{"repository": "https://github.com/openai/math", "commit": "fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb", "retrieved_at": "2026-10-08T10:23:39.060769+00:00", "provenance": "Public official files retrieved separately on the host and supplied as inputs. Not an Open-Science network fetch or proof verification.", "records": [{"path": "lean/ComparatorChallenges/BinaryMatching.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/BinaryMatching.json", "bytes": 355, "sha256": "9ec0cd858949e16333ae9d64e68c2a25a1d1f03b59c6af0c90cc79731f2e9c9a", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.BinaryMatching\",\n  \"solution_module\": \"OAI.Computability.MatchingCount.BinarySolve\",\n  \"theorem_names\": [\n    \"OAI.BinaryMatching.deterministic_approximate_counting\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/BinaryMatching.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/BinaryMatching.lean", "bytes": 2330, "sha256": "05f2ae77b70ff97e2f9cd960662a68fb3664ffae184d2c36bbd6c330955dd090", "content": "import Mathlib\n\nnamespace OAI\n\nuniverse u_1 u_2\n\nnamespace MatchingEntropy\n\nstructure LooplessGraph (V : Type u_1) (E : Type u_2) where\n  left : E → V\n  right : E → V\n  loopless : ∀ e, left e ≠ right e\n\nnamespace LooplessGraph\n\nvariable {V : Type u_1} {E : Type u_2} [Fintype V] [Fintype E] [DecidableEq V] [DecidableEq E]\ndef Incident (G : LooplessGraph V E) (v : V) (e : E) : Prop :=\n  G.left e=v ∨ G.right e=v\ndef IsPerfectMatching (G : LooplessGraph V E) (M : Finset E) : Prop :=\n  ∀ v, ∃! e, e∈M ∧ G.Incident v e\nabbrev Matching (G : LooplessGraph V E) := {M : Finset E // G.IsPerfectMatching M}\nnoncomputable instance matchingFintype (G : LooplessGraph V E) : Fintype G.Matching :=\n  Fintype.ofFinite _\n\nend LooplessGraph\n\nend MatchingEntropy\n\nnamespace BinaryMatching\n\nabbrev Pair (n : ℕ) := {ij : Fin n × Fin n // ij.1 < ij.2}\ndef completeGraph (n : ℕ) : MatchingEntropy.LooplessGraph (Fin n) (Pair n) where\n  left e := e.val.1\n  right e := e.val.2\n  loopless e := ne_of_lt e.property\n\nstructure Record where\n  left : ℕ\n  right : ℕ\n  multiplicity : ℕ\n  deriving DecidableEq\n\nstructure Input where\n  n : ℕ\n  records : List Record\n  valid : ∀ e∈records, e.left < e.right ∧ e.right < n\n  unique : (records.map (fun e => (e.left,e.right))).Nodup\n\ndef multiplicity (G : Input) (e : Pair G.n) : ℕ :=\n  match G.records.find? (fun r => r.left=e.val.1.val && r.right=e.val.2.val) with\n  | none => 0\n  | some r => r.multiplicity\n\nnoncomputable def count (G : Input) : ℕ :=\n  ∑ M : (completeGraph G.n).Matching, ∏ e∈M.val, multiplicity G e\n\ndef encodeNat (n : ℕ) : List Bool :=\n  List.replicate n.bits.length false ++ true :: n.bits\n\ndef encodeRecord (r : Record) : List Bool :=\n  encodeNat r.left ++ encodeNat r.right ++ encodeNat r.multiplicity\n\ndef encodeInput (G : Input) : List Bool :=\n  encodeNat G.n ++ encodeNat G.records.length ++ G.records.flatMap encodeRecord\n\ntheorem deterministic_approximate_counting :\n    ∃ A : Input → ℕ,\n      ∃ machine : Turing.TM2ComputableInPolyTime encodeInput Nat.bits A,\n        (∀ k, Finite (machine.tm.Γ k)) ∧\n        (∃ P : Polynomial ℕ, ∀ G,(A G).bits.length≤P.eval (encodeInput G).length) ∧\n        ∀ G,A G≤count G ∧ count G≤2^(9*G.n)*A G ∧ (A G=0 ↔ count G=0) := by\n  sorry\n\nend BinaryMatching\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/ClassicalON.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/ClassicalON.json", "bytes": 308, "sha256": "47cb3623915c8cfcc35099fabd0c44173afa72b706eb0526c4b8d3855e789252", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.ClassicalON\",\n  \"solution_module\": \"OAI.Probability.ClassicalON.Main\",\n  \"theorem_names\": [\n    \"OAI.ClassicalON.main\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/ClassicalON.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/ClassicalON.lean", "bytes": 1961, "sha256": "c2f5f88455b811eb6b56c1ae80ff91f4e398e18e7c7fbeee6daca8da4122d696", "content": "import Mathlib\n\nnamespace OAI\n\nnoncomputable section\nopen MeasureTheory\nopen scoped BigOperators InnerProductSpace\n\nnamespace ClassicalON\n\nabbrev Site := ℤ × ℤ\n\ndef PositiveNeighbor (x y : Site) : Prop :=\n  (y.1 = x.1 + 1 ∧ y.2 = x.2) ∨ (y.1 = x.1 ∧ y.2 = x.2 + 1)\n\nstructure LatticeGraph where\n  vertices : Finset Site\n  edges : Finset (vertices × vertices)\n  nearest : ∀ e ∈ edges, PositiveNeighbor e.1.val e.2.val\n\nabbrev Spin (n : ℕ) := ↥(Metric.sphere (0 : EuclideanSpace ℝ (Fin n)) 1)\nabbrev Configuration (n : ℕ) (G : LatticeGraph) := G.vertices → Spin n\n\ndef sphereProbability (n : ℕ) : Measure (Spin n) :=\n  let surface := (volume : Measure (EuclideanSpace ℝ (Fin n))).toSphere\n  (surface Set.univ)⁻¹ • surface\n\ndef referenceLaw (n : ℕ) (G : LatticeGraph) : Measure (Configuration n G) :=\n  Measure.pi (fun _ => sphereProbability n)\n\ndef interaction (n : ℕ) (G : LatticeGraph) (b : G.edges → ℝ)\n    (σ : Configuration n G) : ℝ :=\n  ∑ e : G.edges, b e * ⟪(σ e.val.1).val, (σ e.val.2).val⟫_ℝ\n\ndef partition (n : ℕ) (G : LatticeGraph) (b : G.edges → ℝ) : ℝ :=\n  ∫ σ, Real.exp (interaction n G b σ) ∂referenceLaw n G\n\ndef correlation (n : ℕ) (G : LatticeGraph) (b : G.edges → ℝ)\n    (x y : G.vertices) : ℝ :=\n  (∫ σ, ⟪(σ x).val, (σ y).val⟫_ℝ * Real.exp (interaction n G b σ)\n    ∂referenceLaw n G) / partition n G b\n\ndef siteDistance (x y : Site) : ℝ :=\n  Real.sqrt (((x.1 : ℝ) - (y.1 : ℝ)) ^ 2 + ((x.2 : ℝ) - (y.2 : ℝ)) ^ 2)\n\ndef ExponentialDecay : Prop :=\n  ∀ (n : ℕ), 3 ≤ n → ∀ (β : ℝ), 0 < β →\n    ∃ A m : ℝ, 0 < m ∧ ∀ (G : LatticeGraph) (b : G.edges → ℝ),\n      (∀ e, 0 ≤ b e ∧ b e ≤ β) → ∀ x y : G.vertices,\n        0 ≤ correlation n G b x y ∧\n          correlation n G b x y ≤ A * Real.exp (-m * siteDistance x.val y.val)\n\ntheorem main : ExponentialDecay := by\n  sorry\n\nend ClassicalON\n\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/ContingencyTables.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/ContingencyTables.json", "bytes": 433, "sha256": "4baaa8be9da6f2c3b8664c94cad82b8f2c549b69315374584d6d01e381719a57", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.ContingencyTables\",\n  \"solution_module\": \"OAI.Combinatorics.ContingencyTables.UnconditionalMain\",\n  \"theorem_names\": [\n    \"OAI.ContingencyTables.boundedSampling\",\n    \"OAI.ContingencyTables.exactSampling\",\n    \"OAI.ContingencyTables.counting\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/ContingencyTables.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/ContingencyTables.lean", "bytes": 9515, "sha256": "809f0a2575fb97e579e883d47aa6f38717c9df57f4b186d21dcdeb59743118b5", "content": "import Mathlib\n\nnamespace OAI\n\nnamespace MatchingFPRAS\n\nabbrev Symbol := Fin 8\n\n/-- Binary numeral followed by a delimiter. Tape blank is 0; bits use 1 and 2. -/\ndef encodeNat (n : ℕ) : List Symbol :=\n  (Nat.bits n).map (fun b => if b then 2 else 1) ++ [3]\n\ndef encodeInt (z : ℤ) : List Symbol :=\n  (if z < 0 then [5] else [4]) ++ encodeNat z.natAbs\n\n/-- A rational encoded by its reduced numerator and positive denominator. -/\ndef encodeRat (q : ℚ) : List Symbol := encodeInt q.num ++ encodeNat q.den\n\n/-- A uniform randomized Post-Turing machine: a single finite transition table. -/\nstructure RandomMachine where\n  states : ℕ\n  transition : Fin (states + 1) → Symbol → Bool →\n    Option (Fin (states + 1) × Turing.TM0.Stmt Symbol)\n\nstructure Configuration (A : RandomMachine) where\n  state : Option (Fin (A.states + 1))\n  tape : Turing.Tape Symbol\n\n/-- A halted configuration is absorbing. Each nonhalting tick is exactly one\nwrite or one move, or the detection of halt. -/\ndef tick (A : RandomMachine) (c : Configuration A) (bit : Bool) : Configuration A :=\n  match c.state with\n  | none => c\n  | some s => match A.transition s c.tape.head bit with\n    | none => ⟨none, c.tape⟩\n    | some (s', action) =>\n      ⟨some s', match action with\n        | .move d => c.tape.move d\n        | .write a => c.tape.write a⟩\n\ndef initial (A : RandomMachine) (input : List Symbol) : Configuration A :=\n  ⟨some 0, Turing.Tape.mk₁ input⟩\n\n/-- Bounded execution on a finite prefix of the random tape. -/\ndef run (A : RandomMachine) (input : List Symbol) {t : ℕ}\n    (bits : Fin t → Bool) : Configuration A :=\n  (List.ofFn bits).foldl (tick A) (initial A input)\n\ndef Outputs (A : RandomMachine) (input : List Symbol) {t : ℕ}\n    (bits : Fin t → Bool) (q : ℚ) : Prop :=\n  (run A input bits).state = none ∧\n    (run A input bits).tape.right₀ = Turing.ListBlank.mk (encodeRat q)\n\nend MatchingFPRAS\n\nnamespace ContingencyTables\n\nopen Finset\nvariable {I J : Type*} [Fintype I] [Fintype J]\n\ndef HasMargins (X : I → J → ℕ) (r : I → ℕ) (c : J → ℕ) : Prop :=\n  (∀ i, ∑ j, X i j = r i) ∧ (∀ j, ∑ i, X i j = c j)\n\ndef Table (r : I → ℕ) (c : J → ℕ) := {X : I → J → ℕ // HasMargins X r c}\n\ndef BoundedTable (r : I → ℕ) (c : J → ℕ) (b : I → J → ℕ) :=\n  {X : Table r c // ∀ i j, X.val i j ≤ b i j}\n\ntheorem entry_le_row {X : I → J → ℕ} {r : I → ℕ} {c : J → ℕ}\n    (h : HasMargins X r c) (i : I) (j : J) : X i j ≤ r i := by\n  rw [← h.1 i]\n  exact single_le_sum (fun _ _ => Nat.zero_le _) (mem_univ j)\n\ntheorem entry_le_total {X : I → J → ℕ} {r : I → ℕ} {c : J → ℕ}\n    (h : HasMargins X r c) (i : I) (j : J) : X i j ≤ ∑ i, r i :=\n  (entry_le_row h i j).trans (single_le_sum (fun _ _ => Nat.zero_le _) (mem_univ i))\n\ninstance table_finite (r : I → ℕ) (c : J → ℕ) : Finite (Table r c) := by\n  let encode : Table r c → I → J → Fin ((∑ i, r i) + 1) :=\n    fun X i j => ⟨X.val i j, Nat.lt_succ_of_le (entry_le_total X.property i j)⟩\n  apply Finite.of_injective encode\n  intro X Y h\n  apply Subtype.ext\n  funext i j\n  exact congrArg Fin.val (congrFun (congrFun h i) j)\n\nnoncomputable instance table_fintype (r : I → ℕ) (c : J → ℕ) : Fintype (Table r c) :=\n  Fintype.ofFinite _\n\nnoncomputable instance boundedTable_fintype (r : I → ℕ) (c : J → ℕ) (b : I → J → ℕ) :\n    Fintype (BoundedTable r c b) := by\n  classical\n  unfold BoundedTable\n  infer_instance\n\nend ContingencyTables\n\nnamespace ContingencyTables.Algorithms\n\nopen scoped BigOperators\nopen Filter\n\nabbrev Machine := MatchingFPRAS.RandomMachine\nabbrev Symbol := MatchingFPRAS.Symbol\n\ndef encodeMatrix {m n : ℕ} (X : Fin m → Fin n → ℕ) : List Symbol :=\n  ((List.ofFn fun i => (List.ofFn fun j => MatchingFPRAS.encodeNat (X i j)).flatten)).flatten\n\ndef encodeMargins {m n : ℕ} (r : Fin m → ℕ) (c : Fin n → ℕ) : List Symbol :=\n  MatchingFPRAS.encodeNat m ++ MatchingFPRAS.encodeNat n ++\n    (List.ofFn fun i => MatchingFPRAS.encodeNat (r i)).flatten ++\n    (List.ofFn fun j => MatchingFPRAS.encodeNat (c j)).flatten\n\ndef encodeSamplingInput {m n : ℕ} (r : Fin m → ℕ) (c : Fin n → ℕ) (k : ℕ) : List Symbol :=\n  encodeMargins r c ++ MatchingFPRAS.encodeNat k\n\ndef encodeCountingInput {m n : ℕ} (r : Fin m → ℕ) (c : Fin n → ℕ)\n    (b : Fin m → Fin n → ℕ) (ε δ : ℚ) : List Symbol :=\n  encodeMargins r c ++ encodeMatrix b ++ MatchingFPRAS.encodeRat ε ++ MatchingFPRAS.encodeRat δ\n\n/-- The tape alphabet has positive cardinality. -/\ntheorem alphabetSize_neZero : NeZero (8 : ℕ) := inferInstance\n\n/-- A halted tape contains the row-major binary matrix encoding. -/\ndef OutputsTable (A : Machine) (input : List Symbol) {t m n : ℕ}\n    (bits : Fin t → Bool) (X : Fin m → Fin n → ℕ) : Prop :=\n  (MatchingFPRAS.run A input bits).state = none ∧\n    @Turing.Tape.right₀ MatchingFPRAS.Symbol (@Fin.instInhabited 8 alphabetSize_neZero)\n      (MatchingFPRAS.run A input bits).tape =\n    @Turing.ListBlank.mk Symbol (@Fin.instInhabited 8 alphabetSize_neZero) (encodeMatrix X)\n\nnoncomputable def tableMass (A : Machine) (input : List Symbol) (t : ℕ)\n    {m n : ℕ} (X : Fin m → Fin n → ℕ) : ℝ := by\n  classical\n  exact ((Finset.univ.filter fun bits : Fin t → Bool => OutputsTable A input bits X).card : ℝ) /\n    (2 : ℝ) ^ t\n\nnoncomputable def haltMass (A : Machine) (input : List Symbol) (t : ℕ) : ℝ := by\n  classical\n  exact ((Finset.univ.filter fun bits : Fin t → Bool =>\n      (MatchingFPRAS.run A input bits).state = none).card : ℝ) / (2 : ℝ) ^ t\n\ndef samplingSize (n : ℕ) {m : ℕ} (r : Fin m → ℕ) (k : ℕ) : ℕ :=\n  m + n + Nat.clog 2 ((∑ i, r i) + 1) + k + 1\n\n/-- Paper 200-01, Theorem main(i): bounded runtime on every random tape\nand total variation at most `2⁻ᵏ`, with freely varying dimensions. -/\ndef BoundedSamplingStatement : Prop :=\n  ∃ (A : Machine) (C d : ℕ), 0 < C ∧\n    ∀ (m n : ℕ) (r : Fin m → ℕ) (c : Fin n → ℕ),\n      (∑ i, r i) = ∑ j, c j → ∀ k : ℕ, 1 ≤ k →\n        let input := encodeSamplingInput r c k\n        let t := C * (samplingSize n r k) ^ d\n        (∀ bits : Fin t → Bool, ∃ X : Table r c, OutputsTable A input bits X.val) ∧\n        (∑ X : Table r c,\n          |tableMass A input t X.val - (Fintype.card (Table r c) : ℝ)⁻¹|) / 2 ≤\n            ((2 : ℝ) ^ k)⁻¹\n\n/-- Paper 200-01, Theorem main(ii): every halted output is feasible, each\ntable has limiting mass `1/|Ω|`, termination has probability one, and the\nexpected number of bit operations is bounded by a uniform polynomial. -/\ndef ExactSamplingStatement : Prop :=\n  ∃ (A : Machine) (C d : ℕ), 0 < C ∧\n    ∀ (m n : ℕ) (r : Fin m → ℕ) (c : Fin n → ℕ),\n      (∑ i, r i) = ∑ j, c j →\n        let input := encodeMargins r c\n        let tail : ℕ → ℝ := fun t => 1 - haltMass A input t\n        (∀ t (bits : Fin t → Bool), (MatchingFPRAS.run A input bits).state = none →\n          ∃ X : Table r c, OutputsTable A input bits X.val) ∧\n        (∀ X : Table r c, Tendsto (fun t => tableMass A input t X.val) atTop\n          (nhds ((Fintype.card (Table r c) : ℝ)⁻¹))) ∧\n        Tendsto (haltMass A input) atTop (nhds 1) ∧\n        Summable tail ∧\n        tsum tail ≤ C * (samplingSize n r 0) ^ d\n\nnoncomputable def count {m n : ℕ} (r : Fin m → ℕ) (c : Fin n → ℕ)\n    (b : Fin m → Fin n → ℕ) : ℕ := Fintype.card (BoundedTable r c b)\n\ndef countingTime (C d : ℕ) {m n : ℕ} (r : Fin m → ℕ) (c : Fin n → ℕ)\n    (b : Fin m → Fin n → ℕ) (ε δ : ℚ) : ℕ :=\n  C * ((encodeCountingInput r c b ε δ).length + ⌈ε⁻¹⌉₊ +\n    Nat.clog 2 ⌈δ⁻¹⌉₊ + 1) ^ d\n\nnoncomputable def goodCountingTapes (A : Machine) {m n : ℕ}\n    (r : Fin m → ℕ) (c : Fin n → ℕ) (b : Fin m → Fin n → ℕ)\n    (ε δ : ℚ) (t : ℕ) : Finset (Fin t → Bool) := by\n  classical\n  exact Finset.univ.filter fun bits => ∃ q : ℚ,\n    MatchingFPRAS.Outputs A (encodeCountingInput r c b ε δ) bits q ∧\n      (1 - ε) * count r c b ≤ q ∧ q ≤ (1 + ε) * count r c b\n\n/-- Paper 200-02, Theorem main. The input length includes the rational\naccuracy parameters, and infeasible inputs always produce exactly zero. -/\ndef CountingStatement : Prop :=\n  ∃ (A : Machine) (C d : ℕ), 0 < C ∧\n    ∀ (m n : ℕ), 0 < m → 0 < n →\n    ∀ (r : Fin m → ℕ) (c : Fin n → ℕ) (b : Fin m → Fin n → ℕ),\n      (∑ i, r i) = ∑ j, c j →\n      ∀ ε δ : ℚ, 0 < ε → ε < 1 → 0 < δ → δ < 1 →\n        let input := encodeCountingInput r c b ε δ\n        let t := countingTime C d r c b ε δ\n        (∀ bits : Fin t → Bool, ∃ q : ℚ, 0 ≤ q ∧ MatchingFPRAS.Outputs A input bits q) ∧\n        (count r c b = 0 → ∀ bits : Fin t → Bool, MatchingFPRAS.Outputs A input bits 0) ∧\n        ((goodCountingTapes A r c b ε δ t).card : ℚ) / (2 : ℚ) ^ t ≥ 1 - δ\n\nend ContingencyTables.Algorithms\n\nnamespace ContingencyTables\n\n/-- An almost-uniform sampler with polynomial cost on every execution. -/\ntheorem boundedSampling : Algorithms.BoundedSamplingStatement := by sorry\n\n/-- Exact uniform sampling in polynomial expected bit time. -/\ntheorem exactSampling : Algorithms.ExactSamplingStatement := by sorry\n\n/-- A cell-bounded FPRAS, with zero output on every infeasible execution. -/\ntheorem counting : Algorithms.CountingStatement := by sorry\n\nend ContingencyTables\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/DukePrimeDegree.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/DukePrimeDegree.json", "bytes": 471, "sha256": "1e365bb14f0d1c1e12bffc592ba31509a42bbb3b11b322ec00d7f5746d9fc5e8", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.DukePrimeDegree\",\n  \"solution_module\": \"OAI.NumberTheory.DukePrimeDegree.MainUnconditional\",\n  \"theorem_names\": [\n    \"OAI.DukePrimeDegree.prime_degree_packet_measure_equidistribution_unconditional\",\n    \"OAI.DukePrimeDegree.prime_degree_packet_measures_tendsto_haar_unconditional\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/DukePrimeDegree.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/DukePrimeDegree.lean", "bytes": 9711, "sha256": "a6a7425910ce1b6b486efc1fe05d0c262c523635eeef0a14481741557a1c966d", "content": "import Mathlib\n\nnamespace OAI\n\nsection\nnoncomputable section\nopen scoped Matrix Topology BigOperators Classical TensorProduct ENNReal\nopen MeasureTheory Filter Set Module\nnamespace PrimeDegreePackets\n\nabbrev G (n : ℕ) := Matrix.SpecialLinearGroup (Fin n) ℝ\n\ndef integralGroup (n : ℕ) : Subgroup (G n) :=\n  (Matrix.SpecialLinearGroup.map (Int.castRingHom ℝ)).range\n\nabbrev X (n : ℕ) := Quotient (QuotientGroup.rightRel (integralGroup n))\n\ninstance xMeasurableSpace (n : ℕ) : MeasurableSpace (X n) := borel (X n)\ninstance xBorelSpace (n : ℕ) : BorelSpace (X n) := ⟨rfl⟩\n\ndef rightAction {n : ℕ} (x : X n) (g : G n) : X n :=\n  Quotient.map (fun h => h * g) (by\n    intro a b hab\n    change (QuotientGroup.rightRel (integralGroup n)) a b at hab\n    change (QuotientGroup.rightRel (integralGroup n)) (a * g) (b * g)\n    rw [QuotientGroup.rightRel_apply] at hab ⊢\n    simpa only [mul_inv_rev, mul_assoc, mul_inv_cancel_left] using hab) x\n\n@[simp] theorem rightAction_one {n : ℕ} (x : X n) : rightAction x 1 = x := by\n  induction x using Quotient.inductionOn with\n  | h a => exact congrArg (Quotient.mk _) (mul_one a)\n\n@[simp] theorem rightAction_mul {n : ℕ} (x : X n) (g h : G n) :\n    rightAction (rightAction x g) h = rightAction x (g * h) := by\n  induction x using Quotient.inductionOn with\n  | h a => exact congrArg (Quotient.mk _) (mul_assoc a g h)\n\ndef signMatrices (n : ℕ) : Set (G n) :=\n  {g | (∀ i j, i ≠ j → g.1 i j = 0) ∧ ∀ i, g.1 i i = 1 ∨ g.1 i i = -1}\n\ndef rowLattice {n : ℕ} (g : Matrix (Fin n) (Fin n) ℝ) : Submodule ℤ (Fin n → ℝ) :=\n  Submodule.span ℤ (Set.range fun i => g i)\n\nstructure FullLattice (n : ℕ) (K : Type*) [Field K] [NumberField K] where\n  carrier : Submodule ℤ K\n  basis : Basis (Fin n) ℤ carrier\n  spans : Submodule.span ℚ (carrier : Set K) = ⊤\n\ndef multiplierOrder {n : ℕ} {K : Type*} [Field K] [NumberField K] (M : FullLattice n K) : AddSubgroup K where\n  carrier := {a | ∀ x ∈ M.carrier, a * x ∈ M.carrier}\n  zero_mem' := by simp\n  add_mem' := by\n    intro a b ha hb x hx\n    simpa only [add_mul] using M.carrier.add_mem (ha x hx) (hb x hx)\n  neg_mem' := by\n    intro a ha x hx\n    simpa only [neg_mul] using M.carrier.neg_mem (ha x hx)\n\ndef integralElements (K : Type*) [Field K] : AddSubgroup K :=\n  (integralClosure ℤ K).toSubring.toAddSubgroup\n\ndef multiplierDiscriminant {n : ℕ} {K : Type*} [Field K] [NumberField K]\n    (M : FullLattice n K) : ℝ :=\n  |(NumberField.discr K : ℝ)| *\n    ((multiplierOrder M).relIndex (integralElements K) : ℝ) ^ 2\n\nabbrev OrderedEmbeddings (n : ℕ) (K : Type*) [Field K] := Fin n ≃ (K →+* ℝ)\n\ndef embeddingMatrix {n : ℕ} {K : Type*} [Field K] [NumberField K]\n    (M : FullLattice n K) (σ : OrderedEmbeddings n K) : Matrix (Fin n) (Fin n) ℝ :=\n  fun i j => σ j (M.basis i)\n\ndef normalizedLattice {n : ℕ} {K : Type*} [Field K] [NumberField K]\n    (M : FullLattice n K) (σ : OrderedEmbeddings n K) : Submodule ℤ (Fin n → ℝ) :=\n  let B := embeddingMatrix M σ\n  rowLattice (|(Matrix.det B)| ^ (-(1 : ℝ) / n) • B)\n\ndef latticePoint {n : ℕ} {K : Type*} [Field K] [NumberField K]\n    (M : FullLattice n K) (σ : OrderedEmbeddings n K) : X n :=\n  if h : ∃ g : G n, rowLattice g.1 = normalizedLattice M σ then\n    Quotient.mk _ h.choose\n  else Quotient.mk _ (1 : G n)\n\nabbrev LogCoordinates (d : ℕ) := Fin d → ℝ\n\ndef diagonalFlow {d : ℕ} (u : LogCoordinates d) : G (d + 1) :=\n  ⟨Matrix.diagonal (Fin.snoc (fun i => Real.exp (u i)) (Real.exp (-∑ i, u i))), by\n    rw [Matrix.det_diagonal, Fin.prod_univ_castSucc]\n    simp only [Fin.snoc_castSucc, Fin.snoc_last]\n    rw [← Real.exp_sum, ← Real.exp_add, add_neg_cancel, Real.exp_zero]⟩\n\n@[simp] theorem diagonalFlow_zero (d : ℕ) : diagonalFlow (0 : LogCoordinates d) = 1 := by\n  apply Subtype.ext\n  change Matrix.diagonal _ = (1 : Matrix (Fin (d + 1)) (Fin (d + 1)) ℝ)\n  simp\n  ext i\n  refine Fin.lastCases ?_ (fun j => ?_) i <;> simp\n\n@[simp] theorem diagonalFlow_add {d : ℕ} (u v : LogCoordinates d) :\n    diagonalFlow (u + v) = diagonalFlow u * diagonalFlow v := by\n  apply Subtype.ext\n  change Matrix.diagonal _ = Matrix.diagonal _ * Matrix.diagonal _\n  rw [Matrix.diagonal_mul_diagonal]\n  congr 1\n  funext i\n  refine Fin.lastCases ?_ (fun j => ?_) i\n  · simp [Real.exp_add, Finset.sum_add_distrib, neg_add_rev, mul_comm]\n  · simp [Real.exp_add]\n\ndef orbitMap {d : ℕ} (x : X (d + 1)) (u : LogCoordinates d) : X (d + 1) :=\n  rightAction x (diagonalFlow u)\n\n@[simp] theorem orbitMap_zero {d : ℕ} (x : X (d + 1)) : orbitMap x 0 = x := by\n  simp [orbitMap]\n\n@[simp] theorem orbitMap_add {d : ℕ} (x : X (d + 1)) (u v : LogCoordinates d) :\n    orbitMap x (u + v) = orbitMap (orbitMap x u) v := by\n  simp [orbitMap]\n\ndef periodGroup {d : ℕ} (x : X (d + 1)) : AddSubgroup (LogCoordinates d) where\n  carrier := {u | orbitMap x u = x}\n  zero_mem' := orbitMap_zero x\n  add_mem' := by\n    intro u v hu hv\n    change orbitMap x (u + v) = x\n    rw [orbitMap_add, hu, hv]\n  neg_mem' := by\n    intro u hu\n    have h := congrArg (fun y => orbitMap y (-u)) hu\n    rw [← orbitMap_add, add_neg_cancel, orbitMap_zero] at h\n    exact h.symm\n\ndef diagonalOrbit {d : ℕ} (x : X (d + 1)) : Set (X (d + 1)) := Set.range (orbitMap x)\n\ndef orbitVolume {d : ℕ} (x : X (d + 1)) : ℝ≥0∞ :=\n  addCovolume (periodGroup x) (LogCoordinates d) volume\n\ndef orbitalHaar {d : ℕ} (x : X (d + 1)) : Measure (X (d + 1)) :=\n  if h : HasAddFundamentalDomain (periodGroup x) (LogCoordinates d) volume then\n    Measure.map (orbitMap x) (volume.restrict h.ExistsIsAddFundamentalDomain.choose)\n  else 0\n\ndef orbitProbability {d : ℕ} (x : X (d + 1)) : Measure (X (d + 1)) :=\n  (orbitVolume x)⁻¹ • orbitalHaar x\n\nabbrev LocalAlgebra (p : ℕ) [Fact p.Prime] (K : Type*) [Field K] [NumberField K] :=\n  Padic p ⊗[ℚ] K\n\ndef completedLattice {n : ℕ} {K : Type*} [Field K] [NumberField K]\n    (p : ℕ) [Fact p.Prime] (M : FullLattice n K) :\n    Submodule (PadicInt p) (LocalAlgebra p K) :=\n  Submodule.span (PadicInt p)\n    ((Algebra.TensorProduct.includeRight : K →ₐ[ℚ] LocalAlgebra p K) '' (M.carrier : Set K))\n\ndef LocallyHomothetic {n : ℕ} {K : Type*} [Field K] [NumberField K]\n    (M M' : FullLattice n K) : Prop :=\n  ∀ (p : ℕ) (hp : p.Prime), letI : Fact p.Prime := ⟨hp⟩\n    ∃ c : (LocalAlgebra p K)ˣ,\n      (completedLattice p M' : Set (LocalAlgebra p K)) =\n        (fun z => (c : LocalAlgebra p K) * z) '' (completedLattice p M : Set (LocalAlgebra p K))\n\ndef packetOrbits {d : ℕ} {K : Type*} [Field K] [NumberField K]\n    (M : FullLattice (d + 1) K) (σ : OrderedEmbeddings (d + 1) K) :\n    Set (Set (X (d + 1))) :=\n  {O | ∃ M' : FullLattice (d + 1) K, LocallyHomothetic M M' ∧\n    ∃ w ∈ signMatrices (d + 1), O = diagonalOrbit (rightAction (latticePoint M' σ) w)}\n\ndef orbitBasepoint {d : ℕ} {K : Type*} [Field K] [NumberField K]\n    {M : FullLattice (d + 1) K} {σ : OrderedEmbeddings (d + 1) K}\n    (O : packetOrbits M σ) : X (d + 1) :=\n  Classical.epsilon (fun x => diagonalOrbit x = O.1)\n\ndef packetMeasure {d : ℕ} {K : Type*} [Field K] [NumberField K]\n    (M : FullLattice (d + 1) K) (σ : OrderedEmbeddings (d + 1) K) : Measure (X (d + 1)) :=\n  (∑' O : packetOrbits M σ, orbitVolume (orbitBasepoint O))⁻¹ •\n    Measure.sum (fun O : packetOrbits M σ =>\n      orbitVolume (orbitBasepoint O) • orbitProbability (orbitBasepoint O))\n\ndef IsHaarProbability {n : ℕ} (μ : Measure (X n)) : Prop :=\n  IsProbabilityMeasure μ ∧ ∀ g : G n, Measure.map (fun x => rightAction x g) μ = μ\n\ndef WeakProbabilityConvergence {n : ℕ} (μ : ℕ → Measure (X n)) (m : Measure (X n)) : Prop :=\n  (∀ i, IsProbabilityMeasure (μ i)) ∧ IsProbabilityMeasure m ∧\n    ∀ f : BoundedContinuousFunction (X n) ℝ,\n    Tendsto (fun i => ∫ x, f x ∂μ i) atTop (𝓝 (∫ x, f x ∂m))\n\nend PrimeDegreePackets\nend\nend\n\nopen MeasureTheory Filter\nopen scoped Topology\nnamespace DukePrimeDegree\nopen PrimeDegreePackets\n\nuniverse u\n\n/-- Theorem 1.1 as weak convergence of the actual complete packet measures,\nincluding tightness of the entire family. -/\ntheorem prime_degree_packet_measure_equidistribution_unconditional\n    (d : ℕ) (hprime : Nat.Prime (d+1)) (hfive : 5 ≤ d+1)\n    (K : ℕ → Type u) [∀ i, Field (K i)] [∀ i, NumberField (K i)]\n    [∀ i, NumberField.IsTotallyReal (K i)]\n    (M : ∀ i, PrimeDegreePackets.FullLattice (d+1) (K i))\n    (σ : ∀ i, OrderedEmbeddings (d+1) (K i))\n    (hn : ∀ i, Module.finrank ℚ (K i)=d+1)\n    (hD : Tendsto (fun i => PrimeDegreePackets.multiplierDiscriminant (M i)) atTop atTop) :\n    ∃ ν : Measure (X (d+1)), IsHaarProbability ν ∧\n      WeakProbabilityConvergence (fun i => packetMeasure (M i) (σ i)) ν ∧\n      IsTightMeasureSet (Set.range (fun i => packetMeasure (M i) (σ i))) := by\n  sorry\n\n/-- The complete packet measures converge weakly to any specified Haar\nprobability, in the formulation by bounded continuous test functions. -/\ntheorem prime_degree_packet_measures_tendsto_haar_unconditional\n    (d : ℕ) (hprime : Nat.Prime (d+1)) (hfive : 5 ≤ d+1)\n    (K : ℕ → Type u) [∀ i, Field (K i)] [∀ i, NumberField (K i)]\n    [∀ i, NumberField.IsTotallyReal (K i)]\n    (M : ∀ i, PrimeDegreePackets.FullLattice (d+1) (K i))\n    (σ : ∀ i, OrderedEmbeddings (d+1) (K i))\n    (hn : ∀ i, Module.finrank ℚ (K i)=d+1)\n    (hD : Tendsto (fun i => PrimeDegreePackets.multiplierDiscriminant (M i)) atTop atTop)\n    (m : ProbabilityMeasure (X (d+1)))\n    (hm : IsHaarProbability (m : Measure (X (d+1)))) :\n    WeakProbabilityConvergence (fun i => packetMeasure (M i) (σ i))\n      (m : Measure (X (d+1))) := by\n  sorry\n\nend DukePrimeDegree\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/ElasticityUniqueness.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/ElasticityUniqueness.json", "bytes": 342, "sha256": "91b68c7c74c9e3a29813b2a205d9d078072bc69de056d9ef980304258fdf8444", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.ElasticityUniqueness\",\n  \"solution_module\": \"OAI.MathematicalPhysics.Elasticity.Uniqueness\",\n  \"theorem_names\": [\n    \"OAI.Elasticity.global_uniqueness\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/ElasticityUniqueness.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/ElasticityUniqueness.lean", "bytes": 3681, "sha256": "c4c928a811cce5bd5bc46642e8dbf7e6ffd698856f1bf6ff12a511dddfaccb20", "content": "import Mathlib\n\nnamespace OAI\n\n/-!\n# Global uniqueness for smooth isotropic elasticity\n\nThe physical Dirichlet-to-Neumann pairing acts on the variational trace\nquotient. Its quotient norm is not identified here with boundary-chart\nfractional Sobolev norms.\n-/\n\nnoncomputable section\nopen MeasureTheory Set\nopen scoped BigOperators\n\nnamespace Elasticity\n\nabbrev X := EuclideanSpace ℝ (Fin 3)\nabbrev V := X\n\ndef coordVector (i : Fin 3) : X := EuclideanSpace.single i 1\n\ndef SmoothBoundary (Ω : Set X) : Prop :=\n  ∀ x ∈ frontier Ω, ∃ e : OpenPartialHomeomorph X X,\n    x ∈ e.source ∧\n    ContDiffOn ℝ (⊤ : ℕ∞) e e.source ∧\n    ContDiffOn ℝ (⊤ : ℕ∞) e.symm e.target ∧\n    e x 0 = 0 ∧\n    ∀ y ∈ e.source, y ∈ Ω ↔ 0 < e y 0\n\ndef Domain (Ω : Set X) : Prop :=\n  IsOpen Ω ∧ IsConnected Ω ∧ Bornology.IsBounded Ω ∧ SmoothBoundary Ω\n\ndef SmoothUpTo (Ω : Set X) (f : X → ℝ) : Prop :=\n  ∃ U : Set X, IsOpen U ∧ closure Ω ⊆ U ∧ ContDiffOn ℝ (⊤ : ℕ∞) f U\n\ndef Admissible (Ω : Set X) (lam mu : X → ℝ) : Prop :=\n  SmoothUpTo Ω lam ∧ SmoothUpTo Ω mu ∧\n    ∀ x ∈ closure Ω, 0 < mu x ∧ 0 < 3 * lam x + 2 * mu x\n\nabbrev Ambient (Ω : Set X) :=\n  Lp V 2 (volume.restrict Ω) × (Fin 3 → Lp V 2 (volume.restrict Ω))\n\ndef coordDeriv (f : X → V) (i : Fin 3) (x : X) : V :=\n  fderiv ℝ f x (coordVector i)\n\ndef SmoothJets (Ω : Set X) : Set (Ambient Ω) :=\n  {j | ∃ (f : X → V) (hf : MemLp f 2 (volume.restrict Ω))\n      (hd : ∀ i, MemLp (coordDeriv f i) 2 (volume.restrict Ω)),\n    ContDiff ℝ (⊤ : ℕ∞) f ∧\n    j.1 = hf.toLp f ∧ j.2 = fun i => (hd i).toLp (coordDeriv f i)}\n\ndef TestJets (Ω : Set X) : Set (Ambient Ω) :=\n  {j | ∃ (f : X → V) (hf : MemLp f 2 (volume.restrict Ω))\n      (hd : ∀ i, MemLp (coordDeriv f i) 2 (volume.restrict Ω)),\n    ContDiff ℝ (⊤ : ℕ∞) f ∧ HasCompactSupport f ∧ tsupport f ⊆ Ω ∧\n    j.1 = hf.toLp f ∧ j.2 = fun i => (hd i).toLp (coordDeriv f i)}\n\nabbrev H1 (Ω : Set X) := {j : Ambient Ω // j ∈ closure (SmoothJets Ω)}\n\ndef HasZeroTrace (Ω : Set X) (u : H1 Ω) : Prop :=\n  u.val ∈ closure (TestJets Ω)\n\ndef SameTrace (Ω : Set X) (u v : H1 Ω) : Prop :=\n  u.val - v.val ∈ closure (TestJets Ω)\n\nabbrev BoundaryData (Ω : Set X) := Quot (SameTrace Ω)\n\ndef trace (Ω : Set X) (u : H1 Ω) : BoundaryData Ω := Quot.mk _ u\n\ndef div (Ω : Set X) (u : H1 Ω) (x : X) : ℝ :=\n  ∑ i, (u.val.2 i x) i\n\ndef strain (Ω : Set X) (u : H1 Ω) (x : X) (i j : Fin 3) : ℝ :=\n  ((u.val.2 j x) i + (u.val.2 i x) j) / 2\n\ndef energy (Ω : Set X) (lam mu : X → ℝ) (u v : H1 Ω) : ℝ :=\n  ∫ x, (lam x * div Ω u x * div Ω v x +\n    2 * mu x * ∑ i, ∑ j, strain Ω u x i j * strain Ω v x i j)\n    ∂(volume.restrict Ω)\n\ndef WeakSolution (Ω : Set X) (lam mu : X → ℝ) (u : H1 Ω) : Prop :=\n  ∀ v : H1 Ω, HasZeroTrace Ω v → energy Ω lam mu u v = 0\n\ndef dirichletSolution (Ω : Set X) (lam mu : X → ℝ)\n    (f : BoundaryData Ω) : H1 Ω := by\n  classical\n  exact if h : ∃ u : H1 Ω, trace Ω u = f ∧ WeakSolution Ω lam mu u\n  then Classical.choose h\n  else f.out\n\ndef DN (Ω : Set X) (lam mu : X → ℝ) : BoundaryData Ω → BoundaryData Ω → ℝ :=\n  fun f g => energy Ω lam mu (dirichletSolution Ω lam mu f) g.out\n\ndef MainClaim : Prop :=\n  ∀ (Ω : Set X), Domain Ω →\n  ∀ (lam₁ mu₁ lam₂ mu₂ : X → ℝ),\n    Admissible Ω lam₁ mu₁ → Admissible Ω lam₂ mu₂ →\n    DN Ω lam₁ mu₁ = DN Ω lam₂ mu₂ →\n    (∀ x ∈ Ω, lam₁ x = lam₂ x) ∧ (∀ x ∈ Ω, mu₁ x = mu₂ x)\n\ntheorem global_uniqueness : MainClaim := by\n  sorry\n\nend Elasticity\n\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/FactorGeneration.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/FactorGeneration.json", "bytes": 351, "sha256": "4088aa35a7a507a148ff9ca928421bc372bd765c0b0e41c6cf1a0888fc11ef10", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.FactorGeneration\",\n  \"solution_module\": \"OAI.Analysis.FactorGeneration.Main\",\n  \"theorem_names\": [\n    \"OAI.Generator.single_generation_of_II1_separable_predual\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/FactorGeneration.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/FactorGeneration.lean", "bytes": 1738, "sha256": "2e6b0087e27aa2814dd212e5957af3b56670ea2ce07ae049a47908ada5581bb5", "content": "import Mathlib.Analysis.CStarAlgebra.ContinuousLinearMap\nimport Mathlib.Analysis.InnerProductSpace.WeakOperatorTopology\nimport Mathlib.Analysis.VonNeumannAlgebra.Basic\n\nnamespace OAI\n\nnoncomputable section\n\nnamespace Generator\n\nuniverse u\n\nvariable {H : Type u} [NormedAddCommGroup H] [InnerProductSpace ℂ H] [CompleteSpace H]\n\nattribute [-instance] instCStarAlgebraContinuousLinearMapComplexIdOfCompleteSpace\n\ndef WOTClosed (S : StarSubalgebra ℂ (H →L[ℂ] H)) : Prop :=\n  IsClosed {T : H →WOT[ℂ] H | T.toCLM ∈ S}\n\ndef wstar (s : Set (H →L[ℂ] H)) : StarSubalgebra ℂ (H →L[ℂ] H) :=\n  sInf {S | WOTClosed S ∧ s ⊆ S}\n\nstructure IsII1Factor (S : StarSubalgebra ℂ (H →L[ℂ] H)) : Prop where\n  nonzero : (0 : H →L[ℂ] H) ≠ 1\n  weaklyClosed : WOTClosed S\n  factor : ∀ x : S, (∀ y : S, y * x = x * y) → ∃ c : ℂ, x = algebraMap ℂ S c\n  finite : ∀ v : S, star v * v = 1 → v * star v = 1\n  diffuse : ∀ p : S, IsStarProjection p → p ≠ 0 →\n    ∃ q : S, IsStarProjection q ∧ q ≠ 0 ∧ q ≠ p ∧ p * q = q ∧ q * p = q\n\ndef HasSeparablePredual (S : StarSubalgebra ℂ (H →L[ℂ] H)) : Prop :=\n  ∃ (X : Type u) (_ : NormedAddCommGroup X) (_ : NormedSpace ℂ X)\n    (_ : CompleteSpace X) (_ : TopologicalSpace.SeparableSpace X),\n    Nonempty (StrongDual ℂ X ≃ₗᵢ⋆[ℂ] S)\n\ndef SinglyGenerated (S : StarSubalgebra ℂ (H →L[ℂ] H)) : Prop :=\n  ∃ x ∈ S, wstar {x} = S\n\nattribute [instance] instCStarAlgebraContinuousLinearMapComplexIdOfCompleteSpace\n\ntheorem single_generation_of_II1_separable_predual\n    (S : StarSubalgebra ℂ (H →L[ℂ] H)) (hS : IsII1Factor S)\n    (hsep : HasSeparablePredual S) : SinglyGenerated S := by\n  sorry\n\nend Generator\n\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/FoulkesHowe.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/FoulkesHowe.json", "bytes": 354, "sha256": "88d413f643d799a7663353e0ade1d0bb7572a5752e8414fb2ec117244005ff64", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.FoulkesHowe\",\n  \"solution_module\": \"OAI.RepresentationTheory.FoulkesHowe.Stabilization\",\n  \"theorem_names\": [\n    \"OAI.Problem346.canonical_foulkes_howe_surjective\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/FoulkesHowe.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/FoulkesHowe.lean", "bytes": 1809, "sha256": "ef2a1c1ff39cc2caf381d5704476288fe35c00d316d02dd0571e3c5590e7df19", "content": "import Mathlib\n\nnamespace OAI\n\nnoncomputable section\n\nopen scoped BigOperators\nuniverse u\n\nnamespace Problem346\n\ndef symMonomialRaw (n : ℕ) (V : Type u) [AddCommGroup V] [Module ℂ V]\n    (v : Fin n → V) : SymmetricAlgebra ℂ V :=\n  ∏ i : Fin n, SymmetricAlgebra.ι ℂ V (v i)\n\ndef symPowSubmodule (n : ℕ) (V : Type u) [AddCommGroup V] [Module ℂ V] :\n    Submodule ℂ (SymmetricAlgebra ℂ V) :=\n  Submodule.span ℂ (Set.range (symMonomialRaw n V))\n\nabbrev SymPow (n : ℕ) (V : Type u) [AddCommGroup V] [Module ℂ V] :=\n  ↥(symPowSubmodule n V)\n\ndef symMonomial (n : ℕ) (V : Type u) [AddCommGroup V] [Module ℂ V]\n    (v : Fin n → V) : SymPow n V :=\n  ⟨symMonomialRaw n V v, Submodule.subset_span (Set.mem_range_self v)⟩\n\ndef foulkesFormula (a b : ℕ) (V : Type u) [AddCommGroup V] [Module ℂ V]\n    (v : Fin b → Fin a → V) : SymPow a (SymPow b V) := by\n  classical\n  exact\n    ((a.factorial : ℂ) ^ b)⁻¹ •\n      ∑ σ : Fin b → Equiv.Perm (Fin a),\n        symMonomial a (SymPow b V)\n          (fun i => symMonomial b V (fun j => v j ((σ j) i)))\n\ndef IsFoulkesMap (a b : ℕ) (V : Type u) [AddCommGroup V] [Module ℂ V]\n    (μ : SymPow b (SymPow a V) →ₗ[ℂ] SymPow a (SymPow b V)) : Prop :=\n  ∀ v : Fin b → Fin a → V,\n    μ (symMonomial b (SymPow a V) (fun j => symMonomial a V (v j))) =\n      foulkesFormula a b V v\n\ntheorem canonical_foulkes_howe_surjective :\n    ∀ (V : Type u) [AddCommGroup V] [Module ℂ V] [FiniteDimensional ℂ V] (a b : ℕ), 2 ≤ a → a * (a - 1) ≤ b → ∃ μ : SymPow b (SymPow a V) →ₗ[ℂ] SymPow a (SymPow b V), IsFoulkesMap a b V μ ∧ Function.Surjective μ ∧ ∀ ν : SymPow b (SymPow a V) →ₗ[ℂ] SymPow a (SymPow b V), IsFoulkesMap a b V ν → ν = μ := by\n  sorry\n\nend Problem346\n\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/GotsmanLinial.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/GotsmanLinial.json", "bytes": 344, "sha256": "984badd50d1b08ce2b0133740550f4207b64bc3ebf2a4c4cf249402c70dc8b59", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.GotsmanLinial\",\n  \"solution_module\": \"OAI.Combinatorics.GotsmanLinial.Main\",\n  \"theorem_names\": [\n    \"OAI.LeanBlast.GotsmanLinial.gotsmanLinialStatement\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/GotsmanLinial.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/GotsmanLinial.lean", "bytes": 1672, "sha256": "5c63b62186354758cea448deb11dfd42a0e729a2260c6ef5b7df1c0be3b5dcfc", "content": "import Mathlib.Algebra.MvPolynomial.Degrees\nimport Mathlib.Algebra.BigOperators.Field\nimport Mathlib.Data.Fintype.BigOperators\nimport Mathlib.Analysis.Real.Sqrt\nimport Mathlib.Tactic.NormNum\n\nnamespace OAI\n\nopen scoped BigOperators\n\nnamespace LeanBlast.GotsmanLinial\n\nabbrev Cube (n : ℕ) := Fin n → Bool\n\ndef cubeCoord {n : ℕ} (x : Cube n) (i : Fin n) : ℝ :=\n  if x i then 1 else -1\n\ndef flip {n : ℕ} (i : Fin n) (x : Cube n) : Cube n :=\n  Function.update x i (!(x i))\n\nnoncomputable def thresholdSign (t : ℝ) : ℝ :=\n  if 0 ≤ t then 1 else -1\n\nnoncomputable def sensitiveVertices {n : ℕ} (f : Cube n → ℝ) (i : Fin n) :\n    Finset (Cube n) := by\n  classical\n  exact Finset.univ.filter fun x => f x ≠ f (flip i x)\n\nnoncomputable def averageSensitivity {n : ℕ} (f : Cube n → ℝ) : ℝ :=\n  ∑ i : Fin n, ((sensitiveVertices f i).card : ℝ) / (2 : ℝ) ^ n\n\ndef IsMultilinear {n : ℕ} (p : MvPolynomial (Fin n) ℝ) : Prop :=\n  ∀ m ∈ p.support, ∀ i : Fin n, m i ≤ 1\n\nnoncomputable def polynomialValue {n : ℕ} (p : MvPolynomial (Fin n) ℝ)\n    (x : Cube n) : ℝ :=\n  MvPolynomial.eval (cubeCoord x) p\n\nnoncomputable def polynomialThreshold {n : ℕ} (p : MvPolynomial (Fin n) ℝ) :\n    Cube n → ℝ :=\n  fun x => thresholdSign (polynomialValue p x)\n\ndef GotsmanLinialStatement : Prop :=\n  ∀ (n d : ℕ), 1 ≤ n → 1 ≤ d → d ≤ n →\n    ∀ p : MvPolynomial (Fin n) ℝ,\n      IsMultilinear p → p.totalDegree ≤ d →\n        averageSensitivity (polynomialThreshold p) ≤ 8 * (d : ℝ) * Real.sqrt (n : ℝ)\n\ntheorem gotsmanLinialStatement : GotsmanLinialStatement := by\n  sorry\n\nend LeanBlast.GotsmanLinial\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/HotSpots.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/HotSpots.json", "bytes": 310, "sha256": "6227a74dcc4ea3d1afa0e92166110441f3b58424b44c4afc68aae157a600758c", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.HotSpots\",\n  \"solution_module\": \"OAI.Analysis.HotSpots.Main\",\n  \"theorem_names\": [\n    \"OAI.StrictHotSpots.main_theorem\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/HotSpots.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/HotSpots.lean", "bytes": 2094, "sha256": "ef8a14d3717c0acf63c01656f93cc51a7e669aa58aa0a3ad3fd54465e49b1bde", "content": "import Mathlib\n\nnamespace OAI\n\nnoncomputable section\nopen Set MeasureTheory\nopen scoped ContDiff\n\nnamespace StrictHotSpots\n\nabbrev Plane := EuclideanSpace ℝ (Fin 2)\n\ndef SmoothBoundary (Ω : Set Plane) : Prop :=\n  ∀ p ∈ frontier Ω, ∃ (U : Set Plane) (ρ : Plane → ℝ),\n    IsOpen U ∧ p ∈ U ∧ ContDiffOn ℝ ∞ ρ U ∧\n    fderiv ℝ ρ p ≠ 0 ∧ ρ p = 0 ∧\n    Ω ∩ U = {x | ρ x < 0} ∩ U\n\ndef AdmissibleDomain (Ω : Set Plane) : Prop :=\n  Ω.Nonempty ∧ IsOpen Ω ∧ Bornology.IsBounded Ω ∧\n    IsSimplyConnected Ω ∧ SmoothBoundary Ω\n\ndef HasH1Gradient (Ω : Set Plane) (v : Plane → ℝ) (g : Plane → Plane) : Prop :=\n  MemLp v 2 (volume.restrict Ω) ∧ MemLp g 2 (volume.restrict Ω) ∧\n  ∀ φ : Plane → ℝ, ContDiff ℝ ∞ φ → HasCompactSupport φ → tsupport φ ⊆ Ω →\n    ∀ e : Plane,\n      (∫ x in Ω, v x * (fderiv ℝ φ x) e) =\n        -(∫ x in Ω, (inner ℝ (g x) e) * φ x)\n\ndef rayleighValues (Ω : Set Plane) : Set ℝ :=\n  {r | ∃ (v : Plane → ℝ) (g : Plane → Plane),\n    HasH1Gradient Ω v g ∧ (∫ x in Ω, v x) = 0 ∧\n    0 < (∫ x in Ω, (v x) ^ 2) ∧\n    r = (∫ x in Ω, ‖g x‖ ^ 2) / (∫ x in Ω, (v x) ^ 2)}\n\ndef firstPositiveNeumannValue (Ω : Set Plane) : ℝ := sInf (rayleighValues Ω)\n\ndef InFirstNeumannEigenspace (Ω : Set Plane) (u : Plane → ℝ) : Prop :=\n  ContDiffOn ℝ ∞ u (closure Ω) ∧ HasH1Gradient Ω u (gradient u) ∧\n    (∫ x in Ω, u x) = 0 ∧\n    ∀ (v : Plane → ℝ) (g : Plane → Plane), HasH1Gradient Ω v g →\n      (∫ x in Ω, inner ℝ (gradient u x) (g x)) =\n        firstPositiveNeumannValue Ω * (∫ x in Ω, u x * v x)\n\ndef MainConclusion (Ω : Set Plane) (u : Plane → ℝ) : Prop :=\n  (∀ x ∈ Ω, gradient u x ≠ 0) ∧\n    ∀ x ∈ Ω, sInf (u '' frontier Ω) < u x ∧ u x < sSup (u '' frontier Ω)\n\ntheorem main_theorem (Ω : Set Plane) (hΩ : AdmissibleDomain Ω)\n    (u : Plane → ℝ) (hu : InFirstNeumannEigenspace Ω u)\n    (hne : ∃ x ∈ Ω, u x ≠ 0) : MainConclusion Ω u := by\n  sorry\n\nend StrictHotSpots\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/MatchingEntropy.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/MatchingEntropy.json", "bytes": 330, "sha256": "f080baefbec0105edaf37ecc5e2e937b78cb13a9d745dc997bda193c4696e2ee", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.MatchingEntropy\",\n  \"solution_module\": \"OAI.Combinatorics.PerfectMatching.Main\",\n  \"theorem_names\": [\n    \"OAI.MatchingEntropy.entropy_main\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/MatchingEntropy.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/MatchingEntropy.lean", "bytes": 2107, "sha256": "5e8aacabca23f0b2852ffd7e58c1a87a8e94eec7abb8d5f354d787472d6749db", "content": "import Mathlib\n\nnamespace OAI\n\nnoncomputable section\n\nopen scoped BigOperators\n\nnamespace MatchingEntropy\n\n/-- Nonnegative real weights with total mass one. -/\ndef probabilitySimplex (Index : Type*) [Fintype Index] : Set (Index → ℝ) :=\n  {weights | (∀ index, 0 ≤ weights index) ∧ ∑ index, weights index = 1}\n\nstructure LooplessGraph (V E : Type*) where\n  left : E → V\n  right : E → V\n  loopless : ∀ e, left e ≠ right e\n\nvariable {V E : Type*} [Fintype V] [Fintype E] [DecidableEq V] [DecidableEq E]\n\nnamespace LooplessGraph\n\ndef Incident (G : LooplessGraph V E) (v : V) (e : E) : Prop :=\n  G.left e = v ∨ G.right e = v\n\ndef IsPerfectMatching (G : LooplessGraph V E) (M : Finset E) : Prop :=\n  ∀ v, ∃! e, e ∈ M ∧ G.Incident v e\n\nabbrev Matching (G : LooplessGraph V E) := {M : Finset E // G.IsPerfectMatching M}\n\ninstance matchingFintype (G : LooplessGraph V E) : Fintype G.Matching :=\n  Fintype.ofFinite _\n\ndef indicator (G : LooplessGraph V E) (M : G.Matching) (e : E) : ℝ :=\n  if e ∈ M.val then 1 else 0\n\ndef polytope (G : LooplessGraph V E) : Set (E → ℝ) :=\n  convexHull ℝ (Set.range G.indicator)\n\ndef mean (G : LooplessGraph V E) (p : G.Matching → ℝ) (e : E) : ℝ :=\n  ∑ M, p M * G.indicator M e\n\ndef feasibleLaws (G : LooplessGraph V E) (y : E → ℝ) : Set (G.Matching → ℝ) :=\n  {p | p ∈ probabilitySimplex G.Matching ∧ G.mean p = y}\n\nend LooplessGraph\n\ndef entropy {ι : Type*} [Fintype ι] (p : ι → ℝ) : ℝ :=\n  ∑ i, Real.negMulLog (p i)\n\ndef marginalEntropy (y : E → ℝ) : ℝ := entropy y\n\ndef maxMatchingEntropy (G : LooplessGraph V E) (y : E → ℝ) : ℝ :=\n  sSup (entropy '' G.feasibleLaws y)\n\n/-- Main entropy comparison, for every marginal in the actual matching polytope. -/\ntheorem entropy_main (G : LooplessGraph V E) (m : ℕ)\n    (hm : 0 < m) (hV : Fintype.card V = 2 * m)\n    (hPM : Nonempty G.Matching) (y : E → ℝ) (hy : y ∈ G.polytope) :\n    marginalEntropy y - maxMatchingEntropy G y ≤\n      8 * (m : ℝ) * (1 - Real.exp (-marginalEntropy y / (m : ℝ))) := by\n  sorry\n\nend MatchingEntropy\n\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/MatchingEntropyBounds.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/MatchingEntropyBounds.json", "bytes": 355, "sha256": "73dda5ee4bf497674dc8e760ae812cb134327602d27e0e6c23010dbd22ba2e07", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.MatchingEntropyBounds\",\n  \"solution_module\": \"OAI.Combinatorics.MatchingEntropy.Main\",\n  \"theorem_names\": [\n    \"OAI.MatchingEntropyBounds.Refined.pointwise_entropy\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/MatchingEntropyBounds.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/MatchingEntropyBounds.lean", "bytes": 2457, "sha256": "0327e6c5cadc415707c982adf0d966a29631636fa40719fefbaea069efd51b88", "content": "import Mathlib\n\nnamespace OAI\n\nnoncomputable section\n\nopen scoped BigOperators Topology\nopen Filter\n\nuniverse uV uE uIndex uι\n\nnamespace MatchingEntropyBounds\n\nstructure LooplessGraph (V : Type uV) (E : Type uE) where\n  left : E → V\n  right : E → V\n  loopless : ∀ e, left e ≠ right e\n\nvariable {V : Type uV} {E : Type uE}\n  [Fintype V] [Fintype E] [DecidableEq V] [DecidableEq E]\n\nnamespace LooplessGraph\n\ndef Incident (G : LooplessGraph V E) (v : V) (e : E) : Prop :=\n  G.left e = v ∨ G.right e = v\n\ninstance incidentDecidable (G : LooplessGraph V E) (v : V) (e : E) :\n    Decidable (G.Incident v e) := inferInstanceAs (Decidable (G.left e = v ∨ G.right e = v))\n\ndef IsPerfectMatching (G : LooplessGraph V E) (M : Finset E) : Prop :=\n  ∀ v, ∃! e, e ∈ M ∧ G.Incident v e\n\nabbrev Matching (G : LooplessGraph V E) := {M : Finset E // G.IsPerfectMatching M}\n\ninstance matchingFintype (G : LooplessGraph V E) : Fintype G.Matching :=\n  Fintype.ofFinite _\n\ndef indicator (G : LooplessGraph V E) (M : G.Matching) (e : E) : ℝ :=\n  if e ∈ M.val then 1 else 0\n\ndef polytope (G : LooplessGraph V E) : Set (E → ℝ) :=\n  convexHull ℝ (Set.range G.indicator)\n\ndef mean (G : LooplessGraph V E) (p : G.Matching → ℝ) (e : E) : ℝ :=\n  ∑ M, p M * G.indicator M e\n\nend LooplessGraph\n\ndef probabilitySimplex (Index : Type uIndex) [Fintype Index] : Set (Index → ℝ) :=\n  {weights | (∀ index, 0 ≤ weights index) ∧ ∑ index, weights index = 1}\n\ndef LooplessGraph.feasibleLaws (G : LooplessGraph V E) (y : E → ℝ) : Set (G.Matching → ℝ) :=\n  {p | p ∈ probabilitySimplex G.Matching ∧ G.mean p = y}\n\ndef entropy {ι : Type uι} [Fintype ι] (p : ι → ℝ) : ℝ :=\n  ∑ i, Real.negMulLog (p i)\n\ndef maxMatchingEntropy (G : LooplessGraph V E) (y : E → ℝ) : ℝ :=\n  sSup (entropy '' G.feasibleLaws y)\n\nnamespace Refined\n\ndef complementEntropy (x : E → ℝ) : ℝ := entropy (fun e => 1 - x e)\n\nend Refined\n\nopen Module Matrix\n\nvariable {V : Type uV} {E : Type uE} [Fintype V] [Fintype E] [DecidableEq V] [DecidableEq E]\n\nopen LooplessGraph\n\ntheorem Refined.pointwise_entropy (G : LooplessGraph V E) (m : ℕ)\n    (hm : 0 < m) (hV : Fintype.card V = 2 * m) (hPM : Nonempty G.Matching)\n    (x : E → ℝ) (hx : x ∈ G.polytope) :\n    entropy x - (2 - 2 / (m : ℝ)) * complementEntropy x ≤ maxMatchingEntropy G x ∧\n      maxMatchingEntropy G x ≤ entropy x := by\n  sorry\n\nend MatchingEntropyBounds\n\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/MatchingFPRAS.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/MatchingFPRAS.json", "bytes": 320, "sha256": "7f610f6ce6c7a87d1c1f190bdd5676b7f23bec79381c6acf787625a7ebe91fa9", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.MatchingFPRAS\",\n  \"solution_module\": \"OAI.Combinatorics.MatchingCount.Main\",\n  \"theorem_names\": [\n    \"OAI.MatchingFPRAS.thm_main\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/MatchingFPRAS.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/MatchingFPRAS.lean", "bytes": 4429, "sha256": "14624569e906d3702564c39dab9c63bc77ef1d0b621bd1b51a1239fcafbca206", "content": "import Mathlib\n\nnamespace OAI\n\nnamespace MatchingFPRAS\n\n/-- Each unordered edge is represented once, by increasing endpoints. -/\nstructure GraphInput where\n  n : ℕ\n  edges : Finset (Fin n × Fin n)\n  increasing : ∀ e ∈ edges, e.1 < e.2\n\n/-- Exactly one selected edge is incident to every vertex. -/\ndef Perfect (G : GraphInput) (M : Finset (Fin G.n × Fin G.n)) : Prop :=\n  M ⊆ G.edges ∧ ∀ v : Fin G.n, ∃! e, e ∈ M ∧ (e.1 = v ∨ e.2 = v)\n\nnoncomputable def perfectMatchings (G : GraphInput) : Finset (Finset (Fin G.n × Fin G.n)) :=\n  by\n    classical\n    exact G.edges.powerset.filter (Perfect G)\n\n/-- The exact unweighted count, including the empty graph. -/\nnoncomputable def Z (G : GraphInput) : ℕ := (perfectMatchings G).card\n\nabbrev Symbol := Fin 8\n\n/-- Binary numeral followed by a delimiter. Tape blank is 0; bits use 1 and 2. -/\ndef encodeNat (n : ℕ) : List Symbol :=\n  (Nat.bits n).map (fun b => if b then 2 else 1) ++ [3]\n\ndef encodeInt (z : ℤ) : List Symbol :=\n  (if z < 0 then [5] else [4]) ++ encodeNat z.natAbs\n\n/-- A rational encoded by its reduced numerator and positive denominator. -/\ndef encodeRat (q : ℚ) : List Symbol := encodeInt q.num ++ encodeNat q.den\n\n/-- Explicit sparse graph encoding, then the two rational parameters. -/\ndef encodeInput (G : GraphInput) (ε δ : ℚ) : List Symbol :=\n  encodeNat G.n ++ encodeNat G.edges.card ++\n    (((G.edges.map toLex.toEmbedding).sort (· ≤ ·)).flatMap fun e =>\n      encodeNat (ofLex e).1.val ++ encodeNat (ofLex e).2.val) ++\n    encodeRat ε ++ encodeRat δ\n\n/-- A uniform randomized Post-Turing machine: a single finite transition table. -/\nstructure RandomMachine where\n  states : ℕ\n  transition : Fin (states + 1) → Symbol → Bool →\n    Option (Fin (states + 1) × Turing.TM0.Stmt Symbol)\n\nstructure Configuration (A : RandomMachine) where\n  state : Option (Fin (A.states + 1))\n  tape : Turing.Tape Symbol\n\n/-- A halted configuration is absorbing. Each nonhalting tick is exactly one\nwrite or one move, or the detection of halt. -/\ndef tick (A : RandomMachine) (c : Configuration A) (bit : Bool) : Configuration A :=\n  match c.state with\n  | none => c\n  | some s => match A.transition s c.tape.head bit with\n    | none => ⟨none, c.tape⟩\n    | some (s', action) =>\n      ⟨some s', match action with\n        | .move d => c.tape.move d\n        | .write a => c.tape.write a⟩\n\ndef initial (A : RandomMachine) (input : List Symbol) : Configuration A :=\n  ⟨some 0, Turing.Tape.mk₁ input⟩\n\n/-- Bounded execution on a finite prefix of the random tape. -/\ndef run (A : RandomMachine) (input : List Symbol) {t : ℕ}\n    (bits : Fin t → Bool) : Configuration A :=\n  (List.ofFn bits).foldl (tick A) (initial A input)\n\ndef Outputs (A : RandomMachine) (input : List Symbol) {t : ℕ}\n    (bits : Fin t → Bool) (q : ℚ) : Prop :=\n  (run A input bits).state = none ∧\n    (run A input bits).tape.right₀ = Turing.ListBlank.mk (encodeRat q)\n\n/-- A fixed-degree polynomial in input bits, ε⁻¹, and log δ⁻¹.\nNatural ceilings and binary ceiling-log change this bound only by constants. -/\ndef timeBound (C d : ℕ) (G : GraphInput) (ε δ : ℚ) : ℕ :=\n  C * ((encodeInput G ε δ).length + ⌈ε⁻¹⌉₊ + Nat.clog 2 ⌈δ⁻¹⌉₊ + 1) ^ d\n\nnoncomputable def goodTapes (A : RandomMachine) (G : GraphInput) (ε δ : ℚ)\n    (t : ℕ) : Finset (Fin t → Bool) := by\n  classical\n  exact Finset.univ.filter fun bits => ∃ q : ℚ,\n    Outputs A (encodeInput G ε δ) bits q ∧\n    (1 - ε) * (Z G : ℚ) ≤ q ∧ q ≤ (1 + ε) * (Z G : ℚ)\n\n/-- A uniform approximation scheme using independent fair bits. All tapes halt\nwithin the fixed polynomial bound and return a nonnegative rational.\nA zero count always produces encoded zero. -/\ndef MainStatement : Prop :=\n  ∃ (A : RandomMachine) (C d : ℕ), 0 < C ∧\n    ∀ (G : GraphInput) (ε δ : ℚ), 0 < ε → ε < 1 → 0 < δ → δ < 1 / 2 →\n      let t := timeBound C d G ε δ\n      (∀ bits : Fin t → Bool, ∃ q : ℚ, 0 ≤ q ∧ Outputs A (encodeInput G ε δ) bits q) ∧\n      (Z G = 0 → ∀ bits : Fin t → Bool, Outputs A (encodeInput G ε δ) bits 0) ∧\n      ((goodTapes A G ε δ t).card : ℚ) / (2 : ℚ) ^ t ≥ 1 - δ\n\n\n/-- A fully polynomial randomized approximation scheme for perfect matchings\nin every finite simple undirected graph. -/\ntheorem thm_main : MainStatement := by\n  sorry\n\nend MatchingFPRAS\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/RelativeGeneration.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/RelativeGeneration.json", "bytes": 334, "sha256": "10ef647ccd63309fabc6e53378cbba50b3ed6039c6e6aae50515cd7c4753bf5e", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.RelativeGeneration\",\n  \"solution_module\": \"OAI.Analysis.RelativeGeneration.Main\",\n  \"theorem_names\": [\n    \"OAI.RelativeGeneration.main_theorem\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/RelativeGeneration.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/RelativeGeneration.lean", "bytes": 5992, "sha256": "d8777c8421d1b134d75467b82277d46d4e0ae80cc6d3f8d4909a54abd4da6d70", "content": "import Mathlib\n\nnamespace OAI\n\nnoncomputable section\n\nuniverse level\n\nnamespace RelativeGeneration\n\nvariable {Hilbert : Type level} [NormedAddCommGroup Hilbert]\n  [InnerProductSpace ℂ Hilbert] [CompleteSpace Hilbert]\n\n/-- Weak operator closedness of a concrete unital star subalgebra. -/\ndef WOTClosed (algebra : StarSubalgebra ℂ (Hilbert →L[ℂ] Hilbert)) : Prop :=\n  IsClosed {operator : Hilbert →WOT[ℂ] Hilbert | operator.toCLM ∈ algebra}\n\n/-- The weakly closed unital star algebra generated by a set of operators. -/\ndef wstar (operators : Set (Hilbert →L[ℂ] Hilbert)) :\n    StarSubalgebra ℂ (Hilbert →L[ℂ] Hilbert) :=\n  sInf {algebra | WOTClosed algebra ∧ operators ⊆ algebra}\n\n/-- A nonzero, weakly closed, finite diffuse algebra with scalar center. -/\nstructure IsII1Factor (algebra : StarSubalgebra ℂ (Hilbert →L[ℂ] Hilbert)) : Prop where\n  nonzero : (0 : Hilbert →L[ℂ] Hilbert) ≠ 1\n  weaklyClosed : WOTClosed algebra\n  factor : ∀ element : algebra, (∀ other : algebra, other * element = element * other) →\n    ∃ scalar : ℂ, element = algebraMap ℂ algebra scalar\n  finite : ∀ isometry : algebra, star isometry * isometry = 1 → isometry * star isometry = 1\n  diffuse : ∀ projection : algebra, IsStarProjection projection → projection ≠ 0 →\n    ∃ subprojection : algebra, IsStarProjection subprojection ∧ subprojection ≠ 0 ∧\n      subprojection ≠ projection ∧ projection * subprojection = subprojection ∧\n      subprojection * projection = subprojection\n\n/-- A same-universe separable Banach predual, with the conjugate-linear dual convention.\nThis imposes no separability on the Hilbert representation or the operator norm topology. -/\ndef HasSeparablePredual (algebra : StarSubalgebra ℂ (Hilbert →L[ℂ] Hilbert)) : Prop :=\n  ∃ (Banach : Type level) (_ : NormedAddCommGroup Banach) (_ : NormedSpace ℂ Banach)\n    (_ : CompleteSpace Banach) (_ : TopologicalSpace.SeparableSpace Banach),\n    Nonempty (StrongDual ℂ Banach ≃ₗᵢ⋆[ℂ] algebra)\n\n/-- The ultraweak topology is generated by absolutely summable vector functionals. -/\nabbrev ultraweakTopology (Hilbert : Type level) [NormedAddCommGroup Hilbert]\n    [InnerProductSpace ℂ Hilbert] [CompleteSpace Hilbert] :\n    TopologicalSpace (Hilbert →L[ℂ] Hilbert) :=\n  ⨅ (left : ℕ → Hilbert) (right : ℕ → Hilbert)\n    (_ : Summable (fun index => ‖left index‖ * ‖right index‖)),\n    TopologicalSpace.induced\n      (fun operator : Hilbert →L[ℂ] Hilbert =>\n        ∑' index, inner ℂ (left index) (operator (right index))) inferInstance\n\n/-- The concrete algebra carries the induced ultraweak topology. -/\nabbrev algebraUltraweakTopology (algebra : StarSubalgebra ℂ (Hilbert →L[ℂ] Hilbert)) :\n    TopologicalSpace algebra :=\n  TopologicalSpace.induced (fun operator : algebra => (operator : Hilbert →L[ℂ] Hilbert))\n    (ultraweakTopology Hilbert)\n\n/-- A faithful normal normalized trace, not an additional hypothesis on the inclusion. -/\nstructure IsNormalizedTrace (algebra : StarSubalgebra ℂ (Hilbert →L[ℂ] Hilbert))\n    (trace : algebra →L[ℂ] ℂ) : Prop where\n  normalized : trace 1 = 1\n  positive : ∀ element : algebra, 0 ≤ (trace (star element * element)).re\n  star_preserving : ∀ element : algebra, trace (star element) = star (trace element)\n  tracial : ∀ left right : algebra, trace (left * right) = trace (right * left)\n  faithful : ∀ element : algebra, trace (star element * element) = 0 → element = 0\n  normal : @Continuous algebra ℂ (algebraUltraweakTopology algebra) inferInstance trace\n\n/-- The normalized trace two-distance between genuine unitaries of the original algebra. -/\ndef traceDistance (algebra : StarSubalgebra ℂ (Hilbert →L[ℂ] Hilbert))\n    (trace : algebra →L[ℂ] ℂ) (left right : unitary algebra) : ℝ :=\n  Real.sqrt (trace (star ((left : algebra) - (right : algebra)) *\n    ((left : algebra) - (right : algebra)))).re\n\n/-- The topology generated by trace two-distance balls, not the inherited operator norm topology. -/\nabbrev traceTopology (algebra : StarSubalgebra ℂ (Hilbert →L[ℂ] Hilbert))\n    (trace : algebra →L[ℂ] ℂ) : TopologicalSpace (unitary algebra) :=\n  TopologicalSpace.generateFrom {ball | ∃ center : unitary algebra, ∃ radius : ℝ,\n    0 < radius ∧ ball = {point | traceDistance algebra trace center point < radius}}\n\n/-- The entire relative-generator locus in the original unitary group. -/\ndef relativeGeneratorLocus (small large : StarSubalgebra ℂ (Hilbert →L[ℂ] Hilbert)) :\n    Set (unitary large) :=\n  {element | wstar ((small : Set (Hilbert →L[ℂ] Hilbert)) ∪\n    {((element : large) : Hilbert →L[ℂ] Hilbert)}) = large}\n\n/-- Every irreducible inclusion of concrete type II₁ factors with separable predual\nof the larger factor has a dense Gδ full relative-generator locus in the normalized\ntrace two-topology. The normalized faithful normal trace is asserted to exist;\nno representation, transport, cyclicity, or perturbation premise is imposed. -/\ndef MainTarget : Prop :=\n  ∀ (Hilbert : Type level) [NormedAddCommGroup Hilbert] [InnerProductSpace ℂ Hilbert]\n    [CompleteSpace Hilbert] (small large : StarSubalgebra ℂ (Hilbert →L[ℂ] Hilbert)),\n    IsII1Factor small → IsII1Factor large → ∀ inclusion : small ≤ large,\n      (∀ element : large,\n        (∀ member : small, StarSubalgebra.inclusion inclusion member * element =\n          element * StarSubalgebra.inclusion inclusion member) →\n        ∃ scalar : ℂ, element = algebraMap ℂ large scalar) →\n      HasSeparablePredual large →\n      ∃ trace : large →L[ℂ] ℂ, IsNormalizedTrace large trace ∧\n        @IsGδ (unitary large) (traceTopology large trace) (relativeGeneratorLocus small large) ∧\n        @Dense (unitary large) (traceTopology large trace) (relativeGeneratorLocus small large)\n\ntheorem main_theorem : MainTarget.{level} := by\n  sorry\n\nend RelativeGeneration\n\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/SLELowerPositivity.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/SLELowerPositivity.json", "bytes": 338, "sha256": "1225c1b45522cd87c771099ba63d69deb79ef03d91d361c1dfc0878b2e64be3f", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.SLELowerPositivity\",\n  \"solution_module\": \"OAI.Probability.SLE.LowerPositivity\",\n  \"theorem_names\": [\n    \"OAI.SLEExactGauge.sourceLowerMain_proved\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/SLELowerPositivity.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/SLELowerPositivity.lean", "bytes": 3398, "sha256": "f550800b9a60db513d4c9a2a85f710d0036a6a9b8f717ee0e64c38edd35f606a", "content": "import Mathlib\n\nnamespace OAI\n\n/-! Positivity of the explicit Hausdorff gauge on all positive-time SLE segments. -/\n\nnoncomputable section\nopen Set Filter MeasureTheory\nopen scoped Topology ENNReal NNReal\nnamespace SLEExactGauge\nuniverse v_lower\n\nnoncomputable def dimension (κ : ℝ) : ℝ := 1 + κ / 8\n\nnoncomputable def codimension (κ : ℝ) : ℝ := ((2 : ℕ) : ℝ) - dimension κ\n\ndef upperHalfPlane : Set ℂ := {z | 0 < z.im}\n\ndef survivingDomain (γ : ℝ≥0 → ℂ) (t : ℝ≥0) : Set ℂ :=\n  {z | z ∈ upperHalfPlane \\ (γ '' Icc 0 t) ∧\n    ¬ Bornology.IsBounded (connectedComponentIn (upperHalfPlane \\ (γ '' Icc 0 t)) z)}\n\ndef IsCapacityTwoWitness (U : ℝ≥0 → ℝ) (γ : ℝ≥0 → ℂ)\n    (G F : ℝ≥0 → ℂ → ℂ) : Prop :=\n  Continuous γ ∧ γ 0 = 0 ∧ (∀ t, 0 ≤ (γ t).im) ∧\n    (∀ t, DifferentiableOn ℂ (G t) (survivingDomain γ t)) ∧\n    (∀ t, DifferentiableOn ℂ (F t) upperHalfPlane) ∧\n    (∀ t, MapsTo (G t) (survivingDomain γ t) upperHalfPlane) ∧\n    (∀ t, MapsTo (F t) upperHalfPlane (survivingDomain γ t)) ∧\n    (∀ t z, z ∈ survivingDomain γ t → F t (G t z) = z) ∧\n    (∀ t z, z ∈ upperHalfPlane → G t (F t z) = z) ∧\n    (∀ z ∈ upperHalfPlane, G 0 z = z) ∧\n    (∀ t z, z ∈ survivingDomain γ t →\n      HasDerivWithinAt (fun u : ℝ => G (Real.toNNReal u) z)\n        (2 / (G t z - (U t : ℂ))) (Ici 0) (t : ℝ)) ∧\n    (∀ t, Tendsto (fun z : ℂ => z * (G t z - z))\n      (cocompact ℂ ⊓ 𝓟 (survivingDomain γ t)) (𝓝 (2 * (t : ℂ)))) ∧\n    (∀ t, Tendsto (fun y : ℝ => F t ((U t : ℂ) + (y : ℂ) * Complex.I))\n      (𝓝[>] 0) (𝓝 (γ t)))\n\ndef IsCapacityTwoTrace (U : ℝ≥0 → ℝ) (γ : ℝ≥0 → ℂ) : Prop :=\n  ∃ G F : ℝ≥0 → ℂ → ℂ, IsCapacityTwoWitness U γ G F\n\ndef IsOrdinaryChordalSLE {Ω : Type*} [MeasurableSpace Ω]\n    (κ : ℝ) (γ : Ω → ℝ≥0 → ℂ) (P : Measure Ω) : Prop :=\n  (∀ t, Measurable (fun ω => γ ω t)) ∧\n  ∃ B : ℝ≥0 → Ω → ℝ, ProbabilityTheory.IsBrownianReal B P ∧\n    ∀ᵐ ω ∂P, IsCapacityTwoTrace (fun t => Real.sqrt κ * B t ω) (γ ω)\n\nstructure IsGauge (h : ℝ → ℝ) : Prop where\n  continuousOn : ContinuousOn h (Ici 0)\n  monotoneOn : MonotoneOn h (Ici 0)\n  zero : h 0 = 0\n  positive : ∀ r, 0 < r → 0 < h r\n\nnoncomputable def hFormula (κ r : ℝ) : ℝ :=\n  r ^ dimension κ * (Real.log (Real.log (1 / r))) ^ (codimension κ / ((2 : ℕ) : ℝ))\n\ndef HasSmallRadiusFormula (h f : ℝ → ℝ) : Prop :=\n  h =ᶠ[𝓝[>] 0] f\n\nnoncomputable def extendedGauge (h : ℝ → ℝ) (r : ℝ≥0∞) : ℝ≥0∞ :=\n  ENNReal.ofReal (h r.toReal)\n\nnoncomputable def hausdorffGauge (h : ℝ → ℝ) : Measure ℂ :=\n  Measure.mkMetric (extendedGauge h)\n\ndef segment (γ : ℝ≥0 → ℂ) (s t : ℝ≥0) : Set ℂ := γ '' Icc s t\n\ndef SourceLowerMainTarget : Prop :=\n  ∀ (Ω : Type v_lower) (_ : MeasurableSpace Ω) (P : Measure Ω), IsProbabilityMeasure P →\n  ∀ κ : ℝ, 0 < κ → κ < ((8 : ℕ) : ℝ) →\n  ∀ (γ : Ω → ℝ≥0 → ℂ), IsOrdinaryChordalSLE κ γ P →\n  ∀ h : ℝ → ℝ, IsGauge h → HasSmallRadiusFormula h (hFormula κ) →\n    ∀ᵐ ω ∂P, ∀ s t : ℝ≥0, 0 < s → s < t →\n      0 < hausdorffGauge h (segment (γ ω) s t)\n\ntheorem sourceLowerMain_proved : SourceLowerMainTarget := by\n  sorry\n\nend SLEExactGauge\n\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/SelfSimilar.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/SelfSimilar.json", "bytes": 337, "sha256": "5352e95649d1dd075b7a7951db9bf72c85dbbe386c258f655ed1c01365490d61", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.SelfSimilar\",\n  \"solution_module\": \"OAI.MeasureTheory.SelfSimilar.Main\",\n  \"theorem_names\": [\n    \"OAI.EntropyRateDimension.entropy_rate_dimension\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/SelfSimilar.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/SelfSimilar.lean", "bytes": 2253, "sha256": "95798462a1dfc33be8d3e67efc137b6193d9800776beb2fe6b3a27912fecb18d", "content": "import Mathlib\n\nnamespace OAI\n\nuniverse uι\n\nopen scoped BigOperators ENNReal\nopen MeasureTheory\n\nnamespace EntropyRateDimension\n\nstructure System (ι : Type uι) [Fintype ι] where\n  ratio : ι → ℝ\n  offset : ι → ℝ\n  weight : ι → ℝ\n  ratio_pos : ∀ i, 0 < |ratio i|\n  ratio_lt_one : ∀ i, |ratio i| < 1\n  weight_pos : ∀ i, 0 < weight i\n  weight_sum : ∑ i, weight i = 1\n\nnamespace System\n\nvariable {ι : Type uι} [Fintype ι]\n\ndef affine (S : System ι) (i : ι) (x : ℝ) : ℝ := S.ratio i * x + S.offset i\n\ndef wordAffine (S : System ι) : List ι → ℝ × ℝ\n  | [] => (1, 0)\n  | i :: w => (S.ratio i * (S.wordAffine w).1,\n      S.ratio i * (S.wordAffine w).2 + S.offset i)\n\ndef completeMap (S : System ι) {n : ℕ} (w : Fin n → ι) : ℝ × ℝ :=\n  S.wordAffine (List.ofFn w)\n\nnoncomputable def wordWeight (S : System ι) {n : ℕ} (w : Fin n → ι) : ℝ :=\n  ∏ j, S.weight (w j)\n\nnoncomputable def mapSupport (S : System ι) (n : ℕ) : Finset (ℝ × ℝ) := by\n  classical\n  exact Finset.univ.image (S.completeMap (n := n))\n\nnoncomputable def mapMass (S : System ι) (n : ℕ) (g : ℝ × ℝ) : ℝ := by\n  classical\n  exact ∑ w ∈ (Finset.univ : Finset (Fin n → ι)).filter (S.completeMap · = g),\n    S.wordWeight w\n\nnoncomputable def walkEntropy (S : System ι) (n : ℕ) : ℝ :=\n  (∑ g ∈ S.mapSupport n, Real.negMulLog (S.mapMass n g)) / Real.log 2\n\nnoncomputable def entropyRate (S : System ι) : ℝ :=\n  sInf (Set.range fun n : ℕ => S.walkEntropy (n + 1) / (n + 1 : ℕ))\n\nnoncomputable def lyapunov (S : System ι) : ℝ :=\n  -(∑ i, S.weight i * (Real.log |S.ratio i| / Real.log 2))\n\ndef SelfSimilar (S : System ι) (μ : Measure ℝ) : Prop :=\n  μ = ∑ i, ENNReal.ofReal (S.weight i) • μ.map (S.affine i)\n\nend System\n\nnoncomputable def lowerHausdorffDimension (μ : Measure ℝ) : ℝ≥0∞ :=\n  ⨅ (E : Set ℝ) (_ : MeasurableSet E) (_ : 0 < μ E), dimH E\nopen MeasureTheory\n\ntheorem entropy_rate_dimension {ι : Type uι} [Fintype ι] [Nonempty ι]\n    (S : System ι) (μ : Measure ℝ) [IsProbabilityMeasure μ]\n    (hμ : S.SelfSimilar μ) :\n    lowerHausdorffDimension μ = ENNReal.ofReal (min 1 (S.entropyRate / S.lyapunov)) := by\n  sorry\n\nend EntropyRateDimension\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/SelfSimilarCorollaries.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/SelfSimilarCorollaries.json", "bytes": 416, "sha256": "e1e9808a01f811cf030cf7d45eddd8a8cf1464a3eaf34f155d03883444f5a9f0", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.SelfSimilarCorollaries\",\n  \"solution_module\": \"OAI.MeasureTheory.SelfSimilar.DimensionCorollaries\",\n  \"theorem_names\": [\n    \"OAI.EntropyRateDimension.Extensions.homogeneous_dimension_direct\",\n    \"OAI.CorSetReference.cor_set\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Classical.choice\",\n    \"Quot.sound\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/SelfSimilarCorollaries.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/SelfSimilarCorollaries.lean", "bytes": 2674, "sha256": "400d5597f3beb90ea9e4e11aacaf9094737bb7c579245e8ceb795f268c8e481b", "content": "import Mathlib\n\nnamespace OAI\n\nuniverse uι\n\nsection\n\nopen scoped BigOperators ENNReal\nopen MeasureTheory\n\nnamespace EntropyRateDimension\n\nstructure System (ι : Type uι) [Fintype ι] where\n  ratio : ι → ℝ\n  offset : ι → ℝ\n  weight : ι → ℝ\n  ratio_pos : ∀ i, 0 < |ratio i|\n  ratio_lt_one : ∀ i, |ratio i| < 1\n  weight_pos : ∀ i, 0 < weight i\n  weight_sum : ∑ i, weight i = 1\n\nnamespace System\nvariable {ι : Type uι} [Fintype ι]\n\ndef affine (S : System ι) (i : ι) (x : ℝ) : ℝ := S.ratio i * x + S.offset i\n\ndef wordAffine (S : System ι) : List ι → ℝ × ℝ\n  | [] => (1, 0)\n  | i :: w => (S.ratio i * (S.wordAffine w).1,\n      S.ratio i * (S.wordAffine w).2 + S.offset i)\n\ndef SelfSimilar (S : System ι) (μ : Measure ℝ) : Prop :=\n  μ = ∑ i, ENNReal.ofReal (S.weight i) • μ.map (S.affine i)\n\nend System\n\nnoncomputable def lowerHausdorffDimension (μ : Measure ℝ) : ℝ≥0∞ :=\n  ⨅ (E : Set ℝ) (_ : MeasurableSet E) (_ : 0 < μ E), dimH E\n\nnamespace Extensions\nvariable {ι : Type uι} [Fintype ι]\n\ndef NoExactOverlaps (S : System ι) : Prop := Function.Injective S.wordAffine\n\nnoncomputable def symbolEntropy (S : System ι) : ℝ :=\n  ∑ i, Real.negMulLog (S.weight i)\n\ntheorem homogeneous_dimension_direct {ι : Type uι} [Fintype ι] [Nonempty ι] (S : System ι)\n    {lam : ℝ} (hlam : 0 < lam) (hr : ∀ i, S.ratio i = lam)\n    (μ : Measure ℝ) [IsProbabilityMeasure μ] (hμ : S.SelfSimilar μ)\n    (h : NoExactOverlaps S) :\n    lowerHausdorffDimension μ =\n      ENNReal.ofReal (min 1 (symbolEntropy S / Real.log (1/lam))) := by\n  sorry\n\nend Extensions\nend EntropyRateDimension\nend\n\nopen scoped BigOperators ENNReal\n\nnamespace CorSetReference\nvariable {ι : Type uι} [Fintype ι] [Nonempty ι]\n\nnoncomputable def codingPoint (r t : ι → ℝ) (ω : ℕ → ι) : ℝ :=\n  ∑' n, (∏ j ∈ Finset.range n, r (ω j)) * t (ω n)\n\n\nnoncomputable def attractor (r t : ι → ℝ) : Set ℝ :=\n  Set.range (codingPoint r t)\n\n\ndef word (r t : ι → ℝ) : List ι → ℝ × ℝ :=\n  List.foldr (fun symbol coefficients =>\n    (r symbol * coefficients.1, r symbol * coefficients.2 + t symbol)) (1, 0)\n\ntheorem cor_set {ι : Type uι} [Fintype ι] [Nonempty ι]\n    (r t : ι → ℝ) (hr0 : ∀ i, 0 < |r i|) (hr1 : ∀ i, |r i| < 1)\n    (hno : ∀ u v : List ι, u ≠ [] → v ≠ [] →\n      word r t u = word r t v → u = v) :\n    IsCompact (attractor r t) ∧ (attractor r t).Nonempty ∧\n    (∃! s : ℝ, 0 ≤ s ∧ ∑ i, |r i| ^ s = 1) ∧\n    (∀ s : ℝ, 0 ≤ s → (∑ i, |r i| ^ s = 1) →\n      dimH (attractor r t) = ENNReal.ofReal (min 1 s)) := by\n  sorry\n\nend CorSetReference\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/SingletonLoopMatching.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/SingletonLoopMatching.json", "bytes": 334, "sha256": "99893f2e245ea894af1af8adb5a883f5e647cb8a44f2c71d0eb3990d2ad9c3c3", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.SingletonLoopMatching\",\n  \"solution_module\": \"OAI.Computability.LoopMatching.Endpoint\",\n  \"theorem_names\": [\n    \"OAI.LoopMatching.fullEndpoint\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/SingletonLoopMatching.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/SingletonLoopMatching.lean", "bytes": 4057, "sha256": "19d38b8703538dea486feb2d3281dbb2317e6372e59d733062199f500f60cbc6", "content": "import Mathlib\n\nnamespace OAI\n\nuniverse u_1 u_2\n\nnamespace MatchingEntropy\nstructure LooplessGraph (V : Type u_1) (E : Type u_2) where\n  left : E → V\n  right : E → V\n  loopless : ∀ e, left e ≠ right e\nnamespace LooplessGraph\nvariable {V : Type u_1} {E : Type u_2} [Fintype V] [Fintype E] [DecidableEq V] [DecidableEq E]\ndef Incident (G : LooplessGraph V E) (v : V) (e : E) : Prop :=\n  G.left e=v ∨ G.right e=v\nend LooplessGraph\nend MatchingEntropy\n\nnamespace BinaryMatching\nabbrev Pair (n : ℕ) := {ij : Fin n × Fin n // ij.1 < ij.2}\ndef completeGraph (n : ℕ) : MatchingEntropy.LooplessGraph (Fin n) (Pair n) where\n  left e := e.val.1\n  right e := e.val.2\n  loopless e := ne_of_lt e.property\n\nstructure Record where\n  left : ℕ\n  right : ℕ\n  multiplicity : ℕ\n  deriving DecidableEq\n\nstructure Input where\n  n : ℕ\n  records : List Record\n  valid : ∀ e∈records, e.left < e.right ∧ e.right < n\n  unique : (records.map (fun e => (e.left,e.right))).Nodup\n\ndef multiplicity (G : Input) (e : Pair G.n) : ℕ :=\n  match G.records.find? (fun r => r.left=e.val.1.val && r.right=e.val.2.val) with\n  | none => 0\n  | some r => r.multiplicity\n\n\ndef encodeNat (n : ℕ) : List Bool :=\n  List.replicate n.bits.length false ++ true :: n.bits\n\ndef encodeRecord (r : Record) : List Bool :=\n  encodeNat r.left ++ encodeNat r.right ++ encodeNat r.multiplicity\n\ndef encodeInput (G : Input) : List Bool :=\n  encodeNat G.n ++ encodeNat G.records.length ++ G.records.flatMap encodeRecord\n\nend BinaryMatching\n\nopen scoped BigOperators\n\nnamespace LoopMatching\n\nstructure LoopRecord where\n  vertex : ℕ\n  multiplicity : ℕ\n  deriving DecidableEq\n\nstructure Input where\n  ordinary : BinaryMatching.Input\n  loops : List LoopRecord\n  valid : ∀ e ∈ loops, e.vertex < ordinary.n\n  unique : (loops.map LoopRecord.vertex).Nodup\n\nabbrev Input.n (G : Input) : ℕ := G.ordinary.n\n\ndef loopMultiplicity (G : Input) (v : Fin G.n) : ℕ :=\n  match G.loops.find? (fun r => r.vertex == v.val) with\n  | none => 0\n  | some r => r.multiplicity\n\ndef IsPartialMatching (n : ℕ) (M : Finset (BinaryMatching.Pair n)) : Prop :=\n  ∀ v : Fin n, ∀ e ∈ M, ∀ f ∈ M,\n    (BinaryMatching.completeGraph n).Incident v e →\n    (BinaryMatching.completeGraph n).Incident v f → e = f\n\nabbrev PartialMatching (n : ℕ) :=\n  { M : Finset (BinaryMatching.Pair n) // IsPartialMatching n M }\n\nnoncomputable instance partialMatchingFintype (n : ℕ) : Fintype (PartialMatching n) :=\n  Fintype.ofFinite _\n\nnoncomputable def loopVertices {n : ℕ} (M : PartialMatching n) : Finset (Fin n) := by\n  classical\n  exact Finset.univ.filter (fun v =>\n    ∀ e ∈ M.val, ¬ (BinaryMatching.completeGraph n).Incident v e)\n\nnoncomputable def weight (G : Input) (M : PartialMatching G.n) : ℕ :=\n  (∏ e ∈ M.val, BinaryMatching.multiplicity G.ordinary e) *\n    ∏ v ∈ loopVertices M, loopMultiplicity G v\n\nnoncomputable def count (G : Input) : ℕ := ∑ M : PartialMatching G.n, weight G M\n\n\ndef encodeLoopRecord (r : LoopRecord) : List Bool :=\n  BinaryMatching.encodeNat r.vertex ++ BinaryMatching.encodeNat r.multiplicity\n\ndef encodeInput (G : Input) : List Bool :=\n  BinaryMatching.encodeInput G.ordinary ++\n    BinaryMatching.encodeNat G.loops.length ++ G.loops.flatMap encodeLoopRecord\n\ndef encodeOutput (a : ℕ × ℕ) : List Bool :=\n  BinaryMatching.encodeNat a.1 ++ BinaryMatching.encodeNat a.2\n\n\ndef FullEndpoint : Prop :=\n  ∃ A : Input → ℕ × ℕ,\n    ∃ machine : Turing.TM2ComputableInPolyTime encodeInput encodeOutput A,\n      (∀ k, Finite (machine.tm.Γ k)) ∧\n      (∃ P : Polynomial ℕ, ∀ G,\n        (encodeOutput (A G)).length ≤ P.eval (encodeInput G).length) ∧\n      ∀ G, 0 < (A G).2 ∧\n        (A G).1 ≤ (A G).2 * count G ∧\n        (A G).2 * count G ≤ 2 ^ (18 * G.n) * (A G).1 ∧\n        ((A G).1 = 0 ↔ count G = 0)\n\nend LoopMatching\n\nnamespace LoopMatching\n\n/-- A finite-alphabet polynomial-time rational approximation for singleton-loop matchings. -/\ntheorem fullEndpoint : FullEndpoint := by\n  sorry\n\nend LoopMatching\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/SquareRootDegree.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/SquareRootDegree.json", "bytes": 325, "sha256": "d4e3e15f79b609ea5224bf8511d12e08344e9a5e684c392a2a2123601bcc35c9", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.SquareRootDegree\",\n  \"solution_module\": \"OAI.Combinatorics.BooleanFunctions.Main\",\n  \"theorem_names\": [\n    \"OAI.SquareRootDegree.main\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/SquareRootDegree.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/SquareRootDegree.lean", "bytes": 1944, "sha256": "72f48e513f3159bf80d25c7ed5afee9f3224f5c09c672777ead3f1ec7309ac4f", "content": "import Mathlib\n\nnamespace OAI\n\n/-!\nBool encodes a sign: false is +1 and true is -1. The average is uniform,\nand degree is ordinary real Fourier degree, not F₂ degree or threshold degree.\n-/\n\nopen scoped BigOperators\n\nnoncomputable section\n\nnamespace SquareRootDegree\n\nabbrev Cube (ι : Type*) := ι → Bool\n\ndef sign (b : Bool) : ℝ := if b then -1 else 1\n\ndef average {α : Type*} [Fintype α] (f : α → ℝ) : ℝ :=\n  (Fintype.card α : ℝ)⁻¹ * ∑ x, f x\n\ndef character {ι : Type*} (s : Finset ι) (x : Cube ι) : ℝ :=\n  ∏ i ∈ s, sign (x i)\n\ndef fourierCoeff {ι : Type*} [Fintype ι] (f : Cube ι → ℝ) (s : Finset ι) : ℝ := by\n  classical\n  exact average (fun x => f x * character s x)\n\ndef fourierDegree {ι : Type*} [Fintype ι] (f : Cube ι → ℝ) : ℕ := by\n  classical\n  exact (Finset.univ.filter (fun s : Finset ι => fourierCoeff f s ≠ 0)).sup Finset.card\n\ndef IsBoolean {α : Type*} (f : α → ℝ) : Prop :=\n  ∀ x, f x = -1 ∨ f x = 1\n\ndef Nonconstant {α : Type*} (f : α → ℝ) : Prop :=\n  ∃ x y, f x ≠ f y\n\ndef singletonSum {ι : Type*} [Fintype ι] (f : Cube ι → ℝ) : ℝ :=\n  ∑ i, fourierCoeff f {i}\n\ndef absoluteSingletonSum {ι : Type*} [Fintype ι] (f : Cube ι → ℝ) : ℝ :=\n  ∑ i, |fourierCoeff f {i}|\n\ndef SignedViolations : Prop :=\n  ∀ C : ℝ, 0 < C → ∃ n : ℕ, 0 < n ∧ ∃ f : Cube (Fin n) → ℝ,\n    IsBoolean f ∧ Nonconstant f ∧ C * Real.sqrt (fourierDegree f) < singletonSum f\n\ndef AbsoluteRatios : Set ℝ :=\n  {r | ∃ n : ℕ, 0 < n ∧ ∃ f : Cube (Fin n) → ℝ,\n    IsBoolean f ∧ 0 < fourierDegree f ∧\n      r = absoluteSingletonSum f / Real.sqrt (fourierDegree f)}\n\n/-- The two clauses of the literal main theorem. Unboundedness above is the\nreal-valued formulation of the displayed supremum being positive infinity. -/\ntheorem main : SignedViolations ∧ ¬ BddAbove AbsoluteRatios := by\n  sorry\n\nend SquareRootDegree\n\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/TotientAsymptotic.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/TotientAsymptotic.json", "bytes": 526, "sha256": "eaa9cc94213102bbd130f1c3d8f3932896daed2077ee981386b5d60c2af6eac3", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.TotientAsymptotic\",\n  \"solution_module\": \"OAI.NumberTheory.TotientAsymptotic.UnconditionalMain\",\n  \"theorem_names\": [\n    \"OAI.TotientAsymptotic.totient_asymptotic_formula\",\n    \"OAI.TotientAsymptotic.weighted_totient_asymptotic\",\n    \"OAI.TotientAsymptotic.weighted_totient_one_two\",\n    \"OAI.TotientAsymptotic.coefficient_nonnegative\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/TotientAsymptotic.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/TotientAsymptotic.lean", "bytes": 5434, "sha256": "c0be7ff8588f6d0bbf4d0a0e4bb805c0d68e448008113eb4afa82b30580a735e", "content": "import Mathlib\n\nnamespace OAI\n\n/-! Totient counting and least-preimage asymptotics. -/\n\nnoncomputable section\nopen scoped BigOperators Topology\nopen Filter\n\nnamespace TotientAsymptotic\n\ndef IsTotient (v : ℕ) : Prop := ∃ n : ℕ, 0 < n ∧ n.totient = v\n\ndef V (x : ℝ) : ℝ := by\n  classical\n  exact (((Finset.Icc 1 ⌊x⌋₊).filter IsTotient).card : ℝ)\n\ndef ell (v : ℕ) : ℕ := by\n  classical\n  exact if h : IsTotient v then Nat.find h else 0\n\ndef a (j : ℕ) : ℝ := (j + 1 : ℝ) * Real.log (j + 1) - j * Real.log j - 1\n\ndef RenewalRootSet : Set ℝ :=\n  {z | 0 < z ∧ z < 1 ∧ (∑' j : ℕ, a (j+1) * z^(j+1)) = 1}\n\ndef rho : ℝ := sInf RenewalRootSet\n\ndef lam : ℝ := Real.log (1 / rho)\n\ndef gamma : ℝ := (∑' j : ℕ, (j+1 : ℝ) * a (j+1) * rho^(j+1))⁻¹\n\ndef g : ℕ → ℝ\n  | 0 => 1\n  | n+1 => ∑ d ∈ Finset.range (n+1), a (d+1) * g (n-d)\ntermination_by n => n\n\ndef B (x : ℝ) : ℝ := Real.log (Real.log x)\ndef psi (b : ℝ) : ℝ := (Real.log b - Real.log (Real.log b)) / lam\n\ndef m (x : ℝ) : ℕ := ⌊psi (B x)⌋₊\ndef theta (x : ℝ) : ℝ := psi (B x) - m x\n\ndef G (x : ℝ) (j : ℕ) : ℝ :=\n  (B x)^j / ((Nat.factorial j : ℝ) * ∏ i ∈ Finset.Icc 1 j, g i)\n\ndef P (H : ℕ) : ℕ := ⌊Real.log (Real.log (H : ℝ))⌋₊\ndef alpha (s : ℝ) : ℝ := lam * Real.exp (lam * s)\n\ndef largestPrimeFactor (n : ℕ) : ℕ := max 1 (n.primeFactors.sup id)\n\nstructure TailDatum (H : ℕ) where\n  Q : Fin H → ℕ\n  cofactor : ℕ\n\ndef tailPrime {H : ℕ} (η : TailDatum H) (h : ℕ) : ℕ :=\n  if hH : h < H then η.Q ⟨h, hH⟩ else 1\n\ndef tailLog {H : ℕ} (η : TailDatum H) (h : ℕ) : ℝ :=\n  Real.log (Real.log (tailPrime η h : ℝ))\n\ndef IsWitness (H : ℕ) (s : ℝ) (η : TailDatum H) : Prop :=\n  (∀ h : Fin H, h.val < P H → η.Q h = 1) ∧\n  0 < η.cofactor ∧\n  (∀ h ∈ Finset.Ico (P H) H, (tailPrime η h).Prime ∧\n    (9 / 10 : ℝ) * alpha s * h * (rho^h)⁻¹ ≤ tailLog η h ∧\n    tailLog η h ≤ (11 / 10 : ℝ) * alpha s * h * (rho^h)⁻¹ ∧\n    (∑ l ∈ Finset.Ico (P H) h, a (h-l) * tailLog η l) ≤\n      (1 + (1 / 10000 : ℝ) * Real.exp (-(h : ℝ) / 40)) * tailLog η h) ∧\n  largestPrimeFactor η.cofactor ≤ tailPrime η (P H) ∧\n  Real.log (η.cofactor : ℝ) ≤\n    Real.exp (2 * alpha s * (P H : ℝ) * (rho^(P H))⁻¹)\n\ndef w {H : ℕ} (η : TailDatum H) : ℕ :=\n  η.cofactor * ∏ h ∈ Finset.Ico (P H) H, tailPrime η h\n\ndef witnesses (H : ℕ) (s : ℝ) (d : ℕ) : Set (TailDatum H) :=\n  {η | IsWitness H s η ∧ (w η).totient = d}\n\ndef D {H : ℕ} (h : ℕ) (η : TailDatum H) : ℝ :=\n  ∑ l ∈ Finset.Ico (P H) H, a (h-l) * tailLog η l\n\ndef maxD {H : ℕ} (h : ℕ) (T : Finset (TailDatum H)) : ℝ := by\n  classical\n  exact if ht : T.Nonempty then T.sup' ht (D h) else 0\n\ndef AH (H : ℕ) (f : ℝ → ℝ) (s : ℝ) : ℝ := by\n  classical\n  exact rho^(H*(H-1)/2) * (gamma / alpha s)^H *\n    ∑ᶠ d : ℕ,\n      if IsTotient d then\n        f ((ell d : ℝ) / d) / d *\n          ∑ᶠ T : Finset (TailDatum H),\n            if T.Nonempty ∧ (↑T : Set (TailDatum H)) ⊆ witnesses H s d then\n              (-1 : ℝ)^(T.card-1) *\n               Real.exp (-(gamma / alpha s) *\n                 ∑' n : ℕ, rho^(H+n) * maxD (H+n) T)\n            else 0\n      else 0\n\ndef A (f : ℝ → ℝ) (s : ℝ) : ℝ := limUnder atTop (fun H : ℕ => AH H f s)\n\ndef mainTerm (x : ℝ) : ℝ := x / Real.log x * G x (m x) * A (fun _ => 1) (theta x)\n\ndef N (k : ℕ) (x : ℝ) : ℝ := by\n  classical\n  exact (((Finset.Icc 1 ⌊x⌋₊).filter fun v =>\n    IsTotient v ∧ (k : ℝ) * x < ell v ∧ (ell v : ℝ) ≤ (k+1 : ℝ) * x).card : ℝ)\n\ndef fk (k : ℕ) (r : ℝ) : ℝ := min 1 ((k+1 : ℝ) / r) - min 1 ((k : ℝ) / r)\n\ndef normalizedCount (F : ℝ → ℝ) (x : ℝ) : ℝ :=\n  F x / (x / Real.log x * G x (m x))\n\ndef tupleNormalization (x : ℝ) : ℝ := x / Real.log x * G x (m x)\n\ntheorem totient_asymptotic_formula\n    :\n    TendstoUniformlyOn (fun H => AH H (fun _ => 1)) (A (fun _ => 1))\n      atTop (Set.Ico (0 : ℝ) 1) ∧\n    (∃ cMinus cPlus : ℝ, 0 < cMinus ∧ ∀ s ∈ Set.Ico (0 : ℝ) 1,\n      cMinus ≤ A (fun _ => 1) s ∧ A (fun _ => 1) s ≤ cPlus) ∧\n    Tendsto (fun x => V x/mainTerm x) atTop (nhds 1) ∧\n    ∀ c : ℝ, 0 < c → Tendsto (fun x => V (c*x)/V x) atTop (nhds c) := by\n  sorry\n\ntheorem weighted_totient_asymptotic\n    (k : ℕ) (hk : 1≤k) :\n    TendstoUniformlyOn (fun H => AH H (fk k)) (A (fk k)) atTop (Set.Ico (0 : ℝ) 1) ∧\n    Tendsto (fun x => normalizedCount (N k) x-A (fk k) (theta x)) atTop (nhds 0) ∧\n    ((∃ d : ℕ, IsTotient d ∧ k*d<ell d) →\n      ∃ cMinus cPlus : ℝ, 0<cMinus ∧ 0<cPlus ∧\n        (∀ s∈Set.Ico (0 : ℝ) 1, cMinus≤A (fk k) s ∧ A (fk k) s≤cPlus) ∧\n        Tendsto (fun x => N k x/(tupleNormalization x*A (fk k) (theta x))) atTop (nhds 1) ∧\n        ∀ᶠ x : ℝ in atTop, (cMinus/cPlus)*V x≤N k x ∧ N k x≤V x) ∧\n    ((¬∃ d : ℕ, IsTotient d ∧ k*d<ell d) →\n      (∀ x : ℝ, 0<x → N k x=0) ∧ (∀ s∈Set.Ico (0 : ℝ) 1, A (fk k) s=0)) := by\n  sorry\n\ntheorem weighted_totient_one_two\n    :\n    ∀ k∈({1,2} : Finset ℕ), ∃ c : ℝ, 0<c ∧\n      ∀ s∈Set.Ico (0 : ℝ) 1, c≤A (fk k) s := by\n  sorry\n\ntheorem coefficient_nonnegative (H : ℕ) {s : ℝ}\n    (hs : s ∈ Set.Ico (0 : ℝ) 1) : 0 ≤ AH H (fun _ => 1) s := by\n  sorry\n\nend TotientAsymptotic\n\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/TotientCompanionZero.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/TotientCompanionZero.json", "bytes": 345, "sha256": "0326ed0af3b2bcad141be4c3e5f6f0ac4771b1d3ee5fea781c90b536f6d0dad6", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.TotientCompanionZero\",\n  \"solution_module\": \"OAI.NumberTheory.TotientAsymptotic.Main\",\n  \"theorem_names\": [\n    \"OAI.TotientAsymptotic.companion_zero_case\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/TotientCompanionZero.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/TotientCompanionZero.lean", "bytes": 3544, "sha256": "6e6e993b423efc7a30384d656956721918f9c484fca64036169715106799c878", "content": "import Mathlib\n\nnamespace OAI\n\n/-! The no-seed alternative of the weighted totient companion theorem. -/\n\nnoncomputable section\nopen scoped BigOperators Topology\nopen Filter\n\nnamespace TotientAsymptotic\n\ndef IsTotient (v : ℕ) : Prop := ∃ n : ℕ, 0 < n ∧ n.totient = v\n\ndef ell (v : ℕ) : ℕ := by\n  classical\n  exact if h : IsTotient v then Nat.find h else 0\n\ndef a (j : ℕ) : ℝ := (j + 1 : ℝ) * Real.log (j + 1) - j * Real.log j - 1\n\ndef RenewalRootSet : Set ℝ :=\n  {z | 0 < z ∧ z < 1 ∧ (∑' j : ℕ, a (j+1) * z^(j+1)) = 1}\n\ndef rho : ℝ := sInf RenewalRootSet\n\ndef lam : ℝ := Real.log (1 / rho)\n\ndef gamma : ℝ := (∑' j : ℕ, (j+1 : ℝ) * a (j+1) * rho^(j+1))⁻¹\n\ndef P (H : ℕ) : ℕ := ⌊Real.log (Real.log (H : ℝ))⌋₊\ndef alpha (s : ℝ) : ℝ := lam * Real.exp (lam * s)\n\ndef largestPrimeFactor (n : ℕ) : ℕ := max 1 (n.primeFactors.sup id)\n\nstructure TailDatum (H : ℕ) where\n  Q : Fin H → ℕ\n  cofactor : ℕ\n\ndef tailPrime {H : ℕ} (η : TailDatum H) (h : ℕ) : ℕ :=\n  if hH : h < H then η.Q ⟨h, hH⟩ else 1\n\ndef tailLog {H : ℕ} (η : TailDatum H) (h : ℕ) : ℝ :=\n  Real.log (Real.log (tailPrime η h : ℝ))\n\ndef IsWitness (H : ℕ) (s : ℝ) (η : TailDatum H) : Prop :=\n  (∀ h : Fin H, h.val < P H → η.Q h = 1) ∧\n  0 < η.cofactor ∧\n  (∀ h ∈ Finset.Ico (P H) H, (tailPrime η h).Prime ∧\n    (9 / 10 : ℝ) * alpha s * h * (rho^h)⁻¹ ≤ tailLog η h ∧\n    tailLog η h ≤ (11 / 10 : ℝ) * alpha s * h * (rho^h)⁻¹ ∧\n    (∑ l ∈ Finset.Ico (P H) h, a (h-l) * tailLog η l) ≤\n      (1 + (1 / 10000 : ℝ) * Real.exp (-(h : ℝ) / 40)) * tailLog η h) ∧\n  largestPrimeFactor η.cofactor ≤ tailPrime η (P H) ∧\n  Real.log (η.cofactor : ℝ) ≤\n    Real.exp (2 * alpha s * (P H : ℝ) * (rho^(P H))⁻¹)\n\ndef w {H : ℕ} (η : TailDatum H) : ℕ :=\n  η.cofactor * ∏ h ∈ Finset.Ico (P H) H, tailPrime η h\n\ndef witnesses (H : ℕ) (s : ℝ) (d : ℕ) : Set (TailDatum H) :=\n  {η | IsWitness H s η ∧ (w η).totient = d}\n\ndef D {H : ℕ} (h : ℕ) (η : TailDatum H) : ℝ :=\n  ∑ l ∈ Finset.Ico (P H) H, a (h-l) * tailLog η l\n\ndef maxD {H : ℕ} (h : ℕ) (T : Finset (TailDatum H)) : ℝ := by\n  classical\n  exact if ht : T.Nonempty then T.sup' ht (D h) else 0\n\ndef AH (H : ℕ) (f : ℝ → ℝ) (s : ℝ) : ℝ := by\n  classical\n  exact rho^(H*(H-1)/2) * (gamma / alpha s)^H *\n    ∑ᶠ d : ℕ,\n      if IsTotient d then\n        f ((ell d : ℝ) / d) / d *\n          ∑ᶠ T : Finset (TailDatum H),\n            if T.Nonempty ∧ (↑T : Set (TailDatum H)) ⊆ witnesses H s d then\n              (-1 : ℝ)^(T.card-1) *\n               Real.exp (-(gamma / alpha s) *\n                 ∑' n : ℕ, rho^(H+n) * maxD (H+n) T)\n            else 0\n      else 0\n\ndef A (f : ℝ → ℝ) (s : ℝ) : ℝ := limUnder atTop (fun H : ℕ => AH H f s)\n\ndef N (k : ℕ) (x : ℝ) : ℝ := by\n  classical\n  exact (((Finset.Icc 1 ⌊x⌋₊).filter fun v =>\n    IsTotient v ∧ (k : ℝ) * x < ell v ∧ (ell v : ℝ) ≤ (k+1 : ℝ) * x).card : ℝ)\n\ndef fk (k : ℕ) (r : ℝ) : ℝ := min 1 ((k+1 : ℝ) / r) - min 1 ((k : ℝ) / r)\n\n/-- Without a totient value whose least preimage exceeds its prescribed multiple,\nboth the weighted count and its limiting coefficient vanish. -/\ntheorem companion_zero_case (k : ℕ) (_hk : 0 < k)\n    (hno : ¬ ∃ d : ℕ, IsTotient d ∧ k*d < ell d) :\n    (∀ x : ℝ, 0 < x → N k x = 0) ∧\n    (∀ s ∈ Set.Ico (0 : ℝ) 1, A (fk k) s = 0) := by\n  sorry\n\nend TotientAsymptotic\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/TriangleFace.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/TriangleFace.json", "bytes": 335, "sha256": "82f371b8cab6dd027f6d61cc6d9fd9d3a34885b834a4cfb5f94b96ec453699bd", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.TriangleFace\",\n  \"solution_module\": \"OAI.Combinatorics.TriangleFace.Main\",\n  \"theorem_names\": [\n    \"OAI.TriangleFace198.triangle_expansion_face\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/TriangleFace.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/TriangleFace.lean", "bytes": 6324, "sha256": "0b6b19f837991da8842a33f72947e2feeb595ea31f912ce2d6f607f40422391b", "content": "import Mathlib\n\nnamespace OAI\n\nnoncomputable section\n\nopen scoped BigOperators Topology\nopen Filter\n\nuniverse uV uE uIndex uι\n\nnamespace MatchingEntropyBounds\n\nstructure LooplessGraph (V : Type uV) (E : Type uE) where\n  left : E → V\n  right : E → V\n  loopless : ∀ e, left e ≠ right e\n\nvariable {V : Type uV} {E : Type uE}\n  [Fintype V] [Fintype E] [DecidableEq V] [DecidableEq E]\n\nnamespace LooplessGraph\n\ndef Incident (G : LooplessGraph V E) (v : V) (e : E) : Prop :=\n  G.left e = v ∨ G.right e = v\n\ninstance incidentDecidable (G : LooplessGraph V E) (v : V) (e : E) :\n    Decidable (G.Incident v e) := inferInstanceAs (Decidable (G.left e = v ∨ G.right e = v))\n\ndef IsPerfectMatching (G : LooplessGraph V E) (M : Finset E) : Prop :=\n  ∀ v, ∃! e, e ∈ M ∧ G.Incident v e\n\nabbrev Matching (G : LooplessGraph V E) := {M : Finset E // G.IsPerfectMatching M}\n\ninstance matchingFintype (G : LooplessGraph V E) : Fintype G.Matching :=\n  Fintype.ofFinite _\n\ndef indicator (G : LooplessGraph V E) (M : G.Matching) (e : E) : ℝ :=\n  if e ∈ M.val then 1 else 0\n\ndef polytope (G : LooplessGraph V E) : Set (E → ℝ) :=\n  convexHull ℝ (Set.range G.indicator)\n\nend LooplessGraph\nend MatchingEntropyBounds\nend\n\nnoncomputable section\n\nopen scoped BigOperators Topology\nopen Filter\n\nuniverse uV uE uI uJ uA uB uC uW uF uD uι\n\nnamespace MatchingEntropyBounds.LooplessGraph\n\nvariable {V : Type uV} {E : Type uE} [Fintype V] [Fintype E] [DecidableEq V] [DecidableEq E]\n\ndef Crosses (G : LooplessGraph V E) (S : Finset V) (e : E) : Prop :=\n  (G.left e ∈ S ∧ G.right e ∉ S) ∨ (G.left e ∉ S ∧ G.right e ∈ S)\n\ninstance crossesDecidable (G : LooplessGraph V E) (S : Finset V) (e : E) :\n    Decidable (G.Crosses S e) := inferInstanceAs (Decidable\n      ((G.left e ∈ S ∧ G.right e ∉ S) ∨ (G.left e ∉ S ∧ G.right e ∈ S)))\n\ndef cut (G : LooplessGraph V E) (S : Finset V) : Finset E :=\n  Finset.univ.filter (G.Crosses S)\n\ndef degree (G : LooplessGraph V E) (x : E → ℝ) (v : V) : ℝ :=\n  ∑ e, if G.Incident v e then x e else 0\n\ndef cutMass (G : LooplessGraph V E) (x : E → ℝ) (S : Finset V) : ℝ :=\n  ∑ e ∈ G.cut S, x e\n\nend MatchingEntropyBounds.LooplessGraph\nend\n\nnoncomputable section\n\nopen scoped BigOperators Topology\nopen Filter\n\nuniverse uV uE uI uJ uA uB uC uW uF uD uι\n\nnamespace MatchingEntropyBounds.Refined\n\nsection TriangleExpansion\nvariable {V : Type uV} {E : Type uE} [Fintype V] [Fintype E] [DecidableEq V] [DecidableEq E]\n\ndef triangleTip (v : V) (i : Fin 3) : V ⊕ Fin 2 := ![Sum.inl v, Sum.inr 0, Sum.inr 1] i\n\ndef triangleCollapse (v : V) : V ⊕ Fin 2 → V := Sum.elim id (fun _ => v)\n\ndef reattach (v : V) (i : Fin 3) (w : V) : V ⊕ Fin 2 :=\n  if w = v then triangleTip v i else Sum.inl w\n\nomit [Fintype V] [Fintype E] [DecidableEq E] in\ntheorem collapse_reattach (v : V) (i : Fin 3) (w : V) :\n    triangleCollapse v (reattach v i w) = w := by\n  by_cases h : w = v\n  · subst w\n    fin_cases i <;> simp [reattach,triangleTip,triangleCollapse]\n  · simp [reattach,h,triangleCollapse]\n\ndef triangleExpansion (G : LooplessGraph V E) (c : E → Fin 3) (v : V) :\n    LooplessGraph (V ⊕ Fin 2) (E ⊕ Fin 3) where\n  left := Sum.elim (fun e => reattach v (c e) (G.left e)) (fun i => triangleTip v (i + 1))\n  right := Sum.elim (fun e => reattach v (c e) (G.right e)) (fun i => triangleTip v (i + 2))\n  loopless := by\n    intro e h\n    rcases e with e | i\n    · have hh := congrArg (triangleCollapse v) h\n      exact G.loopless e ((collapse_reattach v (c e) (G.left e)).symm.trans\n        (hh.trans (collapse_reattach v (c e) (G.right e))))\n    · fin_cases i <;> simp [triangleTip] at h\n\ndef triangleVertices (v : V) : Finset (V ⊕ Fin 2) := Finset.univ.image (triangleTip v)\n\nend TriangleExpansion\nend MatchingEntropyBounds.Refined\nend\n\nnamespace CanonicalFace198\n\nvariable {A B : Type*} [AddCommGroup A] [Module ℝ A]\n  [AddCommGroup B] [Module ℝ B]\n\ndef facesThrough (P : Set A) (x : A) : Set (Set A) :=\n  {F | Convex ℝ F ∧ IsExtreme ℝ P F ∧ x ∈ F}\n\ndef minFace (P : Set A) (x : A) : Set A := ⋂₀ facesThrough P x\n\nend CanonicalFace198\n\nnoncomputable section\nopen scoped BigOperators\nopen MatchingEntropyBounds MatchingEntropyBounds.Refined\nnamespace TriangleFace198\nvariable {V E : Type*} [Fintype V] [Fintype E] [DecidableEq V] [DecidableEq E]\n\ndef LocalLabels (G : LooplessGraph V E) (v : V) (c : E → Fin 3) : Prop :=\n  ∀ i, ∃! e, G.Incident v e ∧ c e = i\n\ndef localEdge (G : LooplessGraph V E) (c : E → Fin 3) (v : V)\n    (hc : LocalLabels G v c) (i : Fin 3) : E := Classical.choose (hc i)\n\ndef localExtension (G : LooplessGraph V E) (c : E → Fin 3) (v : V)\n    (hc : LocalLabels G v c) : (E → ℝ) →ₗ[ℝ] (E ⊕ Fin 3 → ℝ) where\n  toFun x := Sum.elim x (fun i => x (localEdge G c v hc i))\n  map_add' x y := by ext e; cases e <;> rfl\n  map_smul' a x := by ext e; cases e <;> rfl\n\ndef oldRestriction : (E ⊕ Fin 3 → ℝ) →ₗ[ℝ] (E → ℝ) where\n  toFun y e := y (Sum.inl e)\n  map_add' _ _ := rfl\n  map_smul' _ _ := rfl\n\nend TriangleFace198\n\nend\n\nnoncomputable section\nopen scoped BigOperators\nopen MatchingEntropyBounds MatchingEntropyBounds.Refined\nnamespace TriangleFace198\nuniverse uV uE\nvariable {V : Type uV} {E : Type uE} [instFintypeV : Fintype V] [Fintype E] [DecidableEq V] [instDecidableEqE : DecidableEq E]\n\ntheorem triangle_expansion_face {V : Type uV} {E : Type uE}\n    [Fintype V] [Fintype E] [DecidableEq V] [DecidableEq E] (G : LooplessGraph V E) (v : V)\n    (hdeg : Fintype.card {e : E // G.Incident v e} = 3)\n    (_hmatching : Nonempty G.Matching) :\n    ∃ (c : E → Fin 3) (hc : LocalLabels G v c),\n      let H := triangleExpansion G c v\n      let f := localExtension G c v hc\n      let Q := {y | y ∈ H.polytope ∧ H.cutMass y (triangleVertices v) = 1}\n      f '' G.polytope = Q ∧\n      Convex ℝ Q ∧ IsExtreme ℝ H.polytope Q ∧\n      (∀ x, oldRestriction (f x) = x) ∧\n      (∀ y ∈ Q, f (oldRestriction y) = y) ∧\n      (∀ x e, f x (Sum.inl e) = x e) ∧\n      (∀ x i, f x (Sum.inr i) = x (localEdge G c v hc i)) ∧\n      (∀ x, (∀ e, x e = (1/3 : ℝ)) → ∀ e, f x e = (1/3 : ℝ)) ∧\n      (∀ x ∈ G.polytope, CanonicalFace198.minFace H.polytope (f x) = f '' CanonicalFace198.minFace G.polytope x)  := by\n  sorry\n\nend TriangleFace198\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/TriangularHilbert.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/TriangularHilbert.json", "bytes": 332, "sha256": "79a0bdc2c1be0ea4f51012679983d60b9e3fe72781293a49063e4e7a5c9a3dad", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.TriangularHilbert\",\n  \"solution_module\": \"OAI.Analysis.TriangularHilbert.Main\",\n  \"theorem_names\": [\n    \"OAI.TriangularHilbert.main_estimate\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/TriangularHilbert.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/TriangularHilbert.lean", "bytes": 1471, "sha256": "768eef032a27b868cbeba17c167787a2183aacb6368c2d1733b400d55d264acc", "content": "import Mathlib\n\nnamespace OAI\n\nnoncomputable section\n\nopen MeasureTheory\nopen scoped ENNReal NNReal\n\nnamespace TriangularHilbert\n\nabbrev Plane := ℝ × ℝ\n\nstructure Endpoints where\n  lower : ℝ\n  upper : ℝ\n  lower_pos : 0 < lower\n  lower_lt_upper : lower < upper\n\ndef annulus (ε R : ℝ) : Set ℝ := {t | ε < |t| ∧ |t| < R}\n\ndef integrand (F G : Plane → ℂ) (z : Plane) (t : ℝ) : ℂ :=\n  F (z.1 + t, z.2) * G (z.1, z.2 + t) / (t : ℂ)\n\ndef truncation (F G : Plane → ℂ) (E : Endpoints) (z : Plane) : ℂ :=\n  ∫ t in annulus E.lower E.upper, integrand F G z t\n\ndef GoodPoint (F G : Plane → ℂ) (z : Plane) : Prop :=\n  ∀ n : ℕ, IntegrableOn (integrand F G z)\n    (annulus (1 / ((n : ℝ) + 2)) ((n : ℝ) + 2))\n\ndef maximal (F G : Plane → ℂ) (z : Plane) : ℝ≥0∞ := by\n  classical\n  exact if GoodPoint F G z then\n    ⨆ E : Endpoints, ENNReal.ofReal ‖truncation F G E z‖ else 0\n\ndef maximalNorm (F G : Plane → ℂ) : ℝ≥0∞ :=\n  (∫⁻ z : Plane, maximal F G z ^ ((3 : ℝ) / 2)) ^ ((2 : ℝ) / 3)\n\ndef MainEstimate : Prop :=\n  ∃ C : ℝ≥0, ∀ F G : Plane → ℂ,\n    MemLp F 3 volume → MemLp G 3 volume →\n    (∀ᵐ z ∂volume, GoodPoint F G z) ∧\n    AEMeasurable (maximal F G) volume ∧\n    maximalNorm F G ≤ (C : ℝ≥0∞) * eLpNorm F 3 volume * eLpNorm G 3 volume\n\nopen MeasureTheory Filter\nopen scoped ENNReal\n\ntheorem main_estimate : MainEstimate := by\n  sorry\n\nend TriangularHilbert\n\nend\n\nend OAI\n"}, {"path": "lean/ComparatorChallenges/TypeSystemNormalization.json", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/TypeSystemNormalization.json", "bytes": 348, "sha256": "a0c2f4178f0638fe1ccc16336c851e8e8310d6555a5d1aded59b2b839f69b0cc", "content": "{\n  \"challenge_module\": \"ComparatorChallenges.TypeSystemNormalization\",\n  \"solution_module\": \"OAI.Computability.TypeSystem.Normalization\",\n  \"theorem_names\": [\n    \"OAI.PureTypeSystem.weak_implies_strong\"\n  ],\n  \"definition_names\": [],\n  \"permitted_axioms\": [\n    \"propext\",\n    \"Quot.sound\",\n    \"Classical.choice\"\n  ],\n  \"enable_nanoda\": false\n}\n"}, {"path": "lean/ComparatorChallenges/TypeSystemNormalization.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/ComparatorChallenges/TypeSystemNormalization.lean", "bytes": 4493, "sha256": "9e17d5b15e6437488e6337fff8556d9cba5c07108fb551b90d1423603e85ac5d", "content": "import Mathlib\n\nnamespace OAI\n\n/-!\nFull annotated PTS syntax, modulo alpha-renaming via de Bruijn indices.\nContexts are stored newest-first, each declaration in its prefix context.\nNo functionality assumptions on axioms or product rules.\n-/\n\nnamespace PureTypeSystem\n\nuniverse u\n\ninductive Expr (Sorts : Type u) : Type u\n  | var : Nat → Expr Sorts\n  | sort : Sorts → Expr Sorts\n  | app : Expr Sorts → Expr Sorts → Expr Sorts\n  | lam : Expr Sorts → Expr Sorts → Expr Sorts\n  | pi : Expr Sorts → Expr Sorts → Expr Sorts\n  deriving DecidableEq\n\nnamespace Expr\n\nvariable {S : Type u}\n\ndef liftRen (ρ : Nat → Nat) : Nat → Nat\n  | 0 => 0\n  | n + 1 => ρ n + 1\n\ndef rename (ρ : Nat → Nat) : Expr S → Expr S\n  | var n => var (ρ n)\n  | sort s => sort s\n  | app f a => app (rename ρ f) (rename ρ a)\n  | lam A b => lam (rename ρ A) (rename (liftRen ρ) b)\n  | pi A B => pi (rename ρ A) (rename (liftRen ρ) B)\n\ndef liftSub (σ : Nat → Expr S) : Nat → Expr S\n  | 0 => var 0\n  | n + 1 => rename Nat.succ (σ n)\n\ndef subst (σ : Nat → Expr S) : Expr S → Expr S\n  | var n => σ n\n  | sort s => sort s\n  | app f a => app (subst σ f) (subst σ a)\n  | lam A b => lam (subst σ A) (subst (liftSub σ) b)\n  | pi A B => pi (subst σ A) (subst (liftSub σ) B)\n\ndef single (a : Expr S) : Nat → Expr S\n  | 0 => a\n  | n + 1 => var n\n\ndef instantiate (b a : Expr S) : Expr S := subst (single a) b\n\nend Expr\n\nstructure Specification (S : Type u) where\n  axioms : S → S → Prop\n  rule : S → S → S → Prop\n\ninductive Beta {S : Type u} : Expr S → Expr S → Prop\n  | head (A b a) : Beta (.app (.lam A b) a) (b.instantiate a)\n  | app_left {f f' a} : Beta f f' → Beta (.app f a) (.app f' a)\n  | app_right {f a a'} : Beta a a' → Beta (.app f a) (.app f a')\n  | lam_domain {A A' b} : Beta A A' → Beta (.lam A b) (.lam A' b)\n  | lam_body {A b b'} : Beta b b' → Beta (.lam A b) (.lam A b')\n  | pi_domain {A A' B} : Beta A A' → Beta (.pi A B) (.pi A' B)\n  | pi_body {A B B'} : Beta B B' → Beta (.pi A B) (.pi A B')\n\ndef Converts {S : Type u} : Expr S → Expr S → Prop := Relation.EqvGen Beta\n\ndef Normal {S : Type u} (M : Expr S) : Prop := ∀ N, ¬ Beta M N\n\ndef WeaklyNormalizing {S : Type u} (M : Expr S) : Prop :=\n  ∃ N, Relation.ReflTransGen Beta M N ∧ Normal N\n\n/-- Accessibility for the converse of reduction: strong normalization,\nincluding reductions in all type annotations. -/\ndef StronglyNormalizing {S : Type u} (M : Expr S) : Prop :=\n  Acc (fun N M : Expr S => Beta M N) M\n\ninductive HasType {S : Type u} (P : Specification S) :\n    List (Expr S) → Expr S → Expr S → Prop\n  | ax {s t} : P.axioms s t → HasType P [] (.sort s) (.sort t)\n  | var {Γ A s} : HasType P Γ A (.sort s) →\n      HasType P (A :: Γ) (.var 0) (A.rename Nat.succ)\n  | weaken {Γ M B A s} : HasType P Γ M B → HasType P Γ A (.sort s) →\n      HasType P (A :: Γ) (M.rename Nat.succ) (B.rename Nat.succ)\n  | product {Γ A B s₁ s₂ s₃} : HasType P Γ A (.sort s₁) →\n      HasType P (A :: Γ) B (.sort s₂) → P.rule s₁ s₂ s₃ →\n      HasType P Γ (.pi A B) (.sort s₃)\n  | abstraction {Γ A b B s} : HasType P (A :: Γ) b B →\n      HasType P Γ (.pi A B) (.sort s) →\n      HasType P Γ (.lam A b) (.pi A B)\n  | application {Γ f a A B} : HasType P Γ f (.pi A B) →\n      HasType P Γ a A → HasType P Γ (.app f a) (B.instantiate a)\n  | conversion {Γ M A B s} : HasType P Γ M A → HasType P Γ B (.sort s) →\n      Converts A B → HasType P Γ M B\n\ninductive ValidContext {S : Type u} (P : Specification S) : List (Expr S) → Prop\n  | nil : ValidContext P []\n  | cons {Γ A s} : ValidContext P Γ → HasType P Γ A (.sort s) →\n      ValidContext P (A :: Γ)\n\ndef Legal {S : Type u} (P : Specification S) (Γ : List (Expr S)) (M : Expr S) : Prop :=\n  ∃ A, HasType P Γ M A ∨ HasType P Γ A M\n\ndef SystemWeaklyNormalizing {S : Type u} (P : Specification S) : Prop :=\n  ∀ Γ, ValidContext P Γ → ∀ M, Legal P Γ M → WeaklyNormalizing M\n\ndef SystemStronglyNormalizing {S : Type u} (P : Specification S) : Prop :=\n  ∀ Γ, ValidContext P Γ → ∀ M, Legal P Γ M → StronglyNormalizing M\n\n/-- System-wide weak normalization implies strong normalization for every PTS,\nincluding arbitrary sort types, nonfunctional rules and annotated terms. -/\ntheorem weak_implies_strong {S : Type u} (P : Specification S)\n    (h : SystemWeaklyNormalizing P) : SystemStronglyNormalizing P := by\n  sorry\n\nend PureTypeSystem\n\nend OAI\n"}, {"path": "lean/OAI/Analysis/FactorGeneration/Main.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/OAI/Analysis/FactorGeneration/Main.lean", "bytes": 2052, "sha256": "4753cdbc2e8b15aa5c4d9c21bee3ee37eb9ff95c1dc9d43cb6758d687d0a8392", "content": "import OAI.Analysis.FactorGeneration.Generation\nimport OAI.Analysis.FactorGeneration.PredualContinuity\nimport OAI.Analysis.FactorGeneration.TraceTransport\n\nnamespace OAI\n\nnamespace Generator\n\n/-! Single generation of a finite diffuse factor with a separable Banach predual,\nusing its faithful normal trace and given-predual continuity. -/\nnoncomputable section\nuniverse u\nvariable {H : Type u} [NormedAddCommGroup H] [InnerProductSpace ℂ H] [CompleteSpace H]\n\ntheorem single_generation_of_II1_separable_predual\n    (S : StarSubalgebra ℂ (H →L[ℂ] H)) (hS : IsII1Factor S)\n    (hsep : HasSeparablePredual S) : SinglyGenerated S := by\n  let M := vonNeumannOfWOTClosed S hS.weaklyClosed\n  have hf : ∀ c : M.toStarSubalgebra,\n      (∀ a : M.toStarSubalgebra, a*c=c*a) → ∃ z : ℂ, c=z • (1 : M.toStarSubalgebra) := by\n    intro c hc\n    obtain ⟨z, hz⟩ := hS.factor c hc\n    exact ⟨z, hz.trans (Algebra.algebraMap_eq_smul_one z)⟩\n  have hunit : (1 : M.toStarSubalgebra) ≠ 0 := by\n    intro h\n    exact hS.nonzero (congrArg Subtype.val h).symm\n  let E := Tracial.TraceFreePreHilbert hf hS.diffuse hS.finite hunit\n  let : NormedAddCommGroup E :=\n    Tracial.traceFreePreHilbertNormedAddCommGroup hf hS.diffuse hS.finite hunit\n  let : InnerProductSpace ℂ E :=\n    Tracial.traceFreePreHilbertInnerProductSpace hf hS.diffuse hS.finite hunit\n  let N := Tracial.traceFreeHilbertClosure hf hS.diffuse hS.finite hunit\n  let τ := Tracial.traceFreeHilbertClosureTrace hf hS.diffuse hS.finite hunit\n  have hN := Tracial.traceFreeHilbertClosure_isII1Factor hf hS.diffuse hS.finite hunit\n  have hpre := Tracial.traceFreeHilbertClosure_hasSeparablePredual hf hS.diffuse hS.finite hunit hsep\n  have htrace := Tracial.traceOrbit_isSeparable_of_separablePredual τ hpre\n  exact Tracial.traceFreeHilbert_singlyGenerated_pullback hf hS.diffuse hS.finite hunit\n    (Tracial.singlyGenerated_of_tracialRepresentation hN τ htrace)\n\ntheorem mainTarget : @MainTarget H _ _ _ :=\n  single_generation_of_II1_separable_predual\n\nend\n\nend Generator\n\nend OAI\n"}, {"path": "lean/OAI/Analysis/TriangularHilbert/Main.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/OAI/Analysis/TriangularHilbert/Main.lean", "bytes": 2039, "sha256": "49dcd89df22c7a7567df9033079ca50e83ee41df1092be2a417ab6c54f66ed12", "content": "import OAI.Analysis.TriangularHilbert.TruncationCoordinates\n\nnamespace OAI\n\nsection\nnamespace TriangularHilbert\nopen MeasureTheory Heat Filter\nopen scoped ENNReal\nnoncomputable section\n\nlemma maximal_eq_cyclicMax_ae (F G : Plane → ℂ) (hF : MemLp F 3 volume) (hG : MemLp G 3 volume) :\n    maximal F G =ᵐ[volume] (fun z => cyclicMax (cyclicB F) (cyclicC G) (-z)) := by\n  filter_upwards [measurePreserving_reflect.quasiMeasurePreserving.ae\n    (ae_cubicSliceGood (cyclicB F) (cyclicC G) (memLp_cyclicB hF) (memLp_cyclicC hG))] with z hz\n  rw [maximal,ite_eq_left hz.goodPoint]\n  simp_rw [truncation_eq_cyclicHard]\n  exact (cyclicMax_eq_iSup hz).symm\n\nlemma aemeasurable_maximal (F G : Plane → ℂ) (hF : MemLp F 3 volume) (hG : MemLp G 3 volume) :\n    AEMeasurable (maximal F G) volume := by\n  have h := (aemeasurable_cyclicMax (cyclicB F) (cyclicC G) (memLp_cyclicB hF) (memLp_cyclicC hG)).comp_quasiMeasurePreserving\n    measurePreserving_reflect.quasiMeasurePreserving\n  exact h.congr (maximal_eq_cyclicMax_ae F G hF hG).symm\n\nlemma maximalNorm_eq_cyclicNorm (F G : Plane → ℂ) (hF : MemLp F 3 volume) (hG : MemLp G 3 volume) :\n    maximalNorm F G = cyclicNorm (cyclicB F) (cyclicC G) := by\n  unfold maximalNorm cyclicNorm\n  congr 1\n  calc\n    (∫⁻ z : Plane, maximal F G z ^ ((3:ℝ)/2)) =\n        ∫⁻ z : Plane, cyclicMax (cyclicB F) (cyclicC G) (-z)^((3:ℝ)/2) := by\n      apply lintegral_congr_ae\n      filter_upwards [maximal_eq_cyclicMax_ae F G hF hG] with z hz\n      rw [hz]\n    _ = _ := measurePreserving_reflect.lintegral_comp_emb (Homeomorph.neg Plane).measurableEmbedding\n      (fun z => cyclicMax (cyclicB F) (cyclicC G) z^((3:ℝ)/2))\n\ntheorem main_estimate : MainEstimate := by\n  refine ⟨mainConstant,fun F G hF hG => ⟨ae_goodPoint F G hF hG,aemeasurable_maximal F G hF hG,?_⟩⟩\n  rw [maximalNorm_eq_cyclicNorm F G hF hG]\n  simpa only [eLpNorm_cyclicB hF,eLpNorm_cyclicC hG] using\n    cyclicNorm_le (cyclicB F) (cyclicC G) (memLp_cyclicB hF) (memLp_cyclicC hG)\nend\nend TriangularHilbert\nend\n\nend OAI\n"}, {"path": "lean/OAI/Combinatorics/GotsmanLinial/Main.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/OAI/Combinatorics/GotsmanLinial/Main.lean", "bytes": 6437, "sha256": "5e9f32c670a76d0e271b9f1565788533da99669bf85854ef1fe35ed02b554c0b", "content": "import OAI.Combinatorics.GotsmanLinial.Statement\nimport OAI.Combinatorics.GotsmanLinial.BlockCoupling\nimport OAI.Combinatorics.GotsmanLinial.Commutator\nimport OAI.Combinatorics.GotsmanLinial.CubeOperators\nimport OAI.Combinatorics.GotsmanLinial.EdgeRecovery\nimport OAI.Combinatorics.GotsmanLinial.GradingShift\nimport OAI.Combinatorics.GotsmanLinial.BlockVanishing\nimport Mathlib.Tactic.Ring\n\nnamespace OAI\n\n/-!\n# Assembly of the Gotsman--Linial average-sensitivity bound\n\nThe first lemmas are explicitly conditional assembly bridges. The final\ntheorems discharge their matrix and counting hypotheses using the concrete\nweighted Boolean-cube grading and a sign-preserving constant perturbation.\n-/\n\nnamespace LeanBlast.GotsmanLinial\n\nopen scoped BigOperators\n\n/-- The two analytic constants combine to the constant `8` after\nnormalizing by the number of cube vertices. This is only a scalar bridge. -/\ntheorem normalized_edge_bound {N C T a : ℝ} (hN : 0 < N)\n    (hedge : C ≤ 2 * T) (henergy : T ≤ 4 * a * N) :\n    C / N ≤ 8 * a := by\n  apply (div_le_iff₀ hN).mpr\n  calc\n    C ≤ 2 * T := hedge\n    _ ≤ 2 * (4 * a * N) := mul_le_mul_of_nonneg_left henergy (by norm_num)\n    _ = 8 * a * N := by ring\n\n/-- Convert a counted-edge identity, edge recovery, and an anticommutator\nenergy estimate into the desired sensitivity inequality. The counting and energy premises are explicit. -/\ntheorem sensitivity_bound_of_counting_energy {n d : ℕ}\n    (f : Cube n → ℝ) (C T : ℝ)\n    (hcount : averageSensitivity f = C / (2 : ℝ) ^ n)\n    (hedge : C ≤ 2 * T)\n    (henergy : T ≤ 4 * ((d : ℝ) * Real.sqrt (n : ℝ)) * (2 : ℝ) ^ n) :\n    averageSensitivity f ≤ 8 * (d : ℝ) * Real.sqrt (n : ℝ) := by\n  rw [hcount]\n  simpa only [mul_assoc] using\n    normalized_edge_bound (pow_pos (by norm_num : (0 : ℝ) < 2) n) hedge henergy\n\n/-- The finite-matrix bound assuming an orthogonal projection family with\nbinomial multiplicities, coordinate tridiagonality, and low-block vanishing. -/\ntheorem sensitivity_bound_of_cube_grading {n d : ℕ}\n    (f : Cube n → ℝ) (hf : ∀ x, f x = 1 ∨ f x = -1)\n    (P : Fin (n + 1) → Matrix (Cube n) (Cube n) ℂ)\n    (hP : OrthogonalProjectionFamily P)\n    (htrace : ∀ k, (Matrix.trace (P k)).re = (n.choose k.val : ℝ))\n    (htri : ∀ i : Fin n, BlockTridiagonal P (coordinateSignMatrix i))\n    (hvanish : ∀ r s : Fin (n + 1), r.val + s.val < n - d →\n      P s * signMatrix (fun x => fullParity x * f x) * P r = 0) :\n    averageSensitivity f ≤ 8 * (d : ℝ) * Real.sqrt (n : ℝ) := by\n  classical\n  let h : Cube n → ℝ := fun x => fullParity x * f x\n  let H : Matrix (Cube n) (Cube n) ℂ := signMatrix h\n  let M : Matrix (Cube n) (Cube n) ℂ := centeredGrading P\n  have hh (x : Cube n) : h x = 1 ∨ h x = -1 := parity_twist_cases hf x\n  have htotal : hsNormSq M = (n : ℝ) * (2 : ℝ) ^ n / 4 := by\n    change hsNormSq (centeredGrading P) = _\n    rw [hsNormSq_centeredGrading_trace hP]\n    simp_rw [htrace]\n    simpa only [mul_comm] using sum_choose_centered_sq n\n  have hcoordinate (i : Fin n) :\n      hsNormSq (M * coordinateSignMatrix i - coordinateSignMatrix i * M) ≤ (2 : ℝ) ^ n := by\n    simpa only [M, card_cube, Nat.cast_pow, Nat.cast_ofNat] using\n      hsNormSq_centeredGrading_commutator_le_card hP (coordinateSignMatrix i)\n        (htri i) (coordinateSignMatrix_unitary i)\n  have hdistance :\n      (∑ x : Cube n, ∑ y : Cube n, (hammingDist x y : ℝ) * ‖M x y‖ ^ 2) ≤ hsNormSq M := by\n    rw [htotal]\n    exact distance_energy_le_of_coordinate_commutator_bound M hcoordinate\n  have hcap (x y : Cube n) (hxy : x ≠ y) : ‖M x y‖ ^ 2 ≤ (1 : ℝ) / 4 := by\n    apply off_diagonal_sq_norm_le_quarter_of_commutator_bound M _ x y hxy\n    intro i a b\n    exact sq_norm_centeredGrading_commutator_entry_le_one hP (coordinateSignMatrix i)\n      (htri i) (coordinateSignMatrix_unitary i) a b\n  have hedge : ((orderedConstantEdges h).card : ℝ) ≤\n      2 * hsNormSq (M * H + H * M) := edge_recovery M h hh htotal hdistance hcap\n  have henergy : hsNormSq (M * H + H * M) ≤\n      4 * (d : ℝ) * Real.sqrt (n : ℝ) * (2 : ℝ) ^ n :=\n    hsNormSq_centeredGrading_anticommutator_le_of_trace n d P hP H\n      (signMatrix_conjTranspose_mul h hh) (signMatrix_mul_conjTranspose h hh) htrace hvanish\n  apply sensitivity_bound_of_counting_energy f ((orderedConstantEdges h).card : ℝ)\n    (hsNormSq (M * H + H * M)) (averageSensitivity_eq_parity_edges hf) hedge\n  simpa only [mul_assoc] using henergy\n\n/-- The bound for a polynomial without cube zeros. The projection\nfamily and both block-vanishing properties are constructed from its absolute\nvalue, rather than retained as hypotheses. -/\ntheorem polynomialThreshold_averageSensitivity_le_of_nonzero {n d : ℕ}\n    (p : MvPolynomial (Fin n) ℝ) (hp : IsMultilinear p) (hd : p.totalDegree ≤ d)\n    (hnz : ∀ x : Cube n, polynomialValue p x ≠ 0) :\n    averageSensitivity (polynomialThreshold p) ≤ 8 * (d : ℝ) * Real.sqrt (n : ℝ) := by\n  let w : Cube n → ℝ := fun x => |polynomialValue p x|\n  have hw (x : Cube n) : 0 < w x := abs_pos.mpr (hnz x)\n  apply sensitivity_bound_of_cube_grading (polynomialThreshold p)\n    (fun x => thresholdSign_cases (polynomialValue p x))\n    (weightedProjectionMatrix w hw)\n    (weightedProjectionMatrix_family w hw)\n    (weightedProjectionMatrix_trace_re w hw)\n  · intro i\n    exact weightedProjectionMatrix_tridiagonal w hw i\n  · intro r s hrs\n    exact weightedProjectionMatrix_block_eq_zero_of_polynomial_degree p hp hd hnz r s hrs\n\n/-- Every real multilinear polynomial of degree at most `d` has average\nsensitivity at most `8*d*sqrt(n)`, including cube zeros under `sign(0)=1`.\nThe argument in fact covers all natural `n` and `d`. -/\ntheorem polynomialThreshold_averageSensitivity_le {n d : ℕ}\n    (p : MvPolynomial (Fin n) ℝ) (hp : IsMultilinear p) (hd : p.totalDegree ≤ d) :\n    averageSensitivity (polynomialThreshold p) ≤ 8 * (d : ℝ) * Real.sqrt (n : ℝ) := by\n  obtain ⟨q, hq, hqd, hqnz, hqf⟩ := exists_nonzero_polynomial_same_threshold hp hd\n  rw [← hqf]\n  exact polynomialThreshold_averageSensitivity_le_of_nonzero q hq hqd hqnz\n\n/-- The average-sensitivity bound for `1 ≤ d ≤ n`, without auxiliary\nconstruction hypotheses. -/\ntheorem gotsmanLinialStatement : GotsmanLinialStatement := by\n  intro n d _ _ _ p hp hd\n  exact polynomialThreshold_averageSensitivity_le p hp hd\n\nend LeanBlast.GotsmanLinial\n\nend OAI\n"}, {"path": "lean/OAI/Combinatorics/PerfectMatching/Main.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/OAI/Combinatorics/PerfectMatching/Main.lean", "bytes": 9832, "sha256": "c986879d1e060ab63989c6f21ab9e8a58d7a341364dc36bd31ea85f12637ab0d", "content": "import OAI.Combinatorics.PerfectMatching.MatchingCovariance\n\nnamespace OAI\n\n/-!\nThe nonlinear entropy defect bound\n`F - H ≤ 8m (1 - exp (-F/m))` for finite loopless multigraphs on `2m` vertices,\nand the perfect matching count bound `[k exp (-8 (1 - 1/k))]^m`,\nwith its coarse bound `(exp (-8) k)^m`, when the entire edge set\nis partitioned into `k ≥ 1` perfect matchings.\n-/\n\nnoncomputable section\n\nopen scoped BigOperators Topology\nopen Filter\n\nnamespace MatchingEntropy\n\nopen Matrix\n\nvariable {V E : Type*} [Fintype V] [Fintype E] [DecidableEq V] [DecidableEq E]\n\n/-- The small-entropy regime of the nonlinear barrier function. -/\ntheorem entropy_barrier_ge_id {t : ℝ} (ht : 0 ≤ t) (h8 : t ≤ Real.log 8) :\n    t ≤ 8 * (1-Real.exp (-t)) := by\n  have he : 1 ≤ Real.exp t := Real.one_le_exp_iff.mpr ht\n  have he8 : Real.exp t ≤ 8 := by\n    have hh := Real.exp_le_exp.mpr h8\n    rw [Real.exp_log (by norm_num : (0 : ℝ) < 8)] at hh\n    exact hh\n  have hlog := Real.log_le_sub_one_of_pos (Real.exp_pos t)\n  rw [Real.log_exp] at hlog\n  have hin := Real.exp_neg t\n  rw [hin]\n  have hp := Real.exp_pos t\n  apply (mul_le_mul_iff_right₀ hp).mp\n  have hmain : t * Real.exp t ≤ 8 * (Real.exp t - 1) := by\n    have : t ≤ Real.exp t - 1 := hlog\n    nlinarith\n  have hprod : Real.exp t * (8*(1-(Real.exp t)⁻¹)) = 8*(Real.exp t-1) := by\n    field_simp [hp.ne']\n  calc\n    Real.exp t * t = t * Real.exp t := mul_comm _ _\n    _ ≤ 8 * (Real.exp t-1) := hmain\n    _ = _ := hprod.symm\n\n\n/-- Entropy on s positive edge coordinates of total mass m is at most m log(s/m). -/\ntheorem marginal_entropy_support_bound (G : LooplessGraph V E) {m : ℕ}\n    (hm : 0 < m) (hV : Fintype.card V=2*m) {x : E → ℝ} (hx : x ∈ G.polytope) :\n    0 ≤ entropy x ∧ 0 < Fintype.card {e // 0 < x e} ∧\n      entropy x ≤ (m:ℝ)*Real.log ((Fintype.card {e // 0 < x e}:ℝ)/(m:ℝ)) := by\n  have hmR : (0:ℝ) < m := by exact_mod_cast hm\n  have hmass := G.marginal_sum hV hx\n  have hn : ∀ e, 0 ≤ x e := fun e => (G.coordinate_mem_Icc hx e).1\n  have hz : ∀ e, ¬ 0 < x e → x e=0 := fun e he => le_antisymm (le_of_not_gt he) (hn e)\n  have hsum : (∑ e : {e // 0 < x e}, x e.val)=(m:ℝ) :=\n    (sum_positive_support x x hz).symm.trans hmass\n  have hent : entropy (fun e : {e // 0 < x e} => x e.val)=entropy x := by\n    unfold entropy\n    exact (sum_positive_support x (fun e => Real.negMulLog (x e))\n      (fun e he => by rw [hz e he,Real.negMulLog_zero])).symm\n  have hpossum : 0 < ∑ e, x e := by rw [hmass]; exact hmR\n  obtain ⟨e,_,he⟩ := (Finset.sum_pos_iff_of_nonneg (fun e _ => hn e)).mp hpossum\n  have hs : 0 < Fintype.card {e // 0 < x e} := Fintype.card_pos_iff.mpr ⟨⟨e,he⟩⟩\n  have hsR : (0:ℝ) < Fintype.card {e // 0 < x e} := by exact_mod_cast hs\n  refine ⟨Finset.sum_nonneg (fun e _ => Real.negMulLog_nonneg (hn e) (G.coordinate_mem_Icc hx e).2),hs,?_⟩\n  let c : ℝ := (Fintype.card {e // 0 < x e}:ℝ)/(m:ℝ)\n  have hc : 0 < c := div_pos hsR hmR\n  have hcm : c*(m:ℝ)=Fintype.card {e // 0 < x e} := div_mul_cancel₀ _ hmR.ne'\n  have hb := Finset.sum_le_sum (s := Finset.univ) (fun e : {e // 0 < x e} => fun _ =>\n    Real.negMulLog_le_one_sub_self (mul_nonneg hc.le e.property.le))\n  simp_rw [Real.negMulLog_mul] at hb\n  rw [Finset.sum_add_distrib,← Finset.sum_mul,← Finset.mul_sum,hsum,\n    Finset.sum_sub_distrib,← Finset.mul_sum,hsum] at hb\n  simp only [Finset.sum_const,Finset.card_univ,nsmul_eq_mul,mul_one] at hb\n  change (m:ℝ)*Real.negMulLog c+c*entropy (fun e : {e // 0 < x e} => x e.val) ≤\n    (Fintype.card {e // 0 < x e}:ℝ)-c*(m:ℝ) at hb\n  rw [hent,Real.negMulLog,hcm,sub_self] at hb\n  change entropy x ≤ (m:ℝ)*Real.log c\n  nlinarith\n\n/-- Main entropy comparison, for every marginal in the actual matching polytope. -/\ntheorem entropy_main (G : LooplessGraph V E) (m : ℕ)\n    (hm : 0 < m) (hV : Fintype.card V = 2 * m)\n    (hPM : Nonempty G.Matching) (y : E → ℝ) (hy : y ∈ G.polytope) :\n    marginalEntropy y - maxMatchingEntropy G y ≤\n      8 * (m : ℝ) * (1 - Real.exp (-marginalEntropy y / (m : ℝ))) := by\n  have hmR : (0:ℝ) < m := by exact_mod_cast hm\n  have heven : Even (Fintype.card V) := by rw [hV]; exact even_two_mul m\n  obtain ⟨x,hx,hmax⟩ := entropyDefect_attains_max G heven hPM (m:ℝ)\n  by_contra htarget\n  have hypos : 0 < entropyDefect G (m:ℝ) y := by\n    dsimp [entropyDefect,entropyBarrier,marginalEntropy] at *\n    linarith\n  have hpos : 0 < entropyDefect G (m:ℝ) x := hypos.trans_le (hmax y hy)\n  obtain ⟨p,hp,hopt⟩ := maxMatchingEntropy_attained G hx\n  obtain ⟨hxnon,hspos,hentropy⟩ := marginal_entropy_support_bound G hm hV hx\n  have hH := maxMatchingEntropy_nonneg G hx\n  let s : ℝ := Fintype.card {e // 0 < x e}\n  have hsR : 0 < s := by dsimp only [s]; exact_mod_cast hspos\n  have hrpos : 0 < s/(m:ℝ) := div_pos hsR hmR\n  have hu : entropy x/(m:ℝ) ≤ Real.log (s/(m:ℝ)) := by\n    apply (div_le_iff₀ hmR).mpr\n    simpa only [mul_comm] using hentropy\n  have hlo : Real.log 8 < entropy x/(m:ℝ) := by\n    by_contra hle\n    have hb := entropy_barrier_ge_id (div_nonneg hxnon hmR.le) (le_of_not_gt hle)\n    have hb' := (div_le_iff₀ hmR).mp hb\n    dsimp only [entropyDefect,entropyBarrier] at hpos\n    rw [neg_div] at hpos\n    nlinarith\n  have hs8 : 8*(m:ℝ) < s := by\n    have hh := (Real.log_lt_log_iff (by norm_num : (0:ℝ)<8) hrpos).mp (hlo.trans_le hu)\n    exact (lt_div_iff₀ hmR).mp hh\n  have ha : 8*(m:ℝ)/s ≤ 8*Real.exp (-entropy x/(m:ℝ)) := by\n    have hh := Real.exp_le_exp.mpr (neg_le_neg hu)\n    rw [Real.exp_neg,Real.exp_log hrpos,inv_div] at hh\n    rw [neg_div,mul_div_assoc]\n    exact mul_le_mul_of_nonneg_left hh (by norm_num)\n  have hrankNat := matching_covariance_rank G heven hp hopt\n  rw [hV] at hrankNat\n  have hrankR : s ≤ 2*(2*(m:ℝ))+(covariance p (matchingNormalized G x)).rank := by\n    dsimp only [s]\n    exact_mod_cast hrankNat\n  have hrank : s-4*(m:ℝ) ≤ (covariance p (matchingNormalized G x)).rank := by linarith\n  exact covariance_contradiction (covariance_posSemidef p (matchingNormalized G x)) hmR hs8\n    hrank (matching_covariance_trace G hp hV) ha\n    (matching_covariance_second_variation G hp hopt hmR hmax)\n\n\nomit [Fintype V] [Fintype E] [DecidableEq V] in\n/-- An edge partition into k matchings places the exact uniform edge vector in P. -/\ntheorem uniform_decomposition_mem (G : LooplessGraph V E) {k : ℕ} (hk : 1 ≤ k)\n    (M : Fin k → G.Matching)\n    (hpartition : ∀ e : E, ∃! i : Fin k, e ∈ (M i).val) :\n    (fun _ : E => 1/(k:ℝ)) ∈ G.polytope := by\n  classical\n  have hkR : (0:ℝ) < k := by exact_mod_cast (show 0 < k by omega)\n  have hsum : (∑ i : Fin k, (1/(k:ℝ)))=1 := by\n    simp only [Finset.sum_const,Finset.card_univ,Fintype.card_fin,nsmul_eq_mul]\n    field_simp\n  have hmean := (convex_convexHull ℝ (Set.range G.indicator)).sum_mem\n    (fun (i : Fin k) _ => (one_div_pos.mpr hkR).le) hsum\n    (fun i _ => subset_convexHull ℝ _ (Set.mem_range_self (M i)))\n  have heq : (∑ i : Fin k, (1/(k:ℝ)) • G.indicator (M i)) = (fun _ : E => 1/(k:ℝ)) := by\n    ext e\n    simp only [Finset.sum_apply,Pi.smul_apply,smul_eq_mul]\n    rw [← Finset.mul_sum]\n    have hi : (∑ i : Fin k, G.indicator (M i) e)=1 := by\n      obtain ⟨i,hi,hu⟩ := hpartition e\n      rw [Finset.sum_eq_single i]\n      · simp [LooplessGraph.indicator,hi]\n      · intro j _ hji\n        have hj : e ∉ (M j).val := fun h => hji (hu j h)\n        simp [LooplessGraph.indicator,hj]\n      · simp\n    rw [hi,mul_one]\n  rwa [heq] at hmean\n\n/-- The exact decomposable-graph count theorem, including its coarser bound. -/\ntheorem count_main (G : LooplessGraph V E) (m k : ℕ)\n    (hm : 0 < m) (hV : Fintype.card V = 2 * m) (hk : 1 ≤ k)\n    (M : Fin k → G.Matching)\n    (hpartition : ∀ e : E, ∃! i : Fin k, e ∈ (M i).val) :\n    ((k : ℝ) * Real.exp (-8 * (1 - 1 / (k : ℝ)))) ^ m ≤\n        (Fintype.card G.Matching : ℝ) ∧\n      (Real.exp (-8) * (k : ℝ)) ^ m ≤\n        ((k : ℝ) * Real.exp (-8 * (1 - 1 / (k : ℝ)))) ^ m := by\n  have hkR : (0:ℝ) < k := by exact_mod_cast (show 0 < k by omega)\n  have hmR : (0:ℝ) < m := by exact_mod_cast hm\n  have hPM : Nonempty G.Matching := ⟨M ⟨0,by omega⟩⟩\n  let x : E → ℝ := fun _ => 1/(k:ℝ)\n  have hx : x ∈ G.polytope := uniform_decomposition_mem G hk M hpartition\n  have hh : marginalEntropy x=(m:ℝ)*Real.log (k:ℝ) := by\n    have hlog : Real.negMulLog (1/(k:ℝ))=(1/(k:ℝ))*Real.log (k:ℝ) := by\n      rw [one_div,Real.negMulLog,Real.log_inv]\n      ring\n    change (∑ e : E, Real.negMulLog (1/(k:ℝ)))=_\n    simp_rw [hlog]\n    rw [← Finset.sum_mul]\n    exact congrArg (fun t : ℝ => t*Real.log (k:ℝ)) (G.marginal_sum hV hx)\n  have hi := entropy_main G m hm hV hPM x hx\n  have hH := maxMatchingEntropy_le_log_card G hx\n  have hexp : Real.exp (-((m:ℝ)*Real.log (k:ℝ))/(m:ℝ))=1/(k:ℝ) := by\n    have he : -((m:ℝ)*Real.log (k:ℝ))/(m:ℝ) = -Real.log (k:ℝ) := by\n      field_simp\n    rw [he,Real.exp_neg,Real.exp_log hkR]\n    simp only [one_div]\n  rw [hh,hexp] at hi\n  have hlogbound : (m:ℝ)*(Real.log (k:ℝ)+(-8*(1-1/(k:ℝ)))) ≤\n      Real.log (Fintype.card G.Matching:ℝ) := by nlinarith\n  have hN : (0:ℝ) < Fintype.card G.Matching := by\n    exact_mod_cast Fintype.card_pos_iff.mpr hPM\n  constructor\n  · have he := Real.exp_le_exp.mpr hlogbound\n    rw [Real.exp_log hN,Real.exp_nat_mul,Real.exp_add,Real.exp_log hkR] at he\n    exact he\n  · apply pow_le_pow_left₀ (mul_nonneg (Real.exp_pos _).le hkR.le)\n    have he : Real.exp (-8) ≤ Real.exp (-8*(1-1/(k:ℝ))) := by\n      apply Real.exp_le_exp.mpr\n      have hq := (one_div_pos.mpr hkR).le\n      nlinarith\n    calc\n      Real.exp (-8)*(k:ℝ) = (k:ℝ)*Real.exp (-8) := mul_comm _ _\n      _ ≤ _ := mul_le_mul_of_nonneg_left he hkR.le\n\nend MatchingEntropy\n\nend\n\nend OAI\n"}, {"path": "lean/OAI/Computability/LoopMatching/Main.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/OAI/Computability/LoopMatching/Main.lean", "bytes": 1109, "sha256": "b89372f420ffa395508467274e015d2ed6b5f9d5a1810ab1edcbb8901f363e84", "content": "import OAI.Computability.LoopMatching.MachineMain\nimport OAI.Computability.LoopMatching.ExpandCount\nimport OAI.Computability.LoopMatching.SparseBoundary\n\nnamespace OAI\n\n/-! Polynomial-time approximation of weighted matchings with singleton loops. -/\n\nnamespace LoopMatching\n\ntheorem actual_algorithm_correct (G : Input) :\n    0 < (MachineMain.A G).2 ∧\n    (MachineMain.A G).1 ≤ (MachineMain.A G).2 * count G ∧\n    (MachineMain.A G).2 * count G ≤ 2 ^ (18 * G.n) * (MachineMain.A G).1 ∧\n    ((MachineMain.A G).1 = 0 ↔ count G = 0) := by\n  by_cases h : 2 * G.ordinary.records.length + G.loops.length < G.n\n  · have hz := count_eq_zero_of_sparse_bound G h\n    simp [MachineMain.A, h, hz]\n  · rw [MachineMain.A, ite_eq_right h]\n    exact ⟨Approximation.output_den_pos G,\n      Approximation.output_bounds_of_expanded_count G\n        (fun j _ => expanded_count G j)⟩\n\ntheorem fullEndpoint : FullEndpoint := by\n  obtain ⟨machine, hfinite, hlength⟩ := MachineMain.computational_endpoint\n  exact ⟨MachineMain.A, machine, hfinite, hlength, actual_algorithm_correct⟩\n\nend LoopMatching\n\nend OAI\n"}, {"path": "lean/OAI/Computability/MatchingCount/BinarySolve.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/OAI/Computability/MatchingCount/BinarySolve.lean", "bytes": 6571, "sha256": "299750c99714c599fb9dd9d988bbc20c0ba617b9aad8be4f05da8cc43bd36eb0", "content": "import OAI.Computability.MatchingCount.RawInput\n\nnamespace OAI\n\nsection\n\nnamespace BinaryMatching.Raw.InputData\nopen StackCode Algorithm Optimize\n\ndef powerBits (k : ℕ) : List Bool := List.replicate k false++[true]\nlemma powerBits_eq (k : ℕ) : powerBits k=(2^k).bits := by\n  induction k with\n  | zero => simp [powerBits]\n  | succ k ih =>\n    rw [pow_succ,Nat.mul_comm,Nat.bit0_bits (2^k) (by positivity)]\n    simpa only [powerBits,List.replicate_succ,List.cons_append] using congrArg (List.cons false) ih\n@[fun_prop] theorem powerBits_poly : Poly powerBits := by unfold powerBits; fun_prop\n\ndef smallSolve (z : Data) : List Bool :=\n  if z.1=0 then [true] else\n  if z.1%2=1 then [] else\n  if z.1=2 then multiplicityBits z.2 0 1 else\n  let r := result (parameters z)\n  if r < -10*(z.1:ℤ) then [] else powerBits (r-5*(z.1:ℤ)).toNat\n\ndef smallEstimate (G : Input) : ℕ :=\n  if G.n=0 then 1 else\n  if G.n%2=1 then 0 else\n  if G.n=2 then rawMultiplicity G 0 1 else integralEstimate G\nlemma smallSolve_repr (G : Input) : smallSolve (G.n,G.records.map repr)=(smallEstimate G).bits := by\n  unfold smallSolve smallEstimate\n  dsimp only\n  by_cases hn0 : G.n=0\n  · simp only [hn0,ite_true]; rfl\n  simp only [hn0,ite_false]\n  by_cases ho : G.n%2=1\n  · simp only [ho,ite_true]; rfl\n  simp only [ho,ite_false]\n  by_cases hn2 : G.n=2\n  · simpa only [hn2,ite_true] using multiplicityBits_repr G 0 1\n  simp only [hn2,ite_false]\n  have he : Even G.n := Nat.even_iff.mpr (by omega)\n  have hn : 2≤G.n := by omega\n  rw [parameters_repr,result_rep G hn he]\n  dsimp only [integralEstimate]\n  split_ifs\n  · rfl\n  · exact powerBits_eq _\n@[fun_prop] theorem smallSolve_poly : Poly smallSolve := by\n  have h : Poly (fun z : Data => if decide (z.1=0) then [true] else\n      if decide (z.1%2=1) then [] else if decide (z.1=2) then multiplicityBits z.2 0 1 else\n      if decide (result (parameters z) < -10*(z.1:ℤ)) then [] else\n        powerBits (result (parameters z)-5*(z.1:ℤ)).toNat) := by fun_prop\n  exact h.congr (fun z => by simp only [smallSolve,decide_eq_true_eq])\n\ndef solve (z : Data) : List Bool := if 2*z.2.length<z.1 then [] else smallSolve z\n\ndef sparseEstimate (G : Input) : ℕ := if 2*G.records.length<G.n then 0 else smallEstimate G\nlemma sparseEstimate_correct (G : Input) :\n    sparseEstimate G≤count G ∧ count G≤2^(9*G.n)*sparseEstimate G ∧ (sparseEstimate G=0 ↔ count G=0) := by\n  unfold sparseEstimate\n  split_ifs with hguard\n  · have hz := count_zero_of_uncovered G (lt_of_le_of_lt (covered_card_le_records G) hguard)\n    simp [hz]\n  · unfold smallEstimate\n    split_ifs with hn0 hodd hn2\n    · rw [count_zero_vertices G hn0,hn0]\n      norm_num\n    · rw [count_odd_vertices G (Nat.odd_iff.mpr hodd)]\n      simp\n    · rw [count_two_vertices G hn2]\n      refine ⟨le_rfl,?_,Iff.rfl⟩\n      exact Nat.le_mul_of_pos_left _ (by positivity)\n    · have hn : 4≤G.n := by omega\n      have he : Even G.n := Nat.even_iff.mpr (by omega)\n      rw [integralEstimate_eq G (by omega)]\n      exact estimate_correct G hn he\n\nlemma sparseEstimate_eq_approximate (G : Input) : sparseEstimate G=approximate G := by\n  unfold sparseEstimate\n  split_ifs with hg\n  · symm\n    exact (approximate_correct G).2.2.mpr\n      (count_zero_of_uncovered G (lt_of_le_of_lt (covered_card_le_records G) hg))\n  · unfold smallEstimate approximate\n    simp only [Nat.odd_iff]\n    split_ifs with hn0 ho hn2 hc\n    · rfl\n    · rfl\n    · rfl\n    · rw [integralEstimate_eq G (by omega)]\n      exact (estimate_correct G (by omega) (Nat.even_iff.mpr (by omega))).2.2.mpr\n        (count_zero_of_uncovered G hc)\n    · exact integralEstimate_eq G (by omega)\n\nlemma solve_decoded (G : Input) :\n    solve (decode (G.n.bits,G.records.map Parse.recordRepr))=(sparseEstimate G).bits := by\n  unfold solve sparseEstimate\n  rw [decode_length,List.length_map,decode_n_encoded]\n  by_cases hg : 2*G.records.length<G.n\n  · rw [ite_eq_left hg,ite_eq_left (by omega)]\n    rfl\n  · rw [ite_eq_right hg,ite_eq_right (by omega),decode_encoded G (by omega),smallSolve_repr]\n@[fun_prop] theorem solve_poly : Poly solve := by\n  have hh : Poly (fun z : Data => if decide (2*z.2.length<z.1) then [] else smallSolve z) := by fun_prop\n  exact hh.congr (fun z => by simp only [solve,decide_eq_true_eq])\n\ndef runInput (l : List Bool) : List Bool := solve (decode (Parse.input l))\nlemma runInput_encoded (G : Input) : runInput (encodeInput G)=(sparseEstimate G).bits := by\n  rw [runInput,Parse.input_encoded,solve_decoded]\n@[fun_prop] theorem runInput_poly : Poly runInput := by unfold runInput; fun_prop\n\ndef bitAlgorithm (b : Bits) : Bits := ⟨runInput b.data⟩\ntheorem bitAlgorithm_poly : Poly bitAlgorithm := by\n  change Poly (fun b : Bits => Bits.mk (runInput b.data))\n  exact Poly.comp Bits.mk (fun b : Bits => runInput b.data) Poly.bitsEncode\n    (Poly.comp runInput Bits.data runInput_poly Poly.bitsDecode)\n\nnoncomputable def binaryRoutine : Routine Bits.data Bits.data bitAlgorithm := bitAlgorithm_poly.routine\nlemma bitAlgorithm_encoded (G : Input) :\n    (bitAlgorithm ⟨encodeInput G⟩).data=(sparseEstimate G).bits := by\n  change runInput (encodeInput G)=(sparseEstimate G).bits\n  exact runInput_encoded G\n\nnoncomputable def countingRoutine : Routine encodeInput Nat.bits sparseEstimate :=\n  binaryRoutine.reindex encodeInput Nat.bits sparseEstimate (fun G => Bits.mk (encodeInput G))\n    (fun _ => rfl) bitAlgorithm_encoded\n\ntheorem deterministic_approximate_counting :\n    ∃ A : Input → ℕ,\n      ∃ machine : Turing.TM2ComputableInPolyTime encodeInput Nat.bits A,\n        (∀ k, Finite (machine.tm.Γ k)) ∧\n        (∃ P : Polynomial ℕ, ∀ G,(A G).bits.length≤P.eval (encodeInput G).length) ∧\n        ∀ G,A G≤count G ∧ count G≤2^(9*G.n)*A G ∧ (A G=0 ↔ count G=0) := by\n  refine ⟨sparseEstimate,countingRoutine.machine,countingRoutine.machine_alphabets_finite,?_,sparseEstimate_correct⟩\n  refine ⟨Polynomial.X+countingRoutine.time,?_⟩\n  intro G\n  simpa only [Polynomial.eval_add,Polynomial.eval_X] using countingRoutine.length_bound G\nend BinaryMatching.Raw.InputData\n\nnamespace BinaryMatching\n\ntheorem deterministic_approximate_counting :\n    ∃ A : Input → ℕ,\n      ∃ machine : Turing.TM2ComputableInPolyTime encodeInput Nat.bits A,\n        (∀ k, Finite (machine.tm.Γ k)) ∧\n        (∃ P : Polynomial ℕ, ∀ G,(A G).bits.length≤P.eval (encodeInput G).length) ∧\n        ∀ G,A G≤count G ∧ count G≤2^(9*G.n)*A G ∧ (A G=0 ↔ count G=0) :=\n  Raw.InputData.deterministic_approximate_counting\nend BinaryMatching\nend\n\nend OAI\n"}, {"path": "lean/OAI/MeasureTheory/SelfSimilar/Main.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/OAI/MeasureTheory/SelfSimilar/Main.lean", "bytes": 3054, "sha256": "dd4616b74c7e17364aca272822ecc162a2e21a35208890ab3d5798880dafc3f2", "content": "import OAI.MeasureTheory.SelfSimilar.EntropyGap\n\nnamespace OAI\n\nuniverse uι\n\nnamespace EntropyRateDimension\nopen MeasureTheory MeasureEntropy\nopen scoped BigOperators ENNReal\n\ntheorem exists_slightly_larger_depth {d χ h : ℝ} (hd : 0 ≤ d) (hgap : d*χ < h) :\n    ∃ a : ℝ, χ < a ∧ d*a < h := by\n  let t := (h-d*χ)/(2*(d+1))\n  have hp : 0 < 2*(d+1) := by linarith\n  have ht : 0 < t := div_pos (sub_pos.mpr hgap) hp\n  have he : t*(2*(d+1))=h-d*χ := div_mul_cancel₀ _ hp.ne'\n  refine ⟨χ+t,by linarith,?_⟩\n  nlinarith\n\nnamespace System\nvariable {ι : Type uι} [Fintype ι] [Nonempty ι]\nlocal instance : MeasurableSpace ι := ⊤\nlocal instance : MeasurableSingletonClass ι := ⟨fun _ => trivial⟩\n\ntheorem exponent_lower_bound (S : System ι) (μ : Measure ℝ) [IsProbabilityMeasure μ]\n    (hμ : S.SelfSimilar μ) : min 1 (S.entropyRate/S.lyapunov) ≤ Coding.exponent S := by\n  classical\n  by_contra! hstrict\n  obtain ⟨d,hed,hdm⟩ := exists_between hstrict\n  have hd : 0 ≤ d := (Coding.exponent_nonneg S).trans hed.le\n  have hd1 : d < 1 := hdm.trans_le (min_le_left _ _)\n  have hdr : d < S.entropyRate/S.lyapunov := hdm.trans_le (min_le_right _ _)\n  have hdh : d*S.lyapunov < S.entropyRate := (lt_div_iff₀ S.lyapunov_pos).mp hdr\n  obtain ⟨a,hχa,hda⟩ := exists_slightly_larger_depth hd hdh\n  have hap : 0 < a := S.lyapunov_pos.trans hχa\n  have hl : 0 < Real.log 2 := Real.log_pos (by norm_num)\n  have hε : 0 < (S.entropyRate-d*a)*Real.log 2 := mul_pos (sub_pos.mpr hda) hl\n  obtain ⟨b,hb,hbtype⟩ := exists_nat_log_small hε (Fintype.card ι)\n  have hbR : (0 : ℝ)<b := by exact_mod_cast hb\n  have htype : Real.log (Fintype.card (ι → Fin (b+1)))=\n      (Fintype.card ι : ℝ)*Real.log ((b : ℝ)+1) := by\n    simp only [Fintype.card_fun,Fintype.card_fin,Nat.cast_pow,Real.log_pow,Nat.cast_add,Nat.cast_one]\n  have hblock : (b : ℝ)*S.entropyRate ≤ (S.blocks hb).entropyRate := S.blocks_entropyRate_lower hb\n  have hgap : d*((b : ℝ)*a)*Real.log 2 < (S.blocks hb).entropyRate*Real.log 2-\n      Real.log (Fintype.card (ι → Fin (b+1))) := by\n    rw [htype]\n    have hh := mul_le_mul_of_nonneg_right hblock hl.le\n    nlinarith\n  obtain ⟨C,hC⟩ := S.selfSimilar_entropy_bound μ hμ hed\n  apply Typed.no_strict_entropy_gap (S.blocks hb) (wordType (ι := ι)) (S.ratio_wordType hb)\n    μ (S.selfSimilar_blocks μ hμ hb) hd hd1 (mul_pos hbR hap)\n    (A' := (b : ℝ)*((S.lyapunov+a)/2)) _ _ hC hgap\n  · rw [S.blocks_lyapunov]\n    exact mul_lt_mul_of_pos_left (by linarith) hbR\n  · exact mul_lt_mul_of_pos_left (by linarith) hbR\n\nend System\n\ntheorem entropy_rate_dimension {ι : Type uι} [Fintype ι] [Nonempty ι]\n    (S : System ι) (μ : Measure ℝ) [IsProbabilityMeasure μ]\n    (hμ : S.SelfSimilar μ) :\n    lowerHausdorffDimension μ = ENNReal.ofReal (min 1 (S.entropyRate / S.lyapunov)) := by\n  rw [S.selfSimilar_dimension μ hμ]\n  congr 1\n  exact le_antisymm (le_min (S.exponent_le_one μ hμ) (S.exponent_le_rate μ hμ)) (S.exponent_lower_bound μ hμ)\n\nend EntropyRateDimension\n\nend OAI\n"}, {"path": "lean/OAI/Probability/ClassicalON/Main.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/OAI/Probability/ClassicalON/Main.lean", "bytes": 2050, "sha256": "0e4bca8c6b8fe89ffec32eda5d3222af85062a2e3e685729f41578076b528c8a", "content": "import OAI.Probability.ClassicalON.BlockReduction\n\nnamespace OAI\n\nnoncomputable section\nopen MeasureTheory\nopen scoped InnerProductSpace BigOperators Classical\nnamespace ClassicalON\n\ntheorem correlation_eq_freeSpinMean (n : ℕ) (G : LatticeGraph) (b : G.edges → ℝ) (x y : G.vertices) :\n    correlation n G b x y=freeSpinMean n (fun e : G.edges => e.val.1) (fun e => e.val.2) b\n      (fun s => ⟪(s x).val,(s y).val⟫_ℝ) := by\n  unfold correlation partition interaction referenceLaw freeSpinMean weightedMean freeSpinEnergy freeSpinReference\n  congr 1\n  apply integral_congr_ae\n  filter_upwards with s\n  exact mul_comm _ _\n\ntheorem main : ExponentialDecay := by\n  intro n hn β hβ\n  obtain ⟨k,rfl⟩ := Nat.exists_eq_add_of_le hn\n  let : NeZero (3+k) := ⟨by omega⟩\n  obtain ⟨m,hm,hexp⟩ := LatticeGraph.connection_exponential β hβ.le\n  refine ⟨(3+k:ℕ)*4,m,hm,?_⟩\n  intro G b hb x y\n  have hthree (c : G.edges → ℝ) (hc : ∀ e,0 ≤ c e ∧ c e ≤ b e) :\n      0 ≤ freeSpinMean 3 (fun e : G.edges => e.val.1) (fun e => e.val.2) c (firstSpinProduct x y) ∧\n      freeSpinMean 3 (fun e : G.edges => e.val.1) (fun e => e.val.2) c (firstSpinProduct x y) ≤\n        4*Real.exp (-m*siteDistance x.val y.val) := by\n    have hfirst := firstSpinMean_bounds (fun e : G.edges => e.val.1) (fun e => e.val.2) c\n      (fun e => (hc e).1) x y\n    exact ⟨hfirst.1,hfirst.2.trans (hexp G c (fun e => ⟨(hc e).1,(hc e).2.trans (hb e).2⟩) x y)⟩\n  have hbound := coordinateSpinMean_block_bounds k (fun e : G.edges => e.val.1) (fun e => e.val.2) b\n    (fun e => (hb e).1) x y (4*Real.exp (-m*siteDistance x.val y.val)) hthree\n  rw [correlation_eq_freeSpinMean,innerSpinMean_eq_coordinate (3+k)\n    (fun e : G.edges => e.val.1) (fun e => e.val.2) b ⟨0,by omega⟩ x y]\n  constructor\n  · exact mul_nonneg (Nat.cast_nonneg _) hbound.1\n  · calc\n      _ ≤ (3+k:ℕ)*(4*Real.exp (-m*siteDistance x.val y.val)) :=\n        mul_le_mul_of_nonneg_left hbound.2 (Nat.cast_nonneg _)\n      _ = _ := by ring\n\nend ClassicalON\n\nend\n\nend OAI\n"}, {"path": "lean/OAI/RepresentationTheory/FoulkesHowe/Stabilization.lean", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/OAI/RepresentationTheory/FoulkesHowe/Stabilization.lean", "bytes": 2722, "sha256": "14360c9dda2885195e2ed9fbeabae30fa48b06689dce4954bda2a99a14275c92", "content": "import OAI.RepresentationTheory.FoulkesHowe.Model\nimport OAI.RepresentationTheory.FoulkesHowe.LinearCase\nimport OAI.RepresentationTheory.FoulkesHowe.PairingConstruction\nimport OAI.RepresentationTheory.FoulkesHowe.Surjectivity\nimport OAI.RepresentationTheory.FoulkesHowe.ProductVanishing\n\nnamespace OAI\n\nnoncomputable section\n\nuniverse u\n\nnamespace Problem346\n\ntheorem canonical_foulkes_howe_surjective :\n    ∀ (V : Type u) [AddCommGroup V] [Module ℂ V] [FiniteDimensional ℂ V] (a b : ℕ), 2 ≤ a → a * (a - 1) ≤ b → ∃ μ : SymPow b (SymPow a V) →ₗ[ℂ] SymPow a (SymPow b V), IsFoulkesMap a b V μ ∧ Function.Surjective μ ∧ ∀ ν : SymPow b (SymPow a V) →ₗ[ℂ] SymPow a (SymPow b V), IsFoulkesMap a b V ν → ν = μ := by\n  intro V _ _ _ a b ha hab\n  apply canonical_surjective_unique_of_vanishing a b V\n  have hb : 1 ≤ b := by\n    have hpred : 1 ≤ a - 1 := by omega\n    have hprod := Nat.mul_le_mul ha hpred\n    omega\n  exact vanishing_on_products_aux V a b (by omega) hb hab\n\ntheorem canonical_foulkes_howe_a_one :\n    ∀ (V : Type u) [AddCommGroup V] [Module ℂ V] [FiniteDimensional ℂ V] (b : ℕ), ∃ μ : SymPow b (SymPow 1 V) →ₗ[ℂ] SymPow 1 (SymPow b V), IsFoulkesMap 1 b V μ ∧ Function.Bijective μ ∧ ∀ ν : SymPow b (SymPow 1 V) →ₗ[ℂ] SymPow 1 (SymPow b V), IsFoulkesMap 1 b V ν → ν = μ := by\n  intro V _ _ _ b\n  exact canonical_foulkes_howe_a_one_aux V b\n\ntheorem vanishing_on_products :\n    ∀ (V : Type u) [AddCommGroup V] [Module ℂ V] [FiniteDimensional ℂ V] (a b : ℕ), 1 ≤ a → 1 ≤ b → a * (a - 1) ≤ b → ∀ T : SymmetricMultilinearForm a b V, IsSymmetricMultilinearForm a b V T → (∀ u : Fin b → V, T (fun _ => symMonomial b V u) = 0) → T = 0 := by\n  intro V _ _ _ a b ha hb hab T hT hdiag\n  exact vanishing_on_products_aux V a b ha hb hab T hT hdiag\n\ntheorem foulkes_comparison_embedding :\n    ∀ (V : Type u) [AddCommGroup V] [Module ℂ V] [FiniteDimensional ℂ V] (a b : ℕ), 2 ≤ a → a * (a - 1) ≤ b → ∃ ι : SymPow a (SymPow b V) →ₗ[ℂ] SymPow b (SymPow a V), IsGLEquivariantEmbedding a b V ι := by\n  intro V _ _ _ a b ha hab\n  obtain ⟨μ, hμ, hsurj, _⟩ :=\n    canonical_foulkes_howe_surjective (Module.Dual ℂ V) a b ha hab\n  exact PairingConstruction.comparison_of_surjection V a b ⟨μ, hμ, hsurj⟩\n\ntheorem sixth_symmetric_power_comparison :\n    ∀ (V : Type u) [AddCommGroup V] [Module ℂ V] [FiniteDimensional ℂ V] (b : ℕ), 30 ≤ b → ∃ ι : SymPow 6 (SymPow b V) →ₗ[ℂ] SymPow b (SymPow 6 V), IsGLEquivariantEmbedding 6 b V ι := by\n  intro V _ _ _ b hb\n  exact foulkes_comparison_embedding V 6 b (by norm_num) (by simpa using hb)\n\nend Problem346\n\nend\n\nend OAI\n"}, {"path": "lean/docs/015.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/015.md", "bytes": 1014, "sha256": "2eb9097c27c5ff8603aab35c72e7dd5830e45f2d3b4ff7af224c55aabc3615a0", "content": "# Torus-packet equidistribution in prime, quartic, and sextic degrees\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [Equidistribution of Prime-Degree Torus Packets with Arbitrary Local Type](../../preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/paper.pdf)\n\n## Scope\n\nThe formalization proves equidistribution of volume-weighted torus packets for totally real number fields of every fixed prime degree at least five. For any sequence of full lattices whose multiplier-order discriminants tend to infinity, the packet measures converge weakly to Haar probability measure and form a tight family, so no mass escapes. Arbitrary local homothety types are allowed, and the fields may vary along the sequence.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Equidistribution of prime-degree torus packets | [DukePrimeDegree.lean](../ComparatorChallenges/DukePrimeDegree.lean) |\n"}, {"path": "lean/docs/024.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/024.md", "bytes": 1415, "sha256": "13056416c388c8fac013d201ead954e5bcd521f542220b989d569b64eadbdd40", "content": "# An asymptotic formula for the number of totients\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [An asymptotic formula for the number of totients](../../preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/An-asymptotic-formula-for-the-number-of-totients-September-25-2026.pdf)\n\n## Scope\n\nLet $V(x)$ count the distinct values of Euler's totient function up to $x$. The formalization constructs the paper's explicit positive main term from finite arithmetic approximants and proves that their ratio tends to one. In particular, $V(cx)/V(x)\\to c$ for every fixed $c>0$, answering the Erdős–Hall regular-variation question.\n\nThe formalization also gives asymptotics for totients $v\\le x$ whose least preimage $\\ell(v)=\\min\\{n\\ge1:\\varphi(n)=v\\}$ lies between $kx$ and $(k+1)x$. The associated coefficient is positive under the stated existence condition; when no totient $d$ satisfies $kd<\\ell(d)$, the count and coefficient are identically zero. The cases $k=1,2$ have positive coefficients.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Totient-count asymptotics and regular variation | [TotientAsymptotic.lean](../ComparatorChallenges/TotientAsymptotic.lean) |\n| Exact zero case for the companion totient counts | [TotientCompanionZero.lean](../ComparatorChallenges/TotientCompanionZero.lean) |\n"}, {"path": "lean/docs/082.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/082.md", "bytes": 1008, "sha256": "a010a21dcde470de99bcbf7db425e0213980e6b504462ba5a0d290634b0c2d97", "content": "# Annular variation and dyadic absolute bounds for the triangular Hilbert transform\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [The maximal triangular Hilbert transform at the symmetric point](../../preprints/The-maximal-triangular-Hilbert-transform-at-the-symmetric-point-September-24-2026/paper.pdf)\n\n## Scope\n\nThe formalized result proves the symmetric maximal estimate for the triangular Hilbert transform: for arbitrary complex $F,G\\in L^3(\\mathbb R^2)$, the $L^{3/2}$ norm of the supremum over all finite hard-truncation intervals is at most $C\\|F\\|_3\\|G\\|_3$ for one absolute constant $C$. It also establishes a common full-measure set on which the truncated integrals are defined and almost-everywhere measurability of the maximal output.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Maximal triangular Hilbert transform bound | [TriangularHilbert.lean](../ComparatorChallenges/TriangularHilbert.lean) |\n"}, {"path": "lean/docs/113.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/113.md", "bytes": 2781, "sha256": "988234abb376528ffa180cd07778d5349ed15190b9d21c961117dd9d011c7e00", "content": "# Approximate counting and entropy of perfect matchings\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [A Fully Polynomial Randomized Approximation Scheme for Perfect Matchings in General Graphs](../../preprints/A-Fully-Polynomial-Randomized-Approximation-Scheme-for-Perfect-Matchings-in-General-Graphs-September-23-2026/main.pdf)\n- [Entropy and Face Dimension of the Perfect-Matching Polytope](../../preprints/Entropy-and-Face-Dimension-of-the-Perfect-Matching-Polytope-September-23-2026/main.pdf)\n\n## Scope\n\nThe formalization gives a fully polynomial randomized approximation scheme for counting perfect matchings in every finite simple undirected graph. For rational $0<\\varepsilon<1$ and $0<\\delta<1/2$, it returns a nonnegative rational estimate with relative error at most $\\varepsilon$ with probability at least $1-\\delta$. If the graph has no perfect matching, every execution returns zero.\n\nThe algorithm is a fixed finite-alphabet randomized machine. Its worst-case running time is polynomial in the binary input length, $\\varepsilon^{-1}$, and $\\log(\\delta^{-1})$, including on unsuccessful random tapes.\n\nFor feasible edge marginals $x$ of perfect matchings in a loopless multigraph on $2m$ vertices, let $F(x)=-\\sum_e x_e\\log x_e$, $B(x)=-\\sum_e(1-x_e)\\log(1-x_e)$, and let $H(x)$ be the maximum entropy of a matching law with those marginals. The formalization proves $F(x)-(2-2/m)B(x)\\le H(x)\\le F(x)$ for $m\\ge1$, including boundary points of the polytope. It retains the earlier coefficient-eight entropy bound and the deterministic counting approximations with factors $512^n$ for loopless graphs and $2^{18n}$ with singleton loops.\n\nA further statement identifies the exact face obtained by expanding a degree-three vertex into a triangle and shows that minimal faces are carried to minimal faces by the expansion. The paper's sharp global face-dimension bound is not part of that selected statement.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Randomized approximation of the perfect-matching count | [MatchingFPRAS.lean](../ComparatorChallenges/MatchingFPRAS.lean) |\n| Perfect-matching entropy bound | [MatchingEntropy.lean](../ComparatorChallenges/MatchingEntropy.lean) |\n| Deterministic approximate matching count | [BinaryMatching.lean](../ComparatorChallenges/BinaryMatching.lean) |\n| Approximate counting with singleton loops | [SingletonLoopMatching.lean](../ComparatorChallenges/SingletonLoopMatching.lean) |\n| Refined pointwise perfect-matching entropy bounds | [MatchingEntropyBounds.lean](../ComparatorChallenges/MatchingEntropyBounds.lean) |\n| Face correspondence under triangle expansion | [TriangleFace.lean](../ComparatorChallenges/TriangleFace.lean) |\n"}, {"path": "lean/docs/115.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/115.md", "bytes": 2251, "sha256": "7503f6655041af845dd16fb16746316e877356e75bcb3793ba68917585e02fb6", "content": "# Sampling and counting contingency tables with arbitrary margins\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [Exact Uniform Sampling of Contingency Tables with Arbitrary Margins](../../preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/main.pdf)\n- [An FPRAS for Cell-Bounded Contingency Tables](../../preprints/An-FPRAS-for-Cell-Bounded-Contingency-Tables-September-24-2026/main.pdf)\n\n## Scope\n\nThe formalization gives an exact uniform sampler for nonnegative integer contingency tables with arbitrary prescribed row and column sums having equal totals. It terminates almost surely, every halted output is feasible, and each table has exactly the uniform limiting probability. Expected bit complexity is polynomial in the dimensions and the binary length of the margins.\n\nIt also gives a sampler with a fixed polynomial time bound on every execution whose total-variation error is at most $2^{-k}$ for requested precision $k\\ge1$. The same Comparator file includes the companion approximation scheme for counting tables with individual cell bounds.\n\nThe formalization gives a fully polynomial randomized approximation scheme for counting nonnegative integer matrices with prescribed row sums, column sums, and individual entry bounds. Both dimensions may vary, the margins and bounds are binary encoded, and zero entry bounds are allowed. For rational $0<\\varepsilon,\\delta<1$, the algorithm returns a nonnegative estimate with relative error at most $\\varepsilon$ with probability at least $1-\\delta$, and returns zero on every execution when no table exists.\n\nThe running time is polynomial in the encoded input size, $\\varepsilon^{-1}$, and $\\log(\\delta^{-1})$ on every random tape. The same Comparator file also contains the companion sampling results for tables without cell bounds.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Exact and bounded-time sampling of contingency tables | [ContingencyTables.lean](../ComparatorChallenges/ContingencyTables.lean) |\n| Randomized approximate counting of cell-bounded contingency tables | [ContingencyTables.lean](../ComparatorChallenges/ContingencyTables.lean) |\n"}, {"path": "lean/docs/127.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/127.md", "bytes": 888, "sha256": "a612cbc2218486fd07d4b118e3102776bf303a4e2f01d4e1f9817918a4a9d524", "content": "# Average sensitivity of polynomial threshold functions\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [Average sensitivity of polynomial threshold functions](../../preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/main.pdf)\n\n## Scope\n\nThe formalized result proves the average-sensitivity bound $8d\\sqrt n$ for every Boolean threshold function defined by a real multilinear polynomial of degree at most $d$ on the $n$-dimensional cube. The sign convention assigns value $1$ at zero. This establishes the stated asymptotic bound, rather than the separate conjecture identifying exact extremizers.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Average sensitivity of polynomial threshold functions | [GotsmanLinial.lean](../ComparatorChallenges/GotsmanLinial.lean) |\n"}, {"path": "lean/docs/148.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/148.md", "bytes": 1453, "sha256": "340f3919c849c2485886b3df087cbb840cc770408f84c3fbbad7a4a0de898e60", "content": "# The entropy-rate dimension formula for self-similar measures\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [The entropy-rate dimension formula for self-similar measures on the line](../../preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/main.pdf)\n\n## Scope\n\nThe formalization proves $\\dim_H\\mu=\\min\\{1,h_{\\mathrm{RW}}/\\chi\\}$ for the lower Hausdorff dimension of every finite real self-similar probability measure with positive weights and nonzero contraction ratios of absolute value below one. Here $h_{\\mathrm{RW}}$ is the entropy rate of random affine compositions and $\\chi$ is the corresponding Lyapunov exponent in the same logarithmic base. Ratios may be signed and unequal, and exact overlaps are allowed.\n\nIt also gives two consequences without exact overlaps: the usual entropy-over-Lyapunov formula for a common positive contraction ratio, and the attractor formula $\\dim_H K=\\min\\{1,s\\}$, where $s\\ge0$ is the unique solution of $\\sum_i|r_i|^s=1$. Absolute continuity is outside these statements.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Entropy-rate dimension formula | [SelfSimilar.lean](../ComparatorChallenges/SelfSimilar.lean) |\n| Homogeneous-measure and self-similar-attractor dimension formulas | [SelfSimilarCorollaries.lean](../ComparatorChallenges/SelfSimilarCorollaries.lean) |\n"}, {"path": "lean/docs/192.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/192.md", "bytes": 1097, "sha256": "2541e439837779913e446e3584cdf4c2454b4734d3e83dccf775497efccf26cf", "content": "# Boolean functions violate the square-root degree bound by arbitrary factors\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [Unbounded Violations of the Square-Root Degree Bound](../../preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026.pdf)\n\n## Scope\n\nThe formalization disproves the Gopalan–Servedio square-root degree conjecture by an unbounded factor. For every $C>0$, it gives a nonconstant Boolean function on a finite sign cube for which the sum of its linear Fourier coefficients exceeds $C\\sqrt{\\deg f}$, where $\\deg f$ is its real multilinear degree.\n\nIt also proves that the ratio of the sum of the absolute values of those coefficients to $\\sqrt{\\deg f}$ is unbounded among positive-degree Boolean functions.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Unbounded violations of the square-root Fourier-degree bound | [SquareRootDegree.lean](../ComparatorChallenges/SquareRootDegree.lean) |\n"}, {"path": "lean/docs/210.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/210.md", "bytes": 1048, "sha256": "9b8b9f5be621d2f91791632a668f718f5664624ea3ce1fae7c0187af6aa65ad2", "content": "# Foulkes' conjecture for sixth powers and quadratic stabilization\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [Quadratic stabilization of the canonical Foulkes–Howe map](../../preprints/Quadratic-Stabilization-of-the-Canonical-Foulkes-Howe-Map-September-25-2026/paper.pdf)\n\n## Scope\n\nThe formalized result proves surjectivity of the canonical averaged Foulkes–Howe map $\\mathrm{Sym}^b(\\mathrm{Sym}^a\\,V)\\to\\mathrm{Sym}^a(\\mathrm{Sym}^b\\,V)$ for every finite-dimensional complex vector space, $a\\ge2$, and $b\\ge a(a-1)$. It also covers the bijective $a=1$ case, the vanishing-on-products consequence, and the corresponding equivariant embedding in the reverse direction. The sixth-power specialization is covered for $b\\ge30$; the companion's full range $b\\ge6$ is not included.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Quadratic stabilization of the canonical Foulkes–Howe map | [FoulkesHowe.lean](../ComparatorChallenges/FoulkesHowe.lean) |\n"}, {"path": "lean/docs/215.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/215.md", "bytes": 1031, "sha256": "41ed31b3d5b0f9c0ce5df3604527f17b26eec23b225a7a2184f5a9c2d936ac30", "content": "# Canonical $`O(3)`$ continuum limit and exact $`O(4)`$ mass asymptotics\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [Exponential decay in two-dimensional classical $O(n)$ models](../../preprints/Exponential-decay-in-two-dimensional-classical-On-models-September-23-2026/paper.pdf)\n\n## Scope\n\nThe formalized result proves exponential decay of spin correlations for the two-dimensional classical $O(n)$ model at every temperature when $n\\ge3$. For each interaction bound $\\beta>0$, constants $A$ and $m>0$ work for every finite square-lattice subgraph with free boundary and nonnegative edge strengths at most $\\beta$: the correlation between sites $x,y$ is at most $Ae^{-m|x-y|}$. The infinite-volume limit and the separate $O(4)$ spectral-gap claim are not included.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Exponential correlation decay for classical $O(n)$ models | [ClassicalON.lean](../ComparatorChallenges/ClassicalON.lean) |\n"}, {"path": "lean/docs/230.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/230.md", "bytes": 1159, "sha256": "49930254a299e52e97bd89d72c7b7fa4b4b498ebc2bac782d48b93b40ca773e4", "content": "# Exact Hausdorff gauges for SLE\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [An exact Hausdorff gauge for SLE: A moment-integral and finite-batch construction](../../preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/An-exact-Hausdorff-gauge-for-SLE-September-25-2026.pdf)\n- [An explicit exact Hausdorff gauge for SLE: A regular formula from quantitative tails and dense visits](../../preprints/An-explicit-exact-Hausdorff-gauge-for-SLE-September-26-2026/An-explicit-exact-Hausdorff-gauge-for-SLE-September-26-2026.pdf)\n\n## Scope\n\nFor $0<\\kappa<8$, put $d=1+\\kappa/8$. The formalization proves that every continuous nondecreasing Hausdorff gauge agreeing with $h(r)=r^d(\\log\\log(1/r))^{(2-d)/2}$ at sufficiently small positive radii almost surely assigns positive measure to every nontrivial compact positive-time segment of ordinary chordal $\\mathrm{SLE}_\\kappa$.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Positivity of the explicit gauge on every positive-time segment | [SLELowerPositivity.lean](../ComparatorChallenges/SLELowerPositivity.lean) |\n"}, {"path": "lean/docs/245.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/245.md", "bytes": 980, "sha256": "d05302b4c350e522680f61473d849ae2eae567d20a73da624948f533fee813f6", "content": "# Weak normalization implies strong normalization in pure type systems\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [Weak and strong normalization in pure type systems](../../preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/paper.pdf)\n\n## Scope\n\nThe formalization proves the $\\beta$-Barendregt–Geuvers–Klop conjecture for pure type systems: if every legal expression in every valid context has some terminating $\\beta$-reduction sequence, then every $\\beta$-reduction sequence from every such expression terminates. Reduction is allowed inside type annotations, and the specification may have arbitrary sorts and nonfunctional axioms or rules.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Weak normalization implies strong normalization in every pure type system | [TypeSystemNormalization.lean](../ComparatorChallenges/TypeSystemNormalization.lean) |\n"}, {"path": "lean/docs/296.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/296.md", "bytes": 1391, "sha256": "aaaf849da96ce5bece74765383abe338a9680488b34df1037bb2317186ed5a10", "content": "# The generator problem for finite factors\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [Relative generation and the generator problem for finite factors](../../preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/paper.pdf)\n\n## Scope\n\nThe formalization answers the generator problem affirmatively for type $\\mathrm{II}_1$ factors with separable predual: one bounded operator generates the factor under adjoints and weak-operator closure. It imposes no separability assumption on the representing Hilbert space or on the operator-norm topology.\n\nIt also proves the paper's relative-generation result. For an irreducible inclusion of type $\\mathrm{II}_1$ factors with separable predual for the larger factor, the unitaries that generate the larger factor together with the smaller one form a dense $G_\\delta$ set in the normalized trace two-norm topology. The normalized faithful normal trace is included in the assertion.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Single generation of separable-predual $\\mathrm{II}_1$ factors | [FactorGeneration.lean](../ComparatorChallenges/FactorGeneration.lean) |\n| Dense relative generators for irreducible inclusions of finite factors | [RelativeGeneration.lean](../ComparatorChallenges/RelativeGeneration.lean) |\n"}, {"path": "lean/docs/369.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/369.md", "bytes": 1133, "sha256": "3a79656f6ff91a6f687969cc79e40171531596880e16b2c6f4ac0b889e5ed3aa", "content": "# The hot spots conjecture for simply connected planar domains\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [Strict hot spots and absence of interior critical points on smooth simply connected planar domains](../../preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/main.pdf)\n\n## Scope\n\nThe strict hot-spots conjecture asks whether extrema of a first nonconstant Neumann eigenfunction occur only on the boundary. The formalization proves the stronger interior statement on every nonempty smooth bounded simply connected planar domain: every nonzero eigenfunction in the first positive Neumann eigenspace has nonvanishing gradient throughout the interior. Consequently all global maxima and minima lie on the boundary. The conclusion applies to every eigenfunction even when the eigenvalue is multiple.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Strict hot spots and absence of interior critical points | [HotSpots.lean](../ComparatorChallenges/HotSpots.lean) |\n"}, {"path": "lean/docs/372.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/lean/docs/372.md", "bytes": 1004, "sha256": "5c6a8e21366181a580d561cfdc50d32306ac2f428ea487084db21bc5d603e3d6", "content": "# Global uniqueness in smooth isotropic elasticity\n\nThe following describes the scope of the Lean formalization related to the following accompanying paper(s):\n\n- [Global Uniqueness for the Smooth Isotropic Elasticity Inverse Problem](../../preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/article.pdf)\n\n## Scope\n\nThe formalization proves global uniqueness in the three-dimensional static isotropic elasticity inverse problem. On a bounded connected smooth domain, let two pairs of smooth real Lamé moduli satisfy $\\mu>0$ and $3\\lambda+2\\mu>0$ on the closure. If their full displacement-to-traction maps agree, then both Lamé moduli agree throughout the domain. The selected statement is uniqueness; it does not supply a reconstruction algorithm.\n\n## Comparator links\n\n| Result | Comparator statement |\n| --- | --- |\n| Global uniqueness of smooth isotropic Lamé moduli | [ElasticityUniqueness.lean](../ComparatorChallenges/ElasticityUniqueness.lean) |\n"}, {"path": "preprints/A-Fully-Polynomial-Randomized-Approximation-Scheme-for-Perfect-Matchings-in-General-Graphs-September-23-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-Fully-Polynomial-Randomized-Approximation-Scheme-for-Perfect-Matchings-in-General-Graphs-September-23-2026/README.md", "bytes": 808, "sha256": "1163a09eb4c9ba6538606c07e9497c7489c9bd223ed461ae140bb8c1deeb1ee3", "content": "# [A Fully Polynomial Randomized Approximation Scheme for Perfect Matchings in General Graphs](main.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 23, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:A-Fully-Polynomial-Randomized-Approximation-Scheme-for-Perfect-Matchings-in-General-Graphs-September-23-2026,\n  author = {{OpenAI}},\n  title = {{A Fully Polynomial Randomized Approximation Scheme for Perfect Matchings in General Graphs}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/A-Fully-Polynomial-Randomized-Approximation-Scheme-for-Perfect-Matchings-in-General-Graphs-September-23-2026/main.pdf}{OAI:A-Fully-Polynomial-Randomized-Approximation-Scheme-for-Perfect-Matchings-in-General-Graphs-September-23-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/A-Fully-Polynomial-Randomized-Approximation-Scheme-for-Perfect-Matchings-in-General-Graphs-September-23-2026/build/main.tex", "url": 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d}\n\\newcommand{\\bit}{\\operatorname{bit}}\n\\newcommand{\\PM}{\\operatorname{PM}}\n\n\\setlist{topsep=4pt,itemsep=3pt,parsep=0pt}\n\\renewcommand{\\labelitemi}{\\(\\bullet\\)}\n\\numberwithin{equation}{section}\n\\hypersetup{pdftitle={A Fully Polynomial Randomized Approximation Scheme for Perfect Matchings in General Graphs},pdfauthor={OpenAI}}\n\\title{A Fully Polynomial Randomized Approximation Scheme\\\\\nfor Perfect Matchings in General Graphs}\n\\author{OpenAI}\n\\date{September 23, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe give a fully polynomial randomized approximation scheme (FPRAS) for\ncounting perfect matchings in arbitrary finite simple undirected graphs,\nresolving the general-graph perfect-matching approximation problem.\nThe algorithm returns zero with certainty when no perfect matching exists.\nOtherwise, it achieves relative error $\\eps$ with failure probability at\nmost $\\delta$ in worst-case bit time polynomial in the input length,\n$\\eps^{-1}$, and $\\log\\delta^{-1}$.\n\\end{abstract}\n\\tableofcontents\n\\section{Introduction}\n\\label{sec:introduction}\n\nLet $G=(V,E)$ be a finite simple undirected graph. A perfect matching is a\nset of edges incident to each vertex exactly once. Write\n\\[\n Z(G)=\\#\\{M\\subseteq E:M\\text{ is a perfect matching of }G\\}.\n\\]\nA fully polynomial randomized approximation scheme approximates $Z(G)$\nwithin a prescribed relative error, with a prescribed success probability,\nin time polynomial in the input length and the reciprocal error.\nThe confidence parameter enters through its logarithm. We prove the\nfollowing theorem, including a bound on the time of every execution.\n\n\\begin{theorem}\\label{thm:main}\nThere is a uniform classical randomized algorithm which, given $G$ and\nrational parameters $0<\\eps<1$ and $0<\\delta<1/2$, returns a nonnegative\nrational number $\\widehat Z$ such that\n\\[\n \\Prb\\bigl[(1-\\eps)Z(G)\\le \\widehat Z\\le(1+\\eps)Z(G)\\bigr]\n \\ge 1-\\delta.\n\\]\nIf $Z(G)=0$, the output is zero with certainty. The worst-case bit running\ntime is polynomial in the input encoding length, $\\eps^{-1}$, and\n$\\log\\delta^{-1}$.\n\\end{theorem}\n\nAs consequences, \\cref{sec:prescribed-degree} gives counting schemes for\nedge subsets with prescribed vertex degrees and for matchings of a\nspecified size. On feasible inputs, its samplers always return a\nfeasible object and approximate the corresponding uniform law in total\nvariation.\n\n\\subsection{History and significance}\nPerfect matchings illustrate the distinction between finding a\ncombinatorial structure and counting its realizations. Edmonds gave a\npolynomial-time algorithm for finding a maximum matching in a general\ngraph~\\cite{Edmonds1965}. Valiant proved that exact counting is\n\\#P-complete even for bipartite graphs, where it is the permanent of a\nzero--one matrix~\\cite{Valiant1979}. Pfaffian methods give\npolynomial-time exact counting for planar graphs~\\cite{Kasteleyn1963}.\n\nFor approximation, three problems must be distinguished: counting all\nmatchings, counting bipartite perfect matchings, and counting perfect\nmatchings in general graphs. Jerrum and Sinclair gave an FPRAS for the\ntotal number of matchings in an arbitrary graph, summed over all\ncardinalities~\\cite[Corollary~4.5]{JerrumSinclair1989}.\nTheir perfect-matching algorithm applied when the ratio of near-perfect\nmatchings, which leave exactly two vertices unmatched, to perfect\nmatchings was bounded by a fixed polynomial~\\cite[Theorem~5.3]{JerrumSinclair1989}.\nWithout such a bound, perfect matchings can carry too little stationary\nmass for this sampling approach. Approximating the total number of\nmatchings therefore does not isolate its perfect-matching contribution\nwith relative accuracy.\n\nJerrum, Sinclair, and Vigoda removed the ratio restriction in the\nbipartite setting and obtained an FPRAS for the permanent of every\nnonnegative matrix~\\cite[Theorem~1.1]{JerrumSinclairVigoda2004}.\nTheir method assigns weights to near-perfect states according to their\ntwo unmatched vertices and learns suitable weights while gradually\nchanging edge activities. The general-graph question was explicitly\nraised by Jerrum and Sinclair~\\cite[Section~7(i)]{JerrumSinclair1989}\nand remained a benchmark for approximate counting. Cai and Liu relate\nit to approximation for the eight-vertex model~\\cite{CaiLiu2020};\nFei, Goldberg, and Lu distinguish the spin-system parameter line\nequivalent to general perfect-matching approximation from the region\ncovered by their FPRAS~\\cite[Proposition~1.2 and Theorem~1.5]{FeiGoldbergLu2025}.\n\\Cref{thm:main} answers the general-graph perfect-matching approximation\nproblem affirmatively.\n\nA direct extension of the bipartite chain faces a precise obstruction.\n\\v{S}tefankovi\\v{c}, Vigoda, and Wilmes constructed graphs on which every\nJSV-type chain has either exponentially small stationary probability\nof perfect matchings or exponentially slow mixing, even with arbitrary\nweights depending on the hole pattern~\\cite[Definition~2.1 and\nTheorem~2.2 in the full version]{StefankovicVigodaWilmes2018}.\nHere JSV type means the perfect-and-near-perfect state space with the\nspecified Broder proposals and a Metropolis acceptance rule. The\nsampler below uses a different state space: a product of\nperfect-matching spaces on an enlarged colored graph, with exchanges\nof alternating cycles between coordinates. Its energy comparison is\nproved for these states and moves.\n\nSubsequent permanent algorithms sharpen the bipartite result.\nBez\\'{a}kov\\'{a}, \\v{S}tefankovi\\v{c}, Vazirani, and Vigoda accelerated the\ncooling schedule and improved the analysis of the matching\nchain~\\cite{BezakovaStefankovicVaziraniVigoda2008}.\nChen, Vigoda, and Yang obtain a further improvement through restricted\nPoincar\\'e inequalities and coupled flows~\\cite{ChenVigodaYang2026};\nChen, Guo, Vigoda, and Yang develop an energy-weighted\nmulticommodity-flow comparison and improve the running time\nagain~\\cite{ChenGuoVigodaYang2026}. These results concern bipartite\nperfect matchings.\n\nFor nonbipartite graphs, Yi gives deterministic relative approximation\nfor hafnians, the weighted sums over perfect matchings, when the\n$n$-vertex support has minimum degree at least $(1/2+\\gamma)n$ and\nnonzero weights lie in $[\\theta,1]$~\\cite[Theorem~1.1]{Yi2026}.\nHere $0<\\gamma<1/2$ and $0<\\theta\\le1$ are fixed constants, and the\npolynomial running-time exponent may depend on them.\n\\Cref{thm:main} has no density promise on its simple unweighted input.\n\nThe proof below also builds on the methodological framework of weighted\nmatching chains. Alternating overlays and weight-preserving switches\nunderlie the encodings of Jerrum and Sinclair and of Jerrum, Sinclair,\nand Vigoda~\\cite{JerrumSinclair1989,JerrumSinclairVigoda2004}.\nTheir sampling-to-counting strategy learns local partition ratios while\ngradually changing activities.\nHere these ratios are exposed by marked edges in an enlarged graph,\nand vertex scaling maintains a row-wise balance condition. The\ncapacity bound, subdivision and marked-edge identities needed for this\nconstruction are proved explicitly.\n\nEnergy comparison through weighted demands has classical antecedents\nin multicommodity-flow bounds for mixing~\\cite{Sinclair1992}.\nOur comparison differs from routing every demand along a path of chain\ntransitions: long-arc conversions enter a signed identity and need not\nbe legal single updates. The resulting estimate initially controls\nonly additive functions of two matching coordinates. The separate\nproduct-space argument is essential to obtain a spectral gap for\narbitrary functions. It uses the classical orthogonal decomposition\ninto coordinate interactions~\\cite[Section~2]{EfronStein1981}; the\nordered assignment of residual terms and the replication estimate are\nproved below.\n\n\\subsection{Proof architecture}\n\nThe counting algorithm starts with the complete graph, whose perfect\nmatchings are easy to count, and gradually suppresses the nonedges of\n$G$ by decreasing their positive activities. It estimates the ratios\nof successive partition functions and multiplies them. To keep the\nobservations statistically useful, it updates vertex scales from\nestimates of the two-hole partition ratios: partition functions after\ndeleting two vertices, divided by the current partition function.\nAccurate estimates keep each row maximum between two absolute\nconstants. A vertex scale\nmultiplies every complete matching weight by the same factor, preserving\nthe probability law used to estimate the current counting ratio.\n\nThe purpose of balance is to control the extra mass introduced by an\nauxiliary graph. We replace each logical edge by a weighted path, with\na bijection between logical perfect matchings and matchings using only\nthese real path edges. We then add weak clique edges in overlapping\nbags indexed by a tree. Under balance, the added edges increase the\npartition function by at most a factor of two, so a sample uses only\nreal edges with probability at least one half. A specially marked\nauxiliary edge exposes the corresponding two-hole partition ratio.\nThese are exact event identities; they provide both the counting\nestimate and the data for the next scale update.\n\nTo prove the inflation bound, \\cref{thm:logical-hole-bound} assigns a\npairing capacity to each even hole set using the strongest bottleneck\npaths between its vertices. The capacity pays for the weights of\npossible added edges on those holes. Subdivision transfers this bound\nto the real graph while making incident effective strengths comparable.\nThe resulting geometry supplies the tree of clique bags. Thus the\nhole estimates control the probability of the observable events,\nwhile the tree-bag structure supplies a sampler for their law.\n\nThat sampler uses local switches in one matching and exchanges\nalternating cycles between two matching coordinates. Its energy\ncomparison initially controls only additive functions of the two\ncoordinates. A quadrangulation adapted to the bag labels gives an exact\nsigned identity for a whole-cycle difference\n(\\cref{lem:quadrangulation,thm:pair-energy}). The identity is not a\nroute through chain transitions: long arc conversions may change many\nedges. Two-matching encodings instead compare its terms with local\nswitches and cycle exchanges, with polynomially bounded multiplicity.\n\nA ladder of nearby weight distributions then places many independent\ncoordinates at each tier. An ordered orthogonal decomposition ensures\nthat each coordinate-interaction term is charged to at most one pair.\nReplication reduces the coefficient of their combined contribution\nenough to absorb it. This yields a spectral gap for\narbitrary functions of the product state (\\cref{lem:replica-gap}).\nThe bottom tier has unit activities and admits exact counting and\nideal sampling by a tree dynamic program. The sampler requires no\nbalance assumption. It therefore remains defined, with the same time\nbound, after an inaccurate empirical estimate; balance is used only\nto prove that accurate estimates propagate through the counting stages.\n\n\\Cref{tab:graph-laws} summarizes the changes of law. Finally,\n\\cref{sec:counting,sec:complexity} account for all arithmetic and\nfinite-bit random choices. Every polynomial has an absolute degree;\nthe constants are chosen for transparent estimates rather than\npractical efficiency.\n\n\\begin{table}[ht]\n\\centering\n\\small\n\\begin{tabular}{@{}p{.18\\textwidth}cp{.61\\textwidth}@{}}\n\\toprule\nObject & Vertices & Relation of matching laws \\\\\n\\midrule\nInput $G$ & $n$ & The target is its unweighted count $Z(G)$. \\\\\nComplete graph & $n$ & Raw activities $w^{(j)}$ gradually suppress the nonedges of $G$. \\\\\nVertex scaling & $n$ & Activities $\\lambda$ preserve the normalized raw matching law. \\\\\nReal paths & $N$ & Subdivision gives a matching bijection with a common weight multiplier. \\\\\nColored bags & $N$ & Their activities $W$ recover the real law when every sampled edge is revealed as real. \\\\\nWeight tiers & $N$ & Common-interval clamps of $W$ connect the unit-activity law to the bag law on one colored state space. \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{The successive graphs and probability laws. Here $n$ is the\noriginal vertex count and $N$ the enlarged count. Scaling, subdivision,\nconditioning, and clamping have distinct roles.}\n\\label{tab:graph-laws}\n\\end{table}\n\nThe specialized combinatorial and sampling arguments are proved here.\nWe use Edmonds's algorithm for the initial matching-existence test\nand Hoeffding's bounded-variable inequality for concentration and\nconfidence amplification~\\cite{Edmonds1965,Hoeffding1963}.\n\\section{Preliminaries}\n\\label{sec:preliminaries}\n\nLogarithms are natural unless a base is indicated.\n\n\\subsection{Weighted matchings and holes}\nWe allow parallel edges distinguished by colors. A matching specifies\nthe actual colored edges it uses. For positive rational activities\n$a=(a_e)$, put\n\\[\n \\wt_a(M)=\\prod_{e\\in M}a_e,\\qquad\n Z_a(-U)=\\sum_{M\\in\\calM(H-U)}\\wt_a(M),\n \\qquad Z_a=Z_a(-\\varnothing),\n\\]\nwhere $H$ is the underlying graph and $\\calM(H-U)$ is its set of perfect\nmatchings after deleting $U$. The empty graph has one perfect matching,\nof weight one. Whenever $Z_a>0$, define\n\\[\n g_a(U)=\\frac{Z_a(-U)}{Z_a},\\qquad\n \\pi_a(M)=\\frac{\\wt_a(M)}{Z_a}.\n\\]\nThese weighted colored graphs are intermediate spaces constructed from\nthe simple input graph. Their representation is explicit: vertices,\ncolored edges, and rational activities are stored individually. The\nmain theorem retains the finite simple undirected input model; it does\nnot assert a counting algorithm for compressed edge multiplicities or\nfor singleton-covering loops.\n\nWe call the vertices in $U$ \\emph{holes}. For distinct vertices we abbreviate\n$g_a(\\{i_1,\\ldots,i_k\\})$ to $g_a(i_1\\cdots i_k)$.\nCollapsing parallel activities into their\nsum preserves every partition function. Conditional on a collapsed\nedge, its original type can be recovered with probabilities\nproportional to the summands.\n\nThe union of two matchings, with common edges retained twice, has\nmaximum degree two. Its nontrivial components are alternating paths\nand even alternating cycles. Swapping the layers along a component\npreserves the product of their weights. In the union of two perfect\nmatchings, deleting common identical edges leaves disjoint alternating\ncycles; different colors on the same pair of vertices form a cycle\nof length two. We fix a vertex order and a color order once and for\nall. They resolve every combinatorial tie in the proof.\n\n\\subsection{Bottleneck similarities}\nFor a positive weighted connected graph define\n\\begin{equation}\\label{eq:bottleneck}\n B_{ij}=\\max\\left\\{1,\\ \\max_{P:i\\leadsto j}\\ \\min_{e\\in P}a_e\\right\\}\n \\quad (i\\ne j).\n\\end{equation}\nSimple paths suffice. This symmetric similarity satisfies\n$B_{ij}\\ge\\min\\{B_{ik},B_{kj}\\}$: concatenating two paths and erasing\nloops cannot lower their minimum activity. For $t>1$, its equivalence\nclasses are the connected components of the graph retaining edges of\nactivity at least $t$. Singletons are included. At $t=1$ we take one\nclass containing every vertex, consistently with the floor at one.\nFor an even set $U$ define its \\emph{pairing capacity}\n\\begin{equation}\\label{eq:pairing-capacity}\n \\Phi_B(U)=\\max_{\\mathcal P}\\prod_{ij\\in\\mathcal P}B_{ij},\n \\qquad \\Phi_B(\\varnothing)=1,\n\\end{equation}\nwhere $\\mathcal P$ runs over pairings of $U$. We use this quantity only\nin proofs; the counting algorithm never needs to enumerate pairings\nor compute $\\Phi_B(U)$ for a general hole set.\n\n\\subsection{Tree bags}\n\\begin{definition}\\label{def:tree-bags}\nA \\emph{tree-bag graph} consists of a finite rooted tree of labels and\na finite even vertex set. A label $l$ at level $k$ has height $h_l=2^k$;\nthe root has level zero and a child has level one greater than its\nparent. Each vertex belongs to either one label or two adjacent\nlabels. The bag at $l$ is the set of vertices belonging to $l$.\nFor each unordered pair of distinct vertices and each common label\n$l$, there is one edge of color $l$. There are no other edges.\nFor an integer parameter $D\\ge1$, its reference activities obey\n\\begin{equation}\\label{eq:bag-weight-range}\n \\frac{h_l}{D}\\le W_{xy,l}\\le3h_l.\n\\end{equation}\nWe also consider activities obtained by clamping all $W_e$ to one\ncommon interval $[a,b]\\subset(0,\\infty)$.\n\\end{definition}\n\nEvery vertex pair has at most two edge colors. At any bag, the\ninterfaces with its parent and its different children are disjoint:\na vertex in two such interfaces would have at least three memberships.\nEmpty bags and repeated underlying vertex sets are permitted.\nClamping is monotone and does not increase the ratio of a larger\nactivity to a smaller one. In particular, reference activities of\nnearby labels remain comparable after clamping.\n\n\\subsection{Dirichlet energy and approximation}\nFor a finite reversible Markov kernel $P$ with stationary law $\\mu$,\nits Dirichlet energy is\n\\begin{equation}\\label{eq:dirichlet}\n \\calE_{\\mu,P}(f)\n =\\frac12\\sum_{x,y}\\mu(x)P(x,y)(f(x)-f(y))^2.\n\\end{equation}\nA Poincar\\'e inequality $\\Var_\\mu f\\le\\gamma^{-1}\\calE_{\\mu,P}(f)$\nmeans that the spectral gap is at least $\\gamma$. A symmetric proposal\n$Q$ has Metropolis acceptance probability~\\cite{MetropolisEtAl1953,Hastings1970} $\\min\\{1,\\mu(y)/\\mu(x)\\}$\nfor a proposed move from $x$ to $y$. Its off-diagonal stationary capacity is\n\\begin{equation}\\label{eq:metropolis-capacity}\n \\mu(x)P(x,y)=Q(x,y)\\min\\{\\mu(x),\\mu(y)\\}.\n\\end{equation}\nWe regard inapplicable proposals as holding moves. All state spaces\nused below contain at least one perfect matching; thus their positive\nactivities define probability laws with full support on that space.\n\nTotal variation is\n$\\|\\mu-\\rho\\|_{\\TV}=\\frac12\\sum_x|\\mu(x)-\\rho(x)|$.\nA deterministic map, or a common randomized postprocessing kernel,\ndoes not increase total variation, by the triangle inequality.\nTwo finite laws at total variation distance $\\eta$ admit a coupling\nthat disagrees with probability $\\eta$: first match the common masses\n$\\min(\\mu(x),\\rho(x))$, then couple the remainders. Applying this\nconstruction successively bounds the discrepancy probability of an\nadaptive simulation by the sum of its conditional one-step errors.\n\\section{Controlling the cost of unmatched vertices}\n\\label{sec:holes}\n\nThis section proves the bound that will control the additional edges in\nour sampling graph.  It uses only alternating paths and differentiation\nof a finite partition function.\n\nLet $V$ have even cardinality $n\\ge2$, and give every edge of the complete\nlogical graph $K_V$ a positive activity $\\lambda_{ij}$. Use the weighted\nmatching notation of \\cref{sec:preliminaries}, abbreviating\n$\\calM(K_V-U)$ to $\\mathcal M(-U)$. For any positive activity array $a$\non this graph, define the row maxima\n\\[\n r_i(a)=\\max_{j\\ne i}g_a(ij).\n\\]\nThe activities $a$ are \\emph{balanced} if $1/4\\le r_i(a)\\le1$ for\nevery $i$. Let $B$ be the bottleneck similarity of $\\lambda$ from\n\\eqref{eq:bottleneck}, and let $\\Phi_B$ be its pairing capacity from\n\\eqref{eq:pairing-capacity}.\n\nThe estimate we need controls every even hole set, although balance\nonly bounds the two-hole ratios:\n\n\\begin{theorem}[Logical hole bound]\n\\label{thm:logical-hole-bound}\nLet $\\lambda$ be balanced, let $B$ be its bottleneck similarity, and set\n\\[\n D_0=10^8(n+1)^4.\n\\]\nFor every even set $U\\subseteq V$,\n\\begin{equation}\n\\label{eq:logical-hole-bound}\n g_\\lambda(U)\\Phi_B(U)\\le 2D_0^{|U|/2}.\n\\end{equation}\n\\end{theorem}\n\nTo prove this, we will add a small activity proportional to $B_{ij}$\non every pair. Bounds for two and four holes keep the resulting\npartition inflation below two. Any pairing of an arbitrary hole set\ncan then be inserted using the added edges, giving the stated bound.\n\nWe use two elementary alternating-path comparisons. Such weight-preserving\noverlays also underlie the weighted matching encodings in\n\\cite{JerrumSinclair1989,JerrumSinclairVigoda2004}; the comparisons and\ntheir constants needed here are proved below. In an overlay of\ntwo matchings, edges retain their layer.  Common edges can be kept as\ntwo parallel copies.  A path starting at a vertex unmatched in exactly\none layer is then uniquely determined, and switching its edges between\nthe layers preserves the product of their weights.\n\n\\begin{lemma}[Four-hole inequality]\n\\label{lem:four-holes}\nFor any positive activities $\\mu$ and four distinct vertices $a,b,c,d$,\n\\[\n g_\\mu(abcd)\\le\n g_\\mu(ab)g_\\mu(cd)+g_\\mu(ac)g_\\mu(bd)\n                         +g_\\mu(ad)g_\\mu(bc).\n\\]\nConsequently, if every row maximum is at most $4$, then\n\\begin{equation}\n\\label{eq:small-hole-bounds}\n g_\\mu(\\varnothing)=1,\\qquad\n g_\\mu(U)\\le4\\quad(|U|=2),\\qquad\n g_\\mu(U)\\le48\\quad(|U|=4).\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nOverlay $M\\in\\mathcal M(-\\{a,b,c,d\\})$ and\n$P\\in\\mathcal M(-\\varnothing)$.  The alternating path from $a$ ends at\none of $b,c,d$, say $b$.  Switch that path between the layers.  The\noutput layers miss $\\{c,d\\}$ and $\\{a,b\\}$, respectively, and their\nproduct weight is unchanged.  This map is injective for the specified\nendpoint $b$: in the output overlay, follow the path from $a$ and switch\nit back.  The same argument applies to endpoints $c,d$.\nSumming the three injective comparisons and dividing by $Z_\\mu^2$\nproves the inequality.  The remaining assertions follow immediately.\n\\end{proof}\n\nThe four-hole comparison controls small hole sets by row maxima. We\nnext need an additional factor when two holes are joined by a strong\npath. Normalizing by the row maximum at the moving hole will make\nthe successive relocation errors add, without multiplying along the\npath.\n\nThe next lemma is stated with $\\mu\\ge\\lambda$ so that it continues to\napply while we add activities to the graph.  Its bottleneck similarity\nalways remains the one defined from $\\lambda$.\n\n\\begin{lemma}[Relocating a hole along a strong path]\n\\label{lem:hole-relocation}\nLet $B$ be defined by \\eqref{eq:bottleneck}, and suppose that\n$\\mu_e\\ge\\lambda_e$ for every edge and\n\\[\n \\frac18\\le r_i(\\mu)\\le4\\qquad(i\\in V).\n\\]\nIf $|U|\\in\\{2,4\\}$ and $i,j\\in U$ are distinct, then\n\\begin{equation}\n\\label{eq:hole-relocation-bound}\n g_\\mu(U)\\le\\frac{10^5n}{B_{ij}}.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nThroughout this proof, omit the subscript $\\mu$ from $g$ and $r$.\nWrite $B=B_{ij}$.  For $B\\le8$, the assertion follows from\n\\eqref{eq:small-hole-bounds}.  Suppose therefore that $B>8$, and choose\na simple path from $i$ to $j$ whose $\\lambda$-activities, and hence its\n$\\mu$-activities, are all at least $B$.\n\nWe first give one step of the relocation.  Let $a$ be the hole currently\non this path, let $S$ be the current hole set, and let $z$ be the next\nvertex.  Thus $a\\in S$ and $\\mu_{az}\\ge B$.\nIf $z\\in S$, inserting $az$ in a matching missing $S$ is injective and\nincreases its weight by $\\mu_{az}$.  Hence\n\\begin{equation}\n\\label{eq:adjacent-hole-fill}\n \\frac{g(S)}{r_a}\\le\n \\frac{g(S\\setminus\\{a,z\\})}{B r_a}\\le\\frac{32}{B}.\n\\end{equation}\nThe last inequality uses $|S|\\le4$, \\eqref{eq:small-hole-bounds}, and\n$r_a\\ge1/8$.\n\nNow suppose $z\\notin S$.  Choose $p\\ne z$ with $g(zp)=r_z$.\nInserting $az$ gives $g(az)\\le1/\\mu_{az}\\le1/B<1/8$, so $p\\ne a$.\nOverlay $M\\in\\mathcal M(-S)$ and $P\\in\\mathcal M(-\\{z,p\\})$.\nThe path from $a$ starts with an edge of $P$.  Its endpoint belongs to\nexactly one of the following three cases.\n\\begin{enumerate}[label=\\textup{(\\roman*)},leftmargin=*]\n\\item It ends at $z$.  Switching the path gives layers missing\n$S\\setminus\\{a\\}\\cup\\{z\\}$ and $\\{a,p\\}$.\nThe product weight is unchanged.\n\\item It ends at $p$, which requires $p\\notin S$.\nSwitching gives layers missing $S\\setminus\\{a\\}\\cup\\{p\\}$ and\n$\\{a,z\\}$.  Insert $az$ in the second layer, making it perfect.\nThe product weight increases by $\\mu_{az}\\ge B$.\n\\item It ends at $x\\in S\\setminus\\{a,p\\}$.\nSwitching gives layers missing $S\\setminus\\{a,x\\}$ and\n$\\{z,p,a,x\\}$.  Insert $az$ in the second layer; its remaining holes\nare $\\{p,x\\}$.  Again the product weight increases by at least $B$.\n\\end{enumerate}\nThese cases exhaust the possibilities.  Indeed, the degree-one\nvertices in the overlay are exactly the symmetric difference of the\ntwo hole sets; if $p\\in S$, then $p$ is isolated and cannot be an\nendpoint of the path.\n\nThe edge $az$ is absent from both input layers, since the first misses\n$a$ and the second misses $z$. Switching cannot create an edge absent\nfrom the overlay. Each map is injective when its case and endpoint are specified.\nFor case (i), the inverse switches the output path from $a$.\nFor cases (ii) and (iii), first remove the inserted edge $az$ from the\nsecond output layer, then switch the path from $a$ in the resulting\noverlay.  This reconstructs both input layers, including their common\nedges.  Summing over all inputs, using $g(ap)\\le r_a$, and dividing by\n$Z_\\mu^2r_z$ yields\n\\begin{equation}\n\\label{eq:relocation-step}\n\\begin{split}\n g(S)\\le{}&\\frac{r_a}{r_z}\n                g(S\\setminus\\{a\\}\\cup\\{z\\})\\\\\n &+\\frac{1}{B r_z}\\left[\n   \\mathbf 1_{\\{p\\notin S\\}}g(S\\setminus\\{a\\}\\cup\\{p\\})\n   +\\sum_{x\\in S\\setminus\\{a,p\\}}\n        g(S\\setminus\\{a,x\\})g(px)\\right].\n\\end{split}\n\\end{equation}\nAn inapplicable first term inside the brackets is omitted entirely.\nThe brackets are at most $96$: for $|S|=4$ their first term is at most\n$48$ and their at most three product terms are each at most $16$;\nfor $|S|=2$ the corresponding bound is $8$.\nAfter division by $r_a$, therefore,\n\\begin{equation}\n\\label{eq:normalized-relocation}\n \\frac{g(S)}{r_a}\\le\n \\frac{g(S\\setminus\\{a\\}\\cup\\{z\\})}{r_z}\n       +\\frac{6144}{B}.\n\\end{equation}\n\nStarting at $a=i$, apply this recurrence along the chosen path, keeping\nall holes in $U\\setminus\\{i\\}$ fixed.  Stop just before reaching the\nfirst of these fixed holes; such a hole exists because the path ends\nat $j$.  The path is simple, so there are at most $n-2$ recurrence\nsteps, followed by \\eqref{eq:adjacent-hole-fill}.  The normalization by\nthe current row maximum makes the leading terms telescope.  Since\n$r_i\\le4$,\n\\[\n g(U)\\le\\frac{4\\bigl(6144(n-2)+32\\bigr)}{B}\n       \\le\\frac{10^5n}{B},\n\\]\nas required.\n\\end{proof}\n\n\n\\begin{proof}[Proof of \\cref{thm:logical-hole-bound}]\nTemporarily add a distinguished parallel edge of activity $tB_{ij}/D_0$\nto every pair $ij$, where $0\\le t\\le1$.  Collapsing parallel edges gives\nactivities\n\\[\n \\mu_{ij}(t)=\\lambda_{ij}+tB_{ij}/D_0.\n\\]\nWrite $Z(t)=Z_{\\mu(t)}$ and $g_t=g_{\\mu(t)}$; $B$ remains fixed.\nAll partition functions involved are polynomials in $t$, and $Z(t)>0$.\nDifferentiation of their finite sums gives\n\\begin{equation}\n\\label{eq:partition-derivative}\n \\frac{d}{dt}\\log Z(t)=\\frac1{D_0}\\sum_{ij}B_{ij}g_t(ij),\n\\end{equation}\nwhere sums over edges are unordered, and for distinct $x,y$,\n\\begin{equation}\n\\label{eq:hole-derivative}\n \\frac{d}{dt}g_t(xy)\n =\\frac1{D_0}\\sum_{ij:\\{i,j\\}\\cap\\{x,y\\}=\\varnothing}\n                  B_{ij}g_t(xyij)\n       -g_t(xy)\\frac{d}{dt}\\log Z(t).\n\\end{equation}\n\nOn any initial time interval during which all row maxima belong to\n$[1/8,4]$, Lemma~\\ref{lem:hole-relocation} bounds every summand\n$B_{ij}g_t(ij)$ and every summand $B_{ij}g_t(xyij)$ by $10^5n$.\nSince\n\\[\n \\frac{\\binom n2\\,10^5n}{D_0}<\\eta,\n \\qquad \\eta=10^{-3},\n\\]\nwe obtain, throughout that interval,\n\\[\n 0\\le\\frac{d}{dt}\\log Z(t)\\le\\eta,\n \\qquad\n -\\eta g_t(xy)\\le\\frac{d}{dt}g_t(xy)\\le\\eta.\n\\]\nIntegrating gives\n\\[\n e^{-\\eta t}g_0(xy)\\le g_t(xy)\\le g_0(xy)+\\eta t.\n\\]\nFor the lower row bound, fix a neighbor maximizing the row at time zero;\nthe moving row maximum is at least this one evolving entry.\nConsequently, balance at time zero implies\n\\[\n \\frac14e^{-\\eta}\\le r_i(\\mu(t))\\le1+\\eta.\n\\]\nBoth bounds lie strictly inside $[1/8,4]$.  By continuity, the row maxima\ncannot first leave this interval before time $1$: at a proposed first\nexit the displayed strict bounds still hold and persist on a\nneighborhood.  Thus all preceding estimates hold on $[0,1]$, and\n\\begin{equation}\n\\label{eq:logical-inflation}\n \\frac{Z(1)}{Z(0)}\\le e^\\eta<2.\n\\end{equation}\n\nChoose a pairing $Q$ of $U$ attaining $\\Phi_B(U)$.  At time $1$, keep the\noriginal and added parallel edges distinguished.  To each original\nmatching in $\\mathcal M(-U)$, add the edges of $Q$ using their\n\\emph{added} copies, of activities $B_{ij}/D_0$.  This is an injective\nmap into perfect matchings at time $1$, and it multiplies every weight\nby $\\Phi_B(U)/D_0^{|U|/2}$.  Hence\n\\[\n Z_\\lambda(-U)\\frac{\\Phi_B(U)}{D_0^{|U|/2}}\\le Z(1).\n\\]\nDivide by $Z(0)=Z_\\lambda$ and apply\n\\eqref{eq:logical-inflation}.\n\\end{proof}\n\\section{A subdivision with controlled holes}\n\\label{sec:enlargement}\n\nThe sampler will use additional clique edges. To control the mass these\nedges introduce, we first replace each logical edge by a path. The\npath profile has two roles: equal adjacent factors make the ratio of\nits two matching patterns telescope, and the gradual change from each\nendpoint to the middle makes incident effective strengths comparable.\nWe first construct the enlarged graph and identify its marked laws,\nthen bound the mass introduced by its additional edges.\n\nThroughout this section the logical graph is the complete graph on an\neven number $n\\ge2$ of vertices, with positive rational activities\n$\\lambda_{uv}\\le4^K$, where $K\\ge0$ is an integer. Write\n$B$ for its bottleneck similarity, and put\n\\begin{equation}\\label{eq:enlargement-parameters}\n H_u=\\max_{v\\ne u}B_{uv},\\qquad\n p=2K+2,\\qquad\n N=n+4p\\binom n2,\\qquad D=100N^2D_0.\n\\end{equation}\nHere $D_0=10^8(n+1)^4$ is the constant in\n\\cref{thm:logical-hole-bound}. Thus $1\\le B_{uv}\\le H_u\\le4^K$.\n\n\\subsection{The path profile}\n\nOrient each logical edge $e=uv$ by the fixed vertex order, and replace\nit by a path\n\\[\n x_0=u,x_1,\\ldots,x_{4p},x_{4p+1}=v.\n\\]\nInternal vertices of different paths are distinct. Denote the activity\nof $x_{j-1}x_j$ by $t_j=t_j^e$. For $1\\le r\\le p$, set\n\\begin{align}\n t_{2r-1}=t_{2r}\n   &=\\max\\{B_{uv},H_u2^{-(r-1)}\\},\\notag\\\\\n t_{4p+3-2r}=t_{4p+2-2r}\n   &=\\max\\{B_{uv},H_v2^{-(r-1)}\\},\\label{eq:path-profile}\\\\\n t_{2p+1}&=\\lambda_{uv}.\\notag\n\\end{align}\nWe call these the \\emph{real} edges and write $Z'$ and $g'$ for their\npartition function and hole ratios. Let $B'$ be their bottleneck\nsimilarity. The two innermost equal pairs have activity $B_{uv}$,\nbecause $H_u2^{-(p-1)},H_v2^{-(p-1)}\\le1/2$.\n\n\\begin{lemma}\\label{lem:subdivision}\nThe real graph has $N$ vertices. Its perfect matchings are in\nbijection with logical perfect matchings, and\n\\begin{equation}\\label{eq:real-partition}\n Z'=C_0 Z_\\lambda,\n \\qquad\n C_0=\\prod_e\\prod_{\\substack{1\\le j\\le4p+1\\\\j\\text{ even}}}t_j^e.\n\\end{equation}\nOn a noncentral real edge, its effective strength $B'$ equals its\nactivity. On the central edge of the path for $uv$, it equals\n$B_{uv}$. Incident effective strengths differ by a factor of at most\ntwo. For an internal vertex $x_j$, its maximum incident effective\nstrength is\n\\[\n T_j=\n \\begin{cases}\n t_j,&1\\le j\\le2p,\\\\\n t_{j+1},&2p+1\\le j\\le4p.\n \\end{cases}\n\\]\nIts \\emph{home terminal} is $u$ in the first case and $v$ in the\nsecond. Every edge on its leg to its home has activity at least $T_j$.\n\\end{lemma}\n\n\\begin{proof}\nAn intact path has exactly two ways to match its internal vertices:\nthe even-indexed edges, which use neither terminal, or the\nodd-indexed edges, which use both. The ratio of the latter weight to\nthe former is $\\lambda_{uv}$, since all the noncentral factors cancel\nin equal adjacent pairs. A real perfect matching therefore chooses\nthe odd tiling precisely on a logical perfect matching. This proves\nthe bijection and \\cref{eq:real-partition}.\n\nEvery noncentral edge has an equal-activity twin sharing an internal\nvertex of degree two. A path connecting its endpoints that avoids\nthat edge must use the twin. Its bottleneck therefore cannot exceed\nthe edge activity, and the direct edge attains that activity.\n\nThe minimum activity on the full path replacing $e$ is $\\lambda_e$.\nConsequently, for every $t>1$, real edges of activity at least $t$\nconnect two terminals exactly when logical edges of activity at least\n$t$ connect them: a simple path between terminals traverses complete\nsubdivision paths. In particular $B'$ restricted to terminals is $B$.\nLet $a=x_{2p}$ and $b=x_{2p+1}$ be the ends of the central edge.\nThe legs from $a,b$ to $u,v$ have minimum activity $B_{uv}$.\nIf $B_{uv}>\\max\\{1,\\lambda_{uv}\\}$, a logical path attaining\n$B_{uv}$ avoids $uv$, and its subdivision together with these legs\nconnects $a$ to $b$ with bottleneck $B_{uv}$. If\n$B_{uv}=\\max\\{1,\\lambda_{uv}\\}$, the central edge and the floor at\none already give the lower bound. For the upper bound, any\nalternative path from $a$ to $b$ must first use an edge on a leg\nof activity $B_{uv}$. Thus $B'_{ab}=B_{uv}$ in all cases.\n\nAlong each half of a path the displayed profile is nonincreasing\ntoward the center and successive values differ by at most two.\nThe effective strength at the center agrees with both innermost\npairs. At a terminal $u$, every first edge has activity $H_u$.\nThese facts give all remaining assertions.\n\\end{proof}\n\n\\subsection{The enlarged matching law}\n\nFor each integer $0\\le k\\le\\lfloor\\log_2\\max_uH_u\\rfloor$, take\none label for every component of the enlarged graph whose retained\nedges have effective strength at least $2^k$, including singleton\ncomponents. A label at level $k>0$ has as parent its containing\ncomponent at level $k-1$. At level zero there is one component;\nthese labels therefore form a rooted tree. Components recurring at\ndifferent levels are still different labels.\n\nLabel each real edge $xy$ by the component containing it at level\n$\\lfloor\\log_2B'_{xy}\\rfloor$. A vertex belongs precisely to the\nlabels of its incident real edges. Equal-level incident labels are\nidentical, and the factor-two bound in \\cref{lem:subdivision} implies\nthat there are at most two incident levels. If there are two, they\nare consecutive and their labels are parent and child. Thus these\nmemberships satisfy \\cref{def:tree-bags}. In particular, a bag is\nthe set of vertices with that membership; it need not be the entire\nthreshold component defining its label.\n\nWithin every bag of label $l$ and height $h_l$, add a virtual edge\nof activity $h_l/D$ for each vertex pair. Collapse any real edge\nof color $l$ into the virtual edge with the same endpoints and\ncolor, adding its activity. This gives one colored edge per common\nlabel, of activity\n\\[\n W_{xy,l}=h_l/D+\n \\begin{cases}\n t_{xy},&xy\\text{ is a real edge with label }l,\\\\\n 0,&\\text{otherwise}.\n \\end{cases}\n\\]\nSince a real edge assigned to $l$ has\n$t_{xy}\\le B'_{xy}<2h_l$, these weights satisfy\n\\cref{eq:bag-weight-range}. Also\n$1/D\\le W_{xy,l}\\le3\\cdot4^K$.\n\nFor each logical edge $e$, split off activity $1/D$ from the\nvirtual activity on its central colored edge, and mark that portion\nas the \\emph{probe for $e$}. This is possible because $h_l\\ge1$.\nAfter sampling a colored matching, independently reveal the portion\nof each used edge in proportion to its real, probe, and remaining\nvirtual activities; zero portions are omitted.\n\n\\Cref{fig:subdivision-probe-example} shows the two real tilings and\nthe probe on one illustrative path.\n\\begin{figure}[ht]\n\\centering\n\\begin{tikzpicture}[x=.59cm,y=1cm,\n  reference/.style={draw=black!25,line width=.45pt},\n  selected/.style={draw=black,line width=1.5pt},\n  vertex/.style={circle,fill=black,inner sep=1.1pt}]\n  \\foreach \\row/\\height in {even/3.6,odd/1.8,probe/0} {\n    \\foreach \\i in {0,...,17} {\n      \\coordinate (\\row\\i) at (\\i,\\height);\n    }\n    \\foreach \\j [evaluate=\\j as \\i using int(\\j-1)] in {1,...,17}\n      \\draw[reference] (\\row\\i)--(\\row\\j);\n  }\n  \\foreach \\j [evaluate=\\j as \\i using int(\\j-1)] in {2,4,...,16}\n    \\draw[selected] (even\\i)--(even\\j);\n  \\foreach \\j [evaluate=\\j as \\i using int(\\j-1)] in {1,3,...,17}\n    \\draw[selected] (odd\\i)--(odd\\j);\n  \\foreach \\j [evaluate=\\j as \\i using int(\\j-1)] in {1,3,5,7,11,13,15,17}\n    \\draw[selected] (probe\\i)--(probe\\j);\n  \\draw[draw=black,densely dotted,line width=1.8pt]\n    (probe8)--(probe9) node[midway,above=4pt] {$1/D$};\n  \\foreach \\row in {even,odd,probe} {\n    \\foreach \\i in {0,...,17}\n      \\node[vertex] at (\\row\\i) {};\n    \\node[below=3pt] at (\\row0) {$u$};\n    \\node[below=3pt] at (\\row8) {$x_8$};\n    \\node[below=3pt] at (\\row9) {$x_9$};\n    \\node[below=3pt] at (\\row17) {$v$};\n  }\n  \\foreach \\i in {0,17}\n    \\draw[fill=white,line width=.7pt] (even\\i) circle[radius=1.7pt];\n  \\foreach \\j [evaluate=\\j as \\i using int(\\j-1)] in {1,...,17} {\n    \\ifnum\\j<3\n      \\node[font=\\scriptsize,above=3pt] at ($(even\\i)!.5!(even\\j)$) {$4$};\n    \\else\\ifnum\\j=9\n      \\node[font=\\scriptsize,above=3pt] at ($(even\\i)!.5!(even\\j)$) {$\\tfrac12$};\n    \\else\n      \\node[font=\\scriptsize,above=3pt] at ($(even\\i)!.5!(even\\j)$) {$2$};\n    \\fi\\fi\n  }\n  \\node[anchor=east] at (-.9,3.6) {even tiling};\n  \\node[anchor=east] at (-.9,1.8) {odd tiling};\n  \\node[anchor=east] at (-.9,0) {sole probe};\n\\end{tikzpicture}\n\\caption{One subdivision path with $K=1$, $p=4$ and $4p+1=17$ edges.\nThe top labels are the real activities: $H_u=4$,\n$H_v=B_{uv}=2$, and $\\lambda_{uv}=1/2$; the central edge has\neffective strength $B'_{x_8x_9}=2$. Solid edges show the selected\nreal tiling. The even tiling uses neither terminal, and the odd tiling\nuses both; the odd-to-even weight ratio is $\\lambda_{uv}$.\nThe last row uses the probe portion of the central colored bag edge\nand real portions elsewhere. Here the central label has height $h_l=2$,\nso its collapsed activity $1/2+2/D$ consists of real activity $1/2$,\nprobe activity $1/D$, and remaining virtual activity $1/D$.\nThe probe forces the real edges on the two outer legs to use both\nterminals. Their combined weight equals the even-tiling weight,\nso the remaining paths represent a logical matching with $u,v$ deleted.}\n\\label{fig:subdivision-probe-example}\n\\end{figure}\n\nLet $Z_{\\mathrm{bag}}$ be the colored partition function of the enlarged\ngraph, and put $I=Z_{\\mathrm{bag}}/Z'$. This ratio measures the mass\nintroduced by the auxiliary edges.\n\n\\begin{proposition}[Marked matching laws]\\label{prop:marked-laws}\nFor arbitrary positive logical activities, sample from the bag matching\nlaw and reveal the edge portions as above. The all-real event has\nprobability $1/I$;\nconditional on this event, the decoded logical matching has law\n$\\pi_\\lambda$. For each logical edge $uv$,\n\\begin{equation}\\label{eq:probe}\n \\Prb(\\text{probe for }uv\\text{ is used and all other edges are real})\n =\\frac{g_\\lambda(uv)}{DI}.\n\\end{equation}\nConditional on this event, the residual logical matching has the\n$\\lambda$-weighted perfect-matching law on the vertices other than\n$u,v$.\n\\end{proposition}\n\n\\begin{proof}\nRevealing portions recovers the weighted law before the real and virtual\nactivities were collapsed. The all-real matchings have total weight\n$Z'=C_0Z_\\lambda$, and their normalized law is transported to\n$\\pi_\\lambda$ by \\cref{lem:subdivision}.\n\nNow require the probe on the path for $uv$ and real edges everywhere\nelse. The probe matches $x_{2p}$ to $x_{2p+1}$. The remaining vertices on\nits path have unique real tilings: the left one uses $u$, and the right\none uses $v$. Equal adjacent activities in \\eqref{eq:path-profile}\nshow that their combined weight is exactly the even-edge baseline\nweight of this path. Other paths incident to $u$ or $v$ must use their\neven tilings. The remaining paths retain their even and odd alternatives,\nwith ratio $\\lambda_e$. Consequently these marked\nmatchings are in bijection with logical matchings missing $u,v$,\nand every weight is multiplied by $C_0/D$. Their total mass is\n$C_0 Z_\\lambda(-uv)/D$. Dividing by $Z_{\\mathrm{bag}}=IC_0Z_\\lambda$\nproves \\eqref{eq:probe}; the same constant weight multiplier proves\nthe conditional-law assertion.\n\\end{proof}\n\nThe identities show what the enlarged graph provides to the counting\nalgorithm. To use its all-real event with polynomially many samples,\nwe also need a uniform lower bound on that event's probability.\nThe next lower-bound estimate requires balance; the construction and\nthe exact laws above do not.\n\n\\begin{proposition}[Bounded inflation]\\label{prop:bag-inflation}\nIf the logical activities are balanced, then\n\\begin{equation}\\label{eq:inflation}\n 1\\le I\\le2.\n\\end{equation}\n\\end{proposition}\n\nWe prove this bound in the remainder of the section. A collection of\nvirtual edges leaves a set $U$ of endpoints to be removed from a real\nmatching. Its added weight must therefore be compared with $g'(U)$.\nThe next two subsections transfer the logical hole estimate to these\nenlarged hole sets.\n\n\\subsection{Exact accounting for deleted vertices}\n\nWe next describe exactly what deleting internal vertices does to the\nlogical graph. A path is \\emph{broken} if at least one of its internal\nvertices is deleted. Let $E_{\\mathrm{br}}(U)$ be the set of logical\nedges whose paths are broken by a hole set $U$.\n\nConsider the ordered internal holes on a broken path. If a real\ncompletion exists, successive hole indices alternate in parity,\nbecause the intervening internal run must have even order. A first\nhole of even index forces the prefix to match the terminal $u$;\nwe say that this hole \\emph{consumes} $u$ and map it to $u$.\nA last hole of odd index similarly consumes and maps to $v$.\nRemove all consumed holes (zero, one, or two) from the list. The remaining holes\nsplit, in their order, into pairs $(j,k)$ with $j$ odd and $k$ even;\ncall these \\emph{neutral pairs}. There is no ambiguity when the\nlist has one hole, since it cannot both have even and odd index.\n\nLet $R$ be the set of explicitly deleted terminals together with all\nconsumed terminals. Feasibility requires these terminals to be\ndistinct. To each consumed hole $x_j$ assign the factor\n\\[\n f_j=\n \\begin{cases}\n 1,&\\text{if it maps to its home terminal},\\\\\n \\lambda_e/T_j,&\\text{if it maps to the other terminal}.\n \\end{cases}\n\\]\nFor a neutral pair $(j,k)$ assign\n\\begin{equation}\\label{eq:neutral-factors}\n f_{jk}=\n \\begin{cases}\n T_j^{-1},&j,k\\le2p,\\\\\n T_k^{-1},&j,k\\ge2p+1,\\\\\n \\lambda_e/(T_jT_k),&j\\le2p<k.\n \\end{cases}\n\\end{equation}\nLet $F=F(U)$ be the product of all these factors on all broken paths;\nan empty product is one. Both $R$ and $F$ depend only on $U$ and\nthe fixed path profiles, not on a choice of completion.\n\nFor a set $E_0$ of logical edges, write\n$Z_\\lambda(-R;E_0\\text{ forbidden})$ for the partition function\nafter deleting $R$ and forbidding those edges.\n\n\\begin{lemma}\\label{lem:deletion-accounting}\nIf an even hole set $U$ has a real completion, then $R$ is even,\n$|R|\\le|U|$, and\n\\begin{equation}\\label{eq:deletion-identity}\n Z'(-U)\n =C_0 F(U)\n Z_\\lambda\\bigl(-R;E_{\\mathrm{br}}(U)\\text{ forbidden}\\bigr).\n\\end{equation}\nIn particular,\n$g'(U)\\le F(U)g_\\lambda(R)$.\n\\end{lemma}\n\n\\begin{proof}\nFix a broken path. A prefix ending immediately before a first hole\nof index $j$ has $j-1$ internal vertices. If $j$ is odd it uses the\neven tiling and leaves $u$ unused; if $j$ is even it uses the odd\ntiling and consumes $u$. The suffix has the analogous alternatives.\nEvery run between consecutive holes has a unique tiling, since its\norder is even. These parity conditions prove the stated description\nof consumed holes and neutral pairs. On each path the number of\nconsumed holes has the same parity as the number of internal holes.\nAdding explicit terminal holes gives $|R|\\equiv|U|\\pmod2$.\nDistinctness and $|R|\\le|U|$ follow from feasibility and the fact\nthat each consumed terminal is charged to a different hole.\n\nFor completeness, all factors can be obtained from one prefix\nratio. For even $j$, define\n\\[\n P(j)=\n \\frac{\\prod_{i\\le j,\\ i\\text{ odd}}t_i}\n      {\\prod_{i\\le j,\\ i\\text{ even}}t_i},\n \\qquad P(0)=1.\n\\]\nThe profile gives\n\\[\n P(j)=\n \\begin{cases}\n 1,&j\\le2p,\\\\\n \\lambda_e/T_j,&j\\ge2p+2.\n \\end{cases}\n\\]\nThis is the factor for a first even-indexed hole consuming $u$;\nreflection gives the factor for a last odd-indexed hole consuming\n$v$. For a neutral pair $(j,k)$, its shifted tiling relative to\nthe even-edge baseline contributes\n\\[\n \\frac{\\prod_{i=j+2,j+4,\\ldots,k-1}t_i}\n      {\\prod_{i=j+1,j+3,\\ldots,k}t_i}\n =\n \\frac{P(k)}{P(j-1)t_j}.\n\\]\nIf $j$ is odd on the left, its denominator is $T_j$; if $j$ is odd\non the right, its denominator is $\\lambda_e$, also when\n$j=2p+1$. Substitution proves \\cref{eq:neutral-factors}.\nPrefixes and suffixes that do not consume a terminal, and runs\noutside these shifted intervals, retain their baseline even edges.\nThus the product $F$ accounts for all changes to the baseline\non broken paths.\n\nEvery completion now determines a logical matching on the intact\npaths, using exactly the terminals outside $R$. Conversely, take\nany logical perfect matching on the vertices outside $R$ that uses\nno broken edge. Give its selected intact paths their odd tilings,\ngive other intact paths their even tilings, and fill all broken\npaths by the uniquely forced runs just described. The distinctness\nconditions ensure that no terminal is used twice or is both used\nand deleted. This reconstructs a real completion and is inverse\nto the first map. Its weight is $C_0F$ times the logical weight,\nwhich proves \\cref{eq:deletion-identity}. Omitting the forbidden-edge\ncondition and dividing by \\cref{eq:real-partition} gives the inequality.\n\\end{proof}\n\n\\subsection{Charging the pairing capacity}\n\nThe deletion identity gives a factor $F(U)$ and a logical hole set $R$.\nTo transfer the logical bound, we must compare the pairing capacity of\n$U$ with that of $R$. The next lemma expresses both capacities through\nthreshold classes. Throughout the comparison, these classes belong to\nthe original undeleted graphs, so they are independent of the completion\nbeing counted.\n\n\\begin{lemma}[Pairings in a hierarchy]\n\\label{lem:ultrametric-pairing}\nLet $B\\ge1$ be a symmetric similarity on a finite vertex set $X$,\nsatisfying $B_{ij}\\ge\\min\\{B_{ik},B_{kj}\\}$. Let $\\mathcal C_t$ be\nthe equivalence classes of $B\\ge t$, including singletons.\nFor every even set $U\\subseteq X$,\n\\begin{equation}\n\\label{eq:threshold-pairing}\n \\log\\Phi_B(U)\n =\\int_1^\\infty\\sum_{C\\in\\mathcal C_t}\n       \\left\\lfloor\\frac{|C\\cap U|}{2}\\right\\rfloor\\frac{dt}{t}.\n\\end{equation}\nThere is one pairing that attains the maximum number of internal pairs\nin every threshold class simultaneously.\n\\end{lemma}\n\n\\begin{proof}\nFor any pairing $Q$,\n\\[\n \\log\\prod_{ij\\in Q}B_{ij}\n =\\int_1^\\infty\n       |\\{ij\\in Q:B_{ij}\\ge t\\}|\\,\\frac{dt}{t}.\n\\]\nInside a threshold class $C$, there are at most\n$\\lfloor |C\\cap U|/2\\rfloor$ pairs of $Q$, proving the upper bound.\n\nThe threshold classes form a laminar family: any two are disjoint or one\ncontains the other.  Process its finitely many distinct classes from\nsmaller to larger, starting with the singletons.  In each class, pair\nits currently unpaired elements of $U$ arbitrarily until at most one\nremains.  When a class $C$ has been processed, exactly\n$\\lfloor|C\\cap U|/2\\rfloor$ pairs lie inside it.  Subsequent processing\ncannot create another pair inside $C$, because at most one element of\n$C\\cap U$ remains unpaired.  The final class is $X$, and $|U|$ is even,\nso the procedure produces a pairing.  It attains every floor count,\nand therefore attains the integral upper bound.\n\\end{proof}\n\n\\begin{lemma}\\label{lem:lifted-hole-bound}\nIf the logical activities are balanced, then for every even set\n$U$ of enlarged vertices,\n\\[\n g'(U)\\Phi_{B'}(U)\\le2D_0^{|U|/2}.\n\\]\nMore precisely, for every $U$ with a real completion, the deterministic\nquantities above satisfy\n\\begin{equation}\\label{eq:threshold-charge}\n F(U)\\Phi_{B'}(U)\\le\\Phi_B(R).\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nThe assertion is immediate if $U$ has no completion, so suppose\notherwise. For a threshold $t>1$ and any set $A$ of enlarged vertices,\nlet\n\\[\n S'_t(A)=\\sum_{C}\\left\\lfloor\\frac{|A\\cap C|}{2}\\right\\rfloor,\n\\]\nwhere $C$ ranges over the threshold classes of $B'$. Define $S_t$ on\nlogical vertices in the same way using $B$. These definitions make\nsense even when $A$ has odd cardinality.\nThe threshold partitions remain those of the undeleted graphs\nthroughout this proof; only the sets of holes are changed.\n\nBy \\cref{lem:subdivision}, an internal vertex $x_j$ is isolated in\nthe threshold partition precisely when $T_j<t$; otherwise it is\nconnected to its home terminal. Call it \\emph{active} in the latter\ncase. The threshold relation induced on terminals is exactly the\nlogical relation. These statements can equivalently be checked using\nraw real edges of activity at least $t$, since taking the bottleneck\nclosure leaves threshold components unchanged.\n\nRemove the neutral pairs one at a time from the current hole set.\nFor a pair on the same side, its active vertices lie in one class.\nRemoving it reduces $S'_t$ by at most\n\\[\n \\one_{\\{\\max(T_j,T_k)\\ge t\\}}.\n\\]\nIndeed, two active holes are removed from one class, one active hole\nreduces a class floor by at most one, and an inactive hole is in\na singleton class and contributes zero. On the left\n$\\max(T_j,T_k)=T_j$; on the right it is $T_k$. For a cross pair, the\nloss is at most\n\\[\n \\one_{\\{T_j\\ge t\\}}+\\one_{\\{T_k\\ge t\\}}\n -\\one_{\\{\\lambda_e\\ge t\\}}.\n\\]\nTo see the subtracted term, when $\\lambda_e\\ge t$ both vertices\nare active and are connected by the full path, so their removal\nreduces a single class floor by one. Otherwise the bound is the\nsum of the separate active-hole bounds. These arguments apply to\nthe current set after any preceding removals.\n\nNext consider each consumed hole mapping away from home. If it is\nactive and $\\lambda_e<t$, discard it. The floor loss is at most\n\\[\n \\one_{\\{T_j\\ge t\\}}-\\one_{\\{\\lambda_e\\ge t\\}}.\n\\]\nThe expression is nonnegative, since $T_j\\ge B_e\\ge\\lambda_e$.\nEvery remaining active consumed hole can be mapped to its consumed\nterminal within its threshold class: a home mapping always has this\nproperty, and an away mapping retained by the rule has\n$\\lambda_e\\ge t$. Together with explicitly deleted terminals, these\nmaps are injective, by the distinctness in\n\\cref{lem:deletion-accounting}. Inactive internal holes lie in\nsingleton classes and contribute no floor. Hence the floor sum\nremaining after the removals and discards is at most $S_t(R)$.\n\nIntegrate these bounds against $dt/t$ on $(1,\\infty)$. By\n\\cref{lem:ultrametric-pairing}, the integral of $S'_t(U)$ is\n$\\log\\Phi_{B'}(U)$, and that of $S_t(R)$ is $\\log\\Phi_B(R)$.\nFor a same-side neutral pair the integrated charge is exactly\n$-\\log f_{jk}$. For a cross pair it is\n\\[\n \\log T_j+\\log T_k-\\log\\max\\{1,\\lambda_e\\}\n \\le-\\log f_{jk}.\n\\]\nFor an away-mapped hole it is\n\\[\n \\log T_j-\\log\\max\\{1,\\lambda_e\\}\\le-\\log f_j.\n\\]\nHome mappings cost zero. Summing proves\n$\\log\\Phi_{B'}(U)\\le\\log\\Phi_B(R)-\\log F$,\nwhich is \\cref{eq:threshold-charge}. Finally,\n\\cref{lem:deletion-accounting,thm:logical-hole-bound} give\n\\[\n g'(U)\\Phi_{B'}(U)\n \\le g_\\lambda(R)\\Phi_B(R)\n \\le2D_0^{|R|/2}\n \\le2D_0^{|U|/2}.\n\\]\n\\end{proof}\n\n\\begin{proof}[Proof of \\cref{prop:bag-inflation}]\nExpand the partition function before collapsing real and virtual\nportions. A nonempty partial matching $Q$ of $a$ colored virtual\nedges, with endpoint set $U$, contributes relative to $Z'$ the mass\n\\[\n g'(U)\\prod_{xy,l\\in Q}\\frac{h_l}{D}.\n\\]\nVertices sharing $l$ lie in the same threshold component at $h_l$,\nso $h_l\\le B'_{xy}$. Their pairing therefore gives\n\\[\n g'(U)\\prod_{xy,l\\in Q}\\frac{h_l}{D}\n \\le D^{-a}g'(U)\\Phi_{B'}(U)\n \\le2(D_0/D)^a\n\\]\nby \\cref{lem:lifted-hole-bound}. A vertex pair has at most two\ncommon labels, so there are fewer than $N^2$ colored virtual edges\nand at most $N^{2a}$ choices for $Q$. The empty $Q$ contributes one.\nConsequently,\n\\[\n 1\\le I\\le1+2\\sum_{a\\ge1}(N^2D_0/D)^a\n =1+\\frac2{99}<2.\n\\]\n\n\\end{proof}\n\n\\section{A quadrangulation adapted to the labels}\n\\label{sec:cells}\n\nWe now turn to the sampling branch of the proof. Its input is a\ntree-bag graph as in \\cref{def:tree-bags}; no balance assumption is\nneeded. To compare two alternating matchings on a cycle, we shall\nsubdivide its polygon into four-corner cells. A cell side may stand\nfor a long arc of the original cycle. We first describe the matching\npatterns on such an arc and the endpoint repairs the cells must\npermit, then construct a quadrangulation with those properties.\n\nLet $\\mathcal C$ be a simple even cycle of length $m\\ge4$ in the colored\nbag graph.  Each edge retains its color, which is a node of the label\ntree.  For a vertex $v$ of $\\mathcal C$, define\n\\[\n S_v=\\{\\text{labels of the two edges of $\\mathcal C$ incident to $v$}\\}.\n\\]\nThus $S_v$ is a singleton or consists of two adjacent tree nodes.\nThese sets refer to the \\emph{original full cycle} throughout the\nconstruction; adding a diagonal never changes them.  In particular,\nthe cyclic sequence of edge labels is a walk on the tree, with repetitions\nallowed.\n\nAn \\emph{arc} is one of the two paths in $\\mathcal C$ between distinct\nvertices, specified together with its endpoints. Its length is its\nnumber of edges. We use only odd arcs. An arc is \\emph{admissible} if\nits endpoints $u,v$ satisfy $S_u\\cap S_v\\ne\\varnothing$. The clique\nproperty then supplies an edge joining them with any chosen label in\nthis intersection.\n\nFor an admissible odd arc $J=(v_0,\\ldots,v_s)$, choose such a colored\nendpoint chord $e_J$ and put\n\\[\n T_J=\\{v_0v_1,v_2v_3,\\ldots,v_{s-1}v_s\\},\\qquad\n N_J=\\{v_1v_2,v_3v_4,\\ldots,v_{s-2}v_{s-1}\\},\n \\qquad C_J=N_J\\cup\\{e_J\\}.\n\\]\nAll path edges retain their cycle colors. The \\emph{through pattern}\n$T_J$ and the \\emph{closed pattern} $C_J$ both perfectly match the\nvertices of $J$; the \\emph{interior pattern} $N_J$ leaves only its\ntwo endpoints unmatched. The symbol $C_J$ denotes a matching, not the\noriginal cycle. Call $J$ \\emph{short} if $s=1$ and \\emph{long} otherwise.\nOn a short arc choose its actual cycle edge as $e_J$, so $T_J=C_J$.\nWhen a diagonal later represents two complementary arcs, we use the\nsame colored chord in both patterns. \\Cref{fig:odd-arc-patterns} shows\nthese patterns on a long arc.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tikzpicture}[x=1.2cm,y=1cm,\n  reference/.style={draw=black!28,densely dashed,line width=.5pt},\n  selected/.style={draw=black,line width=1.4pt},\n  vertex/.style={circle,fill=black,inner sep=1.6pt}]\n  \\foreach \\i in {0,...,5} {\n    \\coordinate (t\\i) at (\\i,1.7);\n    \\coordinate (c\\i) at (\\i,0);\n  }\n  \\foreach \\i [evaluate=\\i as \\j using int(\\i+1)] in {0,...,4} {\n    \\draw[reference] (t\\i)--(t\\j);\n    \\draw[reference] (c\\i)--(c\\j);\n  }\n  \\foreach \\i [evaluate=\\i as \\j using int(\\i+1)] in {0,2,4}\n    \\draw[selected] (t\\i)--(t\\j);\n  \\foreach \\i [evaluate=\\i as \\j using int(\\i+1)] in {1,3}\n    \\draw[selected] (c\\i)--(c\\j);\n  \\draw[selected] (c0) .. controls +(1,1) and +(-1,1) .. (c5)\n    node[pos=.5,above=2pt] {$e_J$};\n  \\foreach \\i in {0,...,5} {\n    \\node[vertex,label=below:{$v_{\\i}$}] at (t\\i) {};\n    \\node[vertex,label=below:{$v_{\\i}$}] at (c\\i) {};\n  }\n  \\node[anchor=east] at (-.5,1.7) {$T_J$};\n  \\node[anchor=east] at (-.5,0) {$C_J$};\n  \\node[anchor=west] at (5.5,1.7) {through};\n  \\node[anchor=west] at (5.5,0) {$N_J\\cup\\{e_J\\}$};\n\\end{tikzpicture}\n\\caption{The two patterns on a long odd arc, illustrated for\n$J=(v_0,\\ldots,v_5)$. Solid edges form the indicated matching;\nfaint dashed edges only locate the underlying path. Both $T_J$ and\n$C_J$ perfectly match the same vertices. The latter uses the interior\npattern $N_J$ and the endpoint chord $e_J$; their union\n$T_J\\cup C_J$ is the alternating cycle consisting of $J$ and its chord.\nReplacing $T_J$ by $C_J$ can change interior edges; this replacement\nneed not be a single chain update.}\n\\label{fig:odd-arc-patterns}\n\\end{figure}\n\nBesides closing an arc with a chord, we shall sometimes need to free\nits endpoints while starting from its through pattern. Removing the\nfirst and last edges of a long arc from $T_J$ uncovers its endpoints\nand its two \\emph{leaves} $v_1,v_{s-1}$. Call $J$ \\emph{good} if it is\nshort or if\n\\[\n S_{v_1}\\cap S_{v_{s-1}}\\ne\\varnothing.\n\\]\nFor a good long arc, choose an edge between its leaves with a label in\nthis intersection, called a \\emph{leaf repair}. After the two boundary\nedges are removed, inserting this repair matches all internal vertices\nand leaves both endpoints free. For a short arc, deleting its sole\nedge does the same. This is the matching operation encoded by the\ngood-arc condition. The \\emph{boundary edges of an arc} mean only its\nfirst and last edges, which coincide for a short arc.\n\nRealize the vertices of $\\mathcal C$ in their cyclic order as a convex\npolygon.  A quadrangulation is a subdivision of this polygon into\nquadrilaterals by noncrossing diagonals.  A side of a cell represents the\narc of the original cycle from one corner to the next corner, containing\nno other corner of that cell.  Consequently the four sides of each cell\nrepresent four consecutive arcs partitioning the entire cycle.  An\ninterior diagonal has two occurrences as a cell side, and their arcs\nare complementary in the original cycle.\n\n\\begin{lemma}[Label-adapted quadrangulation]\n\\label{lem:quadrangulation}\nThe polygon of $\\mathcal C$ has a quadrangulation such that every cell\nside represents an admissible odd arc and the following hold in every\ncell:\n\\begin{enumerate}[label=\\textup{(\\roman*)}]\n \\item some opposite pair of sides consists of good arcs;\n \\item if both arcs in an opposite pair are long, both arcs in the\n other opposite pair are good.\n\\end{enumerate}\nThere are $(m-2)/2$ cells and $(m-4)/2$ interior diagonals.  One can\nconstruct the quadrangulation in time polynomial in $m$ and the encoding\nlength of the label tree.\n\nEach diagonal may be assigned any common label from the fixed sets at\nits endpoints, used consistently in its two occurrences.  For a short\nside, use the color of its actual cycle edge.  These choices need not\ncoincide with the auxiliary labels used in the construction.\n\\end{lemma}\n\n\\begin{proof}\nWe give a recursive splitting rule.  A subproblem is an admissible odd\narc $J=(v_0,\\ldots,v_s)$; its \\emph{exterior} is its complementary arc\nin the full cycle.  Choose a label $P\\in S_{v_0}\\cap S_{v_s}$, and\nwrite $a_i$ for the label of $v_iv_{i+1}$, $0\\le i<s$.\nAdjacent terms of this sequence are equal or adjacent in the tree.\nIf $s=1$, no cell is needed.  For $s\\ge3$, we find indices\n\\begin{equation}\n\\label{eq:cell-split-indices}\n 0<p_1<p_2<s,\\qquad p_1\\text{ odd},\\quad p_2\\text{ even},\n\\end{equation}\nsuch that the three subarcs with successive endpoint pairs\n\\[\n (v_0,v_{p_1}),\\qquad (v_{p_1},v_{p_2}),\\qquad\n (v_{p_2},v_s)\n\\]\nare admissible.  They and the exterior arc form the four sides of the\nnew cell.  We call these the first, middle, third, and exterior sides,\nrespectively.\n\nWe repeatedly use the following elementary fact about a tree.\nIf a contiguous part of the label walk avoids $P$ and its first and\nlast labels are adjacent to $P$, these two labels coincide: the walk\nlies in a single component of the tree with $P$ removed, and that\ncomponent has just one node adjacent to $P$.\n\n\\paragraph{The exterior is good.}\nIt suffices to make either the middle side short or both the first and\nthird sides short.  Either alternative immediately implies\n\\textup{(i)} and \\textup{(ii)}, because the exterior is good.\n\nFirst ensure that some $a_i$ equals $P$.  If none does, $a_0$ and\n$a_{s-1}$ are adjacent to $P$, because the endpoint sets contain $P$.\nThe elementary tree fact gives $a_0=a_{s-1}=Q$ for a neighbor $Q$ of\n$P$.  Replace $P$ by $Q$, which also lies in both endpoint sets.\nWe now distinguish the exhaustive possibilities below.\n\\begin{enumerate}[label=\\textup{(\\alph*)}]\n \\item Suppose $a_i=P$ for an odd $i$.\n Set $p_1=i$ and $p_2=i+1$.  The middle side is short.  The first\n side's two endpoints contain $P$, as do the third side's two\n endpoints, so all three sides are admissible.\n\n \\item Otherwise all occurrences of $P$ have even indices.\n If their first index is $i>0$, set $p_1=i-1$ and $p_2=i$.\n The prefix $a_0,\\ldots,a_{i-1}$ avoids $P$, and its first and last\n labels are adjacent to $P$.  They therefore equal the same neighbor\n $Q$.  The first side's endpoints both contain $Q$, and the third\n side's endpoints both contain $P$.  The middle side is short.\n\n \\item Suppose the first occurrence is at $0$, but the last occurrence\n is at $j<s-1$.  Set $p_1=j+1$ and $p_2=j+2$.\n The suffix $a_{j+1},\\ldots,a_{s-1}$ avoids $P$ and has its two end\n labels adjacent to $P$, so they equal one neighbor $Q$.\n The first side's endpoints contain $P$, the third side's endpoints\n contain $Q$, and the middle side is short.\n\n \\item In the remaining case $a_0=a_{s-1}=P$.\n Set $p_1=1$ and $p_2=s-1$.  Both outer sides are short, and the\n middle side's endpoints both contain $P$.\n\\end{enumerate}\nAll indices satisfy \\eqref{eq:cell-split-indices}.  For example, in\n\\textup{(b)} the even index $i$ is at least $2$, and in \\textup{(c)}\nthe even index $j$ is at most $s-3$.\n\n\\paragraph{The exterior is not good.}\nThe exterior is then long.  At least one of its leaves lacks $P$ in\nits fixed label set, since otherwise its two leaves would share $P$.\nReverse the indexing of $J$ if necessary so that this leaf is the\nexterior neighbor of $v_0$.  The exterior edge at $v_0$ cannot have\nlabel $P$.  Since $P\\in S_{v_0}$, the first edge of $J$ must have\nlabel $a_0=P$.\n\nIf $a_{s-1}=P$ as well, choose $p_1=1,p_2=s-1$.  Both outer sides\nare short, and the middle side is admissible.  Otherwise let $t-1$\nbe the last index with $a_{t-1}=P$.  Thus $1\\le t\\le s-1$.\nThe suffix $a_t,\\ldots,a_{s-1}$ avoids $P$.  Its first label is\nadjacent to $P$, and its last label is adjacent to $P$ because\n$P\\in S_{v_s}$.  Hence\n\\[\n a_t=a_{s-1}=Q\n\\]\nfor the same neighbor $Q$ of $P$.\n\\begin{itemize}\n \\item If $t$ is even, set $p_1=1,p_2=t$.\n The first side is short.  The middle side's endpoints both contain\n $P$, and the third side's endpoints both contain $Q$.\n The third side is good: if long, both its boundary edges have\n label $Q$, so both its leaves contain $Q$.\n\n \\item If $t$ is odd, set $p_1=t,p_2=s-1$.\n The third side is short.  The first side's endpoints both contain\n $P$, and the middle side's endpoints both contain $Q$.\n The first side is good because, if long, both its boundary edges\n have label $P$.\n\\end{itemize}\nThe required strict inequalities follow from $t\\ge2$ in the even\ncase and $t\\le s-2$ in the odd case.  In either case the first and\nthird sides are good and at least one is short.  This proves\n\\textup{(i)}.  The only opposite pair that can contain two long sides\nis the middle--exterior pair, and its other pair is good, proving\n\\textup{(ii)}.\n\n\\paragraph{Recursion and consistent diagonals.}\nStart with any arc of length $m-1$, whose exterior is the remaining\nsingle cycle edge.  Its endpoints share that edge's label.\nApply the splitting rule and recurse on each of the three subarcs\nwhose length exceeds one, always using the original sets $S_v$ and\nthe complement in the original cycle as exterior.  The three\nsubpolygons have disjoint interiors, so all diagonals are noncrossing.\nEach recursive child has odd length and admissible endpoints by the\nconstruction.  The splitting label $P$ is only an auxiliary witness\nto admissibility; it does not constrain the color of the closing\ndiagonal.  Thus each diagonal can be colored once and the same color\nused on both sides.\n\nFor an odd arc of length $s$, the resulting number of cells is\n$(s-1)/2$: this is zero for $s=1$, and a split into odd lengths\n$s_1+s_2+s_3=s$ gives\n\\[\n 1+\\sum_{i=1}^{3}\\frac{s_i-1}{2}=\\frac{s-1}{2}.\n\\]\nThe root therefore gives $(m-2)/2$ cells.  Each nonroot recursive\nsubproblem that creates a cell has one closing diagonal, giving one\nfewer interior diagonals than cells. A direct implementation scans\nat most $m$ labels in each of the $(m-2)/2$ nontrivial subproblems;\nthe recursion also has $m-1$ short leaf arcs. Fixing total orders for\nany choices makes the construction deterministic, with $O(m^2)$\nlabel comparisons and polynomial bit cost.\n\\end{proof}\n\nFor each cell, choose its colored side chords as in\n\\cref{lem:quadrangulation}; for each good long side, choose one leaf\nrepair.  These are precisely the additional edges needed for the\nlocal matching constructions in the next section.\n\n\\begin{lemma}[Comparability within a cell]\n\\label{lem:cell-comparability}\nConsider the four colored side chords of a cell, the first and last\ncycle edges of each of its four arcs, and one leaf repair for each\ngood long arc.  The labels of any two edges in this collection have\ntree distance at most $6$.  Consequently their activities lie within\na factor\n\\[\n A_0=1000D\n\\]\nof one another.  The same conclusion holds after clamping every edge\nactivity to any common interval $[a,b]\\subset(0,\\infty)$.\n\\end{lemma}\n\n\\begin{proof}\nTwo consecutive side chords have labels in the same fixed set $S_v$\nat their common cell corner, so their labels are equal or adjacent.\nAny two of the four side-chord labels therefore have distance at\nmost $2$.  A boundary edge of an arc and its side chord have labels\nin the same $S_v$ at the relevant endpoint, giving distance at most\n$1$.  A leaf repair and the boundary edge at either of its leaves\nhave labels in that leaf's fixed set, again giving distance at most\n$1$.  Thus each edge in the stated collection is at distance at\nmost $2$ from its side chord, and the diameter is at most\n$2+2+2=6$.\n\nAdjacent labels have heights differing by a factor $2$.  If edges\n$e,f$ have labels $\\ell,\\ell'$ with distance at most $6$, the\nassumed activity bounds imply\n\\[\n \\frac{W_e}{W_f}\n \\le \\frac{3h_\\ell}{h_{\\ell'}/D}\n \\le 3D\\,2^6=192D\\le A_0.\n\\]\nThe same bound holds with $e,f$ exchanged.  Finally the common clamp\n$\\psi(x)=\\max\\{a,\\min\\{b,x\\}\\}$ satisfies\n\\[\n 1\\le\\frac{\\psi(x)}{\\psi(y)}\\le\\frac{x}{y}\n \\qquad (x\\ge y>0).\n\\]\nFor example this follows by observing that\n$\\log\\psi(e^u)$ is the clamp of $u$ to\n$[\\log a,\\log b]$, a nondecreasing $1$-Lipschitz function.\nClamping therefore preserves the factor bound.\n\\end{proof}\n\\section{A two-coordinate energy inequality}\n\\label{sec:energy}\n\nThis section proves the sampling argument's main inequality. The four\nside chords of each cell give a two-edge switch whose changed edges\nhave comparable activities. Long arc conversions are used in an exact\nsigned identity and need not be individual chain moves. We control the\nresulting context errors by swapping an alternating cycle with an\nindependent matching. Encodings by two matchings change only a bounded\nnumber of edges in their multiset union, and their preimage counts give\npolynomial load bounds.\nThe variance-to-energy comparison is related to the flow method\nof~\\cite{Sinclair1992}, but we do not invoke a path-congestion theorem:\nthe signed identity and its context-error estimates are established\nfor the present pair kernel.\n\nFix a tree-bag graph on $N\\ge2$ vertices, with at least one perfect\nmatching, and let $w$ be either its reference activities or any common\nclamp of them.  Write $\\pi(M)=\\wt_w(M)/Z_w$ and $\\calM$ for its colored\nperfect matchings.  Throughout this section put\n\\begin{equation}\n\\label{eq:energy-constants}\n A_0=1000D,\\qquad \\calT=(10N)^{30},\\qquad\n L=(10^4ND)^{100}.\n\\end{equation}\n\n\\subsection{The pair kernel}\n\nOn $(A,B)\\in\\calM^2$, choose each of the following proposal types with\nprobability $1/3$.\n\\begin{enumerate}[label=\\textup{(\\arabic*)}]\n \\item \\emph{Two-edge switch.}  Choose an unordered pair of distinct\n edges of $A$ uniformly, and choose either alternative pairing of\n their four endpoints with probability $1/2$.  For each proposed\n edge, choose index $1$ or $2$ with probability $1/2$ from the ordered\n list of common labels of its endpoints.  If an index is absent,\n stay put.  If $A$ has fewer than two edges, stay put.\n\n \\item \\emph{Color change.}  Choose an edge of $A$ uniformly and\n choose index $1$ or $2$ from the common-label list with probability\n $1/2$.  An absent index gives a holding move.\n\n \\item \\emph{Cycle swap.}  Choose uniformly one discrepancy cycle of\n $A,B$ and interchange its two layers.  If there is no discrepancy\n cycle, stay put.\n\\end{enumerate}\nAccept each proposal by the Metropolis rule for $\\pi\\otimes\\pi$.\nDenote the resulting kernel by $P$ and its energy by $\\calE$.\nThe proposal is symmetric.  For the first two types, the reverse\nchoice has the same edge, pairing, and color-index probabilities;\nthe possible holding choices do not change this fact.  For the third\ntype, the discrepancy cycles and their number are unchanged by the\nswap.  In particular, a cycle swap always has acceptance probability\none, since its product weight is unchanged.\n\n\\begin{theorem}[Pair energy inequality]\n\\label{thm:pair-energy}\nFor every two real functions $f_1,f_2$ on $\\calM$,\n\\begin{equation}\n\\label{eq:pair-energy}\n \\Var_\\pi f_1\\le L\\,\n \\calE\\bigl((A,B)\\mapsto f_1(A)+f_2(B)\\bigr).\n\\end{equation}\nThe same constant works for every common-interval clamp of the\nreference activities.\n\\end{theorem}\n\nIn the proof write $F(A,B)=f_1(A)+f_2(B)$ and $E=\\calE(F)$.\nA two-edge switch is called \\emph{safe} if its four removed or\ninserted colored edges have activities within a factor $A_0$ of one\nanother.  Let $\\mathcal S(A)$ be its possible safe destinations, and\nlet $\\mathcal K(A)$ be its possible destinations by changing one\nedge color.  Consecutive labels have comparable heights, so every\ncolor change has activity ratio in $[A_0^{-1},A_0]$.\n\n\\begin{lemma}[Elementary energy bounds]\n\\label{lem:elementary-pair-energy}\nWith the notation above,\n\\begin{align}\n \\sum_A\\pi(A)\\sum_{A'\\in\\mathcal S(A)}\n       (f_1(A)-f_1(A'))^2\n &\\le60N^2A_0^2 E,\n \\label{eq:safe-switch-energy}\\\\\n \\sum_A\\pi(A)\\sum_{A'\\in\\mathcal K(A)}\n       (f_1(A)-f_1(A'))^2\n &\\le12NA_0 E,\n \\label{eq:color-energy}\\\\\n \\sum_{A,B}\\pi(A)\\pi(B)\\sum_{C\\in\\mathcal C(A,B)}\n       \\bigl(F(A,B)-F((A,B)^C)\\bigr)^2\n &\\le6NE.\n \\label{eq:cycle-energy}\n\\end{align}\nHere $\\mathcal C(A,B)$ denotes the discrepancy cycles, and $(A,B)^C$\nis the pair after swapping $C$.\n\\end{lemma}\n\n\\begin{proof}\nEvery specified legal two-edge switch has proposal probability\nat least $1/(30N^2)$; its Metropolis acceptance is at least\n$A_0^{-2}$ when safe.  Every specified color change has proposal\nprobability at least $1/(6N)$ and acceptance at least $A_0^{-1}$.\nThese moves change only the $f_1$ summand of $F$, so summing the\nsecond-coordinate law and using the factor $1/2$ in the energy proves\nthe first two bounds.  There are at most $N$ discrepancy cycles.\nEach specified swap therefore has proposal probability at least\n$1/(3N)$ and is accepted; this proves the third bound.\n\\end{proof}\n\n\\subsection{An exact identity on a cycle}\n\nFix a pair of demand matchings $(I_1,I_2)$ and a simple discrepancy\ncycle $C$ of length at least four.  All orientations and choices below\nare deterministic, using the orders fixed in \\cref{sec:preliminaries}.\nLet $R$ be the edges of $I_1$ outside $C$, and let $O_0=I_1$ and\n$O_1$ be the matching obtained by toggling $C$ in $I_1$.  Thus both\norientations use $R$ outside $C$.  Put\n\\[\n D_C=f_1(O_0)-f_1(O_1).\n\\]\nApply \\cref{lem:quadrangulation} and fix the colors of its diagonals\nconsistently.  A cell's four sides always mean the four odd arcs of\nthe \\emph{original whole cycle}, not paths formed from earlier\ndiagonals.\n\nUse the through, interior, and closed patterns $T_J,N_J,C_J$ from\n\\cref{sec:cells}, with the chosen chord colors. A side $J$ is\n\\emph{through} in $O_\\varepsilon$ if $T_J\\subseteq O_\\varepsilon$.\nExactly one orientation $O_\\varepsilon$ contains $T_J$; define\n\\begin{equation}\n\\label{eq:canonical-gain}\n H_J=(O_\\varepsilon\\setminus T_J)\\cup C_J,\n \\qquad g_J=f_1(O_\\varepsilon)-f_1(H_J).\n\\end{equation}\nFor a short side, the chord is its actual cycle edge, so $g_J=0$.\n\nThe reason for using the original cycle on every cell side is already\nvisible on a six-cycle $C=(v_0,\\ldots,v_5,v_0)$. Take\n\\[\n O_0=R\\cup\\{v_0v_1,v_2v_3,v_4v_5\\},\\qquad\n O_1=R\\cup\\{v_1v_2,v_3v_4,v_5v_0\\},\n\\]\nand suppose the diagonal $e=v_0v_3$ is admissible. In the two cells\nit represents the complementary arcs\n$J=(v_3,v_4,v_5,v_0)$ and $\\bar J=(v_0,v_1,v_2,v_3)$, respectively.\nBoth conversions yield the same matching\n\\[\n H_J=H_{\\bar J}=H=R\\cup\\{v_1v_2,e,v_4v_5\\}.\n\\]\nThus $g_{\\bar J}-g_J=f_1(O_0)-f_1(O_1)=D_C$; the shared diagonal\ncontributes one whole-cycle difference, not zero.\n\\Cref{fig:complementary-arcs} displays this common converted matching.\nThis example isolates the complementary-gain identity. Its cells have\nonly one long side each, so the context errors introduced below vanish;\ncells with two opposite long sides require a further estimate.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tikzpicture}[x=1cm,y=1cm,\n  vertex/.style={circle,fill=black,inner sep=1.3pt},\n  selected/.style={draw=black,line width=1.3pt}]\n  \\foreach \\prefix/\\shift in {a/0,b/6.2} {\n    \\coordinate (\\prefix0) at (\\shift,0);\n    \\coordinate (\\prefix1) at (\\shift+.9,1.15);\n    \\coordinate (\\prefix2) at (\\shift+2.6,1.15);\n    \\coordinate (\\prefix3) at (\\shift+3.5,0);\n    \\coordinate (\\prefix4) at (\\shift+2.6,-1.15);\n    \\coordinate (\\prefix5) at (\\shift+.9,-1.15);\n  }\n  \\fill[black!4] (a0)--(a1)--(a2)--(a3)--cycle;\n  \\fill[black!8] (a0)--(a3)--(a4)--(a5)--cycle;\n  \\draw (a0)--(a1)--(a2)--(a3)--(a4)--(a5)--cycle;\n  \\draw[densely dashed,line width=.8pt] (a0)--(a3)\n    node[midway,fill=white,inner sep=2pt] {$e$};\n  \\node at (1.75,1.75) {$\\bar J$};\n  \\node at (1.75,-1.75) {$J$};\n  \\draw[black!25,densely dashed]\n    (b0)--(b1)--(b2)--(b3)--(b4)--(b5)--cycle;\n  \\draw[selected] (b1)--(b2);\n  \\draw[selected] (b0)--(b3) node[midway,above=2pt] {$e$};\n  \\draw[selected] (b4)--(b5);\n  \\foreach \\prefix in {a,b} {\n    \\node[vertex,label=left:{$v_0$}] at (\\prefix0) {};\n    \\node[vertex,label=above:{$v_1$}] at (\\prefix1) {};\n    \\node[vertex,label=above:{$v_2$}] at (\\prefix2) {};\n    \\node[vertex,label=right:{$v_3$}] at (\\prefix3) {};\n    \\node[vertex,label=below:{$v_4$}] at (\\prefix4) {};\n    \\node[vertex,label=below:{$v_5$}] at (\\prefix5) {};\n  }\n  \\node at (1.75,-2.35) {two cells, complementary arcs};\n  \\node at (7.95,-2.35) {common converted matching $H$};\n\\end{tikzpicture}\n\\caption{An admissible diagonal $e=v_0v_3$ cuts a six-cycle into two\ncells. In the upper cell its side follows the lower boundary arc $J$;\nin the lower cell its side follows the upper boundary arc $\\bar J$.\nThe two occurrences use the same colored edge $e$. Converting the\nthrough pattern on either arc gives the restriction of $H$ shown on\nthe right (the common outside matching $R$ is omitted),\nso their signed gains sum to $D_C$. The drawing concerns the signed\nidentity, not a sequence of chain transitions.}\n\\label{fig:complementary-arcs}\n\\end{figure}\n\n\nIn a cell, let $P$ be its opposite pair of sides through in $O_0$,\nand $Q$ its opposite pair through in $O_1$.  Let $A_P$ use $N_J$ on\nevery side and the chords of the two sides of $P$, together with $R$.\nDefine $A_Q$ analogously and put\n\\[\n \\Delta=f_1(A_P)-f_1(A_Q).\n\\]\nThese are legal perfect matchings: the interior vertices are matched\nby the $N_J$'s, and the two chosen chords match the four corners.\nTheir difference is a safe two-edge switch by\n\\cref{lem:cell-comparability}.\n\nOrder each opposite pair as $(Y,X)$.  For the orientation in which\nboth are through, first convert $Y$ and then $X$.\nWrite $A^{(0)}=O_\\varepsilon$ and $A^{(1)}=H_Y$ for the two\npre-conversion contexts of $X$.  For a matching $R'$ on the vertices\noutside $X$, define\n\\begin{equation}\n\\label{eq:arc-derivative}\n \\partial_X f(R')=f(T_X\\cup R')-f(C_X\\cup R').\n\\end{equation}\nThe context error of the ordered opposite pair is\n\\begin{equation}\n\\label{eq:context-error}\n E_{Y,X}\n =\\partial_X f_1(A^{(1)}\\setminus T_X)\n  -\\partial_X f_1(A^{(0)}\\setminus T_X).\n\\end{equation}\nIt is zero if either side is short.  Denote these errors by $E_P,E_Q$\nfor the two ordered pairs of the cell.\n\n\\begin{lemma}[Cell identity and cancellation]\n\\label{lem:cell-identity}\nThe quantities just defined satisfy\n\\begin{equation}\n\\label{eq:single-cell-identity}\n D_C=\\Delta+\\sum_{J\\in P}g_J-\\sum_{J\\in Q}g_J+E_P-E_Q\n\\end{equation}\nfor each cell.  Consequently,\n\\begin{equation}\n\\label{eq:global-cell-identity}\n D_C=\\sum_{\\text{cells of }C}(\\Delta+E_P-E_Q).\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nThe first conversion in an opposite pair has its canonical gain.\nThe second has its canonical gain plus the error in\n\\eqref{eq:context-error}.  Thus\n\\[\n f_1(O_0)-f_1(A_P)=\\sum_{J\\in P}g_J+E_P,\n \\qquad\n f_1(O_1)-f_1(A_Q)=\\sum_{J\\in Q}g_J+E_Q.\n\\]\nSubtracting proves \\eqref{eq:single-cell-identity}.\n\nConsider an interior diagonal with complementary arcs $J,\\bar J$.\nThey are through in opposite whole-cycle orientations, and their\nconverted matchings coincide:\n\\[\n H_J=H_{\\bar J}=R\\cup N_J\\cup N_{\\bar J}\\cup\\{e_J\\}.\n\\]\nTherefore the two signed canonical gains contributed by this\ndiagonal sum to $f_1(O_0)-f_1(O_1)=D_C$.  The signs here are precisely\nthose in \\eqref{eq:single-cell-identity}: the arc through in $O_0$\nhas positive sign and the other negative sign.  Boundary sides have\nzero gain.  Summing \\eqref{eq:single-cell-identity} over the cells\ntherefore gives one copy of $D_C$ per cell on the left and one per\ninterior diagonal on the right, in addition to the displayed local\nterms.  The number of cells is one more than the number of diagonals,\nwhich proves \\eqref{eq:global-cell-identity}.\n\\end{proof}\n\n\\subsection{Encoding the demands}\n\nA \\emph{switch demand} specifies $(I_1,I_2)$, a simple discrepancy\ncycle $C$, and one cell of its fixed quadrangulation.  Its value is\n$\\Delta$ above.  An \\emph{error demand} specifies these data and one\nopposite pair with two long sides, in its fixed order $(Y,X)$.\nIts value is $E_{Y,X}$.  The weight of either demand is\n$\\rho(d)=\\pi(I_1)\\pi(I_2)$.  Repeated demands from different cells or\ndifferent cycles are included separately in all sums.\n\n\\begin{lemma}[Encodings with a bounded number of preimages]\n\\label{lem:encoding}\nEvery switch demand has an encoding $(A_P,B)$ by two perfect\nmatchings, and every error demand has encodings\n$(A^{(j)},B^{(j)})$ for $j=0,1$, with the following properties.\n\\begin{enumerate}[label=\\textup{(\\roman*)}]\n \\item For each encoding $(A,B)$,\n $\\rho(d)\\le A_0^4\\pi(A)\\pi(B)$.\n \\item For each encoding type and each fixed output pair, there are\n at most $\\calT$ demand preimages.  This bound includes the choices\n of cycle, cell, opposite pair, and context.\n \\item For an error demand, both guide matchings satisfy\n $C_X\\subseteq B^{(j)}$.\n\\end{enumerate}\n\\end{lemma}\n\n\\begin{proof}\nWe first describe a repair operation.  Start with the union of the\nfour through patterns $T_J$ in a cell.  Each corner is covered twice,\nonce by each incident side.  Select a good opposite pair of sides.\nFor each selected short side delete its sole edge.  For each selected\nlong side delete its first and last edges, and insert its chosen leaf\nrepair.  Each corner loses exactly one incident edge.  On a long\nselected side the only newly uncovered internal vertices are its\ntwo leaves, which the repair matches.  Thus the resulting edges form\na perfect matching of all cycle vertices.  This verifies legality\nalso for a side of length three: its two leaves are distinct and\nits repair is an ordinary colored edge between them.\n\n\\paragraph{Switch demands.}\nUse $A_P$ as the first output.  In the second output retain the edges\nof $I_2$ outside $C$, start with all four through patterns, and apply\nthe preceding repair operation to a good opposite pair, which exists\nby \\cref{lem:quadrangulation}(i).  Call the result $B$.\nBefore these modifications, the original multiset union contains\nexactly one $T_J$ and one $N_J$ on each side.  The output union adds\nthe two $P$-chords and the leaf repairs and drops the selected boundary\nedges.  If $r\\in\\{0,1,2\\}$ selected sides are long, the numbers added\nand dropped are both $2+r\\le4$.\n\n\\paragraph{Error demands, context zero.}\nThe first output is $A^{(0)}=O_\\varepsilon$, the canonical orientation\nin which $X,Y$ are through.  Start the second output with the\ncomplementary cycle orientation and the edges of $I_2$ outside $C$.\nIt has the $N$ patterns on $X,Y$.  Insert their two chords.  The\nother opposite pair is good by \\cref{lem:quadrangulation}(ii),\nbecause $X,Y$ are both long.  On that other pair delete boundary\nedges and insert leaf repairs as above.  The result is a perfect\nmatching $B^{(0)}$, containing both $C_X$ and $C_Y$.  Again, the\nmultiset union differs from the original by equally many added\nand dropped edges, at most four of each.\n\n\\paragraph{Error demands, context one.}\nInterchange the patterns $T_Y$ and $C_Y$ between\n$A^{(0)},B^{(0)}$.  Each pattern perfectly matches the entire vertex\nset of $Y$, so the interchange is legal and gives\n$A^{(1)}=H_Y$ and a guide $B^{(1)}$.  The vertex sets of the opposite\narcs $X,Y$ are disjoint; hence $B^{(1)}$ still contains $C_X$.\nThis interchange does not change the multiset union or product weight.\n\nIn every case all added and dropped edges are among the chords,\nboundary edges, and good-side leaf repairs of this one cell.\nThey have pairwise activity ratio at most $A_0$ by\n\\cref{lem:cell-comparability}.  Since the two lists have equal\nlength at most four, the input product weight is at most $A_0^4$\ntimes the output product weight.  Division by $Z_w^2$ proves (i).\n\nFor completeness we give an inverse, including the information it\nneeds.  Attach a tag consisting of the four cell corners in cyclic\norder, lists of the added and dropped colored edges padded to length\nfour each, one orientation bit, and fixed flags for the encoding type\n(switch, error context zero, or error context one), opposite pair, and\norder.  An edge in these lists is an occurrence in\nthe multiset union; coincident colored edges retain their multiplicities.\nGiven the output pair and tag, remove the added occurrences and\nreinsert the dropped occurrences in its multiset union.  This recovers\nthe original colored union $I_1\\uplus I_2$.  Its component containing\nthe first tagged corner is the active simple cycle $C$.  The\norientation bit specifies which of the two alternating classes at\nthat corner belongs to $I_1$, so alternation determines both original\nlayers on all of $C$.  Outside $C$, the output layers were unchanged;\nthey recover $I_1,I_2$ there directly.  The fixed quadrangulation can\nnow be reconstructed.  Its tagged corners identify the cell, and\nthe flags identify the term and, if appropriate, its ordered pair.\nThus this is an inverse on every tag produced by the construction.\n\nThere are at most $2N^2$ possible colored edges.  Allowing a padding\nsymbol, the tags have at most\n\\[\n 128N^4(1+2N^2)^8\n \\le128\\cdot3^8N^{20}< (10N)^{30}=\\calT\n\\]\npossibilities for $N\\ge2$.  Here the factor $128$ more than covers\nthe orientation bit and the fixed flags; the lists themselves may\nbe stored in the fixed colored-edge order.  This proves (ii), and\nthe constructions already proved (iii).\n\\end{proof}\n\nThe encodings provide two kinds of control. Their bounded preimage\ncount converts a sum over demands to a sum over pairs of matchings.\nFor context errors, the guide also contains a prescribed closed arc;\nthis additional condition must survive that summation. We use the\nbounded preimage count for switches below, then retain the closed-arc\ncondition when treating context errors in the next subsection.\n\nWe apply this lemma first to switches.  For a fixed encoded pair\n$(A,B)$ there are at most $\\calT$ demands, and each has its associated\ndestination $A_Q$ in $\\mathcal S(A)$.  Therefore\n\\begin{align}\n \\sum_{d\\text{ switch}}\\rho(d)\\Delta_d^2\n &\\le A_0^4\\calT\\sum_{A,B}\\pi(A)\\pi(B)\n       \\sum_{A'\\in\\mathcal S(A)}(f_1(A)-f_1(A'))^2 \\notag\\\\\n &\\le60N^2A_0^6\\calT E.\n \\label{eq:switch-demand-load}\n\\end{align}\n\n\\subsection{Controlling context errors by cycle swaps}\n\nHere an identity $X$ means a directed, colored, simple odd path of\nlength at least three together with a specified colored edge joining\nits endpoints.  It determines $T_X,C_X$ and \\eqref{eq:arc-derivative}.\nWe may sum over all such identities: this is a finite proof device,\nand the algorithm does not enumerate them.  Put\n\\[\n p_X=\\pi\\{B:C_X\\subseteq B\\}.\n\\]\nWe omit identities with $p_X=0$.  Every identity arising from an error\ndemand has $p_X>0$, by the guide constructed in \\cref{lem:encoding}.\nFor any first matching $A\\supseteq T_X$ and either $j=0,1$,\nthat lemma gives the following \\emph{conditional demand-mass bound}:\n\\begin{equation}\n\\label{eq:conditional-demand-mass}\n \\sum_{\\substack{d\\text{ error}:X(d)=X\\\\ A^{(j)}(d)=A}}\n       \\rho(d)\n \\le A_0^4\\calT\\,\\pi(A)p_X.\n\\end{equation}\nIndeed, for each guide $B\\supseteq C_X$ there are at most $\\calT$\npreimages of $(A,B)$; now sum its product weight over these guides.\nThe factor $p_X$ is essential and will cancel the conditioning below.\n\nThe next comparison averages over precisely those guides counted by\n$p_X$. That conditional law can be concentrated on a very rare event.\nWe therefore retain $p_X$ in the demand bound instead of estimating it\nfrom below.\n\nFor a fresh guide $B\\sim\\pi$, define\n\\[\n \\mu_X=\\E\\bigl[\\partial_X f_2(B\\setminus C_X)\n                    \\mid C_X\\subseteq B\\bigr].\n\\]\nFor an error demand, \\eqref{eq:context-error} and\n$(a-b)^2\\le2(a-c)^2+2(b-c)^2$ imply\n\\[\n E_d^2\\le 2\\sum_{j=0}^1\n  \\bigl(\\partial_X f_1(A^{(j)}\\setminus T_X)-\\mu_X\\bigr)^2.\n\\]\nApplying \\eqref{eq:conditional-demand-mass}, and then Jensen's\ninequality to the conditional expectation defining $\\mu_X$, gives\n\\begin{align}\n \\sum_{d\\text{ error}}\\rho(d)E_d^2\n &\\le4A_0^4\\calT\n    \\sum_X\\sum_{A\\supseteq T_X}\\pi(A)p_X\n    \\bigl(\\partial_X f_1(A\\setminus T_X)-\\mu_X\\bigr)^2\n       \\notag\\\\\n &\\le4A_0^4\\calT\\,\\Sigma_{\\rm swap},\n \\label{eq:error-demand-load}\n\\end{align}\nwhere\n\\begin{equation}\n\\label{eq:swap-sum}\n \\Sigma_{\\rm swap}=\n \\sum_X\\sum_{\\substack{A\\supseteq T_X\\\\ B\\supseteq C_X}}\n \\pi(A)\\pi(B)\n \\bigl(\\partial_X f_1(A\\setminus T_X)\n       -\\partial_X f_2(B\\setminus C_X)\\bigr)^2.\n\\end{equation}\nIn detail, for a fixed $X,A$ Jensen bounds the squared difference\nfrom $\\mu_X$ by\n\\[\n \\frac1{p_X}\\sum_{B\\supseteq C_X}\\pi(B)\n \\bigl(\\partial_X f_1(A\\setminus T_X)\n       -\\partial_X f_2(B\\setminus C_X)\\bigr)^2.\n\\]\nMultiplication by the $p_X$ in \\eqref{eq:conditional-demand-mass}\nremoves this denominator exactly.  No lower bound on $p_X$ is used.\n\n\\begin{lemma}[Swap sum]\n\\label{lem:swap-sum}\nThe sum in \\eqref{eq:swap-sum} satisfies $\\Sigma_{\\rm swap}\\le12N^2E$.\n\\end{lemma}\n\n\\begin{proof}\nFor a pair in \\eqref{eq:swap-sum}, the matchings $T_X$ and $C_X$\ncover exactly the same vertex set, and their union is one alternating\ncycle: the path $X$ together with its closing chord.  Their edges\nare disjoint since $X$ is long and simple.  Thus this is an entire\ndiscrepancy component of $A,B$, not a portion of a larger one.\nSwapping it changes $F$ by exactly\n\\[\n \\partial_X f_1(A\\setminus T_X)\n       -\\partial_X f_2(B\\setminus C_X).\n\\]\nFor a fixed fresh pair $(A,B)$ and one of its discrepancy cycles,\nan identity $X$ is determined by marking the closing chord in the\nsecond matching and choosing a direction around that cycle.  The\nremaining colored path is then read from the pair.  There are at\nmost $2N$ such choices.  Consequently\n\\[\n \\Sigma_{\\rm swap}\\le2N\\sum_{A,B}\\pi(A)\\pi(B)\n     \\sum_{C\\in\\mathcal C(A,B)}\n       \\bigl(F(A,B)-F((A,B)^C)\\bigr)^2\n \\le12N^2E\n\\]\nby \\eqref{eq:cycle-energy}.\n\\end{proof}\n\n\\subsection{Completion of the energy proof}\n\n\\begin{proof}[Proof of \\cref{thm:pair-energy}]\nTake independent $I_1,I_2$ with law $\\pi$.  Toggle their discrepancy\ncycles in the order of their least vertices, converting $I_1$ to\n$I_2$.  Cauchy--Schwarz over the at most $N$ cycles gives\n\\[\n (f_1(I_1)-f_1(I_2))^2\n \\le N\\sum_C\n   (\\text{the $f_1$ difference at the step for $C$})^2.\n\\]\nFor each selected $C$, toggle all earlier cycles in \\emph{both}\ndemand matchings.  This preserves their product weight, their union,\nand the selected cycle.  It is its own inverse, since the union fixes\nthe same cycle order.  The term at that step becomes $D_C^2$ for the\nnew demand pair, with the definition used above.  Since\n$\\Var_\\pi f_1=\\tfrac12\\E(f_1(I_1)-f_1(I_2))^2$, we obtain the\nconvenient slightly weaker bound\n\\begin{equation}\n\\label{eq:cycle-demand-variance}\n \\Var_\\pi f_1\\le\n N\\sum_{I_1,I_2}\\pi(I_1)\\pi(I_2)\n       \\sum_{C\\in\\mathcal C(I_1,I_2)}D_C^2.\n\\end{equation}\n\nA length-two discrepancy cycle is a color change in $I_1$.\nFor a fixed $I_1$ and specified change, summing the mass of $I_2$\nthat supplies the other color costs at most one.  Its total\ncontribution to the sum in \\eqref{eq:cycle-demand-variance} is\ntherefore at most $12NA_0E$, by \\eqref{eq:color-energy}.\n\nFor a simple cycle, there are fewer than $N$ cells, so\n\\eqref{eq:global-cell-identity} and Cauchy--Schwarz give\n\\[\n D_C^2\\le3N\\sum_{\\text{cells}}\n                 (\\Delta^2+E_P^2+E_Q^2).\n\\]\nSum over all such cycle demands.  The errors with a short side\nvanish; all others are precisely the error demands already bounded.\nEquations \\eqref{eq:switch-demand-load},\n\\eqref{eq:error-demand-load}, and \\cref{lem:swap-sum} bound the\nsimple-cycle contribution by\n\\[\n 3N\\bigl(60N^2A_0^6\\calT+48N^2A_0^4\\calT\\bigr)E.\n\\]\nCombining the two cycle types in \\eqref{eq:cycle-demand-variance}\nand using $A_0,\\calT\\ge1$ yields\n\\[\n \\Var_\\pi f_1\n \\le\\bigl(12N^2A_0+324N^4A_0^6\\calT\\bigr)E\n \\le1000N^4A_0^6\\calT E.\n\\]\nFinally\n\\[\n 1000N^4A_0^6\\calT\n =10^{51}N^{34}D^6\n \\le(10^4ND)^{100}=L.\n\\]\nAll comparisons used only \\cref{lem:cell-comparability} and the\nadjacent-label comparison for color changes.  Both hold under any\ncommon clamp, proving the final assertion as well.\n\\end{proof}\n\\section{A sampler from replicated adjacent tiers}\n\\label{sec:ladder}\n\nThe two-coordinate inequality controls an additive function of a pair;\nit is not a Poincar\\'e inequality for all functions of that pair.\nWe now turn it into a Poincar\\'e inequality on a larger product space.\nThe construction uses adjacent weight tiers, with many independent\ncopies at each tier. Orthogonality of product projections controls the\nterms involving both members of a pair. The slot order ensures that\nno orthogonal interaction component occurs in two different pair\nresiduals. Averaging over many guides then makes their total\ncontribution small enough to absorb. Using several adjacent\ndistributions in one product chain has antecedents in replica Monte\nCarlo~\\cite{SwendsenWang1986,WangSwendsen2005}. Here the coordinates\nare colored perfect matchings, the pair moves exchange alternating\ncycles, and the effect of replication is quantified by the proof below.\n\nThroughout this section, the tree-bag graph has $N\\ge2$ vertices,\n$b_{\\rm tree}$ labels, maximum level at most $2K$, and the integer\nparameter $D$ of \\cref{def:tree-bags}, where $K$ is a nonnegative integer.\nIts reference activities are\npositive rationals. Let $\\calM$ denote its common, nonempty set of\ncolored perfect matchings. By \\cref{eq:bag-weight-range},\n\\begin{equation}\\label{eq:ladder-global-range}\n  D^{-1}\\le W_e\\le 3\\cdot4^K.\n\\end{equation}\n\n\\subsection{An exact refresh at unit activities}\n\n\\begin{lemma}\\label{lem:base-refresh}\nThe unit-activity partition function is computable by a dynamic program\non the label tree. With exact rational categorical choices, its tables\nalso produce an exact sample. The tables\ncan be computed with $O((b_{\\rm tree}+1)(N+1)^3)$ arithmetic\noperations on counts of $O(N\\log(N+1))$ bits. Label and loop indices\nuse $O(\\log(b_{\\rm tree}+1)+\\log(N+1))$ additional bits. Once the tables are\navailable, a sample uses polynomially many integer operations and at\nmost $4(b_{\\rm tree}+N+1)$ rational categorical choices, each with at\nmost $N+1$ outcomes. A positive table also yields a matching by\nentirely deterministic backtracking.\n\\end{lemma}\n\n\\begin{proof}\nA colored matching assigns each vertex to the color of its matched\nedge. Conversely, assign each vertex to one of its one or two bags,\nand pair the vertices assigned to each bag. These two operations are\ninverse. Thus, if $a_l$ vertices are assigned to bag $l$, the number of\nmatchings for that assignment is $\\prod_l J(a_l)$, where\n\\[\n J(0)=1,\\qquad J(a)=(a-1)!!\\quad\\text{for even }a>0,\n \\qquad J(a)=0\\quad\\text{for odd }a.\n\\]\nThe values of $J$ count pairings of a complete graph.\n\nFor a nonroot bag $C$, write $S_C$ for its interface with its parent,\nand put $s_C=|S_C|$; for the root use $S_C=\\varnothing$.\nLet $u_C$ be the number of vertices belonging to $C$ alone.\nAll interfaces incident to a bag are disjoint.\nFor a fixed subset of $k$ vertices of $S_C$ assigned to $C$, let\n$F_C(k)$ count the assignments and pairings within the subtree of $C$;\nthe other vertices of $S_C$ are assigned to its parent.\nThe answer depends only on $k$: vertices of $S_C$ belong to no bag\nother than $C$ and its parent, and they are interchangeable in the\ncomplete clique of color $C$.\n\nIf $C_1,\\ldots,C_d$ are the children of $C$, set $s_i=s_{C_i}$.\nChoosing $\\ell_i$ vertices of the $i$th interface to be assigned\ndownward gives the recurrence\n\\begin{equation}\\label{eq:base-dp}\n F_C(k)=\\sum_{\\ell_1=0}^{s_1}\\cdots\\sum_{\\ell_d=0}^{s_d}\n J\\!\\left(u_C+k+\\sum_{i=1}^d(s_i-\\ell_i)\\right)\n \\prod_{i=1}^d\\binom{s_i}{\\ell_i}F_{C_i}(\\ell_i).\n\\end{equation}\nThe required partition function is $F_{\\rm root}(0)$.\nThere is no exponential enumeration in evaluating this recurrence.\nForm the polynomial\n\\[\n H_C(z)=\\prod_{i=1}^d\n \\left(\\sum_{\\ell=0}^{s_i}\n       \\binom{s_i}{\\ell}F_{C_i}(\\ell)z^{s_i-\\ell}\\right).\n\\]\nIts degree is at most $N$, and\n\\[\n F_C(k)=\\sum_{a=0}^N[z^a]H_C(z)\\,J(u_C+k+a),\n\\]\nwith impossible indices contributing zero. Naive convolutions suffice\nfor the asserted arithmetic bound. The intermediate counts enumerate\nassignments and partial pairings on at most $N$ vertices. There are\nat most $2^N$ assignments, and for any one assignment at most $N!$\nsuch pairings, so $2^N N!$ bounds these counts. This gives the bit bound.\n\nFor sampling, begin at the root with $k=0$. At a visited bag, choose\n$a$ with probability proportional to $[z^a]H_C(z)J(u_C+k+a)$.\nBacktrack the stored convolutions to choose the individual $\\ell_i$,\nthen choose uniformly the corresponding interface subsets. Recurse\non each child with its chosen subset, and independently choose a\nuniform pairing of the vertices assigned to the current bag. Every\nchoice is proportional to its number of completions in\n\\cref{eq:base-dp}; induction on the subtree therefore gives the\nuniform law on colored matchings.\nA uniform subset is generated by sequential inclusion choices, and a\nuniform pairing by repeatedly pairing the least remaining vertex\nwith a uniformly chosen other vertex. The total number of interface\nvertices is at most $N$, since a shared vertex belongs to exactly one\ninterface. There are at most $N/2$ partner choices, $b_{\\rm tree}$ total-size\nchoices, and $b_{\\rm tree}-1$ convolution choices. This proves the stated draw bound. Choosing any\npositive-count branch instead yields a deterministic matching.\n\\end{proof}\n\n\\subsection{The product chain}\n\nSet\n\\begin{equation}\\label{eq:ladder-parameters}\n c=1+N^{-2},\\qquad\n T=\\min\\{t\\in\\mathbb Z_{\\ge0}:c^t\\ge\\max(D,3\\cdot4^K)\\},\n \\qquad q=32L,\n\\end{equation}\nwhere $L=(10^4ND)^{100}$ is the integer in\n\\cref{thm:pair-energy}. At tier $t\\in\\{0,\\ldots,T\\}$, clamp every\nreference activity to $[c^{-t},c^t]$ and denote the matching law by\n$\\pi_t$. Thus $\\pi_0$ is the uniform colored matching law and\n$\\pi_T$ is the desired law. Moreover,\n\\begin{equation}\\label{eq:adjacent-density}\n \\frac12\\le\\frac{\\pi_{t-1}(A)}{\\pi_t(A)}\\le2\n \\qquad(A\\in\\calM,\\ 1\\le t\\le T).\n\\end{equation}\nIndeed, an individual activity changes by a factor in $[c^{-1},c]$,\nso unnormalized matching weights and partition functions each change\nby factors in $[c^{-N/2},c^{N/2}]$. The probability ratio is therefore\nin $[c^{-N},c^N]$, and\n$c^N\\le\\exp(1/N)<2$.\nAlso $\\log(1+N^{-2})\\ge(2N^2)^{-1}$, whence\n\\begin{equation}\\label{eq:ladder-tier-bound}\n T\\le1+2N^2\\log\\max(D,3\\cdot4^K)\n   =O\\bigl(N^2(K+\\log D+1)\\bigr).\n\\end{equation}\nHere and below, unbased logarithms are natural.\n\nUse $q$ slots at each tier, with product stationary law\n\\[\n \\nu=\\bigotimes_{t=0}^T\\pi_t^{\\otimes q}.\n\\]\nAn allowed pair $(x,y)$ consists of a slot $x$ at tier $t\\ge1$ and\nany slot $y$ at tier $t-1$. On this pair use exactly the symmetric\nproposal of \\cref{thm:pair-energy}, with local proposals acting on\n$x$, and accept by Metropolis for $\\pi_t\\otimes\\pi_{t-1}$.\nAcross unequal tiers a cycle swap uses this Metropolis acceptance\nratio as well; the acceptance-one observation for two identical laws\nin \\cref{sec:energy} no longer applies. For each base slot there is\ninstead the update that refreshes it from $\\pi_0$, using\n\\cref{lem:base-refresh}. There are\n\\begin{equation}\\label{eq:ladder-update-count}\n M=Tq^2+q\n\\end{equation}\nupdates. The chain $P$ holds with probability $1/2$; otherwise it\nchooses one of these $M$ updates uniformly. Every update preserves\n$\\nu$ and is reversible, so the same is true of $P$.\n\nWrite $\\calE_{xy}$ for the energy of one allowed pair update, with\nall other coordinates integrated against $\\nu$, and $\\calE_i$ for\nthe energy of a refresh of base slot $i$. In this notation\n\\begin{equation}\\label{eq:ladder-scan-energy}\n \\calE_{\\nu,P}(f)\n   =\\frac1{2M}\\left(\\sum_{xy}\\calE_{xy}(f)\n                         +\\sum_{i\\text{ base}}\\calE_i(f)\\right).\n\\end{equation}\n\nFor later use, the two-coordinate inequality also applies across\nadjacent tiers. The pair state space and symmetric proposal are\nidentical for the targets $\\pi_t\\otimes\\pi_t$ and\n$\\pi_t\\otimes\\pi_{t-1}$. By \\cref{eq:adjacent-density}, the latter\ntarget density is at least half the former at every pair state.\nThe Metropolis capacity formula \\cref{eq:metropolis-capacity} thus\nshows that the unequal-tier energy dominates half the equal-tier\nenergy. Consequently, for any functions $a$ and $b$ of the two slots,\n\\begin{equation}\\label{eq:unequal-pair-energy}\n \\Var_{\\pi_t}a\n \\le 2L\\,\\calE_{xy}\\bigl(a(x)+b(y)\\bigr).\n\\end{equation}\nIn this display the energy is on the two slots alone; it can also be\napplied conditionally on any fixed values of other slots.\n\n\\begin{lemma}\\label{lem:replica-gap}\nThe lazy product chain has spectral gap at least $3/(8M)$, and hence\nat least $1/(20LM)$. The dependence on $L$ in the stronger bound\nis carried by $q=32L$, and hence by $M$.\n\\end{lemma}\n\n\\begin{proof}\nOrder the slots from highest tier to lowest, resolving ties\narbitrarily. Thus every allowed guide $y$ follows its upper slot $x$.\nFor each slot $x$, let $U_x$ be the set of its predecessors and put\n\\[\n h_x=\\E[f\\mid U_x,x]-\\E[f\\mid U_x].\n\\]\nThese are the orthogonal martingale increments, so\n\\begin{equation}\\label{eq:ladder-martingale}\n \\Var_\\nu f=\\sum_x\\|h_x\\|_{L^2(\\nu)}^2.\n\\end{equation}\nFix an allowed pair $(x,y)$, abbreviate $U=U_x$, and define\n\\[\n k_{xy}=\\E[f\\mid U,y]-\\E[f\\mid U],\\qquad\n g_{xy}=\\E[f\\mid U,x,y],\n\\]\n\\begin{equation}\\label{eq:ladder-residual}\n r_{xy}=g_{xy}-\\E[f\\mid U]-h_x-k_{xy}.\n\\end{equation}\nFor fixed $U$, $h_x$ is centered over $x$ and $k_{xy}$ over $y$.\nApplying \\cref{eq:unequal-pair-energy} and then averaging over $U$\ngives\n\\[\n \\|h_x\\|_2^2\\le2L\\calE_{xy}(h_x+k_{xy}).\n\\]\nConditional expectation onto $U,x,y$ decreases $\\calE_{xy}$: the\npair kernel is independent of all the other coordinates, and Jensen's\ninequality applies to each difference across a transition.\nFor any stationary Markov kernel, \\cref{eq:dirichlet} and\n$(a-b)^2\\le2a^2+2b^2$ give $\\calE(v)\\le2\\|v\\|_2^2$.\nUsing \\cref{eq:ladder-residual}, and observing that functions of $U$\nhave zero pair energy, we conclude that\n\\begin{align}\n \\|h_x\\|_2^2\n &\\le 2L\\bigl(2\\calE_{xy}(g_{xy})+2\\calE_{xy}(r_{xy})\\bigr)\n \\nonumber\\\\\n &\\le4L\\calE_{xy}(f)+8L\\|r_{xy}\\|_2^2.\n \\label{eq:ladder-one-pair}\n\\end{align}\n\nWe now have an estimate for one upper slot and one guide, but it\ncontains a residual involving both slots. Replication helps only if\nthese residual terms can be charged collectively. The ordering of\nslots makes that collective estimate possible.\n\nThe key point is that the residuals in this bound are orthogonal\n\\emph{over all allowed pairs}. To verify it explicitly, use the\northogonal product (ANOVA) decomposition\nof Efron and Stein~\\cite[Section~2]{EfronStein1981}, written here as\n\\[\n f=\\sum_{S}f_S,\\qquad\n f_S=\\sum_{A\\subseteq S}(-1)^{|S|-|A|}\\E[f\\mid A],\n\\]\nwhere $S$ ranges over subsets of the slot set.\nEach $f_S$ depends only on the coordinates in $S$ and is centered in\neach coordinate of $S$. Therefore distinct $f_S$ are orthogonal, and\n$\\E[f\\mid A]=\\sum_{S\\subseteq A}f_S$.\nSubstitution into \\cref{eq:ladder-residual} gives\n\\begin{equation}\\label{eq:ladder-anova}\n r_{xy}=\\sum_{\\substack{S\\subseteq U_x\\cup\\{x,y\\}\\\\x,y\\in S}}f_S.\n\\end{equation}\nEvery set $S$ in this sum has $y$ last and $x$ penultimate in the\nfixed slot order: all its other coordinates lie in $U_x$. These two\npositions determine the allowed pair uniquely. Thus even pairs that\nshare a slot use disjoint sets of orthogonal components. Since every\ncomponent here is nonconstant,\n\\begin{equation}\\label{eq:ladder-residual-sum}\n \\sum_{xy}\\|r_{xy}\\|_2^2\\le\\Var_\\nu f.\n\\end{equation}\nThis decomposition is only an identity in the proof; the algorithm\ndoes not compute it.\n\nFor a base slot $i$, conditional expectation onto $U_i,i$ similarly\ndecreases its refresh energy. The refresh energy of $h_i$ equals\n$\\|h_i\\|_2^2$, because $h_i$ is centered over $i$ given $U_i$.\nHence $\\|h_i\\|_2^2\\le\\calE_i(f)$.\nAverage \\cref{eq:ladder-one-pair} over the $q$ guides of each nonbase\nslot and sum over slots.\n\\cref{eq:ladder-martingale,eq:ladder-residual-sum} yield\n\\[\n \\Var_\\nu f\n \\le\\frac{4L}{q}\\sum_{xy}\\calE_{xy}(f)\n       +\\frac{8L}{q}\\Var_\\nu f\n       +\\sum_{i\\text{ base}}\\calE_i(f).\n\\]\nSince $q=32L$, absorption gives\n\\[\n \\Var_\\nu f\n \\le\\frac16\\sum_{xy}\\calE_{xy}(f)\n       +\\frac43\\sum_{i\\text{ base}}\\calE_i(f)\n \\le\\frac{8M}{3}\\calE_{\\nu,P}(f).\n\\]\nThis proves the first gap bound. The second follows from $L\\ge1$.\n\\end{proof}\n\n\\subsection{A quantitative sampling guarantee}\n\n\\begin{theorem}\\label{thm:bag-sampler}\nDefine $T,q,M$ by\n\\cref{eq:ladder-parameters,eq:ladder-update-count}, and put\n\\begin{align}\n \\Lambda&=10Nq(T+1)\n       \\bigl(K+\\lceil\\log_2(ND+2)\\rceil+1\\bigr),\n       \\label{eq:ladder-lambda}\\\\\n s(\\xi)&=\\left\\lceil20LM\n       \\bigl(\\Lambda+\\lceil\\log_2(1/\\xi)\\rceil+1\\bigr)\\right\\rceil\n       \\qquad(0<\\xi<1).\n       \\label{eq:ladder-steps}\n\\end{align}\nInitialize all slots to any one colored perfect matching and run the\nexact chain for $s(\\xi)$ steps. The resulting product law is at total\nvariation distance at most $\\xi$ from $\\nu$. In particular, any\ntop-tier slot has law within $\\xi$ of the desired weighted matching\nlaw.\n\nAll tables, state representations, and transition probabilities are\ncomputable by rational arithmetic. Their sizes, arithmetic operation\ncounts, and rational bit lengths are polynomial in $N,D,K$,\n$b_{\\rm tree}$, $\\log(1/\\xi)$, and the bit lengths of the reference\nactivities. Exact rational choices here define an ideal process;\nthe bounded-bit implementation in \\cref{sec:complexity} adds at most\n$\\xi$ to the total variation error.\n\\end{theorem}\n\n\\begin{proof}\nEvery colored matching consists of $N/2$ vertex pairs, each with at\nmost two color choices. In particular, $|\\calM|\\le(2N^2)^N$.\nAt every tier, all activities remain in the interval\n\\cref{eq:ladder-global-range}. For any $A\\in\\calM$ this gives\n\\[\n \\pi_t(A)\\ge\n \\frac1{(2N^2)^N(3D4^K)^{N/2}}.\n\\]\nTaking the product over $q(T+1)$ slots and using $N\\ge2,D\\ge1$\nshows that\n\\begin{equation}\\label{eq:ladder-minimum-mass}\n \\log\\frac1{\\min\\nu}\\le\\Lambda.\n\\end{equation}\nFor example, with $a=\\lceil\\log_2(ND+2)\\rceil$, the logarithm of the\ndenominator in the preceding display is at most\n$N(K+\\tfrac52a+2)$, which is at most $10N(K+a+1)$.\n\nFor completeness, the finite-state spectral argument gives a direct\nmixing bound. Reversibility makes $P$ self-adjoint on $L^2(\\nu)$.\nIts laziness makes its eigenvalues nonnegative, and\n\\cref{lem:replica-gap} bounds every eigenvalue on the centered subspace above by\n$1-\\gamma$, where $\\gamma=1/(20LM)$.\nStarting from a point $z$, the centered initial density has squared\nnorm $1/\\nu(z)-1$. By diagonalization, reversibility, and\n\\cref{eq:ladder-minimum-mass},\n\\[\n \\|P^s(z,\\cdot)-\\nu\\|_{\\TV}\n \\le\\frac12(1-\\gamma)^s\\sqrt{\\frac1{\\nu(z)}-1}\n \\le\\frac12\\exp(-\\gamma s+\\Lambda/2).\n\\]\nSubstitution of \\cref{eq:ladder-steps} makes this at most $\\xi$.\nProjection onto a chosen top-tier coordinate cannot increase total\nvariation.\n\nIt remains to specify the computation represented by this chain.\nThe unit-activity tables are supplied by \\cref{lem:base-refresh}; a\npositive table supplies an initial matching if one has not already\nbeen constructed. There are at most $N^2$ colored edges and\n$q(T+1)$ matching coordinates. Precompute the clamped activities at\nevery tier using rational powers of $c$. The tier bound\n\\cref{eq:ladder-tier-bound} is polynomial in the stated parameters,\nand these powers have polynomial bit length.\nA scan choice can be made by an integer in $\\{1,\\ldots,M\\}$,\ndecoding either a base slot or an upper tier and two slot indices.\nA pair proposal is implemented by inspecting two matchings: the\nalternating components can be listed by following the union of their\nedges, and the local switch and color proposals require only finite\nlists of edges and at most two color choices per new edge.\nMetropolis acceptance uses a ratio of products of at most $2N$\nactivities; the unknown partition functions cancel. A refresh uses\nthe precomputed integer tables. Thus a step has polynomial arithmetic\ncost and rational probabilities of polynomial bit length.\nThe bound $s(\\xi)$ is itself polynomial in the displayed parameters,\nwhich proves the assertion about the ideal computation.\n\nFor the finite-bit assertion in this generality, including arbitrarily\nmany empty bags, use the draw and option bounds\n\\[\n R_{\\rm draw}^{\\rm gen}=100(s(\\xi)+1)(b_{\\rm tree}+N+1),\\qquad\n R_{\\rm opt}=10(M+N^2+1).\n\\]\nIndeed a refresh uses at most $4(b_{\\rm tree}+N+1)$ categorical\nchoices, and laziness and the scan use two more. A pair step uses at\nmost eight choices. A final revelation of edge types, when present,\nuses at most $N$ further choices. These fit the displayed draw bound.\nEvery choice has at most $R_{\\rm opt}$ options. Take a fixed integer\n$k$ with $2^k\\ge2R_{\\rm draw}^{\\rm gen}R_{\\rm opt}/\\xi$.\n\\Cref{lem:finite-random-choice} and successive coupling then bound\nthe cumulative simulation discrepancy by $\\xi$. This precision and\nthe resulting bit cost are polynomial in the stated parameters,\nincluding $b_{\\rm tree}$. For the hierarchy constructed in\n\\cref{sec:enlargement}, the smaller specialized bookkeeping in\n\\cref{sec:complexity} follows from\n$b_{\\rm tree}\\le N(2K+1)$.\n\\end{proof}\n\\section{From samples to the number of perfect matchings}\n\\label{sec:counting}\n\nWe now give the counting algorithm. Its sampling calls use only the\ntree-bag sampler proved above. In particular, neither balancing nor\nestimating a partition ratio requires an additional oracle.\nPositive completion, gradual suppression of nonedges, and estimation\nof successive partition ratios follow the annealing framework used\nfor the permanent~\\cite{JerrumSinclairVigoda2004}. The marked observables\nand vertex-scale update below supply the general-graph implementation\nand its balance guarantees.\n\n\\subsection{The schedule and vertex scales}\nIf $n=|V|=0$, return one. If a binary vertex count is used and\n$n>2|E|$, return zero: the listed edges cannot cover all vertices.\nOn the remaining nonempty input, perform a deterministic\nperfect-matching existence test and return zero if it fails. Thus the\nremaining case has even $n\\ge2$ and $Z(G)\\ge1$.\nSet\n\\begin{equation}\\label{eq:schedule}\n b=1+\\frac1n,\\qquad\n K=\\min\\left\\{k\\in\\mathbb N:k\\ge1,\\quad\n                    n^{n/2}b^{-k}\\le\\frac\\eps{32}\\right\\}.\n\\end{equation}\nSince $\\log(1+1/n)\\ge1/(2n)$,\n\\begin{equation}\\label{eq:schedule-length}\n K\\le 1+2n\\left(\\frac n2\\log n+\\log(32/\\eps)\\right).\n\\end{equation}\nAt stage $j\\in\\{0,\\ldots,K\\}$ the complete logical graph has raw\nactivities\n\\[\n w^{(j)}_{uv}=\n \\begin{cases}1,&uv\\in E,\\\\ b^{-j},&uv\\notin E.\\end{cases}\n\\]\nWrite $Z_j$ for its perfect-matching partition function. Then\n\\begin{equation}\\label{eq:raw-telescoping}\n Z_0=(n-1)!!,\\qquad\n Z_K=(n-1)!!\\prod_{j=0}^{K-1}\\frac{Z_{j+1}}{Z_j}.\n\\end{equation}\n\nFor sampling, we additionally maintain positive vertex scales $s_i$ and\nuse activities $\\lambda_{ij}=w^{(j)}_{ij}s_i s_j$. Every perfect matching\nhas the same vertex factor $\\prod_i s_i$. Consequently\n\\begin{equation}\\label{eq:scaling-identities}\n Z_\\lambda=Z_j\\prod_i s_i,\n \\qquad\n g_\\lambda(ij)=\\frac{g_{w^{(j)}}(ij)}{s_i s_j}.\n\\end{equation}\nInitialize $s_i=1/\\floor{\\sqrt{n-1}}$. At stage zero,\n\\[\n g_\\lambda(ij)=\\frac{\\floor{\\sqrt{n-1}}^2}{n-1}\\in[1/4,1],\n\\]\nso the logical graph is balanced. The update defined below always\nmultiplies each scale by a positive number at most two, whether or not\nthe estimates succeed. Starting from scales at most one and making at\nmost $K$ updates, we therefore have at every sampling call\n\\begin{equation}\\label{eq:unconditional-height-bound}\n 0<\\lambda_{ij}\\le4^K.\n\\end{equation}\nWe use the same structural parameters throughout a trial:\n\\begin{equation}\\label{eq:counting-structural-parameters}\n D_0=10^8(n+1)^4,\\qquad p=2K+2,\\qquad\n N=n+4p\\binom n2,\\qquad D=100N^2D_0.\n\\end{equation}\nFor each current $\\lambda$, construct the enlarged graph and marked\nbag edges of \\cref{sec:enlargement}.\n\n\\subsection{Observables from one marked sample}\nLet $\\lambda^+$ denote the activities at the next raw stage, with the\n\\emph{current} vertex scales retained. If $P$ is a logical perfect\nmatching, or a logical matching missing one specified pair of vertices,\nput\n\\[\n u(P)=b^{-\\#\\{e\\in P:e\\notin E\\}}.\n\\]\nThus $\\wt_{\\lambda^+}(P)=u(P)\\wt_\\lambda(P)$ and\n\\begin{equation}\\label{eq:bounded-reweighting}\n \\frac12<b^{-n/2}\\le u(P)\\le1.\n\\end{equation}\nThe strict left inequality follows from\n$(1+1/n)^{n/2}<\\mathrm e^{1/2}<2$.\n\nSample a colored perfect matching from the bag graph and reveal the\nreal, virtual, and probe portions of its edges. Define the following\nobservables, all in $[0,1]$:\n\\begin{align*}\n a&=\\one_{\\{\\text{all edges real}\\}},\\\\\n d&=\\one_{\\{\\text{all edges real}\\}}u(P),\\\\\n z_{ij}&=\\one_{\\{\\text{probe }ij\\text{ and all other edges real}\\}}u(P).\n\\end{align*}\nHere $P$ is decoded only on the indicated event; elsewhere the observable\nis zero. On the all-real event it is a full logical perfect matching.\nOn the sole-probe event it is a logical perfect matching missing $i,j$.\nWrite $I=Z_{\\rm bag}/Z'$ for the inflation factor. Under the exact\nmarked bag law, the means satisfy\n\\begin{equation}\\label{eq:observable-means}\n \\E a=\\frac1I,\\qquad\n \\E d=\\frac{Z_{\\lambda^+}}{I Z_\\lambda},\\qquad\n \\E z_{ij}=\\frac{Z_{\\lambda^+}(-ij)}{D I Z_\\lambda}.\n\\end{equation}\nThe all-real probability in \\cref{prop:marked-laws} gives the first\nequality. The other two follow by multiplying the weights in that event,\nor in the sole-probe\nidentity \\eqref{eq:probe}, by $u(P)$ and summing. In particular,\n\\begin{equation}\\label{eq:observable-ratios}\n R:=\\frac{Z_{j+1}}{Z_j}\n   =\\frac{Z_{\\lambda^+}}{Z_\\lambda}\n   =\\frac{\\E d}{\\E a},\\qquad\n g_{\\lambda^+}(ij)=D\\frac{\\E z_{ij}}{\\E d}.\n\\end{equation}\nThus one batch of marked samples will estimate both the next raw\npartition ratio and the two-hole ratios needed to choose the next\nvertex scales.\n\n\\subsection{Estimating and restoring balance}\nUse\n\\begin{equation}\\label{eq:statistical-parameters}\n \\alpha=10^{-5},\\qquad\n \\sigma=\\min\\left\\{\\frac\\alpha{100D},\\frac\\eps{1000K}\\right\\},\\qquad\n S=\\ceil{10\\sigma^{-2}(n+K+1)},\\qquad\n \\xi=\\frac1{1000KS}.\n\\end{equation}\nAt each stage make $S$ independent fresh sampler runs, each with marked\noutput law at total variation distance at most $3\\xi$ from the exact\nmarked bag law. \\Cref{sec:complexity} implements these runs in bounded\nbit time. Let bars denote the empirical means of the observables.\nSet\n\\begin{equation}\\label{eq:ratio-estimate}\n \\widehat R=\n \\begin{cases}\\bar d/\\bar a,&\\bar a>0,\\\\1,&\\bar a=0.\n \\end{cases}\n\\end{equation}\nIf $\\bar d>0$, form the symmetric array\n\\begin{equation}\\label{eq:upper-estimate}\n G_{ij}=\\max\\left\\{\\alpha,\n             \\min\\left\\{3,D\\frac{\\bar z_{ij}}{\\bar d}+\\alpha\\right\\}\n                  \\right\\}\n \\quad (i\\ne j).\n\\end{equation}\nIf $\\bar d=0$, set all $G_{ij}=1$. The following elementary balancing\nprocedure will be useful.\n\n\\begin{lemma}\\label{lem:one-pass-scaling}\nSuppose $G$ is symmetric and $\\alpha\\le G_{ij}\\le3$ for $i\\ne j$.\nInitialize $v_i=2$ for every vertex and visit the vertices in their\nfixed order, setting\n\\begin{equation}\\label{eq:scale-update}\n v_i\\leftarrow\\max_{j\\ne i}\\frac{G_{ij}}{v_j}.\n\\end{equation}\nEvery coordinate only decreases, all final coordinates lie in\n$[\\alpha/2,2]$, and the final vector satisfies\n$v_i v_j\\ge G_{ij}$ with an equality in every row.\n\\end{lemma}\n\\begin{proof}\nInitially all products equal four and hence dominate $G$. At an update,\nfeasibility shows that the new value is at most the old value. The\ndefinition restores all constraints involving $i$ and leaves all other\nconstraints unchanged. Since every other coordinate is at most two,\nthe new value is at least $\\alpha/2$. At least one constraint involving\n$i$ is tight. If its neighbor is visited later, that neighbor cannot\ndecrease without violating this tight constraint. Thus the equality\npersists to the end.\n\\end{proof}\n\nAfter estimating the current ratio, perform this procedure and replace\n$s_i$ by $s_i v_i$. These scales are used with the next raw stage.\n\n\\paragraph{Algorithm outline.}\nAfter the zero test, one trial uses the parameters in\n\\cref{eq:schedule,eq:counting-structural-parameters,eq:statistical-parameters}\nand the initial scales above. For $j=0,\\ldots,K-1$:\n\\begin{enumerate}[label=\\textup{\\arabic*.},leftmargin=*]\n\\item Form $\\lambda_{uv}=w^{(j)}_{uv}s_us_v$ and its marked bag graph.\n\\item Obtain $S$ independent fresh marked samples using the bounded-bit\nimplementation of \\cref{thm:bag-sampler}. Compute the empirical\nmeans, then $\\widehat R_j$ and $G$ by\n\\cref{eq:ratio-estimate,eq:upper-estimate}, including their\nzero-denominator conventions.\n\\item Run the one-pass update \\eqref{eq:scale-update} on $G$, replace\n$s_i$ by $s_iv_i$, and advance to the next raw stage.\n\\end{enumerate}\nThe trial returns $Y=(n-1)!!\\prod_{j=0}^{K-1}\\widehat R_j$;\nthe final algorithm takes the median of independent trials as specified\nbelow. The current scales are held fixed during each ratio estimate,\nso their common factors cancel. Every empirical history executes these\nsame steps with the unconditional caps above. Balance is used only to\nprove the accuracy of histories whose estimates succeed.\n\n\\begin{lemma}\\label{lem:successful-stage}\nSuppose the current logical activities are balanced and every empirical\nmean is within $\\sigma$ of its exact mean. Then the next scaled\nactivities are balanced and\n\\begin{equation}\\label{eq:stage-relative-error}\n \\left|\\frac{\\widehat R}{R}-1\\right|\n \\le10\\sigma\\le\\frac\\eps{100K}.\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nBy \\cref{prop:bag-inflation} and \\eqref{eq:bounded-reweighting},\n\\[\n \\E a\\ge\\frac12,\\qquad \\E d\\ge\\frac14,\n \\qquad\\frac12\\le R\\le1.\n\\]\nReweighting both a full matching and a matching with two holes by\nfactors in $[1/2,1]$ gives\n\\[\n \\frac12 g_\\lambda(ij)\\le g_{\\lambda^+}(ij)\n                         \\le2g_\\lambda(ij).\n\\]\nIn particular the pre-update row maxima lie in $[1/8,2]$.\nWrite $g^+_{ij}=g_{\\lambda^+}(ij)$. Since $\\bar d\\ge\\E d-\\sigma>0$,\n\\begin{align*}\n \\left|D\\frac{\\bar z_{ij}}{\\bar d}-g^+_{ij}\\right|\n &\\le\\frac{D|\\bar z_{ij}-\\E z_{ij}|\n              +g^+_{ij}|\\bar d-\\E d|}{\\E d-\\sigma}\\\\\n &\\le\\frac{(D+2)\\sigma}{1/4-\\sigma}<\\alpha.\n\\end{align*}\nThe last inequality follows from $D\\ge1$ and\n$\\sigma\\le\\alpha/(100D)$. Since $0\\le g^+_{ij}\\le2$, both truncations\nin \\eqref{eq:upper-estimate} preserve\n\\begin{equation}\\label{eq:upper-estimate-accuracy}\n g^+_{ij}\\le G_{ij}\\le g^+_{ij}+2\\alpha.\n\\end{equation}\n\nDuring the scaling procedure every updated coordinate satisfies\n$v_i\\ge\\max_jG_{ij}/2\\ge1/16$. At the end all entries of the newly\nscaled two-hole matrix are at most one, by feasibility and\n\\eqref{eq:scaling-identities}. In each row choose a final tight neighbor.\nFor that pair,\n\\[\n \\frac{g^+_{ij}}{v_i v_j}\n \\ge1-\\frac{2\\alpha}{v_i v_j}\n \\ge1-512\\alpha>\\frac14.\n\\]\nThis proves balance.\n\nFor the ratio estimate, using $R\\ge1/2$ and $\\bar a\\ge1/2-\\sigma$,\n\\[\n \\left|\\frac{\\widehat R}{R}-1\\right|\n \\le\\frac{|\\bar d-\\E d|+R|\\bar a-\\E a|}\n            {R(\\E a-\\sigma)}\n \\le\\frac{2\\sigma}{(1/2)(1/2-\\sigma)}\n <10\\sigma.\n\\]\nThe final bound in \\eqref{eq:stage-relative-error} is the other choice\nin \\eqref{eq:statistical-parameters}.\n\\end{proof}\n\n\\subsection{Success probability and output}\nThe form of Hoeffding's inequality~\\cite[Theorem~2]{Hoeffding1963}\nused here is:\nif $X_1,\\ldots,X_S$ are independent, take values in $[0,1]$, and have\na common mean $\\mu$, then, for every $t>0$,\n\\begin{equation}\\label{eq:hoeffding}\n \\Prb\\left[\\left|S^{-1}\\sum_{i=1}^S X_i-\\mu\\right|>t\\right]\n \\le2\\exp(-2St^2).\n\\end{equation}\nThe theorem applies separately to each observable; the observables\nwithin a single sample need not be independent.\n\nCondition on any history in which all preceding stages succeeded.\nBy \\cref{lem:successful-stage}, the current activities are balanced.\nFor independent ideal samples, a union bound over the at most\n$n^2+2$ observables gives failure probability at most\n\\[\n 2(n^2+2)\\exp(-2S\\sigma^2)\n \\le2(n^2+2)\\exp(-20(n+K+1))<\\frac1{100K}.\n\\]\nOne elementary verification of the last strict inequality is\n$\\log(200K(n^2+2))\\le6+K+2n<20(n+K+1)$ for $n\\ge2,K\\ge1$.\nThe actual fresh runs can be coupled to these ideal samples with\ndisagreement probability at most $3S\\xi=3/(1000K)$.\nTherefore the conditional probability of failure at the first\nunsuccessful stage is at most $13/(1000K)$. Summing over all $K$\nstages proves that a whole trial succeeds with probability at least\n$1-13/1000>0.9$.\n\nThe trial output is the nonnegative rational\n\\begin{equation}\\label{eq:trial-output}\n Y=(n-1)!!\\prod_{j=0}^{K-1}\\widehat R_j.\n\\end{equation}\nOn success, \\cref{lem:successful-stage} and\n$\\prod_i(1-t_i)\\ge1-\\sum_i t_i$ for $t_i\\in[0,1]$ imply\n\\[\n (1-\\eps/100)Z_K\\le Y\\le\\exp(\\eps/100)Z_K.\n\\]\nEvery matching using a nonedge has raw final weight at most $b^{-K}$.\nThere are at most $(n-1)!!\\le n^{n/2}$ perfect matchings on the\ncomplete graph, so\n\\begin{equation}\\label{eq:padding-bias}\n Z(G)\\le Z_K\\le Z(G)+n^{n/2}b^{-K}\n             \\le(1+\\eps/32)Z(G).\n\\end{equation}\nFor $0<\\eps<1$, the resulting lower bound exceeds\n$(1-\\eps)Z(G)$, and the upper bound is at most $(1+\\eps)Z(G)$.\nFor example, $\\exp(\\eps/100)\\le1+\\eps/50$, which gives\n\\[\n \\exp(\\eps/100)(1+\\eps/32)\n \\le1+\\left(\\frac1{50}+\\frac1{32}+\\frac1{1600}\\right)\\eps\n <1+\\eps.\n\\]\n\nTo amplify, let $\\ell=\\ceil{\\log_2(1/\\delta)}$, computed by rational\ncomparisons with powers of two. Run\n$r=10(\\ell+1)+1$ independent trials and return their median.\nThis is an odd number of trials. Since each succeeds with probability\nat least $0.9$, Hoeffding's inequality for their success indicators\nbounds the probability of fewer than half succeeding by\n\\[\n \\exp\\bigl(-2(0.4)^2r\\bigr)\n \\le\\exp\\bigl(-3.2(\\ell+1)\\bigr)\n <2^{-\\ell}\\le\\delta.\n\\]\nWhenever a majority succeed, the median belongs to the desired\ninterval. The deterministic zero branch was taken before any trials.\nThis proves the correctness and probability assertions of\n\\cref{thm:main}, subject only to the bit implementation established\nnext.\n\\section{A bounded-bit implementation}\n\\label{sec:complexity}\n\nWe finish by specifying a classical finite-bit implementation and proving\na worst-case bound. The argument is uniform over all empirical histories,\nincluding histories on which balance is lost. Balance controls the\nstatistical accuracy of a successful trial; the unconditional caps in\nthe algorithm control its running time.\n\n\\subsection{Sizes of the parameters and state spaces}\nLet $\\mathsf b$ be the total binary input length. We use a standard\nexplicit encoding of a finite graph and of the two rationals. If isolated\nvertices are specified by a binary vertex count, first return zero when\n$n>2|E|$. This test is valid for every nonempty graph admitting a perfect\nmatching and ensures that the nonzero branch has $n\\le2|E|=O(\\mathsf b)$.\nThe deterministic existence test~\\cite{Edmonds1965} therefore has\npolynomial bit cost on the original graph. It is the only\nmatching-existence oracle used.\n\n\\Cref{eq:schedule-length} bounds $K$ polynomially in $\\mathsf b$.\nIt can be found without logarithms by iterating rational powers of $b$\nand comparing the two sides of \\eqref{eq:schedule}. The parameters\n$p,N,D_0,D$ are integers of polynomial numerical size in $n,K$.\nThe hierarchy has at most\n\\begin{equation}\\label{eq:hierarchy-size}\n H_{\\rm tree}=N(2K+1)\n\\end{equation}\nnodes, since its levels are $0,\\ldots,2K$ and every level has at most\n$N$ components. The number of colored edges is less than $N^2$.\n\nUse the ladder parameters $c,T,L,q,M,\\Lambda$ from\n\\cref{eq:energy-constants,eq:ladder-parameters,eq:ladder-update-count,eq:ladder-lambda}, and write\n\\[\n t_{\\rm mix}=s(\\xi),\n\\]\nwhere $s(\\xi)$ is defined in \\eqref{eq:ladder-steps}. The structural\nparameters $N,D,K$ are fixed throughout a trial. Hence the same $T$\nmakes the final tier unclamped on every empirical history, by\n\\cref{eq:unconditional-height-bound,eq:ladder-global-range}.\nIt can be found by iterating rational powers of $c$ until the defining\ninequality in \\eqref{eq:ladder-parameters} holds. The bound\n\\eqref{eq:ladder-tier-bound} is polynomial in $N,K,D$.\nCeilings of binary logarithms are found by integer or rational\ncomparisons with powers of two.\nThese bounds and the product-state size\n$q(T+1)$ are polynomial in $\\mathsf b$ and $\\eps^{-1}$.\nIndeed,\n\\[\n \\sigma^{-1}\\le C(D+K\\eps^{-1}),\\qquad\n S=O\\bigl((D^2+K^2\\eps^{-2})(n+K+1)\\bigr),\\qquad\n \\xi^{-1}=1000KS,\n\\]\nfor an absolute constant $C$. The exponent $100$ in $L$ is fixed.\n\nThere is no initial-sampling problem at the upper tiers. Pair the logical\nvertices in their fixed order and expand this matching by the path\ntilings. All logical activities are positive, so this is an available\nreal perfect matching with assigned colors. Initialize every slot to\nthat matching. The mixing bound applies from this deterministic product\nstate.\n\n\\subsection{Rational bit lengths}\nWe record explicitly why the adaptive scales do not create an\narithmetic blowup. Put\n\\begin{equation}\\label{eq:empirical-bit-bound}\n B_G=1+n\\ceil{\\log_2(n+1)}+\\ceil{\\log_2(S+1)}\n                      +\\ceil{\\log_2(D+1)}.\n\\end{equation}\nAbsolute multiplicative constants in the bit bounds below are\nindependent of the input.\n\nEvery nonzero sample contribution is $(n/(n+1))^a$ for some\n$0\\le a\\le n/2$. These fractions have the common denominator\n$Q=(n+1)^{n/2}$. Thus empirical means, their ratios, and the capped\nentries $G_{ij}$ have $O(B_G)$ numerator and denominator bits. This\nbound does not involve the current scales.\n\nAn assignment in \\eqref{eq:scale-update} chooses one maximizer $j$ and\nsets $v_i=G_{ij}/v_j$. At that moment $v_j$ is either the initial value\ntwo or an expression formed at an earlier update. Following such\ndependencies strictly decreases the update index, so it produces a\nchain of at most $n$ factors. Each $v_i$ consequently has $O(nB_G)$\nbits. Accumulating at most $K$ scale updates gives $O(KnB_G)$ bits per\nscale. The raw activities have $O(K\\log(n+1))$ bits. These estimates\napply even when all observations are unfavorable, because the clipping\nand branch conventions are unconditional.\n\nBottleneck entries $B_{ij}$ and the maxima $H_i=\\max_{j\\ne i}B_{ij}$\nselect from finitely many input activities and one; they do not involve\nsums over paths. Initialize $B_{ij}=\\max\\{1,\\lambda_{ij}\\}$ and,\nfor each vertex $k$, update the entries with distinct $i,j\\ne k$ by\n\\[\n B_{ij}\\leftarrow\\max\\{B_{ij},\\min\\{B_{ik},B_{kj}\\}\\}.\n\\]\nThis is the max--min Floyd--Warshall recurrence: after processing $k$,\nthe entries allow the processed vertices as internal path vertices.\nIt uses $O(n^3)$ rational comparisons. The path profiles add only powers of two of\nbit length $O(K)$. Bag activities are sums of a real activity and a\ndyadic height divided by $D$. Finally $c^{\\pm t}$ has\n$O(T\\log(N+1))$ bits for $t\\le T$.\n\nIt follows that every single-edge activity used by the sampler has\nnumerator and denominator lengths at most the integer bound\n\\begin{equation}\\label{eq:edge-bit-bound}\n B_W=C_1\\bigl(KnB_G+K+\\ceil{\\log_2(D+1)}\n                       +T\\ceil{\\log_2(N+1)}+1\\bigr),\n\\end{equation}\nwhere $C_1$ is a sufficiently large absolute positive integer.\nA product of at most $N$ such activities has $O(NB_W)$ bits. In a\nMetropolis acceptance ratio the unknown normalizing constants cancel.\nThus even a cycle exchange at unequal tiers requires only rational\nproducts of this size. The real and probe revelation probabilities\nare likewise rational, respectively the real portion divided by the\ncollapsed activity and $1/(DW)$ for the probe portion.\n\nBy \\cref{lem:base-refresh}, the bottom-tier dynamic program uses\ncounts of $O(N\\log(N+1))$ bits. Its categorical normalizers count\nthe same partial completions and obey the same bound. Label and loop\nindices use an additional\n$O(\\log(H_{\\rm tree}+1)+\\log(N+1))$ bits, since\n$b_{\\rm tree}\\le H_{\\rm tree}$ in the constructed hierarchy.\nThe table computation and sampling therefore use polynomially many\ninteger operations with polynomial bit lengths.\n\n\\subsection{Random choices with a fixed number of bits}\n\\begin{lemma}\\label{lem:finite-random-choice}\nLet $p_1,\\ldots,p_r$ be nonnegative rational numbers summing to one.\nChoose a uniform integer $J\\in\\{0,\\ldots,2^k-1\\}$ using exactly $k$\nfair bits, and return the least index $i$ such that\n$J/2^k<\\sum_{j\\le i}p_j$. The output law is at total variation\ndistance at most $2r\\,2^{-k}$ from $(p_i)_{i=1}^r$.\n\\end{lemma}\n\\begin{proof}\nFor any interval $[a,b)\\subseteq[0,1)$, the number of grid points\n$j/2^k$ in the interval differs from $2^k(b-a)$ by at most two.\nApply this to the successive cumulative-probability intervals and sum\nthe absolute probability discrepancies. The factor one half in total\nvariation only improves the stated bound. An outcome with $p_i=0$\nhas an empty interval and is never returned.\n\\end{proof}\n\nThe exact categorical choices in the unit-tier dynamic program define\nan ideal transition law. The actual algorithm implements each such\nchoice, along with every proposal and acceptance choice, by the fixed\ngrid in \\cref{lem:finite-random-choice}. Hence exactness of the dynamic\nprogram is a counting identity; sampling from its tables in bounded\nbit time incurs the error budget derived below.\n\nEvery choice in the chain is either uniform on an explicitly indexed\nfinite set, Bernoulli, or proportional to a list of nonnegative\nintegers supplied by the dynamic program. For each used edge, final\nrevelation is a rational categorical choice with at most three outcomes.\nSubsets and pairings are sampled sequentially, so no exponential list is needed. Uniform choices\namong the $M$ scan positions can be implemented by integer arithmetic\non the index rather than by enumerating the scan positions.\n\nA bound on the number of random choices in one complete sampler run,\nincluding marking its output, is\n\\begin{equation}\\label{eq:draw-bound}\n R_{\\rm draw}=100(t_{\\rm mix}+1)(N+1)^2(K+2).\n\\end{equation}\nTo see this, laziness and the scan take two choices per step, and a\npair update takes at most eight further choices. By\n\\cref{lem:base-refresh}, a refresh takes at most\n$4(H_{\\rm tree}+N+1)$ choices. Allowing both bounds at every step\nand adding the at most $N$ choices for final marking gives\n\\[\n t_{\\rm mix}\\bigl(10+4(H_{\\rm tree}+N+1)\\bigr)+N.\n\\]\nSince $H_{\\rm tree}=N(2K+1)$, this is bounded by\n\\eqref{eq:draw-bound}. The bound therefore includes every categorical\nchoice made by the implemented sampler.\nThe number of options in any one choice is at most\n\\begin{equation}\\label{eq:option-bound}\n R_{\\rm opt}=10(M+N^2+1).\n\\end{equation}\nChoose the same bit budget $k$ for all these choices, with\n\\begin{equation}\\label{eq:coin-precision}\n 2^k\\ge\\frac{2R_{\\rm draw}R_{\\rm opt}}\\xi.\n\\end{equation}\nThis integer is found by doubling; $k$ is polynomially bounded.\nBy \\cref{lem:finite-random-choice}, the sum of all conditional\nsimulation errors is at most $\\xi$. Successive coupling, while the\nsimulated and exact histories agree, bounds the total variation error\nof the whole run and its marking by $\\xi$. The exact-kernel mixing\nerror is at most $\\xi$, so the implemented marked sample is within\n$2\\xi$, and in particular $3\\xi$, of the required law.\nThe number of fair bits used has a deterministic bound\n$kR_{\\rm draw}$. There is no rejection loop or random convergence test.\n\n\\subsection{Total bit cost}\nThe preceding bounds already give a polynomial-time implementation.\nTo make the accounting explicit, take a common bit bound\n\\[\n B_* = C\\bigl(NB_W+N\\log(N+1)+\\mathsf b\n                 +\\log(M+1)+\\log(t_{\\rm mix}+1)\n                 +\\log(\\xi^{-1}+1)+1\\bigr)\n\\]\nwith a sufficiently large absolute constant $C$ and $B_W$ from\n\\eqref{eq:edge-bit-bound}. In particular $NB_W$ dominates $KB_G$,\nthe bit cost of multiplying the $K$ empirical ratios.\nThe bound $B_*$ covers all integers\nused in weight products, dynamic-program counts, cumulative choices,\nacceptance comparisons, and output products. All categorical laws just\ndescribed admit common-denominator integer weights, so computing their\ncumulative sums respects the same bound. Schoolbook arithmetic and the\nEuclidean algorithm perform the needed operations within $O(B_*^3)$\nbit time per operation.\n\nA deliberately loose bound of\n$O((H_{\\rm tree}+1)(N+1)^4)$ arithmetic operations covers graph\nconstruction, the bottom-tier tables, and any one transition or refresh;\nstoring the product state additionally takes\n$O(q(T+1)N B_*)$ bits. Thus a trial, including all $K$ stages and $S$\nfresh runs at each stage, has bit cost bounded by a fixed polynomial\nin\n\\[\n K,\\ S,\\ t_{\\rm mix},\\ H_{\\rm tree},\\ N,\\ q(T+1),\\ B_*.\n\\]\nFor example, the sum of\n\\[\n CKS(t_{\\rm mix}+1)(H_{\\rm tree}+1)(N+1)^4B_*^3\n \\quad\\text{and}\\quad\n CKSq(T+1)(N+1)B_*^3\n\\]\nis a valid coarse envelope for arithmetic, repeated initialization,\nand storage operations. Each displayed parameter is polynomial in\n$\\mathsf b$ and $\\eps^{-1}$. The\n$r=10(\\ceil{\\log_2(1/\\delta)}+1)+1$ repetitions and the rational\ncomparisons used to select their median add a factor polynomial in\n$\\log\\delta^{-1}$ and the input length.\n\nAll data structures, loop bounds, and rational comparisons have now been\nspecified by finite procedures. Every auxiliary parameter is bounded\nby a polynomial of fixed degree, and the bound holds on every random\nhistory. Together with \\cref{sec:counting}, this completes the proof\nof \\cref{thm:main}.\n\\section{Prescribed-degree subgraphs and fixed-cardinality matchings}\n\\label{sec:prescribed-degree}\n\nWe record two counting consequences of \\cref{thm:main} and a corresponding\noutput-sampling guarantee. Throughout this section, the input host\n$G=(V,E)$ is a finite simple undirected unweighted graph whose vertices and\nedges are explicitly listed, including any isolated vertices. Only edges\nin $E$ may be selected, so absent edges may be forbidden arbitrarily.\nThe objects counted are labeled edge subsets, not isomorphism classes.\nFor binary-encoded integer demands $f=(f_v)_{v\\in V}$ and a binary-encoded\ninteger $k$, define\n\\[\n\\begin{aligned}\n \\mathcal F(G,f)&=\\{F\\subseteq E:\\deg_F(v)=f_v\\text{ for every }v\\in V\\},\\\\\n \\mathcal M_k(G)&=\\{M\\subseteq E:M\\text{ is a matching and }|M|=k\\}.\n\\end{aligned}\n\\]\n\n\\begin{corollary}\\label{cor:prescribed-degree}\nFor either family $\\mathcal A=\\mathcal F(G,f)$ or\n$\\mathcal A=\\mathcal M_k(G)$, there are uniform classical randomized\nalgorithms with the following guarantees.\n\\begin{enumerate}[label=(\\roman*)]\n\\item Given rational $0<\\eps<1$ and $0<\\delta<1/2$, the counting algorithm\nreturns a nonnegative rational $\\widehat N$ such that\n\\[\n \\Prb[(1-\\eps)|\\mathcal A|\\le \\widehat N\\le(1+\\eps)|\\mathcal A|]\n \\ge1-\\delta.\n\\]\nIf $\\mathcal A$ is empty, it returns zero with certainty. Its bit running\ntime on every execution is polynomial in the full input encoding length,\n$\\eps^{-1}$, and $\\log\\delta^{-1}$.\n\\item Given rational $0<\\tau<1$, the sampling algorithm reports infeasibility\nexactly when $\\mathcal A$ is empty. Otherwise it returns a member $X$ of\n$\\mathcal A$ on every execution, and\n\\[\n \\TV(\\mathcal L(X),U_{\\mathcal A})\\le\\tau,\n\\]\nwhere $U_{\\mathcal A}$ is the uniform law on $\\mathcal A$ and\n$\\TV(\\mu,\\nu)=\\frac12\\sum_x|\\mu(x)-\\nu(x)|$. Its bit running time on every\nexecution is polynomial in the full input encoding length and $\\tau^{-1}$.\n\\end{enumerate}\n\\end{corollary}\n\nOnly the stated total-variation and polynomial-in-$\\tau^{-1}$ sampling\nguarantee is asserted. In particular, the statement does not assert exact\nor pointwise almost-uniform sampling. The explicit simple unweighted host\nmodel does not encode edge weights or compressed multiplicities.\n\n\\subsection{Constant-fibre reductions}\n\nWrite $n=|V|$, $m=|E|$, and $d_v=\\deg_G(v)$.\n\n\\paragraph{The prescribed-degree reduction.}\nWe use Tutte's reduction of prescribed degrees to perfect\nmatchings~\\cite[pp.~348--349]{Tutte1954}, keeping the dummy vertices\nlabeled so that its counting multiplicity is explicit.\nIf some $f_v$ is outside $0\\le f_v\\le d_v$, the family is empty and we\nreturn zero or report infeasibility, as appropriate. For valid demands put\n$s_v=d_v-f_v$. Construct a simple unweighted graph $H_f$ with one tagged\nport $(v,e)$ for every incidence of an edge $e$ at $v$. For each original\nedge $e=\\{u,v\\}$, join $(u,e)$ to $(v,e)$ by a cross edge. At each $v$, add\n$s_v$ labeled private dummy vertices, each adjacent to all $d_v$ ports at\n$v$. There are no other edges. The tags distinguish ports and dummies, so\nsimplicity of $G$ makes $H_f$ simple. Its sizes satisfy\n\\[\n |V(H_f)|=2m+\\sum_v s_v=4m-\\sum_v f_v\\le4m,\n \\qquad\n |E(H_f)|=m+\\sum_v d_vs_v\\le m+2m(n-1).\n\\]\nThus the gadget is explicit and polynomial in the host size; the initial\ndemand check prevents expansion of arbitrarily large binary demands.\n\nProject a perfect matching of $H_f$ to the original edges whose cross edges\nit contains. Each dummy at $v$ uses a distinct $v$-port. The remaining\n$f_v$ ports must use cross edges, so the projection lies in $\\mathcal F(G,f)$.\nConversely, an $f$-factor fixes its cross edges. Its remaining $s_v$ ports at\n$v$ can be bijected to the labeled dummies in $s_v!$ ways, independently at\neach vertex. Hence every factor has the same number of preimages, and\n\\begin{equation}\\label{eq:prescribed-degree-fibre}\n C_f=\\prod_v(d_v-f_v)!,\\qquad Z(H_f)=C_f|\\mathcal F(G,f)|.\n\\end{equation}\nThe fibre may be factorial, but $\\log C_f=O(m\\log(n+1))$, so $C_f$ has\npolynomial bit length. For an edgeless host with all demands zero, both the\nunique factor and the gadget's unique perfect matching are empty, and\n$C_f=1$ by $0!=1$. Other infeasible valid inputs give gadgets\nwithout perfect matchings by the same correspondence.\n\n\\paragraph{The fixed-cardinality reduction.}\nIf $k<0$ or $2k>n$, the family is empty, so return zero or report\ninfeasibility, as appropriate. Otherwise put $q=n-2k$. Form $H_k$\nby adding $q$ labeled dummy vertices to $G$, joining each dummy to every\noriginal vertex, and adding no edges between dummies. Keep all original\nedges. This is a simple unweighted graph with at most $2n$ vertices and\n$m+n^2$ edges. In every perfect matching, the dummies use $q$ distinct\noriginal vertices; the remaining $2k$ original vertices are paired by\nexactly $k$ host edges. Conversely, a $k$-matching leaves $q$ original\nvertices, which can be bijected to the dummies in $q!$ ways. Therefore\n\\begin{equation}\\label{eq:cardinality-fibre}\n C_k=(n-2k)!,\\qquad Z(H_k)=C_k|\\mathcal M_k(G)|.\n\\end{equation}\nHere $\\log C_k=O(n\\log(n+1))$. The same bijection covers $k=0$, $2k=n$,\nand $n=k=0$, using $0!=1$.\n\n\\begin{proof}[Proof of \\cref{cor:prescribed-degree}\\textup{(i)}]\nApply \\cref{thm:main} to the appropriate gadget and divide its nonnegative\nrational output by the corresponding positive integer $C_f$ or $C_k$.\nCompute these factorial products exactly and perform exact rational\ndivision, without rounding to an integer. Their displayed bit lengths and\nthe polynomial gadget sizes preserve the asserted relative error,\nconfidence, certain-zero behavior, and every-execution bit bound. This\nproves the counting assertion. The correspondences also make feasibility\nequivalent to perfect-matching feasibility in the gadget.\n\\end{proof}\n\n\\subsection{A deletion sampler for perfect matchings}\n\nThe conversion from counts to samples uses the self-reduction framework\nof Jerrum, Valiant, and Vazirani~\\cite[Section~6]{JerrumValiantVazirani1986}.\nWe give the deletion procedure explicitly to retain feasibility on every\nexecution and to track its total-variation and bit-time bounds. It uses\n\\cref{thm:main} only through its counting guarantee.\n\n\\begin{lemma}\\label{lem:deletion-sampler}\nGiven a finite simple undirected unweighted graph $H_0$ whose vertices\nand edges are explicitly listed, and a rational $0<\\tau<1$, there is a\nuniform classical randomized algorithm that reports infeasibility exactly\nwhen $H_0$ has no perfect matching. Otherwise it returns a perfect\nmatching on every execution, and its output law is at total variation\ndistance at most $\\tau$ from the uniform perfect-matching law. Its bit\nrunning time on every execution is polynomial in the full input encoding\nlength and $\\tau^{-1}$.\n\\end{lemma}\n\n\\begin{proof}\nFirst decide perfect-matching feasibility by the deterministic\npolynomial-time matching algorithm of Edmonds~\\cite{Edmonds1965}. Report\ninfeasibility if appropriate, and return the empty matching when\n$V(H_0)=\\varnothing$. Otherwise set $m_0=|E(H_0)|$ and\n$m_*=\\max\\{1,m_0\\}$. Choose\n\\[\n \\eta=\\gamma=\\frac{\\tau}{8m_*},\\qquad\n t=\\min\\{j\\in\\mathbb N:2^j\\ge 8m_*/\\tau\\}.\n\\]\nThe integer $t$ is computed by doubling and exact rational comparison.\n\nMaintain a feasible residual graph $H$ and the already selected edges.\nChoose the first residual edge $e=uv$ in a fixed deterministic ordering.\nPerfect matchings of $H$ excluding $e$ are counted by $Z(H-e)$; those\nincluding $e$ correspond bijectively, after deleting $e$, to perfect\nmatchings of $H-u-v$. Test both child graphs for feasibility exactly.\nAt least one is feasible. If only one is feasible, take it. If both are\nfeasible, make two fresh calls to \\cref{thm:main}, with relative error\n$\\eta$ and failure probability $\\gamma$, obtaining nonnegative rational\nestimates $A$ for $Z(H-u-v)$ and $B$ for $Z(H-e)$. If $A+B=0$, take the\nexclusion child. Otherwise put $\\widehat p=A/(A+B)$, draw exactly $t$\nfresh fair bits as a uniform integer $J\\in\\{0,\\ldots,2^t-1\\}$, and take the\ninclusion child precisely when\n\\[\n J<\\floor{2^t\\widehat p}.\n\\]\nReplace $H$ by the chosen child. Whenever the inclusion child is taken,\nincluding a forced choice, append $e$ to the selected edges. All ratio and\nfloor operations are exact rational or integer operations.\n\nEvery chosen child is feasible, regardless of the estimates. Each decision\nremoves at least one residual edge, so there are at most $m_0$ decisions.\nWhen no residual edges remain, feasibility forces the residual vertex set\nto be empty. The selected edges then form a perfect matching of $H_0$ on\nevery execution.\n\nFor the error bound, compare this process to the ideal deletion sampler\nthat takes the inclusion child with its exact count ratio. These exact\nratios telescope, giving the uniform perfect-matching law. At a history\nwhere both children are feasible, write their positive counts as $z_+$ and\n$z_-$, with $p=z_+/(z_++z_-)$. When both estimates succeed, write\n$A=z_+(1+a)$ and $B=z_-(1+b)$, where $|a|,|b|\\le\\eta$. Then\n\\[\n |\\widehat p-p|\n =\\frac{z_+z_-|a-b|}\n {(z_++z_-)(z_+(1+a)+z_-(1+b))}\n \\le\\frac{\\eta}{2(1-\\eta)}.\n\\]\nThe last step uses $z_+z_-\\le(z_++z_-)^2/4$. Fresh call randomness bounds\nthe conditional probability that either estimate fails by $2\\gamma$,\nat every adaptive history. On failure the branch law can change by at most\none in total variation, while the dyadic floor changes the successful\nbranch probability by at most $2^{-t}$. The fallback $A+B=0$ occurs only\non a failed estimate when both true counts are positive. Thus the\nconditional branch-law error is at most\n\\[\n \\beta=\\frac{\\eta}{2(1-\\eta)}+2\\gamma+2^{-t}.\n\\]\nForced branches have zero error. Couple the two deletion processes until\ntheir prefixes differ. With at most $m_0$ decisions, the output\ntotal-variation error is at most $m_0\\beta$. Since $\\eta<1/8$ and\n$2^{-t}\\le\\eta$, we have $\\beta\\le4\\eta$ and hence\n\\[\n m_0\\beta\\le4m_*\\eta=\\tau/2<\\tau.\n\\]\n\nThere are at most $2m_0$ counting calls and $2m_0+1$ exact feasibility\ntests, all on simple graphs no larger than $H_0$. The inverse relative\nerror is $8m_*/\\tau$, and the logarithmic confidence cost is\n$\\log(8m_*/\\tau)$. The every-execution bit bound of \\cref{thm:main} also\nbounds the bit lengths of its outputs on failed calls. Therefore the\nexact ratio and floor computations, the $t=O(\\log(m_*/\\tau))$ fresh bits per\nbranch, and all graph updates have bit cost polynomial in the full input\nlength and $\\tau^{-1}$ on every execution. No rejection loop or\nrandom stopping criterion is used.\n\n\\end{proof}\n\n\\begin{proof}[Proof of \\cref{cor:prescribed-degree}\\textup{(ii)}]\nFor sampling, apply \\cref{lem:deletion-sampler} to $H_f$ or $H_k$ and\ndecode by keeping the original edges represented by selected cross edges,\nor by discarding the dummy-incident edges, respectively. Exact feasibility\ntesting of the\ngadget reports infeasibility precisely for an empty target family.\nUniform perfect matchings project to the uniform target law because the\nfibres in \\eqref{eq:prescribed-degree-fibre} and\n\\eqref{eq:cardinality-fibre} are constant across target members.\nDeterministic decoding cannot increase total variation and takes polynomial\ntime, without enumerating any fibre. Every decoded output is feasible, which\nproves the sampling assertion.\n\\end{proof}\n\\begin{thebibliography}{99}\n\\providecommand{\\natexlab}[1]{#1}\n\\providecommand{\\url}[1]{\\texttt{#1}}\n\\expandafter\\ifx\\csname urlstyle\\endcsname\\relax\n  \\providecommand{\\doi}[1]{doi: #1}\\else\n  \\providecommand{\\doi}{doi: \\begingroup \\urlstyle{rm}\\Url}\\fi\n\n\\bibitem[Bez\\'{a}kov\\'{a} et~al.(2008)Bez\\'{a}kov\\'{a},\n  \\v{S}tefankovi\\v{c}, Vazirani, and Vigoda]{BezakovaStefankovicVaziraniVigoda2008}\nIvona Bez\\'{a}kov\\'{a}, Daniel \\v{S}tefankovi\\v{c}, Vijay~V. 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Swendsen.\n\\newblock Replica Monte Carlo simulation (revisited).\n\\newblock \\emph{Progress of Theoretical Physics Supplement}, 157:\\penalty0\n  317--323, 2005.\n\\newblock \\doi{10.1143/PTPS.157.317}.\n\\newblock Preprint version: \\url{https://arxiv.org/abs/cond-mat/0407273v1}.\n\n\\bibitem[Yi(2026)]{Yi2026}\nZihong Yi.\n\\newblock Diffuse Gaussian truncation for deterministic approximate counting.\n\\newblock Preprint, arXiv:2609.04079v1, 2026.\n\\newblock URL \\url{https://arxiv.org/abs/2609.04079v1}.\n\n\\end{thebibliography}\n\\end{document}\n"}, {"path": "preprints/A-Fully-Polynomial-Randomized-Approximation-Scheme-for-Perfect-Matchings-in-General-Graphs-September-23-2026/main.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-Fully-Polynomial-Randomized-Approximation-Scheme-for-Perfect-Matchings-in-General-Graphs-September-23-2026/main.pdf", "bytes": 617073, "sha256": 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{"path": "preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/README.md", "bytes": 749, "sha256": "0262af80e7c74ef38ac778899aaad4b9d0ad1a2f8c57c270cd9c800cf3eaa643", "content": "# [A closed Ricci flow with bounded scalar curvature and finite-time curvature blowup](paper.pdf)\n\nOpenAI  \nSeptember 24, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026,\n  author = {{OpenAI}},\n  title = {{A closed Ricci flow with bounded scalar curvature and finite-time curvature blowup}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/paper.pdf}{OAI:A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/figures/drift-cylinder.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/figures/drift-cylinder.tex", "bytes": 938, "sha256": "7e7a60d43ba2b80693120555d9b0233b13f41aaf1e5d4324a23f072389ad682d", "content": "\\begin{tikzpicture}[x=1cm,y=1cm,>=Latex,font=\\small]\n  \\draw[->] (0,0)--(10.2,0) node[right] {$r$};\n  \\draw[->] (0,0)--(0,4.5) node[above] {$t$};\n  \\fill[blue!10] (7.2,.5) .. 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Reine Angew. Math. \\textbf{709} (2015), 81--117.\n\\href{https://arxiv.org/abs/1208.4312v1}{arXiv:1208.4312v1}.\n\n\\bibitem{AK2004}\nS.~Angenent and D.~Knopf,\n\\emph{An example of neckpinching for Ricci flow on \\(S^{n+1}\\)},\nMath. Res. Lett. \\textbf{11} (2004), no.~4, 493--518.\n\\href{https://web.ma.utexas.edu/users/danknopf/Neckpinch.pdf}{Author's text};\n\\href{https://web.ma.utexas.edu/users/danknopf/Corrigendum.pdf}{corrigendum}.\n\n\\bibitem{Bamler2018}\nR.~H. Bamler,\n\\emph{Convergence of Ricci flows with bounded scalar curvature},\nAnn. of Math. (2) \\textbf{188} (2018), no.~3, 753--831.\n\\href{https://doi.org/10.4007/annals.2018.188.3.2}{doi:10.4007/annals.2018.188.3.2}.\n\n\\bibitem{Bohm1999}\nC.~B\\\"ohm,\n\\emph{Non-compact cohomogeneity one Einstein manifolds},\nBull. Soc. Math. France \\textbf{127} (1999), no.~1, 135--177.\n\\href{https://doi.org/10.24033/bsmf.2345}{doi:10.24033/bsmf.2345}.\n\n\\bibitem{CaoZhu2006}\nH.-D. Cao and X.-P. Zhu,\n\\emph{A complete proof of the Poincar\\'e and geometrization conjectures---application\nof the Hamilton--Perelman theory of the Ricci flow},\nAsian J. Math. \\textbf{10} (2006), no.~2, 165--492.\n\\href{https://www.renyi.hu/~stipsicz/perelman/cao-zhu.pdf}{Full text}.\n\n\\bibitem{GuZhu2008}\nH.-L.~Gu and X.-P. Zhu,\n\\emph{The existence of Type II singularities for the Ricci flow on\n\\(S^{n+1}\\)},\nComm. Anal. Geom. \\textbf{16} (2008), no.~3, 467--494.\n\\href{https://arxiv.org/abs/0707.0033v1}{arXiv:0707.0033v1}.\n\n\\bibitem{Hamilton1995}\nR.~S. Hamilton,\n\\emph{The formation of singularities in the Ricci flow},\nSurveys in Differential Geometry, Vol.~II (Cambridge, MA, 1993),\nInternational Press, Cambridge, MA, 1995, 7--136.\n\\href{https://intlpress.com/site/pub/files/_fulltext/journals/sdg/1993/0002/0001/SDG-1993-0002-0001-a002.pdf}{Full text}.\n\n\\bibitem{Krylov2006}\nN.~V. Krylov,\n\\emph{Parabolic equations with VMO coefficients in spaces with mixed norms},\n\\href{https://arxiv.org/abs/math/0610955v1}{arXiv:math/0610955v1}, 2006.\nTheorem numbering refers to this version.\nPublished as \\emph{Parabolic equations with VMO coefficients in Sobolev\nspaces with mixed norms}, J. Funct. Anal. \\textbf{250} (2007), no.~2,\n521--558.\n\\href{https://doi.org/10.1016/j.jfa.2007.04.003}{doi:10.1016/j.jfa.2007.04.003}.\n\n\\bibitem{LSU1968}\nO.~A. Ladyzhenskaya, V.~A. Solonnikov, and N.~N. Ural'tseva,\n\\emph{Linear and Quasi-linear Equations of Parabolic Type},\nTranslations of Mathematical Monographs, vol.~23,\nAmerican Mathematical Society, Providence, RI, 1968;\ncorrected reprint, 1988.\n\\href{https://doi.org/10.1090/mmono/023}{doi:10.1090/mmono/023}.\n\n\\bibitem{Sesum2005}\nN.~\\v{S}e\\v{s}um,\n\\emph{Curvature tensor under the Ricci flow},\nAmer. J. Math. \\textbf{127} (2005), no.~6, 1315--1324.\n\\href{https://doi.org/10.1353/ajm.2005.0042}{doi:10.1353/ajm.2005.0042}.\n\n\\bibitem{Shi1989}\nW.-X. Shi,\n\\emph{Deforming the metric on complete Riemannian manifolds},\nJ. Differential Geom. \\textbf{30} (1989), no.~1, 223--301.\n\\href{https://doi.org/10.4310/jdg/1214443292}{doi:10.4310/jdg/1214443292}.\n\n\\bibitem{Stolarski2019}\nM.~Stolarski,\n\\emph{Curvature Blow-up in Doubly-warped Product Metrics Evolving by Ricci Flow},\n\\href{https://arxiv.org/abs/1905.00087v1}{arXiv:1905.00087v1}, 2019.\nAll numbered source references in this paper use this version.\nPublished in Mem. Amer. Math. Soc. \\textbf{295} (2024), no.~1470.\n\\href{https://doi.org/10.1090/memo/1470}{doi:10.1090/memo/1470}.\n\n\\bibitem{Wang2012}\nB.~Wang,\n\\emph{On the conditions to extend Ricci flow (II)},\nInt. Math. Res. Not. IMRN \\textbf{2012}, no.~14, 3192--3223.\n\\href{https://doi.org/10.1093/imrn/rnr141}{doi:10.1093/imrn/rnr141}.\n\\href{https://arxiv.org/abs/1107.5107v1}{arXiv:1107.5107v1};\ntheorem numbering refers to this preprint version.\n\n\\end{thebibliography}\n"}, {"path": "preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/sections/01-introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/sections/01-introduction.tex", "bytes": 7848, "sha256": "efa396bab4c8d615d3c4ed4ce08e16bb4848b00307293ffdd77bb7302e434871", "content": "\\begin{abstract}\nWe disprove the scalar-curvature extension conjecture in its unrestricted\nall-dimensions form. In sufficiently high dimension, we construct a Ricci\nflow on a closed manifold whose scalar curvature remains uniformly bounded\nwhile full curvature diverges at a finite maximal time. In one fixed\nsufficiently high dimension, the examples have two-sided power-law curvature\nblowup with arbitrarily large exponents.\n\\end{abstract}\n\n\\section{Introduction}\n\nA Ricci flow on a smooth manifold is a family of Riemannian metrics\n\\(g(t)\\) satisfying \\(\\partial_tg=-2\\Ric_g\\). Here a closed manifold\nis compact and has no boundary, \\(\\Rm\\) denotes its full curvature\ntensor, and \\(R=\\operatorname{tr}_g\\Ric\\) denotes scalar curvature.\nA smooth Ricci flow on a closed manifold can fail to extend at a finite time\nonly if its full curvature becomes unbounded~\\cite{Hamilton1995}.\nThe scalar-curvature extension conjecture asks whether scalar curvature\nalone must detect such a singularity; see~\\cite[p.~756]{Bamler2018}.\nA uniform bound for the Ricci tensor is sufficient for extension\nby \\v{S}e\\v{s}um's Theorem~\\cite{Sesum2005}, but the trace of that tensor\ncontains substantially less information. The question concerns smooth\nextension on the original manifold; continuation through a singular space\nis a different conclusion.\n\nBounded scalar curvature imposes a necessary restriction on the rate of\nfull-curvature blowup. Wang proved that, at a finite singular time \\(T\\),\nthe product of \\(T-t\\), the square root of the maximum full curvature,\nand the square root of the maximum absolute scalar curvature has a\nstrictly positive upper limit~\\cite[Theorem~3]{Wang2012}.\nConsequently, a closed flow with bounded scalar curvature must satisfy\n\\[\n\\limsup_{t\\uparrow T}(T-t)^2\\max_M|\\Rm|(\\cdot,t)>0.\n\\]\nThis is a subsequential restriction; it does not assert a lower bound at\nevery sufficiently late time. It explains why examples with curvature\nconcentration faster than the parabolic rate are relevant to the\nextension question.\n\nSymmetry has made it possible to construct and study singularities with\nseveral distinct geometric scales. Angenent and Knopf constructed\nrotationally symmetric neckpinches on spheres~\\cite{AK2004}.\nA finite-time singularity is of\n\\emph{Type II} when \\((T-t)\\max_M|\\Rm|\\) is unbounded.\nGu and Zhu constructed rotationally symmetric Type-II singularities on\nspheres~\\cite{GuZhu2008}. Angenent, Isenberg, and Knopf later constructed\ndegenerate neckpinches with prescribed rates, a Bryant-soliton tip model,\nand a shrinking-cylinder parabolic model~\\cite{AIK2015}.\nThe Type-II constructions establish that full curvature can concentrate faster\nthan the parabolic scale, but do not supply the scalar bound in\nTheorem~\\ref{thm:main}.\n\nStolarski constructed closed, doubly warped Ricci flows that develop\nconical singularities with arbitrarily fast curvature blow-up and approach\na Ricci-flat cone at the parabolic scale~\\cite{Stolarski2019}.\nStolarski's discussion identifies bounded scalar curvature as a possible\nfeature of these examples. The construction does not supply the\nscalar-curvature bound proved below.\nWe use its quantitative profile estimates and establish that bound for a\nchoice of sufficiently large dimension and mode index.\nThe geometric cap models considered in his formal discussion belong to\nthe cohomogeneity-one Ricci-flat setting developed by\nB\\\"ohm~\\cite{Bohm1999,Stolarski2019}. Our cap estimate uses only\nthe quantitative construction inputs stated in\nProposition~\\ref{prop:input}; it does not require convergence to a\nprescribed complete cap metric.\n\n\\begin{theorem}\\label{thm:main}\nThere are an integer \\(q\\ge10\\), a time \\(T>0\\), and a smooth Ricci flow\n\\(g(t)\\), \\(0\\le t<T\\), on the closed connected manifold\n\\(M=\\Sp^2\\times\\Sp^{q+1}\\), with smooth initial metric, such that\n\\[\n\\sup_{M\\times[0,T)}|R_{g(t)}|<\\infty,\n\\qquad\n\\lim_{t\\uparrow T}\\max_M|\\Rm_{g(t)}|=\\infty.\n\\]\nIn particular, \\(T\\) is the finite maximal existence time of this flow.\n\\end{theorem}\n\nTheorem~\\ref{thm:main} refutes this conjecture in its unrestricted\nall-dimensions formulation: the assertion that every finite-time\nsingularity of a closed Ricci flow in dimension at least four has unbounded\nscalar curvature. The example is in sufficiently high dimension; no\ndimension-four conclusion is asserted.\n\nThe estimates also determine the full-curvature rate of the examples.\nCorollary~\\ref{cor:rates} gives two-sided power-law bounds with an unbounded\ndiscrete set of exponents, while the dimension remains fixed. Thus the\nscalar bound is compatible with arbitrarily fast power-law Type-II\nsingularities, and the upper rate is controlled as well as the lower rate.\n\n\\subsection*{The two scales and the proof}\n\nThe examples have two sphere factors whose radii vary along an interval.\nOff the two pole orbits the metric is\n\\[\nds^2+\\phi(s,t)^2g_{\\Sp^2}+r(s,t)^2g_{\\Sp^q},\n\\]\nwhere \\(s\\) is arclength at the indicated time and the sphere metrics\nhave sectional curvature one. At each endpoint the \\(\\Sp^q\\) factor\ncollapses smoothly, leaving a positive-radius \\(\\Sp^2\\) orbit.\nThe singularity develops when that remaining orbit also shrinks.\n\nWrite \\(\\delta=T-t\\). The parabolic length scale is \\(\\sqrt\\delta\\),\nwhere the rescaled metric closely approximates a Ricci-flat cone.\nThe smaller cap scale is \\(\\theta=\\delta^\\sigma\\), with a fixed\nexponent \\(\\sigma>1/2\\) supplied by the construction. These two\nscales have different roles: the cone approximation gives small Ricci\ncurvature on fixed parabolic annuli, whereas smoothness at the pole must\nbe controlled on the cap scale. A small Ricci tensor at the larger scale\ndoes not by itself control the smaller cap.\n\nThe additional estimates have two features. First, Stolarski's strict\none-sided comparison with the cone and monotonicity of a logarithmic\nslope, together with the ordinary scalar lower bound, control the size of\nthe cap. We adapt the scalar-sign obstruction in his\nProposition~5.3~\\cite{Stolarski2019} to obtain this quantitative estimate.\nCurvature\npoint-picking then gives a full-curvature bound without assuming convergence\nto a cap model. Second, on radial cylinders moving with the dominant drift,\none-dimensional parabolic estimates give a Ricci reaction bound with leading\ncoefficient \\(2q\\). The corresponding diffusion coefficient leaves a positive\nmargin in large dimension. This yields\n\\( |\\Ric|\\le Cr^{-e}\\) for an exponent \\(0<e<1\\), where \\(r\\) is\nthe radius of the \\(\\Sp^q\\) factor in the doubly warped metric.\nThe square of this bound admits a bounded scalar-curvature supersolution.\nThese are the new estimates beyond the imported singular-flow construction.\n\nSection~\\ref{sec:construction} states the precise construction input and\ntranslates its scales. Section~\\ref{sec:geometry} obtains the warping\nidentities, the strict cone gap, and the parabolic-annulus estimates.\nSection~\\ref{sec:cap} proves the cap and radius curvature bounds by two\ncurvature-record arguments and Shi's derivative estimates.\nSection~\\ref{sec:reaction} proves the dimension-explicit reaction bound;\nthe analytic estimates take place in one space dimension, which is what\nkeeps their constants independent of \\(q\\).\nSection~\\ref{sec:barriers} fixes the parameters in order and completes\nthe Ricci and scalar comparisons, then records the resulting curvature rates.\n\n\\paragraph{Conventions.}\nAll tensor norms and geometric differential operators are taken with respect\nto \\(g(t)\\), and \\(\\dt g=-2\\Ric\\).\nThe unit sphere has sectional curvature one. A constant \\(C\\) may change\nbetween occurrences. Unless explicitly stated otherwise, it may depend on\nall fixed parameters of the chosen flow, but never on \\(t\\uparrow T\\).\nConstants used to choose the dimension will be identified as independent\nof both the dimension parameter \\(q\\) and the mode index \\(k\\).\n"}, {"path": "preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/sections/02-construction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/sections/02-construction.tex", "bytes": 7040, "sha256": "d78b68fdc3fe2078f20e01de88a2478fecdea8a6800d973e89ff8fe90102d1de", "content": "\\section{The construction used as input}\\label{sec:construction}\n\nWe record precisely the part of Stolarski's construction that is needed.\nThe role of this input is to produce a single smooth singular flow with\ncompatible inner signs and cone profiles. The curvature estimates proved\nin the later sections will be consequences of these data.\nThroughout, set \\(p=2\\) and write\n\\begin{equation}\\label{eq:parameters}\n\\begin{gathered}\nm=p+q,\\qquad A^2=\\frac{p-1}{m-1},\\qquad B^2=\\frac{q-1}{m-1},\\\\\nd=\\frac{m-1-\\sqrt{(m-1)(m-9)}}2,\\qquad\n\\nu=k-d/2,\\qquad \\sigma=k/d.\n\\end{gathered}\n\\end{equation}\nHere \\(q\\ge10\\), and \\(k\\) is a sufficiently large even positive integer.\nIn particular \\(\\nu>0\\). We take \\(A,B>0\\) and use the time-dependent scales\n\\begin{equation}\\label{eq:scales}\n\\delta=T-t,\\qquad \\tau=-\\log\\delta,\\qquad\n\\theta=\\delta^\\sigma,\\qquad \\gamma=r/\\sqrt\\delta.\n\\end{equation}\nThe dimension of the manifold is \\(m+1\\).\nThe identities \\(\\nu/d=\\sigma-1/2\\) and\n\\(\\sqrt\\delta\\,\\delta^{\\nu/d}=\\theta\\) relate the two scales.\n\n\\begin{proposition}[Construction input]\\label{prop:input}\nFor the parameters above, Stolarski's construction supplies a smooth Ricci\nflow up to a finite time \\(T\\) on \\(\\Sp^p\\times\\Sp^{q+1}\\) with the\nfollowing properties, after translating its starting time to zero.\n\n\\begin{enumerate}[label=\\textup{(\\roman*)}]\n\\item Off the two pole orbits it has the form\n\\[\ng=ds^2+\\phi^2g_{\\Sp^p}+r^2g_{\\Sp^q}.\n\\]\nHere \\(s\\) is radial arclength at each time. The metric is invariant under\nreflection exchanging the poles. On each open hemisphere, oriented from\nits pole toward the equator, \\(r_s>0\\). At a pole, \\(r=0\\), \\(r_s=1\\),\nand \\(\\phi_s=0\\); the usual odd and even polar expansions hold for \\(r\\)\nand \\(\\phi\\), respectively, and \\(\\phi>0\\) for every \\(t<T\\).\n\n\\item Write \\(u=\\log\\phi\\), \\(z=r_s^2\\), and\n\\[\n\\Ut=\\log\\frac{\\phi}{(A/B)r},\\qquad \\Zt=z-B^2.\n\\]\nThere are positive tolerances \\(\\eta_j^U,\\eta_j^Z\\), large fixed numbers\n\\(1<Y_1\\le Y_2\\), and constants \\(M_0>0\\), \\(0<\\beta<1/2\\), such that\nfor \\(j=0,1,2\\),\n\\begin{equation}\\label{eq:profile-bounds}\n\\begin{aligned}\n\\left|\\partial_\\gamma^j(\\Ut-\\delta^\\nu U_k)\\right|\n&\\le\\eta_j^U\\delta^\\nu\n       \\bigl(\\gamma^{-d-j}+\\gamma^{2k-d}\\bigr),\\\\\n\\left|\\partial_\\gamma^j(\\Zt-\\delta^\\nu Z_k)\\right|\n&\\le\\eta_j^Z\\delta^\\nu\n       \\bigl(\\gamma^{-d-j}+\\gamma^{2k-d}\\bigr).\n\\end{aligned}\n\\end{equation}\nThe first estimate holds from \\(Y_1\\delta^{\\nu/d}\\) to\n\\(M_0e^{\\beta\\tau}\\), and the second from \\(Y_2\\delta^{\\nu/d}\\) to that\nupper endpoint, within the hemisphere. The profiles are smooth on\n\\(\\gamma>0\\). The function \\(U_k\\) is \\(\\gamma^{-d}\\) times a polynomial\nof degree \\(k\\) in \\(\\gamma^2\\), with positive leading coefficients at\nzero and infinity:\n\\[\nU_k(\\gamma)\\sim c_1\\gamma^{-d}\\quad(\\gamma\\downarrow0),\\qquad\nU_k(\\gamma)\\sim c_2\\gamma^{2k-d}\\quad(\\gamma\\to\\infty),\\qquad c_1,c_2>0.\n\\]\nAlso \\(Z_k(\\gamma)=O_{q,k}(\\gamma^{-d})\\) as \\(\\gamma\\downarrow0\\).\n\n\\item For all sufficiently late times, putting \\(f=ru_r\\), one has\n\\begin{equation}\\label{eq:inner-inputs}\n\\begin{gathered}\n\\phi/r\\ge A/B,\\qquad 0\\le f\\le1,\\qquad f_r\\ge0\n       \\quad(0<r\\le Y_1\\theta),\\\\\nB^2\\le z\\le1\\quad(0<r\\le Y_2\\theta).\n\\end{gathered}\n\\end{equation}\nThese inner intervals lie strictly below the equator.\n\n\\item At the initial time \\(\\tau_0=-\\log T\\), the same weighted profile\nbounds hold up to an initial outer cutoff\n\\(G_{\\mathrm{init}}=M_0e^{\\beta_{\\mathrm{init}}\\tau_0}\\),\nwhere \\(0<\\beta_{\\mathrm{init}}<1/2\\), and \\(\\Ut\\ge0\\) beyond that\ncutoff. The exponents are chosen in the ranges permitted by the\nconstruction; in particular, one may take\n\\[\n0<\\beta<\\frac{\\nu}{2\\nu+1}\\le\\beta_{\\mathrm{init}}<\\frac12.\n\\]\nThey are therefore distinct choices, not two arbitrary exponents.\nThe tolerances can be\nchosen sufficiently small depending on \\(q,k\\). The inner cutoffs can then\nbe increased in order, first \\(Y_1\\) and then \\(Y_2\\); the initial\nrescaled time can subsequently be increased. Each such increase is\nsubject only to lower-size requirements from the construction.\n\\end{enumerate}\n\\end{proposition}\n\n\\begin{proof}[Source and change of notation]\nThe existence statement is the solution furnished by Lemma~3.12 and the\nproof of Theorem~1.1 in~\\cite{Stolarski2019}. The weighted estimates are\nDefinition~3.1. The initial family is Definition~3.5 and Lemma~3.6,\nwith the outer conditions of Definition~3.2.\nThe inner inequalities are the Inner Region Barriers I in Definition~3.3,\nas propagated by Lemmas~4.5--4.9 and explicitly invoked in the proof of\nTheorem~4.3 on page~25. In particular that proof gives the barrier\nconditions on the inner intervals; the shorter list in Definition~4.1\nof its final class \\(\\mathcal P\\) is not the only information being used.\nThe eigenprofile properties are Propositions~A.12, A.13, and A.21;\nthe parameter order is listed in Appendix~B.\n\nFor completeness, the initial weighted bounds hold up to the initial\nexpansion's own upper endpoint, not just the smaller propagated endpoint.\nThe lower \\(U\\)-modes in Definition~3.5 are \\(\\gamma^{-d}\\) times\npolynomials in \\(\\gamma^2\\) of degree below \\(k\\).\nBy Propositions~A.10 and~A.19, the homogeneous lower \\(Z\\)-modes are\n\\(\\gamma^2\\) times polynomials, with growth exponents strictly below\n\\(2k-d\\). Each of these finitely many modes and its first two\nderivatives is therefore bounded by the corresponding weight in\n\\eqref{eq:profile-bounds} on all \\(\\gamma>0\\).\nTaking the lower-mode coefficient bound at the initial time to be\n\\(\\epsilon_0\\delta^\\nu\\), with \\(\\epsilon_0\\) sufficiently small,\ngives the required tolerances. This choice is compatible with the degree\nargument in Lemma~3.10: the projection errors in Lemmas~6.13--6.14 can\nsubsequently be reduced by increasing the initial rescaled time.\nDefinition~3.5 imposes the outer conditions starting at that same initial\nexpansion endpoint. Thus the initial profile bounds and outer sign meet\nat \\(G_{\\mathrm{init}}\\); no identification of\n\\(\\beta_{\\mathrm{init}}\\) with \\(\\beta\\) is required.\nThe relation between their permitted ranges is specified in the proof\nof Theorem~4.3, page~26, of~\\cite{Stolarski2019}.\n\nIn the source's notation,\n\\(B^2\\lambda_k=-\\nu\\), \\(\\alpha_k=\\nu/d\\), and\n\\(Y_1=\\Upsilon_U\\), \\(Y_2=\\Upsilon_Z\\).\nThus \\(\\sqrt\\delta\\,Y_i e^{-\\alpha_k\\tau}=Y_i\\theta\\).\nMoreover\n\\[\nf=1+\\gamma\\Ut_\\gamma,\\qquad\nf_r=\\frac{\\gamma}{\\sqrt\\delta}\n   \\left(\\Ut_{\\gamma\\gamma}+\\frac{\\Ut_\\gamma}{\\gamma}\\right),\n\\]\nwhich translates the source's log-radius convexity into the last slope\ninequality in~\\eqref{eq:inner-inputs}.\nThe mode index \\(k\\) here is the index in the construction, rather than\nthe arbitrary curvature blow-up exponent in the statement of its\nTheorem~1.1.\n\\end{proof}\n\n\\begin{remark}\\label{rem:input-scope}\nProposition~\\ref{prop:input} is the external existence input to this paper.\nWe do not import a scalar-curvature upper bound, a full-curvature bound at\nthe cap scale, or convergence to a particular complete cap metric.\nThe estimates establishing those curvature bounds are given below.\n\\end{remark}\n"}, {"path": "preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/sections/03-geometry.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/sections/03-geometry.tex", "bytes": 9387, "sha256": "774075bbd1f9d1ca0ee1a62424de1e2182c75327fc4d53a0241e419763d6f714", "content": "\\section{Geometric identities and profile consequences}\\label{sec:geometry}\n\nWe now extract three consequences needed in the curvature estimates:\nuniform bounds for the warping slopes, a strict inner separation from the\ncone, and small Ricci curvature on fixed parabolic annuli. We distinguish\ntime differentiation at a fixed point of the manifold from differentiation\nat a fixed value of the evolving radius \\(r\\).\n\n\\subsection{Warping equations}\n\nLet \\(x\\) be a radial coordinate fixed on the manifold, so that\n\\(ds=\\chi(x,t)\\,dx\\). Subscripts \\(s\\) denote arclength differentiation\nat the indicated time. On a regular orbit the sectional curvatures are\n\\begin{equation}\\label{eq:sectional}\n\\begin{gathered}\nh=-\\phi_{ss}/\\phi,\\qquad h_q=-r_{ss}/r,\\qquad\nj=(1-\\phi_s^2)/\\phi^2,\\\\\n\\ell=(1-r_s^2)/r^2,\\qquad\n\\mu=-\\phi_s r_s/(\\phi r).\n\\end{gathered}\n\\end{equation}\nThey correspond, respectively, to radial--\\(\\Sp^p\\), radial--\\(\\Sp^q\\),\n\\(\\Sp^p\\)-tangent, \\(\\Sp^q\\)-tangent, and mixed planes. These formulas\nalso follow directly by computing the connection of the warped metric.\nThe Ricci eigenvalues in the radial and the two fiber directions are\n\\begin{equation}\\label{eq:ricci-eigenvalues}\n\\lambda_0=ph+qh_q,\\qquad\n\\lambda_p=h+(p-1)j+q\\mu,\\qquad\n\\lambda_q=h_q+(q-1)\\ell+p\\mu.\n\\end{equation}\nConsequently\n\\begin{equation}\\label{eq:warping-flow}\n\\begin{aligned}\n\\dt\\big|_x r&=r_{ss}+(q-1)(r_s^2-1)/r+p\\phi_s r_s/\\phi,\\\\\n\\dt\\big|_x\\phi&=\\phi_{ss}+(p-1)(\\phi_s^2-1)/\\phi+q r_s\\phi_s/r,\\\\\n[\\dt\\big|_x,\\partial_s]&=\\lambda_0\\partial_s.\n\\end{aligned}\n\\end{equation}\nThese are also the usual doubly warped Ricci-flow equations\nin~\\cite[Section~2]{Stolarski2019}.\n\n\\begin{lemma}[Global elementary bounds]\\label{lem:gradients}\nFor each flow in Proposition~\\ref{prop:input}, there is a time-independent\nconstant \\(C\\) such that\n\\[\nr\\le C,\\qquad |r_s|+|\\phi_s|\\le C.\n\\]\nIn particular, \\(r\\) is uniformly Lipschitz in the metric \\(g(t)\\).\nAlso \\(R\\ge-C\\) throughout the flow.\n\\end{lemma}\n\n\\begin{proof}\nAt a positive maximum of \\(r\\), its first derivative vanishes and\n\\eqref{eq:warping-flow} gives \\(r_t\\le-(q-1)/r\\).\nDifferentiating the first equation with the stated commutator gives\n\\begin{equation}\\label{eq:slope-flow}\n\\begin{split}\n\\dt\\big|_x r_s={}&(r_s)_{ss}\n +\\left(p\\frac{\\phi_s}{\\phi}+(q-2)\\frac{r_s}{r}\\right)(r_s)_s\\\\\n&+\\left[\\frac{(q-1)(1-r_s^2)}{r^2}\n       -p\\frac{\\phi_s^2}{\\phi^2}\\right]r_s.\n\\end{split}\n\\end{equation}\nAt a positive interior maximum with \\(r_s>1\\), the reaction has negative\nsign; the corresponding sign is positive at a negative minimum below\n\\(-1\\). Thus \\(|r_s|\\) is bounded by the larger of one and its initial\nsupremum. Interchanging \\((r,q)\\) and \\((\\phi,p)\\) proves the same bound\nfor \\(|\\phi_s|\\). On the complete interval between poles, the endpoint\nvalues are \\(r_s=1,-1\\) and \\(\\phi_s=0\\), so every offending extremum is\ninterior. The argument is applied first on a compact time slab; the\nsingular radial coefficients at the endpoints cause no extra boundary\ncondition. Finally\n\\[\n(\\dt-\\Delta)R=2|\\Ric|^2\\ge0\n\\]\nand the compact maximum principle imply\n\\(R(\\cdot,t)\\ge\\min_M R(\\cdot,0)\\).\n\\end{proof}\n\nWhere \\(r_s>0\\), regard \\(u=\\log\\phi\\) and \\(v=r_s=\\sqrt z\\) as functions\nof \\((r,t)\\). From now on their time derivatives fix \\(r\\).\nSubtracting the advection \\((\\dt|_x r)\\partial_r\\) in\n\\eqref{eq:warping-flow} gives\n\\begin{equation}\\label{eq:sideways}\n\\begin{aligned}\nu_t&=zu_{rr}+\\frac{q-1+z}{r}u_r-(p-1)e^{-2u},\\\\\nv_t&=zv_{rr}+\\frac{q-1-z}{r}v_r\n       +\\frac{(q-1)v(1-v^2)}{r^2}-pv^3u_r^2.\n\\end{aligned}\n\\end{equation}\nFor later use, the geometric heat operator on a scalar composition with\n\\(r>0\\) is\n\\begin{equation}\\label{eq:operator}\n\\D F:= (\\dt-\\Delta)F(r,t)\n=F_t|_r-zF_{rr}-\\frac{q-1+z}{r}F_r.\n\\end{equation}\nIndeed \\(\\Delta r=r_{ss}+q r_s^2/r+p\\phi_s r_s/\\phi\\), so\n\\(r_t-\\Delta r=-(q-1+z)/r\\).\nEquations~\\eqref{eq:sideways} are written in a monotone radius coordinate.\nThe composition identity~\\eqref{eq:operator} is geometric and remains valid\nacross the equator. The same warping equations give\nthe global identity, off the poles,\n\\begin{equation}\\label{eq:ratio-heat}\n(\\dt-\\Delta)\\log(\\phi/r)=\\frac{q-1}{r^2}-\\frac{p-1}{\\phi^2}.\n\\end{equation}\n\n\\subsection{Consequences of the construction}\n\nThe first estimate is Stolarski's strict cone separation\n\\cite[Proposition~5.1(2)]{Stolarski2019}, expressed in the present scales.\nWe include its short proof to record the fixed positive gap as the cap\nshrinks. Its constant may be small and may depend on the chosen flow;\nit will be used only after the dimension and mode have been fixed.\n\n\\begin{lemma}[A strict inner gap]\\label{lem:gap}\nAfter choosing the tolerances sufficiently small, there are constants\n\\(\\varepsilon_1>0\\) and \\(C<\\infty\\) such that, at all sufficiently late\ntimes,\n\\begin{equation}\\label{eq:strict-gap}\n\\phi/r\\ge A/B+\\varepsilon_1\\quad(0<r\\le Y_1\\theta),\n\\qquad \\phi(Y_1\\theta,t)\\le C\\theta.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nAt \\(\\gamma_1=Y_1\\delta^{\\nu/d}\\),\n\\(\\delta^\\nu\\gamma_1^{-d}=Y_1^{-d}\\), while the relative error in the\nleading small-\\(\\gamma\\) asymptotic of \\(U_k\\) tends to zero.\nChoose \\(\\eta_0^U<c_1/4\\). Equation\n\\eqref{eq:profile-bounds} then gives\n\\[\n0<\\tfrac12c_1Y_1^{-d}\\le\\Ut(\\gamma_1,\\tau)\\le C_{q,k,Y_1}\n\\]\nat late times. This proves both assertions at the interface. Since\n\\(\\partial_{\\log r}\\log(\\phi/r)=f-1\\le0\\), the lower bound propagates\ninward. For example, a fixed positive number below\n\\((A/B)(\\exp(c_1Y_1^{-d}/2)-1)\\) is admissible as \\(\\varepsilon_1\\).\n\\end{proof}\n\n\\begin{lemma}[Bounds used in the dimension estimate]\\label{lem:uniform}\nThe construction parameters can be chosen so that, for all sufficiently\nlarge \\(q\\) and all sufficiently late times,\n\\begin{equation}\\label{eq:uniform}\n|z-1|\\le C_0/q,\\qquad r|u_r|\\le2,\\qquad\n(p-1)r^2e^{-2u}\\le(q-1)(1+C_0/q)\n\\end{equation}\non \\(0<r\\le\\sqrt\\delta\\), where \\(C_0\\) is a numerical constant\nindependent of \\(q\\) and \\(k\\). This interval is below the equator.\n\\end{lemma}\n\n\\begin{proof}\nFor \\(0<\\gamma\\le1\\), the fixed profiles and their errors satisfy\n\\[\n|\\Ut|+\\gamma|\\Ut_\\gamma|\n\\le C_{q,k}\\delta^\\nu\\gamma^{-d},\\qquad\n|\\Zt|\\le C'_{q,k}\\delta^\\nu\\gamma^{-d}\n\\]\nabove their respective lower cutoffs. Enlarge \\(Y_1\\), then \\(Y_2\\), until\nthe right sides at those cutoffs are at most \\(1/q\\).\nThey only decrease as \\(\\gamma\\) increases. In the complementary inner\nranges use~\\eqref{eq:inner-inputs}. Since\n\\[\n1-B^2=\\frac{2}{q+1},\\qquad\nru_r=1+\\gamma\\Ut_\\gamma,\\qquad\n(p-1)r^2e^{-2u}=(q-1)e^{-2\\Ut},\n\\]\nwe obtain \\(|z-1|\\le3/q\\), \\(r|u_r|\\le1+1/q\\), and the last bound with\n\\(e^{2/q}\\le1+4/q\\) for large \\(q\\). Thus \\(C_0=4\\) suffices.\nAll dimension- or mode-dependent profile constants have been absorbed by\nthe cutoff choices; they do not enter \\(C_0\\).\n\nThe lower cutoffs tend to zero and the upper cutoff exceeds one at late\ntimes. If the equator occurred before \\(\\gamma=1\\), the same estimate up\nto that endpoint would contradict its value \\(z=0\\), by continuity.\nThus the stated interval is available in the \\(r\\) coordinate.\n\\end{proof}\n\n\\begin{lemma}[Global ratio and parabolic annuli]\\label{lem:annuli}\nFor a flow chosen as above, \\(\\phi/r\\ge c_0>0\\) globally off the poles.\nFor every fixed \\(0<a_1<a_2<\\infty\\), at all sufficiently late times,\n\\begin{equation}\\label{eq:annular-ricci}\n|\\Ric|\\le C_{a_1,a_2}\\delta^{\\nu-1}\n\\qquad(a_1\\sqrt\\delta\\le r\\le a_2\\sqrt\\delta).\n\\end{equation}\nThe constants and the required starting time may depend on the annulus.\n\\end{lemma}\n\n\\begin{proof}\nThe positive large-\\(\\gamma\\) coefficient of \\(U_k\\), with a tolerance\nsmall compared with that coefficient, gives \\(\\Ut\\ge0\\) for all\n\\(\\gamma\\ge\\Gamma_0\\) in the profile region, where \\(\\Gamma_0\\) is a\nfixed sufficiently large number. At the initial time use the initial\nprofile bounds up to \\(G_{\\mathrm{init}}\\), followed by the outer sign\ncondition beyond that same endpoint. Increase the construction's\nstarting rescaled time so that \\(\\Gamma_0\\) belongs to the controlled\nregion for every subsequent time. On the full exterior\n\\(\\{r\\ge\\Gamma_0\\sqrt\\delta\\}\\), compare \\(\\log(\\phi/r)\\) with\n\\(\\log(A/B)\\) in~\\eqref{eq:ratio-heat}. The latter is an exact equilibrium\nvalue of its reaction. Initial and moving lateral data have the correct\nsign. On each compact time slab the reaction is locally Lipschitz there,\nso comparison applies. Both hemispheres are included, and the equator is\nnot a boundary. Thus \\(\\phi/r\\ge A/B\\) on the exterior.\nOn \\(1\\le\\gamma\\le\\Gamma_0\\), profile control bounds \\(\\Ut\\) below;\non \\(\\gamma\\le1\\) use Lemma~\\ref{lem:uniform}.\nEarlier compact time intervals have a positive lower ratio by smoothness,\npositivity of \\(\\phi\\), and boundedness of \\(r\\).\n\nFor a fixed positive compact \\(\\gamma\\)-interval, the pulled-back metric\n\\(\\delta^{-1}g\\) is\n\\[\n\\frac{d\\gamma^2}{z}+(A/B)^2\\gamma^2e^{2\\Ut}g_{\\Sp^p}\n+\\gamma^2g_{\\Sp^q}.\n\\]\nIt differs in \\(C^2\\) by \\(O(\\delta^\\nu)\\) from the metric with\n\\(z=B^2\\), \\(\\Ut=0\\). The latter is the Ricci-flat cone with link\n\\(A^2g_{\\Sp^p}+B^2g_{\\Sp^q}\\). Curvature depends smoothly on a positive\nmetric and its first two derivatives on this fixed annulus. Hence its\nRicci norm is \\(O(\\delta^\\nu)\\) in the rescaled metric. Scaling back gives\n\\eqref{eq:annular-ricci}.\nAny prescribed fixed annulus eventually lies inside the profile region;\nas above, positivity of \\(z\\) there excludes the equatorial endpoint.\n\\end{proof}\n"}, {"path": "preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/sections/04-cap.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/sections/04-cap.tex", "bytes": 10973, "sha256": "7c5918606e2686082aac6f1d96fffe306e3130cc70cb00c982a476e30938de29", "content": "\\section{Curvature at the radius and cap scales}\\label{sec:cap}\n\n\\begin{proposition}\\label{prop:full-curvature}\nFor every flow with the choices made above, and \\(q\\) sufficiently large,\nthere is a constant \\(C\\), allowed to depend on that flow, such that\n\\begin{equation}\\label{eq:full-curvature}\n(r^2+\\theta^2)|\\Rm|\\le C\n\\end{equation}\non \\(M\\times[0,T)\\).\n\\end{proposition}\n\nWe prove the two scale bounds separately. We use Shi's curvature derivative\nestimates~\\cite{Shi1989} in their global and local forms; precise statements\nand proofs are also given in~\\cite[Theorems~1.4.1--1.4.2]{CaoZhu2006}.\nA uniform curvature\nbound on a parabolic neighborhood gives a bound for \\(|\\nabla\\Rm|\\) on a\nsmaller neighborhood away from its initial time. The constants depend on\nthe dimension, curvature bound, spatial margin, and elapsed time, and do\nnot require an injectivity-radius lower bound. In each application below,\nthe requisite parabolic neighborhood is established before the derivative\nestimate is used.\n\nStolarski proves a radius curvature bound in the inner region\n\\cite[Proposition~5.1(3)]{Stolarski2019}. We first establish a global\nradius bound and then the cap-scale bound in\nProposition~\\ref{prop:full-curvature}.\nIf the radius bound failed, both rescaled sphere radii\nwould diverge while their arclength derivatives remained bounded. A\npersistent nonzero radial sectional curvature would then force a large\nchange in one of those derivatives. For the cap bound, we first need a\nlower bound for the radius of the pole orbit and a quantitative estimate\nshowing that \\(r_s\\) approaches one near the pole. The same radial\nintegration argument can then be used at the cap scale.\n\n\\begin{lemma}\\label{lem:r-curvature}\nThere is a time-independent constant \\(C\\) such that \\(r^2|\\Rm|\\le C\\).\n\\end{lemma}\n\n\\begin{proof}\nSuppose otherwise. Choose record points \\((x_i,t_i)\\), with \\(t_i\\uparrow T\\),\nand put \\(r_i=r(x_i,t_i)>0\\), \\(Q_i=|\\Rm|(x_i,t_i)\\), so that\n\\begin{equation}\\label{eq:r-record}\nr_i^2Q_i=\\max_{M\\times[0,t_i]}r^2|\\Rm|\\longrightarrow\\infty.\n\\end{equation}\nSuch points exist by compactness on each closed time slab. Boundedness of\n\\(r\\) implies \\(Q_i\\to\\infty\\). By the \\(\\Sp^q\\) component of the\nmetric evolution,\n\\[\n|\\dt|_x r^2|=2|\\lambda_q|r^2\\le C_mr^2|\\Rm|.\n\\]\nThe identity extends to the poles since \\(r^2\\) is smooth. For fixed\nsufficiently small \\(b>0\\), Equation~\\eqref{eq:r-record} gives\n\\begin{equation}\\label{eq:r-time-variation}\n|r(x,t)^2-r(x,t_i)^2|\\le r_i^2/16\n\\quad\\text{for }t_i-b/Q_i\\le t\\le t_i,\n\\end{equation}\nat every fixed manifold point \\(x\\). These time intervals lie in the\nflow for all large \\(i\\).\n\nRescale by \\(Q_i\\) and translate \\(t_i\\) to zero. On a fixed ball of\nradius \\(R\\) about \\(x_i\\) in the rescaled metric at time \\(-b\\), the\nLipschitz bound for \\(r\\), Equation~\\eqref{eq:r-time-variation}, and\n\\(r_i\\sqrt{Q_i}\\to\\infty\\) imply \\(r\\ge r_i/2\\) throughout\n\\([-b,0]\\), for large \\(i\\). The record bound thus gives\n\\(|\\Rm|/Q_i\\le4\\) on this fixed spatial domain throughout the interval.\nIntegrating the metric evolution makes the metrics uniformly comparable\nthere, with a length-comparison factor \\(E\\) independent of \\(R\\) and \\(i\\).\nChoose \\(R>E+2\\). A final-time curve of length at most one cannot leave\nthis initial ball: up to its first exit its initial length would be at\nleast \\(R\\), and hence its final length at least \\(R/E>1\\).\nThe final unit ball therefore lies inside the initial ball, with a fixed\npositive margin measured in the initial metric. Take \\(b\\) below the\ndimension-dependent time threshold in the local derivative estimate.\nThat estimate gives a uniform\nbound for the final rescaled \\(|\\nabla\\Rm|\\) on that ball.\n\nIn the final rescaled metric the warping functions are\n\\(F_1=\\sqrt{Q_i}\\phi\\) and \\(F_2=\\sqrt{Q_i}r\\). Both tend to infinity\nat the center by Lemma~\\ref{lem:annuli}. Their derivatives with respect\nto rescaled arclength are the original \\(\\phi_s,r_s\\), hence uniformly\nbounded. Therefore both functions tend uniformly to infinity on any fixed\nshort radial interval about the center. Such intervals exist: the\nLipschitz bound makes the rescaled distance to either pole tend to\ninfinity. Crossing the equator presents no obstruction.\n\nThe rescaled tangential curvatures \\(j,\\ell,\\mu\\) tend to zero. Since\nthe rescaled norm of curvature is one at the center, the identity\n\\begin{equation}\\label{eq:curvature-norm}\n|\\Rm|^2=4\\left(ph^2+qh_q^2+\\frac{p(p-1)}2j^2\n +\\frac{q(q-1)}2\\ell^2+pq\\mu^2\\right)\n\\end{equation}\nshows that a radial curvature \\(-F_a''/F_a\\) has absolute value bounded\nbelow by a positive dimension-dependent constant. Along a radial geodesic,\nthe radial vector and a fixed sphere-tangent vector divided by its warping\nfactor are parallel. The derivative estimate therefore keeps that same\nsectional curvature of one sign and bounded away from zero on a fixed\nshorter interval. On that interval \\(F_a\\to\\infty\\) uniformly. Integrating\n\\(F_a''\\) forces an unbounded change in \\(F_a'\\), contradicting the\nslope bound.\n\\end{proof}\n\nThe next proof quantifies the scalar-sign obstruction in\n\\cite[Proposition~5.3]{Stolarski2019}. There, the strict cone gap and\nmonotone logarithmic slope rule out an ancient limit with nonnegative\nscalar curvature. Here we use the lower bound \\(R\\ge-C\\) on the given\nflow and integrate over a finite annulus whose logarithmic width would\ndiverge if the pole orbit were too small.\n\n\\begin{lemma}[Size of the pole orbit]\\label{lem:pole-size}\nAt all sufficiently late times,\n\\begin{equation}\\label{eq:pole-size}\nc\\theta\\le\\phi(0,t)\\le C\\theta.\n\\end{equation}\nMoreover \\(\\phi(r,t)\\ge c\\theta\\) for \\(0\\le r\\le Y_1\\theta\\).\n\\end{lemma}\n\n\\begin{proof}\nThe upper bound follows from Lemma~\\ref{lem:gap} and \\(f=ru_r\\ge0\\).\nFor the lower bound, the scalar curvature computed from\n\\eqref{eq:sectional} is\n\\begin{equation}\\label{eq:scalar-f}\n\\begin{split}\nr^2R={}&p(p-1)(r/\\phi)^2+q(q-1)(1-z)\\\\\n&-pz\\bigl[(p+1)f^2+(2q-2)f+2rf_r\\bigr]\n -(pf+q)rz_r.\n\\end{split}\n\\end{equation}\nFor example,\n\\(\\phi_{ss}/\\phi=z(u_{rr}+u_r^2)+z_ru_r/2\\) and\n\\(r_{ss}=z_r/2\\); substituting \\(u_r=f/r\\) gives this formula directly.\n\nIf the lower bound failed, choose times tending to \\(T\\) for which\n\\(L_0=\\phi(0,t)=o(\\theta)\\). The inequalities \\(r_s\\ge B\\) and\n\\(|\\phi_s|\\le C\\) imply\n\\[\n\\phi(L_0,t)/L_0\\le1+C/B.\n\\]\nLet \\(r_*=(L_0\\theta)^{1/2}\\). Since\n\\(\\partial_{\\log r}\\log(\\phi/r)=f-1\\), the lower ratio bound and the\nlast upper bound give\n\\[\n0\\le\\int_{L_0}^{r_*}(1-f)\\,\\frac{dr}{r}\\le C.\n\\]\nThe integrand is nonnegative and nonincreasing, because \\(0\\le f\\le1\\)\nand \\(f_r\\ge0\\). Thus\n\\[\n0\\le1-f(r_*,t)\\le\\frac{C}{\\log(r_*/L_0)}\\longrightarrow0.\n\\]\nConsequently \\(f=1+o(1)\\) uniformly on \\([r_*,\\theta]\\).\n\nAt \\(f=1\\), the nondifferentiated terms in the first two lines of\n\\eqref{eq:scalar-f}, before the \\(z_r\\) term, reduce to\n\\[\n\\frac{p(p-1)}{(\\phi/r)^2}+q(q-1)-m(m-1)z.\n\\]\nThey vanish at \\(\\phi/r=A/B\\), \\(z=B^2\\). By the fixed strict gap\n\\eqref{eq:strict-gap} and \\(z\\ge B^2\\), they are at most\n\\(-\\kappa\\) for some fixed \\(\\kappa>0\\). The term involving \\(f_r\\)\nis nonpositive. The uniform error \\(f-1=o(1)\\) is harmless with the flow\nand dimension fixed. Using \\(R\\ge-C\\) from Lemma~\\ref{lem:gradients},\nwe conclude, after reducing \\(\\kappa\\) if necessary, that\n\\[\n(pf+q)rz_r\\le-\\kappa+C\\theta^2\\le-\\kappa/2\n\\quad(r_*\\le r\\le\\theta).\n\\]\nSince \\(pf+q\\le m\\), this implies \\(rz_r\\le-\\kappa/(2m)\\).\nIntegration contradicts \\(B^2\\le z\\le1\\), because\n\\(\\log(\\theta/r_*)=\\tfrac12\\log(\\theta/L_0)\\to\\infty\\).\nThis proves the pole lower bound. Monotonicity of \\(\\phi\\) extends it\nto the entire indicated inner interval.\n\\end{proof}\n\n\\begin{lemma}[Radial slope at the cap scale]\\label{lem:cap-slope}\nFor sufficiently large \\(q\\), at late times,\n\\begin{equation}\\label{eq:cap-slope}\n0\\le1-v\\le C r/\\theta\\qquad(0\\le r\\le\\theta).\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nLet \\(w=1-v\\). Equation~\\eqref{eq:sideways} gives\n\\begin{equation}\\label{eq:w-operator}\n\\left(\\partial_t-z\\partial_{rr}-\\frac{q-1-z}{r}\\partial_r\n       +\\frac{(q-1)v(1+v)}{r^2}\\right)w\n=pv^3u_r^2\\le C\\theta^{-2}.\n\\end{equation}\nThe last inequality uses \\(u_r=\\phi_s/(\\phi v)\\),\nLemma~\\ref{lem:pole-size}, and \\(B\\le v\\le1\\).\nApplying this operator to \\(r/\\theta\\) gives exactly\n\\[\n\\frac{\\sigma r}{\\delta\\theta}\n+\\frac{(q-1)[v(1+v)-1]+z}{r\\theta}.\n\\]\nFor large \\(q\\), \\(B(1+B)>1\\). Thus this expression is bounded below\nby a fixed positive multiple of \\(\\theta^{-2}\\) when \\(r\\le\\theta\\).\nA sufficiently large multiple of \\(r/\\theta\\) dominates the source,\nthe data at \\(r=\\theta\\), and the data on a fixed late starting slice.\n\nTo justify comparison at the pole, fix a compact time slab. Smooth polar\nexpansions give \\(w=O(r^2)\\) uniformly on that slab. The proposed upper\nbarrier therefore dominates on a sufficiently small inner boundary\n\\(r=\\eta\\). Compare on \\(\\eta\\le r\\le\\theta(t)\\) and let\n\\(\\eta\\downarrow0\\). The auxiliary radius may depend on the slab, but\nthe barrier multiplier does not. This proves~\\eqref{eq:cap-slope}.\n\\end{proof}\n\n\\begin{proof}[Proof of Proposition~\\ref{prop:full-curvature}]\nIt remains to bound \\(\\theta^2|\\Rm|\\). If this were unbounded, choose\nrecord points with \\(Q_i=|\\Rm|(x_i,t_i)\\) such that\n\\[\n\\theta_i^2Q_i=\\max_{M\\times[0,t_i]}\\theta(t)^2|\\Rm|(x,t)\\to\\infty,\n\\qquad \\theta_i=\\theta(t_i).\n\\]\nSince \\(\\theta\\) decreases, for every \\(t\\le t_i\\) we have\n\\[\n|\\Rm|(x,t)\\le\\frac{\\theta_i^2}{\\theta(t)^2}Q_i\\le Q_i.\n\\]\nThe global derivative estimate therefore bounds \\(|\\nabla\\Rm|\\) at\nthe final time after rescaling by \\(Q_i\\), uniformly in \\(i\\).\nFor all large \\(i\\), a fixed backward interval in rescaled time lies\ninside the flow.\n\nLemma~\\ref{lem:r-curvature} gives \\(r_i\\sqrt{Q_i}\\le C\\), whereas\n\\(\\theta_i\\sqrt{Q_i}\\to\\infty\\). Any fixed short outward radial\ninterval from the center consequently lies in \\(r\\le\\theta_i\\).\nOn such intervals the rescaled first warping function satisfies\n\\(\\sqrt{Q_i}\\phi\\ge c\\sqrt{Q_i}\\theta_i\\to\\infty\\).\nThe rescaled second warping function is bounded above on each such\ninterval, and its arclength derivative satisfies\n\\[\nv\\longrightarrow1\n\\]\nuniformly by Lemma~\\ref{lem:cap-slope}.\nIf the center is on a pole orbit, choose any outward radial ray.\n\nMove a fixed sufficiently small positive distance along the ray. The\nderivative estimate keeps the curvature norm at least \\(1/2\\), while\nthe rescaled radius is now bounded below by a fixed positive number.\nAt this new center the three tangential curvatures tend to zero. By\n\\eqref{eq:curvature-norm}, some radial curvature has nonzero magnitude\nand constant sign on a fixed subsequent interval. For\n\\(F=\\sqrt{Q_i}\\phi\\), integration of \\(F''\\) contradicts bounded\n\\(F'\\) because \\(F\\to\\infty\\). For \\(F=\\sqrt{Q_i}r\\), it contradicts\nthe uniform convergence \\(F'=v\\to1\\), because \\(F\\) stays bounded below\nby a positive constant on that interval. Both alternatives are impossible.\nTogether with Lemma~\\ref{lem:r-curvature}, this proves~\\eqref{eq:full-curvature}.\n\\end{proof}\n"}, {"path": "preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/sections/05-reaction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/sections/05-reaction.tex", "bytes": 10322, "sha256": "e1f0df308ed17e772059647c80b187b7943a7475729c4df43ae91d9ceb6bc589", "content": "\\section{A dimension-explicit Ricci reaction bound}\\label{sec:reaction}\n\nThe constant in Proposition~\\ref{prop:full-curvature} may grow arbitrarily\nwith the dimension. We next obtain a different estimate whose leading\ndimension dependence is controlled.\nOnly the latter estimate will be used to choose the dimension. The\ncap estimate will enter afterward, with its flow-dependent constant,\nthrough an independently chosen small parameter.\n\n\\subsection{A one-dimensional interior estimate}\n\nFor \\(R>0\\), let \\(P_R=(-R,R)\\times[-R^2,0]\\), with space coordinate\n\\(y\\) and time coordinate \\(\\rho\\).\n\n\\begin{lemma}\\label{lem:heat-perturbation}\nThere is \\(\\epsilon_0>0\\) with the following property. If a smooth\nfunction \\(H\\) on \\(P_2\\) satisfies\n\\[\nH_\\rho=aH_{yy}+bH_y+F,\\qquad\n|a-1|\\le\\epsilon_0,\\qquad |H|+|b|+|F|\\le K,\n\\]\nthen \\(|H_y|\\le C(K)\\) on \\(P_1\\), including the terminal time by\ncontinuity from below. No bounds on derivatives of the coefficients are\nrequired. The same conclusion holds after any fixed rescaling of the\ncylinders.\n\\end{lemma}\n\n\\begin{proof}\nFix an exponent \\(s>3\\). The constant-coefficient heat estimate is\n\\begin{equation}\\label{eq:heat-Lp}\n\\|V_{yy}\\|_{L^s}+\\|V_\\rho\\|_{L^s}\n\\le C_s\\|V_\\rho-V_{yy}\\|_{L^s}\n\\end{equation}\nfor smooth Sobolev functions compactly supported in space and vanishing in the distant\npast, with norms over times up to zero. This is the classical parabolic\nCalder\\'on--Zygmund estimate for the heat operator; its constants here are\none-dimensional; see~\\cite[Theorem~5.5]{Krylov2006}, with time reversed.\nIt follows equivalently from the \\(L^s\\) boundedness of\nthe second spatial derivative of the causal heat potential. The\nhalf-infinite time version follows by extending its forcing to future\ntimes by zero. The function itself need not vanish at time zero.\n\nFor \\(1\\le R_1<R_2<2\\), put \\(d_R=R_2-R_1\\) and choose a cutoff\n\\(\\zeta\\) equal to one on \\(P_{R_1}\\), supported spatially and in the\npast inside \\(P_{R_2}\\), with\n\\[\n|\\zeta_y|\\le C d_R^{-1},\\qquad\n|\\zeta_{yy}|+|\\zeta_\\rho|\\le C d_R^{-2}.\n\\]\nWriting \\(V=\\zeta H\\), direct expansion gives\n\\begin{equation}\\label{eq:cutoff-heat}\n\\begin{split}\n(\\partial_\\rho-\\partial_{yy})V={}&(a-1)V_{yy}+bV_y+\\zeta F\\\\\n&+(\\zeta_\\rho-a\\zeta_{yy}-b\\zeta_y)H-2a\\zeta_yH_y.\n\\end{split}\n\\end{equation}\nChoose \\(\\epsilon_0\\) so that \\(C_s\\epsilon_0<1/2\\) and absorb the first\nterm using~\\eqref{eq:heat-Lp}. If\n\\(E(R)=\\|H_{yy}\\|_{L^s(P_R)}+\\|H_\\rho\\|_{L^s(P_R)}\\), the remaining\nterms yield\n\\[\nE(R_1)\\le C(K)d_R^{-1}\\|H_y\\|_{L^s(P_{R_2})}+C(K)d_R^{-2}.\n\\]\nSpatial interpolation on intervals of radius between one and two gives\n\\[\n\\|H_y\\|_{L^s(P_R)}\\le h\\|H_{yy}\\|_{L^s(P_R)}\n       +C h^{-1}\\|H\\|_{L^s(P_R)}\n\\]\nfor sufficiently small \\(h>0\\), without boundary conditions on \\(H\\).\nTaking \\(h\\) to be a sufficiently small multiple of \\(d_R\\) gives\n\\begin{equation}\\label{eq:interior-iteration}\nE(R_1)\\le\\tfrac1{16}E(R_2)+C(K)d_R^{-2}.\n\\end{equation}\nIterate with \\(R_j=7/4-2^{-j}/4\\). The error terms are summable since\ntheir factors grow as \\(4^j\\) whereas the iteration contributes\n\\(16^{-j}\\). The remainder tends to zero because\n\\(E(R_j)\\le E(7/4)<\\infty\\) for each individual smooth function. This\nfiniteness is not a presumed uniform bound. Hence \\(E(3/2)\\le C(K)\\).\nInterpolation also controls the first derivative norm. Parabolic Sobolev\nembedding~\\cite[Chapter~II, Section~3, Lemma~3.3]{LSU1968},\nwith \\(s>1+2\\), bounds the spatial first derivative on\n\\(P_1\\). This proves the claim.\n\\end{proof}\n\n\\subsection{Cylinders moving with the radial drift}\n\n\\begin{lemma}\\label{lem:largeq-derivatives}\nFor all sufficiently large \\(q\\), and sufficiently late times,\n\\begin{equation}\\label{eq:largeq-derivatives}\n|rz_r|\\le Cq^{-1/2},\\qquad r^2|u_{rr}|\\le Cq^{1/2}\n\\quad\\left(0<r\\le\\tfrac18\\sqrt\\delta\\right),\n\\end{equation}\nwhere \\(C\\) is independent of both \\(q\\) and \\(k\\).\n\\end{lemma}\n\n\\begin{proof}\nFix \\((r_c,t_c)\\) in the indicated region and set\n\\begin{equation}\\label{eq:moving-cylinder}\nh_c=\\frac{r_c}{\\sqrt q},\\qquad\nt=t_c+h_c^2\\rho,\\qquad\nr=R_c(t)+h_cy,\\qquad\nR_c(t)=\\sqrt{r_c^2+2(q-1)(t_c-t)}.\n\\end{equation}\nOn \\(P_2\\), \\(1\\le R_c/r_c\\le3\\). For \\(q\\ge16\\),\n\\begin{equation}\\label{eq:cylinder-inclusion}\n\\frac12r_c\\le r\\le\\frac72r_c,\n\\qquad r^2\\le\\frac{49}{256}\\delta_c<\\delta(t),\n\\qquad \\delta_c=T-t_c.\n\\end{equation}\nMoreover its earliest time is at least\n\\(t_c-\\delta_c/(16q)\\). Thus a single sufficiently late threshold for\n\\(t_c\\), for the chosen flow, puts every such cylinder inside the\nregion of Lemma~\\ref{lem:uniform}.\n\nThe moving center in~\\eqref{eq:moving-cylinder} follows the radial drift.\nFigure~\\ref{fig:drift} depicts the resulting cylinder. A cylinder centered\nat a fixed radius would leave an uncontrolled coefficient of size\n\\(\\sqrt q\\); the movement cancels this coefficient before the\none-dimensional estimate is applied.\n\n\\begin{figure}[htbp]\n\\centering\n\\input{figures/drift-cylinder}\n\\caption{Schematic image of the fixed cylinder \\(P_2\\) under\n\\eqref{eq:moving-cylinder}. Its radial width is \\(4h_c\\) and its time\nlength is \\(4h_c^2\\). Moving with \\(R_c'(t)=-(q-1)/R_c(t)\\) leaves\na bounded drift after rescaling. The drawing is not to scale.}\n\\label{fig:drift}\n\\end{figure}\n\nSince \\(R_c'=-(q-1)/R_c\\), the function \\(H=q(v-1)\\), pulled back to\nthis cylinder, satisfies Lemma~\\ref{lem:heat-perturbation} with\n\\begin{equation}\\label{eq:H-coefficients}\n\\begin{aligned}\na&=z,\\\\\nb&=h_c\\left[(q-1)\\left(\\frac1r-\\frac1{R_c}\\right)-\\frac zr\\right],\\\\\nF&=qh_c^2\\left[\\frac{(q-1)v(1-z)}{r^2}-pv^3u_r^2\\right].\n\\end{aligned}\n\\end{equation}\nIndeed \\(|H|\\le C_0\\), since\n\\(q|v-1|=q|z-1|/(v+1)\\), and \\(|a-1|\\le C_0/q\\).\nThe apparently large drift is bounded by\n\\[\n\\left|h_c(q-1)\\left(\\frac1r-\\frac1{R_c}\\right)\\right|\n=\\frac{(q-1)h_c^2|y|}{rR_c}\\le C.\n\\]\nFor the source use \\(qh_c^2=r_c^2\\),\n\\((q-1)|1-z|\\le C_0\\), and \\(r|u_r|\\le2\\).\nAll these coefficients and amplitudes are therefore bounded by\none numerical constant. Increasing \\(q\\) meets its smallness hypothesis.\nIt follows that \\(|H_y|\\le C\\) on \\(P_1\\), and hence\n\\begin{equation}\\label{eq:zr-cylinder}\n|z_r|=\\frac{2v}{qh_c}|H_y|\\le\\frac{C}{r_c\\sqrt q}\n\\quad\\text{on }P_1.\n\\end{equation}\n\nDifferentiate the first equation of~\\eqref{eq:sideways}. With \\(w=u_r\\),\n\\begin{equation}\\label{eq:ur-equation}\nw_t=zw_{rr}+\\left[z_r+\\frac{q-1+z}{r}\\right]w_r\n+\\left[\\frac{z_r}{r}-\\frac{q-1+z}{r^2}\n          +2(p-1)e^{-2u}\\right]w.\n\\end{equation}\nOn the smaller cylinder \\(P_1\\), set \\(W=r_cw\\). Its transformed drift is\n\\[\nb_W=h_c(q-1)(1/r-1/R_c)+h_c(z_r+z/r),\n\\]\nand its zeroth-order coefficient is\n\\[\nc_W=h_c^2\\left[z_r/r-(q-1+z)/r^2+2(p-1)e^{-2u}\\right].\n\\]\nEquations~\\eqref{eq:uniform}, \\eqref{eq:cylinder-inclusion}, and\n\\eqref{eq:zr-cylinder} bound \\(|W|\\), \\(|b_W|\\), and \\(|c_W|\\)\nnumerically. In particular \\(h_c^2(p-1)e^{-2u}\\le C\\).\nTreat \\(c_WW\\) as a bounded source and apply a fixed rescaled version of\nLemma~\\ref{lem:heat-perturbation} on \\(P_1\\). Evaluating at its center\ngives\n\\[\n|W_y(0,0)|=r_ch_c|u_{rr}(r_c,t_c)|\\le C.\n\\]\nThis proves the second estimate. The first follows from\n\\eqref{eq:zr-cylinder} at the center. Only the numerical \\(C_0\\) and\nthe one-dimensional estimates entered the constants, so neither\n\\(q\\) nor \\(k\\) occurs in \\(C\\).\n\\end{proof}\n\n\\subsection{The Ricci norm and its reaction matrix}\n\nThe derivative estimates now control the radial sectional curvatures.\nThe remaining task is algebraic: write the curvature action on invariant\ndiagonal tensors in coordinates that include their multiplicities in the\nnorm. This isolates the leading term in \\(q\\).\n\nThe tensor evolution and the evolving metric give\n\\[\n(\\dt-\\Delta)|\\Ric|^2=-2|\\nabla\\Ric|^2\n                         +4\\langle\\Rm(\\Ric),\\Ric\\rangle.\n\\]\nThe inverse-metric derivatives in the squared norm cancel the cubic\nRicci term from the covariant tensor evolution. Kato's inequality then\ngives, wherever \\(|\\Ric|>0\\),\n\\begin{equation}\\label{eq:ricci-norm}\n(\\dt-\\Delta)|\\Ric|\n\\le \\frac{2}{|\\Ric|}\\langle\\Rm(\\Ric),\\Ric\\rangle.\n\\end{equation}\nFor diagonal tensors, our curvature convention is\n\\(\\langle\\Rm(D),D\\rangle=\\sum_{i,j}R_{ijij}D_iD_j\\), with\n\\(R_{ijij}\\) the sectional curvature for \\(i\\ne j\\).\n\n\\begin{proposition}\\label{prop:reaction}\nAt late times on \\(0<r\\le\\frac18\\sqrt\\delta\\), the reaction quotient\n\\[\nc(x,t)=\\frac{2\\langle\\Rm(\\Ric),\\Ric\\rangle}{|\\Ric|^2}\n\\quad\\text{where }|\\Ric|>0\n\\]\nsatisfies both bounds\n\\begin{equation}\\label{eq:reaction-two}\nc\\le\\frac{2(q-1)+C\\sqrt q}{r^2},\\qquad\nc\\le\\frac{C_*}{\\theta^2}.\n\\end{equation}\nHere \\(C\\) is independent of \\(q,k\\); \\(C_*\\) may depend on the entire\nchosen flow. The second bound holds at the pole orbits as well.\nGlobally at positive \\(r\\), one also has \\(c\\le D_1/r^2\\) for some\nflow-dependent \\(D_1>0\\).\n\\end{proposition}\n\n\\begin{proof}\nThe Ricci tensor is diagonal and scalar on each sphere factor. For a tensor\nwith entries \\((\\alpha,\\beta^{\\times p},\\chi^{\\times q})\\), use the\nEuclidean coordinates \\((\\alpha,\\sqrt p\\beta,\\sqrt q\\chi)\\), so its\nnorm is the ordinary Euclidean norm. The curvature form has matrix\n\\begin{equation}\\label{eq:reaction-matrix}\n\\begin{pmatrix}\n0&\\sqrt p\\,h&\\sqrt q\\,h_q\\\\\n\\sqrt p\\,h&(p-1)j&\\sqrt{pq}\\,\\mu\\\\\n\\sqrt q\\,h_q&\\sqrt{pq}\\,\\mu&(q-1)\\ell\n\\end{pmatrix}.\n\\end{equation}\nIndeed its mixed terms are \\(2ph\\alpha\\beta\\),\n\\(2qh_q\\alpha\\chi\\), and \\(2pq\\mu\\beta\\chi\\); this accounts for\nthe ordered pairs and all multiplicities.\n\nUsing \\(r\\) as the spatial coordinate,\n\\begin{equation}\\label{eq:curv-sideways}\n\\begin{gathered}\nh=-z(u_{rr}+u_r^2)-\\tfrac12z_ru_r,\n\\qquad h_q=-z_r/(2r),\\qquad \\mu=-zu_r/r,\\\\\nj=e^{-2u}-zu_r^2,\\qquad \\ell=(1-z)/r^2.\n\\end{gathered}\n\\end{equation}\nBy Lemmas~\\ref{lem:uniform} and~\\ref{lem:largeq-derivatives}, after\nmultiplication by \\(r^2\\) the diagonal entries of\n\\eqref{eq:reaction-matrix} are bounded above by\n\\(0,q-1+C_0,C_0\\), respectively. Its off-diagonal entries have sizes\n\\(O(\\sqrt q),O(1),O(\\sqrt q)\\), respectively, with numerical constants.\nThe largest eigenvalue is therefore at most \\(q-1+C\\sqrt q\\).\nEquation~\\eqref{eq:ricci-norm} proves the first bound.\nThe curvature contraction is bounded in absolute value by a\ndimension-dependent constant times \\(|\\Rm||\\Ric|^2\\).\nProposition~\\ref{prop:full-curvature} gives the other two bounds, including\nthe bound at a pole by continuity of the geometric curvature tensor.\n\\end{proof}\n"}, {"path": "preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/sections/06-barriers.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/build/sections/06-barriers.tex", "bytes": 12924, "sha256": "3b81367fa500b336d77904b33273c3e3551e7c4afc1d06eb68927d2e2aa03ab0", "content": "\\section{Comparison and the scalar-curvature bound}\\label{sec:barriers}\n\nWe combine the two reaction bounds with small Ricci curvature on\nparabolic annuli. The inner comparison gives a spatial Ricci exponent\nstrictly below one; integrating its square through a scalar supersolution\nthen gives the required bounded scalar curvature.\n\nFor the inner Ricci comparison we will use a positive function of the form\n\\[\nH=\\delta^L(r^2+b_0\\theta^2)^{-a/2},\n\\qquad a,L,b_0>0.\n\\]\nIts radial diffusion has leading coefficient \\(a(q-1)\\), so we require\n\\(a>2\\) to dominate the leading reaction coefficient \\(2(q-1)\\).\nAt the cap scale, the identity \\(\\delta^L=\\theta^{L/\\sigma}\\) makes\nits size proportional to \\(\\theta^{-(a-L/\\sigma)}\\). We will arrange\nthat this exponent lies between zero and one, allowing a bounded scalar\nbarrier to dominate the resulting Ricci-square source. The choices below also make the\nparabolic-annulus estimate supply the lateral data for \\(H\\).\n\n\\subsection{Order of the choices}\n\nWe now choose one flow to prove Theorem~\\ref{thm:main}. Set \\(a=5/2\\).\nSince \\(d\\to2\\) as \\(q\\to\\infty\\), choose \\(q\\) large enough that\n\\(2<d<a\\), all universal thresholds above hold, and\n\\begin{equation}\\label{eq:dimension-margin}\n\\mathfrak m:=(a-2)(q-1)-C\\sqrt q-2a^2-1>0,\n\\end{equation}\nwhere \\(C\\) is the universal constant in Proposition~\\ref{prop:reaction}.\nNext choose a sufficiently large even \\(k\\) so that, with\n\\begin{equation}\\label{eq:barrier-exponents}\nL=k-1,\\qquad e=a-L/\\sigma=a-d+d/k,\n\\end{equation}\nwe have \\(0<e<1\\), \\(\\nu>1\\), and \\(\\sigma>1/2\\).\nThese requirements are compatible because \\(d>2\\).\nDefine\n\\[\n\\xi=\\min\\{1/8,1/(2\\sqrt L)\\}.\n\\]\nChoose the tolerances and cutoffs as in Section~\\ref{sec:geometry}, and\nfix the resulting flow. In particular \\(C_*\\) and \\(D_1\\) in\nProposition~\\ref{prop:reaction} are now fixed finite numbers.\nOnly after this step choose \\(b_0>0\\) so that\n\\begin{equation}\\label{eq:b0-choice}\nb_0C_*<\\mathfrak m.\n\\end{equation}\nThe remaining constants below are chosen for this same flow. Whenever a\nlater starting time is needed, we restrict to a later slice of the flow\nalready chosen; we do not repeat the construction or change its parameters.\n\nIn Figure~\\ref{fig:scales}, \\(\\Gamma>\\xi\\) denotes a fixed outer\ncomparison radius whose size will be chosen in\n\\eqref{eq:exterior-choices}. The annular estimate is available for\nevery such fixed radius after moving the starting time later.\n\n\\begin{figure}[htbp]\n\\centering\n\\input{figures/scales}\n\\caption{The comparison regions at a late time, shown on a schematic\nradius axis. The cap scale \\(\\theta\\) is much smaller than the\nparabolic scale \\(\\sqrt\\delta\\). The inner Ricci estimate bridges these\nscales. Fixed parabolic-annulus control supplies lateral data for both\nthe inner and exterior comparisons; \\(\\Gamma\\) is chosen later in\n\\eqref{eq:exterior-choices}.}\n\\label{fig:scales}\n\\end{figure}\n\n\\subsection{The inner Ricci estimate}\n\n\\begin{proposition}\\label{prop:ricci-barrier}\nThere is a constant \\(C\\) such that, at all sufficiently late times,\n\\begin{equation}\\label{eq:inner-ricci}\n|\\Ric|\\le C\\delta^L(r^2+b_0\\theta^2)^{-a/2}\n\\qquad(0\\le r\\le\\xi\\sqrt\\delta).\n\\end{equation}\nIn particular,\n\\begin{equation}\\label{eq:ricci-power}\n|\\Ric|\\le Cr^{-e}\\qquad(0<r\\le\\xi\\sqrt\\delta).\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nPut \\(D_0=r^2+b_0\\theta^2\\) and \\(H=\\delta^LD_0^{-a/2}\\).\nThis is a positive smooth geometric function on both closed caps,\nincluding their pole orbits, because \\(r^2\\) is smooth there.\nUsing \\(\\theta_t=-\\sigma\\theta/\\delta\\) and\nEquation~\\eqref{eq:operator}, direct differentiation gives\n\\begin{equation}\\label{eq:inner-barrier-computation}\n\\begin{split}\n\\frac{\\D H}{H}\n&=-\\frac L\\delta+\\frac{a\\sigma b_0\\theta^2}{\\delta D_0}\n +\\frac{a(q-1)+2az}{D_0}-\\frac{a(a+2)zr^2}{D_0^2}\\\\\n&\\ge-\\frac L\\delta+\\frac{a(q-1)-a^2z}{D_0}.\n\\end{split}\n\\end{equation}\nThe first line has a nonnegative extra time term, and \\(r^2\\le D_0\\)\ngives the second. The identities extend to a pole by continuity; in\nparticular the apparent \\(r^{-1}H_r\\) term has a finite limit.\n\nIn the indicated region \\(z\\le2\\) for large \\(q\\), and\n\\[\nLD_0/\\delta\\le L\\xi^2+Lb_0\\delta^{2\\sigma-1}\\le1\n\\]\nat sufficiently late times. Where \\(|\\Ric|>0\\) and \\(r>0\\), the two\nbounds in~\\eqref{eq:reaction-two} imply\n\\[\ncD_0=cr^2+b_0c\\theta^2\n\\le2(q-1)+C\\sqrt q+b_0C_*.\n\\]\nBoth inequalities may be added even if \\(c\\) is negative. At a pole,\nthe second reaction bound alone implies the same upper bound for\n\\(cD_0\\). Therefore\n\\begin{equation}\\label{eq:strict-ricci-super}\n\\frac{\\D H}{H}-c\n\\ge\\frac{-1+a(q-1)-2a^2-2(q-1)-C\\sqrt q-b_0C_*}{D_0}>0\n\\end{equation}\nby~\\eqref{eq:dimension-margin} and~\\eqref{eq:b0-choice}.\n\nOn the moving lateral boundary \\(r=\\xi\\sqrt\\delta\\),\n\\[\nH=\\delta^{L-a/2}\n    (\\xi^2+b_0\\delta^{2\\sigma-1})^{-a/2}\n\\asymp\\delta^{L-a/2}.\n\\]\nLemma~\\ref{lem:annuli} and \\(\\nu=k-d/2\\) give\n\\begin{equation}\\label{eq:lateral-ratio}\n\\frac{|\\Ric|}{H}\\le C\\delta^{\\nu-1-(L-a/2)}\n=C\\delta^{(a-d)/2}.\n\\end{equation}\nThis ratio is uniformly bounded at late times. Choose a sufficiently\nlate fixed starting time \\(t_0\\). On that compact starting slice, \\(H\\)\nhas a positive minimum on the caps, so a single multiple of \\(H\\)\nstrictly dominates both initial and lateral data for every subsequent\ncompact time slab.\n\nAt a first contact of \\(|\\Ric|\\) with this positive barrier, the Ricci\nnorm is nonzero and hence smooth nearby. Equation~\\eqref{eq:ricci-norm}\nand the strict inequality~\\eqref{eq:strict-ricci-super} rule out the\ncontact by the maximum principle. This argument also applies at a pole,\nwhich is interior to the smooth manifold. It proves~\\eqref{eq:inner-ricci}\nwith one multiplier for all times \\(t_0\\le t<T\\).\n\nFinally \\(\\delta^L=\\theta^{L/\\sigma}=\\theta^{a-e}\\), with \\(a-e>0\\).\nFor \\(r\\ge\\theta\\),\n\\[\nH\\le\\theta^{a-e}r^{-a}\\le r^{-e}.\n\\]\nFor \\(0<r\\le\\theta\\),\n\\[\nH\\le b_0^{-a/2}\\theta^{-e}\\le b_0^{-a/2}r^{-e}.\n\\]\nThese prove~\\eqref{eq:ricci-power}.\n\\end{proof}\n\n\\subsection{A scalar barrier at the pole}\n\n\\begin{proposition}\\label{prop:scalar-inner}\nThe scalar curvature is uniformly bounded above on\n\\(0\\le r\\le\\xi\\sqrt\\delta\\) at late times.\n\\end{proposition}\n\n\\begin{proof}\nFor a real exponent \\(\\alpha\\), Equation~\\eqref{eq:operator} gives\n\\begin{equation}\\label{eq:power-operator}\n\\D(r^\\alpha)=-\\alpha(q-1+\\alpha z)r^{\\alpha-2}\n\\qquad(r>0).\n\\end{equation}\nPut \\(\\ell_0=2-2e\\in(0,2)\\). In particular,\n\\begin{equation}\\label{eq:scalar-powers}\n\\D(-r^{\\ell_0})=\\ell_0(q-1+\\ell_0z)r^{-2e},\n\\qquad \\D(r^{-1})=(q-1-z)r^{-3}.\n\\end{equation}\nWrite \\(|\\Ric|\\le K_Rr^{-e}\\) as in Proposition~\\ref{prop:ricci-barrier}.\nChoose\n\\[\nC_2\\ge\\frac{2K_R^2}{\\ell_0(q-1)}.\n\\]\nSince \\(0\\le z\\le2<q-1\\), every function\n\\begin{equation}\\label{eq:scalar-barrier}\nS_\\varepsilon=C_1-C_2r^{\\ell_0}+\\varepsilon r^{-1},\n\\qquad \\varepsilon>0,\n\\end{equation}\nsatisfies \\(\\D S_\\varepsilon\\ge2|\\Ric|^2\\) at positive radii in the\ninner region.\n\nChoose one late starting time \\(t_0\\) and let\n\\(\\rho_0=\\xi\\sqrt{T-t_0}\\). Smoothness bounds scalar curvature on\nthe starting slice by a constant \\(K_0\\). On the moving lateral boundary,\nLemma~\\ref{lem:annuli}, \\(\\nu>1\\), and\n\\(|R|\\le\\sqrt{m+1}|\\Ric|\\) give a uniform upper bound \\(K_\\partial\\).\nThe choice\n\\begin{equation}\\label{eq:C1-choice}\nC_1>\\max\\{K_0,K_\\partial\\}+C_2\\rho_0^{\\ell_0}\n\\end{equation}\ntherefore dominates the initial and lateral scalar data for every\n\\(\\varepsilon>0\\).\n\nFix \\(\\varepsilon>0\\) and \\(t_1<T\\). Scalar curvature is bounded on\nthe compact slab \\([t_0,t_1]\\). For sufficiently small \\(\\eta>0\\), the\nlevel \\(r=\\eta\\) lies in the regular inner region throughout that slab\nand \\(\\varepsilon/\\eta\\) makes \\(S_\\varepsilon\\) dominate its scalar\ndata. Apply comparison to \\(R-S_\\varepsilon\\) on\n\\[\n\\{(x,t):t_0\\le t\\le t_1,\\ \\eta\\le r(x,t)\\le\\xi\\sqrt{T-t}\\}.\n\\]\nThe moving outer boundary is regular in the monotone inner region.\nBoth initial and lateral boundaries have been controlled, so the usual\ninterior first-maximum argument applies. Let \\(\\eta\\downarrow0\\).\nThis proves \\(R\\le S_\\varepsilon\\) at every positive radius on the slab.\n\nOnly the auxiliary excision radius depends on \\(\\varepsilon,t_1\\);\nthe constants in~\\eqref{eq:C1-choice} do not. Letting the slab range up to\n\\(T\\) and then sending \\(\\varepsilon\\downarrow0\\) pointwise gives\n\\[\nR\\le C_1-C_2r^{\\ell_0}\\le C_1\\qquad(r>0).\n\\]\nAt every fixed time \\(t<T\\), continuity gives the same bound at the\npole orbits. No smoothness of \\(r^{\\ell_0}\\) at the pole or uniform\nregularity at \\(t=T\\) was used.\n\\end{proof}\n\n\\subsection{The exterior and the maximal time}\n\n\\begin{proposition}\\label{prop:exterior}\nThere is a constant \\(C\\) such that\n\\(|\\Ric|\\le C\\) on \\(r\\ge\\xi\\sqrt\\delta\\) at late times.\n\\end{proposition}\n\n\\begin{proof}\nChoose \\(b_1>2D_1+1\\), where \\(D_1\\) is the global reaction constant\nin Proposition~\\ref{prop:reaction}, and choose\n\\begin{equation}\\label{eq:exterior-choices}\n\\Gamma>\\xi,\\qquad \\Gamma^2\\ge\\max\\{2b_1,4(q-1)\\}.\n\\end{equation}\nOn the entire geometric exterior \\(r\\ge\\Gamma\\sqrt\\delta\\), put\n\\(H_o=1-b_1\\delta/r^2\\). This is smooth across the equator and satisfies\n\\(1/2\\le H_o\\le1\\). Equation~\\eqref{eq:operator} gives\n\\begin{equation}\\label{eq:outer-barrier}\n\\D H_o=\\frac{b_1}{r^2}\n\\left[1-\\bigl(2(q-1)-4z\\bigr)\\frac{\\delta}{r^2}\\right]\n\\ge\\frac{b_1}{2r^2}>\\frac{D_1H_o}{r^2}.\n\\end{equation}\nHere only \\(z\\ge0\\) was needed; no global smallness of \\(z-1\\) is\nrequired.\n\nThe fixed parabolic radius \\(\\Gamma\\) eventually lies in the region of\nLemma~\\ref{lem:annuli}. The annular estimate and \\(\\nu>1\\) bound \\(|\\Ric|\\) on\nthe moving lateral boundary uniformly. On a sufficiently late starting\nslice, compactness and \\(H_o\\ge1/2\\) allow one multiplier to dominate\nthe initial data. The same first-contact argument as in\nProposition~\\ref{prop:ricci-barrier} proves \\(|\\Ric|\\le CH_o\\le C\\)\nthroughout the exterior. The equator is interior, since both hemispheres\nare included. On the remaining fixed annulus\n\\(\\xi\\le r/\\sqrt\\delta\\le\\Gamma\\), use Lemma~\\ref{lem:annuli}\ndirectly.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\nTake the single flow selected at the start of Section~\\ref{sec:barriers}.\nPropositions~\\ref{prop:scalar-inner} and~\\ref{prop:exterior} give a\nuniform scalar upper bound on all of \\(M\\) for sufficiently late times.\nThere are only finitely many required starting-time restrictions, and\nevery parameter and constant has already been fixed, so a common such\ntime exists. Earlier times form a compact smooth interval and contribute\na finite bound. Lemma~\\ref{lem:gradients} supplies the scalar lower bound.\nThus \\(\\sup_{M\\times[0,T)}|R|<\\infty\\).\n\nBy Lemma~\\ref{lem:pole-size}, \\(\\phi(0,t)\\le C\\theta(t)\\to0\\).\nAt a pole orbit, smoothness gives \\(\\phi_s=0\\). Since \\(p=2\\), a\ntwo-plane tangent to the \\(\\Sp^p\\) orbit has sectional curvature\n\\[\nj(0,t)=\\phi(0,t)^{-2}\\longrightarrow\\infty.\n\\]\nConsequently \\(\\max_M|\\Rm|\\to\\infty\\). A smooth extension on the same\ncompact manifold would bound curvature on a compact time neighborhood\nof \\(T\\), which is impossible. The constructed flow exists smoothly for\nevery \\(t<T\\); closed-manifold uniqueness therefore identifies \\(T\\)\nwith the maximal existence time for its smooth initial metric. The product\n\\(\\Sp^2\\times\\Sp^{q+1}\\) is closed, connected, and has dimension\n\\(q+3\\ge4\\), as required.\n\\end{proof}\n\n\\begin{corollary}[Curvature rates in a fixed dimension]\\label{cor:rates}\nFor every sufficiently large integer \\(q\\), set\n\\[\nd_q=\\frac{q+1-\\sqrt{(q+1)(q-7)}}2.\n\\]\nFor every sufficiently large even integer \\(k\\), with the threshold\nallowed to depend on \\(q\\), there is a smooth Ricci flow on\n\\(\\Sp^2\\times\\Sp^{q+1}\\) with finite maximal time \\(T\\), uniformly\nbounded scalar curvature, and constants \\(0<c\\le C<\\infty\\) such that\n\\[\nc(T-t)^{-2k/d_q}\\le\\max_M|\\Rm|(\\cdot,t)\n\\le C(T-t)^{-2k/d_q}\n\\]\nfor all sufficiently late times. In particular, one fixed dimension\nadmits such flows with an unbounded discrete set of power-law Type-II\ncurvature exponents. The flow, \\(T\\), and the constants may depend on\n\\(q\\) and \\(k\\).\n\\end{corollary}\n\n\\begin{proof}\nEvery threshold used to choose \\(q\\) at the beginning of this section\nis independent of \\(k\\). Once such a \\(q\\) is fixed, all sufficiently\nlarge even \\(k\\) satisfy the requirements in\n\\eqref{eq:barrier-exponents}; Proposition~\\ref{prop:input} then permits\nthe remaining choices. The preceding comparisons apply to each resulting\nflow, with constants that may depend on it.\n\nHere \\(d=d_q\\) and \\(\\theta=(T-t)^{k/d_q}\\).\nProposition~\\ref{prop:full-curvature} gives\n\\(\\max_M|\\Rm|\\le C\\theta^{-2}\\).\nAt either pole orbit, Lemma~\\ref{lem:pole-size} gives\n\\(\\phi(0,t)\\le C\\theta\\), and the tangential sectional curvature is\n\\(j(0,t)=\\phi(0,t)^{-2}\\). Hence\n\\(\\max_M|\\Rm|\\ge c\\theta^{-2}\\).\nThese are the claimed two-sided bounds. Since \\(2k/d_q>1\\) and tends\nto infinity with \\(k\\), the flows are Type~II and their exponents are\nunbounded with \\(q\\) fixed.\n\\end{proof}\n"}, {"path": "preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/paper.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-closed-Ricci-flow-with-bounded-scalar-curvature-and-finite-time-curvature-blowup-September-24-2026/paper.pdf", "bytes": 221862, "sha256": "88b1d8270cb2bc5fb315b3421f984f9db84a4431d4780f89b1bcf6504839510b", "base64": 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"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/README.md", "bytes": 804, "sha256": "f6c0211659e3f696a8c07b624f265f940425cedc64b9df5392c3ece40f978cbb", "content": "# [A projective fourfold with large fundamental group and non-Stein universal cover](large-fundamental-group-non-stein.pdf)\n\n**Author:** OpenAI\n\n**Date:** October 5, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026,\n  author = {{OpenAI}},\n  title = {{A projective fourfold with large fundamental group and non-Stein universal cover}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/large-fundamental-group-non-stein.pdf}{OAI:A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/figures/arrangement.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/figures/arrangement.tex", "bytes": 2852, "sha256": "f9b63a12d4e9e3c29e11527beb3655f7e242baaf7a704000e2e52d1afe27009c", "content": "\\begin{figure}[htbp]\n\\centering\n\\begingroup\n\\definecolor{arrred}{RGB}{165,58,37}\n\\definecolor{arrblue}{RGB}{31,87,145}\n\\begin{tikzpicture}[x=1cm,y=1cm,>=Latex,\n  every node/.style={font=\\small},\n  redline/.style={arrred,line width=.8pt},\n  blueline/.style={arrblue,dashed,line width=.8pt},\n  redpoint/.style={circle,fill=arrred,draw=arrred,inner sep=1.8pt},\n  bluepoint/.style={rectangle,fill=arrblue,draw=arrblue,inner sep=1.8pt}]\n  \\node at (2.55,4.55) {The six marked directions};\n  \\draw[->,black!65] (0,2.6)--(5.1,2.6);\n  \\node[right] at (5.1,2.6) {$\\lambda$};\n  \\draw[black!45,rounded corners=9pt] (1.35,2.12) rectangle (3.75,3.08);\n  \\foreach \\x in {1.7,2.25,2.8,3.35}\n    \\node[redpoint] at (\\x,2.6) {};\n  \\node[bluepoint] at (.35,2.6) {};\n  \\node[bluepoint] at (4.75,2.6) {};\n  \\node[align=center,arrred] at (2.55,3.6)\n    {four red points\\\\$|\\lambda_i|<0.01$};\n  \\node[align=center,arrblue] at (2.55,1.45)\n    {two blue points: $|\\lambda_i|>4$};\n  \\node[align=center] at (2.55,.45)\n    {$C\\longrightarrow\\mathbb P^1_{\\lambda}$\\\\double cover branched at these points};\n\n  \\begin{scope}[shift={(9.15,2.25)}]\n    \\node at (0,2.3) {One pencil and its couplings};\n    \\draw[black!40,line width=.6pt] (0,0) circle (2.05);\n    \\foreach \\angle in {-13,-4,4,13}\n      \\draw[redline] (\\angle:2.05)--({\\angle+180}:2.05);\n    \\foreach \\angle in {78,102}\n      \\draw[blueline] (\\angle:2.05)--({\\angle+180}:2.05);\n    \\draw[line width=1pt] (-1.78,-1.017)--(-1.78,1.017);\n    \\draw[line width=1pt] (1.78,-1.017)--(1.78,1.017);\n    \\foreach \\angle in {-13,-4,4,13}{\n      \\fill[arrred] (1.78,{1.78*tan(\\angle)}) circle (1.9pt);\n      \\fill[arrred] (-1.78,{-1.78*tan(\\angle)}) circle (1.9pt);\n    }\n    \\fill (0,0) circle (2pt);\n    \\node[fill=white,inner sep=1.5pt,below=8pt] at (0,0)\n      {$L_z$};\n    \\node[align=center,below=4pt,fill=white,inner sep=1.5pt] at (-1.78,-1.017)\n      {$z_1=-c$};\n    \\node[align=center,below=4pt,fill=white,inner sep=1.5pt] at (1.78,-1.017)\n      {$z_1=c$};\n    \\node[arrblue,fill=white,inner sep=1pt] at (.86,1.64) {blue};\n    \\node[arrred,fill=white,inner sep=1pt] at (.92,.57) {red};\n    \\node at (0,-2.55) {$z_2=\\lambda_i z_1$};\n  \\end{scope}\n\\end{tikzpicture}\n\\endgroup\n\\caption{The finite arrangement in one coordinate pair. Left: a\n  schematic placement of the six branch points on the real direction\n  axis, grouped into four red circles and two blue squares. Right:\n  the slice $w=0$, $z_1,z_2\\in\\mathbb R$ of the $z$ pencil in the\n  ball; solid red slopes meet both coupling\n  hyperplanes, whereas dashed blue slopes meet neither. The red\n  angular separation is exaggerated to show these incidences.\n  The center represents the slice of $L_z$. The $w$ family is an\n  identical construction in the other coordinate pair; the full\n  arrangement lies in complex dimension four.}\n\\label{fig:arrangement}\n\\end{figure}\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/figures/blowdown.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/figures/blowdown.tex", "bytes": 2189, "sha256": "b4b5c0e3ce43b92445bde24651e191ab1b8e5580af72c1194053225b298d80f3", "content": "\\begin{figure}[htbp]\n\\centering\n\\begingroup\n\\definecolor{blowaccent}{RGB}{22,91,119}\n\\begin{tikzpicture}[x=1cm,y=1cm,>=Latex,\n  every node/.style={font=\\small},\n  marked/.style={blowaccent,line width=1.5pt}]\n  \\coordinate (sw) at (3.6,.65);\n  \\coordinate (se) at (7.8,.65);\n  \\coordinate (nw) at (4.5,3.25);\n  \\coordinate (ne) at (8.7,3.25);\n  \\fill[black!3] (sw)--(se)--(ne)--(nw)--cycle;\n  \\foreach \\t in {.25,.5,.75}{\n    \\draw[black!23] ($(sw)!\\t!(nw)$)--($(se)!\\t!(ne)$);\n    \\draw[black!23] ($(sw)!\\t!(se)$)--($(nw)!\\t!(ne)$);\n  }\n  \\draw[black!55] (sw)--(se)--(ne)--(nw)--cycle;\n  \\coordinate (el) at ($(sw)!.5!(nw)$);\n  \\coordinate (er) at ($(se)!.5!(ne)$);\n  \\draw[marked] (el)--(er);\n  \\node[above=6pt] at (6.65,3.25)\n    {$G=\\mathbb P^1_{\\mathrm{curve}}\\times\\mathbb P^1_{\\mathrm{normal}}$};\n  \\node[fill=white,inner sep=2pt,text=blowaccent] at (6.15,1.95)\n    {$\\widehat Y\\cap G=E\\times\\{q\\}$};\n  \\node[below=4pt] at (5.7,.65) {curve direction};\n\n  \\draw[marked] (0,.7)--(2.05,.7);\n  \\node[below=5pt] at (1.025,.7) {$E\\subset Y_1\\subset T$};\n  \\draw[->,line width=.75pt] (3.45,1.65)--(1.65,1.05);\n  \\node[align=center] at (1.6,2.35)\n    {original blowdown\\\\$G\\longrightarrow\\mathbb P^1_{\\mathrm{curve}}$};\n\n  \\draw[black!65,line width=.8pt] (11.1,.75)--(11.7,2.65);\n  \\fill[blowaccent] (11.4,1.7) circle (2.8pt);\n  \\node[right=5pt,align=left] at (11.4,1.7) {$q$\\\\in $Y_0$};\n  \\draw[->,line width=.75pt] (8.7,1.9)--(10.7,1.9);\n  \\node[align=center] at (10.35,3.65)\n    {other blowdown\\\\$G\\longrightarrow\\mathbb P^1_{\\mathrm{normal}}$};\n  \\node[below=6pt,align=center] at (11.1,.6)\n    {image curve\\\\in $T'$};\n\\end{tikzpicture}\n\\endgroup\n\\caption{The two rulings of the last exceptional divisor in\n  $\\widehat T$. The original blowdown contracts the normal-direction\n  fibers and leaves the curve $E$ in $Y_1$ unchanged. The other\n  blowdown, whose target threefold $T'$ is constructed below,\n  contracts the horizontal ruling, including\n  $\\widehat Y\\cap G=E\\times\\{q\\}$, so the surface undergoes its\n  point blowdown $Y_1\\to Y_0$. The parallelogram records only the\n  product structure of $G$, whose normal bundle is\n  $\\mathcal O_G(-1,-1)$.}\n\\label{fig:blowdown}\n\\end{figure}\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/figures/path-ladder.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/figures/path-ladder.tex", "bytes": 2100, "sha256": "d765c7c16ebd7e81b8853fe0fcd4d025ec95577952ad8d01f0528b0d751741b1", "content": "\\begin{figure}[htbp]\n\\centering\n\\begingroup\n\\definecolor{ladderaccent}{RGB}{22,91,119}\n\\begin{tikzpicture}[x=1cm,y=1cm,>=Latex,\n  every node/.style={font=\\small},\n  guide/.style={line width=.9pt},\n  oldrung/.style={densely dashed,line width=.65pt,black!65},\n  newrung/.style={line width=1.1pt,ladderaccent}]\n  \\coordinate (am) at (0,2.05);\n  \\coordinate (ai) at (5.4,2.05);\n  \\coordinate (ap) at (10.8,2.05);\n  \\coordinate (bm) at (0,.2);\n  \\coordinate (bo) at (6.7,.2);\n  \\coordinate (bn) at (5.4,.2);\n  \\coordinate (bp) at (10.8,.2);\n  \\draw[guide] (am)--(ap);\n  \\draw[guide] (bm)--(bp);\n  \\draw[oldrung] (am)--node[left] {$\\le h$} (bm);\n  \\draw[oldrung] (ap)--node[right] {$\\le h$} (bp);\n  \\draw[oldrung] (ai)--node[pos=.50,right,fill=white,inner sep=1.5pt]\n    {old: $\\le h$} (bo);\n  \\draw[newrung] (ai)--node[pos=.55,left,fill=white,inner sep=1.5pt]\n    {new: $\\le 2\\Delta$} (bn);\n  \\foreach \\p in {am,ai,ap,bm,bo,bp}\n    \\fill (\\p) circle (1.7pt);\n  \\draw[newrung,fill=white] (bn) circle (2.2pt);\n  \\node[above=4pt] at (am) {$x_{i-1}$};\n  \\node[above=4pt] at (ai) {$x_i$};\n  \\node[above=4pt] at (ap) {$x_{i+1}$};\n  \\node[below=5pt] at (bm) {$b_{i-1}$};\n  \\node[below=5pt] at (bn) {$b_i'$};\n  \\node[below=5pt] at (bo) {$b_i$};\n  \\node[below=5pt] at (bp) {$b_{i+1}$};\n  \\node[left=18pt] at (am) {$\\alpha$};\n  \\node[left=18pt] at (bm) {$\\beta$};\n  \\draw[decorate,decoration={brace,amplitude=4pt}]\n    (0,2.76)--node[above=5pt] {length in $[L,2L]$} (5.4,2.76);\n  \\draw[decorate,decoration={brace,amplitude=4pt}]\n    (5.4,2.76)--node[above=5pt] {length in $[L,2L]$} (10.8,2.76);\n\\end{tikzpicture}\n\\endgroup\n\\caption{Narrowing a ladder in a single chart. Both displayed guide\n  pieces read as geodesics. The interior mark $x_i$ is far from the\n  end rungs, so quadrangle slimness supplies a new mate $b_i'$ on\n  the opposite side, within $2\\Delta$ of $x_i$. The old scheduled\n  mate $b_i$ is at distance at most $h+2\\Delta$ from $b_i'$ along\n  that side. The drawing is\n  schematic: the long side intervals and the two rung bounds use\n  different visual scales.}\n\\label{fig:path-ladder}\n\\end{figure}\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/main.tex", "bytes": 1118, "sha256": "af356ca33bc7ccd1eec8806d0ce8b01c93f336fda857082aecec545af868c1d3", "content": "\\newcommand{\\DoNotLoadEpstopdf}{}\n\\documentclass[11pt,reqno]{amsart}\n\\input{preamble}\n\\title[Large fundamental group and non-Stein universal cover]{A projective fourfold with large fundamental group\\protect\\\\\nand non-Stein universal cover}\n\\author{OpenAI}\n\\date{October 5, 2026}\n\\subjclass[2020]{32Q30, 14F35, 20F67, 14K12}\n\\keywords{Shafarevich conjecture, universal cover, large fundamental group,\ncomplex hyperbolic arrangement, Stein manifold}\n\\begin{document}\n\\begin{abstract}\nWe construct a smooth projective complex fourfold with large fundamental group\nwhose universal cover is not Stein. The universal cover contains no\npositive-dimensional compact complex-analytic subvariety, so this disproves\nShafarevich's holomorphic-convexity conjecture even under the\nlarge-fundamental-group hypothesis.\n\\end{abstract}\n\\maketitle\n\\tableofcontents\n\\input{sections/01-introduction}\n\\input{sections/02-reduction}\n\\input{sections/03-paths}\n\\input{sections/04-geometry}\n\\input{sections/05-models}\n\\input{sections/06-image}\n\\input{sections/07-projective}\n\\bibliographystyle{amsplain}\n\\bibliography{references}\n\\end{document}\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/preamble.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/preamble.tex", "bytes": 2102, "sha256": "9f32c7ae258a550cb2490d90d5c1ec59da6ced05d1efa6ffe5713b346cf2d507", "content": "\\usepackage[T1]{fontenc}\n\\usepackage{lmodern}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage[margin=1.1in]{geometry}\n\\usepackage{microtype}\n\\usepackage{enumitem}\n\\usepackage{booktabs,array}\n\\usepackage{xcolor}\n\\usepackage{tikz}\n\\usetikzlibrary{arrows.meta,calc,positioning,decorations.pathreplacing,patterns}\n\\usepackage{flafter}\n\\usepackage{placeins}\n\\usepackage{hyperref}\n\\setcounter{tocdepth}{1}\n\\hypersetup{colorlinks=true,linkcolor=blue!45!black,citecolor=blue!45!black,\n  bookmarksdepth=2,\n  urlcolor=blue!45!black,pdfauthor={OpenAI},\n  pdftitle={A projective fourfold with large fundamental group and non-Stein universal cover},\n  pdfsubject={A counterexample to the holomorphic-convexity conjecture},\n  pdfkeywords={Shafarevich conjecture, large fundamental group, Stein manifold}}\n\\numberwithin{equation}{section}\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\\newtheorem{definition}[theorem]{Definition}\n\\newtheorem{construction}[theorem]{Construction}\n\\newtheorem{example}[theorem]{Example}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\newtheorem{convention}[theorem]{Convention}\n\\newcommand{\\C}{\\mathbb C}\n\\newcommand{\\R}{\\mathbb R}\n\\newcommand{\\Q}{\\mathbb Q}\n\\newcommand{\\Z}{\\mathbb Z}\n\\newcommand{\\BB}{\\mathbb B}\n\\newcommand{\\PP}{\\mathbb P}\n\\newcommand{\\cD}{\\mathcal D}\n\\newcommand{\\cS}{\\mathcal S}\n\\DeclareMathOperator{\\im}{im}\n\\DeclareMathOperator{\\id}{id}\n\\DeclareMathOperator{\\diam}{diam}\n\\DeclareMathOperator{\\dist}{dist}\n\\DeclareMathOperator{\\Sym}{Sym}\n\\DeclareMathOperator{\\Jac}{Jac}\n\\DeclareMathOperator{\\Pic}{Pic}\n\\DeclareMathOperator{\\End}{End}\n\\DeclareMathOperator{\\Span}{span}\n\\DeclareMathOperator{\\Gal}{Gal}\n\\DeclareMathOperator{\\Aut}{Aut}\n\\DeclareMathOperator{\\Hom}{Hom}\n\\DeclareMathOperator{\\Spec}{Spec}\n\\DeclareMathOperator{\\codim}{codim}\n\\setlist[enumerate]{label=\\textup{(\\arabic*)},leftmargin=*,itemsep=3pt}\n\\setlist[itemize]{leftmargin=*,itemsep=3pt}\n\\emergencystretch=1.2em\n\\allowdisplaybreaks[1]\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/references.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/references.bib", "bytes": 9519, "sha256": "cad9d9c624d785dc32b6979d7a7ed8ac9b873f44a89d7822530b9b59e86a2fdc", "content": "@book{BridsonHaefliger,\n  author = {Bridson, Martin R. and Haefliger, Andr{\\'e}},\n  title = {Metric Spaces of Non-Positive Curvature},\n  series = {Grundlehren der mathematischen Wissenschaften},\n  volume = {319},\n  publisher = {Springer-Verlag},\n  address = {Berlin},\n  year = {1999},\n  doi = {10.1007/978-3-662-12494-9}\n}\n\n@incollection{Gromov1987,\n  author = {Gromov, Mikhail},\n  title = {Hyperbolic groups},\n  booktitle = {Essays in Group Theory},\n  editor = {Gersten, S. M.},\n  series = {Mathematical Sciences Research Institute Publications},\n  volume = {8},\n  pages = {75--263},\n  publisher = {Springer-Verlag},\n  address = {New York},\n  year = {1987},\n  doi = {10.1007/978-1-4613-9586-7_3}\n}\n\n@article{BonkSchramm2000,\n  author = {Bonk, Mario and Schramm, Oded},\n  title = {Embeddings of {Gromov} hyperbolic spaces},\n  journal = {Geometric and Functional Analysis},\n  volume = {10},\n  number = {2},\n  pages = {266--306},\n  year = {2000},\n  doi = {10.1007/s000390050009}\n}\n\n@article{BorelHarishChandra1962,\n  author = {Borel, Armand and {Harish-Chandra}},\n  title = {Arithmetic subgroups of algebraic groups},\n  journal = {Annals of Mathematics},\n  series = {2},\n  volume = {75},\n  number = {3},\n  pages = {485--535},\n  year = {1962},\n  doi = {10.2307/1970210}\n}\n\n@article{BailyBorel1966,\n  author = {Baily, Jr., Walter L. and Borel, Armand},\n  title = {Compactification of arithmetic quotients of bounded symmetric domains},\n  journal = {Annals of Mathematics},\n  series = {2},\n  volume = {84},\n  number = {3},\n  pages = {442--528},\n  year = {1966},\n  doi = {10.2307/1970457}\n}\n\n@book{Goldman1999,\n  author = {Goldman, William M.},\n  title = {Complex Hyperbolic Geometry},\n  series = {Oxford Mathematical Monographs},\n  publisher = {The Clarendon Press, Oxford University Press},\n  address = {New York},\n  year = {1999},\n  doi = {10.1093/oso/9780198537939.001.0001}\n}\n\n@article{Zarhin,\n  author = {Zarhin, Yuri G.},\n  title = {Hyperelliptic {Jacobians} without complex multiplication},\n  journal = {Mathematical Research Letters},\n  volume = {7},\n  number = {1},\n  pages = {123--132},\n  year = {2000},\n  doi = {10.4310/MRL.2000.v7.n1.a11}\n}\n\n@article{StoverToledo,\n  author = {Stover, Matthew and Toledo, Domingo},\n  title = {Residual finiteness for central extensions of lattices in {PU(n,1)} and negatively curved projective varieties},\n  journal = {Pure and Applied Mathematics Quarterly},\n  volume = {18},\n  number = {4},\n  pages = {1771--1797},\n  year = {2022},\n  doi = {10.4310/PAMQ.2022.v18.n4.a15}\n}\n\n@article{LlosaPy,\n  author = {Llosa Isenrich, Claudio and Py, Pierre},\n  title = {Groups with exotic finiteness properties from complex {Morse} theory},\n  journal = {Journal of Topology},\n  volume = {18},\n  number = {1},\n  pages = {e70013},\n  year = {2025},\n  doi = {10.1112/topo.70013}\n}\n\n@article{HammLe,\n  author = {Hamm, Helmut A. and {L{\\^e} D{\\~u}ng Tr{\\'a}ng}},\n  title = {Lefschetz theorems on quasi-projective varieties},\n  journal = {Bulletin de la Soci{\\'e}t{\\'e} Math{\\'e}matique de France},\n  volume = {113},\n  pages = {123--142},\n  year = {1985},\n  doi = {10.24033/bsmf.2023}\n}\n\n@article{AndreottiFrankel1959,\n  author = {Andreotti, Aldo and Frankel, Theodore},\n  title = {The {Lefschetz} theorem on hyperplane sections},\n  journal = {Annals of Mathematics},\n  series = {2},\n  volume = {69},\n  number = {3},\n  pages = {713--717},\n  year = {1959},\n  doi = {10.2307/1970034}\n}\n\n@book{MilnorMorse,\n  author = {Milnor, John},\n  title = {Morse Theory},\n  series = {Annals of Mathematics Studies},\n  volume = {51},\n  publisher = {Princeton University Press},\n  address = {Princeton, NJ},\n  year = {1963}\n}\n\n@incollection{Totaro1999,\n  author = {Totaro, Burt},\n  title = {The {Chow} ring of a classifying space},\n  booktitle = {Algebraic $K$-Theory ({Seattle}, {WA}, 1997)},\n  series = {Proceedings of Symposia in Pure Mathematics},\n  volume = {67},\n  pages = {249--281},\n  publisher = {American Mathematical Society},\n  address = {Providence, RI},\n  year = {1999},\n  doi = {10.1090/pspum/067/1743244}\n}\n\n@incollection{Serre1958,\n  author = {Serre, Jean-Pierre},\n  title = {Sur la topologie des vari{\\'e}t{\\'e}s alg{\\'e}briques en caract{\\'e}ristique $p$},\n  booktitle = {Symposium Internacional de Topolog{\\'i}a Algebraica},\n  pages = {24--53},\n  publisher = {Universidad Nacional Aut{\\'o}noma de M{\\'e}xico and UNESCO},\n  address = {Mexico City},\n  year = {1958}\n}\n\n@book{GrauertRemmert1979,\n  author = {Grauert, Hans and Remmert, Reinhold},\n  title = {Theory of {Stein} Spaces},\n  series = {Grundlehren der mathematischen Wissenschaften},\n  volume = {236},\n  publisher = {Springer-Verlag},\n  address = {New York},\n  year = {1979},\n  note = {Translated by Alan Huckleberry},\n  doi = {10.1007/978-1-4757-4357-9}\n}\n\n@book{Kollar1995,\n  author = {Koll{\\'a}r, J{\\'a}nos},\n  title = {Shafarevich Maps and Automorphic Forms},\n  series = {M. B. Porter Lectures},\n  publisher = {Princeton University Press},\n  address = {Princeton, NJ},\n  year = {1995},\n  doi = {10.1515/9781400864195}\n}\n\n@article{Kollar1993,\n  author = {Koll{\\'a}r, J{\\'a}nos},\n  title = {Shafarevich maps and plurigenera of algebraic varieties},\n  journal = {Inventiones Mathematicae},\n  volume = {113},\n  pages = {177--215},\n  year = {1993},\n  doi = {10.1007/BF01244307}\n}\n\n@article{Campana1994,\n  author = {Campana, Fr{\\'e}d{\\'e}ric},\n  title = {Remarques sur le rev{\\^e}tement universel des vari{\\'e}t{\\'e}s k{\\\"a}hl{\\'e}riennes compactes},\n  journal = {Bulletin de la Soci{\\'e}t{\\'e} Math{\\'e}matique de France},\n  volume = {122},\n  number = {2},\n  pages = {255--284},\n  year = {1994},\n  doi = {10.24033/bsmf.2232}\n}\n\n@article{Napier1990,\n  author = {Napier, Terrence},\n  title = {Convexity properties of coverings of smooth projective varieties},\n  journal = {Mathematische Annalen},\n  volume = {286},\n  pages = {433--479},\n  year = {1990},\n  doi = {10.1007/BF01453583}\n}\n\n@article{Katzarkov1997,\n  author = {Katzarkov, Ludmil},\n  title = {Nilpotent groups and universal coverings of smooth projective varieties},\n  journal = {Journal of Differential Geometry},\n  volume = {45},\n  number = {2},\n  pages = {336--348},\n  year = {1997},\n  doi = {10.4310/jdg/1214459801}\n}\n\n@article{KatzarkovRamachandran1998,\n  author = {Katzarkov, Ludmil and Ramachandran, Mohan},\n  title = {On the universal coverings of algebraic surfaces},\n  journal = {Annales scientifiques de l'{\\'E}cole Normale Sup{\\'e}rieure},\n  series = {4},\n  volume = {31},\n  number = {4},\n  pages = {525--535},\n  year = {1998},\n  doi = {10.1016/S0012-9593(98)80105-5}\n}\n\n@article{Eyssidieux2004,\n  author = {Eyssidieux, Philippe},\n  title = {Sur la convexit{\\'e} holomorphe des rev{\\^e}tements lin{\\'e}aires r{\\'e}ductifs d'une vari{\\'e}t{\\'e} projective alg{\\'e}brique complexe},\n  journal = {Inventiones Mathematicae},\n  volume = {156},\n  pages = {503--564},\n  year = {2004},\n  doi = {10.1007/s00222-003-0345-0}\n}\n\n@article{EKPR2012,\n  author = {Eyssidieux, Philippe and Katzarkov, Ludmil and Pantev, Tony and Ramachandran, Mohan},\n  title = {Linear {Shafarevich} conjecture},\n  journal = {Annals of Mathematics},\n  series = {2},\n  volume = {176},\n  number = {3},\n  pages = {1545--1581},\n  year = {2012},\n  doi = {10.4007/annals.2012.176.3.4}\n}\n\n@misc{BBT2024,\n  author = {Bakker, Benjamin and Brunebarbe, Yohan and Tsimerman, Jacob},\n  title = {The linear {Shafarevich} conjecture for quasiprojective varieties and algebraicity of {Shafarevich} morphisms},\n  year = {2024},\n  note = {Preprint, arXiv:2408.16441; author manuscript revised September 9, 2026},\n  url = {https://arxiv.org/abs/2408.16441}\n}\n\n@article{Atiyah1958,\n  author = {Atiyah, Michael F.},\n  title = {On analytic surfaces with double points},\n  journal = {Proceedings of the Royal Society of London. Series A},\n  volume = {247},\n  number = {1249},\n  pages = {237--244},\n  year = {1958},\n  doi = {10.1098/rspa.1958.0181}\n}\n\n@book{MilnorSingular,\n  author = {Milnor, John},\n  title = {Singular Points of Complex Hypersurfaces},\n  series = {Annals of Mathematics Studies},\n  volume = {61},\n  publisher = {Princeton University Press},\n  address = {Princeton, NJ},\n  year = {1968}\n}\n\n@book{Hartshorne,\n  author = {Hartshorne, Robin},\n  title = {Algebraic Geometry},\n  series = {Graduate Texts in Mathematics},\n  volume = {52},\n  publisher = {Springer-Verlag},\n  address = {New York},\n  year = {1977},\n  doi = {10.1007/978-1-4757-3849-0}\n}\n\n@article{BogomolovKatzarkov1998,\n  author = {Bogomolov, Fedor and Katzarkov, Ludmil},\n  title = {Complex projective surfaces and infinite groups},\n  journal = {Geometric and Functional Analysis},\n  volume = {8},\n  number = {2},\n  pages = {243--272},\n  year = {1998},\n  doi = {10.1007/s000390050055}\n}\n\n@article{Nakano1971,\n  author = {Nakano, Shigeo},\n  title = {On the inverse of monoidal transformation},\n  journal = {Publications of the Research Institute for Mathematical Sciences},\n  volume = {6},\n  number = {3},\n  pages = {483--502},\n  year = {1971},\n  doi = {10.2977/PRIMS/1195193917}\n}\n\n@article{FujikiNakano1972,\n  author = {Fujiki, Akira and Nakano, Shigeo},\n  title = {Supplement to ``{On} the inverse of monoidal transformation''},\n  journal = {Publications of the Research Institute for Mathematical Sciences},\n  volume = {7},\n  number = {3},\n  pages = {637--644},\n  year = {1972},\n  doi = {10.2977/PRIMS/1195193401}\n}\n\n@article{Noohi2014,\n  author = {Noohi, Behrang},\n  title = {Fibrations of topological stacks},\n  journal = {Advances in Mathematics},\n  volume = {252},\n  pages = {612--640},\n  year = {2014},\n  doi = {10.1016/j.aim.2013.11.008}\n}\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/01-introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/01-introduction.tex", "bytes": 10737, "sha256": "5e67956a0477b4be343eabbbf7c6629b6ff589750a9d7a2ae10f46b9a624ded4", "content": "\\section{Introduction}\\label{sec:introduction}\n\nLet $X$ be a smooth connected projective complex variety, and let\n$\\widetilde X$ denote its simply connected topological universal cover,\nequipped with the lifted complex structure.  Shafarevich's\nholomorphic-convexity conjecture asks whether $\\widetilde X$ is\nholomorphically convex.  Its particularly direct form for varieties with\nlarge fundamental group asks whether $\\widetilde X$ is Stein when it\ncontains no positive-dimensional compact complex analytic subvariety.\n\nRecall that $X$ has \\emph{large fundamental group} if, for every\npositive-dimensional closed irreducible subvariety $Z\\subset X$, the image\nof $\\pi_1(Z^\\nu)\\to\\pi_1(X)$ is infinite; here $Z^\\nu$ is the\nnormalization.  This is equivalent to the absence of positive-dimensional\ncompact analytic subvarieties in $\\widetilde X$.  We prove the following.\n\n\\begin{theorem}\\label{thm:main}\nThere exist a smooth connected projective complex fourfold $X$ and a\nsimple abelian surface $A\\subset X$ such that\n\\begin{enumerate}\n\\item $X$ has large fundamental group;\n\\item $\\im\\bigl(\\pi_1(A)\\to\\pi_1(X)\\bigr)\\simeq\\Z$.\n\\end{enumerate}\nThe universal cover $\\widetilde X$ contains no positive-dimensional\ncompact complex analytic subvariety and is not Stein.\n\\end{theorem}\n\nThe obstruction to Steinness is topological and occurs in a closed\ncomplex surface inside $\\widetilde X$.  Since $\\pi_1(A)\\simeq\\Z^4$,\nthe kernel of the homomorphism in Theorem~\\ref{thm:main} has rank three.\nThe corresponding cover of $A$ is therefore diffeomorphic to\n$\\R\\times(S^1)^3$ and has nonzero third homology.  A Stein surface has\nthe homotopy type of a CW complex of real dimension at most two.\nThis contradiction rules out Steinness of the lifted surface and hence\nof $\\widetilde X$.  Simplicity of $A$ ensures that this topological\nobstruction introduces no compact complex curve in that covering.\n\n\\subsection{Historical context}\nShafarevich's question extends the uniformization viewpoint for curves\nto universal covers of projective varieties.  Koll\\'ar's account\n\\cite[Introduction, Section~0.3]{Kollar1995} explains both the\nholomorphic-convexity conjecture and its formulation for large\nfundamental groups.  These formulations are related by Remmert\nreduction: a holomorphically convex complex manifold admits a proper\nholomorphic map to a Stein space with connected compact analytic\nfibers \\cite[Introduction, p.~1546]{EKPR2012}.  If there are no positive-dimensional\ncompact analytic subvarieties, all those fibers are points and the\nreduction is an isomorphism.  Theorem~\\ref{thm:main} therefore also\ndisproves the holomorphic-convexity conjecture.\n\nAn important development was the construction of almost-holomorphic reduction maps by\nCampana and Koll\\'ar \\cite{Campana1994,Kollar1993,Kollar1995}.\nThey describe, through a very general point, the normalized subvarieties\nwhose fundamental groups have finite image in the ambient group.\nTheir existence provides an algebraic\nand geometric description of the part of the variety that such\nsubvarieties fill.  Holomorphic convexity asks in addition for a\nproper holomorphic reduction of the universal cover to a Stein space.\nThe large case makes this additional analytic requirement especially\nvisible: the expected reduction has no positive-dimensional fibers.\n\nLinearity of the fundamental group supports strong affirmative\nresults.  Katzarkov and Ramachandran proved holomorphic convexity for\nprojective surfaces whose fundamental group admits a faithful complex\nrepresentation with reductive Zariski closure\n\\cite{KatzarkovRamachandran1998}.  Eyssidieux treated the reductive\nlinear case in arbitrary dimension \\cite{Eyssidieux2004}.\nEyssidieux, Katzarkov, Pantev, and\nRamachandran proved the linear Shafarevich conjecture for smooth\nprojective varieties \\cite{EKPR2012}.  More recently, Bakker,\nBrunebarbe, and Tsimerman developed linear Shafarevich theory for\nnormal algebraic spaces and quasiprojective varieties\n\\cite{BBT2024}.  The construction here concerns the unrestricted\nfundamental group.  Its infinite-order argument uses compatible\nlocal covers of an arrangement, so it does not require a faithful\nlinear representation of that group.\n\nA closely related line of inquiry appears in Bogomolov and Katzarkov's\nconstructions of projective surfaces and their potential counterexamples\nto the Shafarevich conjecture\n\\cite[Section~4]{BogomolovKatzarkov1998}.  Their proposed obstruction\nuses infinite chains of compact curves and depends on group-infiniteness\nstatements left conjectural there.\nHere the obstruction is a rank-three lattice cover of a simple abelian\nsurface.  The group-theoretic task is to prove that its cyclic image\nsurvives all global relations; the local path criterion supplies that\ninfiniteness argument.\n\nSeveral established tools make the construction possible.  Arithmetic\nball quotients supply arbitrarily large regions with exactly prescribed\nfinite hyperplane configurations.  Stover and Toledo's virtual\nramified-cover theorem supplies the required sign covers\n\\cite{StoverToledo,LlosaPy}, and Zarhin's theorem supplies a\ngenus-two curve with simple Jacobian \\cite{Zarhin}.  Standard\nhyperbolic geometry \\cite{BridsonHaefliger} underlies the local path\ncriterion.  Hamm--L\\^e's quasi-projective Lefschetz theorem\n\\cite{HammLe}, together with a deformation argument, allows us to\nretain a prescribed surface while passing\nfrom an orbifold to a smooth projective ambient variety.  Each use is\nstated with its hypotheses at the relevant point below.\n\n\\subsection{The central construction}\nThe principal task is to embed a simple abelian surface in a smooth\nprojective variety so that its fundamental group has infinite cyclic\nimage.  Once such an embedding is available, an ample complete\nintersection in a product with a sufficiently large abelian variety\ngives Theorem~\\ref{thm:main}.  The complete intersection contains the\nprescribed surface, and its other fibers over the added abelian variety\nare finite.  This separates the two reasons that a subvariety has\ninfinite fundamental-group image: it either moves in the added abelian\nvariety or lies in the simple surface.  Section~\\ref{sec:reduction}\nproves this reduction first.\n\nTo obtain the cyclic image, we begin with a genus-two curve $C$ whose\nJacobian is simple.  Its presentation as a double cover of $\\PP^1$\nuses four branch points in one small disk and two outside that disk.\nWe call the corresponding meridians red and blue.  A complex hyperbolic\narrangement realizes this marked line as an exceptional fiber.  Extra\nhyperplanes meeting the red part of the pencil impose relations that\nidentify all four red meridians.  The sphere relation then identifies\nthe two blue meridians.  The image of the genus-two surface group is\ntherefore cyclic: it lies in the cyclic subgroup generated by the product\nof the resulting red and blue involutions.\n\nThese relations alone give only an upper bound.  The central difficulty\nis to prove that the cyclic image is infinite after all global\nidentifications and fillings.  We use covers defined separately on\nlarge local models of the arrangement.  We model their monodromies\nusing the infinite dihedral group generated by two involutions $r,b$;\nits even words $(rb)^n$ carry the integer translation label $n$.\nThese local monodromies need not\ndefine a representation of the full projective fundamental group.\nInstead, they agree on the endpoint tests needed to read short paths\nin overlapping charts.  The graphs of these local covers are uniformly\nGromov hyperbolic.  A local path criterion then prevents a loop detected\nby one chart from becoming trivial through global relations.\n\nThe edges of these graphs are paths with bounded \\emph{projected\ndiameter}, rather than bounded length.  Consequently every power of a\nfixed loop near the distinguished fiber is a single edge.  The local\ncriterion detects every nonzero power separately and gives the\ninfinite-order lower bound.  This feature is essential to the argument;\nneither a global coloring nor a family of finite quotients is needed\nfor that lower bound.\n\nTwo copies of the pencil give a distinguished fiber $C\\times C$.\nAn order-four symmetry exchanges the factors and rotates their normal\ndirections.  Resolving its coarse quotient over this fiber produces a\nmap from a point blowup of\n$\\Sym^2 C$, itself the blowup of $\\Jac(C)$ at a point.  The same local\nargument detects the sum of the two translation labels.  We then\nrealize the resulting surface map in an ordinary smooth projective\nambient variety and remove its point blowups by projective\nmodifications.  Throughout these operations we preserve the actual\nhomomorphism from the surface group.\n\nThe local path criterion and the ambient blowdown construction are\nformulated separately from the example.  The former converts bounded\nchart compatibility and hyperbolicity into nontriviality of loops.\nThe latter realizes a point blowdown of an embedded surface inside a\nnew smooth projective ambient variety, preserving the ambient\nfundamental group and the induced surface homomorphism.  These two\nstatements isolate the group-theoretic and projective-geometric\nmechanisms of the construction.\n\n\\subsection{Structure of the proof}\nSection~\\ref{sec:paths} proves the local path criterion independently\nof the geometric application.  Its hypotheses are bounded-range path\nreadings, compatibility of endpoint equality, and uniform hyperbolicity\nof the reading graphs; no local compactness is required.\nSection~\\ref{sec:geometry} constructs the arithmetic arrangement,\nits sign covers, and the distinguished surface map.\nSection~\\ref{sec:models} defines the local monodromy labels, proves\ntheir buffered compatibility, and establishes hyperbolicity of the\nassociated graphs.  Section~\\ref{sec:image} verifies the path\ncriterion for the constructed orbifold and proves that the surface\nimage is infinite cyclic.  Section~\\ref{sec:projective} carries this\nimage into an ordinary smooth projective variety, removes the point\nblowups, and completes the proof using Section~\\ref{sec:reduction}.\n\n\\subsection{Conventions}\nAll varieties are over $\\C$ unless a field is specified, and all\nfundamental groups are taken in the usual topology.  For a smooth\ncomplex orbifold, equivalently a smooth Deligne--Mumford stack in the\nconstructions below, the fundamental group means that of its\nclassifying space.  An \\emph{ordinary} point has trivial stabilizer.\nFundamental-group image statements are understood up to a common\nchoice of basepoints and connecting paths.  Projective bundles\nparametrize lines.  Complex hyperbolic distance is normalized so that\na point of Euclidean radius $\\tanh r$ in the unit ball has distance\n$r$ from its center.\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/02-reduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/02-reduction.tex", "bytes": 9163, "sha256": "6afb862728fcc9c290571f8842e3647e202c736f61ce160cf0a7740ddd28d25f", "content": "\\section{Reduction to an abelian surface with cyclic image}\n\\label{sec:reduction}\n\nThe main construction will produce a simple abelian surface inside a\nsmooth projective variety, with infinite cyclic image on fundamental\ngroups.  We first explain why that is enough.  This separates the\ngeometric obstruction to Steinness from the group-theoretic construction\nthat occupies the intervening sections.\n\n\\begin{proposition}\\label{prop:large-reduction}\nLet $A$ be a simple abelian surface and let $A\\hookrightarrow S'$ be a\nclosed embedding in a smooth connected projective complex variety.\nSuppose that\n\\[\n \\im\\bigl(\\pi_1(A)\\longrightarrow\\pi_1(S')\\bigr)\\simeq\\Z.\n\\]\nThere exist a smooth connected projective fourfold $X$ with large\nfundamental group and a closed embedding $A\\hookrightarrow X$ with\ninfinite cyclic image on fundamental groups, such that the universal\ncover of $X$ is not Stein.\n\\end{proposition}\n\nWe begin with the equivalence between largeness and the condition on\nthe universal cover used in Theorem~\\ref{thm:main}.\n\n\\begin{lemma}\\label{red:compact-criterion}\nLet $X$ be a smooth connected projective complex variety.  Its universal\ncover contains no positive-dimensional compact complex-analytic\nsubvariety if and only if $X$ has large fundamental group.\n\\end{lemma}\n\n\\begin{proof}\nSuppose that $Z\\subset X$ has positive dimension and that\n$\\pi_1(Z^\\nu)$ has finite image in $\\pi_1(X)$.  The covering of $Z^\\nu$\ncorresponding to the kernel has finite degree and is compact.  Its map\nto $X$ lifts holomorphically to $\\widetilde X$.  The image of the lift\nis a positive-dimensional compact analytic subvariety, by the proper\nmapping theorem.\n\nConversely, let $K\\subset\\widetilde X$ be a positive-dimensional\nirreducible compact analytic subvariety.  Its image $Z$ in $X$ is\nanalytic by the same theorem and algebraic by Chow's theorem.  The\nrestriction $K\\to Z$ is finite: it is proper and its fibers are discrete,\nsince the covering map is locally biholomorphic.  Thus\n$K^\\nu\\to Z^\\nu$ is finite and surjective.  Such a map has finite-index\nimage on fundamental groups.  To see the latter assertion, remove a\nproper analytic subset of the target so that the map becomes a finite\nunramified cover of smooth connected spaces.  On these open sets the\nindex is its covering degree.  Their fundamental groups surject onto\nthose of the normal spaces, by general position for loops; passing to\nthese quotients can only decrease the index.  But the composite\n$\\pi_1(K^\\nu)\\to\\pi_1(X)$ is zero, because $K^\\nu$ maps to\n$\\widetilde X$.  Hence $\\pi_1(Z^\\nu)$ has finite image in $\\pi_1(X)$.\n\\end{proof}\n\nThe next elementary interpolation observation allows us to control all\nfibers of a general complete intersection simultaneously.\n\n\\begin{lemma}\\label{red:interpolation}\nLet $W$ be a projective variety, $F\\subset W$ a closed subscheme, and\n$L$ a very ample line bundle.  Fix a positive integer $\\ell$.  For all\nsufficiently large $m$ and every set of distinct points\n$p_1,\\ldots,p_\\ell\\in W\\setminus F$, the evaluation map\n\\[\n H^0(W,\\mathcal I_F\\otimes L^m)\n \\longrightarrow \\bigoplus_{i=1}^{\\ell}L^m|_{p_i}\n\\]\nis surjective.  The same bound on $m$ works for every such set.\n\\end{lemma}\n\n\\begin{proof}\nChoose $m_0$ so that $\\mathcal I_F\\otimes L^{m_0}$ is globally\ngenerated.  For each $i$, choose a section $a_i$ nonzero at $p_i$.\nFor every $j\\ne i$, very ampleness gives a section of $L$ vanishing at\n$p_j$ and nonzero at $p_i$.  Their product $b_i$ vanishes at all the\n$p_j$ with $j\\ne i$ and is nonzero at $p_i$.  The sections $a_ib_i$\ntherefore give a diagonal basis for evaluation in degree\n$m_0+\\ell-1$.  Multiplying each by a section of the remaining power\nof $L$ nonzero at $p_i$ gives the assertion in every higher degree.\n\\end{proof}\n\n\\begin{proof}[Proof of Proposition~\\ref{prop:large-reduction}]\nPut $d=\\dim S'$, choose an abelian variety $B$ of dimension $g=6$, and\nwrite\n\\[\n W=S'\\times B,\\qquad F=A\\times\\{0\\},\\qquad\n c=d+g-4=d+2.\n\\]\nFix a very ample line bundle $L$ on $W$.  We shall take $X$ to be the\ncommon zero locus of $c$ general sections of\n$\\mathcal I_F\\otimes L^m$, for a sufficiently large $m$.\nThere are two requirements: $X$ must be smooth with the expected\nfundamental group, and its fibers over $B$ must be finite away from $F$.\n\n\\smallskip\n\\noindent\\emph{Smoothness and the fundamental group.}\nFor large $m$, the sections generate the ideal away from $F$ and their\nnormal derivatives generate\n$N^*_{F/W}\\otimes L^m|_F$.  Bertini's theorem gives smoothness away\nfrom $F$.  Along $F$, smoothness is the independence of $c$ general\nvectors in a bundle of rank $d+g-2=c+2$.  The rank-deficient matrices\nhave codimension $3$, larger than $\\dim F=2$, so no rank loss occurs\nfor a general tuple.  The same count for each prefix of the tuple\nshows that the successive intersections are smooth.  They are ample\nhypersurfaces in their predecessors, with final dimension four.\nThe Lefschetz hyperplane theorem consequently gives connectedness and\nan isomorphism, induced by inclusion,\n\\begin{equation}\\label{red:fundamental-group}\n \\pi_1(X)\\xrightarrow{\\ \\sim\\ }\n \\pi_1(S'\\times B)=\\pi_1(S')\\times\\pi_1(B).\n\\end{equation}\nHere and below we use sufficiently high powers of a very ample line\nbundle to obtain both ideal generation and generation of the indicated\nnormal derivatives; these are the usual applications of Serre\nvanishing and Bertini's theorem \\cite{Hartshorne}.\n\n\\smallskip\n\\noindent\\emph{Fibers away from the fixed surface.}\nWe claim that a general such tuple excludes four distinct points of\n$W\\setminus F$ in any one fiber of $W\\to B$.  Excluding four points\nis enough to exclude every positive-dimensional fiber component\noutside $F$; the choice $g=6$ makes the following incidence count\nstrict.  The parameter space of\nordered quadruples in one fiber has dimension at most $4d+g=4d+6$.\nBy Lemma~\\ref{red:interpolation}, requiring all $c$ equations to\nvanish at such a quadruple imposes exactly $4c=4d+8$ independent\nlinear conditions on the space of tuples of sections.  The incidence\nvariety of tuples and quadruples thus has dimension strictly less than\nthe section parameter space.  Its constructible image has closure of\nstrictly smaller dimension as well.  A general tuple lies outside this\nclosure.  This condition is compatible with the open smoothness\nconditions already imposed.\n\nIt follows that each fiber of $X\\to B$ contains at most three points\noutside $F$.  In particular, every positive-dimensional subvariety of\na fiber is contained in $F$.\n\n\\smallskip\n\\noindent\\emph{Largeness.}\nLet $Z\\subset X$ be a positive-dimensional closed irreducible\nsubvariety.  If its projection to $B$ is nonconstant, then\n$\\pi_1(Z^\\nu)\\to\\pi_1(B)$ has infinite image.  Otherwise its image,\na finite subgroup of the torsion-free group $\\pi_1(B)$, would be\ntrivial.  The map $Z^\\nu\\to B$ would then lift holomorphically to\n$\\C^g$.  Every holomorphic function on the connected compact normal\nspace $Z^\\nu$ is constant, a contradiction.\n\nIf the projection is constant, the fiber property gives $Z\\subset F$.\nFor $Z=F$, its image in $\\pi_1(X)$ is infinite cyclic by\n\\eqref{red:fundamental-group} and the hypothesis.  The only other\npossibility is an irreducible curve in $A$.  Let $D$ be its smooth\nnormalization.  After a translation in $A$, the nonconstant map\n$D\\to A$ extends to a homomorphism $\\Jac(D)\\to A$.  Its image is a\nnonzero abelian subvariety, and therefore all of $A$ by simplicity.\nA surjective homomorphism of complex tori carries the source integral\nlattice to a finite-index subgroup of the target lattice.  Consequently\n$\\pi_1(D)$ has finite-index image in $\\pi_1(A)\\simeq\\Z^4$, and its\nimage under the homomorphism onto the infinite cyclic group remains\ninfinite.  This proves largeness in every case.  Lemma~\\ref{red:compact-criterion}\nthen gives the required absence of compact analytic subvarieties in\n$\\widetilde X$.\n\n\\smallskip\n\\noindent\\emph{The obstruction to Steinness.}\nLet $K$ be the kernel of $\\pi_1(A)\\to\\pi_1(X)$.  Its quotient is\ninfinite cyclic, so $K\\simeq\\Z^3$.  A connected component of the\ninverse image of $F$ in $\\widetilde X$ is the connected covering\n\\[\n A_K=\\C^2/K.\n\\]\nIt is a closed complex submanifold of $\\widetilde X$: the full inverse\nimage of $F$ is closed, and its components are closed and locally\nseparated in covering charts.  The real span of $K$ has dimension\nthree, so, as a real manifold,\n\\[\n A_K\\simeq(\\R^3/\\Z^3)\\times\\R,\n \\qquad H_3(A_K,\\Z)\\simeq\\Z.\n\\]\nBy the Andreotti--Frankel theorem \\cite{AndreottiFrankel1959}, a Stein\nmanifold of complex dimension two has the homotopy type of a CW complex of real dimension at most\ntwo; see \\cite[Theorem~7.2]{MilnorMorse}.  Thus $A_K$ is not Stein.\nA closed complex submanifold of a Stein manifold is Stein, so\n$\\widetilde X$ cannot be Stein either.\n\\end{proof}\n\nProposition~\\ref{prop:large-reduction} leaves one concrete task: embed a\nsimple abelian surface in a smooth projective variety so that its\nfundamental group has infinite cyclic image.  We first construct the\ncorresponding map from a point blowup of the surface to a smooth\norbifold, and then convert it to the required embedding in\nSection~\\ref{sec:projective}.\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/03-paths.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/03-paths.tex", "bytes": 23965, "sha256": "611a7bb6cb25709d3d7f528de3ca0011cef64dfe732d7b382e3f59c1951d4061", "content": "\\section{A local criterion for nontrivial loops}\\label{sec:paths}\n\nThe geometric construction will attach group labels to paths only within\nbounded regions.  We therefore need a criterion that detects a nontrivial\nloop using such local information.  The criterion below uses hyperbolic\ngraphs as the local models.  It does not require a global map to a model,\nlocal finiteness of a graph, or a bound on the information carried by one\nedge.\n\nA model to keep in mind is a graph whose vertices are points of a space\nand whose edges are paths confined to small regions.  A reading lifts\nsuch paths to a covering space chosen near the initial vertex; its\nendpoint records both the actual endpoint and the sheet of the cover.\nThe argument follows the coarse local-to-global viewpoint of hyperbolic\ngeometry \\cite{Gromov1987}; we prove here the version for bounded path\nreadings in compatible charts.\n\n\\subsection{Paths and their readings}\n\nAll graphs have oriented edges with specified reverses; multiple edges and\nloops are allowed.  Each edge has length one.  Paths are finite edge paths,\nand their lengths count edges.  A constant path has length zero.  The path\ngroupoid of a graph is obtained by cancelling consecutive reverse edges.\nWe use the path metric on each connected component of a graph.\n\nFix a positive integer $N$.  At each vertex $v$ of a graph $G$, suppose that\nwe are given a pointed graph $(D_v,d_v)$ and a rule for reading every path\nof length at most $N$ starting at $v$ as an edge path in $D_v$ starting at\n$d_v$.  Write $[p]_v$ for the terminal vertex of the reading of $p$.\nThe following are the required properties.\n\\begin{enumerate}\n\\item[\\textnormal{(P1)}] Readings respect prefixes and reverses of edges,\nand read a backtrack as a backtrack.  If $p$ starts at $v$ and $|p|<N$,\nreading gives a bijection between the edges leaving the actual endpoint of\n$p$ and the edges leaving $[p]_v$.  If $[p]_v=[p']_v$, then $p$ and $p'$\nhave the same actual endpoint in $G$, and their edge bijections agree\nwhenever both are defined.\n\\item[\\textnormal{(P2)}] Suppose that $p$ runs from $v$ to $w$ and that\n$a,b$ start at $w$.  If each of $p,a,b$ has length at most $N/2$, then\n\\begin{equation}\\label{path:change-root}\n [pa]_v=[pb]_v\\quad\\Longleftrightarrow\\quad [a]_w=[b]_w.\n\\end{equation}\n\\item[\\textnormal{(P3)}] All connected components of all the graphs $D_v$\nare Gromov hyperbolic with a common constant.\n\\end{enumerate}\nThese rules concern paths within the stated range.  They do not assert\nthat the whole graph $G$ maps to $D_v$, or that the whole graph $D_v$ maps\nto $G$.\n\nA loop $p$ at $v$ \\emph{closes on reading} if $[p]_v=d_v$.  Let\n$\\mathcal Q$ be the quotient of the path groupoid of $G$ by the relations\nthat every such loop of length at most three is the constant path.\n\n\\begin{lemma}[Local path criterion]\\label{thm:path-lemma}\nThere is a bound $N_0$, depending only on the common hyperbolicity\nconstant, with the following property.  If the readings satisfy\n\\textnormal{(P1)--(P3)} with $N\\ge N_0$, and $e$ is a single-edge loop at\n$v$ satisfying $[e]_v\\ne d_v$, then $e$ is nontrivial in $\\mathcal Q$.\n\\end{lemma}\n\nThe proof constructs a locally geodesic representative for each path,\nunique up to a uniformly narrow comparison.  Such representatives can be\ncontinued one edge at a time.  The comparison class is unchanged by the\nrelations defining $\\mathcal Q$, but distinguishes the edge in the lemma\nfrom the constant path.\n\n\\subsection{Calculations within one chart}\n\nWe first record precisely how the reading rules allow short calculations\nto move between charts.  By (P1), any edge path in $D_v$ starting at a\nstate already reached by reading can be lifted to a path in $G$, as long\nas the total reading length is at most $N$.  If two such readings have the\nsame endpoint, their lifted paths have the same actual endpoint, and any\nfurther common continuation reads identically.  Together with (P2), this\nhas three useful consequences.\n\nFirst, a short circuit closes on reading if and only if its two\ncomplementary paths from one corner to another have equal read endpoints.\nIndeed one may read one path and the reverse of the other, using (P1).\nClosure can be tested at any corner: a cyclic change of starting corner\namounts to prefixing by a side, applying (P2), and cancelling that side\nusing (P1).  Here and below, ``short'' means that all prefixes and\ncontinuations in the comparison lie within the ranges of (P1) and (P2).\n\nSecond, geodesicity of a short reading is independent of a short prefix.\nFor example, if the reading of $a$ from $w$ is geodesic but its reading\nafter $p:v\\to w$ has a shorter competitor, lift that competitor from\n$[p]_v$.  It gives a path $b$ from $w$ with\n$[pa]_v=[pb]_v$.  Equation~\\eqref{path:change-root} gives\n$[a]_w=[b]_w$, contradicting geodesicity.  The reverse implication uses a\nshorter competitor in $D_w$ and the same argument.  For distance\ncomparison, suppose the states are reached by paths $a,b$ from $w$.\nLift a shortest connector $c$ between their read states, starting after\n$a$ (or after $pa$).  Its length is at most $|a|+|b|$, and apply (P2)\nto $ac$ and $b$.  Doing this in both charts proves equality of the\ndistances whenever $|p|\\le N/2$ and $2|a|+|b|\\le N/2$.\nThese bounds, as well as their versions with $a,b$ interchanged, hold\nin every use below.\n\nThird, a short strip of closed circuits can be calculated in one chart\nwithout adding the lengths of all its transverse paths.  Here is the\nexplicit bookkeeping.  Let $a_j,b_j$ be the prefixes down the two sides\nof the strip, and let $\\rho_j$ be its transverse path from the endpoint\nof $a_j$ to that of $b_j$.  Starting in the chart at the beginning of the\nfirst side, the equalities to prove are\n\\begin{equation}\\label{path:strip-calculation}\n [a_j\\rho_j]=[\\rho_0b_j].\n\\end{equation}\nClosure of the next elementary circuit, transported after $a_j$ by (P2),\ncompares its two routes across the next step.  Explicitly, write\n$a_{j+1}=a_js_j$ and $b_{j+1}=b_jt_j$, where $s_j,t_j$ are the next\nside portions.  Closure and then the preceding equality give\n\\[\n \\begin{aligned}\n [a_{j+1}\\rho_{j+1}]&=[a_j\\rho_jt_j]\\\\\n                    &=[\\rho_0b_jt_j]=[\\rho_0b_{j+1}],\n \\end{aligned}\n\\]\nusing (P1) for the common continuation, edge by edge.  This proves\n\\eqref{path:strip-calculation} at the next step.  Thus only the side\nprefixes and each individual transverse path enter the range bound.\nConversely, transverse paths found in this one chart lift to $G$ and\ngive circuits that close when read from their own corners.  These facts\nwill justify all uses of diagrams below.\n\nChoose an integer $\\Delta\\ge10$ large enough for the following standard\nconsequences of the common hyperbolicity bound.  In every component of\nevery chart, geodesic triangles are $\\Delta$-slim on vertices: every\nvertex on one side lies within $\\Delta$ of a vertex on one of the other\ntwo sides.  Dividing a geodesic quadrangle by a diagonal then shows that\neach side lies within $2\\Delta$ of the union of the other three sides.\nThe Gromov product\n\\[\n (x\\mid y)_u=\\tfrac12\\bigl(d(u,x)+d(u,y)-d(x,y)\\bigr)\n\\]\nsatisfies\n\\begin{equation}\\label{path:product}\n (x\\mid z)_u\\ge\n \\min\\{(x\\mid y)_u,(y\\mid z)_u\\}-\\Delta.\n\\end{equation}\nMoreover, for a geodesic segment $[x,z]$, there is a vertex on that segment\nat distance at most $(x\\mid z)_u+10\\Delta$ from $u$.  Enlarging $\\Delta$\nabsorbs all rounding to vertices.  These are the usual equivalent forms\nof hyperbolicity; see \\cite[Chapter III.H]{BridsonHaefliger}.\n\nWe will also use the following elementary comparison.  If two geodesic\nsegments $\\gamma_1,\\gamma_2$, of lengths $\\ell_1,\\ell_2$, have\ncorresponding endpoints at distance at most $h$, then for every integer\n$t\\ge0$,\n\\begin{equation}\\label{path:geodesic-comparison}\n d\\bigl(\\gamma_1(\\min\\{t,\\ell_1\\}),\n        \\gamma_2(\\min\\{t,\\ell_2\\})\\bigr)\\le10(h+\\Delta).\n\\end{equation}\nThis is a \\emph{synchronous comparison} of width $10(h+\\Delta)$:\nthe parameters advance by one on both segments until the shorter\nsegment ends, and then that segment waits at its endpoint.\nTo check the bound, join the\ncorresponding endpoints by geodesics of length at most $h$.  The resulting\nquadrangle is $2\\Delta$-slim.  A point close to the opposite geodesic has\na mate whose distance from that geodesic's initial endpoint differs from\nits own parameter by at most $h+2\\Delta$.  A point close to an end\nconnector is within $h+2\\Delta$ of the corresponding end.  These estimates,\ntogether with the bound $2h$ on the difference of the lengths, imply\n\\eqref{path:geodesic-comparison}, also when one segment has ended.\n\nFix integers\n\\begin{equation}\\label{path:constants}\n H=1000\\Delta,\\qquad L\\ge10^6H,\\qquad k=20L,\n \\qquad N\\ge10^5k.\n\\end{equation}\nTaking $L=10^6H$, for example, gives a threshold $N_0=10^5(20L)$\ndepending only on the original hyperbolicity constant.\nA \\emph{guide} is a path each of whose subpaths of length at most $k$\nreads as a geodesic in the chart at its initial vertex.  Its entire\nlength may be arbitrarily large.  Every individual chart calculation\nbelow uses paths, including prefixes, of length less than $1000k$.\nConsequently (P1) and (P2) always apply.  A long guide is handled in\nseparate short pieces, never by reading its whole prefix.\n\n\\subsection{Narrowing comparisons between guides}\n\nFor paths $\\alpha,\\beta$ with the same initial and terminal vertices, a\n\\emph{ladder of width $h$} consists of the following data.  A finite\nschedule of pairs of positions begins at $(0,0)$ and ends at\n$(|\\alpha|,|\\beta|)$; at each step each position advances by zero or one.\nAt each scheduled pair there is a path, called a rung, from the vertex\non $\\alpha$ to the vertex on $\\beta$, of length at most $h$.  The first\nand last rungs are empty.  Each elementary circuit, formed by consecutive\nrungs and the intervening portions of the two paths, must close on\nreading.  Pausing both paths is allowed.  In particular, this definition\ndoes not require an elementary circuit to have length at most three.\nLadders are comparisons of readings, rather than expressions in the\nrelations defining $\\mathcal Q$.\n\nFor the widths used below, ladders are symmetric, by changing corners of\ntheir elementary circuits.  They compose with addition of widths whenever\nthe sum is at most $1000H$, which suffices for every composition below.\nIndeed, given ladders from\n$\\alpha$ to $\\beta$ and from $\\beta$ to $\\gamma$, synchronize the advances\nalong $\\beta$, inserting pauses in the other sides as necessary, and\nconcatenate the two rungs.  Each resulting circuit is a union of one or\ntwo elementary circuits from the given ladders, so closes by\n\\eqref{path:strip-calculation}.  Appending the same trailing path to both\nsides preserves the width: use empty rungs along that trailing path.\n\n\\begin{lemma}[Narrowing]\\label{path:narrowing}\nIf two guides have a ladder of width at most $1000H$, then they have one\nof width at most $H$.\n\\end{lemma}\n\n\\begin{proof}\nLet $\\alpha,\\beta$ be the guides, and let the given width be\n$h\\le1000H$.  Consider an interval of the schedule on which $\\alpha$\nadvances by at most $8L$.  On this interval $\\beta$ advances by less than\n$k$.  Otherwise stop at its first advance of $k$ edges.  The strip up to\nthat point calculates in one chart: its sides have lengths at most\n$8L$ and $k$, and its rungs have length at most $h$.  The second side is\ngeodesic there, whereas its endpoints can be joined using the first side\nand the two end rungs.  This gives the contradiction\n\\[\n k\\le8L+2h<20L=k.\n\\]\nIt follows also that on every such schedule interval both sides read as\ngeodesics, and their lengths differ by at most $2h$.\n\nSuppose first that $|\\alpha|\\ge L$.  Mark $\\alpha$ at\n$x_0,\\ldots,x_m$, including its endpoints, so that consecutive marked\nintervals have lengths in $[L,2L]$.  Choose scheduled mates\n$b_0,\\ldots,b_m$ on $\\beta$, using its actual endpoints for $b_0,b_m$.\nThe distance along $\\beta$ between consecutive old mates is at least\n$L-2h$.\n\n\\input{figures/path-ladder.tex}\n\nAt an interior mark $x_i$, calculate the part between $x_{i-1}$ and\n$x_{i+1}$ in one chart, as in Figure~\\ref{fig:path-ladder}.\nReplace its end rungs by geodesics of length at\nmost $h$.  The point $x_i$ is at distance at least $L-h>2\\Delta$ from\neither end connector.  Quadrangle slimness therefore puts it within\n$2\\Delta$ of a vertex $b'_i$ on the opposite geodesic, the corresponding\npiece of $\\beta$.  Its old mate $b_i$ was within $h$ of $x_i$, so\ngeodesicity gives\n\\begin{equation}\\label{path:mate-shift}\n |b'_i-b_i|_{\\beta}\\le h+2\\Delta.\n\\end{equation}\nHere the left side denotes distance in the parameter of the path\n$\\beta$.  The new mates remain in order, since the difference between\nconsecutive new positions is at least\n\\[\n L-2h-2(h+2\\Delta)=L-4h-4\\Delta>0.\n\\]\nThe same estimate at the ends puts them strictly between the unchanged\nendpoint mates.  Lift short paths from $x_i$ to $b'_i$ to obtain new\nrungs of length at most $2\\Delta$; use empty rungs at $i=0,m$.\n\nThe new rungs agree with the old calculation: following an old rung and\nthe relevant portion of $\\beta$ has the same read endpoint as the new\nrung.  On a marked interval this equality can be checked together with\nboth adjacent marked intervals.  Their union has first-side length at\nmost $6L$, so the preceding bound and the short-strip calculation apply.\nThus between successive new mates both sides are geodesic, and their\nend rungs have length at most $2\\Delta$.  Apply\n\\eqref{path:geodesic-comparison}, lift the comparison rungs, and retain\nthe specified rungs at the marked endpoints.  Each elementary circuit\ncloses in this chart and hence at its own corner.  The resulting width\nis at most $30\\Delta<H$.\n\nIf $|\\alpha|<L$, the initial schedule argument applies to the whole\nladder.  Both sides calculate as geodesics in one chart with identical\nendpoints, and \\eqref{path:geodesic-comparison} directly gives width at\nmost $10\\Delta<H$.\n\\end{proof}\n\nIn particular, existence of a width-$H$ ladder is an equivalence relation\non guides with fixed endpoints.  Reflexivity uses empty rungs, symmetry\nwas noted above, and transitivity follows by composing two ladders and\napplying Lemma~\\ref{path:narrowing}.\n\n\\subsection{Continuing a guide by one edge}\n\nWe next show that this comparison class can be continued along any edge.\nThe difficulty is that appending an edge need not preserve local\ngeodesicity.  We repair the guide by choosing nearby vertices at widely\nspaced marks and minimizing the total distance between those choices.\nAll nearby choices are specified by paths in the charts; distance in\n$G$ alone would forget the states that must be compared.\n\n\\begin{lemma}[Extension]\\label{path:extension}\nIf $\\alpha$ is a guide and $e$ is an edge starting at its terminal vertex,\nthere is a guide $\\beta$ to the endpoint of $\\alpha e$ and a ladder\nbetween $\\alpha e$ and $\\beta$ of width at most $50H$.\n\\end{lemma}\n\n\\begin{proof}\nIf $|\\alpha|<L$, read $\\alpha e$ in its initial chart and join its\nendpoints by a geodesic.  Lift that geodesic to $G$.  The result is a\nguide by transport of geodesicity.  Compare it with $\\alpha$, whose\nfinal endpoint differs by at most one in the chart, using\n\\eqref{path:geodesic-comparison}.  Finish the comparison by letting the\nfirst side traverse $e$ while the second waits.  This proves the claim\nin this case.\n\nSuppose $|\\alpha|\\ge L$, and mark it at\n$a_0,\\ldots,a_m$ in intervals $\\alpha_i$ of lengths\n$\\ell_i\\in[L,2L]$, where $\\alpha_i$ runs from $a_i$ to $a_{i+1}$.\nAt each mark choose a path $c_i$ starting there, with $|c_i|\\le H$.\nRequire $c_0$ to be empty and $c_m=e$.  For adjacent marks define\n\\[\n F_i(c_i,c_{i+1})=\n d_{D_{a_i}}\\bigl([c_i]_{a_i},[\\alpha_i c_{i+1}]_{a_i}\\bigr).\n\\]\nChoose the paths $c_i$ to minimize $\\sum_{i=0}^{m-1}F_i$.  A minimum\nexists because the set of choices is nonempty and the objective takes\nnonnegative integer values; no compactness assumption is needed.\n\nThe point of this minimization is that, on each short block,\nhyperbolicity will supply a geodesic in one chart between its chosen\nouter endpoints, with points lying in order within $H$ of the interior\nmarks.  Replacing the interior choices by short paths to those points\nwill force equality in the triangle inequality for the minimizing\nchoices.\n\nFor each $i$, lift a geodesic realizing $F_i$, starting after $c_i$ in\nthe chart at $a_i$, to a path $\\beta_i$ in $G$.  Equality of read\nendpoints shows that $\\beta_i$ ends at the actual endpoint of $c_{i+1}$.\nThus the paths concatenate to a path\n$\\beta=\\beta_0\\cdots\\beta_{m-1}$ from $a_0$ to the endpoint of $e$.\nThe comparison circuit between $\\alpha_i$ and $\\beta_i$, using\n$c_i,c_{i+1}$, closes on reading.  The paths $c_i$, rather than just\ntheir actual endpoints, retain the states used in this assertion.\n\nWe claim that on every block of at most $100$ consecutive marked\nintervals the concatenation of the $\\beta_i$ is geodesic in a single\nchart calculation.  Read such a block in the chart at its first mark.\nThe paths $\\alpha_i$, $c_i$, and $\\beta_i$ all calculate there\nconsistently, by \\eqref{path:strip-calculation}; each side has length\nbounded by $100(2L+2H)$, well within the prescribed range.  Distances\ncomputed here agree with the single-interval distances $F_i$, by\ntransport of short competitors.\n\nFor the hyperbolic calculation, denote the read marks on this block by\n$x_0,\\ldots,x_q$, where $q\\le100$.  Their consecutive distances are in\n$[L,2L]$, and\n\\begin{equation}\\label{path:zero-products}\n (x_{i-1}\\mid x_{i+1})_{x_i}=0\\qquad(1\\le i<q),\n\\end{equation}\nbecause two consecutive marked intervals of $\\alpha$ have total length\nat most $4L<k$.  We record the consequences of\n\\eqref{path:zero-products} explicitly.  Induction using\n\\eqref{path:product} gives\n\\begin{equation}\\label{path:forward-product}\n (x_0\\mid x_{i+1})_{x_i}\\le\\Delta\\qquad(1\\le i<q).\n\\end{equation}\nThe first case follows from \\eqref{path:zero-products}.  For the next\ncase, the preceding estimate and the identity\n\\[\n (x_0\\mid x_{i-1})_{x_i}\n =d(x_{i-1},x_i)-(x_0\\mid x_i)_{x_{i-1}}\n \\ge L-\\Delta\n\\]\nshow that this product exceeds $\\Delta$.  Applying\n\\eqref{path:product} to the zero product\n$(x_{i-1}\\mid x_{i+1})_{x_i}$ forces\n\\eqref{path:forward-product}.  The same argument backwards, followed\nby another application of \\eqref{path:product}, gives\n\\begin{equation}\\label{path:block-products}\n (x_0\\mid x_q)_{x_i}\\le2\\Delta\\qquad(1\\le i<q).\n\\end{equation}\nFor the last implication, use\n$(x_{i-1}\\mid x_q)_{x_i}\\le\\Delta$ and\n$(x_{i-1}\\mid x_0)_{x_i}\\ge L-\\Delta>2\\Delta$.\nAlso, the distances from $x_0$ increase at every mark by at least\n$L-2\\Delta$, since\n\\[\n d(x_0,x_{i+1})-d(x_0,x_i)\n =d(x_i,x_{i+1})-2(x_0\\mid x_{i+1})_{x_i}.\n\\]\nThe analogous estimate holds from the other endpoint.\n\nLet $A,B$ be the read endpoints of the chosen paths at the two ends of\nthe block.  They are at distance at most $H$ from $x_0,x_q$,\nrespectively.  By \\eqref{path:block-products}, every interior $x_i$\nhas a mate on a geodesic $[x_0,x_q]$ at distance at most $12\\Delta$.\nSuch a mate is farther than $2\\Delta$ from either of the geodesic\nconnectors $[x_0,A]$ and $[x_q,B]$: its distance from those connectors\nis at least $L-H-14\\Delta$.  Quadrangle slimness therefore gives a\nvertex $y_i$ on $[A,B]$ with\n\\begin{equation}\\label{path:block-mates}\n d(x_i,y_i)\\le20\\Delta<H.\n\\end{equation}\nThe vertices $y_i$ occur in increasing order along $[A,B]$.  Indeed\ntheir distances from $A$ differ from the corresponding distances\n$d(x_0,x_i)$ by at most $H+20\\Delta$.  Consequently each consecutive\ndifference is at least\n\\[\n L-2\\Delta-2H-40\\Delta>0.\n\\]\nThe same inequalities keep them strictly between $A$ and $B$.\n\nLift short paths from the interior $x_i$ to the $y_i$ to replace the\ninterior choices $c_i$, keeping the two outer choices fixed.  These are\nlegal choices by \\eqref{path:block-mates}.  For them the sum of the\nsuccessive distances equals $d(A,B)$, since their endpoints occur in\norder on a geodesic.  Transport of distances shows that this is also\ntheir sum of single-interval objectives $F_i$.  Minimality of the\noriginal total objective implies that its sum on this block is at most\n$d(A,B)$: changing only interior choices leaves every term outside the\nblock unchanged.  The triangle inequality gives the reverse inequality.\nThus the original joins concatenate geodesically on the block, as\nclaimed.\n\nEach join $\\beta_i$ has length at least $L-2H$.  A subpath of $\\beta$\nof length at most $k=20L$ therefore meets fewer than $100$ consecutive\njoins: apart from at most two end joins, every join it meets is entirely\ncontained in that subpath.  The block result and transport of\ngeodesicity prove that $\\beta$ is a guide.\n\nFinally compare $\\alpha_i$ and $\\beta_i$ in their single-interval\nchart.  Their corresponding endpoints are joined by the given paths\n$c_i,c_{i+1}$ of length at most $H$.  Formula~\\eqref{path:geodesic-comparison}\ngives comparison rungs of length at most $10(H+\\Delta)$; retain the\ngiven choice paths as rungs at the marks.  Lift these comparisons and\nconcatenate them.  At the last mark the rung is $e$; let the first\nside traverse $e$ and finish with an empty rung.  This gives the required\nladder of width at most $50H$.\n\\end{proof}\n\n\\subsection{The invariant of a path}\n\nWe can now finish the local criterion.  Fix an initial vertex $v$ and\nconsider guides starting at $v$, modulo width-$H$ ladders.  By\nLemma~\\ref{path:extension}, appending an edge $e$ defines a continuation\nof such a class.  It is independent of both choices involved.  If\n$\\alpha,\\alpha'$ represent the same class, append $e$ to their\nwidth-$H$ ladder.  If $\\beta,\\beta'$ are any two guides supplied by\nLemma~\\ref{path:extension}, composition gives a ladder between them of\nwidth at most\n\\[\n 50H+H+50H=101H.\n\\]\nLemma~\\ref{path:narrowing} reduces this to width $H$.  The same argument\ncovers different choices of the extension for a fixed guide.\n\nLet $q$ be a loop of length at most three that closes on reading.\nIt has a ladder of width at most three with the constant path.  One\nexplicit schedule traverses $q$ on the first side while the second\nwaits: after a noninitial position use the remaining suffix of $q$ as\nthe rung, and use empty first and last rungs.  The first circuit closes\nbecause $q$ does, and the subsequent ones cancel consecutive reverse\nedges.  The short-chart rules justify the same assertion at each corner.\nAppending this ladder after any guide $\\alpha$ gives a width-three\nladder between $\\alpha q$ and $\\alpha$.\n\nSuccessively applying Lemma~\\ref{path:extension} along $q$ produces a\nguide $\\beta$ with a ladder to $\\alpha q$ of width at most $150H$.\nTo see the bound, at each step append the remaining common trailing\nedges to the comparison and compose; each of at most three steps adds\n$50H$.  Composing with the preceding width-three ladder and narrowing\nshows that $\\beta$ and $\\alpha$ represent the same class.  The same\nreasoning applies to a backtrack, which closes on reading by (P1).\n\nStarting from the empty guide at $v$ and continuing along a path thus\ngives a class unchanged by every defining relation of $\\mathcal Q$.\nInsertion of a relation in the middle of a path causes no problem:\nthe classes agree after the inserted loop, and the well-defined\ncontinuations along the remaining edges preserve that agreement.\n\n\\begin{proof}[Proof of Lemma~\\ref{thm:path-lemma}]\nLet $e$ be the edge in the statement.  Its reading has distinct\nendpoints and length one, so $e$ itself is a guide.  Its class is\ntherefore the continuation of the empty guide along $e$.  If $e$ were\ntrivial in $\\mathcal Q$, the invariant just constructed would give a\nwidth-$H$ ladder between $e$ and the constant path.  This entire\ncomparison calculates in the chart at $v$: its sides have lengths one\nand zero and its individual rungs have length at most $H$.\nEquation~\\eqref{path:strip-calculation}, with the empty final rung,\nwould then give $[e]_v=d_v$, a contradiction.\n\\end{proof}\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/04-geometry.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/04-geometry.tex", "bytes": 26575, "sha256": "fca62ffc49421c85121b5def43baf2d617a618cf88c0407fdbcd894f0d1ada93", "content": "\\section{An arithmetic arrangement and a distinguished surface}\\label{sec:geometry}\n\nWe construct a smooth proper effective orbifold $\\cS$ with projective\ncoarse space and a morphism $f:Y\\to\\cS$, where $Y$ is obtained from a\nsimple abelian surface by point blowups.  The image of $f$ will lie in\nthe ordinary locus of $\\cS$.  The arrangement constructed here provides\nboth the relations and the local covering spaces used to prove that\n$f_*\\pi_1(Y)$ is infinite cyclic.  The fundamental-group calculation is\nmade in Theorem~\\ref{thm:orbifold-cyclic}.\n\n\\subsection{Two pencils and their coupling hyperplanes}\\label{geo:prototype}\n\nThe two pencils will use the same six marked directions on $\\PP^1$.\nWe first choose these markings so that their double cover has simple\nJacobian, and then choose the arithmetic ball containing the pencils.\nChoose six distinct real algebraic numbers $\\lambda_1,\\ldots,\\lambda_6$ with\n\\begin{equation}\\label{geo:slopes}\n |\\lambda_i|<0.01\\quad(1\\le i\\le4),\n \\qquad \\lambda_5<-4,\\quad \\lambda_6>4,\n\\end{equation}\nand such that the smooth double cover\n\\begin{equation}\\label{geo:curve}\n \\pi:C\\longrightarrow\\PP^1,\n \\qquad y^2=\\prod_{i=1}^6(t-\\lambda_i),\n\\end{equation}\nhas simple Jacobian.  We call the first four slopes \\emph{red} and the\nlast two \\emph{blue}.  These requirements can be imposed simultaneously,\nas follows.\n\nStart with six disjoint real open intervals satisfying\n\\eqref{geo:slopes}.  Monic real polynomials with one root in each interval\nform a nonempty open set in coefficient space.  At three sufficiently\nlarge distinct primes prescribe squarefree degree-six reductions with\nfactor degrees\n\\[\n (6),\\qquad(5,1),\\qquad(2,1,1,1,1).\n\\]\nWeak approximation produces a monic polynomial in $\\Q[t]$, integral at\nthese primes, with these reductions and with its real coefficients in\nthe chosen open set.  Its Galois group contains a six-cycle, a five-cycle,\nand a transposition, by the Frobenius cycle criterion.  The six-cycle\nmakes the group transitive.  The stabilizer of the fixed point of the\nfive-cycle is transitive on the remaining five points, so the group is\ntwo-transitive.  Conjugating its transposition then gives every\ntransposition, and the Galois group is $S_6$.  Let $F$ be its splitting\nfield.  Then $F$ is totally real, $F\\ne\\Q$, and all six slopes belong\nto $F$.  Zarhin's theorem gives\n$\\End(\\Jac(C))=\\Z$ over an algebraic closure, hence $\\Jac(C)$ is simple\n\\cite[Theorem~2.1]{Zarhin}.\n\nUse the inclusion $F\\subset\\R$ containing the chosen roots as the\ndistinguished real embedding.  Choose a CM extension $E/F$ and an\nelement $a\\in F$ which is positive at this embedding and negative at\nevery other real embedding; weak approximation supplies $a$.  On $E^5$ put\n\\begin{equation}\\label{geo:hermitian}\n h=\\operatorname{diag}(1,1,1,1,-a).\n\\end{equation}\nAt the distinguished embedding the negative lines form complex\nhyperbolic space of dimension four.  In the affine chart with last\nhomogeneous coordinate one, write this ball as\n\\[\n \\BB=\\{(z,w)\\in\\C^2\\times\\C^2:|z|^2+|w|^2<a\\},\n \\qquad o=(0,0).\n\\]\nThe metric is normalized so that in the unit-radius ball a point at\ndistance $r$ from the origin has Euclidean radius $\\tanh r$.\n\nChoose $c\\in F$ with\n\\begin{equation}\\label{geo:coupling-size}\n                 0.85<c/\\sqrt a<0.9.\n\\end{equation}\nThe \\emph{$z$-family} consists of the six slope hyperplanes and two\ncoupling hyperplanes\n\\[\n H^z_i=\\{z_2=\\lambda_i z_1\\}\\cap\\BB\\quad(1\\le i\\le6),\n \\qquad K^z_\\pm=\\{z_1=\\pm c\\}\\cap\\BB.\n\\]\nDefine the $w$-family by the same equations in $w$.  Let $\\cD$ denote\nthese sixteen labeled complex hyperbolic hyperplanes.  They are all\ndefined over $E$ and are preserved, with their labels permuted, by\n\\begin{equation}\\label{geo:sigma}\n                    \\sigma(z,w)=(w,-z).\n\\end{equation}\nThis is an order-four isometry represented by an integral unitary\nmatrix.  The two pencils have centers\n\\[\n L_z=\\{z=0\\}\\cap\\BB,\n \\qquad L_w=\\{w=0\\}\\cap\\BB.\n\\]\n\nThe intersection pattern is elementary but important.  The $z$ slopes\nall meet along $L_z$.  On $K^z_\\pm$, a slope of value $\\lambda$ meets the\nball exactly when $c^2(1+|\\lambda|^2)<a$.  Thus each coupling hyperplane\nmeets the four red slopes, in distinct subspaces disjoint from $L_z$,\nand meets neither blue slope.  The two $z$ coupling hyperplanes are\ndisjoint.  These statements hold also in the $w$-family.  A $z$ coupling\nhyperplane and a $w$ coupling hyperplane are disjoint because $2c^2>a$.\nEvery intersection involving the two families has a local product\ndescription in the $z$ and $w$ variables.  In particular, the phrase\n``product'' here concerns local analytic coordinates, rather than a\nproduct decomposition of the hyperbolic metric.\n\nFigure~\\ref{fig:arrangement} shows the incidences in one family; the\nsecond family is obtained by using the other coordinate pair.\n\\input{figures/arrangement}\n\n\\subsection{Exact models in congruence quotients}\\label{geo:separation}\n\nThe arithmetic lattice theorem of Borel--Harish-Chandra\n\\cite[Corollary~12.4]{BorelHarishChandra1962}, applied to\n\\eqref{geo:hermitian}, gives a\ncocompact arithmetic lattice of simplest type in $\\mathrm{PU}(4,1)$.\nIndeed all the other archimedean factors are compact, and the form is\nanisotropic over $E$: a nonzero $E$-rational isotropic vector would\nremain isotropic at a definite conjugate embedding.  Torsion-free\nprincipal congruence subgroups therefore give compact complex\nhyperbolic manifolds, which are projective by the Baily--Borel theorem\n\\cite[Theorem~10.11]{BailyBorel1966}.  One can\nalso obtain projectivity from the positive canonical bundle of a\ncompact ball quotient.  We use principal congruence subgroups normalized\nby $\\sigma$, and may take their levels arbitrarily deep.  These standard\narithmetic facts are recalled in \\cite[Section~3.1]{StoverToledo}.\n\nFix positive normal vectors $n_i\\in E^5$ for the members of $\\cD$,\nscaled to have integral coordinates.  We always label a translate of\nthe $i$th hyperplane by $i$, and use the prescribed normal $\\gamma n_i$\nfor its translate by $\\gamma$.  Congruence subgroups may be chosen to\navoid the finite scalar kernel of the projective action.\n\n\\begin{proposition}[Exact local arrangements]\\label{prop:arrangement-local}\nGiven $W>0$ and $R_0>0$, there is a torsion-free principal congruence\nsubgroup $\\Gamma_0$, normalized by $\\sigma$, with the following\nproperties.  Let $\\mathcal A_0$ be the union of all $\\Gamma_0$-translates\nof the labeled hyperplanes of $\\cD$.\n\\begin{enumerate}\n\\item The arrangement $\\mathcal A_0$ is locally finite.  The hyperplanes\nmeeting any ball of radius $W$ have distinct labels and are carried to\nthe corresponding subarrangement of $\\cD$ by a holomorphic isometry\ninduced by a unitary map sending each prescribed translated normal to\nits prototype normal.\n\\item The injectivity radius of $\\Gamma_0\\backslash\\BB$ is greater than\n$R_0$.\n\\item In the ball of radius $R_0$ about $o$, the arrangement is exactly\n$\\cD$ restricted to that ball.  The analogous assertion holds in every\n$\\Gamma_0$-translate of this ball.\n\\end{enumerate}\nAll these conclusions concerning the lifted arrangement remain valid\non any subsequent finite cover of $\\Gamma_0\\backslash\\BB$, provided one\nuses the full inverse image of the arrangement.\n\\end{proposition}\n\n\\begin{proof}\nFirst fix a radius $W$ and a finite collection of hyperplanes meeting a\n$W$-ball.  At its viewpoint, represented by a unit negative\nvector $x$, the distance to the hyperplane with positive normal $n$\nsatisfies\n\\[\n  \\sinh d(x,H_n)=\\frac{|h(n,x)|}{\\sqrt{h(n,n)}}.\n\\]\nFor a normal of one of our finitely many fixed lengths, intersection\nwith the $W$-ball bounds $|h(n,x)|$.  Write $n=n_++\\alpha x$ with\n$n_+\\in x^\\perp$.  Since\n\\[\n h(n_+,n_+)=h(n,n)+|\\alpha|^2,\n\\]\nthe positive component is bounded as well.  Cauchy--Schwarz in\n$x^\\perp$ now bounds every Gram entry of two normals visible from this\nball, by a constant depending only on $W$ and their labels.\n\nAt every other archimedean place, definiteness and the fixed normal\nlengths bound the same Gram entries.  All entries belong to one fixed\nfractional ideal of $E$.  Its image under the archimedean embeddings\nis a lattice, so only finitely many Gram entries satisfy these bounds.\nIf $\\gamma,\\gamma'$ are in a sufficiently deep integer principal\ncongruence subgroup, then\n\\[\n h(\\gamma n_i,\\gamma'n_j)\\equiv h(n_i,n_j)\n\\]\nmodulo the level, in this fractional ideal.  Choose the level to\nseparate all the finitely many possible nonzero differences.  We obtain\nexact equality:\n\\begin{equation}\\label{geo:gram}\n            h(\\gamma n_i,\\gamma'n_j)=h(n_i,n_j)\n\\end{equation}\nwhenever the corresponding hyperplanes are visible together.\n\nHere equality of Gram matrices gives the asserted isometry even for\ndependent collections.  Every $E$-rational subspace of $E^5$ is\nnondegenerate for $h$: a nonzero radical, being defined by linear\nequations over $E$, would persist at a definite embedding.  Consequently\nthe kernel of a Gram matrix is exactly the relation space of its\nvectors.  Equality \\eqref{geo:gram} thus defines an isometry between\ntheir spans, which extends to a unitary isometry of the ambient\nHermitian space.  Its projectivization is a holomorphic ball isometry.\nTwo visible copies of the same label must in fact have the same normal:\ntheir difference has norm zero by \\eqref{geo:gram}, and anisotropy over\n$E$ makes that difference zero.  Two different labels cannot coincide,\nsince this would contradict equality of their relation spaces with\nthose of the distinct prototypes.\n\nLocal finiteness follows from the same boundedness argument applied to\nnormals themselves at a fixed viewpoint.  The normals have integral\ncoordinates; their distinguished components are bounded by the distance\nbound and their conjugate components by definiteness.  Only finitely\nmany can meet a fixed bounded ball.\n\nTo obtain exact agreement at $o$, choose an auxiliary ball about $o$\nlarge enough to contain the prescribed $R_0$-ball and to meet every\nprototype hyperplane.  Apply the Gram argument to this ball.  The\nprototype normals span $E^5$: the slope normals span the four positive\ncoordinate directions, and a coupling normal supplies the last\nhomogeneous direction.  An additional translated normal visible in\nthis ball has, by \\eqref{geo:gram}, the same inner products with this\nspanning set as its prototype.  It therefore equals that prototype.\nThis proves the third assertion.\n\nFinally fix a torsion-free congruence subgroup in advance and a compact\nfundamental set for its action on $\\BB$.  A group element moving a point\nof this set a bounded distance belongs to a finite set.  A nested\nsequence of normal principal congruence subgroups has trivial\nintersection, after the scalar kernel has been removed.  Deep enough\nlevels avoid every nonidentity element of the finite set.  Normality\nallows an arbitrary viewpoint to be moved into the compact fundamental\nset without changing subgroup membership.  This proves the required\nuniform lower bound on injectivity radius.  Increasing the level\naccommodates all the preceding requirements simultaneously.  Passing\nto a subgroup preserves them when the arrangement is pulled back in\nfull.\n\\end{proof}\n\nWe next record the consequences for the divisors and centers that we\nwill blow up.  In $M_0=\\Gamma_0\\backslash\\BB$, each label gives a closed\nembedded smooth totally geodesic divisor, possibly disconnected.  Its\nlifts are locally finite and have disjoint sheets by\nProposition~\\ref{prop:arrangement-local}.  The divisors are algebraic\nbecause $M_0$ is projective.\n\nWithin one family the intersection centers have complex codimension\ntwo.  A center is either a common intersection of at least two slopes,\nor an intersection of a red slope with a coupling hyperplane.  At a\nslope center only a subset of the six slopes may occur.  The subset is\nconstant along a connected center: if another slope meets it, the\nlocal model shows that it contains the intersection, and the\ncontainment extends along its lifts.  Distinct centers within one\nfamily are disjoint; otherwise all participating hyperplanes would be\nvisible near a point of intersection, contradicting the prototype\npattern.  Coincident centers are counted once.  Thus these centers are\nclosed embedded smooth subvarieties.  Centers from different families\nhave the product structure already described.  These assertions apply\nto the full pulled-back arrangement after any finite cover.\n\n\\subsection{Two global sign covers}\\label{geo:signs}\n\nFor the later local models we need global parity characters on the\narrangement complement.  We obtain these from the following theorem\nof Stover--Toledo, in the form stated by Llosa Isenrich--Py\n\\cite[Theorem~4.2]{LlosaPy}; see also \\cite{StoverToledo}.\n\n\\begin{theorem}[Virtual cyclic ramified covers]\\label{geo:stover-toledo}\nLet $M_0$ be a compact ball quotient of complex dimension at least two\nby a torsion-free congruence arithmetic lattice of simplest type.  Let\n$D$ be a nonempty totally geodesic divisor whose components are smooth,\nembedded, and pairwise disjoint.  For every integer $d\\ge2$ there is a\nfinite unramified cover $M_1\\to M_0$ such that $M_1$ admits a cyclic\ncover of degree $d$, branched over the full inverse image of $D$.\n\\end{theorem}\n\nApply Theorem~\\ref{geo:stover-toledo} with $d=2$ to each of the sixteen\nlabel divisors separately, on the initial congruence quotient $M_0$.\nEach application satisfies the disjoint-component hypothesis.  We do\nnot apply the theorem to the union of the intersecting divisors.  Take\na common finite unramified cover of the resulting base covers.  The\nsum, in $\\Z/2$, of the eight characters for the $z$ labels gives a\ncharacter $\\chi_z$ on the complement of the $z$ arrangement.  It has\nvalue one on a meridian of every $z$ hyperplane.  Pull it back to the\ncomplement of both families.  The same procedure gives a $w$ character.\nSubsequent finite covers need not be congruence: every use of\nTheorem~\\ref{geo:stover-toledo} has already taken place on $M_0$.\n\nWe arrange equivariance before fixing the signs.  First intersect the\nfinitely many $\\sigma$-conjugates of the subgroup defining our common\ncover, so that $\\sigma$ acts on it.  Since $\\sigma^2$ preserves the\n$z$-family, the character\n\\[\n                 \\delta=\\chi_z+\\sigma^{2*}\\chi_z\n\\]\nis zero on all meridians.  The kernel of the map from the fundamental\ngroup of a divisor complement to that of the ambient smooth variety\nis normally generated by meridians.  Hence $\\delta$ factors through\nthe fundamental group of the compact ball quotient.  Pass to a further\nfinite cover by intersecting the kernel of this character with its\n$\\sigma$-conjugates.  On that cover the pulled-back $z$ character\nsatisfies $\\sigma^{2*}\\chi_z=\\chi_z$.  Define\n\\begin{equation}\\label{geo:sign-equivariance}\n                  \\chi_w=\\sigma^*\\chi_z.\n\\end{equation}\nThen $\\sigma$ exchanges the two characters.  They have the prescribed\nmeridian values in their respective families, and each is defined\nusing only that family.\n\nWrite $M=\\Gamma\\backslash\\BB$ for this final finite cover, still with\nthe full pulled-back arrangement.  The image of $o$ in $M$, also denoted\n$o$, is fixed by $\\sigma$.  It is an isolated component even of the\nfixed locus of $\\sigma^2$, because the derivative of $\\sigma^2$ at $o$\nis $-\\id$.  No assertion of this kind is needed for the other fixed\nloci of the symmetry on $M$.\n\n\\subsection{Filling the arrangement}\\label{geo:fill}\n\nBlow up all intersection centers within the $z$-family, and then the\ntransforms of those within the $w$-family.  Product coordinates show\nthat the operations for the two families commute.  The branch rule\nfor the $z$ character is as follows.  Every strict transform of a\n$z$ hyperplane is a branch divisor of order two.  If $k$ such\nhyperplanes pass through a center, its exceptional meridian is the\nproduct of their meridians, and therefore has sign $k\\bmod2$.\nAccordingly the exceptional divisor is branched precisely when $k$\nis odd.  In that case blow up each meeting of this exceptional divisor\nwith a strict transform of a hyperplane.  The two meeting divisors\nboth have sign one; their new exceptional divisor has sign zero.\nThe branch components within this family are now smooth and disjoint.\nApply the identical rule to the $w$ character.  This prescription\nincludes partial pencils.  In particular an ordinary red--coupling\npair has $k=2$, so its first exceptional divisor is unbranched.\n\nLet $\\widehat M$ denote the resulting smooth projective base.  Extend\nthe two sign covers from the complement by normalization over\n$\\widehat M$, and take them together.  Finite topological covers of\nsmooth complex algebraic varieties algebraize, and normalization in\nthe resulting finite extensions gives finite algebraic covers; thus\nthis procedure is projective.  Locally along a branch divisor the\nnormalization is the smooth double-cover model $t=u^2$.  Branch\ndivisors within a family are disjoint, and across the families the\nlocal equations and the two signs are independent.  Their combined\nnormalization is therefore smooth, including at crossings, where it\nhas product equations $(t_1,t_2)=(u_1^2,u_2^2)$.  We obtain a smooth\nprojective variety\n\\begin{equation}\\label{geo:V}\n                 V\\longrightarrow\\widehat M\n\\end{equation}\nwith deck group $(\\Z/2)^2$.  It is connected: a meridian at a general\npoint of a $z$ divisor has sign $(1,0)$, and a meridian at a general\npoint of a $w$ divisor has sign $(0,1)$, so the combined monodromy is\nsurjective.\n\nNear $o$ all six slopes in each pencil occur and the coupling\nhyperplanes are absent after the neighborhood has been made small.\nThe blowups are the product of the blowups of the two coordinate\nplanes at their origins.  The exceptional fiber of $\\widehat M\\to M$\nis $\\PP^1\\times\\PP^1$.  Each exceptional line has six branch markings\nand has even meridian sign.  Its inverse image in $V$ is consequently\n\\begin{equation}\\label{geo:Fstar}\n                            F_*=C\\times C.\n\\end{equation}\nPut\n\\[\n       L=\\pi^*\\mathcal O_{\\PP^1}(-1),\n       \\qquad N=N_{F_*/V}\n          =\\operatorname{pr}_1^*L\\oplus\\operatorname{pr}_2^*L.\n\\]\nIn fact a neighborhood of $F_*$ has the product line-bundle description\nprovided by these two tautological lines.  Indeed, in each factor, after\nthe unbranched radial disks have been filled, their disk bundle retracts onto its\nzero section.  Away from the six marked directions its cover is thus\npulled back from the direction line.  Normalization gives the\nsix-branch cover in that direction variable, with the radial line\nbundle pulled back unchanged.\n\nThe equivariance \\eqref{geo:sign-equivariance} makes the kernel of the\ncombined sign character invariant, so $\\sigma$ lifts on the complement.\nThe equivariant blowups and normalization extend the lift to an\nautomorphism $s$ of $V$.  Its restriction to $F_*$ exchanges the two\ncopies of $C$, possibly followed by either hyperelliptic deck\ninvolution.  Composing with a deck transformation makes this\nrestriction the pure swap.  To determine the lift in the whole local\nmodel, note that $u,v$ are actual tautological vectors: the covering\nmap in these coordinates is\n\\[\n (p,q;u,v)\\longmapsto(\\pi(p),\\pi(q);u,v).\n\\]\nThe pure swap determines the lifted direction maps on the connected\nlocal cover.  Since the base map sends the radial vectors to $(v,-u)$,\nthere is no further radial multiplier.  Thus\n\\begin{equation}\\label{geo:line-action}\n             s(p,q;u,v)=(q,p;v,-u).\n\\end{equation}\nIts fourth power is a deck transformation fixing $F_*$ pointwise,\nso $s^4=1$; a nonidentity element of $(\\Z/2)^2$ does not fix $C\\times C$\npointwise.  Its square is the identity on $F_*$ and is multiplication\nby $-1$ on both normal lines.\n\n\\subsection{An ordinary neighborhood of the distinguished fiber}\n\\label{geo:resolution}\n\nThe quotient stack $[V/\\langle s\\rangle]$ is a smooth proper orbifold\nwith projective coarse space, but a surface mapping into its distinguished\nfiber would\nmeet stabilizers.  We now replace just that fiber by an ordinary\nsmooth resolution.  Retaining the quotient stack elsewhere allows\nall other fixed loci to be left in place.\n\nFirst blow up $F_*$ in $V$, and take the coarse quotient by\n$\\langle s^2\\rangle$.  The exceptional divisor is\n\\begin{equation}\\label{geo:P}\n                          P=\\PP_{F_*}(N),\n\\end{equation}\nwhere projectivization parametrizes lines.  Before taking the quotient,\n$s^2$ acts trivially on $P$ and as $t\\mapsto-t$ on its transverse\ncoordinate.  The quotient is thus smooth near $P$, with transverse\ncoordinate $t^2$.\n\nThe residual involution induced by $s$ has fixed points near $P$ only\nin $P$: away from the fiber, a fixed point would project to a\nnontrivial fixed point of the symmetry near $o$ in $M$.  Its fixed\npoints in $P$ project to the diagonal of $C\\times C$.  On the\nprojective normal line over that diagonal, it acts by\n\\[\n                       [u:v]\\longmapsto[v:-u].\n\\]\nThere are exactly two eigensections, with eigenvalues $i$ and $-i$\nbefore projectivization.  These give two disjoint smooth fixed curves.\nOn the three normal directions to either curve the involution acts\nby $-1$: one direction is antisymmetric in the base, one is tangent\nto the projective fiber, and the transverse coordinate to $P$ has\neigenvalue $(\\pm i)^2=-1$.  Blow up both fixed curves and take the\ncoarse quotient by the residual involution.  This quotient is smooth\nnear the distinguished fiber.  Indeed, for the linear local model\n\\[\n              (x,t_1,t_2,t_3)\\longmapsto(x,-t_1,-t_2,-t_3),\n\\]\nblowing up the fixed curve $\\{t_1=t_2=t_3=0\\}$ makes the action a\nreflection in the coordinate normal to the exceptional divisor, and\nthe coarse quotient is smooth.  Finite-order holomorphic actions are\nlocally linearizable, so this model applies.\n\nAll centers just used are closed and lie over $o$.  The blowups and\nfinite coarse quotients can therefore be performed globally,\nproducing a projective coarse space $S_0$ which modifies\n$V/\\langle s\\rangle$ only over $o$.  The intermediate coarse spaces\nmay retain quotient singularities elsewhere; the preceding calculation\nestablishes smoothness on a neighborhood of the distinguished fiber.\nTo form $\\cS$, use this smooth scheme near that fiber and use\n$[V/\\langle s\\rangle]$ away from it.  This is an algebraic gluing.\nMore explicitly, in $M$ remove all components of the nontrivial fixed\nloci other than the isolated point $o$, and take the resulting invariant\nZariski neighborhood.  Outside $o$ in this neighborhood the action is\nfree, so both constructions restrict to the same ordinary quotient.\nTheir gluing is a smooth connected effective orbifold $\\cS$ with\ncoarse space $S_0$.  The map $\\cS\\to S_0$ is separated and proper:\nthese properties are local on $S_0$, where it is either the coarse map of a finite-group\nquotient stack or the identity.  Since $S_0$ is projective,\n$\\cS$ is separated and proper over $\\C$.  In particular the whole distinguished\nfiber lies in the ordinary smooth locus of $\\cS$.\n\n\\subsection{The surface mapping into the ordinary fiber}\\label{geo:surface}\n\nLet $A=\\Jac(C)$.  For a genus-two curve, the Abel map\n\\[\n        \\Sym^2C\\longrightarrow\\Pic^2(C)\\simeq A\n\\]\nis the blowup at the canonical class, after choosing the displayed\ntranslation.  To recall the geometry, Riemann--Roch gives a unique\neffective divisor in every degree-two class except $K_C$; the latter\nhas the pencil $E_C=|K_C|\\simeq\\PP^1$.  For the degree-two quotient\n$q:C\\times C\\to\\Sym^2 C$, the inverse image of $E_C$ is the graph\n$\\Gamma_\\iota$ of the hyperelliptic involution.  The map $q$ is generically\nunramified along this graph, so $q^*E_C=\\Gamma_\\iota$ as divisors;\nramification at its six diagonal points does not change the generic\nmultiplicity.  The projection formula gives\n\\[\n  2E_C^2=(q^*E_C)^2=\\Gamma_\\iota^2=2-2g(C)=-2.\n\\]\nThe Abel map is therefore a birational\nmorphism between smooth surfaces with one exceptional $(-1)$-curve,\nand is the stated point blowup.\n\nChoose a nonzero rational section $\\tau$ of $L$.  On the open subset\nof $C\\times C$ where both values are defined and nonzero, put\n\\begin{equation}\\label{geo:rational-section}\n                 (p,q)\\longmapsto[\\tau(p),i\\tau(q)]\\in P.\n\\end{equation}\nThis section is equivariant for swapping the two factors and the\ninvolution on $P$.  Indeed \\eqref{geo:line-action} gives\n\\[\n       [\\tau(p),i\\tau(q)]\\longmapsto[i\\tau(q),-\\tau(p)]\n                            =[\\tau(q),i\\tau(p)].\n\\]\nAway from the diagonal it avoids the two fixed curves, and thus gives\na rational map from $\\Sym^2C$ to the smooth resolution constructed\nabove.  All the bundles and maps in this description are algebraic:\nthe normal-bundle identification is algebraic as well as analytic,\nby GAGA on the projective fiber.  Resolve the rational map by a finite\nsequence of point blowups of its smooth projective source, using the\nprojective coarse space as target.  The resolved image remains in its\nclosed distinguished fiber, where the coarse space agrees with the\nordinary locus of $\\cS$.  We therefore obtain a morphism\n\\begin{equation}\\label{geo:Ymap}\n                        f:Y\\longrightarrow\\cS\n\\end{equation}\nwhose image lies entirely over $o$.  Thus $Y$ is an iterated point\nblowup of the simple abelian surface $A$, and $f(Y)$ lies in the\nordinary locus.\n\n\\begin{proposition}[The geometric construction]\\label{prop:orbifold-surface}\nAfter any prescribed finite lower bounds on the arrangement radii and\ninjectivity radius, the construction above gives a smooth connected\nproper effective orbifold $\\cS$ with projective coarse space, a simple\nabelian surface $A=\\Jac(C)$, an iterated point blowup $Y\\to A$, and a\nmorphism $f:Y\\to\\cS$ with image in its ordinary locus.  The distinguished\nfiber is obtained from the product $C\\times C$, its normal bundle\n$\\operatorname{pr}_1^*L\\oplus\\operatorname{pr}_2^*L$, and the action\n\\eqref{geo:line-action} by the explicit blowups and coarse quotients\nabove.  Away from that fiber the orbifold is $[V/\\langle s\\rangle]$,\nwith $V$ given by the two parity covers and the filling rules of\nSection~\\ref{geo:fill}.\n\\end{proposition}\n\nAll parameters used in the finite arrangement have now been fixed.\nThe depth of $\\Gamma_0$ has not: Proposition~\\ref{prop:arrangement-local}\npermits its choice after the constants required for the finite-model\ngraphs in the next section.  Passing to the finite covers which produce\nthe signs preserves these estimates.  This order of choices will\nallow the local calculations to establish\n\\[\n                    \\im(f_*:\\pi_1(Y)\\to\\pi_1(\\cS))\\simeq\\Z.\n\\]\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/05-models.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/05-models.tex", "bytes": 34051, "sha256": "1f88976e8e8ea02ea59216d8033a672d18acad318bce9da2fef7c65987f3c664", "content": "\\section{Local covers and hyperbolic path graphs}\\label{sec:models}\n\nThe geometric construction gives relations among the loops in the\nsurface.  To show that these relations leave an element of infinite\norder, we will read paths in auxiliary covers of finite arrangements.\nThis section constructs those covers and proves the two properties\nneeded for Lemma~\\ref{thm:path-lemma}: their kernels agree on sufficiently\nbuffered overlaps, and their path graphs are uniformly hyperbolic.\nAll constants in this section depend only on the finite arrangement\n$\\cD$.  In particular, they are fixed before choosing the arithmetic\nlevel.\n\n\\subsection{Dihedral labels on a finite arrangement}\n\nFor a subset $T\\subseteq\\cD$, set\n\\[\n U_T=\\BB\\setminus\\bigcup_{H\\in T}H.\n\\]\nThere are characters $\\epsilon_z,\\epsilon_w:\\pi_1(U_T)\\to\\Z/2$,\nwith $\\epsilon_z$ equal to one on a meridian of a $z$ hyperplane and\nzero on a meridian of a $w$ hyperplane, and conversely for\n$\\epsilon_w$.  One can define each character by the parity of the\nwinding number of the product of defining affine equations of the\nhyperplanes in its family.  Let $E_T\\to U_T$ denote the associated\nfour-sheet cover; it may be disconnected.  These signs restrict\ncanonically to any ball patch, up to a choice of sheets.  Indeed,\nmeridians normally generate the complement group in a simply connected\nball, as is seen by filling a loop with a disk transverse to the\nhypersurfaces.  Thus their values determine a sign character there.\n\nWe refine these signs using the infinite dihedral group\n\\[\n D_\\infty=\\langle r,b\\mid r^2=b^2=1\\rangle,\n \\qquad \\epsilon(r)=\\epsilon(b)=1.\n\\]\nIts even subgroup is the infinite cyclic translation subgroup\n$\\langle rb\\rangle$.  Conjugation in $D_\\infty$ preserves this subgroup\nand changes its integer coordinate at most by a sign.\n\nWe first construct $q_z:\\pi_1(U_T)\\to D_\\infty$.  The construction\nignores all $w$ hyperplanes.  If $T$ contains fewer than all six $z$\nslope hyperplanes, define\n\\[\n q_z(\\gamma)=r^{\\epsilon_z(\\gamma)}.\n\\]\nSuppose instead that the whole $z$ pencil is present.  With only its\nsix hyperplanes removed, projection to the direction\n$\\lambda=z_2/z_1$ gives a map to the six-punctured projective line.\nChoose its standard meridians so that the four red punctures occur\nfirst, based together in a small red disk, and the two blue punctures\noccur last.  Assign $r$ to each red meridian and $b$ to each blue\nmeridian.  The sphere relation is respected because $r^4b^2=1$.\nWe fix this choice of the direction-line representation once, and use\nthe same choice for every complete pencil and for both families.\n\nIt remains to add the coupling hyperplanes that belong to $T$.\nThey lie over the red-direction disk: on $z_1=\\pm c$ in the ball,\n\\[\n |\\lambda|<0.7.\n\\]\nOver $|\\lambda|<1.5$, the pencil local system has a reduction of\nstructure group to $\\langle r\\rangle$.  In this reduction, multiply\nits transition functions by the two-sheet systems defined, for the\ncoupling hyperplanes that are present, by\n\\begin{equation}\\label{mod:coupling-functions}\n (1-z_1/c)^{1/2},\\qquad (1+z_1/c)^{1/2}.\n\\end{equation}\nThis means adding their winding parities to the exponent of $r$.\nOver $|\\lambda|>1$ one has $|z_1|<c$, so both systems in\n\\eqref{mod:coupling-functions} have the distinguished trivializations\nobtained by the branch of the square root near $1$.  These\ntrivializations identify the modified system with the unmodified\npencil system on $1<|\\lambda|<1.5$.  They therefore glue the two\nsystems.  This constructs $q_z$, up to conjugacy, on all of $U_T$.\nIts parity is $\\epsilon_z$: this is true on every meridian, and\nmeridians normally generate.  Interchanging $z$ and $w$ gives $q_w$.\n\nOn each component of $E_T$, both representations take values in\ntranslations.  Choose integer coordinates on the translation groups\nand write the resulting homomorphisms as\n\\[\n \\ell_z,\\ell_w:\\pi_1(E_T)\\longrightarrow\\Z.\n\\]\nThe notation is componentwise.  Their individual kernels are\nindependent of conjugating the dihedral representations or changing\nsheets.  The signs of the integer coordinates are immaterial until\nwe form a sum in the central model.\n\nFor each $T$, apply the intersection-center blowups, parity branching,\nand normalization of Section~\\ref{geo:fill} to the arrangement $T$ in\n$\\BB$ and its sign cover $E_T$.  The resulting smooth space is its\n\\emph{filled finite model}.\n\n\\begin{lemma}\\label{mod:extension}\nThe homomorphisms $\\ell_z$ and $\\ell_w$ extend across the blowups and\nbranched fillings of the finite model prescribed in\nSection~\\ref{geo:fill}.\n\\end{lemma}\n\\begin{proof}\nIt suffices to check the meridians of the divisors being added to the\nsign cover.  Removing subsets of complex codimension at least two\ndoes not change the fundamental group of a smooth manifold, and\nadding a smooth divisor kills its meridian.  These assertions follow\nby general position for paths and disks, so they also apply to the\nsuccessive local modifications here.\n\nFor an incomplete $z$ pencil, $q_z$ is the sign character in\n$\\langle r\\rangle$; hence $\\ell_z$ is already zero on the sign cover.\nFor a complete pencil, a meridian of the exceptional divisor over\n$L_z$ circles the common intersection at a fixed direction.  Its\nimage under direction projection is constant, and the coupling\nsystems trivialize near $L_z$.  Its $q_z$ label is therefore the\nidentity.  At an intersection of a coupling hyperplane with a red\nslope hyperplane, the two local meridians both have label $r$ in the\nsame reduction.  The meridian of their exceptional divisor is the\nproduct of these two meridians, and its label is $r^2=1$.  Every\nmeridian that is filled after branching of order two is the square\nof a meridian with reflection label, so again has trivial label.\nThese are all the complete-pencil centers.  The additional blowups\nneeded for odd partial pencils cause no difficulty, since the label\non the corresponding sign cover is zero.  The $w$ calculation is\nidentical, and intersections involving both families are products\nof these local calculations.  Thus both labels kill every required\nmeridian.\n\\end{proof}\n\n\\subsection{The sum cover at the distinguished fiber}\n\nFor the full model $T=\\cD$, let $\\overline E$ denote its filled sign\ncover.  Its fiber over $o$ is $F_*=C\\times C$, and it has the lift\n$s$ of $\\sigma(z,w)=(w,-z)$ described in Section~\\ref{sec:geometry}.\nOn $F_*$, the action of $s$ interchanges the factors.  The first\ntranslation label is nonzero on the first factor and zero on the\nsecond; the reverse holds for the second label.  For example, the\nproduct of one red and one blue meridian has even parity and label\n$rb$, so its lift to $C$ gives a nonzero translation.  This calculation\non $F_*$ agrees with that on the complement: push a representative\npath slightly in a nonzero normal direction, keeping its directions\naway from the branch points.  The coupling square roots are\ntrivial there.\n\nTo pass from this filled model to the resolved distinguished fiber,\nwe need an ordinary covering across the fixed points of the coarse\nquotient.  The descent argument below will achieve this by making the\nlifted symmetry commute with deck translations.  We will orient the\ntwo labels so that their sum is $s$-invariant.\n\nThe construction of $q_z$ is unchanged under $z\\mapsto-z$, with\nthe two coupling hyperplanes interchanged.  Indeed this map fixes\nthe direction $\\lambda$, interchanges the functions in\n\\eqref{mod:coupling-functions}, and preserves their distinguished\ntrivializations.  The analogous statement holds for $w$.\nConsequently $s$ interchanges the two translation homomorphisms up\nto signs.  Orient them by the preceding comparison on $F_*$ so that\n\\[\n \\ell_z\\circ s_* =\\ell_w,\n \\qquad \\ell_w\\circ s_* =\\ell_z.\n\\]\nThe nonzero restrictions to the two factors determine these signs.\nIn particular the homomorphism\n\\begin{equation}\\label{mod:sum-label}\n \\ell=\\ell_z+\\ell_w:\\pi_1(\\overline E)\\longrightarrow\\Z\n\\end{equation}\nis $s$-invariant.\n\n\\begin{lemma}\\label{mod:central}\nThe regular covering associated with $\\ker\\ell$ admits an order-four\nlift of $s$ commuting with its deck translations.  Its quotient by\nthis lift is an ordinary topological covering of the coarse space\n$\\overline E/\\langle s\\rangle$.  Consequently it pulls back to a\ncovering of the resolution used over the distinguished fiber.\n\\end{lemma}\n\\begin{proof}\nChoose an $s$-fixed point of $F_*$, for example a point on its\ndiagonal, and a point above it in the $\\ker\\ell$ cover.  Invariance\nof $\\ell$ gives a unique lift $\\widehat s$ fixing this point.  Since\n$s^4=1$, its fourth power is a deck transformation fixing a point,\nhence is the identity.  Its order is four because it projects to\n$s$.  Invariance of the integer label also says that conjugation\nby $\\widehat s$ acts trivially on the deck group.\n\nFor descent to a topological cover, consider a point fixed by a\nsubgroup $H\\subseteq\\langle s\\rangle$.  On the fiber over that\npoint, the lifted $H$ action commutes with deck translations, and\ntherefore acts by translations of this integer torsor.  Each such\ntranslation has finite order because the lift is an action of the\nfinite group $\\langle s\\rangle$.  It is thus the identity.  Choose\na sufficiently small invariant neighborhood at the point on which\nthe original cover is trivial.  The stabilizer acts trivially on\nthe discrete factor, so its quotient is again a product with that\nfactor.  Translating these neighborhoods under the finite group\nproves that the quotient map is a covering.  Pullback of a covering\nalong the resolution map remains a covering.\n\\end{proof}\n\nThe model using both kernels will be called an \\emph{individual\nmodel}.  The full model using the sum kernel and then the lifted\n$s$ quotient will be called the \\emph{central model}.  The distinction\nis essential: the sum is used only near a distinguished fiber.\n\n\\subsection{How kernels compare on bounded balls}\n\nBefore constructing graph metrics, we prove that these local labels\ncan be compared without a global coloring of the arithmetic\narrangement.  There are two possible sources of disagreement:\nnormal-matching isometries may not be unique, and a larger chart\nmay contain hyperplanes absent from a smaller one.\n\nFirst suppose the prescribed normal vectors of a complete pencil\nhave been matched as in Proposition~\\ref{prop:arrangement-local}.\nTwo distinct slope normals span its positive\nnormal plane and specify its two coordinate functionals, hence the\nratio $z_2/z_1$.  Thus the direction-line local system is independent\nof the choice of an isometry extending the normal match.  If a\ncoupling normal is also matched, its homogeneous linear equation,\ntogether with those two functionals, specifies the ratio $z_1/c$.\nExplicitly, if $X_1$ is the first coordinate functional, $T_0$ is\nthe last homogeneous coordinate, and $F_c=X_1-cT_0$ defines the\npositive coupling hyperplane, then\n\\[\n z_1/c=\\frac{X_1}{cT_0}=\\frac{X_1}{X_1-F_c}.\n\\]\nThe negative coupling gives the same conclusion with the signs reversed.\nThe gluing rule \\eqref{mod:coupling-functions} is therefore intrinsic\nto these matched data as well.  The corresponding assertions hold\nfor the $w$ family.  Under $\\sigma$, the two families are interchanged\nand the $w$ coordinates are negated.  If the chosen representative\nof a relabeled normal differs by a scalar, use that same scalar on\nboth sides of the match.  The resulting coordinate ratios transform\nas just described, so the individual kernels are carried to the\ncorresponding individual kernels.  This is the equivariance needed\nbelow; it does not require a globally chosen orientation of a\ntranslation label.\n\nNext, consider a coupling hyperplane absent from a metric ball $Q$.\nA metric ball in the ball model is a Euclidean ellipsoid and is\nconvex.  Its $z_1$ image is therefore convex and avoids the relevant\nvalue $c$ or $-c$.  On that image the corresponding function in\n\\eqref{mod:coupling-functions} has a single-valued square root.\nWhere the image meets $|z_1|<c$, choose its sign to agree with the\ndistinguished square root.  The intersection is convex, so one\nchoice works throughout it.  If the intersection is empty, there\nis no overlap requiring a prescribed sign.  This trivializes the\nextra coupling system compatibly with the red-disk gluing.  Hence\nincluding or omitting a coupling hyperplane missing $Q$ gives\nisomorphic label systems on $Q$.\n\n\\begin{lemma}\\label{mod:buffer-bound}\nFor every $R>0$ there is $B_1(R)$ with the following property.\nIn any finite model with a complete $z$ pencil, if a loop in the\nsign cover projects into $B(x,R)$ and has nonzero $z$ translation,\nthen\n\\[\n \\dist(x,L_z)\\le B_1(R).\n\\]\nThe analogous assertion holds for $w$.  The same bounds hold if\nthe loops must avoid any additional analytic subsets.\n\\end{lemma}\n\\begin{proof}\nIf the ball meets at most one hyperplane of the relevant family,\nits complement for that family has trivial or cyclic fundamental\ngroup generated by the one meridian.  One way to see the latter\nassertion is to use the convexity of the ball and a transverse\ncomplex coordinate for the hyperplane: its projection has an\nellipse as image, and the centers of the convex fibers give a\ncontinuous section.  The complement thus has the homotopy type\nof a punctured ellipse.  Thus its dihedral image lies in a\nconjugate of an order-two subgroup.  It has zero translation on\nthe sign lift.  Omitting hyperplanes from the other family does\nnot alter this conclusion, because the representation factors\nthrough the complement of the relevant family.\n\nIf two distinct slope hyperplanes meet $B(x,R)$, take a unit\nnegative homogeneous vector representing $x$.  Its inner products\nwith their fixed positive normals are bounded in terms of $R$:\nfor a normal $n$ the normalized absolute inner product is\n$\\sinh\\dist(x,H_n)$.  The two normals form a basis of the positive\nnormal plane of $L_z$.  Their inner-product bounds therefore bound\nthe positive projection of $x$ onto that plane, and hence bound\n$\\dist(x,L_z)$.  There are only finitely many pairs of normals,\nso this bound is uniform.\n\nFor the remaining case, suppose toward a contradiction that there\nare balls supporting nonzero translation whose centers leave every\nbounded neighborhood of $L_z$.  After passing to a subsequence,\nat least one fixed coupling hyperplane meets each ball: otherwise\nthe preceding two cases apply.  The centers tend to a boundary\npoint $\\xi$, and a fixed-radius hyperbolic ball converges to the\nsame boundary point as its center.  Thus $\\xi$ belongs to the\nclosure of that coupling hyperplane.  The closures of the two\ncoupling hyperplanes are disjoint.  At $\\xi$ the direction coordinate\nis defined and lies strictly inside the red disk, since $z_1=\\pm c$\nand the boundary estimate is uniform:\n\\[\n |z_2/z_1|\\le\\frac{\\sqrt{a-c^2}}{c}\n <\\frac{\\sqrt{1-0.85^2}}{0.85}<0.62<0.7.\n\\]\nEventually the whole ball lies over this red disk and misses the\nother coupling hyperplane.  Its label system consequently reduces\nto $\\langle r\\rangle$, contradicting the assumed nonzero\ntranslation.  This proves the bound.  Restricting the class of\nallowed loops does not weaken it.\n\\end{proof}\n\n\\begin{proposition}[Compatibility of kernels]\\label{prop:kernel-compatibility}\nFor every $R>0$ there are constants $B(R)>R$ and $J_0(R)$, depending\nonly on $\\cD$, with the following properties.\n\\begin{enumerate}\n\\item Suppose two finite-model descriptions, with their prescribed\nnormal matches, agree on all hyperplanes meeting $B(x,B(R))$.\nIdentify their sign covers there by a fixed choice of sheets.\nOn loops in this sign cover that project into $B(x,R)$, their\nindividual $z$ kernels agree and their individual $w$ kernels\nagree.  More precisely, for each family the corresponding integer\nhomomorphisms agree up to sign.\nThe descriptions may include additional hyperplanes outside the\nbuffered ball.\n\\item In the full model, if $\\dist(x,o)>J_0(R)$, then on such loops\n\\[\n \\ker(\\ell_z+\\ell_w)=\\ker\\ell_z\\cap\\ker\\ell_w.\n\\]\n\\end{enumerate}\nBoth statements remain valid after restricting the loops to avoid\nany further analytic subsets.  The first statement is equivariant\nunder the symmetry interchanging the families.\n\\end{proposition}\n\\begin{proof}\nChoose $B(R)>R+B_1(R)+1$, enlarging it if needed to include the\ncorresponding bound for both families.  Use the subset of hyperplanes\nvisible in the buffered ball as an intermediate description.\nIf a larger description has an incomplete pencil, so does this\nsubset, and both corresponding translation labels vanish.\nIf the larger pencil is complete but the buffered pencil is not,\na nonzero translation on an $R$-ball would, by\nLemma~\\ref{mod:buffer-bound}, put its center within $B_1(R)$ of the\ncommon pencil intersection.  Since every slope hyperplane contains\nthat intersection, all six slopes would then meet the buffered\nball, a contradiction.  Thus again both labels vanish on these\nloops.  If both pencils are complete, the normal match fixes the\ndirection-line system, and extra coupling hyperplanes can be\nomitted by the square-root trivialization just proved.  The only\nremaining change is conjugation of a dihedral label, which changes\na translation coordinate at most by sign.  This proves the first\nassertion, including its equivariance and its compatibility with\nthe chosen sign sheets.\n\nThe sets\n\\[\n \\{x:\\dist(x,L_z)\\le B_1(R)\\},\\qquad\n \\{x:\\dist(x,L_w)\\le B_1(R)\\}\n\\]\nhave bounded intersection.  Indeed, in homogeneous coordinates\nthe two normal planes together form the positive four-dimensional\nspace.  Bounds on both positive projections bound the distance\nfrom $o$.  Choose $J_0(R)$ beyond that intersection.  Outside it,\nat least one of $\\ell_z,\\ell_w$ vanishes on every loop over\n$B(x,R)$, by Lemma~\\ref{mod:buffer-bound}.  The displayed equality\nof kernels follows.  Every argument concerns the same individual\nloops before and after imposing additional omissions, so those\nomissions preserve the conclusions.\n\\end{proof}\n\n\\subsection{Graphs defined by projected diameter}\n\nWe now turn the local covers into graphs to which the path lemma\napplies.  Over each connected component of $E_T$, let\n$\\widehat E_T$ be the connected regular cover defined by\n$\\ker\\ell_z\\cap\\ker\\ell_w$.  For the full arrangement we also use\nthe cover defined by $\\ker(\\ell_z+\\ell_w)$ and its lifted-$s$\nquotient.  Restrict all these covers to the open arrangement\ncomplement when defining graph vertices.\n\nFor $u>0$, vertices are all points of the relevant covering space.\nAn oriented edge is a parametrized continuous path whose projection\nto $\\BB$ has diameter strictly less than $u$; its reverse is the\nreverse path.  Multiple edges are retained.  In the central case\nwe take the quotient graph by $\\widehat s$.  The symmetry acts\nfreely on the open arrangement complement, so edges out of a\nquotient vertex correspond bijectively to edges out of any chosen\nrepresentative.  Denote these graphs by $\\mathcal G_T(u)$, with\n$\\mathcal G_{\\mathrm{cen}}(u)$ for the central quotient.\n\nThe use of diameter has an important consequence.  A word of\narbitrary length in loops supported in one fixed bounded region\ncan be one edge.  It is this feature that prevents the unbounded\ntranslation labels from producing large flat regions in the\ngraphs.\n\n\\begin{proposition}[Hyperbolicity of the models]\\label{prop:model-hyperbolicity}\nThere are $u>0$ and $\\delta\\ge0$, depending only on $\\cD$, such that\nevery connected component of every $\\mathcal G_T(u)$ and of\n$\\mathcal G_{\\mathrm{cen}}(u)$ is $\\delta$-hyperbolic.\nThe same $\\delta$ can be used if an arbitrary locally finite union\nof proper complex analytic subsets of $\\BB$ is additionally\nomitted, with invariant omissions in the central case.  On the\nvertices retained after such an omission, graph distances are\nunchanged.\n\\end{proposition}\n\nThe proof has three parts.  First, paths carrying arbitrary deck\ntranslations inside a fixed bounded region give bounded graph diameter\nover every bounded radial region.  Next, an arrangement-preserving\ncollar identifies the remaining vertices with points of a covering of\nthe sphere complement, together with a radial coordinate; the angular\ndiameter permitted by a graph edge decays exponentially with that\ncoordinate.  Finally, comparison with the cone graph defined below\nproves uniform hyperbolicity.  No properness or local finiteness of the\ngraphs is required.\n\n\\begin{lemma}\\label{mod:bounded-core}\nOne can choose $u$, simultaneously for all the finite models, so\nthat the vertices projecting into any fixed bounded radial region\nhave bounded graph diameter within each connected component.\n\\end{lemma}\n\\begin{proof}\nThe deck group over a component of $E_T$ is a subgroup of $\\Z^2$,\nor of $\\Z$ for the sum cover, and is finitely generated.  At a\nbasepoint choose loops representing a finite set of its generators.\nThere are only finitely many sign sheets; also choose finitely\nmany paths joining the sheets that belong to the same component.\nAll these projected paths lie in a fixed bounded region.  Choose\n$u$ greater than its diameter, for each of the finitely many models.\nAn arbitrary word in the generator loops then defines a single\nedge.  Thus all deck translates over a basepoint have uniformly\nbounded graph distance, including the finitely many sign choices.\n\nFor a fixed radial bound, any point in the ball region can be\njoined to a chosen basepoint by a uniformly bounded chain of small\nballs.  Within each ball, paths can avoid the finite complex\nhypersurface arrangement: its complement is path connected by\ngeneral position.  Choose successive junctions off the arrangement\nin the overlaps.  The resulting paths have uniformly bounded\nedge count and lift to the covering.  Any discrepancy in their\nterminal sheet is corrected using the preceding generator loops.\nThis gives the asserted diameter bound.  One can take the chain\nbound uniformly, for example by covering the compact closure of\nthe radial region and a path to the basepoint by finitely many\nsmall balls with connected overlap graph.  No positive lower\nbound on distance to the arrangement is needed.\n\\end{proof}\n\n\\subsection{The arrangement at the sphere at infinity}\n\nRescale the ball to unit radius and write\n\\[\n x=\\tanh(r)\\xi,\\qquad |\\xi|=1.\n\\]\nLet $\\Sigma$ be its unit sphere.  Each affine intersection of the\nhyperplanes that reaches $\\Sigma$ is transverse to $\\Sigma$.\nIndeed its homogeneous Hermitian subspace is nondegenerate by the\narithmetic normal-space property in the proof of\nProposition~\\ref{prop:arrangement-local}.  More explicitly, write\nan affine intersection as $a_0+V_0$ with $a_0\\perp V_0$.  On its\nhomogeneous span, the Hermitian form is\n\\[\n |v|^2+(|a_0|^2-1)|t|^2.\n\\]\nNondegeneracy excludes $|a_0|=1$, precisely the tangency case.\nIf the intersection reaches the sphere, then $|a_0|<1$, and the\nintersection is transverse there.  This also excludes intersections\nsupported only on the boundary.\n\nThere is an arrangement-preserving product collar near $\\Sigma$.\nHere is a direct construction, including the estimate needed for\nthe graph metric.  At each $\\xi\\in\\Sigma$, choose a constant real\nvector tangent to all affine hyperplanes through $\\xi$ and with\npositive outward radial component.  Transversality of their\nintersection supplies this vector.  Use a small neighborhood\nmissing every other hyperplane.  A partition of unity combines\nthese local vectors into a smooth vector field tangent to each\nhyperplane and transverse to the spheres.  Normalize it so that\nthe derivative of the Euclidean radius is one.  Its flow yields,\nfor $r\\ge r_0$ with $r_0$ sufficiently large, diffeomorphisms\nbetween the ideal arrangement complement and the complement on\nthe radius-$r$ sphere.  They preserve all arrangement strata.\nBecause the vector field is bounded on a compact collar, their\nangular displacement from the original ideal point is\n\\begin{equation}\\label{mod:collar-shift}\n O(1-\\tanh r)=O(e^{-2r})\n \\quad\\hbox{in Euclidean distance.}\n\\end{equation}\nIn the central model, average the field under $\\sigma$ before\nnormalizing.  This retains tangency and positive radial component\nand makes the collar equivariant.  The collar lifts to each\ncovering; the lifted end is a radial product with a possibly\ndisconnected covering $I$ of the ideal complement.\n\nOn $\\Sigma$ use the metric\n\\[\n d_K(\\xi,\\eta)=|1-\\langle\\xi,\\eta\\rangle|^{1/2}.\n\\]\nFor completeness, it satisfies the triangle inequality.  If\n$a=d_K(\\xi,\\zeta)$ and $b=d_K(\\zeta,\\eta)$, then\n$|\\xi-\\zeta|\\le\\sqrt2a$ and\n$|\\zeta-\\eta|\\le\\sqrt2b$.  Expanding the Hermitian inner product\naround $\\zeta$ gives\n\\[\n |1-\\langle\\xi,\\eta\\rangle|\n \\le a^2+b^2+|\\xi-\\zeta|\\,|\\eta-\\zeta|\n \\le(a+b)^2.\n\\]\nPositivity and symmetry are immediate.  In particular,\n\\eqref{mod:collar-shift} is an $O(e^{-r})$ displacement in $d_K$.\n\nThe ball distance in our normalization satisfies\n\\begin{equation}\\label{mod:ball-distance}\n \\cosh d(x,y)=\n \\frac{|1-\\langle x,y\\rangle|}\n {\\sqrt{1-|x|^2}\\sqrt{1-|y|^2}}.\n\\end{equation}\nConsequently, for points of radii $r+O(1)$, bounded hyperbolic\ndistance is equivalent, with uniform constants, to angular\n$d_K$ distance $O(e^{-r})$.  To check both implications, write\n$x=\\rho\\xi$, $y=\\tau\\eta$, where $\\rho=\\tanh r$ and\n$\\tau=\\tanh r'$.  Then\n\\[\n 1-\\langle x,y\\rangle\n =1-\\rho\\tau+\\rho\\tau(1-\\langle\\xi,\\eta\\rangle).\n\\]\nThe second parenthesis has nonnegative real part.  Thus the\nabsolute value is at least $\\rho\\tau d_K(\\xi,\\eta)^2$ and at most\n$1-\\rho\\tau+d_K(\\xi,\\eta)^2$.  For $|r-r'|$ bounded and both\nradii large, the denominator in \\eqref{mod:ball-distance} and\n$1-\\rho\\tau$ are comparable to $e^{-2r}$.  These inequalities\nprove the assertion.  Conversely a bounded ball distance bounds\n$|r-r'|$ by the triangle inequality for radial distance, so no\nseparate radial hypothesis is needed in that direction.\n\nOn the lifted ideal complement $I$, define\n\\begin{equation}\\label{mod:ideal-metric}\n d_I(\\xi',\\eta')=\n \\min\\left\\{1,\\inf_\\alpha\\diam_{d_K}(\\pi\\alpha)\\right\\},\n\\end{equation}\nwhere $\\alpha$ runs over paths in $I$ joining $\\xi'$ to $\\eta'$\nand $\\pi$ is projection to $\\Sigma$.  Put $d_I=1$ for points in\ndifferent components.  The triangle inequality follows by\nconcatenation, because the projected paths meet and the diameter\nof their union is at most the sum of their diameters.  Distinct\nprojected endpoints give positivity directly.  For distinct lifts\nof the same endpoint, take a small evenly covered neighborhood\nin the ideal complement.  A path changing the lift must leave\nthis neighborhood, so its projected diameter has a positive\nlower bound.  Therefore $d_I$ is a metric, bounded by one.\n\n\\subsection{A cone over an arbitrary bounded metric space}\n\nWe isolate the metric calculation, a discrete form of the familiar\nhyperbolic-cone construction; compare \\cite[Section~7]{BonkSchramm2000}.\nWe give the proof for arbitrary bounded metric spaces, as no\ncompactness hypothesis is available here.  For a metric space $(I,d_I)$\nwith $d_I\\le1$, let $\\mathcal C(I)$ have vertices $(\\xi',t)$,\n$\\xi'\\in I$, $t\\in\\Z_{\\ge0}$.  Add vertical unit edges between\nsuccessive heights at the same point, and horizontal unit edges\nat height $t$ whenever $d_I(\\xi',\\eta')\\le e^{-t}$.\nIn particular all vertices at height zero are pairwise adjacent.\n\n\\begin{lemma}\\label{mod:cone}\nThe graph $\\mathcal C(I)$ is hyperbolic with an absolute constant,\nindependent of the bounded metric space $I$.  More precisely, put\n\\[\n j=\\min\\{t,t',-\\log d_I(\\xi',\\eta')\\},\n \\qquad -\\log0=+\\infty.\n\\]\nIts distance $D$ satisfies\n\\begin{equation}\\label{mod:cone-distance}\n t+t'-2j\\le\n D\\bigl((\\xi',t),(\\eta',t')\\bigr)\n \\le t+t'-2j+3.\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nIf $\\xi'=\\eta'$, both the formula without its additive error and\nthe claim are immediate from vertical distance.  Otherwise,\ndescend to height $\\lfloor j\\rfloor$, take one horizontal edge,\nand ascend.  This gives the upper bound.\n\nFor the lower bound, let any connecting path have minimum height\n$m$ and $n\\ge1$ horizontal edges.  The number of its vertical\nedges is at least $t+t'-2m$.  By the triangle inequality in $I$,\n\\[\n d_I(\\xi',\\eta')\\le n e^{-m}.\n\\]\nSince also $m\\le t,t'$, this implies $m\\le j+\\log n$.\nThe path length is therefore at least\n\\[\n t+t'-2j+n-2\\log n\\ge t+t'-2j.\n\\]\nThe last inequality holds for every integer $n\\ge1$ (indeed its\nminimum over positive real $n$ is $2-2\\log2>0$).\n\nTo deduce hyperbolicity, fix a height-zero basepoint $p$.\nIts distance to $(\\xi',t)$ differs from $t$ by at most one.\nIt follows from \\eqref{mod:cone-distance} that the Gromov product\nof two vertices differs by at most $3/2$ from their corresponding\n$j$ value.  For three base points, the metric triangle inequality\nimplies\n\\[\n -\\log d_I(\\xi',\\eta')\n \\ge\\min\\{-\\log d_I(\\xi',\\zeta'),\n                -\\log d_I(\\zeta',\\eta')\\}-\\log2.\n\\]\nTaking the minimum with the relevant heights preserves this\ninequality.  Thus the Gromov products satisfy the hyperbolicity\ninequality with an absolute additive constant.  The standard\nGromov-product characterization of hyperbolicity for graphs\ncompletes the proof; see \\cite[Chapter~III.H]{BridsonHaefliger}.\n\\end{proof}\n\n\\subsection{Comparison with the model graphs}\n\nWe can now complete the proof of\nProposition~\\ref{prop:model-hyperbolicity}.  Work first before the\nfinite symmetry quotient, in a connected component.  Using the\ncollar coordinates, send a cone vertex $(\\xi',t)$ to the covering\npoint of radius $r_0+t$ above its ideal coordinate.  A horizontal\nedge at positive height is represented by a path whose projected\nideal diameter is at most $2e^{-t}$: the factor two allows the\ninfimum in \\eqref{mod:ideal-metric} to be unattained.  Place that\npath at radius $r_0+t$ with the collar.  The angular estimates and\n\\eqref{mod:ball-distance} give a uniform bound on its projected\nball diameter.  Vertical edges likewise have uniformly bounded\nprojected diameter.  Enlarge $u$ to exceed these bounds strictly.\nAt height zero, even when ideal components are different,\nLemma~\\ref{mod:bounded-core} gives a uniform bound on the graph\ndistance of the two images.  The cone map therefore sends adjacent\nvertices to pairs at uniformly bounded graph distance.\n\nIn the opposite direction, round a point of radius at least $r_0$\nto the nearest integer layer, retaining its lifted ideal coordinate.\nMap the bounded radial part to one fixed height-zero vertex.\nThe two maps are coarse inverses.  Radial rounding has uniformly\nbounded graph displacement, and the bounded-core lemma handles\nall other points.  To verify that this inverse also sends edges\nto pairs of bounded distance, consider an edge whose projection\nlies sufficiently far out.  If one endpoint has radius $r$, its\nwhole projected path has radii in $[r-u,r+u]$.  Formula\n\\eqref{mod:ball-distance} bounds the angular diameter of this path\nby $O_u(e^{-r})$.  The collar changes this by another term of the\nsame order.  Following the lifted path in the collar consequently\ngives\n\\[\n d_I(\\xi',\\eta')\\le C_u e^{-r}.\n\\]\nIts rounded heights differ by a bounded amount, so\n\\eqref{mod:cone-distance} bounds their cone distance uniformly.\nIf an edge meets the bounded radial part, both endpoints have\nbounded radius and the same conclusion follows through height\nzero.  Hence the two graphs are quasi-isometric.  Hyperbolicity\nof the cone and quasi-isometry invariance for geodesic spaces\n\\cite[Chapter~III.H]{BridsonHaefliger} prove hyperbolicity of the\nmodel graph.\n\nFor the central model, the collar and the comparison maps on the\nend are equivariant under $\\widehat s$.  The lifted action preserves\n$d_I$, since its projection is unitary and hence preserves $d_K$.\nFor the finite group $H=\\langle\\widehat s\\rangle$, the formula\n\\[\n \\overline d_I(H\\xi',H\\eta')\n   =\\min_{h\\in H}d_I(\\xi',h\\eta')\n\\]\ndefines a metric on $I/H$.  Positivity uses finiteness of $H$, and\nthe triangle inequality follows by translating and concatenating\nminimizers.  The quotient of the cone graph is the cone graph of this quotient\nmetric, possibly with multiple edges and loops, which do not affect\nvertex distances.\nThe preceding comparison thus descends, while the quotient radial\ncore remains bounded.  Lemma~\\ref{mod:cone} proves hyperbolicity\nalso in the central case.  There are only finitely many subsets\n$T$ and one central model, so one choice of $u$ and one\nhyperbolicity constant work for all of them.\n\nFinally, let an additional locally finite union of proper complex\nanalytic subsets be omitted.  Removing vertices and restricting\nedges cannot decrease distances.  For the reverse inequality,\ntake a finite edge path whose endpoints are retained.  Perturb\nits finitely many junctions and constituent paths, relative to\nthe endpoints, to miss the omitted loci.  General position permits\nthis because proper complex analytic subsets have real codimension\nat least two.  Locally finite omissions cause no extra difficulty:\na compact path meets only finitely many members, and the\nperturbation can be made in finitely many coordinate neighborhoods.\nMake this perturbation as a homotopy of the whole concatenated\nchain in the existing arrangement complement, relative to its two\noverall endpoints.  Homotopy lifting preserves those two endpoints\nin the cover; the intermediate junctions move coherently.  Each\noriginal projected path has diameter strictly smaller than $u$, so a\nsufficiently small perturbation preserves that strict inequality.\nThe perturbed path has exactly the same number of edges.  Thus\nall distances between retained vertices are unchanged.  For a\ncentral quotient edge path, lift the whole chain to the cover, perturb it relative to its overall endpoints, and project\nback; invariance of the omitted set ensures that the projected\nchain avoids it.  The four-point inequality for hyperbolicity\nrestricts to the retained vertex set with the same constant, proving the\nlast assertion of the proposition.\n\nWe now fix such a value of $u$, a common hyperbolicity constant,\nand a reading radius $N$ supplied by Lemma~\\ref{thm:path-lemma}.\nOnly after these choices will we choose the arithmetic level and\nthe geometric sizes of the charts.\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/06-image.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/06-image.tex", "bytes": 28587, "sha256": "f94ffe0f3cb2d1c01e66a32e89c140111c963f96ff46cb5fda567583b28aa337", "content": "\\section{An infinite cyclic image}\\label{sec:image}\n\nWe now compute the image of the surface constructed in\nProposition~\\ref{prop:orbifold-surface}.  The relations supplied by a\ncoupling hyperplane show that this image is cyclic.  Proving that the\ncyclic group is infinite requires a separate argument: the translation\nlabels of the preceding section exist in charts, and need not define a\nhomomorphism on the fundamental group of the whole orbifold.\n\n\\begin{theorem}\\label{thm:orbifold-cyclic}\nFor a sufficiently deep choice of the arithmetic level in the\nconstruction of Proposition~\\ref{prop:orbifold-surface}, the map\n$f:Y\\longrightarrow\\cS$ satisfies\n\\[\n \\im\\bigl(f_*:\\pi_1(Y)\\longrightarrow\\pi_1(\\cS)\\bigr)\\cong\\Z.\n\\]\nHere $Y$ is an iterated point blowup of the simple abelian surface\n$\\Jac(C)$, and its image lies in the ordinary locus of $\\cS$.\n\\end{theorem}\n\nThroughout the proof, fundamental groups are based at points in the\nordinary locus.  Changes of basepoint are made along specified common\npaths; in particular, the comparisons below concern homomorphisms with\none common conjugation, rather than separate conjugations of individual\ngenerators.\n\n\\subsection{Surface loops and the cyclic upper bound}\n\nRecall the distinguished fiber $F_*=C\\times C$ in $V$, the order-four\nlift $s$, and the rational section used to construct $Y$ in\nSection~\\ref{geo:surface}.  Put\n\\[\n \\mathcal K=[V/\\langle s\\rangle].\n\\]\nThere is a natural homomorphism\n\\begin{equation}\\label{app:comparison-map}\n \\pi_1(\\mathcal K)\\longrightarrow\\pi_1(\\cS).\n\\end{equation}\nIndeed, the substack $[F_*/\\langle s\\rangle]$ has complex codimension\ntwo in the smooth stack $\\mathcal K$.  Removing it does not change the\norbifold fundamental group, and its complement is the open substack on\nwhich the construction of $\\cS$ leaves $\\mathcal K$ unchanged.\n\nChoose a general point $q_0\\in C$.  The map\n\\[\n C\\longrightarrow\\Sym^2 C\\longrightarrow\\Jac(C),\n \\qquad p\\longmapsto p+q_0,\n\\]\nis, up to translation, an Abel map.  Its map on fundamental groups\nsurjects onto $\\pi_1(\\Jac(C))=H_1(C,\\Z)$.  Since point blowups preserve\nfundamental groups, loops from $C\\times\\{q_0\\}$ supply generators of\n$\\pi_1(Y)$.  They may be represented by a finite bouquet avoiding the\nbranch points, $p=q_0$, the zeros and poles of the rational section,\nand the finitely many points where the rational map or its resolution\nmust be avoided.  Removing finitely many points from a smooth curve is\nsurjective on fundamental groups, so these restrictions do not lose any\ngenerators.\n\n\\begin{lemma}\\label{app:surface-comparison}\nWith common choices of basing, the images in $\\pi_1(\\cS)$ of the\npreceding generators of $\\pi_1(Y)$ equal the images, under\n\\eqref{app:comparison-map}, of the corresponding loops in\n$C\\times\\{q_0\\}\\subset V$.  The loops can also be represented in the\nsign-covered arrangement complement, arbitrarily close to $F_*$, with\nboth normal coordinates nonzero.\n\\end{lemma}\n\n\\begin{proof}\nLift the bouquet by the rational section to the exceptional divisor of\n$\\operatorname{Bl}_{F_*}V$.  Over this bouquet the section has the form\n\\[\n [\\tau(p),\\sqrt{-1}\\,\\tau(q_0)],\n\\]\nwith both entries nonzero.  It avoids the fixed curves involved in the\nlast resolution step, because the bouquet avoids the diagonal of\n$C\\times C$.\n\nA complex line bundle on a finite graph is topologically trivial.\nThus the normal line to this exceptional divisor admits one continuous\nnonvanishing section over the entire lifted bouquet.  In the product normal coordinates, this push can be written\nexplicitly as\n\\[\n \\varepsilon\\bigl(\\tau(p),\\sqrt{-1}\\,\\tau(q_0)\\bigr).\n\\]\nOne sufficiently small $\\varepsilon>0$ works over the compact bouquet\nand gives a closed based bouquet off the exceptional divisor.  On blowing down to $V$, the pushed bouquet is\nhomotopic to the original one in $F_*$.  On passing to the resolution\nneighborhood in $\\cS$, it is homotopic to the bouquet obtained from\n$Y$.  These homotopies use the same push of the common basepoint.\n\nThe two components of the displayed section are nonzero, and the\nchosen directions avoid the marked slopes.  Taking the push small\ntherefore puts its projection off the arrangement.  The distinguished\npoint is an isolated component of the fixed locus of every nonidentity\npower of $\\sigma$ in a sufficiently small neighborhood.  Hence the\npushed bouquet can also avoid the inverse images of all those fixed\nloci.  This proves all the assertions, including the common basing.\n\\end{proof}\n\nWe next compute an upper bound before taking the quotient by $s$.\nWrite $H=(\\Z/2)^2$ for the sign deck group.  The map\n\\[\n V\\longrightarrow[V/H]\n\\]\nis an orbifold covering, so it induces an injection of $\\pi_1(V)$ into\n$\\pi_1([V/H])$.  This remains true over branch points: the target is the\nquotient stack, which retains their isotropy groups.\n\nThe pencil through $L_z$ gives the orbifold line\n$[C/(\\Z/2)]$, with six order-two points.  Number the four red points\nfirst.  Its fundamental group has the presentation\n\\begin{equation}\\label{app:pencil-group}\n Q=\\langle h_1,\\ldots,h_6\\mid\n       h_i^2=1,\\ h_1h_2h_3h_4h_5h_6=1\\rangle.\n\\end{equation}\nThe kernel of the parity character sending every $h_i$ to $1\\in\\Z/2$\nis $\\pi_1(C)$.  With the $w$ sign point fixed over a general direction,\nthe map of this orbifold pencil to $[V/H]$ identifies the homomorphism\nfrom this even subgroup with the homomorphism from\n$\\pi_1(C\\times\\{q_0\\})$ to $\\pi_1(V)$, followed by the injection just\ndescribed.\n\n\\begin{lemma}\\label{app:cyclic-upper}\nThe image of $\\pi_1(C\\times\\{q_0\\})$ in $\\pi_1(V)$ is cyclic.\nConsequently the image of $\\pi_1(Y)$ in $\\pi_1(\\cS)$ is cyclic.\n\\end{lemma}\n\n\\begin{proof}\nWe show that the four red meridians in \\eqref{app:pencil-group} have\nthe same image in $\\pi_1([V/H])$.  First move the $w$ coordinate a little\noff its exceptional direction line, keeping it fixed, generic, and\nsmall.  Push the red pencil meridians off the $z$ exceptional divisor.\nThey can be represented with\n\\[\n t=z_1\\ne0\\quad\\hbox{fixed and small},\\qquad\n \\lambda=z_2/z_1\n\\]\nand with the red meridians based by paths in a small disk containing\nexactly the four red values of $\\lambda$.\n\nSlide this calculation in the $t$ variable to the coupling hyperplane\n$t=c$.  The disk of red directions can be chosen small enough that\nthis entire motion, with the chosen small $w$, remains inside the\nball: the red slopes have modulus less than $0.01$, whereas\n$c/\\sqrt a<0.9$.  The motion and the finitely many paths used here lie\nin a fixed compact part of the full finite model.  We include this\ncompact set among the regions that must agree with the actual\narrangement when the arithmetic level is chosen.\n\nBefore blowing up the pair intersections, the relevant local divisor\nis\n\\[\n \\{t=c\\}\\ \\cup\\ \\bigcup_{i=1}^4\\{\\lambda=\\lambda_i\\}.\n\\]\nLet $k$ be the meridian in the $t$ variable, with one common choice of\nbasing.  The product structure makes $k$ commute with each red\nmeridian.  More precisely, the same $t$ circle can be transported along\neach of the based paths in the direction disk; this gives one element\n$k$, not four unrelated conjugates.\n\nBlowing up $\\{t=c,\\lambda=\\lambda_i\\}$ introduces an exceptional\ndivisor whose meridian is $kh_i$: in the two transverse coordinates,\nthe exceptional meridian circles both coordinates once.  The filling\nrules of Section~\\ref{geo:fill} impose no branching along this\nexceptional divisor, since two branch components met there.  Thus its\nmeridian is killed.  Therefore\n\\[\n kh_i=1\\qquad(1\\le i\\le4).\n\\]\nThe strict transforms retain order two, so $k^2=h_i^2=1$.  All four\nred images are consequently one involution, denoted $r$.\n\nThe product relation in \\eqref{app:pencil-group} now gives\n$h_5h_6=1$, and the two blue images are one involution $b$.  The image\nof $Q$ is thus a quotient of\n\\[\n \\langle r,b\\mid r^2=b^2=1\\rangle.\n\\]\nEvery even word in $r,b$ is a power of $rb$.  Hence the image of the\neven subgroup $\\pi_1(C)$ is cyclic.  Injectivity of\n$\\pi_1(V)\\to\\pi_1([V/H])$ gives the assertion in $\\pi_1(V)$.\nLemma~\\ref{app:surface-comparison} and the surjectivity from the chosen\n$C$ loops onto $\\pi_1(Y)$ give the final assertion.\n\\end{proof}\n\nThis argument allows the cyclic image to be finite.  We will exclude\nthat possibility by applying Lemma~\\ref{thm:path-lemma} to paths in a\ndense open subset of $\\cS$.\n\n\\subsection{Paths in the ordinary open set}\n\nLet $O\\subset\\cS$ be the sign-covered arrangement complement,\nquotiented by $s$, after also removing the full inverse image of the\nfixed loci in $M$ of all nonidentity powers of $\\sigma$.  The\nmodifications defining $\\cS$ are isomorphisms over this set.  In\nparticular $O$ is a dense connected smooth variety, and all quotient\nactions used over it are free.\n\nThere is a useful space for specifying lifts.  Over the complement in\n$\\BB$ of the lifted arrangement and the lifted omitted fixed loci,\npull back the sign data from $V$, and call the resulting space\n$O^\\#$.  Then\n\\[\n O^\\#\\longrightarrow O\n\\]\nis an ordinary covering.  Changes of lift are generated by the ball\ndeck group $\\Gamma$ and by $\\sigma$, the latter acting on the sign data\nthrough $s$.  These are precisely the transformations used to form\n$O$; we do not divide out the two sign deck transformations.  The group generated by $\\Gamma$ and $\\sigma$ acts freely on the\nretained ball region.\n\nFix the diameter bound $u$ supplied by\nProposition~\\ref{prop:model-hyperbolicity}.  Define a graph $G$ whose\nvertices are the points of $O$.  An edge is a parametrized continuous\npath in $O$ whose lifted projection to $\\BB$ has diameter strictly\nless than $u$.  All graphs use the same parametrization and reversal\nconventions.  The diameter condition is independent of the lift,\nbecause changes of lift act by ball isometries.  No bound is imposed\non path length or winding.  In particular, arbitrarily high powers of\na loop can be individual edges when their projections stay in one\nsmall region.\n\n\\subsection{Choosing and comparing the charts}\n\nWe make the order of the parameters explicit.  First fix $u$, then a\ncommon hyperbolicity constant for the model graphs, and then the\ninteger $N$ required by Lemma~\\ref{thm:path-lemma}.  Set\n\\begin{equation}\\label{app:radius}\n R=(2N+100)(u+1).\n\\end{equation}\nA lift of a graph path with at most $N$ edges stays within distance\n$Nu$ of its initial ball point, regardless of the lengths of the\nindividual parametrized paths.\n\nThe special points of $\\BB$ are the points of $\\Gamma o$, the lifts\nof the distinguished point of $M$.  Choose $J>10R$ so large that, in\nthe central model, both of the following hold at every point of\ndistance at least $J-3R$ from $o$:\n\\begin{enumerate}\n\\item On sign loops projected into the $R$ ball about that point, the\nkernel of the sum translation equals the intersection of the two\nindividual translation kernels.\n\\item For $j=1,2,3$, the displacement by $\\sigma^j$ exceeds $10R$.\n\\end{enumerate}\nThe first requirement follows from\nProposition~\\ref{prop:kernel-compatibility}.  For the second, none of\n$\\sigma,\\sigma^2,\\sigma^3$ has a boundary fixed point.  The ball\ndistance formula then implies that its displacement tends uniformly\nto infinity as the viewpoint tends to the boundary.\n\nLet $B(R)$ be the buffer radius in\nProposition~\\ref{prop:kernel-compatibility}, enlarged so that\n$B(R)>R$, and choose\n\\begin{equation}\\label{app:chart-radius}\n K>10\\bigl(J+R+B(R)\\bigr).\n\\end{equation}\nAll these constants depend only on the finite models.  Now take the\ninitial arithmetic level deep enough that\nProposition~\\ref{prop:arrangement-local} holds on balls of radius\n$10K$, that such balls inject into the torsion-free ball quotient,\nthat the full finite arrangement agrees with the lifted arrangement\nin these neighborhoods of every special point, and that distinct\nspecial points have distance greater than $100K$.  Enlarge this last\nchoice, if necessary, to include the fixed compact paths used in\nLemma~\\ref{app:cyclic-upper}.  Subsequent finite covers preserve all\nthese requirements.\n\nFor a vertex $v\\in G$, choose a lift with ball coordinate $x$ and its\nactual sign data.  Its chart has one of two forms.\n\\begin{description}\n\\item[Central chart.] If $\\dist(x,\\Gamma o)<J$, use the full finite\nmodel centered at the unique nearby special point.  Read in the\nsum-translation cover and then quotient by the lifted order-four\nsymmetry, as in Lemma~\\ref{mod:central}.\n\\item[Individual chart.] Otherwise take all lifted hyperplanes\nmeeting $B_{\\BB}(x,K)$, identify them, with their prescribed normal\nvectors, with the corresponding subset of $\\cD$, and use the model\ncover given by the two individual translation kernels.  There is no\nsymmetry quotient in this chart.\n\\end{description}\nThese chart graphs are the graphs of\nProposition~\\ref{prop:model-hyperbolicity}, with the appropriate\nadditional analytic omissions.  One can transport the whole locally\nfinite union of lifted omitted fixed loci to model coordinates; any\nextra lifted arrangement hyperplanes outside the comparison region\nmay be omitted as well.  Such omissions are globally defined on the\nmodel ball.  In central coordinates they are invariant under the\nsymmetry.  Proposition~\\ref{prop:model-hyperbolicity} shows that these\nomissions do not enlarge the uniform hyperbolicity constant.\n\nFor central charts, fix the identifications of sign data once on the\nlarge comparison ball about $o$.  Sign characters on that ball\ncomplement are determined by their meridians.  Choose the sheet\nidentifications to agree with the local construction near $F_*$;\nthey then intertwine the actual lift $s$ with the model lift\nthroughout this connected region.  Transport the choices to all\nspecial points by $\\Gamma$.  These choices are also compatible with\n$\\sigma$, by the symmetry of the construction.\n\nWithin the comparison ball about a special point, the only quotient\ntransformations that can identify two ball points are the four\nsymmetry powers centered there.  Indeed, if $g\\in\\langle\\Gamma,\n\\sigma\\rangle$ identifies two such points, it carries the special\npoint to another one at distance at most twice the comparison radius.\nSeparation forces it to fix that special point.  Its stabilizer is\nexactly the corresponding conjugate of $\\langle\\sigma\\rangle$,\nsince $\\Gamma$ is torsion free.\n\nTo read a path of at most $N$ graph edges from $v$, lift it from the\nchosen point of $O^\\#$.  Its ball projection remains in the comparison\nregion.  Identify the resulting sign path with the model sign path,\nlift further from a chosen initial point in the translation cover,\nand, in a central chart, take the class modulo the lifted symmetry.\nThis is a path in a model graph $D_v$, with initial state $d_v$.\nWrite $[p]_v$ for its terminal state.\n\nCentral models are centered only at points of $\\Gamma o$.  Other\nfull pencil fibers, including those over\n$\\Gamma_0o\\setminus\\Gamma o$, and the other quotient fixed loci\nremain unmodified in $[V/\\langle s\\rangle]$.  An ordinary-open loop around such a locus may have a ball or\nsign lift with different endpoints; an individual chart retains this\ndifference.  Equality of actual endpoints is not required to imply\nequality of read states.  For orbifold relations, the relevant\nmeridian power lifts to a closed loop in a smooth chart in $V$;\nits closure will be checked separately below.\n\n\\begin{lemma}\\label{app:chart-axioms}\nThe readings just constructed satisfy the three chart hypotheses of\nLemma~\\ref{thm:path-lemma}.\n\\end{lemma}\n\n\\begin{proof}\nThe uniform hyperbolicity assertion is\nProposition~\\ref{prop:model-hyperbolicity}.  We verify the two path\ncompatibility assertions.\n\n\\emph{Continuation from a state.}\nPath lifting is consistent with prefixes and reversals.  Equal read\nstates project to the same actual point of $O$.  In an individual\nchart, equality means equality in the model covering, and hence also\nin the ball and sign data.  Every outgoing model edge at a state\nreached after fewer than $N$ edges gives an actual edge of $G$: its\nprojection stays within $u$ of its starting ball point and therefore\ninside the comparison region.  It avoids precisely the arrangement\nand extra loci excluded in $O$.  Lifting and projecting parametrized\npaths are mutually inverse once the starting lift is fixed.  This is\nthe required bijection on outgoing edges, and it depends only on the\nread state.\n\nIn a central chart the symmetry acts freely on the open complement.\nAn outgoing edge of its quotient graph consequently has a unique\nrepresentative from any chosen representative of the starting\nvertex.  Representatives at equal states differ by a symmetry power,\nwhich respects the identification with the actual open set.  Thus the\nsame outgoing-edge bijection holds, including its dependence only on\nthe state.  This proves the first chart hypothesis.\n\n\\emph{Comparison after a prefix.}\nLet $p$ go from $v$ to a vertex $v'$, and let $a,b$ start at $v'$,\nwith\n\\[\n |p|,|a|,|b|\\le N/2.\n\\]\nWe must prove\n\\begin{equation}\\label{app:prefix-compatibility}\n [pa]_v=[pb]_v\\quad\\Longleftrightarrow\\quad[a]_{v'}=[b]_{v'}.\n\\end{equation}\nFor this calculation, use for $v'$ the lift reached by lifting $p$.\nThe equality tests do not depend on this change from its initially\nchosen lift.  For $\\Gamma$ this follows from transport of the normal\ndata.  For $\\sigma$ the two families are interchanged and the finite\narrangement has the same symmetry.  If relabeling changes a chosen\nnormal vector by a scalar, it changes the corresponding translated\nnormal by the same scalar, because $\\sigma$ normalizes the\narithmetic group.  Thus the intrinsic prescriptions of\nProposition~\\ref{prop:kernel-compatibility} give the same individual\nkernels, with possible reversals of translation orientations.\nIndividual sign-sheet choices likewise do not affect a kernel test\non a closed sign loop.  For central charts the fixed, equivariant\nidentifications above give the same conclusion for the sum cover.\nFinally, the choice of initial point in a translation cover is\nirrelevant because the covers are regular, and the deck translations\ncommute with the lifted central symmetry.\n\nAll projected paths in the comparison are contained in the $R$ ball\nabout the endpoint of the lifted $p$.  If both charts are individual,\nequality first requires coincident endpoints in the actual ball and\nsign data.  If this requirement fails, both tests fail.  If it holds,\ncompare the two sign paths by the loop $a\\overline b$.  The further\nlifts end together exactly when both translations of this loop\nvanish.  The common prefix does not change that condition.  Both\ncharts contain the required buffered ball about this new viewpoint,\nby \\eqref{app:chart-radius}; therefore\nProposition~\\ref{prop:kernel-compatibility} identifies the two kernel\ntests.  This proves \\eqref{app:prefix-compatibility} in this case.\n\nIf both charts are central, their special points coincide.  Otherwise\nthese two special points would be at distance at most $2J+Nu$,\ncontrary to their separation.  The two readings then use restrictions\nof the same covering with the same lifted symmetry, so their endpoint\nequality tests agree.\n\nIt remains to compare a central and an individual chart.  The\nviewpoint at the end of $p$ is at distance at least $J-R$ from the\nspecial point of the central chart.  This follows directly from the\ncriterion for the individual root and from the bound $Nu<R$ on the\ndistance between the two roots.  Every endpoint being compared is\ntherefore at distance at least $J-2R$ from that special point.  Two\nsuch endpoints in the comparison ball cannot differ by a nontrivial\nsymmetry power: their mutual distance is at most $2R$, whereas the\nrelevant displacement exceeds $10R$.  Thus the central test, too,\nrequires equality of the ball and sign endpoints.  On the remaining\nsign loop, the sum kernel equals the intersection of the individual\nkernels by the choice of $J$.  The buffered comparison of individual\nkernels now applies as before.  This proves\n\\eqref{app:prefix-compatibility} in the mixed case, and completes the\nverification of the chart hypotheses.\n\\end{proof}\n\n\\subsection{From orbifold relations to path relations}\n\nLet $\\mathcal P(G)$ be the path groupoid of $G$ modulo backtracking\nand every edge loop of at most three edges that closes in its starting\nchart.  Lemma~\\ref{thm:path-lemma} and\nLemma~\\ref{app:chart-axioms} detect nontrivial elements of this\ngroupoid.  To use that conclusion for $\\cS$, we need the following\ndirection of comparison: every loop that is trivial in\n$\\pi_1(\\cS)$ must already be trivial in $\\mathcal P(G)$.\n\nWe recall explicitly the topological fact about orbifolds used in\nthis comparison.\n\n\\begin{lemma}\\label{app:orbifold-meridians}\nLet $\\mathcal T$ be a connected smooth effective complex orbifold, and\nlet $U$ be a dense ordinary open subset whose complement is complex\nanalytic.  Then $\\pi_1(U)\\to\\pi_1(\\mathcal T)$ is surjective.  Its\nkernel is normally generated by the loops obtained from boundaries\nof small transverse disks in smooth orbifold charts at general\npoints of the omitted divisors.  Equivalently, if the generic\nstabilizer along such a divisor has order $m$, one kills the $m$th\npower of a small meridian in the ordinary quotient.  Omitted strata\nof complex codimension at least two add no relations.\n\\end{lemma}\n\n\\begin{proof}\nOne may apply ordinary general position to a smooth frame\npresentation of $\\mathcal T$.  Effectiveness and finite-group\nlinearization imply that the action of each chart group on tangent\nframes is free.  Consequently the frame space is a manifold, with a\nlocally free action of the connected group $\\operatorname{GL}_n(\\C)$,\nand its quotient stack is $\\mathcal T$.  The homotopy fibration from\nthis presentation, as in \\cite[Example~5.6]{Noohi2014}, computes $\\pi_1(\\mathcal T)$ as the fundamental\ngroup of the frame space modulo the image of\n$\\pi_1(\\operatorname{GL}_n(\\C))$; the same description applies over\n$U$.\n\nIn the frame space, paths can avoid real-codimension-two loci, and a\nnullhomotopy disk can meet such loci transversely in finitely many\ngeneral divisor points.  Removing small disks around those\nintersection points expresses its boundary as a product of\nconjugates of transverse meridians.  Real codimension at least four\ncan be avoided by the entire disk.  Passing through the preceding\nquotient of fundamental groups proves the assertion.  At a general\npoint of a divisor, the effective stabilizer is cyclic and acts\nfaithfully on the transverse line.  A circle in that line projects\nto the $m$th power of a quotient meridian, giving the last description.\n\\end{proof}\n\n\\begin{lemma}\\label{app:true-relations}\nA loop of edges of $G$ that is nullhomotopic in $\\cS$ is trivial in\n$\\mathcal P(G)$.\n\\end{lemma}\n\n\\begin{proof}\nFirst consider homotopies within $O$.  Every ordinary path admits a\nfinite subdivision into graph edges.  An already specified edge may\nitself be subdivided without changing its class in $\\mathcal P(G)$:\nif $e=e_1e_2$ is a subdivision, then $e_1,e_2$ are still edges, and\n$e_1e_2\\overline e$ is a three-edge loop that closes on reading,\nsince all three pieces lift the same parametrized path.  Repeating\nthis observation allows arbitrary finite subdivision even when an\noriginal edge winds arbitrarily often.\n\nA homotopy in $O$ can now be subdivided into sufficiently small path\ntriangles.  To choose the neighborhoods uniformly over its compact\nimage, take lifted ball patches avoiding the arrangement and the\nomitted fixed loci, each disjoint from its distinct quotient\ntranslates.  For every root in a smaller patch, the oversized chart\nagrees with this same arrangement-free ball patch.  The sign and\ntranslation covers trivialize there, and a central symmetry quotient\nmakes no additional identifications inside the patch.  A finite\ncover of the homotopy image by these smaller neighborhoods supplies\nthe required subdivision.  Each triangle consequently closes in its\nstarting chart and is one of the imposed relations.  This is the\nusual edge-path proof that ordinary homotopy relations are respected.\n\nIt remains, by Lemma~\\ref{app:orbifold-meridians}, to check the\ntransverse-disk relations.  Away from the distinguished fiber,\n$\\cS$ is $[V/\\langle s\\rangle]$.  Even at points with inertia, the\nrequired power of a quotient meridian lifts to a closed small loop\nbounding a disk in the smooth local uniformizing space in $V$.\nIts projection to $M$ bounds a disk in an arbitrarily small\nsimply connected ball patch, so its further lift to $\\BB$ is closed\nand lies in the corresponding small region.  The finite model there\nhas exactly the same participating hyperplanes and the same blowups, sign branching, and fillings as\n$V$.  This local identification extends across the fillings by the\nexplicit local rules, or equivalently by uniqueness of the\nnormalizations extending the sign covers.  Lemma~\\ref{mod:extension}\ntherefore says that the translation covers extend over this disk.\nIts boundary closes in an individual chart, and also closes in a\ncentral chart by the sum rule.  This argument includes unbranched\nexceptional divisors, order-two branch divisors, and the powers\nrequired by any further quotient inertia.  Additional analytic loci\nremoved in defining $O$ cause no new obstruction: the extension test\nis made after those additional omissions are restored.\n\nOver the distinguished point, the relevant chart is central.  By\nLemma~\\ref{mod:central}, its sum cover descends to the coarse quotient\nand pulls back to a covering of the resolution neighborhood.  Thus\nit extends over every transverse disk used for the ordinary smooth\nspace there.  Those meridians close in the central graph as well.\nA sufficiently small representative is a single edge: its ball lift\nstays arbitrarily close to the distinguished point, even when its\nendpoint differs from its start by a central symmetry power.\nAway from this fiber the same diameter assertion follows from the\nsmall local chart in $V$, including for a power of a meridian.\n\nEvery required transverse-disk boundary is consequently a\nsingle-edge loop that closes in its chart, and is killed in\n$\\mathcal P(G)$.  Normal generation, together with the homotopy\nargument in $O$, proves the lemma.\n\\end{proof}\n\nCombining the last two lemmas with Lemma~\\ref{thm:path-lemma} gives\n\\begin{equation}\\label{app:detection}\n \\text{a single edge loop with distinct read endpoints represents a\n nonidentity element of }\\pi_1(\\cS).\n\\end{equation}\nThis conclusion does not require any of the translation labels to\nextend to all of $\\pi_1(\\cS)$.\n\n\\subsection{Infinite order and completion of the proof}\n\nChoose a based loop $\\alpha$ in $C\\times\\{q_0\\}$ on which the first\ntranslation label is nonzero.  Such a loop comes from lifting a\nproduct of a red and a blue meridian in the six-point pencil: its\nparity is even and its dihedral label is $rb$, a nontrivial\ntranslation.  The second label is zero on this factor.  The loop\ncan be represented in the good locus used in\nLemma~\\ref{app:surface-comparison}, since deleting its finite\nexceptional set is surjective on $\\pi_1(C)$ and the translation label\nextends over the filled curve.\n\nPush $\\alpha$ to a loop $e$ in $O$ as in that lemma, so close to the\ndistinguished fiber that its ball projection has diameter less than\n$u$.  Its basepoint has a central chart.  The sum label on the pushed\nloop is the same nonzero integer as on $\\alpha$, by extension of the\nsum covering over the filled model.  For every nonzero integer $n$,\nthe parametrized loop $e^n$ has the same projected image as $e$, and\nhence is itself one edge of $G$.\n\nThe read endpoints of $e^n$ are distinct in the central quotient\ngraph.  Before taking the symmetry quotient they differ by the\nnonzero translation $n\\,\\ell(\\alpha)$.  The lift of $e$ is closed in\nthe actual ball and sign data, because the push is closed upstairs in $V$ and its projection lies\ninside the prescribed small simply connected ball about $o$.  If the two translated endpoints became\nequal after quotienting by a lifted symmetry power, that power would\nfix their common ball point.  This point lies in the ordinary open\nset where the symmetry acts freely, so the power would have to be\nthe identity.  A nonzero deck translation cannot fix a point of a\ncovering.  Thus the endpoints remain distinct.\n\nBy \\eqref{app:detection}, every nonzero power of the class of $e$ is\nnontrivial in $\\pi_1(\\cS)$.  Lemma~\\ref{app:surface-comparison} puts\nthis infinite-order element in the image of $\\pi_1(Y)$.  That image\nis cyclic by Lemma~\\ref{app:cyclic-upper}; it is therefore infinite\ncyclic.  This proves Theorem~\\ref{thm:orbifold-cyclic}.\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/07-projective.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/build/sections/07-projective.tex", "bytes": 22193, "sha256": "baa0f20dabc60db1997ed29466c6b9e48150429b88243f530bdcc26d56223432", "content": "\\section{Projective realization and removal of the surface blowups}\n\\label{sec:projective}\n\nTheorem~\\ref{thm:orbifold-cyclic} supplies a map $Y\\to\\cS$, where $Y$\nis obtained by point blowups from the simple abelian surface $A$, and\nwhere the image of $\\pi_1(Y)$ is infinite cyclic.  We now make two changes\nof ambient space.  The first replaces the orbifold by a smooth projective\nvariety containing $Y$.  The second removes the point blowups from $Y$\nwhile preserving the homomorphism on fundamental groups.  Together they\nproduce the embedding required by Proposition~\\ref{prop:large-reduction}.\n\n\\subsection{Replacing the orbifold ambient space}\n\nThe projective-bundle construction below is a relative version of the\nfree-locus method in the Godeaux--Serre construction; compare\n\\cite[\\S20]{Serre1958} and \\cite[Remark~1.4 and \\S5]{Totaro1999}.\nWe include the containment and fundamental-group arguments, since the\nspecified surface must survive the replacement.\n\n\\begin{lemma}\\label{lem:ambient-realization}\nLet $\\mathcal T$ be a smooth connected effective proper complex\nDeligne--Mumford stack with projective coarse space.  Let $Y$ be a smooth\nprojective surface and let $f:Y\\to\\mathcal T$ have image in the locus\nwith trivial stabilizers.  There exist a smooth connected projective\nvariety $S$, an embedding $i:Y\\hookrightarrow S$, and a morphism\n$h:S\\to\\mathcal T$ such that $h\\circ i=f$ and\n\\[\n h_*:\\pi_1(S)\\xrightarrow{\\ \\sim\\ }\\pi_1(\\mathcal T).\n\\]\nOne may take $\\dim S=5$.\n\\end{lemma}\n\n\\begin{proof}\nWe first pass to a projective bundle whose nonordinary locus has\narbitrarily large codimension, without changing the fundamental group.\nChoose an embedding $Y\\hookrightarrow\\PP^{b-1}$ and form\n\\[\n \\mathcal E=(\\mathcal O_{\\mathcal T}\\oplus T_{\\mathcal T})^{\\oplus a}\n                 \\oplus\\mathcal O_{\\mathcal T}^{\\oplus b},\n \\qquad \\mathcal P=\\PP_{\\mathcal T}(\\mathcal E),\n\\]\nusing the convention that projectivization parametrizes lines.  The\ntrivial summands give a closed substack\n$\\mathcal T\\times\\PP^{b-1}\\subset\\mathcal P$, in which the graph of\n$f$ is a closed copy of $Y$.  This copy lies in the ordinary locus,\nbecause $f(Y)$ does.\n\nThe representation of each stabilizer on the tangent space of\n$\\mathcal T$ is faithful; in particular, every nonidentity element acts\nnontrivially.  Indeed a finite holomorphic action is linearizable at a\nfixed point, and effectiveness rules out an element with trivial tangent\naction.  The added trivial line ensures\nthat its action on $\\mathcal O\\oplus T_{\\mathcal T}$ is not scalar.\nConsequently every eigenspace in $\\mathcal E$ has codimension at least\n$a$, including after the extra trivial summands are added.  The\nprojective fixed locus of each such element is a union of projective\neigenspaces.  It follows, in local finite quotient charts, that the\nnonordinary locus of $\\mathcal P$ has codimension at least $a$.\nThe same bound holds in its coarse space $P$.\n\nThe coarse space $P$ is projective.  Stabilizer orders are bounded on\nthe proper stack, so a sufficiently divisible power of the tautological\npolarization has trivial stabilizer actions and\ndescends to $P$; it is relatively ample over the coarse space of\n$\\mathcal T$.  These statements can be checked on finite quotient\ncharts, where descent is precisely triviality of the stabilizer action\non the fibers of the line bundle.  Twisting by a sufficiently ample\nbundle from the projective base gives a polarization of $P$.\nLet $U\\subset P$ be the ordinary locus.  It is smooth and identifies\nwith the ordinary locus of $\\mathcal P$.\n\nChoose $a>5$.  Removing a subset of complex codimension at least two\nfrom a smooth orbifold does not change its orbifold fundamental group:\npaths and their homotopies can be moved off that subset in smooth\ncharts, or in a smooth frame presentation.  Also, the projective-space\nbundle $\\mathcal P\\to\\mathcal T$ induces an isomorphism on fundamental\ngroups, since its fibers are simply connected.  Thus the natural maps\ngive\n\\begin{equation}\\label{proj:ordinary-group}\n \\pi_1(U)\\simeq\\pi_1(\\mathcal P)\\simeq\\pi_1(\\mathcal T).\n\\end{equation}\n\nIt remains to cut down inside $U$ while retaining the embedded surface.\nPut $N=\\dim P$.  In a sufficiently high projective embedding of $P$,\ntake $N-5$ general hyperplane sections through $Y$.  Their linear systems\nare free off $Y$ and generate the normal differentials along $Y$.\nThe locus where $r$ general normal differentials fail to be independent\nhas expected codimension\n\\begin{equation}\\label{proj:rank-loss}\n (N-2)-r+1\n\\end{equation}\non the surface.  For $r=N-5$ this is $4>2$, so the final intersection\nis smooth along $Y$.  Bertini gives smoothness elsewhere in $U$.\nSince $P\\setminus U$ has codimension greater than five and is disjoint\nfrom $Y$, a general constrained intersection avoids it.  We obtain a\nsmooth projective fivefold $S\\subset U$ containing $Y$.\n\nTo verify the assertion about $\\pi_1$, first take unconstrained general\nhyperplane sections in this same embedding.  The quasi-projective\nLefschetz theorem of Hamm and L\\^e\n\\cite[Theorem~1.1.3(ii)]{HammLe} says that a smooth open variety of\ncomplex dimension $n$ is obtained, up to homotopy, from a general\nhyperplane section by attaching cells of dimensions at least $n$.\nAt every stage here the dimension before cutting is at least six.\nThe successive inclusions therefore preserve connectedness and induce\nisomorphisms on fundamental groups.  The final intersection avoids\n$P\\setminus U$, by the same dimension count.\n\nNow consider the full product of the hyperplane parameter spaces,\nwithout a containment requirement.  The tuples whose intersections\nhave dimension five, are smooth, and avoid $P\\setminus U$ form a\nnonempty Zariski-open set $\\Omega$.  It contains both a general\nunconstrained tuple and the constrained tuple just constructed, and it\nis path connected.  The universal complete intersection over $\\Omega$\nis a smooth proper family with a map to $U$.  Along a path in $\\Omega$,\nEhresmann's theorem identifies its fibers by diffeomorphisms and gives\na homotopy of their inclusions into $U$.  Thus the isomorphism on\nfundamental groups for an unconstrained fiber also holds for $S$.\nComposing $S\\hookrightarrow U\\subset\\mathcal P$ with the bundle\nprojection gives $h$, with $h\\circ i=f$, and\n\\eqref{proj:ordinary-group} proves the lemma.\n\\end{proof}\n\nApplied to $Y\\to\\cS$, this gives a smooth connected projective ambient\nvariety $S$ with an embedding of $Y$ and\n\\begin{equation}\\label{proj:blownup-image}\n \\im\\bigl(\\pi_1(Y)\\longrightarrow\\pi_1(S)\\bigr)\\simeq\\Z.\n\\end{equation}\nThe remaining issue is that Proposition~\\ref{prop:large-reduction}\nrequires $A$ itself.  We address one point blowup at a time.\n\n\\subsection{Realizing a surface blowdown in a new ambient variety}\n\n\\begin{lemma}\\label{lem:ambient-blowdown}\nLet $Y_1\\hookrightarrow S$ be an embedding of a smooth projective\nsurface in a smooth connected projective variety, and let\n$b:Y_1\\to Y_0$ be the blowdown of a $(-1)$-curve to a smooth projective\nsurface.  There exist a smooth connected projective variety $S_0$, an\nembedding $Y_0\\hookrightarrow S_0$, and an isomorphism\n$\\theta:\\pi_1(S)\\xrightarrow{\\sim}\\pi_1(S_0)$ such that\n\\[\n \\theta\\circ(\\pi_1(Y_1)\\to\\pi_1(S))\n = (\\pi_1(Y_0)\\to\\pi_1(S_0))\\circ b_*.\n\\]\nThe equality is understood with compatible basepoints, or up to\nconjugation.\n\\end{lemma}\n\nThe proof first places $Y_1$ in a projective threefold and adjusts the\nnormal bundle of its exceptional curve to\n$\\mathcal O(-1)\\oplus\\mathcal O(-1)$.  Blowing up that curve gives an\nexceptional $\\PP^1\\times\\PP^1$.  Contracting the other ruling then\nperforms $b$ on the surface.  This is the classical local two-ruling\nconstruction; compare \\cite{Atiyah1958}.  The essential global point\nhere is to construct a polarization that makes this second contraction\nprojective.\n\n\\begin{proof}\nWrite $E\\subset Y_1$ for the exceptional curve.  Replace $S$ by its\nproduct with a projective space and embed $Y_1$ by the graph of\n\\[\n Y_1\\xrightarrow{b}Y_0\\hookrightarrow\\PP^r.\n\\]\nThe new ambient fundamental group is naturally $\\pi_1(S)$.  Let $Q$\nbe the pullback of $\\mathcal O_{\\PP^r}(1)$; it is nef, and\n$Q|_{Y_1}=b^*L_0$ for a very ample line bundle $L_0$ on $Y_0$.\nFurther projective-space factors allow us to make the ambient dimension\nas large as convenient.\n\n\\smallskip\n\\noindent\\emph{A threefold containing the unchanged surface.}\nCut to a smooth projective fourfold $W$ containing $Y_1$, using high\nample degrees.  For each successive cut the rank-loss count\n\\eqref{proj:rank-loss}, with final dimension four, is at least three;\nthus it exceeds $\\dim Y_1=2$.  Together with Bertini, this gives smooth\nsuccessive cuts, and the Lefschetz theorem preserves the fundamental\ngroup throughout.\n\nFor a final threefold, the rank-loss codimension is now two, equal to\nthe dimension of the surface.  We therefore allow isolated singularities\nin the last cut and resolve them while leaving $Y_1$ unchanged.\nTake one more sufficiently ample hypersurface $Z\\subset W$ through\n$Y_1$.  On the surface, its two normal derivatives form a general\nsection of the rank-two bundle\n$N^*_{Y_1/W}\\otimes\\mathcal O_W(Z)|_{Y_1}$.  In sufficiently high\ndegree, generation of its first jets makes the zeros transverse and\nisolated; since $\\dim E=1$, they can also avoid $E$.  Bertini gives\nsmoothness away from $Y_1$.  At a zero choose coordinates with\n$Y_1=(x=y=0)$.  Writing the equation as $xa+yb=0$, transversality says\nthat $a,b$ restrict to coordinates on $Y_1$.  Hence $x,y,u=a,v=b$ are\ncoordinates on $W$ and the hypersurface equation is\n\\begin{equation}\\label{proj:node}\n xu+yv=0,\\qquad Y_1=(x=y=0).\n\\end{equation}\nThus $Z$ is a connected normal threefold with only ordinary double\npoints, all disjoint from $E$.\n\nWe require the small resolution of these nodes that leaves $Y_1$\nunchanged.  It exists projectively as follows.  Choose a high-degree\nCartier divisor on $Z$ containing $Y_1$, general in its ideal at the\nfinitely many nodes.  At each node its equation can be taken to be\n$x$, after a change of the coordinates in \\eqref{proj:node}.  This\ndivisor is the sum of $Y_1$ and a residual effective Weil divisor $R$.\nThe latter is Cartier away from the nodes, and at a node its ideal is\n$(x,v)$.  Blow up this coherent ideal.  The blowup is projective, is an\nisomorphism away from the nodes, and is smooth at every node.  Indeed\nits two charts are\n\\[\n x=tv,\\quad y=-tu\n \\qquad\\text{and}\\qquad\n v=sx,\\quad u=-sy,\n\\]\nwith coordinates $(t,u,v)$ and $(s,x,y)$, respectively.  The strict\ntransform of $Y_1$ is $t=0$ in the first chart and misses the second\nchart.  It therefore maps isomorphically to the original $(u,v)$-plane.\nDenote the resulting smooth projective threefold by $T$ and keep the\nnotation $Y_1\\subset T$.\n\nThese operations preserve the required fundamental group.  For the\nlast cut, deform $Z$ to a smooth ample hypersurface in $W$.  Away from\ndisjoint small neighborhoods of its nodes the family is locally\ntrivial.  A three-dimensional ordinary double point has simply\nconnected link and simply connected Milnor fiber, and the small\nresolution neighborhood retracts to $\\PP^1$.  For completeness, after\nwriting the node as $\\sum_{j=0}^3 z_j^2=0$, its link is the space of\northonormal two-frames in $\\R^4$, hence the unit tangent bundle of\n$S^3\\simeq\\mathrm{SU}(2)$, which is $S^3\\times S^2$.  Its smoothing\nretracts to $S^3$; writing $z=x+iy$ identifies it with the tangent\nbundle of $S^3$ before truncation.  These are also the elementary\nquadratic case of the Milnor-fiber theorem \\cite{MilnorSingular}.\nVan Kampen therefore gives the same fundamental group for the nodal\nhypersurface, its smoothing, and its small resolution, compatibly with\nthe maps to $W$.  Lefschetz for the smoothing yields\n$\\pi_1(T)\\simeq\\pi_1(W)\\simeq\\pi_1(S)$, with the prescribed map\nfrom $\\pi_1(Y_1)$.\n\n\\smallskip\n\\noindent\\emph{Adjusting the normal bundle.}\nThe hypersurface degree can be chosen so large that\n\\[\n N_{Y_1/T}|_E\\simeq\\mathcal O_E(-m),\\qquad m\\ge1.\n\\]\nIn fact there are no nodes on $E$, and the normal exact sequence there\nsubtracts $\\deg\\mathcal O_W(Z)|_E$ from the fixed degree of\n$\\det N_{Y_1/W}|_E$.  The sequence\n\\[\n 0\\longrightarrow\\mathcal O_E(-1)\n \\longrightarrow N_{E/T}\n \\longrightarrow\\mathcal O_E(-m)\\longrightarrow0\n\\]\nsplits because\n$H^1(E,\\mathcal O_E(m-1))=0$.  Blow up the threefold along $E$.\nSince $E$ is a Cartier divisor on $Y_1$, the strict transform of $Y_1$\nis isomorphic to $Y_1$.  Its normal line bundle becomes\n$N_{Y_1/T}\\otimes\\mathcal O_{Y_1}(-E)$, so its degree on $E$ increases\nby one, because $E^2=-1$.  Repeating $m-1$ times gives a smooth\nprojective threefold, still denoted $T$, in which\n\\[\n N_{E/T}\\simeq\\mathcal O_E(-1)\\oplus\\mathcal O_E(-1),\n\\]\nwith the first summand the tangent-normal direction inside $Y_1$.\nAt each step the same extension vanishing justifies this splitting.\n\nBlow up $E$ once more, obtaining $\\widehat T$, and let $\\widehat Y$\nbe the strict transform of $Y_1$.  The exceptional divisor is\n\\begin{equation}\\label{proj:exceptional-quadric}\n G=\\PP^1_{\\mathrm{curve}}\\times\\PP^1_{\\mathrm{normal}},\\qquad\n N_{G/\\widehat T}=\\mathcal O_G(-1,-1),\\qquad\n \\widehat Y\\cap G=\\PP^1_{\\mathrm{curve}}\\times\\{q\\}.\n\\end{equation}\nThe intersection is transverse.  Moreover\n$\\mathcal O_{\\widehat T}(\\widehat Y)|_G=\\mathcal O_G(0,1)$, and\n$N_{\\widehat Y/\\widehat T}|_E=\\mathcal O_E$.\nWe continue to denote by $Q$ the pullback of the earlier nef bundle;\nit is trivial on $G$ and restricts on $\\widehat Y\\simeq Y_1$ to\n$b^*L_0$.  Figure~\\ref{fig:blowdown} shows the two rulings and the\ncurve on the surface that the second contraction must remove.\n\n\\input{figures/blowdown}\n\n\\smallskip\n\\noindent\\emph{A polarization for the other contraction.}\nWe seek an ample line bundle $H$ restricting to\n$\\mathcal O_G(\\alpha,\\beta)$, with $\\beta>\\alpha>0$.\nSince $\\mathcal O_{\\widehat T}(G)|_G=\\mathcal O_G(-1,-1)$,\nadding $\\alpha G$ will then give the restriction\n$\\mathcal O_G(0,\\beta-\\alpha)$: it is trivial on the curves to be\ncontracted and positive on the retained factor.\nChoose an ample integral line bundle $H_0$ on $\\widehat T$, with\n$H_0|_G=\\mathcal O_G(\\alpha,\\beta_0)$, where $\\alpha,\\beta_0>0$.\nAdding $k\\widehat Y$ increases the second degree by $k$ and leaves the\nfirst unchanged.  Choose $k\\ge0$ with $\\beta:=\\beta_0+k>\\alpha$.\nOn $\\widehat Y$, the line bundle $H_0+k\\widehat Y$ still has positive\ndegree on $E$, because $N_{\\widehat Y/\\widehat T}|_E$ is trivial.\nIt is therefore relatively ample for $b:\\widehat Y\\to Y_0$, whose\nonly positive-dimensional fiber is $E$.  Adding a sufficiently large\nmultiple $lQ$ makes its restriction to $\\widehat Y$ ample.\n\nWe claim that\n\\[\n H=H_0+k\\widehat Y+lQ\n\\]\nis ample on all of $\\widehat T$.  Here $H_0+lQ$ is ample since $Q$\nis nef.  We use the following consequence of the Nakai--Moishezon\ncriterion: if $A_0$ is ample, $D_0$ is an effective Cartier divisor,\n$k\\ge0$, and $(A_0+kD_0)|_{D_0}$ is ample, then $A_0+kD_0$ is ample.\nIndeed, for an irreducible $r$-dimensional subvariety $V$ not contained\nin $D_0$, the difference of top intersections is\n\\[\n \\bigl((A_0+kD_0)^r-A_0^r\\bigr)\\cdot V\n = kD_0\\cdot V\\cdot\n   \\sum_{j=0}^{r-1}(A_0+kD_0)^j A_0^{r-1-j}\\ge0.\n\\]\nEvery term on the right is a mixed intersection of ample restrictions\non the effective cycle $D_0\\cap V$.  For $V\\subset D_0$, positivity\nfollows directly from the restriction hypothesis.  Nakai--Moishezon\nnow applies.  Taking $D_0=\\widehat Y$ proves the claim.  We have obtained\n\\begin{equation}\\label{proj:ample-degrees}\n H|_G=\\mathcal O_G(\\alpha,\\beta),\\qquad \\beta>\\alpha>0.\n\\end{equation}\nThe relative ampleness, Serre vanishing, and numerical ampleness facts\nused here are standard projective criteria; see \\cite{Hartshorne}.\n\nSet $D=H+\\alpha G$.  We shall prove directly that a high multiple of\n$D$ contracts $G$ to its second factor and has smooth projective image.\nFor a sufficiently large integer $n$, put\n$L_j=nD-jG$.  Then\n\\[\n L_{n\\alpha}=nH,\n \\qquad L_j|_G=\\mathcal O_G\\bigl(j,n(\\beta-\\alpha)+j\\bigr).\n\\]\nSerre vanishing gives $H^1(\\widehat T,L_{n\\alpha})=0$.\nFor $0\\le j<n\\alpha$, the restriction exact sequences and\n$H^1(G,\\mathcal O_G(j,n(\\beta-\\alpha)+j))=0$ propagate this\nvanishing downwards:\n\\begin{equation}\\label{proj:vanishing}\n H^1(\\widehat T,L_j)=0\\qquad(0\\le j\\le n\\alpha).\n\\end{equation}\nChoose also $n\\alpha\\ge2$ and $nH$ very ample.  In particular the\nrestriction maps for $L_0$ and $L_1$ are surjective.  The former gives\nall sections of $\\mathcal O_G(0,n(\\beta-\\alpha))$.  Hence $|nD|$ is\nbasepoint free on $G$ and restricts to the second projection followed\nby a Veronese embedding.  Away from $G$, it contains the system\nobtained by multiplying sections of $nH$ by the canonical section of\n$n\\alpha G$.  This subsystem embeds the complement of $G$ and\nseparates each of its points from $G$.  Thus $|nD|$ defines a projective\nmorphism\n\\[\n \\varphi:\\widehat T\\longrightarrow T'\n\\]\nthat is an isomorphism off $G$ and whose nontrivial fibers are exactly\nthe curves $\\PP^1_{\\mathrm{curve}}\\times\\{t\\}$.\n\n\\smallskip\n\\noindent\\emph{Smoothness of the contracted space and of the surface.}\nWe check the local model, rather than assume that an analytic\ncontraction is projective or smooth.  Fix a fiber\n$F_t=\\PP^1_{\\mathrm{curve}}\\times\\{t\\}$ of $G\\to\\PP^1_{\\mathrm{normal}}$.\nUse a section of $nD$ nonzero on $F_t$ as an affine denominator.\nThe restriction surjection for $L_0$ gives a section ratio $z$ whose\nrestriction to $G$ is a local coordinate on its second factor.\nThe restriction surjection for $L_1=nD-G$ gives two section ratios\n$x,y$ vanishing on $G$, whose first normal coefficients on $F_t$ are\na basis of $H^0(\\PP^1,\\mathcal O(1))$.\n\nIf $e=0$ is a local equation for $G$, write $x=ea$, $y=eb$.\nThe functions $a,b$ have no common zero near $F_t$.  Thus\n$[x:y]=[a:b]$ extends across $G$, and the map $(z,x,y)$ lifts to the\nblowup of $\\C^3$ along the axis $x=y=0$.  On $F_t$, its projective\ncoordinate is the standard isomorphism to the exceptional $\\PP^1$.\nIn the chart $a\\ne0$, the coordinates $(z,x,y/x)$ have nonsingular\nJacobian along the fiber: they respectively detect its base direction,\nthe direction normal to $G$, and the tangent direction of the fiber.\nThe same holds in the other chart.  If injectivity failed on every\nsmaller neighborhood of $F_t$, colliding pairs would have subsequences\nconverging to two points of this compact fiber.  Injectivity on the fiber\nidentifies their limits, contradicting local biholomorphism there; hence\nthe lifted map is an isomorphism of neighborhoods.  Properness of the\nstandard blowup then allows us to shrink its image to the full inverse\nimage of a neighborhood of the point on the axis.\n\nAll the other affine section ratios defining $\\varphi$ are holomorphic\non this blowup neighborhood and descend to holomorphic functions on\nthe unblown-up neighborhood.  One may use here the elementary identity\n$\\pi_*\\mathcal O_{\\operatorname{Bl}_{\\mathrm{axis}}\\C^3}\n=\\mathcal O_{\\C^3}$: a function descends off the codimension-two axis\nand extends across it by normality.  Since the entire fiber of\n$\\varphi$ over $\\varphi(F_t)$ is $F_t$, properness ensures that the\nresulting neighborhood computes the germ of its projective image.\nIn these coordinates that image is a graph over $(z,x,y)$.\nConsequently $T'$ is smooth, and $\\varphi$ is the ordinary smooth\nblowdown of $G$ along its other ruling.\n\nAt $F_q=\\widehat Y\\cap G$, transversality in\n\\eqref{proj:exceptional-quadric} makes projection to the blowup of the\n$(x,y)$-plane a local isomorphism on $\\widehat Y$ along $F_q$, and it\nis an isomorphism on that fiber.  The same compactness and shrinking\nargument gives an isomorphism of neighborhoods with the surface\nblowup.  Its remaining coordinate $z$ is a holomorphic function there and\ndescends to the plane, by the same extension argument.  Thus the\nimage of $\\widehat Y$ is a smooth surface, and its restriction is\nexactly the point blowdown of $E$.  By uniqueness of that blowdown,\nthis image identifies with $Y_0$.\n\nFinally, smooth blowups and blowdowns along smooth centers of complex\ncodimension at least two preserve fundamental groups.  This follows\nfrom the usual tubular-neighborhood model and van Kampen, or from\ngeneral position together with the simply connected projective-space\nfibers.  The earlier threefold construction preserved the groups\ncompatibly with the surface inclusion.  The final diagram\n\\[\n \\widehat Y\\longrightarrow\\widehat T\\longrightarrow T',\n \\qquad \\widehat Y\\simeq Y_1\\xrightarrow{b}Y_0\\subset T'\n\\]\nshows that the induced identifications also preserve the homomorphism\nfrom the surface.  Taking $S_0=T'$ gives the required $\\theta$.\n\\end{proof}\n\n\\subsection{Completion of the construction}\n\n\\begin{theorem}\\label{thm:cyclic-embedding}\nThere exist a simple abelian surface $A$, a smooth connected projective\ncomplex variety $S'$, and a closed embedding $A\\hookrightarrow S'$\nsuch that\n\\[\n \\im\\bigl(\\pi_1(A)\\longrightarrow\\pi_1(S')\\bigr)\\simeq\\Z.\n\\]\n\\end{theorem}\n\n\\begin{proof}\nTheorem~\\ref{thm:orbifold-cyclic} gives an iterated point blowup $Y$ of\nthe simple abelian surface $A$, a smooth connected effective proper\norbifold $\\cS$ with projective coarse space, and a map $Y\\to\\cS$\nwhose image lies in the ordinary locus and whose fundamental-group\nimage is infinite cyclic.  Lemma~\\ref{lem:ambient-realization} replaces\nthis map by an embedding $Y\\hookrightarrow S$ with the same group\nimage.  Apply Lemma~\\ref{lem:ambient-blowdown} to the point blowups in\nreverse order.  Every step preserves both the ambient fundamental\ngroup and the homomorphism from the surface.  After the last step the\nsurface is $A$, giving the asserted embedding and cyclic image.\n\\end{proof}\n\n\\begin{samepage}\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\nApply Proposition~\\ref{prop:large-reduction} to the embedding supplied\nby Theorem~\\ref{thm:cyclic-embedding}.  The resulting smooth connected\nprojective fourfold contains $A$ with infinite cyclic fundamental-group\nimage, has large fundamental group, and has a non-Stein universal cover.  By Lemma~\\ref{red:compact-criterion}, its universal\ncover contains no positive-dimensional compact complex-analytic\nsubvariety.\n\\end{proof}\n\\end{samepage}\n"}, {"path": "preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/large-fundamental-group-non-stein.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/A-projective-fourfold-with-large-fundamental-group-and-non-Stein-universal-cover-October-5-2026/large-fundamental-group-non-stein.pdf", "bytes": 593161, "sha256": "bb2a84e535643f767166d6b2918821d3de18dbff884958cdcc6100f15d1e64a7", "base64": 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"}, {"path": "preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/README.md", "bytes": 722, "sha256": "62d22a9d31e21565b6e5256061128d1701d879c73a30b4bfcc83b31912cadb09", "content": "# [An asymptotic formula for the number of totients](An-asymptotic-formula-for-the-number-of-totients-September-25-2026.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 25, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:An-asymptotic-formula-for-the-number-of-totients-September-25-2026,\n  author = {{OpenAI}},\n  title = {{An asymptotic formula for the number of totients}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/An-asymptotic-formula-for-the-number-of-totients-September-25-2026.pdf}{OAI:An-asymptotic-formula-for-the-number-of-totients-September-25-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/collisions.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/collisions.tex", "bytes": 17977, "sha256": "177b2803225c098a3f40d9083d43df5675101396fdfadec9ef4af607377763ee", "content": "\\section{Uniqueness of the continuous prefix}\\label{sec:collisions}\n\nWe now pass from representations to distinct totient values.  We retain the\ntuple sets $\\mathcal T(t)$, $\\mathcal G(t)$ and $\\mathcal D(t)$ of\nSection~\\ref{sec:extraction}: the first consists of the distinct tuples\n$(p_0,\\ldots,p_R,d)$ admitting a basic witness, the second consists of those\nfor which every basic witness satisfies \\eqref{eq:good-conditions}, and\n$\\mathcal D(t)=\\mathcal T(t)\\setminus\\mathcal G(t)$.  Their value map is\n\\[\n F(p_0,\\ldots,p_R,d)=d\\prod_{j=0}^R(p_j-1).\n\\]\nThe tail preimages of $d$ are not part of a tuple.  This distinction is\nessential: we shall prove that almost every value has one such tuple,\nwhile allowing several tail preimages.\n\n\\subsection{Counting pairs with the same value}\nThe alignment of largest factors and cancellation follow the argument\naround (5.34)--(5.37) in Ford's Section~5 \\cite{FordTotients2013}.\nHere we count all ordered pairs of distinct tuples, including the data\ninside both residual factors.\nWe first cancel the common prefix and align the large factors in the\nremaining shifts. After applying the comparison estimate, we recover\nthe tuple coordinates inside the residual factors and then restore\nthe common prefix.\n\n\\begin{proposition}[Prefix collisions]\\label{prop:collisions}\nFix $c>1$, and form all parameters from $x$, with $H$ fixed before $x$\ntends to infinity.  For $t=x$ or $t=x/c$, let\n\\[\n K(t)=\\#\\{(\\tau,\\sigma)\\in\\mathcal G(t)^2:\n             \\tau\\ne\\sigma,\\ F(\\tau)=F(\\sigma)\\}.\n\\]\nThere is a quantity $\\epsilon_H\\to0$, independent of the phase, such that\n\\[\n K(t)\\le\\bigl(\\epsilon_H+o_{x;H}(1)\\bigr)\n                   \\frac{xG_m}{\\log x}.\n\\]\nThe pairs counted here are ordered pairs of distinct tuples.\n\\end{proposition}\n\n\\begin{proof}\nChoose one basic witness for each tuple under consideration.  All choices\nsatisfy \\eqref{eq:good-conditions}, by the definition of $\\mathcal G(t)$.\nIf two distinct tuples have the same value, some displayed prime differs:\nequality of all the primes $p_0,\\ldots,p_R$ would also force equality of\n$d$.  Let $i$ be the first unequal prime index, and write $q_j$ for the\nprimes in the other witness.  Put\n\\[\n h=m-i,\\qquad J=\\lceil30\\log h\\rceil,\\qquad\n z=z_i=\\exp\\exp(0.7b_{i+J}),\\qquad S=S_i=\\exp\\exp(b_i^{1/3}).\n\\]\nThus $h\\ge H$.  The residual integers in the good conditions are\n\\[\n D_i=\\varphi(p_{i+J+1}\\cdots p_La),\\qquad\n D_i'=\\varphi(q_{i+J+1}\\cdots q_La').\n\\]\nCanceling the common prefix leaves the identity\n\\begin{equation}\\label{eq:suffix-identity}\n v^{(i)}=D_i\\prod_{j=0}^{J}(p_{i+j}-1)\n        =D_i'\\prod_{j=0}^{J}(q_{i+j}-1).\n\\end{equation}\nWe will apply Proposition~\\ref{prop:comparison} to this identity.  First\nwe verify its size and interval hypotheses; then we count all ways in\nwhich its solutions can give tuple pairs.\n\n\\paragraph{Sizes of the suffix and its layers.}\nFor sufficiently large $H$, uniformly for $h\\ge H$,\n\\begin{equation}\\label{eq:collision-scales}\n b_i\\asymp h\\rho^{-h},\\qquad\n \\log b_i=O(h),\\qquad\n c_1h^{-20}\\le\\frac{b_{i+J}}{b_i}\n       =(1-J/h)\\rho^J\\le C_1h^{-18}.\n\\end{equation}\nIndeed $30\\lambda$ lies strictly between $18$ and $20$; the ceiling in\n$J$ changes only constant factors.  In particular $J<h/2$ and\n$i+J<L$.  The constants in this proof are independent of $H,x,i$ and the\nphase, once $H$ and then $x$ are sufficiently large.\n\nFor $i=0$ set $y=x$.  For $i\\ge1$ place $v^{(i)}$ in a dyadic interval\n$(y/2,y]$.  The coarse bands in \\eqref{eq:basic} imply that the logarithm\nof the preimage suffix after $p_i$ is at most\n\\[\n h\\exp(0.62b_i)+\\exp(2\\widetilde b_L).\n\\]\nTo see the first term, use\n$1.1\\widetilde b_{i+1}<0.62b_i$ for large $x$, and bound the number of\ndisplayed factors by $h$.  The bound for $a$ gives the second term.\nThese quantities are negligible compared with\n$\\log p_i\\ge\\exp(0.88b_i)$.  Consequently, writing $B_y=\\log_2y$,\n\\begin{equation}\\label{eq:suffix-sizes}\n p_i,q_i\\ge y^{0.9},\\qquad\n 0.88b_i\\le B_y\\le1.12b_i,\\qquad u_i\\le B_y+O(1).\n\\end{equation}\nFor $i=0$ the first inequality is part of the basic witness conditions,\nand $B_y=b_0=u_0=B$ by convention.\n\nOur target is a bound of the form\n\\[\n \\frac{y}{\\log y}\\exp(-\\kappa B_y/h^4)\n\\]\nfor the suffix pairs, with an absolute $\\kappa>0$.\nThe strict simplex slack will supply the exponential saving.\nSince $B_y\\asymp b_i$, this saving can absorb the\n$\\exp(O(b_i\\log h/h^8))$ cost of recovering tuple coordinates\ninside $D_i$ and $D_i'$.\n\nLet\n\\[\n \\delta=\\sqrt{\\log_2S/B_y}\\asymp b_i^{-1/3}.\n\\]\nIt follows from \\eqref{eq:collision-scales} that $S<z$ and\n$J\\delta=o(h^{-20})$.  Normality gives\n$\\Omega(p_{i+j}-1),\\Omega(q_{i+j}-1)\\le4B_y$ for $0\\le j\\le J$.\nFor example, split the prime factors at $S$, apply the two normality\nconditions, and use $p_{i+j}-1\\le v^{(i)}\\le y$.  If\n\\[\n A_j=P^+(p_{i+j}-1),\\qquad A_j'=P^+(q_{i+j}-1),\n\\]\nthen\n\\begin{equation}\\label{eq:largest-factor-band}\n \\log_2A_j=u_{i+j}+O(\\log b_i),\\qquad\n \\log_2A_j'=\\log_2q_{i+j}+O(\\log b_i).\n\\end{equation}\nThis follows from\n$(p_{i+j}-1)\\le A_j^{\\Omega(p_{i+j}-1)}$ and the reverse bound\n$A_j\\le p_{i+j}-1$.  When $i=j=0$, the convention $u_0=B$\ncauses only an $O(1)$ difference from $\\log_2p_0$, since\n$x^{0.9}\\le p_0\\le x+1$.\n\nThe bands in \\eqref{eq:largest-factor-band} are disjoint and decrease\nwith $j$.  Their smallest separation is a constant multiple of\n$b_i h^{-20}$, which dominates the error $O(\\log b_i)$.  All the $A_j,A_j'$\nexceed $z$, whereas\n\\[\n P^+(D_i),P^+(D_i')<z.\n\\]\nFor the latter assertion, every prime in the residual preimage is at\nmost $p_{i+J+1}$ or $q_{i+J+1}$, and\n$1.1\\widetilde b_{i+J+1}<0.7b_{i+J}$; taking a totient cannot increase\nthe largest prime factor.  Repetitions of the last prime in $a$ or $a'$\ndo not affect this conclusion.\n\n\\paragraph{Aligning the largest factors on the two sides.}\nWe claim that\n\\begin{equation}\\label{eq:factor-alignment}\n |\\log_2 A_j-\\log_2 A_j'|\\le(2j+1)\\delta B_y\n                 \\qquad(0\\le j\\le J).\n\\end{equation}\nSuppose, for example, that $A_j>A_j'$, and put\n$e=(\\log_2 A_j-\\log_2 A_j')/B_y$.  On the first side of\n\\eqref{eq:suffix-identity}, each of its first $j+1$ shifts contributes\nat least $(e-\\delta)B_y$ prime factors, with multiplicity, to\n$(A_j',A_j]$.  On the other side only its first $j$ shifts can\ncontribute, each at most $(e+\\delta)B_y$.  The later shifts have smaller\nlargest factors, and both residual integers are $z$-smooth.  The lower\nendpoint exceeds $S$, so normality applies.  Equality of the two sides\ntherefore gives\n\\[\n (j+1)(e-\\delta)\\le j(e+\\delta),\n\\]\nwhich proves \\eqref{eq:factor-alignment}.  Interchanging the sides\nhandles the other order.\n\nCancel any equal primes among the two lists in\n\\eqref{eq:suffix-identity}, and let $r$ be the product of their shifts.\nThe disjoint prime bands show that equality is possible only at the\nsame original index.  The index $i$ survives, since $p_i\\ne q_i$.\nLet $b\\le J+1\\le B_y$ be the length of the remaining lists.  Their corresponding\nprimes are unequal, as required by Proposition~\\ref{prop:comparison}.\n\nSet $Y_b=z$, $Y_0=y$ and $\\log_2U_0=0.8B_y$.  At each remaining\npositive index, choose an interval on a $\\delta$-grid in normalized\ndouble logarithms which contains the corresponding pair from\n\\eqref{eq:factor-alignment}.  Its width is $O(J\\delta)$.  The intervals\nremain strictly separated, because $J\\delta=o(h^{-20})$.  Each lies\nbelow height $0.8$, by \\eqref{eq:suffix-sizes} and the upper bound\n$u_{i+1}\\le0.62b_i$.  At index zero,\n\\eqref{eq:suffix-sizes} and \\eqref{eq:largest-factor-band} give largest\nfactors above height $0.9$.  Thus all the ordered-cutoff hypotheses of\nProposition~\\ref{prop:comparison} hold.  If $b=1$, there are no positive\nindices to grid and $Y_1=z$.\n\nThe part of $p_i-1$ supported on primes at most $Y_1$ has logarithm\nat most $4B_y\\log Y_1\\le4B_y\\exp(0.8B_y)$.  Since $p_i\\ge y^{0.9}$,\nthe complementary part exceeds $\\sqrt y$ for large $H$.  The whole\ncommon product is squarefree above $Y_b$, by the good conditions;\ncanceling the equal shifts preserves this property.  This verifies\nthe last two hypotheses of Proposition~\\ref{prop:comparison}.\n\nApply that proposition to the remaining lists with $D=D_i$, $D'=D_i'$\nand the factor $r$ just canceled; their common product is at most $y/r$.\nWrite $\\nu_k=\\log_2Y_k/B_y$ and $\\mu_k=\\log_2U_k/B_y$ for the\nnormalized interval endpoints.\n\nWrite $j_1<\\cdots<j_{b-1}$ for the surviving positive original\nindices.  Since $j_k\\ge k$ and $a_k\\le a_{j_k}$, the grid intervals\nand the strict slack condition in \\eqref{eq:good-conditions} give\n\\begin{align}\n \\sum_{k=1}^{b-1}a_k\\nu_k\n &\\le \\frac{1}{B_y}\\sum_{j=1}^{J}a_j u_{i+j}\n                          +O(J^2\\log(2J)\\delta)\\notag\\\\\n &\\le1-h^{-4}+O(B_y^{-1}+J^2\\log(2J)\\delta).\n \\label{eq:collision-slack}\n\\end{align}\nHere $a_j$ is increasing and $\\sum_{j\\le J}a_j=O(J\\log(2J))$.\nThe grid widths and the explicit $\\delta$ term in\n\\eqref{eq:comparison} contribute another\n$O(J^2\\log(2J)\\delta)=o(h^{-4})$ to its exponent of $\\log y$.\nThus, for some absolute $\\kappa>0$, its logarithmic factor is at most\n\\[\n (\\log y)^{-1-\\kappa h^{-4}}\n =\\frac1{\\log y}\\exp(-\\kappa B_y/h^4).\n\\]\nIt remains to show that the other factors and the recovery of tuple\ncoordinates use only a smaller part of this saving.\n\n\\paragraph{Recovering tuples and summing the remaining choices.}\nThe comparison estimate counts its displayed primes and its residual\ninteger $D_i'$.  We must also account for the tuple data forgotten\ninside $D_i,D_i'$.  If $R\\le i+J$, all displayed tuple primes are\nalready determined.  Their tail totient is\n\\[\n d=D_i\\prod_{j=R+1}^{i+J}(p_j-1),\n\\]\nand similarly on the other side, with an empty product when $R=i+J$.\nIf $R>i+J$, the missing tuple data give an ordered factorization\n\\begin{equation}\\label{eq:residual-factorization}\n D_i=d\\prod_{j=i+J+1}^{R}(p_j-1).\n\\end{equation}\nThere are at most $(h+1)^{\\Omega(D_i)}$ such factorizations.  Indeed,\nfor any integer $n$, assigning its $\\Omega(n)$ prime occurrences to\n$k$ labeled slots bounds the number of ordered multiplicative\nfactorizations into $k$ factors by $k^{\\Omega(n)}$.  Once a factor\n$p_j-1$ is fixed, $p_j$ is fixed as well.  This argument also covers\nrepeated prime factors.  Consequently the two missing tails cost at most\n\\begin{equation}\\label{eq:tuple-recovery-cost}\n (h+1)^{\\Omega(D_i)+\\Omega(D_i')}\n       \\le\\exp\\{2b_i\\log(h+1)/h^8\\}.\n\\end{equation}\nThe factorizations in \\eqref{eq:residual-factorization} are exact:\nthe primes up to $R$ are distinct and the remaining preimage tail is\nsmaller than $p_R$, by \\eqref{eq:cofactor-size}.  All repetitions inside\nthat tail are kept in its totient $d$.\n\nWe now sum the comparison bound over the canceled primes, the grids,\nand $D_i$.  The canceled index sets contribute at most $2^{J+1}$.\nEach canceled prime has reciprocal shift sum $O(b_i)$ by Mertens'\ntheorem.  The number of grid choices is at most\n$(C/\\delta)^{2(J+1)}$.  Thus their total cost, including the factors\n$(CB_y^6)^b$ in \\eqref{eq:comparison}, is $\\exp(O(h^2))$.\nFor the residual integer, the finite Euler product gives\n\\[\n \\sum_{P^+(D)\\le z}\\frac1D\\ll\\log z.\n\\]\nBefore enlarging this sum we use the good-condition bounds\n$\\Omega(D_i),\\Omega(D_i')\\le b_i/h^8$ in both\n\\eqref{eq:tuple-recovery-cost} and the factor $(b+1)^{\\Omega(D_i)}$\nof the comparison estimate.\n\nThe logarithm of all the remaining costs, apart from the factor\n$y/(\\log y)$ and the saving from \\eqref{eq:collision-slack}, is at most\n\\begin{equation}\\label{eq:collision-costs}\n O(h^2)+O\\left(\\frac{b_i\\log h}{h^8}\\right)\n       +O\\left(\\frac{b_i\\log h\\log\\log(3h)}{h^{18}}\\right)\n       =o\\left(\\frac{b_i}{h^4}\\right).\n\\end{equation}\nThe last term uses\n$\\log_2z\\ll b_i h^{-18}$ to bound the factor\n$(\\log Y_b)^{1+3b\\log(b(b+1))}$ in \\eqref{eq:comparison}, as well\nas the sum over $D_i$.  All little-oh estimates here are uniform for\n$h\\ge H$ as $H\\to\\infty$, because $b_i\\asymp h\\rho^{-h}$.\nWe have therefore obtained, for some absolute $c_2>0$,\n\\begin{equation}\\label{eq:suffix-pair-count}\n \\#\\{\\text{ordered suffix tuple pairs with }y/2<v^{(i)}\\le y\\}\n \\ll\\frac{y}{\\log y}\\exp\\{-c_2B_y/h^4\\}.\n\\end{equation}\nFor $i=0$ the same expression bounds all the pairs with first unequal\nindex zero, using $y=x$.  Choosing one witness for each tuple has not\nintroduced multiplicity into the desired count: comparison data and\nthe factorizations just counted determine each ordered pair of tuples.\n\n\\paragraph{Summing suffixes and restoring the common prefix.}\nFor $i\\ge1$ the required sum over suffix pairs is weighted by\n$1/v^{(i)}$.  A dyadic interval in \\eqref{eq:suffix-pair-count}\ncontributes at most\n\\[\n \\frac{C}{\\log y}\\exp\\{-c_2\\log_2y/h^4\\}.\n\\]\nWriting the dyadic endpoints as $y=2^n$, and comparing the sum with\nan integral, gives\n\\begin{align}\n \\sum_{\\text{suffix pairs}}\\frac1{v^{(i)}}\n &\\ll\\sum_{\\log(n\\log2)\\ge0.88b_i-O(1)}\n       \\frac{\\exp\\{-c_2\\log(n\\log2)/h^4\\}}{n\\log2}\\notag\\\\\n &\\ll\\int_{0.88b_i-O(1)}^\\infty\\exp(-c_2u/h^4)\\,\\dd u\n  \\ll h^4\\exp(-c_3b_i/h^4).\n \\label{eq:dyadic-suffix-mass}\n\\end{align}\nIn this change of variable, $u=\\log(n\\log2)$, the factor\n$1/\\log y$ exactly compensates for the density of dyadic endpoints.\nIn particular, no factor of order $\\exp(b_i)$ is lost.\n\nFor fixed suffix data and common primes $p_1,\\ldots,p_{i-1}$, the\nnumber of possible $p_0$ is at most\n\\[\n \\frac{Cx}{\\log x}\\,\n \\frac1{v^{(i)}\\prod_{j=1}^{i-1}(p_j-1)}.\n\\]\nIndeed the upper endpoint for $p_0$ is\n$1+x/(v^{(i)}\\prod_{j=1}^{i-1}(p_j-1))$; if the range is nonempty,\nits restriction $p_0\\ge x^{0.9}$ makes its logarithm comparable to\n$\\log x$, so the prime number theorem gives this upper bound.\nThe projected-prefix estimate \\eqref{eq:projected-mass} and\n\\eqref{eq:dyadic-suffix-mass} now show that the contribution for this\n$i$ is at most\n\\[\n \\frac{CxG_m}{\\log x}\\,h^4\\exp(-c_3b_i/h^4).\n\\]\nThis also applies to the empty projected prefix when $i=1$.\nSumming over $h\\ge H$ yields the convergent tail\n\\[\n C\\sum_{h\\ge H}h^4\\exp\\{-c_4\\rho^{-h}/h^3\\}\\longrightarrow0.\n\\]\nFor $i=0$, \\eqref{eq:suffix-pair-count} gives\n$O((x/\\log x)\\exp\\{-c_2B/m^4\\})$, which is\n$o(xG_m/\\log x)$.  This proves the proposition, uniformly for both\nendpoints $t$.\n\\end{proof}\n\n\\subsection{From tuples to values and least preimages}\n\nThe following elementary lemma keeps track of exceptional tuples as\nwell as exceptional values.  This is needed because a small set of\nvalues could otherwise carry many representations.\n\n\\begin{lemma}[Finite-map counting]\\label{lem:finite-map}\nLet $T$ and $W$ be finite sets, let $F:T\\to W$, and write\n$T=G\\sqcup D$.  Suppose that every element of $W\\setminus E$ is\nin $F(T)$, where $E\\subset W$.  For $w\\in W$ put\n$r_w=|G\\cap F^{-1}(w)|$, and let\n\\[\n K=\\sum_{w\\in W}r_w(r_w-1).\n\\]\nThen\n\\[\n -|E|\\le |T|-|W|\\le |D|+K/2.\n\\]\nMoreover, with\n$E^*=E\\cup F(D)\\cup\\{w:r_w\\ge2\\}$, one has\n\\[\n |E^*|\\le|E|+|D|+K/2,\\qquad\n |F^{-1}(E^*)|\\le|E|+2|D|+K.\n\\]\nThe restriction of $F$ to $T\\setminus F^{-1}(E^*)$ is a bijection\nonto $W\\setminus E^*$.\n\\end{lemma}\n\n\\begin{proof}\nCoverage gives the lower bound.  For every positive integer $r$,\n$r-1\\le r(r-1)/2$, and hence\n\\[\n |G|-|F(G)|\\le K/2.\n\\]\nAdding the at most $|D|$ remaining elements proves the upper bound.\nThere are at most $K/2$ fibers with $r_w\\ge2$, which proves the\nbound for $|E^*|$.  Also\n\\[\n \\sum_{w\\in E^*}r_w\\le|E^*|+K/2,\n\\]\nby $r\\le1+r(r-1)/2$ for integers $r\\ge0$.  Adding the elements of\n$D$ proves the second bound.  Outside $E^*$ coverage, absence of\ndiscarded elements, and $r_w\\le1$ give exactly one preimage under $F$.\n\\end{proof}\n\n\\begin{proposition}[A common prefix for all preimages]\\label{prop:unique-prefix}\nFor $t=x$ or $t=x/c$, with the same parameters formed from $x$,\n\\begin{equation}\\label{eq:tuple-cardinality}\n \\bigl|\\,|\\mathcal T(t)|-V(t)\\,\\bigr|\n       \\le\\bigl(\\epsilon_H+o_{x;H}(1)\\bigr)\\frac{xG_m}{\\log x},\n \\qquad\\epsilon_H\\longrightarrow0.\n\\end{equation}\nThere is a set $E^*(t)\\subset\\mathcal V\\cap[1,t]$ for which both\n$|E^*(t)|$ and the number of tuples in $\\mathcal T(t)$ with values in\n$E^*(t)$ are at most\n$(\\epsilon_H+o_{x;H}(1))xG_m/\\log x$, after enlarging $\\epsilon_H$.\nFor every $v\\in(\\mathcal V\\cap[1,t])\\setminus E^*(t)$ there is a unique tuple\n$(p_0,\\ldots,p_R,d)\\in\\mathcal T(t)$ of value $v$, and every preimage\nof $v$ has this prefix and a remaining factor of totient $d$.\nIn particular,\n\\begin{equation}\\label{eq:least-factorization}\n \\ell(v)=p_0\\cdots p_R\\ell(d).\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nApply Lemma~\\ref{lem:finite-map} with $T=\\mathcal T(t)$,\n$G=\\mathcal G(t)$, $D=\\mathcal D(t)$ and\n$W=\\mathcal V\\cap[1,t]$.  Let $E$ be the exceptional values in\nProposition~\\ref{prop:basic}, restricted to $[1,t]$.  Every preimage\nof each $v\\notin E$ is basic, so it induces an element of $T$.\nThe size of $E$ is negligible on the scale $xG_m/\\log x$, by\n\\eqref{eq:ford-scale}; Proposition~\\ref{prop:discard} gives the same\nbound for $|D|$, and Proposition~\\ref{prop:collisions} gives it for\n$K$.  The counting conclusions therefore follow from the lemma.\n\nFix $v\\notin E^*(t)$.  Every preimage is basic and so induces a tuple\nin $T$.  It cannot induce a tuple in $D$, since $v\\notin F(D)$.\nThe remaining tuple is unique.  Thus, writing $Q=p_0\\cdots p_R$,\nevery preimage of $v$ has the form $Qz$, where $\\varphi(z)=d$.\nIt follows that every such preimage is at least $Q\\ell(d)$.\n\nFor the reverse inequality choose one basic witness with tail $w$.\nThe deterministic size bound \\eqref{eq:cofactor-size} gives\n$\\ell(d)\\le w<p_R$.  The prefix primes are distinct, so\n$\\gcd(Q,\\ell(d))=1$ and\n\\[\n \\varphi(Q\\ell(d))=\\varphi(Q)\\varphi(\\ell(d))\n                 =d\\prod_{j=0}^R(p_j-1)=v.\n\\]\nHence $\\ell(v)\\le Q\\ell(d)$, proving \\eqref{eq:least-factorization}.\n\\end{proof}\n\nThe universal quantifier on preimages in Proposition~\\ref{prop:basic}\nis used only after the counting argument, to obtain the common-prefix\nstatement and \\eqref{eq:least-factorization}.  A single basic preimage\nwould give the upper bound for $\\ell(v)$ but would not give the reverse\ninequality.\n"}, {"path": "preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/companion.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/companion.tex", "bytes": 12649, "sha256": "ced026f90fb14f93415990f96d6cc8c5fdd5ce9d8669ca732c235925bbee0168", "content": "\\section{Least preimages}\\label{sec:companion}\n\nWe now prove Theorem~\\ref{thm:companion}.  The prime that dominates a\nbasic representation also determines the interval in which its least\npreimage lies.  Counting that prime first gives the weight \\(f_k\\).\nThe resulting approximation is additive; a separate argument will show\nwhen its coefficient is bounded away from zero.\n\nThroughout this section, \\(k\\geq1\\) is a fixed integer.  The mass\n\\(M_f(x;H)\\) is defined in \\eqref{eq:mass}; its summands correspond to\ndistinct data \\((d,p_1,\\ldots,p_R)\\), regardless of how many basic\nwitnesses yield the same data.  Write\n\\[\n f_k(r)=\n \\begin{cases}\n  0,&1\\leq r\\leq k,\\\\\n  1-k/r,&k<r<k+1,\\\\\n  1/r,&r\\geq k+1.\n \\end{cases}\n\\]\nThus \\(0\\leq f_k\\leq1/(k+1)\\), and \\(f_k(r)>0\\) exactly when \\(r>k\\).\nEquivalently, \\(f_k(r)\\) is the length of\n\\[\n \\{t\\in[0,1]:k<rt\\leq k+1\\}.\n\\]\nIt is therefore the permitted fraction of the normalized value interval\nwhen the least-preimage ratio is fixed at \\(r\\).\n\n\\subsection{Counting the interval for the largest prime}\n\n\\begin{lemma}\\label{lem:weighted-endpoint}\nFor each sufficiently large fixed \\(H\\), as \\(x\\) tends to infinity,\n\\begin{equation}\\label{eq:weighted-mass}\n \\left|N_k(x)-\\frac{x}{\\log x}M_{f_k}(x;H)\\right|\n \\leq\\bigl(\\epsilon_{H,k}+o_{x\\to\\infty;H,k}(1)\\bigr)\n       \\frac{xG_m}{\\log x},\n\\end{equation}\nwhere \\(\\epsilon_{H,k}\\geq0\\) tends to zero as \\(H\\to\\infty\\).\n\\end{lemma}\n\n\\begin{proof}\nProposition~\\ref{prop:unique-prefix} allows us, with an error of\n\\((\\epsilon_H+o_{x\\to\\infty;H}(1))xG_m/\\log x\\), to count tuples\n\\((p_0,\\ldots,p_R,d)\\) satisfying\n\\[\n (p_0-1)\\cdots(p_R-1)d\\leq x,\n \\qquad\n kx<p_0\\cdots p_R\\ell(d)\\leq(k+1)x.\n\\]\nBoth the exceptional values and the tuples above them are controlled\nthere.  This use of the least-preimage identity requires that every\npreimage have the same prefix.  The identity would not follow from the\nexistence of a single basic witness.\n\nFix the remaining data \\((d,p_1,\\ldots,p_R)\\), and put\n\\[\n D=d\\prod_{i=1}^R(p_i-1),\\qquad\n A=\\ell(d)\\prod_{i=1}^Rp_i,\\qquad\n r=\\frac{\\ell(d)}d,\\qquad\n q=\\prod_{i=1}^R\\left(1-\\frac1{p_i}\\right).\n\\]\nHere \\(r\\geq1\\).  Set \\(T=x/D\\) and \\(U=x/A=Tq/r\\).\nThe admissible largest primes satisfy exactly\n\\begin{equation}\\label{eq:largest-prime-interval}\n p_0\\geq x^{0.9},\\qquad\n kU<p_0\\leq\\min\\{T+1,(k+1)U\\}.\n\\end{equation}\nOnce the lower bound for \\(p_0\\) holds, the basic-witness restrictions\non the remaining data do not change with \\(p_0\\); recall that their\nzeroth simplex coordinate is \\(B\\).\n\nIgnoring the lower cutoff and replacing \\(T+1\\) by \\(T\\), the interval\nhas length\n\\[\n \\min\\{T,(k+1)U\\}-\\min\\{T,kU\\}=T f_k(r/q).\n\\]\nThe replacement changes its length by at most one.  Moreover,\n\\(z\\mapsto f_k(1/z)\\) is \\((k+1)\\)-Lipschitz on \\([0,1]\\),\nwhere its value at zero is zero.  Consequently\n\\begin{equation}\\label{eq:weight-perturbation}\n |f_k(r/q)-f_k(r)|\n \\leq\\frac{(k+1)(1-q)}r\n \\leq(k+1)\\sum_{i=1}^R\\frac1{p_i}.\n\\end{equation}\n\nFor fixed \\(H\\), the deterministic size bounds in\n\\eqref{eq:cofactor-size} give \\(T=x^{1-o(1)}\\) uniformly in the\nremaining data.  The tail has a witness \\(w\\) bounded in terms of\n\\(H\\) only, so \\(\\ell(d)\\leq w\\) bounds \\(r\\) in terms of \\(H\\).\nThe prime bands bound \\(q\\) away from zero.  Hence\n\\(U=x^{1-o(1)}\\) uniformly as well, and \\(kU>x^{0.9}\\) for all\nsufficiently large \\(x\\).  The lower cutoff in\n\\eqref{eq:largest-prime-interval} is then inactive.\n\nApply the prime number theorem at its two endpoints, taking the positive\npart of the difference if the interval is empty.  Each nonzero endpoint\nis \\(x^{1-o(1)}\\), and each is \\(O_k(T)\\).  Thus this gives, uniformly\nin the remaining data,\n\\[\n \\#\\{p_0\\text{ satisfying \\eqref{eq:largest-prime-interval}}\\}\n =\\frac{T}{\\log x}\n   \\left(f_k(r)+O_k\\left(\\sum_{i=1}^R\\frac1{p_i}\\right)\n                +o_{x\\to\\infty;H,k}(1)\\right).\n\\]\nThe shift by one is included in the last error.  This is an additive\nestimate relative to \\(T/\\log x\\), obtained from the ordinary prime\nnumber theorem; no relative estimate on a short prime interval is\nrequired.\n\nThe geometric separation of the bands implies\n\\[\n \\sup\\sum_{i=1}^R\\frac1{p_i}\n       \\ll \\exp(-cH\\rho^{-H}).\n\\]\nIndeed \\(b_{i-1}/b_i\\geq\\rho^{-1}\\), and\n\\(p_i\\geq\\exp\\exp(c b_i)\\); the resulting reciprocal series is\nbounded by a constant times its smallest-scale bound.  In particular,\nthis estimate is uniform in the growing number \\(R\\) of primes.\nSince \\(M_1\\ll K_HG_m\\) and \\(K_H=H^{o(1)}\\), summing the displayed\nprime count gives an error bounded by\n\\[\n \\left(O_k\\bigl(K_H\\exp(-cH\\rho^{-H})\\bigr)\n           +o_{x\\to\\infty;H,k}(1)\\right)\\frac{xG_m}{\\log x}.\n\\]\nTogether with the tuple-to-value error this proves\n\\eqref{eq:weighted-mass}.\n\\end{proof}\n\nThe additive form is necessary at this stage.  For example, when\n\\(r=k\\) and \\(q<1\\), one has \\(f_k(r)=0\\) but \\(f_k(r/q)>0\\).\nInequality~\\eqref{eq:weight-perturbation} controls this boundary without\nasserting a relative approximation to a zero term.\n\nApply Lemma~\\ref{lem:coefficient-convergence} with the function\n\\[\n C_{f_k}(x)=\\frac{N_k(x)\\log x}{xG_m},\n\\]\nwhich is independent of \\(H\\).  Lemma~\\ref{lem:weighted-endpoint}\nprovides its required comparison with \\(M_{f_k}/G_m\\).  We obtain the\nuniform convergence of \\(A_H(f_k;s)\\) on \\(0\\leq s<1\\) and\n\\begin{equation}\\label{eq:companion-additive}\n N_k(x)=\\frac{xG_m}{\\log x}\n          \\bigl(A(f_k;\\theta)+o(1)\\bigr).\n\\end{equation}\nIt remains to distinguish a coefficient bounded away from zero from an\nidentically zero count.\n\n\\subsection{Seed propagation and bounded ratios}\nThe positivity argument needs two distinct facts.  A seed with\n\\(\\ell(d)/d>k\\) must produce a positive proportion of values whose\nratios stay away from \\(k\\).  These ratios must also remain bounded\non a positive proportion of values, since \\(f_k(r)\\to0\\) as\n\\(r\\to\\infty\\).  The next two lemmas supply these facts.\n\nPreservation of a complete inverse fiber under multiplication by a prime\nalready appears in Erd\\H{o}s's proof of Theorem~4 \\cite{Erdos1958}.\nFord's construction gives such preservation on a positive proportion\nof the distinct-totient scale. A later simultaneous two-seed form appears\nin Pollack, Pomerance and Trevi\\~no \\cite[Lemma~4.1]{PPT2013},\nagain using Ford's construction. We use the following single-seed\nconsequence of Ford's proof.\n\n\n\\begin{lemma}[Ford's inverse-fiber propagation]\\label{lem:fiber-propagation}\nFix a totient \\(d\\), and write\n\\(\\varphi^{-1}(d)=\\{d_1,\\ldots,d_\\kappa\\}\\).\nThere are constants \\(\\eta_d>0\\) and \\(x_d\\) such that, for every\n\\(x\\geq x_d\\), at least \\(\\eta_dV(x)\\) distinct totients \\(v\\leq x\\)\nhave the form\n\\[\n v=d\\varphi(b),\\qquad\n \\varphi^{-1}(v)=\\{bd_1,\\ldots,bd_\\kappa\\}\n\\]\nfor some positive integer \\(b\\).  In particular, these values satisfy\n\\(\\ell(v)/v\\geq\\ell(d)/d\\).\n\\end{lemma}\n\n\\begin{proof}\nUse the construction in the proof of Theorem~2 of\n\\cite[Section~5, pp.~24--26, Equations~(5.10)--(5.19)]{FordTotients2013}.\nFor the fixed seed \\(d\\), Ford constructs a set \\(\\mathcal B\\) and\nexcludes at most half its members.  For each remaining \\(b\\), every\npreimage of \\(d\\varphi(b)\\) is one of the products \\(bd_i\\).\nThe paragraph following Equation~(5.18) also proves that these members\ngive distinct totients.  The concluding bound on p.~26 is\n\\(\\lvert\\mathcal B\\rvert/2\\gg_\\varepsilon d^{-1-\\varepsilon}V(x)\\)\nfor sufficiently large \\(x\\).  This full-fiber conclusion is recorded\nexplicitly in \\cite[Section~7.3, p.~39]{FordTotients2013}.\nFinally,\n\\[\n \\frac{\\ell(v)}v\n    =\\frac{b}{\\varphi(b)}\\frac{\\ell(d)}d\n    \\geq\\frac{\\ell(d)}d.\n\\]\n\\end{proof}\n\nWe also need to keep these ratios in a bounded interval.  This follows\nfrom the existence of a bounded arithmetic tail and does not require\nuniqueness of the prefix.\n\n\\begin{lemma}\\label{lem:ratio-tightness}\nFor every \\(\\varepsilon>0\\), there are \\(C<\\infty\\) and \\(x_0\\)\nsuch that\n\\[\n \\#\\{v\\in\\mathcal V:v\\leq x,\\ \\ell(v)/v>C\\}\n     \\leq\\varepsilon V(x)\\qquad(x\\geq x_0).\n\\]\n\\end{lemma}\n\n\\begin{proof}\nChoose one sufficiently large fixed cut \\(H_0\\) in\nProposition~\\ref{prop:basic}, so that its exceptional set has at most\n\\(\\varepsilon V(x)\\) members for all sufficiently large \\(x\\).\nEvery remaining value has a basic witness\n\\(n=p_0\\cdots p_{R_0}w\\), where \\(R_0=m-H_0\\),\n\\(w<p_{R_0}\\), and \\(w\\) is bounded in terms of \\(H_0\\) only.\nThe prefix primes are distinct, and their bands give a uniform bound\nfor \\(\\prod_{i=0}^{R_0}p_i/(p_i-1)\\).  Therefore\n\\[\n \\frac{\\ell(v)}v\\leq\\frac{n}{\\varphi(n)}\n   =\\frac{w}{\\varphi(w)}\n      \\prod_{i=0}^{R_0}\\frac{p_i}{p_i-1}\\leq C(H_0).\n\\]\nThe last bound is independent of \\(x\\), as required.\n\\end{proof}\n\n\\subsection{The positive and zero alternatives}\n\nSuppose first that there is a totient \\(d_*\\) with\n\\(\\ell(d_*)>kd_*\\).  Choose \\(\\delta>0\\) such that\n\\(\\ell(d_*)/d_*>k+2\\delta\\).\nLemma~\\ref{lem:fiber-propagation} gives a fixed \\(\\eta>0\\) such that\nat least \\(\\eta V(x)\\) values satisfy\n\\(\\ell(v)/v>k+2\\delta\\) for every sufficiently large \\(x\\).\nApply Lemma~\\ref{lem:ratio-tightness} with \\(\\varepsilon=\\eta/4\\).\nIt supplies a fixed \\(C\\) for which at least\n\\(3\\eta V(x)/4\\) values satisfy\n\\begin{equation}\\label{eq:bounded-positive-ratios}\n k+2\\delta<\\frac{\\ell(v)}v\\leq C.\n\\end{equation}\nThe numbers \\(\\eta,\\delta,C\\) are fixed before the cut \\(H\\) is\nallowed to grow.\n\nFor every sufficiently large \\(H\\), and then sufficiently large \\(x\\),\nProposition~\\ref{prop:unique-prefix} removes at most \\(\\eta V(x)/4\\)\nof these values.  For each remaining value its unique tuple satisfies\n\\[\n \\frac{\\ell(v)}v=\\frac{\\ell(d)}d\n                 \\prod_{i=0}^R\\frac{p_i}{p_i-1}.\n\\]\nThe product is \\(1+O(\\epsilon_H)+o_{x\\to\\infty;H}(1)\\), uniformly\nin the tuple.  Increasing \\(H\\) and then \\(x\\), we may bound it by\n\\((k+2\\delta)/(k+\\delta)\\).  Thus at least \\(\\eta V(x)/2\\)\ndistinct tuples have their tail ratio in the fixed interval\n\\[\n I=[k+\\delta,C].\n\\]\n\nLet \\(M_{\\one_I}\\) denote the mass \\eqref{eq:mass} with weight\n\\(\\one_I(r)\\).  The ordinary prime number theorem, counting all\nadmissible largest primes for data whose tail ratio lies in \\(I\\),\nbounds the number of such tuples above by\n\\[\n \\bigl(1+o_{x\\to\\infty;H}(1)\\bigr)\n       \\frac{x}{\\log x}M_{\\one_I}(x;H).\n\\]\nComparison with \\(\\eta V(x)/2\\), followed by\n\\eqref{eq:ford-scale}, gives\n\\[\n M_{\\one_I}(x;H)\\geq c_1G_m\n\\]\nfor all sufficiently large \\(H\\) and then sufficiently large \\(x\\),\nwhere \\(c_1>0\\) is independent of \\(H\\).  Since\n\\[\n \\inf_{r\\in I}f_k(r)\n      \\geq\\min\\left\\{\\frac{\\delta}{k+\\delta},\\frac1C\\right\\}>0,\n\\]\nwe conclude that\n\\begin{equation}\\label{eq:weighted-mass-positive}\n M_{f_k}(x;H)\\geq c_2G_m\n\\end{equation}\nwith \\(c_2>0\\) independent of sufficiently large \\(H\\).\n\nWe transfer this lower bound first to the normalized count\n\\(C_{f_k}(x)=N_k(x)\\log x/(xG_m)\\), which does not depend on \\(H\\).\nFix a sufficiently large \\(H\\) such that the error\n\\(\\epsilon_{H,k}\\) in \\eqref{eq:weighted-mass} is less than \\(c_2/4\\),\nand then take \\(x\\) large enough that its remaining error is less\nthan \\(c_2/4\\).  Equations~\\eqref{eq:weighted-mass} and\n\\eqref{eq:weighted-mass-positive} give\n\\(C_{f_k}(x)\\geq c_2/2\\) for every sufficiently large \\(x\\).\nThis count is also bounded above, since \\(N_k(x)\\leq V(x)\\) and\n\\eqref{eq:ford-scale} holds.  The bounds clause of\nLemma~\\ref{lem:coefficient-convergence} therefore gives\n\\[\n \\inf_{0\\leq s<1}A(f_k;s)\\geq c_2/2>0.\n\\]\nTherefore the additive formula \\eqref{eq:companion-additive} is\nmultiplicative in this case.  Its positive lower bound, the inequality\n\\(N_k(x)\\leq V(x)\\), and \\eqref{eq:ford-scale} give\n\\(N_k(x)\\asymp_k V(x)\\).\n\nIf instead \\(\\ell(d)\\leq kd\\) for every totient \\(d\\), then\n\\(\\ell(v)\\leq kv\\leq kx\\) for every \\(v\\leq x\\).  Hence\n\\(N_k(x)=0\\) for every \\(x\\).  Every weight\n\\(f_k(\\ell(d)/d)\\) in the finite arithmetic formula is also zero,\nso \\(A_H(f_k;s)=A(f_k;s)=0\\).  This proves the dichotomy in Theorem~\\ref{thm:companion}.\nIts final assertion is established next.\n\n\\begin{remark}\\label{rem:least-preimage-scope}\nThe positive alternative occurs for \\(k=1\\) and \\(k=2\\).  Ford gives\n\\(d=2^{18}\\cdot257\\) as a totient all of whose preimages are divisible\nby \\(8\\) \\cite[Section~7.3, p.~39]{FordTotients2013}.  Each such\npreimage \\(n\\) is even and has an odd prime factor: a power of \\(2\\)\nwould have a power of \\(2\\) as its totient.  Thus\n\\(n/\\varphi(n)=\\prod_{p\\mid n}p/(p-1)>2\\), and \\(\\ell(d)>2d\\).\n\nWe do not determine all integers \\(k\\) for which the positive\nalternative occurs.  Its occurrence for every integer \\(k\\geq1\\)\nis equivalent to the unboundedness of \\(\\ell(d)/d\\) over totients\n\\(d\\).  Neither that unboundedness nor the complete classification\nof \\(k\\) is established here.\n\\end{remark}\n"}, {"path": "preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/extraction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/extraction.tex", "bytes": 26712, "sha256": "1d5f111b5de36c1ad7408f871464015ab5594bca5eb552a69eb4480419702776", "content": "\\section{Extracting a long prime prefix}\\label{sec:extraction}\n\nOur first task is to represent almost every totient by a long prime\nprefix and a bounded arithmetic tail.  The exceptional set must be\nsmall in a stronger sense: outside it, \\emph{every} preimage must have\nthe required form.  Later we shall count distinct prefix--tail tuples,\nand remove tuples that fail the conditions needed to compare two\nrepresentations.\n\nThroughout this section \\(H\\) is a sufficiently large fixed integer,\n\\(P=\\lfloor\\log\\log H\\rfloor\\), and\n\\begin{equation}\\label{eq:cuts}\n R=m-H,\\qquad L=m-P.\n\\end{equation}\nWe first let \\(x\\to\\infty\\), and then let \\(H\\to\\infty\\).\nWrite \\(h_i=m-i\\) and\n\\[\n \\alpha=\\frac{B\\rho^m}{m},\\qquad\n b_i=\\alpha h_i\\rho^{-h_i},\\qquad\n \\widetilde b_i=\\alpha_\\theta h_i\\rho^{-h_i},\\qquad\n \\xi_i=1+10^{-4}\\e^{-h_i/40}.\n\\]\nIn particular \\(b_0=B\\).  The definitions in\n\\eqref{eq:scales} give\n\\begin{equation}\\label{eq:alpha-comparison}\n \\frac{\\alpha}{\\alpha_\\theta}\n =\\frac{\\log B}{\\lambda m}\n =1+\\frac{\\lambda\\theta+\\log\\log B}{\\lambda m}\n =1+O\\left(\\frac{\\log m}{m}\\right).\n\\end{equation}\nThus \\(\\alpha\\) and \\(\\alpha_\\theta\\) stay in a fixed compact\nsubinterval of \\((0,\\infty)\\), uniformly in the phase.\nWe shall only need the coarse bounds\n\\begin{equation}\\label{eq:rho-bounds}\n 0.54<\\rho<0.545,\n\\end{equation}\nwhich follow from the numerical value recorded in\n\\cite[(1.4)]{FordTotients2013}.  They imply\n\\(\\lambda<1\\), \\(18<30\\lambda<20\\), and \\(1.1\\rho<0.6\\).\nTogether with \\eqref{eq:alpha-comparison}, these inequalities\njustify the fixed constants \\(0.61\\) and \\(0.62\\) used below.\nWe use\n\\[\n K_H=\\exp(CP\\rho^{-P})=H^{o(1)},\n\\]\nwhere the absolute constant \\(C\\) may be enlarged from one upper bound\nto the next.  The last equality follows from\n\\(\\lambda=\\log(1/\\rho)<1\\).\nThe notation \\(\\varepsilon_H\\) denotes a positive quantity tending to\nzero with \\(H\\), and \\(o_{x;H}(1)\\) denotes a quantity tending to zero\nas \\(x\\to\\infty\\) for fixed \\(H\\).  All such estimates below are uniform\nin the phase.\n\n\\subsection{Structure estimates from Ford}\n\nFord's order estimate and fundamental-simplex estimates give\n\\begin{equation}\\label{eq:ford-scale}\n V(x)\\asymp \\frac{x}{\\log x}G_m,\n\\end{equation}\nand his recurrence estimate gives\n\\begin{equation}\\label{eq:g-asymptotic}\n g_j=\\gamma\\rho^{-j}+O(1),\\qquad g_j\\asymp\\rho^{-j}.\n\\end{equation}\nSee \\cite[Theorem 1, Lemmas 3.4 and 3.7, and Corollary 3.5]\n{FordTotients2013}.  Ford denotes our constant \\(\\gamma\\) by\n\\(\\lambda\\).  In particular, for \\(0\\le j\\le m\\),\n\\begin{equation}\\label{eq:G-uniform}\n \\frac{G_j}{G_m}\n =\\prod_{i=j+1}^m\\frac{ig_i}{B}\n \\le C^{m-j}\\rho^{(m-j)(m-j-1)/2}\\ll1.\n\\end{equation}\n\nWe shall use two quantitative structure results of Ford.\nOrder the prime factors of a preimage \\(n\\) with repetitions as\n\\(p_0\\ge p_1\\ge\\cdots\\).\nFor each fixed \\(0<\\eta\\le1/8\\), his Theorem 10, together with the\nmissing-prime exception in Theorem 16, implies\n\\begin{equation}\\label{eq:ford-bands}\n \\#\\left\\{v\\le x:\\begin{array}{l}\n v\\in\\mathcal V,\\ \\text{some preimage has}\\\\\n |\\log_2p_i/b_i-1|>\\eta\\text{ for some }1\\le i\\le m-P\n \\end{array}\\right\\}\n \\ll_\\eta V(x)\\e^{-c_\\eta P}.\n\\end{equation}\nHere and below a missing indicated prime is a failure of the condition.\nFor completeness, the summation giving \\eqref{eq:ford-bands} is short.\nFor \\(i\\le3\\eta m\\), Theorem 10(b) contributes at most\n\\(Cm\\e^{-\\eta m/13}V(x)\\).  For \\(i>3\\eta m\\), Theorem 10(a)\ncontributes at most\n\\[\n C V(x)\\frac{i}{\\eta m}\n \\exp\\left\\{-\\frac{\\eta^2m(m-i)}{4i}\\right\\}\n \\le \\frac{CV(x)}{\\eta}\\e^{-\\eta^2(m-i)/4}.\n\\]\nSumming over \\(m-i\\ge P\\) proves the assertion for sufficiently\nlarge \\(x\\) when the indicated primes exist.  A missing \\(p_L\\) is\nincluded in the \\(O(V(x)\\e^{-P^2/4})\\) exceptional set of\nTheorem 16, which is absorbed in the displayed bound.\nThe convention preceding Ford's Theorem 10 counts values\nwith \\emph{some} failing preimage; hence the complement of this set\ncontrols every preimage.\n\nFord's Theorem 16, with its parameter \\(\\Psi=P\\), states that all but\n\\(O(V(x)\\e^{-P^2/4})\\) values have every preimage satisfying\n\\begin{equation}\\label{eq:ford-simplex}\n \\log_2p_L>2,\\qquad\n \\sum_{r=i+1}^L a_{r-i}u_r\\le\\xi_i u_i\\quad(0\\le i<L),\n \\qquad u_0=B,\\quad u_i=\\log_2p_i\\ (i\\ge1).\n\\end{equation}\nIts final adjacent-coordinate inequality has coefficient \\(1\\)\nin place of \\(a_1<1\\); weakening that inequality gives\n\\eqref{eq:ford-simplex}.\n\nWrite \\(\\Omega(n,U,T)\\) for the number of prime factors of \\(n\\)\nbelonging to \\((U,T]\\), counted with multiplicity, and put\n\\(\\Omega(n)=\\Omega(n,1,n)\\), including \\(\\Omega(1)=0\\).\n\n\\begin{definition}\\label{def:normal}\nFor \\(S\\ge\\e^\\e\\), a prime \\(p\\) is \\(S\\)-normal if\n\\[\n \\Omega(p-1,1,S)\\le2\\log_2S\n\\]\nand, for every \\(S\\le U<T\\le p-1\\),\n\\[\n \\left|\\Omega(p-1,U,T)-(\\log_2T-\\log_2U)\\right|\n <\\sqrt{\\log_2S\\,\\log_2T}.\n\\]\n\\end{definition}\nWe use Definition 1 in the 2013 revision \\cite{FordTotients2013}.\nIts Lemma 2.6 gives, uniformly for \\(z\\ge3\\),\n\\begin{equation}\\label{eq:normal-primes}\n \\#\\{p\\le z:p\\text{ is not }S\\text{-normal}\\}\n \\ll \\frac{z}{\\log z}(\\log_2z)^5(\\log S)^{-1/6}.\n\\end{equation}\nThe function \\(\\Omega\\) in these statements counts prime factors with\nmultiplicity.\n\n\\subsection{Basic witnesses and the coverage target}\n\nWe now specify the representations that these structure estimates will\nprovide. The simplex notation records the inequalities on their prime\ncoordinates; the bounds on the last factor will make the arithmetic\ntail finite for fixed \\(H\\).\n\nFor integers \\(1\\le j\\le m\\), \\(u_0=B\\), and \\(\\beta_i\\ge1\\) for \\(0\\le i<j\\),\nput \\(\\beta_j=1\\) and define the simplex\n\\[\n S_j(\\boldsymbol\\beta)=\n \\left\\{(u_1,\\ldots,u_j):\n s_i:=\\beta_i u_i-\\sum_{r=i+1}^j a_{r-i}u_r\\ge0\n \\quad(0\\le i\\le j)\\right\\}.\n\\]\nThe terminal inequality is \\(u_j\\ge0\\).\nFor \\(j=0\\), the simplex consists of the empty tuple and has volume\n\\(G_0=1\\).\n\n\\begin{definition}\\label{def:basic}\nA \\emph{basic witness} is a choice of primes \\(p_0,\\ldots,p_L\\)\nand an integer \\(a\\ge1\\) satisfying\n\\begin{equation}\\label{eq:basic}\n \\begin{gathered}\n p_0\\ge x^{0.9},\\qquad\n 0.9\\widetilde b_i\\le\\log_2p_i\\le1.1\\widetilde b_i\n \\quad(1\\le i\\le L),\\\\\n (u_1,\\ldots,u_L)\\in S_L(\\boldsymbol\\xi),\\qquad\n P^+(a)\\le p_L,\\qquad \\log a\\le\\exp(2\\widetilde b_L).\n \\end{gathered}\n\\end{equation}\nHere \\(u_0=B\\), and \\(u_i=\\log_2p_i\\) for \\(i\\ge1\\).\n\\end{definition}\nFor our parameter range the prime bands are disjoint and decreasing;\nalso \\(p_0>p_1\\).  Thus the displayed prime prefix is strictly\ndecreasing.  Repetitions of \\(p_L\\) inside \\(a\\) are allowed.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tikzpicture}[x=1cm,y=1cm,font=\\small]\n \\node at (0.4,0.45) {$p_0$};\n \\node at (3.2,0.45) {$p_1\\ \\cdots\\ p_R$};\n \\node at (8,0.45) {$p_{R+1}\\ \\cdots\\ p_L$};\n \\node at (11.5,0.45) {$a$};\n \\draw[dashed] (5.55,-0.1)--(5.55,1.05);\n \\draw[dashed] (10.45,-0.1)--(10.45,1.05);\n \\node[above] at (5.55,1.05) {$R=m-H$};\n \\node[above] at (10.45,1.05) {$L=m-P$};\n \\draw[decorate,decoration={brace,mirror,amplitude=4pt}]\n       (0,-0.1)--(5.2,-0.1);\n \\draw[decorate,decoration={brace,mirror,amplitude=4pt}]\n       (5.9,-0.1)--(11.9,-0.1);\n \\node[below] at (2.6,-0.35) {prefix};\n \\node[below] at (8.9,-0.35) {finite arithmetic tail};\n\\end{tikzpicture}\n\\caption{The two cuts in a basic witness. We count $p_0$ by the prime\nnumber theorem and approximate the $p_1,\\ldots,p_R$ coordinates by volume.\nThe later primes and the smooth integer $a$ remain discrete.}\n\\label{fig:two-cuts}\n\\end{figure}\n\n\\begin{proposition}\\label{prop:basic}\nThere is a set \\(E_x\\subseteq\\mathcal V\\cap[1,x]\\) with\n\\[\n |E_x|\\le\\{\\varepsilon_H+o_{x;H}(1)\\}V(x)\n\\]\nsuch that every preimage of every\n\\(v\\in(\\mathcal V\\cap[1,x])\\setminus E_x\\) is\n\\(p_0\\cdots p_La\\) for a basic witness.  One may take\n\\[\n \\varepsilon_H\\ll\n \\e^{-cP}+K_H\\exp\\{-\\exp(cP\\rho^{-P})\\}.\n\\]\nFor every basic witness, putting \\(w=p_{R+1}\\cdots p_La\\), we have\n\\begin{equation}\\label{eq:cofactor-size}\n \\begin{gathered}\n p_1\\cdots p_La\\le\\exp((\\log x)^{0.8}),\\\\\n \\log w\\le H\\exp(0.62b_R)+\\exp(2\\widetilde b_L),\n \\qquad w<p_R.\n \\end{gathered}\n\\end{equation}\nIn particular \\(w\\) is bounded in terms of \\(H\\) alone, uniformly\nin the phase.\n\\end{proposition}\n\nWe prove the proposition after the volume and smooth-cofactor estimates\nbelow. Those estimates also prepare a second task: we shall retain the\nshortened data \\((p_0,\\ldots,p_R,\\varphi(w))\\), then delete those\nthat fail the strict inequalities and normality conditions needed for\nthe collision argument. Coverage concerns \\emph{values}; this later\ndeletion must count the distinct shortened tuples, including all tuples\nthat represent the same value.\n\n\\subsection{Simplex volume and concentration}\nThe coordinate scaling follows Ford's fundamental-simplex method\n\\cite[Lemma~3.2 and Corollary~3.3]{FordTotients2013}.\nOur last adjacent coefficient is $a_1$ rather than $1$, so the exact\nvolume is slightly different from his; we derive it from the slacks\nbelow. The recurrence estimate \\eqref{eq:g-asymptotic} is Ford's\nLemma~3.7; his Remark~1 credits its streamlined proof to Lau's solution\nof a recurrence problem \\cite{FordLau2000}.\n\nThe next estimates turn sums over the allowed prime coordinates into\nsimplex volumes. We also bound the mass near a coordinate boundary or\na simplex face, so that removing these regions has a controlled cost.\n\n\\begin{lemma}\\label{lem:simplex}\nLet \\(1\\le j\\le m\\).  The true simplex \\(S_j(\\boldsymbol1)\\) has volume \\(G_j\\).\nUnder its uniform volume measure the variables \\(W_i=g_is_i/B\\)\nare uniform on \\(\\{W_i\\ge0:\\sum_{i=0}^jW_i=1\\}\\).\nMoreover,\n\\[\n \\operatorname{Vol}S_j(\\boldsymbol\\beta)\n \\le G_j\\prod_{r<j}\\beta_r^{j-r}.\n\\]\nWithin \\(S_j(\\boldsymbol\\beta)\\), a specified slack \\(s_t\\) in an\ninterval of length \\(D\\) occupies at most a fraction \\(jg_tD/B\\)\nof the volume.  A specified coordinate \\(u_t\\), \\(1\\le t\\le j\\),\nin such an interval occupies at most \\(jg_t\\beta_tD/B\\).\n\\end{lemma}\n\n\\begin{proof}\nFor the true simplex the triangular inversion is\n\\[\n u_i=\\sum_{t=i}^j g_{t-i}s_t,\\qquad\n B=\\sum_{t=0}^j g_ts_t.\n\\]\nThe transformation from \\(u_1,\\ldots,u_j\\) to \\(s_1,\\ldots,s_j\\)\nhas determinant \\(1\\).  This proves the volume and distribution\nstatements.\n\nSet \\(K_i=\\prod_{r<i}\\beta_r\\), \\(K_0=1\\).\nThe map \\(u_i\\mapsto u_i/K_i\\) sends\n\\(S_j(\\boldsymbol\\beta)\\) into \\(S_j(\\boldsymbol1)\\), because\n\\(K_r\\ge\\beta_iK_i\\) when \\(r>i\\).  Its inverse Jacobian is\n\\(\\prod_{r<j}\\beta_r^{j-r}\\).\n\nFor the slice estimates, define\n\\(\\gamma'_0=1\\) and\n\\(\\gamma'_t=\\beta_t^{-1}\\sum_{i<t}\\gamma'_ia_{t-i}\\).\nThen \\(0<\\gamma'_t\\le g_t\\) and\n\\[\n \\sum_{t=0}^j\\gamma'_ts_t=\\beta_0B.\n\\]\nThe marginal density of \\(s_t\\), under the uniform measure on this\nsimplex, is at most \\(j\\gamma'_t/(\\beta_0B)\\).\nFor the coordinate assertion, fix all other positive-index slacks.\nThe coordinate \\(u_t\\) is affine in \\(s_t\\) with slope \\(1/\\beta_t\\).\nIntegrating first in this fiber and bounding by the full projection\nvolume gives the additional factor \\(\\beta_t\\).\n\\end{proof}\n\nThe following concentration estimate will be used in Section~\\ref{sec:limits}\nto remove the prefix bands after replacing prime sums by volumes.\nWrite $z_+=\\max\\{z,0\\}$.\n\n\\begin{lemma}\\label{lem:concentration}\nFix \\(\\eta>0\\).  There are constants \\(C_\\eta,c_\\eta>0\\) such that,\nif \\(Q\\) is an integer with \\(0\\le Q<m\\), \\(j=m-Q\\),\n\\(1\\le i\\le j\\), and \\(h_i\\ge C_\\eta(Q+1)\\), then\nuniform volume measure on \\(S_j(\\boldsymbol1)\\) satisfies\n\\[\n \\Pr(|u_i/b_i-1|>\\eta)\\le C_\\eta\\e^{-c_\\eta h_i}.\n\\]\nAlso, for \\(T\\ge0\\),\n\\[\n \\Pr(u_j\\ge T)=(1-g_jT/B)_+^j\\le\\e^{-jg_jT/B}.\n\\]\n\\end{lemma}\n\n\\begin{proof}\nBy \\eqref{eq:g-asymptotic} and Lemma~\\ref{lem:simplex},\n\\[\n \\frac{u_i}{B\\rho^i}\n =\\sum_{t=i}^j(1+O(\\rho^{t-i}))W_t.\n\\]\nRepresent \\(W_t=E_t/\\sum_{r=0}^jE_r\\), where the \\(E_t\\) are\nindependent mean-one exponential variables.  The main numerator has\n\\(j-i+1=h_i-Q+1\\) terms, and\n\\[\n \\frac{(j-i+1)/(j+1)}{h_i/m}\n =1+O((Q+1)/h_i).\n\\]\nExponential moment bounds give exponentially small probabilities\nfor a fixed relative deviation of either the numerator or the\ndenominator.  The error numerator is bounded in absolute value by\n\\(C\\sum_{r\\ge0}\\rho^rE_{i+r}\\); this sum has a uniformly bounded\nexponential moment, since\n\\(\\prod_{r\\ge0}(1-z\\rho^r)^{-1}<\\infty\\) for \\(0<z<1\\).\nOnce \\(h_i\\ge C_\\eta(Q+1)\\), these three bounds give the first\nassertion.  The second is the exact marginal tail of \\(W_j\\).\n\\end{proof}\n\n\\subsection{Prime boxes and smooth cofactors}\nThe use of reciprocal prime sums and enlarged coordinate boxes follows\nFord's Lemma~3.1 and the thickening arguments of his Section~3\n\\cite{FordTotients2013}. We record the quantitative bounds for our\nsimplexes, including the boundary errors needed for the tail unions.\n\nWe use unit boxes in the coordinates \\(\\log_2p_i\\).\nMertens' theorem gives\n\\begin{equation}\\label{eq:mertens-box}\n \\sum_{b\\le\\log_2p<b+1}\\frac1{p-1}=1+O(\\e^{-b})\n \\qquad(b\\ge2).\n\\end{equation}\nThe replacement of \\(1/p\\) by \\(1/(p-1)\\) changes the sum by\n\\(O(\\exp(-\\exp b))\\).  When the lower endpoints grow geometrically\nbackwards through the coordinates, multiplying\n\\eqref{eq:mertens-box} costs only a bounded factor; if their sum\nof errors is small, it is the relative product error.\n\n\\begin{lemma}\\label{lem:boxes}\nLet \\(0\\le j\\le L\\).  Sum over primes \\(p_1,\\ldots,p_j\\) whose\ncoordinates satisfy\n\\[\n 0.9\\widetilde b_i\\le u_i\\le1.1\\widetilde b_i,\\qquad\n (u_1,\\ldots,u_j)\\in S_j(\\boldsymbol\\xi).\n\\]\nThen\n\\begin{equation}\\label{eq:projected-mass}\n \\sum_{p_1,\\ldots,p_j}\\prod_{i=1}^j\\frac1{p_i-1}\\ll G_m.\n\\end{equation}\nEvery unit box meeting this region lies in\n\\(S_j(\\boldsymbol\\beta)\\), where, with a sufficiently large absolute\nconstant,\n\\begin{equation}\\label{eq:box-enlargement}\n \\beta_t=1+C\\bigl(\\e^{-h_t/40}+h_t^2\\rho^{h_t}\\bigr)\n \\quad(0\\le t<j),\\qquad \\beta_j=1.\n\\end{equation}\nThis enlargement has bounded volume cost.  Its coordinate scaling\nfactors through \\(R\\) differ from \\(1\\) by\n\\(O(\\e^{-H/40}+H^2\\rho^H)\\).\n\nThere is also the following shell estimate in \\(L\\) coordinates.\nFix \\(A>0\\).  Among unit boxes meeting the displayed region, retain\nthose with a point satisfying, for some \\(i\\le R\\), at least one of\nthese conditions:\n\\begin{enumerate}[label=(\\roman*)]\n\\item \\(i\\ge1\\) and \\(u_i\\) is within \\(A\\) of a coarse band endpoint;\n\\item the absolute value of\n\\(\\xi_i u_i-\\sum_{r=i+1}^L a_{r-i}u_r\\) is at most \\(Ah_i^2\\);\n\\item \\(\\sum_{r=i+1}^L a_{r-i}u_r\\ge(1-h_i^{-4})u_i\\).\n\\end{enumerate}\nThe total volume of these boxes, and their total reciprocal-prime\nweight, is\n\\begin{equation}\\label{eq:boundary-mass}\n O_A(H^{-2}G_m).\n\\end{equation}\nThe same estimate holds when any subset of coordinates uses\nreciprocal-prime measure and the others use Lebesgue measure.\nFor conditions (i)--(ii) alone, or for the region lost by replacing\nthe prefix coefficients \\(\\xi_i\\), \\(0\\le i\\le R\\), with \\(1\\),\nthe stronger bound is\n\\[\n O_A\\left(G_m\\sum_{h\\ge H}\n       (h\\e^{-h/40}+h^3\\rho^h)\\right).\n\\]\n\\end{lemma}\n\n\\begin{proof}\nThe band lower bounds are \\(u_t\\gg h_t\\rho^{-h_t}\\).\nChanging each coordinate by at most \\(1\\) changes inequality \\(t\\)\nby \\(O(h_t^2)\\).  These errors are absorbed by\n\\eqref{eq:box-enlargement}, including at \\(t=0\\), where \\(u_0=B\\)\nis fixed.  The logarithm of its Jacobian upper bound is at most\n\\[\n C\\sum_{h\\ge P}h\\bigl(\\e^{-h/40}+h^2\\rho^h\\bigr)\\ll1.\n\\]\nLemma~\\ref{lem:simplex}, \\eqref{eq:G-uniform}, and\n\\eqref{eq:mertens-box} prove \\eqref{eq:projected-mass}.\nThe coordinate scaling factor is \\(\\prod_{t<i}\\beta_t\\), without\nthe Jacobian exponents; summing for \\(h_t>h_i\\ge H\\) proves its\nstated bound.\n\nFor the shells put \\(h=h_i\\).  Throughout a box meeting the bands,\n\\(u_i\\ll b_i\\).  Conditions (ii) and (iii), together with the unit\nperturbations, put the enlarged slack in an interval starting at\nzero of length at most\n\\[\n C_A\\{b_i(h^{-4}+\\e^{-h/40})+h^3\\}.\n\\]\nHere the \\(h^3\\) term includes\n\\(h^2\\rho^hu_i=O(h^3)\\).\nSince \\(Lg_i/B\\ll\\rho^h\\), Lemma~\\ref{lem:simplex} bounds the\nrelative volume by\n\\[\n C_A(h^{-3}+h\\e^{-h/40}+h^3\\rho^h).\n\\]\nCondition (i), after expanding its interval by \\(2\\), costs only\n\\(O_A(\\rho^h)\\).  Sum these estimates over \\(h\\ge H\\), and use\nthe bounded enlargement cost.  The prime-weight assertion follows\nagain from \\eqref{eq:mertens-box}.\nFor the stronger assertion, omit \\(h^{-4}\\) from the slack interval.\nChanging \\(\\xi_i\\) to \\(1\\), for \\(i\\le R\\), only removes points whose old slack is\nbetween \\(0\\) and \\((\\xi_i-1)u_i\\), which obey the same bound.\nFinally every full unit box has uniformly bounded mass when an\narbitrary subset of its coordinates uses reciprocal-prime measure:\nmultiply the factors in \\eqref{eq:mertens-box} for precisely that\nsubset.  This proves the mixed-measure assertion as well.\n\\end{proof}\n\n\\begin{lemma}\\label{lem:smooth-tail}\nUniformly for \\(Y\\ge\\exp(\\exp2)\\) and \\(Z\\ge0\\),\n\\[\n \\sum_{P^+(a)\\le Y}\\frac{a^{1/\\log Y}}{\\varphi(a)}\\ll\\log Y,\n \\qquad\n \\sum_{\\substack{P^+(a)\\le Y\\\\\\log a>Z}}\\frac1{\\varphi(a)}\n \\ll(\\log Y)\\exp(-Z/\\log Y).\n\\]\nIn particular, with \\(Y=\\exp\\exp(1.1\\widetilde b_L)\\),\n\\begin{equation}\\label{eq:smooth-cost}\n \\sum_{P^+(a)\\le Y}\\frac1{\\varphi(a)}\\le K_H,\\qquad\n \\sum_{\\substack{P^+(a)\\le Y\\\\\\log a>\\exp(2\\widetilde b_L)}}\n \\frac1{\\varphi(a)}\n \\le K_H\\exp\\{-\\exp(cP\\rho^{-P})\\}.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nPut \\(\\sigma=1/\\log Y\\).  The Euler factor is\n\\[\n 1+\\frac{p^\\sigma}{(p-1)(1-p^{\\sigma-1})},\n\\]\nwhose logarithm is \\(p^{-1+\\sigma}+O(p^{-2+2\\sigma})\\), uniformly\nin this range of \\(\\sigma\\).  Partial summation gives\n\\(\\sum_{p\\le Y}(p^{-1+\\sigma}-p^{-1})=O(1)\\); hence the product\nis \\(O(\\log Y)\\).  Rankin's inequality gives the tail bound.\nFor the last assertion the exponent is\n\\[\n 1.1\\widetilde b_L-\\exp(0.9\\widetilde b_L),\n\\]\nand \\(\\widetilde b_L\\asymp P\\rho^{-P}\\).\n\\end{proof}\n\nCombining Lemmas~\\ref{lem:boxes} and \\ref{lem:smooth-tail}, the shell\nestimates remain valid after summing a smooth cofactor with weight\n\\(1/\\varphi(a)\\), at a cost of at most \\(K_H\\).\nIn particular their weighted mass is bounded by\n\\begin{equation}\\label{eq:weighted-boundary}\n C K_HG_m\\left\\{H^{-2}+\n       \\sum_{h\\ge H}(h\\e^{-h/40}+h^3\\rho^h)\\right\\}.\n\\end{equation}\nThe \\(H^{-2}\\) term is needed only for condition (iii).\n\n\\subsection{Coverage of the values}\n\n\\begin{proof}[Proof of Proposition~\\ref{prop:basic}]\nTake \\(\\eta=0.04\\) in \\eqref{eq:ford-bands}.  By\n\\eqref{eq:alpha-comparison}, its complementary bands imply the\ncoarse bands in \\eqref{eq:basic}.  Apply\n\\eqref{eq:ford-simplex} as well.\nBoth conclusions hold for all preimages outside the union of their\nexceptional sets.\n\nWe may also remove \\(v\\le x^{0.95}\\) and \\(\\Omega(v)>5B\\), at a\ncost \\(o(V(x))\\).  For the latter, the elementary reciprocal moment\nbound with \\(z=1.9<2\\) gives\n\\[\n \\#\\{v\\le x:\\Omega(v)>5B\\}\n \\le z^{-5B}x\\sum_{r\\le x}\\frac{z^{\\Omega(r)}}r\n \\ll x\\exp((z-5\\log z)B)=o(x/\\log x).\n\\]\nThe Euler product bounds the reciprocal sum by \\(O((\\log x)^z)\\),\nand \\(z-5\\log z<-1\\).\nFor every preimage, \\(\\Omega(n)\\le\\Omega(\\varphi(n))+1\\le6B\\).\nThe \\(p_1\\) band gives \\(\\log p_1\\le(\\log x)^{0.61}\\), so\n\\[\n \\log p_0\\ge0.95\\log x-6B(\\log x)^{0.61}>0.9\\log x.\n\\]\n\nIt remains to impose the size restriction on \\(a\\).  For any\npreimage failing it, fix \\(p_1,\\ldots,p_L,a\\).\nThe prime \\(p_0\\) is distinct from the others and from the factors\nof \\(a\\).  Supermultiplicativity of \\(\\varphi\\) gives\n\\[\n \\varphi(p_0\\cdots p_La)\n \\ge(p_0-1)\\varphi(a)\\prod_{i=1}^L(p_i-1).\n\\]\nThe prime number theorem upper bound therefore gives at most\n\\[\n \\frac{Cx}{\\log x}\\,\n \\frac1{\\varphi(a)\\prod_{i=1}^L(p_i-1)}\n\\]\nchoices of \\(p_0\\); if the interval is nonempty its upper endpoint\nis at least \\(x^{0.9}\\).  Sum the prefix by\nLemma~\\ref{lem:boxes}, and the cofactor by\nLemma~\\ref{lem:smooth-tail}.  This bounds all failing preimages,\nand therefore all values having at least one such preimage.\nIt proves the exceptional-value assertion.\n\nFinally \\(b_{i+1}/b_i=(1-1/h_i)\\rho\\).\nThe bands and \\eqref{eq:alpha-comparison} imply that the largest\ndouble logarithm after index \\(R\\) is at most \\(0.62b_R\\).\nThere are \\(H-P\\le H\\) such primes, proving the second line of\n\\eqref{eq:cofactor-size}.  Its right-hand side is smaller than\n\\(\\exp(0.89b_R)<\\log p_R\\) for large \\(H\\), then large \\(x\\).\nFor the first line use the \\(p_1\\) bound and\n\\(L\\ll\\log B\\), while the \\(a\\) bound depends only on \\(H\\).\n\\end{proof}\n\n\\subsection{Tuples and their discarded part}\n\nFor \\(t=x\\) or \\(t=x/c\\), where \\(c>1\\) is fixed, keep all\nparameters formed from \\(x\\).  Let \\(\\mathcal T(t)\\) be the set\nof distinct tuples\n\\[\n \\tau=(p_0,\\ldots,p_R,d)\n\\]\nadmitting at least one basic witness with\n\\[\n d=\\varphi(w),\\qquad w=p_{R+1}\\cdots p_La,\\qquad\n d\\prod_{i=0}^R(p_i-1)\\le t.\n\\]\nEach tuple represents a totient, because \\(w<p_R\\) makes its tail\ncoprime to its prefix.  Several witnesses may give the same tuple;\n\\(\\mathcal T(t)\\) counts that tuple once.\n\nWe next remove tuples for which the comparison argument might fail.\nFor each \\(0\\le i\\le R\\), put \\(h=h_i\\) and\n\\[\n J_i=\\lceil30\\log h\\rceil,\\qquad\n S_i=\\exp\\exp(b_i^{1/3}),\\qquad\n z_i=\\exp\\exp(0.7b_{i+J_i}),\n\\]\nand, for a basic witness, put\n\\[\n D_i=\\varphi(p_{i+J_i+1}\\cdots p_La).\n\\]\nFor large \\(H\\) these indices exist:\n\\(J_i<h_i/2\\) and \\(i+J_i<L\\).\nWe call a tuple good if \\emph{every} basic witness giving it satisfies,\nfor every \\(0\\le i\\le R\\),\n\\begin{equation}\\label{eq:good-conditions}\n \\begin{gathered}\n \\sum_{r=i+1}^L a_{r-i}u_r\\le(1-h_i^{-4})u_i,\\\\\n p_i,\\ldots,p_{i+J_i}\\text{ are }S_i\\text{-normal},\\\\\n (p_i-1)\\cdots(p_{i+J_i}-1)\n \\text{ is squarefree on primes exceeding }z_i,\\\\\n \\Omega(D_i)\\le b_i/h_i^8 .\n \\end{gathered}\n\\end{equation}\nAs before, the first condition uses \\(u_0=B\\).\nDenote the good tuples by \\(\\mathcal G(t)\\), and put\n\\(\\mathcal D(t)=\\mathcal T(t)\\setminus\\mathcal G(t)\\).\n\n\\begin{proposition}\\label{prop:discard}\nFor \\(t=x\\) and \\(t=x/c\\), with the same \\(x\\)-dependent parameters,\n\\[\n |\\mathcal D(t)|\n \\le\\{\\varepsilon_H+o_{x;H}(1)\\}\\frac{xG_m}{\\log x}.\n\\]\nOne may take \\(\\varepsilon_H\\ll K_HH^{-2}\\).\nFor all the witnesses in \\eqref{eq:good-conditions} we also have\n\\begin{equation}\\label{eq:tail-cutoffs}\n P^+(D_i)<z_i,\\qquad S_i<z_i,\\qquad\n c h_i^{-20}\\le b_{i+J_i}/b_i\\le C h_i^{-18}.\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nThe last comparison follows from\n\\[\n \\frac{b_{i+J_i}}{b_i}=(1-J_i/h_i)\\rho^{J_i},\n \\qquad 18<30\\log(1/\\rho)<20.\n\\]\nIt gives \\(S_i<z_i\\).  The next prime band has upper endpoint,\nin double logarithms, less than \\(0.7b_{i+J_i}\\); all remaining\nprimes, including those in \\(a\\), are smaller.  Taking totients\ncannot introduce a prime above this bound.  This proves\n\\eqref{eq:tail-cutoffs}.\n\nA bad tuple has at least one failing full witness.  We bound the\nnumber of these witnesses; this is an upper bound for the number of\nbad tuples, regardless of witness multiplicity.\nThe prime-count upper bound for \\(p_0\\) contributes\n\\(Cx/\\log x\\), with the product of reciprocal factors for all\nother variables.  The smooth \\(a\\)-sum contributes at most \\(K_H\\).\nFailure of the first condition in \\eqref{eq:good-conditions}\nis covered by Lemma~\\ref{lem:boxes}, condition (iii).\nIts contribution is therefore \\(O(K_HH^{-2}xG_m/\\log x)\\).\n\nFor the other conditions fix \\(i\\), write \\(h=h_i\\), \\(b=b_i\\),\nand \\(J=J_i\\).  If \\(i\\ge1\\), the reciprocal sum for the common\ninitial segment \\(p_1,\\ldots,p_{i-1}\\) is \\(O(G_m)\\), by\n\\eqref{eq:projected-mass}.  All of \\(p_i,\\ldots,p_L\\) are at most\n\\(\\exp\\exp(1.2b)\\).  Their unrestricted reciprocal sums together\ncost \\(\\exp(Ch^2)\\), since there are at most \\(h\\) variables and\n\\(\\log b=O(h)\\).  For \\(i=0\\), omit \\(p_0\\) from these reciprocal\nsums and use the same estimate at \\(b=B\\).\n\nPartial summation of \\eqref{eq:normal-primes} gives\n\\[\n \\sum_{\\substack{p\\le\\exp\\exp(1.2b)\\\\\n                   p\\ {\\rm not}\\ S_i\\text{-normal}}}\\frac1{p-1}\n \\ll(1+b)^6\\e^{-b^{1/3}/6}\n \\le\\e^{-c b^{1/3}}.\n\\]\nFor a failure involving \\(p_0\\), apply\n\\eqref{eq:normal-primes} directly to its counting interval;\nits upper endpoint has logarithm comparable with \\(\\log x\\).\nThe normality failures thus contribute, relative to \\(xG_m/\\log x\\),\nat most\n\\begin{equation}\\label{eq:normal-discard}\n K_H\\sum_{h\\ge H}\\exp(Ch^2-cb(h)^{1/3}),\n \\qquad b(h)=\\alpha h\\rho^{-h}.\n\\end{equation}\nPolynomial factors from choosing an index are included in \\(Ch^2\\).\n\nA square of a prime \\(q>z_i\\) in the shifted-prime product either\ndivides one shift twice or divides two different shifts.\nFor \\(\\ell=1,2\\) and \\(Z\\ge2\\), the elementary estimate\n\\[\n \\sum_{\\substack{p\\le Z\\\\q^\\ell\\mid p-1}}\\frac1{p-1}\n \\le\\frac{1+\\log Z}{q^\\ell}\n\\]\nfollows by summing over all positive multiples of \\(q^\\ell\\).\nIf the shift is \\(p_0-1\\le T\\), its integer count is at most\n\\(\\lfloor T/q^\\ell\\rfloor\\le T/q^\\ell\\).\nRelative to prime counting this loses at most \\(\\log x=\\e^b\\)\nwhen \\(i=0\\).  Both types of square therefore cost \\(q^{-2}\\),\nup to \\(\\exp(Cb+Ch^2)\\).  Summing over \\(q>z_i\\), their total\nrelative contribution is at most\n\\begin{equation}\\label{eq:square-discard}\n K_H\\sum_{h\\ge H}\n \\exp\\{Ch^2+Cb(h)-\\exp(c b(h)h^{-20})\\}.\n\\end{equation}\n\nFinally \\(\\Omega(\\varphi(a))\\ll\\log a\\le\\e^{2\\widetilde b_L}\n=H^{o(1)}\\), whereas \\(b_i/h_i^8\\) is exponentially large in\n\\(h_i\\ge H\\).  In the identity for \\(D_i\\), overlap between \\(a\\)\nand the displayed primes can occur only at \\(p_L\\).\nThat overlap changes the sum of factor counts by\n\\(1-\\Omega(p_L-1)\\le0\\).\nFailure of the last condition in \\eqref{eq:good-conditions}\ntherefore forces some \\(i+J<j\\le L\\) to satisfy\n\\(\\Omega(p_j-1)>b_i/(2h^9)\\).\nThe Euler product gives\n\\[\n \\sum_{n\\le Z}\\frac{(3/2)^{\\Omega(n)}}n\\ll(\\log Z)^{3/2}.\n\\]\nUsing the band bound for \\(p_j\\), this exceptional reciprocal sum\nis at most\n\\[\n \\exp(Cb_{i+J}-cb_i/h^9)\\le\\exp(-c'b_i/h^9),\n\\]\nbecause \\(b_{i+J}\\ll b_i h^{-18}\\).\nThe resulting relative contribution is at most\n\\begin{equation}\\label{eq:omega-discard}\n K_H\\sum_{h\\ge H}\\exp(Ch^2-cb(h)/h^9).\n\\end{equation}\nEach sum in \\eqref{eq:normal-discard}--\\eqref{eq:omega-discard}\ndecreases faster than every inverse power of \\(H\\), uniformly for\n\\(\\alpha\\) in its compact range.  Together with the thin-slack\nbound, this proves the proposition.  Replacing the endpoint \\(x\\)\nby \\(x/c\\) only decreases the witness sets, so the same bound holds\nwith unchanged parameters.\n\\end{proof}\n\nThe two cuts have now served different purposes.  The cut at \\(L\\)\nkeeps the smooth-cofactor cost at \\(K_H=H^{o(1)}\\); the earlier\ncut at \\(R\\) supplies the summable \\(H^{-2}\\) loss and the much\nstronger savings in the remaining discards.  Proposition~\\ref{prop:basic}\ncontrols exceptional \\emph{values}, while\nProposition~\\ref{prop:discard} controls exceptional \\emph{tuples}.\nNeither yet asserts uniqueness of a representation.  That is the\npurpose of the comparison and collision arguments that follow.\n"}, {"path": "preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/introduction.tex", "bytes": 5873, "sha256": "3a8bc61da6450e2562014998163d5eddd321581b0ca48693aadff374197c17e8", "content": "\\section{Introduction}\\label{sec:introduction}\nEuler's totient function $\\varphi(n)$ counts the integers in $\\{1,\\ldots,n\\}$ that are relatively prime to~$n$. We write\n\\[\n \\mathcal V=\\{\\varphi(n):n\\ge1\\},\\qquad\n V(x)=\\#\\{v\\in\\mathcal V:v\\le x\\}.\n\\]\nCounting distinct values requires more than counting integers with a\nprescribed factorization: different integers can have the same totient.\nThis distinction is decisive in an asymptotic formula for~$V(x)$.\n\nThroughout the paper logarithms are natural, and $\\log_j$ denotes\nthe $j$-fold iterated logarithm.\n\n\\paragraph{The counting problem.}\nSince $\\varphi(p)=p-1$ for primes~$p$, the prime number theorem gives\n$V(x)\\ge\\pi(x+1)\\sim x/\\log x$.\nPillai proved that the totient values have density zero\n\\cite{Pillai1929}. Erd\\H{o}s used the normal number of prime factors\nof $p-1$ to sharpen the upper bound to\n$V(x)\\ll_\\varepsilon x/(\\log x)^{1-\\varepsilon}$ for every\n$\\varepsilon>0$ \\cite[Section~2]{Erdos1935}.\nHis later count of distinct values $\\varphi(pr)$ with $p,r$ prime and\n$pr\\le x$ gives the lower bound\n$(1+o(1))x\\log_2x/\\log x$ \\cite[pp.~542--543]{Erdos1945}.\nErd\\H{o}s and Hall obtained a much larger factor\n$\\exp\\{a(\\log_3x)^2\\}$ for every $0<a<1/\\log16$\n\\cite[pp.~1--3]{ErdosHall1976}. Pomerance brought the upper bound onto\nthe same logarithmic scale \\cite[Equation~(1.4)]{Pomerance1986}.\nMaier and Pomerance then determined the leading constant\n\\cite[Section~1]{MaierPomerance1988}:\n\\[\n V(x)=\\frac{x}{\\log x}\n       \\exp\\{(C_0+o(1))(\\log_3x)^2\\},\\qquad\n C_0=0.8178146\\ldots.\n\\]\n\nIn 1998, Ford refined this to an estimate with only a bounded multiplicative\nuncertainty \\cite[Theorem~1]{FordTotients2013}:\n\\begin{equation}\\label{eq:classical-order}\n V(x)=\\frac{x}{\\log x}\\exp\\left\\{\n \\begin{aligned}\n &C_0(\\log_3x-\\log_4x)^2+D_0\\log_3x\\\\\n &\\hspace{12pt}-(D_0+\\tfrac12-2C_0)\\log_4x+O(1)\n \\end{aligned}\\right\\},\n\\end{equation}\nwhere $D_0=2.1769687\\ldots$; both constants are specified in the\nnotation of Section~\\ref{sec:statements} below. Ford also described\nthe typical prime factorizations of preimages\n\\cite[Theorems~10 and~16]{FordTotients2013}, which supply the\nstructural starting point of our proof. All numbered references to\nhis distribution paper use the revised arXiv version of 14 July 2013.\n\n\\paragraph{Fixed dilation.}\nErd\\H{o}s and Hall asked whether\n\\[\n \\frac{V(cx)}{V(x)}\\longrightarrow c\n \\qquad(c>1\\text{ fixed})\n\\]\nin the final paragraph of \\cite[p.~3]{ErdosHall1976}.\nErd\\H{o}s repeated the question in \\cite[p.~80]{Erdos1979}.\nFord proved that $V(cx)-V(x)\\asymp_c V(x)$ for every fixed $c>1$\n\\cite[Theorem~4]{FordTotients2013}. This controls the number of\nvalues in a fixed multiplicative interval, but neither it nor\n\\eqref{eq:classical-order} determines the limiting ratio.\n\nWe obtain an explicit asymptotic equivalent and prove the fixed-scale limit, thereby answering this question of Erd\\H{o}s and Hall positively. The coefficient in the equivalent retains finite arithmetic information. It is defined in Section~\\ref{sec:statements} by bounded prime and integer sums, inclusion--exclusion, and a uniformly convergent limit; its definition does not use~$V$.\n\n\\paragraph{The source of the coefficient.}\nOrder the prime factors of a typical preimage from largest to smallest, and separate a long prefix from a shorter tail. Ford's normal-structure estimates put the double logarithms of the large primes in a simplex. Most of that simplex can be counted by volume. Near its last coordinates the primes remain discrete, however, and several tail factorizations may give the same totient. We retain these tails exactly and take the volume of the union of the regions that they permit. Inclusion--exclusion of this finite union produces the arithmetic coefficient.\n\nErd\\H{o}s's semiprime argument already controls collisions between\nproducts of shifted primes \\cite[Lemma~1]{Erdos1945}. The quantitative\nshifted-prime normality and layered comparison used here were developed\nby Maier and Pomerance \\cite[Sections~2--3]{MaierPomerance1988}\nand Ford \\cite[Section~5]{FordTotients2013}. Here the comparison is\nproved under the hypotheses needed for the long prefix, with explicit\ncosts for recovering the tuple data inside the residual factors.\nThe resulting control of all ordered prefix collisions, combined with\nthe exact finite-tail union, converts the volume into a count of\ndistinct values. Counting the largest prime with common endpoint\nparameters first gives regular variation. Identifying the resulting\nmass with the arithmetic coefficient then gives the explicit equivalent.\nThe errors are uniform in the phase; neither conclusion assumes\ncontinuity of the coefficient.\n\nThe prefix uniqueness used here is not uniqueness of the entire preimage:\ndifferent arithmetic tails may still give the same value. A separate\nresult on extreme complete fibers gives, for every fixed $\\varepsilon>0$,\ninfinitely many positive integers $v$ with\n$\\#\\{n\\ge1:\\varphi(n)=v\\}>v^{1-\\varepsilon}$\n\\cite[Theorem~1.1]{OpenAITotientFibers2026}. That result concerns\nlarge individual fibers, rather than the asymptotic number of distinct\nvalues, and is not an input to the present proof.\n\n\\paragraph{Least preimages.}\nFor $v\\in\\mathcal V$, let\n\\[\n \\ell(v)=\\min\\{n\\ge1:\\varphi(n)=v\\}.\n\\]\nFor each fixed positive integer~$k$, the same passage of Erd\\H{o}s\n\\cite[p.~80]{Erdos1979} asks about the number of values $v\\le x$ for\nwhich $kx<\\ell(v)\\le(k+1)x$. Section~\\ref{sec:companion} gives a\nweighted asymptotic and proves that this count has order $V(x)$\nprecisely when some totient~$d$ satisfies $\\ell(d)>kd$; otherwise\nthe count vanishes identically. The positive alternative holds for\n$k=1,2$. We do not determine which other integers~$k$ satisfy this\nseed condition. In particular, the separate question whether\n$\\ell(d)/d$ is unbounded, posed in \\cite[Section~I.9]{Erdos1995},\nremains unresolved here.\n"}, {"path": "preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/layers.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/layers.tex", "bytes": 16583, "sha256": "065a7f375c56f3d2c5f138469b670ed5288c1edd8900910fb47327d63cbb8d1b", "content": "\\section{Comparing products of shifted primes}\\label{sec:layers}\n\nWe next bound the number of ways that two ordered lists of primes can\ngive the same product of their shifts.  The largest prime factors of the\nshifts lie in separated intervals.  We expose the factors one interval\nat a time, starting with the smallest interval.  At each stage only two\nor three simultaneous prime conditions are needed.  This keeps the\nconstants uniform when the number of intervals grows.\n\nThe layer decomposition and the comparison of dual factorizations\ngo back to Maier and Pomerance \\cite[Section~3]{MaierPomerance1988}\nand were developed further in Ford's proof of Lemma~5.1\n\\cite{FordTotients2013}. We prove the version needed here,\nincluding its dependence on the number of layers.  In particular, we\nassume squarefreeness of the whole product above the last cutoff;\nsquarefreeness of each individual shift would not suffice for our\nfactor-allocation estimate.\n\n\\subsection{A uniform sieve for two or three forms}\n\n\\begin{lemma}\\label{lem:sieve}\nThere are absolute constants $C$ and $y_0$ with the following property.\nLet $y\\ge y_0$, put $B_y=\\log_2 y$, and let $1\\le A,A'\\le y$ be\nintegers.  Consider either the two forms\n\\[\n n,\\quad An+1,\n\\]\nor the three forms\n\\[\n n,\\quad An+1,\\quad A'n+1,\\qquad A\\ne A'.\n\\]\nWrite $k=2$ or $3$, respectively, and let $\\mathcal P$ be the set of\npositive integers at which all the indicated forms are prime.  Then,\nuniformly for $X\\ge3$,\n\\begin{equation}\\label{eq:sieve-count}\n \\#(\\mathcal P\\cap[1,X])\n \\le C\\frac{X B_y^k}{(\\log X)^k}.\n\\end{equation}\nMoreover, uniformly for $3\\le U\\le Y$,\n\\begin{equation}\\label{eq:sieve-reciprocal}\n \\sum_{\\substack{n\\in\\mathcal P\\\\U\\le n\\le Y}}\\frac1n\n \\le C\\frac{B_y^k}{(\\log U)^{k-1}}.\n\\end{equation}\nThe coefficients need not be bounded in terms of $X$ or $U$.\n\\end{lemma}\n\n\\begin{proof}\nFor primitive, distinct, admissible integral linear forms\n$a_jn+b_j$ with positive leading coefficients and nonzero determinant\n$\\Delta$ as defined below, Ford's uniform upper sieve\n\\cite[Theorem~2.5]{FordSieve2023} gives\n\\begin{equation}\\label{eq:sieve-determinant}\n \\begin{gathered}\n \\#\\{1\\le n\\le X:a_jn+b_j\\text{ prime for every }j\\}\n \\ll_k\\frac{X}{(\\log X)^k}\n \\left(\\frac{\\Delta}{\\varphi(\\Delta)}\\right)^k,\\\\\n \\Delta=\\left|\\prod_j a_j\n       \\prod_{j<l}(a_jb_l-a_lb_j)\\right|.\n \\end{gathered}\n\\end{equation}\nHere admissibility means that no prime divides the product of the\nforms for every integer input; the implied constant depends only\non $k$, including when the coefficients vary.  For our forms,\nprimitivity is automatic, and the determinants are $A$ and\n$AA'(A-A')$, up to sign.  They are nonzero and have absolute value at\nmost $y^3$.\n\nIf our forms are inadmissible, an offending prime is at most $k$, since\neach primitive linear form has at most one root modulo that prime.\nAt every simultaneous-prime input one of the forms must equal this\nprime.  Each such equality has at most one solution, so there are at\nmost $k$ inputs.  Their contribution is absorbed in\n\\eqref{eq:sieve-count}.\n\nFor admissible forms, the elementary estimate\n\\[\n \\frac{d}{\\varphi(d)}\\ll\\log_2(d+10)\\qquad(d\\ge1)\n\\]\nand \\eqref{eq:sieve-determinant} prove \\eqref{eq:sieve-count}.\nTo recall its uniformity, split the product over prime divisors of\n$d$ at $\\log(d+10)$.  Mertens' product estimate bounds the first part\nby $O(\\log_2(d+10))$.  For the remaining part, use\n$\\sum_{p\\mid d}\\log p\\le\\log d$ to bound the sum of reciprocal\nprime divisors, giving a bounded factor.  Since $k$ takes only the\nvalues $2$ and $3$, all constants are absolute.\n\nFinally, partial summation of \\eqref{eq:sieve-count}, with the\ncoefficients fixed, gives\n\\[\n \\sum_{\\substack{n\\in\\mathcal P\\\\U\\le n\\le Y}}\\frac1n\n \\ll B_y^k\\left(\\frac1{(\\log Y)^k}\n       +\\int_U^Y\\frac{\\dd t}{t(\\log t)^k}\\right)\n \\ll\\frac{B_y^k}{(\\log U)^{k-1}}.\n\\]\nThis also includes a possible prime at the lower endpoint.\n\\end{proof}\n\n\\subsection{The comparison estimate}\n\nFor a positive integer $n$ and a real number $Z$, its part supported\nabove $Z$ means $\\prod_{p^a\\parallel n,\\ p>Z}p^a$.  We use\n$S$-normality as defined in Definition~\\ref{def:normal}.\n\nThe key term in the bound below is the exponent of $\\log y$.\nIn its application, a strict simplex inequality makes\nthe weighted cutoff sum plus its error smaller than $1$ by a\ncontrolled amount.\nThe exponent is consequently smaller than $-1$; that extra saving\nabsorbs the other factors, even after recovering the tuple data hidden\nin the smooth remainder. We need an estimate for ordered pairs with\nmultiplicity: a bound only on the number of values admitting a collision\nwould not control how many tuples lie over those values.\n\n\\begin{proposition}\\label{prop:comparison}\nThere are absolute constants $C$ and $y_0$ such that the following\nholds.  Let $y\\ge y_0$, put $B_y=\\log_2 y$, and let $b$ be an integer\nwith $1\\le b\\le B_y$.  Suppose that real cutoffs satisfy\n\\[\n Y_0=y,\\qquad \\e^\\e\\le S\\le Y_b,\n \\qquad Y_{j+1}<U_j<Y_j\\quad(0\\le j<b).\n\\]\nPut\n\\[\n \\nu_j=\\frac{\\log_2 Y_j}{B_y}\\quad(0\\le j\\le b),\n \\qquad\n \\mu_j=\\frac{\\log_2 U_j}{B_y}\\quad(0\\le j<b),\n \\qquad\n \\delta=\\sqrt{\\frac{\\log_2 S}{B_y}}.\n\\]\nFix a positive integer $D$ with $P^+(D)\\le Y_b$ and a real $r\\ge1$.\nLet $N$ count the ordered tuples\n\\[\n (p_0,\\ldots,p_{b-1},q_0,\\ldots,q_{b-1},D')\n\\]\nin which $D'$ is a positive integer and the following conditions hold:\n\\begin{enumerate}[label=\\textup{(\\roman*)}]\n \\item The $p_j,q_j$ are $S$-normal primes, $p_j\\ne q_j$, and\n \\[\n  U_j\\le P^+(p_j-1),P^+(q_j-1)\\le Y_j\n  \\qquad(0\\le j<b).\n \\]\n \\item $P^+(D')\\le Y_b$ and\n \\[\n  D\\prod_{j=0}^{b-1}(p_j-1)\n  =D'\\prod_{j=0}^{b-1}(q_j-1)\\le\\frac yr.\n \\]\n \\item The common product in \\textup{(ii)} is squarefree on primes\n greater than $Y_b$.\n \\item The part of $p_0-1$ supported above $Y_1$ exceeds $\\sqrt y$.\n\\end{enumerate}\nThen\n\\begin{equation}\\label{eq:comparison}\n \\begin{split}\n N\\le{}&\\frac{y}{rD}(CB_y^6)^b(b+1)^{\\Omega(D)}\n       (\\log Y_b)^{1+3b\\log(b(b+1))}\\\\\n &\\quad\\times\n (\\log y)^{-2+\\sum_{j=1}^{b-1}a_j\\nu_j+E_b},\n \\end{split}\n\\end{equation}\nwhere\n\\begin{equation}\\label{eq:comparison-error}\n E_b=2\\sum_{j=1}^{b-1}(\\nu_j-\\mu_j)\n      +\\delta\\sum_{i=2}^b(i\\log i+i).\n\\end{equation}\nHere $a_j$ is as defined in \\eqref{eq:aj}.  Empty sums are zero;\nin particular, when $b=1$ the cutoff in\n\\textup{(iv)} is $Y_1=Y_b$.\n\\end{proposition}\n\n\\begin{proof}\nWe first describe the layers and the weighted summation that combines\nthem.  We then count the top layer, estimate a general lower layer,\nand finally sum over the small prime factors.\n\n\\paragraph{The layer configurations.}\nFor $0\\le i\\le b$ and $0\\le j<b$, let\n\\[\n s_{i,j}=\\prod_{\\substack{p^a\\parallel p_j-1\\\\p\\le Y_i}}p^a,\n \\qquad\n s'_{i,j}=\\prod_{\\substack{p^a\\parallel q_j-1\\\\p\\le Y_i}}p^a.\n\\]\nRestricting the equality in condition~\\textup{(ii)} to primes at most\n$Y_i$ gives the common product\n\\[\n s_i^\\#=D\\prod_{j=0}^{b-1}s_{i,j}\n        =D'\\prod_{j=0}^{b-1}s'_{i,j}.\n\\]\nFor $1\\le i\\le b$, put\n\\[\n t_{i,j}=\\frac{s_{i-1,j}}{s_{i,j}},\\qquad\n t'_{i,j}=\\frac{s'_{i-1,j}}{s'_{i,j}},\\qquad\n t_i^\\#=\\frac{s_{i-1}^\\#}{s_i^\\#}.\n\\]\nEach $t_i^\\#$ is squarefree and supported on $(Y_i,Y_{i-1}]$.\nFor $j\\ge i$ the factors $t_{i,j}$ and $t'_{i,j}$ equal $1$, so\n\\begin{equation}\\label{eq:layer-two-allocations}\n t_i^\\#=\\prod_{j=0}^{i-1}t_{i,j}\n        =\\prod_{j=0}^{i-1}t'_{i,j}.\n\\end{equation}\nThus a layer consists of two allocations of the same squarefree\ninteger among $i$ labeled slots.\n\nLet $\\mathcal C_i$ be the set of configurations\n\\[\n \\sigma_i=(D',s_{i,0},\\ldots,s_{i,b-1},\n                   s'_{i,0},\\ldots,s'_{i,b-1})\n\\]\narising from tuples counted by $N$.  Restriction of prime factors\nmaps $\\mathcal C_{i-1}$ into $\\mathcal C_i$; the two allocations in\n\\eqref{eq:layer-two-allocations} determine each extension uniquely.\nReconstructing a tuple from its small prime factors runs from\n$\\mathcal C_b$ back to $\\mathcal C_0$; the weighted count below\nsums in the opposite direction, starting with the top layer.\nThe original tuples are in bijection with $\\mathcal C_0$, since\n$p_j=s_{0,j}+1$ and $q_j=s'_{0,j}+1$.  For $i\\ge2$ we will bound,\nuniformly in $\\sigma_i$, the reciprocal extension sum\n\\[\n \\sum_{\\substack{\\sigma_{i-1}\\in\\mathcal C_{i-1}\\\\\n                  \\sigma_{i-1}\\text{ restricts to }\\sigma_i}}\n       \\frac1{t_i^\\#}.\n\\]\nThis is the appropriate weight because\n$1/s_{i-1}^\\#=1/(s_i^\\#t_i^\\#)$.\n\nEvery configuration considered here extends to a genuine solution.\nIn particular, its partial common product is at most $y/r$, each\nshift is at most $y$, and every fixed or conditional coefficient\nused below is at most $y$.  When enlarging a sum for an upper bound,\nwe retain any necessary restriction on a conditional coefficient\nuntil the corresponding sieve estimate has been applied.\n\n\\paragraph{The top layer.}\nOnly the zeroth shift can have a prime factor greater than $Y_1$.\nConsequently $t_1^\\#$ is the same part of $p_0-1$ and $q_0-1$,\nand $t_1^\\#>\\sqrt y$.  Normality, applied with endpoint $p_0-1$,\ngives\n\\[\n \\Omega(p_0-1)\n \\le B_y+\\log_2 S+\\sqrt{B_y\\log_2 S}\\le3B_y.\n\\]\nWrite $t_1^\\#=t'Q$, with $Q=P^+(t_1^\\#)$.  The weaker bound\n$\\Omega(t_1^\\#)\\le4B_y$ already gives\n\\[\n Q\\ge y^{1/(8B_y)}.\n\\]\nAfter $\\sigma_1$ and $t'$ are fixed, the completed primes have the\nforms $AQ+1,A'Q+1$.  Their inequality implies $A\\ne A'$, which\nis exactly the nonzero determinant condition for the three-form\nsieve.  With\n\\[\n X=\\frac{y}{r s_1^\\#t'},\n\\]\na nonempty extension has $X\\ge Q\\ge y^{1/(8B_y)}$.\nLemma~\\ref{lem:sieve} therefore bounds the number of $Q$ by\n\\[\n \\ll\\frac{X B_y^3}{(\\log X)^3}\n \\ll\\frac{y}{r s_1^\\#t'}\\frac{B_y^6}{(\\log y)^3}.\n\\]\nThe integer $t'$ is squarefree and supported on $(Y_1,y]$.\nMertens' estimate gives\n\\[\n \\sum_{t'}\\frac1{t'}\n \\le\\prod_{Y_1<p\\le y}\\left(1+\\frac1p\\right)\n \\ll\\frac{\\log y}{\\log Y_1}.\n\\]\nIt follows that, for each $\\sigma_1$,\n\\begin{equation}\\label{eq:top-layer}\n \\#\\{\\sigma_0\\text{ restricting to }\\sigma_1\\}\n \\le CB_y^6\\frac{y}{r s_1^\\#}\n       (\\log y)^{-2-\\nu_1}.\n\\end{equation}\n\n\\paragraph{The prime conditions in an intermediate layer.}\nFix $2\\le i\\le b$ and $\\sigma_i$.  The last slot, of index\n$i-1$, is complete after this layer.  Let $Q_1,Q_2$ be its largest\nprime factors on the two sides; both lie in\n$[U_{i-1},Y_{i-1}]$.\n\nSuppose first that $Q_1=Q_2=Q$.  Remove the unique occurrence of\n$Q$ from $t_i^\\#$, and call the result $t$.  For fixed allocations\nof $t$, the completed primes are $AQ+1,A'Q+1$ with $A\\ne A'$.\nLemma~\\ref{lem:sieve} gives\n\\begin{equation}\\label{eq:shared-layer-prime}\n \\sum_Q\\frac1Q\\ll\\frac{B_y^3}{(\\log U_{i-1})^2}.\n\\end{equation}\n\nNow suppose $Q_1\\ne Q_2$ and remove both, obtaining $t$.\nIn addition to its two allocations, fix the position of $Q_1$ on\nthe second side and of $Q_2$ on the first side.  There are at most\n$i^2$ choices.  If neither prime lies in the opposite last slot,\nthe two completed prime conditions are\n$AQ_1+1$ and $A'Q_2+1$; two applications of\n\\eqref{eq:sieve-reciprocal} with $k=2$ give\n\\begin{equation}\\label{eq:distinct-layer-primes}\n \\sum_{Q_1,Q_2}\\frac1{Q_1Q_2}\n \\ll\\frac{B_y^4}{(\\log U_{i-1})^2}.\n\\end{equation}\nIf $Q_2$ lies in the last slot on the first side, then $Q_2<Q_1$.\nThe prime $Q_1$ cannot lie in the last slot on the second side,\nwhose largest prime is $Q_2$.  The completed prime conditions are\nnow $AQ_2Q_1+1$ and $A'Q_2+1$.  First sum over $Q_1$ conditional\non $Q_2$, using the forms $Q_1,AQ_2Q_1+1$.  Feasibility ensures\n$AQ_2\\le y$, so the inner reciprocal sum is uniformly\n$O(B_y^2/\\log U_{i-1})$.  Only after this estimate do we enlarge\nthe range of $Q_2$ and apply the two-form bound to\n$Q_2,A'Q_2+1$.  This proves \\eqref{eq:distinct-layer-primes} again.\nThe reversed incidence is treated by reversing the order of\nsummation.  Both incidences cannot occur simultaneously, as they\nwould imply both $Q_1<Q_2$ and $Q_2<Q_1$.\n\n\\paragraph{Counting the allocations.}\nPut $\\Delta_i=\\nu_{i-1}-\\nu_i$.  Normality bounds the number of\nprime factors of an individual shift in $(Y_i,Y_{i-1}]$ by\n$(\\Delta_i+\\delta)B_y$.  Indeed, if the shift ends before\n$Y_{i-1}$, apply normality only up to its endpoint; if it ends\nbefore $Y_i$, the count is zero.  In either case the error is at\nmost $\\sqrt{\\log_2 S\\,B_y}=\\delta B_y$.\nOnly $i$ shifts contribute to this layer, and removing the\ndistinguished primes decreases the count.  Thus\n\\[\n \\Omega(t)\\le I_i:=i(\\Delta_i+\\delta)B_y.\n\\]\nSince $t$ is squarefree, each of its prime factors chooses one of\n$i$ slots on each side, giving at most $i^{2\\Omega(t)}$ dual\nallocations.  Write\n\\[\n M_i=\\sum_{Y_i<p\\le Y_{i-1}}\\frac1p.\n\\]\nMertens' estimate gives $M_i\\le\\Delta_i B_y+O(1)$, uniformly in\nthe cutoffs.  Since $\\delta B_y\\ge\\sqrt{B_y}$, increasing the\nabsolute threshold $y_0$ ensures\n$M_i\\le(\\Delta_i+\\delta)B_y$.  Hence\n\\begin{align}\n \\sum_t\\frac{i^{2\\Omega(t)}}t\n &\\le\\sum_{n\\le I_i}\\frac{(i^2M_i)^n}{n!}\n \\le i^{I_i}\\exp(iM_i)\\notag\\\\\n &\\le\\exp\\bigl((i\\log i+i)(\\Delta_i+\\delta)B_y\\bigr).\n \\label{eq:allocation-entropy}\n\\end{align}\nThe middle inequality follows by writing $i^{2n}=i^ni^n$,\nusing $i^n\\le i^{I_i}$, and extending the remaining exponential\nseries.  It is valid without any lower bound on\n$\\Delta_i/\\delta$.\n\nCombining \\eqref{eq:shared-layer-prime},\n\\eqref{eq:distinct-layer-primes}, and\n\\eqref{eq:allocation-entropy}, and using $i\\le b\\le B_y$ to\nabsorb the $i^2$ position choices, gives\n\\begin{equation}\\label{eq:intermediate-layer}\n \\sum_{\\substack{\\sigma_{i-1}\\text{ restricting}\\\\\n                  \\text{to }\\sigma_i}}\\frac1{t_i^\\#}\n \\le CB_y^6\n (\\log y)^{-2\\mu_{i-1}+(i\\log i+i)(\\nu_{i-1}-\\nu_i+\\delta)}.\n\\end{equation}\nThe squarefree hypothesis was used both to remove a single\noccurrence of each distinguished prime and to obtain the\nfactorial denominator in \\eqref{eq:allocation-entropy}.\n\n\\paragraph{Multiplying the layer estimates.}\nStarting from \\eqref{eq:top-layer}, sum over $\\mathcal C_1$.\nFor each subsequent layer use the exact weighted identity\n\\[\n \\sum_{\\sigma_{i-1}\\in\\mathcal C_{i-1}}\\frac1{s_{i-1}^\\#}\n =\\sum_{\\sigma_i\\in\\mathcal C_i}\\frac1{s_i^\\#}\n   \\sum_{\\sigma_{i-1}\\text{ restricting to }\\sigma_i}\n          \\frac1{t_i^\\#}.\n\\]\nAll bounds in \\eqref{eq:intermediate-layer} are uniform in the\nlower configuration.  Their constants therefore multiply to at\nmost $C^bB_y^{6b}$.  If $c_i=i\\log i+i$, so that $c_1=1$, the\npower of $\\log y$ accumulated in this procedure is\n\\begin{align*}\n &-2-\\nu_1+\n   \\sum_{i=2}^b\\{-2\\mu_{i-1}+c_i(\\nu_{i-1}-\\nu_i+\\delta)\\}\\\\\n &\\qquad=-2+\\sum_{j=1}^{b-1}a_j\\nu_j\n     +2\\sum_{j=1}^{b-1}(\\nu_j-\\mu_j)\n     +\\delta\\sum_{i=2}^b c_i-c_b\\nu_b.\n\\end{align*}\nHere $c_{j+1}-c_j-2=a_j$, including $j=1$.  The identity also\nholds for $b=1$.  Discard the nonpositive final term\n$-c_b\\nu_b$.  It remains to bound\n$\\sum_{\\sigma_b\\in\\mathcal C_b}1/s_b^\\#$.\n\n\\paragraph{The smooth parts and repeated factors.}\nWrite $s=s_b^\\#/D$ and $L_b=\\log_2Y_b$.  For a fixed $s$, its\nallocation among the $b$ left slots has at most $b^{\\Omega(s)}$\npossibilities.  The allocation of $sD$ among the $b$ right slots\nand the additional slot $D'$ has at most\n$(b+1)^{\\Omega(sD)}$ possibilities.  These bounds allow prime\npowers: assigning labeled copies of a repeated prime to slots\novercounts every possible distribution of its exponent.\n\nEach left shift has largest prime factor above $Y_b$.  Its number\nof prime factors at most $Y_b$ is bounded by normality as follows:\n\\[\n \\Omega(p_j-1,1,Y_b)\n \\le 2\\log_2 S+(L_b-\\log_2 S)\n       +\\sqrt{L_b\\log_2 S}\\le3L_b.\n\\]\nIf $S=Y_b$, the first normality inequality alone gives the same\nbound.  Thus $\\Omega(s)\\le3bL_b$.  The ordinary smooth-number\nEuler product, including all powers, now gives\n\\begin{align*}\n \\sum_{\\sigma_b\\in\\mathcal C_b}\\frac1{s_b^\\#}\n &\\le\\frac{(b+1)^{\\Omega(D)}}D\n   [b(b+1)]^{3bL_b}\n   \\sum_{P^+(s)\\le Y_b}\\frac1s\\\\\n &=\\frac{(b+1)^{\\Omega(D)}}D\n   [b(b+1)]^{3bL_b}\n   \\prod_{p\\le Y_b}\\left(1-\\frac1p\\right)^{-1}\\\\\n &\\ll\\frac{(b+1)^{\\Omega(D)}}D\n   (\\log Y_b)^{1+3b\\log(b(b+1))}.\n\\end{align*}\nNo squarefreeness is assumed below $Y_b$.  Combining this estimate\nwith the layer bounds proves \\eqref{eq:comparison}, after increasing\nthe absolute constant $C$.\n\\end{proof}\n\n\\begin{remark}\\label{rem:comparison-uniformity}\nThe explicit error in \\eqref{eq:comparison-error} satisfies\n\\[\n E_b=2\\sum_{j=1}^{b-1}(\\nu_j-\\mu_j)\n       +O\\bigl(\\delta b^2\\log(2b)\\bigr),\n\\]\nand the smooth-factor exponent is $O(b\\log(2b))$.  Thus all\ndependence on a growing $b$ is displayed in\n\\eqref{eq:comparison}.  No restriction such as\n$r\\le y^{1/10}$ or $D\\le y^{1/100}$ is needed: the large top\npart supplies the lower bound for the actual sieve endpoint.\n\\end{remark}\n"}, {"path": "preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/limits.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/limits.tex", "bytes": 19676, "sha256": "e11b183b4f99ed85da42fa785f47a91889118be46a42f25f7f768c092e3625ee", "content": "\\section{The arithmetic coefficient and the limiting formula}\n\\label{sec:limits}\n\nThe preceding sections reduce the count of values to distinct tuples with\na common long prime prefix.  We now sum over the largest prime and replace\nthe remaining prefix by a volume.  The shorter tail stays discrete: its\ndifferent witnesses are combined by a union before taking that volume.\nThroughout this section, $H$ is fixed first, $x$ tends to infinity next,\nand $H$ tends to infinity last.  In particular, $P=\\lfloor\\log\\log H\\rfloor$,\n$R=m-H$, and $L=m-P$ always refer to the two cuts in \\eqref{eq:cuts}.\n\n\\subsection{Summing over the largest prime}\n\nLet $\\mathcal D_H(x)$ be the set of distinct data\n$(d,p_1,\\ldots,p_R)$ for which there are primes\n$p_{R+1},\\ldots,p_L$ and a positive integer $a$ satisfying\nthe basic conditions \\eqref{eq:basic} with $p_0$ omitted, and\n\\[\n d=\\varphi(w),\\qquad w=p_{R+1}\\cdots p_La.\n\\]\nThe convention $u_0=B$ is retained in these conditions.  Thus the\ninequality with index zero is a condition on the displayed data and\ntheir witness, independent of the eventual choice of $p_0$.\nFor any function $f:[1,\\infty)\\to[0,1]$, define\n\\begin{equation}\n M_f(x;H)=\n \\sum_{(d,p_1,\\ldots,p_R)\\in\\mathcal D_H(x)}\n \\frac{f(\\ell(d)/d)}{d\\prod_{i=1}^R(p_i-1)}.\n \\label{eq:mass}\n\\end{equation}\nEach datum is included once, regardless of its number of witnesses.\nThe function $f=1$ makes the least-preimage ratio irrelevant to this\ndefinition.\n\nWe use $K_H=\\exp(CP\\rho^{-P})=H^{o(1)}$, allowing the absolute constant\n$C$ to increase in upper bounds.  Summing over witnesses, the totient\ninequality\n\\begin{equation}\n \\frac{1}{\\varphi(p_{R+1}\\cdots p_La)}\n \\le \\frac{1}{\\varphi(a)}\n       \\prod_{i=R+1}^L\\frac{1}{p_i-1}\n \\label{eq:tail-reciprocal-majorant}\n\\end{equation}\nand Lemma~\\ref{lem:boxes} give\n\\begin{equation}\n 0\\le M_f(x;H)\\le M_1(x;H)\\ll K_HG_m.\n \\label{eq:mass-upper}\n\\end{equation}\nIndeed, the reciprocal sum for the full $L$ prime coordinates is\n$O(G_m)$ by \\eqref{eq:projected-mass}, while the unrestricted reciprocal\nsum for the allowed smooth cofactor is $O(K_H)$ by\n\\eqref{eq:smooth-cost}.  The inequality in\n\\eqref{eq:tail-reciprocal-majorant}, rather than an equality, also\ncovers the possibility that $p_L$ divides $a$.\n\n\\begin{proposition}[The unweighted prime sum]\n\\label{prop:mass}\nFor each fixed $c>1$, both $t=x$ and $t=x/c$ satisfy\n\\begin{equation}\n V(t)=\\frac{t}{\\log x}\n \\bigl(M_1(x;H)+E_H(t;x)G_m\\bigr),\n \\qquad\n \\lim_{H\\to\\infty}\\limsup_{x\\to\\infty}\n \\max_{t\\in\\{x,x/c\\}}|E_H(t;x)|=0.\n \\label{eq:mass-count}\n\\end{equation}\nAll parameters in the two formulas are formed from the same $x$.\n\\end{proposition}\n\n\\begin{proof}\nFor fixed data in $\\mathcal D_H(x)$ put\n\\[\n D=d\\prod_{i=1}^R(p_i-1).\n\\]\nSince $d\\le w$, the deterministic bound \\eqref{eq:cofactor-size}\nimplies\n\\[\n 1\\le D\\le p_1\\cdots p_La\\le\\exp((\\log x)^{0.8}).\n\\]\nThe possible largest primes are precisely\n$x^{0.9}\\le p_0\\le 1+t/D$.  The remaining prime factors are smaller\nthan $x^{0.9}$ for large $x$, so this choice introduces neither a\nrepeated prefix prime nor another ordering condition.  The endpoints\n$t/D$ lie uniformly in $[x^{1-o(1)}/c,x]$.  The prime number theorem,\nwith $\\log(t/D)=\\log x+o(\\log x)$ uniformly, therefore gives\n\\[\n \\#\\{p_0:x^{0.9}\\le p_0\\le1+t/D\\}\n =\\frac{t}{D\\log x}\\bigl(1+o_{x\\to\\infty;H,c}(1)\\bigr).\n\\]\nThe lower cutoff costs a relative\n$O(x^{-0.1+o(1)})$, uniformly in the data.  Summing this formula and\nusing \\eqref{eq:mass-upper} counts $\\mathcal T(t)$ with main term\n$tM_1/\\log x$ and error $o_{x\\to\\infty;H,c}(xG_m/\\log x)$.\nProposition~\\ref{prop:unique-prefix} replaces $\\#\\mathcal T(t)$ by\n$V(t)$ with an error whose normalized iterated limit is zero.  Since\n$t$ is either $x$ or $x/c$, this proves \\eqref{eq:mass-count}.\n\\end{proof}\n\nThe common mass in Proposition~\\ref{prop:mass} already gives the\nfixed-scaling conclusion. Its proof does not require identifying that\nmass with the arithmetic coefficient.\n\n\\begin{corollary}[Fixed scaling]\\label{cor:fixed-scaling}\nFor every fixed real \\(c>0\\),\n\\[\n \\frac{V(cx)}{V(x)}\\longrightarrow c.\n\\]\n\\end{corollary}\n\n\\begin{proof}\nFirst let \\(c>1\\). Apply Proposition~\\ref{prop:mass} at \\(t=x\\)\nand \\(t=x/c\\), using the same \\(M_1(x;H)\\) and \\(G_m(x)\\).\nSubtracting gives\n\\[\n \\frac{(V(x)-cV(x/c))\\log x}{xG_m}\n =E_H(x;x)-E_H(x/c;x).\n\\]\nFord's estimate \\eqref{eq:ford-scale} supplies a positive constant\n\\(c_-\\) with \\(V(x)\\log x/(xG_m)\\ge c_-\\) for large \\(x\\).\nConsequently\n\\[\n \\limsup_{x\\to\\infty}\n \\left|1-\\frac{cV(x/c)}{V(x)}\\right|\n \\le\\frac1{c_-}\\limsup_{x\\to\\infty}\n       \\bigl(|E_H(x;x)|+|E_H(x/c;x)|\\bigr).\n\\]\nLet \\(H\\) tend to infinity and then replace \\(x\\) by \\(cx\\) to\nobtain the assertion. This also covers arguments at which\n\\(m(x)\\) and \\(m(x/c)\\) differ: the latter integer never enters\nthe comparison. Neither continuity of the phase coefficient nor\nmatching values at the ends of the phase interval is required.\n\nThe case \\(c=1\\) is immediate. If \\(0<c<1\\), apply the result\njust proved to the multiplier \\(1/c>1\\) and the argument\n\\(y=cx\\); taking reciprocals gives the stated limit.\n\\end{proof}\n\n\\subsection{A finite tail and a continuous prefix}\n\nThe phase-dependent witness set in \\eqref{eq:AH} changes with the\nphase, but for fixed $H$ all its members belong to one finite set.\nThis observation will permit uniform limits without any continuity\nassumption.\n\n\\begin{lemma}[Uniformly finite tail data]\n\\label{lem:finite-tail-data}\nFor each sufficiently large fixed $H$, the union of all\n$\\mathcal W_{H,s}(d)$, over $0\\le s<1$ and all $d$, is finite.\nAll its prime and integer coordinates, and all its totient values,\nhave explicit bounds depending only on $H$.  Uniformly over these\ndata,\n\\begin{equation}\n 0\\le D(h,\\eta)\\ll_H\\log(2h)\\qquad(h\\ge H).\n \\label{eq:tail-D-bound}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nWrite $a_*=\\lambda/\\rho$, so that\n$\\lambda\\le\\alpha_s<a_*$.  Let\n\\[\n q_H=\\left\\lceil\\exp\\exp(1.1a_*H\\rho^{-H})\\right\\rceil,\n \\qquad\n A_H^*=\\left\\lceil\\exp\\exp(2a_*P\\rho^{-P})\\right\\rceil.\n\\]\nEvery witness belongs to the finite set\n$\\{1,\\ldots,q_H\\}^{H-P}\\times\\{1,\\ldots,A_H^*\\}$.\nIts corresponding integer $w$ and totient $d$ satisfy\n$d\\le w\\le A_H^*q_H^{H-P}$.  These bounds do not involve $x$ or $s$.\nFinally,\n$a_j=\\int_j^{j+1}\\log t\\,\\dd t\\le\\log(j+1)$, so the definition\n\\eqref{eq:tail-D} and the bounded tail coordinates give\n\\eqref{eq:tail-D-bound}.\n\\end{proof}\n\nFor $s=\\theta(x)$ and $\\eta\\in\\mathcal W_{H,s}(d)$, define\nthe region $\\mathcal K_\\eta\\subset\\mathbb R^R$ by $u_0=B$ and\n\\begin{equation}\n u_i-\\sum_{r=i+1}^Ra_{r-i}u_r\\ge D(m-i,\\eta)\n \\qquad(0\\le i\\le R).\n \\label{eq:prefix-region}\n\\end{equation}\nThese inequalities imply $u_i\\ge0$.  The coordinates $u_i$ here are\nreal; the coarse prime bands have been removed.  We write\n$\\operatorname{Vol}_R$ for $R$-dimensional Lebesgue measure.\n\n\\begin{lemma}[The volume of the witness union]\n\\label{lem:tail-volume}\nThere are numbers $\\delta_H\\to0$ such that, uniformly for\n$f:[1,\\infty)\\to[0,1]$,\n\\begin{equation}\n \\limsup_{x\\to\\infty}\n \\left|\\frac{M_f(x;H)}{G_m}\n -\\frac1{G_m}\\sum_{d:\\mathcal W_{H,\\theta}(d)\\ne\\varnothing}\n \\frac{f(\\ell(d)/d)}d\n \\operatorname{Vol}_R\n \\bigcup_{\\eta\\in\\mathcal W_{H,\\theta}(d)}\\mathcal K_\\eta\n \\right|\\le\\delta_H.\n \\label{eq:mass-volume}\n\\end{equation}\nMoreover, for every nonempty finite\n$T\\subseteq\\mathcal W_{H,\\theta}(d)$,\n\\begin{equation}\n \\operatorname{Vol}_R\\bigcap_{\\eta\\in T}\\mathcal K_\\eta\n =G_R\\left(1-\\frac1B\\sum_{h=H}^m g_{m-h}\n       \\max_{\\eta\\in T}D(h,\\eta)\\right)_+^R.\n \\label{eq:intersection-volume}\n\\end{equation}\nHere $z_+=\\max(z,0)$.\n\\end{lemma}\n\n\\begin{proof}\nAn intersection imposes the largest of the witness thresholds at each\nslack coordinate.\n\n\\paragraph{Intersection volumes.}\nSet $C_i=\\max_{\\eta\\in T}D(m-i,\\eta)$ and introduce the prefix\nslacks\n\\[\n t_i=u_i-\\sum_{r=i+1}^Ra_{r-i}u_r\\qquad(0\\le i\\le R).\n\\]\nLemma~\\ref{lem:simplex} gives\n$\\sum_{i=0}^Rg_it_i=B$, and the substitution from\n$(u_1,\\ldots,u_R)$ to $(t_1,\\ldots,t_R)$ has determinant one.\nThe intersection is $t_i\\ge C_i$ for every $i$, including $i=0$.\nTranslation by $C_i$ leaves a simplex of available size\n$B-\\sum_i g_iC_i$. Its volume is\n\\[\n \\frac{(B-\\sum_{i=0}^Rg_iC_i)_+^R}\n {R!\\prod_{i=1}^Rg_i},\n\\]\nwhich proves \\eqref{eq:intersection-volume} after putting $h=m-i$.\nIt remains to compare the volume of the union with the prime mass.\n\n\\paragraph{From primes to volume.}\nIdentify $Q_h=p_{m-h}$ for $P\\le h<H$.  These are exactly the\nprimes with indices $R+1,\\ldots,L$.  For fixed tail witness $\\eta$,\nthe original prefix conditions are its coarse bands together with\n\\begin{equation}\n \\xi_i u_i-\\sum_{r=i+1}^Ra_{r-i}u_r\\ge D(m-i,\\eta)\n \\qquad(0\\le i\\le R).\n \\label{eq:perturbed-prefix-region}\n\\end{equation}\nTaking the union over $\\eta\\in\\mathcal W_{H,\\theta}(d)$ is essential:\na prime prefix with several witnesses contributes just once to\n\\eqref{eq:mass}.\n\nFirst replace the reciprocal prime sum by the volume of this union\nwith its bands and its $\\xi_i$.  In a unit box wholly contained in\nthe union, the product of the prime reciprocal\nsums differs from its volume by a relative\n$O(\\sum_{i=1}^R e^{-c b_i})=O(e^{-c' b_R})$, by\nLemma~\\ref{lem:boxes}.  The geometric growth of $b_i$ as $i$\ndecreases makes this estimate independent of $R$.  Both the total\nmass and the total volume before this replacement are\n$O(K_HG_m)$, by summing over witnesses as in\n\\eqref{eq:tail-reciprocal-majorant}.\n\nIf a unit box meets the union without being contained in it,\nchoose a witness whose region meets the box.  That witness's\nregion does not contain the box.  Consequently the box lies in\na shell at a prefix band endpoint, or within $O(h_i^2)$ of equality\nin one of \\eqref{eq:perturbed-prefix-region}.  For an upper bound,\nsum these shells over witnesses using\n\\eqref{eq:tail-reciprocal-majorant}.  Thicken the discrete tail\ncoordinates into unit boxes as well and sum $1/\\varphi(a)$ at a\ncost $O(K_H)$.  The resulting full $L$-coordinate boxes are covered\nby the shell bounds of Lemma~\\ref{lem:boxes} and\nProposition~\\ref{prop:discard}.  Their contribution is $o_H(1)G_m$.\nMore explicitly, the slice coefficient for an index with $h=m-i$\nis $Lg_i/B\\ll\\rho^h$.  Summing the shell bounds therefore costs\nat most\n\\[\n O\\!\\left(K_H\\sum_{h\\ge H}\n       (h e^{-h/40}+h^3\\rho^h)\\right)G_m.\n\\]\nThe broader $O(K_HH^{-2})G_m$ shell estimate in\nProposition~\\ref{prop:discard} also suffices.\n\n\\paragraph{Removing the slack perturbation.}\nReplace $\\xi_i$ by $1$ for $i\\le R$, retaining the bands.\nThe removed region lies in a shell of thickness\n$O(e^{-h_i/40}b_i)$, with the same box-thickening errors.\nIts normalized contribution is at most\n\\[\n O\\!\\left(K_H\\sum_{h\\ge H}\n       (h e^{-h/40}+h^3\\rho^h)\\right)=o_H(1).\n\\]\nBoth shell estimates are valid for the union because upper bounds\nmay sum over witnesses, while $0\\le f\\le1$.\n\n\\paragraph{Removing the prefix bands.}\nFor every point satisfying\nthe now unperturbed prefix inequalities, recurrence iteration gives\n\\begin{equation}\n u_i\\ge g_{R+1-i}v_{H-1}\\gg H\\rho^{-h_i}\n \\qquad(0\\le i\\le R),\n \\label{eq:unbanded-prefix-lower}\n\\end{equation}\nwhere $v_{H-1}=\\log_2Q_{H-1}$.  To see this, retain the contribution\nof the first tail coordinate in each prefix inequality, discard\nthe other nonnegative tail contributions, and apply the recurrence\nfor $g$.  The tail band for $v_{H-1}$ and\n\\eqref{eq:g-asymptotic} give the displayed lower bound.\nThickening the $H-P$ tail coordinates changes inequality $i$ by\n$O(H\\log(h_i+1))$. The enlargement term\n$C h_i^2\\rho^{h_i}u_i$ is at least a constant times $CHh_i^2$,\nso it absorbs this error. Together with the retained tail bands,\nthis places the full region inside the enlarged simplex of\nLemma~\\ref{lem:boxes}.\n\nTo sum these regions without a factor for the number of witnesses,\nuse \\eqref{eq:tail-reciprocal-majorant} and first fix $a$. Partition\nthe tail coordinates into half-open unit boxes\n$\\mathcal C=\\prod_{h=P}^{H-1}\\mathcal C_h$. Let\n$\\mathcal U_{\\mathcal C}$ be the union of $\\mathcal K_\\eta$ over\nall allowed witnesses $\\eta=((Q_h),a)$ with $(\\log_2Q_h)_h\\in\\mathcal C$.\nLet $E$ be the set of prefix points violating at least one removed\nband. Aggregating the reciprocal weights within a box gives\n\\begin{align*}\n &\\sum_{\\substack{\\eta=((Q_h),a)\\text{ allowed}\\\\\n                   (\\log_2Q_h)_h\\in\\mathcal C}}\n \\frac{\\operatorname{Vol}_R(\\mathcal K_\\eta\\cap E)}\n      {\\prod_h(Q_h-1)}\\\\\n &\\quad\\le\n \\left(\\prod_{h=P}^{H-1}\n       \\sum_{\\log_2q\\in\\mathcal C_h}\\frac1{q-1}\\right)\n       \\operatorname{Vol}_R(\\mathcal U_{\\mathcal C}\\cap E)\\\\\n &\\quad\\ll\\operatorname{Vol}_L\n       \\bigl((\\mathcal U_{\\mathcal C}\\cap E)\\times\\mathcal C\\bigr).\n\\end{align*}\nThe sums over $q$ are over primes. The product is $O(1)$ uniformly\nin the number of tail coordinates, by \\eqref{eq:mertens-box} and\ntheir geometrically growing lower endpoints. Here $R+(H-P)=L$\nand $\\mathcal C$ has unit volume.\nBy the preceding enlargement argument, every set on the right lies\nin the same enlarged $L$-simplex. Since the tail boxes are disjoint,\nsumming their volumes counts a subset of that simplex once. Finally,\nsumming $1/\\varphi(a)$ over the common smooth envelope costs $O(K_H)$.\nAll these bounds hold at each phase; they do not compare witness sets\nat nearby phases.\n\nScale that enlarged simplex into the true $L$-coordinate simplex.\nIts Jacobian cost is bounded, and its coordinate scaling factors\nthrough index $R$ are\n\\[\n 1+O\\!\\left(\\sum_{h\\ge H}\n       (e^{-h/40}+h^2\\rho^h)\\right)=1+o_H(1).\n\\]\nFor large $H$, and then large $x$, a violation of a removed\n$[0.9\\widetilde b_i,1.1\\widetilde b_i]$ band therefore becomes a\nviolation of the\n$[0.95b_i,1.05b_i]$ band in the true simplex.  Lemma~\\ref{lem:concentration}\napplies with dimension $L=m-P$, since $h_i\\ge H$ and $P/H\\to0$.\nSumming its bounds gives $O(e^{-cH})$ of the simplex volume.\nAfter the smooth-cofactor factor, the additional volume is at most\n$O(K_He^{-cH})G_m=o_H(1)G_m$.\nThis proves \\eqref{eq:mass-volume}.  All constants used in these\nestimates are uniform in $\\theta$, since\n$\\lambda\\le\\alpha_\\theta<\\lambda/\\rho$ and\n$\\alpha/\\alpha_\\theta=1+o_{x\\to\\infty;H}(1)$.\n\n\\end{proof}\n\n\\subsection{The arithmetic approximation}\n\n\\begin{proposition}[Approximation by the finite coefficient]\n\\label{prop:tail-limit}\nThere are numbers $\\varepsilon_H\\to0$ such that, for every\n$f:[1,\\infty)\\to[0,1]$,\n\\begin{equation}\n \\limsup_{x\\to\\infty}\n \\left|\\frac{M_f(x;H)}{G_m}-A_H(f;\\theta(x))\\right|\n \\le\\varepsilon_H.\n \\label{eq:uniform-approximation}\n\\end{equation}\nThe bound is independent of $f$ and of phase.  Each $A_H(f;s)$ is a finite sum involving values of $f$ at explicitly\nbounded rational arguments and absolutely convergent series in its\nexponential weights, and $A_H(f;s)\\ge0$.\n\\end{proposition}\n\n\\begin{proof}\nApply finite inclusion--exclusion to the union in\n\\eqref{eq:mass-volume}, and then use \\eqref{eq:intersection-volume}.\nFor a nonempty subset $T$ of witnesses, write\n\\[\n C_T(h)=\\max_{\\eta\\in T}D(h,\\eta),\\qquad\n S_T=\\sum_{h=H}^{\\infty}\\rho^hC_T(h),\\qquad\n q_{m,T}=\\frac1B\\sum_{h=H}^mg_{m-h}C_T(h).\n\\]\nThe series for $S_T$ converges absolutely by\n\\eqref{eq:tail-D-bound}.  The finite universe in\nLemma~\\ref{lem:finite-tail-data} gives a uniform bound on the number\nof possible witnesses and their subsets for fixed $H$; it also\nmakes all the following estimates uniform in phase, even when\nmembership in a witness set changes.\n\nUsing $B=\\alpha m\\rho^{-m}$ and \\eqref{eq:g-asymptotic}, we obtain\n\\begin{align*}\n q_{m,T}\n &=\\frac{\\gamma}{\\alpha m}\n       \\sum_{h=H}^m\\rho^h C_T(h)\n       +O_H\\!\\left(\\frac{m\\log(2m)}B\\right)\\\\\n &=\\frac{\\gamma}{\\alpha m}S_T+o_{x\\to\\infty;H}(m^{-1}).\n\\end{align*}\nThe omitted geometric tail is $O_H(\\rho^m\\log(2m))$.\nIn particular, $q_{m,T}=O_H(1/m)$, and hence\n\\[\n (1-q_{m,T})_+^{m-H}\n =\\exp\\!\\left(-\\frac\\gamma{\\alpha_\\theta}S_T\\right)\n       +o_{x\\to\\infty;H}(1).\n\\]\nHere we used the uniform scale relation\n$\\alpha/\\alpha_\\theta=1+O(\\log m/m)$ and\n$(m-H)q_{m,T}^2=O_H(1/m)$.  The prefactor satisfies\n\\begin{equation}\n \\frac{G_{m-H}}{G_m}\n =\\prod_{l=0}^{H-1}\\frac{(m-l)g_{m-l}}B\n =\\left(\\frac\\gamma{\\alpha_\\theta}\\right)^H\n    \\rho^{H(H-1)/2}+o_{x\\to\\infty;H}(1).\n \\label{eq:volume-prefactor-limit}\n\\end{equation}\nThere are only boundedly many terms for fixed $H$.  Thus the\ninclusion--exclusion expression differs from $A_H(f;\\theta(x))$\nin \\eqref{eq:AH} by $o_{x\\to\\infty;H}(1)$, uniformly in phase;\nno witness multiplicity has been introduced.\nTogether with Lemma~\\ref{lem:tail-volume}, this proves\n\\eqref{eq:uniform-approximation}.\n\nNonnegativity follows from the finite-union probability interpretation\nin \\eqref{eq:tail-events}: both the union probabilities and the outer\nweights are nonnegative.\n\\end{proof}\n\n\\subsection{Uniform convergence and the asymptotic equivalent}\n\nThe preceding approximation holds for every bounded nonnegative\nweight.  Convergence as $H$ grows requires a comparison quantity\nthat does not depend on $H$.  For $f=1$ this quantity is the\nnormalized count of totients.  Section~\\ref{sec:companion} will\nsupply a different comparison quantity for each $f_k$.\n\n\\begin{lemma}[Convergence from a common comparison]\n\\label{lem:coefficient-convergence}\nFix $f:[1,\\infty)\\to[0,1]$.  Suppose a real function $C_f(x)$,\nindependent of $H$, and numbers $\\eta_H\\to0$ satisfy\n\\begin{equation}\n \\limsup_{x\\to\\infty}\n \\left|C_f(x)-\\frac{M_f(x;H)}{G_m}\\right|\\le\\eta_H.\n \\label{eq:common-comparison}\n\\end{equation}\nThen $A_H(f;s)$ converges uniformly for $0\\le s<1$ to the\nlimit $A(f;s)$ defined in Section~\\ref{sec:statements}, and\n\\begin{equation}\n C_f(x)=A(f;\\theta(x))+o(1).\n \\label{eq:comparison-limit}\n\\end{equation}\nIf additionally $c_-\\le C_f(x)\\le c_+$ for all sufficiently large\n$x$, then $c_-\\le A(f;s)\\le c_+$ for every $s\\in[0,1)$.\n\\end{lemma}\n\n\\begin{proof}\nBy Proposition~\\ref{prop:tail-limit},\n\\[\n \\limsup_{x\\to\\infty}\n |C_f(x)-A_H(f;\\theta(x))|\\le\\zeta_H,\n \\qquad \\zeta_H=\\eta_H+\\varepsilon_H\\longrightarrow0.\n\\]\nEvery phase occurs at arbitrarily large real arguments.  Indeed,\n\\[\n \\psi(B)=\\frac{\\log B-\\log\\log B}{\\lambda}\n\\]\nis continuous and strictly increasing for $B>e$, and tends to\ninfinity.  For any $s\\in[0,1)$ solve $\\psi(B_n)=n+s$ and put\n$x_n=\\exp(\\exp B_n)$.  Then $m(x_n)=n$ and $\\theta(x_n)=s$\nexactly.  Comparing indices $H$ and $K$ along this same sequence\ngives\n\\[\n |A_H(f;s)-A_K(f;s)|\\le\\zeta_H+\\zeta_K.\n\\]\nThis bound is independent of $s$, so the functions form a uniformly\nCauchy sequence.  In particular,\n$\\sup_s|A_H(f;s)-A(f;s)|\\le\\zeta_H$.\nCombining this inequality with the preceding limsup bound and\nthen sending $H$ to infinity proves \\eqref{eq:comparison-limit}.\nIf $C_f$ lies between $c_-$ and $c_+$, apply those bounds on each\nexact-phase sequence and let $H$ grow to obtain the asserted bounds\nfor $A$.  No continuity in the phase was used.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\nSet\n\\[\n C_1(x)=\\frac{V(x)\\log x}{xG_m}.\n\\]\nProposition~\\ref{prop:mass} with $t=x$ supplies\n\\eqref{eq:common-comparison}.  Lemma~\\ref{lem:coefficient-convergence}\ntherefore proves uniform existence of $A(1;s)$ and\n$C_1(x)=A(1;\\theta(x))+o(1)$.  Ford's two-sided estimate\n\\eqref{eq:ford-scale} supplies positive absolute constants\n$c_-,c_+$ bounding $C_1$.  The same lemma gives\n\\[\n 0<c_-\\le A(1;s)\\le c_+<\\infty\\qquad(0\\le s<1).\n\\]\nThe additive approximation is consequently equivalent to\n\\[\n V(x)\\sim\\frac{x}{\\log x}G_mA(1;\\theta(x)).\n\\]\nThe coefficient in this formula was defined by the finite arithmetic\nexpressions \\eqref{eq:AH}; the count $V$ has been used to establish\ntheir convergence and bounds, not to define them.\n\nCorollary~\\ref{cor:fixed-scaling} supplies the remaining assertion of\nthe theorem for every \\(c>0\\).\n\\end{proof}\n"}, {"path": "preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/main.tex", "bytes": 1862, "sha256": "9d27e9707cc113d25ad3940be63f8cd3fda1c5a95546e0516489b508f3303ba6", "content": "\\pdftrailerid{}\n\\documentclass[11pt]{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{lmodern}\n\\usepackage[margin=1.05in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools,bm}\n\\usepackage{microtype,booktabs,graphicx,enumitem}\n\\usepackage{tikz}\n\\usetikzlibrary{decorations.pathreplacing,arrows.meta,positioning}\n\\usepackage[colorlinks=true,linkcolor=blue!45!black,citecolor=blue!45!black,urlcolor=blue!45!black]{hyperref}\n\\hypersetup{pdftitle={An asymptotic formula for the number of totients},pdfauthor={OpenAI}}\n\\usepackage[nameinlink,capitalise,noabbrev]{cleveref}\n\\numberwithin{equation}{section}\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\\newtheorem{definition}[theorem]{Definition}\n\\newtheorem{remark}[theorem]{Remark}\n\\newcommand{\\dd}{\\,\\mathrm d}\n\\newcommand{\\e}{\\mathrm e}\n\\newcommand{\\one}{\\mathbf 1}\n\\DeclareMathOperator{\\vol}{vol}\n\\setlist[enumerate]{itemsep=3pt,topsep=5pt}\n\\setlength{\\emergencystretch}{1.5em}\n\\title{An asymptotic formula for the number of totients}\n\\author{OpenAI}\n\\date{September 25, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nLet $V(x)$ count the distinct values of Euler's totient function up to~$x$. We give an explicit asymptotic equivalent for $V(x)$. Its coefficient is a uniform limit of functions defined from finite arithmetic data. We also prove that $V(cx)/V(x)\\to c$ as $x\\to\\infty$ for every fixed $c>0$, answering a question of Erd\\H{o}s and Hall.\n\\end{abstract}\n\\begingroup\n\\small\n\\setlength{\\baselineskip}{10.5pt}\n\\tableofcontents\n\\endgroup\n\\clearpage\n\\input{introduction}\n\\input{statements}\n\\input{extraction}\n\\input{layers}\n\\input{collisions}\n\\input{limits}\n\\input{companion}\n\\clearpage\n\\bibliographystyle{plain}\n\\bibliography{references}\n\\end{document}\n"}, {"path": "preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/references.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/references.bib", "bytes": 4973, "sha256": "468646d0b86943e900fa96d14f511b7e40d2579b1ac112429b4dcaa6ef28c491", "content": "@article{FordTotients2013,\n  author  = {Ford, Kevin},\n  title   = {The distribution of totients},\n  journal = {Ramanujan Journal},\n  volume  = {2},\n  pages   = {67--151},\n  year    = {1998},\n  note    = {Revised version: \\url{https://arxiv.org/abs/1104.3264v2},\n             14 July 2013. Theorem and page references are to this revision}\n}\n\n@misc{FordSieve2023,\n  author       = {Ford, Kevin},\n  title        = {Sieve methods lecture notes},\n  year         = {2023},\n  howpublished = {University of Illinois Urbana-Champaign},\n  note         = {Spring 2023. \\url{https://ford126.web.illinois.edu/sieve2023.pdf}}\n}\n\n@incollection{Erdos1979,\n  author    = {Erd{\\H o}s, Paul},\n  title     = {Some unconventional problems in number theory},\n  booktitle = {Journ{\\'e}es Arithm{\\'e}tiques de Luminy},\n  series    = {Ast{\\'e}risque},\n  number    = {61},\n  pages     = {73--82},\n  year      = {1979},\n  publisher = {Soci{\\'e}t{\\'e} math{\\'e}matique de France},\n  note      = {\\url{https://www.numdam.org/item/AST_1979__61__73_0/}}\n}\n\n@article{Erdos1995,\n  author  = {Erd{\\H o}s, Paul},\n  title   = {Some of my favourite problems in number theory,\n             combinatorics, and geometry},\n  journal = {Resenhas do Instituto de Matem{\\'a}tica e Estat{\\'i}stica\n             da Universidade de S{\\~a}o Paulo},\n  volume  = {2},\n  number  = {2},\n  pages   = {165--186},\n  year    = {1995},\n  doi     = {10.11606/resimeusp.v2i2.74798},\n  note    = {\\url{https://doi.org/10.11606/resimeusp.v2i2.74798}}\n}\n\n\n@article{FordLau2000,\n  author = {Ford, Kevin and Lau, Kee-Wai},\n  title = {Asymptotics of a recurrent sequence: 10682},\n  journal = {American Mathematical Monthly},\n  volume = {107},\n  number = {4},\n  pages = {374--375},\n  year = {2000},\n  note = {\\url{https://doi.org/10.2307/2589199}}\n}\n\n@article{PPT2013,\n  author = {Pollack, Paul and Pomerance, Carl and Trevi{\\~n}o, Enrique},\n  title = {Sets of monotonicity for {Euler}'s totient function},\n  journal = {Ramanujan Journal},\n  volume = {30},\n  pages = {379--398},\n  year = {2013},\n  note = {\\url{https://doi.org/10.1007/s11139-012-9386-6}. Author version:\n          \\url{https://math.dartmouth.edu/~carlp/MonotonePhi.pdf}}\n}\n\n\n@article{Pillai1929,\n  author = {Pillai, S. S.},\n  title = {On some functions connected with {$\\phi(n)$}},\n  journal = {Bulletin of the American Mathematical Society},\n  volume = {35},\n  number = {6},\n  year = {1929},\n  pages = {832--836},\n  note = {\\url{https://www.ams.org/journals/bull/1929-35-06/S0002-9904-1929-04799-2/S0002-9904-1929-04799-2.pdf}}\n}\n\n\n@article{Erdos1935,\n  author = {Erd{\\H{o}}s, Paul},\n  title = {On the normal number of prime factors of {$p-1$} and some related problems concerning {Euler}'s {$\\phi$}-function},\n  journal = {The Quarterly Journal of Mathematics},\n  volume = {6},\n  year = {1935},\n  pages = {205--213},\n  doi = {10.1093/qmath/os-6.1.205},\n  note = {\\url{https://www.renyi.hu/~p_erdos/1935-08.pdf}}\n}\n\n\n@article{Erdos1958,\n  author = {Erd{\\H{o}}s, Paul},\n  title = {Some remarks on {Euler}'s {$\\phi$} function},\n  journal = {Acta Arithmetica},\n  volume = {4},\n  year = {1958},\n  pages = {10--19},\n  note = {\\url{https://users.renyi.hu/~p_erdos/1958-18.pdf}}\n}\n\n\n@article{Erdos1945,\n  author = {Erd{\\H{o}}s, Paul},\n  title = {Some remarks on {Euler}'s {$\\phi$} function and some related problems},\n  journal = {Bulletin of the American Mathematical Society},\n  volume = {51},\n  year = {1945},\n  pages = {540--544},\n  note = {\\url{https://users.renyi.hu/~p_erdos/1945-08.pdf}}\n}\n\n\n@article{ErdosHall1976,\n  author = {Erd{\\H{o}}s, Paul and Hall, R. R.},\n  title = {Distinct values of {Euler}'s {$\\phi$}-function},\n  journal = {Mathematika},\n  volume = {23},\n  number = {1},\n  year = {1976},\n  pages = {1--3},\n  doi = {10.1112/S0025579300006100},\n  note = {\\url{https://users.renyi.hu/~p_erdos/1976-11.pdf}}\n}\n\n\n@article{Pomerance1986,\n  author = {Pomerance, Carl},\n  title = {On the distribution of the values of {Euler}'s function},\n  journal = {Acta Arithmetica},\n  volume = {47},\n  number = {1},\n  year = {1986},\n  pages = {63--70},\n  doi = {10.4064/aa-47-1-63-70},\n  note = {\\url{https://math.dartmouth.edu/~carlp/PDF/57.pdf}}\n}\n\n\n@article{MaierPomerance1988,\n  author = {Maier, Helmut and Pomerance, Carl},\n  title = {On the number of distinct values of {Euler}'s {$\\varphi$}-function},\n  journal = {Acta Arithmetica},\n  volume = {49},\n  number = {3},\n  year = {1988},\n  pages = {263--275},\n  doi = {10.4064/aa-49-3-263-275},\n  note = {\\url{https://math.dartmouth.edu/~carlp/PDF/67.pdf}}\n}\n\n\n@misc{OpenAITotientFibers2026,\n  author = {{OpenAI}},\n  title = {{Weighted dilation graphs, smooth shifted primes and totient fibers}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Weighted-Dilation-Graphs-Smooth-Shifted-Primes-and-Totient-Fibers-September-24-2026/paper.pdf}{OAI:Weighted-Dilation-Graphs-Smooth-Shifted-Primes-and-Totient-Fibers-September-24-2026}},\n  year = {2026}\n}\n"}, {"path": "preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/statements.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/build/source/statements.tex", "bytes": 9702, "sha256": "28b7a2f32fbe2d7c51e21b0aebb68bc0c12b06e7c4a9ec4d81be489fffe4cd9c", "content": "\\section{The explicit main term}\\label{sec:statements}\nWe first give the counting scale and the main theorem, then construct its\narithmetic coefficient. Definitions involving~$x$ are used only for\nsufficiently large~$x$.\n\n\\subsection{The scale}\nFor $j\\ge1$, put\n\\begin{equation}\\label{eq:aj}\n a_j=(j+1)\\log(j+1)-j\\log j-1\n     =\\int_j^{j+1}\\log t\\dd t.\n\\end{equation}\nThese numbers are positive. The increasing function $\\sum_{j\\ge1}a_jz^j$ goes from zero to infinity on $0<z<1$, so there is a unique $\\rho\\in(0,1)$ such that\n\\[\n \\sum_{j\\ge1}a_j\\rho^j=1.\n\\]\nThese coefficients and this root occur in Maier and Pomerance's\nasymptotic estimate \\cite[Section~1]{MaierPomerance1988}.\nDefine\n\\begin{equation}\\label{eq:renewal}\n \\lambda=\\log(1/\\rho),\\qquad\n \\gamma=\\left(\\sum_{j\\ge1}ja_j\\rho^j\\right)^{-1},\\qquad\n g_0=1,\\quad g_j=\\sum_{d=1}^j a_dg_{j-d}.\n\\end{equation}\nOur $\\gamma$ is the constant denoted by $\\lambda$ in Ford's recurrence estimates \\cite[Section~3]{FordTotients2013}. Set\n\\[\n C_0=\\frac1{2\\lambda},\\qquad\n D_0=\\frac{1+\\log(\\lambda/\\gamma)}{\\lambda}-\\frac12;\n\\]\nthese are the constants in the classical estimate\n\\eqref{eq:classical-order}. For the scale used in our theorem, put\n\\begin{equation}\\label{eq:scales}\n B=\\log_2x,\\qquad\n m=\\left\\lfloor\\frac{\\log B-\\log_2B}{\\lambda}\\right\\rfloor,\n \\qquad\n \\theta=\\frac{\\log B-\\log_2B}{\\lambda}-m,\n\\end{equation}\nso that $0\\le\\theta<1$, and write\n\\begin{equation}\\label{eq:Gj}\n G_j=\\frac{B^j}{j!\\prod_{i=1}^j g_i}\\quad(0\\le j\\le m),\\qquad G_0=1.\n\\end{equation}\nThe factor $xG_m/\\log x$ is Ford's counting scale. The following theorem\nresolves its bounded multiplicative uncertainty by an explicitly constructed\nfunction of the phase~$\\theta$.\n\n\\begin{theorem}\\label{thm:main}\nFor sufficiently large positive integers $H$, there are nonnegative\nfunctions $A_H(1;\\cdot):[0,1)\\to\\mathbb R$, given by the finite\narithmetic formula \\eqref{eq:AH} below,\nwhich converge uniformly as $H\\to\\infty$ to a function $A(1;\\cdot)$\nsatisfying\n\\[\n 0<\\inf_{0\\le s<1}A(1;s)\n   \\le\\sup_{0\\le s<1}A(1;s)<\\infty.\n\\]\nWith the parameters \\eqref{eq:renewal}--\\eqref{eq:Gj},\n\\[\n V(x)\\sim\\frac{x}{\\log x}\\,G_m A(1;\\theta).\n\\]\nMoreover, for every fixed real $c>0$,\n\\[\n \\frac{V(cx)}{V(x)}\\longrightarrow c.\n\\]\n\\end{theorem}\n\nThe first argument of $A$ records a weight; the main theorem uses the\nconstant weight~$1$. Its arithmetic construction below does not use~$V$.\nThe fixed-scale conclusion has a separate mechanism: it compares the same\nrepresentation count at two endpoints. No regularity of\n$s\\mapsto A(1;s)$ is asserted or needed.\n\n\\subsection{The arithmetic coefficient}\\label{subsec:coefficient}\nThe tail index runs in the opposite direction from the ordered prime\nindex. In the proof we write a preimage as\n$p_0p_1\\cdots p_La$, with the primes decreasing, and use two cuts\n\\[\n R=m-H,\\qquad L=m-P,\\qquad P=\\lfloor\\log\\log H\\rfloor.\n\\]\nThe long prefix ends at $p_R$, whereas the exact arithmetic tail\ncontains $p_{R+1},\\ldots,p_L$ and $a$. Thus the notation below is\n$Q_h=p_{m-h}$: its indices run from $P$ to $H-1$, and $Q_P$ is the\nsmallest retained prime. For fixed $H$, these tail primes are bounded\nindependently of $x$. We first let $x$ tend to infinity with $H$ fixed,\nand only afterwards let $H$ tend to infinity. The following definition\nuses the phase $s$ directly, without reference to $x$.\n\nFix $s\\in[0,1)$ and put $\\alpha_s=\\lambda\\e^{\\lambda s}$. Let $H$ be a sufficiently large positive integer and $P=\\lfloor\\log\\log H\\rfloor$. A \\emph{tail witness} is a choice\n\\[\n \\eta=((Q_h)_{P\\le h<H},a)\n\\]\nof primes $Q_h$ and a positive integer $a$ with the following properties. Writing $v_h=\\log_2Q_h$, we require\n\\begin{equation}\\label{eq:tail-bands}\n \\begin{aligned}\n 0.9\\alpha_s h\\rho^{-h}&\\le v_h\\le1.1\\alpha_s h\\rho^{-h}\n          &&(P\\le h<H),\\\\\n \\sum_{l=P}^{h-1}a_{h-l}v_l\n   &\\le(1+10^{-4}\\e^{-h/40})v_h\n          &&(P\\le h<H),\\\\\n P^+(a)&\\le Q_P,\\qquad\n \\log a\\le\\exp(2\\alpha_sP\\rho^{-P}).\n \\end{aligned}\n\\end{equation}\nHere $P^+(a)$ is the largest prime factor of~$a$, with $P^+(1)=1$; an empty sum is zero. Associate to~$\\eta$ the integer\n\\[\n w(\\eta)=a\\prod_{h=P}^{H-1}Q_h.\n\\]\nFor $d\\in\\mathcal V$, let $\\mathcal W_{H,s}(d)$ be the set of tail witnesses for which $\\varphi(w(\\eta))=d$. For $h\\ge H$, define\n\\begin{equation}\\label{eq:tail-D}\n D(h,\\eta)=\\sum_{l=P}^{H-1}a_{h-l}v_l.\n\\end{equation}\nThe set of all possible witnesses, even as~$s$ varies, is finite for each fixed~$H$: $\\alpha_s$ lies between $\\lambda$ and $\\lambda/\\rho$, and every prime and integer in \\eqref{eq:tail-bands} is consequently bounded in terms of~$H$. Also $D(h,\\eta)=O_H(\\log(h+1))$. Thus the series used below converges absolutely, uniformly over this finite set and over~$s$.\n\nA tail totient must contribute once even when it has several witnesses.\nThe union that enforces this can be described by independent exponential\nrandom variables $E_h$ of mean one, indexed by $h\\ge H$. For each witness\nput\n\\begin{equation}\\label{eq:tail-events}\n \\mathcal E_\\eta=\n \\bigcap_{h\\ge H}\n \\left\\{E_h\\ge\\frac\\gamma{\\alpha_s}\\rho^hD(h,\\eta)\\right\\}.\n\\end{equation}\nFor a function $f:[1,\\infty)\\to[0,1]$, the coefficient is\n\\[\n A_H(f;s)=\\rho^{H(H-1)/2}\n       \\left(\\frac\\gamma{\\alpha_s}\\right)^H\n       \\sum_{\\substack{d\\in\\mathcal V\\\\\\mathcal W_{H,s}(d)\\ne\\varnothing}}\n       \\frac{f(\\ell(d)/d)}d\\,\n       \\Pr\\left(\\bigcup_{\\eta\\in\\mathcal W_{H,s}(d)}\n                    \\mathcal E_\\eta\\right).\n\\]\nAn intersection of events requires each $E_h$ to exceed the largest\nof the corresponding thresholds. Independence and absolute convergence\nof their sum therefore turn finite inclusion--exclusion into the\nexplicit expression\n\\begin{equation}\\label{eq:AH}\n\\begin{split}\n A_H(f;s)={}&\\rho^{H(H-1)/2}\n       \\left(\\frac{\\gamma}{\\alpha_s}\\right)^H\n       \\sum_{\\substack{d\\in\\mathcal V\\\\\\mathcal W_{H,s}(d)\\ne\\varnothing}}\n       \\frac{f(\\ell(d)/d)}d\\\\[-2pt]\n &\\quad\\times\n \\sum_{\\varnothing\\ne T\\subseteq\\mathcal W_{H,s}(d)}\n       (-1)^{|T|-1}\n \\exp\\left\\{-\\frac{\\gamma}{\\alpha_s}\n       \\sum_{h=H}^{\\infty}\\rho^h\n                    \\max_{\\eta\\in T}D(h,\\eta)\\right\\}.\n\\end{split}\n\\end{equation}\nFor $f=1$, the factor involving~$\\ell$ is simply~$1$: no least-preimage\nsearch is part of the main coefficient. For other weights, any witness\nwith totient~$d$ supplies the finite search bound $\\ell(d)\\le w(\\eta)$.\nThe probability expression also shows directly that $A_H(f;s)\\ge0$.\nSection~\\ref{sec:limits} identifies these probabilities with limiting\nnormalized prefix volumes: $1/d$ comes from counting the largest prime, and the\nprefactor is the limiting ratio of the prefix volume to~$G_m$.\n\nWhen the limit exists, write\n\\[\n A(f;s)=\\lim_{H\\to\\infty}A_H(f;s).\n\\]\nEach approximant uses finite arithmetic data and an absolutely convergent\nseries, not an unspecified counting constant. Theorem~\\ref{thm:main}\njustifies the limit for $f=1$; the next result does so for the weights\nneeded to count least preimages. Convergence for arbitrary $f$ is not\nrequired.\n\n\\subsection{Least-preimage intervals}\nFor a positive integer~$k$, define\n\\begin{equation}\\label{eq:fk}\n \\begin{aligned}\n N_k(x)&=\\#\\{v\\in\\mathcal V:v\\le x,\\ kx<\\ell(v)\\le(k+1)x\\},\\\\\n f_k(r)&=\\min\\{1,(k+1)/r\\}-\\min\\{1,k/r\\}.\n \\end{aligned}\n\\end{equation}\nThe function $f_k$ is nonnegative and bounded by~$1$.\n\n\\begin{theorem}\\label{thm:companion}\nFor each fixed positive integer $k$, the functions $A_H(f_k;s)$ converge uniformly for $0\\le s<1$, and\n\\[\n N_k(x)=\\frac{x}{\\log x}\\,G_m\\bigl(A(f_k;\\theta)+o(1)\\bigr).\n\\]\nThere are two possibilities.\n\\begin{enumerate}[label=\\textup{(\\roman*)}]\n \\item If some $d\\in\\mathcal V$ satisfies $\\ell(d)>kd$, then\n \\[\n  \\inf_{0\\le s<1}A(f_k;s)>0,\\qquad\n  N_k(x)\\sim\\frac{x}{\\log x}\\,G_m A(f_k;\\theta)\n       \\asymp_k V(x).\n \\]\n \\item If no such $d$ exists, then $N_k(x)=0$ for every $x>0$, and $A(f_k;s)=0$ for every $s\\in[0,1)$.\n\\end{enumerate}\nThe first alternative holds for $k=1,2$.\n\\end{theorem}\n\nThis theorem does not determine which other integers~$k$ satisfy the first alternative. Whether the first alternative holds for every positive integer~$k$ is equivalent to the unboundedness of $\\ell(d)/d$; the weighted formula does not settle this question.\n\n\\subsection{Proof strategy}\nTwo parts of the argument ensure that this construction counts values once. First, outside an exceptional set, \\emph{every} preimage has the required form. Second, collisions between different long prefixes have negligible total multiplicity. For the latter we use the layered shifted-prime method of\nMaier and Pomerance \\cite[Section~3]{MaierPomerance1988}, in the form\ndeveloped by Ford, and adapt his comparison \\cite[Lemma~5.1]{FordTotients2013} to a different set of hypotheses, proving the necessary uniform bound in Section~\\ref{sec:layers}. The number of layers grows, but each sieve application concerns only two or three linear forms. The comparison and the subsequent control of tuple multiplicities are the ingredients that turn a volume estimate into an asymptotic for distinct values.\n\nThe end of the continuous prefix and the end of the retained prime list\nare different cuts. Their separation makes the exceptional-set estimates\nsmall enough to absorb the smooth tail. Section~\\ref{sec:extraction}\nobtains the required preimages directly from Ford's Theorems~10 and~16\nand develops the volume estimates. Section~\\ref{sec:collisions} proves\nprefix uniqueness and transfers tuple counts to distinct values.\nSection~\\ref{sec:limits} counts the largest prime using common endpoint\nparameters, obtaining fixed dilation before identifying the arithmetic\nlimit. Finally, Section~\\ref{sec:companion} weights that prime count to\nlocate least preimages and proves the positive and zero alternatives.\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/An-exact-Hausdorff-gauge-for-SLE-September-25-2026.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/An-exact-Hausdorff-gauge-for-SLE-September-25-2026.pdf", "bytes": 561334, "sha256": "513ca9aedce0f3421f979721c2274be13c3e9f03a754e8a4ff582f528c280a53", "base64": 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"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/README.md", "bytes": 687, "sha256": "6df96941335fde8cc7c52f27ef211331b76fd8e5073bbe487e12ece27f78c83c", "content": "# [An exact Hausdorff gauge for SLE: A moment-integral and finite-batch construction](An-exact-Hausdorff-gauge-for-SLE-September-25-2026.pdf)\n\nOpenAI  \nSeptember 25, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:An-exact-Hausdorff-gauge-for-SLE-September-25-2026,\n  author = {{OpenAI}},\n  title = {{An exact Hausdorff gauge for SLE: A moment-integral and finite-batch construction}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/An-exact-Hausdorff-gauge-for-SLE-September-25-2026.pdf}{OAI:An-exact-Hausdorff-gauge-for-SLE-September-25-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/figures/batch-timeline.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/figures/batch-timeline.tex", "bytes": 1750, "sha256": "fd0a0bc5886a340c3a1403449a1cc8b434b32989474c2eafdc7df1d3ddce9d36", "content": "\\begin{figure}[t]\n\\centering\n\\begin{tikzpicture}[x=1cm,y=1cm,>=Latex,font=\\small]\n  \\draw[->,thick] (0,0)--(13,0) node[right] {\\(s\\)};\n  \\foreach \\x/\\y in {0.8/2.6,3.8/5.6,7.3/9.1} {\n    \\fill[blue!9] (\\x,0.15) rectangle (\\y,0.82);\n    \\draw[blue!60!black] (\\x,0.15) rectangle (\\y,0.82);\n    \\node at ({(\\x+\\y)/2},0.49) {test: \\(\\ell_n\\)};\n    \\draw (\\x,0.07)--(\\x,-0.07);\n    \\draw (\\y,0.07)--(\\y,-0.07);\n  }\n  \\node[below] at (0.8,-0.1) {\\(\\sigma_1\\)};\n  \\node[below] at (2.6,-0.1) {\\(\\sigma_1+\\ell_n\\)};\n  \\node[below] at (3.8,-0.1) {\\(\\sigma_2\\)};\n  \\node[below] at (5.6,-0.1) {\\(\\sigma_2+\\ell_n\\)};\n  \\node[below] at (7.3,-0.1) {\\(\\sigma_J\\)};\n  \\node[below] at (9.1,-0.1) {\\(\\sigma_J+\\ell_n\\)};\n  \\node at (6.45,0.48) {\\(\\cdots\\)};\n  \\draw[<->] (2.6,1.1)--(3.8,1.1)\n    node[midway,above] {\\(n\\)};\n  \\draw[<->] (9.1,1.1)--(12.2,1.1)\n    node[midway,above] {\\(n+\\frac{1}{2a}\\log\\frac{100}{8}\\)};\n  \\draw[dashed] (12.2,-0.07)--(12.2,0.85);\n  \\node[below] at (12.2,-0.1) {\\(\\sigma_{\\mathrm f}\\)};\n  \\node[align=center,font=\\footnotesize] at (6.5,-1.15)\n    {All failures are known at \\(T_J\\). The final gap controls\\\\\n     the inverse-angle factor in the change back to the original law.};\n\\end{tikzpicture}\n\\caption{One batch at a fixed grid center, in inner time (schematic).\nThere are \\(J=I_n-1\\) mass tests, each of length \\(\\ell_n\\),\nwith gaps \\(n\\) between consecutive tests. The coordinates are\n\\(s_z(S_i)=\\sigma_i\\), \\(s_z(T_i)=\\sigma_i+\\ell_n\\), and\n\\(s_z(\\tau_{\\mathrm f})=\\sigma_{\\mathrm f}\\); the final coordinate\ncorresponds to conformal radius \\(8r_{n,I_n}\\).\nThe first restart is preceded by a mixing gap as well.\nThe tests are conditioned on their actual past; independence is unnecessary.}\n\\label{fig:batch-timeline}\n\\end{figure}\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/figures/individual-stops.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/figures/individual-stops.tex", "bytes": 1059, "sha256": "49d5c3d8e8caeca85e1e89c695b48dde969420f13cbd4f8e68474db1b36c161f", "content": "\\begin{figure}[t]\n\\centering\n\\begin{tikzpicture}[x=0.85cm,y=0.72cm,>=Stealth,font=\\small]\n  \\draw[->] (0,1) -- (8.2,1) node[right] {$t$};\n  \\foreach \\y/\\i/\\start/\\finish in {0/1/1/4.2,-1.4/2/2.5/6.5,-2.8/3/0.6/5.2} {\n    \\node[left] at (0,\\y) {$x_{\\i}$};\n    \\draw[gray!60] (0,\\y)--(\\start,\\y);\n    \\draw[very thick,blue!65!black] (\\start,\\y)--(\\finish,\\y);\n    \\draw[dashed,thick] (\\finish,\\y)--(7.6,\\y);\n    \\fill (\\start,\\y) circle(1.5pt);\n    \\fill (\\finish,\\y) circle(1.5pt);\n    \\node[above] at (\\start,\\y) {$\\sigma_{\\i}$};\n    \\node[above] at (\\finish,\\y) {$T_{\\i}$};\n  }\n  \\node[align=center] at (2.2,-4) {solid: active factor};\n  \\node[align=center] at (6,-4) {dashed: frozen factor};\n\\end{tikzpicture}\n\\caption{The multipoint event uses each point's own activation and target\nstop. The solid intervals may overlap in any order. After $T_i$ the\nfactor for $x_i$ is constant, and no further survival of that point is\nrequired. The axis is schematic physical time, not the individual\nconformal-radius clocks.}\n\\label{fig:individual-stops}\n\\end{figure}\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/macros.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/macros.tex", "bytes": 839, "sha256": "7c9f85670b9135974e0ac9565511ec48f5bc104076124d55f7dd98616ab7916c", "content": "\\newcommand{\\HH}{\\mathbb H}\n\\newcommand{\\DD}{\\mathbb D}\n\\newcommand{\\PP}{\\mathbb P}\n\\newcommand{\\EE}{\\mathbb E}\n\\newcommand{\\dd}{\\mathop{}\\!\\mathrm{d}}\n\\newcommand{\\ind}{\\mathbf 1}\n\\newcommand{\\SLE}{\\mathrm{SLE}}\n\\newcommand{\\Haus}{\\mathcal H}\n\\newcommand{\\supp}{\\operatorname{supp}}\n\\newcommand{\\diam}{\\operatorname{diam}}\n\\newcommand{\\dist}{\\operatorname{dist}}\n\\newcommand{\\Var}{\\operatorname{Var}}\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\\newtheorem{definition}[theorem]{Definition}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\numberwithin{equation}{section}\n\\setlist[enumerate]{itemsep=3pt,topsep=5pt}\n\\setlist[itemize]{itemsep=3pt,topsep=5pt}\n\\allowdisplaybreaks[2]\n\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/main.tex", "bytes": 1645, "sha256": "2caefee67525b302bae6c0b73e148ca7bd5a5ac028bc7062088edaa1897cfbe6", "content": "\\documentclass[11pt]{article}\n\\usepackage[T1]{fontenc}\n\\usepackage[utf8]{inputenc}\n\\usepackage{lmodern}\n\\input{glyphtounicode.tex}\n\\input{glyphtounicode-cmex.tex}\n\\pdfglyphtounicode{mapsto}{007C}\n\\pdfglyphtounicode{vextendsingle}{23D0}\n\\pdfglyphtounicode{vextenddouble}{2016}\n\\pdfgentounicode=1\n\\usepackage[margin=1in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage{microtype}\n\\usepackage{booktabs}\n\\usepackage{enumitem}\n\\usepackage{tikz}\n\\usetikzlibrary{arrows.meta,calc,positioning}\n\\usepackage{xcolor}\n\\definecolor{linkblue}{RGB}{24,64,111}\n\\usepackage[colorlinks=true,linkcolor=linkblue,citecolor=linkblue,urlcolor=linkblue,\n  pdftitle={An exact Hausdorff gauge for SLE: a moment-integral and finite-batch construction},pdfauthor={OpenAI}]{hyperref}\n\\input{macros}\n\\title{An exact Hausdorff gauge for SLE\\\\[4pt]\n\\large A moment-integral and finite-batch construction}\n\\author{OpenAI}\n\\date{September 25, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nFor each \\(0<\\kappa<8\\), we construct a deterministic Hausdorff gauge\nthat almost surely assigns positive finite measure to every nontrivial\ncompact positive-time segment of chordal Schramm--Loewner evolution.\nThis answers Schramm's Hausdorff-measure existence problem in this\nparameter range. The entire trace has finite expected gauge measure\nin each bounded box.\n\\end{abstract}\n\\input{sections/introduction}\n\\input{sections/onepoint}\n\\input{sections/multipoint}\n\\input{sections/mass}\n\\input{sections/coefficients}\n\\input{sections/gauge}\n\\input{sections/hausdorff}\n\\input{sections/upper-cover}\n\\bibliographystyle{plain}\n\\bibliography{refs/sources}\n\\end{document}\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/refs/sources.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/refs/sources.bib", "bytes": 9656, "sha256": "f0d6168407b2d177cadd804051b082da2823ee9113a64f6203f929ea1e759a6f", "content": "\n@article{Beffara2008,\n  author  = {Beffara, Vincent},\n  title   = {The dimension of the {SLE} curves},\n  journal = {The Annals of Probability},\n  volume  = {36},\n  number  = {4},\n  year    = {2008},\n  pages   = {1421--1452},\n  doi     = {10.1214/07-AOP364},\n  url     = {https://arxiv.org/abs/math/0211322v3},\n  note    = {\\href{https://doi.org/10.1214/07-AOP364}{doi:10.1214/07-AOP364}.\n             arXiv version: \\href{https://arxiv.org/abs/math/0211322v3}{arXiv:math/0211322v3}, 27 August 2008}\n}\n\n@incollection{Schramm2006,\n  author    = {Schramm, Oded},\n  title     = {Conformally invariant scaling limits: an overview and a collection of problems},\n  booktitle = {Proceedings of the International Congress of Mathematicians, Madrid, August 22--30, 2006},\n  volume    = {I},\n  editor    = {Sanz-Sol{\\'e}, Marta and Soria, Javier and Varona, Juan L. and Verdera, Joan},\n  publisher = {European Mathematical Society},\n  year      = {2007},\n  pages     = {513--543},\n  doi       = {10.4171/022-1/20},\n  url       = {https://arxiv.org/abs/math/0602151v2},\n  note      = {\\href{https://doi.org/10.4171/022-1/20}{doi:10.4171/022-1/20}.\n               arXiv version: \\href{https://arxiv.org/abs/math/0602151v2}{arXiv:math/0602151v2}, 10 February 2006}\n}\n\n@article{Rezaei2018,\n  author  = {Rezaei, Mohammad A.},\n  title   = {Hausdorff measure of {SLE} curves},\n  journal = {Stochastic Processes and their Applications},\n  volume  = {128},\n  number  = {3},\n  year    = {2018},\n  pages   = {884--896},\n  doi     = {10.1016/j.spa.2017.06.010},\n  url     = {https://arxiv.org/abs/1212.5847v2},\n  note    = {\\href{https://doi.org/10.1016/j.spa.2017.06.010}{doi:10.1016/j.spa.2017.06.010}.\n             arXiv version: \\href{https://arxiv.org/abs/1212.5847v2}{arXiv:1212.5847v2}, 11 June 2017}\n}\n\n@article{HoldenYuan2026,\n  author  = {Holden, Nina and Yuan, Yizheng},\n  title   = {Regularity of the {Schramm--Loewner} evolution: Up-to-constant variation and modulus of continuity},\n  journal = {The Annals of Probability},\n  volume  = {54},\n  number  = {1},\n  year    = {2026},\n  pages   = {367--420},\n  doi     = {10.1214/25-AOP1768},\n  url     = {https://arxiv.org/abs/2211.15609v4},\n  note    = {\\href{https://doi.org/10.1214/25-AOP1768}{doi:10.1214/25-AOP1768}.\n             arXiv version: \\href{https://arxiv.org/abs/2211.15609v4}{arXiv:2211.15609v4}, 20 February 2025}\n}\n\n@article{LawlerRezaei2015,\n  author  = {Lawler, Gregory F. and Rezaei, Mohammad A.},\n  title   = {Minkowski content and natural parameterization for the {Schramm--Loewner} evolution},\n  journal = {The Annals of Probability},\n  volume  = {43},\n  number  = {3},\n  year    = {2015},\n  pages   = {1082--1120},\n  doi     = {10.1214/13-AOP874},\n  url     = {https://arxiv.org/abs/1211.4146v2},\n  note    = {\\href{https://doi.org/10.1214/13-AOP874}{doi:10.1214/13-AOP874}.\n             arXiv version: \\href{https://arxiv.org/abs/1211.4146v2}{arXiv:1211.4146v2}, 19 June 2015}\n}\n\n@article{LawlerSheffield2011,\n  author = {Lawler, Gregory F. and Sheffield, Scott},\n  title = {A natural parametrization for the {Schramm--Loewner} evolution},\n  journal = {The Annals of Probability},\n  volume = {39},\n  number = {5},\n  year = {2011},\n  pages = {1896--1937},\n  doi = {10.1214/10-AOP560},\n  url = {https://arxiv.org/abs/0906.3804v1},\n  note = {\\href{https://doi.org/10.1214/10-AOP560}{doi:10.1214/10-AOP560}.\n          Numbered reference is to \\href{https://arxiv.org/abs/0906.3804v1}{arXiv:0906.3804v1},\n          22 June 2009, titled ``The natural parametrization for the Schramm--Loewner evolution''}\n}\n\n@article{LawlerZhou2013,\n  author = {Lawler, Gregory F. and Zhou, Wang},\n  title = {{SLE} curves and natural parametrization},\n  journal = {The Annals of Probability},\n  volume = {41},\n  number = {3A},\n  year = {2013},\n  pages = {1556--1584},\n  doi = {10.1214/12-AOP742},\n  url = {https://arxiv.org/abs/1006.4936v2},\n  note = {\\href{https://doi.org/10.1214/12-AOP742}{doi:10.1214/12-AOP742}.\n          Electronic reprint: \\href{https://arxiv.org/abs/1006.4936v2}{arXiv:1006.4936v2},\n          27 May 2013; reprint pagination differs from the journal}\n}\n\n@article{RohdeSchramm2005,\n  author = {Rohde, Steffen and Schramm, Oded},\n  title = {Basic properties of {SLE}},\n  journal = {Annals of Mathematics},\n  series = {2},\n  volume = {161},\n  number = {2},\n  year = {2005},\n  pages = {883--924},\n  doi = {10.4007/annals.2005.161.883},\n  url = {https://annals.math.princeton.edu/wp-content/uploads/annals-v161-n2-p07.pdf},\n  note = {\\href{https://doi.org/10.4007/annals.2005.161.883}{doi:10.4007/annals.2005.161.883}. Published Theorem 5.1, p. 899; normalization in Section 2.1, pp. 886--887}\n}\n\n@misc{Beliaev2015Notes,\n  author = {Beliaev, Dmitry},\n  title = {Conformal Maps and Geometry},\n  year = {2015},\n  howpublished = {Lecture notes, University of Oxford},\n  url = {https://people.maths.ox.ac.uk/belyaev/TCC/lecture_notes.pdf},\n  note = {\\href{https://people.maths.ox.ac.uk/belyaev/TCC/lecture_notes.pdf}{Author lecture notes}. Version dated June 8, 2015, Section 3.2: Theorem 3.2.5, Corollary 3.2.6, and Theorems 3.2.9 and 3.2.11, pp. 39--43}\n}\n\n@article{PeresSolomyak2005,\n  author = {Peres, Yuval and Solomyak, Boris},\n  title = {The sharp {Hausdorff} measure condition for length of projections},\n  journal = {Proceedings of the American Mathematical Society},\n  volume = {133},\n  number = {11},\n  year = {2005},\n  pages = {3371--3379},\n  doi = {10.1090/S0002-9939-05-08073-1},\n  url = {https://arxiv.org/abs/math/0406375v1},\n  note = {\\href{https://doi.org/10.1090/S0002-9939-05-08073-1}{Published version}.\n          Numbered references use \\href{https://arxiv.org/abs/math/0406375v1}{arXiv:math/0406375v1},\n          18 June 2004, Lemma 1.2 and Section 5}\n}\n\n@article{LawlerWerness2013,\n author={Lawler, Gregory F. and Werness, Brent M.},\n title={Multi-point {Green}'s functions for {SLE} and an estimate of {Beffara}},\n journal={The Annals of Probability}, volume={41}, number={3A},\n year={2013}, pages={1513--1555}, doi={10.1214/11-AOP695},\n url={https://arxiv.org/abs/1011.3551},\n note={\\href{https://doi.org/10.1214/11-AOP695}{doi:10.1214/11-AOP695}. Numbered references use \\href{https://arxiv.org/abs/1011.3551v4}{arXiv:1011.3551v4}}\n}\n\n@article{RezaeiZhan2017,\n author={Rezaei, Mohammad A. and Zhan, Dapeng},\n title={Higher moments of the natural parameterization for {SLE} curves},\n journal={Annales de l'Institut Henri Poincar{\\'e}, Probabilit{\\'e}s et Statistiques},\n volume={53}, number={1}, year={2017}, pages={182--199},\n doi={10.1214/15-AIHP712}, url={https://arxiv.org/abs/1412.3700},\n note={\\href{https://doi.org/10.1214/15-AIHP712}{doi:10.1214/15-AIHP712}. Numbered references use \\href{https://arxiv.org/abs/1412.3700v2}{arXiv:1412.3700v2}}\n}\n\n@misc{Field2016,\n  author = {Field, Laurence S.},\n  title = {Two-sided radial {SLE} and length-biased chordal {SLE}},\n  year = {2016},\n  howpublished = {Preprint, arXiv:1601.03374v1},\n  url = {https://arxiv.org/abs/1601.03374v1},\n  note = {\\href{https://arxiv.org/abs/1601.03374v1}{arXiv:1601.03374v1},\n          submitted 13 January 2016; manuscript dated 18 April 2015}\n}\n\n@article{Zhan2019Decomposition,\n  author = {Zhan, Dapeng},\n  title = {Decomposition of {Schramm--Loewner} evolution along its curve},\n  journal = {Stochastic Processes and their Applications},\n  volume = {129}, year = {2019}, pages = {129--152},\n  doi = {10.1016/j.spa.2018.02.010},\n  note = {\\href{https://doi.org/10.1016/j.spa.2018.02.010}{doi:10.1016/j.spa.2018.02.010}.\n          Numbered references use the \\href{https://users.math.msu.edu/users/zhan/decomposition-SLE.pdf}{author-hosted published article}}\n}\n\n@article{Zhan2019Optimal,\n  author = {Zhan, Dapeng},\n  title = {Optimal {H}{\\\"o}lder continuity and dimension properties for {SLE} with {Minkowski} content parametrization},\n  journal = {Probability Theory and Related Fields},\n  volume = {175}, pages = {447--466}, year = {2019},\n  doi = {10.1007/s00440-018-0895-0},\n  note = {\\href{https://doi.org/10.1007/s00440-018-0895-0}{doi:10.1007/s00440-018-0895-0}.\n          Numbered references use \\href{https://arxiv.org/abs/1706.05603v3}{arXiv:1706.05603v3}}\n}\n\n@misc{LawlerRezaei2012Basic,\n  author = {Lawler, Gregory F. and Rezaei, Mohammad A.},\n  title = {Basic properties of the natural parametrization for the {Schramm--Loewner} evolution},\n  year = {2012},\n  howpublished = {Preprint, arXiv:1203.3259v2},\n  url = {https://arxiv.org/abs/1203.3259v2},\n  note = {\\href{https://arxiv.org/abs/1203.3259v2}{arXiv:1203.3259v2},\n          11 September 2012; numbered references use this version}\n}\n\n\n@article{RezaeiZhan2018Green,\n  author = {Rezaei, Mohammad A. and Zhan, Dapeng},\n  title = {Green's functions for chordal {SLE} curves},\n  journal = {Probability Theory and Related Fields},\n  volume = {171},\n  year = {2018},\n  pages = {1093--1155},\n  doi = {10.1007/s00440-017-0802-0},\n  url = {https://doi.org/10.1007/s00440-017-0802-0},\n  note = {\\href{https://doi.org/10.1007/s00440-017-0802-0}{doi:10.1007/s00440-017-0802-0}.\n          Numbered references use the\n          \\href{https://users.math.msu.edu/users/zhan/Green\\%27s\\%20function.pdf}{author-hosted published article}}\n}\n\n@misc{OpenAI2026Explicit,\n  author = {{OpenAI}},\n  title = {{An explicit exact Hausdorff gauge for SLE: A regular formula from quantitative tails and dense visits}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/An-explicit-exact-Hausdorff-gauge-for-SLE-September-26-2026/An-explicit-exact-Hausdorff-gauge-for-SLE-September-26-2026.pdf}{OAI:An-explicit-exact-Hausdorff-gauge-for-SLE-September-26-2026}},\n  year = {2026}\n}\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/coefficients.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/coefficients.tex", "bytes": 13308, "sha256": "ae3c5c13fcecd5cdcc428f2be29d81de055290b5813701145fa06cc1cab1df8a", "content": "\\section{Disk moments and deterministic coefficients}\n\\label{coef:section}\n\nWe now supply the deterministic numbers that select the gauge. First,\nthe physical-domain multipoint estimate proves that one centered disk\nmass has positive finite moments. Then its conditional Green potential\nrecovers those moments through an explicit sequence of integrals.\nThe last subsection realizes the integrands by normal coordinates and\nordinary differential equation limits.\n\n\\subsection{The centered disk model}\n\nFor $0<\\theta<\\pi$, use the conformal identification\n$J_\\theta:\\HH\\to\\DD$ below.  It takes $e^{i\\theta}$ to the marked center\n$0$.  Denote the ordinary law in these coordinates by $\\PP_\\theta$, and\nits centered finite-level laws at $0$ by $\\PP_\\theta^*$.\nAverage the latter over the invariant angle law $\\pi_a$, and write\n$\\PP_{\\mathrm{mix}}^*$ for the resulting consistent stopped laws.\nDefine\n\\begin{equation}\n \\begin{gathered}\n J_\\theta(\\zeta)=\\frac{\\zeta-e^{i\\theta}}{\\zeta-e^{-i\\theta}},\n \\qquad K=\\{x\\in\\DD:|x|\\le\\tfrac14\\},\n \\qquad \\PP_{\\mathrm{mix}}^*=\\int_0^\\pi\\PP_\\theta^*\\,\\pi_a(\\dd\\theta),\n \\\\\n L_\\ell=\\mu^{\\DD}_{\\tau_\\ell(0)}(K),\n \\qquad L=\\lim_{\\ell\\to\\infty}L_\\ell.\n \\end{gathered}\n \\label{coef:disk-model}\n\\end{equation}\nThe disk mass is the weighted pushforward of the single canonical\nhalf-plane functional in Section~\\ref{mass:section}.\nEach $L_\\ell$ is a raw finite-stopped-path quantity; its increasing limit\nis defined on the consistent collection of such quantities.  In\nparticular \\eqref{coef:disk-model} does not require a Brownian\ncontinuation after the centered terminal time.\n\n\\begin{proposition}[Finite, positive disk moments]\\label{coef:positive-moments}\nFor every integer $n\\ge1$, the deterministic numbers\n\\begin{equation}\n m_n(\\ell):=\\EE_{\\mathrm{mix}}^*L_\\ell^n,\n \\qquad\n m_n:=\\EE_{\\mathrm{mix}}^*L^n\n       =\\lim_{\\ell\\to\\infty}m_n(\\ell)\n       \\quad\\text{satisfy}\\quad 0<m_n<\\infty.\n \\label{coef:moments}\n\\end{equation}\nMoreover $L>0$ almost surely, and, for each fixed angle,\n\\begin{equation}\n \\sup_{\\ell>0}\\EE_\\theta^*L_\\ell^n\n                  \\le C_{n,a}\\sin^{-p}\\theta.\n \\label{coef:angle-moments}\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nWe apply the multipoint estimate directly in the physical disk.\nIts initial conformal radius is $R_0(x)=1-|x|^2$, independently of\n$\\theta$.  Choose a nonnegative continuous cutoff $f\\ge\\ind_K$ supported\nin a fixed compact subdisk.  Apply the center-hit bound of Corollary~\\ref{mp:level-moments} with\n$m=n$, $x_0=0$, $e_0=e^{-2a\\ell}$, and level weight\n$A_j(f)=M_\\infty^{(j),\\DD}(f)$.  The proof of that corollary includes\n$0$ as one extra fixed point in the multipoint estimate.  Its caps and\ncollision integrals can be chosen in the physical disk, where $K$,\n$\\supp f$, and the initial radii are independent of $\\theta$.  Thus\n\\[\n \\EE_\\theta\\!\\left[\n   \\ind_{\\{\\tau_\\ell(0)<\\infty\\}}\n   \\bigl(M_\\infty^{(j),\\DD}(f)\\bigr)^n\\right]\n       \\le C_{n,a}e^{-2a\\alpha\\ell},\n\\]\nuniformly in $j,\\ell,\\theta$.\n\nThere is no angle-dependent extraction in taking this limit.\nEvery disk approximant is exactly the weighted pushforward of the\nhalf-plane approximant.  Convergence of the fixed convex combinations\nholds for every compact continuous half-plane test, and therefore for\nthe pullback of this $f$ under every fixed $J_\\theta$.\nApply the displayed estimate before taking convex combinations;\nconvexity and Fatou's Lemma, with the center-hit event retained, yield\n\\begin{equation}\n \\EE_\\theta\\bigl[\n   \\ind_{\\{\\tau_\\ell(0)<\\infty\\}}\\mu_\\infty^{\\DD}(K)^n\\bigr]\n       \\le C_{n,a}e^{-2a\\alpha\\ell}.\n \\label{coef:ordinary-hit-moment}\n\\end{equation}\nThus the uniform constant has not been obtained by transporting a\nhalf-plane estimate through maps that degenerate near endpoint angles.\n\nSince $R_0(0)=1$, the density at $\\tau_\\ell(0)$ is at most\n$e^{2a\\alpha\\ell}\\sin^{-p}\\theta$ on the center-hit event.\nAlso $\\mu_{\\tau_\\ell}^{\\DD}(K)\\le\\mu_\\infty^{\\DD}(K)$ under the ordinary\nlaw.  The finite-level change of law and\n\\eqref{coef:ordinary-hit-moment} prove \\eqref{coef:angle-moments}.\nThe bound is integrable against $\\pi_a$, because\n\\[\n \\int_0^\\pi\\sin^{-p}\\theta\\,\\pi_a(\\dd\\theta)\n    =c_a\\int_0^\\pi\\sin^{4a-p}\\theta\\,\\dd\\theta\n    =c_a\\int_0^\\pi\\sin\\theta\\,\\dd\\theta<\\infty.\n\\]\nMonotone convergence gives finite moments of $L$ of every integer order.\n\nFor positivity, choose a finite inner level with\n$e^{-2a\\ell}<1/4$.  Its attainment under the centered law is certain.\nAt that time, the distance from $0$ to the surviving domain boundary is\nless than $1/4$.  The nearby boundary cannot be part of the original\nunit circle, so the past trace has entered the interior of $K$.\nContinuity gives a nontrivial time interval, contained in this finite\nstopped piece, whose trace lies in the interior of $K$.\nThe positive-increment assertion of Proposition~\\ref{mass:positive}\nand the increment-support assertion of Proposition~\\ref{mass:construction}\ntransfer to this stopped piece by its finite-level density.  They give\npositive mass in $K$.  Hence $L>0$ almost surely, and $m_n>0$.\n\\end{proof}\n\n\\subsection{A Wiener-integral formula}\n\nWe use the discrete potential compensator appearing in\nLawler--Zhou \\cite[Theorem~1 and its proof]{LawlerZhou2013}.\nWe prove its convergence at a random radius level and then combine it\nwith the finite-level Green weight to specify the centered moments.\n\nLet $W$ be standard Wiener measure on\n$C_0([0,\\infty),\\mathbb R)$.  For a fixed $\\theta$ and driver $b$, solve\nthe ordinary Loewner equation with numerator $a$, using $J_\\theta$ for\nthe disk identification, and form\n\\[\n \\Psi_t(\\theta,b)=\\int_K G_t(x;\\theta,b)\\,\\dd A(x),\n \\qquad \\tau_\\ell=\\tau_\\ell(0),\n \\qquad\n P_\\ell(\\theta,b)\n =\\ind_{\\{\\tau_\\ell<\\infty\\}}e^{2a\\alpha\\ell}\n       \\frac{\\sin^p\\theta_{\\tau_\\ell}(0)}{\\sin^p\\theta}.\n\\]\nThe last expression is zero on failure.  For paths $b,c$, let\n$b\\oplus_t c$ agree with $b$ through time $t$, and equal\n$b(t)+c(\\,\\cdot-t)$ thereafter.  Put\n\\[\n \\delta_h=2^{-h},\\qquad N_h=h2^h,\\qquad t_i=i\\delta_h,\n\\]\nand define the potential differences and their stopped cumulative sum by\n\\begin{align}\n q_{h,i}(\\theta,b)\n   &=\\Psi_{t_i}(\\theta,b)\n       -\\int\\Psi_{t_{i+1}}(\\theta,b\\oplus_{t_i}c)\\,\\dd W(c),\n       \\label{coef:increments}\\\\\n D_h(\\theta,b;\\ell)\n   &=\\sum_{i=0}^{N_h-1}\\ind_{\\{t_{i+1}\\le\\tau_\\ell\\}}\n                      q_{h,i}(\\theta,b).\n       \\label{coef:discrete-mass}\n\\end{align}\nSet undefined differences to zero.  For every fixed $\\theta$, these\nexceptions have Wiener measure zero by the compact first moment and\nthe conditional kernel.  All integrands are measurable in $\\theta$ and\nthe paths.  Write $[x]_0^u=\\min\\{\\max\\{x,0\\},u\\}$.\n\nThe next estimate explains why a potential formula recovers mass at a\nrandom endpoint. For fixed $\\theta$, write $A_t=\\mu_t^{\\DD}(K)$ and\ninterpret $A_{\\tau_\\ell}$ as $A_\\infty$ when $\\tau_\\ell=\\infty$.\n\n\\begin{lemma}[Recovery at a random level]\\label{coef:recovery}\nFor every fixed $\\theta\\in(0,\\pi)$ and finite $\\ell>0$,\n\\[\n D_h(\\theta,\\cdot;\\ell)\\longrightarrow A_{\\tau_\\ell}\n \\quad\\text{in }L^2(\\PP_\\theta).\n\\]\n\\end{lemma}\n\n\\begin{proof}\nThe process $A_t$ is continuous,\nincreasing, starts at zero, and has a finite terminal limit with\n$\\EE_\\theta A_\\infty^2<\\infty$.  Equation~\\eqref{mass:kernel} gives\n\\[\n \\Psi_t=\\EE_\\theta[A_\\infty-A_t\\mid\\mathcal F_t].\n\\]\nIndependent Brownian continuation identifies the Wiener integral in\n\\eqref{coef:increments} with its conditional expectation.\nThe tower property therefore yields\n\\[\n q_{h,i}=\\EE_\\theta[\\Delta_{h,i}A\\mid\\mathcal F_{t_i}],\n \\qquad \\Delta_{h,i}A=A_{t_{i+1}}-A_{t_i}.\n\\]\nPut $C_{h,j}=\\sum_{i<j}q_{h,i}$.  The grid process\n$A_{t_j}-C_{h,j}$ is a square-integrable discrete martingale starting\nat zero. Its increments are centered conditional mass increments;\ntheir variances are bounded by the second moments of $\\Delta_{h,i}A$.\nOrthogonality of its increments and the maximal inequality imply\n\\begin{equation}\n \\EE_\\theta\\max_{j\\le N_h}|C_{h,j}-A_{t_j}|^2\n       \\le4\\EE_\\theta\\sum_{i<N_h}(\\Delta_{h,i}A)^2\n       \\longrightarrow0.\n \\label{coef:maximal-error}\n\\end{equation}\nTo justify the limit despite the growing time horizon, set\n\\[\n \\omega_A(\\delta)\n   =\\sup_{\\substack{s,t\\ge0\\\\|s-t|\\le\\delta}}|A_t-A_s|.\n\\]\nContinuity and the finite terminal limit give\n$\\omega_A(\\delta)\\to0$ almost surely.  One can first use uniform\ncontinuity on a fixed finite interval and then make the total variation\nof the remaining tail small.  Moreover,\n\\[\n \\sum_{i<N_h}(\\Delta_{h,i}A)^2\n       \\le A_\\infty\\omega_A(\\delta_h)\\le A_\\infty^2,\n\\]\nso dominated convergence proves \\eqref{coef:maximal-error}.\n\nLet $j_h=\\min\\{N_h,\\lfloor\\tau_\\ell/\\delta_h\\rfloor\\}$, with\n$j_h=N_h$ if $\\tau_\\ell=\\infty$.  Then $D_h=C_{h,j_h}$.\nOn a finite hit $t_{j_h}\\to\\tau_\\ell$; on failure $t_{j_h}=h\\to\\infty$.\nThus $A_{t_{j_h}}\\to A_{\\tau_\\ell}$ in $L^2$, interpreting the latter as\n$A_\\infty$ on failure.  The maximum in \\eqref{coef:maximal-error} controls\nevery selected index, so\n\\[\n D_h(\\theta,b;\\ell)\\longrightarrow A_{\\tau_\\ell}\n             \\quad\\text{in }L^2(\\PP_\\theta).\n\\]\nNo predictability or stopping-time property of $j_h$ is used.\n\n\\end{proof}\n\n\\begin{proposition}[Explicit coefficients]\\label{coef:explicit}\nThe moments in \\eqref{coef:moments} are given by\n\\begin{equation}\n \\begin{aligned}\n m_n(\\ell)\n   &=\\lim_{u\\to\\infty}\\lim_{h\\to\\infty}\n       \\int_0^\\pi\\int P_\\ell(\\theta,b)\n       \\bigl([D_h(\\theta,b;\\ell)]_0^u\\bigr)^n\n       \\,\\dd W(b)\\,\\pi_a(\\dd\\theta),\\\\\n m_n&=\\lim_{\\ell\\to\\infty}m_n(\\ell).\n \\end{aligned}\n \\label{coef:formula}\n\\end{equation}\nAll limit indices may be restricted to positive integers.  Thus these\ncoefficients depend only on $\\kappa$ and the specified Gaussian, angle\nand area integrals.\n\\end{proposition}\n\n\\begin{proof}\nFix $\\theta,\\ell,u$. The function $f_u(x)=([x]_0^u)^n$ is bounded\nand continuous. Lemma~\\ref{coef:recovery} gives\n$f_u(D_h)\\to f_u(A_{\\tau_\\ell})$ in probability. This convergence\npasses through the fixed integrable density $P_\\ell$: truncate this density at a constant $R$,\nuse bounded convergence in probability there, and then let $R\\to\\infty$.\nSince $\\int P_\\ell\\,\\dd W=1$ for each fixed angle, the resulting\ntruncated Wiener integrals are bounded by $u^n$.  Dominated convergence\ntherefore permits the angle average, without a convergence estimate\nuniform in $\\theta$.  The finite-level change of law identifies the\nlimit as $\\EE_{\\mathrm{mix}}^*([L_\\ell]_0^u)^n$.\nFinally, monotone convergence in $u$ and then in $\\ell$ proves\n\\eqref{coef:formula}.\n\\end{proof}\n\n\\subsection{A prescription using only normal coordinates and ODE limits}\n\nThe Wiener integrals in \\eqref{coef:formula} are deterministic\nintegrals. The following construction specifies their integrands without\nusing a sample of the trace in Theorem~\\ref{thm:main}.  They can be realized\nas countable product standard-normal integrals: choose each unit\nincrement of $b$ as an independent standard normal, and recursively set\nthe midpoint of a dyadic interval of length $2^{-k}$ equal to the average\nof its endpoint values plus $2^{-k/2-1}$ times a new independent standard\nnormal.  The polygonal interpolants converge uniformly on compacts almost\nsurely.  Indeed Gaussian tails and Borel--Cantelli bound the largest\nlevel-$k$ normal on finitely many unit intervals by $O(\\sqrt{k+1})$,\nand $\\sum_k2^{-k/2}\\sqrt{k+1}<\\infty$.  The limit has Gaussian covariance\n$\\min\\{s,t\\}$ and hence Wiener law.  Use the zero path on the null\nexceptional set.\n\nThe Loewner quantities in the integrands do not require constructing the\ntrace.  For $\\zeta=J_\\theta^{-1}(x)$ and an integer $j\\ge1$, use the\ntruncated vector field\n\\[\n F_j(t,z)=\\frac{a\\,\\overline{z-b(t)}}\n                    {\\max\\{|z-b(t)|^2,j^{-4}\\}}.\n\\]\nStarting at $\\zeta$, perform the Euler iteration\n$g_{k+1}=g_k+2^{-N}F_j(k2^{-N},g_k)$ and interpolate linearly.\nFor fixed $j$, its compact-uniform limit $g^{(j)}$ exists as $N\\to\\infty$:\nthe vector field is bounded, continuous in time, and globally Lipschitz\nin its state variable.  A given finite time $t$ is surviving exactly when\nsome $j$ satisfies\n\\[\n \\min_{0\\le u\\le t}\\operatorname{Im}g_u^{(j)}>j^{-1}.\n\\]\nFor such a witness, $|g^{(j)}-b|^2>j^{-2}\\ge j^{-4}$, so the cutoff is\ninactive and uniqueness gives the genuine Loewner solution on $[0,t]$.\nConversely, a surviving solution on this compact interval has positive\nminimum imaginary part, and a sufficiently large $j$ recovers it.\n\nOn survival use this solution to compute\n\\begin{align*}\n s_x(t)&=\\int_0^t\\frac{(\\operatorname{Im}g_u)^2}{|g_u-b(u)|^4}\\,\\dd u,\\\\\n R_t(x)&=2\\operatorname{Im}\\zeta\\,|J_\\theta'(\\zeta)|e^{-2as_x(t)},\\\\\n G_t(x)&=R_t(x)^{-\\alpha}\\sin^p\\arg(g_t-b(t)),\n\\end{align*}\nand put $G_t(x)=0$ off survival.  At the marked center the level time is\n\\[\n \\tau_\\ell=\n \\inf\\{q\\in\\mathbb Q_{>0}:q\\text{ is surviving and }s_0(q)>\\ell\\},\n \\qquad \\inf\\varnothing=\\infty.\n\\]\nContinuity and strict increase of the clock on its open lifetime identify\nthis infimum with attainment of level $\\ell$ while alive.  These rational\ntests, deterministic limits and integrals specify every integrand in\n\\eqref{coef:formula}.  This is an infinite-limit prescription; no finite computational error\nbound is asserted. The coefficients retain neither an unspecified\nnormalization of trace mass nor a choice of convex extraction.\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/gauge.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/gauge.tex", "bytes": 7355, "sha256": "c898a43682c901d5f02c96a2018e0fc58f5c2eccddaea89c96ec3665f36ec4bc", "content": "\\section{Moment batches and their full gauge}\\label{gauge:section}\n\nWe now turn the positive finite moments into a deterministic gauge.\nEach finite batch must balance two requirements: moderately large masses\nshould provide many chances for a successful test, whereas exceeding a\nlarger multiple of the threshold should have summable probability.\nDeterministic repetitions of dyadic thresholds achieve both.\n\nWrite \\(\\PP^*_{\\mathrm{mix}}\\) for the law of the disk model in\nEquation~\\eqref{coef:disk-model}, with initial angle distributed according\nto \\(\\pi_a\\), and write \\(\\EE^*_{\\mathrm{mix}}\\) for its expectation.\nWe use the increasing masses \\(L_\\ell\\uparrow L\\) and their moments\n\\(m_n(\\ell),m_n\\) from Equation~\\eqref{coef:moments}. All the numbers below\nare deterministic: the moments are specified by the explicit integrals\nin Equation~\\eqref{coef:formula}.\n\n\\subsection{The finite lists and their radii}\n\nFor integers \\(n\\ge1\\) and \\(k\\in\\mathbb Z\\), put\n\\begin{equation}\\label{gauge:coefficients}\n\\ell_n=1+\\sum_{j=1}^\\infty\n\\ind_{\\{m_n(j)\\le 3m_n/4\\}},\n\\qquad A_k=2^k,\n\\qquad T_n=\\frac{4m_{n+1}}{m_n}.\n\\end{equation}\nThe sum is finite, and \\(m_n(\\ell_n)>3m_n/4\\), since\n\\(m_n(j)\\uparrow m_n\\in(0,\\infty)\\). Form a list, in increasing\norder of \\(k\\), containing \\(A_k\\) with multiplicity\n\\begin{equation}\\label{gauge:batch}\nN_{nk}=\\ind_{\\{2^k\\le T_n\\}}\n\\left\\lfloor\\frac{4^n2^{kn}}{m_n}\\right\\rfloor.\n\\end{equation}\nAppend the number \\(n\\) once, as the final \\emph{cleanup entry},\nand denote the resulting entries by \\(b_{ni}\\), \\(1\\le i\\le I_n\\).\nThis list is finite. Indeed, \\(N_{nk}>0\\) implies\n\\[\n\\frac{m_n^{1/n}}4\\le A_k\\le T_n,\n\\]\nand Jensen's Inequality gives \\(m_n^{1/n}\\ge m_1>0\\).\nThus \\(c_0:=\\min\\{1,m_1/4\\}\\) is a positive lower bound for every\nentry \\(b_{ni}\\).\n\nAssign the radii\n\\begin{equation}\\label{gauge:radii}\nr_{ni}=\\exp\\{-n^2-2a(i-1)(\\ell_n+n)\\},\n\\qquad 1\\le i\\le I_n,\n\\end{equation}\nand define\n\\begin{equation}\\label{gauge:definition}\n\\boxed{\\quad\nh_\\kappa(r)=\n\\inf_{\\substack{n\\ge1\\\\1\\le i\\le I_n}}\nb_{ni}r_{ni}^{\\,d}\\max\\{1,(r/r_{ni})^2\\}\n\\quad(r>0),\\qquad h_\\kappa(0)=0.\n\\quad}\n\\end{equation}\nThe infimum includes every entry of every batch.\n\nThe quadratic envelope has a planar covering antecedent in\nPeres--Solomyak \\cite[Lemma~1.2 and Section~5]{PeresSolomyak2005}.\nIndeed, for $0<s\\le e^{-1}$ put\n$\\phi(s)=\\inf_{r_{ni}\\ge s}b_{ni}r_{ni}^{d}$. Exchanging infima gives\n\\[\n h_\\kappa(s)=\\inf_{0<r\\le s}(s/r)^2\\phi(r),\n\\]\nwhich is their quadratic regularization of this step profile.\nThe batch data, nondegeneracy of the envelope, and control after finite\nexclusions are established here; in particular, positivity is proved\ndirectly rather than inferred for an arbitrary input profile.\n\n\\subsection{Admissibility and restoration of early entries}\n\nThe envelope must remain positive at every positive diameter and tend\nto zero at the origin. Its behavior after a finite set of entries is\nexcluded will also be needed in the lower-cover proof.\n\n\\begin{proposition}[Gauge properties and finite exclusions]\\label{gauge:admissible}\nThe function \\(h_\\kappa\\) is a Hausdorff gauge and satisfies\n\\begin{equation}\\label{gauge:doubling}\nh_\\kappa(Cr)\\le C^2h_\\kappa(r)\n\\qquad(C\\ge1,\\ r\\ge0).\n\\end{equation}\nThe radii from distinct batches may interleave. Set\n\\[\nH_{ni}(r)=b_{ni}r_{ni}^{\\,d}\\max\\{1,(r/r_{ni})^2\\}.\n\\]\nIf \\(0\\le F(r)\\le M<\\infty\\) and\n\\(F(r)\\le C H_{ni}(r)\\) for every \\(n\\ge n_0\\), every\n\\(1\\le i\\le I_n\\), and every \\(r>0\\), then\n\\(F(r)\\le C' h_\\kappa(r)\\) for all \\(r>0\\), for some finite\n\\(C'\\).\n\\end{proposition}\n\n\\begin{proof}\nThere are only finitely many entries with \\(r_{ni}\\ge\\varepsilon\\)\nfor each \\(\\varepsilon>0\\), since \\(r_{ni}\\le e^{-n^2}\\) and\nevery batch is finite. For a fixed \\(r>0\\), entries with\n\\(r_{ni}<r\\) satisfy\n\\[\nH_{ni}(r)\\ge c_0r^2r_{ni}^{d-2}\\longrightarrow\\infty\n\\qquad\\text{as }r_{ni}\\longrightarrow0,\n\\]\nbecause \\(d<2\\). Comparing with any one entry reduces the infimum\nto finitely many positive terms. Hence \\(0<h_\\kappa(r)<\\infty\\).\n\nEach \\(H_{ni}\\) is nondecreasing and satisfies\n\\(H_{ni}(Cr)\\le C^2H_{ni}(r)\\). Taking infima proves\nmonotonicity and Equation~\\eqref{gauge:doubling}. In particular,\nfor \\(0<s<r\\),\n\\[\n(s/r)^2h_\\kappa(r)\\le h_\\kappa(s)\\le h_\\kappa(r),\n\\]\nwhich gives continuity on \\((0,\\infty)\\). At the cleanup radius\n\\(\\rho_n=r_{nI_n}\\),\n\\[\nh_\\kappa(\\rho_n)\\le n\\rho_n^d\\le ne^{-dn^2}\\longrightarrow0.\n\\]\nSince \\(\\rho_n\\to0\\), monotonicity gives continuity at zero.\n\nFor the last assertion the discarded entries, those with \\(n<n_0\\),\nare a finite set, and \\(H_{ni}(r)\\ge b_{ni}r_{ni}^d>0\\).\nThus one may take\n\\[\nC'=\\max\\left\\{C,\n\\max_{\\substack{1\\le n<n_0\\\\1\\le i\\le I_n}}\n\\frac{M}{b_{ni}r_{ni}^d}\\right\\},\n\\]\nomitting the inner maximum if its index set is empty.\nThen \\(F(r)\\le C'H_{ni}(r)\\) holds for every entry, and taking\nthe full infimum proves the claim. No ordering between batches\nis used.\n\\end{proof}\n\n\\subsection{The opposing probability estimates}\n\nThe multiplicities supply the two bounds advertised at the start of\nthis section. These are estimates under the one mixed disk law; the\nconditional tests along the curve are constructed in\nSection~\\ref{hd:cover-section}.\n\n\\begin{lemma}[Batch probability estimates]\\label{gauge:multiplicity}\nThe non-cleanup multiplicities satisfy, for every \\(n\\ge1\\),\n\\begin{equation}\\label{gauge:upper-tail}\n\\sum_{k\\in\\mathbb Z}N_{nk}\\,\n\\PP^*_{\\mathrm{mix}}\\{L\\ge16A_k\\}\n\\le\\frac{4^n16^{-n}}{1-2^{-n}}.\n\\end{equation}\nFor \\(n\\ge3\\), they also satisfy\n\\begin{equation}\\label{gauge:success}\n\\sum_{k\\in\\mathbb Z}N_{nk}\\,\n\\PP^*_{\\mathrm{mix}}\\{L_{\\ell_n}\\ge A_k\\}\n\\ge 2^{n-2}-\\frac{1}{2(1-2^{-n})}\n\\ge\\frac{2^n}{8}.\n\\end{equation}\nIncluding the cleanup entries,\n\\begin{equation}\\label{gauge:tail-summability}\n\\sum_{n\\ge1}\\sum_{i=1}^{I_n}\n\\PP^*_{\\mathrm{mix}}\\{L\\ge16b_{ni}\\}<\\infty.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nFor \\(x\\ge0\\), the geometric sum gives\n\\[\n\\sum_{k:\\,2^k\\le x}2^{kn}\n\\le\\frac{x^n}{1-2^{-n}}.\n\\]\nDiscarding the cutoff and the floors in Equation~\\eqref{gauge:batch},\nthen applying this inequality with \\(x=L/16\\) and taking\nexpectations, proves Equation~\\eqref{gauge:upper-tail}.\n\nFor the lower bound put \\(Y=L_{\\ell_n}\\). Since \\(Y\\le L\\),\n\\[\n\\EE^*_{\\mathrm{mix}}[Y^n;Y>T_n]\n\\le\\frac{\\EE^*_{\\mathrm{mix}}[Y^{n+1}]}{T_n}\n\\le\\frac{m_n}{4},\n\\qquad\n\\EE^*_{\\mathrm{mix}}[Y^n;Y\\le T_n]\\ge\\frac{m_n}{2}.\n\\]\nWrite \\(w_k=4^nA_k^n/m_n\\). Whenever \\(0<Y\\le T_n\\),\nsome dyadic level satisfies \\(Y/2<A_k\\le Y\\); it is allowed\nby the cutoff, and its weight is at least \\(2^nY^n/m_n\\).\nConsequently\n\\[\n\\EE^*_{\\mathrm{mix}}\\left[\n\\sum_{k:\\,A_k\\le T_n}w_k\\ind_{\\{Y\\ge A_k\\}}\\right]\n\\ge2^{n-1}.\n\\]\nThe weights with \\(w_k<1\\) have total at most\n\\((1-2^{-n})^{-1}\\), by another geometric sum. For the other\nweights, \\(\\lfloor w_k\\rfloor\\ge w_k/2\\). Subtracting the former\nweights and halving therefore gives\n\\[\n\\sum_kN_{nk}\\PP^*_{\\mathrm{mix}}\\{Y\\ge A_k\\}\n\\ge2^{n-2}-\\frac{1}{2(1-2^{-n})}.\n\\]\nFor \\(n\\ge3\\), the subtracted term is at most \\(4/7\\le2^{n-3}\\),\nwhich proves Equation~\\eqref{gauge:success}.\nFinally, Tonelli's Theorem gives\n\\[\n\\sum_{n\\ge1}\\PP^*_{\\mathrm{mix}}\\{L\\ge16n\\}\n=\\EE^*_{\\mathrm{mix}}\\!\\left[\\left\\lfloor\\frac L{16}\\right\\rfloor\\right]\n\\le\\frac{m_1}{16}.\n\\]\nTogether with the summable bounds in Equation~\\eqref{gauge:upper-tail},\nthis proves Equation~\\eqref{gauge:tail-summability}.\n\\end{proof}\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/hausdorff.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/hausdorff.tex", "bytes": 12842, "sha256": "0f4cd5c1a0c227e9523af4b074e524f8f1b2f97af1083cff9df8f83bd6cf236a", "content": "\\section{Past mass and the arbitrary-cover lower bound}\n\\label{hd:section}\n\nWe now compare the gauge with the actual mass of small pieces of the\ntrace. The lower bound uses mass accumulated before a first visit.\nSection~\\ref{hd:cover-section} will use successive finite tests for\nthe complementary upper bound. Throughout this section,\n\\(\\Gamma=\\gamma([0,\\infty))\\), and \\(h=h_\\kappa\\) is the full\ninfimum in Equation~\\eqref{gauge:definition}.\nThe notation \\(\\PP^*_{\\mathrm{mix}}\\) continues to mean the tilted\ndisk law with initial angle distributed according to \\(\\pi_a\\).\n\n\\subsection{A spatial comparison at a stopped restart}\n\\label{hd:spatial}\n\nFix the numerical constants\n\\begin{equation}\n B_*=100,\\qquad C_*=200,\\qquad\n c_*=\\frac{192}{5},\\qquad C_*'=\\frac{8000}{27}.\n \\label{hd:koebe-constants}\n\\end{equation}\nFor \\(z\\in\\HH\\) and \\(100r<R_0(z)=2\\operatorname{Im}z\\), let \\(S\\)\nbe the first time that \\(R_S(z)=100r\\) while \\(z\\) remains alive.\nOn \\(\\{S<\\infty\\}\\), choose a disk map\n\\(F:\\DD\\longrightarrow H_S\\) with \\(F(0)=z\\), normalized so that\nthe restarted half-plane coordinate at \\(z\\), after positive\nrescaling, is \\(e^{i\\theta_S(z)}\\). Thus the disk has exactly the\nmarkings of Equation~\\eqref{coef:disk-model}, and\n\\(|F'(0)|=100r\\).\nFor \\(K=\\{|w|\\le1/4\\}\\), the growth and distortion estimates give\n\\begin{align}\n \\overline B(z,16r)&\\subset F(K)\n       \\subset\\overline B\\left(z,\\frac{400}{9}r\\right)\n       \\subset\\overline B(z,C_*r), \\label{hd:koebe-sets}\\\\\n c_*r&\\le |F'(w)|\\le C_*'r\\qquad(w\\in K).\n \\label{hd:koebe-derivatives}\n\\end{align}\nIndeed the inner and outer growth bounds are\n\\(100r(1/4)/(1\\pm1/4)^2\\), and the derivative bounds are\n\\(100r(1-1/4)/(1+1/4)^3\\) and\n\\(100r(1+1/4)/(1-1/4)^3\\).\nIn particular, \\(F(K)\\) contains every closed grid square of side\n\\(r\\) containing \\(z\\).\n\nSince \\(F(K)\\) is compactly contained in \\(H_S\\), its accumulated\nmass at time \\(S\\) is zero. Proposition~\\ref{mass:restart} identifies\nthe actual subsequent measure there, simultaneously at all later\ntimes, with the pushforward of the disk measure weighted by\n\\(|F'|^d\\). Consequently, if \\(T\\) is the time of an additional\ninner-clock increment \\(0<\\ell<\\infty\\) at \\(z\\), then, on\n\\(\\{S<T<\\infty\\}\\),\n\\begin{equation}\n (c_*r)^d L_\\ell\n \\ \\le\\ (\\mu_T-\\mu_S)(F(K))\n \\ \\le\\ (C_*'r)^d L_\\ell\n \\label{hd:spatial-comparison}\n\\end{equation}\nin these restarted coordinates. The event that this test endpoint\nis reached has probability one under the corresponding finite\ncentered law. Conditional on the stopped past\nunder \\(\\PP_z^*\\), the variable on the right is the disk-model\nvariable with initial angle \\(\\theta_S(z)\\).\nThis is an equality of actual measures before applying distortion,\nso it also permits random Borel sets and random derivative weights.\nThe clock increment is exactly \\(\\ell\\): the derivative at \\(z\\)\ncancels from ratios of conformal radii.\n\nAll uses of Equation~\\eqref{hd:spatial-comparison} under a centered\nlaw are first made on a finite stopped piece, using the raw mass\nfunctional of Proposition~\\ref{mass:construction}.\nFor example, if \\(q<T^*=\\lim_{\\ell\\to\\infty}\\tau_\\ell(z)\\), some\nfinite stopped piece extends beyond \\(q\\). Monotonicity therefore\nbounds the mass accumulated in \\(F(K)\\) before \\(q\\) by\n\\((C_*'r)^dL\\), with \\(L\\) the increasing limit in the restarted\ndisk model. No mass after \\(T^*\\) is used.\n\n\\subsection{Past mass and first visits}\n\\label{hd:palm-section}\n\nThe relation between natural-length-biased chordal SLE and integrated\nGreen-weighted two-sided radial laws was proved by\nField \\cite[Theorem~1]{Field2016} for $0<\\kappa\\le4$ in bounded\ndomains with analytic boundary, and by\nZhan \\cite[Theorem~4.1]{Zhan2019Decomposition} for $0<\\kappa<8$.\nZhan's proof connects mass strictly after an observation time to\ncentered stopped laws through the conditional remaining Green potential\n\\cite[Proposition~2.2, equation~(2.1), and equations~(4.3)--(4.5)]{Zhan2019Decomposition}.\nWe use this mechanism to derive the required past-mass identity\ndirectly for our canonical mass, from its conditional measure kernel\nand finite stopped changes of law. Retaining the strict time inequality\nkeeps each centered-law mass evaluation before its terminal time.\n\nLet \\(Q\\Subset\\HH\\) be a deterministic closed square and let\n\\(H>0\\). Write \\(M_q=\\mu_q(Q)\\), and let \\(v(z)\\) denote the first\nvisit time of \\(z\\). We shall use the following identity:\n\\begin{align}\n &\\EE\\int_Q\n   \\ind_{\\{\\exists q\\in\\mathbb Q_{\\ge0}:\n                 \\ q<v(z),\\ M_q>H\\}}\\,\\mu_\\infty(\\dd z)\n \\notag\\\\\n &\\hspace{12mm}=\n \\int_Q G_0(z)\\,\n   \\PP_z^*\\{\\exists q\\in\\mathbb Q_{\\ge0}:\n                 \\ q<T^*,\\ M_q>H\\}\\,\\dd A(z).\n \\label{hd:palm}\n\\end{align}\nThe strict inequality \\(q<T^*\\) is part of the statement.\nHere is a derivation that also specifies the measurable kernel\non its right-hand side.\n\nFirst take a finite set of rational times\n\\(q_1<\\cdots<q_N\\), and set\n\\[\n A_j=\\{M_{q_j}>H,\\ M_{q_k}\\le H\\text{ for }k<j\\}.\n\\]\nEach \\(A_j\\) belongs to \\(\\mathcal F_{q_j}\\) and does not depend on\nthe integration point \\(z\\). If a successful test in this finite\nset precedes \\(v(z)\\), its first successful test also precedes\n\\(v(z)\\). The first-visit identity in\nCorollary~\\ref{mass:first-visit} thus\nidentifies the corresponding random mass with\n\\[\n \\sum_{j=1}^N\n    \\ind_{A_j}(\\mu_\\infty-\\mu_{q_j})(Q).\n\\]\nApply Equation~\\eqref{mass:kernel} with the event \\(A_j\\), followed\nby the past-test identity in Proposition~\\ref{op:potential}.\nThe expectation becomes\n\\[\n \\int_Q G_0(z)\n       \\sum_{j=1}^N\\PP_z^*(A_j,\\ q_j<T^*)\\,\\dd A(z).\n\\]\nThese events are disjoint. If a later successful test occurs before\n\\(T^*\\), then the first successful test does too.\n\nFor completeness, for a fixed \\(A\\in\\mathcal F_q\\) the term just\nused has the measurable version\n\\begin{equation}\n \\PP_z^*(A,q<T^*)=\n \\lim_{\\ell\\to\\infty}\n \\frac{1}{G_0(z)}\n \\EE\\!\\left[\n   \\ind_A G_q(z)\n   \\ind_{\\{z\\in H_q,\\ s_z(q)<\\ell\\}}\n \\right].\n \\label{hd:palm-kernel}\n\\end{equation}\nAt a finite level the expectation also equals\n\\(\\EE[\\ind_A\\ind_{\\{q<\\tau_\\ell(z)<\\infty\\}}\nG_{\\tau_\\ell(z)}(z)]\\).\nThis is a stopped-past test of the finite tilted law.\nThe raw mass functional is fixed once, and the Loewner quantities\nare jointly measurable in \\(z\\) and the driving path.\nThus Equation~\\eqref{hd:palm-kernel} is a measurable function of\n\\(z\\), and Tonelli's Theorem applies without a common exceptional\nset for uncountably many marked points.\nFinally, exhaust \\(\\mathbb Q_{\\ge0}\\) by increasing finite sets,\nsorting each set before making the first-success selection.\nThe existence events increase even though their finite partitions\nchange. Monotone convergence proves Equation~\\eqref{hd:palm}.\n\n\\begin{lemma}[Summable mass of bad cells]\n\\label{hd:bad-cells}\nFix a compact set \\(E\\Subset\\HH\\). Set\n\\[\n D=16(C_*')^d.\n\\]\nFor an entry \\((n,i)\\), write \\(r=r_{ni}\\), \\(b=b_{ni}\\), and use\nthe square grid of side \\(r\\). Call a cell \\(Q\\) bad when\n\\(\\mu_\\infty(Q)>2Dbr^d\\).\nFor every sufficiently large \\(n\\), uniformly over all its entries\nand all cells meeting \\(E\\),\n\\begin{equation}\n \\EE[\\mu_\\infty(Q);\\ Q\\text{ bad}]\n \\le\n 4\\left(\\int_QG_0(z)\\,\\dd A(z)\\right)\n       \\PP^*_{\\mathrm{mix}}(L\\ge16b).\n \\label{hd:bad-mass}\n\\end{equation}\nMoreover, almost surely, \\(\\mu_\\infty\\)-almost every point of \\(E\\)\nbelongs to no bad cell for any entry of any sufficiently late\nbatch.\n\\end{lemma}\n\n\\begin{proof}\nAll cells in question lie in a fixed larger compact subset of\n\\(\\HH\\) for sufficiently large \\(n\\).\nTake \\(H=Dbr^d\\) in Equation~\\eqref{hd:palm}.\nContinuity of the increasing process \\(q\\mapsto\\mu_q(Q)\\) and\nthe first-visit identity show that, when \\(\\mu_\\infty(Q)>2H\\),\nat least half the terminal mass of \\(Q\\) arrives after the\naccumulated mass has exceeded \\(H\\).\nMore explicitly, if its terminal value is \\(M>H\\), the mass\ndetected by the existence event on the left of\nEquation~\\eqref{hd:palm} is \\(M-H\\): rational times detect strict\ncrossings, and continuity lets the accumulated mass at such\ncrossings decrease to \\(H\\).\nIt follows that the left side of Equation~\\eqref{hd:bad-mass}\nis at most twice the left side of Equation~\\eqref{hd:palm}.\n\nFor \\(z\\in Q\\), restart at conformal radius \\(100r\\).\nEquation~\\eqref{hd:koebe-sets} gives \\(Q\\subset F(K)\\).\nThere is no mass in \\(Q\\) before the restart, and the spatial\ncomparison gives\n\\[\n \\mu_q(Q)\\le (C_*'r)^dL\\qquad(q<T^*)\n\\]\nin the restarted model. The time before the restart in the inner\nclock is\n\\[\n \\frac{1}{2a}\\log\\frac{R_0(z)}{100r}.\n\\]\nSince \\(r_{ni}\\le e^{-n^2}\\), this tends uniformly to infinity for\nall the relevant \\(z\\) and entries as \\(n\\) increases.\nLemma~\\ref{op:mixing} therefore bounds the restart-angle law by\n\\(2\\pi_a\\). The event in the right side of\nEquation~\\eqref{hd:palm} has probability at most\n\\(2\\PP^*_{\\mathrm{mix}}(L\\ge16b)\\).\nTogether with the first factor of two this proves\nEquation~\\eqref{hd:bad-mass}.\n\nThe countable collection of grid lines has zero terminal mass\nalmost surely, by\n\\(\\EE\\mu_\\infty(\\dd z)=G_0(z)\\dd A(z)\\).\nConsequently boundaries do not affect sums over closed cells.\nThe integrals over cells meeting \\(E\\) are bounded by the integral\nof \\(G_0\\) over a fixed larger compact. Summing\nEquation~\\eqref{hd:bad-mass} over every non-cleanup entry,\nincluding all repetitions, is summable in \\(n\\) by\nLemma~\\ref{gauge:multiplicity}.\nThe cleanup entries are summable as well, since\n\\begin{equation}\n \\sum_{n=1}^{\\infty}\\PP^*_{\\mathrm{mix}}(L\\ge16n)\n =\\EE^*_{\\mathrm{mix}}\\left\\lfloor\\frac{L}{16}\\right\\rfloor\n \\le\\frac{m_1}{16}.\n \\label{hd:cleanup-tail}\n\\end{equation}\nThus the expected integral, against \\(\\mu_\\infty|_E\\), of the\nnumber of bad-cell memberships in this entire countable family\nis finite. Tonelli's Theorem implies that this number is finite\nat \\(\\mu_\\infty\\)-almost every point, almost surely.\nEach batch is finite, so every such point has an eventual batch\ncutoff as asserted.\n\\end{proof}\n\n\\begin{proposition}[The lower Hausdorff bound]\n\\label{hd:lower}\nAlmost surely,\n\\[\n \\Haus^h(\\gamma([s,t]))>0\n \\qquad\\text{for every real }0<s<t<\\infty.\n\\]\n\\end{proposition}\n\n\\begin{proof}\nApply Lemma~\\ref{hd:bad-cells} on a countable compact exhaustion of\n\\(\\HH\\), and work on the resulting probability-one event together\nwith the conclusions about the mass in\nPropositions~\\ref{mass:construction} and~\\ref{mass:positive}.\nFix a nontrivial interval \\([u,v]\\) with rational endpoints,\n\\(0<u<v\\).\nThe increment \\(\\mu_v-\\mu_u\\) has positive mass on some compact\n\\(E\\Subset\\HH\\) in this exhaustion, and that mass is finite.\nFor each $N$, let $S_N$ be $E$ minus the union of all bad cells for\nentries $(n,i)$ with $n\\ge N$. The countable cell families make $S_N$\nBorel, and $S_N$ increases with $N$. Lemma~\\ref{hd:bad-cells} says\nthat their union carries the increment on $E$. Choose $n_0$ with\n$(\\mu_v-\\mu_u)(S_{n_0})>0$, and set\n$\\nu=(\\mu_v-\\mu_u)|_{S_{n_0}}$. This is a finite nonatomic measure,\ncarried by $\\gamma([u,v])$, with $0<\\nu(\\HH)<\\infty$.\n\nFor every entry \\((n,i)\\) with \\(n\\ge n_0\\), every grid cell has\n\\[\n \\nu(Q)\\le2D b_{ni}r_{ni}^d.\n\\]\nA bad cell has zero \\(\\nu\\)-mass by the restriction, a good cell\nhas the displayed bound for \\(\\mu_\\infty\\), and a cell disjoint\nfrom \\(E\\) has no \\(\\nu\\)-mass. A set \\(U\\) of diameter \\(s>0\\)\nmeets at most\n\\(16\\max\\{1,(s/r_{ni})^2\\}\\) cells of this grid: each coordinate\nprojection has length at most \\(s\\), and it meets at most\n\\(s/r_{ni}+2\\) grid intervals. Hence its outer measure satisfies\n\\begin{equation}\n \\nu^*(U)\\le\n 32D b_{ni}r_{ni}^d\n       \\max\\{1,(s/r_{ni})^2\\}\n \\qquad(n\\ge n_0).\n \\label{hd:retained-density}\n\\end{equation}\n\nTo pass to the full gauge, apply the finite-exclusion assertion of\nProposition~\\ref{gauge:admissible} to\n$F(s)=\\sup_{\\diam U\\le s}\\nu^*(U)$, bounded by $\\nu(\\HH)$.\nThe constant supplied by that proposition is\n\\[\n C_\\nu=\\max\\left\\{32D,\\\n       \\max_{\\substack{n<n_0\\\\1\\le i\\le I_n}}\n          \\frac{\\nu(\\HH)}{b_{ni}r_{ni}^d}\\right\\},\n\\]\nwhere the maximum over an empty set is zero.\nIt bounds $F$ by every quadratic expression, including all the\nfinitely many excluded entries. Taking the full infimum therefore gives\n\\begin{equation}\n \\nu^*(U)\\le C_\\nu h(\\diam U).\n \\label{hd:full-density}\n\\end{equation}\nThis also holds for sets of diameter zero by nonatomicity.\nThe argument uses finiteness of the excluded set, and does not\norder its radii relative to those of later batches.\n\nFor any countable cover \\(\\{U_j\\}\\) of \\(\\gamma([u,v])\\),\nouter-measure subadditivity and Equation~\\eqref{hd:full-density}\ngive\n\\[\n 0<\\nu(\\HH)\\le C_\\nu\\sum_j h(\\diam U_j).\n\\]\nTaking the infimum over covers with any diameter cutoff proves\n\\(\\Haus^h(\\gamma([u,v]))\\ge\\nu(\\HH)/C_\\nu>0\\).\nThere are only countably many rational intervals. Every real\n\\(0<s<t\\) contains a nontrivial rational closed subinterval, so\nmonotonicity of Hausdorff measure proves the simultaneous claim.\n\\end{proof}\n\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/introduction.tex", "bytes": 13837, "sha256": "2b3f3b6ac47b68ed59cfae595f5f48cfe9f8519ed71791f9dbad429d1a9dcf8a", "content": "\\section{Introduction}\\label{sec:introduction}\n\nA Hausdorff dimension identifies a critical power of distance, but that\npower need not assign nonzero measure to a random curve. For chordal\nSchramm--Loewner evolution $\\SLE_\\kappa$, $0<\\kappa<8$, Beffara\n\\cite{Beffara2008} proved that the almost-sure dimension is\n\\[\n d=1+\\frac\\kappa8.\n\\]\nSchramm asked whether the trace has a sigma-finite Hausdorff measure\nand suggested $r^d\\log\\log(1/r)$ as a possible gauge\n\\cite[Problem~7.1 and the following paragraph]{Schramm2006}.\nRezaei subsequently proved that the critical power $r^d$ itself gives\nzero Hausdorff measure \\cite[Theorem~1.1]{Rezaei2018}.\nThe question therefore asks for a deterministic refinement of dimension\nthat measures the trace through arbitrary spatial covers.\n\nWe answer the existence question positively by constructing the scales\nof the gauge from moments of a local trace mass. The gauge is explicit\nin terms of deterministic integrals, although its asymptotics need not\nhave a regular form. The main mechanism uses finite moments of every\norder in two ways: moderately large masses provide repeated opportunities\nfor an economical cover, while exceedances of a larger threshold have a\nsummable total cost. A sharp tail asymptotic is not needed.\n\n\\subsection{The result}\n\nA \\emph{Hausdorff gauge} is a continuous nondecreasing function\n$h:[0,\\infty)\\to[0,\\infty)$ with $h(0)=0$ and $h(r)>0$ for $r>0$.\nWe use diameters in the definition\n\\[\n \\Haus^h(E)=\\lim_{\\delta\\downarrow0}\\Haus^h_\\delta(E),\\qquad\n \\Haus^h_\\delta(E)=\\inf\\left\\{\\sum_j h(\\diam U_j):\n E\\subset\\bigcup_jU_j,\\quad\\diam U_j\\le\\delta\\right\\},\n\\]\nwhere covers are countable and their sets may be arbitrary.\n\n\\begin{theorem}[An exact moment gauge]\\label{thm:main}\nFix $0<\\kappa<8$, and let $\\gamma$ be chordal $\\SLE_\\kappa$ from\n$0$ to $\\infty$ in the upper half-plane $\\HH$, parametrized by half-plane capacity $2t$.\nThere is a deterministic Hausdorff gauge $h_\\kappa$, depending only on\n$\\kappa$, such that almost surely, simultaneously for every real\n$0<s<t<\\infty$,\n\\[\n 0<\\Haus^{h_\\kappa}(\\gamma([s,t]))<\\infty.\n\\]\n\\end{theorem}\n\nThe gauge is the full infimum in \\eqref{gauge:definition}, with\ncoefficients specified by the iterated integrals in \\eqref{coef:formula}.\nThe exceptional event may depend on the fixed $\\kappa$; the gauge\nis independent of both the sample and the interval.\nIn particular, the trace outside its initial point has a nonzero\nsigma-finite Hausdorff measure.\n\nThe upper argument measures the entire trace in a bounded region.\nFor $\\Gamma=\\gamma([0,\\infty))$ and\n$B_m=[-m,m]+i[0,m]$, Proposition~\\ref{hd:upper} proves\n\\begin{equation}\n \\EE\\,\\Haus^{h_\\kappa}(\\Gamma\\cap B_m)<\\infty\n \\qquad(m\\in\\mathbb N).\n \\label{intro:box-mean}\n\\end{equation}\nThus all these bounded-box measures are finite on one event, including\nat real-boundary trace points. Positivity in Theorem~\\ref{thm:main}\nis asserted for the stated positive-time intervals.\n\n\\subsection{Multipoint estimates and one canonical mass}\n\nThe gauge construction needs a trace mass with moments of every order\nand a way to compare its future in a small region with one fixed disk\nmodel. We establish these inputs through a multipoint probability bound\nand a conformally restarting mass. Both are also useful independently.\nWe first give a direct product-supermartingale proof of the fixed-order\nestimate needed in an arbitrary physical domain.\nFor a conformal map $J:\\HH\\to D$, let $R_D(x)$ denote full conformal\nradius: $R_D(x)=|F'(0)|$ when $F:\\DD\\to D$ is conformal,\n$\\DD$ is the unit disk, and $F(0)=x$.\nWrite $H_t$ for the surviving half-plane domain after the trace up to\ntime $t$, and $D_t=J(H_t)$. A point is \\emph{alive} at $t$ when it\nbelongs to $D_t$; its evolving radius is $R_{D_t}(x)$.\nSet $\\alpha=2-d$ and $a=2/\\kappa$.\nFor distinct interior points $x_1,\\ldots,x_m$, choose caps satisfying\n\\[\n 0<b_i\\le R_D(x_i),\\qquad b_i\\le |x_i-x_j|\\quad(j\\ne i).\n\\]\nProposition~\\ref{mp:estimate} proves that the probability that each point\nreaches its own radius target $e_i>0$ while alive is at most\n\\begin{equation}\n C_{m,a}\\prod_{i=1}^m\\min\\{1,(e_i/b_i)^\\alpha\\}.\n \\label{intro:multipoint}\n\\end{equation}\nThe constant depends only on the fixed order and parameter. It is\nuniform in the domain, the initial angles, and the order and times of\nthe hits. A completed point may be swallowed before another target is\nreached. No sharp growth estimate in $m$ is claimed. Integrating the\ncollision singularities in this bound gives all the compact moments\nused in the mass construction.\n\nThe second ingredient is a single adapted increasing measure process\n$\\mu_t$ on the open domain, starting at zero. In the half-plane,\nProposition~\\ref{mass:construction} constructs it with nonatomic terminal\nmeasure, local total-variation continuity, and increments carried by the\ncorresponding trace segments. Every nontrivial time interval has positive\nincrement mass by Proposition~\\ref{mass:positive}.\nTo describe its conditional mean, let $g_S$ be the Loewner map of the\nsurviving half-plane domain $H_S$, with driving point $U_S$ in the\ncapacity normalization. For $x\\in H_S$ put\n$\\theta_S(x)=\\arg(g_S(x)-U_S)$ and\n$G_S(x)=R_{H_S}(x)^{-\\alpha}\\sin^{8/\\kappa-1}\\theta_S(x)$;\nput $G_S(x)=0$ outside $H_S$. Its remaining conditional mean is\n\\[\n \\EE[\\mu_\\infty-\\mu_S\\mid\\mathcal F_S]=G_S\\,\\dd A\n\\]\nfor every fixed stopping time $S$, including $S=\\infty$ with\n$G_\\infty=0$. Here $\\dd A$ is planar area. The equality holds as a\nconditional measure kernel, so it includes nonnegative tests known\nat $S$.\n\nConformal covariance weights the measure by $|J'|^d$.\nAt each fixed finite stopping time, Proposition~\\ref{mass:restart}\nidentifies the actual future increment with the conformally transported\ncanonical mass of the fresh driver, simultaneously at every later time.\nThis permits comparison of the actual mass near a marked point with\none disk model after stopping at a prescribed conformal radius.\nThe lower bound also uses first-visit accounting: mass already\naccumulated on the trace is not charged again at a later geometric return.\n\n\\subsection{Why the moments determine the gauge}\n\nTo bias the curve toward a marked interior point, stop when that point's\nconformal radius reaches a prescribed smaller value and reweight the\nstopped law by the ratio of its terminal and initial Green weights.\nPaths that fail to reach the radius level receive weight zero.\nSection~\\ref{op:section} proves that the weight has mean one and that\nthese finite stopped laws are consistent as the radius decreases.\nWe call them the \\emph{centered laws}.\n\nPlace the marked point at the origin of a unit disk and measure mass in\nits radius-$1/4$ subdisk. The \\emph{inner time} is the logarithmic radius\nclock $\\ell=(2a)^{-1}\\log(R_0/R)$, where $R$ is the marked point's current\nconformal radius. Let $L_\\ell$ be the mass accumulated in that subdisk\nby inner time $\\ell$. Under the centered law, the marked point's angle\nin the corresponding half-plane coordinates evolves as a diffusion\nwith invariant density proportional to\n$\\sin^{4a}\\theta$. We average the initial angle against this distribution\nand write\n$\\PP_{\\mathrm{mix}}^*,\\EE_{\\mathrm{mix}}^*$ for this averaged centered\nlaw and expectation. Then\n\\[\n L_\\ell\\uparrow L,\\qquad 0<\\EE_{\\mathrm{mix}}^*L^n<\\infty\\quad(n\\ge1).\n\\]\nSection~\\ref{coef:section} specifies these numbers by Wiener, angle and\narea integrals, and then by product-normal coordinates and ordinary\ndifferential equation limits. This deterministic infinite-limit\nprescription fixes the normalization\nof the moments without referring to an unspecified random measure.\n\nFor each order $n$, retain more than three quarters of its moment at a\nfinite level $\\ell_n$. Repeat dyadic thresholds $A_k=2^k$ in a finite\nbatch with multiplicities $N_{nk}$ determined by the $n$th and\n$(n+1)$st moments. Lemma~\\ref{gauge:multiplicity} gives the complementary\nbounds\n\\[\n \\sum_kN_{nk}\\PP_{\\mathrm{mix}}^*\\{L\\ge16A_k\\}\n \\le\\frac{4^{-n}}{1-2^{-n}},\\qquad\n \\sum_kN_{nk}\\PP_{\\mathrm{mix}}^*\\{L_{\\ell_n}\\ge A_k\\}\\ge\\frac{2^n}{8}\n \\quad(n\\ge3).\n\\]\nThe first sum is summable over batches. The second supplies many\nopportunities for a success. Tests along the actual curve are separated\nin conformal-radius time so that angle mixing gives a conditional lower\nbound after each preceding test. This uses their actual stopped pasts,\nnot independence. One final cleanup scale in each batch pays for points\nmissed by all of its tests.\n\nWriting $b_{ni}$ and $r_{ni}$ for the resulting thresholds and radii,\nthe gauge interpolates their costs by the quadratic expressions\n$b_{ni}r_{ni}^d\\max\\{1,(r/r_{ni})^2\\}$ and takes the infimum over\n\\emph{every} entry. The quadratic factor accounts for subdivision in\nthe plane. Radii in different batches may interleave.\n\nFor the lower bound, a strict past-mass identity uses the conditional\nGreen kernel to bound expected mass in overfull squares through centered\nprobabilities that a mass threshold is exceeded before the marked point\nis reached. The upper tails make these expected losses summable over\nall batch entries. Positive mass\ntherefore remains on a set that avoids all overfull squares after one\ncommon batch cutoff. The resulting finite nonatomic measure is bounded\nby every late quadratic expression. Its finite total mass supplies the\nbounds for the finitely many excluded entries, so taking the full\ninfimum is legitimate. Summing this one density bound over arbitrary\ncountable covers proves positivity.\n\nFor the upper bound, successful finite tests force terminal-mass\ncandidates. A greedy selection makes their charging balls disjoint, and\none actual mass in a larger bounded region pays for their enlarged\ncovering balls. Only adapted test failures enter the finite change of\nlaw; terminal candidate membership is used afterward through inclusion.\nThe cleanup scale covers every missed cell, including the bottom row\nat the real boundary. These measurable covers have diameters tending\nto zero and uniformly bounded expected cost. Their lower limiting cost\nand Fatou's Lemma give \\eqref{intro:box-mean}.\n\n\\subsection{Prior work and proof organization}\n\nThe Green-potential approach to trace mass has its roots in natural\nparametrization. Lawler and Sheffield constructed natural parametrization\ninitially for $0<\\kappa<4(7-\\sqrt{33})$\n\\cite[Theorem~3.1]{LawlerSheffield2011}; Lawler and Zhou obtained the\nfull range $0<\\kappa<8$ \\cite[Theorem~1]{LawlerZhou2013}.\nLawler and Rezaei constructed $d$-dimensional Minkowski content and\nidentified it, up to normalization, with natural parametrization\n\\cite[Theorem~1.1 and following discussion]{LawlerRezaei2015}.\nOur common-kernel construction proves the properties needed here\nlocally; identifying its mass with those measures is unnecessary.\nMultipoint estimates and their higher-moment consequences also have\nsubstantial predecessors \\cite{LawlerWerness2013,RezaeiZhan2017}.\nRezaei--Zhan subsequently proved existence, local H\\\"older continuity,\nand bounds up to constants for chordal multipoint Green functions at\nevery finite order \\cite[Theorem~1.1]{RezaeiZhan2018Green}.\nThe domain-uniform bound in \\eqref{intro:multipoint} also follows from\nRezaei--Zhan \\cite[Theorem~1.1]{RezaeiZhan2017} and the Koebe estimates.\nWe give a direct product-supermartingale proof with each point frozen\nafter its own target hit.\n\nThe use of regions carrying large mass together with a fine residual\ncover appears in Rezaei \\cite[Section~3, equations~(9)--(11)]{Rezaei2018}.\nZhan \\cite[Remark~4.5]{Zhan2019Optimal} gives a conditional covering\nsketch for zero $d$-dimensional Hausdorff measure.\nThe present finite batches, actual restart identities and all-time\nboundary cleanup supply the covering proof for the specified gauge.\nThe quadratic envelope is related to the planar regularization of\nPeres--Solomyak \\cite[Lemma~1.2 and Section~5]{PeresSolomyak2005};\nthe strict past-mass calculation uses the same remaining-potential\nmechanism as Zhan's decomposition along the SLE curve\n\\cite[Proposition~2.2 and equations~(4.3)--(4.5)]{Zhan2019Decomposition}.\n\nHolden and Yuan identify a positive fine-mesh variation, up to a\ndeterministic constant, with natural parametrization\n\\cite[Theorems~1.7--1.8]{HoldenYuan2026}. In the half-plane their\nresult includes positive-time intervals that touch the boundary.\nIts ordered time partitions differ from the arbitrary spatial covers\nused here. The moment prescription proves exactness for its own gauge;\nit asserts neither a log-log asymptotic nor identification of Hausdorff\nmeasure with natural parametrization.\n\nA later companion \\cite[Theorem~1.1]{OpenAI2026Explicit} gives\nexactness for the regular closed-form gauge\n\\[\n r^d\\bigl(\\log\\log(1/r)\\bigr)^{(2-d)/2}\n\\]\nat small radii, by quantitative mass tails and dense visits.\nIts argument establishes its own stopped-law, mass and covering estimates.\nThe present argument leaves the asymptotic relation between that formula\nand the moment-defined gauge $h_\\kappa$ undetermined.\n\nSection~\\ref{op:section} proves finite centered laws, mixing and the\nremaining-potential identity in the required normalization.\nSections~\\ref{mp:section} and~\\ref{mass:section} establish\n\\eqref{intro:multipoint}, the common mass kernel and actual restart.\nSection~\\ref{coef:section} recovers the deterministic disk moments;\nSection~\\ref{gauge:section} turns them into batches and the gauge.\nSection~\\ref{hd:section} proves the strict past-mass comparison and\narbitrary-cover lower bound. Section~\\ref{hd:cover-section} constructs\nthe all-time upper covers and assembles the theorem.\nThe external geometric inputs are continuous trace generation\n\\cite[Theorem~5.1]{RohdeSchramm2005} and the classical Koebe estimates\n\\cite[Section~3.2]{Beliaev2015Notes}. Every subsequent centered-law,\nmultipoint, mass and covering argument is proved below.\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/mass.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/mass.tex", "bytes": 20894, "sha256": "ab227dca4bd8b036d8944c3979588d9ba55c29c0b23a0a725456aa03cdead926", "content": "\\section{A canonical mass on the trace}\n\\label{mass:section}\n\nWe now turn the Green potential into an increasing measure on the trace.\nThe conditional Green-potential approach goes back to\nLawler--Sheffield \\cite[Proposition~2.2 and Section~3]{LawlerSheffield2011}\nand Lawler--Zhou \\cite[Proposition~A and Theorem~1]{LawlerZhou2013};\nsee also the Minkowski-content construction in \\cite{LawlerRezaei2015}.\nHere one common sequence of convex combinations supplies the mass and\nall of its versions. We construct the measure and prove the\nproperties needed below directly; no identification with Minkowski content\nis required.  All measures in this section are measures on the open domain.\nWrite $\\lambda(f)=\\int f\\,\\dd\\lambda$ for integration against a measure.\n\n\\begin{proposition}[Construction and conditional kernel]\n\\label{mass:construction}\nThere is an adapted increasing family $(\\mu_t)_{0\\le t\\le\\infty}$ of\nlocally finite measures on $\\HH$, starting at zero, with the following\nproperties.  Its terminal measure is nonatomic and has moments of every\ninteger order on compact sets.  Almost surely, simultaneously for all\n$0\\le v<t<\\infty$, the increment $\\mu_t-\\mu_v$ is carried by\n$\\gamma([v,t])\\cap\\HH$.  The process is locally continuous in total\nvariation, and $\\mu_t\\uparrow\\mu_\\infty$ as $t\\to\\infty$.\nFor every stopping time $S$, possibly infinite,\n\\begin{equation}\n \\EE\\bigl[\\mu_\\infty(\\dd x)-\\mu_S(\\dd x)\\mid\\mathcal F_S\\bigr]\n   =G_S(x)\\,\\dd A(x),\n \\qquad G_\\infty:=0.\n \\label{mass:kernel}\n\\end{equation}\nThis is an equality of conditional measure kernels; in particular it\nallows nonnegative tests known at time $S$.  Moreover,\n\\begin{equation}\n \\EE\\mu_\\infty(\\dd x)=G_0(x)\\,\\dd A(x).\n \\label{mass:mean}\n\\end{equation}\n\\end{proposition}\n\n\\subsection{Deterministic convex extraction}\n\nThe compact moment bounds permit one simultaneous limit for all\nrational times and a determining family of spatial tests. Keeping the\nsame convex coefficients is what later makes the kernels and conformal\nimages properties of one measure rather than separately chosen versions.\n\nFor an integer $j\\ge1$ define\n\\begin{equation}\n M_t^{(j)}(\\dd x)\n =G_{\\tau_j(x)}(x)\n   \\ind_{\\{\\tau_j(x)\\le t,\\ \\tau_j(x)<\\infty\\}}\\,\\dd A(x),\n \\qquad 0\\le t\\le\\infty.\n \\label{mass:approximants}\n\\end{equation}\nThe density is zero on failure to reach the level.  For a compact\n$C\\Subset\\HH$, fix $c_C>0$ with $c_C\\le\\inf_C R_0$.  Given distinct\n$x_1,\\ldots,x_m\\in C$, set\n\\[\n b_i=\\min\\bigl(c_C,\\min_{k\\ne i}|x_i-x_k|\\bigr),\n\\]\nwhere the second minimum is omitted when $m=1$.\nThe terminal approximant is the level weight $A_j$ of\nCorollary~\\ref{mp:level-moments}.  The collision-kernel estimate in its\nproof gives, for every bounded nonnegative $f$ supported in $C$,\n\\begin{equation}\n \\sup_j\\EE\\bigl[M_\\infty^{(j)}(f)^m\\bigr]\n \\le C_{m,a}\\int_{C^m}\n       \\prod_{i=1}^m f(x_i)b_i^{-\\alpha}\n       \\,\\dd A(x_1)\\cdots\\dd A(x_m)<\\infty.\n \\label{mass:moment-bound}\n\\end{equation}\nThe last assertion is Lemma~\\ref{mp:kernel-integral}.\nAll constants are independent of $j$.\n\nWe obtain one sequence of convex combinations as follows.  Choose a\ncountable family of continuous compactly supported tests, uniformly\ndense among tests supported in each member of a compact exhaustion.\nInclude nonnegative cutoff functions equal to one on that exhaustion,\nand rational linear combinations of the chosen tests.  Enumerate the\npairs of these tests with times in $\\mathbb Q_+\\cup\\{\\infty\\}$ as\n$(f_r,t_r)$, $r\\ge1$, and set\n$X_r^{(j)}=M_{t_r}^{(j)}(f_r)$.\nEach coordinate is bounded in $L^2$ by \\eqref{mass:moment-bound}.\nA diagonal subsequence $j_l$ has a weak limit $X_r$ in every coordinate.\n\nFor each $k$, consider the first $k$ coordinates as a vector in\n$(L^2)^k$.  Its weak limit $(X_1,\\ldots,X_k)$ lies in the norm closure\nof the convex hull of every tail.  Hence there are deterministic\ncoefficients $c_{kl}\\ge0$, supported on finitely many $l\\ge k$, with\n$\\sum_lc_{kl}=1$, such that\n\\[\n \\sum_{r=1}^k\\left\\|\n       \\sum_{l\\ge k}c_{kl}X_r^{(j_l)}-X_r\n                    \\right\\|_2^2\\le4^{-k}.\n\\]\nDefine\n\\[\n \\overline M_t^{(k)}=\\sum_{l\\ge k}c_{kl}M_t^{(j_l)}.\n\\]\nFor every listed pair, the summable squared errors give both $L^2$\nand almost-sure convergence; intersecting the resulting events over\n$r$ gives simultaneous convergence on the whole countable list.\nThe same coefficient row is used for every time and spatial test.\n\nOn this event, the cutoff tests bound all local masses.  Density therefore\nextends convergence to every continuous compactly supported test.\nThe limiting positive functionals are Radon measures, with almost-sure\nvague convergence at all listed times.  Positivity and ordering in time\npass to the limit.  The limits at finite rational times are adapted, since\nthe corresponding $L^2(\\mathcal F_t)$ subspaces are closed.  Convexity and\nFatou's Lemma preserve \\eqref{mass:moment-bound} for the terminal measure\nand continuous nonnegative tests.  Approximation then gives the compact\nmoment assertions.\n\nWe also obtain nonatomicity, which will be needed to pass from rational\ntimes to all times.  For two points in a fixed larger compact the kernel\nin \\eqref{mass:moment-bound} is bounded by\n$C(1+|x-y|^{-2\\alpha})$.  Cover a given smaller compact by mesh squares\nof side $\\delta$, and majorize each square's indicator by a continuous\ncutoff in a slightly enlarged square.  The enlarged squares have bounded\noverlap.  The expected sum of their squared masses is bounded by\n\\[\n C\\iint_{|x-y|\\le C\\delta}\n       (1+|x-y|^{-2\\alpha})\\,\\dd A(x)\\dd A(y),\n\\]\nwhere both variables lie in the larger compact.  This tends to zero\nbecause $2\\alpha<2$.  Every atom in the smaller compact contributes the\nsquare of its mass to the corresponding sum at every mesh size.\nFatou's Lemma, followed by a countable compact exhaustion, proves that\n$\\mu_\\infty$ has no atoms.\n\n\\subsection{Support, continuity, and stopping times}\n\nThe finite-level conditional weight identity of\nLemma~\\ref{op:finite-tilt} gives, at a finite rational time $t$,\n\\begin{equation}\n \\EE\\bigl[M_\\infty^{(j)}(\\dd x)-M_t^{(j)}(\\dd x)\n             \\mid\\mathcal F_t\\bigr]\n =G_t(x)\\ind_{\\{x\\in H_t,\\ R_t(x)>R_0(x)e^{-2aj}\\}}\\,\\dd A(x).\n \\label{mass:approx-kernel}\n\\end{equation}\nThe strict inequality excludes a level already attained at $t$.\nAt every surviving point the indicator tends to one.  Since\n$\\EE G_t(x)\\le G_0(x)$, dominated convergence on compact tests applies to\nthe right side.  Local $L^2$ bounds give uniform integrability on the\nleft.  Passing through the tail convex combinations proves\n\\eqref{mass:kernel} at rational times.  The mean-one finite-level weights\nalso give \\eqref{mass:mean}.\n\nThese identities initially hold for countably many deterministic tests.\nThey identify the conditional measure kernel, and hence, by the Monotone\nClass Theorem, hold against every nonnegative\n$\\mathcal F_t\\otimes\\mathcal B(\\HH)$-measurable test.  In particular,\ntesting the complement of the known surviving domain shows that\n\\begin{equation}\n \\mu_\\infty-\\mu_t\\text{ is carried by }H_t\n \\quad\\text{at every finite rational }t,\n \\label{mass:future-support}\n\\end{equation}\non one event of probability one.\n\nAt the same times, $\\mu_t$ is carried by the past trace.  Indeed, on a\nfixed compact, a point receiving mass in $M_t^{(j)}$ has, at its level\ntime, distance at most $R_0(x)e^{-2aj}$ from the current domain boundary.\nFor large $j$ that nearby boundary cannot be the real line, so it belongs\nto $\\gamma([0,t])$.  Thus the approximants, and their tail convex\ncombinations, are carried locally by shrinking neighborhoods of this\nclosed trace segment.  Vague convergence gives the claimed support.\nFor rational $v<t$, the increment is dominated by $\\mu_t$ and by\n$\\mu_\\infty-\\mu_v$, so it is carried by $\\gamma([v,t])\\cap\\HH$.\n\nTake increasing rational left limits and decreasing rational right limits\nof the measure process.  At any finite time $w$, their difference is\ndominated by increments over every rational interval containing $w$.\nContinuity of the trace forces its support to lie in the singleton\n$\\{\\gamma(w)\\}\\cap\\HH$.  The difference vanishes by nonatomicity.\nAt zero use zero as the left limit; the same argument gives zero, since\n$\\gamma(0)\\notin\\HH$.  This argument is pathwise on the one event already\nconstructed and therefore works simultaneously for every $w$.\nIt defines an increasing process on all times, locally continuous in total\nvariation: on a compact the variation of a positive increment is just its\nmass, and the possible one-sided jump measures have been shown to vanish.\nAdaptedness follows from the rational left limits.\nThe support assertion for arbitrary intervals follows by enclosing them\nin rational intervals and taking limits.\n\nThe terminal value is exhausted by finite times.  By\nProposition~\\ref{op:potential}, $\\EE G_t(x)\\to0$ as $t\\to\\infty$.\nApply \\eqref{mass:kernel} at integer times and use\n$\\EE G_t\\le G_0$ on compact tests.  The nonnegative measure\n$\\mu_\\infty-\\lim_{t\\to\\infty}\\mu_t$ has zero mean, and hence is zero.\nThis also gives local total-variation convergence at infinity.\n\nFor completeness, let $S$ be any stopping time.  On the event of rational\nconvergence, monotonicity squeezes $\\overline M_S^{(k)}(f)$ between its\nvalues at rational times approaching each realized finite $S$.\nContinuity gives convergence to $\\mu_S(f)$ for every nonnegative\ncontinuous compact test.  On $\\{S=\\infty\\}$ use terminal convergence of\nthe extracted sequence.  Uniform integrability again follows from the\nterminal $L^2$ bound.  The finite-level identity\n\\eqref{mass:approx-kernel} holds with $t$ replaced by $S$, and with zero\nright side on $\\{S=\\infty\\}$.  The bound\n$\\EE[G_S(x);S<\\infty]\\le G_0(x)$ from\nProposition~\\ref{op:potential} permits the same dominated convergence.\nThis proves \\eqref{mass:kernel} at $S$, including its conditional-kernel\ninterpretation, and completes Proposition~\\ref{mass:construction}.\n\n\\begin{corollary}[First-visit accounting]\n\\label{mass:first-visit}\nPut $v(x)=\\inf\\{t\\ge0:\\gamma(t)=x\\}$, with value infinity if the set is\nempty.  Almost surely $v(x)<\\infty$ for $\\mu_\\infty$-almost every $x$, and\nsimultaneously for every finite $t$,\n\\begin{equation}\n \\mu_t(\\dd x)=\\ind_{\\{v(x)\\le t\\}}\\mu_\\infty(\\dd x).\n \\label{mass:first-visit-identity}\n\\end{equation}\n\\end{corollary}\n\n\\begin{proof}\nAt rational $t$, past mass is carried by $\\gamma([0,t])$, while remaining\nmass is carried by the open set $H_t$, disjoint from that past trace.\nThis proves the identity there.  Increasing finite times exhaust the\nterminal measure.  Right approximation by rational times, continuity of\nthe measure process and of the trace, then proves the identity at all\ntimes.  In particular a later geometric return to an already visited\npoint introduces no new mass there.\n\\end{proof}\n\n\\subsection{Versions, conformal maps, and restart}\n\nWe have constructed the continuous mass and its conditional kernel.\nTo use them at centered stops and in random future domains, we next\nfix a measurable path functional and establish equality of the actual\nrestarted measures.\n\nWe fix one nonanticipating measurable version of this construction.\nOn the canonical continuous-path space each $M^{(j)}$, tested on an\ninterior compact, is a raw adapted c\\`adl\\`ag process: its terminal density\nhas a deterministic finite bound there, and one-sided continuity follows\nfrom dominated convergence of the hitting-time indicators.\nThe same holds for every deterministic convex combination.\nAt each finite time, take the vague limit of $\\overline M_t^{(k)}$\nwhere it exists, and use the zero measure otherwise.  The space of\nlocally finite positive Radon measures is Polish in the vague topology;\nits convergence set and limit map are therefore Borel.  This defines\na raw progressively measurable measure-valued functional.  At infinity use the same limit rule for\n$\\overline M_\\infty^{(k)}$.  Rational squeezing as above shows that it\nagrees, simultaneously at every time under the ordinary Brownian law,\nwith the continuous process just constructed.\n\nAll evaluations under a centered law use this raw functional of the\nstopped path.  At each finite inner level the density in\nLemma~\\ref{op:finite-tilt} transfers the required statements about\nthat stopped piece.  Consistent laws of the stopped pieces, and increasing\nlimits of their coordinates, suffice thereafter.  We do not extend the\ncentered law to the original Brownian space completed by infinite-future\nnull sets.  This distinction also ensures parameter measurability:\nLoewner solutions, level times and conformal maps are measurable in the\ndriver and marked point, and progressive evaluation of the fixed mass\nfunctional at those times is measurable.\n\nFor a fixed conformal identification $J:\\HH\\to D$, define\n\\begin{equation}\n \\mu_t^D=J_*\\bigl(|J'|^d\\mu_t\\bigr).\n \\label{mass:conformal}\n\\end{equation}\nThis rule holds exactly for the approximants: conformal radius contributes\n$|J'|^{-\\alpha}$ and area contributes $|J'|^2$, so their product is\n$|J'|^d$.  Inner-clock ratios are unchanged.\nA compact continuous test in $D$ pulls back to such a test in $\\HH$.\nThus the same deterministic convex combinations converge in every fixed\nimage domain, on the event giving convergence for all compact continuous\ntests.  The kernel, local continuity, nonatomicity and support statements\nhold there with the corresponding $G_t^D$. More precisely, its increments\nare carried by $J(\\gamma([v,t])\\cap\\HH)$. The map is applied only to\ninterior trace points; an arbitrary $J$ need not extend continuously\nto the boundary.\nThe construction is jointly measurable in a measurable family of maps\n$J$; it requires no new extraction for different maps.\n\nThe kernel also proves uniqueness.  If $A_t$ and $\\widetilde A_t$ are\ntwo continuous increasing adapted test-mass processes starting at zero,\nwith integrable terminal values and the same remaining conditional\npotential, then\n\\[\n A_t-\\widetilde A_t\n   =\\EE[A_\\infty-\\widetilde A_\\infty\\mid\\mathcal F_t].\n\\]\nTheir difference is a continuous martingale of finite variation, hence\nvanishes.  Applying this to a countable determining set of tests proves\nuniqueness of the measure process.  In particular it removes dependence\non the convex extraction.  It also gives Brownian scaling: if\n$B^{(c)}_t=c^{-1}B_{c^2t}$, then, for each $c>0$,\n\\begin{equation}\n \\mu_t(B^{(c)})(A)=c^{-d}\\mu_{c^2t}(B)(cA)\n \\quad\\text{almost surely, for all }t\\text{ and Borel }A\\subset\\HH.\n \\label{mass:scaling}\n\\end{equation}\nIndeed the scaled process has the same kernel, continuity and terminal\nintegrability as the canonical process for $B^{(c)}$.\n\nFor natural parametrization, Lawler--Rezaei\n\\cite[Proposition~3.13]{LawlerRezaei2012Basic} prove conformal restart\nat an almost surely finite stopping time, simultaneously over later\ntime intervals.  We prove the following measure-valued identity for\nthe mass constructed here directly from its conditional kernel.\n\n\\begin{proposition}[Pathwise interior restart]\n\\label{mass:restart}\nFix a stopping time $S$.  On $\\{S<\\infty\\}$ put\n$J_S=(g_S-B_S)^{-1}:\\HH\\to H_S$, and let $\\widetilde\\mu$ be the canonical\nmass functional of the fresh Brownian motion $B_{S+t}-B_S$.\nAlmost surely, as measures on $H_S$, simultaneously for all\n$0\\le t\\le\\infty$,\n\\begin{equation}\n (\\mu_{S+t}-\\mu_S)|_{H_S}\n       =(J_S)_*\\bigl(|J_S'|^d\\widetilde\\mu_t\\bigr).\n \\label{mass:restart-identity}\n\\end{equation}\nThe same assertion holds when the initial domain is a fixed conformal\nimage of $\\HH$.  Once this equality of measures has been obtained,\none may integrate any random nonnegative Borel weight on the restart\ndomain and evaluate at any later random time.\n\\end{proposition}\n\n\\begin{proof}\nWrite $\\nu_t$ for the right side of \\eqref{mass:restart-identity} on\n$\\{S<\\infty\\}$, and set both processes in the comparison below to zero\non $\\{S=\\infty\\}$.  Take a countable collection of rational balls\n$U\\Subset\\HH$, and dense\ncountable families of nonnegative tests $\\phi\\in C_c(U)$, including local\ncutoffs.  Localize each comparison to\n\\[\n E_U=\\{S<\\infty,\\ \\overline U\\subset H_S\\}\\in\\mathcal F_S.\n\\]\nWe first check terminal integrability, before invoking uniqueness.\nConditioning on $\\mathcal F_S$, the strong Markov property, the canonical\nmean measure and change of variables give\n\\begin{equation}\n \\EE[\\ind_{E_U}\\nu_\\infty(\\phi)\\mid\\mathcal F_S]\n       =\\ind_{E_U}\\int\\phi(x)G_S(x)\\,\\dd A(x).\n \\label{mass:restart-integrability}\n\\end{equation}\nThe pulled-back test is random, with potentially random derivative and\nsupport bounds.  The formula remains valid: first use nonnegative simple\nfunctions of the restart data and source point, and then apply conditional\nTonelli and monotone convergence.  Its expectation is at most\n$\\int\\phi G_0\\,\\dd A<\\infty$.  Thus the candidate terminal test mass is\nintegrable.  The actual terminal increment is bounded by\n$\\mu_\\infty(\\phi)$ and is integrable as well.\n\nAt $S+t$, the candidate and actual increment have the same remaining\nconditional potential.  For the candidate, this follows from the\ncanonical kernel for the fresh driver, with its $\\mathcal F_S$-measurable\npulled-back test; the Loewner composition rule and\n\\eqref{mass:conformal} transform that potential into $G_{S+t}$.\nFor the actual process it is \\eqref{mass:kernel} at $S+t$.\nConsequently\n\\[\n D_t:=\\ind_{E_U}\\bigl[(\\mu_{S+t}-\\mu_S)(\\phi)-\\nu_t(\\phi)\\bigr]\n       =\\EE[D_\\infty\\mid\\mathcal F_{S+t}].\n\\]\nThis continuous finite-variation martingale starts at zero and is\nidentically zero.  Intersect the resulting events over the countable\nballs and tests, and use continuity to obtain equality for all times.\nThose balls whose closures lie in the realized $H_S$ cover it; their\ntests determine its locally finite measures.  This proves the pathwise\nmeasure equality throughout the random domain.\n\nOnly now do we integrate random weights.  In particular, if\n$F:\\DD\\to H_S$ is a random conformal map and $K\\Subset\\DD$, the Borel\nweight\n\\[\n \\ind_{F(K)}(x)\\,|F'(F^{-1}(x))|^{-d}\n\\]\nis bounded on its realized compact support.  Its integration follows\ndirectly from equality of the realized measures, without a further\nexceptional event indexed by $F$ or $K$.  The all-time equality similarly\npermits substitution of a later random time.  Composition with a fixed\ninitial identification proves the last domain assertion.\n\\end{proof}\n\nTo obtain the centered restart rule, first work under the ordinary law.\nFor a stopping time $S$, work on $\\{S<\\infty,\\ z\\in H_S\\}$ and write\n$g_S(z)-B_S=\\lambda e^{i\\theta}$, where $\\lambda>0$.\nConditionally on $\\mathcal F_S$, the rescaled fresh driver\n$\\lambda^{-1}(B_{S+\\lambda^2u}-B_S)$ is standard Brownian motion.\nWe may apply \\eqref{mass:scaling} at this random scale: after conditioning,\n$\\lambda$ is fixed, the fresh driver is independent of $\\mathcal F_S$,\nand the scaling equality holds almost surely at each fixed scale.\nJoint measurability of the mass functional permits this conditioning.\nTogether with Proposition~\\ref{mass:restart}, scaling identifies the\nactual future mass in these coordinates.\n\nNow apply the finite change of law through the next radius target of\n$z$.  The ratio of Green weights is conformally invariant, since the\nderivative at the marked point cancels between the two weights.\nLemma~\\ref{op:finite-tilt} therefore identifies, conditionally on the\nstopped past under the centered law, the future mass pulled back to a\ndisk centered at $z$ with the canonical disk law of starting angle\n$\\theta$.  This assertion uses only data through the finite test endpoint.\n\n\\begin{proposition}[Positive mass on every interval]\n\\label{mass:positive}\nAlmost surely, for every \\mbox{$0\\le v<t<\\infty$},\n\\[\n (\\mu_t-\\mu_v)(\\HH)>0.\n\\]\nThe positive mass can always be detected on an interior compact.  The\nsame statement holds in every fixed conformal image, in its associated\nhalf-plane time.\n\\end{proposition}\n\n\\begin{proof}\nEquation~\\eqref{mass:mean} gives positive terminal expectation on a\ncompact with nonempty interior.  Terminal exhaustion therefore gives\npositive probability of positive interior mass by some finite\ndeterministic time.  Scaling shows that\n$\\PP\\{\\mu_t(\\HH)>0\\}$ has the same positive value for every $t>0$.\nThe raw event $\\limsup_{n\\to\\infty}\\{\\mu_{1/n}(\\HH)>0\\}$ belongs\nto the Brownian germ sigma field. On the common event of monotonicity it\nequals $\\bigcap_{n\\ge1}\\{\\mu_{1/n}(\\HH)>0\\}$, so has this same\nprobability. The germ zero--one law\nmakes it one.  Hence positive mass is present before every positive time.\nRestart at each rational time and apply\nProposition~\\ref{mass:restart}.  The strictly positive conformal weight\npreserves positivity, giving positive increments on all rational\nintervals on one event.  Every nontrivial real interval contains a\nnontrivial rational interval.  A countable compact exhaustion detects\nthe positive increment mass; \\eqref{mass:conformal} proves the image-domain\nassertion.\n\\end{proof}\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/multipoint.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/multipoint.tex", "bytes": 18136, "sha256": "fe6bcfbaf38e29f78bdfd730398edb7c0bede38e02b1375ec4b9930951ad366a", "content": "\\section{A uniform estimate for several radius hits}\n\\label{mp:section}\n\nThe construction of a trace measure will require moments of every fixed\ninteger order. The necessary probability estimate allows each point to\nreach its own target at a different time. Its constant is uniform over\nthe physical domain, a feature that will later be essential for disk\nmodels with arbitrary boundary angles.\n\nMultipoint Green estimates and their moment consequences have a substantial\nhistory. Lawler--Werness \\cite[Theorems~1--2]{LawlerWerness2013}\nprove ordered two-point Green limits and unequal-radius approach estimates;\nRezaei--Zhan \\cite[Theorems~1.1--1.2]{RezaeiZhan2017} prove all-order\nEuclidean approach bounds and finite moments of natural content.\nThe estimate needed here is a consequence of those Euclidean bounds and\nKoebe's estimates; a comparison after the proof gives the deduction.\nWe give a direct proof in arbitrary physical domains using regularized\nproducts. Each completed point is frozen at its own hit, and a conformal\ninteraction potential controls the remaining factors. We then integrate\nthe collision kernel to obtain the required moments.\n\n\\begin{proposition}[Multipoint radius estimate]\\label{mp:estimate}\nLet $D=J(\\HH)$ as in Section~\\ref{op:section}, and let\n$x_1,\\ldots,x_m\\in D$ be distinct. Suppose\n\\[\n 0<b_i\\le R_D(x_i),\\qquad b_i\\le |x_i-x_j|\\quad(j\\ne i).\n\\]\nFor targets $0<e_i\\le b_i$, let $E$ be the event that each $x_i$\nreaches conformal radius $e_i$ while alive. Then\n\\begin{equation}\n \\PP(E)\\le C_{m,a}\\prod_{i=1}^m(e_i/b_i)^\\alpha.\n \\label{mp:hit-bound}\n\\end{equation}\nThe constant depends only on $m$ and $a$, independently of $D$, $J$,\nthe initial angles, the radii, and the order and times of the hits.\nA point need not remain alive after reaching its own target.\nFor targets exceeding some caps, the corresponding constraints may be\ndropped, giving, after enlarging $C_{m,a}$,\n\\begin{equation}\n \\PP(E)\\le C_{m,a}\n                \\prod_{i=1}^m\\min\\{1,(e_i/b_i)^\\alpha\\}.\n \\label{mp:capped-hit-bound}\n\\end{equation}\n\\end{proposition}\n\n\\subsection{Controlling the interaction of two active points}\n\nLet $\\sigma_i$ and $T_i$ be the first times when point $i$ reaches\nradius $b_i$ and $e_i$, respectively, while alive, with value infinity\nif the corresponding radius is never reached. The point is active on\n$[\\sigma_i,T_i)$ while it remains alive. Put\n\\[\n U_i=\\frac1{2a}\\log\\frac{b_i}{e_i},\\qquad\n u_i(t)=\\int_{\\sigma_i}^t V_i(v)^2\\,\\dd v\n\\]\non its active interval. Thus $R_t(x_i)=b_i e^{-2au_i(t)}$, and it\nfinishes when $u_i=U_i$. We first take $e_i<b_i$; constraints with\n$e_i=b_i$ may be dropped.\n\nFor two surviving points put\n\\[\n L_{ij}(t)=\\log\\left|\n       \\frac{Z_i(t)-\\overline{Z_j(t)}}{Z_i(t)-Z_j(t)}\\right|\\ge0.\n\\]\nThe common Brownian driver cancels in the two differences. Consequently\n\\begin{equation}\n \\frac{\\dd}{\\dd t}L_{ij}\n =\\operatorname{Re}\\left(-\\frac a{Z_i\\overline{Z_j}}\n                            +\\frac a{Z_iZ_j}\\right)\n =-2aV_iV_j.\n \\label{mp:interaction-derivative}\n\\end{equation}\nThis is also a conformal invariant: for a disk uniformization\n$F:\\DD\\to D_t$ with $F(0)=x_i$ and $F(w)=x_j$, it equals\n$-\\log|w|$. Write $\\rho=|w|$ and $L=-\\log\\rho$. The growth estimate\n\\eqref{op:koebe-growth} gives\n\\[\n \\frac{|x_i-x_j|}{R_t(x_i)}\n \\le\\frac{\\rho}{(1-\\rho)^2}\n =\\frac1{4\\sinh^2(L/2)}.\n\\]\nDuring joint activity, the separation assumption and\n$R_t(x_i)=b_i e^{-2au_i}$ imply\n\\[\n L_{ij}\\le2\\operatorname{arsinh}\\left(\n       \\frac12\\sqrt{\\frac{R_t(x_i)}{|x_i-x_j|}}\\right)\n \\le e^{-au_i}.\n\\]\nInterchanging the two points proves\n\\begin{equation}\n L_{ij}(t)\\le e^{-a\\max(u_i(t),u_j(t))}\n \\qquad\\text{during joint activity}.\n \\label{mp:interaction-decay}\n\\end{equation}\nIn particular, for $c\\ge0$ with $2c<a$,\n\\begin{equation}\n \\int_{\\text{joint activity}}\n       e^{c(u_i+u_j)}V_iV_j\\,\\dd t\n \\le I(a,c):=\\frac{e^{2c}}{2a(1-e^{2c-a})}.\n \\label{mp:interaction-integral}\n\\end{equation}\nTo see this, joint activity is an interval, and\n$v=\\max(u_i,u_j)$ increases on it. On its portion with\n$k\\le v<k+1$, the total decrease available to $L_{ij}$ is at most\n$e^{-ak}$, by \\eqref{mp:interaction-decay}. Equation~\\eqref{mp:interaction-derivative}\nbounds the unweighted integral there by $e^{-ak}/(2a)$, while the\nweight is at most $e^{2c(k+1)}$. Summing over $k\\ge0$ proves\n\\eqref{mp:interaction-integral}, also for any truncated part of the\njoint active interval. This argument is made in $D_t$ itself; no\nbound on the derivative of the initial map $J$ enters it.\n\n\\subsection{Regularized factors and the product supermartingale}\n\nThe factor $\\sin^p\\theta$ in the one-point martingale has no positive\nlower bound uniform in the angle at a target hit. We add a positive\nregularizing term at both ends of each active interval, so activation\nand completion have angle-uniform bounds. The added term decays away\nfrom those ends: this keeps its accumulated drift bounded independently\nof the interval length. The symmetric distance\n$\\min\\{u,U_i-u\\}$ records exactly that choice.\n\nThe exponent $q$ will supply negative angular drift. Young's Inequality\nwill introduce a regularization loss with exponent $M$, and we choose\nthe decay rate $\\beta$ so that the pair interaction can absorb that loss.\nUse the explicit values\n\\begin{equation}\n q=\\tfrac12\\min(1,p),\\qquad\n \\eta=\\frac{1-q}{2-q}\\in(0,\\tfrac12),\\qquad\n M=\\frac{1+\\eta}{1-2\\eta}=\\frac3q-2,\\qquad\n \\beta=\\min\\left(1,\\frac a{4M}\\right).\n \\label{mp:parameters}\n\\end{equation}\nThus $0<q<\\min(1,p)$ and $2M\\beta\\le a/2<a$. For an active point\ndefine\n\\begin{equation}\n \\delta_i(u)=e^{-\\beta\\min(u,U_i-u)},\\qquad\n f_i(u,\\theta)=\\sin^p\\theta+\\delta_i(u)(1+\\sin^q\\theta),\\qquad\n D_i=\\frac{\\delta_i}{f_i}\\sin^{q-2}\\theta_i.\n \\label{mp:factors}\n\\end{equation}\nThe factor for point $i$ is $1$ before activation,\n\\[\n Q_i(t)=\\frac13 e^{pu_i(t)/2}f_i(u_i(t),\\theta_i(t))\n\\]\nduring activity, and the constant $e^{pU_i/2}/3$ after completion.\nThe activation jump is downward because $f_i(0,\\theta)\\le3$;\nthe completion jump is downward because $f_i(U_i,\\theta)\\ge1$.\nAfter completion the point's factor is frozen, including if that point\nis swallowed later. Figure~\\ref{fig:individual-stops} illustrates the\nseparate endpoints.\n\\input{figures/individual-stops}\n\nPut $Q=\\prod_{i=1}^m Q_i$. If all points finish, this product has\nthe deterministic value\n\\[\n Q=3^{-m}\\prod_i(b_i/e_i)^\\alpha.\n\\]\nThus a uniform bound on the expectation of a discounted product will\ngive the desired probability estimate, provided the total discount is\ndeterministically bounded. The individual negative drifts absorb\nthe singular angular terms, and the pair interaction estimate pays for\nwhat remains. We give both estimates explicitly. Write\n$s=\\sin\\theta$, $k=q(p-q)/2$, and $\\sigma=\\delta'/\\delta$, so\n$|\\sigma|\\le\\beta\\le1$ almost everywhere. Formula~\\eqref{op:sine-generator}\ngives\n\\begin{equation}\n \\frac{(\\partial_u+\\mathcal L+p/2)f}{f}\n =\\frac\\delta f\\left[\n \\frac p2+\\frac{(p-q)(1+q)}2s^q-ks^{q-2}\n                         +\\sigma(1+s^q)\\right].\n \\label{mp:exact-drift}\n\\end{equation}\nLet\n\\[\n A=\\frac p2+\\frac{(p-q)(1+q)}2+2,\n \\qquad h_0=\\left(\\frac{k}{2A}\\right)^{1/(2-q)}\\in(0,1).\n\\]\nThe bracket in \\eqref{mp:exact-drift} is at most $A-ks^{q-2}$.\nFor $s<h_0$, adding $(k/2)D$ leaves a nonpositive expression.\nFor $s\\ge h_0$, use $f\\ge s^p\\ge h_0^p$. We obtain\n\\begin{equation}\n \\frac{(\\partial_u+\\mathcal L+p/2)f}{f}\n \\le C_1\\delta-c_1D,\n \\qquad C_1=Ah_0^{-p},\\quad c_1=k/2>0.\n \\label{mp:drift-bound}\n\\end{equation}\nAll these constants depend only on $a$. The corner of $\\delta$ at\n$U_i/2$ causes no additional term: it is an absolutely continuous\nfunction of the finite-variation clock $u_i$.\n\nBecause $q<p$, $q<1$, and $0<\\delta\\le1$, angular differentiation\ngives $|\\partial_\\theta f|\\le(p+q)s^{q-1}$. Also $\\delta\\le f\\le3$,\nand $\\eta(q-2)=q-1$. Therefore\n\\begin{equation}\n \\frac{|\\partial_\\theta f|}{f}\n \\le(p+q)\\delta^{-1}s^{q-1}\n \\le(p+q)3^\\eta\\delta^{-1-\\eta}D^\\eta.\n \\label{mp:derivative-bound}\n\\end{equation}\nThis bound includes the singular angular behavior when $p<1$.\n\nOn active intervals, the normalized\nBrownian coefficient of $Q_i$ is\n$(\\partial_\\theta f_i/f_i)V_i$. It\\^o's Product Formula thus gives\none cross term for each unordered active pair. Its absolute value is\nbounded, by \\eqref{mp:derivative-bound}, by\n\\[\n C_2(\\delta_i\\delta_j)^{-1-\\eta}\n    (D_iV_i^2)^\\eta(D_jV_j^2)^\\eta(V_iV_j)^{1-2\\eta}.\n\\]\nWeighted Young's Inequality with weights $\\eta,\\eta,1-2\\eta$ shows\nthat for any $\\epsilon>0$ this is at most\n\\begin{equation}\n \\epsilon(D_iV_i^2+D_jV_j^2)\n       +C_\\epsilon(\\delta_i\\delta_j)^{-M}V_iV_j.\n \\label{mp:young}\n\\end{equation}\nIndeed, putting $r=1-2\\eta$, the weighted arithmetic--geometric\nmean inequality gives the displayed bound with\n$C_\\epsilon=rC_2^{1/r}(\\eta/\\epsilon)^{2\\eta/r}$, and raises\n$\\delta_i\\delta_j$ to exponent $-(1+\\eta)/r=-M$.\nFor $m>1$ choose $\\epsilon=c_1/[2(m-1)]$; for $m=1$ there are no\ncross terms. Summing \\eqref{mp:drift-bound} and \\eqref{mp:young}\nabsorbs the pair contributions into the negative terms. A nonnegative\nupper bound for the remaining normalized physical-time drift is\n\\[\n k_t=C_1\\sum_{i\\text{ active}}\\delta_iV_i^2\n       +C_3\\sum_{\\substack{i<j\\\\i,j\\text{ active}}}\n                     (\\delta_i\\delta_j)^{-M}V_iV_j,\n\\]\nwhere $C_3$ depends only on $m,a$ (and the pair sum is zero for $m=1$).\nIts total integral has a deterministic bound. Namely,\n\\[\n \\int_0^{U_i}\\delta_i(u)\\,\\dd u\n =\\frac2\\beta(1-e^{-\\beta U_i/2})\\le\\frac2\\beta,\n \\qquad\n (\\delta_i\\delta_j)^{-M}\\le e^{M\\beta(u_i+u_j)}.\n\\]\nEquation~\\eqref{mp:interaction-integral}, with $c=M\\beta$, yields\n\\begin{equation}\n \\int k_t\\,\\dd t\\le\n K_{m,a}:=\\frac{2mC_1}{\\beta}\n             +C_3\\binom m2 I(a,M\\beta)<\\infty.\n \\label{mp:discount}\n\\end{equation}\nIt follows that\n$Q_t\\exp(-\\int_0^t k_v\\,\\dd v)$ is a nonnegative local\nsupermartingale on every nonsingular interval, including its downward\nactivation and completion jumps.\n\n\\begin{proof}[Completion of the proof of Proposition~\\ref{mp:estimate}]\nTo justify stopping the product, monitor \\emph{all unfinished points},\nincluding those not yet active. Stop if any of their angles leaves\n$(1/N,\\pi-1/N)$, if any of their heights $Y_i$ falls to $1/N$, or at\nphysical time $N$. Denote this cutoff by $\\zeta_N$, and also stop when\nall targets have been completed. No cutoff is imposed on a completed\npoint. At these stops every stochastic integral used above is\nlegitimate; a further standard bounded localizer, followed by Fatou,\ngives expectation at most the initial value, which is at most $1$.\nThere is no need to continue a factor through the singular lifetime of\nan unsuccessful unfinished point.\n\nPut $T_{\\rm all}=\\max_iT_i$. On $\\{T_{\\rm all}<\\zeta_N\\}$, the final\ndiscounted product is at least\n\\[\n e^{-K_{m,a}}3^{-m}\\exp\\left(\\frac p2\\sum_iU_i\\right).\n\\]\nConsequently\n\\[\n \\PP(T_{\\rm all}<\\zeta_N)\n \\le 3^m e^{K_{m,a}}\n                   \\exp\\left(-\\frac p2\\sum_iU_i\\right).\n\\]\nEvery successful path belongs to these events for all sufficiently\nlarge $N$. Indeed, each point remains alive through its own finite\n$T_i$, so its continuous height and its distance in angle from the\nendpoints have strictly positive minima on $[0,T_i]$. There are only\nfinitely many points, and later swallowing of a finished point is\nirrelevant. Thus cutoff exhaustion covers precisely all the success\npaths needed for the upper bound. Since $p/(4a)=\\alpha$, passage to\nthe limit gives \\eqref{mp:hit-bound} with\n$C_{m,a}=3^m e^{K_{m,a}}$ when all $e_i<b_i$.\n\nFor equality targets, or targets larger than their caps, retain only\nthe strict constraints $e_i<b_i$. The original event is contained in\nthe retained event, and all separation assumptions still hold.\nTaking the maximum of the constants for at most $m$ retained points,\nincluding the empty set, proves \\eqref{mp:capped-hit-bound} and the\nfull stated proposition.\n\\end{proof}\n\n\\paragraph{Comparison with the Euclidean estimate.}\nTo deduce the proposition from Rezaei--Zhan, put $f=J^{-1}$,\n$z_i=f(x_i)$, $\\lambda_i=|f'(x_i)|$, and $z_0=0$.\nKoebe's Quarter Theorem applied to $f$ on $B(x_i,b_i/4)\\subset D$\ngives\n\\begin{align*}\n |z_i-z_j|&\\ge\\lambda_i b_i/16\n                   &&(1\\le j\\le m,\\ j\\ne i),\\\\\n \\operatorname{Im}z_i\n &=\\lambda_iR_D(x_i)/2\\ge\\lambda_i b_i/2.\n\\end{align*}\nIndeed, $f(B(x_i,b_i/4))$ contains the disk with center $z_i$ and\nradius $\\lambda_i b_i/16$, whereas every other $x_j$ lies outside\n$B(x_i,b_i/4)$. Retain only targets with $e_i<b_i/32$ and relabel\nthe retained points; if none remain, the trivial probability bound\nsuffices. Their preceding-point distances satisfy\n$l_i=\\min_{0\\le j<i}|z_i-z_j|\\ge\\lambda_i b_i/16$.\nAt a successful hit, radius covariance gives\n$R_{H_t}(z_i)=\\lambda_i e_i$. Koebe's distance\nbound then puts the past half-plane trace within\n$r_i=\\lambda_i e_i$ of $z_i$: the real line is farther away.\nTheorem~1.1 and equation~(1.5) of \\cite{RezaeiZhan2017} give\n\\[\n \\PP\\{\\dist(z_i,\\gamma)\\le r_i\\text{ for all retained }i\\}\n \\le C_{m,a}\\prod_{i\\text{ retained}}(r_i/l_i)^\\alpha\n \\le C_{m,a}16^{m\\alpha}\n             \\prod_{i\\text{ retained}}(e_i/b_i)^\\alpha.\n\\]\nEach omitted target contributes at least $32^{-\\alpha}$ to the capped\nproduct, so it can\nbe restored by enlarging the constant. This proves the stated\ndomain-uniform consequence without a boundary extension of $J$.\n\n\\subsection{Integrating the collision singularities}\n\n\\begin{lemma}[Capped distance kernels]\\label{mp:kernel-integral}\nLet $0<\\alpha<1$, $b>0$, and let $K\\subset\\mathbb C$ be a bounded Borel set.\nFor distinct $x_1,\\ldots,x_m$ set\n\\[\n b_i=\\min\\bigl(b,\\min_{j\\ne i}|x_i-x_j|\\bigr),\\qquad\n F_b(x_1,\\ldots,x_m)=\\prod_{i=1}^m b_i^{-\\alpha},\n\\]\nwhere the inner minimum is omitted when $m=1$. Then\n\\begin{equation}\n \\int_{K^m}F_b(x_1,\\ldots,x_m)\\prod_i\\dd A(x_i)<\\infty.\n \\label{mp:all-variable-integral}\n\\end{equation}\nThe same assertion holds with one point fixed: for bounded $K_0$,\n\\begin{equation}\n \\sup_{x_0\\in K_0}\\int_{K^m}\n F_b(x_0,x_1,\\ldots,x_m)\\prod_{i=1}^m\\dd A(x_i)<\\infty.\n \\label{mp:fixed-center-integral}\n\\end{equation}\nValues on diagonals may be set to infinity; they do not affect these\nintegrals.\n\\end{lemma}\n\n\\begin{proof}\nFor each factor,\n$b_i^{-\\alpha}\\le b^{-\\alpha}+\\sum_{j\\ne i}|x_i-x_j|^{-\\alpha}$.\nExpand their product. Each resulting term corresponds to a directed\ngraph with at most one outgoing edge per vertex, no self-edge, and a\nfactor $|x_i-x_j|^{-\\alpha}$ for each edge $i\\to j$.\nIntegrate a vertex with no incoming edge: if it has one outgoing\nedge its integral is bounded uniformly in the other endpoint because\n$\\alpha<2$; if isolated it contributes only the area of a containing\nbox. Repeating removes all trees attached to directed cycles and all\ncomponents without cycles.\n\nFor a remaining cycle of length at least three, one vertex has two\nincident factors. Their integral is uniformly bounded by\n\\[\n \\int_K|x-y|^{-\\alpha}|x-z|^{-\\alpha}\\,\\dd A(x)\n \\le\\left(\\int_K|x-y|^{-2\\alpha}\\,\\dd A(x)\\right)^{1/2}\n      \\left(\\int_K|x-z|^{-2\\alpha}\\,\\dd A(x)\\right)^{1/2}\n \\le C_K,\n\\]\nsince $2\\alpha<2$. Removing this vertex leaves a chain, whose\nendpoints can be integrated successively. A two-cycle contributes\n$|x-y|^{-2\\alpha}$, so the same integrability applies directly.\nThere are finitely many graph terms, proving\n\\eqref{mp:all-variable-integral}.\n\nFor the fixed point, choose $R<\\infty$ such that\n$K-x_0\\subset B(0,R)$ for every $x_0\\in K_0$. The kernel $F_b$ is\ninvariant under simultaneous translation, with the cap $b$ unchanged.\nThus the integral in \\eqref{mp:fixed-center-integral} is bounded by\n\\[\n \\int_{B(0,R)^m}F_b(0,y_1,\\ldots,y_m)\\prod_i\\dd A(y_i).\n\\]\nAverage a simultaneous translation by $v\\in B(0,1)$. A change of\nvariables bounds the last display by\n\\[\n \\frac1{|B(0,1)|}\n \\int_{B(0,R+1)^{m+1}}F_b(v,x_1,\\ldots,x_m)\n                       \\,\\dd A(v)\\prod_i\\dd A(x_i),\n\\]\nwhich is finite by the already proved assertion. This is translation\ninvariance of a deterministic distance kernel; no translation\ninvariance of a chordal SLE law is required.\n\\end{proof}\n\nWe record precisely the moment consequence used in the next sections.\nFor $l>0$ let\n\\[\n A_l(\\dd x)=G_{\\tau_l(x)}(x)\n                       \\ind_{\\{\\tau_l(x)<\\infty\\}}\\,\\dd A(x).\n\\]\n\n\\begin{corollary}[Integrated level weights]\\label{mp:level-moments}\nLet $f\\ge0$ be bounded and supported in a compact $K\\Subset D$.\nThen, for every integer $m\\ge1$,\n\\begin{equation}\n \\sup_{l>0}\\EE[A_l(f)^m]<\\infty.\n \\label{mp:ordinary-moments}\n\\end{equation}\nFix also $x_0\\in D$ and $0<e_0\\le R_D(x_0)$, and let $E_0$ be the\nevent that $x_0$ reaches radius $e_0$ while alive. Then\n\\begin{equation}\n \\sup_{l>0}\\EE[\\ind_{E_0} A_l(f)^m]\\le C e_0^\\alpha.\n \\label{mp:center-moments}\n\\end{equation}\nHere $C$ can be chosen using only $m,a$, the size and support of $f$,\nthe bounded location of $x_0$, and a positive lower bound for\n$R_D$ on $K\\cup\\{x_0\\}$. In particular, in a fixed disk these bounds\nare uniform over all initial boundary angles.\n\\end{corollary}\n\n\\begin{proof}\nChoose a fixed cap $b>0$ below the indicated initial radii, and cap\nit further by nearest-neighbor distances for each integration tuple.\nAt point $x_i$, the target is $e_i=R_D(x_i)e^{-2al}$, and\n$G_{\\tau_l(x_i)}(x_i)\\le e_i^{-\\alpha}$ on success. Tonelli and\n\\eqref{mp:capped-hit-bound} therefore bound the $m$th moment by\n\\[\n C_{m,a}\\int_{K^m}\\prod_{i=1}^m f(x_i)b_i^{-\\alpha}\n                                                 \\prod_i\\dd A(x_i),\n\\]\nbecause $e_i^{-\\alpha}\\min\\{1,(e_i/b_i)^\\alpha\\}\\le b_i^{-\\alpha}$.\nThis proves \\eqref{mp:ordinary-moments} by\nLemma~\\ref{mp:kernel-integral}. For the additional fixed center, apply\nthe same argument to $m+1$ points and use\n\\[\n \\min\\{1,(e_0/b_0)^\\alpha\\}\\le e_0^\\alpha b_0^{-\\alpha}.\n\\]\nThe fixed-center integral proves \\eqref{mp:center-moments}, even\nwhen the center's target exceeds its distance cap. No relative order\nof the center hit and the other hits is imposed. Finally, in $\\DD$\none has $R_D(x)=1-|x|^2$, independently of its boundary marks. The\nproof was carried out directly in the physical domain, so a\ndegenerating half-plane identification introduces no additional\nconstant.\n\\end{proof}\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/onepoint.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/onepoint.tex", "bytes": 15466, "sha256": "07d7e728c1e8ce0126f3175b1c1b428023298e64a10114f60aebc3721986d5ec", "content": "\\section{Conformal radius and the law centered at one point}\n\\label{op:section}\n\nOur first goal is a finite change of law toward one interior point.\nThe same calculation will supply a conditional Green potential, uniform\nangle mixing, and a finite physical lifetime. These are the inputs for\nboth the mass construction and the eventual tests at successive radii.\n\nFix $0<\\kappa<8$ throughout, and put\n\\begin{equation}\n a=\\frac2\\kappa>\\frac14,\\qquad d=1+\\frac1{4a},\\qquad\n \\alpha=2-d\\in(0,1),\\qquad p=4a-1>0.\n \\label{op:parameters}\n\\end{equation}\nIn particular, $2a\\alpha=p/2$. If $t_{\\rm cap}$ denotes the\ncapacity-$2t_{\\rm cap}$ time of Theorem~\\ref{thm:main}, put\n$u=\\kappa t_{\\rm cap}$ and\n$B^{\\rm int}_u=\\sqrt\\kappa B^{\\rm cap}_{u/\\kappa}$.\nThis driver is standard Brownian motion. Writing $t$ for the internal\ntime $u$ and $B$ for $B^{\\rm int}$ below, the equation becomes\n\\begin{equation}\n \\partial_t g_t(z)=\\frac{a}{g_t(z)-B_t},\\qquad g_0(z)=z,\n \\label{op:loewner}\n\\end{equation}\nwith a standard Brownian driver and half-plane capacity $at$.\nThe original normalization and continuous trace generation are given by\nRohde--Schramm \\cite[Section~2.1 and Theorem~5.1]{RohdeSchramm2005}.\nA constant change of time preserves the\nassertion about all nontrivial finite time intervals.\n\nWe first specify the geometric and probabilistic conventions. Write $H_t$\nfor the survival domain of \\eqref{op:loewner}. It is the unbounded component\nof $\\HH\\setminus\\gamma([0,t])$, and $g_t:H_t\\to\\HH$ is conformal.\nMoreover,\n\\begin{equation}\n \\gamma([t,\\infty))\\subset\\overline{H_t}.\n \\label{op:future-trace}\n\\end{equation}\nThese are the filled-hull conventions for a Loewner evolution generated\nby a continuous trace. The domain identification is part of\nRohde--Schramm \\cite[Theorem~5.1]{RohdeSchramm2005}, including when\nthe trace has self-touches. To obtain \\eqref{op:future-trace}, for\n$u\\ge t$ and $\\varepsilon>0$ observe that\n$g_u^{-1}(B_u+i\\varepsilon)\\in H_u\\subset H_t$.\nThe inverse-map definition of $\\gamma(u)$ and the limit\n$\\varepsilon\\downarrow0$ give the asserted containment.\n\nWe also work in a fixed simply connected proper domain\n$D=J(\\HH)$, where $J:\\HH\\to D$ is conformal, and set $D_t=J(H_t)$.\nOnly the interior domain evolution is needed for an arbitrary $J$;\nno boundary extension of $J$ is being assumed. At a finite stopping time\n$S$, the map\n\\[\n J_S=J\\circ(g_S-B_S)^{-1}:\\HH\\longrightarrow D_S\n\\]\nidentifies the remaining domain with a fresh copy of the half-plane.\nContinuing the ODE gives the composition rule, and the strong Markov\nproperty gives the fresh driver $B_{S+t}-B_S$.\n\nFor a simply connected domain $U$ and $x\\in U$, our conformal radius is\n$R_U(x)=|F'(0)|$, where $F:\\DD\\to U$ is conformal with $F(0)=x$.\nWe use the classical Koebe estimates in this convention\n\\cite[Theorem~3.2.5, Corollary~3.2.6, and Theorems~3.2.9\nand~3.2.11]{Beliaev2015Notes}:\n\\begin{align}\n R_U(x)/4&\\le\\operatorname{dist}(x,\\partial U)\\le R_U(x),\n \\label{op:koebe-distance}\\\\\n R_U(x)\\frac{r}{(1+r)^2}\n &\\le |F(w)-x|\\le R_U(x)\\frac{r}{(1-r)^2},\n \\quad |w|=r<1, \\label{op:koebe-growth}\\\\\n R_U(x)\\frac{1-r}{(1+r)^3}\n &\\le |F'(w)|\\le R_U(x)\\frac{1+r}{(1-r)^3},\n \\quad |w|\\le r<1. \\label{op:koebe-derivative}\n\\end{align}\n\nAll path functionals used under a changed law are taken on the canonical\ncontinuous-driver space with its \\emph{raw} natural filtration\n$(\\mathcal F_t)$. Usual augmentations may be used for stochastic-calculus\narguments under the original Wiener law, followed by raw versions of the\nresulting adapted quantities. The centered laws below are laws of finite\nstopped data. Their consistent limits do not assert absolute continuity\non the original completed infinite-future sigma field. In particular,\nlater adapted mass functionals will be evaluated using one raw\nnonanticipating version, rather than choosing separate versions for\ndifferent centers.\n\n\\subsection{The local martingale and finite stopped laws}\n\nThe finite-radius Green tilt and its centered angular diffusion are\nstandard in the study of two-sided radial SLE; see\nLawler--Zhou \\cite[Section~2.1]{LawlerZhou2013} and\nLawler--Rezaei \\cite[Section~4.2, equations~(38)--(40)]{LawlerRezaei2015}.\nWe give the cutoff argument and uniform mixing proof in the present\nfull-conformal-radius normalization, with all changes of law made at\nfinite stopped levels.\n\nFix $x\\in D$, put $z=J^{-1}(x)$, and let $T_x$ be its lifetime. Until\n$T_x$, write\n\\[\n Z_t=X_t+iY_t=g_t(z)-B_t,\\qquad\n \\theta_t=\\arg Z_t,\\qquad V_t=\\frac{Y_t}{|Z_t|^2},\\qquad\n s_x(t)=\\int_0^t V_u^2\\,\\dd u.\n\\]\nSet $R_t(x)=R_{D_t}(x)$. Conformal covariance of radius gives\n\\[\n R_t(x)=\\frac{2Y_t|J'(z)|}{|g_t'(z)|}.\n\\]\nSince $\\dd Z_t=aZ_t^{-1}\\dd t-\\dd B_t$, differentiation of this\nformula and It\\^o's Formula for $\\log Z_t$ give\n\\begin{align}\n \\dd\\log R_t(x)&=-2aV_t^2\\,\\dd t,\n &R_t(x)&=R_0(x)e^{-2as_x(t)},\\label{op:radius-clock}\\\\\n \\dd\\theta_t&=V_t\\,\\dd B_t+(1-2a)\\cot\\theta_t\\,V_t^2\\,\\dd t.\n \\label{op:angle-sde}\n\\end{align}\nThus the angle in inner time has generator\n\\[\n \\mathcal L=\\frac12\\partial_\\theta^2\n                +(1-2a)\\cot\\theta\\,\\partial_\\theta.\n\\]\nFor every real $r$, direct differentiation yields\n\\begin{equation}\n \\frac{\\mathcal L\\sin^r\\theta}{\\sin^r\\theta}\n =-\\frac r2+\\frac{r(r-p)}2\\cot^2\\theta.\n \\label{op:sine-generator}\n\\end{equation}\nConsequently\n\\begin{equation}\n G_t(x)=R_t(x)^{-\\alpha}\\sin^p\\theta_t(x)\n \\quad (x\\in D_t),\\qquad G_t(x)=0\\quad(x\\notin D_t)\n \\label{op:green}\n\\end{equation}\nis a local martingale during survival, with stochastic differential\n$\\dd G_t=G_t p\\cot\\theta_t V_t\\,\\dd B_t$.\n\nFor $s\\ge0$, let $\\tau_s(x)$ be the time at which the radius first\nattains $R_0(x)e^{-2as}$ while $x$ is alive; it is infinity if this\nnever occurs. This is the inverse inner clock on successful paths.\n\n\\begin{lemma}[Finite stopped change of law]\\label{op:finite-tilt}\nFor every finite $s\\ge0$, the nonnegative weight\n\\begin{equation}\n M_s(x)=\\frac{G_{\\tau_s(x)}(x)}{G_0(x)}\n                 \\ind_{\\{\\tau_s(x)<\\infty\\}}\n \\label{op:finite-density}\n\\end{equation}\nhas expectation one. It defines a law $\\PP_x^*$ on the path stopped at\n$\\tau_s(x)$, consistently as $s$ increases. Under these laws, the angle\nin inner time solves\n\\begin{equation}\n \\dd\\theta_s=\\dd W_s+2a\\cot\\theta_s\\,\\dd s,\n \\qquad\n \\mathcal L^*=\\tfrac12\\partial_\\theta^2\n                       +2a\\cot\\theta\\,\\partial_\\theta,\n \\label{op:tilted-angle}\n\\end{equation}\nand neither endpoint is reached in finite inner time.\n\nFor an arbitrary stopping time $S$, on $\\{S<\\infty\\}$ one has\n\\begin{equation}\n \\EE\\!\\left[G_{\\tau_s(x)}(x)\n       \\ind_{\\{\\tau_s(x)<\\infty,\\ S<\\tau_s(x)\\}}\n                  \\mid\\mathcal F_S\\right]\n =G_S(x)\\ind_{\\{x\\in D_S,\\ s_x(S)<s\\}}.\n \\label{op:conditional-weight}\n\\end{equation}\nConditionally on such a surviving state, the remainder of the centered\nlaw is the finite stopped change of law in the domain $D_S$, with\nremaining inner duration $s-s_x(S)$. Indeed,\n\\[\n R_S(x)e^{-2a(s-s_x(S))}=R_0(x)e^{-2as},\n\\]\nso the restarted target is the original radius level.\n\\end{lemma}\n\n\\begin{proof}\nLocalize first by stopping when the angle exits $(1/N,\\pi-1/N)$,\nwhen $Y$ falls below $1/N$, or when physical time reaches $N$.\nWrite $\\zeta_N$ for this cutoff and $\\PP_x^{*,N}$ for its changed law.\nThe stochastic coefficient of $G/G_0$ is bounded on this stopped\ninterval. Girsanov and \\eqref{op:angle-sde} change the inner-time drift\nfrom $(1-2a)\\cot\\theta$ to $(1-2a+p)\\cot\\theta=2a\\cot\\theta$.\n\nWe verify that removing the cutoffs loses no probability at any finite\ninner horizon. For $0<q<p$, set $F_q(\\theta)=\\sin^{-q}\\theta$.\nThe new generator satisfies\n\\begin{equation}\n \\mathcal L^*F_q\n =F_q\\left(\\frac q2-\\frac{q(p-q)}2\\cot^2\\theta\\right)\n \\le\\frac q2 F_q.\n \\label{op:lyapunov}\n\\end{equation}\nThe locally defined diffusion \\eqref{op:tilted-angle} therefore obeys,\nfor its first exit $\\sigma_\\delta$ from $(\\delta,\\pi-\\delta)$,\n\\[\n \\PP^*_{\\theta_0}(\\sigma_\\delta\\le s)\n \\le e^{qs/2}F_q(\\theta_0)\\sin^q\\delta.\n\\]\nThis follows by stopping the nonnegative local supermartingale\n$e^{-qu/2}F_q(\\theta_u)$. The bound tends to zero and proves global\nexistence in inner time and endpoint nonattainment.\n\nThe physical clock cannot obstruct this argument. Along every compact\ninner interval on which the angle stays in $(0,\\pi)$,\n\\begin{equation}\n \\frac{\\dd}{\\dd s}\\log Y_s=-a\\sin^{-2}\\theta_s,\n \\qquad\n \\frac{\\dd t}{\\dd s}=Y_s^2\\sin^{-4}\\theta_s.\n \\label{op:physical-clock}\n\\end{equation}\nThe first equation follows from $\\dd Y_t=-aY_t|Z_t|^{-2}\\dd t$;\nthe second is $V_t^{-2}$, using $V_t=\\sin^2\\theta_t/Y_t$.\nThese formulas keep $Y$ strictly positive and physical time finite on\nthat compact interval. Thus, for $E_N=\\{\\tau_s<\\zeta_N\\}$, the\nlocalized changed laws satisfy $\\PP_x^{*,N}(E_N)\\to1$.\nOn $E_N$ the localized density at the target is exactly $M_s$, so\n\\[\n \\EE[M_s\\ind_{E_N}]=\\PP_x^{*,N}(E_N)\\longrightarrow1.\n\\]\nUnder the original law, $E_N$ increases to $\\{\\tau_s<\\infty\\}$:\na point alive through its finite target time has positive height and\nangle minima on that compact interval. Monotone convergence therefore\ngives $\\EE M_s=1$. This calculation uses the localized density only\nbefore the cutoff; it does not continue the weight through a lifetime.\n\nRepeating this calculation after $S$, using the Loewner composition\nrule and strong Markov property, gives \\eqref{op:conditional-weight}.\nIf the point is already dead, or the level has already been reached,\nboth sides are zero. Applying the same calculation between two finite\ninner levels proves consistency and the asserted conditional restart.\nOnly stopped paths through a finite inner level enter any of these\nchanges of law.\n\\end{proof}\n\n\\subsection{Uniform mixing and the remaining potential}\n\nThe stationary density and uniform mixing are also described in\n\\cite[Section~4.2]{LawlerRezaei2015}. The orthogonal-polynomial\nproof has a direct counterpart in\nZhan \\cite[Proposition~4.2]{Zhan2019Optimal}. Writing $X$ for the\ndiffusion in that proposition, the substitution $\\theta_s=X_{4s/\\kappa}/2$\ngives our centered diffusion. We prove mixing uniformly over the initial\nangle and give the finite-lifetime estimates needed for the subsequent\npotential identity.\n\n\\begin{lemma}[Uniform angle mixing]\\label{op:mixing}\nLet\n\\begin{equation}\n \\pi_a(\\dd\\theta)=c_a\\sin^{4a}\\theta\\,\\dd\\theta,\n \\qquad c_a^{-1}=\\int_0^\\pi\\sin^{4a}\\theta\\,\\dd\\theta.\n \\label{op:stationary}\n\\end{equation}\nThere is a deterministic $s_a<\\infty$ such that, for all $s\\ge s_a$\nand every initial angle in $(0,\\pi)$, the transition law of\n\\eqref{op:tilted-angle} has density between $1/2$ and $2$ relative to\n$\\pi_a$. In particular, after such a gap the conditional expectation\nof any nonnegative angle function lies between one half and twice its\n$\\pi_a$ integral. Moreover,\n\\begin{equation}\n \\int_0^\\pi\\sin^{-p}\\theta\\,\\pi_a(\\dd\\theta)\n =c_a\\int_0^\\pi\\sin\\theta\\,\\dd\\theta<\\infty.\n \\label{op:inverse-angle}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nFor $v=\\cos\\theta$, the generator and candidate invariant probability\nmeasure become\n\\[\n \\mathcal A=\\frac{1-v^2}{2}\\partial_v^2\n              -\\left(\\frac12+2a\\right)v\\partial_v,\n \\qquad \\varpi_a(\\dd v)=\\widetilde c_a\n              (1-v^2)^{2a-1/2}\\,\\dd v.\n\\]\nIntegration by parts makes $\\mathcal A$ symmetric on polynomials; the\nboundary term vanishes because $(1-v^2)^{2a+1/2}$ tends to zero.\nLet $P_m$ be the orthonormal polynomials for $\\varpi_a$, with $P_0=1$.\nThe operator preserves degree and acts on the leading degree-$m$\ncoefficient by $-\\lambda_m$, where\n$\\lambda_m=m(m+4a)/2$. Symmetry and orthogonality therefore give\n$\\mathcal AP_m=-\\lambda_mP_m$.\n\nHere is a sufficient elementary bound on these polynomials. Choose\n$m+1$ disjoint intervals of length comparable to $1/(m+1)$, separated\nby the same order, in $[-1/2,1/2]$. The density of $\\varpi_a$ is bounded\nbelow there. The unit $L^2$ norm supplies in each interval a point\nwhere $|P_m|\\le C_a\\sqrt{m+1}$. Lagrange interpolation at these points,\nwhose pairwise separation is at least $c/(m+1)$, gives\n\\[\n \\|P_m\\|_{\\infty,[-1,1]}\n \\le \\exp\\{C_a(m+1)\\log(m+2)\\}.\n\\]\nConsequently, for every $s>0$ the series\n\\[\n k_s(v,w)=1+\\sum_{m\\ge1}e^{-s\\lambda_m}P_m(v)P_m(w)\n\\]\nconverges absolutely and uniformly in $(v,w)\\in[-1,1]^2$.\nIt\\^o's Formula for bounded polynomials gives\n$\\EE_vP_m(v_s)=e^{-s\\lambda_m}P_m(v)$, so the actual transition\nprobability and $k_s(v,w)\\varpi_a(\\dd w)$ have identical polynomial\nmoments. Polynomial density in $C([-1,1])$ identifies the measures;\nthis also proves that the displayed kernel represents a nonnegative\nmeasure. Its nonconstant part tends uniformly to zero as\n$s\\to\\infty$, proving the bounds. The conditional assertion follows\nfrom the Markov property. Finally $4a-p=1$ proves\n\\eqref{op:inverse-angle}.\n\\end{proof}\n\n\\begin{proposition}[Finite lifetime and tested potential identity]\n\\label{op:potential}\nUnder the consistent centered laws,\n$T^*(x)=\\lim_{s\\to\\infty}\\tau_s(x)$ is finite almost surely.\nFor every finite deterministic $t$ and bounded raw\n$\\mathcal F_t$-measurable random variable $H$,\n\\begin{equation}\n \\EE[H G_t(x)]\n =G_0(x)\\EE_x^*[H\\ind_{\\{t<T^*(x)\\}}].\n \\label{op:tested-potential}\n\\end{equation}\nOn the right, $H$ is evaluated only on stopped data extending beyond\n$t$. Thus\n\\begin{equation}\n \\EE G_t(x)=G_0(x)\\PP_x^*(T^*(x)>t)\\longrightarrow0\n \\quad(t\\to\\infty).\n \\label{op:potential-decay}\n\\end{equation}\nFor every stopping time $S$,\n\\begin{equation}\n \\EE[G_S(x)\\ind_{\\{S<\\infty\\}}]\\le G_0(x).\n \\label{op:optional-bound}\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nChoose $0<q<p$ and $\\varepsilon\\in(0,a/2)$. Lemma~\\ref{op:mixing}\ngives a uniform bound on $\\EE_x^*F_q(\\theta_m)$ for all sufficiently\nlarge integers $m$, since $F_q$ is integrable against $\\pi_a$.\nThe nonnegative supermartingale from \\eqref{op:lyapunov} and its\nmaximal inequality imply\n\\[\n \\PP_x^*\\!\\left(\\sup_{m\\le s\\le m+1}F_q(\\theta_s)\n                      >e^{q\\varepsilon m}\\right)\n \\le C e^{-q\\varepsilon m}.\n\\]\nBorel--Cantelli shows that eventually\n$\\sin^{-4}\\theta_s\\le e^{4\\varepsilon s}$.\nEquation~\\eqref{op:physical-clock} also gives $Y_s\\le Y_0e^{-as}$.\nTherefore\n\\[\n T^*(x)=\\int_0^\\infty Y_s^2\\sin^{-4}\\theta_s\\,\\dd s<\\infty.\n\\]\nThe integral over each initial compact interval is finite by endpoint\nnonattainment; the remaining integrand is bounded by a constant times\n$e^{-(2a-4\\varepsilon)s}$.\n\nFor a finite inner horizon $l$, Lemma~\\ref{op:finite-tilt} gives\n\\[\n G_0(x)\\EE_x^*[H\\ind_{\\{t<\\tau_l(x)\\}}]\n =\\EE[H G_t(x)\\ind_{\\{x\\in D_t,\\ s_x(t)<l\\}}].\n\\]\nFirst take $H=1$. Monotone convergence as $l\\to\\infty$ proves the\nuntested identity and the integrable bound $\\EE G_t\\le G_0$.\nThe same passage for bounded signed $H$ then follows by dominated\nconvergence. The union of the events on the left is exactly\n$\\{t<T^*\\}$; no value at $T^*$ is used. Decay follows from\n$T^*<\\infty$. Applying \\eqref{op:conditional-weight} at $S$, taking\nexpectations, and increasing $l$ proves \\eqref{op:optional-bound}.\n\nThese identities also make the parameter measurability needed below\nexplicit. For a raw $A\\in\\mathcal F_t$,\n\\[\n \\PP_x^*(A,t<T^*)\n =\\lim_{l\\to\\infty}\\frac1{G_0(x)}\n   \\EE[\\ind_A G_t(x)\n                 \\ind_{\\{x\\in D_t,\\ s_x(t)<l\\}}].\n\\]\nThe ODE, its spatial derivative, and the inverse clock are measurable\nin the driver and the marked point. The right side is consequently a\nmeasurable kernel in $x$. This permits integration over centers and\ndoes not require a common exceptional-set assertion for all centers.\n\\end{proof}\n"}, {"path": "preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/upper-cover.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/An-exact-Hausdorff-gauge-for-SLE-September-25-2026/build/sections/upper-cover.tex", "bytes": 16471, "sha256": "70b99c94f8d823bc4f47788a901730eace33ffa930e58240527cb1ddeed76a5b", "content": "\\section{All-time upper covers and theorem assembly}\n\\label{hd:cover-section}\n\nWe now construct finite covers of the whole trace in each bounded box.\nThe two charges are different: disjoint successful balls are paid for\nby the same actual terminal mass, while missed cells are paid for by\nthe cleanup entry. The finite changes of law estimate adapted test\nfailures; the cover itself is selected afterward.\n\nThe use of regions with large trace mass together with a finer residual\ncover already appears in Rezaei\n\\cite[Section~3, equations~(9)--(11)]{Rezaei2018}; see also the\nconditional covering sketch in\n\\cite[Remark~4.5]{Zhan2019Optimal}.\nHere the all-moment batches choose the thresholds and scales, and finite\nadapted tests give the conditional failure bound. We then account for\nevery missed cell, including cells near the real boundary.\n\nWe include cells meeting the real axis. Their centers will always\nlie strictly in \\(\\HH\\), even though the cells themselves may\ncontain boundary points.\n\n\\begin{lemma}[A cell visit forces a conformal-radius crossing]\n\\label{hd:visit}\nLet \\(Q\\) be a closed square of side \\(r\\), centered at\n\\(z=x+iy\\), where \\(y>0\\).\nIf \\(y>4r\\) and \\(Q\\cap\\Gamma\\ne\\varnothing\\), then \\(z\\)\nreaches conformal radius \\(8r\\) while alive.\nFor every \\(r>0\\) and \\(y>0\\),\n\\begin{equation}\n \\PP(Q\\cap\\Gamma\\ne\\varnothing)\n \\le C_a r^\\alpha y^{-\\alpha}.\n \\label{hd:one-cell}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nSuppose the radius never reaches \\(8r\\) before the lifetime\n\\(T_z\\) of \\(z\\). Since \\(R_0(z)=2y>8r\\), continuity and Koebe's\nQuarter Theorem give\n\\[\n B(z,2r)\\subset H_t\\qquad(t<T_z).\n\\]\nThe past trace avoids this open ball. If \\(T_z<\\infty\\),\ncontinuity prevents the trace from entering its interior at\ntime \\(T_z\\) as well.\nThe ball lies in \\(\\HH\\), is connected, and avoids\n\\(\\gamma([0,T_z])\\). It therefore lies in the same complementary\ncomponent as \\(z\\). This component is bounded, since \\(z\\) has\nbeen swallowed. Each point of the ball has an open neighborhood\nin that component, disjoint from \\(H_{T_z}\\); hence\n\\[\n B(z,2r)\\cap\\overline{H_{T_z}}=\\varnothing.\n\\]\nThe future trace lies in \\(\\overline{H_{T_z}}\\), so it cannot\nenter the ball. If \\(T_z=\\infty\\), the ball is avoided at every\nfinite time directly. In both cases \\(Q\\), which lies strictly\ninside this ball, is unvisited.\nThis proves the crossing assertion, including when\n\\(4<\\kappa<8\\).\n\nIf \\(y/r\\) exceeds a sufficiently large constant depending on\n\\(a\\), the inner time to radius \\(8r\\) is\n\\(\\sigma=(2a)^{-1}\\log(y/(4r))\\), long enough for\nLemma~\\ref{op:mixing}. At its hitting time \\(\\tau\\),\nLemma~\\ref{op:finite-tilt} gives\n\\[\n \\PP(\\tau<\\infty)\n =(8r)^\\alpha G_0(z)\\,\n     \\EE_z^*[\\sin^{-p}\\theta_\\tau(z)]\n \\le C_a r^\\alpha y^{-\\alpha}.\n\\]\nHere \\(G_0(z)\\le(2y)^{-\\alpha}\\), and\n\\[\n \\int_0^\\pi\\sin^{-p}\\theta\\,\\pi_a(\\dd\\theta)\n =c_a\\int_0^\\pi\\sin\\theta\\,\\dd\\theta<\\infty.\n\\]\nFor the remaining values of \\(y/r\\), a larger \\(C_a\\) makes the\nright side of Equation~\\eqref{hd:one-cell} at least one, so the\ntrivial probability bound completes the proof.\n\\end{proof}\n\n\\begin{proposition}[The upper Hausdorff bound]\n\\label{hd:upper}\nFor every integer $m\\ge1$, the random variable\n$\\Haus^h(\\Gamma\\cap B_m)$, where $B_m=[-m,m]+i[0,m]$, is measurable\nand satisfies\n\\begin{equation}\n \\EE\\,\\Haus^h(\\Gamma\\cap B_m)<\\infty.\n \\label{hd:expected-box}\n\\end{equation}\nIn particular, these whole-trace bounded-box measures are almost\nsurely finite simultaneously for all $m$.\n\\end{proposition}\n\n\\begin{proof}\nFix $m$. We first estimate finite adapted test failures at the final\nmesh of a batch. Then we choose a finite terminal-mass cover and bound\nits expected cost. The last step establishes measurability and passes\nto arbitrarily small diameters.\n\n\\paragraph{Finite tests at the final mesh.}\nTake a sufficiently large batch $n$ and write\n\\[\n J=I_n-1,\\qquad r_{\\mathrm f}=r_{n,I_n},\\qquad\n r_i=r_{ni},\\quad b_i=b_{ni}\\quad(1\\le i\\le J).\n\\]\nThere is at least one test for all sufficiently large \\(n\\), by\nLemma~\\ref{gauge:multiplicity}. Cover the box\n\\(B_m=[-m,m]+i[0,m]\\) by finitely many closed squares of side\n\\(r_{\\mathrm f}\\), beginning at \\(x=-m\\) and at height zero,\nand retain the whole squares in the outermost rows and columns.\nTheir centers have heights\n\\[\n y_j=(j+\\tfrac12)r_{\\mathrm f},\\qquad j\\ge0.\n\\]\nAt every grid center \\(z\\), the closed ball\n\\(\\overline B(z,C_*r_i)\\) is a \\emph{candidate} for entry \\(i\\)\nif\n\\begin{equation}\n \\mu_\\infty\\bigl(\\overline B(z,C_*r_i)\\cap\\HH\\bigr)\n       \\ge c_*^d b_i r_i^d.\n \\label{hd:candidate}\n\\end{equation}\nEvery such ball contains the final cell centered at \\(z\\).\nThere are only finitely many candidate checks in a batch.\nThey involve terminal mass; candidate selection itself is made\nafter the sample has been realized.\n\nWe estimate the probability that a cell is visited and has no\ncandidate through finite stopped tests. Consider a center \\(z\\) with \\(y\\ge e^{-n}\\).\nFor large \\(n\\), Lemma~\\ref{hd:visit} shows that a visit requires\nthe finite hitting time \\(\\tau_{\\mathrm f}\\) of radius\n\\(8r_{\\mathrm f}\\). For each \\(i\\), let \\(S_i\\) and \\(T_i\\) be\nthe first times, while \\(z\\) remains alive, that its conformal radius\nreaches \\(100r_i\\) and \\(100r_i e^{-2a\\ell_n}\\), respectively;\nset either time to infinity on failure. On\n\\(\\{\\tau_{\\mathrm f}<\\infty\\}\\), both times are finite and the\ninner-clock interval from \\(S_i\\) to \\(T_i\\) has length \\(\\ell_n\\).\nOn \\(\\{T_i<\\infty\\}\\), let \\(F_i:\\DD\\to H_{S_i}\\) be the disk map used in\nEquation~\\eqref{hd:spatial-comparison}, and define the stopped test mass\n\\begin{equation}\n X_i=\\int_{F_i(K)}\n       |F_i'(F_i^{-1}(x))|^{-d}\n       (\\mu_{T_i}-\\mu_{S_i})(\\dd x).\n \\label{hd:stopped-test}\n\\end{equation}\nSet \\(X_i=0\\) on \\(\\{T_i=\\infty\\}\\).\nThe variable is measurable at \\(T_i\\) and, on a reached endpoint,\nis the canonical disk mass \\(L_{\\ell_n}\\) for the restart angle.\nOnly paths reaching\n\\(\\tau_{\\mathrm f}\\) will enter the stopped event below.\nSuccess, \\(X_i\\ge b_i\\), implies\n\\[\n (\\mu_{T_i}-\\mu_{S_i})(F_i(K))\\ge c_*^d b_i r_i^d,\n\\]\nso Equation~\\eqref{hd:koebe-sets} and monotonicity of mass force\nthe ball \\(\\overline B(z,C_*r_i)\\) to qualify in\nEquation~\\eqref{hd:candidate}.\n\nThe gaps in the inner clock are deterministic;\nFigure~\\ref{fig:batch-timeline} shows their order.\nWriting \\(\\sigma_i=s_z(S_i)\\) and\n\\(\\sigma_{\\mathrm f}=s_z(\\tau_{\\mathrm f})\\), the radius formula\ngives exactly\n\\begin{align}\n \\sigma_1\n   &=\\frac{1}{2a}\\log\\frac{2y}{100e^{-n^2}}\n     \\ \\ge\\ \\frac{n^2-n-\\log50}{2a}, \\notag\\\\\n \\sigma_{i+1}-(\\sigma_i+\\ell_n)&=n\n       \\qquad(1\\le i<J), \\label{hd:test-gaps}\\\\\n \\sigma_{\\mathrm f}-(\\sigma_J+\\ell_n)\n   &=n+\\frac{1}{2a}\\log\\frac{100}{8}. \\notag\n\\end{align}\n\\input{figures/batch-timeline}\nFor the failure estimate, we now work under the finite centered law\nstopped at \\(\\tau_{\\mathrm f}\\), under which all these levels are reached.\nFor all sufficiently large \\(n\\), each of these gaps permits\nthe uniform mixing estimate. Conditional on the entire stopped\nhistory through \\(T_{i-1}\\), the angle at \\(S_i\\) has density at\nleast \\(1/2\\) relative to \\(\\pi_a\\); the first test has the same\nbound from its initial gap.\nConditional on the history through \\(S_i\\), the disk law of\n\\(X_i\\) depends only on that angle, by\nProposition~\\ref{mass:restart} and the finite conditional tilt.\nThus, with\n\\[\n q_i=\\PP^*_{\\mathrm{mix}}(L_{\\ell_n}\\ge b_i),\n\\]\nthe conditional probability of success is at least \\(q_i/2\\).\nIterated conditional expectations, followed by\nLemma~\\ref{gauge:multiplicity}, give\n\\begin{equation}\n \\PP_z^*(X_i<b_i\\text{ for every }i\\le J)\n \\le\\prod_{i=1}^J(1-q_i/2)\n \\le \\exp\\left(-\\tfrac12\\sum_{i=1}^Jq_i\\right)\n \\le \\exp(-2^n/16).\n \\label{hd:test-failure}\n\\end{equation}\nThis argument conditions on the actual past at each test.\nIt requires no independence between tests or between centers.\n\n\\paragraph{Changing law for the failure event.}\nThe event that a cell is visited and has no candidate is\ncontained in the larger event\n\\begin{equation}\n A=\\{\\tau_{\\mathrm f}<\\infty\\}\n       \\cap\\bigcap_{i=1}^J\\{X_i<b_i\\}.\n \\label{hd:adapted-enlargement}\n\\end{equation}\nAll test endpoints precede \\(\\tau_{\\mathrm f}\\), so \\(A\\) is\nmeasurable at that stopping time. Only this enlarged event is\nused in changing law. In particular, no terminal candidate\ninformation is put into a finite Radon--Nikodym formula.\nLet \\(\\widehat\\theta=\\theta_{\\tau_{\\mathrm f}}(z)\\) and\n\\(F_{\\mathrm{fail}}=\\bigcap_i\\{X_i<b_i\\}\\).\nThe final gap in Equation~\\eqref{hd:test-gaps}, conditional on\nthe history through \\(T_J\\), gives\n\\[\n \\EE_z^*[\\sin^{-p}\\widehat\\theta\\mid\\mathcal F_{T_J}]\n \\le 2\\int\\sin^{-p}\\theta\\,\\pi_a(\\dd\\theta).\n\\]\nSince \\(F_{\\mathrm{fail}}\\) is known at \\(T_J\\), the density at\n\\(\\tau_{\\mathrm f}\\) and Equation~\\eqref{hd:test-failure} yield\n\\begin{align}\n &\\PP(\\text{the cell at }z\\text{ is visited and has no candidate})\n \\notag\\\\\n &\\quad\\le\\PP(A)\n  =(8r_{\\mathrm f})^\\alpha G_0(z)\\,\n       \\EE_z^*[\\sin^{-p}\\widehat\\theta;\\ F_{\\mathrm{fail}}]\n \\notag\\\\\n &\\quad\\le C_a r_{\\mathrm f}^{\\alpha}y^{-\\alpha}\n                       \\exp(-2^n/16)\n       \\qquad(y\\ge e^{-n}).\n \\label{hd:upper-cell}\n\\end{align}\nFor centers below \\(e^{-n}\\), use the unconditional estimate\n\\begin{equation}\n \\PP(\\text{the cell at }z\\text{ is visited})\n \\le C_a r_{\\mathrm f}^{\\alpha}y^{-\\alpha}\n \\label{hd:lower-cell}\n\\end{equation}\nfrom Lemma~\\ref{hd:visit}. This includes the bottom row:\nits center has height \\(r_{\\mathrm f}/2\\), and the trivial\nprobability bound is absorbed by the displayed estimate.\n\n\\paragraph{Selecting and charging the cover.}\nOrder all candidate balls by\ndecreasing radius, using a fixed deterministic order for ties.\nChoose the first ball, delete every ball meeting it, and\ncontinue until no candidates remain. The selected closed balls\nare pairwise disjoint. A deleted ball has radius no greater\nthan that of the ball that deletes it, so it is contained in\nthe concentric triple of that selected ball.\nThe triples therefore cover every final cell having a candidate.\nFor a chosen ball associated with \\((b_i,r_i)\\), its triple\nhas diameter \\(6C_*r_i\\); Equation~\\eqref{gauge:definition}\nand its qualification give\n\\begin{align}\n h(6C_*r_i)\n &\\le (6C_*)^2 b_i r_i^d \\notag\\\\\n &\\le \\frac{(6C_*)^2}{c_*^d}\\,\n       \\mu_\\infty\\bigl(\\overline B(z,C_*r_i)\\cap\\HH\\bigr).\n \\label{hd:candidate-cost}\n\\end{align}\nFor large \\(n\\) all these balls lie, after intersection with\n\\(\\HH\\), in the fixed bounded set\n\\[\n \\widetilde B_m=[-m-2,m+2]+i(0,m+2].\n\\]\nDisjointness thus bounds their total gauge cost by\n\\[\n \\frac{(6C_*)^2}{c_*^d}\\,\\mu_\\infty(\\widetilde B_m).\n\\]\nThis is the actual mass of a fixed region on the same sample.\nIt has finite expectation, even at the real boundary:\n\\begin{equation}\n \\EE\\mu_\\infty(\\widetilde B_m)\n =\\int_{\\widetilde B_m}G_0(z)\\,\\dd A(z)\n \\le \\frac{2^{-\\alpha}(2m+4)(m+2)^{1-\\alpha}}{1-\\alpha}\n <\\infty.\n \\label{hd:boundary-integrability}\n\\end{equation}\nThe mean identity on this set follows by an interior compact\nexhaustion and monotone convergence.\n\n\\paragraph{Paying for missed cells and the boundary.}\nAdd every final cell that meets $\\Gamma$ and has no candidate.\nThe cleanup entry gives the individual cost\n\\[\n h(\\sqrt2\\,r_{\\mathrm f})\\le2n r_{\\mathrm f}^d.\n\\]\nThere are \\(O_m(r_{\\mathrm f}^{-1})\\) columns. Since\n\\(0<\\alpha<1\\), summing the half-integer row heights gives\n\\begin{align*}\n \\sum_{\\text{all cells}}y^{-\\alpha}\n     &\\le C_{a,m}r_{\\mathrm f}^{-2},\\\\\n \\sum_{y<e^{-n}}y^{-\\alpha}\n     &\\le C_{a,m}r_{\\mathrm f}^{-2}\n                        (e^{-n}+r_{\\mathrm f})^{1-\\alpha}.\n\\end{align*}\nFor example,\n\\(\\sum_{j=0}^N(j+\\tfrac12)^{-\\alpha}\n\\le C_\\alpha(N+1)^{1-\\alpha}\\) proves both bounds.\nMultiplying Equations~\\eqref{hd:upper-cell} and\n\\eqref{hd:lower-cell} by the cleanup cost, and using\n\\(d+\\alpha=2\\), bounds the expected added cost by\n\\begin{equation}\n C_{a,m}\\left[\n       n e^{-2^n/16}\n       +n(e^{-n}+r_{\\mathrm f})^{1-\\alpha}\n       \\right].\n \\label{hd:cleanup-cost}\n\\end{equation}\nIt is bounded uniformly in \\(n\\) and tends to zero.\nIn particular, the bottom row contributes at most\n\\(C_m n r_{\\mathrm f}^{d-1}\\). Thus points of the trace on\nthe real axis are included in the cover, although the auxiliary\nmass itself is defined on \\(\\HH\\).\n\n\\paragraph{Measurability and the limiting cover.}\nThese constructions are measurable. The candidate family is\nfinite with measurable terminal-mass indicators, and the\ngreedy order is deterministic. For a fixed closed cell, the\nevent of meeting the trace is measurable on every compact time\ninterval, by continuity; a countable union over integer time\nhorizons gives the full trace event.\nAll upper-bound centers, entries, and boxes constitute a\ncountable family, so the stopped measure identities used in\nconstructing their tests can be imposed on one event.\n\nLet \\(C_n\\) be the total gauge cost of the resulting cover of\n\\(\\Gamma\\cap B_m\\). Equations~\\eqref{hd:candidate-cost}--%\n\\eqref{hd:cleanup-cost} show that\n\\(\\sup_n\\EE C_n<\\infty\\), after omitting finitely many initial\nbatches. Every cover diameter is at most\n\\[\n \\delta_n=6C_*e^{-n^2},\n\\]\nwhich tends deterministically to zero.\nFatou's Lemma gives\n\\[\n \\EE[\\liminf_{n\\to\\infty}C_n]\n \\le\\liminf_{n\\to\\infty}\\EE C_n<\\infty,\n\\]\nso the liminf cost is finite almost surely.\nFor every fixed \\(\\delta>0\\), all sufficiently late covers are\nadmissible for \\(\\Haus^h_\\delta(\\Gamma\\cap B_m)\\), and hence\n\\[\n \\Haus^h_\\delta(\\Gamma\\cap B_m)\n \\le\\liminf_{n\\to\\infty}C_n.\n\\]\nLetting $\\delta\\downarrow0$ gives the pathwise domination\n\\begin{equation}\n \\Haus^h(\\Gamma\\cap B_m)\\le\\liminf_{n\\to\\infty}C_n.\n \\label{hd:liminf-domination}\n\\end{equation}\n\nWe justify ordinary expectation of the left side. For an integer $T$,\n$K_T=\\gamma([0,T])\\cap B_m$ is a measurable compact random set.\nIndeed, for a fixed closed $F$, the event $K_T\\cap F\\ne\\varnothing$\nis the measurable event\n$\\min_{0\\le t\\le T}\\dist(\\gamma(t),B_m\\cap F)=0$; the minimum can\nbe computed on rational times. Empty target sets give the empty event.\nHit events for open sets follow by an increasing union of closed\nsubsets, so these tests give the required compact-set measurability.\nIntersection with $B_m$ need not depend continuously on the path.\n\nFor completeness, $K\\mapsto\\Haus^h(K)$ is measurable on compact sets.\nUse the countable family of finite unions of open balls with rational\ncenters and radii. The limit of the corresponding countable infima\nover finite covers, with their diameter cutoff tending to zero, equals\n$\\Haus^h(K)$. To see this, start with any countable cover of the compact\n$K$ at a smaller cutoff. Enlarge its sets to open neighborhoods with\narbitrarily small diameter slack and summable cost slack, using\ncontinuity of $h$ (also at zero). Compactness gives a finite subcover.\nShrink that finite cover on $K$ to compact pieces, and cover each piece\nby finitely many rational balls inside its assigned neighborhood.\nThe grouped balls remain inside that neighborhood, preserving its\ndiameter bound and cost. This proves equality after letting the cutoff\nand slack tend to zero; equality at an unchanged fixed cutoff is not\nneeded. Each admissibility event $K\\subset O$ for a fixed open $O$\nis measurable, so the countable infima and their limit are measurable.\n\nFinally $\\Gamma\\cap B_m=\\bigcup_{T\\in\\mathbb N}K_T$ is an increasing\nunion of compact sets. Hausdorff outer measure is a measure on Borel\nsets, so continuity from below gives\n$\\Haus^h(\\Gamma\\cap B_m)=\\lim_T\\Haus^h(K_T)$.\nThis is measurable. Taking expectations in\n\\eqref{hd:liminf-domination} and applying the preceding Fatou bound\nproves \\eqref{hd:expected-box}. A countable intersection now gives\nalmost-sure finiteness in every integer box.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\nEquation~\\eqref{coef:formula} specifies the coefficients\ndeterministically from \\(\\kappa\\), and\nProposition~\\ref{gauge:admissible} shows that\nEquation~\\eqref{gauge:definition} defines an admissible gauge.\nIntersect the probability-one events of\nPropositions~\\ref{hd:lower} and~\\ref{hd:upper}.\nOn this one event, the lower bound holds for every real\n\\(0<s<t<\\infty\\).\nThe image of each finite time interval is bounded by continuity\nof \\(\\gamma\\), and therefore lies in one of the integer boxes\nof Proposition~\\ref{hd:upper}; its Hausdorff measure is finite.\nThus the required positive finite measure holds simultaneously\nfor every such interval. The constant change of capacity time\nused at the start preserves this all-interval assertion.\n\\end{proof}\n"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/README.md", "bytes": 752, "sha256": "bfcf7488cac15bd5253c9f69f54a99f49f3b847f838f97152e033f6e507ec54c", "content": "# [Annular variation of the triangular Hilbert transform at the symmetric point](annular-variation.pdf)\n\n**Author:** OpenAI\n\n**Date:** October 5, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026,\n  author = {{OpenAI}},\n  title = {{Annular variation of the triangular Hilbert transform at the symmetric point}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/annular-variation.pdf}{OAI:Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/annular-variation.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/annular-variation.pdf", "bytes": 523253, "sha256": "f5857c58037fa5289f2f9fe1fe4421af11df8d9fa9b2914ba041469f1195ea31", "base64": 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"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/main.tex", "bytes": 2269, "sha256": "5fced11c7fa05911d7517983b84d04ad562fc80d264a8b8f8e4d1d265d30abdc", "content": "\\newcommand{\\DoNotLoadEpstopdf}{}\n\\documentclass[11pt]{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{lmodern}\n\\usepackage[margin=1in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage{microtype}\n\\usepackage{xcolor}\n\\usepackage{graphicx}\n\\usepackage{tikz}\n\\usetikzlibrary{arrows.meta,positioning,calc,decorations.pathreplacing}\n\\usepackage{enumitem}\n\\usepackage{needspace}\n\\usepackage[hyphens]{url}\n\\usepackage[colorlinks=true,linkcolor=blue!45!black,citecolor=green!35!black,urlcolor=blue!45!black]{hyperref}\n\\input{glyphtounicode.tex}\n\\input{glyphtounicode-cmex.tex}\n\\pdfgentounicode=1\n\\hypersetup{pdftitle={Annular variation of the triangular Hilbert transform at the symmetric point},pdfauthor={OpenAI}}\n\\newcommand{\\R}{\\mathbb R}\n\\newcommand{\\C}{\\mathbb C}\n\\newcommand{\\dd}{\\,\\mathrm d}\n\\DeclareMathOperator{\\tr}{tr}\n\\DeclareMathOperator{\\diag}{diag}\n\\DeclareMathOperator{\\supp}{supp}\n\\DeclareMathOperator{\\Dil}{Dil}\n\\newcommand{\\HS}{\\mathrm{HS}}\n\\newcommand{\\op}{\\mathrm{op}}\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\\newtheorem{definition}[theorem]{Definition}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\numberwithin{equation}{section}\n\\setlength{\\emergencystretch}{2em}\n\\title{Annular variation of the triangular Hilbert transform\\\\at the symmetric point}\n\\author{OpenAI}\n\\date{October 5, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe prove the annular $r$-variation estimate for the triangular Hilbert\ntransform from complex $L^3\\times L^3$ to $L^{3/2}$ for every $r>2$.\nThe partitions may depend on the output point and range over all positive\nscales. The estimate yields the two-endpoint maximal bound and joint\nalmost-everywhere and norm principal values, and resolves the symmetric\nscalar triangular Hilbert transform problem.\n\\end{abstract}\n\\tableofcontents\n\\input{sections/introduction}\n\\input{sections/preliminaries}\n\\input{sections/matrix}\n\\input{sections/heat}\n\\input{sections/smooth}\n\\input{sections/frequency}\n\\input{sections/rough}\n\\input{sections/completion}\n\\input{sections/consequences}\n\\bibliographystyle{plain}\n\\bibliography{references}\n\\end{document}\n"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/references.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/references.bib", "bytes": 7612, "sha256": "ca3157ee079b1cccf11308592b76f7d1d2eaa75298f078d85ead053034a9440b", 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{10.1090/tran/6850},\n  url = {https://arxiv.org/abs/1506.08150}\n}\n\n@article{Lepingle1976,\n  author = {L\\'epingle, Dominique},\n  title = {La variation d'ordre {$p$} des semi-martingales},\n  journal = {Zeitschrift f\\\"ur Wahrscheinlichkeitstheorie und Verwandte Gebiete},\n  volume = {36},\n  number = {4},\n  pages = {295--316},\n  year = {1976},\n  doi = {10.1007/BF00532696}\n}\n\n@article{JonesSeegerWright2008,\n  author = {Jones, Roger L. and Seeger, Andreas and Wright, James},\n  title = {Strong variational and jump inequalities in harmonic analysis},\n  journal = {Transactions of the American Mathematical Society},\n  volume = {360},\n  number = {12},\n  pages = {6711--6742},\n  year = {2008},\n  doi = {10.1090/S0002-9947-08-04538-8},\n  url = {https://people.math.wisc.edu/~seeger/papers/jsw.pdf}\n}\n\n@article{OberlinSeegerTaoThieleWright2012,\n  author = {Oberlin, Richard and Seeger, Andreas and Tao, Terence and Thiele, Christoph and Wright, James},\n  title = {A variation norm {Carleson} theorem},\n  journal = {Journal of the European Mathematical Society},\n  volume = {14},\n  number = {2},\n  pages = {421--464},\n  year = {2012},\n  doi = {10.4171/JEMS/307},\n  url = {https://arxiv.org/abs/0910.1555}\n}\n\n@article{Daletskii1957,\n  author = {Daletskii, Yu. L.},\n  title = {Integration and differentiation of functions of {Hermitian} operators depending on a parameter},\n  journal = {Uspekhi Matematicheskikh Nauk},\n  volume = {12},\n  number = {1(73)},\n  pages = {182--186},\n  year = {1957},\n  url = {https://www.mathnet.ru/eng/rm7543},\n  note = {In Russian}\n}\n\n@article{Lewis1995,\n  author = {Lewis, Adrian S.},\n  title = {The convex analysis of unitarily invariant matrix functions},\n  journal = {Journal of Convex Analysis},\n  volume = {2},\n  number = {1--2},\n  pages = {173--183},\n  year = {1995},\n  url = {https://www.heldermann-verlag.de/jca/jca02/jca02012.pdf}\n}\n\n@article{HardyLittlewood1930,\n  author = {Hardy, G. H. and Littlewood, J. E.},\n  title = {A maximal theorem with function-theoretic applications},\n  journal = {Acta Mathematica},\n  volume = {54},\n  pages = {81--116},\n  year = {1930},\n  doi = {10.1007/BF02547518}\n}\n\n@article{DurcikRoos2021,\n  author = {Durcik, Polona and Roos, Joris},\n  title = {Averages of simplex {Hilbert} transforms},\n  journal = {Proceedings of the American Mathematical Society},\n  volume = {149},\n  number = {2},\n  pages = {633--647},\n  year = {2021},\n  doi = {10.1090/proc/15196},\n  url = {https://arxiv.org/abs/1812.11701v3}\n}\n\n@article{ChristDurcikRoos2021,\n  author = {Christ, Michael and Durcik, Polona and Roos, Joris},\n  title = {Trilinear smoothing inequalities and a variant of the triangular {Hilbert} transform},\n  journal = {Advances in Mathematics},\n  volume = {390},\n  pages = {107863},\n  year = {2021},\n  doi = {10.1016/j.aim.2021.107863},\n  url = {https://arxiv.org/abs/2008.10140v2}\n}\n\n@misc{HsuLin2026,\n  author = {Hsu, Martin and Lin, Fred Yu-Hsiang},\n  title = {Smoothing inequalities for corner-type bilinear averages: geometric characterization and applications},\n  year = {2026},\n  howpublished = {arXiv:2410.15791v2},\n  url = {https://arxiv.org/abs/2410.15791v2},\n  note = {Revised July 18, 2026}\n}\n\n@misc{LinSlavikova2026,\n  author = {Lin, Fred Yu-Hsiang and Slav\\'ikov\\'a, Lenka},\n  title = {Rough averages of triangular {Hilbert} transforms},\n  year = {2026},\n  howpublished = {arXiv:2607.20206},\n  url = {https://arxiv.org/abs/2607.20206},\n  note = {July 22, 2026}\n}\n\n@article{Kwong1975,\n  author = {Kwong, Man Kam},\n  title = {Inequalities for the powers of nonnegative {Hermitian} operators},\n  journal = {Proceedings of the American Mathematical Society},\n  volume = {51},\n  number = {2},\n  pages = {401--406},\n  year = {1975},\n  doi = {10.1090/S0002-9939-1975-0374970-X},\n  url = {https://doi.org/10.1090/S0002-9939-1975-0374970-X}\n}\n"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/completion.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/completion.tex", "bytes": 4373, "sha256": "0d14e5d2a234b36044627628ad1dd960929dd357f3b5fc4e85feb84fe3a7c7d3", "content": "\\section{From the count estimate to full variation}\\label{sec:completion}\n\nWe now assemble the smooth estimate and the endpoint estimate, remove\nthe restrictions on choices and inputs, and sum the count bounds to\nobtain the full variation. No selection of a common partition for\ndifferent output points is made in this argument.\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:count}]\nFirst let $F,G\\in C_c^\\infty(\\R^2)$, use the pair maps\n\\eqref{eq:pair-maps}, and take $G_0\\in C_c^\\infty(\\R^2)$.\nFor step choices of disjoint annuli and coefficients, the identity\n\\eqref{eq:hard-smooth} writes the hard linearization, multiplied by\n$c_0\\ne0$, as its smooth linearization minus its endpoint errors.\nPropositions~\\ref{prop:smooth-switches} and~\\ref{prop:endpoint-errors}\ntherefore give\n\\begin{equation}\\label{eq:hard-linearized-bound}\n \\left|\\iiint G_0(a,b)G_1(b,c)G_2(c,a)\n     \\sum_j\\frac{\\beta_j\\mathbf1_{\\{\\varepsilon_j<|a+b+c|<R_j\\}}}\n                       {a+b+c}\\dd a\\dd b\\dd c\\right|\n \\le C\\sqrt n\\log(2+n)\\prod_v\\|G_v\\|_3.\n\\end{equation}\nThe interval list and coefficients in the sum may vary with $(a,b)$.\n\nFix a finite menu of such choices. Its measurable choice sets can be\napproximated in measure on a compact set by finite unions of rectangles.\nTo see this, approximate each of the finitely many sets using Lebesgue\nregularity and then refine all rectangle boundaries to one coordinate\npartition. Resolve overlaps by a fixed ordering of the menu; the total\nmismeasured set remains bounded by the sum of the approximation errors.\nAssign any menu item on the remaining cells. For the fixed menu, the\nannuli are uniformly bounded away from zero, and the integrals against\nthe smooth inputs are bounded on the compact support needed for $G_0$.\nConsequently the corresponding linearized integrals converge.\nThus \\eqref{eq:hard-linearized-bound} holds for arbitrary measurable\nchoices from a fixed finite menu.\n\nNow fix a finite collection of lists of disjoint rational annuli, each\nof length at most $n$. The maximum of their sums of absolute increments\nis measurable. Choose a maximizing list measurably by resolving ties in\na fixed order. For each of its increments choose a coefficient from\n$\\{1,-1,i,-i\\}$ whose product with that increment has real part at least\nhalf its modulus. These choices are again measurable and range over\na finite menu. Testing \\eqref{eq:hard-linearized-bound} against\nnonnegative smooth compactly supported $G_0$ shows, by $L^3$ duality,\nthat this finite maximum has $L^{3/2}$ norm at most\n$C\\sqrt n\\log(2+n)\\|F\\|_3\\|G\\|_3$.\nThe reflection $(x,y)=(-a,-b)$ preserves that norm.\nExhausting the countable family of all rational lists and applying\nmonotone convergence proves the count bound for smooth inputs.\n\nFor general complex $F,G\\in L^3$, choose smooth compactly supported\napproximants $F_m,G_m$. For every fixed annulus, bilinearity and\n\\eqref{eq:finite-annulus-bound} give convergence of\n$B_{\\varepsilon,R}(F_m,G_m)$ in $L^{3/2}$.\nFor a fixed finite collection of lists, the difference of the two\nfinite maxima is bounded by a finite sum of such truncation differences.\nIt therefore converges in $L^{3/2}$. Pass the estimate to the limit\nfor this finite maximum, then exhaust the rational lists once more.\nLemma~\\ref{lem:finite-truncations} supplies the common representatives\nrequired in the definition of $\\mathcal S_n$.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:variation}]\nFor any finite rational partition, arrange the absolute annular\nincrements in nonincreasing order, denoting them by\n$d_1\\ge d_2\\ge\\cdots\\ge0$. Whenever the index $2^j$ exists,\nthe corresponding largest increments are a permissible list in\n$\\mathcal S_{2^j}$, so\n\\[\n d_{2^j}\\le2^{-j}\\mathcal S_{2^j}(F,G).\n\\]\nThe group of indices $2^j\\le l<2^{j+1}$ has at most $2^j$ members.\nBounding the full $\\ell^r$ norm by the sum of the group norms yields,\nuniformly in the chosen partition,\n\\begin{equation}\\label{eq:rank-variation}\n V_r(F,G)\\le\\sum_{j\\ge0}2^{j/r-j}\\mathcal S_{2^j}(F,G)\n \\quad\\text{almost everywhere}.\n\\end{equation}\nTheorem~\\ref{thm:count} and Minkowski's inequality now imply\n\\[\n \\|V_r(F,G)\\|_{3/2}\n \\le C\\|F\\|_3\\|G\\|_3\n       \\sum_{j\\ge0}(1+j)2^{j(1/r-1/2)}.\n\\]\nThe series converges for every $r>2$. In particular, neither a bound\non the number of increments nor a restriction to dyadic endpoints\nsurvives in the conclusion.\n\\end{proof}\n"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/consequences.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/consequences.tex", "bytes": 8415, "sha256": "b2ce161f18a7e204d893a5792e5d1832ea2c9b3de24eec26c6278c202341c64f", "content": "\\section{Maximal estimates and principal values}\\label{sec:consequences}\n\nThe counting estimate already contains the maximal estimate, by taking\na single annulus. We first record its continuity with respect to the\ninputs, then use that continuity to transfer joint principal-value\nconvergence from smooth functions to all complex $L^3$ inputs.\nThroughout this section, write $p=3/2$.\n\n\\begin{corollary}[The hard maximal operator]\\label{cor:maximal}\nFor complex $F,G\\in L^3(\\R^2)$, define\n\\[\n B_*(F,G)(x,y)\n =\\sup_{0<\\varepsilon<R<\\infty}|B_{\\varepsilon,R}(F,G)(x,y)|\n\\]\non the common full-measure set of Lemma~\\ref{lem:finite-truncations},\nand set it to zero elsewhere. This function is measurable, and there is\nan absolute constant $C_M$ such that\n\\begin{equation}\\label{eq:hard-maximal-bound}\n \\|B_*(F,G)\\|_p\\le C_M\\|F\\|_3\\|G\\|_3.\n\\end{equation}\nThe supremum includes both finite hard truncation endpoints. Moreover,\n$B_*$ is a continuous map from $L^3(\\R^2)\\times L^3(\\R^2)$ to\n$L^p(\\R^2)$ and is the unique continuous extension of its values on\ncompactly supported smooth pairs.\n\\end{corollary}\n\n\\begin{proof}\nEndpoint continuity in Lemma~\\ref{lem:finite-truncations} identifies the\nsupremum with its restriction to rational endpoint pairs. Consequently\nit is measurable and equals $\\mathcal S_1(F,G)$. Theorem~\\ref{thm:count}\ngives \\eqref{eq:hard-maximal-bound}, with its absolute factor\n$\\log 3$ absorbed into $C_M$.\n\nFor two pairs of inputs, bilinearity of each finite truncation gives,\nalmost everywhere,\n\\begin{equation}\\label{eq:maximal-continuity}\n |B_*(F,G)-B_*(f,g)|\n \\le B_*(F-f,G)+B_*(f,G-g).\n\\end{equation}\nTaking $L^p$ norms and using \\eqref{eq:hard-maximal-bound} proves\ncontinuity. Density of compactly supported smooth functions gives the\nasserted uniqueness.\n\\end{proof}\n\n\\begin{corollary}[Joint bilinear principal values]\\label{cor:bilinear-pv}\nFor every complex $F,G\\in L^3(\\R^2)$, the joint limit\n\\[\n B(F,G)=\\lim_{\\substack{\\varepsilon\\downarrow0\\\\R\\uparrow\\infty}}\n B_{\\varepsilon,R}(F,G)\n\\]\nexists almost everywhere and in $L^p(\\R^2)$, and\n\\[\n \\|B(F,G)\\|_p\\le C_M\\|F\\|_3\\|G\\|_3.\n\\]\nMore precisely, the entire tail converges in the maximal sense\n\\begin{equation}\\label{eq:maximal-tail-convergence}\n \\left\\|\\sup_{\\substack{0<\\varepsilon<1/n\\\\R>n}}\n |B_{\\varepsilon,R}(F,G)-B(F,G)|\\right\\|_p\n \\longrightarrow0.\n\\end{equation}\nThe operator $B$ is the unique bounded complex bilinear extension of\nthe principal-value operator on compactly supported smooth inputs.\n\\end{corollary}\n\n\\begin{proof}\nFor compactly supported smooth $f,g$ and fixed $(x,y)$, put\n$h(t)=f(x+t,y)g(x,y+t)$. Then\n\\[\n B_{\\varepsilon,R}(f,g)(x,y)\n =\\int_\\varepsilon^R\\frac{h(t)-h(-t)}t\\,\\dd t.\n\\]\nThe numerator is $O(t)$ at zero and vanishes for large $t$, so the joint\nlimit exists at every point.\n\nFor general $F,G$, define the tail oscillation\n\\[\n \\Omega_n(F,G)=\n \\sup_{\\substack{0<\\varepsilon,\\varepsilon'<1/n\\\\R,R'>n}}\n |B_{\\varepsilon,R}(F,G)-B_{\\varepsilon',R'}(F,G)|.\n\\]\nThese functions are measurable by endpoint continuity, decrease with\n$n$, and satisfy $0\\le\\Omega_n(F,G)\\le2B_*(F,G)$. Choose compactly\nsupported smooth $f_m\\to F$ and $g_m\\to G$ in $L^3$. Take a common\nfull-measure set for the pairs $(F,G)$, $(f_m,g_m)$, $(F-f_m,G)$,\nand $(f_m,G-g_m)$, for all $m$. On this set, bilinearity gives\n\\[\n \\Omega_n(F,G)\\le\\Omega_n(f_m,g_m)\n       +2B_*(F-f_m,G)+2B_*(f_m,G-g_m).\n\\]\nThe smooth joint convergence implies $\\Omega_n(f_m,g_m)\\to0$ pointwise.\nThus, writing $\\Omega_\\infty=\\lim_n\\Omega_n(F,G)$, we obtain\n\\[\n \\|\\Omega_\\infty\\|_p\n \\le2C_M\\bigl(\\|F-f_m\\|_3\\|G\\|_3\n             +\\|f_m\\|_3\\|G-g_m\\|_3\\bigr).\n\\]\nLetting $m\\to\\infty$ shows that $\\Omega_\\infty=0$ almost everywhere.\nThis is the joint Cauchy criterion for the two endpoints, and hence\ndefines $B(F,G)$ almost everywhere. Taking, for example, the cofinal\nsequence $(\\varepsilon,R)=(1/(2n),2n)$ shows that the limit is measurable;\nits magnitude is at most $B_*(F,G)$. Define it to be zero on the\nremaining null set.\n\nLetting $(\\varepsilon',R')$ tend jointly to $(0,\\infty)$ in the\ndefinition of $\\Omega_n$ gives\n\\[\n \\sup_{\\substack{0<\\varepsilon<1/n\\\\R>n}}\n |B_{\\varepsilon,R}(F,G)-B(F,G)|\\le\\Omega_n(F,G).\n\\]\nThe supremum is measurable, again by endpoint continuity.\nDominated convergence applies because\n$\\Omega_n(F,G)\\le2B_*(F,G)\\in L^p$, proving\n\\eqref{eq:maximal-tail-convergence} and therefore joint norm convergence.\nThe norm bound follows from $|B(F,G)|\\le B_*(F,G)$. Passing to the\n$L^p$ limit in the bilinear identities for finite truncations proves\ncomplex bilinearity. The bound and density then prove uniqueness.\n\\end{proof}\n\n\\subsection{Flat and simplex scalar forms}\n\nFor complex $F_0,F_1,F_2\\in L^3(\\R^2)$, let\n\\[\n \\mathcal L_{\\varepsilon,R}(F_0,F_1,F_2)\n =\\iiint_{\\varepsilon<|t|<R}\n F_0(x,y)F_1(x+t,y)F_2(x,y+t)\n \\,\\dd x\\,\\dd y\\,\\frac{\\dd t}{t}.\n\\]\nNo input is conjugated in this scalar form.\n\n\\begin{corollary}[Flat scalar principal values]\\label{cor:scalar-pv}\nFor every complex $L^3$ triple, each finite flat truncation is absolutely\nintegrable, and\n\\[\n \\sup_{0<\\varepsilon<R<\\infty}\n |\\mathcal L_{\\varepsilon,R}(F_0,F_1,F_2)|\n \\le C_M\\prod_{v=0}^2\\|F_v\\|_3.\n\\]\nIts joint scalar principal value exists and equals\n$\\int_{\\R^2}F_0 B(F_1,F_2)$. This limiting form is the unique bounded\ncomplex trilinear extension of its compactly supported smooth definition.\n\\end{corollary}\n\n\\begin{proof}\nH\\\"older's inequality in $(x,y)$ bounds the integral of the absolute\nvalue by $2\\log(R/\\varepsilon)\\prod_v\\|F_v\\|_3$. Fubini's Theorem\ntherefore identifies the finite form with\n$\\int F_0 B_{\\varepsilon,R}(F_1,F_2)$.\nCorollary~\\ref{cor:maximal} gives its uniform bound, and pairing the\njoint $L^p$ limit in Corollary~\\ref{cor:bilinear-pv} with $F_0$ gives\nthe scalar principal value. The limiting form is bounded and complex\ntrilinear; density proves uniqueness.\n\\end{proof}\n\nIn simplex coordinates the corresponding scalar form is\n\\[\n \\Lambda_{\\varepsilon,R}(G_0,G_1,G_2)\n =\\iiint_{\\varepsilon<|a+b+c|<R}\n G_0(a,b)G_1(b,c)G_2(c,a)\n \\,\\frac{\\dd a\\,\\dd b\\,\\dd c}{a+b+c}.\n\\]\nThe equivalence of the flat and simplex formulations, including their\noptimal constants, is described in\n\\cite[Appendix~B.1]{KovacThieleZorinKranich2015}. We record the\nnorm-preserving maps to track both hard endpoints and their joint limit.\n\n\\begin{proposition}[Scalar equivalence]\\label{prop:flat-equivalence}\nFor every complex $G_0,G_1,G_2\\in L^3(\\R^2)$, each finite simplex\ntruncation is absolutely integrable, and\n\\[\n \\sup_{0<\\varepsilon<R<\\infty}\n |\\Lambda_{\\varepsilon,R}(G_0,G_1,G_2)|\n \\le C_M\\prod_{v=0}^2\\|G_v\\|_3.\n\\]\nIts joint scalar principal value exists and is the unique bounded\ncomplex trilinear extension of its compactly supported smooth\ndefinition. The optimal constants in the uniform finite flat and\nsimplex scalar estimates are equal.\n\\end{proposition}\n\n\\begin{proof}\nUse the determinant-one change of variables\n\\[\n x=-a,\\qquad y=-b,\\qquad t=a+b+c,\n \\qquad\n a=-x,\\quad b=-y,\\quad c=t+x+y.\n\\]\nFor flat inputs set\n\\[\n \\begin{aligned}\n G_0(u,v)&=F_0(-u,-v),\\\\\n G_1(u,v)&=F_1(u+v,-u),\\\\\n G_2(u,v)&=F_2(-v,u+v).\n \\end{aligned}\n\\]\nEach pair map has determinant one. The inverse formulas are\n\\[\n \\begin{aligned}\n F_0(r,s)&=G_0(-r,-s),\\\\\n F_1(r,s)&=G_1(-s,r+s),\\\\\n F_2(r,s)&=G_2(r+s,-r).\n \\end{aligned}\n\\]\nThese maps preserve $L^3$ norms, compact smoothness, and the Schwartz\nclass. Direct substitution identifies both the integrands and the\nfinite truncation regions. Absolute integrability, the uniform bound,\nand joint scalar convergence therefore follow from\nCorollary~\\ref{cor:scalar-pv}. The inverse maps realize every simplex\ntriple, so the optimal constants are equal. Density proves uniqueness\nof the bounded trilinear extension.\n\\end{proof}\n\nFor completeness, let $C_S$ denote this common optimal scalar constant.\nIt is also the optimal constant for the uniform family of individual\nbilinear bounds\n\\[\n \\|B_{\\varepsilon,R}(F,G)\\|_p\n \\le C_S\\|F\\|_3\\|G\\|_3.\n\\]\nIndeed, H\\\"older's inequality gives one direction. For the other, if\n$U\\in L^p$ is nonzero, then\n\\[\n H=\\frac{\\overline U}{|U|}\\,\n       \\frac{|U|^{1/2}}{\\|U\\|_p^{1/2}},\n\\]\ndefined to be zero where $U=0$, satisfies $\\|H\\|_3=1$ and\n$\\int HU=\\|U\\|_p$. Apply this duality formula to each finite bilinear\ntruncation. In particular, the optimal pointwise maximal constant is\nat least $C_S$; equality of the scalar constants does not assert\nequality with the maximal or variation constants.\n"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/frequency.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/frequency.tex", "bytes": 20971, "sha256": "d7c8163675bcee6040efcab72c6ad0146aed9eccc00ee42d933382f0e6213ec2", "content": "\\section{A uniform frequency-block estimate}\\label{sec:frequency}\n\nThe rough kernels arising from hard truncation will be decomposed into\nfrequency blocks.  The estimate in this section permits their coefficients\nto depend on the output point and on frequency.  Its decisive hypothesis is\na pointwise square-sum bound over scales and frequencies; no sum of\nfrequency suprema is required.\n\nFix $q>0$, let $D\\geq1$, and let $\\Xi_D$ be a measurable subset of\n$[-2D,2D]$, equipped with the measure $\\dd\\gamma(\\xi)=\\dd\\xi/D$.\nThus $\\gamma(\\Xi_D)\\leq4$.  Define\n\\begin{equation}\\label{eq:frequency-windows}\n \\ell_\\xi(z)=D^{-1}\\frac{\\dd}{\\dd z}\n                 \\bigl(g_q(z)e^{i\\xi z}\\bigr),\n \\qquad H_\\xi=\\ell_\\xi*\\ell_\\xi*\\ell_\\xi.\n\\end{equation}\nThe parameters $q,D$ are suppressed in this notation.  Constants below\nmay depend on $q$, but not on $D$.\n\n\\begin{proposition}[Frequency-block estimate]\\label{prop:frequency-block}\nLet $\\mathcal I\\subset\\mathbb Z$ be finite and nonempty, and set\n$L_k=2^k$.  Let $A_0,A_1,A_2$ be finite arrays on the lattice\n$u_i=ih$, with the norms $n_v$ defined in the heat-flow preliminaries.\nSuppose that measurable coefficients $\\mu_{ij,k}:\\Xi_D\\to\\C$ satisfy\n\\begin{equation}\\label{eq:frequency-coefficient-condition}\n |\\mu_{ij,k}(\\xi)|\\leq C_1,\n \\qquad\n \\sum_{k\\in\\mathcal I}\\int_{\\Xi_D}\n           |\\mu_{ij,k}(\\xi)|^2\\,\\dd\\gamma(\\xi)\\leq C_1^2\n\\end{equation}\nfor every output index pair $(i,j)$, with the first inequality holding\nalmost everywhere.  If\n\\begin{equation}\\label{eq:frequency-mesh}\n h^2\\leq\\frac{q\\min_{k\\in\\mathcal I}L_k^2}{16D^2},\n\\end{equation}\nthen\n\\begin{equation}\\label{eq:frequency-finite-estimate}\n \\left|h^3\\int_{\\Xi_D}\\sum_{k\\in\\mathcal I}\\sum_{i,j,l}\n A_0(i,j)\\mu_{ij,k}(\\xi)A_1(j,l)A_2(l,i)\n \\Dil_{L_k}H_\\xi(u_i+u_j+u_l)\\,\\dd\\gamma(\\xi)\\right|\n \\leq C(q,C_1)\\prod_{v=0}^2n_v.\n\\end{equation}\n\\end{proposition}\n\nThere are two reasons for refining one Gaussian variance in the proof.\nFirst, a window oscillating at frequency $D$ becomes an average of score\ninsertions at variance comparable to $D^{-2}s$.  Second, the boundary\nenergy produced by changing the output array can still be estimated\nuniformly at this small variance.  More precisely, the refined row\nscores will be controlled by the heat identity through single-edge\nenergies and the changes of star energy at slab boundaries.  Such a\nchange contains the square of the changing edge norm times the norm\nof the unchanged edge.  We isolate a uniform estimate for that mixed\nterm before beginning the main proof.\n\n\\begin{lemma}[A uniform mixed boundary estimate]\n\\label{lem:frequency-boundary}\nFix distinct vertices $v,w\\in\\{0,1\\}$, so that $\\{v,w\\}=\\{0,1\\}$.\nLet the edge joining $v,w$ have array $B$, indexed in the original\nendpoint order $(0,1)$, and let the edge joining $v,2$ have array $E$,\nindexed in the order $(v,2)$.  Use variances\n$(\\sigma_v,\\sigma_w,\\sigma_2)=(\\theta s,s,s)$, where\n$0<\\theta\\leq1/16$ and $h^2\\leq\\theta s$.  Write\n$\\dd\\nu_s(d)=g_{(1-\\theta)s}(d)\\,\\dd d$.\nThere is a nonnegative array $\\mathcal H_E(i,j)$, independent of\n$s,\\theta$, such that\n\\begin{align}\n &\\int_{\\R}\\int_{\\Pi_d}\n       \\|C_{v,w}\\|_{\\HS}^2\\|C_{v,2}\\|_{\\HS}\n                  \\,\\dd\\pi_d\\,\\dd\\nu_s(d)\n \\leq C\\sum_{i,j}h^2|B(i,j)|^2\\mathcal H_E(i,j),\n \\label{eq:frequency-boundary-mixed}\\\\\n &\\left(\\sum_{i,j}h^2\\mathcal H_E(i,j)^3\\right)^{1/3}\n \\leq C\\left(\\sum_{m,l}h^2|E(m,l)|^3\\right)^{1/3}.\n \\label{eq:frequency-boundary-norm}\n\\end{align}\nHere $(i,j)$ are always in the original order of the endpoints of\nedge $0$; $i_v,i_w$ denote the same two indices in the order $(v,w)$.\nThe input arrays $B,E$ are extended by zero to $\\mathbb Z^2$.\n\\end{lemma}\n\n\\begin{proof}\nExpand the square of the first Hilbert--Schmidt norm.  For each pair\n$(i,j)$, the measure\n\\[\n h^{-2}w_v(i_v)w_w(i_w)\\,\\dd\\pi_d\\,\\dd\\nu_s(d)\n\\]\nis a probability measure.  Indeed, with $p_v,p_w$ as the free\ncoordinates on $\\Pi_d$, it makes $p_v,p_w,d$ independent normal\nvariables with respective means $u_{i_v},u_{i_w},0$ and variances\n$\\theta s,s,(1-\\theta)s$.  The remaining center is\n$p_2=d-p_v-p_w$.\n\nUnder this probability measure, the expectation of\n$h^{-2}w_v(m)w_2(l)$ is a bivariate Gaussian density, evaluated at\n$(u_m,u_l)$.  Its mean is\n$(u_{i_v},-u_{i_v}-u_{i_w})$, and its covariance matrix is\n\\[\n s\\begin{pmatrix}2\\theta&-\\theta\\\\-\\theta&3\\end{pmatrix}.\n\\]\nCall this covariance matrix $\\Sigma$.  The bounds\n$\\Sigma\\leq4s\\operatorname{diag}(\\theta,1)$ and\n$\\det\\Sigma=\\theta(6-\\theta)s^2$ show, directly from the Gaussian\ndensity formula, that\n\\begin{equation}\\label{eq:frequency-covariance-domination}\n \\mathbb E[w_v(m)w_2(l)]\n \\leq Ch^2g_{C'\\theta s}(u_m-u_{i_v})\n                g_{C's}(u_l+u_{i_v}+u_{i_w}),\n\\end{equation}\nwhere $C$ is absolute and one may take $C'=4$.\n\nLet $M_{\\mathbb Z}^{(1)}$ and $M_{\\mathbb Z}^{(2)}$ be the discrete\nline maximal operators in the two coordinates.  By\nLemma~\\ref{lem:line-maximal}, we can take\n\\begin{equation}\\label{eq:frequency-boundary-majorant}\n \\mathcal H_E(i,j)^2\n =C\\bigl(M_{\\mathbb Z}^{(1)}M_{\\mathbb Z}^{(2)}|E|^2\\bigr)\n                 (i_v,-i_v-i_w).\n\\end{equation}\nIn fact, \\eqref{eq:frequency-covariance-domination} and\n$h^2\\leq\\theta s$ show that\n$\\mathbb E\\|C_{v,2}\\|_{\\HS}^2\\leq\\mathcal H_E(i,j)^2$.\nCauchy--Schwarz under this probability measure now proves\n\\eqref{eq:frequency-boundary-mixed}.  The map\n$(i,j)\\mapsto(i_v,-i_v-i_w)$ is a bijection of $\\mathbb Z^2$.\nThe $\\ell^{3/2}$ bounds for the two line maximal operators, applied\nto $|E|^2$, prove \\eqref{eq:frequency-boundary-norm}.\n\\end{proof}\n\n\\begin{proof}[Proof of Proposition~\\ref{prop:frequency-block}]\nWe may discard a common null set of frequencies so that all pointwise\nbounds in \\eqref{eq:frequency-coefficient-condition} hold.  Set\n\\begin{equation}\\label{eq:frequency-slabs}\n B_{0,k}^\\xi(i,j)=A_0(i,j)\\mu_{ij,k}(\\xi),\\qquad\n a_k=qL_k^2,\\qquad\n \\theta=\\frac1{16D^2}.\n\\end{equation}\nFor each $\\xi$, use $B_{0,k}^\\xi$ on the open slab\n$2a_k<s<3a_k$, and use the zero array on edge $0$ outside these\nslabs.  The slabs are pairwise disjoint because $a_{k+1}=4a_k$.\nThe other two arrays remain $A_1,A_2$ throughout.  All scale\nintegrals below are over\n\\[\n [s_-,s_+]=[\\min_{k\\in\\mathcal I}a_k,\n                         4\\max_{k\\in\\mathcal I}a_k].\n\\]\nThe mesh hypothesis gives $h^2\\leq\\theta s$ on this entire interval.\n\nWe first turn the oscillatory windows into refined row scores.  We\nthen bound the integrated row scores by telescoping the star energies,\nusing Lemma~\\ref{lem:frequency-boundary} at every slab boundary.\n\n\\paragraph{Refining a row variance.}\nFix a slab, put $L=L_k$, $a=a_k$, and $\\omega=\\xi/L$.  The window\nat each vertex is\n\\begin{equation}\\label{eq:frequency-scaled-window}\n f_{\\xi,k}(u):=\\Dil_L\\ell_\\xi(u)\n   =\\frac LD\\frac{\\dd}{\\dd u}\n                  \\bigl(g_a(u)e^{i\\omega u}\\bigr).\n\\end{equation}\nInitially give all three vertices the variance $s$.  The diagonal\ninsertion with entries $f_{\\xi,k}(u_i-p_v)/g_s(u_i-p_v)$ has\noperator norm at most $C(q)$, uniformly on $2a<s<3a$.  Indeed,\n$|L\\omega/D|\\leq2$, and the remaining factor is bounded using\nGaussian decay in $g_a/g_s$.  Split this diagonal into its real\nand imaginary parts before applying Lemma~\\ref{lem:mixed-trace}.\nThe resulting eight terms involve only Hermitian insertions of\nuniformly bounded norm.\n\nConsider the cost belonging to the row star $R_v$.  Replace its row\nvariance $s$ by $b'=\\theta s$, leave both column variances equal\nto $s$, and shift the row center from $p_v$ to $p_v+d$.  Denote\nthe refined Gram and score insertion by $T_v^d$ and $U_v^d$.\nAddition of Gaussian variances gives\n\\begin{equation}\\label{eq:frequency-gram-average}\n T_v=\\int_{\\R}T_v^d\\,\\dd\\nu_s(d),\n \\qquad \\dd\\nu_s(d)=g_{s-b'}(d)\\,\\dd d.\n\\end{equation}\nAll these Grams have a common support: changing a strictly positive\nrow density amounts to invertible row scaling and leaves the kernel\nof the row matrix unchanged.\n\nTo represent the inserted Gram in the same way, put\n$\\omega'=a\\omega/(a-b')$.  Since $b'<a/2$, completing the square\ngives the exact identity\n\\begin{equation}\\label{eq:frequency-modulated-gaussian}\n g_a(u)e^{i\\omega u}\n =\\exp\\!\\left(\\frac{\\omega^2ab'}{2(a-b')}\\right)\n       \\int_{\\R}g_{b'}(u-d)e^{i\\omega'd}g_{a-b'}(d)\\,\\dd d.\n\\end{equation}\nThe exponential prefactor is at most $C(q)$, because\n$\\omega^2b'\\leq C(q)$.  Also\n\\[\n g_{a-b'}(d)\\leq Cg_{s-b'}(d),\\qquad\n \\frac LD\\leq C(q)\\sqrt{\\theta s}.\n\\]\nDifferentiate \\eqref{eq:frequency-modulated-gaussian} in $u$.\nThe derivative of the first Gaussian supplies the negative refined\nrow score $-(u-d)/b'$.  Consequently, for either the real or the\nimaginary diagonal insertion, its inserted Gram $K_v$ has the form\n\\[\n K_v=\\int_{\\R}\\alpha(d)U_v^d\\,\\dd\\nu_s(d),\n \\qquad \\alpha(d)=\\operatorname{Re}c(d)\n       \\quad\\hbox{or}\\quad\\operatorname{Im}c(d),\n\\]\nwhere the scalar coefficient is explicitly\n\\[\n c(d)=-\\frac LD\n       \\exp\\!\\left(\\frac{\\omega^2ab'}{2(a-b')}\\right)\n       e^{i\\omega'd}\\frac{g_{a-b'}(d)}{g_{s-b'}(d)}.\n\\]\nThe preceding bounds give\n$|\\alpha(d)|\\leq C(q)\\sqrt{\\theta s}$.\nThe averaged matrices are integrable, since the finite-array Gaussian\nweights dominate the polynomial score factors.  By\n\\eqref{eq:frequency-gram-average},\nLemma~\\ref{lem:joint-convexity}, and the quadratic homogeneity of\n$b_T$ in its second argument,\n\\begin{equation}\\label{eq:frequency-score-refinement}\n b_{T_v}(K_v)\n \\leq C(q)\\theta s\\int_{\\R}\n           V_v(p_0,\\ldots,p_v+d,\\ldots,p_2;s)\\,\\dd\\nu_s(d).\n\\end{equation}\nThe cost on the right uses variances\n\\begin{equation}\\label{eq:frequency-anisotropic-variances}\n \\sigma_v=\\theta s,\\qquad \\sigma_w=s\\quad(w\\ne v).\n\\end{equation}\nThus the factor $\\theta$ in the refined score is gained at exactly\nthe scale needed to accommodate the oscillation.\n\n\\paragraph{Integrated refined scores.}\nFix $v$ and use \\eqref{eq:frequency-anisotropic-variances} for the\nrest of this part of the proof.  All stars, energies, and costs\nrefer to the slab-dependent edge $0$ just defined.  Dependence\non $\\xi$ is suppressed until frequency integration is needed.\nFor a center-dependent cost $Q$, write\n\\[\n \\widetilde Q(s)=\\int_{\\R}\\int_{\\Pi_d}Q(p,s)\n                         \\,\\dd\\pi_d\\,\\dd\\nu_s(d),\\qquad\n \\widetilde J_v(s)=\\int_{\\R}J_v(d,s)\\,\\dd\\nu_s(d).\n\\]\nOn each open interval where the arrays are fixed, the heat identities\n\\eqref{eq:heat-plane-identities} give\n\\begin{equation}\\label{eq:frequency-averaged-heat}\n 2\\partial_s\\widetilde J_v\n   =3\\widetilde Z_v-\\theta\\widetilde V_v\n                           -\\sum_{w\\ne v}\\widetilde Y_{v,w}.\n\\end{equation}\nHere the coefficient $3$ has a useful interpretation.  The original\nplane second derivative has coefficient $2+\\theta$.  Differentiating\n$\\nu_s$, applying its heat equation, and integrating twice by parts\nadds $1-\\theta$.  The second heat identity identifies the averaged\nplane second derivative with $\\widetilde Z_v$.\n\nFor fixed arrays on a compact positive scale interval,\nLemma~\\ref{lem:heat-identities} bounds the plane integrals of the\nenergies, costs, and relevant absolute derivatives by polynomials in\n$|d|$. The density $\\nu_s$ and its derivatives have Gaussian decay.\nThese bounds justify differentiation under the integral and the two\nintegrations by parts in $d$, and give the required one-sided limits\nat slab boundaries. Their constants may depend on the mesh and\n$\\theta$; they justify the identities and do not enter the estimates.\n\nThe metric Cauchy--Schwarz inequality gives\n$2Z_v\\leq\\sum_{w\\ne v}Y_{v,w}$.  Hence\n\\begin{equation}\\label{eq:frequency-score-differential}\n \\theta\\widetilde V_v\n \\leq-2\\partial_s\\widetilde J_v\n                         +\\frac12\\sum_{w\\ne v}\\widetilde Y_{v,w}.\n\\end{equation}\nThe column costs in this inequality can be paid for by individual\nedges.  Indeed, $S_v\\geq C_{v,w}C_{v,w}^*$, so the order comparison\nof Lemma~\\ref{lem:order} yields\n\\begin{equation}\\label{eq:frequency-edge-cost}\n Y_{v,w}\\leq\n b_{C_{v,w}C_{v,w}^*}(C_{v,w}N_wC_{v,w}^*)=:Y^e_{v,w}.\n\\end{equation}\nThe inserted matrix is supported on the single-edge Gram, so this\ncomparison also applies if the larger star Gram has a larger\nsupport.  Define the integrated single-edge energy\n\\[\n J^e_{v,w}(s)=\\iint_{\\R^2}\n                  \\Phi(C_{v,w}C_{v,w}^*)\\,\\dd p_v\\,\\dd p_w.\n\\]\nThe integral of $Y^e_{v,w}$ over $\\Pi_d$ is independent of $d$,\nbecause only the two endpoint centers occur.  With just this edge\npresent, the heat identities give\n\\begin{equation}\\label{eq:frequency-edge-dissipation}\n -2\\partial_sJ^e_{v,w}(s)\n \\geq\\iint_{\\R^2}Y^e_{v,w}(p,s)\\,\\dd p_v\\,\\dd p_w.\n\\end{equation}\nThe coefficient of this column cost is $1$; the remaining row cost\nis nonnegative.  Thus the estimate is independent of the small\nvariance ratio $\\theta$.\n\nIf the edge is unchanged throughout $[s_-,s_+]$, integration of\n\\eqref{eq:frequency-edge-dissipation} and\nLemma~\\ref{lem:single-edge-energy} bound its total cost by\n$C(n_1^3+n_2^3)$.  If it is edge $0$, apply the same argument\nseparately on its slabs.  The result is bounded by\n\\[\n C\\sum_{k\\in\\mathcal I}\\sum_{i,j}h^2\n                         |B_{0,k}^\\xi(i,j)|^3.\n\\]\nAfter integration in $\\xi$, this is at most $C(C_1)n_0^3$, since\n\\begin{equation}\\label{eq:frequency-cubic-summation}\n \\sum_k\\int_{\\Xi_D}|B_{0,k}^\\xi(i,j)|^3\\,\\dd\\gamma\n \\leq C_1|A_0(i,j)|^3\n       \\sum_k\\int_{\\Xi_D}|\\mu_{ij,k}(\\xi)|^2\\,\\dd\\gamma\n \\leq C_1^3|A_0(i,j)|^3.\n\\end{equation}\nWe have therefore controlled all column terms in\n\\eqref{eq:frequency-score-differential}.  The remaining issue is\nthe boundary energy of the stars.\n\n\\paragraph{Summing the slab boundaries.}\nBefore the first slab, edge $0$ is zero.  By\nLemma~\\ref{lem:initial-energy},\n$\\widetilde J_v(s_-)\\leq C(n_1^3+n_2^3)$.\nThe star at vertex $2$ has no jumps.  For $v\\in\\{0,1\\}$, let\n$w$ be the other vertex of edge $0$.  At either boundary of a\nslab, write $C_{\\rm sw}=C_{v,w}$ for the slab-side weighted block and\n$C_{\\rm fix}=C_{v,2}$ for the unchanged weighted block.\nThe absolute change in the pointwise star\nenergy is at most\n\\begin{equation}\\label{eq:frequency-pointwise-jump}\n C\\bigl(\\|C_{\\rm sw}\\|_{\\HS}^3\n       +\\|C_{\\rm sw}\\|_{\\HS}^2\\|C_{\\rm fix}\\|_{\\HS}\\bigr).\n\\end{equation}\nTo verify this bound, integrate the derivative of\n$\\Phi(C_{\\rm fix}C_{\\rm fix}^*+tC_{\\rm sw}C_{\\rm sw}^*)$\nfor $0<t\\leq1$. Its support is fixed there,\nand the derivative is\n\\[\n \\begin{aligned}\n &\\frac32\\tr\\bigl((C_{\\rm fix}C_{\\rm fix}^*\n                   +tC_{\\rm sw}C_{\\rm sw}^*)^{1/2}\n                    C_{\\rm sw}C_{\\rm sw}^*\\bigr)\\\\\n &\\qquad\\leq\\frac32\\bigl(\\|C_{\\rm fix}\\|_{\\HS}\n                    +\\|C_{\\rm sw}\\|_{\\HS}\\bigr)\n                      \\|C_{\\rm sw}\\|_{\\HS}^2.\n \\end{aligned}\n\\]\nContinuity gives the endpoint at $t=0$.  Removing the edge has\nthe same absolute change as adding it.\n\nThe integrated cubic term in\n\\eqref{eq:frequency-pointwise-jump} is bounded by the cube of\nthe norm of $B_{0,k}^\\xi$, by\nLemma~\\ref{lem:initial-energy}.  Summing both boundaries of\nevery slab and using \\eqref{eq:frequency-cubic-summation} gives\n$C(C_1)n_0^3$ after frequency integration.\n\nFor the mixed term, let $A^{(v,2)}$ be the fixed unweighted array on\nthe edge joining $v,2$, indexed in that order. Apply\nLemma~\\ref{lem:frequency-boundary} with this array and denote its\nmajorant by $\\mathcal H_v$. It is independent of $k,\\xi,s$, and its cube norm is\nat most $C(n_1+n_2)$.  Both boundaries together consequently\ncontribute at most a constant times\n\\begin{align*}\n &\\sum_{i,j}h^2|A_0(i,j)|^2\\mathcal H_v(i,j)\n       \\sum_k\\int_{\\Xi_D}|\\mu_{ij,k}(\\xi)|^2\\,\\dd\\gamma\\\\\n &\\hspace{25mm}\\leq C(C_1)n_0^2(n_1+n_2),\n\\end{align*}\nby \\eqref{eq:frequency-coefficient-condition} and H\\\"older's\ninequality.  This is the point at which averaging coefficient\nsquares over frequency is essential.  Taking a separate supremum\nin frequency for each scale would not give this bound.\n\nIntegrate \\eqref{eq:frequency-score-differential} on the intervals\nbetween consecutive boundaries and telescope.  The final energy\nis nonnegative and may be discarded.  The initial energy and\nthe absolute jumps just estimated, together with the single-edge\ncosts, give\n\\begin{equation}\\label{eq:frequency-integrated-score}\n \\int_{\\Xi_D}\\int_{s_-}^{s_+}\n        \\theta\\widetilde V_v(s)\\,\\dd s\\,\\dd\\gamma(\\xi)\n \\leq C(C_1)\\sum_{j=0}^2n_j^3.\n\\end{equation}\nHere we absorbed $n_0^2(n_1+n_2)$ by Young's inequality.\nValues at the finitely many boundaries are immaterial.\nAll integrands are measurable in $\\xi$: the arrays are measurable,\nand the finite-matrix energies and supported quadratic forms are\nBorel functions of their entries.  The latter can also be obtained\nas limits of positive-definite regularizations.  Thus every\nnonnegative frequency integration above is justified by Tonelli's\nTheorem, without any regularity assumption on the coefficient\nfunctions beyond measurability.\n\n\\paragraph{Recovering the frequency-block form.}\nReturn to the equal-variance stars and apply\nLemma~\\ref{lem:mixed-trace} to the real and imaginary parts of\nthe three window insertions.  Integrate the bound over base centers\non $\\Pi_0$.  Shifting the row center in\n\\eqref{eq:frequency-score-refinement} maps this plane to $\\Pi_d$;\nusing the two column centers as free coordinates shows that its\nJacobian is $1$.  The integrated cost at vertex $v$ is therefore\nat most\n$C(q)\\theta s\\widetilde V_v(s)$.\nIntegrate over every slab with $\\dd s/s$, and then over $\\Xi_D$\nwith $\\dd\\gamma$.  The sum of the resulting bounds is controlled\nby \\eqref{eq:frequency-integrated-score}.\n\nOn the other side, multiplying the three weighted windows in\nthe trace and integrating the centers gives exactly\n\\[\n h^3\\sum_{i,j,l}B_{0,k}^\\xi(i,j)A_1(j,l)A_2(l,i)\n                \\Dil_{L_k}H_\\xi(u_i+u_j+u_l).\n\\]\nThis is the convolution identity on $\\Pi_0$: the three arguments\nof the windows sum to $u_i+u_j+u_l$.  The expression is independent\nof $s$ within its slab, whose logarithmic length is\n$\\int_{2a_k}^{3a_k}\\dd s/s=\\log(3/2)$.\nAll signed center integrations are absolutely convergent by\nGaussian decay.  We have proved\n\\eqref{eq:frequency-finite-estimate} with its right side replaced\nby $C(q,C_1)\\sum_v n_v^3$.\n\nFinally, if all $n_v$ are nonzero, apply this estimate to\n$A_v/n_v$.  The coefficients and mesh hypothesis are unchanged,\nand trilinearity gives the product bound claimed in the\nProposition.  If any norm is zero, the form is zero.\n\\end{proof}\n\n\\begin{corollary}[Continuous frequency-block estimate]\n\\label{cor:frequency-continuum}\nLet $q,D,\\Xi_D,\\mathcal I$ be as in\nProposition~\\ref{prop:frequency-block}.  Suppose that\n$\\mu_{a,b,k}(\\xi)$ is constant in $(a,b)$ on every cell of one\nfinite rectangular partition, independent of $\\xi$ and common to\nall $k$.  Suppose its values on these cells are bounded measurable\nfunctions of $\\xi$, and that\n\\begin{equation}\\label{eq:frequency-continuous-condition}\n |\\mu_{a,b,k}(\\xi)|\\leq C_1,\n \\qquad\n \\sum_k\\int_{\\Xi_D}|\\mu_{a,b,k}(\\xi)|^2\\,\\dd\\gamma(\\xi)\n \\leq C_1^2.\n\\end{equation}\nDefine\n\\begin{equation}\\label{eq:frequency-continuous-kernel}\n \\mathcal K_{a,b}(t)\n   =\\int_{\\Xi_D}\\sum_{k\\in\\mathcal I}\n          \\mu_{a,b,k}(\\xi)\\Dil_{L_k}H_\\xi(t)\\,\\dd\\gamma(\\xi).\n\\end{equation}\nThen, for complex $G_v\\in L^3(\\R^2)$,\n\\begin{equation}\\label{eq:frequency-continuous-estimate}\n |\\Lambda_{\\mathcal K}(G_0,G_1,G_2)|\n       \\leq C(q,C_1)\\prod_{v=0}^2\\|G_v\\|_3.\n\\end{equation}\n\\end{corollary}\n\n\\begin{proof}\nFirst take smooth compactly supported $G_v$.  Within any fixed\nstep choice, the kernel in\n\\eqref{eq:frequency-continuous-kernel} is continuous in $t$:\nthe frequency block is bounded, the coefficients are bounded and\nmeasurable, and the Gaussian formulas supply a common majorant\nfor dominated convergence.  After performing the frequency\nintegration, the trilinear integral is therefore the limit of\nits lattice Riemann sums.  The finitely many rectangular step\nboundaries have measure zero.  The mesh condition\n\\eqref{eq:frequency-mesh} holds for all sufficiently fine lattices,\nand the discrete norms converge to $\\|G_v\\|_3$.\nProposition~\\ref{prop:frequency-block} proves\n\\eqref{eq:frequency-continuous-estimate} for these inputs.\n\nTo pass to general $L^3$ inputs, note that for the fixed finite\nblock and scale set there is an integrable majorant $k_*(t)$\nwith $|\\mathcal K_{a,b}(t)|\\leq k_*(t)$, uniformly in $(a,b)$.\nOne may take a constant times the finite sum of the dilates of\n$\\sup_{\\xi\\in\\Xi_D}|H_\\xi|$, which has Gaussian decay.\nFor every integrable nonnegative $k$,\n\\[\n \\iiint |G_0(a,b)G_1(b,c)G_2(c,a)|k(a+b+c)\n                         \\,\\dd a\\,\\dd b\\,\\dd c\n \\leq\\|k\\|_1\\prod_v\\|G_v\\|_3.\n\\]\nIndeed, set $t=a+b+c$, apply H\\\"older's inequality in $(a,b)$\nfor fixed $t$, and use the determinant-one changes of variables\nfor the other two edge functions.  Thus the defining integral\nis absolutely convergent and continuous in the three $L^3$\ninputs.  Smooth approximation completes the proof.  The final\nconstant comes from the Proposition and remains independent of\n$D$, the number of scales, and the particular step choices.\n\\end{proof}\n"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/heat.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/heat.tex", "bytes": 14921, "sha256": "c6a85b01fdecf3a7b03bf3ba25ec03a3da68ab71c3c85c5d82da139ce1ebd3c6", "content": "\\section{Gaussian heat dissipation}\\label{sec:heat}\n\nWe use Gaussian weights to turn the matrix costs of the preceding section\ninto derivatives of an energy.  Integration over a plane of Gaussian\ncenters then gives two complementary estimates: strict dissipation of the\nsum of the three star energies when the variances are comparable, and\nsingle-edge dissipation for arbitrary positive variance rates.  Both\nestimates are uniform in the matrix dimension.  We retain arbitrary\npositive rates in the differential identities, since both forms of\ndissipation will be needed below.  Continuous Gaussian\ntelescoping for entangled forms also appears in\nDurcik~\\cite[Lemma~3 and Section~3]{Durcik2015}.\n\nFix $h>0$, an integer $N\\ge0$, and nodes $u_i=ih$, $-N\\le i\\le N$.\nThe vertex index $v$ is taken in $\\mathbb Z/3\\mathbb Z$.\nFor finite complex edge arrays $A_v(i,j)$, put\n\\[\n n_v=\\left(\\sum_{i,j=-N}^N h^2|A_v(i,j)|^3\\right)^{1/3}.\n\\]\nFor centers $p_v\\in\\R$ and variances $\\sigma_v>0$, define\n\\begin{equation}\\label{eq:weights}\n \\begin{gathered}\n g_\\sigma(t)=(2\\pi\\sigma)^{-1/2}e^{-t^2/(2\\sigma)},\n \\qquad w_v(i)=h g_{\\sigma_v}(u_i-p_v),\\\\\n W_v(i,j)=A_v(i,j)\\sqrt{w_v(i)w_{v+1}(j)}.\n \\end{gathered}\n\\end{equation}\nForm the stars and their Grams as in\n\\eqref{eq:row-star}--\\eqref{eq:star-grams}, and set\n$e_v=\\Phi(T_v)=\\Phi(S_v)$.\nTo distinguish the two blocks of the star, write\n\\[\n C_{v,v+1}=W_v,\\qquad C_{v,v-1}=W_{v-1}^*,\n \\qquad R_v=[C_{v,v+1}\\ C_{v,v-1}].\n\\]\nThe diagonal Gaussian scores and the corresponding inserted Grams are\n\\[\n N_v=\\operatorname{diag}_i\\frac{u_i-p_v}{\\sigma_v},\\qquad\n U_v=R_v^*N_vR_v,\\qquad\n P_{v,w}=C_{v,w}N_wC_{v,w}^*\\quad(w\\ne v).\n\\]\nTheir quadratic costs are\n\\begin{equation}\\label{eq:heat-costs}\n V_v=b_{T_v}(U_v),\\qquad\n Y_{v,w}=b_{S_v}(P_{v,w}),\\qquad\n Z_v=\\mathcal B_{S_v}(P_{v,v+1},P_{v,v-1}).\n\\end{equation}\nAll these insertions are supported on their respective Grams: an\ninserted Gram $B^*DB$ annihilates $\\ker B$ on both sides.\n\nFor $d\\in\\R$, equip the plane\n$\\Pi_d=\\{p_0+p_1+p_2=d\\}$ with the measure\n\\begin{equation}\\label{eq:heat-plane-measure}\n \\int_{\\Pi_d}H\\,\\dd\\pi_d\n =\\int_{\\R^2}H(p_0,p_1,d-p_0-p_1)\\,\\dd p_0\\,\\dd p_1.\n\\end{equation}\nAny pair of centers can be the free coordinates, since the changes\nbetween these charts have absolute determinant one.\nGiven positive rates $\\boldsymbol\\alpha=(\\alpha_0,\\alpha_1,\\alpha_2)$,\nwe evaluate the weights at $\\sigma_i=\\alpha_i s$ and write\n\\[\n J_v(d,s)=\\int_{\\Pi_d}e_v\\,\\dd\\pi_d,\n \\qquad J(d,s)=\\sum_vJ_v(d,s),\n \\qquad \\Sigma_\\alpha=\\sum_i\\alpha_i.\n\\]\nThe arrays remain fixed whenever a scale derivative is taken.\n\n\\subsection{Differentiation and the plane identities}\n\n\\begin{lemma}[Heat identities]\\label{lem:heat-identities}\nFor fixed finite arrays and arbitrary positive rates, the energies $e_v$\nare smooth in the centers and positive variances.  For $s>0$,\n\\begin{align}\n 2\\partial_s e_v\n &=\\sum_i\\alpha_i\\partial_{p_i}^2e_v\n   -\\alpha_vV_v-\\sum_{w\\ne v}\\alpha_wY_{v,w},\n   \\label{eq:heat-identity}\\\\\n \\partial_{p_{v+1}}\\partial_{p_{v-1}}e_v&=Z_v.\n   \\label{eq:heat-mixed}\n\\end{align}\nThe plane integrals are finite, continuously differentiable in $s$, and\ntwice continuously differentiable in $d$, with\n\\begin{equation}\\label{eq:heat-plane-identities}\n \\begin{aligned}\n 2\\partial_sJ_v(d,s)\n &=\\Sigma_\\alpha\\partial_d^2J_v(d,s)\n   -\\int_{\\Pi_d}\\left(\\alpha_vV_v+\n                  \\sum_{w\\ne v}\\alpha_wY_{v,w}\\right)\\dd\\pi_d,\\\\\n \\partial_d^2J_v(d,s)&=\\int_{\\Pi_d}Z_v\\,\\dd\\pi_d.\n \\end{aligned}\n\\end{equation}\nOn compact positive scale intervals, the plane integrals of the absolute\nvalues of all derivatives used here grow at most polynomially in $|d|$.\n\\end{lemma}\n\n\\begin{proof}\nWe first check that the possible kernels of the Grams cause no\ndifferentiability problem.  Each star has the form\n\\[\n R_v=D_{\\rm row}R_v^0D_{\\rm col},\\qquad\n R_v^0=[A_v\\ A_{v-1}^*],\n\\]\nwhere both diagonal multipliers are positive and invertible.  With the\ncolumn parameters fixed, $\\ker R_v$ is fixed, so $T_v$ has fixed\nsupport as the row parameters vary.  With the row parameters fixed,\n$\\operatorname{ran}R_v$ is fixed, so $S_v$ has fixed support as either\nor both column parameters vary.  For joint smoothness, factor a nonzero\n$R_v^0$ as $XY^*$ with $X,Y$ of full column rank.  Set\n$X'=D_{\\rm row}X$, $Y'=D_{\\rm col}Y$, $P'=X'^*X'$, and $Q'=Y'^*Y'$.\nThese last two matrices are positive definite and smooth, and the\nnonzero eigenvalues of $R_vR_v^*$ are those of\n$Q'^{1/2}P'Q'^{1/2}$.  Consequently\n\\[\n e_v=\\Phi(Q'^{1/2}P'Q'^{1/2})\n\\]\nis smooth.  The rank-zero case is immediate.\n\nFor one Gaussian density $w=h g_\\sigma(u-p)$ and its score\n$N=(u-p)/\\sigma$,\n\\[\n \\partial_pw=Nw,\\qquad\n \\partial_p^2w=(N^2-\\sigma^{-1})w,\\qquad\n 2\\partial_\\sigma w=\\partial_p^2w.\n\\]\nWith the columns fixed, $T_v$ is linear in the row density, and hence\n\\[\n \\partial_{p_v}T_v=U_v,\\qquad\n 2\\partial_{\\sigma_v}T_v=\\partial_{p_v}^2T_v.\n\\]\nThe chain rule on its fixed support and Lemma~\\ref{lem:hessian} give\n\\[\n 2\\partial_{\\sigma_v}e_v=\\partial_{p_v}^2e_v-V_v.\n\\]\nFor a column vertex $w\\ne v$, use $S_v$ instead.  It is linear in that\ncolumn density, has fixed support, and satisfies\n$\\partial_{p_w}S_v=P_{v,w}$.  Thus\n$2\\partial_{\\sigma_w}e_v=\\partial_{p_w}^2e_v-Y_{v,w}$.\nTaking the variance derivative along $\\sigma_i=\\alpha_i s$ proves\n\\eqref{eq:heat-identity}.  The two summands of\n$S_v=C_{v,v+1}C_{v,v+1}^*+C_{v,v-1}C_{v,v-1}^*$ depend on different\ncolumn densities.  Its mixed derivative in those two centers is zero;\nthe Hessian term in the chain rule is therefore exactly $Z_v$, proving\n\\eqref{eq:heat-mixed}.\n\nBefore integrating these identities, we give the domination needed for\ndifferentiation under the integral.  Lemma~\\ref{lem:insertion} yields\n\\[\n V_v\\le\\tfrac34\\|N_v\\|_{\\op}^2e_v,\n \\qquad\n Y_{v,w}\\le\\tfrac34\\|N_w\\|_{\\op}^2e_v,\n \\qquad\n 2|Z_v|\\le Y_{v,v+1}+Y_{v,v-1}.\n\\]\nFor the second inequality, apply the insertion bound to $R_v^*$,\npadding $N_w$ by zero on the other column block.\nFor any Hermitian $B$ on the row space,\n\\[\n \\big|\\tr(\\sqrt{T_v}R_v^*BR_v)\\big|\n \\le\\|B\\|_{\\op}e_v,\n\\]\nbecause $R_v\\sqrt{T_v}R_v^*$ is positive and has trace $e_v$.\nThe corresponding estimate for a column insertion follows by using\n$S_v$.  The Gaussian derivative formulas and the Hessian chain rule\ntherefore bound every first center derivative, pure second center\nderivative, mixed column derivative, and first scale derivative used\nabove by a polynomial in the centers times $e_v$, locally uniformly in\n$s>0$.\n\nThe energy itself satisfies\n\\begin{equation}\\label{eq:heat-energy-bound}\n e_v\\le\\left(\\|W_v\\|_{\\HS}^2+\n                  \\|W_{v-1}\\|_{\\HS}^2\\right)^{3/2}\n \\le\\sqrt2\\left(\\|W_v\\|_{\\HS}^3+\n                       \\|W_{v-1}\\|_{\\HS}^3\\right).\n\\end{equation}\nSince the node set is finite, each edge norm cubed is bounded by a\nGaussian in its two endpoint centers, locally uniformly for positive\nvariances.  On $\\Pi_d$, with $|d|\\le D$, each pair of distinct indices\n$i,j$ satisfies\n\\[\n p_0^2+p_1^2+p_2^2\\le4(p_i^2+p_j^2)+3D^2.\n\\]\nThus the preceding bounds have integrable majorants in every plane\nchart, locally uniformly in $(d,s)$.  Dominated convergence justifies\nall the asserted derivatives.  To obtain polynomial growth in $d$,\nuse the two endpoints of each bounding Gaussian as free coordinates;\nthe third center is $d$ minus their sum.  Integrating a polynomial times\nthat Gaussian leaves at most polynomial growth in $|d|$.  Constants in\nthese differentiability arguments may depend on the fixed arrays,\nmesh, rates, and compact scale interval, but do not enter the estimates\nbelow.\n\nChoosing $p_i$ as the dependent coordinate gives, for each $i$,\n\\[\n \\partial_d^2J_v=\\int_{\\Pi_d}\\partial_{p_i}^2e_v\\,\\dd\\pi_d.\n\\]\nTo obtain the mixed derivative, first choose $p_{v+1}$ dependent and\ndifferentiate once.  Reparametrize the resulting integral with\n$p_{v-1}$ dependent and differentiate once more.  This gives\n\\[\n \\partial_d^2J_v\n =\\int_{\\Pi_d}\\partial_{p_{v-1}}\\partial_{p_{v+1}}e_v\\,\\dd\\pi_d\n =\\int_{\\Pi_d}Z_v\\,\\dd\\pi_d.\n\\]\nIntegrating \\eqref{eq:heat-identity} proves the first identity in\n\\eqref{eq:heat-plane-identities}.  These comparisons concern integrated\nderivatives; no pointwise equality of pure and mixed center derivatives\nis asserted.\n\\end{proof}\n\n\\subsection{Strict dissipation at comparable variances}\n\nThe plane identities contain mixed terms $Z_v$ whose signs are not\ncontrolled.  We bound them by column costs and then compare those costs\nwith the row cost at the neighboring vertex.  The resulting loss is\n$\\sqrt2$, small enough to leave strict dissipation for the variance\nratios we use.\n\n\\begin{lemma}[Plane dissipation]\\label{lem:plane-dissipation}\nFor fixed finite arrays and arbitrary positive rates,\n\\begin{equation}\\label{eq:heat-plane-dissipation}\n 2\\partial_sJ(d,s)\\le\\int_{\\Pi_d}\\sum_w\n \\left[-\\alpha_w+\n \\sqrt2\\max\\{0,\\Sigma_\\alpha/2-\\alpha_w\\}\\right]V_w\\,\\dd\\pi_d.\n\\end{equation}\nIn particular, put $\\lambda=11/10$.  For each permutation of\n$(1,\\lambda,\\lambda)$,\n\\begin{equation}\\label{eq:heat-strict}\n \\begin{gathered}\n -\\partial_sJ(d,s)\\ge c_*\\int_{\\Pi_d}\\sum_wV_w\\,\\dd\\pi_d,\\\\\n c_* =\\tfrac12\\min\\{1-\\tfrac35\\sqrt2,\n                      \\tfrac{11}{10}-\\tfrac12\\sqrt2\\}>0.\n \\end{gathered}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nWe first prove the neighbor comparison\n\\begin{equation}\\label{eq:heat-neighbors}\n Y_{w+1,w}+Y_{w-1,w}\\le\\sqrt2\\,V_w.\n\\end{equation}\nWrite $A=W_w$, $B=W_{w-1}^*$, and $N=N_w$.  The elementary inequality\n$\\|Az+Bz'\\|^2\\le2\\|Az\\|^2+2\\|Bz'\\|^2$ gives\n\\[\n T_w\\le2Q,\\qquad Q=\\operatorname{diag}(A^*A,B^*B).\n\\]\nBy Lemma~\\ref{lem:order},\n$V_w\\ge2^{-1/2}b_Q([A\\ B]^*N[A\\ B])$.\nIn a block-preserving eigenbasis of $Q$, the Hessian cost is a sum of\nnonnegative squared entries.  Discarding the off-diagonal blocks gives\n\\[\n b_Q([A\\ B]^*N[A\\ B])\n \\ge b_{A^*A}(A^*NA)+b_{B^*B}(B^*NB).\n\\]\nNow $A^*A\\le S_{w+1}$ and $B^*B\\le S_{w-1}$; the respective insertions\nare $P_{w+1,w}$ and $P_{w-1,w}$.  A second use of\nLemma~\\ref{lem:order}, with comparison factor one, proves\n\\eqref{eq:heat-neighbors}.\n\nIn \\eqref{eq:heat-plane-identities}, replace\n$2Z_v$ by its upper bound $Y_{v,v+1}+Y_{v,v-1}$ and sum over $v$.\nThe two column costs carrying the score at vertex $w$ have common\ncoefficient $\\Sigma_\\alpha/2-\\alpha_w$.  If this coefficient is\nnegative, discard those terms; otherwise apply\n\\eqref{eq:heat-neighbors}.  This proves\n\\eqref{eq:heat-plane-dissipation}.\nFor the specified rates, $\\Sigma_\\alpha=16/5$, and the two possible\ncoefficients are $-1+(3/5)\\sqrt2$ and $-11/10+(1/2)\\sqrt2$.\nBoth are negative, giving \\eqref{eq:heat-strict}.\n\\end{proof}\n\n\\subsection{Energy bounds and the single-edge case}\n\nThe decrease in \\eqref{eq:heat-strict} controls the accumulated costs by\nan initial energy.  We now bound that energy uniformly once the\nGaussian variances are no smaller than the mesh scale.\n\n\\begin{lemma}[Initial energy]\\label{lem:initial-energy}\nFor all $d\\in\\R$ and all variances $\\sigma_v\\ge h^2$,\n\\begin{equation}\\label{eq:initial-energy}\n \\begin{gathered}\n \\int_{\\Pi_d}\\|W_v\\|_{\\HS}^3\\,\\dd\\pi_d\\le M n_v^3,\n \\qquad M=1+\\sqrt{2/\\pi},\\\\\n \\int_{\\Pi_d}\\sum_ve_v\\,\\dd\\pi_d\n       \\le2\\sqrt2 M\\sum_v n_v^3.\n \\end{gathered}\n\\end{equation}\nThese bounds also hold with the original right-hand sides if any entries\nare replaced by entries of smaller absolute value.\n\\end{lemma}\n\n\\begin{proof}\nThe Gaussian grid mass obeys\n\\begin{equation}\\label{eq:heat-mesh-mass}\n \\sum_{i=-N}^Nh g_\\sigma(u_i-p)\n \\le1+\\frac{2h}{\\sqrt{2\\pi\\sigma}}.\n\\end{equation}\nIndeed, for $u_i\\ge p+h$ compare the summand with the integral over the\npreceding interval of length $h$; for $u_i\\le p-h$, use the succeeding\ninterval.  These intervals are disjoint, and the at most two remaining\nnodes contribute at most $2h\\sup g_\\sigma$.\nFor $\\sigma\\ge h^2$, the right side is at most $M$.\nWeighted H\\\"older therefore gives\n\\[\n \\|W_v\\|_{\\HS}^3\n =\\left(\\sum_{i,j}|A_v(i,j)|^2w_v(i)w_{v+1}(j)\\right)^{3/2}\n \\le M\\sum_{i,j}|A_v(i,j)|^3w_v(i)w_{v+1}(j).\n\\]\nUse $p_v,p_{v+1}$ as free coordinates on $\\Pi_d$.  Each density\nintegrates to $h$, proving the first estimate in\n\\eqref{eq:initial-energy}.  Each edge occurs in two stars, so\n\\eqref{eq:heat-energy-bound} proves the second.  The same upper bounds\ndecrease when the entrywise absolute values decrease.\n\\end{proof}\n\nFor example, if $h^2\\le s_0<s_1$ and the rates are a permutation of\n$(1,\\lambda,\\lambda)$, these two lemmas imply\n\\begin{equation}\\label{eq:heat-fixed-array-integral}\n \\int_{s_0}^{s_1}\\int_{\\Pi_d}\\sum_vV_v\\,\\dd\\pi_d\\,\\dd s\n \\le\\frac{J(d,s_0)-J(d,s_1)}{c_*}\n \\le\\frac{2\\sqrt2 M}{c_*}\\sum_v n_v^3.\n\\end{equation}\nThe first inequality retains the terminal energy, which is essential\nwhen arrays are changed at finitely many scales.\n\nFor much more unequal variance rates,\n\\eqref{eq:heat-plane-dissipation} does not guarantee that the summed\nstar energy decreases.  A single edge nevertheless has no mixed term\nand retains exact dissipation.  We state that consequence separately.\n\n\\begin{lemma}[Single-edge energy]\\label{lem:single-edge-energy}\nFix $v\\ne w$, retain the weighted block $C=C_{v,w}$, and delete the\nother block of the star at $v$.  For arbitrary positive rates, set\n\\[\n \\begin{aligned}\n e^e_{v,w}&=\\Phi(CC^*),\\qquad\n V^e_{v,w}=b_{C^*C}(C^*N_vC),\\qquad\n Y^e_{v,w}=b_{CC^*}(CN_wC^*),\\\\\n J^e_{v,w}(s)&=\\iint_{\\R^2}e^e_{v,w}\\,\\dd p_v\\,\\dd p_w.\n \\end{aligned}\n\\]\nThe plane integrals of $e^e_{v,w}$, $V^e_{v,w}$, and $Y^e_{v,w}$ are\nindependent of $d$.\nMoreover,\n\\begin{equation}\\label{eq:heat-single-edge}\n -2\\partial_sJ^e_{v,w}(s)\n =\\iint_{\\R^2}\n      (\\alpha_vV^e_{v,w}+\\alpha_wY^e_{v,w})\\,\\dd p_v\\,\\dd p_w.\n\\end{equation}\nIf $n_e$ denotes the discrete $L^3$ norm of this edge's unweighted\narray, then, whenever $\\alpha_vs_0,\\alpha_ws_0\\ge h^2$ and $s_1>s_0$,\n\\begin{equation}\\label{eq:heat-single-edge-integral}\n \\int_{s_0}^{s_1}\\iint_{\\R^2}\n      (\\alpha_vV^e_{v,w}+\\alpha_wY^e_{v,w})\n          \\,\\dd p_v\\,\\dd p_w\\,\\dd s\n \\le2M n_e^3.\n\\end{equation}\nFor the original two-block star, $Y_{v,w}\\le Y^e_{v,w}$.\n\\end{lemma}\n\n\\begin{proof}\nThe three integrands depend only on the two endpoint centers.\nUsing these as free coordinates proves independence of $d$.\nIn the one-edge star the other column insertion is zero, so $Z_v=0$.\nAfter permuting column blocks, its matrices are\n\\[\n R=[C\\ 0],\\qquad T=\\operatorname{diag}(C^*C,0),\\qquad\n U=\\operatorname{diag}(C^*N_vC,0),\\qquad S=CC^*.\n\\]\nThe Hessian cost is unchanged by zero extension, so its row and column\ncosts are exactly $V^e_{v,w}$ and $Y^e_{v,w}$.\nEquation~\\eqref{eq:heat-plane-identities} now proves\n\\eqref{eq:heat-single-edge}.\nAlso $e^e_{v,w}\\le\\|C\\|_{\\HS}^3$, so\nLemma~\\ref{lem:initial-energy} gives $J^e_{v,w}(s_0)\\le M n_e^3$.\nIntegrate \\eqref{eq:heat-single-edge} and discard the nonnegative\nterminal energy to obtain \\eqref{eq:heat-single-edge-integral}.\nFinally $CC^*\\le S_v$, while $CN_wC^*$ is supported on $CC^*$.\nLemma~\\ref{lem:order} with factor one gives $Y_{v,w}\\le Y^e_{v,w}$.\n\\end{proof}\n"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/introduction.tex", "bytes": 10021, "sha256": "0c1678042ab9a0e3c5c21c1acd280d1a43b8e3c65e019bf78f7509a49f26422e", "content": "\\section{Introduction}\\label{sec:introduction}\n\nFor complex functions $F,G\\in L^3(\\R^2)$ and\n$0<\\varepsilon<R<\\infty$, define the annular triangular Hilbert transform by\n\\begin{equation}\\label{eq:bilinear-truncation}\n B_{\\varepsilon,R}(F,G)(x,y)\n =\\int_{\\varepsilon<|t|<R}F(x+t,y)G(x,y+t)\\,\\frac{\\dd t}{t}.\n\\end{equation}\nThe two inputs are translated along different coordinate directions.\nEvery finite integral is absolutely convergent outside one common\nnull set; we use its usual representative there and set all quantities\nto zero on the exceptional set. Lemma~\\ref{lem:finite-truncations}\njustifies this convention and continuity in the endpoints.\n\nFor $r>2$, its annular variation is\n\\begin{equation}\\label{eq:variation-definition}\n V_r(F,G)(x,y)\n =\\sup_{\\substack{J\\ge1\\,,\\ 0<t_0<\\cdots<t_J\\\\t_j\\in\\mathbb Q}}\n    \\left(\\sum_{j=1}^J\n          |B_{t_{j-1},t_j}(F,G)(x,y)|^r\\right)^{1/r}.\n\\end{equation}\nThe supremum is pointwise: the partition may depend on $(x,y)$, and\nthere is no restriction on the number of endpoints within a dyadic\nscale interval. The use of rational endpoints makes measurability\nimmediate and, by endpoint continuity, does not change the supremum.\n\n\\begin{theorem}[Full annular variation]\\label{thm:variation}\nFor every real $r>2$ there is a finite constant $C_r$ such that\n\\[\n \\|V_r(F,G)\\|_{L^{3/2}(\\R^2)}\n \\le C_r\\|F\\|_{L^3(\\R^2)}\\|G\\|_{L^3(\\R^2)}\n\\]\nfor all complex $F,G\\in L^3(\\R^2)$.\n\\end{theorem}\n\nTaking a single increment gives the maximal estimate with the supremum\nover both hard truncation endpoints. In Section~\\ref{sec:consequences}\nwe prove that the joint principal value\n\\[\n B(F,G)=\\lim_{\\substack{\\varepsilon\\downarrow0\\\\R\\uparrow\\infty}}\n                   B_{\\varepsilon,R}(F,G)\n\\]\nexists almost everywhere and in $L^{3/2}$. In fact, the supremum of\n$|B_{\\varepsilon,R}(F,G)-B(F,G)|$ over\n$0<\\varepsilon<1/m$, $R>m$, tends to zero in $L^{3/2}$.\n\nPairing the bilinear output with a third $L^3$ input gives a scalar\nform. Its symmetric, or simplex, coordinates are\n\\begin{equation}\\label{eq:intro-simplex}\n \\Lambda_{\\varepsilon,R}(G_0,G_1,G_2)\n =\\iiint_{\\varepsilon<|a+b+c|<R}\n  G_0(a,b)G_1(b,c)G_2(c,a)\n             \\frac{\\dd a\\dd b\\dd c}{a+b+c}.\n\\end{equation}\nThe three equal exponents $3$ form the symmetric point of the scalar\nH\\\"older relation $1/p_0+1/p_1+1/p_2=1$.\nWe obtain a uniform bound by $C\\prod_v\\|G_v\\|_3$ and the joint scalar\nprincipal value for every complex $L^3$ triple.\nProposition~\\ref{prop:flat-equivalence} gives the norm-preserving\ncoordinate maps and equality of the optimal constants for the flat\nand simplex scalar formulations. In particular, this proves the\nsymmetric scalar estimate in Thiele's Problem~13 in\n\\cite[Section~9]{GrafakosEtAl2017}.\n\n\\subsection{History and related work}\n\nThe flat operator occurs in Demeter and Thiele's study of bilinear\naverages for two commuting transformations\n\\cite[Section~6]{DemeterThiele2010}. The scalar form\n\\eqref{eq:intro-simplex} is a basic entangled singular integral: each\ninput sees one pair of the three variables, and the pairs form a cycle.\nThis dependence differs from the bipartite configurations in many\nmultilinear estimates. Kova\\v{c}, Thiele and Zorin-Kranich established\nestimates for a Walsh model with two general inputs and a third input\nof specified structure \\cite[Theorem~1.7]{KovacThieleZorinKranich2015}.\nTheir paper also records the cyclic trace formulation and the\nequivalence of the flat and simplex forms.\n\nQuantitative cancellation over a finite range of scales was developed\nin several stages. Tao proved sublogarithmic bounds for one-dimensional\nmultilinear Hilbert transforms \\cite{Tao2016Cancellation}, and\nZorin-Kranich obtained sublogarithmic cancellation for simplex Hilbert\ntransforms \\cite{ZorinKranich2017}. Durcik, Kova\\v{c}, \\v{S}kreb and\nThiele proved square-function and norm-variation estimates that yield\nsquare-root cancellation for the triangular form over finitely many\nsmooth scales \\cite[Corollary~5]{DurcikKovacSkrebThiele2019}.\nDurcik, Kova\\v{c} and Thiele obtained power-type cancellation for hard\ntruncations \\cite[Theorem~1]{DurcikKovacThiele2019}. Permuting and\ninterpolating their $(4,4,2)$ bounds gives a\n$\\sqrt{\\log(R/\\varepsilon)}$ bound at the symmetric tuple.\nThese estimates retain a dependence on the scale range. Moreover,\nnorm-variation estimates for a fixed list of scales do not place the\nsupremum over output-dependent partitions inside the output norm as\nin \\eqref{eq:variation-definition}.\n\nOther results gain cancellation by averaging a direction parameter\n\\cite{DurcikRoos2021,LinSlavikova2026} or by introducing curvature\n\\cite{ChristDurcikRoos2021,HsuLin2026}. Those mechanisms are distinct\nfrom the flat problem considered here. The broader role of variation\nin strengthening maximal estimates and controlling convergence is\nillustrated by L\\'epingle's martingale inequality\n\\cite{Lepingle1976}, the variational inequalities of Jones, Seeger and\nWright \\cite{JonesSeegerWright2008}, and the variation-norm Carleson\ntheorem \\cite{OberlinSeegerTaoThieleWright2012}. These results provide\ncontext rather than an input to our proof.\n\nThe energy method here is related to telescoping arguments for\nentangled forms: Kova\\v{c}'s twisted paraproduct and Bellman-function\nframework \\cite{Kovac2012Twisted,Kovac2011}, and Durcik's continuous\nGaussian telescoping \\cite{Durcik2015,Durcik2017}. The obstruction\npresented by a triangular cycle is discussed in\n\\cite[Section~6.2]{Kovac2011}. We work with the finite matrix energy\n$\\tr(P^{3/2})$ for $P\\ge0$, establish a mixed trace inequality, and compare\nits heat derivatives at neighboring vertices. All matrix and heat\nestimates required below are proved in this paper.\n\n\\subsection{The count estimate and the new ingredients}\n\nThe proof reduces variation to a quantitative estimate for finitely\nmany disjoint annuli. For an integer $n\\ge1$, define\n\\begin{equation}\\label{eq:count-definition}\n \\mathcal S_n(F,G)\n =\\sup\\left\\{\\sum_{j=1}^m|B_{\\varepsilon_j,R_j}(F,G)|:\n \\begin{array}{l}\n 0\\le m\\le n,\\quad 0<\\varepsilon_j<R_j,\\quad\n     \\varepsilon_j,R_j\\in\\mathbb Q,\\\\\n (\\varepsilon_j,R_j)\\text{ pairwise disjoint}\n \\end{array}\\right\\}.\n\\end{equation}\nIntervals may share endpoints, and the empty sum is zero. As in the\nvariation, the entire choice is made separately at every output point.\n\n\\begin{theorem}[Disjoint-annulus count estimate]\\label{thm:count}\nThere is an absolute constant $C$ such that, for every integer $n\\ge1$\nand every complex $F,G\\in L^3(\\R^2)$,\n\\begin{equation}\\label{eq:count-bound}\n \\|\\mathcal S_n(F,G)\\|_{3/2}\n \\le C\\sqrt n\\log(2+n)\\|F\\|_3\\|G\\|_3.\n\\end{equation}\n\\end{theorem}\n\nTheorem~\\ref{thm:variation} follows by arranging the increments of\neach partition in decreasing order and grouping their ranks dyadically.\nThe resulting series has terms bounded by\n$C(1+j)2^{j(1/r-1/2)}\\|F\\|_3\\|G\\|_3$.\nThe substance of the paper is therefore \\eqref{eq:count-bound}.\n\nFor smooth annuli, we write the kernel as a scale integral of\nconvolutions of three one-variable Gaussian derivative windows.\nEach convolution is an integral over window centers whose sum is zero;\nwith the centers fixed, the three window factors depend separately on\n$a,b,c$. On a finite grid the resulting sums are cyclic matrix traces:\nthe sampled pairwise inputs supply three matrices, and the window\nfactors supply diagonal insertions. We localize the matrices with\npositive Gaussian weights. At each vertex we place the two incident\nweighted matrices side by side to form a row matrix $R$.\nThe energy $\\tr((R^*R)^{3/2})$ has the same degree-three scaling as the\ntrilinear form. A mixed trace inequality bounds the localized trace by\nquadratic costs from the Hessians of these energies. With the matrices\nfixed, heat identities control the accumulated\ncosts by initial energies after integration over centers. Those\nintegrated energies are bounded by sums of cubed $L^3$ norms.\n\nThere are two different costs in passing from one annulus to many.\nFor smooth annuli, entries of the matrix formed from the dual input\nswitch on and off at their chosen endpoints. We estimate the energy jumps entrywise,\nthen rescale the dual input to obtain a $\\sqrt n$ bound.\nFor hard annuli, estimating each endpoint error separately would\ngive a linear loss. We instead group endpoints in dyadic radius\nintervals. Disjointness gives uniform size and derivative bounds\nfor each group even when it has many jumps.\n\nThe main additional analytic tool is the frequency-block estimate\nof Proposition~\\ref{prop:frequency-block}. It handles modulated\nGaussian windows with coefficients depending jointly on scale,\nfrequency, and output position. For uniformly bounded coefficients,\na pointwise square-integral bound over scales and frequencies suffices,\nuniformly in the frequency size.\nTo obtain that uniformity, we narrow one Gaussian variance and\naverage its center shifts. This expresses each oscillatory insertion\nas an average of derivatives of narrower Gaussians. Auxiliary heat\nflows retaining only one input edge control the extra derivative\nterms. A two-dimensional Gaussian estimate then bounds the energy\nchanges at the ends of the scale intervals without a loss from the\nnarrow variance.\nThis estimate and the rough-kernel family bound in\nProposition~\\ref{prop:rough-family} are formulated separately from\nthe annular application.\n\n\\subsection{Organization}\n\nSection~\\ref{sec:preliminaries} fixes truncations, coordinates, and\nline maximal estimates. Section~\\ref{sec:matrix} proves the finite\nmatrix inequalities; Section~\\ref{sec:heat} develops their Gaussian\nheat flow. Section~\\ref{sec:smooth} proves the repeated-mask estimate\nfor smooth annuli. Sections~\\ref{sec:frequency} and~\\ref{sec:rough}\nestablish the uniform frequency-block estimate and control the hard\nendpoint errors. Section~\\ref{sec:completion} proves\nTheorems~\\ref{thm:count} and~\\ref{thm:variation} by linearization,\ndensity, and rank summation. Section~\\ref{sec:consequences} derives\nthe maximal, principal-value, and scalar conclusions.\n"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/matrix.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/matrix.tex", "bytes": 16488, "sha256": "914af3c5e87cd4af52b1c7a9e7f2a4ac03d82b9456b8be50b69ffbbbee59f9ad", "content": "\\section{Dimension-free matrix inequalities}\\label{sec:matrix}\n\nThe finite-dimensional part of the argument has two tasks: to bound a\ncyclic product by positive quadratic costs, and to keep those costs\nunder control when matrices change or are averaged.  We give all the\nneeded estimates here, including the support and complex-phase\narguments required for arbitrary complex input matrices.\nThe averaging inequality in Lemma~\\ref{lem:joint-convexity} will permit\na Gaussian window to be replaced by a probability average of narrower\nwindows without paying for their relative widths.\n\nAll spaces in this section are finite-dimensional complex Hilbert\nspaces.  Matrix inequalities mean inequalities of Hermitian quadratic\nforms.  Set\n\\[\n \\Phi(P)=\\tr(P^{3/2}),\\qquad \\supp P=(\\ker P)^\\perp\n \\quad(P\\ge0).\n\\]\nThe application uses three spaces $H_v$, indexed by\n$v\\in\\mathbb Z/3\\mathbb Z$, and maps $W_v:H_{v+1}\\to H_v$.\nThe two matrices incident to vertex $v$ form its \\emph{row star}\n\\begin{equation}\\label{eq:row-star}\n R_v=[\\,W_v\\quad W_{v-1}^*\\,]:\n H_{v+1}\\oplus H_{v-1}\\longrightarrow H_v.\n\\end{equation}\nWe use both Gram matrices\n\\begin{equation}\\label{eq:star-grams}\n T_v=R_v^*R_v,\\qquad S_v=R_vR_v^*.\n\\end{equation}\nTheir nonzero eigenvalues agree, including multiplicities, so\n$\\Phi(T_v)=\\Phi(S_v)$. Our eventual objective in this section is to\nbound the cyclic trace formed from the $W_v$ by second-derivative\ncosts of these three energies. We first develop those costs for an\narbitrary positive matrix $P$.\n\nA matrix is \\emph{supported on $P$} if it vanishes on $\\ker P$ on\nboth sides.  The Hilbert--Schmidt inner product is\n$\\langle K,L\\rangle_{\\HS}=\\tr(K^*L)$.  Write\n$\\mathsf L_A(X)=AX$ and $\\mathsf R_A(X)=XA$.  For Hermitian matrices\n$K,L$ supported on $P$, define\n\\begin{equation}\\label{eq:hessian-form}\n \\mathcal B_P(K,L)=\\frac32\\operatorname{Re}\n \\left\\langle K,\n (\\mathsf L_{\\sqrt P}+\\mathsf R_{\\sqrt P})^{-1}L\n \\right\\rangle_{\\HS},\n \\qquad b_P(K)=\\mathcal B_P(K,K).\n\\end{equation}\nThe inverse is taken on matrices acting on $\\supp P$, where the\noperator is positive definite.  If $P=0$, its only supported direction\nis zero and these expressions are zero.\n\n\\subsection{The energy and its quadratic costs}\n\n\\begin{lemma}[Supported Hessian]\\label{lem:hessian}\nOn the cone of matrices positive definite on a fixed support,\n$\\Phi$ is smooth and\n\\[\n d\\Phi_P(K)=\\frac32\\tr(\\sqrt P K),\\qquad\n d^2\\Phi_P(K,L)=\\mathcal B_P(K,L).\n\\]\nIn an orthonormal eigenbasis for the positive eigenvalues $p_i$ of\n$P$,\n\\begin{equation}\\label{eq:hessian-metric}\n b_P(K)=\\frac32\\sum_{i,j}\n \\frac{|K_{ij}|^2}{\\sqrt{p_i}+\\sqrt{p_j}}.\n\\end{equation}\nIn particular, $\\mathcal B_P$ is a positive definite symmetric\nbilinear form on the real space of supported Hermitian directions,\nand\n\\[\n |\\mathcal B_P(K,L)|\\le b_P(K)^{1/2}b_P(L)^{1/2}.\n\\]\n\\end{lemma}\n\n\\begin{proof}\nRestrict to the support and put $A=\\sqrt P$.  The derivative of the\nsquaring map at $A$ is $X\\mapsto AX+XA$; in an eigenbasis, it\nmultiplies entry $(i,j)$ by $\\sqrt{p_i}+\\sqrt{p_j}>0$.\nThe inverse function theorem gives a smooth positive square root,\nwhose derivative $X_K$ solves\n\\[\n AX_K+X_KA=K.\n\\]\nMultiplying by $A$ and taking traces gives\n$\\tr(PX_K)=\\tfrac12\\tr(AK)$.  Hence differentiation of\n$\\Phi(P)=\\tr(P\\sqrt P)$ yields\n$d\\Phi_P(K)=\\tfrac32\\tr(AK)$.  Differentiating once more proves\nthe Hessian formula.  Formula~\\eqref{eq:hessian-metric} follows by\ndiagonalizing $A$, and proves the remaining assertions.\nThis is the square-root case of the classical divided-difference\ncalculus~\\cite[Section~1]{Daletskii1957}.\n\\end{proof}\n\nBoth $\\Phi$ and $\\mathcal B$ are invariant under simultaneous unitary\nconjugation of their arguments.  They are also invariant under\nextension of every argument by zero: their positive eigenvalues and\nsupported entries do not change.  These facts will let us compare\nGrams on different subspaces.\n\n\\begin{lemma}[Order comparison]\\label{lem:order}\nIf $P,Q\\ge0$ on the same space, $a\\ge1$, and $P\\le aQ$, then every\nHermitian $K$ supported on $P$ is supported on $Q$, and\n\\begin{equation}\\label{eq:matrix-order}\n b_P(K)\\ge a^{-1/2}b_Q(K).\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nFor $x\\in\\ker Q$ we have $0\\le\\langle Px,x\\rangle\n\\le a\\langle Qx,x\\rangle=0$, so $\\ker Q\\subseteq\\ker P$.\nThis proves the support assertion.\n\nWe recall the order facts used in the proof.  Inversion reverses\norder for positive definite matrices, as follows by conjugating\n$0<X\\le Y$ by $X^{-1/2}$, inverting, and conjugating back.  Also\n\\[\n \\sqrt X=\\frac1\\pi\\int_0^\\infty\n X(X+tI)^{-1}\\,\\frac{\\dd t}{\\sqrt t}\\qquad(X\\ge0).\n\\]\nSpectral calculus reduces this norm-convergent formula to the scalar\nintegral.  Since $X(X+tI)^{-1}=I-t(X+tI)^{-1}$, inversion order\nshows that square root preserves order. This is the classical\nLoewner--Heinz square-root monotonicity; see, for example,\nKwong~\\cite[Theorem~1 and Section~2]{Kwong1975}. The supported\nHessian comparison below is derived from these order facts here.\n\nFor $\\eta>0$,\n\\[\n P+\\eta I\\le a(Q+\\eta I),\\qquad\n \\sqrt{P+\\eta I}\\le\\sqrt a\\sqrt{Q+\\eta I}.\n\\]\nIf $A\\le B$, then\n$\\mathsf L_A+\\mathsf R_A\\le\\mathsf L_B+\\mathsf R_B$ on\nHilbert--Schmidt space: the difference has quadratic form\n$\\tr(X^*(B-A)X)+\\tr(X^*X(B-A))\\ge0$.  Invert these positive\noperators to obtain\n\\[\n b_{P+\\eta I}(K)\\ge a^{-1/2}b_{Q+\\eta I}(K).\n\\]\nLet $\\eta\\downarrow0$.  In an eigenbasis, every entry of $K$ meeting\nthe kernel of the corresponding limiting matrix is zero.  Thus\nFormula~\\eqref{eq:hessian-metric} gives precisely the supported costs\non both sides of \\eqref{eq:matrix-order}.\n\\end{proof}\n\n\\begin{lemma}[Inserted Gram bound]\\label{lem:insertion}\nFor a linear map $R:E\\to F$ and a Hermitian map $L:F\\to F$, the\nmatrix $R^*LR$ is supported on $R^*R$, and\n\\begin{equation}\\label{eq:insertion-bound}\n b_{R^*R}(R^*LR)\n \\le\\frac34\\lVert L\\rVert_{\\op}^2\\Phi(R^*R).\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nThe insertion is Hermitian and annihilates $\\ker R=\\ker(R^*R)$.\nIf $R=0$, there is nothing to prove.  Otherwise let $\\rho_i>0$ be\nthe positive singular values of $R$, and let $C$ be the compression\nof $L$ to the corresponding left singular vectors.  Then\n$\\lVert C\\rVert_{\\op}\\le\\lVert L\\rVert_{\\op}$ and\n\\[\n b_{R^*R}(R^*LR)\n =\\frac32\\sum_{i,j}\n \\frac{\\rho_i^2\\rho_j^2}{\\rho_i+\\rho_j}|C_{ij}|^2.\n\\]\nFor $x,y>0$,\n$x^2y^2/(x+y)\\le (xy)^{3/2}/2\\le(x^3+y^3)/4$.\nEvery row and column of $C$ has squared Euclidean norm at most\n$\\lVert L\\rVert_{\\op}^2$.  Therefore\n\\[\n b_{R^*R}(R^*LR)\n \\le\\frac38\\sum_{i,j}(\\rho_i^3+\\rho_j^3)|C_{ij}|^2\n \\le\\frac34\\lVert L\\rVert_{\\op}^2\\sum_i\\rho_i^3,\n\\]\nas required.\n\\end{proof}\n\nWe next show that averaging a Gram and its inserted direction can\nonly decrease the corresponding cost.  No uniform lower bound on\nthe positive eigenvalues is required.\n\n\\begin{lemma}[Joint convexity under probability averages]\n\\label{lem:joint-convexity}\nLet $(\\Omega,\\nu)$ be a probability space and $E$ a fixed subspace\nof a finite-dimensional complex Hilbert space.  Suppose\n$T_\\omega\\ge0$ and $K_\\omega=K_\\omega^*$ are measurable matrices,\n$\\supp T_\\omega=E$ and $K_\\omega$ is supported on $T_\\omega$ almost\neverywhere, and\n\\[\n \\int_\\Omega\\bigl(\\lVert T_\\omega\\rVert_{\\op}\n                  +\\lVert K_\\omega\\rVert_{\\op}\\bigr)\\,\\dd\\nu(\\omega)\n <\\infty.\n\\]\nThen $\\overline T=\\int T_\\omega\\,\\dd\\nu$ has support $E$,\n$\\overline K=\\int K_\\omega\\,\\dd\\nu$ is supported on $\\overline T$, and\n\\begin{equation}\\label{eq:joint-convexity}\n b_{\\overline T}(\\overline K)\n \\le\\int_\\Omega b_{T_\\omega}(K_\\omega)\\,\\dd\\nu(\\omega).\n\\end{equation}\nThe right side may be infinite.\n\\end{lemma}\n\n\\begin{proof}\nIf $E=\\{0\\}$, all matrices and costs vanish.  Otherwise restrict\nevery matrix to $E$.  For every nonzero $x\\in E$, the positive\nquantity $\\langle T_\\omega x,x\\rangle$ has positive integral, so\n$\\overline T$ is positive definite on $E$.  All averages vanish on\n$E^\\perp$, proving the support assertions.\n\nPut $A_\\omega=\\sqrt{T_\\omega}$ and $A=\\int A_\\omega\\,\\dd\\nu$.\nThe integral exists by the stated integrability and Cauchy--Schwarz.\nFor every vector $x$, the vector-valued Cauchy--Schwarz inequality\ngives\n\\[\n \\lVert Ax\\rVert^2\n \\le\\int\\lVert A_\\omega x\\rVert^2\\,\\dd\\nu\n =\\langle\\overline T x,x\\rangle.\n\\]\nConsequently $A^2\\le\\overline T$, and square-root order,\nproved in Lemma~\\ref{lem:order}, implies\n\\begin{equation}\\label{eq:square-root-average}\n \\sqrt{\\overline T}\\ge\\int\\sqrt{T_\\omega}\\,\\dd\\nu.\n\\end{equation}\nFor a positive definite selfadjoint operator $\\mathsf M$ on a real\nHilbert space, completing the square proves\n\\begin{equation}\\label{eq:inverse-variational}\n \\langle K,\\mathsf M^{-1}K\\rangle\n =\\sup_X\\{2\\langle K,X\\rangle-\\langle X,\\mathsf M X\\rangle\\}.\n\\end{equation}\nApply this identity on Hermitian matrices with real Hilbert--Schmidt\ninner product.  The operators\n$\\mathsf L_{\\sqrt T}+\\mathsf R_{\\sqrt T}$\npreserve that real space.  By \\eqref{eq:square-root-average}, for\neach Hermitian $X$ the expression inside the supremum for\n$(\\overline T,\\overline K)$ is at most the average of the\ncorresponding expressions for $(T_\\omega,K_\\omega)$.\nTaking the supremum and then bounding the supremum of an integral\nby the integral of the pointwise suprema gives\n\\[\n b_{\\overline T}(\\overline K)\n \\le\\int b_{T_\\omega}(K_\\omega)\\,\\dd\\nu.\n\\]\nEvery inverse in this argument is taken on matrices acting on $E$;\nuniform invertibility over $\\omega$ was never assumed.\n\\end{proof}\n\n\\subsection{Changes that alter the rank}\n\nThe Hessian was defined on a fixed support, but switching matrix\nentries on or off can change that support.  The following\nfirst-derivative formula remains valid across such changes.  It is\na special case of the gradient formula for unitarily invariant\nmatrix functions~\\cite[Theorem~3.1]{Lewis1995}.\n\n\\begin{lemma}[Rank changes and entrywise derivatives]\n\\label{lem:rank-change}\nFor rectangular complex matrices $R_-,R_+$ of the same size, set\n$R(z)=(1-z)R_-+zR_+$ and $D=R_+-R_-$.  Then\n\\begin{equation}\\label{eq:rank-change-derivative}\n \\Phi(R_+^*R_+)-\\Phi(R_-^*R_-)\n =3\\int_0^1\\operatorname{Re}\n \\langle R(z)\\sqrt{R(z)^*R(z)},D\\rangle_{\\HS}\\,\\dd z.\n\\end{equation}\nFor every rectangular matrix $R$ and every entry $(i,j)$,\n\\begin{equation}\\label{eq:entry-gradient}\n |(R\\sqrt{R^*R})_{ij}|\n \\le\\lVert\\operatorname{row}_iR\\rVert_2\n       \\lVert\\operatorname{col}_jR\\rVert_2.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nFor $\\eta>0$, Lemma~\\ref{lem:hessian} gives\n\\[\n \\frac{\\dd}{\\dd z}\\tr((R(z)^*R(z)+\\eta I)^{3/2})\n =3\\operatorname{Re}\n \\langle R(z)\\sqrt{R(z)^*R(z)+\\eta I},D\\rangle_{\\HS}.\n\\]\nSpectral calculus implies\n$\\lVert\\sqrt{R^*R+\\eta I}-\\sqrt{R^*R}\\rVert_{\\op}\\le\\sqrt\\eta$.\nThe matrices $R(z)$ stay bounded for $0\\le z\\le1$, so the\nderivatives converge uniformly as $\\eta\\downarrow0$, and the\nendpoint energies converge.  Integrating first and passing to the\nlimit proves \\eqref{eq:rank-change-derivative}.\n\nFor \\eqref{eq:entry-gradient}, Cauchy--Schwarz in the matrix product\nbounds the entry by the norm of row $i$ of $R$ times the norm of\ncolumn $j$ of $\\sqrt{R^*R}$.  The latter column has squared norm\n$(R^*R)_{jj}$, also the squared norm of column $j$ of $R$.\n\\end{proof}\n\n\\subsection{The cyclic product}\n\nReturn to the three maps $W_v$ and the row stars\n\\eqref{eq:row-star}--\\eqref{eq:star-grams}. The reason for combining\nthe two incident matrices is the following estimate: three inserted\nstar costs control the entire cyclic product.\n\n\\begin{lemma}[Mixed trace inequality]\\label{lem:mixed-trace}\nFor Hermitian maps $D_v:H_v\\to H_v$, put\n$D_* =\\max_v\\lVert D_v\\rVert_{\\op}$.  Then\n\\begin{equation}\\label{eq:mixed-trace}\n \\left|\\tr(D_0W_0D_1W_1D_2W_2)\\right|\n \\le\\frac53D_*\\sum_{v=0}^2b_{T_v}(R_v^*D_vR_v).\n\\end{equation}\nThe constant is independent of the dimensions and no Gram is\nassumed invertible.\n\\end{lemma}\n\n\\begin{proof}\nWe put the three costs in one quadratic form, bound a cubic trace,\nand then recover the complex phase of the cyclic product.\n\n\\paragraph{One quadratic form.}\nOn $H=H_0\\oplus H_1\\oplus H_2$, define\n\\[\n M=\\begin{pmatrix}\n 0&W_0&W_2^*\\\\\n W_0^*&0&W_1\\\\\n W_2&W_1^*&0\n \\end{pmatrix},\\qquad\n D=\\operatorname{diag}(D_0,D_1,D_2).\n\\]\nBoth are Hermitian and $\\lVert D\\rVert_{\\op}=D_*$.  If $P_v$ is\nthe orthogonal projection onto $H_v$, put\n$A_v=MP_vM$ and $K_v=MP_vDM$.  After permuting blocks and extending\nby zero, these are respectively $T_v$ and $R_v^*D_vR_v$.\nThe matrix $K_v$ is Hermitian because $P_v$ commutes with $D$.\nMoreover,\n\\[\n \\ker A_v=\\ker(P_vM)\\subseteq\\ker K_v,\n \\qquad 0\\le A_v\\le M^2.\n\\]\nThus $K_v$ is supported on $A_v$ and on $M^2$.  Lemma~\\ref{lem:order}\nand the inequality $\\sum_{v=0}^2 b(X_v)\\ge\\tfrac13 b(\\sum_vX_v)$\nfor a positive quadratic form give\n\\begin{equation}\\label{eq:trace-common-metric}\n \\mathcal E:=\\sum_v b_{T_v}(R_v^*D_vR_v)\n \\ge\\sum_v b_{M^2}(K_v)\n \\ge\\frac13 b_{M^2}(MDM).\n\\end{equation}\n\n\\paragraph{A cubic trace estimate.}\nChoose an orthonormal eigenbasis of $M$, with real eigenvalues $m_i$\nordered so that $q_i=|m_i|$ is nonincreasing.  Ties are arbitrary.\nIn that basis Formula~\\eqref{eq:hessian-metric} yields\n\\begin{equation}\\label{eq:trace-Q}\n \\mathcal E\\ge\\frac12Q,\\qquad\n Q:=\\sum_{i,j:q_i+q_j>0}\n \\frac{q_i^2q_j^2}{q_i+q_j}|D_{ij}|^2.\n\\end{equation}\nAny term with one zero $q_i$ is zero; pairs with both zero are\nomitted.  Write\n\\[\n A_i=q_i^3|D_{ii}|^2,\\quad\n E_i=q_i\\sum_{j>i}q_j^2|D_{ij}|^2,\\quad\n A=\\sum_iA_i,\\quad E=\\sum_iE_i.\n\\]\nHermitian symmetry and $q_j\\le q_i$ for $j>i$ imply\n\\begin{equation}\\label{eq:trace-AE}\n Q=\\frac A2+\n 2\\sum_{\\substack{i<j\\\\q_i+q_j>0}}\n \\frac{q_i^2q_j^2}{q_i+q_j}|D_{ij}|^2\n \\ge\\frac A2+E.\n\\end{equation}\nExpand\n\\[\n \\tr((DM)^3)=\\sum_{i,j,k}m_im_jm_kD_{ij}D_{jk}D_{ki}\n\\]\nand group triples by the multiplicity of their smallest index $i$.\nIf it occurs once, its three cyclic positions give\n\\[\n 3m_i\\sum_{j,k>i}m_jm_kD_{ij}D_{jk}D_{ki}.\n\\]\nThe inner sum is $x^*D_{>i}x$ for\n$x=(m_jD_{ji})_{j>i}$, where $D_{>i}$ is the corresponding\ncompression of $D$.  Since $\\lVert D_{>i}\\rVert_{\\op}\\le D_*$,\nthis contribution has modulus at most $3D_*E_i$.  The argument\nallows $j=k$ and retains the signs of the $m_j$.\n\nIf the smallest index occurs twice, its contribution is\n\\[\n 3m_i^2D_{ii}\\sum_{k>i}m_k|D_{ik}|^2.\n\\]\nBy Cauchy--Schwarz and\n$\\sum_k|D_{ik}|^2=\\lVert De_i\\rVert^2\\le D_*^2$,\n\\[\n \\left|\\sum_{k>i}m_k|D_{ik}|^2\\right|\n \\le D_*\\left(\\sum_{k>i}q_k^2|D_{ik}|^2\\right)^{1/2}.\n\\]\nThe contribution is therefore at most $3D_*\\sqrt{A_iE_i}$, also\nwhen $q_i=0$.  Finally, if all three indices equal $i$, the modulus\nis at most $D_*A_i$.  These cases exhaust all triples.  Summing and\nusing Cauchy--Schwarz gives\n\\begin{equation}\\label{eq:trace-cubic}\n \\begin{aligned}\n |\\tr((DM)^3)|\n &\\le D_*\\bigl(A+3E+3\\sqrt{AE}\\bigr)\\\\\n &\\le D_*\\left(\\frac52A+\\frac92E\\right)\n \\le5D_*\\left(\\frac A2+E\\right)\n \\le5D_*Q.\n \\end{aligned}\n\\end{equation}\n\n\\paragraph{Recovering the complex phase.}\nSet $\\tau=\\tr(D_0W_0D_1W_1D_2W_2)$.  Since $M$ has zero\ndiagonal blocks, every nonzero closed three-step block product\nvisits all three vertices.  The three starting vertices in one\norientation give $\\tau$, by cyclicity of trace; the reverse\norientation gives $\\overline\\tau$, because the $D_v$ are Hermitian.\nConsequently\n\\[\n \\tr((DM)^3)=6\\operatorname{Re}\\tau.\n\\]\nWhen $\\tau\\ne0$, choose $|z|=1$ so that $z\\tau=|\\tau|$ and replace\n$W_0$ by $zW_0$.  This changes the stars by\n\\[\n R_0\\longmapsto R_0\\operatorname{diag}(zI_{H_1},I_{H_2}),\\qquad\n R_1\\longmapsto R_1\\operatorname{diag}(I_{H_2},\\overline zI_{H_0}),\n\\]\nand leaves $R_2$ unchanged.  These right multipliers are unitary;\neach conjugates the Gram and its insertion by the same unitary.\nThus $\\mathcal E$ and $D_*$ are unchanged.  Apply\n\\eqref{eq:trace-Q} and \\eqref{eq:trace-cubic} to the modified\nmatrices, with their own value of $Q$, to obtain\n\\[\n 6|\\tau|\\le5D_*Q\\le10D_*\\mathcal E.\n\\]\nThis is \\eqref{eq:mixed-trace}.  The case $\\tau=0$ is immediate.\n\\end{proof}\n\nIn the applications a diagonal insertion may be complex.  Write\n$D_v=D_v^{(0)}+iD_v^{(1)}$ for its real and imaginary diagonal\nparts, and put $D_* =\\max_v\\lVert D_v\\rVert_{\\op}$.  Expanding\ngives eight products with Hermitian diagonals, each bounded by\nLemma~\\ref{lem:mixed-trace}.  Both parts have operator norm at most\n$D_*$, and each of their six costs occurs four times in the sum.\nThus\n\\begin{equation}\\label{eq:complex-mixed-trace}\n \\left|\\tr(D_0W_0D_1W_1D_2W_2)\\right|\n \\le\\frac{20}{3}D_*\\sum_{v=0}^2\\sum_{\\epsilon=0}^1\n b_{T_v}(R_v^*D_v^{(\\epsilon)}R_v).\n\\end{equation}\nAll directions appearing in this bound remain Hermitian.\n"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/preliminaries.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/preliminaries.tex", "bytes": 6664, "sha256": "305281fb7f26046eea88b4087beba843ab6e48a62be6158f3a987bd91a68ddb0", "content": "\\section{Truncations, coordinates, and maximal averages}\n\\label{sec:preliminaries}\n\nWe first fix the representatives of all finite truncations and the\ncoordinate convention for their linearizations. We also record the\none-dimensional maximal estimates that will control changes of the\nmatrix energy. Constants denoted by $C$ may change from line to line;\ntheir additional dependences are indicated explicitly.\nFor an integrable function $f$ and $L>0$, set\n\\[\n \\Dil_Lf(t)=L^{-1}f(t/L).\n\\]\nAll inputs and matrices may be complex. Scalar forms are complex\nmultilinear, so pairings with dual inputs do not include a conjugate.\n\n\\begin{lemma}[Finite truncations]\\label{lem:finite-truncations}\nFor $F,G\\in L^3(\\R^2)$, all finite annular integrals defining\n$B_{\\varepsilon,R}(F,G)$ are absolutely convergent outside one common\nnull set. On its complement they depend continuously on $(\\varepsilon,R)$ in\n$0<\\varepsilon<R<\\infty$. Changing representatives changes this family\nonly on a common null set. Moreover,\n\\begin{equation}\\label{eq:finite-annulus-bound}\n \\left\\|\\int_{\\varepsilon<|t|<R}\n |F(x+t,y)G(x,y+t)|\\,\\frac{\\dd t}{|t|}\\right\\|_{3/2}\n \\le 2\\log(R/\\varepsilon)\\|F\\|_3\\|G\\|_3.\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nFor each fixed $t$, H\\\"older's inequality and translation invariance\nbound the $L^{3/2}$ norm of the product by $\\|F\\|_3\\|G\\|_3$.\nMinkowski's integral inequality gives \\eqref{eq:finite-annulus-bound}.\nApply this estimate to the countably many annuli $1/m<|t|<m$, $m\\ge2$.\nTheir union of exceptional null sets works for every finite annulus.\nAbsolute continuity of the integral gives endpoint continuity.\nThe same estimate applied to a zero $L^3$ difference proves the assertion\nabout representatives, first on those countably many annuli and hence\non all finite annuli.\n\\end{proof}\n\nIn particular, suprema over rational endpoint pairs agree with suprema\nover all endpoint pairs whenever only finitely many endpoints occur\nin a choice. Rational exhaustion makes the variation and the count\nquantities in the introduction measurable.\n\n\\subsection{The triangular coordinates}\n\nFor most of the proof the inputs are smooth and compactly supported.\nThe change of variables\n\\[\n x=-a,\\qquad y=-b,\\qquad t=a+b+c\n\\]\nhas absolute determinant one. Define\n\\begin{equation}\\label{eq:pair-maps}\n G_1(b,c)=F(b+c,-b),\\qquad G_2(c,a)=G(-a,c+a).\n\\end{equation}\nBoth pair maps preserve $L^3$ norms. With a dual input $G_0(a,b)$,\nthe expressions to be estimated take the form\n\\begin{equation}\\label{eq:triangular-form}\n \\Lambda_{\\mathcal K}(G_0,G_1,G_2)\n =\\iiint G_0(a,b)G_1(b,c)G_2(c,a)\n       \\mathcal K_{a,b}(a+b+c)\\dd a\\dd b\\dd c.\n\\end{equation}\nThe maps in \\eqref{eq:pair-maps} are invertible, so estimates for\narbitrary smooth compactly supported $G_v$ are equivalent to the\ncorresponding dual estimates in the original coordinates.\n\nA \\emph{step choice} is a choice from a finite menu that is constant on\neach cell of a partition of $\\R^2$ by finitely many horizontal and\nvertical lines. Unbounded cells are allowed. For annular linearizations,\neach menu item is a list of at most $n$ disjoint radius intervals\n$(\\varepsilon_j,R_j)$, together with coefficients $|\\beta_j|\\le1$.\nAt a fixed output point the intervals are disjoint up to endpoints.\nWe first prove all estimates for step choices. Values on cell boundaries\ncan be assigned any menu item; they do not affect the integrals.\n\nFor such choices and smooth compactly supported inputs, finite-mesh\nestimates pass to \\eqref{eq:triangular-form} by ordinary Riemann sums.\nIndeed, choose $T$ so large that all coordinates in the input supports\nlie in $[-T,T]$, put $h=T/N$, and sample at the nodes $u_i=ih$.\nAfter any auxiliary frequency integration over a fixed bounded block,\nthe finitely many kernels used below are continuous in their scalar\nvariable. The resulting integrands on $[-T,T]^3$ are bounded and\ncontinuous off finitely many coordinate planes. Their Riemann sums\nconverge, as do the discrete $L^3$ norm sums. No limit of matrix\nenergies is taken.\n\n\\subsection{Maximal averages on a line}\n\nFor functions on $\\R$ and sequences on $\\mathbb Z$, respectively, let\n\\[\n M_{\\R}f(x)=\\sup_{r>0}\\frac1{2r}\\int_{x-r}^{x+r}|f(t)|\\dd t,\n \\qquad\n M_{\\mathbb Z}f(j)=\\sup_{r\\in\\mathbb Z_{\\ge0}}\n       \\frac1{2r+1}\\sum_{|m-j|\\le r}|f(m)|.\n\\]\nThe discrete operator includes singleton averages. We use these\noperators on individual coordinates of a function or array as well.\n\n\\begin{lemma}[Line maximal estimates]\\label{lem:line-maximal}\nOn either space, the centered maximal operator satisfies\n\\[\n \\mu\\{Mf>\\rho\\}\\le\\frac3\\rho\\|f\\|_1,\n \\qquad\n \\|Mf\\|_p^p\\le\\frac{6p\\,2^{p-1}}{p-1}\\|f\\|_p^p\n \\quad(1<p<\\infty).\n\\]\nLet $g_\\sigma(t)=(2\\pi\\sigma)^{-1/2}e^{-t^2/(2\\sigma)}$.\nThere is an absolute constant $C$ such that\n\\begin{align}\n \\sum_m h g_\\sigma((m-j)h)|f(m)|\n   &\\le C M_{\\mathbb Z}f(j), &&\\sigma\\ge h^2,\n   \\label{eq:discrete-gaussian-maximal}\\\\\n \\int g_\\sigma(t)|f(x-t)|\\dd t\n   &\\le C M_{\\R}f(x), &&\\sigma>0.\n   \\label{eq:continuous-gaussian-maximal}\n\\end{align}\n\\end{lemma}\n\\begin{proof}\nWe recall the covering proof of the Hardy--Littlewood estimates\n\\cite{HardyLittlewood1930}. From any finite family of intervals, repeatedly\nselect one of largest radius and discard those meeting it. The selected\nintervals are disjoint, and the union of the family lies in their\ntriple-radius enlargements. Each enlargement has at most three times\nthe original measure, also for integer intervals. Apply this to witnessing\nintervals centered at a finite subset of a discrete level set, or to a\nfinite witnessing cover of a compact subset of a continuous level set.\nThe latter level set is open because fixed-radius averages are continuous.\nTaking suprema over the finite or compact subsets proves the weak bound.\n\nFor $f\\in L^p$, split at height $\\rho/2$. The low part has maximal\nfunction at most $\\rho/2$, while the high part is integrable. Thus\n\\[\n \\mu\\{Mf>\\rho\\}\n \\le\\frac6\\rho\\int_{|f|>\\rho/2}|f|\\dd\\mu.\n\\]\nThe distribution formula and Tonelli's theorem give the strong bound\nafter integrating $p\\rho^{p-1}\\dd\\rho$. Truncation of the distribution\nintegral first makes this argument independent of any a priori\nfiniteness assertion.\n\nFor \\eqref{eq:discrete-gaussian-maximal}, put $a=\\sqrt\\sigma/h\\ge1$.\nThe kernel is $(\\sqrt{2\\pi}a)^{-1}e^{-(m-j)^2/(2a^2)}$.\nSplit the sum into $|m-j|\\le a$ and the annuli\n$2^{l-1}a<|m-j|\\le2^la$, $l\\ge1$. The sum of $|f|$ in the\ncorresponding ball is at most $3\\cdot2^la M_{\\mathbb Z}f(j)$.\nThe resulting series is bounded by a constant times\n$1+\\sum_{l\\ge1}2^l e^{-4^{l-1}/2}$.\nThe same shell argument with integrals proves\n\\eqref{eq:continuous-gaussian-maximal}.\n\\end{proof}\n"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/rough.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/rough.tex", "bytes": 18283, "sha256": "f5a80e111befed4781b7a9b0d75f2b8d15646c08580c3ad98fe95d48bc877064", "content": "\\section{Rough kernels and the annular endpoint errors}\n\\label{sec:rough}\n\nThe comparison \\eqref{eq:hard-smooth} leaves one error kernel at each\nannular endpoint.  Estimating these kernels separately would cost the\nnumber of endpoints.  We instead collect endpoints at comparable scales.\nDisjointness of the annuli gives a Gaussian bound for each resulting\nkernel, independent of the number of its discontinuities.  The frequency\nestimate from the preceding section then controls the groups with few\ndiscontinuities; the remaining groups are few enough to estimate\npointwise.\n\n\\subsection{A pointwise Gaussian estimate}\n\nFor a locally integrable function on $\\R^2$, let $M_x$ and $M_y$ denote\nthe centered Hardy--Littlewood maximal operators in the first and second\ncoordinates.  For $F,G\\in L^3(\\R^2)$, put\n\\begin{equation}\n \\mathcal M_{F,G}(x,y)\n =\\bigl(M_x(|F|^{5/2})(x,y)\\bigr)^{2/5}\n  \\bigl(M_y(|G|^{5/2})(x,y)\\bigr)^{2/5}.\n \\label{eq:rough-maximal-envelope}\n\\end{equation}\nThe one-dimensional maximal inequality on $L^{6/5}$, followed by\nH\\\"older's inequality on $\\R^2$, gives\n\\begin{equation}\n \\|\\mathcal M_{F,G}\\|_{3/2}\n \\le C\\|F\\|_3\\|G\\|_3.\n \\label{eq:rough-maximal-norm}\n\\end{equation}\n\n\\begin{lemma}[Gaussian envelope]\n\\label{lem:gaussian-envelope}\nSuppose that $c>0$ and that a measurable kernel $f$ satisfies\n$|f(z)|\\le e^{-cz^2}w(z)$, where $w\\ge0$ and $\\|w\\|_5\\le\\delta$.\nThen, for every $L>0$,\n\\begin{equation}\n \\int_{\\R}|F(x+t,y)G(x,y+t)|\\,|\\Dil_L f(t)|\\,\\dd t\n \\le C_c\\delta\\,\\mathcal M_{F,G}(x,y)\n \\label{eq:gaussian-envelope}\n\\end{equation}\nat every point where the right side is finite.  In particular, a bound\n$|f(z)|\\le A e^{-cz^2}$ gives \\eqref{eq:gaussian-envelope} with\n$C_c\\delta$ replaced by $C_cA$.\n\\end{lemma}\n\n\\begin{proof}\nAfter setting $t=Lz$, apply H\\\"older's inequality with exponents\n$5/2,5/2,5$ to\n\\[\n |F(x+Lz,y)|e^{-cz^2/2},\\qquad\n |G(x,y+Lz)|e^{-cz^2/2},\\qquad w(z).\n\\]\nThe first resulting integral is\n$\\int |F(x+Lz,y)|^{5/2}e^{-5cz^2/4}\\,\\dd z$.\nSplitting $\\R$ into $|z|\\le1$ and\n$2^j<|z|\\le2^{j+1}$ bounds it by\n$C_cM_x(|F|^{5/2})(x,y)$, uniformly in $L$.\nThe second integral has the corresponding bound with $G$ and $M_y$.\nThis proves the first assertion.  For the last assertion, retain half\nthe Gaussian decay in $w$, whose $L^5$ norm is then at most $C_cA$.\n\\end{proof}\n\nWe will use this estimate inside the triangular form.  Recall that\n$G_1(b,c)=F(b+c,-b)$ and $G_2(c,a)=G(-a,c+a)$, and that these changes\nof coordinates preserve the $L^3$ norms.  With $t=a+b+c$, the absolute\nvalue of the $c$-integral is bounded by\n$C_c\\delta\\mathcal M_{F,G}(-a,-b)$.\nConsequently, integrating against $|G_0(a,b)|$ costs at most\n$C_c\\delta\\prod_{v=0}^2\\|G_v\\|_3$.\nThe kernel, its dilation, and its envelope may depend on $(a,b)$:\nthe same assertion holds whenever the bound for $\\delta$ is uniform.\n\n\\subsection{Families with a controlled number of jumps}\n\nThe next proposition turns the frequency estimate into an estimate for\npiecewise smooth kernels.  The pointwise bound on the number of jumps\nat one scale and the bound on their total number play different roles:\nthey will give, respectively, the uniform bound and the square-sum\nbound for the frequency coefficients.\n\n\\begin{proposition}[Rough kernel families]\n\\label{prop:rough-family}\nFix $A\\ge1$ and $c>0$.  Let $n\\ge1$, and let $\\mathcal I\\subset\\mathbb Z$\nbe finite.  For $k\\in\\mathcal I$, let $K_{a,b,k}\\colon\\R\\to\\C$ and\n$m_{a,b,k}\\in\\{0,1,2,\\ldots\\}$ be finite-valued step choices in\n$(a,b)$, with $K_{a,b,k}=0$ whenever $m_{a,b,k}=0$.\nWhen $m_{a,b,k}\\ge1$, suppose that $K_{a,b,k}$ is $C^1$ outside a set\nof at most $A m_{a,b,k}$ points and that, outside that set,\n\\begin{equation}\n |K_{a,b,k}(z)|+|K_{a,b,k}'(z)|\\le A e^{-cz^2}.\n \\label{eq:rough-family-decay}\n\\end{equation}\nAssume also that\n\\begin{equation}\n \\int_{\\R}z^\\ell K_{a,b,k}(z)\\,\\dd z=0\n \\quad(\\ell=0,1,2),\n \\qquad\n \\sum_{k\\in\\mathcal I}m_{a,b,k}\\le An,\n \\quad m_{a,b,k}\\le A\\sqrt n.\n \\label{eq:rough-family-hypotheses}\n\\end{equation}\nFor\n\\[\n \\mathcal K_{a,b}(t)\n =\\sum_{k\\in\\mathcal I}\\Dil_{2^k}K_{a,b,k}(t),\n\\]\nand smooth compactly supported $G_0,G_1,G_2$, one has\n\\begin{equation}\n |\\Lambda_{\\mathcal K}(G_0,G_1,G_2)|\n \\le C_{A,c}\\sqrt n\\log(2+n)\\prod_{v=0}^2\\|G_v\\|_3.\n \\label{eq:rough-family-bound}\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nWe first obtain Fourier estimates for a single kernel $K=K_{a,b,k}$;\nwrite $m=m_{a,b,k}$.  All constants below depend only on $A,c$.\nThere is nothing to prove for $m=0$, so suppose that $m\\ge1$.\n\n\\paragraph{A Gaussian-weighted primitive.}\nWe seek a representation $K=(g_{3q}f)'''$ for a suitable fixed $q$:\nthe Fourier modes of $f$ then produce exactly the differentiated\nGaussian windows controlled by Corollary~\\ref{cor:frequency-continuum}.\nDefine\n\\[\n U(z)=\\frac12\\int_{-\\infty}^z(z-u)^2K(u)\\,\\dd u.\n\\]\nThe three moment conditions imply, for $j=0,1,2$,\n\\[\n U^{(j)}(z)\n =-\\frac1{(2-j)!}\\int_z^\\infty(z-u)^{2-j}K(u)\\,\\dd u.\n\\]\nUse the defining integral when $z\\le0$ and this tail integral when\n$z\\ge0$.  Gaussian tail estimates give\n$|U^{(j)}(z)|\\le C e^{-c_1z^2}$ for $j=0,1,2$, with $c_1>0$ fixed.\nMoreover, $U'''=K$ as a weak derivative.\nChoose $q>0$ sufficiently large, depending only on $A,c$, and put\n\\[\n f(z)=\\frac{U(z)}{g_{3q}(z)}.\n\\]\nThe quotient and product rules, with a slight reduction in the\nGaussian exponent, give\n\\begin{equation}\n |f^{(j)}(z)|\\le C e^{-c_2z^2}\\quad(0\\le j\\le3).\n \\label{eq:rough-f-decay}\n\\end{equation}\nHere and below derivatives of order three are weak derivatives, or\ntheir almost-everywhere representatives.  The functions $f,f',f''$\nare continuous.  Between the exceptional points, the same calculation\nusing $K'$ bounds the classical derivative of $f'''$ by\n$Ce^{-c_2z^2}$.  Its jumps are bounded by $C$, and there are at most\n$Am$ of them.  Thus, for $0\\le j\\le3$,\n\\begin{equation}\n \\|f^{(j)}\\|_\\infty\\le C,\n \\qquad \\operatorname{Var}(f^{(j)})\\le Cm.\n \\label{eq:rough-f-bv}\n\\end{equation}\nThe same bounds hold with zero right sides when $m=0$.\n\n\\paragraph{Fourier bounds.}\nUse the normalization\n$\\widehat f(\\xi)=(2\\pi)^{-1}\\int f(z)e^{-i\\xi z}\\,\\dd z$.\nLet\n\\[\n \\Xi_1=\\{\\xi:|\\xi|<2\\},\\qquad\n \\Xi_D=\\{\\xi:D\\le|\\xi|<2D\\}\n \\quad(D=2,4,8,\\ldots).\n\\]\nWe claim that\n\\begin{equation}\n \\sup_{\\xi\\in\\Xi_D}|D^3\\widehat f(\\xi)|\\le\\frac{Cm}{D},\n \\qquad\n \\int_{\\Xi_D}|D^3\\widehat f(\\xi)|^2\\,\\dd\\xi\n \\le\\frac{Cm}{D}.\n \\label{eq:rough-fourier-bounds}\n\\end{equation}\nFor $D\\ge2$, the distributional derivative of $f'''$ is a finite\nmeasure of total variation at most $Cm$.  Taking its Fourier transform\ngives $|\\xi|^4|\\widehat f(\\xi)|\\le Cm$, proving the first bound.\nFor the second, the translation inequality for functions of bounded\nvariation gives\n\\[\n \\|f'''(\\,\\cdot+D^{-1})-f'''\\|_1\\le\\frac{Cm}{D}.\n\\]\nIndeed, integrate the absolute derivative measure over each traversed\ninterval and apply Fubini's theorem.  By the uniform supremum bound\nin \\eqref{eq:rough-f-bv}, the squared $L^2$ norm of this difference is\nalso at most $Cm/D$.  Plancherel's identity and\n$\\widehat{f'''}(\\xi)=(i\\xi)^3\\widehat f(\\xi)$ now yield the second\nbound, since $|e^{i\\xi/D}-1|\\ge2\\sin(1/2)$ on $\\Xi_D$.\nWhen $D=1$, both assertions follow from\n\\eqref{eq:rough-f-decay} and $m\\ge1$.\n\n\\paragraph{A finite frequency cutoff.}\nChoose $\\chi\\in C_c^\\infty(\\R)$ supported on $[-2,2]$ and equal to one\non $[-1,1]$, and set\n\\[\n P_*=(2+n)^8,\\qquad\n \\widehat{f_*}(\\xi)=\\widehat f(\\xi)\\chi(\\xi/P_*),\\qquad\n K_*=(g_{3q}f_*)'''.\n\\]\nWe show that this replacement has a summable pointwise error.\nLet $\\eta$ be the Schwartz convolution kernel of the multiplier\n$\\chi$, normalized so that $\\int\\eta=1$, and let\n$\\eta_{P_*}(z)=P_*\\eta(P_*z)$.  For $0\\le j\\le3$,\n$f_*^{(j)}=f^{(j)}*\\eta_{P_*}$.  The translation inequality gives\n\\[\n \\|f^{(j)}-f_*^{(j)}\\|_1\n \\le\\operatorname{Var}(f^{(j)})\n      \\int |u|\\,|\\eta_{P_*}(u)|\\,\\dd u\n \\le\\frac{Cm}{P_*}.\n\\]\nTheir supremum norms are uniformly bounded, by\n\\eqref{eq:rough-f-bv} and the $L^1$ norm of $\\eta$.  Interpolation\ntherefore gives\n\\[\n \\|f^{(j)}-f_*^{(j)}\\|_5\n \\le C(m/P_*)^{1/5}.\n\\]\nSince every derivative of $g_{3q}$ is bounded by a constant times a\nslightly wider Gaussian, the product rule shows that\n\\begin{equation}\n |K(z)-K_*(z)|\\le e^{-c_3z^2}w(z),\n \\qquad \\|w\\|_5\\le C(m/P_*)^{1/5},\n \\label{eq:rough-cutoff-error}\n\\end{equation}\nfor a nonnegative $w$.  This calculation also holds for weak\nderivatives, so no additional terms occur at the jumps of $K$.\n\nApply this construction separately to every $K_{a,b,k}$.\nBy \\eqref{eq:rough-family-hypotheses},\n$(m_{a,b,k}/P_*)^{1/5}\\le C/n$ whenever $m_{a,b,k}\\ne0$, and at\neach $(a,b)$ there are at most $An$ such indices.  Lemma\n\\ref{lem:gaussian-envelope} bounds the sum of all replacement errors\npointwise by $C\\mathcal M_{F,G}(-a,-b)$ after integration in $c$.\nTheir contribution to $\\Lambda_{\\mathcal K}$ is consequently at most\n$C\\prod_v\\|G_v\\|_3$.\n\n\\paragraph{Applying the frequency estimate.}\nIt remains to estimate the kernels $K_*$.  For each dyadic $D$ use\nthe kernels from Corollary~\\ref{cor:frequency-continuum}, namely\n\\[\n \\ell_\\xi(z)=D^{-1}\\frac{\\dd}{\\dd z}\n                 \\bigl(g_q(z)e^{i\\xi z}\\bigr),\n \\qquad H_\\xi=\\ell_\\xi*\\ell_\\xi*\\ell_\\xi.\n\\]\nGaussian convolution and modulation give\n\\[\n (g_{3q}(z)e^{i\\xi z})'''=D^3H_\\xi(z).\n\\]\nFor the block $\\Xi_D$, the contribution to $K_{a,b,k,*}$ is therefore\n\\[\n \\int_{\\Xi_D}c_{a,b,k}(\\xi)H_\\xi(z)\\,\\dd\\xi,\n \\qquad\n c_{a,b,k}(\\xi)\n =D^3\\widehat f_{a,b,k}(\\xi)\\chi(\\xi/P_*).\n\\]\nSet $\\mu_{a,b,k}(\\xi)=D c_{a,b,k}(\\xi)/\\sqrt n$.\nThe two estimates in \\eqref{eq:rough-fourier-bounds} give precisely\n\\[\n |\\mu_{a,b,k}(\\xi)|\n \\le\\frac{Cm_{a,b,k}}{\\sqrt n}\\le C,\n \\qquad\n \\sum_k\\int_{\\Xi_D}|\\mu_{a,b,k}(\\xi)|^2\\frac{\\dd\\xi}{D}\n \\le C\\sum_k\\frac{m_{a,b,k}}n\\le C.\n\\]\nAll transformations were applied separately to finitely many kernel\nvalues.  The coefficients $\\mu_{a,b,k}$ therefore remain step choices\non a common finite rectangular refinement of the original partitions.\nThe block kernel is $\\sqrt n\\int_{\\Xi_D}\\mu_{a,b,k}(\\xi)\nH_\\xi\\,\\dd\\xi/D$.  Thus Corollary~\\ref{cor:frequency-continuum},\nwith measure $\\dd\\xi/D$, bounds this block by\n$C\\sqrt n\\prod_v\\|G_v\\|_3$.\nOnly $O(\\log(2+n))$ blocks meet the support of the cutoff.\nSumming their bounds and adding the replacement error proves\n\\eqref{eq:rough-family-bound}.\n\\end{proof}\n\n\\subsection{Removing the first moment of an odd kernel}\n\nThe endpoint kernels are odd but need not have vanishing first moment.\nA short dilation identity reduces them to the preceding proposition.\n\n\\begin{corollary}[Odd kernel families]\n\\label{cor:odd-family}\nProposition~\\ref{prop:rough-family} remains valid if the three moment\nconditions in \\eqref{eq:rough-family-hypotheses} are replaced by the\nassumption that every $K_{a,b,k}$ is odd almost everywhere.\n\\end{corollary}\n\n\\begin{proof}\nChoose a real, smooth, compactly supported odd function $\\varphi$ with\n$\\int z\\varphi(z)\\,\\dd z=1$.  Put\n\\[\n u_{a,b,k}=\\int zK_{a,b,k}(z)\\,\\dd z.\n\\]\nThese coefficients are uniformly bounded and vanish when\n$m_{a,b,k}=0$.  The kernels $K_{a,b,k}-u_{a,b,k}\\varphi$ satisfy all\nthree moment conditions and the other hypotheses of\nProposition~\\ref{prop:rough-family}, with enlarged fixed constants\nand unchanged counts.  It remains to bound the subtracted terms.\n\nDefine $\\Psi=\\varphi-2^{-1}\\Dil_2\\varphi$.\nOddness gives its zeroth and second moments, and its first moment is\n$1-2^{-1}\\cdot2=0$.  For every integer $J\\ge1$,\n\\[\n \\Dil_{2^k}\\varphi\n =\\sum_{j=0}^{J-1}2^{-j}\\Dil_{2^{k+j}}\\Psi\n   +2^{-J}\\Dil_{2^{k+J}}\\varphi.\n\\]\nFor each fixed $j$, apply Proposition~\\ref{prop:rough-family} to the\nshifted scales $k+j$ with kernels $u_{a,b,k}\\Psi$ and the same shifted\ncounts $m_{a,b,k}$.  The constants are independent of $j$, and the\nouter coefficients $2^{-j}$ are summable.  The remainder contributes\nat most $C2^{-J}n\\prod_v\\|G_v\\|_3$ by Lemma\n\\ref{lem:gaussian-envelope}, since there are at most $An$ nonzero\ncounts at any output point.  Letting $J\\to\\infty$ proves the claim.\n\\end{proof}\n\n\\subsection{Grouping the endpoint errors}\n\nWe now verify that the errors in \\eqref{eq:hard-smooth} have the\nstructure just proved.  Recall\n\\[\n E_\\rho(t)\n =\\frac{P(|t|/\\rho)-c_0\\mathbf1_{\\{|t|>\\rho\\}}}{t},\n \\qquad\n P(v)=2\\int_0^v\\psi(u)\\,\\dd u,\n \\qquad \\psi=-g_3'''.\n\\]\nWe take the continuous extension at $t=0$; values at the jump points\ndo not affect any integral.\n\n\\begin{proposition}[Endpoint errors]\n\\label{prop:endpoint-errors}\nAt each $(a,b)$, choose at most $n$ pairwise disjoint open radius\nintervals $(\\varepsilon_j,R_j)$ with $0<\\varepsilon_j<R_j<\\infty$,\nand complex coefficients\n$\\beta_j$ with $|\\beta_j|\\le1$.  Suppose that these choices form a\nfinite-valued step function of $(a,b)$.  Then the kernel\n\\[\n \\mathcal E_{a,b}(t)\n =\\sum_j\\beta_j\n       \\bigl(E_{\\varepsilon_j}(t)-E_{R_j}(t)\\bigr)\n\\]\nsatisfies\n\\begin{equation}\n |\\Lambda_{\\mathcal E}(G_0,G_1,G_2)|\n \\le C\\sqrt n\\log(2+n)\\prod_{v=0}^2\\|G_v\\|_3\n \\label{eq:endpoint-error-bound}\n\\end{equation}\nfor smooth compactly supported inputs.\n\\end{proposition}\n\n\\begin{proof}\nPlace each endpoint $\\rho$ in the unique half-open dyadic interval\n$[L_k,2L_k)$, where $L_k=2^k$.  Let $m_{a,b,k}$ count these endpoints,\nwith multiplicity, and collect their contributions into\n$\\Dil_{L_k}K_{a,b,k}$.  Thus $K_{a,b,k}$ is a linear combination of\n\\begin{equation}\n e_\\alpha(z)\n =\\frac{P(|z|/\\alpha)-c_0\\mathbf1_{\\{|z|>\\alpha\\}}}{z},\n \\qquad 1\\le\\alpha<2,\n \\label{eq:dimensionless-endpoint}\n\\end{equation}\nwith coefficient $\\beta_j$ at a lower endpoint and $-\\beta_j$ at its\nupper endpoint.  Every kernel is odd, its possible jump points are\nthe $\\pm\\alpha$, and\n\\begin{equation}\n \\sum_km_{a,b,k}\\le2n.\n \\label{eq:endpoint-count}\n\\end{equation}\n\nThe important assertion is the uniform bound\n\\begin{equation}\n |K_{a,b,k}(z)|+|K_{a,b,k}'(z)|\\le Ce^{-cz^2}\n \\label{eq:paired-endpoint-bound}\n\\end{equation}\naway from at most $2m_{a,b,k}$ points, with constants independent of\n$m_{a,b,k}$.  Fix $(a,b)$ and $k$.  Pair the two endpoints of every\nannulus whose endpoints both lie in $[L_k,2L_k)$.  After scaling, its\ncontribution is $\\beta_j(e_{\\alpha_j}-e_{\\gamma_j})$ with\n$1\\le\\alpha_j<\\gamma_j<2$.\nThe intervals $(\\alpha_j,\\gamma_j)$ are disjoint, so\n\\begin{equation}\n \\sum_j(\\gamma_j-\\alpha_j)\\le1.\n \\label{eq:paired-length}\n\\end{equation}\nThere are at most two unpaired endpoint contributions.  Their\nintervals must satisfy, respectively,\n\\[\n \\varepsilon_j<L_k\\le R_j<2L_k,\n \\qquad\\text{or}\\qquad\n L_k\\le\\varepsilon_j<2L_k\\le R_j.\n\\]\nDisjointness allows at most one interval of each type.  An interval\nwith $\\varepsilon_j<L_k$ and $R_j\\ge2L_k$ has no endpoint in this\ngroup.  Figure~\\ref{fig:endpoint-pairing} illustrates the decomposition.\n\n\\begin{figure}[t]\n\\centering\n\\begin{tikzpicture}[x=1cm,y=1cm,>=Stealth]\n \\fill[gray!10] (1,-.25) rectangle (8,1.55);\n \\draw[->] (0,0)--(9,0) node[right] {$\\rho$};\n \\draw[densely dashed] (1,-.25)--(1,.93);\n \\draw[densely dashed] (8,-.25)--(8,.93);\n \\node[below] at (1,0) {$L_k$};\n \\node[below] at (8,0) {$2L_k$};\n \\draw[thick] (.25,.55)--(1.65,.55);\n \\draw[thick] (2.3,.55)--(3.55,.55);\n \\draw[thick] (4.15,.55)--(4.8,.55);\n \\draw[thick] (5.45,.55)--(6.4,.55);\n \\draw[thick] (7.15,.55)--(8.75,.55);\n \\foreach \\x in {.25,1.65,2.3,3.55,4.15,4.8,5.45,6.4,7.15,8.75}\n   \\fill (\\x,.55) circle (1.5pt);\n \\draw[decorate,decoration={brace,amplitude=4pt}]\n   (2.3,.88)--(6.4,.88);\n \\node[above] at (4.35,1.04) {paired endpoints};\n \\node[above,align=center,font=\\small] at (1.05,1.03) {one unpaired};\n \\node[above,align=center,font=\\small] at (7.95,1.03) {one unpaired};\n\\end{tikzpicture}\n\\caption{Endpoint grouping inside one dyadic interval.  Intervals with\nboth endpoints in the group contribute differences; their total\nlength is at most $L_k$.  Only intervals reaching a boundary of the\ndyadic interval can leave unpaired endpoints.  The picture is schematic.}\n\\label{fig:endpoint-pairing}\n\\end{figure}\n\nFor $|z|\\le3$, first consider the smooth term\n$s_\\alpha(z)=P(|z|/\\alpha)/z$ in \\eqref{eq:dimensionless-endpoint}.\nThe function $P$ has a smooth even extension and vanishes quadratically\nat zero.  Hence $s_\\alpha$ extends smoothly across $z=0$, and\n\\[\n \\sup_{\\substack{|z|\\le3\\\\1\\le\\alpha\\le2}}\n \\bigl(|\\partial_\\alpha s_\\alpha(z)|\n       +|\\partial_z\\partial_\\alpha s_\\alpha(z)|\\bigr)<\\infty.\n\\]\nThe fundamental theorem of calculus in $\\alpha$, followed by\n\\eqref{eq:paired-length}, bounds the sum of the paired smooth terms\nand their $z$-derivatives independently of their number.\nFor the hard terms, the paired difference is\n\\[\n -\\frac{c_0}{z}\n    \\bigl(\\mathbf1_{\\{|z|>\\alpha_j\\}}\n          -\\mathbf1_{\\{|z|>\\gamma_j\\}}\\bigr).\n\\]\nAway from endpoints, at most one such difference is nonzero at any\nfixed $|z|$, because the intervals $(\\alpha_j,\\gamma_j)$ are disjoint.\nOn its support $|z|\\ge1$, so its size and its classical derivative\nare uniformly bounded.  The at most two unpaired terms have the\nsame bounds on this compact range.\n\nFor $|z|>3$, all indicators in \\eqref{eq:dimensionless-endpoint}\nequal one, and\n\\[\n P(|z|/\\alpha)-c_0=-2g_3''(|z|/\\alpha).\n\\]\nConsequently $e_\\alpha$, $\\partial_z e_\\alpha$,\n$\\partial_\\alpha e_\\alpha$, and\n$\\partial_z\\partial_\\alpha e_\\alpha$ are bounded by\n$Ce^{-cz^2}$, uniformly for $1\\le\\alpha\\le2$.\nThe paired-length estimate and the two unpaired contributions prove\nthe same bound for their sum.  Combining the compact and tail\nestimates proves \\eqref{eq:paired-endpoint-bound}.\n\nWe finish by separating the groups according to their counts.\nAt each output point, \\eqref{eq:endpoint-count} implies that there\nare at most $2\\sqrt n$ indices with $m_{a,b,k}>\\sqrt n$.\nThe uniform Gaussian bound \\eqref{eq:paired-endpoint-bound} and\nLemma~\\ref{lem:gaussian-envelope} bound all these groups together,\nafter integration in $c$, by\n$C\\sqrt n\\mathcal M_{F,G}(-a,-b)$.\nTheir trilinear contribution is at most\n$C\\sqrt n\\prod_v\\|G_v\\|_3$.\n\nSet the kernels and counts of those groups to zero.  The remaining\nfamily satisfies \\eqref{eq:rough-family-decay}, has odd kernels,\nand has counts satisfying\n$\\sum_km_{a,b,k}\\le2n$ and $m_{a,b,k}\\le\\sqrt n$.\nCorollary~\\ref{cor:odd-family} applies and proves\n\\eqref{eq:endpoint-error-bound}.  Every grouping and deletion is made\nat the output point, so the proof imposes no common choice of\nendpoints or dyadic groups at different output points.\n\\end{proof}\n"}, {"path": "preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/smooth.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Annular-variation-of-the-triangular-Hilbert-transform-at-the-symmetric-point-October-5-2026/build/sections/smooth.tex", "bytes": 8844, "sha256": "dc964effac57c11797de4dba23fa8137978baec49c430f43a46324f49ecc08ee", "content": "\\section{Repeated masks and smooth annuli}\\label{sec:smooth}\n\nThe fixed-array dissipation from Section~\\ref{sec:heat} controls smooth\nannuli as long as we also pay for changes in the dual array. We now\nallow up to $n$ disjoint intervals at every output point. The useful\nfeature of the jump estimate is its dependence on the dual norm:\nevery mixed jump term contains that norm twice. A rescaling will then\nturn the linear switch count into a square-root loss.\n\nDefine the odd windows and their scale integrals by\n\\begin{equation}\\label{eq:smooth-kernels}\n k_s(t)=\\frac{t}{\\sqrt s}g_s(t),\\qquad\n I_{\\varepsilon,R}(t)=\\int_{\\varepsilon^2}^{R^2}\n       (k_s*k_s*k_s)(t)\\,\\frac{\\dd s}{s}.\n\\end{equation}\n\n\\begin{proposition}[Smooth annuli with repeated choices]\n\\label{prop:smooth-switches}\nLet $G_v\\in C_c^\\infty(\\R^2)$ be complex. At each output pair $(a,b)$,\nmake a step choice of at most $n\\ge1$ disjoint radius intervals\n$(\\varepsilon_j,R_j)$ and coefficients $|\\beta_j|\\le1$. For\n\\[\n \\mathcal K_{a,b}(t)=\\sum_j\\beta_j I_{\\varepsilon_j,R_j}(t)\n\\]\none has\n\\[\n |\\Lambda_{\\mathcal K}(G_0,G_1,G_2)|\n \\le C\\sqrt n\\prod_{v=0}^2\\|G_v\\|_3.\n\\]\nThe constant is independent of the step partition, the menu of choices,\nand all endpoints.\n\\end{proposition}\n\n\\subsection{The cost of the switches}\n\nWork first on the finite grid of Section~\\ref{sec:heat}. Make the stated\ninterval and coefficient choices separately for each pair $(i,j)$ and put\n\\begin{equation}\\label{eq:repeated-mask}\n A_0^s(i,j)=A_0(i,j)\\sum_{\\ell}\\beta_\\ell\n                  \\mathbf1_{\\{\\varepsilon_\\ell^2<s<R_\\ell^2\\}}.\n\\end{equation}\nThe sum runs through the list chosen at this particular index pair.\nKeep $A_1,A_2$ fixed. Choose a compact positive scale interval\n$[s_-,s_+]$ containing all switch scales in its interior, and take\n$h^2\\le s_-$. By disjointness,\n\\begin{equation}\\label{eq:mask-switch-count}\n |A_0^s(i,j)|\\le |A_0(i,j)|,\n \\qquad\n \\sum_\\tau|A_0^{\\tau+}(i,j)-A_0^{\\tau-}(i,j)|\n \\le2n|A_0(i,j)|.\n\\end{equation}\nThe sum is over the distinct switch scales, and one-sided values are\nused. Both inequalities hold for complex coefficients, including\nsimultaneous openings and closings.\n\n\\begin{lemma}[Repeated-mask dissipation]\\label{lem:repeated-dissipation}\nUse the masked arrays above and variances $\\sigma_v=\\alpha_vs$, where\n$\\boldsymbol\\alpha$ is any permutation of $(1,11/10,11/10)$.\nWith $n_v$ denoting the norms of the original arrays,\n\\begin{equation}\\label{eq:repeated-dissipation}\n \\int_{s_-}^{s_+}\\int_{\\Pi_0}\\sum_vV_v\\dd\\pi_0\\dd s\n \\le C\\left(\\sum_vn_v^3+n\\bigl(n_0^3+n_0^2(n_1+n_2)\\bigr)\\right).\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nBetween switch scales the arrays are fixed, so\n\\eqref{eq:heat-strict} applies. It remains to bound the energy jumps.\nAt a switch $\\tau$, freeze the weights and set\n$\\delta A(i,j)=A_0^{\\tau+}(i,j)-A_0^{\\tau-}(i,j)$.\nInterpolate all changed entries linearly. Their moduli stay bounded\nby $|A_0|$ by convexity. For fixed centers and $(i,j)$ let\n\\begin{align*}\n X^2&=\\sum_m|A_0(i,m)|^2w_1(m),&\n Y^2&=\\sum_m|A_0(m,j)|^2w_0(m),\\\\\n H^2&=\\sum_m|A_2(m,i)|^2w_2(m),&\n Q^2&=\\sum_m|A_1(j,m)|^2w_2(m).\n\\end{align*}\nThese quantities use the original arrays, and square roots are\nnonnegative. In $R_0=[W_0\\ W_2^*]$, the changing entry has row norm\nat most $\\sqrt{w_0(i)}(X+H)$, column norm at most\n$\\sqrt{w_1(j)}Y$, and derivative modulus\n$\\sqrt{w_0(i)w_1(j)}|\\delta A(i,j)|$.\nThe adjoint entry in $R_1=[W_1\\ W_0^*]$ instead has row and column\nbounds $\\sqrt{w_1(j)}(Y+Q)$ and $\\sqrt{w_0(i)}X$.\nThe star $R_2$ is unchanged. Lemma~\\ref{lem:rank-change} therefore gives\n\\begin{equation}\\label{eq:switch-entry-bound}\n |\\Delta e_0|+|\\Delta e_1|\n \\le C\\sum_{i,j}w_0(i)w_1(j)|\\delta A(i,j)|\n                    (2XY+HY+QX).\n\\end{equation}\n\nThe measure $h^{-2}w_0(i)w_1(j)\\dd\\pi_0$ is a probability measure.\nUnder it, $p_0,p_1$ are independent normals with means $u_i,u_j$\nand variances $\\sigma_0,\\sigma_1$, while $p_2=-p_0-p_1$.\nAdding Gaussian variances gives\n\\begin{align*}\n \\mathbb E w_0(m)&=h g_{2\\sigma_0}(u_m-u_i),\\\\\n \\mathbb E w_1(m)&=h g_{2\\sigma_1}(u_m-u_j),\\\\\n \\mathbb E w_2(m)&=h g_{\\sigma_0+\\sigma_1+\\sigma_2}(u_m+u_i+u_j).\n\\end{align*}\nExtend all arrays by zero to $\\mathbb Z^2$. Lemma~\\ref{lem:line-maximal}\nbounds $\\mathbb E X^2,\\mathbb E Y^2,\\mathbb E H^2,\\mathbb E Q^2$\nby constant multiples of, respectively,\n\\begin{align*}\n X_*^2(i,j)&=M_{\\mathbb Z}(|A_0(i,\\cdot)|^2)(j),&\n Y_*^2(i,j)&=M_{\\mathbb Z}(|A_0(\\cdot,j)|^2)(i),\\\\\n H_*^2(i,j)&=M_{\\mathbb Z}(|A_2(\\cdot,i)|^2)(-i-j),&\n Q_*^2(i,j)&=M_{\\mathbb Z}(|A_1(j,\\cdot)|^2)(-i-j).\n\\end{align*}\nAll these majorants are independent of the switch scale.\nCauchy--Schwarz under the probability measure controls the three\nproducts in \\eqref{eq:switch-entry-bound}. Their square-root majorants\nhave $L^3(\\mathbb Z^2,h^2)$ norms at most\n$Cn_0,Cn_0,Cn_2,Cn_1$. This follows from the $L^{3/2}$ line maximal\ninequality, applied on slices; the changes $j\\mapsto-i-j$ or\n$i\\mapsto-i-j$ are bijections on the relevant slices.\n\nIntegrate \\eqref{eq:switch-entry-bound}, sum the jumps using\n\\eqref{eq:mask-switch-count}, and apply H\\\"older on the grid. The result is\n\\[\n \\sum_\\tau\\left|\\Delta\\sum_vJ_v(0,\\tau)\\right|\n \\le Cn\\bigl(n_0^3+n_0^2(n_1+n_2)\\bigr).\n\\]\nFinally integrate \\eqref{eq:heat-strict} between consecutive switches.\nThe resulting sum is bounded by the initial energy plus the sum of\nabsolute jumps, after discarding the nonnegative terminal energy.\nLemma~\\ref{lem:initial-energy} bounds the initial energy by\n$C\\sum_vn_v^3$. This proves \\eqref{eq:repeated-dissipation}.\n\\end{proof}\n\n\\subsection{From the energy to the smooth kernel}\n\n\\begin{proof}[Proof of Proposition~\\ref{prop:smooth-switches}]\nPut $\\lambda=11/10$ and use base variances $\\lambda s$ at all vertices.\nIn Lemma~\\ref{lem:mixed-trace}, insert the diagonal matrix with entries\n$k_s(u_i-p_v)/g_{\\lambda s}(u_i-p_v)$ at vertex $v$.\nIt is Hermitian and uniformly bounded: the polynomial factor in $k_s$\nis absorbed by the extra Gaussian decay relative to $g_{\\lambda s}$.\n\nFor the cost at vertex $v$, narrow just its row variance to $s$.\nWrite $T_v^{\\rm b}$ for the base Gram and $T_v^{\\rm n}$ for the\nnarrowed-row Gram. Directly from the densities,\n\\[\n (R_v^{\\rm b})^*D_vR_v^{\\rm b}=\\sqrt s\\,U_v^{\\rm n},\n \\qquad T_v^{\\rm n}\\le\\sqrt\\lambda\\,T_v^{\\rm b}.\n\\]\nInvertible row scaling preserves the support. Lemma~\\ref{lem:order}\ntherefore bounds the base cost by $C sV_v^{\\rm n}$.\nThe narrowed variances are one of the permutations allowed in\nLemma~\\ref{lem:repeated-dissipation}.\n\nIntegrate the mixed trace inequality over base centers in $\\Pi_0$ and\nover $s$ with measure $\\dd s/s$. Its right side is bounded by\n\\eqref{eq:repeated-dissipation}. On the left, the density at each\nvertex is now $h k_s(u_i-p_v)$. Integration over the center plane\nconvolves the three windows, giving\n\\[\n h^3\\sum_{i,j,l} A_0(i,j)A_1(j,l)A_2(l,i)\n       \\sum_{\\text{chosen at }(i,j)}\\beta\\,\n       I_{\\varepsilon,R}(u_i+u_j+u_l).\n\\]\nAll signed integrals here are absolutely convergent for fixed arrays\non the compact positive scale interval.\n\nIf the original norms are nonzero, apply this bound to arrays normalized\nto have norms $n^{-1/2},1,1$. Its right side is bounded by an absolute\nconstant, since $n(n^{-3/2}+2n^{-1})$ is bounded. Rescaling gives\n$C\\sqrt n\\prod_vn_v$. The zero-norm case is immediate.\nThe Riemann passage described in Section~\\ref{sec:preliminaries}\nnow proves the proposition. The finitely many endpoints are fixed\nbefore the mesh tends to zero, so its scale restriction causes no loss.\n\\end{proof}\n\n\\subsection{The hard endpoint errors}\n\nThe smooth annuli differ from the hard Hilbert annuli by a difference\nof two localized odd kernels. We record the exact identity because\nthe next two sections must estimate their sum, rather than pay a\nconstant separately for each of the up to $2n$ endpoints.\n\nPut\n\\[\n \\psi=k_1*k_1*k_1=-g_3''',\\qquad\n P(v)=2\\int_0^v\\psi(z)\\dd z\\quad(v\\ge0),\\qquad\n c_0=P(\\infty)=2g_3''(0)\\ne0.\n\\]\nThe function $P$ has a smooth even extension and vanishes to second\norder at zero. For $\\rho>0$ define\n\\begin{equation}\\label{eq:endpoint-kernel}\n E_\\rho(t)=\\frac{P(|t|/\\rho)-c_0\\mathbf1_{\\{|t|>\\rho\\}}}{t},\n\\end{equation}\nwith the continuous value zero at $t=0$.\nGaussian scaling and the substitution $z=|t|/\\sqrt s$ in\n\\eqref{eq:smooth-kernels} yield, away from endpoint values,\n\\begin{equation}\\label{eq:hard-smooth}\n I_{\\varepsilon,R}(t)\n =\\frac{P(|t|/\\varepsilon)-P(|t|/R)}{t}\n =\\frac{c_0}{t}\\mathbf1_{\\{\\varepsilon<|t|<R\\}}\n       +E_\\varepsilon(t)-E_R(t).\n\\end{equation}\nHere $k_1=-g_1'$, so convolution gives $\\psi=-g_3'''$; integrating\nthis derivative gives the stated value of $c_0$.\nEach $E_\\rho$ is an odd dilate of a kernel with Gaussian decay and\ntwo jumps. The next section proves a frequency-block estimate that\ncan accommodate many such jumps without a linear loss in their number.\n"}, {"path": "preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/README.md", "bytes": 622, "sha256": "84469ed726d3f2f3123abf4f8ab614367bee6497618dc1e09e51dab7050d20b6", "content": "# [Average sensitivity of polynomial threshold functions](main.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 25, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026,\n  author = {{OpenAI}},\n  title = {{Average sensitivity of polynomial threshold functions}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/main.pdf}{OAI:Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026}},\n  year = {2026}\n}\n```\n"}, {"path": "preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/figures/grade-coupling.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/figures/grade-coupling.tex", "bytes": 2769, "sha256": "e3ac5a116f88be2f47f06ad5a7821177f0875ce230c56741fcd78e9594c322ff", "content": "\\begin{figure}[!htbp]\n  \\centering\n  \\begin{tikzpicture}[x=1cm,y=.9cm,line cap=round,line join=round,\n                      font=\\small]\n    \\begin{scope}\n      \\fill[gray!17] (0,0) -- (3,0) -- (0,3) -- cycle;\n      \\draw[step=1,gray!25,very thin] (0,0) grid (4,4);\n      \\draw[gray!65] (0,4) -- (4,4) -- (4,0);\n      \\draw[->] (0,0) -- (4.25,0) node[right] {$r$};\n      \\draw[->] (0,0) -- (0,4.25) node[above] {$s$};\n      \\node[below left=2pt] at (0,0) {$0$};\n      \\draw (3,.05) -- (3,-.05) node[below=2pt] {$n-d$};\n      \\draw (4,.05) -- (4,-.05) node[below=2pt] {$n$};\n      \\draw (.05,3) -- (-.05,3) node[left=2pt] {$n-d$};\n      \\draw (.05,4) -- (-.05,4) node[left=2pt] {$n$};\n      \\draw[thick,dashed] (0,3) -- (3,0)\n        node[midway,above=2pt,sloped,fill=white,inner sep=1pt]\n        {$r+s=n-d$};\n      \\draw[thick] (0,4) -- (4,0)\n        node[midway,above=2pt,sloped,fill=white,inner sep=1pt]\n        {$r+s=n$};\n      \\node[align=center] at (.85,.85) {zero\\\\blocks};\n      \\node[align=center,font=\\scriptsize] at (2.95,3.25)\n        {other blocks\\\\not specified};\n      \\node[above=10pt] at (2,4.25) {Grade blocks $\\Pi_sH\\Pi_r$};\n    \\end{scope}\n\n    \\draw[->,thick] (4.48,2) -- (5.57,2);\n    \\node[align=center,font=\\scriptsize] at (5.02,2.63)\n      {$u=s$\\\\$v=n-r$};\n\n    \\begin{scope}[xshift=6cm]\n      \\fill[gray!17] (0,1) -- (0,4) -- (3,4) -- cycle;\n      \\draw[step=1,gray!25,very thin] (0,0) grid (4,4);\n      \\draw[gray!65] (0,4) -- (4,4) -- (4,0);\n      \\draw[->] (0,0) -- (4.25,0) node[right] {$u$};\n      \\draw[->] (0,0) -- (0,4.25) node[above] {$v$};\n      \\node[below left=2pt] at (0,0) {$0$};\n      \\draw (4,.05) -- (4,-.05) node[below=2pt] {$n$};\n      \\draw (.05,1) -- (-.05,1) node[left=2pt] {$d$};\n      \\draw (.05,4) -- (-.05,4) node[left=2pt] {$n$};\n      \\draw[thick,dashed] (0,1) -- (3,4)\n        node[midway,above=2pt,sloped,fill=white,inner sep=1pt]\n        {$v=u+d$};\n      \\draw[thick] (0,0) -- (4,4)\n        node[midway,below=2pt,sloped,fill=white,inner sep=1pt]\n        {$v=u$};\n      \\node[align=center] at (.85,3.1) {zero\\\\mass};\n      \\node at (2.65,.95) {$v-u\\le d$};\n      \\node[above=10pt] at (2,4.25) {Reflected coupling $(U,V)$};\n    \\end{scope}\n  \\end{tikzpicture}\n  \\[\n    \\mathbb P\\{R=r,S=s\\}\n      =2^{-n}\\|\\Pi_sH\\Pi_r\\|_{\\mathrm{HS}}^2,\n    \\qquad r+s-n=u-v.\n  \\]\n  \\caption{Grade blocks and their reflected coupling, schematic for $0<d<n$.\n  Under $U=S$, $V=n-R$, the zero-block region $r+s<n-d$ becomes the\n  zero-mass region $v-u>d$, giving $V-U\\le d$ almost surely.\n  Dashed boundaries are allowed; solid diagonals mark $r+s-n=u-v=0$.\n  Each marginal is $\\operatorname{Bin}(n,1/2)$. Unshaded locations need\n  not carry positive mass, and grid spacing is illustrative.}\n  \\label{fig:grade-coupling}\n\\end{figure}\n"}, {"path": "preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/main.tex", "bytes": 2714, "sha256": "801abc393d2a78359838565a1373c1663655b0c19e166c8cbd8588d93ba0c18a", "content": "\\documentclass[11pt]{article}\n\\pdftrailerid{}\n\\usepackage[T1]{fontenc}\n\\usepackage{lmodern}\n\\pdfglyphtounicode{parenleftbig}{0028 FE01}\n\\pdfglyphtounicode{parenrightbig}{0029 FE01}\n\\pdfglyphtounicode{bracketleftbig}{005B FE01}\n\\pdfglyphtounicode{bracketrightbig}{005D FE01}\n\\pdfglyphtounicode{parenleftbigg}{0028 FE03}\n\\pdfglyphtounicode{parenrightbigg}{0029 FE03}\n\\pdfglyphtounicode{parenleftBigg}{0028 FE04}\n\\pdfglyphtounicode{parenrightBigg}{0029 FE04}\n\\pdfglyphtounicode{braceleftBigg}{007B FE04}\n\\pdfglyphtounicode{circleplusdisplay}{2A01 FE02}\n\\pdfglyphtounicode{summationtext}{2211 FE01}\n\\pdfglyphtounicode{producttext}{220F FE01}\n\\pdfglyphtounicode{summationdisplay}{2211 FE02}\n\\pdfglyphtounicode{integraldisplay}{222B FE02}\n\\pdfglyphtounicode{hatwide}{02C6 FE01}\n\\pdfglyphtounicode{ceilingleftBig}{2308 FE02}\n\\pdfglyphtounicode{ceilingrightBig}{2309 FE02}\n\\pdfglyphtounicode{braceleftBig}{007B FE02}\n\\pdfglyphtounicode{bracerightBig}{007D FE02}\n\\pdfglyphtounicode{radicalBig}{221A FE02}\n\\pdfglyphtounicode{productdisplay}{220F FE02}\n\\pdfglyphtounicode{hatwider}{02C6 FE02}\n\\pdfgentounicode=1\n\\usepackage[margin=1.05in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm}\n\\usepackage{microtype}\n\\usepackage{needspace}\n\\usepackage{tikz}\n\\usepackage[hidelinks]{hyperref}\n\n\\newtheorem{theorem}{Theorem}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\newcommand{\\E}{\\mathbb E}\n\\newcommand{\\Prb}{\\mathbb P}\n\\newcommand{\\Id}{\\mathrm{Id}}\n\\newcommand{\\HS}{\\mathrm{HS}}\n\\newcommand{\\op}{\\mathrm{op}}\n\\newcommand{\\sgn}{\\operatorname{sgn}}\n\\newcommand{\\norm}[1]{\\lVert #1\\rVert}\n\\newcommand{\\abs}[1]{\\lvert #1\\rvert}\n\\newcommand{\\ip}[2]{\\langle #1,#2\\rangle}\n\n\\hypersetup{\n  pdftitle={Average sensitivity of polynomial threshold functions},\n  pdfauthor={OpenAI},\n  pdfsubject={Polynomial threshold functions and total influence on the Boolean cube},\n  pdfkeywords={polynomial threshold function, average sensitivity, total influence, Boolean cube}\n}\n\\urlstyle{same}\n\\title{Average sensitivity of polynomial threshold functions}\n\\author{OpenAI}\n\\date{September 25, 2026}\n\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nFor every $n\\ge1$ and $1\\le d\\le n$, we prove that a polynomial\nthreshold function of degree at most $d$ on the uniform Boolean cube has\naverage sensitivity at most $8d\\sqrt n$. This proves the asymptotic form\nof the Gotsman--Linial conjecture. The bound is uniform in both parameters\nand uses the convention $\\sgn(0)=1$.\n\\end{abstract}\n\n\\input{sections/01-introduction}\n\\input{sections/02-coupling}\n\\input{sections/03-weighted-grading}\n\\input{sections/04-edge-recovery}\n\\input{sections/05-parity-coupling}\n\\input{sections/06-consequences}\n\\input{references}\n\\end{document}\n"}, {"path": "preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/references.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/references.tex", "bytes": 3801, "sha256": "3b65d65dfb5b23afe7f2861e522c066c7e303bb1f04ff76b0beb20c02d1f2fb4", "content": "\\begin{thebibliography}{10}\n\n\\bibitem{ChangSloteVolbergZhang2026}\nFan Chang, Joseph Slote, Alexander Volberg, and Haonan Zhang,\n\\emph{The Boolean surface area of polynomial threshold functions},\npreprint, 2026.\n\\href{https://arxiv.org/abs/2604.08095v2}{arXiv:2604.08095v2}.\n\n\\bibitem{Chapman2018}\nBrynmor Chapman,\n\\emph{The Gotsman--Linial conjecture is false},\nin Proceedings of the Twenty-Ninth Annual ACM--SIAM Symposium on Discrete\nAlgorithms (SODA 2018), Society for Industrial and Applied Mathematics,\n2018, 692--699.\n\\href{https://doi.org/10.1137/1.9781611975031.45}{\\nolinkurl{doi:10.1137/1.9781611975031.45}}.\nPreprint: \\href{https://arxiv.org/abs/2108.02288v1}{arXiv:2108.02288v1}.\n\n\\bibitem{DRST2014}\nIlias Diakonikolas, Prasad Raghavendra, Rocco A. Servedio, and Li-Yang Tan,\n\\emph{Average sensitivity and noise sensitivity of polynomial threshold functions},\nSIAM Journal on Computing \\textbf{43} (2014), no.~1, 231--253.\n\\href{https://doi.org/10.1137/110855223}{\\nolinkurl{doi:10.1137/110855223}}.\nPreprint: \\href{https://arxiv.org/abs/0909.5011v2}{arXiv:0909.5011v2}.\n\n\\bibitem{GotsmanLinial1994}\nCraig Gotsman and Nathan Linial,\n\\emph{Spectral properties of threshold functions},\nCombinatorica \\textbf{14} (1994), no.~1, 35--50.\n\\href{https://doi.org/10.1007/BF01305949}{\\nolinkurl{doi:10.1007/BF01305949}}.\n\n\\bibitem{HarshaKlivansMeka2014}\nPrahladh Harsha, Adam Klivans, and Raghu Meka,\n\\emph{Bounding the sensitivity of polynomial threshold functions},\nTheory of Computing \\textbf{10} (2014), no.~1, 1--26.\n\\href{https://doi.org/10.4086/toc.2014.v010a001}{\\nolinkurl{doi:10.4086/toc.2014.v010a001}}.\n\n\\bibitem{KKMS2008}\nAdam Tauman Kalai, Adam R. Klivans, Yishay Mansour, and Rocco A. Servedio,\n\\emph{Agnostically learning halfspaces},\nSIAM Journal on Computing \\textbf{37} (2008), no.~6, 1777--1805.\n\\href{https://doi.org/10.1137/060649057}{\\nolinkurl{doi:10.1137/060649057}}.\n\n\\bibitem{Kane2011}\nDaniel M. Kane,\n\\emph{The Gaussian surface area and noise sensitivity of degree-$d$\npolynomial threshold functions},\nComputational Complexity \\textbf{20} (2011), 389--412.\n\\href{https://doi.org/10.1007/s00037-011-0012-6}{\\nolinkurl{doi:10.1007/s00037-011-0012-6}}.\nPreprint (titled \\emph{The Gaussian surface area and noise sensitivity of\ndegree-$d$ polynomials}):\n\\href{https://arxiv.org/abs/0912.2709v1}{arXiv:0912.2709v1}.\n\n\\bibitem{Kane2014}\nDaniel M. Kane,\n\\emph{The correct exponent for the Gotsman--Linial conjecture},\nComputational Complexity \\textbf{23} (2014), no.~2, 151--175.\n\\href{https://doi.org/10.1007/s00037-014-0086-z}{\\nolinkurl{doi:10.1007/s00037-014-0086-z}}.\nPreprint: \\href{https://arxiv.org/abs/1210.1283v1}{arXiv:1210.1283v1}.\n\n\\bibitem{KimMaldonadoWellens2017}\nH. W. Kim, C. Maldonado, and J. Wellens,\n\\emph{On graphs and the Gotsman--Linial conjecture for $d=2$},\npreprint, 2017.\n\\href{https://arxiv.org/abs/1709.06650v1}{arXiv:1709.06650v1}.\n\n\\bibitem{Rollin2008}\nAdrian R\\\"ollin,\n\\emph{A note on the exchangeability condition in Stein's method},\nStatistics \\& Probability Letters \\textbf{78} (2008), no.~13, 1800--1806.\n\\href{https://doi.org/10.1016/j.spl.2008.01.043}{\\nolinkurl{doi:10.1016/j.spl.2008.01.043}}.\nPreprint: \\href{https://arxiv.org/abs/math/0611050v2}{arXiv:math/0611050v2}.\n\n\\bibitem{TsengVolberg2026}\nChun-Kai Tseng and Alexander Volberg,\n\\emph{Small moments of the sensitivity of polynomial threshold functions},\npreprint, 2026.\n\\href{https://arxiv.org/abs/2606.16004v2}{arXiv:2606.16004v2}.\n\n\\bibitem{Xu2004}\nYuan Xu,\n\\emph{On discrete orthogonal polynomials of several variables},\nAdvances in Applied Mathematics \\textbf{33} (2004), 615--632.\n\\href{https://doi.org/10.1016/j.aam.2004.03.002}{\\nolinkurl{doi:10.1016/j.aam.2004.03.002}}.\nPreprint: \\href{https://arxiv.org/abs/math/0401418v2}{arXiv:math/0401418v2}.\n\n\\end{thebibliography}\n"}, {"path": "preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/sections/01-introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/sections/01-introduction.tex", "bytes": 8807, "sha256": "f7155b070e24527bf2411097914b8e096f96c7cc9ca19f2e30b3620bdefa0b69", "content": "\\section{Introduction}\\label{sec:introduction}\n\nA polynomial threshold function assigns a sign to each vertex of the\nBoolean cube by evaluating a real polynomial. On the cube, the identities\n$x_i^2=1$ allow every polynomial to be replaced by a multilinear polynomial\nof no larger degree without changing its values. Average sensitivity\nmeasures the expected number of coordinate changes that reverse that sign.\nFor $x\\in\\{-1,1\\}^n$, let $x^{\\oplus i}$ be obtained by reversing\ncoordinate $i$. For $f:\\{-1,1\\}^n\\to\\{-1,1\\}$, define\n\\begin{equation}\\label{eq:influence-definition}\n I(f)=\\sum_{i=1}^n\\Prb\\{f(X)\\ne f(X^{\\oplus i})\\},\n \\qquad X\\text{ uniform on }\\{-1,1\\}^n.\n\\end{equation}\nThis quantity is also called the total influence of $f$. Throughout,\n\\[\n \\sgn(t)=\\begin{cases}1,&t\\ge0,\\\\-1,&t<0.\\end{cases}\n\\]\nWe prove the following estimate.\n\n\\begin{theorem}\\label{thm:main}\nLet $n\\ge1$ and $1\\le d\\le n$ be integers. Let $p$ be a real multilinear\npolynomial of degree at most $d$, and define\n$f:\\{-1,1\\}^n\\to\\{-1,1\\}$ by $f(x)=\\sgn(p(x))$, with $\\sgn(0)=1$.\nThen, for average sensitivity under the uniform law,\n\\[\n I(f)\\le 8d\\sqrt n.\n\\]\n\\end{theorem}\n\nThe constant is independent of both $d$ and $n$. In particular, the degree\nmay grow with the dimension. The polynomial may vanish on the cube, and\nno regularity condition is imposed on $p$.\n\nGotsman and Linial proposed an exact extremal bound for average\nsensitivity~\\cite[Section~5, p.~47]{GotsmanLinial1994}.\nTheir candidate is symmetric: it is the sign of a degree-$d$ polynomial\nin $x_1+\\cdots+x_n$ whose roots lie between the central levels of the\ncube. The proposal asserts that this function maximizes influence among\nall degree-$d$ polynomial threshold functions in $n$ variables.\nChapman constructed counterexamples to this exact assertion and\nexplicitly distinguished it from the asymptotic bound\n$I(f)=O(d\\sqrt n)$\n\\cite[Conjectures~1.1--1.2 and Theorem~1.1]{Chapman2018}.\nKim, Maldonado, and Wellens independently constructed quadratic\ncounterexamples for odd $n\\ge5$ and proved influence bounds for\nquadratic polynomials with restricted support\ngraphs~\\cite[Theorems~1.1--1.3]{KimMaldonadoWellens2017}.\nTheorem~\\ref{thm:main} establishes the asymptotic bound with an absolute\nconstant; it makes no assertion about exact maximizers.\nNumbered locators in references with a listed preprint refer to that\npreprint version.\n\nThe classical symmetric examples explain the scale of the theorem.\nLet $1\\le d\\le\\sqrt n$, let $J$ consist of the $d$ indices nearest\n$(n-1)/2$ in $\\{0,\\ldots,n-1\\}$, breaking a tie arbitrarily, and put\n\\[\n t(x)=\\frac{n+x_1+\\cdots+x_n}{2},\\qquad\n p_J(x)=\\prod_{j\\in J}\\bigl(t(x)-j-\\tfrac12\\bigr).\n\\]\nHere $t(x)$ is the number of coordinates of $x$ equal to $+1$.\nMultilinearization preserves the values of $p_J$ on the cube and has\ndegree at most $d$. Its sign changes exactly across the boundaries\nbetween Hamming weights $j$ and $j+1$ for $j\\in J$. There are\n$(n-j)\\binom nj=n\\binom{n-1}j$ unoriented edges at such a boundary,\nso the ordered-edge normalization in~\\eqref{eq:influence-definition}\ngives\n\\[\n I(\\sgn p_J)=n\\,2^{-(n-1)}\\sum_{j\\in J}\\binom{n-1}j\n \\asymp d\\sqrt n.\n\\]\nHere the comparison constants are absolute. The central-binomial\nestimate from Stirling's formula, together with the ratios of consecutive\nbinomial coefficients, shows that the binomial masses are comparable\nto $n^{-1/2}$ within $\\sqrt n/2$ of the center, which includes all\nselected indices; the case $n=1$ follows directly from the count.\nThus the order $d\\sqrt n$ is sharp throughout\nthis degree range; see also\n\\cite[p.~3, discussion following Theorem~1.1]{KimMaldonadoWellens2017}.\nFor every Boolean function, the additional bound $I(f)\\le n$ holds.\n\nEarly general bounds established sublinear influence for every fixed\ndegree. For $n>1$, Harsha, Klivans, and Meka proved\n$I(f)\\le2^{O(d)}n^{1-1/(4d+6)}$\n\\cite[Theorem~1.6]{HarshaKlivansMeka2014}, while the independent work of\nDiakonikolas, Raghavendra, Servedio, and Tan gave\n$I(f)\\le2^{O(d)}(\\log n)n^{1-1/(4d+2)}$\n\\cite[Theorem~1.1]{DRST2014}.\nThese results first circulated in 2009, before their 2014 journal\npublications. Their proofs use regularity and invariance arguments to\ncompare sufficiently regular Boolean polynomials with Gaussian ones.\nKane subsequently established the square-root exponent in the dimension:\n\\begin{equation}\\label{eq:kane-bound}\n I(f)\\le\\sqrt n\\,(\\log n)^{O(d\\log d)}2^{O(d^2\\log d)}\n \\qquad(n>1)\n\\end{equation}\n\\cite[Theorem~2]{Kane2014}.\nFor each fixed $d\\ge2$, this is an $n^{1/2+o(1)}$ bound with a\npolylogarithmic loss. His proof combines random restrictions, regularity,\nand invariance estimates. Theorem~\\ref{thm:main} removes that loss and\ngives linear dependence on $d$ with an absolute constant.\n\nSmall moments of the local sensitivity\n$s_f(x)=|\\{i:f(x)\\ne f(x^{\\oplus i})\\}|$ give further information\nabout its distribution. Chang, Slote, Volberg, and Zhang bounded the\nBoolean surface area $\\E\\sqrt{s_f(X)}$ by a polynomial in $\\log(en)$\nfor each fixed degree\n\\cite[Theorem~1.1, equation~(1.6)]{ChangSloteVolbergZhang2026}.\nTseng and Volberg studied a wider range of sensitivity moments\n\\cite{TsengVolberg2026}.\nFor comparison with the first moment, the pointwise inequality\n$s_f\\le\\sqrt n\\sqrt{s_f}$ converts the surface-area estimate into an\ninfluence bound with a logarithmic loss. At moment order one, the\nestimate in~\\cite[Section~2.5, equation~(11)]{TsengVolberg2026}\nis $C(d)\\sqrt n(\\log n)^{Cd\\log d}$ for $n>1$, which likewise retains a\nlogarithmic factor for $d\\ge2$.\n\n\\paragraph{Proof strategy.}\nThe proof separates a probability estimate from a finite-dimensional\noperator construction. The probability estimate says that equal-law\nrandom variables of variance $\\sigma^2$ satisfy\n$\\E(U-V)^2\\le8a\\sigma$ whenever $V-U\\le a$ almost surely.\nA bound on increments in the opposite direction is not assumed.\nEquality of the marginal laws supplies the needed balance through an\nincreasing function whose increments dominate squared displacement.\nSection~\\ref{sec:coupling} proves this estimate directly.\n\nFor the operator construction, begin with the spaces of cube polynomials\nof degrees at most $k$, for $0\\le k\\le n$. Multiply these spaces by\n$\\sqrt w$ for a positive weight $w$, and take their successive orthogonal\nincrements in the counting inner product. The increments have dimensions\n$\\binom nk$, although the weight changes their positions. Give the\n$k$th increment eigenvalue $k-n/2$; this defines a self-adjoint operator\n$M$ with fixed squared Hilbert--Schmidt norm $n2^n/4$.\nIts squared Hilbert--Schmidt norm is the sum of the squared moduli of\nall matrix entries.\nMultiplication by a coordinate connects only equal or adjacent grades,\nbecause it raises polynomial degree by at most one and is self-adjoint.\nThese are the two features of the grading used in the proof: fixed\ndimensions and locality under coordinate multiplication\n(Section~\\ref{sec:grading}).\n\nWhen $w\\equiv1$, $M$ is minus one-half of the cube adjacency matrix.\nFor a general weight, some edge entries may have smaller squared modulus,\nand other entries may appear. Section~\\ref{sec:edges} shows that the total squared\nentry mass between vertices at distance at least two is bounded by the\nmass on the diagonal.\nTogether with a bound on each edge entry, this controls the total deficit\nfrom the unweighted squared edge value $1/4$.\nIf $H$ multiplies by a sign function $h$, the anticommutator $MH+HM$\nretains diagonal entries and equal-sign edge entries, multiplying them by\n$2$ or $-2$. Its squared norm therefore bounds the number of equal-sign\nedges even for an arbitrary positive weight.\n\nFinally, multiply $f$ by full parity to obtain\n$h(x)=(\\prod_{i=1}^n x_i)f(x)$, and use $w=|p|$ after removing zeros\nwithout changing signs. The equal-sign edges of $h$ are exactly the\nsensitive edges of $f$. Moreover, multiplication by parity turns the\nFourier support of $p$ into degrees at least $n-d$.\nThis forces the block of $H$ between grades $r$ and $s$ to vanish when\n$r+s<n-d$. The normalized squared block norms define a probability law\nfor $(R,S)$ whose two marginals are $\\operatorname{Bin}(n,1/2)$.\nReflecting one index gives $U=S$ and $V=n-R$, with the same marginals and\n$V-U\\le d$. The coupling estimate then bounds the anticommutator norm,\nand the edge estimate gives the theorem. Section~\\ref{sec:parity}\nperforms this assembly, keeping track of ordered edges.\n\n\\paragraph{Consequences.}\nSection~\\ref{sec:consequences} derives a dimension-free Boolean noise\nsensitivity bound and a quantitative learning guarantee from the influence\nestimate. The learning statement concerns independent labeled samples\nwhose input marginal is uniform, while the labels may be arbitrary.\nIt follows by combining noise smoothing with polynomial regression.\n"}, {"path": "preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/sections/02-coupling.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/sections/02-coupling.tex", "bytes": 2564, "sha256": "0950fe55cbf65b1dd981e6e484bf0ff2fa82d9dfcd33c889a31c1980b116fac9", "content": "\\section{A one-sided coupling estimate}\\label{sec:coupling}\n\nThe next lemma controls mean square displacement from an upper bound in\nonly one direction. Large downward displacements are permitted, so\nbounding the positive and negative displacements separately would lose\ninformation. Equality of the marginal laws instead balances the expected\nchange of any integrable function applied to the two variables. We use\nan increasing function whose increments dominate squared displacement;\nits increments in the bounded direction can be estimated using the common\nvariance. Neither independence nor exchangeability is assumed.\n\n\\Needspace{5\\baselineskip}\n\\begin{lemma}\\label{lem:coupling}\nLet $U,V$ be real random variables with the same distribution, finite mean\n$m$, and finite variance $\\sigma^2$. If $V-U\\le a$ almost surely, where\n$a\\ge0$, then\n\\[\n \\E(U-V)^2\\le 8a\\sigma.\n\\]\n\\end{lemma}\n\n\\begin{proof}\nThe function $|t-m|$ grows linearly away from the common mean. Its\nprimitive is the increasing function\n\\[\n g(t)=\\int_m^t\\abs{z-m}\\,dz=\\tfrac12(t-m)\\abs{t-m}.\n\\]\nFor $u<v$, we have\n\\begin{equation}\\label{eq:g-increments}\n \\frac{(v-u)^2}{4}\\le g(v)-g(u)\n \\le \\frac{v-u}{2}\\bigl(\\abs{u-m}+\\abs{v-m}\\bigr).\n\\end{equation}\nFor the lower bound, if $u\\le m\\le v$, the integral is\n$\\bigl((m-u)^2+(v-m)^2\\bigr)/2\\ge(v-u)^2/4$; if the interval lies on one\nside of $m$, the integral is at least $(v-u)^2/2$.\nFor the upper bound, convexity places $\\abs{z-m}$ below the chord joining\nits endpoint values, whose integral is the displayed upper bound.\n\nThe second-moment assumption makes $g(U)$ and $g(V)$ integrable.\nTheir expectations agree. For the integrable random variable\n$Y=g(V)-g(U)$, write $Y_+=\\max\\{Y,0\\}$. The identity $\\E Y=0$ implies\n$\\E|Y|=2\\E Y_+$. Since $g$ is strictly increasing, $Y>0$ exactly when\n$V>U$, and hence\n\\begin{align*}\n \\E\\abs{g(U)-g(V)}\n &=2\\E\\bigl[\\mathbf1_{\\{V>U\\}}(g(V)-g(U))\\bigr]\\\\\n &\\le a\\E\\bigl[\\abs{U-m}+\\abs{V-m}\\bigr]\n \\le 2a\\sigma.\n\\end{align*}\nThe first inequality uses \\eqref{eq:g-increments} and $V-U\\le a$ on\n$\\{V>U\\}$; the last is Cauchy--Schwarz.\nThe lower bound in \\eqref{eq:g-increments}, with the endpoints put in\nincreasing order, gives $(U-V)^2\\le4|g(U)-g(V)|$ pointwise. Taking\nexpectations proves the lemma.\n\\end{proof}\n\n\nThe cancellation $\\E[g(V)-g(U)]=0$ uses only equality of marginal laws.\nPrimitive differences with this property also appear in R\\\"ollin's\nformulation of Stein's method~\\cite[Section~1]{Rollin2008}.\nThe present one-sided second-moment estimate follows from the elementary\nincrement bounds above.\n"}, {"path": "preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/sections/03-weighted-grading.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/sections/03-weighted-grading.tex", "bytes": 4403, "sha256": "f00cb46648ee1f960255ce21d758ca91fce68e27377529822ce8c1f08c188387", "content": "\\section{Weighted Fourier grading}\\label{sec:grading}\n\nWe next construct an operator whose spectrum is fixed by the dimensions\nof polynomial spaces, while its matrix entries respond to an arbitrary\npositive weight. The construction is the cube instance of the usual degree\nfiltration for discrete multivariate orthogonal polynomials, whose\ncoordinate multipliers satisfy a three-term recurrence; compare\nXu~\\cite[Section~3]{Xu2004}. We give the finite-dimensional argument in\nthe present notation.\n\n\nWrite $\\Omega=\\{-1,1\\}^n$ and $N=2^n$. We work in\n$\\mathcal H=\\mathbb C^\\Omega$ with the counting inner product\n\\[\n \\ip{a}{b}=\\sum_{x\\in\\Omega}\\overline{a(x)}b(x).\n\\]\nThus the point masses $\\delta_x$ form an orthonormal basis. For an operator\n$B$, write $B_{xy}=\\ip{\\delta_x}{B\\delta_y}$ and let\n\\[\n \\norm B_{\\HS}^2=\\sum_{x,y\\in\\Omega}\\abs{B_{xy}}^2\n\\]\nbe its Hilbert--Schmidt norm squared; $\\norm B_{\\op}$ denotes its operator\nnorm. The Hilbert--Schmidt norm is independent of the orthonormal basis.\n\nFor $S\\subseteq[n]=\\{1,\\ldots,n\\}$, set\n$\\chi_S(x)=\\prod_{j\\in S}x_j$. These real characters satisfy\n\\[\n \\ip{\\chi_S}{\\chi_T}=N\\mathbf1_{\\{S=T\\}},\\qquad\n \\chi_S\\chi_T=\\chi_{S\\mathbin\\triangle T}.\n\\]\nIndeed, a nonconstant character sums to zero by pairing vertices that\ndiffer in one of its coordinates. The displayed product rule therefore\ngives orthogonality, and the $N$ characters form a basis of the\n$N$-dimensional space $\\mathcal H$. Let\n\\[\n V_k=\\operatorname{span}\\{\\chi_S:\\abs S\\le k\\},\\qquad 0\\le k\\le n,\n\\]\nand set $V_k=\\mathcal H$ for $k\\ge n$. Membership in $V_k$ means that a\nfunction has Fourier degree at most $k$. The character multiplication rule\nshows that $a\\in V_r$ and $b\\in V_s$ imply $\\overline b a\\in V_{r+s}$.\n\nFix any function $w:\\Omega\\to(0,\\infty)$, and let $D$ be multiplication\nby $\\sqrt w$. The identity\n\\[\n \\ip{Da}{Db}=\\sum_{x\\in\\Omega}w(x)\\overline{a(x)}b(x)\n\\]\nidentifies the weighted inner product with the counting inner product\nafter applying $D$. We can therefore orthogonalize polynomial degrees for the weight\n$w$ while retaining the orthonormal point basis used to count edges.\nForm the nested subspaces\n\\[\n K_k=DV_k,\\qquad K_{-1}=\\{0\\},\\qquad\n E_k=K_k\\cap K_{k-1}^{\\perp}\\quad(0\\le k\\le n).\n\\]\nLet $\\Pi_k$ be the orthogonal projection onto $E_k$. The spaces $K_k$\nare nested, and $K_k=K_{k-1}\\oplus E_k$. Since $D$ is invertible,\n$\\dim K_k=\\dim V_k=\\sum_{j=0}^k\\binom nj$ and $K_n=\\mathcal H$.\nSubtracting consecutive dimensions gives\n\\[\n\\mathcal H=\\bigoplus_{k=0}^n E_k,\\qquad\n \\dim E_k=\\binom nk.\n\\]\nChoosing an orthonormal basis in each $E_k$ and decomposing the image of\neach basis vector gives, for every operator $B$,\n\\begin{equation}\\label{eq:block-parseval}\n \\norm B_{\\HS}^2\n =\\sum_{r,s=0}^n\\norm{\\Pi_s B\\Pi_r}_{\\HS}^2.\n\\end{equation}\nDefine the grading operator and its centered version by\n\\begin{equation}\\label{eq:grading}\n L=\\sum_{k=0}^n k\\Pi_k,\\qquad M=L-\\frac n2\\Id.\n\\end{equation}\nThey are self-adjoint. Their prescribed eigenvalue multiplicities give\n\\begin{equation}\\label{eq:total-energy}\n \\norm M_{\\HS}^2=\\sum_{k=0}^n\\binom nk\\left(k-\\frac n2\\right)^2\n =\\frac{nN}{4}.\n\\end{equation}\nThe last equality is the variance identity for a sum of $n$ independent\nBernoulli random variables of parameter $1/2$.\n\nLet $\\rho(x,y)$ denote Hamming distance. All pairs of vertices in what\nfollows are \\emph{ordered}. When $w\\equiv1$, the identity\n$\\sum_{y:\\rho(x,y)=1}\\chi_S(y)=(n-2\\abs S)\\chi_S(x)$ shows that $M$ is\nminus one-half of the cube adjacency matrix. Thus every edge entry has\nsquared modulus $1/4$; the proof below controls the total deficit from\nthis value for arbitrary positive weights.\n\nThe key local property is that multiplying by a coordinate connects\nonly equal or adjacent grades.\n\nFor each $i\\in[n]$, let $Z_i$ be multiplication by $x_i$. It is a\nself-adjoint unitary and commutes with $D$. Multiplication by $x_i$ raises\nFourier degree by at most one, so $Z_iK_r\\subseteq K_{r+1}$.\nFor $s>r+1$, the inclusion $K_{r+1}\\subseteq K_{s-1}$ and the\northogonality $E_s\\perp K_{s-1}$ give $\\Pi_sZ_i\\Pi_r=0$.\nTaking adjoints gives the opposite triangular vanishing, so\n\\begin{equation}\\label{eq:tridiagonal}\n \\Pi_sZ_i\\Pi_r=0\\qquad\\text{whenever }\\abs{s-r}>1.\n\\end{equation}\n\nTogether with the fixed total energy in \\eqref{eq:total-energy}, this\nrestriction will control the entries of $M$ between vertices at Hamming\ndistance at least two.\n"}, {"path": "preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/sections/04-edge-recovery.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/sections/04-edge-recovery.tex", "bytes": 4553, "sha256": "29db4cc58b6d4cf57defab07afad01f58cbdc12701114f97c35cafe05e964bbb", "content": "\\section{Recovering edges from the anticommutator}\\label{sec:edges}\n\nKeep the grading of Section~\\ref{sec:grading}, with no restriction on the\npositive weight $w$. For an operator $H$ that multiplies by a sign, the\nanticommutator $MH+HM$ vanishes between opposite signs and multiplies\nentries of $M$ by $2$ or $-2$ between equal signs. The following lemma turns this observation into an edge\ncount even though an arbitrary weight can change the individual edge\nentries.\n\n\\begin{lemma}[Recovering edges from the grading]\\label{lem:edges}\nLet $n\\ge1$, $\\Omega=\\{-1,1\\}^n$, and $w:\\Omega\\to(0,\\infty)$.\nLet $M$ be the centered grading operator \\eqref{eq:grading} constructed\nfrom $w$. For $h:\\Omega\\to\\{-1,1\\}$, let $H$ be multiplication by $h$\nand set\n\\[\n \\mathcal E_h=\\{(x,y)\\in\\Omega^2:\\rho(x,y)=1,\\ h(x)=h(y)\\}.\n\\]\nThen, counting ordered pairs,\n\\begin{equation}\\label{eq:edge-recovery}\n \\abs{\\mathcal E_h}\\le 2\\norm{MH+HM}_{\\HS}^2.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nWe compare $M$ with the unweighted grading, whose edge entries have\nsquared modulus $1/4$. First we show that every edge entry of $M$ has\nsquared modulus at most $1/4$. We then bound the sum of the deficits by\ndiagonal energy, which\nthe anticommutator detects for every choice of signs.\n\n\\smallskip\\noindent\\emph{A bound for each off-diagonal entry.}\nWrite $C_i=[L,Z_i]=[M,Z_i]$, where $[A,B]=AB-BA$.\nIts grade blocks are\n\\[\n \\Pi_sC_i\\Pi_r=(s-r)\\Pi_sZ_i\\Pi_r.\n\\]\nWe will prove $\\norm{C_i}_{\\op}\\le1$. The commutator keeps only\nadjacent-grade blocks, with opposite signs in the two directions.\nWe first remove the equal-grade blocks by a norm-nonincreasing average,\nthen supply those opposite signs, up to a common phase, by unitary\nconjugation. Introduce the unitaries\n\\[\n J=\\sum_{k=0}^n(-1)^k\\Pi_k,\\qquad\n W=\\sum_{k=0}^n\\mathrm i^k\\Pi_k,\n \\qquad \\mathrm i^2=-1.\n\\]\nSet $O_i=(Z_i-JZ_iJ)/2$. The triangle inequality gives\n$\\norm{O_i}_{\\op}\\le(\\norm{Z_i}_{\\op}+\\norm{JZ_iJ}_{\\op})/2=1$.\nIn the $(s,r)$ block, the factor defining $O_i$ is\n$(1-(-1)^{s+r})/2$. It is zero on the diagonal and one when\n$|s-r|=1$. By \\eqref{eq:tridiagonal}, $O_i$ therefore consists precisely\nof the blocks of $Z_i$ with\n$\\abs{s-r}=1$. On these blocks, conjugation by $W$ multiplies by\n$\\mathrm i^{s-r}=\\mathrm i(s-r)$. Therefore\n\\[\n WO_iW^*=\\mathrm i C_i,\n \\qquad\\text{and hence}\\qquad \\norm{C_i}_{\\op}\\le1.\n\\]\nIn the point basis, $(C_i)_{xy}=(y_i-x_i)M_{xy}$.\nIf $x\\ne y$, choose $i$ with $x_i\\ne y_i$ to obtain\n\\[\n 2\\abs{M_{xy}}=\\abs{(C_i)_{xy}}\\le\\norm{C_i}_{\\op}\\le1.\n\\]\nIn particular, each neighbor deficit $1/4-\\abs{M_{xy}}^2$ is nonnegative.\n\n\\smallskip\\noindent\\emph{The total neighbor deficit.}\nThe same grade blocks also give an aggregate bound. By\n\\eqref{eq:block-parseval} and \\eqref{eq:tridiagonal},\n\\[\n \\norm{C_i}_{\\HS}^2\n =\\sum_{r,s=0}^n(s-r)^2\\norm{\\Pi_sZ_i\\Pi_r}_{\\HS}^2\n \\le\\norm{Z_i}_{\\HS}^2=N.\n\\]\nSumming the squared point-basis entries of $C_i$ over coordinates gives\n\\begin{equation}\\label{eq:distance-energy}\n \\sum_{x,y}\\rho(x,y)\\abs{M_{xy}}^2\n =\\frac14\\sum_{i=1}^n\\norm{C_i}_{\\HS}^2\n \\le\\frac{nN}{4}=\\sum_{x,y}\\abs{M_{xy}}^2,\n\\end{equation}\nwhere the last equality is \\eqref{eq:total-energy}. Set\n\\[\n A_0=\\sum_x\\abs{M_{xx}}^2,\\qquad\n F=\\sum_{\\rho(x,y)\\ge2}\\abs{M_{xy}}^2.\n\\]\nThe difference between the outermost sums in\n\\eqref{eq:distance-energy} is\n$-A_0+\\sum_{\\rho(x,y)\\ge2}(\\rho(x,y)-1)|M_{xy}|^2\\le0$.\nThus energy beyond neighboring pairs is bounded by diagonal energy:\n\\begin{equation}\\label{eq:far-energy}\n F\\le\\sum_{\\rho(x,y)\\ge2}(\\rho(x,y)-1)\\abs{M_{xy}}^2\\le A_0.\n\\end{equation}\n\nThere are $nN$ ordered neighbor pairs, and thus\n\\begin{align}\n \\sum_{\\rho(x,y)=1}\\left(\\frac14-\\abs{M_{xy}}^2\\right)\n &=\\frac{nN}{4}-\\sum_{\\rho(x,y)=1}\\abs{M_{xy}}^2\\notag\\\\\n &=A_0+F\\le2A_0.\\label{eq:deficit}\n\\end{align}\nHere we used \\eqref{eq:total-energy} and \\eqref{eq:far-energy}.\nThe fixed total energy consequently controls the full deficit from the\nunweighted edge value $1/4$.\n\n\\smallskip\\noindent\\emph{Recovering the equal-sign edges.}\nLet $A_h=\\sum_{(x,y)\\in\\mathcal E_h}\\abs{M_{xy}}^2$.\nBecause the individual deficits are nonnegative, their sum over\n$\\mathcal E_h$ is at most the full sum in \\eqref{eq:deficit}. Hence\n\\begin{equation}\\label{eq:count-deficit}\n \\frac{\\abs{\\mathcal E_h}}4\\le A_h+2A_0\\le2(A_h+A_0).\n\\end{equation}\nFor $T=MH+HM$ we have\n$T_{xy}=(h(x)+h(y))M_{xy}$. On both the diagonal and $\\mathcal E_h$,\nthe squared factor is $4$, so\n\\[\n 4(A_0+A_h)\\le\\norm T_{\\HS}^2.\n\\]\nTogether with \\eqref{eq:count-deficit}, this proves\n\\eqref{eq:edge-recovery}.\n\\end{proof}\n"}, {"path": "preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/sections/05-parity-coupling.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/sections/05-parity-coupling.tex", "bytes": 3743, "sha256": "be96e3479406ea84f1aa40585f56ea642263c80449640ffd55e8a24d5e1f60f6", "content": "\\section{Parity and the binomial coupling}\\label{sec:parity}\n\nWe now choose the weight from the polynomial. Parity will convert the\nsensitivity problem into the equal-sign edge count of\nLemma~\\ref{lem:edges}, while the polynomial degree will produce the\none-sided constraint needed for Lemma~\\ref{lem:coupling}.\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\n\nWe may assume that $p(x)\\ne0$ for every $x\\in\\Omega$.\nIndeed, if $p$ takes negative values, add a positive constant smaller than\n$\\min\\{\\abs{p(x)}:p(x)<0\\}$; otherwise add any positive constant.\nNegative values remain negative, while zero values become positive and\npositive values stay positive. Thus the perturbation preserves $f$ under\nour convention $\\sgn(0)=1$, does not increase the degree, and makes $p$\nnonzero at every vertex. We henceforth use $p$ for the perturbed polynomial.\n\nLet $\\chi=\\chi_{[n]}$ be full parity and set\n\\[\n w=\\abs p,\\qquad h=\\chi f,\\qquad q=\\chi p.\n\\]\nFull parity changes sign across every edge, so $h(x)=h(y)$ for neighbors\n$x,y$ exactly when $f(x)\\ne f(y)$. Consequently\n\\begin{equation}\\label{eq:sensitivity-count}\n I(f)=\\frac{\\abs{\\mathcal E_h}}N.\n\\end{equation}\nHere both sides count sensitive edges in both directions.\nWe have $w>0$ and $hw=q$. Construct the weighted grading\n\\eqref{eq:grading} from $w$, and let $H$ be multiplication by $h$.\nSince $\\chi\\chi_S=\\chi_{[n]\\setminus S}$ and $\\deg p\\le d$, the Fourier\nsupport of $q$ lies in degrees at least $n-d$.\n\nWe claim that\n\\begin{equation}\\label{eq:vanishing}\n \\Pi_sH\\Pi_r=0\\qquad\\text{if }r+s<n-d.\n\\end{equation}\nTo see this, write elements of $E_r\\subseteq DV_r$ and $E_s\\subseteq DV_s$\nas $Da$ and $Db$, with $a\\in V_r$ and $b\\in V_s$. Then\n\\[\n \\ip{Db}{HDa}=\\sum_{x\\in\\Omega}\\overline{b(x)}a(x)w(x)h(x)\n =\\sum_{x\\in\\Omega}\\overline{b(x)}a(x)q(x)=0.\n\\]\nThe last equality follows from character orthogonality: the product\n$\\overline b a$ has Fourier degree at most $r+s<n-d$.\n\nSet $T=MH+HM$. Its blocks are\n\\begin{equation}\\label{eq:T-blocks}\n \\Pi_sT\\Pi_r=(s+r-n)\\Pi_sH\\Pi_r.\n\\end{equation}\nThus the anticommutator energy is a weighted second moment of the sum of\nthe two grade indices:\n\\[\n \\frac1N\\norm T_{\\HS}^2\n =\\sum_{r,s=0}^n(s+r-n)^2\\,\n   \\frac{\\norm{\\Pi_sH\\Pi_r}_{\\HS}^2}{N}.\n\\]\nThe coefficients on the right define a probability distribution. Indeed,\nbecause $H$ is unitary and the projections are orthogonal,\n\\[\n \\sum_s\\norm{\\Pi_sH\\Pi_r}_{\\HS}^2\n =\\norm{H\\Pi_r}_{\\HS}^2=\\binom nr.\n\\]\nDecomposing the domain into grades and taking adjoints likewise gives\n\\[\n \\sum_r\\norm{\\Pi_sH\\Pi_r}_{\\HS}^2\n =\\norm{\\Pi_sH}_{\\HS}^2\n =\\norm{H^*\\Pi_s}_{\\HS}^2=\\binom ns.\n\\]\nSince $\\sum_r\\binom nr=N$, we may therefore define random indices\n$R,S\\in\\{0,\\ldots,n\\}$ by\n\\begin{equation}\\label{eq:block-coupling}\n \\Prb\\{R=r,S=s\\}=\\frac1N\\norm{\\Pi_sH\\Pi_r}_{\\HS}^2.\n\\end{equation}\nBoth marginals are $\\operatorname{Bin}(n,1/2)$, and the preceding energy\nidentity becomes\n\\[\n \\frac1N\\norm T_{\\HS}^2=\\E(R+S-n)^2.\n\\]\n\nTo use Lemma~\\ref{lem:coupling}, reflect the first index: put $U=S$ and\n$V=n-R$. Binomial symmetry gives $U$ and $V$ the same distribution,\nwith mean $n/2$ and variance $n/4$. Meanwhile,\n\\eqref{eq:vanishing} gives $R+S\\ge n-d$ almost surely, which becomes\n$V-U\\le d$. Figure~\\ref{fig:grade-coupling} shows how the zero-block\nregion becomes this one-sided support constraint.\n\n\\input{figures/grade-coupling}\n\nApply Lemma~\\ref{lem:coupling} with $a=d$ and $\\sigma=\\sqrt n/2$ to obtain\n\\begin{equation}\\label{eq:coupling-bound}\n \\frac1N\\norm T_{\\HS}^2\n =\\E(R+S-n)^2=\\E(U-V)^2\\le4d\\sqrt n.\n\\end{equation}\nFinally, \\eqref{eq:sensitivity-count} and Lemma~\\ref{lem:edges} give\n\\[\n I(f)=\\frac{\\abs{\\mathcal E_h}}N\n \\le\\frac2N\\norm T_{\\HS}^2\n \\le8d\\sqrt n.\n\\]\nThis completes the proof.\n\\end{proof}\n\n\n"}, {"path": "preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/sections/06-consequences.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/build/sections/06-consequences.tex", "bytes": 7811, "sha256": "63c0dcfe187326ddd2879b14b452b60fcec3bd3705721b4792f52db5d0662892", "content": "\\section{Noise sensitivity and agnostic learning}\\label{sec:consequences}\n\nThe influence bound has two consequences that are independent of the\nweighted grading used to prove it. A random-bucketing construction\ntransfers the bound from one-bit changes to independent noise.\nSmoothing by this noise then gives low-degree polynomial approximants,\nwhich yield an agnostic learner by $L_1$ regression.\n\n\\subsection{Noise sensitivity}\n\nFor $0\\le\\eta\\le1/2$, define the noise sensitivity\n$\\operatorname{NS}_{\\eta}(f)=\\Prb\\{f(X)\\ne f(Y)\\}$, where $X$ is uniform\non the cube and $Y$ is obtained by flipping each coordinate of $X$\nindependently with probability $\\eta$.\n\n\\begin{corollary}[Boolean noise sensitivity]\\label{cor:noise-sensitivity}\nLet $n\\ge1$ and $d\\ge0$ be integers. If $f=\\sgn(p)$ on\n$\\{-1,1\\}^n$ for a real polynomial $p$ of degree at most $d$, with\n$\\sgn(0)=1$, then\n\\[\n \\operatorname{NS}_{\\eta}(f)\\le C d\\sqrt\\eta\n \\qquad(0<\\eta\\le1/2),\n\\]\nwhere $C$ is an absolute constant.\n\\end{corollary}\n\n\\begin{proof}\nConstants have zero noise sensitivity. For an integer $q\\ge2$, use the\nrandom-bucketing reduction of Diakonikolas, Raghavendra, Servedio, and\nTan~\\cite[Section~7.1, equation~(14)]{DRST2014}: choose independent uniform\nsigns $a_i$ and independent uniform buckets $b(i)\\in\\{1,\\ldots,q\\}$,\nand set\n\\[\n g(z)=f(a_1z_{b(1)},\\ldots,a_nz_{b(n)}),\n \\qquad z\\in\\{-1,1\\}^q.\n\\]\nMultilinearizing the substituted polynomial preserves its cube values,\nincluding zeros, and gives degree at most $\\min(d,q)$.\nTheorem~\\ref{thm:main} therefore gives $I(g)\\le8d\\sqrt q$; a constant\nsubstitution contributes zero.\n\nIndependently choose uniform $z\\in\\{-1,1\\}^q$ and $J\\in\\{1,\\ldots,q\\}$.\nPut $X_i=a_i z_{b(i)}$ and\n$Y_i=X_i(-1)^{\\mathbf1_{\\{b(i)=J\\}}}$.\nConditional on $J$, the flip indicators are independent Bernoulli\nvariables of parameter $1/q$, and their joint law does not depend on $J$.\nConditional on $b,J,z$, the random signs make $X$ uniform, so $X$ is\nindependent of the flip vector. Thus $(X,Y)$ has exactly the\nnoise law at rate $1/q$. Since replacing $z$ by $z^{\\oplus J}$ produces\n$Y$, averaging first over $z,J$ gives\n\\[\n \\operatorname{NS}_{1/q}(f)=\\frac{\\E_{a,b}I(g)}q\n \\le\\frac{8d}{\\sqrt q}.\n\\]\n\nFor the rest of this section, Fourier coefficients and $L_1,L_2$ norms\nare normalized by uniform probability on the cube. In particular,\n$\\widehat f(S)=\\E[f(X)\\chi_S(X)]$ and\n\\[\n \\operatorname{NS}_{\\eta}(f)\n =\\frac12\\sum_{S\\subseteq[n]}\n \\bigl(1-(1-2\\eta)^{|S|}\\bigr)\\widehat f(S)^2.\n\\]\nThis expression is nondecreasing for $0\\le\\eta\\le1/2$.\nTaking $q=\\lfloor1/\\eta\\rfloor$, so that\n$\\eta\\le1/q\\le1/2$ and $q\\ge1/(2\\eta)$, proves the claim with\n$C=8\\sqrt2$.\n\\end{proof}\n\nFor comparison, Kane proved an absolute $O(d\\sqrt\\varepsilon)$ noise\nsensitivity bound for degree-$d$ polynomial threshold functions under\nstandard Gaussian noise~\\cite[Theorem~1]{Kane2011}.\nHere $0<\\varepsilon\\le1$, and the input pair is\n$X$ and $(1-\\varepsilon)X+\\sqrt{2\\varepsilon-\\varepsilon^2}\\,Z$,\nwith $X,Z$ independent standard Gaussian vectors.\nHis proof counts sign changes along Gaussian rotations.\nCorollary~\\ref{cor:noise-sensitivity} gives the same dependence on degree\nand noise rate for independent coordinate flips on the Boolean cube.\n\n\\subsection{Agnostic learning}\n\nIn agnostic learning the labels may be arbitrary: the objective is to\npredict nearly as well as the best function in a specified class.\nWe use the $L_1$ polynomial regression method of Kalai, Klivans,\nMansour, and Servedio~\\cite{KKMS2008}, which was applied to polynomial\nthreshold functions in~\\cite[Section~8]{DRST2014}.\n\n\\begin{corollary}[Uniform-marginal agnostic learning]\\label{cor:agnostic-learning}\nLet $n,d\\ge1$ be integers, and let $\\mathcal C_{n,d}$ be the class of\nfunctions $\\sgn(p)$ on $\\{-1,1\\}^n$ with $\\deg p\\le d$.\nLet $D$ be any distribution on $\\{-1,1\\}^n\\times\\{-1,1\\}$ whose first\nmarginal is uniform, and set\n\\[\n \\mathrm{OPT}=\\min_{f\\in\\mathcal C_{n,d}}\\Prb_{(X,Y)\\sim D}\\{f(X)\\ne Y\\}.\n\\]\nThere is an absolute constant $A$ such that, for $0<\\alpha,\\delta<1$,\na learner using independent labeled samples from $D$ returns, with\nprobability at least $1-\\delta$, a Boolean hypothesis $h$ satisfying\n\\[\n \\Prb_{(X,Y)\\sim D}\\{h(X)\\ne Y\\}\\le\\mathrm{OPT}+\\alpha.\n\\]\nIts running time and sample size are polynomial in\n$(n+1)^k$, $1/\\alpha$, and $\\log(1/\\delta)$, where\n\\[\n k=\\min\\left\\{n,\\left\\lceil A d^2\\alpha^{-2}\\log(4/\\alpha)\\right\\rceil\\right\\}.\n\\]\nThe labels are unrestricted, and $h$ need not belong to $\\mathcal C_{n,d}$.\n\\end{corollary}\n\n\\begin{proof}\nWe first construct an $L_1$ approximant to each $f\\in\\mathcal C_{n,d}$.\nFor $0<\\eta<1/2$ and $\\rho=1-2\\eta$, the noise operator is\n\\[\n T_\\rho f=\\sum_{S\\subseteq[n]}\\rho^{|S|}\\widehat f(S)\\chi_S.\n\\]\nEquivalently, $T_\\rho f(x)$ is the expected value of $f$ after independent\nnoise of rate $\\eta$ is applied to $x$. Since $f$ is Boolean and\n$T_\\rho f$ takes values in $[-1,1]$,\n$|f-T_\\rho f|=1-fT_\\rho f$ pointwise. Corollary~\\ref{cor:noise-sensitivity}\ntherefore gives, with $C=8\\sqrt2$,\n\\[\n \\norm{f-T_\\rho f}_1=2\\operatorname{NS}_\\eta(f)\\le2Cd\\sqrt\\eta.\n\\]\nTake $\\eta=(\\alpha/(8Cd))^2$ and\n$K=\\lceil\\log(4/\\alpha)/(2\\eta)\\rceil$.\nLet $q$ be the Fourier truncation of $T_\\rho f$ to degrees at most\n$\\min(K,n)$. If $K<n$, Parseval's identity gives\n\\[\n \\norm{T_\\rho f-q}_1\\le\\norm{T_\\rho f-q}_2\n \\le\\rho^{K+1}\\le e^{-2\\eta(K+1)}\\le\\alpha/4;\n\\]\nif $K\\ge n$, the truncation error is zero.\nThe first error is also at most $\\alpha/4$, so\n\\begin{equation}\\label{eq:learning-approximation}\n \\norm{f-q}_1\\le\\alpha/2.\n\\end{equation}\nTaking $A=32C^2$ makes $\\min(K,n)=k$ in the statement.\n\nFor completeness, we explain how this direct $L_1$ estimate enters the\nregression argument of~\\cite[proof of Theorem~5]{KKMS2008}.\nUse the $M=\\sum_{j=0}^k\\binom nj\\le(n+1)^k$ characters of degree at\nmost $k$ as features. On a training sample $Z$ of size $m$, fit a\npolynomial $P$ in these features by minimizing empirical absolute loss,\nthen choose $t\\in[-1,1]$ to minimize the empirical error of\n$h=\\sgn(P-t)$. The fit is a linear program, and the threshold is found by\nsorting the fitted values. Write $\\widehat{\\E}_Z$ and\n$\\widehat{\\operatorname{err}}_Z$ for empirical means and errors.\nA uniformly random threshold has error at most half the absolute loss,\nso the minimizing threshold satisfies\n\\[\n \\widehat{\\operatorname{err}}_Z(h)\n \\le\\tfrac12\\widehat{\\E}_Z|Y-P(X)|\n \\le\\tfrac12\\widehat{\\E}_Z|Y-q(X)|.\n\\]\nChoose an optimal $f\\in\\mathcal C_{n,d}$ and its fixed approximant $q$.\nThe triangle inequality and \\eqref{eq:learning-approximation} imply\n$\\E_Z\\widehat{\\operatorname{err}}_Z(h)\\le\\mathrm{OPT}+\\alpha/4$.\nThese hypotheses are halfspaces in the $M$ character features, so their\nVC dimension is at most $M+1$.\nThe usual expected uniform generalization bound, as used\nin~\\cite[proof of Theorem~5]{KKMS2008}, makes\n$\\E_Z\\operatorname{err}_D(h)\\le\\mathrm{OPT}+3\\alpha/8$\nfor $m$ polynomial in $M$ and $1/\\alpha$.\n\nApply Markov's inequality to the nonnegative random variable\n$\\operatorname{err}_D(h)$. Since $\\mathrm{OPT}\\le1$ and $\\alpha<1$,\n\\[\n \\Prb_Z\\{\\operatorname{err}_D(h)>\\mathrm{OPT}+\\alpha/2\\}\n \\le\\frac{\\mathrm{OPT}+3\\alpha/8}{\\mathrm{OPT}+\\alpha/2}\n \\le1-\\alpha/12.\n\\]\nThus one trial succeeds with probability at least $\\alpha/12$.\nRepeat independently\n$r=\\lceil(12/\\alpha)\\log(2/\\delta)\\rceil$ times.\nWith probability at least $1-\\delta/2$, one candidate has this error.\nOn an independent validation sample of size\n$\\lceil8\\alpha^{-2}\\log(4r/\\delta)\\rceil$, Hoeffding's inequality and a\nunion bound estimate all $r$ errors within $\\alpha/4$, except with\nprobability $\\delta/2$. Selecting the best validation score therefore\ngives error at most $\\mathrm{OPT}+\\alpha$. All the training,\nregression, repetition and validation costs have the stated polynomial\nbounds.\n\\end{proof}\n"}, {"path": "preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/main.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Average-Sensitivity-of-Polynomial-Threshold-Functions-September-25-2026/main.pdf", "bytes": 354487, "sha256": "441f9f8a40930603ba88967f00514a974f13a8f8ab901f71206b2b7dc82e4884", "base64": 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"}, {"path": "preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/README.md", "bytes": 720, "sha256": "a9674e6a35ed15fe1910a05dda14b88c0e6a667e01e6ebfe738f65f72eb6aee4", "content": "# [Equidistribution of Prime-Degree Torus Packets with Arbitrary Local Type](paper.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 24, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026,\n  author = {{OpenAI}},\n  title = {{Equidistribution of Prime-Degree Torus Packets with Arbitrary Local Type}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/paper.pdf}{OAI:Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/adelic.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/adelic.tex", "bytes": 6827, "sha256": "de04130652e5e0ac810f1f8143576056b39d094903b3dbb1e60bb5d62fbe1ea9", "content": "\\section{Adelic formulation and choices}\\label{subsec:adelic-packet}\n\nThe model of Lemma~\\ref{lem:packet-model} also identifies the measure obtained from adelic\ntorus Haar probability. We give the identification for arbitrary local\ntype, so the lattice formulation includes all finite lattice and real\ndiagonalization choices in the usual adelic formulation.\n\nLet $\\mathbb A_f$ be the finite adeles of $\\Q$, let\n$\\widehat\\Z=\\prod_\\ell\\Z_\\ell$, and put\n$\\mathbb A_\\Q=\\R\\times\\mathbb A_f$ and\n$\\mathbb A_K=K\\otimes_\\Q\\mathbb A_\\Q$.\nWrite $\\widehat R=R\\otimes\\widehat\\Z$.\nChoose a rational basis $b_1,\\ldots,b_n$ of $K$, and let\n$\\rho(\\alpha)$ denote multiplication by $\\alpha$ on row coefficient\nvectors. It embeds the torus\n\\[\n \\mathbf T_K=(\\operatorname{Res}_{K/\\Q}\\mathbb G_m)/\\mathbb G_m\n \\quad\\hbox{in}\\quad G=\\operatorname{PGL}_{n/\\Q}.\n\\]\nThe embedding matrix $B_\\sigma=(\\sigma_j(b_i))_{i,j}$ satisfies\n\\begin{equation}\\label{eq:regular-diagonalization}\n \\rho(\\alpha)B_\\sigma\n   =B_\\sigma\\operatorname{diag}(\\sigma_1(\\alpha),\\ldots,\\sigma_n(\\alpha)).\n\\end{equation}\nFor $g_f\\in G(\\mathbb A_f)$, choose a lift to\n$\\operatorname{GL}_n(\\mathbb A_f)$ and set\n\\[\n \\widehat M(g_f)=\\widehat\\Z^{\\,n}g_f^{-1},\\qquad\n M(g_f)=K\\cap\\widehat M(g_f),\n\\]\nusing the chosen rational basis to identify coefficient vectors with\nelements of $K$. This is a full lattice, and its completion is\n$\\widehat M(g_f)$: choose an integer $d\\ge1$ with\n$d\\widehat\\Z^{\\,n}\\subset\\widehat M(g_f)\n\\subset d^{-1}\\widehat\\Z^{\\,n}$, and patch in the finite quotient\n$d^{-1}\\Z^n/d\\Z^n$. Changing the projective lift multiplies it by a\nscalar finite idele. Every such idele is a rational scalar times an\nelement of $\\widehat\\Z^\\times$; the latter preserves every completed\nfull lattice. Consequently this change only rescales $M(g_f)$ by\na rational number.\n\nChoose $g_\\infty\\in G(\\R)$ with\n$g_\\infty^{-1}\\mathbf T_K(\\R)^\\circ g_\\infty=A_n$, using the\nodd-degree identification of $G(\\R)$ with $\\SL_n(\\R)$.\nFor $g=(g_\\infty,g_f)$, the adelic packet is the projection of\n$\\mathbf T_K(\\Q)\\backslash\\mathbf T_K(\\mathbb A_\\Q)g$ to\n$G(\\Q)\\backslash G(\\mathbb A_\\Q)/G(\\widehat\\Z)$, equipped with\nthe pushforward of torus Haar probability.\n\n\\begin{proposition}\\label{prop:adelic-packet}\nThe adelic torus quotient is compact. Its packet and canonical\nprobability agree with $\\mathcal P_{K,M(g_f),\\sigma'}$ and\n$\\mu_{K,M(g_f),\\sigma'}$ for an ordering $\\sigma'$ of the real\nembeddings. Every prescribed local homothety type occurs this way.\nChanging the rational basis or the projective lifts does not change\nthe packet; changing the allowed real diagonalizer only reorders the\nembeddings. Thus Theorem~\\ref{thm:main} includes every such adelic choice.\n\\end{proposition}\n\n\\begin{proof}\nWrite $M=M(g_f)$ and $\\widehat M=\\widehat M(g_f)$.\nFirst identify the ambient quotient with $X_n$. Given a finite matrix\nlift $g_f$, choose a basis matrix $c\\in\\operatorname{GL}_n(\\Q)$\nfor $\\Q^n\\cap\\widehat\\Z^{\\,n}g_f^{-1}$. Then\n$cg_f\\in\\operatorname{GL}_n(\\widehat\\Z)$, and the corresponding\nreal lattice is $\\Z^ncg_\\infty$, normalized to covolume one.\nChanging this basis or either matrix lift has no effect on that\nlattice. This constructs the usual quotient identification directly.\nFor odd $n$, the signed real $n$th root gives\n$[h]\\mapsto(\\det h)^{-1/n}h$, identifying\n$G(\\R)$ with $\\SL_n(\\R)$ and $G(\\Z)$ with $\\SL_n(\\Z)$.\n\nMultiplication matrices modulo scalars give\n\\begin{equation}\\label{eq:idelic-torus-quotient}\n \\mathbf T_K(\\Q)\\backslash\\mathbf T_K(\\mathbb A_\\Q)\n \\simeq K^\\times\\backslash\\mathbb A_K^\\times/\\mathbb A_\\Q^\\times.\n\\end{equation}\nHere the pointwise lifts can also be seen by elementary linear algebra:\nover any ground field $F_0$ containing $\\Q$, a matrix whose projective\nclass lies in $\\mathbf T_K$ becomes a scalar multiple of a multiplication\noperator over an algebraic closure. It therefore commutes exactly with\nthe regular representation already over $F_0$. The centralizer of that\nrepresentation is the algebra of multiplication operators, so the matrix\nbelongs to $(K\\otimes_\\Q F_0)^\\times$, with scalar ambiguity $F_0^\\times$.\nFinite lifts may be chosen integral at almost all primes.\n\nA finite idele $t_f$ acts on the completed lattice by\n$\\widehat M\\mapsto t_f^{-1}\\widehat M$. Hence its translates are\nexactly the prescribed local homothety type. The stabilizer of\n$\\widehat M$ among finite ideles is\n\\[\n \\{z:z\\widehat M=\\widehat M\\}\n       =(\\cO(M)\\otimes\\widehat\\Z)^\\times:\n\\]\nequality holds precisely when both $z$ and $z^{-1}$ preserve\n$\\widehat M$. In the projective torus the stabilizer is the image\nof this group, since a representative that preserves the lattice\nup to a scalar idele can be rescaled to preserve it exactly.\nThe unit subgroup is compact open in the finite ideles and has finite\nindex in $\\widehat R^\\times$; at each prime not dividing $q$ it equals\n$R_\\ell^\\times$. Its projective image is compact open as well.\nNo invertibility of $M$ over $\\cO(M)$ is used.\n\nTo describe the quotient and its measure, first divide\n\\eqref{eq:idelic-torus-quotient} by the image of\n$\\widehat R^\\times$. Fractional-ideal factorization and\n$\\mathbb A_\\Q^\\times\n=\\Q^\\times\\R^\\times\\widehat\\Z^\\times$\ngive one real fiber $E\\backslash C$ for each ordinary ideal class.\nThese fibers are compact by the unit theorem, and the omitted\nfinite-unit group is compact. Thus the torus quotient is compact.\nAt the stabilizer of $\\widehat M$, the finite fibers instead retain\nthe translates $\\mathcal N(I)$. The same decomposition becomes\n\\[\n \\coprod_{[I]}(\\mathcal N(I)\\times C)/E,\n\\]\nwith exactly the action in Lemma~\\ref{lem:packet-model}.\nHaar measure pushes to uniform counting measure on each finite\nunit orbit and to the stated logarithmic Haar measure on $C$.\nAfter normalization it is therefore precisely the probability in\nthat lemma. For $g_\\infty=[B_\\sigma]$,\n\\eqref{eq:regular-diagonalization} identifies the resulting real\nlattices with its displayed map. This proves both the packet\nidentification and the equality of measures, including the orbit\nvolume weights.\n\nFinally, every full $M$ is obtained by choosing a basis matrix for\nits completed lattice and taking its inverse as $g_f$.\nAny other allowed real diagonalizer has the form $[B_\\sigma dP]$,\nwhere $d$ is diagonal and $P$ is a permutation matrix, because it\nmust permute the joint eigenspaces. The diagonal factor translates\nthe real torus and permutes its sign components, preserving its\nHaar probability; the permutation reorders the embeddings.\nIf a rational change of basis has matrix $c$, then\n$B'_\\sigma=cB_\\sigma$ and $\\rho'=c\\rho c^{-1}$, while the same\npacket data have representatives $g'_\\infty=cg_\\infty$ and\n$g'_f=cg_f$. Thus the torus is conjugated and the translated packet\nis left-multiplied by $c$, which disappears in the ambient rational quotient.\n\\end{proof}\n"}, {"path": "preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/analytic.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/analytic.tex", "bytes": 7493, "sha256": "da2e6ef51da7b9898178301173cf047f23b0a56c8bd7b6fb46dce6290128e874", "content": "\\section{Residue-relative weighted ideal counts}\\label{sec:analytic}\n\nWrite\n\\[\n a_K(m)=\\#\\{\\mathfrak a\\subseteq R:\\N\\mathfrak a=m\\},\n \\qquad \\kappaK=\\operatorname*{Res}_{s=1}\\zeta_K(s)>0.\n\\]\nWe first retain the residue in the analytic estimates. All constants\nsubscripted by $n$ are uniform over the fields under consideration.\n\n\\begin{lemma}\\label{lem:residue}\nFor $D$ sufficiently large in terms of $n$,\n\\begin{equation}\\label{eq:residue}\n h\\zeta_K(1+h)\\asymp_n\\kappaK\n \\quad\\left(0<h\\le\\frac4{\\log D}\\right),\n \\qquad \\kappaK^{-1}\\ll_n\\log D.\n\\end{equation}\nFor the remaining fields, the first comparison holds uniformly for\nsufficiently small positive $h$, and $\\kappaK^{-1}\\ll_n1$.\n\\end{lemma}\n\n\\begin{proof}\nPut $L=\\log D$. Stark's Lemma~3 gives at most one zero in\n$\\operatorname{Re}s\\ge1-(4L)^{-1}$,\n$|\\operatorname{Im}s|\\le(4L)^{-1}$; such a zero is real and simple.\nHis Lemma~8, which also applies to nonnormal fields, says that a real\nzero $\\beta\\ge1-(4n!L)^{-1}$ forces a quadratic subfield of $K$\n\\cite[Lemmas~3 and~8]{Stark1974}. Since $n$ is odd, there is no such\nsubfield. Consequently, for a constant $c_n>0$, every nontrivial zero\n$\\rho$ satisfies $|1-\\rho|\\ge c_n/L$.\n\nThe real logarithmic derivative of the completed zeta function gives\n\\[\n S_K(\\sigma):=\\sum_\\rho\\operatorname{Re}\\frac1{\\sigma-\\rho}\n =\\frac1\\sigma+\\frac1{\\sigma-1}+\\frac L2\n   +\\frac{\\zeta_K'}{\\zeta_K}(\\sigma)+G_n(\\sigma),\\qquad \\sigma>1,\n\\]\nwhere zeros are counted with multiplicity and\n\\[\n G_n(\\sigma)=\\frac n2\n \\left(\\frac{\\Gamma'(\\sigma/2)}{\\Gamma(\\sigma/2)}-\\log\\pi\\right).\n\\]\nThis is the Hadamard identity in\n\\cite[Equation~(9)]{Stark1974}. The zero terms are nonnegative,\n$G_n$ is bounded on $[1,2]$, and the Euler product gives\n$\\zeta_K'/\\zeta_K\\le0$ there. Thus, at $\\sigma_0=1+L^{-1}$,\n$S_K(\\sigma_0)\\ll_n L$.\n\nThis bound persists uniformly for $1<\\sigma\\le1+4/L$.\nIndeed, for $\\rho=\\beta+i\\gamma$, set\n$X=L(1-\\beta)$, $Y=L\\gamma$, and $U=L(\\sigma-1)$.\nThen $X\\ge0$, $0<U\\le4$, and $|X+iY|\\ge c_n$. Hence\n\\[\n \\frac{\\operatorname{Re}(\\sigma-\\rho)^{-1}}\n      {\\operatorname{Re}(\\sigma_0-\\rho)^{-1}}\n =\\frac{X+U}{X+1}\n   \\frac{(X+1)^2+Y^2}{(X+U)^2+Y^2}\n \\le4(1+3/c_n)^2.\n\\]\nSumming gives $0\\le S_K(\\sigma)\\ll_n L$.\nFor $F_K(\\sigma)=(\\sigma-1)\\zeta_K(\\sigma)$, it follows that\n\\[\n \\frac{F_K'}{F_K}(\\sigma)\n =S_K(\\sigma)-\\frac1\\sigma-\\frac L2-G_n(\\sigma)\n =O_n(L).\n\\]\nSince $F_K$ extends analytically to $1$ with $F_K(1)=\\kappaK>0$,\nintegration over an interval of length at most $4/L$ proves the\ncomparison in \\eqref{eq:residue}. At $\\sigma_0$, the inequality\n$\\zeta_K(\\sigma_0)\\ge1$ also gives $\\kappaK\\gg_n L^{-1}$.\n\nFor completeness, only finitely many fields of this fixed degree\nhave discriminant below the cutoff. One can see this directly here:\nnonzero vectors of $D^{-1/(2n)}\\sigma(R)$ have absolute coordinate\nproduct at least $D^{-1/2}$, so Mahler's criterion puts these lattices\nin a fixed compact set. Local basis lifts and a finite cover give\nbases of $R$ with uniformly bounded conjugates. A basis element\noutside $\\Q$ generates $K$, since $n$ is prime; its monic integral\nminimal polynomial therefore has bounded coefficients. There are\nonly finitely many such polynomials. The simple pole of each of\nthese finitely many zeta functions now gives the remaining claims\nuniformly for sufficiently small $h>0$.\n\\end{proof}\n\nWe also use the following uniform form of Shiu's theorem\n\\cite{Shiu1980,Pollack2020}: if $b$ is nonnegative and multiplicative\nand $b(m)\\le d_n(m)$, where $d_n$ is the $n$-fold divisor function,\nthen\n\\begin{equation}\\label{eq:shiu}\n \\sum_{u-v<m\\le u}b(m)\n \\ll_n\\frac v{\\log u}\n       \\exp\\left(\\sum_{\\ell\\le u}\\frac{b(\\ell)}\\ell\\right)\n \\qquad(u^{1/2}\\le v\\le u).\n\\end{equation}\nHere $\\ell$ denotes a rational prime, and the threshold for $u$\ndepends only on $n$. To check uniformity, the growth conditions in\n\\cite[Theorem~1.1]{Pollack2020}, with all excluded residue sets\nempty, are supplied by\n$d_n(\\ell^j)=\\binom{j+n-1}{n-1}\\le n^j$ and\n$d_n(m)\\ll_{n,\\eps}m^\\eps$. Its strict interval condition\n$u^\\beta<v\\le u$ is satisfied by taking $\\beta=1/4$; thus\n\\eqref{eq:shiu} includes both endpoints of the displayed range.\n\n\\begin{proposition}\\label{prop:weighted-count}\nFix $A_0\\ge1$ and $0<\\eta\\le1$. For $Q$ sufficiently large in terms\nof $n,A_0,\\eta$,\n\\begin{equation}\\label{eq:weighted-count}\n \\sum_{x-y<\\N\\mathfrak a\\le x}P(\\mathfrak a)\n \\ll_n \\frac{\\kappaK y}{q}\n \\qquad\n (Q^{3/4}\\le x\\le A_0Q,\\quad \\eta x\\le y\\le x).\n\\end{equation}\nThe implied constant is independent of $A_0$ and $\\eta$.\n\\end{proposition}\n\n\\begin{proof}\nSet $b(m)=a_K(m)\\one_{(m,q)=1}$. This function is nonnegative,\nmultiplicative, and bounded by $d_n$. Indeed, at every prime $\\ell$,\nincluding the ramified primes, the local generating series for\n$a_K$ is $\\prod_{\\mathfrak p\\mid\\ell}(1-T^{f_{\\mathfrak p}})^{-1}$;\nit is coefficientwise bounded by $(1-T)^{-n}$.\nDecomposing an ideal into its part over $S$ and its coprime part,\nthe sum in \\eqref{eq:weighted-count} becomes\n\\begin{equation}\\label{eq:weighted-split}\n \\sum_{\\mathbf k}P(\\mathbf k)\n       \\sum_{(x-y)/d(\\mathbf k)<m\\le x/d(\\mathbf k)}b(m).\n\\end{equation}\n\nFirst suppose $d=d(\\mathbf k)\\le Q^{1/8}$, and put $u=x/d$.\nThen $u\\ge Q^{5/8}\\ge D^{1/4}$. For $h=1/\\log u$,\nLemma~\\ref{lem:residue} gives\n$\\zeta_K(1+h)\\ll_n\\kappaK\\log u$.\nThere is no upper restriction on $u$: increasing $u$ only decreases\n$h$, so the lemma applies even when $D$ stays bounded and $q$ grows.\nPositivity of the Euler logarithm yields\n\\[\n \\sum_{\\ell\\le u}a_K(\\ell)\\ell^{-1-h}\\le\\log\\zeta_K(1+h).\n\\]\nSince $a_K(\\ell)\\le n$ and\n$\\sum_{\\ell\\le u}(\\log\\ell)/\\ell\\ll\\log u$, replacing\n$\\ell^{-1-h}$ by $\\ell^{-1}$ costs only $O_n(1)$.\nThe latter prime-sum bound follows, for example, by comparing\n$\\sum_{\\ell\\le u}\\lfloor u/\\ell\\rfloor\\log\\ell$ with\n$\\log(\\lfloor u\\rfloor!)$. Therefore\n\\[\n \\exp\\left(\\sum_{\\ell\\le u}\\frac{a_K(\\ell)}\\ell\\right)\n \\ll_n\\kappaK\\log u.\n\\]\nFurthermore,\n\\[\n \\log Z_S=\\sum_{\\ell\\in S}\\frac{a_K(\\ell)}\\ell+O_n(1),\n \\qquad\n \\sum_{\\substack{\\ell\\in S\\\\\\ell>u}}\n       \\frac{a_K(\\ell)}\\ell\n \\ll_n\\frac{\\log(2q)}{Q^{5/8}}\\ll_n1.\n\\]\nThe first error is bounded by a constant times\n$\\sum_\\ell\\ell^{-2}$. Removing the primes of $S$ thus gives\n\\begin{equation}\\label{eq:coprime-euler}\n \\exp\\left(\\sum_{\\ell\\le u}\\frac{b(\\ell)}\\ell\\right)\n \\ll_n\\frac{\\kappaK\\log u}{Z_S}.\n\\end{equation}\nFor $Q$ sufficiently large at fixed $\\eta$, the length\n$v=y/d\\ge\\eta u$ is at least $u^{1/2}$.\nApplying \\eqref{eq:shiu}, then \\eqref{eq:coprime-euler} and\n\\eqref{eq:shell-sum}, bounds the contribution of these $d$ by\n\\[\n C_n\\frac{\\kappaK y}{Z_S}\n       \\sum_{d(\\mathbf k)\\le Q^{1/8}}\\frac{P(\\mathbf k)}{d(\\mathbf k)}\n \\le C_n\\frac{\\kappaK y}{q}.\n\\]\n\nFor $d>Q^{1/8}$, terms with $d>x$ vanish because the inner sum\nin \\eqref{eq:weighted-split} then contains no positive integer.\nFor the remaining terms, $x/d\\ge1$, and the elementary bound\n$\\sum_{m\\le z}d_n(m)\\le z(1+\\log z)^{n-1}$ for $z\\ge1$\nfollows by summing reciprocals in the first $n-1$ factors.\nUsing \\eqref{eq:weight-tail}, with $s=1/(2n)$, the remaining\ncontribution to \\eqref{eq:weighted-split} is at most\n\\[\n C_n\\frac{x}{q}Q^{-s/16}(1+\\log x)^{n-1}\n =o\\left(\\frac{\\kappaK x}{q}\\right).\n\\]\nThis is uniform in the stated range: Lemma~\\ref{lem:residue}\ngives $\\kappaK^{-1}\\ll_n\\log(3D)$, and $D\\le Q^2$.\nAt fixed $A_0,\\eta$, increase the threshold until this term is at\nmost $\\kappaK\\eta x/q\\le\\kappaK y/q$. The implied constant has\nnot changed with $\\eta$, proving the proposition.\n\\end{proof}\n"}, {"path": "preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/entropy.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/entropy.tex", "bytes": 7312, "sha256": "fa15e90c1202ae3da92e2d0018de705d02f826a556da51af0ece31d319a55f54", "content": "\\section{Entropy and identification of the limit}\\label{sec:entropy}\n\nThe ball estimate has exponent $n$, whereas $A_n$ has dimension $n-1$.\nWe follow the ordinary-ball strategy of\n\\cite[Section~2.7.1]{ELMV2011}. We retain the estimate for every invariant\nprobability dominated by a finite multiple of the limit, so it forces\npositive entropy after restriction to any positive-weight collection of\nergodic components. Measure classification then identifies the limit.\n\nFor an invariant probability $\\nu$, write $h_\\nu(a)$ for the\nmeasure-theoretic entropy of the map $x\\mapsto xa$.\nFix distinct real numbers $\\lambda_1,\\ldots,\\lambda_n$ with sum zero and put\n\\[\n a(t)=\\operatorname{diag}(e^{\\lambda_1t},\\ldots,e^{\\lambda_nt}),\n \\qquad \\lambda=\\min_{i\\ne j}|\\lambda_i-\\lambda_j|>0.\n\\]\n\n\\begin{proposition}[Entropy from ordinary balls]\\label{prop:entropy-from-balls}\nLet $n\\ge3$ and let $\\mu$ be an $A_n$-invariant probability on $X_n$.\nSuppose that for every compact $\\Omega\\subset X_n$ there are\n$C_\\Omega,r_\\Omega>0$ such that\n\\[\n \\mu(xB(r))\\le C_\\Omega r^n\n \\qquad(x\\in\\Omega,\\ 0<r<r_\\Omega).\n\\]\nThen every $A_n$-invariant probability $\\xi$ satisfying $\\xi\\le c\\mu$\nfor some finite $c$ has $h_\\xi(a(1))\\ge\\lambda/3$.\n\\end{proposition}\n\n\\begin{proof}\nFix $0<b<1/4$ and consider the two-sided neighborhoods\n\\[\n T_t=a(t)B(b)a(-t)\\cap a(-t)B(b)a(t),\\qquad t\\ge0.\n\\]\nIf $h\\in T_t$, then\n\\begin{equation}\\label{eq:tube-entries}\n |h_{ii}-1|<b,\\qquad\n |h_{ij}|<b e^{-|\\lambda_i-\\lambda_j|t}\\quad(i\\ne j).\n\\end{equation}\nSet $r=e^{-\\lambda t}$.  Every term in the determinant expansion other\nthan the product of the diagonal entries contains at least two\noff-diagonal entries.  Since $\\det h=1$, it follows that\n$\\prod_i h_{ii}=1+O_{n,b}(r^2)$.  Thus\n\\[\n d(h)=\\operatorname{diag}\\left(h_{11},\\ldots,h_{n-1,n-1},\n                   \\Bigl(\\prod_{i<n}h_{ii}\\Bigr)^{-1}\\right)\n\\]\nbelongs to a fixed compact subset $\\mathcal D\\subset A_n$, and\n$d(h)^{-1}h\\in B(Cr)$ for a constant depending only on $n,b$.\n\nIn logarithmic coordinates, $\\mathcal D$ is a bounded subset of $\\R^{n-1}$.\nCovering it by a mesh of size comparable to $r$, and enlarging $C$ to\nallow for matrix multiplication, gives\n\\begin{equation}\\label{eq:tube-cover}\n T_t\\subset\\bigcup_{j=1}^{N_t}d_jB(Cr),\n \\qquad d_j\\in\\mathcal D,\\qquad N_t\\le Cr^{-(n-1)}.\n\\end{equation}\nFor $x$ in a compact set $\\Omega$, all the centers $xd_j$ lie in the\nfixed compact set $\\Omega\\mathcal D$.  The assumed ball estimate and domination\ntherefore imply, for all sufficiently large $t$,\n\\begin{equation}\\label{eq:tube-mass}\n \\xi(xT_t)\\le C_{\\Omega,b,c}\\,r^{-(n-1)}r^n\n             =C_{\\Omega,b,c}e^{-\\lambda t}\n \\qquad(x\\in\\Omega).\n\\end{equation}\nSince $\\xi$ is a probability, the constant can be enlarged to cover\nthe finitely many integer times before this estimate holds.\n\nWe use the entropy criterion of\nEinsiedler--Lindenstrauss--Michel--Venkatesh\n\\cite[Corollary~3.3]{ELMV2009}.  In the notation\n\\[\n B^{(s,t)}=a(-s)Ba(s)\\cap a(-t)Ba(t),\n\\]\nthe criterion states the following: if flow-invariant probabilities\n$\\xi_i$ converge weakly to a probability $\\xi$, and $t_i\\to\\infty$,\nthen $h_\\xi(a(1))\\ge\\eta$ provided that, for every compact set $\\Omega$,\nthere are an identity neighborhood $B$ and a constant $C_\\Omega$ such that\n\\[\n \\xi_i\\bigl(xB^{(-t_i,t_i)}\\bigr)\n       \\le C_\\Omega e^{-2\\eta t_i}\\qquad(x\\in\\Omega).\n\\]\nThis criterion applies on the noncompact lattice space; its requirement\nthat the limit be a probability is essential.  Here take the constant\nsequence $\\xi_i=\\xi$, the neighborhood $B(b)$, $t_i=i$, and\n$\\eta=\\lambda/3$.  Since $B(b)^{(-t,t)}=T_t$,\nEquation~\\eqref{eq:tube-mass} proves the required estimate.\n\\end{proof}\n\n\\begin{lemma}[Entropy in the ergodic components]\\label{lem:entropy-components}\nUnder the hypotheses of Proposition~\\ref{prop:entropy-from-balls},\nalmost every $A_n$-ergodic component of $\\mu$ has positive entropy\nfor $a(1)$.\n\\end{lemma}\n\n\\begin{proof}\nThe group $A_n\\simeq\\R^{n-1}$ is locally compact and second countable,\nand its action on the standard Borel space $X_n$ is continuous.\nThe ergodic decomposition theorem\n\\cite[Theorem~5.2]{GreschonigSchmidt2000}, specialized to invariant\nprobabilities, therefore supplies a measurable decomposition\n\\[\n \\mu=\\int_Z\\nu_z\\,\\dd\\tau(z)\n\\]\ninto $A_n$-invariant, $A_n$-ergodic probabilities over a factor fixed\nby $A_n$. Set $T(x)=xa(1)$.\nThe function $z\\mapsto h_{\\nu_z}(T)$ is measurable.  Indeed, choose\nincreasing finite Borel partitions $\\mathcal P_k$ generating the Borel\n$\\sigma$-algebra of $X_n$.  For every $T$-invariant probability $\\nu$,\n\\[\n h_\\nu(T)=\\sup_k\\inf_{N\\ge1}\\frac1N\n H_\\nu\\left(\\bigvee_{j=0}^{N-1}T^{-j}\\mathcal P_k\\right),\n\\]\nwhere $H_\\nu$ denotes the Shannon entropy of a finite partition.\nThe right side is a countable combination of Borel functions of $\\nu$.\n\nThe component partition is fixed by $T$, since $a(1)\\in A_n$.\nThe entropy integral formula for a fixed invariant partition\n\\cite[Theorem~9.8]{Rokhlin1967} consequently gives\n\\begin{equation}\\label{eq:entropy-decomposition}\n h_\\mu(T)=\\int_Z h_{\\nu_z}(T)\\,\\dd\\tau(z).\n\\end{equation}\nThe same formula holds after restricting to any measurable collection\nof components.  It does not require the $\\nu_z$ to be $T$-ergodic:\nthe induced transformation on the component space is the identity\nand contributes zero entropy.\n\nIf $Z_0=\\{z:h_{\\nu_z}(T)=0\\}$ had weight $w=\\tau(Z_0)>0$, then\n\\[\n \\xi=\\frac1w\\int_{Z_0}\\nu_z\\,\\dd\\tau(z)\n\\]\nwould be an $A_n$-invariant probability with $\\xi\\le w^{-1}\\mu$.\nEquation~\\eqref{eq:entropy-decomposition}, applied to this restricted\ndecomposition, would give $h_\\xi(T)=0$, contradicting\nProposition~\\ref{prop:entropy-from-balls}.\n\\end{proof}\n\n\\paragraph{Measure classification.}\nEinsiedler--Katok--Lindenstrauss\n\\cite[Theorem~1.3 and Corollary~1.4]{EKL2006} prove that, for prime\n$n\\ge3$, an $A_n$-invariant and $A_n$-ergodic probability on\n$\\operatorname{SL}_n(\\R)/\\operatorname{SL}_n(\\Z)$ is Haar whenever\nsome diagonal one-parameter subgroup has positive entropy.\nTheir action is on the left.  The map\n$\\Gamma g\\mapsto g^{-1}\\Gamma$, where $\\Gamma=\\operatorname{SL}_n(\\Z)$,\nchanges our right action by $a(t)$ into the left action by $a(-t)$.\nSince entropy is unchanged by inversion of an invertible transformation,\ntheir result applies to our convention without any additional hypothesis.\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\nLet $\\mu_i$ be the packet probabilities for a sequence in the theorem.\nEquation~\\eqref{eq:discriminant-scale} gives $Q_i\\to\\infty$.\nBy Proposition~\\ref{prop:tightness}, this sequence is tight and every\nweak subsequential limit $\\mu$ is an $A_n$-invariant probability.\nProposition~\\ref{prop:ball-bound} supplies the hypotheses of\nProposition~\\ref{prop:entropy-from-balls}.\nLemma~\\ref{lem:entropy-components} shows that almost every\n$A_n$-ergodic component of $\\mu$ has positive entropy for $a(1)$.\nAs $n$ is prime, the classification just stated makes every such\ncomponent equal to $m_n$.  Integrating the components gives $\\mu=m_n$.\n\nThus every convergent subsequence has the same probability limit.\nTogether with tightness, this proves $\\mu_i\\to m_n$ and excludes\nloss of mass.  All preceding estimates were for each prescribed local\nhomothety packet separately, so no averaging over different local\nmodule types is used.\n\\end{proof}\n"}, {"path": "preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/geometry.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/geometry.tex", "bytes": 5944, "sha256": "1b5b899650f7d8f1fee8d9d8f8c7e8c52cb490b3c241727d8edb00c6061a8a3b", "content": "\\section{Nonescape and ordinary ball bounds}\\label{sec:geometry}\n\nWe apply the weighted count in two ways. Cubes about zero control\nthe cusp; small boxes away from the coordinate hyperplanes control\nordinary neighborhoods in the lattice space.\n\n\\begin{proposition}\\label{prop:tightness}\nEvery sequence of the packet probabilities with $Q\\to\\infty$\nis tight. More precisely, for $0<\\theta<1$,\n\\begin{equation}\\label{eq:cusp}\n \\limsup_{Q\\to\\infty}\\mu_*\n \\{\\Lambda:\\exists\\,0\\ne v\\in\\Lambda,\\ \\|v\\|_\\infty<\\theta\\}\n       \\le C_n\\theta^n.\n\\end{equation}\nEvery subsequential probability limit is $A_n$-invariant.\n\\end{proposition}\n\n\\begin{proof}\nFor $f=\\one_{[-\\theta,\\theta]^n}$, the first $n-1$ logarithms\nin \\eqref{eq:product-fiber} are at most $\\log\\theta$.\nThe last coordinate bounds the sum of their gaps from\n$\\log\\theta$ by $\\log(\\theta^n/z)$. Thus\n\\[\n \\cV_f(z)=\\frac{2^n}{(n-1)!}\n           \\bigl(\\max\\{0,\\log(\\theta^n/z)\\}\\bigr)^{n-1}.\n\\]\nPut $X=\\theta^nQ$ and $U=Q^{3/4}$. Up to an $n$-dependent\nconstant, Proposition~\\ref{prop:unfolding} reduces the first\nmoment to\n\\[\n \\frac1{\\sqrt D\\,\\kappaK}\n \\sum_{\\N\\mathfrak a\\le X}\n     P(\\mathfrak a)\\left(\\log\\frac X{\\N\\mathfrak a}\\right)^{n-1}.\n\\]\nIf $X<1$, the sum is empty. Otherwise its logarithmic factor is\nat most $(\\log Q)^{n-1}$, since $\\theta<1$. The terms with\n$\\N\\mathfrak a\\le\\min(U,X)$ contribute at most\n$C_nQ^{-1/4}(\\log Q)^{n-1}$, by applying\nProposition~\\ref{prop:weighted-count} at $x=y=U$.\nWhen $X>U$, define the truncated cumulative sum\n\\[\n A_U(t)=\\sum_{U<\\N\\mathfrak a\\le t}P(\\mathfrak a),\n \\qquad U\\le t\\le X.\n\\]\nWriting each logarithmic power as an integral and applying Tonelli gives\n\\begin{align*}\n \\sum_{U<\\N\\mathfrak a\\le X}\n   P(\\mathfrak a)\\left(\\log\\frac X{\\N\\mathfrak a}\\right)^{n-1}\n &= (n-1)\\int_U^X A_U(t)\n      \\left(\\log\\frac Xt\\right)^{n-2}\\frac{dt}{t} \\\\\n &\\ll_n \\frac{\\kappaK}{q}\n      \\int_U^X\\left(\\log\\frac Xt\\right)^{n-2}\\dd t\n \\ll_n \\frac{\\kappaK}{q}X.\n\\end{align*}\nHere the same proposition at $x=y=t$ bounds $A_U(t)$, and the\nlast inequality follows from\n$\\int_0^1(\\log(1/v))^{n-2}\\dd v<\\infty$.\nThere is no lower boundary term, since $A_U(U)=0$, and the\nlogarithmic weight vanishes at $X$. If $X\\le U$, this part is absent.\nDividing by $\\sqrt D\\,\\kappaK$ gives $O_n(\\theta^n)$.\nSince $E_f\\ge1$ whenever the displayed cusp event occurs,\nthis proves \\eqref{eq:cusp}.\n\nMahler's compactness criterion \\cite[Section~3, Theorem~2]{Mahler1946} says that the\ncomplement of this cusp event is compact. Given a desired\nmass error, choose $\\theta$ first and then $Q$ sufficiently large.\nThe finitely many earlier packet supports are compact by\nLemma~\\ref{lem:packet-model}, so the whole sequence is tight.\nThe lattice space is locally compact and second countable;\nweak compactness for tight probabilities gives probability\nsubsequential limits. Invariance passes to them by testing\nagainst continuous compactly supported functions.\n\\end{proof}\n\nFor $r>0$, let\n\\[\n B(r)=\\{h\\in\\SL_n(\\R):\n             \\max_{i,j}|h_{ij}-\\delta_{ij}|<r\\}.\n\\]\n\n\\begin{proposition}\\label{prop:ball-bound}\nLet $\\mu$ be a weak limit of packet probabilities along\n$Q\\to\\infty$. For every compact $\\Omega\\subset X_n$ there are\n$C_\\Omega,r_\\Omega>0$ such that\n\\begin{equation}\\label{eq:ball-bound}\n \\mu(xB(r))\\le C_\\Omega r^n\n       \\qquad(x\\in\\Omega,\\ 0<r<r_\\Omega).\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nFirst fix a compact $V\\subset(\\R^\\times)^n$ and $b_0\\ge1$.\nFor $w\\in V$ and small $r$, take\n$f=\\one_{w+[-b_0r,b_0r]^n}$. Each coordinate has a fixed sign,\nis uniformly separated from zero, and has logarithmic width\n$O_{V,b_0}(r)$. Its absolute product lies within $Cr$ of\n$z_w=\\prod_j|w_j|$, so\n\\[\n \\cV_f(z)\\le Cr^{n-1}\n       \\one_{[z_w-Cr,z_w+Cr]}(z)\n\\]\nfor some $C\\ge1$ depending only on $V,b_0,n$.\nChoose $r_0$ small enough that $z_w>4Cr_0$ on $V$.\nUse Proposition~\\ref{prop:weighted-count} with\n\\[\n x=(z_w+2Cr)Q,\\qquad y=4CrQ.\n\\]\nThis slightly enlarged interval contains every contributing\nideal norm. Uniformly in $w$, for sufficiently large $Q$ it has\n$Q^{3/4}\\le x\\le A_0Q$ with a fixed $A_0\\ge1$, and\n$\\eta x\\le y\\le x$ with $\\eta=4Cr/A_0\\le1$, after decreasing\n$r_0$ if necessary. For every fixed $r\\in(0,r_0)$,\nProposition~\\ref{prop:unfolding} therefore gives, eventually,\n\\begin{equation}\\label{eq:vector-box}\n \\int E_{\\one_{w+[-b_0r,b_0r]^n}}\\dd\\mu_*\n       \\ll_{V,b_0,n}\n       \\frac{r^{n-1}}{\\sqrt D\\,\\kappaK}\n       \\frac{\\kappaK rQ}{q}=r^n .\n\\end{equation}\nHere $Q=q\\sqrt D$. The implicit constant is independent of $r$. The threshold for $Q$\nmay depend on $r$, which is all that will be needed.\n\nWe next choose a suitable lattice vector at each $x\\in\\Omega$.\nEvery full lattice has a vector with no zero coordinate:\nfor any basis, its combination with coefficients\n$(1,N,\\ldots,N^{n-1})$ has coordinates that are nonzero\npolynomials in $N$, so an integer $N$ can avoid their finitely\nmany roots. Near each center choose a continuous local lift to basis\nmatrices, and continue the chosen vector using its fixed integer\nbasis coefficients. On a sufficiently small neighborhood with compact\nclosure in that chart, its coordinates remain bounded and bounded away\nfrom zero. A finite cover of $\\Omega$ by such neighborhoods therefore\ngives choices $w_x$ lying in one compact\n$V\\subset(\\R^\\times)^n$. This pointwise selection need not be\ncontinuous or measurable.\n\nFor $h$ in $B(r)$, each coordinate of $w_xh-w_x$ has absolute\nvalue at most $r\\sum_i|(w_x)_i|$. Thus one fixed $b_0$ makes\nevery lattice in $xB(r)$ contain a nonzero vector in\n$w_x+[-b_0r,b_0r]^n$. Equation~\\eqref{eq:vector-box} bounds\nthe packet mass of $xB(r)$ by $C_\\Omega r^n$.\nThis set is open, so Portmanteau gives\n\\[\n \\mu(xB(r))\\le\\liminf_i\\mu_i(xB(r))\\le C_\\Omega r^n.\n\\]\nThe argument holds for every fixed $x$ and $r$ with the same\nconstant on $\\Omega$. It proves \\eqref{eq:ball-bound} for\nall $0<r<r_\\Omega$, without any exchange of shrinking-radius\nand packet limits.\n\\end{proof}\n"}, {"path": "preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/local.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/local.tex", "bytes": 3798, "sha256": "b9753e6ed4cb8cc78c3c06e6369ac193171f1abb158f17c389236c921735db07", "content": "\\section{Uniform control of the local weights}\\label{sec:local}\n\nThe ideal sum in Proposition~\\ref{prop:unfolding} involves arbitrary\nlocal modules. We now isolate two estimates that are uniform in\nthose modules and in their indices. The first gives the total\nnormalization; the second makes large local norm contributions\nnegligible.\n\nLet $\\mathbf k=(\\mathbf k_\\ell)_{\\ell\\in S}$ range over nonnegative\nvaluation tuples, and write\n\\[\n d_\\ell(\\mathbf k_\\ell)\n   =\\ell^{\\sum_{\\mathfrak p\\mid\\ell}f_{\\mathfrak p}k_{\\mathfrak p}},\n \\qquad d(\\mathbf k)=\\prod_{\\ell\\in S}d_\\ell(\\mathbf k_\\ell),\n \\qquad P(\\mathbf k)=\\prod_{\\ell\\in S}P_\\ell(\\mathbf k_\\ell).\n\\]\nThus $d$ is the norm of the $S$-supported part of an ideal. Also set\n\\[\n Z_\\ell=\\prod_{\\mathfrak p\\mid\\ell}\n          (1-\\ell^{-f_{\\mathfrak p}})^{-1},\\qquad\n Z_S=\\prod_{\\ell\\in S}Z_\\ell.\n\\]\nEmpty products are one.\n\n\\begin{lemma}\\label{lem:local-moment}\nFor the weights just defined,\n\\begin{equation}\\label{eq:shell-sum}\n \\sum_{\\mathbf k}\\frac{P(\\mathbf k)}{d(\\mathbf k)}=\\frac{Z_S}{q}.\n\\end{equation}\nIf $s=1/(2n)$, then, for every $\\eps>0$,\n\\begin{equation}\\label{eq:weight-moment}\n \\sum_{\\mathbf k}P(\\mathbf k)d(\\mathbf k)^{s-1}\n       \\ll_{n,\\eps}\\frac{q^\\eps}{q}.\n\\end{equation}\nIn particular,\n\\begin{equation}\\label{eq:weight-tail}\n \\sum_{d(\\mathbf k)>Q^{1/8}}\\frac{P(\\mathbf k)}{d(\\mathbf k)}\n       \\ll_n \\frac1q Q^{-s/16}.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nNormalize additive Haar measure on $R_\\ell$ to have mass one.\nThe valuation shell $\\mathbf k_\\ell$ has mass\n$Z_\\ell^{-1}/d_\\ell(\\mathbf k_\\ell)$. Its conditional law is\ninvariant under the transitive unit action, so $P_\\ell$ is\nexactly the fraction of this shell lying in $L_\\ell$.\nSince $L_\\ell$ has mass $1/q_\\ell$, summing over shells and\nthen multiplying over $S$ proves \\eqref{eq:shell-sum}.\nElements with a zero component have additive measure zero.\n\nChoose an additive-Haar uniform element of $L_\\ell$, and denote\nits component valuations by $v_{\\mathfrak p}$. The condition\n$R_\\ell L_\\ell=R_\\ell$ implies that the projection to every\ncomponent contains a unit. Modulo the $k$th power of its maximal\nideal, the projection therefore contains all $\\Z_\\ell$ multiples\nof that unit, at least $\\ell^{\\lceil k/e_{\\mathfrak p}\\rceil}$\nelements. Uniform measure on $L_\\ell$ induces uniform measure\non this finite additive image. Consequently\n\\begin{equation}\\label{eq:valuation-tail}\n \\Pr(v_{\\mathfrak p}\\ge k)\n       \\le\\ell^{-\\lceil k/e_{\\mathfrak p}\\rceil}\\qquad(k\\ge1).\n\\end{equation}\nThis argument does not require the component projections to be\nindependent, or even to equal their full integer rings.\n\nPut $j=\\max_{\\mathfrak p\\mid\\ell}\n\\lceil v_{\\mathfrak p}/e_{\\mathfrak p}\\rceil$. Then\n$d_\\ell(\\mathbf v_\\ell)\\le\\ell^{nj}$. If $j\\ge a\\ge1$, some\n$v_{\\mathfrak p}$ is at least $e_{\\mathfrak p}(a-1)+1$.\nEquation~\\eqref{eq:valuation-tail} and a union bound give\n$\\Pr(j\\ge a)\\le n\\ell^{-a}$. Hence\n\\begin{equation}\\label{eq:local-moment}\n \\mathbb E_{L_\\ell}d_\\ell(\\mathbf v_\\ell)^s\n \\le1+n\\sum_{a\\ge1}\\ell^{(ns-1)a}\n \\le1+C_n\\ell^{-1/2}.\n\\end{equation}\nUsing the shell masses once more yields the exact factorization\n\\[\n \\sum_{\\mathbf k}P(\\mathbf k)d(\\mathbf k)^{s-1}\n   =\\frac1q\\prod_{\\ell\\in S}\n       \\left(Z_\\ell\\,\\mathbb E_{L_\\ell}d_\\ell(\\mathbf v_\\ell)^s\\right).\n\\]\nEach factor is at most\n$(1-\\ell^{-1})^{-n}(1+C_n\\ell^{-1/2})$. For fixed $\\eps>0$\nthis is at most $\\ell^\\eps$ at all sufficiently large primes;\nthe finitely many remaining primes contribute a constant\ndepending only on $n,\\eps$. As $\\prod_{\\ell\\in S}\\ell\\le q$,\nthis proves \\eqref{eq:weight-moment}. Finally, take\n$\\eps=s/16$ and use $q\\le Q$ to obtain\n\\[\n \\sum_{d>Q^{1/8}}\\frac{P(\\mathbf k)}d\n \\le Q^{-s/8}\\sum_{\\mathbf k}P(\\mathbf k)d^{s-1}\n \\ll_n q^{-1}Q^{-s/16}.\n \\qedhere\n\\]\n\\end{proof}\n"}, {"path": "preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/packets.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/packets.tex", "bytes": 9264, "sha256": "6fa7df6b3b0ed49a576a9dbeb2354a356b5d4f36130c0cdaeaaecb73a8062ffb", "content": "\\section{The packet as a weighted ideal sum}\\label{sec:packets}\n\nWe first identify the packet measure exactly, including its signs and\norbit volumes. Throughout the proof, $K$ is totally real of the fixed\nprime degree $n$, and\n\\[\n R=\\cO_K,\\quad E=R^\\times,\\quad D=|\\Disc(K)|,\\quad\n \\kappaK=\\operatorname*{Res}_{s=1}\\zeta_K(s).\n\\]\nThe absolute norm $\\N I$ of a nonzero fractional $R$-ideal is extended\nmultiplicatively from integral ideals. Thus\n$\\operatorname{covol}(\\sigma(I))=\\sqrt D\\,\\N I$.\nWe use standard ideal theory and the unit theorem\n\\cite[Chapters~3--5]{Milne2020}, together with the analytic\nclass-number formula \\cite[Theorem~19.12]{Sutherland2021}.\nThe classical packet interpretation is discussed in\n\\cite[Sections~2 and~5]{ELMV2011}.\n\n\\subsection{Saturation and local choices}\n\nLet $J_0=RM$, $q=[J_0:M]$, and $Q=q\\sqrt D$. For a rational prime\n$\\ell$, write $(J_0)_\\ell=j_\\ell R_\\ell$, and put\n$L_\\ell=j_\\ell^{-1}M_\\ell$. Then\n\\begin{equation}\\label{eq:local-normalization}\n L_\\ell\\subset R_\\ell,\\qquad R_\\ell L_\\ell=R_\\ell,\\qquad\n [R_\\ell:L_\\ell]=q_\\ell,\\qquad q=\\prod_\\ell q_\\ell.\n\\end{equation}\nSet $S=\\{\\ell:\\ell\\mid q\\}$; off $S$, $L_\\ell=R_\\ell$.\nSince $qJ_0\\subset M$, one has $qR\\subset\\cO(M)$.\nConversely, a multiplier of $M$ has an integral endomorphism matrix\non $M$ and hence is an algebraic integer. It follows that\n\\begin{equation}\\label{eq:discriminant-scale}\n qR\\subset\\cO(M)\\subset R,\\qquad\n D(M)\\le Dq^{2n}\\le Q^{2n}.\n\\end{equation}\nOnly the last implication, $D(M)\\to\\infty\\Rightarrow Q\\to\\infty$,\nwill be needed.\n\nThese quantities are constant within the prescribed local type.\nIndeed, if $M'_\\ell=c_\\ell M_\\ell$, multiplication by $c_\\ell$\nidentifies $(RM)_\\ell/M_\\ell$ with $(RM')_\\ell/M'_\\ell$.\nIt also identifies the local multiplier orders, since multiplication\nin $K\\otimes_\\Q\\Q_\\ell$ is commutative. The multiplier order commutes\nwith completion: in a $\\Z$-basis of $M$, its defining condition is\nthat the matrix of multiplication have integral entries, and the\nsame condition over $\\Z_\\ell$ defines the local order. Thus $q$,\n$Q$, $\\cO(M)$, and $D(M)$ are unchanged by local homothety.\nDifferent local types can nevertheless have the same multiplier order.\n\nChoose one fractional ideal $I$ in each ordinary ideal class of $R$.\nLet $\\mathcal N(I)$ be the set of lattices $N$ locally homothetic to $M$\nwith $RN=I$. If $I_\\ell=i_\\ell R_\\ell$, its possible completions are\nexactly\n\\begin{equation}\\label{eq:local-choices}\n N_\\ell=i_\\ell z_\\ell L_\\ell,\\qquad z_\\ell\\in R_\\ell^\\times.\n\\end{equation}\nIndeed, taking maximal-order spans in a local homothety forces the\nremaining multiplier to be a unit. Conversely, these local lattices\nlie between $qI_\\ell$ and $I_\\ell$ and patch uniquely by the primary\ndecomposition of $I/qI$. Thus $\\mathcal N(I)$ is finite,\n$[I:N]=q$ for all its members, and its cardinality\n$T=|\\mathcal N(I)|$ is independent of $I$. The label count $T$ need\nnot equal the saturation index $q$. Local unit Haar probability induces\nuniform probability on each finite set of local choices, so the choices at different\nprimes are independent. Every global homothety class in the\npacket occurs in these sets, by moving its maximal-order span\nto the chosen ideal-class representative.\n\n\\subsection{Real parameters and measure}\n\nLet\n\\[\n C=\\{t\\in(\\R^\\times)^n:\\textstyle\\prod_j|t_j|=1\\}.\n\\]\nGive $C$ counting measure on its $2^n$ sign components and measure\n$du_1\\cdots du_{n-1}$ on the coordinates $u_j=\\log|t_j|$.\nThe embedding of $E$ is a discrete cocompact subgroup of $C$.\nIf $F$ is a measurable fundamental domain, the unit theorem and\nthe totally real case of the analytic class-number formula\n\\cite[Theorem~19.12]{Sutherland2021} give\n\\begin{equation}\\label{eq:unit-volume}\n \\vol(F)=2^{n-1}R_K,\\qquad\n h_K\\vol(F)=\\sqrt D\\,\\kappaK,\n\\end{equation}\nwhere $h_K$ is the ordinary class number and $R_K$ the regulator\nin deleted logarithmic coordinates. The factor $2^{n-1}$ comes\nfrom the $2^n$ signs and the logarithmic kernel $\\{1,-1\\}$.\nIn the class-number formula this is the factor $2^n/w_K$, since\nthe roots of unity in a totally real field are exactly $\\{1,-1\\}$.\n\n\\begin{lemma}\\label{lem:packet-model}\nThe maps\n\\[\n (N,t)\\longmapsto (Q\\N I)^{-1/n}\\sigma(N)t\n \\qquad (N\\in\\mathcal N(I),\\ t\\in C)\n\\]\ninduce a bijection from the disjoint union of the quotients by\n\\[\n \\epsilon:(N,t)\\longmapsto\n       (\\epsilon N,\\sigma(\\epsilon)^{-1}t),\\qquad \\epsilon\\in E,\n\\]\nonto the union of the packet orbits.\nCounting measure on the $N$ labels times the stated measure on $C$\ndescends to the sum of the orbit Haar measures. In particular,\nthe packet probability is obtained by averaging uniformly over $I$\nand $\\mathcal N(I)$ and integrating over $F$ with measure\n$dt/\\vol(F)$.\n\\end{lemma}\n\n\\begin{proof}\nSurjectivity follows from \\eqref{eq:local-choices}. A global\nhomothety changes a normalized embedded lattice by an element\nof $C$. Since $n$ is odd, each pair of opposite sign patterns has\nexactly one representative in $W_n$; a common negative sign\ndoes not change a lattice.\n\nFor injectivity, suppose two displayed normalized lattices coincide.\nAbsorb the ratio of the normalizing scalars into the real diagonal\nmap carrying $\\sigma(N)$ onto $\\sigma(N')$. Applying this map\nto one nonzero $\\alpha\\in N$ gives $\\sigma(\\beta)$ for some\n$\\beta\\in N'$. None of the coordinates of $\\sigma(\\alpha)$\nvanishes, so the map is multiplication by $\\sigma(\\beta/\\alpha)$.\nThus $N'=bN$ for $b\\in K^\\times$. Their maximal-order spans\nrepresent the same ideal class and therefore equal the same\nchosen $I$. Hence $bI=I$, so $b\\in E$, and the parameters differ\nby exactly the stated action.\n\nCombine an $N$ label with a sign component of $C$. Its stabilizer in $E$\nis the totally positive subgroup of\n$\\{\\epsilon\\in E:\\epsilon N=N\\}$, and has finite index in $E$.\nIt acts faithfully by logarithmic translations. By injectivity,\nthese are all periods of the corresponding $A_n$-orbit.\nEach orbit of labels therefore contributes exactly one compact\n$A_n$-orbit, with precisely its prescribed Haar volume.\nThis proves both finiteness and the measure assertion.\nOne may equally use $\\mathcal N(I)\\times F$ as a fundamental\ndomain, since the $E$-action preserves counting measure times $dt$.\nThe total measure of these fundamental domains is\n$h_KT\\vol(F)$. Dividing by this quantity gives precisely the\nuniform averages over ideal classes and finite labels, followed\nby integration against $dt/\\vol(F)$.\n\\end{proof}\n\n\\subsection{Containment probabilities and unfolding}\n\nWrite $\\mathfrak p\\mid\\ell$ for the primes of $R$ over $\\ell$,\nwith ramification indices $e_{\\mathfrak p}$ and residue degrees\n$f_{\\mathfrak p}$. Normalize $v_{\\mathfrak p}$ by a uniformizer,\nso $v_{\\mathfrak p}(\\ell)=e_{\\mathfrak p}$ and\n$\\sum_{\\mathfrak p\\mid\\ell}e_{\\mathfrak p}f_{\\mathfrak p}=n$.\nFor a tuple $\\mathbf k_\\ell=(k_{\\mathfrak p})_{\\mathfrak p\\mid\\ell}$\nof nonnegative integers, define\n\\[\n P_\\ell(\\mathbf k_\\ell)\n =\\Pr_{z\\in R_\\ell^\\times}\\{x\\in zL_\\ell\\},\n\\]\nwhere $x$ has those component valuations and the probability is\nnormalized unit Haar measure. The definition is independent of $x$\nbecause the units act transitively on each valuation shell.\nFor an integral nonzero $R$-ideal $\\mathfrak a$, set\n\\[\n P(\\mathfrak a)=\\prod_{\\ell\\in S}\n       P_\\ell((v_{\\mathfrak p}(\\mathfrak a))_{\\mathfrak p\\mid\\ell}).\n\\]\nFor $\\alpha\\in I\\setminus\\{0\\}$, the probability that a uniform\n$N\\in\\mathcal N(I)$ contains $\\alpha$ is\n$P((\\alpha)I^{-1})$.\n\nFor a nonnegative Borel function $f$ on $\\R^n$, put\n$E_f(\\Lambda)=\\sum_{0\\ne v\\in\\Lambda}f(v)$.\nThe product-fiber integral below integrates $f$ over the $C$-orbit\nwith absolute coordinate product $z$, using logarithmic Haar measure\nand summing over all sign components:\n\\begin{equation}\\label{eq:product-fiber}\n \\cV_f(z)=\\sum_{\\omega\\in\\{\\pm1\\}^n}\n \\int_{\\R^{n-1}}\n f\\left(\\omega_1e^{u_1},\\ldots,\\omega_{n-1}e^{u_{n-1}},\n      \\omega_n z e^{-\\sum_{j<n}u_j}\\right)\n \\dd u_1\\cdots\\dd u_{n-1},\\qquad z>0.\n\\end{equation}\n\n\\begin{proposition}\\label{prop:unfolding}\nWriting $\\mu_*=\\mu_{K,M,\\sigma}$, one has\n\\begin{equation}\\label{eq:unfolding}\n \\int_{X_n}E_f\\dd\\mu_*\n =\\frac1{\\sqrt D\\,\\kappaK}\n       \\sum_{\\mathfrak a\\subset R,\\ \\mathfrak a\\ne0}\n           P(\\mathfrak a)\\cV_f(\\N\\mathfrak a/Q).\n\\end{equation}\nThe identity holds for nonnegative extended integrals.\n\\end{proposition}\n\n\\begin{proof}\nLemma~\\ref{lem:packet-model} expresses the left side as\n\\[\n \\frac1{h_K\\vol(F)}\n \\sum_I\\int_F\\sum_{\\alpha\\in I\\setminus\\{0\\}}\n   P((\\alpha)I^{-1})\n   f((Q\\N I)^{-1/n}\\sigma(\\alpha)t)\\dd t.\n\\]\nThe map $\\alpha\\mapsto(\\alpha)I^{-1}$ runs over the integral\nideals in the inverse class of $I$, each fiber being one free\n$E$-orbit. Summing that orbit and integrating over $F$ unfolds\nto an integral over $C$, by Tonelli. The absolute coordinate\nproduct of the normalized vector is\n$\\N((\\alpha)I^{-1})/Q$. Translating the first $n-1$ logarithms\nand relabeling the signs identifies the $C$-integral with\n\\eqref{eq:product-fiber}, with Jacobian one.\nEvery integral ideal occurs in exactly one inverse class.\nEquation~\\eqref{eq:unit-volume} supplies the coefficient in\n\\eqref{eq:unfolding}.\n\\end{proof}\n\nWhen $M$ is an $R$-ideal, $q=1$ and $P(\\mathfrak a)=1$.\nThus \\eqref{eq:unfolding} recovers the maximal-order formula.\n"}, {"path": "preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/paper.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/paper.tex", "bytes": 11173, "sha256": "0d9ebbcc1a7f6e3d39d63e0041f07c5d73e0634cf4d3df081207733f45976f37", "content": "\\documentclass[11pt]{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{lmodern}\n\\usepackage{amsmath,amssymb,amsthm}\n\\usepackage[margin=1in]{geometry}\n\\usepackage{microtype}\n\\usepackage[colorlinks=true,linkcolor=blue,citecolor=blue,urlcolor=blue]{hyperref}\n\\hypersetup{\n pdftitle={Equidistribution of Prime-Degree Torus Packets with Arbitrary Local Type},\n pdfauthor={OpenAI},\n pdfsubject={Full-packet equidistribution for arbitrary local module types},\n pdfkeywords={torus packets, equidistribution, prime degree, nonmaximal orders, entropy}\n}\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\newcommand{\\R}{\\mathbb R}\n\\newcommand{\\Z}{\\mathbb Z}\n\\newcommand{\\Q}{\\mathbb Q}\n\\DeclareMathOperator{\\N}{N}\n\\DeclareMathOperator{\\vol}{vol}\n\\DeclareMathOperator{\\Disc}{Disc}\n\\DeclareMathOperator{\\SL}{SL}\n\\DeclareMathOperator{\\diag}{diag}\n\\newcommand{\\kappaK}{\\kappa_K}\n\\newcommand{\\dd}{\\,d}\n\\newcommand{\\one}{\\mathbf 1}\n\\newcommand{\\eps}{\\varepsilon}\n\\newcommand{\\cO}{\\mathcal O}\n\\newcommand{\\cV}{\\mathcal V}\n\\numberwithin{equation}{section}\n\\title{Equidistribution of Prime-Degree Torus Packets\\\\\nwith Arbitrary Local Type}\n\\author{OpenAI}\n\\date{September 24, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe prove the packet form of the higher-dimensional Duke equidistribution\nproblem for totally real fields of any fixed prime degree at least five,\nallowing arbitrary local homothety types of full lattices. As the\nmultiplier-order discriminant tends to infinity, the volume-weighted packet\nmeasures converge to Haar probability measure, with no escape of mass.\n\\end{abstract}\n\n\\section{Introduction}\\label{sec:intro}\n\nCompact diagonal orbits in the space of unimodular lattices encode\nideal classes and units of totally real number fields. The higher-dimensional\nDuke equidistribution problem asks whether arithmetically complete families\nof these orbits become uniformly distributed as their discriminants grow.\nThe grouping matters: one local homothety type specifies a complete packet,\nwhereas a multiplier order alone can combine several packets. We prove the\nprime-degree packet statement for every such local type.\n\nFix a prime $n\\ge5$, and write\n\\[\n X_n=\\SL_n(\\Z)\\backslash\\SL_n(\\R),\\qquad\n A_n=\\{\\diag(e^{u_1},\\ldots,e^{u_n}):\\textstyle\\sum_j u_j=0\\}.\n\\]\nWe regard $X_n$ as the space of covolume-one row lattices, with the\nright action of $A_n$, and let $m_n$ be its $\\SL_n(\\R)$-invariant\nprobability measure. Haar measure on $A_n$ is\n$du_1\\cdots du_{n-1}$ in the displayed logarithmic coordinates.\n\nLet $K$ be a totally real field of degree $n$, let\n$\\sigma=(\\sigma_1,\\ldots,\\sigma_n)$ order its real embeddings, and let\n$M\\subset K$ be a free rank-$n$ $\\Z$-submodule spanning $K$ over $\\Q$.\nWe call such an $M$ a full lattice. Its multiplier order and discriminant\nare\n\\[\n \\cO(M)=\\{\\alpha\\in K:\\alpha M\\subset M\\},\\qquad\n D(M)=|\\Disc(K)|[\\cO_K:\\cO(M)]^2.\n\\]\nTwo such lattices are \\emph{locally homothetic} if\n$M'_\\ell=c_\\ell M_\\ell$ for every rational prime $\\ell$, where\n$c_\\ell\\in(K\\otimes_\\Q\\Q_\\ell)^\\times$ and subscripts denote completion.\nChoose representatives $M'$ for their $K^\\times$-homothety classes\nwithin the local type of $M$, and normalize\n\\[\n \\Lambda_{M',\\sigma}\n =\\operatorname{covol}(\\sigma(M'))^{-1/n}\\sigma(M').\n\\]\nThe packet $\\mathcal P_{K,M,\\sigma}$ consists of the distinct orbits\n$\\Lambda_{M',\\sigma}wA_n$, with\n\\[\n w\\in W_n=\\{\\diag(\\epsilon_1,\\ldots,\\epsilon_n):\n       \\epsilon_j\\in\\{\\pm1\\},\\ \\textstyle\\prod_j\\epsilon_j=1\\}.\n\\]\nThese orbits are compact and their number is finite; the\nparametrization in Section~\\ref{sec:packets} also proves these facts.\nFor an orbit $O$, let $\\nu_O$ be its invariant probability and\n$\\vol(O)$ its volume for the specified Haar measure. Set\n\\begin{equation}\\label{eq:packet-probability}\n \\mu_{K,M,\\sigma}\n =\\frac{\\sum_{O\\in\\mathcal P_{K,M,\\sigma}}\\vol(O)\\nu_O}\n        {\\sum_{O\\in\\mathcal P_{K,M,\\sigma}}\\vol(O)}.\n\\end{equation}\n\n\\begin{theorem}\\label{thm:main}\nFor every fixed prime $n\\ge5$ and every sequence\n$(K_i,M_i,\\sigma_i)$ as above with \\mbox{$D(M_i)\\to\\infty$},\n\\[\n \\mu_{K_i,M_i,\\sigma_i}\\longrightarrow m_n.\n\\]\nThe convergence is weak convergence of probability measures. In\nparticular, it holds against every continuous compactly supported\nfunction, and no mass escapes into the cusp.\n\\end{theorem}\n\nTheorem~\\ref{thm:main} permits nonmaximal orders of unbounded index\nand arbitrary local module types. The field discriminants may remain\nbounded or grow independently of those indices. The statement is\nseparate for each local homothety type; it makes no assertion about an\nindividual orbit, a convergence rate, or composite degrees.\n\n\\subsection*{The maximal-order specialization}\n\nTake $M=\\cO_K$. Every lattice in its local type is a fractional\n$\\cO_K$-ideal, and its homothety class is an ordinary ideal class.\nThus the packet is obtained by embedding one representative of each\nideal class, normalizing its covolume to one, and taking the distinct\nsign translates of its positive diagonal orbit. For every sequence of\ntotally real fields of degree $n$ with $|\\Disc(K)|\\to\\infty$, the\nvolume-weighted measures of these packets converge to $m_n$ by\nTheorem~\\ref{thm:main}. This construction also explains why an individual\nideal class is not the object being averaged.\n\nFor general $M$, the same maximal-order ideal classes remain the global\nparameters, but each carries a finite set of local module choices.\nProposition~\\ref{prop:adelic-packet} in Appendix~\\ref{subsec:adelic-packet}\nidentifies this construction with the\npushforward of adelic torus Haar probability, independently of the\nrational basis and finite-lattice presentation. The theorem therefore\nincludes all these presentations of the maximal-order specialization,\nas well as arbitrary noninvertible modules over nonmaximal orders.\n\n\\subsection*{Context and prior work}\n\nLinnik's ergodic approach connected arithmetic distribution problems\nwith dynamics \\cite{Linnik1968}. Duke proved equidistribution of the\nlength-weighted closed geodesics associated with fundamental\nreal-quadratic discriminants on the modular surface\n\\cite[Theorem~1]{Duke1988}. In higher rank, Einsiedler, Lindenstrauss,\nMichel, and Venkatesh developed the relationship between arithmetic\nseparation and entropy for periodic torus orbits \\cite{ELMV2009}.\nTheir cubic equidistribution theorem combines subconvexity, local\nanalysis, and measure classification\n\\cite[Theorem~1.4]{ELMV2011}. Their packet construction already\ndistinguishes local homothety from the coarser condition of having the\nsame multiplier order \\cite[Section~5.5]{ELMV2011}.\n\nThe higher-prime-degree extension described in\n\\cite[Section~1.6.3]{ELMV2011} is conditional on subconvexity.\nKhayutin subsequently obtained entropy bounds using arithmetic\nseparation and Galois invariants, under maximal-order and\ntwo-transitivity hypotheses \\cite[Theorem~1.1]{Khayutin2019}.\nLemke Oliver, Thorner, and Zaman proved equidistribution for sequences\nof orders in totally real fields of each fixed prime degree at least five,\noutside a small\nexceptional family of fields \\cite[Theorem~2.3]{LOTZ2024}.\nTheir result allows nonmaximal orders; the exceptional family has at\nmost $O_{n,\\eps}(X^\\eps)$ fields of discriminant at most $X$.\nTheorem~\\ref{thm:main} treats every field and every full-lattice local\ntype in the stated prime degrees. It gives an unconditional affirmative\nanswer to this packet form of the higher-dimensional Duke problem,\nincluding sequences in a fixed field with increasing order index.\n\n\\subsection*{The arithmetic ingredient}\n\nThe proof follows the counting-to-rigidity strategy of\nEinsiedler--Lindenstrauss--Michel--Venkatesh\n\\cite[Section~2.7.1]{ELMV2011}: packet averages of lattice-vector\ncounts control the cusp and small ordinary balls, and these bounds\nfeed into positive-entropy rigidity. Their cubic argument obtains\nasymptotics for these averages using global and local subconvexity\n\\cite[Sections~2.7.2--2.7.3]{ELMV2011}. For the same rigidity step,\nwe obtain the needed upper bounds by retaining the Dedekind-zeta\nresidue in the unfolded ideal count. Stark's exceptional-zero descent\n\\cite{Stark1974}, together with Shiu's theorem in Pollack's uniform\nformulation \\cite{Shiu1980,Pollack2020}, supplies the required bounds\nwithout a subconvexity hypothesis. Odd degree excludes the quadratic\nsubfields that occur in Stark's exceptional-zero alternative.\n\nFor an arbitrary prescribed local type, we keep maximal-order ideal\ncounting but insert local containment probabilities: at each prime,\nthe weight records the probability that a vector belongs to a randomly\nchosen unit translate of the prescribed module.\nThe essential estimate is a small positive moment of the local norm,\nuniform over full local modules whose maximal-order span is the full\nring. Its proof uses a unit in each component projection, not\nindependence of the components or invertibility of the module.\nThe unit-mass argument in \\cite[Lemma~9.7]{ELMV2011} is an antecedent;\nhere we prove the exact shell normalization and the positive moment\nfor arbitrary local modules with this full-span condition.\n\nThe exact unfolding belongs to the classical Hecke method, presented\nin the packet setting in \\cite[Section~10]{ELMV2011}. The dynamical\nconclusion uses the two-sided tube criterion of\n\\cite[Corollary~3.3]{ELMV2009} and the prime-degree measure\nclassification of Einsiedler, Katok, and Lindenstrauss\n\\cite[Corollary~1.4]{EKL2006}. We give the residue estimate,\nlocal-module bounds, and the passage to entropy in almost every\nergodic component below.\n\n\\subsection*{Proof outline}\n\nPut $R=\\cO_K$, $D=|\\Disc(K)|$, and\n$q=[RM:M]$. The natural counting scale is $Q=q\\sqrt D$, and\n$D(M)\\le Q^{2n}$. Section~\\ref{sec:packets} expresses the packet\nmean of a lattice-vector count as a maximal-order ideal sum with\nlocal weights. Section~\\ref{sec:local} proves an exact total-mass\nidentity and a power-saving tail for these weights.\nSection~\\ref{sec:analytic} combines them with a residue-relative\nzeta estimate and Shiu's theorem. Removing the Euler factors at\nprimes dividing $q$ produces precisely the factor needed to cancel\nthe local normalization.\n\nThe resulting count controls both short lattice vectors and small\nboxes about vectors with nonzero coordinates. Section~\\ref{sec:geometry}\ndeduces tightness and an $O(r^n)$ bound for the mass of ordinary balls\nin any limit measure. Since $A_n$ has dimension $n-1$, this bound\nforces positive entropy in almost every $A_n$-ergodic component.\nSection~\\ref{sec:entropy} gives this implication and applies the\nprime-degree measure-classification theorem. In that step it is\nessential to prove positive entropy for every invariant probability\ndominated by a finite multiple of the limit, not only for the limit\nitself: this rules out a positive-weight family of zero-entropy\ncomponents.\n\n\\input{packets}\n\\input{local}\n\\input{analytic}\n\\input{geometry}\n\\input{entropy}\n\n\\appendix\n\\input{adelic}\n\n{\\small\n\\bibliographystyle{plain}\n\\bibliography{references}\n}\n\\end{document}\n"}, {"path": "preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/references.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/build/references.bib", "bytes": 4775, "sha256": "a132f928be749117a6c6ae33018ea638f875cae03d9092853400585520e88930", "content": "@book{Linnik1968,\n  author = {Linnik, Yurij V.},\n  title = {Ergodic Properties of Algebraic Fields},\n  series = {Ergebnisse der Mathematik und ihrer Grenzgebiete},\n  volume = {45}, publisher = {Springer}, address = {Berlin--Heidelberg},\n  year = {1968},\n  url = {https://link.springer.com/book/9783642866333}\n}\n\n@article{Duke1988,\n  author = {Duke, William},\n  title = {Hyperbolic distribution problems and half-integral weight {Maass} forms},\n  journal = {Inventiones Mathematicae},\n  volume = {92}, number = {1}, year = {1988}, pages = {73--90},\n  doi = {10.1007/BF01393993}\n}\n\n@article{ELMV2011,\n  author = {Einsiedler, Manfred and Lindenstrauss, Elon and Michel, Philippe and Venkatesh, Akshay},\n  title = {Distribution of periodic torus orbits and {Duke}'s theorem for cubic fields},\n  journal = {Annals of Mathematics},\n  volume = {173}, number = {2}, year = {2011}, pages = {815--885},\n  doi = {10.4007/annals.2011.173.2.5}\n}\n\n@article{ELMV2009,\n  author = {Einsiedler, Manfred and Lindenstrauss, Elon and Michel, Philippe and Venkatesh, Akshay},\n  title = {Distribution of periodic torus orbits on homogeneous spaces},\n  journal = {Duke Mathematical Journal},\n  volume = {148}, number = {1}, year = {2009}, pages = {119--174},\n  doi = {10.1215/00127094-2009-023}\n}\n\n@article{EKL2006,\n  author = {Einsiedler, Manfred and Katok, Anatole and Lindenstrauss, Elon},\n  title = {Invariant measures and the set of exceptions to {Littlewood}'s conjecture},\n  journal = {Annals of Mathematics},\n  volume = {164}, number = {2}, year = {2006}, pages = {513--560},\n  doi = {10.4007/annals.2006.164.513}\n}\n\n@article{LOTZ2024,\n  author = {Lemke Oliver, Robert J. and Thorner, Jesse and Zaman, Asif},\n  title = {An approximate form of {Artin}'s holomorphy conjecture and non-vanishing of {Artin} {$L$}-functions},\n  journal = {Inventiones Mathematicae},\n  volume = {235}, number = {3}, year = {2024}, pages = {893--971},\n  doi = {10.1007/s00222-023-01232-2},\n  eprint = {2012.14422}, archivePrefix = {arXiv}, primaryClass = {math.NT}\n}\n\n@article{Khayutin2019,\n  author = {Khayutin, Ilya},\n  title = {Arithmetic of double torus quotients and the distribution of periodic torus orbits},\n  journal = {Duke Mathematical Journal},\n  volume = {168}, number = {12}, year = {2019}, pages = {2365--2432},\n  doi = {10.1215/00127094-2019-0016},\n  eprint = {1510.08481}, archivePrefix = {arXiv}, primaryClass = {math.NT}\n}\n\n@article{Stark1974,\n  author = {Stark, H. 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Series A},\n  volume = {187}, number = {1009}, year = {1946}, pages = {151--187},\n  doi = {10.1098/rspa.1946.0072}\n}\n\n@article{Rokhlin1967,\n  author = {Rokhlin, V. A.},\n  title = {Lectures on the entropy theory of measure-preserving transformations},\n  journal = {Russian Mathematical Surveys},\n  volume = {22}, number = {5}, year = {1967}, pages = {1--52},\n  doi = {10.1070/RM1967v022n05ABEH001224}\n}\n\n@article{GreschonigSchmidt2000,\n  author = {Greschonig, Gernot and Schmidt, Klaus},\n  title = {Ergodic decomposition of quasi-invariant probability measures},\n  journal = {Colloquium Mathematicum},\n  volume = {84/85}, number = {2}, year = {2000}, pages = {495--514},\n  doi = {10.4064/cm-84/85-2-495-514}\n}\n"}, {"path": "preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/paper.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Equidistribution-of-Prime-Degree-Torus-Packets-with-Arbitrary-Local-Type-September-24-2026/paper.pdf", "bytes": 404189, "sha256": "7e6a0188abd45ae4520d4592c773d264e029751e3ef6fb2623388aee8b0f56c0", "base64": 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"}, {"path": "preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/README.md", "bytes": 719, "sha256": "9daa75b7b040bf2711186b3ea39cf8775970d78467a456c142928b273aa534e3", "content": "# [Exact Uniform Sampling of Contingency Tables with Arbitrary Margins](main.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 24, 2026\n\n## Citation\n\nTo cite this manuscript:\n\n```bibtex\n@misc{OAI:Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026,\n  author = {{OpenAI}},\n  title = {{Exact Uniform Sampling of Contingency Tables with Arbitrary Margins}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/main.pdf}{OAI:Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/figures/repair.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/figures/repair.tex", "bytes": 1792, "sha256": "d9d0eb410aaab7d3d9e8c61e8080879c4a74f7ef3cea004c8d9588a7632d335b", "content": "\\begin{figure}[tb]\n\\centering\n\\begin{tikzpicture}[x=1cm,y=1cm,font=\\small,\n cell/.style={minimum width=2.4cm,minimum height=1.12cm,inner sep=1pt},\n every path/.style={line width=.55pt}]\n \\begin{scope}\n  \\fill[blue!9] (2.4,-2.24) rectangle (4.8,-1.12);\n  \\draw (0,0) rectangle (4.8,-2.24);\n  \\draw (2.4,0)--(2.4,-2.24);\n  \\draw (0,-1.12)--(4.8,-1.12);\n  \\node at (2.4,.88) {\\textbf{Defect}};\n  \\node at (1.2,.3) {$c_{j'}<U$};\n  \\node at (3.6,.3) {$c_j\\ge U$};\n  \\node[anchor=east] at (-.15,-.56) {$r_i<U$};\n  \\node[anchor=east] at (-.15,-1.68) {$r_{i'}\\ge U$};\n  \\node[cell] at (1.2,-.56) {unchanged};\n  \\node[cell,align=center] at (3.6,-.56) {$s:\\ (a,a+1)$\\\\[-1pt]\\scriptsize negative};\n  \\node[cell,align=center] at (1.2,-1.68) {$t:\\ (b+1,b)$\\\\[-1pt]\\scriptsize positive};\n  \\node[cell,align=center] at (3.6,-1.68) {$z$\\\\[-1pt]\\scriptsize large entry};\n \\end{scope}\n \\draw[-{Stealth[length=2mm]},thick] (5.0,-1.12)--(5.9,-1.12);\n \\begin{scope}[xshift=6.2cm]\n  \\fill[blue!9] (2.4,-2.24) rectangle (4.8,-1.12);\n  \\draw (0,0) rectangle (4.8,-2.24);\n  \\draw (2.4,0)--(2.4,-2.24);\n  \\draw (0,-1.12)--(4.8,-1.12);\n  \\node at (2.4,.88) {\\textbf{Repaired transversal}};\n  \\node at (1.2,.3) {$j'$};\n  \\node at (3.6,.3) {$j$};\n  \\node[cell] at (1.2,-.56) {unchanged};\n  \\node[cell] at (3.6,-.56) {$(a,a)$};\n  \\node[cell] at (1.2,-1.68) {$(b,b)$};\n  \\node[cell] at (3.6,-1.68) {$z+1$};\n \\end{scope}\n\\end{tikzpicture}\n\\caption{The repair when the receiving cell $(i',j)$ is large.\nSmall cells display $(x,q)$, and the shaded cell displays a completion\nentry. Decreasing $x_t$ and matching the viewpoints increases the\nresidual row margin at $i'$ and column margin at $j$ by one; adding\none to $z$ gives an injective map of completions.\nThe other cells remain unchanged.}\n\\label{fig:repair}\n\\end{figure}\n"}, {"path": "preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/macros.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/macros.tex", "bytes": 896, "sha256": "12c025c5f10a88e18256a93c8288c17102837216d00ee19056692adf26ce0930", "content": "\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\\newtheorem{definition}[theorem]{Definition}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\newcommand{\\R}{\\mathbb{R}}\n\\newcommand{\\Z}{\\mathbb{Z}}\n\\newcommand{\\N}{\\mathbb{N}}\n\\newcommand{\\E}{\\mathbb{E}}\n\\newcommand{\\Pp}{\\mathbb{P}}\n\\newcommand{\\one}{\\mathbf{1}}\n\\newcommand{\\eps}{\\varepsilon}\n\\newcommand{\\norm}[1]{\\lVert#1\\rVert}\n\\newcommand{\\abs}[1]{\\lvert#1\\rvert}\n\\DeclareMathOperator{\\Var}{Var}\n\\DeclareMathOperator{\\TV}{TV}\n\\DeclareMathOperator{\\diag}{diag}\n\\DeclareMathOperator{\\vol}{vol}\n\\DeclareMathOperator{\\supp}{supp}\n\\setlist[itemize]{topsep=4pt,itemsep=2pt,parsep=0pt}\n\\setlist[enumerate]{topsep=4pt,itemsep=3pt,parsep=0pt}\n\\numberwithin{equation}{section}\n\\emergencystretch=1em\n"}, {"path": "preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/main.tex", "bytes": 1543, "sha256": "d935f2e6e7b6052b3ec9ada2d7c0f5510c9b65251273fb9c806b2e82c1b6fdeb", "content": "\\documentclass[11pt]{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{lmodern}\n\\usepackage[margin=1in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage{booktabs,enumitem,microtype,needspace}\n\\usepackage{tikz}\n\\usetikzlibrary{arrows.meta,positioning,calc,fit}\n\\usepackage{xcolor}\n\\definecolor{linkblue}{RGB}{28,70,105}\n\\usepackage[colorlinks=true,linkcolor=linkblue,citecolor=linkblue,urlcolor=linkblue]{hyperref}\n\\hypersetup{pdftitle={Exact Uniform Sampling of Contingency Tables with Arbitrary Margins},pdfauthor={OpenAI}}\n\\ifdefined\\pdfinfoomitdate\\pdfinfoomitdate=1\\fi\n\\ifdefined\\pdftrailerid\\pdftrailerid{}\\fi\n\\ifdefined\\pdfsuppressptexinfo\\pdfsuppressptexinfo=15\\fi\n\\input{macros}\n\\title{Exact Uniform Sampling of Contingency Tables\\\\with Arbitrary Margins}\n\\author{OpenAI}\n\\date{September 24, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe give an exact uniform sampler for nonnegative integer contingency tables\nwith arbitrary prescribed margins. It terminates almost surely and has expected\nbit complexity polynomial in both dimensions and the binary length of the\nmargins. No positivity, balance, sparsity, or fixed-dimension assumption is\nrequired.\n\\end{abstract}\n\\input{sections/introduction}\n\\input{sections/model-and-graph}\n\\input{sections/signatures}\n\\input{sections/transport}\n\\input{sections/dense}\n\\input{sections/algorithms}\n\\Needspace{12\\baselineskip}\n\\AddToHook{cmd/thebibliography/after}{\\addcontentsline{toc}{section}{\\refname}}\n\\bibliographystyle{plain}\n\\bibliography{refs/sources}\n\\end{document}\n"}, {"path": "preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/refs/sources.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/refs/sources.bib", "bytes": 6734, "sha256": "ef5615628091750d7d55b7b535c141fd6501d31a2deeb8287cee12f43b2cea57", "content": "@misc{AGW2021,\n  author        = {Arman, Andrii and Gao, Pu and Wormald, Nicholas},\n  title         = {Linear-time uniform generation of random sparse\n                   contingency tables with specified marginals},\n  howpublished  = {\\href{https://arxiv.org/abs/2104.09413v2}{arXiv:2104.09413v2}},\n  year          = {2021},\n  eprint        = {2104.09413},\n  archivePrefix = {arXiv},\n  primaryClass  = {math.CO},\n  note          = {Version 2, 15 June 2021},\n  url           = {https://arxiv.org/abs/2104.09413v2}\n}\n\n@techreport{KM2003,\n  author      = {Kijima, Shuji and Matsui, Tomomi},\n  title       = {{Polynomial Time Perfect Sampling Algorithm\n                 for Two-rowed Contingency Tables}},\n  institution = {Department of Mathematical Informatics,\n                 Graduate School of Information Science and Technology,\n                 The University of Tokyo},\n  type        = {Mathematical Engineering Technical Reports},\n  number      = {METR 2003-15},\n  year        = {2003},\n  month       = apr,\n  note        = {\\url{https://www.keisu.t.u-tokyo.ac.jp/data/2003/METR03-15.pdf}},\n  url         = {https://www.keisu.t.u-tokyo.ac.jp/data/2003/METR03-15.pdf}\n}\n\n@incollection{DiaconisGangolli1995,\n  author = {Diaconis, Persi and Gangolli, Anil},\n  title = {Rectangular Arrays with Fixed Margins},\n  booktitle = {Discrete Probability and Algorithms},\n  editor = {Aldous, David and Diaconis, Persi and Spencer, Joel and Steele, J. Michael},\n  series = {The IMA Volumes in Mathematics and its Applications},\n  volume = {72},\n  pages = {15--41},\n  publisher = {Springer},\n  address = {New York},\n  year = {1995},\n  doi = {10.1007/978-1-4612-0801-3_3},\n  note = {\\href{https://doi.org/10.1007/978-1-4612-0801-3_3}{doi:10.1007/978-1-4612-0801-3\\_3}}\n}\n\n@article{DyerGreenhill2000,\n  author = {Dyer, Martin and Greenhill, Catherine},\n  title = {Polynomial-time counting and sampling of two-rowed contingency tables},\n  journal = {Theoretical Computer Science},\n  volume = {246},\n  pages = {265--278},\n  year = {2000},\n  note = {\\href{https://web.maths.unsw.edu.au/~csg/papers/contingency.pdf}{Author version}},\n  url = {https://web.maths.unsw.edu.au/~csg/papers/contingency.pdf}\n}\n\n@article{DKM1997,\n  author = {Dyer, Martin and Kannan, Ravi and Mount, John},\n  title = {Sampling contingency tables},\n  journal = {Random Structures \\& Algorithms},\n  volume = {10},\n  number = {4},\n  pages = {487--506},\n  year = {1997},\n  note = {\\href{https://www.math.cmu.edu/~af1p/Teaching/MCC17/Papers/contingency.pdf}{Published article}},\n  url = {https://www.math.cmu.edu/~af1p/Teaching/MCC17/Papers/contingency.pdf}\n}\n\n@article{Morris2002,\n  author = {Morris, Ben},\n  title = {Improved bounds for sampling contingency tables},\n  journal = {Random Structures \\& Algorithms},\n  volume = {21},\n  number = {2},\n  pages = {135--146},\n  year = {2002},\n  doi = {10.1002/rsa.10049},\n  note = {\\href{https://doi.org/10.1002/rsa.10049}{doi:10.1002/rsa.10049}},\n  url = {https://doi.org/10.1002/rsa.10049}\n}\n\n@article{Prekopa1973,\n  author = {Pr{\\'e}kopa, Andr{\\'a}s},\n  title = {On logarithmic concave measures and functions},\n  journal = {Acta Scientiarum Mathematicarum (Szeged)},\n  volume = {34},\n  pages = {335--343},\n  year = {1973},\n  note = {\\href{https://rutcor.rutgers.edu/Prekopa/pdf/SCIENT2.pdf}{Author-hosted version}},\n  url = {https://rutcor.rutgers.edu/Prekopa/pdf/SCIENT2.pdf}\n}\n\n@article{Leindler1972,\n  author = {Leindler, L{\\'a}szl{\\'o}},\n  title = {On a certain converse of {H\\\"older}'s inequality. {II}},\n  journal = {Acta Scientiarum Mathematicarum},\n  volume = {33},\n  number = {3--4},\n  pages = {217--223},\n  year = {1972},\n  note = {\\href{https://acta.bibl.u-szeged.hu/14358/}{Publisher archive}},\n  url = {https://acta.bibl.u-szeged.hu/14358/}\n}\n\n@misc{GLMP2024,\n  author = {G{\\\"o}bel, Andreas and Liu, Jingcheng and Manurangsi, Pasin and Pappik, Marcus},\n  title = {Perfect sampling from rapidly mixing {Markov} chains},\n  year = {2024},\n  howpublished = {\\href{https://arxiv.org/abs/2410.00882v2}{arXiv:2410.00882v2}},\n  eprint = {2410.00882},\n  archivePrefix = {arXiv},\n  primaryClass = {cs.CC},\n  note = {Version 2, December 6, 2024},\n  url = {https://arxiv.org/abs/2410.00882v2}\n}\n\n@article{BH2020,\n  author = {Br{\\\"a}nd{\\'e}n, Petter and Huh, June},\n  title = {Lorentzian polynomials},\n  journal = {Annals of Mathematics (2)},\n  volume = {192},\n  number = {3},\n  pages = {821--891},\n  year = {2020},\n  doi = {10.4007/annals.2020.192.3.4},\n  note = {\\href{https://doi.org/10.4007/annals.2020.192.3.4}{doi:10.4007/annals.2020.192.3.4}}\n}\n\n@misc{AF2002,\n  author = {Aldous, David and Fill, James Allen},\n  title = {Reversible {Markov} Chains and Random Walks on Graphs},\n  year = {2002},\n  howpublished = {Unfinished monograph},\n  note = {Recompiled 2014. \\url{https://www.stat.berkeley.edu/~aldous/RWG/book.html}},\n  url = {https://www.stat.berkeley.edu/~aldous/RWG/book.html}\n}\n\n@misc{DZ2016,\n  author = {DeSalvo, Stephen and Zhao, James Y.},\n  title = {Random Sampling of Contingency Tables via Probabilistic Divide-and-Conquer},\n  year = {2016},\n  howpublished = {\\href{https://arxiv.org/abs/1507.00070v4}{arXiv:1507.00070v4}},\n  eprint = {1507.00070},\n  archivePrefix = {arXiv},\n  primaryClass = {math.ST},\n  note = {Version 4, 29 February 2016},\n  url = {https://arxiv.org/abs/1507.00070v4}\n}\n\n@article{CD2003,\n  author = {Cryan, Mary and Dyer, Martin},\n  title = {A polynomial-time algorithm to approximately count contingency\n           tables when the number of rows is constant},\n  journal = {Journal of Computer and System Sciences},\n  volume = {67},\n  number = {2},\n  pages = {291--310},\n  year = {2003},\n  doi = {10.1016/S0022-0000(03)00014-X},\n  note = {\\href{https://doi.org/10.1016/S0022-0000(03)00014-X}{doi:10.1016/S0022-0000(03)00014-X}}\n}\n\n@inproceedings{Dyer2003,\n  author = {Dyer, Martin},\n  title = {Approximate counting by dynamic programming},\n  booktitle = {Proceedings of the Thirty-Fifth Annual ACM Symposium on\n               Theory of Computing},\n  pages = {693--699},\n  publisher = {ACM},\n  year = {2003},\n  note = {\\href{https://www.math.cmu.edu/~af1p/Teaching/MCC17/Papers/knapsack.pdf}{Published article}},\n  url = {https://www.math.cmu.edu/~af1p/Teaching/MCC17/Papers/knapsack.pdf}\n}\n\n@article{CDGJM2006,\n  author = {Cryan, Mary and Dyer, Martin and Goldberg, Leslie Ann\n            and Jerrum, Mark and Martin, Russell},\n  title = {Rapidly Mixing {Markov} Chains for Sampling Contingency Tables\n           with a Constant Number of Rows},\n  journal = {SIAM Journal on Computing},\n  volume = {36},\n  number = {1},\n  pages = {247--278},\n  year = {2006},\n  doi = {10.1137/S0097539703434243},\n  note = {\\href{https://doi.org/10.1137/S0097539703434243}{doi:10.1137/S0097539703434243}}\n}\n"}, {"path": "preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/sections/algorithms.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/sections/algorithms.tex", "bytes": 20054, "sha256": "ed5a846b4d6de2ff2ea2bed21aaa441107115e0a2745328cd3413e5a963c8a46", "content": "\\section{Sampling algorithms and exact correction}\\label{sec:algorithms}\n\nWe now turn the weighted graph bound into an algorithm using unbiased random\nbits. All deterministic choices, including greedy completions, neighbor\norders, and tie breaking, are fixed once and for all. Abbreviate\n$\\Omega(r,c)$ by $\\Omega$. Write\n$\\nu_X$ for the uniform law on $\\mathcal F_X$ and $z_0$ for a greedy original\ntable. Its restriction to the small slots gives a starting transversal $X_0$.\n\n\\subsection{The ideal chain}\n\nSet\n\\[\n q_{\\rm s}=\\lceil\\log_2(32d^2)\\rceil,\n \\qquad p=2^{-q_{\\rm s}}.\n\\]\nList the distinct feasible neighbors of a state $X$ in a fixed order.\nAssign one of the $2^{q_{\\rm s}}$ equally likely binary strings to each\nneighbor and let all remaining strings propose a hold. This gives every\nedge the same proposal probability $p$ in both directions. As explained in\nLemma~\\ref{lem:repair}, the list is obtained by testing all unit exchanges,\nthe repair of $X$ when it is a defect, and the uniquely reconstructed inverse\nrepair of each possible ordered defect type. There are at most $5d^2+1$\nneighbors. Each test checks only coordinate bounds, the allowed occupancy\npattern, and residual margins; it does not evaluate $f$.\n\nHere is the completion adjustment used for a nonholding proposal $X\\to Y$.\nFor a nonempty block $\\mathcal B=I\\times J$, fix a reference row $i_0$ and\ncolumn $j_0$, independently of $X$. Let\n$\\Delta R_i=R_i(Y)-R_i(X)$ and $\\Delta P_j=P_j(Y)-P_j(X)$ be the changes in\nits padded margins. Define the integer matrix $D_{XY}$ by\n\\[\n (D_{XY})_{ij}=\\begin{cases}\n \\Delta R_i,&i\\ne i_0,\\ j=j_0,\\\\\n \\Delta P_j,&i=i_0,\\ j\\ne j_0,\\\\\n \\Delta R_{i_0}-\\sum_{j'\\ne j_0}\\Delta P_{j'},&i=i_0,\\ j=j_0,\\\\\n 0,&i\\ne i_0,\\ j\\ne j_0.\n \\end{cases}\n\\]\nThe equality of total row and column changes verifies all its row and column\nsums, including the reference column. Also $D_{YX}=-D_{XY}$. Along a unit\nexchange or repair edge each individual residual changes by at most two;\nhence every displayed entry has magnitude at most $10d$. These formulas\nalso apply to a block with a single row or column.\n\nThe ideal transition draws $a\\sim\\nu_X$ and accepts $Y$ precisely when\n$a+D_{XY}$ is nonnegative. It then belongs to $\\mathcal F_Y$ because its\nmargins are already correct. A rejected proposal holds at $X$. If the block\nis empty, every edge proposal is accepted. Denote this ideal kernel by $P$.\n\n\\begin{proposition}[Small-chain bound]\\label{prop:small-chain}\nThe kernel $P$ is lazy and reversible for $\\pi(X)=f(X)/\\Lambda$. On every\nedge it satisfies\n\\[\n P(X,Y)\\ge \\frac{1}{128d^2}\n                  \\min\\left\\{1,\\frac{f(Y)}{f(X)}\\right\\}.\n\\]\nIf there is more than one state, its inverse spectral gap is at most\n$d^{160}$. Moreover\n$\\pi_{\\min}\\ge(C+1)^{-2d}$, and, for every $t\\ge0$,\n\\begin{equation}\\label{eq:small-mixing}\n \\TV\\bigl(P^t(X_0,\\cdot),\\pi\\bigr)\n \\le (C+1)^{2d}\\exp(-t/d^{160}).\n\\end{equation}\nA one-state chain is already stationary.\n\\end{proposition}\n\n\\begin{proof}\nThe proposal holds with probability at least\n$1-(5d^2+1)/(32d^2)>1/2$, and rejection can only increase this probability.\nTranslation by $D_{XY}$ bijects the set of acceptable completions in\n$\\mathcal F_X$ with the corresponding set in $\\mathcal F_Y$. Calling its\ncardinality $c_{XY}=c_{YX}$ gives\n$f(X)P(X,Y)=p c_{XY}=f(Y)P(Y,X)$.\n\nEvery nonempty completion-block margin is at least $L$. Rejection implies\nthat one of at most $d$ entries of a uniform completion is less than $10d$.\nLemma~\\ref{lem:small-entry}, with $a=L$ and threshold $10d\\le L/2$, gives\n\\[\n \\Pp(\\text{rejection})\\le \\frac{40d^5}{L}\n             =\\frac{40}{d^7}<\\frac12.\n\\]\nConsequently $c_{XY}\\ge f(X)/2$, and in particular\n$c_{XY}\\ge\\min(f(X),f(Y))/2$. Since $p\\ge1/(64d^2)$, both the stated\ntransition bound and the Dirichlet-form comparison\n\\[\n \\mathcal E_P(H)\\ge \\frac{E(H)}{128d^2\\Lambda}\n\\]\nfollow. Theorem~\\ref{thm:poincare} and $U=d^{20}$ now give an inverse-gap\nbound of\n\\begin{align*}\n128d^2\\bigl((1+2d^2)4d^2(U+1)^6+2\\bigr)\n &\\le 98{,}560d^{126}\\le d^{160}.\n\\end{align*}\nIndeed the coefficient inside parentheses is at most $770d^4U^6$.\nThe graph is connected by Theorem~\\ref{thm:poincare}, and\nProposition~\\ref{prop:stationary-success} gives\n$\\pi_{\\min}\\ge(C+1)^{-2d}$. Lemma~\\ref{lem:mixing} now gives\n\\eqref{eq:small-mixing}.\n\\end{proof}\n\n\\subsection{A bounded almost-uniform sampler}\n\nFor an integer $k\\ge1$, put\n\\begin{equation}\\label{eq:outer-parameters}\n J_{\\rm o}=d^4(k+1),\\qquad\n T=d^{200}(k+b)^2,\\qquad h=d^4(k+b)^2.\n\\end{equation}\nThe implemented chain replaces every ideal completion draw by the\nsubroutine of Theorem~\\ref{thm:dense-draw} with accuracy $h$; write $\\mu_X$\nfor its output law on $\\mathcal F_X$ and $Q$ for the resulting kernel.\nStarting anew at $X_0$ on each trial, perform the following bounded procedure.\n\\begin{enumerate}\n\\item Run $Q$ for $T$ transitions and then make one further, independent\ncompletion draw with law $\\mu_X$ at the final state.\n\\item If $X$ is a transversal and every large completion entry is at least\n$L$, subtract $L$ on the large block and return the resulting original\ntable. Otherwise this trial fails.\n\\item Repeat for at most $J_{\\rm o}$ independent trials, and return $z_0$\nif all of them fail.\n\\end{enumerate}\nAll randomness in different draws and trials is fresh. In particular, a\ncompletion used to test a transition is discarded after that test; the\ncurrent small state is the chain's entire persistent state.\n\n\\begin{theorem}[Almost-uniform sampling]\\label{thm:almost-uniform}\nThe preceding procedure returns a feasible table with law $p_k$ satisfying\n\\[\n \\TV\\bigl(p_k,\\operatorname{Uniform}(\\Omega)\\bigr)\\le2^{-k}.\n\\]\nIts worst-case number of bit operations is bounded by a fixed polynomial\nin $d,b,k$.\n\\end{theorem}\n\n\\begin{proof}\nWe first control adaptive approximation. The dense guarantee is uniform\nover feasible states:\n$\\TV(\\mu_X,\\nu_X)\\le\\eta:=2^{-h}$.\nFrom a common state, couple the proposal and holding decision identically,\nand maximally couple the two completion draws if there is a nonholding\nproposal. Agreement of these draws gives the same acceptance decision and\nthe same next state. Thus\n\\[\n \\sup_X\\TV\\bigl(Q(X,\\cdot),P(X,\\cdot)\\bigr)\\le\\eta.\n\\]\nCouple successively until the first disagreement. Conditional on agreement\nso far, the common state may be adaptive, but the same uniform conditional\nbound still applies. After $T$ transitions and the terminal completion, the\nprobability of a disagreement is at most $(T+1)\\eta$. This argument uses\nneither reversibility nor a stationary-law assertion for $Q$.\nBy Proposition~\\ref{prop:small-chain}, the additional error from replacing\nthe ideal terminal state by a stationary one is at most\n$\\tau=(C+1)^{2d}e^{-T/d^{160}}$.\n\nIn a stationary ideal trial every pair $(X,a)$ has probability\n$\\pi(X)/f(X)=1/\\Lambda$. By Proposition~\\ref{prop:stationary-success},\nsuccessful pairs are in bijection with $\\Omega$, and the success probability\n$s_*=|\\Omega|/\\Lambda$ is at least $1/[2(1+d^2)]$.\nThe first success among $J_{\\rm o}$ independent stationary trials therefore\nhas, including the fallback, the exact law\n\\[\n \\bigl[1-(1-s_*)^{J_{\\rm o}}\\bigr]\n          \\operatorname{Uniform}(\\Omega)\n       +(1-s_*)^{J_{\\rm o}}\\delta_{z_0}.\n\\]\nFor clarity, each particular table receives first-success mass\n$\\sum_{j=0}^{J_{\\rm o}-1}(1-s_*)^j/\\Lambda\n=[1-(1-s_*)^{J_{\\rm o}}]/|\\Omega|$.\nPregenerate all potential trials in both procedures, then apply the same\ndeterministic first-success-or-fallback map. The union bound and contraction\nof total variation under a deterministic map give total error at most\n\\begin{equation}\\label{eq:outer-error}\n e^{-J_{\\rm o}/[2(1+d^2)]}\n +J_{\\rm o}(C+1)^{2d}e^{-T/d^{160}}\n +J_{\\rm o}(T+1)2^{-h}.\n\\end{equation}\nIn particular, no error is divided by a success probability.\n\nEach term in \\eqref{eq:outer-error} is at most $2^{-k-2}$. Here are explicit\nchecks. Put $t=k+b\\ge15$, so $d\\le t$, $k+2\\le t$, and\n$\\log_2(C+1)<b$. First,\n\\[\n \\frac{J_{\\rm o}}{2(1+d^2)}\n \\ge\\frac{d^2(k+1)}4\\ge49(k+1)\\ge(k+2)\\ln2.\n\\]\nFor the second term,\n\\[\n \\ln\\bigl(J_{\\rm o}(C+1)^{2d}\\bigr)+(k+2)\\ln2\n \\le2t^2+6t\\le3t^2\\le d^{40}t^2=T/d^{160}.\n\\]\nFinally, since $T+1\\le2T$,\n\\begin{align*}\n \\log_2\\bigl(J_{\\rm o}(T+1)\\bigr)+k+2\n &\\le204\\log_2d+2\\log_2t+\\log_2(k+1)+k+3\\\\\n &\\le209t\\le d^4t^2=h.\n\\end{align*}\nTheir sum is less than $2^{-k}$.\n\nFor the running time, neighbor lists have polynomial size, and all state\ncoordinates, residuals, and completion entries have $O(b+\\log d)$ bits.\nThe proposal uses exactly $q_{\\rm s}$ unbiased bits. Each completion call\nhas bounded polynomial bit cost by Theorem~\\ref{thm:dense-draw}; the numbers\nof steps, trials, and calls in \\eqref{eq:outer-parameters} are fixed\npolynomials. Comparisons, additions, integer multiplications and divisions,\nand loop counters all have polynomial bit cost, for example with schoolbook\narithmetic. Only feasible completions and the feasible fallback can be\nreturned. Empty and singleton-dimensional blocks use their deterministic\ncompletion rule. This proves the claimed worst-case bound.\n\\end{proof}\n\n\\subsection{Exact tabulation of the implemented law}\n\nThe exact correction will need the law of the actual bounded algorithm,\nincluding approximate offsets, adaptive transitions, and both stopping\nrules. Only the small state persists between transitions. We can therefore\ncache the dense completion law for each small state, propagate the resulting\nfinite kernel, and then apply the terminal and first-success rules. The spaces\nenumerated in this calculation do not grow with the accuracy request;\naccuracy changes only the iteration counts and the lengths of integers.\n\n\\begin{proposition}[Law tabulation]\\label{prop:law-tabulation}\nFor fixed margins and $k$, the entire law $p_k$ can be computed exactly in\n\\[\n 2^{500db}\\operatorname{poly}(d,b,k)\n\\]\nbit operations. It has a common dyadic denominator:\n$p_k(z)=a_z/2^R$, where $a_z$ are nonnegative integers and $R$ is bounded by\na fixed polynomial in $d,b,k$. The sizes of all enumerated state spaces\nare independent of $k$.\n\\end{proposition}\n\n\\begin{proof}\n\\emph{The finite spaces.}\nUse the dense sampler's notation $A=d^4$ and $K_0=4d^8$.\nThere are at most $\\mathsf S:=2^{2db}$ feasible small states, since their\nnumber is at most $(C+1)^{2d}$. A completion fiber, the original table\nspace, an offset space, and the bin space each have at most\n$\\mathsf V:=2^{db}$ elements: use $(C+1)^d\\le2^{db}$ and $K_0<C$.\nThey can be enumerated by listing coordinate arrays and retaining those\nthat pass their defining tests. None of these coordinate ranges depends\non $k$. This exhaustive calculation is used only for the exact correction,\nnot during an ordinary run of the bounded sampler.\n\n\\emph{Caching a dense completion law.}\nThe following denominator allowances are uniform over all feasible small\nstates, with $h$ as in \\eqref{eq:outer-parameters}:\n\\begin{align}\n Q_0&=\\lceil\\log_2(8d)\\rceil+8Ad,\n &r_{\\max}&=b+1+4(h+d),\\nonumber\\\\\n H&=d^{50}(h+1)^2,\n &J_{\\rm D}&=d^4(h+1),\\nonumber\\\\\n R_1&=H Q_0+d r_{\\max},\n &R_{\\rm D}&=J_{\\rm D}R_1.\\label{eq:bit-schedule}\n\\end{align}\nEvery bin transition has denominator dividing $2^{Q_0}$: its proposal\nneeds $\\lceil\\log_2(8d)\\rceil$ bits and every dyadic acceptance test needs\nat most $8Ad$ bits. For each input $X$, enumerate its bins, compute their\ntransition matrix as $B_X/2^{Q_0}$ with integer entries, and propagate the\npoint mass at zero for $H$ steps. At step $t$ the vector uses denominator\n$2^{tQ_0}$; its next numerator is the previous integer vector multiplied\nby $B_X$. Adding contributions from many paths changes the numerator,\nnot the denominator exponent.\n\nOffsets are tabulated without enumerating their random bits. For width\n$w$ and the actual bit count\n$r=\\lceil\\log_2(w+1)\\rceil+4(h+d)\\le r_{\\max}$, the probability of offset\n$u\\in\\{0,\\ldots,w-1\\}$ is\n\\begin{equation}\\label{eq:offset-count}\n 2^{-r}\\left(\n \\left\\lceil\\frac{(u+1)2^r}{w}\\right\\rceil\n -\\left\\lceil\\frac{u2^r}{w}\\right\\rceil\\right).\n\\end{equation}\nIndeed these two endpoints count the integers $z$ with\n$u\\le wz/2^r<u+1$. Integer ceiling division evaluates the count exactly.\nEnlarge its denominator to $2^{r_{\\max}}$, pad unused free coordinates to\n$d$, and combine with the propagated bin law. Enumerating bin and offset\ntuples, constructing the candidate table, and applying its feasibility\ntest gives the implemented dense trial's success numerators $\\alpha_X(a)$ and failure\nnumerator $\\phi_X$, all with denominator $2^{R_1}$.\nSorting of margins and its inverse are deterministic functions of $X$;\nwe always record completions in the original block coordinates.\n\nLet $a_X^0$ be the dense call's fixed greedy fallback. The complete dense\noutput law is exactly\n\\begin{equation}\\label{eq:cached-dense-law}\n \\mu_X(a)=\\frac{\n \\displaystyle\\sum_{j=0}^{J_{\\rm D}-1}\n    \\phi_X^j\\alpha_X(a)2^{R_1(J_{\\rm D}-j-1)}\n    +\\one_{\\{a=a_X^0\\}}\\phi_X^{J_{\\rm D}}\n }{2^{R_{\\rm D}}}.\n\\end{equation}\nThis is a finite first-success sum and the all-fail mass. It introduces\nno division by a success probability. A deterministic empty or\nsingleton-dimensional completion call uses the same denominator by\nignoring the reserved bits. Cache \\eqref{eq:cached-dense-law} for each $X$.\n\n\\emph{Propagating the implemented chain.}\nFor a distinct neighbor $Y$ the implemented transition is\n\\begin{equation}\\label{eq:implemented-kernel}\n \\begin{aligned}\n Q(X,Y)=p\\sum_{a\\in\\mathcal F_X}\\mu_X(a)\n                \\one_{\\{a+D_{XY}\\in\\mathcal F_Y\\}},\\\\\n Q(X,X)=1-\\sum_{Y\\ne X}Q(X,Y).\n \\end{aligned}\n\\end{equation}\nFor an empty block interpret the indicator as one. These entries share\ndenominator $2^{R_{\\rm D}+q_{\\rm s}}$. Since only $X$ persists between\nsteps and completion randomness is fresh, ordinary vector propagation\n$v_{t+1}=v_tQ$, starting at $X_0$, computes the exact adaptive state law.\nAppend the terminal completion draw and the unpadding test. This yields\nthe one-outer-trial success masses $g(z)$ and failure mass\n$f_0=1-\\sum_z g(z)$ with common denominator $2^{R_{\\rm T}}$, where\n\\[\n R_{\\rm T}=T(R_{\\rm D}+q_{\\rm s})+R_{\\rm D}.\n\\]\nExplicitly, $g(z)$ is the sum of $v_T(X)\\mu_X(a)$ over pairs passing that\ntest and producing $z$. The final law is\n\\begin{equation}\\label{eq:outer-geometric-law}\n p_k(z)=g(z)\\sum_{j=0}^{J_{\\rm o}-1}f_0^j\n                 +\\one_{\\{z=z_0\\}}f_0^{J_{\\rm o}},\n\\end{equation}\nwhose denominator divides $2^R$ for $R=J_{\\rm o}R_{\\rm T}$.\nBoth geometric sums are evaluated by bounded integer multiplication and\naddition. Algebraically, the common denominators reserve ignored bits for\nholds, shorter draws, and stopped trials; neither tabulation nor sampling\nenumerates those bit strings.\n\n\\emph{Bit complexity.}\nThe denominator exponents are bounded by fixed polynomials. With\n$q=d+b+k+2\\ge31$, one has $h\\le q^6$, $h+1\\le q^7$,\n$Q_0\\le q^6$, $r_{\\max}\\le q^7$, $H\\le q^{64}$, and hence\n\\[\n R_1\\le q^{71},\\quad R_{\\rm D}\\le q^{82},\\quad\n R_{\\rm T}\\le q^{285},\\quad R\\le q^{290}\\le q^{300}.\n\\]\nHere we used $T\\le q^{202}$, $J_{\\rm o}\\le q^5$, and\n$q_{\\rm s}\\le q^2$. Probability numerators never exceed their common\ndenominator. Intermediate products and sums may be stored with the\nprescribed stage's denominator, so their bit lengths, including the\noffset counts in \\eqref{eq:offset-count}, are polynomial as well.\nCandidate tables that fail feasibility can have negative dependent\nentries, but their entries are integer sums of $O(d)$ quantities of size\n$O(C)$ and still have $O(b+\\log d)$ bits.\n\nFor each state, dense matrix propagation costs at most\n$H\\mathsf V^2$ arithmetic operations. Bin--offset summation, even with\na scan of all completions to identify the output, costs at most\n$\\mathsf V^3$; the geometric composition costs at most\n$J_{\\rm D}\\mathsf V$. Constructing \\eqref{eq:implemented-kernel} uses at\nmost $\\mathsf S^2\\mathsf V$ terms. Small-state propagation uses\n$T\\mathsf S^2$, terminal completion and output lookup at most\n$\\mathsf S\\mathsf V^2$, and the last geometric composition at most\n$J_{\\rm o}\\mathsf V$. Including the cache calculation for all\n$\\mathsf S$ states, the total number of arithmetic operations is at most\n$2^{10db}\\operatorname{poly}(d,b,k)$; multiplying by the established\npolynomial bit cost per operation gives the stated, looser $2^{500db}$\nbound. The iteration counts $H,J_{\\rm D},T,J_{\\rm o}$ depend polynomially\non $k$; none of the enumerated spaces does.\n\\end{proof}\n\n\\subsection{Exact uniform sampling}\n\nThe final step uses the rare residual-mixture construction of G\\\"obel,\nLiu, Manurangsi, and Pappik~\\cite[Section~3.1, Algorithm~1 and\nTheorem~6]{GLMP2024}. A sufficiently accurate approximate law is dominated\npointwise by a slight enlargement of the target law. Its remaining mass\ncan therefore be sampled exactly on a rare branch. Here we verify this\ndomination, implement the mixture with unbiased bits, and use\nProposition~\\ref{prop:law-tabulation} to account for the exhaustive work.\n\n\\begin{theorem}[Exact sampling]\\label{thm:exact-sampling}\nThere is an algorithm using unbiased random bits that terminates almost\nsurely, outputs an exactly uniform element of $\\Omega$, and has expected\nbit running time polynomial in $d,b$.\n\\end{theorem}\n\n\\begin{proof}\nSet\n\\[\n D=d^6b^2,\\qquad k=2D,\\qquad\\delta=2^{-D},\\qquad M=|\\Omega|.\n\\]\nTheorem~\\ref{thm:almost-uniform} gives, for every $z\\in\\Omega$,\n$p_k(z)\\le1/M+\\delta^2$. Also $M\\le(C+1)^d\\le2^{db}$ and $D\\ge db$,\nso $M\\delta\\le1$. Therefore\n\\[\n (1-\\delta)p_k(z)\n \\le\\frac{1-\\delta}{M}+(1-\\delta)\\delta^2\n \\le\\frac1M.\n\\]\nIt follows that\n$r(z)=[1/M-(1-\\delta)p_k(z)]/\\delta$ is a probability law.\n\nUse $D$ independent bits to enter a correction branch precisely when\nall are zero. On the complementary event run the bounded sampler with\naccuracy $k$. On the correction branch, enumerate $\\Omega$ to obtain\n$M$ and use Proposition~\\ref{prop:law-tabulation} to compute\n$p_k(z)=a_z/2^R$. Form the integer weights\n\\begin{equation}\\label{eq:residual-integers}\n n_z=2^{R+D}-M(2^D-1)a_z,\n \\qquad \\sum_{z\\in\\Omega}n_z=M2^R.\n\\end{equation}\nThey are nonnegative by the pointwise domination just proved, and the\nsum follows from $\\sum_z a_z=2^R$. Thus $r(z)=n_z/(M2^R)$.\nAll integers in \\eqref{eq:residual-integers} have\n$O(R+D+\\log M)$ bits; the fact that $M$ can be large as a number does not\nmake its binary representation long.\n\nTo sample this law exactly, put $V=M2^R$ and draw a uniform integer in\n$\\{0,\\ldots,V-1\\}$ by using $\\lceil\\log_2V\\rceil$ bits and repeating\nwhenever their value is at least $V$. If $V=1$, use zero bits.\nThe acceptance probability is greater than $1/2$, so the expected number\nof attempts is less than two, and termination is almost sure. A scan of\ncumulative weights $n_z$ then selects the output exactly according to\n$r$. Use fresh bits on each attempt and after the branch decision.\n\nThe output law is\n$(1-\\delta)p_k+\\delta r=\\operatorname{Uniform}(\\Omega)$.\nThe correction branch has conditional expected cost at most\n$2^{500db}\\operatorname{poly}(d,b,k)$, including enumeration, integer\narithmetic, and the exact integer draw. Its contribution to the\nunconditional expectation is polynomial, since\n\\[\n \\delta\\,2^{500db}\n =2^{-db(d^5b-500)}\\le1\n \\qquad(d\\ge14,\\ b\\ge d).\n\\]\nSubstituting $k=2d^6b^2$ into a fixed polynomial preserves polynomial\ndependence on $d,b$. The other branch and the branch decision also have\npolynomial cost. This proves the expectation and termination claims.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\nIf either dimension is empty, if $N=0$, or if one dimension is one,\nreturn the unique feasible table as in Section~\\ref{sec:graph}.\nOtherwise the parameters in \\eqref{eq:parameters} satisfy\n$d=O(mn+m+n+1)$ and $b=O(d+\\log(N+1)+\\log d)$.\nFor the supplied integer $k\\ge1$, Theorem~\\ref{thm:almost-uniform}\ngives the first assertion. Theorem~\\ref{thm:exact-sampling} gives the\nsecond. Their bounds therefore have the stated dependence on dimensions\nand binary margin size. All outputs are feasible, and the exact\nalgorithm's only unbounded loop is the almost-surely terminating integer\nrejection on its correction branch.\n\\end{proof}\n"}, {"path": "preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/sections/dense.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/sections/dense.tex", "bytes": 26193, "sha256": "2e53b2bb7f67244ef0d74dcd11516e547230c857495e3865f87c044a4695bee5", "content": "\\section{Sampling a dense completion block}\\label{sec:dense}\n\nThe completion distributions required by the small-state chain have large\nminimum margins, but their margins need not have comparable sizes.  We now\ngive the sampling subroutine, using the global parameters in\nEquation~\\eqref{eq:parameters}.  All random choices in its implementation\nuse finitely many independent unbiased bits.\n\n\\begin{theorem}[Dense completion draw]\\label{thm:dense-draw}\nLet a rectangular block have at most $m$ rows and $n$ columns, nonnegative\ninteger margins of equal total less than $C$, and every margin at least\n$L=d^{12}$.  For each integer $h\\ge1$, there is an algorithm that always\noutputs a feasible integer table and whose law is within $2^{-h}$ in\ntotal variation of the uniform law on the block's feasible tables.\nIts worst-case bit running time is polynomial in $d,b,h$.  Empty blocks,\nand blocks having a single row or a single column, have deterministic\ncompletion algorithms with the same guarantee.\n\\end{theorem}\n\nThe proof first groups integer tables into scaled bins and gives a\nconstant lower bound on rejection-sampling acceptance. A log-concave\ncontinuous density controls the bin normalizer and the conductance of the\nfinite bin chain. We then implement its transitions and the within-bin offsets\nwith bounded numbers of random bits.\nThe scaled-coordinate and softened-density construction follows the\ngeometric approach of Dyer, Kannan, and Mount~\\cite[Sections~4--5]{DKM1997};\nexpanded-polytope rounding is also central to Morris~\\cite[Sections~1.3--2]{Morris2002}.\nThe estimates and finite-bit sampler needed here are proved below.\n\n\\subsection{Scaled coordinates and rejection from bins}\n\nThe deterministic cases require no argument beyond the prescribed\nmargins, so suppose the block has $a,c\\ge2$ rows and columns.  Write its\nmargins as $R_i,P_j$.  Reorder the rows and columns so that $R_a$ and $P_c$\nare maximum, breaking ties by their original indices, and restore the\noriginal order when returning a table.  The entries with $i<a,j<c$\ndetermine the entire table.  We group their possible integer values into\nintervals of lengths\n\\begin{equation}\\label{eq:dense-scales}\n \\begin{gathered}\n s_{ij}=\\min(R_i,P_j)\\quad(1\\le i\\le a,\\ 1\\le j\\le c),\\\\\n B=d^8,\\quad K_0=4B,\\quad A=d^4,\\\\\n w_{ij}=\\left\\lfloor\\frac{s_{ij}}B\\right\\rfloor\n \\quad(i<a,\\ j<c).\n \\end{gathered}\n\\end{equation}\nThere are $e=(a-1)(c-1)\\le d$ free entries.  Since $s_{ij}\\ge L\\ge2B$,\n\\begin{equation}\\label{eq:width-bounds}\n \\frac{s_{ij}}{2B}\\le w_{ij}\\le\\frac{s_{ij}}B.\n\\end{equation}\nEach nonnegative integer free entry has a unique representation\n$X_{ij}=w_{ij}v_{ij}+\\beta_{ij}$ with integer $v_{ij}\\ge0$ and\n$0\\le\\beta_{ij}<w_{ij}$.  For a feasible table, $X_{ij}\\le s_{ij}$,\nso $v_{ij}\\le2B<K_0$.  Thus all feasible tables are represented by bins\nindexed by\n\\[\n \\mathcal V=\\{0,\\ldots,K_0-1\\}^e\n\\]\nand an offset in each free coordinate.  The number of bins depends only\non the dimensions, although an offset range can depend on the margins.\nWe will give weight one to every bin containing a feasible table and\nchoose the other weights by penalizing violations of nonnegativity.\nAn ideal proposal then samples a weighted bin and uniform offsets,\nrejecting an infeasible table.\n\nTo define these weights, pass temporarily to scaled real coordinates.\nFor $u\\in\\R^e$, define the affine table $X(u)$ by\n\\begin{align*}\n X_{ij}(u)&=w_{ij}u_{ij} &&(i<a,\\ j<c),\\\\\n X_{ic}(u)&=R_i-\\sum_{j<c}w_{ij}u_{ij} &&(i<a),\\\\\n X_{aj}(u)&=P_j-\\sum_{i<a}w_{ij}u_{ij} &&(j<c),\\\\\n X_{ac}(u)&=R_a-\\sum_{j<c}P_j+\n                    \\sum_{i<a,\\,j<c}w_{ij}u_{ij}.\n\\end{align*}\nThese formulas impose all the margins.  Let $T_{ij}=X_{ij}/s_{ij}$,\nincluding the dependent entries, and set\n\\[\n \\mathcal P=\\{u\\in\\R^e:T_{ij}(u)\\ge0\\text{ for every }i,j\\}.\n\\]\nEvery free scale contributing to a dependent entry is at most that\nentry's scale.  For example, $s_{ij}\\le s_{ic}$ follows from\n$P_j\\le P_c$, $s_{ij}\\le s_{aj}$ from $R_i\\le R_a$, and\n$s_{ij}\\le s_{ac}$ follows from both inequalities.  Thus every coefficient\nin each normalized affine entry has absolute value at most $1/B$, and\nthere are at most $d$ of them.  Consequently\n\\begin{equation}\\label{eq:dense-lipschitz}\n |T_{ij}(u)-T_{ij}(v)|\\le\\frac dB\\norm{u-v}_\\infty.\n\\end{equation}\nFeasibility and Equation~\\eqref{eq:width-bounds} imply\n$\\mathcal P\\subset[0,2B]^e$.  The integer representation above belongs\nto the closed unit bin $Q_v=v+[0,1]^e$, at the point with coordinates\n$u_{ij}=v_{ij}+\\beta_{ij}/w_{ij}$.\n\nThe feasible polytope has a point $u_*$ with uniform positive slack:\n\\begin{equation}\\label{eq:dense-interior}\n T_{ij}(u_*)\\ge\\frac1{2d}.\n\\end{equation}\nTo construct it, first assign $s_{ij}/(2d)$ to every cell.  This uses at\nmost $cR_i/(2d)\\le R_i/2$ in row $i$ and at most\n$aP_j/(2d)\\le P_j/2$ in column $j$.  The remaining nonnegative real\nmargins have equal totals and can be completed by the greedy\nsupply--demand construction.  The resulting table determines $u_*$.\nThe strict slack and Equation~\\eqref{eq:dense-lipschitz} also show that\n$\\mathcal P$ has positive $e$-dimensional volume.\n\nOn the cube $[0,K_0]^e$ define\n\\begin{equation}\\label{eq:dense-penalty}\n p(u)=\\max\\bigl(0,\\max_{ij}(-T_{ij}(u)-d/B)\\bigr),\n \\qquad \\rho(u)=2^{-Ap(u)}.\n\\end{equation}\nThe allowance $d/B$ makes $p$ vanish at the lower corner of every bin\nmeeting $\\mathcal P$, by Equation~\\eqref{eq:dense-lipschitz}.\nThe function $p$ is convex and $(d/B)$-Lipschitz in the sup norm;\ntherefore $\\rho$ is positive, continuous, and log-concave.  Comparison\nwith $u_*\\in[0,2B]^e$ gives $0\\le p(u)\\le4d$ on the cube.\nAssign the dyadic bin weights\n\\begin{equation}\\label{eq:dense-bin-weights}\n E_v=\\lfloor Ap(v)\\rfloor,\\qquad W(v)=2^{-E_v},\n \\qquad Z_B=\\sum_{v\\in\\mathcal V}W(v).\n\\end{equation}\nIn particular $W(v)=1$ whenever $Q_v$ contains a feasible point, even\non its boundary.  For $u\\in Q_v$,\n\\[\n \\bigl|Ap(u)-\\lfloor Ap(v)\\rfloor\\bigr|\n \\le 1+Ad/B=1+d^{-3}<2,\n\\]\nso\n\\begin{equation}\\label{eq:bin-density-comparison}\n \\tfrac14W(v)\\le\\rho(u)\\le4W(v).\n\\end{equation}\nThis comparison lets integrals of $\\rho$ control sums of bin weights;\noverlapping bin faces have zero volume in these integrals.\n\nAn \\emph{ideal trial} draws $v$ with probability $W(v)/Z_B$, then draws\nindependent uniform offsets $\\beta_{ij}\\in\\{0,\\ldots,w_{ij}-1\\}$ and sets\n\\begin{equation}\\label{eq:dense-offset-output}\n X_{ij}=w_{ij}v_{ij}+\\beta_{ij}\\qquad(i<a,\\ j<c).\n\\end{equation}\nIt fills the remaining entries to give the prescribed margins and\naccepts exactly when all entries are nonnegative.\n\n\\begin{proposition}[Bin rejection]\\label{prop:bin-acceptance}\nAn ideal trial succeeds with probability at least $1/64$.  Conditional on\nsuccess, its output is exactly uniform on the feasible integer tables.\n\\end{proposition}\n\\begin{proof}\nThe unique bin-and-offset representation of a feasible integer table\nhas a bin of weight one.  The table's probability before rejection is\ntherefore\n\\begin{equation}\\label{eq:dense-equal-table-mass}\n \\frac1{Z_B}\\prod_{i<a,\\,j<c}\\frac1{w_{ij}},\n\\end{equation}\nindependent of the table.  The dependent entries are integers because\nthey are integer sums and differences.  This proves conditional\nuniformity, including tables on faces and widths of different sizes.\n\nFor the success bound, let $n_{\\rm full}$ be the number of bins whose\nentire closure is contained in $\\mathcal P$.  Each has weight one and\naccepts every offset, so its contribution to the success probability is\n$1/Z_B$.  We compare $n_{\\rm full}$ and $Z_B$ with $\\vol(\\mathcal P)$.\nSet\n$\\mathcal P^- =\\{u:T_{ij}(u)\\ge2d/B\\text{ for every }i,j\\}$.\nThe affine map\n$u\\mapsto(1-\\alpha)u+\\alpha u_*$ with $\\alpha=4d^2/B$\nsends $\\mathcal P$ into $\\mathcal P^-$ by\nEquation~\\eqref{eq:dense-interior}.  Hence Bernoulli's inequality gives\n\\begin{equation}\\label{eq:dense-inner-volume}\n \\vol(\\mathcal P^-)\\ge(1-4d^2/B)^e\\vol(\\mathcal P)\n \\ge(1-4d^3/B)\\vol(\\mathcal P)\n \\ge\\tfrac12\\vol(\\mathcal P).\n\\end{equation}\nExcept on grid boundaries, every point of $\\mathcal P^-$ lies in a bin\nwhose entire closure is in $\\mathcal P$: the change of any normalized\nentry across that bin is at most $d/B$.  These bins cover $\\mathcal P^-$\nup to a null set and each has volume one, so\n$n_{\\rm full}\\ge\\vol(\\mathcal P^-)\\ge\\vol(\\mathcal P)/2$.\n\nTo bound $Z_B$, for any $\\gamma\\ge0$ consider the entire relaxed\npolytope $\\mathcal P_\\gamma=\\{u:T_{ij}(u)\\ge-\\gamma\\text{ for all }i,j\\}$\nin $\\R^e$, without a cube restriction.  For $u\\in\\mathcal P_\\gamma$ the\npoint\n\\[\n z=\\frac{u+2d\\gamma u_*}{1+2d\\gamma}\n\\]\nbelongs to $\\mathcal P$, since the affine coefficients sum to one and\neach $T_{ij}(z)\\ge0$.  Thus\n$\\mathcal P_\\gamma\\subset\nu_*+(1+2d\\gamma)(\\mathcal P-u_*)$, and\n\\begin{equation}\\label{eq:dense-relaxed-volume}\n \\vol(\\mathcal P_\\gamma)\n \\le(1+2d\\gamma)^e\\vol(\\mathcal P)\n \\le\\exp(2d^2\\gamma)\\vol(\\mathcal P).\n\\end{equation}\nThis argument also covers points with negative free coordinates.\nThe sublevel set $\\{p\\le(j+1)/A\\}$ inside the sampling cube is contained\nin $\\mathcal P_{d/B+(j+1)/A}$.  Summing the upper bounds for the penalty\nlayers and using Equation~\\eqref{eq:bin-density-comparison} gives\n\\begin{align*}\n Z_B&\\le4\\int_{[0,K_0]^e}\\rho(u)\\,du\\\\\n &\\le4\\sum_{j\\ge0}2^{-j}\n       \\exp\\bigl(2d^2(d/B+(j+1)/A)\\bigr)\\vol(\\mathcal P)\n \\le32\\vol(\\mathcal P).\n\\end{align*}\nFor the last numerical bound put $a_0=2/d^2$ and $b_0=2/d^5$.\nSince $a_0+b_0\\le1/4$, both $\\exp(a_0)$ and\n$\\exp(a_0+b_0)$ are at most $4/3$.  The geometric sum is at most\n$4(4/3)/(1-(4/3)/2)=16$, which is stronger than asserted.\nConsequently\n\\[\n \\Pp\\{\\text{success}\\}\\ge\\frac{n_{\\rm full}}{Z_B}\n \\ge\\frac{\\vol(\\mathcal P)/2}{32\\vol(\\mathcal P)}=\\frac1{64}.\n\\]\n\\end{proof}\n\n\\subsection{An integral interpolation inequality}\n\nWe supply the continuous inequality used to mix the bins.  It is an\nanalytic proof device; the algorithm never integrates or samples a real\ndensity. This finite-box statement is a special case of the\nPr\\'ekopa--Leindler inequality~\\cite{Leindler1972,Prekopa1973}. We include\nthe one-dimensional transport argument and induction on slices to specify\nthe regularity needed here.\n\n\\begin{lemma}[Finite-box integral interpolation]\\label{lem:integral-interpolation}\nLet $S_1,S_2,S_0$ be bounded finite unions of open axis-aligned boxes in\n$\\R^q$, and let $0<t<1$, with\n$(1-t)S_1+tS_2\\subset S_0$.  Let $f_1,f_2,f_0$ be positive continuous\nfunctions on a compact box containing the closures of these sets.\nIf\n\\[\n f_0((1-t)x+ty)\\ge f_1(x)^{1-t}f_2(y)^t\n \\quad(x\\in S_1,\\ y\\in S_2),\n\\]\nthen\n\\begin{equation}\\label{eq:integral-interpolation}\n \\int_{S_0}f_0\\ge\n       \\left(\\int_{S_1}f_1\\right)^{1-t}\n       \\left(\\int_{S_2}f_2\\right)^t.\n\\end{equation}\nIn particular, for finite unions $S,D_0$ of open boxes in the sampling\ncube,\n\\begin{equation}\\label{eq:dense-logconcave-interpolation}\n \\int_{(1-t)S+tD_0}\\rho\\ge\n       \\left(\\int_S\\rho\\right)^{1-t}\n       \\left(\\int_{D_0}\\rho\\right)^t.\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nWe first prove the one-dimensional integral assertion in the slightly\nlarger class needed for slices.  Suppose $F,G,H$ are nonnegative,\nsupported on bounded finite unions of intervals, and, after removal of\nfinitely many endpoints, are continuous and either zero or bounded\nabove and below by positive constants on each interval piece.  Assume\n$H((1-t)x+ty)\\ge F(x)^{1-t}G(y)^t$ whenever $F(x)G(y)>0$, apart from\npossible source endpoints.  Source integrals zero give a trivial\ninequality; otherwise write $M_F=\\int F>0$, $M_G=\\int G>0$.\n\nLet $u(a),v(a)$, $0<a<1$, be the quantiles of $F/M_F$ and $G/M_G$.\nSplit $(0,1)$ at the cumulative probabilities of the finitely many\nsupport endpoints and continuity breakpoints of both densities.  On\neach remaining open interval $I$, the quantiles are continuously\ndifferentiable, strictly increasing, and satisfy\n\\[\n u'(a)=\\frac{M_F}{F(u(a))},\\qquad\n v'(a)=\\frac{M_G}{G(v(a))}.\n\\]\nThese statements follow directly by inverting the continuously\ndifferentiable cumulative function on a positive interval piece, whose\nderivative is positive.  The function\n$z(a)=(1-t)u(a)+tv(a)$ is also strictly increasing and continuously\ndifferentiable on $I$.  Its images $z(I)$ for distinct pieces are\ndisjoint.  Change of variables on each piece, followed by the weighted\narithmetic--geometric mean inequality, yields\n\\begin{align*}\n \\int H\n &\\ge\\sum_I\\int_I H(z(a))z'(a)\\,da\\\\\n &\\ge\\sum_I\\int_I\n F(u(a))^{1-t}G(v(a))^t\n \\left(\\frac{M_F}{F(u(a))}\\right)^{1-t}\n \\left(\\frac{M_G}{G(v(a))}\\right)^t da\\\\\n &=M_F^{1-t}M_G^t.\n\\end{align*}\nThe pieces have total parameter length one.  A gap in a source support\ncauses a quantile jump at one of the excluded parameter values; the\ncorresponding gaps between image intervals merely omit nonnegative\ncontributions to $\\int H$.  Thus neither disconnected supports nor\ndensity jumps require continuity across a gap.\n\nThe assertion for dimension $q=1$ follows by restricting the ambient\ncontinuous weights to their sets.  Induct on $q$.  For each first\ncoordinate $x$, let $S_i(x)$ be the slice in the remaining $q-1$\ncoordinates and put\n\\[\n F_i(x)=\\int_{S_i(x)}f_i(x,x')\\,dx'.\n\\]\nWhenever $S_1(x)$ and $S_2(y)$ are nonempty, their interpolation is\ncontained in $S_0((1-t)x+ty)$.  The induction hypothesis for these slices\ntherefore gives\n\\[\n F_0((1-t)x+ty)\\ge F_1(x)^{1-t}F_2(y)^t.\n\\]\nTo justify the one-dimensional step for the marginals, partition the\nfirst-coordinate axis by all endpoints of all the boxes.  On each open\npiece, a slice domain is a fixed union of boxes or is empty.  In the\nnonempty case it has fixed positive volume.  Each ambient weight has a\npositive minimum and finite maximum on the compact box, so its marginal\nis bounded above and below by positive constants there.  Uniform\ncontinuity of the weight on the compact box proves continuity of the\nmarginal on that piece by integration over this fixed domain.  These are\nexactly the hypotheses of the one-dimensional argument just proved.\nApplying that argument and then iterated integration proves\nEquation~\\eqref{eq:integral-interpolation}.\n\nFinally, the interpolation of two finite unions of open axis-aligned\nboxes is the finite union of all pairwise interpolations of their boxes,\neach again such a box.  Convexity of $p$ gives the required pointwise\ninequality for $f_0=f_1=f_2=\\rho$, proving\nEquation~\\eqref{eq:dense-logconcave-interpolation}.\n\\end{proof}\n\n\\subsection{Conductance of the bin chain}\n\nThe following finite-chain estimate converts a cut bound into a mixing\nbound. We will use it for the bin chain here and for the small-state\nchain in Section~\\ref{sec:algorithms}.\n\n\\begin{lemma}[Conductance and mixing]\\label{lem:mixing}\nLet $P$ be a reversible kernel on a nonempty finite set $V$, with\nstationary probabilities $\\pi(x)>0$ and $P(x,x)\\ge1/2$.\nSet $Q(x,y)=\\pi(x)P(x,y)$ and\n\\[\n \\mathcal E_P(H)=\\sum_{\\{x,y\\}\\subseteq V}\n Q(x,y)(H(x)-H(y))^2,\n\\]\nwhere unordered pairs have distinct endpoints.  If\n$\\Var_\\pi H\\le K\\mathcal E_P(H)$ for all $H$, with $K\\ge1$, then,\nwriting $\\pi_{\\min}=\\min_{x\\in V}\\pi(x)$, for every integer $t\\ge0$,\n\\begin{equation}\\label{eq:elementary-mixing}\n \\norm{P^t(x,\\cdot)-\\pi}_{\\TV}\n \\le \\pi_{\\min}^{-1}e^{-t/K}.\n\\end{equation}\nIf, for some $\\kappa>0$, every cut satisfies\n\\[\n Q(A,A^c):=\\sum_{x\\in A,\\,y\\notin A}Q(x,y)\n \\ge\\kappa\\min\\{\\pi(A),\\pi(A^c)\\},\n\\]\nthen the variance inequality holds with $K=2/\\kappa^2$.\n\\end{lemma}\n\\begin{proof}\nFor nonnegative $g$ with $\\pi(g>0)\\le1/2$, integrate the cut inequality\nover $A_u=\\{x:g(x)^2>u\\}$ to obtain\n\\[\n \\kappa\\norm{g}_{L^2(\\pi)}^2\n \\le\\sum_{\\{x,y\\}}Q(x,y)|g(x)^2-g(y)^2|\n \\le\\bigl(2\\mathcal E_P(g)\\norm{g}_{L^2(\\pi)}^2\\bigr)^{1/2}.\n\\]\nThe last step is Cauchy--Schwarz and\n\\[\n \\sum_{\\{x,y\\}}Q(x,y)(g(x)+g(y))^2\n \\le2\\sum_x\\pi(x)(1-P(x,x))g(x)^2\n \\le2\\norm{g}_{L^2(\\pi)}^2.\n\\]\nConsequently $\\norm{g}_{L^2(\\pi)}^2\\le2\\kappa^{-2}\\mathcal E_P(g)$,\nalso when $g=0$.  Choose a $\\pi$-median $a$ of $H$ and apply this to\n$(H-a)_+$ and $(a-H)_+$.  Their squared norms sum to\n$\\norm{H-a}_{L^2(\\pi)}^2$, and their energies sum to at most\n$\\mathcal E_P(H)$, by the corresponding inequality on each edge.\nSince the variance is the minimum squared deviation from a constant,\nthis proves the cut assertion.\n\nFor mixing, reversibility makes $P$ self-adjoint on $L^2(\\pi)$ and gives\n$\\mathcal E_P(H)=\\langle H,(I-P)H\\rangle_\\pi$.\nThe stochastic kernel $2P-I$ is an $L^2(\\pi)$ contraction by Jensen's\ninequality and stationarity.  Hence the eigenvalues of $P$ are in $[0,1]$.\nThe variance inequality bounds those on the mean-zero subspace by\n$1-1/K\\le e^{-1/K}$.  For\n$g_x(y)=\\one_{\\{y=x\\}}/\\pi(x)-1$, reversibility gives\n\\[\n (P^tg_x)(y)=\\frac{P^t(x,y)}{\\pi(y)}-1,\n \\qquad \\norm{g_x}_{L^2(\\pi)}^2=\\pi(x)^{-1}-1.\n\\]\nThe $L^2$ contraction followed by Cauchy--Schwarz bounds the total\nvariation distance by\n\\[\n \\tfrac12\\sqrt{\\pi(x)^{-1}-1}\\,e^{-t/K},\n\\]\nwhich implies \\eqref{eq:elementary-mixing}.\nOn a singleton all asserted variances and\ndistances vanish; it may simply be regarded as already mixed.\n\\end{proof}\n\nUse the axis-neighbor Metropolis chain on $\\mathcal V$.  Each of the\n$2e$ signed coordinate directions is proposed with probability\n\\[\n q_B=2^{-q_{\\rm bin}},\\qquad\n q_{\\rm bin}=\\lceil\\log_2(8d)\\rceil;\n\\]\nthe unused probability, or a proposal leaving the cube, holds.  A\nproposal $v'$ within the cube is accepted with probability\n$\\min(1,W(v')/W(v))$.  This chain is reversible for\n$\\pi_B(v)=W(v)/Z_B$ and holds with probability at least $3/4$.\n\n\\Needspace{8\\baselineskip}\n\\begin{proposition}[Bin conductance and mixing]\\label{prop:bin-gap}\nFor every nontrivial cut of the bin states, its stationary edge\nconductance divided by the smaller stationary side mass is at least\n\\[\n \\kappa_B=\\frac1{10^5d^2K_0}.\n\\]\nStarting at any bin, after\n$H=d^{50}(h+1)^2$ steps the total-variation distance from $\\pi_B$ is at\nmost $2^{-4(h+d)}$.\n\\end{proposition}\n\\begin{proof}\nWrite $\\mu(E)=\\int_E\\rho$ and $D=(0,K_0)^e$.  For a cut\n$\\mathcal V=V_1\\sqcup V_2$, choose $S$ to be the union of the open bins\non the side of smaller \\emph{continuous} mass.  Then\n$0<\\mu(S)\\le\\mu(D)/2$.  Set\n\\[\n t=\\frac1{4dK_0},\\qquad r=tK_0=\\frac1{4d},\\qquad\n M=(1-t)S+tD.\n\\]\nWe have $S\\subset M$, by writing $x=(1-t)x+tx$ for $x\\in S$.\nLemma~\\ref{lem:integral-interpolation} gives\n\\begin{equation}\\label{eq:dense-mass-gain}\n \\mu(M\\setminus S)\\ge(2^t-1)\\mu(S)\n                    \\ge t\\log(2)\\mu(S).\n\\end{equation}\nThis is actual gained mass even though $S$ need not be convex.\n\nWe next cover this gain by neighborhoods of cut faces.  Ignore the\nfinite union of grid hyperplanes, a null set.  If $z\\in M\\setminus S$,\nwrite $z=(1-t)x+ty$ with $x\\in S$, $y\\in D$.  The segment from $x$ to\n$z$ stays inside the open convex cube and has sup-norm length less than\n$r$.  Its initial bin lies on the selected side and its terminal bin\nlies on the other side.  Follow the segment through its finitely many\ngrid crossings.  At a crossing in several coordinates simultaneously,\nthe incident bins are indexed by the two choices in each crossing\ncoordinate, and are connected by axis-neighbor faces through that same\npoint.  If the incoming and outgoing bins have different labels, a face\npath between them contains an opposite-label pair.  Its common closed\nface contains the crossing point.  Since some crossing changes the\nlabel, $z$ is at sup distance less than $r$ from a cut face.  No exterior\nface is needed: the entire segment lies in $D$.\n\nFor such a closed unit face $F$, take its sup-distance-$r$ neighborhood\nintersected with $D$, denoted $F^{(r)}$.  Its volume is at most\n\\[\n 2r(1+2r)^{e-1}\n \\le\\frac1{2d}\\exp\\left(\\frac{e-1}{2d}\\right)\\le1.\n\\]\nIf $v$ is the lower corner of either bin adjacent to $F$, every point\n$z\\in F^{(r)}$ satisfies $\\norm{z-v}_\\infty\\le1+r$.  Hence\n\\[\n \\rho(z)\\le2^{(Ad/B)(1+r)}\\rho(v)\\le4W(v),\n\\]\nand the same inequality holds for the other adjacent corner $v'$.\nThus $\\mu(F^{(r)})\\le4\\min(W(v),W(v'))$.  Combining the face cover with\nEquation~\\eqref{eq:dense-mass-gain}, and writing\n$D_i=\\sum_{v\\in V_i}W(v)$, gives\n\\begin{align*}\n \\sum_{\\substack{vv'\\text{ axis-neighbors}\\\\v\\in V_1,\\ v'\\in V_2}}\n             \\min(W(v),W(v'))\n &\\ge\\frac{t\\log(2)}4\\mu(S)\\\\\n &\\ge\\frac{t\\log(2)}{16}\\min(D_1,D_2).\n\\end{align*}\nThe second inequality follows from\nEquation~\\eqref{eq:bin-density-comparison}: the selected continuous side\nhas mass at least one quarter of its own discrete weight, which is at\nleast $\\min(D_1,D_2)$.  It does not require the two notions of smaller\nside to agree.  Stationary edge conductance is\n$q_B\\min(W(v),W(v'))/Z_B$.  Since $q_B\\ge1/(16d)$, the cut ratio is at\nleast $\\log(2)/(1024d^2K_0)\\ge\\kappa_B$.\n\nThe weights satisfy $2^{-4Ad}\\le W(v)\\le1$, so\n\\[\n \\pi_{B,\\min}\\ge\\frac{2^{-4Ad}}{K_0^e},\\qquad\n \\log\\pi_{B,\\min}^{-1}\\le4d^5\\log2+d\\log(4d^8)\\le4d^5.\n\\]\nLemma~\\ref{lem:mixing} supplies inverse gap at most\n\\[\n K_B=2\\kappa_B^{-2}=3.2\\cdot10^{11}d^{20}\\le d^{32}\n \\quad(d\\ge14).\n\\]\nIt bounds the error at time $H$ by\n$\\exp(4d^5-H/K_B)$.  Since $d^{18}\\ge4d^5$ and\n$d^5h(h+2)\\ge(d+1)h\\ge h+d$, we have\n\\[\n \\frac H{K_B}-4d^5\\ge d^{18}(h+1)^2-4d^5\n \\ge4d^5h(h+2)\\ge4(h+d)\\log2.\n\\]\nThis proves the asserted error.\n\\end{proof}\n\n\\subsection{A bounded random-bit implementation}\n\nWe finish the proof of Theorem~\\ref{thm:dense-draw}, keeping explicit\nbounds that will also describe the finite output law in\nSection~\\ref{sec:algorithms}.  Use the integer schedule\n\\begin{equation}\\label{eq:dense-schedule}\n J=d^4(h+1),\\qquad H=d^{50}(h+1)^2.\n\\end{equation}\nRun at most $J$ independent trials, each running $H$ bin-chain steps from $0$.\nAt the end of each bin walk use Equation~\\eqref{eq:dense-offset-output},\nbut obtain each offset from the bounded draw\n\\begin{equation}\\label{eq:dense-offset-bits}\n \\begin{gathered}\n k_{ij}=\\lceil\\log_2(w_{ij}+1)\\rceil+4(h+d),\\quad\n Z_{ij}\\text{ uniform on }\\{0,\\ldots,2^{k_{ij}}-1\\},\\\\\n \\beta_{ij}=\\left\\lfloor\\frac{w_{ij}Z_{ij}}{2^{k_{ij}}}\\right\\rfloor.\n \\end{gathered}\n\\end{equation}\nReturn the first feasible table.  If all $J$ trials fail, return a fixed\ngreedy feasible table with the required margins.\n\nEach offset probability differs from $1/w_{ij}$ by at most $2^{-k_{ij}}$:\nits number of preimages is either the floor or the ceiling of\n$2^{k_{ij}}/w_{ij}$.  Thus its total-variation error is at most\n$w_{ij}/2^{k_{ij}}\\le2^{-4(h+d)}$.  Coupling the independent offsets\ncoordinate by coordinate bounds their joint error by\n$d2^{-4(h+d)}$.  Proposition~\\ref{prop:bin-gap} bounds the bin error by\n$2^{-4(h+d)}$, so a complete implemented trial, including its success\nflag, differs from an ideal trial by at most\n$(1+d)2^{-4(h+d)}$.\n\nCouple the $J$ independent trial records until the first discrepancy.\nThis bounds the difference between the implemented first-success\nprocedure and its ideal counterpart by\n$J(1+d)2^{-4(h+d)}$, without conditioning on an approximate success event.\nBy Proposition~\\ref{prop:bin-acceptance}, the ideal procedure has uniform\nlaw on success and failure probability at most $(63/64)^J$.  Its greedy\nfallback therefore changes the uniform law by at most this probability.\nThe total error is at most\n\\begin{equation}\\label{eq:dense-total-error}\n (63/64)^J+J(1+d)2^{-4(h+d)}\\le2^{-h}.\n\\end{equation}\nFor explicit numerical bounds, $d^4\\ge128$ gives\n$(63/64)^J\\le\\exp(-J/64)\\le2^{-2(h+1)}\\le2^{-h-2}$.\nAlso $d\\le2^{d/2}$, $d+1\\le2^d$, and $h+1\\le2^h$ give\n$J(1+d)2^{-4(h+d)}\\le2^{-3h-d}\\le2^{-h-2}$.\nAll actual outputs are feasible by the acceptance test or the fallback.\n\nIt remains to account for every computation in the bit model.\nAt an integer bin corner $v$, the entries $X_{ij}(v)$ are integer sums\nof products $w_{ij}v_{ij}$ and margins.  Since $w_{ij}<C/B$ and\n$v_{ij}<4B$, their absolute values are at most a fixed multiple of $dC$;\nthey have $O(b+\\log d)$ bits.  The candidates for the maximum defining\n$p(v)$ are the rational numbers\n\\[\n 0\\quad\\text{and}\\quad\n \\frac{-BX_{ij}(v)-ds_{ij}}{Bs_{ij}}.\n\\]\nCross multiplication, comparison, multiplication by $A$, and integer\ndivision compute $E_v=\\lfloor Ap(v)\\rfloor$ exactly with polynomial bit\ncost.  Denominators have $O(b+\\log d)$ bits and there are at most $d$\ncandidates.  In particular no evaluation of an exponential or integral\nis required.  We have $0\\le E_v\\le4Ad$, so a Metropolis acceptance is\neither certain or has probability $2^{-(E_{v'}-E_v)}$, implemented by\nrequiring that many fresh unbiased bits to be zero.  The difference has\nabsolute value at most $8Ad$, a convenient uniform allowance.  A draw\nof $q_{\\rm bin}$ bits proposes each signed coordinate direction by one\ncode, with the remaining codes holding; this implements the stated\nproposal exactly.\n\nThe offset draw in Equation~\\eqref{eq:dense-offset-bits} uses\n$O(b+h+d)$ bits and an integer multiplication and division on numbers\nof polynomial bit length.  Table construction, feasibility tests, the\ngreedy fallback, and the bounded loop counters also use polynomially\nmany operations on polynomial-length integers.  There are $JH$\ntransitions and at most $dJ$ offsets.  Schoolbook integer arithmetic\nalready yields a worst-case polynomial bound in $d,b,h$.\n\nFor later use, a uniform upper bound on the number of bits per transition\nand per offset is respectively\n\\begin{equation}\\label{eq:dense-denominator-bounds}\n \\begin{gathered}\n Q_0=q_{\\rm bin}+8Ad,\\qquad\n r_{\\max}=b+1+4(h+d),\\\\\n R_1=HQ_0+dr_{\\max},\\qquad R_D=JR_1.\n \\end{gathered}\n\\end{equation}\nUnused transition bits, missing free coordinates, and unused trials\nafter the first success can be padded with unread independent bits.\nThus a trial has probabilities with common denominator $2^{R_1}$, and\nthe whole subroutine has probabilities with common denominator\n$2^{R_D}$.  This padding changes no output law.  For an offset of width\n$w$ and actual bit length $k$, its exact probability of value $\\beta$ is\n\\begin{equation}\\label{eq:dense-offset-count}\n 2^{-k}\\left(\n \\left\\lceil\\frac{(\\beta+1)2^k}{w}\\right\\rceil-\n \\left\\lceil\\frac{\\beta2^k}{w}\\right\\rceil\\right),\n \\qquad 0\\le\\beta<w.\n\\end{equation}\nIt can therefore be tabulated by integer arithmetic without enumerating\nthe random bit strings.  The deterministic completion cases can use the\nsame denominator allowances.  This completes the proof of\nTheorem~\\ref{thm:dense-draw}.\n"}, {"path": "preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/sections/introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/sections/introduction.tex", "bytes": 9047, "sha256": "173a0eaf3df62e2e7833be0b13abbaa4db0650f082c77a3d1faddda9ed65f242", "content": "\\section{Introduction}\\label{sec:introduction}\n\nFor nonnegative integer vectors $r=(r_1,\\ldots,r_m)$ and\n$c=(c_1,\\ldots,c_n)$ with common total $N$, write\n\\[\n \\Omega(r,c)=\\left\\{X\\in\\Z_{\\ge0}^{m\\times n}:\n       \\sum_jX_{ij}=r_i,\\quad \\sum_iX_{ij}=c_j\\right\\}.\n\\]\nThe uniform distribution on this finite set gives every table equal mass.\nWe study sampling when both dimensions vary and the margins are encoded\nin binary. In particular, a polynomial bound in the numeric total $N$\nwould not, in general, give a polynomial bound in the input length.\nThere are no cell bounds or forbidden positions: every nonnegative integer\nmatrix with the prescribed margins is allowed. For probability laws on a\nfinite set, we use the convention\n$\\TV(\\mu,\\nu)=\\tfrac12\\sum_x|\\mu(x)-\\nu(x)|$.\n\n\\begin{theorem}\\label{thm:main}\nFor arbitrary nonnegative integer margins of equal total, the following\nalgorithms exist in the model of unbiased random bits and bit operations.\n\\begin{enumerate}[label=\\textup{(\\roman*)}]\n \\item Given an integer $k\\ge1$, a bounded-time algorithm outputs a table\n in $\\Omega(r,c)$ whose law is within $2^{-k}$ in total variation of the\n uniform law. Its running time is polynomial in\n $m,n,\\log(N+1),k$.\n \\item An algorithm outputs an exactly uniform table in $\\Omega(r,c)$,\n terminates almost surely, and has expected running time polynomial in\n $m,n,\\log(N+1)$.\n\\end{enumerate}\nThe polynomials are uniform over all margin vectors. No positivity,\nsparsity, balance, or fixed-dimension hypothesis is imposed.\n\\end{theorem}\n\nFor a rational tolerance $0<\\eps<1$ encoded by a binary numerator and\ndenominator, integer comparisons compute\n$k=\\lceil\\log_2(1/\\eps)\\rceil$ in time polynomial in that encoding length.\nThereafter part~\\textup{(i)} has accuracy dependence polynomial in\n$\\log(1/\\eps)$.\nThe algorithms below are explicit, although their exponents are deliberately\nlarge. Part~\\textup{(ii)} concerns expected time: an exceptionally rare\nbranch performs exhaustive computation.\n\n\\paragraph{Counting and sampling fixed-margin arrays.}\nContingency tables are a basic family of integer arrays with linear\nconstraints. Their enumeration and exact and approximate generation are\ntreated, for example, by Diaconis and Gangolli~\\cite{DiaconisGangolli1995}.\nThe algorithmic question involves more than producing one feasible table:\nthe law must give all feasible tables nearly or exactly the same mass,\neven when their number and the coordinate ranges are exponentially large\nin the margin bit length.\n\nFor two rows, Dyer and Greenhill~\\cite{DyerGreenhill2000} give polynomial-time\napproximate counting and sampling. Their heat-bath chain has mixing time\nat most $n(n-1)\\log(N/\\eps)/2$ in the author version's Theorem~4.1.\nDependence on $\\log N$ rather than $N$ makes this a polynomial bound in\nthe binary input length.\nKijima and Matsui~\\cite[Theorems~3.1 and~5.1]{KM2003} give exact uniform\nsampling for two rows by monotone coupling from the past, after sorting\nthe columns. Their expected $O(n^3\\ln N)$ bound is stated for pseudocode\nusing uniform real draws. These results allow arbitrary two-row margin\nsizes after deleting zero margins.\n\nThe fixed-row case extends beyond two rows. Cryan and\nDyer~\\cite{CD2003} combine dynamic programming with volume estimation for\napproximate counting and sampling. Dyer~\\cite[Section~3]{Dyer2003} gives a\ndynamic-programming rejection sampler that is exactly uniform, analyzed\nin arithmetic operations. Cryan, Dyer, Goldberg,\nJerrum, and Martin~\\cite{CDGJM2006} prove rapid mixing of the\n$2\\times2$ heat-bath chain for every fixed number of rows.\nThe polynomial bounds in these results depend on that fixed row count;\nthey do not give a bound polynomial jointly in both dimensions.\n\nDeSalvo and Zhao~\\cite[Theorem~2.3 and Remarks~2.4--2.5]{DZ2016} give a\ngeneral exact divide-and-conquer construction using $O(mn\\log M)$ expected\nrandom bits, where $M$ is the largest margin. In the cited arXiv version,\ntheir polynomial total-runtime conclusion is conditional on efficient\nevaluation of the parity-restricted counting quantities in Conjecture~2.6;\nthe bit-time bounds in Theorem~\\ref{thm:main} are unconditional.\n\nAnother line of work uses the geometry of the transportation polytope\nwhen margins are large. Dyer, Kannan, and Mount~\\cite{DKM1997} develop\nscaled coordinates, softened constraints, lattice walks, and rounding\ncomparisons for dense tables. Morris~\\cite{Morris2002} improves the dense\nsampling bounds through expanded polytopes and rounding. These methods\nare close antecedents of the dense completion routine below. Our use of\nthat routine is conditional: a padding construction makes every\ncompletion problem dense, while a separate chain handles all cells\nincident to small margins.\n\nFor sparse margins, Arman, Gao, and Wormald~\\cite[Theorem~1 and\nRemark~2]{AGW2021} give exact uniform generation under $5\\Delta^4<N$,\nwhere $\\Delta$ is the largest margin, in expected $O(N)$ time with\nunit-cost arithmetic on integers of magnitude $O(N)$. The condition is\na genuine restriction, although within this family it also bounds $N$\npolynomially in the dimensions. Their 2021 introduction surveys fixed-row,\ndense, and sparse algorithms and reports that no polynomial-time\napproximately uniform sampler for arbitrary margins was then known.\nTheorem~\\ref{thm:main} removes those margin and dimension restrictions\nand includes all random bits and integer arithmetic in its cost.\n\n\\paragraph{Approximation and exact correction.}\nExactness and bounded running time are separate issues. Our approximate\nalgorithm has a deterministic polynomial work bound for each accuracy\nrequest. The final exact correction uses the residual-mixture construction\nof G\\\"obel, Liu, Manurangsi, and Pappik~\\cite[Section~3.1, Algorithm~1 and\nTheorem~6]{GLMP2024}. That construction combines a sufficiently accurate\nsampler with a rare exhaustive draw from the remaining probability mass.\nIts application here requires an additional property: the actual\nimplemented output law can be tabulated in time with an exponential\ninput-size factor but only polynomial dependence on the accuracy bits.\nWe prove that property and give the dyadic implementation explicitly.\nThus the exact algorithm has polynomial expected bit cost, not a\npolynomial bound on every execution.\n\n\\paragraph{Proof strategy.}\nOnly cells incident to a margin below a polynomial threshold are explicit\ncoordinates of the auxiliary chain. The remaining cells form a complete\nrectangle. We pad this rectangle so every conditional completion problem\nhas large margins. Each explicit cell has two bounded integer values,\none for its row and one for its column. Besides states where these values\nagree, called transversals, we allow a positive unit discrepancy at one\ncell and a negative unit discrepancy at another.\n\nThe target law on these small states assigns weight equal to the number\nof padded completions. Sampling a state from that law and then a uniform\ncompletion makes all state/completion pairs equally likely. Accept only\na transversal whose large completion entries are all at least the\npadding. Subtracting it then gives a uniform original table conditional\non acceptance.\nSection~\\ref{sec:graph} constructs a repair map for the defect states and\nuses it to bound their weight and the probability of successful unpadding.\nThe bounded sampler never evaluates completion counts: an ideal transition\nuses a uniform completion draw to test a proposed move.\n\nTo prove that this chain mixes in polynomial time, we expose the small-cell\nvalues of a transversal in a fixed order. After fixing a prefix, we compare\nthe averages of any real function on the state graph over the two fibers\nthat assign adjacent values to the next cell. The comparison comes from\nquadratic forms indexed by pairs of unit removals.\nSection~\\ref{sec:signatures} proves that these forms have at most\none positive eigenvalue and that the property survives summing out a\nbalanced pair. A weighted telescoping calculation then bounds the energy\nof a signed flow between neighboring conditional fibers.\nSection~\\ref{sec:transport} combines this bound with conditional-variance\ndecomposition and repair to obtain a Poincar\\'e inequality for the\ncompletion-count law. This supplies the small chain's mixing bound.\n\nSection~\\ref{sec:dense} implements the completion draws approximately on\na finite scaled grid, using rational arithmetic and dyadic random choices.\nA continuous log-concave density serves only as an analytic tool for the\nrejection and conductance bounds. Large coordinate ranges appear in\nfinite sums in the proof but are never enumerated in an ordinary run.\nSection~\\ref{sec:algorithms} couples each implemented completion draw to\nits ideal counterpart and bounds the number of sampling and unpadding\nattempts, giving the bounded almost-uniform algorithm. For exactness,\nit also shows how to tabulate that algorithm's actual finite law,\nincluding its stopping rules and fallbacks. This exhaustive computation\nis performed only on the rare correction branch.\n"}, {"path": "preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/sections/model-and-graph.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/sections/model-and-graph.tex", "bytes": 13055, "sha256": "417050fcb6ad3f4830ae339fbc7c0078d87f960fa438d0c8ce1dfd03d45afc4b", "content": "\\section{The binary model and the padded graph}\\label{sec:graph}\n\nThis section separates bounded small coordinates from a complete rectangle\nof large coordinates. Its repair map controls the mass of defect states;\npadding then gives a uniform lower bound on the chance of recovering an\noriginal table from a stationary state and its completion.\n\nWe count bit operations and use independent unbiased random bits.\nAll margins are nonnegative integers given in binary, with common total\n$N$.  Accuracy is specified by an integer $k\\ge1$, meaning error at most\n$2^{-k}$.\nAn empty dimension forces $N=0$ and gives a unique empty table.\nIf $N=0$, or if one dimension is one, the unique table can likewise be\nreturned directly.  In all remaining constructions the dimensions are\npositive, and we use\n\\begin{equation}\\label{eq:parameters}\n\\begin{gathered}\n d=10+(m+1)(n+1),\\qquad L=d^{12},\\qquad U=d^{20},\\\\\n C=N+dL+U+2,\\qquad b=d+\\lceil\\log_2(C+2)\\rceil.\n\\end{gathered}\n\\end{equation}\nHere $d$ is a dimension allowance, $L$ the padding, $U$ the small-margin\nthreshold, $C$ a coordinate cap, and $b$ a bit-size allowance.\nThus $d\\ge14$, $mn<d$, $U<C$, and\n$b=O(d+\\log(N+1))$.  Polynomial bounds in $d,b,k$ have the required\nbinary-input dependence.  A fixed feasible table is obtained greedily:\nchoose a row and column of positive remaining margins, place the minimum\nof those two margins in their cell, and subtract it from both margins.\nEach placement exhausts a row or column.  This uses at most $m+n-1$\npositive placements and polynomial bit work, including initialization of\nthe output.  We fix index order for this construction and all later ties.\n\n\\subsection{Small states and large completions}\n\nPut $I=\\{i:r_i\\ge U\\}$ and $J=\\{j:c_j\\ge U\\}$.\nThe \\emph{large slots} are the cells in the complete rectangle\n$\\mathcal B=I\\times J$; the remaining cells form the set $\\mathcal S$ of\n\\emph{small slots}.  Set $B_s=C$ on $\\mathcal B$ and $B_s=U$ on\n$\\mathcal S$.  Associate to each slot two coordinates\n$x_s,y_s\\in\\{0,\\ldots,B_s\\}$, and put $q_s=B_s-y_s$.\nHere $x$ is the row viewpoint and $q$ is the column viewpoint.\nA slot is \\emph{balanced} when $x_s=q_s$, equivalently\n$x_s+y_s=B_s$.\nThe balanced representation of a value $a\\in\\{0,\\ldots,B_s\\}$ is\n$(x_s,y_s)=(a,B_s-a)$; both $x_s$ and $q_s$ then equal $a$.\n\nA graph state retains only the coordinates on $\\mathcal S$.\nIts allowed pattern is either a \\emph{transversal}, with all slots\nbalanced, or a \\emph{defect}, with distinct slots $t,s$ satisfying\n\\[\n x_t=q_t+1,\\qquad x_s=q_s-1,\n \\qquad x_v=q_v\\quad(v\\notin\\{s,t\\}).\n\\]\nWe call $t$ positive and $s$ negative.  Both patterns have total doubled\noccupancy $\\sum_{s\\in\\mathcal S}(x_s+y_s)=U|\\mathcal S|$.\nDefine the original residual margins by\n\\[\n R_i^0(X)=r_i-\\sum_{j:(i,j)\\in\\mathcal S}x_{ij},\\qquad\n P_j^0(X)=c_j-\\sum_{i:(i,j)\\in\\mathcal S}q_{ij}.\n\\]\nTheir totals agree because $\\sum_{s\\in\\mathcal S}(x_s-q_s)=0$.\nLet $k_i$ and $\\ell_j$ be the numbers of large slots in row $i$ and\ncolumn $j$.  A state is \\emph{feasible} when all residuals are nonnegative\nand $R_i^0=0$ if $k_i=0$, $P_j^0=0$ if $\\ell_j=0$.\nIts padded residuals and enlarged full margins are\n\\begin{align*}\n R_i(X)&=R_i^0(X)+k_iL,& P_j(X)&=P_j^0(X)+\\ell_jL,\\\\\n \\bar r_i&=r_i+k_iL,&\\bar c_j&=c_j+\\ell_jL.\n\\end{align*}\nWrite $\\mathcal F_X$ for the set of nonnegative integer tables on\n$\\mathcal B$ with margins $R_i(X),P_j(X)$, and set\n$f(X)=|\\mathcal F_X|$.  On infeasible states set $f(X)=0$.\nThe complete rectangle admits a greedy completion whenever these\nresidual tests hold.  If $\\mathcal B$ is empty the only completion is\nthe empty table, so feasible states have weight one.\n\nEvery completion has total at most $N+|\\mathcal B|L<C$, and every\nnonempty block margin is at least $L$.  In particular the formal large\ncapacity $C$ excludes no completion: its doubled coordinates can be\ntaken as $x_s=A_s,y_s=C-A_s$ for $A\\in\\mathcal F_X$.\nThe bounded sampler will draw completions of this block without\nenumerating its large coordinate ranges.  One-row or one-column blocks have a\nunique completion and need no random draw.\n\nLet $\\mathcal X$ be the feasible states, $\\mathcal T\\subseteq\\mathcal X$\nthe transversals, and\n\\begin{equation}\\label{eq:graph-masses}\n Z_0=\\sum_{X\\in\\mathcal T}f(X),\\qquad\n \\Lambda=\\sum_{X\\in\\mathcal X}f(X),\\qquad\n \\pi(X)=\\frac{f(X)}{\\Lambda}.\n\\end{equation}\nAn original feasible table gives a transversal and a completion by\nretaining its small entries and adding $L$ to each large entry.\nThe small entries respect their capacity because every small cell has\nan incident original margin below $U$.  This construction also gives a\nspecified starting transversal, using the greedy original table.\n\n\\subsection{Repairs and graph edges}\n\nFor a defect with negative slot $s=(i,j)$ and positive slot\n$t=(i',j')$, put $v=(i',j)$.  With $e_w$ denoting a small-slot unit\nvector, define its repair $\\mathcal R(X)$ by\n\\begin{equation}\\label{eq:repair-map}\n x'=x-e_t+\\one_{\\{v\\in\\mathcal S\\}}e_v,\n \\qquad q'=x',\\qquad y'=U\\one-q'.\n\\end{equation}\nThe term involving $e_v$ is omitted if $v$ is large.\nThe undirected graph on $\\mathcal X$ contains all pairs that differ by\nlowering one doubled small coordinate by one and raising another by\none, and also every pair $\\{X,\\mathcal R(X)\\}$ with $X$ a defect.\nRepeated edges are counted only once.  Its unnormalized energy is\n\\begin{equation}\\label{eq:graph-energy}\n E(H)=\\sum_{\\{X,Y\\}\\text{ an edge}}\n \\min\\{f(X),f(Y)\\}(H(X)-H(Y))^2.\n\\end{equation}\n\n\\begin{lemma}[Repair and preservation of prefixes]\\label{lem:repair}\nThe repair is a feasible transversal.  For each fixed ordered pair\n$(s,t)$ it is injective on small states and extends to an injection\n\\[\n (X,A)\\longmapsto\n \\begin{cases}\n (\\mathcal R(X),A),&v\\in\\mathcal S,\\\\\n (\\mathcal R(X),A+e_v),&v\\in\\mathcal B\n \\end{cases}\n \\quad(A\\in\\mathcal F_X).\n\\]\nThus $f(X)\\le f(\\mathcal R(X))$.\nOrder small slots by row and then by column.  If $t$ follows $s$, the\nrepair leaves every slot preceding $s$ unchanged.  When additionally\n$x_s=l-1,q_s=l$, its repaired value at $s$ is $l$ if $i'=i$ and $l-1$\notherwise.\n\\end{lemma}\n\\begin{proof}\nThe defect relation is $q=x-e_t+e_s$, and $x_t\\ge1$.\nIf $v$ is small, the changes to $x$ lie in row $i'$, with zero total;\nthe changes to $q$ are $q'-q=e_v-e_s$, in column $j$, also with zero\ntotal.  Hence all residual margins are unchanged.\nThe new common entries are nonnegative.  Their row totals are the old\n$x$ row totals and their column totals are the old $q$ column totals.\nAt each small slot, one of these totals is bounded by an original\nmargin strictly below $U$, proving the capacity constraint.\nIf $j'=j$, then $v=t$ and the two changes to $x$ cancel; the reset of\n$q$ is confined to that column.  If $i'=i$, then $v=s$: the transfer\nincrements the negative slot, decrements the positive slot, and leaves\n$q$ unchanged.  These include both possible coincidences.\n\nIf $v$ is large, it differs from $s,t$, and\n$x'=x-e_t$, $q'=q-e_s$.  These vectors remain nonnegative and within\ncapacity, since $x_t\\ge1$ and $q_s=x_s+1\\ge1$.\nOnly the residuals of row $i'$ and column $j$ increase, each by one.\nBoth lines meet the large block.  Adding one to completion entry $v$\ntherefore gives a valid target completion.  Its entries remain below\n$C$ by the total bound already proved.\n\nFor fixed $s,t$, the repaired vector recovers\n$x=x'+e_t-\\one_{\\{v\\in\\mathcal S\\}}e_v$, followed by\n$q=x-e_t+e_s$ and $y=U\\one-q$.  In the large-receiver case subtract\n$e_v$ from the target completion; otherwise retain it.  These inverses\non the image prove both injections.\n\nFinally, $t>s$ implies either $i'=i,j'>j$, in which case $v=s$, or\n$i'>i$, in which case every small receiver $v$ follows $s$.\nOnly $s,t,v$ can change, proving prefix preservation and the claimed\nspecial value.\n\\end{proof}\n\nFor later use, fix a balanced prefix $\\sigma$ before $s$, and let\n$z_-,z_+$ be the total transversal weights extending it with special\nvalues $x_s=l-1,l$, respectively.  Let $c_{st,l}^{\\sigma}$ be the total\nweight of defects extending that prefix with negative $s$ satisfying\n$x_s=l-1,q_s=l$ and positive $t>s$.  Lemma~\\ref{lem:repair} maps this\nentire class injectively, with completions, into one fixed child.  Hence\n\\begin{equation}\\label{eq:conditional-repair}\n c_{st,l}^{\\sigma}\\le\n \\begin{cases}z_+,&i'=i,\\\\z_-,&i'\\ne i,\\end{cases}\n \\qquad\\text{and in particular}\\qquad\n c_{st,l}^{\\sigma}\\le\\max\\{z_-,z_+\\}.\n\\end{equation}\nFigure~\\ref{fig:repair} illustrates the large-receiver case.\n\\input{figures/repair}\n\nThere are fewer than $4d^2$ ordered unit exchanges.  A defect has one\ndesignated repair; a transversal has at most $d^2$ inverse repairs,\none for each ordered defect type.  Thus the degree is at most\n$5d^2+1$.  Neighbors can be listed with polynomial bit work using\n\\eqref{eq:repair-map} and its inverse, testing the boxes, patterns and\nresiduals, and removing duplicates.  No completion count is needed.\nOn a repair edge the weight in \\eqref{eq:graph-energy} equals the\nsource defect's weight.\n\n\\subsection{Stationary mass and unpadding}\n\nThe repair bounds defect mass by transversal mass. To recover an original\ntable from a padded completion, we also need to control the probability\nthat a large entry is smaller than the padding. The following estimate\napplies to unrestricted tables with any prescribed margins.\n\n\\begin{lemma}[Small entries]\\label{lem:small-entry}\nConsider the uniform law on all nonnegative integer tables with prescribed\nmargins, with at most $m$ rows and $n$ columns and no entry restrictions.\nIf both margins incident to a cell are at least $a\\ge4d$, then, for every\ninteger $t$ with $0<t\\le a/2$,\n\\[\n \\Pp\\{\\text{the cell's entry is less than }t\\}\\le\\frac{4td^3}{a}.\n\\]\n\\end{lemma}\n\\begin{proof}\nLet the marked cell be $(i,j)$ and its entry be $h<t$.\nThe other entries in its row have sum at least $a/2$, as do the other\nentries in its column.  Unless this event is empty, choose deterministically\n$j'\\ne j$ and $i'\\ne i$ whose donor entries are both at least $a/(2d)$.\nSuch choices exist because each line has fewer than $d$ entries.  If there\nis only one row or column, the event is empty instead.\nFor every $v=1,\\ldots,\\lfloor a/(2d)\\rfloor$, subtract $v$ from the\nentries $(i,j')$ and $(i',j)$ and add $v$ at $(i,j)$ and $(i',j')$.\nThe resulting table is nonnegative with the same margins.  Given this\ntable and the labels $(i',j',h)$, its marked entry determines $v$, and\nreversing the four changes recovers the source.  There are at most $td^2$\npossible labels for each output.  Since\n$\\lfloor a/(2d)\\rfloor\\ge a/(4d)$, double counting these moves gives\nthe asserted bound.  No upper entry bound is imposed in this argument.\n\\end{proof}\n\n\\begin{proposition}[Stationary mass and unpadding]\\label{prop:stationary-success}\nThe masses satisfy\n\\begin{equation}\\label{eq:stationary-mass-bounds}\n 0<Z_0\\le\\Lambda\\le(1+d^2)Z_0,\n \\qquad \\pi_{\\min}\\ge(C+1)^{-2d}.\n\\end{equation}\nDraw $X$ with law $\\pi$ and then a uniform $A\\in\\mathcal F_X$.\nDeclare success if $X$ is a transversal and every large entry of $A$\nis at least $L$.  Subtracting $L$ from these entries gives a uniform\noriginal table conditional on success, and\n\\begin{equation}\\label{eq:unpadding-success}\n \\Pp\\{\\text{success}\\}\\ge\\frac{1}{2(1+d^2)}.\n\\end{equation}\n\\end{proposition}\n\\begin{proof}\nFor each of at most $|\\mathcal S|(|\\mathcal S|-1)\\le d^2$ defect types,\nthe joint injection in Lemma~\\ref{lem:repair} bounds its total weight\nby $Z_0$.  This proves the upper bound on $\\Lambda$; the other mass\ninequalities follow from inclusion and the greedy starting table.\nThere are $\\Lambda$ joint objects $(X,A)$, each specified by\n$2|\\mathcal S|+|\\mathcal B|\\le2mn<2d$ integer coordinates in $[0,C]$.\nThus $\\Lambda\\le(C+1)^{2d}$, and the positive integer weights $f(X)$\ngive the minimum-mass bound.\n\nConsider all unrestricted tables with the enlarged margins\n$\\bar r,\\bar c$, and call their number $F$.  Every transversal and\ncompletion gives one such table, so $Z_0\\le F$.\nEach large cell has both enlarged margins at least $U$.\nLemma~\\ref{lem:small-entry} and a union bound show that the proportion\nof these enlarged tables with some large entry below $L$ is at most\n\\[\n |\\mathcal B|\\frac{4Ld^3}{U}\\le\\frac{4Ld^4}{U}\n =\\frac4{d^4}<\\frac12.\n\\]\nHere $L\\le U/2$ and $U\\ge4d$; an empty block has no bad cells.\nEvery enlarged table passing this test can be unpadded to an original\ntable.  Conversely, padding every original table gives exactly one\npassing table and a feasible transversal with its completion.\nConsequently the successful pairs are in bijection with\n$\\Omega(r,c)$ and number at least $F/2\\ge Z_0/2$.\nUnder the stated joint law every pair has probability\n$(f(X)/\\Lambda)(1/f(X))=1/\\Lambda$, proving conditional uniformity\nand \\eqref{eq:unpadding-success}.\n\\end{proof}\n\nIf there are no small slots, $\\mathcal X$ has one state and all graph\nenergies vanish; the completion and unpadding step still applies.\nIf there is no large block, the completion step is deterministic.\nThese conventions will also be used for the Markov chains below.\n"}, {"path": "preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/sections/signatures.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/sections/signatures.tex", "bytes": 13953, "sha256": "a1551bb45dc44b4791ad4abe70c89c7f9e41d614aba4630f0559ee9a89d7bfaf", "content": "\\section{Quadratic signatures and balanced summation}\n\\label{sec:signatures}\n\nThe transport construction uses quadratic forms with at most one positive\neigenvalue. We prove the required property for integer coordinates and then\nshow that it survives summation over balanced slots. Integer coordinates\nallow two units to be removed at the same location, so the diagonal entries\nand the endpoints of the summation require explicit treatment. Large\ncapacities occur only in finite mathematical sums in this section; the\nalgorithm will not enumerate those sums.\nThe one-positive-eigenvalue viewpoint is central to the theory of\nLorentzian polynomials developed by Br\\\"and\\'en and Huh~\\cite{BH2020}.\nHere the needed closure is a discrete balanced-pair summation, established\nby the laminar decomposition and telescoping calculation below.\n\n\\begin{definition}[Softened weights]\\label{def:soft-weight}\nLet $\\mathcal B$ and $\\mathcal S$ be the large and small slots from\nSection~\\ref{sec:graph}, and put\n\\[\n T=\\sum_s B_s,\\qquad\n \\mathcal Q=\\prod_s\\{0,\\ldots,B_s\\}^2.\n\\]\nFor a full coordinate vector $\\xi=(x_s,y_s)_s$, write\n$|\\xi|=\\sum_s(x_s+y_s)$ and $q_s=B_s-y_s$.\nFor $a\\in\\R$, let $a_+=\\max(a,0)$.  Define\n\\begin{align*}\n D(\\xi)={}&\\sum_i\\left|\\sum_jx_{ij}-\\bar r_i\\right|\n       +\\sum_j\\left|\\sum_iq_{ij}-\\bar c_j\\right|\\\\\n &+\\sum_i\\left(\\sum_{j:(i,j)\\in\\mathcal S}x_{ij}-r_i\\right)_+\n       +\\sum_j\\left(\\sum_{i:(i,j)\\in\\mathcal S}q_{ij}-c_j\\right)_+.\n\\end{align*}\nFor $0<\\eta<1$, set\n\\[\n F_\\eta(\\xi)=\n \\begin{cases}\n \\eta^{D(\\xi)},&\\xi\\in\\mathcal Q,\\quad |\\xi|=T,\\\\\n 0,&\\text{otherwise}.\n \\end{cases}\n\\]\nFor a small-coordinate configuration $X$ of total $\\sum_{s\\in\\mathcal S}B_s$,\ndefine\n\\begin{equation}\\label{eq:soft-marginal}\n f_\\eta(X)=\n \\sum_{\\substack{(x_s,y_s)=(a_s,B_s-a_s),\\ 0\\le a_s\\le B_s\\\\\n                  s\\in\\mathcal B}}\n F_\\eta(X,(x_s,y_s)_{s\\in\\mathcal B}).\n\\end{equation}\nAn empty product of choices contributes one term.\n\\end{definition}\n\nEvery box-valid full vector of total $T$ has strictly positive weight,\nregardless of its margin deviations or slot occupancies.  Thus\n$f_\\eta(X)>0$ for every box-valid $X$ of the indicated total.  On the\ntransversal and defect patterns,\n\\begin{equation}\\label{eq:soft-limit}\n \\lim_{\\eta\\downarrow0}f_\\eta(X)=f(X).\n\\end{equation}\nIndeed, a term has $D=0$ exactly when the full row and column margins are\n$\\bar r,\\bar c$ and the small row and column totals do not exceed $r,c$.\nWhen the large slots are balanced, these conditions specify precisely the\nnonnegative residuals and padded completions defining $f$.  On a line\nwithout a large slot, the full-margin equality also makes the residual\nzero.  The capacity $C$ excludes no such completion, by its choice in\n\\eqref{eq:parameters}.  Each zero-penalty term tends to one, all other\nterms tend to zero, and the sum is finite.\n\nWe use the following convention throughout.  Some coordinates have fixed\nbox-valid values $\\sigma$, of total $c=|\\sigma|$; a disjoint collection\n$\\mathcal R$ of whole slots is summed over its balanced profiles; and all\nremaining coordinates are active.  For an assignment $v$ to the active\ncoordinates, let\n\\[\n \\Phi_{\\sigma,\\mathcal R}(v)\n   =\\sum_{b\\in\\prod_{s\\in\\mathcal R}\n                  \\{(a,B_s-a):0\\le a\\le B_s\\}}\n        F_\\eta(\\sigma,v,b).\n\\]\nThus the summation domain is fixed, even when $v$ changes.\nA box-valid active display $p$ for two removals satisfies\n\\begin{equation}\\label{eq:two-removal-total}\n c+|p|+\\sum_{s\\in\\mathcal R}B_s=T+2.\n\\end{equation}\nIts two-removal matrix has entries\n$M_{ih}=\\Phi_{\\sigma,\\mathcal R}(p-e_i-e_h)$.\nIn particular, two removals at the same coordinate give a diagonal entry;\nan invalid removal gives zero.  The display has total $T+2$, whereas\nevery nonzero entry is evaluated at total $T$.\n\n\\begin{lemma}[Laminar factors]\\label{lem:laminar-signature}\nWith no balanced slots summed, every two-removal matrix just defined has\nat most one positive eigenvalue.\n\\end{lemma}\n\\begin{proof}\nBefore imposing the box and total, the formula $\\eta^{D(\\xi)}$ is a\nproduct of positive log-concave sequences in sums over a laminar family\nof coordinate sets.  Here laminar means that any two sets are disjoint\nor one contains the other.  The sets are the $x$ coordinates of each row\nand their small-slot subsets, and the $y$ coordinates of each column and\ntheir small-slot subsets.  The row and column families are disjoint\nbecause they use different coordinates.  For example, the column\nfactors have the form\n\\[\n \\eta^{|K-z-\\bar c_j|},\\qquad\n \\eta^{(K'-z-c_j)_+},\n\\]\nwhere $z$ is the relevant sum of $y$ coordinates and $K,K'$ are the\ncorresponding sums of capacities.  All exponents are convex in $z$;\nsince $\\log\\eta<0$, the factors are log-concave on all integers.\n\nAn active coordinate with $p_i=0$ gives a zero row and column of $M$;\ndiscard these indices temporarily.  Restrict the laminar sets to the\nremaining indices and absorb fixed coordinates into shifts of their\nscalar factors.  Combine factors whose restricted sets coincide, and\nabsorb empty sets into a positive constant.  We obtain distinct\nnonempty laminar sets $G$, positive log-concave sequences $\\psi_G$, and\narguments $t_G$ at the display.  Evaluate their formula even for invalid\nremovals for the moment.  Let $W>0$ be its value at the display and put\n\\[\n \\alpha_i=\\prod_{G\\ni i}\\frac{\\psi_G(t_G-1)}{\\psi_G(t_G)},\n \\qquad\n \\gamma_G=\\frac{\\psi_G(t_G)\\psi_G(t_G-2)}{\\psi_G(t_G-1)^2}\\in(0,1].\n\\]\nThe extended two-removal matrix is\n$W\\diag(\\alpha)K\\diag(\\alpha)$, where\n\\[\n K_{ih}=\\prod_{G\\supseteq\\{i,h\\}}\\gamma_G.\n\\]\nFor each set define\n\\[\n a_G=(1-\\gamma_G)\\prod_{G'\\supsetneq G}\\gamma_{G'}\\ge0.\n\\]\nThe sets containing any given pair of indices form a chain.  Telescoping\nthe product along this chain gives the exact identity\n\\begin{equation}\\label{eq:laminar-blocks}\n K=\\one\\one^{\\mathsf T}\n       -\\sum_G a_G\\one_G\\one_G^{\\mathsf T}.\n\\end{equation}\nConsequently $K$ is nonpositive on $\\{v:\\one^{\\mathsf T}v=0\\}$ and\nhas at most one positive eigenvalue.  The only still-invalid removals\nare diagonal ones at coordinates with $p_i=1$.  Replacing each such\nentry by zero subtracts a nonnegative diagonal matrix from $K$, so preserves\nthat conclusion.  Positive diagonal congruence and restoration of the\nzero rows and columns complete the proof.\n\\end{proof}\n\nWe next sum over a fresh slot of capacity $B$.  Two occupancies are\nneeded.  At occupancy $B$, summing the profiles $(j,B-j)$ eliminates a\nbalanced slot and preserves the signature.  At occupancy $B+1$, the\nsame calculation gives a weighted quadratic estimate; this is the\nestimate used to bound the transport construction in the next section.\nBoth conclusions follow from a discrete second-difference inequality.\n\n\\begin{lemma}[Pair summation]\\label{lem:pair-summation}\nFix a box-valid display $p$ on the old active coordinates, a fresh slot\nof capacity $B\\ge2$, and fixed context and balanced sums as above.\nChoose its displayed occupancy $D'\\in\\{B,B+1\\}$ so that\n\\begin{equation}\\label{eq:pair-total}\n c+|p|+D'+\\sum_{s\\in\\mathcal R}B_s=T+2.\n\\end{equation}\nWrite $\\Phi(r,a,b)$ for the weight after the balanced sums, with old\nvalues $r$ and fresh pair $(a,b)$.  The box-valid displays\n$(p,j,D'-j)$ are indexed by\n\\[\n \\mathcal J=\n \\begin{cases}\n \\{0,\\ldots,D'\\},&D'=B,\\\\\n \\{1,\\ldots,D'-1\\},&D'=B+1.\n \\end{cases}\n\\]\nTheir two-removal matrices have the blocks\n\\begin{align*}\n (V_j)_{ih}&=\\Phi(p-e_i-e_h,j,D'-j),\\\\\n S(l)_i&=\\Phi(p-e_i,l,D'-1-l),\\\\\n u_j&=\\Phi(p,j-1,D'-1-j).\n\\end{align*}\nThus $V_j$ records two old removals, $S(l)$ one old and one fresh\nremoval, and $u_j$ one removal from each fresh coordinate of display\n$j$.  Put $S(l)=0$ outside\n$0\\le l\\le D'-1$ and $u_j=0$ outside $1\\le j\\le D'-1$.\nSuppose each matrix\n\\begin{equation}\\label{eq:pair-block}\n M_j=\\begin{pmatrix}\n V_j&S(j-1)&S(j)\\\\\n S(j-1)^{\\mathsf T}&u_{j-1}&u_j\\\\\n S(j)^{\\mathsf T}&u_j&u_{j+1}\n \\end{pmatrix},\\qquad j\\in\\mathcal J,\n\\end{equation}\nhas at most one positive eigenvalue.  The entries $u_{j-1}$ and\n$u_{j+1}$ are the double removals at the first and second fresh\ncoordinates, respectively.\nFor every real old vector $h$ satisfying\n$\\sum_{l=0}^{D'-1}h^{\\mathsf T}S(l)=0$, define\n\\begin{equation}\\label{eq:pair-cumulative}\n g_j=\\frac{\\sum_{l=0}^{j-1}h^{\\mathsf T}S(l)}{u_j}\n       \\quad(1\\le j\\le D'-1),\\qquad\n g_j=0\\quad\\text{otherwise}.\n\\end{equation}\nAll these divisions are valid, and\n\\begin{equation}\\label{eq:pair-one}\n -h^{\\mathsf T}V_jh\\ \\ge\\\n 2u_jg_j^2-u_{j-1}g_{j-1}^2-u_{j+1}g_{j+1}^2,\n \\qquad j\\in\\mathcal J.\n\\end{equation}\nIf $D'=B$, the matrix $\\sum_{j=0}^B V_j$ has at most one positive\neigenvalue.\nIf $D'=B+1$, then\n\\begin{equation}\\label{eq:pair-weighted}\n 2\\sum_{j=1}^{D'-1}u_jg_j^2\n \\ \\le\\\n -h^{\\mathsf T}\\left(\\sum_{j=1}^{D'-1}j(D'-j)V_j\\right)h.\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nFirst, $u_j>0$ for every $1\\le j\\le D'-1$.  Its pair values\n$(j-1,D'-1-j)$ are within $[0,B]^2$ in both cases.  They have total\n$D'-2$, so \\eqref{eq:pair-total} puts the full configuration on the\ntotal-$T$ slice.  Choose any balanced box-valid profile, for example\n$(0,B_s)$, at each summed slot.  The unchanged old profile and fixed\ncontext then give a positive term of $F_\\eta$.  This proves positivity\neven when the context violates the original margins or some old\ndisplayed coordinates are zero.\n\nThe cumulative definition and the balance condition imply, including\n$l=0,D'-1$,\n\\begin{equation}\\label{eq:pair-difference}\n h^{\\mathsf T}S(l)=u_{l+1}g_{l+1}-u_lg_l.\n\\end{equation}\nWe use an elementary consequence of the signature assumption.  If a\nsymmetric matrix $M$ has at most one positive eigenvalue and\n$e^{\\mathsf T}Me>0$, then\n\\begin{equation}\\label{eq:positive-projection}\n v^{\\mathsf T}Mv\\le\n       \\frac{(v^{\\mathsf T}Me)^2}{e^{\\mathsf T}Me}.\n\\end{equation}\nIndeed, subtract from $v$ its $M$-orthogonal projection onto $e$.\nThe remaining vector must have nonpositive square, since otherwise it\nand $e$ span a positive definite two-dimensional subspace.\n\nFor $j\\in\\mathcal J$, put $v=(h,g_j,-g_j)$, and let $e_a,e_b$ denote\nthe two fresh coordinate directions.  Equations\n\\eqref{eq:pair-block} and \\eqref{eq:pair-difference} give\n\\[\n v^{\\mathsf T}M_je_a=u_{j-1}(g_j-g_{j-1}),\\qquad\n v^{\\mathsf T}M_je_b=u_{j+1}(g_{j+1}-g_j).\n\\]\nIf $u_{j-1}>0$, apply \\eqref{eq:positive-projection} to $e_a$; if\n$u_{j+1}>0$, apply it to $e_b$.  Either bound is at most\n\\[\n u_{j-1}(g_j-g_{j-1})^2+u_{j+1}(g_j-g_{j+1})^2.\n\\]\nIf both diagonals vanish, then $u_j>0$ and $e_a+e_b$ has square $2u_j$\nand is $M_j$-orthogonal to $v$.  The same upper bound, now zero, follows.\nThese cases cover all admissible $j$: at $j=0$ or $D'$ the inward\ndiagonal is positive, and at every other index $u_j>0$.\nAn exact expansion now yields\n\\begin{align*}\n &v^{\\mathsf T}M_jv\n   -u_{j-1}(g_j-g_{j-1})^2-u_{j+1}(g_j-g_{j+1})^2\\\\\n &\\hspace{2em}=h^{\\mathsf T}V_jh\n                 +2u_jg_j^2-u_{j-1}g_{j-1}^2-u_{j+1}g_{j+1}^2,\n\\end{align*}\nproving \\eqref{eq:pair-one}.\n\nFor clarity, the smallest capacity is covered without a positive\ndiagonal at every display.  When $B=D'=2$, the displays $j=0,2$ use\nthe diagonal $u_1>0$, and $j=1$ uses the positive sum direction with\nsquare $2u_1$.  When $B=2,D'=3$, the only displays are $j=1,2$;\nthey have, respectively, positive diagonal $u_2$ and $u_1$.\nThe out-of-box displays $j=0,3$ are never evaluated.\n\nWrite $a_j=u_jg_j^2$, zero outside $1\\le j\\le D'-1$.\nFor $D'=B$, summing \\eqref{eq:pair-one} over $0\\le j\\le D'$ gives\n\\[\n -h^{\\mathsf T}\\Bigl(\\sum_{j=0}^{D'}V_j\\Bigr)h\n \\ge\\sum_{j=0}^{D'}(2a_j-a_{j-1}-a_{j+1})=0.\n\\]\nThus the sum is nonpositive on the kernel of\n$h\\mapsto h^{\\mathsf T}\\sum_l S(l)$, a subspace of codimension at most\none.  A positive eigenspace of dimension two would intersect that\nkernel nontrivially, so the sum has at most one positive eigenvalue.\nFor $D'=B+1$, let $w_j=j(D'-j)$, with $w_0=w_{D'}=0$.\nMultiplying \\eqref{eq:pair-one} by $w_j$ and summing only over the\nadmissible indices gives\n\\[\n \\sum_{j=1}^{D'-1}w_j(2a_j-a_{j-1}-a_{j+1})\n =\\sum_{j=1}^{D'-1}(2w_j-w_{j-1}-w_{j+1})a_j\n =2\\sum_{j=1}^{D'-1}a_j.\n\\]\nThis proves \\eqref{eq:pair-weighted}, using only scalar zero endpoint\nweights, never endpoint matrices outside the box.\n\\end{proof}\n\n\\begin{proposition}[Signature after balanced summation]\n\\label{prop:signature}\nFix $0<\\eta<1$.  For arbitrary box-valid fixed context $\\sigma$,\narbitrary balanced-slot summation set $\\mathcal R$, and arbitrary\nbox-valid active display $p$ satisfying \\eqref{eq:two-removal-total},\nthe matrix\n\\[\n \\bigl(\\Phi_{\\sigma,\\mathcal R}(p-e_i-e_h)\\bigr)_{i,h}\n\\]\nhas at most one positive eigenvalue.\n\\end{proposition}\n\\begin{proof}\nInduct on $|\\mathcal R|$.  Lemma~\\ref{lem:laminar-signature} proves\nthe empty case.  For a nonempty set choose $t\\in\\mathcal R$, regard\nits two coordinates as active at $(j,B_t-j)$, and sum only over\n$\\mathcal R\\setminus\\{t\\}$.  Every resulting matrix $M_j$ has at most\none positive eigenvalue by induction; its total is $T+2$ by\n\\eqref{eq:two-removal-total}.  Apply Lemma~\\ref{lem:pair-summation}\nwith $D'=B_t$.  The matrix $\\sum_{j=0}^{B_t}V_j$ is exactly the required\nmatrix after also summing slot $t$, and has the asserted signature.\nAll capacities here are at least two.  Zero active coordinates give\nzero rows and columns throughout and need no additional assumption.\n\\end{proof}\n\nThis is a specific closure under balanced-pair summation, proved by\n\\eqref{eq:pair-one}; arbitrary sums of matrices with this signature\nneed not preserve it.  In the transport argument, a weighted matrix\n$V_t=\\sum_j j(D'-j)V_j$ will be tested against real vectors $h$ whose\nentries may have either sign.  Equation~\\eqref{eq:pair-weighted}\ncontrols these quadratic evaluations under the stated balance condition.\nWe make no signature assertion for $V_t$ itself.  In particular,\nProposition~\\ref{prop:signature} is applied before this weighted sum,\non the entire softened total-$T$ box, even when the resulting\ntwo-removal configurations lie outside the graph patterns.\n"}, {"path": "preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/sections/transport.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/build/sections/transport.tex", "bytes": 16376, "sha256": "1791a326f1e5ac907808c450e371ae3176e592a9fe8994c6d47b5ac65523a882", "content": "\\section{Transport and the graph variance bound}\\label{sec:transport}\n\nWe now turn the quadratic property into a variance bound for the graph of\nSection~\\ref{sec:graph}. A signed flow compares adjacent conditional means;\ndecomposing variance over successive conditions gives a bound on the\ntransversals, and the repair map extends it to the full graph. All large\nslots are summed with their balances throughout. The remaining slots are\nexposed in row-major order. Fix a real-valued function $H$ on the feasible\nsmall-state graph.\nThe flow-energy comparison is the standard quadratic electrical-network\nmethod; see Aldous and Fill~\\cite[Section~3.7.1]{AF2002}. The recursive\nsigned demands and their bound for integer slots are constructed here.\nA \\emph{context} $\\sigma$ fixes balanced profiles on a prefix of small slots.\nWrite $\\mathcal T_\\sigma$ for the transversals with this context and\n$z_\\sigma=\\sum_{X\\in\\mathcal T_\\sigma}f(X)$.  If $s$ is the next slot,\nits child $j$ fixes $(x_s,y_s)=(j,U-j)$, and has mass $z_j$ and weighted\nmean $H_j$.  Means are used only for positive masses.\n\nLet $E_\\sigma(H)$ be the sum of\n$\\min(f(X),f(Y))(H(X)-H(Y))^2$ over unordered \\emph{unit-exchange} edges\nwhose endpoints respect $\\sigma$.  In particular $E_\\sigma(H)\\le E(H)$;\nrepair edges need not be included in $E_\\sigma$.  A superscript $\\eta$ on\nmasses or means, and $E_{\\eta,\\sigma}$ on this energy, will indicate the\nsoftened weights from Definition~\\ref{def:soft-weight}.  For the softened\nenergy we include all box-admissible transversal and defect patterns with\nthe context, including states whose limiting weight is zero.\n\n\\subsection{Child weights and adjacent means}\n\n\\begin{lemma}[Child log-concavity]\\label{lem:child-logconcavity}\nAt every context with positive transversal mass, the child masses\n$(z_j)_{j=0}^U$ are log-concave and their positive support is an integer\ninterval.  If $z_a,z_b>0$ and $a\\le j\\le b$, then\n$z_j\\ge\\min(z_a,z_b)$.\n\\end{lemma}\n\\begin{proof}\nFor $0<\\eta<1$ every child mass is positive.  For $1\\le j<U$, display\nthe next pair as $(j+1,U-j+1)$ and sum every other unexposed slot with its\nbalance.  The display is in its box and has two excess units.  Its\ntwo-removal matrix is\n\\[\n \\begin{pmatrix}z_{j-1}^\\eta&z_j^\\eta\\\\\n                 z_j^\\eta&z_{j+1}^\\eta\\end{pmatrix}.\n\\]\nProposition~\\ref{prop:signature} gives at most one positive eigenvalue.\nThe trace is positive, so the determinant is nonpositive:\n$(z_j^\\eta)^2\\ge z_{j-1}^\\eta z_{j+1}^\\eta$.\nConsequently the successive differences of $\\log z_j^\\eta$ are\nnonincreasing, and for $a<j<b$,\n\\[\n z_j^\\eta\\ge\n (z_a^\\eta)^{(b-j)/(b-a)}(z_b^\\eta)^{(j-a)/(b-a)}.\n\\]\nFinite sums converge as $\\eta\\downarrow0$.  The local inequalities give\nlog-concavity in the limit; the displayed interpolation gives positivity\nbetween positive endpoints and the asserted lower bound.\n\\end{proof}\n\n\\begin{lemma}[Conditional transport]\\label{lem:transport}\nLet $\\sigma$ be a row-major context, and let children $l-1,l$ of its\nnext slot have positive masses $z_-,z_+$ and means $H_-,H_+$.  Then\n\\begin{equation}\\label{eq:conditional-transport}\n \\min(z_-,z_+)(H_--H_+)^2\n \\le A_0 E_\\sigma(H),\\qquad A_0=2+2d(U+1)^2.\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nWe first work at a fixed $\\eta>0$. Extend $H$ by arbitrary fixed finite\nvalues to the box-admissible graph patterns whose limiting weights are\nzero, and suppress $\\eta$ on masses. We seek a signed flow with divergence\n$f_\\eta(X)/z_-$ at each transversal in the minus child,\n$-f_\\eta(X)/z_+$ at each transversal in the plus child, and zero elsewhere.\nFor such a flow $J$, summation by parts and Cauchy--Schwarz give\n\\[\n (H_-^\\eta-H_+^\\eta)^2\n \\le \\left(\\sum_{XY}\\frac{J(X,Y)^2}\n                    {\\min(f_\\eta(X),f_\\eta(Y))}\\right)\n                         E_{\\eta,\\sigma}(H),\n\\]\nwhere each unit-exchange edge is oriented once. Thus it is enough to\nbound the flow's resistance energy. We build its signed demands by\nsuccessively exposing the remaining small slots, and charge its energy\nto a quadratic potential along that recursion.\n\nDisplay the special slot $s$ as $(l,U+1-l)$, where $1\\le l\\le U$.\nRemoving its first or second coordinate gives the minus or plus child,\nrespectively. The flow uses these endpoint transversals and defects\nwith $s$ positive and one other unexposed small slot negative.\n\n\\emph{Balanced recursion.}\nA recursion node $\\nu$ displays the special pair and some subsequent\nsmall pairs, the latter all with occupancy $U$. Let $p$ be this display,\nand call its coordinates the old indices. With the context included,\nand all undisplayed slots assigned their balanced occupancies, the total\nis $T+1$, where $T=\\sum_s B_s$. For an old index $i$, let $s_i$ be the\ntotal softened weight obtained by removing one unit there and summing\nall undisplayed slots with their balances. Invalid removals have weight\nzero. Assign coefficients $h_i$ satisfying\n\\begin{equation}\\label{eq:balanced-demands}\n \\sum_i s_i h_i=0,\\qquad \\Phi_\\nu=\\sum_i s_i h_i^2.\n\\end{equation}\nThe quantity $s_i h_i$ is the signed demand on that one-removal fiber;\nthe first equality requires total demand zero at the node. At the root\nthe two coefficients are $1/z_-$ and $-1/z_+$, giving the prescribed\nendpoint demands and root potential $1/z_-+1/z_+$.\n\nTo process the next ordinary slot $v$, branch over its balanced profiles\n$(j,U-j)$, $0\\le j\\le U$.  In the notation of\nLemma~\\ref{lem:pair-summation}, use the auxiliary occupancy $D'=U+1$.\nThen $S(j)$ is the vector of old-hole weights in branch $j$, and $u_j$\nis the no-old-hole weight with pair $(j-1,U-j)$.  Thus $u_1,\\ldots,u_U$\nare positive and $u_0=u_{U+1}=0$.  Since $\\sum_j S_i(j)=s_i$, define\n\\begin{equation}\\label{eq:transport-g}\n g_j=\\frac{\\sum_{a=0}^{j-1}h^T S(a)}{u_j}\\quad(1\\le j\\le U),\n \\qquad g_0=g_{U+1}=0.\n\\end{equation}\nBalance gives $h^TS(j)=u_{j+1}g_{j+1}-u_jg_j$, including both endpoints.\nIn child $j$, retain all old coefficients and give the two new\ncoordinates coefficients $g_j,-g_{j+1}$.  Their one-hole masses are\n$u_j,u_{j+1}$, so the child is balanced.  Summing child potentials\nconserves the old terms and adds exactly\n\\begin{equation}\\label{eq:potential-increment}\n \\sum_{j=0}^U\\Phi_{\\nu j}-\\Phi_\\nu\n =2\\sum_{j=1}^Uu_jg_j^2\n \\le -h^TV_{\\nu,v}h.\n\\end{equation}\nHere $V_{\\nu,v}$ is the matrix of two-old-hole weights with $v$ displayed\nat $(a,U+1-a)$, summed over $1\\le a\\le U$ with multiplier\n$a(U+1-a)$ and over all other undisplayed slots with their balances.\nThe inequality is precisely Equation~\\eqref{eq:pair-weighted}.\nEach individual display before the two removals has total $T+2$.\n\n\\emph{A future quadratic form.}\nFor any slot $t$ still to be processed, define\n$Q_t(\\nu)=h^TV_{\\nu,t}h$ by the same rule.  If a different slot $v$ is\nsplit now, we claim\n\\begin{equation}\\label{eq:future-monotonicity}\n \\sum_{j=0}^U Q_t(\\nu j)\\ge Q_t(\\nu).\n\\end{equation}\nFor an explicit common summation kernel, put\n\\[\n K_t(r,w)=\\sum_{a=1}^U a(U+1-a)\n       \\sum_{b}F_\\eta\\bigl(\\sigma,r,w,(a,U+1-a),b\\bigr).\n\\]\nHere $r$ assigns the old coordinates, $w$ assigns the current pair $v$,\nand $b$ ranges over the Cartesian product of balanced profiles of every\nremaining slot other than $t$, including all large slots.\nAs usual, an invalid coordinate assignment contributes zero.  This is\na single fixed summation domain; all penalties are evaluated on the\nfull configurations.  Every kernel entry used below has total $T$.\n\nThe old--old terms in the children partition the parent's balanced sum\nover $v$.  For a mixed term, removing old index $i$ and the first new\ncoordinate in branch $j$ gives\n$K_t(p-e_i,(j-1,U-j))$ with coefficient $2h_i g_j$.\nRemoving $i$ and the second new coordinate in branch $j-1$ gives the\nidentical kernel entry with coefficient $-2h_i g_j$.\nThese terms cancel.  All legal resulting pairs have occupancy $U-1$;\nthe unmatched formal endpoint removals are invalid and have weight zero.\nFor the new--new terms, each resulting pair $(k,U-2-k)$, $0\\le k\\le U-2$,\nhas exactly the following contributions:\n\\begin{center}\n\\begin{tabular}{lll}\n\\toprule\nRemoved coordinates & Branch & Coefficient\\\\\n\\midrule\nFirst twice & $k+2$ & $g_{k+2}^2$\\\\\nFirst and second & $k+1$ & $-2g_{k+1}g_{k+2}$\\\\\nSecond twice & $k$ & $g_{k+1}^2$\\\\\n\\bottomrule\n\\end{tabular}\n\\end{center}\nThey all multiply $K_t(p,(k,U-2-k))$.  Hence the exact identity is\n\\begin{equation}\\label{eq:future-square}\n \\sum_{j=0}^U Q_t(\\nu j)-Q_t(\\nu)\n =\\sum_{k=0}^{U-2}K_t(p,(k,U-2-k))\n                      (g_{k+2}-g_{k+1})^2\\ge0.\n\\end{equation}\nAll omitted boundary removals are invalid.  Auxiliary entries with two\nholes in an ordinary pair need not be graph states: they belong to the\nfull softened total-$T$ slice where the signature proposition applies.\nNo signature property of the weighted sum $V_{\\nu,t}$ is being asserted;\nEquation~\\eqref{eq:potential-increment} and the explicit square identity\nare the properties of that sum that are used.\n\n\\emph{The root bound.}\nIterating Equation~\\eqref{eq:future-monotonicity} shows that the sum of\n$Q_t$ at the level just before processing $t$ is at least its root value.\nThus Equation~\\eqref{eq:potential-increment}, summed over levels, bounds\nthe total leaf potential by the root potential minus the sum of the\nroot $Q_t$'s.  Let $c_t=c_{st,l}^{\\eta,\\sigma}$ be the total weight of\ndefects respecting $\\sigma$ with special pair $(l-1,U-l)$ negative,\nslot $t$ positive, and every other small slot balanced.\nThese auxiliary defects differ from the special-positive defects used by\nthe leaf flow: the off-diagonal entry removes both special coordinates\nfrom $(l,U+1-l)$, leaving $(l-1,U-l)$.\nAt the root, the off-diagonal entry of $V_{\\nu,t}$ is this sum with\nmultiplier $a(U+1-a)$ on the positive pair $t$, so it is at most\n$(U+1)^2c_t$.  Its diagonal entries are nonnegative.  Substituting the\nroot coefficients and dropping their nonpositive contributions to\n$-Q_t$ gives\n\\begin{equation}\\label{eq:leaf-potential}\n \\sum_{\\nu\\text{ leaf}}\\Phi_\\nu\n \\le \\frac1{z_-}+\\frac1{z_+}\n       +\\frac{2(U+1)^2}{z_-z_+}\\sum_{t>s}c_t.\n\\end{equation}\nThe sum ranges only over the other unexposed small slots.  With no such\nslots the recursion consists of its root and this bound still holds.\n\n\\emph{Leaf flows and their divergences.}\nAt a leaf let $D$ denote the entire small-coordinate display, including\nthe fixed context. For its unfixed coordinate indices $i$, the valid\none-removal states $X_i=D-e_i$ have\nweights $s_i=f_\\eta(X_i)>0$, and form a clique of unit exchanges.\nChoose a maximum-weight vertex as hub.  On the edge from each nonhub\n$X_i$ to the hub send signed flow $s_i h_i$; its conductance is exactly\n$s_i$.  The flow's divergence at $X_i$ is $s_i h_i$, and balance gives\nthe same prescribed divergence at the hub.  Its resistance energy is\nat most $\\sum_i s_i h_i^2=\\Phi_\\nu$.\nDifferent leaf cliques have disjoint edge sets: the coordinatewise\nmaximum of the endpoints recovers $D$, which uniquely determines the\nleaf.  Their flows can therefore be added without an energy multiplier.\n\nAn endpoint transversal has a unique leaf representation.  Its ordinary\nbalanced profiles determine all branches, and its special hole is in\nthe first coordinate for the minus child and the second for the plus\nchild.  Its coefficient remains $1/z_-$ or $-1/z_+$ throughout.\nEvery other used state has special slot positive and a unique ordinary\nnegative pair, say $(a,U-1-a)$ in slot $v$.  It has exactly two leaf\nrepresentations: fill the first coordinate, giving branch $a+1$ and\ncoefficient $g_{a+1}$, or fill the second, giving branch $a$ and\ncoefficient $-g_{a+1}$.  All earlier profiles are determined by the state,\nso both coefficients come from the same recursion parent.  Later splits\nretain them.  Both representations carry the identical weight\n$f_\\eta(X)$, and their divergences cancel.  This also holds for\n$a=0,U-1$.\n\nConsequently the summed flow $J$ has divergence $f_\\eta/z_-$ on the\nminus child, $-f_\\eta/z_+$ on the plus child, and zero elsewhere.\nThe resistance-energy comparison from the start of the proof and\nEquation~\\eqref{eq:leaf-potential} now eliminate the flow and all\nrecursive coefficients:\n\\begin{equation}\\label{eq:soft-transport}\n (H_-^\\eta-H_+^\\eta)^2\n \\le\\left(\\frac1{z_-^\\eta}+\\frac1{z_+^\\eta}\n       +\\frac{2(U+1)^2\\sum_{t>s}c_{st,l}^{\\eta,\\sigma}}\n                    {z_-^\\eta z_+^\\eta}\\right)E_{\\eta,\\sigma}(H).\n\\end{equation}\n\n\\emph{The limit and conditional repair.}\nThe extension of $H$ was fixed independently of $\\eta$. For this fixed\ninput all sums are finite.  Since the two limiting\nchild masses are positive, every quantity in\nEquation~\\eqref{eq:soft-transport} converges as $\\eta\\downarrow0$.\nWe take this limit only after obtaining that inequality; convergence\nof the demands, the $g_j$'s, or the flows is unnecessary.\nFor the limiting $c_{st,l}^\\sigma$, Lemma~\\ref{lem:repair} injects\nstate/completion pairs into one of the two child fibers.  Specifically,\nsince $t$ follows $s$ in row-major order, the receiver is either $s$\nitself or lies in a later row, and all earlier assignments are preserved.\nThe repaired special value is $l$ in the first case and $l-1$ in the\nsecond, with the choice fixed by $s,t$.  Thus\n\\[\n c_{st,l}^\\sigma\\le\\max(z_-,z_+).\n\\]\nThere are fewer than $d$ possible slots $t$.  Multiplication of the\nlimiting inequality by $\\min(z_-,z_+)$ now yields\nEquation~\\eqref{eq:conditional-transport}.\n\\end{proof}\n\n\\subsection{From conditional transport to variance}\n\n\\begin{theorem}[Graph Poincar\\'e inequality]\\label{thm:poincare}\nFor every real function $H$ on the feasible small graph,\n\\begin{equation}\\label{eq:graph-poincare}\n \\Lambda\\Var_{f/\\Lambda}H\n \\le \\left((1+2d^2)4d^2(U+1)^6+2\\right)E(H)\n \\le 770d^4U^6 E(H).\n\\end{equation}\nIn particular, the feasible graph is connected.\n\\end{theorem}\n\\begin{proof}\nFirst restrict to transversals.  At a positive-mass context $\\sigma$,\nput $p_j=z_j/z_\\sigma$.  The between-child contribution to its\nunnormalized variance is\n\\begin{equation}\\label{eq:child-variance}\n z_\\sigma\\Var_p(H_j)\n =\\frac1{z_\\sigma}\\sum_{a<b}z_a z_b(H_a-H_b)^2.\n\\end{equation}\nZero-mass children may be omitted.  For positive endpoints $a<b$,\nLemma~\\ref{lem:child-logconcavity} makes every intervening mass at least\n$\\min(z_a,z_b)$.  Telescoping and Cauchy--Schwarz therefore give\n\\begin{align*}\n \\frac{z_a z_b}{z_\\sigma}(H_a-H_b)^2\n &\\le \\min(z_a,z_b)(b-a)\n             \\sum_{j=a}^{b-1}(H_j-H_{j+1})^2\\\\\n &\\le U\\sum_{j=a}^{b-1}\\min(z_j,z_{j+1})(H_j-H_{j+1})^2\\\\\n &\\le U^2 A_0 E_\\sigma(H).\n\\end{align*}\nThere are at most $(U+1)^2$ pairs.  Thus the contribution in\nEquation~\\eqref{eq:child-variance} is at most\n$(U+1)^4A_0 E_\\sigma(H)$.\n\nIterating the identity\n$\\Var H=\\E(\\Var(H\\mid j))+\\Var(\\E(H\\mid j))$ down the exposure tree\nexpresses the full unnormalized transversal variance as the sum of\nthese contributions.  At any fixed depth, the energies $E_\\sigma$\nuse disjoint edge sets, because distinct contexts assign different\nprefix coordinates.  There are at most $d$ internal levels, and\n$A_0\\le4d(U+1)^2$.  Hence, writing $\\Var_{\\mathcal T}$ for variance\nunder the normalized transversal weights,\n\\begin{equation}\\label{eq:transversal-variance}\n Z_0\\Var_{\\mathcal T}H\\le4d^2(U+1)^6E(H).\n\\end{equation}\nIf there are no small slots, the transversal space is a singleton and\nthis inequality holds directly.\n\nLet $\\overline H$ be the transversal mean, and compare each defect $X$\nto its repair $\\mathcal R(X)$.  By Lemma~\\ref{lem:repair},\n$f(X)\\le f(\\mathcal R(X))$, the repair map is injective within each\nof fewer than $d^2$ defect types, and its edge has conductance $f(X)$.\nDistinct defects give distinct repair edges.  It follows that\n\\begin{align*}\n \\Lambda\\Var_{f/\\Lambda}H\n &\\le\\sum_{X\\in\\mathcal X}f(X)(H(X)-\\overline H)^2\\\\\n &\\le Z_0\\Var_{\\mathcal T}H\n      +2\\sum_{X\\notin\\mathcal T}f(X)\n                         (H(X)-H(\\mathcal R(X)))^2\\\\\n &\\hspace{1.5em}+2\\sum_{X\\notin\\mathcal T}f(X)\n                         (H(\\mathcal R(X))-\\overline H)^2\\\\\n &\\le (1+2d^2)Z_0\\Var_{\\mathcal T}H+2E(H).\n\\end{align*}\nInsert Equation~\\eqref{eq:transversal-variance} to obtain the first\nbound in Equation~\\eqref{eq:graph-poincare}.  Since $U\\ge1$,\n$1+2d^2\\le3d^2$ and $(U+1)^6\\le64U^6$ give the second bound.\nFinally, the indicator of a nonempty proper connected component would\nhave positive variance and zero energy, which is impossible.  The\none-state case is already connected.\n\\end{proof}\n"}, {"path": "preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/main.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Exact-Uniform-Sampling-of-Contingency-Tables-with-Arbitrary-Margins-September-24-2026/main.pdf", "bytes": 525125, "sha256": "be1d677ab515ceb23e7ee9fd369d705345ec0c9254585ac96ded3aa97732cd89", "base64": 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"}, {"path": "preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/README.md", "bytes": 1605, "sha256": "5a4b848bc17d626f37680a7c2253e3af83ef3e3dee92c1183a35c63ab237bbcf", "content": "# [Foulkes' conjecture for the sixth symmetric power](main.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 25, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026,\n  author = {{OpenAI}},\n  title = {{Foulkes' conjecture for the sixth symmetric power}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/main.pdf}{OAI:Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026}},\n  year = {2026}\n}\n```\n\n## Verification\n\nRequires Python 3.9 or later. Run the supplied read-only checks from this paper\ndirectory:\n\n```sh\npython3 -B verification/verify_computations.py --check\n```\n\nSuccess prints `PASS` and confirms the bundled source and reference-stream\nidentities, all 62 expected numeric rows, agreement with the manuscript tables,\nand the stated numerical bounds. This command does not execute the C++ programs.\n\nTo compile and run both complete exact computations, GNU C++ with signed\n128-bit integers and Boost headers is also required:\n\n```sh\npython3 -B verification/verify_computations.py --run --serial\n```\n\nAlternatively, replace `--serial` with `--threads 2` to use OpenMP for Appendix A.\nAssertions remain enabled. A successful full run prints `PASS` after both\nprograms exit successfully and their complete output streams match the reference\nbytes. It records compiler and execution details in a new temporary directory\noutside the paper; `--output PATH` selects a new external directory instead.\n"}, {"path": "preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/main.tex", "bytes": 4826, "sha256": "d0a56cb219a4bac1f78ee09cfa881a3d245d477375c5a88ed47d73a81cc8bf73", "content": "\\documentclass[11pt]{article}\n\\usepackage[margin=1in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage{microtype}\n\\input{glyphtounicode}\n\\pdfgentounicode=1\n\\pdfglyphtounicode{mu}{03BC}\n\\pdfglyphtounicode{bardbl}{2016}\n\\pdfglyphtounicode{parenleftbig}{0028}\n\\pdfglyphtounicode{parenrightbig}{0029}\n\\pdfglyphtounicode{parenleftBig}{0028}\n\\pdfglyphtounicode{parenrightBig}{0029}\n\\pdfglyphtounicode{parenleftbigg}{0028}\n\\pdfglyphtounicode{parenrightbigg}{0029}\n\\pdfglyphtounicode{floorleftbigg}{230A}\n\\pdfglyphtounicode{floorrightbigg}{230B}\n\\pdfglyphtounicode{braceleftbigg}{007B}\n\\pdfglyphtounicode{bracerightbigg}{007D}\n\\pdfglyphtounicode{bracketleftbt}{23A3}\n\\pdfglyphtounicode{bracketrightbt}{23A6}\n\\pdfglyphtounicode{bracketleftex}{23A2}\n\\pdfglyphtounicode{bracketrightex}{23A5}\n\\pdfglyphtounicode{circleplusdisplay}{2A01}\n\\pdfglyphtounicode{summationdisplay}{2211}\n\\pdfglyphtounicode{summationtext}{2211}\n\\pdfglyphtounicode{productdisplay}{220F}\n\\pdfglyphtounicode{producttext}{220F}\n\\pdfglyphtounicode{tildewide}{0303}\n\\usepackage{xcolor}\n\\usepackage{listings}\n\\usepackage{xurl}\n\\usepackage[colorlinks=true,linkcolor=blue!45!black,citecolor=blue!45!black,urlcolor=blue!45!black]{hyperref}\n\\usepackage{accsupp}\n\\NewCommandCopy{\\OriginalHookrightarrow}{\\hookrightarrow}\n\\NewCommandCopy{\\OriginalMapsto}{\\mapsto}\n\\NewCommandCopy{\\OriginalLongmapsto}{\\longmapsto}\n\\NewCommandCopy{\\OriginalLStroke}{\\l}\n\\renewcommand{\\hookrightarrow}{\\mathrel{%\n  \\BeginAccSupp{method=hex,unicode,ActualText=21AA}%\n  \\OriginalHookrightarrow\\EndAccSupp{}}}\n\\renewcommand{\\mapsto}{\\mathrel{%\n  \\BeginAccSupp{method=hex,unicode,ActualText=21A6}%\n  \\OriginalMapsto\\EndAccSupp{}}}\n\\renewcommand{\\longmapsto}{\\mathrel{%\n  \\BeginAccSupp{method=hex,unicode,ActualText=27FC}%\n  \\OriginalLongmapsto\\EndAccSupp{}}}\n\\renewcommand{\\l}{%\n  \\BeginAccSupp{method=hex,unicode,ActualText=0142}%\n  \\OriginalLStroke\\EndAccSupp{}}\n\\hypersetup{pdftitle={Foulkes' conjecture for the sixth symmetric power},pdfauthor={OpenAI}}\n\\pdfinfoomitdate=1\n\\pdftrailerid{}\n\\pdfsuppressptexinfo=15\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\newtheorem{verification}[theorem]{Finite verification}\n\\theoremstyle{definition}\n\\newtheorem{definition}[theorem]{Definition}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\DeclareMathOperator{\\Sym}{Sym}\n\\DeclareMathOperator{\\GL}{GL}\n\\DeclareMathOperator{\\im}{im}\n\\DeclareMathOperator{\\conv}{conv}\n\\DeclareMathOperator{\\aff}{aff}\n\\DeclareMathOperator{\\sgn}{sgn}\n\\newcommand{\\NN}{\\mathbb Z_{\\ge0}}\n\\newcommand{\\CC}{\\mathbb C}\n\\newcommand{\\SSS}{\\mathfrak S}\n\\newcommand{\\Schur}{\\mathbf S}\n\\newcommand{\\st}{\\operatorname{st}}\n\\newcommand{\\HW}{\\operatorname{HW}}\n\\newcommand{\\one}{\\boldsymbol 1}\n\\lstdefinestyle{proofcode}{\n  language=C++,basicstyle=\\ttfamily\\scriptsize,\n  numbers=left,numberstyle=\\tiny\\color{gray},numbersep=6pt,\n  keywordstyle=\\bfseries,commentstyle=\\color{black!65},\n  stringstyle=\\color{black},showstringspaces=false,\n  breaklines=true,breakatwhitespace=false,columns=fullflexible,\n  keepspaces=true,tabsize=2,frame=none,literate={λ}{{$\\lambda$}}1,\n  aboveskip=8pt,belowskip=8pt\n}\n\\title{Foulkes' conjecture for the sixth symmetric power}\n\\author{OpenAI}\n\\date{September 25, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe prove the sixth-symmetric-power case of Foulkes' conjecture. For every\ninteger \\(b\\ge6\\) and every finite-dimensional complex vector space \\(V\\),\nthere is a \\(\\GL(V)\\)-equivariant injection\n\\(\\Sym^6(\\Sym^b V)\\hookrightarrow\\Sym^b(\\Sym^6 V)\\).\n\\end{abstract}\n\\input{sections/introduction}\n\\input{sections/reduction}\n\\input{sections/upper}\n\\input{sections/lower}\n\\input{sections/certificates}\n\\input{sections/band}\n\\input{sections/verification}\n\\bibliographystyle{plain}\n\\bibliography{references}\n\\appendix\n\\section{The bounded certificate program}\\label{app:certificate}\nThe following is the complete first program. It uses GNU C++17 signed\n128-bit integers and optional OpenMP; assertions must remain enabled.\nThe source file is \\nolinkurl{verification/programs/appendix-a.cpp}.\nSee Sections~\\ref{sec:upper}--\\ref{sec:certificates} for the construction\nand Section~\\ref{sec:verification} for arithmetic and reproduction.\n\\lstinputlisting[style=proofcode]{../verification/programs/appendix-a.cpp}\n\\section{The tail-band program}\\label{app:band}\nThe following is the complete second program. It uses GNU C++17 and\nBoost.Multiprecision for the final coefficient sums.\nThe source file is \\texttt{verification/programs/appendix-b.cpp}.\nSee Section~\\ref{sec:band} for the construction and\nSection~\\ref{sec:verification} for arithmetic and reproduction.\n\\lstinputlisting[style=proofcode]{../verification/programs/appendix-b.cpp}\n\\end{document}\n"}, {"path": "preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/references.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/references.bib", "bytes": 13151, "sha256": "1e02e43d1d340aab8463677d2c186280b4915df5f85a350fbcb4946c8f4f8416", "content": "@article{Foulkes1950,\n  author = {Foulkes, H. 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M. and Render, H.},\n  title = {A {Fischer} type decomposition theorem from the apolar inner product},\n  journal = {Analysis and Mathematical Physics},\n  volume = {13},\n  year = {2023},\n  pages = {91},\n  note = {Article 91. \\href{https://doi.org/10.1007/s13324-023-00844-4}{doi: 10.1007/s13324-023-00844-4}},\n  doi = {10.1007/s13324-023-00844-4}\n}\n\n@misc{GanglGutierrezSzwej2026,\n  author = {Gangl, Moritz and Guti{\\'e}rrez, {\\'A}lvaro and Szwej, Micha{\\l}},\n  title = {On the generalised {Foulkes} conjecture for {$\\mathrm{SL}_2(\\mathbb{C})$} under divisibility conditions},\n  year = {2026},\n  howpublished = {arXiv:2507.06220v3},\n  eprint = {2507.06220v3},\n  archivePrefix = {arXiv},\n  primaryClass = {math.CO},\n  note = {Version 3, July 1, 2026. \\href{https://doi.org/10.48550/arXiv.2507.06220}{doi: 10.48550/arXiv.2507.06220}},\n  doi = {10.48550/arXiv.2507.06220},\n  url = {https://arxiv.org/abs/2507.06220v3}\n}\n\n@misc{RaicuSamWeymanYang2026,\n  author = {Raicu, Claudiu and Sam, Steven V. and Weyman, Jerzy and Yang, Fuxiang},\n  title = {Powers of binary forms and derived {Hermite} reciprocity},\n  year = {2026},\n  howpublished = {arXiv:2602.15175v2},\n  eprint = {2602.15175v2},\n  archivePrefix = {arXiv},\n  primaryClass = {math.AC},\n  note = {Version 2, September 2, 2026. \\href{https://doi.org/10.48550/arXiv.2602.15175}{doi: 10.48550/arXiv.2602.15175}},\n  doi = {10.48550/arXiv.2602.15175},\n  url = {https://arxiv.org/abs/2602.15175v2}\n}\n\n@misc{OpenAIQuadratic2026,\n  author = {{OpenAI}},\n  title = {{Quadratic stabilization of the canonical Foulkes--Howe map}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Quadratic-Stabilization-of-the-Canonical-Foulkes-Howe-Map-September-25-2026/paper.pdf}{OAI:Quadratic-Stabilization-of-the-Canonical-Foulkes-Howe-Map-September-25-2026}},\n  year = {2026},\n  note = {Theorem~1}\n}\n"}, {"path": "preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/band.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/band.tex", "bytes": 11025, "sha256": "412495904d153d85b043e6a9d8b88baf240e837348578d8002894bdf61785538", "content": "\\section{Exact differences in the small-tail band}\\label{sec:band}\n\nWe compute the entire band\n\\[\n\\begin{gathered}\n 26\\le b\\le149,\\qquad\n \\lambda=(6b-B-|\\eta|,B,\\eta),\\\\\n \\eta_1\\ge\\cdots\\ge\\eta_4\\ge0,\\quad\n |\\eta|\\le27,\\quad\n \\eta_1\\le B\\le\\left\\lfloor\\frac{6b-|\\eta|}{2}\\right\\rfloor .\n\\end{gathered}\n\\]\nIn particular it contains every flagged partition of\nVerification~\\ref{ver:certificates}.\nFor each fixed tail \\(\\eta\\), we will construct seven Laurent series\nindependent of \\(b\\). Their coefficients at\n\\(B,B-b,\\ldots,B-6b\\) will sum to \\(720(Q(\\lambda)-F(\\lambda))\\).\nThe same seven rows therefore serve every degree and every second part\nin the displayed range.\nWrite \\(\\mathcal Q_b=h_b[h_6]\\), \\(\\mathcal F_b=h_6[h_b]\\), and set\n\\[\n \\mathcal C(q,y)=(1-q)\\prod_{\\ell=1}^4(1-y_\\ell)(1-y_\\ell/q),\\quad\n \\mathcal A(y)=\\prod_{\\ell<h}(1-y_h/y_\\ell),\\quad\n D(q)=\\prod_{h=1}^6(1-q^h).\n\\]\nWe first expand the characters and geometric products in the tail\nvariables \\(y\\), and then expand their rational coefficients in \\(q\\)\nas Laurent series at \\(q=0\\). In particular, \\(D(q)^{-1}\\) has only\nnonnegative powers.\n\n\\subsection{Coefficient extraction and truncation}\nThe normalized alternating Schur formula gives\n\\begin{equation}\\label{eq:bandextract}\n Q(\\lambda)-F(\\lambda)\n =[q^By^\\eta]\\mathcal C(q,y)\\mathcal A(y)\n                       (\\mathcal Q_b-\\mathcal F_b)(1,q,y).\n\\end{equation}\nIndeed in six variables it extracts the coefficient of \\(x^\\lambda\\)\nafter multiplication by \\(\\prod_{i<j}(1-x_j/x_i)\\). Distinct decreasing\nshifted partitions cannot be permutations of each other, so only the\nSchur term at \\(\\lambda\\) contributes. All Laurent monomials have total\ndegree \\(6b\\); setting the first variable to one loses no information\nabout its exponent once the remaining five are fixed.\n\nGive each \\(y_\\ell\\) degree one and \\(q\\) degree zero.\nThe monomials of \\(\\mathcal A\\) have total \\(y\\)-degree zero; those of\n\\(\\mathcal C\\) have nonnegative total \\(y\\)-degree. Character terms above\ndegree \\(27\\) therefore cannot contribute to \\eqref{eq:bandextract}.\nThis uses total degree even when individual exponents in the alternant\nare negative. Write \\(\\equiv_{27}\\) for equality of coefficients of\nnonnegative \\(y\\)-monomials through total degree \\(27\\), with coefficients\nin \\(\\mathbb Q(q)\\).\n\n\\subsection{Seven numerators independent of the degree}\nFor \\(0\\le j\\le6\\) define\n\\[\n L_j(q,y)=\n \\prod_{\\substack{\\tau\\in\\NN^4,\\ 1\\le|\\tau|\\le6\\\\0\\le u\\le6-|\\tau|}}\n                         (1-y^\\tau q^{u-j})^{-1}.\n\\]\nThen\n\\begin{equation}\\label{eq:targetband}\n \\mathcal Q_b(1,q,y)\\equiv_{27}\n \\frac1{D(q)}\\sum_{j=0}^6q^{jb}(-1)^j\n                  e_j(q,q^2,\\ldots,q^6)L_j(q,y).\n\\end{equation}\nTo prove this, fix a multiset of \\(k\\) inner monomials with nonzero tail.\nThe remaining \\(b-k\\) entries come from \\(1,q,\\ldots,q^6\\), with\ngenerating polynomial\n\\[\n h_{b-k}(1,q,\\ldots,q^6)=\n                    \\prod_{h=1}^6\\frac{1-q^{b-k+h}}{1-q^h}\n                                      \\quad(b-k\\ge0).\n\\]\nThis Gaussian polynomial counts partitions in a \\(6\\)-by-\\((b-k)\\)\nrectangle. In general their generating polynomial \\(P_{r,s}\\), with at\nmost \\(r\\) rows of length at most \\(s\\), satisfies\n\\(P_{r,s}=P_{r,s-1}+q^sP_{r-1,s}\\), by removing a row of length \\(s\\)\nwhen one is present, and \\(P_{0,s}=P_{r,0}=1\\).\nThe displayed product with \\(r=b-k,s=6\\) satisfies the same recurrence\nand boundary values, proving the formula.\nExpanding the numerator gives the \\(e_j\\) in \\eqref{eq:targetband};\nthe factor \\(q^{-jk}\\) contributes \\(q^{-j}\\) per selected tail monomial,\nand hence produces \\(L_j\\).\nWithin the cutoff \\(k\\le27\\) and \\(b\\ge26\\), so the only possible\nnegative value of \\(b-k\\) is \\(-1\\). Its numerator has the factor\n\\(1-q^0\\) and vanishes, exactly as required. Thus the formula includes\nthe boundary \\(b=26,k=27\\); its cancellation is across the seven\n\\(j\\)-terms.\n\nSimilarly, the binary complement of one tail monomial gives\n\\begin{equation}\\label{eq:binarysource}\n h_b(1,q,y)\\equiv_{27}\n \\frac{\\prod_\\ell(1-y_\\ell)^{-1}\n       -q^{b+1}\\prod_\\ell(1-y_\\ell/q)^{-1}}{1-q}.\n\\end{equation}\nAt tail degree \\(t\\), its coefficient is\n\\((1-q^{b+1-t})/(1-q)\\). It counts the binary monomials when \\(t\\le b\\)\nand is zero at \\(t=b+1\\), the only extra possible degree.\n\nFor a cycle type \\(I=(i_1,\\ldots,i_v)\\vdash6\\), let\n\\[\n n_I=\\frac{720}{\\prod_{r\\ge1}r^{m_r}m_r!},\\qquad\n E_I(q)=\\frac{D(q)}{\\prod_{h=1}^v(1-q^{i_h})},\n\\]\nwhere \\(m_r\\) counts cycles of length \\(r\\).\nThe quotient \\(E_I\\) is an integer polynomial of degree \\(15\\):\nthe multiplicity of the cyclotomic factor \\(\\Phi_d\\) in its denominator\nis the number of cycle lengths divisible by \\(d\\), which is at most\n\\(\\lfloor6/d\\rfloor\\), its multiplicity in \\(D\\).\nApply \\eqref{eq:binarysource} with variables raised to each cycle length\nin \\eqref{eq:cycle}. Together with \\eqref{eq:targetband} this yields\n\\begin{equation}\\label{eq:seven}\n 720D(q)(\\mathcal Q_b-\\mathcal F_b)(1,q,y)\n                         \\equiv_{27}\\sum_{j=0}^6q^{jb}G_j(q,y),\n\\end{equation}\nwhere the seven numerators are explicitly\n\\begin{align}\nG_j(q,y)={}&720(-1)^je_j(q,\\ldots,q^6)L_j(q,y)\\notag\\\\\n &-\\sum_{I\\vdash6}n_I\n   \\sum_{\\substack{J\\subseteq\\{1,\\ldots,v\\}\\\\\\sum_{h\\in J}i_h=j}}\n   (-1)^{|J|}q^j E_I(q)\n   \\prod_{\\ell=1}^4\\prod_{h=1}^v\n       (1-y_\\ell^{i_h}q^{-i_h\\one_{h\\in J}})^{-1}.\n                                                     \\label{eq:G}\n\\end{align}\nHere \\(\\one_{h\\in J}\\) is the indicator of membership.\nEvery selected second term of \\eqref{eq:binarysource} contributes\n\\(-q^{i_h(b+1)}\\), explaining the sign and the residual \\(q^j\\)\nafter \\(q^{jb}\\) is factored out. Equal-length cycles still have distinct\npositions in the subset sum. These formulas involve eleven cycle types\nand seven values of \\(j\\), regardless of \\(b\\).\n\n\\subsection{Computing the numerators}\nWrite \\(L_{j,a}=[y^a]L_j\\). Euler differentiation of the product gives\n\\begin{equation}\\label{eq:euler}\n L_{j,0}=1,\\qquad\n |a|L_{j,a}=\n \\sum_{\\tau,u}\\ \\sum_{\\substack{i\\ge1\\\\i\\tau\\le a}}\n              |\\tau|q^{i(u-j)}L_{j,a-i\\tau}\\quad(|a|>0).\n\\end{equation}\nFor example, this follows by differentiating the logarithmic series\n\\(\\sum_{i\\ge1}(y^\\tau q^{u-j})^i/i\\). Its denominator \\(i\\) cancels\nthe extra \\(i\\) from differentiation. Every lookup has smaller total\ntail degree, and the division by \\(|a|\\) is exact.\n\nFor the source product in \\eqref{eq:G}, put\n\\[\n A_J(m)=[z^m]\\prod_{h\\in J}(1-z^{i_h})^{-1}.\n\\]\nAt a single tail-coordinate exponent \\(m\\), its Laurent coefficient is\n\\[\n          \\sum_{p=0}^m A_J(p)A_{\\bar J}(m-p)q^{-p}.\n\\]\nMultiply these four one-coordinate polynomials and then\n\\((-1)^{|J|}n_Iq^jE_I(q)\\). These are the \\texttt{ways} arrays and\nconvolutions of \\texttt{source} in Appendix~\\ref{app:band}.\nThe routines \\texttt{target} and \\texttt{gen\\_source} implement\n\\eqref{eq:euler} and the cycle-type enumeration.\n\nAll raw arrays are symmetric in the four tail variables, so they need\nonly be stored at decreasing nonnegative exponent vectors; arbitrary\norders are looked up by sorting, without a sign.\nTo multiply by the cross factors in \\(\\mathcal C\\), the coefficient\nat a shift \\(v\\in\\{0,1,2\\}^4\\) is\n\\[\n                  (-1)^{n_1}q^{-n_2}(1+q^{-1})^{n_1},\n       \\qquad n_s=\\#\\{\\ell:v_\\ell=s\\}.\n\\]\nAn in-place sweep in decreasing tail degree reads unmodified lower-degree\nrows. A subsequent decreasing-exponent sweep multiplies by \\(1-q\\).\nThis is the routine \\texttt{cross}.\n\nThe cross factors preserve symmetry in the four tail variables. To\nextract a Schur coefficient, we now apply the alternating factor\n\\(\\mathcal A\\); the permutation signs enter at this step, rather than in the\nsorted lookups.\nPut \\(\\rho=(3,2,1,0)\\), and let \\(M_{j,a}=[y^a]\\mathcal C G_j\\),\nwith value zero at a vector with a negative coordinate.\nThe exact row produced by \\texttt{schur\\_row} is\n\\begin{equation}\\label{eq:row}\n R_{j,\\eta}(q)=\\frac1{D(q)}\n       \\sum_{\\pi\\in\\SSS_4}\\sgn(\\pi)\n                     M_{j,\\eta+\\rho-\\pi\\rho}(q).\n\\end{equation}\nThis follows from\n\\(\\mathcal A(y)=y^{-\\rho}\\sum_\\pi\\sgn(\\pi)y^{\\pi\\rho}\\).\nEvery accepted index in \\eqref{eq:row} has nonnegative entries and total\ndegree \\(|\\eta|\\le27\\); in particular every coordinate is at most \\(27\\).\nThere are \\(1908\\) decreasing four-coordinate tails in this range.\nEquations~\\eqref{eq:bandextract} and \\eqref{eq:seven} show that the final\ninteger tested is precisely\n\\begin{equation}\\label{eq:finalinteger}\n \\sum_{j=0}^6[q^{B-jb}]R_{j,\\eta}(q)\n       =720\\bigl(Q(6b-B-|\\eta|,B,\\eta)-F(6b-B-|\\eta|,B,\\eta)\\bigr).\n\\end{equation}\nThe notation on the right specifies partitions rather than gap vectors.\n\n\\subsection{The Laurent window}\nAt total tail degree \\(t\\), the coefficients and individual Euler\nsummands of \\(L_j\\) have \\(q\\)-support in \\([-6t,5t]\\).\nThe elementary factor adds at most \\(21\\). A cross term consuming\n\\(s\\) tail units shifts down by at most \\(s\\), while its raw input has\ndegree \\(t-s\\); the factor \\(1-q\\) adds at most one.\nThus all target numerator summands lie in\n\\[\n                      [-6t,5t+22].\n\\]\nThe source has raw support in \\([-t,21]\\) and final support in\n\\([-t,22]\\), using \\(\\deg E_I=15\\) and \\(0\\le j\\le6\\).\nAlternation does not change total tail degree or \\(q\\)-support.\nAt \\(t\\le27\\) all these supports are therefore contained in\n\\[\n                       [-162,157]\\subset[-168,161],\n\\]\nthe interval stored by the program. Its clipping operation discards\nonly zero terms. In particular all terms of the boundary cancellation\nat \\(b=26,k=27\\) are retained separately.\n\nSince \\(D(0)=1\\), its inverse is a power series with no negative\nexponents. Starting from exponent \\(-168\\), six forward passes\n\\(a_n\\gets a_n+a_{n-h}\\), for \\(h=1,\\ldots,6\\), divide by \\(D\\)\nexactly. The numerator is zero above \\(161\\).\nExpansion through \\(447\\) suffices because\n\\[\n                         B-jb\\le B\\le3b\\le447.\n\\]\nEntries below \\(-168\\) are zero; every accessed nonnegative array index\nis at most \\(168+447=615\\), within the \\(616\\) allocated entries.\nNeither division nor the shift \\(q^{jb}\\) can bring a discarded high\nexponent down to a requested one. The program converts to\narbitrary-precision integers before the denominator passes and uses them\nfor all final sums and divisibility tests.\n\nFinally, \\texttt{run\\_thin} visits each of the \\(1908\\) tails once,\nconstructs its seven rows, and loops over the displayed ranges of\n\\(b\\) and \\(B\\). For a fixed \\(b,\\eta\\), the number of permitted\nsecond parts is\n\\[\n       3b-\\left\\lceil\\frac{|\\eta|}{2}\\right\\rceil-\\eta_1+1.\n\\]\nThis is positive throughout the band. Summing it over the tails and\n\\(26\\le b\\le149\\) gives \\(57065668\\) distinct partitions; there is\nno dependence on which partitions the first program flagged.\n\n\\begin{verification}\\label{ver:band}\nThe complete program in Appendix~\\ref{app:band} checks \\(57065668\\)\ncoefficients in this band. Of these, \\(177134\\) are zero and none are\nnegative; every integer in \\eqref{eq:finalinteger} is divisible by \\(720\\).\nThe grouped counts appear in Table~\\ref{tab:band}.\n\\end{verification}\n"}, {"path": "preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/certificates.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/certificates.tex", "bytes": 8725, "sha256": "2bf9183c91f9bfc1277cd03e27e09e1a111b6856d5b605a787d24bdf31434356", "content": "\\section{Certificates in degrees below 150}\\label{sec:certificates}\n\nWe now combine the upper bound \\(F\\le U\\), exact target multiplicities,\nand highest-weight multiplication. The base interval \\(6\\le b\\le25\\)\nis checked directly. Beyond it, exact target values supply lower-bound\nseeds: they are computed from the target recurrence without assuming\nthe desired comparison. The resulting certificates leave a set of\npartitions whose exact differences will be checked in\nSection~\\ref{sec:band}. We retain the full interval through \\(b=149\\)\nfor the independent chart proof; only \\(b\\le29\\) is needed with\nquadratic stabilization.\n\n\\subsection{Initial tables and the base interval}\nCompute \\(Q\\) using \\eqref{eq:targetrec} on the following four sets.\nAll partitions have length at most six.\n\\begin{equation}\\label{eq:tables}\n\\begin{array}{c|l|c}\n\\text{degrees}&\\text{additional restriction}&\\text{variables used}\\\\ \\hline\n0\\le b\\le25&\\text{none}&6\\\\\n0\\le b\\le149&\\operatorname{length}(\\lambda)\\le3&3\\\\\n0\\le b\\le55&\\operatorname{length}(\\lambda)\\le4&4\\\\\n0\\le b\\le45&t_3(d)\\le18&6\n\\end{array}\n\\end{equation}\nPrevious-degree lookups remain in the applicable set by\nSection~\\ref{sec:upper}. The first table supplies the direct base\ncomparisons; all four supply lower-bound seeds for larger degrees.\nOverlaps are immaterial.\n\nFor \\(6\\le b\\le25\\), every partition is first tested against \\(Q(d)\\ge\nU(d)\\). When this fails, compute \\(F(d)=H_6(\\lambda(d))\\) exactly by\n\\eqref{eq:sourcerec}. Only these requested values at the last stage\n\\(j=6\\) are needed. Table~\\ref{tab:base} records all the fallback\ncomparisons and their equalities.\n\n\\begin{table}[ht]\n\\centering\n\\begin{tabular}{r|r|r@{\\qquad}r|r|r}\n\\(b\\)&exact comparisons&equalities&\\(b\\)&exact comparisons&equalities\\\\ \\hline\n6&2428&2428&16&7370&354\\\\\n7&4476&315&17&7599&369\\\\\n8&6820&255&18&7801&384\\\\\n9&6899&250&19&8057&399\\\\\n10&6886&264&20&8233&414\\\\\n11&6814&279&21&8427&429\\\\\n12&6850&294&22&8650&444\\\\\n13&6872&309&23&8850&459\\\\\n14&6973&324&24&9031&474\\\\\n15&7131&339&25&9268&489\n\\end{tabular}\n\\caption{The base interval: \\(145435\\) exact fallback comparisons,\n\\(9272\\) equalities, and no negative difference. All other partitions\npass \\(Q\\ge U\\).}\\label{tab:base}\n\\end{table}\n\n\\subsection{Residues and transport of lower bounds}\nFor \\(26\\le b\\le149\\) we need only consider \\(d_6<6\\), by\nLemma~\\ref{lem:rectangles}. There is a unique expression\n\\[\n d=r+6m,\\qquad\n r\\in\\mathcal R=\\{0,\\ldots,5\\}^6\\cap\\{6\\mid w(r)\\},\\qquad\n m\\in\\mathcal G=\\{m\\in\\NN^6:m_6=0,\\ w(m)\\le149\\}.\n\\]\nWrite \\(b_0(r)=w(r)/6\\), so that \\(b=b_0(r)+w(m)\\).\nThere are \\(7776\\) residues: choose \\(r_2,\\ldots,r_6\\), and \\(r_1\\) is\nuniquely determined modulo six. Moreover \\(b_0(r)\\le17\\).\nThe grid \\(\\mathcal G\\) has \\(6611697\\) points. We call its indices\n\\(m\\) modes: fixing \\(m\\) groups the \\(7776\\) possible residues for\nsimultaneous testing. Pairs whose degree lies outside\n\\(26\\le b\\le149\\) will not require a test.\n\nFor an array on a downward-closed set, \\emph{propagation} replaces its\nvalue at \\(m\\) by the maximum of all values at indices \\(u\\le m\\).\nExtending an array means filling new positions by zero and propagating.\nAn increasing-\\(w\\) sweep taking maxima with the five immediate\npredecessors performs this operation. A reverse sweep through the same\npredecessors forms the downward closure of a set.\nLemma~\\ref{lem:rectangles} shows that propagation preserves every lower\nbound for \\(Q(r+6m)\\), with \\(r\\) fixed.\n\nThe same multiplication lemma also transports a multiplicity from\nresidue zero to residue \\(r\\). Suppose \\(J(u)\\le Q(6u)\\) for\n\\(u\\in\\mathcal G\\), \\(M(m)\\le Q(r+6m)\\), and \\(E\\subseteq\\mathcal G\\)\nis a set of indices satisfying\n\\(Q(r+6c)>0\\) for \\(c\\in E\\). Multiplication by one nonzero\nhighest-weight polynomial at \\(r+6c\\) gives\n\\begin{equation}\\label{eq:transport}\n Q(r+6m)\\ge\n \\max\\left(\\{M(m)\\}\\cup\n      \\{J(m-c):c\\in E,\\ c\\le m\\}\\right).\n\\end{equation}\nThe right side certifies the comparison whenever it is at least\n\\(U(r+6m)\\). No product of two multiplicity dimensions is used: a\nsingle nonzero factor gives each injection.\n\nWe first construct \\(J\\) and choices of \\(M,E\\) valid for every residue,\nso that one test can discard a whole mode. For the modes left over we\nuse more precise choices depending on \\(r\\). The following construction\nspecifies all arrays and tests; the common cap \\(Z=2^{100}\\) is applied\nonly to lower bounds.\n\n\\subsection{Certificates shared by all residues}\n\n\\paragraph{A shared power table.}\nInitialize \\(J(m)=1\\) on \\(\\mathcal G\\), using the rectangle occurrences\nand the nonzero constant at \\(m=0\\). For every gap vector \\(x\\) with\n\\(x_6=0\\), \\(Q(x)>1\\), and known value in one of the first three sets\nof \\eqref{eq:tables}, put \\(d_0=w(x)/6\\) and \\(C=\\min(Q(x),Z)\\).\nFor each integer \\(k\\ge1\\) with \\(kd_0\\le149\\) and all entries of \\(kx\\)\ndivisible by six, update\n\\[\n             J(kx/6)\\ \\gets\\\n                 \\max\\{J(kx/6),\\ \\min(Z,f(C,z,k))\\},\n\\]\nwhere \\(z\\) is as in Proposition~\\ref{prop:power}. Then propagate.\nIt follows that\n\\begin{equation}\\label{eq:Jbound}\n                         J(m)\\le Q(6m)\\quad(m\\in\\mathcal G).\n\\end{equation}\n\n\\paragraph{Common multiplicities and occurrences.}\nFor each \\(r\\), initialize the exact array \\(Q(r+6u)\\) on\n\\(w(u)\\le25-b_0(r)\\), then extend it to \\(w(m)\\le42\\).\nTake the pointwise minimum of these propagated arrays over all \\(r\\),\nwith a cap at \\(Z\\), to obtain \\(M_0\\) on that grid.\nLet \\(E_0\\) be the minimal points where \\(M_0>0\\), before extending\n\\(M_0\\) to all of \\(\\mathcal G\\). Then\n\\begin{equation}\\label{eq:Mbound}\n M_0(m)\\le Q(r+6m)\\quad\\text{for every }r,\\qquad\n Q(r+6c)>0\\quad\\text{for every }r\\text{ and }c\\in E_0.\n\\end{equation}\nThe exact entries supplying the minimum may come from different\npredecessors for different \\(r\\). Their common consequence is precisely\nthe two universal inequalities in \\eqref{eq:Mbound}.\n\n\\paragraph{Selecting modes.}\nKeep a mode \\(m\\in\\mathcal G\\) unless either \\(w(m)+17\\le25\\), or\n\\begin{equation}\\label{eq:sharedtest}\n \\max\\left(\\{M_0(m)\\}\\cup\n       \\{J(m-c):c\\in E_0,\\ c\\le m\\}\\right)\n                   \\ge U(6m+(5,5,5,5,5,5)).\n\\end{equation}\nThe first condition puts every residue in the already settled base\nrange whenever its degree is at least six. For the second, use\n\\eqref{eq:transport} with \\(M=M_0\\) and \\(E=E_0\\).\nProposition~\\ref{prop:upper} bounds \\(F(r+6m)\\) by\n\\(U(6m+(5,5,5,5,5,5))\\), because \\(r\\le(5,5,5,5,5,5)\\).\nThe high-residue gap vector need not be admissible; \\(U\\) is defined and\nmonotone there nonetheless.\n\n\\subsection{Tests for each remaining residue}\nForm the downward closure of the retained modes. On this closure,\ninitialize \\(Q(r+6m)\\) wherever it is available in\n\\eqref{eq:tables}, use zero elsewhere, and propagate to obtain \\(M_r\\).\nLet \\(E_r\\) be the minimal positive locations of the exact array\n\\(Q(r+6u)\\) on \\(w(u)\\le25-b_0(r)\\).\nFor each originally retained mode with \\(26\\le b_0(r)+w(m)\\le149\\),\nput \\(d=r+6m\\) and clear the pair if either\n\\[\n t_1(d)\\le b\n \\quad\\text{or}\\quad\n \\max\\left(\\{M_r(m)\\}\\cup\n          \\{J(m-c):c\\in E_r,\\ c\\le m\\}\\right)\\ge U(d).\n\\]\nFlag it otherwise. The first test is Corollary~\\ref{cor:first-row}.\nThe second is \\eqref{eq:transport} with \\(M=M_r\\) and \\(E=E_r\\).\n\nMinimal positive locations can be found by checking the immediate\npredecessors. Indeed positive exact multiplicities form an upper set\nwithin the small grid, by the rectangular shifts; the propagated common\narray has the same property. The set used for \\(M_r\\) retains every\npredecessor needed for propagation. Each difference \\(m-c\\) in a mixed\ntest is nonnegative and lies in \\(\\mathcal G\\).\n\nIn Appendix~\\ref{app:certificate}, \\texttt{powerAll} stores \\(J\\),\n\\texttt{uniBase} and \\texttt{uOcc} store \\(M_0\\) and \\(E_0\\).\nThe routine \\texttt{setup} retains the modes and their downward closure,\n\\texttt{init\\_occ} constructs \\(E_r\\), and \\texttt{TestSpec} propagates\n\\(M_r\\) and tests the remaining pairs. The routine \\texttt{run} performs\nthe base-interval comparisons.\n\n\\begin{verification}\\label{ver:certificates}\nThe complete program in Appendix~\\ref{app:certificate} verifies the\nbase comparisons in Table~\\ref{tab:base}. Its mode list has \\(87354\\)\npoints, or \\(114314\\) after taking the downward closure. It flags\n\\(4969698\\) pairs in the middle interval, all with \\(t_2(d)\\le27\\).\nTheir counts by degree range appear in Table~\\ref{tab:band}.\n\\end{verification}\n\nEvery pair in \\(26\\le b\\le149\\) cleared by these tests satisfies\n\\(Q\\ge F\\), by the proved upper and lower bounds or the chart.\nVerification~\\ref{ver:certificates}\nsupplies the separate finite assertion that every pair left over has\n\\(t_2\\le27\\). Thus it remains only to check the entire band\n\\(t_2\\le27\\) in \\(26\\le b\\le149\\).\n"}, {"path": "preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/introduction.tex", "bytes": 8212, "sha256": "f0447bbe5113462ce532fd1c5ee8f3cb241e6ddbaff2b103d29dd263b8229472", "content": "\\section{The result and its context}\n\nFoulkes' conjecture compares two ways of composing symmetric powers.\nFor a finite-dimensional complex vector space \\(V\\) and integers\n\\(1\\le a\\le b\\), it asks for a \\(\\GL(V)\\)-equivariant injection\n\\[\n             \\Sym^a(\\Sym^b V)\\hookrightarrow\\Sym^b(\\Sym^a V).\n\\]\nOver \\(\\CC\\), complete reducibility makes this equivalent to an\ninequality between the multiplicities of every irreducible polynomial\nrepresentation. Write \\(\\Schur_\\lambda V\\) for the Schur module indexed\nby a partition \\(\\lambda\\), and \\(s_\\lambda\\) for its character.\nIf \\(h_r\\) denotes the complete homogeneous symmetric function, then\n\\(h_a[h_b]\\) is the stable character of \\(\\Sym^a(\\Sym^b V)\\), where\nbrackets denote plethysm. The conjecture says that\n\\(h_b[h_a]-h_a[h_b]\\) is Schur-positive: all its Schur coefficients\nare nonnegative integers. We prove the sixth case.\n\n\\begin{theorem}\\label{thm:main}\nFor every integer \\(b\\ge6\\) and every finite-dimensional complex vector\nspace \\(V\\), there is a \\(\\GL(V)\\)-equivariant injection\n\\[\n                 \\Sym^6(\\Sym^b V)\\ \\hookrightarrow\\ \\Sym^b(\\Sym^6 V).\n\\]\nEquivalently, \\(h_b[h_6]-h_6[h_b]\\) is Schur-positive for every \\(b\\ge6\\).\n\\end{theorem}\n\nTheorem~\\ref{thm:main} resolves this case of Foulkes' conjecture\npositively, with no restriction on \\(\\dim V\\). The diagonal case\n\\(b=6\\) is immediate; the content is the comparison for every larger\n\\(b\\).\n\n\\paragraph{History and the canonical map.}\nThe conjecture originates in Foulkes' study of concomitants\n\\cite{Foulkes1950}. For binary forms, classical Hermite reciprocity\ngives an isomorphism between the two symmetric powers for all \\(a,b\\);\nsee \\cite[Introduction]{RaicuSamWeyman2022}. In higher dimension one\nseeks a multiplicity inequality instead.\nThe case \\(a=1\\) is immediate. Thrall's decomposition formulas imply\n\\(a=2\\) \\cite{Thrall1942}; see also \\cite[p.~237]{DentSiemons2000}.\nDent and Siemons proved \\(a=3\\) \\cite{DentSiemons2000}.\n\nA natural approach uses the canonical Foulkes--Howe map\n\\[\n \\Psi_{a,b,V}:\\Sym^a(\\Sym^b V)\\longrightarrow\\Sym^b(\\Sym^a V).\n\\]\nRealize the source as symmetric tensors in \\(V^{\\otimes ab}\\), arrange\nthe tensor positions in \\(a\\) rows of length \\(b\\), transpose the array,\nand symmetrize the resulting blocks. McKay proved that injectivity\nfor every \\(V\\) at a seed \\((a,b_0)\\), with \\(b_0\\ge a\\), propagates to every\n\\((a,b)\\) with \\(b\\ge b_0\\)\n\\cite{McKay2008,Ikenmeyer2015}.\nThe computation at \\((4,4)\\) by M\\\"uller and Neunh\\\"offer therefore\nyields \\(a=4\\) \\cite{MuellerNeunhoeffer2005};\nCheung, Ikenmeyer, and Mkrtchyan's injectivity computation at \\((5,6)\\)\nyields \\(a=5\\), with the diagonal comparison again immediate\n\\cite[preprint, Theorem~6(a)]{CheungIkenmeyerMkrtchyan2017}.\nCanonical injectivity is stronger than Foulkes' multiplicity comparison.\nThe canonical maps at \\((5,5)\\) and \\((6,6)\\) have nonzero kernels in\nsuitable dimensions, although their source and target representations\ncoincide \\cite{MuellerNeunhoeffer2005,CheungIkenmeyerMkrtchyan2017}.\nFor the sixth diagonal this already occurs when \\(\\dim V\\ge6\\)\n\\cite[preprint, Theorem~6(c)]{CheungIkenmeyerMkrtchyan2017}.\nOur finite comparison does not require injectivity of a prescribed map.\n\nExact character calculations give another approach.\nEvseev, Paget, and Wildon verified Foulkes' multiplicity inequalities\nwhen the two parameters sum to at most \\(19\\), including the sixth\ncase through \\(b=13\\)\n\\cite[Corollary~5.2]{EvseevPagetWildon2014}.\nTheir character-deflation recurrence is a predecessor of the exact\nsource calculation used here. The present certificates combine such\ncalculations with bounds on entire families of multiplicities, leaving\nonly a small band for exact comparison.\n\nBrion proved eventual truth for fixed \\(a\\)\n\\cite[Corollary~1.3]{Brion1993} and later gave a dimension-dependent\nbound for surjectivity of the canonical map in the opposite direction\n\\cite[Theorem~3.3]{Brion1997}.\nThat map, \\(\\Psi_{b,a,V}\\), is realized as multiplication in the invariant\nring associated with the normalization of the Chow variety\n\\cite[Section~7]{Landsberg2015}; \\cite[Section~2]{RaicuSamWeyman2022}.\nThe companion paper proves its surjectivity for \\(b\\ge a(a-1)\\),\nuniformly in dimension \\cite[Theorem~1]{OpenAIQuadratic2026}.\nAt \\(a=6\\), an invariant complement to its kernel supplies an\nembedding for \\(b\\ge30\\). The remaining task is to compare all\nmultiplicities for \\(6\\le b\\le29\\).\n\nFor binary forms, recent generalized comparisons concern plethysms\nwith different parameter pairs: Raicu, Sam, Weyman, and Yang prove\nmaximal rank under divisibility conditions\n\\cite[Theorem~1.4]{RaicuSamWeymanYang2026}, and Gangl, Guti\\'errez,\nand Szwej identify the dual map through a substitution construction\n\\cite[Theorem~1.2]{GanglGutierrezSzwej2026}.\nThese results concern a two-dimensional underlying space; the theorem\nabove treats every finite dimension.\n\n\\paragraph{Proof strategy.}\nThe source \\(\\Sym^6(\\Sym^b V)\\) has Schur support of length at most\nsix. Let \\(F(\\lambda)\\) and \\(Q(\\lambda)\\) denote its multiplicity and\nthe corresponding target multiplicity, respectively, for a partition\n\\(\\lambda\\) of \\(6b\\). Section~\\ref{sec:reduction} reduces the problem\nto these six-part partitions and supplies rectangular highest-weight\nshifts of known comparisons and a chart certificate\nfor partitions with \\(\\lambda_2+\\cdots+\\lambda_6\\le b\\).\nThe canonical multiplication map also proves the main large-degree\nrange in Corollary~\\ref{cor:quadratic-range}.\n\nThe finite argument compares upper bounds for \\(F\\) with lower bounds\nfor \\(Q\\). Section~\\ref{sec:upper} bounds source multiplicities by\ncounting possible intermediate partitions in a signed strip recurrence,\nand gives exact recurrences for both characters.\nSection~\\ref{sec:lower} amplifies known target multiplicities:\na basis of highest-weight polynomials can be chosen with distinct leading\nmonomials, so a sumset bound supplies many independent products of those\npolynomials.\nMultiplication by a single nonzero highest-weight polynomial then\ntransfers these lower bounds to further weights.\nSection~\\ref{sec:certificates} organizes the resulting tests into a\nfinite computation. It checks every partition for \\(6\\le b\\le25\\).\nIn increasing degrees from \\(26\\) onward, rectangular shifts reduce\nthe remaining problem to \\(\\lambda_6<6\\); the certificates leave only\npartitions in the band \\(\\lambda_3+\\cdots+\\lambda_6\\le27\\).\nSection~\\ref{sec:band} computes exact differences throughout this band\nby reusing seven truncated rational-function numerators.\nSection~\\ref{sec:verification} proves the arithmetic bounds and\nassembles the range coverage.\n\nAn independent route is retained in full. The chart calculation used\nin the finite comparison, combined with polarization, proves canonical\nsurjectivity for \\(b\\ge150\\) in Proposition~\\ref{prop:large}.\nThe polarization argument is related to the successive weight shifts\nin the proof of McKay's theorem \\cite[Section~5]{Ikenmeyer2015}.\nBoth certificate programs run through \\(b=149\\), so the extended finite\nverification and this chart bound prove Theorem~\\ref{thm:main} without\nthe quadratic-stabilization input. The main route needs only the\nfinite comparisons through \\(b=29\\).\nThe complete programs are printed in the appendices and supplied with\nreference outputs and a reproduction script. The formulas explain\nwhat the programs compute; their exact execution supplies the finite\nnonnegativity assertions.\n\nThroughout, we use characteristic-zero complete reducibility and\nhighest-weight theory for polynomial \\(\\GL\\)-representations\n\\cite[Theorem~9.19 and Proposition~14.13]{FultonHarris1991},\nSchur functors and the Cauchy decomposition\n\\cite[Theorem~6.3 and Exercise~6.11(b)]{FultonHarris1991},\nand the Schur alternant formula \\cite[Chapter~I, (3.1)]{Macdonald1995}.\nStable Schur coefficients are unchanged on specializing extra character\nvariables to zero whenever the partition length remains admissible\n\\cite[Chapter~I, (3.2)--(3.3)]{Macdonald1995}.\nThe character interpretation of plethysm is recalled in\n\\cite[Chapter~I, Appendix~A, Sections~7--8]{Macdonald1995}.\nWe also write \\(e_r\\) for the elementary symmetric function and set\n\\(h_0=e_0=1\\).\n"}, {"path": "preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/lower.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/lower.tex", "bytes": 5212, "sha256": "912a69db7319bae79afad44c5a5458cc59e2a7f63ce2b8498fce8b29a5c731e1", "content": "\\section{A power bound from leading monomials}\\label{sec:lower}\n\nRectangular shifts give a first lower bound for \\(Q\\). The following\nbound amplifies a known multiplicity.\n\nThe sumset estimate is the repeated-set case of the Matolcsi--Ruzsa\ninequality \\cite[preprint, Theorem~1.5]{MatolcsiRuzsa2010};\ntake \\(A=B=D\\) and \\(r=k-1\\) there when \\(k\\ge2\\).\nSee also \\cite[author version, Corollary~2]{BoroczkySantosSerra2014}.\nWe include a self-contained triangulation proof. Its generic-point\nassignment is closely related to the half-open-simplex count used in\n\\cite[author version, Theorem~20]{BoroczkySantosSerra2014}, where the\nregions are instead obtained from a shelling.\n\n\\begin{lemma}[Sumsets]\\label{lem:sumset}\nLet \\(D\\) be a nonempty finite set of \\(C\\) points in a real vector space,\nof affine dimension \\(h\\). Write\n\\(kD=\\{v_1+\\cdots+v_k:v_1,\\ldots,v_k\\in D\\}\\).\nFor this \\(k\\)-fold sumset, \\(k\\ge1\\), one has\n\\[\n |kD|\\ \\ge\\\n f(C,h,k):=\\binom{k+h}{h}+(C-1-h)\\binom{k+h-1}{h}.\n\\]\n\\end{lemma}\n\\begin{proof}\nThe case \\(h=0\\) is immediate. Triangulate \\(\\conv D\\) using every point\nof \\(D\\) as a vertex and no other vertices. Such a triangulation can be\nbuilt by starting with a full-dimensional simplex from \\(D\\), then\ninserting points. For a point in the current hull, subdivide the simplices\nthrough its minimal containing face in the current triangulation by\nconing from it, retaining the induced common face subdivisions.\nFor a point outside the hull, cone from it to the\nboundary simplices on strictly visible facets. Rays from the new point\ngive the intersection and coverage assertions in the latter construction.\nBoth operations preserve all previous vertices.\n\nChoose \\(p\\) in the relative interior of the hull, off all hyperplanes\nof facets of full-dimensional simplices. Assign \\(x\\in\\conv D\\) to the\nunique full simplex whose interior contains \\((1-\\epsilon)x+\\epsilon p\\)\nfor all sufficiently small positive \\(\\epsilon\\). There are finitely many\nfacet hyperplanes, so this rule is well defined.\n\nIn barycentric coordinates of one full simplex, this assigned region\nrequires nonnegative coordinates, with strict positivity exactly at the\nindices where the corresponding coordinate of \\(p\\) is negative.\nAssign a sum of \\(k\\) vertices according to its average, which lies in\n\\(\\conv D\\). If there are \\(e\\) strict indices, the sums of \\(k\\) vertices\nof this simplex whose averages are assigned to it number\n\\(\\binom{k-e+h}{h}\\), interpreted as zero if \\(k<e\\).\nAffine independence makes these points distinct; the assignment makes\nthe counted sets for different simplices disjoint subsets of \\(kD\\).\n\nThere is exactly one simplex with \\(e=0\\), namely the one containing\n\\(p\\). At \\(k=1\\) the counting rule counts precisely the \\(C\\) vertices.\nTo see this, a vertex of the complex lying in a full simplex must be a\nvertex of that simplex, by the common-face property. Its assigned\nsimplex therefore counts it. A simplex with \\(e=0\\) contributes \\(h+1\\),\none with \\(e=1\\) contributes \\(1\\), and all others contribute zero.\nThus exactly \\(C-h-1\\) simplices have \\(e=1\\). Keeping just the \\(e=0,1\\)\ncontributions for arbitrary \\(k\\) proves the bound.\n\\end{proof}\n\nThe leading-monomial argument in the next proposition is an elementary\ninstance of the value-semigroup method: dimensions are counted by\ndistinct values, and multiplication adds values; see\n\\cite[preprint, Propositions~2.3, 2.6, and~2.10]{KavehKhovanskii2012}.\nWe give the finite-degree argument and the dimension estimate explicitly,\nsince their quantitative form is used in the certificate.\n\n\\begin{proposition}[Power certificate]\\label{prop:power}\nSuppose \\(Q(x)\\ge C>0\\), where \\(C\\) is an integer and\n\\(w(x)=6d_0\\) with \\(d_0>0\\). Put\n\\[\n z=\\min\\left(18,\\ \\min\\left\\{h\\ge0:\n                              \\binom{d_0+h}{h}\\ge C\\right\\}\\right).\n\\]\nThen for all \\(k\\ge1\\),\n\\begin{equation}\\label{eq:power}\n                Q(kx)\\ge f(C,z,k).\n\\end{equation}\n\\end{proposition}\nThe cap at \\(18\\) only weakens the lower bound; it limits the integer\narithmetic in Section~\\ref{sec:verification}.\n\n\\begin{proof}\nChoose \\(C\\) independent highest-weight polynomials at \\(x\\) in the\npolynomial algebra \\(S=\\Sym(\\Sym^6\\CC^6)\\). Row reduction with respect to\na fixed monomial order gives a basis with distinct leading monomials.\nTheir exponent vectors form a set \\(D\\) of size \\(C\\), all with\nnonnegative integer entries summing to \\(d_0\\).\nFor each point of \\(kD\\), choose a product of \\(k\\) basis elements with\nthat leading exponent. The products have distinct leading monomials\nand hence are independent highest-weight polynomials at \\(kx\\).\nThus \\(Q(kx)\\ge|kD|\\).\n\nLet \\(h=\\dim\\aff D\\). Some projection onto \\(h\\) coordinate positions\nis injective on \\(\\aff D\\), by a nonzero maximal minor of its direction\nspace. The projected integer points are nonnegative with sum at most\n\\(d_0\\), so\n\\[\n                     C\\le\\binom{d_0+h}{h},\\qquad z\\le h\\le C-1.\n\\]\nLemma~\\ref{lem:sumset} applies. Finally, for \\(0\\le j<h\\) one computes\n\\[\n \\frac{f(C,j+1,k)-f(C,j,k)}{\\binom{k+j-1}{j}}\n                  =\\frac{(k-1)(C-j-1)}{j+1}\\ge0.\n\\]\nThis proves \\(f(C,h,k)\\ge f(C,z,k)\\). The formula also covers \\(C=1\\),\nwhen \\(h=z=0\\), and \\(k=1\\).\n\\end{proof}\n"}, {"path": "preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/reduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/reduction.tex", "bytes": 13562, "sha256": "0ea80c214a9cf4d05a28f8de31671c79c009e7d75f0cc850b77d9fcf0d91e864", "content": "\\section{The large-degree range and finite reductions}\\label{sec:reduction}\n\nThe proof has a representation-theoretic reduction and a finite\nmultiplicity comparison. We first specify the canonical map supplied by\nthe companion and reduce the remaining comparison to partitions with at\nmost six parts. A chart of the same invariant ring gives one of the finite\ncertificates. The final subsection proves the independent large-degree\nbound used with the extended computation.\n\n\\subsection{The multiplication map}\nFor the moment let \\(a\\ge2\\), and set\n\\[\n P=(\\Sym V)^{\\otimes a},\\qquad\n W_j=P_{(j,\\ldots,j)},\\qquad\n R_j=W_j^{\\SSS_a},\\qquad R=\\bigoplus_{j\\ge0}R_j.\n\\]\nThe symmetric group permutes the tensor factors. We identify\n\\(\\Sym^a W\\) with \\((W^{\\otimes a})^{\\SSS_a}\\) by the normalized average\n\\[\n w_1\\cdots w_a\\longmapsto\n \\frac1{a!}\\sum_{\\sigma\\in\\SSS_a}\n w_{\\sigma(1)}\\otimes\\cdots\\otimes w_{\\sigma(a)}.\n\\]\nIts inverse is the restriction of the symmetric-product quotient.\nLet \\(A\\subset R\\)\nbe the graded subalgebra generated by \\(R_1=\\Sym^a V\\).\nFor \\(v\\in V\\), put \\(z(v)=v^{\\otimes a}\\in R_1\\).\nMultiplication defines the canonical Foulkes--Howe map\n\\begin{equation}\\label{eq:multmap}\n       \\mu_{a,b,V}:\\Sym^b(\\Sym^a V)\\longrightarrow\n       R_b=\\Sym^a(\\Sym^b V),\n\\end{equation}\nwhose image is \\(A_b\\). Thus \\(\\mu_{a,b,V}=\\Psi_{b,a,V}\\) in the\nnotation of the introduction. It is equivariant, so \\(A_b=R_b\\) suffices\nfor the desired embedding, by complete\nreducibility. This is the equal-multidegree invariant construction\nunderlying the Chow normalization; see \\cite[Section~2]{RaicuSamWeyman2022}.\nWe will use its explicit polynomial realization.\n\n\\begin{corollary}[The main large-degree range]\\label{cor:quadratic-range}\nFor every finite-dimensional complex \\(V\\) and every \\(b\\ge30\\), there\nis a \\(\\GL(V)\\)-equivariant injection\n\\(\\Sym^6(\\Sym^b V)\\hookrightarrow\\Sym^b(\\Sym^6 V)\\).\n\\end{corollary}\n\\begin{proof}\nThe map in~\\eqref{eq:multmap}, with exactly the averaging convention\nabove, is the canonical multiplication map of\n\\cite[Theorem~1]{OpenAIQuadratic2026}. That theorem gives \\(A_b=R_b\\)\nfor \\(a=6\\) and \\(b\\ge6(6-1)=30\\), including dimension zero.\nIn positive dimension its kernel has an invariant complement, by\ncomplete reducibility of complex polynomial \\(\\GL(V)\\)-representations.\nRestriction to this complement is an isomorphism onto \\(R_b\\);\nits inverse gives an equivariant section and hence the stated injection.\nThere is no assertion that this section is a prescribed canonical map.\n\\end{proof}\n\n\\subsection{Six variables and rectangular shifts}\nFor the finite comparison take \\(a=6\\). If \\(b\\ge0\\) and\n\\(\\lambda\\) is a partition of \\(6b\\), the Cauchy decomposition of\n\\(P=\\Sym(V\\otimes\\CC^6)\\)\n\\cite[Exercise~6.11(b)]{FultonHarris1991} gives the source multiplicity\n\\begin{equation}\\label{eq:label-multiplicity}\n F(\\lambda)=\\dim\\left((\\Schur_\\lambda\\CC^6)_{(b,\\ldots,b)}^{\\SSS_6}\\right).\n\\end{equation}\nIn particular it vanishes for partitions of length greater than six.\nStable Schur coefficients allow us to work in \\(V=\\CC^6\\): extra\nvariables are specialized to zero, while longer target constituents\nalready have nonnegative multiplicities. This covers every dimension.\n\nPut \\(S=\\Sym(\\Sym^6\\CC^6)\\) with its ordinary symmetric-algebra grading,\nand let \\(Q(\\lambda)\\) be the multiplicity of \\(\\Schur_\\lambda\\CC^6\\)\nin \\(S_b\\). Pad every partition by zeros to six parts and write\n\\[\n d_i=\\lambda_i-\\lambda_{i+1},\\quad \\lambda_7=0,\\qquad\n \\lambda(d)_i=\\sum_{j=i}^6d_j,\\qquad\n w(d)=\\sum_{i=1}^6 i\\,d_i.\n\\]\nAlso put \\(t_s(d)=\\sum_{j>s}\\lambda(d)_j\\). We abbreviate\n\\(F(d)=F(\\lambda(d))\\) and \\(Q(d)=Q(\\lambda(d))\\) when \\(6\\mid w(d)\\);\nthen \\(b=w(d)/6\\). Gap-vector inequalities are componentwise, and \\(e_i\\)\ndenotes the \\(i\\)-th unit vector in gap coordinates.\n\n\\begin{lemma}\\label{lem:rectangles}\nLet \\(c,d\\in\\NN^6\\) satisfy \\(6\\mid w(c)\\) and \\(6\\mid w(d)\\).\nIf \\(Q(c)>0\\), then \\(Q(d+c)\\ge Q(d)\\). In particular\n\\[\n          Q(d+6e_i)\\ge Q(d)\\quad(1\\le i\\le6),\n          \\qquad F(d+6e_6)=F(d).\n\\]\n\\end{lemma}\n\\begin{proof}\nMultiplication by one nonzero highest-weight polynomial of weight\n\\(\\lambda(c)\\) injects the highest-weight space at \\(\\lambda(d)\\)\ninto that at their sum, because \\(S\\) is a domain.\nThis is the usual highest-weight multiplication argument; compare\n\\cite[preprint, Lemma~2.2]{BurgisserIkenmeyerPanova2019}.\n\nThe rectangular occurrence \\(Q(6e_i)>0\\) is a special case of the\neven-partition theorem of B\\\"urgisser, Christandl, and Ikenmeyer\n\\cite[Section~1.1, Theorem]{BurgisserChristandlIkenmeyer2011}.\nWe give the determinant-tensor construction of\n\\cite[preprint, Corollary~4.8]{BurgisserIkenmeyerPanova2019}\nin the form needed here. Take six copies of the determinant tensor on\nthe first \\(i\\) basis vectors. Regroup the tensor positions into \\(i\\)\nblocks, each taking the same position from every copy, and symmetrize\nto \\(\\Sym^i(\\Sym^6\\CC^6)\\). This is a highest vector of weight\n\\((6,\\ldots,6)\\) with \\(i\\) parts. Pair each block with the same sum of\npure sixth powers of the first \\(i\\) dual basis vectors. A surviving\nterm uses the same permutation in all six determinant copies, so its\nsign is \\(+1\\); such terms exist. The vector is nonzero.\n\nAdding \\(6e_6\\) adds six to every partition part.\nIn~\\eqref{eq:label-multiplicity}, this tensors with \\(\\det^6\\),\nshifts the equal label weight by six, and is trivial on permutation\nmatrices. Thus \\(F(d+6e_6)=F(d)\\).\n\\end{proof}\n\nIt follows that, after checking all partitions for \\(6\\le b\\le25\\),\ninduction in \\(26\\le b\\le29\\) need only treat \\(d_6<6\\).\nIf \\(d_6\\ge6\\), subtract \\(6e_6\\); the degree becomes \\(b-6\\ge20\\),\nand Lemma~\\ref{lem:rectangles} imports the earlier comparison.\nThe same induction works throughout \\(26\\le b\\le149\\), the full\nrange retained in the programs for the independent route.\n\n\\subsection{A chart certificate for the first row}\nWe return to arbitrary \\(a\\ge2\\) and \\(V\\) for the chart calculation.\nIts factor bound will show, at \\(a=6\\), that the multiplication image\ncontains every source highest-weight space with \\(\\lambda_1\\ge5b\\).\nIt will also provide the independent large-degree bound in the next\nsubsection. Dimension zero is immediate, so assume \\(V\\ne0\\).\n\nFix \\(0\\ne v\\in V\\), use it as the first basis vector, and write\n\\(x_{i0},x_{i1},\\ldots\\) for the corresponding polynomial variables in\nrow \\(i\\) of \\(P\\). Put \\(z=z(v)=\\prod_i x_{i0}\\).\nAfter inverting \\(z\\), every \\(x_{i0}\\) is invertible. Write\n\\(Y_i=(x_{i1}/x_{i0},x_{i2}/x_{i0},\\ldots)\\).\n\n\\begin{lemma}\\label{lem:chart}\nFor every integer \\(t\\ge0\\), every row-permutation-invariant polynomial\nin the lists \\(Y_i\\) of total degree at most \\(t\\) is a linear combination of products of at\nmost \\(t\\) elements of \\(z^{-1}R_1\\).\nConsequently, for integers \\(k,L\\ge0\\) and \\(f\\in R_k\\),\n\\[\n             z^Lf\\in A_{k+L}\\qquad\\text{if }k+L\\ge ak.\n\\]\n\\end{lemma}\n\\begin{proof}\nThe multisymmetric generators are described in\n\\cite[Remark~2.9]{RaicuSamWeyman2022}; we include the factor-length\nargument needed here.\nFor the general multisymmetric generator theorems, see\n\\cite[Theorem~1]{Vaccarino2005} and \\cite[Corollary~8.4]{Rydh2007}.\nFor each linear form \\(\\ell\\) in one list, the coefficients of\n\\[\n \\frac{z(v+tu)}{z}=\\prod_{i=1}^a(1+t\\ell(Y_i))\n\\]\nshow that \\(e_h(\\ell(Y_1),\\ldots,\\ell(Y_a))\\), and all their\npolarizations, lie in \\(z^{-1}R_1\\), for \\(1\\le h\\le a\\).\nHere \\(u\\) ranges over the span of the remaining basis vectors.\n\nGive \\(e_h\\) weight \\(h\\). Newton identities express\n\\(\\sum_i\\ell(Y_i)^d\\) as a polynomial in \\(e_1,\\ldots,e_a\\)\nof weighted degree \\(d\\), including when \\(d>a\\). Each product has\nat most \\(d\\) factors. Polarizing in \\(\\ell\\) therefore expresses\n\\[\n                         p_m=\\sum_i m(Y_i)\n\\]\nfor every degree-\\(d\\) monomial \\(m\\) using at most \\(d\\) polarized\nelementary factors.\n\nInvariant monomial orbit sums are scalar multiples of expressions\n\\[\n        \\sum_{\\substack{i_1,\\ldots,i_h\\\\\\text{distinct}}}\n                    \\prod_{\\nu=1}^h m_\\nu(Y_{i_\\nu}),\n\\]\nwhere the \\(m_\\nu\\) have positive degree. Inclusion-exclusion over\ncollisions of the indices expresses this in products of \\(p_m\\)'s;\na collision replaces monomials by their product and preserves total\ndegree. Thus total degree at most \\(t\\) requires at most \\(t\\) elementary\nfactors. Constants require no factors.\n\nFor \\(f\\in R_k\\), the polynomial \\(f/z^k\\) is invariant and has total\nlist degree at most \\(ak\\). A term with \\(m\\le ak\\) ratio factors has\nthe form \\(z^{-m}g_1\\cdots g_m\\), with \\(g_\\nu\\in R_1\\).\nMultiplication by \\(z^{k+L}\\) gives an element of \\(A_{k+L}\\) when\n\\(k+L\\ge ak\\). Equality in the localization descends to \\(P\\), a domain.\n\\end{proof}\n\n\\begin{corollary}[First-row certificate]\\label{cor:first-row}\nFor every integer \\(b\\ge1\\) and partition \\(\\lambda\\) of \\(6b\\) with\n\\(\\sum_{i\\ge2}\\lambda_i\\le b\\), one has\n\\[\n             [s_\\lambda]h_6[h_b]\\le [s_\\lambda]h_b[h_6].\n\\]\n\\end{corollary}\n\\begin{proof}\nPartitions of length greater than six have zero source multiplicity, so\nwork in \\(V=\\CC^6\\) with its standard ordered basis and put \\(a=6\\).\nChoose \\(v\\) to be the first basis vector in the chart above.\nFor every weight-\\(\\lambda\\) polynomial \\(f\\in R_b\\), the total\nlist degree of \\(f/z^b\\) is\n\\(t=\\lambda_2+\\cdots+\\lambda_6\\).\nThe first assertion of Lemma~\\ref{lem:chart} writes this ratio as a\nlinear combination of terms \\(z^{-m}g_1\\cdots g_m\\), with\n\\(m\\le t\\le b\\) and \\(g_\\nu\\in R_1\\). Thus every corresponding\nterm of \\(f\\) is\n\\[\n                     z^{b-m}g_1\\cdots g_m\\in A_b.\n\\]\nThe entire weight-\\(\\lambda\\) space of \\(R_b\\) therefore lies in\n\\(A_b\\). Since \\(A_b\\) is a \\(\\GL(V)\\)-submodule of \\(R_b\\), their\nhighest-weight spaces at \\(\\lambda\\) are equal: intersect the common\nweight space with the kernels of the positive simple-root operators.\nThe surjection onto \\(A_b\\) induced by~\\eqref{eq:multmap} then gives\n\\(Q(\\lambda)\\ge F(\\lambda)\\).\n\\end{proof}\n\n\\subsection{An independent large-degree bound}\nWe now prove surjectivity of \\(\\mu_{a,b,V}\\) for\n\\(b\\ge a(a-1)^2\\), without using the companion. The chart will clear\ndenominators uniformly once we know that \\(R\\), as an \\(A\\)-module,\nis generated in degrees below \\(a\\). Polarization supplies that\nfinite-generation statement and then turns the chart calculation into\nsurjectivity in large degree. We continue with arbitrary \\(a\\ge2\\);\nthe zero-dimensional case is immediate.\n\n\\begin{lemma}[Polarization]\\label{lem:polarization}\nFor integers \\(L\\ge1\\) and \\(j\\ge aL\\),\n\\[\n       W_j=\\sum_{v\\in V}z(v)^L W_{j-L},\n       \\qquad\n       R_j=\\sum_{v\\in V}z(v)^L R_{j-L}.\n\\]\nThe sums mean linear spans.\n\\end{lemma}\n\\begin{proof}\nThe operator argument is related to the successive weight shifts in\n\\cite[Section~5]{Ikenmeyer2015}. Polarize the degree-\\(aL\\) polynomial\n\\(v\\mapsto z(v)^L\\), and multiply, to obtain a linear map\n\\[\n                 \\Sym^{aL}V\\otimes W_{j-L}\\longrightarrow W_j.\n\\]\nIts image is the first displayed span. Identify \\(W_j^*\\) with\npolynomials \\(f(x_1,\\ldots,x_a)\\), separately homogeneous of degree \\(j\\).\nIntroduce one auxiliary vector variable \\(y\\). The dual map is, up to a nonzero scalar,\n\\[\n                f\\longmapsto\\prod_{i=1}^a\n                       (y\\cdot\\partial_{x_i})^L f.\n\\]\nIndeed, evaluation of the dual pairing extracts the coefficient of\n\\(t_1^L\\cdots t_a^L\\) in \\(f(x_1+t_1y,\\ldots,x_a+t_ay)\\).\n\nOn the polynomial space of total degree \\(aj\\) in\n\\(x_1,\\ldots,x_a,y\\), the operators\n\\[\n D_i=y\\cdot\\partial_{x_i},\\quad\n E_i=x_i\\cdot\\partial_y,\\quad\n H_i=\\deg(x_i)-\\deg(y)\n\\]\nform an \\(\\mathfrak{sl}_2\\), with \\(D_i\\) lowering weight by two.\nThe other lists are spectator variables. The standard weight strings\nare described in \\cite[Section~11.1, (11.5)]{FultonHarris1991}.\nIn an irreducible module of highest weight \\(n\\), a weight \\(w\\ge L\\)\nis at distance \\((n+w)/2\\ge L\\) from the bottom. Therefore \\(D_i^L\\)\nis injective on the full weight-\\(w\\) space of any finite-dimensional\nmodule. After the first \\(i-1\\) shifts, the relevant weight is\n\\(j-(i-1)L\\ge L\\). Every shift is injective on a space containing the\npreceding image, so their product is injective. Duality proves the\nfirst identity. Average its coefficients over \\(\\SSS_a\\); each\n\\(z(v)^L\\) is invariant, proving the second identity.\n\\end{proof}\n\n\\begin{corollary}\\label{cor:module-generation}\nThe \\(A\\)-module \\(R\\) is generated by \\(R_0,\\ldots,R_{a-1}\\).\n\\end{corollary}\n\\begin{proof}\nLemma~\\ref{lem:polarization} with \\(L=1\\) gives\n\\(R_j=R_1R_{j-1}\\) for \\(j\\ge a\\); apply induction on \\(j\\).\n\\end{proof}\n\n\\begin{proposition}[Independent chart bound]\\label{prop:large}\nFor \\(a\\ge2\\), one has \\(R_b=A_b\\) whenever\n\\(b\\ge a(a-1)^2\\).\n\\end{proposition}\n\\begin{proof}\nTake \\(L=(a-1)^2\\), so that \\(L\\ge(a-1)k\\) for every\n\\(0\\le k<a\\). Lemma~\\ref{lem:chart} therefore applies to each of\nthese degrees. For any fixed nonzero \\(v\\), multiply an \\(A\\)-linear\nexpression in the generators of Corollary~\\ref{cor:module-generation}\nby \\(z(v)^L\\); this gives \\(z(v)^LR\\subset A\\).\nThe same assertion is trivial for \\(v=0\\). If \\(b\\ge aL\\),\nLemma~\\ref{lem:polarization} now yields\n\\[\n                  R_b=\\sum_v z(v)^L R_{b-L}\\subseteq A_b.\n\\]\nThe opposite inclusion is definitional.\n\\end{proof}\n\nFor \\(a=6\\), Proposition~\\ref{prop:large} supplies the independent\nlarge-degree range \\(b\\ge150\\). Combined with the retained finite\nverification through \\(b=149\\), it proves Theorem~\\ref{thm:main}\nwithout the quadratic-stabilization input. The main proof needs only\n\\(6\\le b\\le29\\), since Corollary~\\ref{cor:quadratic-range} applies\nfrom \\(b=30\\).\n"}, {"path": "preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/upper.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/upper.tex", "bytes": 9526, "sha256": "94465d3b42d4f25924492ea9d5903a2aeeb8b417e3eadfa47880116f980b123f", "content": "\\section{An upper bound and two exact recurrences}\\label{sec:upper}\n\nWe compare the source and target multiplicities without computing both\nat every partition. This section supplies a gap-monotone upper bound\n\\(U(d)\\ge F(d)\\), together with exact recurrences for the target tables\nand for the source values needed when that bound is inconclusive.\nAll three constructions come from coefficient extraction with Schur\nalternants.\n\n\\subsection{The signed strip rule}\nThe coefficient rule below is the complete-symmetric case of the\nplethystic Murnaghan--Nakayama rule; see\n\\cite[pp.~28--29]{DesarmenienLeclercThibon1994} and\n\\cite[arXiv:1408.3554v2, equation~(2)]{Wildon2016}.\nIts residue-class determinant formulation also appears in\n\\cite[Theorem~3.4 and Corollary~3.11]{CaoJingLiu2025}.\nWe include the proof and the containment inequalities in the form\nneeded by the exact calculation.\n\nFor \\(n\\) variables put \\(\\delta_n=(n-1,\\ldots,0)\\).\nThe next rule bounds a single removal step in a product of factors\n\\(h_b(X^i)\\), and also enumerates that step exactly.\n\n\\begin{lemma}[Signed strips]\\label{lem:strips}\nLet \\(\\gamma,\\mu\\) be partitions of length at most \\(n\\), and suppose\n\\(|\\gamma|-|\\mu|=ib\\), where \\(i,b\\ge1\\). Then\n\\[\n                  [s_\\gamma]\\,h_b(X^i)s_\\mu(X)\\in\\{0,1,-1\\}.\n\\]\nFor a nonzero coefficient one necessarily has\n\\[\n        \\gamma_j\\ge\\mu_j,\\qquad \\mu_j\\ge\\gamma_{j+i}\n                         \\quad\\text{whenever the subscripts are in range}.\n\\]\nHere \\(X^i=(x_1^i,\\ldots,x_n^i)\\).\n\\end{lemma}\n\\begin{proof}\nSet \\(l=\\gamma+\\delta_n\\) and \\(a=\\mu+\\delta_n\\).\nMultiplying by the Schur alternant denominator\n\\cite[Chapter~I, equation~(3.1)]{Macdonald1995} gives\n\\[\n [s_\\gamma]h_b(X^i)s_\\mu(X)\n =\\det\\bigl[\\one_{\\{a_k\\le l_j,\\ a_k\\equiv l_j\\pmod i\\}}\\bigr]_{j,k=1}^n.\n\\]\nIndeed its determinant expansion assigns the entries of \\(a\\) to\npositions \\(j\\), with assigned values \\(\\beta_j\\) satisfying\n\\[\n                  \\beta_j\\le l_j,\\qquad \\beta_j\\equiv l_j\\pmod i.\n\\]\nThe size condition makes the sum of the decrement quotients equal to\n\\(b\\), so there is no further constraint from \\(h_b(X^i)\\).\n\nGroup the rows and columns by residue modulo \\(i\\), preserving decreasing\norder within each group. Unequal row and column counts in a residue give\nzero, and empty blocks contribute one. In a square block of size \\(m\\),\neach row support is a terminal interval of columns, and the supports\ndecrease with the row index. A zero row or repeated support makes the\ndeterminant zero. Otherwise their sizes are \\(m,m-1,\\ldots,1\\): the block\nis upper triangular with diagonal entries one, and its only allowed\nmatching is the diagonal. Translating this matching back to the original\npositions gives\n\\begin{equation}\\label{eq:stripconditions}\n \\beta_j\\le l_j,\\qquad \\beta_j>l_{j'}\n      \\quad\\text{if \\(j'\\) is the next position of the same residue}.\n\\end{equation}\nThere is therefore just one surviving assignment. The sign from sorting\nthe assembled \\(\\beta\\)'s is the remaining row-and-column ordering sign,\nwhich proves the asserted coefficient values.\n\nSorting the componentwise inequalities \\(\\beta_j\\le l_j\\) gives\n\\(\\mu_j\\le\\gamma_j\\). For the other inequality, consider the first \\(j+i\\)\nentries of \\(l\\). At most one per residue lacks a preceding entry of\nthat residue. Each of the remaining at least \\(j\\) entries has a\npredecessor position whose assigned value, by\n\\eqref{eq:stripconditions}, is at least \\(l_{j+i}+i\\).\nThe \\(j\\)-th largest assigned value is therefore at least\n\\(l_{j+i}+i\\), which after removing the staircases says\n\\(\\mu_j\\ge\\gamma_{j+i}\\).\n\\end{proof}\n\n\\subsection{A monotone upper certificate}\nAveraging the permutations of six tensor factors projects onto\n\\(\\Sym^6(\\Sym^b V)\\). Taking traces gives the cycle formula\n\\begin{equation}\\label{eq:cycle}\n        h_6[h_b](X)=\\frac1{720}\\sum_{\\pi\\in\\SSS_6}\n                   \\prod_{\\text{cycles }c\\text{ of }\\pi}h_b(X^{|c|}).\n\\end{equation}\nWe bound a coefficient of each product by removing its factors one at\na time. The signed strip rule restricts the intermediate partitions;\nthe bound below counts their possible coordinates while discarding\ntheir signs and some of their constraints.\n\nFor a nonnegative gap vector \\(d\\), define\n\\[\n h_{sj}=1+\\lambda(d)_j-\\lambda(d)_{j+6-s},\\qquad\n N_s(d)=\\frac{\\prod_{j=1}^s h_{sj}}{\\max_{1\\le j\\le s}h_{sj}}\n            \\quad(2\\le s\\le5),\\qquad N_0=N_1=1.\n\\]\nFor an ordered list \\(I=(i_1,\\ldots,i_v)\\) with positive entries summing\nto six put\n\\[\n B_I(d)=\\prod_{u=1}^v N_{6-i_1-\\cdots-i_u}(d),\\qquad\n U(d)=\\left\\lfloor\\frac1{720}\n       \\sum_{\\pi\\in\\SSS_6}\\min_{I\\sim\\pi}B_I(d)\\right\\rfloor ,\n\\]\nwhere \\(I\\sim\\pi\\) means any ordering of the cycle lengths of \\(\\pi\\).\nThis definition makes sense even when \\(6\\nmid w(d)\\).\n\n\\begin{proposition}\\label{prop:upper}\nThe function \\(U\\) is nondecreasing in every gap. For every nonnegative\ngap vector \\(d\\) with \\(6\\mid w(d)\\), one has \\(F(d)\\le U(d)\\).\n\\end{proposition}\n\\begin{proof}\nEach \\(N_s\\) is the minimum of the products obtained by omitting one of\nthe widths \\(h_{sj}\\). Every width is nondecreasing in the gaps, proving\nmonotonicity through the products, minima, sum, and floor.\n\nThe zero vector has \\(F(0)=U(0)=1\\), so assume \\(b=w(d)/6\\ge1\\).\nRemove the factors of one product in \\eqref{eq:cycle} in an order \\(I\\).\nAfter lengths totaling \\(6-s\\) have been removed, the remaining product\nis the trace of a permutation of the \\(s\\) factors on\n\\((\\Sym^b V)^{\\otimes s}\\), together with the diagonal \\(\\GL(V)\\)-action.\nThe permutation commutes with that action, so its Schur support lies\namong the constituents of the tensor power. Those have length at most\n\\(s\\) by Pieri's rule\n\\cite[Chapter~I, equation~(5.16)]{Macdonald1995}.\nIterating Lemma~\\ref{lem:strips}, every possible intermediate\npartition \\(\\mu\\) satisfies\n\\[\n       |\\mu|=sb,\\qquad\n       \\lambda_{j+6-s}\\le\\mu_j\\le\\lambda_j\\quad(1\\le j\\le s).\n\\]\nFix all but one coordinate; the sum fixes the remaining one.\nThus there are at most \\(N_s(d)\\) choices for \\(2\\le s\\le5\\), and at most\none for \\(s=0,1\\). Each chain has absolute coefficient at most one.\nThe coefficient of the product consequently has absolute value at most\n\\(B_I(d)\\), for every order \\(I\\). Average these bounds in\n\\eqref{eq:cycle} and use the integrality of \\(F(d)\\).\n\\end{proof}\n\nIn Appendix~\\ref{app:certificate}, \\texttt{init\\_masks} lists the eleven\ncycle types with their class sizes and the remaining lengths\n\\(s\\in\\{2,3,4,5\\}\\) for each removal order. The routine\n\\texttt{ub\\_cycle} forms the corresponding products of \\(N_s\\), takes\ntheir minima, and performs the weighted sum and division by \\(720\\).\n\n\\subsection{Exact coefficients}\nThe target tables and the remaining source comparisons use Newton's\nidentity in place of a bound. For an integer vector \\(v\\) of length\n\\(n\\), a \\emph{signed lookup} is zero if \\(v\\) has a negative entry or a\nrepeated entry. Otherwise sort \\(v\\) decreasingly, subtract\n\\(\\delta_n\\), and multiply the coefficient at that partition by the\nsorting sign. A strictly decreasing nonnegative integer list is at\nleast \\(\\delta_n\\) componentwise, so this subtraction always gives a\npartition.\n\nWrite \\(Q_b(\\lambda)=[s_\\lambda]h_b[h_6]\\) and\n\\(\\widetilde Q_b(v)\\) for its signed lookup in \\(n\\) variables.\nThe initial value is\n\\(Q_0(0)=1\\). Newton's identity\n\\cite[Chapter~I, equation~(2.11)]{Macdonald1995}, multiplied by the alternating\ndenominator, gives\n\\begin{equation}\\label{eq:targetrec}\n bQ_b(\\lambda)=\n \\sum_{i=1}^b\\ \\sum_{\\substack{\\alpha\\in\\NN^n\\\\|\\alpha|=6}}\n       \\widetilde Q_{b-i}(\\lambda+\\delta_n-i\\alpha).\n\\end{equation}\nIndeed \\(h_6(X^i)=\\sum_{|\\alpha|=6}X^{i\\alpha}\\).\nEvery nonzero previous partition \\(\\mu\\) in this formula satisfies\n\\(\\mu\\le\\lambda\\) componentwise: subtracting a nonnegative vector cannot\nincrease any decreasing order statistic, and the same staircase is then\nsubtracted. In particular, a bound on a partition's tail is preserved in\nall previous lookups.\n\nFor the source, fix \\(b\\) and set\n\\[\n H_j(\\gamma)=[s_\\gamma]h_j[h_b],\\qquad H_0(0)=1.\n\\]\nNewton's identity now gives the source recurrence; compare the\ncharacter formulation in\n\\cite[Proposition~5.1]{EvseevPagetWildon2014}:\n\\begin{equation}\\label{eq:sourcerec}\n jH_j(\\gamma)=\\sum_{i=1}^j\\ \\sum_{\\mu}\n       [s_\\gamma]\\bigl(h_b(X^i)s_\\mu(X)\\bigr)H_{j-i}(\\mu),\n       \\qquad |\\mu|=(j-i)b.\n\\end{equation}\nAt stage \\(j\\), \\(n=j\\) variables suffice. Lemma~\\ref{lem:strips} gives\nan economical enumeration. Put \\(l=\\gamma+\\delta_j\\).\nIn each residue modulo \\(i\\), its last assigned\nvalue must be that residue's least nonnegative representative: the\npartition \\(\\mu\\) has length at most \\(j-i\\), so its shifted list ends in\n\\(i-1,\\ldots,0\\). If any residue is absent, there is no term. At every\nother position \\(h\\), whose next position in its residue is \\(h'\\), choose\n\\[\n \\beta_h=l_h-ix_h,\\qquad\n               0\\le x_h\\le(l_h-l_{h'})/i-1.\n\\]\nThe sum of all decrement quotients, including the fixed last ones, is\n\\(b\\). These choices have distinct values: different residues cannot\ncoincide, and in the same residue\n\\(\\beta_h>l_{h'}\\ge\\beta_{h'}\\). The fixed representatives\n\\(0,\\ldots,i-1\\) are the smallest \\(i\\) values. Thus sorting and\nsubtracting \\(\\delta_j\\) gives a partition \\(\\mu\\) of length at most\n\\(j-i\\), with the sorting sign. Every such choice contributes once.\nThis is exactly the routine \\texttt{strip} in Appendix~\\ref{app:certificate}.\nEquation~\\eqref{eq:targetrec} is implemented by \\texttt{gen},\n\\texttt{Make}, and \\texttt{QTail}; \\eqref{eq:sourcerec} by \\texttt{Source}.\n"}, {"path": "preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/verification.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/build/sections/verification.tex", "bytes": 12568, "sha256": "671d980ba5c3ce3a12a920997fd9bc86fc9b2982eaf8f23821e5d086f66c97c3", "content": "\\section{Finite arithmetic, reproduction, and conclusion}\\label{sec:verification}\n\n\\subsection{Indexing and arithmetic in the certificate program}\nAppendix~\\ref{app:certificate} lists the complete program used for\nVerification~\\ref{ver:certificates}; it has no external data input.\nIts partition order reads parts from last to first. If \\(p_j(u)\\)\ncounts partitions of \\(u\\) into at most \\(j\\) parts, the prefix table\ncounts those whose last padded part is \\(<k\\), by\n\\[\n N_j(u,k)=N_j(u,k-1)+p_{j-1}(u-j(k-1)).\n\\]\nThe second summand subtracts the last part from all \\(j\\) positions.\nNegative arguments count as zero and \\(p_0(0)=1\\).\nSubtracting two prefix counts ranks the next part after the already\nfixed smaller neighbor. A shorter partition padded by zeros has the\nsame rank. For the tail-restricted table, first group by its exact last\nthree parts, subtract their largest part from the preceding three, and\nuse the three-part rank. The grid similarly groups by \\(w(m)\\) and ranks\nthe partition corresponding to its first five gaps.\n\nThe allocated prefix array has size parameter \\(1250\\). Its terminal\nweight need not supply a complete unrestricted count; all counts used\nfor multiplicities and grid indices have weight at most \\(894\\), strictly\nbelow that terminal boundary. The prefix entries are bounded by\n\\(\\binom{1254}{5}<2^{63}\\).\nThe actual largest complete levels used in six, four, and three variables\nhave respectively \\(1229120\\), \\(261072\\), and \\(67051\\) partitions.\nThe largest tail-restricted level has \\(1263138\\) entries, and the grid\nhas \\(6611697\\). These ranks, offsets, and predecessor indices fit signed\n32-bit integers. Independently of these evaluated counts, coarse bounds\nfor the three complete levels, the grid, and any tail level are\n\\[\n \\max\\left\\{\\binom{155}{5},\\binom{333}{3},\\binom{896}{2},\n             \\binom{154}{5},\\binom{21}{3}\\binom{272}{2}\\right\\}<2^{31}.\n\\]\nThese count unrestricted weak compositions. For the grid,\n\\(w(m)\\le149\\) implies \\(\\sum_i m_i\\le149\\); for a tail level there\nare at most \\(\\binom{21}{3}\\) ordered tails of size at most \\(18\\), and\neach remaining three-part prefix has weight at most \\(270\\).\n\nMultiplicity arithmetic is signed 128-bit. The target recurrences check\nevery accumulation for overflow, and test integrality and nonnegativity\nbefore division. For the source, only \\(b\\le25\\) is needed.\nEach intermediate multiplicity is at most \\(151^{10}\\): it is bounded\nby its multiplicity in \\((\\Sym^b V)^{\\otimes j}\\), \\(j\\le6\\), and the\nsuccessive interlacing partitions have at most\n\\(151^{0+1+2+3+4}\\) choices. In one source Newton step, there are fewer\nthan \\(6\\cdot151^4+6\\) strip terms, by fixing one coordinate with the\nsize constraint. Thus every signed accumulation is bounded absolutely by\n\\[\n                   151^{10}(6\\cdot151^4+6)<2^{127}.\n\\]\nFor \\(U\\), every width in every actual or high-residue call is at most\n\\(920\\). A chain has at most \\(1+2+3+4=10\\) width factors, so\n\\[\n                      B_I\\le920^{10}<Z=2^{100}.\n\\]\nThe minimum initialized at \\(Z\\) therefore never clips an upper bound,\nand summing class weights costs a factor at most \\(720\\).\n\nFor the power bound, \\(S\\) has\n\\(\\dim\\Sym^6\\CC^6=\\binom{11}{5}=462\\) polynomial generators.\nConsequently \\(\\binom{d_0+461}{461}\\ge Q(x)\\), so the binomial search\nfor \\(z\\), before its cap at \\(18\\), stops by \\(461\\).\nWith \\(C\\le Z\\) and \\(d_0\\le149\\), its first crossing is at most\n\\(150Z\\); predivision multiplication costs at most another factor \\(610\\),\nstill below \\(2^{127}\\). After the cap, the other binomials are at most\n\\(\\binom{167}{18}\\). Products in the final lower bound are screened\nagainst \\(Z\\) before multiplication. Capping can only weaken a lower\ncertificate. These observations cover the unchecked arithmetic as well\nas the program's explicit assertions.\n\nOpenMP distributes distinct coefficient destinations within one degree.\nAll previous degrees are read-only, and each work-sharing loop has its\nbarrier before the next degree. The mathematical result is therefore\nindependent of the thread count.\n\n\\subsection{Integer bounds for the band program}\nIt remains to bound the signed 128-bit arithmetic before the\narbitrary-precision division described in Section~\\ref{sec:band}.\nFor a Laurent series truncated to total tail degree at most \\(27\\),\nuse the sum of absolute coefficients as its norm.\nThe norm of \\(L_j\\) is bounded by\n\\[\n N=\\sum_{n=0}^{27}[z^n]P(z),\\qquad\n P(z)=\\prod_{h=1}^6(1-z^h)^{-M_h},\\qquad\n M_h=(7-h)\\binom{h+3}{3}.\n\\]\nHere \\((M_1,\\ldots,M_6)=(24,50,80,105,112,84)\\).\nThere is a convenient purely elementary estimate\n\\begin{equation}\\label{eq:normbound}\n                         N\\le4^{27}P(1/4)<2^{79}.\n\\end{equation}\nThe first inequality follows from nonnegative coefficients.\nFor the second, put \\(r_h=4^h/2\\). Bernoulli's inequality gives\n\\((1-4^{-h})^{r_h}>1/2\\).\nThus the \\(h\\)-th factor of \\(P(1/4)\\) is at most\n\\(2^{\\lceil M_h/r_h\\rceil}\\), with strict total inequality.\nThe six ceilings are \\(12,7,3,1,1,1\\), whose sum is \\(25\\);\ncombine this with \\(4^{27}=2^{54}\\).\n\nEach four-coordinate source product has at most \\(24\\) geometric factors,\neach consuming at least one tail unit, so its norm is at most\n\\[\n                         S_0=\\binom{51}{24}<2^{51}.\n\\]\nUsing \\(\\|D\\|_1\\le64\\), the quotient \\(E_I\\) has norm at most\n\\[\n             Q_0=64\\binom{21}{6}=3472896<2^{22}.\n\\]\nIndeed dominate all reciprocal cycle factors by \\((1-q)^{-6}\\) and\nsum through degree \\(15\\). The partial quotient calculations through\ndegree \\(21\\) have bound \\(64\\binom{27}{6}=18944640\\), and the\none-coordinate arrays \\(A_J\\) have coefficients at most\n\\(\\binom{32}{5}=201376\\). Their additions fit signed 32-bit integers.\nClass-size divisions are exact: for each partial cycle multiset of\nsize \\(r\\le6\\), its centralizer order divides \\(r!\\), hence divides \\(720\\).\n\nThe undivided Euler step costs at most \\(27N\\).\nThe absolute weight of all target elementary masks is \\(720\\cdot64\\),\nand the class-subset weight is\n\\[\n \\sum_{I\\vdash6}n_I2^{v(I)}\n  =\\sum_{\\sigma\\in\\SSS_6}2^{\\#\\text{cycles}(\\sigma)}\n  =2\\cdot3\\cdot4\\cdot5\\cdot6\\cdot7=5040.\n\\]\nThe last identity follows inductively by inserting the new largest\nelement into an existing cycle or making it a new cycle.\nCross multiplication including \\(1-q\\) has norm at most \\(512\\);\nalternation costs at most \\(24\\). A bound for every predivision\ncoefficient, partial sum, and product is therefore\n\\begin{equation}\\label{eq:allbounds}\n        512\\cdot24\\bigl(27\\cdot720\\cdot64N+5040Q_0S_0\\bigr)\n                         <2^{115}<2^{127}.\n\\end{equation}\nThe source convolutions have nonnegative factors before their final\nsigns, so this also bounds their intermediate products.\nPotentially large products in the code already have a 128-bit operand.\nThe row divisions and final sums then use arbitrary precision.\nEach output counter covers at most six degrees, \\(1908\\) tails, and\n\\(448\\) second parts per degree and tail. Thus even the coarse bound\n\\(1908\\cdot6\\cdot448<2^{31}\\) keeps these counters within signed\n32-bit range.\n\n\\subsection{Recorded computations and reproduction}\nThe reference output streams contain \\(20\\) base rows, \\(21\\) flag rows,\nand \\(21\\) band rows. Tables~\\ref{tab:base} and~\\ref{tab:band} present\nall \\(62\\) numeric rows, including every tested degree. The reproduction\nscript checks these streams against fixed hashes and compares their\nnumeric contents with both manuscript tables.\n\nA full reproduction compiled the two printed sources with\nGNU C++~13.3.0 and executed both programs over their entire stated ranges,\nwith assertions enabled. The first used two OpenMP threads and the\nsecond was serial. Both exited with status zero, and all four output\nstreams matched the reference streams byte for byte. This execution\nreproduced all \\(62\\) numeric rows in the tables.\n\nThe programs retain the complete ranges \\(6\\le b\\le149\\).\nFor the main proof using Corollary~\\ref{cor:quadratic-range}, only the\nbase comparisons \\(6\\le b\\le25\\) and the first row of\nTable~\\ref{tab:band}, for \\(26\\le b\\le29\\), are required.\nThat row contains \\(29814\\) flags and \\(467068\\) band coefficients;\nthe band differences are nonnegative, with \\(2114\\) equalities.\nThe later rows give the additional finite checks for the independent\nroute through Proposition~\\ref{prop:large}. Exact target tables at higher\ndegrees are computed from the recurrences, not assumed from Foulkes'\nconjecture, so their use in lower certificates introduces no circularity.\n\n\\begin{table}[ht]\n\\centering\n\\begin{tabular}{r|r|r|r|r}\n\\(b\\) range&flagged pairs&largest \\(t_2\\)&band coefficients&zero differences\\\\ \\hline\n26--29&29814&25&467068&2114\\\\\n30--35&46805&25&872322&3621\\\\\n36--41&50091&25&1078386&4161\\\\\n42--47&54443&25&1284450&4701\\\\\n48--53&63027&25&1490514&5241\\\\\n54--59&74729&20&1696578&5781\\\\\n60--65&90684&18&1902642&6321\\\\\n66--71&109505&18&2108706&6861\\\\\n72--77&130920&19&2314770&7401\\\\\n78--83&155274&20&2520834&7941\\\\\n84--89&182871&21&2726898&8481\\\\\n90--95&214444&22&2932962&9021\\\\\n96--101&248073&23&3139026&9561\\\\\n102--107&285382&24&3345090&10101\\\\\n108--113&324614&25&3551154&10641\\\\\n114--119&366623&26&3757218&11181\\\\\n120--125&410723&26&3963282&11721\\\\\n126--131&457715&26&4169346&12261\\\\\n132--137&506088&26&4375410&12801\\\\\n138--143&557588&26&4581474&13341\\\\\n144--149&610285&27&4787538&13881\\\\ \\hline\ntotal&4969698&27&57065668&177134\n\\end{tabular}\n\\caption{Full retained verification range. The first row is the residual\nrange required with quadratic stabilization; subsequent rows support the\nindependent chart route. All flags lie in the checked band, and no\nnegative difference is found. The program's first row is labeled \\(24\\), the\nmultiple of six below the first tested degree; the explicit degree\nranges here avoid that convention.}\\label{tab:band}\n\\end{table}\n\nThe supporting package contains the complete program sources, reference\noutput streams, their hashes and numeric tables, and a reproduction\nscript. A new full run creates its own compiler and execution metadata.\nThe commands, run from the paper directory, are\n\\begin{verbatim}\npython3 -B verification/verify_computations.py --check\npython3 -B verification/verify_computations.py --run --threads 2\n\\end{verbatim}\nThe first checks source and reference-stream identities, all \\(62\\)\nnumeric rows, agreement with the manuscript tables, and the numerical\nbounds above; it does not execute the mathematical programs.\nThe second compiles both unchanged sources with\n\\texttt{-std=c++17 -O3 -UNDEBUG}, enables OpenMP only for the first,\nexecutes them, and requires both complete output streams to match\nthe reference streams byte for byte. It keeps each run in a new external\ndirectory, with the compiler version, build and execution commands,\nexit statuses, timings, hashes, and captured streams. The option\n\\texttt{--serial} may replace \\texttt{--threads 2} to compile and run\nthe first program without OpenMP.\nThe two C++ files are also printed in full below, so every finite step\nis specified in this paper. The coefficient formulas, indexing\narguments, and arithmetic bounds establish what they check; successful\nexecution supplies the finite nonnegativity assertions.\n\n\\subsection{Completion of the proof}\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\nCorollary~\\ref{cor:quadratic-range} settles \\(b\\ge30\\).\nVerification~\\ref{ver:certificates} settles \\(6\\le b\\le25\\).\nProceed by increasing \\(b\\) from \\(26\\) through \\(29\\).\nIf \\(d_6\\ge6\\), Lemma~\\ref{lem:rectangles} reduces the comparison to\ndegree \\(b-6\\ge20\\). Otherwise \\(d=r+6m\\) lies in the exhaustive\nclassification of Section~\\ref{sec:certificates}. Every pair is either\ncleared there by a proved certificate or the chart, or flagged.\nThe flags have \\(t_2\\le27\\) and are covered by\nVerification~\\ref{ver:band}. Hence \\(Q(\\lambda)\\ge F(\\lambda)\\) for\nevery source partition and every \\(b\\ge6\\).\nThe source has no constituents of length greater than six;\nspecialization at extra variables zero transfers the comparisons to\nevery finite dimension. Complete reducibility gives the required\nequivariant injection.\n\\end{proof}\n\n\\begin{remark}[The independent full-range route]\nThe same finite argument, with no change of predicate or arithmetic,\ncontinues from \\(b=30\\) through \\(b=149\\), as recorded in the full\ntables. Combining it with Proposition~\\ref{prop:large} for \\(b\\ge150\\)\nproves the theorem without using the quadratic companion.\nThe chart and certificate methods are therefore retained as a complete\nindependent proof, while Corollary~\\ref{cor:quadratic-range} gives the\nshorter large-degree route.\n\\end{remark}\n"}, {"path": "preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/main.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Foulkes-Conjecture-for-the-Sixth-Symmetric-Power-September-25-2026/main.pdf", "bytes": 451463, "sha256": "6439c44d77b7de4bd1d9a3425a35e2f2ef2e21036609f77652167f95c8daa94d", "base64": 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gbXv3Ifrl/uP7v0AntewETxlMIO7kXWZibfN5zAHGFMP9o9YmKuinL8stPf0qrOD5gkUlTya1jlOzmuzt/aNM0uiJBlpxl8Z5KmicjVWe3j3KONEJ7Vdc2a+0sESy2jDsSz7gFm30WzXsL24xy+JstcVTWzNxkqmTO8sCcsNm9N2i169C2VPrf3hUaQYUvXw6JbPYJkm4PT7ZS9e+FLtiKHu6jKGl3/Lzvcqisnu97Y60MnBH6uIlnd2fNiIW+gT1ae1rxNepiqXKTlI/pCuR6xIxT3OU0tjpd3GUESACsJlgUx9w0BvsIGJj77rd3fT81x/fTAcyIo31aqjMeSzJaLAUXK+S+fp6b1sK7y1Zr2XOr6VtnKRh94/x/aOxMnq/L4mnNu3hZRyKobqXUdtQG0g/zAk8LaTluVIRp8rcJbmNTa6mWxfm0mVnwrFyMOjtqbtYrpRBZ3gJBhlmMJXDlas0eqXnn+4fge//eW+ViMYaDlDDoWTU0dei2dLDf5djs//nvcl11P4qpC6xgxbRsC+78rntSur2WpX1tr/IvMLGOl3trbh2IC1NLLM8HPUfsPXURoZ2ciOvaZ3FUqThTEqEDBAsbeDq5eoGPr6lRb/bA112D3T4P39CupbNoXx1D93Diog/feqGDX7a0wUA4Yvmm94zivJ8ou6STMa50bRY1fy93AzV52rw92aSmeccwxlL5gY+IEfZSzegpIYbWK1xjqXUQqWq1ZiEOQoWvX7vKoFVMxnOUFxbFTSd1atl4dq1MJFF6oIUOm3pNjE4eYSHbVXs2qaoiTBAKeToF+Dtoqt6olcSgRGo+LE6HEoYM/DwGzkJNgYKc3Wi9IiR1NQ9i42yYfeHU3efZLHODXV46cqXDvYJFz84C5ag2gF5O5CtwDsQBV4EaaU8+q5o2qbauNNDh2PmwUY2fv3Qtc2OZNkC/YqG2n+4z1TUjjUxdP8NtR7wbayH6qWuNsF8yIxE29gdCC4z1/5EYERQ3qwBxMCnmjXncrM2OhQv/aQ+3e9Vbpa5ilMgXzD/2zPGatqUzNPYWhmOst4EWxZZXj+5yNdGxlZmv3N9ZZJYJyuqJPP6+wKVA0ESagE6fS27ll4+EVZpyprJVbHV6cdqKJ7qkq5lWx3KpgcN0TPrFPUAUrBvx93eX3lZddOVZQsmhGOp3NLG+nbsNuWKMkPR7Uq+JWTSsi+JTb0d7GnNTVs1m2rLw2+ULYAtcW5tuI21ll5uWWV5nCUmHHAjJlB5AlomDcem59fSAiQ4X9HIiZ/JUXRM1E4Sxcfuqy8Dk3yhnFyPynN83ZXF9pWI1qJpizabsWMq/rYH6jpdsbgjBbjZJLQ+3PVpjIeA9JfT4DCRyRIcntPCUqZA2yRcL4F95Tffp9Qws1xNcV5XSg3igap12R142Ei11ojYNGtE2JHTiBuvERMjov8ZO/oEwMU0lVPz8LZBq+AUF7Ar24UcjAcyrwNFzkXIUfQGeujKf4xVx8OPFSs0ts/0WxzdFQIYcNK8Uu83XfVEqzh50qj7JuU5uZUgBQDAEyHixGQ0+PsvxYbFboNqYF900EA6HKStqDdj7WAB89WuWqoR0LcI2VgdoE0Eh6igiZwBUdH3n8FV/cwa42cHUkDQvQYh8U+iv1X11mNddp0qpG/J6gHkwLIJyZwJOTZ0NktjkfOxjoxLhjQ1Eq6mAbIXdTVUeCFGChIG980JGDa5S/gN/7TUANdaHEqgS089+/HAPVtqQAuD74e2H6hF5shPGlfc1OO2anY8ghaRXoqxbVP05fq28Eipjq3J6UjDvnOa9pL1kgrsQB6Ou+Y+SgXuI0CpYBCC4UxHUhPUvFUuTQbWWYdTSXHWHZI2B2u3OqdjDRl913ZtXRcdOxY2Vl4nvndWxrUGLPu4LYFlrAex8LkrQeV1ZeOsDfR0yhEhNP2A4GzLTdn3LU/n5A1+SdHCQ+mk4wSvJWJly4xcCgs1jL1jX3hCtwZ3n+YMVoyMvI3LooHHk6uENtXx/sZBSOAT/gzAEzo/AQNTQz8y+l+vzgM8lJXRk9P/7dhs+VN7pP0tQAjwQulUvC1STmD5nxHsOthboU9XTdsCReNohgoHh8wSB9L1QIPrktCHEwA3oqlf6cnHsg5FXVPLE2sEHT2T1TN8B67tFDCUoXJzMFqwX/VtR3cBCgb00oyBWOlwkIYOO042tBvJuCZ+D4lT9V9g2EWPJAOfXocbOBdaymdkksbGyHDUragGnHkDaC8Yq46BxtzfxhnIXtCfFfFS2uBVxvpjoKFtBKzlFf1uBScTushkRoggii9kjpqt53GWIqEXTCfzWFvl9RQOQCYNGEADn3ezbcyi1yXfGQ+KdAj+nQGkh6pZdWxfXkgeesfRwNDDhHA7XIh4Rv+eeJOEtuA0+giNTAcHjBHbPA/7s7C838N2nHh5p0xH4OmS6kNnrRim403enkxn58imsBEBFy20DwlJyTG5dBnlnQNCSgAPCQMKjqHR09vi7S/nYih+LQkoQptwrYelms1A/YKd/eqkDc0+K99AUUzKOqN7Qk7Zl1MD3NLne2vR0y4Io/HMpEzcrD2jW/S4eSnWfMvJmrY74G4WC5K2pz7HKt6AJ6gs+1Lf7VtCBI9GCdySwS2VHqth480m0oK1zcLpZXIeu1oLIpyG/RFYGBO988xqlIzSj8A5b+E5yW5WIBiYzVZHVersXpQG9xI44epenKlOZUrGDqnjNHczdXgpSARRo+Bnp53xrl2AgNqqoafOgfxP9Eb1cEEd4w2C15cKezGgrUVyIaCNX68FlcD1T6QMF7uakkGXD7YSDCrOhNxd+kCIa16VAu8xM+GU0udgDEhcg5oOqHZw1tcYEjijZ61NzbcyjwU3LtXhgkqeZx6wj2m2uhXe2+xxwXYo9JQl0Z8mu3E2+msT0F7JaTqeo5RObKzlaiMuCiiihDdUMGnOqSBDWKQuD+XcxtkfHDacisIaWFha1rEUC8OYCw3txxfQh85HxOgrR2FL58cQfCu35ENg7yvMr9Dh1asVz0pAdovXrpDUSodTghuOVzWBWvRpD0XV8D4xqtN/YmbjkxGJLEO5kj4S/IPWlds20QMBKB05EHCLwcApgnApY2XOp//OprrS5UB1PFDl8VFShlGo0QAGLfvnFP50LkaK8B3BNCVFJKVNUieWDi/DM0CQmangZ1c2ZTcb0mSBgnvqsWnBw+kaegGmBOOHOKk/8HdvDlMQdcq2zH4HjvC+7SmeVSa2gp3ql6Lq+n+7f0xAOP9aVJsRdT94ie+KAz/97T4D+PJ6KBpucPARwwD/h0QonA9s1ULfOwiJTYfiS3VA8IYvXdF8oieAg85IwOMWLEBfPVX1bAegFQ6/rZzpcS4JNt0acVcpeJ9JeEalr4bcg/7HIXcKUBtkF5tPAWrSbwaAtIzNR6RO4qkDbT8CZWpu/HEcKqQLMJ++x4bSM0vnnr66fmoe/O6ri1SUf8fXJKq2HkKyF/f8Sh8o8IAxQ+frGIqQ+29dO8I4jOQaAvTGBYCf+qEaRkffY+7QYPx8GhPuoQfXycNn4IObg3kAgzMbTCb1BUAEiDU3QXeUnNwszAisLilgYHNnXvqSWsHnBvXS08ssN5kTSfxxLgZFfh4nw+h8CvjomLF+JfWWiaifMkEu+lci/BLpDCgXlMp0nCU+kFPO4L5gt4c9qjRiJ2UA8Dywi13Sh449n0XcUS+sd0yaCkzy6TgP7gLApPAB0J8p/M8wOMHsDSDo3et9nkbxaa0nBQAvp+sBavjYx/KkSiauFCFY6N3r4VRRgJv4zsSaKgKSa1UYBgQrC2f2OGuRPgFvRGfn1k8vrP90ykIA9obTPE6drhXuqAykOzVHu5REsT36QPjwjgJX9+AOYTTZUXN0bmkOlmKgFrwY/K3LZocKHJ8L/kZhRjeu+hKTsP5XeREsGZeXCLf2w5XzoHcHBvYk0c+tY0UeK3l0U6cRxnItaWObyXO0Q3V3Ge9r4BDjg+L/e63ESsSJMuGYayfLjEOwqzFXD5ZLFx5YLyXZJM/a2mUKUAW5FvzofCB8ODhWWcezUWy5HCD1ri7igw7UHDMTzsq40WXTOMsG3VYBO5oSLPsD1SLwFGCf0fVyhhoch8xGXIZA4RlFsR9CEUPl7cRZ3tDa1QDdREKMmaSL6wVznfu0BUV/RHQxmZrlcZ6k4cCnayvlJk5MHg5y0Rc5+bgXzdoHt6+VE/2BSbodN1QpJnw4yvm+TzWZA8H1Y+5rz99BwB+RuByz9mTmaTg+J5YOhFtqMwCmGOuCK2v21Q6mHB6PsgwEV7AiRvvkyW/utmEAMp1bF6DVvnomZhRsMWT0iVgWDWZDLSto6reHD9wBp9zzaTDkDaeZY92a/Xr3bWYoijUrebWUDcurhFXhaWb2CuwPZsxNUJOmrtWkmQxcHxnO/i+nvIdpPxYQY5Kv9yMkFfb5p4Serqye6ljm+enVT1MDMIeV6c3U0AE1klsrK29wrc6rfI11n8EGnaxR5GiNoYSA3gyp19FechwxlhvEFbHOxAeCi7pv6WlOBlC4yQEuHx4+FD5iXKO2fNyWu67kb+CX7MowrDxHzq+qBR2peBUg+RCxD5k4v/FRciJsO5e1qBXgg02Nh9mB08r70nyQ8WWOrbmUSpD84cD66XAXzPXDaeRn0imfBDPULaPkjFdRi+h9Tw0XVtGnrTPfvbldz6p1mP3D0V5OZB0USFLqU11zMcqpFJY/m69NQlqP7Fb5dBZZybYH17TmC6uoy+S4udRZucUAXOGvMVTknp8UpVI0aP1dQ3kqhci8Yh6eM5oP3J1g0coxUwmG9Tm14FL2HFeVyufTjFzO1lODU77YiUno6nvoE+UaOdXaYy1Erm+9KRxv1jeFyxQHILXL8/MGPjUUaG9oGx6v4CcScSwpXVwQRiNE5gskQhqYJE4sZyaeit5FoCxbLztbRHKenbWjmAt84wRR/dq0h6qoeeTGheXggQNy3OoI02Iu172zmMDTtgLnudnwnHVZMBCzWCbAM7uyQ3C+Wmov6Mc5lqPnnJ6A/cqZU4ClpM+8BMkzyWggSNWKHDRb44EjvFXNiSSd+4LqEcEjoJWBhzqiCcyMtw6XSOH5fqKRE14d/bRO7UikFxbz0nNBE/VAippncjCi+Uri0p46qsxj6xMOi3sLUYqhPeXznqhxcPUd7gn0d//sijj4gzsMPE4aLSeNZuiS8zlpztgMfp/Hjote4IWGuF0A/RwR7M1ygRPYIw2G+fluV7h4/VenOfop272giQBX24ev2XilHN9g3QTsDITq6ZF1UktvxZzX9pYlXRYBu3OI6M8Df3Huaul+PvGEGJXIOTjFaeMZsrkenMFNT2OJGbSJWCarA/3BkG12NmS7IJ5JqHosWFBZf2QuGADB7coC2XRiSDtTH+tEjQ+DE0JwZMmi5651WD6PVEItbTNZy6LbPlDjCqBnM8B2H9FRoFn4arMgtA6vC7chx2u9CIsBVaThnq+j4jTEgfY0DuRe/34O6dkJCmNUYEW45C2dhc+Yh/5Af0ITSKzkFOw4+soS5kOVG64twaelfcV3tK/469bChye0xpeDCgq4IgUTEqx6M+X0tWCRzlzKLpj9oj+hwAMRuNVwP3/In1CJciWEJ1c/TY1UOpf4j1HjrD+hlnx0QXynjWOxdZaH2wBvHYNg2a2aFxkgWWtebCQdWDLTMFbCxzl1svFfuSKvHWfznCxYFfOMPgfkC3PTqcIJ03q+mgX19thPWrufCpNIiXdjs+HiBuzqFHRRPz5D86xsGxchOpTwue16/p+im5F0ui6fpcagaooPMNkacPSH/aEcqs3aUwMvOc5EtoYmjHSnEq+i7znBWR95YZOnhVcy76ErdmW8BnucdJMoTz7l8qeGolMe5aQe5XBeLQGkMQ4cMaOClcRhdWfQhgKNb+OeXQCDa+Khz/NY1zElSchBU3bCxjxzWAgIDVwI6LZDv1OwbmGB59QeRTwkRe9O/VOHxP87mxIvRMPK+QtG+nwfPnkcC3NS5Yur1hBHDjD2XXjURmau6sKTwxVf3MtoyrvJ9FoKWqcpGNc82OkZs401sjdkoHUOIDBLgxmlFbSbqqHfn7vWF3dVt3kmKTjl6bFT/piKOQixskFSAmDhHXgUPFGYK0y9k84BtHyqPe3Kei5UcnBsLjhFqm8olQTupLdr2OE4JJcvQnK+eGpRufrSceF5PtU5+erWnzb/6ezm6zf9VADFCanbS30xeZrgTS6JceE/HzF5ak0a9p8r2Gbqcj0NZW7majwAylQEJ6JvnWhQLEIchRAporrrioMPhY6Nj6leLaDW0sbAjsEmzkWM5//dBT/MJMEY/l9CaXKOplPkCSvMObgPz+WXAXUSx2+XUFwENfBzMEtM7n7IkZmJRaKWlkZlcz3/QK9LFxHfZx1A6Cmb/xfsmvHMMIstj8xnRv/pjfbQvRDUgod/jMUWzdbmsR8KTOB7UcGPVQMGl1NY7/0ICsLhnigvPfAOm7KkoM4iNzz9H42OFRYkAa6RJ6thv3//5v8BTJBu7AplbmRzdHJlYW0KZW5kb2JqCjIwMSAwIG9iago8PAovTGVuZ3RoIDQ0MjQgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjarVvdc9s2En/3X6F7o2csFp8EmLSdSXNNrzftTC/xtDeT5oGWYJk9iXRIKk7af/52sSBFUJTstH2xQRIfi93F7m8XK7bYLNjiuwsW/n9zffHFKykWnKU5y/ni+nZhxMKwPGUKntaLt0ldbT9dLqVUSXfnqPEr46oqu/C0qnf3RVO2ddXiCw39LnnS1PvN3eW763/D/Ho8/1vf5yZ8EvEnYfLkq0v/T+Tp5VIpk1zfjRbaun7Z+6beNMWupaeiGV6X1aUwSefW9AIeY+qL+3tYwSauWpcr1yIhC54yAQxhC477z3Kdqlwsrnew/6LCiUyWtPv7+23ppzUmeShhm/59425d46qVow/1vrvfdy19C4NNUvR9gWzcX7Ler7qyruhru2rK+w72q60O+4XOt3Wzw63st0VL/dzH+y3wtijDuIe7oqO+uDu/kdEuLE9zJWkXY25Jz8l9554PnCkb+uA+Fquub7rVPpAIncLuwwSBmXKiChUQV1eV2xRd+aHskPhP1K9oW9fgbG06Zbi1aS6zRZbZVOaKyL2+8/oDvLwCJdAsebhEiUFbZMkeVgE6OD3rZIXroM4VDVDv8EsDT6Xv02Frha3l7w5570cMmoRPjQNhlDfldqAY33rRYeOu3Ny5tls+4EcHD0hKR9+AD3UTBoC4+q2NpaBValXY1r0Xfb39VNW7sthGEuPCptaYeMB3P0yn5EqlQmdxtyXqlWsdaT4w38vBy4olb3GKCxzNUqUXzWYxtF9/dzGsL4RJpZysz0UYTOZiTIiQNhVqQghIS4ocVbhu3I4e8pTn1KLTAI2fmprOYN2WQcXgLVcpl+/CFG9WyOY7r/8NvbrdV6uubtrJZN7ckPaDKbFj3iueilwTaS+LfT9lTlqZJ2sHmoC6MaUGvj2RbdzKNNeTtaZsG9PFc+BXxuMBV7TmgW/wkKUSGorBTmGDa2p/C82Pg4p7vS5Hx4HGcf4r0+wG/vB3YWbilsz7kzticE4Mxk7bzjUVLEcKjq/IBlm0QZ/FFg3alIHhj9liTrNFgzppUKMJW4JU83wkVZjagOKRVO+KeyCaHM/3V71/0kym3O/fOxGbvOmKm62jbsPObdg5GgR/Mh2Ys4yY6ogLsXvRCaigH1xUG3IwOhksZHArKzjZ5e9ltaHP7mPXFGEVGuqtVEODPlxqMDVNidS1gw6PNouHIje02a4mGZAhw9bDnavcB5ShaybyBZ/cDepsk62rNt5jwSdQMXAhbRD6ele2bRm4Y58qYKl0KqcEzgm476+zVIC0o/5BOUdCzIMQbRCiQCH+QW05CFTLHhXkE6bOcJCxNFOWFixJqq4Bc4l2kkyPSOpb/C8T9ApkIPyJ2tHXsqWvjVsV262XOzyVYexT7WsmUpPpmJ4zDBNGpplRcX9vHLMRw4AOzzD4/2KEaz6GV1dE4hvnwUbYhvnD9sfil0sAHI46Fdu2pg4PDXr0Gex2OKwslVkW0xZGaLMwaW6YH6BTZvLFkoMtMAQnmzngt1SZTBUwbMkBs3BJXdGhjtGGStzW7cLBLJqALNpPu53rGvTx2MU7iaD1sjd70Mt1p8CoTAJO1WxEuZBAtAq0Z0QQm6MdjFEmF6NeX80vJIwdWDReiGepzvXT1hHxOn5OnlLvHLFBKhV0Qo4K4KjJgH6p/Dn1QwQoNRcDxNTJtmg2brl2m8aFNw1aNmoG8KrHIM8EuETqNEMmIoTh9AG1zIAvAGRnBmTn1c0mPRCmo4dHuHfs9HyANDaho7r0aMt1XtQi94T0TpAgD7YqGh7gge2nG+0BngKuvsSBAGtXB6wKHw8BDZ4SMDFwSkTiDvM0bTcTNWhgvxXBc7UE9cAZ3OKslgclhsaqqOqqBEtCj7viHhtsHF/A65ueoMNQRpRVpN3wnvYIDS8SN+lMVp78EC477Cr0q2eMpWbgfU3wrYMHaXEvfciDLYw68P+ubkOrRZODDRzUer6B+aFXvYUOXVHg+N8T6scWu9Ai8ww+0Xif2AMR/NT4jWBrU5LDa0n/68qdmPcgck8Fxh/4alV0LkQh482TrioL50xkI10VNigPCkxYIHd/07pgY/AjKrKnuicK3nX9yLJau8Esu2FDME189LDvDYHQPUoVn/etC63AeWsOEwOswBlDBwro/CkZmQOwpVqLseEGESsNQIuH0yhSPjUJu0vJ4WjQuehdJL4HPZ057wxMDfMWzFuYV5dWJhO7LUFPdiE2oeftOWtchE/22H7+yoQ+lTiwibh6mtEHURugWqdahhzHT/OTKm7IyhqPRt582s1TzTOT/HyCLnCsCFvoazY2/GCgWaYWKtVMERmgZmLGib4dWBI7Y55qK8F3HiaAgF4gMb/MzCIzaEZu5rd5N6O4eKI7+2nOnaF2gdsRqRD9rjSb3dVvYH6fP3v27PlvM/O8XWoz4ly8vIAYB2KFwxr9xl/PbVylE//6lzce+Mvh2xgxQPwsQZ5GB3G8CdRYwEG5QbMKUgczA9RgTBPWKWZoXtoUiFkacKY6pjlWAYhy9AJQsjazfDjSRkB7YVuw+uiTylMNQI0jrAvE/zhDFkst9F/CTjXnEV3ZRHbjsxrLdUA38U4C+2FDKuxlTpYWYgg1K8toNoA8NpLlsxNQRcTWH+xcys3Y+ismKLg+oEx8tWnq/T01g3UPYXKHHkCxYPvwO5jpFg0ivrwtfAID3GOWCYQVlMOCXuU6GEjEC+O0kMwZKIGKSRssUcQc4bcGmxcQLo9VK2ZOllqYbznq9ssj3FFwhBiodkQD+SSk/XDA56MFlalUGR0P/2VGb8CiWQCPmFCxT7SIJ0kGP25hrWjNgzmJT+5AaA4WheE+85TL4CJPHWI1d4hP0qMZYGA+oafHeF5NjhO4MgOt7LFIVTc7zCu44NsKHPvBZ0WbYjMfrUkDxxnMCBwuLcKpepg5k8AqHgVpfM4FQ5yi+WLU61fGED1kZmhl1DrpJB/mtFakUutoec/Mi2+vL754A3D3yy+/eLHq9sX2GjDPl6++ffVKmFcvv/7662/++TJcYER0Cq1SDuKRCrS2V3EDhLFLpOEf/eTf/vhy7v4DvD3YQrAFmoPW5iOG4KD3F0M8D5LMGUhUwYECKa12F2/fscUaPiKwVGB6HnzXHVgtA66XLbaLNxf/CWvG/qyfC5x0xs+p1Fuif2q/MfSwGq2oVWH4f+fcCkAOOIhowLN8cM9SztpwmSVi7rTMHwlYWppZxza1PlJTpCp0rJHmSCPHGKInUh8RieaHj5BCpAwAR2xmF+OZwKIEWHZCfeGrHFmeIwAAY+doBnUz9nOIPmfYJj7zFBDiWWrYeHuzji6O8SQ4WUzNerPyvc9wMj6EXhjE+PRy68KH0IHAPMNgs0VHGCIDeOEj+GDC/IshJbOkIN/62y769n5fd31y1SehePLDPEwfPKC1oK0ypvvFuSBBitPQ7fUjDFJwNhiwNFqNIjM32eamKXwA5re8vym2G3fTFH1mAFDegeUMnLgNlnzjKjDZ4X6S9U7gDAPQjnMl41lez+gJoiM5a8inbmkgjmtAXNbGc38VX0yB+8Z8e9Tlb0YgPz9Gp8DEBKwVEeH1Rwwx5xkWCoE2WMXDP8zrkOQqOalA5gmkaszoTdbyoSlPIEg/S6bFGELHQ39/dEE4IHYqw8dAmcgxduDzHDm9VA5LSX60FBpNk82HTMM5BheRWTG7YuR6dJqB3/0cFGgnvjDjEn3dMMHj9uAohgUCP+sgIVgTuZxV0B+n6RQ49mvnk0p0oc77S0zfPiQIJ4BQWNAsGeYmrd9v/+cxYPvHv3wWqr8nx1T4IWMzyRvkqchwexDGqbxnschmWAzrGW9RjOmx2fOb57NHAOwm+NLFqOszStCdMBU+HYG5iwADZitClA+DR72iPEw8IQBrLcYznrc9Q7cnZW7mHc0YUp5XrwlQoITEwKmb+YQE93h76PXVOX5KkSqh5rY/YSjCsz/L0Zu5jQIGlPmf4GjMreT5/CDAlyKGeDNxu9AizbNQsPNwV7chWV/uik3fbM+6WGV8Mi2a6MVsAoJb8bjsRiRynXtsHk0dLhauqb7BX+Cfoy4HHgNej6b46yd2RKQAWRvN4xW8vvlqLz6z0OBIIP7h4PQFxEvc9oDpefH4mhkwBvxktGYZrm+6/prIlxQdrmrtcF809Ah3uiOkWYabGwvwdpC+/+/e78vhyp0SLlf0pa3PSQCNr8xTxeVfVg0MkPrKJ4iZdDzxWQ+aI4ix8YBTWTL5WZRwDCDVZOp278shyEXZ/jrW9Gl94LVry4ZwLLDWJ8ACUl6vy2oTLvJvhoIbG5Xw2bjwykIfq/3J0AJORhmW7f+HVQ3KsNguQ1VKV4YblOP7ONRpm6nDxb9JJqLHzAuWFVYQ0PTleJim21dr12w/+Rsn7BFyeTJ5eVfjwAd63adkvHI+J1ffOkcfn1pGgdhNyJhYcVx1JYZ0YJ4aOdkc1shByPrGDXsABPvOZxmz4fISXz6U2y219m3YUtm1RzCDi1TycJKx5NDfj/qsU6iTGgrY/MvGDVxII8K/eAWmaHJXJFLdJzhe1k293fqLfMWYv4uadQHSSu8BhkLQHRVA4kVSfH+NxOD99cHLxASg4Y0J5JxP7ny0AWiF14mo7Rp0+YPzpQb6cIe8XJc7V7WwX88C+LK61GCmULc/nrjYgk7BIB4tqS0Ld1bDcjNzsBiw3fy5WzKTSDZPhg9RtD9nDe7G0aM/ftqeyEbBJ5N898OpYOMMFJDjbOyUlKVDCt6jYyw/+PPaBWqq3/wnUvQTFI0hUlTSAcBL6wxvB4iH2TxCMtLfqskn4SN+mO48OpJPxkbnMp/8xYso8xmtJTObKrXgJkv75NrVrJo8JQ8Kpw7CWB7N9kjULx9B8vwkW9kc7JxISU+T0Z/H1Rk1i40BRIxYggzGOLJSP/nTUPs/t1PzcTKNxBkAKpv7/IqRUe2L1KHmQ2bolqapFoZVPuNR/eE67Uz6/PSwk0PCMKLJWAhYeTz7FRESytmh5UvAqeRf9043w+uG/HDdMBQ3gu+M04YVtvooF3wt4oEcuDeMDuuRV8/6BXRUFQPvd74uNQqbjz08V+wAOQNPJQFE+P/UAl7wdspOJhP8pAfmeM+bT/qHwtNR/a5MQqkillYUXV8QQVE+qNLQzZezYAR/PlsDLjmP1/x7whPJTGqliad+JI+T+4vVaMRfgKFjYiScGrRg46lvz6f2JIQPxvB4zGPXcf5qbSp0jHcAamR9FYmvuj53r8nCPfN4klN+2Z72y+NrS1CuI2lkaAGy2VRXP0xxqg4cD/PpiUeWQ97xiSh5yOcBKzxHBECGKzqkZbXa7tdYagcG4Lh6jIuMrtxxmgEjwRxS+JplPA/cJN+HV/dkG7Dovjcg+HoyEAGqb1Ciq6nclp59wSA2ir7nieox+ERxhy9Cyn3IJ7QaAhPfwR/Knf95jT+gXaAmxCiIRIYwpc/wZ6OtAzZjNpQ1kwXqV/1ID1PoHNl9q3xBSzTL4bcfw0Ic0L8Wk9UezfQyMPd5POZkErtfCWMAbezRSnx5XJfpy/2UTl5HF0OwZapXxxsTMveRHGxfDNZ/K8Kosq13dXMPY3YzOoYckKHimJxPdzZyz7W3DuNhj9gqez5kPnh4MLZGRzPjD6q4JZWVjFITg9ukCzUzMvj+0W+bDamJQXdzzx/4MBQbUu81Ne7CL85YHxnDu7YL10rMeKQ8ZEKUr5dvQrcyUFfVYc7+t1nTQNACeLVha93IiZXhVxEHygYxSl9v63+H5/BXbTdUBht+ixc5eYkuOz2+WNdSgDCyfvXJvboEAfT36ogasV6J7tXjGRSYM3/FIc/NADBSZcPN/PEMNvO3Y39qhjwFKPm5mzjzw0yJP4eAeFZBoMzDda+MYAqw8f+Wqb6yCmVuZHN0cmVhbQplbmRvYmoKMiAwIG9iago8PAovVHlwZSAvT2JqU3RtCi9OIDEwMAovRmlyc3QgODQ3Ci9MZW5ndGggMjcyNSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrFWltv20YWftevmMcWaKi5X4CggJPU2WCTbjcO0O0mfpAlxtZGF4Oikri/fr9DU5RHvMkKmz6Y1NDDme/cvjlnhoJxZpjizDLhFQtMCsUEZ9oEhl86aCY0s1Iw4Zg11It5Q92YD3okJROCeyYN7loxiQ5Kok1vG08DC+MdUxjUorPCcE6j7XD3aHsmgpZMY/zg3Ag/JfcC02MGgbtlUmFmjRkV3jMcUynDDKYyDtA1k85ZZhyTHv2LKSWQCtytHFlMIS2kg4wyGGYtIDjPbGDKADeGUA6gnMIdD51mKuDiMCXnaHumJcAQJOn5CJC1Bh5vcDeSeQuoUIcPuOMSoDkPnAGq8+gM9RkBMAHQRbAseEBHQ3Bg1UqOBJfMWIgtOKSyjv4FcbygJwE/oFRomFkOxQtYxHLoQpBNhIPSBGSSHFoVlllFRiM76WBHAi9Yq2AQEZh1QCwkxglkIljPcXpd4YfEGzAafkCLCjeAF5p0QlaDME4RLEnaEW4koCtXIIY9XSA0UjHPPf3QzEt6C/7glaeZLHSlaW4ozUJ8Ae/wHjMJ2C5wCCKMYUFYIIZ7BZgNT6BBremHZ8EEciLo1ilMAWMGT3NBq4JzchxLuhLkqZZ8V5GEFi2uqQ3TQ7cBwGF8wZ2iN0jNnv5jSaucYwZLuhOFg6IFvOTypGENHQtHDm5IWEcaJVsLR85voRdJ78Ovw+jpUza+YOOX63drNn7Bftik03y+XiXiR/bzz6MfPsDJ8Oc+YMZ3dLmhS0qXD1xzumfVkw1dtnRZ0CV/2GtClxVdZg8fz6uOm4ePp3RZV6/k1RRfi+aPrbDl0bAXFapCgOuqw5MKZFr9Yy9jXfS9ZNe1Di1Sf6xEX8VKaFHsrFLstOo6j1W0adDJ9qpSyyPsuWywYjHZbfVsXkGZtOGJRit63fYgbDDdRTXw14djfo5NN6+aVxXCtOZSLabYK3mv2gOLbhvcZff6ptLhvDJsfow9VF3as1oE3MST5rUOafy/eexcBxZKay64rsmTN/vEfsyskjqvqbEY7kuP4LpF8FWNFlZxIN5Wvw7+saqhGSK2ryqJtvs524lHHSPWtpLjNrZe56RHeHEh/Zfq5d3jPWVOYg9v8f5t9SyLtbt3tm6/Vo/gmU1l6OtqnrQm2d7VspiIIhH2IZr24JM9cbeMaWxdTb+OQR5t0hPitEeEBlf7pc/Q+wU1reY+ADBvCKhNu8PrHkXe7hnhRF//GBPLshbhaSXwLCaN6z6LLuN1Y9Ejq6nL+vw0+t00s9zuSTc1bWrqSyv0e03vOgi6GLrwTocyTTH7qiE5OdBXxD2nLr8t681V9cqm9r95Q1qYxWnBokfgBhJ4G8Oex2nX4+Q5SAs3cTjUl/J15TeRe58UPJse0dVf48qbhnSlTuYF6ruaGhc1n8qOsEaPoLplIUrjVWVzRFKUxsR604x0n+keZPZ1SuoRr52I7AnUP4vt+TG2duMa301Secd6vmww6pOYJeY1a18dBFSnZW0TXT0/bXmr50kH8TvtK296aWAb67exXuqRt60wSivSS2u4VtXcy9jekzihyTrsfVLqXeeyFndpX+p61KG6zL+s4G5jTdeDsAXX4zTXg1UfnQ2/rqbYNsRmpOIvDWZqLb1KKK4O5PyoTYiDMnuvtL128jjQfmpmuts4mztiS+OnIwLtYKNoWnHLNmbXIwLNdedBs5goWpxq0lG2PUqLj6bfb9wPODDPdez2yx7NyS7N5XGkPyaZOX3H4qrDGt8mq+pKHqcxyFlzHjTt5KvWPbXH7Wc9LuB6hNbHke6iwZ3ri+bRa8SBCPfv1ZFObm/T1Wz+NTk7mm5bXa7FWN8xsCppnh0tTVNy9WToKHhPRzecvWXj83l+SdCePh2N393dpmz82+Q6HY2fr1d5uso3TChOPUfjt+lmvc2m6aY4jSkevUln88mz9Vf2nvpYQUcy8nKEITK8y4Qu+52tVmsM9b44KKJp6Zzo/u7Le7i/S17eRXG/HEXQinFG44vtVV60X89Xn0bjZ+tslmYFBH45/sf41fj5e1E0CPQ0h7RKJz4oJpVPHB1lGZEITqd4PNHcod8ZOzRdfpOus3SZiHIpOREIZ+IBEKeS4BQTHoW6UszbxNPZTtCJMLYRxnSep8n5erv4lG5EMHw4LMKrxOjinCkxXgMFchw68PM8sS1KKdC8ncyn24vJ8vf0bjlZSS7lcKC0DYkzis7eEq4M014m1tBRmUu48e2g3t1kk8WCjnOHA2OUTHjYgzEqQGP9WF7A+S/m6XK92kjasRkMkIR2NJ1ea51ob5l0cCAynYEpeTgJ0UMy+M8f/2VOMGdc4q1kq+1icdnaj85seW8fI6BDF3r6wQPp0FzZxCnbO6YUKpHSx/3OQViF8OcaCkFw3dObpuNlXTYo9NSO+JREw+4acDHldg0whPK7Bh1Uh12DPkjgZSPgP1qUDTpT1nL3HwygdwjoSwKhd7MaWFTvZrXUuJ8IUox/y9bTixR2Bgm/OGfjd+nX/JACD9nZ6Ro7O3kqO0tZsq8q77q8m/Jesrcs2VuW7G3KfqbsZ8p+puxndv1Klrcly1tR3st5bTmvLcez5Xi2HM+W49lyPFuO58rx3DeuGgdcxFGgCs0cR/jDgFqERGIBcwLxbzvi/830n5M7xJkfkBelSpx9gAWhYu0RWF59SlfL9C7NJBdmODxKocSCY1ssXTpY+p4l4eRhHEwkunSzTReLNPs13a5u1unHjwSMmyE5W2CJNcxiFQsINOJsDb+2PiTBdlDk8xtAuq7U9eZTlk9v7mh9E25ABncuCQgDq3kiQ2AycFCtoI9mEiXl36c3GUyRDeyAKS6T4H0/sO+jN6ScSYAVJVaIgqoTBTKwyiTO/f3gtKOMiRknE0PJi+UJfd9lPNY115HX/fJ5k6afidLz3+eL2Zpw6SGdDb5P361RTHDDJOWb9FEYbBxkR5A+y1ApihDUgAQGMqUVB+SQKKwPGmu4AXEYCVO60I9lUHtp5HWC6SASaagcsAmEZYbD971ox/J6spptrtLselgylZJizVZ4JNzaI/Z68fyVmbg0VJw4psFYjj6ypMwciRzyTiRpHaD+hZL37NW/t5NZNsnnU6CyA9I7CMBIRsshMsuK3TUydaPMsZr6Y7K6HhYX8sVEedAmFVH0ESsqTFTJmhsk7B0M8BJIFi+3+TzNsvTPiz+/pP/7VmCHpS+te0xJVMDFB5UJfdpImbZp5s3467tBMGhrqb7FYgdXQtpNRR339LEsWU90lt/y28rvAyBY5JBrSo9621O8KyRPjtHa5123OtRwKJQi59X0DTHUoJnSAiUSAswjwHQnCj2gLjwyOMwqbFFSaiRytGahrkqQ53eiMAPqAnESis9esZJT/Bjk2PRpK9DplmUhPkAdRhlILxx9DWxlgqIHCoFl6MNbhYWyG4Ub0j01knr62hl5Q6DamRcUjGoW64DsiRM3LMta2hoCACrG7jc+6FvwgMBx36HaiBUjQffgiyKlRxpDaOAzQSZW6cds3tFeglPH7XVE/SRTiE4TTNueQ7x/8HBnISr4o52FaMvgwdbEiVsBUHltKyCcvFEbypI6lCV6KEv0UJbooSzRQ1mih7JED25fgv8foouGkAplbmRzdHJlYW0KZW5kb2JqCjIxNyAwIG9iago8PAovTGVuZ3RoIDUxOTAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjaxVzdk9s2kn/3X6FHqjLi4hugfU7Vbm6d8tVeVbKeyl6d4weOxJHoSOQsSXk8ebl//boBkCI4/Bh5U3cvEkUR6Eaj0f3rRoNktV+R1Y+viP/+y+2rP71L6IrSOJGSrW7vV5qtNEliIujqdrf6GLGYrTeUMhl9yL+uN1zL6MtasSit8vTumNXuVlrs3EWVbZu02J+PaYU3RAQP0OiQ3zf1+tPtf/zpHWcrSuKEAFWgBlfGACtkRZGyMiYWQq1uT0D53dqIqMRuuIqaQ4YXOvqVUFHkjf+1LU8PwEldFu53k/62ZibKPC3Zp0U9HUZ1nIgkJJYucceYirWQYau36w0zNFLxeiMkid7fz9JVIEsiwh7ufAvTb/Fxw7SBkTK5yJWhseA07JM4WeCczLDDYdKxSdD0JTQ5w4Zhu7z2NN0XzEmTN/maRu3ElPfuW82xJIiICeXjEprkRxATq2QwMzdeH1q1+SE9b1EzDmuWRE/u3i5D9cEpjMoa2AVegVTHDeqjFrFJtOuyHJ3dj9AVj34aY/LjRlBtdQRm88PT6VciyWgX0S/jWiCEQH1n7t9kqCOMRT+4vyRZgU5rYv9JSKzEisOKTtwSVmPMiVjDjU3vMWCPru1oPmKDVygFEoPKV/tVd/33H1/Bo46lzoy08hbRX79m1TavM9ePiinFQd9h15/cvX3+xa7Q2jXwE8SjujxX28zdPOET52OTPxzzbd7gr6cxwWm4ZMkK5BszZtwo3o3PBOVJNC3/KbVvhYITaOcRlCY/+elQ/SclyJ+JFWUxJZ08GRnrVMWSytUmfLTlTPOQ/gc/wbo3wbBwGYW5o7HWuqMlx7REgnWAG5cHxxRGx0YnoBCSyGWF6T1mZWOfpCDQS38iZoLDp1H+wXYQHPhJtFZOCgpsIYyBEh1QDVjb+Md0rBkfyioQChiKN69fv35zN9JLn9Vw2sCo4xWFLy06UdIxLTExqGY4ba8ndA0sErd8spYuGqyE9wwL2FAivGF5XzgfZ63m1rtO+A2aD9/ob6UCa57XB7dqVHTfukVvaMvC/+HMrIqOWbFvDu56X2Vpk1Wur+aQemp1/hXdlgDT1KAnd3c/tFYSFqCnsS2ticzAEqltnhX4X+Pppcdjib8f3c9zPTpYomMBemkH25SOkUdsVla/uV95MecXKBOgxyLs6JcJv9BRplzFTA7Ivx1bJR0dAXpGTNhiesUsmdiAGUViTpOw69cofx1lX5sq7U/1BVrBrbTK3EXtXFW2zdNj/nu28wbUy/P3rCpvvGgP+dG3OZbFvpv6tNpnXqW2oDGgOOf+bAqYTVCV3dPQC8qEW8BkeT6k2MLZcNeqKIsi26dNfrnJhyY8z2qrbDK6PTiswFGvXE8ggcrfy1wf1ZPreocQ4oQIMitq0PLYsRYT5rkDl59wtZIGnJPj76dzM6tKYDwNrN9eiw9jc9ejgdpHDOs3eRuIiHINACTo8uk05JRKQCn9Z1o7dukG8GHC9Eg/A+1jlk00s0mofqFWg5+RaK+7p75dk3vjYAS4BELhSMBHCk7QDNAI5wxtD+BSMGG1uyirHdzOixSnFm/UT6dT1lT5dpMe99kdLgC8va9S9+D+BjG1Q7G272M2O7FCJwDVaJ+vnxeGIkB70BWPTMoklYTFBpxb0GQcO/QJJQkgfzUmMwr4doMrwA0S4dBw9SkVG0lds1FkZJdKi04HAMKuqjEQod3MWxBhXgoizEsUycwoEjhRZRzq9I+NW/5pvmkSA6oO+L4bhyuU0T7T1v4IQOog7XQ3Ym74xZs6kd514uXWvNZ9i8vRgV6aefvl4mB46LHCGHVkZBATWD2Fp8CoOtZ2I+OUsQLOYZiKe3yUj4NJAVCw99TbcXGGAWUoUZy6cVIDNTCgw72nfiWETFCD0GSaGgMdHaE2gGzfjaIwCvaM0D4XbzA9oXSfXmAsAa8xFtDTY4IEz68GgrRCm4gYgCqjSs1HDhNtdzOhxnOBKdBkxb9dEwJpWOlr8FjwmAp9R4CLN9KKAxYqTpZ97r/GJhMmA55iHEJDny/6PDaZG2k6dkLQno9Osoo5wHHgkhhzzRL5PNabiLlhA41hqDGP4/Cd6W+cOxsfjkldgfkXf6DQCaCEQOjjK+gtnRCuBDDXF24OSwjksZuINRFGjGhf0CsHIQfL8vWCT9Q0ljB8kWgIhHzS5s/H2tpWEz0soTjQ47BtM8I8WFieBLzXi1hPAmriYddLoIAygB4mbLNbIsSRkHlGCLUooV2YokbpSRkTChCMSJf3sLh3fPIkVSAAGkNI2i4RXIzf1xOqoYFD0D+mhpBgeiQmidWVAsOAAnT4OokBiFJEjkhsZOAdWEUVwOkEDEN8BPN5EeECSqEDQogeVBL9Y22oC3FMlN7dVdmXPG3ms82YjpODsb5bYgHGSo2+TqocBiupuE6qnAClhC3o4QRBBiGNZNcNjUOwRM2VK4wL1ORhm0XV5AKhjbiSlmQWYlwnRumTaEMxtklVs5iOT2icmAGvixEMAeUi5roBYpYdg+arBijgtqLim/QEApuYaH7l0Dhm9a8dmgCvxuTViiJEAv4ruZKY5LGm18pRAqVEzCnK4yEr3JUag+EteQkrVw8W3+cJOJ5MgJ0eYxKzHMmV61KCBRDsOglIjoRGPN+bNndo1CX6FQZgTrsD06BkZthJENrIsM3k9lFLhMJyYGig+63ezqMPQJ4IEvotHhfpAGDTQzrL0AL3KsM2u0VKEFoqwAdDSnSWEthlg2ZvTAzTpGDBIT4MWrntUBr9mD5svuB+W7ZtXMLaQLif/fOcHm1G0G8ZVt6fXjbjCp+WxLaPdjeJtht6S1bUACQig6Fno7EDeUF03R8qB33FVFo4F1lRNu1IfPJm2l6I2NDBZC6SFdLEcrgQNi63r6Nz4TYJ2oxsK2jt8yr9RBLgF9Al38M+feiysFbm1S4vAMrUcbCzN8wn9QE89Aj+VQDrtse/ZacTZrGNwJqBYTeU0sHeJijI39aSRPNpParA8zMaktq+CQKWZ5vnJomm9kwTEv33SLDEcauSjeRBVf8pghtRRMshNh7EXX5FD4bch3fUZXqDQdVpk9f3T+MxKeCLeW/AJMTM6NT7XU66A7LoDpgGf8/D7paMFdMALORgWNtFQokN44eU6KgQnSQuRuBaMXEK2J6YP0xMnDMAYOw6OcE82X3roNEiwhRISb9UTjZg0UslKVxp5/v6nS4hM64lhLv0yjEb4gzzNboBtuS5oJYcGU+YzRAHjb5fBNIAStRgTGRCtOiKZLKIRgQELXq4hhZhrySARPV1wkXPotGzXKNQQrHYqMGMfDe/xQL2MUmum0OhTazZ5BSOrlJhFCjLMyF0MH5koS7JVRIVC62uk6uEe4bw6+QqKVAaWR5LC/W5vwYYZyBSth08rMFfXuoDRpjGPSbMHlKO+37WQ/38x2SmbTr/u7X9UuMNxyCWYDYjbUGWmgFZl9ShGtaVLJTFjexxXDtkOp/RpYS5So5JhuzGg5hmKJ8diZhKQUPwN2XoXAKbUfpttU7/8hyH+wlYmZEEkzxZPcSfTbIvrJoQnv4jBxhNi/P1PFbdcAiNmU25M7tfbPn/qcIVWdqP+yHafV4HYrpoE5CmYTaaMNTnbv+z3dFN281HjpuPut18hICoK7XAjUj345DvD1ndbGwlTQY/XMxk/3uwm8rl8akoT3l67LaI3d5k2GDO4kN8T4bcztSIdsMUEORjLeag4YIbw5SPCdtsFylBBCoIfUaJurHmxWcIivzurC82vIiuyzhcNiUQYyHuYCppA/tOWokVL3zVD6mtVYTLdD58Ecrm2oIe54psuxQ/t8HmoN1CskCCx5ED9ndLlBSPsXxvSIm64UEYicMu3a/mkDbdsOHboEjzygvlfLJBuitpwGA0PdfZfAlyElM1kM6HBYaBlJ3+oJGtKUK2HFe78pTmha/K9hVHOJi6Y9rdONdnuzjgElVibXP7NHKLKgnXSN81c4iONE1GajJ6Kzit9ucTZjJ01Lxpg20sV89eWGUrJ4psH4DPyjJqr3JH4sb12obhcAlh+Kd4qOJY8yKETVhTnwu4RXEwNSjeZwosBmZ3o+32XFVZsV2oqgdTwXjQ8c8LU4k19UrooA0q+WyROMNYXQZt5hM8aiHB0/YrRKwoHTIzG2swCRAfl2mvzfdLdGDBcRrOAHHyR/1EsafuZ7/gz93ZprWfKrTl+GjTzp0vZSk2vSIW/0BZZaew1V/svP7PWoIjPVf7vK6z6qYr3exleDhYV8V88dEPhyqvQUF2x14yzl68d6cuilP25Apq3O0FDb/s0gkLswNiIlT+Pl8MBMukCZ/3LH0AFc7bQwc0pv72rZPCJ5+Z/AceLfEnA/a9usXegYFd1mTVKS9Sl4rcNFlRt7k1Wz1ZnbfdkQGc3r55sBserDs2sOGCvlQYRmOtQNCDfCaKjhjEuioZPH7j6D1U2UPlzbe/9UNZlcejLcHDnyD1T+4qR4EJ4kcPN+7L6uSuiizb2VpT+PuQVZmVoIpu10ZG7WEb+6Arh4KntuWDS+4K2g2+67gvVudO8d9WtsIeGPGVk0T2JIpVC4x32wA4D8yeBarqhUoBgAdahx3kEyFcR5EyCPw0D1vdpbUrXWW9ZKsra+XR37N9VZ4f3N8dgxeloVFw0MT3kzvlKuf3I2EE/MoBcAUDYGowgKPL9doS79/qG8dDlvqSb8dx+lte7AfDqNOTv/K4sn9aJsgIc6wO1Z7efVWeXLlxUGeH1ccPDt0aFoWmRHR1ob/7CmYnnJ7FJTEwEhK6lMnqsTJZPuMAgkwkOAD0YP2un1XpQsQSJ3KKPpmhP1mkS7FKl8+XVppranQRVxFGn42E2sWbdDXYMmq/0wAXt7XoWJfa7ii04YOt3NceGuk23lj02pKAtxMD4fo0/mv8Uu5Lt19vFrcRaczNYCJU/7zFANQzk8SC+fLbx7w5zO4kylgzE7ZZAhAUOIFYKWhjS0MvlacmSi1ahti9t+woGyxN90RQTI0PufVIu/WIV+eTu7CTA40ezlX7V/61behNjzuogbjWltt3rXo9v8Ci4l457mNeJRtwGbERA4HuHPjG4XvTCgMITKs3MFSFRWMMM9C4DWOLxhymr8/Vl/yLs11oUzPrwBCZ11kI+00rP7jyCKtyGL5p0TszziXihB2PvpH1bxhc9FyYWwA4cdbp3fhHS99HU7dt9223/s539E3L9xa7OQxDDDwtyllb3AYE2xMSXwGDdWcpfCjhhYYK45crj9pjFj5nMBoIcA6w17jTYVaWu52TILhxNb9bDItKh43H8kPPtlnV0iISMqZswFa629WOKQ8yqItI4UbfsyDoCQ+e+jvW7MlhipVjQSYu2D6p1t7PxWXPDj+OWR3ONHiTQe83LRrCIw51H/z4H61duqw2PJomaNjNrt0zDXNxIDkskoIFRl8mbMFlnDA5ymN9yO/R9tQDAGe3753sj6kP9I+h8np7y6XNmg2TKF2Oql9eD/Pqg1sPBC76C1jgi09h8agtzXc4xK5b3Vu3ePAoxUMlWWt0b62XOtfThwz+/5OomNFQjL0sicquTKKa6QGab0uiumTnX29f/fNV5905i5lR3ZxvT68+fiKrHfyJi4KDxj3aR08rHDhXYnVcfXj186ALYVAXwe5BqDfTBVb5oEeY7AK4EPNcTHeRxGh9rh2HfZHCczhuwmNquO+vqQ/T3jcuLrgv7RlKHT3W7gYm2XwiLb33BzepQwaHzDqLFqJT7504HRwEpe0JUbqwHc6E8+V9xub3MyZfWJBM7mf0JcGlhICEhRSZ9OPNi925i999WArfbHYQAqxXQtj/4SDwrJlSA7GxxDGLAbO7Kovj03MIw2wlMx7pcK4dT+fOQlHA8tKEjXajrnZ4GmLUBGm7J9176t8mRktV//wjFSEHtsYLMz/38+lvY0+7/gvMm1nmZ2o7L/xrPCkx5N9CNe1eT3IGR3fXVOnWg7mF/KOIk4SG3X079ulziilITIQFXb8JwCsmpvZV5q/bRHt5yur5+ukk7HRqATDaP9U1yShXyhYrhyKdtTQazzuosMVLZo8bLLkaiJsRj7QBg+MOrr12me957Galhq+0CRGcg+Ynn91wp/lYV9Bnk49Jb/0aUFfaznxaHfPMY26LwG2O3wM8i0NpFOJ10SVUeGjxRHcqvtuwqsrz/lCePWSaN4S2mgd0LuDvGw2hnjSEfWHgplsiQ4JUJDftuz3WtD0kfn8+eghXpcXe36yyJs2LrAV8zyogqGYxUSLI+vHooSr3VXrypzHv23in+x9Emvm4Dq6KDnECaRBkFvs0TPDWI7LaAAhjlLcvPeL+pUd/dq812q7Bnh/Ax/qfGXhbjJW3qX0TEdxyfGjp+YCLNpZ270UqsYepIuFEXrAzoFYwuczWZdmBX5LVuEPTnKuiy4z5FwTc5WC8qqdZS2yYhfpBz+k3vXyoYxZP17ABs8zzNF+myziYZJaETX9ZpAexoZEDgn7+W7ljdtEiJTdX+DM9Ys1MY18fYN+38L7xWa/7tM1uSW9Ky7MLPyREYk5hJURCLnPi364w760lQAIDYGRUzpMjkwzcRNjGHj9N0E+1FqiPIIixL6rwCyNtBiIY35KUYOLSfSclZ5xwdcAirEcTtZeX8siZcgPV7Ydbuc1n1gROohgMYaKUVgHCDk++0glUoOlLUEFfigJzSSEXcr74TdmjZEGLu0UqBtiiAzr+tSvvm6GmpXj+cWT3I8HI3jcG62dfm5HvsjZcyDrwPGr3KATq1T7bdMCh26Fvtf2CtLvOiuyrb1yf72q3qWb5ZtG/5yf3Ng73f1uQ4jMa+H06ZTs8F3czNOgojfYYXu2P0ad1fT5lU/G5mHo3liRkHFBdTm6HVTlgdKg7eg4xnq2k3z3bZoSg9n8BraoeSgplbmRzdHJlYW0KZW5kb2JqCjIzMSAwIG9iago8PAovTGVuZ3RoIDU0MjkgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnja1Vzrj9tGkv/uv2K+HQdrcfv9sOEDckGc20Pu4HiN3Q0cA+FI9AwveoxJyo7z119Vd5NiU01SspMPhyCWhupHdXV11a8eTXJzf0Nuvn9Cwud/vHny15ec3VCSW2LpzZv3NzQnDH4jN/TGmNxydaOJzYmAH3c3b7OX1W+3K0FsRm7fvfkv6GyizqEnpTY3MG7UVYUeU9MlO70InWRyGs6hZdzhY4qutyshecZSY8FP1GT/WKJN8dwYHU/1FDjBbHZsSs+SqvWfReM/24fww8+EiroJv94VTRUafLxlOivX7aEOQxX7jf/lU1215dzSuba54jYm6DffQwKdudXEdVA5gxFWFFqrmzcbaFWFVmTQ6m2/n9FsOlcUHpz6Pr9dUaWTE1Fgl+EXzESnZlKJmZ7hh/If+Nf8Ngkuck5HEvH+UI/2Y32o67J5vF0B+w/7TbW/x3FvYEhYgB9JsxtlZS4s84NgY5Mdtp/3h11VbG9XnBvYP6myoq6Ku23Z+EfVHj9tVh9uYT8/zW0hIzKn2sbzVKkVDgljlOVSqbjX4f3sREzAweJxl1eLE8Hh4nLEhhwYKFn26tjOTqgYSB2Nu/6+OKEmuYHNi3rNnn9mWE6NvHYeo3MN+iHq9TORZHYqS/FZ3Onj4lRW59KcT0VRmni3OKuSJ5yqnEo84TxXWvi+P6YOHsk5wYNHc0FMdPLitcic3fCcsXDC/ghtMblyLmQuaVp6vnnflnV/XBieI/inrNtwEKf2QDCVC33tdgvOQEGM9uCpnx70AcXZLcz+eXZqpXMyls0/lYHCCJhpdGirJsk2UD+502AduRpEQQC1BoRWcN/3n7cgcVNm5S0My7OfEgviKlegbtyKdCxcEe2wNbDdg1YvUL2ayYOVZh8DgmGxidmuMyIdDROGShl7wRzkojkuNImzM7F547u0mq9ajEws5gKr6/WYE7seTv71peYTlhntKVgFbY2Xxh/K3a4AdaBExnI+HoZSOsJolmQvb7XMnC1nNCs/4ol1X6s9yLQk2T0olTkNDnrAgJ6MyGgn0CJBwWVyAW4wKXLKR0MGRo+X4NAiAIMB4TVSffi0esTPst4d26KtDvtVtf+IoGLf+mbu5wHy8Gv2ny0qMAdr4I9t1bTNHAtwE4TNmQik/pRUYKAJUgpstKChnwCGCo3ccGhAJMBFlrWH1tEMXzd+l9yqS/+Pe160/nN3aOZRBQF8oHQ8T7tEHAMoyEbEefwNM/uPbbV39BS1/3vtGL67q/ZuP/zDbkGPftfwn81x3Tbxr5euRQiSI83XrUUIk0sm417lttyV+wEhM9NKYnIOZzQaIFhQNdQXYLwlB7BgAYA4IfiZEHK5IgalxClopcEArxO9DexMLGt0iQWSQycVL2Bk+0iuKWw4l6AYrW/x7WEPTpqk2YcjsGr7Gc2/1N4z4NIEFUJRhTQT5hFa/QqNaacif0hrDm/y0poDf9VT+sHNgP7f5PTv0zNyI6c8WyTmddLKgmfPnJnVOmj+X5NkgYeCRr1v9TQ10UqB1ebiZsVILhT3LX9PzOulSuQSnHbX6IfUcAChoNdq0OzLVv5NauUmJ+CYJBYeizV6hn9J9O9JHvGJ21wrM+RU9X5OkNLSIXk36dl6GJ+WOD2UuAQjChRc5s34tJFdMdBJAsGr28bA+lc1nouD++f92Eyfo9cuXAKurQaMJa0AjgTw+gbtFAODv0Pcety2VfN5tyvbulr75/flvqyL9gBnEP6WoI9Dh03ZrOvqzrng5cY/QwuIjd4GmuC/XMib+v6m//76+ycnbwqsP3gCMUWMRQsaroAL8H4Fjds/RUhrs9flrqh/9dOz3L57joShs8+cR+GpW2+Pm/BH6xcus/cFxnpW23J/3z54pRUhJAl4HXbaT1bU90dQ7BgiahH1y2xflhtcPucKbH5dojulGOAiQx0uwjZuLmzgmbn1f6Q4fmrU+s4KOx/qctcE76gpSz/ohTwWSuVS6HgZTMU8HpozrWEIFbcPc7/xpHi66LtYwwOnwB/rnWIXNUP/ZYFOIEbExITpBBiI+rDd3lLg+mf/xOTiHTJYCGQwDwzmWVmskZkPvhVChyL8AtZkN+1b/ZI+8ziIjxnBFPvSf0Eg99R/DfspsvXBi//PhKp1VToHsG1Cx7TRBy+QKtDy4FQJqiO1PCYDVjntqH2cUldmWl0B8jtOuw1uF7578+TDkx7cwXlDYqUANAB6f7178vYdudnAjxiYEwAQPrmmO3DonY9NbrY3f3/yY4hhRwRg/IkA1JcccIgVMysHe6AAqIAcGt65rQkfC716cPr4yVEqUqEbsFmaUYQ9TIfhUhAboZ+BbeEgmHTOZ3tBkw4Izw0Yb/ggRnfoTBKMKQHb53bkl8kdmdj5FPUCeGtpykOIxwX9aegwoosb323+UNXicSZwnG3QAM3DAYX7E6zHAtEPxSySpmCt0OhGQ5RJ8EFMZPgfpkIwfXaBOkGLRl6KE1KAndyOVvTL4kwWAJS5ciYhYCNG5P2UkCP06EQKZU/TIwH3KXpGD52lR1oH+qNOE/EE9/HcP1xkjjE5JepK5lgGboJIM2fe3S0W6GGE5xKGPGMO/o9aG91AtEjM8qwAoxLEuKxq/6xLJYCP/7tzMJvQa1uVvgUYhLnUj7K5kaPdmXLixFc6cebLnbhJDnILaJmPJCXwwLlkyAM6l18UnID7x8Z74M17GgQ/zPprkx31ojwIBT61HJ1DRA6AYv4TUNq8P05yM5LS48J8Eh4zOupUF/v7shmjJAHgFdREiMgMQsYeUgRswbPmsdj3SCL+CTBYUe0xMO/+DMlLbOKHQkDb5L1KV3HqWBgCZ0V6Ar6vfJfZc0uBYiXjjhPaXNvL1LnqLQWGDkw8tgft1f2Dw1OzpAGoQIsWdV+eFKQD4+dRL5cH4dn/lJ/aQ8gXVpsA6aq28rlEm5W/PdZl08wmiUCAOLjoAhjuODFI7Y3DxJKqkCTSs0kiaq1LE0nf6pelJXLAlxKlbbjEJQXNGXiIcsSXq+KRMwSBDQY3ZEwPTbGl72JzhbEJI5zSdH02ixMJME3QOpqpCLtX+I9E4tguKHjQHHCyafoQkNQhOFO90biASoGFy7n1oVVOnTkGBp7Ry4yl6tNYKhdj+fdaxg4Pn81al4zbJNxil3wTKmxKeV+X8zoEFCMFxRh3Wyw+YbnRNO6ENsmq4Mx7HWhY9umh3M9jDziLZ9OvuGbZv/uPYimvwBScZhkPAUpDgYH8Dv1P7fxPg9Hog6tiwFC0f/Lg5M/4UDQHx3UpFI1wQlB6Hbuw/ISL0db48EYTCH2FZHqU07NuQe4R6WkVD/rLUsmHVSCgIu7UYngEoER5ZhElOF026MmgX8t0zsRqp1lXYJFQuzlxf0xaIjHKJu6SJAPN1MQJ0pRi93oac/pdJPVfSa0oYJiUC3mG4ShCHvQVzQx11zqB4HSh4lvMCqMTGGWFewdwxRQsAjYMxDdnfaS3D7P42igfjeHh1K/SBG6mQiso/EHtTsRlxAQ7XOdj0+Med5hwPNRREwfKd5ohZgj8yxCy8tkbF+QrusX258gJr3JIagVbKEWA4X8L6X8pXfWRBw638YI9nKvvqtbDtea4C9EiF1Z1sG9dbIuABfsI4WMoZTphwXBM0FFJSiyqAoInAJ3YaZFdgWOM1ioV9gBvCtrB0UfRyo33J2iHU1KGG2CixOhC3+j5s2fPnnfjmcF4cH6VcCUZgqgIKka2dMUA3jGYGFSvDNBnUzUtKP82VRYhXMoDPowR0ahnISHJrA8JsemQ0IrkGlcNzEEfOhwVppO5CSVPkaFRxM8tAUyZ7ZawS0xmQaGZyIyfporlFsBgOL6nGM4VWgIaSK2S1TDmfMdhNKsX6WHQXHEea5Nzcp4veFEaw4wacB/JiQxO1Ce0Gpjj087/WQg64cqi7lOsthexOgoHAYBgLB79oQj+m80CjUGdNOAxBMcKHqsOIIGecuFrCdoC4Ase31X5W/jmB3AliiePEJ6sD9tt5U+6G8upgMAO96Dab6q1UxDw9GQ/Q6Oq6Vp1DqEVJ9uLBW0upefqKWsfzw45dNQz86lriuhTjgaZMMdm2RwPaaMCJJDpeOx/a5775EfRxd8Da/zTunzcFp4T8FendcNK7pCnn/uETBV07HDNYWyfucCfyqas/V6EMUPlBH7d3FJfqnZfd99c8keo7I1zXI9nrj8nQQk4ONQNxXvpwEl7ozZr0NrpxEVdfjhWdWcvLhlOTA93soWmt4VigCkxE4N5/LYY5D56GvzU+0PCgibiEswA5iWBOy9vjcD0zqxLgVEuEfebzEqr6ay0Tefj0Y2Uqaz0TGBBAFKUNibpqferQvTm3PmcLRJm4Abw8RJXsPTsRSqZD8ZGopI6RQcXaWbCuJqMaI6q6X1iD2r0CNTY7piY4NzgCjuJti5Z5n89ybYNwmiXC3GUL5GMSCoWVyI0Bx+Pxd1CWOcbT433Z/HA7oK3W7UPC9VZTMvcSBXZkzP5gimmI5b4a5r8yNnluQKTG01XYywa/VPaHR//h+c5fPFShT9PJDqHuTyrRsNfW2UUV12k50PPTEYB6vtklISRdIDaxGUnUo7yVZhTw5hI9035b0nGY7v71DGxuRYx2totOdeYYTAjaXgagg8LQsSxXtfGXe+TZnLMlAEwifhicwJAd9CQTUteKj9ArYs4LiYIouiCzIlS8SrgdEllsv/2Tkq1DrV5KJB36D/N12/DeRUsKZGJCqVznXZlddDkyjDgL5Ue7U/lnd1wzor92JhjPpsTPaz367XjFG7yJjVVA6Vcoz+gBmqUSkdnLCoWmwqUedImi6DEXA77srK7dBFUGnj0OPm7D0csANmCx+zEyVHZ1Wb0dRhbH3AD1zlk7/xjLFEq95sAhdrD9LpfpemgYCFCMUoRoNoBUy/5ea2E5AzQiOrFYlQrwcGR6GolcEO5EqFUIh5CGAlDwOkECZsZAmt6VF9tkRqCAWpWXzaEzcFfvXod0fXFuOQ8ritwJW1KdUWgWOPjkSXFyi0xIYHWOj/3ZVU37aq7yLViimRrvG2Bt/lA+5Qn5zMmIRSkzpazE1fObjKf7ezL2fGrr0VloLVpNl/RDkICnnO8yLvry1KHmVnJ4RCPhqQTBatIbEh0uyJ1BrgN01eH+SAvE2AQaTzFibTRLMM8Kxc5ozru2Os+dk6bmiVDYlGUSjJvhgYJAjui/WSR0yk6YUAlMo99xVKGTiYzdCrO1p+4dck1EnBqSXTva9B9lGhT6YiuGd/zoXFEdy7tvshSV9DNRtv6dHybCR0TpnJLu313pXLSewQpk4s3n8wobPo2HSpqEswAh0zwyEROnR6ZCx4V/r5Lz5KKOWLBTBweS96RFs7VGLR6Oz+FTk3R03eXnoIymlrFfLlEorz9CiZjCpjaP4XLfwQL3l63kfKyjYwSsROrWKzUNiQ3TLiQOxXy6kptAKSkjyMQ5YO8FtQr6y8HA7zstLnDNCykA2gWqpfdd/C3i3CX01Xq7f3jBl8SgF8waKmzPjoJjX4v60NodDjW69J/32GzDtgj/sLwmeEeEDFo6pu5fPEBS6/x6UIyUYPjAiovWtfUNf+eG8JiQc6oV5eoM+kSHh83iXp8m5ARnRtthq6FWiJGUqwdofHQnblxLeUJ6lCQCaVDsLhqw6ZhtGxT1BvPv0O9KWufeWKnUh7WxVdY9ogIeP4SFqhpK+PZptLZPXlMeCc46uXuh+pMOfTNsm8fDiGePf/eA4pX7VQ81MclAjhjOcVNGvZqD4ERDtCXnRCHL/07GyJe0cE7G4IUAsf2o76+QhzOT2Cvv7kwKMDKE1UOVGnQQKKPSTJ/15Go/pay8flQfPTJUeEqJ3TWrubrMo27UBONf8HlRqpQudC4X3hnwmUBRe2S1FH/iZipZGT67Rw8GTPlQKo2y2p9WFahmFO9EUXuzRuqezeEOl1YVCGyCGCsjyzi0/k0BcfLkKgp4lUvhFGjkMPSMgQcAa5GAlM1S6D7rEu7+DINvDAr404v5m81Shf4mxC1cVXyCG6yJXoUgS0fjf6X2TJRQI56LMILYb3J2Q1eEqDp2dPcsHiA7BdyY+ltOa4MVIx2B0tvtHA3rYKCoQNvmIJ3hwHTQEin30D6i6ZxSaU6BDRYSP3BJ17Uxt+Khbs1rjEI5uiCjXvs3maD+pP1WUJ42gWc4WERHhX+z/5WDTxaHxw4OF3GPREHg5X1bl7wJQOZEfHS50PR5mtD0WY+FB1v82QoOnax/rBI9CWVSgOpke71Ajzm31PP/AvyGRTQAmFyNp+xVIFtpi7ph4uPy85nKtc4fdOWiyT3tHYFb5dEzMHeUnZBxHx4PTeKmJtcGrUcMT9zJ0KW9I1DBcfmVEllukoq4d591CWR41cg+chlnLWajy6/n7pMRifsEWByqvkXXBS+mwiEXHVGjbPMw4vF/6/SReasnNBeKGSpLABwA6O3KfwkZ93iqXdwATVRaQ1YP9K9E8Nd/eUohqHIvQ5/D3CsWcKxYP61isedgbE9ORRfCABCN+zXPBbrQMHCq7OwchK872Hn18l4UbgEswhGe7oYgzGlioY+1a062rZVZzUXbgQRnWtmo6G+SUe1DLuOSg5HlcDPw6HD5Za/O9dnPe+uSZVbof4UyjTAUxtvjYcWxoOVExGWw5mWUcvvf+gRUjegIKh3xUjA5q8xIBa3I2H/xxLlgqFRPZuIrprj3a6r595eJJ/CguQR9mfIJwJMReKte+pp8jVN5y6skT7Y6urV4FCXTbuKPdVwsfyxK50y2cLtTtg6CfpnOPIlL+bBd6YJHXULZa+ggz4ci+0zrNXwL9JyMLIp14G44MUb8OKx1ne38yk+M7ytEK+k+xma45HwwTGswvF2Fr//6m3wvtyGAhYfTTNdoaGLiA+guo5fOxjV+HGVObRe7R5vMRyH31ehtOzQ+gYH3zG8TsEVppCghbH7sf5fWG9IXiIAcOr3MB94AYtnVU/ZV5zi4VrxvQz4Sptu2Gq/Oa67lxyEOrroLIMbBpKsR6/tm7v0T8d3/qNLyH1qh+DFXd2P3GLy2r8koerq8mYdDUyM2L77j0sLF0rmRJyvZLpQCXOlYtB+OpvXz6FB/uHsanZ2hTftLxsshExNkQAVL5dmx8ioUfLiFeKFRiL1VSuU1NV1DBc4kzCfT3Qv5coRvCrxNalysFh4ue7LM+XXLGDmHb9YYIRvKBAYFQ1V6TI6JcC+/wOfxRkbCmVuZHN0cmVhbQplbmRvYmoKMjQ4IDAgb2JqCjw8Ci9MZW5ndGggNDkxOSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrtXFtz3LaSfvevmPNG1Xpo3AHGSWp1Euust7y1WVuV7JZPHqgZSmIytyU5luVfv90AyCEokJRk1zkvW67ykCAujUajLx8aIoubBVn87QXxv3+9fPHqIqMLStNMSra4vF5ottAkS4mgi8v14mPCUnG2pJTJ5Hx3tuRaJuVuXRzgkSYFPO3O4KFxXzZ5dVMs18VNVRSu5MrW2x9367PfL//91QVnC0rSjMCYMBZNCQM6yIIuNE2FEAtlDPyqxeUWhv7tzLDEdqST3f6M6eQOX1RyqOzbJ/yvcEX1sfqjWDXlp7LB0ntXur/2w8pgWD8mozqljIeD/p0w5RpJvdBppolto1Kp+WJJU621Y0z++ur1rzOzYsLAy2CE6301SZWSqYRPQZsr38L0W3xccsWQYBnrD7/yJJ+jMGMp12LIA0kmScxUinwJGuVxEplU0B8hM3RwChzmWdglBUKoXwzSW4yWDs5UKoC7MAdBpGvD5gbiNKWKhQO9BHExWXJXNrf7Y+Nk51iXuxsnfM2tl7LVfnvId+V+l54tpZHJ5a0XzxUK3W1eNTg6DkflSbAV0QulRWoy7Ya7KzcbaMeyZLUp8so9wlbab8td3uyr2pUcdyXIynZz7173u1Xhnu5awYfnP087A96a27yZWjnOWSqZCcl5P8Kybg5cEKgtw1Yv3Yi5JzbfTY6rYaWyARvOZ8c1IlU6C1stt/uzJcx4fdwUSAMnSelpuCl2RZU3xdq9lruWs6iQars0MJDpdA5IgaIpF6bdZEtmkmIDDDXA0InpUAISR3XYPLrV+uNRYlJqRNgKJElInvxyhisMCrT8kjcgYDixLIF50eR4RpPDYVMWOEtB3Brbz38nVOzKplj6iZ/aNcCFbYGtd9hx41rmu7X7DgLtazbHald3ha7aCudvhdmWr/LN6rhxvXvp7nNQ6FRnmZsMcBxWpoEF4pLEdDKU2lWBX2ss3KNbINxTRKHWp4n/sNq7DrFfkxxtKbXb1H3Pq6uyqfLqflr2SCqhKKB0RFtxavoKdVSJZGgIeNgle31mFbLjI1D3paj2y3W5LXY18C7f+DnldlV9ndKJJe0xlIuUeWVWbrfFuoS1TLFWZ7ZfXcDYfSFbLBlLmfYm+12x3eao0ThY7+hUYKJGWDX/CzK2J3cnjRsOgYIakkApDbsUoOkvztDqokaTGla6Kc5gvW6Kqh6xUNIk70bshh5fCffV0/mADhw67xyOyIh/jKw9lxPGFEfM38WHBF0Ykz9GUgKqH0xgasC3sWvzW8S3gDacWOdCceWq/RGbtgadAwWnWj+4WpkKh02JtvRx5Qf978igS9gUUgFxPFVgEGy9T66e6ttadCNaoxp08DH5NTprkyqjF5SnrB3+S3wNhRh1NDpSHogtOhOdTxAwMUs1aGSRSuKHfRfrWcHegiXpVZtZEZMFKzLgjTQ952bAnuj44OZoYHevY9AajFHaWuGgEy7Bv5H/Lxn/HMmIrYhJmfkHCcZ3UWUDFlpBHfxNSeaX2/mhMGZ93NZO+W6t6s13rnxT7tx75esd3Nc61OowPLIqsCa/VFh1b/+7HhqBB84OGMPW2ZGphqlIBuxX3tlxdHKR7A/Wh0OnxVoLKAJ34AimEu2RdTy4c+vgS1VsvFMHhda5gEJnZaGgPq5W1qDWdfkJW/ty5ySXN7e9Huvb8rrxvVo/BMo++inBv1TIRXWz6J7fI0f8fATMA+YZzoeKkH19Bgh0mqkJG7x0Y39At2jvCZC/ow8oZNKzxcXQK5AgH4x6T6Obu3OclvGN0lqrBzsFCHDs32/uMeYAzyRuK6Gi3W0v3ly+ePUBIp/vv391vmqO+eay+Nx8f/Hm4oLRc/Xjjz/+9eefHJgQGlVGQFalOVGPAqX/0vb55j/aVsHIjKHDpsNW/0hVwdNMZqGqYENVgTvwVO2lWw/vYItka/3VTVMeIHoDV8uvuxNenuyvmryVv9z94A7N/VbY5ge7/ktw3xSofwYeIQRFdqQP99sI4cwS19dxeVzLMIdj9Cr+GveGUCAhvmAj3hBjUcMJ4aVg8hvpx4AiXBoZ6Efbniqd/GXcY4vS6HgVM6pBDyLlJjCq381hWGwhlLZaz27Tt6hqhJLgxucY6thHX2S3sFDCxnBV3bjSdVkfNrkLllB7rV0dGzAczlBhg6owEDa8ReDNRkbX07EPQnk8C+n6LbIilFpl1RNpYC+PeQc0lSCLSw1xuphwSQLIhYAGHDDHBXE46YEuqqfmI6ygm7Cr65nxBQP/iPGw0RzGJTjYQhm2+RzBokAXDDw0GhcmzXlfmMDtQ9n9Dn+U+9Htz2tX+DkuvELrYLw5iE9ok1IoHU6fglISWZbUxSFH1GRzHwnvhQTdI1yb2/12j0jD/li76HJ/7X494jsFmBgHKfe7mxMbmomUwpbvtwHxV0Qkb53wVy0SZIExJGjnH/Lj53JTIjBgXx1SvHK+hn2XEhyOMr/aTJLNsyylPCT7fo7dRKRG0xjZzv1Bhh0tGgBPqOrtQ1mjjch0cvQF1lYYj+D2XQF0LBQOiz3v9juEGbx7s8rBe4gHo0aBEKAtoeDaecm5njPu+uKnCeOO8oRrCqpZtOZJh1o5sPKhxcp4ShS45Rx+JQ1EOQxbwFjBrMHaMrBcttr/RDYGSSnoLwxaRNtbGdmuEBrR6ALy1DCKP8SYVgGO+RX3Y1C3NWp03GT+a8x0Q5glffTvjxbajQ+OE1TD1bIKRNl933kCZWwe0JlasNTgYY6fRMzRQdFkzwiJYMNLFjeEiHSehJQbA608vP52ty6KtQWMRVLA5lNJvjm2eCUjTpEw6h1bKPAbBIoOeVk5IB6KQTCrfGU9+H7tFeqBLCnAlqpVWXSQJ6MzJ0Ac0XngZ0BtE1O7aQZW9KFfGMgWWSxVagwLzUAgJAYicAnrkzHeWVnqlH77pCZkCOvFyIPQWhj5gD5HkZJThqK3bAKcAEQzA26Uu2l7DDqaDxh4PTcOGDE1aDNvjkGrguEPGj3NHo+SgyCuoWHX/zJJjASLSlhcbMgDsXmUcxB4mmi21ISL0H2LuggYNZl5F6HHAgluYpbxJ7BAgiPGwQF85M6JETNgQYZav8+CGZJBIDKTPZAimgb2Er5m0CXXUFv6E6T/9JFX06IRw2jYFtaHfOXDbKueLPbQtF/HXZ6PLqr7YyT2zOjIlnLtYiLNGGxvxp4hR9/eyZxeND/e/Wjc/TLgPHc4kPE4UNTzX1KNh4AmjIJ/jlCNkQ4NiC7jwXscK42Fj9/U0GPcyULV9DRDL6OGnp5Yj7x/E3cxDH0ka+gjWROTG6oxZIoNEzBQgiYPxObpvARLxYQKhrofkVA9FFDk0r/FuAQWhslnCZDlCKgE6H0Kj4ruNVRfen5UPkQjTp7dA/FkbDTlwn2dJ/V+FjqzYGfP4WPKZha0KS5b3OC6SwugI3EhZakiNGxdbyJKUENQZQIvmY1YiA6BZhL20KDvl44siz5MxasSVtkMZjWmdFhAVjlHlmYp+PtBz23KgVWFztsFTrv3PoKtkyt8vnffm67R3iLIWYuvgy0DDVu5WpuydpA3LEblP9de7a6aFn9XiXPLXUxcD/urm3y3zqt1l0MxSG/hsI6ZbqGdCN11g/OydJgTHeuitth9dQZW8co58Wv3ycGj5rHwPIO4VkFIExDi0MsAnu8IZ5gJI2nY4KUbswfP6wRUDfXluF3gVeIWsKC9pAhFuDn56mVVFevjqkRkwX6w6SrGpqv4tbl2H26BQ0XduJc7jFlOPJsQTdhrLIOIm3mad2Py1jUADSNM2OilW/M8KmhT+wJdEYxg+p3djZynk+nzdB0H1/ukU8VSCosTDOeOhYD4xv2uS5RO56up+dw1WHcl6RN5yAQGigPOTzrITLJUcB7n1Pg4ioBy0GGrnqYfyRgUFogJGv0wO5QmKQX3LWjFotB718KAmmND4qYyJmZXmBOw5GTQ53W133oVd+uX1eVm7Ztmv7W6yaBuqgowNMV02AgRuRRh93E9znkkgF76RIAe2l3OTUmA7SBax4W23LmUqO6gUiVO0bgjgVa99va6AXepNatHmzfodmyW2PyuElSn37jL6Z0rUolbqdff3UyMRTGU1CJo04VFyisyhaqvNUrylJMWJj5Bta1DaUARnlFHu1WhLDm/bpyxkqf1bo9FJsFZWC7FA+rKEY/I+jpxjygAQAh4GTLokTp63Mmx9x46Kqt2JhtEtay346GnqFYdYOsMPAHOaScgE3M1HLzELGwxlsZE+GMSbm0KoxkQMac8KYHtRAeEj7BcPCrvF8VSMBF2Sec0HoXgn0N4FLR6N5bWZaZypPmIGerTqCRsfx2OhtmKgkG4Rf3pgj1rUMxJinu0mx5/u03v0nahyG56+M19I7+r4LHNeczLnfMEu5RCeDhUxapYd+X2YBGFUtOk3nd1y6pTJH2vQZpUIzqJ5B+q1i1Z+TTP0qeBBhrKZ6f+fMw3mMSZuV2euQx8k7hKQf6oT0ut6rbbtc/paNsb2ea8nrvmlT0ctXUb39XKEdcDdKED/60d2Ou/ajK+QKQONHow9Q8jbnwWuPGjkFnXNUiDgngx6Pu1o7HIXQ7tJIJGuM3DCtp/mRtVEpNmKIv9VnPbVuKxhh6w4dPsUNTYI/fhUNHEiQ4WRGwbPBJmPZJgX44PBC4ZIgLBQA8TY5mAeA48qi7T2JzO8ZyEeHSpFU4fSPWAvrqA3eUTiQLBRPNluM9xfXP54n9f9IljRnWjr7YvPv5OFmv4iDuMg/G4s1W3CwxXcUU3iw8v/mvQBcLz6BLyTE91ATGhUPA02oXAjED2vC4ymMDT5xHcFQqTgnFRYMG18pDET/tqv9m4U9csS2B/PCJpWNsoczRZ93wk5RavAUD4ZcOqkca6zeB7mCKslNN3MIDNnT8DNyR32cprV3x1P07T+xhIK1SqsxDIId8KpH0fT7WRTMVA2jDVpp9nE36IJ1BT8H1oAJoNcgSX6NgaPJJ9Zprgx6UkxGepTwb3DhL3OewnAlzxGITj8tbejeWN6xauo04l3JStERvF5N9HT9wwIFSPS83lj8RUY3KFeU+ZeQb2P529OqB5MjsrKjUDb8mkRvRHH7nk5hj6/AT86Ko6q8uT/HCwd6UwuRPcsh36Nh7GEcl+N0/PUEr1pEEgIKeZ/CqDQHSqwD35GoNAMBlEfI1BeOo8JgxCGLpSjbcffRz1S4VnPXjkU5d+VTIKe1uPn5OhW/PWXTbFHbpbn7KDB7eU4NUDBMfd+muusCh7hcUk01c0OWaLCB1OMH/6PZYeu0BEUwqiEHTJRq+bgAWTPtsJHm7zyeCRY+4PyErQ96R66C65Xs1FzATV4IDsHyYRGcRbQZaCFudRYqhhMWIG7OhTw7KUDpbl7hbs+6dicj2FBpvHw3ZXj1rOx+qo4KydpIYNGDDrwRNhHeZHSdzE8U+fDoi8ybDLuRu3kvNUoqYH85cZPSWmwVm9gXBdhUMNbx709IaUEHjoVm885fqBGdw+CLq6xNgz/9NBb5PoAua4Dhq/m8PKBHAEFU6/Eboahs/DKhK8BK3CtiOrS7NHQVkURBoTHoIu51YXlEQqkAxMg1cT96n7A2UIIw/mbVO9jMtbRliCydmbyYjLmzjLn6dRmQSXaMCA2cMBiEAzZr7lQoDHmQpiRhdihBBQ2Rnup36rP+eGyvBoeiBG1/Z8jynMu0PMxAZn8EqmkH4uwDVgasg8JsbV3p/QLcmS788epQS50SkZSrw/dHxcYAAiBdH0g8CA2TMDfzBgX/Pu7jRK4b5DxlRP4Qhw6YjXSx12Y5HD9rAOOq39gZ6/Tu7P/y7weoc7QzV9JNxYGOyzO840XZ7u5JqDQsY171MzitKorlUGDmDYyB9XBpdQWvqm/ATYMVwP+jqfI4ArvKUyIHt5us4CyvbzocLbWXt/VNoe7XpYxrT39t0fPkCI7zqCluPRIzrLtv8T0jAbQDLEIR4GkKw9Sp/UywJ8ZxkO/WUWLSYpAUEPGs1aACXsxZmg0ae5kfAEj4gHI00BdJhNhQASxWNQaiZsW38gQ+y1m2Cg1+09PAccMxdJ65FI+qTmlT0WfRJL8fQVBe5JLEVAhlP+NJYyCSNl7EksZRIGGuFoLJ26d8j4fsTCWdPCx+5eGTX6pzNOf9YFIhQ5EEKvsXxSB0vqfOuf8rouqjZWpkm7ok1VfvL5kGzuz9YIhbsuexq7hQI7owbstk4TTYgllyRvr6cdY+kS9/s9jPnuRk357kaPXJnsEYye7IN9+nKIVFMqU9qi318Fcwn7h4+M/fMuUOe+LDbr+P7CP6DEEX4U9obfPAQ0HeNpiEYxFbCrFb9R7i6Dzd0ol6nSwsKFePGuf6F8MAnYfITZS4pC/nNuiMvHXYeQz2DwOCz7uOuPwqYydr2CJLNxd+w8Pk3lEOoHaz+AEm1+Y1cpet1jiSomQwAatoJsT80uT4m9DrVBtMcXlbvV5lh3gJxTMnh5zKcM2FSBCcBNs6/B2nSWgpH9GqhNMwh0sq9A2p4ygQBkG2SCEncVCvwtSv1lk9DBAfb9H3zUuAsKZW5kc3RyZWFtCmVuZG9iagoyNTkgMCBvYmoKPDwKL0xlbmd0aCA0MDc1ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42u1bS5PjthG+76/QkVNe0cSb3C2nynFlHaecxBWvK64a+8CROBruSuSYpPblP5+vAVAiOJA0s0kll1xGJAigG41+fN3AZIvNIlt8+yzzv398/ezLV4IvWJYWWcEWr28XLM04vmULtsjztBB6YbIizSQ+7hbXyaurnCdtd/Xr679gpApG+mEM/aWajSv9iFO0GCtSrkU46KurpdAi0c/xW6jkh669v1pyk7R9PdRtQ1M+o+EZ6C26zeLw/I9vny2ulwKs8vSKJcb3dIv2X/r9/f22rnrMnbNkuKvcQ92sK6KSJ1Wzxlg0s6S5wvtwZcdtyw7NG9u+XFebrvIju7JB6xnBSClSJotwjTd+RD4dAQ5NlvyScXVBbFLLVBR5OCVTWXq1VEWRfNPuriCvm7qp1jTTgRXDUp2ZhS5UKgvuhr2vhzusRHAvDDx01VDasfbtl4zJph78t3c0c9XV1Loq7X74wV2739zFBHFtO9zE1nSNfTW04dzkCYREWy5EUg9u0vuupS14R3/slqHt9V3VdtXuohZgGkZawB5oAb7Qotv9MFv3b/ty3WFNq6WT2lHghi90ztJCCie0fihv6m39yQtAihwadL8fsAFG58Sja9xBju6JlmKV+Na9N1W17ukRTc32o2vUMa0YN09wmebFjBEoi4zLXGp9SsukNtOBp7RMaJbmWRES5LRHUqukJ0vA6liyoqfKreWbtmu3sJWPl/aHlsvTB5tDzeVoofRyCwJduztnYLQ5OodByNC2Hmha7jTNJAKWYvvgG4MpSc2pT7ZYCrwZLHWNeQTWxbhKviYVNyqB58ADltq59xv8wCvum7V7L8eH4Qrt76/QtXUN1YdyNbjHrlrtu65qVlhgXPyZPFqrlHKhYeRaF25t/7zKRVI5K1m1u/uy8y9eiUXSt/tuZZ8lOIL01u55mDiwwXXdkV3tt0MNea/gW52JBdbhqOyHutm41xvrJFvnM0RS+l6Vs1Hsu30FX4N11jAIKTUMou4jNqUKnTLvh/pq5b2JUlMvjbeSfmSyKe+Xu7Zph7ap3Ae7I9YfufcbZ2K0I2f0RWRZqhgLqP90yRawCeTEp2N+yVR2lg4TqTQ6GLO+RAcBkSk5p8POOwaTZoLNxhyCyNwXQ6NfXeJCmhSzPW21Cl6Ci6etVhkEJP5gtRQGNBxzu6mg135zfaCCIozmpEJzsg23DqXMFS3nqTI+UFpTkZkmkwABeobNljdb63PQbg2ZHmgy93UcMloXPb+7It3c7sdh5NIrP/L9XdWMQ8vBtU20077XvetRN6u2WW33fe2CK2xGG5F8vd2OMxDYCOI4VqQR+rmXGcb3Q7e3BtQfzNY7gVtyn77NIRzEbr2qqwO8ESTSoStHA/QuwD39uKJud9ZVdN6tbIeqa0o3vp+4UmiAUqMr5YB2bHSl8PTOmdrQSI6wrzcOYtDz0NX33j/ut1VMa/BUFCGMUQQemXf6bl4wd2aRmNtBO3pxjqzaWoTx3rXVvfeVdxMXu62Gammj3cfdrgKnK/+t7P1UFNMnw2RyDzIYdkfb+bEfxhF/3UNqm7uy+f1v5VurPET7I/0pd2VMbTMF16/cCkkyLwkSG/hKK0SRXJ8Ps2wCsxFGwvmKMPZOUTlXaQZUHnQnm8xVcn+fOtI8/50Xv9KzHCPfo/nhjKc8NyEBbk4yxDkUqDBRhsru5/rdCyYzIACl5Dvu2yugOa/PYIy8FyfX8ithZKOT74befekqh2QoUu6rpQUz27L3X9fY/G5XW2U30KOHewQgnTKmHU9wGBZ677cjbUCtctu3/mmMV2XXu5ba93m04ESa5UVIVJ+WG8tTBQgRdCf5AAONONqSR6DyHNqd1FMg5zswJzplCINwa0U6nwBAMq392jc7S8C4KewN5qceM5cmDbjV5uDSrOUCQO8CI0Zigp3dTuBK3QQQSLpdsE2jS3bIxRpbaOBjKLH2vF35fbMujVxOHubEwL1pxswhJ1YXc+Ic+ZkJxzUn3Ft+MNY8ZQCBwSDEGgivq32UElgKUNlZ2oTaWRFOQ07RDVJmYdLCZHYMEguZL5Zwq1ikddgXmaQUUsyYJHSdi4tIgVObOCGVWarCkM7+kmXZBXY4EL3IZlOys0wIuBLEqmAE3CuDbr6gH+1+zPjz8hILqkB6NpNIRi6HMLDKfITilPp9GNyTDXb2ycNqYAO/u4R5eYEo2WzGoIVXGCziVUHqAJiydV37gUoX9GQtYRw85pwF/NpqeGBs4Evq3LHpAphIbmEMbdefSuJlchdRHiVTrhRpjzHGaU80A5OAeFCcY69TenKd/BwhgzyNQTsFIEbhxtcxKiIVOUMed+w2AkrKVpr1iGCsSyYHYH3Lflch8a8O4X9Ma7xoR0eBPB36nQb+9stXyBdPARXImcN1KJ88f1/tdrQ3mESkLJ6mCm9BP474yLIBfNQf84CQ4vUDjhhjs4wf3uP7KwU9Gs4bhUg1YHLANHyGguPXzjo0pRbeh8zITFcOjUg5glcw043lgHA6AgM9j1mixe65U0P8wiio+IagsnGQ37g8E592bX92BTJDpIMvC+g2F9kl9KCR8ATDnjuSTmnAAyWlxHXbV+fSMaR8SIRMONebuKJb2UYdH+T9BsbLmXN/9unkJDxaODpSPYmkFWINZzNevzonYAVYb5DmBSPqm4sSVgJRTYu5hGfVSZ5jDxhCS+Z7vKf0z2pN3Cdhf2qvlyeKXVT2OVVSdV9ZlHmyqqXU1m9HoyHTEo6ewqYAyPLO5jq+RX3EoUEegGbToDvRhoCSSgUM/tjv17gwKErFHDTSMibkf8tByxQo/bKDnnSb1DceIbYCMF/PxHbQ/5nYVBHI7eJ65tphVSdefHGqw13l+jY6PDlB7GWcGG0fOz9iDo6sN46DoyP8mfmETZzEi6gRwO0ogFHsF8+QVvgNe3WF/KHtvHt0P03bfHKm2jpvuXLPZLtjKu59uC/fERCiz7Zj1fdAt5Q2f3Tf7sooDMl1mnPYHCf9yedmE+KSVM9A7ZuYREwqGQ+15FTpzLuSGIKGH6W8LEZNhTYm8oDaS+va2al5EZANl49bhXjKKqIiY8b66Ag1lU26ITrlyReR8QeTn6kR1ROygD2qj1XvKq9DLh3DQ7+/6VdWD+4HDwrK7oAaPMDFr207dfB2WpmpfKBYqD1/vhhgYs4O2UORq4toVGNbQjTqz7pOu6IPEWocqVzBI9SCbckWS1gH1Mv2YHH1Mwgl3BZqR/U7k/HYxhhDSLckosScIceCVueSSGWVgANumbn7j0TgoLKxRFoMoxbkllLFvDf6oTt4mvZ2joRPJ7CZThlkJfCZFz4P+vE8PGbQmyzT4ZjtpUwZslLzQWchFpMszdWMtVPYYEpJUj00HPbFWUIKqJcXDwidKhF4/2YeWSIwPM1mk5fnT2ZYwdF3toTyUuKdMfhe8wQBU/lRwg5m646jiCklBEH2FAFzgGUlHi3f/Eny5RIhmclwdsKsttJsT/U+2mM78jdj8Yuerbelh1lpn5qC0r5rWldNSxXQofV9HlmrpDxI5zNxsVmRd7IemdOhU9j9OdUvWPLNXXk/VJ78d8/ntQxe6NRIP2RS9xUuk0aqbSu/LrnfUHRxBy3+SBLawDQPo8L/EMdHMTxQL9P/h/CfD+GPx/+k0d7NI+OfCg8YzmjsZy4O0st4rOKClUPZl5OefvV6Go91yg3FqpT7kHwbhUzev6kcqy0MGLA6ZbAQSEhmfuFvIwTABvF17HS81DGjsY3QKODLrN7yMZK+iUV6LJpoHDq9pGqmjnINCcGIP4tt9h9ge0oDNi0Mm/JNmrOj3CQbj6VDYvZkOQarptqtww+bmBoBsnOoN7LuTB31SER9vlUjnhpWTHw+oSgSJXJ2vz6WvHwbxeFcJ19FDQ+I0VA93LoNO8uLC2fx5EelsXpn/eh3zbqy9Udp7OGYxeN4np+IoVEn1Yf7sumd10WfsqfT3d59c+AerZUb0bmDG3xpb89eKJBAW0aFXJUXbxQIiyyDQUPrGAiuEfZny4h0Kyhn4TRvLt3KY0UqEbmCQbaIyMbz9INsRslO7hBcKLpJOVsV3cCLKBVgLRQ/ljAGfC+lhqRs+YrOiXw60CN09rcWNsTY0YDdelbxOsNGAGcuJN9m7sFiaes2HrPMuGDzqMzbHDNvZm/CRRcguPXun7UE9hlLiFIKVU0BGM1IOZ8GsLY+dWgj4jH8XMy+6Crc/QueZfZWweT+RZ4lff3J3wekt1XbrGsPx/C6K9965GVfD9dv+/3OtdgjgOPFXPjqatVVu+rgbND0274dat/iJ7JHwn5ke9awuYAliZD3m0uGzYHxuAoH2dvJMrHnSG4p3XiX2DPV+E+3+87eYpqhVga8m+npBR66LDoedAt3b+dJJ3E5gJnxKDF/FErM/82TuPwCSuSu9nEkw04drTMDn+IvaH3btXs6dssoV/GHn3h2l4Hfk3Sz8SYBmlftdr9rfOvhkJ/6V3293h/Hj5ay37bnr+0pIHoWcFRfyMUEAxYsimAMKQiTyT3YqLp37gJlljuFLnv/TnXUtS2D4RMFCVvhyuiKiE/O7NuGJPJAdKREqkgL7Sn+1Ix2kIlRWu7FC0t4YY3P+4YOk60RZf4eBXWmHzkTHyVO76h35Tp9qrrWrpAdJ69298NB/iK52TqB24W87UeijmZX3+wHP1fbVPaeKAPoCFjofyP6dMd1XLmaLF1q63qdCQe0yID8cSJ+4ZHOXtBnQGwsm024O7Xj6lBt4Uhvw0HPHcHJ3ol8shHEi7uyCua6wbW4O2eujo4fD662/qNzCNAfe/9jGy7Ma76n6g+3x2rqjJin4rXPd3g/3nwa7sabTQD7RwlTDNRicmtSCDZdj/u/iQ9280TytWsizYj0tOcF1Dj+n0U5uONtNvJpRqGwIE4IdqQ9BZ/eUTqCnoW/k6N9X/fVYVztyZISHK4gs8kBPd2NE+MtQq9qpzSlACovZDhk50uouxOnRJyfPKOZcsK4Ad7Iw7nZJbUVlHpMRzz5BkvAA13HFcWMhxfuZr/bA/q3krmpca/EdO/NK5z1aA7sl81mT/88Y989DCYvXJeb1uk5n6cG3J4RPXefnFoTjcETcf8vYe+5bMfrmzTH+kGEzVRq9LhP5eCs0l9ll1bx5xc+R7asPsnkNd336JDa0L2w8Qr8cDfeE53PCZ9XHj2QdInH9HJ+29Wb0brlPB/x5anxv10iW29kWnD24PTraWnAxdMv+dno9cknX4/E3+fmfXDy9Yery6zmggdTIoIeKwwizXO54CnXPsWJGy+yU6DxJSXqHo7Xt6dB+JtIAYGABl2+ONbRshOHSCIst9X97Majv1JG/3nhHH5fH2ug4+2uQ+++3Hl9dDG+ekIacL1UJjOurjpmEMdyLvLUjGpeorBFUctsWPH90+tn/wLjFkb8CmVuZHN0cmVhbQplbmRvYmoKMjc1IDAgb2JqCjw8Ci9MZW5ndGggNDc3OCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrNXN2T2zaSf5+/Qm/LqbO4xBdBJJWH7FWc8tVWKrv23u2VkweOxNHQ0cdElOx4//r7NQBSBAWSMx5X7T3Ykkh8NLob3b9uNCZbbBbZ4sebzH/+5d3Nn18LvmBZajLDFu/uFyzNON5lC7bQLM0zvdCZSTOJl7vF++TdQ3WsbpdC5End0KdKTvTo/tA+/nC+ZQleseTk3h/2/k1zPn7Eu/pjvd+4J2XT1Jv9rtrfcp2c0tulzDlN4TrSO9fu/oh+h50f5nA8+RHs5N1Q1Y6GudtW69tf3/0XFqaChflVSaNSoUW4rF8yJn2nMW6ojKeMmbDfnxrPC7dm+koUYaRuPs0XOaaUhrsux2pX1nu/AJEcD7e8SD4ty/16uTpszzu7ZJkcjuvq2LUiVrxyXz891Cvq8uDaPboBPtJ/VeOenR5ATuW+E1+OJ7DEdl4dbpfUEOvNVzXxvXBikhhBgYvbc9Wkln4wwfglFEVqRL7IC5YaKdwy3l6kkF+ksDrsHu0MELofnFZXN63O7Kvfz+W2PtWgdUJIXOeYvginvAhJQStTozPbJ0+ZXiyhq0Iv3q3R7oMfueiPrFPJxKLX6peMyxgJ75dcF8k2MhGTqY7PFOhMj3GCiVTkLFzGpm6FNcEAIUxaSD1kAM+jDFDQ56/NAXBbxZigU5aL53FB044bCJM2uzTJ69sCFuI4UKMD2ZSBxhDPPt8WMnnVqtq+qbFH3GbrWS3st7xIhZFuIj+osOpzbE5TXGcQMeM6HODDjGFgnKWa5WGn/5icRnAId0BnPTeNzFKRFWEnbzmPtdv6IjncT06MnZxzEY6xnZvY8DQzg04kPqWS7+30burdofHfvMWHcYIt0IkTJUxdBYGdvTS25Yr6/ubpLt3H47FaVeQ71uQonHmRiTclx8/eNN67xqeH8hSM7In6wQ39ENMMJcF5vwY7jhJeQ5QITDN+2tHxua3KGbUx2IKMhaPPqQ3PVKqUCjuF8sTcD6X/UroPMGgNFjWN3TP0wLL40MCkHvbu0aeHQ+NX5Bystf74BROvrImnLZRnyZ3bU31vJQ22ep6HVP2SqYxa3VCbLJVqcdwsuu9///EGbYRv4ICF68SGEgCTwIrB6ESMMg5OOK5791P05CZZyo3yCjsnD6yBNkrQJ2LKRMrBf2vJ8sCSqazX6v1SFe12DrqPbNke4Yzlac4G1E9bBq5TMCHsMTuNYEAmLOxk94K0YGpqvpzGycOuH+bmy+FyswGRyxMBEplhYx83lbUEkvX1D6+gf9rqn3tpJY7HfQCJn6XvOytmUcAwFfz/gaBlhrGZfIagJeMQtHqeoCX0ibEB4185rvVhITHx/mTNLvgIqwaIaBIPu/EkBlGheFrmbsjmVNbHVdm0TsWi+dIaiyYOGJx/jaITXaSS8xj3r+EJX/RaYTz1InjyFWUNHFeogDoLkm9+eHfz+03LRgW3zmkzeV6udjfvf80Wa7wkeyYgx0+26W5BtIhcLraLtzd/GwwhC6iFkClmnhgBHgdTZOMjgAgD6/xFQ5iUQMJzl2GjSUNmLzUK3CKh8oUyRaoKDxWxE6FLjBNyWAIXAjTsDyeHF/Dr/Ah3JphDDPi9QuhSE25bladqDGXml3BV4YfSWLjxKPN7B7WP5eY6YnSBSnXcUZPzqSQH6kNahzAoAv3DI9Nq7zwuHt6Xq9Ph6FtS+LVE4PShWp3azg6tHIItJiiYliak7u3nXUQ3uV3eQmKLG8e1fG7pAh6PDwZvnfbF5MBIKabGSNADElifgrvYRlSICovFstfsv+cIlQL7U7ErSpkNB4rk3W0BBPBbvQf8a2PLY7mqPGuD0KlvvlQuUymHiH/1ebX1SBTuxcl5W0bNMujSwLZL2A/FC7eah4hwcuxwFUQ/UeEgUhS8H2u9j1qyboo+800qEE1gCq2LkPuDKRB4LHqtfm0lfj3NP2MDvF9aMwqUZg3qd5ankqd5Jmlb+1HZtZ3j0GWyAkoa6LQYGggJE9kaCDgssg9Z30KEVHSDwehIIdykmvuFQHH6bWFfEelCRoX0ZvifEQYuEV2h45KLNKesi3MmgrmmeegHoGHcvWDgWt8RvPVDC1J0rXPvXohYCIdlofwHlEIuxYJDDdqt8b8RZVoylRbMEkoBpG1ndbaJeSagY5msos6M3rSRX6TTZfGhYkht43JKUigR6ryO6fwzFXJWHwNRUOgNzZGpgv4Fjjv0z6uYGOOYtUgLzchAdUO2Qck1Sd/EtwhjUmd2JTIW0EB5yewo6HkmtTM//3NbcJt9yxUYZaOz854QcC5tFIfHNg2n+2k4F0XnPrjFZ9UGsbaDdTP4uT6vfLs7evvZfXcIT7cID09q64zyvrOisfeerPLUI0cmp3pHsTP2s0++4lmH29G8QUD62C49kz2zmwFsYD+4xObZGVtCnNTDO0Te2mMK6K1nhMut1jV5dPv6sYSTt873W9esa3/nUomOe93vautSpq73Cq/tqE3XuT76gX1o3NR326pt7Z4d1/UeBHgCgZ23V4hYUtZT+rWt62ZVUqdNm3rws3hwTJlqnwEGV/c+29ocdtUwZdH1o/wVfJvniU+6Qo8o07rEloQ0nM6+Jp94OAZ5kv1hv682gCzOH7pJNuWje+sekuDH0LpM1lF9T3ySeU2qKffR2BEmnZkFYFrK+ROMRoeYm3hGBOYf0KXX7jvnj8gtcR6Pot7bV70AYbCKccuzHu3AIotQWVqAuNnYJU+LLA9jlyz7dxIO4UirR08Jg/KoRb0sYWCAoyEgV6nOVJ8F2Myc5Tr5KUaeAnlhTNjEXYpEVB3w9Uv4c8E3Qz8tM8BRRW6dM6/Mf4tFlZR6RVQpbOsLvbAVyyLlis+w+Luo08GsGTwmYiKupjdSHtlIA0xmCJmohZSwIIAmE5gsh81mk5CsHUvkAFB+0l35RzxWsYzpUcdGtInLqDZ9iKIyVYz3iOsf4XbRV5Xn8PKKA+AYlmXsgV+neDy29dtsxPhRiu4onu7Ir9+qcbwy3F59/hUmHQgli82BkCfnoeUdTbf8FBc+J/h6LfzZebyFfzoSW0oC0xIeRUA2OYu5xr0/faCTU3fgKZPtrcsz0mn0aObqTVwCAnG9J3Xc7tQRxjBwJh/kvOIAvAsTL0aThPoNfeTuQ7cf37qHdSxeL4DLQkl8jNtTsg2hPWX+WLk+PfQPbeyJwkdClT6Ybg9h6vaUuTnvdhdAdGgPqv9oR2HJOcrzpUbUoxBEwbxY+omQv0RWBcuqpV1U4Xfhm5FY21gmFuZrOYnQWUnEsIqipCFzQ1+COEPnpKAgm4dBXxCdAlOAXgr6RGtdzjHHMe41lMQCOCXZXPeoDXDE9GT9TDdfx8JfBKqZDMLfmLmXNsfTa0ST4B/r/ZuetOhNCkZR0ntJeSQd8CuUIzAFaONYsHiJHkxa23+MRImZ+XKt03FooouUaUU5D8qgXCRImMNlbq+fqatnpvCqKEfS2oaTgLN270TSPUIpBNCUwjSwwPJl6Z5usNykSsgnp3v0E9M94uune0ws3TNUPZMbymDIdrPBMsZwZJph4CXQZivSN1EqrccR8S0ylsxhCFc5Ze66sf/9NjUiVErz5CJQax1Rax1Rax1R6+gJEeCPobRayg1r/Wr8MB5SDWrdBAICw32l1idb6zZ1eppR5oyHvd7EwZ5QuifT+ImWYHNkMiHSAsYwmHFXlXuf/Sj3bVoIP/olZMynIHrZmC47zpNttd+cHpqu4dShK2aWTIUUPIV0ISncGXS0aX8q+at9KsYnHtpqBpCzK3+71LbxpKn2jV9A5eCJbwdp7Yc5HEHH6dofM7SJ2ggKX45L5dMIOs+/bEukQZnEn1+TfYue9RL1mUqZ9Me8Px8PNrvTK/UwnM7ThiNSJUBAqgRo9Gm9PLk/71e2+1TxHQcww6Ngfu/5BqP3CObCpEIOelmxYtr9Yb+ubhVPVkf6H1+pNsWnK6nIy31WHytb5IOvm/KRlENzgHyIwYL8sIXNgdFoNg9Wdd3cl492jhXhUpYcp5YLTsNOpKo9UF9HV0o7uHAoeSRYKDrfUFyHgB+er2H6JUZ3SL2rsil8ChhfqLAoHhDBUL0egTnCvAhUPTNwLr4+3IrzJtxC5GEIerTHgD9DZWEV7X/3w912XashO+eQpwxuigPpiNyfDdjStKLNTYxXBxoYyqDjT1FfPoT4zVgBSUeUdBguGNvZXtN6BkPgpXanpTv3wPmOXgt3HlHY8wjf+3B3Kmt3ZIBfvrTMvdnVp7ZW2LTFgZFBP9VrckKTtdsIrPWApy/JPvd5o2C4qE4oGNyWUYnkh49UQUOmpy2NHhTJoyMcsz8tsAuhNWnPW+0M4OpYtUaPXu3dp1+/JtvVvHJf26rurrGvk6hXbSGs73o8nDcPg3EC4fgBrUhL/wORu/1W2KOKgc/kEroPNNuWXCs32OGYDg+/igLYDLrEoYi8ux5AJUYq+VdFRNC33mGE/T1iePolqjozwaivZytUgXpF2IfsQ+bTGxmPp7b6Dq8Y9P/HXPkm5bAB5ifnxIdImK3QMknjGVI24P8krhTcQC9FMPbdDD2QJn4UQZ/JRQuVpZkMF/BpbhKFoFRdrXlyGko3ax30Wc9Nkxc20T6Yh03Oo6nuiUfXPzoPVaGIkLaoD79UHEJRcjkgLH6Y05/HoDkLabMVLyZL/m7Pbu0FDpdqk22Jkuyf3kp/hijdfZrrClnOALu0v5bStwCUYrBHr/19zririQx6PaXMV46W+fYLRAHxsyIPRydzx3Vm07REkY1Opquq6fLEYJQ3c7WpBsEf02EnYrXIku9dTSRN3gU8RNHpcCq31tLSq2kFYLTr5ZBvY4d9RT7ukrt9S+mLIhzxofQqYUgliCp/9F155nU1nda4rl/RW+n0xr9uC9lpgRF1aGIqRFWsnPWLqJh09Vfuq9VA5soGmEsUG1dAZ9oCurDhabr6mPZDTra+P/Ecw7iw1d1Bn8s+ock9EVcFcBzoQbGw53j9my3BEzPlb4ayvmK6+K2TNLwMI4Pbn/9i1sJimE473AGbvSxQdD6ZTx6MjRIgGQaTIQHknBhA2GFT+Us3YJ0/DPASxBflLmSUm8O+3IZVhVoidBooz49/HaqXLODSNLtafDYN96imaCDrOTZTTYphETYvSxv5plea7xAcAY8sxDFex4dFovSG6jd2/mHVXHPNVtiglZvTM9kiANZW5+DL2xVtnQc7jGd9Y2tgLeA6uSfb2t3mc/UlNCbA4CYQj3TVJPXpTKc0JmnHH+zDwX5nJDrusyOXClfmF+2rbOwWtzCYgS3uZgmMaWeifPlJ5m2qz0OdfPLmMF3Cz3ONLalDUmYtgMZGgmMIOl0Cjiz5ua6O9Z98OqstS8pcDea4c7tkuIqUw8IEwzM9iBF720rROfFgDb6O5j/pJmb56GrxQcybVz519fu5VSXhystg/XPS0l89q9+gT2mjpgE+t1c2tC91/Wu125UzPtsSIlI29Nyv/OUqXyL92d/AcrK/FE0JW7alB2Vb4lK29ZQbAVfZEjo/xPIayvZV0fNxugqDcBWxuMsYB9U3ahjb83juJV4X+L5/1hfLOzR37sAn76G78FIDAE6mYrcgv159TbASQo5CPef2ZvQyBivsUc+T7oryZ802chmj0Gb+riigD2Xqg+lU9sVZow8jGXihnlin8eRjwW9m7yTgDeNp3tYRvm6Px8vt1m2ju7O/f+3zIt0d7bZG8dv2TnfVHbtf7rP+cbny7V8fW2dhESB+tJcoMb7NtlMy2DqcS8+jn7k8tpfG2yudNNCMCZdkjVm4zJlslZ7OVl1uCwA0cKhrMPY8aBBwKTLstJ6dCnEDM/xqKuYY4rJ2h3rVXj2wQUYnYwo62jLYe+tFYTD5VCBhsMMKGXScVuovqCHqE8kIagNB9idUrxyhrl6V507sXLc3eakKxColXt0fJmM1ToV0iPL7wzdzNHE4WgGE1u9k621Ekk2DBmUvafT7fTs7GUy20HnQycbgQl+ysnbNru66tOk5rNzdwCU23TWH7fnkGXL9lxRcCfcYG+1UeXsfc4DECuXKfYmoYGBbW9zuaAftaESP7Hi777mrt7FhnmmLxLlDhYAaHhRuP7unfkUyWJEMbmdybwO4vdY9Wj51yYuINFN5sI6Z0+di4vS5D64MrAsPOTRrATIqTAu6rGcvcCKaU8VwGuZ3iN/TDiwZD5a4nM9hUBF5oYJh51ZMQQ9VSPT7+NtRl9tsV9XqbeACfWi8Nb9UzzcdjrPG7MvzPL7Ozpe3n5sqcD6iK/DfHC9/saGtfx9FiV/7sGniZia49IJLmRouIpcvuZSpOZyMecGdzOcsYOKP/IhM2NIaSX/SxldSFIHEwb7/AyHXrFkKZW5kc3RyZWFtCmVuZG9iagoyOTAgMCBvYmoKPDwKL0xlbmd0aCA0NzAzICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42rU8XXPcuJHv+hXzFqqiYfAN0C5fle/Km9vUXW6dqOq2TusHaoaSaM+QWpIj2/n11w2AHIICSSm7efGIJIBuNPq7Gyab+w3Z/PmC+N9/v7740w+cbShJM5LRzfXdBv4yAj6SDd0Yk2ZcbTTJUiLg63Fzk/xYXW65IMn7x8fLLTNJUe3Lb5efrv9ygXNIKuSmud8Mf//tzxebmy3nWfLeD3KQYaUrfPGnH6jSY/jUw6ZKpVyLEHhZlZ1d5sP1xa8Xw1CTpZm2I5nY7I4XN5/IZg+f/gI48MxsvtqBxw1PBWyHbA6bv198dJufQs9gDNFnqHuAeszbL61Ddp5WjMlUmSzE91C2Xevo1T0Ul5YOxaF4urSEcx9233eHwo+5ZFnyHekKw1o3/GvZPQwrlI17ubvc0uSQt35MW/6j8GDyaj+B1xTHHOhW3eMONjQlzGOs2UZlMhUZ88gW1X330G9URg+F0pQLHs7rZ5jxjJut4DJhl/AjkrsY8UaYUG5SpVS4LFtERIiUTfB4uwZFwMloE07ii1CkSJVgrwQjMxgczhGLUJRMiRSvhKKA58kENRk7iQGMhpFTIt+vgTEylWZC6Lsa2FBQ4NF8d8l08uCegNFqfHy6lDLJD+5l3eyLJr3casqTa2RJO7I+dWVVLMo/1zSVMgR8un0u/NzwVGWOEAqW+E3iz41OKRuxCIq/E9BlOgkG2o7pZ3Q6tm7HXb/1Xd00RYsirpMaVGcvliOZNDTNQMbsEo9NbbXs/rSzioTzpL6LMdON/fZX90mCAgONSOwX0KMC8N7SVHHtdhRVZiIVVGxGo64cvC7/YtUVqhyPwqCJeHIE1XLM/VCnfEDg3f7slOaS9oTAL14t8eSrXbS8f0CW6Yq9e9uejpOl9jC/fCrbsq7ci1uc+N2N0kB2t5cMlVOaSclwL1tNUiPUBhRpSjLp9sNTDghRJpMP3/JdBytoCecBvzQpfiFU7UrQyfDQzSn7bDgmIBNYCcVZykBy7VFdu50pIFhzX+DyoAC7/PbgNLnqtyR7GqiRarbv2/rU7PynXX18zJuyravWfTy1/stfi69dXf3Bvy73gDMQpAPD2NNFgZ10Xx8Peb8gsI19lbvHW8tW9anap71dINmICalJNfE7++HSANvZA0cDA79gnwCEPbj7onEvnvC52HUwcEnZaTDrqOzGAJ5mpGvACNUQqq7xJLchkhyAQSyngfWCPx+WoDNOUy0mC1Vr0BmYJ5ZNcL7y5PBKjNKJNDI0y/eg5CRJ8NwlTw41PtRfTo8xiDAnY0nZOmr+o0DJRwjl3dKOOBiPzKhX0hM4H3Y0OeaHvO33ZFGoivu8K92xujeFO/Pmu3scWMI9NoX3CHMU556p+IipiEwp6HQLzbMtrAXMBQZCCJ78D0hG87VsvYpo66Zb5iUN5tmE687unQ+8pNJMiXDWvtg1Rd6CKB4sQvZ0Gaij265BXbHEU5lIKZXheqhOIrpYgFYyThUrp5SqNXQ5AR8kXNwj5/UJ6GCk5OnQlY+H7+6T1zDMqjc8kkG94fn56V0/1v3FE1A4IECgSbyu5b2uxbNEX3ekIGSGKlA5hDw4bU/MqzNj2R8PFnj+vf/cNeWuc0jqnuYouuUliq+bVtXVwHnW7ODY0nGL1zbwAj1r/6l183L/fIA1Fw9MUA22erKF33ZgY+II0DESdEywPKpzp3Erz/j4D7L6VU85t4vuoWynHoHUBhxE7b1tz5LDGeUHa0pzPNrv3kDfe+J5o5sP5+sON52GAj7Qk0qkkhgH6H8vDUuasisWRZDh1mk482OEkODgM4mE1OCBW0LergYFPNVsgtQvRJJFfLixvvRkEpOrsQG6P/oZMIqKSSXv3M/NsjYCFxD0QLBEGyEFSDI4WmNSvARBA6/VBMFPi/gYAaQw4YyHmIOYMvPKowGVJ9SEysvEyTR48zKODIkhM7ihagUZRkwqUUpCykDgSa2KdO6hiqpuAeGs5jBVp0z1him2jy1LKfwFP1p6vOa4XLyKlEyC18r067icSWX9zmDS0xokBZC4iLI4pz6ogz9KDDTwD+u+7N3fh9pFLOi7uFHVoleSMdCBE96r1mIowlOTsemuMJhsSutHYxwpMh9HWiRAndlIEx582HkqInkOKQGi9EuWiykOCCcNp+GMjxFGHY56sAxkZX8gAtTocGU8aeIOgesM9Qw3JKGwUQX6e+TqZ9S7+tq5+tq6+vD2Zjn3Rsc+LJ7+GDrVYU5uzC0iSylEocF4sFWC6OQ/HvLHzhpigP/jlfstfj3l3ijBE+4LRIbi1j75aUfE2rkppY33YNwQzMHfzouAP/IDrF7lXSQ4lhxsN7ix3mmraog/cwg5rqZ2L3bCIO0ZRSGGSFBnXj5jYkxhAFjuVTkGx4+Bbhsr8rjgzqv4XgSZNnD6z1HhmLRBu3bms9uYStuqlIB52PKUgUKz436O7QxOWFsKCOmzm2WEuW+SdzQGhQE2MtuMp8egbCG60MKCUZr1pKE+16ZCSD7Pl5ng7f/5ZUGHwFutLPLKpnNYaigPnDEg0HkUQfAQmU9Nq+SjQYO0BshsKboc4O+BrCi/v8+R/S3s5nOcmipKTQxfBP5Q4c/2X2J8Jij+QgiJbqqMKjCeyuy3MvmWMZb8MZ63wk8z3jeD6BQ05tgbqGIpTvD5UEbOo85bjEErz6c3xVM4cYzu7k18CgVHy1iiyH7uxPoI8AxR9/r6yb4o9ou+Ejg0HBgimDbnK6nABK35SpRmKQfHLVh61adGMYZXwaSY2GdgvsWGg73IAtWy4OJjKK6eoYPWkNJeH8Z9Nwo+kMGpBrxuH2X9FOF3CtEYRY+S2sLO7y/TIdGAVqBA4B8m5/Xjc0LNceTYIsNrrrKQWOAoAN8lH5zZs4kZCLchhPb5I3h4bIqnsj7Z+lB2jgEX3TdwgrIpKBDuNfbiGBfwCXvZXKQkLrK1OGA62PkCufvUgq1vgQSiWHTNJJyhAiM3h9akAMWpxq9iJl3uvq7FXSi+wGnKyCCQ1z6Qx2wK/IOB/BtMdWTn+LzPZ+TP0xouoQYvRzlT+7zLq6ruXN2urGx6xI/Mq97ng4dztsoNtUUWn3TpkJZdubuauk5C8JRyLylD7sinbgScwbFPu2FGpssxJ9PYQmPeJ+TKcxK/6vP1brfF3qbwpCsP97mGHZywc82GHMQ57+yrGVU8RfGHvtSQlwc3ovRvgJ/bonGUc2lGYg23FL0wDYkRP/Ox6VPET7gpJwvw/uCSYzaooW7bPjOibDrErurFeJQD5+cihsvY+x0iA3+br83cziR9x8WOtohnrjhm/zLrUGmsNyBG/xlVLESZwF5+jvut3LDQXs6bdDpj0qUaXFeNgQt6sDfxRWJZEHTkWYjrHCyZch6Y909xMA9RinAqX0YRPgZx80+AeGWwMLOLt0BKqvRwvmOrA0GcVuF2SByW5s8OmIwjDfvzGkcn1IlcmVQyHxaew1QhRViRsioL31YoeFZVuud7rwftJC9R+L6vgeHLptidmqaodgXQRIK+dkUxP9JP4YmrQedWDTXum7MwmBT3MalFrHLjXxgxS/DLJXgWwVYpmY2YJRyOAKsfjH+mhbkUcJo+gP2pqX1WuB0l3UHWP72JnYxh4IpjjIf5EdVz81bqqC6gwHB9Kk29SBmo300ZRONYhuwvUkZDQZzEsRCjcRvHGvkvjGOJpc1aHIuKhwEUhhOHeDLuBWH9H2Bg3ksuqpA2GrpRESaTlnTh+LA+xbbHUwIK06RmLR6SGOoOo1aSw/pFym3Vovw8w6uwI+RrsRA6cGBViINHw+ajtTauqaWakDl+nBIkjb9MKH6OS4Q2I+QmIYyx+Sek/LA6oQvno15k8UMhD2OYrTSvj/uNfLFeiFNx6Xzexp12a/o+r4IJZ8UV283YM5lH/vMMIoaspRFeu+XbmQyCEpRYBNWQQMCqGj83m0hgIIj1ge+95Xjvaoi9y3+/XKQD8QKvIVjg81wZk49SBRqCrWDW1XKeADs2J3CqVThgGTJQ9cGsd8u9fzrlTLx2P1JZUQlmPV2C8eyrC76X8mRr5YWtXLPkv4rjMV9vbzUJBFeTBlf7OqjEGtc9A7/Frsbc9S4/+KbQCls/T8eiyV2B1gL/6bRcyNYSlEMWbumwRggBbgSfzlokuMgg7KDhhDn7NIaEJQDMWAQT/7gc4XPbSzEFNVeaz9bdmwAhsOSYWQuW97T+sRoE7+ytMQ2m34fMttnRYLMjJxAZF225PxXu4dg36UFEudjomYEc0mDVcg7noaoMPko4BcNNkrk6HUI/5LYVgmCg3tfs8AmYW7mCmEPSOsT9UBeH+0++BeS8LRt9E+Z7KeyXcf4icxNZMgTWGEA3rkXQsXI3NHC8iWREGfbIUjruHlHiZbkpagtZOlziBbkpKsCSYu1iPM+1PAFo1/vs/ra0UNhWA9upl3tJmAB3nvFw1RmLwolcsihCr+ZLmdKpMjSEhuyguWsgAewdW8Af7UN55/oqcX+uXQZ25Vrl4Lj2ftxaJRczxhOQ5YzJFHq8wdk6L9UpIRMmoIsKCORWKhXOgJgQfYU3+KPcj+5/1rqnhVCpISZckGAyFbbw453ng6ovTsLDWd6V8BVk2xEqRkwtQB0r3/ub37Y+DL4amj4b34pZ+t7NqvafiuZo02dmZNdlUoySuvBY4wpugTBoXDo94mpLAWoPc92t/V44xU5pGs7y2/j6UPeNqFXxzWP6LIiVPgmnPDvCizMB8Wu7Zm+w6BnDWj0vgHBXoJ5Nhoy3hiZJChbdmlPvPifYLnpUjNBU4JEDY1KiBzslohEU1UEAFaW/sWXt0ah3M2oCfNlDPILScTCBkGpgexbGaYsu7rdYVY7YmwcxWHISqmozhvV2xktHiSUzKkUv5fCRGDEMJZvF0EyJbkJqDKDYc1DzMUT0RCA0o/x3P5KZw6fK5g8M5wGooDIOHhNWz9lS5hD8L3CXtuDcjlKHM/HMuzk7YCu6cTvgvr4m8Yjim4V99zSDCFqyoO9+uDsgfGf4qABg6yXHvvHSfvv1VHelf9P6BH5Z7Q6nva+qjLL8NrFf+BS997Zs6aIYZrbLNtS4dMIY7du1vWI7FINwKpjlqyyw574m42/ggKdS7sZ3NB5y3Nu5hxaogHWhyjUzB/4Y8B5Vtl/QED4ERgJ9x96/a99gR1IGa2ClEixZ30ks2GAbW/fU17AEA+RqAFjusUKC1s3WOXCMTcWyPocLA9vcelv+qbcVSzaCuCRKgPWCBqZBgj5+SYCkjKhxhv7fXFt6VOQgglDZi0VOZaHMTdsXsC2J2OtJ04bQiQpAfGa2CQpHGPFPozTrKxmgCJjKgNLYiQcMcN2fXy8iz119IP35jkxT2OqdY56hFmr7DWVCVq/isSxcbtkFtC/nVFT2IlfVtq3qCVTbMAmRlS1E4H0f144ok/YIGqdw5dPlGwPYiUnDVdd8f05IKnEb40leTEFKT4WvX27hrOBcbH29dQgOXfmIpJNBfDsqVC86Y3gdbUKC9W75pWLbuGhCFDxMdjWuDSGhh8B8FJlTqc83V14WOGJVF/XpeOZc4DiGRrFBCz2Z8URnY/Q5cERE/Q2E41rQSLE1koYLzqUhtVl0COh8BsEMrZ8iRckZQ/O3Dvzl4p6JJ1c49HCFQ2RBiwl+OQ3ZELyt5lzncucX2dX+dk95e+pc7gs7JlxWzXbwnkOo8aFCND3ccumjpAIv67k7I+LcrvCSK6RUA0Nm4ap4BeVxJefCMGoiU3R8o4K795699N67eH7vPXULfegbZscVQs4w3A4h927nAiB383cMJMgk94tj1IsSECMzdkYc+1TOwRr9o0/lFP7e4q29nr5IcoHZXDIBcV9UKxQXkqfETPa9/P8DCAjdsXsymPLf+ZdiDRRWUSWfghpuaC7BlCSzHcTB3I/X2KKyDBRrw1LocOLbsDZMUs1gO5yMLyWtn7yKnvzk9tazHQFrgj/2d1dsj5YxRvdqBSZNmdPcDJwILECzvg1F+Du179192kd7nbbGa7Rf8R+bstDIO/a96/uB57umPrq/DkXufW54ONq0eH5oVwgKykJ7l/hvxa7Lq3tsNcJST+YSX+2lreDcD7fI8Et+6cs6VDR9x0/nPh1qeyHL5jYbN2zcqoTPd+hkL15uxbwyKo4etY9riiYT9oqTHnn33N+TtxDrA+KF/wkEEiiGVH608lr67jn3zm3pS+X2VLm3R1thcO33u/62sJHuoEcdDYO/4VrJBdoqj18WDP1wffH/qafLnwplbmRzdHJlYW0KZW5kb2JqCjI5OSAwIG9iago8PAovTGVuZ3RoIDQ3NjggICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjavTvbkts2su/+Cj1SlREXVxK0T7YqcTZb2bNblcRTtQ+OH2iJM2IsUWOS8iXn57cbDZAExYudU7UvMxQIoC9o9J1s87hhm78/Y+7/9/fP/vKjFBvO4oxlfHP/sOExE/CObfjGmDiTySZlWcwUvDxvXkf3x2K7k4mJmuu52e54VLT0u2ja8py37m3Z4P8sav30unja7kQaFTDlsAtW7vPGTbo8jFb9K28vp31T/t+v1z+anF6WVfH+mp+2PCrbLez4mYZfb9/c/+MZIs5ipTf146Z7/vXvz4AaokkZFhtjQqK4cYuJK0MeqIzHaZaG8+8AZJpFT0BUXVZbYaL2DncYLkx5nLB0k2Q6VpnomHepizOsztKIx/rNi+1OSRG1+TukpMA94Dx0cB5uP55ksdRZuOF3bsXcCXLDY5OKcNG3i2AyFZsxmO9XwAhmYsa/BowQMJaZcMUPa2CkilMdrsmrwyIcncYsG62p1+AkSZxo/jXkpFmcyNFpv3MrzHDF651kPPqNMbaChGQsZnKEOSfRgftRF/T48VhUS5jBLYozrb4IMyUywEzoNcwyEXMx2lLE212ikuhV4RDLT82Fnr70ZmoV6/SG4NmLmSB/wtlwLxVXUX5tj5eagH+wF6tuyktFVxRIMv6KgsTCBcmUpOUvL/XldMpr1CiKR+IN0KR0Ev17a3RU0GBZ7U/Xg/uBGkmJqClOD7v9BRQBqK4c9NOBXrd1mVeP11PeAnQaeqqBKQI1nd07jX5qG3rzWFRFXe53T/TeqRUHp2nKx+pcDMdKt25/ugD8z179sCFxcPYyU07oixOqXlgkAbOL+28VLTwccyDhQrCLateU56dT8Yne7S/XDjD8vDZ+l7Ki/194wCjRKgmxuj3fjgDJdJxxFc6/I4jdCcNzcMJ2pFez8EOwN274o7s1Q7rr4hHWNcS9AedAFo1yMHNaJYHepi1yS7yKLm+7o4ZXD/XlTE85/bMW7ghGqsCHkzVX1WMckPuXH1M5vGMommKAhBSxFBkh8c/ifMatMxapmE/d0Nf25W9Ms1dgmou2gUc3MQTzOhqjwTkfaYJER//caoE2ekntgVHkKQ8xdVp8tOeQNiFErNA2Dpe9teDQlivLQ/hXXari/NR+pl+/Ma6Ql1UJvGytA0EvGnIkFLoPS5pQ81ilWQj15SqyMpGgQE247AmRhTtqLy9AtjehR7wmWuAP+Cl25IP9tW+t0AJzG7tDvt9qGRV3NEbeD2wClCZVEcokj3kCd4GbODWEw6EEhYBSP0U0CIM2IH9T1OH5wwmz6N912RZzq1OwEjvwTX6YkTWmO5M4MiMiNdHkSbyGu2qHNdukcZYyOh8JnlUmNztuTej9wauFMdA05jrdDGZ9MwNfCDSwYCt5knZPCT1NEiOE7DbTt5t5rNMB1krE4Fsg0ikeyGFgVYPNDSC9GUx6PgMGeDbJHB5LlU3yJtgBHCOE2896QdQ/J9KfD36l9C6ZJEtksZaTVJklqsQ8VT/MHJKJHpel88dtmkb2voAkt8eyWZLUyZ1QfHcPlxPqaJjVWLV4N39dZh22ebeIaOQzlNwR8qDG6OGYN1MgdlxrcGz1BkDFGg8Cefr79BXCW8kXuPo7+GEyGSI8ojSRurueI1o4M9aATEN+iWLjBevohOjdjHrAKwjmB9gDYucURZYEepkloFBhnY4laFtLM2A9KUoGXb0d/GVi4a4B/VotXWSrDzfgqoPc7jiY+ETSdsdJBMH/gAlgrEyiO/zkpHeMzo7Gg+xp+WbrNNE8P6fPMFOz8QGRwRf1HpujX3oyR3tGvaMQMgDCIAmc+m8f0CplCywJLT8YFvCj1k9aAIWgBmdOeoLQIUueLztTO4m2WwEaoFtN5rb/GVwEHl3sn4exOzYfzoNLnJpsozW4xEYNMzIQetk8ylJor4UNlYLVx7UMQgLBnh6B/BYAMhUxgkuRCPw/n4tDCUEGKvBMRPdbAw62D4FCf0bCmTA1wsWGUJ0dHMsHN3w1Q4A+HuMy3PbagOdNKBYuWLARno4oJWVjrZTiqmTFiVQAwPAR3mtYqUTFGUQwwaLccS2nfx+Q8qJubdCFw5TXCDJKAFaBKANmdocKgzgwlBdMB9CjI68t90UDp6BB1b+67nHwSBNy+jeOTGFon7uHt5Sncz+u5an14z7ZxqKmzQGI5Sr8+li24fYP19Np1/mn5APDIhdUTpAleSxMQmTZUGrhCDJjfZ1gzdoJcExQJUm4CK2zkRgLIuWpwPiu8GSlPAojcctPGP1xawS5JbAip383cmQ3o10o1ISB/bWui+GMI+4LnHJ4NFfi/KH8UB7cmm6x5R2eqh+vL9fH4xQreQrsSYnC0sYnYIvPZVWe7TnAD3vNUpupIFrBn3jI9z7IheiqookuSh6jjm9G8gNDb5Gazx0Et7PoImPgSHtHIOpiAHsApqwO131x8Gka0JU9WQyiXpN6PXE+E1jdIZ44/hnk35bSI9wSA+E9KiRl8OgUHR0szGnVTboFXl2ubUNHAD8cdhqPK42uWwzm72jtnhw7xMIRqTEnbd9RhmWwnJC7XKsDJZhgaHiosIQiOUu57ClXGQOv3sV8DTB+357c+g9IHtH5FvEqepa0RHMa/Zrbc2lCPFWPV1V8pIdbTqjosSS9FNBiw12cU9RNsfcSoK3Ksq/3F1LjVqPlj2513tjbhdkW4lTVYzJKv6g0jZlxsTYIWWv1G0gOcBxYcLVALYE6+v5ilQ+89OmrOu+gSMzRA9ieCgivT6fu1YfycnUTvQIm1Tm6WK4SohIZa6e/Xx4vdOtXjK4wsYB9hkuf1rSVxIAsXGO5ZfwZwIPN5/XHYyxD8YYWdWll3LiMglH9IpJhEuAsiy6/gbGkN8QVP+Xz1qu/on465ZUVUaPchl7EhkD0rdK/zbYqlYFwuyJKJ/02E6qi72ySc9mB0bHRJtzm03wSQsxHbGuuBps2KQNyBAfHyIyQsbdeMcdweLhW5fure0YG0RPd2fOTu7Q8+kTjH4+Xxk3urhgdJowMEswNjWBoMRkL9N4KuJyShzguhQjoZvN0hXKV6pgnZryr5ouOU5pBtJdMn90sJAMXwehw0TeLYNA7EHqMG0+fVkBpOE6RZuHCB897vB1jHSWTWPmCYHPFnN2+dBl6UtAqas69siHt2pTdnZ1OQ8DUuSOgzAiovHufyJZ9bhqzo1XZFh70Oa86gyzpvtIj3W9XjHWX+86he6EpNtlClvp68irfjXy0yBNVKjoUFm5xiG+MtleZGFT5+sxPFVm6t2AC945XYNC8NSUW1YeygnDBG8UHbxydmX8gE+xsPrmUFn90TnjkUE9czYTy84nL8/vn99ey9gCqS1UVj0NVGuKym9JjDITZV40ssoCBgJCHPGEBkQpZanxmwdljzdoeC74oPuVkzXFF7mZb3YEDoAI4eDPWP8Dfrn4RzNlfwDNrHIDqYH0qnGAJyDpm0uBKaGNrxUlI3Nq1Qe9EJiOOlA3BG/I1vmEjjzVE4TKDkNZXd5wJ2EmeWv+Mo1d3pt/LyHNlbIwfbPduBXkO8ZhIwzVDN4AwyPd7y8ZHwsOqeBgm39oiSs5ORs4OyiLo0Y9HkGsYO9KkU+m3K6swEE5VrDkPkfiTgfDwXFgKXqMKt7XqI4l+eiBMfBUZaaiLZeXNQKuGmxWrViID+zjCwN8KhDkUbx5NFWelAZ2sXLRLJlUkrvFEilWREFkseLjJqkRINB8jyCOREE4picQrSkClL5jCsDfjwgpHNvCE3XrS2vi6V1LCxQwioRgC3lp7D/IPq986Z2wyT+k5rnSsDDAcXElzY+yDND+mmkFXJ0GuLhkWHtBNmlvdHX5Qqugs82iui1IZ3E+OKcLEpwjnkq6pguMRoB2MT8VN120CR0EAzTI8N3sTs96TAn+/7aPLoZwlgFWmB3kZbqI/ivriBHVZzCDgBoMdboJJepZF/7O1zubaVeFKxEmiwy3cZf3OFQPdlTnYNipQNfBU7d3wGduHsAOLMG+7snPjJowyGTTtYKPktqz27QvklI8t4NVNv8EYSNrPpe4A8EstxMPYTZLgs3PlMi4NBQ3okFAYDh5EiTFIUfewsHg+DIxxTvP7MC61uRK/11zZl3ypsFoibqolXaTHs95v0QrUlkL7lHYpVn9j/YXvTLdVRFVxowZScgYWe5N0nIGwBqCKmdiww08kKmZ6tOpbFDQZsTuvOMCdONsSOaGYePU5wHWUBlpS/xwz10kI8mkNUYVZdTPiJEo1+FffUS5pCSb44ZKLcPW7VZiYYU2mmcOJbH87LAuQRZerzXjhK8eHsXqAEJ4pl4FxHnEn+P19AvWyLxtiuL2Fy3kBlDNhws1frsWcDOuDSbgoTFxYpXGPDUsXwqMpCo9QaR19QT0q3IQ5Z+7aLx3qjkQvzvDj9Nn5QIaSEagn6J8LbBFYOVhgDde1aaeyWpj/V1nX+rHrXW+Xl/PeOI/q1quzT2FI0B7zdioM6DORXuytR485w12fLnyqu3yRc8mNsuKpmWvKugkiBl1R6UDL1v7hAbONFJCloXhINOqzLjCcaqYy39Fp87FBA9aaIgH9Giv0WoYbrRodbLeTPFyEV8VIW9jxieIMQ7S3VwzIlnSZhDgAtdJwt+OaOIO3ZVIZLsKyKZhO7nLFLu5bZwK2i3GQqK9iguQyBkM9yQQ+zQSLjMfOpTy5TypyKsY0vnHOh7i4lMbQrZgSBOwVFiaNu6IeXR5c1MeJSzIgwfnTYrjHXH051Us5IKZmq7BsvjA9FKw0iTmc6gATPhBqb9ZZdMz7NPGynVQ2hTDYcO1cRSJtKXqwxJ+qveI8+t+ieHIVCBb9jlqqawxc6abVQn0NKhLYgZHYLSpsEVIGkin0cNmLNUiZmWF8IMM+N869E8YgHnlbtjXWJRYw0hJChjSQ0kl7vFMJtxN3HNPXGTnx2PjanXYT1CDkoDZCpfC/3T97/6yHK2zNDmx4bDTf7M/PXr9hmwO8xEsEVG8+2qnnDYYjMlGb0+bVs19GWyiDzERnQS9tkQDKCTzNbpGi+/XndoB4VH49GRPfaKAxHZUmhOax9JXhe1/cORU5Zi52YPou55IKgBqO+/E6cO6l618cVquq4pMrhnV20uavLtUwHYg1H/pfnAq/Y1fewnbZnIIU6coEFoTzv7CAtdVgY0/XYte1Lw9CByHTODVd6HAuH+vL9Ykk5ly0R8rRHZ5TUrSrPDRBZlT1htj36vb+gQ0tbATka0BaW3yaUL8r58uc2hIVWFf1hAmHQzNe+8LFKEUx4UwKoUAKnGf6hY3TIHJZGq7kOmycHgAxGoxqFk6/c3Fg8MGKHfp5dLzWmRWRiKWbAKHpnfP2LDM4hyHObG+8Un1vPCwaVAu5L3xz0aWmd4fisS7c4EgGYb7bXvQLD1jfpDCma3QdxpYCrkRinBs8+P4ImO/8RR7heZVoO0Hh3vno0gml1UClKy2+v1LGvC3bIBmsUE263m6fBPct8P7iyF6V7dETR4r3thFmrd27JwYDAp66RDmdCro//aVLIzjQmW47SX17PyPWF1T1lKavt7abLMDp/9MVDnu+uj5R8/Ny2VNkPBbg0gRU/bJmtTMqVwWL5vrner8PzJLJwkWfVv0+FsMFuYG0WE2D2xArfoPeXJ8lZn5eYvkoi/66ho4WEM+NaGALTaXuawZqM1/0IsBajTCeb3nvcsdJzOSIOZT3EKTwBX4LAVdNy+jRluaFXPsQC0t5SowE4uNqdUFjO/JXyQNElVYpfJU8aPzWRMgJebCemnXYVJQsukfY3jfm9WEiT6pd+yl4hS7/On3SO2wb47YxHvwp7voX58Ig6mCeggd4yVRPAQx3AGObyM1g1l/ny+dsuXX75+tkbsckACIBTAxYONfl/Mdc/7CgvI3B3qWpRLFiMctmm1PFuGeTh02b9MXnbU/ui2l0wOefwwO8OqApREPO9MhyI0I0jvPNC0sd5ylG6Djp+SRGACBN/lzb7lcJ7NznGUn4ecZsI/aO2QbrL+jENnbvmf7cUFmDE5mMDntNR091BKN0jXvTpZzrCuYTIvZ8+lZLJWMF7txOaGtN7Nx76kGEG0wxmU7tl0MQp57+q58rTDZRM/u1p43mHPN/me0jZ0sfLXxaaz9fuQd6/jujP/shQwIKCPxY/1XD3NcMfLa/pf+iZ4xCwjm16KR+LfAwxW/QQFBAlDXX3eE73zF/ci1qLh4BLeWSYL6tg/w6V4qpfByNfQ/OWT3ZMLtz/3CI2koxun7hnFa3/ak8l74g0zmwvovs0ffc5TWYHIi3qE9Ddn17r1zv32IIQ1WYNAxWxq4mOPKYAMs2oGdjjQ3SVkWzYNLf7p/9B60FVKcKZW5kc3RyZWFtCmVuZG9iagoyMDcgMCBvYmoKPDwKL1R5cGUgL09ialN0bQovTiAxMDAKL0ZpcnN0IDg5MAovTGVuZ3RoIDI3MTcgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjaxVrfj9u4EX73X8HH9oUmh5whCQQH5EfTSy+HS7NBr+0hDzqvkDWysVPbGyT31/cbWrtZObHseJU9IFlJ1JgcDYfffDOkL84444s3PiZcyVCJuAYTgz5Hw6ngyiYR4SomJ8gWvHMuGXJo9JwMntE48XhHQdsFNwl/cjY+kP4E3QTxhjxaWbQFrxKeyEODLAE3XnuEDl7/Z8YftIZIE/LRUExZmw2JZ/SBK6tIwQ0+gDAKpYDe0ZCS9ou3WQcIzgRHaKZogtdXxCYE4gmRmBAFfVIyQRxGoWxCiugrkAk5oY8QTCjaWcDPi46JvkKBUaCSiWol/egYIoYI2cRYwoRCMVGig4wzMSUYBZ3GUj+EDDvWV8Wwz3gVDKsGhEYmNQV7w8EFGAYiIaIlCm4yOuZsmLO24FcJzRQxTbm2sBFH6JjJiM4LcTAS1AYcjcTaAhlxkBFnJOGnGFdSjpPgjWRYCObFjegNeilqE/xOSoSVUjRJPYVY4BH64bBdikG7KyaxYKTkTEpOJ8Eb9CsTdYRUMiaBTa7qwRrZid5kk71jnTjcRJ2wYnJIOpVkMr4cN2gRdEroIWe1X/YmF8HsZTLFwwcJH1/U9JSjKZg+uLE3heGimIbCqjF8urDaGloXweTCfEV0XuCjJXl9g5sSYWK4Hvy76JjwJ7ftWX0+1BWib1mVLfpW0HOAQb3L6lbO653oHRYHOtG7oHf1F9pLKW7y4MFk+urT+9ZMHy4Wy81kenb1+6Y+P58v3k6mj5ar83b1my5P93r64/TZ9DEePB4m05ftbGN+g+/aAJ8SDjbr5IRi1SdEyGKVQu6hefDATM/M9O/LV0szfWL+MptvWvv06nKzXPzYrFbzNdal/6v54YcJ/t1dJZ/JFrgG/N5SqcBhszpPLDYQ/RkqYTlZwfxdq8SwUl0LBzT6uZmdLxfN5TnU4fHUCTHagPUgPljgCyera0CILFG4d22w1G3CwuVSrLAuumgF0+UyLvH+jZOTzQqDFG0GsETnbCL1bHh4GfDol818dnXWvPu1/fSuWWgMuYtSvj7c+LQkGxxiFTvrNMDBRgkg6rHMiv/6nLX/u2o28+XCjrm2vLdR4QgOrFHBw0hBw2vMFgFzv3F+ed8uHj7751VzvoJSM1hHbmv1BG7gdNyXZvrv//zXKGrDLTPAf3F1efl6rxyCmGSLmHRQLiYHpBoWAwexpOEjC/qUg32SC+hzZ+yny8WmGuEp8B10Y/uLp4g7HgF6+wA48gjA3QNsHK/FKgvy179R4pK7B1Gx6weP7jD6dQ9wT4TIrgfoD7zfPiCkqWj3I/WcuH2CstMXq+XsrMW0mumLJ0/N9FX7cWNe9z3lRfOmnUwf45vaxWYNPlM/Qf1hvbxazdp1pTi16ef2fN48Wn401YME/CkVdYkXzQq/xchlK1edb41RlX2pLkq+tlffXam7hu66/bbXIzlxAuAhukoKtmikLM4qARINHBT/jABBNSCA0EkB2FSiZQsmMqRswa72q/ToavVmvl63q2dv28W79lO7etEslh8acr6MqB78PCuPiWSFlO9inSCABfZWYj5CvccXsNmmWZxf3igKFUe0IFasUtBAxTqEthKs8vEQsnUl37/9+vAN8gvPysqNrQMbDgqbyuqBceDyw/A9YhhBLgCWppRIrAMQxOytU27tkk3h63psLtrlqn1nqa+IoqGX4xC7JwdUAPselAHVtUphBcxAU6tDHUZxtoCPH5LzrMxiL1z3oXcvdvdAWfPHGL4K5HthuAf+O0COpU/uK3Ddg/9TsTv4L7CbyonYrWlsxWbi7irdNXXXPCZmhwDiAVMxFrbm1ZXCBmWJZFMp98XK+kpFYcuaJGfQenx3BFpHTCFnJEG8X6d/NbNZs5ovluATIzJX5Dk1QRepXo4E0KrHcGKbBgz06fwCeqQRyWohzA1XslqzfSjCHgqJsqVDKBNGhF3QOZc0sFtN/0OBHnBbgJ5P+Rs4s0JJOJKdhj5DlKT5cTooh2gKYs/HMcnbAHQsrTyKFO6QzB4a9TnmZ9g7FYy2o/TAaKvtKWDUWYA6s2jdq15jRyw7WKfOOCOBUqSs2ZdhXJPGoehtxiRyQDgtAwBwmwONmUmTAx2Do4OelUw1ZWXYOZCGeb4fYruDBcBpjxChjCNgIj1CtauYAJ4m6QAW8IjUBwGkqCJM1iEPjDFZVpQEhuc9FPa7gJLHJGm1VlPnUlev2Oi4ptRODughI+qBGShYw5rSl4KZIRgCnNQTqHOmb5kZxbOYjsPHntweLrgjg4wDuJ0PylFQH+ODcprlc0p3pIE9rO1Bcg94B7C2h8l94L2NtbcZ4qlgy+ULsOV0FNiSxF2w7YBf063ttWOA3GXt3L3n7j3z3UC377cwjI0wfHKs2bqpoIchk09W9pTCPvvtiDQneq6ppijdSUCUoCiboRhCucRBRbwdEWMjF4V33UCxWv2PIHxalJHiwLsO8a1Rq4PZqgMxLCCi1UGkmazbS5qND1QHn7TrZvWuXczbxfN2dtmuZq8u5r8vF4hFccTwqFULrO5r9bTCq7uMB9X7dX55vkTu4GVMZRCHdIsTYFR3GuuOADsEhzigyuNm+Y/54s3z+RVSmRGpg/qMZN0lKjX1jhlcOSuVQCpOp1ThFXg5HxcUenIAfEZI4nhQjhnJIKeDcoALy+7AuF5LbuRNiTY7+faSbr8scDuG9CLFQDzYT9j3BwfdzJa7xYbEX8SGFE6NDdxVAaQj4tIR8dQR8eTHjAXstEC0dRhPurIBQAAcEUDwno21mxRvVE4ncDBf66JRzzCAeJe4RZgQy7AeccyNvVTt4ROI//YwgfVeN/CxAPgettL2UExd9yyfKaYo2A1z3TBmhPS6O63HEbaFAK91xVKOKgTEXUxL8ThM68lhaAXWWIblNCtKet4F8xaFD3aq6Cwl3nH/aT957cHYfkQaqGHuqxp8h52peoqoj2M5n4pjqeOyqcOv3OFX7goJuXufO66b45i4ppUzgYNec1zNmlMc5rjN+/ft4nz+0T4cc1sKeALX4IiFLFqlAp6Eokd/bMl873vzeo4CyzgIcvfg6zGKSHhOaOYBkvK3D+u2/aAOs7nhcnE8eNETC6HoIR5kAnp0AMTbbQ/pWJf9/eEcwTAAGiwl4AdWPyX1FzwD/0L4nn6zU3gNBD3kRo8QXd33GtLjBnFHDEC6wZtqGpKxYAH30adaaEk5DivxxemEXI6D/Z4cVSYiiYflCAlIAQIiZFs96jgorJtYjK8KoXKcgxpofQfr9agY4UWdlw7S21746AWJkxhtfxNtD73tBZaTY8SXdZBych0kd3WP3HHe3BWhS9deZNRicyo2F6175O3ebaHKFTQmOMlDWLxZXs7W85dXf6x1X9uNmDViPavDgoPYiM+NIFaOBDoG3fEZ2HZfYvL+ePvprFlsluuzdrVq7grJO5GrIDSInkNMFZqDk7rEdD9mKL/+7opVSFJHDlZ3JbLY7fFesiQDEeyn5kN78dPF8kOzWL+dz6HTiMcCNG0CNJtaF0W092z1TCk03ONXa/xMUTLtomQ5MuEvOwl/1MQgH5TDZ9tEfliOlDw7xXmQJvF3zOSPzdf7kLgH6oZh6/+IUlebCmVuZHN0cmVhbQplbmRvYmoKMzE2IDAgb2JqCjw8Ci9MZW5ndGggNDE5NiAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrNW1tv3EaWfvev6Ec2NmJYd5Y9ftjJjLO7GGAmiRcBxpkHqpuSaPdFIbsj279+v1On2CLZJFtyPMhCgLpYrMu536qYLW4X2eL7F9ng989vX3z7RgixEFnqMy8Wb28WTi5c5tNM42m9eJf8o14akezDv5t0+a+3/3Oa/u0bJbtTRZpJvMgWtKBNRd5ZaoulvrvbL6+kS/ZNSetguulNb+dqk/rBzDh+cjvjUykG21W7dXkfNizR2i3ROCyvtDTJXXV7VzaHqwfqK/FwemcTnrHffNrtt1WxaXhGcZiDWDmT6lz3t/94AWaV21QbPYSZ9zvclaPgcGexuS2v62IOJCN8Kqzpr/7TBZCMEqnSg0mvadJpXYiH9cDWy7jkpy2/TzOh4hgvafH+uF8yk/UW8i6VmRtd7Ns3Jlu41LssACgDrAudKpZIy2N83sXCpsYsrk5jvhtZx6W58wuVeuN7C52R44SIUADSuTNMRAo+6Cz5cb+UefKwvFIqS+pyfVwdqj04qKRPHqrDHbfqsiEu5km5OnDPYc9TCn78JRP6Y7nmh+2+5TSN2NfrpUggCyKp+f1t9RttWja9Ja6Lpoo9jxuvq+ZQ7VaHlkGyw8NcpF4rxmhTFutqd4tJOj/t3wBHk2XJ27uyqvlV+TFK4+6kTdTN8KwIxsO+BrgEiPbJzb7e8oiCf5pyXou0SEU2gO0vl7TIgOeiP2d/E/erPs8aGpXnqfKDyZdMjfI29TbvT/oGGxoFvdzwzpEHaO32u115WxyIa44YSZ0Vk6+8LevAG2yUd3hjCSzNKxOl8+RQV4HhAsQ9brfMLCFJkOYMqdWphuz3FlyPaIZJrcsXVyK1yrFqZGNU6IApnE5zMYA1aIVO3ixzA8FlCMtitYQo3vET68G+YqS4D9yawUECBz9A4QNNHJeMDogyz1JJjOpOJUYJl6xo/+CN8uCNAiBFhLHmftLmp0CosE+u1BDEeeCUz+AfB8C1Skxk25TbyPqoUOiMUkV8vysibK3utmKkOnIE1ch1K0cfI+13cVlilpGk3mQ28oi2D2g33HVX0EhWb+45WRQ8+Y7dUB27we+K3TouUu5WZeyr4zKtZ87Zsu2A4ro8yQRGdhy05zEVxpCfpiFDfwS7Ib1hPCOWHfetlJpw3+/CO5ImOe6tTyL9Nux8bKZW0ckP4/Nbt3c2KQrxx8lpIr7J+3tJl+OtNMvQej+39l8mVnicdobswFHAPnplF1a61GnLBP7bvBEXCB2c9v05d5fCN7h4q11/Uj/uEAqOW+j+kHUFByOsSwq4UDXOHAn2XXIiwmFRUuHu4mC99TL5ab+lUKw1DEYn78vo5qlzz6Z81g5Ln2P5/Hk0USCkzQaEXMUAuobeFYcWrhhUBzU5kKLsdxS2CpVUDQ+pdgHoVpXpFQDv0lcjGlV2wOvfSVYjyMgN1oQF1tC6ayLbJ4auOIsgSauFToV3PAle9HNJ5FdAYFt8rLYhQKKHiqxH8Dak9zf8WwUTRka8qstTUOaS5r5YBXNCeg1DxNZP5j3eciTW99I8iAkdvVfcgY2a7Ht6NpgyBgJDe5WRtOeMGdz5yUQFQ7LdNzPGasx7w4XA7F/03hoyiI7HUd/wxs2o7DqLRS1WZRPQDaqHBoViPZgkHcNy230rVSqkQn6WKmSDYRUMlWM75mkGLPA/iwN/R6TiUmFcF9f/mJBiKVkRF1fotkRF+Ewbk4i7MYxAEFpbSowzJ4zUFEYCS3aQfwURIoP1eYKUUnRIeQYtLP7drFuYmTjBPOkVJmajVCT6qESM+5iXYzOAHKyWANISGZOISP+t3G6LWDnAH7LeRX27OLV//P5FlG5ofK/AEH1rcX+/QfzbhpcwfZvNp2WuWgm+CeqPRvalxHlP803yp2WPxvJc/xA+8Var/fb+eChHIwKBNoIvEEGm0uRMhJvxRUWWzwQJ35F7I6ER1k54bfjzDOIdRHmCVa/YSX6YQEvMhRtYtSMeI+rze/FyRHzxdBhf/PXti19fnMKCXAZSG+mhZH6x2r54969sscZLioc1TNdDGLoFV3wqFE3cLH568QMXvwbWSmqkDBKrwRlK1Wp3iz7UvmOJFleISHw0jR9GbBWJVmt4elPftay0/eHGdTfrvWtZ21tHkMs0ZDNtazPfj5os2DSqlsjU+PyElBgjtaFcxoByiMJ5ZAzDZOqg1g5EbheYDmzHJQnedc7QQHqJxXMyM2HA9AUJfT9lFfxTADqXOQUX4KR4iszlDiS3fZHrg6hAcYO4K9TrnO9x8QwmQBw0/dEo93AyHqJhwSSdn2QXmcLEWnmSPcOyU255imHgWDO30F7CwcU45u1dSF11FkIpZG1tlWouVwABpbH9hW4u7S6USZ0T/VlTYvM4CbRBhN+b1LNCd9HKfri4v/VcjxzsP8oS0ckwqLY1mHTizohPukwIb2DQ5DMJ4UFzr2cI8ZncbPJUakiZpwbrnFEj1BdUjLAhFqEaCLk4bgruQHK+59ZqH+sMyPXrWYlRBmFFlg+hvwCjsg6GciBnr6lQAEX6hmF4uCt3cztrKL3SA2LfXdpZI/OxJh/uPLcPBDUTg30+X9xHk6ExoxhmEUOqyMxt7EXq8oFWX2S/QS6T63HKpuem0yhERPkjJwamU8E9taaT3J+yOlrO/hI6J1QhyNComSVsavSj8R1bAtAgQ/iiJbC7ej4eHHTQGRekCZEGH7hpAzGxMZQw0DwhTfJdWR8qOhpYFYdQ/HUmHA/R77q8rcu28zpUe8rNfomfB+4TJruQnMOII5qGvKlUiVgk/HmZy5C5Got0tj3bwAOiXcqFr6sQ/pp4PkWN4308Yqt55DXnyMd5YZMagVYm+ru/GffT2pqZ6J2y+v+9gKpEMuud6e9GWmFVUn4sQjmTcCrq2zK2A7rHzaFC5rGqDsg+4niua5rRM0QuX4IIcfZSJIfqauT0RcPZOxsBoR0KKlGEmoRpaxKWysGxFSsOZf3bElEUlz7s6fhq1OFI2CuJhLq31XwSdD0VJ7nuxEki6zz1yOZ6G1KdMiAQiiWG6u7wtGX4+RAIt+YXXKehrC7WZkzy5/LTMhw2xSHV4RterGUZmieWoQ3aGNDmGNQCzw1EEwuOUD+zqRSxXgytwSbMxPqqI72wmTppynLdvAQ8TpDIf+JeLvmgQWqB3OUejD5y2Uib5Kbeb/k1Kwk1WjDxGnge6zoWxfGOKkT74yGu3DTHeLqjScn47I/a67IBidYjx0UKNigYDy4Qbu+LumqiNGmWJgmq1mVD8kxr0+NqYF2ob1MWj5EbdxX8Qyd3oUEFNvrFJoeKhDZOfbgLxyjUjOyh5pp2UGXAth1Y0RkZta7bA4AATjiQKcNZ0Yew/3qEc8ohSBbu8ahcOZ38FAt8l+oLGGoH1QUSNQPJ/5nEruTl6vJQtEszA9G4OYaTPbTaomAdxY3N7eGu3h/5hGIueoHV97C5PTSuL9WBpUKAZfuTXpOdEYnQnve/aWugzjwCPX7/gd6wGoKF/MhHP5D7G0R98F3QOVacKUSIFdYgb8n7OMzXXeS5XZGei55VPKfZQd+CHindnnah99djsa5hIlexYHkorqtN9TkaTU54BWXD3pjgUrPFFaUWImZBCJOjT/3vHYT2xLTietP6UDbrj9QzrfVlpwvHGlhODmAzGhNhxyyEYFwp5erQdB33h6kSk06OTTQA7hTMz8h1YoYyHVKRWKziX8YJS9/sNxt26/HYjmpnx1g8g45zic0k/xmkHX1dNadnOhF0nRNBBZuxu2351Cth85rVx3ioBCmmo+grqVMkGbHAG6OYszBRarpvYReS8mfp58MrAf3Qvpdh96gqkWFr5xYSAa7xUSSK9TrgVUREYR0PdfVoSPoJP7IOBPFfBRplKd8XfWhgSyy8StUKZJACtoCDcheMgEcWQqCoeVCkkbAXZlB76FuZnLITio/wq22/nv7sGuoXhA88kZPgCQlwoCeyja8jAV08d1TKnWT119hWgcXU7G1r+bRB0v046MEVLWJ+F+Xdl5rfPPiPadJDUjMlvxLprQYtIg3YZkxX+iarVpfPxWd8jZrm9tfA9MTtLqaq5Tasn2Zma/9H6ZmZ0zNtQ9Xw6zCbapra/ZHM1tPM/hqYnpjdxVT/f2K2nmO2kqEY+lwSmFFmKyTX7bHCYeTsQmEvz0e2Xa0YO55Wi86oaZFZf315Efm0wHwhtcYFpkst9gUCXRBKuIlUdg5X8hadQbqHqBaRVLyL+o+6/K3aH5srDqUQOmSeUtpwB+rD8b7hnrrcck6DdvvLQSEa4XR1RaEHP4dcjxrXFOZ94vZTkywMVSNJltZtJTjj6651Ezc5dDY+8jnvAD4uDQwvUVAdOzfqdHGucyzL+W/zigOpoo1lO5EuVwY4fm3z/7zN//M2/w8xMfL/3iGzTjaUzMeVYgQbT6b/znc8601x3947qyNc1XaLPBsR3mbqkpWgCzjZqRSWS07olJ+v8yAlTF2u+9NnbIqfDhd8/pQ6D2IGRC4DeKUJRWYk0ZEEn+KlwjZ/iPf+4uXARwlQVNZquHaCdnELOW0u3BLOU+tMf/8fLt4ShnJ5P6TS/FGJsrBG0vUnrS/tZBGZZ+Zsp9nTIUW3ad3ZpMnTIX+55qm8gVURz8QYSmWsfx7GOqOd3DnGpBUuS36mkxXm9F0rADdFtWmiyKymc+WuvjuZOmP6leIpkHJ4ZdWfcgn1HByAse/NuYS5z0JlZrgPZd+Zmz/moSsiiq4odef+14j39Glm+xee7MXLlEixh6hcPBaUeTgSGUwaD826mym6Y+yfuZmis277PGILTRzS59SOFA/fJxShhhweovfqug0pQEY9DutcicWOllgGZlzKEC30FicN8D75+46BcuTUmvgNQF3+emwtX/iIIlT2uXqNkUX8uCLWkHO4nSZ2NYfidlZdtLFpbkUflveX1NiaENP0Jr1mUbbxM446wsJlurTvk/lSQPgSK4vV6Ld0SSs4+Cdc/hq5+kV1mdW+XjcDVx4LWnDK6LouQvH4A4/pBADRBbe+/BA/ZMEgkL7YhFOd8fIhCKGGJUQZS4g/lk21Pp7XDetiR5/5KCrEHrgv3EbFb3syR6d0dffMjgKN5gnlxDdEx/aSm7R/SJ0g7P3QfsQSa7VtnbGNpsJHQPsdf7Igkqrz8dKzrrQKjza8aDdrsKPXVFNne1nDn6axsPG64CmuhSQ8+WqiTOVoXBtOWOqyX8Yu+Oe4q6DjUeQ+3tdl08QYeogGuG1SS4Yexhuxf+8C7Bgur6dTxnpilrLh8lCWj1PyXbJtL6fWE5dTM5NIviH542XI8ifeaHk3dQ0p3kB5ybdSXvaeXk1cOsQrM7p/cjsmZTaHji2I4rovY3lfxjIYtKvOsF/g9piUEyjNqOj7ac49TFF1Jhee4nY+nQ63tBghuCQab6eBj+z/fur0wj2F/UOxmy5xyLEvPXmxf47yU6UOFvucob2LljCuLtw+GFyRG9y4HL9AoRG2LWRqfYzGXk6zc+xbVgE1V/moXZOzdu31cu7K3isKNNQXCdD231BM4bLykwXv5exX5VcCGZ+he64akYmLtlH0w4W/vn3xf27rgNUKZW5kc3RyZWFtCmVuZG9iagozMjYgMCBvYmoKPDwKL0xlbmd0aCA0NTM3ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42s1cW4/cOHZ+96+oRzXiZni/jDELTAbewRpxsJPtRYCZ2Qe5St2tuG6jUrnb8+tzDkmpJLVEdTnaIDDsUkkiv8PDj+dGlunqYUVXP72hg89/u3vzr38WasUocdSx1d39iglJjGErA/cU06u7zerX7NPNP+4+vHl/9+b3Nwwa0hVbMamIFPAe1UQqtlrv3vz6D7rawMMPK0qEs6sn/+oOXxdESbfarv725ucIynugihFJbR+0eM7X9c2tECJbH3bHvCpPh/3ppSRcSqKoWUYSriiRVg0k+f2cb8u6LDz6UGO/3nJl2JiOQHRinF5GMiEcTIxI6ejmlmXHG5ZNqkoKTqzhywgkhSVpTQ0YwzlROmiDK54CF1oTqw1cT1MGdOucW2mnoVcb4PU0T7UDdCr+l+xgmhjG+qBccjvNymVwjSDwdwT3llNKM6anybeIAJIqQo3pC2CEodMcWwZXG0Id7+MKJRF2hbSHp2J1G7sJUiU4YCxxSi7EAQvgLIJKaXSCA4vgQieGmz6uYKqhgElQYAn8lgJdfKOcS1BgEdyGAr1xa5eggE1QQMMnVQtRQDsiTTQ9ltMEAxaBbRjQgeWqJYBNEGAJ+JYAHXhjKUvM/yKwzfx3YIUdswAqPHSJ2Yc4wyq90OwrRUCuOPtj66Cd/UVgm9nvwEK00sy+S8z+EvDt7HfgLVUmMfuLwDaz34EVrl38Flrb/uJnNDH/0EDQpeZfciIgUo4EsCkHsAhuQ4AuLtcyMmDM/LQMWAK/ZUAX33IhEhRYBLehQBdXMpniAEtwgEOCo8xCHOAQA/PWCAShJjiwCG7DgS4uN67hAEtwYAn8lgNdfCt5ygwsgttwoIsredIO8AQHGCMc8s1lOMCUD84jB1QqDFgEt+FAF5e71g7wBAeWwG850MW3WqVygUVwGw50caVM2gExzQHlDNHKLcQBymBOZMMBw6c5sAxuw4EurqCtHRDTHFgEv+VAF9/aFAeWwW040MWVaswOxGhwzCS3FLCSMLYMA5S1hJmGAc6IBAOWgI0E6MEK3hoBmSDAAvDN/PfgHRWJZGAR2Dj9PVhpkiZAJebfUKL0UgQwkmgeWWfYmCpaAiyC2zCgiytEawJUggFL4LcU6OI7rm2CAovgNhzo4kobTAAjlEdQw4hGXSsO9/Tqbgev3d1YnuWftsXNrZA2Y9/d3Eqjs7vHeONTfvJXJiv3N9xkdVF9uVEqy7f+TQPWREmhwitN7RXa3efb7ad8fcNt9jnc6VSs38IdxUE14BVCw7ZI6h+JLN9vwpP9IX4WD3ldfkERijCuZlAwbmA5iBIGtCl/o0wUVbFfFwRElDL7YbvFgrDMDvVjUYXaMIhSAyDWhOP300QpGxv+HB/ZQZUbxv8bDbwaqYEbm/09PuL9R4y6jHhWNPsOg3kCsVcK6CQxusdh/RfOU1XWxRhWu4opJwrigF7LT6GFoitDnKGBMURbDlaBaBHL1HRM0I5MDOIVZ02/69+ookl5wBAINhhJNYfEGVHCvUBiMFGgtu+TgJLB2lH9tk9zgNIRCQvuuqEpAWG3vnJoyhGqRkaWAtICcryBdN/PAWlICuBpr5H2a0tmJ7+kXFY/5nUKmTMHdoePsmkSmXOIafS4tOMouImj+D+Dsxwmlg2FmZtYDhMrnbhuYrkCz8PsGGe54Nm/JAGNBlcgr+Msh9DBmSuXI7eOCDpA2s0hOaCSFS+HBsZVUYaeoioCofLmwhijw1VVnMrNuTh5Z+GyNdrvR+AffBxOSVsm0Qq60WkY0MLxPi34WLeQceMQL2+9u7ll2mTf4YcOH6b5eBdujsFxpJTs4ekZDSrIzoZaj0sR3VxCB+DbpGN+gFeMn83IYxURWvV7Lk/BQ55vWLYvfz8X26/hxqYAj78r98Um+MkdTp7NNvji9hDeOZXP6GmVzj4equKAc/wFHX9RpQYn4B7joi/HMgtfoOUSpt/13OoQHCz6UC9zC18AH6h1L5DYWMBwCTo1uGk2bBQylBSYtIQPmjETVR8iNpihh6rcpNAlhJPcDrr5aQZackhVuO03esxj8KQ1eHpnAhuOYXXHcPEUxfPxS4jcAMDqDsMhahdYvMAu13mI1WxW1r5zB3HnplyPnzO4uH1OOGX9rkYNWxebKViQzA1aRX4HkwVBKkST8rncPyQNK+7jS3ElPheKGD1QwEN1OB9PQQV1mNLWnsKto5fucDqVIWQXFxMbXrg/VOH2qdzhGjxvYaHW+b44nKM+6+JUw3hIGN5f8aW8rE79oDpkClJLCHJkdEmPh1Ok2KZ4qIoiTPe2LCILDucaJIm3uU6GzHI6ZP401dBOrpHwFOK4IMhTGUgkIHuIR0EqyDDKKsqWh3uoCNIy0jTW0RIHdkaCg+MmDv3PNxaShypoON8HPeYVaLbKUX9fw53DPr4Rvm4O+OwJ1oEDf45qrja36y1ocROnqKjfhuFAdD1MOlx2rG5ggg5H/DfH7Af6Hx28ZOCriuM2XxfNmqkjHSBX05CrnSNX0gGfFLDIneuPfZLGjcKkNESCSei1+tSqpUPjXf4cSbkLTw73rfo79kAYyOJV6CeYAwj724GcwvccJ5bRV1kHrYDFrN/x+VUsUy/JO6mPBs1pwtAjdNF8Iiqy9891AfKCMfGyeybhWKoLjeDrrsh9VgpvoPHZbkMDeLIvnsL9xgy0GSw8/NTp4o+iOjQYm3DrWB2OnkW49Idah4zL6WDkTSwM/IDCcQkKXldFfoJmt0klQxhPKev38TSnK0V9vtprdPJrpSiOAb/OP4fxw7UnEK4urmCR14/xDc8vuEBthcAjir7bFZsyr4vQ4lgVmwK4cjpUp/BCUGOBh8RwMYPt3J3Cu/VjGd85xJcqvwBJU1NhZqA7GKHmsQbxAzY1sCZDvaLydhOucGimGRo4x0cw9g+PzZfmpXwXr4YC24uEnfcvZsZkvn8wM+EJ2ppzFV+DteY/84hS1EhKYbJ/L3ag01CLgD9EqlX1sGqv//OnN2iPoAn42EHJ4rJohTOEQpYaZvAxiBRjSp9nhiJLw8FgKfFGccKCEio4vl3E0PFreGMbugp6DLc+BfKf9zEkRZc3UbkRbeWmbzIng8GJiA8PNFJI427B07fx/qDhbhKJvQ1D85SdlHQCWGgWYpBiQ4YFveirhBVE8aj8EAdimh94pCBmR+2dt3V53JbrRvtwf+tnPiQiW18VgJt1BdYnxnCVdyMqeMxhR2WN376GJ/cVnrXEV2M4Em5HOwS369h98zhhSpSAQBxMSW9Yc4G4QvsD5rbX6IXCkKnaQWbhwgt/Ox+Pr0lGsZCFYX6v8YfZQpYAT2D6jWbrStwQSBj7jc5zSFjF1i+AkkkIlsqcG7aZ8nxC2fGl1Cs6CXAB4uV4dXLAhhPr5JUDNpCGCzcy4lth5JRFaMN1yMQZteOQ9uXAsUgNH3M5EodZUHog1NukIBLuKdZv8XG21sT9Rt9VtOIK7PMQabb0ozX4NHcdryAEhDR6SV5xB/GbEdcNWFBKBot9NpenFsLfAS3Q5kuX6XTlAPjkrtOtwB8VaPtSt+grrJ6rDAltgWsDM/F+nMKSKZyBV7JYUpg/MejZB0Mm+AH4gOAhXPigwmCQCOETBuIYSvl3BfifGptBWAXPvs5k0GiYlSYyZsH/d157Pe21p1PUP00nofQVMcl6Ovfg06Dvx0GVCdmFyj5GzwzqXqPeQf9wGUIt2cnIIFPfx0x4f9hHFw13H8uHR0iJb0P8DV/ws+4VdbZf94ddmcfttPFsMhnNfOuEhMpW3P8bzfYcGF/wgbBaiaLxWPz/i+AvmWiqaSZBshPK0B1x+51k9+PCfJzedJwe+rUjyB5QISL7NehlQpQP46I4/QpJhhrjWIyjdGKFwNPUWvYq/W56dX3LmjSwJm/BpL7DnRCa6uJ1JYUXCr7SNIUdjFGFG8mMV7lrmo8cDBDUEMlkJ4+QHDLjiyXAr6HEh1froqpxs11i6iY5DSmpf9Ju9YcHT4/FvsmFwxtlHZ6Up/A9j71vi/yUrFEJKwlFJ92V9e9zztZByISJUrfRbASBKa0W/UbVrPdU/md7vUZoOixNxxCSg4hyIOJcEIGnSASGgoNx4d6cgQn/j0PQ6rEKW26b8zrqHR03PqnbqsFheKqCu7AfEiXxteRL5hd9iQSvvyv2p8tBijJ+nk/F5rvgmGK9FWtI26IpzLaOB8L1fF0fYnLvbXynGhCOkDzGvvf/XawvZRjQiRuUbUHPRDDdnpmQvhYklE+hq1P0Zt49HjBm8QFKjcUfVE0yTTF+w7UH8GGivtVKxSzuC8t+q1CS82L43dBy3Yz2cJ+O4WGMdNDZR7A9stmwfD8nD7eG8EEPsbJaboKi4gbCoBCjmsQ9ljJ8tUCotrYjm5hC+bJ6HF8olSvgyGkdqlK++j5ufTDbMFq3mw2BKjzsyAB3/WkexbAYz2Mxnje15c5bp/B9W9zX4coXkL5cCkg8VpPC9TnsaTDooIq3jlWxLk/xy2COOCCEWKgt5CLEPrm0NSVKyP4AZw2JVsQo128UNXDXDPr+sA3bsLiIY4Q9qKbjcQbOYl1yDau0rs7rWAJisCZxMC6DATeGHO+GqjteNJXp5oGnLgt7J6d3WEoUcQ6Y/73trul4nR+TxhXP3ED60xPvl5lysQBFYkm91+h7NK6icdK9jeMWzKB51D4JbsvMjNI5ODCATus+XNlo4giWsIjaOOz9djmLZS7WKVu6wDomO2XLEwnIDgtLxCnFB6kQhyCW0mjPFRGwuhlX2Y+Nw13ntZ8qo7LTY155MeD60w3w+Gu4DlNo1GV70ENCGj3ON0SlAtTUVrAlLLOmdwkaxqI1WE3EeMJ/vCuHd2o8PUgm4jzLsr+gnS1ryBrKP4q5GgXayo4YH2YrFJJwiJu6bWZLITF17raZrYQYC6LJIQ5DJ2qQgviB35i7GIPxvX9u/C5Zt6efXlErkKzX5G3YUDqfvA1C2Bh8UZjwdZ0HT+vF8bRbr8+VPxJ5Cnf9Qm5ituEWKHCBWYdVHI/V8dUmmBCw601qaGf2FP1JNN7vcHZLkVnmTyN3G/l1zjJ68QIqegHTd1U2e8iP4X647YOLZKlIEy1VD+15TkRwn0Sx/rim6vsXoweTatkYUM9uaeC0mj9i1BNH4dFjN6qxZAlSALOl68v08ywahBGM9TU2H04zIqi4UssQg+NBngFQ8sAkFrCM6gv3p9lNbJhO3scJuzamCdds9nl/8Fv5+3Zr3bRb6xZLYP51jINigENdZ0EZPAobV68vmwnaRC+0G53i3Xi4gmKh7RSu4lZ5u6wsniTV/W6bKUhs5MUfgrdbeBd19gTmlEPUOOj+bZDkeE6fGuWgQu76TTcjLFdEG/uqY10XsSR4Yj3oO326VGE1mvVbPM3iaBi+luPanfYrGFurfqPnWSgDRt2+QGLpE6MGt3tGtTANZDGKGYinw5TOFZuZgCxmAPjjHCBWxSE8HBGzdxDODWXalfsXfPTmzenrJkQoRyD06zf6eVZscFh4kOE6KMMJt/zKuUfbq5i5bvKF5cS4QaN4mPWXWcS4lfIC0ZeQaTe3or3cm/qff4DPLx7icYmUBYasF7f0eiifJ/bXfFFyvA7alVxRTBgGGmZBtDm/i+YXVjSetYuiYCQ7ZpMgHMerMaPUk9v4zjpvvfJkWt6cWcibn6QUQa3V5RTd/fTPUILgz9MV8k35pWyPBvZr/qfy+W2szIQTLZt84scklBINkU2vkv4tdVyUlmfP34+XMfV0bVwzPPIg6TgmPsXa+DUV8X+u+IOG7yY2aihF2zYN+Asu4qaUcz9Vw7cJkX/s9vAHLunGMnye4Az3qz9V5H/9EG8Zpz6p7zHnyf8+YHKf6I8JwWDVNJXE5pRxGXey/lodmkMbZXMoMnl2CdrI4dmleCZPYjFl3z+dVMT9tL/UzV7edvvyQNPYykEvIBZYOd+6m5Q4VZvYCvuGLS3gIaXfsIVjDB6mQHl+mlgkbnp/Y2pvg0nqPI8po03jy0QrDSZdr24lpCxc9/4jhOad93dv/geXiuueCmVuZHN0cmVhbQplbmRvYmoKMzQ1IDAgb2JqCjw8Ci9MZW5ndGggNDg0MSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrtPGuP40Zy3/dX6COFzPD6/bDhAN7DrmEnxsXxXAJk7z5wJM4McxKlIynPzv36VHU3KTaHj5F2nXwJFtjhq7qqq6vr3SKrxxVZ/fCOhL/v79794aPmK0pSSyxd3T2s4MoKeElWdKXZShObEgFv9qtPyR8P+/2hXN9yI5P9mrPktGuK467YFE2R1/55Vm79xQH+8GSzOVVVXm7yOl3/9e6nP3zkrI/t0y0lRCUf10YkhwohZJJnmzUzyVP4Xva/p4EyLlRKuYjJq8Yw9OfDJU25tDHUjcdalDCJbFf8I/f3zVO4yD9nm8ZfZlW1pkm2Zjp5maNOaJ1qGqP5ZYk4YVQKSGKovxBJZjFZkkrJLuSDsCaldkDgP61vGeOJmkMnqUiFGgCeltBJRlJN1KuZUc9VkKgZlCCDyspUWObhnqewhe8NSSkRMcwSF41I+RDmtIAHGS/sKzw0gJkxPBbEwtAhDBMLqCjwjVkTwzE5h4kylkrGh6jIKBs+wcKz5N6/kmSlU6uJH06mEr65panianW3hUHIErGCpUboyxaACg2SMQCqljBJlrKheOASuB2tcQeX/ir/3OROLXGVFE14e5gjiIF+seoyyWNSpkTxy2bOJKztEGi/hEkZ4DG7TPiYVinR4mLpY0Y7IYjgYJHXt0LY5A41d/Y3VNh5x3Z/cQSFYpJDUeLLZs1s8lyAkaD+Q5XsQeXu8d1p7wEOD90IdTtGdThmj1mTb5FKR5bsKQZDUyu4JwmUc4ajvdRBreDNb44wZ1iAzN1uXgg5COFg1EkhlB2UgQXUMVSwKc9F8xRQ+web7Bjum1mhYDAW4fGY/7VECeOg+vgoIc3Boz3cN1kxq26Z4SBaNB7l5zHVAPrPcK8a9IJqaCnkhIECNPHgzqEA2pqnLJjZx6rYOvFSyb/ms4ziIJtCynjADyPUmpTIC4kVBDgxFLJ7J9N5S3FwEZwkZzv/NBb7IIzPT3mVL5k52NUcjOY1LI/GBCsDGqX31T+Pu15Mm4Q4CeHtxB4OVe4feJ1ZlI/jFgM/GaWRguLUepnVMFdl+0R6GeVul3phfRhTZx71D+MzkjZxgiOTO1T+I5TfghazSqxuYYtx6jEv8XrO8olUc8/r8NWUwp/Q6tN6269PT0EPbbbW407lHA3VhCRIMuv/zROPPjxJHrz/DpITlO7LtORU6EJ/CwipUl9/EmxqEpvFFZCvV2Bu81w8dTFFPFfUhU6TLNtMywibFpAxXciFBSdejsq2nJXtb6ZYQYVbLkpbVgIUYa1qgxEIGEgmU62lV293TnUa00VXeFmiVDRVCCVNUp+OR4i3di+ohdyTpgULvoP2vgM+CFEZ3Ohkc9i3fgbcPVSH8NG2+AuhHDSxx+QfHqt8m0N8Wh+qgNcv58j3c5ERB2sB/I1mObrWPc5IDgERkzFQ6vk3YB4FX5epjnlFELhNG5GDZG0OZZ3//YTBtn9Z1K1FogmqdZpsijrfvfi3wXpxdM0MOLcww6CBT2UR5Lj2Rg2GKmFkiI5DnA+DFqV/027Td0gvAWu8qh5X3fW///AOCKY0fOGzDmHLhYTAMP3ALahdBuaBMi91v+a7fNMEGeDJHqlkyXYuo0CSf8nzY8/18mAwp20+vcX2c1vM/ZkwO9wyYNoOZMijzMHvy6tpRM9T++hK6+F0XlB9VL/JmIxoMyaDL3BwpK8URrjeUBLrl2KffQYxUhGdMZ7kYZz8MRsLQYSAvbDoKqAeYn1X4evZ2OTRc+2T/zNB/E/jTLNvWS/zOtKej8M3swsN6/TNtK2aNBJ62kiYUSNBqU0lMcvO5qvFAbMuYQNurvVp9lMLdZ33JKfn/ecp/WHdul7kCfW3IAKPowXmjKOELfX7Q6jkf4UqXJJJl2rKhwBza7wPwWZ8CCloyqzq+xCcABgVVd34G7CCWzBT3iiS5HjyIRjt+WbutsrrYnsKIxTh66YdMttVebZ98Td13jS7fOvh7rM6fFNl5WO4hPCuxPF1yDXAiB4tAYPzWOW5hy3AEAdqfAIgmiJbSQahmwjT2+WZn5NN6uIzhjWaY6KchUS5DdQKJBBnjdpbgCGq88iDYBRi5OHgb7HadsxoD6lmXKbUqHh0l/eYSzKgabEDoJ8XnCWmZaqYjYG+m89laLBgfBTNVcFejxpOFHw8GHvCge+yFejVcBHDfFhCJEjKtLpg2lyylNpRLFM5kbdOWgmAlvHQQTL/rTpg5gOcyNpvv3npcgLMUzaQMff43o9zKrf1Uu2DgNaPqPm4MAUsfUhlxnfDpHNPWGqG8rro3MNjNIoRkLMQaqHIAgBc0xhwvxhKqNQK+2pedEzNUAECotuM1lI1C3cR6t4+1J8XyLEqpbDUEcykTe0SsLCnCB9ALU2cUpNSOiDPsVlOG+IOIwRgYBdj4G+XMAqREjogcx4NRNxSmwvRSJEKpi9BIy2m7S/DAlvacHsJFk2c234ZGlDeTNJL0BiSmiGXF9EYsBGKD9C0DsmNt5ZeveSbLFjK6RoQ6Eo1WLVqIifJxGR01bdhyriy9at9McsK8FVdmuAiVjADOkuLCzjOjEkF1xeisaDEjboEjcXi1YViyglPlTUXoOFYNFX0QjT48SVSyiFMUppfiIWJVDI2IqVoS5VpPVudPBWPT7c9T9Ukj76CpBPvbW4a7wzqpMy9h2qS8hBKnEHQ43wSoCVgmIQJLst2X9R1cb/Lv53OV0wESkK0OSYObi564aWngYcuEC6S/QHJocmhPBdN2hJDCQ/hFvMmIZVjUZ+nFjsaMBfk8kA6ZOllKta3lIJav1tbkeS187C1DOk67RtHwLV+8ndVvs+K0qeN3K3n4njVx5zZI7BJpK33gL8twd/G/CGz5/BgizNyabMyJM9sklXbEIHsDvXJzRGeuppmBwoXVY4luDx822Wlalx6SZI/le33jrNwtcHE3Q4RuuLpqUvm3YSg4ty4MucscYGcjCb3ywIzYH2dvxoBLXakgNbEMnEEVC1hAlsHj2MgNOJEzvtKAoyRVgNs+yVsYFpGpkU9v12p7lw3hidF0xafTa/4DKoy1W25zm8Dg71BFramVElW7DLYVX4n+ppr5HYoQeMh3hKSmcmQrE8clRzimXj0G0/fqS2s/yOvDv5RvqtzN+fwSdi6vdq7f+7rc/oNNWQOi4KqNyKgjbv0fNw1KSldDRlMPAcvJxrcbR66VC0WFJtidAz6YYSu15HRIlkCNZUaSEWvWmzOfRG9arFpq8UYM3mZ61azJ2lgL5gI5YGd1xibDIMspyJEUDJtQRou2qoKXGbYshY6I2bNMlhYdIUjZEsagoG21pbGQIstL1RCqGJioCUNwRj4GUMg0BAUTMGshmDcwCg8BjwtYRPgrjLzal408Hu+hUJhw4yIgZ+XMGJ2A3yByzipbWqkvHBuBmKzkZnNtQ45J+8VOyZbh0zng0FwQQa8n29c41S7+HyAaiphTi2Zalxjgr2p4aMjlmO2iF22AlzABrhMkrF11TL5egVc54JpXY6wkbEX1jqXBuWuKh6LMtu5Eh7c93wJJnoVLnfbJuH6pUSN2g/zEm0Olanr0vRmlOuuAUl/1YLOVK3fRJWvyWLK1660XVlao8KG2trx1Ez72dvpEb6bXomr2yEmKhk9/52DM5tnVdTuBHPI2vJz8bBU8OQsFYZG7TbNmOSkltvI4tJ5wVnsttl+7W6bTuRflQGtdfuVYvXjyyuksYNkUyrVsisCAaUUv08b0v+XSHslUj1VIh0sjpxdnC8rkZqvWCLVX1YivXbrvaUmeMsphDEsbkD4uMseg+ppQpcgap7nos6d+RRt7oT3q4Kgu3J/dU5Y/PFQHXa7zLdqzRcqRAJKbBh5DdH5zmasw8V4rq+0OcxTpbSoA/N1MVVPGww9qmm4AWZbu6xp/MmR3lezXWsfpkyMmiNwbLdJ5tIKb9KEsk9fOoyoDAQE8JJLzMKFNNjPbUzGrB6JydxjF3nZc+SFzzZZ6S+6UI9ZbCA7oRj45wDyEj52x5py9+dvLjGFT51ZxYtiv8+3hYu3HRW+Lw3FyvWmocBxm/xYbl2mb5zQ18Ej1g+ttn6aIS5khiW+8x7Pb2GLXXeEC189uIQXXrnZwV9sxLsNGRG8r/MwCopnET7yE8G3e9fDi5fYyn2DxGKHsQtB408xOPWdaU1WPmJua5e1OJ6Kh6YGVckJAvgP8cXgTEBc2+JMp4KF6XbdcYKH4wG+RRBun7LaX4QiOmzgbB+ujm0pM68aDwJYkf1Ghv2OnyML8OJUOwccrh4O1XysIFMJKjgi8UvSIoPKrLA8HtuHB2GebXvCSzvHruvRP8D88WAi/aQxKmMTNku7ALANghb80B7Zczrr3Da5uaLbbsH68wnrH9oUQ/o59PwVn51wBZU8NAICM9AgV2WOk/H8Wcfu767f73hVN/pARFvlQ0jKeTgK9yNSrUjy/TGIXbktPi+aJQQY2o0bTwj4n6MlNWVTA68i5MfDc159352MmazzYKEDBCCCrZtDldfzx5tAiJWIwX5awiV9Li0Cmp8aUz6nFIGcyuJ9VueLNR/ijv1EoJ1Rm8AHH8J2HQCd/rTZLCGDqRkjRtg4f8xFpULTGOorNbIIIlNt7MT0JxKp2GQwmPzX6TARYK0kkfHQoGQkb1UvbKrqcGqKMp9dIEnBaxwKHujs03Hq3E/PZmKKTBs21KLnGLiXbAkvuoJX0zZngx6MS0SmLRGJtkS0INOuVcXG1GC1x+36D3fv/v7u3FnEUqqk+9RCuL3Zv/v0V7LawsufXEu1WT27T/cQaQtYDLLarX5994s/AD7Az5lMFelxARfuMC7afcZhYksMmIc96U112jSzWoLjTK2KQRcS83o+MX8u9oAgMCLisW/Oiza3AgL78wWPYe/Aivx6zBfZIbCcbnUM3Pku9cJhMKZ5yjh/m5Og3+Sv67O/3pnDepDa6RdNQ6anPp+oCt/MbL9wvOdUTtlofz4OHRH0MofosbnyNpycy6vf1lIm7QkE8OWOWVXUIFBpZPaGJwhif5BJnkJsE6q5uHvcqRCa/Ia/X5BX6KoIdOnRT/C/bUASmdIhDkrpoPgtSauRJLgkcAcE7nI3vIRZVu4R/vfoLrO9f+FcFABBYw+P32brAe77V51yMMhvYQJOC8n2Rwvg63uHsh6Q5x6iy+3Y6N+F0mQ/aCApA44xEH7DQyXrbq1BfWE9c4lUKhI6FitLlvzoxI065SmZT1TD3a5wbhlcoVM+Ljfw0miYWBC64WLg+yOOeSjKpr5ps+XTg1Eq4M/MaNlD48q/cNlkPlhz10+B6O3huXwGXuI8/BOv0/FB3s3Xv4EVktnjzNSEVfDPjJLT1wlMwcqEFYHp0gS3Jy6jCWLVnr6WeEJqC4TBN7u8/QK3VLa78be4r/CtB5tJM0gzmrHF/iwV5wvY/3HKlo2k9PT4GjuRVKQ7SiUxMQjsgMC9CRy9f/GsATGFF7iJMQKV/liZxLQ7LoBvAUd+Hp0Aui+yqr8oBnePftvukeE0X7x75gtKIaRgBMxvu18/9JrdcW/4jD0N6ofKicLPOVLQIGk2HvNtyfELlqg3BwoeAIG3EUIqbLu18qxywSlQ3mZU8Lr9pQAqOmsGT2tQ53VQi7N90jaFmDzG+ct1h0n0eONxv+xnwTAJEWNzmooMpxTWrGp/wcAm7dz7eRinpcrwYndwv68Qulban0qwZ91BLYPAQ/b7EnyDNRjX9kRB70ygD+ezqgkB/n+sDe9by7f0d3Owo2Br6OsWb+6OdraxdYeWhSTq0dmo0HjiE7nBbmNoXtd51Z7vYN2vBvD4fEe/SLXLH5owzcBQ3TKpp1ypBmngoSx6NkQjiYsxbci4O7o/pg3N+NH8ThvOHsnTXZrZpevaXETT99YCEw+lP9kpupP0Ub5x4Gnl3skqqhDP3M+mcUenLFVqhPkd5ryOcjlfXKK+3Gpgv/a5IVGAllCuIfGWWQiWFcwZT/uqUJNQoS3xQ2i60TLKgIUuRTcd3e5vuHBZ0luILXf+vrcCcR0Ef+FJAW6gA4Ifh/I/3XY8O8andnd08Wm8vrwbfUWBdOxFv2UQF6oQ3V3LY30tj11JfOpoGf4sQFSYGlmi79YBx+Q5vftr84vvp6YUAN0Q/z2OFIie8FW4moL51heM32NR0N+o/jivfihgppDmenhhaVVKMTZkKKWm23R0ZBfLVFA9Wnhf3MOyXQJCW6L9lQ5XyycwJ4gSwoXsfarE26ma+bmG2bOMgwWjOKXrlvkth631tDDEAhBxBpwIxsR16zWjKd9PnWroT8OqKEskwJ9g2GjF2xwRfKrGZowdXatbcPAIazXmVTv2yzbeIGNmUPLlCqy+omyYMMMUWZcwgxlyE2fMBiEO5hgl/lpYKgiNLGHMM0ZTaTGOg20pO56NSoKBHYzVzh57vxmkJAZmgjKbSvDwbrHTWwcYyiMgYMH/ABCB0WkKZW5kc3RyZWFtCmVuZG9iagozNTggMCBvYmoKPDwKL0xlbmd0aCA0NDA0ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42r1cW3PcxrF+16/YR7AiInO/mNGD7USuk/JDHKtyTkKryuAuSEJeYkXsriX616d7ZoCdAQcASVvnQVpc5tLT09P99QUkq5sVWX33ioTfb969+vNbzlaUlJZYunp3vaIlYfCOrOhKs5UmtiQCXtytLov/ac/OueDFx6o7NOvjtur8fXPwv+tde8Z0caiadu+f1L+eMVPUZ+e06M5o8YBPRfEToRLuqpsb92YTRsVHMPCh2YV5dtf+919nhhZ110A/sa7c+/fv/v4KiSSlkKvuZjVc//O7V6tL10uWNDTza4UFlGfnwoji7ZlhxS4QX1drJPrW3+EUn+tAESxki0MAi2TCosAfYXkpuEiZ9BNhNHSa4quwtmRixNzXOKcuPiExtZ//U7N188PKrBGnTVFWlsIy3w+Yvj90xzXuAbfFPrC89bffV8cOruHJ8L5r6r2/btpN/fHs3DXfJK2A8zPrZpaUktiUjqvcomPCGbSm8DjphTsiefHutm46P/V65ymCnVDrJlAVCK4Oc2RxYKulKp3gm7NzSYoLkDOl4cZ3N3H3y3OmGWwbIbnB4S3jxZUbQBVf+XHiH7U4OI8Hn2SPMKo0PCVezUsfA37qZ+6CJLB3dLR3TtC8eBzvnMSBtNJI4AwtreC+8WGHTUmhGfmJSDJHIiWqZMCQpP8PE6dj6ERFaRRPOy3ORG1JhR53YnJpMiaBHfTRZDS3mUMnzkpG2bjTtABRUDkLdEhaKvHcRUtZWmaev2gF9NvMovEfnkhB8ET6bd5Xd3jFgmrRQbWQotvh0fy093eH27qrr3fd0Lb7tddk8DYope7B327qm64OLat2c2qjQxs3Ami28AoNjn/atF48I9lUpuRWBNn0VPNi0+w/biuc9MGNuvGPu6q9qd0KRfG/ZwYeNIc6f26xeRBVqVe6tJp4PnJbKq5W57TU2qzebaZOnS5hU1fYSvtWbybEQxuwPpmJdGng6jwaITuPKClL5rnMzjPMQaI5bMmpxClwRa6zyk+hOV9Frd6/nmba2/xaJNfLa3kJz0iWZ8sLYvGCFnimRzxTL9iW96+9DAaBFyDhhxwXzxnl0IuvAAOATgsEfpub4/JcmkktcVncwzyDeXrIDwANnLoDlmrgtuMsjkgnrBlbMpX38/M83jMjYBtgIH7aDeGbWZXwBcyCZMCVEuy1b/fvzPack5JqEAEGTKSB9T9npgXZyuM0UYJKBWq45L7372AH69mekMhAY2mbiNDPOVJ4aaFf1Cro6D+WGjBmirEsNXKWmjdP2eqxTF7kSacool9nx5sWcPoEoTa9CkmFCYyGsviAKzErS4DxR7L0l9sJsbFM/r/LzW1uBAnNTLJTudGggyH8aVJoxlI4tbl+I/+a3xYwxHPK6slbmZxjhRy3ifpQv099GDNSH7fPUR+yxBZ/mBzc5+UA4ZsAJ0ykYsAei8F51Gx6576a8ht6oEVLwEwrBd4DhYPjwBYAKOkAHtXOb+72B39Tf/7ojRxce0QGF2sEY7dVV60Pdbf3z4ZWN/Xurj50zdrffuy8E7gBtzY0bdrReM45d1e/nklVVF1TXW3Bs53zmJgoORzSZBUPCysXnJREjTqhKYfNiVfZZtcOXm1wp6KgigRXT9EwVOcCGhWuRTHv/erY+9Xe+4V3HvtOOgMGJZamg98vRCKo5SUhI4qqMF8fO9AuKgAPhtgBXM+74gzGBX//ecRwAm4JuCNJpzeIlWhBALRLwXwICqY/haBez4cEQGWaEVP+uhSeIRo4qdJOS76YoKwkY+KXViwAr3I4nOOZwiFVCbQdhBhhK2wZBmHQAUmdz5GSWgxFcQWiPmLQbS8Bu3b74K/aXdvWNyCqwasbOWFSgxxp39tHlLxz6L2+fRkUMvrpYE1ADeMZAyULTrsMOrukaDyYLL7F+AKNjgDcoABqUDafD6g/fIAQ7v1Jg4tDd2xPkcHxekGnc2fGgWM2qMJ3vafY7rq7atv8VgdUXm1BP7UwVnvjG/zoNRf+d+x8m+szWkA3ZAWIoG9203gfN6+BKJodcIBAA1FiPQk/PBPuTLr20xGLp1iYt3nDC37uy0kJ1tr9XM7a+8SwgQojAEUji/VNrrMGXqaGLYdvwBCLVWL88oFZWRIuktHeZ1n5xV2w7KTPxcQLk0T9zAhL54IdypaGs6yzm3RXpRQyxYggcrF4pdDTlFq+wIUe+z8Z+AlsRp7LZ/H8KWDo8two4wEs5X3fkWEXWsLxNn22ZFOjUhFEOPSCv/vms78AzCJOmMW3OfjfoOTCU4d38GKEDDBx0kfs8fV8xJ4DZNSGpwR+zm14yeAwIOS3w6mRS3ZbgO8jR6uvrkGNetKCnjw0H7dNUNLu+ZUPz2Ww+mDrFDhgRq6EQTiRhI/Hrgx3rgygVBJkpfnLhyXb5yIPNqV7Eqyfwu66lMyMe01qWLBOn/MRMfQv4kjVh9wIgFhNEql6kx2NojsbD9YsLF5yWLx9tAyM/2YyTkLRUsoePzV7sI4u4SQwlrvu6mrvzSXc728bsI6w+xt//7HP6e39/bpq213oe+VTPT71gLjBAloAcbHF8VBFfXwikPhcnXG5OkxSYcz5dZh1F1p6xCJIcBTgAu23jew3vsNZ+mXqaJnCYJTcC/AswDWyZGDH4w5TJyWew0I3wOhxN5c0hWPcNVfHQ713CTFafO0TMnqcwDPF3a7d3TXVdu9vb12Qu0+z4pMDcJcWzqGAuyHUDtez6SQuAYuylAdXS+sBaAj+g0g6XfjJ9vWhx1Cmj8ubyE/kSLXUvRLsafe/uzY82O72HlHFaFOA7IJE+unancdfTXuNSC6AQ4yzeunaHQ+hgc8iooINELWNGMvhdh0gYaBWFF19VzXtgASR+BOnUX/Xpxef6035SKaMKS2cSAHI09KQbPuuiXdrEOk5146bUsjRMHnIw4VJMhM/L+0gJp4EaIVk7Fhohq1AsD3n8jEAXCMa75dmZ6iF1Mzsv9Xdzh0J1iP2RydgsHxZdc2dW5xO8PWiWFsFPn/aKXvQqvn6AKFcMCi3b9OTw1pLIUa9zsdMufA68HCLrsge0UA9SJ+Jjgr4snBI/SDznAJryKlIe3w7RWw/hfURlaTTmFM68R5t/3CRfYwArgBzngz+sEQRg+dKjHoF9jlJIsW3fRgqEALWYO8ve50RFjDQeuK+LpgO3aKMJ9wOdg2vE50+7EuEFMEfLQ09ZdRjDQe2hYKtTZr0uH2m6KWgfFzvMsDUeHYKbJWSp+MPdRhovIPVPe7rcBWZFBFlb0H+omoTePPptq87adpN82uzOVahruCRzg0jN20vx2HA4H7HxShezULjQYZgxqAUXAxyKokbmQxuSMmUGJfopIkohzVjEAVbPR8ctKw0kqajX7vKIgo03sPyAdWDGD34Jw7HwK/D8jaO8lnPkahNfGZMUc8eFAN6RoqUjKW4JgPahR11Oo91K0W73e2ON7fhxh1YdzmIAFwzHaKhn5rDbbQ+M/ZV3BpHppwrIB1jQDh7H+C006n4p/vB8/mEJ0WkWIhI/QhSjhGpEHVyEanjXd1Vh52LZMOzUErlWm2SmJXbTwxROQmHi8C73O6EEFUg4C0WKDhhgvWTqeoixFRMTDkfevAqHvFUxh3Z42GVBzkb3EXRntEJGRRAscAQAWiVPrHyfQab6FIwmfV32Ky/8ztjK3IxE8hLap6QCsTcASB+/DViiEdwkQnVYjpTFmwQ5uRN8Z8wOge4ZjVAJp81UlyvmFfIUQocUO6pFciHdLHfkWeeDHXZi8qoaAEtKY4f/PqLXLgYVKoqcsFnN5v4kBl4lgmq+HCSsVFk2jkjIA8SvGnmXHcSMCuZJCA7/3GpeWbeTHNCnr08mRdhwG8SZBUry9QXTAVrTdPwJhf5ZDAFwYjDm/dZz4GpZLjjJJuyXPowkQs1oBLyGchkZOHqYZN2CwmNUXgdoKtNJsrG8MBMwelFijjYPjlkAtoseiAAJIl0OtnINGafRq2xy3I92CmaaR4LxXQkM2g49ZLocW8ecmgHfBMquVfIeg7uMHTxJWgQ0ufDfV313969un81QBABvAcXhoE5QMFc3726fE9WG3iJ6FOANv/kmt6BzwcnHfttVz+++iGUoCfrHwbjYFuCGZgqKTAvLyl4zBNOYadhXlpSNldKgJ5uKCUIB/z/MnIBxDPlKgkEYMTY5I20rgTTlE8u6pK4mhJizDPO7oczHDIrhMbt5ehE5rHFZNUyCEDEv0QrAaRkIiVlMqYZE1Fnl0VMIp4LeEE/Cy/c56oHbaloQj7Lk4/FgzH5F0k9eFokHk5vbj4AmzhENNBMseLT6ji+nwgOK/3lWLkAvZ6TYtFwYrxeFHGKhapTGYpCHAO+vVAhaP3Ofc2BLogvHGH6FHtAJ/K22b/2b12Yzl9W/mdITWAtpnuy8OUDSjg4iQkBv0yVW6shysVKykXaqwH3rgtURE4X3AY/CmhH1L1rfxu+n9mFJVXNFv1frkNQDDqdQpVzASmDClWlhFxN4RKyUNO+uG5JjPviI5kuVJS4SpKRM4jVKLYvQljv7kJ09brb3XlfZM5aqlQov8odyZmzaAFQwbi+2G3xLEbNQlVv2DQg8qZu0TUcosYh8LF98Jucd6JKTXBYDr4UTYvORtk5ky09fipM+2UCpnFmR3nW5+RYp3md17MCS6hfqvYmCwEB2klGfSGgfmIhIP+ChYDGBTYAPGLt/Ak3/WFFgKIkwsap2pfJwgiLwNn8U7b57WPQxwn3koM+NMCKGdAngZtCjEBfWmYKh4DAxlFwn4xlX5JjVNKZskmGSgudEc7lkh28eil9v0xEZSiZLq1x4Rzy/BpOmxpPypT7bswbT7COPmb9XXXc75vKxWPVI6UVwtrH6HM8laZ2eajRhOeVv1XnPsN/vlQ7xwF3azmi7HezdooNXAJ8t+lkXrMg0V29PlTtzdZ/JWR9wSGsyWv17RD6958rqljbP64HoZSVSphTXZxesANDwo+UjOm0/z+yQTXMvsbIrjvjpLjYL9V9Cg0elkgnAEsmqAmWTBCCKXD3e7fbz1d7oo5Uo9G6BRIYGDtMc6WdHHz7tPfzYugUf7d1e/NcmjhoHDEiaYkrKBlS5rmyhz3eI4BcKDEm4Lbg9yNfaucEiC1+HJRM8GaWJG5LA5jsxSQh+twv2ZZnlp1qeKxGm/+neejKQGJH6572hiOAts/7cAI8vRjHPZUjuVAgJ4usSK0pTbg6XaSDKWCisgJ5hSflYfCQIq1jbUlsf6DqO+8ReTgK6rjyH1KGk+afuRwFPAwHbU4zWeWqROI5plYx0MTwUCiZdAqpQpjUFxgAFc4SwYOPXb0PXsLr0zeis9Vt2n3HGo//j8xmRNupklDzCOpcLK6ICwPtTTLj7CHkyid2ciTqWRLDGcyJFlkk09CSj4gEIwSOlHMfbe8+2uF7WRQp9J03/Vb4qsON//sC8MRZh5mFgvwZ0KLCzliBKAuJyUcpky6zfLTAmaT1FEygcg4mUAUwAbN1wUvZL5BJGSaHR2QCQuI+S6YT89BDBA8QwvfTHlocu64OZUb91y/QpC9UajeV/0Jah9Ko7bF2sQtu3F/H8F9ch6Mcz3HtitENFqOHvPjf3GcoPSpB9MOjP+rBkq+lpS8k7TOa/tGpnN3N4OpM6vnvT4zTUf3g8yG9uRRgTC0jAA0FHUYdff/NwGnggOz6108qkxBzZRKnmQGfaLjrh77wfBl4dl2tkVkLfyaDgnDrtBoqsR0na2UfJT7GGRYM6v6yxDB0o9BaaZYtL5zVURp1xzK97In0LlEK7DFaDdOFv0QS5G9fbwGJ+48yZP+xFwhiHyt77V8Ey3DazZNvSlxNumAl7YuoRjsPvux/AVY5WX8KZW5kc3RyZWFtCmVuZG9iagozNzEgMCBvYmoKPDwKL0xlbmd0aCA0MTIxICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42tVc63PbxhH/rr+C/QZNTPTeD3vUmbRx0nj6IWk0k3YUz4QiYQkJHxYJ2lH++u7eHUAceAdSsjNtv5gQcI+9vX38dm/PZHI3IZNvLkj4/ev1xZ+/5mxCSWmJpZPrdxNaEgbfyIRONJtoYksi4MNqclPcV+t5dTnl0hTvt5vLKdPFYj+vdpdvr9/AMDIaJoxBJSkZ1fFA//A9JLwurSaugy4Fk5MpLRVXk+sFtPoljJsjjyoGf4h46PJyKpktfqyb+3rtaW3uA9HzfbP5iVA+Ri9TQC8z8aC/hh6m3+NmyhUrfgIKTtDJDC2t5PGQTHuaZuvFGDmcGuDQYI23GXI4RXLkCXI4tyXMPCBHvQB6lCp2mwHTNuvlY9hzt+Ob3a6+XYaP6+pu1tQfLuF9ePPhUspitty3vd+NrQ4ETFlZCstGF0aNgYURkhrKf/01tWZ4MmEia0pgYjRXvUtN1oktMIdrHXfp0ZCbiFJdGjVYFgWhFMIU3zY74ApjxfqSmWK/qrazZrPFV7y4n4VvnvHw5t1s3n2lY7QyzUtNVJbWBL8eggKSngJSWWqrJxxk1XoFPLVYZnWpjIwnRoF2NIMkaPir3t1XOxQuporqN1iTEyf43i54Wz3s6221CEy6vnfMOeLGZrtyH5YzpCoSIdQvwcO2rufL/aLC7pyH7lyAZDnZ3a8Xs+1jmi++WWLFN9BVF1c4ggFFSfYuXl1OqdJpQYSxie0G0K88bXUTiJzPwKwul6BIm7Wntg5fZvMtqNtgKbvqAzKiWueWwdOW82YK+jhtqu1qV3oWqtKCrZ1SXkoRtvyHelUvZ1vYI8OLF9HMvLit18g9T/Rm9X5ZreAL9eLc+Peg8P53HchtZvXSv1pt1ptVPQt/3dV+GUnvYUVpDJ8A30oFWuhIu084DVUy45yGblsld1CUlIFuHlr9RCShoxv5gPar/eMxs6sKB6Jpo4V7DdaYJjRNqJJKFvk6cAeunVWRhvOSWj4Bm8JA6VzD7xNcgB6cALcoLQUJK/w5RTLqNmo4A097xIch/YyNGBH8+pgghRkwmzbakRFK4h0JhKiIVSUDze7ZpANJEUtvMiYSLKNAOUoNkFrTQ4q9sF2RXbxNEvAFTW6hKrXCZTD+/76BcnQDrx7OUJJob6lwLD17cy9eX188XHReT4LZAgctSCmEmMxXFzdvyWQB396AjxKgWB9dyxWoD7DXKHheTn64+D4FesE5lRoYqMDgMC48Nc/mq+MEYg5BOeJaE7b+ZZpDRhrrN1K2nEK81nk4WkqQKUV0Sbj2Xu5LRFxocQHqtgaWFIvqbltVo2hcUGdKosGaDGA8dJLAFB13euGnDE6MgD9wHrYCfK3mNTinQCC2CU2ystptKVBnrIwnep6+qicorIpf94RQRx9O8YkZViphhuRLOhpvGPTCPO50dWomy0Cij2caZy8nolSEP4O9WUI4I6XWxyt2WI4A4G2FY+8xQpCEgChID1GQDh6ENh/v0winnVrCNpsh45pRXywyC4WvSdgAaxBujggieYCLAG7nEc7vFQbDDq81eUzW5KCl6bb7XLr83nxx6X7oi4Cz7i9pETBXiNlg1uq3Zjvzjxi+mV74Bu28wcijwa8RBboYBBcXgN/jvO3e4J4++nGr/Mq/zTgGqVpMjKKbRmN1KlAh4KNMBJ+SKibApMOch1YBzb3EH+V/dPvzyr+s0y6RwFN/vg9plGmJ7s83Dg5/zguECsh7WSUFCuSy5LBHU2ZLo6mfbJ2Ex1J4RpkAH5N7ASErc4xqWzmBnHAJ3SV85eG1ZiThh6l2HaVkED+qMUeM+RpOYz8co6XDYGBXlH4aYtqmzDnwqJcUyWDG2MBoAN2iD7K2CQLAJTJshEDeNVqFRkA8NALM53NaMOYUxrYxjUNBVRI0j8FgMh6sP6MFuBNL/TYthRJG6bX6U3pKxlQfm4D8M9SD14mZOWgSOy1G0MpGYpRX6xNYMW8VwbVIzXsy+VVGi8zzZz8ScdC10lJxlohDqCb5mIgfBjtTxPlBLJzZgcGnBmZhcVgcS/YVTXpuQEaA/j5X/PCQNpaATvqKUScUg/nwHpVXRcuI6ZUluBkXi4hjkxrLBeyCieV5PAsKqBqQA8iTKQF/e/QAkGM7Dp8BQRHB4l6foKv9ZDYD9R8O7XGT9rgJ3KXzve5Z+0QL/C6r9V1zPwoxOQQ3w6FP0YNY3KoBg1xyXRbXPjesiof9pumw/oksNjUyHixlacDTSn7a0kTZbHC7INPR0HXg0Wzt6aw9idVdtfUvAhYClESLLinVMVV5YISprbBQKgEmCCsdcnU66CbtUpCSgKgS2wqEy1M29ftlPa89SAq47F2L1QKCwg3dNEDA3P/d5XzhGSKpVBxyiH/RKsPCCQA2HpKvi0zG9pCyVaj8Mb11m3Vsdi0sRJ6sZx0pLdLt6A4JbPz31sO/bbzCHkr0AtoOXX+oEYA6rBoCXdJjJPhLzJeE0wDHuRG5MmhtadRpkZGXbhIInSyzUR+HtiioP2wDyMm9/9MvmiKod7+rza4ZPTKA8E/wwQpOEENxBWAp+p3UqAUC8tGG9jtcLZIGvJifmp0BkNYgXAleBFmgKMw6JcwUxWZM5QkrwbVEY391iiAOcM7weHUulGTFl+/fLx+jzD8Hb2xUzLvW519gE/DMcrK9m3TP//zmAkGfDC38EWjPqcS0aHBXJN7Pj3UThMMfc23r2a03yPBqO6t31cI/Nxv/W836EuWVYnB6ITR4TBqO4YIx9+HlurUDp9ckUktynBPFtQvi7ipQ3hDJtcvg5/ErOXgv6Gytw2NdLRc7vz6mYFESwIsoqeZR+HCMxz4/huv1GyhFCmmBIGGGLXWMEPVWLh6KktYAjxwU+jplqVG7nnc4Qc87oFBt2PqZDygAmnIrEwcUGBxpafGskLXbqlJp76kEpWEIXrt2/0qwiJSSYeTDQdxodOof41k8uroimWiNSMzzExPY95CO1hTF0IjIdhYc8jY1Ikb7sPZe42+SSEXCfp0sV4BA3bBBTiAr0d2G5k+cZBYBZyDvzdTCo8f5qjN0U05FaSh3CmpMWKfHv7nTRvfeAan9JfoFREf+AJsWwQLM2u7Vb95dLJPuWylXtzFlIPr8f5bHIQh1P2C68iYlVxYApvOwWXGtiy4Veq6+PCaDdFxYXxKrpGwT88cxKZEsUwcTdKyq3IL66P7SVCZXgHnVXrO8XKeqhQAoKKX+y/oXycEUS0YsuhEGZFI+PNEaHAu6RxfjK5G3jxjoGGcfNdVRQDTIch3yiYNEQBpN+sQgWE0u+VnpQxtNPkja4ZkZuHwXu2WXMoUGkluv9WEz3iTXYhHzMPZuLGU3OJp59fLly1cfkqPBnt6FDzwiB9SGooMiKoSa3yUSFaa0SLMphbJRogLcyaEVuMdE32598ZZKh1qZSw0+PUESFxI4tNxrdJXkTrqQDiCehhgAZhLsSDeeY+IixsO4Amw8KriIHXtMW1YEkhYRHp1L6A2bc/csMXfWvIZmJ1IS9qzkp/3E5OfxNoOCSUA3oK6dGRNJ0AXyILkHXWFL/53SRIL1DJFR+Xk0iRgn+DAbJyYA8G271A8ZYrSS5xBjUA/71DwppQlAE1r0TdkfURMBQifBbPGzNFbmC5YcO4ArEPYbOlJeYUpFsDRCtmUjDwk9ORLz7NF1imDrbFrCxFDCx2wMTYyFIJKlxnqKmRxsLFaHOPkh1pysEcqx4ZzzHlpyaiM7kCnUEFxKL126Q9K9ckgQc05g49rD6L932WQIgRIpEqtddVfUh6aL3DSP4sj7ZOECGzOw+YpV0DwObj4iw4X0TLXll5hBXdTztlpV+TQf/K6q1SWzLgdoIRrY3dfvQxnnax82uMN9aLirltW8cekR99d8sw7PWJDYDdrPjQg8iOMDwj4xwdNbtxCkhJAoHh7IcnUK2/p231SjFcLCylJKPiSPjBv9GApgdVpUqPIZocDB/wwEIlcM0wO2OVmRHEIypLi/5hdt6qx3bYBZjEpCNh6DwVm9rtd3IBrE59Ddw66+W+OT8DUV0bdttasX+9ly9GBD+pPTaLY8Hujx+dTVAqZMacCORyPP3oGsjtJjMWUonkVPPx2RPetABKAHVKGqIsP80UEV+LjZN6CIStni9QNwcdomFvFbe4rktqCpl0v/eD/D2N6rbXfGIXp7aqgD8D7NXkPP9dylxrvjlF2NJcw7/86nL1WvLgX0fn+7q5r2eeVMhcXjpF1o0KvyPgzDjKdqswzEuQ9gUHqZCXU4cVBxXUoYJ1TtqDihoTCRq9x9hdDu5IUFpmAfAxdydda6CEU52+putl0AA5buNKmt5U5P4qtl0hU/RRkwFl4xQMfInGq6EhAqg9tSJYdZKJPF3zar9/vGaRzXQavwYX3JaXfxYJeseQKs6YoR4NcGIPMjZpC3dTNS45OK0bl2pwtxjE6LV7OkmIMtxFLnQ9uQerlJ29MkTiOuHLynV7NMnQ6G6b1mb5+QeEBHI5+ReAjZ+Nf7ZXtWtqjxMhKABO9z6u4CwKGKvkvD9e5bNf7NaP08+AygEZPwLGDv3JUr4Zeie/uT9A8ks2mSmUlvgLBpJzKUQ1SOOaVf0l1m6YD0l/N3TKEDNcfLzIshi1eUSt+AKaTSp29Gch54pI/pG+aSPcEtcwHGXhWv9plcioSm/R65vJAP4WScRDiqZR2vfIpjE+YyAhCb6LYAsHbkpiJ0SmWvpHIAMZKbBksTSuCPsWokLA88St8EozzXKQmwdAnAsh8a1EluZLHS/mkVwtmzi1xyk0nA3jSKPZ6gqLOnUdfbzEH1uywNREC94fOZoM+hps5E/CV/1Yrkc8GZyExZ6Q8zqclEZlTo0ujgt7++NDLUtTK8lIZXmboyWn+cyQCLLJcbjG4+Bq99i388+o8J2+2cLW+vrMHDcgPOv27uQ3UHA+ixrVt7PbAn3aEzuHPYBCpVSVQ4FP4unf9GA+TrH82nmIAs2AdcDl5ExJzLZbW6qAgvldIBux+TEFiL+OoHF+nCLqpjZ30WoH6e4uZ5IajbjyEv6EiBjsBrE6hWEosaWrSevkvDfSq6I/+qPkWQ1O78LiIIIYak4ZopCmmvjmcc1FIOeIXHJOam1ngjWUdd/A1GnJXQIP/w4GvPx4pZ8K41gr4nzE6xwjKe/d12s/IzJhFVuIvQT0hA0+XGxyy/7t/7F/4CLjzsVrOlx2i4mE3jasPw0d+zwWlc2TwaDGJDYNFft69x2qyHlR4UL0uYRG2Tyd/gfJqZZcc3WturpO7q7ZmV/t2Fz91mv51nEWi9fkIRiU7lZEKZO4QqSTEB2eao/AyLCQLM+jKZoqeGRSn6N+lqUqP5eSn6VToIG5wL61xw8nuCSLAg1EZpylW6Ft8iWO01e5tyGLAWZiP8mUmu2/b4UH2m1CGVpf186fbf0+l2aXgKtT2/hPi8LDL/xCyyOc4idyJ/FGhriA4omn387xja3Hqcunx9ffEf8rmB5gplbmRzdHJlYW0KZW5kb2JqCjM4NCAwIG9iago8PAovTGVuZ3RoIDQyMTkgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjazVvdb+PGEX/3XyGgLzR6YveTu8whD770EvSQAm1yDwUcA6Ul2uZFomSSuov713dmZ0lx6aVkX1KgTyKX+zE7Ox+/mR2xxf2CLX64YP733ceLv3wvxYKzNGc5X3y8Wxie6jxbGJanTEHLenGdXF0Km3SXSylVUuCPTNqqvt+U9NwV1Wa52l0uodeuWVd10fkv5W97aq3Lmua4+fgBVtTjFa/dvFv/KSDmOnlDE1VdSw8/Foemn8uN8+uWvzCerarRJxjU0pzaLEyaG+am5JalSpnFklvYaE4b9IvDvkeLL1XKpVosZSqMpH7/ikzIUilhOiFTyS112/tubNTtOvmWxXbPTSpkvuAizbXu2f18FZtyK4DoNLOe5g8xhunUGmg49vqF6eiyA5FThsMAHh9wFdmVQB7B7llquepXFFlkA0B6jjsQKeP6zA7kQqRWnN1BLzI2lCYhBAxi8UHu66u3/kjN2XjrSqRcZQuVajbsvF802PmR1eHB81Qqi1I2TPBNjC6eMgHKyhZ8AUebyRzkDFizhf5/P2y6ar95AnHPVNI9lC3qXaaTu92hoUbQvIhqwgdUTJPsNk/1blsVm5YGFvV6mKymphH/A0ZzT5TMWCqYDikb8X9uMzKzaZaHw/jxCAJeD2sZUFrLF5lCpbQ06FOU5x8i01wvc9V3nxwGS7UxqOu59nJXR6bNUo1HNtKvv8VmU6nJAy18jMylU5HBjo4LfopPJVFrRt3eR60D0zJK1xz3lQCDY7PpqcVVjQ+DDHRW4aDHcytJAbx9Jh+ap5dLrfLk41Fsi6YcpI9m5ZmJEaLBamJTMOeX4qmN0QJPdqRAQqdw0m5E0TQF+osnlH4uvfRzdCrOj3y+FDmoyKGrdnVLX3Z3JwnjOQi1DRZpQRdX5RnChMxSmZtgYFXTkld7cqJlva5+w3kucAx4Mb1o7hfD808/XKArhQHvfCfy8TBTStsDTtOMzQ62VJftya1II53FHpPUFc192Z3ZiwLLloFnDTgNnD21GAoJKvR4zH3pVPDi/ceLx4uho2ZgdolROZiP1fbi+oYt1vDxA7pioOKL67oF1VIyg6fN4ueLfxLUmayutARzIodV1y89LpWD8QbVC45ru9+U2xHOGTZnWGoEaA34eCF4qGonzjLhdnqQzjw6bEOiKlFVLnlSeiz0tNqUy86JtJcZj8McVYctvPKkKToYAiKdOipH27I2zYFlGoXYeEKvNhuYQ+AwnOMLvQSaAx6VNBc+tE/bbdk11YraK1ippsfuwXfxzgmbADhS2+dLbWCSqrjdlC1iPqHhJIaBT9SrLss1te3qjW+7HfYJzW23a6gLkNhR27pcNWWBeHW6XbAG2uqUC0FbrXd1Xd4XXfX50k/JuIewuYewucOWTCTUZQXrtW9JqYrmtuqaonmiDuBty6alOYg70LjZ0Vy/4kyOUGg87On3Fhv96HbXgIbeIydYnnypugdQWfpU+B7VPRzgUimWfLy0OtlR89YdtIcFgRDCZhHbI5TFzd664/MA/qEXoGbXeqB9V7jNUXtVx6EWdvwuCqaW2hJ2n0joPFovJgHGQ3U3Gy5I4H+cIDgIgcjGJndxkDeDJd/Gd8GzLOGvHGGAghhxyX0MQOcAFqwZ4wAVm9imEqzdGAdQdINxDAcbhMgakaR9BprtFOTGkVkAvgJwAUhBgfka4dQeGkkAMbkxGcEZLh004sxbUh7HM5nGbaCzG0OjAKcRNHoJso5RAu7HCB2QImI7BlJUHtCCXAP7iiHCn+ejhxjFwqYinyM5DAN5FC9xh2OWwQTR01CpUPKPO4wJB8CWCZTgGPiVgLm542sGToKcZWwvBuJrtRj1+pYU8k9xgbyLq9e/4+qFE30zczbG9GYh5JgCk+/oNr2Tj06eQTeg7djr27l17LDxuIZP93I2rqOciwZN09rDiSt0m7kG07vcb4pVSW/tl0uI2spy33+k35GPc+/esbov903pB0OXdUuPh3q7cwHgugJTrJwrgmbwT9BGazTLYCx9acHlZOByrjw5h9u2fDygHTdT0AP+BnNIzEdpRxKXz7NCZPFHe4PXkSerSu+UbrGHd1r8a6P/x7hkySxx/lQDTq7aIYMUOkiPnKNYlhwTudCogSW8NTp3j7dUrgH0eT4RSIdtrhxWA9e5a/GppdajW4aXfQOhU/PZsQ1bbA+/nui7Ew88mQf/uYdeZoBeqDJaDdALOSANIgruEAXO/BugmlVHLwXN8/MK13xwJ9RQ04rkaeTZYZE30x2DpGdgkhT4LOCLj9t6+iHwq70AupdiP85t+LZNVzZ10ZGoQwMx5FSCQmjUaRUuenVGJUUGQNxOBiHSs/JIDOVQymaLRB+6AuNE+oLorKVHz4rSJ2QczHE7qnyHtiv3CJWMQmj+0HfsHgo/GaEvpI7po3apDDGBj8tIPgk3OmWWHmyClv162LtzVSr5x6E7Fd9zm6eWZ8HUvzApZng1UINRrwIXMh7njL5xLlWeWlNwXDMY+vbcekIgjMiDQeLkIhKjxteuItFxhKvwk6sokBnz2lWUdq5/PIih739Dp+gDPZlsypNnJ+AMLLi58Tx/j+W/mQt5x178ExiZt8U5QqXAnBuPHfL1KcKkNM8Ie4rmw4zMxzj4PEUK4lQRUnRzMlUJliDLQvn+Lu4oIHz5IcY9EAltJ9w7R6bBt3BVf7gY2F36eEZnYNwOXo//Q5H6zstA53/phwy+M3vBLIJYNnbBSivQLh/KT4LbPhwzQ2Z48H/D9Q0ZfnhsBsMML/uGTMv6sCrXg2e2/bE+94vKJO3q4dA8z+egdIBkAaE6BRb/rnSOBP0D/DvsGU+n2X2ZcfejC6Lw8JdccjRJsENwAR4L/hTNugrwEM806Rcm4hgfqMvzMTCev9qYxyiUAkIM6uDpQoJQAU3SjnB+yGKhTapBFASgMf2MxcjUnsWg4czykMchZf1cHIxNfzv41xmkbr9+f5HbODiMHJ44GBNl5m/jliK1MBCv47Jc9myWPHobAMt5t8GBwHGQ9nMsrjROHsdRlYrfYGFiEiyFGsKk+3qeFUfqTnJjOixm2xV3bviEROoJA3Qf6E7i6qPXD1k2H4m7bRDJccAAim5y9YeI/8uDrOslZzaXFNnn/WBMAgKfAkgqc5aybADhhPzxJm2DQZGF0Mc3NLvtKR/DQUNyrsL5rmbyyQMRPMvAlU1GnbuO4YalVk8GPZ1dymQQbT5fCq2KZfGg93i9AWAl09EVJxkcANwjX35KbERM3bl01+IqFcJr8T9i7hgCZ5eSAMFnZqrtz9ISSDlMqAetfCYMQuUAFOXrzkFomUpwtpNBcTKC1XTurlieH8Wp1TJgChfRM5gCqskZnNHQgDQDpMnJSTt8YJP3l9xjEBdoglIUq1W594GHy0pwiPd/G97GoEQxsI9AYJTDJ29D8uhtyPQEFToKUM1g/ofCa+/zFD80+ixEU9GNmANvozsCiQrTXzJ6OM6SbtcVG3r0eZKTRkFSlnc81ac5fyBmUhtwdJ/I9UMfNQc77RDM4b08D9cU5i3R7FID8Lsvmq5aHTZFQ+/l+GBZMi4duMQ7Fccy7hMjzCNTlmx3rX8SxkkJRxTprj2wV//Ac2Z7ngW3MmGRg8xEmvs8GKYslhOcClNouj1CMjSn3cCvD6rhY1PU94hnNWfJ+8dDQVe4Y0EUuQFXZILFXiSHclYOx9UNAk4FDclodn8JegxHlACp4K+nIHsRBeBSmM2C2duHAcY7brnj0/3dHDRhMrB2cg2tFelFee9SEtitbEnJNe+vH0axhgTbqfr7zX1Trqq23DxFcIfzuBCC5qlU3hZn0QIssGCZeEEBlhZUgKU0D2KyCdqBmO7beFWKBmfD0eewHu9fnwQm0zDAp/79Zt5FwabK570gqDUQdxujTXDnW5aj+W/itP0Ud5CSy1fFKPaPjFBcesAIFjtehUkcDeyz0kxRQrgaePhRr3/OVm+xLE7r7demit/NDDSSBrpOX2fHZ+4U3Y3jO7Rb/cvcUU3h8Cs3J5PvZwCztF/FS/m1vBT/Q14ax0vWv7yQl5PIE5SIQeAeCio/L6gzEYmShqrqBBvs9lLmOZgxNHYsNcYr+pCIqXdDclliTV9wKyGTprp/GJXEtj4v7W4Q3P0OXcDTJQLmb9Dp90VNOL5PO7tJC7/MfbHvc1NDrUPqGcTRsuRaC18BC6H2UuSAS72dzlIF63HRJ5Ngx75yF8BDRw1fqnq9u8T81cuuxzBTanXWR1TuxmmpWN5DMQWunq41sDEAZTPhk0gZQORg3u4MLRg9MdhlMOgN3powOg9c+3mVA9Da0jcHIPEBdl99rtaHwhP8/rDBM8BdtIftFvr5Ebu7kzV5MkvtZBM/RjyBcZf4L0lcDoV7AFoFvAVTPxSX7s7IoedTZEH8YCA0CwafLRW0CuidnPSyPex9sWrTEX8mIQW4RyFFKplHOdevrXyYMXXdiRIPPTMkvsCNv3/52N/TlL5UDGUY64OexkU3Ppe7XrfjyhgxgGyZCO4nvKIvo7qdDq+jqBH0+7D1CDteRdPO5UaH2ix4OtRD9b0rymmpeU1pkTpSVSW4TU1/oTkUGnFDO+GWdnJKNYUGldfhROeKOzmaTgh/gkFvqAjsy0Pl/q0Aa2NJmtsDPB9r2jjelu4Pnj6KF6HtvBUREGbJ6apd3FlxF7rNOUk+qiuY26NkWNxiwtXeUr0cuQOgfpAi2AA/XbqtUj7h8mn6Hs/RZ6WD0sGUXpKn5++edjXdOmSgG1j/dvAnU7iyQ25GJaeBvjOA7P21bF/cCG5s572YuzI/wElv8QGMKWYkfDHbpvJu05sRCJJdgAHIJ9UqD/H//40N8VVJ7keIm1fkQMe6SblOjr8w9lhwIHLWV766Z6cAENa4AoReSVwnMsY5GWNswdgbf69P3sFz/AOLDFeeFbUZrgX10lgyocP5BL8hUpyPxYchnB2RbnvS+ctIx7A21/wPI11mJhXgikPSxY2r9WSgAENOZKinVq6APhgAhmlGSy2P/kVAZKm05vx/BMZVzgqU2E4oxZjOyoRrYuHA6pP/EQGs69LbEx4OSaxIhdWnuYpTPZv9GpGupUwtmxxa9qzg2SNLrq1LlfiCZ19h4mA20+joHJpr6RWwOD6oZIVq8YB5JvrQZwTZUH8FnYYaKmgdylRmLvAB9GAB2piYxxf4Aw42Kxi0HEu6s66i/wvfqXv6DMxfpsKpurmgzb7kFKQFhD2lThjiBll4dvzfEtOBdWnpq6+ddu6tKe92TTn1BVyBfvQX6/6fG3AAdX813aNFI91/Rn6fmeeZeHU5MNfmZo6PZjYbT1z+CgLt6wnM+M0rBoVgj+OXTIwLkeAA+txhQ5X9G1+eNtTn58dyd+xHw3KsLLh3Lsf5bSe7efI3JwtYY7ap9nuq/ELhJlFBt0+6ihUJVbsqmrXv3/9LwCb/KV0thSOpr6tzS7T9Gn6CIB8uCIdM5C2HUIr7KnpE2x4bQ5jmy93LviTCqeChXg/4flXUq3KzGWJ5h+1n/xJ7OycWpi+pFdnJ3MevM/Aegsp+AuPJaPpUQnlUHZW0JfCj6DCNaxXVLx7TzkJYUCS9WGrt/gxD9Qdhbvr9x4v/AhoxEo4KZW5kc3RyZWFtCmVuZG9iagozOTUgMCBvYmoKPDwKL0xlbmd0aCA0MDAwICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42s1aS3PjNhK++1foSNVaDPEGMskhqWS2sods7Y5rLzM50BItc6JXSGps59dvN9CgCJqSZlJJVS4iBeLR6OfXDRSz9ayY/fOmoOf3dzdfvRV8xorcFY7N7h5m8GbhWzFjM2tzJ/TMFC4vJHzczt5n7+rdspr/cvcvGKeG4xgNYkznxpp01A804txKjOuccZYO+lCoooAfNl9wK7Jv8SEzdjtfCFFkddfCCzdZvZtzm32ac5dVTVtho81q+liGxwHHZnvs+IQ/VRPa26qpK+r6VHePYfBuzqBveK3WZVeHyUNL9UyT7Sq/cNfm84WULnvXlU1X79Zhtodmvw1vrwYQL+wU9wyfaady6XjkQVFM8Y7lRcGjmIpcWpWOY9p6PvGsrZ/Dy8O+8Xsvm1VoOJRtC5u/JEsncmFlOnUZRigQce5M4Qeo3Gg9W7Bcg77craDXbmqXNtfazAa9PhSymCLg/YIbO7kSc7kpppc6yyAuVA6TJLv4x6V9c6mBpeIL9m2MSYjRxaDXUIzJ8PfZ41XatcuFGmnFbS/Ri7twPDfApGTo1QVFwXOwuXQU2p4xGbu0nGBouy4d+Ga+YNpkX+NDh4eJjzdXSZEi525EiqbNr+pP9aoK7/eo1y+XiJOFyAtt06l+uEaABF9WiBHzq+dy2W1e5uCSvOWL7O6R6AD7NtkRvMcWGljWlB16EvQ1QqBLguUSS7csd1KEaX+vmn3oWN6fvNUnnLEK7Uwzv6DMfnw+lLu23u/wg8xwlcdmf1w/hv9SmjCiPX4omF56DwftOC/MtiyP7bQLNyYvuJstuMxVYYNCfz9lyGiffOicxtbLRfZxvlAG1jwz3MJwLs8Zvzm3LrrgwUD+eloxOednEjIxH3Jzcsavz6gPMwMRC547roOIf/T60Z1CThB0taHAdCEuMMlyqV0634Xg0NPApMudkulAiA4hmJUNRTXUvjcUr4LONS/UZQn601Yriof7XR8SbQyJZdOUOIZG1LtV9UyxuaUeXXhu9y29eRI4s+CI4eGCznITwjw8NFO3wV7y3hwNOP0C/Dg4GoH+FreCURsQwEIylXVoh5JJGKxDS7kB3qLSL8sOt4BtVZQCWI03Uwzg2rns5wrmQmSAvVbwFTxMMDJs2O2b0SrtY/3QXfSI1uXM6JTg3ybDCNfQHQTlgtGh7dheZcfC7d1TAfaqRvMvSyL4vvFgBCktaU91u4ToHxghs8d67aOBB2J6oLOFyi230duN0Qu4Epet9giJnnbhX7cPT0RbwmZN9duxaj3DsRWGot9SPDpKlx2a/bopt6H7ch8RHAIzD+v6OVG57uuuKZuXxaGplnX0es7jPujmRbgG5Be6k0lBaKSlUFwjv6sgMHLjwgZX1W6/rXdlF/00wSLvMcvdKjQeqUngfNvw8SGOAC0LL+Bt5a6kPy0S5oOBVzLqGidceeW6rzd1F4JXmDwoJGLKKJgUiytjIaYHyt8C1RsMRCq7DZrCEF1NOA9uclHIZHBz9Ejl5se7m99uTl5GgcIK39FBIFhub97/UsxW8BGJEUDNk++6BWWVAnVmM3t385+QR4xXlwD3CtevinrtbfVKKmAk2EVKLLIqwH2RVeUSGfYY/u0fwtMbpeCQHrjCUlNZb2jMHnKW2+CRQNnarjku43T9vG0VFHAX/jbBJ7d+mCa5wfybkBuA5u0PcfZB/hE6TemcBtZaymzAEA+bMsgd11wFhWrK3TqqGWxsMjTit/vpYBUV9uy476fHqSIjXPEWQU2v1YEo1Onn6sKs94TxIIqyM4TpgFijmODFW/xxO6cgaALrYDkvULTCYMfNtu7IkaCIQHqraKXkKWQPrGzIIUE2uXUqKNwXggE+BDVOD79y0HjQbwUJEY/Jiyim5jG5Aj++sB5IkjufpGLAMfuKYx9fmyfHeKJmSsDUTIytE+0xWidjuQW0kJhnKhVMDgQEIQW+wRZknnxq48BN6Mc5zKj6jbPp9M5YC9lQwqPzEJEPGKCG+RKgbrC5JMeb1CwD3EjSScQSMC37QryWwgsF4U84stS7xwBhXBafB6+ue3BIdQ/OMS4GAL4/dqEbOSSX3YNJonlBKHp33G6pQADzUccBzG/i0IBM/Ht0Yi66IJdxfQkpSmZzDBTDXVxC2vY8MjZnkfGAaxIAJgNFTxYE1BloXRNQpD0oyN2VBug38o7SgcJK3nvHrt4tu5OhA6/Bbb/pfUiI7TLIBL0JZU6ritwGIFAsUwWPsgsfnx7rPnIk81ISFblOPq9pIwEnrOIjvCrX62rlA3RfR/vqLYSqUTwTg+1hEh/BN4TtGuO8cAXIHbLGqqlxQYCoAdtAO3iy8QKMsVQ+UisCVFpmyznAq/32sKk6ajk0vgl/1v7VbwHGIFrGDt8dDtgMvKqfaa0C3YiaNetZ//7ff954fyRD9OgJosYlEACTLH9tz7l+lQh9Yh8n8j05PmOtq5337vApkts9kp8fYAWwWKaAvUqFAszWu3bca7A5LbJ/YzyRHrW31e0ZIqXLIPdlQp4jESYoPROrMBukS3MlfJEQP3nLlBqzI+qQ9IZG6OyzpgqVGL98CgmWRHl0nodrb/++YdLketAPsEqydNNYKr0iw4wXIwFmvrY6teMBi6VgueEyXc7bnVQRxG5on/cv5/irMsOLaeZmJKi7MWZiOVcC3Atg9ZiMoCID/Doe8FHhDzJeGdQfbA9KA5lTeTh1KZvQ5hUJ+t7NDeA5JPqa0kNnPuZZTiESwXIOMueBXwawAewT0jAKfJDNMsbVydoNKgUkmNuqq5e3oaGpDr7oA/sARArWT+1BoeAF8M5yc/Q5T78sgzRRhWV9pcYoRkvmjBb9CTPwEGmGs53WD/89RwxltH45SL+iL6Km6P2mQbsbpOYKYIPQPFeMggA6GPLGn+FhAPJ898rDQOMGooF30SaiRw1s6T0d/Bs4aI2J2iq8UXpmsv/NrRw52c8gBuD6WPZvwsQ+csPzsWzDSwg/WOnvqobyP52tyq6kAbvDsfMQ22Y/de2rsgafCciVnCa+9ZEpFBX3kLE34bWpylUbi/eEf0X20GBOQgFqU4a4xSmB5jGYUQr+08PFikUhc2XUiJ7JioW0LkFoH68UKwTYi5Q6nTr6rfM1ZcCUEFqTQcdrK3EF8MS+WokFfizBT/gK0ICTJyDAz2RebOAPpdJfRpGUzCfPyaA6UEFSKklqvkJ2YX0FEjJupDHXeK+YAGirXqtZeztGYkKAaBkhsT5dOzSVzwMJKoH3PIGlwE+bdX2BZN/Sp6f4etJLACYwtFytYtKLZAwL5NNJ5jdzD1V/PRNgeEwwL5wEcJ1DVpLUtn+e0GxQOMhzrmq29DlOcpQ1rcqgHZQgnyOekXLiBr89D9OniEV5grD+SmrHWYESZ+vOIaljtBsupg/ZQq8pp6JFziF4TOwmPU7D2ujgRC09apvMFCEl5cXnMuBzagNTW/p4prxiLyz2B7ksApdPAO6zct0UOWOuyy2k2k7HXLeKp+JUZ+E6ayFnpcwTPhzvu6ak2pmOOa6Jxg1NZM2DA3Dty6OXfDxYpFYjWj5eoR9sPedgp8kgqlS3wZvTsfzPo+MKg4CQZetjOKMLReQ2FgeP4T91pH3+jh3xeO514OaGQ+JKsPhM8S1u1IETNjYdcpjQ4UFspUPl4op/Z4XIrXPp1Mn1CefPVSCz9EeIWmfvSJQBJSK4wmj0hD+EZoY+34yipsFa5a8pLDOQ5zx3NJacOvDwoauaUb9yg0DmJfyJ9UX/p92CslRNn+epAaeVy7mjRGBX4dkFj+X+fePRDQMEGaZ53Df9sgmcwr8x9pgYLfw7noDRhgjY9RTbrC231WnftNhbrLzvx5vDis2iqdquqZddXAgjpq9AC9sXF8a5LGxRQo4l+1wH85wY1Fw8IahjmPXH0AEzxNiKsbepqkGB9DbE2Gi3fQGlbuLIZl21g0pLRJQRTp6qIh4ELKsVKQytdfv6nCQZSBbD3GCXgufMuYgwYJJF9Bq6Zy+gZnJHGjhRR2dUb2sgefMSPwCLyEbvwwHkxesQOlfCpes/natuuQEi1lKko67C1gJxlk4Hba8uBWBXSflqKZYcQeje9IYe+OCdWq/n3pc1oIVUrdyR2HDIPjzr7tV9BA4EYI03rDsogeGf3oOCuNflgU6oBk6ITqh4AZlTwQYhRRb9QaztD2ILNXAwvsfpEBn/ehuUBcr8d5oD9ghm6O0aP0CiW+DJrWE+qfJdOjww8BkY9ngikllG3qIL7bsqkrDbd+GlPR4Om5fwuYym6QaOnjnIcBXh4jT/PO5Sc9cnSEwZIx0Q6lOAac9kqxrUBE9GNl192NRLkGdFXcnEenPAvBJEDpJdxi6P5ekcNbQEDpx27yeiZ8w0RhGNGTq4wI1aJ6ML8Rv0lucv3PgzG7y4gEEj5JuPMYc5SSF4L6+Cx92qbF7mlmfpYTBPAg3PYlS2/UUJPItqquHCOFm16u/+9IafHlz0KZvSuQR7wgMiK9z47kQae2cLAYKPpw5c+ZLgDECGYniko+NRiZpcsciVhRaeK+f6A5BJiGZzJvyMNgb5b64lkQ7GWJkKiE8dn/SpovDhhOE1o5j1aXE1V+S5YaNlBiJ7HbaYhsxe0VEtRJkjSh6A2Sm64J8lyhXsBnR242WM5uN7VeHsFM85uDVkE/gF61P4bOvnW0QvACr3x4ZevT3gV4p5+PpprjRWuTDatqEJbcL1NoEt5BVhxWW82YlqjUszziGdKmgFrllh+O3UjhWyTp5Qnw/y2lCNwQ5qCpFzuOM+ltph4LXTkGFD59GbBJNYoFELJixVnE4QFo2FEAZRZE+gJPoMe8I2WjOmncnPXLJhwoDOq96Nt/7SCwvB5xYjNM/2YLairbr4368rWYEWvaq8wKu2DfdmCrwWFFwVToMG34XXtl7vwpUUlgm+uK+pPdzr7Sq824F+3lpf1wwRjTbO8PqGh6XeecMoPD/Gp6/3jwMc44CTC8oWqqAtm2PZxVrEqZRB5YTlvmxiMePketrR/Y++TNLDL5mECHESZkRk/RCUzO3obki5i7epRLjGMJqEMFcT9udkrphJj7235fPkGTb3J+SjQ2z+oeB80itoyMd5cpLNlHd8mKMorJLI3OorHhFeR6fHXEx7RPCtyeHxm6kZYUX0ackWpsnHCVlCPXCxp16m1Is/gXr2F5NvnT5HPv9bkj9SHfk3UB33x4gP9xRmEBq5+QzFwUjNX5F+wc4KnYoaMvk/Q9Rg3ZMMY5BLyxHLvjl/BZZPX9JwZobFVEmMOINzMBFaDPp9PTrYThdc4E1XV9jZQrHcRBTFTDLox7ub/wPqBCXbCmVuZHN0cmVhbQplbmRvYmoKNDAxIDAgb2JqCjw8Ci9MZW5ndGggNDUxOCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqtW1mT47YRft9fobdwakcMcYN2NlW2K4dTWWcTby5v8sCROBp6JWpMUl6vf3260QBFcEBpx5UnUiCORnejj6+hYrVbFas/vCj888u3L379e8FXrMjLomSrt/crlhccvhUrtjIs14VZmaLMCwkfD6t32duHuq9v1kIU2eZ4am+4yQb6eWq7uh+6ZjPUW2r5gF/r6n3ofni8WUPLsW+G5tj2+c1aSpX9/sby7NhRp+HBT77rmu3tzX/f/gnoUxF9njihyry0ZUzdBz9iaUdC27woRTzoP4UqLq5keC55POZwbSHL8rK0TxZifpxNLmTLXDI1H8TllcVkAc0zTjBZEiObw+O+qXuaotSpdSWwsrAa5G1yzgVN8IZGKJB/XpqCBrBcMb1aw1OCYrzdQr8mxTqVMyBIwGyKeh0Ss5W5MhYmy7XQ8VwRd3QuDVtNen0CR1QhQGTqCUs+R57w7B7UDSaIdJ2vdKlyWXLqXHl9rJo9ve3rH2+4zer9qKidV9VqfPEn4XDsh0v85kw6qkwhc8FtkHPhtVAVEyap1VqAcD0budOfFc8ZA85ZOJ6CPojUejoXCjjHcwWa6FlXsCus40LnMHnEjGO3hd1uzyzp6fV4T8+++RlZwMuYBe6N2Vt8YVnV+gnqaoOG4YFGdPWhatqm3QW+dnW9fqy6gSQ0EQ8eKem187Gr/1Mw+RMOEtlD5QiSaHFARM3uAZ8DfSSS5EiSyLgp8iD/wvoVrM1L0DANFkKUklZ5fdoPDRyfTTPghB/9RrpmeDjUQ7Oh301Pz77ZtcQkljFu13fN4EwcR6NJzUPV7eqB3rt6c+q6ut3Ufjxxpd7gUu+pCXUOTGgXVt5sTgf8fNpXaEKpNWgziNNO2KVkbqXfyBEHwVwlzAV8U+73BxAMLwsSDDdlNtTIIW5s1rTYd6h3XbUf944ffF+btce2rXdAxY/R9zt4gASApJoattChB1KRE8B6MPbKGXv85ow9vvTHU7epHTksO7b7j5fMsdRwejiLN3iXshzv3ORL5mLCLmnBVEkbT8mV50Xvd1zX23qbpwyHZDnHI4rDfnfWbnBuDXnIujvU26Yaamo9YONUt4zTLeyPuiA1KS32JKWFFhZ8R2QeRpNSilxxUF+pcq78DlhxzUsV4NnAHUYb+AwkZUTWDDFFd+S7T+3W6bhr8WcC3puhv7q1lp7B3Z4Fag1QIWMqvv2YchncbWMFZpPs2V3a+ZSiXK3HTv+45iy4yBkv4/XPvlqn+K040AykRfyGk8UTRL/Lvr9GgbDOXUUUXIx+UOuABCM1df4+rf9C6Evukgk/XQnu1cp4Sn3rvZqz22gOQmzWnzZgtPqGjBN9HBV9X21GY45mvLkBs4fhHjU9VNiPXGnCa7q5Lqu6sDw3DPYvWG4Kz6ziJXvJX4qXV3cqBeiFiXdKB/bYwKacwS6yr1vv4Fq/PbJQ4exDDHQWAzhzVhqa6Zv6w+DMMmyvH+rHW3oNwQK8UrAA693XFB53oUvlx+lLISJYqdyYMl71glcfKQUlhWjMxAOv2BRwk1aBdWA2Z8rzSl5dyECIXch4oZdgPBhuzXOmax79tsEy9p5Loz3haLTBuZMewS8vBgs5BFmhbtu0ZE3Rj4E7Jp6ShuIKGJDM4weIkrX00dYGFHLoKtRaIMJnIm9d2HDqKUrw8R75Xjnx7iLph4WPA6T3gZgcsYyMJTRWd/1xfxrq/Ufq7DfraIT8QmiwWBwiNWCsM1oLooGQTguwfwo4TP2SNh5iGW3QBI790OrqBSfJ+aIK0dc0MUI4QztZJKkcBpQnpgW1AWbVk3xIPfXbv1mgBz7xBDUSpFvoiDXcJE0o+PqInM+uWGfQHaVUrqw38yFhvWCewRszq+Jhf7+yDCtYbvlsrVt0xkXQxZs1hI2gVR8prPzQbFHzpWTOueJzorT4E2IRGDKcYEy1p0EYfOGnB4ySWbYOc7rJ+2Z7onfsuqn2ez997+cb6Em2Gt9KcMk3azBJ2RfU4MLXhyoQ5EJznCwaGh1NSCYV8EryvBDenDPSEK8oHB8i/BLRN0nB2St6wGlwp8tzBl/vq81w7JyNwVN8TIoNbJYCMdMJZKQWXyZcuc25US5ltSX1+jp1powT5KTXkg8mdQYeJhQaPJQ1ZfKsx8Qj0qAjjf4NceO79JJCloFfqYMkSvCMMxtT/H8OEuFJstSwGB/xJAwLwf1DEkjBIyjggdqgaWggA/mZQk4XK1w4dRyyfYmkTxf47lq+y1H/4jHOX1MOg6u24VTRz82+eew9PS09kebHR8p9HJJVxl4AtU8KH0pJjKAOB/Jubr6q9/NN8lfwZv3chWGeIgNAs4GD5F1VRcpNqj7NesWY9crM8OJJ8uKTXqlL0LWZeQOCmPeocHw9dId0fTgzg0/SgovxKishrZ4t9O01yaA44bhFg9CeTLnCOXQqZjNvQZPWTJtJFjFzFZRF8NKfT+8ZyxnwBGZpPen1VWIuOqTgCVU81fKuSghfgdCI4FeX4CJRyNxAdiDBShn2KWiRB8rYGS0yhBb54EL939AiwTXkbvPNQB6vM6k56RCdi+P+Y3s8NOiGsHFXt3VXoWXG4Avcx1cQkdU/nGqKyAIEkOYIHgWNCe8TZphFZmx9D4H5oTHaKQJs2aA5ZwHNTDEV4lhkzLnTS6mJs2sgTbOItf7TnHI4eyDBBeZGWlcic2FGO4E7VTpKgqAx++u1qALynRISHQnxv7by0xBvBj6Ao45OB/10bSVIhRUeq9lKDI1fKdD1roUNJsUW2V0zKgT86uuqG5ET+H1/OcLitsxFIeLVfr569iRkNDNmePIi5AoJIEgDXjbVI704m2oDpFlCNDEcH32nu4B0wDvoACi1gjPwT0xNLuwCYvJcGxYT9FU6TMe8dBJHPNEFe9XV4bGRoMchykJ3dDmI1UBENGSbMjqwBY/jx6foSVBk9WrS6xNwfAapp5UspoHJ0kVzyosIfBumixjE9i7cJc+36Y5976GIc3I28YuTvuzyeeAlA5utYjK+uwquFZAIz1juChAG4esAjHpyprDZJKccnbxMePSqPWKkMqISYiJniB6V9Yue44Ii06zwuE4/NBjeI7rhdX9PsDB95heQAchtcl6AagiWq4CXntOtRfAFowCJJmJKXU55wRf3Qx0VAYEuOHm3s7og7di9BvvR+9LDc8swEhNYoEIK8GGcP8exajP3rB5rZDa5YgHb1WT+y6u+9YxVKbAO1ibZ9aYjKGR72gweWmvaM6+CSkw0AotOQo9IpWwrL/39LKpD1o5R3Yy3/aara8JBsHkHeV5/OSZXpUutouWXjs4ZeYKYnJfxIKIpGGiULyEwkzPj0buvqsdHjx2hDo1YnsNeoIm2Wr13+Fvrt+KrfP4IEDL3FOrHUqu0HtXa1N3QICth9RotvlJjfRyCz+NdsEh19+MNfKsCDgofN6Ek4yFAHdArrbNTey4Fmey9o3ZLfaLaE3St/HREsTvPk9ZxxsfuuOuqw698e/2TR+dn+YWwkAQJD5FBUoL7c4X6UF4Scd4gjMgF5rjY/y8+/2lfv0FeYukHgb6700AFLuMamnYz0GeH5UF/YKB2MEVTU9XJj66x88gyQTgf6bjxiKDrtgONRMErnn3hGED29cfmeOqnffwPVOWncsXbBFb4nXR1tV07dYFMyKVuJqRuYlq9RAjmxsGR79f9A4iGMjqBWuTOzyP9pPKkFKFKAllT1XUNpVBiptjQ4JMuAYnnTwO9nbdpZciZOabJiMk8BPjmAOzawLGei1VqSGLC3no4MrFPnKa72Nxu6yDLrY/H6QvVewOBbiTyKriqU0BTyQQyB00ojNLvgRSYEM66D5NNDskBY1wh1I6F0Xrn2GEUsgMa0Pb01HB/9F9oWexC0oA3r9gL9b1SnZkAKUluSp87f+02pH3x2avncKTGMXPf+nbPX3w/o8A61HjpQ1wU1rNwUoSzCF+qDgZ1VfdxDVq6GWMAnU0jAoP6v4HTQ7qBwt0mirzcFS69A6YCG8u+rTcugIDuL7BngfhGt1uN73/7wwsHAzFKVsfbQN67KHUu02Knih5/rk7dWRsQxCA8shnBQ2oeOrBfFV0CQgThGJ5DyP38pQrJvFr7ZQZ6hpInhCDmlvT8NBpSv8SDH9OfDvTiNBMn8TD7/AhwSFB1qKxFlqfxewpRlodUmoCttMfuMNYH6nPbeCLS4ThM9efUDRprcqFldOvl+zSCXzA7vfXS9KFwELQzVCPOpYQ5HWsmEIrUELwIMHc+8/xmARa0JuAQMQ5pQcUUon0jLSHSi6MciJA06CI8uPHh0L8SLICkmcHbmoPvYJ6kNrHuu+xVun6tHNgCwRTYFhr+LimEkA7OEFxlI3CzTa2h3F6n2OZ/00u8SfMSEujFJHshTcUEL1usiGDiwBHRWlzQ/oIFFS0IJiYt+lLlEK1PJa+TgpeuTjgV/L/TgodgFuVumJ/uIS13tiB3g7QIJc4lLXaxpFUsVJfga0o3+BPleEgrh8B8Ny6upS4LSDg40XQLQNW77LVvtRNwCmJfw0q0FIhvRuTMrjuAsUHmFwFaCrryOrEWGATuKhjG2At7pDxt0ssXDJDn5pfyPLnSlHuxWknQPtAnkLuwfIQMeGp+sJYC87AixBcPy8XMsYyELgKivgJ5DBqsL12lAyI4mG3OoZ8aaRFLtIB5m5KdLIysZWFyyHBAdBwSSD/rH+sQhi0f59cL5U8W+5WkJQGlEnzqVz4npPwzfGh6mPD4nBpT6wlJjoU9sQ3xLp+sFyyOnaoU+PcFYAtoSBs8IG+JPxeG2OcOgTCvUM9ehjH+fNIkssaFGi7EH8PxEJmEMLs9Z45tE8XnjxChhQsG9b4+1HQpp+pCG6RUh4qiozltWP9CIz4tgX6zcKsIIoXLFU2ZiiNK57An5jBdn8e7C7HvveJkFzzmq7S1kb/w1oFJImIK/JG2002Z8tM29dnCplRR0LY4C5RGQT/VUJmCGctyWkNlhmBQFz3Dj6atfzhNrm9i2/0RMmRG10E/9L6tOx7o7XynM1zPYmYhWHZqqid5AgsZDrz0NSipq3lyBjp5ER9C8I2D/4o21CUcl8xLzshxmQs2fnqrU5a50rOp0ehYMSqoSaCbUgNJoKlMY4jp0ZAl14uRwKT69+oaTcbkhZ3Jj3tufll37fG03ze/6sfryROMAgssUoVcbyZcwbOdv5DX08/F8GhEcTVzeOR03okDn4NzZ1Ae/w8xGycv3JVksOcSvCaTpYsqLtXsFqQaLW/BkPA52eMNpZREWSnxTntEQJcIuYS7t3E14rKUwoGhDKHob68RzRmWP1hENLuIm2J6JE004NXVVSCCtUW8CimXcJfbjL/cxgNGvFxds5DLqWiiq4LB6lopojFruoXDo6sJ6Zz5/L8eOH063sOba0uDPHJj5ypRsMt/65Ggxvp5HBYGvVhMHXkUt82QpFMJgo+1Gh6cR/KAoKHC8DPSz+2nJwost6VJqe3MgGJJedLpVeoIQPSqVGquWWYzm6u+xji8umJkfGpviTX+/iawjf4+NsdumGAwpb/4EGAkh1NGNhCs+jl4Cnc2/R9ENnWzb9pdkE7nvz87StOZeXZcJ54fPT4/Rn3+Grf+ptzDsQ/cOh2mEafIuPqcvmyOrtZy17R1AIBDVHoWXirmk5gwgeWdeMiFKiFeYjMzV7p4RY3hXa3ppEou3YLV0ZxjJaOcVTKw0CzCfRL3Fw7rIH6BMdOpW/soaHLxGMFguhTuKw7UYwvh1WnjQWH/vyTj7YEZ7YHBdMc9d/XxUKPa+8UmtxUN1RpGQvDO8slfG5vMua+r/umZKeCIFuHvW6PY/L/ZZHZq4diw7Hwrcgp6ygwvrXmMU86MmpzUVWeGtYR4QmAKAZIKyN63CfHpXJdR5lik7w0bHWWO6UtS+JdEtQQS8ERiPkEJQngdAwAhsXlSw8VLY5+CAABNhY4RgGdeZiYFx2xBTSm9kFWoSVYxAvpPIQfYJryuIXs3hk9L1uOY37198T8ubhPqCmVuZHN0cmVhbQplbmRvYmoKNDExIDAgb2JqCjw8Ci9MZW5ndGggMzgzNyAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNq9Wt2T27YRf/dfoU4fypucGOIbTJqHxHUyzUzTNHFmOhN3Wp5ES6wlUuGH7ctf313sgiJ11J3jdvoiASAILPbztwtmq90qW33zLOP/r14++/RrJVciS/MsF6uXr1dOrlyWp5mG3nb1c/JTV9W7m3+8/BYm+unEn9dKqeQNPzKzR8mflt+QPr5gspVLc5eFBwbmCLFai9QqS9uKpQVc6iwQe571KpOaJ8qLnZxPrL69CUT2+5IavwxNX5X1jXRJv0h4mPWCSXQTEnOf5rA3kuhz2vzPSzv7VMt8NZm1Lzrau27aI7Z0UvQ0cmy6RSrWLlXCrtYqS03GYvjbAtt8aqWecS1bIukB1764YfbQ7NxOZ0sJJwByTKq8HLksl8+aqdUafjOeKIPUVjJ10gFdOvVW0RO7tJVPBWwBG3prxp3U0k5CpJlFBZlQxadQ2kmf22VpSueSP15XELnAVJunNncrDaxnosbDmznxuQUZTeZ9tkg5cMeArWUrgaZltUuFBDkcUYHqbVluURdksm2OVV30JfWKw4FUpC031alt8KjJpjjQ08395sAK/brY9E3b0fjdDdjXPT14lZnsRiybkWB6FLBVKzUn6lWWZVeYKcGAnjqjki61mbtc0zAhdsrqkQ4lUu/EyhpYyZtLOmbiGVXpERK0Tb264HVRM6O74UiNft82w27P7C93bck8BSW7WWutk5d7FsepaPsKuI/KDTvJiTyVSo1ztMfMu6yV8SiyzXAo+qqpUUYmn+wKnS1ICLYQya6NrZJeBEsKM/YFrvYWBVvSyF3QhWYI54H+shWPrNUiFWBdVoNZC/0oaw14nDTP2UlIx7YshEMjt9ljpmxTZcRKyNTkeTTlTFyRVOSfsiqVxsyZCGatpEmEz7W2OkMPbh2LL/CPOdTU5XqDhgHNdku2syAf4VPnow60bWDnfbek4JFlIrOp027+6pcLIYHc1zQifPvEgYU0sLSdLz0XsdYm2ZC9l68yYTdRoTrSSYwdOOccO5YFLw3wFI0A7VHK3yB4JUfBexI8O1zzhOD9Bwse3HIqIeDP+ACC1xY0PxPKWbBA53O0wKrlg2+3FdsRdoE3mlnRVbsa3SgOK7m+q3pqV8S4cle2XbqkG5lJpeLdnx+Krlt31a+oXl4n2+pt1bHZegWqU1KjfA8e97ObtdF58rppaXJZbHCrPU2J3iJ0oq+GWUecM4ChH/qqC8be05zmNf2H3R/RTI24zKk54e0y0tKZvIqOJmzQgDFyfbGkRaPLfQKMFElHxG9YDdviAFS2THi7LVvi7JmtJkeLYGtGNm7L7jrWaq9EZ5/8jsHbvqw3JQGnuFoYdzIjqcJ7nrf3EJXhFMaBbQJHAgnBi0s4DrjM8P4Wu3nyYjjgOfBJ15cnam2aLgimoy5aG/4HawtvkVu8JiHpDbhZOSfgu6UjToiWOaAae0F1CEHZmfjirmsOQ8+9d8FbVBBH4J9pRC1CCgN2wIG+aHclPywP5RElGEbv+VBFOOubIORLIVoANujWkJbqzPErsF6qq2aPT+UZinMcHjE5aBZZ3nDXlYyLJ6ejSApjVbfo7TKVepMHYCgsO6C/L/hqVHkIY1KlTtgZfJ8hkp/B6yT/ehR2zOVuU3AEK6HAC3J4rBd2x1n+6dxBpy6X09xBLqwFCmZ0gKeaJr1dIheCJkHAhwuMm5sHJz/jtAuH4VOPwWCy6xeL4tCpgvgPI8rq68JYA+L3OkjD5ipGDaUWBQIHYTYI4P30GD/y0gp4kjtnAwWQOjmDrBYAQqd4ZX4ieMPmSK+OSdFSLoC8NlNe/z648+4qZ8+nWBTHInM16KS54O00O3lobPIJY1Mf96JK9MfuaD72RfuxDsURjwKrXAKsW05aPiApEyFfCcwfwwWCfnBLPbUgZlDwg4AYMywJ0f9waHD4HadfVb0dNn1FSO5w/zAnq+quBGhQ72ICwjvV5bu4JXjsuGtw2eiga3bytEfoNNGZ8tT3VXded0wPJQToywgJvl3nGei1oOMeizf8osV4D/8mKajLZBlaEAOSkcnztuk6en5EUgYAM6dDtQk5Dk2v6s1h2I6rPp6CAvTPwXRnND2SgoorKejkhOCJUwu7zZbkKozFKgzEuyN1qBBjObwj7UbIz7EFAfcA8/oggLaG5nhAiyChfxAxNYDWPGOgPavwhCgnNSeVX9KTO8pchhgOCUvCxJL0p2WdObVlhKI0wDnPPDng4HpGntCBRHcec3USKwmopzQSQzvoYlsCDZxDCQxFGC/RK3FYBdZ8rJXLxRxVQcwG+UBsOZfzssUKlsE8ZJw1QrDfTMd/BWCW/ct3y68oJZJPbsg3XvVNSzU9CFupdVTUc/OinrnACoqKejzrx6VaVoASC2vNBYFrKRzQTj3I4cxSxW+UxW8srgFe4toahzohzBVcJZWYBcWP2Aki/HSrK9BdpZByzrb6bHkr760MoErKGMvR78CBZpFEQ8btY1LL0QSS1GZoN9zeNMGLB0NHUM9pJjzBWoA/l3tgpG7qutwVMazQ4KTqZ9iTkPEGm+4pacZnmCXX5BAoT+5uIzn03+8rXqY4xLFzhamLk3DBeNypz0PAF7NGDk1lewSPxZVMwy7HB5fTBRfok+/DwXvOR8BhUbA0FP8W3otxZSQn8pGmlPEEbVls769xsqA/IT2VCLDTnIh5LbhIJg4l9rBWoLMsdTGpbSnsYw7s54WCnH2t9xPWwyj44o6GgfiaxgZKrqnkB0+KFshqQ3aG3ROWfnFhoMtKkbyY1BgwcPSnoaeFNiAqqnS09HDTcEXJwcl434Inx7rRxfmUd2BxnHt21XssACiuiqLOaJElIs88jgNyKapDHKUDw2RgHjW6EhScBzEiddykolY7XZra4xK4Lh5XYe01VEv4XQ6KNU/b84ubpmg7bk/qorPILAKGV05RWoXnCwf5fwNWZM+VOwp/3bm5Refmcsiz1Aqgk+FMUS3nbTKUbdeTiW8CI8tTN/qKrozAYogWzA/fVeAeGHbEIhu2Y5Et1G+KelemHFUEXtTkxsiQ5eUi4K+1zFPAbbQ9pGeg8kKa5AfQkjYUYyBhg82PoM9joRxGSCmg0ZbBF0BADfC6qa/hPzX3wwrSS+/FzA/nsNprQDpcUZrYETzqevAfwU6Vjz4aNLKmAYAOYdZd0cWlwMxyQP+31A0le/gHwzeIF3fnWT7O8hGLxeke1osjcWZA2iJ5eeN1UtwdqHr2DM+GgHbV7lZj+4dvngWw4Qllj/e6YZCc0Qe8K+fvXngGzOYV41pwSt35gkNysQkbVuK/T8LD4Vi21YYesLMM54fuNDmAbsle6p66PWQ/QSWgTR4ieGXJ8sPlyimIJVQMw92mrU5M0wbZuC+Du3zT8cJBzy8gu8wtOHXFTu8sffBIO5B7hO7oyd9H3YdEYh8rkCOuRu0t2vKMpSn+KuYGJRzg6IkpwdbY1OhvYm+z7CB2j0WYN4ynxF2CapwroHJeApUecI3S8fYiqMPrga4ViYtAFTIynzAy6H0g9QRPq0PJmtlH6wn57ztU+4YGTm0M+zyVcE5HnXggn3zz3U809PyTT6iBNbOUbYoZ6ekK7H28C9sMvKwPDMkDQx7GLolXtvHaELRj15IcPeS/56uVlkaicKAZk3oq7UvQgIL2g3Zwa0FncxuPAcNFFzJ4jva4BEghkou3eRi9sqiuXgbdaYMiQecc8cMuWP8POALjFs34KwfJ+i/fLyTu0tgUi2DhoP0e0Q7WqZEuZKD0XFPARgzC2A711KKLD1pAXGhWoHFfBRXD4fJ9FQ4f5ldxFFky8Iu/lm1zi2LQ5+2o1uyxGDK01IoelV5nm8LOsejZMrcPDFHL1ETIPH6sMXPVem6gVFbp+REnz+fhlG6GXu5jflu+LzdDPybR0YsEfBlNmy/dNfkysl5/9mX42qTeo5IYGsdK9tQkscQkLkwSAmGm3CwgqanCBsJixFHn+tDEJg9lX04CL79lHyuxyNynWl9sP7kaWoAjd1dADsCRR+6UxvOCw02F9/Mdhc65AvL1Tbg6mhXB0MfFc0fRhGsx6A/dWNp63rTN4YAY+cmwphKZXgbF5HbJfQiTas/Qt6kpF3GRMAfMEEkxAvVQ/kFvX3Wctbkn2I93o0LPt3mE/bm5yv7cfMiVntSAewBvzTaUhkhlT4vHu+EDuYmTgs45uYEOicAiFhEBizzNd3cJJ5DrFH5I5jDhUYZpJUB71P+PYdpK2OhCEWTOVNPNLzKm/GWAWLE927edA06ZAfhVebTvgku2DOuiKVPQKSpgf82FY7BQoamJ4LHY8fj4zYq2LrOeC8rj6PJnAp9fFJfP8zE+VTBZERZBzxo3ojOKWbUBVn5oLnRSMO5UxWyKo6MTAGnDMRxEk1+G4lD1FXrDtbE5uzuHJW5KU2H6xJtCb8d7Uq+P8+O1PxrhgYZCZl31PJOy4gnagzHStOkyVb0tY2zdnhFsoGLgL0ewuDH5VEs4nXo9Btv4rZBNvm+5bNB0VcxHHrcJC77IXVpFgPlZ8gI/KKCF44UttQPyn5Wp99VuH5gHbU7NeUI7cU0RNNnkddtwoZu1weL3ZENLor+9jMLCilTmnDDVDeNMgDwQAhnoxhUVO/HhQPlk94cR1/673PRDW3LpOfjN5gEuHjoOmcEMqHlgO4lgDXE1gi0U9wa05hx1EW9Owvf4fSW/VLX4zVWLSweAdY+J1Bko23lUFtqlTstZVHZJN5xYxuOFjUtORQAwb24MONIdz5wkit1F5EBpxIDtYqAnOYwwWZB3dHO0g4UMJJ9zUxfBz+24w6QgN4llQulU5Gq88+BUZfT79hLTOFa0W1akGB6KSNSIk4Ccc57gOOFiHf5ydmHkxkQD9G3g+Rugv4/UVD0rLom8PrOrOkQFL+oHMFHILLWaT3eB6I4AnLZFX4zIb6xqHCF72tIBdYJZz1BfKvMIQk5FLAhSfwsOH0ut96jt8TalLWemHP8//VpYN6tJrNYSYz9/Hnq67/dNDc7XSJOsv6L/tyCI13x79mno3P9zWgtJT/f8wnoDTu4NCX21huwJPzz4nywcZETNManA7jyOXwZQKT2QgBfXED0tXy+IfPbOi5fP/gOFymS+CmVuZHN0cmVhbQplbmRvYmoKMzE5IDAgb2JqCjw8Ci9UeXBlIC9PYmpTdG0KL04gMTAwCi9GaXJzdCA4NzgKL0xlbmd0aCAyNDEyICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42r1a348UuRF+n7/Cj8mLx/XTtoROgkMkkRLpBDxcgnjgYJRDR3bJskjkv89XPb0wvbvTPew2K7G4e7raLttVX31VbiFLJUmpiVzQNvzFfU8qloRKskpoKdWiaDk1xXOS1CvkCe9RCEerfWOEtnU8aImYezJDW+NNTRT/CeMXbRUXkHXICXOiauiNJVFr8UtNXCAoXHDhthFuiWMg62gtuomLjguBCEv8orioGFtxodBQhBJ7TEk4QQn0J5K4NUwoXijUNjLMMgYXj04xN4FuyrgTvGnOuECn3nEXU28YUBRd9Fgw9aSlx6A1Ycy4aLio6FmxLNAMfRRcQBj/NFZasEoaLzjekVgKxRPleIJfvFAogDWF2oK5anP0i5W0UnQjWAKLkbAvRjFfxyZhLVKFBDsm5ngisV3muIAeUiFiMWdMxzwWAMpYi9XHC9YN/bolLz0eeXLGgktl6IyfBVvtWuJCcYH5SIX22i1hfLdYltpw0UosVHKPvWt4qUlFx5DtLQZvUJpDGMpy7CJeqBJjQqVqMWZDnx5jNk+1xphYoFoxCfRXa+x441SbQGPctMLxdkuNPN4OsZg5NrLpsEuU2mDCHaZbY/W7p4ZnuKi4gF4YoHVYT4h2j35h3Vhq7GzBj6EeNr9Lib3m1KNfTL0rlNYCWQsdYATdoLjCZ3qNPUcPvWGQWDkqRcrm0aPN9uX/Pu7S9pc3/95ttj+fn13uzi4/wQQdbvh8s32++3T++eLt7tPgMcNP/9i9e//myfmX9KrgB4cL1s6vN+jiAu+m2PFB7vHZ2Tm6ejV4M34ZvHnf9n1LZWxpbHlsZWhfb3766UDFob/N9sXn3y6H+7+/P/tjs31yfvFudzGoUl5v/7r92/bnVzTchPJvL9MruGnusGgYXCZsGrHlFrbpijY0f5wePUrbF2n7l/OX52n7NP3p8vfd+cXuP1kz/TlBi3UUYcktbKBa7tge4E+OTYdFZW/9VkU+4c3352fZV1TDJSv8BcudC8CFaslhbgCs3KXdqsbuv5/fDHrYenpIpUyBoplbuHLLAB9AJuVedXYxZD0lmGAEcG3yjsWAOsw5wJga1BCZX4w1N6WU7PADMs8F8EGl5WJoveZab9+Uyze/fditaaAA9xy4QcWxJ2GoJUfQIqrYk1lH4cyHejxFZ0Mkf562v/7zXxEbqtXcAPdnnz98eH1ULsIMwy1pUQ6BDe68IId4BN+PaIvBEdCWOiWl3BC6JnLPgIrDxJ9R7JCOrzxDOCTxqxtgqtTxBmhLevUEWE0qV2K4kXb1xMEmrnpDqCXl8cajg69iMegeIqHR9peL87cvdtguoPbTZ2n7cvfl8jpWXodzvgnnfGc45xGueYRrXhWuDfAccaxmC36FDWGEcKWeqffvscF7OmQHECCYEszRwiF7zwga1CzrEaz+igttRXzCOhC2AKw2e5BOxJACu6HAKWoLweuGT/KJPnkoR+QZiAgukQ0rMC87CIGrKpbpNifmqb+xIBbLCXLQoVufl2NFfJWAL8611WNOPPHUqQ8fOufEh6eOf+CQ22fgoiR2L+/cdzzxzj1gLHqnGF/3Thm9U0bvFBlbHVsbWx/bkZyNE4/kYmh1JGfjRFfybkV4LyWYfYN/A+1Fcgc/dPwOgjrvV7RirGMuuSLfMLCeAgRDggFHAysUDzY0r0hfTw8VwBZygSs9VGNhZFaPKweXVQFPEXdbZIBQBLHIykCEVKHHEZb8QwCvgZDBqpEHZok8GMSoRrKGCM5mCwFA16SnPRv1b4o0LARS0TlFfoSBENIWDWbaMX7AEOh7jQQcHtTL7Qz1zcePu7N377/kx2uuh+QAB4Zh8JAQS3YYKjuY8pH1+BF69BoBGJHD4b8IhAVZC3Ca4Tda/GGIsiH6KtUhW7CobjBoJpKYCGfqPKPEig7LSB8tOGsDM3FQA8XKYGEKkNTm2bpNFyMCqeppzGAih2gUK9DnxQjcrYK7uIPDIBwt9Wmxxb4gR8BNAyupnqP+ttSntvCaZTl2eJksz5tg9VzbMY5xQArm8oTjhGNCMSapATLCFAHjVmLyLaG4KxexdoOLmN+Vi4xKR9Fw39qaXCKQ2DHdWmxfwOjgf4CEinTVSb7HA+6nSAvosSjygZsDkShHyUtBQ3unBUqzai0D84YteINpwhKkwENqapSpLpR1SK/jgdXT8GAiB981y1XrvBxFjEdMNwJRR0hd6jRS/dpsoVMkagybZAMsdjnK/k90xkOXm3rmYdJ+LE+/lvVb8Mty9U6cAsi9nLTSDSf1flcn9dFJR53ER2f1MVFwX9VpDYBNcYjQhuqjROENTmvgeYvVR7I1q7Gjm1bNcf5DSADqcLpRsyxl+Lqm10Zkoq96MHv2OJo5RQ9aURFFRiTBtINRxQ4hNePmcagWsPpwOzPgWBSmiyAVansgi7yeHDi7tCL1OpLVchqSHcpRk6EO6BblJ18Q7gBcsgTWNZSKZoWNBYsVR2CgzyrzwkFIQLd9UV1HBIQQgAl8tH0/9k3gboaVHMU+0AYuevVE4ozjJtyBrpR45DdLJ6tUOZvegMXGd4bFsS7iY12kjnWRNtZF2qp1kcjuFEvlUdeLE9LiubAnRzYOGjWfXT1Zz/dglb0O1RmN40gfSvFRnJG+5HgrFgHg5YC2OuTcUR1TrEqJVek9sy5B0YrJd1QfgqmIDQPH0eTARARkA8b/cNxONOyhflPEkP1zP1ERv46JTU7DxEM5joID9kI6jELmZYEs+6NOicS4zwsHytU2l06dCFzT85RD4JpA2gS4punUhKgFNnGR9fCp36zz9jvXeduIS23EpT7iUh/rv+Oc1qJtjrCF6SvyYy57u4vvNjQO1np7MHxSon2V0oAIwdZY4ZANeunRKtRXP+CyZv2YMrhJeEPk4FE+9vjUBuG6PUz9h8DXhg+NAAlhRwSaFOcGca5e2gMWBqMUEp8ljXpEWl4Qd+f0mClE9RMLUf16gSeOjX1ZDvzLvS7K4T6zr5pGzuDOceSacKSjx1B3RCQtNxPJfudEsrdVP9sJBzOsSyuZeJ+fxSZxp6PpyK1fyzwNPnFaDjCV4/gAD8xe5+XYkCvERncLRLifwRz/JGBiFdO6w9FSw7SK+I2i39Vc6Ia5aDnNXJSuf0agoy46TleLj20d21XNyZDVFho+4RwKwYZAEkUTEc5q8kDnBnHaeajE+HXDshJf48b/AegDPLEKZW5kc3RyZWFtCmVuZG9iago0MjQgMCBvYmoKPDwKL0xlbmd0aCAzOTYxICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42q1cS5fbthXez6/QrtSxB8H70ZwsmtROk9M2STPpxsmCljgzPJEohaRiO/rzvXhxQI4G49SwFyJBAB9w74d7P/AxeHW3wquvr/Di98ubq89eM7EiGBlsyOrmdkWYQVjRlYIyQeTqZrt6U71d/3LzLdSkac0314yxqq+7u8Zevnp1c/XbFYF+8YqsiDKIC7NSWMIvWW32V29+wastXPx2hREzevXOVd3b6gwJbla71Y9XP4QxzZCIFogzOR/Tz5iI+u6u2a7dOI512w+Px0EFQQKLMuOgQiGuF7bZ1f1dM4zBQuKxhcIlgVcKGYXdFTAygY6uCZJM+X7o48EzrBBTvMzgGVRgYunYugv22xzW11RXDZhVbtqmW8PJeMGgAIqI0mXGxDFHRIj5mP5o+oMf07aF0bCmb7pNc2EohBrEKHVDoTCzzFCYsJNXcPw0yTj0gvVKGomE1MEp8kzN0+yWhgK72CeyimgYPl8AG03403QuA2ww0FktgMXTPCyCygTYTrE5KpfgRP0024ogc8GR1gtkSrydV9cMaySVgEXp+3GXGT4zkSGA0siA78oQQDOEiYkW0VhkCFAEOBIgBc4SoATqRIAUVSsKSzlDgBLIEwFSZCYpyRFAnjnJEEBCagJ/lCGAtJbxuALDxYz/i+BG/ye4WfeXAGXgBEz0DJRgpZmWGfeXQJ7cnyBzIi95X4Sr9MxVxvngDC1kIecLgQwNzuecs4zzi+BG5ye4WeeXAJ2cn4ASqjkXOOP8EsiT8xNkDmoss/S5PguW8T7TiOFS3ucUrEw8sGSYqoz7iwBH96fAWf+XQJ38n6ISbrC4pHYmApSAngiQQgvKcwwQ/Cxy8o9ypEBVlmEA1UjTwEzF1SXdOTGgCHBkQApMcYYBJVAnBqSoBCSZUDn9VwJ6YkAKDbA5Bkh8ljn9R4jbNJRhABGIgjBywAb0cG4DUAQ4MiAFJjrDgBKoEwNmqAZTWJkZBpSAnhiQQkuWFYBSnlVGAAqjkITfT2QARTBFTJCiPEoiI3IbgDK4kQAz5AwBiqBOBEhRKcEa9oBPE6AI9ESAFFrqnAZU9KwyGlBojggp4X6h7USjExg2l2Lx5P4SsMH7c2CT8X4B0Oj8GShlhCuVkYAlkKPvZ8iKZyWg0medkYBCYSRkGe8rjiQNnCNCUJWJ/mVwo/tT5Fz+L4I6+X+GKiC0s4wCLAI9ESCFViarADU/64wCFELBbMoQQBKElYwxmGqV2f6XwY0ESJEpyRCgBOpEgBmqolKbjAAsAj0RIIXWPCsADT6bjAAUnCEuaRECcIVg2Uc3cPiXIUAR3EiAGXLmQUQR1IkAM1TDqJEZ/VcEeiJACg3KMxKAQJahZkEAeSY4JwCpQYawIgxgDBkVb0xzjVXmJlAZ3MiAGTLLMKAE6sSAFJURZvcAGQaUgJ4YkEIbMQlAYhBojTkDCKZAgZwGJBIxyYtQgIJDaLwVrwXTNEOBIriRAjNknqFACdSJAikqY1xgk5OBJaA5B6/RBTQscXwpCkRBhvWZkJwSxOA9IopQAEukVTQJ5TL3ILAMbqRAipy7F1gEdaLAbL5CECJySrAE9ESBFJpgyXOJgBAOHMiIQa4NorIIB7hhiLH4BEpKmkkEZWADBVLgi8E4MKAIaGTAbLZKKEoySrAIciRAikwI0Vn/U3wmNKMFuRJIEVnE/9Cl0iQ+o8IqS4AiuJEAKXKWASVQJwakqMxIRnVGCxaBniiQQhOiaJ4DoAZZRg2CREVEqiIckBJRFh9VCaVI5nZgGdzIgRQ5y4ESqBMHZvMl0jCeUYNFoCcOpNCEUpnlAAM5yDJykHPYRxJdhAOCIqlZfCVAYq0zHCiCGzmQImc5UAJ14kCKyiEXQOzLcKAE9MSBFJpQnZWDDOQgz8hBzgTCsshTAW7fFYw5SgglshQoghspkCJnKVACdaJAisqFJlxl5GAR6IkCKTQc5OUgBznIc3KQgk+K3BniVLhdqX9aAdtRncsEJWAjA1LgS68kTAwoADoRIAXlSivYBGcIUAB58n+KDGtck6dfOrW47JPfOQWfYajCSfIi8HgY612GVmBykMif6GDrNy7mwNxI+J+LLkWgI7dS6Cy3SqAySBVC8jmqUFgKmXvvtAg25/Ze0wKbgJ7zTzygP+jO4yqogdWKwR4H25p7qHmz1qKq3+4a+1K0qehf19dcsOq1LT7tdr60b8a67fzL8Kb63b693fT29Wm+qcf20IVadbcm1R30RCrku7m5D/3auv0whoqHNTXVO3/SDvDLcTXeQ+PGH/fN0G5P9W7q966JA/nt1PZxIO9aaOSOfjvVW2jew2g2btbAfBpnTVeQ6oEV2s94GOu37a79w438c2gPMx1ObwfoOryYDmVSulGq6p0dnrQ1jsf1NRQc+nB9GjAct9228ZcbOOpsw9G329jj+7oPp/3hNDZgHCFo9TdnXij0HxsMvqtd2/jStnuM43tr3M+vzg3byckyma8kyDDj52vfwn9pzaSr8D6+qrqDL+iaOzCDd6gvSd+K9yXOQfB7C8M4nLqtcy2JrtXVsT/c9fX+L6Fa4mnlbKi9px862tVv/dcAO+9IXVEeBjj6TlW1t+1Ou7E97gLM4db/Du177+FkurD8ptmGrh+AWeyVp2OD0mYY4/cd2+aub5rPfa1QnVXN++Ou3bRjWsdXcZQcfPk9GMsf1dYtzpiHdhuh6zF+AtFNlwPPgH/IRYf4xcyMtmGxEoGYzRpusbqR8dTIcDIjxeDLhsOp34TK3utw0De33rHXw9g39d6XtpGw7dg2g3UELNzacROuSup/XZXTHpb9JnQWlkdsYI2zf6A+1AjLk3u3LmMRuA0TpJnwU9vXzianYdO3R9sBAebbuOQAKA2zsKXOCERVvoEbkgsVcM1631iWAle3g68W2OYI4a1v/Ux55RwLFbbh45RQvzuE8uZ9s4HVukDd13C4t4HGg6q4AAYUIw/hcx9SrRGF9Zn4EKw0uEAJtNj6881hf2x3bhRwFgbtLGgt34VA0vlvkmx75+JQ3dnafQlkt62pCIg5h0DQhzHMxgI82H6xefGCKIhIVFTX37Fw8NO///7qy5++vvRlVjpBLjDShMx7femH1HTOffZEV9/F6Piv7y8wgSqOVMxIh273wUf120PvD7zxITEE3r/0xcFBw1Rp/9JXC3FuyhehRmBHTBrW4LtmDKkFAvPxFBKUXx4xMR18Ifh8Y+lzn47JYYRVdSHxwF4fEa1i4om9SgpjAXN8cOBw5mfqi7UttimC0+qb0Rf7WN8cQ+Om9mzwZ/2p8wdt+K39T9e8C9Xfj03fObrC2RYMshkP/Ye1ps5XisSlClf9tOAg0DGMK+T8AULWyzhPbJKJcgXqI2TYt6d25xwweQIHXwWtgG3vsOK3bnEzSG/vfZTFNjuPp8Gvejgd233b3cWz+3q4b2KbqetNfRxPQRTgaGWbpcB5IUtB5jo68NwSkXZ/BiuEwU5Bh3RyfT1AfPGKebkQEg4TokFSkXnbfe2caRcyZMLmuKs9R54cAFESwX50OYLxHmZko5ldmfSZkVBO7Y2EWRejC3A6utSfOPO5kVn62BLnenswZRd7OUQ3f8Xy5HAa/Ylb09qvadBQyJfe+F5sFnAZ2IXeC0ueYtC1fnxfvXjhI6kF3sU4XPch9Na7wUbKgz879q3Pns7hUM+xHi7cerlK1EIDhPwxhPZNoPKHB8iuHUNwB0VwDL1CBgkDGY4ubjQbp3iXsON9G+od62CQ3pFPBvIRdfnzx0tGwRIZ61JrFYgJQQXVlvLUMCcz38N6sL3BAujvTvv4LWWoEbwKMaAHV+0bq4fdecgnoN8G39plH6uFYaK7drj31d45vWKPgA0ffE0f9Rr386vNnRganjaQewawua/9sLwX2owYhjgPU7IiGvTtcEGWBR9AcXfogixtx7CAnLgaYCVaBJ9nP3tt7LJDBpS0WwIrmD+iJL7ijjjMj8CC+cpHeR95lPA6En49Ohwc3QehxF5ItRisUkhs6SIDBOCsCXfpvu/XwpJSEN+lCBJVKOd3W+xqNPvQLfyHLfaqv1tNx/8ByffGNYU0MVeCbyq0GM6jLBzzDNEUKaxXBHaCksU1degPu10NNH8OHbYV9BG63/M047jz38Y++pA7hhstEIa+Ztjxg269+FyZW1f7Z1yZ6TBsn7/weZcMh03Tf9eWu7PN57MTFJW4NEE5TdCdyEujjvPkkN8MkfNBwWT4E19mw1TzVuDPWIFrUFZ0jkdFMML3lrJWMto13IQ94Fx5ELAY1iI6xIoNCErUbis3kE4GG0UyfiWcIAEJbdFNfswE1KDhat7otj/sPTKV9tdGTNgF392HQgNzUgykzm2WZyClFefzrrcXvn0XSCo9+/T9ol8NLGSYxkOtjyImRkyY+RjkS7sdxtU/GxA0z1HRzp4iuqSitQPol5NT895CjbeOzdgQyQcbvNyFgy8Pe9GMwSwFiAQfyuyapNSmQIyfmbrBII3IvMfsejEaaWnmDT7CxMR+qk8X7Sh2txxM9R3YpX/XDtmJ22gobDRMu9g+h2sIYnIxwS+yhMQMEbNo0T8DQ6FYm8XYQPtQyiZzXkajCgi7aLh/Do1btIUxY/oN95cebncIyOH39WlIbwcBAUF8DIs7fSGHQtMfYSPxMREYmojH6c359NXvNjZ5OQY92j+0EYY3RIEEGfeB05i6W11uLk1rCREH2tRhD+Byex8m8HaSEMLti5hP+NO9gNDCR1LQF3aqa+LP6zF0cuinfmN1K4nq3u9EHypMfzbEKUAVtx/pLT44ua8f8HNetw+OpVxM+uIf/HB3+1zQk8nf+1guToWU5Kuk1lN5KDW6fSNGazIfBFXBol2wnxXrj5MQNOVxW785hNtf1tvO7LA9m7wDx/9HZodW8pJwuuag6/4R9+RPLirYKilIV+kof3huTQnivu9M2/yMBc7iCEhKsHjnbZ6NhVRShCVdQpFc2KWwh5VSPgW1VCkYV6+fGwV0ZYT+cxM2HIEw/LMTtvlVcvN4ws7R/hYJHDRJxIDThzudGKJHb+9iujA13QaaNcmtNq0QkXP4pzQcxh8zIfcCoJ73KB07/d4wbgCTFaMEtIgPKpKZ3dfhHpa7c8/tDZQOQvXoHlkY+7d0XKkLzPC7W4cNnnsi42/D4go0A4S0Pj5vqcNzm6F9/3mwZdjAbto6PiAJlhzjHbexr33R72th9+htvMkHRfFv68B4+robbpvwN5Mebm8nnhb2ASFsPOyObf/wbVCs++rm6n/n/HdFCmVuZHN0cmVhbQplbmRvYmoKNDQ1IDAgb2JqCjw8Ci9MZW5ndGggMzI3NCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqtWltv21YSfvev0NOCAir63Eka2Ic4TdOmSZqNA3SLtA+0REtMRNLhxYnjh/71/ebMkXWJZDnFomhInfuZ+eabCy1G85EYPT8R4Xn+7uT0J61GUsSZyOTo3dVIxkKhT4zkKFGjRGSxMOioRu+jflGMJ9qZaNpU13lbdk3dUYOO+oY7ipuxSqKiveXmP4U0ddmHWbOyKuqubOp4PLHGRU+xyrLgXh21xWyYlpflsuxpjVueMy/HMuJFO24Jh6AJ44mMPqF/KDE5HODTUN6MrY1wvLymef34r3cvcJn7K7nMxiZTfKWy/lBMe38mDDt59u7k08lqrNUqVqm7nzCtTt7/JUYzdL4YiVhn6eizH1rh/yTG9qPl6OLkPztLmNRCjCp21j20hIshE7wdWoJO4aCif7REFiv9/ffYQomU8hBMpF/DJTrWxrJc346tiIoqbz+SXHcx9n5itY2SWI4nRgvAxIp3pFYjNTQyg0q9bq/HE1J8PSugyRSa9AOuhuVy0ub1PExom6EvsITknXbO+T5iza7u8u1RTGai+927vApva+waqQCn+VAVDKgf6NQm+lz2Cx5aN/yc0ikX65M1V4y9LYtyNouTzLGYrgHccpp7G8iSqGnpmWI3iADLV0VfTkkStKUku+MTlP4xeJPAtKu2qYKY7ZaOwq7KYUu7s/XlPsVsHFUlaZwkcnvSv2lDEWnBG/cLCH++eGhvbUUssu/bWmOw3J0UtpYm88KAhYfbt8W0aWeeAPALovEihG48iuidEBMOnF8uiy7epxbjYmXCXmAmEvBlWZf1HDMTC23wk7VOb2+wQdsQRtOo6UoikYA0/BfDENr56P797fMTQI2mqTjZAmRovsJqUP+DWgSHJMn2SVeiTLcxrR1ZlbLHdJxqzylbS0or+ILXbUNSuCFUe6ihjdmXX5q2qIJQCKPE1P0CxshtQ3cvu/tJn4Z81uaE6T0KAM+5LOEzeAdTs6swNmP71OCMKZ1okbe0CWwwr2ehvWj7kkw2GBOaYDyLxjPIrOOWvC14Gg7UFldNG0a2RZ+XdRGW8riitcPKay+FX2CnYouW9jkYCfiadGXhDSPk6gdaQUefF+Wy4NenTdsslzm85THc4DgqhnzlN9DR5CPXKsKqLG20d4um7YuWW7HNvJjMinlbhN09b8aMEGBAGrC3U4QQMZpoA+WAy2fE5cUVxFVPscFDTArnYu1oorAOuMbPfC//2j7whsozExtlRtYJTzNeWL8Cs3psYOY5JEMhQKqjFzE/z//2PU0bntOvH2/HqSJuJNP+aZzCHvN6WnbThmdchAig6cIYhgt6fmvLZhkGFW2bYwsn0FxzWzdUXdGTPFOznhTYl4X9hTsX9AP0Eu93Pn7VH8tusgv4TMfW4vYmiZUKsci09U6TnYGM/sUPipKGPid6yZfc9LyggQ0AztD5xqd5mUAcVp0lwt4lKgsNChYSH0ObdTZ6MsB4CDqJDBduKXLzyyj0zofO2zkIOQmNtPYOPKP4OCOKaNaUZ1s2BOGkSYawBeyU6MBKAi5BJKedEMaaCfaaZE7riTq0ZWiYSOdxNlESOMsCLtVxXEoZp5nhzV+VzDtFcCPnLVOTMya68D6F269X/oDYaOWdEQVwJ1hk4UPbrjpDBJy4qKmDW6Lmz/RPww2AGsWlQ7u9SEooV1EzLD9ySHwQdjjYq7weumlbXvf5rnQT50YmS9fh2qscrFF5ZrYyyg+DCryRijNtkjsgIzTIDEseU7TBibyi/dtKm+c/CS2UszZ9hBo1brmtRn1UjSZBUOzcHjU6d69GWGJ0QXkEAd4l3pnAIXT8qwpOZFhyCuKieZvPBlqJ6Qq5SBHG5l3Ho6fl/v7hCy/BboFWLfvQ191W23P6tvw0kI5xQB39Uh/QNc19MlY2Ws7HytF8Nca1o+KS+WRFPcJtAACcgz1iIYPDrT0UrYs8AJqq8pRzU5CKkU3N/VLNcE29/jLWe/O690fkqUXPz7K+QfYFeYJ+ERf0YThF+W/9/LLyhIyhgI5dh+57ARfSv2Y5VAX/Vvzg6HqfQLSCOlkUECUMpqjKOi/bzcMX/a5RSAOZmCw2Ll1FgTXufS/S1EbsAQ9axnU+XzlhaZM7ieiKPfEFxwAEijScKlF0hK2fPJNMcbud7BJS5u5ZcO4gAuPdnVcRGt7QJErK11aZPMIqdfRmuFyW3YIDBRPC6aPmiGQ0TXbM0Rw3R5VgXsbyfeM5zEcoDvHquZfR31ASPGo7L7uuaHGXxFNZ3y9KCsycg0dc4JY93PIydHsPTUtwDyX+fuAvzJJ1VdxyWQLqgJaz6BlXFGqedY1w0ofvYf2y/sZbh9PDecrgEjbZ/CAN4y160Qyt99wm0Dg9g70GEz3Mtxbo0yol05FkJ2daqTvt3bnFCnC58qiO6RB5+9/y5gyUq2NjEnOjvttRm9QF/k5s4G/pTrdtKEhJiNhmIaL7EH/I/T3zGIelWbHQ8hFkn7lsB132KLo0UtZUqm/QZWD3555uCV3JJrqIHTZQQyO/QU0Y5lFmrIqeU77AY/0mmAg24tAQsqFgMhU2et3w8OAQpkPrg2ie2Fx2fTtMA+qoJaAOAEh3r+VsrFyIg+aFj/uoLKCNCNnJl9JTCQXMOuPU7Jb44RAu6Vj3uMTyHF6si2uWAOqQQdE+Vnkip9bNMAGtS54EdsOvaVn0D0SjOKtWZ9LpO5np0ABEZEeAt4MtbYEIGUQRQO0EchVntLzR3x9+EqOGqMQyqjNx+oF8V0W+qzvNRPpdaE0DWt1xtKosznQIwP6guI44nuphT/OGMAf/+DovOcPw4ETXCyTVoY/xCDZ77RNt6n1ZDjFRooreMD0BE50vIVHvK1L4fJHXd6/zjx6pDBqCcJXzmHZYhkPclDkvH0q6PVKeQIty9yYUnKUhyGpCeow8v2kPMqMxcgOBOItHIJ6bzAgteJyjGVnQJXw4liynDzlhETl1ZpPwrpDDUgUBmz0Bx0858RaRTR6BDHGPDLHOPqRIhZzsoz3EsXGqgxCwL6UoqZh8/kfoSY6iR6Vos+kqfK8nvxOC8hIXhM6eLorBA4WuscFv1LeP32gY57kYcFFwBf0jt7/62PZc2eS6vGc8L1WEWbeVZ6OvXOvBZJ8NcZiLX7Tmfl+qXBJbkW5+WNA2sr64RlWXvAtNHhjarsfspECrKs0qYTrMedkm4tKAuJTucOmj2yWDLd1MuR+AWkKZUKr1XWp0aAChJd9HaMrqOLPZFqFZ+uaiM2Nu5P+D0OCmidDiD50vJVMZCed0Me0i1D+CZ3ocnlrFMg1BwMXwtSRWQVLyNObnj+uSfrKiMry8aEBPNSmVfl2URQX/SP40yXxxxg+mx6bGudmHVng+MkOmFdehWcpwoOcOAR0CwA4DSKtGSqax0+HOSrl11Ka0u1O+cE36UQIR0qPyZYortnQpoMv8so0R3GcIdVz2CPVRUXVbfdmO+ijmcNv6k5mLUxsc7Yt8GrI8nUY/biQnGQSct9V2S10Wvk6EsecF5NvO+MfLAvzbTkNXqNxCCQXI6w84mSwYM1V7y0sumjLPZNHP+XJ597Ls+2XxmXYLNVX8m0Vh+auBwjAP8Z6+nvC3Sb3mILrlziWRoGkRLvlr0/XwiiC3O0DIBgiFsiqgdO03a5a3dVOV+TK0+s8NGNUW1+3qs1VH2CYm9ATiu1chmT0ckiGj3UhaM7tOWrVPDeEEX1Kc1fQLZHn1vCx8xh4yQnaROIH3knSKhjLwlmYU34Fh6T8VhRwHAdu5VtN1QmliLt/oNCS8148iJMhTR/wNhurTxZf+GGyV8qfZTgCkOEo70iRxloSY6kmo/tbsx57ddEVx4y+jorfNfcmtqHMewMH8vOjDmABReED6jOlrDr+Xy1kAJbgMisinocIuoYc/hbR5v0LeyqNqyZ8SJGL3GmHRsvx671r2XUHTcxVM+bJfFpxflh6N4WgwxXCHC4ObtCcDbSInen5f4fE/6YML/X6gwEyzZUIk5zzJSaHtnRSJ+IGXeFStmc5zqP4rlcAzWdd/rbSnH+b9YkJLTwQZyjEcSb/SDo6Of5UAu4osBCc/x5zm/Bbz3x34Dw2b7EA+dWINWWCNfKzsw2eH8AcLO7mVjj4NZfiMO+Uexgl6Os8fX+hD2qqPFOJnh7+wuP+Cg3f/zZCLloaZaL0NfSzmQByodNOyWJ1pP+pSG1uTbYDOrD4k4WXeNoM/It6vmrY6yGHpVlrpwtVXsRsaXnpQ1TNvk/i9mVCKaJWNUkIpjiaUQBjVDoVmFyvsnRJZ6JGZfYSLTX2sBCHuhV+i1siTUp1+WFYdEoAJUgodY7sj2FMCrtc5YA+eOg2uV21HdM/enfwPZzSf1QplbmRzdHJlYW0KZW5kb2JqCjQ3NyAwIG9iago8PAovTGVuZ3RoIDM0MzIgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjanVpZc9tGEn73r+CbwSoBmnsAbypV8iFbSmgnljbZXTsPMAlRiHFwcciW9LB/fbunB7xEmrLLVeZgLkxPd3/9dUNsNB+x0esnbOv3+eWT41MpRpxFCUv46PJqZMXIsiRiCp5mow/BBy7+Gv91eb5cM4K5MbTYiI8SFSmhVgtKWPBnXhR5Wo5DYZLgdBzroC+6uqLntBrzYEbt8xp+kyCjpzdp08BY3o5DHkT4xuNTzvn60T6EUprg/ViLIFs0Yy2DrM2qLu1y3F4yFlxeZzhaN7fPxqGCySfUf5o3bYdvS4IXdd+0Ge2/KfmH4AimxDK4GYs4qIu+9EfjIsEG9F3tORfOet3gq9NZn3Zue7gef0kissyOTKIjlQi6pMuxtUH2tQNppY6DvKLfSdpdZyUING13nzACsZQMLhZNXs2zJvxjDOfNmiKdw9mllMHb7As1/o0jdfPZ9/Mk4dGmHkPB3MFCwSOTqEHd8qG6k2RT38bKyIIROFEmdZN3d/gWEbxOq3lxtCE9NyZScF/GmkhrTWs+MiHdrPXNudFgRXZz95PiZqxNkDY1yfG673JUj9tBWRS+ye68kGk1o3NM8ilOuk6dKK/Ayi8WafXTT8cn065Pi0u4+Z9OX52eMq7Ezz///Pzli12usH44yUTEnBhrh/vIFBsLGwfF8J5Xk0fsFEdGb4l5cfcFD5z97RQsgncVCQLmQI15VmVNWuRguqHzIOwE71JB3Ref3VocaGlgWld/ZyBq41df1c2mSXo9GhYJlfgj/Eomp9nIRoll7uBcRLFNRiFYiLRkIWKXZXK4HD3oERYxqTd3/8g0o4Ug8vrC5SITSYCQjUUvDr5KqEiIB2/iIDUgS1/Nsoaas/wmb/NPeZF3oLDglnrhmmY5okeL1y4Bnpp/5TeAQs+EZjZiRgh2I51xsUAwYfw08DqAoKYl3IGN3Bz4Pe8LvzX3PbSKHAr+RUqPmvlo2X4PDvaBTljnzzZ1JN3VG2UiS5LBJahYa3bsjhmtzrjpsB+CB47OSYubjq62HB0uVmzZhwCPZMYr47rJ2y4HBA8NmPzZZ7zIrCqz26X1IYDDFXEVkwXjvAn0TXHwlxTn3T5tqX/R1It07rEbO8DU6yYr6QENduilxineOdk6vNZvsrLzaKd9cw6X7M/vLu0Z1y5WJdp6zBBgTQcVpJQCn+MB6gge9LYqVrseVIVwJ9tShd6BuVsxVsdJZI0PH7/kfZk23tnxYi0FreyaupZAeNK6+6oL//w68muua5wP2Kphcvs5zx3wcIwfEKzv3zlIqfuKltNct/ITxsigdv/P8qw9om7CpTKfN3UPF7Vo6XYhYrphCHAwv8vmgGFL4N/SloYIIbgkCRf0Gr+O3mIDWD7LnCgQEoq5e+cneF2TtjTBC26XL4RYP/X8AHqdjd0iau5jGErFQBqECaoqLXBTI0gIww9HZ39Kbg3CnUAkepYIfZ/Y2A+BtYkD1rZ9K0ZExqh1G2YJU5GUGrBpn7nthxpQymzDjBmzx46WocCROyBHTUT6x2zZHIQVreDSPKado90BtZr4319Bgy3aGDh5M4dO8LLgdVaXWddAUHdzpnW5KLKvSyjHPtIsiGV5jCzTdXojaLy19gMUdUg0az/JQQ025u4tYDH7bCPWSCiFdraRb4O1juORFhYk9ER4lhXjUPOgePrPKr/BcNEBaxFjzXmQEt+b5fhrAdysQSbjGCT8jxP2W5gKDEcD487AjL7n/DvRbMsGONpAy7k2OgRKFDKIWqF9RFRBmbfUbw+rn+nICq//s4iiKwET+th0VsP1zvY7qA4ubsvBHqRWdHt95fy8pS7CAWi8Sb0S8FEGv9Wo/wL0f1vVZY4uvp9n6+DdV7CO2XCywftxSzzzpK7qcFfcAZoPPuSpHWDW4rqNSD8virTJqhnhERynyVqX8Hj1rd4nAzQah+loOC50ku7dmo35/gGQrib4U0FGvGZF/vXjnDd+qD2+FYiUxdjnAWlCPDwBg3XeSI9GIR+Q8AyXVhfTNqcn0go0zsrGT/hPRL/v+7s2dfRKBRd92WYuNVpfQ1QYGiClD3iYQlHfNV5QXxRuBx2cVdT9kkJj7rd4QdNmVe2628+3q3c4NYK0W6kO0IcokV7a1002x/gBi8yuzYChHNEg3n/dtEf78liBSKJZMEM93aBg8PC2Lz+h7zvSqn02y4ZsFlpg6Bq4T9dOgYtddTSLckcWvAFrxIUqoYAFXXRl0LjIv3Z51l0PUooVbNk4GQEgRDL2udnzvOmuZyniKmduK/ydZMXNbUXt5xH9vgWPAPIA6tiHVYrZAFge0jUJNiIEvxcAkfgkggvkCZTHuqkxohc7iF5GmCEOxohdwMNu+PfHQTjOPsqtRBLF0ixJt4NHiOEhC2VsQwNjPFRDyvffJ8NyEC8SIyWZS3+m5ZMPf7HRDIaQ7kggdF/cxHIkI2EltIrRxZPfd+WLAvIaSG5XJ0Hv5OYQInMt3LItp04OFnDg8uEil7WJWAc1EHEJgWoy9bwdO1HvzFByCmMQhDvnBLetn73i4y11OEvkoOsurYrsljrJpbnLX80qf90bd/Gd53XfVAS8sWeVCRE1oIGfKHDuD5ncBpInGDO1i5kCWvfbyA23N5LwqxPPQQWzfBlX4YYelSX4uKrIcLg5/jv6O3VnTIFcQcaG2TGTj+BWeJ5NTQp2UJPSQsae+FT43PGe/4013FnfzLOKrm2y3l0UDm4w+XVqUcylbOlXar/N+uoapiIUfGRcZg0aAYDURV1mtA7pWE9VuJYWAUymDXAb4JRz6nF1QNJ0App+Sr1b6dsu1cObXn1duJJfk4fb3mphvtRJJD12lVgOLB5ZSxuCo0K7kGQX1t6LWK6U/mOpIWcxO+ZMx0bpOHLbgCmIOBHxI0gVCrWld/4wLENavql4aSMz5LrvKH3KqpMzX7L4vU9nDV4GkZm2S7EYcjfUTiXzTsWHkhM4c1rV1cB2OKpQrjnr/RuXFX5xUZhmlOnCv23t/SvyRK95nxVZSgVYlwSqLTEA+Tjz5HDRZBggHIk/COhgT+9w5snZs6Ww4cW6nGF9FYJ04YtBsHCzgNaGS5l8QSOcpAvQbYhtDFeuE3u6rETBfaIy1D7cPKFDLPpsK/poJ9yAsWhtB7ihAhOw88uhFoJcjj8OG3aV6bfIjIjjKDYe3YCK9rO8J0R9n+bT/ojaF04+FOXGeWtF3X9Efjgt/cRlsn2+vIE7bNxS7594udltmVYR0Zd3fqfWQ0cclENmVriAgWP9wqf+TUeZPqRxCCTU9MYZA/vyqvKMhum1m1UQi62OJPe8jcocDTAgH8rkN/J/GfwBEyv8giH1euCBPI2IlXk0tmi2KgRAvnavBff1ElC2eFSiRlRHcIzQsD17yHX+QTsWdJfTFInnrqxEGMAW45Gyya4I92XQ1WRn3XXuqzbrfvf9FQWfTTKIFpBJ8hB0I2wofqiaIOTBdFIo4FuWbxs1wPLSqBVYbZdRwlDRmLNm/JJC1gyk+Txr7m5p8E9X30XL9at9aFRUhey/5ikFNoWfWbDchpUKBWTmN1c2wxcRMLoaHUxzhgO/n/IqbfxbIHUr2wEHH7gqYIEWnpF5RzPBDIIgJYW+403WlHnnfEIHjXPDaY4A1dDHtSmmjkngS16gIesMCiaATRmGQMKtvhFHtN0SgizWuNWqxg27C1/CusgW3QCFJW7+iT7jNZvTHlf4hgX7WLgAopRYubv0vTz6D5W+hTrIo3gCXN56HvXSUWBKI8Ha3xPIQQJx7fH6hDyozLGOlpc4TmETs/KOBim+Kv8lxzmpe3b1J/K/jBqYE22ucffcF13erlU+cO1Q9dgPZ2JVznQYayDpzB7wKK1H3NrICi9x8fSsaru86zsiU6f4vRKQMM+abwOeRcAzDvC4VfyeW5Os6JT9fg6tYyaO0/wqEpAxPIY9gSikbTloWx/EEA5ZPhsyvvee4U582Lq8blJXWYiZcjEMBwdF3KEn4qxfmrrKpuj09IWioXkLxxAGRIgJEXDA+TQ2nNpxh7Zrev/NDvtd5r0+ftVkfujX3DfQUnarfk25BgYUgGvs5XP2UGZoRO4cpOG1OOcTLE0fbxxzA38vXRubCB/fDHwqMOqZtPxexvGyBKXE9xcmhYRQImAnk/BH6N48IM6Hi9Ecv4IPn+UdvDdpNc3aKUZFeHLf+tLpFOhDVYMR6MS6wgwM8sFTY5/oxg4R7QNPhf6Dnor7LovL6KmKOU+FX77hjvjsCkHfdscdBAB4dSSUR1StV2mP5fbeSv7dac9uPwXRfizW24OoHOtID995J2nzGSMPx784wYqq+/YI+S+iMXZDXooehyEX0DZH2xYJww+PRDCvaNrwm/rhVVHDZUs4NEHXmF+n1f3b9PMYeKAviQAop2VKcxrgsHv/bAUOdXF2MsFKRLL0tCEDkmvfELTCP8Bx8rm4C2j9Mm+n7pNA5uI89DyOfepAspWKIWEw91xLuYz03DxCyWZFFzgWiSSc8Eb8wFcns7QW6zgil/YYdkxiCOX/OmAufPmXE2BmsCcgCef+lsTmYV5dPvk/et3cWQplbmRzdHJlYW0KZW5kb2JqCjQ4NCAwIG9iago8PAovTGVuZ3RoIDE5NjIgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjazVrdbuO2Er7PU+igaCrXkULq32sri7Zptz2Fu0WbXrlCoFi0Q0d/kOg4xaLvfoYinThGaovMpjjAIqYpcWa+meE3Q3qRsTSQ8eEEyc9vr07OfxhhA3u26wWOcbUwQscI0chGHjauMmNmfjOwMHZ88+qWDCw39M0b+MBmtS4zkomZOWkY/RNhb54y+VLdVMsmLQbJ1X/Pf3AdAyN7hEARKMA2ckA5MrARYjtAoRFEnh1EI+OqAH2dHg9F5qLK82rghOaGlksxRVvxybbvzKuizgnrvo1MbkLTMvFIWmAPrCBC5k9ydt0SKePDL3+IwXfDIQ7FsKXLkoPiY+xE1g1lQjItBw6oJUvStGImLQfYzDg+A6BF0RaRYwSACnsCTFUzWpVpDk5xA/NjPbBADCmnv47FTNq23HdV2YrvBVez5hD4t4YUKWjuxqRMb3KSARzPG8lgwHRbrZu5HHP4uRyDpzrX4yB85ntppocc2/fCZ7bek4YuKIQQzDmX3mvP07rDCEJRt8LCru37IjVqUmb0wQKTAIk9r+uXws29I7ViD9uO4xuBH9q+DHcHKDB/J9xwB8FgLvwBok74MmR7vtEsjcfxbx9OjBmk4Mh05Usil0HYpyOrTH9vRSdmUTWDbtClFR/MwQDWrDtLxExaZmIg7etjXnhQWdpQdlsQRud7GhoCzu8SJRMW2EKO7XNJ8PcD7FnfNXx7FIbBblQDx3aDkRF4yMZ+IPyLnxmxKwQmrJHtBTKYzt6LrnjTFfowZHgIClEX05kV+sg8Pz+w5OVZWOkDeXwH25ZCqmou39k1mhLkbjqwHDxypi39sFz78OOl/uqDVh2ROxzqCrYOC27ZET/Hhx/Pj9mNwzey/KP7RoKLtJnfvsopJdD0PXkj8xYVMHtR60q/f1X6yzLyImXt8Z4kME8QmPsvEtjHmhdx3dWPXcFBR4x1xbeEaTOr7sKiykjDuz9dh05/vf7lj+n11Y+/ff/N5e+6Yv5EPjrs1f9PbvbAcKyY9rJue8+WvZzoX9Bynq8z6G64sklBiqr56+JYW/F8e/nqeuZA/Tm96atIAgrUFa0ZzSlTRRSqK6IV9IUkLRQhReqa7qHPrBpFRCN1PWnTpMcdZ7l24DvPMGGkoSxfVrzthaNToQgNY738oyVTDBZ2NDTdNlVZqUJytSA1cBxWhaRBEnPRZati8nU2VZGWtFbFpMET5bqAQ+5cFZMGURSpMp4+JLFuu7sQrqJMC9LW6VxqhFQfK8IaqSmkgRfDbgq8azZWg+YgRU3YieLra1AGexGbUX+FEprThywklZ8OZE/G+5eqmYDCCW8j9h6f8U8HDiMwLvkFDf9GMXx1IvEl7Wp49/YnRf8cYxzwB3hlMXjscdIJetLWELZuSmnnZLJ/KfLV877i6YH1Tw++2hcxmVipaghcZUwXoydMgCM9x0g10bw+Svf8BWnWqf8Sdz5VxdmH8P5WhNGH2ubbNAQvlnLwcxycidE0dhD6+uehj+TEZRyoIgvVdu1PZUYe4q6f4JsIjDq7vNjqfKHn1T19OWZGWr4P+e1judRtueu0Ya2uBd05A5tD3fUtS2kDAqDIEm0ZvI2iC6a7fnNLQECpu7wkJCPbO/B/PhLpH2ycPgURipIkwbS8u2bpzexyiJMZ2DVNZtNENeX7lMT7imbbfUc76tAmfhcp3Vl07KV/abF10eGAzY5cPB1+nBxZvnnd8rsjy3Vd88VrWIS+6iq6Wuiu3OguzEm5ZEfuHyexfh4ctpoeVH34ZqtI85y07N/3tTZNTtQSuu9dJ+7V6Gw5ESX839QCWsRJjBW7KrdXe7qoGs6Fj71IBnqySXw5zobDpyLQG6Cro3MTo/FmMh1vpMpucv+luxifsXgzvuNdynR8NxyeMSvOFGzc+sVTCkKWzDbJ7C4BrfH+pIUTbjFi3Cb0Hr17egEezRgPHj9rJBodqvsGHarbp0NlpKjzlJHJTcVPTflAti3Q9GN4mMWLNG+J4mHc7dOVPsaau7Hmjt3plrs+VR7r8rO9DnqjX8ujXgeerSKaPcTb3rxuyH2MVMM60tkhq/jSwuPVBWzOlWVpg/WOnehxhJ0tWMes4xxCPlslXCPiGQDhf8/HYI214ma80zl8ebiPGemaVcIOFg92NuSKd4qbRJENPacX9OxhGLNZnVjwB8Kr2ol6bh8tGyuux9LFHBmkUa0KpxeJ/a1ova96Dgd/qRoefEZa29rdh1w6/pCXbjyTeRrvzOX6myrS2dEUmINOLsdU1Lx8RnmFGfJ9blHVrBupxi1XjJqPPn8x8rFKSWhZk9Ll7U7YsHkq9tALofuM9xa4u7d43aXFq5pnuiz1uk3fUStt5b0o7aq54erlP97Nf7WdtwXoKZWzB1HOaCIvhVe8rFBVtH4vhr+lOeF4Vxfo9PThIp+toCHkQD/l/JZjlcRiavzk+//AH/kVavxYkQP9oJcvFoPHizAwje+h04c4fjJvlyRUr3H9sI8JoCuJHxQZzo8+Z7nb2qtMmxCh9xZ+p9rvBG/AnkEv9sTiVw5sLgnb66fFL9UTKn6ruZBcCg3Wfm+9nThYLB/NeP6/SB0P25ETGZaLbBy6L/7G8P3Vyf8AXScTcgplbmRzdHJlYW0KZW5kb2JqCjQxNSAwIG9iago8PAovVHlwZSAvT2JqU3RtCi9OIDEwMAovRmlyc3QgOTEzCi9MZW5ndGggMjk2MCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrNWtuOHLcRfd+v4KP90GxWkSySgWBAtqHYQAwolgMoEYSgd7alHXl2ZjMXXfLgb88pTs96V7deTU8AGV5193Q1eVisOqwqMjgxzgSXDHnBNRuWaAIx7vFH3oTAphQTcsJPxUTGlb1JFPBaTBa8I3zunJwFyoYo6IfFECe8YmcoEJpkMhQj+uJoSHI0PnpDSZ+c3iR9xYYy4SYEQyXoV2KYOZ8FdMteoTEgRqfCBVgzugi4yUImeGe88+jdk/FUABQPXnsO3qM/dBqAwCdRmWB88WjZ63idyoj2hxFHPNQnjybEaTvZhJRUBv2VoFpxJpKqKRB0ogqKbGKQXBUWxUMbGGDMDs0HMbEk/SoZcVWpGTcxAW/EDaBSZiPk9cbjJmG40LIw6Q1eMYRZ8DmXdEbZGfEqjC7EqzBUI3UeMuEm6ivIBFUC4ErUoSQxIqrVGIwUzEzADCVXMIoYTWJhYBaTVFvaRIpFhbNJSccVIVwwbqjEYJYw/4CR6yxD+1mHG6DZLF5lvMlZVSfBFJfCWQDCwqo/EVOqhQFqiRntYFwlRb3JsDVYVYBVwaKSqh0G5Op30D961ZnC7+SgY8NO78R5qERtUCBH9a0Alodxk0tVK0HvVIdOW05FVa+/ZdVi0m+z/pv0balfqGGWqkjttxRBH2gP1ipqinoHhRKmnfCf/hb1Dppk0bekykv6m74JmFYir6pJ+oWqLCT9rSo5AQucoZw9eHDW/vbuujftw+VytT1rn+zOt/X5b/Pl72ft96v1Rb9+5uC17nn7U/tz+8Mzqg9n7a/9bGueRfY2YgaZo8XURl+szjP7ZGFpEHtoHjww7RPT/nX128q0P5pvtpf9at1fWbb0rfnuuzP8Px2Hz2I9HJmJLZQMZ7NAwcE6+TiI7nzRWz4dAEoOisAEJLIJzkQKqLqat7C/EU2k20B+NM/AEWj9V9M+/ee/wH5wjmQzzH25Wyyef0pOu1Rbg/FbpY3PCsMufARE957co9VyW4E+Ap0pTdcvHoGTCH/DA4YJJe8fonLuQUwtGIy0fygwUzDO/kFU7NAAoQVGz8NHaj7u8BEphR/k4Ktc9u0BX/t4vZo96aFt0z7+8ZFpf+vfbs3zuxP4uHvZn7U/YBj9crsByQb9XKdps9qtZ/2mMn/96Zf+Yt59v3pr6sQKWCIVnanH3RrfgqiHT6tNbNCrrkGKRblvf83DteyvwzB0FdDr8xPZVsxWlzUKsDGlixRsgWZBB5Y/ZeCDbdEpvYw9WaXgAxAOyWJBvA+OE3t7sFlZccABnrYuyL2AxJMCid4qk7PYAlv1UmzBQgNXsRLTiEJOyD5YCm3GYgXSt2qUWMisV2cELWOx+SiQDb6cr5Y2npAEwUAOMHK0GrKxwyAJ6y60QfGz2pC7s6Jsxf5+FHhbDkud1WUa8aSNWEA/K6xLYwJgLKKfosDbRHeHD++w3m2aep/BSJ/ihyx6LJ3t27pDZ8EfS2c80BkPdMYDnfFAZ2G4+oHWPA1XHq57bWqcu38OwzUO16H9ePhu6GfQgQa4+36G7we1aXS7fx6u0Z+STjVWSVicJHhLGtgHhtXiOYJVKH/JUj01ZiigU4TZBCtEyFCi1YhROCGEOJpNP9YvrNXQLdaK6C8ggwhWQ2tQKIImRG9RFROe3wyljqR9+OBB7aF9WAmjfdL+49ef9e+by+32evOXtt1t+vXGwsreze3lrv1j1q0Xq0272V1t+m0zWy1f928v4WLNi/myW9jrixffHg0+Mly7+D/BYw1AvHw8+IvV3K7WL1tyVrOAduMccpbGUWgK8p6Gj8eKKFR9CikbAjKNR7lOcITB5UhfFVQOGUtX1oTTIhFE3grnQFYeXbII8qdj/f6R8w6ZY8zHgwSx2FBTW0SuEXaLQLcmjVj9vxzh5uqF7a+67aV9sW6vd+eL+axTUTXcdTPr19tuvtw0V6uL3aLfNC/X3cUO126zWc3merN721yv8Xa+3TSbd1f9dj3/DySOH59HYKcGg0jdhpqZwqZrYcNbX5npC4fYrd/OX9dp6M43mAoHg0T693qCoSCjtJzjDcaD/x2N8Y6hkLSv7Ktu8bI/X3dwlvojyIomOyGSsWo7hGtGphxcsST+68WL2Logts6I/RFSenE2F5psAuI0J8ZK/tofD5GAUWsPA0b4tU05HI/xjkqLa191V5u2uClMAavMWnUjtiJ8Y6Uea3+UU6wSCKsdNY6j0m92zZvpM44sRktHcR/DenSVxX+VUDk6W8tSg3WywCB8mmydEU7pfAnhNU23zgNIj1UjUzwe5Ec8fjNTZ0fYBrxuyrKrmSuzYdKUPlesWo9kxIC5FtAmTT6wdudrS6UUqEDKFKBQpsA6Rax4Tf0r3WsB1HqWLwf65s0bi8XX7pbz1/PedjPbbds/NotZu7rurhFPthvPi3626Nez00SM5J1Nzt9wAWEI4iZyAWKQ9tXL7WXDGoXBFvxk5yKE4yABpM+wL600AzeS1K8H50GhGXzFf0YAiBwTT3QvIm5fLcD+G2pA1R5eFicrVDLSLIGP+UpWgA2r/b/i1FJDCPerX9yVs74oYMFCGkZk2QjiORdlVC7CSTUpH5Vj9Iskf0wuFLLR+XE5eBh7HpUD4yOeH8fnkfPlNI6PS4Ypjver9Ut427gcEQKcMiqHeNJGkOKoHNje+Xivyvt7FaQ7FanbRflbZadja0spfVBbSvHY2lIcakFpqPHEoSYUDzWhoRYUh1pQHORkqAXl4SpDTUiGWpAMNScZ2pehXRnazQf5oX0Z2peh/TS0mz5Xor8fB4puf0ZBfhxvSFCkgHOmsWDIMTqERk8RGrEm4E6Y3XQaDGyL8qBmagXIERZN5MFTI1VHE4WKppJuo0tAuG6EHYKOcDKo+zgzlJim13ZitmBq3eZznMC0GsPz1EAYSRAY0Uc3KUvLUKKjA0Qlz+LkeIi3lal5RbdcdouNxsFstZCOoGBC6UNrkBl+BOOUuomJ9FxrPB7+VcopsiCKUjMgZEKOmwlTH2oBWc9taEyca/1UuTdEsiVOzoJcVsyw1ylJ0CFGu8F4KNMci/F9fZaUG9f4nBrJXqgJ/yaZ7E0BAQiR1yMP1uuBDA8/pfC1oUU0jaQnIm7RFL3o+R+r55WC0+1AOnFNCVxIukMxgQo0INKdBq38C0J2rMoIHJFuIjSOPLVolx2uMYsWYgFXtxyJc+EJ9RvSPFgPVIH5CyIvPVgSwWXeY9lNR9DBy/n2cnduZ6sr5Jf9spu3Wvltzxerc9zNl+31ur9ezxEHtX/fdRfrbjufNU+23fl8Mf9vLQs3qxfN9rJvfuiWq+V81i2aR6vd4vd+0/y0etM3v3TXzZP+ettfnfdr5ARItFjamspOS191c5s14wZvR2WdIjZhUeQcbCiTlxrG/GnVjdy0mgsyjKiFIa3F097Q9MxUcnoW5SRFLEce1E1IXiOnKXsdusZo4KyBULi1hcQwOldOF2awONbMO01PYRkBC9ctpBq76U4STdxBOjVSLggvfainU3Q71et25v4gVHHTKCZmx203f4H2aMK8+6jHRHw9npHq2Uukk1Errog6JxaF2cNI4aYkZZIbxVoCxjoS92ks1cN/AiOgE6kQCeUEFXLZ753vSyqBoMnMR5dU3iv/BlARmGPa/pQ6NVaOAaH6tm4Tn6To45NiLBnu8vS9ak+S+1V77sghi2Y3KiMZvl/yuBzWyDIuhcxCYhqV0/10LOnjcpqbMY3KgW6wko+3p/Onp17H5LxmN8WPy0UEwnFczIky4qgcgzOK3EMO7VEah6fnRu8xCo2HJI/PGlZJq9WmL68s3TnRBGuj/bGio4tJ+cNzl/noc5dpKP6kofiTwkkPBCntuFwPAOmpRoKug5ZyZNgh/vThOX/Cw5Xg0lLKDQqkQnrq/j4o4imPViLaDHRA4TMShFTuheLmZNT/AHgOpR4KZW5kc3RyZWFtCmVuZG9iago1NTIgMCBvYmoKPDwKL0xlbmd0aCAxNTA3ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42sVZW3OjNhR+z69w22nGTgyRwBgIJp3JpNlpdzZPefN6PGDLNjYBD+CYNpv/3iMJfNs4kbSb9CEmIKHz6Vw+nXNAjWkDNT6doPqqW/Q/+P10cn1/cnFrmQ1Ld22727ifNCxHt7HdsJGrow5u3I8b/WbXaA3u/94scHGLHadhwysI01f6GjYc3IySoqVZJm7mfl5kQTSdFV+RhWL4wR4fiSb0yS85v/v2jV9jPQxGCzpCp/bYb0aKVZbwceRx8Tu4D0CjhubqnW6ngmtKwY1KPwuSxbJXZCtyxSC3w7PPLdzkyMVkW5XsjpDsCRdNheXfb1grgrAflQPZfVsisncFScqp99h9Q45toeazJHZ7b83d9w6lOwLSC/KwjIOC9EYx2DHImccZzdsrSVSugKzHNBpzdY7Sh+UwAsnUrhv/Wrf59a9kTMrTyuWrZ5tJ8+rBLQdK4wTzJ09yxrGRmANSiHPfv9EwFfUU9+cDfw268ibboOWu4kma0sYiCCZptqel0kde2fPXXnl+zhBJbvtNinKw0WS7LCXDyjZFlt6z/Vor23F7fo7bEwUOsYU45FlyUevnB63d/YBQrPHb0qGYvxiGt/yyG2EsMqvA9PYNCWZElRFlleN8oHLkeGoZZMX/xlOVehw5nkI7LOVN+NmsxlKVyhxFltry5h/oEvCcAxY8YEDKMzZ4zhhVgcpqzbwDldWbFqKyPfeoqEyTpLJ6L+9BZc47UJnzgVTm2NLRqkZl+4YEM95szCirHhEyeySjIs169ALqgf8isO7VFUczJcneHq5r0IZshuO4Ij61AwYuoBaAREqOxmjmK7gNKaBrHq6Sfu0iFfaIfOxFPf/aizgxKCV4LhZNR5jTfG73Tyk9oV3PYKy0RVeRfXnJBsozP6KJYL6C4mCgL1f5bFgXaJxxn1U0ZrwDE7imvCuAU8LunNoToASC2/wHXEGI4kBM3kcD/wk/S3K2K1TV/bbMgulDwA0Jtt9QRxDHJJbcUVfFuUNw7pA6d6iUwLtvF4A1PEckACpcRvPOzyhZJ4shrXZvBn2o7Qf9LxqWLXtdV0TuS4YAvqERlUxj8pZIzdS78GB3vxghEcHMw8KBnpE8+pdQ09wp1B4YYVE7YCSUqGwZGOi3csvcW+YUKMkefwCpcDLDifAsPEqFgIbRHD0UKqbLZ9Fk279Spj2MOupeM0kBUFZ50GhG6Bm2ipnGxv8kwUM0amOWGMtqzhLBdBjgc6jQ5707b66U1WL0FqsYZqcD51aSFy/UIf4yh4SXHks0qaio229JNAc3m7dFcGw2DyIqImGnN9z2/HD/AK/mBqsireCOSVwEl/wAVdLVWxRnOLbV3HHjZUYeqx5rmKZx5UcLn7Y0pRXkigh/2TtuqHdUnVNeu9E/io6WK7Rm0Zhu+jBnPti2e58A6ySIc+KFGQkWsukpxkgEM0eULjji08pYw2G4iuIiSobBeDxMHwkNukmcrunkoj0ljAY4vWrRoB1qMCFq001R6O3TomaIIEyzou5jy6odYxG/lFaMIbIqV0zRQy3alS9+D5lN8mJ8eTkiWdbr7S0C6/66f0hsB8JgfGzoKzY7x8aSIwP4QBJM9fa1/CyrZlNEIdzaoI0B9dviIpSlfSxE+9LgLeHDGXffId/G2Fb4xJBLK8/5iQX+BrorrDsDiX6MwMZhumTyuaZ1FPjFxSuvvPwU3mQf1mbg+SR75XWApL0+HJNkWsxUEYA16xxW/uVVHiVTZckzZbl58EDUN5zBAaD6dkZGqywjyYgcdajXI8EwZNpSu9lKItmUwoYpICqHhGJUnfhfggVRav1io6NUtjuwu52y/am1mcMaTVBay9bW2LAkkByUMLKb7iq1rWg2stO2Ys0K6V3aUh/E6dfw5LBOOvwQsN727yBnp6gKuW7eRi9y5Xw0LkGYj9r8FrIw6ga+dBlguGo1UKJhb34FpcBc077r4W0WM40GRrqLXLaY0bH0joOhuDd0B9kcwUEQ/Hl/8h9bC9CPCmVuZHN0cmVhbQplbmRvYmoKNTQ5IDAgb2JqCjw8Ci9UeXBlIC9PYmpTdG0KL04gMTAwCi9GaXJzdCA4NzAKL0xlbmd0aCAxMTUwICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o2Yy4pcNxCG9/0UeoJq1VUSDF4kZnYB43iRxHhhnCYEzEywx+C8ff7ShNwIlGBA1ae/rnPqcn6Vxqa33riPZr3ZjDYCy2jMuc7G7s0WiJUrN9FcpcnIVZtyrtbU/WJgdHP47ebgdXOzORvW1dytee/NV67cQnOVFiNXbYNztTbcLt69jc1Fm5sbbW5utrW51ZYrfPe2Vq6MSDQNyZDSUETCum/P7HpxPC/zhqOxbBjRyoYRruKnzguGwxAErisNeDZNA55tpAHPeR8XyyTJxQWefcPwHBuG59gwPI8Nw/Nwbq7wPFYa8Dw1DXieIw14XpyGZdr54sglrw1Hk77hAWPDswlveMFIz6ijMCrmhmqJpoFyyUhDUT9Ow7KQ/eLmMDYMz7ZheLYNw3Nm2CZ+npcd3iU8YGjWn3cxZUeBLpEdBb4WRHFxh58dhcPP2jCecEcRvemOIhhGRhGCRko4dkfh7oGW2lEEempHEQFj4JljNN1RxISx4QUjYTy37ijgVHcUyL5mfzgKjB5NI5t1ohPRmRqSBjxHrIsPeB49r8DzsDTgeSQ84XkmPOF5RhrwvBKe8LwSnvC8Noye76i5432yHvNyd3e5vmxvDWhvr9v1hx9/QqejyQdNxP7w5ePHd5cXL/6fE/z1kgkbxHjxam7RwS3DOrEfuNNOgaKWnDChCWuOmcbJ83Uhga5UHF5ImnijSm4qKcpackNpQZ9KLozsAHOjhUYsOXNKeSs5DepWlw0aRdnBJcfoKq7LBmmm8DrN2AqIZ102bD00pM6fjUXiddksFo3lB1yng24x73hx6zSbMVmvy2bKtLROs4lQ7j4lx0q9H6S5K+WuUHHQN+qjTrNOo+C6bDoc6lJj4RTzgPOAuNRpVgsaB6KmOiAudZqxH9E8EDXlCXE5SHOfNA9EDdsomdZplrloHYiazE45bJXcYDrQPgmm3FRLzoX4QNTEhLC91ZwqxKUuG6YiGnyQZjaIS1026UY5JFQchjnSA1Hj6TQPRI1HkB6IGkdAXWoM04gdiBrbhLjUZWOd5AeixrIgLnXZMLZTHIgaM0aXA1HjjtHlQNQWJpcDTZsYXP5Ti/vHh6d2d9eu9yvPD/zM3+ekj0n++QMOCC215vlTTtYY9v78Chuloa/yEzxeX316/PD97am9bddXL+/b9c3t61P762Zvfv/thi/e/3K7XL/FjW8PT58xX2zPl+vr2+fHL58+3PJSf7703e3nX99/8/i1vc0LgePJWPIOt3n/Cb/FHmKb+zviPACcjK//5uAW8pdnuJLraATjkgvIX87qNYdG4Pr5AvIXNmsO8oezWs1B/wavmoP+4WRVc9C/ceAO8qfiNQf5m3GQZsifrrpsAflbWqfZIX8WddlwaoK+HPiD/OX5t+RiUP4zoOTQztHrPONkDH2py4Znw/RSY5A/4bpsDvnLQ3fNdZJZl80hf1PqNBv0T70um0EA56rTjPmVTOq8WAiGlzrN5kr5n6WSM8ywWucZgxWdhCsYYQ9EzRgz7D9F7Q+uQEIJCmVuZHN0cmVhbQplbmRvYmoKNjI1IDAgb2JqCjw8Ci9MZW5ndGggMTYwMCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNq1Wt9vm0gQfs9f4evpLCc2zi7ml4s3J0W6VndS+3Cy1AefZQHGDZhgAjhJk/Z/v5kFEztpkh3UvBDMsjsz387OfDOEdb52WOfjEdv9HZp4B9ePR+fTo9MP5qhjDse2bXWmq45pDplldGw2HjKDd6bLzqzHR+x4Pv2nWeH0A3ecjg1zGMc5M00fGUYvSstjzRzxXiaSWTzX/mMmS495T+NaDPfcrRbZE/9INuto46EB0mupXEWqty03ldhS5F66XpQg0vNncZ/PZzdzN1re9kU5y+YaXGAoD6/nqpqYO010FU1uNJG5tQi4o1o7ekUGd7je+0HV3FBZNQ/LbZ5WKAJeZNXN14ToDqerbqms+slbh+hojfOdo6u9L7Z+gY/P+xx/V0P3VLtsFfCCzWUmZVmDWXeON3+ny/C2ErlE6fdUyx0Vd1tt8spw8Dhpu96LROpG8HNiuVG/j6KjFb6znEWoGK/2mby945bqxIK7Maojzt240ud+D5tELN1mVr17kWBuNElRfZiIMxLQ/UTELu4oRJVhti0uFr4Ho8EapyYytFCdy2BKp61N2DL4W5wGQ1dZNYJrBaRXPvLEZNCA7D8ciWCTFiXtdDQaHQYsNhxVM0fm84Cenr4w5+dPYSr6UtJuIu8VF9GqDJdtp/vf2s5E/F+YCxCmLw9rfDAcMrlXh+8pe6JS8K/dAm+u4VSJlZcUITVDGqaKqKenne9OOyVONvZZJFpyKzCWDKofsYjINtpKLOAiSmRGAizjM9bt3p4BG9K4DMD3SIyQHWkwyoGcpNfiN7i4saYpx7DGfKU0UWWAuNs9RgTEgy77SZ+RsVDKCdLaWxBMjaEmewsyZHKVVXeEUgdCibkMuKQ/nyG1TGW+GfgnFuI3Jxulk7lYev2nVr4vqbtjjn5lCmr0N1RWLUqvjIJK/+tNtKzuIHsvZCRuslK3TkvVwZf3L5ABGQXhbTjAfZFCRQGnV75D9gElrvqgeBrelgdK+e35pGmR4vFnkWMpgyUN+GA6n1kn4IifNE6uX0wlIpsVwyAJPbkHkvjAgzwswvxaxrPP9R6Q3d5REd4wFb13B0LCfHP/w828vFxEl1ki6fWJP7iD5zA6AA/4CdmufOnA1Sp2CFPAlpo5HtBGCsFr0ByrGAR7NgxBdS8IG6GgRBHdhRWzlRhL+VWIrgd3I78JCbjnb9ALymZTiOhbTDXmdSv0r5ADoPI7pdtAZClF2t+z3Pt66VWbB1VUpUCG3D73kiRMmrBQjRTBBbrGclsl2OW31LuMgoHO9kiSOjB6G8ayhpC0nnx21zTG0uAyUkmbey6diayYred47HbcHipO3CLm+ptNjdDmOsxpvK1BwWhdavKq1BT+Lj7XbzW+BAdS1uF18UnG6rVIrTu2uY9VCRGDar6lIuOxE8QAfgx5KW6K1PsSmU6GXS/kPPWJxocIEdulDL33dYPtqZpfpJssxNhEzWGWraI1OsV3sVj42yjBfYvShbdcLvDxKtncyK2SYaUc+EhHI9RyEAy6Qas446johBa/JwcTJa5JZTM2U2fPgBnGJLnTQZjnk8nBXFju3WGJ9jCwg/un40/fr2t3ytv+c6LFcwPvHms/mfguJpqHLEN1SZuroxlMsDb6/j34w2+DqO8tKfBcKaPQEoLGoZSaxFcQzQUG8FOfeg7sX9ohbtQ23qJdZb8WvG2T9cjh2rae7YQ8UcBWUKCUBK0MJ/uZLT2jauUoiCrKfBvU+WPqRcm/wOvb9d3ssVIncJespoMbKswOU6qRwqDc5ABdeVazkNWK6tIOJ0iSqb6WVRZko3Rq9dpAmJawYcUBBfjShtE4I1L9lzLsP1haOrgS5HzsGO0acrLiZjtKd3UivvTJ7N8xqY2OK7J51ltELccmHS2gVOCRMqU9tNGfNDf2Gu3UhoHj0Bq4ghHq88bocRtPiQXw34m1+64TiegEx6fV97Y+Mk4kwkSDx4zcIqOaO+Zv0Rsb6yTPeSvHaYykRZq1wKqkUSppUwCMaR8bVkK6SDof3Ikb8h6av8Bl7zT5bwpkO5WaeLD46iQl22XTzvsSe+sC8i6Qyvq7Rob/fLBr5zfrjPQOZ8MxG8t1dMMcGg7vaCN96DC7Eq4fYvrX9Oh/mzTjuwplbmRzdHJlYW0KZW5kb2JqCjYyMiAwIG9iago8PAovVHlwZSAvT2JqU3RtCi9OIDEwMAovRmlyc3QgODcwCi9MZW5ndGggMTExOCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqNl9GKHTcMhu/nKfwEOpYsyTYsuWjD3hVCmou2IRchPZRC2C3JBtK37y8HGrI+xnNlzew3//FIGutfazXlZK0ljaWnhqXnxBIrJ/ZYJUmOtSQJrGuS2rFaKhyrp2L9sF5T6XENtRJrTwrO41mOlZNZrDL+5rkkL7Fq8sFZqtyweqrWDs811R7XLbUSK7ZXsXJOPTjm1C1WST04LolzgKwIqiOwxMwROALzw7ki6HGn4SVLBB1BwHhlLgEL3ruYYbNxB2/rAkEdDHSsMgLoxAZcoOPGhwt0HCnzgqdqiQA6NeAiiVvABTtsFgEE24Cxwz5gKPeaEVRkmyNoCCwfkSPJKIoj+cIlAkYQsKI0ErCiNmIRoDgyYEtSBuwIBlyjfki0Qlm1H65QVlTd8aSYRABl8wigHBl3g3KAblD2AUO5DhjKdcBQbgOGclPUDnWWhrZyNJB0iQDK3SOQVHKOoCDQCBTBgKOjBoyW4gGjpwSN4LgoovVw/F4RtK3jlUqRCBiBRwBlDbhCWTUCKOuAoWwDjmYdMJR9wFBGko7oweINVcZHUKpEAOXqEUC55Qig3DQCKLcBQ7kPGMp9wBVfUzT1+KxUj7u74/IyvTUkN6fX6fLb738k7FWzExokPXz5+PHd8eLFioMqVTT5lmuVUM49VytVvMiW80bxgW85a9TQHVtOO5UTcqVTFzvBZVJ01ZaTTB2f/JZjJisn0pyFsu/LhranOOO2XCvEZV82qYWiobecK+G82HOmVHWfZ1EjObG9YtR4n2YRp2L7sgk7tXYizbmSyr5sOF0pxsWWa42078vGtVOWfZoxFMhcT3CZ9llhyxRDY8sp42w5oVeYat5XjUVwtpzIMgvVE2caBjXOln2We6F24khrSjiWf8TuHx+e0t1dutzHCMN0Hw/cc5zKGGPfrmI0Y/TGBR67vPr0+OHX61N6my6vXt6ny5vr16f0v+Kbf/+54g/v/7oel5+hfn14+gwHYPH4cXl9/fz45dOHa9wq3279cv3z7/c/PX5Nb+PXw6jULu/wM+8/4dnk/duj398rnMb392L4oErN57T/yEn4JrIbZZy4jKZV3nLe0bQ3yjhzaFre7w8jlfxGHSeuom1vjKaJc7TtjdE0cYa2vTGaJk7RtifkCrr2xmiaOEHb+ok0M/q278sGi0y97NMMS46RuC+bNaecT+hVx0jclw2GinLdl83Qzp73eTZtxLovG/ZGfmJ70kl4Xzb8l0LV+ASXMRH3ZbOcqck+zdoZI3FfNm1Mre/TrFUwEvd5URfqvk+zWsFI3JdNVSmXfZ61KJ15XTHiE4easpGfONRWfvs5t/LbE7fw2xO38NsTt/DbE7fw2xN3229P2MJvz9xtvz1xC789cQu/PXELv/2cW/ntiVv47Ylb+O2JW/jtiVv47Ym77bcnbOG3J27htydu4bcnbuG3n3Mrvz1xC789cQu/PXELvz1zt/z2RC389sQ999v/AWg2PegKZW5kc3RyZWFtCmVuZG9iago2OTkgMCBvYmoKPDwKL0xlbmd0aCAxNzM3ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42rVZbW+jRhD+fr/CvUqRHYPD8uKX4E2lk3qnq9QPrfyNQxFgnGA7mMA6yTnNf+/MLmDsS867tPkQB8GyM/vMzDMvGJ2bjtH58sGo/g8cvILfLx8+zT5cfHasjjOYjEbDzmzRcZyBMbQ7I2MyMGzSmc07XpdMxj1/9ke9w8VnMh53RvCOQfAdTydjYnYXm/yb4RhJynq6Y5HukqY6cZdXFH50HR6RZ7FNQ4Ej6UZHnwxskF/KnZyQa1q23a0FZnTtLX194UqKcYQY0zBkxARbthFyGM2DdHXNeqQbhN6yT3xv57vJ/KlPmZf5OvzAozx+8F21A5sGkdFkp9PMLUXAleppTRljvqhqbsnsmsdsm6cCRcBLWXX7lBBzTNRVd2R2jTZpUfpZ7XBFsovR5dG3xZ3GqufmYTeLxSAMolW12n1RPfpQRslZkKz/Bt88iMNHQ6uvGZd++YgLHvm1NsPrXvUIl5ndZ1UIRzLWRxDyuAItZaBsgVeoAekTTQctCEen1hechBru13QeP5Ux/vyi7DVS7FWFNzop6BFR78xH5fZhn8fRHsjl/nKHKutXD5tkroybHMEtUBEgUtArVeHRCgFykuDGI2fv1gu6pNT5zbjMOLmBVFXICZEReJwwVnThrs75WftErw47pTN9JxacnQkLlYtgRZ+IJY/6zl31+xwc0NqnK1eYERei4ZY60Xb9lVLsVVYipoyVXoSO8bqISwJQBc1SstKKsgTjAk+Y8XNBfMGRvZVPkVtZMci2xe11RTtijerRbSmdMPWJnBiABqGX+h5YRE/PM7j0vT914iu7kCMHuuJ5hlLJT1nZkVz2q93R0YxW5hjL8mwz2dAWqZZM3iPVmobMrg9xxDb5lLP+VUn7Qc6K6jyqgWVKVVVCKrjvgeDNlrnwh3krzh944mpmfZ4y5fmxxkGKUUp+/JqChDIBmt3sEuvOohUOagSzo4/uKzW9u5wO3WW/D1qgEliMAuEqIyBFLGj26+QuW6MSOw1EZho0FVqZmxt1wZoDgpaqSA8W8whYC85XCbcasHfhIHMon/ThQHCOQdPlfqHHDvgcxXk+nR7sCoI+HtzYPxB166sPf1zMfS7cghI3b2zX/UYs+61n6VsPPh5r6wbhJmet62NzJAPqQUkOka1qufH/SYq16qeoduQY8imp0tU6pNrmi8cKWERCARZDGAYsniZIR0yQUXqlqpUpIapg+TYqCecvbGna1VOWJWOtmtpmbpl3qiZqml6VSQBLm0LZALZCriv/JXAP0BVyzS4LQlXKsqSaWY7qAbHfBU/hvqXhHdnlrG7O1AxQIyBVbHFwm/kVVRF1vfLhR23mVCF0mOGUolg3FD2E0AkbCHT6KK6L6PB8yBtm5crNehfqsCZKLv7pjWEFNvzkgBwFAM1Eo6v3TbZUtcd9ngu9idlxVteOJi7hfkaB2iMTVV5pCbUsZ5+3Li6OOLC55vW7/E3oPDdpGt8ELOlhiLbbxewWtwl2ZCyet90BeAE3gLr07Q0Aaq3t/umGk3nbt+MoRhsUQZ6sv7fFGug+idjPD3jx88cFlA4/gCwbRDaRYY3SBcGolLtdHiQ3t2xfYIp67RdemDcjyVBlCttsQ2IJkFgChXkiCGztJb5OHT1RpVBbeY5bnEOa8kLf45wBF4N6LNEr63Ll7sC234MtbedkSVQrIDVuxZGfACGNn9hhUmnToNkj2UllOQjDdhVQpxX07n5amRX87oB3zqJXazVysKVGDuACg/goV2YigTRkc/m1+qR7T/G9g7m4KmITaZM6hsxBfs3y4OYuKFuEu6xsunGslQfrdVzWohB+JRtEt/Aonm/XPFPOv6fBXRJpxBB5Vu00DmkT+isI/dU0O0zX9TRU0dyO1GSiOZCnmOVYaeWs8Fa+4MM6rUdYX202JXKbhzini2BdxKq2diyFoUmzS0koqIODHRpW/KgMi9S44uBbwesfEARypFsGMDvxRQEnP6os4pxqAayJNe7i1jxl7SgVUzW0zD/0+jrcJmvG8bsO5vNrvL1Yb/gno7JCY1qoJ+XoS4u0s6jVWN0ZyuiJ/FGO1M1yKNLDORRtNQp0TjGsbQ8nXeYZkD13LQZ7zlhmf3EMlILNnjjJEfoc+p4Il7fQR+jLbxzKxOlMZIHotwJiaMgY9z9/MxkSKbyrwIKE4y197YHeYeLkNcq9vms3ix+aMqJLOnKZh17rX9EHF1XQaSIKxfqLAMPppvhKxRfct7Hq0JKz6tKn98rnteUsqqixI0Os1fSrXmWZHWIMJsaEr4LKbmCPSUe3zMHYGFVbH7zz++zDv8UMLVUKZW5kc3RyZWFtCmVuZG9iago3NzEgMCBvYmoKPDwKL0xlbmd0aCAxNjI1ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42sUa23KbOPQ9X+HdmXbcGBwJjIECfujMprP7unlzmQ7GJBbm4gKuPeP43/cIYWwnra3DNN2HGCKkc9e56Ij0nnqk9/mGHJ5Dg7/B7+ebTw83d/eG3jOGtmmOew+PPcMYkvGoZxJ7SEa09zDvTfvaePzBf/inhXB3Ty2rZ8IaQvmaqapZptEvovALMQg8lJViKB9of3zLYIQ6YvUJ3hdISU+1hyNA26Azr6HTR6P+XhKqcYBqyUBlj5yH/HsE5Bec+F0YFYXrnq0FcH+eDZx8gKXFY5Jvfvj99XzV0OkLYADEdWeuG2XzxAmAjlleVJyqWpZ7rDBtebZDlwC65+fw3awL67NgjuH6WxWw5GewXonECQ5iABJRkjgYgElkJPFtuvS9EHDczbB2a9IrCKhFNbTdmto1qJpF+1izMHUZqAx+P9TKCvPosbaRv7N5tBVjiSKeLKvEi7AaLH8jGak1OLR+5XEDcB5zvjtJizr2Mid2x048GHAiqoGXTGMfrUFDipZaENXkQaB+fhaUzSafTsyT/xVRtS4yMYs4WLmMZWg5RVEFs+nMnxZBtizhZci4qjhJCScG6MLLw/yVttdyds0Tmwbp79HyOnd0pwtfsmURCQKqKF0lQRW5YQKiC8pSaPl+gqTLohLIvudsLpRYVgVbXdto2csBpgjqjluxmXEvHo+1BdSvO6QNWNoLuepipW783CLu7i6s+fFoExRYFRUg9K7LwzwD+c3WFcuzrjCqvOvK1Vd2YSkIc3r58+Lr7PIE//by9zJPo2rBsqfL02o3dWkGmFyUPVWLy2Bc7/L37PJn9fJndpUHenmGglYjbATzzJi5Ur8QzcDsgP4VqSRdres3qETpbPkFy4uuizcRe1pUXVdfNeb/2w6v7OnhT8PWlbgil8EdAkISlNV07CtsvuWPDZtXiymv0fwmdKQiscJGh5EMFSexbOUlSrreocO7Zcggep0WEkgLs0NauIKccABceplK1RjN67g7CexAQq0HICH2PZWihWCidF5EqXdIBMo1KBjNsdWNYxCvE08446rapSawbFRNUHgrkCjo9h1Tll4t4QKd69pEPvdfeqC8LozZVKYGTdfAi8dPIBxQoepxlNx01fpE4o6hWdOkCtMoKSM0Q7rU8QI4ndTnGy92hOdJ/SNXKyi5a8ZgAlPRx0X2SIYEsP+Bt+EYBHpnMEix2882fmWB39IvVeg1Vu3FaKLNtzg/sKU8g9gt6R/gENhpsVy7JpecD4GGXBg+Fs4HTn8Q9LsXGGm3hRAfue2UaVCFi664vUlX5JDYA/qo6/IiKqG+pP11VHYFkeVV9+QwKqMM0FcdUx1burTXCZGxy2ANRZ4oGIMk8abv/cP5KtL/6UTqzK/Nf0CT3Auma6ST04mGioglewI8mYfNMHSiy+B5HfHpaZq1wzIn5cFbbFuvhLABkZ6nUWgGDRlcmwVLouY0b/n+/XZSTpcqraPUruQnxOJ/njAvVdUBcWeeyn+x5/M6GUtFL45zi8jPW3bNNwhZOrG6WclZMs6VqIpMvAtjUiliHX9KhWumQydKp1LeBFvL6JTinFQRhQcfdTaotKJdHl83x9c04Me/VJ3w40XspqQaKh9ORccI3Gnbqmqi+B4rHR3l6mKPgflAgrn0HWBY9USCufTRyh51MWpwRtX840chakKUjdpI3dm63EFtwHu4R5q2OA/ZysRA1Q11Ws1ut2gRSDmjttELmEJlOaDA9FY5sI3l7E0cFLXeZuPaMmBPGuFEgYxWgcy26cBgFaLJdCqwG0yj0umUpkngL6tiHTbb4d98XYQRrtPQ0oU70Zo536Owygu3efBm5QRkPBF+YR5UwQ7+Pf26o/s9WgVSJ1yC7zPPUHdDP/JfIhqjaD0ZuB5tFm2rV52jY/Q9toDCPCsrbDzQpHzDScfYw7Y8dU3q4kfrgeuKQrS9IP/kUcCF8pgJD/uim5YoscKUmdKEUS4imN/k5OlaOYVWdslhNUvmjkw48MpbbpnTWOX1pOjW1hQCEfwRq/WdmdvaZHy0BKVue+y75EI66XKRJP4NF0lK4XNwN0lEm550uUmi61QuTB5b8+FdjJa39ga9d12X8rHHdvRTkW/Ok/iTzdEC0bUeJUOb2DUQbWQMRxbtqbo2tIh56M+frfnr4eY/w7uxhgplbmRzdHJlYW0KZW5kb2JqCjY5NiAwIG9iago8PAovVHlwZSAvT2JqU3RtCi9OIDEwMAovRmlyc3QgODczCi9MZW5ndGggMTEyNSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrFl8FuHDcMhu/7FHoCriiSogQYObSBbwWCNIe2Rg5BuigKBHZhO0D69v2pQ1vvWJDQS0/i7H7z74ikuP/U1lNOteeksXBqsZTEJVZJXGPVxL2nWiQVabhuSfC9Z9zFhpWTmp08l6Q9riWZxKrJPFZLdXA1VYvVoRFrSz64njw4zqmxYsVjmJ6cS2o9riV1iVVT91gtcR5gRWAROIKBtsQ82I4gYDwol4ALIzA5eYntYW+O/bBIBNigeARQVo4AymoRQFkHDGUbMJQtYIFyDVigXENZoBzZc3AcV45HYfcy9smNI4Byswig3AYM5T5gKPeAUYuSA1ZGYHxyLQhQHlfUgCUCReARWCqFI6gILAJHMOCGgg24IwjYoKwBgysayniUoqi/Y7dITwRQNo8AypUjgHK1CKBcBwxlHzCUPWA0SmkBVyg3bSevUG4NNa5Q7iUCKPcagSXJUf5aEWgEjmDAaC4u0SH4hPF9qEsJBqkRUeQZ2RdpyDwKLFpktI9ojQCCliPA7aYRQNAG3JNEDzt6XGrAjZNEt3qDMnJ8cnASV96g3EoEUG41Aij3HAGUu0YA5T7gjuMT8DhHASP7ygGjwMpQvrk5nd+mu4pWy+l9Ov/08y9oJbQdU5yL+69fvnw8vXkz5Uqhgt9cclwotr7kspCgd1dcF0Itl1hTEmTsBXb7cP+cbm7S+TY6C8dw3HDLyJqOqRNXcWJwIuICt53fPT58/vHynO7S+d3b23T+cPn2nP5W/PDnHxd88em3y+n8PdQv989P6JOhdTq/vzw9fH38fHkarTM++uHy6++fvnv4lu7i1yuPenzEz3x6xL1jCAX3r31htP2zrzgmTlH8w/5fcCXmDtlrZbzmcqeMQ73isCmy18p44DKh5GuuZaqv1fGacyZusuYq2hYjdskZ2tZ0zSnadkNO0LWY0UuuoG3rRpoZfdvXZatZKSb0irNupHVdNmuVct7Q80om67JZdcq+LpuhnWte59m0Eeu6bHg2qhuPVzoVXpfNuFP8Ma25TKWty2Y5U8zwFaedSWxdNm1Mra/TrF5IyzovWgv1uk4zjAhpX5dNVSnLOs8qSjvbLUa8MdTgATFc1mXDPyPxxlDDiSTfGGrSnMrGUBN38o2hJrXhP3GdZrgJahtDTbRjuqwx6dQ3hppIxnBZpxnWnPrGUBNmDJeNNOdCeWOoweJhuKzTXJoQbww1+Euqvk5zqUplY6jhf4jCLC85Ndo4vEWMGq/TXErFcFmXrXCl1jbSnB3DZV02vElQt/U+4MdIN4YaO6xLWacZL0BkG0MNb5a0zgobjMvGSMNbG8Xb0JKb+e1rbua3r7mZ377mZn77ipv57Svsf/Tbjixf+W2v/T/67Xi32/HbL7m53z5wE799zc389pF73W8fuInfPnATv33gJn77wE389oF73W8fsInfPnATv33gJn77wE389jU389sHbuK3D9zEbx+4id8+cBO/feAmfvvAXfntvwDx3FuECmVuZHN0cmVhbQplbmRvYmoKODQ0IDAgb2JqCjw8Ci9MZW5ndGggMTQ0NiAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNq1WUtzo0YQvvtXUJXKlmxAmuFNJHRIbby1ScUn3xSVCwGy0ANYQJaiLee3Zx6AZCUrT09ZFz2YGfrr7q+7Z3qQ8qwg5csNar/7Nv1FPr/c/Pp4M7i3TcXu+67rKI9zxbb7yLEUF/l9ZGHlMVYmPdO0bqePv3dvGNxjz1NcsgZhumaiYw8bvXBb57e6bRq9XXCLe8u7mfYQpFn9F7JRGWarpzqcTZbTyW5KRid/6nhKRvCQv/oE1BkipOh+3yKYGiy2CJaXJKrzcvQ1i5P9mILCvaIaFlW/TKqkfEkopgcuHvfYLI68IH+/v4pisltMjgimIizrp3RTrKnwnVZoSx1rk0/UCuhrRgR3KOgM/P2WYu4X22rxNAujFZ3GBoavUnZzRTDGYR32E4IxjJJOKkFRpQcCh5mNCmcAqL8/cdt+C9jCdgEEX2dDTwTfT0UZPm9CLjXfFI3XQgKuDNfrZM1H5nnJR6poQXHH2zXDHv+dhZs00jhEqAV9EYRENJVEeM+hrAI0XI0ehitVpTK/TVbTIEv2LCoo2yryQFvKWMxC7+ExPNx7BWpp4Xfe6tqoB44Qy3jz1tOF/wFgvju1e6slgDUlZuCumKVZvnnjnEw7+onHHNBYtogLjgiigHi5ocUmzSiWlZbpKyn3OyKyz+mYEgTpKFgNU87HFld0R6dleqpiQko6MkjBiFwRRGVSb8uskQvNYpYnwk4obl/gpVGeVXWyL8rGjp1Pvz7cB/QftR9LjMR+oxFGCKqbjUBsXs3ybRYz53bPiiOfZ2fU5j+/A01jYyGCz1k2CwKWVk8dDDeCISKwaKOn0Ij5ZaLHNoUUa014COCaWCISdouUl6YuOc3Ug3ZoeVS0rjN6qkpLMFhPW9yBhzGmLMaHAHutHJMLMln6nTBbDAZnCfp0zv8/bVaGL3kayy7OX5Jyvs53suujRZ5XyYXVxFwshh1X43a4OBf3/rk8oZcY5uUJQ1lVCOfllhrECglhUbSqflhk32G1UNVpjwK0snTEXrXE1vjQ4s0QQUW2wwepWHbFOM7FsrShY/0wJmYcLM4zFnkIjnQPWvea7EVkaZHaAaJQ7ogdFlJG8D+wMraaOUIlqU2ScfJMdWGVsjEmPVk1Z4RSvgo5GJSsd/Bk7Rgym6klEbQcfR4u+V5qpwZ0eElcqLKdwF1JzrtQRzomlE07sLbWFXZRji3w0u6oTXJEHF9gS9gkiR8Mz+Bk6pQHZ7AQ7EL3A9gUEe6ohErBDECiTknvalvxTslrJBwXwUi02a55++RIK8kjXauWiwG+O2mB7H8Jqbj9HTlbQZU2oM4CM9I1r+EsC1Id6jBdXwj4+Oi3LDDka4Vrg2pFDa8VriMX3dlpdDfSm4qhYj1rq38sUTNc8Mm7Bmt9jZO368PCnbZQz+I9lunbeEjOhZ91Y7gc0zyt61RuPKGN7qkaEKepGOw2D0PdFkPd5hlXcJsnkk2aFjz9IkYiv2ibYtw042m7+AmsCyjfnOSZKCwCw9Y24X4WYMsHu8kGNWaiPJnPL2U6KcY6wC1wsxmPZc4SnlA6Casq4eG4+9lpej9gl3ri7YndmPjxzpFuMXXa+eCoo2yd7AZ/TCf0Iqtoe/dNNmI21nb0E7xR89EV4tMX6eHX7JqnTkbR+pY5k7eZ7sdQDUQyDGv88GNgUqXxNqnO0njZ7PePybZ5cN/c6Mjs5XxTJs3vSX7fj5zhnldqaKT6Qpel9GQY7KHU9YUuP3m4LJuQlDsl+e/lG8O0rEYSSTX8zgCxM36XDI6e4wNgbYWuLF85b5N1lbxhGL1bY6LZPetcJjv53kfesHVqXeHgZCEkemtmIZHsUNXlNmrLJ0nzLYuO1Yz/XA/f3ocYvZdwzfrU7M/r8EyWaSgY9X3kM1mGZfctDyu6afQ95HKAxlur//Z48y/jiHjhCmVuZHN0cmVhbQplbmRvYmoKODQxIDAgb2JqCjw8Ci9UeXBlIC9PYmpTdG0KL04gMTAwCi9GaXJzdCA4NzAKL0xlbmd0aCAxMTM1ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o2XwW4cNwyG7/sUegKuSEokBRg5tIFvBYI0h7ZGDkayKAoEcZE4QPr2/bkJGnQ1A81JHPnbfySSI/32oaUWH620HHqJHKyw5OiFLccoUnMcRYAFYokcuajkKEWtnqIqVPIZajow9tI8Ryudc/TSe45R+shxFEuOa7HkmIsnx1K8j1OwXt8d3Epojlie52hlcI5eRs8xyrhyo3BNEIvimqRwYU5UBEGPU4giSFgaNqmOoCPwDLBt5Qywb+0ZBIKRAZRbwgrllrBCuSesUO7dT6FQ7plBhbJpBlA2zwDKnquHBHvv1+3wNd3YPycYSCyHt+KGmYGtR9NMO9bcehFsDDOGoGXgCCIDlEYkA9RGDEFHcTThzggS7oIg4Q7BlnBvCExPgaVIR/miQ7m3DKDcIwMom2QAZTMEaAXxhA3KnjBmxRPGiyVzHAZlTJ/CoDyuMJRHYwRQHpFBFK2SwUAAsfBalGsG6ChO2NFSnLAreixhpEbF+BRIqOoVNgRX2BFkQ6Lk2rIj0U3asN7ArPbsSWhpz+bFcrUnjIyoJRxQtuzfgLJfYSh7dmZA2bM1A8rZXxFQzpTEgHJkFw8oj4QHlEfCI7+DhK+fFTr57u50flkeHPus5XU5//b7HwU77DJI0BYfv3z48Pb04sUux4Mc5VxzlZD9NVcrZbVWXBtM+R0suWDKTS85F2r4ppacCQ100pLrSg0lWHKtUdV1nps2OrJd6cR1nebGnayty9aqEfv6vTqMnNdl03CStk4zOps81mVTC1JepxmfFeXRsuTaID0gp4OG9ANcpWbrNOOqojHWZVNm6nogzVWo2rpsMoSsrtMsocS6Lpu4kvk6zWKNpK7zjHOfvK3zLK3TgY9XtFPe0EtODIfLumzCRnkOL7nqOFzWZePhlK5hyUXgcFmXDdc4VVmnmW1QP3CowXHROivcK9mBIw2XKc6WA3rK5AfONBbB2XIgyyzkB8402BqcLessD6U4cKRFI42bHN8/fXwud3flfJ/+R77/4B71xcU1vj/h2Gds69tDWjpYtnyAxvnVp6d3v16ey0M5v3p5X85vLl+fy3/yb/75+4I/PP55OZ1/xqsuH58/Yx0tf346v758fvry6d0lp+Tb1C+X9389/vT0tTzkUizN7pC3eM3jJ/y2GFxhcj82mUbwxybhkbtTWq/bZPyfgywH9Y2aTlxFB2/cU7ecDXTwRk1nDh3M6/VZoIc3ijpxjh7euKcmztDDG/fUxHX08MY9NXENPXxATtHCG/fUxAl62A6kmdHEY102q42GrtOM/0lwP67L1sOo1gN6brgf12Xr5lR9XTb884T7cZ3n3oK4rcuGtZEdWN6O+Z64HfM9c9vme+J2zPctt2e+J27HfE/cjvmeuB3zPXE75nvidsz3xG2b7wnbMd8Tt2O+J27HfN9ye+Z74nbM98TtmO+J2zHfE7djvidu23xP2I75nrlt8z1xO+Z74nbM98TtmO9bbs98T9yO+Z64HfM9cbfm+19/bEIzCmVuZHN0cmVhbQplbmRvYmoKOTE3IDAgb2JqCjw8Ci9MZW5ndGggMTM5OCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrFWd1zqkYUf89fQV8y5gpmgV3QIN5pe5tMO71veaNOBhXTjQYQUDPN+L/3HBaQa3LHXXozfYjyse45v/P5OxuiPWpEu7sg9feA4RV83l38cn9xfctsjQ1Gruto90uNsQFxqOaS0YBQU7tfaEGPEutqev/HuzvAA2M0oPCTaqn9zdLrW3M41FzYnpi4fWC4jPR+jxfRy5XBbLO34MvlX4SR1qNQF9/zJM4LcVm+vRTXM1huvi6TDH/G42oF94nHx1883r8ye31cEgZ8avgz+PSyqNhmcbW7dxAatkCc2KCCxGpIVB49k0CfF9l2Xql9l/GFuHpV1co5I8q0hmCW2j5r/swLT1JGA8eVkbGL5kWSjUsfTYS0NAHBuZ5m0Q7c4alCGyqI5Q6thBaJOsCRjCR00jfBBsbECLspjYpvqgf6ValF9aRv4bNu3jXJOcWGptU7TYI9JMF+7Atf7/tlGrwqmsQ0z0i2bEoRZbDHVDOnfnk97WdhvHoowlnApnALr4KvhjlV9b1pyQA/qIKyZXYVUTvIojzKdhFaFqANZuF8hdeACO2Jf8qgqJR4yJbvCa8ld8gmk/1voeTIhFK7Gbwe9PT10AWkKyMqDbPigcP2z+ka0e71VKd6cDk96UDrTmjPVS1r6LLeIqBTfw0fyghHMtu/3xYptMV+Fb8LaIigAPRG+OjLJ2gN0yIyelSplG7zvx9AcB3Giy7ZY5ndkfu0xQjwhdVT9at1riLZ0It7hoGGVYZmy+yN3vI5hqeyDRsQVEZQv98JBJPxj2rJthypOMOaWUbZLAQ3iyhbd4oyqQpy6GT8oczWvOTBkDiAA3Mn5/9Eden/STRZSNeS2c4SWJMV9VtlqKOP6LE2keFSquTbNpVobpMl3xkeFkdK1lqhWhJsq0tL3d2U0oWfd+PSde3JxFB2pC1Fa4TABfCy7hIb5FJMpoJsAYtYQKRGj626JdxUqrSf1AT6v9pBiuK0ZbQ4a1qmXSjSqVRS33fJctv5kPiXGsGwVjxUYVbXjSbunsMX3zDfD/y2TWDdmHyuOneOZehYgG7QYM88rkYbXuiwGl+IGcBTLhZDiUH5oBwHI4ldjyP3p0dVH1Ny9iig1oWaikcBX5M46VaOqKVUI2NVs1JbZR6Hm2og3yibl8oIKiKg72ERjefrMM+FqNtK5C6pnRu9hPPiNA/0W3G5VCH4jRmYTBuP/UdjkkNWVGlY5Ym3wdGuehZ3qS9Uappqmg4eRYgiXDJhuB3HOAag6A0SyrIEg64i4QPUeNqFTVD3R9a9Bq3cGVDj7jRL0vARoqKuWMreHf0w86rmLyNdBpwnkPs0Zt5TM9qpQmbnSpQ9soc9wUcF1hSCG24nSLshhoKnqVfRVkFnoLUU5fptpBrezJJRZoPnShC8IqsIBrK+CdJOgcvsj5hbGJWaI1R1ZR+RZMxRS7LopYjixWlRbZ1yiuL3pvLhtHJS/N7kq/KxPJOqO9iOqoP/Qn0uaIgs9wEDYDuZwtv0iU98cnnJx/HnjSimN0SZFrEPoUVMhhYd6Uea7KPs5/VaNYMdIiGmYgn4BUZCvhAXk0kdS/kq/5PnhSpAx5SXjPKO4n5NtmLcVgZryYhsMofHvHgo8XXtT44UCTttEivf9FZj3/FW3VqTIzXvvTFtOVOtuhRlR2qUC7dFIhIzi/DYx6/OcOvnWG/memOFNMmPN+toWbTuEtTSmKCrlI3jqJzrICf0/VUn37synUoIQnj1RI11N1aObKlzbA6RIcql71rEayIAedFEuKast27H/1o43Y67b9KShvjza3xeMqMCOMIn3orFZkvb0kwyGJFRuaVF2YAOTc2wrcGQuEKPk6Oy3+4v/gXXa1PMCmVuZHN0cmVhbQplbmRvYmoKOTg5IDAgb2JqCjw8Ci9MZW5ndGggMTU5OCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNq1WVlzm0gQfvevwKnalGIheQYYDqNx1Sa7SWVrjxe/sSoXoJE9kgCZw1LW8X/fORCWlTiaoZwXSQzQ3V/f3QLGjQGMTydg9z1G/Bf7/HTy/urk/COyDTQOPM81ruYGQmPgOoYHgjFwoHE1M6KB44F306s/OgrnH6HvGx57B0D+TjSyfA8N7klaF+WE5vXluxGy4SCLq2UVylf3mB5wBMYoGDuMZ8sLqvCaFQ+KdNGOrnWErh3Y/oC+g4O85uJbgwq7JrvMMNDGYKvwmjPiRfkvQIBKlnBAL9bsGkr+2gidI1wdxw0G1QhTbTxIhTKdcyzVJbYYMA4j+4rhZCIORxY/CHURuSp6fNRF46l6ggA0p/mMfwtvHifkhub8kuMxs5g9xY+JfEYe8k+M5fPtjVYjQivifN1Ut9dJnC4F6V7K8VUC5VE61uaWrghnlZNtfb0mZdbUTKa4poVAsz4Att5HJFxSAtD2nEBFSKGSP2lVM7Vwhe5pht/pox0fqDL+UDR5zQzCGO/bJO0D11dKXiWpmzLXRnQsgVm24ygHQ0fVVqE63+UppqQ2O1K8KkJ2SSd4ReZ1SIdDrrGHdbQuqinLMSVhd7kWQUlSkx0OocmfHFGTCt3qxq1/LL9BH1qDR229IhWyDMMOCjBdE/byjmPpDFo+1Legp0JVJuc4TZusWcU12QUXs1EbAIeZTUYGN/jz9CY+T7FniR8PKSnLyeSZCEYEB2+enTzdGaSC23fvfvu0yF3zmK5eonahQ+jNgZSDsPXruKkLmSfvL55SAof3hM6w3bHvwmcWvf8G+Mu4zZdufCtVyxQOmNpXYZwUZb3T/vGgGe3L2fnIsVrhIaBdR/1AgShl3idV2yTX6ZdUVqG0yKu25fmcz8j2rfw9Ex6l6f8BUPL/To67CLpT3dANoAqTuwhMsXapCiwV2vPDVpG1pXTidHlXF5CtkvXoUy/M+6GhFXb8NxGamgmpavX2uAOslMgPAS8Yo8WkChc9ASOVQreJFsyCDKo2JrdnHV3iRciz8GQxdEdVuJTouBxDPIuW+p7qqQgiywHjwjhPLzcRN+SUMxYWXWij95VKsy6UQMlH28hmHoMZGt3wQwC8gjdKfS5OMddf2y/PzzA3o6YBEYB95OFTajZh4xYNMynSXZTxbwBFKREt1xSzw+nZXFtF1ms2Lh1QWy9xN9qZBgGnT2qVtn0aSiquv/9IV4P7ZSAEkJ4719ydP//9URu028uBLjrAzGk5vhpnsiGs+RqEe06PphcBT0WaZog58F1XKhuwVo6zWj8fI+D/FJcNVKjK+a512nPWKOsKD8Hrt2sIQgWi9wWdScHnhMyu18WGCDdhXRrvR7fSM7cm5Qlu56jrHm0bgs9zChjb8k0bvazY8/MfvPP907b13/7gRSYN62UwBn2Jl+SuoSWZ/ZhH2Jd8Ud+Ssu/L1S2dH45b6m/f07i/Uio6a0hv1hVZxyUbVFdfDigo+7utPhevJxh+/bplbsBdmccvc2/tFQ2CSuWmS7kJnpEbzn7LuZ67uqkVoj7VbckmlOVZMsFZvE14v9mu9rQDWKnM7EZrazDj5SxrVgKwuexTS6BSLUmKYiVZFryxxnXZEG1L+n1qqJzJUM+ZDMFAvXWfRXwgm/7iCkbFEs/jVUXCpCTxMtRcISELqDBmLKfn3IjajmrB1xwPOqktpX5KqOu0EA6XFnlNc3V36ADoTcsbfMPUNLqkfLchrNVjhY0spTFZlOhfV6vxXbSZ8pgW+UwcMhnac3OZsI5K7PDWZiKDr08AWugnbDCR5f6EdsfyVNod+Zcl30ExZW0vpfnKSttUKvu1vaVXlzGaFc2wY2nbQWX19leRt1vNJqfv40o7CdpAXYUpKXmb3mqw+SdNdTHZev0pzWl9nRZZVnRLa90KZiuNtTc4JxvJ81NJ278Ct0mf8LFttfFBdk5t3022D4/mbzyfQDN6O+1OpUilAF4e/KFYtiuI8LFP4rEd9c6Jca72ZuNT7DGabUfxfHOtqyulVNNlP7KN07q1DWOfmNGhprZSU3uz2W7ilKmwn6qU/tJp4+9JSB72TEoVIfkCYF+0jpNtGRCMAxAITpaDxo4PjZFtjX3gSfEOAvj3q5P/AWCq3d8KZW5kc3RyZWFtCmVuZG9iago5MTQgMCBvYmoKPDwKL1R5cGUgL09ialN0bQovTiAxMDAKL0ZpcnN0IDg3MwovTGVuZ3RoIDExMjAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjaxZfBbhw3DIbv8xR6Aq4okpIIGDk0gW8FgjSHtkYORrIoAgR24ThA+vb9KQMNvGNVg1560u/db//RkBRFd7eUU/eaNJaWmmPpiTlWT2yePOfEHiunIrGWVFqskoRj1STmm2dLMji4ScPakrZYezKO1ZMZVs7JPFZONTguqQbHkhrX1LWkZrZ56PF9Ty4FK7wb1oL9ZA7BEBaiQHgIwdaDLQoxYEtcBlwhrGxeGsSA8Z4iDIEXlQYhcFYOAWc1Hs9l9RBwtoAFzjZgONcBw7kab47Nch0wnNuA4dxaTo7wcucQcO4WAs7dQ8A5nuwKZx+wpZIHXCEsb64NYsA9FR6wQwQMr4LvIJCeoiGQn9JDCBJWQihEDQFnHTCcVZE0JKTogOFsA4azVcS8wrnmEHCuGgLOtYeAcysh4NwGDOc+YDh37ZtXOPcBw9kHDOf4K15SMsrDEUfJ2kZeJfcQUVQlBKqKawiUVRlwhdC2OYpFyoB7EhmwQ2AL3uGsOQScVUPAWXsIOFsJEfU6YDjXAcO5qqLk4hOAjn1LCwZBl45IOPIqcRQcpSORT3cYxgPdFcenhDCIAeMI8IBxBljzdnW1nd6km45k5/QunX797feEdy6m1LDLu29fvnzYXr2acmqECC4xMeo4NkuuVBLUy5LjSh2BXnK5keIQrjiUN0WhLbneSRHeJdecMmp2yVUnQ6muuUzrqLBlqrJOGloJcTvgJ0wtr7PGpVDRA1HmQq2ts8ZZSHgdZRfCgV5iXUn6RYyv7+8e09VVOl1HL8IRGT+4Rn5TNJ2nv6LHoofGH/jZ6e3D/cdfzo/pJp3evrlOp/fn74/pH8f3f/15xhe3f5y302u4n+8ev+JqaPHz7fTu/PX+28PHc3xkTx/9fP70+fan++/pJp5ecRU0Lx/wmNsH/DZVf+J+vFfcOz/ei3EXNep1H/bnHGy5k72Qxh2XUbTKS646ivaFNO45FC2v91c7yvaFPO64hrLtsuYqypZ9zRnK1nTNKcr2gJ2gaoutuYKyrQfCzKhbX6etZqUYB1acuZHWddqsV8r5gF+rZLJOm9VGua3ThhGMal7H2bQT6zpt2BvVA9srToXXaTN2asYHuIwbcZ02y5l6WYcZQx6uxHXatDN1X4dZW8GVuI6L1kJe12FWE1yJ67SpKmVZx1lF6cjrFiM+0NSUDc1lnTaMR7gT18/FiURzWadNesOduA6ztEbtQFPD9EdyoKmJdeoHmpqoo7usMXHyA01NJKO5rMOMgZn8QFMTZjSXA2HOhfKBpoY5H81lHebShfhAUytNqLZ1mPHvCZUDTW02b++4l+ftHTaZt3fcZN7ecZN5e8dN5u1LbjZv77jJvL3jJvP2jpvM23vupXl7R03m7R03mbd33GTe3nGTeXvHTebtHTeZty+5ybx9if2f8/aT17N5u7f/Om9jc4fm7Wfcv8zbl9xs3r7gpvP2jpvM25fcbN6+5Gbz9iV3OW//DUS8WwsKZW5kc3RyZWFtCmVuZG9iagoxMDYxIDAgb2JqCjw8Ci9MZW5ndGggMTYyNiAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNq9WVlv20YQfvevUPIgKDFF81qKjEgBDVAHKdAWBfymCgKPlbw2tWR4SIod//fOLg8ddZxdNi6MiAzF2W+/mdm5pA3WA23w6UJrrypid/D56eLjzcXVNTIHSHUnE3twsxogpGq2NZhorqpZ+uAmHsxHyJy8W9z81q1wda07zmACMprOZOZj3XD00e8pTd+NkamPigxHZEWiaS12BHiGpg3GrmoBXoPjiOCs0vxvDWmfaYz3NV7+IS/gkf4oiIdaPPdHeI5ujAgtGYwxCjX/nT6K8ZrBsz3oV7YsRUsTgWwVqOJ9EJUMLgqycagp8yHsYHFGf9OHu6X/YCOGaVmjHJdVTmuYKMWrFYOGLQRxzJWgbKqE3WwUm22i/SetFUNEK08yS3c8TSEbc2L5HC18X2MwjZM1ttdHxNemxKvtAvzBNHRKLi/Zq/ULK4zjZZbuMJc71w+IrMezLIX1ijlZHLSlNKaGFyL1C3zVS32WpFOVmPL9VQnZ9FIqEgF8SYfHCqwo0+jHoMBcA/6GUL452jyDb5nOOsHearJFwsuTrC5Og6OmmrWkib6PcXX1gszzT0GUn3VCySZI+opH6WaT0r7SaRRVeY5phIuzJYRNIBPhzxyn9QfwG/CI2nWko54rHg3edA7Y+FuU0pLQCsCx7IFBQmE/TNOkTjUbRpFb2i/zCst6OtL7nM87UPOdh6Z3/XSLDJGM0qFlPouIOd7Wx3t+t5BmaQoBcmNmM5a7teHwYNSMG7U5UOzbVZAU8pYVir1PstSQuJ82DHgk/TOK1KwqbpdhEAGje1464Lx8PMk+TAcsiTe+3Suvop8aTDveQpUmMMo9j5MtyANmJBkFDzh5J/Kw5NvTSPWdKw9wBo+usMi/AuzLUhBUmdB5XH1ZCGIpCD0fTl+S1Ec7Ut7KvB8kUmxymbVXOXCXeB+K52dfB0N5vsxCb8/tDP4AxYznQWUDek2k3VkoMbXB41BBbYI9wIV9yhHkikDykvKXJFGzPM2CdVBiwGs9Xpal/aM0NEGa9Jm1dYFFtzgq09xjF9g/3BFgN5vVqeAL6DMP5vZCmo8hAN1lnD00UQDiP2oK+4N96JarIKQY6EnWeLYpgFziTZaAxTzCKtymi6QN6W1K4vpujemSa6A1q2zmtS2h/jy4x16LvoH/MDiuE9qrnLZRn1ou9PVp6PkN7jS8ZLUcT8SwI5WCHtjrYS/vFkpJjbNR6DPSLa7P8D2r6dQy6HWO7clPPFQdF0fkUHUuxLvPzoc6ZVMZb+r4uP3sCha9PLItG1SAeXuVkhOhcrkbgRgjFvMfn5Ts8Wl60jVIUp8I1c1ZkJdLAkebe+p7W8kUOtaV+XDRtPpHG0vqaC2vAaFiOqjKZuS2hRKWeTbAceeeh4s5tNfSNfVEoqamMxPghsNkDvTbwUk9MZI9uhNLBDbmQIDp15DS5JAISufZh5jNOyNWXQJq2x7F0LHAPu4WY/i41Lm7y7K2xZUNBgaI2ec/rhk4MzfcSitgIqQAPspiabqZZsXKtu4vpAk6P32u11FxX2OY42j/W03WEnH0V0gfjlBhBGTq6IH3WUIiUi75jLchczRhjvvkEccU0WUXrnd+M1nnYEro765shfq2rM85QsVQfaggWUGuOoSts0E33wloQ9qkQpXR7pYkuI6jvjUcvuHRrRnv63Q8liZuixMHSFST/fatyd+zpiQ7fbx749vvw3MNSSdWR6ivP4boyjSWyfKA3meNW7Lse3CTHfuUDryO8wp9kON+dyh6ju+K9GFH5++WrG8fkVL/gR7gIt2xuLrUnJrtoP+YuiB0neCy/6j5vw2qExag+wqXuCglx9udXUXCbjvlhTwSFAWOz3/NU+rrcfi9JX0CsCsWgLssUPlVuIy+RnVUakCVrX/0+0zAj16v34xcibi8nfnVeczpMwd3ZVrUQzH98IHNFvuU7a4tV7ZnfkzqXMNGiMqDmvQpIVyhsm7rb4J906Fsla6GqU2ayf502zF2XmMO7Lqy+YK5jOTebe0VxmG2poumAVsTCRcVJV8qvMzKnM23/7oJSOKZ3eSsnMpuT2RwRWyrmSsAWrEMv84NtID+up0d7eEJbIY/lFa6JT4uhOKwIVqmcVpMzyRNY6Brqqu5XNKwkGo5+mBsGqqjTWo489Qav95c/APntCeoCmVuZHN0cmVhbQplbmRvYmoKMTA1OCAwIG9iago8PAovVHlwZSAvT2JqU3RtCi9OIDEwMAovRmlyc3QgOTY3Ci9MZW5ndGggMTE1NCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqNV02LXTcM3b9f4V+gZ0u2LMGQRRtmVwhpFm1DFyF9lELIlGQC6b/vsW5pIH7Gd3PnvDvHkvVh+Vx3Szm5e6o5lZxz6j7+llRKAE6lBZBUPEBNLAFa4oOsSQ5yT9L8AmBJDjLsBrnkVLsNUFIrATi1FkBS8wA1qQRoSQ+ypn6Qe+rNYBm/+kH2ZEHmnCzIXJKDB8DJWwBBZAEqHhKo4dEDKUI8+HiU1mGdbYQd77BpjhWCvHDXgZAUKYGQFWmBkBbxQPBRJRB81GMFHu1YAVOtKXyMTbdjBXxorBi511hR4aOXsckKH70Fgo/ugeDDJBAe1gNhmR8r4MNbg49qo1jxzhPHhnLLQF0GKolLCcRALZAAeSDUlyUQCszHCkXNjxUdqPHFbQDHbrPCcA2awlzr8Q7mFBUGgjkNFwpz6qMSCnM9NqWw0iNwtcQW5VBs2aLYHZYt+ghG2aNDO3y4BZIko+hAFUiHN2REymjV3EdTjnyNakuxqDvakkfdM/pWWEfJYUokj/6xAlSHN2MgHz4MPqoEgo8ascGUtIjN4KNFbBadP7wZfGjEZvCBosIHTEmP2JAw6dH1Dh89YkPWxSI2GBCL2FA78YgNnYL+Gt7QlzWOYcYBqNELOc6ttcvDw+X6Mr11G6f5dbr+8utvCZnSxsQo6McvHz78fnnxYsmrTEjJliZCghO05bGQadnzSqVxeLa8XMlx5na85o0qWmrLM6Ux2ba8rtRwXrc87TTacctrnTTv8wyfpCfcshOO755XnHorJ3iZ2PblaDmT8T591QtJ25ejWiHzffpqZ6q8z0tVJtd919cmVH1fjlorjRm65UmlM+FyI0y5Pa800rovW81Kpe/94qRRL/uyiXXiuk+z9E7d9mUTNRr6YMtrRnZiWGFGY2rsaeLkJ4YVpj+Gxj7Nwpn8xLCSUjA0TqQ5M+UTw4qdMTT2aWYTKieGFW5e0r5PM2slPjGscL9Qr/s8c2104vBCXZCVfZpxj2O47MvGRcnsRJpzx3DZlw3ykLzt4yhmGC77skHZ0VCbW546tRNDrWimfVZKy6QnRlqpBbPlhD0p1E/MtMIQJPVElgsEyYmZBq1HQ5jueA5BcmKkGfSIfZfjx6ePz+nhIV0fh0bG10YseER9cXH5f7/ig0DjB5ZdX316ev/z7Tm9TddXLx/T9c3t63P63+Kbf/6+4R/v/rxdrj/C+u3j8+ehe8tYf7m+vn1++vLp/W28a368++n2x1/vfnj6mt4O/0NZd+ff4ejdJ6xGuuRY/C200OXfYiv4fuoQZXPqvyPy+OKidqeWMzGjde9cUBNRHb17p5p3iGjecmKPaujfOwWdiR0NfOeSmomKDr5zS83EhaaeifdF9cxbqOqZuJDVM3Ghq2fiQlhPxJWynokLaT0TF9p6Ji7E9UxcqOuZWI3Gp+GeeF+Hz7yFEJ+JCyV+h3hfis/EhRafiCsxPhMXanwmLuT4TFzo8Zm4EOQzcaHIZ+J9ST7zFpp8Ji5E+UxcqPKJuJLlM3Ghy2fi98L8X1oyWMoKZW5kc3RyZWFtCmVuZG9iagoxMTM1IDAgb2JqCjw8Ci9MZW5ndGggMTQzNiAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrNWUtv20YQvvtXsDkYMkzSXL5lkToUaIIU6Km6KYIhi7RNWyIpklIEJfnvndldrl5ptEvbaA+2xOfM981jv1lZ2qNmaZ8urPbT9PAb/P908fvo4uaj52ie2Q8CXxs9aJ5nWr6rBVbftFyijRJt3PMt72oy+lO84eYjCUMtgGcsgs+MjcCzeut01hRVlOXN8MrwHNJLiq+5zr6WVbr+nCfpFelt6Cm79/UpreAwHbBX7zl15JGlGX3TBZ+4L768L63JITOJDpVwDM7IWvVaq4GM1SJLGN46bVblF8uz4I98U0UYnrFF7JBQ/lKzSutsm6KlR2NYZwCOHaFd3SD4oQy1L2P+JNpNkRRENZjEkrH1UFSICWwxU1lsDbKIAt6hHSD462sVwlvEhJzzIiR2b7pqCpZHixhMgfmyAJfqcTZRhm2rGUzQ4GI1R6wL3afB5TX2lMXTJMELif6UPT51iThxZNzh9Nu9+zjBwn2k3qjYE/BdKXsPaOD+mgRRPJuWDO/377yhTOs6TRgfwAF6gX+zIm+yfIVFr0yCJ+MUrTqIOGQgTXizXNVPd/dTMDh7oUnaiRBfpgx+qCIKuhXXUWEZUFpDTEAoOsPoVF+hfLwFwbHRNaKC1b6M2WP8zxDa58gbPHfrJfa5jmY7rtsT1kpkG1dHwAeox8+TAeOhHMaUfcZHOWEBUGTAlmpsqnll22+ZrcJX5/+wFNjuf5KqtlTzQTmzYT0YZUybOgoLkIDpv0Fp8ESFnOW5Cj7hUcxzFr4qr4x2oNSFUdGZB6FXJUKqNVHhCEy33Z61+rJLq7f7sgYPjWVd0DnWe1SqQ7pUanlLF0yEgV9qhAcJs0PYdrpOQKVa0iytqig6uBMe/nBwYu8CdfOnF09vphA/HL86ihhUyFBUTG2SKvigq9jHrJG/3z7xl4DDJxUVRWmezFXT3HEk5iXVhcc5bM77D57Y987eKt6qMFDiB0SSj5abIZ99itkMrihzFMj7GCqNn1meNXfgVNcJ1JEaARnqvRG0gkwHdvaSp0stu6+YCavafKUQaClwFWfCCgVaRYfBv4q84NsApXxWCPhSsyG+2Uw301mDWGE8MhI2kCEr5MbXUU9eYi+1hGqAEZIyUaXNqsrZmVmRPtBlnA+RlS7GTHiD38aQkvmjy3rnOl2UxhaiuY0oyHyw7SblXFdKiVP0v9UoqMDacryd7Gs5ZbiejNH7opjziECpLqZzTJ6mkjcnMPoy5gS7aIWlK5VycKg+6Aik55SaHQbebtBZC7W6xSkHNTKjfg3i8fKShno5XlPykRTc8ABeHqbzWp2VUIYVVdnj9uXzaUG5xsgiHtYooTXsJB1c5Zt2fBNp20n3eNZbDngtUo+8x3aEJ6XRRMIsIEN54mKXveT96paRSdMkXkw3nMiFDg/SVniwAKiuQYIB5x30JHqroOYY0Ffqz4WafBOxct9QvglS5TWZJ6PJauiYM54to7Ru/oYewo5Ue5kntVO2t5BWyoSGKpvqcDBUVxACjcoGPtOxQsaq4vKlxFobnUPFhoV5WzHhCM2R7WpTL/CcaJp0Z6FTWH0iLaVAxKbVmvaNkwmItRjl3uzbXZTPC7S9l9MpbPDSbZX2HZlVeq/BLuLdmoTb/ODI+GWivMHku7J2+e8Y8a8FqGoN+J6UOoHsZPaXuJag2XRTzrNZhlPenRDHCc3NZWMMxalyWjV87WFXuf5OmP7uOvz4vozfv9iY48mqnCfnOqDTd8IeqrL2V6AyTgo4ZBtjkCKnW9fAKdzAaF3qWGYdd3f8UIYU1eXI78u8lXaH4x95ll1QBO+i1YJ30WqBlFbbjTH4cxxVX0Ve85ykC0vbVPR/uZDoeyW4a/R7d387csSxNWKZfatPHbFdz3RDohmObYZWwLw/2gz8Y3TxD5eAvwYKZW5kc3RyZWFtCmVuZG9iagoxMTMyIDAgb2JqCjw8Ci9UeXBlIC9PYmpTdG0KL04gMTAwCi9GaXJzdCA5NzEKL0xlbmd0aCAxMTc2ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o1Xy25dNwzc+yv0BbwSSb0AI4s28K5AkGbR1sjCSC+KAoFdOA6Q/n2HPEUf1hUuV1fneA5HJEfUuJScU06l5JK02C+nwfYrqbAvNJUmtqiJ7aHkllirLXri0WwxknC3xUzSxk3BKikesEDcagSFk05jKJKqWOSiqXZf1NSKUZSWWjWK0lObRlFG6mIUZabeOyJzTgNvsShpVKPANmc2CpY0xd9omsPfIEZmY2UEyc3Yjo0aHVsYNT62r0ZFeMmWt31msbnZJgQQUfvMtqKeu+BRPXlBlOpBjbs2D4oozfO3ojYvgIK8zQIOBXn3Ehh577YXCzqK7UURdHgVjHJ4GRRBp9dBEXR2Y0NQzl6JWrDyUlROjI6CowpW3lGE4uI9reggs7FVtJCbcVT0ULytdWDluSEUi+fWwKGeWwOHem4oItc8wQFZcPXcEIqrdxcF4+a5NXA0z62Bo3tuCMDdc2vg6J5bB8fw3Do4hucGWfAsJiQE4Om5dU2Svc+9YuW59YaV54bP5BBshxgPxXao8ZAsxCCHZkfBynPDZwLVgmMIVp7bAMeh2wGOQ7gAy6HcAY5DugMch3aHKd7FO8FxqBdgOeQ7wQH9gmOCo3tuExyHggGRQ8ITHIeGJzgOEU9wHCqeE4fJVMzZjpWpmP28+iHFiVUI6eb29ub0Nt1D5Xai36fTTz//krBHZEaC4/P49fPnjzdv3uyBdZAJ7zpQJ0kkoEyaOEsBYCaFwq4DOdNEsa4DSyFr1XVgZsoQ6FUgT6aGgl8HDiEbCteBXaj1QL1xUolzoOBclboGCo6zQBzZolQaJVBvTBOSGuggl0ZjROqdOykHOogjQ7MGksGpJZ2BDmJwUOZAvTHFqDaNADMFioOZTk0C/cNkJxuX14FSqOdAAzE7CaM+ACxMNsiuA7Ng7gTKPYWGBvo3lGS8Kvbd0+NLur1Npzu78PjvD+5sQCtG8/FkIxsXjD3gs9O756dPP55f0n06vXt7l04fzt9e0j8RP/z5xxl/ePjtfHP6HtHPjy9f7G6v9v3N6f35y9PX509nfyfHux/Ov/7+8N3Tt3Rv/A1Op0/+CKKHZ3xtwOLA/+RmnuLf3ErqtdNoF8r/fyAil0H1UkMXYIaItVwHtgkRX2roCoSIS2CPbUDGlzq6ADtkPCQAbJDxpTtsAVbI+NIdtgAVMo4EFKj40h22ABkybpF6F+h4BjrYstKUQL3rrLg+Ax2so1HOkYi94foMdLC2TrkHOlgh8JYDBa86yHzxdaAMapEt8iQugQ7WMqnXEgFmMg95HZgzDQ7UG/4S12eggzoKjRmot3bG9Rkojzam2QL11iq4PgMdVFXKEii4ilIoaa5UIjNPS8XgCXQQJppKZObhoGLwBDooo+P+DNQb/wVQj8y8rW9fgDvfvgA3vn3B7Xz7Ctz49gW48+0LcOfbF+DOt78Gbn37Atz59gW48+0LcOfbF+DOty/AjW9fcDvfvgB3vn0B7nz7Atz59tfArW9fgDvfvgB3vn0B7nz7Crzo2xfYzrcvwJ1vX4A7374Ad759Ae58+wJ87dv/Aq3FYBoKZW5kc3RyZWFtCmVuZG9iagoxMjEwIDAgb2JqCjw8Ci9MZW5ndGggMTY3NCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrFWltz2jgUfs+vYLozHVrAWL4TbGZ2Z7ad3bedZvaFZTKyLYiCscGW06Qp/32PLGMCKYlkmt2HJsRCOp++cz+u3ll09M7nC333W7P5J/j5+eK3q4vhJ9vs2NrIdZ3O1bxj25ruWB1XH2m6hTpXcWfadVzzw+zqz+aE4SfkeR0X9uiI75kOkIeMLi5Z9mFgm0a3DD6gbhleRw9RQv7RbT2GH2gsDnki/ki23hmMNAuk11ItGal0zgVsJkHJZeSElXnKUaAuy0siK9PeybQPZOqaKXaa9mkEw+ELe378FLZynmKaE2AqYm0PiG5wDge8tB9uddn2+DRLXz558OIy6s5pXrS+HIObYZq03e4HL2MP2x2MukU5n9OIK644OkLash15y2ZAQeVCfcQN3A9ismhcauhURg9QGrsXvLUxfVcG1DzLuXTu6++Fm0WXWRRxGALvOvtK8l+TRMOM/wlYYjqvVlb96mv835ne2vDoyUB+KmWOk0KdmdFrYgwPdbeK4D1d5tS7jMYCel6mnEXOW018lhbsUfEuHlLQMk2ZELW8ZFmcFVz2o+o1jVcEGqZldZ/Y0ypYDCbrDAwHxBfTOPuaTpezmarSPFNWrhAbBzjmPqXnfRC9KhNhss7OZFVt07Nk5DcMh4d+DRAc5RvbUhIrXwx9nqIjvP7+PZys8H3IpYJBMZqqRw7PkRe8xgUPnILcuF+sSQTa3ZGsiqAh2z0ZiZ+B9WTA/rLO8WLFE5CIqdlqXTtdThmNcKIKcCQjVdWZR6+FEMNz7W6vN4ccUlyHD9MQbjR0ZmOucfgLPgbwsVIGfwKr1cM+dayD3LPTTxtHGCGZq29Vr27IxDHVmDwyf2akb7C+FgpcW+9ulXm1JU7dp48owUVB5w+7HKJsa44MNTSl7DrKVqusSVbK93JlBC1Iek3uWY59c7KXVN20WQJz9q3j1TkhsVjmC2a1sn8GW/hjSwV4w5AnA7yAamTdYKr4EvWTyOyoq8yXVHWyYeDpS3JdpnRTEv+vK35VmgB7FUGW3UdehWjDBhOGw+AR1h/RdqvIgqtLlTXHBUYYoDHkJMseh70exwEwAMAkBa1U+aqFJYFEGSgi1GOBo4nza5zjJCFJbTRZLj4U0Q2Jy7qpfUjxikb9ylhUWTLasEQDfUz9vNAK+o3sbQiIEqQ9XpGCfYGUWpfU1ebqO0x7Uj6Ot6pEmu3QQn0xdACxYY+pQMiNSpUqqTFAlJXM952P1PcPvg4nvDusDU78rpu7eYIXhcqGd8fifL9Jt3R2JhrIymdiEZmezngs9EkaJ8pOZMv3qjthE8PlysZhljPVNNCo3XmDpsvVXZlUrArVe7X6bOSPJORD9VXn7BsSLUkc6P26NaIFgyBeQBhQhIh0pWKhDhb78Nyi83ORVPj9kpV5ROroug/1x8HkFhLErR/Y49teTyRpVGiLPPvKv3XbxsCQVAje6wK0QDbyzDcsSMXOOxKxLPf/SGNyP6mjN3TcyneyZIRBbmMV1c7HsD99z7svnUsGXu9Fu5O00rdUpKiuKKTEIDBIfqBqHVRdaZrDiKe3s0GQTOHLtz00U6ZEYdYWZUSMqqqGx382wa4zGNeMti6Lm+sQR0u+mrQpT5BUobttZdvef1L5GPZ+GpXDasR4LSJK/95lBP6iUBo1tIzaFBtLMJqlX2nmaXHE6+llXRwpcmjoiuaccHPmCKbLWT9+0bB5xfbUtlUMe8eTgdReyqz5xKfQdhV1IkZbdWbZBIe2r2pwhiHvZevJpk14MaQGelGvp4zd+h8mQq6hMK2DvM/DDk/9vZ6PRP8ekTyXLS9h4W+aJbznY/T4pc5LNaVx/Jpkf2BwYuGHZWh4ZiGcqKJ4DuKZO95dVmFb8HinQGVfXmbEX83I6+lUU3L+7X1/fSaIzc8AsambENUS23DeYNznGq5C1AqCKmyRTYsI471FL2NIJcq6h+gFTW6sZ1xtwrwpNdwRvfi5Ls8yhk+6ffVq8cQaucfHb9QPNqIzW+qKSJ5Ln9QZ5zo4pgmJz8QVnQmCbEqcUPZwJgyyaTdoMNEbtOamId2am6ZMa75LHitM05ajdNe0ZEfph3PiFS6WRf1SvXoaY4avgwWpkER43aYLMe1281qHz2tB5m5gm5eAKlWe1TacyL1e2JVDVS10/F8I1O8u1YEdvzlRvponb9gnvG34yTQ6SNdG+qjaY1i2ZnmoM7CRBtQJQUcG/PvVxb/NRrHgCmVuZHN0cmVhbQplbmRvYmoKMTI4MCAwIG9iago8PAovTGVuZ3RoIDE5NTggICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjatVlbk5s4Fn7vX8HWVLpwDBghbm6Mp9Izm3SmspPUTu+T4+qisWyrbQMLoseTrvz3OZLAl062GlHZB9tCCJ3znfOdi7CtrTRbe3dhN7/Xtxejt2OkIdfCru9ot0stcLTAHlu2i7TbhTbTrwcmQo6n367JwMSBp7OEbs37JFvIy6LMV2WyG8xvfxu9xY6GbGtsw5awFbJsB8TYGtICZPl2oPmha/nhWLvdwc5yx7GtL/PtNh84gf4nzVZyilb8F+msXZPmu2JLGJHTFUlzoQDcaRSwBqbv+Pp7JlfUFank/Xe//0dO/TIcokDOJe3D1/nABME5rAVh1r8G8OSW0aIkKa1onnFYZzgczQcsyJUQlnnJrYAbPQNX/2wjN0u28iKVuxOY9FNKMo6RyQeqeleByl7gt5Z19Sqvy/Rkny2Ra8EYwrrID87M2+iE7dBy3OBMsUdS0iVNEwYYRo2FqlFSFCRb0L15b6VF8YLLPCCF6/hn2zYa/0GINMyJVbzA8hrH/kFS1tjugi+wLdfTypV2GP/73YU2MzHGut8sknQUk25jVdxaFQbg7YqVtdxWzEgPwmIFYcG3wnArzNWTkrL1jjCanknAeknAgODIUF9IDSy5j+XxneD7HQSRhzXPGgeBz61oji0fayYKLBe7MozQmWzwZRhqATxgC7PPzMCz9Z9olm7rBWD2MNIn93lesdGuBk6eUnIErrujGbPW4EKkT19SxubquL4n9XDU9ajYguxTwoWxrtIa1Fhd2rLOpEORnmwVwbnq4lJAB8ZUxOWpC6LgzJJAolR2ma8uLNmuck5nELZThBb0kFaWyV+KoEJ1MY8Q6XmpCGfcgxJJVZHyZUqY2PJh4hQVsjuIqytR5risLNmRqkjSRjRwMVKDh5CawPcZi+949kBOCOS4Y5Ga25CjJu+armKRx66uds9r69VVk8lUMWM1Hd7EgqATkGSADu5UFXOXtCIqlJQHcuTgNnYC42OM/ND4EGNsg3Dj9+vYdYPhxyFSRe2pof7UoH4vUX84oMZSIPZkDfSwo49GZ3ufr/n+rHgS6f/t+yDZF3lGMlb13cAEs1oW8tGzDbo6tUtWlRln8mYqTVokJatU3dYlnx4YQzMotXczJ5zzD7itHapSNlSSuth/tj37TYPSONyo6CqLbbiHnhRRv5R2If3A9nnJuODCuicrmvEhl2UUFiCHRvUwsYLCyUjJI3jaTvKPolUcu4tWdNnolKSbVthEfJeE8RxWZgMRNaZqCDuoi3yQ0og48KGY2fM5fKO54EQxc8QlnqvywumSvr8qosIqXPuTnVJNUOsUMEc65ECHHCSAHXKY0VdFmF0ydqNDVd+fkb8ZJMco2MSCbE9wUuArj2ET2xGduBEdDvn9Yka5wma8eZ3AMDqDFaka1fufae051i6JjBE4PUMITdIt76urSjL4bZPYHnO6aGDzmlGdwWQGFPCtHOflgpQEtlgYb+UWyx7ZwQm6xEHrkaevqiQPu2yf1CyXEqAfiWeXcw76bPLIAHocbsmSHa/u81qkKWROhQ2RrmqKFxNliJwmJdE4doW15eU/uCr8WuYrkQ9Lnjg57VQjBttd9HgeAXuIgP0k5opcXsJAmCPa87Ad9uAFfik/OiF/4QTBFe8hvFKui0AMrhoig6th7g3J0cXP+yt+YO1RJrDTxRaKAY1xF1KqUh273SqKNBXYyTaYoWSTVn3vBxaOVnmFFuxTk6myejcL5qrKd+nBTnLgQnQeny6b/JcbJ819O7ss890xD+zq7UnXtKYyMJ8U7dEpb31bhXaJaN9swzxIjihwf7KTPdUH48PpHRmaLJ/BEjoUN+bDGBC85ph45VK07vjHU8O1lTzGknJFWNusKWYdF/Ux+wOkvodJ7EcPaqmuBdgpxzwj/41I9PwQYlX0CznrhG+gceIe/TiPVWPbxX1yP3RF0WbyrTbRRhpEoRa2NnkpmTnYdU/qdhEL6Rz1Zh4d+5VY9phFjzTnel1UOJiCFxjZBq3BGvxtGzDi8nI9iVm0lmY4NlRrYyneaVbEaLqNttXswx+/S6n8NldA/L8W6ong71Ok3ZeSKXZt/9jvf4mb02XTZxcGdNbH85tUS3QwX+Qh615U9GSjWAndsItazw1SQxTX4DNzHdWNv3gTJ/L/zWwzN25mX+bG2qCv+UxtPqiePFujjbu4SzFJenYXsn7t0QF5SCEMEv4HBcsvZSA8Xt3wI+pmLqgl36TyVY+vWCxfJUSPo5ifo1WPRN4PbctapL1y3y6pNsAc/jPxXfEr2cPLriDz3d19TbeMZndFXqTQFrfVyebWyviLFSIODvxZ/hvHDz2C0XO78OqgNwGlv3+K9aEv4G27ACEQcMWmU3op6E6GMR0qs8jrk6O4YZu6Agp9t7Q0DRrvBR/msw0s44EK0frwyvnZDBz7Cj4G6RGnnv//4Fin465qNIQ/vuvyxkpdl/y7mnvipD9uWhYeCBmbNklhSwTpjy1ymrEebPe7vb9rhfw6c5x5/IRUD1U+6teHQ23lDWFTVpv7g4NCjp7FDlyiKJvGNMpMky/7dZbNzRi+TfH2KHom+vzfecdFVuiEmoltCwX4u2fAf95e/A0JsxMMCmVuZHN0cmVhbQplbmRvYmoKMTIwNSAwIG9iago8PAovVHlwZSAvT2JqU3RtCi9OIDEwMAovRmlyc3QgOTcyCi9MZW5ndGggMTMzMyAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrFWFuLnDcMfZ9f4cf2RZ9lSZYNSyAXti20EJI8tF3ykCZDCQ0zZXcD6b/vkRfSXPlUKPRpNbPHOpZkH8nDrUqphVvVovjLImWsz7NwbzCYS+vxDbcilcOQIrr+pUWGhGFFm4bRi3Y7wPBitcc3o5h6GLPYGDBaLb3NMLj0voxWnIOiSXELCjjzGRTNypCgaL0MV3huXib4YIwyLSjgbM6gEPioEhwCJ9XDt8AL87LghlckEltdoUg4WrFIeBoCAoErWdGEK+kBETjQFU8kSVdACge6IgoHtkJScNiKSeGgr6AUDvqKSuGgzwaOcOArLoUD92BTLBsrMsOysUKLZWPFZuCYsiwsmx68pgVhBJsZrBUbljWuDA5zWCs2G7BWoWyW1lZsACOnwdZRYFmxod7ITXB0gTWWBQ5twQYwIgi2Dg7jCo4ODluxdXDYKhkgWB9sDo6+YnNw+IrNweG2vgOHz/UdOMYqgINjePA6OCYPcOBjmxa8deAE1ogSpZU6gg0bl7byXL2IRLLbYBzQOMU4u2IttgesmI1VY1gz3A0c3y4TFMNgrWM/4M7vVsCdrxuAGsuoEdAA7YjKtllhjSCb4JgtNo/oZfbY/BRcprqsuFYaYSBSrSN4Jy4Jx1ltqKyyB9scuEEcbNiarhojVFhwwHHtVOKsSm2wkBtY4MDisMCh5oeLi8P2qFzhDiuu9JOy/fzLrwX5nkIDx+z09s2b54d7976KG0qI7mPc5fl0Wy4uynaJLcXVXQsuGTlRBHP3Ke4YyhcfsGx7fH1++fR4W67K9vjRZdmeHd/dlvcen/315xH/ePH78bA9hPfj6fYm5GG5PmxPjjfnt9cvjzd39V7f/XR89frFg/O7chWgjjRBHJ6D6MU1Vodo8QJ+EFvo1z+xMWTFaaCanyXhY2AI0aBQrn1gnVShAbvAPicZDnECWIk5sUccVepfquhnQGeKi78P7EwOJdkHWqPQnX2gNvKMQxESqPw+sOEY90y+Ged4JirYq9KURL5tGmlPVNBGp1ozHr2TSaKC1p2qJypoOOC9JhJuOggdLAGUQT2zxTYpJoB9IE+K9p4AVgol3gfWSqMl8q2TSSxRQR1MYybyrd4o+uI+sDeaPZFvNSGdiQqqKlVJJFxFKRV0M+KM5ikbhCdRQcxKxBnNw0WF8CQqiGmEWkbzxJ08o3no+CQZzRMbNDKaJzqhPAmcTJoZzROpEJ5EvqVVmhnNE2YITybftVHNaB7GHghPIt+YvIgzmofJkGJs3Ad2pZbRPHQsck0kHMM6ZS41Hg40OJFvzNgQnkQF8XSiMTL5rg7hSVQQjwSalggGwxyEJ1FBdow8LZFvvHLIMpqH9xglksOGgSejeKwYeDzjUTDwZCQPz0uKB94+kDHwZCQPT1TojvxPc/b4wpzt/3LO3u6fTmd4u7p7f8V+1vMrjE/4F/KwPX372+36/OPr0x+H7cH5+tXxerHU59v32w/bwyteH2JjLxESY7pmjAAdN1d9vdwpHpW9o7F0Ae7+StfTsn13fnYuyPQ3N1j5+nyi/m2k5z/ZBh6kkCJ+v42lYTxS2/APt7EOgmffJP7JUwPnYRfU1b8yPnwGnJRh7Yq7aT0BRJfqkxPAxhS/3+wD0aU8tUd0qXg77wINXSp+SNoHosLSE2k0dKlZNQFEl9IMDmdsuiWAaFLGiXyb4DmiiQoaupSNRL6NHec/UUFDl+qWyLdOPEdGooKKLuUtkUdFl4ofuvaB6FLxA1QCWClzdhRtavQP8v03jOa2SQplbmRzdHJlYW0KZW5kb2JqCjEzNDcgMCBvYmoKPDwKL0xlbmd0aCAxODkwICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42rVZbW+bSBD+3l/h+9DKicHZBWMgsDld1Gt1p6qtTvnmQ5FNaExigw+w3cbyf7+Z3QW/1HfZRYmagnmdmWfenllI575DOh/fkHrfd/AXbD++ub55c/HBsTtO33fdYefmW8fx+i51Oy7x+2RAOzd3nVF3aJ1FN382L7j4QD2v48IjhOIjI5NaHu1+y4u/iUPSrDozHZt208tZkpVwih5fyVgaZCGz6BntBlmvh/e8H2VRj8HWTKNAiNvT80hJ0jH9/mA4kOrZbdSbj8tHRgLchTQMZ6ALqNsv06cE70Od+EWh3kZRJUeqNHhOJY9aXamL1V2Pf4D4cmRFo5sejTbbAM6UIxLBH1vDJTyi/IjqguNoafLACIgzYhZnVaBp81BF0rEb0AePoTyFSM9+8gIopOmF2nb3GY0sezDo7gKWoezRY4QIPDF0/tXV4zuqi4OnJPUb2veEVj2AvB5LAXQz1vWuryKrwRwEST/zFITDkN3UGchD7inieYi/4So/1EhIiYBLVCJhq2eqS18kvBbjojqMLyxC7bLctVTQHxfF+EcIUv7IKgPS+0potshnkOewFVndaHyX3IPOmtjYLcNgfgl44CGGfRsEnqtzluc6EgFhPggTCFjdCuzXFOeoiPu53RBsNwAslhIZ7T91K7gpDdk8SMV1TQc8V/vsARl2q1HWSyG70OlZdF5XfSjsKT/iFX/eIt9cFVhAKqsCQKHH5hjzmhYqVbWtpuJtyxf3Fi9f6NX0tEsTuCkJGXWCpIdxp+9Wj6gAmy3no4cIEmj0BcU89BJwocnic/QzePb9KNH1qEdfoYJ6SjRO04Xec6XHdYi2ogOFd67y9E74+T7Jbst8WcS8pK8S8EFc5UUIgXD1TtyCjd1oAqNI5ruDWb7eHcSiSWhWQc9RAVZ0/F9AOBex01hST0MKD4qkWhZZoAvasA0LnjKwP5iGDNQKpk2a6ALgapHMOYtBTr7k9hJO+CbJfZrVHdngp9CP2V1zborbni799TwVzbi8xbKc3k7G8SOK5NICHllc1dpV6CeAypwaU/TXBZ6EG6bn3Kn8IfGyfNG8S5BYPUD9l0xViYVPXjBVpaI+VXhnnGdl3WbrwFuMy3g8GzkR/LHNBlnY1thQg/KtJfc2/EPGwA8GxhD+0+1WMwZ8S6ucxEVelrXfNBPBbzWKPrJTBBWrQq2GSYPHK6SyptliDvKVptHfRHIu2B4l1Axb32nD0fkkLobt0GszdNdmDlW6dSO24hOekVFGjMxiYDIJJAbTzVaDIdXGu3rkVLam1SWvNZsVq97aKLW6YHaQ0R5bMUAls/gPC6mrbr57WnikLL37jrqVywnuFqLk1iOSaF9pyOMRMrpKs2WiC5Hfhr8/QOA/hGwYPGB7Okn1SrilDFlGg1KEzviOdw6gZjjGcnZmSJ6WRga/RN9av5pYXC65jeeyImVAx8vIMDPLLGU3fNZK0+4P4cQ++JS86BDs1G9tNQXvIfgf8H2CClNeYSsLSlFjdqy2BDLLj+CqOGFS3TU6Sl6DfFLyCuyTEhX6uSzT7F6g91e+ZmLEvU7vce3o8/VVoGuIoyATBEl/xdNlcVvka/TiXnddJUh8cS76Kpiv1c2wf8qyllTjFmWVEiVm+Vu9fDGrxhsbO7hBtoZs9sHpcXuAo5tMsgUkJgMNYdfjLxlpz8GUuCqafpVTwGarHcSeyvvv8o2u3r4Wf4ZKDaAdFWuOWUNCD6o1DkSaBbu2mBItzWJG9Rsnpa2K2gpCaAUhtDpd09ZweR2ugnXTNAQmIrbWUSh+rPiyF84j+quvlFoqisteVBgZ9p7YaDMQUKrELLfraTrjrHFRJKvbRVLMlxV+P6jSPGusP5q24BzyvXE/2Y1bTSDpIjJQUbMpZLgEeLo0fKpXdSoADb1TtKgH1Gk3E1OciIc4D+9F1hndz8Ap50Th5+t9PXsMtuZUuzlSpQor1gUkcNpQuK/QKamn8NId4c4r4FeWE222RvLPLRIzfqBtiq81zxXL7Laa7uJdt/1ZRMU5YmyLZ8m4OJ75bSHJRlFwO+p0cXHwzsN7Tp+VT4I1bR/Nszg5elYVcou2SaQKMrnCL0yVJOVAksDrP/h0XRlVsUyM0TsswESyhwV3UJrdJd9vR4sRiTD14QeNIthafGtHEeNow6XDD4XCB806jiAWhL8z2Narvx172PeG9MDHCgR/76EGFJ3P4uNllTcTH7BpVGfVL5LagLM6hPYtalOBLaVGUY2L+6RqkBPEMRRL7PLzDK5jYZE7XF7lK2BDIHeuRerx6GDFRDebrYEmijJSLjlcrRJaaZVCMHroVIYrv9gB3y6D/xutJB54H4xO7IClNwOUsWiFUquP6xNmDYNJyOjADyanadI1wzRDP1vn12DH+cRcV7u0uW61EkMtpS/vOC6JJTf1L54NIF7LT0fcYfzTUTMOb/a4x5fetflwPqkJ9BVrGHSPScciEdkeSbetDiV9n/hcujVw+gMPyoVt9T3iCpXtw7D7/ebNv/tfhQkKZW5kc3RyZWFtCmVuZG9iagoxMzQ0IDAgb2JqCjw8Ci9UeXBlIC9PYmpTdG0KL04gMTAwCi9GaXJzdCA5NzEKL0xlbmd0aCAxMjE3ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o1YwYocRwy9z1fUF2hKKklVBYsPsdlbwDg+JFl8WOwhGMxuWK/B+fs8aUxiQ0LrNFXdr/XqSdWvVcOjW+uNR/emHL+zLYnf1VhGDHZjVwy4NwEYA26iHgNpsmYMRhuSA23D1wkDaxpPDkZcCwKeTXcw8Go2goJ3sxkU0ptzUAg3t6AQab6DAkuYIyKLtjknIou1hRAYeFsWFGDePShktZ0iZLedKgYu95QxcL2njuDmFDJwg1PJwA1eEX7ghqSWgRviwZQLSDUjpilnxDT1KKaagoJbU5FiailJMTXDSCaetT3AEZSz5xO4tnLtingrFx/hV67eMN25esN05+ptoAq5egSVnqtHyqRnIcybsEQBbGLksUhbTSRXj2yJ5OodtZRcvaOYmZLhqObI1Ts4NFcPStGsiINDsyQODhuRKweHhSzUvIlzcIBSPMsywTFT2wTHTG0THDO1gUhWapvgWKltgmOntgmOrQ6OCY6d2kA0ugTb3BilttUb9mWOGKPUhvDYWsG2YlumtqWxQYNtWRsjtS3HyAwcCI96hrYFDs1yL3DojNEGh+WOQ1A8kIUHh6e2DQ5PbTv3frBtcMzUhqDQouDY4FipbYNjZd02OFZoU4RCzByBY8euU2wQXN8xGhiFNu0ar1VsM4RSDm2ab+4cp5ub0/lVu8NG23ij37Tzr7/93pBwVaYI+/Dl06d3pxcv/h84mDbEHwNFSPF7DORBHU5yDOyDDK/KIRBZpo5rx8ClFKZyDJxGcKtjnBv5qgDNSaSQb/gATbyxx8AxSXYh39jjtKRQQZgwxWt+DOyL1i5UUPYmHYV8y9q0fVeAnawXEi6TqVeYnclmId9iQsyFCooKuRbyDYslXoUKigyaXMk3K8GGC8CuNFch37yN4jN7DFxG8eU9Bk6nMMNjoDucp4CzSeqFCrIuGE+hgjB6sornsWwYT6GCzJu84nloQogrnse9k1c8bzNJxfIWw3d+hN0+Pjy3m5t2vs2vC1/xt9fG4NuE43sPG7rOohFB4/btFj6cuq4siHh+/fT4/pfLc7tr59evbtv57eXrc/uH7O1ff15w4/6Py+n8EsSXh+fP0QLNeP50fnP5/Pjl6f0lr9n12s+XDx/vf3r82u6C0tF5zi3vQHT/hKeRy5FL/l51tFn/quY2sXmW/0cFfwQiMszRuheAHXtC+RjoMEebswLEnuDCGh3m6LoKQJgjr1EAwh0n7wIQ7iimBSDccVYCwhyjrT8GwhyXV/INc4ze6xgIc9yjkG+DOaoXKmjL4TyViDBHG4UKmk84T6GChg3uvZBwgzuyFiqI9aHnKeBgjsKFChrMcRpXgJ1wKigAYY5LCvnG4ZPiVHQMhD2uXcg3WmBSKaRHXShOb8dAG6S7UEFVtMGjkHC0ZFQSLeiCK56njDa44nk4lxBXPA8vKoynUEGc00gqnodDI1qeQgVxXqNR8TwcotHyFCqIczycp4Ab6IIrnjdGh/EU8j2k06543mC0wRXPw2mbesXz0BTAeAr5loU2uOJ5OKuj5SnkWxxtcMXz8MWiqYWEixpVXmoZ6IK5kG8RdMEVzxN2ir8DjoEdbXDF83hP2lYQg/4NxlOoIE+0PPJdvv8GWUJpqgplbmRzdHJlYW0KZW5kb2JqCjE0MTkgMCBvYmoKPDwKL0xlbmd0aCAzNzYgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42q1TTW+CQBC98yu2Jk2wBJhddvmQ5dKkmvTMzRojiJVEIaWYHqr/vbMQiR9pxaQXlpmdeTNv3iyQdwJkosHxtIT6w+9Ee441eywcIqzA81wSr4gQFriceBBYwCmJl2SqUxYMZ/Frh2CPqe8TD3OAqpypyRzO9Xz1BgJSCUOq7/fpo8fgIQL00XpdlV9DUzhU/6yXo1G1K+p8m80zjKyqslJ5ZxWw6ODMcXJRr/Pit7vBJYqqHrbOE9oXnIGYgcWRdcvWgT5ss4/5NLGRgDszIsWN6WmEZgRha9VljeZig1EYYvTtQhy7oDe6oD5l+uFebuwWKvNpb9SuV6cP6qoVOi/qdhWSiIeJZCJMDEPplJY7NTEp3adEyn77cL0DDfTVHkiJcigtZv+P3GwC4mbFEtvf3L1u/MbwPAH3KyJ6gHZKbBd5oaRRKnzj45w3b4zqR194+HNE9thhhIIVQNCgMy4s7lNiuuiknLU9Oe4ZyEus/QBw+wYvCmVuZHN0cmVhbQplbmRvYmoKMTQ1MSAwIG9iago8PAovTGVuZ3RoMSAxOTMxCi9MZW5ndGgyIDE0NjA4Ci9MZW5ndGgzIDAKL0xlbmd0aCAxNTgwMiAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o32BVAc2tIFCmPBNbgPGtzd3d3dbbDBLbi7EyS4W3CH4O4Ed3cnQPA8cq6cc7//r3qvqIJZ3at7r+7dvQcKEiVVemEzkIm5BMjehZ6ZgYkHICovosXMBGBiYmVgYmKBp6BQA7rYmv/HDk+hYe7kDATZ8/yDIepkbuzybhMzdnknyoPsATKutgBmVgAzBw8zJw8TE4CFiYn7P0SQEw9AzNgNaAaQZwDIgOzNneEpREEOnk5ASyuX93P+8xFAZUoNYObm5qT7KxwgbGfuBDQ1tgfIG7tYmdu9n2hqbAtQBZkCzV08/ycFFZ+Vi4sDDyOju7s7g7GdMwPIyVKAmg7gDnSxAqiYO5s7uZmbAf6UDFAwtjP/d2kM8BQANSug878cqiALF3djJ3PAu8EWaGpu7/we4mpvZu4EeD8doCotB1B0MLf/F1nuXwQ6wL+bA2BmYP5vun9H/0kEtP8r2NjUFGTnYGzvCbS3BFgAbc0BihJyDC4eLnQAY3uzP0RjW2fQe7yxmzHQ1tjknfCXdGOAhLAywPi9wn/X52zqBHRwcWZwBtr+qZHxT5r3Novbm4mC7OzM7V2c4f/oEwM6mZu+992T8d+Xa2MPcrf//B9kAbQ3s/hThpmrA6O6PdDR1Vxa7N+cdxP83zZLcxcAOxMTEycHF8DcEWDuYWrF+OcANU8H87+czH/M7zX4fHYAOQAs3ssw9wFamL//gf/sbOxmDnBxcjX3+fxPx/8ieGZmgBnQ1AVgYm4JtIf/O/u72dziX/j9/p2AHgBdpvfxYwYw/fn57yf99wkzA9nbev5N/+uKGcWktEVENWj/XfJ/nSIiIA/AZ3p2DgA9CzsTgJmZgw3A+f7B53/zKBkD/63jH7HS9hYgAPe/5L736T+S3f49A1T/XhBqwP/mUgC9T645gOrvQddjYmcyff/F/P953P8K+f835X+y/L8O+v9VJOFqa/uXn+pfhP8fv7Ed0Nbz34z3yXV1ed8CedD7Ltj/X6qm+b9WVwRka/Z/fdIuxu+7IGxvafvfNgKdJYAe5mZKQBdTq3+Ny7/s6n8WzRZob64Ecgb+eVoA9MxMTP/H975dpjbvz4fz+0z+5TJ/X57/PVLc3hRk9mfLWN5v2NjJydgT/v2S3xE74DPz+zqamXv8NcUARgZ7kMt7COC9OB+ABcgJ/s+NcrADGIX/mP6FOAGMov9FnEwARom/EQuAUepvxAFglPsv4npnKv2NWAGMqn8jNgCj2n8R9/sJxn8jLgCjyd+IG8Bo+l/0pxuMZv+AzABG83/AdzkWf8M/CPhfyM76B7r9TWf/4we5Ov0j/p1i+Q/4rtLqH/C9LcB/wHedtv+A70Lt/obv28Zo/w/4LhT0X8j2zn3/BviH+12Kw9/u9z46vK8B6B+VMr9r+YdS5nctzn9X8geZu5n/fSD7O935/bX4O+A9p8vf7nc5LlZO5v9oxrteF3fQPwLeL8X1H/C9Wrd/wPcK3P/R6fd8nn/B/xlEU1cnp/fn+K+n4n1K/4P/evvNzT3MTeGXF0CmvMHWdcEdDzXC+O70+5P8sxT7mmnU9J+Xnb67PiHDpFBXZwZuOt0Lp4z0oq7tilPdCa0Qv34+bW2ACWtLUm5/9n4xTFCZ2W+HX5rGGpwqPBWuHyCEI6BXEzrwfnX01giwgWwF75KhyHV05UJWykd/cO+X9KgfKF8dD13YVz6o5pBFeCn/QR+jHq0XUDJHkWeSNY9DCu1CTwhL8/HKA2Xu7n72Y87Ub2KZBFp4n7MY1qLPOlsssY/zXuuVaizO3bjkuDo4hJB3H8dnKD+LHH2RwV78XFocvcm/mM0ZETe8EtyfOC+rgUvqq4TjFFxGz//VX64Lnbw7x930q5OthumUAoDyE3E97OcQilJJi6QqspFSwywCoN/jKxmhaqIWuBnt9x6i7rML9S4a78Q4HB7mmMNJO6J7fvSdzixfjpdHaMYK4xlJuQ8pFmAbZCsKilg/JlTzfMRVRpxjGgo/6w78zmMo/kVvDWkd5l3sYqe3wdOP4MGFrTT5bKLUcJR0ef5a1BVvNaLbWINxhxk0jWrYyR2+O+3C8saa1MR5HUwnzYItgnxekcU1LJO2yTSPYedMoA6hOa38ZKZ1RkIwMyCIPPua1XleLriqfPHxjO3cXCVKKgZBfzaSId3Bz3B6gkAwM2CP6yKbB3TM06yO51oolqSja5duwDhs3Di7duC3wJYK22h7yzacOI+17yLH50tJrJkRUukuZ8xt+VEJtsfRalvZP2MyF6vb2q1qhjrkUt+51lGF9HW6eiiE7CXe/KKbsDnl8fWDjqyfktRVgpPEV75f4jL5dc8ZvPhbMwwKvqdl/hqzNY6wcjK0hjOw1rMNO/Aub9pussv5xN/N9Vxh0+WulB1j+TQSpYUXQvBOHrmNO2AMNz5RnkiINXNLkmm0Fz0KudlEjri0MIgTeHtVdOnkA7/yZivUIirKEjNyPeGSXsHOoJZJCa9wdi4ICpBWZBdkYbucfRlx56WZ2Q+3ZUfoQpCQQBLR3HtiDlElInkbgChkoWk8w4eemzDCj+bi0YpvC7ixVa/Ag2kJls6/FdR1r4DA2xk3haPUOTQqwZ0py+ncqEF0NpMylyhO/YTVQuN8cIT0YzpAzYx7TsR4qzAKNTBkRTd23DiB/ItlHXP7iwUKjN5RCgzTMRznOZGxypQBGlXGixy2NRrZGImd+wZLO1m7/ZjTNrl31O8ZtY/ncjDJSfrTnyu0Sox+P7ENMEimuKM3Ve6teis9sIicWK41uP80semWqMlGQAhQqCyxsOaNNRMki7clpeU4qyDstYQ4iPv8eIdG87hxcZ6KO4OATqeqvIfMUtye/1xJ+anU/huunlGdvnJUcBDPEfLOemsvhy2uPZMJd6idfsfVNd0p40ppkZ42OQeeLm2Aungk2WBLtHye4hkV0lQdg9jLtlz1TjmO3h3NttCDF+unkmLK6RSO4uArltQ16Mguig+0VCm3GTb+RnU4YptvjHXDECk2VB9HEmPjbNJQHaqRXYzuDY5F+p7CcHbma/WkAnxPtNEN78vGEhveMGGf0U0pulF8qTBIVuBl3BadkVF80oa4HRfcD6a3Pu/mcoR+rJp0nkxCkw9Oh3ugVKbvcwg8/BRBGZkuT1nH2YDWivbFRWfzGUrPidDPmz1FvqSFnbQ+kKazGqWL8GzlhK8mejJFLDa6nq5d7qeFNW1ywtlo5/euMfD705yRLFIPpt5fVmSQFZuuukIrPttHy6QLQfAjZtIJ4jRwdADrrgP9gwE3hh4Xj1ZYzDK4y0Qb8R5t2/zqYh+zMAZ6cxmEw/qhO9H4FBi/Yn7iMtldrEYxJGv7amlSTwytMHzENbEm7wfKjrzwvjsbgSNgVkfv8hkqGAa6R030qUmra68gfnHoTG3iMIAu/VKj1TnLqzvlZV/YFMmvHUmSaBYxf5DP1LZs8HKqichLVVSq2QyyVi6mlAMq68u4y0A3sR7Emv60Vu6wreUc5Oh3m0R9beoFKKfluw9cny7bBDa3OKVyctuNwPtjYTr8PvU3UaM5wbftNP2Irc+RHFmohijKh9Tx/Sp6mAofpc4AhSQeJdq+zZrzwWfBytz+S3/Jjjh6QiXJY4Doi67H+IRHlX2vqCNZI5kasI0B9Rb4oLnIeA3cpwkSlDVaGcnO60ddfsX/7XNsyTvZlVCHUpkkVPg9WmBBqtq/suf0+fGybNXOnnucOf2JPuzXfu4zZpkuaiIEeOX5AFIf4qGLNScCLOuA/Lrnjldx8AHdtqDOVbPQwZMKhpIIUODn3vRp0eXZqlu4hsS8nrCVTS0oranqwxZqJhcUMTREOtTlJQ101yBhhII0nrJbCTcbhFf77wi2xV92ASgAey5qQrGsk00T/aHlbJjffJcT6lSW+3dLjxtUhUaKLKlRra+FOKteJqwfmHYRxu7ocdQ0aj53VMM9GEpdxkcZhOl2mqPbigzt7CClxhK+sEUrXIcA9rqfwrFaBQS6+M1zSliVk6bpMz4xec5snpfh5MSS3cnAagKYYDfXw/E59Z5DPrpcsVet4bLGJPMTx/ZNduAutUFhl6L0LnjrqmRs1wqwB0jHoAeXZzk13yF6oFpVWXmS7NpojKkOV3GGSSM8qFkNG6sl3WGsg05HAn4Fx1JVIWqaOatLP/Bf6j/NpqPKe89+yEgwk3nQ0y5XQa6Q9PRHaWYzo0LdVIjbJWj5TIpl+6gXCdC3Ga7i0oBPfwC/vBK+5OefenC6A2jsGfjd0+zbedxRmf+qJLjh1xscrSF5fMvtE2nKNuHyn26d0UJaSJcvq/neEP3t41LJzkcgvEPsVloU9ZDTZRliru/eOvPPDITexbgMGD5ePzO6aoNZjqJ9iyw6pW6HyVLvqyqbDhWuNs7GWyIBIEI4dHhTrTnAoCu4PjNL4Uc+gVB7jdXHoyyjal0RibDODTo5ijk7dYLMjiP0BhFz/w4a8hpie5DHNqssAPWu96cGeaduvm5P9GtImoU6Ly7bW1RMXTauMrR168/Ep+D6hEhoy/YwYsvUTdDGGhIJBaE6HkZ3h1VEUVn7GtVcFoDl7qh1Mae+qzYov4QOsN2wlf5dEUd3Ojxx0lBPMFbTZYxvOISRtzUGsZH0QIXgm916b1XOV+qq+zKMr+Kxek7DmsyIPbMziaDkoml0MM/dhYhlapHxyCu9TqEVy4Dj6CkZQTuAuKPjcnLwLRebwcLM6A5K14xYThOB8Qs3J+JuYUv39OdvIyVfP1bokDQYGtE7rdgux9FctjuCB37Fyd4jWG5bisxzQYSSEwW3/AHPY28iAxf+JNfW7xiHlzzkdGjNbtDPDVmuoKJRL4Ny+I3THHPrJt6+juJse6oGXXswxXXYZcHvC9EqVDg222BL1C6fI6mmi4geb55w6cMH/ZeCgg9EdXf4Fjgj7p6e3lIYeZ9PQtV9sfhsu8aN3+SvCT+kIYJ1+qNdOK8uti5toRjOdPQ/T84LSExG9adGuiUqh41Yo+S8oeFBJD5VZXFgUmSb+kOSFQ75NsYjpEaOKR58QII0h/gRkRD2yXhZlTnjZso8DHDQK4HgiuRsACTFOT4J8Wxwj6dLbgzyhRO9Kkr03VEZTDTqoMN/tSYNol0erpS1iUFSZseOyByVDiq3HnsRD702UaO+QKlGySWNCa0XaHQ/Xnd7rt/2JsdmYDtJohGadRZchQc+hE85gDWYDOzoOcHFcCMLV/Rt6fA9utGWj0JXy4nutadM0pYSrnJBxGF8IXj0jGLwEP2xPJhDGdilXpris6wnvY8X7kScNfo0ii1jiY+Y1b3Nc7AqJoiDVzYKSISh+nqDh9bD8OWMkD1GSwXRiHH16XvpZxhuoyquLUyUuKid/hWJj2KUcoHj3obzbwwjlbmJbsYnW6r7qbyc6hTjNryQu3KIIiJGFy42hT5w4r37KI3+euFSKkiDE6QS6SfnWqg1Yd5rnlOXrr77ymhWFTZdbmwaMLOFueCYxx9lNdsms7iuS5P9elnRY8Gf0ea1XwyWEujPFZapfNIIfRDmkxh+JmgCKzCNC38E4/ODuweVCBJU4PmOZ+pAslUCLi4aLxVQJwQ8eDeQR+BLfMMzTRlzpY6mKmiJYscpROyOm+g5pxGxslUzWC5fQ2muB8STh0CORLxd4fPtU4wHaLFtHCe6pYhyqAsXqKCWHvxmNcSbNdiyPImI5DoB/g7U6yWuq6ZI8vjsHLP9KEoyJuIZ0EOyi6tuw6zyxPizuPoQCQHOwJge8Ua8M7q7+O+fD5XVy2ccbRWaizEIoejKXHBI5/V8iBQetpQB+CXwC7ffUC98Rax2+LckGvI3SVn0WvXp9vxma4WczKlKkW0lDmkGIrU5X5ZjVpaMChFqHVQFKVqA3mNRjy7eYLqwv7BM+B7xOeX5HzN/cGI7j0zbN+IFTqM2zZS7s7BtoC6GdJsxmlyI+epSVz/CWLKV5LEeva5LUoIxOigE9VKgpBAu837rcEk6QAJP5QGPxnE4OkE8wXjs9XGXz8BEYwT7DFc0VelfwMRgUpa6a02isxsrxBdVeIAUeP/m5blgO0f9+5W0DrB6dSebhz61G9S9rm4H+pF+AbvMUghmCpHqQ+2ftXvolbTFg8uHVvvr8m41WOKRGC4X1Hs1EIRalr13JCmH4jmqtsJ5wye+ernz1u0Yoekr2pD/aKeN+5P/0HyXwYziCfGASeB3Me8meldcczP53rXBJESuAJ12JgOhxPNaciF4Rg4FeLJkH3YbhnUnHSXYBZY65aRqZTkTykk6fnRFmuq3CctOvd2W3ufI9NP1GShaKy6GMAySkZIGwYTfbD4faAfGR27z0i7NRMcX8njvT4kk+ztrCvwLBwd0yIQbEnHyyuovf6CaO4q3CqBAdKnIlcMFvvnLLgmRey+LaX5Lj5q9ZVRSqIc2mE5hpWGSvdsLOO5/XRtspvaZhaySOZa3JP1Bzv7EdW3YaVkh6swi2shJJ5uVgzKfLQg72T45RZ17kBLOtoH4hLzGTAbeC2qr+NLajI0pTaxlmqX0a8k3opkZUHAMa3wTuGdQNMD2ijnCQTibTG1NqFi8YowJ6DdGMgB5vsYY5Fb8oORG30yNuIM9HKqgyO/i43PfKeW3EUyehFx9sxaqVPImsLnH8mMrkHg7C3X2odF67VuSLbYuSf9p6qDv55vOxdcttBvfk8baVGDdus+SFRZIkQLOzakweUXu5Rtv0NsTCSFgJ9cnp7DL/csz4rS+Bzf0DBYMYLxIDT1FrP5CLcYgOLEAtsDdkVVtwaZyz1J+GTuj1do6jgTrTkaEYqjihQ+rM+5K7bi99RnwhH6PfAfLarWrerb59VAJcwtNbHQg0QOamgt29qwfNoliIbV3P15Zn28O1D9uMR3lu2Dxld8x83szhkx/RaUYic5q57ysGP7ID9OeXydU+3z5WjhD8TgMWUfE01LHrUisUljhQ2AhZTJxd72QSpwPiSlkY0RxKrQtpurbFYRqatvgVNnUxE0v4rIGZqsUDd2kN2+qUQyyA+U5lN6cjmHu85KvuEtfn7LJtzQiZhTyOpfebO1+cDiy6EmH+m2lRretgU10nRWShgsZs1L3w+cigCtwHslw1hNDzW0zc5btL8g2eNGI8+JsPo5O4KAZgSicidBHVAQpKbCwPXuww8u1NFPlNZZrfETLJh6JJ8ayLLuOyiwo6Yb1lhYuf27x6TH2n/mvNmjlY34T5EgETV30bUsavm34574s2F0lEd7Xtp8p2JJQzS5nfblUeZ4Tm+/I1qcPA/+1p/TTgvrTmdsLW+RURY+4jIbfpC53l10RL0T4mVM8shMESYz52cQZb4OHwY+LoDWFoLTx9OPKBGtOzeITsq/1PxKhZ2GSF0frR4baRDQkqfy2gHvYunvSJZrCfEItR7tf+Ws47gWr0Y8twdCiyZHURiXLTj/mBTApOMux3y2DTR4nyBH/dI2kjwXnWLA0dHuCgJc7ABWJV7cM5vtKUzfIkrBqXU+6f/+IACan/Gv/4+dfPWkuk+bJDPj3poN69LAlwjnCaT2fwFjlUUbQvdfrdtfNC8scbmudMdENuGSZ0549aSwZky/7S1c4K09UAYh8QAxd6+Fk0/5XTTfZIjjc4dVvHlpag2QQ0qAQziiE2O77NbT5bYRSGdMdnhFaVaKuoHZoKkDqkYNxySYU6dmwsz5lavXqjn6mzc/zXtUGuUvPsw3DN+uHsTSVxSa+y13AzXjrPKwTY3NFD/ZXS5ieNU/C3Q3ZAjrM+F4QBruqFBKZbA4Kti4l4mIlzsGt6QBiLu6HG16+r2atxKhQfXWbO4PCy5+OaQm4hDw9lVVPXUfRLpOntLzesu2Ol1k9Fm8jgLIkMd+cXaOmE82i6pf5nXBEa7UEf9p2BKWMa/hpmnmur7LLoMTXzxzZrJ0ync48IKi2tVuprsu27sVQt2B+8CfyX3wqx4tIabRtMdl2vsKvefsMfsoj/bnBiBKg6vA2P4dYB56fllClvWFZAinAXfZregKAuuY0YUESxFOwiOm+ZY9MvMKL0poCOBYSfpKFyxC0jO2FUkhwDD1JhWIDq+NCpe6PUyuagBswBev+uF6apqLaOiU7OsFzasNW8BNjTRX2i88yLyvfl9WT0Ua+k1SAqroBxGRUHMjhFrXhtwUpu5BwFFz+wbWJI8YJuD7BfafmAo2GYhyR/DeXlQU61StLQ1hD/cZcppDIqhN6yVGt+Qev+iNaSe9E2DwLw0QnfXHJhNVNY8fVzFjL+BI7uHn9Rnn7gt+psG5UNWJ1jA23K3ImFWh+x1GLZjuXyyrfeFclvwoKGsv0aaihbL95GhytfvvdbFlg57uVYVSnwxJ9tD0d7h31YfrznrCMaaqyykMhoqO9GdUGymqmZ/pN8z1IbMJIrVxGNA5q7adUmJeK8drt0Cyh0kvda8FiMuriScSox2CmqFD7W3SSvMMkn25cDNOUYOmr+RAvhI7ayW0cbUiZim6+zYPlwFRJzG8MzKAZ7GTjDyFdpOWOyAa1JgtXNZ2by8NwNLsvfrhvZ06/zpavvat4eJpRNT4oxIso9qh7IY+sSZlcGUtWi0khV5+Tc5Czuch1NKJWy5iAN8J3SF8oSOOILmQ6NhozyKaj0Q1+QxTwF030hLdNgeX8lG1nkYRA2QAaZR7YLuMJOQC+/QgpQ9t3JaxDsutZN1eY8gfPSYcQYuYFiIg8n2Fe+OhQ5c1h4yuhs3aDC1hE7MEald/auT97Qi5V1m116Sf5yDHBOoK2ZRbPhwDpt8uXW7hse+saoiIiAWVyh88eE8zOhmWVuFR6LKO7OnudOgyza/CSgr/Wd/ADTszxsqIbXJuauI6RUFi3HL68cKGJOdIlDXZSPqyj6LLKI95uO0P3uywwok+V97SoIfIeXiQ+N6GI8mIm64uJ1boyrUPAFCDOFyq/6PJ7+b+NFinl0ciKqjsm2uSCJQftkk1hCB7atNYgIFHLw12fP0sSqBDVDGlKIxGTqmhzXpVovrWv/QCbPVkYrDmNuEzfZaIVmqyfV1tXUBsSvMWOLly9CDBul6nw8PRt5cIddnVozPdGahNkv6zB/U2uS+38qmDGl0UWWz29daxeIT9HxTWNRJRuIt6lfCr1cR8a1YWh14LjEO9sIa9PHWN8CXEoh2Y/z2PXbypHEbgYb9TsxKhnCpqKUOUZJ4MSC/jFLyR7L7zPkvOxuXgv+xjxG7KlHKGUneJyWkV5rgkyN6xSfG4nHkzLMzGodyhlBj5lZCV4WhEDuDooXdrPla2irlrqicVysktrH0OSlERRdbtxE2U4FPeVchtNi89xltgn7vXVZURJpOEoKr0rfshonrPoHrxogD4DIsJCoZjBmU52tpDk6JrH0ZXrAETf8GVkfH0xYjbajBd7FxVpeeNrM8614jWqozie9OUME6m72hN+LyZyF/0wVDmihcMXP1s+GmfU8UGZhkDc8478zE4LvHkaU0dkRR/8pRDfvqaP+rTNxrJKVlx0bmZipFU46TlvnGloEQPddskHEFJYavuc0THovjxhn1THV3nylPMLGdd2BzLV7VXY1X1y1fNsWcpJ5RAqokwk3Oz0IYPGfhEzazfWrhhixbykzOCl3OQzX5a5hn5gwvAShnW4RXssgWVL7k5Pe8au5Q9zngETZHJzwwkXWA+n8bKG7hI+ZEVNFiwJVE+h8THJ9D9aYhjz3OCnN0D4JntsGhQ9yR5kIXO5NZC9ApUn+axM6qVT03uZDc/xYXNboHm32jXKida2qD4BBiJsUFOgYvLNHtbFMZ9x7MvBKAOi1UrbeB1gTRIFTjQffhqmW0gwJCSwRE1Tb4cVCg3mUwezR4yOgixm9jpKBO9g6px9KYmwXnwO4evNK0u3UwfjF7nV8iGa9J/KS+TOLCd+gWGzkZDIL7xWMjmeZT7iuef0d/J+/G0CwjHcMmBXzk1VqSnNrCKDu+Pdsr/5rb7V0+uaNjTwTa/nPCJiSM0ZuGR4Z6+35VpACYrNVLP81vgsRhaH9Du6afFTUMEMC8c9z7NkAJykSuV4f44ezWuncBFuTIqhakPIcaFE6d4YuDhXW4vMl97lDrVm5KXGr11wiJ6G+ntgmbrukTeZT8FF8aGt+Kii3hdz5LS6lau1ZrPai/UQiOar8LJVhHKffrMU/UgrtbTRIxYTe9jwDPzzvyUN4IYvnNVsoyyKMmG6OsO+qsIk9XqDzMPw8yG16xlSnf1rQXFWKImMI6bgo6+hO5kbwy77B3zuJq12hBn6FxaakhvzM084NTO948y6tUBmtnboH9Xit/R762/DWtZv4Q8wuMO0wpbWMj8PTX6vS+zEMcUbQS7HoVgTXMiYiXXd8kU5X+PB3ETr+N9+56NTNmjsBA8R9CMqLbLpZV/iDY4NSHDtP953jOyV9ieFdYPLKjJzkg73MDWhrGT7lqf9mZZM2Oi8WjyNhugE4Czij7leLJwr4N/L9O01+xhKewue0afLqQ6XOJKKyBSjwMaKKn3aVzB6OyFE6g3XC8Ve7eKxPiyTrW/MaW3H1OhhoOlVn1oi9uMoubr1Q75ViLc3OLvxy878uNZUbSjQrl5yjkidoG72Tq9reGxq8bG6iVa2z+ZlHczkepma+NiCR7GCErZdTY6xchIMG1zpcB5MhMtGa/NxyqqWKydEjNyIT0f4vJ2wVJLxe0KHxZa6VEl4I6IN9Em/Wu8ZgyCIa0PtC765lWkO/9P9jNmYdJyMV6fcAEd0qo/s6aTXN+uO0zB5f8eKualPLxtMcjoE7OcxNo3ra24/c1h2oB5eWL0vkGYX50/vxBPli4p/1BF4NyTVH8s0FBFgQnyssOcyjmrMKuNJXU8DqndpbPjLpkogETUrkWKZN5HXb5lv7eloYLai50IZH5XoiwSybDwVgy4zbEI1SqmW3Ptdi5+qN9GjIEueMpF3wzqLRzIQ0wGTNiLkN4+iX2eQO4FE/AKSiD2qPoX3p9x7c5SRgqsP1xXOeoGaBSzt3R+x17XWvN02p86f+VOzgAhY9ELmgq1WsjzCHgZ7uz10aZjfp8UIWxSQ2iQC0hFdjT+IUqyiPeaPYh0VtD6USZG3lQ/waYlp1moYbtGcDxbETipJSoIAbG8B5oeYVAaYQl5JSCDvWLgy8JIUNVkIbM0KTzm8zdtxiDlaROrP2dKLj6J0pGeIZiWYSJnldiz64ZA4XV1F/XFwsHTcLVBavBYaR49hFQomnedjfewnH/pz2JSuIhmNhZHIjX7uSa+3XmAe6x4szD7HNIIzUzbdjOgGf7i2XcMHtpf6rm0e70eEldkkC5+Ug58T94VmOphd91ATI3j0QmCQ3SzTBJaZBU4HYm2qnkMHDEhRa6ZzaYC588CPHvwwLIfnmGNSzqQx5r3T+HzbsIYViSsFahDmfGxkiTn+Bk8GWsjowtDgHWCD9GRNTIQKDncwZy6Au3sZtCxqQW0yVyb6Vjh7r+6GAEk4ifaoMQ3jaBKwXFyH5ip+Salvt9BxPi1iqbNtRb4e3CRj49MX077noFdnCeaK9+hri4xzHx8rTgWtY2o0+GYn+GEU6bdOLU+cCTIEok1wuH9eXekoDUoEYZ90YW7KsYAfynzbErmjqPhRVkucE/JNIXXiFW3ZZjEhR2wJVnSBpXcaYUqx++hrWb08HUSxSl1/qzR2K5yDhPj9MgGfvewYBhE0PnWfP7FVSw7EHr91uez3DMK1YVU7jGCNysmIlUAT7OpfgWMUWzSDp1jQFbTKlmHfRYVbyT0PaSofK/v5ubh7s6EeA/SbVonqh8auGX4XGr/64sxv4Wm30WE2EdXH/sxbJ6tE+XU7l7cVgAA+oPbKexPj4JuaZJYL17RLsHwjVCDpusLmvFIXRMaaW7Ec+aDfVSjPmIlkixdfDRzUlWXwrekp76x+WJh76n2Eui4MkHOMtJ/RnWJsk5pZZ9q7gP9xg/k7oQEdfOJORapVs7CoRVmyVJaa1MUCXBVZ/7fWolwys16tkVfADOD7b3S6lCAKWhVUfBDrPQ9xIuN6UAQZG30UWVXoTgm8QoocH7ge/Pb+E3wU7X5T4S28t2+4khcXAlt47FPMKDp+f3pON5FaMffqTocTrraOxErceh0s7g2rC0sB5dnkZ5xd0jslqMP2uxnpQOoxz4amclGdV3um7K7wOFsqwSS3HuHaLw4aoAIweM2KHlj2CkWI2LsiK8Dyb4n8jQeiUTfZywj4mBJcg9K1eEu87K8NNG91/rM1vtNugSOPYvJ7+Hohgbp+uTw7dV3hzgNbtzZdLix7YaD6wRCjhMkrMPgecqOJq95Hw2Q9fiUlD3Pns2YGL5CcXm/jvYZjZIzS5Ymo/K7OJzIZRdTt8itZhnHUJGGpuHgzpW8OETSulSdhUml5EZzpJAlF+cZqk2rVy6t8sdnn8uuQBxIFseHfx3y2RMoD/XY01UthLSGUU3bAoxyz/ECmq2gvP13x9P1Lg1NJzincm74/Su4wlGTRxVUMfiTfZ0o3mzRB+d7UzqPG7d423nyjDs68ZC19pW3Zf9PTeGejQ2ObRZ8tpMqYBf/xqn6zKAESTRzZmXEQWk351882bwkhNiomdfKhZhGmQgTYRQNSLTldSx22TWR0xS5tktjm1u+B/Hvje67Hta3egSOzwnn9pdoTgxVI1pAxE9BGSZhz/jV3CBEvTin80X56aa4g2ecMM4hFFTfW+m6UR1Hq2MjGj4ORLs3XEl5IqNZTso7+uqvn7diiOljcNXAV3wWGLqq0r78m7BMBdZGQd/SdG/d77HczJjm8bhCxMEPGxs+H2NHH2khT+/uTPeFXX3IVPOr20p5c0930wFKRIQAnDxiVNvDe2td9uCXeEByT+Tvh3obyzMzPR0tu26T1MnJQ7XhpUZa6SBgxgpvrROI/1Z4dzk+MMRhJ8DxK/eEPElQUxuiRE7KY2xb5x0/z1wjEXui9D9lPCjKr4Iwe7WxffbOGDePpJNfl447tkDLRgso8uVBm++JxiwVckKan3cOXs2eaCGLAM1NpeLqTkGcqHSJjNZDtxmEfPXGspj1Eoe7OpjJs+cB8tX2V2KR9vTety52J8WAW5yOJvwBDXUJkzggp6ko8MGmhD24sgm3FrJE9g6eiFMIY8J0fekFWDfmT16ZKRV1cn3JnKEvbPMIwaXMhCBu8iCBxfuoo35hSNMfhUT0e1F/+TPnJYpy4qKk8gJwxh5D+wujnwz5kmUxYdeumdHTwjXihlJiwNE8oufSJJiidUkhEtH5OhZmIAhIqZhu8C+3yd4u89JO97G1fUtmhTpM3RufmBWisaCo2IHaIEuXrtwEYfS6uRq57j7BeVgNHrhkuRzohYgKRNB/F559VsKzw5FbOk9ZXEFR2ZpycHEbmaDDaYpoFb1qExWpfYC/NfCIpKvJUDtzxfGV+2OdlU8c7rrbW9OPg9TzKNBchuOXmL7ZZNRPa/1JqiloHchK+9bBK87ChDWbBdUyWT6swBqBscLwIOQXH93DALEWWaEkG/EzzhE5NwF1yxGKH4EP1w8hDN6PTYFOHVl6OuHuJ+9kBbbE+mD6mxj2QVmVvSYnY6ngcNuJLZJoRD1mLTWRQLiXYRQ89MefBa1KnUD+wK8e6jvfmHB5OaEnc49O5A/PWT59ut9t+APb0sqOPN+fAFmq1p7yEPBUtfu2KhneEAOyAlqZ26dMdV1jyZ6SovbwEQcXZnerH6ZOVJpHxHvJ1ZtXXYod/ohIWiKiUPm2/brCP0+ceXfB+4n1grKJuC8u4Wjf+OBxz5yfi/KDtxBqp8uT1Nc5pkNVDk68kWr1flD08Lhd9qTOup8jeG+wtkExm9jRSNwF1uTB3oGXANPaAxU6YLrB7QoW8zLQWpAxZRQ/x5YAcms8XbABITUH1Wzk5fD7KbDHFlUjA4zHU4dEQwUMdwh5IBpsLtdr9YxDZTKfBX+mavMr7ETmKvE8cjki94NCeRKgkA6wnUbcCMqKhYvuHJIpmDYag5TkSN19ane+VLT5OckiFPKW4t6os82rUHeyOsA1n46XiEFmUHY+FwNYW11b6/t1qC88lThT4IbU6F6+Hxo7psPqNNGL9yUJy+eLATC8UOh/8N6uiUkEGoNW0eT3wFmTInHs75ikxinB99N1/jlXXhR4HzwoqEIIMwL2v9pEiVJpVMHuvhekYPfZZtTjZoURZWVUAcFTo3Dkz96JYlkv3pZtoo8dIOiJ6MtEof9vS+u4C9hM2w1DIJ2h/e8WgzMEviyPMFtuOgleDab2lz/CtOVnuqZl34mroHbq6L8TAEPZs3+99dqHncNJi0lprxZYHOB2bLRjCuWhJeka3JJFm0QPzv+Xqeo6ENL+oi9gG5VPUCeP4+AmU2cttE5oVV0EM7qs1J1KkqUVQwYxigB+USOFH6KIy7ocidTrmErRVdbsE3DJ+4VqQWlGnrq9CsmxdqQM1IK5D5Px8RD76AjPmw3fJglwYxZrFlyeK28dqVpBgdrxKtEUtAnWc6Vp0dA604rYryEltZlem65b5SAlv1OSmZyY48FbIYvWCZ5rTgfc0MiFnTYBlmh0qPeSsT4tYcyBo6VJd7ZBK8IDSxDuLIr30OMmAxv7NEVJmxWAUrG3z6OmX0FFi6E5iwhT69wsSeYsNf/RNw0YhSLpDhWHtIB14lXbbXMVlHU8eKpy9Woyi6My4xC8SeL7BkAIMq9/UJrET4MxY9XQZvCh31AmRclIhdpdFoAVGXe+xOCcedmzrCd0jdfkQx3HE8FnpmE1rUpY3XJSK/Y0ZQKQseZw7g5AWP0+1ZH6WIG4nUBYLe/rXux6prEy6SbxiS+kwTOd6dQQ9iHvIPmRX2+Z5iLkgujlVjhpPgAck5AJY7e2IxtIBIpFmXRC3PNwXi5umjsZ9JYc7gOrJjqKNs2fgTltQDVqvyrVc0luWyHQG91UZgr3/WOvwqzotmeVnIVZezB36StMNAT/RLkom26xQyOsywSegp698FRSRABYlN8PklOsKyVW/xOF9jj8SqlhMP0mPCSqSCnptXj5+JVqvAtF53Z2whdR0blRv6tWlaFHZF8+DJ5/12aYxy/A3hetmQ/QevqN19gnP6VNJ8PHCFl21VyhNxaAC2AuGDi1YLhZ11zvqrOG3gFAaWp16RhLPJzTHUX2UHOoY6MuL2NNZUR+/OH6Chwjv86/hlg50Q4XkSN4n8w1CbVzjE867TzlW1GRQEkDyJhaBwkNPChWW7wMv4ceEC1jpT3Z483RLfSkS2JfQmK4dLdErlTx+Uy/O9nOlaj+SrZZjn8ohn7P4PA5wE3ZzZN2X1DI/Z+Svxw+lSvtjfkz/cZ78VNyPKwRaCRvJ3dVQurtt7f9A/QRhyT7jYxyP6g+jdUG2WkGQBj3tC5DyIPbLQ6RI1v4iOX/bM1p/Ssn+igebB8VA75VNP8gaQRz5rRtT5exjAPEGM97qhrT670J7A/yrsDd1G+aRqCkv1KliCePG0k30MvgGgbN9I8wg4nX324XlKq8HEC96Ul5ayALE2ink6p0/fteNAad2sS8zttDtJ5zYYVEI2LCfqIJiBbHmPDtxcjcjNieTW5n8bsaQQyPhdzzbuWf4w79u7EKTFKf9Msm/WIeh5NyIC/XUsnDrWuDoPkhcMDrp7ZLr2SrS6Vdd9eRHeY/5aVQ1KUEowPlbOKOUtfmTuBS34H5tgTf2StqFv/t2tZMWnYEeDnMsm7WSJ6cJHzbUB7Re4sPh3cCvRXvW7a3HPMvh9rDNl2ZbzttIt2DAsca6OGCAiy6Ne2jukJZH9pP6KXkTxYgrXZP57QV4sIqYWXA8IDHedmHfTX1i5KT9WhA4EgOJCD9Q00uDFfYRymvuCM4V1Y6EFkFXsO0+Q3d6uOZ1p95h2nnRujfC/RvYLSrX7d1qysTni81Dl9k7+IWmHUj2cmURG3vmsw+os66rLp3KCReX5l2+I/1HVFfq8HGSUofLsauCZsEbRhxuv4Nv1sjiN0ZCqU1Rgq4clAoP1XFL0tFmobp1kpQqdoKsP06wfAt9jquidfm2wQALurMhrdC4Rms8hT3+SuA9T6QdIoRjvLajgDiThq8exTNQxtxQJQumSCbfK9W/lSgFPrwUaIn/EcwlNAfpepMHSXvFLQOa8Pfj+oLGV/GCBy4V16b8eIJcHAHTadoOXqf5wTeG+bgfsMd3GU8HNwdA6h0M3YDcgc2MXxYK0Lv92tbHq7F6o9aHyEgitUijBTNIgNDZKpxqo22uYJylH3bPtWIpMSSoj6oNkWLKGPKL6eNuw5nVNTHNftdhc+W+bvXRdNOXwhcc86Pp49FjVJ3wKMtTnjlU38QJOCnElJC9hGgpva7KYYRZy44H5SL9j34qE6Sw9tUana4WbIwdEBOGjdQJWl3k3PJR2BOybnVkM8omqXQ3jxwer5PMhM43WgU4XqEi53vL0VLq5jR9bGJXbHIoWFjEI06bQDzXV5LMaja8F1NswxsY7B0aCwX2G2CUHl/dZl9MlCcUAgqO96Y0BXq9c5iREO6rdlR4igZ89mKWs2dvEQjjUpsKjeGrsUkmLfu3DX4hTYcq2Kez0CkqCOIb+nPXBAOw7bCeDDPE7OCUFwWY99OGdV6LWHFxUMoLD76LY7g3rJ06pOAFuwOXMgs8KtO7o9Ul4qh/f78Ok1Fp6kMAGqhB+6f5ukuxv1LVkRoU9iXClqAsdjOQaXpTJtiqiJH6XVjdj4TDqpTOmHxWpFQg1ZHgsRePNJrnN2xgETyw01CbfLhdwr2x/+0DViqYMV/NZnuXsopvnbptfrK2YMEhQtEh2D8uBuWPvCmMZa7XqzPaNOgwUZW/ms5BYF9LhP5ZqWhsjh3n9I4kwRNqZV7I2wkYrtj1teD3AjnAZT9G8Q2J/zedsinfd9E6K8WL6qhTVbZPZTTxuixti/eL4ZRhKcPO2iQGgcFKGi9IR+cvbB2jlXwHfLZreP7GOZh6FhbCTveihoUCedQBHPewK+Nk1cMIpG3GWBU53zjM4vPkmpl71hjslBCvPliPIbbssDQzj0fMsurlUwkPlxkXuornfoCipQs5XXSgpwFqDkSPZD5v1Zio6XLEBC7wiqUy8lYdbnVG+r9++uJ2njgMRK9dwt88bnTJfWymUyIRtqVET9N8oEXqpRcRgbjpzywJjONzs2ltjsXPP7g2EO8zNycLl4+iD0Va+sId+JG4SIvpM4kpCp0HnZ7iCdQwtvQ3Lfi3A8w6UR/ZXfidrycaGYiWS6H+GwvnyI5QcN9lWXXjA3FKzqc/khcpix358nTs1kFomqmJwh1As6Lnz+pKybc/C/FYkU7nNcJJevvXBbvutcFNc4oDW7JgOmrFGYe7UZ/tyere9MTP+RGjnHFHS5gdYQXzriE76RsscmmZC2dRodC7eCV3aQ4KSVtjw+UGFJFuTCJz1jRmuMU81r1goMN1vmfXUF3BIjHJJ3a9hGcoiXTvxiE5nkxSs4mzAaAYpH9flsjnvYxlg8aTX+K/vp2NApCWOK6CyKFd5mNISUCjaiJzWtIv63XfRRG/CJD3/WTJUY8mq+vNXvy5k+OU7hnX7Za4WVSdjrAn2PgDY0xuIafY4fr3Zd0Nf+Pe+hXBAKIoxxUfrpsFzoC4eTebxoLHMrsJhSfjFCoqrDiehFCs6anVhj/hGtpMnqcTk36Ul5HlGt0txM5peVIJapVbBG6pJgqV11G2ylXO2IixyHOMnChdkDIkhG6fIvKH1GBTCR7R3DItKHoiHcRyyx9k1EsENjCmOOuAZZyHmU64p1ijjHaRFK09/2iFwR9sIluCPfF3jBH4s7kHzthZYN4tRb00NAfAt+37deDOk8zXwLfGPrAV/nAfo3pKWc7OMm+jwQlueW9ZR6PJ0+8yEztOs+yM0ZBywgX0drIYPbG/P1AP1MzJa1OdJ+xysSZ24Vskm9GeOZh73hJ/jEgWIHTKn/h6llMoWAylxk0TBR/05kpYYCGlAckRiwxwOLLzAggOmxRKexhiaFDY8DKT4qs3LkQX9SzK4XQOcDT76rbjPGtxNtW7+H/Qntu95sV/TrkvoL2RJAzEQN7bhuqhxbqB39BW2rh/YzVx2zi0WTXMOIoTr3jrf7gnYiCIuoJ0PyeNgw5onvFJXPjeWGMpVVv0c+VbxmaJBgMHFuLH1RAkV3hoBs7AU5p18XkWrMoA3dFDo+Ixi83rtR/9cZ2pPF9IKHL7+oMyIFhNXYo4I71WXbxaOh5Oiew+Ha+s9clz5lPdhMIYzXfr+CWr28nvUCKVgujJUuhXbtaB5qxyQFWsW8fMeC1S/ZAP1V0U1P235ZOTduVKx3B+9Am0dx+zStu/2rA5K3PmXdaKWGsrnqCDGKaHt5jBxukB8qLIFEDPYZ6byM7gey96/uRPXcarXuupe7mXHKmJ+lMCiHJd62VStB+EDrjQyhBjMrdMdDrWBIi4c41xY/kYelMrlEn2NPfaVQ15RG9KClv81A0/OMODPm3rQNgkiQLzzRD2UYVJlD4vTtLUoz+G33xjM9J98vmFAH6zElBc3+mBH0bdWUyo1/zhUJ6fHy4V6abqCqEqRuueSGu8GIVgjrqUqetYcZXrqWn/Hmv2R61H3m8mb61GJqeI5KG3ELlxdd8XxbDWsCDWlE8Jk7RYPU73uO4W58I0xUVW1kNOeoKycHrbLh+OYW9SNkTADSSHGi7xi3u452S72s6I08F6rC93i59hIQmiWi8r7V3wfiNKA6J3P4WOvIQ3JfBbcR+nrQ0AJs6Ba679obqEJYSPIiCbr0m6oCAM1wAjC6bE/fYn75xBruz2OKLLCiCf/ioPsJUvbwZmB7SeRuGdV1zdF1y3qzzlGKTK/3yVJ0q7f+UZotqjZHHIzlzeNylqTaXlM7bYS+HcAjKSCthz8NPSSImeBeciHRtbrD/Ajj/SboGix/+aCG6KCeq5SGIi+mAbkHlonSZPyt6Migib46hbFI1xsEdRkyAaKy3O9WN7Unxa/Vspt0eV2llqifDwHkppneujbQAuLrSJh2HXooDhpOxhEXs0BQzT9EzrzyUE4rlv329z5i1VjnBDTnLCc6HoE2mkX1MeruGQrHLt7p2CtFlIVYY59JR578igvQ3gTedCXkbCSuihfjG1UOF/13BqqrFVJ9f/be4iyXZ+zjd+OHZiWvMA6vLV5yAURTXv0M9VUo19gcYBAbfHMP2x3FibRXx2WI2J+TFKE51Soh9P132drerrBr00cLBMx2XqyTztyVefTR8NE1jm9bMiCZ036GCsw58anNe59SwlBT6AjuusRup1H7Ljtz5EPKZcr2ow/9jdDPi+t/G8mEvRsrDYulgVHCyfAFuvUWHCYoxj9TETLRS9MW3gIS+9LxkNI2uoi+qnLBUGc65aEE5slKfuc8UEl47KbV4A7iFj8A5iQY4FZIVOmiBV1evirwgIP+p+dlmUiN0Cz35FF+DeqYNQQ+LdQZ9ZAvHoA9nFDgaHHA77OONqOLpyX33larIAlX9ysi+pkw1xvcp5IkJS+Fb9JPhTFWivqhJlNS4FXiwC4mz3CUv1DG7F6VMWLMT2eYBdO9RB0DDxunoqiNA0bRnu81Waphi5Ll+78NZXv9UFdjyJe+5UZ/S7IGdxZLl4A64maCVAkGm7Bkk95thlMN9mQ8eBzBa48IexpEZ6Cl/BCsTDzXz2JEFw/CkyvN4a13QwBLFW+I4POYwUAxiGkXDhZDLjVYgFA9mGfYLz44/kqnM7E7ddwGtl56JaSLffvxq3aNCtA6XIX2GqKj7JcmKVFfIls1To/1q1HWSFMBOPB18waNltXQ4hVI6S7lVMCnvJrUkfvNw7WKmOJWYW1z/zuzQY0oCke45VaV9r94Arb5fWp9kQj0NABnxVqoqlJiZVjMpLlNjDDRWguUceaIIqIjrYKn6iBA4KbODehY9tB6/Z6j7GBq0kTV9gtiQKQYfBQRlYDg2M1hAazAepDpam9d8espf1+2cqnCbkk30/PHK2+innl31FrWKB1EL34XuD+AFIwGz/0XCd5mlxrMek8eCb1NRPsCgtzRYWvkFItJySeXTErUcX0kGfZi1m6ucgr7fsK98hdNhGXpMDiB97kEXML2rOgnIB7C6AaPTf0bLH+drYZlBfYidwGh8/y7rBrHFW2dRUqDjeXXNZDuzeuETgfg2prbMKfXm1nmlrxFYMqvkKveS2o2cKdrjfZr40OIG2k+Zans2Pk7eucJOh+FasNbYCPRIOQzJe3QaOg8BGffK98jM97v7gQUBlS872yTEiji9uf/7ZcOKE5ahGmgFgAtMfga9WxNfuO0vjqIOxEy4CxRVcUCRtHDi4U+tYkPMLioCmYaH9Z/r8igz8Ym5V9jHlLgPgBxOcQa6+8EMJ7XoxtLXbw8Ztmp4njhcs6vNOni/3OPsrpxtXUY9f1Ct817YeazWZynso9WNg5Ap2r0IpKARjJ0OSB7IDc1ogwcs9WaJw0sb5z3Vf6oK+tJlt1juvaVqx6qZczy9zHFBMNko8U2/5R7SoFE7b9Gu9mmz5aIOYiIeK6Tu5wRteMOY28wefPeSzeKa2lBW/93eFHGaeKV0q3jGRb84MjEV4bOSJeMz1039f50txk89sL1ptvVDIVC39hB0zErBWTQRVo6pHooQwC4bpPdChnnneA2vgzbw3I+3U1PSSymGNHUSVnWdIVmz6g/SE+dNZTyWs5qavKBrIWFt2861zmxrIv0ltPCc3/X0/MNtEsCq4KW44OZmpUILW9mcbRtwK1ZXFlZ2iRCwUP3LHHBFWoX7cEw9KxWXttTIJyd486bSw3+dPtCelA9lq0szkX4OFSB2aEL6A96eyhSynSoC1qsXNJH0u41bh7TEao70cCQBvtaCluUkFo7vj/V7CX+3GOHRUq+Suc7dz22j+4YgnGWtZ+62GG7PaeeeIlvpM/gT7C40iUN+IaaM2qOXm8RUTG3wky5JpeLGVrfH89AUFXvPLw7p29AAOkPCoBN87lrtdKdaqLgSicuKrS6tLpaGd8GPMKPmpE/0C+6iPYY8UQUbqKmwaQWIuSaXOR4dzMLp5gppRMqtsHkeXsu+7aEXll0oRFta5HnpPMBTUSxeg0CO/WQ4qR8HH/wcUE8AvCmVuZHN0cmVhbQplbmRvYmoKMTQ1MyAwIG9iago8PAovTGVuZ3RoMSAyMDU5Ci9MZW5ndGgyIDE0MDA1Ci9MZW5ndGgzIDAKL0xlbmd0aCAxNTI1MyAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o33A3Sd69YGDMdWY2fFttM0aWzbXLFts2HbOI1t27Ztq7Ebf+ne+5zu8/7/GN831hhrPdec1+Q95/2MRU4sr0QvYGxrCBS1tXGiZ2Zg4gEIyQiqM7MAmJhYGZiYWODIyZXNnayA/5HDkasCHRzNbW14/sUQcgAaOL3LhA2c3okytjYASWcrADMrgJmDh5mTh4kJwMLExP0foq0DD0DYwMXcGCDDAJC0tQE6wpEL2dq5O5ibmjm9x/nPI4DKiBrAzM3NSfeXOUDAGuhgbmRgA5AxcDIDWr9HNDKwAijZGpkDndz/xwUVr5mTkx0PI6OrqyuDgbUjg62DKR81HcDV3MkMoAh0BDq4AI0Bv0sGyBpYA/8pjQGOHKBsZu74t0LJ1sTJ1cABCHgXWJkbAW0c302cbYyBDoD36AAlCWmAnB3Q5m+y9N8EOsA/zQEwMzD/190/1r8dmdv8ZWxgZGRrbWdg425uYwowMbcCAuREpRmc3JzoAAY2xr+JBlaOtu/2Bi4G5lYGhu+Ev1I3AIgKKAAM3iv8pz5HIwdzOydHBkdzq981Mv52895mERtjIVtra6CNkyPc7/yEzR2ARu99d2f853AtbWxdbTz/g0zMbYxNfpdh7GzHqGJjbu8MlBD+h/MugvsjMwU6AdiZmJg4ObgBQHsA0M3IjPF3AGV3O+BfSubf4vcavD3tbO0AJu9lAL3NTYDvP3CejgYuQICTgzPQ2/Pfiv9FcMzMAGNzIyeAIdDU3Abuj/d3MdDkb/x+/g7mbgAtpvfxYwYw/f7890nnfcKMbW2s3P/Q/zpiRgkNEVVREdp/Sv6vUlDQ1g3gSc/OCqBnYWcGMDOzcgM42ZkA3v/rR97A/J88mP7YStiY2AK4/073vU//Sdnlnxmg+mdBqAH/60vW9n1ygQCqP4OuzcTOZPT+xfz/edz/Mvn/N+W/vfy/Dvr/zUjU2crqLz3V34T/H72BtbmV+z+M98l1dnrfAhnb912w+b9UNeDfqytoa2X8f3USTgbvuyBgY2r13zaaO4qauwGN5c2djMz+Hpe/5Sq/F83K3AYob+to/vtqAdAzMzH9H937dhlZvl8fju8z+ZcK+L48/xtSxMbI1vj3lrGwcwAMHBwM3OGY3keJhZ0d4Mn8vo7GQLe/phjAyGBj6/RuAngvzhtgYusA9/tEOdgBjAK/RX8jDgCj4B/ECWAU+oO4AYwi/0WcTABG0T+I9X38/qB3L9L/RVwsAEbFP+idqfQHsQEYlf+LuN/jGfxBXABGwz/oPbrRfxHbu937BWP9h/27d4zG/4LMAEbgv+B7EiZ/4LvS5F+Q7Tc0/4N/k//A9815hy5/vLH/1ts6O/zL/TvF9F/w3aHZn2TfW2zmbmf2fpX+YbzL/hWQ6b1Wq3/B92Kt/5Xte2n/sv2dve0f7+/c9zfMv9Tv2dn9Ub+fhd37mtn+qze/6/1X8szvuTj+Ke43Arr8K1n2d7rj+230x+Ddp9Mf9Xs6TmYOwH/15z1fJ1fbfxm8H6zzv+B7tS7/gu8VuP6r+e/W/wrG8u7e/U8576YeQIe/ff/PIhg5Ozi8vw7+uqret+Q/+K93DxDoBjSCW5q3NfoYZFEd1PqrUgDPlX5v/NMM+Z5aEjW955JDm/MjEnQ8dcWPgA2HO4H4oW6U1R0RqtvPy0QvnsdNtdChzd8VWp68nvW+Kk7ttcAtTmL2T+QeC9T0EcDi0yt/3vd6sfdS9bcEbwLtkCTPtHfmQpLPRvvl2ivmVtNXvDIaMr+nsF/BIQX/XDxNH6USqe1fMEueZZg2h00C5URPAEODeuGGPHt7N4OaMfFGJPmVFs77JIo1z1NzkyX6Yc5jrVSZxbEThwxHE5sA/BZ1dIrCU/AwQRJrwbMwP0YyJCLfhEB3voGvDdnAnq2KuGOJt3hkYmsE+0jPX6gYeWcqJMZIYZtccOjISimjHRs1s4azYPqFWS8HzlGh/eAF0yOibPaHQyB/v6XTqTxLaHLPNyvCoFsqfqj2w9DDhAWORZzs/j5un2nK2m9vvp+/LrIYZjlpdXkauCGetoNx/rr0npgQs4FJ17pR1jjlU54EwR8iNRhmNiPO+Ailf5quNthfjvpmyQeKVxQ1BSZlMVq1IdnhgfTWI5t+bBTIv6zLu//wGQFsc8Feec85aNG4WAs4FZ1iwSPZZQMxIvX0hZRFKbtSK+64L03QEJV/tFQGNMA2qJqzotDAIDchPJsStzHU9KbgXp6AZ7x2qmg6DP7IuELQ+JvXlETx5FmwuK5rmAxyeckEkFRY+Jw5rYRV7XtOZzjD6OR1hyQDJsdC+SwBb5IyjszQCUkdX5ADNhVOqWzAAEZ6AsJ2IGkqX4OmRdedWfZQHW2SyqtrxdvlDtxzLVnsbs/ktvaWmiyDUB6V6rT2uez+Pn5mW3cj6wckyFkB/isrPRTHriKZEPmG+2L49mh3EyX3wq35DsPND2M9+tzOBHbR/Xfy24OxlbLODvPU0p+czYqi5wKmB4XHl7Kau+RnI3QLvLEbGokgOztn4XbpC8c9VGjGVghZSw987nbE81yxAj32FHjA5oagW4iQ5rfGGOKmMIzcmosxc1ywE4txqVICUHk1eb9wLKawS63MptSAztB6PH8Qa8tiAqXu9HXQKP3+hWehAn3IKBsUUxCxKItTQnXO/wVyfi9T7a0pVVEHse4Hdy+kpmYqgbWXEZZtUGhVrP9QgIS2dYgsSIiPTXLcz71ThLtMHCZtnBcTaYRUB4jFeYj4Vn+53jcfmOW1sdPo1LJOZFDwj6BBVMOUy/tE/oldJlWBiM+MeUZqiYHCSQMvOIVcoIHDj4r9VNrIhUHN42jZEL1L0x0pz2R2F2jfiSdJCFavuWsiFQGfODWvvIt+rMbq0B57AIOxVaG1kodhcaGs6+jbaTZ2OjfZ8tHJluJUhTXpKxK6ypNn1ZalEuzqKhDOshvrs3yUne/zLubljmuYMb6WEj/44/sYpFJwsuUGHkBGHq1Od/RB031qph0pVhXFMtpn7WOtHMEZ4LLsgzc70YM8hbJqwTgQsIGJ3TBI2asLaLiT7RxAnO3gucgq0EomG8sw5pk0N8KYT7Z4EXqSYdnVH2V3KJeOuebJwd6tyR183CxrH/PZfDXKRF2Q0Yct3AxHIzbh1uOT/1nhDSOaW3RXxge5uJ+EFl+fBL0Hw/a9lYDLAwimAkMriX0K+YBj+QEHgZePTtynXLdkEFSFr8LnA+q6DzUEN1aCWAY9ul4Q7cRsKFQiy6/WWOsU8/2H8TRmEIeq+KKTmz4+zZKz0XX+iegxrm15TVDrw1goUmugsPzNVEphGEcz3te4B+58l09mIp166lT97rEhsr+SoKm4cQsOsIzZ53mz19XZiiFb2NGIYy4Q1IVSoSpFCCXvokvaG1C0F2d11LPQsZsTCGvLU5+XrGgaZ0fxeDTw6+j58O2GOO/iG6Wbn2oHFjXTB4R8agasbh9uS7+gthgnM+eO8TD6PwJMienQB+DaVr2jspAUxmkvoLlZVT0B7fEKUVbzx2LldfXbiJwgxB+JwEDd7Wm39ksuEacla+N9BY4hoWD3Xe4tGB0m55Z/BGoSyKNQs1WazNPK2anrhTn5ANgTtGIUNcl4zTKF1l2T2ogun9jkBdDiXM0MO3zFH1jNRbyDAuq0kPOsTzzu4xUxfpERzDyDa4lksnmiBffaeSgyqj0cIdHDtvgXGuhJQO7Xfbc2+14MNLmibG30O9CyT20ogGFImxdicsZXyb4nOGmjKN5boROYgnWlJ6FlqyFbz+ObbspN+kRaDvwsRaMG1ioSXuEEoTdEL9kWNwBvSDCeE5pzrWkcc1FkFpoQWx0R08vnJntalL1utxPjCJ6f1iQm7bckl2fGd50xYxt1M3OcWZiG2wmyLPnxQpJW2xpunAhfEueJA3u42xIHLXre2p+bBEoWAaAvPN2l1XYBODTjcPUUQDbi/bRe7uKdOoosDyKUtwEzTfPFy1FgIfHERKqf9qaMmdi/LKrgu5LNQ27fjg9plK/eB5oQHYL0Om3zC2KMB8kx8x/D9iNJ1pIAqRqASVtmmDuN0gMokvdduLmYKoerQQMo1haZdXWICy+Kh6YCnq703AGT7E9F+5Q74hUaziFnabnW8U+GvmM+Z896a5KwL0IBEvy87Jd5kPDKCB6OBbWn3s7S+gkyi37F7SSc6VLtM/DgZ/cHLVe7IWtc7nKACM+rq9hcr0wXfWrCbgbhEHx81qyl/Xgpsngc9JxD2ORWAB2rb3VKwvSaY0+aMFbL5oziW/j3vG5lxHq/UaMq6BKWk6+B5P7P7sLaMrUflLjFFy8Uolrn0hxMbsQKdJ+12dlD40l3llwwtII1JmqpMQ4wpXaV8nTxa31smpLLZHDVn+i4KHI4DRD7jcleE9fi4XWdGNVQ/RfdJzp8O3qxC2CuS9kaFaQ8qZc+jYEEqwm3GvO12OICfy5rptotxeOaB9EDkT/Lf5N+M4zxKr0jtYMk4eiQikKSJpJXH9nwMETKz9AMMZq+937zP6Umx5JelDKRY0FeK/ff3gRQyc7WqsRW0NcC39ocfDMir5wRFEua3COVLa0w+ZopN6vFT2cQ1rmnAujjqB86npyr4DqdadGtao1vd9nqjho9ioykHUO/Y/5SloTS4oEsdRnHfQ7ej+RXC5f9FSGDQdOMWCkLaX5+Jm1ybSI6y1jAD58DMhpnizI+qmcwRpy5irgt+NNJ59YjQLsNKxWydD1OkTHZgS33+PAYU+axMAFzDiR1QdTVd/AcyE4ZrXPlW6vZKT9GaDcIGr2wUJ/vUME/KB9rEDSEPC79nZIeHK8PbsII40NxxOo9q5h3njIozgFlynJWjE0n6aAuH1jMdOY8ZSZ9aiOE37rfquivdlcZOeXYCmFHyaQRBbzm2seuQE9o2Cf/NuX19oYblnJVnK/lso4b09FPV8i+7eSCgctmkVcrZPgLdFvXfBn7objXtkdSicsk4mkTFUizyJmAxhKWcUGUQpUxTkrJKqr68xaCgdSpqzIjZnmJtditewarTIVZUIshUXKLjEL5WjjmsxYPQ5ah8io1v3UZWmuphqCm/gETOpJVRlafnN6Kj093CZzZjJxdZZ9GbOkPFMn9vuxv2iTgSwrW43+22Qd94xBgOuo260RXFOWuzFCeR/CMpcqmSLog2wiO9eUZuTHrwL1/u42RebPoPIAuzEBYxDshjxUFw1INelsOE9sedGkIgOLXV+LZPOaRBNop9o/WDyH/8t3mTi0BicwcWePVuNeyGUysL7sZWAeLPoZBn9ls35oP5PpJ9thBSgKEmfyEOmdJAE9wabJuEpSVEBOZMQJfjloUUIsg74onUsddcDtBJtsStMyb/VL4FkZioQ7Zou4I/iLjgLv4U6Si9iRtTvqZ5JD4hMYEmzaVGFRFIPzrxDAYAh0/K2rnjhtnwiUHKV7HmwHx9ZdPYTukg2hPL/BD8UPOeVgRhtmNjN83xDpqVkkB4vL32kXa+oZbO3Nkdw6f2qnH+GMZ83HvuaN5U23RBFVQfh4vcZYzZLIX7WAL1u8oSgCSa/K4DUiwVYnRdigI3D4wm+4XHJfaJ1nGf0oIOYmkB2gyt4ZEVbSuiWudl6fsihqANyu5fJ7OMT+qdz2Ydid9MU55A+seOQ9BIXcc3vBJ1QYHPoT/mtGhij5fhuPZuzJgac7ln0JbXfUMztzbEcuYT4f5XM3Tn2VV+tjnaLDPdbjULtY8rfrFpOVG8kNr1mr7cd6oBCivftNZND3/2a3OR/rrY0woMW8lMkIDL4T7LVuEhXpLZ8pz11SUl4pEd38Q0Z9JQet2PbUshOhN33u9JWjWa7EEhSb43PlT5PSGQCFb2RJRn3m246sMRQ4yVXWCmOunVS8eDOkCSUKlbEWZjA1jDXCbT3DeRjPWTdjEdEdLLb9ReR70jPGswsEk172u69ADJRsWDXHjR4z85/gxqjKeR82G7h1pUcezfb889uvVOaam9ytVY6UdEtAZOBW6m4NWXU6NOrw2kddeibLiPJui5GXkrUdQZMt4L64UDrJmCFsOmulHaWc4OBvzbVx+vDdvEsDTaz8Lx4CzFdWbSjayvJVNnY34RWSjn/3osoAxR82SEr+S/G2XjPQUkeNhyvP1HOa55GC7vF5b1OVW5USNAL7Dz+oMjtwNUsu0dhblNNiCwycPVQh+FmOV5Ns3XUsroepzd+TmVXL8VyLqSXKberU50R9yjCDoMijJ7mW+Q+izBz7XmvZtp0XLROYS2cH+ZPkZQiPKqTRugcV9Ok/FBwnUN0TmGaCgTmyaqQmqILM61nvh/qozIF+X5Z6++/uV37ZSm4KYvzbBXZHTg0mq9GdqmVQqq3POMjLgBhuFYkb4w6hAhNHdm3Gec8lks038xIf1OOTm2Rmt2bJ4tXZwuU2Vv4kmReT7lglj6Od21bKjd9+SPyLd09ibAe9eElL8PbDyZoIdeUrxM6OnybGC0KV3ifFwJ8zgwp3P4o5DzRFxBThby0vGVOxtS6SJfI5tw6lfh8wMrS4lXRLSGYce44qap9ni2ND5EGP4VMrPUYuDGiXv6ddTyVAP+CnHkC9z9btftuYtsGAL12t8PFKlPe0FtEEUp83IqCN4PGSVJKXX3PEtdNN74wa+EdSBxvbjE1JPK0/6TEppggtgPMozQcYEQEnKY/pQ8flwb1P05K27dG3rvpHABEK2U7eFqrMf4H0yA5edDjCQRVmsLiKPYobcnneBYl41deE/8GPjqOH0F7/7inlM/HpdJ3vz0mWRvS3HRlDYhr7Ubrj2tpfbFAXqlCV65X61X7N3e0lx7nntqqBFq/lF8lu2fSMcbdPWZ/jzHWsbug0LGRsREwkEa/hCRTGCChWIsloNpAqu7pRfssFYhTlvaVIoX3jwdHpzBYmL+7Bk+f0kMb6cH1AuCDDcWXZ68soPdify75ExGmbvkBsp1vqHbNm0EkPQBO5cpWRxo6NzfSFalUV21lNO+5nCXI9ARHFGAwnMldWlKHCW8Lp9CKJ9m2cqqiOB54EHjTXGUoK+KCvp5ihXcb2VIxFZePIlmLy4Ov4kGtPD20+UiTylRRLsxoQO8mHnBT75VtPTMyCq52adiCEIG+GUa2SAsl4TtrZrTM+A3NQY0afFN9Zw51BKbbbkWCXTI+QK9C47Ylg2LST7jk4CMAk8M4c9ALPiq38+KNsy2skJ6YSkZbQSzJzyQQf3CL6n38+Eb5eF/KGGgaZm1OIQM4l2FByOFcnv261KZ+wHqk32Mb/UCV9ZsZMcXYarS/jEt3FSOsNRwzNcuJaUDf5XoLCnQZG2xccGMps8FOPhDMqxMSkRhARKu+97CAYzhHerekIfsECZ542RkJ6Hnw1ERJBW2Ug68nm1fg6VekNy+nGSLzYnH/hZMrJI1FuGU5YVuIIb8o4KxqPeeQ181Nj+Cl/eYdUp6rlYTIdx92M/jxsOjZKOL9DqGD9H097+c6U+Zjg2ulBE5RkdEgfOsteVCs5htRbLQJHnr8bRvX3PG70a/C/vw22V1qrVSXWbtCpSuhMc+4OiVoqYgcnpiLo0LxbF5OJ2bvaaMx4q2mrKJkXGdU+d9we81xBix6+sxot1V2wW0pZhHq+RyVaO9awFNvGHyrxZS4dLH4kgEJHoPOub2csUl7LwCkO6bq0Ut+IA/61BFI3gzw1Jmw740lgNnZSPRKwjPPp5ApxCMT7qtHr7Se2KpGK7Aqq9TjpfhNeNEdtriWHnmfy2afydNyBXPG4Md7EqA5Iq8Y5LHDSmTvOrZb9UZtSTX0/hmMKAXA3iqqhTmI2FccQuyrUfXD9uzqqNs1TpLmP/igVF8hCrOH0GmMtmTo+XvLy4m7TpKb2RLPE9MZjq3+EKcogdwL0MHgvGEz4PGqufxdah30Y3qYt4g1T9OA0O3geqobboHWZYWSkUoXG8QJnbFgb5fxPlrYnFZZ1NftYEf13bliXHXFeA0nUdx14LjOjxTKUQKYvkybb8BEAOP8K7TMEXLKxqkPDKi4fL8JlbTq0wkSoyt3TLA8SceXRZ1zUC4GdYH5dqfKxPLeEUc+jzfBt2HFrEq44dE5bDl581C/ZKixkgdF67dLCyeYnrMcyoRwE9XqkmWuIYxVarIawr/sNaG6Ee/Uh9jACogvNAjZeClgS6Q+OpUO3rDg7bqKTTitnFzsMmXJFuLhCxOS2UaTQkk9oC989+nOpamQgoEaDQR8aoapQcLZIChU9it5KM5/BkPkIE0cS1296ZiDt+EDJSNfACDZo8d5k47V2IX7/aeqOPlosqZK3zRChFYn5vkDdTgsYc3192aXpJUckxrwHlKsFePq/l0NG9j7uQGO1pLf0e6IwcTJOiyNu1cbVdT3LOwwB+7DOe/AuGouwpZKlOGTYORBpPlrtGzOcTrcF4m0EW4+NCSAtVLdwbXAf48MybA3exWePE4D7Bdo5N2zQ5vJQP7+qIW9K2s7uBLOmlghEW16TNA1mCll5+QHdZ86trSLiKVpeM8cHpsUJ78uu6SW049GSdK8/aGukIEoXOoUzzx1SaBo6vt8/bujLd0+5PYsZNt9L4BRzE1eSEUut2m1/pUnZs7LowCaV91IRpvRyHjdXEOMqr3ZmhDUvQodXZIKXE8qpJe884pw5Y5+gdMFFQvrKR2CCOCyRIXi+h4kHFbxFhfGTwAY0RlpoDHVm1DdDMn/yY42rodt16jBlI/BzUrcZoHd893NGPlyiy2crsdI6JLoTlogPHGiEzd0Gry3G/OHFvlt7XTlyr5CuxKa3cmI8V8MjiHkBvBoMGXTxbwVADEaZjWO7i8LFtiz38pmq72SMmFq/5SG73R12TNUrr5DFrhj0VrRwBC3zhTWRQ8sIN9XGqDvpTuCNiQgR7EgQoMrW0oZV/ioyulhVGo9/Yt+Aeg13MdggiqJxeb8b9FXUs6CHXB6sdRhLB8HJvSHiquHolF6f9+Ks+XVUB6+JDgz3y8nOdiiZ6eh3Z4EH0+fP6WEQ1bjvTHh1r6UyKl9LaU5C8Kb+f+jm6GZwqlGlC4XahYv4+9w9mvqzMP0e2Zi4oFL2QSMA/dr3te00RXRY4E/XaowdBa5aRCcj6F0x74DgIY8Qek98svWmqgumsRuQFZBGRqWeSJ8hEmIRoM6ZgfyujqVGM9g3GdzZp9seUwDb74hukcXoe462cTpzLXE8ttSDPgDjEJ4NufTyV5OAG0Yp9uV11/9AV5El+ceKshxOCYlc9B6FFgAJX14rBnuOjz3rMzGe3cWpwXGbxZGyrPD7wM5liUWezA0ZSa/C1ck3r0O/zQAcFqSiX+Q6sLndqPe/3SNAg1yloIG0WfYFVQ5tglvGPVBe0c75dwSLoWBa7LUItEizMa2yLBYP+iqTq5Qmgo2XgKxsTFFoZfwDyTZn+woJteg7q1Fdp3TlgePKHEHi5wMklg4oB0wMwyR1ohWsJRy2rm9swd4mAMCKP54l1t94l+VI5TcwdWRENLOcHU3QogWeoyE2nRB38uQNECAINHkY218TrV18ZyJAkiJ0ga5oMfnc0btmUoFCwDtFUMvaJOQZ2MZu1p+S2Nepfb10X5ttayzniTQdIfjzzn8QKdh9uL8SxIvJME456BVzeBqdyGLYxLddr60igz1Tq+5sUrQ0X71nu0ZE259Vb8kALPVNq2lV+XqUuAXyaF4RdQooLU92XO9IX8+dCDQO+HdS2cDvY9AccPhx+9OeM18rpc5yLBw6irSwWwJbXz+VTJHJ3dluvlbt6ojbuQSr8wvFCYHwj2ORp8Ou+KMX+nJeyp8LeFzjTJbBahymkY7vjgln7jT0cNitRwbyCFsGs08ul3K7CenrVvsxJFa/dhzNMYr9Hsn5IrGjFzSi05t43kGUVKdJ4+uhW3Kl/IatzF4RdByYkMD1HC5e4xb9Bo22nWf7eBY9p3KSbhzgFgz0UvkEvGSDaLdX9keFz3pBSuHesGoXkEodHh9pqq42hs1qmO//3XP7ahzOGK/Ds5YvZ1JstYQ+bQzzU9nzvSjLQFaLLLV1HWglaK5OK+rolxNpIZb8NgxrmIzJVK7gBvHvFahbNrL7BY+XvRjyuEYGsFOP+k7tKrNq2KJxvKd0NnONZhquGXIcsLNB8iurrdbcFJtdAw4UpBdrK64xKk/aaxzm03HHUuYs+p3BUwwHmdYcc37z6cccQvC+MVrziiaa8ddjmPE2Ly6HPHT+uGrh3OMf2O7nqKnJKVxPf0tbnSfSrdepFBshZe3caEr/NqhQNrQsJSnBw3QaiFycC9rPzKWa6uqM81uQGVkFfzmM4PNnn0el4EYlXHziD51oyUFiV4LOXKHgsUQkG8KpY7PA/gAp4pSKirihcxiOJ3PRYGn8ivDA6aa4Us1jM+TKzdHqdqJpqN9rD0FHxNG2np3VaQ4j0HBP0aQDza0Xjkl+vKdKZH37zfTOHxvkAa7spsXlsPMYdtPzgARr6mfL3C9Rh9agMjQSHaDSdJoaI0M2BX2Y6P6+EsCIU0SBODWGv8IIEXKqeG6/CpU9JTT47VxmHCFklSZzm3866trxWqg2/yBZ6W/n3O/CGVrptY7Nr2SVxOm2gCZ91kkvKQcSsvdw9isTDNrlVghaItd68TXL6UB3K2vSszWabFjivxqt/vfjovxtySgGWpdnoGwH/YrUVjtHjNgZYIYXwzL6za0crtnoaf2070NqYVa1PWULaZ1Ttox7BkEb9Zqn3oygeMYo11hFtY9jtJxANWS/SwgzkEvFsAJNWpQKmI4cRqg3HpB2UJ7pWFxkHbYdQ13Xf+3lk3YlbK1CVWvNSe8RJOqVKJ55LNv0UVwDLAaGYvQIJwRzd9ywkijFSHf82a3/6FN0Vkdg2KjowrnJXPz0wsXuZD/mE2de1Z6d3eBxXVDbj7XV+lZg+v0duB28i88xknlJ6qCtO3h7WpDrScFF9L7yQjRoTHQP8k4RVFkbKq7GOJjEh6ghEIK5JBPV90oAC23VMLH9DUORmukhoI4zAgXdxjsJWGljJDjOKpvBqb0nubrfSjCIRovI3w0815SE3e0i6BF2Bw2K4DR2Djn6Cm9U/vv2qfy5ez5mSO+RHUC3rDj0WV5qUHx2UQtW9Fr2DObEPSmKm6gTqJ0FXs3xrSG4pGgwx3eriOobt9OXPFLcWlBNDqNS1BGWb8wEROlGxz7RTIL0ucQMvbERuN1PC3zFOjutc9XMgzwpWBlFyV0jRFIGXBBcz7eCqcSpSdyy7PloobeU/lawa+EUw4I+5jrwCNVC3XcndhsPPSF+EREO3ZhkChA4zrc0keybT+iXICIB4nSMSKjU8ZsYmvfFuYM+UcMANRYjs+EZhTCYK1l9woz8/fwwjs5KG8j1diRkKekYKpUyrCn/bLQrkxyDhxyhDoY0NvskJCr5dH1qzRFU2oYMI81GMPUaKOChi05+a1ukWZbFI2UYOVENcKazqxWrcI6t1Xk+Jn+V7g0x62emqCActJ+7xWNWS1izwxGPpVTaFhqiFLmSK93ssNPob5EZLqHDwGg22oulS8nB+fRySFufGMB3qDmcfLeOSHzznKngkiVvD7rwLvqy+ooo1FJZP/LmwX+bOzenoxEayVgs/tNIwBIxY44HZzd05S+lTwDJNDgezQCPjNSkVmvdndvNpZjNbuUorKEDKOuQutj6uFVMkP3YR3iphfwxq/X4ENbvcBmYhAK+/Ytzs7y11grq7qnF49Mpcyv+j1HrPD7+/d3v4yLgqZK6w7bhJ9EdVk/S6vq31yDmaeVe/l6RhsVlehXaBCEHoII+rdCISDhIz1szNiGOaRBdVnsQX71NoxK4MFnbLtXp1VTS8vLdtZCUn+bfCc2rb2p8ajxkhxz/wQ+gQPtCJUsfF9pGodfEr5iLs6/TAo0kfVWvkPyIM7RSPx6/CVVEfce0/thEHct1W2YxqFV44FhDCimj5T6xQX873Eq5+JDq/WVpa0ZrKPuOOGbPHzASnI/P51uWDkfYZcv6Ddt9mcK9cWI97Q3pds6DRpexjcfJoWZuqNjsY0hd4GgXyMy98/LSjcd0KD1Qt3PBNynF4KfzA7oDWVYKKDPR24WohVGr1BvD6wVdJ7zAIiwbaX88HGGAerO5NVuroQ/Q0U/0V4/zskoU4Mc9zXPXCcbSWiYykW1+C+dfc1I0QGWG1kk4rG/bVdR1vNTTkM+IFWfRIYO0zWdBzaA5A7rWlknJ6K4pXwV8v72c3lrBck6cZh+OrF0FxUN0WTeuEKaVuYnGY5AQMHiDkO5IHKXORm+ztuqrB4gmqM/p+mbJNmaV/JUZPjbPHOMoVLbhcLceQuEojvBNuevokNk8h0yrWMQMVdJTYoyba4UsTXBcrM66jMbu1nJbmrSTOJDLJgipGtJ6a7T6aAs9INczSKfPC/0kZHil6xL50BWZ2EYnwpKA0MJQrd7LFT5qnHaMzv+R+pBOa7loh7lTgDFta/uOb+NAYW0zUo/qMTv4X1NcXp3tLfY7O3XmJ/khBx8I4XOTmD8MSKqRGuH5krO6uRqhxeXTn/Q69hBZEZ1baamw8SBL0+QsHEqnfY+6dP37v2FhOT/mkkp2AJ3zaWl3PwYi75fADwuvmTPfH/IwtPLZogCPkQJUdZNw3eucpy5Rt785flhV9dNi0qzP6WpGMQy0Vzv5xsuO5ZqzYqOmo644AFGU4DpFCvq5ERg3hH9EYMzTfFZwgLei6UeuK6rEXWrQMVa9LQrkcNPmKnGTDGnvFYA1dE9a7UgNU1VNr7sq/7INzorf9xDkrVKIZ67O5M4dgqUqGtkf/jM3L2twpBN0Zoom1nNzl5RUqcTav6DSHMjNNsqKkzGdQEbYCngxo7AlcspK1wpi3aeCdc+QO5KJfhg/U6XOAcbRAycGKv16FgvOja2cwojd5a0tEKlSwJaQHi4narA9VqSRW5QWFMcR/Wu+qoPE9b+REkpeHu4o6GYILkNczihgSFPEKoDmxATJfaymcsqjq3vpOyTQWhyKhrQTzLyMcCVchxVdpurfvrXR4PzE2nRlZcmA7pQqCw0Ck57chwVpWe57B1WTIaRgK2INiDnfd4R5rf4O7N1+he10tU+D/wKNQSqP7GSN6tfn56Y0qGJ7sjMBaFwQPq3DxOSlHpIe92tQBhElNV4IEgofHmV4Nzi3hCH1stgZ93lB3O5ZTn0baYO+lcAtqT9Tp6BXHhlThXghkV+3TWf0eON6K5SXHmh+cxW20NTRh4JicO/zKwZcDE1ReH12/htr9F2M0BJDj/i9s+RjskA413X2VLvSfontZToXY9jchvHwU59XG7Statrk7V57UTNhgun6M8dM71ksUKnS8rkp+7qMHnZjHCT2holmy3erPhh39UfyzlCSs6lgC6Evme9bxgM2jDSXa/S3TQ4bqwp3c0r7fXlh5fXICs03tiH9/WqJOCP5VaGWbFPoTXo+m/5p/1rcY50hSM3k/nho6aPkT9mbNvPI4BY2dclGb6M0d9+szUnrZfh9KJNI4sdAKapoZzfYilBKI/gwkV89Dr3jujRTCpcnhNPfG/gVGpjfYI/4RIhSTT51bMEn9T285K4UtZXLZ5pNjuTFbJL0K6ZJ04jcm0IyNCV0jnQ7YO3B33oLw5fSfvkeiGk4ZoBVT5iw0SBW6cMX4TN84ucB74jN1+6B47ZfEHvZSLPOqql4JZhVeeosWOK+HPSd1B1I8Y3/coBPlzFD90ei6N7BDdPSpTZv0oZxwN5tG1TEBdS6KN+HN0rBOCSKXI4t4CouObmqHmKTD3yA8ZxoNrgD67aALal9/c+9BLzbLvwd6dL6RayHXcQJni54+3oB4F4iN1uP/lKrt2iUEEc9A5tCbwhl+qaQKnKf0DkatOPBTD7SQs0S1gfwa90WTvQvTj61ZJU7Ii8dTzKN7/Qf9vGz257bedW8C+v5b4CQI9AWv0wxF6eGmkYEyFz7NNJMtxxJO57pn4BLTAgK+GalQooniNWMBNL1GipXExaJLqpnm1qElynKzqF/gg9LnHeGLOKwCUcQgUxttTrxGhZhzMBTeHtbxr66P4uio1yn2ZlsFjzRwRJ0RLcNy25NaZyyj4qleGW+xNx6QxvQX5QbE1xt4UZQp89ORIgPpvx6nAhg72EptMsQ+N5pzK0nu2jc6HFfjEWxvmnUH0l69VlmCvAlXVwTo1/q0FkrbeYhp0oOVYIgEp2chiEDrcn1wh2IrxmrWRQg/tS3dJMyjPY2siFfAaH5xsGbiGk/BMtXruFrh3L+E/iaN5PC5tfuHRu7iiyxtwDU18hV7kKeNFj2Y59gHoPJWN7wpMV7YQuNkX0EPvMMRvLGZuEG3GDn+HdoBuNDCCJZubdYL2leYkD4Fz500EOnZk61hw4i1zPZnHRMegIp+VwPOvtYdSM9jdr8vWxP9jeArjZt1JP3XBuL1NRi6qpdpg8e46gCguLNFAIq0+8VBFfvtgPs5ZGIo+ttYi8SHA36Jh68S3E0vCbhHWQZ4+3e3s0JVzGURMfIe8DDsSlAWLrzdz0dIVC5R4Tobe8iI7PNRhDyLPjuX/AIRbnvxD1FMpLM/QGIHWCStJlB0WDV/HMON66xYSryl8E6V2uq3UxTHuJRUjJkK0mcU2zbkre98dglT0xTQaOxEf8PD3K0Y7EIR7t23cOPq6gGCpAiSeHWV2s/YwNyiKWx3aKrh81MgP24US4LSFQiiwVndXnXOCR0kiqETabngAN4glMVa7GpBakM4A0EfqdQrIsJidtyAjFvFlD5M5Rw7M0u4v9RSTOGXFHz8cHqpEpLa91uQE1z3fVFkzsy5WUjsfHCG27iOENnrv4+q8R58PFAy1Jk3JI2GdK6PO5wM681axNn/DG/883p6flqPcVbPEPr4zMTLvgK9WY9SEKVznnfLl0aDiw8hDMKu5zaTL/vTxrP+4jo2anfA9lKzxChf5v0SLrnlAusS9tN+YvIwH3mKiBJuxzfdLFB/LYXIde5VpV0OdrIKwfVSXSJow0kFpmsDukdO2NXX8a/G54f1rf0WH/hYw+cBC6RsO0NM/nktXWJmvyrSVXDxX4SvxxMYFwKliqJs8GYxaGsS0mO/XY226+p9Q3n0rqBlxCKopQDV6yToa3wgujXsRyeIPCrIaIoS4qBioVtwyMc+hISZL02cEfDIziLGMDwOpcfv+cgH+5FYderJKBGRRwFW/NveaOlrKt5TQkIOVNuPMv6kRfqLm4ot45WiC5GYHW1I+PW79iOsJ9zl0VnpTa5JwaLgjk/VN4J1OYg55IgHNzuKitU6btJzONlxQzS55F9jBw40aOvgLwsg2LrUxoKSX+aTVt0QbdkcvqAeMhMXsqvEOUN79lUiWllWtJHZ09dXxtrhPFEotsMy9ujmd1vAyBP+kn9hQevCbMj4+vGb5VooW+MBkb++GQxBWJQ2poPiLlG/X2L7IjfRwVcJqkhaFLXcqTG9q4lOw5Tw2IQMLZKAWiaRbYYV3MTU75Ts/dPJhghkzQQ/g0fkdrfF5Y5e1wPZkAPxnjAJuVA5ZOqdb2eSlQHQMuhKfdUNh0WvJi99vBtG8dQeLHIj5GHNudE6BNKps8VlB9aEUm7flhKEq8k7Z06Gns5/yJ+1zd87pbVReLmNN2QjbJeyEKmDRojSaNrTpXWoa82DNimjbwXMwW7tiqHXSGOGVS6aBB56URv6ydgFEMxH4qgeaX5lXAKrd1kUSl7ErvSZ7ZBLItpEv30cgOoXEj902Jpk5gH0NB2aErzOC3dxatY9mfqpCGil2vQSsQfHCsxYubfdaKeKSqVlhZX2aYMNKf4Uf47DytEO1NWTtFKK2kPgG9P4pq/7XTo/9NOACWwoHglh+Gv8h48cQrI9FlXYCfy9lchQpnEVdxlBcTjJgJ7ELD8wwQEnQV1O/rbncx4eQNVU/XP49mmrqDgYknHHpP5lc9dmUPdk4HaaabNrfx4lCd4pA0qVfFFP9sfRWhIX4hmnNNFGnBc0uWQ7uyMfJU+U/aWq+txpeeGi0l6rcKgATVTKg2N4HVDILpLmlDkufHW2muaqg2Sx5J8pv9xW/fa/56WTOvPHPd76BBaGVRUwZno8kuPkRyTVhuzX1PUGHPXLSD5KBd7esVs0Hs7QlSLZxtj3BTbCl8gqy++e2hWdozYnLWWm6uy6rCKBzPus5RTU2Qafu/0wXxrCIyH2QvJrcsTix9fo4CC2BZc5dHNqBsUefM62lfOkoJMbKY11ydDXsqBsR4arGfjSyMNXFmKJEC1GXewa8LmlBmc9Hjjuxs9J6Ch+b2mGhJwViwUtDD7ombBF2idY2r/sVNXeHXb5+EvjZOWjSjojUoaq5dPNRdvtdY8/oW87JlOyww0a3yJ8g06X/8K88EYnj5ISX76dTOkTyBOSiowBYL3Au0LNTJJOl61bvtnlCvyLl9Ajt6r7+OQKyy3MTNllq+ns1wcsMjP774aIqQ/7uHFPPnzRWJ+oC6Y5F7WOfiFe4CyMYS0iEHGo9NBgJKU7RMG7KDmHQQdHjas9WB+WC3NxZu9RTuLXRT/+gHkukAjsJCTnnbs5TSPQexFti03+MBC7UvvyZFzbGwUH0QcYoQKz/QQRCtJ6lFnQ+pUX4hL75ADsfupg7hNURBau+ctH9gpMf5YGKVXmgfpbygZZMoZAtrW6pKMXBoKm/lZ9RX+nNcOGVNjVD36LQ4JDGlXwoGUTYlp7yJq3SXwOQ2ncW1rxvPNqYVZO+Q/PQUQ/xcmGeVS2LD8rPSev7s72vI1mqjTJQHMnyqMnT4mLQVbE5oP/nIXl5VgNrNWHRKi5rUz1rLdad8g5feCnqzMdQez2SSBP9v6gINYq9r1ZLviE6Rd2S8NXnHZWTlQsMmoHEgyEDdGdT9ibxd9EbfM+J06D/oTDl6VVCO5j75hQtjKl3i8/r4gdPwvs+JZ9iKcCSljMJZMsRI/L+vThe1KSPGYY6NyP/TtE/CohuMKiAz9i8ZW6FFkuFEWTQD99/pUU5GZ+j/fLfGKXX3E36tjpCAHhwJUjTv3GeN8q0vIHSntMkv8VW0kQuDuqITtFjLJrufF0WuXPNdYSzymnsJ4TyigemjR3zMjTUQmE0U8W5/rS/Xk0rjDGTW/3jRXF3Woq5GBg6eGpTt7eIJICBEnCBUNpO86r50fUqL7IVXNrUrUZyMpHxR0SHqnHWDX5YfE/pjaF6/hyrvaQdTLt1kq9oLe9TW6xgZadbeZcLjQ4uYGFZW8huW/2ijauB1gf1qZPeRvfQEN1gEmiszt1CO1R/nwcb9Q/dhR0So4+fxEPORlVzS249SFrF+peYCrso8BJD7hJaNQFW/xAXAaeXJJn9f7vvvmcOyUZVD0u2gMWs6vJx7X+w8l4uowRwxp2P/ssqlfyba3d0+MHkRSN76RonY3BsTkc4M7g1zvCGrHBCbn05OaYspRLdfqu0Q4tyyD2D09aG/iso7mP5vb+x4niw6Q+gVPpdGrBAvmm+up+eQcI8P1Sxfe+s6I1yEU/pCM9Z8LYcJNeDRd+hkkaev9KkZzIohjWgeqwC5ot0a0KemaPGp2ZH3/OSLNApfkiFvKTPphHTezJt+OBUF7w1QK+W1tL7Grg4KfX3kxYqEfvIJOpafH2iMcltDel2LNxpWdUBKNIdej8h20QoD6stB18hYzboWUiPFtVZwmV3+ch7bwcj83oGl64jAanpCcZqt3xqs8SMGcmZOAQpHWJG54Bdqrk4xsP9AUyXbBp1Z8hlOb5zUlz7cyGaTsEqQ9Z9zOKVWsLoZ4iXwZWXfdWyv2XM3he402+05NZ8JLnSiJc83AvrEkKArhADyiIqejrmHpSAx+lblQTjgcIf2lc2sff77ezV78y7wzu7Houdfh3V6Be0dKVaAdxdW7MftTEWzyHuN0eVKt/ZEvNeDJIFC4ficHkarUKB+FC1Q3P1eg/JitqmyspOm9ywfkQbW9ZoTY2G6nHWz2EaJWHdvKpFvw8EY2u9vlJRYOokatSL4oJCqhgCsSaktoD4mLfufQQQuon9x6EDVMiTczNNXelfDPVb2XmfK5YT5C+q3qAFt+FN4LYJhgEveuwNHRHxvCb/ug2eRpQ0eSn4yuh8dU/o1skhajws2VzCz115q1z260I20vT6MMvYMHATwtxSoKN/jU5/PvtQlTrfp78lGIn0QA9eXgGflSkHQqhMOwupL66w/I4NdweitfezXPLLDylIWonrq+zL+GUsqDPP8KIS/jHyeNobQuT6NO7TpRtMSY3F9r1w07ZDHUk5fyjBoMr4cfghL5bdNyPaJyIqGDNC6MyjuWuOhBm/Oyd0aWPryVNuSla6eGYid+4lzcJ9DGIaQMZjxh96ucOMyALK9/ef8ObZScluqyamJQl+rbafAbxoSzcsLTVz7vDpxSy42NIhMdFWfCM53fXeAdV0FKBhzYX94WbweGINM6uUerUNLjATCbWVL7aQ13StSsPpBPMCPy7KA2TrCYYzUjX69ioGnE+vLvIw1FFzBYZGt64X3xaaF/IGnx7Eo8XvAfYaEtSSgv5sMfi7j9ky2t0XJqsREGd4kLYue/aD9BT7HhsNIqHY9ntuq2Y1roeDwbOk8Dl8QM5wWhdOqNe+0lvVqAyQkK6uNePEtNZdr9Yp4+tudqHg+ze5jbuo6NFOVUyjw7mfPnl6E1ZhXsX4/VTdanCwi2Wkl00SIV5sNp5UYqTdmxWOzVChtvXdX7ixlFApb4BTpFfl8jEHNJInzrxohCHZHo7wLGtZyF6kEf8QFmqf2dzqHhe5BREvkNlH9XJaqOcBjI7XGaxraKjtan3WkwNTV3ckaOkg/yLeCWVzU51xpk8W8vCrJefDaTL4y0d2sn2jt4I0K5ZcdnGIlm7xALmesq7w3IaLw2On44A0c2YbPbBNqJ97eFcsueqmNRdfXT1yyTUfKbNUjgoxHKAJzj4cpMCZ9DrmC8BlVUCKY4hNE73F+K+TX5eumek2C2J3MwfeYFNMVe8UjO2u67NZn08RsR4YtG1s94ETF8MXOKyEC9pqfqjzqK0KLrffEZTFdMXbO82mEAYd9Oq8L3yZEJb+l/cJ3TtT/DLg/3YifSXLT6siz/HNtXG5hehW3sOJ2UErHC7hX4RshXUGZAiv0QDlAgpInYsSgQyOhGL9yxges1whNhJ54rmDBtKop7cT+WmHpRZd5sHirbePU5UJU0EZ0U3I7mtINaNZTSfX1Z8Ig3NvevLP4eisgQ8d8m28bJKhaRFLue74ZvbuMZS4sTyWD6Mkksrxt7Xmspz7RqNcgt/d/4Wmx+NiETP01DUXRA+9/3FODsnF+3B1XHPHk+Bo0EJu3DKcbeXj3lwlkBiDaa5C1R2QMydTMdNsdVLyPFXPCc9ZNtTh7qoIN70dYJYWntRE1st1LO+EB1ci9dp9e4y06/NMj3BcJFxG9Cx0q/iwpUyamoXVj24GTguKYARMbu5BaQcdNEarwWpD5hWPXG+OObVI/NN/vp7uoUbdyFd8CrIYHpnWFBWgS7UTlvHE3yy9AXRY153d+iiqXc61ERfSV50PpkcyNqkGLiR9WU2wZlcvKmyJuvL7SyktsM3+tP77D01WQ/9gMqaIy2wikg79GJvyo40O33h3h04mSbm8JspOj3ZqyK6bMhrg7ZuciuEiKusZMrECIQYZBdQTKWpK+4gZNhzd6gEDh/UlJxP99EPK/DVV/HB5o/d2HZP9Np/mS7bt7IOvnFFy1o7JOZPNQc8s3ZhiZKYUJy6QQ1lhLk2kXMzK/gZnjIz1VobgkOmBzIsRumAPSw3PxLpOxWjPWVbJzgSF5t6hm4XJevIEt4fSET66mtJDUKiHkcnnTyKlkWjhlD1NreYQH+r2DxAqkRxmsV2+iiZMI041lnW7sGenSJ0wnN0efoTGIKLyg9ZduddE72EY7ZsVKMwDToR+U0YeP3IyEUQsWCTt7vHoE/pSJRvmkrg6pXceGPbzJSlx8oG7MbD0oZNhCsFvf9E7WEcKXMmIYQZUoUKBh1klMFRLTW/TMVscb3VWg6UWPTcv9b2nMDaeMXxNtshAX1iSN/F3x7xAZhoT8NmFQ6bdYQEKh8RfEdznghEnQMbugU7DmTrAZHBTaGtcyTc+ZxrWAOTDnpbRkG/PxGDVLj2JDM+Db8hOYP7uF+TYMQZQg03bp4ko3K1bXTXvhEBxIUd7oDL9k5cAlfMJGl04lktnFc7ovNfh6yixThjP4ULPZL2g6I8EOuxtJ6atEUnRdXN4s6ucu0+Ewh1bvhupJ6Gohsa4AK2FCRkzMYIHbueIHOc2lFSeKHI3uNiuKKhVUvqiwgWpoqY196czgUu0tUaX/kQuk/h1Q+EI9wx+ehi4pLDJ94kWPk1I56YhTN4Csz9/wHg/ugxCmVuZHN0cmVhbQplbmRvYmoKMTQ1NSAwIG9iago8PAovTGVuZ3RoMSAxMzYzCi9MZW5ndGgyIDU5NDQKL0xlbmd0aDMgMAovTGVuZ3RoIDY4ODEgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjajXcHNBze9q4gyuii19E7g2jRe/ROdMMMRplhjE4QvXeiJETvNUoQIlES0TvRoiRKEiGI7j8p997/77631ntr1po5Z+9v77P32d931hoOFn0jAUUIwh6qhoCjBIQFQfeAyjpKDySBIJCoIAgkAuDgMIah3KB/zQAOUyjSC4aA3/tfAGUkFIxC21TAKDROBwEHanq7AYVFgcLi94Ql7oFAQBEQSOpfQATyHlAF7AODAHUEgZoIONQLwKGM8PBHwpycUehj/rUEcjvwAIWlpCT4f4cDFd2hSJgDGA7UAaOcoe7oEx3AbkAjhAMMivL/RwpuGWcUyuOekJCvr68g2N1LEIF0kuPhB/rCUM5AQ6gXFOkDhQB/NQzUBbtD/3QmCOAAGjvDvP7YjRCOKF8wEgpEG9xgDlC4FzrCGw6BIoHow4FGGtpAPQ8o/A9Y+w+AH/j3boDCgsL/Tvc3+lciGPx3MNjBAeHuAYb7w+BOQEeYGxSop6YtiPJD8QPBcMgvINjNC4GOB/uAYW5gezTgd+VgoJqiARCMbvBve14OSJgHykvQC+b2q0WhX2nQt6wKhygj3N2hcJQX4Fd9KjAk1AF97f5CfybrCkf4wgP/bhxhcIjjryYg3h5CJnCYpzdUQ+UvBG0C/MfmBEUBxUAgkIS4OBDqCYT6OTgL/Upv7O8B/e0U/mVGdxAc6IHwADqim4AGwxyh6B9AoBfYBwpEIb2hwYH/2/HPHUBYGAiBOaCA9lAnGBzwn+xoM9Txzx49fCTMD2gJQnNPGAj69fn3yhpNLwgC7ub/H/jv+QqZqhqq6Wrz/en43z4lJYQfMFBATAooICIGAgqLiIoBJdCL4H+m0QfD/pYB+k+sBtwRAZT6Uy36mv5Vsc9fAnD/FQcP8J+5dBFo1kKB3P8huRVIDOSA/hL+/6b675D/G8N/Zfl/kfy/C1LzdnP77eb+7f8/3GB3mJv/XwCatN4otAB0EGgZwP8bagb9I1olhBvkv30aKDBaBopwJ7d/XyLMSw3mB4Xow1AOzn+48sdu8ktjbjA4VB/hBfv1qAAFhEGg//KhheXgin44vNCE/O2ConXzzyNV4Q4IyC+BiYiJA8FIJNgfgB4xeicGDBRGKxEC9ftNYaCQIByBQocA0c0FAx0RSMCved6VAgqhn6ZfRsA/Ejt4I5FoZf0ePPrUf+1/yxgK9YM6ABZmEQ7SES5NEV2nDYr0vgJbo7JTHFtmj3kEAheQL73PiXEzeerzH60iTxQz370mXdpQ5T5WWGS+Ctx78Rw3uiPdoPMi6NI21XBiqxMwP041MFayp9jcz4jPIGCs8CnoyjPINMwV68WtHk2OQk9vSWL9ojunvn3qfs39VR+Go2a3DD7Vi2sRXFZNCiSaJFiFlU9zPLN/MkPDioMSYMTjJf/uRzJ9fDJFXjB2w6yZygcI/pIoWhposSaSdDYTsFxjLOL1ipad1oKGEeuYfHiCM1BpO0uTei6woiz2+bPpmfL7/vudm4dbg+ONkA5aOXr3wopRRuI5ZCZ+0NfJwKnA9Eu42TPzuOtqamzmfmom8xxrglAH6YcZYbxC5en56whB9ufbOmsDwRy9em++c7FsjdpXktCxL7nPWrHeVWPb1SGgm/XubwezXiy/vTUbQoxz71YkRvLz4YSRzkwGR5D+LEmsUJRA2YBBhs31d0ttNtpB8v6ZTyiF8D7lRw/YtNkIbp0yiORB96+Mu9auDaM5fLKKUqB8UkRVD0+sxL6rxJ1RRByZ7LOwFgTLD4m0pxdHOVp6nY6n3ZyMy+ukC1L13cdKNabXjWQEevmuIjtdCLDZJ2FQH93+RVbbB9pYymCUcSPslWPs3aDqkOdWHPt3A+LiTnuyx1nMjbA/YTR7H9VpqPvVjkg9nxZnmUlEtO5EUX/QMbjm32YfuDeX97xNMftci0Jj2ZS9M7+GLk41R0LF7vXGXZ4r1fMMvgKjXK2txEcVtViHy8WJkJc9zbMZCakML0r17UtFyd6urAwNwolpXK46vrpY542dvTi4GQUShijGxws5hlXbRqtl3440Cr8TcNo0xp61lm6GnzfGs2bxBQHwkC+LGiTToxV3u22rV9MzhTf+kkKqsWtiXOUntZUac2kGQ7Nhdrsk8mewfYi8oMT6jqYWOUGW/oaOcso1aFZHkPp1lZahR6R0f06vnfNtgjr7kCFahddpkv1fns+Z6LXgWGDIwiJvY15c3jJOrr82LHJVqafdJ8cdpua1477LVpZza+JrmuXtGNqsz+95DIsA31elv21Cwouk09UzecZxvcSrGs2duSi4Ceu/bG5+xDiVucTateWUgbU3Cy7PLDZPzp2J4ErHVOK+4gPc8DZIxQ1Bucp88sBrSJ/Ez7ZcbvR0tJDRnruWMfQvlP2PmlAySpwskvlrs8PyvgkSuBE0vh4j4u1n4dg+Y8SPBUvasRnswOttp8J719OfJnsSXruSefhQsLXxVM5phVK4245peEyW3zoU61OQqf3eEXpS7fEORJhP1m9RCeSVw7GXud9DIyJdIl0LkDyb1i5b5znI1z9qTIoK1+nZlkiR/cA14D6tsbAcS8cIYJ+kLiqf8aYVEZrx02HqXmlrKFi+UxVN4ZAzQB98Uwu+vTNkjVTc4BdoGt9tqVgC+4RRVk9H4RF23u4fFgRoImhVWWdCU7RA86dEEeQUc4mx4wkFeUKVdL19tCtpFj6Snk33b5YYD8JpWPE2aaGi5V4Hd5u3TLO48yY4rFX3ziovs4aIodqejadMMeqEtJzMBBj1+IXOhTl8vINg8xj9TxzB0+9247BtHox+fM3W+0b5oczPn8tht2SSdlhrw1L63LY+HiNR4ZFciHFF43DV/cdHIhE4tNOFADafS64j+PqHwaMoqsOReHmPNVJqK1RgWrBGfn4k0aon4riDfmej6+38526KxvoDHjbGLmhe/D7djtaDESkQcoIGLG0XhHTlOdX/JLLbD0vb3V1Aug3qC8GNg4LrcwP6VMX9UkSUBtOTlpRDAg7j9tfZbxgxxfl9cz/MtMrhv+b9GRr/gdw35W2HY3C4e166jVaXT3F631NroPspbJ3Mrd1LPJifQ/4g49XUBRFFW+FwqjwWtvXk7vQRcIzXLHMeUqvTZyucJEfsK0dqMBu85edczFJ4dcj7/n7bh0Xl8bEydjGPb0E6+OF7jdb9+Bk9D2gtomVpCqwK5Fj1AHotDte+son0bwmUyIeTBMxrs/UUIVQztKJEfjT1p70WH+VGFZXDAibiVEoWCEcX3AoymAjSbjckbSTvzm0MnYnZ9OvMQVsWi9MdR5RIRNsG7shX2HhDZMqK3PvTzGB5BZNtUo9zVXYwaibWQoaTYJbd2B/74nb2CjaNNXbCrMFhHCASVZN9aDxWv/hXlBxnQnMdD+paNa5Zk6ZZ5ZN7GHb4CotFFHdHdtSeiyzieOPyTlrnTGTSpCcDPkQ+Z8Pnanyiebt9T7LPr5vnGmP1oq7hyRgWFfbLIQi35JvlZ+/5pxxqtNum+wyNtyaZlciN6+kgCN1rOYSdZ/ZALL+u+XnFbLHY1WKNpcL7oja7dbiVh0HCR2FDLGVWrNzvimWRby1JsvH1b+e9ebEYVvii7O009cDIqtq19/0FZfcfNSJFVZgmxNRXjRZsRydm5O5s9VfvZ4hWPvrMpJUCkih8XEEx5Yb1y30pU4lbSQdbRuFibbV1nwuu344fFr5KPZlVmbNpfpRpe35P265x5JhDQ70cuoktdxd5LPxw1DtsnjR+P2pIzNVWfqzNascoVWKieXWhOhPfVZ3lMm/D/igvSKmwAZPR/jMRmJh2qwmTsr3B8YZsqpqNNB5zxPSKW7OAYU+y0GhRL+pp+BLzCpEaL7DhVDoYkH1jeMAVMhwbsKao4wbOGz2r0oWTiUqq3iTpJ8Q25kPobsdBJA05bovxxviOaqY7yMg+NdAJtTavKO9pql9L3T9JmUyN8tjB1zFE5rzXumZdCP9GDQh68CrsIPdFgmT5Cf0HQpekkTufM2UTZzFTjtZnhOfN+DaKmIdTdrGjLS0gKwVDJ3ezR2i/0b2tXlr4Nq3N7f7IQIHt2CsjFNfXOJYAo68uU3yVlGZZACzktyKjmrpj3PLxDUam2e03bA9n3yJUoofX1198zvIF/GBqp1xxy3it6Lx691MUzrvCeQ9J5j17X9iZ1TspV+OLH/xlAy+/kA0+iQ26uz4OICC60OR7+jjjUi2pNyZlpD1RNSoi5wX9VIo2sT1dIk81aaia4Zgz+0sWv8TL5Y+G364OJSMcF/mZTd3UOel3bAX3a2uzcceDilk40+qXcb58LVgBM5yxNhHSXOl6a1X0Sh6xXOiWyOxN1SHZoyIdBO4ImpMqMychEQESBHT1h5EhXuvZ0vZ+XAd7O2RNiT1ZfmzGj18Bp58LxShazZhWH4hQq24CZeL8suNTBSsUQzPkZh0nMk+QusU8egWTdwDU92f8W0Vd/Glvby8cUkoEFQeX1NXCg1oXvzCwenwInrG47KoNP1X1f+zwcslAc63UmzCh53l7Is2C71Sro/TAiOPXLO61l5z8P6KNsLsXmkJUoIAdCp17oG3gXF5kqAPvoZhRqkYJSkqescTs69AT45fJmw7QXu6xhATMS41XmWqzZez9ZRtjWRl0b2SZVj9Gq0ucs8/a4ihIt953KovObg4uy1KV/FYuNMV/uhi2JxLHMvuJ4ZP3qxS6uXPZDbhZglXPPkOLxIm2UVP8h6SSB5zDS51I8ieF50unZiKVEtZ8A89FaMRyflzaSNGpLY6vVM9P6xsGir9BVol419zpNm5plbuhlxEDLUEfyBAQFI12LZ+Fi6w0lLyXdgkFuuMHzAWGvBqq5j1kO6oFf2CYChLKHFXHHKabfKDGvT/OrI6wPHEwLGcdEFAlS46yb9m7ilVtC9VgKZiLEKbEizZNeh+hY7OinW2wZiD9/Y751sBGxrwEjtjJ1y5FplILq2emzIfPdZdocbvqvVi9CCzEUSAvqpn+iMMx7gZjb29cVe/gbwePpT8lerYvCG2/gpWeiqYGIr12ZYQH0hA4RT9Zv5yRzV22Hz+lie1sVvt57C1zlhivHaMY4aOGyWWnx1au/2zg27k8fnuhRdqe55L70tzi1fJCn0mpP7MAtExNonlRpOOdxAGcCv2X1c8MnCqidg+vwIu71S4l7G7yGxo4uY1a+LrO5fXJ0YrLwx8wYF3WwTxb4sArEZ33xXnm6T/AhjK8ehMJAbFcby17fYm6KIncGig7qWhDAnPFeQZd+ons9LnMacBE8dHmI0Wpwo5hqoOnuU8Kv+wZaCWKVb3FUut+sA7LOkr2QBbdOb/JPoyoebP2IprVJdLz1jnW6mzmy+5SvTiyaohsZ0Zr4uQiVKqEwSDE1LIVoFlQ4BC7OZMjYZ3SQSxgTOX+6Nl2JwvfK6czi3vJmP73xWlGv6a7xKWmgjQttjYG+8+SfZZqpYDkD/InoxXfHXt4E5S6XXN861s2j8ZOomMppd3B3ZVSVo7m9F5JavpqyyNVW9V8uLasGlZ2jNQDjoSykacHMTiVl/lHfa3s/7RXq3Cy2RjTmG6JXfDMVU+493uJQFZJhp3G7Yz0zalP+k0XZSTu5Kay5V41GzMxAtx9XrNHD6fe4Ka1a8cc3L8aJSpaXa2pPfVpFFPGqXShJCfeOmkQWucbgj/Uk7DxOvfVgHliil9rknmzO2k1wxqldaF0KZ/PtGbouuMplURy+St2RlOSj8kDPudHYbhVNT36qvbDPlandvXyYsC5zdXKIYLlA9OWfUN1RJeky+lXdqnw7e3kEr3EHgxdnXQBbIKCaHvrIFdnEqQvZqCe1mtPhbl3Fo+ZaUMzLm+1d+Nbnj3dT7l/cNCbzD+64UQF19gQcDRQb8LbKpidekyiHzfySHhogZDf0WY4UYk2V0YRT2EIo03ZpcP50RPGAUzu2RiPknsx/Xl2JHMy2f6WXux3YK0MRJ6RiHfLvHfnIxqtM0M2i990upqQti96Dvin+SJyzfcxI0Ld/FY/uWUHVktnPGqPKg9LkQX7BRkT6Ee1GxkZp5IwjYdbWaBY9KsTVisXTd1E+IhZFgeeBR1YUWGLnxcayGtby8QF4+a8nTV/YtdunbRMsxKKg5so4r1WXVfRBGqj1WtcN6Dc6VFiMhm06GKh1VWgVx4TaBCT/NrqZmmtXCPRzL0wlxHiAX/p9ymZUpf5onQ0vu+KUX6uYnZ3DqsRz9StidMN+GPXxPuh3nr0ej5VSku2SVCJgiDuAyX5kqqPqxP2nVhdX/bEpSlIxZLk3m8xDOG01ePixgRgj757l1CSZbi62ewq+qjKtM+01/zcS1mEtVXUnVOFoC+cOpkxRgCZUqpTSDmPL44V9LqdOHppetkkKwt0vhepgjSwmheo189p4OPD6O/VpebIzqLb9Su0/UHizhARRK7glPYOXhHeSLS0rJedKm3ZTPT08Gvl9Mi3nLIgP9ioydxPDd1JtVUHSsgbuyy9qtm1jAELY6yj6deAeBLLcNnN79Hbwc+aHZkW968jJ/nBti6xLHSApkhQwMcalebAEkYOhrWibcuSzyvefH6TTZJBfHgZ/e0O9LgUTPzR4xhMTv0GChp4uqtDmccT+u7HRUPaCxAqQgq1L0MDFnX0N08h4/QCk8TsJPBF8tJqdxORnmofDvvwE1lFG1THKbHCud3yzRW9tLQ+zuZAlalHDzmGyb4/O50Lf0qe9LOnOVqfQaKlWSSy5sxZies/mZr9L7OPgmV4luhKVUMK4Nnp3zD7njkad/wUL0u6acYRw18rLsLEX9wvvrbW9sk7+aA3y/PmdcGkRPZ8nmgIYTzGluUDSos35hYvYubhy7r6d4KKFN9eqtO5lgwTcK+unP+IvKM6Oc2tvEvpjNfSl3V4EOGWJ0iKdJYQmsmY4zazXZSPt3nOGMtqPXxKq+HYZQjNJ/psJOEiRB2ELJevK2mOJBoUw8BQaApW7ZjxAFbXPeoii3i4Y6O/g3V87ERIK4r0IOwzqVeUYhARP79qPtZoYrp55lzJTTTQAroifBVD+cn3bpDn5snwtgcf30PSuQfyyRjlBDQWU/wj6mTP18LKtMp48k8K/bNFtEaqBsmAGOW9fMlB9Tmi3cihZWxRWJLoTGxwhF2fhsyt/nbZGs+2xJaQE8Tx5jSLHF20V9FuMrV2Sq17YOf40NUFoq7chTSyP7Sv8j0ZwlSvtRuDxa+g8shepPMLLsf2Mzs2JARw5leFLRZeVRBFGepiouvvZGbpQtiiScVC9Q4crwvISPviOjA6iaolcWbsyHVMLwwoHm9lZgTKEaGKWcpnHvmJkl5skvfwyXYnfmTmI95nZKGazN+3rjKeKa9XCKuw6SLvlB3yncBggblqham+zxB4iXHxiGd9g5qiN2rjNQmV4zI+drLsauPOE70YYoHlDukIsALgfimjj5hfZfJsR3diYrWV37X0e7gqUyp8e2H/JE8I72cpOZZN1uPPppZ+ZLHvGR/Hj74NoJWtdyiHAHIPGDQyuHSTUiyqVeac6jT5YXHbbXKPte1x8RSe7jAke/6c7KxL8VE++7rOSBWyGY5KxsjhFp1/+tRqcYXNNLPV7ecpGf1hS3jUepbCvBDjPAxj7VtI0Ug8K30ye6tmmmQniMjmx5yijemcazghL9/dLMxmoWcTdoHChhiJWU511nh7z34QSQSEFX1p17GTbTd1Wcul42SujHv7sHIeQtljIWjXm3HtyAG0mnjt3/vi2um+XKDEwRgZS156rq76SNKFHe4h0/VR87FBSw3zm9Dr2qlmSV9n4OuXNQDDx4955HpclozIJBb0jpjq+8qo+b+19EF8LwkGK5hiA8GGSz9EmQ38mULiVyDUM0+Ts6ZXzOK8wwObt3sipnE46+v2ztzT8uCJFeqmP78zuvaMZl3qt7o8xZKzMVP+QE26ndmqqZxyxG0Cndawg3l8d9+Rvo1hdVLhpPkOi4vFG/rGkXtgnK7LoLRIkoveNS4hhDPdvZfZ/TO0IXmSEYsMx04ioGt8oKZlIx/vzHcnUDFZMvQVMJz38dyLuy+1rWc9tqTzwy5XHGivY3dTDcyNy8ZoSwN/5gj6DMloqHtyqmkodoubbrg/3Alkm3eLS/iSIUga912AWR1X2U4bSMQGEVbz+sLnYo+bHC3VDg3oQXT4KNE7jnAbYmgIa1iUPwtoMi8cL99ZirJo10u9c75MGVaj2dA0/iBicFdSoqWwWZ+PXpjH3NgyX2Bs/6CjsOMlD0a3rBmbp7J/l+Zl9WzbxfA5bZm1uIfWM2rad+FE03y7m1VdyfT4rsVOL7oxoi61lJNVpu+y7JtaPOKWTEx/GVM4K5kTLu7VLY7hPbYR+g3F0jo1SNcoy3UjSHh78JbQOCdMSnKOqzOlQUeYVtHAj6IuwUgOB8azvokCPll5Z/hAnJKS1+xhMddyePmql3kAQ5rtrfGmwFcp83WZ+nZtVyHdP1zcKd9/S64zGhy/om4aTcrVudBwtzleujThlnwpJ/OZoQOv2wPR12l9zwmPavmiwrTCQ+AnMvzjOOn66DSLElThXHWOKy6A5hxmH1mo/yPoqzO7jSi9gZ+0UawoJsnrgM4WtvyemZ3EQ7iKUrVtxc9IHZsJG0EdtRCxMLvPgv75ppvjEmTlKqyQySXJlhRylsYlkCMffzbpa0m7Us873Q5S5zjbm7Ir1Ik1QRPyeiJYyW/vbX58Jc7D6qemLq51WXvxZBOb/6sMxHi1n1DYetSY0jvzJsQMHG60QFTBqd58u/r9bN/ThZVRUvpPlsXv/Z7Pm43njl2kgmvyV6pXiVuQ/axXLJZMBFU4MjKIdekuaMDmIAGGgbyQYfyNlDr1/nQElgMbUniPZcu/DMt2Rg0sPAV4Sau8Y6Ote+oUmF/ZJe2KMAAkp2LTN9PCVMd6BHIPGfC4rzuVguqvFbx78DtataY1u4dJygtHFJ9Y+pTQaqUf2Ha/PkwRJFmU5G0ISbcxK65mHqiurjP4MMQ5ni0wVO66E3TFs92xvk+cednMdfLQ0mSIZpwt5KALhWxKr/KQM+hNV6OZLt5cuz4NrVR+k1hcbdH4fmQJyorxcKrQKjthP9Jpn6KQsDmiPoBi1XjkyVqOWE1R7ZDM+7mjd2mDrvq+ER7fV18rXUm9A93kwq900x5Gpq3y/A+srk+2CmVuZHN0cmVhbQplbmRvYmoKMTQ1NyAwIG9iago8PAovTGVuZ3RoMSAxOTkyCi9MZW5ndGgyIDg5MzkKL0xlbmd0aDMgMAovTGVuZ3RoIDEwMTA1ICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjajZUHNBzeE+91QRC9s4ToZfXee/Rew2Kx2rJWF0JEb9F7F72XhOg1CNG7IEr0Ei1KeJJffvX/znnvOGftnc/M3PneO3OXgVZDm13KCmoBloc6wdmBHFzCABlVOQMgF4CLi4eDi4sbk4FBBwJ3AP9px2TQA8NcIVAn4X94yMDAIPi9TRYEv3dUhToBlN0cAEAeAJBfGCggzMUF4ObiEvrTEQoTBsiC3CFWAFUOgDLUCeyKySADdfaCQWxs4ff7/PkVwGTJDAAKCQmw/QoHSDmCYRBLkBNAFQS3BTve72gJcgBoQy0hYLjXv1IwidrC4c7CnJweHh4cIEdXDijMRpyZDeABgdsCtMCuYJg72ArwUzJADeQI/i2NA5MBoGMLcf0DaEOt4R4gGBhwb3CAWIKdXO9D3JyswDDA/e4AbSUVgLoz2OkPZ5U/HNgAvw8HAOQA/pXud/TPRBCnX8EgS0uoozPIyQviZAOwhjiAAeryKhxwTzgbAORk9dMR5OAKvY8HuYMgDiCLe4dfpYMA8lKaANC9wt/6XC1hEGe4K4crxOGnRs6fae6PWc7JSgbq6Ah2grti/qxPFgIDW96fuxfn78u1d4J6OPn8ubKGOFlZ/5Rh5ebMqesEcXEDK8n+9rk3Yf5tswHDAXxcQtwC/PwAsAsA7Glpy/lzAx0vZ/AvCPxpvtfw3McZ6gywvpcBfg6xBt//w/RxBbmDAXCYG/i5zz/Bv1eYQCDACmIJB1iAbSBOmH9nvzeDrf9Y398/DOIJMOa6bz8ggOvn31/fTO87zArq5OD1t/uvK+ZU0jEykDdk/S35LygtDfUE+LBz8wLYuYX4uQBAXj5egIAAN+D5vxNpgCC/C/lHsJKTNRQg9Ee99wf1Z83uv5uA6feEMAP+nUsNet+6YADT351uwsXHZXn/Afz/7vdfIf+3Nv+Z5f/Z6f+tSN7NweEXZ/rD4X84yBHi4PXb47513eD3Y6AKvR8Gp/+66oP/mF1VsBXEzfG/VAkOuh8HKScbh78OEuIqD/EEW2lA4Ja2f3TMH3bdn7PmAHECa0BdIT9fFwA7kIvrP+x+wCzt718Q1/u2/IXA9/Pz7y3lnCyhVj8HjZuPHwCCwUBemPfXfL/iA/gA7yfSCuz5q5EBnBxOUPh9COBe3nOANRSG+fNOufkBnBYwkCXYAWwNt4DY2PzEfxCB3+TXHf4b/UHswfDfUX8Rvv8g+N+E91/kvqo/Cc9f4M+d/g76L/tHPr5/o38kvO92Tksw5P4Abf5XFd9f7H91CdxLtoTALB3Azg5urlYQV2cHkNffoffarB2gUNj/JuX5Tf43JfD+fJ3v31mnn0HS/5DG9Q/7PyUDBf8N/kEEfpNfu/wzF/Cf4F/JhP5D/kaCPxEMauVmCf+vUEHgX+j+WP8+csH70lzdHB1//Rj+TxDXP+C/woD3bxcnHOJgBfaAWIF/mf/VwpZusPsK4b+emfv+/nP964cDDPYEW2LOTUMtRYLsaoNavldLUXiwb4yITTBs6Kcws/vMwVrdrnDQE5mrMgKXYedSiQNdjxbX5JjOJOdpfvjsNtWjhzTHa76/9r0xe601tvEec3aUuO9Twa5UXS8VBiW7juSm7w8XX70Ae+QmxHZlhhwXN0EcjTyC7x49Cp51vaULH4OnNzQ3q/ifYt2UjrNH6UaaBBRNMuRaZE6R0qHB2akesOAfeeJOnp1P4Gd/uqNRfs2K+XwviqfQx2iFO/pyynupXIfbtYOMnsyIlAr5DP/j2BMf6a0kZZIZn+LCz/mTtNHWzncKEsbnOgFwe7cMRj5i9YcaAOC3YWOOSukB6cQ9aShXwjD2Rz3ogyMFkYD8bx7ZuxRn5TKqVk/RKZGDn5gY6vg8di/HdhwAtKDJ8Qk0K/gt5z5kDeQx7i18lSYQydObSSz8xE0t11R6HUMZjIdVMHLrft3FhP8lFElAGUEJR16dE9XeiFkG6Y19QLx3WSqLQKGBm7tHWg92ZNFc8HebJ9MR5bo3B4SsshX6UoW9K8uTddT7VOv9UxqZniL5DKawTDzJghVE9LvobLxcBfTEypUX+xt+1lfKvEG7grOXPu4J8qqned/9v395S5fuV/4VNW7HI829/Ev/ZMgtEbBh97ZnVwkB4aGjsoE9OYY9SjTh1HYvH5BINisq9DNpn/GQPtTZYe0Z0/6hCNG4R+CqqP4PdYPFRxjFxM4vDNMAQWgLxB65/TOrkvHIlskljEpEY/sJV+34eV5vA2p6mT+buynl40kljqHbQPcY0M+ckjZiHjPckDKraFRVchOHvrMoMtsjNU120mObvLogoHzZamc8SaRYO1mn90o1zs5CsHBY3X/WD35mJL2RxvR9C7JaaJ3B3hLQYKyBoVDA3PniuxiKGeZh1Iq31ljbJuMgjZ2UOI6JPNr06ZCS94Xiq5JqL/rkPHoY6sN6ijkOzhpPu2FBnvRH6GcLB6viL73FyTdbcYUu1MYy3Nnnv7nnrzDf6Ojky7JQvVdSeZ7d6EJZ/aV4wcJzRvzinMoN53lrbOWTbeGRXTEH0ynqvdQxqPDbt/UhOwVy8REVkIWifeMYqWdJn36cY4f2Gydz4m7IaQ2LfDjW2o4SScCh6+Nn6i2pe0ohIUM7LC5iY8vcNvVQAdR0iCHkpFlCs9QhGihJsnkUnMlEQCM6jxgalU7hyxrM/HxKo28ohJD7SUmNFXWSi6PlzfLjNSUc29rGre4N+XOw0jV7qHYdClXW3MMgGqf1rx99fQotaUKtvdyjeLkqr7t2Td3VQySOlyKM8D+FDao9VjqoZEQ0EVr0GLjufqVRiewcc3KL2my+0wqp2ENhWuceUkMlQ/S2Lsz7VhUp1TL42ljitf/l4N3zZP8JAbUF5l52+BlzwbMyOSWmZ4txqsa4y4Dx1ne7OtbFxm277RRD3kvbPjmLWLBHMtPcle2GJ5ZfZHoL+lELe8upK5AurTd+kFy6DzlV6BzY6EjbzD2zbg5SJyzQL8d69aiN5+mSzdc6iNvR1mw8WJzi/FKt6VXsTOJ3/PI3NBcuxcBZbY6h6TzWmBauD2H+oqsXRaaNH92zKqcd0Y0uDgpzBh6mV9Fci7Xq69ka4j7RuvWpoOX2X1hs2WLcVYPxUw3UxFhE5dgXmL0knS8ZW3uwXX1LnE3SUUI+vLrL2CEZSgbsI1rlafDBOVJ9uhT7wuz82myuRwG0pNMuhWyBOBg+zGVULO2zXpCLbTW90YrGrXyeVLkufg0Q0Jp4EDbytFwgchUlZSYm9sHYLEmnziORD9VbexWsxryx0icsCJSJ7Q9ceP1EBHLqZAF8yK/0JZC0L94/meFJLB77fGyssrF/WXZ+9IzRJ/JGg1nvY71frjRbOpDLyJRK5zpqSoabECwiJjkh8Nnpqoof05cs+2yKepFNDi8gp+EqkHXwPUPViRmONPZIswATM1bw9StPoqf8qQk3Ll3dmj+OCByWhEkqMd4MmLvsT3odQrJPzNTz21AEtgm/qD6lbeerZnLdIbUWHZTjevBe0eOz0a64TX1Rz7AiWiGxJpBGLUKIdkNEEEtyfjlV+MWn8ic77DFMsi+5uN4eFR9Z28tQoRMaB2RXJi16kkUuMYp5Bnfq+yt+K8GC+GG64hSF4xP9KPcIuYyKyq9TL2h9obdTGfoKkj6pHKyyQjpxxRCXSOYz5bWUgiSPQHZDJ4xZOQs0XrztPszENcZ2mnZ9Bc5aNWWX3eBI+paSYQl/YpYk00uftZapZCo7m9JXVsqYFxP0euTwXFYhRButR5N+/IUzIkXpVFA7Or/o3dfv1X7oR3Sy2+02U1lAwhVi6CQKJ6vgSWXAqviiYWZrIBK5QQZr5afRneqK8/FXbWFtncLWJGUGeKqdi3s4uYFM+EV+cDQ/z8ierVPLYtZSc0XL1+vuhHQZrKPItJs2a9HIVA6PSEUY7LdGmrAfB0amue/RYQS+Y1hQLelCkYrjnw5W8/F/9YGhOdE0ilT2utZcZFNcabelbjO/b8OmkTYjV0YbsjXbnQK1Hu23IQl3IHUThvLkeCVR6R4t5c0cmYCyUYk7RVD3GONXwqzLJFfTU91nv5SH3xr9qLLDUpOuNX1ZSHjE4fUOcqbP032NwecTGvmBng6BQIRie8bwxrQs2jyHFgI89rmprTEKqnbC9T1BKA/S02Zzr+Jf7P3C0Q9ypPddYyCcEx8b/7LPrOMv0/41adKeLv81Dw3hGrRWkkVLd4Ytepo/dF2U4jFvF+8D80hP2b6jHkSosNw0fIHaRzWI/7FwSHJwZZqpx9fPWWfpMlKlWYmMdqa5YtfXT3lDUU+pDuSkusUKakaEh1TVJ0F946x6BgPGa28s2JaoHEFzLE3hpHHD/ELJRuHVpoMF71tCX/pWmVMPfpIgJI6Aux/Lb+V5zI7AuJn7RqlowqinOdfZ15DV3Wv4kHvxOiW9/EsiAoUAps2PR+8QZ8Y+XJ4gevoF+yAc0mIM5XsPiBohnEGjOeAW/dLWzeRHmo0rflmDXXnVDOPlWqG+Jg+lIU9Z1BiO/HapcMp8zMafWMxlWcNEKddhSIH0FCBrrdj4/p6r2nnMcLTcrHYhACbWY5rod02nsFEH/d0XxC3N+lxkqD9EXlRfZ9VJKEapkbz1Wenf30S2pcjuU0pefOwAqT/c8qqLJ5l7QH8HI1gWrYeTpYdDDBBB3Tn7nwjRuLNFf9QlxYH7F9ICXwyHxSitvxrDLlmZIYqM3DBFo5onre5kQ5Y/uRCuVsydjZt2MMSWT1LU9j8gglB+eE0e3kEmnKhAaLh2+iOo+ZtCHaL3PkKYj74RgjbQeL2nEcOArhgvSGd+kKjhStiq/V0NLbhJNStwZr9qsMpdBUUH6w0ZfmK7Cd76i1gyL06E7cWTHT2Xaai+t5HlFz++57RbqTrEzU11ag0YD+TiSqOnsPScU2N3RxxPTtnXMVaHchJDoijWSeRC6icjK4Rip4hrNLnWoqnxn7Nx5tEl1OgvjjpYtDwsyMa31iITzBwUJNOLlCDSUTUR0TXr3FBJ9u/dw7Izhc69sqHK64GhHXwaZynw/d7eYoeRZpPwMEAkG8LK5GW9QnBgaySL8BDhyCqgKamZ64huf/P9Q7f8TCu2164a88XWzDYHkiGBSArqT73PEjFfrG92NGFo5z07Sjgbj2Oj0Zqyjt1rvJXrMkmvvMuFIFbTuZfoJg+JCG8cGmsea01sCM1S2iNYtQVfenQkDpp+hXgQTJN9EaAEarT+qHlslaJmNd8w1CFRLhCYXy3tm3yzk3Q4ORDo0SLl5vZB3EenV9ZUoPlud+DIanXPJ/6y4sE42Zo+nOjbS1VnguAcyHwAE0HuBasJGwm2s5Gp4Nv27rymCg9LBzM58r3Zm453Iy8UkB5TXM5MTtR0djXi71SkiZkaCuCzZERPw15q3gJo9TUXJ4JgvWel5zaIK1aqQtQdE07kLO81agH1bZTRSBkr4RbLPEFl29LNnii44EcPzyWBWrbqdqjfXxE/eHVpcpEyLO1l6YGT4k2cidM3aAd4BG5omJhQnJGI7d3Gr8dI1E184EfovP1RLpASd2v9DRFHub1/v1J85Yolr43o9PZ7kvUF6Z3mS6Ndk3YkaSLqsld8toGZsMZgXDK5/JwgIqtnBL0epV3hSHOQhreOgDwR9DPuh6R0Er07mKbmZjXEJUYAUnRBDWpEk+2RU4+UDOYAypdCDEI5eCQn6VZB2vz+k2QGURLPgyNZ17JUBgyLlT8985IEXg5u4dAAdplsfXRryW9GwbVkjmX7bPKcVuspVOGTVHSKAuQSXXYPM/SeB54ncWNuOmCq1ztYMMkbCg4r4F0t1+zzVnP0YoO1cR4g1JoWi9bUxhFaSPp8HCiQokHk0A7FHrAWj4R2nKvk9+XyJ3MufW1P6O4lUFesBEutFoSg9J+OPH4kbHewgVkvgFXYwd+AS2m43jH6TMudCla/LYFaT6L2fVpvXGlszhvY8MbzWaaG1PbI1N2yX/nm96ynmTSobvA47bvX5kYvVxS0zKl1Em149hQ7eVczZxjHwE+1sM1LFwPsM1nfln0mW/38CM/Od7VJYBG68l5RTQPNBT8sFk/ZZhSNd9x/f3sl3+eSLvIrktqDY/Ql5cP5WY7t4xNU5NOVURoFqPG5F4F7zWsOpUztFtbYfqFHRHYjuvUqVqyhDfHWbgktQ4U9fo6auTXW1N2uCseqIxajzDtahtyrPeKVXtxWYWIo716OkL3XxU08RskF7+/O3nxrQhAMlmQRZZFoWN+kPYuJ5djzyjbVsI0mUZseLRsDEnx0edF9uAkzJLA5qK1Wja7X6v/CGYGDv9OJeWpmMDA7sv6+bRfmxii17OwGJmAdIjc3RBd3CQbofzNQaOG3Ogy9aDN6FIfdfkxchgPGztggCLl7EJhwdnyVodP0PW9g/9As5FWX3bMI9s0xRmOnAicvInWjfGH19dtJCd3ohFL/otg2Hm8ReRhotfQRStHzWE4z7HT2lVHZbEZoLS2l+romY8RgjcrIuWTKw5JZDlpdz9FvfrhgLTsxXbGRQlmXzZqnb5bjnmS5CW1fowy7pI/IigovAxqqIvBWVSxaOXhaju/8gyzXb1EZ5j1Ny2bCRFAqKzp7kB8ekbRugPYbU7S8grOxF4zmhaeiuh1eR7edPwu4dJrxfoRDO8QJu9GNfIM09eaAl7nXVv1tnCq6qlXP8KOmzTj/2+mHu0Des3pOg+uwt66galIuugMUqbLd8ob+MzAsdbUxziEaWVoNNzwHli2YObZUbfjVD+YlFGEb0SuxY2k7l5VEexVEY4lJaIjyNfKVeuSj1oUrywL8s8XnLrhtfKjZyUkmjVdJTM1eU7TEMT/ePPsyt4vMpFLUiAXk45mzcw/xPrUNH6ec2hbafptQGb6ZTCEwTWB0JmN4joa4EVX3oumjeudKqTAb7du4H7qEr2ToWeZtjRUCuhgQeaO21INP2x6zFnjFJ/HTjYprabHjCT8eMFBhaDfMIsqLHZANsyqrYDP+jHZTXlkZIp+ns5NTEBBqM2RgGTha094qXEAGFeoPSTTxOQiqOLprsWVwFzpjhBOjL+lSSxMW1hdq9iY+eXklf+fcO1m4MMkmqB+xsZvceL4r3/RJPjsgJb6DXbNATr9lVFau1nuMzzXrMYcOmizwkRZX0FJ4eY3wNf3Sc9aXMJbtCv0E/6z0c9QXnyt1LdozTEzlit1d0X27JVTsobUF0eqVBZO3kH75qLBwz8RhJsEwb+DqJZHVy+4rQQ90DI5M8iOLF3QZ1QxPHzyD8L3qFuqU8b0wpVL+uo5EwpvrDLfrDe9+LZOQ8iEkISBiGLOngnxSgQa8FbbBSQegRgl4/I0QY1mSFkSkShNs8U2uhuu4ojMdaRxdV6LKBLBqYVgw0bobcqH0ssBxMPmMA7/f3JPsG+ssQWA8t4uRMIrdWs2bbbi+Kmlr9dNSkT7Bqr43UhkPXfeeuNyYQF2Km6P17FA1AQyQSh77gDgEeqezkzNSRsJusUi5xyzdmPp6eg9bc5RWNDQFd0lfnK79QBafXPGHPJziJbVwxOjCK3CEXPtvpA22jXFomNfqcw2PMXKfBHNtTi8ijLhfezhwmIIjHFJHA/33qS6XqHppKw2Tk73Tp+mGfryUX7bXGk5tnBrZYKtIM7dfZYhIUXKzAN04mdxqzYe4kVqRfUbFHhaGL0q8rd4zfKMdfXWdy6lv4ECsvh2DU2na6VNsF4vZUWOdnBhqFubsm7CxI2aknt7PdMha+sN8VxEDs5kP2y8tY4w6RMDK9sEHpPbpMlO9BWD91GTQXgZ3Ht6oLNOIvnE3JyFcAVMfkMDUgL3nnZP99DtK1XTUVideKY8RbQSV+Gm0SlHEsv4MCEe+SKQy0COP6/O6ZXvdFpVUUnXCYuDe8KIKnmXAezmQyFr1Q1HYrPYzFJxUlBLMxWkQm3ldLFJYD15KsFCByV5jVynBZjr5JFJOU4SYu/8I+zfGbVn5RzYWYg5kH67VKTcxPAa9RWJEn+6/MXiGOeqH8+VJjKHiJWc/JXUZ7W34bZ+b/Ke90rUuxBPcWn9jmuFeZiylqE/SMexIUk8son2kXTs4tlEXP+uHdHf5KGZ+9uL2e9QydVBU8alzkWP60jm2rfEO8fwTZ3xPVwo3FcSMHShzLNO51y0vF+He/3ykoQ/k/zry4C3/gB7+yM45BvFWvcBzfXjZxm1RlIDj6fmcr65s+OfUgDfjfhA/17LTg4b0MEGQ6Zq531the/qjzJD3XrEtrPAQk0BikBrIaZIBQyRLxldoalPRREXG/avSVrR98MFLDs+2vXCoKSk9jppTtsy0oy+jCuNXFWaXCEaEDeTWr59PLfGyFsn5NN/PoSKQkDbY+ZR6yXHuPpYc81vKxoPiB18/JUYCS1EAV3JOSJtSQ+ojskPhqqALhyEsBAPLO/YDiU5LMnt8nFUN/tQWf0FzmrtPvGBNBIasD+8uc9fNZ0ff9/fqSXBoa+4dHzXShBAgFG8mVMtbponaBO8oT32p0COFfY8ZGze1vZhfXYYvu2rwXEDNi37g8ud/5olBZN8obcX+XkfD1To7GPJZgLdF5Vb1MHmFDMgizhIgq9DA90LxkcH4kz7Xd69JPT6wHH9Pzo3rGloXodhZd2WqiHqsIT3XBrCFqsMZwpVj3ndjdJO6jSGc43zmEL45Pk0KXXj5EvvuCXhtw5XatUquoG+DB/QxkSX1VmMn3T2BpdDe9AHfOYLsNf9mlES55ybPis8Cw9vRKrmT4gaI3l1Hzp1d30RxVVNRgkb+S2o1T+O0c6zD0RKnU388jZl5INVjffRR98J0KcHP7fy5Vt7x7zJV9o/hCgcWfBo3mR5sHJmtMY9t3qOVvYq8HZUXnus4QD7i/SHsRiCtdyEJolTfiBWh65GbOY/sCf9m9wFFyy6oceddYjNI0O199OPMa7NetBxk+wAAihaOJL9JQB3twXh6io7uN2oiZ26bwJND6lsuToiKdp0wwlf4oyLbeEcRBYWoiVyagsEOILNj/4I9ecLCgOU1RS5iRDWnONJOVXXbJyfuK6IGUedWtCPrADLhS2FdNNseVHaxpPwR/qO3CLs2KOsREys5G7e83uisqPWFw9nYgusojVbaETzUT7GJERG6Fnha9DueoMSWm3eUu/S73clV9YKFlBjncOo+TYePG8Y3PrZQWV46JnnG7aeZZyDaeT7ZOb6xA7TDoJQLn0gCLQFMIsmthufIk0ktLXQhp+6V5nYj1AS6VdavX6GYmzrghMqevbHYKFBbWM61l4Tmy3sUYvSFStXXvSQjIzGbNdporM+Mw1m330D2/6rcNcj5zcwA6k8jeZ0P7WjGHKMZecO40fAdWRdnIMrik7A7iyQQPVkuZcMVKdbsURGMQ2h+nolEVpluoMk1SpcCeSdcFb4I51oJ4Z+Njztm9WpV7iKLsmbqVaHc6U9FQ3m1Eb9wOfss6dq+4Jbm81lA0BV565mK3X4IxccUzzFfoE6oBJUXhLgcKnFpFn9Qky7dt4wZTznuNPSg6tlLB8H3Lj5I67OVzm9qsSbGebIm8834myvgb94moxpkqUoG7CG4L7jlPgt+LAXLQhag6lHdE8UxcT5MoR8ljc7rkFTH5NS4mfIFDIyFscVOAVWuSxyC3ARc9247juxgrqyF1JRKihMKWVmYmRml7ShK4qL7+08DYSnt5IyaA1QUd9USGClpd1gaDRSH+nHxN+blWGHhz5KpCOAv2ImU4zuMcLTPDynoZBk5gXPxMdipC0qpzEEt+vacEuEDYTveqX0zW+w8OIerrnUdvY24Gc7fup4vmKC+DNGAsPuSXVd/HKtR6tNM6sfYa3P3GYWU4zAT0q+2yoqNlnJmPe9OxNprS6wS3iywM1A8hdlUfDC828x3PbVRHvONB2iiGLNbeH3PjSxZks9b2Bjq9qm5PQpKAzyxR8WlfZC8LvJFep26RfCcfIIE1+lr72Q7dao3/mvZl1vKs3lo0w2ku6J6ZU92ZG01wLStRJQHW/nFb1t4YJ3ylURT5ZRMpZ1HK3x25aTKz1OYdzSuXL4g7egvqIZnarlTihlzS36TmuEVnEHY9iHV/lG78vnCJEYWW3KP65s7pQgPeT2vH+Ie7jU20bty+oin6AnJX9jUBFypp62FI3x6NQIw6TRfan7ZA9Za+NNsOks1i+6TlODjy92uD5af+zMVSDkU4xzPDzSNC9+/w+3ChlGhFw0oUdBM9y4jIJOWB3bZ80x0iV0y/C5X5tMjdIEHrUstlPxskzH6OUsS7i94Jz+0axbxXuvYyyvjrTFtlkuew89Yy7Y4584krFjs4liCg3BRe5aKP8qDEXssXBFv6vM/Edi7988wyqtJ+9unZQel0BU0ayWpKkwPtOGhpdO3CM2Rj+23CgmWKvOeVJrONw5lTxFADWoyyUJtfXJKbz2LoGyf3rqtPgNXYtFpkEoVXByS8keyioZ6RuTx6JtgGQTxTLEava+NSTDCedPOoZjOIiYa/0O15+1D3rK1aGEfKlsR7RBPi3TOMnJ2HD2Q4Bfw2Cw/aL7onWjhclwUSgrBKYawDSJCxKUkG1fF/tBJ1eqOCWJtXlNuo8Q6VfL+6+yYC501h+CKrKnD0Tiqu5IsBTWKjgdUtcSUFZvt2481JpTSSQ63yW4bU8aVBlKDbnW93uGLymyVF3HcjEUzlsutSWD1mCLTcNVckx+keMpSBiSm2gnm4F4lSE1miRGqUo8iTXqdao9YdcXK15RAZxvkFIIQZvYnVhxs3s71RZ82TMhdgfF2sHlGIqg2kxMbfAeqNMjfH0wwp36SYgfjtj3aGKsUKODZKHrILBMtLeqOsOEroRyQdFxin7onxEMynEJphwBMGVz6ah3RFo4OKNd+PQkxVEMmiG2xkUosF3zeOFKCjF6Vaq0da7y8YxXxZTAwaaFBabGE2Z5GChs42ZfuNC2WmMfssDFLBDYVN8R4yqDpGNd/myacFtGCI/Lko4X/rmLP8xBGsg1oUs5RxqnuIx7pGGJrBgHalx8cA7QEpngXFLGarVGoPVIlgkTtZ4/FB1pGV1JefL/tvAAwqMP5GySnnyP6UWAZDaA70HJ8fWtYlVyk3xh2pXsaLlXRxmfj8VIjluAd44pFw3KZRoeZQbYXawS/ZnlS9GvfJtTmWbmIuwNv6drwHpxrEn60cAVEQpm9uzg0TuPoEZ6wknY2xcojgUs7SaRL6RBd1wM2Mv5XLeDJROYu5942+M67mflpiqpGVoL1vKMep9y0lEBiFYIEkrOEmXiyqLQ0mHuX0hrN6qM4DjNRJOYSWRF6CmqOA/+wFrQUC/1w8STCCNEQnTPbxabRh0z2HPPRpyFy0N0PacrUcFMrdx5B0ycN9NIvCBbitXvmfgxddnWvWh81juU0OYRmGQAuPQ90fDGaTvGtaOsolgwPgyip+5jn5SvzEGTxuLbB8cvd2ejAVewn+XmrFVjGFK1kpC+/LW+5q3u7VWSQH30Eo0uwn35Ml3R9TwhLn/z6IRffWzenwKy8l+E7s5Kb50eGdqY4r+tYQLjZ9/Xe+q2KrMhn9MmRV3KUzOoJeJNprATOOyF6V0Xlyt8GGeRwtujz8gAYxfZXZmy3dInNAu2MoBHjY3Fip5wlcZs0enMj5aTpm4s2vnoIE5FEvTynd/zBnqnq92PPLz7UFikM7eCwvilrYsmD13ugINuo3a/1B49/rHy9zPD3Elb4BA1HfeLwkYtGD0M7xiqanSSsCMwjY6TtY/bVgxJhkVMfiqghSp5YdayxWzMaJ1XTobLgYPjFvFSCA5evyoMOC2FrvcqpMg8LDzE2iFIMDDxFoDWmwMNPEJbjR80WzT3x7ReIqAjgSW1VYLzJkKISjTybcUSnGuq67brrNtxroqCLAPRT2gfkm4dJ1PVoIWPkV9Nu0b4voNU4s2cpA6EdlipQDjeQQBxCXtydaHoxXD2V7Tk/cKvYaBuHRPPWAhiT9etXkcYEq1rDmb9syUHbdFo7J1uqL+Flkolx0dZynNISOCH6uyHYFMQD15P0gWhOvMdirGw+3GfFYgIuQPlB3+h2GYY9Q1DYQBN7nnkYZku+huHxUD1v7Er2jX13SJs63n7fJbvEDLG4hB+iGZYs1OXyVk5FcbGMj3/v47OZ9bSq1yYiknseY0f88wNf6j8IJxwr3mDFOVV5MS4te3TF6ND0GGxPfdwXIuQhiYul5rm6c/iWlRQLmw3eY0wtcm4bg/JiiuFEmiXpGpCuOcweW1UZilzFN146znwjjCyKNKqq/BpnarASzSGJ05gb/fm1UkOyo8qU3aKUtNmFWRl7863Ni3hlkWv5uy+FN+bMEbmedwsNagvNuttF4Uf5Qts5LoRLtVLTWgd46x/LYo+uzsR+rLThxUNF3cknNqzkO4JAnfCTpTGCmGGxAB6BSxY0AXQprXHnEmIkToFvTwghuGcslicvLJU0XxdLmxvIX8Ok4xypjhsPiWh28/Rn276/Pvru2xO1TyhCwbeLHfk6E0Sh/bx+4ZNgy+hwgpF8GBJx5qmM8QsNEvYLR3l+/YqhZ2Xkn3Ulf4gshs8ndJG74L41p3hjajmQZh3wYWXTR5U+qQzPRfbuS4PsEu14gFKSV/A7FUlDoyIJWqkQXx/vjO5J7Ill7uKh12waoYpk4so9joHoSh8Pu8p2jwmkO8pHYvLqSyHokoJRY9dzqGTYfsXRGTLoWW8c6Of7NwiIlYVwlzQeWKtORHk2FFmQn+uWvlG3MyGtoX9H4f/gnaxB7qiUTfjE6uzkpUmlvKixWwgmsr7pahEAcezd9wPx8m8lJcbFHxs53xEoMt1UUkbXBqfcgezO6ILtnyvLXYnCq56WYO+UkHL6VHHy6ZgmhHeePeldY8T4ut3VPnOxMD62x8mEG++fVD4ykcq+jC2zdoLGJbnQzVtxV9xMUqIXEAw4mwdWHxMtp8W8oWEBfFyzVy+Ao5qgrL9t7ieqm7bHsVt75JlWlSgo+tnxrLFbNyn7qu0jhZirQaEDMmkTRvcbPAG+fPpXUsc2S1w6A8k/+tPHHdgFRpE+V5AatioSLyt3eDJEq+HZX5DwYve7TEeTOgdLQjYTYUfdY28khMnPQhqpJCuyLQ25uvpd1Txw1KQEepqeL1Mamp20Vfb57NiHmc+/00+868PGb1AoMI+MgxOAcGs2WMfQRRhqZ5QAVdxqs2TnzBkvzGXXMHrDj0/8dSJO9BDtIQCx5ibi5kE/j5DXwP6VNoc+CbNX5qUE5ElHhX4Z3/niHIukXMKz6JsXdT/Z5OiCYTJNh97m5mi+X1EQNaXp4UtlQ1M7Jkjm7PEgi3NXx+t6mbqmC74UneG49+b9cSzOKsU7+ws9zkPs5NSFcSmYlm7BaRRuFUyHxqLFyvO+b9bmr9F71x0vqphPCOTrEreEjuCz78L6HggEAegvNosKezF1KpJns02Hpl0aXU0dqdTSSjTxZLOKhZStWAb/D6AU7bwKZW5kc3RyZWFtCmVuZG9iagoxNDU5IDAgb2JqCjw8Ci9MZW5ndGgxIDEzNzYKL0xlbmd0aDIgNTkwNgovTGVuZ3RoMyAwCi9MZW5ndGggNjg1MSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqNdAVUVN/6tnSHdCgcSkCJIYbuRpohpBlmBhgcZmBgSElBlJISpUEBaaUbQUBCWhQJEUQESemWP8a99/+737fW962z1jlnv+/z1t7Ps/m4jCHCqnCME0ILg/YWFhMByQHqBpq3ZAAQSEIEBBIn5+MzQ3qjEH/N5HwWCKwXEoOW+18AdSwC6n1h04B6X+AMMGjgJg4FiEkAYlJyYtJyIBAgDgLJ/guIwcoBGlAfJBwwEAFuYtAIL3I+dYyHPxbp4up9UeZfv4AATBAQk5WVFvodDqi6I7BIGBQNGEC9XRHuFxVhUBQAwcCQCG//f6QQUHD19vaQExX19fUVgbp7iWCwLkqCQoAv0tsVMEV4IbA+CDjwa2DAEOqO+DOZCDkfYOaK9Ppjh2CcvX2hWARwYUAhYQi010UEDg1HYIGL4gBEVx8w8kCg/4D1/wCEgL97A4iJiP073d/oX4mQ6N/BUBgM4+4BRfsj0S6AMxKFAIy09EW8/byFACga/gsIRXlhLuKhPlAkCup0AfjdORTQUjUBoBcD/h3PC4ZFenh7iXghUb9GFP2V5mKXNdFwdYy7OwLt7UX+qz8NJBYBu9h2f9E/J3sbjfFFB/5dOCPRcOdfQ8BxHqLmaKQnDqGr8RdyYSL/j80F4Q2AQbLi0lKSAMITQPjBXEV/pTfz90D8dor9Ml9MEBTogfEAnC+GQAQhnREXH/JAL6gPAvDG4hBBgf/b8c8VuZgYAEfCvAEnhAsSTf6f7BdmhPOf9cXhY5F+gA3ogntiAOjX8+8/uwt6wTFolP9/4L/PV1Td1Bhiqn/jz8T/9qmpYfyAQGFxaUBYXBYMBsTAklKAtLQYEPTPPMZQ5N8+QP8J1kU7YwDZP+1e7NO/Wvb5ywCBv+oQBP6ZyxBzQVsEIPAfltuCwCDYxUvs/5vrv0P+bxT/leX/xfL/bkgLh0L9dgv89v8fbqg7EuX/F3DBWpz3hQIMMBc6QP831BLxR7UGCDgS5/7fXl1v6IUSVNEuqH9vI9JLC+mHgBsjvWGuf+jyx27+S2YoJBphjPFC/rpXAGExEOi/fBfagt2+uDu8Ljj524W4kM4/S2qiYRj4L42Jg6UAKBYL9ScHXVBJ/OKoA8UuxAhH+P1mMSAqgsZ4X4QAF+MFAc4YLPmvE5UBAaJeOHf331ebN8LP+5eb/B8lYDgs9kJmv0lwUf9f69+aRiD8EDDyyQ8YmHyEW1VEy+FLVXZf4cUhcUmqN9vRu6SWXzVZfZZMwtUVjnYknsV8VG0f/xZzNEe3Kl50Xn/7FhPotv3nO2kxckyTNS1Ezk4LQndG6BSPV730ceLf9F69snaUQMNdmDYbTHt9d1XeCTILeW4tvs/8rq1wraHioNx1k4S/bk2EEn8oLoaERUZGghI/olU1s7VCrVL13XibLq/XtsrQevJVre9Had1vxcFf3kyVCOEh59PYV1aoPPR2M1eUniy/lmLTcxwB3vF9fa1JRj5XquuI0ZlVoKm5ya6RG3WSudB1tscYe6clnXZhhUfiaW6AKftIO9KfXJ9bUtxF88WmR2hnZujnfZssqrPr0MdduEsBZX6SNy9TKjyM+XpIadPBKeWHFlbetbySg+0yNhHLSOz+WaSbg0qwEOkpVPLtFu6OtY5B73ZGz73SB0z8aPGGq6Zzt5ci3YY6/Tkkp7KCfe7RZON6YqxfVuUMjk4LESBoZUeq2nhQsc7an/aIjEhfN3MtNCw1z5srxx7Z63KdqX7YLIpTpmePFgu8ZrUfIpzpKuCOnHcXQWnhbR8q7hAcBoKnPr/QCESvynBU1nOinga8mwht7K4OzTm759rrCDu5cmNp2exTedkAg+K5gjxvyUqqppCE6stx7p6cyRUDepazfXtZd9YN4zOq98yXOQsL06fOds4mXiXpyJPTBuqfhBPKgnJiXkxEjysl29WN6VJsSwVsnvUdl1Pi5hh0ApskWCVN9MAMd1IxNyJr2SlyLIuEBYQEM9p2tqzRONtdvLSehOR9qXxDzgSUZ0WwgHjt3sZjd19r54phHkohZzbF5Ud+8cF7L0R1CjJC3gQGNHlxJ5JF0jObDNW8xADVnkUbQ2KzLEGXJRw2w280NL6adIWQNdOxGhfZQksd1lVZoWyt14djtEsK6u+oXS6Nd/NdIVtoGj7Opff4DGk3Xjnlwfs5dE3xlq/8WwbA1a/pvKHvu+CbsSMEN2a3kwpwW58PPbtT6TzwTBs+Tg3h8AJgTpS9/d/v8XBFrMgPhOB7jrwhskhIaBoyUOHo4gl0hwtNTWZUgR2yHb4XvmElR/ngfR3aFku02l9Z8hOeEU4/Ur0nw/+2Xvb22a0VqsfR+hJy9Qqq4yTjtWEblAF4MvWjPEbglDuZDY70lYtSKZHm98NJD+6EZjOXvQ6YuEeYNPRWzBhdD28tejoEynDhtECyBiecZqpQQUwfeD21/ik4ur76AHZ3T0eoWYZBZBExhuoajcpCFfy4i+yhmq2memf3BKykZUz7lepem96+CaNNt45B8eejsrSERt1lGtl3R+O0i/abPLxOSKPEXW3fZ/dN6uk+Z3P43SXN3DE3lman8wwdaInhU+NZ02m8/4kl2V8wpDO7bcTfMDsTp7xg3Xnf97qo13X819o4lEHfqWwhmBb2rS/YhrrQyfAaofeg1O3dS51OSTr00/S4TEG48D1bHS/HmA6OGxGkXNyt6On1iVFCZsK4nY56DULHDOy1o6/eueTHLyScLaoGihetGPc3fkI+NiukEiliNgWcp5xwPwkf0u/gsQiqHckNf5HCuB2xb3pCkMUVtfRbcpJ3+bxtIZvD4L1QE1GWsPjsyhns1Hb2+iyVWcSHydfR5EhSNZrTLQGZ0t4eSNKPA7+IxtD+0tS2BBZyp22zJ0UWVAadRfXLa4ieq9O7SQRixafcLUSlth3GpHJvmB9ZkBh1EEeBAiw+a+d3fY08ViAnW1K267IqPxP+SCxEcFurcpq3fCyu8x479XMhpybzUnH2++tRNOYzkqp9Y45VVft1zZKene6cqEvl8rmlONlaWWpICGeGQzPxWSAb+I4xffxEnEjcNXj/kNtLOmvkprwhn+rp8Rh+Yrn+PvLo+gNi57RdWWJy3T4X6xBo3/DM5yuKOnXs9587vz3bfcT6VIW1gdqiMHAzIizsmPAjdf+bZ5EWIBULV9i8mh1QTNSfQDqJ94Kyigpfj9OkzDgl+nDfRGoseeFaqw2jdpjG6V0RvLv93zIoRu8QvsuXC0Q3DnvwBlrw6GD7nxrsq5IpQy3GLz27w1YSbUUAo44nX3gaXZkyAFo3nyjvf/LSnm7hue97vpuLEiu5JRxxRrSsiZsjEmHI+Hq53rTgxH5qXVsmsrm8eNpb1ucrH94xXrdqylHac04w+rhxXbqdc3qLC6a1VJdpPcWpqPfNMIuL9jyBiVmQPniJoltCtHJ1fKO54XVjuAmR7YFuToHiUbBVTsEx5xhnPgf1gf0XIYsRvN44Y/E8AxJ+dQZC4wJbIxKxbGkrFddvUNryHm2hD9wtWxxjkb3bpsebXkqnfvu+TCJoSauGQjavHjrk5nQXAddqtis0ql+gf1LLLdxpNHAz6X7aACWEMbKcr5nOaq78JsHedi2W38A0vBzXGhsrYz8IO3wQxDz/YGl2/3aLBf/V4xNYBSgjpx60UFfZnLruolGs2M7mZqC55kRqZI/PEiNzJYSAajl2Mm1I/1bwNV5g0OPTAFNzM9tldxvG7kRrp2JHCtNXAYa2Ngo3nQ+TzJMkWur4hhg5Ut7jgY/zl8fnnPOvAXt31VDc/hi7FObQdJyDqkqSSX+ZKK38lMNOSDOzzLGBS+9zz4KfgkrPy9+psPkTSrKGWYC8G6MlIiFHddhP9tqPTjd8lMvnNKS/kjC6p/dQH1Yw5ULIkbQBMQ98TX8Qkz9IXJN3s8n0oJSajlltIB9raVnq6bXjae3iNaPQyxJlPAhNTcZJLcgpQopzEk6UBMWjteU3SdB8xEQ3wqEsnO1905tHWO6JPUpMp2MW5Ea20VVpu0yysNQARZpqkvd28Op9yCCE4PYVQ4GO+zG7SWiHx9d8aZY8ZmkVHO9vd9fd0blvksylvhA8mXj0g66Rv7jg5X0G6/klZ674+KVIP7k8PFeHDu2RRHwunzDar89306qIG6z4irjVMFXZhg+iODLrTAKIsqHLmwplwRXPiK+w3ga6DvYP1jV0ZtixfW5KgdMF4h71ixrp3DmzjGlUXUSk3ufbGiZfanVCxBR2SsxXaF2WW5NIwnRe1SxYx9jyM6rA7XRm6qHJiPdk/ox7vbLYVMpVZTQkll08YZcHRJqt3dIhzX1bft/+LQUWT6tUJnEpRF5c7eSHTdxyMSdF22alP+1xSbkN7/zUz3O+AXizOQPk3skDauOlOK4WZ1LTsfFXD5OCbh+mplmXXC08bB3Wq2LqkjgZss59P2jU5pcUctAVfXaD60FYOvucLk6xyuR7qFBbuhmD5Z1XxCG0UsV3a4ofdG91fp+pH3s2orHc2PTSFOSTYkvshGaz1nhUvB/HeC+XofgmgvOKtY00SQxMqsM90TYinHIioxzgHhvUqFW3s/UAg/zA5atdmI4QgRJQMfP29Ujse+QSRQ7rVLG1Pd8i5v5i8lps7TVz5f4in0treYeZD0B8FjmPe+IXg/PLHwbwRwcmSS1kpOR5cMuvs36kidtgi/fBn3/P/onwSZG7YnvAznIRphfGdKtd7zmSJ/esr59Y53SwQ+QNrwPckuu8yFIRb4HimaJRXo3FgefiT5rUoDEH/1KKlTj1wCbp7evKURLmysFJcT2apP73W13MdM2HJwoD8OdcTuzf8uEBVs0D66Mb+G1lo4TZRrOohbhVGkjkua2RT86eNax9MVBt2E07gi2+TianzwYUvZ8dmWFwZqbt8D4ywtbINpV7fNLxDimDRtxJPlMPL7/Uvfmr17rTaJjAVx/d6+wKlq7jnQTlK/HvbEQZ6XbRrnsmiGa9Y6eytMkDjfbX8lEm9oFfPSXFuPk8dwLPuu/sI2jLEpyqsoe/0QaWfpz56I4pF+XpPDUs0n0ppTBfS10o8gJxUkOjroj26w+94tRkau3ejotC5QWJKe3fWkiqQBVoHiRjo9Mi2Hu4TSykXn5phsAMTfOBeDJODm7la8+CY4Yft+yW61N+6xHNIsrgsbxMnnglPX1zjo4nJMkOPnPwreILZAw/rNrqneN3q16dymNy/+j41g0CIgU/83hHrEU7Q9rxWwSVoCl/uM0rYlbF8iWS+Ij0eTLFSn16D9Ep4VnB3DIJxiEH+jqZ+aCYUhf7axTLbLJMDQICWusupsv38CJCGiNmE99C2wS6JX9sCcsn5pbQEDUvm6oAA4Eh1HDvmm26zvZcl4cMVUafeI2+Z9d/2+qT+zFiQ8kuknNFT89xNlULZMvhwWMNI6XO0d6JAe8o+WoU6L9u8hIZKL305bwutCqxFPX2CscHl5s5Da64ayBTUb00naTVfW2v+c5vN69/beSiIu4OWkt+i9s0jM3qYlO5RiUlG6FEFNgxAt6zaCYz3ldgy2WBEoBIf7DSp4DjvXBkjyOuN70eeLIYeulUQJwrma/kJ0xdp5qdjf4WNJUpMK8gyOft9zbZwi0I65x5wVpLlPxl8tCZ77aWK1nuea7yQp+o7mbPig8LqGRbf2r3l9jS0tBYazQ4/K4SyU918CpgJe+zDn0dossFRJKAnem4S30DMsxPjzjTc9c3X0z6KSepMPv+rf/17x7nByX703028I0uXwLR3JB756ytUzwfa6wsta7fBh80bZ0RgeFT6fLlWS/aUb7aibBHZdDbjj7rPPBLqwNqxmF5xS6MzWdyHJi2ZhLqihl33xfxXnUPJBTLhcPtTpVUwTdsXFSdtRZ4P5eEnevUsM7Ng/r4wTz8RFuSy8+2AkxK5jcura5iu06NY5Jp3KcuMW6Y4H8GqALf+xEPkl5Ol/WFSwOu+Lxnw3RTQRSzTUR4SdNRrfVwg87Rz/rHAaGB9BP5X4SIZK+8/BTQO4gtgo4/ub4WdbN6V6bYrPXT4ryJ9xSRqTBy9J08DDUDD0qkCts2qhB+0geImwKdBC2dTfEhT0Kd9QTVyB9GHsUSgHK+5BaatqfXVbCy+VDqjT7M8IALjjsSDf0YyjekZqZPeDmZhd9ZcFPqu+IIZeS1R8uVD6nU40tiTD6h9+2UGRaoxhkS32XebetVStT3u5z6dTXbLjp1u8DEQIvX8RayzMbsJIWi/dzrsQS33FS1ljq7ffjNdr2yHCGnvAVd2ul855RW52q4yDcWwTKT6VcSykMfHjmTT6BW8pcCuAf9dmqIOdVxFZduWhDRPaE3H/naUH93s0wUHE7F46kyKJ2NO8Gscr2IO103/BwjFDvi4XiAH1To9824fGt1TbtlmP+VfPWX4XBlKnYDsHkKK2RZ0D95TnZu5W4Dfen650/5vA81D81QnjIZ0ooAaJ+6McJitVfQMt6calJISsgztTz/U30hdhlHqiJOWT6WZu1aN2TGJsesrzPCsbGVE/4ELZGRVu9ApgZxUJKHZqSUwyqeoVdw1DojR4TvIpdJXwrKZdnntIBr+KnCKk+L2swWfboTR1ndZ99JqH22sjXWkw+sZYdLfS9QHO1J3YI/L8QvTh8A+WiSOlwt2tqQ3DoD9T+dEOpiP2X9yN/5/DjjfDRorKKLLZd1ojKy3Y016qqL7hNWocfKt8h6NvW5HVRDDzqKSr/mP8kiPnFbA9G0Pk7ae+ew3akGVF2W/zYyFJYnBZQQKiRxeAgYBaLB0TkYXZ2nXQpie7EZbWLuN4QPZ/J5SnkaVJwDNqej2KdZU5Jdz9MyPwQVXUUYKLH1V8cFOlEddgkKwOhddz15WsSC7N/bEbDw4QT0YOTTSp4ielcH/KOV7zUMrpze369Qka2IgoxcXkx3MuOve6Dbm+VDlui18mm7+pw2zORrrbCdy+ubQt+F+/hUBfCnb6Di23i/Ha//oDjaTQzPE9ghs7xuPtA1OSXPHPB0q1Cw0YvvkKWrqaXodfESDb1K4qcON2QJLtob1KoziKpJlt1+KKDEIUfUNz13NeIIPgC69YF/EC9xakGiOnpWrtbN6/RGsGlNgyRg1KJa6vciKpko92mViKoajY7j3BUZ9TUfoeoxGHvaV3zVO0fMHguFTkHZrk/UZ9d0xu4/oik6U356onHkyOBQgltaxrP0BZ/QmISrt8KZ7I4hmmh3VpOtAIlS0YR74bQByHzHJw3GeLLaZpLh6dCTmYJkhBu0pyJ2n51fkq3vYI8qQLeg5tJHQ8H2EY17P5LsfcP4uo6bJq3TiaP0bQ42eUfTbfVdnWaCNoni+gTcNHFHkjNbfDdq96LgRi7+I+HMcf0/ZCRUwS20KQDeOlqXpAEnEsPr7/DQnlGpt1WdiqzgvnW/FOzjt3HUV78DtTlkMKrgKZUlOY8gJyYpwYfzkvV1xavqCF/CsruWxblPSrEBKCUSNV9xsYc1uOHqq+fj1VVWojdzaEpl140CNWsbN6S4X2GiN6q3Deeen3VnJTV9/5DF/+hFBck9rqVmAxOrn9eY7rpPqEx0q2WVCiV41k6jF72ejg20aHFrFUmf2kv78EymqHl25EjE97ZwYwZ10q1eJ3FN0onAU2d6cqKsGYOHq9+w193Q72CBdoiR7h+B6dRyizUXdNTqM85oUuXzJ26dN4VkKv5wlsTn8vIyJRYIUpfS9HQyTKkg8E5dLjOsama2Y4oeAouYDN64U2o/jrOEKF15AphRLKwFuMT5Y8WDGj0OPi40tH7IKPqkBHLoXUR637TssqC2xHswShlMyyNsy1e3+oEkfulK3ObDcvuSp1PninEEBdWxrSqd5Fkvs/E5MuIBqtY7FZv6hi6ZD8HRhGHaCzGxbBSFGBMw3JJoq4hvTUtzWn36Gep1o0BB+xHFjYPteu2yDgiT8GSMp/2OyaV1U/EvFDFhIwabHajkH69tU0snlt5KP8b5nz8D+c43RrxYbG2Ny38WHfeA4lzlZUKI/Ew5KPujfuT+7l22nmYsNmvmYN7zEsdc/1r7AiPP1U/esbnCXNXfcSRGVUnDbRoEIX0D7OLYc4t9iCSTR+TiYUaeKvMivmfXkQmVIithQd6KKn5J36EBZNyJX+gbRImDwq8weug2DcgAcsjaWafcC8pTip++FNuWzGO8fZbg5Yq5pPTCkdSiv0jojERhmXNz5KUpun2jRq9NN6RSheKUTmy33iiTmoWATt+ZATMfvgV9AdvT3j/OqhXzPqyBuXCWRNIeX7n/+rVl78NacpVrHevngbk9PHWr+zDzn66sT47Mqqerv1Th1N497Jw8Cb4d5jHKasAOX5EpcFlMFq5nu6O90cHinPpjWdnDpaQ73S2Avetp4Owg58L9BW2uRywuuSdskY1PwvX2TTjZKuY+tj67hconZpDezhcfrwAds7TT5EkUWGLevZ0MqxXwxej5lvAS3PhohnXhivOZhDi9taC9ZUmK1WTeYjaioa/3bWJqTOrvzozfUIajK4DHSd3DbwrILCbmuLmEb54pe8xpnVlWZvMuV8gXKV+XEirg8L6DUrFtHfVkJX3feF1emGElZco3KuPIIncZY69Q46JAw7KHC60TuW1HfLqnMRRrS3p7bXG2AN5c+9XikKUVmxFsp/bzNSc711QlAfYG1d7hY9sTB2YnDX2zlc0spaXIZdhixnFKHhm+dtet+rnzQW9fvSVCwYfwj76vPygsPFZj6jI0E+kylLeNtTvqWCyinQBLtp2X3/nS7bZJAvF8fczNsM95mldmEbLfdlS2yWxf+dwxy4vXMZVKUIniw+okgxlqrUZvR0n/M313opQ+OEUkvKiPJ7Q6mr1qMnXw/mFoKyhHuev7M4WeY1lxahL30KXYHSsP8bpCLk03GzqWE7mhjxycL77ZPovVump0JhK24JpfWbeULb7i99bM7pXh42d2Hc0cz5KgFPANBc4GLGNOaMZpOBkouSkvBT/250i86dDC4hs6JahirJUwwvHL2yGyBoQFyxWZqYeFlVOm2FixajkX4unlDFVn7RJEPd+5UIGqgjzvy2Nwtk+vF0vgdeTb5R1Q3yOuybOWdH73YakKNUIjEqZWGcU22fwhyaT/ARziI1sKZW5kc3RyZWFtCmVuZG9iagoxNDYxIDAgb2JqCjw8Ci9MZW5ndGgxIDI0MjQKL0xlbmd0aDIgMTk3NzAKL0xlbmd0aDMgMAovTGVuZ3RoIDIxMTkwICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjajPcFUFzN1oALE4K7uwwJENzd3d0dBhjc3d3dJWiCBHeH4BbcSXCCQ3D3O3mPJOf7/6p7a6qY/XQv77V6DxRkSqr0wqb2xiAJezsXemYGJh6AqLy8NDMTgImJlYGJiQWBgkLN0sUG9J91BAoNkJOzpb0dz18Sok4goAt4TQzoAhaUt7cDyLjaAJhZAcwcPMycPExMABYmJu7/CNo78QDEgG6WpgB5BoCMvR3IGYFC1N7B08nS3MIF7Oc/jwAqE2oAMzc3J90/6gBhW5CTpQnQDiAPdLEA2YI9mgBtAKr2JpYgF8//MUHFZ+Hi4sDDyOju7s4AtHVmsHcyF6CmA7hbulgAVEDOICc3kCngd8oABaAt6N+pMSBQANQsLJ3/taFqb+biDnQCAcALNpYmIDtnsIqrnSnICQD2DlCVlgMoOoDs/iUs9y8BOsC/iwNgZmD+r7l/a/82ZGn3jzLQxMTe1gFo52lpZw4ws7QBARQl5BhcPFzoAEA709+CQBtne7A+0A1oaQM0Bgv8EzoQICGsDACCM/x3fs4mTpYOLs4MzpY2v3Nk/G0GXGZxO1NRe1tbkJ2LM8Lv+MQsnUAm4Lp7Mv77cK3t7N3tvP9DZpZ2pma/0zB1dWBUt7N0dAVJi/1bBryE8GfNHOQCYGfi4mTlYgeAHAEgDxMLxt8O1DwdQP9sMv9eBufg6+1g7wAwA6cB8rU0A4G/ELydgW4ggIuTK8jX+++N/yUEZmaAqaWJC8AYZG5ph/DHOngZZPYvBp+/k6UHQJcJ3H7MAKbfn/8+6YM7zNTezsbzj/g/RwzOVUdSQ4n23yn/d1NExN4D4E3PygKgZ2FnAjAzsXEBOMEPvv9rRwlo+e84/tKVtjOzB/w29jtecKH+E7Pbv5uA6t8TQg34X2MK9uDWBQGo/nS6HhM7kwn4D/P/537/R+X/X5v/tvL/2un/NyIJVxubf/ap/iXw/7MPtLW08fy3BLh1XV3AYyBvDx4Gu/8rqgn61+zKg0wtXW3/7660CxA8DsJ25uCWpmdmY2Bi+9e6pbOEpQfIVMnSxcTiX23zr3X13wNnY2kHUrJ3tvx9xYC1mJj+zx54ykyswdeIM7g3/7UFdAaPnMs/B/mbQeCh+t84xO1M7E1/Tx8LOwcA6OQE9EQAHz6Y2AHezOAxNQV5/NPdAEYGO3sXsAoAnLMvwMzeCeH3QXOwAxiFfy/9izgAjCJ/iBPcbH+IC8Ao9oe4AYzi/yVOJgCjxB9iBjBK/iEWAKPUH2IFMEr/ITYAo8wfAnuX+0Ng7/J/COxd4b/EBfan9IfA/pT/ENifyh8C+1P9Q+Bs1f8Q2J/GHwL70/xDYH9afwicrfZ/iRvsXecPgfWA/yXw0DMCbRws/qywgXMEn4u9u4W9vbUNyMzljybYh/EfTXDcxiCXP4rcYKcm/yX23wS+Cv9yBW4fRtM/CPZjCrJx+VsAHAzoD4KTBzmAL1h7O+Y/i+DoQf+jAw7D7C8E18/8D/4m4N9BcIDFzX+/C8Fj9EcJHIrFXwh2bPkXgstu9ReCI7D+C8FFsfkvsoAzsAHaGpv+5RGctc3vEflvncD2bP71/vjLDLhctn/MgKO0df3rkMA27P5C8LbDHwfg6B2ATi6WQBtTS7M/tWAHR+YAvsjs/9ScBZyZw1+p/a6P418ItvSnLCzgOJ0s7P/aBmv/SYMDnKqzDdD5r8IxgzX+5MQC9u8C/DsLcOX+RvC+218IroD7X90FTtnjLwR78/wLwRXw+gf/53YxcXVyAl8//7wWwFfPf/ifFz0I5AEyQfixYG/CG2JVF9JxVyNM6E6/M8EHfZpxp8VCP1FoAOcyID5juJmkmpO9JFsm8aOfWcLAqktBxPEud23x0nu7nrTBk+2GnlRi35zUOGHh9ebNXLL3LdG7BdQWiE+aaSIkPF+cBiCUiKPRuuEFTc0HVClQfT7U9X1PdqfEKpHglAntUOjsrinJlsMh4tb49VPFpdV4WG2H/QrvQ95P2/jgrRi9yHSFj+9k+TrvEZLcq9E2hkaGV9D7iXuiA2XcgnbOyMNc0MV5h4nE2GCrNVY+ozzlfXE7+ojhbSyJKXRkOlC+Tp12V6dwYbF9caRPRpXSzWlhlGIWw/zL654Y3WEFSyTDKb9gN8p0OTec9G3zkEDyrbNGX6alVO9a5U9ia5nwUFnPpbm+6Y0nV9l0ad3JwRAqOvkk/peK7V++jRRfY93MwiDbPtSkoW+L0+JkUx/6mgV/HiS3yVm5oQqd02TR5cqw6FATKF7hDmkL0EJnZUV2s8yoUkdhbNwbgi3SvvH2Ynv5fNnZKUK55FgdbReLEeO1Ub/i87Hw08MpM+O3BpPM2hhRL3fbzRufao9NfbkhW/si30qvDlv2441F9LgYex4AaW7pYVdUSCweQ1vku8rlrf6Ia5IKyARTiBKF/PKHq0Cc8UH7DghWBLeSwJiHI6/nI8iPAAUEhwYv6DIlbF6NmS5zjqHPA4iQAj/oXs8F2tIVA2TYbIWZxp5VWJsWWmmwhr07aT/jtG1jcKNvhlq67kzKp2wrCqFa+NJjLlPFIQdhKQ8HHyTFx3QIb1y15kVTz9YSzh0jdZTwB8IxcJfuZR9dsZoTElxm86OQ6nYakr4av9obYp+JEGkUrj8MBo3CNl1oviM9xofcbsrJ6t0AXhzY60g4KyZSh6QUUpyasly0uscotFfOSglci/len8Et5yQOaKeidBw31DJb6VQFm5egwHQxG3e2ZsuUAX2MOi1ivxj7U/e4awefmuhSMlmlnNVpQXB63pkjYGZY4Ks2h36mhPb3xXB4oZ85NFswAAAfFwwRBght2Lw0Di737VQuN7qIDWNBQtX1NJBePmmJqZqWDM3cY9RxQ5lZImGqcevOrJPx8db5dAYxxkkfz7xGW+GAiKOhbIYiMFuHmNAYK4+DEw/Mp7TTS+izb1B/aTbWsYw42GeLPWaXR77t9BAYMVuUqn/QJdVXE7/psfqoMhzlx0j7iLi9jXMI+JiBtmrDte2ltZLkUHeVl5UjRaZxuxJo2kTWFMBryva5QK0I9Qc7D93WgxvWexPZeJqVBV5ar0lsWK2YagwvZGsAbqHy7Lr7gADQcdCYY4c5VvqhdUIWjTJUEDQ1f5vIH3Fo1trEd2Yl+xQ3S27dYnOzRmF4ZUuuj5bJvUXfH5y/g2sNlBv1J1i5z1CbVGL2ngrEJMCyy4+Mm8+VWPI2Vvs61mEGE/bzOL/SO1Ju5ogyorsbYUQrh3trpHjj4fakcV1AMBBzabtBLf/7sP2JivvH2U0ehHCSLFDrKQLjBXxk2z4xUQ8ejufjcQuKb0h+uMvuktbn4LZ3nAEXji4hG2YPvt0Yu9Mp1DVhXFCqKrNaqZ64GHzZzg0sVqmKBh2bwW1oFN1I8eo5baEb3gJzsYz5FdWlorb26ztMExQVvp8clnFN97wtcNgEojCiYA3cxYrPhEDKV6Yo/W8PgZam2tMGG0JdjYJwDkiWrLxEdYEiA63CxKDHR5Khq2ukt9PvKmVNDnYNERCuBJ6+PanZT/MX0hBhtnwbffAsDPjx3gEThjxTfYIIWBxQF5DDrsyiw8qDl9NMOMynLTntzXz3aqIhjV/zloPK0En+3V2kS6hpQEDwapnCURiLvoklcPOG7fKV/5R43Cmz3c1zudrCgKZHSvZHrY/e1XftSHfMRgL5owJWOprG/ig3dl+GsiLuYtyavHccwI4GdBSQ9sFH34S3HKUsvrLBTCeGGjU4E+0xsMtjjF84uv1GI5kurEWSIgZfbE4E3JPWtA75fkppx9YMQnm4YXzF9A3YlqAnz3qXZYxFRtXoZS2mrGLswls/hHJgVdjSn2ONuK2Af8NWOQvDdp95tV6/8molTJNlZD4TrlxWWwdPVbzHOxpYo4ysThoi6mC8Py7Ee9cnCsENOqCU0KWKRvj8FHqfcF8sOIEX6wQRdZzR0WZ4VRUKiz5jpginuTYFuVWSV+J6z7R6mxhrzZhURVnNfo30babkvnH+O3xgLf26nLaGTpnxLzatXN0xJtcxKxwB8TnruoA6Mq2BbrmEzqa7T0OJ6uk1N1lbAPlwmwc1KaJhu5kXz6x+kPrg4OuTc6gw1Ep2lH0oK+mEjz4poFo/c3uX0Ks1cFWhijKVIEVyXp1ZqDGMgZEbkndoWvrN4aSEVwp5cWeA+ndGWci3NotmHB6OG/nE8J4Ide6+tUFSUoz1NBQPy8ap1snVT9q1nvYeU01WVSfpHgV1ql8z3up5NlLb4Qq25WrsB8k04ZPcw9CSDhNaA0PzdXG8VsbMMmoDMNIXzaRpVnXN8Pkh5TFwWGepKTrOS6vQkh3LqpoUqaC79Oh1yGLwSXKPZPoQpKiJnJRvEhqJldlvCvzV8IzlfBkFJtitMX7h3fIru39WfOHEYX+KeE/Ik7pI91ndEEdOSTCxcWgEV0G0aldV/mbe8nQeeUkgFQV9cPI2Ygdp55yVZtHqvWZfjtgh1340+cWsz0UuWbNE1PrIWFTLC8HBklwyr6E/gqlymow0eVNbWp/2MNV3PBxbFcd9zNsCGV28JD+zBbS33HRp5Rz1P3ENmR8KZv3cAYjtR6VEqnTQgszxgsFwiJcLwnMD6542VEFbcm9aJ13a80nfnqonYJz82ofvxi+3FZqhwXlMUMPiTlgWb4Us0lQ0p19KW+iJQa2ROnDYxUMBSsGQvIPT8yp87kVjnJklCRG30oD2zmGOvGQeC1arTdWQe55h99SU9fEKmLncSj/zWWpygS20EjE9CNXgr/L6wbrUrbYg49exq1vX2fgyz2r07Jb/4mDnFKyOJK+hWZZ1w7CHqU3vsp+ZcVBTWcfilTN/nhhSBBGOwKfK4cLcaNO/atRu/HE3CiKvDUL6CGea2JA6Ryfzcs4QhCaOouqCXKRuwv5rXHRTKDrIfoAdbchDTmy8Szgjqsn/eUChmHhKBQ4gF32cbTboscsiRpUWNGaXX7e0kL1suRJbNjldGuOM3PejIzb45wOD0xpczVuSS3HdUvq3Fc/Nv65R9oNLAr2+pu3EO1VBox5uzJAXiZQhykEyRiXs8bSI4FwDFtYWKQMkCXNnJphdOZj0nY0CEZKuUn0+tiVT1eKw5a1/oTo4E6l7pke37mhvzR1Msy7avHpslyWpso1eFVozLBT+sLvYrdM2240BR0euAoK3nRPTfVsZ2x3r4Dd6VnUWiQijtZIs/MxlK+z143ovG8q0U4H33XamHx7MdFJJp8N6x006dgu+qGQxSkCywlReTVz/ug8vQBb2hAOtW3j9dm7IyGi704qHERESF0KnPj7g9UW9mILmgYNQ7/tlk8juZvJSIt/I4Y5SDLnDW6vKgO7BA/c+l1Bay5I1y6pCmbK53OanOcBX9aKB0TPnfe22HKtQ388Ier0uUqKHsKcj9ySZjAhimzuA9hF4b0tQ/xZS5tsKvk/VhJ2Hk88+vQze6SY14TZ3THEFEVRfcJb8ReEyaE/GiVqIfui0vDiQAyjP0YBfUBvIxcuSJKRibSGo13HXClmhK1zWVXYss2zImSg8rMmeRTN5ArgP9gwn6Nt9rf3kpoX2jit6kUcl+fsNyi2+H1JbV2e1WsJzGc8m3gk5G+DeeUi+MiSGwaEe4Snehn3UiT6xqt6SwFJb6ZhCpiMHngYTqCzXPDibpTQUFH7Xsw0wsWa2r6Xd9YcLH9tdhwroET8MNz6NphOtcrzsowx71sI8ki7IGpzLgHL6qrfc1Bv07fXnowrI3sgtabPNyRTXQfDX5FxVdbbZvOgENkxt6UKrJvSNQ9jcpTXM5+CJYeF8UjSqH/ry2/iL6KjwaFg3KwkGgZXT84qptTark1Oe/ERXJQZe7OIxgfhe5C9BoybHa0yj1qpRDvFfhc8gLb44OSOGfo0wgpYZT36xju5oHZMUt5ij3BfEDg4FFGBuVBEapeZsCXFHk2XY6wnJJQPV0vVVXBB/oXp1VX8goOm5VecNoxefJtgJTbZvVzP+udrFvl3d3PX0lSWlU6D8l6yHI2tf0cY3Q5cvxJVZBwlTPtOaixQRTAVj/LviiULVbSkMpywGG071RTx5VMRSAhWg/SWlsdX6Od7q2+9Di59CfG01sw/ozxvlN4UnvJyNnI19E3Jjfjz3ObcIYRkmO3cwQqbnbklEdIWgKiaJ95an25xEr1noSRoZv7kBknAVfmwmp1q7GDZQZ8WsdvzYMHd9unUpCv2u/NWtK7rl2uoFHSJWMCPvYWVUmHi2eZedocSZp1N/iJBXiqUq9GudoE1XQ50JFsb2C4RS0e0VGQiuYPmQ3oU57jsjAeNQojFfWGryyrP1I+OO9zrMwXSKfdZEPmZI/Pjg261fRVySFOiRDhMd344Gm+XPeWZTH+yee8lNVAOOCNnJypJy25hSo34uCYWSpTX5bYvBz77n4kXy/3TR8HrpDEWcZ3BYw28uevY5sSFwVfB+/2jRC5Y31lA/ucPJD2QGkEqujmR0eULsu9znEC3llDSPa824/0bIg3ovws9Nfg17PnSER55F08yReP0wbOGEek0WDE8KquDdH0UJmNEcYCgx9buVfGESz9icgIcoXBNi3m4qivWwrInSKHNEShxrqS5PqURbcNgzL1R44+6pv76gl73izOJoe/IimMvwvUi26eGG1/v6Wq2VLQgtrDuvN/6LwufTObQNmE7ro41+0w2PHmkLcf6h4C8RRfpJ+k4AtaRuX+S2E+mgx+MsllX8qvfIReFQmS+HA7rwNekI80kmrs+ereoqgdYbbxPAv0gaRyIJy8cDu8JCfDq0lObyNb4OUB14rU0f2nq7fY3rU3ChyRs7/2iCBiGWWTtGkIRoyOtiPbdGn5VzyEZM8eVLE7REyf1pJj7S3epxHcGbt8KKeVRVYc7fzGopqFYeHSbElqXesVBCZ9A7RCHKnvrVRBpvXbGS8buKXyofpKTDUR2J8fHWW9X0jzQ/rP+Q3rlnccYi/ty0/ijcYB4mmTyuoDoUZTLIMvfUtuiEwRC2usU5LxpnICqSvxm9X4PFv+qUMG2UdoGVbeNYScWiv9qVqQAizJHdT2GqJ/4l+GXGgTnt9hzRIDbjAgULG91IyU/1lZRfdvVVUPx+V5YwK4uynvYyssBJj5wFnlx3MYbRY8GBtRPi6Ke2+qcKx2DoRhpIo3wnQOmtMGJlsjrX4NCAfQKNgzMS0T2yeNGNjfUXYyE7ZdrLMV6VxBi73KRIFs/JsGqckB9NviTt+7IH32U11ILfl3MRbMbkpjqI83S6j+TkTuOIm2AgJuf1VfTxS18fRH6RGTI6+5aiRa3icbqWdJDwtt8Dnn2aYcmi5D1cm5gU/mHeD6ZhSJNGv55WHJkipK2wltPAIh5HKJzBYQtP2dWGFwlePu3XvKnQJ78+n4vy5nX4OXTMKzVGp67nYtLqU+F+48OOoYKGx22bCgobryumj0A/571kMSVRBTZZvabVi11B8ffnKnYzt6nvDjet36/mXSUJqeY/+MggUr9eSelBEcJg+dKlzLipC4rxUQeMpp3qF2Y9F56ktp8lVfxcO5CcGkLvSYaGaP6oRr4vLX+z6RkN8ZELh41RntinprnrfR6/EQfBIDQkdKILMuYEl/UIieXnfRg+4/vU7LzWNwhbeXgRtCS8/MhZUJLL/v5eXGicqk4OlGF15JHW19vjQltwjcUZ0UD5gIjeNFys9yN1IJ/HjCvj6p8kmYhILnLhXNO1TOkjET6Ts4ediiTU645TO2gSqS7waYp5kAX8HnVFbQPQ0UvI34sNdwf4X5PRnmnTq2cUuXfChH3H8D7pIW56GqDS8XsYxvSUq0O3x3evf+JQhAu3Elna4hIMFQzZ2xGDbo+FSCTSjbvyFEv50QVrq1ls+D7JOBr4mubjTo0ubbSBWtlnCQT6AAedv9IzyOSoObxjHRYWotz1FPp6jPNzNogRBiYUsjvNnvEm3mWaANEDMfXymnYMh4az6VRq0vBOme8Kj9nNWTGgNVjtTf2y9Tx7uqmW80OaVFxtsKm1pgPnjqOnygzfEMmmByL/FX22WwTUk+Qklm+NduuJl0nm3FX1JD21JeS5rcbCOlWF3Me0LU3z6NI3L04T/Iy1qF2UzBJqvuL65x2sn9a/qFIQUcq3eMYWaIsWpsAb32+1FMfSwgu7Y8sWINx+BRBVJmbDF4hzbBPbaDqF88GwvdzJjxgS6KtFZPp9yHCrJn4UVPZEsNoIncot3Ezx70HM8KGvMZFDgwq5alCDQs878fIbRkeHzH4llHsgCOOcK6qUpNXpHs0Tialdj+o65G+Bo6EcC1TjLOfy8w9PeJ8xszHYZZPay0WK3URaN/C1xULmsbm4J5/oM5VV7KvxDg0Ov49LDCGdyUwyE8YAk03zaU3DuDOfRps7RMUQ80zK7SdSGtJ4Cfzgi3T6kr7l+nt0fy979uDhopXyetOKt2ji/smPI5R9WEHfYQa/FN75y7UyCDI1rNLPby2pGokiQQRxOoLUPhbifOEzLkJ0cHCpa16bqXFR01Q0CiOaOWNjUPQg90jy2EM/j7vTasHPrxg155KVxDI8YaonC1ywv/Iq1gw+Gxu15BmC0eygvYBKl63v6HpaUINy3yt1dgxXq+i0COJwWjq8kEum5NHUjurvyEwEbJmXFTbKmQrERIZDysJmIQs9ss7rnagq8HB9KsD5YA51ULwOpy64Rr09p2JMMf24ZB/THJCxLxDFgbzVP+RHnQGvmIRQXjqk5c4+zvhudNPHOrOECoRPzoi3JlPeKYnYspFMMnTdk0vEJH9t/97CYVrtgKL/YucNvUo+lUARzcQD/EzJr2EW9giLjv3TNh9pBnbXHiNlpYLbhSBqo/PSdGXloaYDqifYW7ZkdrzUeoMvwWshQVjy69X2EcRw7JI0RoLjKhLyJcgbfQEv82PmW3GHDt1mS+uQdm9fIDrZB5lRTSsKWxOPZfuPMHTlEDxbHOEbRLqbyPuEPuBHjHAckGkebSBMJDyIYzcNSreectzRPUqr9ZCHx75MeGb9CD9JfiOV5mDZuvuqYWKRq49nQ7m8/kmib5WntRdNsfCA/P1wFByZGE8JRDQ6RCgFNSQsFhBvK0veI/l5eOidoVz6gyBRrykGzQNP+ALTKYs+pwfVrzY2B8QqJa4OGiIGKDd24lznUWO+67h6fTnlMKZTmrsm2ePURdpmIQEQA/2Sfdls/P6LKH4VPipU2qfBAJsRtNPBaT0/fBVabHK3Pvs0saLMJfOwdbgXz8D7w/x9IOayj11eMrUAc9qnGpLSAljDex8Lt2gx9rp32Eou7z9chO10tkYyvJH8LH4CpyozqTmaOlZGoIAsDKXzc9op9P7nFtk3++MWnF+F7/KCtZ4lZvro7y6o86HIDbRRNmt9LOMBKqWDwlDsr/zoiuaH46J2QC4vmgqWSdALR+EKrP5cEEm2w6XJB1U9aYBPHd3irOMMtp0cFLL/XXQVhdQnNS8uQq6orqfwBVxNx8YR5/R04s+QDKM9+/b72A7juIi9BnaiXk83IXox8jApQpNBtHykDFQr+lNX7sHobiHflhKoyF8tUj1FlnV/FCSVfMh87HymhOi19lMGcC4X+FNUqdAIuOgezSIRFnTsrgO/6CHEGeB50+xmLnpuWAYRVrT6OJlOSIfif+BaQ3ILew/J5TwyNq/EsTYzxukPsHRmB1oJi8R9/WxTt4s0dDz1FeixqCzyhno95GtCJh8Ljh4ouxh3w5yr1tptsAjI1VW9yGqbT9ZLegdrdlxDtfda8oahUJ/dpTrJLLIlwOYnsmBRNVxjqGVwU0hwowQ+60oupSJMo2MFFwkm32Oyvz9u7R5/Dtz2o+AS69vdt8vfNqPcxFqGjGOx8lqExTMqk8IJDf1lfpimLRMKvd0XTNcONWcwfdQp7BB2REaL3nqqU6qaA4yWZIf5DwPLh3VciXYtqM08iqUpDpY1xl/90/0DbEbZWmrphSTLuzyfPluJA6+I3pGeHnpmPsTgaKBPfK5C3rlFdDnQsBcRZwWZPe+IOz9JiY+6RywTd6V/S9FEi8xoPT4KXGHgA6aMYdzOrJrOrpTcS5pqK5fjX79k7GiLtEz7NMsuFXZBAjbj91fW3qxD7xMoF1xzSE2hSv/cNoTm55yi6+NLWy/RVMdhruZk3Z500W+EyM+tD8sf76QRXNSqeE2/zEieZ0n+4rUiEsXb9pOwc6FHKvfMIbJocOBB5ePltia5xkYEzGtUlRHpwCTo49y1q7Co4WrgY5JJseMyrwW/mpPvoHWR1IcqW3HsKierPBn60uuJaVTlfBu/8GUbVgJQKee5dC4Pr++R2ahBB/VMD7GqgtjUIb7DKC4xhf6h2ymmG4xyEHuWxF6r30V4oERxYwBBMBv1Q62renALzCCC5eG1WF1gvywXN9e9BOmLJsMv5FgVmTF7YO7YUksvNepGCQ7a07iM5pbzEevYOnH/uFr1V/zsTpzoqa7StWnBxE1Y3u5ZSp38sSVhARU4rl0mZhMk2Ka8oLFCbALodg5p0Yn0quqg7gAjuBbzYX/XisEJ4or7rxd1TeXTddvbn6vSB7D5fLcat42QknnVGEOMw1F2GnibIrY+xWFy2GtlHAgewu0/iHTUpWwIuK7aQXMjbCUSdjwkMBsRX3CNINm0xJ+ny0yRWZ6hJM9NVgTEad/kkHyQG2sP/pykqZkNQMziLWnlCE/m4jFpIIXPDFiU6NirbEE4OubaGBwcVqnJeqtVjncg4HIsqoMjK+rsRRnFpsAQxCxNjNxfhtsY3lF80XjlVEpqWew2X4sGlTwenEGN3cqbh28sWRvflkPUJ/eUVaHJUrOw3jliFczSNuaIQrFZY6T4aXN5rch5+OHBo99BqkzgKz/jxbPHoysSt2GGZHtL58M02zD3YN9GCCgMdpJ/+0NYpi0ZJvnA4ud5T7vAg8vqT+uk8oWYQkzIUgHMUpq7PEVvvVVj8DH06Pc3ayeVTAIzCqGeF7CYz3i7KDTcJoK2EVzDGKRqVRjMM+Ur6SoEDcKDnh/x4Qr7lPgcxbkqqVfI5UWKeuuBzKf18E1tGc/swObKuu+pKm+/5a9/FhAJQ9Z72XhF/SBXrE9gL0xtXNkHccbb3e5qiLxR6AGIaUoLu6yMY3TLcDsD+X9TB8g7cRnxs4mj93k4SF6dlIP697NawsTseXCa5JmvUNpgqvogVJwHRXA/FLnF6zzAvp0JSiGTTx2uUUjvXbUjAXgN4m9hs6wc00tOckqvxARFXVac6exGngFc0hGdTIWEl5pzmb7iGermOu6H537wzqmJvdhv3JdpQntptI6kTqTCXrBR96riNxSFBVrnELYGAhCZxfCiuvG9k1XELRgEhw7sRm0Vim6zsSCdAFKV+SzwktLTifonh34MeU62g2g3paFwaVmO0Qof8j7wxwt0BB9HgZzdXU7bZy4bpxg7k2yjFXmaKpd8Ji9PyW/Ir/LCr+ovL31hcQbaI4iSpkbWYLkcRsKlhkbQacRE38nmJ3A/0d7F2TRpvDAVoCSzsvFRuaM1UN2/aoX/wveH7Z9O3K/afgtMGS5U/rIdJm35xgwfdAP1iEAq48ZaOUuvzH6yciRa1Z9Q8Gzov5KSC+jBZp6dSEipbQEpwxgbiazNH/F+gUv6cKGP8d0dBin4wCUhKwCoP8VM2veqc87JY+1S5mMec794mbzDjhqdjx67LGs5bjRRUKcWf6U+0unb1bSG25tp1VXBXsBd4fcBIydxdnotZhbZzKBiljOb7HjnZVctq6GX6VmQ7n2whK8lgMqdekbE98so6SOXqdCWNokrFb+eU7Z7cca+nKqWZNKIYjPFV11Wmu93gvY8w1niSDGmlHJCOy/TdGLowcxd3JtfNQart3bWn2e3Pnth4hvgnZOyefrJCJNx2gonG2+6ySEvqiW1CYMw3CINssOgMt9gpA6IvsvIJwUFRo4idZ0uPT4Grn3nbZuAfRIXZ1YdImZabI+EO/981rWZBF9S6oKQ2AdDRxA4fPhEz8pwCi/Yi15IkIR9I1VyO2mwyuN2EsiulSAOeWlPka8fal95oGrxlU2f8yUMHjuuIer2LHjog5u2N9wZO2TGT3XrrCT3mlg1rM8LGBBj34binY7R9+hWg4lLBt6ymDXXtB9of8dG6v8UWBUc9X2LKH7UysPHR3SDpN0gDpXY28N9A4lSRyi959Dg0e8ANp8oJqbaNui6XPO0e/x+96A1ysqX/slP5UsmhGe3eJDUAFaMjXCI8OEn5Pe1D5SNrXnDc7j5ZtwFmmz4iyxWm7SR9sL1xx2Ic7DQzHP7PzljAUWXTjN3ipXHNEosPRX2o5mNxlqM7cxDsow0Cdoi260ElI1XxoBMkm56Fd1R2IsrX4Lhtcei5+nwYF5pPVkqofDtzvbnCb/lIK1KSHSbojW+jKaFCZn5x5psHLpvBNv6rYiRarBTy4YWF98bD3j2htCzLe3OJc8bd0ado+G5D2bjW0zNUBJQYNzacMf2cJ5YhbeeIJ2Y2HQvcluTqd0EOizn9uo6es589op2PX/4jzekL6AA5WszWE0PYDFVKz7TaVcuVTjpw2xXdVIjCXzht+GDqcTCtf2s2dkS3QuT0piS3L0orLXV+gPjaDF6NnG4f4lwVgp3/EogzSlZ4EtvNLMFtmp7nmwhbp4puxXWOiMDvMX0sGeJYP6p1HSeAH/1+JJfKD7UHhFpTLTTVqt2FtrotOewqr/eMEpp5x1wdhDuDQEZTKdSKB6FDIwH2rysTITkJ/YXB4Ggt73eK7bkNmm+me+cRGvMS34tIpqtbHIht784hiV8N8FoxtpdhIHCml5QDQ5zUH3eX6/103IrCa6D9ct3zO2tKJlvfVofUXOF/UnUzAeP6rzNxIWaNSX38HNTCZs1PaxDD2M5ajC+beeFAx4HtoYTtsGiGObIh/78m6WXGR2eiZ6Ub+xMNqdfDBEX3Nk7FQkzycvKDzq05jkSKcdYRR2J3L56guMwY8ggXRaoz4gqAfboF+EC3Un7lg7jaUhxrmNTPtSTkUWLGiV2SMZo+zapVW+rYRSRK6HCg0iRr/cHc/CO2c8KqbRGWiXr6U3hZT4XaIY8lctEd9PIMXK70IS35ZmtxhOCPgQ+6OA8nSuUbz9ot+vkI1fJ8Qy98XVepRUtjCuhdvRQoGbpgYhkSEsRHkepcMlWibmNv6SjrBvbmSQnMooQclaXnZrsl2L4JXqX35oS88imy8Oxn0BfM3qSaRssQ6yvpfcqSARwO+QZVdpVD2OZndBCirO/ExPgbKB9gzHaJI+hQ48Uf3raLjORuyIUjiwZNCiWxa4ahnqDEPprFXeort9V7zII7vAVfsa94x0kdb7jO6EtnWWqFW81mUL9+HJ8JuMa87Usg3LgS4J2nAieCQJcgOQ3yQIlSw3cDRumdbZObb75EGw3lGuoHP5m0hfIKbHoK9WnH4XnzMzrkaYYt45vt37M3QlOH4Z9VMMBEi0bOsHGecwMfk2ELunx1oSAlfbh8VHM80O6r0mczKaX7oNiceuyyeoblsfB+jTHbWn8fqiZvp/JAUZiWnfyFZ6+HLDFSdltR0iyOemj0pwO7WO0cvvr7XddmJWZPeA1r3m6VOF1WUaF3fqd3vWvY1ziefJMC94h98J3j+ULOWQqSmmbdZD5/aNrjKc4lagxbZfJ+4fi73UPLzCO1+uPKyLd9shTeZc3CXX7AeTkXXilMVHQMcUNPLNYe+tMBOiT7b8OiEkFUULnEHQSOH+1ftISIq68rZshXtwNlw/iEE03/ab3kSrk03bs61OtpgVmZESvQZh8BEv4XgCBt7GglooI2XVUfguM8rcqKlmV18TvyIk5KfOu0lCC3WRFBGilqFhJ1gdkO5xfF+liB7pren1P8AmzOSfXyTMNF6B2MLa0K+XLHiUGPTBFlsd0B61p4QW13a/zAwVod6iYIXdwa6SmuoZl0gl/KTfbHGnfQQ2N9o8mfyHsM9Yj8X9fNnBTqpRIlOLc48KA/nUyBk73ss7LOFpMJC3qzCTCq8E5wIihzG6v5ilbZ1Ex8dv77Kuepfhm9q9bXW8/h2LrPLt2kAANy9aZKoKCBsNbJIZ3Erp6v3xqceYk2EdRnsxGxkE3IxlGOi91KP3sj/DphG2M5qswe23H+Mnh5a9QnwLExu2a5he0LH5vmEvMdG/Jks4Z7SJVlFI/OeHVgJd2v5PAb031Tbvv/d8p1VE8Rap9bC7kTlkmufEsDpaJa/YUE+wJYfIZrCF2/+V/Yx7Luig7LLOj1/u5PXz/wERpjf1eKuZ5dqg+I5SKgY1elEWtAdeKpy5GlKx+Slc+pPrZ0ZolAbsi/Fo+JmmgNJd9+SNkUNFndwX+HTfY/ZymVMjN3pbJTmcg8gt0G7eYj8tQE3ZCIWlkXGckjYtj/PK8SJ3uUwBm2VOwVKcqwhjjB8HKoSIBhCQsIie8wXn0dAVRNK/lUij3fM/wUMtUuIFz0PqYCGOk15q+qyKgOc57PKCZqtkL9jTtYx/wlN9F76xtXmZoQrvkpwtse3M3QXG8avnrNUqE//VusklLxdS1j/iikOH5L2J3ncCeC1VbQmeJJIOR7/qT8T/lh/kFunvg0IXDHBbdQflwPk6f9ZGEmDjNG7mYmHjg5QDIs1pu3ojAfArLeOHo2iqTRy3Izq0rodrpJmGzW/TR8VZASMKwerJS6qHKup3NYgq0KndRKLW/KfWDSrREZKay722VK79hc1lZy7BZ18RsGqz/xDsu16f3e6xcPuys5tKrrKYTep+q7OfjJMI2E4JU+N3wYweRaCmUsDS7v88lIZ9+03gy91UgxwhIJan7xXoXJOgaUWqcQgVJqA+dgDCNGxTkSeBsjXsOg3FzOh1mpTvt72SEfyUarCCcosGBiJP3HE4TfrmuhVRS+Eg2KYf20LyGVlMbsHPzKCVWJsrp21OkTAnxa6N43o+bGzMJmeeSHiRrd0kxqMeMd4qetYcKmZC913Tvyg5XEwxQhbOXDZYD6p3PfCMpq0r8ssE+J+3y031OiYTwQmwLh793ZpST5JOZGuluiHKfRISWfiKAeR0ji476k4f9a6ix+AUeBSajec1uQo12oJ4Ya3sitrLVAhkdCPOa4l1FiiofI6RNfBMaMjTB9q/Jd9nTF5/QxFCC8UQ2YLpf5Ot6GGzConWMLzdeEd4LVocqxoR06DR/t7slZ/DANNlr+OGqP1DEd/62Gzcyh0dkW8N/s50ZRlKH87yTVj7c9ytrPuQbHb7SuGVeaa7xlm4rHyTusqormyO9H0NhMe5kW8fN7FY6BQgiibP8HXLKqvZO/KXNqc7DmXnSV45fNsmrjKmCfIpRiOwPaYmRNJnUHcp22o5aMmElZ0p7RORUdjP6vLF6xLdcRQ3TrPLLl0N/Zr0lHrf4ee7lvF56hz+vavGluWqqw0PW+z3GtGVkl3ZRAWnR6E4We3kq/SLO8X0d7TfseqfFbipx6t7cIPkH1ipXkq2Hxyv9K7GR48iwilHXkqf3Uttzgm6zWoBMXs2F6LsfQQ988tT6d15B141keQYogjbDyrOTDghZ5nLviC/K8T7PCWiYMTM4clhV/3qfV/HT8yOW1/tWaD0I6N5axf4fdaM4gJQRc/gT/B7sClkV54yvb4zMsXKLqC8xK3JNIhA48ZuWiixLfNEanFuJq7keb87wUlYQX+yPu3J7lz+ZigXq5yXONlQDAmo9f5KjnDRWXtqQ/vLSf+0vpceUbyHh8Xdo9V4eudquzwyL/7wZ5zoz9vnmDXxvvV0NZmCk9n0U3SbHpuhAY7tbXzq8mj5OBYpwZwgJn7Fg+Uv4gwDAaJIOrnLOPVMwXTpwuj7wxuG18ANrZG1gF+7xHOQG/Ge84cK67rwoVYBXdriVjNwUPbK71KE6W3F0glnr4BWUAMfW9+Mfi6ShIMXXzJexNg/frLSQiiBY6oeUtaeawrQU2XewLnyl04MmYVkyHwcwbGxY4EsuArgkyugf6Lx+1sezOKhrzl3gzt80ZH3bN7TR4GdRU83gOyutBojyPQsbzM2BVvMJIpThVMn9xvSfeLK2F4tsRFurUYWXeZGqjMxS0rmeHfvt9bHZLaIdCv22LOXOunva2PbgbuntA1w8yVr9QdeZmrNpC3XjU+YlAbhaFDkVa2MJH0ISyiaBqZ9Im8iS+1Ri6xT3BHRPKwoKh6Uot6035wPkKIxDWDtoOvpXmxMyJyJNnz5o/URa+OakuzOsRSWE3ZNRMuT4TTcFAhIlthMbpTRKqVx5jkRPwOHlc9D1qXu83djZ+yBijL0p3ovv+xerZ6SqY5cWuxBtRwJYFXcGvMw/Gp8usnYs1deR3kdc+QNs9BW8bIzHpTLmK2aD2QFblajrRuL6K7k/D7PTtU1PzOXbTYYV+XD74Esw2FHoX81k6hMEv9ol3za/9rAvPn9LD+6gXAwy32x6i8E9EkO4NE6HR+zy9JjMwSFD+K7ocrzUwF5d+sZ2eNbn2A9Nj+cxvwWWFT/roxABNLtfqHVZnu9z0RvFPH63lLoGrvoGmtq+97Zl5zJBcAc71F6X1BlRFp0vuFsYDnwjWrIvGWZlYc2NygTClHltURNT6lgqI01DAgiH+qXxX0maTnALPySHopLiuN5Gz2H7pK9uMx9taNLmRBFoeyAKzdOptCm/twIZuG2/OXCsfkdH7sqqPjS7IYdn9Z2ImHZrAH6Nn1bvyw+SdQeLBrwjTlpHVIztQamvwm/06qIqF+A0mrJpAgjdWy+5ESSa3PwXtkswhGQuhnJNXII17LOgFjR1D7TKrxlITQzkt+jpsLj1WgR457XoIEeQ6IxelJ4Q7q/LVVH61RceAlBgnBNRk0CcqM5B0T7Dikfz3Mga0R9Ea4ixE8vVpykXbmzr7yi37Y8UuY0jJAGgoliNvPq1H/fVSyy0us1kqX6p1Z9w8RDfKU+Rv/FZnbtbcf8l128B9dHDaqj2cYobBGIGDinkzZu/YYQaf2mh+VwYx55q9o4IP4fAVkwRjjelaX15y0AwoAeX9e69zEyG7nKyb71teTCD5OQFjHl2SOhPkKY27Ff4B2Y+MtVUOwq520dzhWUOyymoUooymRFn9xCYLZeyZejIBKOMcdnVHc1kdHz6whUTWUPd13R4FgQ7qukfsEzD6IKFug+eF6465SGzivKkc4LNAMiYoTAGQGcOSp0eVd2dn3iK49YEYUHft/HkVL+vEZxk7+dele1zcVyj/CK0v1dx/9x3hpN8O/hVtEfnI0RhsczK+tHizdAKwQmSaX/7+AlaFKprlUBsBx5+oFbLtaHOpySp42/Ec/bZhlgIl9CG4hsk2qteq8b4SZCk7WcYYpgQi4GIqa8gnY5R9Cs+LbYG46YQhdtcCDx8neLAxC/KeOa+9yZfWQeiEw28UH26Gqnb/W7WMI04Ah+/CHBD0O44ridFFnJjSjtc2j/BKnxWn5vFuB9CRPxZnHeYkzPZF8b8A4nwsY4CkIa734dHaieSRT/abMpWQy7lkPfx2IyExT7oyxWTB/Dda7oyOfaZbogBgoqfxwQU2QybBZqpr4G8y+5e8d5loUNHrBpygx3VuEVF8mMVAiXpSXg0Rgd9lzZHPkbqnIr+i5Le+8BfN+6u7SJI7NT03AIid+2oapyE7+SjraUd9tLDhgpglZ6ERBKKVImmmLWzETtvkJ9rsmogoAc1qBg5b3146RS5Xo6kk2U47WsMd3nFxr5sBTCFFwcwsYTH7niTyVwRdfYs6OHiuFycuE8O8hceKV8Pn/B47WSb6dm569og+NYHQ7qYBgxf0HgdXLvd0HxrUYvVrUr8PiWis7283zxiFSSj6VqLjP7ebrKr+M3dT1D9Ds3zG07k5lZIQqVOmsK5aFxxaMVCoJthRbK0vo4dE6bzugGjofp3o9vqoBXJUQMkfh/OHY+dl3Wi91vU+sVlIPPHb8ghEIOam3ACQsE9/Ba4DEyJykEyzZ1xC3A0bXXTtiSAcYEtI+sUmaepM+nynWcaw5brPAy/83H1PCS2/iXO8q3gsjT7VezKQhxQ/HXObZQIBaz8kiS0N++rPnsvhRp/OJTg6de6MqCILleJ+TJc9+rEeAVeranFcDgBjPEEd+9HXZjXH+rfiQ6dSq6q89kc8Ht8IvykhYHubDDrDnUV9vzHahb0CnpE3uKnZqPr3SlOGnPfJdrX874XzFSEXmwu9hppiVVDCbxjipEHbRSp8KmnBUl3nLZPM4n2nT0isSifHfVVyfgnddsg3cUt/XDmdzeM2F4/KnoNwArBJRAP8ym5k7LBJ2lo3nPQYpJppknnt5QweIcI2cvK/1ENC+8SB8khNAu+WGgjNWIjmOpHToxlnHCvob91/NvA2oG3RYVjl4O0Yh9Gdu4LupJS0tpN4dnbsLYmIZvZVHaJ5xYEvJ01RZLVOy+ahY89nY89Qrpps2hPT+ohdwx1tCOoBaaDw+71bi3YQoscvw2do2g25n9rqHzA57HEDX6C67wOj3It/5A9lLF5iNPqeiSFVbVRvIiGdReWihbrfCvZ9YW912KZCBWD/vGcknqMCOsD4NiBPjhWrLI1H7B3bh9Z3I+2aP/U05KssspkOGMRv3hBgPAuvVcozoOUirSMeiNuiWhKbvO1LxsC98VZO7R1lUnabsDyy1BnAmY15nYpKQGB5k+JcurZJlmttkCHwD2I+GtJYbiNcpWCIy5cPVzFdT7ahM5Lep7x9+McBEfHH7W7gg/UdFMl0lecib7hjRvnNNapfK2HxgpLSnybRyY7cPZ1bqylKTsoypAZ8eFs6UwQ4PyL8yU+KWTPYsUdoa4nWsP9dHGl6wtTDeWMk1/LYvIX2zD5LEgB+XJ+rA86GN8Zf+HsycE9rZTmhRfj11Srq+LPP2PpVrwrsHTgrFHFnVi5qRSmis/r8/vFG8SLfR8rt8qOV1cJDZ3umlHv11avu5FB96t++sKUeG9QBDl1T1nHvM/PhR46aO6BuQl4KEaXpnOD1hunhklSdhej/k1gDlv/wRoK68cgcbvTRISmvNrHomV6Z56KZzyeN68vThA/gaPpiM2hizmbhjU/6k2njSrPi757fW1o04MkX9Myc5h8VLqztXgX8cZpOmdo/B2MS1H9KwLNMUaI89ivMZy5MbSN3nXkCgVIydsa5Jy19/e+HBxVHEnoCZyMvUw0XI8ObhRpODUuCDPI9gOQ2j9eBHcVyMuz9rl1mK6SDiwV+bDoB1kJuo5lcv2Qn+FIBBZRiOEtGvnJEYZ+9GTftnX7xvJjlT6GE89oXCnEO5r7j5zaXz3mwnLwGXjwyqqZy8F80Q4dhJaEXbBzfnl507nDVXK9cjeVvjOkMvN+ONLnk1G4j9M9rw8K8Ru3vdJ+J0GGnbPKTNBaPe1EyiVUY5U17bqXbIspStRHwlzPS0uViTjSEsQoe+ix813LupWFfi3KgdLrGxmXEtNVWzQyDBRpFL4fbrL+tiuSw5WC4RR8IihRL4M1l4g1MZTYS1Jt7NmYSLLs0tFqWFDRt6iRBFYp+p9q0YsCwquF5wPsTDPk3ca1K2CpzFI9/FqNaChMpcy2cfLPZ3pS6EycWCw2Fn0CFFFwe9ACjnk3bjPvk4Ii/Pi+a2QoYDW4tiiDxBUfT+AG+TGMGsrQyvYk0HFbn5lsM0pTG+i+t2wML/fJytJxoTs9KpkWDNAXQP6obQ3my0AoIGdT6bd+NBLxmihD012twFCXGIAl9Qwbr3zldXKmCW+6plF0v6M56tOQE24kLSRRrtZhLaMVI08xDmsgTiwdwKt38Ue0/HIymOoz+Ax7F76gv6PhNb5C8QIBYb/66hO1a+rV6hS6lixpSkbH2KVCJlYebApQtZpYey4H6J0LlU38dI5acpvHNYGky4MSDlKMPkv9GAvvI5Q570NhKIqttcfS00BErnhSYrXqKNsqidmuUymr5I7DsFHVLMkeDsS6xiEuIRQkb1aCUQr+mZB09qYAKLldmxaJjYAmZ6VLQ/pYgq1x6RdzAius6IEGreBAjsWS94H4sI90oFbHgkH+em8moimTizqWcVBTAJD5uD4k9rZld7OsBKKsSztGP24f/dyGDlgzsrHEmVnuE2q2t+yr4HOXYiNxufKDmBrybuVWL1+98L37O5NMjIUiV+tJh0/bm6gfFxKLBoVCF9oQLvSDQTzl712Ki/O/oKKMZcLUxH/15FAUX8295/et4ie0Rdc2mOxHIS3uh8631v3BsGdOmqhn8PZTjNIYlRkvBJokGpOJnmqtEIWWNWcuk+ZS7EfozMr9jxgM6QwheAgdzgSSsUWlsErrripV6ycrnpXmxM5QlveiwsD0pVmYkTZ6giz4cPLzgzPkLgVc+p9tkZv0+lNd2BOvp17cqBtxWi4XvxIFvd2ya6LrrHrRTo2I59T63tBMkmTy+CwJ4Uu5pO65hnmbo6FHRuQcMil7Bzi0ZLjPx7ch9kfNcRqIsI7LvJenI2q9pjq/Wd18hZETy+wr8pVW2xXSqXDy2JWq195BubcYedOElbWWhl9DWMT6bYFvDuVz8ARqYSVXHkBvtqc19swPVRxWd1LBXFlDWB2xLQfCF3/9RvxyajxSl9LCP4THYxrYs/Z1q25Tn2P1qYpbwfpkoPRpiAlZsfG4CylESFgJ/5djJTPwUfHxaMTpp4KLiaH0QXMBm5HWyHzSj+5lJlqLC/v46R2YfQcx7KuoDv3AUaj0X7BQeciYYnulJ13KL/AN89QTCGNMTIqSAoHOZRkP86u0aCriCxLLFVREfd9LdiL7C40qFvSepxEXekgSRIDStQ4POtXP3vkBV3PT9YL+q82aBeNzqQvxApDsphTBGG5h+IyyQVwGGyaY6goG0pFXjGY2k4xhnWwHo/h3Or/8MN/mIZHOPBo/v6rCP929qxjhOOgqwwyjbrDA1o4yjF5JZ7V1g8cYZmHA7mJZjy30icxAudWj9aucJhqPXUmwkCxFky/aQU6YrrnQlfQvTAqvbfa3Xt3QDWdaKaZNSZbU3j9Q2CezfDmdJ3y3xLkx4OZaGXrxZdxa78HGAh+XmfDbtznxndajrVWXCdqPH7Yn1/K/9ax9pLyRhHOuDtwV5D81XZHUMk+XWcOu0soSbsNqm1cpgy+qZm67Ug6oJ1BEKD83r/dbRZZpKkljkGj3v8m+IhlNxrRD/ZhUgyBV0RYW2nF9FNtZwAZ0PCKM4y0LaCLZSnK8vDRBIXivptvaQ7KAQzy+4P/oAk8aoW+pvGa0QXZj6B8eH/Itkd6suKNw/XtJXowoN91txw/hEKNUUijpRj6Uc2FlbqVcGv1vtAS5qGGyaD1EMIXzMdc0Ly2GfQQf6B3WjMkT4GuEnl/LZNBDAg7RP1bpCJ/cG8kuikKtUA+HexJrzFVLzXt3MdMeDS/F2DRz1NX+cluRR89hpjU2ph6SLZRRsV2HOQifGrUGtbwpbosEbXXAHfTmEwj00ZbIFF6iE3BGOn+pv9PqJupGnkmjhrUb6I8RDmHu5HGVaZhPN2nOjF0CBpBQIrRtDO6czMnJtax8++JONoZ3Smwnx7ghxL92MkyZLMGXxjGYnT8lFRQnE+exZ/Xadg41JawY0bjn4WxsX67Dk1fru3uXVcUe/MGwTwpPVthwbgVta4RMCUYvUjkM4/Y5rxSr4VlbaFwnY8EJxVDgdfGgPC73ZkIoScWgw52QKWL0bNL/hKSYsP1A4yIHH1dqQJzWPrpg7PpF2TxbNSo0GZg5NH90y8vjchIu6UV4bxeYWl9etpJdNDnPB8i6RbV77BsyYTc00eXLd8F8Kv7VYrwNdxUUF9FfEINOrw0luBzoDUrLQEZKKByfChhAtfaB4Af/78dHCRp0EuV6uPRlxm5ZY+jaEj3eLXiBTCqBMhdpfEmD7yuCwulHNc4EPj+aF5PIXjLiPeEoY80bYq1x113LX/CTmB2ktlD3ylv+INtsefuBFJIbkDNK2Gu/m1/1kMhbpYODEhH+WPS9ECmAwb2dBAsS5yAjKu8TA45p1rAL1wita3Fcc68Ji/PHn1VntlOdvDRtct6KCcfYFlCv4yXQUXqy822JtGGUXUwbdTmEKh2MnjW+gcc/nUD6DdjDH+u1tvC5IbmI6iEOuAPY2mXf4iROkdEwCF79gtFhBeDSD37QcqBMDtj19Oy0O+LzpiSj7NcDxoRbDtvRXK0Unwe7jb2PJsiS6py6jKLaHVydfW+6Jgn1WM+xN6jIfpjyFFIMIO+Zqni6z44j58iLcv3Onix5IZn8YiCcqK6OPpuxTxj/SHE+tNJGZQF54YHQwlOwpPttbcd5DQFiq9cq8MZEZixFH4GfvNsoizq+vvYom9NO0MUB9V0IgQa6+GMLyxqG3hixwe4Hm7VhYlppDgcLqhisusLSPgNd6MCyrtGpFjURmuFt/KdK0wb0OWMHwVzlPBGHjdkfJKk028OaQPS1VcYqpPbvyYFVhFYOGv9PABUR6u7avAukxGFIZBGthkXDXfWVq8Z2354dW7mIe4RIwLyg0y07oDvtjrSdGNAWe+w4vRCGHAclUT1Zxu3hf3ocHDXd5mPseZh57ZM2AX0qoDYjh3ug7447XIJFGdg8akez6dtIM/3rKsmdic67CS7d9itjrJSO8xtCE8HkkteGlgfqiPJvwlXZeh4uLUeHAQjfc2Am2aZHMDVnggrovZKUfRenXfZH7f8x2+58cwEwJGN5td5tOxDU3H0KW6keHZHS482xDAQPPrTRoj1OYeuUqPaHniE1N0y7eTu8A3Ku9qQD22dFxZ6Cv8DXenMTSl2dRSuDHm/sEDGT5ZXtYTPX87dCNp18ehTwsl+LggTJL2SW2isg+8qbfmLHSahOhqBOIywDcHU/i/ecIUxt9yWZbuU8kR29+GCMiSprCkGy1iIDn+/DNQ79S+zODgUckbo1abHqWt5vvcWuIWb9Y0t5CdOsRFzMMWBoS7UDKiQdp38w2DKKM07Ye08aWl0U6Ye3oYqPHK1Plz8e/Rc7PBEcDXDxpy2jO3LLAwoPrjHoMMjGiLwqQoPZG4s7xCNQu2x2gK3sKopmWLce2gsRBcSrhgrrd2M/Q/BzoscxHSA+rpG0r8pKSyTVahyTbj03ewFLQ8GZxcA7yWPpTmax6YJrmkVv3kc4PmGF6yLIv3KXJt4vLaItMD72hKkqJf3X7NGvnrep/HGBcP9aoq4ni1C5rh1lDWeDtGj5TjwFT7yLNh/KZ1nMFZdhtbFreMaUdsey+7NPPOL3+PzX4Xa8bd0Ul7etLYAKt7m7yUa2CEvyYMbraTm0s2xm+gchz2ptIOiulpH8PblyVEhtkaG1XErNbky+6/uZaXiFh+6LnAJAwdRI2Mt1aBHCcS7ApxW0O5c/ssVc+aeavYUNUo4O2bLRrVl/iZmUhkhnm8RnTbWlgBN7zMRblhli0+t3rbl75lCFo+MX1PbrfTkpfdVEjVOrY1GPftyudvZIU88esxEIkiFjaIWV5txhNw85/2gpmdi2Ymy9ZZLNPihjX7lfilTynyEr9Ex5i6h4JpXyvN3GFP+Hjhu+rUskhPDnh0ModjXZ8/0LhPDe5T+Pbu8EUFds5Izg01cnou2GVCJyYsPYdhxvtEe7K4TunBsljkuBx4FkdTw7ycIRsiXtpBVJha/UTFgbls+sA2HMzlFQLSNxGzZfqoyOs0nNvhkZzEImi04hS0DWSqoXNEkex5LQDcJB3bYZBJ8NMDIQCm/L6WKbrt0Vl+eYORkmYESvkwZuT9+qj41TH6HD8qd/FRGsS7i2WVyAjdY/60MViLO+/N1IjwRnOMDvbHC2Y0AS2zpqgLd7MuGmrXPxli3gpNqeR1y5jqKF/jXHeBnAYm4uBEEROMTRSK/DF4sXygZOuk4dHu0rx+SVdfxCyBOO9LPY2DkeQy/mWI09L6cnkUO96ZHWT/4q7bWaYeQGPCZ83PGoxVHXIgkp1CZF0wACABFxAIWWVasodUZrMlD5qGxnmhxDV1JBpPlK2bzbLschW8eysNMu2D9aIgtJsPeo3PRADnDFwbsB9WT52qtBOaJojQiBw7FnwJV5SW+DWZBXl5R8XNs6O//HTF1LGd8H2195R4YvNpjnJWmi9p5gg97KSk2ypcWDj6jOoW4YW4smnWHmX2DOeNLOe7UsGF9srja0WlGIcUzlwZiGXZ2jrp9OSo0MtcGn5XAsMp05k70YqAdiDAk+YAzGhcmDqLZbeQr/6vHqA4cCDywNHdNCHasK6ObtOXGFc1+6bXryYzF/MOBVqcFureg760aOqo+v0JIfRP67lrJi/bkBdS0qYG5GBhw3DxD6Qkd2+ZgoVQZLw1nw5+K4eCtwthLkx4ONJAqD5Uevi/WZ/ntbLtLRtoo/2jz2KlU5S/gH81KTaFacl8SBN8tU0xNarx7+dH16vrDcxMmaBQEXA4CRnxzr0VJ8mPEAkfDUCoH8aKPllmJJe34ScnUDjTw7DAKowqR19GScvDIOmZc7VoNs45PlY+w1IpvVE89S+oMXL/m+F15ayEjZHkh2SsB16SPB/0nE0ukeFDk5dH9LBI+oN/e98D0Nw5vwgqPZndmeLWYbcsEhCd9UomNn1IKmArf1MTnMVFjH4FqRjRzqnCupQ+n3G0ogGuRsPplvikivrS0dO7dEt5SVxlDEeNxcQZSxcTtNccKUWrOpBkJCOsI0r47RmY0qoCFYE/+TPq/SwSx+20FBXy4Daqc9s2H4ACseyTfQ6CgEYDHxINE2c295DeeVyqElnBR7aeaD8GAl4WpjfqNw94H/HU3ROomNOp+IqnYQMF34ph00LFH0MnHEaYx8krE5b9dzbqROqjL1tC2Zds8v/fw6pMxz8f37bUDEqVqma+mJxBubwl0/kwZk4GLu8A2XDdxdyFfMzENTQPmykMMTS7KD1RxH/oEx8bLK+OtnZMzVz1kE2KIfysVm5mYHmFfIWj3A438Gp5yGCQOiWDEIizrHWtKgTyagVcpmUCe86sUOs++WyM+p9prn4LzN/QCFZSini+YauwI0m1SA6GXp340kEbe3guHYDNwYxFXPP6ibg2d5znLke6WG1A5IpH4NrQcpcNL2QOD0JrezL3NSpgF6t/BFYAqdua79mO9O769AiEyd2W9SsIr6Op+nqC+vqP81lRjxiMQjT+qAfLpHkrJFZAU65CGzkOQmTzUn7/MBOGZn6JPhUk1vlsgeWuVGZMlfc1PSOZaJ9iLUj+od8bhvSKv9dSXCHfoIlKRqRGHQ6jIqBp1+YR62HUxaRRr/ygKVG14fsgf4npd3Wu2sa5MBL6aHgTk3oTcmAufOig8psXCXcqz5brLEXFhMpE3R1+nDd+pLxlqwsfdx2NaW6EAC1rOz74mEiIPpc9caS1DRP7hlgCWUzQ+wCIQZPXJtnY/sCD+NrTxHQyZsP8U6SK/Zwk5dgxWJv+VATQUEkuQShSTcnhh9Ktomgb0Xwb9b6WzFzdYxbghoD65NtK1vwSv5RaZrulyQOBHH+9gKcU+pdk0fLFQbmkhbu21EJ0BmoKnOP72PWv/ZHby23QoIf4hJSSkc+xJdJ5aZTOrNoZzijryyKmzJLEYL0WWrZZVEaOPugCfGHCHbGUx2Q3fe1tcErUbsLd0nHcVjvjPVXtI9PjlNHXWMc3GKNgJCjk+4CYq6v/zR/fERi3fGNonUgfXund5V8pIkitnYb77jIKBwiQmTWSIVzNX29xUiOnA0pVLaKGCiBnQmNwRRAdAicnKeXoLIUjPGfr1qEFsjCRImZlcrnIns96VpGejwHa/LC+OMbC0kGcb7TvAH67ho2txsg1E/Y5W7IDk+5+/UXSa91LiH2lq1FKYP14ed9zOsbx6nwNaOizlKwt6Bu/pU3GXTYCg5Tcv/TNXts1kip2cQ9XuNJLkz1mw3ZatjfoXJ4WBnqB+uPaQ1dtbWhx4EwVzs7K707ZUKJxrBq/yODzwTnhhYDN0ALg9j1Z/QII+0hUQIAp3WIkKx1P4y+7hAilG7isj2OmF9Ul0uVMkIbN4kyupbmdvADjiapkne3TFqRSW9WhuURvgUXivV4VTU1/VcXBCBzkAeBB78xLOcQUe9ve4qpb8r0a0WWFeB0QdH5UF5rMJkk/8CHU53/MWW9CIZmjXvRHkyLbU6wMGa1t1+Wf8Cq3NlMG9F8DqKAU6gw/h55jLbgVY8NLQB99ychuYlOHsUALoz+UhbBtGqDxFRyKfRCvEyQSORez8MEDmN4IFL5bbX1u/GrGdNIlgylayT5gKV09/dfdYmrs+9TKzt74d6SRHfB1028+aNve3w/g/3+HnpsY6m+U9NjjLDvXzJ7GA+alT80bTDVtoG5474K6AZeiomEGBHPCH6uhb27n6iWvxJYfYtNH+wOW+L/NE06hNFQXbJbyJdZ09kU1nqArxg4eGzb+sUinyO98bNH32q+ZSx9TvJIZM+9EXc2yyTKbIhrLQ3tGVINBR4k0sn3RYIXmH5Z4EB2UT81Mm8xPiIqhbOfOxRmWh6hvB/CJP6w0Lx9UV6LbUCGloyTxgusMqKvNQnkp9BddkS8Ehbt8LQbBsFFiKTAQWR2BLFZGfz96zDkBBx43kn4OS+63x/605HixBUhiER3UqjFfftsVi6/tGMDHcTDQKnNoBjQ6oTUxqyhXiyTll1b0BHFpdKHa30tMdhclqxd4dU1Rnii5Xmykya2ab9DpA26tpnlcx9xtrPgnP+QtOJnfpza7acXBLGl3Am32i9pfahA9U9yLWoWFfovtwCnNtByMdD1vpvTxmrt3YyZPFICOtNDSNNwF/djjOBUl7Av6Sbi6xSJLhnAO9Q9mv1Xkf2KmoxT7+uuBZjPKjwohPFTR62uYX1xWA0TTPcNaD3Ulw7o7D69iWyv76JvOsK9kWUADxIFteKy269EidtCBUiIFuuCQWzV4lsEvWq9hD9zmv1fUfzo3W92/d2buDs6bInLvWPrBEVr/k3QpsXU7Me/Jx4jGASJ58DBPhTiFAPsVptuAHXKRUrcMRUMk0/PS2hcuUkKPVJ75sA1CHh6vzrTNqJWZFZEefMI8MdusnNrs7Tp67sOGzZVqXMUo7Zk7wUnCSvOY4Sed//zjipuxHveNuV8Zv5nF9wxLsQvQsnzLFatKQpfc2XBdErMPXE8uEhtDZlAREAweE1kmUpsKMujajOjS97iBFlo24RN0BmY3u5po8v4j4Oyd+Nw2cNQEwsqIsq92AD3pHmuJf2IpkKF1sqc5nn+SDYLC+IzImAg5HeQV5sWqSlatKZwKMyXCtGDgDin7NmPpk7PJbSXOd3mWuLrrAfDyLhrcXsc0Jn/X6J2wX9XLLQ7fI8h7Zkxc6LX4F3RXfZwy1/aWMjqF9jbKtJ+rTps27uBP7z0UWJUaUk8HPPjW2mysPvuJrDilDAW3nCdJ0c7Obb0Wqudsvqa4aMKolHHrrNkKe6iwnlxcJllTbPLPdjtcWxpTwtgirLP/VU6Kfzh9ZbfaL5qlV5VC0w9exgKQcuRuruhRSs/BjhSVnYDKeCggiz24Aku+3BwI9xEzN2CT+iXjQkfu1aIMG7AlJRHZmrTRR4CuXNUkIfEcgIj5KYSC9LQOyAFlsXcVMnN8rE/oWRAe4dl5sxhVRZsET3T83K3fZD56X2/BhxYrOVyj7CbsW4r9/X5jfNfIBAerOnQzmmXzHD1khnOI5TKlz1BLOy/we/rl12HKIABnrZWjn2pEfWGoo34gM24BNPxkPOi46xI7wvflggTzvHP20mFvS3dKMVpFWAwbGAEm6WpveDywNNFIUbZtTGcN2LPzf0gWM+EBIq3S6GfruRFtrXVEpqf6EYl1ipBTlHBNqvmFdjGzDqT821RLVQ73498leJmnPUGpHg1Oe7c1ixI+ZMsYOV47VzIraIUCMlzbuYrQSpmNB5YUwbfwWsK7Y6+tbMdkYdac+TNGFSUdWosb4cd3AAGCxa2vXmCfjA6ne1yWuY6dt+h1ExKnO3lg4HcnKaF58A3jDJR+TrzwCvCZxpycbXZ2oOQ71rhHarJ1QxTsF/7lTMfo/zYX4wsJnB8gnELP1AM21z7beBa7pdIJrLpR9vYo3cIrpFXk2giMuzeP8/4jTz6GAecs9cUIRdj2COZPwiJPtQ1XGBkGHriag0nfDebrLVbLCYj2lkDwHy30nchsV+krz+xduVAeJ3roD4z2cGvrPIJsGanrjtCZAyEGtUWS6qA4p9GIvJKPdzAaAOpPdz1PSGFqSCd9lH34/dGoAjWTlj18lUOp3+iwGKodHxBramLLIjSj3cgeI+Y6tccWiVOzuCpEL+o+WvEh0BKJvYPZt4R3CBtLpulOkKZW5kc3RyZWFtCmVuZG9iagoxNDYzIDAgb2JqCjw8Ci9MZW5ndGgxIDE1MTgKL0xlbmd0aDIgNzk5OQovTGVuZ3RoMyAwCi9MZW5ndGggOTAwNyAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqNtgVUFNoaNkxKC9LNIC3d0g0SQ5eExDDAEDPE0NLd3d0hLZ1S0iAgCAhKiDRIg4B+o557zzn3/9f6vjVrzeznrf0+ez/vXsNEr6nDKWMFswQrwqBwTl4uHlGAHBCoLATg4eHn4uHhw2Zi0oXAHcB/mbGZ9MEurhAYVPQfAXIuYAs4wiZvAUfEAWFQgIqbA4CXH8ArJMorLMrDA+Dj4RH5TyDMRRQgb+EOsQIAuQAqMCjYFZtJDubk5QKxsYUjtvnPEsAKYgPwiogIc/xOB8g4gl0gIAsoAGgBtwU7InYEWTgAdGAgCBju9a8SrOK2cLiTKDe3h4cHl4WjKxfMxUaSjQPgAYHbArTBrmAXd7AV4BdhgLqFI/gPMy5sJoCuLcT1j10HZg33sHABAxAGBwgIDHVFZLhBrcAuAMTmAB1lNYCGExj6J1jtTwAH4K+zAfBy8f633F/ZvwpBoL+TLUAgmKOTBdQLArUBWEMcwAANRTUuuCecA2ABtfoVaOHgCkPkW7hbQBwsLBEBvzu3ACjKaAEsEAT/oucKcoE4wV25XCEOvyhy/yqDOGUFqJUczNERDIW7Yv/qTx7iAgYhjt2L+8/N2kNhHlCfv4A1BGpl/YuElZsTtx4U4uwGVpb/KwRhwv7bZgOGAwR5ngrzP+UFgJ0BYE+QLfev8rpeTuDfzt9mBANfHyeYE8AaQQLsC7EGI36wfVwt3MEAuIsb2Nfnn45/I2xeXoAVBAQHWIJtIFDsv6sjzGDrPxhx+S4QT4AxD0J7vACeX5//rkwR8rKCQR28/g7/fb/cwOfKKs+02P8w/q9PVhbmCfBB7MnJJ8gD4OUT4AUIIxa+/66iaQH5qwuev1OVodYwAC/Pn24Rx/Sfjt3/EgDrX8PBBvh3MXUYQrVgAOvfIjfhEeQBIb54/5+l/jvl/0/hv6r830T+vw0pujk4/Haz/vb/f9wWjhAHr78CEKJ1gyMGAAhDjAH0f0MNwH+GFgi2grg5/q9XGW6BGAQZqA1CzJy8Alw8An/sEFdFiCfYShMCB9n+kcwfu96vUXOAQMGaMFfIr7cFkcXD8z8+xHyB7BHvhytCl39cFq6IYYP/vsZfGIwYp3/3oQAFwax+zR2foBDAwsXFwgsbcfUIJIhQAmJArcCev5UN4OaCwuCIFACCsy/AGuaC/euahQUA3Cq/TL+RiDCA2+K/iBfBidv2H1AQwA35BxQCcNv9AyJy7f+GCC1xQ/8L+fgRyO0fXkRll39ARO4f77/IgdxcXBDsf2sSwfw/+PcLAwZ7gkHYSwswkFiIXWNI13W9DJUH59aUOPpxxrUhH+dUyQtM+JDCrNl6kk5uzrJqleLSIK/iC7tedVnn67xPH858vryma/ISuOSkU9yxobNMWPh5ifw+2eeK+vECfhtSoUGaLK1oucsQkiZNNMEbLCkrmyEdJvyXLI0Di8kezMQVisIqoV3qPW/qK3LUSKlF9A82tOHtliO6W4Ln5Cz5G47xwZsxJpHp6lmPVcV7brCTPOoI1obHRlYeDdL0RQequAdtfWMMgz9SEBuhlhfAqNNfKXp4l1/uvp9F6GOpRCRHqgMspNcmxx6RK0ppPyY3uWPAMLTLibRNxt4nx+8uytoutYUFpN/wC/YHP03Pf/843CbKx0p8VVE7oKf9Q/jYLNnVEvra2BcHnDfvOllzGF7aVlec+vuNy+hE22XUgow4uQwKwAtEhYOETzxAPDhX7Uq86eKeeUMlpxk9oaJ6tiwmP/Or6NqJtSE0NMswS9yEeWPWA8Cp500iRjdqYOY8fzpcv2u0yr9nAdOi9J7iYYjbaRutPS2qNJlYTdmp2Rl/Gu6D7yTO0/k4xu92UDyMCfv9P7vsd8uzBVfGjT1B2dU6ne78iLnCqHBCflS6WloRG9A6xXPVK1gjjV2V/TEDqoILsvyU3f/i1SxmMkxrOwsjoymYZcyYV0XWtOnxUgItQbAMJBJYhfSEiFU4wB63KMx9d/6DlruMAp1OO+8wxaKcD3Pa52/Vy+MjISbL0czc2DLPRmtkrTFNNXirKqX1k4vGljZgvHDRScrabnP5xpsvscdM+KFbSMGgkTNTk2K6Sz8b2/06EitsthuNjxwT/EXUWqmVu7eKuJHxZuI1pR2rxbg1OIAMhbSfshbiaZ/JW65Z2yMnYlxNB3yo4Wpsq4CSk60P90mzn4AsVlQrBxLForFcHjiMoS/Y3rDip3tHsJGwd6SgxmMqTzXhMmShG2hJ842mUikhS1MuIy/zn1o6nHr0q8ztD7fe5+ZyM1t013qjfn7UFRvba1eQ1nEbypnkzhabAkCcavZ+r3oVb/bHFMK3PsNXLfwU0K2GBS40mfDhuJ3BoQ5ob7zUkWL393vOSDrRHSsT38qYqZE6gYYjkfRn8gzUdPPRUnEJGFixjcmx4fMTlLUq1H25ApcBF8ApiqPZY1d5ieW0pBod8CWLmUQaotG2OCOxUNLjUTZGTiI+Xy8xEbLBA6dBdPTYbGGP6MI+Nm/qd8YP2XkJ+xmrxgmQJeQ6/YJ6LalVxTTB6cW++C9cAp994iFLukHLn+Rq5nyljGY+td44snTlKqSe0vxV5Bk8CLRcjkxMknEnErvH5yXuPN60FSrRdAl9can+FuYKHPwczbjyXislUavt8VPRshiza/de4iyxwAn6wkeoefCjGT69jXqSVm6ZXWzbnxGemh5qhd47MWF9PwYZM6Y09u7WUaRfBHEuEtUdoDnz6Tden5O8A9l5hEweFQUhaxoZSgkex6A93ou879J5y33f65m07eGilX2JVh4CWaMa/om8m8cG3ru8cdFiT3N4Spy1jWTayC5kwzHN/Wy8IIuewLobsKHcfFcwlZBkcsN7Qsz0LAlHF8kNlh95u7L1IsqVjrLW3aWBI2Z3h8j0PQV80qKnPMcyhDBPXM041VmGqOctlYLobi3Ba6OPuOBYXypxlNHY1qW3M36xirlnpc1jNfpjxMlOwYoc1EBVpmkMlAk5j4pMW0s0N/Kr9l6OXbJ12ObIi+7TstCHayb6TXzHrExealQV6srCJV8kWwwW1AbIvsLHkC/svmaE/VSawWV7v5lH6SCIO5hFIrC/K1RYZWOt+ZOsq+3iVitbPklNtO9YBfua7bUux7tyZ/2Du1LroWc7YL2tw7iVKqWxtCqGpMinzfEr0VdUPw+5dBPLtjg0XdE4dinpH77FSqTuDnldUx4PPy0AZszR1J46+6rSCHUv28M96nb3LHJFjIvf+UVLqld/z0lyOJi8oPVyeIA+a2dCNlH97Gpdre4y4RSUtf+2E/V4PgFlE4lH6h0s5ntGCFMuaXSEZFU5+ixdplrgPeVhO5WbfxOI76sb4aLej32KQnvV03me1dflnhFpE3i0nZ8zib2nqnCLeCw8wmLmBVVgyC2pxNPZjGY354eUpQuZBg2nQKNA21umRiXpohQ1P24lvtEcf6sOG8E8ygt1N6hjHs8wET0Fnvld+r46r0S72UkuwYuc8Py39h0i3xpvE38q4rUz8kmPeNj/RApFswqlkD8hzAeCa71At0Er6O8BRwGrB2WzEcxWBXMZx8zfiQzzngul733Fvutr/hxHwzw0tXxVNDJzP6EmyET13VYhxI5M4O0aQyeF3pPiexar0c7zMRuqrMvXoXmNRUd0tgz6ax0m2fyOn42d1Vcfigvx0P/MHed7JelnTWcchHLCabyY3at1EONhv+aU7irSWjgvp6aocxE62LF15TyiM1CG38eQUOtS9tAG7ZEPQw6tdYFl29ePFO/L/Qxj5JVTmapFzWkc1/pdgNyLymt2U8OoD2Fv4t52RaeMVXp9Vzt1nFF+14yWwG24+2xim697rmuN+Z6+7lszBdvgvTG4nNNPf/Z1x245o9SHdUZYHwOJ/qGczXcdiniNDKVzW62XFJS3iWjaDYXCbsRH224xuLVJ8kPOB5yeMeJUOqffdR2/bWmeCl0rH0UWs58vDi7Ek5YXRhI+W9Ppf8dMfjYjY8kVuJAUzlCDqWhxfymkGjEjqRRSpTNN3vwKJ3ujD9JSt/bpyWADaobtcrfXBEdCl/YZ0ZfmL+ul1OU2VnNJxHGUuh/F5jtl+n1Mq7gylc8vpr5UGrx+vFk3bzkkak32yvARsO8tybmC5F3ykrpZoAh3euRbehKkOuJ0jbUatseZwCOsAGizSnrazJkYVdFTEQ6j9Ty8qOrlLnwSPJmy+CD5hJIYeTiLclbPgCRI+W6eo/pqdmQseO2Isjdli6Qka6MixCeIWKOD2i8VtcuA7lruPrlPhkG2apawSo+9bUxA1mS4kAYNRa89ZYBz3gTpxdbCifaW5MQwrvFClsF44vaD03yVO967yUQaJzL9wK90XqCG1eE8TLmLKO+b7UayZUrKufeHpa10AdxSeVYXnHcCLTHebnmNI4zbahNKxxzo4gnH7JF6FRVzmItMGYI6jgEnY77i/eEkGjguAXfhKTd0eWyDeCzdPQYNTWuH4MYyZ9L7xQwrwhvPgX71izoh1fcU1hTHSlgMwAOwX2pl5/NqnF3Z2T4S+3fPx6qxUuzzpew99lJdFqIHBzHmiW9Hau3KHguRraniK0KImaQaViSqruY5zJ9hbyd6fgUfRu2XsuXrTrWp1T7LBovlU850N8kMxZZ/PcARsAuPoMXQw7TH9ynNa3ZaulSwYGkX6+OpJGvBu0gOEXaOOowCzzxqWbZXAdp8LyZJO+deHhCeXZIDk4Mwql6DfHsYK0I3axQ90yISrcz6E63YWaYDLL6+8ZED3uCfbHmuH75h+zDtw1ByPaeVPZDgm+g8d1Pd27PvnuHdRr8SmcWQmDc12UPG+PjmR8GAzM4MUVm2wQT7A7iijYi9a/4St3pZmqOseXUcm+7eS3CYSNISCCUpgtmyN66U0qAoyyexVI+KqehKz8n/a6VcNq7lvZJ3kPhXbkw2ckUrQ/a5YqyvJElk5uDTeI6dyVfhQZ4tci4UdMRW21iae9LugpHTCpY4QzfmGE3LbsVWa5BS2gMGAiP0uYKLmbwolkwVFMObaDaed/qV5lqfHlTBNZ0Go+5WUqQdbMfcFgcAxcg8JP4q6C+RaD9W5sWGcLSY5arpgvi+Zx+bIqvzdCkkYA3u9IpdjHYiXo/2Xb0qUfPEr52iIx2GS3Ivq5wcALIBXAdU7Ws+PyOVv6vRZM1XCK2nKwOqsKPwIkif02ptrFzdIMe8bqKjMU7QOFCMS5ORoWHkilH61Jbo1c9gscz9CqUpUlMRUzPhESHEaMBqul2n/0N8fWC9on3pa54ZokBibfPpR1zBygsKp2Na1dc2XBjQgU45aRv6Sv+zMffJnyoKxK79yY3RtOuKSnlww6CpGfanc0X5+GFh+1Wpo40Co2QYtdVBRMkVlKKwlbwC3hOikN7TR1Ped3wo8cYjFJkSe91VxcQHnjqVOxvUcqL68HnjTdp9sk1HOp0eiO/t4UgUKpeCVdUryyvKVx36Q1EYW330ApWLXI5mH70+Bg68JrkTXR1o0a1PgBTwkwQTJaXyi+pF6mQwfsbjCrslAS6JNB+bqTHQYER8Evw5+9q81O5za1O3t6yvzQsDfn+y/Sklr9IrM7GqtRF2SAX3hhSmNInid963T4Au00+C2ASDXwW/f1ZPrI0DgXoEejQFNwClsTrtBxmFWh5//irmI5qr/KTZCYqZ2f1jLDSCa4X1uE6IBaMpYtqx62eJY+l5crsWpdGLYp7aUPtmG4pvfuX8MbVWLzk2Sp9fBeNSnhF9rUnNOjF/952vT8E8LWvzMxIj8nWXQfs2kMkxfhtTWYIaWjL14WwyUocMOU/jTTluPWctKZaTruJ351fv4zbXu/q6hAJaAx4nvXfZCVDFnjrIvsdPs9bL5iiW5CY1iSH3ezCCPcLlPpDXjiZ9M3GZqk6lJ4G67RnAmOt5CfmBROJvO7+vQB7VNACgXqlKa8ISxhuke9eV0ZPnhRJPgmB+VGp7bDg9UJq37dTDd1VlTNU+123vF5663fD8SdDUgxqcrm9Ls0CQ7LSL/s8L2um8kQdFDEmeUqf134yCYiVrOMqsvGTwr9/3vdzFpoMb4ES3W8l9hiy23+UrySSi6CIPhRzfJW77ji+YybXDHCIaYiGLHz6dkDjym4WE6MwW7UOrQISm5zotffSiaoo/WH2mSK+Olmw095wKpvKsvkmyvb0LTZzD1MtxjX19h5THejiTZMALiWrhmvGsuqlD3iOIDW4qBoiswkir65ztHUaTIlWjBe7ncSbrgjLyZFK/5eAkgX8IBR+PvCUBS7gF1XirsEajWfaEe6K1wKTPV9ToiAD9P5paHwzOv0vcAZN5YRfJFy+t81lx1mh9Wd1DO0A6ZpGkyfBvEKHl2nGnJ52cEl5viFh4ckXoxeyz86HV4JFI1hml137qdQEwcwJJ/OnlTybQOeNHvNvo0k1yo3c96aIo5zct3u9o7XOhTkL9+pQ6yQRbQ2LTIKSX7qWS/vEk4rmpWgwnp1wTLh1hpQ4OgD6a720/1LEnch5wht2l0LTLqrRcEb3y6RHE244Wgd68/twkRtZllY+0zHxrBDkv0uJVI3QUJSkouePbl/7CjuPl96wo6zazUdXtYUu8gTupOkiTMYLMElmYOXKiEv8MhaSRf7PKcXJOy6K3ptQwcLV/Zya44HFLNmW2p6fFyDCG/zietziKHNURw84hGVNvJK6demHotG2bS1yikNDncqVXEzRmpBjktEKxH4LQRlxXfzDQOz99zazU/Ao54/52Fr1raLhPrbN0WzRoI9M+GtD9RN+WelMnR/VocSiZPz/o5gbaqjigGGd+sCHgtC/Z2b1PanrKRv0irktrVSayuOJlZcHJcvb7723DtorYQUocRyZmnX2VIx8pyF7blQtxDF5UbVUa7wzBzcstk17ovBIpwHxZKfsRK4zZzZF0oImFnXP2+q0geuKjUzsWal0b8qKyLaBt23CoFW7EJvYrrXnt3BkG7aI75VyO6x+KhsMs+cWKYjijp9j9QnsnydZxjRs2Dl5iLfEDazHKUiORpMBI11j4MhfonJBdmL9cEClV2dupeJFtJZUiZGlcGD3Q0rZnwhj3Aw214lv29ktClqu6ruemyYndhFoXetXDQpEJNistfed918jZaYPoltqorYVKP3zTJ8A4JlkyFHi87+YteQsg43i0GxMp7oGqMj8Kyut9ym2sad3wL9sGY2/KjCuhdr6gnpnV+vt3QtpSl10zNEb2epida6jrkZmTC2b3Kfk+xqeLY9XXF7FLSCaFlBOikkAjbPfV/oz4ktSmpUkUGUWlL13juHrI7I0ixhAQ03P4BnwoYXm3PF2hIFXKWCXaQ92r2KngcxTMv+sxinaloPk2hd0PiclPzO/0A7vO26qurd0uG3niK7vswaig1Od5pJXXYb3sVGUFamC7i57lclqXqSC1HZRl8hZFBXP9Znr2cI0nus2ldCiWs0WyofpK2UMjNmeL8InnIWMsruaxnDgRuzJGavLovaZEBwLGQnWlh96YxFlD2qf+mi87n9qyuZln35bhokGEtnRjZb3Xv+Zq6eM2ufl9Ozd78+wuzZ27v8nppnG9hgmNcASjY7l3TiMOXzfNWNEEy508LklZkMl9IHudgIL9+DIYlJtwV5wh+DORTaccpsr/NMLTANbf+eiV8HjObt1CTmxDr+ji81nJ4UnhKyXT7o3rV1yOO3dRfDLWC77g9a9K/fgE69bBX17S1f34vC0mrqRCF7C72KTDw/TeucqC4BP9vvW+NOhi0rvCk7a3nerjTr3azlTy6v7noDFIezsmGTfOAaWpFQbhlSea31PWl5yJZUyoFekrk+75ODy1mHXpRZVVD2M9zOPsKg1meSLrWx4WJYTeceYG776gU772XY8X5dtsKo3J1z3jisWCL4oxfvwyY1k/2mr/sMEtqHGBAlgaER20BduuyEQvBFWT5ra5jiiuNymvExDnegcygcQLdNuoXDs6F2TPK81KcxYyAV8bVAbJo8ifaNcZW7u8v5fPfMnyYikkq/tiTvkL1zc8TBKjfl7UXmOe7p5B2E7jHVUh3aQ8shOTPTgkTNr6nq11AoglT8Gk8/7HzEp3nAVlnoAbtoq+d/UUJEXHHJTyLuf8uQh0QSLz2v9rWqIsdwcyqi5b+eP80snwMi1afUyxp5zGjRNqQ6cLAlMjrUwESzmpzvFIr3fujjJfB18oMAQCkIBEB1pPwx146xXkr5ieTtS0yQjITeMXE39ZWk1nKGrLfXB2GTrdDAsqZBUSyPB/geRAbRJNuzPdP4fKmkNbQNwtIvywF9Nc0q+in3XRsOKZ2dtjzpBO+jLC5meclMHCW2F8NBMrjiKyVfEMl7zx2JOK/bMm6QprN8QnQnbXQFWj/ceBDKoF8aMSCjL4Z7IagVX8LhpvD1CKeiaMJBXdCwRW1qfGrT0GxQ1Duw0hXi5c4S5ohy6yE71Z0fulL9ekqFod9QzrdMH5zL5mDLGb8pHb85KyNsslxXm7MnIF6uoCXb68HWKYtkpiaGMtaFNNMs6EJail3p3zNn4xXFigjRJadpSg5cTZtB+J4uZL9MrFA9njEwcE0DGy28oTrrLxD5XyurSKtc9itckJ+wg5jDZTzsQEBqDLHNLHUY+GZfhYEwbeB5InjmB+w6CvUnuPW0zSsGyiYRPo+Ca2btiiFDkdj8jU01Dw0BmIRC6z2p6uswDA5efeR6p9tWixYWIgnTCLXVKKzZGN6oshoHsUdi3ZVpD86YbKlUz+tBdN3rjNw9suJ7YAJSjp9Kwb/FCZSwMrCoWDH61dEO2S2O3hXd4XiGFRSOerlXWJ7KfTnaajnx/IG/HEY8xeCb32eX79huSU8+soo0S1L9FubY9Ho246Pd/Wy/XBy77g6PsfHbkf75ZQxAUPPLtIpHDXm425jVFffMAk2xnNrL1mSy4aqId9qnvitjE9dDk3Y9e+3pyp+636DKAlwau75vG0aDPXblQ2n1IwOX+BamsofNHKTdUD2D7Z//FTHZUUMeTpR2FFoOUpDsOyCXaBxH4iKV0udBWvqu6wnn1mMjN58+y5SSaC1RBZVslla/EScAGM4cqz0Oez715ozaZLqWmV8Gp9vCW+Aq933PeQ69tCAIPDTJsm0jb5dcDTQCz/hJHL7CCZejRKS7szSVW+1qf9h/YSVvtF5XnLRAkO5sdnHiVsp/l4pwbsBX5Jt9+mvyHpv2Rl6B58YORwtWb/wzFdEuUuTm+dIgGATBTrc/3TZL00fUTh6XvHYFAeXK2EehZq6owZ9T0WS3ZrX0L6HB17M7NwZuUQWVdzSCwPM+fRkFElQJJilYhmuUQDZ62wZm8iOfPBSZE45JBgP0aWcIq7Ns179XM5zLHi/L7QWUpbi4b5R8ZbKBlHWN4O+X0eMCufq+/HmTOI5qknOk4itOZjruCJFjHl7Rz1tlxFzVaIZKVNxPF09YM9nY5a2/0zTvMUg6rdhQup20OQKuUduzmHWGRAaS70Iwq2pJolBk48D0V+kMYiB/Xr+nfxps4bkyfWcvVDbk2XcWOtJuuOqzMcYqZJoaiSM3ipKEbTeRTGzT9MWSNPLj6Nq9KTZ4QPF0QZ2gfa5ct+ozkTtvhiGnX50O/uEP9hXgYBdrPWiNG0mEn7k3L7xLwPl4I2WnwPZ+XmWCXj9aAznTSobrXgscNbwklOL862h7PsrajP2pjajJuVgSjAB5S8yoyGqyth8ieDPdW8BnSvDsLpbIM+0cgkaakFxXZMbtU3mVJFK7BBDaP9kXS6n9ejW7djNBvS2b72mjv88RhwYvKs1IJPRPqAJsClNIRONgW3ywMX5qRGrFfZr6VLqpvLfcpashFXvK+/TaE82ZCQCyBJy1Iex2nwAOUOWgEYQ513bMWcRO5pfIcnErah4hWnke+kl90cr+ofiO10RKiSpY58pppa9a9v5y7YGssxf/yRjYphk8mlYzXwQ4/IxiNq9rjQ8+pn7URPsd4O9+5DxmEo8W3QSoFpIoYZt56jL2Hmmubf6IDe9Y/0G/36NLeRCJAa34bYphcHf1B4SKFEd0VK1cO7zoVMwsLH+4kn9MQ7s3Vppz1GwwxMn+pKUPOeWeVEHvMizB/U6iMibXj3gXkh4gjd4BJ2mmk7tiuqMnAVQyoFj9lr029msxQQeLoenSxxjOtLxe86f8S0avyu8LD55XtsK14o4n9FVJ2qe6ETheVyJhSGd02fnM1wXcnvtl9n/BO0gvqA4oLpxSObHYDRxKCZ3lBYX+lXgBIrjVaexspou0ro1K0n9vrg9kylSBqJh5JT8MsA1/osX+b0gBBt+Zk2JhaIn+W3iJ1XuCr757pnud7vm2sNwpmMytH3opXsXGQ5lx5hu2nzJZqVGF2gA6AE+K+/LoazDC+yTkk9c5YucQd5dCkS1j6WaByxdV+oo4k8rmg1q/iUWBiMAnROCDRvOFojaL8hHxdxwjPv1pBk1wQPKK75BTcRemNNin7DGYVhrIsV+OB9iWlFt2n6pEkYmnnir1jHkV+Lpac+dGc3JsAtUkN0piyH+fGmWXZSqNXZnHmSiV68cElIKe1lOdksneQyGVQa+ZWyyFrkePlUe7jKvGFfgU5m1fwbVR9K2hiRlB1Gw0HSFy1HL7hYhAIFZI84FyrsPHIKl629N3OEmAfRp9M6yUe2ijP24EX7bMRybkUBOUOHwRoGDFA1UzxWTOIpW5bk2xpugp9u/USxl+480oshlnh24xodRxouq5tPmmuZ0c5IwR7IdDes7kNz3cRkkxTgNIlRghdszdHfbcBd3CSds98briukt6HDw9ArVWAzYCvDrt+c43KEy+aEeytKXptFmU1cBld4nXCeRSp2MWAgxiLrIuW73uyh3XyfyjdWj71uZonAU4yuvedLKpUR2gPPevWIUHIJ1z9hrLGL1HCVhsr7RGVs5w3Rp8yaf0LpDTfUVY3ca6Um7/asdD5jhuUFqIr23mu0mvcpaG6OHYFoePfejPGe4KLkZd2gbawNFmmGDbmWjwm7SNx4vLkyABkiW9VCPbvU0xPWnqTICMaoUsBss7oWP+ZtMXz/VCZjrTUsE47XgLxfEFzsoiVvy+NXpxcWrxGfePVI9JFCez9oVKgkc2mXGFNG6RyKUt9UJibBqlc4gHT9xMHuYbc7M3F+weXGD28X3ci+45WlnCU9i8jRTr10wXsXJVVsljc+yfRo5v49Bi0WbjtkBeFcP1Wd4HmNK3xSD9bM14a3rqMbZ7tNYtKWONxz6d84puWUt0rJCCZEbMcOiOUMLRlboc33JE37yQWTz8mHuPqvJ4aj9s/sPFbyUD4+RZeaBJMWC+IW8mJFnbI1MQ3AayO5gUgx8DeoRsvG/rczN8VJ4kC94JTk6EWaMnaTI1VWNAPUOJ2wGsPkc1B9tbZR0mMC1DdTMpsD5OOkfBYtKV6SrsVzF4BeymvnscVhmnOzHOXmwJiGR65JU7CD/iJBsHgorsrmwYW3FMV9Cb0efkaOXYPorRXuRoz4q7atmrjwDUGL91s/QEhtLbpxvAGG8bg1WqwSjTIUNA5mHV9SyVrk6cvCxHwHHb+3fISrpDxvEcS1jcidZjDU6jLb4Sn+UsfqqlkvbFk0VUV7WEJ9wTCpP+FcRRPbfj+kjLZd5mYXYsav1FnUpVKE9zKe3mFamy4mtei8nCTzaD/bWbZyJchj/4SwYw2DV2I4tRFDyOpRBh9DQ6lpL0aGsQRmF7Yaw8TSs2FCd2Dn9wgS8lXRkeknExAIVr2p/wARlun8d6InRY7HWGbFW3B1PFKpmm+kNB1zh7WJ9XnVhPhJd2Zh2wL2vjkftFRD2NNyH+Z0L+8G12SdPSpKwAxjwK9lPEYusa9nOM6lruyS8s9l6XIOcNTeHzWK8bwtXZ+MykxB2osYPY6okdhzBa2G0bim7xEm1cfHvPC/0wRF91go436LcgDSxJ9vxyziFN97ZfCuhBvQ71ZzocvZeyyGyYqF5/le60j4i/Gr1rMUh5w+UKAUZks7TpC56v8/Q52JxQplbmRzdHJlYW0KZW5kb2JqCjE0NjUgMCBvYmoKPDwKL0xlbmd0aDEgMTM4NAovTGVuZ3RoMiA2MDE3Ci9MZW5ndGgzIDAKL0xlbmd0aCA2OTY5ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o10BVSU7/YuKYiESJeMwNAxQ5c0SHcqAsMwwBAzxNAgIY2EICUgSne3dDeCSCshIQKS0vEf45zz/51717p3zVrffHvvZ+9373c/zwd8oK3HI2uFtIQpIREoHjAvSBwgr6GhIgIAgQR4QSB+AiBQH45ygP11EwANYS6ucCRC/H8B5F1gEBTapwBBoXEaSARA1c0BABYAgIXFwSLiIBCAHwQS+xcQ6SIOUIC4w60AGrwAVSQC5koAlEc6ebnAbWxR6GP+9Qpgh3IAwGJiIty/0wGyjjAXOBSCAGhAULYwR/SJUIgDQA8JhcNQXv8owS5pi0I5ifPxeXh48EIcXXmRLjZSHNwADzjKFqALc4W5uMOsAL8GBmhCHGF/JuMlAAL0beGuf/x6SGuUB8QFBkA7HOBQGMIVneGGsIK5ANCHA/RU1AFaTjDEH7D6HwA34O/dAMC84H+X+5v9qxAc8TsZAoUiHZ0gCC84wgZgDXeAAbSU1HlRnihuAARh9QsIcXBFovMh7hC4A8QSDfjdOQSgJKsDgKAH/DueK9QF7oRy5XWFO/wake9XGfQtKyKs5JGOjjAEypXgV38KcBcYFH3tXnx/NmuPQHogfP4a1nCElfWvIazcnPgMEHBnN5iKwl8I2kXwH58NDAUQAomKCIjyA2DOAJgn1JbvV3l9LyfY7yD4lxs9gZ+PE9IJYI0eAuYHt4ah/wh8XCHuMADKxQ3m5/O/A/+0CMBggBUcigJYwmzgCIL/VEe7YdZ/bPTyXeCegCcgNPfAANCv37/fnqLpZYVEOHj9B/57v3zyBo91Hxly/Zn43zE5OaQnwIcHDODhFwIBwGARMEAE/eL3zyraEPjfLkD/SVVBWCMBYNCfbtHX9K+O3f8SgP2vODgA/yymiUSzFgZg/w/JTUFCICj6Af7/pvrvlP8bw39V+X+R/L8bUnJzcPgdZv8d/z/CEEe4g9dfAJq0bii0ADSQaBkg/htqBPsjWg2YFdzN8b+jKigIWgiyCBs0mXnAgrwgwT9+uKsS3BNmpQ1HQW3/UOaP3+CX1BzgCJg20hX+69uCzgKB/iuG1hfUHv39cEXz8k8I4ooWG+r3Gn/ZMLSc/tmHIgKKtPqlO34hYQDExQXiRYBePdoSAviA0QK1gnn+ZjaAjxeBRKFTAOiZ/QDWSBeCX2sWEwXwWf5yEfyjLNTNxQV97m82oM/8l/1b2zCYJwxKMDuFhEoE21UFN59WyNJ58KyNSuLuppwa8/OM5pjho3oUJ8yX4/Uy0ufUipRmu8FKZnZtmnLOp2++TB/6rFYz1ngJHvMwKn2zYbSMm7o5xpxM8DmhZ5oiacB4Z5Qkd18836UHQ5sh6m77bWkrmx49IIkvW1XXTIIHK3mBkohqSLNma3tFQbo6Jb2Y4faKLqrRsl9/TeiImi1zxTH2+dcXphHJmq+Z1CRbzwjiPcrvLvUN9i+QdjN0RAWquget7bGEokgVJfrpFQTxyg0XsogvM/Pdt17f87F8RCanzPFkgiOhIG4trcdoC8c/rHzQcT163S1bojPAbqvoMl32c3ANgzCJU7qFznDv2o/qysPABdUjxptBauzAW82xZQJ7Mm/ub+ccKDeD6MsduouJAQYr9A/1zhFg8aPXt6cZvQ34Yx6UTA+WE6wNNzL0YeZukwov7LLuGD+wevWk6FlUl03C54qJs9zIZJutx9f6VlTSNX6c1BFyl/YfC/qBUgHj8kPQkGGecNksDY2ZphzRW+bR5I18YmLauBm69Yx3HPatsyt5jqyeBhHfLrlaLFU06KG76jYfp3pm/ZkxY4vz3O6EvPTsJdHbiGcWTez2L11lvrHjv44zUa77Vlgb3uqzWSjV7jo5U5CTKVb95WQsWYftFbAzx+zFnbHC2gi20ntr2M5v9CluT6r/lHsJDKnAC4fvGzlQ7LZIcPfmqEt2a5sDKCcfkkTeLw8dOXsDEoOH+fYUfuswFmHU1s0RZ/vYTbZNIbnYGRH/MKmNWMlyn3i5RDATP02zz0flAgMKMHxCOfhD15RQkMrkRZnKQhRXJbkv0SKAHSwXpaPLiixpCPa3KikyTX1eu+ij0ODqDcDMziL6As26TfhT07pFM+ugmT0nKHtIDH8d+5LD0sibyfXy8aFYeb9IHOuJDl4dBS0r1vX6mDv96SWfeQI/ls8FvcBcQOHO0Of6iFBSXrYui3ikaNgI0fOdthtTDsn95XuRvkUCBwo44oIrhhcVXR4s884Z2pmu0PrOzc2MsPxMoGJBsQ29lu8Pb36Xzww6U6sf5bqg5zYpEw/fxN/QyHw1vRdjYwa4GaVXFDzEeCRGvc7/eg5z8YHsx7njGphKEoaIAaaHkC39EJeBheYi7UXhfpzkfWefmdXes4CD/JjAHb6kcPp7FDhxweSJahdXW6LdXoPfZ5jmeg3eXd/mSutLnBMvbQn2z3pCLo1xSZruO+AoIbmowTc3ZQN5ub1LhLRNJ0gVN3IfrhlYYLopqSLhsEt5N6HTvBl9WTjpUfv63QNYY0SURDXejJI2ubBylQZetOOjx8Y3YjruWFbO1xsi8k8KBfvT3xg9ZcqyTABHpkM1LyNVrviAPD/e0C9iXEgRVzemsQ4d1zU3pT8ZSbjAVs8/xa4xGRlof9BGos7bSqy4DDPPkn/T7wI9VGGaYxZycghlIW5e7QhjNrrsogzbujqa7inLMuQko00343msbPs07uwqXKPLaoy1wsMa+Xy19nG1uWdqnVZW7cfTR0jDhGUgfFrU2+INuWRfaHMomd4SVd+eD7vdGK+arsmLn22XFMxuoT8SGzu/vspLRuiOfpp6Fd6tDqvz3Gr7tJF5jR/87q4RI10Lv2bnnYUGR8obgM/KcxN+l+/DTkp67eXW+YRYJ1mT7MT55QVCloIhEvxNfiz79FTUnaQE2PIUzpQUZh8tMD7hGtZ4mvMsBOEsJjuZ9szl3kTHnId82LMW7v9k34BD8lGxMtpoeW4LK78JPOq5fYQ4Q6TuYxv3ZdtFkEpXQOXmS5bfpvxs2sWm3hPU9Iihbl4WQQnyeL7SUvPlpe1UVU4GjqQK89qdhCjDcYqu9t5JOdj8YL2e9ajI2w0PmfQNFZI3ouPIepEclBt4fhA+Mi2c6u53RJ4nyC8XLkWM1VMwMXzoXALcf/ZVsrTUc0TQYlAW2TX6zqG8LVF3RvPWdAYVzbfaLuUrLsoOIE9eQmjy+2PDjAkX4qjCd0acVjiIQlT6p9LldVpqBbsQ8BIDdlrLlss19XzBQwRRZWkq4gMvaaFGUzMmfmI5S4yqXmphnKZt1vJlOVGv1Wzfa6zk435jee4IPhayWEbGPXvqFsIxMSWh0W4gnexa+1LQl7A2f5H8poGlyfpKJ+I1mXfLxWGomztckzH5MRHFz9oOib+aCRK956ZKGWUKZk5BPKht+Zk7pO+s0irPwlkpSixy5XLKONRIW08pTB1imCPT4Kdx6QiS3GL4rgVQg62x5cNL/aJ2aVUZBLS1krTeyAkgKTJ6vxtZOt9wCRF7fsbyMqCcdZWSJV/U4sWyrGuIkHtgpUHR1aKmNo397kGN7tSZadZdnmOxLco0CYaQQsw+HQISj8agcHWgXbnksjX2hCOKljtlWoJ+Ty5I++Fg3qPhSUFZ0BfSCAGVx6kGZNifTqW3Hy1YUbljz7G301tOuoQFVJ3XmEWTRYqdNsCCNLY5wpmvl2JL73WuTh585yp2dF0XL1oPxgK9NjCgwm7pC6HnbYyP9J2XNWtCUIZgCiGqXw51uwYrQxZNeuMIPEGbkjhjajkTIT3brGoYCLIh8/rBq4rCuZjVZqoghs76hzvdjoyas3qleOINRriSXZPikSTiyPN48C3RyCmNlLcXcDa5nwItwqj376I+WizVX9pzSpvpWT5swIqnzFrJ/UZKl/v+2p6bQzTrHuPHvsEJwo0aD3P5elwazVjxiXWTZxFAKwAGm9wxyfHd0Lm1dDJw9XPh3cOyU7Fh3YqP0unbn9yd3e6Q+8rWTzd6N/VpritUkAtR0JmRF1wcj6g0BmE9evt6Yw42QI3fVbP03CKpfDpljDqjF34TGSSey3DUMu8tjB0p1PZq/UUh/kjRh3rJQyx7osxpXZL+PfGd9wnfIKp2D+tDRjNm5Ekdx+ms0oaVtfEn6xyhz/MMT650vu4HScAxi+C23N0ud1HpzfOLkvqiHJsjScWY5ceuUm9HEzqOC55B6aNWt0CPt2eY31umRp07VbrQ2AgE293XTL7PFkgu2SaRpJ8xt9KdQPf8Dc+4cR7qyn9K6cGLkFaMOQLp20NZT/gZe/24eLTvMF783NxWnxdTWr/fi71ibfg55iSnVbB34EAQYOzfFlfBUf1hnwvPhgdnaJZ6aLaSM8Jx2Y893oA47lbrmEn3SwOziQEpAq72XNX7SlYJ1GPA1urIYobhDfGH/d9sFDWiVjEq3W504p0MAymdWEWeiDnPk+ogVX5cXdYaJuHEavA/hXTHngE62DEYSy+XA658K61n+VaUYl2KzvBfkegaul++NDs6xNkru415POzbXcjzymnG89HO1S52MUl0Bp6/NBveRpRNm/cMpUNVQULY6d537rdrdKPVzA15rxtl6+SVSlOTTBzfhJjd2ZZ5wUuWN+M61RS3K0Vn/eAp/cy0KunbiY/ydx9dVbhRN21+0qGMmfCSj20uM3KdpfxK1FWxwhpRFaiiT0eqNBKgutMKGzok+RbNylMFYzF8O2YPpKq63b1AuZPk7sB8zCa8n3Ynqlr0NCGKSHmxVTZoRiQt+2kFfDqcVABmgj/nOTS8uhbO50Vj/XSP4LvUKaaz2ZpF/EHgeVUnBun6M7bqDD9si0OBa5yOzYNJ9vVVcPHUPOOzzk3vSV07WY9TtzKIKDNKc7DC+d0u7jhxflAOy13ML4nPyV/qaaV/0KLI+i7zsrQtqvY93aoXKYMPeEepp3gYL6nrOUekmZJaYICfKu/pBxmGQUFvksU5cOurrwoCLFj2NNyBoL08io60WhXIlCjOu2KayLNyevqXoU7an3VcauU74SWp/TP2T53lnjGy5j4nDy+7evPKtF5b38jUj61JiLj3+11A35x8pndGLExyl73xM0U7Lr2rfVbON+Q+Xo3Yg+DTGWlyz+Dx5BXF2le1d0uftGF2rcS/OW5QlWJdYoqQC9q/Ta0HfT0deURzf9DPX0ghcM1RwSCp2XZyU80frMFh37s3g8+hfYwP5OFzIPVFbeE2pFxDWy6o/BOW9QnNbdsXbSsx96PtD5LD2poXXjLUhQ3I11Y6AI6WIHAcVns2UMtuSm3yNmHsboW7STjSXox1cEbguJq8TIh/xrhP1OVyxH/zqfAGmFORCisJOzvM2D6Y8OV8n9UtCx5Xo681Ly02cUwnoth6rzeHdloerCjVfBpPta4UlWuYsp4N6V7IZ+fAqC5PZdKJXgJajstQ9zhzxZtp1wvLflh1Yyzq9lvCCdYWf8SOg2v3fE6ZU9UPwmShOzdpnlmLIh7oaXqEcUSfnoe/5aOxKMqowFmly326CuQpvi4j1Z36MuVvPpal7d2jM2y/tojjfGtSg8MQJQpoNdug/H5CVGXqoBabGY1VGoQSYX66upVVdM9TM1HWUEHbDhWmUytbEUuqPys6CdkLfz2KT1QkR6041RiyZg4tw9WNuZvG7655r9dB6UuaASNbw6qX7/qgIkb2MdmsAN0lQ6WJEvKtRedTJ1eCSKKIRGDz0cCa97wCUaswZljOx5KBlO2lV4+x2k19qpq2FHictAYkY8abQkI++6V+UKO/6ddhNSDMWe3vp7zFR9j4ScIrJzH7qnscRuutpJ7iBXx1tPFCfffMpkxzLuN9LtUFApeaAievRY36mPl7a/HZ0ZR5mzhZOPEjs0oO3BU90nui/c2Dj5kLa2RtOSq/S9KWmU5RRdGsm2azxs8l6bk3HV8D6dSSdb5HrrAB1rP9K/GuaAXFollaPkVTKOMrDIBMvGw52Ac3JPafSCQVpDRWjF9Okm54cSsu7Z8tfTK86PnQr+ofQTxy0nOId76qxbPABWd1uIW6D0fFssxUjV6H3Kajfkc5yR3ut75fJbl0/mRslcWLoS9zrpArioM9HtfTGaT2czVws/tqYXy6BVUa8RaVlhCX18x0lw8VzKcRmFA6dW/I4/3czzMC2ACDCJcVDr0R47Osn5bswmN94dKzUPNRu6xzgqfzLYafbB/yZAWxX5r3mRW9YBS38BmRzruRazO4wJvGY33XhHlrbeHoePL69qyYcrezAf/BGc8mBjgZmhfQbLWnUHcnh0Z+rV33VMhdbV3xHG745cF5w8ZZY02lhHMiSfx9Evx1VNylFlDEyDvhMHnH7lll566BjYlzc/DazkZsjF4HfVLN4p7RCLmk9acNuFbz8I5UrrFajPouP1OPlFg5S0QGg/jnvVKJr/63UgEWcfYEikJEShUdmPTUcnYOaaS+CoHT55JFbjWtanN0uzTbTA8vMUVFuXgFqK7eN5nNkPJuCX44obJIp1l/Je8d/2w8YuUWbeTkacm8QCe2D8sAmUHTs4uCVPjaJ15B5pHQ3b7ROtaHtI5xt8f0q07muXE39l6MPxzpLCh+fU8+g9Isa8WO6GJ9SWnFpmq/wcdk8MyhFjblzuex6NQT6vNzb3TLYuy93/u0FEGBWkaljqouAfZJHG7oYQKdCtNosXRm9kPInsxCaI1V76AivV9atHaEDqcuJ8v0JLTksmNuloi0UyVshpAnrjstYOdQe3yYCRfF1CE5nZmo2uiVuNHLa6aRQnBmMV9+NCkT+4ULbplTTev8Psra3gulXrZt6WpcSfIOFGKeo0yx388Vcz+OmjC1oJ6kD0ihHvXF9/y+s4NHbmbojQTlgRsPN3DkQyYupQMdkw6SI5C75rj9QUlJ7o9sjPT526SEV8qvY4YaD3XEswfeMJATfgNN/ahX+AJeWVET78HjenjJfjQT/j3Y+OQuUXFNpcDXGR5TuBCTQ15FN2Q69wWczVujM016B//MvscU5Kpxz5cB4RE1OzSpKC19WivqE5vG+TmkK9KYqCojjzz51TotOIh7UbXzoDBc61y3puQwuj63uQSUR/pB/ICemPpAnLj1LcAiZ/OWHaahwNd3dSaO+8xuzI2R6hYL9VqY+bJFUkcwcQKGEVn7G81BQaqdukLNEco96iZ7qJbz6QumyCBRunxWDpW57/Ey8SVvX+QSOLI/LHgiKOhc7+/8+L7b4BpI25zv5Wz+lvQck1tC405IXUuOhMyLGkD56KeZ9czIpAgmS2F2zZgbPytDVvgx3TItkBMoVEyezc27tuEfVTBkGI+0Pg6B8krkdHz40RB5hx5yxhdV7gTs3/B3KHh7M3+b042JWmPY9kw4hXv1Mo8ugly450uujpSd9ZEuaf5PWv2OH+obOz35c8Q1ZJM++K7EjD1Y+XaxGOwd64XFCVR1/MDNXVBPEC/NSwtktY9mEA7HMQ1sz82GrO4bZWyCHNsK1bnlg2AOyaTgsAcy/ZxigPOcWe5kAhIeoXNJvHKfYG+X9sQNc+pgdtZ+W8wAjxEwruSyjezCj77SgXnR9+aGIgkllIMwzu/MR/sn2ntOpZY8ygnmLwI9D4ZqWq/JRYPC97iZ00wPHpUNz4CFCMLWFh4y8xBvlAV1cfDPPurG3WwgMkWAFZ5nn4xIms9704ZD611XYsMSX19zMGWM51VKSFCsHZMl+ApcF2kH89oF1KyfgH1+XKq8AZAexzgx0yeQQKXg2vNaOCbqSaRmTlWwspySWxtynhM1Ml7cgqMYzJyczdom/LeLVjJSy/oDRo+9Vmxm9KWKj05ZTp+UJt0zTvBRAG+iKPak1crcrwCVQz7XWdNNE3Zf0lOvNynCmAYEgT7yWz5YhzY9RdrUjfApiXv67zSYzCWxeJU1DZRSZhLC9sfCu7vDExdAmqnqXHwOjfe+6gxtlzq3GpNPE1TtpnkcjY+YGW15dF/uFekoPKVO63omI/qM/RXT9eOJK4MMCqGO12pMmBNCCvrXhwTw5BQ3WpcHoQ5K3HZ7kuX+xtQ61p7yX5sTjcXvHKYDPzoEJA68dx5qmL0e30a8u5tAQPe9taENciBp1dZtNrbtzIJHQ9LUYEvzpCcmJUUk0Rf/HIOyIQZokvVtNmA5d8uL8FDrsi/AkAxr9CDIcqIYvDOL/VTML/siDHff/04o/zHlZHGcvixZaXZu8+jgGeH9TByEt7R6Ep6OVCjh4+fh7XoamyttkB+nZ6I99D1M9qk28uqTsQQfZy6YCsYS3sbeH3MgZ07/noUnRpkgF6Bh12Ic2O7/XWCidK6i/3gLoUjT5G+hv+Va8KS2jmxkLvMDPX6VZvnip2SY3Qqvwrehe275RHf4tXSCRyE0mUd2UL0UK+aCmjPkk6c7TB17W37xtYFdL3HFt0J2OXcoyWu9QERbbX4/UrZ2bl0XKjzlRmc2f11KNlMU39FwNNYToe/DwDBgLaMnWwvIT8PeFq61XT3ttH4eF3HWQivj2FPtBntS0UUcAfbnNS63WQuX2Xu78a3AXkd5QpPMsHlb57ztBUR/0FIUc02joVTVz4nByb/v7vstLi76XCr4pt5qm57duYzYVKrpZxvyQ5H5ZNXw812pKarXUnRYoY+JLoYgCnEpuzOVtZMxMt4zOGnOk/wfZs4qXyS1TSaL+Rie2HfwW/AcD1Y79aGM+r+YQDyPM5KcHGoXx1i02e7Um2dYMzEQjE783JNvvDVeSgkMSah6eFlzPUYTmdsZfbszeClpTvXGWS+/dlIhYtC5Kjpas92z0VcgWrMymYiW0Nne4HXCaSHV5/zjLwZvyKYeueS2ZeBEYVQPtk40NU8Mm32N/pFSSVyprQHHwL5Hv8Xp2DOwavszTuLi/KjwWHqN/+Q5VXvZl1yKC2txHAwGeSDQUhbcWnJ/G2WWwzpdlJyQcoActyXE1VIEWEoNzCX2NYWNfmBoyZgSK3fI7vCpp0cpHIuXfKF0EX8DMsOmE3SxNU3SOdmWihJOQSoGGdU6FWJRDMbkQEVugBb2goQF91O3cxQouHhe7TgD79QJkqT4xWA801OnUA3QEzwJQlwmvy76NGEbmBfhjr/TS0xIBInnmQrofVwvaBss9jEa5XNwtN5bSmSK1aJ14l2uSPdy5X1k++OI8rTbZqzzK7dC69Gjr2JHKiaa812NSlfyK6zTYjzrJg2Sx1I0TkszXJaKibP3J7DKolc69mmTpb3P55hpOayuOeMxq/wsJsGwXRqk6NeVcUoTQAcynBp7JFtnXrWsRe3pNdsUsJn9P+YUehEKZW5kc3RyZWFtCmVuZG9iagoxNDY3IDAgb2JqCjw8Ci9MZW5ndGgxIDIwMzAKL0xlbmd0aDIgMTM0NDYKL0xlbmd0aDMgMAovTGVuZ3RoIDE0Njk0ICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjajbUFUJ3bsi1MIAR394W7u7u7u7Nw1sI1uAV3Ce4QCBbcXYK7BbdgwSVYeGTvfc/Ouf9f9V5RBd9oGbNHz+4JJamKOqOoBdgMKAUGuTKyMrHwAcQVFWV5ACws7EwsLGzwlJQaNq72wH/M8JRaQGcXGzCI748AcWegqeurTcLU9TVOEQwCyLnZA1jZAaxcfKzcfCwsADYWFt7/CQQ78wEkTN1tLACKTAA5MAjoAk8pDnb0craxsnZ9PeZ/PgE05rQAVl5eboa/0gGiDkBnG3NTEEDR1NUa6PB6ormpPUAdbG4DdPX6LwoaAWtXV0c+ZmYPDw8mUwcXJrCzlRAtA8DDxtUaoAZ0ATq7Ay0AvwUDlEwdgH8rY4KnBGhY27j8bVcHW7p6mDoDAa8GextzIMjlNcMNZAF0BrweDlCXVQAoOwJBfwcr/B3AAPinNwBWJtb/0P2T/ZvIBvRXsqm5OdjB0RTkZQOyAlja2AMBylIKTK6ergwAU5DF70BTexfwa76pu6mNvanZa8BflZsCpERVAaavAv+R52LubOPo6sLkYmP/WyLzb5rXLkuCLMTBDg5AkKsL/O/6JGycgeavbfdi/vtm7UBgD5DPP8DSBmRh+VuEhZsjsybIxskNKCvxT8irCf5fmxXQFcDJwsPNzsMOADoBgJ7m1sy/6TW8HIF/OVl/m18V+Po4gh0Blq8igL42lsDXP/A+LqbuQICrsxvQ1+dPx38jeFZWgIWNuSvADGhlA4L/l/3VDLT8G79evrONJ0Cf5XX2WAEsv3/+82X4Ol4WYJC917/hf90vs7SmpKKYNv3fiv/jExMDewJ8GNk4AIxsnCwAVlZWFgD364fvf9OomNr8UwbLv7myIEsw4DX+r3Jf+/Q/Jbv/MwE0/2wHLeC/yZTAr2MLBND8O+UGLJws5q+/WP+fZ/2vlP+/Ef/N8n+b8v9dkJSbvf1fbpq//P8ft6mDjb3XPwGvU+vm+roBiuDXPQD971Bt4N9bqwi0sHFz+N9eWVfT100QBVm9TjMjKwcTC8ffdhsXKRtPoIWKjau59d8z87dd8/eu2duAgCpgF5vfj8trFgvL//K9Lpi53esD4vI6mH+7TF1et831r2v8jYGv+/TfdUiCzMEWvxePjZMLYOrsbOoF/3r1r4gT4MP6uqEWQM+/RhvAzAQCu76mAF41+wIswc7wv6+ZiwvALPbb9BfiZgcwy/6LOADMcv+i10iFfxE3gFnxP4jn1af1H8T76jP9D3rdAWZTe0frfy28PABms38RL4DZ/D+I8zd6XfY/8l+7xGzxB3zlA/4LX88Cuv4R/arAyvTPfC62V8vvd/r1ov8leZVm/QfkBDDb/AFf5dj+AV/PsPsDvpZv/x/I9lqOvamDmcUfJ74WbP/7Ev8j8ZXP/u/H7Q+aV6UO/9K8Vung9kfTXjlA/3pfVYH+9L5GO/7bs9eCHF/3C/xvl9heBTn+oYj1Ve+/8tle63G2Bv/hfg3/t1y2V70uNlZ/9vBVpIu9qcsfLWN95fhXDdtrCa6mf1b4yvEnfPW7/wvZXtV5/gFf6b3+gv812+Zuzs6vw//Xk/Q6+P+D//oPAwR6As3hlxfA5vwhtl9C2n/WiBJ4MO5NCECfpf/UYWOcKDKCdR2QnDHeSlTPzlqRL5da7meVMrLtUhJz+pmzvnjls1tHUu/FcctIInVgRWIWv/By+2YuyeeOkGwBpRkiXztVjJiv1HkAQoUoCrUbTtjCakCdEuU99Ze+pSQPKswyKW650Halzu6asiwFbEJerZNtNdcWs2GNPc5rXOrcbYe44J1og4g0pQwyeYHOe/hEj2rUzaGR4VW0fqKeqEA596C9c4owVzRJ/mFCCQ6Yaq3VAuSn3FL34wx0HzNpDHFsdcV8UjVc+GHxguSWM1yDJ3IYHdusCOsk+GNclI6CjO/F1uCAtHt2zt5gnrTcObJwq0gfC4E1KbWAzpbF8JEZnLtl6M2RXXuE7qk2mizy99afyy79/UZF1aNs06vM9RiZtPOACxj5/eh0HuYsCHct0qxpAp45A0WX6Z2hfJrWgDseQdVHLoW0Sy8TYg/CsndxKIwEbrJ2+2ii7rk9MW4x6tFb4zZh+VwqZYSwWtZRhFg74dfl6MQRCzt4fFSSi9QLojSl4TwrZT7VSpEJfgOIczA3lcSYvCQuyIIyORZETj5m2yIkO+i6AbkQY+TOwM0kSdi4nyQUdN6gecbPfW1LzoyoIPISIaXF0OyDCJcP1KeBVNA6PFdtRBBouNdP3yBrvtyyt+anpzUzd3WZ1IqjxbNzX2vZk40ErBzG396bQZfP4ygBVYhE5NtgtMxz1EJ4JKx5eMuPH28K8hy9d/IyFM4ypbL9Ded309vbHfQJdJ9u8w/u5+sN+5LvAFe3kcaX1lomi7KsP+Rud5/Hl8tL6AT6HXN93IXvEA0I26S70gx/Hh5mCUkA4PBcxxE0S8h/nfGM4wQzVK08AjsKDqQZAbQLrOIvHOQHvNvzjWJ+aeXJBxJRsmawv27eGSNwMcQK8q9GgRmmlG5kFbNs442f3LXAEHuUw2jW+aXDqjZvAC2ZnDoC64Pln+2TWDIUCIJD+uFGb9Fa188SMbehBUwTWx5V+DLrH81EciAdyCGof+LsXK1aTtZHVZIBd284SWkvbXAxUvHl+LLO2T0oUNeJHoqRXcJT9vePviNTvi8KuM6Zbvg4X075gpSpjEpWfeKh5fGFRe7jr2wDWOOVGsn2LYLGU8vLkEEUFTS4Q/IRVVen7iY3IXMWeooR81y+H/Mc5GPsavTWfm+83rBz1lY9b3OwyYd8agyuv3XYO1rT9mYiKLDaI7WNEX2Ln/9+dtI9ljcNiPbW6XZ0bdBLSt+pdxllsDcN0t42FP7wdq8UIilrnzCO1nbmY4kLlJB9aQ1BWSrbl3Kr79Pp0I4a+10isIrMPvptZqfyrEraOc8dRAQPRouuGAESpURfKb5d8m/1RzMwPIdWHjWK6apysqe/8+1W5e50UQO0jb7nIDLtb4l6JK7+IMiz+Ik1JU/3LKgeQH3Jb7/ATk7kKB+Bnwk1jW8BxoiIA7AR+fSoDAovrNrdjzzHNKDKu/VrUkKuYJX5t3GKGE+KmabTnUHP+WgmtsdlBqMzDjWvQUoXhFWw9igQRHBqdkuxHD7OhAgdKarhIL5ZRJlipbVwyrkzZRBYDCJRS02Z6RUr1fVfNs7/kprX1DL3k5W/QOvTV22sBzpDnU/Cj0jCd7BkEPJ6zR+CwyBsRVR5qIOB622u2zptDYEHzQKr2MhL963HKXb7qD63X10Y9SBz8x2cnEKd+2XRHdbmnCBcmXlNc8OUMKPeWZnrnWHPSYVbVhFJvLcvO1KI173vEbO9s6p8CK3JLPU1qT4P2smqsabAydinyIjTkBlgRKcW6LMyltLx5nPYvRu7e+wij9PPxuNr8oMXNCiTvN29ma0wG4KBRoE5Xi7f8sBbq4/1usp/ppz6SOpjWe4XSOOBr+mrOBjC9QSLIUbykQVypeDH8cB6DVUBeFSxRoxFEsIDAuezp0ysMS4ls3DTQl+UOefbr56Xcg9Q0+OyUxrP/H7J/FRoBg8JNHCK77xX7dRpPry5b46UK5xmNdK03TU7AAZzsQUR0VfzCTdfIVUPs1O97DKeS3boXDLrtz8XuvdkejeJyF90RluSH7U9+8Ncr9K7SMXzj/mylMmr5lYb4HatFuihZhGQjon7HXBQ3gV823hBJdyUQn15lK6cRlV6YjvC14wMTOQNko2fE/seQMTJdUm+8/h5nVxvnZOKjDrsdqXaFy20x+qz/vfvfsiBW3Vt2zJQtL4/1GeDF378mCPwoisYVAAI9Kh/nRomfE9rEjBgL72ls3T/dffL7WV9LpNvD7MPvmFR/II9blUjHM50KRoZNoWqu+TMrFYH4eagbq7I6I9eyO++hPSUvPezz5poLJd4gxvrL4rmcH3b/W0hHaxGz9WIi2hzFhWEurjvqYOT7oMrBJ6uddFysSVRlYsERBkp3n6Jp/66Ft2DqmyAXXbfKSYr/nY3yL/mvUc0z1adZuZsd95VkZpIrQiTWK5v2dviaK6C4MAgtpFCjRDlsoFfBEF4TRdIhvuJWkpDortUsnNjbGqsV49LCDErayJl3TCjYrl4ZIsn4onGQVk4VnlrbuLBQ9qJOtYyA5GfFZh+bFNqmDv7EboM64tnyKqN4TZF1o9QL+gsWUxwVVGnGl0KRUEm3CaobXa7aS16kl9mCXk5RLUuZFJtvrvxbH2XexaVDB2fRXwN65UXZnff2PkyZ+cpM8PNbhx1vBQx32DEkq8XFK8+SbmWulIhmGb7zpNTR1tXIqvgAIb0w02zAE3gPXDBuo8rJqKwwbopr3FRrPFRBAYLLzxvn0356KsaFg3pIa48Jl3NajVx7HN6c7Jr34jRd5gl5IsfZPykLHQEI/DvIFyl6x7hO19w7Kt55HRR4t8nrpDAt1ZwyvNGhcQRnc2jr15cOmTB6M5+GhoDYQKkhu+EGIVpG8mGS9818WolD70jnKUrifGwRZrDGLX6ObqSgpcaDc5S2Ouh1PWD1FMcfe4B8CEV59THN48htg1+74YAhE6e33XkR2PmHLSTqpB2TtrHsLL4SPW4HlHrHBXlJLR9l4fsl1SEWuSZ2dpzvVkY/UDzBJGryk5ip1eGAJf+YT3zKJ/jS+6UjTEv9iQxlVfhB5cRe9G5kTR2ug/bkCKedgwikO89u3dXmfNClqdnR1IXaZUXBERDe/HQdK615rWi3/KFhtkb2vZy2KvZoT+geAY6xecUGRw+o9rTLwYrE7Q/CEsl63ZDmmqPn8o4BGcf5X8xsM9ZgOMWMv9E9ygBlCwmfg9n+VST90baHZkLwwA6JHwKuUeT55isoz97itiB9cbf9IDl2fobME60yVJ8eJ4eMuusIJ5z82R0iBMmD3zYp9SZ6oE+bhRll9XOh7EhjfQooK5OLuOR7841BD86BPOltR7AGg4biBB91h4jjXABXCMfDzpmAMiS9BqPJfyaM6vm6oHGHSB+1l6ERMrpNAuV78PK2rWreLgOviYPD21Ah39843q/NXa2v2lSt/R8R/yA4RLbWGrJFFHRCwM6sTb/PPpSVgC6kuSM0eOII6eAefJlp+4uaarsV5xoL/ELuyyEtJq7Nc9OVz0oqz/UZ9qK52Miw/sc0lZX7AlPGGmn13ldu7lESQmBYSzlKFeqIlgO1TJsX4ESvhq4eUy9MohnkYBGv6FfF1r6XB7Mx38FMTAqrNzqzbmPeYeqPEXrIoM7iKg65Yi99+6X0V1/E2hCoY4UojjfArfHm1AgmZ2kN3bJnDv4vPM7JTK8uLp+xMVyBQhP4hjuMuqRjXuJiu0zmbcG73vbWoXRxu+O2IYPaiu1k7j50grrtUxRPFMOeknnxs/hok2+jN6rl0hxKxZn/Pyeryv/0TFt6JIf4LeQLjAzeULZtR3m7Mm01MlbQr+mEVjxUnZSsuIjOgjBgXfnHUOba55iWuwfpgCwpeRrhWIItxAugwAzLqZDy4K+knjxkOk6gRbIXmA3pn7+bCKQmFpI+dmmqDpvi9we/jKIa8sUm/qQdSuSlZLgq3r+a4jeIVeKhHdeYj5/9NN51S4cUow9dEEXNumnlT1d9brnMJASVPFuUPT+dio5qwcFE1e9rGcmGtSYGUnAGN+oxNel6ZBPgu1H1C+q2D9OwsxhF+g0jO382TZcj2pWgDWU7/1tZUKKyLwSgMssDAmc09qlSHdMo1f3mmPZD3FZ4QIxBsgl8hwHA19lLzHfaBkkahZC+/lV6ETivFzwkPbcssf0F3XfVRZG8Yq7Ufb6KQX0DTfSqB6QeZM+WG7dbhe3oLn4S3TLduLNZAPIO4pTLpjRypdHZvUKXObF4S2wbshb7j1nc/HXFad2OpOYgvmbkuj94OwQUb2qvfeU+ydQJr6517LssFre680zSk0Zq2zKF0d8VZAHiARRaPa+k26RI13LUYTqvovZRyxCLsXZLL1xo26dcCh/fLNnBalbT8h06Pa1UBHfnUgvuuSk2HKMSulxdSREuu6nFwSkyOGqsMTjhZZfYTYeJbjaqeg0lQPHZWT946xq6sPelIh751AUhSPpXCWOehAP+GEmhbKeTdh0w3aWcWEL0o/POvljxj3rOEYTh/xoIZXEd7eAb3NE2zApkHjqkbqbqJso1j6LOaGTlF2XngmGwe4JdALJQE1xJ5GBaKrDHXGmR2qQdlEw/qWWkAHOI4spOQRBDtKHrki8U19Dz6cgdzXLJlrXQtRCF4WJg6LHNXGX7BhjbRF/8v6DhnxuG+XUzq4RndLlzWRXKh+YADObmYK2Nl2QVo/T2+agsbxNUp1gvDh98I/xt4UeT8anALIKkc3daQzDaZQ8OY688KMZZ3XJ2nFEeogqhgZ0ltRx3s8lqRLav6S+kIWFi7+Mi9is7uvPpcGJQP3w4wlMxr/1dHWPzylnisSw1KyjQEIw8uz3cTiEqDMJi418ICE/CNT7tscwfvGs+vhL7Ute5eRK49tBpIiADyhVgGzKQOIQ9WHzHyxbbdIFBftMK7P7/N8E706Tj2p4bl28jrsaqa/0DFFFPetGPPQWjmwJpBtylGUHw7gjSxjGOPfA+7efNrpqewVm7ERKKF1kDUxMRGX9YWulGFNkjiF3MAjNTD5vHYMCph94WNA7iy8ov6m2xBg5Zk4O9rsFuRgm16lzZDLXEeAsx7l1icMc1l1x0Ee9i8ZU1wtwOnc0ma7CKzwPjLSXqNpMnvnIwsGo5rFKwZLC94y+dNSLNfOLfNL7vCeSAVdaYVwBFtYD5mkmP0PeaEFpKval3OwihXazK+5ANR2cMsL55XpH0z+bO2h/5wV19APdpKh0GUcvq3O8Q+zhC9KP8SxBk6lvwbICV6h0WpWCNKM8BT/J6X/S73lxHwqDhKcoJUQDXIqlK9fT+dlzsLZ+cUIZ0dkOZ8kc32tGssJAgIgHmNNRazwg60zUGzMTrcpFCEw/U7vMEMq4t3vCSe73ta8Y0aa0Oga9nYmbvco6L6OleEirGyDCOdRzz5cSa1VAlk7jcWer6CI35dSB/z45xLESknPN+9NC6asIe6YBr7ZrTlxG9YKGwh2a/zkl349d7JlM3KXCqTkdKN4bNEbDOB8q4/t9Vnwzbl9vqrAKcg5DS3zMr/icNDX2Ap8Q343Dd0tQMRpi1LzJm+WvTVAeSFfjuuxMmrxOOxIDh/EXJqYDglNjKdGZTN9wMLf1MOp7XzoaW6TVbasP7bLtbdiZdqjEgmm9TX3DhPxXvoek20mfLflJpHN2XXDOgYUgRQxFZTsdjGcakz7DnfWfElYXlcsV1QSBTyKN3n5NdGiaDCMGR3VGCTiewQTKaYu0VI1BUA6kra3l2EGAMd00wnZXZ2MCCKBvQmxWESskmYfrI5v3bqo4bEF7H1S7XPs7lpSlnoTIudBUNEXfkOksH2U2jz1+WGUWM7rvoYGKi04RQQZRkEfOoOCcyfSsrx0UJbPA7tS3WfYsODMs7FpAEG/FXI6tktWGHJ85dfykT3d5EkYAFZWhd/hFD99LJKKlK0RXX8J5J4ovAyqzoh63E2OjvcrEJUd/XchtFCxNeVbMVGfIudxlkpZeHjzRTBGlzKULba9YuRjeTfAQmfBnor+bq8TWghBsXouztvsSmp0UpwEj9/NWRxGdLoVnzGjZ+9gc+DEG/ct0qCRtuVoKYhdpGtfbAY/Gi/zQ7QsSgsw59lS0DmpqLjKXr50Oh8FEsiaCLOaUVlOeD8GW5zgJbDRWrD9Qf0Hvq1LhYJGj/cpw/KAc3XgF5a4rkieD9zJnluD/OP9BWv/ujGI6w0CBc6QgFXPiYeVxHuIDvGUxJHMvChea8u2tV3LCdESwxs4Yb/r7mEdD5QXoIZNk28rD5TpWVPoP4RnaWMHUFiVKGTvLfTTRpfYbA6zlVWJfVyRwjQy3Ctu9YS/pexQ4aljTwgrQVT9qojvBRxOX32BKZySJ4RK3vyCtJmmJUuefqt+UPjnNoqUg2rrv/ewJpo3FrkPnOK25QJ5Fnopqx0MAvp8ntBnoIrA3cUMLZo7aT4YGt7kJGS/VaUDSGqlDTzMSh4I8fJ8ZED7oKTVLYk2Jv7muvkPQRYhRgwY2ePfO3Z6zt/90aSRyDJh6GKBAPH1mCx1XkxHGYQLkO4aW5JL30Cuh9uKkPawBhVQuC51xv13JDlN64R7fi7yF+ARirKgprurfVueoXe5tnPNpmliIKdY918fF6VM+tQ4plvy0YROde2iAhkeHLuYZASm+MPWR/NMYjd8MueBgD/5kAtJNUt+gjkHp8ILXpupEI+fhSbnxsyOVGmdvhLrcghEgY2J3rsn5OkWna99tES+yIHYdKStxJ7uANgd9MJn1M4NYKfcpinRIrfK8xBCKJGBvwq5zYtSgbkw5dLj0UhcvWuSnNSg9hY/uonS/PlJEVTgjJbhBRRJXIkD9yqA66uHTEWBtKW83t7gmpFq8DvfhKvRK0VpVNSl3xlNxMu2jhPuS4WF0W97i7UPVTEnFllw5XYvGdgH5KtnVjPU3gtDFCxttY06MkY8p+Lim8uMaZeX4VxSL5OEQ4S1M0EengMDBtHMEXShTaIfvAX2CkPGTlpfa2+T92Bhxh66DtlTe9qGymzoieK6ZgQMbzZuNyf74gTV0D2enHZDUQibOvcw6xEQZqA1aND+gXMDlUKHSIb1qxRChF92w80Dq+t7th7wihpx0J65YIB8CNqf95uOvaZXTjt4xZKoU7oh7ZqyqLLwvGbcn8JJJnnS6megwd2dlFEt7vQNrJd1bMbMFJXk/cq5O/cvDzliTOV+GcbMMhLBKZm+ta8j3WaelxmIMckxEK+1ghtSFbZtltlOX+OjVESWa+AzpZYo1BjhxMHujLLESDLTi+AxGrCpvWIbPqj5tA5JlZ764+sL31YMl+uxQLl1ehOjnvmIT90ssPfOsPnBu+KbYb0X/CFJnEDkrMGDqudXJW18bPuI7nORH/5DROI6w7ksO553GbyEseHDorCOGklkwqOKWgcDWv4lv8UAy8Clvb7uqv7BAowG21wB6y7z6y1XZBGlpURoFmjNGkwpjlTdtod9EuGACzC94qNTN1lMaUwNmTj4NlFOTbbbtAj0Xy86ex9tIzizyjv39D/mJyaQ4u9B02RYNCDIRkqe+vBx+5Xbkc9Lv640TpeomuxwxTVAzNdncsAqJ5mPwuqhvsmpM7V0+OyChCWUnBMjaZSSIARUcBODT3nTHnI0JIKFVX1X1jFzIobyhgX4oWmdK8oNylWbbuXraZ1Kkg27o+Umrl5KnVWhdx46fISupzs2k/khSfRMl8FE+Ns40/ahOj05uFVHD7AXVV3sKUsdzyVOkxm3OTS6oOGSucO5nhLzblNkHKLh08pcKuCgm/3m7LeR2osuB4euNLdqgGfdl2xsykgr4DQP5KZhMT30OXLzbKqfeAzdrBBq4xGZEKPq53P5e41xIAluJtguMI77Fhj1/4Y1PKOG3L1y2ZB1pLB1fh9WMtDnSGCp0xHtVvGDz2gyueZqrIsoKmI3BBoMPn4zSuLqLAtnW3BHcR/s8c45S0R8ymbveLF8UTnp9QSAPJHsp7CLYXGspYRO7TX2sTI3Jm4va6zM3YsS4/GKnWhdQt33s7liRQkLKEab/nClWNs/flybJ8W6VYdoPmhI1qISAFEqRd5CitPfkbUBe4XZuPMY61HNIdpulDTkTpfmYRT+qvZm6MZn2UOVTJa0t6s9ZQHqWeUNaBEISUSIQV8P5o311DRNCQVEA39dTccEd3U1ymUPm1UWK776r5xljGQKnKwqySW0MQY5vIAQ6NOm/pIMhyfJoJiY+rclt1l8P8gHlECkfJLFRea+D+Rf0PAiXngm7DKzTEBDNCnKbiZ7LtQRackTUz77arzbUcWUGYm51zAXAQIytdPgGJcpOaH68UxpnpX7o9blf8Rb4WBdWGMVuXAcixVa2UPyMoyeTXEa7Yz+Hv97gZ++xny1zInEOggBekQ/SZV7wtdGBQnxIQibFDlL2S8uvqcgX7QvQVDq4J5VhYV6ecBgHyXHJpkiuj/A1BPayfhW5SnK35ir/aPFyl3DKKDwrEu2tGNkdCR0JCTQZHJhUF7xcQm4Yy2LkXzitpBzPygt9GLLvYur7FRQXr8YAiKWMQvr+5JXtO8r9tYUptKqt1S5ZnsLWKwkvXqlEnDI6DGJwL5PI9fgHdk8Z6cTAoj2lDCa4fLphUorU9wFxXxJWYVZ9v/pCRuxS70M6uF9Ht69aE5TGo/SFKmHw9OKqqzZAjrbXwDT6tmIkx0DVMKkZG11VSFiGmot/QZhFMg4pQs/xm9IAk64n/sIt0rdTOXGvX4f+aHRjfJ8ysOm4dlmU7lhZfemAaA+urDE09F/xvb8L2zwsz6uMoDEm7R4K9wS37LkOF1lHvPQAW51yvAWROsthA1G6QzwkMCd4aufnbwY1nU4i3j6SZsGofx64hYu8SW/OLHzfIHSti8ZPoKToWCG48FSnz1tBWxkHJ0sE6+E8IcA7hi45aRUuARW01QC/WB1naP4J45mVgsmyrCpXBEZkXO0GV6HGewEBxFjXe2LM1Xpx3cBQFLTaGO+c1i15Mz+hB1fIwoViCxZvAYfbdSMQCsvaORHzdtwf0cl8+PGSMCNklNXYalqJZazPAMIWdLivqnOS7gy63Qf+yiE2/7UxMnMEL7ioHb3sU5curBoy6mFWXmXLO1In8exP4dizJo9IpyVPLfYZdzUwPzH+yUf1cFUKZgPqdmcmy7B4Chlpklu6zDkg9iYKwttUUG+itrsMMvOtPVDehDLsdjC3riX92MQJexw+pAiFQQnOOEUvZWXurZ0CFW4tHpfECQ1l9R6i4p7eRnvkQwZ5CIWIHbszbEAXFQRUJoKf24t7hxSPFUQFAQxcNKD7unJGEfoWm3pcJBi/9DtN+AoevvJAjLkn/uce9s1MSzmMplOC04PCCdLzy9Uxpim8m2Qnv6H3P2FB8CNyGL6Fxd6XYJWiimfcazclu2IHmxiS1n13FB5jJ4QJW+jNEl1dSMkqVYhTk1L73HDMrb4qJgOIgUkf+3zkAN33Expay/thvo6Hq9bbpR3oarBDtZ2LTmhNC0vPeoX8enWYEVarqr3zBnl3343hizV7t3HS4uNR6yGUn4p8Zr6e4PNTPsDExMBZc/oQ0zAJs+YywAiT4WHCdmJzppapsPvaaL+sNhiMNYMxWXPapcU2jcJGroCC7LzrGNT1c8QqsG2zfZVyn+e7OEZEg5SH8DIQJN6qfUlC//ihjKpNxeSR5MvUMWO3SiVxAGAIyXb0vnfd8iODeoN9/DDslvqXfTqXj2vQfn7u6TVJdSZatV58IjyahCJXKDSXKopH/LPXQ7KYZxxLrWb5G+aOD6akFTyjufpnJ4hT7jzxPHYQCumwUPjLfRER9dO5AtB1+owIo1JMBgwqxtPXc1MZCgZcW+9vG3Y/EKUzHT9fz3+Z5ibaocXpdpiM6yRi1JpN+nDNAycosBenIcJIZOc30hFu5orvrBvHnMa4PXOLm2pMqeU3PuMQkn2iT9x/0vA92JdxMBow+0ZCqYAo+wgF7rPe7mA6lZbiCL491SDUHj6b3TeOgW9XoOUAMV6zKBprtSV9IKFVvIqML5+fJqtZyab4Cgys9Dia+qBbvR2cTJnIdOwTlEOBMMfaSkKmHR4Z7laR2tKXcsRCR29snkNvV87NfcuENxzrIRHvURX8LR7ML+aWke/NQpHHthMVw5LvMPYnPeV65NeREOr4Ho6IM0IgZMfz6D6fGnOqb7rhF2V9rjZDje4bFbFNyr3T31ZbNLVvl9P6RSVx0a8z+RMt2gSDGEKOSliGSoBaHJ2w60h64p6XnzFsBT7FAcLIl8wpAe8Qbpfu22riV/JHpxYKcruFDvNTZnGgnYCizLBWV3eZKKV9zWSAFJkUusOS6ejsKo6tWM2OSkzoQal42RCBwJn/BF+5tH0r37NxupgZ5lUGErUxm2vIEye5gFMVNpOgqhWrTxo8TPcH3uSVvjEs+66rA6nNBxcsb196UFQ9J30BgH7Mr1qiP/4uhgGH2veIC6LmFJGDQBWitcRD1URKd3LhczlfBHpk9Nx3SyJZuo1G9znCrm0YKwGbPXenY6fC1b63gXDh5vac+fcL33yWtQVZBz6a6c8G+bhhR70XYOZbymlUKJVTpRa9Bzelk86Iso9SD0dk/KrEJiflerYow2mihAdohRfn+vPraVENYbAMXmXkUACNFpK60N8cnvCu13ukxmuNDSbkLVOb7cPMRfyojFG5KG/K3NWhILZNKRfBWf1hN9zTq2C87JOSJ7J4uP+MEHeJgFWPyYylAFryYkgU42w2zJnWKDM+G3SDhYUuSxtf0n5nw1vROt/FKL9gDzXz+dD5IXDfI0+y414v+emzRPNIHzM/0/ZHkBk5wtHyyraYK0tMTb0nfclcJGUokt0mf1ug//0mBgwVpEoGWRlbULTl8dAmp4sG1eCymonMkZK41hmepWlYBu3AhRiEdBsONp2UulXI2OhmaBDE6dx7gcweOJQHEkiKQSyY6/sVMvM933gLOlBmVVF0EFKiArce44Pn8brFqKdcU1tc6f5uGMTHz4WyQWd7o2C+p7fnw9/Wd1/QpmtxPvb4w3FbJ2/bZCk3Soi4sWht5EB3KH1ez7jzUIq83PbgOYK9gxE3hT1faPZ5uVFt6TY646ZlpgdSbFAtkyJ7tBZD5Jp85VGSsxyKi/T9OcMqyWnXdp5T9dKUBSb5SMQJC1FS2mjg9cKw0zeOAzRo7gP3d6rcf6BID7XvPLOrckgi4rEMmrpauEKEE+r41Onlm60174HMm3HcoQDJI0gj3Kt6Gnt4ZGHaH+49fvhIRdhqZBO80pov+rODqW1CrpuLeaNpPE/spFtzg+ZGwqf0myOHGGYo5fyASNiVdrbVD2dCWOzTK4P1CZSvRcsxowhW26LzBoRTisoX4W4Xgw00s8YUdHCB10dnHhYoCd4mHYz2WsQtyaUNEwcIO0a7KRjJXG7DmbVn33OnFRmFi9iyMNxUQc6J7aszn/qXJPE+HvFJL+trsRTz7z8pCdVNAkHfThypn9M2BZBL7+BfGNsMGne3wb677je8IUqidFhDnqIzhNgjhgiNQTVL3XCGpVToIBd1J+JAjxbp96sB1ZT551RSqAWyjHukULkLk8HLFewfHOnQ3rixVIQeJFIAsYQALalqv8Tf2WbOXaiGekteiY2EmjQKOicvynpADF2QQPNku2tB+gkb8WjwHNbWnmW37BJFirL4GHDj0HpdfMJjdYjJ0+MUyKJhbnzTIcJeZ683GR5qeCuJnu4R993ZGy6V+KmkGJFsfL+ARWBHPM21ZIn6xmfySGdCmZJ5Gqq2U9UXFQcmz228TvBZ40TuOYzO560R/NRcvz7QFfNsnwNJmm9TAk1LbPfXndP03N1sxGalYltBEz+xNDUrYQ3UL5x4FXShpZs9tYii2iQs2xI6TYrRHd2LOM9P4lFMhgRys2Yyb9s8ZH9ow2+O2A3tmnO6zVx6yzmZdQE+w6yD4WcKer00poIgtvSXBF17UZO34Z0jjBIOpVnRcm0Gl8Z1z3oP0ZJ1z2CWPJEL90ztHGvAgSxxcDF3GcLGlKPLUiqw7Sm6VtXWibAeXD8tkOoJf5zRdhS0st5VXh8Ebb18Imm9/YGpYLMp/4tF1eex+9vMQf9zWKtXjO/RxFVpl6myHOZem4rRu0upPuwdpLz34sn0qXJVng9qT6lvVQ2ZLUS8JPzeyCKRbmN7Ft1u89lXEEUxlzA9LPj1qw2T1M3VLDsyBux0G0En9Rz0+C7E1gq2WeSRrzHHPG2ok7RXa8MvWfPJi80zUYwAZRmwtKfq0yetJ1OeNMmr0G9AtNAy9jovecschkBNM6+mMCQyw+rsYA7lmIjnRdLp+/UYIiu4vg+qychc1Tt0LZddRNOStsgQKJKehNBs0xad4Tr4rIQez9fbCNTPYbU92ZMToxFFLHQL+44/NDdLOLvXsVH92Q/HlwE1d9t9qD94hqrpNw2TJb6+KIbrEp4dbK4TzRmtH5nZdpxmrO5og7C1jPJ69PcGAeS7CbfJHKpPLAoCCCFtDPHVWON2BNhl9kE14NOio7M+Zs/rIRBtT+D0Qkh4lbIALOVcRGBxr3PCgWF5B5OmBxdCON2qizxfzfqi9ulFIJVyWXR3X23toKQmVPT23UvGLqfOgdcmQgTMQJKEzdvqmAdopR0aa63Pu8iO4sWYPw2K0OyC0CBm3srr5uVqtT8z0yAUNjV0V2F6e/o8yPvKtN4pqCJhdbWHzwal1/W0hufbDZ+EsDylVbgIVYJs6Ok+D62gzTdnkVwQtRG1a+vq7hRYmiWli6otm1WkSqiCfYRfMBFXBZWYrotHWddWohofZKskNyaeh66DClyO0CtpYUZzQ/GkybAcdb5RNhr1mJqr5KDHHqA4vdjdxJ6kZublb3h1QFkDQfFSRYRrTfI0EsW8hcHKX9rMGsP4FB52htUYUJC8OHhdljWbhlMpR8l7Ue8TkDlnVeEiv333Ew7l3miBVhdteRMVKPddbeBkoPG84T2NpXrNCJa7uxOP3LJ3v6kk+jyj5HF9grlLjbxrwrIomQNHb/5zv+Ek4aMw3lhzO33md68SH+QohowkO69i8p4dBGKZ8PiPY588UH0BTzs95AwrSendCalqPAzipPZhdszt+7XpKMmHmRckFlSf4pS6YiaOdMLrh20Mm4PJOuFin+hC6DDSxyGKBAklMm5r2PtYi3BM/XqcSVCe0iKYkXn80m1wUS+A1p6e6N/cY5hvJrRFWMN572MMgBvUpTQrudPUnkmxioJX2x4gzVtHbwt94z6d50kaiLSJJFXYbJ2btsjxo0kaskinPUfX43TKCmuMY2U5AILiTuzuRHwqKQtZdOoufbDPGGEULFPgKewBCMXIT4aYIp3xSY+q1VTnhA7bWpxfXb4tSAWMus5B3o5p0INjBLGhRyMMzCCtiLHiGZGnshKNeQ7lKzn+FEyJK4LrP6ApC//cz+tEEHkZydnpZUs/BD4dY5hqdTFFrDKusNt6BxfcDjw4JEU0AgMUPKArTwdTBbcKjuk2zUR3wVTUVfjJRJbM3GR9DA5gDKNmPkIUiVq/uXb6H3SdDO9iATdvy7vGV472S4dJWvNuiIZjEr7ly8vUO+7480Q/e4y31Um+OEwHPxx/7hw6kfqVbRC5JKoRSEYCbTV5BPuunsy/Sbywm9KCKeTHe98VmFNs0UvfFwp/2UZSdW+mKMPYGaS3RRZ7/TDRNGUukYzi5K2Tg5mp2PuPckovZIhq/RmWrnLCO7MWxQMLJl28nVNEtkdWG1sPx0KcFx+2KQ0VHGLKl6SH9MIBPvIFe4gV/CkZ9ZZF+2E+kyfZ5VWbQlFTnUym7mkouSn01Uf5HzSkrHVENMS4bPnNvCmg3FFANaa8esqaz/obNTlhNOGu75QRto1L6x68r/FZG5LiUUQgJoyouvZ9xM4h8LetSlSObpcuIEyF5n3gglSbRb5L9WHgXnsHsN7EixgavKs7nbjfhRaQ8WeyyXETVFR6wP4QN16+FwD/cpThUvuN/TrP8+3M1VaDoO08Yzv1jwIhU0pQYEQpYivv4ZJwHMTxeNyEuWbYx4aBIMsC7l7AXKnrORttYX4+ZkIBgdPjLbOjD9w1P2GlQWsV2mcueSWGCuh5h7DNcHXwEgFdNOAEpK90s4dpITSMLGugrP5y+g2Hz+UCMskNFII0vNDzVlx6R6mUrvPSwTViPY3voEnEGj5M0RLJi9Gc1qlJqNokTJDm66C/O7YN4Ror9NLwHjyyIGRgtoIZu2XuQKxD2GrA1UELbxYvmqcJW1tPOyUlUxELuyGGd9R3B0+rT9kVK9WXm18VUE6BnRymMnzd6tmEd0KOomfrRlNiY232nrE/7/XUjGX7KUPdc77f5SNxUc0gez2gQik9V4dtnLryZXiOjjNNo3E0ARMbDO1NzJ0rJPJhO+oTZUbe8bbPyRZncZ0vDLnhZyaANc6OuUt+FZfaZ320evfN3xkJ/8j28+zoRrzlod40CRP0fTH2EH5orw78inzjJpEgZP27OmiSU5y5zZWogaQb0nNLPe/nXiuQNDzjTH4jdyp3Ck9jY99xb6FFWVrUleQcAgGV2oIBMx8fPC6Uu8pEzsQQk8cGw2bfBCL/TlG0qJ7exzVjCNmHnHfAl1vGE+9ftCSRGpt3gyx5exsRkQx1vwjAhLxn3e8tmCRTRmtQrxjSghXD54bIqQppWMhC9L36brg+N2YhlBP3Tn9TLLVVblM0n0UiiIwLwaaVrgoMFXVwuRy2WNvxZd22+DqQpaQFXbY9HbpfRZmT17M5eyGopVIUzEWz4EQvrcueazjcQkVTJ6+OTeWNva6Q915A/cE2K/LRTkSjZr5FcsSLnmzkAvIuG+5kT1nKQ4V9EDl1NIlScPSImXa4cIGRPfyCf0w/Cd4EK3r2MKujxjH3UBQS/AhpEwlrM3osHbuk97D7TnI2fLxgPDzWG/YDJw5XMVyW5+6mU63ZcjV9/Kq3uAzM1qRNi+TXxuMV5QW0kp1qCExJkIjcbdGia4kdwRvGwa5hUufaOnh80OI5trStW0EjkjVMkr5vxzU1B6U3ib5jLYfGcpKUQiGAEapwWXyfB8M99evDNwe+xgpvI0J/i5LigscOFYdLXN6YoO/bHaxta6yc23P9BkL4n0vJH6n6KqpQ9kx5oYk7GjWPhETxODT6fslrL/8ARuRPIDOBVpYQmzK8AteS0HVG+zI5dZDN+AgI0G4MnE7DGua4GSCgD3JjV8CMnI/Fl0GZRJWn0TMKiIMrVlPWX30bQLwDL62EwfnZ4OLD4oy0xqiJS+Fz+HyOLYUb7GmqtU8yDSgR2WOgUsmWgZcPhisHOAoq+VVq7rM9WWzPiprGLCrGl3Tw1mU3EuH5csPKpvf8gTkfx/LvQqGERbB2M2yao8zTJCBuJPZEWKB6o29Y9ramSb/t7rynMpRLVwi0JvsOLX+IyqzI/26euh918LatD/t4bcXCNKGuK9Go6zZzZM9nO7yigyedSo12X0v/Z3UCq0gBtZVS5YbUEN6JjbiJKdvPxpeRrij5PfEcp4yUfEnOnxtJuTOPNsuIc+I62sjCHcPmUIZoaQvnk1NXDllh4HyuBl0r4luSNa69ZbC5qJfyvJpFM17d5IbHYPvPCSvvSfVsYPiUrV/A25L74KNwW/WvKhjwg7+EG9rXV66pUzkXWrTbBFsKHOxMYTWltbLiyxTmY+vnmi3rldYmy0AzVB3GeSy1QlpgCVT5T3uVpU0GiZ3HE84q82OP8QCf21QPF7XdE9SvCoarcFMxVzjvW0Pe5JYWAArDNiqjZi2WoqEmb4gjmDZUMRVqVeitqtY8venHglhZj2gYyxAS/T41DLzUChlposaYEKZfox/QNiQp8dnt2MkZvlMr3iHAKlZwoVfKNHyz8PY5PYTuXRgj18uE/egtdaDqNUOT/MNYYWnftmGE+RbJ/UD1Vn3V97jvEVdf/Cq/JWrWuSILJ0HB2FzP1BBBSCffFxGMMoqxeNarBGOgUXHOYbwBlyNy6h6/YS9u22y0g+5wP/BnCTO3Y/1YixrSH3q/pMpWSx01gjoHU+0yBwNHqXSW7P61k8hmXceTMZw3nHQFDhlNMJYgTk4+/U0tlPIXj7YN6BnN5MZjPWBRgu5sqL91oZ1GpjYPcmKX/KceQLac8f5tMOuw+NDCg9f0m1TnxnJk8gPtskQ6Sjtkw/09BWsrJAdD2h6fWLy08K/+2kj5o/RSVh9Lnu9HdPis3fSSYd1/ykWEXpv/GkjMqEZaR2RKax45EaZfKots/rx8oxXJOjUzfNJKbYa//7SV5b/IVrAyRHaanP2D5e3pM9ZTepCsxqAnDBqzJNUMFGmbQr8plVcTqno1XaJNfrU+xK0znaPCycf0T4I7WY+ZWSISuvDG8gNQgZa9GzZ691PWqHigiGJuKa/oGZMkJy4BsfEZzpbufe8vmPjygvMjOGoZkB9Ci81y/JzreLV13rJV3n4S3RqVcSsL/sIMhpOEh8BZepfp3nzYXKWpXpDHv8ZMi7xzH8ezGQhpJjB6jTtZPvW8dzywgv9r3svxKP9lS8SKwULSrUfd2r25I3Y0VfGUCEt3UiHlixoL2Qr9JwGedS0ZSX1b7FTcyEXrcWI0gn7N7mmdtCucYguqAR45s8m2gs/fW37xoH15MYU4Y9pBft+HguyKNZK2RLOyLzHbZpFNMoFtaD7m/i4VCvMLMlmWjwsyfaXBbZ7g+bPsbtzPKhH6nxJMVYq0lzh6C5RldxcfRWiGjqOwTg7Hua7OhbwcWTvcmPvh+RgkY0+emCYZLnJ10YVQRqlcV893bkdpVuhTZVOgHeuQO246cE+hxoyi6oOn5yd8LHLcoiIshHMoZ01byISaqCH79xkU1yXAVswiXiUmd27NB/c5QueKKpGTHWXmK9RKXUkt81HK75shUiJifNR3HBRLs33QjL2U5dTMKtNm8t9kHJNJigRavIHUgCueWZMoyMkF7m0JMn3wo6vGP1YWVpZKnUgm3ltriV13STbfzUT1asDKgnxEODdgEU3PNGP0MFVZ7IJz1dYqvrcZn4J3ok1eghFe5CSkUIXZSxDNGLU5KzYitV/nwkmZ4WTx1P1R4Jd4XLisNcqYpo1IJe4F1m+VxabFgYeOWtw1NhH1P4F96sD7sU1YOmndNjwyw80b6t0XhwnfzTL4E46SnDCKKg+vRe8EgwET0ipQjvGG7K2jQ8pmW+vZNZXqjF+XpiUEii2e0L1AvN+X/Pp7eSohBm/B7IJbrK4GH2Op7Ecre1VO569cRiFBzzrUCSr69/TTEXrYzLik1EGzXHbhgNwSCHMRFddapXc9Fl+P0R7z4rvif0CO3Y1VRz4+DdHXUKzhAlStzOXOfoAxAxLVqNC2e/zzsNUliPsa/ObfIFrONzl+QePH/ln5LUEyxOW4aeS+edifqG6wJtoQNlrwXHur27G/XDFDOeO8RT/6sAoHON+wSyi2p+gND4X6fGF03j/jFF5a/20nnmptmi1bEVEhpmgSprG2D0lz03DcMyA1SlMS16riRe+kbLfv22bc3SeLBMlZjY58MatFbuMWUqSnrWmeuBXIX4HNVkXYNryhjD+L+YxUDkQYm6BgtHy1WpO9pXEsrNpzAiUqoBzX08SG+eHVm9EoIPpPH/AqwYXMAN5yWBmjVnsJBCzqHuyfYwdf3t9yUyoP2a5+IgQhpRgu21ZRBDcacK0/yXN4pIXwJkw2DxZ8xF0kd0bvdCiW2iwSfumF9Rvo8eBuSb90AL1FNXk43w3mhbZF+sYNhFifL/mlXMRHmymHUrlORceyHFLLNdMCbe24KyMZMo//4oaFHYjROD28xYsAEoIWUWjWPq7i6OZVqXFqb+oxU0v1CsJzDoV4KJl8gZIsH5cYOHWXd4FWPV3WTBvfsCzrsxkT3mVMFsZKsmTYRIesXOAZNPCG7ftWAikegH8baZ3OPWkrLWO9DH+T7UxeK4D/7v8A9eGxFwplbmRzdHJlYW0KZW5kb2JqCjE0NjkgMCBvYmoKPDwKL0xlbmd0aDEgMjc1MAovTGVuZ3RoMiAyNDI3NAovTGVuZ3RoMyAwCi9MZW5ndGggMjU4MjggICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqM9gN0Xe/2BgrHtp0d22ps23Z2bLtx06hp0ti22lhNg8ZqbNtojLt7fuec9Py/b4x7R8bYWc98J56pdy0qMhV1JlELJzOglJOjOxMbMysfQFxRjY0VwMrKwczKyo5ARaVh424P/LcYgUoL6Opm4+TI95eCuCvQ1B0kkzB1B+kpOjkC5DzsAWwcADZuPjYePlZWADsrK+9/FJ1c+QASpp42FgBFZoCckyPQDYFK3MnZx9XGytodFOY/jwBaczoAGy8vD+O/zAGiDkBXG3NTR4Ciqbs10AEU0dzUHqDuZG4DdPf5Hxe0Atbu7s58LCxeXl7Mpg5uzE6uVkJ0jAAvG3drgBrQDejqCbQA/EkYoGTqAPwnM2YEKoCGtY3bP3J1J0t3L1NXIAAksLcxBzq6gSw8HC2ArgBQcIC6rAJA2Rno+I+ywj8KjIB/1wbAxsz2X3f/tv7jyMbxX8am5uZODs6mjj42jlYASxt7IEBZSoHZ3dudEWDqaPFH0dTezQlkb+ppamNvagZS+BdzU4CUqCrAFJTgv9NzM3e1cXZ3Y3azsf+TIssfN6AqSzpaiDs5OAAd3d0Q/vCTsHEFmoPK7sPyT2ftHJ28HP3+DSxtHC0s/yRh4eHMoulo4+IBlJX4twpIhPAmswK6A7hYWVl5eDkAQBcA0NvcmuWPew0fZ+C/Dtn+iEEZBPg5OzkDLEFJAANsLIGgfwh+bqaeQIC7qwcwwO/vg/9FCGxsAAsbc3eAGdDKxhHhzTtIDLT8B4Oa72rjDdBnBc0eG4D1z99/nwxB42Xh5Gjv86b+r/6yiCmLaUuJMfyT8X/PxMScvAF+TJysACZ2LlYA258h4wE9BPyvGxVTm3/T+MtW1tHSCcD7D1tQmf7D2PPfA0D77+WgA/yvLyUn0NQCAbRvQ27AysVqDvph+/886v8y+f834X+8/L8N+f8lJOVhb/+vY9p/nf//HJs62Nj7/FsBNLQe7qAFUHQCrYHj/1XVBv6ztIpACxsPh/97KutuCloEUUcr+/+W0cZNysYbaKFi425u/c+0/CPX/LNl9jaOQBUnN5s/1wqACdSa/3MGWi1zO9DV4QYayX8dAUGb878hJR3NnSz+rBg7FzfA1NXV1AcB1GQQ4gL4sYF20QLo/a8hBrAwOzq5g0wAoPQCAJZOrgh/OsrNBWAR/SP6B3GDpugN8QBYxN/QOwCLxBviBbBI/hfxsAJYpN4QG4BF+g2xA1hk3hAHgEX2DXECWOTeEIiL/BsCcVF4QyAuim8IxEXpDYG4KP8XvQNxUXlDIGBt818I0lRxe4PvQExV3xCIqdobAjFVf0MgphpvCMRU8w2BmGq9IRBT7TcEIqDzhkDxdd/IgJjqvSGQnel/ERtI09QcNIhv5yBPZm8IxNzM1dTcDgh6a1m6v8k5/iv/Z/P+ewByaP5fxAVyZu5kD5q6/0g4Of9IHBz+ogAaRxaLN8gOImhhA3QFuoHu7jclEJE3kqAZZAH+T1w2dhAj0Niaulm/Dc8fGxcP0LL/R8LxR8nb3N7U4S/PoHZYvkGQjeVfkPMPtPkrDAi/QS6OP9DzjRjbH8FbPK4/6k4ern9FAylY/QVB/t8Ic4L6be3jbA10/EsDJPsrPisoc9u/IKhYdn9BUL3t/4KgZvyVKei6ZnnzzAUydQQt/1txQJEcPRzM/ty6Vn8x+FMSpzeOIJ9Of1mx/SmJ89sxKIYz6FXv+D/jwsn2b+n/DgsnKBln0OXs9NZ+TlB9nO09/ur8n5K6vJEEhXfxcALdnP/b/j+N+qvMbKBc3nxwgYzcgA42/zuKXH90gJ5/FZsL5MQN9D78LxlQidzs/x4pDlCybh7Ozq5/Lsu3aKA83riA3j4s7tauwL/GFVQUdy+nvwxAjj3+gqC2ef4FQXS9/ho5kLX3XxDk3ucvCGLk+8YY5MkX6PpPqP+5wc09XEH1d//XOxa0J//B//piAgK9geYIC7NO5vxhtl/D2u9qRQm9mHbGBKeodrRT6Zj8Flw7PB5QYJPoajJD1lxvRJMGu9GWtyRpf4sskj77HbXUw35oTVRte/R/Mv6sNrnThjA/gfNzvPBI9FsfMTwRk4bIrv+zi79WsB1kC/h3OapcF493KCr5mHdevdLe3/rKl0YiZndUd2u45RGfyn8xxWrGGASXTFPlmWXN4JHDuDMRw9FjnHujTv++mcLIGX8llfvMgBBwHMtR5Ke3zh53P+O7UqnB7taFT4mvh0cM+RtjZJLaT2w/WQ53zq+0aPnnvHeLQBFpDjJjyjITGvM+e0aNjVq0Y1NPnefIQifbdm4SIIyG8CfmdmJdWROWsSu5MnZNm3E0pnsdhx2QeL/TUmyl/Ubrh6VtCtEi769vr4BpZF3ro6DmH36P9S4rg0M/mW4jUlrvBpuGtn4EaJcI9wkTWXmx0Uta60ctL/FlEKdAl7uPQnRpA2Wx0BgXPZPePUGbhgv4hVyIPRCwbRaysuq9g+4BHyE65cr1v4gLqGo4V+pcn0afs/fCMAx9DeVWejX7nnJ+XohvzSFpkhlBm5ch+eNj3uonGlX+r8CG6dBylvP1KhxmiTIfUoWETa75i37KIk5Z6Z6duerBKIMQH9t73gLmU60qzbZ4rgJVsu3jaKbR/YbgAbHQ8F7eG4v9zZuKCjEj7AMnrfvzKq9IscG4DQ2esLyPnWpBOV1uymnSotEQxbdbMz3wZhQnGmKeZA5Nn1C1NQQftka7ip+8GMtlJahoihYU++QPBD74LxjOSxT14NcGieB8/N0UxP61Q/mOFgwpE20ljipzdwmSwrhjsEnr4oVAjFBd4y6fwncdrGfu1+gW2fDkFqWffZVBCWdvM4CfKyYN0v4zNdncVlD61ZogbUzUi2VU86qml2j9IlFdxvRP4NrxwAiUGAVsBwcrltDYsQygsIwmc1WhiAHrdTYx23bTN0vkgfRTs2C1m8HXVazfAuEVggZduuCUaxLCkZqH8BO7T9xJOb+8pfNUHfsrCNOj8Ldtaqhmv2STzU0iKLCD4dbpZ6ejX28Vvacji7EM1wmBM99Hj4YD2xKYocEa+Pop+UINU4VDMo8sjImvDp0Gff1lQhRFDrWGGXqh96u43BiYnHs5GHWPHw9MPHcdAkWw1kTPRNfRzKzMDGHiVTRL2RCK1JyVF7kU15dPXhfqkZT+Na/BTqoCY3tjKYiIkyaBjQJZJGysZf5waa8CbQcOZ/VN9/OZq4PW56u5YWtVB0FwDQ21tRnCg/gmwlc8yUc4iMYjudD6SZ1X77xVBDjZZoivBN3H72TAFlGR+bnTWpoDXOU92A44HtM1xcGgbdJx6mXcq75OTgXc+OCRzL4EmpNQGay5nAUZtLbucipzepLpZxjfyrbm1559q3nC/rlrHk3AukZccakod6+Yo8taOfLJ0497De/LrMFdDwDCNcdlMJ2cA1/wFvJLlsYvXXnmZXm8w0G6H4y40kPyfqaR4npi0tkdCCTEPku0KFHyNBE97MqsU1op1dyyKpSU9o8HD5XNTIm46/pdeEfTwNHB6Lt+Ajh8+ZE5anYqqy9mNb4fbuVG8NMYPZwCFSvZe/F8Vz0S0nI/THPTp7fRFyP3dSNXTL7yfh8rXRPuLZD/gmNOhzc8frthcG6V415FLVy2FTfee/3oCKNx5NWRuNtOmWuhZCDenFE4tqh8MzsuUqTN7k26/86FTMHiV/UWodNR4DabFkvMgJigSN31wwkZa8IP3Twn33vKk/uJ2/4gqt6q75OD2F4bqxvshk7lPWuAauNnYVnrYVSP/Twxh1MpGFy+BjYUuPlkC7iyAR/u25CuxVUAsRRyaRiegiOX+qUXdRnT0VRtTba0710p7yGe4ABNwjbtyHO77JO10kP9rN+2MgYAJZUNUsHTfMYBG9qmobiLeoBc8fjQoVekSXsmogqvsMph2YyKdSEVmv7BJP0ItjcvcS2RxfJ3T5Z9B5WKJHseH4mWKJ26mALLrkA5zikXZrl4tmjB2m2WPX+133AXIi1F67wOtKMUTAI5XDRYKljYOz+1eacE9B+9Gri028ba5xBmoi/v5f2dFBGNMWenUrTR116IPncg+2wr7r9yB1Xk1hl13Jq6Z5PCiSizHeWxZIFJXZB+aeTwIaFI2zJHVVScm5Du03te0Yd0qHxt9R2PkXoPA49hX35fQd4tJ1QNhJrXEvukLUYUsKdFFw+1rD6wZIPPmHcWvFpypoaB04xT1NEmFdsPWd6hksW836EANRnkfQdVjGesP6ajFYmO2391rZpbg6yh8vrbMzT3HgE4xJjg/LJIEOr63jrxoTnRA+ciPizGhMHejwDuFqh6OxtvLGKJy81BILOmYuo56YuvOYptXJszivVTr4KNhL58uz8Wo+aTIkO/mXNQBL/fSvkIfHCVKgfOvpYW/3xe+2jkFAoyRrUys3L2ug1j+7t3FbMkocc9EeFWOYIGgusi5FLrD/JSdxp5Zo+pnzu1guPsae2tqn9kLZr9qopdIWveL59Jnhk22Hi+N+VUZcOtHQkEVPBuumgVNZeMFAES3IzsTvLdLOIyHFF8P/+Ag60q2CvkmyVSyPbFN4xDPy93RYwqyi8P9VSS6IxWlObhpZRVsHmhThSdtyLccLTn9gH/IAf/SUq54VJAHn7nSGW0UDlOVBRvyDMi8f3qL/t86r2GO92duXqahTEw7cWJnVDqrnzWYHnACz52OosNvM3CLw94s2yCbnGEd/29vqIyRTM5HOAIPsyX+rneLZsr7myiHlXlWUjOouPM+pwF6vPsZl5fGMwB7mWFp0pAAjxrIomRryuIfeRpZCSB2xX8DDzOBoVe3vgYupvSCEYrXJLVo8v7idgYRDnGyy3J0g23G+cHQbQkokj2TZiTp2wmy+88mK88GE63FsgfroT4zxnf+w7WT1IhOLXVrOV6Qw6wprqFoM8O4GNos0l5j59O76esAaxeHD7HNnhr+fxuWESfzCI3pPANxEspcfa8/IGcRRzxOe1mvq+U2VWM9yMltmOzrniVONtDr3u5hQjFbek7sAk7pQ/T0fK14LlT/AVdh9OJ9sFSD7QwiPMxSldRxeD86xtFFi+V3Y8n4AlVaE9r9O1RAj+L85K2+jDDMMsh3bqKeQUapD8hi0uahGsd5Fuvdwz2UmGrJo5JFTmpdPRuTqKHvqcsj+8gn+TgaL9V4Z3jPdaLdeP9AE5jslVFgnzsuqh9z6f0peh4wG3qd/+XJlIRGMtQpEwjYu2VcGp2Ti+MMntfxF3KaXbtM8yvXivhP4+Vec1eC3bIlYtzMIdGtXvopTAMV1iKTXzKY1Tr4CMHvxioJnUTStGXL9to4FzE7hspAW4sjAsUTUILI9SpNL4ptcrzfKqBY1BLmz3Wdy3KeBrQ/d2HeaLrAgxor5snQiqKvp3X/aDycI0wJgWuKZl/fPUScz4lH9o1gyoTQ/O1QQhcT1VkDFV2Yd2dqmD7NTmzS7+UjqeXuvMp13rMMQupDv9Al2Z6ybS7UaPngkbH9f30WhqLo7FjEqScQh4OIhxtsgjC173c4Yl0hKoGJWI/eamGtaVzI7sMs9Vux8NAnBMThZoCQ7gl00LNPPCWPr+nSAzPzTOFteXW3XzOxGu2VMr3mK4Ooq+nbklY3T+vLh311iDp09oEy26H3H6fO2VgmYn4ffaZRYQjExcnjfbDNYdj1n6v13h8RrLLiuuIuG7mHG7lppJqQck0aXBTMzadQa0g4PwdCqMZ1R4Tyb8lMKP+3f4D82BvciOD5Vi/r0+pZyjphvFXMd/qoQ85Edfswp7T7XAwEIdX7b/nPvTogJsWkL3Dnyl0QEhe+oBn5bVABosAd8ezIwWvK+Kjbhh4EZ4DkVZ1b+8kNntQmQhPmpzlScZ6v98UJfUlBGll7eG0THLNVD8yxPJOxZszSCY8F9v/EFb7qZ5/SZpkFgtNF5OcQ+yaa+HqafduQ8aAfKpCH+aOLXks7cScS+mOt9xZiJ1Hrk3s+arBjQXyNKrRiva6zBtblRHFXUi/evPdaKK0W19zMB+pGiWvITcWdeSDg081nU0DfpTn4IFt9412abRCr5qYEbGamKn99yPvb8a5c7pZfF+5FEtqoNZ2T5ewzqJgMePaqlAbCN+XPrLYFoav9y3kkQWsmnw5PZKg1iYKGijfnlXVaaGEkVkr2UMdcZctrGqHfz8ww7ygS3QhMu+IXeGajGZ8fey4HNwNh9yp/LTlLqoEzaSA7JgcnSIMbbyKY2QcvmSFldAQmI3z8xN/rAlJ7cRJqbzYh9VzbmBv449l0prPzQhCX/btv6s6f44gqMh/P0XXFdGYdd9QdMkSyw+rjk+DZNTym2/bHUquzlbkEMu74luoJC2r9BD/WCv2kqoLRFxZ/xc7DZqW82w0/myBeBdCx/ltj2B3ewVxtkSdXgDXKEEKgsvnuV6XX0XBwtvRE75s4uoMXUQFKmw9gSTAQQh19lFEjgq5ZMXFobAke9qLqQZKOJJW4KDqDouDsxKXPA+49jMwyp+uZ2l+1TRKJrlqXa9lqobTI1Yz4Htf717PdSkMliZgkmw4WTfp+BDsnmwaXBZuWnendCoHX55q37xIsqb5mOQxQ1BsYUTWkDBIHYbtFkBxNdWKbo0yoq8aoWBdrsu0ExKo7vriY8oFUFTRlS6bmeAn4YWKJTKEKpvE1QXDiQ4X4QpKWFmbpCVAuMv8LISY1RPaNCghY/shH23LDdsVO8R7ORlhl7lbaHFitqYmb9wupXrRvpefOrmGQmBvMb32EhhPZzm5LeWbYdLU3yScvyVrDiaiLkOdLPAK3Z5wCgX0dAR7hxmcJRVCZLClVVfO0XdlWiVK5uPTvsV3McWAlW1OVuB9Dp0cYjau+DL7cYPFMoBjLTnjaLi4f4tcuiwZvWqPxlLRbRJ2mlRHW/M54OmmoalZt7ch1ggxaHqOyna0n0ym8yxaWeYVbnA0jtT4vjGaf//D17HL95835Zo981C+BhsuUyBQLsCWoy7RiFsLB3OlCYSp4z0Tt50M1/64EEmswsuCTzetrA9iwkoTfW8eFXFg76QNXq9Ie/kJxhWsqYRiyWRuwlHXz3bJhMPfHsKTuNqklUbWPvf5/Xod2U79U97vKZXuoWO/L/kwkjWpZ4Jjzqb7RojB89ZHynaQJllNpxlbzncPSFXvPHyWKV0vk1eukjwQLAPMXXoYuH74BJI9rxjOZD0Q3nUdo2gyVoF3uufS7drwyVzmdiqH9y7RVhsDlYMPiKW0Ol9cJmBwhCo99td7maT5Ijcuen1Ds8MzZPQMqD5pnnjOd4GRIE/4bhnhrQ+LxfpQBSlpbDY3cgjiVescrQu3O2DJJdzhME1xhN0S22zNyYgsxwdbjoc+2zWrMR9l/H72pAnqd2DGoBlV3+qWaBtZsPd5IsXjScTJR9L9PSv+rarP4aDBqLFG2oPLWDoD9q5hga34EdWdJWYa8WU9dmTzHGGd82MdW5VjpPGjhFCAMwzDrmW5Fa81JfFDaR+4ociZ4r3JvNGvFmn5G36uCphCr/6jNqhtWVeXM9FSDkvRkDVddBuPyqDule2ey47Js+NfGLquaAfEEr9v+D7FLlqQwKoVQa4AEkXd0ZGOmMrjGbHoNltNywsHW603x3n1p0s8XuHU9/B45RPww9B7NeTLTHUot72sBhMblUP1NyG8owld+kMLfKBkaZNf223RuBRKymW7zBsC/K1Juig/+0psYmqOd8d7tenGMnQ8V+rxjZ3t9Y4X9mWyfb9ssH+/AOwi7Ac38bgTgSDwh6o8JsWeXY7cr0//abTITXjKku0z+Whiep6HUTyAHKVfMxaFZvKrc0Qyu6Dbi+AH9LEpCSFSnu+uzaWCaRguRoDtUO3eoMhjQdROQN97b9TdM+1GaNi0HccPbnGprI70KH4oNfbCemW+V9FJQxjRQs17705InZkUJqkHCh4hz+RNhAAm9aVU3SbJ+acr3wfdyoVbPYfbfhoZPH2kT8CefVYd2ihVMrMMZ1hq71wNy3Yih49dKNGEb/edvLI6tBo13Y6L96iXyp1ME+jB1HnwWWkur5yAeKBvhxiwunnlFNZhN+yO5yyVSR/Nz5i7TDf6MeGiAHT6mpmqeXIViHRblJAwwSN0TPez8CpNxjE+G6K1tEqUh/zATyJCTYCF3f1Zsa//+t5tpzjo0Y5XVuc6wbKgLHMScvN1/PmRAXOhWEfyuCQZbYD5cIOsa6KHnF5uSZ0HpWtRl6QGjmgf+sx2CZmY+teC6L4cEkPFbuT3ljY4jEUMwd/JSnk7lNyeX9UJNHTRP1Is4VUrpLxyjpWc7glngI3T3Bzf9ty/l8K10JJWmGVk2MNxw66UfilcN2JpLnOp74EavAG3julDrby/1yOp9A2TKHMXQeK4BWKkZ+TsK86LCNnSCWhbm2wrtkcKvc7lD74GbgDXT7Qk5Gb8U1pbdVADJkvEXaMGcH9qDIgO7CNa4j1DpBYkfFEJ4IeOIp9ql7+Zohj7rJHbhCJ1+2CwR/B8azSdCElMAInInwbf5X+oBpAw6A1BlvGY5n5V7XueSKXJUuUYMOP3pmiOI+W4IlUf4Su3NUM/WaJqdpf7DMsiNJSn/HqaSeyqtRrcje9tWNsC0y5Et3mW86PBMMg+6x6Sy6kTTX8vb0KKH41RibqUBtOmLMqgTkn1h+ylzPTo9QRpl7AG34nIvNQZ7p7SKyKsus6PFdY6gUq6Nut17VmY4A5OyxYpiSbrT0vM8980IOiM1KEnmUjCHb0CnhkRI/WUmiWxJ8TBr2tuEXURY9WggQ2+3dM35xztvwwQGufdT15edDe4TD7hSgUaZ3j/iA2F1Mz2j6bRSWjo/xAmLYKqOYvGLYtoQJoWtrd8I/TEN6hl/AgxKDRTzEsvyxqAM9VgQO43jw13xqG56PvzYtBjWyrTEnJkoTD8t8pa6RCdZHzkO9SQJ82izAcvuupzK8yoYWyXab51C1uKopaaZexIa1iXfsSizHY3J2SmFpEEBHlIaZ5PW+RV/qoTHyB2yx92AHqXvm7+4SEBBb0KM/XqmFFFfEGW5TMO7SXdohxnn2bP1RHZewXxP8/qf7Nomk98kss0WP6GQ1JFdIljfryIF/R0j+oPM+zeacs2+TNDdM/1JQq7umuy+gV/RM6zm1Ge1B8c/rI+fDxJazU+PhMh9SlDNaxef3lVQPOHDh8RVMjikrA5ww3Veh38iVLbspg6F2/EXeiPb+Cx5dnkPbO9md+s9jUO2le2jczKtPvgYJZov9JTAmK8YCrv9umwo2ISt8KNsJTIXKt6dH2LBIoiqHFdU/hFLWTFCU1YIoMDEJhsAeDcvgbD9peoI89RmfKcBPSjwfJM+HMr2kXqOyrRo9Wi5dI0ob04vcsPuN8mfo1InrHPT8io8PzArSE50d3WHmT6KjWIIan4ec4wfUDs42YOJQaY4QU7lYBqy+5m4C6ktdEGydbByHHt+4J+isV+P4tiZ6/JMGIwmwebQ1InOF7XxFFNP0ff4QBudcMrQRXjrROc4I8NGoB2VQ1n1SIlNf2SqFtlp8JnbWXucmaLnxtVBCJke3Mp+6JGBNW4e/KI1cviTfxU7ssjs+FtLNC76gzN2j+Q9Zsx8wDIR0h2YzJiFGZTrAikgdL3iJL8ceUkA2lQSim/pNqxu663LZzu7q37JOZ9wMWbZDhUHTd3EEaCthlHUKz1ccSpkeEb+3LAbHL8X16ugAf17muhHyk4maIpqiM3SGoiN8tnxWKNTzPl2hKDxfeJk2MoiWRsz/LPTxtmRiZvUKcWe2CSCaM1tkRsmwuZTOxVY1TeaX8nhhHt94xpSoXQ+0Y/0tHRUMyeUnuKWf1zT1wXyAs1eUiYpdf2ck25oN1A7YVOG9IULBLzuX0JDEWu6bdPp4h8viMX4/esXOpl32mA/EhrDvLtzX1s2ej2ipiY87lbrNBRixEvBdJH5CSN8azvI8kN2AYeqbNz6FiTphvsvYTqmvwQTeAB/V5hyZMlaMjpnw3pPx36snOWmd/e9CtwUl6wL0nVwVQceQnMm2yWrvPQoVRWGPcsILHmYslOXJ3N64W22Vu2pNascHWerRvsn7ifBmoJIg+1Uy8Tfip4wdr8yqGV8Yo9qwsRveUpuctrfsDjvfYI+rI92/NJQFtDqhcKyO5tAnbEdPRW7VHPLjuePzQAOzg63ZGoCaZPKPc7nrkgRm66CcBFw29OrobxeSyGM1qkJoudbq+8wRiMR3+07HkZCnMbeEtSrrcZddE0Cu7eiXzcXS/ytZ2hqn+UylJe+3DwrVwqmx8rB0USEnO+c0vWsQDD0yMlKMCCSyOEw6upCAytzxrcV952A5VK9BKPM23QOB2KGIG0eid+fFXAtLVoNL0RFoh6x4K+xlDgvDLrKoCtEI+M9x0bxYecSwbBs/o0SE6MosbhIA14/CT46WqHW4M79WsR2lPIwKJ9wVAOLlLZzOPFzvKFfO2DOJg86XTh/kGq3MPpaQ1xCroMoyoXWt89rNrtr+YQfRhpzrsNVlUmT4weXYaNzZc5ZU0hnCNP1Gd5ZKWI6k2cL+SC13zdiEvQoXF6GNwSvocWLNrq7rgvbZ4JftjEFnfOrmjm9zcilx3RNh2WnrmzDh3xouQC1crzOMRB+Nfo11gGIsDrA6JvDO4Y6any4dh5ws3i4xSvAinC5ASukj/xdTZcXhj6IdqFrewezd30odfD+mEqRzNN0RJiMCtRwYEEEPS9/eIzYrXaRTi3Y5k8QUf68rXSsNRVpHFQmseky/4jCqmlDKVl1crDLlDEabOLkVcSBv5ZyTaUF5SWBzT6qNG8tRpmGEpOScQzOTCZyIzKZbihn9YnQvzclYsnFL/wuZzr2yi4hTzMUdioK/YzeV4iX66VIyDIiaOcyVQjpkWKx/S37s31hbiBsyk5WmIx38KvGKearaCcqTNX1mkvVv7adONuYcTLuxBTsqgMLJ6GOw6BQ/RszTc35mb9NaZlN1Z7wiZJsU/U7Gc3O0ntrgzlidTG5v+r4LWs+mEJ5n6gs3lDj9spTOj5/sRlehs7tWIo16FCKT2ATCuHI4cQ8QYlueb5+Xj0fGvXpbRN6dmuso9nFNJWUpFDI+WxFJF7Gq/mgDLlC5ecNLPGsz6vj0586YmFn0+FKDOqMJZQeLHH2lbWb6kCZW4MKsozSXuJnhknFXA0t/l14ZuVZiYzqeKPcdhsWGxhxPt4UervXN1t07oiAhMjXcDZWoTIbUJpK5L58bIt3Kbpo/nBI8AdSj7DkseeEMr1kpPubTY+J3y63SFqs9pDpEMYiHW7/uFG1mUw+BKbPukAtYr2gebZH4y0p+xeT00yBZ76E70GhKbr08KNIj09NN8gQq6RvuZ6y4TYaq6iDCzalE2B6w+tDJyTeNVY+oBcGYZmRbbb1PL0Uka4Gl7n8diM/t9IlXF4xmkFGvq4H0/Rv2egMmwAars8U+zqi9inbfGMP5B9BuI/aeFzQhZ+Tob2MXIcE6pPldHR19fmefHm8cSN4lbxy68LR6O2PjwKClPHV7jxct0dkqQvvQyka2tlVDuFfKRThqtz+IWB8GuA3J4AKhwA3sGMs0OwG/DFplR3xme+H7e5sBsKf5aXHu9Zm+Z+kf1G2uCLIbvD/hU0APYeTQG5+P32WYDTOZhmuD0s7UzChyBVsY0M1eHXbXfDZtrfz3TD2qOEH88m8REodx5IPq2f2sYF4wS05b/wTTlmXG5S7jsYfy064lihVEB1gEQtpKejJTIYLY3QulGQKBVwhxKtaOwYwjh6nR7gJxte01gn/kzudH/qRno9K9EcZAO5xvC+99opuGAjLyfYbGdeUj5Qn2ZZnYCa/WBl7uSwbY9lJv2FyL04svPwKnCSWEFLDU6FhMCePDHLJQkSmDm9oGeX0S31nJ5MsvG7zfWwGUn8aHvR+bncrBK5+AK6rSmOiDzBHTxah08zSDh0UFMcCpVcw+CFPbzmpHhxAoKkpMQa0c/sJ3ub4pZz/FWRA8zjT43zjQ5VjalXDwnaaru0RHjwNucD+UvOXgPOQxirVPs0maCX6EmZOUvezwkNB9aQRgE761wIYEYKRsDuNGLmmk+k1H0wq0OMXwMTXhb0DWNwXtZd0Tmu5kNxUnymOil3qyx83p1yIKpEkeyJPOPvtq4hxq4pVJWVbSWbVurYZXnFemc3598Rp2fxlz+URsSJIyIinKvaXWTscgkM3AWTYyO963HH9u6jw7f1udAdR33ChC24mqxrljU5f4Sqv7OoIiGO+dqMkwkYNl1pyPuAzq91ylJuEhfPYgK7umdYFq7cc1tdEWtRudAr872Sv9SQEEtNL55P0xdlJot0/la2jVMgd1BIKARJ4LsN8qRjDbyWEiFJOMP7aqzA8luWlJ5yNCQmpTT4Y70SMkdOhhlOD4tec9wN3iDXstkRbIX91cKHvhfuTw5b21k2VdwIGgL+BpBS8DJwbb3VhZG4PawL2rLQNmWzVjjFYpvnT2g8KTUc2udO0backr/fQzXiGyBmReWbKI5fn7zPm+V/4QzS8r5vTua7B8ScDXXIq8DjI2Mj+RiGMZyslgvh+qsOMbMS/Ei/lO865auvFBX8vjx8MzMwKpoDMxl3nI+qW37b4lUrAkixPSHev6/2v/4imOb6uqjnSlDiUqR9dS1UqjPaH9/oIb9zRKNdtCZoliBW1+VjyK57DglgczlzktRoxqyeumg4Fi7vd7MGUxtbgIw5mx5s5mdlvAD/FYIk/zmL34dqVAEzVKeXLwesYEWp8Uf56Id0UVqlOes6TQOI8p5x4gAtijlJ+wsNffjKWcHqA+16m7yNcWPSGJmhnJOkOW4bQuY4zqmJYWRNnBvsF6k2GC4kZU7dn74+nk3X3Yfs90lL7JhIAj3XgpKSXpV2zFuv5KM8WPEyK++U/b5wm/Wk7uuEJ2+VbD+iUgJS3WQPRgbFPLPPnu8DkOQdhs1fuGSQneXxVku0y5qr+pewozeuz4Poqn068DP7FcRTBRP09YOVv20bW8XxNBE4UEtdyQC217hzNLkBGmQjSD7k35vgr6HNZlwNXz+mp7t6dstukWap0tdWA8mjcTCZLn/z8wV1AcSxXslyAyiGRFobFnSB3tFuzmruFpJLu079Tvc3KtFhGEWUPwTwiNQWlhcwG8DaqFEO2I1/CRul3lVZYPnnl/i/2jVAcautibItsAXd2GLuO74I4DKw2yOeKQRh6Pbio8BEkcrNziC6FYawh5/gcIRQMLYhvxfqjy4TiXt0tKo0ZZTzK8gxFNMwRB5c22agB0K34v+2HNJsxqDFrr569YLSivgozFzHIcA38BNnCtOle7c/rIWXuPTn7vDQcUnd03BFG4yNbC6jEp/xbFCL0xJqJ5il+koc3MWL6+3+wqDfzoLHVM9TjAM+2ywmX1SI3VleBCRqT77Hwm934cZMl3jKWrAXtdzlb2gtmvEr+FXXiQ5MSIRrRPWqXB9ITa9R2jxOa/M44Ee+VpyJoqbbKxKBL7Sf5F54uQRo2lN22Xsbm9gvOOqyCiUIXu17drdW/4TBnO8vFWg+RF5IUaNWJaH72PhpzB3W1GsGt37vR5r/Bb/OdlOfVep7NdOvm8FFvHfxbDyLnwtMxO9MZmZk6k5CPwdn1iaczkxCE0SdeXMTQPx0UfuidMYy0BC0zU6rEQcWhGwbjQnb/ayGdhjz1WVXMyrVf36CZy2+TNxRHEV80hHZG7mUqpIzcOPdclI2+Qw+NGIMFMyQmMDX4Kb0ZrJ4Luw1v33BLDwaWomkB4u0Q+5Z+99SJNIeeRooPxw62BBLPl2jdyVcrDTeLD7g/mhuY5Q9ji1ptENo8GTnk9j2RrngjA1Jq3PK9SNZ8wTiQsmTpOCDSxv2XoYif3gJA3sYJ+XqAcvxMEAKyYe7vrtqrZxMKnI97JaMiJBEYz4/KnmPGfhR/lqMMk9vRzOvuqdwkCkrFE1mXS3oyv31xPjDNlNQx6d9BYzM4MJ0N1hyzcu7TwS70sf86quYdKzakcU13y3QdypXj7LIMI4LunesHnTKcmobI2ZbDq1sh9d3xfW2q9cfPa5/iHHUJ4Wwhg8z7L2PURkcgxlWXTBrCpLDeS69lxLWrIRyg5UQT8KEnVmsRjbhrCV6Fj4YMG6MpGi5l2jtD4NK9dRK5ORY1fQhN21ChyqC2d7rDk5ech+nwVvMjUidiy4nCmIZ4Xj5QNT7lTHX8OdxFwsOrtRQ55QG2UX5nDp3S7d7Eydq9mb6xi9JHFOkrpv+0TR9+npzFrO8woRjSoVa1HbLi3yMl+gsZYhJ77mwuagZs9G5T/v+Dc8+ib+p1RPW2S2qyedJPHLldkMlyi6McciQkBrbjRiPZ6PTlL9/PBdjNzuk7DA0xDWNSl+ZgDZvSWGViXrZbLg4IliPbh2E337o3EumH9Wh1SwO3vNP2u1HS7HdU2QQF4I8QNbKYiI8JuGgyVjIbmpLQBDjnPnCctLbenW7lLceJVYd/iXCO5/Zr+OJk0QY3igQVah22P/8U06+hd1JUNB0t9oCt+2HWdbcXcCt4yj7qgFWANyl/nehlQtKORQX+gPCOrxE/xJZroKK0jKoRJpJadgc16PE1EZHWnCm7efJ47qjlAbHrY4cZER9bb7DTcE2MF5/mSj0T+apZwY/JkZ9G7QMj7/bBRsSOhSxm3biBfrEQZLH2UVj1dtGziSOU7OhcKpxy78+7vFoJkqBObRS4NEvyZZfXrSgh5T+esAhemIyQ+igMDeDU1fZRLP7pNnurUOm8kJNKhqnEAtbeRU/muJ4kUXhPfTtq2aNcEMx6s/7rHpHiTLeQVknLtV3U2P3dwj2OvQ5H3pPZdpH1Y9n0qx3OMevtl9aZ7i0RxpNfYquxYImS9bFX5AQZauSfgakn9c/5q7LEq58qkMw1Sf8lAFFZmzUfpjeVHxfc2rleaJMU6ILbvn5pYKTlNgV3pxTlgr4/RtNs9vUjbtNuYYAQav83KsPnGt6G1qzCgM4JiVMujLj8fvB/t9E88s4k4Qj27UntFIwRCrzcUjfWO3Q9eIts6NwXOqP5YcPmjvxE/nzImDAojCzjPyCsh/VhnY5rAH3MfilSfIG02M8Ypts7FGhhLhLoiKF0PGeB3C22DNGXteuL0SoU1VQx7I1E59IHyVIjpjqd2zZbrBTLFDp17x/9GuFbYTf6if7iiEdmPJtOH9Q4Y7lvn4ss5MsoHwn/BmGe/U+4AN1kYvzO4aL2m2tMLDmHGsvSZyqyyFdsKmWBQ97HX8fmPKQmGQkJdiDATEXHdsbpuD450IiAmn0UuMdXfAvlU2fVSnqxN2Fu295xLsp66JoszliVQQyMXGivGNRTQewfr2ygT5rlO4gHzUvoAVoAzU9bCdHsQ/8sd0dudWca+3ODj4uK187rOTCM6rgPHMTiRSTvbML8Z6DFyKbPniP2g5LV2M6JfzbZKnG3dR6tzpBdMSCpmpdNkYF4KlfHyEUViityq8Q75To5X8yoPmF0WPoui2MGAE3ZtcD1+Id7kMzvVe9IWbZ5vPQnB2tw6vBdqoBxezNQQ6EXj4plfxpVwhDC+/vUa3BG/j9bgDBL76AyegWk6xUTJfcDmJnsSaL0/dnH150M0rIYY2kqxLhTzggLyjMoWd9G04ypS1lYxrWIH0cz5pq+Fk2aS5Tna3kET98XPl8VLv3+h5r7OTDgLbBFS2V2QEVO95BDpNq5G1H2hS4sUPMiMbe7Ol71ROEUke/xlo5RJ8MhCVLCx23dDDhB2RmapmgaZWIwqm5gMr681VlYWplzkn/uz17sfoIff7qvrZwipKUceZS4NEiH8q+1TgR0yVEHlRuclackAxczrrO9lmtREoi+oVlq8XvpLnsqSkZHayPFKbKvgcxpzNS+0vMyhN9tZb1T3YSX0eL/NmHmtK9g+4o3Q6sum63t6OoQxmP0+E69fudcomLs2UIFWR0GNlkQmaq+ZsVW2aZU4JeHbbudo49YXoEkyOVe+0H7X5c+gXHW3qgaIP3Sd8LJhPokO2x+jME2wV/QdUDa8uS/3J0pzS+NrHP+NPqwPGTimjvYh93kLwgVMVcDWWMzDQpOVL0l9OAxw91ZgReizIdtr45jq1rz0t7k+YnQoR6roWo4MxusNVT6u3sVF8wbzsSAa88v8TufBE6azuMx6UQK2KO1VgTWUvOzPKm/Ym//NpUsBrsvrZdgjaTsrvvgT+vsyXgHf6ZIJ1tQJg5Ityu+nsR3CW6w3yelnX2CYca4vSSP0ogj2pzucm88Hobj56XoY5fQIIbeOlFhR7f/q6WXUQRSbL6QPxFmeIKrOj2aKJkdVEI8xesDuQyxLOUp+EHqjmyrcf9CO9hKN6BndQW1sMjEc0emzzRtD6F73j0G9INT8RY1yUregfyv4YBrjkeD58/GMRlDJO6pmVD0hhCGQBfh+jVtI5CqZQSSfT9y41jMJnblmQiixtimBUzcta1RRL7vGIcm6+vVTEtnXX0AjaIyj7L+jpPZDU0HvAQK/9GDSuBmpOwl9o0d2zOPggHX3A/XPHju3COgpHspQ/H2OAafcDohol1chv/WCH6cKlVtd500hf3PgcQ2p+L//Ej1bevKwHORHdX9HpPnwAZ73bzNSgpq80+ihbq/GxtqkE8tg8mU59F+r57/mxrianzbuJBnf37XSQsq7VamxoQBspk3OtsqGINwus1TjvCltf54i4qaog2n3+tmPKCkQj/2rno/OTg+5bB68N8juGXphRzB4JMhsj3mFuyhn5rhM7qLjwRzv5HOnl1Rsg7yiHCNkcXdN8iCz60Qwtxt90LBPFUiUv7NYzqDLM6GTLZVzfqfKNEgtMeQFP6GJrasZ71zRRn8DzUL0vth0lwU/BuqEWyw+Go+QX37lShF81LNLRgDjdTLBWm4PQQY3pSe9uhofvRV1FM6x8DFeB5rBYAFF6KwzMtFE8UiwwYmIHFr8VgXUUUvcL7nQqJ6iXi2fA/tbauY+9otc3Tds2FzXAQOoFRjTeATz7vVnl6cOzQ9NBd8sl4TzokuWm7xyy0SBvkcJjlNRTgHWmvX3zz5idaLNLc7k0Q4BV7er0J3WV2menef3DUSREQ6CdxI0uQKCPwUmIlQ6NIJNudaSIMfb8Rbh4jBcUeNs+hWDVCo4JU7dGAeE6ReRPuk9nevdTc9bn9FuO8kbwXIPF7mnU8Rq3q1Dp/Z65QETZOzoXEn+9zDN8CwaGLgLbGB0du8pzrKpEXlEPx8ImhxFi+yLUap1XMGeUx6KBxPRmGU5GHlm8fTdCdQzEzEKTjru/JJwD0cb2aT8X1OD/ZJc2nW6VQlbRZww5jEDLHOViU0bmWe139FPsSi4DX5I+eIU6k9xWdWJmnXERfxHntN7ZxisNOxwR0wJ89nVNmP47soTAENtBXklKFOQgmz9sS1ge39M7D1TofwBA5G8d07IXrzOocxt0nHqJa+T8zM5msbRR3JuNtCrxfMFjas2khLbhZCMX18XE2aeT3v21SSJMOtDCj4Hm6ek8XHfSo6MciAFm25CdcwHhrBMmVAOu1xJmhA8nT6StbgOXaoNcLUNOs2FaAuGxaqhsmN7YvPpq7bdEt1rHQLz+YTVk6PgtLk5tuYDsX6319mH5CxDLkI6FSMLANFoHDNv4ut6K86RxCmhtuOLrvF+Ev/R5WhQoXzkrxciHen4lPFCnz7OcVxpXknWNWBb081+1EI1Zx9p6A2DUN9OOaJmJe/sdNBzHsj809+l1ucElqa69+K52Q3aa2CUie/oCfNsO8GfG72TLb9rrjCrG0qNevUfYnDHUiJDtyuREWn6J/OBm5eMNg+zVZVGZovhdSOdpyLhMsYSRMqm2aCIijxl5I+WrHC2U0UvjF/Wg3FuGGTI/7OMKS5LfQ7FGmk6mZGCN8uHT+0ImLvUR30k+uMoRAdBcimOxYOdKOBa19GaiyUiEq7GBOBU45htTVI2s8zybAXVYn3la3GIlbmYo6C11q2Lfaldh2yUC0i3KNH7Ap5ZY8ShPw2OdsV9+duPI4pWucOcsEN4mmtz9LKCSjXw5EOrVcbSxjyzLQBFOOS/Fx/xZpCmyZoJrQHyIW4k+DxbgP/ErNXWK9/IWKFYCYH6gB2fV5N6bqIbe7+ntlAs5jHIO3S2CeHKNk1HoThocg9EHu5BcLkeEGfaca3GupB/VnXEFyyNbEkrhe/+iCfmy+tcnr0v12zzBnG7tHScu8WE4P9h3jSjNaWQgJYRa+TwOJcBxLIiPGi5ywZOQQZeLxJLK4Z8I2mxDz2w7pSnb4NEyENEGLtoMmBYODyG7YvMkclZwBCIO1kTz2ZRAa3ydSwNBFcqymnS/xL2+H4vWebVALQxu+4ub++NaAor6JX84kbf9wWey5hOayzbbORIOQIF0V4Y2gi2JYVENpUxSANkg3OM38RYvmEx0013gxoqfvR9dgRW8K7sF2RQ2/9qaIJdM4hmrRpnrRMbphSE8rcb/VBrVNON5GlZ1Bs3eMNyaxkuMImXB1H2P40Fk4GdBii7EE623VpzAiRw11vHk6glWShb27vWf6IloSb+jzsHCzSvjyMBJhiPioxk9/CnpBZSftZDVtJVShx+Kvu39eLjy1qDgJSbMZYur0MJGYpdy3fCcVBcG28cmdrIqD9NvM96oe+s7THo2JXmc54YgKrN7WZfa2dIwNl5I8py8V6lUGKv6KiTArggI8KgxT+QxLY98PWUkZcGa9WCfigkkFFrndwIKlvcl+ZFcqp+CqVS7CrkLQ3xLfyzJ+aS8S5L15Sc2//N5vsBYS7Wx6JzDaihsJDF6COWiGwyV0X1A5c/XKgW0H049uPjPFTQ9OFKksgcKmqGkscj4KOoxZPTqboL1cr1d5+EI//srR429K8PRdF45huNs1denRyB92mcZIHZGBy5UcrQaZw0RFPuJBjohlzB6c9RQvPrrOOjmDaQwZ48QFUzYQFjujCFoik/vLbX9dBXScPDnzt40UNPr6Oq5Y0TjF00Gb4XB62FbaJDfYzwXi7DzqyhKbGUcWjWfFjWyhBoIDfZOuW7fXq5DTsikwxB/7GV0J4JO6SA6y9w8sSblfX8Se9fHFJI5/5Pv2YWyqlE8PZaQPbLR5f9g875FN01UQpTDfC9k/PxKC4zUdXtqmD23FKma2lu9d3Vqx8M52vyZM/Bm8nmJJWtqGV0LmdUN3Jr7gRV5sWvidfh9V62GFnY5909MQTGS7xi8RsPHAqo19u/7h/ld6M2Jj0pl/B/EKeMtzxnwP8bzvt3pYTOCrvNwQxnFz0u1Sm5fdmB5Jyc5M4UJa8H1L7zmGBwZSsrWVtDbb9+Hb8eIPKMK3DHIdXkjUptBQVrAP/nK82ngP2YyQKbyUNj0B1Z9iIIrLeFL1k3L3vpJuM4C1LpoTBBXAyAzP6Th8r6a0usT47OxqASm7FH+CmJFDa+Xp4s/VMmwEzOmE7NEqwYePZCZ8XMmT8SLxkk7fuVnrMy08Xd8yUTOp5PhQRIpKkLKA9KN0THjrpM36ckpDFTeUuHTS5LcLAjj4hkrBhH04qWNJE/b1QhoOzrH3NWmkww5RQqy32bG5bVManBRD4+nLHpvsXkdMNo6IUGtQWcN7nyT24s5Lza+YN8AGp1FszrEHA1QXyYsamBks3/h9iPqW7ibaPUD1Col4OmaR2cJv83LawHc5MUuu88DCNISw76sUMOG2dYjsxqc0OEy/a6Y5rHOlkoBNQxUa/FtPI2Vi5bidjEMVDnoM+mgHrp00waXvB9QraRexektnpb5b8BNUalTQo7mX17Di7RCVkAvdoETuDhYME5fQMR946Vt1yk3YKdIJRPCL+CckmGQ/m85V4exXrNBSiU1wW6qr/cRcjL8wtRiNjwlS6mv6VinbU8SVGtEBlbaD0I+omjV1lMUOvrUGTpFgo0EpQQ/bJyl6/lSXWD5rolMJHllGP9K5o+1lFBIfPS+rMAe36yXpAJ2ESK5ByDP6uBAZ8VPycptjPNWnblDqrCWZMur4Y3aTXcMeo9fv6xIEPiL7lfYefCx+KzFAQVvOlLKzy7RYwEG3XJUJ+CNbwyuaKAzLmZsOMIcWhk53yv6bbm3k9xaIpovcwBdP/8wAG1iKn1xawtfRET5X95Gu0fjC5XSsMQtCIwOhvvoEuTppGnl+TS9jHZBqScEvLwL6P3MjDj1DJnD0p7bSW0NJUq6tDqqBDLW4eyZV6SUdOR4+3XT0+gw6e4lzHKxj+PP6kJGFQobsq9uu08WSODjltvRFycGaNIvoWGo4XQWtsJGxvXVsyb+rUBCGPmYfcCkXBfuDM8KafFZcY2agSS7xMW0hbJqpB1gWjKAoOTTtfy3gnNmsZHzETb7gVqd0KgtIs6JVOzwFQ0IYI4dSK7QjjQMHiySwMZoAFjVTUl3HmjixBYhdCAUFoZH38XK7fSwR0cIwU0q5g9QMsczSz8RVquT9/jnHuHiqPSmcAlPlZTpRIEPGtVP0K3u6miAMVyj9pBR7Jnr8S2HaMnuQxvcTIP0ZpLFUsqR0uh2lXzeDOc3iea+sb/NZU5ODCgw3EbxV4JwZf+7vXISTyx8tYle0bvUlq3id2ZCq3uKcU1TI+ivZwZ9IOiHw1y6xvGOouDwnE1y/M2igOuny5hZn7LPAXb1E3k2MPNEPR3rqUnR+xaRUHM78Mj7QV6rXkl7Of5J1MfkSwoTmCQMZRG8jXoXmwKsKydo36vM1ykblEteC/b7jidej3/2X8AhZFCFp1gQnm4BKJpMrikEI24pDoPfhrKE9QpFmPJsTsVfHskhs5Sh61M82hZl9AZizleJZh7ElNaaFpP6wbEUnt9Rk+zz9lhq50RhpQxHZKMtc6opYxAnJXToaauJTNo9lFLogEW149Yv52eGGAXshSxnTsBb+rt7UaG17qanOX/vMN8wavehdRXs8k4NKORjo5JuB2DW28/kY491fIwIXdeVxfnuDvS6jZZP2Pa/qNSC68X3ItkIqocguAXxDy5NofIE3FfyM5hVHTZXvq8WaiBzPNV77he6wRZ91OW0v0GXQJ6lMFV8+q2Nfxzxi2K7fRDHs3QcnfOi7Ghrqw6e5xRhO23ESrslYW4oT6nDXufxSr07ZG4TavbBcmfGMZBkN5AdVt1rcyOC+5MRlnNYBbnSkW218L/ynsq/C7vXC5q5oPCT5XdYQbF0hY45gER/QsxBqmaI1yz1hfAv6+BKlGzoZ6vQe6bLWCmevqgOYv3NkgFMeDtwFKs/gKJ4LU744OvaA/2ze33J8UH30j+/O+HBeVOTK2p2MyVD3bSJcjRXBB3YPszMbyT34EEUSh0ER/Ja3f+QXPhXvptEzViCEUZuDq56wDrhXryXiNTnd5sxUycLy/TbgbHvoNCsXtYxbK06IFIpV9ITG6aHEIXiVU89vop/gWCZVmfiiNH2alJMjTcPcvBTFYRwOf/ZF9312q9Wlxbu25gynRaZilpWBn8MKv5v2O6Hk5T5q46+Nd9NYUiGyMmFMFwv5gbE1A3Cniit2nUo1o4bcnMyTMa5pqIigWt9r5yhbjHFI0tE/g82G7qpK8ycQmbGL0nXGdyUa1w8VkRMOOXKmWy8+t08esHzGDaPittDfRbwC+BQlGjqzdDTiKKaiNZ3feFVqmbd31UKqO1z1SExAVoWwSHCJdK5foasoY7iqV4yhB77Mq5K2M/bo2fpM1ykXfrW5weBqzB/KgDNiy4zeOsuEbEcYKoA+Hv1ilRT2pGuznucB5kOzVA4uIOgnmUN0z8iMuoyXWTE+ug1XWd+H/vOEbJtJfbqhrC50FbbbJXGqUpIwd1qm/bDrKHMxev0m2lDMg3Au6uyM5L0JGW4yOQVFxf7ZiKBamew6psI+YN6RmFMaP6qM1JBW3WM/+K79Egp+wx5Ka6Hi1YfwCbr4MYKu83z9/TOhWtMIZDzwbns/UTAStnqKNWSAODpA0rg+QGlESLVHqzzFmdPnmMt3fGRSKCqgiL2w8KS495sD0tPpAmE0gWFn1LmOTgqbTHO6pZ09iSs/Q8973GuEUIHye9Me/9XGtgEzj33P5a/8V3uS/YNOGc17XZXdsiR4uGFgcg0rZ6kqNiOLnWniV1QDnpWGWTexHTKTYUYf7ekd9ILvE1Ei9FVXJ8b5KtHq2eH62hHaFQzo1COtSNt+l4rb/uh+ctoWx8SGz39CgsoJiGcRVdvEGQciD+8Oi0c2TPtG6DE5DIt9k/EbCkryuMQXo6MkJxnd4Zam/qU2gb9hZmfb53cV0AZLPq+TlzJNaNo4K9zp1hrnWSPzahSKyn7L1DnTmITMxMzqNAbBGC9eelpS0Z/ER4i/bVjFTKU6X6zTawTbcjkDx5s5Q/j4iTF3DXxIVDiHrChZbUWk9yAodG4o7iD/Y3MGzJXYJgFX5y8woLTr6JGi1eEC5/q9fhCEUTzOxPKLCyexaLSlubPagzjdwPdE0rpIkZjFdj8hZrwA3a0j174f/moe1Nz1ZEW/MnL4iGOfA3buRq0SvFxyZu0Rb+9JB7HNurWdm/+fAPwiA90DkM/d0iF/JR0ALLlZOLwJhxVRyJlpz8bILzmRNU9Uf6woumEQOH9put1IwnApHuu7zhLUxN07KD4Nnd9H4Y3xRmvQEPbjBTK61UgCqMeH8Ml2Bk2liVTSKEwOZVALT0GWmrKi1InxUn5a4BvNN4tEIrDHv9LK9fCJia2CY4e7BUNskjv8RWXfLzM7XotYZeV2qkUYtE2fsTqvxQ/iCYKewPx7VK+zbh9BlxqIf0bI57Uf6Mg9YdS/uQ3gIkORPrMyED9Z2R0BR6quq2umNR/0P6MZN7xjiiDJtH/Zz2b27A2z7KFmyGnJFXvFtMMH3o1eEZ5Kjh8dwZk7c3cAcKv49MsAVhHh2ef6czz+0nslqtJWXjLI5/kyMyvJoyp7INHd94VP9CgSND9nC9mxOXjrWrlORHHqcQpm+hJsvJBDQiVJANVHK71QxovVLzjhtL46Zy1nEX73C1J0RRzx50R+DXk2kCMI5PgKZZcoo2JxgAISgghORtvAM/DFDVLcakfLwMEeacJ67f3kuQNkP4ruEiI31VF6IrqG41F1I580tvDwd1JFzTq5zeBEsOUv0gMLRsOJNAwbT9diCAoU/RAk4hDAua2QabRcof6IPFD+B+CXvYkI5av13JhB8ed4aN832D3tPpLgQnBwMyqRy7o8Rc2dV6YqV+7aNsW0YMTPK2L9fZXiewWd1GZGtoTCAE32qVRr0Iu1Jn1TjuMjj5Jeu6Zd9U5MDs3qnogTyJM2/gerHg4K2XZJEOidlg16CzzGD7mMVJ7S5qkX2hChCeUc2+ICXM3RR0IITqcEb5LMbYdVOsiuQ81ZF142WlyC8eKx4vXaADmgSYce17y223sQ7MA8KSJIwgfDXPCBKB8PTyeh5lt/w3S0AxwgNiuKiSzvr489keiuriWlENk3Sr357Dz3mDjuqYLZsT7lnYiLY6cTDsIDnCqdfTr68f2ZFp1pOXcxqyDnzC4+nTz2grTBtxbWbdghwV2U9YlZ68KpVlEsj+7TBrypZ4vKTObMzWLbNRBNe057oSar8vmpTK+u4nXHIF8Fif4M4QaCdQtEyKejeEoMh4T56HAWy4fjMKiYZTaCo3ZQdc70iTk4Z7qhKO5I5LVT6NUTcZtEwDCAQwc9RSM+yKpWDRUiVvCtWeYESAVpsflByX/uGRw82a6fwNJgM9YMNH2VgPp/I0kWulZKEU82Sb/B/sqcWmoChO/anoYqa17DEVGqlNdI7yMmcA3/A7wMo+Nc3IrNz/ZXtmk8VqcLGB06ueFOfgL6WyX2sIotnlVWs/ZriCT4ndZhAg55Lxmxy6PZ5+p3W7qV2l7uXnTZOn6vnKgFPrkzFsT4bkxfTOkeRv2NCLtoCjLc1eGIyD2SYmUW79ic337CF/fws5wLYZ+8tW2wxwKmGA7wVYA4UeAS/1W+/JUVrbHfOvuamipMblP08fBQIkIfl2G/IVgxoNophZYIvpmPs7wDOMeT8CP6KDBs/ie7suhGql982SupYyDweXwZo+u+/FsFfgdbKEv3pgAik7pTnT1/eFnJTH4ie6+BhV7hUy8SllC/5QpHD/3lprEG+8gss+bVp21EgA2lWRwWmGP2rtFuB9gtG/YrNhh39mqgfynYqOYmjfI8X3SSShpQTlKb/9zpjJC9W+7sdAiAz8o0w7Q8qM9mf9t73e1rFZMD8wuzEvmBctazA1amxq8+o0xEpHlBO+WN7bdVzAC3osyrHSabS7t48pvKZgy28YmmOuIRrLDQUrbQgmnWlFsrUPa4sTVxA9Vn3jrbQWwQRo2HzHUzG9iNZUAIKF1E6UY9L60t6s/gbw393fYTo1POKZINpI+TFTc4odVmLkJAmu4c+StRYdRwY0LKvX5SepNqhSlh1p/s2hg5FtrMS/cGh9Er1CtIx13QWUwMzpkEAXOp5ki1wmYU7YE72eX+AwKpZIk6T34DyluOWVZFDiyn8rXkRKkmhA2vfGyzDT7f5hC3uq1rHiMAFnjhnLtmcoBc2IrEBYwZe6VIOgYyabevUivBJk2qbkp1a5wmOl8WpPmbosS7mEbge2+ngZn97D7LbL8AbbPSf/6h653cVOzyPkYDjFPlU+PJY7FNNlzrt9b1jsieCpLqmcP0w0omen6lWjZ8LksZYvbI9AbcaKq3NYQYNvDPgkx6TBHMARdw6/nml5enotdcPvrYBESzEA7lR4NwRboOG0NnU0QInHUMj0+GJNTPJlEritPVV75WXMRjRhbqSstcQx541dGcOm7IURzocecB7bUsiBFT6Ooq9qF2iFztRINFESfSiNRHzO0hxILjslBl5QVuD+BfuXtT6BaZh/ae/ihp8eDq9G2Hw6PmmdhZhOoaOOatfPW4AMVor2vdIt3RIIXMlPIGRVSQ4XOkNEKJvBHDWSD74XCZGPkJL1t92NNveoHxAG3lGae3OQLyzFa172wMeXo0Zxn9xAxh5TZot/t7OLNqwKWhLoitMth1EeWUdlEOkxC1CkyMLT85XDrqVR2JISlZwpZvtX9S6F1lEbBQzWJJQtR5Z3uNJEXotAzV+EiiLbM+/PC1c5ZGqC1UXZ/7ZyaVZYR8RUGb7FP17q6A6hCKMBFEZEPacN6v7wTwH0a/CUHOvyf6M2tnouLAaRQRh/RmOyB7Vty+JH7MHiioX/LdXe5Y7pZJgqWBJM9tmTshUgmNTFMg30Jm4GdoE9eSsBEnfcE8W2gzyUeIignhtha5QjT5GHG/9cVldvV4VYAAjNjXz6j9YC7EZp8OOXEM9qYK6FG3fK12ZjaVWOngI32r+JN1WI2BTJ2Oo3nGOR7SW1sZl5Jhn0ZYqwKoEqfGLx25Wlg5Kra/9FvS5xUixJrdO7ldY/brwyJGZmdv7+0MJchmXIz8DfO7m2vsp2AeOiSrcNURdXdFE1Ff6r/r5xJh/yUiGNHO0W8BO9n/shvz927MXARhpKSn7Z7pVcNgkpHgTMi83sgFWnxDj63Xu+rzIUI9lF/fzJuOedbNnMKq0zoMQ4GIOm8zxArLfbIYszPlm/7Io8NBSMgUnuFqGW1bsz07mpQRdlvrhfcPKtZFjpbQIEr77IXGaGNmnHuJ6yD59XyUn6peplLyX63DVFpgbTsDoQfSJHN3cxdEhlE3OVtE7876jQUIMID72bWj2EOf/egbR5hV56uZ390ssW8VopSQDMq53nxXhtPYmR3W2Lcf0xD9K44PWvUAzS27zVhcs96oefYLZIhr679tg3zJl2r4BwA2vlQRbeBO08SEbvHzn859KCbxhS2paFFaB+8Qp5AGR67aQ/2x/aSJGVfJCsTwq2i5XKl4jd+gRfd7Q2jnN3QNSSmV2r5XO937euC2YkRyI+64yEM2yilkJnWGWSknu0I6cqi0CUa2I2Tps/JZylfoR0xsOvn/6tFRF2nlrJmiAPrV9ha7lIztEMVO2s09e+NnAPn563zhJJxBQHh8aWliijEOB1JnDdEmROvqZ4EDn95WSJQ3t+CZoWRj/kBPufTz2n00Vxv2VsiVJmS3fxebXiUSnrslYbJVO2+PJRTT0YYrgkqqAbScek644WQj8DXpkkkgCk9furUU74k0lnclmr0WDxGJ9ptU0syewZ5Q25SLyNT5a+CuhqaQRskx2zbLt+zf9cgYQ7Lh8ALw7IRSzpDonKO/5ZOVzi8iNC8t0kkLkIKyjOAGhsr2YTAOMa4ohFCE8yrXaglVsbMVR0oD9SeBrdjjguEURCFA6LRY3gPwgztjcpJIpGiZ7iXBWdi+9tn8+w/dso4iUxlDEjbqugkOGj5RroLSt5oTV5vV/ymjBz7aw0F7laQkwY7yr1SZ+qirLc1ViGTp8IErJmBG4Rr/Q/XAbnW/AEWQq0Hie68p2Sd+JwI9dMWQ8uFfugzgXFRSO1qWw9KT+bvlyVGf8hHjfvsv/QDJWfpnpNGnCmBNyiecGcO2DsxL5OZejmqmpATYuM4MVcPMUIPUJQqPTfOmS6pwPltzvkdRSfwQ7+qsdPpwTGTJFoO4ZeFo4eqRg7pmjq26gf/1AXCpkQYHfOMoH1YgUbD0S8HYu78fMXwMBHKBIDU5cvaDpb4KZ8jFNV/LvSgm+yIWPq12YrcFBYaOwNgMnrEFqd3VdLyHd7A5WlIRHTfDOGlzPqxRRSpgtFZ0yrIoQ32ne9/B1zHYQoKP85mMPUDd4WLoojxB6l+piWwxiDmikwYhKYWnAj46GtEcgUnb9bx3a3u/7a2fWByJ6g5nwGpW+/4tjZvAvPK1ZERjZEi8I2dflg8KRypX1FEB59hsiAR9CeJBF62HuNFpvNZIfkFJ7LGU7No6PikAVHL0nbCda5UUJYIChLbqDVnR0nSd6GbL5xZwMMwNlAZuZWcRoxt7nxww7LOypusRXp7DMDapgvY4joa2QWnWLv59+6fRNUgaMVFvLR1Eby0alkmOtTcqlSbAPCSptoyad61NJpaw3emBmuF7QMXKqkpyAkz/XeRvqIkMoU25zYzORu8CdfyonNw42+KFx95pzs9Ld+U/OJLF/ipRlU6OUdiObgaqRuWs4FmLoldbFQ2j/FClz9h7rkoNguHR+cggRufrxE8VZkaRPvX9hXwT1VPg7FbwWEXHybOlgkAAMg7t+55BVDjD33TB3yxZiMUPYQDt8LIl64SIchUCochXSK+zx/B73+Mo9CjZLNrFTHOykh19XMJXFhEjJ56mKyu85CLNQONadTC1zYB4BlbVLFpT82uFKQXWWTGCrIEFgqpn2BGj59oOErfl/e+M2h8sj/C0ahtRKK5GL7Mf1jxSikRW88Eis6DQAS3yKO2sISn/uFkfF5iJY2eGCJQE6QtzREJR/9VLJ0eVV+7N/5T4Ry1nkOLNc+NSxNzRsmxxaqYYyWX2c79yAYufLeOUT8GyvD6aChhVKefiA7hpzLr6uTy3TtcE1SX2Lv5s7qs3Eb7g6M41R9lYECm3PjvQKKzug91V3CLo01mmeVl0WFu5/PiyILcrlbzBHC0O0bQzlMxN6W6ugfTHFhWxHtufsHVOCe4xlhJZw6y5DGRhnLlE9XbIueRrrlPKYdC5KoyK/T6v4QKHWCjxN0JCc4USx6iNzYqCcdExSA8WG2J9Iu49h8rc+nFcWVv53F8dLEXhF3INfe7U08Y2MwiHX6d91RAE9VAIDzxqCh4zMd+gbjMraNIai5PfCqwYJzfjaC2RLOTm3h3brma8Tzz8Y2xVfnzW32aWOYVhIaluB2ZY4yohTKed+e8paFgfBhR1meaA/MiTT55auEbwhKP9UZATw15UH/wALwenXy70inb0YJ25OtQKv5j/KjtyVWctBgeAY6kenNNozTNmjJdt7tdTKUXABoN++RoHgnFWKljoFxwxi4GwNrOD+kfoq4Q4abUgeIuS77UxNjhmVmiVDeBWpMFoNzak2CB90Bcb2ao76oGR8e7lCTTUZzt3+DtD/iWP5gRX7IgjaXvcRtW+OkFmgKp0k6qEFICtL//pQ3eAV1r6dS2Yvt69Jzfpwx3HoiT/zEIFh55czpbWJ4TfuHiMzATRLtHZUr/AF38JGpOXDDKfsyUM8fAoPVIzvyHVye7TFN9cv4nv32cHU5wSKQXSBAJw6B67M80urGt1wawkACAfObtGsRtDbo5A8p9kicu+0YGN22ukdPZe9joe3wlhV/c1PrONBc9Yi6HaRUB07SGHl1ucn+f+jVg4PF3J/rbB4rHmTxcoxhZCi4Dq99OciFlxrWBeb3+eJMwqBqru2r5odzaaq2PEvrHwCvh2fIj8iY3UhZabQb6z62fEcQCcVQXOyyfsWA8//QtlfUzqa3TBKw81TGCxfqGB5tvaWi1/ac/hhIL+KYIn4WCVbe1cHJggx+F4lRRMD8c0IgDNXhhvjnFYNBqeh16hTVMTUEGHZPX8lh8OObxEaoFJ8/RjDf6L9fq/O3agW4jdIUjzPVA6RnlvooZN8nTKvcJjgRaektwf5Y59o7ABcVAm2kz8upra2U4UYsizd+q1sLOJrCKxROSaR4KlU9GMICp0iqpWDqxlIh+SbPqUF1TLVr7hYkrmBA9W1zhGRnozkEYCPS3Bmgg1hWNzm4RekklpTUyDoeAW32aYZFDD4MV4vonpcbZEinXAlOjLdst8Np8JcdW7cyqKLyWlTNVSoQwd/9099k1bfEZRW7XxMajwbqJC3LXHutTo2nJOm+OIoplVossr/GZ4qU0o8frGTSxp99bxdYsWkR+0lmCSp/lFqJzF+A4pRHyCx4QGeD46M7xXtAheNYkGfRPQaVJ8mPWiACAOIvYgrI6N2sC/rguE5wOMPDSSyRQqLwVIBn/zMufB1Yx8By9HoDrVPsLX7bdN4D9nwqy69gZsPV4y9SJTSwqAq0xJCDYc4EEchS6jSwPsCuuW9fuOwgDgEJKPiEoN5HDW0WnUv7+RRHcUktteuVmkEmmM+qgJjbXjtbgkNsYVmLrGF6CBGTVsCbejU7zdtq1C6zG4A30KJ6ZvHWkM2Dti3SF/pa4mQtt7IsAdFumEGkDtyUvSoQKkOD101Zkg11UxawjBXGynV6g8qIgewUY2v9WYmL4ylUvuqIn/Jt0xJHvGaVa0mEDqIquoYcrMKsRh6Fzvbzh9XIB48EaP05REkfd4+6xpS44UU4qs5X310xAmPoFz1sWEpONurFjdXxCSugy7AM6iT+lfJTbeBS6zyHuAxnk9VXSvCK726EcC6CkaO4r9HSzDDAxyJvCccCBHJyRz+FaFsd5IjuWXJNBGU6IPUSGF88amrut20FJ1cUZRa6xHXeu14qY+FH5Ahv3fPJzJMI+54uMbofZo6q3ipCpDoybUvqCzt9u33B9GNfdOCmJRANqkfzgpPzqe9/ehHf4CzZPEfOXKgbLdU2QS9qB/WaKd1CL/hWnzQlSk0yYGcXDTpAgAGv95KdQButQYOFUHNZlkm1mWWXI2OIsUHesrH9Dqhm6R3MSEjWZ1xcz4dLLalrngHxNu3xWWLoPS1V4WvGTLtp7FJR2dWuudzhUqa7KU5qsd2tExAAZFazEt4Dz7FPDJLwhwBUi4AViQXuXMoPWKpYhFD4kKnK4dCSOozhzyxzAi0kLfwghkfcQ411t5ByglTh1Sc4J3OLfEjaN5mU0LC9X0t085Hhkhu5Wtl0fQIC/21eFAEl/SXRt3R3SRsjFohmYmJjD0Csa/kg9EVim0TbdHeEQ5tkomVGzH5SbQlr56t1uFlaAAwZXl/C2ANdc5xQpamTex8EdTXCiyBeRe8mjAt5rENnhopWSqrhaGkNEUuIF8JxC5RK0mpQ3ElJXlI1aXHpCWciJjB4xnuu3KINyALhYxaATJ2I8O6ww+LIV1IIL5zZKgGonKp+Q0gBKP4VMSJ4lQt2LesZc3z97StCU2WzhddmQ6TS4rHlT4QJdohY7/qde3+ZcMsGlVjONj/63xl1INSTWZDawJvpMyYM97611APcjCqOvSIf0VcqDsjxTksAQ0AdQUN7z/D+bXPK0XmVYAUhRmyjElFxlrsXCWxbg3rTxmCQlGV9rz3WF898VvkD/eHO1tqAM7IrxdU2vWG4hQsOX4lw21JO8KvVtfhcPN8hW5CnhvzOp2+W/wkdJ3XopExtM2RKbJeZOPD3hG29am+59FbnoLSSSZQnau6+llSZbBwSXawqPnKP/ZRGM6FLMZyT7rt5PqTbRAdLAYb2wSnye0jGiuw8Iv2uTkMstJrpAmOEDMTNrc+KLMPMPquIqKge7KtfCPyEt19BCouMEvSHpa2ttohHhZ2F6HI3kIC4TQrKuqvkDz58xgrW2d12JIWcoQEAqb3L5bujy2XzUOVpKnUBgoanC242qJWGzlD8lmeh0uBf91vqoFlEJZEcubYxF/CrsIFrxRvDctEbEEfeqvALH7QJsGLCV9sUPXZMefdbJB3UTy033wkcKhY4f5rDZR8BeRdT6Fl8YGkEirgJIJK7oLlxlpVRPncyjbqFEelm7gjyzO5WVt2fiNfls/Tjj+3Z5kSQgKGz0QUWmpz/mkqRWHqybauI9bldACnVa3ztAARNqEAnpGTVckRYbthHjD7OvmhYaLuSBTILDaRXF/sDY4M1jPCDGSEO+dMjE716c9mSK4FgLgLc0pVeF9d5Hb4eHVCsEtDcL4USnwYfn/eDWl5TLG9lOJ9zL5bvBlD71PXG3Sbb0+dN9VJjH8Hk1dvN/qBLSe7Z9tU/SgtBS3NvQKz3nPKw/eJuiVSWALhyEtiuMnQUk+WK4JpVpuRZyokstIeeSdzvRZ6EXx+FDMLbQ6f65xlNeEqEFfzPkqEiR2b6SWb19jiwsi33w5QfJz07+t1Llp7/kaKAGnZZDLdNB+VQDJ/1YtYoXwvSM6wLVmbyhuEihTNOaTIHQyVinb5x+IVBvUbHqfkt7IwYxu+f64gsgR2MPT7oGw4eG5c2gzGgRqKQqV8PsOF0O5l6ewmIVrvzwd/gJJM1sVILGISBqV4RjrM1OZ86LcPQC5KbTAYQCeasvEZ2eWubE1Cv4ySZj6d7Cul0imMkQhn6CgUpxBYdW2EQgUBEuyr2ATlentfKB7tAMPqzjPi2biT4j5InB5H00xjZH0IH5HcMGVGdWZ0FmMKdpecAgIlz7QSa0BxseqnlxQdvvzJ5OWbtksVoHuLAvEpZg3MqFgB2jIpz5PzlaAiLjh5eqElRio1HCVyqrCUhl4zoaTEpuMNskreD3zgsFYrmR4kX9iDJNJFSGJx8vK4Tfq0SMIq3QwamtDnm6CGglyHLaKhvjavpIlMuOD6AobvEeQw4RoMonU4/Rkm6XeuzCpvQj4R9FYqz9uivAMB6nXZ/jkRR7YZAywFfbNkvcAY9pnwN1P+ZIaVUaMnPB4iQ44SbB3CZoDstQBOC75CtAXtOoYeru29nfZxWbeTKKlmmnQ4yl8qTKTtwnBMZKR7UpiIuAVSI5RuzECh7umrNV7rCHFu/Zp8p5ui4HJdZnHh3rQZ0cp1n7ubApMZep+tTqKhGFLzk1Lv+e8efK7o51Jt6EaSI/ah+TXyey4fonaCKrhcySVaY4BLGt5A+wu0lP4si0EGsth2pSE5iuKcvxkjt1Uob6cige9BFiPqc9pJ7mCWWPjhopKAZp7u/eyxmhaBTarCg8DlXALRPMFVcPve81VqVVlcvEulCdu8U7OUOChowb98TeprBCwsPvlUuM5tFIihSNOUio4klc02P4q90gZPemDU8wUR2JVCkoVjx4tuDoX189T27OkBvE8HVrvNIziTozqj/fDH7ZZyXbbegtf9RoT9RuCUreIi812ClqrT2/OOpTbQrgnjrtJaTbi0TkO6RfKqwqkiXqUSYosTv11ij7j1hF+QFw1BtY4X6QIA/mbJO3VVyu3D5yQTvPr5QSXLmsEBUBlHHu+MD+GLp93M9jN78GZXLLoFKFlLbejHA1jVNItQQlJu2KK+/KBkadlu/mLdJ3Ka5YSrfEr45lc1PptyBZgYjPJyGKE5gsQw68WCBr+i4h8A46kvHV7+YOGSMN29BjD955xlhkvqsuYyi7SJQIpKGWes4RIoATWja06NJDxIpq2lTqVP1Un47ZUEgOsQhjYIi06A0hCysnUKNsUXh9DoZ6KJTz9ulBs7dmovW1nKX+3CYbROU7LuvJucomHAswHArXzceaDUZhcl2bzstPXYDw5UYfyjs4q0itY2/ib+i91KAeak6qHFtefwuVruWgmxFErHfRbDzbq2siygYzDdi92UUP32brtvWakbHWIcD6yjSkIw6kq9L6usCfEI9OQTawKG1DYGQzJR2i1OIkaacYWcLfGoV5BTkO2NmxUgNPWSwCK0vcTSXifLaYtBw1lEo6Jh1mZeGky2ptfR004KG1g7Tq0GP7+80W7VZZy+2fegz2N9DtrxZXhlnIMS7TpPF8+2mwsKkEzz3w2bPewiksU1wWZu8XNlmIeYJIujRHSdHkZrlJpNBCyBAYAI5hPYu+1kELPFhLumrCVkI3MPT+ve5y3S2ke/dCWWldp77Ui4Mt/87FbUzEggeuFFP/j7WRQhdySW5PiEVabBThSc3l+icHGH/D9r/wq/Bkk/4W/C0ADce1ZOgfRxnpiOZakwS21EIQsgT9v7Li+6AtU3cmLTvPwJY6aHB9ObgvpjnrOBVtwlhzaUm+lrAAlaTkHcbbxcymTbuQhG07cBLUGFXsSlZKJX+1BRcLjkMvIlgAwqoW+Ta98pZGJiB9EwEE30NRLeB1MTVitIq3VIuYy/1K01vYyZoD+tl6xCP89pNBSyy2uAB/WG1LLAi3V1CfP83kF6u4LMSpG8k4KmvguHxWH6N/njHuTxbb6JH1BeccJJdLdTzQzrCD62zUFvr2/pEngpcBG/RqaL5mTtpboBRZB6mr5VbS6X1BFbIzsNDwnZ5Ry/ABHXHfm2V7erIG2cOgST0Vu65V1WN66meifiaoIRGk4fmmi+X6KKN45EmNcp4mE/4TGROtx/fO26KmuJ/+v7h+Yl+dNNzCbP0AFuazQuX2cNLaY43r5GlqhFkMfuBMlKyolYS147FSRejk+u1Ju4RHyihpJ5iCnS74wiecd7ZwrGDmRAQNccpldUEnlkzcTffjiVE/Ax1oDngomBi5lSBA7pVyBvhoiOVFMP+zXMFWfU+q3BWq8Rnlm37WnkhnHzhIeDE6k3KTVQil3pfSfFrYSmwFhr/fbreV1ZGZrgyF3QhIytTRA+IO2nspkZbIgzbQ23naBXwESJKBsoczIDqdizSPlCvO732jMQDTRLqfmu80MkTCqZdlnlg6Yk9iICRucGx1z7adNTbepam1z+4MnjyOTAJDd21lQ/zpLRAbsDu3IWmjsELEDE5RS/wIhELl8G9ciBMRmzf4E5P7nEvJ9Y2SANEDISIb8Nuzgc0I9sxdB2yTHkMpUB/iWqA9bovzwFlfgXiLXU4ebDAZeZqIBNVFy0u3qMSR0etTd1P684npUj5GliSb4gZR9BHZShQDOE9gBnMI8FCpupDc6MVGYprrBxAlfPnA0jIaSUE9Qfbzy3zHb67TrAMKCxYWvbR5xCoE+uIfuVAqRAII2aF3lSKz0lPsBEv2rsUGti4iN0nXF3IM2Xc9p8baQtR7pE7h/FArYxLHB7CaoO7rGxLtJoCO9jbSJy/q/yfHRr+FoPQZgP4VXWxvRN5GT9ZiJ1UUdV8ixa23g6CAIfCseNpiyEoKUczaJDJz+41T3UTFrElGLopLxeJ+xKZn79mPlaIsjhEcQnz9tpOo2eYfIPOMC8vzxo21ngAn8ecaG1ZWm7VDAj3gEgsI/alm5iMi9jH+c+CDkEVjRo8DMXlNUeOTDvCO1TnfG3/nWqD4wowmp2a5B0Bs75+rIiexkfMXxFZ5+PstJXhxP8Vtw5h8cTUpxU2rQGYCILzAowC+WSkASPNBHGWCRC2hLWzS6oPFZgEux8ezDs5cSPbWLrxCCUxTyXeVqM5A2pI6qXU2qZzomSipVWGEda8qJGrBTGPnZ7ueXkeKMJ5P03aod7Ce9kVIMjS0GU1aE1Jfbu/IkThAYX23VwhrkTwTk0o9gm+y44sJByscPREGf6bLdBQynJpvlnhKJyV5mJ2BwSHcyGmMnF5XK2pSsD/xos97leEE5YWq09flVre2M3XUMjyWFFmMXIkex1JGcKDMSqTnG5fINCKe0lNrwPZExQMK/7ZgnQC4XTA/vD3xLtqshmZ8hhq5LVI4WdSPsE/ygv1rTitLpFkzP5AVpU6Ji06/Ec0gu35YiLGhhvbzL9UjJaJqpxgOTCRQdtofTG5c7QlD9IKT0N31+t9UfqV+DiaPVJYKU0/SlEf6FHxwBBz/OdX0d5K2ZeXbmBjG4p0mnG68cabpulLCBVxpizr1I1i/4SZljvNwmnRvN8anKmL5GzOiHRo97NihvqntPQ+ZhP7VCkXDa1pYSeRphFU9rlPKit4A9xs+h9R/xwTnIwSNFd86k1592645RZru1v7agfgplbmRzdHJlYW0KZW5kb2JqCjE0NzEgMCBvYmoKPDwKL0xlbmd0aDEgMTU5MgovTGVuZ3RoMiA4NjcwCi9MZW5ndGgzIDAKL0xlbmd0aCA5NzI1ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o22BVSUaxc2TAhISzcM0j10iDQS0iFIyQADDDEDw9Dd3dINIkqntHQeEAnphqFEkW750HPec97z/v9a37dmrZnn2nE9e+/72vcaFkYtXR5ZK5gF+BkMiuDh5wVKAOTVdfgFAECgIC8QKIDDwqIHQTiA/zLjsLwAw10gMKjEfwXIw8EgxL1NAYS4j1OHQQGqrg4AfkEAv4gEv6gEEAgQAALF/xMIg0sAFEBuECuAOi9AFQYFu+CwyMOcPOEQG1vE/Wv+8whgt+QA8IuLi3L/TgfIOoLhEEsQFKAOQtiCHe/faAlyAOjCLCFghOe/KNglbREIJwk+Pnd3d16QowsvDG4jxcENcIcgbAE6YBcw3A1sBfjVMEAD5Aj+szNeHBaAni3E5U+7Lswa4Q6CgwH3BgeIJRjqcp/hCrUCwwH3LwfoqqgBNJ3A0D+D1f4M4Ab8NRsAPy//33R/Zf8igkB/J4MsLWGOTiCoJwRqA7CGOIABms/UeBEeCG4ACGr1KxDk4AK7zwe5gSAOIIv7gN+VgwDPZLUBoPsG/2rPxRIOcUK48LpAHH61yPeL5n7KilAreZijIxiKcMH5VZ8CBA62vB+7J9+fJ2sPhblDvf8C1hColfWvJqxcnfj0oRBnV7CKwl8h9yacf2w2YARAGAgEiooLAcDOALCHpS3fL3o9Tyfwbyf/L/N9B77eTjAngPV9E2BfiDX4/gfH2wXkBgYg4K5gX+//dvwb4fDzA6wglgiABdgGAsX5h/3eDLb+E98fPhziATAG3muPHwD89fn7yfReXlYwqIPnP+G/z5fPQEtPVusZ158d/+2Tk4N5ALx5BIUAPALC/ABxMTGAqDAQ4PtvFi0Q5K8qgP+kqkCtYQDxP4u9n9J/Cnb76/zZ/9oNDsC/uTRg96IFA9j/0bgJUBhoef/F//+s9N8p/38C/8Xyf9P4/xb0zNXB4beb/bf//+MGOUIcPP8KuNesK+Je/+qw+y2A/m+oAfjPnVUHW0FcHf/Xq4IA3e+BLNTG4e8xQlyeQTzAVloQhKXtn2L5067/a8kcIFCwFswF8utWAfDwA4H/47vfLEv7+5vD5V6Rv13g+8X59ysVoZYwq18bJiAsAgDB4SBPHOC9kASEhQHe/PeraAX2+K1hAB8vFIa4TwHct+cLsIbBcX6dqIgwgE/2l+k3EhUE8Kn8g8QBfJp/I7F7n+7fSFwMwGfxNxISAvDdL7sj6G8L/71W+cB/Q+H7ZGuIG/i//Pfkjv9AfiCAD/pfUADA5/Rf8J4f/g/ZPXK5X45/3CIAPsQ/7nsqhDvsn+LuS/UCw/80/Gt4lq5w+P318Vve95P9D/59V4HBHmBLnLlpmOWTELvakLaLalkadx7kp6eTLEiDdA4e7zl4u+sVAVYKR1V20Ar8TDZlqPvR4oYi+6nMPMOt99fmeqzwlmTt1mufm1eJOuPIVpzZz+T9Y2++ytb10WHT8ujJbPncOvu8CLRHb0btUGXJd3YVI9AqJLlw71XyqOsrXRgJm0Zqb1WJPMe9KZ3gidWPMQksmWIpsMj5QvkYE8FD95CT+IcH4dTp2SRx3tgdg2oiF47vfqxgsbfRqkDc5RevpXI9AZdOKmYqI0o69FPikXFWb7mdVFWKGe93xSvwkbQv5O2ja46v+R122L2QGjoX8N0jM1aWMQl2Kko+dFKV6JiVFuUkxxLGJEyhnB+LqiseKkeizRYu3JpJkDtNB9E6YR1ccWTXVV6d1w4WO1mtIWe4ji1ZSh0DtEc4nA41r46VIKIIaTNwE0uxbDJguTS8RNrQJE5grkjX3B6wbpNd6x64u6THOLToMagydajqNRHhtrvB6UD648QyC9bfsTXqYuVIZYaawtLQEPR0p7olJU8Bl4smmrwuh871TLyZBoM6liklOzO/WbBBC3fglWkt8isuR1bVTmufO85FRN/eBmmpfumdbFVFvlg20RtsZaz4uMtGtPC1UVdypc9rEgrLxOFrqhFxP9nDCo8NKrqPRz8eH5gW8QAleUttdOUI8TQcXpMY5WomqB1MFsQJ05iJ1/eQ/pQoed7CieHz/RlOUMidd08kYD48oHRakXuFKvfauQ2NQqrdDmiW5vE1tVa77yebYc/uN//d0Mg2CBXe0BMqT6MhrVNE04ONQjRyqbua2UWnbXr+QN0X1ai+8QMSbzuXS0HHVkDb3hlvf39JR8wvAu7fRvJSr8LIQm2E+/3Qg1IHD1BwLoNpacSVgmwDYvzGvh1PcUgK6o5T4odadZShqfagTFOo0TtzRp92sNCEbki6s/HnuwQem2kouBV9iMl9lM2r1FuvC23zMDr4/iaJ3Gsy65o3Rzc6Wf2EgSNsQXKoMea7263WpG0AkyS/CpPmK6TL0pvW3sFn5zau1T0b38gtVu0WuBCYYRd3yUkFP67x8xy1yjg82K424lkFRLzNnaynbQkjKc7Hbh5hxUIe3Noc0qmXjZm0amCVHxMc0tdIv8h8N0HhMZFlCP5uSeMEeTv8HK8rg3A3TXSxW1o7nZU8Swit16h3iiPt5xsu6khH7V42EnGRtVkVUcxCkBiSTSEbbfwxgyGw6HC4KfOSa2/MaslUbjeVdAJfeiDJpgTk+eFA9m3tuNlmY6k5ZvypOHmmMO8uJIUrOOajKoZHFZ5EV9HW4tjBS1+d7TGbAaS4X9XnXcSug/4VmRdjqSlbOFz6WprTmmHlwifhy9k4e6HiiVJXf4cMRiseEdQ+y89rB/xtPqqvSPNMqmd5QzU3pk42jyP3U3pZuHEvNmZrOWkmnX5kz9NbDM+DWErhUzKn1z+adPL0z5uOSFgHgG0t70gSgthSeVaz3teKkaSMI1TuZD04R4qY1xgXbRh00CmVA3RRaMue6HS9QPmmvxqwL83o8DZWfa58XMCGRHNngZc0VK9XNJny6oP7WLI/h3QzerH8w5X81BcWpKJm6/ZZue9PVZ+UW+LjaHS+5jzGn4tVou18+2Z1zicraaqHPkL/zZ3laOVK4Q7B0DuHKf+1ZJEJ20wRDGNsgbmVhs95CfQMRqtu+ieZ6w5tWj4sHC8Gmggcf7TMlOXQERM/ZW0D4WyXk5+oxvW4pIe8oisaqU3KwPCM85+j9vHWCSqMFemtzx1Ok0uLF7VTOafGCqQIDxhMF0ijvq0DkUbelLDg3aXLiArhytyhkO0JsSfRtvE+Rf0OGGUwz52wZXMqn9jg8khxGzX0DauVjh4hUpoWJFKgk0gLMvsQ31AwmZfAydosdOg0C1/3iL1ZEwaj0+fHm9kSZtnLHOq+GolDMsg7mJlhjvjOf+BxFpdpGV8B3y1jy9S0/nyLld/oE+WuHgVPtiQVX9EiY9/MlbQXa8WqepJSKjqgzJdc1CiUpjOvTNkq/LFiMgBzGqnfQDbD/YzWZ5RzMTgmBJ2kPArEgHSKJKAM1tDF+2zMyFqBQZJAi+xj9vf5cu7Igial1z5mX732cBfuGlr9UkYdGtXo+snAnEWamlBbjO7EXUnQhi7g55M2XKtizKZFh2EY9ih1s8AtXrL+2vGpa6fIerr8dDQLdd9LvVM2nZOYAWq7CZQ82DQ/nXop1FftZzwXyo9kCc2fDXwj5ucywduTeZNxSDIUsiGhprj6MlUcghA5vOiRQeayl6Av1cpk2LKJ/jZAchXt11THkstPMNOu3Xwkv4lgTSnNbcogYp0Nw2Kk1obinVQoKliq2ekNT9xShrdEffnrxVEdymSrns9XBqAyiBQTPUxNNU3uesSVebVa45B48kqE7JgtlerrZ8FFc3tOPbwP7JXvTXN7CEWEdWZ5jLvYFSv2ZVUZBV0kMMCcsoV7i0UjeBhGP0sHFV3qNcZjHOstP5ce8vIuXErhhXdHTB5lS2l3hLM206QOeFQiv440e0HTCToiwuzl31VbOxvwzKXcBos4R8KwONOduHjPbOwcjPLh9LYImvhP0oISAsWIucM5T1aaZxmM7pJnzgwHBN+qW5/xqk8SeFO+X3uSKQuEXlkkVUq79ZK81PjEpZrWv1FF0ifCms+8n3WdIInrSbzH5ER4PlaNDqUOPrGr5tXbTMFFOQ2n3GEtIwoXQ07ledX05SjBIsQ7R6Yem1FUXAboPHpD4hHuY8Ro1kf40V0RNGNdM/F9qBHieJcdKyREw2dRqXG3z0adcYb+fKFbENlOdHi0WxNYpmk7pio7kJFZ1yZlVkIs7pY3meDJSYPFpuKIaJqslLDSYDpICyp6rTZJswXS8cjvYQy9sWJs18FFfZ3lH5HZwHmYjxnYsSm+lU+hW9mnYEViP9tDnWTBXH314nAgrNQMkSrkaysMTlxW1oiA9ypXqBRzazZiibCtFlOtjRE5FNYxhGXhhNyFaWjuUc4zeYBQsIc19XIfvkDytLI3Mqo2NBp99DRcwW5/x2ha5TWzKDPD+yHg/SUZnvC3SJQxnvhkBbV64VfwOWXkboWzTVBJFR89fLsk6NGOzWiVFktytAeg3GqvI7XTI39K9tbya1/6cMtYQeHNS6sl9hBOidwLWKyVeURhoaz6lZZFSEwYD/RNYvhuFeJBkilTQ3Fkv4MN69Ak+F1aBeFOqoWwFck4p6YitnACfo2+nu0V1ZKDW4d4K80rDtTLKaoRZif5rMz+r3SPuEbYm9TjsL+0zWtdv4hO0nQqGB5AWMqbt3zJXwdmxfXWBlSKCZe/lGXvY9tNiuBXzcTlwKOuInk9+XQYKq3oFBe3BnImKowyqbrcY+P445XQHN1H3+cvC6q4Gqympcwsl19Ei/QTAxWN+hHcg7ekJFQc5ZAzdLA/QJOhrmIyTxhK9JYGIiYn/JNzqz8kzWMV2i7D0KV4jaagdSfVkg9mJlU5Xdg3a4LOdYJwX5ZKkV0UFNozrVa6S3c59azV340m99yuIRpyyOkWOhlf8QPaJnut4LXexTdWeEkb3MkTMkemXLMfEtuj5BcIvZpPoUTeD3aBOvv6lR3PD2NouVm/BM7v0jBUm1D3VyQsbn4u5FeTqy4bI2ZS3axFbfjRefyz57CJzomCAkg2CiBF+WQYqkpMlPhc1EhN4TGoKOXEekuF/x2t9JQdoodtSEyCjz1GJVepIG8eCxNluEEkauABVXg6vmQwV0Hte/gZ4CLbI3fWLvCPpxH1/lWt4h1Irx6Vj4wP3vZ/gBOBlTQ6DDWcZCNThTD1XUwtntgkotLH8qhJbMec85GVz1eOOJGAZlMfEkpsfOb3uBx4fTS4aZ6I+aIz5ZBVUREtL1D6g+UjSoLpC1I8+YW0gQxR0Voe84E55gdVFepuK1h7dHvkPnWhrC69MZ/wKkUMGXttmKBWY+HGye5EmyGBUPYQq7m4NNgUjOG85FXo/muYAMGG5FJrKTZaRA3tAH4XIRp9uId6gkqHGU77SYUxs5WC3p7R9KNLvIkeu97G1aBkiI8m77aiuLuFLlqLS58cllfC2nTmYD1jQ6aAzQ+0N9M3pCLvHq0ujFpgWcLTGZqPFhINqPkJ/GUfr0ANC0cO+RtQW4VCoYmBz+m6NOV5yDVk3689FaewEX76zcDMY3SEHcMjsL73bj5X0Z++umseIUbxyFcj72RaJU5hOOR1UnpkR1lmMUMHY2AC5RDaMkbAY1Nf1A/PSDv6OhPnFPVIBAYddSCS2G+74YIgcuDA1z5Geuv1pIEdjNYo3WxWAzLvFzTTKJWYC6/u8EsJVntLGvjSMgwscQLxkfzJs8J5nolidTR8eC3TmC9aNqpKTuZKmnuOAoNyHnGaPfOlZmCSxtOi2YprCuDBvDnwS6V27PGz+/E6O+nhMzfaF/0AhsjnP2Z5H2GZsL0sPOuPOzEKisojiaWLjJj22SIWZ8XKbu8jWLaGixKufQjQbvODNvIA1IQjtYmR3/iMo9DaaSmNk4MCNHVcwSUUN1JIpd4x3GIvlKsqO60/qrjtDLYDX5PrzFe6itY74++3Xtg5OuCERWUK5jEu/qAShyeFfKWOhWDvPjJ37RVjrBVmpq6zfPDuUl8/40Zz+Imf78I7wiaiCyupkKhB6ofDBCOpl/gMNi0fsTBQHfRvYG7Ft1F5XER1j7VZvJ72Mr/TR0F1lWwkUxXRUUo7G65fHAl3c1zerKNgOb3sac2rmyA1LX+qJe8bcrP4SmBxYMr+9pSUhPVrPxru7hNwjZLoTBJGsUrqRUw7UNu6i68l8Qw76pqBauUgKxfMqLvuIxCX/Ozzk9jpBChZgKDEqZJdQvg5h599SubInDqbbHTJgo6UPG3LirqZ75VpmJWanGsTu3v6YB8r4IuSKifhwdRH3VigD+mOvEc/rKMYohET8FxGQyY95itp+rl1Ve/m0mWwwHL1m27INLxAdBwjjIW2kMQjanxUqFg0+pvYH43fQgJqwgcLhe39vkj8bM9RcHd6XPbE7rBa9sFVBfYnc10dkw8vHxDU8E64a6xS3MrXJNojPUnK/JZdlZl22cUIxjRE6wBuhCt8KhT0uDZPdpKB6tbU3rNJ188E6bCTY4N13ZWhTr4KNcBvWFdfTzxPN0HWb4ID3mXLCkornsXx6jzKkyNAXcFABqZhWI7p+2geveNq1qdjroePP50Lj0yopIEm3NZvC5uPpBRxr67xTf0wgz8RlqYWH579QSilna0xo2QEwQouXznPTrRJC3N66h02+YpwMAM4pTwqxf5HvatB+2Sxl0+W3kj9xgsV9Y0qa86VohCRbjKsEcvtynkzybRsHeFRNx73Sk6kOuFsfMTlcSp/0IvCt3iQcOnNwiBVOevQfKqcr3Q6b9GwIN6bykz1Qra47KGo+TQ2fX5tOGIfeabjZvZC20dJZS7YffezKG8it8j1sjH1cNXTskO0KmqCzA+cpiIKzL1ivH+yZWVHeOBFHpBxZSbh/EEh+V0SMZMkrpE51zWy4qCiTJe/5JLT8/TABUxYPbI+8oDizP8Nv+qn0vecsTPVHe/VjDMae2NdOC1tTmVm/Rztrd3eyYstDQ+QhITh44vrORnJxJsRBY/0o5eE5ENEnRrLBD6kBGmrldWJ1r5x6sRvJLeS2ebQMe26s5WeN3YMdZJm2vcoSgNyP3DfkoZ51R1lNS4+VFl31ZBTJmTbR637EUfR98KglCyG0VBkCpxp2HrM5+/rcuE5mpP5jX3BV5w/8ls7sHPTZS49ohfz3BUjcuuEYrvaZaBICd8zwuQNhfyRKko+RyDrSqejeiQLzYON/JEOrEyTB7IdXZ9HjkSd5Mvzi9RKiItV5ZqBp2dEp1k6fpdFjnciT4LjKrDdtNY5SJSqbjaQWpQshLXWpizxmohA//hm5Aalg3WbrD3WegGx1NB0XuhaA4PvB5mIV2ONtRt3GO+PDUhZt99xYDBdfpoJqM6Mmb7mKqBgHkoQ2IK8yngvl+Pl9uDAnNZUfnfA1JJrJyBwPKYlpB6DM3j4sqESqU4DbG4LOy+YMRkd/IkJ62dYOI47dmx5gfOeu1jPuO0Ys2huGhRywNYHj5v/ONf46SlXPu8u4lXDBDdbwNtuLY75488luXDOSaI1IYNQpxnmfFxafW86npgP78aD5CJpjgF5MWHUzvP5GYU4IHPNER/njUirdIyC5bVPgrvU6JM9Lcxre3zHPhPqOleNPOYxNKtVeO+fiGypX/n05fXY+B0FnL8EuJYa1zgcscPa5u4wpwJtqDMcFAn33ZMbm7qmQ7gCYvydTqzm6wwdSOMVDF0ZSAtzlXCb6S0E1NHAFfjISE7wzMzFCkRnwdBDrFl2J00RqkucYqJDVoCZ75mmOLlGGJjVjZ7RHUxjN3ajJ85PeaCVXAKv+jB3Yqrn0zvuCszwWjVQj9LdflqdkIx+DKVBXaIOnrDPusz62Rv7EDwVy70T2/vlgkG2aPpZzqx9PZ+FyenGTq2bytL51hBaIy56gTbxMvFxDqpCzdTe8bX9hUtqoa3Uh0zz0MQ8UOx7rWp2PzsWrcqwpsEhKUKcBiUDhxS1wHFptIyc8M2GH5dvzx4sJb5n7YqydT1FYW2nl0UNY34UExQ7xXIVJ25pooX3WIn98TJe33rp5e1pQ9jaPsZQZxdpysQjFK/QB7QxeWlcj5M3A2iaSOebgktfrCtHZSSdzlM1xPIrzeedK3tErb8kbNvVqx2dKBtCdUng6XHSjcEs65Kvq813lsAJQhcX9wBbYprqTHp7ydegh7dIe2q26E7zbnGt6jGZ35g/ngUEYvfbJ2bbC8hmSSFZaYPNapd2qHS8xp9aE0XupnJc2NjJLiWzeYRbyGlZSVZtFJtNxHFPMhL2FLh9QTrO4qoyJtZwSlbW8OnWog12oz1VHxH3NaQzXtARXLuSFeDSCLb6+njdDeWZCLbiaZLxAS9hwadJIw2NIAUauo3ubQZbSrKYIh8G4h/Pt6WEJ6J5hdmfr12RTUpxYYUFcwm1en6ReUe2jJET91nuy3fjlwxNba9PscvpeMxviGJSIl8nHo4mMhaX8Pi8P9V5oQ4XwbZiJrKLPS9zrwRT57VUxH5jZ+E15z07wkd0JuOYUx4O+kfk7pzVx0P5kbYAQqjX+AJYJ2c1gAl66bhJuqxZjfZzLozSvdtd05VTY+CCwB7FwzIqJ+PuRdFUl3i5crezWLXA0JsxIsHDwk9HRzP+X7JOCoN5A3UzMbkGKWRYCdujEk16x007cywMVgoYCzWWi53myEA934FJ1vFtDvFL0NZJHEWFWLx6J6wdebjEYfUxHRFyal/70YOnRZsoiTuk6yM5Bg006ORi3vSJjvL6s41PGrURP2k2cowro4xWSomJ7Poz631eomsLMQxuyUuSGYHHZr9TwDaRVwoxf1iy+Cf7E93O1+5cGgkk7vq7daKzker+oF+SMf8paGj8I33aEUCv0vCgq0zalJc+t84uVY74od9uBCtYaPZOxVcvSH0C8oMlhIsw8WwzYnHPlqefd1hN6ihDOT9mxNJF4PFnWz+ad/Ldq8G0fUOvKWI0/ngzonVBQmdAeDSkh7fsloL+DhHjPzsSXKrmkw0qJlfZFiJfMxvqJsy9bPzDn47iOe/rFGk92ichU8IjKFgWzGC6FrxaoEEcYj8j/DEmehOTzi1OymM+Kek3Nl4bE3sHC7hYi6lJfPi+rQOhz6YNcGqY03um1/jP5RnR8kj4cSTwnBTzv7W0m/Pi+byVzs0tJuy99TQg3vvJj0znEivv6B019H+ZS4hbnD1ynXPW06OnxkTblBMRu1b0re9OBKPixrnvIr33oYaLkA2ebQs5PpxRCPX5YEXGbKPQsnY1fRTwGZp3Nwv5A5JyBsO9wpRCA3L6uotXd9tDA/2fxkBkRpA5I3Oea9/v4aaokMBLuo0dOg7xF8/FlIZlyjeqLac+drx/LWJh+P4qjKmMgu62fhb2TGqFWdusugOVfLDTwWA7au+ZoFINZXyOSkDPODT33KXgjpnpAyEfFL+5jLU9afqWVm9Xf/EP95B2mw6z1y/MDXOl5LgjxkWQIWsVObnravxwSzJNn6UP1MTpp/ziKmRfGT0rvh+y430VxpQSsEvMYSYhO0KqkmigZ3MaCtrmbfZr0zunL0Lj6avu6Az9kSzSyA8j+EXaI164Vm8Ie92FgihTPOesCdkvx7aF34MZpLFxNq5+OnWnA23dnbAeNmktE/TcBLzJ269je3SwqyyYAq1Wu0T7mHixmFTfFZ5Gf4wV7VdWEcdQjS3R+LXE4G45L1MukRKlPOTaIPrzxMSQ5vuv3oQc1Ye8skYfw48bU9GZ8APN2BkI8cDPhEchWyfsWNuA2/jaVZNNWbRlGLNsC7nUoa1wR9dXemVz9AFc6F2DGX+Wm/tKoTvCWgO0F0D1MMZseI5wYNf4PCiPwuKTmv2x07i/YkpO4mM7R7owG4DaEkhNWVgK/2HAQbGv/PVmwdtPzoFy+uYv52JK2hGw3NYzoI1/37BRaiNj5RfhXgVoXJfJUAHijJje9Zgh+DbTjitQbQkN/RxgGlMdFA0WqHlUXv5Q5pBkY+jIqOdsjaPiLe4oCur6jFFIHO3p3JcG+cNB6MeA/IOfWHaI4uhr4ZqbUKqXuKpeQqFPn4y75I9oJ2mLu6y8HuERnS4mxYikrFLJ3yMZJYhirbLwMVpB3nnqbYv5ezn8HA+antbDmHev8eOr8bxuwabGDKo5sKC96z9q/4gy4KcQ3THu6vu+YJrr3XDdWqeKQPBaEDtLi/fYuuk2i0KopeoYjczbxt0fiY5WMBrxDKZnFgkzFCTHlwk7410Tzc/H5p9qHXgE2givyYRWzT88ORovdBzXpC+mH+z5SnRNLbvx3frNZD/1wN2SuX7Pt9zoDt7Zs0dvoUKMxOZOqoPlatqnyksVBG2VTHmoqDQ5fC0Y5xkCEpeoj2VOkloKJl1Su0OGKSb6lDEj91c9D1k7PE5UJr9yrb/d71NxRS7Qn16wUbjyGwhNY2+xBJvWstVHnNvtZ8D192CxOlgjnzyc1KXPIpuNdYMTpzWvWtGMVGXQ9Ihtucy6n/VcMuRMOolROOhUWBGkifHs1sPOVbCUS2LcDtOJaXSMdL0ymeln0R5YQWo9UrLAD/yiJ0s+Jh5ESB5zM9HY0ib6ojG28UrEn5y+nK/ZebUl9nbexvYW79KNR5qa/6TIXpoo77v+97HoxY/arGFNUQRygSZzAY0tCImHvWp2Hf7Ns8zETdmo9JTA8/HAo50KegJcCc8Zn6bcR/lj7TjajxN9MAmqaKlRNHCj9EvPaz9TIsXkjIWskPzdM4fSZiW36N6dCUBdLxiSv9mjc/GmGwr48eka56EbXwtpWLS6K2dBCLFu86fzd+VfLmeGH3y+SRGAnn4iB2/cfc72uJNCkZ44OXBL1tWelL/xOFCKxrTkRHM0kbamm6bIOh9GPTdoVT2sp5kydDI1+u6+jzgdQiozckWZqn9lgHd+5KHdcigs5GKzIno1wo37IO6d7OLbr37a3oFqiUtfcAPVXfev6x5mkWlm1CE0wdiNgVwuKx3HRdOe1gVuxjzPO7+/xNrYthMvMFbC+qHz1nDlFdq5sKZKcoD0jlzfdbYAdrBPQ4QSVZB80uATeWP5SM7k4ho59xOqx92GF/L831YriKOYxw4nB7tgb15CH0/0L9NII+1BfuUPeji211FiVtXmHwNyuaNNymDOmhmWM4KGhEhMF+C8VOkhQFQ1ux+CuXn7+olCrPtD0hhKUdHJoSvD/GzNcA2H85+pm2LrnXuQkN6rBiYyQtpJeXY9zDlcknDOxWX6ptpxnaTmkr3XtwdZhbcmYk3jmD2Kgc3Nr6jbWFj4s4f7OjoVBalFcZFH+9J9DMNHlkYs5hRczTmHayt8thmY8Hr6rzs0MZs8EN2C1cvASGPCHAQ4qPect6jQ9fNFToh+CX0pjUTEwTSbaxm2zHFunnbQdTREeYoo9WhfQ8EmPTP9dCyL+ZDr2mhspocKA//y28n82/F1cWn169NSL3aUvfFCus27N5WfvyDR60eUA+7wpAh4eySNdIymgZy13U7ReqQ5NWb0938soI4J1MCL7wV22OgofleOV3HECqlzZ20GlVXtDEmj2hzrOybOBZ6ScaCNuZTFC+YIzhvOBWQdpSSFAzUZWr1e8kTtVsp8hJ2Ci/yQcTNvdZfx6rXIFB9dvYaUL+fsHz3qn3zyUhGHrXAex59nZjqjDx+ieIrl9Uhz/9gBNmuWyWrQbHfbRhFaLO7SLVvVIF717ZWYRilgN5NXbbrcXUNm8wC6slujSgOcTftvsHT7r6v6s10WUuiZmtzVX3Yu7Q0TQ5iTTs6Kk9bnWyvaiB2xD/c6Au5esiofO4QSzWNIs2dpo43Sc9l/Hhlbl0/Y8u3cdaHywEgKgWf0+HR8cWw1At1VRrYW2RvpTez6aRnh0FbjKIwmdVCcznUXwrKsQzUE4kXzcCQHCB8xhLmGOMwUmq3eBMqjxp2/ozApz0mynzzHm6DCb9o5Hor/Jho9hff+j6OFM/JZtBeWiq3bBkU2szGC0dRMfLr50lEDwt7DAL0WqoSvplc8awGfU5YVvqxlzTb5IEPC6yBOqG65q0s4uw1skCodgRFmezaggCfg8+vDyugepnjy2YbcFyTdqVYWPvvh40mZK8yumzXZ8e+ESbYmLriC/JAHHe8fv77+PsHVpR/CeFTI1JVq4wE7vPTjirMkQpSzk9fgMHh6j/j4PlLh/El4Hq4mnvCJxlTlu8EnItOKK/a4iSioZiJVCI0uU0ye0VRmmDpVxpe553oNZfsud+Ihd3vMKAK3kxTfxe3IJkpliu82y9uSmk2fR5Chlah0x57ZdJPgQvrT1Vdg3A0se9kt4pzkDRUuQn7fPXYvTiIpvUimjTtoheM9iKT6A5sHEFxRindWBCMStqx4VNFLxwuy00alAglXVvioh0UUvt6iY51UbW9/+j9T8g3D9NWtkQMLGVezQvkfUeT4DzgHO5amLCpzHoRByRg/nI2Z1ABan2iC1nhm2Cj68GFJRaDowwgn7X0HcrJvF8wLwnuXCyiCUzhmBgFLzz0NkIioUpabYnTH7ZYJtoY7nRbc7cuXNi4XqiU2mvNW8xqZdkNsocsQtEz0UbToBTzBvZn9g/kGmkfxXcyej9Gpr390Qu3Ye59bCHHrs/cRVHV9on81xhPmd44Tfa7v3yXeDjopoj8fqVXbL7OgiJF3Jtc7pHyjhW1SuPKeVfomht1+7zgRyvEH7pFOtuXPeuJwbsflE2KjATZ/HZcQZvQk/QMz/bhs0punXThdQfHTlAdS9GxriwUhkYVJZS+N/AF8mNOdLGbJQZiB/nenLqllLOaJaACnGr2WkA499yOap3V+a8Ye2IfOZyNh77syETLdKG7lA6mD8mpwe7xxwVAW8UW18SV9P8bYuRhWkyXbLQMWq3cqjd0zYuKB5Svzu7Qr/fr2zVqCBZO7j9B6N0E13dHJdSaML0f1HfgNL1Oy5Xzp3YFTsIC73vrDvmV2UnzJuJmZFTr9kXOKw6vky0zKQuirl84JZ6cZb7/H1U0lcS/u4CGni/8P5mfHSQplbmRzdHJlYW0KZW5kb2JqCjE0NzMgMCBvYmoKPDwKL0xlbmd0aDEgMTY2OQovTGVuZ3RoMiA5NzUzCi9MZW5ndGgzIDAKL0xlbmd0aCAxMDgyNCAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o22BVQU6hY2DNKd0jGUdIN0d0qnNAMMDDN0d6dISTdKd0o3SHdJhyLS0iEfes49x3v/f63vW7MWM8+u99nvfva7oKNS02SVsISaA2WhEBdWTjYOQYCUigYnH4CDg5uNg4MLlY5OC+QCBv5tRqXTATo5g6AQwT8CpJyAZi5PNmkzl6c4FSgEoOgKBnByAzhfCnLyCXJwALg4OAT+Ewh1EgRIm7mBLAEqbABFKATojEonBXXwdAJZ27g8HfOfnwAGC0YAp4AAH8vvdICEPdAJZGEGAaiYudgA7Z9OtDADAzShFiCgi+d/lWAQtnFxcRBkZ3d3d2czs3dmgzpZizKyANxBLjYADaAz0MkNaAn41TBA1cwe+FdnbKh0AC0bkPNfdk2olYu7mRMQ8GQAgyyAEOenDFeIJdAJ8HQ4QFNBGfDKAQj5K1j5rwAWwN93A+Bk4/yn3N/ZvwqBIL+TzSwsoPYOZhBPEMQaYAUCAwGvZJXZXDxcWABmEMtfgWZgZ+hTvpmbGQhsZv4U8Ju5GUBWQh1g9tTg3+05WziBHFyc2ZxB4F8tsv8q83TLMhBLKai9PRDi4oz6i580yAlo8XTtnux/TdYOAnWHeP8NrEAQS6tfTVi6OrBrQ0COrkAF6b9Dnkyo/9qsgS4AXg4ODj4BXgDQEQD0sLBh/1Vey9MB+NvJ+cv81IGvtwPUAWD11ATQF2QFfPpC9XY2cwMCXJxcgb7efzr+G6FycgIsQRYuAHOgNQiC+m/1JzPQ6i/8NHwnkAfAkONJe5wAjl+ff34ZPcnLEgoBe/4b/nu+7ApKKopaesx/dfyPT1IS6gHwZuXmBrBy8XIABHh4AXw8AgDf/66iZgb6mwXHv6kKECsoQOAvsk+39B/Cbn/Pn+Hv3WAE/HctVeiTaIEAhn81/pqDl8Pi6Q/n/7PSf6f8/wn8V5X/m8b/l5CsKxj8283w2///cZvZg8Cefwc8adbV5Un/KtCnLYD8b6gu8K+dVQFaglzt/9er4GL2tAcSEGvwP9cIcpYFeQAt1UAuFjZ/ieUvu/avJQODIEA1qDPo16sCYOXk4Pgf39NmWdg9vRzOT4r87QI+Lc5/HykDsYBa/towLt6XADMnJzNPVI4nIXHx8gK8OZ9W0RLo8VvDAHY2CNTlKQXw1J4vwArqhPpronwcAHbZX6bfSEAAwG7xD+J80hs78A/IBWC3+gPyANht/oC8AHbQH/AlgN32D8gHYLf7A/ID2MF/wKdz7f+FnE+kIH/AJxrQP+ATDYd/IPdTqqMr9Glivwfzb9QTO6c/4BM75z/gE7s/g5/Yuf4Bn2q6/wu5nuh4/AGf6Hj+hv81CQtXJ6ent+j3rjyN6T/498MHBHoALVCX5qEWQiG2tSFt19USpO6su+MiM3S7uqmMrN5LTu2ut5hIyYxVmUHrTpcSyZ96sFe2ZRguxJcpH7y/f6xHCm9JVG+987k3ideY2m1FXZwkGJgo/C5R10+OQsaqJf7F58HRRyfQDu4jbKciXa6jKz+mWj7etXufnEddf+nn0bD5XfUvVS+V0O5Lp1ljtWNeBxbN0uWZZ80RUSO6sJIjM+GeeGDNXlzO4OZMPFIqxjOj+h7Ecr/3NtjgenMz57VarsXl3EVMS2xARA53gTs69cJbcu+dIuGCd/GHaOxmthGu/AfFLKODSNRFRSoG492AXm/bcaVuJOmDy42oFx7AsPaRL1cBvojIJ16ybVL1yNFfdEP15Ty6RVrs1cmgSZ9w2CyeYRtcdvpcz7keKk4VpROAzAMwRjk61j5GvXnulIKPyiCoY5Bd+taXxdVDc7Gwu+PEOl+mP9Kpl79BUh9HH3N/XW1GLFluOT8XG+GzWhpfB9ZqRveXFFHTaBPks7ZWYtxYIUz7IOHrmHlZLZWtuwmd98Uq8s82aXYWpjwN5NqTLE5jqLSq9TZTrnyt7RwFsuRxCQ8wCOIvvCse2CW8I65Fr8M0Qg9v8N/1vNjWndu/4Ancu2Ty2LKZblvnCW8gV3B/54AxnCCcLc0/f6LPrUO455Y+OFX5oEDBdh96D51cjdCuNVKR7W1kuV/fH2zR7YgJPmXRqpujNB9ENl1jG9ayQiR9eYvDYHlLpqAhACMuzFg6f1/wkyWErEszJvEqoJ45x9psfFnjiDpR+MReLiCL0Z9sZrMOi6S8x0QCBgmv36NKBkT+mk3G+uBsuv7oa8ZwpJrzVyE4e+uaqrbMgaNlDxnNuLSk8y7ffFrrt+JWH1Uhi5gZW2YfiAAfWfR7Mrmg1YEwdG3G3eO0Clu+NUO52pXs+PnP2q6/WH+laFc7eAF1nohu9gs8ixLxKjGirIBbnQP/iH89gPbwPjHeO0pu6Go46gZLxG2JVfQThgA+7uA9rp94M1K0BXaa0ZiRIsk60cib9noRFB7U3NNFd9DJAoU8RtFFOheBane42YWMpdVxeHRUvjNR1pz0GjIe9gSrt6dxsnoLftK2PRkWZYD7WzlMGf8NdMH2vSxUHxLBLuOV5xeyXiYK7rnDi1YB4ndfNgL8K+7phFPidKtk243CFKonzvtJb17iTEWPO7tTTSTJE+G+YhJQGZ6mflaT5yaJF3NR2b8pymhV9A5i5Gs5FRcpuVxHAvrZDsnhTprHk7cVSopxcSWTUA3R9H2+E+pfkk9kXAjbiuvNn2JGN1FuolRtssQtgWyGUCAtlGm0bRzbF0Eoapf/RrvOR+S8MA9qEje46urldUyFtNhOjLmVLZjj3x40evDWlIwch+uDkNq9dBN3xqm4j4t8KALJNNmkixG+Xbe2eHY2nxW3w2sV6SNkzAkZSsNHuyCs0hpWg3H771LynOKksq9E0dB+aNEb1TQdTXO8G1lebhDQmMdD3zJZ6I6On5OTaL88wsSKL3j/qTW52L1IV1+rZ20geDvgyIXqzGd23GaIGYmJZephUQBLz8aTt8zfgpvuPY5N+iHpGp8tyyuPmfCmi5VULdeI5uUOpV519WRf+h96CtrvYeIc/AiLMQIS5pO7Sx4GplugrZcOKxOkKvuBbkl3kNKVgbkD3fBwyzZHgnoE1tmY8mXCLSxXz5DBjHL/kcThABaK0JhihWryo6oeWTsh9CsNct75Zfnyvdc+bw+h8DdIV/vUvYMBu0z5A8G6mRxKI9JhWmllaHXLIXKTIz9WtOaCj1N27K4R+RZ6Z1nzPvzsgvvZ8MoqQ5KxBk57SspKhp+tDkNBl8N7bWtQbvbDUWjSHkoDnmTNVdRYqCSeBrEDjU3rKiIO5n7nlpLJevhFp8r55ZKPtnTUWlrZXDTT2CFT/9JJ97Sah31HdrUTxf7zzWj+CXa9YS3i53ztGiGS7K746O7XelyBV366wzrRNYCWYOMrG2nQ/ll9dycwk1JLqNhwr4Czg2JuW0KhQ+k9njrnafUd/FqQeavOBdzILEhj87hh4KdfwcjoDlyMwCMNPSni66KmkmzMZTSH8f3yeEVe3rJqRGsvOL63gdZy8hURE1IMgW+WZu6W60PZWPPkhTeIkceIsSL5W6UV15YMzBfr/OVmPQhfvWg9ggsK/dSvP7AtsoEzcL8gsY/hidRMBvysYyxrvxIpUnAUQhl4e3uNGFOUbjfKdnl8tyv9gYGjmGiVR1xPJGCK2+Ks9XGVv3akfhfZjrrn2lp2PrfVAh/P3audtfkx+TU9XAFT/AaMDhGMRucW913uhQH9N1f4WPUdnP79sEclIpW6TEtU89G09m6BGthm18RIZy39exuFgg1/CvtQI8vVHuFKdB58XLyKyYvSi7hokZ8FHy+zXT/qjYtoQH/MmhYhfV3LqtS5mSN5b0qLxy7q1WjOt5T9QG/7VYEYkSWSFrQL3eO5m9ru6WBbnhnlKmQU160ooFfVLtsOpRMajvqxnBDKzCEiw1jKxKiWsmRYYC0Crmea11yF+XEz6N6WqZ9AkABW1531ZHoAMgIyYLhAd5wZynB+pqfKinev26F+UrZKNoerfmktmdNvN+wJHYZl59TOTVUH3i5VS8CXB+IwH3AGSBRgPqD7SQy4BcOw90+opDQD0oiMjNlB8NS6lAUntj+KUKFJ2suLwpRzFjsHhQ03euTtnOzC1+0ejbtYeo9S6TnNKkfhesrb1F2iJamreXDssqDRoUYkFXdeE/2pIMfCpHbKGwso4VpnqK15i8ZU64RpbkoZ9bivEdFbSpyEkCiPwi5N+iUuz5xj/JOFF9F9kGWuXZc2W4SAM/UizfnDw/pTuvFjxRqp2d7VMwVf5a64SE3DjopbG6X09b3EYh4G5T7yo6T9iugUvBy2onJqQj6OHHCKrc5ERMt0LVDhjsVjuFuVz7/3s06qxzes1eakt6HzgUNOq6pYNbZQO3iJiMaWhjTKaYDYpwnhGw0OX8NVvoyZPDL/qs3XH+vdgUV9yT/cuae0cje1N7nk8OBn/EeGFL5SvnK80A9OXqpYMQ1FuxbCnEoHk2Sm8b1/acKlIqJL+ELJn9ZncoGetXVsqfRIFCZTowbqwo+DcgKisnVAYN5p6mSDbxGuye4gUf+OusjRcx6hIdKrMMlbavhR2/6Aa6f4yi2G/SbeUC8lKuxbmoKbemIaLmu+ccNXti7GtkSR19ZqmYXlSU2nnowHDdiEKI1M6BFihh3WnXSTiZ+jqSPPAMt+6FHg8c8VXsp4qeOCd85FFu3HMoi7KSL9nqg+MAaPr4iZNypHd5ikgPnpJjTZoq6+qfACKwI2myUC6qm42U9v+Ddk+BpjEv0JPkYHJK2eddP8kjEfbhxtt/thrMaX4pqaTHHVPZLAaHDaZJD/IfmmMz+4EbAb7pvgKEt+spUDA0AUr73A3XkUGJspISZy+6wdriaEG3S4Qphy8xPNk223+xn2rBWJTsEcCWLzBmcRKlxgUP4S3SZFjm68QwSuN4cDquUcrXg69RtBpv5XcRzfFIHLxYlNQT3dqlnqpHYbZfthejnIAeCWS31PsayN4pGlDpXSe3xiN9btZeMqBeD7aVZSJLjAphQxsXod9FhwmZwhddaYlBBtl39eMWKpC8IBQ4P69Htc1IRp3sawNa2yH11WEzfN5+kWo9LhAgqt8JLIM/lXhuxS8Ve2uK/EaKkyoHgNVV3wjt4sP1ezwQVonjUE0pNuES38VLStXflWXNPRu8+XDcw9hag4YDwVBnVXsDSaY7EGZnmxo6+CTS7n5ju2uJJFRx+CT5ZxX9ZlZexcFD/D8R4N4gi9aM0yelFH/C5JJ8SvfHy6S99yhVhVPqkpAMUNI9VNlGGiQhufLg+XmSype7fHsL9zhD9a+i3rLkSbU64xRxntNGpiqER25UFVbH/wE72FGLkJXKPhnBvGjYW7ltCYl+2YXkQjyJpJjoDyKPh50x4kugUxAtTSdpJqV6FdL27QFEgJP4gLSsJu4WpJOHxT5dDa1OTQsFKeNmuaEU+xVPFdBH5KaXuzWRL3R0IFBFFApW/mS/rRe0uU+b6XxgfmBLvAPb3qYk+BG5pM2Kk9+0hE0tpyGSWgSW1RrlQ5oDeHtYoBxp9ecmXtDQ40yyD2nSvNPJ/zvYtSbwEz3FDKzQCj7CGjIALF3bts8aUobI/qkGS61E/97nZj5F9rsZ6XrOMmFYUnWcBXVjQinsMhS9SL9s2loGo8zw9O9xsSaI8Lk9JsVemqabn4KhCyjLmHgKAoj5+CPzQlTCL3SpvH1rKirc6S/5M02e5rtU1Qd1MZ95ZgilpjypBqHl/K1TMu67LS/gqNWt4asOM7khbKvouY4xCxudmF2c1n3tDpTYqtn8iCH1fsw1+gLKaGTrruV2JQOZiIZXdbuOkFsKCM+dlGgtZyKd8PCn+ImMdjISQfMTrV9kk7F5WsCMY+1JQJdl8PEvmMQGv7uKZ9RM/h+TNgRxqhNw8ci2hrmmdEToCp56HViDraegW+74aHfTQQBV0SDDOoijCMwbpnesPEcM6cm33Q/xDsqgYq9+PdT1Z+S+vTfcN9t/XOU2T9fKg9Lm/u3s0gUeYVHGJL5CfrTVgFom9YVjHffuhNcW1VsjsKNNrVb9QeAEkZLN1rmMTULcLKpJsDzOh/bHWja2JEEHKcq2YWTyFKiTV83FD1Qw9kkCGPbrCUFRYeUInf3LvwaUPj03IdtjLXLY6ytNgueuwPjCo6/Bx1gKHWzJY0PHtVtSonldtHAC5I4lP/yALmUNEJv0eNVBuhIMTBZlhoeEi9xXc+olplh0Xf6gpWV/4ximC5IP0RkC1W/loNYSXB4/MzfG3kYIQGbmj9GxUY9BSYrO8MgFMfHQnqBniU7wqwP1hozueilDU22D8UhaPaxrOtNhyH3M6jOfRmxHmsb3mUacbau2ON2x4Wb7/wZ5nDtmlogCb1iXo21RsZXIPglHFYNrAbS9CxxuyCJVR6UGoWA/vh79XtcELR/QWUUz7df5F4k9MkPnKp6HbBAriX7qqbpjWZ8/j4RtOLfWCH1r0/RSa2NApffxMTLAcz8P06Py3Fb8smljr6a0Jtp06lUrDhnLa20EWp2ktOlO2PBVHEbl83hpt4kpAoOxyoXZUYkCA23oFA7It0PHOFfNPjPCnqIdGA/O3rHNGsd7ik22Oalz9KeemLCcZP0gE99ZRZ6Trp3CuQ1Bm76rFTZGvTc1vnsqNxmx1SOuVX6i5vkYk19NEuUz9DGurhybIKN1zJrBHVeF5rJKnQFND5bdF8649Gfvb91clhUIFkSa8BScubdOPB8y5DNNN1EIXkcvwl9HibLyqYQouieCBhPY7WcUgGfWAa8Y4lm9FcyGyG/oKYKKaWvwMyYh+NH93SxPTp8dv2Pg2q/RoxxcWKTTyC+X1ap53md7aMHS8FmEd2kXetxmZOXo2FppjvKB5e5pa9jCebhUgd9613EZj0JA0wk9WJXGk0PCDZE7blbGDX/kjgjFoAVpSbVwbP+OYkQVplw+IUnB++iB66ltcAYJXhrCEjuvNqUgSrqm7rWYVgF09YSUEiVdg4cTtwCqRo6DWOkm/EWd7iD+YQj6uqKwhqi9dr3+ztuPxXhjv1Z7LD83MfdRl7eBqFxQLiiPdW++XH/cNUiBO0S1gsGDl2Iphz+qWfD0SunxPnpfs936hKOxMOBI192vWQNq13VHe3KnZ41y5b+rn5ppSGQTFWnB7Od7JH5EiSgsr0/pTxsKDD6jHwFTvU/oNb5A8s28j6WojgJPBb9vsZ++WjisZnJtwhM5GBUhlV45ieu9FilAOaNJhTTSgBmvqCMQV+oO+F8rYTOrH9FHkMtWe3+WDZcErmHh07wfsxVr705qTuXCH9rMFn7JKXpkK1ERlN16b2m8VuIqRmDmk7l4NFMmh1oBohVSDJ25ZcSVq9w+i3fvsINKgq5niaIRJHV9GyRPxs16sxLzgtxMJoMgipolYkRRBrzn8ObC0ZET4shAsOI8jxQ27rpR2Ttr4j3qsU4JXOqh8ommOPp1caa2XMNKD32lqdTQLX9GkvitrpOY+UaQBGMPlNGeAkidJa1suZyLHsrOnhr+RBHnl3hPIdJCwIFT1EUYyFjQz7OYbquoEKxZ0S9vXsdWZ3XLPSCQ6mj0AxpuMut9zPjKZqLqe8/Nona5I/CMJq9DkFerFy5m7d/LF8fOzL1mpvbidsLM/F10ovBa4uDQBBs2haUjgvInJTSgRjwooEQuK/N3qnbUdLtCFq2uvWfUkJMOKljn5wknFb2o2guWUgW7Y5zcvs0+oCa2Er6jW2CKS1kytG+qXc8khGnDoOlTCnetoVWBEPLTMFHfplVzlSqT8/dnHkD1Jr7XI9RbtQXPUc269KGAKwpB1MB7GVHN2DzHpLZySiIkW+z2IwZwXN5UH41WEr+T3VELrHbk+xaB12ENBsWgZcCxJpjkCX2rN86QnB+dhgldm1cKMl+be0H8EDjtLV20loA9lSSPjAjM22l8YvxwpipYlupCmbHJQKCVMqmYCR9dotUPXqZ7LFSKOvkA6PJw35nt3aWMNupd6f1GpieAcatSZtjuGaCgw1SpxTKouTltGwjYccIJvtt+RnWlAVwXbC551wlC7QpXjwxxNkpVI4/LhPUKE+fIDTVMRzJDqzT9KtTs0Up5lajP9pEOqlhi9RigwflrM7YNRYDxDoEW0GaTqKOWPC7VM/etd+P2LVzWWmaZbBQryyN1ygiDVGnRuLmKa7DxPWMCqyopIXP3RWnOgck+q77KTsgOd5//lnomvejcys64XCFMXi/PJzSqWwn6jHYYfjCWVoYa0EIyE9hSBejIRz5VGXz9Psrwgm6/VxYR7Tv3B5FmvzIK8C+A0yHPs5BX//W5lMBV9NYB74xRKIVWiIKuP0VdYG+H6vr7pc/sW+rPRiFNrGO9MaVcotGLa4R6mu1GcrlwZhKKLJxkoo5xt9mtlKabjCSECk8uWr4JIPW6oL5D0qfaK60er2bthtRTO6tzTBQflykLEM3BFbDfvjWTKbF5++qMi+hhlNVp9l6NfEyqg7hqMTRTvpGnznfPwY1cwHmU23GGKLtO+Cuz61nfkaE9XSte65j719J9PORPAgTyqbgm/Ce2mGyVZnbCzf4/qzJr2jnoHqsDkfjWeDH/EKt2Shn4A8ZVFaFn4ZdfyR0WE936sakD1bI4DVZ39UmLgsE17dtkB6KReFMa7NBJClDwn5HiDKW9f5hl3fjIP107PKolDGrLvXrrC3QZPhX6497fyPrwfPXuQolc6eBjppYsbw9L3RRJ5ovhhaFTGuUeMec6wKlCUdWY7DVRW5vrEj92l/3U9pJXjAvl6/mGrEfFE+Ne+dOCqxg6KSjMUyQkismzTTRU0D82oE+ZKBZCLKS8w1CTz7LDc8D+D8ets3ahBH/e1IXv51FO5dOrHbEmLfmKwd7kJuBob7zDuBHTqgQ4rh+zzS9Lk9KkdOLZUMhJoJ/ZmvUZkN7Y+VP/NJf461a48nJFe6naR+jWeZOduvhYXVeCl0M+77jleXqvXU80AvcLzTr02IozzqO1F1+iDRS0210CUFlB15c39a/86HFe0LBW6B+ImlZXYjqrzxpuxPt+7iC40ibC498dxxG/v6jldz8xU1dY29CVWLRvOcYInL5AB/oaF8s9M+H1UXVFNzjPNMV9NeR8rhypJxJiF0tl36b7XccSbn18mporspYjV441QLtAt+69YXxRKyBJHRr/dn7eD7RpDcpzy/vEMiYW0a+5a/8v5x8j1VnaPW966yia80ne71YhAskoyrs65QLBjOvtnk+NUEV2hyRXBzil4hyql5tRVyerx6y+IPeJ3D9tRS7kkHmXu1lp6WqRObRwyKQ9iBMPQwegO2nWZR6faKoLVHZCCqO4Qh+mgBNuq2vUF86qjyhBGWATM595qMbFdJ95h0aN3Jjmhujmv2ygKLUPYu9Ip324tPLVS3tdmWPP97v1ycCQJjCriMSbXe6/2PhJYEWU0euO380XjLcHrXH0yY0+g9lLa8robwNWKpSxcL7deq6erDGKZrPGODdrIo7dGMnBXVLqJC2Hke6O8gaBpe1xOvEEZtBMWwLldyU9ZPJfiFRd/p4dbabzft0VMOuktq5GPi5rXw0LHYp25prXxL5Qg+c9mMI3pTiUYDikJIk46vQnjWl8rmugIvTNk4MDq/ybqrv5MoHFDCM1OtkLaqHatFlcxJDXFz6SVuZKRoC1aJTqgJtDloYIbymVi+YxUDLVcVD/NchHyfx9ud7sM+eEO0vZWrTFetitIuR1bqIjaorD38MiqorN9NlNjS5tRs/xkc7MZnSL9pvnKpVsNiUr1IDreRUddjiQJC/NlgZdmwptGxGvwd+ZbA4T2b2KZkNGtpQwmNcAem1x7IyQ77omMUQzHPtrKuSbGZsSb3WdtUacL+doXnVWLFx4I1t/Kv9SZkOO1r10ef3nN99pebAsf2t/A4rvUhFkrwTZpS9hLYBetQn7lbN/X7XbD2MdxmPtdQWGYZnpgtX429TIrsX7sqyjNQExf7mT5FMVhLwjDkoM5uHptGG4j2ElRkUR2FkWYsY3ag7Fv/ks3vW/9QH0JwprkZwwjtBTcPnLlEreNcuruBnqf6d56wFT7tEu0u98Z42WJkGtEBzMIumaWXYLjPn87KdX5OqrYXkV2NYz6/+PoscWEkOBzDe83L+mJqEcQ3KlovxXWHcS8Au5JgCmCnSzvPkBthdjb9rh5b9qhoT74GIMFgrGaGOM7sJyHSjl7gfShiysLp2eqHvB+6pu5/mRDbOykczB3sK8RxSr87Yb9o8OGNxYXw6Pft0qYyDkkh3cabBM7yy2g2xjNz5c0xO4CpOgurk4Sa42PpXbqVyFJoPvqgVqb6mz5tPW2qtZ9m7LM3Q3DSjwrZHvCAxMctST8V932ve2vPxzptlBAag1uyTYvb1vlsTc63DkpvMnQoiUwt8T+OHRy445zEYy5KGNUIDELzC0UdvbTDN85XfJ59q3B63Ds04Uu/b5GyR69DJhNWjAK8OFhSr6IO4qsjPYnF4mntWltGk3WGIiqsFN/byni5CJzsK/dyi82TI2qUcy67N5lIrql8USEvVB9143GLyYvv5cExGOX/lEqrz438kCtO5mzHrwM7CkxbcyzMetco3Bcw7fRpg26FWo9JP8mnm+/Q/eScKmW1V0+Gw0bHt5S+fmB8skVB+YbssY9+embfuaVB7EFLJAKNdpEjmlWObd2XLwvzxXfkUUfHAPthS+ZT011Dz9tDiqIsvWbe5YpNkUT7VbLlvNyqkFwv3lwywtVdsWaLHz0OSyOuMWq3KKsvAuqQ9DWpBv183tzKN3C1puNuQmRGQEYtxuan4MLSpalebk0/Qvtjqc7ypprrlou9gxQEXdQ9HcaLRJnVUsIZ7k08wrq3HfQ7pETdfC/qhAiVQ4hlqRp908yDWDFTPLBAPHLDwstBWA03OzcMFT+CkHlaxK46wm5NOUfrRiB8JBXxapqoDE6fGSZ8LjnorFg8kUgdFmD9hTwaKDLxYvt9wntuXWGvkcqaVOrHjBR9Y2RpcccolvuU0fjeOG63JB5VcZwm7S3GRcN+o1VE4Lt7eVQCXGA0x5KhOJyx4GEmmkdpJjeV1oEshVegcDq9fFHyDfeM1Q72I3zpCywz3e4X2YMEWi9tNW2/B0ClP+cH+UsXM5vT0O9zKLLedfIyNjcNLJ1E4L++SuuMZH171FL2ftdX7w5dnlC5inhKnTFPrXuXyM6dvT2pvXjw7KxEZK42i/InMe85K3CZXPnhJ4VkD/cZrNeRSMV9dHjHKiY4vh+QXE972Eapi7ubPFOzQj6EKPN+vWMlmamT/Yz+p/6gFd44oHxkIMDY+IMTNO891ZTEfrwEIvgscSaLzbqt5PZEV8ZEQDy6HZjFTZQ9suDX5+81QGAbjvdqQ9M83dZi8M75/dAPIg0T9A6OfFFqZeyMLgxUmFqtvonPN5KkW7lpcrzi36Rj6xzeO0jnXlRFO6n7Mt14CkqY21IylmM91PpHLGajY8LXm2THSpfaSuzFO2FwZpd8fqBxc67dohHAEdfC1xS2amnP8EwTcplel9L56QJ7eRqYrfQ6M3/PokXUMZE9RnfJ2OwgUnE31dfjcRFMv42ygKrlixU7Axt/ju4bfj4CU0lfxOWPxahnV5LUnBENyfjORaKKuyU0wbOwlxNsY8FkSmDMj+IDJ6u9QasVK+r0zWa+S5KsEo8ZnZeo1dVzoTXyQ8FAqDhar+uD33VHiKZPrluLwochaYvnZnQkGFVykbi4BM8+iLfgB/eS2tyrbk+prOzugeDR98JMstZHcSQQ63PanforwmCWj0yvzi+bPGRuzOid9WqXe8dW/NgO0+PufgT2EsXnxAMCPO1jyfJeTdi/AMW98/8MLkx+bj0MwOOltgtknNXBSE23srb/gn/qsEtUFSdXODjyGi0pSYvpJp2+QM+Qn1LkIJH+wGLkCFPe9MKEedhjMkWWQK2tdnveov+MDFcdiDbPzEVxlXsYOiguy3xCis0+irOlwPxojCigIs+uI+SSKJVV84USby2a8UNU6yPhG4uvTscY/HC7xjw/xs5TfOgmG9J27RQ6Y4ilISlYpl3RrujzppUgsB4rCUk+DOkXnBVcWaUCsDNaKCpCV/xnbOdjQUMNM3OuvaVh1PBBBjtCqjrdxbnWhfOFrc/ObyK/nMhGh9a8eC8+PG26ls5s0rXUPby9+kKy6Zttl84aRXz3dU6NwrwJCdJBPnqrBudX7fCRGfcKu+DGboeU4U8E8A9c+PzU5VKVkb768aqRwbbAwSgFN3q3ET1EWufkgUjzSZGy24usBAIFiIjLjEbh1wI8boNAHjfalcVa/UAuX5+fiVx+mEbJimfIZ3E9wwfkDtZwFCXD8Aypu1BCN+Hwvp9aPq1bx/fJKVZ4hEHLF/2hRr6Ek/rJsXh8E11NmfLgkkG1Rri3xqxq3IH408qCdgfgwAkpPGVmBjNH0MPLOgMQ9+osNaeUTJBWqOJzvjjKXTsL8VZE8a1VwLkIvauQw5f2epEoflLV4EFVhkMyk8sTLuX2nE08jYHb1cs6dIV6xnKGuQJ8v2NelBdZCoLjPDY9sElf25Qr5it0DexYew1dJnzpnXHuR1hkXltqCFC6Ga25MK7l2ftAmbZ7YHzhFjt4TqIix8wcUmZd6cxU2/sLeZCq8ZMLmIHyAY7tamG4hYxrjMnyvQsTYT96ZT94DyyIJr9pmxrUywlTOIkNsZhBa8Vld+RtGOvvNng1tY2EECO4l0zMwMRIGCLCCedylkw08g021EY65wW356Ovx8RuKIZdqTbO65wtn3I8qVYx3OHMHAa/LbwzytzDwGqV6qTm4/529gb06pbLgEZNRYnnzviopZZKKwRrjU4U9CrOlh8/fJxebUU5MzAkfzegKccvs7hvx3cpJoMvxb7C17UQkmW5J83eJoXNqU+DmqFkqUxJhXK5Mq+IPs5jGSxZniXddo94SZoZDHPEhb1khvX9DndDVjewK6Gx8VvutJEHvrnt8sOGz8rt/Ll9Q3QIuj3+kYehFL+5eenBBGowzqI63+NGxzvp4q5OHQ47b2s2FNy3H462LKdiqCRzafqhfDc5+arRRFhcJntsN2lJz7kOPRWQWqwIpbFuX9EMDqHyqOJEinyD2Fp7RroFE79aN6su6I+02TfNLlNuaPbrTabjqtcdRH5B3a4qqlYT2UYhheYHRH7Dr5ANavpc+FWDNyiexemxOrJFX2ylvL/I3Hp1+0mCWOwAYTyCZ0PivSlzkbC9VfwqVU3Wil72X13IX5e4Oe8NhHJ/v/HulyCHYQXkuJ7Dk+r5SsbrrrAUya+1Is/F423y3dFbtlsryVq2l3IdVu+8psgMJ6moownjwEbU1HpF4BtiqlwMllrHjeb+8NPKBe1TpSQNPYywpmYio6hBq9Z23ynAts4AquQQUZxapZqVL8RNQs+gm7wki3zSVwWckh+6EUpfxfxc0fwKgHDP23SRG/OM45FXHR5nf9yq1rdXhYz7wRFlXzmwUj0mjVA0DCiMPlUj5PUmUZmRJJVOz/CFzdtujkkEeY70SuuA3hwQUptFnOPgxxryj87BilHG/cqQmPG8vdr3aS16XYMcZ/owXpoWt7E248yU7wTzw+ntl4fQccjLnpn5HVAx8Zk61hMiEYSL507372E9Fp2kZZgHrJ45vpFe8GQHM0WytxVSHGwrsz//CVs3wvbaFOVhdvTg2yECQcMO2nVZu7vghXHtAR5BpgEYaaHKhB8zt84K682sONu3jxY90zwTaXDi+hgNn2qF3461Ssqnaj8ercHShIi/7e472J/7rLTvqLLwmSo5n63yp5FUhhnn4YnGSRb6YZwSUY4qz8Kyd51oT6ztwte5tZ06JkgNP8zJzCD4cSI8aIWvp59RsGbe1+gqQ++65S3JFSxB3o3+WDxa4uIQB+yt7yN8gyRFilzKRxenfElPr9gkqiyzixa5Nll5rh1ZLpgOu3ZBbtzRTdQVtDvVmlUX1pOJ5vBKhiQkuEHp9MEYtCvzydIQN0nxiE9t1TVW4FTTtxZ8BzNIQT7WrHKmd5FzJUfl2Sdg7nBz3bcgL0ny+gUKl9qiXeoPdVvHnR6rUnIf3BiK9PWkRQQWWDgishwrGRa/L6UJEGesuNuQlS6cei8Qv3tnKO081TVEpD15cYzahGY9wrlYQblMlxGj3Ul31oH9HLGyjNBXX4wpm9GJspN+LhPzaGsPCYaoQ0MnNVZsPpwau5l1KDvC6dorX8wcyau5uqbCLqosJFQzsunDonNdqCKTq8s3pnJsaU0bMzfT+pZwfSuUAHthXncKhA/ykh7POlGjsMEaR+dJElLm6EqvqkkD8QXy4qD9lR9aVwZ0+2TtKx4x/Ipr4uC2vCAJttGDrYoOymMa7F4GHrySy8kyqtGR0mLGofr9EevHfgRJvDoy6q1lsItYRg9X7n1MTFFXHgRJ1HKBhOSTnMFWrPYTjS32Xl/2YFv/D/DHFFkKZW5kc3RyZWFtCmVuZG9iagoxNDc1IDAgb2JqCjw8Ci9MZW5ndGgxIDE1MTEKL0xlbmd0aDIgNzQ3OQovTGVuZ3RoMyAwCi9MZW5ndGggODQ5NSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqNtwVUFOoWNkyngkgIKDDESMPQDdLdJSUMMMAQMzAM3SktXYI0SKeUtEiKdKcBIo00Et+o555zz/3/tb5vzVoz7352vDue/a41QHotXU5pa7glRAEOQ3LycIFEAbLqOoIAEIiPCwTiJQAC9aBIR8gflABoAEG4QuEw0f/SyyIgYCQKkwMjUWbqcBhAxc0RwMMH4BEU5RESBYEAvCCQyH8M4QhRgBzYHWoNUOcCqMBhEFcCoCzc2QsBtbVDom75zxHAYsUK4BEREeL47Q6QdoIgoFZgGEAdjLSDOKFutAI7AnThVlAI0utfIVjE7ZBIZ1Fubg8PDy6wkysXHGErycoB8IAi7QA6EFcIwh1iDfhVLkAD7AT5XRgXARCgZwd1/QPrwm2QHmAEBIACHKFWEJgrysENZg1BAFB3A3SV1QCazhDYH2O1PwYcgL9aA+Dh4vk73F/evwJBYb+dwVZWcCdnMMwLCrMF2EAdIQBNBTUupCeSAwCGWf8yBDu6wlH+YHcw1BFsiTL4nTgYoCCtDQCj6vurOlcrBNQZ6crlCnX8VSH3rzCoJsvDrGXhTk4QGNKV4Fd+clAExArVdS/u32N1gME9YD5/zjZQmLXNrxKs3Zy59WFQFzeIstxfFiiI4B/MFoIECIBAICFhEQDEBQDxtLLj/hVcz8sZ8lvJ8wtG5e/n4wx3BtigSoD4QW0gqB8CH1ewOwSARLhB/Hz+W/FviYCHB2ANtUICLCG2UBjBP9FRMMTmj4yaPALqCTABoYjHAwD9+vx9MkNxyxoOc/T6x/z3cLkVVBX1dbXYfxf8t0pGBu4J8OHkBQE4eQVAAB4eET6AEOrg9+8oWmDoX1mA/vFVhtnAASJ/kkV16T8Ju/81fZa/FoMV8O9YGnAUYyEAln8IbgoSAFmhvnj+n2n+2+X/j92/ovxfCP6/+Si4OTr+1rL8Uv9/tGAnqKPXX3oUX92QKO6rw1EbAPtfU0PIn3VVh1hD3Zz+V6uMBKN2QBpm6/h3E6GuClBPiLUWFGll94cqf3D9XwvmCIVBtOCu0F8PCoCTBwT6Hx1qq6wcUI+GK4qPv1UQ1NL8+0p5mBXc+td28QoIAsAIBNiLADVilCQA8OFBraE1xPM3gwHcXDA4EuUCQJXnB7CBIwh+zRPlxP2nJBT8B+EDcNtAUST+G+BFAXA3xD+AEIAbhsrub4BfBMAN/y8ZdTe3K8Qd9Wb8jfCjEBSb/5Z5ANxIOwTkv3xAKMQD/k9MYQC3NwTxB/hXyVZuCARq4X9TEtWP/8i/XxcIxBNiRTA/A7cSC7WvC207r5F+6MG5/lFiErhumM7K6TOPaHe7JMJNYa3OCl5FnEqnDPbcW/oiz3LyZIHu2me7pQH3eWuS9tufvlfmCTrj628J5sYo+kYLt6Xr39PgP+LUe7Lhe+3iaxDkgNmC3qkCzHVxEybSyic99+hV9Kx/X7b4IXxmXXujWlCV8KpsgjNWP8Y0qGQKmGeZPU3JgIPkpMFju3/gSTx1cjp5P2f0lk4lgZ3AbyeWr8jHeI037mLae7lCj9e1i4qJypiSBvPk/ofxxz4ym6kqD2Z9XhetIj6kTVO0j3xySuZx3GTxXtfQOUd8//HsMXBUlIWKkhuTTDk6ZrVVKdGphD4Rhz/7YEll1VP5h1CLpSuHZiL0VtNRqA6rYrKYfsOS7NEexbPt1Y3xho+cW/KSRHwlSDFF3BjxdTHHQ9F3+OdFmrop9/I/6bJpSr0TacsdXZ3/tJZ2XHM/5zyGbvwR581z7J+lOOceAC9bSmE0NVcj5EyzfDKhi1KNM6hQcvg5Uf86wSaHy9rWYlRLMrFLfE04GX6FE4mxnJnQUGuLpgOnovmy6WfdYdIaL8WsE7VXx9fJmeHxGtTCl7Yvd3i1cfxEv78rvLfheynLfZubm9Pwgfrn1GCC453xpN5Y2YNlXouuwDg1N76RZeK1txRyH7Hx2IinZftLBWjbxzPWXafG1UL0qRwonZTK7yi4LOrZDBkzbR4fVE74ZixtNPN+8HRcqNJ87jbr2ZFoq7jJ7YuOI7rzWAMWcMCkMiivvbHvdfVtJYT/9mhKstTZi/krLV96yK09dJlr8WqSur/4KdawiH/Ul48jliBCt41sSzGFMmEWLQUjDN9ZMjldYKKXxE24LYxeO8g49flY+quaMk3o64jw043K1/FKHOHfwEIWJ6mxG+bG4oekttmHxp3X2yMj9xvK0iE4qzk5AQHTOcEuKRHc/jj2zCbyPMv4Dwc9bDkGVCsDk4+J4E4zkurCDWI1iFJAlOxTMkyaRtr45wYkDuRktnOc3KEVx1s6Tybo5n0dDy3RZIO420a9g2tU82jNscqqMKAczDM8o9qHGULPkvha5VvOJ8v2dwKiaUSYMLuMf5BOW3uZOSJp4xD3ckkkcigzM4ckY0jJVAOBHybA93jaTfdYkMRfKLjkab1fGLJEMAara3xIaBRc8GeblReb6bwdk+2jyq8GP+M58xIoWe5ktmvxsiGodSW5t8AlHDRaI1DVZQUlUWQSGLYF7avqOd5gOl/NPpmXNKO6tfk8j2E48S10UUj7haLBdvJaBazwnv9wjoQZ9nfjeIsYkvy9pHC2DQx/IcJNhlfEuXgvFmKsNubOPGhAzXkYJVHX+j5G2TCuIXNP5U6t3MoSo2JtM2qLNwmlWH3qGvRjOEkUJ92fnnp8Fz3YqNcRC0dWZs5RZiWxm9km1GyRGk+RDYbd3L2Gu3qUJW1q6wexZjUJR1cNgKM+Sz8D97tLevbF9g4rBbeZw4bm/dGCMqL9qPbpbpcConWRmUOK2VFCFXcmVNsohSJ4O2yyK2VfzUFBiuROPG5SwSEPpvkjH6eabX9bSK+2HrlVCFxydtoixW3Wv19NbNecO1mX003F5FFkWq1ijSvM3qWbKy9R0vU08hbWNGyQAJxclJqonslRHDLhD4l5JzfKurg78QInwW8rRDz2DesO0Pk2Xg7ognAYh/8IfcryGCeho7/RCiuKPF782VSrZoiYgjJmYV6TjmzGN9VLTgybE/u2k720Je+PuG+b8sbHaJcAWds7V3ZfYlzZ19iSqPQ5s6rpVolNe69ZHnVLyJlYsz65WgFiIQNPUlkZX88nRqrPp0lLXAhoML2m5NFOc6gktivf6L3v+6g6oJ3VeMBqf4l8ti1awi8+ZVSw6xnGB49CZojblA22UvVdZ3pPruMuXAZX+osZ3dfOc3XuGmh4A2lewin+p8DMV8/DxijIzHedpuSE+5sjEN9rBuvtoj0M3cy7v0wL9phQNCZKNBLEGfSbPTIqb+aVArYRVLa+32KN4lbVSW4J8IVI7C2OYJdh5kVaopuH3jaCdDdDh1IFWqPC+HjmOp0GPTckU+9MpzdoZl+SkNd00T6lN/kSPD+XixVncTHojmXzw9fvk85lPD4/QzQFjj8rCY4b+a77nVV5Jv3yjA7T6DeGYtsxr6gnVEGVdwTfMs0UlWxEx1dqOUi9H/F663UbwR/WSiqOfbq8cjCc6CIhq8gEOHiQ2vzEer67FrvJ/2hQmkdbOFia03c10pLHwruy1h4N0BCfulh8ZWx6L5+rH7FfeifX/2u/a2cGwxvLrQdmOl4ZGRmPEQNyoHt2apm606xbFd6C6hsSn9NIbxiYWzQ1N1Nv+lD9x/fhZU9hujd57FcjPrv2NvHHkKHFad3F1Dv8QZ46CeaD+CyOmWW70nc/Qhz6Xsxvy9rIZwUJXCqYRY1Fe6XC7HwffbDGe4CLM8bN0un/+OZbl1ZNkYzIG9lbz8AYEYKLlS38F0DKT2ccIjm46CSjmniqO5O7apTpS1juj8iUOGLIJCfiwo5jHnEINiRjMLDL9TUfIezUxM1wpdkFN1R6VN6IfFtf7IzfIw/gPbMXp+d25C0vSOv5utUpQN2XUttle2uAntiP/2CdDZ9sJUtY+ZXqqJXpcdaOJt1Vdkn4ZLyfn17q1hn58k9SkexHchMs34etMCZF9vPSXSGzVGHZHrs6z4WFlunrPtbAE1wTWygtkcl3/GXyNQq16M67D5Se2g6M4DLK1AM/ktMmCF+EF6ZAP+cktyTvHF/ZFi3eVUxOThBMFrq5x5MmrsMwE8tsA6vk5M3Sg7A29W8USBv2+QbxiZ8ZcbxKabyQs7z2qIoMGA3kDQPdGuwtzQJrjasyCSVoqCtulAfC+vMcLmWvdveFa4KfSGr70VqGKeSZYG2RHpV6HzE75z7WbUdj029goq2fS6UtQIsLBlvojCtLTL+y3nVPNqebM6VdNMwuPZNZaz2pNSdtF2gseQ8zyThcffxDf+u68Sm08uBzgsbbaiu3j52IA/f7ymlmBsqvV6vCTXX39hiU46pwNtyJAnJc8XESXYycs3BGtcxuBXxvMX0RmguR6IHoVlbfZUDkR7GqBr6zudvh/GcrIFmwvfPn4m3XQWHpvs232WnfiL6MbL25zTPC4OfcmdxWa1j/dqd+pLr3AANKuL+8p2BQ6wPs88R8KI9tfqcyi81iTE3znf7OEUF/smpnpeLQII2rb0CZk0pBD1CAark6cXS/2FranVScyn6tqiO4DpvTEOvVrugqMYcEuYzRVsWNU17ZWvoOhAEhgCSqJK7KDxzcvXxfUof1aEeSY81j3lPZHHACqXeNbCL59GHeM1UHiZbfUKQNiWzDthO7BXCaUGcp2tAkT4Sis1jksV0ccn9pU67z8Tjgh6Zrghl+3vbG7puS5qy1AH04z9nzLi3KhZc/PRF7Z22cF3BIPUCL8XDloWfOkSEz5dqBrTR6whoTpnuXyxgQgzRJguJJOSFAy4vM3KNk7HtJR6BPVEWLc7dg1ur4zg2RGka+euq1U7w64zWBDkHYd6OXAQ1tWeXjTGO5O5WB++HHWA/7fIsFWzJ86dYz4gdeKL4gU05rsui1cXB5CPZ7gmzGNn6czZzQI/EVj68odMoSS7nE9pH+KwG8EEhg5UDfdal0ezX611y/+xxXJT9Yv1GIQqnw+qwO7IxFHTu/V08E3xFXyBSawuJmFz6q7jTN2+424KrPegE3dXhaiAtBb8t9V4XFfr+gb4Ra0KNIphEq2+2s+ZKw9j6xwlB3moannNlIjjjtgx52fCNj0JznxwyagS2s5ybG/jPBySVfwzoynKXOjyybBEXig2TOFF5zUCrGCYH3HnQb4BjNLCX+3EY+utuDGXGqHuBuJclLYcj4mq8C9G0boc5UCca/vatM4eT+cuzHpeZWB8NrDgLSEFGWXKDsduvKfqk6tOnJ/ojpSbZJLc4xwdJJbCQyZWKNXGIzVl/9EGYiY04+wJtqjhlL9ATmli6lNsFBLmeoT/bhJmbKsxmNSsLNL4XRqTFFkGbSU/oujeczbG3jn+xAMvwX25FsPg9Go6abEAAdJXwTNiOvtFbQOrS/EgS2xL5+qCVUOug10xHp7E7rUKK38GFWT4cd+F4Ibe8Kj65uiT6ydoHS5rxBgRhgq7iyhpat1RXNK6ULJPVttATWfLm5Tsvf0XiM7BOVpJTXHDJSUdlXwb4tTDSOSyr7fiujxEmP5u/VNkHlPP0haqIC/UtuaMTaTLX17l0Sb+6twstVvsI1e/KEQrYuFhoVdK2X75mYqwsrpb/qnTc2PRwu82RofH9smc7vz2/2lGi6dSOx44wmXaRKRE+8fSpn6KXqFmZB2ECOCIWJsk+61qVkCHWIAJGOm9Vjlr0yitNJ0FKZezZJ3WFujtiBz3e+a08sv7fmQK4OLMsciGHuHklZPi9E13NHDj1YmLag/CF8J2Pzk8/i267vTZtZfng4nR4wDkOtg1yyKu+XGKkxWWIz7DoRyDCDkaeHXyRGCE0n3IpbZVJw3+8e6bPIuvdhekgpzeLbI2/90eRyCik9kbuXkyGtl1HyWokmA57EaOWT3LF1oXm8TcGsVePsaw1NGcQ5RONDtYOeYR/445vOSoniBZWVbGvg8jPMz/PnYHWWGw97TR0rP6gl2llhbF9ZchOVqfo5bBlk3qzI4bvlEgkysi9+pxSB42JX7PWMQOsp7rPYegk46kYfPm/+6dPRj0to3P6RTjvybkwTKPuN9YlztKEtp1q8ZVGO89lPKi/TRW8cFludEkG8Nch+/8euCtXYNzeKD4olRPR5l0s2PoVQ1LfI1lwX0x7FTysRfu6GO8chmC8WsBR0twfZ7+q3Dw4vMZ039n9sJaUibLETm8cfU0bP88cN+phaGCxKC5c7H9Bvr6SUL79QaPaCi4EWbgJpDqRWFicWnbTGJKkZhhn7U/oua+6SxekJqNCzUZ8HrD+40H3wpER9Q4hR+PHMDmdxyUWuVxDsslfk5crxRdKEQFONYmit3sr01GCoQQDu8X2Cs/K0XXZDtFpdE2rNLSc+8gQtJ+vI7xwk5ezJuoFjlN3XJYd6O3nhDudeJ/d0xJkIyUe+Lgfif+K9BeM9njhtpsgJmpMokp3KPd2v+aTx8a4IhVAjY1ndZ/+hPvXok5OsJb3aLqkGGW/yH7M64yMtufnaZHXe5aWzZvdJXaPL9LYPjie1nmiFZhDhhyly9q8AdETUOwPXvSvXjwLXWfmI4zP31NtOcYuUKl/hej7suTVgxk0s4mKZdK25FFsb9l01x9HUy5l+hft0e0LEglYRO/TOFZOW4XwhxGg/5HBxXMJGHjhVqElNFedsLVe07HTsPYBdOtNr9qmFPLzuzUwRtnfXc9VkdixPZXIDBwIP3FrR42P1Vtxcp/FZ/uGrMiODDsa8i8+tzZkt6lf5B2bF52z9+bMFIl9IIrCQF5QCjAinhc/M8rMQIjjTx8z8kpTlCXZvYRi3hBTMfrye8tU4SyXubO8wCwdFZrFmEK8H4XdxwTwFmFUuD/26dIv3Q4MxnYeX5L3KGW+35urY9Ry64A9EqtIXRYlXBw1NLyLc47nuYXgFSguQJ2/09dj+QLMxk7IXZL3t83xZ5rVIr/FCID/almNSlDc7OjNiTyG2jQT4zW1a3mKvahdtUmbTM8DjDI9luXowPPNxA9GzzKOR4OxgsmiN4hOD0c3axVeFo9IDMncd2B9j9DwZZB4dTw937QkJbw03F2vFzahoIotyOWExL71uzhbdbeOaF2XdC6agNRnHp5bCsrxBvufSOCjG8e2a0pp36vPr6eKOcKteYxSSZtAvf8faGKpnOmvsua10FwH3g/MuPs0Z7x0Gc1+4H7YHyfE56Kccm3RNzLJTc2yX1828o6Wk5BjGxNMk3OUHrj5nwiZVYr56XyuLQ8J9v+GmMToB607MVX73tC9HvrtEy77f9/AsOtmIsqwsLwtBIyIttuaN4xeYSYiVLmFcIJkPnN8qmWejVVP3UKaR3loaF09wu9LMEGjbZbWul/sjayWnsdQa61ZSPYK3u+WZhn3AHlG3icb0k5RcNuM3pzQbYox2dkuqQt8YecWqZZcb84N/1vLe9ZC9/6I6LoFhkq7PpMFLrlxlgINxxDKgJ9I4NMKATQjgSbKarvpV7FV03Fiy17B7YIqIoDKrMgM+E31C7zT364s3wh0n8EQWrXwvna3uBpIQfd0CCyJicboLMnofqqhsxeKxbkgbe3DIBotscL2bdE2PCxbVmOOmnpWbSSrheYr9jMQL+Uycw+kcKt5zWX7g8FFDQEUNOS+7NUJ7r3WCIp+MW6w7ITd+IeHCT/b3n9AnAb63j61C33fux/L8VB7AeKTp14ou8Gnv8d1genGEunlSzaGj7Pj8y2/EgdSD2OSzGsSMxf3xcfX8RMmS7hzs6iuHPFvfupydI82/VMftVIIOoFqBNyMpKfsxwW0Vh8/jm22LOox5Il6IBob5b0BZEh1jor5IOLhUgasrDri6eIi/Zs2tMIuKsc6smPHTHXBsCz5w87T6pjYvXQsOmX9GX3qsRoRWF5nIkOtG8Yru5WeCwSx/iaJR2TG+UJMOABeP8DX6kr3yxaWe5pz2XQLtkFtyulfJXkssRfLkbF0/JTsrmFefr4YIRNyltbsbEGoQmLZswbF54o7g06OJ05v0Wb1t5yPX7Fi83s82b4yNE9/+GDopt8OOX3KLvmJHvkIdWcdHSzoUTCr+aR3KtXXgoUQtWjP5kakNj1eOJajpuliivHKvfJJWFV6R7cCUPXC/pJNNLFJjbCQJwGioQPgYh4y69Ixx4mf9JgklJn8skV++N8JYavDBIOMRo9XhSe79w9GXdAOAdWzw+kZijPOZf7RT5rF3wpsdVZY6SnnIMTDcZl533cP6wt03TaUjO+hOKeR5EumVJly2J3D/igOkjYyHojHF+M0EM/gy2PZqvlSuIuCufS6eUSevVcZGN7v6eMPbbqOAuClTrk0lYpmcK/50KMSKilqu3/jwM0Foy60x9N7Hrdqz1aT3dpk/c03EafX6IHW+gNCFWD3WjwxB2NJb7XOfeAzZFNd8kYQGu6a0ZjkFKdnoHUVSb7PVH62BI+6d4NCeu61/0HQLqxKJpFrI/fr9+1X5j5tN4fEtNX/mWPvF08X8vZs+rJeShc661wH3DBiTut1EJdttLEaJGMxlsuGwZHoPjY40wlEvblmBglGEpdQC2guhnx+fFEixhxkIRE/Tjm4/nKuqzCOQiQkTbB063YUbvTt0Ceh9VimQbOwMaEJwLMkkPnj2WRRT470KFllXQ3rRLehISHo0arUEkTBd4ifPnzvoLV25uUJk+ALx4SvQYkZp3tm49cxZYtAnBx7wjqvL5xsjc9lJvZs6yWacXu3aj3cp77FMgm2PlH3eWatJayT2BeD7WWI+guyEcytI3ZVuNrN0y55xH+wOKi90WU6KN+ETH+VXl9ErJHcGN60hNNpNKkFhq1eagaT8pouFGJvV4TLwvuYPXzIVKghfvMZAy75ePJBMHGZ+j2tdBfNs00iLZ4Tey3Uwl1552ThdH1KT78oo1rbgMXA6koCOzls0eTwnCvsE3PrmrChvtx0stSrTU7CLYY/rn9pXEKn/sy/jKjTg+dPG/c0mhj2Cgq0dWh9jtjsBzFOkTy4m0n0FzuOq1cM1gg+df6wt4Ous3RRJFOyWVb/8bKlh+jm8qQgaclpDgI/OgV1Gmv8u1H1zvQozdYpIZNe6KFklH9yGV5zZjpaRKXjJm+0+rek4spg6+7U1QjByxJHTdglLJ5PtfoApV7bN8bHRySqtLFcMwPb18aX1S6WUy8MjC+0iBzPvqbnYpK4BwyVTDjkD9UPzLywrEXGI922v75Xua3YsXRrys2SacBTE5MqRiUp/8gU7ZL06KG50VjPW6jqumCkra1xhU/qetPnNX7/M67BzRXlAvuF6EncdbgJO3cL/fLJtusL5MLXP/Kf8BreSi8Tb1wBRG41LcO/7IBC954qCmjnRD2uaYQkdU+KQIH3683NZZ+TPipi3VD1btcX7pjFy0ZQOc1WB+sYLtxTtuZDLH8Q5xqAC/I7eeroOrrJ32xMxBazk6sjEDytKEUTCt/ORYex1JEBdXghn7fIgbddE59DKOHwqHukyuKAGJq+PzPZt7TNB9L+zH2rgwHsW0Z5cYeOm1pzwgcG3lO+jxVykSQ8GS5Xg1rK63hbxF1rMGCOYbaroYZWhSgO6a+XIOzyfQWqCZ+s0vQIDiJ8LgiNoaQMik+rY23nVRi94CZCsbhdsFG8GhPM3QA3Aybcz6V+rdESb18Bh0kKakK6U1HNTKWSwGLtGP4WUllHvndNAgxtoa7Wh6LLpawKBd6LVC2scCDHZXOO940fGnq7BfneqL2Q0ruj1okfWbsSwY8m+zgffjwtxIc1UiroEH0Q4vV52PZtUQAYlKS11Ait/aGyPKBjOrm+d8erEZn8JySE3Xn79DWvHVNOemldAitxPCnOW1be4rrslwHeuPNM1Rj1q5l3dTxaM2vHzgPoA3sNQ/51kq9rse7vHrxbb7Hc2eiilRU61hwUxd/Lnkj5VycbPvLzxVKrpXhuO/mSBN99SWVppgIetDpYTH8ghfGXdHO30jQ3t8oKa6Mg9Pqi99LUg8K4NV1bPCA15i5v4qBlXXKtvbIQ9fQdThGGnxWJ65tGJ8IM7ApFpLzi8vr3geg0ki4OX3W+kETtqra61CVeYechQczycS6bt+zjcIVA/T/Mtn4y6z5fsL/hlFJ8yy63xLt6uo0XeIW6VjM8f0h9kKraiCvd6y3s8Kv4cXfcbbdeA1uRHp06kj8E2ACBV9OIwaNahXfXqiP4B1C9gc6hlPk7wkzzt6vRrovQnt2/CjqfMvkPuijDyWizcRoOzphYbo3y6EtN7wl9Xhm+nHg3BR5q7kfZiTKJqoZ4yVdLhpI2hoiK0MYM5LhoYIfds5fOo7WuO+9mqYH2ldFek1+baaVtYOCqy9mzkMe2H29UNVknP0dUx0X+eMIn3JHIsz68w85xeeh4t+J1IS6jWy0xViwpb9dgKqugBVeINbomfOLqSBOqd5fk62L6YS/jJt6E7W46OPhyh0SWLWX47HhYqyNicH0xstE/IKCblnNixeUWowRCljc+vRHzjr38yyGH/agPNY43lkcybN+BEWcUlWb/pVP2bmWAjE/rGFrmPQIezx9HDbkzWY9Q7kaNXsp6pNBuQ8t3CslslP9gWVnttuBPfqf6oL3neHqnLM5PHcAZufzcEAXqgOrKksnj3dGccfXBoJn0So5UCYNYdULsAT3lQDD7LiC95n/KFUifmyraeen4fRrzR3MCLc/Cw86PcUPAtLTVpA311xVOpcx+Vy8FH1gvHt4rG9lcNVp7wt7lv+zMNPmBFCNghpbQWSqVTCVdZfQpCiz/32QTjTCmwTwmFJ70yTWDVuvJPaULSEczilFQ9sDF17EUQ+xAXT7NH7buePiCSEalfJbOikkowD9tyubOuk/zZPT/IFfpEs49mV6HzZfsyCT1cvH1B5sAX/44UOuKp5fvvhlmUL2e+mxBRT++u4fZGEmJfL6R1uH9uSgaxLRtNm2R5a1yCXNC/rnN0195KPhWYIg9+RnyvaslP+EjVTJGbUTdxjde6imFuddiBWkSBgVYJXsYNG1RV8Qv/vNnE9yTK5fUR+pH6zMOeZ19weABgRXZuNcVnrfcvYg/efjUDC0VaqyGeuqK5HCBtJDsykl4SnsnD8tiWW08K1XLQhzjnfpSlKXFznpBmK4757I/slhTjmdrvaBZkUR5uQxvUgNbSJxGsY0KqrT+iVTrEsFsJ/C4A6yK8QXNCM9LfgcMNmB2huXIfVIdTdwb9iPfpAp4+yLKJm+mF3T8PkppsmUnyN7mk7rPZ3HgJA7Loeb9ELstIFhm9W20y8xld0DkadxOCj68WnGZv3BEN27tbNG5H+iaqgOY5T4EMX1CrMXSft35gLZOZSiP5NvCU4d3/AT9yhGEKZW5kc3RyZWFtCmVuZG9iagoxNDc3IDAgb2JqCjw8Ci9MZW5ndGgxIDEzNTcKL0xlbmd0aDIgNTk5NgovTGVuZ3RoMyAwCi9MZW5ndGggNjkzMSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqNVgdUU12zpUkJvUhHIkXpSaiC0nvvVVoICQRIAkmoAURQmqggTQWp0hVRpAjSQUCkSZFepShVBKTKi/r9//e+/7213ltZ6+ac2TNzZs7sfdcVvmhmKanujnGD62DQeEmIFFgJqGlsoQAEg2WkwGBpgLCwFRLvA/9jBQjbwLE4JAat9N9wTSwciifatKB4opsxBg008PcBQmSAEHkliIISGAyUBoMV/+WIwSoBtaABSHegsRTQAIOG4wDCmhjfYCzSwxNPPOVfS6AITBQIUVRUkPgdDlRHwbFIGBQNNIbiPeEo4okwqA/QEgNDwvHB/0ghcs0Tj/dVAoECAwOloCicFAbroSIqAQxE4j2BFnAcHBsAdwf+ahdoAkXBfzcmBRAGWnkicX/MlhgEPhCKhQOJBh8kDI7GEQP80e5wLJB4NtBS3who6gtH/3E2+uMgAfzraoAQKci/0/0V/SsREv07GAqDYVC+UHQwEu0BRCB94EBTHSMpfBBeAghFu/9yhPrgMMR4aAAU6QN1Izr8LhwK1FE3B0KJ/f3VHQ6GRfricVI4pM+vDkG/0hAvWRvtrolBoeBoPA7wqz4tJBYOI956MOj3WL3RmEA04c8agUS7I3614O7vC7JGI/384fpaf3kQTYC/bR5wPFAODAYrKIKBcD8gPAjmCfqV3CrYF/4bhPwyE+sPI/hifIEIYgvwMCQCTvwDEHDQADgQj/WHhxH+O/DPHQACAbojYXigG9wDiQb8nZ1ohiP+7ImTxyKDgNfBROJBgOBfv3+vnIjccsegfYL/dv89XJCZha6NoZ3474b/DWloYIKABElpBaCktBwYCIFISwMViIuwf2YxgyL/qgL8d6w+GoEBKv4plnhL/yo44K/pi/wlDFHgP3OZYIiMhQNF/ia4I1gODCM+IP9vmv8O+d/Y/SvL/0Hw/6xHx9/H5zcq8gv+HygUhfQJ/gsn8tUfT+S+MYaoAPR/utrC/8jVGO6O9Ef9J6qPhxI1oI728Pn3JSJxOsgguLsZEg/z/EOVP3brXwLzQaLhZhgc8tcLBSgJAYP/AyOqCuZNfGngiHz8DcGJovnnkdpoGMb9l7qk5eSBUCwWGgwgjpi4kwMSIEQZusODfjMYCJJCY/DEECCxvTAgAoMF/JqnnCwQhCPSjGgE/CMxzB+LJcrq9+CJp/5r/1vDcHgQHAYYH8XArt7yenXr7UGFOk+g5FKf8pDwku1DUUnCOLbB/4iBKlX0RWbkLHZfPbW7lWlqUVtkT22C/5Sw9uY1VUxdsnn9ceiJS5LF4FI9YGyA/V3/0zX1yg4+Gl5JK7Xl0FO/UJub3uRvSJsMhHP8/K8wmOWxHgS26wZVdpROfogeXTJffiFvSHtS+lHyrnWC482iYeFctycjnAKUeEk+ajGW7SDG4b39IZbs/jN+gyRxQNj6XZkCgsOc9L3DkZDpZ1bSuGYuIS4HTj7yPZYPg5cIGqtpBhyfCMUFM/nDjv6FcTJTzSROuuGjCzQK14KRSVdeqnaRX1z2mP+x3kRb24OW0I0tn0h7unwqUXaj3JdMbnnlBLyX3XWDaZMwylfKUv1KoOnkqYLmwetH5xfk4yIMBC9yKyvvXU7h38bemQjgV5ng193lSiu/GA2i5zen12Ewfh3egQChLvCI5SLviGQJXESWtGQDDCPkWFXoOvJPaNyTJQycy6pu83BRSGz3O0U65wFCVjg4uD+K8dDsVInHenuWDJF5mQauNkz5rZPRbHtfkkhedlGNePThZ3cs7IJAMlp1v7BvxDY2WTQrOW3kDj9rM3cfH3khzV3vtrTl+FTXjbsOz2tS2kC79y1Z6GnTxHpVU6sob8BCqT6nLNl2eGAakzaTTke435e/cmtBXvGE4w4aK8wZkUe3uutkRXVykw5ZA2rf7z+OMKOz2VwqZh9LbPZFeifVunptjWx/dm3OqGrRLrMWxVBvfq2y3ab10jBB3GmZbK6NMuu2ShijrCv8CHUR2vrC9Jy+21/LnS16TOaeOfV+ugaXgyUi2F+WbjWrOKh1rrBufnbQ5PDaz+wEyBjPx4Vcu3nvVAWOIf2MNyrCPCerYUM3AGOXJ5esHp2JloSfF9IX7jvzm3tgpi9YKWnMkty/ahPk3cfQGtSxrjbzdngyk9Vh1kDeM2Lr+cKEsqf6RMVs3wdKJaYmwUSM22hA3QwbGN7oeMq/y5i7Qdqa28EHTu5ePbcHM3uhEULo4M5DkCjPHlS7Q+K/Tus+vkxog3xqVnPv0woPtsM2pmqVmzokFmt3S/UnTimvJYsIOxc3gKRtmms5RGsbuZ98/djR+VlkdnsDtVXHhajjojrHwmtxTxrM6ToTV2NlhNoFke5/Nme9Z/TgPW8kfYjhmDEf6iV/b8HLgsztkzEacecvtaMB6pVvARjtnp+6QfylAbzrG3Y1+kHqZN3fbV9nP6XqOhpRCZrQbC1tWpMbpIhK2d26GJg75Pwlme6Qbay9YCwgRYR8d1BWQjI/Et767e1QfT61KsgibnE9XfWeVqUf3K8lrZX5yd0yXupKydycYuog7Y1NoNDe1MO77Il5YjKYt/PlDyPth8XBBHETqaPTqogOlf7Y2MvKOTdrOvUNjnnbAAinDzGaAjhfdGRFjnQkhIYgc6SsfTLhVb5GJrs37GDyXhpA8mLPMFws8Gad6k/SXfanq5uCaa9zoikUS7mv7gJeU6ifE31sFsxAqxAbNuVchfLn0dnMHOuUPorPTXlWERSOkm+lvGbjbOhDzt8XYnM/+Ef5UwHtgiuMukwHucsdn9tr2hs1Yr0aGFbexrdZPnRNdHRm5D57fCxe4JQlBO2wu2ym6ZUyoUulDhcHKNoLcbkdl1Z/6htIfbjFwJj9aY1Xb9njE+KEsU1cuZQ5eOvsJn6p50OmU7LMl5+0j6zDBjDHuWoJNN+vM+hOOwXld8kpvnpcHYb0LYLuUh3nHIQLvG8CTrC6d9AwuzhlDcJTCXlWqS6xk4g+Xm6Xvu2kQvlLzXaS3FvYxbiXfakHKp/LGPllL1HrdqhiCxxD5TOKGR51GYnCA1sLOZOm2h5qiGOHNRJhK5NZOTK3WwIAGelzF18/UwIT/LmMABfa8gbUL1BNeoUR/GRsnTVISi4/mRY5ZeW0LyDp8kSDR23bLVbvW86rkvizHJJMtNOdkq2nO3TwV1Snxt5HJqFfL468Vx5vqJ3I6XwoupZMqMK9UF5FOUbejExt8mf2cdb1NOniH6NUO16bE+FNT8TRB1HUH8mfTx7nfUnhdmQcc/Myi9hX6urL5KWFHfJLqp+X1tBN6jlvCJlZFY/4JeofQWIJGdr7setSoxZBKBNZrjrv2WURNpJA1ow3PjJg40OmUvKm7MnleUdtkniuSizgbJXs9Sz1oz3f041e/EbwNA33LGaDUeUQQEoo+gl1SX9RY7nf3FIKuK71qEwqbRM/qM/8kFN7qGGTtIc2zunmXUTH0YrN1/KR5LIZLwUbqAu0u/n7noAkQXBjaINjuYhH8ubRecC+0gqvm2n65PVvosybes/fUg2r25FNaK/Vp7SfFf2w9si+5l3lV0RixNVWqVG2PPpezOvZUB2zJ8OofsJlOqFzjWoTSigpqWiQX7pwqa8PUCMCxEPviPiy5G8Au5DewGCYvbT92DPlbkCQE4osRY5uY0VMALt5kFbTlHDV8cqWnH/45tJ1JX5xXc0Ipd1XOuICJkyAgSHuG2sxDg9RXOnqeDXL/tD1/f5s1KS4ckWpTTurHqVtjtnAnMxO6DFZji/qEVigvqDR2l1hpfpTI1fXdRu7rK1UqUJ8wwud941h2JHq4Df24Tn5rzkRE1UNAyPC57uiDkL4927rGs+Zhw8r3zPr2dLq9yH5vGvEbOXbaf/O12Bxn44r1gU2Y/5EaXOWxCv5y2LxrNLIEbCvLCsnjmm1x1FOSdHOehDoFU9mkTHrIei0RWbrV5W55VBbnLRYH/G4pQc9NJSjSf5kdStYICuOeu2Vl6B450Jnl+OjUUJyb8qN4NSV0lSngQZG04NC7slAbfgDX2qb4+/HocAXoSf6Tg7uz1YywVVVmMZldxsDIbXy0K07FxqiORJoxb4WczdmBAdus58yMdMZZjP2X9zJG2EAl6+uyRyQ+wVQpEvplLvw2amn5eqE7d+Rs9UIdiZjCkuwVRkekg4xTfVbpigquuFXEZWfwCOQBbz9E6vfHd49xS9UpIJbpprZ2MunPAeusFcrPqzrKpY7KlllqiHoN9wbI1Nh/RB8biXyFWZA8/X0ehS/urRys8ah6mb+2xYBi1jmCkCLh0u3RHj18Q8/Ke1QIBrzoT/dSv4qUtUu3u2DbNT0bhj+Bzyr+Sc5J9nwxFmNUT8kfO1SspG7Q2/YrnS07fcMZqk12aJ7UVKb9c7CvWIFA3yV7D1a46FbDqqVG7cUbt2zhWyTPqcTo9e6ZrYI6fFyOoybow0xaGQ9SM2Zpvz6QIXvG5sWd7aqmcvZFn4Ak9v73TA+NdEo/jKsNH6vho9kb4Zminc2JucOipPNklKFrUXyScKYYGjSPbqrN/oWrltAyJcm8u6kuu4CqtTUMNV96ANP3u23UsMdNqPTOuI9gTKs09npn0Ut7BgF+WoLD/MUC3Ahst/s4gReCbotMAaSGxGyYCTROhEmz0lI5r2oL2e8S8gxjx9Owr/Z5z2U5vlkNYEO35TaKIsC3mrq4UkNLXXpuMC6zTmbWPvTu1/kuc4ZCoBevCKWdcJrTyJzKCDlQRL+RcZSY+zZxd70nV7czJW3NOJ9i9PR9GmpyIe141+0FN50s2ud1B+WfxdcerE32YzRPjY2Aw+BfUvQpR/uaDvebB8vPxYf41NIe+9CW2/+gzokyqI9jr6fE6mF0XiQqRyak4MWIpd2O2/TltFoy7+7VcVmse2//dh5/Y3TawVpwejnTTHbzwBBCmxLNZba7PVyFfmpplljA1vW75VUMc/96CqZbB0Zf74//22JP9svnqyQg8NsluQ9/treeU/SxarWzt55C4mAFwp3GLXdSCI7lusc5NiUufdU2jIO06kebXeMZrId5Auer/e0ZLFQr1xXkW6foYyiNh1R9WEU8JsRYRzU+moaHbPn771HLusSxj/ZmSVYQkdS4GrSFHsENimjimSqd9FcejaucW1BucxjzdCUAQI6gjZc3LgYM5ws4mrQ8/wTl6JUTKdFfk9xMITKi6rwmw863ajyh+sR4rCfPaI0kvP5aHhvqX99vMFLr0Ctfk6I4naXobOtgtEZqUjVxyZvvdUr2I4bHOuaoAT2TkoX6ttlXwYXL7LSNdyKhni0ebgCaz66fTtX7Z9GU328YOOOsXrjUqYtFF0UDRxkMXLQXLvp9pVhjqnvfiKnaJGg/094/ur0HNWq2LU8JhRlRxgP5dmFsLg71244vK6BMDzbPUh1UqhOX0Sv3j5NXW5iUGaSj9tfg4VfEHR9ToExdq3Vn/v8QFczngvWMad/16JVBMFM+poOwqi6kPpFRZzK3pCKR9OB7bZwaQqOTdT3Y/+PmaDt/TM9Uw4ti0+et4yOyeISYiaZF0pKVAE6El/31SZrEh93tvlILggHMbBW2c75r7wzImu2Ss9Ji1WA5vTKAZYGubp+xrkpL2AOBu+x1xbD7mbGQX8ClrFm9n7VNXysrQie89ljMtbwwixzea8HVP5Mm3EXq+iTCvE3luyNJAS1v/4MiOoZPa29ohrBqDZw7QvzGUq1ZKvt1uOF1trToSb7zXSOZoURZ2twTOc3Y9fGcTuJ8e59DFlI4WMaukkTp+jin1PHlAyaltt5vm6jPUNNXWeOXf2E2MMOTmkJyGy+1SUDiy1N/0s0pyCtu4IV8Ma+5NCe47ALBse3viwMSWKecGaEmPbZW6VpOi0jhztBR+++HgMNuZyrCmPMKaFdmheQuYQEyquk809LWKhuuRbFbFwFqFXaak73/PCOk7bXm2NKSdQVWjMYb98bTk43rVIxFL9JzyuuM/ldilbYcGoj7+yT7QQjw8SXGVuHc3Sp/jBec6a84xGLe2yFiStWTyFZVU+B0ypb2NWo/Yy0jfiEqzoDF3RHi75dPm3kU3rVUZLvfn6d3S6mHx2q/3bPqbc4P8gPtMIJoY45qcMhC02E3HzTIj761lyzdtsDC3YnlejprG0fVl7unCK157j/omtAoqgnN7+d/X1kq1Xh5Sv13b6pTwOAuNQiEMlNxXZlSvVTCkXq4cujmm4OICrFwp9x6EtC3h1lENYZ3ODdleTo+SDRsQvMvmzV0JGLjhvN/AOejaRvdnvTLbFs5NPnMfQ0NP4Ok8aNFAGar1zyfoKsojED7JK2MoRbZ2nzJdXKHS2jgftSAwm4jlaLeck0vthgSPNAxcw04lqPqGxAkXsxf+JMU4sOZcJ4+vr29Y3tRtdxwJzMSEDzQcLVKELaOg9nuMCjNeTwj3HYzFovp563x/zDb5PKoJ55LXqMG4FE6BLOWczl3uFqJeiO56TjNPuWEAfqkoOnyU2qq1SV1YKv1XXlNVSr+Ts6QSxncOeLbJcKyU92xeNcStb70xnG1RtIW/QKWzatbjDuAJnadSpddyLrUst9gRO+rAwX7irW4CWgJbDJomCqsQdpI8/yJrwCFwEnF6SYj8d75ZZpP+Dn81C9E+s+PSv+1sUwPFt8mySoo0dDDkfLKyOR1vdSFza2PMyDeduGZ+eKPpVTHyThvR/CKV4hPg/VPk/XlQzpIp/b31LVVN92qzpPE2R3GQL/IL9QEDGkaG60knvOB2qm46p1zymWKizr/ILDj6RXos/uX/GNLLwewxD4pbZXTKRMoP76pTL/PKVuMfIn2Mf+m5K9NPHKD4urBLCaGmicxwjP/q3tzF6PoYN+9KuSUu2QiWtur/rKoLTfg0PGYxZ4xFleEb9xzkL6nwxPgnRkkxNXN6OV38g/w7wsfsaxc/h0hKGcDzHc65KcaL/WArbTKxGWrRA3ntQjxwducAIHElrujFCLhB36RHxvn3gLDdfIP3UOgiXJoAnixYSHbv3fU8FtxY/LodH39Vkhn70KFx3ZmVjIPd/QWA960RTokfa0l9xP0TeKNB89uZvrKbp6w6Svr3iSfbHUUIUWXIUvdFmkF3M1LaJWkntLG2e8nvV4MSwp9GDJwaTeuvPHvbWLgrP05h8bGDUr0rL0mwBvwpcxPD9ogaFZFPELQoYQVnRnaJBiZl3sxoji03mqHQLULLHK7RyHIQcIwYwapOSUAN9XPUEo+hajriRKyocwWfd1cXqQfWV6KLWi6563ynAngp9b3WDlvLAszbXJgfBin6FRVhfpno0U2yqv0np3Y9O2Jv7PJPFLlpMoeAlPq0Bbwyxtv3IeirRUMTke9h3R7RZ6O0Z1lm4ptRpgz2dOqdYcReaUsfeay0zaswD0yXYtUtTuW8BkU7I9oPuEfGdcvDn5Ktowc1ax1uhmC2tCuRhbmWud5F0IXYT4S/KWnfSMLc5yr46BD5N6O99t8Y4JPzbfDezoNhtEIXy41pca19Qf7SB2ESpQOS1NJqES+TcLPh6Jjaep1WKyW6BcMdaHvItCxnM8ZYU/UQvuQlSVycVCLOAECvjyqI8CFDp1Uh5LL8KzGSgT6/jlkaNd1qJ6V1PGDdkM1w9OLzbSQpMDohejJqQhTaL3abNOb2e7N8hTSCno1HlrLoPmSCBjBnLuGcuc8EzdtpdxR9xRfpHlaW8i+OdJAiKteGabZ7NMOvtXQ9UcG4TJ31FFwzVd1lXaWIXs9q7ANHZqLyMIdAXZ6GzzC68cTN5haSwq76OzC8MsGhIhM1QaIfTLXx5xuAxkJnx6vNK6Isk4RK2dv+fttdy/NoVCews84PkkV+S1D5JOCH+26Hbn/p52+W3F77GCLTppKPvJkpXwUBMt3+fpVrxW8g7C344H9GhgTCcdeK/SnRCZ7MwJx/NAsRSmShC4jrdA0YLvesa7PJm2EdAc7Ivs+GjpRhKJ4czJC+fKG7TzMm5bwhrcakKlxu7V60JXRNZwyXfM3tvCCu3PtpWkUwdNLu34MVP0Tsk+3EsUygUU29gHVLa+06umfMNhCYzk0sXSiqveLyuR7/6ssoAjbF+qKS9Ro7cLYab7fPxK8HMQi56euJZwQoBDuK3pXVUbapkOGXITWp3bQzczFQdIqXIA1wXoBOqrsKhn8MzY9LWHPYSx4Z8yRvzcCvdXPDKfdFamv12zoHA1N7zykt/xYHtzIjUCAfbcy3kJjLhWfnaFLfc2fXDU2DuAYmyP41uz2cN1reg3zs87IUYzkA+3HG8LkTBVaDbquG199zE2Jw25K8Vf17OQu60/+YKk4kotIoCuTHK8b45MeCKrwPTmkIaVzQMKDr2SSXsO8WZfgzBLwGz3K5RvowGXTHRxt/3tU2gejm46u5WiSszS3N6oTsPM5Xqkni2FBku7uQdiGei63cgbzsNcfG/9etCHSfL7IAqyYN9FQFJADpK2ViUjhCvoPctY55L/QI+vc3uj2iNB/8CBa7KLbkwS51BiYaW+z1tvmyQR6hG7OdoRsWoIk5w5D4ZcKT5Xd4GOqigWzuJnWNqFJeYxbhy2TlaOK8FSss2arZuJwyXoMdncmesHHUGG2aWmREIfyf1r1jPF1gU4+A0x/gHMS8O2wclCEW/XioijlzWocPIKpYPFKHDHOZ9Zwr2U+V4ZeckYVp+yYVWOm9vjUU53yehGSDtef78UHwv5/qZXX8rS+JGjaRL85RYHihaTUsn9eWNVcOfjpEVOyo2jEl+p6583rV8KeoULMqqC7EWUyJ1GR2blhI6m4gR7NlugQvIHMM7mfJzkw8H1kwe9DrlZq/of614NJ33l4TnWp8kN+8rzjoKzamfpsoLyCz3lhB4ywRyT6f5i9KD5eJLhaFWQMVXQUyGjMheyBYXtV6by3SJ5s1aUL6r4Rb6OZiOnRr55SD7hpJgpHTYdv0S7WjTijR6apvM+xxEh2vjWgprto9R7D9159/i98KlYm0b1mqghjpYJ5mWG5thsyno38T5AOSdt6nGWRUiuGBMM1f9xrIP4/Vq/W/C+Pd70wXTdi4KayvwKNjMSr6EnG3eHy79bXIQPdt9e7wdJdOK+Mre3crEHHam41hsvYmq8/PJkeorStkE7ONTGxvkXCIn4ctQUdf+zLt1a/h5QVq6/Y7nnwvq7FEc0+YS++LOTz2KTSUJjttTRbizZZikruo8hbJdF+m/Tl1hfcEd4zZ+yU0XKyF5PpH0+UnJh0Pcj8L8AlhpUjgplbmRzdHJlYW0KZW5kb2JqCjE0NzkgMCBvYmoKPDwKL0xlbmd0aDEgMTgxMgovTGVuZ3RoMiAxMDI1NgovTGVuZ3RoMyAwCi9MZW5ndGggMTE0MDkgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqNtAVQnNkWLYy7BXcah+DubsE1BPcGGunGJXgI7hbc3TW4BneHIMGCB0twf2Rm7szc+/9V71VXfX22nrX3XvvQUqppskhYQMyAshCwCwsHK7sgQEpZgx/Azs7Fys7OiUJLqwVysQP+qUWh1QY6OYMgYMF/2aWcgKYuLzppU5cXN2UIGKDgagfg4AJw8Apy8AmyswM42dkF/uMIcRIESJu6gSwAyqwABQgY6IxCKwVx8HQCWVm7vNzynyOAwZwRwCEgwMf8RzhAwh7oBDI3BQOUTV2sgfYvN5qb2gE0IeYgoIvnf6VgELZ2cXEQZGNzd3dnNbV3ZoU4WYkyMgPcQS7WAA2gM9DJDWgB+F0uQMXUHvhHYawotAAta5Dzn2pNiKWLu6kTEPCisAOZA8HOLwGuYAugE+DlboCmvBJA1QEI/tNZ6U8HZsBfrQFwsHL8ne6v6N+JQOA/gk3NzSH2DqZgTxDYCmAJsgMCVGWVWF08XJgBpmCL346mds6Ql3hTN1OQnanZi8MfwE0BshLqANOX+v6qztncCeTg4szqDLL7XSHb7zQvTZYBW0hB7O2BYBdnlN/4pEFOQPOXrnuy/TFWWzDEHez159kSBLaw/F2ChasD21swyNEVKC/9l8eLCuUfnRXQBcDDzs7OJ8ABADoCgB7m1my/k2t5OgD/MP6hfsHv4+UAcQBYvpQA9AFZAl/+ULycTd2AABcnV6CP178N/y2hcHAALEDmLgAzoBUIjPJP9hc10PJP+WXyTiAPgD77C/E4AOy/f3+fDF+4ZQEB23n+4/7HcNnUVXXe6ikw/VHw3yZJSYgHwIuFixfAwsnDDuBg52MH8L0cfP47i5op6C8U7P/EyoMtIQCBP8G+dOk/gN3+mj7DX4vBCPjvXCqQF8YCAQz/ENyAnYfd/OXD8f9M8z9C/v/Y/TvL/4Xg/4tH1tXO7g8rw2/z/8dqag+y8/zL/sJXV5cX7itDXjYA/L+u74B/rqsy0ALkav+/VnkX05cdkABb2f3dRJCzLMgDaKEGcjG3/pMqf+rf/l4wOxAYqAZxBv1+UAAsHOzs/2N72Spz25dHw/mFj3+YgC9L899XyoDNIRa/t4uThxdg6uRk6onyMuIXiQfgxfGyhhZAjz8YDGBjBUNcXkIAL+X5ACwhTii/5ykgAGAz/636Q/qNgM3iXyIHgA34t/hyAduf5f9Hw/vb7uj6MrN/QjgBbJb/hHC9SCC3f+X4bYa4Ov0rgAfABvqXyA9g+3e6F3z2f4ucL9H2puZOL93624PjBfI/Ig/fi/TSub8VXC/pwa72Zr8Ja/XvsBfokL9F7pdrIP+K4n5J6vDyJoLtgJb/lMvN8ZfW6b+6wP1SpIOdq/O/kr9c+o/I81sCugH/hZL7RfOy3P8EvLT2n4QvS8LmYu0E/FfbXvC4uP+Dl4PzxcXzHwQvTXsPdPrT/l/8MHd1esHr8sf+vpDnP/IfTzEQ6AE0R1lagJgLfbSp+9h+UyNB4s6yMyEyS7vzLoWRxWvJqcP1DgMxibE648O605VE0vAXrNXvMgyX4ssUj14/WhoQQ1oT1NvuvR+M4zSmd9pQvk7hD0wW/JCo7ydDJmXREt/1fnT01g6whW2B7lKgzXF05cdQy8O5ce+T86jvL1sZC17YUd+t5lVEfSibYYl6G2kQUDxHm2uWOU9IheDCQob0GvvMA3Pu8moWO3vymUIhjgnF5yiKq9BLb4Mz+nb+/bcKLU7nbiIaIj1CMthL7LFpOi/J/U8KBIteJYXrTmPJ8/gd45v2iRx2+wzvd1Q0bpwOfhnR0U4KMhARssHiykdErre+ibcvpoxH4M48W1VY95D/xddi5sysGg96VrXjq8Mw7ju3mq5P6XDmFdpN+qKW7J+Z3NBc6eOC7dHA3C3vphDO1acLlXU0Um6I8AvlXmT12LeskSIVby/zzZaTndAbZMExBo84NTa14fvEZ6VUkl+D41GaN/L0zmZrJG35i75+ySQI3mpdsA5J+CrmqnjNVZUS48WXl7WSNdoD33upGLWs44omjaReYXrAfCJk0PjumcTc7czI9GY9Qui7PdSX1wRie6JvMjqoQsZoVaLahPjpzc6XkeQQcKL0epO6E8wGLTbQjUBHNJXNZ60XsSBiUyzgVaJzTlPf2magLKOdoA/5czXXtg5KHDw6mbLbDWwJrZzKWediPwHzTFEHm6/JVNg1VfoRrAI8VqwqJxf/ciwIARDJYigv98F8u8OSb3vOv2jsF+fiOHHccr6PFgDjaW8AvbRX/ByZzgMPlwx/vR6b3xrlI96Sl5VpAw/pupL/4Lw7vS+ciGrLhRKhudWN2lbtoVucQK3saPr7OzxarA9lN68+Ar0fQ1WT2WbNTgKriUI6v94fHmojlwcoom/FG7U09wXzz4W0CTMqV3YHRQZ/F0aCsV+HiyoRGtv7FEcaj1npo2rBYainOYG7R26vFmg5zl0z18pIluKjHJauVjS8b8U5VWfHYRU1tiV7OXGQhnG6lueAnqwN1eTzK9KrsVhY/xUeKQ9IQVW3THR8ngcWWyccW6+T1J/m6GqNgyf+lA96w7eFEHFfLwsKhrR0OWnyuAizgOVtkHTPtL0YDNdEw1HpR3qko1ui0M78lq0SHG2sSSaNJRI/uUeHLE9pAtI3k24eXg7+2OZK+HB51xzu7ftfOr/NYBP34YUhN8seKKtYMWhCh28BQsTmGOGOWsWE2sWVccbyjyVV1ivkOnUuY6RBmIcQdFgwGrpa1ZJm+xdNnRVpeH3bH1eRMVaD+icuGT295zi1b42IxOLORoanLTnDm7NGf/JdX4VR+3scv4Xa7+2wb43uuDSxKTQftm2gcRVsRtAow10KCgfJ7L1txWqbz53AyhY+at9qnGc10qWrTVkJJo2FrJEIOIsWoN+O9rZ1xrXuoFTz23S+J5KfXTmQSqaFtM0+lEenqYL5SX5pWFF/EpJu/zRKkjWFxRnHoMAUOHruvTTdgySXkVC8P3+c8QN3w1ZguW4hEPcc6JlH/OqxhG1IgImZxYzLvUXgXewNH4xP5uLhCgDj1fGP9/Ob+S2kzYVP47OlxG89H7Uc7rpdFu+bo8N+Gj8kcXKX7JnyzjiXxIfs4VMizKmmA6jHQIuRNGv1BhsS5w5ONLHuPIklBpijDnahOaG6YA7XAgcL/ZPRYzvbHnhKO42jbbdf/hHckcYeQdjt+8bkNMQI2vqn9wgJb/Qes64Vq++dDNwvc9vj/VxpT4zr0mTRjVBbYUXyt6Hi9nGT5jKHgqWCLTtSaFkWV5q66jVE27fC1WFhJjBdxC9GB/uEW0KknBoeCDdO3ufvbidITwelMOBcAX2qDwUJkWQmocRk/IM6L63w0URenfcs75OZXbFTBU3VOGqpQhsqIRj4d6LgSoyl30xXIMz0KY8nXCpHJwVByimyPZRaOyPCZnxdh8kNA9uMp6eX99ChcQWmWIfLxGebyNNIIgbZIDfQA1nyNCT6eau6zG+Cw7ELO/vll0U505bQGH/kPLSWVhF0FTb0+76P8Ijk7Srys08IwkfUuZLVDk+8kpJR9VAIvkOxkbUv+mjNSKQz3AGbSzo/FRLB3xg4SBaxfPDJ67srYi96RJB5/usdoZJRoNiy2c76IJSddsWT9/XEK/GCvmmWV3mkNj8kLLcEPndqP5hd5NYjY/PDaN2vWNTZEHJTHmi3t6ZsA+suncL13Ob0mIHaRTPBcGLsKHow347f9csVINy6Pa/YNeD4HpwMLIXW6PM4KYX4OCJpnbnZ20gt7JSbKfdhC1plbR739IZcIfdpq/7ABU3s7iUsl2GSV1J7T3vaX7Nwj7Mwj/7MQLUMkr3yeHeHEdVR3vE5ysG5WYcyiteRJeajMib9Cc5jbn/M8joVx+AA83mrs8fwZ3nN/GS49L6MG2ztZyqjYxu+Ha4MmRE3bYTUVAa4PC2jE8Urljxbscifw6dps/Gsm0IWXozzmw5IktYESEzcFfsxrm+RUV/Xle/DbAre2ScUCgmT0Tp/qKVkQSncGubXSR9ncqDjvrJJxdsmarxSNWrnqC3UcnCLkg4ySo7F7etDpIhMW0G/8KBJ+XzMZGl3se0RH0eUu8vXUy2yRcShyStKcuaEd6dMEvIewL2xzm74jJ6cP66TGiRVtDa6SoFz2IPMSeQHP9Bq7PzM4ypdYXRHL/Z9HfnRBuiIf7OP2HN3WQyPVtPOgEOA1HE7m7CkLuNqSevxZZ/BngoJGloSlcHMyVHXkcgV2nCi/A2Z1eBSHxRhgQNfTkJepWCtypK6BJ4FdPtx8Py8btLPgFN1WwZvvKkvxtd+mvlsQ6PX/KYJ2rdDtPSQZTeqt5zRlBVHxbZk1jzqYjHnuWWMu+FJVnQKuTQnFMW4r/znRpk3BldsNA3x6EY4jmQdp8ZeDx2+XhnWSXsdHQMqswv5pWmpkoiT7OWoR4pcTQ8rPrhhAXcDtiNr9nqkUuyTvsiDfu5PQ17gdlnR0o3BJBBIe9bx4bYGqqPZ9RjziSe+0iN921fayVYoYd76egoHqlSix4CbTEhl1qsBgYL7+Tj3AY7nsmK7W6m6Z0mEUpuHFbUz08cJWnHzngy/0PHDtmYq7Gt6jTuRLAx/wc5hF1loY58AVW0WwhMi/yWsxzTZOJGfW9FkQq/1stuiPq715IRjyUEigdcoMtGL0swSNEeAyfOUudvDY8yX0fT2E21hfulNwwhEkvcXWBWkyKmWnQxrImajNWG2xy+vB2UdYe1qbmw5c0AlK4vanY9/243Nv9Pbms/GRzgydlHILMdzHEztQVS4yMpNtHUdkvSso2YqqynleHOkZPrDbEQpGpC4E5aa9jPwcmcA9Z0ZRY0ho3rUq0UbSzBizKiDdQDWjktlqtCEQexq5BW/4CGzV2hdncgjrAKmTCCn/rsq6VYv6X6+1TYygZ4rEnoF/7Ws66mde2qOi0kOm2TBaTDTJbxcHvNi/yRzEt4Ojp+d8xNX+7cPsvLPWm82Yt6QBpnEQ12luWV0Jv0o3RGhnWU13fWEVgyOCav0VJa8BVN2aLraerpR8dSuSL9HWSheJaHnJh6+Xp+A+qjujU63UNI61D3b1KpBaM5lSzfbvqSQk4PGbT+Bfq7/tVs/Ycjae8KQLAOHZ3t9DEVAoXEoqoV86HJ+YRaFvRJj/nu21a9toh5jFFJ38GaX10G7ukQTPBi2uLRFzipB/s2nSENEGfTbGozzOIhfv3bmc9OOfzu3IEMI0qjBd60CXQpEZ+Gu+pzmjU+9Ki1iGyjK2UNox+gH9DUjxo0saVp0cPZSBm4Om2Q3ZTyN1anGC0Ty6K+eWV0lVEYJvsRwV027iD/sF7qm5e2a6K7P4Q+1jUg8b/qfg/P7HDcrumOq5MZOC/gmmSvs62C+yxQfNvMKiCbNPpTwvQkmlkieJ+wATl9Uen2fqsqJEtOeHVXJZ3ZQu9JKWLObyEa1tMoSkn/qWO9JEsujSl2RnF2YlfacqkNTOlIWRZH5xtzPbV600ON9gELsE4cRigG/I3K3V4PbOO6BZ6Z4wvlwAtMefm+0c2L07hrurYQ2X5WHui8Qj/pERmHMko0HWoOzGM0mfW0Qo2EOLA4jizmXJk+HZ+HdmsT08qCZGQUPt6qUMGRqApZtX12m7X7FFV4kCv8YiF7h35oGqHCcZoPHasKPX+MSW4wZSPPtQoRfwMEpIHGOrcnsi9H089HO4GOKv8e4Ow9wm0Ti3w75Zru5WvqTcqiLMHvk2McDIMKn98snEHswo+Egy9D8U9EPygUb37IoMR86XCwznYKUZsdFkEwEFwEsjkec/eNEzO29Hu98kcwFX1691A6ToGLc3GFYQu3m7TdfWQs4FasyDiS5idZd2ivYeKjxxYCSxM9HK5Nr2/0rgRyeW/UEazsI5bihYwHuHVJ28Me7ZRW1WWOOHLI96pY5HHAb/W0RSQIGSaeH4l/a0/coYJZ3GPpDSRuNItNIZcjgpuofaZtrGdAsFd6HjX03IkdNb6BPqq9DgjlrQtcANHCOxwSbfmzHHwzMO9HFxlgI1mvOvXjUIw2cnbYKsuhFfiswy/bdO/diVnuyQ8sYG/8bHTGNcXoFfpAIR+r9e5wJuUW5TY/BfTQ2mDCqusaiatnrSwh8Sb0w8+u4qGqCr1eYS9SLuj8hi4LrpnSxNlLfCAOGqvsLXY8JLxU7TMpqRl2/RMMvTwNg2a4yXO/cTWVcorLaAdR2lP4+H85jy1E3H7NaoCMfgmy+hgRnhOIX078GaljvsGBGsZJb45YIRVkjKBdCBr5WmFfHYE5e9XvcMVCcgboJw91Rm/FuVcaaXbeSvqJfkWudXCLFipRAt/IaAbRTU54KaOoQIs/0SeMtY7cnjheOPt2J6zsv2EL5XXPbKrSdibNVaaqWTYJ1FQyo7IeId07ee3Hk1sOTRCV+foLP2HkNCx+q8KB4shTvDcfQmQfudJfCWWgGDHEP08HYGHd2yFW8WmOt1o2ufDDTP2tR2OUvTiI843tNRUcJ5T8xkVmBfRNFR/PTS+72pChGzMSuAyu7t4wxMFKIztstWBmDsBXxprs3y+AqC0NTE0kmH0MJcma7z/oY2jetkkUSRSduAo01o5kWKXAJzB+9bPj0syZ4aHoyAj+5Mck8I93vG6gjC3UQjHYRDt0J3dlxQkX2+vKpBPY9LoMKQ9ZE730ZbKrJSRrDK4S2shmbT6JS4ckcr6WG00rW5bEzY5FwzVzunUJsYMyT3zl2Xrv4i+nLgyiEHS02Z/N13U+usNIaGlbxOAYCmcTLtgNNfIpmwEy2fS3IR7OK6ehTTA/Orc5MDPfQMpVJQxxMwcoJx741tDv0kPKYMVavAY149NcztNKVRMwT9Z1vD7exl9QGekJIimwQEmUFx+q/xVoSwyHr2556mQS+G31YbN1BJ+E1pZuMIMA/sHR5cqOSxVfjP/XGjWeCfe+rEtC5wiQi6RemjP/q2t3yAvFalOd+8ykkkpdsvdxPYjlNXiGCPsaLRcKujsZa6NSeulK9mZyHwsKCndiVi6mMYkHKPKDsUm8WYzpizpujS7LPUGgXs3EzB10rlLymddqnM+JbQPvhudfri9jsRkpEVaNhngN0oaJu3vWBuT750bjIVHuWAQ/xc5jZlZEY5xn9Lk6o9jDkSBXSGKkls08pTPmgXb+29Dym4cib8mXwK1efjczsyKqzyBMbu3bcxETpzyLDxrBZSD5hSy2rrznCucS2BEqHnXhmzqoThDU9PxuYvR3Whz0CqXUO6HWr8b6PbtLMmXmzF7APiljvKGC/8KjyjtW66iOapZ4YWolcUh8hrRYzBrgqFH2X+lXUzGt/ZvKLx2UaGHOHYxBDQtnYVXPd/nTRjqwe+IxHpGpXERq602dWIK86VI0z9Y3xqy3QxpZdgo/Y7hSLz4tt4SPqFGlSAiI4Y1VNeKG3xy6v8iKCSnP/yogzStE9xG3uVkJIfm1mSaTxYy8xDFCuxGexbwU5cOnIdEbp0yQdtlhc4CiLC5ry9mMGK0JlSh6gx87SG/14K3VTkiYTitEQPgMbbr+rUr++smNfhEd022CksDZ4CFoy75k0bJ2Te5jqVJa6YE0xo1LpWea15IF9KemqO2reN3Pm4Rpr5mxhass2D9onJvJ1JR7unCTP+4MuDSikKOro5HNN5+boIKYpxAubMtqOgaHNhfvF/V4nx51shZrMgK7Yug81mD0B2xgj10Q7iNdvjpKrAkxWEV5peidd0pviUfLpXy3o0fmzSropCgRye/mOV9PLJBLYbV8VrX+7lRRA47X6Eg7WxKTzOR6TVUrnceLw0dnPbBMTmnwzWL1Opqb6Woab1nh2HC5A3TMMhxUQwMTRAR14NuKzFnApND5hoJlipygjXjFCS4DQX6BONYe4vXZ+TKstfV4aVhER3ZqOFzyz2+J5TBkqxI5fbGPCWkrRlHTIcCtQt7r1YIFMWZzILhQT84lNH7ov4lyU2hGyZRFtmKkq2o0g8p6Fsfy5vNGC7KbI6p2dN9gfSWm+Dvnnd9kYmTcz6Qa+dvQkzxgzH+NHcVs2f55O7MS63n/nfVsKLOp/8DuRq5LsBDfFfFFUHgvLfFAlu49MA148fIPP8p8NiPvRqy1mDdHg8fwo9i3VLaevWe9JnL7hhKnb3jOFZ1ucXNAhawav/+wAybHtrYnnOArnBlLuky8Rnfh4g4qLUGBkztONCOxEDepZzPIl5EdPly5TmeLASnpATLhsb79L0Ps+tqb3Grb+freG75mz7HoFuL7QNaAaRaOV40Y9e9730TVXGKDkhjS4MeW76RzitP5wleK8g5WvysE4QvDNviC2CkyNdxa5oWJw2FFvL77KodJQ7z4o1nTk8AgbK2LeO+CwHA7Vl/vQaehBRzGe9MrPNjullUfmoYQe7eF9tKx98l2qYWXY2aq9Vq/X5AfrVA6/Pr3wuBSGAfJq7Ee3DBfVbmJywVzbqTxConRuPwjRWY/as+9cHlr5fm22ndBWxPW3+m9PoxxDJvUXnvLiaeHRlJZnTUrBF8UyvPmLr4QDJ5i+fXfPyEljMw4oZNJROQ/WnlA66NaSF9Md2s0gcrXlpRhw2rAqaTVNDEHGd3EUucXaUxapfhK7c93j9rXmM7WRey51h174FFwwmj+fWvNoxv/Vm++g12//0BgnCbsPWuLX4GdOZ1HxY6evttF0NOCduYkQEnDlUpfJPW7EZLuForSKZVAORfW2Hy/7gD83dRsdb8JlPbfAetXY7NPl+Ab2dx25KtW3z5rV2oECpVn0aXSykdRnbyc3xbUjLzp6nOBugSqMay7FaSM6ZeNjWBdZXVBeZGabUZe74HqyzNmBjcSxphUKb6JNKXpVs0ejSYVzHAeasqbHLcFQzohXPhBn/Hw8p4PgYeAj6g5+YR/XYUBoaybdaWQBXH2q21F9H25RKEpxIM9kdctmOX8Jc5dsIWLrtUuK+0z8CSygtrH6XUeyGNXZGEYdEDIcyXz5sJsO3CAkA/GmAvpbHHTQNjQ9fg2cnOHsGx3ulQnXgv3S7KOHfcOODWbfG+IShhd5bn1945PaNHX8lOHzJqDxYW50s55pIlCy/M2jsCSvSuJQsf+vJVERlaVKhAkb4DzcXP3nCZ6H9YmbNqkPULWmyIeKe7gRkF3+JYXDBeIp5t3sizDg3un+9I8DT3th5QL/CipUHBPyCMSvlAOBYwQEMmbbj+lffmHYiK44SvTDUF4WWtUGY0ucvicylJODc+rq5MkkmTWmonoMmDZm1RQqwqvA73X+cSTEP5jLGFcQ2lDFxRoYZe7gRihqmOj/yJTg4d3rGVQggYAeSZwlx1O+37hud7Lg8WxkCd9uopvmWN0N/xanGD0lRNMRxmmAkE68NSc2bKCCZrO2JGXLwGjW7UGZA4bxBFnbb7eqML1ViVHj0IXyV9ISSUAYeby2yNRQVzSRE4y08DrxfSMPPDKrzjdLnHK3EUxnMmJVOQo9XA3M9ZrbyanYfi+IuXA6AUO8WtNA+8Vxuu7YavEOymropQKGKnrr9WM7zrtccNW2bIyK288qWuPEB4oPI9R2pikZi6Yb7R1882diCbjumtxhShKdijjLrI6pBDutpco0U484dHKL7dhrNiJ+56H2UIaiusI5MQkl4ik1W9T2OiKBjGYZs8Rop9Cyh1au/Dn6yPRUBTzKES3MRO/vBGYofCjR1PKpemZXYna5aXTJ32QBaK31NqccJxbJu7/aVaeJYod3k61gJ3rN8KtLF1UCByMF70SP98nMPofAeX9maMnI5YJTby9Hqm4+DU0yCtAjf9QlMGQnGCRn6zqIrXuLUhLEojH5eHC2vzkzGu/jMoErkttGp5x3rVqsW8t9gPytjIaFD5Rm/PTjKE0QFj7F0NoUKAsqoOhHjVR8fmrRLLKMS5zJjBW04sWQbIeSaYqr90GrHRPrPQ+cv1LytzeeVk2FCHoYW00VlE6HAfEkdOZ83l0pqRixJatocDnRp32bcijsQ/Zl7RHMjsIbPkejsBLZ5Fzt/9gZFLtPw2O5xUFg1/2B0/SHv0kJOe4JJ7vYB1Pjal4GB9MjfVEcByV6TbMiXzskIVWBJ4Iuuw+C22SnvchvmCWTz8O3Y1Bo1dJ7XD++37RRf5zs7UpSIYdpIm4HMGjUV4ra4G8jMWG7G30CEjpWFiddRK0lp5X4FxfCaq0yo82VUNOtwDT4cy3+EjYYKpM3zaNpOsaXF7O+idGF3/pGf6d326kjsQKOFJgemkV0FRv3d9adWDUU9La3W4h+Vqk8FEm/azGRqn6VOz/u8jr5zlryAxndIRtp8NWaIa0F+flyGk3MunhC/NjHznqCJqoKmIw48WtajtmEvETVPfoPA2wkBA1mAODIT7LrLyZtXxpDeFNC21sYufdDHT6pjO9NpX7Qv75v9Cp1SDEiYvvllDd68SgTPwnijGDDmF9u7WsPu4iNPb2Umg+uNlwyP1hI3LWrlUu7vcAzYR3Q+9ROtDqrCNNhcaaTvTa8/KGFoV6/+hDFMt3A7yOOIYf4V3Dl5CuyE0lFv7JKE2NpG2wvewwozI0DI++CZhC/gHCEJZnPkSmxaWK5u7YgnJSwvTynlklCc9bXD5a2Ewmbd+qzBWUlds9mCXdO1CfNQ2Y+GVIlooLZuXFOWdhWXxeboyoDXvfuXGn23D90ElKu3xI3sQUh1FDiI1crsX5RCv6xwa+nxSaxjon2fRkeMSQBubS2tW7Ao6FdLdTsnQSsztSNY5QDeL6mYmQgby6rvIvLcblmm+ncw0rgtDLkNdccTNgv5loWV54EtCKTdLIKmglMHfbbOzTnG2A4L4Uj3XV10frEWc4h7vtfdQ+HeU6zT1Ve/lio25nHcwduNHDGGpFwuep0yAjcS8gL8ZqUc9/ctk1lyOSnCT2I4pc+MT6fIWjgudY6CvZW5HR1kye47qUWkVwMMJwbY2O3fBffnPTAXvaVw9BW5sCVTaXfVAO9gxBn2/Nq5T8TVWGOeOtpiYIxFRL4ttpW6SXf9A1h6rUz55nmSXOqm1he6Vftve+PV8R9bEyONmEQnl78iKoUnonU2tSE5fElMi/8Kl1sjMqYi+RtXLx2HmCFy5ZivvmmscLxA8toTN3APeSsteXatOQjsoWHYulBy1enKQMmHNSjtQjqLE9X1N6l8qZ5kkmZ8DxTXoMUfhsTzgeGgKiNPJ3W3KZwTu3K8lFoqO5dF+5a+AK0Ppe3+OfeOfe638OVxX1hgR98KZTrSKdxqFBlli52YtUOqYVM7fWOW7UKCQU4X10VlHBD3oVetqUXYn2AaIjkLHnwxXoVk8hCK0w6GF4eyCzq1Y/78G1KRhJaPO3dMV/MtpkE4OpA+UUPjJcxdtbjMkYhag93ELtQoxGi9UXWS8jK9Pt3P0ToRfM2ulsJ0BRFvpmGRrUc+mQ9joFMXM+sgYnYjbtw7odzutvmI45fkMbrW5DxedNhr/axpaz/w6+wQ2II81mgAsKxk58b5/xeyls0uEwV19K+VxP8NZ5azTar6JASWdhV+RA5VIkHwQFtXk01E7wfb6DXFc1SWtI2zZqlBt/B2Snp4ipqSdiDfG2idDNHHQx4nzLw4PuzumFLrxNUbr7PYnuJUWiTpHEcLhkKf//8oKyIpxBUOnD7aE0IsrXJ1kO2zT8enOA9Bemu+Jj6yIthLgDl0sq6ilUtPEpPp+3sPVx8OOpuZFpskcTHsg8k7w6qZtoZ1nW3mnQen3f2rCoupGAJdJhMS6nz95CasiETQXVa1OC3oUymv85Jdj/666q1vsnjeMqIF1wCKz0Q9Ww32O/94M88ppGu0pofeGY4j3wLORLhfyaSduyy9z8QQRJsWsp6VJqdufDQKgkLk8LqJU2x119LZY0n2W507rb/1RHqGxd5OC9FBF/m7GVBL16Pu/Oxn8voR12Y+NHHHmPjRv69xOHzUgQ/yc83WWhrC1fpNwvdozFGhABJx8ixtPkG8kLOXCroOWM+In1eOSXq0XmJzOUf9kPl26vqvm46pZrZomQPk5MfC78Y4LcpyOz/Qi62oj+ZsZ0f6Gh58qD5IBoBpdVnQ8ntTgIbMCM5+itW/WSxyoNHyzl/K4jJMamKtA7WGQ2F5npy+borviruuhMh8m07vgVf9eLVT8JkfCCsQYd1rczdszwaCaYGLFvmvgK2fzLiNbNt4C8pjtOWrIJO61oSchQNbzmymeKHAQDtGRN85rrVfXE9kA3AdYyqLbtciNBQrYXJe4wJnggZHcjn6akleFjOubwc6x/V55eWv6IZHBQO0N/52pi+bGxN6ywU5LsxCBe8Ozi5VVhUkNcpn5afM4svl2RPNw5LR5p1LhWjVHBBjOKBypBamYF5qhKEfObjorvdVHTXZ1C8+XkxDDV9vpd2IwBUGOKowGjhhE+7wxGqpfMArvIazVuYe72jbTi88NUrscKmexu0eahsy5sOCNLzGSLFuDmgZTQvUyOPObgU5uTBuqIgLsluf72T/Fj7RrNEvyintypI2MIEalP0h7e1xYa2scO7z4ozvazX69lRvUcOvCtF97Ryo5g9SKGtcLWftNDcxF7tzGkS7/oKiBeZYziJQ2fJ1VEjU4fw+06mPSqZ5KG3lNFkjjksQj1lCLtYFUyFs2vK0yKodsr5koQbThy0nACbRc8aGLikN+diWeXoXfBvM55TwoN0dFPDj1Z+EvDfo+cZ86eIYgy9i40+oV8Tch4Zhdkm7WkKB7/R1JX5xE5IX16KyQjQ0bCmNXamNvL6YthBD0CqsLg5/XEMaJUaQfqZWXEQ3/m+Z0h1IoR9W+389J2nXeyv9epcNsEdB25ahjXqqiviXMLWSqjl0msXb3IBpH47vtdUJf7qMqsuG+zFRZol40Cjj6uJiAPFAHhKqF27N8npLpMXiXIqB+94i3MmPDslwHPhhCL9cORwMvnuFZavngRtYcRXF2QRzaf2ZcM8Bb7SXXz0BdPAJKPmCd0aKAiGcV479BcdcufWh3utIS9Ckq48ZdqodFV6KhLGnyAHQU0H2WvcRpZed5rtsz+SrmTwSt9S1zDnzfIFaXDeOeGXHAgzZkvjfjYbng9WLqf8tY4FffDw85dXy0gmmjnaNxss0SYJp7M7R2oZQ3uN7hIrQtWAiUpquPCQSPoS9PjMwBBe7rdgfDlP1+cvkjq8F6UOxu5YCNX2iAlXnEMr1/le83AWgz02JnYYX1gIYi8ibW6ne0IynDbW86JYLd123cknvC2ldnvIamfUV/2yToqaxd8hvp4NReFjmwxuma1fpfXgp+uxIYR5PnDxHpHzuZOZ1w2/qKf6qTPlWzDLU9csbDBvwrO0YVAaq/Ns/nEsXPoQFl7Psu175y0HdtVCPGLmzwGufuR4Z4zbwZaGhR4YJesGMUIsZPJ55zTM9bucXbdRq4ZrlqETN7E4xDPnlGKOaNdmPDz2lgcPvvhIVPb8/aSOU4etDqQGNlE8NCV24rx03W7Or2Xn04xlX5HP2FB+GYP0Pff18XDmSzO/+xWiQg5OSgdinUVgdpdG8hd2lYb6PYhBNFg/I8IXrc3zsfJP5kyJFuPHgA9447w0iSeQZfxRXpj3YcSoxkTblRFJXlt04cEXzrUwh8GFzw/bdnNES+dfji/8nbLpIqRTAz/yFrOZ0+qJSspTR1bU7nY/+roQGPBuJCTvx8pxdha3xNcklqA/lsmtWcHytqmoVZhV4TytxfC9wvuU3LaYyF6B2iBTv0BatSjJIZMg8Zg/FjGPg/ZlzWvlxyoDSZ9vIWb/F3NkaELIQjKe8wcYLmjsSw+SVw6SJ/wsxnM7iOsZjHEScgKjMvki/OFL6CP0HU4bapb0s+xXkaZYc56JBZPSmD+xlDCNH1TjG0aubKGcEzwR5bve07LukGkTH2qmyzXGDTxQPpPHg69xBhi39UWgFTq1kX3flo3ZC3uGHP7M9Bcr+Gxp/Z146cjqe4WI2VH7LrTUMmZg4gAuh8lJCRUMt98GwiVSo4P89VbFj49NTSzfByMtMT6Tve7sEk4z1MzgzRh4vhLv2glBWGqDW6NbP2332nmOQc4OPawb7kUnow5qgeJeHj3EGmJkedqRp9IZgEfXJQ2gM8eyFrGn/hCQRvGVNg9qr3PYWgRQskR2XQ+MQ2ORtP/0em+2RvdqB31DY7sryH43Za7Vtf1wNfJ2+eqoP02lD++Wq1AwBXKR3g/LP7kJUPQOs3KVnZI3+HzFzrxUaNrcFh99Q7Pir01Izsb0ro4sR8uJMeSOXn8TGYkKSudTdwG9IaB4xPHz1gAIhWTbyLnSiV7GdLBHYNwgzA7uVWBO24nAyoT3OhTXq3TOm6D3n11OBSmt/XC5UX9MlCCjBB7tDeCeEJ5F1/J8gUcbFoZErOvCNlx4j5k1Ho6beKfb1NS4fumyFsTDGYHdMIctQeDlzR3jF6h2EgVTbzCU9hhMTHbzdIKtsBvc5stXZVA0a0GMOE2KJp9CR6herwpVNSLU85s5tCsHlb9LKtTf4BkaX5TFFlpPgBYiYSGXxtvGSUeWIJWn4eTpucvQqp1om2YKPk843B+M99aASi4s7/E7Dvtp9slvaF3snCkoiPK8nAnWsS53PhQMsOQJLsX4EZ+rANsEF3isIRf0bl54mr42+GsIPz9/CYPt0ApLKOnADFLwZgrBU+35RvNs6061VJK5yZKhIb8BLCGdjIoK6STT1/hAnVExEZdSX5WCbvHNyoTR4oGIjZHcdyZUtNNZpsL8bMRMX4Nm3nWkp8KWsBPM85JGk3E8Khd2nfqsGjL4zn/QCoJgpIyhiNY0rzhpk6J0+uIyfjUGIa/dQPKUpBA4kTKYe5NKC0NbV/yQLBM5EriofJd5o3ry0ZQ2EbR6VvtuCDe+Fq67m+nugwQx4ZSgnw9v0Q2hvtHBe5f2Q3o54w5xbvSPbQu6+bFxSMYqlHhaD6qoNJcxP8yyRuL7anaOdU30BXRre6E0Zh6zgByAo0bZvqsFVp7C0yUjl6FEMHgN+m5LapnsvUiafu+/qQHSlLjCRhQpbEc6eqmBKdCcm0yGmsft0U5UCuuVfLCNhzar9/ZqpzMF3HO6nj34FEHQRG+ix0JoPldYU+r2THW7ZJjDu+crKdEybzYHbQi+1aEj2Bk84xom5z09Q0VStm2SIj/c5N+OVMkJZdzORxyhj0DQESGaQofyeTLcrxoR5kcajG4Ctyzs9TADxtLUyXGShc6xKFdbpGGrGD98+z8UI1L7CmVuZHN0cmVhbQplbmRvYmoKMTQ4MSAwIG9iago8PAovTGVuZ3RoMSAxOTY1Ci9MZW5ndGgyIDEwMTQzCi9MZW5ndGgzIDAKL0xlbmd0aCAxMTM5OSAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o24BVCb6xYuTNFCi7s2UNzd3SlOgUIpngCBkCDBobi7a6FQ3CnuDsVd20JxirS4Q2+65eyzz//P3DuZSbKe5et91vtlwkCr9ZJDBgizACnCoHAOHk5uUYCc+ktDHm4ANzcfJzc3LxYDgy4YDgH9jWMx6IOcnMEwqOh/Wcg5gczhCEzeHI4wVIdBAS9cIAAePgCPoCiPkCg3N4CXm1vkb0OYkyhA3twVDASocwJewKAgZywGOZiDhxPY2gaOyPP3VwCzJQuAR0REiP0Pd4CMPcgJbGkOBaibw21A9oiMluYQwEuYJRgE9/hXCGZxGzjcQZSLy83NjdPc3pkT5mQtycIOcAPDbQA6IGeQkysICPjdMkDD3B70V2ucWAwAXRuw85+KlzAruJu5EwiAACBgSxDUGeHiAgWCnACI7ICXKmoATQcQ9E9jtT8N2AF/DQfAw8nzn3B/ef8OBIb+4WxuaQmzdzCHeoCh1gArMAQE0FRU44S7w9kB5lDgb0NziDMM4W/uag6GmFsgDP4o3RygKKMNMEd0+Fd/zpZOYAe4M6czGPK7R67fYRBjVoAC5WD29iAo3Bnrd33yYCeQJWLuHlx/Ha4dFOYG9fpbsgJDgVa/2wC6OHDpQcGOLiAV+b9sEBDWP5g1CA4Q4BYRFBTgAYAcASB3Sxuu3wl0PRxAfyj/gBE9+Hg5wBwAVog2QD5gKxDiA8vL2dwVBIA7uYB8vP5b8W8Ji4cHAARbwgEWIGswFOuf6AgYZPWnjDh/J7A7wIgbQT8eAPfv13++GSMYBoRBIR7/mP9xxFzaurI6Krpsf7X8H6WsLMwd4MXBKwLgEBHkBvDw8AgChIQEAD7/jqNlDv6rjv/yVYFawQAif5aLmNPfJbv+xQHmvxaEBfDvWBowBHNBAOZ/iP6GW4DbEvHG8/9M9z9c/v9Y/jvK/5Xo/1uRogsE8oee+U+D/4/e3B4M8fjLAsFcFzhiC9RhiF2A/q/pK9Cfq6sOAoJd7P9XqwI3R2yDDNQawWgOHn5Obv4/cbCzItgdBNQCwy1t/mTNn7je732DgKEgLZgz+PcNg/Di5v4fHWLJLO0Qt4gzgpp/qECIHfp3XgWoJQz4e9l4BQQB5k5O5h5YiLNGSAIALx7EVgJB7n+QGcDFCYXBES4ARI8+ACuYE9bvgxUUAHDJ/Ib+lIQAXHL/kYS4AVyK/0g8AC6l/0jCCEn7H4kXwKXzH4kPISEqgblBQFbwf1C+v9A/ifE3zMMtCOCyMHf6L0DoDwBoAfkvDBHSwsncEvSvkDzcfH/B/xsTwGUJdrKEgOxdIHCwA2JWf6sQc+RCBPl9gfxjjkgIcnQBu5pDQFAEff/GRYQBXFYQGMzpX0lFRP5C/52TFzEQ69/PAJATIpb5P7WLIEpHbBhicxAX1e9z/o8HohQI4mz/bY44Ny57cwdnOOw/EMLOHgx1cf7Hhh/ABQVZ//FgcoaYO9v80wqAywExDBgQcS//rgT4TzbEmB2cYAits4uFM+i/ukd06QSygoDc/0fBi8jiDEasx38djggiCtzFCeoMR1zt/x4LIrUL9O/+/sVRSxcnJ0Q5f1wmCAL/Lf/xdACB3EGWWEvzMEuxINuPQW1X1TKUbhxbY+irax3hcYb9YQJwxoX3XjZqGBlK046yZsAKspFUrcWSkKl5b0rWH1fj7m+6s/xXUpRz4EgK/RuyRxy5Q9en3dYgGiCMwnpCKDV8A/9nvDY1rpkRqhFT4qv5u2ChHr6dyu38+w7nndci+wB7QOEFHm0KJTxaJrtjHznSkJZ+DT5s+AYvhsDJKM1FV0Vv4h45EhxzKdsWRX7tsWEUkz789eunrIH+VL6TPNXeKsGKEEJyIs8XtNkjCYPkxPFZLxqdkRhlBS7kSpLj6MgL7EMYV1DMhFZuZ/IGWuMH+VzqrTXk/J9ovh1tcKmFCcxLZPesnFCZbmbqXnNhkzUlhNuwV6fSyzJUAZEuhtN+VvFBcnpRSWgA6LUdn3+pz8CKii8N1ZCXG6Iw3gbymWxPkl2ZRWfE8/AaJ0tAhAe/fZAV1ILQOIxYvVq+E08GEFIwEGqpoGjQ2q65jDxn5zz/0A8uM3iWCD3bbfrUNtwTegSj/0FNPO0fZ9Cesd+3F9+oInAxi6/PqiAIr1FdgHWassRmx8746uRko2q1diiJvZYWWZyki9Xnw83VxV/QVdtBStPANBQNlvGbwcMhtPtRQl25XC44+l4ioEkiWdiP++3nyaNH8toHsXg1G0+0e67RTOIsnI64eB+bqOZ0vLYxK9JgegHnjwM37Mfd7coaTvsfCc2v5Mg+6PmaEha/fZNjFX0bc75zVv8zx0CmNuw1JDypc9p3w/TDKHX764EYqrSuyYPedqbkHXfl+psL1CNCi2El0UG6oAJIWUOsQIvc1wHicD2jAXO1trOmx049o1YNZ9dPeYwuxfg3/R1Mdus1fjrcljrKygMIcJkWg0X4NirU/RhFvFCfhun7ymOfCxNgm0/WB/DOQzmRbKwasDYhUqrLJHn7RvSRTRH4tTdKDkcXvVRfCVtqqLheDHpaXqIUBCgK37pq8X35xmWgKfjJQwbWWfPj7f6O8StHSvkA9Wb0MEP+1aLTkCb8PMPYudE8Ay2FI7V73g1T3uYnFc0lMOVkLyweUvdpcqKhPabONhdy71YY9tXFzSuNZ9Q6GQpJJ4WH6c2xM8hnt4xJG2mQZ4q/SN74quEj69jwVNIoZlscF1ZM+LCFQrDlrowBhQWfe5gfB1G5Ws3BAtmsJsiy1PcPn9X6XEKkqCxZV8LoflSY+OqUbxuX2ods4s2Ci4JUSzWHpmNoVZ5EfZz5eLtDdq6D/2AKn3kllt45epw26CLN/LwUzjpaRDjzRO99lpf3xvm5to8535ZiMQQlueNU2B21nBIjfxqeoPxKprinYl2slnZZf2XyAVuuZZgoUB9Wm/rZtSX7IEGqpzbpmVhSVo3EyBdJ9qCBjaKyjjd0ZdcNyRmkUzqHaK46csmLBahZ2vI2WOQ4M9Km7NmDJCNZuO12BCTRFlaQHRmVKy0T5NLadLelDMJCoMqQsqcds+UlX6D7bRxNxJD/wcthPS2VWisv1fGSMWeK4Kc/xgyr2nIPuQ5cu2MnjHKbbxdpmtQYNHOl99OVA0MSCcRXtJhiXnwY+ezrGj/HPkBfR2ejV6QudVa7L7WW6MA6HyTkirNAKvFZi8fZZ0MJN1E6XyNBwOSILzlcd6tW2KolBEDhoV0YGtisyRJUh13OLTSzPc2AE4wZpPQ0/yZBx/MD4emp1Xev0EkTfHmbL2fim3le6enrsskje/M+N10DS9nOvFZMcqceK+vVpoOVM5XSxpY0wok1UAohR6ZA8u1HqmF3/HaaEqSHD2XS83qAhOIvEPavWjCFyBXSbTvk2XRWHKHHjihGy7ToB5c3jeOLuzNvYK0BJgv0jYxRW6cnxvjCkOTYzUbnZsagoG6uuPV1E5YMWt2tPj9z7FPktfIfS5O1ayT+7S/pcsVid5PvXEyQXoZjyQicpAvKSuB/znOFfk7X3XhckR5bM9FGFzx1WUJozIU+xuHr4xU2NTAknTOTwhZJlQHGeXRfYmk+7gAd+rxRN/NtzqsUW6MkAOmnDYPzden3o2wAUvPCmkY+ScDmVrn+8ub4YGCneUmG8dmu1Bz7U+TDEwt4LiQxzQZPMP6XZphrj2kRus730VqRuJcAExStQ1pth0vM7jjKy2Ynwcu2brGC2tcpu50Dm66LNCJ2Tz3wY0jqlRM8HMQo5IcWNgCJtuSHPFmTokc6QcblXzMtPJXkpOgYArxBXwvcaHdrUsYEo0cKdLR26bcbm0V/tc+yDLXHKDxf3C0AMpX3bnVScjQ3PP3Ei8RF2Q9gOI7xCV3qpIxa/EL61H4kuOr1HUVfnhqO8ETX2VK05chp/QSMaAm5PCJTTblgtkX1y9lpQzZL2jmGYzbbwmdLQ4/k/rHWgpzMR1iEpuqjUfdmHNK1GNwE+uj3lExnWRM1kxcl8JfFQ/m4QKmvNQbEqITMgYaBwX2Xdhqv6g27DhUXxXdlfWpTvtvVfu7FWNU5ZF6emzDhXWTA40aWubs/sA9qRYoax/0O8bYPhM18OijhJ9feaiGuCMr1eEfonRi77PqM4Cfb27j8HJq+JIV6y5jZj0soCeoD+wtcFRfUJPvwxWCUO2VQRsiSllzdd1eNHzt0SoI+TwsPsH9g5GQb1/PkypDGdUUlsXnvVDNvNuvPHa9GKBWXQ4QCVK0oXxF5rjjlmB5K3CrReTGV8o+EGKgDCpk7yXqTa0J+kntyfaltWPKcVut9HeTdHC7/iGaAnwP3eo1b4dChIYLi9cubplOZwbXQ8sdhtZ/WyBebZDaTmrvb4ZCOmnHl+1wjJF1/d+dx9TzqYK1w1rr3IFBJbKFVYQeSm91yKBnNlTIPi9Dj4lgwXfRk788Aem7qxQf/urJefSlwJwFBuOfpjvBAv3dngSTwl62Hbka0f2+9vv3gduoauWPRqLXcMHn8afzPxa6WjEcinmitW7phciu+XH3wty3x/WkhaEUwatbDVJGoAaeJOZXIXJhtld6h6gWhu21uCycmO/rYFZsFT/X8x+OnvSqZEHm1n9qvUfj8Lxu0OKCws7aTdibxScP2VXQQxwEP1xVOVI6QfxSrgsDzVGDocT4sJsXLWK5Ukv4BRRHT05p2Moz1A6wFaQbNNmKWQ+PW/3CsYKwCdiIc26/qpm39Ndohi5XHhBAngHM+IuzdeM9+zVM1/fC3Z59+3V3ZYxNBzMexjRmIDjjpCs3iWktMK7UbwVS+FJsOXRf9MTTDGBjOCcgCHpwGgbEoVNNjlSzH21R5urmHSIUXQB7VnDy7srLRCNuvvmV5jymh3J6kpUqV1GvPHMMbY/fNS15pf2fwDJohe/GxedEgnyKvhw+u4sHd1/GBE3XmeXi/UeLzfOpA+ecKswEJDg5Viqxlxec1K2muP500Z4DjUCHok3z8aRQrkUDWKsoDA9Hzjh3JwbYMUdaxFTVqK+SxaOJekQRG8wRbMTZqL+JW7lBMlsllmQTr3mpV1j1/A9Ue5M/6DUyeS69PR2Wol1kwoT8uJp7Uzum2JeH3psd9VhtfxXmx//Pku4k8uPjeqPBI1Fn83Zcm3h7aY6IVvdAb70yklOfo1wL6QftWU3oRe45a/j6Xd9PeEr+iMhC/Z3oENL21etF3nTNEjotoYnw2txJZsajZrB1k/Nir2bslvG3LeAXw5YYChDLP554m/Eypqk9dpMLz5fzOpVhMSTnUPMKdlwLXyVpriUDmpKNyAz21oSDvxNHyew/anLd3OPnxIhRWJ11ds57i/8v0aE9m2pevaF2b+HunLI3+2Vv6IxGFvBaSOFsJrEhjKUDZ7jsvN7QqU1wBvssCxR/WM521/O9k03c3Mw9kissI1todP04cjeA+W+1ameKvx4igCc2Pm/zBU6llRTaLWX7KPv+yP0fwbKMNjeq9U36RUfzXblezb0/b6mtuo0nPOn3dL5Xof+VQBhXsTXD65DaPPxlNaiwm/TLRoTiWnmRZXZMRHyBBVtFtRUv66BnrSHspJzrXjht6X7iOcAuz9NJSZIYntmVTu3SvBqOKfWr21w+SBrSf7JNBx8PS4xASnFe/6Kb15ZR3r94Jz9qRzsWjiXtcZWm4YJsnYewVFLf3cxYO/ESGa9QV9IVBeD/iNdnJqBiHspCqX+H6NrzM2/5lmLIEracIiEZHT4+dYyxmNYmhHoxnurOURsJdJ7tjbBzyFl8d789PveygmaI4rCb4tbdUypZkiVPh1yCe+9aDyf7AWTxd77u/iAOtaQdykXbb8vkmU6FGplLdzzW0NEkc5P7isS5b4zoC6CmfgeSLXE+pJflDPwmNq+BH7RZQbOpoL0dSC0NmSbolYdbdyI6hMbtiD74r2HrEFdIXgN9puPJGD3asvNt685Vxvt/Tdso44n3gey+FBOLhgt5IN/8l8rV0fE56Yv21HRsKeNaFpx29nR29Mf9+FTHqkaVVU+5NFV6rHOqrFOmTkdNbvncXqQ7PINpV7xO1t9PDUS3Wiom5HE4f1Z++p9JNdOxQnBJGNX2Yk8FP6ITuE2rPYvzKs1eN8WL0FBOf68xG97c/2TACC41OkNTtZ304ZmIluXc08NN9BD2Bi1PORdmdcD2WKuG1J2fJHpmwOmuuOkxeW8+78njOvFFlxrDsVUqJV0Tbpk2TxcXN6Lzq3nxmtSNbxlY+5xT5U2H1pxF8atlaU5UqWvsD/Q39Rwkiim8BC1lSe0BpPksWPGO+UooUnL05qjkkBlZ1H2WqXor07wXZYnLAD2tpaENODu3k1TxEtvc5ivgUeJ1hlsHO1PDkQyWmy3W5NyktDiyoFSwX6A9Zps8y8PNYauaYm28yC+qfqKu3YNXXBmE7DrgyNlQNX8qtbvFpIDWovXauddPVcvGKJ3Fua5movtLKjmTjeFdRkqsXmVqjeK53pUXXqjoeEsJW1jZt2AESIpTiLmNOvngMBlJSZqjViVnbYYh0EzAbqnDERUHMKs9/iJnNEe9sy0w+Vhatq3y/ZqhMbcGTWL/eqjzo1eQWOKhjYjiVQY7DjvWeXrF4NkVZfulEn0LK4QTDYyq1mUhrhUFRojWgZ3BANhT9YukiV0l1Ucum9q1GnVETqsqw6V6Yn9zjI10Sz5VpK8wdTPGZlKSoO/9HZvyIp7ICz/wLprb7ta/0Zj46mOi4ARjLvm+K/fDXNXjtrPLMT2EuL4T43vni7dWduK7tdZ75qn5+5ZWlPrPCJ7UQG12ifmJ8LfqKXIwI3/VZXDBeGprBuhhakOyzBMzwIgPCWE6tNRUxqRxa3fwwAOeTRmeSA5QLPePNysAEiSmDiIcn9wkCdV0Rfk5+6nBJD4mqENYNfws5lI63Jtjt8H5KGaeaXAhTw1V/YfLd6aM3Z5eLOUNUav7I+4G5WomMQnA4LvKFi5KnmKo1//LpVDSNWKMLC933WrGbLy+TU3pc20eO7s7l3tZKdb4IbOVKUKQT1OOs5pbcQUc+Y/uiqj0TxE1ONzxTNiWx73/s/H3lMNAd9sJ4baGUdtTtTlDzK6dPZLYffH/EVwD/KE9/faSklq0cv6CxvKLrjlooolbPOSCjcr5kwGNna7gvEUM3J0pO4E7JyXpdD70PT2CUPjY0NYrRuzq2vjPi8dvkr0qlm4vC2al72LlV6OquW6vMtqTwosCNVQD28UQe9Pgw03jcuF8befQVWbDmYjxFmD0OZ9SHmIojTB2jDtLJNJiGuO3z7ccPTAE7fW08FYSLOF1iPs9wj4LfqPu+s5xnFl0ExdZIXQSHbBC+TuenUQ2zDkUt6d/N7ynZwSWUjsrSLU8CzDGZX9AxDKh6m2mr+nzL+7Rx4iIfkpUYjuNzyuh33ycswnk/X9S6Y0HhKmvJ5qvQySb1dEigMIwRInJm0KwlOQm1PsNUUdwRleqBJy3Gob/U0xy3HMCx5ZWzNyr4VqYpJOlv9+rGriCvpB15xKCKKfDjD+pCkixc4h5xvaKPCxqfhdVgQNvPzgs4fHihkk/Tn1NSHKdMwZkMkoHEImwKQjVHGpvoj9OBUcJe/cYnUfbN77hCyZ8MHONlty7EEBcAKVzJIw2RQ9ANUy4enrnBHAjPvqfTpiv0ab0TUpSCkU302WrhiqyfVNBldlsnyka8ixrvX9L6EXMmJmuiHatZksh33ckq+4SvNtAF2opC37iqMoCz9yvHVBMd9GA2i/Usjb5r1SzAh0OEWJurr2BLvRbkly212D04tDddXf+IBu/AZhPFOKLQ4qgT8/57616Z6R6v+NTVWMoAGyreAZyqp7TaaCKrJoz8Re5DiaZ0PoN6vErAyndseGH9oit6YMjcG4pmLouNqd4TdX1F4mFugV/jbHYqW7yvRCStUnxKGgF4k1d9DWd88NCGsl8tubalIYW93nRFddtnP+LmsXIGh4yIZ9QibHKQYwwJiGtZFmECQnbEqw+Jzxl8MZQT3iCxl7uPe585Vzm7HxYoZ4srquJvbIYuoo0d2hQ9aX4tOEkWjGTPvjJl85Lj1WuCWByU8RdkTI3Vl8MSxZtf3DS23tWaQ0pFw+mvNAVWtTaD3HpNJ8MVuCNoVVf4widH6Ey2pq6tHR9EuGusFAaYxRzoJB6GrSwXODPtlJwP5+RG9v03IjA1r4/3zQiGHxc5JKR5Na0WqwiSyqWo3suhcLlVv7Mnwij7Mvxylr5S+5ZmSk7tLO80vonEPc0N81tHIXWwYtNKJhoj9lu6oE2FRMmxEO8GMTNU3P6nrrcMV8+Wq3SNDi3n0Y1/BoCQI+iUUvUobKNXy9CDPCKO4K7uc6IuQO14EpfXHqZEH3WBdImlHyzYdro1Q4wXIVXu35gE3U42BA0+yuF1yqvSaGKQd4daZTJbS7sZWxXqp4/0GO35vt2P4lG8/k5jgivD51qOObiumek6XUXVwPvI9SmW582WbtPKzQj30lcii+cXmY+MMj09JhLm6FFBPOWf7eYEUoE6MgdmzP3B0l5xEmgeryQ7j8I2JW/iq3Y8pLf7fzXrrd08/uMvhV5lsUFLr5QjcQ9ZC6ESCC2je2zLEZp0Sejsz+THHMBc1M/tLpt7b5B6xpC36dJ8L5lcfgx7E9NKfua75a6LJu1t+2AyV5lExjKXzuHVqBnbXK05f8OLp1PcU4fr/ml3//GzMO3+x2dustaT8ett9y/vW7Mz6RZKIZkWRxeeYs01aSoec28UFtC6R/bCHZyK7dSfNkqjS/eAGLve8yWnQXqHw+EvL7f7GKj9D1l2uRx0Bq9WlrpK6ch4612w0hRACQJXbH1L1c5Fr0+YKB4T1TQXnad927EI0/5IcXZU/RJCkGPbfwGII6fwxHAlR8Jb8Vt/y8BJqOr5RoMoNyJo527jkkRhD4N9A6TPO40voU4KI2Qh4vB/YTmo/y1fdJpCb4l3qklyiCds4VMiHW6n9quW94Ga9+dT8fysmp63LZyWTae2lkllhox3q18WFycIYw/2JPaFCJIMydLrLq4+He2xywhAh1VWFSOSubKk7kYj0tewjc2dOes0uG4wd9jVLUG3yW6vavO4p583GQ8vlc7d8bllGUdkxfAmlnXFz1uNFF/1Q5k2Tdsw1V2U27E099UjIXJCKzaVGJhSDt5moMvz+AzmxJaZTHLiRm9W5F0CL2qg9Y+Pvsr9WgQa8uSGlWq72mV1OZmHJEckZLI4nSae/TcsRkxyxRXXasmx/Wbz5cBX9f0spRZNTyX5oa5v9Df1IZqYDbsNqXmiszJprNxyevLh2Zn9DxFksWxR7yfInzQthpHb72LuD4aR7tNSH1G1HplLrjSLURqDu+d6SldxUtB0ph+YdnQIN5rdjVg2BNAoRRoMWi/nFwK79wXFIhj4lBMzXrChozmaGT0DOLQzBrEH3GacPfLKmmfIlLs/FHwUP/5JSL7jlpzn61v2b1vSH9JKA88dgRJW6s8SZ08ihSvp2AqsNiddMOw1ITaDecB7lciI3XGHiOKjtBYq2iF97UNQYEjKVWVHe4Me7eQjVORXxlW/AsXVDbuTyNl85DBWdzxVSaaCopX1PbYSusue1uPNVxgGgaVCaTvslHdpgw77MdPZalHS10rZvmd4PjZYmL775nTnSSsycJtJh8apVE/p8fGkvFYWd1hb4tpzIEqiADWqpXJNiFOFjvc85jFXR4LECOh7+vcvQdl00nEBp/OeSEPAN3GtByHRE0961/3xzsXHefkOL4kTBGKMoJXY8qKmAf4ByZ0u+Lb7P6hrviUcQ5Y8lGqQuauCMbYpUBmZD8dCQu0o4M2hzrQ8kdRsioLyP0ooPvfk4vDd0ZhT9nbbn2ewA3I66rIcXAKwdFoGfJ2OCnoPtD89MGChq/70hMWgHmt+2i8MRa3/BEb6IseKnN1yyX1lBJX8+NRXXXN8FCi45ShqXqE1gWOSMRfDYTm8wSTEtRKNquHcLk06JAq+WWBszG5Q7fNWiiccT4SWrd6E0Rt87UbNCCZQcWArQOmGD46IK8THJMj4Fji0So/feJC12n7ahw0tc79NGmRUkOTItzWXMOh0SVTBPPfxC0zxw0XDt36wvl9NLQy87dLzPBQ4nzvFObU0/TjybV7hcQTKRJJ3xcC7JkyTH6llgOnKL1zbTsRdw1eTnNu1vW1h56tMTT6qyDNfvcZP4uZzQ8DSw+PM68ELp8QvwqRPS6FcugO68/zsj6LYEtjIFBZta+9HYOsjcVGZGEOyelcbYrvMy3FopVJzLYnXr2cOiATdmIQb3haTVexQOmF5tODsaNyMSVwOX6YrWpRROekJNBrtcfJ5pBnPwswwlwbQPqZiq/jnFvsRbbQN7n4TIR5LNzSrK4peH/7ZI+rz6KJUPTms4iSulhq/JuoBTCO2TwmW94YkkPreyfp59xKRCKuIJb8WaMRE2psrS1w+3kgkpjxgtzo6iBdJnC1WvIhS3S1zS7suRyGAFItvEZhPL9whjT+gzLLgfBVnT/LC/DTX9ixfsfv2JWVL17arg2Y6K8QwAQeFvRctp1PfFiqUqGQHDI9+KMxT1ubsLbLAPjDvS/4AuiWErX7yVwNWQa/35YQ2YacnVERNxxVYYCGmoZyMV8g4eJF8bJhKhtOeHdtG+C/8gvdfC30VsYrbcDmO2lZP5KfR+aAF7UDiR537QHTqh9nwXEPwK1n5W4ygbdKP70tQBQUdF+mBtR8kPCaqlpRyyMmqAtb16mglS7mVFi6XOLY+u6S3J/Ke8Xy77RccX9plv2GkFy16PpU6YdxADruIn4+WgYc9l31GIK5pu8LN3xS6KLjwnn/yl99SAAFFcmZqJOuCyaNab5fhB/QJwvaNx7y51No9qM91rmIjH8X1JHzLnDKI1pT1JCie3iHzSuWt0RvhRwkPPsVkalZ6TLM2PtyA7690pP96NrEAPanC5O1NWVtc/iIJJdpoJ970HemhNEg8PGNmuqDQNW3lIrnETrg+tYD8h0jDA0sAfOSLHWZ2vBAPzgjAM0CyiZyT+wRdrL7u7IfomHKl5PMzQQzgVqvRWFnTF6a43UU3yJe0gpN2haXZz2XDdZmqE17juC1Grd3aoDE/TyaWI4YzsLRCoz67FX2C+jtBPbNbvJpjn4kAdO+eAiP9nvMGYeFQUwPvfVpUzqaPd1LLfGY8uwO59t2ORLfaH7QVelm/0YGdZG3IWtuDaSXks6rTH8ntrUG/WWIrDC458W1PCf04KoBjouB8F6ouiHmABWY94fRd/aWOf/shu39qljkC+ZSS6zuDG2bq0cXwFRgtxZw++FD6hEhfBGeBl/lt/3g7U25rEgglqxEglMmfsDTXGCJlMJL65NGTpLy1mCuJTPNNwtZxHEFCAIXPdE0qPIFBM4zMZr9m0WkE9/j2i1yFkTsHmxUN2UDSxLfPRXKRflJ6a7lEXZ8vXzb4xBwUpjj4v/0wq1G1LM6az7LjSGAtb0Vq6/CDWDWBqcz4Xd3bJnONjOWj9yIFJDupXLS2kkb68CubCBqVan6sIe3p8vwkZuVl/1nJmI0LTBITMWxHJwP81GLjQUk8heh1/FwzOdQfqV3V7nyTT6ctma7GVX/h1vGJTOM0vFo8DoVEbkT7MtbVcFHWkVHqhhXaOS3plZ23ntUfT6rgSPfcqP4c+KHNqAxuU+LSc6kRbH7tMX2ujfy2Y0nUix5l510mmDesP+qNNUu+AytP5+NStVN6W6kILP4jBgPTki+nYlR1jpXpCxLvlgxwZTHstiavvhsTDIDp7aw1yhiYRK+ARvgDYvWgO2HHNjH4876PDjHfLylnvk1rmMzZa31o4OHhFUvQsytB8z2N+PwNlPq0lWMQOZ50OufI1PCsdlwBP9MLMqMknKmQ/wpHOmDNHxilFxcxYnh9LzLjmmot8xEb+HZBLmI7RjLobOH1q6SDdHR2ulbju+lVzr7vhm88E/tbanDScidfBZY9Q+scwVbvNvUnOP5QDFuL8GqgWu1Vrk/FLqywRQ5eZHS8JlR1fkc3gcYQx+6aNnnDrvymBJt76oV75uNvUlXEJiPgKHLL2SwcRyk9G6Jvd7ZK1XsrLet2ww6L0lr1EuVm1NANkT20Bxkp3q9fNJRIHr49NKpcdGdkZ1s5ie4tA6egbaWRr4XmLcRNYYS5MU93u2apgkNv6hYANbaQ0YT1wQq/dHDoI0nDEeH1YuwbPg3QC7lkQCr+Jkd/fWMLaDOkTaSRHfcDukiMoN3ngzTHGdZfUZBaHRJx8Yqhlb7MbozXn9XLRYnNMkq1LLC5qkeN2cuZxSl0LXLTMar8Db3RsTbCr5jebQRXDcQE7fF0LUWtWlN7eUC+G6llLI3arpvx1lo/6locWnz27H70KymJLjHxG2QfjOntRSz3TDG+gKr9alLv+JfZYt0rXU5DVNrfdAFVAWyjSvO/DuduCKSh2QGOtBj06u4pmnS3hi76buYo0saAswYFOhjD64XQ41z6JiCNk2gkQNnl076ni8cc5vkkCb+juLpS7pKXO6Xpo5gv9OUXZ9QlvaO4LhXhgokYoLUpIklCXu6Ax5c5FGM8DSWD+dwTYC3fQ8MZE0vLQZlI1YluJskDzFZhsV1tzP4P/WtUXMWSJ3qXpRv2tjknpm/u1hgaWz8zJyvU9DOuC9VFZk9jHBTQHH8KPB6fT+/2Puyg+8H83Fld6KoOyFHZn6z4Hlnk49fjIvaIm/LpdbEG0ju38pdAMj7ir0I6jcG85J+wnxWdZ+5776Xy9hXb1LmQAvnemfPjXXKhDM+R8gpSqIWh3XR1cD50vv6YeNqd1J7TauJXhPexHWj8g7BcwPb1QzFKq8rnBnUUNuezYc73Em9yvJBy7sgy37yI4aWQPNQDMZ5vnQCMbY/9MlzRy5lImbDCPbSf+jcP0ha1qD/1dzY8qtGn/W45ktLnr0G0iBLmZL62ElpFz75fakliVFIvHIJmB2cXqaC5uMQfxso90L4sOCUdAzk5qLcO7R5s58A/ZyItnRSHyUVC04pzJKIMAQFihigKAZWOFvW77PnRVj14oYLK+WG8uUFxSPcooTvEgl+6xsT7HZG9Fz3wsw2vjfzrFtoOTTbzI7jOt2zwwtNJAIfTXNe56MQcRtRpmISao5mT+iWHnAT40IQR+xocp7SnFbnHSOtDldYl0+tbA+b7L+f8sxae/hSmd9dp0Z+z9HmDq0ZCr1Yn4VeVXRdOBsCUphy9WRso1VGOkUzGitFylVfucAgtPR7qU7SOPRzMSl6eT0gwjzEcNjZYY4SnCe6yMHo8xGxplvS9vPLaSC3OW07dLhbPqrE3TFSUT5t2WdF/sQ9w2RF/WIi+DD+PUAyPGMAMlk5OtTl4azHVUCneV7+YY87K0Cw1Xy+h41Qa0uD8uC/2II7kxxlUiO8AOua5eVdaPNFqVviRCXVKzM+GsopfJia8afjm+v7z/HU3M0fNw3cvsqg1lynLceHmnmY5KS2tlfx0fXGOPXSsskya8is/BugugSfOTxCRdFxVGonWG7K6nrfXX+TPiFt0ry8M7WhLji7UQQKnkz5LcEyqGCqubZ5kk5krIVz9vZ/pUx5MT0U+8jC9f0OXHPfpqzPa88v5HL2EjtDkWCVWBhmcybfBnpStYcNdE5uzSL/kdg03mmAui0vkz4YWZc3N5usJ/d+ZX2FUL3btMbdj1U+R5xrlN0UvIyfrPlnmfJTiYDM+OaLKmtTAgLHHfX6REXlrbCXV+nVhm5PyR2QE72AK8YwDJy2wYjGKCpNvocLZyX5Yg+ilANG2uugyqIERF2fVeq5Pr5vSTWXAqlong4RaxNNyNWlU9TxKqgJ1KNYNmOddmCCc2BjRnRfzcnfY9VW4bA+je7qTwOw6rp+05arh+GpiCdIzGBXqcKzCo/fiVCHxWUOj3HWUHxilzIKlP84D8dN0ntN7b8i+024n+aU4sz5+pvW8aUm+b2K/ifkNsMl0NkYFm7rmgzeE+8n2u5VJfi7o63fmLZolvTu2h749jPUfgVA7d1rpLKkifvkssftCYrKfcYneMSPSec+rTdbiNnT0kF7mvGff+YosbRtyr2JZSPBIZ8fSFB1ng/tNmQeeFsW3I+1u+HFKVupTW/PF2Po3zOvh15hToFPi6J/ryJi7+gS/gNp394cUX3sy9HN4MOxornPHnDstrvOXcotF/bFCdYu7zOyzwvE6sGzqQj8VgZe6Kz1V7nBRzBlIg5Y1LT2fMyKNM/uMnWU6WLrhvzBP/EpYZzC/fY4ZHzZktpUqL40tdA1zLVxU0jBc4jmv+YnyYqxV1l/i08taBuSp5hdiZVQmjETNWQ4oFKlHykQ+OGeKBF3vjxODaKV0WXPZ3kGjCvCM3/NtBM1n76laoXwLKzzBCCv3Sco4LtrkrNUxqA9tO34Tp+JDfEydhMe86K02dF3pPKyUv4zn8A3fTyBUl3Osp6p8C6uOjUbM55MRFwtep2SELBzcP9Jsu3EvpFmt2n0qT1+d6nixWl8UnDOgx3TZvzLckEd3R5DSmT0Kmq1sXr3dxlstnXguJn26u/1NESveInB+tpwoYISnL/QtWHBvRWd5vaAEBZJH3EYwW9F2ZL/gFaHOX/wyo/5TkNCAE2rgCyFW8iZmPbzQNmLOEI7TrYarogxsh052S7C2ib2w73GwsVvkZGV3DGRNUAfLyTq6mOsF3pqKp7Lf0dzIyHdbw2dOqEvGdgaHuiGSnmHlGa6/UFvwb5b1LgUGJ9+qtTFPIFm5TbWpsI/3f+h6dzOqyoLyxPEeeGXTCUcf+9ARjAJ7qexF+ylN30BziFP0XqyKdogVnLjHTF5EZrBSjU06OTGuH9lIU9nVeMS41R54+yLILr+a5dBOU+0KUFrWOqiRP7dKpD2CdBl5Oyw5qBMt4pgP5pg20wkfqhrfFimxZRR8mAqsZb9KbNoQ+dx/XBZP6CddU2yUY9Xt/wE5yzD7moo3YRPLYmivh/jwhNKceHI39ZJulOiK+/n31/Jo6hW3p4sN+JTeIx2YaVxALb23+8Ff9iPm0kPKKpWLXYIZT7mN6sOR1rFcr1NLX9VQGr3afNLE4sTVbLwFGnP5GDfbPoSffYqTlCmSLPQ1CX5GQg70Mbt1gkFzFeYmK0L2HimHjHddlyi10DDzTIVY6/u6sIZhgIlYenUHhlAnU6b9QY9bud5FqD8TnLLas/2yMXLMqIDV+dhD8v8AiFtheQplbmRzdHJlYW0KZW5kb2JqCjE0ODMgMCBvYmoKPDwKL0xlbmd0aDEgMTQxNgovTGVuZ3RoMiA2MzcxCi9MZW5ndGgzIDAKL0xlbmd0aCA3MzQ5ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o12B1STW9MuTXqVJgISqtSQ0DtSpXdBikIgAQIpQEIHQaU36YJ0EFGpUqRX6U2QjtJUOiodpP5Rz/nOf75717p3vWvlffczz8ye2fPMXuHlNDIVUYGi7WGaaBRWBAwEyQHU9E0tpQAgkDgQBBIj5+U1g2MRsL9gcl5zmAcGjkbJ/S+CmgcMgsVh6hAsjqePRgF0PBEAsDgALCUHlpYDgQBiIJDs30S0hxxAHeIFhwL0gQAdNAqGIedVQ7v5esCdnLG4bf7+BPA7CADAsrLSwr/dASpImAfcAYIC6EOwzjAkbkcHCAJginaAw7C+/wrBr+CMxbrJiYp6e3sDIUgMEO3hpCQgDPCGY50BJjAMzMMLBgX8KhhgAEHC/lQGJOcFmDnDMX9wU7Qj1hviAQPgAATcAYbC4Dw8UVCYBwC3OcBUWw9g6AZD/SHr/SEIA/46GwAYCP5PuL+8fwWCo347Qxwc0Eg3CMoXjnICOMIRMIChph4Q64MVBkBQ0F9ECAKDxvlDvCBwBMQeR/idOQSgqWIMgOAK/Ks8jIMH3A2LAWLgiF8liv4KgztlDRRUDY1EwlBYDPmv/NThHjAH3LH7iv7prCsK7Y3y/2vhCEdBHX8VAfV0E72Dgrt7wrTV/6LgIPJ/MCcYFiAJkpWSkpAGwNwBMB8HZ9Ff4c183WC/jeBfMK6CQH83tBvAEVcELBDuCMO9yP0xEC8YAOvhCQv0/9+Gf6/IwWAAFO6ABdjDnOAo8n+i42CY4581rvkecB+ANQinPTAA9Ov5z9c9nLygaBTC9x/67/6KGmpbaZhrCf2p+D82VVW0D8BfRAIgIishg5OrmCxAWkYKEPjvKEYQ+F9ZgP5x1UY5ogGyf5LFndLfCXv91X/+v2ZDAPDvWAZonGhhAP5/NG4DkgQ54H7A/99K/+3yfxP4ryj/L43/d0KangjEbzP/b/v/YYYg4Qjfvwg4zXpicfrXR+OmAPXfVAvYn5nVh0Hhnsj/tmpjIbg5UEE54bQsApYAgiT+4HCMJtwHBjWCYx2c/yjmD37n16Qh4CiYERoD/3W14LxAoP+y4cbLwRV3fWBwsvxtguGm59/7aqAc0NBfYyYmKQWAeHhAfMlBODWJSUoC/MG4eYTCfH4LGSAKRKGxOBcArsZAgCPag/xXW3FxRWEI2K9R+mX4jYmBAaJOv+4xmAfM3RPXl78NOA2JunnAcWf1C/lXHg6eHh64GL/lgkvy7/Xv2YfBfGAO5DOTaAf5EJfKkKbjChVWb5HlIeKFpZbIeMuuCEks31Suv7MeybPbH9xV7aCl1waeGk2/ChudDGAV/H487GPTnvlwPlUrB4un0fVFdVskr+/nXrsT7AYUfd3pvfTTyC90PxKM2WnsrImsbyZZTJ6FSneIr5atPD9vwaxayW4CkIAXh7ScqazYWJXslk2CaEtOniVsv6UNbdxVD+s0TzPtO+/PCaLhcUeqTTEsP32/WMel98/N9WZ2dz0V383XfVcuVRpGz8Lgp8OZPZDYw8KYkKlTi8HjU5U8VHuVEs/FUogM45snRFaOg9nYeOI6WUBIQBGby4AKPAI/0UMrV/HGlkuZYpNCwiBy8uW+X8r6+kgWYoEh8t1seqiVw3mht/89ekPrfYJlAJNam7I1adoosyWcSbOce+fYrzTI7SAVL2O0i/Zyk7Ye8povHjtzHk+HwgAfKTwYqhkwJPbzbDEIr2wnJtPNXHW2XRo9NL/cv9kw607suMyX4f1S0AdVtYG16iybvJpRt/32OYEU9wP61CaT4twV5PHtpeQ2NmAeRJ8qJbq2IaDHZp6Qn2zdHDw34Qx8cKVoTz1mWOtQ/yJ0QpokU9lKUvFVio2ufpqBdUnlC4zUy4W1u3I0ESPNLyVDc4e0lxRU3zbaP71JpcEUbtjakNbSJMjRJKa2JU+6pswb4NH62PdG0bd0bUH2z0TgUdO42NIU1y7qDGzlk81oU0/Kg1dCBLJ5IxSLKVLayEfW1HkirsTARxYk0bRJu3LDnwfKX7FUUn/P5HTcx7uasiWr38ssjAmH7l9rLYbcYeOZ1+LT21qybZtUuBbRBxE7De6il2f+JnAgIzzOKyf55nuhwVfKTZS1+SRdrWpk3FfCZuXHEoziHNWu6sIlrK0r7B2IjV0P5GDHxtDu7kqT7Gkab+l5gkluzoAVYb7Cor2hX5BEWYW5YGR3y/CNktvsfq23uewyl0AHGU1cY55byW4PaAUe3rxKTq6yAGPb6dnW18BPDxenJyB0iJ+N19VYUKH10GRoGa8kpi59q3GWtgje3Gtc4KeUKT2h7WW4kF0cGG2qKJq8CSZnK/x0Ayzqq7r8Kdjf1hkaenbvTamZG36Ub1pvon3KmcmSIDfwdpIW7Ve6CJDKs008AjJ6a1tnen585v53JmeRbv70nNuUd3SY1NcsSZcvR/i1PHpYWZvGdE8C7Rgk75bKpXcLuUQ0c4hLlb66Ql+/o9jjOhrSbsHkWfVN4i6B849V91EEVsuDdKm+ao1d191Fiyrg1B754mdO2BkSqRSYt92TRSGflBbWUTergDH+3Pi2EqqelRPCWX8xyk1JVpERpdJcwpcn+/jQoGJmI4xTMvNpoo2qlqBW2CpwTvyELqqDlxLv8nW009sXhluPJ7IUdO2NZ56YZzTucGwYH/jogx49+1rzI5d2JluPphzRS7t/0Lc1HDzHSdEgatmyWqGw7iEQp5UHVXlRTSEvPlmwWRxlTnXxLlWbHRn2PE6VM/DHEWGIYuLJSnu0l63Es0okZmlFurhkaf3KCAHLmH6IvbgtoKwQwzTK/DGSkvZGNdwGfGkEZfW409N437MDPOux/aNeeD0iSmFj+t2jDAo47Q3fu6cW2Pupn4004iGPuAow7BlahDEOVKsOb1ybsAOnHKtGsls7A6ndUGBsSe2MNpan5x6F7rHS+lLU6rGMPeLjFbb1/bJPUVL65VssfLp5dzDAWAqBuFdGUjMaT4gnnrrnP+mTRMOIWLSai8IOuzBT4AE/1inJk5CHEF1Q7Qz2TKZzi/2GerUD4aNRdf5M0jS3YOGQrSeTwcydw22fqpnD88SljmzeKjxY8I857yMXlqyQJN4uUAoKGHjM1tWXfJvhk95l4lKZlPqGz5nrg6CE8olYmq9hAoncU4JdpskEYPUfLu8enDaOz1JZZ/jcomGtaCcXK0i5JjSYw29vUR7LFT3D/YxsI5/hC0IphwosQdg39dwfqcb34a3KimQc3INMLiFSwP/7yY+1glOnergtk5B+2IEx6T2Vvtucwyps++RN1zsGsz78UK8cyPkkIxF4jUtp+7XqQ6TCGvR4+yIQaBn2rQhRAARHj+uqgUOPVTbmd/Ef17FlLksUCsI07wYnK006xgVLCLjKUKSMnaVcNYjjsZM+ffs07T5NciG9nuNl3tSJXHL0U7vXiKKrFJ2PjKGK1zh7mrJt3dw/pUX5XBAc1tdmks3YsEavUVezydL5v+Qy/CDpL9uzIFNyTC+c7z2kwcdihv8CvdKlrX0/iXiJn4n1aF2bkfLLZjzZroGzncD04q0L+4AgqmD824m2nqQNuwcBOSrGXJ8feqcRo+v0SMAirbXXMnX4Yj+38gz/YIMzmOC/C8pbXdXSG/carzsMP+zpMlditWk5WA8HE9mAiEqpRBU++GptBUgtFN3yT88674yRr2f+hv/ZX5OKT4COEQTqF1GX5LnfVdmWPiE4Jqizy1NnK5O1W8h9dEOcnaweXhRK21XPxL6UaJai4TBBUSpcG2aPX9Fs7pua7+Y6IpghBJEuMWqu3MJXbOA2H8G7RnoQrsLIyzg0b5PTTs8WL8Y6P3A6w7anACq854cWato8Opusba4DHX5Y8+y7lLO+KM2/wlZXk6C9qP/1xECVsKz8TRL9u7epc7eNO/rXv+eFVDZGWE5CLGoi3AN0J99KH65rkpZwnoPuX4R+ZZdvG+LjDiy5HBwm1S6y6nvOsEvbHsucQzXQei0sscNBd9pYo7l4r4uUrbQxmEh2oaA/vHaBnoabL/bIm9Jmb8AvEC1ruCtz2m/FjtFUytZ8F6vwPeBTdlskvuns3U8SmxfjT88s+tiUAo4Hpj1H0r6Y7y5ukMhYoAtbJmQsUl2rBmKgTTHyknxFdj5TiftoFkietBWSujw7c1/IKIM8eT2JRJF6xpmYmlzxpS7kcYS1VYN7X7I7iWa4ww0VXq3pXMK3UIcKqcaHwhv91wl7KkI/ModGbfsczXfXbhTEmrlwI/JzR6rN7MKLYhOec9TsFvMzDFnc28VA9xkobHUNTlnnvIY0pK/WkNlX8HEWBEckfYWQriqCPfXw/VboErlahQ7SCsR8CdsL7bIVeYE+T+pHjxEn+k7dj2tYbs8Gjs2I3eLJ3lcOeSTjuL77LWORlKFqzyPYqZFhInahI/VxpGU1Z5Td66AjYgVkSoMnHl44U97PwvvPiGTnK4lA3K0FLhSds4Y8bQOobLe827Wyu69rKLmCXpYEJIufFgcS8xdqEVpnhbkQzpXZ5wM9Zn6u1FzH45i16Ca00rktye6Qsu3oKV/rxi2aU9/BMLn80kme5rp9WJizwGY6wPEbZb0dQ4pxul9+UczpIEoL3anwao0I/bVs7m6hRejRDOHOpXFblcKcEwej1eKUQcteuH4tUR0nxwSnutSj4jXe827f63Mu+evu97pisOW6OfSQHYJZI3quRjqmNcvHS2oRZvyvQg7kaZ/kR7UEej/rOOlP8Z4auhsxaCOUDKsgfs1TU6WuZlESVH6FS63CbP9L7zZWQeLgZusUSXI/AoqnPLBDPl0UxbymLsZZJ4RepaFUUIptGN/JO4v/Nh5y1Y8O+0Bd7+msyRgVHY10GIU7F2GXxCqemFucucsdEAUkgPJoiSd/jhtPeLEcUpTb7alJZ3C2VUq4LunXld9A99GkMFgzR4ydW2xwRp285YrLfR919Q9n2sfvkQb+J+sKxFGl3D321sMkoAuKAd6ow3XtidjKS0O2heQe+fwn13qtJjhmn349EkbO1yJIxnaBE5yMxZF8EdR4VAnLzJNW+wnp1SKmYiWaZT6lg2+aHCtqMWGJlwmoegDgJQFRU17PSYFpRXVSdumi6H7xBRAvSGq22TAHiDeWe4cgZoTaPUjxeUAXdH3uNrdrfAnwvNXOxqZqNCw4YxDPa2Ohvqdm3NL97CgQ5L4JsvW9/uZF4xHyCPvu0jRS5EdN3ILx/Vdt6J3CirzCvixSU1gPpbk/CSOAZtRLyA/uLQ2stiqTSxSKYXBtzMrslWwMSly45yubmMX8YFnZAix/eyYgI/lINIbwxPWuhQ39B4g5qqZKQk56cdiY4hv6Nr3/iBCqsTT3GTQFKT1nOnOhxXEFQnXKMxT76K2+YKFFBmalPbvkPdFzPVW0B17coSIJXoPjvl7ys8UoG1uRNpfS5cXWnmH+XOuue/I3Ox5TXE0Frr/s/blXvfZuMfZSV4TfIRcZii18cXuyzW5B8buXooPeaYPe+wjM+/T0D5+Q1+KbMD+pgSYt8v5XoyZTGRkXG1lY3incmSCXTQ0+P/8uEK2jWRdIb5KW4jqeGdkMdJ5FaBU8bJQY2jNwZ0fTM9T76Xzsh9nay+10+GvOoLJeUpVbqoP9giutfUvkh6uJ8O2ThxLHckrPk5uiTFiJJzmlKMIjo4hEaorPavaXSwJdws6sJSM5PydRNj0v+J4r/m4jyydmjS7a8rzkOM9Z200ypgeNrRFinysR6W2LAe1/6PemyA5ImeQyoswyCX5WamEWWPX4tc7TQW93PpZn3cI5blpPobxgPv3yjQmZD3pZ23Nta/5BRf2fRTpYWng+pF4EnPGnzRmFy+ikw4pTjV600Xy0Fwo/hc73R9y9xHxSu6bLHPHqs6avNUmpttRNpuON5FWfZkbikcD3P3oGve6Ln7uFkrYa372wNLqNypnbJMrXax0Y/FqTiyQ/TnmdYkZOt6s9PuAsQJ3PW3r/M2LQlvzqQmp+oO66CQOsV+pHR0rx6/OMDxdf/XZTIsikjszd3rht1NxciNrj7lwekRydD6uyuSk4N3UiLdZ0X4dEIodn9wrw7nzs3DvEDNmINn7eAw6aS+Ska1G6Isk8llf5GoHky0na+7NUarVTCFGMjRDPlCmTT/Zc2ySJIFMSLXBl6ZNjXGoKBq/H6sgMITJzkNubu8L++KcgCHrn+6Zv+OsMmsqN8ugfATFfrvvxIeO7WnPsUs+oW4N1m5Yp+vLiRkYYtMs+pzR/UPPiH/ZPFVi+90j4G39iR85aPPwMlSXXz2tKyZ5X+mAy2h6kXpbpY/ykEH7Tr5DmXdYaJ+3FQSFoM03fKn+52tKho+K7geTUXgmFPGtYCdtA++qZ2dKsL7NPtAqXCLUPkbpwkX+Z19h7tdftd81SGYX8+lZ4f87uTBYYXJWySyZJ5038Sd2kD3QdR8hwKVq2j7VoHZZEdSnXUPGShJJF+d0isz007fnqaADaObQr+EQqO1c2+VWq3kk20FQjU/ZavVUQMt/rq28xzdHQwU6W1ZRkOeQhY14Q6s7cT5MbkSWF8vzGy893tTBhkndimWR6BqgEN9rt8uRC9vgKpJJdu6yCrUo/DjWU53ekbD1+8dqFCkwCQ/Z2zZP2v7rvW1QdJVOj5foEdT9F6wtzw8RdwwKdKw/fiExfeTqven3bh+1Ek7dK3x5QGmnzlLAN707y3nXWphf2yvx9DnC9VipIyY7Ewe4tgAavG5mHs2E7aMWM5n12fAQJ2s9X4qHpiUnvs26D43Pggtm5q0UHlsXEIXmMd05Qgu55DV8r62S0RMwJkO17U7pmuZlmHoy/+Yeu553JyHqmWzJ4kgc/pWUfr8ttRK8qlBrcSS33DQoxyEhenbzjq0rez3h2zL1BJvv0/kcExw1i9+pGpnDOmQLZAQ//IenmnrU4W+1njy4ALKIJP6HF0qTTpiMDHUFg8wqwCS9ynCBJBNLo3kzqzh9smlaWQMDuCfwSQUD3xOxq+sNKtxcFW5dIJotDwsTuPKF2fXlj9KOGBPOkx/fWPef4LFoVphc7RawuEm0nQscHV2H+8BGJRZ2xBcEbY52ZUl+bRGzAHIdfQh46rT03YyCQm2pQJ30qn99rQ5tz0kbfyUTj9D4SXZdXMD07BrEKe0S4NaKBeXxLCuu4S53lbyf3GQ/7oUmsuJ1bZO8FlII1MO55RHhbTI+BVjHjMKDO5bAztGO9rqKZhEDGKd0rDX3xTYCDsi5Jw12w/eGbbvUPY4TiJPP0vFn13JTflIDOfgMr724MKhEhMjKpMe2pYKtLrj0Q30cT/SDKs4kZoC590l7G/HaE9E3LaYO0vATqwSBfehDzp+RsAkG6MkBSsNCGaOJs+Avqu3PwTgOtyZd1kLyfZGIJyINsTUBTRYFZLUNW5xPKL47U0XTCdfXaL22shtRd9Eip5eCT8QFKCX474Ydr5Ctb7MGaldj45NUyDqeTRMZvJn32yy7Q17408yOHyFjb0yecjWs3IzODGcYFAWYnV1keAx74CgQe9jaBV9U4nk41cqeuy20z3BTzqokSYc5wJP5BXPnqtZurcnJn/DDj5DTdsN/ZNYb3K6Fbi93V+YIZvmoQ2xW6CKWPG8kdW+ZcByOGh0LE44ZoBq/rgGde4j7eTzq9KfqjYQh1nVRdZ8mL2y0WgWzUNXFqwoE2x883lvnyjabj3tPQaO47+RnphFccPinZiV2g+3gh7Wne/zqb9kVw3Kwes/8It4pkBnY4o+GBpRZv3BtVvQ/6b65iSImoyw04BzH9uiXXMc8JAV/0kkwBZORWUtmhtjendZtvkeyVJMotbKqkt1J8atSvuybpmJYQgjdMmy28zBVcWdEkLof7S0Iuq5MniZYe4n7MFmxmMTEVPJypsqTSlFP5AqYY+J3BWdNch6mKKrEoqwsk0tRPukYcnf+zwezgpjTI27dZo0em5W7fYKRsQAEDXUqcotdVUjl7pJXzDRT1m7vGaaLWIspigM9JXNtF3w3fuB4ut8DsqiiYY4mg9RuLb4qLpgXXPIBK11mCUTVYnfCqh0o5MWe6Baxpak+O5oinCyMVe3OGJ7Gc3q+GQrPFbxqc7lY6rWXAhPnVM9zydk8Mvxdgp61HjQQGqAc6+o5O5P0s6F5Vf2M8rj68Ba0vMvNT/ba32WfmMa/zzcQc7SRPV1rxelxg5dAbdel6kbiepMerKJGF7GEpmhjDi3hGbS02WMe5OXE3mMUvqzD+y2i/x6bpol6uMDY2iOnjl13PEqrzbyTI8ItDHwoKxJVUB9HreBUfaG02oKsFH9sUeExE27fukNWMNH9ezVijsPPb+qLs7tn28v0LssAqloG5KrcStlbN/v7KZxzKZ3WUS+4sGQKKUUxzcp9sl0y9QYb8VaKhLe5GHZkmExfd55ktRzFZNRJdIVXYgtjAw2jT526mbZvxmizbr6v3eluWGZtG54q271R+u5Quabptx9wEzTF5zSamsbiSY2L21rr9XgBeiuAo/eL9pAdDAkz0S+O198RmGnm/yw1Xh1ufR/b9fJLif2VBT8rt6VZMo/WDRft8MTXP7NFM7hwH5/Kv58kjDyM+cL7Y1g6WdKju8Esy1uASCHOMX7sdRw4l7kA4oSYtWBzeW8wthyxii325aLsJ1/T1H+uupP7AK9x+ZcXzk7UPs0Atx8ohfm/dNzMgVFSQa+HgRiRlgm+Yn3+8yT027GDWaKtW8VFNcDff9x3+vb2p94v3bcZvbZxMbccxtN0VbVmUrTwPL2Dt2WfroNuNyOnIJCMK5O4t2+hABNKGZFTM3wBSEWtyk8r+GE2WsDgsE7iptNXQ2msqxbMRoeJz2ermJbjjf/I6G5WhfA3V13jQrQV1mftZEF/XGLMJ5NmKufEOALJ3Zj+VE9o8SFCcCxVeXeYMcB4VeUXk5W7Ip+ISxTFcUbp2089f4pFOIN2nCaYoP3rTzIDYj/7V8WowFSLK94sd2psBjrWOjrL0vblNpTtW+PUJ54/k5talzUB6x7papSklsuOW+CDyBi45P2dQNjPwMMK4ypH7m+NW0DgVYAIz9jVJowrf1/qjcPHZzvuCtnx80lSH0QQrG6uMZqe35Nm+w60rQyHCw7otkce3wDIuQVGQ2eN3ufTl08l6coeenBtO4AcvdXWGOAW67ynZtH1f9djq/Kz2bB3ixjseALtrOe174VZj4TZo4Fm1GKawdUudZcnFwD//FmWWw1v58d7lV1s/hqzK6tOrx3OU21SCn4FpSHO5DCTFut80dqQ3pmi8h7JObiRRJ4kEQAsLNk+PdRYzi2R9w4GqUuwsB+nY8m04zbvctJozLpnoFp4T4pjDiX0SM2ORoYUIlnse1c+rdz3kiSVl9/w20k4Rr0p/di0uzKil90NdQ+joxg5ZTNh5vyQqU3/rnPZSEq1k+i7pwhhrodtg8uSpRa06z2nhFO/JeykkJEKx/d4o5mVUts8wVUqMUHZYMZkbRVJAB5O7pHJlSsY9jT5n9k4ITZwym9HaQ1OA6jANLL6feqmeI789bHZCkNx6x2I6XT21IZPef1qFIOnaGysrcJhs30WQiH1QybhyvVpl88zGh3EQr9IYleRXds2h0thkpitFXou2Gg+Zkc/j3feeK1UYvtP5qu7JSvHS5XNyL1s6yCmaG1sTLEAfga/8jNiKJb57eOM+wmC3LrbI60iqCipBeCHckOWYtqmslQLRLOVmWECI11NIErpwb6imeSsbfyfbQ4cjOpNzptESMnvpz+JChI2Ksq4KU9+czmzrl7N9mYVvO93rtKRopD2UjfwfW+Q6ugplbmRzdHJlYW0KZW5kb2JqCjE0ODUgMCBvYmoKPDwKL0xlbmd0aDEgMTc0MwovTGVuZ3RoMiA4MjI4Ci9MZW5ndGgzIDAKL0xlbmd0aCA5MzgzICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o22BVSUaxc2jJSIdEkzhHR3iaS0hIQwhMPMCEPMwMzQ3R0iDaKEgEhKiXSndCkhCBKSSvc36nnPec/7/2t935q1Zp77uvbe9733vvb9zF1W/UcCShCEDfQBAo4WEBEUlgWo6D4ykwYIC4sJCguLEt69awRDO0L/ggnvmkCRKBgCLvtfBipIKAiNwVRBaIydLgIO0HJ1BIiIAUQkZUWkZIWFAaLCwjL/MUQgZQGqIDcYBKArCNBCwKEowrsqCGdPJMzWDo3Z5j+PAG4wD0BERkaK/7c7QMkJioSBQXCALghtB3XC7AgGOQIeIcAwKNrzXyG45e3QaGdZISF3d3dBkBNKEIG0VeDhB7jD0HYAQygKinSDQgC/EgY8BDlB/2QmSHgXYGQHQ/3BHyGeot1BSCgAAzjCwFA4CuPhCodAkQDM5oBHmjoAPWco/I+xzh8DfsBftQGICIr8He4v71+BYPDfziAwGOHkDIJ7wuC2gKcwRyhA74GOINoDzQ8AwSG/DEGOKATGH+QGgjmCbDAGv08OAjxQMgCAMAn+lR4KjIQ5o1GCKJjjrxSFfoXBVFkNDlFBODlB4WgU4a/zqcKQUDCm7J5CfzrrAEe4w73/WjyFwSFPfyUBcXUWMobDXFyhmqp/mWAgwn8wWygaICEsIykpLgOAugCgHmA7oV/hjTydob9JkV8wJgNfb2eEM+ApJgmoL+wpFPND6I0CuUEBaKQr1Nf7v4l/rwhFRAAQGBgNsIHawuCE/0THwNCnf9aY5iNhHgCgMEZ7IgDhX5+/nywx8oIg4I6e/5j/7q+QtpammrYJ35+M/+aUlREeAG8BMWGAgIyEBEBERFoCICUlA/D9dxh9EOyvYwj/46sJf4oAyPw5LaZM/zmx218C4P5rOHgA/471EIFRLRTA/Y/ILYQlhMGYL5H/Z6n/dvn/U/ivKP83kf/vgR64Ojr+prl/8/8fGuQEc/T8ywAjWlc0ZgB0EZgxgP+vqSn0z9DqQiEwV6f/ZTXRIMwgKMFtMWIWEBEXFBb/g8NQD2AeUIg+DA22+yOZP7jxr1FzhMGh+ggU7NfdgvESFv4fDjNfYAfM/YHC6PI3BcWMz7/3VYODEZBfcyYqIQkAIZEgT0JhjJxEMf32FsEMJATq8VvJACFBOAKNcQFgcvQFPEUgCX+1VQwgBEJhcoahHDBdsPtF/sZFhCUBQjYg5H8BohgACQJDHaFP0f8Fi/0F/+n2P7gwQAgMhWGysP0fB5G/if91wRAwJNgR6uTqiIY5YzL8D4XJXgiz76+J/8dcCoO5uMLcQI5QOEZy/8FFMfFtf13DUCSGxqjqbwITxBFTy3+jGNAJBndF/RMXIOSMSQYBwdxqv2JA/mbEpTEUEub0z14iGAQJfeoI9UC52qCg/xxOVBwghIJhpPVfFZTBVBTtioSj0Jgb8e+a/KubYFckErPr76HDtPo/699XKBTqAQUTfppCgOVC7N+FNJ5UKDG4C3z7iP9lqTkywawrQgLNOf3K207nZob6mIvyE0gp7UCq/sybsNEpHwbenZMhD4u2rMCFFI2XaCy1rmXlPYGcvtP9NlsoMwRBbzsslRq5TL77zICJ9AkQF8j13HTqIlSqXWytbDX/shm1Zi6zCXACFByRsaYwoGOVsps3saPNWDmW0P1mFmRxFEhgmquRpvHwJXY0LO5YuTGG7tRzGRiX3j8/35vV3ZUq9jNXu6NcsjSMko7KS4s1eyCxh476WZZWHQqLU1niSOVNcgIb3WunMM4FHFBXxyegTH3rBhpEl3UzdvfZwYO6B6y4igzA5pkxLQGIr81e17t5vVsOgRQijdsROeDEs/ldrQ3uGvxvioTNknE+feK6+XGN+4viT00Ynyn0qJASEOfQy30erPdoaLM3bFKyhUQoCrD7F8u+7Sudi8uMZPRS6jh286g8xCetoAyLvm/4GGoUVp5LzsnQMAmaTi351GJfOUiHT+Ipd2gu+PDAo+YteYpJuI68s4sT4yHYPWa9A2qA68n2NbLjrE9sc5YFazUOV89XAs7+KMCiXYQmbu+No6r9AL1I8FFhSVti93oT56tvH7sLLe7F1LvN22e+jqv7AXGYHtgZuJ1yr1toaZzNvJonN1niKpWG+XMjjm/HHauPi8kC/ve/e410zNwOef4T6VW+uetyw1pICDwrT1w/fcK38DqG9KO1G+hK0Sx68bMo/4bx+wC8jkpPf9t2JYrC5nBuZfysh2ivWOx3zwqP8ruFRwFeX0NyNdrtPt6VYOHc+dEdWOIfhRdklyDapSAvvSOEXSp6ONb/QePVvspA5Zc+a5aq4w2GqrBvhTuC/cZ9fLhD8MLxUPZMNHncp9Nl6nhstU2KN0DXPfjrHPhPkxE7LWwZ8Sxi38sw7HQc50TORHXuLaeoVamsT3jAICdWq3tJMQqb4OooQrage2y3P086dweUzaxpMF9c3kfeB1j5JUQnWwPL6ZpayNODVb0y29wIM5sebRuzWFYHwclN+LmoTEB9OcLBP+wS87vELOUa7KrcD6/iWSDrTbGnThkSkV6FEQ76Q2T0+eKyS0l3yI+khsMkDwUJgnaVJZuoDSYi6IYuPuEGyp+7LDG2kUVxxR1TLjn5vwtwlPIUtuWo+h4op2HcK3jq2EfOezdFWHyML1MEyKXsboYtbStGZGn1ZIJoKJU8oJ+ewFnjPodncNWhgX5ApxiL7QUEmR2ylaDPLCuZoXud0XJTJnRSpdOExV/li7l/nLCgHZLWXlOCEuGoHPAypZcazxtAehT0vNNpImPFJ/dOSYTURq8LyNsTAh0fsbI71pU4hqujwNXsRoW1gs/pCnz2gXQp+vP7wWsNJurL3TcognK1PpNXyruvBf6Ay2Tx2usGoHCB8iS0tJ2WlugNVB5OjoHrqfcLLd/i5wR+ORQln16wFq05v10L0S3GlzD+6MqPpRAJ67gBtA00VrQblIAKWtQvEj7TPsUyKDSURN42n4tiv+gZ2PTqPjPqOGCVD+f1pLFUnsmp9TgfEC56zNyI48Jh3vzxhCUbfEAwW1hUnR+/9MbtlkZAhghvPHZbAvLbZfhCCFfQEo5Vk+TUsOnAUEsyxTaxhZnTDzPD+689GzJY1QESkLs5ieRb2rOHNlPXhYc174A4GtiyQ+r01X4Ay4tO7IAWf6xSHbqM2wfF4OyH4zUxa8TmgfleI0QiwGM58ZVAZ6v1moe7zufFLsqqAApSrpnIk1lqrtjwhY+Z/uSPE087Yx2qffJX1bFwDZK79asDNj5fVnSqMYhHtFjFkzat14+mRS/eu/a5O28+zvMkSgHcEZnvk6a13sRj7egezgFk+babY/LMiLz8RJIp4ikl4fuue3MvX+Be4Cwv88Z//UYEcy7VUUMSdhreD1pxSXJDN43sxSbdRj4iOc4w2mcPbpimKDltEl5JHqS/tM2v1rX8dtebyZayuSjN7CXdZFgxSOwrTialadb7gZ4vP1VfvZnm53xnx9r9KfhNZMzMCP22y7UVoBKPkKFG+5xYafk1np+UycDERFeKcrLHMUHQWB+urqqo5Cjj+Uo+vWcFIv29bHboEGt4DDmb6jJAjtKJ/ed+RjsXVkO6qQrhHQtPFCfTiMOgr3mxv5L6/nL0nXr7udPAla2VxbBUw6NAlvKyvfdOe5zx7951W26GF8BNg4Noe8B5K68lOBtJJNkur6efbRypTpcdnQsXHM+MVpr9eEnB0T3TN1uW/VOVRsH9k+FFYE+AeTjoQzbVN+nO5TZ9VNGsLM9JDNsrNcmPkuzv3duLbBv9TrI5fGclLzRRDEXETl3SRq3aIZ0c+DyJi2PtwdxXt8328NwVv3Y0i9kBO42TuuubbrGHtRyEHBEl2IDT6wh5mlkFF5ka6NcnR01D654sRZGCh/D7UpUW2oM4cNwL79PuVETSKDES5nGcvSmJIy/Va8evUVwDK4wlqImSzE5pZlrWGq70tPD1JnTopD2VvZUrgrrFoNcsATMF6Mz34VxSPZU3J006y3iQhAvN0gij4zoyIKO1l7Fod3eZfvlynvzlyNHKkQvUPTGUcRWUvhujLPiJgnmZjYjHsINDpDV7WXwp8mVZ6OuyVNn2x66XYytvK1Zb33vVPGBjnG1go3nNNWoiJvDdGfbouEWAP33kiv9HlWVIEENKxEfuZVwFETd9Zk5Rx3zGqJ/ONKKaNEzalznKXLPu6sHd9OFP6pBJmsmrB1TxI8DzneJ9HftJ7vUdT2x8G2poWYp++6hTN/fuu4ZUOWjjC0u0dNYqqzFH+EZEGwihQuNzHozEC7tvL9Fk5yQ6LK3nOlIDeevkVmmd+qPozoJId20VSC39HZXN9O1KFP+7Y7BUUpjnjD495cxxQjro8WKzQYreS27m6IEr0hyPo2iHNhcIg4HUKdjtB2HRszsHBJaN3u8TS+VXh4l9mGzX6Ta1H48wI4qcQ7eno/Iq/CfAXpmfvCk6TPGJgLKvgwwLWZO1cg+Eu7GdwSK54BfKnSbaOCasmQRGpe2rjsGdoWa1BWSa2OTn0RGOtd89eSNFb43XjmoFzkHDII2bkiojC9yJZebHZ0s6MuPWiuR0Zi4Kr/zpPDpxmYSyk9dzU/XmpLPKi5nx2RQyvp4dBJQUP3oq8E4vgIuSllP3jlmm4UU/xPSirOqQlSF7a0TxSGiHgjUC26/bxY+96HGXQLOr+ov62DH0l5e7sB+4/G0wTizE42qZH3wgBZSFyJFBGCAmJ2H84fz2d/HO0CFosrBGzdcTdGbv4lZ0YssTqjUNQtz4I7ny4SfXWaUlYWrgeNbzVLKSL88k9ck3Px19pNZlL8uRpk/3GzZtQ1a3tFKe2LrSuKqoQTqGy3aMuEcU1NLOBCVXNu1uxtEsiM2X6CDYgVz1VMttNrHx99ZvPFIodp4JUlvOj2ImF0/8PNFozc4LlHvxdW1P46Pk/IVt6UWB0Vk+s+/K6Ha452Ci4yIjIvp1YoL81+i6JizNxbzZe+kGiKGh8Dc+kCK1s/Ytrphl3sQSS9snA76Kr/SF8eIDio2CbxIllxZJ2yZcYssCDuNGVmqm3gmozkvlno59xK78pM1Fwvp96snPgJZT3dRwi7W6PGnERkgsvVDbVeNT9UO76eXmiITKbIOAnDhiinhc6qs5r3yFi4HaoRCNlsSM/CMgPAj/eQOOaVUP7mxpnh9KRXAsXbHC+KyxUteBcnCS75Xt/pfwI/uqVVsDLBWsGme4+Wy4xZcirworBPZwncSovHHz1zujpaPyzMSGVrnGmBfe5QBsv5RYW/20JqnfOzWCbQM4onaYmWRlh82/faEJzYvhpGCF3+EIvNw3UbB032u+acVWuszZkavqlRs141g8dckRCgFzdHJBu7/4BwSWgNQ9zZTuuUS8eUyQLaVtFHF/icfq01sfvWPuFXFRlmn4YwcZ5x9q7+7pwRaFajwl+KTXsiS1Ta0iJN2K95RQX1fpp0ZShN521PcWplUzGHr0heo7KO6oyvpNauMxWgMks3VCoJ8iJ+JJDYhzlL36OHQi3rj4qhtHpdouw3svCJfqztcPv6gZbcvvDrid31N+CZ85QBb6shFih63qyxN8WEhfOs5yvR8y37fPo099ZDmwXmCT+b6sCb4U8e0O0+u4scMbDF+mu7qKD2iMlJ3HtwhFAxEoT0MpCTpV8tFDbT3ps7K727oWNt0L642fu5r5M+pOoOu3WV3d3qJSvIspUccsKdFln8wed7e94VNfDcDKsyW3U00dultTpfQAK/G5KNsknzBTDVXW7ebTEIX0/OkTAsO1FXlS97HGlz5sbj1YhpdG28OEV9eplE5XDTfvPLiZ+rL+Y325BsPF1HFmVSyFqupcVdf992mF1a/cOFgalnE67xwCC2dWvmiMmY3QW3jpX4cJwuqCE7nrks+DXkwkTIAaM6SoKh93bX39InZ9LlaZE6tEYTQFIim38kyVfYrwNOZz75cbJY1fLbgd1oYMMwoCnjWki1MfUzJa32GHnV26mSkzPZab2npon+3W5UAkG2q0PDuQj01yTRMla2DR6zNLvBzx2ThYrHww1wB2w4c4bFErIndZTbOnBoS9YFuPEzoK4mkhp7i7KbS/+k2V5PV932FzvFvVRwpdKa451kGNku9t+W8t19frGjt3DdALVAp+Lgx1ypN2ynt3JSojew7a8R6vLPE/cB/eNdPIqVgMrU4MWkjJSRBjl2jQCUFQORizm8YNgymTqJuCFPWecWAjX+LPVb4ob+kTYHfe7R7cKkvVKqYCUKypsRJUnfPNXIubpX0e5hIQt7C6f2P+aUrMBvGxPQH7s6QGR3fv5Df6w5SmiBqvrJxgnBdQzkPFbv48Yrb2o8tIIjKKYeQ2IT3kA1ITqd3wjS6p+4eY0PnQsyIg53KTmLRgGg6nWndKRaOf+VXAowDBQJvArfrKbUVCZckJD+nAezVsFzmJiTMBgz4y0uImHzp+6ryNjcxPcCFlCXU9Pa87tBY1vLoYeAJrmJUDLJ4LK7bbnnAL83aORNAXbLTkvj6LCrk8skCZilsFG/fLTpPEC1ClK/mDo8I6KlI2l7KV6P1ItXETH+I9gg7pbYCpXgQBeRXTNoJLeBZ9q92svDNocQnzutpbsl/UPQGNeqyRKkChZqGbWWHE3m139UzUjpfDEtadD/kRNHDJiAGFZpI9Ml883LfUVvsuzeTOJrWXxoxUhBQ9t+J6eh+pjQzcQLYS7Hx/hCjDIQqTyNDqOuC5kYKvfSIVHS+jKdBdw+t40cmRa/Qm5gpUNXwH7cD+44Ds7r6Ek7IuhGroU/JZvEyJJJgr1WRDTuC6dKIn+VQARiNv1b5mQ6an0VMRtEyfkPce2DP8aLXI/GvEJr0i3KEYkvf6wc/7nRcM4ljNydPZsrY/CE4SFCLJd+5VXGVsOS3msuQNDiTulIb2+QtIvpWigyxUnnPWaYOyOoFdencl1sPshsJapgYNt/m0ZAWJZy2P46N5IWrFixFBvdWJYuRCWSq2R9708fcd0Cclz+YTo3fxv6HbDp43fuaQfP/52Hw3WrlJxgw2Mb0vcltniATFOTXeWnfZvCIX7jnRH2PJSNdqxhLGdUvqsFeAotXNWe+mwTEboQXI8+3noe0ey/XnyqWDqXIPIi/plJePKpo/WJtORuWKW3FsLtC+YavczixVblQ2ZB0HE99JowMHrX1SdxnIOXbLC75z03LCr6rzuQbvoAxhSotlD75KZNdUrkDqHPPEG8Mhe+AkrUOggx91CWuSEBvexkTiyQ2RCGcGopiYy4PIF3t9Jndv/CR/S3g63WytJ4a/cjgtrMGr+ozga+FBv6AkqRc7Psqed2SWlbt3zLc0JqCRXaPx3C0uaN/LN64cj2I8r0LjRbLJx4OrgDWsUzx0F1P/KL8gK5/jTJbhaO8cQBAhZCQdkLoRbnB9DlXwArJaPH+StiztYKRSkBiwZeX3WrCzsSrnaOu4EClYAQ7nWE+OXNbMudt78WKqyP5SqSMmw3gvuYEjj1WR39eQLZC0zLtlntqb36E7VKdDtKF6V+vIRNdUFI/4htaKUvxTHybZAs1OCmXqQV7aGTz1DhReDp3q2Dw/DouFn+DPQJlp1VflwE5KuhRwLUcrIx4f7+k+5xTAz32Ql8BaUMfVvLX8xpjmOH/OmGoPTTF11lZjRIj8AmHwdGZNO172IGzscUHOyGp4ebuQ5enigM+0AWq7jscfkeSbvpKjxzfnbklWd4fy023X8pJXRdVCuYTyioHY5iW989tKysO7Dz5WD6LQLPlrSkO+BDS935uTZPJlTxaIryELxeVnmKFpvzT5Ob2r6C4K7hAcqzXRfsTgQf54yLiy5tYIoU7DtXvnJ+ovvh9AVNsZ/YjJXe20frvWOyf8Z/UTOkabRJI3rT6qfOg74F6Dhvak27Cjk3H69zbe3Odyyg3kgqId8dTE3LtadNKipcSCITot2g5crgn7U9hfqd1+1EsSFB2c6VjeqMZJcpDxu8/m23iXoUKK8Eb5kYwSSByLbvQEJ28myNfz3rnpvZJFV7rrb2f4zLnT5KeD7LZrWbN3OFtcX6CKzDRREfHt/rESSO5HO+xCYCNmfJXl0tlaluG+e4fSbTcq9KwjUs08gsfd7kgkcRQSHaQRuipFCF1sKAwWdZWBkDAakJXOgnnuwwNNshqFQqvukoCNNL4IsiuCsEqiQ348n/y3CjwEq21DNibtH6q/a+p0uqpvybYnegrmiivwEU5dUXZN+mYmYXnV7DTq7N27IojacU8iCtCf3924aaPUuF9TiI+Vflsx+HETG8K7svpd63m32CN908WZQxkT+/Z5MuCi2gFjWhVpmT9IutXsrON57OjtAlppKtw6fy35H97tZ7TdYjnMi0aXbBJbIWCymT2l75RPSUxrOm+i9D1zEwvbbObJR/LGlW15SBkTtYcEg2Q110YtDWLK5LiCv6HG2PLYK8oc9jzeyte+qpmd1rP+PMk771YLqd5e7d/52JO/Efi1ZyeSjpdDRFgK56Gv2pNTZSxFDskp06Px9yG1X1d/IB/ImX+TNVQjL2V2g4i8zfXVfK8fRuDh05tre4p2ete+Ud/VY2g8Z9xa0+omqENQUH5MPZEjYa78nPYp9uFP4qa+8BnE9xpiDnbtecgtZWznmLiCqppngWjK5nmSENQVSbbLyTSYGEZ8k24hLVfZSY5GYhouVUf2+KTehz4yBdE5v5zucHYALnBVJaM2cLzBdNjEbRL8M6Bm2J9rTv85UeBpyINRDR0a3/NBCyV6Ute3ewkE8nIWoXsCmRdePJDSCK+xCoJSdQoeD4/P2c9ijIEd/NSe0YEjIWUMXMCm8xWnroH62QqcSp8vj/XVwr1tChEGHmLBrYk982ZHKkwnuWs/iX7ekzRy+jbobDOeqqsRlOMRWuHzU6vUvj52fP3njZbssjU84nVg+WkJkev+W0Y/q7F81jXSqI7l8Z7VZQKAdcACtmqz8vrb1YLzUZElWEYigB1/36HbUS9/e4kzEFT/6t6cXoHWIt+5MZ6qw3gGa82HRJyWqqkbjRnKSuInU80dtfJW6XRUId8fW8oa6EdbCmq/oDAhYCL28WmdE5PjJMAJlOCjzOP074/J6DP2T/XQa8bS/0mtT8rNMhu8FUg7g7X/rrDmO86SA0DTptGuTEjFz6BM7knTDxx52Rxu96+wzk/Ar1wfOnfJzWvJYaafU+fPbIYzWlt5Ze/HEvB2tbUqe4095E9307z9oLa392XKpZr8fjn7Td29jpRhIfIfgO+9eHyDssXqFVtYSOeBZGd9ncpZ0Vyn0M9q91WoczgJmHZFOR5p38EluzHBcGLOV1p8JeGt6kDZG0GyB+WarBWoewN+SifANaP1UnhU4HpPGxL9JZudIkrIE7kdI9mMxRhyr+FDsVzUfc7qCgsqsgUbhgD/Hh2sQXtGnTgmBuFF1W0sPYKfzZD+cmNcz82piqRKvQ/+hT+2ICehVgFHo1FNatcstuLO8x2OU7I86ez1KZPcOdyAqnujH9yis3tzJ7rWZXzL8DwqzI0MrOOqyyvfmcVGHlNHexZ0HChLPjQtF3itLyY/kxnpFJ7ydpqB6GFRwUOeurNL2fdEezTXi6+2tcuNl/Y+aD84M0YcleIa9dAIeX3oGD5NLnABZQCX6jfs8rS3Ysh2etfT0lcZKs0aPkTKuxiLpa4cO91wNbBptAGRPLKy9WkdcH/FmBOkpB97oibeGOBWWtqJqowK89XSial6+c6eUO6ZEYn1ZKKQAf8qyi7sYpXaUNZepLaDTjNbbTlOkyPqajmVtBktGRFCXk4yV/Ctoeh5eUDTdyYIXmuwSfMtqWpnRSt2q5NqFyVObN0RykH5oG7V763WNeNtzT9I57cTFIh/1sII2CWML3DUGnwQhI3MJA/JP0yLkzxEpqu/qPKo3z+3GaA2x72q5g/1EK0SivVkCPtmML2IEzPSmmH60KHUer9O140RKOhmGrlY/fW7S3WB8fPEhvLCmMUGOqwZBfuQN/DW1es+x8bbFEoD88vPKfgUg7lOIeNSgT1S1kpa+8On2ZHcceJY4QQuOZT0ByciEOqOR7vYHG3yo/D8V7TOZMiBXfWX4FCuXSWtmgBEfIoKe/nN6w9J4UlveAXDR6vTibDbqoAGa4Vpt94cuRMJxFKbr90PP9SWYmxRbbi3fzmAH4FNYlk4ykIwtF1I0PmYNbgovtzVdL9zFpsZ+dnRdFZyvm83DKR8gL33IvrB262lGrrYxfHFHz6j5mv2c5Nz+/dbp+KKIuaUfKbpbORIDdQ0pskkJ04NdVL3hOQqWSwbNqcKtVyWdRlS3s5b53A5KhaN2gX2XGrtSb/+OMn9LfxL7EQFm9Lbo+hg4P286QOGAQIXXkOT0PcR4+3sRjyJhUVJe95sqTxs8ynK1nH7gY7m5cYU0rTck6IP1OvOxXm2epmAJzj5IoqdqtvMQyupH4wg3BcMTRb+uDHZom4c1quB9P3osJBo7Vne6IVCGrd6HrKu81q4YOPXtEq/1eW0TV00CdAwTqMgVg4nIswk3FnRSa9FQvl1rTq7wxFgviqcMcqcU5Mmj7izlKFo6fbVskfPlJatmRoxnc73De4PJe8NlwRfTc0bm51JIyH8+U4x6gvJyEV8bRZ+rC6pwpdUHFrzyucDcy68q/UDeWsU3Xh7Mi0r5l9UQ15FFu34+bC189A9k58lduqfdUpkkPLSPlJlfgP50DMhqlThY+Le6Psioae+yAXcAF/ilxskktrZtO3E/Ee5U+v5KmlJPBXtXlpHWj4eVZ/uvPNWRCKYp2DymVZcLD2ZZztO9xhZm01SG+LZFw4UzUum2BvDuOqX/G7oHeP6uMgnZnFLdguXOZE1TTpi3s+f+D7J9OMLfflETPh7QSjLSrHkJsv4x1cxDsQMRoOSRqqIkRWb0sat0/fL7BzY50Nvn9wO4fo0F8Kr3ElHps7QQPaTKfCN0K2FbEVhmZcyOrzNpIu9HxyoPj+sTX1oEXVs+Hqk+8ntMqqJZrKdZqcYeqDT7myIYpgIFpeipuTIXqbU+rieULAm62zpycdJQlFRyn06pdOoXvGHP7qbYD3FhQ9qafcyv4oZ2pgc7nc85/MGtpRMDCLUjWgXCaRbWRo39vQKBzSf4olufsx/9aWBKWBoGxXI1xneq0LlAvhCknLtAQOXSZpRECLWGdOBG7Rf5Cs/A4EOpQPyjb0Nbd/Vgpk7bM08fsrMGoWE2oN6dxQPvk/7HzxsVAB93OuWeqTb+BwgdOhE3zWXAHo4sBHi7LAEKR84OVzfpmGtTksoUr+giTeVFSqy4HuumVX9wrWlyoqpmqclpWpPUzLzdFGguUuZ6JvzcGgsjrnl+0sj4SR7nDkRSr1bxQRyKsEpyrlq5+WKaS/FY7ZHwp63fr15534g0VDqbgyvjXSXS+6DCUtVRiMs2vfZOQOohbMw0Zb5JVpAPclKZ+x4hL5Tz5jfJo2eyIfyyHHxOcLctATm0U/6luOXrzQymQ/d6ndaFG4ozgDaruETY/NGl7LLTpYh+pQx7wa+Jhdf1zOuUzVGapr58VufPB1DZu3TvlBBfR4ZvXeVdotAlJpnhvwqdphdbkLhyntTVo7/Jgz7XSlFWqlcvQcYdd5UWhQv4uCm/D319AU/L8di2ePwCspIvxjO5aF6QUUlDWEQBRF3rZG/i7xtCreycTtJlwWOQubYMsd6HbD8zeDkGiGOi5Z8R52gMIHZvdbDVwVZ+jB0ZCm31pf5CY71q9YbF4Uu1gD/1RHSZ9t3D2h0Stbl8CNcvgZ8i7wyqu8/hWgV3oTX75i5wfrRKw0c/eBewxiElxuJ6Fsa1RVYWNiGCWe1Mc3s3Y1lVfiqezf+HttpzlwO546YrDzCuyVgfTB9lMfvWEgM0jhwxRF2uJdx+DpQ399jpcbY1R/Z9zpbRJNUWfC7SN9JOn2aU0S+uHd0S6mm/YrEbDdnwHL/wjiMyP1auaDhw+y5m9Ak2JPqO1alRHvQt5SIZLI52jskdv7hBTH9rxT7RVxIXzGRWyrYymy6u8CyFGWvJYW+1H17o0fOkeS5tK6kQKtP5Y93yJFvn2p6c2XoYXJxQ+5oK1/fLS32CjOuImLnWwOZthFsDyR005hlR+7gm1KAqhzJ4g3e6ZXg36+nnVxk/CxK/Zqt7OqBhB4DU86oiIUclTf1Arw6ASfu+AQ/WNf4ya15feoXdi7N7lMWMYGS3LMI8qrS4+PzzdFENu7V9ZRCrE22VKP3k9jm5T6m9CXsUuZN1w/jL1xfIX14braLHKamhD8vmtrquxfwkIFlW77hOdTfJzh4EQxNCmwjEfdzkC2DvNGkW9jA/ywgVljMnhEPuL6muZlth03lfmDOMeftid+0Jpd2+Wyc4lu+oFxuqrHQliWQJ2PVRU987fkBM1dHlGa/NtEjxTzbURE1xEe9Z86vcdZ3+O0Wvm0UXZbc7eoMmZ8w1OKO8J564m68pJ5yWMF1kTg5q8bRDMwFtsVcxhnavFvxZjrJy8WyqPi4938AqwxW9wplbmRzdHJlYW0KZW5kb2JqCjE0ODcgMCBvYmoKPDwKL0xlbmd0aDEgMjM3NwovTGVuZ3RoMiAyMDQ3OQovTGVuZ3RoMyAwCi9MZW5ndGggMjE4NjggICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqM9wVQHNrSgIviENw9MLi7e3B3dx1gcHd3CW4Bgru7BXe34AECBAgWXIJzZ+8j2ed/r+reogrma13dq3sBVGQq6kyiFo5mQClHBzcmNmZWfoC4ooYsGyuAlZWDmZWVHZGKSgPkZgf8jxyRSgvo4gpydOD/h4W4C9DUDSyTMHUDGyo6OgDk3O0AbBwANm5+Nh5+VlYAOysr338MHV34ARKmHiALgCIzQM7RAeiKSCXu6OTtArKydgPn+c9HAK05HYCNj4+H8W93gKg90AVkbuoAUDR1swbagzOam9oB1B3NQUA37/8JQSto7ebmxM/C4unpyWxq78rs6GIlTMcI8AS5WQPUgK5AFw+gBeCvkgFKpvbAf5fGjEgF0LAGuf5Loe5o6eZp6gIEgAV2IHOggyvYxd3BAugCAGcHqMsqAJSdgA7/Mlb4lwEj4N/NAbAxs/033L+9/woEcvjb2dTc3NHeydTBG+RgBbAE2QEBylIKzG5ebowAUweLvwxN7Vwdwf6mHqYgO1MzsMHfRzcFSImqAkzBFf67PldzF5CTmyuzK8jurxpZ/goDbrOkg4W4o7090MHNFfGv80mAXIDm4L57s/z7cm0dHD0dfP9DliAHC8u/yrBwd2LRdAA5uwNlJf5tAxYh/pFZAd0AXKysrLzsvACgMwDoZW7N8lcCDW8n4N9Ktr/E4Br8fZ0cnQCW4DKA/iBLIPgHoq+rqQcQ4ObiDvT3/afifwmRjQ1gATJ3A5gBrUAOiH+ig8VAy38x+P5dQF4AfVbw+LEBWP/6+u8nQ/CEWTg62Hn/Mf/7illU9GRUxJUY/l3yf5ViYo5eAF8mDi4AEzsXK4CNjZ0TwAP+4P+/cVRMQf8+xz98ZR0sHQF8/zouuE//ObLHv2eA9t8LQgf431hKjuDJBQJo/wy6ASsXqzn4G9v/53H/2+X/35T/FeX/ddD/74mk3O3s/tbT/svg/0dvag+y8/63BXhy3d3AW6DoCN4Fh/9rqg381+oqAi1A7vb/VyvrZgreBlEHK/BEM7FxMrNy/ksOcpUCeQEtVEBu5tb/mpp/yTX/2jc7kANQxdEV9NcLA/ZiZf0/OvCSmduCXxFX8Gj+rQKCd+h/80o6mDta/LVs7FzcAFMXF1NvRPBdg4kL4MsG3koLoNffwwxgYXZwdAO7AMA1+gMsHV0Q/7pYbi4Ai+hfon8RN4BF7A/xAFjE/xAvgEXiD/EBWCT/SzysABapP8QGYJH+Q+wAFpk/xAFgkf1DnAAWuT8Ezq7wh8DZFf8QOLvSHwJnV/4v8YKzq/whcD61PwTOp/6HwPk0/hC4ds0/BM6u9YfA2bX/S3xgMv0vsYGzm5qDJ+W/Eg7w6UztncDb8dcj918vsNTsD4G9zP9LXGCduaMd+Nr/I+Hk/Etib/+PPOB5YLH4B4L7CvwHggu1/INgd0tL0B/+S/sHuTj+Qo9/uP8lsPtHNDBb/UHw8axcTP9pDx5oFus/pwX3ztrbyRro8A8LsOwfB2AFN9TmHwjuoe0/EJzhn+nB3bH/g+B3jOVPZC6wqwN4Hf6hB7fC8c9hwM6O/6MGF+/0Rw0O5gT+fedgB7R0+yNl+7f0Xw/Vf8XgU4MvEuT4j8b/1SvnP5cNzubs7gh+KP7Hke2vG3D5B4K74fqnBLCTK9Ae9L93zsYGzvYnBHjJWdw8Hf+hBhfu/g8E98zjHwiO6fmPCwd7e/0DwfV5/wPBLfH5G//n/TB3dwG3wO3vhx78uPyH//7NDQR6Ac0R15YdzQXCbBrDuu7rRYk8mfZn2DlRRq5ibhC09yQJPA5UQ8QFH645imJXRfsWf8Y+bGOesJe/tdnq4LLaGm35Zcby4641d8Famu0y+s1hCj2euCq4s/+U7+3VM+FwsLDCPW9XG/O8+bBAh8fofLm/9PlIWpC6vfZ3jfU5PE3rL2ZkqJm4WHh8Xl4OZKiwbtHP3bViDaILiz2ylK5XH2ZOU95LHT1kDk+yc/0Y+VbJCAnaySQ6PkZxkr/5fCz86XCAm1DeZA6wQLU3IIn/lEgbctMVFkxr5jqlLkIhkCO7N3clc7UXJ6tm1S4zug09nAEtvp1IfrH9TjTPy4zmh5EAfa9E+ZeYtTSNEh8OLprKgaoqaSwhqXdIQ6vvpOZ/hTUy52HI+yk0jM8nG2m4puUsZJYwTmZjtaqoX/uusM2bv5uMDXWJBzGKdb/fDVQLhciNg7dZsTPeDJl1a9As7hpEk0GiIkOLuXwiOaEI0Som9uOePZuS5gMV4GRMPbF00bB0jGEms5xANJwRjA5aPOY7LrNdjwxpT8YaS0cNLSMOROc2mCJ7KJA7mUKrP6YzlZfhF1odtEb6u1117nzwJaBOq5jTXbjNMYmWRQz1Wy90xZgHWS68dOwST0YavoU1CUJF4fs8N/wCvWWn2+VcaGe8hb/XbJiyTIpBqvKfLXCOMzJnDP9QneYb1TTJPRBwviUSI6fe21g51ORxS5nadANqIqe8bFT+iFiYRZqntaaaJHICsXmIwA5xVVXNwV3pXgxZPjlj4o/Z6B5Q1NgF8YOkZrcone+2d3+wgWJaUagc3pLZDPNqYaxqlzCwE0WaZAG57aTi115wwSK5PGkREea3x7zKTICpbKaBUhN9z+GFav76KT00tsCQNCchHDo2JgGrHi3lzNGSjgchvfZH17gTFZjXNYfad9Y6r5oChCXMw6akPDqjX7mgfAPgFTN9DmRGkHo1dtDSbm1K00SC8Iae3u9kwRkhLilY19SWPsm64fqjvwfOw+9iU/ccMIqqtN6jqd1e/FLyFgQE8Snu7M4dHIpboseoepGbC35M+bYVoGRdE0PFem7yLVKiZn+bITLe+ezymwOyQBLL5Z1OS375Z0WILxDInnoW9SCO8wGt2Myf5AyUtFxLqH7MqWq/z+JNVqX1q3C8ZleJFflcqVtHefCdqRDohVZab6hSXvLQEHySi+Q4pESpTj4k2ubHfyXdvv5qwRIEVWWYa3U9FBzCPnk3Wipo8BRNtZXiMKtNG4rN5S6Lu0X99btFkey7Ykvt+i63KsOMB0PYnmMOdtJbKLOro4pcWC1UqJZ59YAg5duK2haUQ+pUk5cj620dV2lR8qZdqgO0pJHpDPJ567NOwbeWeC0eHsxaa4r8NAqiTHiN2dmYAjdkGAVxSBKrnMUvamovHIXr/orm/iYDV+lJxS/BmdIVdQYs1vMOLobLXk18FJA5IDwb/6S5YcqHvL0hJJTR3juLijmtHcPa1ooi3XLhC/qyj8mcM9lQk9Ym/FZEqlZZxQem8G5K2+ljfOQdFCWQEvSXlKOVXZ8xf3h7GxQAyvmUIYol0X29yRNzVEU9b1gu50tB+YSMyxlfsoogWrm5ufjjRdSD0F0o2NtuISDD32HEM1wbnvYMT3jgVkc0ftw6WR3qf9USMkTWRMXjDZia2duJwwasX91zRpLDBnEdCLy/xu3tELBZ3ZULfWW4X+1wzCGLPagso/NOZjYSxEKYP857WzSyUrLT4r88DpNB2nikLcZ+JJ23oWROfEjCEl/HGxf+em/hsofRp1vgM8CROi1BaEROSvu0WemdXCFSWbf+6FoFEeEqEYbvVstJwkUML098f7/47GxeUlimVfKp7cT/++iQyvqkLM2rmqT1xpdR6CcS4s4gSAcyp7Mif1Bn70c7Zxma4mThGCj2x1vU9F6Exm+iyrN2KWHsVYC6YoqURzhk/U6iySzXPlLHQuR3EF+eJNyuno5M9+TfB80EZvD7r6F387EorhlannbC6azZWV4VMT1KzfitAgZ1AUiibFf4EaojaWj1LOyvvAB1nKOs/c6KYxajjAULLTFkBH3TcYxiOZ+mdplU1gT3nD2coIY59bZ1x48suibwNFZ0XNgTlUf8BvUh1mh5uqku0t1kaR3eAL6GJZdBa0jznMNm0xYrn8raLUeC2U+UH6IMeZEKq/PUDlolb1Rumi+1rZuHIFanu30MpdBxZFcLen3Wmw5/Ksz4kYtL1nv6i/38PNxdclTLWstB7XTtEc6pyu8+Kv/Js/uGwUF8McCj927ZzREitymEP8O0iRHFO4TUAJ5AF5/3rFsB4Lrf2H8xO1uHG4BnMdmHFdqVNOBUKuQwIQInQXuJnLQnJNwXeDRvHx0EueZToMT1HPUOk7AYGfXrNeMkJTsL+SkEGva1ZWBZHncBKRWdGtWE2Sbvmk1Ffig3fDKp/TlOdoszwqSkKpM4Kcq0a7p82+KbRHDNVYtBdYiQGgfeOM6jhTCZ7vpM/Alfp2KUaiPcTBqL1pJF004tJMLmAMOr2MEGFE7dgzYBdajs5XnIBrHsrVOO6G0ZljV9RwVgkFEbtn7wEIPo6s4YrbgeQ9O2XJt9WZp6P87NtpTBWkuQssJYw6pM4WS11f0F5sbch0/k3Kh5wfu5gVaguoC9YeP0+GaKafU+gKcGb9gQ/TZ4+tilpoxaNW2FVb8ne15U0TDXcknnp0irlyFJLKjjPvPnhYOm8ON5cOEG1UT7C+18n4ZIEkFo5jQzNoopmwIMc7Nn6xTINCL8u0xN1evRbwrxOCYTKyFCPPhay006RI2URknpiMCnSDxgtqy7JHaoo4gUkRXSAldmvytfZVJCMypZ0M2ctK7LYAjlcsFPwHnetcJe3/A9djdvijX/ic170LvPEvvmxJG8tsTM92d2p3UDaH52fhgYScgVTSxV9xtZAjSZtOwqhoLvdhx+LzQs3iPaGQyJP68GbfMXpe18/Ezjx7TgGuIdjrPMBQncnZQoSyUFYjxJRs9ON3J8y0AtKS0kTrsfYi3kSnMg/VIR5FEjnEBPZgFAgYN8dmplYIeaCtg+n+11GacTkBPoyJqP2pZjbzn+6m3IzPaAsAtBAyz8SMN6cABhUH6Mqdjtxk+cEGzVtzUycijFYB9y7JLKsYzbWEGP7eamI0/krgatQJoPu4x+EjJZXjIstsgJuQ8TP6Vij54zZB0NtNt77dWvMeObaP/+oKRW4RLWiUDFKk+4fT7KvNsV/8uMd0ijMFPcN4ZRBcf9lA8JhoocsWckmw1zemj1C8aNUYi8Jobsuk9Hed7anuXDTqPrAS5EL01FphTIU5qafcKshEBCUCcB7XZOZuZ3L7TH4kLIFZKwTgZnxBU+0fAi9Q65m6FHjybH1bhzQcdAItB2Wy/ZjIT9IfCiw17GJprN3/mls8pYhR3Xb9OU91fJeEDU5JRto8rOHafjQ6nxyUEk+i/iH0JC+xg4ce+uM0s8sheTIq/j0K+rxrfWp3nvNbn0O4wXUI+R+ufpjm6AcEL2EzuPEyH8by/tbItx6GZtUCgSpXwveP6TUiaR0Mu9Nay/zQ/a9hwK1TZC+2TvopyavUHqIlM+XM7AlCZ1C+erN+ANKOYNRTVHTos7fqc4U9jR7ZmLmv3y8qQQt6tNT3uW8rVXUZVfZbZrqs/grG/Gs/Suk+Xa44NNu9DM27axQtqHiJLflZ6DSl/b4cL8pTYsdAQQOpImYs48Y0d/ns9QfTFVLDcfzA88YSk8I740MyMJlGfAyc5md/5eZWmJoRoSH4bebwILQ47ocIitgLbiyBITQeMZd4CEUG1Er41VmGraWF7yOSrasfXyKWbnPWpaKprdGTsBXTM1EOsRJ5RhtTn8d4/Va1F2FNLLW9hxD1mjVTNKEI3loIhzk2dntNvKZ/lCvaUnt9+31LDzUPmMghQQL3Q/dD8x+nx6GPaJneuNg4X/WVpHxUaWVgwxmt9a3xShTe7kPvsMSLW/MVVzYSvf2nV5fq7H+iAeSYtklNOG4ta686ZmxFoaETst74Z57rvUi2Yl1vREvrRV9vtcwvKebHtLyC82uUJ1BRF6l//5MoW2951+zGBAuTRGnKsHiU1g5kSKbZUKkhXxLssFfpMQ3DLa9V2isaEmBEl+a3+azzFpCrLGOSOlVsU8H4bN9Nm63lbLTcIb+vu3QCyuHz1bbWVuYUuTqs5TtadrmV+lbc1n7nbOfItz3vmVqX15gHpRgERHOsQbMcT2EBe1PqQcQaPlKvDmE+Y9EeXldTxGY5u8L0atrxyLji5Qn1AeQxb1EBGJzUjN3+pcgtrtSNKydRWy8zD7PlsECcU77tc4NmjQbUzle/G1vEmYmtqHWBYe5/NtPar2o1a3+iNIUzTuXKuIYRNiIZlyTMI/GbnrVvr2+TAHKGSp4Ic2AmA4pdt2RI/NMZI0uWurbDvakPVIxtwjOuM5D7LEkO9qYnDtLJDwyWd3vRyGLX9tPeZk6D8K7OmJ3ucXFnUIdHrUYFlea6fWeYtfHC/ZLFjXiGf8oBEaLuBZhfT6u2KRIO+svtJnrGuhbBt39tgHuPQbAXrTNtlcyGf04tZ070HGZMmEEi/9jnMI4xENcTZ2G1NmTdroMwuNG4L2m/bL/MTzwYw7MRe92TA0PD7fs9KPcei4ZurFOv9vbRQ59AgbiMs8jxFCB5Esfo8t4iU67J+DFie1cfvIPEUzfhHZjo3TaCgKflCfDwmLq+22VeG1JKjqEBapOryuCZ8C7GtAsAfJs7TsHPTK9xqgCG+9j6PMWFwbj1lZPW1QUFdC4RqQEvpOFz1CRrHCCwn0G1+dTlm0akiDM1aqXVaFase63eAnLCjPKYP9nLJuoCn63BsPayH4wRhvonmz9gJxRytMQDH/+wyXj9srlKL9eXoLaOS1Wy5qZbXB8MQTZN9/OKgwWVZwt2Neqtg2n28rnCwjWwOALnMlNq5rMH461r2AYCnG3pC5oJKZRbCMTjEMNMZEJg/fgVzmSmTgk5z7ismuMQQ/1T72U7z5Il65Qz+iaQmrduEe5eorhNWDUKTgxYftJnYFzhGjXuiepTIHKKM43m1sjvC+i6pPP+5KBb3bvE4s9p812iILaL1eXF0ASjbtRxY78fc0m+ZwK36RS2g6uStdmdPHQOX+lZD0HiERqd1tsuxVHkS11xIIo4Dtx1f4s64noKqqAs/JOdZa55UYwGkdosBwrgQtKXm5515m8NdToCjV0a92TrePnOYY/SI6kiE79R6OvzIH7egX8mdG4k7UHA+jSXgf4uqeZdFBD+zNA98utxrDF6K5A6RMkzUOyArhRPud5cZh6r7tMTr8JorvKio4Jaj0O9gb+YFzqz0oi6uDvsAzqxnQXXNr3A+rdk4zZnIFtSFZ7S4zUihzrjAya/EG+w2g0Z3sCBx0/HJ25XId+MWE4chRz3EAHw5C2p/Dh3Jw9ltSbEOWpMbjdyWfN/xMo+kxEVjdKCtK4Q0XePQQ0ZN7sXAf9TZI0PFGRrj3bSispvI7vaF8aZEce+j5aoXRs7eDlUs4OxX7umCMVrjz9UqnDw6M3oEDxJzLuNOuZZAT/MNeStktOXhHofI6puwUj4gI8dDubp60rc+rgEyeyGGsxubdqZLDlR+owp0j3yKw7hmlvjtcextayhi/GAkooXrLaCbDsmxSkNORLL25tcOdM1dMBlX0kc1mgzyjvXJTCd77Bh4je4vfbCKLm/nFzvXg1yCht3VmCjTspvFg2fanpBbv5bzJn6izXXV/2pMz5Wvx2dTucy2aTJ21kKiHRdCRecT8bGCxnpNs0xNIx+G1QeXwQmMHgOjBCZRXLnuss1fDNtiU5XIC4SgHcAnPWX3QXefaS2MgnB/ge/nRvtKWwjre3ARjy/sB9Tm6bhw5/K6eEdU5XXR8IGUDSuIkuuOIU0ms0h3hqm/X2w1tzsfQqbpjrCyVvCQ+nVlkYCp5vm6RZcNuQ99uk/rzATYhZrRaQIK2pxhZ0U3y7ycHwxFeBlkuT11DGQ+sjcZuAU3y0U+kN6HbH5UI8Ps3YDE7LjivsiNmN+k34YZuhXz1cn5pcic4MihQTW8KvdTfA3KpO5s8xkMx3P2rnF3rQ/rKGVV/1EvViaj1SNubfobtvd8AbRPu54p4I3s/n5iFes1ZauIpJCZoY6j+Lv6AFqG5wcRdC1w/Wuv4wiCL2zSS5nZBpm1J4Dly1mYvqjTVFaSwc7PbAf2it6Q7r7CufFla/Ntbrlo1hY64TwUSmh2CUlBntMuaRvydMkk9zBwB6ucz0rcmXFEHfbj5CP6+pAQewHmxbZoVlsNPB2eSkwzYqCpEazHu8yYjm46jb8ReHxeEzURCGmWrk5XDOR2Rv9/8UJYrv/hdfNf0ehDnkrG4IAEdzR8xykti2/HKLhKJr5EJc8b4qFqqe/BOtuPpKKxJTPIIVq6H2qVLz+wd7kS9svqIwGKAsxSf+RjFfWmVuXkGwZflzB4LhdFiM9aR9MlejNis79bDeisXjWqh2HJlowBh+kXpSiSphDWCXV8BcRqgnay5jj0Ag/HzaiR34XIm9Y9CSfuMawcCmDhmNendt2nRCGBjZRjE6HDxEXum9ns/RHmBjAb4inlALYowtFdi8xPZA05h/CgyCpLitOht5qJA3e/V0Rh/VLO3XQ6UnkIW9vWEKFc6OiOs8g2YO9BdRfr32M9sOGrDq7cA2ln84fSR2lHihKT2Q1IrTtL5Rk47lv5hHTVohjk/WUuzMN8C6xlkyYKoEEOuUH0Hxmw/XkOSOvF8RrwusSVIrXFyeqWQEr3FttHxDKvUuu/0Js0iz+uJ8DklKhEx4kjiYZ25V9d0CkuSEt/QBza+nhOqq89AOBK6KZtFv6s0yILXEGJZe7p5CnIdmx++He4jjb1rdasSES9tUrGUwiu7y5p8t8FseppOK062ugUpYdZ/i0A8lQ0nWeFFuGZDddo+r3s6PHAur/2SzHaVHfc9/aCARdfW7UNPPjNfINfVg6fgoCoAAonJXB060O81M8SfHRmizl3TOGHKvHxosIaAgooOXqb/WqCa//hc3F4mk6C1hUOis7qQaMA+5Q11lvYJdlHCd+hbA0EwvtE8xF5uIIYwqszCsVSP+dA3JQkc8Yvvy1Ag0iPXlPmL3bDWNPzGKKjZOj1MXlHu1FR5PUVronR7yd/STjq5i/l3u/nGAp2Ee+7v8VjcjKl7kboaaw12QzLyc4Q/v/XgZ6e8WlZ6EpkZCvuZvD0TZ02NS7keSP9ulfi2mOO75zOXj9sl6JSwRMR/EciFN+9obkTo9LGbdCP27kcLjo0S8q2J2Q+n928wIzYxoZcc2j/71PBgnoRvXiXzUc3CLVHHDeh8TTpbNCxxIIc3A++Cae1gcuHDc2ej3ULFcM5Emo5HxXq/ChaUwD/bdj4+JlJgK2aWkawLugJEXDNnfrixfxQwbE+TZTOcNx4L2MFFKyJggmqunRYxmEmk3C/gjBttr/YY39Ui2rpP4uZdDc28BMoyuUt9UPDDIarrypz5JFxstj6Q81SOypsRkX/eciQbfJJs+MqWjwn9CeO4z0m7AyPuWWjmvLPa0SfGfkFzhuK37wPKaoTLKeHugFkhnOLPoK8CmUyB7hLe09wZBHQeuKJZp/mSdEfE0M1ZapDbGEsSd8NQZHkgX7Sr4s2slQ+HM+r8sLR9WEW9MarqAY9M8UjXbtIMBr4tSJCQLuZvZWFyJbPV+7nfUhc6qUiz8Wh3GMn1gA3Y4YIFvcQajQlcSf31erNCUmkMsglFfoNHQisCIPMqyN/Vctdvp3VP+VGvr11lHeVR8njqoi80rdTdPNTrDcCiILMZ6qXH9qAD595F0UyAYCp0nevxvsLhj/nBITQm1+1KLHYGWo+MSERXcYqa7O37G/JH4looJ2zK0sVcecS0x2STX77Eodq9w4csSzkNEwy7ENHfcVw4tnMpvsOSx/d/qGvsZjPV5iBjUFIo/AqtqRGiq/f0Kp5Te6T5jddz5dv5i5OFMO2KKUV4bM0UqvceSX5vmWoLRKD5nqxhs9j9mAxHJCo39TfNrWqlplQJSxxye84zzeH28/1Hanw8dCYhKfk9XGUTjGfWlxF1lonoPjTGbdQREq7aVHMWgRk4OVRtRts7P5f6R6FuvMrejtWc8ecd922izGVcGHTIRaJ7PYaaylcuXwlbrLEo1Os4nrRqjo/a+QZKhlrFm0PWRKKF6pbClQODZech4tRkbQLP/G7Bemxy6h9KdM+0rtU45KYRb0ywQF78oWekMCggJR7ykuP8sRsR7p0Kt2PfdDbS+Pt3ceXwRSRmvJRjWwPOHU0wl1qwwsWhWQxnhD2nuXzGpO91v+rWfNGuWdEOHm7TNGMd19zqQSnvMM11V0uAqcLoVnumg3/2f/peMwyK4AzFn9C1TeB0rfRmj5dbWxqU1oHpJPv2cHKJeY1M3ZJxEO1xSPcJnyYo8n2UUCfx+G0PFRomK81ZNej9frBHLU5yhszktlq1X5A8rPAWURpQ3q+Wac6vIFEtpIxHX1DCANiWk1483wOH3AT+60HpTRMwk8vrZy3VQInb4OmUz388aVWdpUSM2mS2Pv/eR+3l7VAtijDpojHMdJ5a/yOPBbMOnG+zRaiKnQMGyUbG8NTJ1i0t/mMffb/PaY2vCBsEP4nR5JYATX9ymtlbPveUvuMPy6iFEn2idquheSQ8+HJY8iR2xe9cBTXkmzcI0dP9PweSI+a/emwoV04sFbhrSydVS7P3oE1mvyUqlywPNX07d6bGEY93oSbhqlJxLfA+Q9mGFYUTz0dA0yee/mIVMQef8AGrlvGg/zAm7cOncwuS0bU9UjreoiOxwtW7a52SI6k8aGHiZcY38fZXOryTBdRS4S8MfpYFCfnb19lk6O79XJjurJdxlzYTMXktSZlUoSrTHU5aD7/ZpKKtDX6OnbnEenLT6l2MuiREOleWHjGxGn3IkIck2jt3yde7uaZr6iW46TkQIqfXynIQw3gBkmllRhjjwK/k2CxwydzcVOOHqNQIaXrOvKZU9tgOORgKcys7WJrOnCPlySNTPfKiLf6ExYPEUPrSV39lXn2sLMF7+uw/bbvJnjtik9sQ1YGnQh6SiX+N2x54OqIiwuT7faWOKqfIxgwItP2uTWWMJWnodu2EnaIKHU2QLrCo9Uu7VpGbQUgcEa6SmaNUjOSEs+QZrSudPW05gFF0S9rM0dSXtsqobKuLb+OpvckWPWSVGdZK8EjEg+JxdQ1CsIEH4+m3HQZT7U7D4bEARazrdlm1mZnAAkn7rar8j3KmxA8Cm2MriG0SkeGIyJALHoM1X5ZNkyYrFaAsg/EgBTW/50X50s9xEUnB5HAY0BGgdOzs8y68ewhNUqQOa/by89J55ac+uStZ5IbCxk+R0FGfYwJdOA6g+O96EyW0yLVm2rVNk/qvTa3sw/VnO8IBm1N3HfWqjKvDZtq0ki7Z5tdTiXuyIwUpcDJllX4UO9HPY9UnvlRrytu/02N4tFfs/Jaq+ft6U9bD5DiMFldIW/nE8TXqZqpfl9souv4k3bFoh61RVY22VBa2Sms3uB1HS2Efk2784c07CbU6rXgBwKe4r8S3rgTRbDb1w71oW7LliG4ScmtEyc4Ql+n3vSBuVYzIp2D2HlBr385PZs+3jHX6Eb9Ai+rJqVZ/nOXRYPWj4ccclK95Yusd39iL3mfMJbU56EnwEDB7W0WxTtieWjfhnU8jO7wnfWVVTzZ6Iiakg02NPNCvCgXAvUeJzt2Z1/2l7yXpwfQKwdNzd+l4iCPRa/zIrmVf9DO4lLFCcL3EE/zRo6P5cItVtBk11hs1qlGfzFLlo9BoWItPe05jjT0GDvEwC9NHQV6aGyBKo5jFMjsqI3JUimajSlQm2USGsBEdtTfp205gbBgNZEhRTPbzoUfWSKmVEE5F/KtTDtdOuGZ4ZPJIkIXLqRlEGcdjiuRp3LudAAKZu08BuM2+WZ56XnE3TzE12SKYnxJOpW3HML8MOFBOfIm47ZXgvLkWb8iI5fMUNP2uLCd/8raGz7vtTYi/sb5HtruhZD4B37JVSL6ivydeU0OQLS6fVx8rnvf066oiww7nlvvzrf2olqc1DtIRWfQp861BA7J5c7f8gQSHUKTIh58NJ46brkpFRbF0aZxZwYiVuKlxZnLiHMghvfRDGpzYFXSXguwra2Ih6w6uYfnwIEavPtIyLxDszqeMuyjCSWmHreLy5s6pEWJxcaru2buflicQpakDw674EghspG0apLVz2Lg4nJJd5b3a1OXLEsnwaD/FgnolCO3q8Ro7zpTF9b9/akiGrT8T1mCRWNFKaOcZMBqjDIoP6Hdl4tcN2iBHY3GSDtzUqO+R92vYvkffjUobxYAm5cWIgFONsEYOy+589rPp3MU1xq5lHKels+2Tr+gbkP1S9FnDyOuduyd0bqYMF7TtGqMum67m1KQb9WxHf+rTNiiN7ui7PkREIsJbIjlh/Pwnw56Pz0/i6thlBzYZRxTKKxNu4tZiRF14lN+VFYxd68MTdMeVFLdX4ZpLmnyjliyyIA8f6kz7Bt81lyCl2+eKSw75rb9+yl9L3P6OqqsRWs71Hvmb1ryLWTsVpU8fHg9xZOfTIh6sqCuzDTmvSiK16tIzvR4KxVCg1tlE57ig2xRFzNmSUmmzcnbhd5ZwTSbR6gtJUkTqak/GzoN3YjNwInMD+kV2AaY8B3ko8v7j2Si07SuorOPDVbxWR3jjH3/K7rpzRTby4dZ1975dp+z0pZvDfJJN0pEf9VviWxsIkU2+6VtkGPg2PWPz+iyRmlCv0tIhQbmmX0NdihWn4JVfSuIFKc4XUuR2CmtmXZaJJNJ+VZ/AYAMVMzvY6pnSKhQhTO9jda0clOdVmgeNps9ozZYHCR/MuszDP+Oelyq1L3/VblE0eFCJnc4vPy+tuU/04+rC2Scioj4UOnBOuSnTUhOSlwVi+tr95TtN58Pt1RDSvVTR3qG5jQv6FF1lqG3Wbwj6pTCGCS0JWVTUT8F7pqtF6TgKEGXSLR9cSRhEgEdRS4vDtkvTO65mivORgXlFyjZ2RmN81k2/PhGX9OloDi2uQIz0KyLq5eN6cXN4+PEzErT51o2kzTKMEjwvK3kJcsDF8tjvmeJrJ3F5LWthEWb5tgkeY1lwfr1L92rWvAnSPegTCD23785jh8PTp0Mqb9PlCRKGkHstunbHgMcXsnd6vzMrVnNEXKrGoHdXYuw8kvxZKUUeNluLz5JiyMmr3rw2EqC8hr+LtQeardNI8AmodCzBof6U8WkUf+9XKl8uKc1NfxboKaqPBsUKi9LK2hKXuRwN7bGP0SzINNHrp5vbhipoxCQvpp5Sz4FvsrMF71Dq+eql4zM1p7ZZruqSkQJxkra3Q0598dAm+kJnhgJyskRAx29pcquPq0qIQQZrFfQd2RQo6HbDarIAy7zsQhx1x94TOZUhVCvCgL53cgivIw4vNRhWJh3DELGjU7bYZ7jw5NnF9FINXd13ncUQxuwZTv0aALaEcArPsVnQ+Usgkn2iS9KLCIMQYdAg2WUziU+27AdPZEhKKIVEQTENb6iWLtydt9Vxl34odDOBdt1WZmYAHE5lzbnrGy9QkHaHQTGgZUyZVoBFFRfa6Z73E2s8uo08y8l9v7hdhoEMT38LosMjdmyJ2aB4Y0XJkuAi2hel24JKN/UoSqiJWbp2TOny3LpoqfErrqgewxLsQArcXkji4Q3cyaKrh/IEBMsBsewwgjxndAwp7nfnOp4LcZWFtpI86x3X2hYFFyczuNWJMvWpZRlVvbigMRQL4owbvC33zgTbrzZi6WWRybBwSJrzFZsEgUeIBCihZrgb0eOJa4qD34VLC8sFeG0K76D0Ll+iubzJg8kJDZZ8Ajh4InoX+cyDUOJtfylYxdvoZ88h9Gb3Q792oUL5kM7Wmlo4Nwewv9vKC9by7KQXnWdA7VlIH0UmsV96AZiktMyqcv8m+znnAyIkSIBhDWf+kDcvS1I9gqwmPHF/r7kJ20z2RVcwtrwUzrTk4p05B1OgUdWIabfi7fJJg5mG8piK1n1GfE8d+/YwkSVrAIZCBmPPZPI1Atxs1m7TJ7TcxF7sgn5Mkq8U87+2lnhmKji+U6xRqWqyya6gTUxG7HDwuRFfY6UZAIHiDwG6kX3tHGxadHAoMzwBlndHlyWQWimFnLQ4vELm8JPl9+aSWI53pMc887xrdhWmJxhjCPn3kUayorG/xATvxtLehv1rv2UF+JRx2TjFZ48LAaAapk6lWV1yugyioVQQ7w8zFlSu9hbRVTrRbCsX8CSrdSowJlyhmw2H4hbLWldFMo8RpoYPfe1n9cZuEtFjls1GhG9SPj/y+HSpAmskjITmva9NFn5w5/jGJRg1FV9SuFRZMkHuAT3ejfkVaWo99c8aZrhi/kxjKWxufjiDH/qMy13mBHCf60TetXxO1FNR/IlD+imP0WiRfXY3f3NIMzIRMaX1sAA5eLvYTFfuCYselnRItqUGZ0welbDoVazCaevLoXZ149eHkawPu+SHbFE6Wp2Fwq+F2nMuDsOw4YkDkvTKjLaDsIVh45lhI2YcsLFTq/XGkzTcn7jv1Slrd15qhev8suZTFksC7JFYHhhS8QM8i7lPGlytXqsrUuKOM9oekgFo9unOfZURqGTPRZMEwkZkd+4kHNBSnV/RHRym8WCfpehFyBCcTYu/P/fEyEYi9K6WKKy1k9NzuiajsKUxVib1Oleap0a4+vwQ/2QEodGqHPVRXs20ZbAOGJG2Xbq8OKBrMkz+OfGYOjZCoOeZYpQRVuyQRbdMq5LR/gFZJbgseE0Qcl64WvTL8viiKC8nhmQ00Qi5BMx+22PeUNtnSctgIwRGMlU3oEmiknZLTDxBWqUX2c3UlsoxYhEOj2mKDXKViXiyQOm2t6HoeRnqtM+yQvhoHWVzZ8dg1l6Wcam4R/k4Tmg3CVlD4uIHlQxp7qFVhY3u5q+efY5HPhQeB4eBPwkkmrYLacYaAtX3oTyhrRYU+nKoqRd8K7TezyVc9i34B2UPy9ee9Oc/0k/vQZjdfY7o6HXbXWM461op+8xnOgk/wtJTOsBA8b1/SeSyDNJe+Be1RN4BdUq4MCfPJxhlz/FzrFGFtZT3eVwHpfCeki58GA9woDlK5OyN1S7T4MalxsFBMcExJ/srfMY8kZoijBH/dwdyHU211sYMDwEzaFP1Mwon/uI4K4V6u3T1oyOoqaozlhKSjluL2JbcuTh5xGnms6mXo/VpGy3Tp6W7e8FzmTEzMMx4kbBE9F0elik7DLvWWmX7zlJzsdKYqSeTBwbN3XXywYrGrUf4B0RxBw9q0Q17R7NJr6s2hWxcuvGjruyo1NkCjkrusHl+yTq1mW70qV87a9Gkzs7EUiNrK/E4LL7t0QTQ6wvWcxBUZySHkxIPwQRnw/FjkYUsZ8JqNWmounRn5MBseoyrczgjOvdgHUubX0Dui+2DiBDc9YtsHXMael4XZUGV3fwkUurR+TloaLEjaagTPweGauJWsWI87pb72S2q87HQD43dR2dBy37aTi0JNfqrPY9kOOOeOjMUHPx59bYSu0V1w1Gfp5O+hCuYKa5YozAbG5b+yotvvFrnMDKTqvGw4AuJedBEFm0SdcTKyxgx3e/0/Fp8mr3D60B3YiK52K3hkKZw74/EWxHQF3ymtNMOQkLxr9dE9ldsMNTCYiDW4vJDkNdvTX93OOa12rSYp/DDLLVX0S9uLbYLhjB/9+tYy93STLuPnzW2MX+vgqnhL8KEHwhE70l0OHVxMWxou6neP5yP87PJvtThJ5M1mcto/brZ8zqXZyf/WT47zJ6DHeYYwwhphFzlvgGdvh1ED2mTfFsfWkBA2oH69UHQkScTX8xdBreUjPOzopTCBWvBdbsnrhMqUL9q9qe2OrVcqYEXUkWpCgqzF7QlfVOePf/i1VpNoXj6mgCg8xiSs3Q14w09FhsOBh/J68SwmyDG4LugxeByy8BXiKGxMZxRG9bsa3PgwWIvKqXzCiG0NosJ6WJrMuVB7SLsZTUiRCnrtoYp7NaG/p4WNZ6e4HXQ7XX4t8MPoVWHCU9Z7huu43FUB9nzW6NK9/rGkKXfi+Wi7hlCbQtSIzWhYxKJOUpLBIGWW2wKryul1oYJJY0lBa8zMjbwbbzA8hVRFrRwn0BNNo/PTNYmwd9tqIW8GQcnSlvNS3hFlul792A9TQSJ0wkfH4Sp2exnClAUecffOwKdnDaamZkGRj/1PQG/ZGFQyLCcslvJKOU2CsReftQt2bDDpdraFz95/9BG+RsOzkM2x3cLG1ZrP8dAhvnO6Yc9Dz9HvUhOvX8MEjzTOCMFYIaWp5rc2bCiVahcQ537Acoq/6I0UP/zjU4l+mApiAUG5o5Ychr8/yx6KqijuOd3sddKqApJ98UUKUJa1mBBXsUca7/zMKJV4bSAB+LHJe7T+qgE6e9Ie9vwuNGD5iqOaD9mSNT60j+mytcKY+w3VUnpfDm7ymYmENgn1KLuzM58Fxd8vUDP8ixu7EJHIkxZzCJBYNrOmm6Ky5skTXnNQuocTOe62UhGvPny5bjx94HzcZvoUmeq+UlGH+z+wAFFHQIohf0pAyjHgN7FOiIFwF0oy9DQfdwnDp9vVumMk56vxr3xFr/bH7PzEvCjgE+TRgpy7RexO+XvQOVMiWei6sRmhWnNrdvcL9SNXfdnWi+Ww18ftFJucf8ZcfYUt4TFzNzUiOctFz6PXP6KgJ6ERqklAn/Xf8WQwRPIaxwec4Qo0uXb2dDJ/EEDPXy1WmhxXcuvAzukPaGnVi3zyHf6517+pDapNjEH1dk1dUWr9Arj0NmH/V9TcVCL55GBzt0MLWcsZCM7P0STdZxWJsZM2p0srUK0hZoVw6mVKegeS1PsINbQ0Q8yCXsDgheVvsLv89qbWPkFaVCVHhh8go4SLzJvD1fp7MxHiBV9jmjEeK9+R2KZm1+VJO8shYVNI35W0yV1JQvt6G2WmJSJ28JwBgHjz/tLoCNH7rhp/HXq2j3wvoShpL6OgVZ8wYKz7qvueyO3B0fG/m6iYafKhwvdur2hkfr1VfLKU2azfZnqyOzftMS+a5U/bcqMFhWJwjV4SinMrj68MiEmJh0I4ynQlFb+LN86quNKFnrJuarzWzqgnjwyUfvJCyU/bKeYTcdKFWK0gdyZP5r6HSDJrJetI923yoIaYT63/4DhEP0VtqGvSCFXK3cWAwsmEIfaN7ZdxXzXrC0+S4KL3SQLj/c2xzH0JcRs076Xt1QdX5ERyWvdiagkf6t15hfR1A6cr8fMchR0nLAb+V3iZLkD/K2fauna5g7VFwxPDkTT+246N211TEbGRMhr0b3t1xuLVCcpxfwY22qAcxEUZu8A6o1hBC6h1eYrXKyqfyk6DIvqfbryAtYBdyoTcuGgGDbRZYpj9HXLfaERo2qT29305Cf+7k19perAMfz1xkjyacltsf5pU18Ag9JY6vilo+bmnPz7l2rvzQoAgUruVw3eybvUoVc2L7l9OVNSjWfB6IqmoN/RvMTm9Dr5j/aw5YKPi883fm3n80gHtsOaI2znAZogYtcu53ea+yzTWbDOTYW/Ak2VtPrtjKsWOUY/49K8cY9XM7geBnvipOd7yZ1YW+zO/hZs6Aba0S6Zvwfmv9NN2UaPgyCxE0q0hpJUil1Ke9fC2jYzIf4LRGhd30aSYPO1gHlUdG2tX3SGcfszsiT9xXMgwndYiDRV/oZhKBgiFARFpgKi1d+MOlaQXuTkKvt3X1I33g/TipSqaZ+lenhoL2X2+NVLbT4sDaDiM9pjTdSjpJ7zSXKU6Mx/DWQyPuhZjeNt19mat/ocSuRIT33usuc74Z6kyr2YUwGbWPOp172f4fXHEH2Ub1/olFm+HX1G4mj6o3TKRRyvMlrf2uiV/j4e8OQbNFVZhJgCaIKlsJHyvZa5p5Dqinju63JrVEI7xluhdDOLlEfqximkWDeehgtBpBXb9ESm7tkvKk3seNUauB5Tf+2wX4Frc5XfgkgtZxVUWjbJnBSHD9vjST1d43kgqgh6GiklZX3JTXusNvQwy69SvtUivGZp8tPhlNr6aDcx/RimOjvn1bOZzeUQ0tJgMbJL7Ca+8IzTND6zHIebhzqzqxoKBcmec54lIcuXa5pfe3sBCts+DSmHf3/f2dhP/rBtRpLDX1nWiiDqGP6u7U74mRgRo+42570UtgrVJ/wz5k01WZdsJ8/5RgOeOBmB2UsXGoKAnxIu89vupL5Rh7bV69zQQzQ2dnL55SKzT5DZXwDbZnXoi+YhmFBxsGlQwUbJRV2jevdtDadxuZLUJlb3BVY168boyLKXdR/EROJ+7khjPvNeDdVcWw9ZlCTP4AP31YCUheRrXiHavrfe4dU+2REnHOcB21GquIPqkWrjlrKeB26kCUErWV3YjwjG4eaYEBypm70D1ty6ipjvn78+y25ACLaaaw6942ETcMqxLm1aWa1Xtb4agfaztdfp+k0PjwqiXHG7XWuK0i4M61vQ06Odpt5fZRMxUuNHcOeZnvRLRWE7BAo94UauGnU5xjtdp2P8ro6jyJ9QZ3DNCvRrpbf/tbu1BNiUSjSXLnX+KGAP71W9QWBjGcU2bMj5iHb5cLkcRIa0/Do0sNMHJetFNPJCz85Cp65Y4PsrmGnQyJVrujdvPX5x0yQjqmwXNVd+GvszRfsLhqlraQ67N0z8DIBlYGD8jXiZpMWSpkHTZlgouYj3IX7V9hlST2P4ykag8Bd+Xhgl39QniJ8ucCFGWDHtkl3epd6hBQi+o3axz189138aMllNBa8Hd6gWn1xQoXoz879QLMeQiT9CCnwUu8lzrl+hejcZLCuu8T3zV25flcxdrmq7SEl5t2/7N8paYVckbxk59e39EJ81ZRpWgLQADVZisWR9zOInLi8cl8mm31dv4tccQazPjj4oJpsbJfUyqvc7wIPGaSvhU2chiMeDNGyLRbtH+s25s349ZY6batToKZb59IDy9L70ZAaBuM/6qCTFomcfMTcCEEvVlB+DSNbLLn8RqVZl45bonGKvptvlnaLlfwJgpROT3IoMuSV7SHzrYISz88jDHf895m+6AMPxRcXSBK+PaGaEzgb9zR1ozxPIFIxJkibXOGjSSwdDKzfVpTYWJ1b1Ez2P71In3YCSrLuFTEFdI3c9xAsSGcK8g8Rllv88C3sPA0Y79ZcdUjzzixHJCpSIdPJ1k1PTHELd8ybdh4mh/eDZTUIhXO3VMw0VlF1/mrBik6w7FPcs4CDKQcpNaTx3ghGlyMjeTapohY2h3+EXewYIXAoLBIyGOqafNWW6xE/H/AlfyUWQL1SjrdhHvuiWiJwQlsh/cKgq3YKIjv+FksZ2nQgYmub/LtKGUjTPLLaJzaT70XrRzCh1whWUv7kry3mYqqYDDz0sGzHX1lv9+ON3FRVtIdFUyKhDcxVl15D0DEYoz4+x43TZWIhxFh9f7p1KN3w/uTRh9uzfA3MwnWKBtkvL6tKI1nEm0tEoas4krnoyP/Np5U7OLSD43JLvnkM2NxHW+FU3LYa5Hscpn3fs8fQ5wn7pqUweonYSB9Y21immVxpcIySQwn7y3KkK4o/iJrFBJuQ86mkxEOy5CXbWh9MbtjdU/C7/TmS84eewNm3EMLmz3rGuOd7dQCaEa7JzVnK5JeyNHJk/JGrorxttZki9MBKcvGSJegeBjxDpThemND7W7N5NJ+yf/DEbohMWrI9/XnFQ2aHWbEZ+tSQvBQK4q7GKGwgnIZckXo20hw8h91IUrjDFA6Di0ql1F8MwQrqFMCwNPLgf2ElrOaZyXZ9XxcezxcWwH3Tw7z965SX6RTPv4BwYfZB5fg62nBqu9GzCEnkLI8x+T728/9GZ2Eihzh594vU4EknclL3HnO1RHq+8IUo6a8btIHvnSrW8WU5K7QzusBEfWoyfob0sQWLwrOvgLhmygfBH2Od5OaTwS+9vtvzHPboRV9thwSyuZRU0eNafPczwcE+m5xECnuBiZO89Sc6ueEMNhVNQ4bNfwpWFe7zbP0aoWpRXhpNS3PNyLuy4whDpargr4ZSDykwWFcu+juQyADRnx4SqdvrJQLyLmm355Fa976TJpPxYJSSqu8aWrIGibb8MbdicjmbymR8YC9BHt7ly+Unwcbc+WFsf6JeFpBFRhqp/9v9Kh3dhuNibr7pPKQ7BhDCmIb6gjG6uiNEko5vIY7R2ekzIXUBj/DBhIv+kdlpeJ8iLi6/RXqdZaMyB4zcufYokGFXYVYpPRccijCTG0f6SxKENv5umY6ZSL29hmRymBT/kLlND681wRBavFhvj6B8/pOBccBHSMdOhEx88biRsi40dD8ryxTOkixqYs7Av6UkvsrP2f6DrGlOJINbMRT6Gjs/gW0QWuHaSv8Wh/iVlrxqgZZoC/JUF+xi12Ajt8e7O4pifE72pUi1F40QeZGCyOo7DZSyVPGVeDWVeuT3NxD5gbb/+0v4gG72msZByvMnVdEJzJ/cet7sjpAH08sXcM5fmkJQSztQaaN4NMWgCgRb8QXhW896pHajB+lX/LPfpaKzS22Xcsk5qnXJUzxCoE614pEPlNw+7TgNdKjeKMI7SXokHggiJN6DnSsedJPPf9ShkODVBAXDr6YZIio81ZekNy7+1TXwk75q+am8jr6sLPO9om5XA7iWuVILEqcI96euExMTH+FJVC1xfUZc0FFs7+KLEw9HhQhmfp4faYRrN02dr0reUvKzq5CXpmMz9yKFhHNyeAEknPEsWZfOeDXIFddr1noRW2cw0xNYNHc3v93tOhUuHcKAF6hc36WTOwm+b6WXQuTo9iBgWgaYnbs573zCQfYZzqt6YMXg8XweI/UVqkpkaWqkBqqtup/5rxtjfJQyWiAUvfoRUtLlLJy/2nHZzHi28Rup+q5IX3E3n0VHxRamS7uJKAQHNK5bfUsuSSuxGslenEyZWSutgMjuw9bK2DJ4IS6R4mEBRccdUYjaycGNLx4T6eqmpgSdCHybJq3lvh+vKEHMf2fl4zXUgPv1wshqzdtskGSr9wlcDR4nff2s0MuJMl99lai9vG8/Byw/c+mFOMc/TJRn2S8ZztkVvr2tJJb1QZzX3h5UAo5LyhwyFef5zISLPHPK8p/ctGZMb5vTu4xUAJ2Dpx+uxvbH6A6QcljRa9E6k+N2c2cY6xMhI6EEbrUXCdqdSLRMFXXiabhuSMsORX30suHhSj3Xu6UIb1uubbk1oVRFM1FWFdyy2jSfa/MJfrTr9FCy/QtQRD8vK6wXPKZ0r5g/tLmpzJhpCZl7kZ81dCB8Jlp4fTKNn2HmssXZYMyyuSNobDDY+M0KbJJ7aiGTqcQtra0vDoSjDfH6mVlig/7gpfoX5dYe1xdvXlr6T8PTWOvQnkZHoRHRWc0lT1ukRU8Ad+X01XvjEvZttw5v7LUecp5AzVP5hxvycFMpE4TFp0yzOvsWYbFJPHM7t1RqF2C8ddajywBbVkGatsyhNNngpKE49V/uJYYG+tKwWRrvb/VUIREKjL3PYPpTOO47Qaxp2uqFwmE/WSn7p5de3G9Pbgvd86jkFSaUiVWI+HfGpqR1E3D/xXkW+VLbWI8us9nhx1358LhZJa/NSuyseSu61syKNIVV35VnQpvdzgsmaR5J8UYAbe4R1Nqnn4tc+4GroX28P4DTT0EAy9z2xdOfXx6HozlYbTP+ymPtRyC3avm1SZp2r5Itmoi1D6keQQ1eFNA7EaUuhuStV7WHZA1Ol+nUh/LPphiYXjLlnK6pzUJXzSYdMKaYS+uAhAW3nFyZBKOevBhK3989yq2eyHrv7wlP4n6SZcky4Cx9WSPzj+Lrp3sl7pjs9KloMRt1wpTt8wy0t/BSeh2aEiIaDeF1yc5khkQh3aXoV6hB5jwlg5uD5otVO94626eY8GwItFkrROTHYpOEsqAsifNqODcZojJNTF1/S3USDMCQXzQimoFzOo57Anzd063vYpTHhkTHVo4L8ejKxdZBXA/o9AHI6fU1Iwew3zO8UbB4WEtrWbwiaHZYmix3QtXRqqCLAIOh9qTeEK0yxLwYGZFlQUiif+zIxter7rCPb1hBsyUX9ECXQotdahmNIb8/03JzS9evOPh6kP3/is1z8BkCMeXa9py9j/v0Cze8BnUUNoN5IK8DVXdp6J3/3Cnd6aHp8K/h0wNs+u7WHHYbqvPehfsGSZTbX/H57OCtF5twVQ9hR6eIUMWEbXrznUuYilm+FV4gGi0Si+17nZmfAb5vFtR238qRijKHQaTZCz1NNn1YAxX+Uejvb0oFOUYz4G8pK40ZvAbZm5+LZDnWZFY+CPzvGpQ5JrNEnxshPuA+ZfSmX06a7LY/v7SvHs2E71L5jPk6A5NPdB/xPNOXO6/ldpPjj6EM/Z6KvwUH1UMEJNigJGgEiM5P0f0CloB2JVuv6E1VYu54IUom9wY29V3nLlWXrJZP0hWHy/by8CL8GWRz8fYQ2y+xgeEZna1UkpwxPAquNBmMjFKF/435UyJ2LPq98SjvsMtBvOkXpZ1iSMMmklH7uO+XWDSNv2MG6N2LfoPNm8h+Hv7/nMXy7sjTmjoR5uMBzagDk1ORv9uNgcVCaeoTENv8/AcYTOexJEG96v5pmidbaolX54kJauQqA/6qI4BpI9iJPuOG2ts607pQAutISsooYia/mjBThcxXTJf4uH/TXpRv0zNhR0j/OIZpleT/Ym7luu/5RWf252eLcWyDcZAldRCGYJXa5Ld1mpwt0GWGVKJCxLQB/2PERvmHYzIgzoJN5/lD5PaUdwXymcjiV+48URLAc58nr5JGxjVcJk1Al2XhN1teuYXP1+br0gXyf6f94O4IDXXXKRvES6PFr5kfNt299cKNpHCbslWmU51S1Ltr013xzzL0uCGmqKysCMHNSQhPk/tqU85pdQmmLsWIn032Sql8udyqYThvzujXphQQ5ElLeVPGl1nrcjqGiAKo5qMoSLyLBpRrTjWkTK7PNW0WDcUWGaIRecO5gw7lgD+oUET0meDBv5Lk7VFpDa8JGeNjzXpO6CwgQhsNShZ6T0gn5+M6G0jGA4RpLgGrFTz0fGs8ogB3Y/0l7biV8gSAOoftSpJwU0dKigOKS6uT/zCZcdspvUEFYwGx2BmDiUuPNKsr6L0ZBbTcQvyj3nZ6IeG0idI6HvqzPrpiZ/NDrR8kbKWLuyzGc8Pll3aHXBsiHqj4TJ+nna9ZYHiszhIRlMKiCKLH0sFOltO9BtwadAIG61pxdfd+ksac0yAlJ8a2VJDVwn0YLCMtMYNTMpXcFCiyta7TEuF24euSjBtAQA74j8I/gidNWcg3+6SZgfC7bHgBp6uZNqDzwBQVr79bFxtoK70wsoms7w+8/k9OliJi1dI5rHqlmmuL90WMLfllfLICilh4aOrHBKY3x1+HVoXSkiBUx3RDSm+Prn55SC5eWolwZFEb7GXbplueiIxHCJY/kQD8Ferg6B1iL5MFHKbBLsXJ+XMfVC9rcSEUF9DSgB1gKKdrFlhe3gCi5ahp4aKMvfZbHShb0CBd5jxBlmaRTfx5HWdVVbNi+Iw5AvZcsYtpXKhLckm4kovSxxSFRqzKO39acRuaWi1qs15PPO0W5FcmGnBqr7e1HN8XaH+YtEdUbJZdbBlT67h749VxvFSMoaxVmLfZ2HhNIb1Nf/T+uhDO0S1qtuKnI/PWINBAn9ZgvwugjqoCIQ+oQsqMTlhc9ZNoINkVM4SeMeIB21sRUxVhgEWvlq5KwC3beTK2NE0a9NZiJ+QaF7gZjJL7WArskuEGx/5qjkrWM+xhRsVUZMedBOllx2vTSeJrzhYSSEAoc9s5EfRn9YPlGphmbIiucWyWYWAu/31viPD6m0098et+31SyjXGY5/A2zLoKbb0CYtNxpjX64Q2NITmG9XP80l9hZXsAx5xAJMo1cxsF86NhSzpNIHVX8b4peKsGhUm4yX84RGgNt50/7C1LJmHe7LavzRcGPynH2ab1R77Le4KOCDdeFr4CaNp1/aonbB5LpKOzGLO5mrGmGAH8D7mvAw4n/+uu+/MVanHc5aun4Pk1gLeEPr4Yru9chn2fuQ3Q5eRIHXMdjnCjFwIyGxRVyZXsKM1mFqGqdFJojgXXcALDJs1/nh2Q+sMuXsnFfQ0jYjc2tRIgeZWK7gdA4kkHRW04RVpPNYo8tgpoZHiBI5sr5zbY3bH/HcRnA9hUoP2lyEMo7qBzIGgnUwr1GVTHxL/Ik7/fi52YPN/eiMvT7kaxFVAfkLk3l0XqtZFTpKFeOtP5KcABdlFckGCdKxy0h6jDL79XMMU+YG1K+t+kZo1535c1WzEwh9d9DEk4QUacVOZu9x5LSxF88JhuiSWTfNN3tRodhJYJ+1P4BX8JGRYi6E5t9Gp0l166+9gZDiz0Vk9+BZxsQEouSlGlP2WN2Azvda9qGDEUcefAvtdPbrOYFwnrlLOsOwIEl8vPa/FijNh5ksANjER7fpNds87fpZgxpaXV8i9OUzFy2hk8BUfNBC84EeZyHjy/fGsz+0NUVEpPrcRv/RsVkc3TGSSynhY7eFiZleQhx87z/8CyRxrMgGOtP7ualVrukDUr4RrJGR+O9/AkVR4IdtAmKpCEuODwsyVrL8upKJIS0XMqL6Z5qlKxiu40+UIpgzU5OKowGUE46y+y6MMycR4IsKiPwvwBfRjs8UiTnjeepTtHk5a2aTVgTZBN6bSaBLf249cBZbX3K6CZKdXKBJVFLJwVXbayOG6yacZ/hLmotTFWZaZDd/4/2btefwoisqA+y8HhmvUO4HQ0qToxIsl/bryJTY2yXF66GywXOpbL7r52cAIJ3hAhmQbHl5T392RICz5uJUI+cfljH2J4jyRP+QMktT15jJJg8B65vQ//kkWm6kwXCZ9rLzJHWda8dJxjvCUDcFNQDHlyWB1JWAb7lZu/FOmzS7zTVBxgnQ2/mxBTv6FSpH3cJiUe3TBLWVydJG3u8KJhhcerwg06msTtlMVGOL6iSddqGVwM1bOPTITg6McexLh2T5xjEswzMpfY8nLep60dugs3FCk1M4Lp0AjIc5SjzFem7mRrEq5F3KRpRZqRRPrz2Rdx3heL0Ct3C/3RLkvJ7gUCv0lTz7xRQgyXEd7fv/okFrqAlAQpob4UWltLwmSv5dqSd9OTz0YQOq1XkPXrkioR3CqLnYsdW/aD7/dkPkV9axZrzp1FH9zqieKsBiSDUdVJ/xcP2oP0EhYGAkiZpFXinW3aHbKQypQGtplhH8lDwUayslPlnm3wk4iKtoLtZxj6Vj/6q3OIZ1B4vr6yLBURuZ4OGv1TA6+fettOCettJitnjZRnMVmX81G2D2djw6+CNFx8Itu/VLJ3tLdHhb2VrOId0HMf3rV81md30u6gevO2DtJ5R3iVFAqDlSWC6zwxCsUH9LeNMA+C7JzaQ5zjno3o7ud1xT6MOrGHnzZJbwdjubysOfzaLxQ8oge2Rs9AR0o/H7AuUuLJdaCb0nDbK/V4pHs4h6mOYbeBaWvShm2gsqXg5MabW9Cvp7q1MeMnC7UW1vpAPAj9KFdYs6RzSZARmMUUCA6DzqEudTNGVstpLaW+hq/S4+rukqYCa0XMN1LAcF1uOz5m22dXeMkA0hbrV2xXuUsju0HRz2Mcx8Ck3I9TG6Ug+Uhl0Xjfl9QfmAPBGOZBKKdEVjYXUqHkNFgPwAnx10Eh2ZdMv1uPyQRzKM1gh0ZSD1ulsCycnN2RtVB+Emm8lkaqULtAs++D0+hv8rDSSUDdUQ04wkZskc9SVLIkgfRS4K9mVHJ5A1PYGdook5X4wxyUw+Pa/7PZ12M9kyW+np+6WUp0dTfK+OPsVEPkE+katL2nXGHE+zHd8cT/XCLyQEndFYbKx1+KMAqik4Sy5fkGuqNu5m8DRotmMpmXgdlRC3hBen9FXAqUtEHDwKMWF5YxRHXiRvP/bHJe2v3/VME35B/SECu42AHRcTo12c32HjICbejP7aOnpyo+6pYzYl2xmDn4n6mRNxbR5Jl7gDebGQAL1mdhdJTIXv90LXm4H6q/VmVxC7VrWPEaDEaZ8IfCjZhA9zoYPND64cc4CADHAbAs09w/l20fzzva9C8S5f6c7WNiEuDXqjHv+tLsNXenAujPCi2wAo0LldgF6gYAj7wlVwG9fgRALfBk+dXF0PYltHT01C9J4Ts3uKieKoX4OeZRWdcFhMkau5BpDyakxW9QA+N8lWl9bOOVO8bRjMLyZVyWB8cyTgfIGtExkRIliyb6VJEWS+cgFHHDKG8dAuujc0uk4EQwZOVc5+MZgdH1F/NYQhU27O8i2kAMvgBponyhI6e22qr0GtFj9OsGqzae/Hp3QmwrD6enHCierrqqKPXfhwOPvgh60ww6ZikEUNuOo5Yfd01l9/LCSGhGMNdQ1elcsSfQPrWEQS2UdfbSkizGBmFzQwEMbv8EOCgsJvbTU03gX7QDSKtIpAlSOjZ2SQsbKOwTCMUdB8M1UqbiBWVeMm7ceWx+4+6Pjr8Jp12Rb1gz+3ii+ijinMQR9T9NowWQ1D0yIbqfeZwmEru0SNOr8Pd7lhCOo/ogywd+FhQCajsCLl7P8JJ+BtFh8+/4s2/X27zCKA1IQaMRz3upgFYYSwFaSAstnWUr6oHxwxb/LacEEiPZdDyngT7oFO9W5NhQRndHuNhI1E6eFbQGRWhiy0naTY+OqLp2caWJoDU/bH2o+eRavlV/IuFW5QK315SlHxeeH2z60kvzKFaaroRDGWE/nIKII3UkMAxzL8/hr7w4juisg5U4KK22I/pGokXzHLA08OCv08MK6JJfYKcR+eU6/ALXFjCcXM4pY9alLn3aI8CsYgF8ZnFrJGhTBG1sI5PNzsDbJn03oYvBYaCjBOPorj+UWt92jmZeWK8iwdVKtYYpLBNEPojQSNZr7QtIASUuK6cs7ujMHFO7e2Si6bIMnK5aN5arfAaUYby/OfexvyZHDp5GTzg33scWGyZPC9FMnjClfpOUxv71K0r1XoN/Cnp9musFV0SeYtkkG3aRB1g4B9J5C8c5ORkU9gaEAF6puG7/zoa27F2tQbfOCV+ct5MKkhMCGaJyr5LPmHiXcmaZLyVph/oOi/gmLkEjLcLIXHzQkO1W11hZdQ6+yuDqu5Jp9qOzZhdXCTZB8sUegVpGFBpqmwD38CmbqRtsVQm/L0nNIwSU/YHO0SG4A2JhE/PWXWUW5csnrskO+9x+bJZ2i5+/5gpAsvtIHtSSbrQ2fY25v7lvRZSIAg0+hXYEjmIq9vsb8RJoFvHlK7shOkMGB/kEaEC7Er7uS0vC9HCgrwYCs9YvLJNC4HLCeiqp/EV6JFgeeSMm0y8wKaD9d/goPeOOIMjMFGurPVmVu2Tu9OMx+YyXzE9pKqy4gWE0nDLq7vXNvx/w5PgVGSdpfPMtYvjHl3r8Y0wduXUgGbgI7Q2MKbAV9Ez1xRAw/B8JcgcW4iU+LidLTDPicWoSu1pgKp6ACd5skXM1/7yWBbnoXLrw8v1a9GqXxZ6qjxsHQuh5KyW6y/eK2NnTw8yc4CCj/kBvSKwowKMtzvKhI1z/AhVSXwKaCzl98SFWhEW37SJ21KManeUQih7UbNb0MsbSoEh+sWky1J7XqgdQJXuLVtGQ7wpCp+un3XlvpcWxfWxyll3REkRiVNuvbFbzZa5KjQYPivDfnjfV/v3d8wwXbEwQUUGMp1WVdH11KF23N83rivf2efdx10yOR38f6Pb1lNPg+5eolY+DasgCB8zLkYzAS+2AXm5oRCLYZr09KS6W7K2F9Yy5wuOfhGrZqhiWds06xaGxEH9fh3ZDTg/7iy+lWYBqah0Sr4UTGuR4h72W7+4sUwx0UETbb8YkCN5v24NObLWdH9dmdZ2vX9hXGbtPeT2lb11aBeNmtcjITINvmUXc7AEqguDOj9KdFU/kF4oFSPJQKPQJOzIzbPpFqtMm8BrgBfnNicK+7loLw7WKscvLIgfq000pp940nMTuZB0EbY4dIUNXek82Avh6QPhQBLCujN5jQasHAEytfxK74BCircaVc/jcUga4OStqIf+A/mnGSELpei+Q8dvECvCrx38wnEfIOMryN1+CVQgsCLF2Xlu4RD1o9qaYKX3j/KOal4ft5SwyuI5zs9yBJ3xQVccSSUBtOtGfol5/DfjJw4tyK+/oQIedQ+5uj97acEXUb76l8Zr2AUILYruvfBsSnAVS3VtHZezqgHYGuym+jrlM/2Z3QOLJfpgaATxiCGMZUkLs7L1z7wLjQJsnMY2cgsumddMFcQHVJ1Gi+CO/KS+rHnS18Jdz4oEWm/3sNmSAnir8fvyktZRG6ze3o9jAhzKzCV9FuGL3N4IRVdp7CxEFQ2IJ+4Tc6X++cUBo9FXE7rF0mceA9lUk+59C32gmGE2kNgeESB1UWCV3BK0U8wtj/3N60VJeiKQOEKFdWkef4u1Cng9+rZ9Gny00+79mUTBc6nHJdrjWML/a6B/o9puacGuY1pfG4IcD2cMImck0x9J6D/oAjpn1tCV7A+aQtoMjTfysmXtWN/gqVIJpQNilHfJusQDZkBYZVOO8+7Il8fIBQnFIw0NJm0gdWYmTv/JMB+titoCjrLSPCfWXTY6c7m0clt3379lNueECF0FaorGM5y2+aZhl00uck0TvgBQoaJuiPq2TuqOPVumBjMP+Ko41+P4sb5z+r2M2XKXE3QaAtOrdcP5YwYDeVN450Gugd8jB6Ys7x/fior32d3eFGyFbGrelckQM8+YOtxO8YoO/VHpaKhdOg0sTpRKwl91TVNx4Qtdx7Chq3Bhvgq+JUHSCE4nIyGCzpwbBc3DHWzPcLq94bbK77Owqw1KNbFaRSlixc4eIOkKOc8JK4i0g1OjcEnxzBYWFyZI2iVaelwo6e0JTpfI1KI0MthtH8+DtmnMAnnVG9QL7jnHXcSiqpaob07BaKUm/wBIjfRN3t7wHKuAce9h5h58KHXbZ7qvnQbaExzsxtOttdpczOK1XVMDcLtd8gi0x5zib6bDr6vdjUAob7nseiutzVXlToNRBSH+hCiEoAjDuo9NiKKe79Ox5cZlGTg290lZujgKDMAYGB7QRZD1/jTkMGTH/zKa14OVWXFZkaPHoNvt6SJvADTEU+y386oH4PHjdBFQ4tJV4gEYnPDJrcaDFBAPnVZG19sEIwKg//aDq5xBApoKCJPnwIaDGSP0XW1tDrBsPAMR9BLSyIQEkXMpEW26U85xEeW3l65VVi1unvkv38z9YVxWjmslSv47vIZ1zqCPIMiei6haUEYZ789FtT+0jUB2D8Sqvi5ZFyGzAMT5EqsIrcshtos/6XIwzzAhTKt/5ldTwdtwtAHcDDXqPIeAplbmRzdHJlYW0KZW5kb2JqCjE0ODkgMCBvYmoKPDwKL0xlbmd0aDEgMjA0NgovTGVuZ3RoMiAxNDAxOQovTGVuZ3RoMyAwCi9MZW5ndGggMTUyNzAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqN9wVQXNvWBQrj7k6wxt3dneAQ3L2BxhppXII7AYJLsODuFtxdggcIDsEDhODwOOdKzv3+v+q96qruPaaOudaca+2mpVTXZJW0AlsA5cBOEFZONg4hgLSKlhYnB4CDg5uNg4MLhZZWCwRxAP5HjkKrA3R1A4GdhP5hIe0KNIe8ymTMIa+GKmAngKK7A4CTG8DJJ8TJL8TBAeDi4BD8jyHYVQggY+4BsgKosAEUwU5ANxRaabCztyvIxhbymuc/jwAGS0YAp6AgP8vf7gBJR6AryNLcCaBiDrEFOr5mtDR3AGiCLUFAiPf/hGAQsYVAnIXY2T09PdnMHd3YwK42YowsAE8QxBagAXQDunoArQB/lQxQNXcE/rs0NhRagJYtyO1fCk2wNcTT3BUIeBU4gCyBTm6vLu5OVkBXwGt2gKaCMkDNGej0L2PlfxmwAP69OABONs7/hvu391+BQE5/O5tbWoIdnc2dvEFONgBrkAMQoCanzAbxgrAAzJ2s/jI0d3ADv/qbe5iDHMwtXg3+pm4OkJN8BzB/rfDf9blZuoKcIW5sbiCHv2pk/yvM6zLLOllJgx0dgU4QN5S/+MmAXIGWr+vuzf7vzbV3Ans6+f4HWYOcrKz/KsPK3Zld2wnk4g5UkPm3zasI5Y/MBggB8HJwcAhwcwGALgCgl6Ut+18JtLydgX8rOf8Sv9bg7+sMdgZYv5YB9AdZA19/UHzdzD2AAIirO9Df95+K/0UonJwAK5AlBGABtAE5ofyJ/ioGWv8Lv+6/K8gLYMjx2n6cAI6/Pv99Mn7tMCuwk4P3H/O/t/i1Vk0VGQPmf5f8X6WUFNgL4MvKA2Dl4uYG8HLzA/gE+QD+/xtF3Rz0bxb/8FRwsgYDBP9F9nWV/kPY498dwPDv8WAE/G8sVfBr3wIBDH/a3IiDl8Py9Yvz/3Oz/+3y/6/H/4ry/9rm/5eRnLuDw996hn8Z/P/ozR1BDt7/tnjtW3fI6wyogF8nwen/muoC/zW4KkArkLvj/9UqQMxfZ0HSycbhvwsJcpMDeQGt1EEQS9u/e+NfYu2/5swB5ARUB7uB/jpZAKycHBz/R/c6XJb2r6eH22tL/q0Cvs7O/2aUdbIEW/01ZFy8fABzV1dzbxSO107i4uUF+HK+TqMV0OvvJgawszmBIa8ugNfq/AHWYFeUv7aUjxfALvmX6F+ID8Au9QcJANhl/iBBALvsfxE/J4D97R/ED2BX+YNe/VT/oFc/tf8igVe/d38QN4Bd8w/iAbBr/UGvzLT/iwRfM5j/Qa8ZLP6g1wyW/0V/rSK71T/ga0bgnyr+Qi7ur432x4ALwG79D/jKyeYf8JWU7X8hzyspW29n29dT8Y/Fqwz0D/jK1P4f8JXqP5O9cnX8A1/nlP0foV5PBnbwn2Svtq93xz/Ur0yd/6hfN8v5dYTAf4rleeXu7ODu9g+XV/qu/4CvXP9oef9CQI9/FMPzSt7NwdzN9h8ur2kgf1xeGUJsXYF/WL12JjvEE/wPh9cY7n825zXH33eKmyXY9Z+1vK6Lxz/ga62efyDXa1Cvf8DXrN5/w/9pfkt3V9fXG+Dv8+l1Mv6D/75ugEAvoCXKyiLYUjjUrj6047ZWksSTdW9KdI52TzedkdV3xbXT/R4DMYWxJjt4w/W3ZMpoH9bajizDtcQ3iiff47ZGxIj2pHdfHvweTRM1Zve+oCzPEAxNFx5LNgySIZOyakns+z25+OkE2cO2QXcr0ua5uAtgqBfg3noOvPVqGCxfnQhf3Hu3X8OnhPpY/pU1TjvWKKhknjbf4tMCERUChJUMiQnnpxfm/PXvOZzc6RcKxURmFP+TOO4iX4NNrg93Cz7rlVpcbj3ENMQGRGSw1zgTs3S+Uj9SFQmXfEuLYzdEl3I4GNmgNiYGKfzGuylPhQyVFXAIORxcVwTfTtD4f2lgD9LrR0PO3tM0c2aqdz7GWCJitvb0lHtboNZaPGCyPxKBfBPpymyl4hGPqOrtu7+1Anetf3ytdHxaP23oM2ZovY2Mso2/1iK1LvMU4eKPp4JA+mtfoMev0C6rjKbOt99u+sR5zmvfS7NzqEL2zB0DBbEwJQMTC7cws5mfdjTxug6Xxwi5RvCLBRSyUS+j4SjxNzntVTaXi/1nntP8CYN0AaUHH09P0iZvh+TpYgVeC5wWCjvLxFMD/EoYjIxoliT+3MLF0XKlOqFbagnFVatqidkvcifQkyoRBze9puX5D1bY1DbM9ffHoi0/xI6j4eYNPkWYv29tSdMljVa9xouW59Vn/7YjYlA/VPijMD9iMAmagER2sshwfIouL04BtY/nx8LNb/TYXYRUTCJbZ3vrlkhVw6psWgKpx9Gxyxq5PnEbjQylsgbjqMkEEc059eDWnhfM+Moi0evybcMJ0v6kFeNa6oD0CYO5XoNl7zGvkZrY+oi64SBdOhnDxLlDqCw6nKa99gVhKkHSL2Hq/nf+XYkb3f2z5teLdmymT1Nxed/G5Sma1p3240yOboc840fnVFrmmLwcWxcI9vWr0/AmxE7W0Nlc93ArE5ytfTlkevz4JCoOcg8/CRsfsdd02fpNz7DdlmhIwn0frVWah3XMdvhtskanuxZIGLM+7EveUvDzyVFu4M36eQXZp8a0bpJvCxmj3AlySgEKBOFF/BIEWvySayoPauoY0WzCH9YTYuXHmEHhenU5wv71H29z8PbtBvX1rmWX3CsoOj7zDn9xXk2QUugo3sqP1gQQPJbl8Tu/64zj4bCEls+YX+D56W54gu6p39mfmpmz94mrwxAy83V0LPXb59jGbHHbXxnRrRbZW89MK2QwNIEoVkNFZtE0APUwtLCsKKEgLKrgvIsyxg9lftqz50Nlx7HWhAh3FbgyTYWQW6x5q7S+kjzRyqxs3smrjvfMJM2FZ3b8vbTHIImXNIJt3BFsRmLo2chxLXaoGZfIN9prM3KHdTcJbZzJENnp4K8t6wUS6LMFgYScxP2f6+VMNMRTWT+lfLrsK3YhCEU0yaZ8iIVh4rTxvDng6snnYS/Oy23l7pao2Daq5fdZNfc5muUkPWSMamHbzPh5FRQj6xWsT98+Un5zxBspZ/le1Mmui2u7cwFh0RLvR4InJ1rc4+3ED+5Ai1wF44js6F7v1nqhDxO76NaL+WVrRzFJe0cXxQ+l1jYntN3O+Q8ioAsKNXIGet+y+IX6cQzPimfNfU1gFr2AzMvF+CzJdgPX9FTPvj/5jVxVoNRCKXgurj3xFNcELqQ37cygOBcB9XjlhT4loSocA1rNd1YuParffCtLdyTo4XZamHc9u1DdUXJ+hDlPZ2nMZ33s88csSwKKCCIzbhMtDJRcdQ8Uuje1tTu9R5WJxjcaGlnl0kXScCY1vE+C1L8kT7eYxYeljmkO1oYZjyQ+1guVMOjS77PARkA9Sw2+LUcjZnX9OKGVH56yQKAPCoTDDJVisf8EW3pAh/V+qkKWKsv94suK/uZ9mdm5GPasiRk5VCNDStUcPYJjxj0ZqhI9ptL2HDq9WC52tuymuiqLkMoP32hN7rdcGhgSfEa5m+KsPxdMCPTi6ANXf1y/lyNgc7FN74e/0SfJvXxwCDYvL/WcWFGt1naeLDGrqvq1nakiWIewa4qnaPPVwpNyOw1tosNtIGhM62syVJVMD6xF8KiQrgul8XvQUL7nlySzlx+FZXCIsfR7fWabtiYIfWhaWvEKFU7yUUCH3eYC7M7Jj5cfbvrzzHDWfy/uBKUH/l5aB9CXkjtHbmAwiMJV6HO27f+uiMrT7p6tLGU/UbuH10KhNaM7IfUNZSBSGhRikz4BTSoJFnR7yQ+y4npsyz7O83iZ8pJH7sXnrbQ/RmcOsPzKKbHaduCltxi3WHdhSZX/zQZQS6r9ITC4hayq6nRjMPD2Eh7RxX2SvGW4blElGabrV6/Nw+G8FTssDBsi+DSAaaAzFWervQtzrBuI53w73oTCeVbvD8YMVII6YUg995V8M+OmskNmJg7ARstpGvcnxvLDuczGgcIpNfn6rYTh4Rtnt0euCrTe47KkXQKlDtxViD2v6NEQXZZ+/gmIfMXzKnzcj7CU6FO4h3RNjDrQZ0cOru573KGZXMi2IqIaZU/Fs89Q/nD/Z1k2RoHPktkQM+a0KfDRylEyUnUVemCID57ZPqXf8/sqc6bqSasDbdQEOERGNogCqa1L4tJzJDkTB2yey1lge11gjdhBqCr+sDbyIPcQxUj8Jj/mQKCgGqTZkNsv7ievKwfsVZvz0PLzw7IA4kaKbjqpi4zyYGfHB9+1U7N6vKQRK89kEr+lUPS3ii8+7C3ZP+TpuwxoQqNWoDKWVaYKBeIKNz/EDxlgTUR+gC5EEuHf8K1Rdv6O3X+jw0iLi7P1w35sjTsk9CRDC1z4KO3kzyPWYvDiOlLr+8WVsFfb4EVQtjx3cf5t9ZZ7Io/gN7FNMzCxS9cke3DhVsAhhtd2arxmmF+2tSDFUqV4j94Fm/uIlJf1WwvZRzZPXeGHsAwwSw8yYZGUsr29UpMvoqA1ko3NIwu0w6FfBV0HSeBXIgyVEXOmz/M0v6PIuj2o15rFTvi1TywP30/2NyNWQNG3wOBFOLWqYs5kTHHGZh4U634fqnhGLF150VlDK0tXdUdgFnz5hsnDjx3j/RaTYbbveWMDvTQIva8nmqrKZ2wbA6zWZ5rsV2lp8EzqI1W4HyLRYhAoSbHedbcpYf4Z6JW6R2IhnnAgRjWFnwL9Moqo3xN2fpa9SBh1qFVsI9XoAIe7Eieo/SR3pFgjToVj6Gzch/oNfsx5yjZ2uTdvqudj6UW4NoxA4PRCkk/+wfcu5XDxhsB7mCTCRyFypRjh6uv7fN0X2gm95NBrOJrlCCFCi1FB551GOROrTKkp8kVFDQZoPkNWLaZTHsrsuFlvfSgzepWmJwZY1CqkmtMxFdwKdUnqIevEQC0qa9JYKjhx4E3as14J3UeNrgHZ4WNU41Iu5cCj8AEoPji0C3FbRgG6CXx2KXMs65DfqUmUhO0ckU0/IVgtKiKDMbB6HNA+nZwbNHw0+C3+D0G6IhucdnugbX6+gyeaLp46IHRtYwPgoEx5j8VjKl/L/d0PcJvQ+qXohT9Zk2zjeCnDjubY+CQHW1J3zQfO74yoVg8EMSHUV2MKCJPCXwLVjQlxOkebLtoINKC7n1Mef6S0K8CyJFONiGd9Sgr6vWMhlTs9NfK4JOC3tvp2Ouf7+rmRcLs8GorT0BaCJVfkRo0Xhd4UAl4eJ6W40Cb+FnZb6h4WAYSavH+TQJy2bfYmxSY0WrMbaOiTmQuchArzB4xt2DKm3cucoMHasd6HIYUOnKiF8nlc+7gXJkhYhCDHDCKDvBoth9bNXFdx6Fx7DkjRMRROjhBw4onjv7WZimEjBaFzah5pWsH/giLa06c5CzNjLOyrup4dIFoSgo8mOOtOwTZGvdXAMZ6RU6jWsvf6mWVnY5Ejn2SFxMYghNK7fV6RTcoWOwOTRlvo7NJd/kuM4H2lmMmhjxfLN1+Tj5OWxq5Z23zg6pbwprbr/JHvlRD5JK016afpJUCKmvCMy3aQscAdDZ53a4tUJFjZFT3QV9S6p7mShh4gdzvoz3QdQnvHFIOBc19+D7FyKyDIkoZW9bFs/pJZ8hDM+zZg8v1yX2sOEHDEFUM2b1VBigMdvWNs4eZD3z0itDdtrivZWJ4hxkPEjPIEU8zrviGqSK51eV+VJB+JDy1Qqr/VQuDUijsz7ftYxxqE2XF7j5QDefnqr/54kgwZwC8oUfRqSBFYGpnTIF3dYiM08h0zc19/McSPJKFoKvlFe9jG3RsZ/xjG32e04OoUHK6qSNF09BwzfZnxlCdXZV9Zj9oAf9mlZted1vmmeuPbPnMjoi7wzcKMW5nKgk5aDGP5bXrqVe8mYrnihb7OxXiWQONJD2fvE4IVtaIyJ4E4rkrjiDgu1WCWRcnEu14M0vOuNmvLlpm5XNfGzhpUERuHgLjztERe/RqladEgzb0L42MrFInIwkBTRN4FebfN4IfrNfyhI/bL7faYCXsd0nX3fZvd98lP+TGLZ1j+9DwOKTPJexJ3Nd2magobbfzEWdPsvgkmcD7erFauk0Re6NYk+Z0TFPc4/Iobs4sXt6FLcz03jZqfynboc9+Q+PgStx1bNBXNWdqrWyAFDAVBLaNw5Nf19d8WT/qQ7CjMVVGGomuFtMZolNPMsRSRB5eesOf9kIIqPNkOopNNRxDobcgYflRB9tS+yT7hSVv+NsCSvQkqjtA322hjAi4b6zPHufxSSSxkavkU0E7S47PWLvLdO0h7X3pGIjxN/3M4Sa6Mcfv4hqP7p/Apvgd/tUNiWTYtiyYD7IW2gExsU+5onYLGM+I8hEYSHo1DqngygsL6maajn1sYGOhl4HDFtA+6vXTd+G1+ugYMd6FHM/LKyKMS+xFT/FZq/Z/3x0JddGPCzRMviWvWqghzhNwIaRE9yJmoVEQmEMjTvQ+Xtu0EVRWM4NOHeONsF9YxWY2Sou92lckt5vF7WWps1JzoBfBHH0fKq6opCd/IzofTC5M9h3ESACJ2wrPH3tMTX5kbDAEcDkhL5yp/899R5U9ZBGp9lUilekeS7Ok8ZZeFn7Rgq8d50yBMf106A99oUuyCKTLNcH71/r6X920PaihQAMs0wXzKBJWEwhTZKo3ZoUKLypccxFdr6uGqo7ZKUBCOBJ1gjZB6kBtcbhuyYcOe4fUlv1Z19nRoTptL/NJG+gzvXVAFHrUzir++AqKSYfDdYCRL19yNZPq5hvoEBaK6PUjkRlcLei+pMAF989gsEjKlFvlRfSjTYSL+t5YvkxGHFuokOe2I1Le9PNU8tHpxeI7kNGfil9Nn5Z+NyXqNdejfvt/JJJurIuyJmQfhuHbwLCUtSBDfWXWVp/UmH3jU8OI1wBy1Z/MC8QdkrDWYWyxF1UEwL24Vq3qJAmTPmfMzjo4xgZmrwrJVNcpHA8bZ8n5Vmkl9tXURLQDq65FuxO6Sn7fjZI0lewe1PV+GMMsdDjCu60nuMnYAIzell6GafBgEd9H7P4GFZ73dlNzQgUhS+lo2WOlwCN8Q6vwJtPK1zNOnIoMjcyumYazxs+Ztcou/zD//1Cs2ZYYkc+ZzMUouYvhulN6e71iGLNXmJVaU+qgzvRsvS9Nc0tizYqI4cFA9JVyOt8C1G40ow4Px7HQ3HSyBJSLl2CDiLjLtduwXhWX/sfH+3cWvBnfrX9sFKBf0CsiqcAVftsVHcV3syi8yGFHGPhxnztUz5zTnS+IMz5LNuBZsfynjo5OfSZYWXHR0hffr53etx7l6e0crFHGp6PrcLPKdocLmrUnfQGNDQI2X+iGCSaDS9vd5YsLeTf17/TCZ8SSq29ZkGjxx6xZ+8rRvmRv+2G0f9cZ3wT8iD7gfs+m3mbXCgH781wG5XreCcl0fFxHGkgApeO4oE+fpyaAdJFsoFEFVCDYx2ccwweAv7iDLfbfJXcVATxRLA7/mXjx4GukRw1lyUPQS8GcI6leqG6iH+s2uIeE33H6SiT5yW/o8v3URlzVJ5cfSFbdKcg4ZA17waLNLLAy7sdXE+y8GhfFzNTwjNtqGZat4P4tT5eju+QUKL1Qr8J3FwSTrwdAbWUX/ULr75ebPFUvs+06S0fON4hEEUW/zAz6I2z1WA4cbRivLhTfRwo884Pj+o8gh10MPP7GFpH9YUG7GmyJlvXk1voMHdix6GDMhiun5i06Yxu7GxFLydmj8CwGbWAVomuu3LCkvEypm3v5vj4qzCcfEGS0/rsF/v0rNfOHfUkKlFJAa6/zgAD3KtoYGA74mnyZoWnCP9EBRagnVFJ/kMBApJ+u4VDHJAcvCbC2eJ6OL6EVamQOV0E0ChjdbApmRKTG6NkUc37Z5xHnbcAiKlTRSkXMeFp8aVQGnKhWa0TTdsEelFLRaYhfmPS5xVoA/mvIpkDW+2Hs40PhVgW5OOFycf3aivPOCmnUF78jfv4tsyPDwuTjIGgz5CYxcueCJNysdngCWWdykt4tX6NDXmHv9xCdH62PamiRAHs6H/hIQo7InLf2rIKo6+ZrjK5VrJolb1pSmdPU+jGjfV+8xtAfD+Yd5ExFK1mJOmlpe5eTvKtjYCqxizwQLyQpnUJwRVtTZOIqCZ3QZCvGwYdR1gOlBmw8bzZZMX6SwoZlLq/DX5HSdrMJV/Kc19N0zLNRhW4KrPDIPLMVNbVTseQ3AA+B1vF3S0tyGkK/WeeGwAYQbp2/7xOYfl5uOJyNZcrHyKtN3bOxHjWFkBXBbxGHTc5fNzCNV2c2cEUowD6a2D7m4QLCl95BxZAZ2wwGJHT9h6eINKGNzV76dBb1r9FDdVhaYDumYaCgoNYLfUe9OovTNSg/MFE2lajbJbUvmXS4OcQXQKIVH2S6nh0m6mQa6lVVuKofPD7IsupOLVZSI08x9LcBKWQr7TrPaoeBWEs8FG7JL7o+9C3F+iRpdLmTi2D8r6O/LMPehNe8XwQqLOkYDiKvGoEUtm5GYOEPTDYMm0xO7cNLqz8/JvIU1pfSqAp6YKpG0iMyyQh6xKNFjVKGwrXPl52Xvt5LMi70IhKf10T6YNTii3dRNDQHjLs/Py6gs3DZBLmIsA28MnMbI+kuzuPf7YoEzEwxFdTZDy+CHYvwOgCjNicZdy0tu0/Na5T18TS497LaikKVdOXdxsDUXiurHJiFuaekEYTWsMQ8qkfcEIw2S4oKi3fMR5h/niEZ88e7vVca/RDf/pvNX3ndy/1T8nqiIZzVXx3Bik5mtwDTM4qiUBvK7IoHziK3Zf3RxeK7Xcvaxxne3QkQky+PkZq11g2DCZ/FXhMg96iTURerIzOd1KMEJV4TvRE1BVNkWwZDmjLF0Gu/DmfLFfJ0TgLFuKlHf4td5sc/rOxywOdjX7ynUC6U+tugykRlIjw5lRavlHGkGBSIfczQcBi6YxxO2B08t9rQj7GmG1vFOpdyw+vNHBSPNDxGssPZmMRG2KkhXx/tyjrSFS0anbxgMNuwiVhO7qiUyvEVCfeOUjsO3Q4HXEV8gCNrh3bDAlnlSvamnIKf3VO5DR4feY8Gpt6NKOYTS9WjwVpmysPbIaiu8I6VjWQiXR6wOD8Gabe54axhsHXY6gE+XVKbe49XaSKeAQ5SZ8XJcGA8SZcAcT8v74SMUbOgPyKQ5uhdbhJs1bsw/UHDwqPZW/7XyaBU7Z0gOGwzBbveZ9dwVnrWFsiwSTK051EPTFDjB8f2UiLHwSb9Skcce363tJz2NK3MWJPMpYp+fMZRxw5EuRll34t08+o4yfxJjVYPN78CWoPClq8TJxGYqtsiejhdrQCSWbNYT0zYKZi2hytjvYCF+0ppruzeOWemwaOWUTfTFcN143G1eeAMkIoaugLPdCrizqDziY6eEdZMMt/ZRoVyTNBXffkHZpMEWvFlSmAmB2XWpHYjIN13q4wZdt1Vk7o/+fVhB+wHG06qST7NelMVop9bJP2mfv2YMDN/WevatkKIHnkQe9483sCx3v1vciW3U7a+MKYcez4VPqJDBI6/GICSBxGFwnffDNEzE2zjjwNGJpeF4nejy4gnS2pSyOSjx2Ff0+tasRblqjSuYrTlaO8zeiIim59szbnFZKpLzCGvHpZfjxzgGboQuvCsL2Vch9xdFk2m06fa/kJMy6FIWFNhKQ//weXK82z5CVQy1VS7ovXWU7AFPdUh4dup+juSbr4KskR8ratSznt18MTx/4YM8J09FLzzgXVkfwYUUvsrVwpjHJPrNYJnv4uX0NL4uD3XsMzN9OvXp32Id9uOqGY2POLh5GX+CN8YI+GBEhGVX0vn3zFo1zog3PbaN3mYsorEM82HVRvy2re788aFI2Nh0KtH8IhLRrydVKETQjhY1P6I3Au4hhCrNB3/kZB2uK6Wbn2bXjXkNronmc6HU8B46D9NmhjFO4KjYxJu25ncIWxL5CqxgpAQQlGDWMOJNizeuNlxdrwuUX/YjxJBF7mrLyH/RNWniYhyTrpQelp58HG5CIDT9pxr1UycHAnxb697vcE1HFj0rynmLZQYn6rGWGrmPNmk9L4KWlvV96tJpmnji2MisXNL7KSzWpLm99nL89KXVWP2dfsf614hF4PP0Kd2T1TOrQcaJlflg2IjTnws4K2cW8GflLlFzW6P3I8/aVJAWbAcB2bP843e22S6xv4my7thQya4jqMtNOwsRL4Mg9WdNYu61zKbms80wVNrOFMxV2yFL4Z8jWdr5fZThW/Y0pLAfq7wz8ePF3/i7J4LSdXZlNGva+JpkDOjKcB83AHmfycpiHppku5TRH+g+b8JeNTx0M4QLzwwQaHiX/kY6ICZzkLVKYPxhrZQSpUK+PD25MFiTq7VXRRkQojuSmwHqZrDYIRz9iJZWp6cUAPXt2JAF6al0NzLYA+ew3zNPg4kiMZpz0GWk6MqFrQEmxxYzcQ3PQO19eZEWliNtQjeYdfamow2+JN5UWr0wNvHnA1qJkXN5B+Kyo/OLGhfwfcNghVEepx5yoBkPCv9gmv6gYRTurJ5IPB6V0dmuQeZGxJPHLK0uZVR+En6VoPuhWMxjyuyblMsCTnDfu0iJdgNqe8z2DRw8fTKixZl42qBsXFTfz2XOXBakpyLoEtl1lkc9KQRWF5DPFOcm7f0SwoLHb4IkzPjv9ioISKJpc2eL4kWH9z6vX1EoPVEz6X9TnpJB7E66t+liwtteUNociFC0JyjdaxT0N9XP1NRlWlmZLe7dJfUI5+0pZiRuUWTre7uUnuhdo7Oe/zDo2BUaV+jyMU2sVotHzQK7zFRCMEVhs7BDjK2PiM4yEQHF/tJS8x3CRo+u0sTXonTVmk9ss8w4dYyMAarRxzk81p77qnWjHdJbJXiLlVJoL6aOb7GqnF3C7G74CsicaszI54OQNdrBPL8nu0Z/U1jlQ4ZsPytg0/Uk4P/yk7AN3Am2yW82g044XMN3FWooFoYS7IK7BiIsBjGzRu9UCKrqNIyzTrAncJ96tFiWrlp0DkBaDJ7zP3xu2JqojlBh4W+uyk3zpof+1MCqEuTPhzLwozONz51BlVC2n5MVJAIP+56MVvbQR+3bm4F7ZEwRMC/LE6WOss9J2FLc4in9RoKhO5TRed4XvrhOJ03RFBOziRaO8Df1Yyx4ONs6Bnewvh9kTlLHGCKb2HOpDWJyvtKuLGYBodNWq/tycNLM3O7CnGghk/Wd1vQKTy9xJl76LHxl4QL+H1JtyYO9f7nmfrLdJUQGx6rT4g5TmYnPlrV30/gJKGRiuJYOBfRklqHSsPzy+jBX//Y0iiuVD/OKgwjHneMi7sJuLD7naFj6jcq0lgwZG2sFElzXo+swSNZOqf3n0qM0rnzwiZpFPtmvz2FOvzQO47OrD+42bnVlGfafcsiY/NFp+dNavpbhhxWJ8eMMhVzbM+dLnUW3mFMcwuG7V+XyyQ8tzh8xAyNHHm/tTVg+VxwVD2kQOVofb9o9C5+NO6FXMKTh3lI7Dpte4zQl7JXAKhsUDdt6paNgtZKzST+63D0cpX2De/Asmw2zSVAb8rEWJzknYLwcy7CKHk4jwrr2HFYhP8hGSoiG4rgbu4cSz8MffH1jpmijeld1lYfd7L/qOCnO8Y5MU9cxW8ZxwWnKSvKrcABVKHsaVlZIHf7rP9d+IjzvkqFCmXMhKV0ynPiZo5c27WP3ztgY/ObUttlFxLpjlilW51Km9xQgmSc0XBcH57FPEZFWuXPiWaJiodrTDMHzsRRrL90hZbhGvalZTLZcmZxaHLqPZZa7iTv4DUBKQ5ITtyDDsgTxTKbLjwbXIILlsraalW5B+IgshNT8fOPAWOq+iYNjFSRWIw8n0Q6NDW4eyN6+f/UDquqaLf6Ug/wb8NJgJvMbjTO7sVxT13rR8gTbabMgBcyDIqnM8sBCGbj3BLA/XGOS9TLQvHGlHE/sjeBdOOYuf4cFqm1Ba8q976kZfdcsu4MxUTGkvQfV0Lej87NDGnGosFycbjBiXi4x3eeTbBdF35jR6ZtYUTfLEm6HlZRIn6NPgIa+T19mtOvSk3MPBrMo+riMGdi7myJdSpooPM1o3gERRr2s90+O2Mds6hV/pbW/Q4dEzlh3NzYOeJ2pEGVCWyEad2abSgZJwN/N8r580KHLR3ehvbhzIRfaGhl2E8BlDiDyVYtYu5/OdKqqjYX9Lvep/PxpF5uWbCa89GsZaQJeY1gAlAKqtLkatFDolBQmbqQtMiELw8EIF1l5D2+/fgYGQV77IFW1PBbgInsvn1gplFzzbl0ST6WqznJ01elS8af07otzDSaafMH2KPXP0nl1aAHCk9Qvjw+V3f1VSCzLvEYIazY1NtRM1C6RbV9tCgYIUhKc0sdJKHXYEloc8piO3u8PAgCoUtOTt8EN9eNJIGflH6YFIleNB4ckZndooYGBT/BMOpjW77mJnz8Vr+Zp+6w+ezNCM3Cy7ao5e7s3gFpviDTduVUf4dtFR4KiPnhGRzjf/DJn6/YlGsO0rsAVL/RReJc22immL0TJE0ki9O4cUoI1edPK4Uh1+PJ+NpaPaBZSR/KN355DAy965mKktcErJnYF5/3Z+BMnIcsyEEqVtm9yDDmh7uOtT6uDIL3DPnNbD4JxZrGKxQNlDi/SLvnnFdGeYhQRh9WcfMsP/kGNBuB4zi6v1pTPsqmo+FiacgQw9yNuqTEbv+b9BxMx9y/x4mvuvInzGLT3v6bs6qEGhu/riEYE3PGhynWou1Jl8mO5N/bTUgYbJPVkQvswCL1F0Qr5itSqEHfB5hb3Np325AJB/ncZmIqHA7cyosReyk39pkrdreAA4Zt2kWayqziWHK/vkJGi3Vdhoo/z32Qn1zwCdg4Gq1v6Z7dulV7cbRapf5JkrojFfYseK6BPq27I5D49tSrSRw3KqM2q2Ppul0vZktBXCdvZtqzkp8GPqsBBz4Q6Jw5GsEHeW4IVL2cIZ7Ug9TEZEmQZdqP8kf4dBaSpYPwJSkiV9gzutMZ3Tkkxh2rU+kGuDZqR/rGz/ou+HYiA5sq0X5ykFOo8T2yD7613sL658oL6yXh1KTwLu0l54F3FamwOR+z7KzEuU4R8KIqMZWUVcAGbeWWgZhqV6vb4GjtHXujolRUneRQc3oz56YdGVKhI/tN4R+EnMVzFcisiwb63jqjyw0pH+qrYn+5TaGc1mRq8L1wQlm4Q6QYaBpsj/CXnDx17ZWa01CGoLpBf5AwM8KSj9efb7hCb36tPXFHaXfq5eUpLg+OZNau0KVumcgBgjpVj4t5DOgvJ+noUljei8ZYiVaTIAOzACrIj8B75sF/mZ18xNNWnSSZuwZ6J5p2LZ++OhYAiqhAVM3wqPqxNFsH6NQUtaZ2BvHiMVL0tb9Wvlb/uComnZwgUCZazeYPLO94eks+IubjaP3lSihmmuB/q5FAXN7O/i0rTLSqvklV2acHROfMSxWnblibjsmqVx1Tzqva8m5rc0LKuU941XA1xfdYfMnA7IKkyMhbuMIeO6Q9dvgB4OoDe+K794nLOsdYjm5MmSAdz7wHiWU1Tw4jr5PPzwUPAm/Wu/pDuz0WUTs5CRhRpdWRxizrxPUEzJFrmG6Zri28rzoqM1rn9+gZEqG13krrz5OCP1OOF8wsYPB+/5DQPIXZVlqeD5RALU6DCIKSpax0MxoTD7ia13yZhidgr9smZiC97w1sers6JGMt7NfzNGabo9GpahPNr47qhLWlTGRFH4ess6JYl+pxtL9iapvXHTaQdqMOOP8dEllHpGrGa4t/eh500sg3fBSz3f7i/evlcUi96knYtHdBVlGcVybXRZbVEddCeXBg27z0/3HGmJbKSILW5Hbm7KmnuXJWETv2L65emdaMVoWbkG+BP64NgodSbup9UIWjKCMN+11Tt7Gygdns4PTUfiMehUROKfqYkMQYuzlCz5A9evdhIBteYqOnWiZjOp0Y24rUs3j4+jV5qZGV4Ad9K3JBw8SCBexM+GjW1hLJmkDtb01iM1k1U2xxU3cnsJe4DVjubbzzSEeqPe/lIzs/Et1n08+qEDfAdtpwGLbzepx1iQfQ/vMzvkd/QbrfRBgqUO162HE4P61+DFy8dVUbqUCgc6QR38CedCMU2CwV7JMeTF+bCeXVjvHGGYmig0y6pGS1vpI/YXtYzh1pjK3TeCYcklpRW9aH/LEf01jInm/ttq4khcVS3//aAJXo7mmXJueVNaVUM+2nwRNFnnN345fdLs/YL5vA7Vdi4LWlowZIV+GTVT+vKIXuSV6eDjFzkNsGLNooPpeF9MEmqNfvwZ6bFAsH+4knHchsGMKCxhqzJhaHEp9mx1lYmanxevWnbYxYAi3TYhVSwESEfGbtolDGs6N1Yj/HeatW3AwHTpfifNKpd3DHogGqQASuqPLFqCiFbaftAIsFnBSWngK2fKDDCTwTfe9W0/TyMWNlhVjtylhhniHNlrxAe7AFjcWQ2MX71cJhB7LwDmIYulaHShAwKO6g1xytP1MdCJ7VZJ+GRnfAY5bJWAiuF5+U/YyC+ThdGqOvhscFZilUKiGhoRzJ4V0Xa61SO2hm++Gh22Opf6ZnMzo2+q0QT4NaM2jB3mzO15Kt/c5C12D/d+CzCrNtEr63iYpS2Wi5y4h8glQCLkYGHLpiD6rm+sCeJz/WYvvXiUD67IBDW0yc6JYGILtMDEn86Q/r8czE9JguCJFvdjJFCYM8SkE1/qfP8FCHjopX2TpRopJ1/RVTv6LaLp1lX6DDWEgcGx6M7FzCbiOeXB4NdF8wl8gzFmyWwIWvgFa81vvDC/iMug8RjwvA4dtuyzSvbkvasZ5SpMKw7ebKE0j+FiT/LGnPHfAuJ8hcr3pfuhWIO4ubuYG1ne0UTI7ueANb30XSn6DaXCv5QNJrKLBoeVS2IABvlT6GJqajkruhkaFyI8WMDrh5ISR+RVAcac9IGxSI8jPkJjXeULF/sSeERlN+lqqVQf8jk6IKtbX02/WBLj/lmibGwe+Uq//rHQF3gdhrUVZV8KRJZcxUpWMxhDYaVwUJYHK849Y1O2VH/O429+cfl6muZzfRUr5vJSRJnad68BC3tH2VEpIEZKUp9gYEjtkWqv25drrgN8ZtpKS3bWFTkC01qmgxauHZ1Z8z9LbwsjQrE/daxuCWYBp6VC4fOD7mza/sSytTQdl3nnfwTU1ygfpTOLya9q3cYCPHN3XVz0YyyMbTkQRbLImv3tzfzVscK8ibvOW3KPzOssmrodnDugLL1We/qmpw6JBpvC+56+XWbENr7ExpX5JOMMZrkia1sKuh5aDVZkN6dy+2bpP8T4IXZowRCYQXJV7x0Ks9OqZAVN0STJl/0PrqO8BiIvCQU2vhwOOJyTK0j3n39fkw7AQPFP8vfWJgXnT9buzhBpoO4laKRw0SuG3LWK5VOxZb5ATMmU0qfn5yYx/VTFg1Qyn+wx41ujgXW0iEhs+4mJ3NNUJQ/I4XlW9F6hyxZfu5HAJKrdLbVCD9yZwzka8eRPBSJAuwnB1arilMNk1vV95J7o7uRgll3cxitP9dCYNOOQCTGLeLAcWFPQQ6NhRGNjt/LuoRVJ7YCvb/7qe9ivJGoxxOa5kiFL7tkd9MiKpcGEu4stn+kJ6PMON9nwEqaZMDO0WnHGSGvumgL2nkUOH0W8CNd1TA9CnbpkX+ZUJVBkSjOraTDtQmL9axq1FKhaUidCdtfPqkjg9sb+LL0WAXxOulLnjIbkXU0n8uPwWo4JGCrpQ4JszsDd+3v+yRTC1IM2S4+8YRyaeLEJg59EQkXgcUfNEz9BS0WhvG83blNqSePUNXXnFsYGnLQ5tRirsBmZ6E9qLUMp42tyw19LprCLOw2CbJ/AFWQ4+rar8V+gMolS7V4fzi8qnTCuNVC6b05H8yvlVzWclecuTmM6XIkWXvQV1rychbRcN0n5ZXO02FYNdV02G523q2RVmcjs1XkquR+8+mShXllGZl9tLT8ohcvJlT83aXKiPNzhXO2zjRWHx+GRyj7985leMRhOmi/hveVgzwv8AWxxt9k35vZtRpK8827bewfrmGxZMhpTqz3bqsPEOyum2NiYM9qZ5PBB0wuQb/dyNzvmBe6HiiAkJywdjxeJ68cOrXB+tZ0eF+JIbJz+VZjDKYqAeUrQlYfeF3Oa2ZyM6elLc4v7YfLqqHEdZw2qsHNaij3SLgvcrnNfgwEttSqSsBb4/Ti+sJKF5r7xoqc9oxTxezEtcosC3W4L1iNnbPpAn3Yx6kpcwf4vBThNQQ804LxZcRynjQX5snqU5ob1WETn0xzdq1shdpOTSxCqe7N5ONsre44IKSVukgfxnrwJjYnGhryxFlSp7bS9VOPuzkNRYztCMEVWj06gv7y5L4BHix2V0v6zlNxhFBKd/xcFpvDXW+lhG6Ro6CQ8G+mD7ll8aWb54Tvn6HonFl+hmmaBJqpx5Q+9/8giePlOo0c5xKKS+l2IA0EII/4wrdr4BjzyeU3JkfJODmVmDOnlF8U00BKTucpmC0uEq9ypXHitDGmB0/JYMsvnteN9eil01/k8bFhGHPuoQnO79HeJ95kTKpWyQko3gbpdyU533YzBONJNN97vxErIT88nWMxowUvZYLh+d61T+PlYL7VH+/LMK49lMW1OLC5vP9NvYHcNYKghYHvyCyyI84SXvH00eYOPCyL1F3LfqPiZCtaVCCGFe2t6ql7avC0i1JwJR6JVp2HJtp+ctIaQ5th1c3AdBOVCPZcPDglRDS3IWHWjZPusWBd5/2BX5iGbhq21R3tlv992khlaay803Igx9oEgKA0AN1lGwTCWd8q6uHDRTkfbEyaMIHFoAdEtFHY62HPNOpIjbYOJE/URogpF6c1werssujEmCTHV7QlqCjjc0wvWEEpmuldUpekdaFwfa0AOt070zz4tbQzqsrmUMZWqrIMhF+1Ghhv6q+gnj7Xwnj2RYcZdqdkOdQln6SOcI5DaSVjqLjtcW8Oo/v5is3sySmbpIRM+I+yjixu2iHXogQOOMMqxJqIfTKqnW8S81kCD2osZicajaOqXBKHGZepC/bhecRGRt+D93V6fXfnzLNsE8GnjEMziEiyYm6yumgmR0w0ykRCReqsmAIqGBBTxJ9GBXOTdxYW1vX7oQESeeaIQ4Iapt9oc3Zct1dTsATV2QxlrwQdAGy77nSNEfc5NLwxyBZfp+zKIpZCF02K7ovCoiga8odaDLLdRXLhFAr2dEWxPJESR2ZyicZZSHTG8KfJyeQfMia+cr6jm3YeYiVTq0wTMDwn3ekY/3gqyFCgl2w8Q+zDnIhqJPErDJf+k+IUrHUdHUp0UtySUklqzNtnMTbXIRxLjCjZS+SA61qe2zCsq7KgYqjALufcPJsqnqe6wAW6TABSVRAMr7kE83kiwRdePtRpxhCsjsYf4EJbOCwuZaOeBoJAKlY9aWOyXA+Lgu5Yhi96tdO+rmol0zf2afC5AuBRjFR+KYmehFx7GFAUHoysee7e18w6quhwH6eJqpxYThtWD6EP724SzTKM15zkg9F6dcyDkIpwC3/n07flRjHSbezWox8h8vpQSdXVIDhw6ZExIC+lnOv2JaP8pitp47TF+JgTv2QChxgqf5nCPUmnlFCuPJVWPuHBgd+qe9Fvl5BqRBuRy2oKmUjqVnip87286mrAg8ZK5hkvr1Ju6lqdl1r7XBo1C5qHSvXUJ8lDFhO2JeDHYmGhnUWrpI6+TV9jutDVY/5c5bHb1S1Qv3UQnsealbusBkEsfKxyz9cprRfvJecoPoCAHGhn08kAuA/RjmG3XM3zH2ShffOOTkOgSTdSdLQXlq41yzRmWs5hYRO3HqBz4/lY363CHRfCInrXAmpFfLcwOLNezCSTFGOJFTvZxSMKZwgw48YK519EU7T14C+E5Lg33WuJzl1bIl5c6KZu2qmIGTa/sIKFqKX3+rBVoodn0Qalx8m8feBNuzTJuPDNvpwo9dHP8qhLmYs5ONELr+RpCZ15IflCPB0x0/BMcy0RRCcze0O17aIbrdnKDVfooQTXmPl59Phq/L0tkp3TezeqeisidHYI7MwhXgVdzRr4/twOrvuUxNLfH9T51ZlTYACKpPQQE0cWrINlFEFzMlelWhrbtyBVOvqsoSaH/Lg5uI9fNkREMUir3KS08BijiTDUXRmlI7Vh9KP3Ba9nA5y/GWI7NDd84ltvSWTl40GwBFhr5iVz27d28bc3qU02rRLLI7AvL3v6LLOGr3wIAP6W7orurNBKH43LHP99X1z2aVazKezXrG9sCSa+smxvqD2+ayw7dhHrRhD92XcpUGQNeiUOeobpkkbsm0O6W8HcUZZIi+TUyEGeBOxEym+GqSyL+VZ6zvQyZdgVM4TVHltwUFT0qDRD7VB9MXhrupOqh3e5OSA56Bp2gBDo+ckJB0z6RWVgAHz83iVWqKXUs02O5er3qCNfQecZg0gLIqpl/vZId2ZStBYP8Rh3Fwmh4vjqwkOkh97UCTNLPZ5/9krB+S7kw9ZU3MIbtp9Ekjz1Bvp9Q6xN1KI6kuXrwjdePd8EPmqyf5+I5h0rHAuOd3QBCRhbXXAGispd3K/f2Bhg3plVfwrsP8L0jcDjQKT+zVq+0oDs0/JAfWXQ7NW9gSUlP9jHZ6iI2f9D7y4lgTtiQjgH7sp3leJippLpa106oOxGk4F8CaKeuSAYp1eXF9T8RdaVv3ag+MBINCxGBnrIH/9ox5rW9eYRHmIaTCKCmFtYmEqJEofDMGFsyZ93fBIhO8Fd4e/XnncC3biyxUqxiRngE5gmkPWlP8IFyKkcWdZqadgcM+OwqFehePm5+3tTyYcq9nN1d1MWoqyaWcCFQ6+TcOdh+YQhmkQ99iB/1AZCAybUk//XinyVTHWNaNpmf6nYhbZb1kjvHOkTTM80GMGfCVq/VuRyyTOmTOGr65l/bZPU6USr9r1df2a7m6tiJoCFQh/NRLZiarCroXNIkyrf8uxKfHTeUDz0f7mqTBLCAebSiFHSHHCWcI7/eOt05PVjsWvIhJSxZv6NHy0U609IYO5y/lPzaQFbz+Mjpn3gUB1jQCI+bFoBG2WOTJSw6bEFJzzB06h+ZAUDpowwxPkNCtfKYMaRr18Hourxei7nichJOhf5ic312C20plS/QAe3XO4SZrQ6xHaq4/4FtgACkGRgTyA1qWQ78fghH5D6a8bMovtZxpoTUCkQ3KhcbN69VzIO7gZiKSX5NfhGuLX01xVLZDCwzD3OlZ3BSyeeE8AbrXYQ6rgbdeN9SfLx5f/Q8nZHuLOoJEUJWh7k6rgmQRMGOItdXGUr2MmIE+1LZtUqepduL103ltTh6j/j3AFpHv0F3ZbWm9UthmLZ8ha8f4OEqW1eLJehrtN3a9kJT3U18mLoaqI4brJAARay7sfFpmo8bbfXu/lB1p0SO7FhFPtiwlQHZEny8ygmNFD94jh6JTbSG1rfibsz63WpjPTN0jghW37CsMidLhjpPIf+UTu8yHCslTFxQrJY3OnGEGaYYPaosfXoDXmxkBX9YIRBoIM8UoLTnlH4DRVbyyyqLPvHoezEOBoJ8dPWrhajeeQ7DanydAt11ywJPiVtR7K96h5rkedIqjzSKRp4wzIB/Uvi9zTcQVQ9nMPKaFz7AzRCHbmQkLHZWXh48TARZTbKagrK4rjvKrupDgwNXp3aRMy+LpPnmxkMJYI9gc8OY15eOtnhHyycv2sqvygcbTktlFxs47kYWHEdE1wMmMXQmIriH9VzDvkqbKxEVFNa7ToU6zISObEZHpEczgfHMvF9nQnZN/e3d3kWS33S9L3dk2rgixqQZRjcX+aWnFsuMUf76sbcF4vxDNdtB3X8oUQlvJ6fE/KmIOmpe5p3aYghs9b5zEERMwXmjikocSZ1TXexlspi1d/+kFgXwfuNPkg3ytXUhGMZnZn63KdlVfHF+vcSdmjcV6yg4xTYfgXuhnlUV0NQ88kSy+nnuKnkSyFqUCjTE8cbp1uePHLuVJcL9Yf73A0SLqMDva77znC6eZq8W72kibKh2XAzQcTsiF9HnpplJfzBDKuMD28MvS9D9hd/UwJW2q1a721Q6ETxjXkXFTZV0njy91wr6lqW/GLuv3wY1Pl9HF91qDAV1XMcxv4DSH/25m4PV4RlkyQvtKxEVgwEc9WM5UCvp4jveGNlm32TGtscFQSRzdBTUY/BS5a1E2ELmJJO3yRZW4o6wfnGRvvJrUHNBQ8N1RdaQqPgbC10LQKNAb0ttNV4udutgRP424hmj5r3e+X7s5KT3wHnD9UxWAn09Z9ovy00Nme23FoZr4rhO1W4pfqOEgUuWX+dxy3yFYoK6H8kRDTlvD+dP5kojlJ357BFkknltmA8kin8fwDZRqzeCmVuZHN0cmVhbQplbmRvYmoKMTQ5MSAwIG9iago8PAovTGVuZ3RoMSAyNzk0Ci9MZW5ndGgyIDEzMjE3Ci9MZW5ndGgzIDAKL0xlbmd0aCAxNDc5NyAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o33BVQU2vcGDNMlDQpIjXQPQ3d3pyA9wABDd3d3N4h0p0i3tEVLCNLdjUi84733f/X+vm+t912sBTx777Pz2efM0L5Q02QVN3cwhcg42LuygtjYBQCSylpafAB2dk42dnYODFpaLairLeQfMQbtS4izC9TBXuAPA0lnCNgVJpMCu8LslB3sAQputgAQJwDEIwDiFWBnB3Cws/P/n6GDswBACuwONQcoswEUHOwhLhi0kg6OXs5QSytXWJj/+xfAYMYIAPHz87L8dRwgbgdxhpqB7QHKYFcriB0sohnYFqDpYAaFuHr9xwWDkJWrq6MAEOjh4cEGtnNhc3C2FGFkAXhAXa0AGhAXiLM7xBzwq2CACtgO8ndlbBi0AC0rqMvfck0HC1cPsDMEABPYQs0g9i6wE2725hBnACw4QFNeCaDqCLH/21jpbwMWwD+9AYDYQP+6++f0L0dQ+78Og83MHOwcwfZeUHtLgAXUFgJQlVFic/V0ZQGA7c1/GYJtXRxg58HuYKgt2BRm8FfmYICMuDoADCvwn/JczJyhjq4ubC5Q218lAn+5gXVZ2t5c0sHODmLv6oLxKz8pqDPEDNZ2L+Dfk7Wxd/Cw9/kHWEDtzS1+FWHu5gjUtoc6uUHkpf4xgYkwfsssIa4AbnZ2dj5OdgDECQDxNLMC/nKv5eUI+UsJ+iWGVeDn4+jgCLCAFQHxg1pAYH8wfFzA7hCAq7MbxM/nT8V/EQYIBDCHmrkCTCGWUHuM395hYojF3xg2fGeoJ0CfHcY9EID918+//xnC6GXuYG/r9dv8r/kCdRQl5bRVmf+u+F+dhISDJ8CHlRvAysHJAeDm4gbw8PMD/P7rRA0M/ScJ9t8n5e0tHAD8f+cKa9L/5ev+z/gZ/lkNRsB/fak4wDgLATD8prgBOze7GewX6P8z0f868v+P37+8/L9R/H8TknGztf1LzfCX/v9HDbaD2nr9YwCjrJsrjP7KDrAlsP9fUx3I3yurDDGHutn9r1beFQxbA3F7S9t/2wh1kYF6QszVoK5mVn8R42+x9q8Vs4XaQ9QcXKC/7hQAK4id/X90sL0ys4HdGy4wPv6lgsDW5r8Rpe3NHMx/7RcHNw8A7OwM9sJgh9GIg5sb4AOCLaI5xPMvBgOAbPYOrrAjAFh1fgALB2eMXwPl4QYAxX+J/kY8AKDEb8QLAEr+RnwAoNRvxA8ASv+LeNkBQJnfCAQAyv5GHACg3G/ECQDK/0aw6Iq/ESy60m8Ei678G8Giq/xGsOiq/yI+WHS13wgWXf03gkXX+I1g0TV/Iy4AUOs3guWi/RvBcnn5G8Fy0fkX8cMQ+F/ECcsMbOcI24hfd9r/SUEcMAdgFzMobLa25pB/5Vwcv8QwekFdbH47hLkw/Y1gJqZgMxsXW7CL1R8OuX6Jnf8QwIoxdQabQWwhFq5/iLn/Ef+9eP96Bf0ttoG4/seen/Nf+f8cgPXY7F/EDUvRzMEWRtN/K+H6JbGz+92JX/wF/tEC2GUF/F05jJ5AyH8i8PzSO7nBVv7fXsKSgZHVFmz3hxdYOyx+e4FZWEDd/3D7S+3g9kdf2GEmlr+DwPSWv15jyJ8msNx/txZ2HwKtvBytIPZ/WMBk0D8gLHnrPyCMATZ/QFhzfhfBA+uC7a+d/a2HtfKPikAwg9+huGG+7GG7/rsJsND2bnamv+5Yyz9Sgr0ZQIffScN8OvxxCgSCFer4Ww2L4Qh71u3/M2wu0D/S/46aE5YCjMCw5/sPU56/ZFCH3wPlgjXW0dbtj8Jgn3yATr8L/4XcIC5/3Wb/+ub6JXRwhZib/jFn/n+E/80DBIIZ/zEmEKwTv6Nxww65QOyg/6Uh9y8biPsfs+OGOXGBvZz/pg2r7n92CQQr7ndY2KMEdLVyhvxBKlj3XD0c/jgA8+H2ezVgMf/6iORi5uD85whgPHD/A8IS9vhjMWFOPf+AsKhef0DY+Lx/5wzz5A1x/juD/1z4Zm7OsPm5/vUiw16D/8N/fbqCQDwhZhjzXx3MBEOt34Z23tSLk3qwbn7h4MIaOou+QNPZkCZx31YPlhT6cc5ZFDMn3ju9FfNjBX+fo/yxxUb3GbuN0bJvVozAs/l3ncgWpussvuP4wrf7LkpuHFuKPT16Jpz25pbPjls1RjwuxKYYiVicTjdncndlhehaa69rrI5R6ZsP2DARvsTFoBLz8XFiIoR2ied21Uo0iE9Nd8vTuJyJfTlMIZfZ/ZE1+JGDe21ooZIFHrqaRbq3h+WoeJG7J5K5857nuaLJOGCKduO9NPHPRIbgi87QIAZTl09qyQg/yFiQooLDGtd0YhQ7cSm1qNgVpYbrcLbqJtOjc4pjUG3ktdMXyy2AzDoqwJBx5+SGBa7r8i40p5MZHWKx3jbzxppPcar58YOrpbK+EmBTK68dbhQOZqS4+U8cRDk9bo4t4LcsrBfyg1RRqI0nd/AIDfhvlCXXo2pX2GeT3Q2Sc5ZWNmSTowzqaNHnovUMmBNIBQ95XXNiPoCX7WIVXHSvA2PfqZkh/BR6VYf4M6a0KAbUH2wgGiBln4Bep+be8ZgeCj3EArX31aPJVpkhCbnaVz1nGeSmJ80YCLMNXalPxb2cxfvI2EE1Bql370k4HcQrK3OnmP9kj38iZn9gPL6LX9DTYjvWSD7p73s8I9hlX2l/PMOnPp4FTDMVCKuSER7VnSeBM/w5KLi7PyUnGfbEeBT8LK1NtyfjBaKBg4aYFxTRohKxSFDpLWMSWbOV6xbDh/w3KRo9itf5exOXI8q92D6upbfcGRpMtxWzY8zH3Puul/VMoBiBsodW3K55rBGL/ci2Q7W79f4jUilWbCCe9Zx9samyX5nBgRjnadjXQ9e3ZaIJmFeUbCaHPmG5IuzfVJw+KlV/wpmHHzigrQ1OcF3XiXDdHKhj2c+dHT59EsMouGY6st9xeUmn1Hrkp5L79S3xmewklGb5rYD+OyFmF4NEHieUS9nnabdO9ZjTSA200v5icTE2X99RnNyMonyF//IQeq0PfaU45KQfHzELUHO/KTJ1xpmc9v88vJ41tALyju/kCT4RIWHE2ewI7Tb4WefQ7Olgei0BRQsksDdAfNGqf8wg2EnF3tpdKTByzesRNo7p5aTsYFciFUVj1u70BVdRQ9cZMxv+sc+smYSylxMu3a6wKyQp19NwDD6w9Ow4fC1YJnw8rOOQy9jfvskPGRNRI18igeGDw8irDlFzLhdCC8LDO2OW7actkKOWVOrSdnFEKY6m9eQEzwinNahzfhXeHDgf1VzG0+E9iWn949vWcPSdCMJyZW3QDV0E4xgSz9AU6VdaX6R1b7rWDY24PDkzHfjrLL6g6eQKrxzqJgKN0WDmlC1AHERbfHPbiDigQ3O6tb2fp+dKf+fn8V6d0PY8DXYSRWJSCOMXxaKytO8qVpI5Xo39JDikGzi9dbt2+sVzfavpkgoahndw/N+EMeV51HgIxxqQzSOWKQNT4YqoSyaDn/Gpgr/VkjOg1x7KRt0083F4f3xcsTC1bimdUBFvkrKMzpER8sr2l2Xeujlc1dfPZ+3Ei9YZqbYlj4yDyygRqXSlYqcxqvj6iH/t1vnup69Dq1FfjeO7jfMcFV6WiBrqNC1L57oEcT3dEXg7C99dUY6bhiWP1NzkEqzbTz2ZbIgOXQGenzt3Ly75IR9UkCnnjqmMPa1AAqv4Hsb9L0zVwZXTu86McXLBoUcVaVFVw5PPnYKUqJy+3zVUHth6pnuKf2Nczu9yTruAJ3skiaAmJseiZfholO1rFxn8npP7pVN10095lqGH5GtSm/iAOVln/DuQpvpZ+0BdFDJdsr6FKbW+1YZLM7sN+eAirka1qNVPhAu3kvJLI/4V/A+FXQShvE+eF0sSN08gzwWFvUIIVUIXriK5WObxRUozl/anySJ2MSkNuGYbxrz7shbLv21ErS1glxL8rFRi3D5Jsr9DyNVtbav9BcU7UKah3/6mEEFJDXIiRiQSNyK+m+bGE/OR4BSf+ac4YkoEzVa5DSLbBUhuMQAUgs88lbysYu/V1X8M8j4x98b3XVwffnIg3L+ptTkqh1qC2/O0FhTd4T3QIyIuyfIAp3CXEsYVevquOVW2heEjnIEazobl24hs/Zc86QqfaQA1q89rbwRYqbkidrWQXd3lsYnxsgZrWNG5WrdcfKy3e+U6nxNXIXw4SG6TjX+8cxz1UmV9FkLVONrc/Qq4Xy206Lr6+QjvyyO+yQV60FL2pP9l0Y7UDEYLwZRXAA6N7rM1qs8qNUoNVDnMjWvKnwXeT0thJyYCyZAPk3J/uPWSsmnjRPjbS+r1A3bvqNkDOxye/ACZy7335jHcnaCkDxdXWv5e3zWw8z1qPYGT416ZzSIBYeO5qgB5TEnIQ4QpxoAQMZ0DAa7gPDbz2FIJCyu/7ldvAZuzqTy8vEFpKB5C+wO7YhuepWiAb3XWPLdR0LfRsAvIg5Whl1YltuJxZsrlD6zvDv0cEeX5zlLN6oK5lUxuyKEcqi/7khkTk8LHRQZq264QExe7OKju0g/d0cmdsHcYPzDXb+tNva6RzMarqpfnRAP4vmyhEEeLmK2ZRXl9OY6sdCD51Y2VS1yQ9+nT6RQVTOyWeq6o4m2F+1MGhfSP30osFORqawpPUwjw5mMhhUBSO66jTj1keZzWQCNKvLCnZRTKqggWjgzOyMUI8Myb6w9U25iSJhx7ZYjoVLSRtKDPRzikGMVOnY9JwBHo8abteaAsYTTF6DvfjoPKq2E+5pfpr92z8f2MDjf1SFsp1tZCJOD6GhXPrC85gyB3Eu3oKfoihUevkGI0jA+J+XcFJCBvlNQ1p6cWECwi4dGBRzfBwfqKiVBkQ9vbEhaOmVeSO285JGxSKYp3hb61h3r6zvWaNSzOPRnY7WxvMTimqIIenUHMLMihYiZ5JVmOzYSoUNHhIzCb5TIO4Dl0og+e7CJBa1IL0ffwVZaREz+C3h3aLBqPcQAJ9NTIvJrknmP2mLYG0vjyu8MRsT9WhcT7Gr18mRBqZFb8H+8u6UwZqZOQLT+iNITQ1k7htxMw77mULTa61apTnBYhfEkS1AqfJwexdR2c1wJTi3ZM+s7v3fairHrpWUXJSqqT1Hj3yU1s3KVvV8fN2T1Yz7hxfDlqhgie2iAYnm0ST2y6Vwd4OOzpCKPtcKyYkY2bhz3rFV4OHJNBOCFIbnCOrhtCP1fjISlax1tdVKDhHSUxV96WUkMaGg14ts12eqBvJbQVk3hTraEUcSN5NSrMICZt/XLXwkxxqmUp9iQw6Bp3kG4K2dQpfPcC6zyF1kR7JtB07oZd5CbEa+z2hbhV8bNrJAyDlZlTordSVRh43tjYhynTuRknXG+bg1KjIp7WPccnmDmeDQmw0TmJZqWhVngbtynV067bdf/0eXAN/UfEOBAnVhpm6KzVshSBNLUHJaKcEbVfgAZxUa2lC24zI9qKBAtA1yBIclfoud4Hx0R9NvXi5DHp4ReO3ZsFpgW+Wc3nz+gP6FvsuMvMmNJuHT5UbRGyvE3tSjBiVxVUXJnmwyBozyz3flAtxqMs3lOy1kReXbz7Wt/bfF2HkytyhP/BhQfe2EQhBzglHSRiijgg72o+mrluccLwcaElGbvbY6TnfPubf14KNK++uqw9tUWfz+K8CV5+HLFweqJZNiTVmwcT5cn7FfS5rr531p5l41zbvVdWdW6xd0+4KJqbiD7Y+iO2lVA8cbxYT0zZbnyYo9BhpWRs+zLStW3ViXg2/OxNwNPQy2G5PFIMfZO66rfcEfHlSdKlgfKf0xBr4USf95t1UQi5YOwdR9dQslfyY87fROm+fVrC4K0i9pZ+CwOcT4jQUs3Oqe2lSjc3ltjUsPhImyJrXLxE8bJT66RN2ToeuVJogu8VeI+zWxTK6IepyXEG1Opl2G787pDnuYXGs92MQCHnBy99pFl5Ta21We+StWmPuXc8/D6eM7XcXSlvVs1ahT4Ob1ez4ek8vIbN/tz3H1fVib5wDa/GBA0/eH0WvLETHhnQ8OT9Npe3lc4dlziL7LE3lOjtq5lDiWhZmxWWu2rsybV/sVfDXb+Im4NQjkJnnltenxTJEJBcziuEGyOkWhJYUwIo4oEaGtdC0MLsn5TQ9+GsJBF1Xj67U/Fv+nRqjS4ic3zyUxcsTVuargcvaq2tjhP8DsONK9HMwIQBnkrsdkgGo2B4wGB3A1FI8TtPII4kl0GKOJvqHsmijGG8qhm4ayvQE8FPpmNtNSlZgfAiNnlV6eXneuNk7okvPsu++eyCg9qCPO2LXD4N0gsaAnREGnF1UlPwblxFfpNsdUgdHYnXuW2PbRrRHQY/ggidSXNnumkFAojwi3PiFVuGmBnhrmNzrOCTYso1ZBeJLUpT9zUTWSbLzk2ohkggAX5hlpH1LcNlSyYl+I1253d7/OJoGXICHkzkOiRe+NJjSNH5+lj5M4sxD1nT9hN8IkVtBnbGYk1wcsUTORkfZW98Ako/rDCLGhIetBxx/zR0tN7Y6G0fAGszFEwjcyekV4+6Kn55O06DEjZdqrewfqL3aK2fhvuJ9qLJ2WG1QLgzFFeLQa0eOSvCwTQBDS/I7M27nTh5Xr967ONgwpyIsTkB2uedn/GAOwmsC9C1EGlkI3oua8M5H//VfIgQysF7jqdFCiNq1otNwEm/4vybVpAWKtyIdRdZ+tY2tiSMETgzNlHNILll0BbgRdiiSxKRPPmTi4AUGqeZVu2x9EECzINh/rik3TS7OS2B7952ty0EFTpJpr2lVWX8zEG2r2sak40x1J7bl1G73+5MvXm+hd0ENxBA8YObTU258qEy5RyZRfEgfA4jmRUPakuqTioY8zIT0yV+BOeCdVESHt2JZUBunuOFqpNzFfuZ5inizx0kU2VmUAH6tDhoSsCaZnJX18iCR0ZYdHpeK5V4IrFhMCj0MVHUIMZjK90EisczPk9QGn/28cpP2hEr7yeKigbyeisV//F7HDRR2sqHwq8ERzPisqhn3Npf7C0fhgKid6e91XdYhhljECC+rH5vquuMTbay1xuY4z0eHIOrRuOX+qNfa8Fjh+I/LmTz+4xiEJp39vVnNJ8OotuQ4IOKS4Wdk38Yh2be6915tTR69Eir1I8WX82QZfD41kJIu1xVSBO2Mfsw912/ZgYr44cXSkqWz8xzENf1v3v+qYH/HVtVcfDiVzX6Iyr/F2BWKrx3ILm1dfaL1buIjF5WJI/m++EYCfC1Kv0txMCR485qaZrJ1qMpMCYVQT9X/823CiYSzoXX8HUtxQreaX5LmUfKi0NpJSRe6ufYZG+IPcxMGbJKPxc0t793pyjXWcTu7Q3ldHET9pA0Eqg01HGgOdpqk5fsorZPbBJlMdvJ7Kj6kVnMs3q03CKB2CkTH8us3soqVt3DsyRhs4B9RwVfU+DCvGYiu8dCJeCSv7bnQBxwcQ8d1rlAhnIixISZvblmbrfZeqHuuWH9NlVJVtBooJdKP/gRI+i7DoeLYbmBdpsdh1GR2VwBwEcqjypxwgNX/buODeVEm2LD7ga///QyLRj2dSYvnwygH0/6HZAHOK0tnTV6/2RSi7aeI1eG9IUFiHyEWfYZLjuuv+W9HI6Y6lCnbYrL7M8e6+j1CNb2hFdB+lVsWlHk8nhznz6LFHXn9AcLpVA27vP0Tb6lI7DxfDUlyXI9is+QJMRpRKxrM+lSWHj1ApFmWbQhXBMfIzyLV9Ed/1Lrh895Ct4XB77XYqkrb+wiHkaAD52YMyR4QfzvxUkbgYdanqFv5to+O7xFSxGEXtBxBZ6gCxOHcCuzaj2WCER/ImJ/ERJHSc+oB2ziyildz1ZCOIoNnyKK62QUxNIFm07XxyyZHRrumLKc+NwO+qPX3/ZjY7eWCLykSazFQg+p5v1yk+Gv8KpDmS42ntqkzjIt/gZZheedUXXb1vWjqkGnJe44teRYDQVvbhxqPLqjS+NmS2FaMiGGe2VCg9A0FNwgnSCCiQqyGta6fZMerKDsNRTne1Itg/HGnVNGWqnWg4dgyBk7/UPDD+P2hNawHWEmwgE0IY2P1jZA6zmaZ/GlX9bRxV4Spg7sWuKZEGfsKKAusI5kZEzAj2T49nGx8NOTWH3/fuqQn7T0RmikOW7mvIf6jLf99jva5diQuuLtM+zs+jbzwwtDyUgSswsmrUy1CANIkvJxn0lMpgyaF0+aukQev20uxs24Gv7SO1Vs/6bLn8xJ7PmGKQWF8I77NDKcR5VsqkhWNixSiNYJE7jVsyJdF/rt35vRbIClV4CMza0kQtJJwBw2fJthAsJeEbdPibLpHRvNWmr9mUNiSeA0x4OtN/7tVQqxB4GSonBC+Iqvk7F14sQrm5B1P+gyDcPxOfZRPOC5YZp42PTcsQaQUUaLK4m381DLQ2qMBUvhM0bUs9fudKgqEh0CrLEBE2/FcvwYH3rwjXExZY/HGDJqCDcaOdIdnGJ/PF+39Mvjkj2uNwJK1sGLVdfOJDNzKPefaBmo6luXx1E++8DcIdZpe5AhbXESakH4KlgSy7QrLGEIdUiKyo3nlNMoxotI7UfuPAs3vwc9elPFOywwqsmz24zgQRwjiqiYoJRbTTUjMBsB0Uegi0Lb4IuyK+dRorST/E/l8FiNs5b95LqJc6jHj7kjOF+iDUeEJSmKdd408ypQaT+Xg/ckrSTk8QunKDK53ar0EdF9mJWpUpnpsKpZDIAfGmT3RjaWZAwj8m9KJSiNYEdMRk+5Jr9MhWv2qYCPo8+EthJp53HyR+gEknsmPvHjcpQRocJdu/j5Dbq2P8cc7Wtw3I/ycru63vPwqhpA6ONdPERdmRNk13Sz0YVGZneBtTAXWNXxbIENc6NVYX4dzXSZ8Z29VbU6hWVQw/GcSjZAaNreYFgxUxNZiPUaxFMVcnJNopP0kexsK9Z/ejvf+wtKbw3Jx6YUQjIBK7gngGdOo+khc5YLQ0kW2QrVyxHzjFwKImkODXbCRaKD1L3e320cF5/Ven0r/sqtwGtiru9OPy8MuMswoHJb2U+FjpUl0Zj8EJ7TRD42HnroyaHkceIr8zvFphtJwaQgqCZO5o+GRPYUOVpZRsD58dviT5fWaVWMR7ExiBRnIVnVsqvZiTU+rWuZbOeXa3xaaHhz65J8u/fqs+OE2LvMdtjX14wAgYJYOd/EyPOTpkZabkqEEP789TW65djAFTJKZ++xTqbPjFg8+rcOK161pCMvWUdfe0fcNH48oCuMUqlLQ8o5QLnYWtW91JqN9vBf/RyQsvwdJZ16hClt+ifDQH0o56aJ4CcsYY1cKBHlMovAiEnccAHtNcKbd475HEChsgDzMyyqoqUOnrdkd0yrW3WGJKnLL+Yab2vPR2TNyWUZdiNovgamXjRHf9Um56+xebEDtSwUiVRLjnCuNj9OTEXcQyTtu0Lc25S6kc9+DWbQqi9QcvGuYAw+T+tD30kTZpzow6CzvtisEYQ2bxRoNiMFvCrK6C4PPwdq4zbTPbwdpW7fP6oBnxk9EJuFyar1vRipusePZVSHqG6iRWzuf5WewdYcMs5YRWHuetWBidP7JVp7ZIDg58W7Wr3b8NVbaLtOoATh86yGZF4Wv+Z+3uyQQUJTpd1B/BiPtdXRjjcv8PoqkEmRRsSazZ3ulN2XUXQ/Rr3Y0SR+8WEbnrPxXMjZZZteKwX+eu/Kpl4qJRVIaBl5RrX23a6D7uKj+wqOJhYm4eo8cGLCEjkmivZV1pkSr0K0mrFBOpLw+JlYP86WcpEn5qOp8RI2fGTbl5xtaXlcLbO3T0cHJUz2jF/Oi1+9N3CmYabBk85oZyRMEP3YBkeWq4hFcjt1ql//Fm9wi/LObrvHROAq91Z9xziFNvr1ypLcWrd/zK73ewVKVhmRtv48oeDj7SvEq/vzeSxq5pfTRxUL+KzS4uBhJcsfz9NvSU4ly1SUNanQtJRw3AUtWX7uEK6njpre8UYjMiPXG+9fhmgyZeKVrx/0jlGBPB1t0byvi8WIZsPuj5+lq2DIjX6yC63DpDzoKPimK2c/vS6Kc+G/v+ftvKYvaV+k+cNzpawZGEGfz8y8d34bR/TDYrIlpxObfk/cWy12lTlC4r2H0Ho8RX9FEWmr5qCgMkXKC9pavSlD2/6QDFEnk2NA5BNzWRPQKqW0ypj+cHAaPkIDmEU9TEjoNn+cIPo+JWK6tNyJ1M5Zs0MlLtfpVQH7QmK/WuSNrPhzyycdRd1WismOA5TDyQc/p/MKi2Y6Gs1cF+b0SFnzFbEtDnD9Mu7M6ZBKYpQCZF+nESlTTpHyKgNKoz2B2JzXM6lSNIRNF/dFOxoXCiDtaWKS6n7klvzxmroiIRa5KmcI5kt0fS2fdsqOgD3CvG5demygA2s/mzxlmsiFcrunjHbMOOijhjWPnYob+QC2kZM3bzWtIatEN3auq8ZWmQFx+/y7YXZMgax5s9Ckm0zhb/xhhphVpm79P5WnjLcdpOzYdq+YrmWLwRPZZr3hqXimZ+ycAlnihxLDLKQMM0IlR3BiE98+Ecp3ve91XvLatMLS26QaS60jfkozQv7Ed9jTTysVPSvZFh2nQn6D6445g9svMTUrvthCbp7zULLB3wsOasPwYWUR50vJ2tploNxZWms/6MOP1izfRQxWqRlT0lht0+FWq5kRHTGD3abji4ZsD2On7wxztJRLUeMChFghH9ba1eg2klc+IxiDizje1YWUBpdVo/XmEy8VKbZfroSipgcbkVBabEmDPVQ/2g0LIe/NWY+afhN6Chl6KiD8WQNXH35iply97muPR7hD9OQytvakeCEZ6yPVi6f4z7JVf5LLDd75RXIq8nhFhWTXFnzH4b/ZxRvdLL/eQeuMgbZhrKQhQzzaoY3lfGMWJaRluXUvTXVTJ1vaF23AWOFek1rSvXNrS58TB/OK3aK+Fig1P/v6gKb1jFTK8YUB8tY59Vi2Cs2zChGlXb6WPDByUbeyI2fXPjMgzGIvjocyhkbsVRKdBe/jVwjjYbkTZUizqDtWlbW/+TAGUDgrZDUnoxRJr7HZavGHyE/NmYY3ck9Glk3gZkucfnxLaYCal5yhLh4+2ETRESaGc/jxfyLO5MqIxTamBfHhfU8MHiJz3mrtU3zKsCPB6FeU+9kFxFGrrU3IJLzESlmnQ2KcIsWUg0cJUhJ2lwFtMaCix/niEZdK4JH1CSWHzryuCk3LNHsvjv+zn4njoP3c82hz265LMH0S5+vU+04beSYA1rH18auUekRS5VNbQ87Gl2UWdJQ7kI9br3rwaxWo3tedN2G9cZy65OOImB2q+0o5SbVrXH/YKaaN1SWrC8+4JjLyPJJc8NJuuGJRUm/ZBXF+DouJoa+UKUfnUK/l5eCWs4QDWTIWfHbGa7+Peik/ujf5tvL7/cL9l42ZixYD1iAbWBvNboyYgY/iirQaGMjwj8YIkUfGX9wQkThmT5ZEnI0F9D8zCPRZJUWnLKrM/nzekpi44pJMhpbQu0OIr9yv+S5fncI9H+Rx2xR5KgofNl7zRSrfjtaVQNnTw+Y5Oc+7y2vktgUDpqNvP20VmsY7Nh+U4C5Id3xxwD42GrL0vktOCCbu6/6P6GOq/I1vUaVf39RZ9dKTdxSYaiLrNyp+MLpAzAe/cNfVtEoLqWJfw0sn46m5RXqd/d33K+U6MDCgsKZzEOekcc7hG63CPRX92RcaEMcQZo7NiVmoKiewEpHIRmAMfnkirx7ho9g6F+uKuv4Q3Dzzqum7j0KVkPj8xkXWh8KuMHbHZj2SnPxnRvRXw/AsdW3vyhCD+jQiyZV6ZKHS/BJ48eavqqyTXR8SRLMTqE0oI897bAMQugm5bEKb91rT2sF8bqc32UcCpLl3pvgH2u1DlHJw0oQZ9MBccGghKOZ57QYPtxH49ucNLQhvLesxNCpxBuWHx/coz7C0fDHg2CT/VU6kgGSRVoybS+FAatArZw2ngVA2VayiqpQVeTdKiiUM6Y1IoagbQfXIY80BaJMLqxGHJlXFkRdmkc0TZ8rRGAFEjEMa5qgDj+jk9KMsaOsj9sDrYH3GVTfxQRSzgqHmvlMGYxMaSMvjU6kZc44Fx/GVanPD+IImPdDlBfVj7XhFuze8yvwjojKbIHSTgGQlPFxSsMHx6mmrA07qx+b34ZHStiz70yx232xr1HP7S9insdPw6vYzFCmBT0CixJiac5by3eLaPnSJmPLbsUOZRCwLaqpM251d9PDYWqaXV7sUWGlsGEOC0GNVt1wlkRq7Wgxm+Up5fT0rz3oBuUwVUGtzZe7btRVVpMs9lIHw89o6JJxIkQaJpOffQ5DlfjSGV2U5pXEtzTKjFA/0WigsTSrIl9VKOk8+3c9qA++xi6AljMB5nWR3XS0+Ju7Q8f6UyzcjtAz6WU1QrXvrXi/+iQ8sZrqfIlrftXrTavuApQXdo91EQW+PW5u/7KUW4RsIPT9G2B8mf+Ri8KnzU6oP14rNT30sdVbc6U4eNtWn2JgjHkpru2WAZMBXiw++LOsakpN999qjj1v2bWLmCwdQjLTQyW755s2ltMaM7y2FGGCqch1FEvdyhrxHZt41iua94uzhS94EtA+BS2Tq1/aVJEm3Ag+x4bpVCp9whxzdzDUTrCvRnzxlfkllFIk6dcef8h5fcsMkyCzv/pS1YUctzy7hW/VeT1fLffSPaIxz+gSQd71N+yjmpR7oRs2LvavBgjb7pXP2VkggRIoiNb6fo/6BmxLJ9YO9qUss6R2mvSol4eamv2tjfxQWE6raEXLWybERLjxgslh2kkyMSXykiD6f3vRlbJ9Mk/6iEAkAK6hk5bWZShyZy30eP8Vx3CLuF3x7FS3PAgqACi105qWYDqNk8E++G2JfC+VUr5eBEaTSNrW475dBz7PkyfrNgXePMSrzfUsnCj+Eq28pgfYLqF8EKTkFF5Cas4V0aGSUx+CEs47OMt9i6rFtX6OI4Ity4ltjaEuxB1dVxhnn12n+pLlHQOvQXx9eC+FBU3zzMLtMbbjzWkblYsHO6msttnHnyItrX6bOhHcUTUJq53Br6dcdnkEIyXpywfHXgW8SSV2a892ZQD0EUmOiXBL8lK+DtxD2T7K11EZtdpd9okL5NIVT5Y62LyuaQIuGIaZu/j/f2ITXdMAlJqCRX9O+6q18aLxkaWw3kj4uE3idKKVTefHB1E1Rxgi/+PB1NlgCf3Pi/ovyHFrTF2qPhVL9gG9Gsihp9ZTrhpVCbkeeDYeGGhGl0D1Fp0RNi4nuuVOBZyiL2rmCUIc3XWNrmbFSXO+TFgKz0HK4Q6Wj9aM4A5Qyiz8FirHgomsLosjTvnj49Mp0S/Mcvaqz1BvpzSc4Nb9Rgf4hte0No4ADjBqZATS/64wP0bZs5WKcdDIiJkBslQWiVczanzCeWVExn7UE9EOc/MheXTAxFQ30iCrZtmHueRejFvUPWaO+tfhs7IQrePdT4Hue+OtZq7xPKWIMj5UVFyRHKE0VPvYbbrJULKBgWsEPEcEhTjgJbglqGjRo84969dWynPaOxFafkKlxEzCYW1LsH121TGONmspuqE3c+S1n1eXVs3gK5yCkSRUfGvgkw9mSewsCsVWPGpXxNdQcP+lVyoCzLHfQDS+GcSkjdxH9uzlGgz/xUmpUGSEPh2B/e04JTtDhvCfKFRNu3JonZTARGg5jMTkx2B/9dLiRa/XVu4WpA4Mxhwbk/cztqgc1YxDKl1axSCbwmn+rUonrhDILkOqZ/m5no9DOcxJoePmAgH1pM1EBSqXJ4dShLQp9myIzzq35Tc6byUtBj6b+pmVCMdaVqc0gHPX8o6wzS6220uI2b2Pr1OBZh1UARbJoxsFi4YRrJ1EN1HMoSM46BxETg0AlDe91/MCbYmtRXOthaYk2ifpiIWi8MTxh4KVp4Yd06/VyBsPT1q+G7xoYXL9VWXeuJ65uFaF0wl0wpjWYUulA3hc1Ht2njvYUubzO2xm0mhbOCuMht13MgY+Hd7jXb3+hZG4AwJYiLK9Mgqw2YZyfCPeR6H01fxIWajKw1Gfw5rQyzPGM/RNgw543D+c0zSDjrCJRRH8Q6Homjzkp62NiVYXkNpwzS3JyVJUNLiYe+x5bPSOcntiVqFzqjfF0pvEDFRHUqAEuzl/2qZC/dzab1ROsELFHZs35pb31vSHyMz+k4FPvNsX1U3aPl180ynEh8SzGV+gpUut7ziwvJWjp9VKxtGMgsxZ4kYdXnAplbhiynV1cSOOl8S1Fb2q+12PENHHBtWqvGnythTrJ6lFu14hMSQp+3MWK5apIT8VqxZ0s2cCo2U0K7X7Nx66DHf76rRtf9SvvtuLC0o2H19Gfnm5oeqCknocj3/qp9yg0FIuLhHqDrEZTMR2S0Xy+oewJUjpfdtXjH5RswCWseRvF8HxiOU1/3bWGcg8FNz9zNm5xNeioC3/mPw1dnEhXyc/z+XLg/90J/nhBndBzt5dThD0l4bNE18c1Xvfx7HNB2+ELIelWXQWUrflZoQVlW0/DEvbZ/Bcmrw7aC7KfhxKIpuhKaJSJRX3SwT6jd+yekGVzTb4+I9ModZBvzNfzrhh4jCUpjTRbpHLOlV7dEug8ImFUPEnPU9NWr4ZYr4Y7LhR1SrJ886e4wWqozj6U16hiEcp4K9JLbrgYEfpaja320uq8k/t+5VZmQ1OursaLSEeJGJL0LhRVVZmR40YLmx+no1O2MKf07Ias5mL8K/CbTqmPd3EWqMpifkRNDH+sJ+2jWDjGDd2FBMNXjJvJSA9zYmQOymaULGA7qV2SjJ2JRKuVpIV4s62X1f7AOte8IR9Jl2TXZybvhHTh93PKdMzGF9S6flMRICQRFPNMu7g3VdUTHCNl3fhcl49iF/ICm7H9qYVAtub3Q1Aubul9D4u0CX0VQayBtcSM7hjWSDKOXI0BQnkN3PvttqK0F3Ss2W2ryLIQwcfaQ8yE2thmGfNkk7jtjEIaTDSVl01evvLxAjHo73UQXcZEqL/diRswV6gRJZm9ev4pKWDX4ZXp8MbxKsY9oJDYz89k6crfBy3qJeW9UZlnvha2UqVyRGv9E9Yc4vv9SKnhTm+lurIS3MOYmXfBP15IgEe6+bGJMsdpbI4oPBWQTUetKxnpj/IcCT5Ym5IdXyyP2/URPCgklFXl+WCRBTywK12NAgJyymtUp3qp9LMbuvMUgnogQikBOnvuiQ08nw6tQ+nz8dPbUvF9blqJzCxx6r/PTVbwPjB8L8OQsSejTsotrAu55xuBEjCqCAcaaxZejW8vFKs51lHjnmC0FHDUKmRM7mtMaNTX2ND5qFSqQPNWy7C7fW+U4+oSKjoZO+aWilS6l2YuNB6+MLP2QRoUr9ioZhOBcmsxCK8+CDrsJbScrJCuWZVbVoLpfAiAHELYZ4Nq3HMaMijzQTdeRJ/A7aP7BF/ZZuxxeoGI6F6fesULrUtuiH6q0uiMjVp5+S4EyfHnPog2+prRa+gHsBw/iQStUo2Jfh+PP/DctzoHYey4AvC9saxXyVSjv7M5UuCe7SfhNC5Fn9Skq+KzAwbCqFK21SpEJyDGvCWrihgBeD+tEZspcSy4q9i1+q0iye5JLorpkRbnHmhmD7TRA5zcSflFUfGH6S6CrslN8HaZxAWeSzAFnTxQ3OcxIM2aFHaDjWI64lBFBAhUfoyHouLxkc9a25O9CtzqsbdgJAu9gw/bGlbjd+DdxlCeUFh5rdtxxY/pJNM0VQHIMYbfuCNgXCo5xYFfvR4Kv0ttJzwVzdSWbHYOTxIzGKeSq0+Zez2L4AUigTRiylKeMoqEHznl5ojqfE/EU3U/nXxKa/yN07jPqeaG7004LWlf5lYaroTUxlvRMh/xxQXuAGo87PVV4Zca7B9j7QuPBonJmF05RKtRiBIOOThOQYXvyBSDQ5Cw07ZZFxgm4S6pOZ751Oec85I+h2tCuHitxtO2qG17IOJH9TGuvAK5unWW8q3I9DW/xxO1dpLFYzb2u0CRqahcBmI3M/7mRDnhZcFhTKJTXJy3EyJBPypKMNsvDYE/HBQ3XuWNaEXbqklHU+0qMz2VFVV8I3TD/643+6xMw0xlMH54MGeS7DMifN9NQNPNkMusHZGhJb+yLiuD50vvqiTdHWfNOUxdvjeR7/zo3u+js01yxf9E5wPOZjCOseo5qdh69J9b1K+dC2c12U+L6rj0n39bJwgtYJW7KZl64X+ygQ33US0tZ38Uav6FGkRrzWGhV7oQPd7U7SOnyoe8tD538DEgkQSrvxo3HWvCfc54dJjPXYlET1L8K8NBVrxvcwSSIiefY4lmJ5lte+X9ATKKLZvEWPe7ymvFgSI4aal5UN2Fgww4kdcv20Rt7bJPvTpa+eqTanW2ichOU7DSU3cAIFdKFF2PjksJHcUy9i35WJg7YgE8fABKMZfdmFaLGFrNpVvH+2FNHMXnee0e94reOctS7JwW95vqGVTYLTgIHBs/+btkjyAug7I3Lum0PT2SC6WHz/ccs6MDjzG03djU4NLYQw741q0EG2MVCfBjXZgi6fwkxhRYnzgYZhI8bZU9Dx13RjN+6e6wVSMAd9oUsTeOs3YWmThLvSIVPq/7QIdhkIXrf1zoGMxhuVjniFeDfvaabRy1YLu55+V+dtDngnmZdGp65Nk6YvR0UuN12TQEo3IssFTI9yvirjkv5xNBzpke4Ibwy/sMquvqMkFn6WoL0Uup+QK+/LqiBB7Ol1cOIkSocAkTcvUZsn43Q/1t5c9fJULohw4vEv1jfMBb7jp7cJ7m9S+uPDvIRuNvSk68HIxplzyXEGeBBrH5F2puVqifmS4zXrsm4IFeuxR6VIvuXLPJJjUxSXjJnamp8j3ZM2d2MsS0TB0tcwar74MWUq/wJC2xSa9syNGj3A84py1k0jtkLvVD789PivJZZXPlvva/GYtvDdhKDRGVzwRyY3d+BiJRSoS5W87rR37LnBMQ80yx283sbqHSjrqCuE5nFwYKQ8pO+L0qccgyzKsdM+rbVeu/PEM+Dc3lwOiNEpDy6Ss/0fp0uc8WWa6TGedBNBtaKqdrCy674FuMzlGaHp7q2UFvALAyrUqnErnWk4GRvm9v9YK7uMEoREMlj2TkxU0IO5YmzqZPzXVyEMbMoRTUKmcawS4+UsaqGaHIg8H73x3vLOfZ8u03pTAQjz93B3GcMQ+nZitsUWq1TO5QsTzBi23IdNmA83M//PIIX+M+/ZaBunaILLTmbUcJajN2cOKBoTdGKB5Y80XuKHNt5vlwPHpWYLaUEBwSS4t8nL56c/3LLvHV8lwK89TC/raB2EHmikWr9wN3JdyWO8JuriW5IPKn2zfw4nt1BQ9R10pfqTTDRR6Ct7NVeK41A8eBZWnvdLbPi0V5VSFTxGIct9xZSPpNyMdYA8/V1QLCMDaD16KJR0ReFSwp030/VukiKYkyZiCkpATIpLfRkmyqRheMGXSLzteeN9Y2aMvHMNN75tOxFYJX163kW9f0wg/hON16qJ67O1AFrYjheJjkZJx5vaZhfyp33Ek7zEu9PMLEYuj7zZ3+Obb8XGJDVG+gL5Q14loBrFIYv3g3kDhG+v3TNPXnu/l08VrNjSZ7Nz6pu73T7qiykewRgx/B93RufgEoe++Xdrzf8CwAdK9jHWOQL4PE3mN2CTcqwTWgctyfhrJ97GEsKchLyFc8MFIOfpdFRHqbOhCpsY2kk9jGSIXLVxDBwWlc9CUt32U2wJj0B/1Gg90YUEBMKGUkKxFRJvcy7/4Kj0ET20tw/3S/FFj/A0EhW0hUEtGpfGC+Cd48+CJmqwZoBlFeqK/onlnA5yTenkBLa96o1JhNqRKqsSQwbcR3/yZI+lx/vqJPVJ7ryuCFfXM/upXHSPmKxqs0JiLQohcAiydcfnEDZ/jpYJnRYIyU9tLOyDGzrPJago2uDsTpmca2c27fYBu9FQELkUgpTnz4cmBLDMmQegU2muq0u/Z08SVNzCPTQgHNHvvGXPGyRCyhi7j+gGrSeqVHCgvx5UkL4i2B5U4G+gfvSsbeTwPiU0Nn5YJaE88rbBeFpDrKmwW7pqgm90KoSQiJZCyCJqgWFrSyP0AqHuimViDcWPzr9VOEJO0xbLXZxxR1mQwGbLc/veAXaJ6lEXbuIqE7WO3rhuECFCOJvzn7b0xhnNRMBu94qdjkizAid2j5Fo/y1tviEdQNokXvimnp3Be3tPhfsq9SwlsariTR7FKPfQnYRJEcrYXnXRKj8elqtbDLeLebtM6u3ajx9YCH+sqMPPOCSWYLNYK8dtEeEZnPkakE0SRAWZ3e3f+wkv+GbWuDPEqiazrXMJR6vveHoM3YIm3IPivYf3yMuJbf9qkdKb3ucP+T3Tevq2tG4qznyshJPAdyErosd10/L+QrGhBssvQ4ieFQ9CrQfskyF/zI/ja0E+0DYjyc7oM3+1prrmboZKYk033k2y5si43QACE/+5IXJDoshpYGXSWROJS9AnPIla5X2h9DfTbuH48INZAKjNi4KKSC43be+yq5LUkupAt7CeT5iTKmORzWC/mvSGHZ92DvTQcLCreJo80YGKRy/DiRoW0s/eYLrEpzT8zX6/6ae6J+SAfd2Uw+XXtZrJ5vO7voWB6/TqvEHqAozcuJIFPtQT0QwhmFuu2RdaU38oMFbZdSK/k5uZnOva8YV/nyg9WN61TINl0CKKXfvfAqxN1dVEIR+xVG/BnG5kiszZ0nlo5PyIOMLKL+LAsFAZ3gtm5/756ZsjeRiHFXGPtirhfY7wnLdqG8OYW7MtzHQoWL4MxFvP66AgG1Af4LOlQSwDZa4csKT7tltA7XzKkXr0cENOQ/974XPylEzu53zonCUlqnQEdl2OiVKfvoufh9XVdBN5ScH6uMt6DWW2GL/6QzoDPjk3Hvys0bxMAS74xKJHRyt5YFwipUcb+Pcc7hrBG1qlaPH7QQ5G3RDhutaTJKkFqEV39YDGLah33IlMmwLzsZDA0BTszVmK6sdUQQoUfw91RsZq6LhZ6vBirx7x97dlswK1TDYdYvRby8e/Eh5fMnaIwNj67w0CKT0kSlsLZoDyc3pcKEju789ZNsZeEVXEMUpOqgguBcgP6yYlaXYnWEqqq3Ti5VbKHGORtvn3mQNqlrkxD8hDbsZfYqn8rJ4NeXGbA6LTfcQHXmqUp1TmcHyBS7BVyZVUW0qpj26eVr/UgWQdQTfQHc7v5Z8OGZ5NGU56Gh2KBo7yF3OZNZ92fndr03oVh4fA9erbAvXiNdfenfqck3CNjdvpQHrKS41+RgTXoZdT/VqBMO+LKsyT9gPXekubxde/9j6IRO06Vc9YDRunoID9gdxf3sWmmhZEyEbYBNcI91xjnRIy0LtDGhxJLdUjurDIj7QDRLCofx/rBDhCeOU6hXsiIvEFwVhdyT7WG7smHQW4giEXPaCT1sgSO+5zOFu+X0yBPx5ll2tiWytNSh+MIjQO8222vaa2UoZLIQCa84dlGWUnroGbsiZUyFV1LekM2pXfSz0SdioGGjA0HQ3V8Etfv1Fi0a8m1N0/1VxqmX1oENRqNU3Uy4l5neZGCBtUY/e9gXDHgDtmj9ffw34duBewN3tjwT/E3otpQLM0GlEZ9u/J9946XhBZjrZbpViKn79mEGUykdEPc6TFfZZZXdZEn5t22fGLYuOxqa3DTLOnicP7gzh2UMvQOxbaN8Yg1hbCF/8vNMj7yQexoD1LbtCJw/Id1AQL9hIGVNC/a+X3gvOE2AV2rQqjQsp5IVhNInH2nz/wB49d9ICmVuZHN0cmVhbQplbmRvYmoKMTQ5MyAwIG9iago8PAovTGVuZ3RoMSAxMjcwCi9MZW5ndGgyIDE2MzUKL0xlbmd0aDMgMAovTGVuZ3RoIDI0NDIgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjalVMLXEz5F1+yqZHkUWl6+EXTy9Q8Mr1Deo5KqehBMubeqct075i5UzN62bboqZaiQuy2JYWwveixqdiWIXnEEkobhSKSFbV7p7IP//18/p/9fO7rnPM9555zvt8fRd/Hz8wRwrbArhiKmzHM6bbAZZ2rF4MO6HQLc+ImUSj+CM6HP/lJlPWwUIRgqO3fEE5CmIMTPmcOTgC9MBSsFqOAYQEYTFs6nbgAk85gfAJiQlvgGAELES4HBV4cPByOILK5HD7ww7gIjEtJFCdMIBUiYeG4LfjzExhzTQDDxsaKSlSj21Anav6HQsb24TgusKXRoqKizDkRInNMGLbchAqiEDwc+MIiWBgJQ0C+B7CGEwFPzWtOogD/cEQ0GfDDeHgURwgDwsFHuDAqIlLEKAQLAfF34Mf2BN4CGJ0Ee04CqGBqY4Bhzviz3FS2vBCCTiRzuFwsQsBBpQgaBngIHwberp7muASnAg4KyYEcvggj8jmRHITP2UIAJlrnAFfHtYBDTDg1n4grRAS4yFyE8OUz0uRliN27oJATFhEBo7iIsNZ7iUWcMIItBrFMgiAGS/4iERuFEC4OtsBhCEqiyedgozyMCE/6IbHgUyxyairjKR2YAIJcCEP5UgDBPBJtDUZwAQPjvwjcSGfRucSD8R9pnEj8N/bktf4vgZ/35Srm8yfixpOA/4lzIhC+dAoh5sPCzwFsnEOw54iGEQzQJ12IyBWRwJAPgnPDAY/gCZ70r5NLg4+gsA8mQuQnBJgx6PTPYoQeuNtQWCQCrKlQADwpeC8YQsQRn/0fJsTwWcfEyBP90oLcg4PYvkunjvBE0AXlYpBcUkyWJeAIhRwpiWCbsFggmkFoD4IlAJYQbdPMUQwnUoBAjMcCHiYkycm2tgA0P7lrwmLQrQGN/5dJyIImmjD/2ZEPB0Fxf6ngzwXJW5ywGX/ZBNVCRAI2ADohHwaBpP/tK+RfZly1CpOAaDOmNTBjsqyIZogZrJYxYv8J5YqFQkLkE3IldvXJnjhRMCyBuaRfOjCuXeLW3IN5+GnNI5dkzhvaYuwtLDSZzJ6xH4dt2Lo1vOas5CaFzsVjPwUGum9+EV0Vf89YtkXJIGtzd2YZE7hJKxvtZ+yrrf154yvDvTKvZyU3nmVVNj1xwvuXXkmatupg5qnVkY2XqiOwd235sZse1R3tD/LXCar/svqh040Qv48O99BXTbiNpPj6ci3ZwOq5r595yBhqDWkZBUZCSy3kwt7Al+WdRvwzJ/tBajbZsj3zyJlmh5eRpYyQu8OLUinsn5wUFgwaKx/fY7CaHKaqN0ZTQdixndNyC9sGSjr6VIqHtLP2XtKvZ6Y7VEHqCbtWVg4m/5C34Pn41tbZecGNqtrl5xfGeDi7dI3MMUI/DJoWzc8wzIiIfRtXG55CXpqMxC9ESpWftgiEZjMHKh+ZFhad2nQlw98wxYL0kVVU2a7vH+CBJn9lM3iC9mG/bvDxdnvK4EDU7Zj56Vm9B/rys0+wr3c/V66sZzKuru3XuRzffk46586QwS3f3hzFG5JpdXpU328OhF4dMyrrezMeE1g6L4FteFv2MC+61LCRHLord0fXXm5NhYeseTSpZsamoqCQOycUkeiZ4+mh29pa5m480ZtLry9ehQ5AWMBAx895YBBuXBlw6ouqnv3qPzondj/pRvQ9/ZRnBDPRTnbKkg+zV+bntrz56RmS37f8yWg5RG1T84O649I1KsjeLlCrm0ZTiSvVMTl9U121eHjO483ODgpmmkiB4+uqLw98+13p4RHqnBU645gXs/TeY3cB6dE7+PDryDsq5PeL7kdWD4dto15aECfuPhlVdabLcT4vzF9TpHu1Yp76ka28Qmj+W4uFwdrnahqHNVJASeHB696nc8VPH1mUrHR8qWSde/8jJjwyso48+siopTsSrvt+Q6NEsxjKXaqVlJWh21W2Ue08trj02b5baiS9kBn4ESXWSYXB+7uP5IyO96SWGYbeu1rs47gbUN1IsN+hwIpOM5UDd2Rza84O6kdG2/ckGFpJv72pa+bQqrlH9WBOdtoORS5fdx3F6ctlfXatNrXSBhvSibL1mX1rW26r3m37brO5iKRe++6R7bJdp+K89XuHn2UXtjOBzz7Tu+Ud8Qm3WjElZ7jIm1q9UNSsONQrLrrdC1avv/wFZBXfME1WIMP7FA9/LfA/X7ugTEG6/Q71QcyhhNs/BqSqOIzicYCH79mZ4B0bIIsVJO0dKLloR4l70F9B2Qi99zXZrJKop9Z0byVcqGR8oqte8kWaYk7P7AoLVbcsbv78J28S1+kX/tpaocU+vGIbC2W5N84uOyOZzm3oMNe4kHjoQIJO+lw7S0nY0WSPJqG1kd7umGtxvj28xgC7Jo3EtKDhVYl3f9Ht+eHNri6HQzoHC7dfdplbTSN3rcl2bL66PCx9W0fpA5cNATtzFPfhs46O7gw9Wd1OXv2t+uu3fGgrC7ePtMwublqmxzs/81Xa9GkYTzRPucj0uUOcW022drpNyctja5pqyVea1J0a3EV5a7Vnaqil0BYXqH+dYq89Z8aoUL3dalu9b77P6cTYfddyttS+7/W0pHEUdF8hpqXLMpOjF3jIxoKrjKOfXneep+Jg5JKkuuMo3drrfL1TA8mUu1wpXSYYz7aJT3YQLGl7fHT3EKV8ZM54ont85Wb7a7MGpNPThtRvHjfKvnXpwvsKdp3qqELIhzGHJYdGvL8uLM9YP/tBI9nsu143aMQs1E9nYa9rXWu+yViAJ0tAVphXFVgkkNVWjLa+yOz/sP9s84sD+nE8vcK6i2RzwciuApL+9zWXM0x99WoetBR4eEbbk1ptbv5mUd5nGnjRK7U6UHX32q+0Fo00VQWnLVErisP9R6Ju7NRy594rifhoHbM9ryFn+fEVhVDpwcXVPzALLDdeO7294e5W2slX9GWzDUahGZqB7dMrhf6zzipaOoW11s3dakCTBot6WOdmloeec9OdpfTVgrfvBhP8Lp9V04p9bNpSbrNj4dixIsdWn7jCgdpDD58eW5Hhk9owsDR/RWBp0y27+H6oLhJ9pbxL52VShYr/joLoJVzH34qft7xBh+zvH7vCTfq9dkDbwCf7GyTk5v5uk3Lh6ZcJ+zofPOxa1C6yCvzd25z9WLTqQtva05eGMl90JkSt90nZMj3pWntec8ye8J7wjp1Blkd7lVq2FwsMLwC2svP5syYGqSpvpYBjp6HSEOtB+fXjY7vrF1ttE/8AYPO66AplbmRzdHJlYW0KZW5kb2JqCjE0OTUgMCBvYmoKPDwKL0xlbmd0aDEgMTM1MwovTGVuZ3RoMiAyODI5Ci9MZW5ndGgzIDAKL0xlbmd0aCAzNzE2ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o1TCTiUbfcXKk2WLEmKHkkIszH2fRlL9jVCxswzZhgzzIKxRZYsCS8SqVSUJCJeLZYoopCtF0myE5U1SvQNLe/b+/9f1/ddc13P3Od3fufc59znd6QkrO0U9HAUTxBNIdMVEFC4OmBhp2+BgANwuCIUDkdCpKTsiXQS+BOHSDmCVBqRQlb/B8OACmLoLMwQQ98gUsiAGYMEIBQBhLI6QkUdDgeQcLjaTyKFqg4YYgKIOMACCphRyCANImVA8WNSiV4Eujrw6wjIYGUBhJqaivxmOKDnC1KJWAwZsMDQCaAv60YshgTYUbBEkM78LYWMJoFO91OHwQIDA6EYXxqUQvXSlpUHAol0AmAL0kBqAIgDNloGLDG+4I/WoBApwJ5ApH132FHw9EAMFQRYAImIBck0VgiDjAOpAOt2wM7UHLDyA8nfyebfCfLAj8cBEFDEr3Q/ojcSEcmbwRgsluLrhyEziWQvAE8kgYAV2hxKD6LLAxgyboOIIdEorHhMAIZIwniyCJulYwC0ng2AYXX4oz8alkr0o9OgNCJpo0fYRhrWMxuRcQYUX1+QTKdBNuozJFJBLOvdmbAfw/UhUwLJIT8tPJGMw2+0gWP4wRzIRH8GaGr4g8OCIH9jXiAdQMEVEWqqSAD0B8AgLAG2cYE90w/cdCI2YFYPYSF+FD8Az2oDDCPiQdYfJISGCQABOpUBhoX80/G7BUEgABwRSwc8QS8iGfJ3dhYM4r/brPlTiUHAcThLfggAvvH7dXJjKQxHIZOYf9M3Rwyz0DdAWzrL/Wj5l1NfnxIEhCigUICCEpKlU0UlRUCNdQj7PY81hvijjn/EmpLxFEDle7msd/pZcsAPDcj8WBBZ4PdclhSWckFA5m+hu8JRcCzrg/if5b4Z8v+pfCPLfxX6vytCM0ikTb/Md8L/8WN8iSTmd4YRgwRS/01wAr8vrAWIIzJ8/+01pWNYO6BH9iL9ej4iDU0MAnHWRDqW8F0mmzjIUv/vsUZkLAW3sSZIlDKAoVIxTAicpQUka2AhCNY+4cCgTRkCMCiZQmeFAH4MehiAp1AhGyNRVgFgBhvQpqWKAGA2vyw1OABz+WUpoQAYK4MnhroJ/VYFlkGlspZpc9CsEn/am5sLgkEgFtL3FwWrEe1dFl21ckdvX6DCWBtSiadxPmGRy2nUaG/AhM0pA83PC4rXEnv1HnWPJ35+KzCNLPhW6XNMGO7jPhialagu3FdetRXvOSIf2i6g9WWaZs5Ajh+trXXxUCTjvIQ/3rNtClzU7ZLdI+8/N/YyZ8pY8/C94uXbhI/bpf+cgXKztyUlbhdRVVXkZo+u1supLtYv1evqrjE9RJvXbXufJoae+pzV8ByJGm58VSi/hTiUte/dOx6/o4s577TPT9Yrix71aAe6pEbrjURWU2ROLVZFR8p40lrstIw/+N+tnXzcx3j5pSvSf8SzL29d5FT+krMZz21LcUstYW1F/y4faaXe7f3HTnqnFfZ0CrWUt7kqcd98bexxLqDn7ElBA0o01KfHAFHpmXWh6AKhIPUu+tu2AfnVBjmuLGgiisu6FIrWNGoRVTTr804Yam9ewYulZ6jNpqInB17T8IPrkaIcysTgWPOPqP6CE+0ckHofk76IcmZ8/UL1zaEHT/ueNKeJl0qjW57nRdNl/xi3Cjd5NfjJyYODTtn7aMW14BKp241S7+kQaaG2mK/cNnPVvwkRfrX32ym0iO25WwOLmNIz95tEc7P+nEOtFk3d46qAhrscWIodWwq9Yf/MIKJxIG9WHDVSqR9CfvclUB26ekzjvK1z28661YgOPTEXyvVq3kOoLjYuTc1zZSsmZ3ICcoMyH55V9CdQ8lHVhHPrbgNllyoYp2QVZ3itczg0vty33nXQ9PrDJ94ejewJHQMePGHztkfQ8TamwQX0hX0e555ll2AnuGGxt8a/rQgAfrZ1gvUxzzQ7Y97XjNxx5HmdxAHLSzAWSVrS7Hd+x8eDSzHJWCgeHt//oMYWQB6yL5ZOFlLRgKZxRE/bPr0+OVTkC2e/NdQSyHw53F6885FkdrzJlrlObaTR1oPLtoI731ytmdsPeYp/scM6zQUbmyKKPXJXLL5qLnCLsnPxCWuF8J7Q90x5/boBn7TRe8fZbfMF05V8er3gmk2cU9V6kUyubmZChVKY39h2L7DuyAhe3FVPa+9Wvizejw43Gelv9ltm1hXwAqpuR6NSHdiWJT1k3s/NVAin1a/dXTPXa0niOqxL/DpmWPjSswGpBm8aGOMcl6iIC7VauXL7oQ0weeGZ8KEaiPKrBG/f0145xVGFd75gIv19i+dqE3dpz8/wh4Rk7sjuPzEoMSbktaO9H9nZ/+lJqzL9ceCq6ZczZbuT1M6GtyxEOvGmXPyTn8afvtysEEV0f1PhPNtdsKzcvNvhYmOi7eDJDO6h6lIHk2e9TIbE+c8monufcUwPz30Yy9Yi69RrgMWdZspHas9rW+6pci+9f/0uoVB0IffgXyLBnNKhiX5ikbU7bNYa61XB4LB9cihRr92PC+/33EyVVIor694XeYR/wPY9GyqdRIaz4yitW2gJRfdv21a1d+V+EoKGDYimBfKZ0M9V2kxdJCQ5euluQ9cc7rzjUY8VPtucbM0+q6AdghiMqNIthGuM+zLPGMK5hktYa3ul0GL4eDUvY3kn37YlvdD859ohndaIux1nEk7xcCgQrhb2ph+o7G045op4pVXfIeUIH7O36kqXqHoZesOxR7yIb6J9WWk65uiYtdKBXqGzVYsm0IuZtJgR8l2O8BdzZwQPhQlOR0VvWd+6kyszdckxQzb2QPFas/gpbQjvTehJfpFa9m9XulJvUJNsShN4xfqlJ/ib4isHVYbpfHlD+5wPRxB0dGVTqUXqZjqNUHpuW0uRo6twhWaouygAs8PGy9MVDNkF7c+3CZ+s6IxvmF4QHX0wuELKXH/EiRaYnHa97t6HaHHnG5pPAnGPRIxe7o5R+nDohuorTc4luffNS7fndh8Z7V+VvhCeGRJn5nPy9UULdYX0qK15B2PPfG1izxCUdGWPrcScG7E2TVYXVWrl8DayULTPjKGkNo1AEo/agpN+yer8X6UFykIRToijFQWduzgteC8Yx0m8v7k2FKHcdfGE6bqB4jOuag0Xk2QOoiCBV7ukiV4yDind99p8ikxLXMiM5BH0cwxmCsuWf2591n39tXEqvsl8SUnOOBtto1J9igv5Ib0nO3mi2nSxztaUjXYmLMm6TjJOZ/fFt6e1ua/CpHRGRzrGyAYw0OWMpBlcGHMuP/vD8MuHF3ZVJE7xjLRcCu73JjkTJp97hweZgmKCVRArttEIRXc+lzfMWpGhfpvIPbcBNqGoDKPS7cmqPVAsuAr7y+DGPTnNXsHEjw+KDPkrJFOaxzpPJMnty6rUWW8pCWUgouUyKq8IXT5rVJbj8brR/CZ/vqQmymMmwm/5SsELQiGsP3vHDZ3nbPGOa0/a1DoDxIu6aHq3nh8Z5sgZkjMT7iW2vkMt56YGwtdfGWfdbL2QorsUy375zzpxp/gZI545zSJZq/ldaoerLWLijCXKG7OWBAFJ/WJXSW577yTRhrhYeU0xkXynHn37iUPrq+aivpNNXUm+NH0Qm9V9eHaaKFvreN7HrfJYew7P4OylksKkTBuZcpqRwPOx4rOLSM9edLRgwPQxYVgDSotjQWcmfavQRJs+V3FNzYOook7hpCYhs0cvnlKDhyed7gQI7mfoDj2ddwt/2ucODLqJbTl90KlNSup429GJWrrV9sGJ4KPCHdE8n8saGTEG4p9IwdLDgaluSlbJ1EbX1opVHj2DtVPlmXckiex5qZg8oCgxqTrcWiI+G2Bm11rm3KLsnn80dWf/vZmiKsRflpArDlP6oyAaxzF686XxCl3T+62alrNORranN2PwQxNcRr9ZKNLRWkZu1cQ2Y4cFsSMhlZMY1Z3h7nkNhTLD2w7b0zrkIYs1pMxVxuK18q0TqNnbGsaX6ewUqEt7pcZiBFJMguyUwmc/8/Hx3dSsi1JqwQjlcmD3vYHE4+1LI5L2b0QuSyaZ+cq+IK9H3DPM7XrsPJQzPY14fz59REl47wC2ONhh28eXhvJByD2keB9H7pr4GXYvkuYyOoJNPxs6rjHgLLHLhtO9PMnG42FCfmnniq9cUErUefGOs9RDydWl70SO2zPS7KYFdQWkdqj5/oFsHDZc1go+tHX98Ak1tsKJS3dc4N5PSmRLZXjmJ1QdhfbnTb1IYLuR2+nz9EA3qf+kGW/yMbbKpyrc/hlZs9KXvRyGkXXJbuv169FlUNc/lFN0Snvz7FNpezjfmFcrX8Fue5NmISCVTJrdMoiXppZc7q3hL4mZ+LT2dW13VJjfhMNxHYewcydOg+N2w3nobkVylVUFDyHdgbJYeinoSFPDigTzob5P6ue3XrMDwmgPS5uhBrea+pKZryUPVApP5QuyD/FlJcUtHb8/LdAkQL3uZHf9xdUi88qClGVeI6iwytbbY19bO7S/8a9cYbfaAXExy/8Q84ggezBgQXxVTLq9Ezogj8CpG2eInrkW+zww16NC1o4iag53fX7cyyaOf9t0htgIOC5A3yV3eAv/slql8+H3Mn039ARmy8DKfJnanR/yJnP3qQidrE7YtT39DS9b0v1lhJmQa6nhvXtt+PKQeXiGECU6llGEbO65NVrQ0Mg9NWOkbqkYrAW775l4Pe60Yn2TVkqZpteTXW/sHAdxGtaDrQeUnaGOPog6zW2PFadGpu6bXuP9cGVJ94hEenyyEVy18eSBE8omEm9lzlbOX3b+xu5dYy31+XSOgO6idPNJuzwFkJkm5NWA1vPlCDRpOVoJfcapHIDXrpNqn026Bje/Y+DKidjGySMgOZI7Oowamh1fG7/Q2IRnT48z71UywMErx10/tT6ygPhfl5eWxLuZb0sNjPl8cNXDr+U/Cecl8wplbmRzdHJlYW0KZW5kb2JqCjE0OTcgMCBvYmoKPDwKL0xlbmd0aCA3NDEgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42m1VTW/iMBS851d4D5XaA8V2SAIVQrLzIXHYbVWq1V4hMd1IkKAQpO2/X49fgpdtD6Dx8/h5xh7M3beXzURV7c5MwkfOXs25vXSlmaTft6fg7i5ry8vRNP0PYypTjbPnJ/bSteXG9Ow+XWfrpu4fLHndlIdLZUbW1yRt3uvGU7APu38zvyblcfdH8MnuUh/6uplwkN/q/mBJX84zW2S3ReYW/TTduW6bJyYeOee2kDdV2h5h4xxMBylsOorb103VDXrYDuoCIVlVl/0wct/l0Z4HFm8+zr05rpt9GyyXbPpqJ8999+E0PgTT564yXd28s/tbaXZqczmdDgYyGA9WK1aZve1o/f/YHg2bfunxynn7OBkm3ViQrrKtzPm0LU23bd5NsOR8xZZFsQpMU/03l9CK3X6kJpbK5/gKVbQKljK0WMYocIttAZOhpsLcFsLC4ogKFgfLWFicKFewOFgmmExSNOICPRR6qMV1F6trVJDMR0Xl7203aOfhAss4GkvJY2BJdSjgIWENPCOcA0e0AweOCbs+w85uLXRKIV1PMhHH+GCc+vEC48yPU4zzf/gjp7itgSecBxHiLAQ82JNMgCVxoU84D1zPgJ0HmTqO8yAzaBV0DQqHLBKqK+A5rXUc541njkN3kMOLcN5EBP+CfBWOT55Sx8mJI4HJh9tXCrpt6JeSrjEDTgjDd0g9FTgh9QzBCemMNPREQwRwJhFxInCinPxCQ1SQL/SMOfGhIcmoDn5C/Ax9EtLp7lJx0onzVMLnRkmfGxX63KiZz42KfG5U7HOjEp8bpXxuFGVFQY8avDt+5vOk8ts8qeI2T5rf5kmLz3nS8nOedOjzpGc+TzryedKxz5NOfJ703OdJL3yetPJ50trnSac+TzrzedK5z5MufJ5S7vOUCp+nVPo8pbPrnblfvvul423CS3p99spL19kX0T237qHDE1c35voin9oTVrmPe8rH/w6MnovgL0DMqlgKZW5kc3RyZWFtCmVuZG9iagoxNDk4IDAgb2JqCjw8Ci9MZW5ndGggNzQxICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVU1v4jAUvOdXeA+V2gPFdkgCFUKy8yFx2G1VqtVeITHdSJCgEKTtv1+PX4KXbQ+g8fP4ecYezN23l81EVe3OTMJHzl7Nub10pZmk37en4O4ua8vL0TT9D2MqU42z5yf20rXlxvTsPl1n66buHyx53ZSHS2VG1tckbd7rxlOwD7t/M78m5XH3R8jJ7lIf+rqZcJDf6v5gSV/OM1tkt0XmFv003blumycmHjnntpA3VdoeYeMcTAcpbDqK29dN1Q162A7qAiFZVZf9MHLf5dGeBxZvPs69Oa6bfRssl2z6aifPfffhND4E0+euMl3dvLP7W2l2anM5nQ4GMhgPVitWmb3taP3/2B4Nm37p8cp5+zgZJt1YkK6yrcz5tC1Nt23eTbDkfMWWRbEKTFP9N5fQit1+pCaWyuf4ClW0CpYytFjGKHCLbQGToabC3BbCwuKIChYHy1hYnChXsDhYJphMUjTiAj0UeqjFdRera1SQzEdF5e9tN2jn4QLLOBpLyWNgSXUo4CFhDTwjnANHtAMHjgm7PsPObi10SiFdTzIRx/hgnPrxAuPMj1OM83/4I6e4rYEnnAcR4iwEPNiTTIAlcaFPOA9cz4CdB5k6jvMgM2gVdA0KhywSqivgOa11HOeNZ45Dd5DDi3DeRAT/gnwVjk+eUsfJiSOByYfbVwq6beiXkq4xA04Iw3dIPRU4IfUMwQnpjDT0REMEcCYRcSJwopz8QkNUkC/0jDnxoSHJqA5+QvwMfRLS6e5ScdKJ81TC50ZJnxsV+tyomc+NinxuVOxzoxKfG6V8bhRlRUGPGrw7fubzpPLbPKniNk+a3+ZJi8950vJznnTo86RnPk868nnSsc+TTnye9NznSS98nrTyedLa50mnPk8683nSuc+TLnyeUu7zlAqfp1T6PKWz6525X777peNtwkt6ffbKS9fZF9E9t+6hwxNXN+b6Ip/aE1a5j3vKx/8OjJ6L4C96bqpiCmVuZHN0cmVhbQplbmRvYmoKMTQ5OSAwIG9iago8PAovTGVuZ3RoIDc0MSAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVVNb+IwFLznV3gPldoDxXZIAhVCsvMhcdhtVarVXiEx3UiQoBCk7b9fj1+Cl20PoPHz+HnGHszdt5fNRFXtzkzCR85ezbm9dKWZpN+3p+DuLmvLy9E0/Q9jKlONs+cn9tK15cb07D5dZ+um7h8sed2Uh0tlRtbXJG3e68ZTsA+7fzO/JuVx92c+2V3qQ183Ew7uW90fLOeraWZr7KbG3JKfpjvXbfPExCPn3BbypkrbIzycg+mgg01HZfu6qbpBDNtBWiAkq+qyH0buuzzaw8Dizce5N8d1s2+D5ZJNX+3kue8+nMKHYPrcVaarm3d2f6PMzmwup9PBQAXjwWrFKrO3Da33H9ujYdOvDF4pbx8nw6QbC1JVtpU5n7al6bbNuwmWnK/YsihWgWmq/+YSWrHbj9TEUvkcX6GKVsFShhbLGAVusS1gMtRUmNtCWFgcUcHiYBkLixPlChYHywSTSYpGXKCHQg+1uO5idY0KkvmoqPy97QbtPFxgGUdjKXkMLKkOBTwkrIFnhHPgiHbgwDFh12fY2a2FTimk60km4hgfjFM/XmCc+XGKcf4Pf+QUtzXwhPMgQpyFgAd7kgmwJC70CeeB6xmw8yBTx3EeZAatgq5B4ZBFQnUFPKe1juO88cxx6A5yeBHOm4jgX5CvwvHJU+o4OXEkMPlw+0pBtw39UtI1ZsAJYfgOqacCJ6SeITghnZGGnmiIAM4kIk4ETpSTX2iICvKFnjEnPjQkGdXBT4ifoU9COt1dKk46cZ5K+Nwo6XOjQp8bNfO5UZHPjYp9blTic6OUz42irCjoUYN3x898nlR+mydV3OZJ89s8afE5T1p+zpMOfZ70zOdJRz5POvZ50onPk577POmFz5NWPk9a+zzp1OdJZz5POvd50oXPU8p9nlLh85RKn6d0dr0z98t3v3S8TXhHr69eeek6+yC6x9Y9dHji6sZc3+NTe8Iq93EP+fi3gdFzEfwFCCepiwplbmRzdHJlYW0KZW5kb2JqCjE1MDAgMCBvYmoKPDwKL0xlbmd0aCA1ODQgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42m1UwY7aMBC95yvcAxJ7YLGdEMgKISWYSBy6u1pQ1SskhkYCJ0qCVP6+fuME2IoD1vPzm5nncYbBj8/NKM7LvR75r5x96aa81JkeLX/uKm8wUGV2OWvTvmud67w/bd7YZ11mG92y4XKt1qZoX6x4bbLTJde96rko0cfC3CWow4Zb/XuUnfVfwUf7S3FqCzPiEG+L9mRFT8+ZJdl3klHQL103RWnemHjlnFtiZfJlecY1Gm/cWWHj3tyhMHnd+WF7uPOEZHmRtd2O1uxs+4HgzbVp9XltDqU3n7Pxlz1s2vpKHl+88Ued67owRzb8bs0ebS5VddKwwbi3WLBcH2xGe//33Vmz8dM73jTba6WZpL1wvrIy1021y3S9M0ftzTlfsHmaLjxt8v/OIhexPzxKucDC5WzhzQUI8UhIEP4DEYKY2kX6PLZEDHUs74oE6iR4ICYgwgciAhHfcyjkUzMo/HhCznuPgeg9Z392ta0fU55pAi+Jw8pin249W4H3BeE0BJaOXwH7jo+AAyof+8ATh0kfOiyB3TXhaO7PXKwAjhxeAscOU57EYaq1dJjyKIfJ58rhADh13lA36PxDE3T+0aug849aQed/CoycMubwM0GslIKwIMxT4NkDH935cEKd5vAcpU5DT8ndG3G8kbr3OVnd+6xI40eIVV0t+FfS8fCsfIcRqwKH0RPl6sbIr0KHKU/kMMXSZyFk2H0F9Or4kjF3tyHJLnVt54eGk8YCA1EYfZvfqqwQRT8a/P6fBruP1PsHLyRO2AplbmRzdHJlYW0KZW5kb2JqCjE1MDEgMCBvYmoKPDwKL0xlbmd0aCA1ODQgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42m1U0Y6iMBR95yu6DybOg2MpiDIxJmAl8WFnJqPZ7KtCdUmkEMBk/fvtuQVlNj7YnJ6ee+/pLdfRj8/dJMrKo5p4r5x9qaa81qmarH8eKmc0kmV6LZRu35XKVNafNm/ssy7TnWrZeL2VW523L0a81enlmqle9VwUq3OuHxLUYeO9+j1JC/V3MTle80ub6wmHdp+3F6N5dswMx75xjEJ+qbrJS/3G3FfOuSE2OluXBe7QONPOB5v2zk65zurODDvCmuMKluVp2+1oTQvTDATvbk2riq0+lc5yyaZf5rBp6xs5fHGmH3Wm6lyf2fibM3Oyu1bVRcEF485qxTJ1MgnN3d8PhWLTZxe8S/a3SjFBe9e6SstMNdUhVfVBn5Wz5HzFlkmycpTO/jsLbcTxNJRyFwsXi5WzdEG4Q0KA8AZEAGJuFuHxyBAR1JF4KGKoY39AzEAEAyIEET1ySOSTCyi8aEbOe4++23tO/xxqUz+iPPMYXmKLpcEe3XqxAe+5hJMAWFh+A+xZPgT2qXzkAc8sJn1gsQC214SjpbewsS5waPEaOLKY8sQWU621xZRHWkw+Nxb7wIn1hrp+5x8av/OPXvmdf9TyO/9zYOQUEYefGWKFcAm7hHkCvBjw4YMPZtRpDs9hYjX0lNy+EccbyUef482jz5I0XohY2dWCfyksD8/Ssxix0rcYPZG2boT8MrCY8oQWUyx9Fq4Iuq+AXh1fMqbuPiPpta7N+NBo0lhgIHKt7tNblRWi6Edj3//JYPeROP8Aa4tOCwplbmRzdHJlYW0KZW5kb2JqCjE1MDIgMCBvYmoKPDwKL0xlbmd0aCA2NzggICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42m1UTW/iMBC951d4D5XaA8UfJIEKIdkJkThsW5VqtVdITDcSSVAIh/779RsTrFY9EE2e38y8mUd89+t1O9FVt7cT9cjZmz13l760k+z37hTd3eVdeWlsOzxbW9lqPD0/sde+K7d2YPfZJt+09fDgyJu2PF4qO7J+Jhn7UbeBgj7s/t3+nZRNUws+2V/q41C3Ew7yez0cHenHc+ZA9hVklPTH9ue6a5+YeOScO2DdVlnXYIxzNL1KYdNR3KFuq/6qh+2hLhKSVXU5XN/oWTZuH0jefp4H22zaQxctl2z65g7PQ/9JGh+i6Utf2b5uP9j9V2nuaHs5nY4WMhiPVitW2YOr6OZ/3jWWTX+c8cZ5/zxZJuldeF1lV9nzaVfaftd+2GjJ+Yoti2IV2bb6dibmPmV/GLmp4/I5HkrHKwcYxGsCjHCAQDXhGSYBsACgCcg4AKSI3AMKQOFiKTyQOkAiXS4INRkApEtDANVQ6KLA4FwBmCE9pi58Bh0x2DG6yCTJHZCAkXpGAoZGF70Is2iMYUSYxSgAJszixDggD7MYpGQ8zJJJAOo2i9vpuLxZMi6z/Lfrr3vnaoE0jq5ScjTh0uNzxL7SwiCe+XiNOPayocElUUx1ruNQLi1NSKpZEF5gTOHdyhFL38tQ7HvlmFz6Xjlype9VEO57uSldnPqYOKgpY0PukZ1xBr7y+5bQrIyPYajKvHUU5x4vEK89TnyqI6n+jPv9oG9MvZTEvHHhY+AJcYQAnlIvoaAhpV5SQVuaew72k1IdngOfXy0ErrnXg91qETzSMnikVfBIz4JHOg4e6SR4pNPgkdbBI50Hj4wMHmXi5hH9g+gfg+8Tt8nt0y8vfe9uBbpy6GPHZ1639nYrnboTsuhH19l4f+LtpYj+A9+Dd/AKZW5kc3RyZWFtCmVuZG9iagoxNTAzIDAgb2JqCjw8Ci9MZW5ndGggNjc3ICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVE1v4jAQvedXeA+V2gPFHySBCiHZCZE4bFuVarVXSEw3EklQCIf++/UbE6xWPRBNnt/MvJlHfPfrdTvRVbe3E/XI2Zs9d5e+tJPs9+4U3d3lXXlpbDs8W1vZajw9P7HXviu3dmD32SbftPXw4MibtjxeKjuyfiYZ+1G3gYI+7P7d/p2UTVMnk/2lPg51O+HgvtfD0XF+OmYOY18wRil/bH+uu/aJiUfOuQPWbZV1DWY4R9OrDjYdlR3qtuqvYtge0iIhWVWXw/WNnmXjloHk7ed5sM2mPXTRcsmmb+7wPPSfpPAhmr70le3r9oPdf1HmTraX0+looYLxaLVilT24gm72511j2fSnAW+U98+TZZLehVdVdpU9n3al7Xfth42WnK/YsihWkW2rb2di7lP2h5GbOi6f46F0vHKAQbwmwAgHCFQTnmESAAsAmoCMA0CKyD2gABQulsIDqQMk0uWCUJMBQLo0BFANhS4KDM4VgBnSY+rCZ9ARgx2ji0yS3AEJGKlnJGBodNGLMIvGGEaEWYwCYMIsTowD8jCLQUrGwyyZBKBus7idjsubJeMyy3+7/rp3rhZI4+gqJUcTLj0+R+wrLQzimY/XiGMvGxpcEsVU5zoO5dLShKSaBeEFxhTerRyx9L0Mxb5Xjsml75UjV/peBeG+l5vSxamPiYOaMjbkHtkZZ+Arv28Jzcr4GIaqzFtHce7xAvHa48SnOpLqz7jfD/rG1EtJzBsXPgaeEEcI4Cn1EgoaUuolFbSluedgPynV4Tnw+dVC4Jp7PditFsEjLYNHWgWP9Cx4pOPgkU6CRzoNHmkdPNJ58MjI4FEmbh7RP4j+Mfg+cZfcvvzy0vfuUqALhz52fOZ1a2930qk7IYt+dJmNVyfeXoroP0E/dxkKZW5kc3RyZWFtCmVuZG9iagoxNTA0IDAgb2JqCjw8Ci9MZW5ndGggNjc3ICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVE1v4jAQvedXeA+V2gPFHySBCiHZCZE4bFuVarVXSEw3EklQCIf++/UbE6xWPRBNnt/MvJlHfPfrdTvRVbe3E/XI2Zs9d5e+tJPs9+4U3d3lXXlpbDs8W1vZajw9P7HXviu3dmD32SbftPXw4MibtjxeKjuyfiYZ+1G3gYI+7P7d/p2UTVOnk/2lPg51O+HgvtfD0XF+OmYOY18wRil/bH+uu/aJiUfOuQPWbZV1DWY4R9OrDjYdlR3qtuqvYtge0iIhWVWXw/WNnmXjloHk7ed5sM2mPXTRcsmmb+7wPPSfpPAhmr70le3r9oPdf1HmTraX0+looYLxaLVilT24gm72511j2fSnAW+U98+TZZLehVdVdpU9n3al7Xfth42WnK/YsihWkW2rb2di7lP2h5GbOi6f46F0vHKAQbwmwAgHCFQTnmESAAsAmoCMA0CKyD2gABQulsIDqQMk0uWCUJMBQLo0BFANhS4KDM4VgBnSY+rCZ9ARgx2ji0yS3AEJGKlnJGBodNGLMIvGGEaEWYwCYMIsTowD8jCLQUrGwyyZBKBus7idjsubJeMyy3+7/rp3rhZI4+gqJUcTLj0+R+wrLQzimY/XiGMvGxpcEsVU5zoO5dLShKSaBeEFxhTerRyx9L0Mxb5Xjsml75UjV/peBeG+l5vSxamPiYOaMjbkHtkZZ+Arv28Jzcr4GIaqzFtHce7xAvHa48SnOpLqz7jfD/rG1EtJzBsXPgaeEEcI4Cn1EgoaUuolFbSluedgPynV4Tnw+dVC4Jp7PditFsEjLYNHWgWP9Cx4pOPgkU6CRzoNHmkdPNJ58MjI4FEmbh7RP4j+Mfg+cZfcvvzy0vfuUqALhz52fOZ1a2930qk7IYt+dJmNVyfeXoroP1kadx4KZW5kc3RyZWFtCmVuZG9iagoxNTA1IDAgb2JqCjw8Ci9MZW5ndGggNjc3ICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVE1v4jAQvedXeA+V2gPFHySBCiHZCZE4bFuVarVXSEw3EklQCIf++/UbE6xWPRBNnt/MvJlHfPfrdTvRVbe3E/XI2Zs9d5e+tJPs9+4U3d3lXXlpbDs8W1vZajw9P7HXviu3dmD32SbftPXw4MibtjxeKjuyfiYZ+1G3gYI+7P7d/p2UTVPPJ/tLfRzqdsLBfa+Ho+P8dMwcxr5gjFL+2P5cd+0TE4+ccwes2yrrGsxwjqZXHWw6KjvUbdVfxbA9pEVCsqouh+sbPcvGLQPJ28/zYJtNe+ii5ZJN39zheeg/SeFDNH3pK9vX7Qe7/6LMnWwvp9PRQgXj0WrFKntwBd3sz7vGsulPA94o758nyyS9C6+q7Cp7Pu1K2+/aDxstOV+xZVGsIttW387E3KfsDyM3dVw+x0PpeOUAg3hNgBEOEKgmPMMkABYANAEZB4AUkXtAAShcLIUHUgdIpMsFoSYDgHRpCKAaCl0UGJwrADOkx9SFz6AjBjtGF5kkuQMSMFLPSMDQ6KIXYRaNMYwIsxgFwIRZnBgH5GEWg5SMh1kyCUDdZnE7HZc3S8Zllv92/XXvXC2QxtFVSo4mXHp8jthXWhjEMx+vEcdeNjS4JIqpznUcyqWlCUk1C8ILjCm8Wzli6XsZin2vHJNL3ytHrvS9CsJ9Lzeli1MfEwc1ZWzIPbIzzsBXft8SmpXxMQxVmbeO4tzjBeK1x4lPdSTVn3G/H/SNqZeSmDcufAw8IY4QwFPqJRQ0pNRLKmhLc8/BflKqw3Pg86uFwDX3erBbLYJHWgaPtAoe6VnwSMfBI50Ej3QaPNI6eKTz4JGRwaNM3DyifxD9Y/B94i65ffnlpe/dpUAXDn3s+Mzr1t7upFN3Qhb96DIbr068vRTRf3D1dyMKZW5kc3RyZWFtCmVuZG9iagoxNTA2IDAgb2JqCjw8Ci9MZW5ndGggNzM5ICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVU1v4jAUvOdXeA+V2gPFdkgCVYRk50PisG1VqtVeITHdSJCgEA799+vxI3jZ9gAaP4+fZ+zB3P14XU9U3W3NJHzk7M2cunNfmUn2c3MM7u7yrjofTDs8G1Obepw9PbHXvqvWZmD32Spftc3wYMmrttqfazOyvidp89G0noJ92P27+T2pDr3gk+252Q9NO+HgvjfD3nK+m2a2xm5qzC35ZfpT07VPTDxyzm2haOusO8DDKZhedLDpqGzXtHV/EcO2kBYIyeqmGi4j910d7GFg8frzNJjDqt11QZqy6ZudPA39p1P4EExf+tr0TfvB7m+U2Zn1+XjcG6hgPFguWW12tqH1/rw5GDb9zuCV8v55NEy6sSBVVVeb03FTmX7Tfpgg5XzJ0rJcBqat/5tLaMV2N1ITS+VzfIUqWgapDC2WMQrcYlvAZKipMLeFsLQ4ooLFQRoLixPlChYHaYLJJEMjLtBDoYdaXHexukYFyXxUVP3Z9BftPFxgGUdjKXkMLKkOBTwkrIFnhAvgiHbgwDFh1+eys1sLnVJI15NMxDE+GGd+vMA49+MM4+If/sgpb2vgCedBhDgLAQ/2JBNgSVzoE84D1zNg50FmjuM8yBxaBV2DwiGLhOoKeE5rHcd547nj0B0U8CKcNxHBvyBfpeOTp8xxCuJIYPLh9pWCbhv6paRrzIETwvAdUk8FTkg9Q3BCOiMNPdElAjiTiDgROFFBfqEhKskXesac+NCQ5FQHPyF+jj4J6XR3qTjpxHkq4XOjpM+NCn1u1MznRkU+Nyr2uVGJz41SPjeKsqKgR128O37u86SK2zyp8jZPmt/mSYuvedLya5506POkZz5POvJ50rHPk058nvTc50kvfJ608nnS2udJZz5POvd50oXPky59njLu85QJn6dM+jxls+uduV+++6XjbcI7en31qnPf2wfRPbbuocMT17Tm+h4fuyNWuY97yMe/DYxeyuAv8fOoUAplbmRzdHJlYW0KZW5kb2JqCjE1MDcgMCBvYmoKPDwKL0xlbmd0aCA3MzkgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42m1VTW/iMBS851d4D5XaA8V2SAJVhGTnQ+KwbVWq1V4hMd1IkKAQDv336/EjeNn2ABo/j59n7MHc/XhdT1Tdbc0kfOTszZy6c1+ZSfZzcwzu7vKuOh9MOzwbU5t6nD09sde+q9ZmYPfZKl+1zfBgyau22p9rM7K+J2nz0bSegn3Y/bv5PakOvZCT7bnZD0074eC+N8Pecr6bZrbGbmrMLfll+lPTtU9MPHLObaFo66w7wMMpmF50sOmobNe0dX8Rw7aQFgjJ6qYaLiP3XR3sYWDx+vM0mMOq3XVBmrLpm508Df2nU/gQTF/62vRN+8Hub5TZmfX5eNwbqGA8WC5ZbXa2ofX+vDkYNv3O4JXy/nk0TLqxIFVVV5vTcVOZftN+mCDlfMnSslwGpq3/m0toxXY3UhNL5XN8hSpaBqkMLZYxCtxiW8BkqKkwt4WwtDiigsVBGguLE+UKFgdpgskkQyMu0EOhh1pcd7G6RgXJfFRU/dn0F+08XGAZR2MpeQwsqQ4FPCSsgWeEC+CIduDAMWHX57KzWwudUkjXk0zEMT4YZ368wDj34wzj4h/+yClva+AJ50GEOAsBD/YkE2BJXOgTzgPXM2DnQWaO4zzIHFoFXYPCIYuE6gp4Tmsdx3njuePQHRTwIpw3EcG/IF+l45OnzHEK4khg8uH2lYJuG/qlpGvMgRPC8B1STwVOSD1DcEI6Iw090SUCOJOIOBE4UUF+oSEqyRd6xpz40JDkVAc/IX6OPgnpdHepOOnEeSrhc6Okz40KfW7UzOdGRT43Kva5UYnPjVI+N4qyoqBHXbw7fu7zpIrbPKnyNk+a3+ZJi6950vJrnnTo86RnPk868nnSsc+TTnye9NznSS98nrTyedLa50lnPk8693nShc+TLn2eMu7zlAmfp0z6PGWz6525X777peNtwjt6ffWqc9/bB9E9tu6hwxPXtOb6Hh+7I1a5j3vIx78NjF7K4C8rkKhaCmVuZHN0cmVhbQplbmRvYmoKMTUwOCAwIG9iago8PAovTGVuZ3RoIDc0MCAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVVNb+IwFLznV3gPldoDxXZIDFWEZOdD4rBtVarVXiEx3UiQREk49N+vnx/hLdseQOPn8fOMPZi7H6/bma7avZ2Fj5y92aE996WdpT93XXB3l7Xl+WSb8dnaylbT7PDEXvu23NqR3aebbNPU44Mjb5ryeK7sxPqeZOxH3RAF9mH37/b3rDz1Qs325/o41s2MA/e9Ho+O8900czV2U2N+yS/bD3XbPDHxyDl3hbyp0vYEHoZgftHB5pOyQ91U/UUM24O0QEhW1eV4Gfnv8uQOAxZvP4fRnjbNoQ2ShM3f3OQw9p9e4UMwf+kr29fNB7u/UeZmtueuO1pQwXiwXrPKHlxD5/15d7Js/p3BK+X9s7NM+rFAVWVb2aHblbbfNR82SDhfs6Qo1oFtqv/mFK7YHyaqclS+hK9QR+sgkaHDMoYCd9gVYDI0WFi6Qlg4HGHB4SCJhcNK+4LDQaJgUqXQiAvooaGHXl13cbomBWo5KSr/7PqLdh6uYBmHxlLyGLDEOijgIWIDeIE4BxzhDhxwjNj3uezs14JOKaTviSbiGD4wTmm8gnFG4xTG+T/8iVPc1oAnvAcRwlkI8OBOUgGWyAV9wnvgZgHYe5Cp53gPMgOtAq9BwyELhXUNeIlrPcd745nn4B3k4EV4byIC/wJ9FZ6PnlLPyZEjAaMPv68UeNugX0q8xgywQgy+Q+ypgRNizxA4IZ6RAT3RJQJwJhFyIuBEOfoFDVGBvqBnzJEPGlSGdeAr5GfQR6FOf5eao044Ty0oN1pSbnRIudELyo2OKDc6ptxoRbnRmnKjMSsa9OiLd8/PKE86v82TLm7zZPhtnoz4micjv+bJhJQns6A8mYjyZGLKk1GUJ7OkPJkV5cloypMxlCeTUp5MRnkyOeXJFJSnlFOeUkF5SiXlKV1c78z/8v0vHd4meEevr1557nv3IPrH1j908MTVjb2+x13bwSr/8Q/59LcBo5ci+Au7c6hzCmVuZHN0cmVhbQplbmRvYmoKMTUwOSAwIG9iago8PAovTGVuZ3RoIDczOSAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVVNb+IwFLznV3gPldoDxXZIAlWEZOdD4rDbqlSrvUJiupEgQSEc+u/X40dw2fYAGj+Pn2fswdz9eFlPVN1tzSR85OzVnLpzX5lJ9nNzDO7u8q46H0w7/DKmNvU4e3piL31Xrc3A7rNVvmqb4cGSV221P9dmZH1P0ua9aT0F+7D7N/NnUh36eLI9N/uhaScc1Ldm2FvKN7PMltjnEnMLfpv+1HTtExOPnHNbKNo66w4wcAqmFxFsOsraNW3dX5SwLXQFQrK6qYbLyH1XB3sSWLz+OA3msGp3XZCmbPpqJ09D/+H0PQTT5742fdO+s/vPwuzE+nw87g1EMB4sl6w2O9vP+v61ORg2/cbdlfH2cTRMurEgTVVXm9NxU5l+076bIOV8ydKyXAamrf+bS2jFdjdSE0vlc3yFKloGqQwtljEK3GJbwGSoqTC3hbC0OKKCxUEaC4sT5QoWB2mCySRDIy7QQ6GHWlx3sbpGBcl8VFT93fQX7TxcYBlHYyl5DCypDgU8JKyBZ4QL4Ih24MAxYdfnsrNbC51SSNeTTMQxPhhnfrzAOPfjDOPiE3/klLc18ITzIEKchYAHe5IJsCQu9AnngesZsPMgM8dxHmQOrYKuQeGQRUJ1BTyntY7jvPHccegOCngRzpuI4F+Qr9LxyVPmOAVxJDD5cPtKQbcN/VLSNebACWH4DqmnAiekniE4IZ2Rhp7oEgGcSUScCJyoIL/QEJXkCz1jTnxoSHKqg58QP0efhHS6u1ScdOI8lfC5UdLnRoU+N2rmc6MinxsV+9yoxOdGKZ8bRVlR0KMu3h0/93lSxW2eVHmbJ81v86TF1zxp+TVPOvR50jOfJx35POnY50knPk967vOkFz5PWvk8ae3zpDOfJ537POnC50mXPk8Z93nKhM9TJn2estn1ztwv3/3S8TbhFb0+etW57+176J5a99DhiWtac32Nj90Rq9zHPePjPwZGz2XwD4eGp3kKZW5kc3RyZWFtCmVuZG9iagoxNTEwIDAgb2JqCjw8Ci9MZW5ndGggNzQwICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVU1v4jAUvOdXeA+V2gPFdkgMVYRk50PisNuqVKu9QmK6kSCJQjj036/fewku2x5A4+fx84w9mLsfL9uZrtq9nYWPnL3ac3vpSztLf+664O4ua8vLyTbDL2srW02z5yf20rfl1g7sPt1km6YeHhx505THS2Un1vckY9/rxlNgH3b/Zv/MylOvZvtLfRzqZsaB+lYPR0f5Zpa5EvtcYrjgt+3Pdds8MfHIOXeFvKnS9gQGzsF8FMHmk6xD3VT9qITtQVcgJKvqchhH+F2e3EnA4u3HebCnTXNogyRh81c3eR76D9T3EMyf+8r2dfPO7j8LcxPbS9cdLYhgPFivWWUPrp/z/Wt3smz+jbsr4+2js0ziWJCmsq3suduVtt817zZIOF+zpCjWgW2q/+YUrdgfJqpyVL6Er1BH6yCRocMyhgJ32BVgMjRUWLpCWDgcUcHhIImFw0pjweEgUTCpUmjEBfTQ0EOvrrs4XZMCtZwUlX93/aidhytYxqGxlDwGLKkOCnhI2ABeEM4BR7QDBxwTxj7jzrgWdEohsSeZiGP4wDj14xWMMz9OYZx/4k+c4rYGPIEeRAhnIcCDO0kFWBIX9An0wM0CMHqQKXLQg8xAq6Br0HDIQlFdA17SWuSgN54hh+4gBy8CvYkI/AvyVSCfPKXIyYkjAZMP3FcKum3QLyVdYwZYEQbfIfXUwAmpZwickM7IgJ5ojACcSUScCDhRTn5BQ1SQL+gZc+KDBpVRHfiK+Bn0UaQT71Jz0gnnqYXPjZY+Nzr0udELnxsd+dzo2OdGK58brX1uNGVFgx49ekd+5vOk89s86eI2T4bf5smIr3ky8mueTOjzZBY+TybyeTKxz5NRPk9m6fNkVj5PRvs8GePzZFKfJ5P5PJnc58kUPk8p93lKhc9TKn2e0sX1zvCXj790eJvgFb0+euWl7917iE8tPnTwxNWNvb7GXdvBKvzgMz79Y8DouQj+AaRDp34KZW5kc3RyZWFtCmVuZG9iagoxNTExIDAgb2JqCjw8Ci9MZW5ndGggNzM5ICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVU1v4jAUvOdXeA+V2gPFdkgCVYRk50PisNuqVKu9QmK6kSBBIRz679fjR3DZ9gAaP4+fZ+zB3P14WU9U3W3NJHzk7NWcunNfmUn2c3MM7u7yrjofTDv8MqY29Th7emIvfVetzcDus1W+apvhwZJXbbU/12ZkfU/S5r1pPQX7sPs382dSHfr5ZHtu9kPTTjiob82wt5RvZpktsc8l5hb8Nv2p6donJh4557ZQtHXWHWDgFEwvIth0lLVr2rq/KGFb6AqEZHVTDZeR+64O9iSweP1xGsxh1e66IE3Z9NVOnob+w+l7CKbPfW36pn1n95+F2Yn1+XjcG4hgPFguWW12tp/1/WtzMGz6jbsr4+3jaJh0Y0Gaqq42p+OmMv2mfTdByvmSpWW5DExb/zeX0IrtbqQmlsrn+ApVtAxSGVosYxS4xbaAyVBTYW4LYWlxRAWLgzQWFifKFSwO0gSTSYZGXKCHQg+1uO5idY0KkvmoqPq76S/aebjAMo7GUvIYWFIdCnhIWAPPCBfAEe3AgWPCrs9lZ7cWOqWQrieZiGN8MM78eIFx7scZxsUn/sgpb2vgCedBhDgLAQ/2JBNgSVzoE84D1zNg50FmjuM8yBxaBV2DwiGLhOoKeE5rHcd547nj0B0U8CKcNxHBvyBfpeOTp8xxCuJIYPLh9pWCbhv6paRrzIETwvAdUk8FTkg9Q3BCOiMNPdElAjiTiDgROFFBfqEhKskXesac+NCQ5FQHPyF+jj4J6XR3qTjpxHkq4XOjpM+NCn1u1MznRkU+Nyr2uVGJz41SPjeKsqKgR128O37u86SK2zyp8jZPmt/mSYuvedLya5506POkZz5POvJ50rHPk058nvTc50kvfJ608nnS2udJZz5POvd50oXPky59njLu85QJn6dM+jxls+uduV+++6XjbcIren30qnPf2/fQPbXuocMT17Tm+hofuyNWuY97xsd/DIyey+AfwQCngwplbmRzdHJlYW0KZW5kb2JqCjE1MTIgMCBvYmoKPDwKL0xlbmd0aCA5MDEgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42m1VTW/bOhC861ewhwDpwTU/JFIuDAOkZAM59ANN8PCujsTkCYhlQ7YP+fePs2ubbZFDjNVwuTs7HIZ3n34+zny/f44z80WKX/G4P09dnDXftofi7q7dd+ddHE/fY+xjf109fhU/p333GE/ivnloH8bh9DklP4zd27mP16yPk0J8Hcacgj7i/in+O+t2x3clZ8/n4e00jDOJ5Kfh9JaSPlwXCRR/goI2/ROn47Afvwr1RUqZgPXYN/sdxjgW8wsVMb+SexnGfrrwEc9gVygt+qE7Xb7ot9slPbD58f14iruH8WVfLJdi/istHk/TO3H8XMx/TH2chvFV3P9JLS09ng+HtwgaQharlejjS6qY5v++3UUx/3DGW87T+yEKTd+KeXX7Ph4P2y5O2/E1FkspV2K52ayKOPZ/rWnHW55frrl1ypVN+tF6Ua2KpdIpVoaA2gIoAVQE2BKABeAAeL8B4AEE3qIBoJ5qeYsHsAawIcAB0NQAlbVaGADUwDLgAFA9IqZaZBjUMFzDg0epUlyht5QpLpYVMirO0KhhIYRVvAXELIaz1NtI8LCgYCsGagDQwy4AuDUAB07OsUDo4rDo0FZLLRPgAXifJfSY3DMPmiWgQbBZ04B6oc6aBtQIPmsaUCOss6YB9RqZNW0wV6NvmqbDvp5qra6n3P23nS6G0FqhjlQkWAAvqSluKeYjVxSXhG8o5pNvwFJa3gsZJEuicDSSjlq2LWLiXK0xptywSuQJ5lAiR/GRWNRRrIJpEC84B7pryV6AwlpxjDpac4y9mjmUZKCarUL5C44p33NM+S3vBTe95niBmM9KgZuhvtJgr2Geyb0pZqXJJIa1kgExmbgKZNGKY6pDWhlD+TyjBDfDM0rKZ/N6zG74/kgYyLDtFeHMWYF/yZoY5FScr2GFiu+vxiwVX7sanC2flwNP6zgGN8t9HepbqiMVNLHcl0xsuW9L8ZryK8pnrUrM4i6+AgdHWnkD87qLVvCPY600OLiS7wS85NhXdPldzTpQfmCPgY8jPtZSTssxzsWtOabruOEYfOrfPOMl1eEbqrL/vc7+9yb735fZ/77K/vc2+9+77H9fZ/8Hmf0fVPZ/0Nn/wWT/hzL7P4Ts/9Bk/zcmz9KUtxnpltOtxj93PEW3d6M7T1N6Uui9opcCb8QwxtuTdtgfsIv+6C28Pr74+rEp/geqTeocCmVuZHN0cmVhbQplbmRvYmoKMTUxMyAwIG9iago8PAovTGVuZ3RoIDkwMCAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVVNb9s6ELzrV7CHAOnBNT8kUi4MA6RkAzn0A03w8K6OxOQJiGVDtg/594+za5ttkUOM1XC5Ozschneffj7OfL9/jjPzRYpf8bg/T12cNd+2h+Lurt13510cT99j7GN/XT1+FT+nffcYT+K+eWgfxuH0OSU/jN3buY/XrI+TQnwdxpyCPuL+Kf4763bHdzt7Pg9vp2GcSeQ+Dae3lPPRskiY+AMTtOWfOB2H/fhVqC9SygSsx77Z7zDDsZhfeIj5ldnLMPbThYx4BrVCadEP3enyRb/dLomBzY/vx1PcPYwv+2K5FPNfafF4mt6J4edi/mPq4zSMr+L+D2Zp5fF8OLxFsBCyWK1EH19SwTT79+0uivlHA95Snt4PUWj6Vsyq2/fxeNh2cdqOr7FYSrkSy81mVcSx/2tNO97y/HLNrVOubNKP1otqVSyVTrEyBNQWQAmgIsCWACwAB8D7DQAPIPAWDQD1VMtbPIA1gA0BDoCmBqis1cIAoAaWAQeA6hEx1SLDoIbhGh48SpXiCr2lTHGxrJBRcYZGDQshrOItIGYxnKXeRoKHBQVbMVADgB52AcCtAThwco4FQheHRYe2WmqZAA/A+yyhx+SeedAsAQ2CzZoG1At11jSgRvBZ04AaYZ01DajXyKxpg7kafdM0Hfb1VGt1PeXuv+10MYTWCnWkIsECeElNcUsxH7miuCR8QzGffAOW0vJeyCBZEoWjkXTUsm0RE+dqjTHlhlUiTzCHEjmKj8SijmIVTIN4wTnQXUv2AhTWimPU0Zpj7NXMoSQD1WwVyl9wTPmeY8pveS+46TXHC8R8VgrcDPWVBnsN80zuTTErTSYxrJUMiMnEVSCLVhxTHdLKGMrnGSW4GZ5RUj6b12N2w/dHwkCGba8IZ84K/EvWxCCn4nwNK1R8fzVmqfja1eBs+bwceFrHMbhZ7utQ31IdqaCJ5b5kYst9W4rXlF9RPmtVYhZ38RU4ONLKG5jXXbSCfxxrpcHBlXwn4CXHvqLL72rWgfIDewx8HPGxlnJajnEubs0xXccNx+BT/+YZL6kO31CV/e919r832f++zP73Vfa/t9n/3mX/+zr7P8js/6Cy/4PO/g8m+z+U2f8hZP+HJvu/MXmWprzNSLecbjX+ueMhuj0b3Xma0otCrxW9FHgjhjHeHrTD/oBd9Ecv4fXdxdePTfE/KWbpRQplbmRzdHJlYW0KZW5kb2JqCjE1MTQgMCBvYmoKPDwKL0xlbmd0aCA5MDAgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42m1VTW/bOhC861ewhwDpwTU/JFIuDAOkZAM59ANN8PCujsTkCYhlQ7YP+fePs2ubbZFDjNVwuTs7HIZ3n34+zny/f44z80WKX/G4P09dnDXftofi7q7dd+ddHE/fY+xjf109fhU/p333GE/ivnloH8bh9DklP4zd27mP16yPk0J8Hcacgj7i/in+O+t2x/d69nwe3k7DOJPIfRpObynno2WRMPEHJmjLP3E6Dvvxq1BfpJQJWI99s99hhmMxv/AQ8yuzl2HspwsZ8QxqhdKiH7rT5Yt+u10SA5sf34+nuHsYX/bFcinmv9Li8TS9E8PPxfzH1MdpGF/F/R/M0srj+XB4i2AhZLFaiT6+pIJp9u/bXRTzjwa8pTy9H6LQ9K2YVbfv4/Gw7eK0HV9jsZRyJZabzaqIY//Xmna85fnlmlunXNmkH60X1apYKp1iZQioLYASQEWALQFYAA6A9xsAHkDgLRoA6qmWt3gAawAbAhwATQ1QWauFAUANLAMOANUjYqpFhkENwzU8eJQqxRV6S5niYlkho+IMjRoWQljFW0DMYjhLvY0EDwsKtmKgBgA97AKAWwNw4OQcC4QuDosObbXUMgEegPdZQo/JPfOgWQIaBJs1DagX6qxpQI3gs6YBNcI6axpQr5FZ0wZzNfqmaTrs66nW6nrK3X/b6WIIrRXqSEWCBfCSmuKWYj5yRXFJ+IZiPvkGLKXlvZBBsiQKRyPpqGXbIibO1Rpjyg2rRJ5gDiVyFB+JRR3FKpgG8YJzoLuW7AUorBXHqKM1x9irmUNJBqrZKpS/4JjyPceU3/JecNNrjheI+awUuBnqKw32GuaZ3JtiVppMYlgrGRCTiatAFq04pjqklTGUzzNKcDM8o6R8Nq/H7Ibvj4SBDNteEc6cFfiXrIlBTsX5Glao+P5qzFLxtavB2fJ5OfC0jmNws9zXob6lOlJBE8t9ycSW+7YUrym/onzWqsQs7uIrcHCklTcwr7toBf841kqDgyv5TsBLjn1Fl9/VrAPlB/YY+DjiYy3ltBzjXNyaY7qOG47Bp/7NM15SHb6hKvvf6+x/b7L/fZn976vsf2+z/73L/vd19n+Q2f9BZf8Hnf0fTPZ/KLP/Q8j+D032f2PyLE15m5FuOd1q/HPHQ3R7NrrzNKUXhV4reinwRgxjvD1oh/0Bu+iPXsLru4uvH5vif29+6U8KZW5kc3RyZWFtCmVuZG9iagoxNTE1IDAgb2JqCjw8Ci9MZW5ndGggNzUwICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVU1v4jAQvedXeA+V2gPFHySBKkKy8yFx2LYq1WqvkJhuJEiiJBz679czQ/Cy7YFoPH4zfs9+Gu5+vG5numr3dqYeOXuzQ3vuSztLf+664O4ua8vzyTbjs7WVrabd4Ym99m25tSO7TzfZpqnHBwfeNOXxXNkJ9T3I2I+68RA4h92/29+z8jTWgs/25/o41s2MA/i9Ho8O9O0+c0l2m2RY9Mv2Q902T0w8cs5dIm+qtD2BjCGYX6iw+UTuUDdVf+HD9sAuEJJVdTleVvgtT+4+oHj7OYz2tGkObZAkbP7mNoex/0SOD8H8pa9sXzcf7P6WmtvanrvuaIEG48F6zSp7cB2d/ufdybL5txqvmPfPzjKJa0G8yrayQ7crbb9rPmyQcL5mSVGsA9tU/+3FVLE/TNDYQfkSPkqH6yCRoYtlBAkuMQGbylBi6RKqcHFICRcHSSRcHGtMuDhIYtiMU2jEhXIJDT306nqK4zUxWPKJUfln11+4c7WCMg6NpeQRxJLywIArig3EC4pziEM6gUMcUYx9LidjLfCUQmJPEhFF8IN16tcrWGd+ncI6/wc/YYrbHOAEahAK7kKABi5VDLEkLPATqIGbBcSoQaaIQQ0yA66CnkHDJYuY8hriJdUiBrXxDDH0BjloEahNhKBfkK4C8aQpRUxOGAkx6cBzpaDXBv5S0jNmEBNnCT3lgrhhHBMG7kPRWRpqFZ2loFbR3RngGV6sAXcVEiYETJjTPQC3sCC90DPihAducUZ5wMeEz6BPTPzxjTUnnnDPWng/aen9pJX3k154P+nQ+0lH3k869n7S2vtJk4c08NEX7YjPvM90fuszXdz6zPBbnxnx1WdGfvWZUd5nZuF9ZkLvMxN5n5nY+8wsvc/MyvvMaO8zY7zPTOp9ZjLvM5N7n5nC+yzl3mep8D5LpfdZuri+GU4EnAAws2DCXsdhee57NylxDOMAhNFXN/Y6qbu2gyr84Yif/lNg9VIEfwHpzK8ECmVuZHN0cmVhbQplbmRvYmoKMTUxNiAwIG9iago8PAovTGVuZ3RoIDY3MiAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVTBbqMwEL3zFd5DpfaQxjYBkiqKZBuQcti2aqrVXhNwukiJQUAO/fv1jENmu+oB9Bi/mXnjJ+bux+tupur2YGfxI2dvdmgvfWVn5ue+i+7u8ra6nK0bn62tbT2dDk/stW+rnR3ZvdnmW9eMD568ddXpUtuJ9T1J24/GEQX6sPt3+3tWncdR8Nnh0pzGxs04kN+b8eRJ354zH2RfgwyTftl+aFr3xMQj59wHCleb9gxjDNH8KoXNJ3HHxtX9VQ87gLpISFY31Xj9wnd19vcBybvPYbTnrTu20XrN5m/+cBj7T9T4EM1f+tr2jftg91+l+aPdpetOFmQwHm02rLZHX9HP/7w/Wzb/dsYb5/2zs0zitwi6qra2Q7evbL93HzZac75h67LcRNbV/50lIeNwnKiZp/IlvGKVbKK1FB7LFALcYx+Aw6QMgaUPpMDICgykwFDAUKtbDd91qp/xqV/1Z99flfF4Ffs0jp0kTwHLEIf6PA5YA14EXABOQgcOOA0Y61w7Y66CmkJiTY14BRK5CRj5eZglAxzGUMgJM2p/b2uBOkUMcQE6uYyBL2QYGzSIOPAXgFGnNMhBnTIHPSJcpMKaWYgrwMuQixzUz3PkKMQF6BcaNSSgWRiMl8gP+g1yisCRgIN+7CtBv1xIyJXQV3LfxnvHAwZOFvh4b8t/+IqH+4EZlSC/lCS/VEx+qQX5pRLyS6Xkl8rIL6XIL6WDhhxwmDHwc/JRFeSjKslHzclHLchHLclHHZOPekE+6oR81Cn5qDPyUS/JR70iH7UiH7UmH7UhH3VOPuqCfNQl+Wg4+WgE+Wgk+Whi8sUsbn7hH4Z/FPzhsI9uy6O69L3fK7i0cF3Aomicve21ru0gCx9ciNMGhq+XMvoLr02ExwplbmRzdHJlYW0KZW5kb2JqCjE1MTcgMCBvYmoKPDwKL0xlbmd0aCA2NzIgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42m1UwW6jMBC98xXeQ6X2kMY2AZIqimQbkHLYtmqq1V4TcLpIiUFADv379YxDZrvqAfQYv5l54yfm7sfrbqbq9mBn8SNnb3ZoL31lZ+bnvovu7vK2upytG5+trW09nQ5P7LVvq50d2b3Z5lvXjA+evHXV6VLbifU9SduPxhEF+rD7d/t7Vp3HcTk7XJrT2LgZB+57M54857tj5mPsS4xhyi/bD03rnph45Jz7QOFq055hhiGaX3Ww+aTs2Li6v4phB5AWCcnqphqvX/iuzv4yIHn3OYz2vHXHNlqv2fzNHw5j/4kKH6L5S1/bvnEf7P6LMn+yu3TdyYIKxqPNhtX26Av62Z/3Z8vm3w14o7x/dpZJ/BZBVdXWduj2le337sNGa843bF2Wm8i6+r+zJGQcjhM181S+hFeskk20lsJjmUKAe+wDcJiUIbD0gRQYWYGBFBgKGGp1q+G7TvUzPvWr/uz7qzIer2KfxrGT5ClgGeJQn8cBa8CLgAvASejAAacBY51rZ8xVUFNIrKkRr0AiNwEjPw+zZIDDGAo5YUbt720tUKeIIS5AJ5cx8IUMY4MGEQf+AjDqlAY5qFPmoEeEi1RYMwtxBXgZcpGD+nmOHIW4AP1Co4YENAuD8RL5Qb9BThE4EnDQj30l6JcLCbkS+kru23jveMDAyQIf7235D1/xcD8woxLkl5Lkl4rJL7Ugv1RCfqmU/FIZ+aUU+aV00JADDjMGfk4+qoJ8VCX5qDn5qAX5qCX5qGPyUS/IR52QjzolH3VGPuol+ahX5KNW5KPW5KM25KPOyUddkI+6JB8NJx+NIB+NJB9NTL6Yxc0v/MPwj4I/HLbRbXdUl773awVXFq4LWBSNs7et1rUdZOGD63BavvD1UkZ/AQrXg/oKZW5kc3RyZWFtCmVuZG9iagoxNTE4IDAgb2JqCjw8Ci9MZW5ndGggNDM3ICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptUsFuozAQvfsrvIdI6YFiYEOiCiElJJFy2LZqotVeiT1kLQUbGTjk73fGBNqucsA8v3kz8zz27Mf7MVgre4YgeRb8A1rbOwlB8ats2Gy2tbKvwXSvAArUGG1f+Luz8ggdnxeH7cHo7gnFByOvvYJR9Vi0gYs2nxLqw+cn+BNAX9WRCM69vnbaBILEJ91dUfQwzpHk30nuk36Da7U1Lzx6FkIgsTOqsDUdo2Xh3QoPR3OVNsrd/fAzuWNRzJWW3X3nV1njPCj5eGs7qA+msizLePiBwbZzN+/xiYVvToHT5sLn361h6Ng3zRXIBhcsz7mCCivi+V/LGnj48IyT5nRrgMd+Hw2+pFXQNqUEV5oLsEyInGf7fc7AqP9iqyHjXN2lUYzSKMElFtEqZ/gjHOEiBGIkUiKKgUiJ2CFONgOxQ+InqRcDgZhliy0RO08gZllKiuXaEykpNgvEm3Rqi0ZHS8vRofxbOuxGteIYfbIs2foKCZVM9gPGU2bLgY9JsxKf1tdfcJFM2Dfz1Wk+dJvT6GXvHN6Kv3I/bBqzNjC9isY2lOU//5zG90u7tz37BxNs9w0KZW5kc3RyZWFtCmVuZG9iagoxNTE5IDAgb2JqCjw8Ci9MZW5ndGggODQxICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVU1v4jAQvedXeA+V2gOL7YQ4VAjJzofUw35oW632ConbjVQSFODQf79+M4TAqgeQ/TLz/Gb8bN99+fk8s02/9bP4qxS//KE/DbWf5d82++jurujr0853x+/eN74Zvx4exc+hr5/9UdznT8VT1x4fQvBTV7+fGj9GfR7k/FvbTSFYR9y/+D+z3WG7U3K2PbXvx7abSQS/tMf3EPTpdxFAcQsKSvrth0Pbd49CfZVSBqDsmrzfoYxDND9LEfNR3GvbNcNZj9hCXaS0aNr6eJ7Rf70L/UDy88fh6HdP3WsfrVZi/it8PByHD9L4EM1/DI0f2u5N3N9KC5+eT/v9u4cMIaP1WjT+NTCG+r9vdl7MP63xEvPysfdC01yxrrpv/GG/qf2w6d58tJJyLVZVtY581/z3Ldacsn29jpUq/GmdZusAaAAxAUYCSAAsOKIEkAIwBGQUkQFYXnHkAAoANjMBUFhF8SolIhQ4FHFYtwAADrVkYAnAAnDMQUAJoAKwrMChQaqZNLMAoFSTUps7ABlFX0WAVDsGwBFDR8y1lIiIkRJTiloCSLDAAnlShnG0SlFcSsXpIgUAYSkJ0zmUGnAY5nCIsOCwemqQRX9tMjXZQrVNpyZbaLLZ1GQLPmuvOLCik1OTHUhdMjXZIcXZqckOlbt8arJDGa6cmpxDaa4vTQ4OGq2yyEbr1H83Q9hkJjaFjGPadN4rU45zprXVOK94XXmeK1aapuP8bDEzzkmnpp4rbneCMrXmDV6c43TM85FHc55G4zXnBWOEnaVWhUCM2TUWnY55X2yBMfs+tDaMz50knLWVlEt1q5xwx2OKz3lMnLSuYv6SnQHtMbtEYjMS1iOhbVHQoQvHLtiJbR1khTHpVOw7cofS4EkN+bGCMVI2SULxbLqYbEmeXRSEs07qj+F6FWIM1xujLsP1JhTD9SY4AIZdaVCj4ZNiUJcBf5GZRVHRYTOOanCUQxeATAnnXkjKL6/GFcVX0JHJ6YxaupGkJtMXtx6z5a3HnLr1mNO3HnPm1mMuu/WYqyaP5XLyWB5PevLkopNOA7kfNyvegculXZ+GIdzn9FjQNY0Luu385T3Z93tk0Y8eovHlw+xHFf0Dk5vTdAplbmRzdHJlYW0KZW5kb2JqCjE0MTYgMCBvYmoKPDwKL1R5cGUgL09ialN0bQovTiAxMDAKL0ZpcnN0IDEwMDEKL0xlbmd0aCA2MDEzICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42u1cW3PbxpJ+96/g426dioC5z1SlTlXkWLHjKHF8i+1UHmgJtrihRIWkEmd//XZ/PSAHACGJjPbtVFnDwWCmp2/TN8BQtg6TeqJsHSdW8W+aRL5W1GgeUNR4zR090bXhjploa7ljJzo67riJ0dQxljrePaIRAmcD3dL1xFmeQ8Bc9NzRE69xy0y8j9yxk0D7UsdNguPdtZ+ExLvTxGiwO3WCIcg6TpIDQFrhgKKpuccADF06wzB5UXS8nyF0a7lL+NYAa9xEh5qhGE+oB/TChPD3tIVhXtRYmyY21AzPEh3GMdXEJuc1M8IyJZEhWyIlKYZCjPFJ7jrqMULWEjXOMDxiSVS+pj1snMQIDGyaJIu1RHqKNdPvCHvCmZfwnrWvGQ6TUyfDvHOEkyL8qOtpgaI9uUuNSoDqaZk2OtJWnpZp5xgCs5uo5QmBlpkaWwRaZjxQDbTMxMBbBA+hM/EBHEUL5XCRd0OTwKdome+e5cMydg7oRFrgEpYlWuZNYv7xLR80L+PrYBRG6To4zZgQdiEYKBj1YjQTw7jE2rAG0t2oQnpkWGikG4QqcyQy6rqmVVGEQxsSHdAh6iVSSMUiIHw9Kyr1NCmyIu1QyXg10aSpKjnWQ5K6St6xHvOKyMxnpUqJpKY965MwLrJmQQiOzwYRqRNwZL0hxdC1jxGnRtcRCsC9hDNDdxXt/kiTpDSJyk4MSZ1wIvpxsqCbPhFkYi1RqEhAmpjITCVctQp8rvgEaOIDocgHVEXWR9JYTbSwElkmjNTg668fVd9OfiXKPZ33l5Pq3fsPExa8T0eOULi6mc9/e/Tvf98ysT5S95hm1ZEK95lo1FEg5bl7otZHmoR490SljwKp7t0Ta3NkSPh3TkzmKJKQ75wX7ZGJPR6eLK7Wk6+/nlQnzkC5sOCEBATVkCuDk4cLWla9WC7OXjXrya+T6sW3J5PqdfNlPdlAfP33dUM3pp+bR9Vjgt5crVfMbwB7VL1sVoub5VmzEmuJsdPmfDY9XnyZ/Mr7e9KQkPRvtNF0SatFp3hiQRub7i1tiixXOKIjNWRCdyJBVvHI7RLoYGKdjmqr7p7oE+nmLoEOJ5Juqnvg6GN95HdJdDAxkBpHc4+JntSYRPD/KPtfHR0VM/lP+5/23u1vj36l037k6JcOhkLHUXj1n7+D/oh7ifz+2B+5KPJR7c/uUXLcRym33TvFsu5I8GRRczu6hOLto9AORGOy7Ml8TQ5qaTXFd6RHuVWewOc28B7D1kXDeoe2HDcG484d+QnZ8aPYtinwKrTBeNpZWqdxV9oCZtlyflD+UYRjj2z7Q0kD4SltuapkftlSBE64DVsKkhmJ/BPBndxS2Ml/3jNHbm9jxB73aPP8DHuzRyEbSJZjfr7IbAdj85By8nPbVY0FCZwSEAJ1rOVshnkrXOhud8tP2rGqRlqDGxhhHWWzRBqhaBalA8w1CpsDaRqNRs2Ux8T3LPeDD7Q2qsRSDol4Rtkky9rVrP41AUsAYGMrMr/70OQfQ0B8bsfUY9+2hMlnU0a3PU9HKkyStswlIpnUjdSTRhIftZhALgmeQndex+EPZc+BWRNBT635sEfroQteYZ/A0AzDDJSdJc8MTBpMc2CREQWKPKIThEhc4zyhVsxOwoRYa3gfyn/40BRtl2uOgzyKjiwLAJhRrseiIiGx2IjGmgXia74XgC5lwmwAIBHveWNbCzWJiQ+UgYQJvFULJjAIl6VsaYSSUWYNocjWLeoIULQZgSZhw++RCaA8mxSE1Zr552tKiEgJOcaSX5sSriiXJyxpI2VYEyLz2SYGajFutAf9jB4lWNTXgfK1yLjLbLqGafYwWDCOdJBjhktBHvGEZ5IpJJICkGYu6+RYFuiLux7279Oq2oNThd+//+J7gc4/43fKvbt3Oku7Y96yTkl7yyJNzNTtUKj5dEjLtsXlFnKkyN/kVmYIQmIustEoSJCdo2Xp+WRZvrQHnUMNl0HaSWoGx8f1kFRbuDfHWGjOf2MNuAZGh1J3xtizsKNllQ4W1tOz8qfasxLSiIWt4mqXJvCeDiXT5iwvcIxVqFmNgzc8UlA7bEv+jbrjQjAWGi4tdO+3Np0IifcsV5TckpEEe6VqnL5SgaXNekfssZ1+ObNEoNRTuWucbRF7gMi3xK1z0GoIF62MCPHBlZ5FwTyyMniyS+JxXZ5pIo8766ChTKOHsFJgQyNzMmQt6oQ+jHZWTlFI2Ret4lJkOSBSLZkx7HdYBcBZgkXfaOh1MSczW8ah45k9Ijo5Q8V8mWOVhQDTRozlXSFI+mhZs7yEg/DAEexO7PJNzRbSGPEgzOixVkw51zSdge9S7DukzTMiHyKuoQUETRoBhuzglIUoA/DDscx97I85to7wQNwKxPKu4WrHBtptmN6nzTCLVvYq58iIGI5AHie1NDv2szjzTiMuMBsDYyL4oSU5SFxPZ6+XCkgShmJ93kEnyAuJhewgM9FyBJI6I15hp4KvJVdKmmSEZQa5ba4yvzdj5RoeFcmJhLLmFHwBPFhlkVXWHtOu9h5hrGeOWfAD80gLJfDMUZ6E15vDrTv9gJnDVsLVsaacKeFthpz5DMjFSN6xWCVYuQ0smSdxc4lZZ6XexvzDnQSx/tYcZdNx4SPOPocDNjYS7MisRtDj2VDAQicO1UzwYJGGAnA4SiE1r9Qax5KJ4dXOsmngII+djsTxcK8GQZ5DRJfYO3LARwIjw8Fhts4HuIWQxZP0IFDo/uwwfg/qLcYCMgdKsn2XYNkxD2NAnhkiDpmGzwuczQU+qhY0OwoXLR/K7BMoeMeAhAg1GI0zHQ2CeoocacSKACKsXoQORNFI7BIM1BlOQ9cd7yptl2/8yMvJQXGIo30NPcAhShomsIaHhFpB7sbj0OmE4B4BspIUA/pg4USUQQRoEUJIPilBtIJ5MlB7hyMtgRgixhSR5dM4aybiIE9GjP8SxfZta5ALGGtyeOZGWwnk+CC5YGHuMRra+6pGvsJPyvJ4Cxv+PKq8o8yW/naWxbUtZhQQNr3bMbxPmyEWmIEC0kCdW1QEQGtAkpjp9CyvQIkdh5BwAfwkgmZzmsMPBpjCGNlF++CQ8SFblAnRchYn5SXpR4Ncjv0KzEb0FvMSeOh17ubwOW5lxfuU3CgpYm1LAMpywq+4lkK+PNqROjJDGRGpYN1mrty3DjpHK2KW0vaecEj6aDk84TOEMCWhICqGVZfmfGOSXc/Q397C/Lr8d/9l9wA68Djb0Xa/ne5pe11WkXZOxeNvzBypXSHRkipe4X5cz+OUSGbHlVAscG2STGoV2IZKhY/NG8sGioeEzqH1Asdr1H84zao57466hvpzodAniRyAqIXgYcAYgGclAAoSJ8pWiGg8oiFEwXeU7wqWSdFq2LbcFxcmrW5Vh82bgQH1ErkG1EGwuuaDmE1L8jDKHJkZx6bdwQHnomWtUbOwrQkKHEkFlMxkjE255L+gGNUW7/iu1zBgHnWMpOCgYHxpvclpqG3jhl6ji2PhCj27z5++40g5w7KU4i2K28oiKWPnyYZMI2HmSpZHGRLZgElQPZtNj5HHeJR8SUaONE1osvyAs0b0nHIMq1FCIo4gbxDOSqTFHHFw0x53fUeW0m65IiVwh9ITO0dyuAYQoZcWcua6D7UhZDkaKLRVKs/zFG5EuPGIOhnBiHCntYGmeThqNvjWs9dnV0wyC5AlZnLknqABDkGVwc6c1XcOoS7K8sXBLYu7IiMj52aHJFlGW00vofN1qjWMjELCUgh9qzayzbZXzipZ7ApDhHsFeoeqXqmAZWkks4fVT4oq+TkLoipRBEm2hNk0L4ugoBPRisq153YMazN0eZZRJAbbZxV6s71rDfOW0H6vYAGAFeXz3C+kh9GOTPUWRiwfxGxmywyDhyVG8nQRSHE3a1LxYGarEx0n1Bq9bTE8oYwmrVFi4mD9Obbp/UWYwJz+4blQNMxRKR84OCjLePKbToiqgW6GWsNUxryXQwFdCurbWdYiV0GLHUsIOCjS34Xd2F9evcEiYwsDJq2MCHVRRRQ4QFdUMIOiI6gkodSY2NCI/mBlQs7hISKuF6GsSBtBYdlGqtyPEhrlh1o1AsGQN1ZKfLlggTaAX4K5L/p9qij/yUBFNrmvA2p3rTTzw49Cxh61TxlJuZyKlZvZMsMiAbIOLWRT3hVeSR8tknuN8rydcC8b3H/QMpT2L+CBivUahQWNvAVVXWNRE2yj7GDbpF3LMysJLIw4cc3mHCul2EDHLz/pbOHJygwbboEClxY2z+LX0+raby4cvMaW4G5ve8VgrEHatenpBF+1uY8VGHPKw0JojqRkxWaW3DNIHUyEL4tS49MWz7iczeVbhQfF8uSFoMndmtVb+nL3n7QCp2xlL/a/LrcywkIyiGw42guopntEhIgsapR78WxHZhqNp0LwpiEfADqcbFBwP0PF6tyP8tyoxkPbkIvFLbyEGKUccZ7NQMmJYb8cEci2Rgmi6FOMzhQXc/JaGcdpz7wJKK/I2hIm5rRyk6NqtzOjRMXgB/pofytePOOXmr5tVmfL2fV6sZSXnH6cXtKdb5++P3789l+PT4/fqZpuzKefVxMrM47x9tlXzk++0g5vqnINhXxF9c3qjN9B84lmPp5eP21mny/oMvpHFW/D975SfPPZejqfnX1z9XneTAj8q3Vz+ZZT+kfVu7yIfBLBuJgu+QWq/6q+qR5XJ9XT6ofqRfWqel1Nq4/VWXVeNdWn6tOM/v1JvcXNsvpcXVSzal5dVlfVolpcNdV1dd0sZ4vzalmtqlXzZ3NVrWZfqnW1vlg2TbX+a1HdVH9Wf1V//7fQdzIjrBQFbeV7dHcx7Nn7J29PngjD9AjDDDOMn6mb9DAMq9MtDDsmlj0hpj0jpr0smHa2uLyctqzjf7MhBy/+vr4gPh3IyC/V39X/NstFj59mH36+ffLy5McfwM84ws6U9U8b9zDs1HqUnUR/jxq3l3a8/vDu5D1R82T0OGlL5CRfy/vhdOS39Ni6JKcuiKGMY4QYGwpajCpp+bicnjXz5tP64+zzZ7la8rzN5e/NOt/uXK7Lq+ZLe9Wu7V6vO5c0+6yZzWeMY943X293Ppstz+bN9fxmdT5bXc+nf1ef5ovFcrMAV9vp1/y+K988nhUXH3sX+QrLNhM3GHeu6HK5OL85W7fb50tCfl2tbujQrGeLq/bmZgC317P5efPX7LyvJGEfJXn88sWrlz9ASUZUXgfWEX7X3/GLEUEVOuL215EwriMd8npEpb2IOv3w3dsXRNTpszHNNzof5NrGOw6yue0gf8X+I1OmRykTw/gtTON35FGeVd+TgTytfiTP8jMM5ZvqbfVL9a56X30gkzmdX19Qu1wu/rpYLH5nvSIz+rFZTwtbet7M6bqpmuvVbL64UhXf/kSm9POUJ3xeNtN1s4Rr+p/qd7Kq8+nlx/NpNW9Wq2oufCNLe3lDxvaa1XI9m87PZ2Ses8m9nlV/kN1dXizY9s6nqwu2u9Ob0uJ2xeT3cl+n7599//RniMnvlJJSrbW16oGEFN2okL4nxrfMuqquboj0mx55e3mTx28+vPzuLcgLu5WwJU+FByNPjVvgHi17+ZLv3jw5Pf4FtMRbXAmIUfVDHag4Sszx5gi93RyXbpzBh+GAg0Bivy70f6P9s88EqX8GvvTDN7+X7T3+6fiXk2Ni6ssxK2Xr1krV6Z8wdWN9fdrLRj3PVuonslMvLmbVi9Us26vXfYt1drNu2EZtvXXHGUM2ZKW21mvWLJvVbMWiwoTm6py52/xxM51XzZczEtKOYHG+I14UoXLMeDWjkPHq5vJjsySJFTFk650L77uRMjl/snN/3CzWEpLkUPNyJghvg04R/+rm+nrJmjMSg+6IQv1e3uuXF6+/eXECtRgJ6o2VoD7F+DBa4W7Rimck/Vd8uCC4RuRwCZ+xbGNx4sCQ6rBfLvP89PvX70D1iLU0Rg5DshR623Rvqt0I1c6UVNcl1SekrpyqXHS0a0Ek99RkTZL/a2gHwl6O4uT5d29ecbjy0o/Y1tYMqGQeRODRjApcDuM2M8OR4lNUJF8btd8h9L3cygtykM8h9HBL8AnKtX4QysO4ASTSerTsZc1//undmw/fMy0jHtK0tYs61A9Dix+lRcoU2bDCon4qZCop9uX0bEn2bdRq7rSZbCzHEvFdCXjYy/T9/Pr45bPXxMNX70dTVsrAkbEqxdmI23KRWHpYnMGp7qj5ewx3yE4P4TgYgp4w5ON0yX/nH+fbFLdIb3OCeXkzX8+u539XdP+S8GGZzP6czpurs2abcRbZZhu0iOxmlBEtV80Z50aIWmT4cnq9Wi+qy9kVCeWq+YzcSZyU+DYmnWCcc1ZJI6ubj6uG7Fbzad58yRer2eVsTjSsb5ZXK8oohYCbK4LUFWTcy5r/9OzDk7dPIcgRm0YujP8btDIaTyAeQIxuPP9q2d7h6vVydtnLneNedvv598+ePH8LIseOPBlupM6K3zMN6SGUdfzQT1fsmla/UxJ9IYq5UyO3NZFOPeQ+qtrhX6GI0MCBzhF776FsPQns5z8+PH3x+EeSwOvxRN9tPIh9oLzExwMSfQ6hy8C5DZmnl3wyp1fnOX8pY2SplnaiX8rul1Pqjoe/94l5bwl31/14tief/apLp69Ov/3A8nk9muKQeAy/S8lvehZmwCvVkQ5fbqSjuWp6SILDkvkuy+PnLI1tWb/1lJ3s4vfd9Wh4wqIo3aaGRUpwc3VOoj1bLJuWmz1e7pcWPH/89M1P4OVYcZpZqfGdFJ/SP2blLeHFUM3LTPHnvqJvVHy6OpvNULfcGCvOGSlNFP6NGq17p5XdoGebRhZPGrb1gN4RQj3gkDSSOTlIJ5sV3DUOGocH904w71KinSlm2ss9v3/64f2zl/968ubkdDTOimw3A/63J6VbqrSbXXVKpTqpmO6jTsGV6vSKmL/q0bOXJz49fnzy4/t/nb46HqWHqPiKvyKkjTWTpAs/YH3PD6QtPaNPOsqQ0ZuSmMek/x+qq8WalLlH04hvI6A3H9e45EFi5/F01eDjD4Nnoh0WyBd+8IWOk9lytWYkJlw0+GG6uSBov8zO1xf8OQ8bZfLrxZsrUr5zIBX2R6r/3HGAlB0gpcaR0juQivsj1Xt4N8DJ93GyqcCJLzYo6bQDpXQAn3pP4AY4xT5OdcmmumSTsQOc+H2pvXHqPfDpo+TrPkqxxIkvtijVO1BSB6DUe1wzwEnfoU66VCe/Aym9P1K9hxMDnAYqrk2JExextoxyO3AyBzCq+0RhgNNAxVMscOKLLZvMDpTs/ij1HgwMUIr7WAK1A6cDbGavrt7HKQxVvINSKTkbdqDk90epV9MdoDTQcH4ToNAmX+KUduB0iBXvVlwHOA003KRR0bldpuAAI94thQ5QGtrwUnSuc+Z2KfgBNrxbohxgNNBvVwqOL7Zy22HB1QEWvFto7GMUB9qtxwMCE3egdIAF79ftBkjp2x1d58jtMJbqAAPeK0ENUBoa8NIyuY6j0ztQOsB+9wpGA5T87VwqjYDZYZjUAfa7X0EZ4BTviC9L32t2WCblDgkIumWDPlJpoOPWjOv4Dq+iDjHh3fx7gNNAxU0Xp1J6ete5O8CE9/O4AVIDJY/dKMWVjNoROakDbHg/GRsgNbTirgxTypNnd/gVtcOK89cMV/w5wxtOULcfJnQ6A3g+O1/xCyXQRv5aKH61RND8PUz5Nfla/P5vB22iJayfaPGgE50/1aclq5mYjAR/6vbgTYx4E/7QgPxKVMP/B0F+RfT8IZrDN7Hinib8iV385k2zI57kevnEyWurh23itQD1mT0hUxIlROX/IyO/YooO24Q/fSuSV5JI8X8HTLkTcqf9HGf7ccbbtjJ+A9pm8fpWIlqYo1q6XLvzLi79dLOez64YMM7DJJ9YPg74FCuuZM+0WTlbc0Ehu6Dqm4nKcURBfrZ6L5bNn/iQa3lkZHmOhnh5DmlHlrc+4Ef+lKTq+eIMy21h6Q6sdnFGZBPv/B+EDi8HCmVuZHN0cmVhbQplbmRvYmoKMTUyMiAwIG9iago8PAovVHlwZSAvT2JqU3RtCi9OIDEwMAovRmlyc3QgOTcwCi9MZW5ndGggNDE2MCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqNWl1zJLlxfN9f0Y/iw+01qgqoQoRCEaeTT5bvw+fbCyscDj2MuKPd8XKH8pBca+/XG9kk53oGyYGeOGw0gKrKAroygTTP0zzVMrlOVaZkeYqYZNYpbNLU/psnbW1eJjObXKbcHpaYcntYrP3SV2We3NKUyxSptUsbQSeLqbaHrU+ac/s7TylJG7K0vyGTtrkk6yQxJU1pak3JZH4l7T1zn1J7L1v7294ryaf2WnGb2lseqTWJTik08Muauab4lSfRyPhVJill+eWTVPFX7Vc0f3x5r06qGRNocy5rxa/U3MTkuVmmdXb8al4kWX61mbU51n7lybJj5OaJeYUF6lOeRdsczaBmz9KjTs189Gie51IXd1qMqsB6k6kg1O1Xi6I65m2RKrkafiHACVZZw2U2eQyFS8ltjhZYt7r0qJN7Qo/cEKiLHy3U0QKGXw0HDczbwhwloUc2YIN5c57qnJcepQEfyyg+1TxHmyM38FwRq/ZKrUusGsppTo4uGH+2GX1KM24uQDGX9v4cGb1aYqQ0A8YMFJLO8KEAe2DTfram5Dm1uVoupVSXkJY2uMgMSxESsQUQb7NJWaKFgEqgB9KvDVvhacuLpLpkDIZRhLz9bP+rL0kAQ7RGbbN5IM+WoMEVsyWHAllX4EBGdlkETA8YnZCBOXTJBwz2GKq8dFsGX9oQ6DIvaYO+RVTabIG0tYwe0WYrZQkfwCt1RjfY72kJH1afPyZZbU2+WL0khMcMN2ubLeYlfEinEFsGa29Fy7RXv/3tqy9/3t3fbAFTW9M/vfryq6nl5/Lrx81hu7/H0n7897D9hEW//PPD9h/3wHP573e/Ww3Uxn4exy6MIyfjzGQcPY4z03GWrk/2nPRsUXvqGeWkZ8ujtLKhxIkNT/99szvc3R/t+25z96ufX359+9CG+cLO5svH+eRkvrL2OIiPcYy5x8s9fR3zICH3Y8jdLgyzDjmzxo8R95kOs/R0EvFyjHi5GHFbDxQnAfd1wONivMsx3uU03rZ2txAPyzHeOV7umdfxLiTe+RjvbBeGWcebWZOP8c4zHWbpmUm87RhvuxxvW5twEu+8jne5GG87xtvk0myyNvvJh9OBjuHX0/DLOm5KHNZjxNUu9FxH3EjE9RhxnekwS09mgBwjLhcjfhICWwdc1wF/Mu454Ho22THgchrwtbNC/JNjgFO82DGt01tIeqdjsJO9PMo61syWdIx1mtkoj7suCfWve8nF1F5Pf5LZaR1ouZjZxzinF6Z63KjWNv6w+bi9m/77N3+4vf7izf3mcH/VPF1mnH6z+fvft/u3u3+8/qo9nP3s4e/bw5QeH17v7revf397uL3+5cPnN5v9/e3dm+3hsJE52VVLzrx+7bC73ada9Qrfb9LgaFii/Zfm9e7j7v7MwPPX/3LuzGP7w+Hd7u5ue/j6/WF3d7/Zv73504ft/uP28/bQ7EpXLRt1Pf3z+8e3ftzsbz/Bh4p316Z+vbn9t93+3Xe7B5klt9bi69b324f9u+Mw33843F+//7zZt5EW3+bVu39oAL3ZbT/e7u9a0TdfocQ6ab7bHD5u97vt/rvt9c32cP3z+91fF78N0+bzMP2Trv8To/Oo/sunu+3204+bd9v7P+9u3t7uHyFOsc6Eb24fbj5s71KrpOHQ2t9vHm7ub/f/ujk0w9o0QOGxUnlq/+Nm/+7mjw/3u5Y+21/e/PJ/2/9pIS6YYR3itTst/snXY3y7+bR9/+37ht3+7sNu114RJGGhsXrBoZdH4nH5rgX57q/bw7sngx4/+U/DfL+5bgNvbt42j5Etcz1pvL+9ub7b/fTwyx2SbYatJ72vv918bskR8HMdhO8ftjcNtB9atr2/3f7tb4jGvERjPf6/t/X61Z/+42Hz9rC5310/R5NnzpkbL4/Ao/DTZnf98Gbz8c/bzx+R7yLAf50ap2/8V0P72aCyfuvz2/fNl7ZaRNfPf35/2NzcpGrLuOsV+Z+b6+vNYbe/fQyB6HrLOQJbsGZ4HjDTu96d19v/fWghud2/XhL5ydRfHwJLnc+ftnfV4vwpcjR377ZdUkv3FJvqY/G39mNtzMnLF8xusVLX8/ELntr5U8dTP38aeNp507ZMjfN4NBcl1ZfNzlcnL79stiCwcW6KNL9tPncGQFo6f6rIBDl7iC21cxDZVPVFo2HK+uWXjUby1fOgIqadyQhpOg8pIipPD2/u7m/axt4+Qa+/gNPPkV4/xxKRxw8sNbyZc/Z+Z3pr3j98bNtBm+X1Mo8f51k1tAhY5S2tKVehTQlNSpsETUabFE3d3nVuaN9r4NtsGLfQKTOauHsFTUGbHE2VNjV4yzzTpoqmdNk92Nr3GzgI9LilgK/MFCP0KjPFCNtVmSlG2LPKc8l31mRoKpf96/FjW9jZGxkDU5CwmZWZu+5ooiBhWyuJgoS9rTxXvqdN2GpqHbiXr/puA/ewyZREMcKmVxLFCDtfSRQjAUaJYiTAKNGlgFK3PNKpl/1bNsSu38hBgJQoSAKQEgVJAJJQkAQgCQUJWwhffoowy8i/ckX6DfxToCTUPwVKQv1ToKTcVKCk3D+gpDRZFGFWveyg9gtQy9BBoKQ01xQoKc01BUpKcw0b8kz9M6CkdK0bwvwoxlzwz69Iv4F/qDiLUpQMKBlFyYCScS+AklGUDCgZXdKGMD8KMS87CFv7fiMHgZJRlAwoGUUJ30b+7QD1K0ZRykDJ6GLICLMNdlCY2vcb+JeBUqYoZaCUKUoZKGXuIFDKFKUMlDJdDBlhzoMCBrb2/UYOAqVMUUKZwj/jBShlilIBSpmiVIBSpouhIMyPhOWCf/WK9Bv4V4BSoSgVoFQoSgUoFe47UCoUpQKUCl0MBWEugxKm9CVaGZZoqBh5ReVAqVCUHCgVipIDpUJRcqDkdDE4wuyDErTfQX1YojlAcgqSAySnIDlAch4VgOQUJAdITtdCIMqD9ed9iTZcfgGMnGIUwMgpRgGMnGIUwCgoRgGMgi6FQJR71tfb2vcbOQiQgoIUACkoSAGQgoIUACkoSBUg8RaEOQYlWvQlWh2WaNAiS1CUKlAKihJk7FIpSlBoS6UoQQUsnBdXhLkOSrTal2h1WKJBWC+cVteGkvNqq1Y0MVNBdmi8wAAyJWMCQcDnkXfd9oJ+l70T6AlOt0+BnuCUkAr0BKeZJqDZ/oIXGU3c9YKmywXMYmvfb+Rgi4pTRipQFJwyUoGi4JQ5gjhm7gQkAaecUsDOPdnAvx7ANKqwBeTZKSUVUDanlFTAEzxRlFCceuIOAiXKKQXfNpfLBcxia99v5CBQopRUsOG5UJSghVGJQrDMXChKwNaFLgYM6JIH/nUVNvoN/IOm4EJRgqbgQlGCpuCUGQuYtgv3HShRZiwQBVwvFzCLrX2/kYNAiRJrDJf52oSm4EpRAtF2ypkFooBTziwQBVzLwL+uwka/gX/QFJwSa4Gm4EpRgqbglBkLmLYbDwtQosxYIAq4XS5hFlv7fgMHoSnwbRKaglPKLdAU3ChKINpOObNAFHDKmQWigNvlEkZ698yG7gEkyqsFkoIbBQmSglNiLCDannnAABIlxljQOQ3gs67CHmqEAkXBKeEWKApOCbdAUfBMMQLNdsqYBZKAU14skAQ8X9aYFlv7fiMHARKl1QJFwQsFCYqCU14s4NlOeTG21sy/8JAEvOjAv67CRr+Bf1AUnBJugaLglHALFAUvFCXQbKe8WCAJOOXFAknAfeBg6Uu0MizRoCi4cweBknMHgRIlq/jKZSrgCyQBp3RTIAm4DxK09CWaD0s0KApO2apAUXDKVgWKglO2KiDaHjRBoQk4pZsCTcBjUIN6X6L5sESDpOCUrQokBadsFQVH5oUriLbTQ1OBKOCcPkEU8BhwCO9LtBiWaNAUnLJVgabglK0KNAWnbFXAtJ2e4gpEAad0UyAK+OAUd7G17zdyECjRc1xURJlzCGgKTk94BUTb6bmpQBRwesIrEAViHnCI6Eu0OizRoCkEJeoCTSE4UYemEFQAFnDt4LQZokBw2gxRIAbnuIutfb/LDioOUFl+oozOlOcpNIWYeVNCU9AmQVOlTYAvXYavS04dXjJQKApB60+FohCUpisUhaCfagXPDvp9UEgCQTclhSQQ6TJ42l8y0OElA/CZTAm3QlEIDh8UhXihFzCiNF0hCQQ9+lVIAiGXCWB/xqnDSwYKQSEoS1cICkG5uEJQCMrFFTQ7hIIERSAomQY1zKkO3Ovqax19HBR6QlCSrtATgpJ0hZ4QlKQrWHFQkq7gN0GPrxX8IfQyf9D+koEOCYSi6A1KxRWVVlAqrvi8B6Xiim9K0ONrkPTM0xM7WagP/Ovqax1eMlAkdVCSrkiaoCRdYWoYNxUoGfcPKNHjXYUgEHa5/lSyAIeXDBR6Qhj3Aihl7gVQoiwW/mXhLUCJ8luFIBBZB/519bUOLxko9ISgNFahJwSlsQo9ISiNVdDsoDRWoQgEPd5VKAKRBztMf8lAh5cMFIJCUBqrEBSC0likWubbLmh2FN4ElOjBr0ISiGID/7r6WoeXDBSKQlAaq1AUgtJYhaIQlMYqeHZQGquQBIIe7yokgfBBAdNfMtDhJQOFohBOUYKiwL+AUBSCnvwqaHY47wWUKJlWSALheeBfV1/r8JKBQlEIysUVikJQxq1QFIIe8Cp4dlDKrJAEglJmhSQQMShh+ksGOrxkgG0p82IEikJQLq5QFIJycQXNjuADAiVKphWSQMSgBO130OElA4WgEJRwKwSFoIRbISgEPd9V0OzgjASKQFDGjI9RHpyxaH/JQIfLD3pCUCau0BOCMnGFnhCUiStIdlSKEQSBoFRaIQhEHZQw/SUDHV4yUOgJQfm2Qk8IyrcVekKlVzoVLLtSwoyyIPMKG4JAnQclaH/JQIeXDBR6QqVMXKEnVMrEFXpCpUxcQbLrzL3IaKJrAYJAnQclWn/JQIeXDBR6QuW0GnpCpYdkCj2hUmZsEARoAwQBylYMgkAdnEBof8nAhpcMDHpCpVzcoCdUylUNekKlZMBAs6tw/wAfLXsMkkCVyxWo9ZcMbHjJwKAoVPp9MCgKlZJVg6JQ6VJCrZwpcTRIApUDCEmgSgz86wEcXjIwKAqVslWDolApWzUoCpWyVQPRrvRM2KAJVEo3DZpA1csVqPWXDGx4ycAgKVTKVg2SQqVsFbV85lEB0a70TNggClRKNw2iQNU68K+rsG14ycCgKVTKVg2aQqVHygZNodLL1gamXenJr0EUqPTk1yAKVLv8hbf+koENLxkYNIVKOTcoYOYJCk2hUjZuINqVHgobRIFK6bRBFKh5HvjXVdg2vGRg4NyVsnED0auUcxvYRaVnv4Y6sVLSbKjDKiXNhq9/zZcrUOsvGdjwkgHYeOZ7BT5ZlbJxwz5ZKRs3LM5KT4UNGVEpnTaYUctlBmG9e8NLBgZJoVLKbZAUKqXcBkmh0qNfA9GulDMbNIFKOTOsyAOR0PpLBjYqQA2KQqVc3KAoVMrFDYpCpVzcQLMrvWptkAQqJdMGSaD6wL/+koE9XzL4f7CCjjIKZW5kc3RyZWFtCmVuZG9iagoxNTk5IDAgb2JqCjw8Ci9UeXBlIC9PYmpTdG0KL04gMTAwCi9GaXJzdCAxMDQ5Ci9MZW5ndGggNDI5OSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNqFXMGuHbcN3fsrZtlXIM6QFCkJCLLqrkUX7bLIwkmM1kCctLHz/xXPeK7vvTpvtDHG0kgieUQNeah3xXvb9k1i3zfVkg+yldLzQbeolg+2tfHWeBj9opFPvol5zafxX29oG/9tgra26V7kzXjqY9aqo03G/L7nSiLbaMr5RDftnmuJbSYNbWWzImjzzaKgLTZrFW11K7KjrW3FrI81xsvFI9t030rt+Z7K5rumLKqbq3s+2eYFkg49vQrafPNe0BbbmD0lHcOi7Kn4EDLCdKyhfYsW2Wb7Vvee75ls1RRtulV3tNlWa0Nb2do+VB1PvjW1lMpia6WmDaxuLSCptWFc28ca45UusEbZt2493yuy9YA1im69OdrG9PsOc5QByG6C1qHO7pYIpj57jdSvDBPsvadAaUlRTVQSXikQOGeUaCmnj3+kC1rHPyqYzMdYNSzs4//qkVZNg+qhqI/VbAeyuSFMsUF8rGbF61gtVbKA4WO8ZR2Wj7FaEZg+xmrFKh7HjAW7K2KsVhomS1V8x6aM3G5aU94UxEtPIRP9gWiM1WK85R3mhqUlFwoMsJbvAmNs22N3N8W7o6vuULOO1eqxRMpUjyXqWK2OJfJxrFY79l6i0A6XaGNsU8CS5mzFBLOPR8fCaa1WIVn+v7WOF8ZqfQcWaaKumjK0sdpYITd1ekUfXjlWa2Nsb+mFkbuj956GGtPofmzdsS91P/xyYD58EDMM+XVPdx6PPh6rpN49xuOhfK/jsRfMO1xXxstvvvvuzbd/f/fx/aftX3/65dPnX//4+OP7399+I2+Hr71sfZhu3/6xPXfV7Cq0q2WX066eXcG6xlJudKnYc1DNrh/efPu3Dx8/fOaizuN+ePP99xf6heTMjS6q2dVpl42usQdYV8kuoV2eXUq70szNrhVMWedxKwUTpUZRikSpUZQiUWoUpZoo0flqotSp6jXN3HWhX30h4xb61USp011TE6XORU2UOlW9Jkqdq54o9Uq70sz4ul4omLLO41YKJkqdbsM6UJI8jUlfS5iogm3HKIpTE/TRPdoUfYtNmuLOAxc6NsPUFKpW0MdVcfRRsFqgj6LVKvqo27eGvn6tZko8D1ypCbyE4tUTL6pIB15C8erASyheHXgJdY0Og0tZKNlfyMCFkh14CcWrAy/hagIvoXh14CUUrw68hDpIh8F1v1YzJZ4HXqs5FCmdmd33BLLSHgCpQvsApPIZAaQa7QOQeg2kv5BhC/12wKhOFwWMGrQPMCo3AGDURvsAo3baBxjtGkZIPA9cqCmJFhVIgJZRtARoGUVLgBaNY1yAFv16usDgx5H9upJTCJDjVjoCLnq4uAAuvl8FcL1iHcBlFC4BXIUdc65p777Qcd6vqy+IK9AqFC0FWoWipUCrULQUaBWKlgKtQt1DYe8S11qmxPPAlZqAq1C4FHAVCpcCrkLhUsDlFK5hAedAGgzuslByislz4EJJA15O8TLg5RQvA15O8TLg5RQvA15O/cNgcL9OPSDxPHClJvAKrgrwCq4K8AqqSkm8qBMU4BVUyQKDx0rJKTzPgQslC/AKuikL8Aq6KQvwqnTrFeBVuZrAq9K9U2Dweh3AQuJ54EpN4EWTYS/Ai2bD7okXFdaBF02U3YFXpaeAw+C1LZSconRfn7EOvGhG7A68aErsDrxoTuwOvBo3AfBq1EEcBm+LuMfthQxcqQm8aF6c0jr/WATwoimzB/BqFK8AXo06SMDgbfG59ClKz4ELJQN4dYpXAC+ayHsALx75BvDq3DzAiybjHjB4X0Q+MYd3sQzvkqzgH/AKvGie7xV40UTfK/DqFK8KvGiu7sk4iOyLGHY+YusyvEvCYsxM4UrGYvRRuJKyGH0UrsziRx+3XEcf9Y9kHcoCyDqHd0ufBGchNNF3cBZCE30HZyE00XeQDrJTtEA6CM3UHaSDyCLyaXN415bhHTgLoem8g7MQms47OAsRChdIB6H5eJ4cXqhRQTqILGLYNod3fRnegbMQmug7OAuhib6DsxCa6DtIB1GKF0gH4Wk3SAfRa2ISEs8DV2oCL561g7MQpXiBsxCaeEeSDmzC/Pg4zQoCpIPoAsg+HTw58FrDAGchypcFkDTVD3AWQlP9AO0gttM+AGl8PQBp12EPJJ4HrtQEkMbFBZCFiwsgaTSeAYHTTC1AOwiNCQO0gxRbKDljKas4PcBaCP1UBlgLoblugLUQeoYEeAfh2xK8g7xiAhi8XIc9kHgeuFITeNFcN0BbiFO8kragiWWAeBCarAaIB6HJaoB4EC8LJac4PQculARvITTXDfAWQnPdAG8hTvEC8SBO8QLxkMVs1geDx3XYA4nngSs1gVdQvJK34AYAbyFB8QLxIDQjDxAPQjPyAPEg4Qslpzg9By6UBG8hNG0P8BYSFC/wFkLz7gDxIDTvDhAPQvPuAPEg9TrwgcTzwIWayVvwTQneQmhCH+AthFanA8SD0Iw8QDwIzcgDxIPU6+/lnIvkuJWOgItm7QHaQmgdOkBbCE27A7yD0LQ7wDsITbszw/MFqQV5p2ELHcFaCE3nA6yF0HQ+wFoILVMHaAeh+XiAdhCadQdoB2kLLX1/IQNXagIumrQHWAtpFC6wFkKz7gDtIDTrzqTb+dkL2kEWlfWYr0bE8mpEgLUQHm+CtRCazgdYC6HF9QDtIDTrDtAOQrPuAO0gi/p6zBckYnlBIsBaCE3aA6yF0gJ7gLVQmnbnlM6/h6AdlCbkAdpBFwX2mG9JxPKWRIC1UJrPB1gLFS5SQR/dA+AdVPicgT66B8A76CK1jPmuRCzvSgRoC6WpZYC2UJpaJsweFGUQD0qTzgDxoMrHweC6+FbOlyVieVkiwFsoLRUHeAulSWeAt1CadAaIB+VJJ4gHpdXeAPGgujhj58sSsbwsEeAtlGeQyVvwwA+8hfLcEsSD8twSxIManxMGt8UZO1+WiOVliQBvobRYHOAtlBaLA7yF0vs9AeJBabk3QDwoT6FBPOgqhZ4vS8TyskRN3oLpkaeh01i8grdQmnVXEA9Ks+4K4kFp1l1BPGi5BnI6XuvyskQFa6G0MFDBWmjh6gNGmllX0A5KM+sK2kFpZl1BO2i5hrHOlyXq8rJEfp+cZkcVrIXS+nIFa6E0566gHZTm3BW0g9LMuoJ2UL/+VNYpHKjLyxIVpIXSXL+CtFCaV1aQFkpzmArWQWliXcE6KA3u8hPoURY6TlF6XX1BKjgLpQFKBWeh9GNYwVkoPXgrSAflvgzSQfnOAemgKy3nyxJ1eVmigrNQWiiv4CyU5tUVnIXSvLqCdFCaV2cc5Xw1kA4afaHkFKXX5WWJCs5CacJewVkoLZRXcBZK8+oK0kFpXl1BOijNqytIB63XPEidL0vU5WWJCs5CaTm8grNQmlhn/Ooc5UyBOcaZbXGEM65foThfk1jGrRkGOocwIx0KYMtvKttsGbE6xTXjVaeoZrTqdeGDU6y6DFUz7nNKs2Sc6hTNjFKdYpkxqlOKJCNUpwRJxqfertnHOTpdBqcZ6zllVnrCwvTKwNQpMZJhqVNaJINSp6RIhqTerh1tDkiX8WgGd07ZlAxGnd5gyFDUKRWSgahTIiTDUKc0SAah3q/TpjkEXUSg+haHJLHx6MHRTC40ZFf2GdE6+wR9jfYp+jrty3PX+tWWhLjzsIWGiEGN8EfZ5+jjSgb6uG0q+grta+hz2tfRF9dKPsegx8CFmkCSSwsorVO4MM46hQtBqHUKF4LQQhig7Cvok2stZywXQWi+4ZiZ4oUgtOzcAhV9FC8EoWWneCEILeR6xehDDNHLQkl/mcctlEQUWnYKF6LQslO4EIWWncKFKLQIhQtRaBHqIIhCi+i1ls9R6DFwpSbwEooXotAiFC9EoUUoXohCi1JVENdxt0QYWnSlZbyQgQstEYYWpVoiDC1KtUQYWpSLC8CU7kqEoUXp5kH0ujp8bPbLpVsi9OXnCyJffrwgcuWnC45QfrjgBN0ZwnDItnDIyR9X7ghvNHrkwBmNnjjwRaMHDlzR6HkDTzR63MARba+X2s1+uHRDeKHRAwVOaPQ8gQ8aPU7ggkZPE3hgY4rDAW1xzMz+t3Q/eJ/RMwbOZ/SIge8ZPWHgeiYMIXieCfMC5H8m7VK92fEW2d94AQgJQwi5nylDCOVqo4ckTojGtEO52ZS5AarNpnatXX2Zh11rh0q10eMRhWqjpyPq1EYPRxSajZ6NqDObMjdAmdm0X6r3fDkewxbqASFjCKFCbcYQwlFLI2+UmM0YQqgwmzE3QIHZ7PrUfL4Vr6s/PFL8hcyYlyGE2rQZQwilaTOqHhAyhhAqy2bMDVBYtnKdVTzfhtfVxVvFDdExL0MIXzya/6AmbYUhhJqyFYYQSspWmHaoKJsvtOsv87Br7VCNNqfaASGnOgAhpzoAIWeujFKyOdsoqCSbXwcsMSVLscqVEHjQLBRVaHO2/1CENme4oohsTicEQsGcHCVki+ssaTo16yoYQ/XZggGE4rMFAwi1ZwsGEGrHFgwglI4tmCeDelxk83UKxlZuh6qzBcMHRWcLhg9qzhYMH9SMrTJ8UDK2ypwAFWOr1+FKm4KxtgrGUG22ygBCsdkqAwi1ZqsMINSKrTKAwNdTVgSlYqvXoWabgrG+CsZQZrbKEEKV2SpDCEVmawwhFImtUR2AUGNegBKxtetwpU/BWF8FYygvW2MIobpsNPJFcdnOkOu/7/79/u0Rs943DEBu2dDRMt75Emq+rkKeHefbk+hHxzD7LR46Woa1rTxIM4x8O9uOlmFbO4n0o2WY9Kbe0TIWJzzXbdXbW68Iliz+/rBEEshy35Jbpt6vmdnPLWM+WoaZyoM2mZeV+Y8Ab4ve3uJyZe5WHhRV3NC0+5ZhntCHRYd56gN+mVrVBxNmRtXmvxC6rXp76xXB8ndpzjs8aBlj9MGChl+huZfi+HvieHgJ99fsXvqjIDb/Wc9t3dtrXLSjaNbuN/SRKpUHccEG6b14Y5w+qDRGqd1LO8bofB3+tur5EpdrQHAr56JhIHArpKFh6HY7IT+9/+nzh99+/TO887xgcGvEXoynxnQl5dKla969Nwl460vnm1ZL/zvjwVtj/g3MadEvjSmrPbYMQR9nwz6ZDpL79e/efE3OfK08rZQ/BPK01BAxnpqGIc5k5tMfP96khJWFtKeh/RVpD2Ef354lvu/Pnd3JKrnzbGo32FNIex6jTtpzB8zze84z/0DAs2TPQ65U8bf4QwyyVNbDmAhDxZhVjBStCmnPjeOkfcxf+4UqkOx5yJUqkaK1WbSaonUh7flDV07a8StXpP34PZGj4/O7H3/BF9fmH8N5Fur28iT+0YFdcM77n/e//f7+41s5zovy2Hrs7zNH/No6ZtCTl/namh5q9tyKPTql7TdJHt6cBb515pl6XkH62poHq0+CZIBzpk1nK5zidvXla2uqUp8ULDD0XBd6kObh7VcFL7D2+X07W+FZ9iw4NvWt/HW2Yj+VUl8TpjxYsd4L89cPP483xb+cEeOhnA9+PsT5UM+HKej4y28/ffPPz+9+/zzWef+/P94dR3Yh6/Qvc3whi8aDnA96PpyyzHvi69zTlShhy9mphJ1K2KnEF/pFvvxUTf705PUdAiFr3jNj55rlVKicCpVToXIat5xyLX5BRuYfcZBGljyVKqdS5VTKTzv7KZZf342U+a+aVciO8VMpP5XyUyk/je2nXH59VVHnP/TT849t/g9XXiN+CmVuZHN0cmVhbQplbmRvYmoKMTczMSAwIG9iago8PAovUHJvZHVjZXIgKHBkZlRlWC0xLjQwLjI5KQovQXV0aG9yKFwzNzZcMzc3XDAwME9cMDAwcFwwMDBlXDAwMG5cMDAwQVwwMDBJKS9UaXRsZShcMzc2XDM3N1wwMDBGXDAwMG9cMDAwdVwwMDBsXDAwMGtcMDAwZVwwMDBzXDAwMCdcMDAwXDA0MFwwMDBjXDAwMG9cMDAwblwwMDBqXDAwMGVcMDAwY1wwMDB0XDAwMHVcMDAwclwwMDBlXDAwMFwwNDBcMDAwZlwwMDBvXDAwMHJcMDAwXDA0MFwwMDB0XDAwMGhcMDAwZVwwMDBcMDQwXDAwMHNcMDAwaVwwMDB4XDAwMHRcMDAwaFwwMDBcMDQwXDAwMHNcMDAweVwwMDBtXDAwMG1cMDAwZVwwMDB0XDAwMHJcMDAwaVwwMDBjXDAwMFwwNDBcMDAwcFwwMDBvXDAwMHdcMDAwZVwwMDByKS9TdWJqZWN0KCkvQ3JlYXRvcihMYVRlWCB3aXRoIGh5cGVycmVmKS9LZXl3b3JkcygpCi9UcmFwcGVkIC9GYWxzZQo+PgplbmRvYmoKMTcwMCAwIG9iago8PAovVHlwZSAvT2JqU3RtCi9OIDMxCi9GaXJzdCAyOTkKL0xlbmd0aCA5MDEgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42pWXOW8bMRCFe/0KlnFhiTNDzpCAYSCIu5zIUQUuFHvhCLAsw1KK/PuQK78cJsHA3VtqyTcfDz0uac7OOzJPjkirYMfCVYiTYFUEF1SqiC5arkKd5liFuURz9+Qyz93L7z7wgqy2U0yljcrYbHUEKgNLqkNQeYy+jkHBkTJVVR9lbitDpVgHplJB1lBVcuxne8qOKUvxYO+Yc20rA7AcVQGIvrqxFDX35eBY5wo4Ora5glIuZz//ak48zW3JCXH15eyEKRYP8UWJLc7OFqvXm+u9+0ox1kn7WAjUQxAEQwhEqOJysXqz2W4Opf+L2/3h7sf22/SwPKUlh3jinjSZnVwuzs//8dSI8RTCIBIE6jI/9rT01FN8bj0NVAYqA5UFCNRlOvQUaiwDdSwBZYAyQCVMdkJZiceWgRtPC61nAlQCVAJUwmQn1JXS2NOa5QxeO56gyqDKoMqY7Iy68ngLBW+Np3SWM4MqgyqDKj/OtnoPQWPP0Fhau5zqGcMJRICIEAphY0trlrMc9I7nb5bHOVYCFBEE6iIZekbfcEZpl1MJVAQqAhUZBOqiPPaUZjnLyW49GVQMKgYVY7YZdXEcezaWZSd0LAHFgGJAMSZbUJaMd1DZYY2ndJZTACWAEkAJJltQl4y3UOnaeGpnOQVUAqoAqoDJDqgrjLeQaju3ubOcAVQBVAFUAbMdUFcYb6HGscRY6xjBFMEUwRQx1xFVxfEGsvb/vQcZgRSBFIGEUFWEqupoA/HyiWFp6EEikBWBrAokRKoiUlVt7CiNZyfDFHmsyGM1ICFRFYmqJkPLpxHGy06CKdJYkcZqQEKgKgJVLQ8d5wC7X99MS2qvIYoQVoSwJpAgRhUxqqnZM8dx6+1jP10dNru7ZefEI3QVoasJtSM2FbGpudkkGLke9cP3afcwbZdh2Vmn3Fwf/rzOf3W2bmfQZtAiXhXxqohXzc2l4WJ3dfrpsH44NJe/0Fnf/IhtniAYQiACRHzuhbN3WzDEsnmDSBAoB8Fq9NzbQu+v3pDKRoBCrhpy1ZCrRs/8q++eVUMoG4EJsWqIVWP+/xEd7RVDHBvi2Bg4SFRjG22R7vAX0768OH+2lK5o/fzzfnKrV+vD+nZ3s1h9KCeuvBSPWIvV+x+H283dsYmOTe/W22l//OiZn2uXt7vrafVlP+H10vF+uns5n6zyYRfg+Av0SW8/CmVuZHN0cmVhbQplbmRvYmoKMTczMiAwIG9iago8PAovVHlwZSAvWFJlZgovSW5kZXggWzAgMTczM10KL1NpemUgMTczMwovVyBbMSAzIDFdCi9Sb290IDE3MzAgMCBSCi9JbmZvIDE3MzEgMCBSCgovTGVuZ3RoIDM4MTggICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjaJdlZyCTdQYDhPv0ts+/7zD/zz77v+941/+z7vu8zEKKWpRiIICYYRWhwIZbeSNRSab1JSCBw9AjJTTCSGLASA7lQIUrUG6MX5UVEcOnn5Oal6kx/3afrfU9939Tp9Xq9/+v3ev1e6B36dM/RR/3eVLfSUYD+GO0sYyucTsCksdnGljudgmljy40tczoDZhpbZWyp01kw29geY0uczoG5xk4ZW+x0Hsw3dtrYIqcLYKGxy8Yc9fM/+In2gbH8kvymPq19bMy79POExlMLg/w98tvnL7PSWP4eZtBfBauNzTWWp7YGPjC2zJjZ99fCOmM7jeWv9SGsN7bbmG/e3wAbjR0yli/JJths7IgxV62/BbYay9c+X85tsN3YLWOueH8H7DT2xFhWsQt2G3tqjK3+Hthr7JWxrHEf7B+PNVPGsukDcNDY+HJODd87PQSH++NJBWPvnB6Bo/3eZLxh7K3TY3AcTsBJOAWn4QychQFchitwFa7BdbgBN+Fi6PUW5FkVcM4H3YT8ka7L8DqwP7wLt+EqnIdr8MTP5lNFDO84veDoPjz1VY9CPnoB+fvegtuQf+Iu3IP78AAewiN4DPmDnsIzeA4v4CW8gteQr9+l0Bt36OgNvPW5j+AuPIZs4QjsAfKa+/AEnoGptXml7Afv11Lb5hUqhjavS/m0eTUKrrUq2pz8+9Db8C/5x2a6GtbH8ApcNDYn9L5OQGhugy/d5vVmlbV5lVlbbV5bq2F+6J2t8/tZYO0Kb+VKtpZVk1tTXfsBWDPtOrBS2vWwGdaE3v0d+V0sl3YjbPKzL8GFbbeCBdFuB8ug3QnibxlsD4NOW1G3W0Lv41/Kb7ob9kKey1HQbqvdVrvtOdBpezD0fvFY/jE9typuz/pa9yBbOAyvIH/QR3AeLkC+kqJuNd4Woffrn8jvdwmsgNYKaCXf5mulujarVV2rulZSbf7mkmq11l4PvabKb5VTlmOboclWda3qWou4fRp6X16XXyzCVnWtVdu+M/GH4JbR3HOPCNCHCZiEKZiGGTAPxr8hvr1WIIOZMCeEn5yRT5fAAlgIiyDfWceB/OPf5pcshqWQb8MrIN9o10C+va6FfFP9EPKtdAPkG6giBvm2uQ8sksGq0Ov+Ib/9Fsg30G2Qb5s7YJdr9dxXzfPT5OAAHIO9IUwvz29g+Q2oHbg9DE7ASTgFp+EMXILLcAGOh7Dic/kNzsIACh95CfJLzsF5uAJX4RpYZYOLIWyfym9wHW7ATXBnGGQ97gKDB8DbQISDOyEc/07+MbeRgTvIwN1s8Aaeh3Dla/lfLaHBayB+8N6FyEuc84bzJkt+G8Kzv8j/IIFm0uxfhPCnn8pjdDezYDbMgbkgi4buZkYIn/jn/OL5oIiG7mYZcN7km8IqIL4hvuG84bzhvKG7obvJNwC6G2obVhvKmsUh/MoX8wfx2/hl2OwCSTVCapTTiKHRQcNlQ2NDY0Njw1bDVlPAR8BRQ1lzEZhumG4oayhrOGo4ajhq8n08r6h8YV3nZlzEZ7+Z53cLNNRIoLniV2r+tDch/MEv95xm9GEC8mV/5GgKpmEGzIRZMBvmwFyYB361bFkAC2ERLIYlsBSWwXJYASthFayGNfABrIV18CGshw2wETbBZtgCW2EbbIcdsBN2wW7YA3thH+yHA3AQDkH+U+MIHIVjcBxOwEk4BafhDJyFQQjpy65flWd1LoQvfzpfzo/gPFyAi3AJLkO2cBWuwXW4ATfhFtyGO3AX7sF9eAAPITt6DE/gKTyD5/ACXsIreA1v4C28g/cmnmcfQAKVBKpJYL9iv2K/Yr9iv2K/Yr9iv2K/Yr9iv2K/Yr9iv2K/Yr9iv2K/Yr9iv2K/Yr9iv2K/Yr9iv2K/Yr9iv9oYwl8eNN2UZ7U5hK/+UZ69DiodVDqodFDpoNJBpYNKB5UOKh1UOqh0UOmg0kGlg0oHlQ4qHVQ6qHRQ6aDSQaWDSgeVDiodVAMo4BywX7FfsV+xX7FfsV+xX7FfsV+xX7FfsV+xX7FfsV+xX7FfsV+xX7FfsV+xX7FfsV+xX7FfsV+xX7FfsV+xX7FfsV+xn/LlZD+xn9hP7Cd/ng9fhvC9r+eXmFoa316/GfKpGJIYkhiSGJIYkhiSGJIYkhiSGJIYkhiSGJIYkhiSGJIYkhiSGJIYkhiSGJIYkhiSGJIYkltBsuiSW0GSQJJAkkCSQJJAkkCSQJJAkkCSQJJAkkCSQJJAkkCSQJJAkkCSQJJAkkCSQJJAkkCSQJJAkkCSQJJAkkCSQJJAkkCSQJJAkkCSQJJAkkAaJzBxNU/oVgjfXpOvsw6SDpIOkg6SDpIOkg6SDpIOkg6SDpIOkg6SDpIOkg6SDpIOkg6SDtJ7n9uDAH2YgEmYgmmYATNhFsyGOTAX5sF8WAALYREshiWwFJbBclgBK2EVrIY18AGshXXwIayHDbARNsFm2AJbYRtshx2wE3bBbtgDe2FfCP/k/wsTdZ7VgRD+/nP56x+EQ3AYjsBROAbH4QSchFNwGs7AWRhAAefgIzgPF+AiXILLcAWuwjW4DjmBm3ALbsMduAv34D48gIfwCB7DE3gKz+A5vICX8Apewxt4C++A/R9fDfZr9mv2a/Zr9mv2a/Zr9mv2a/Zr9mv2a/Zr9mv2a/Zr9uvxH0rdN/Lbm1W9NIR//bl8qoNaB7UOah3UOqh1UOug1kGtg1oHtQ5qHdQ6qHVQ66DWQa2DWge1Dmod1DqodVDroNZBrYNaB/U+2A8HgP2a/Zr9mv2a/Zr9mv2a/Zr9mv2a/Zr9mv2a/Zr9mv2a/Zr9mv2a/Zr9mv2a/Zr9mv2a/Zr9mv2a/Zr9mv2a/Zr9mv2a/Zr9mv2a/Zr9emx/cmOe1fhv7x+eyNdZArUEagnUEqglUEuglkD93o/1IEAfJmASpmAaZsBMmAWzYQ7MhXkwHxbAQlgEi2EJLIVlsBxWwEpYBathDXwAa2EdfAjrYQNshE2wGbbAVtgG22EH7IRdsBv2wF7YB/vhAByEQ3AYjoCnTBuPwXE4ASfhVAj/e9G1KvOszoTwo9/Nl+4sDKCAc/ARnIcLcBEuwWW4AlfhGlyHG3ATbsFtuAN34R7chwfwEB7BY3gCOYFn8BxewEt4Ba/hDbyFd8B+mb8R+yX7Jfsl+yX7Jfsl+yX7Jfsl+yX7Jfsl+yX7Jfsl+yX7Jfsl+yX7Jfs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"preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/README.md", "bytes": 709, "sha256": "7ad925c0e5d494e8c642e767990255b06011b9cc147edd5247f9773571c6966d", "content": "# [Global Uniqueness for the Smooth Isotropic Elasticity Inverse Problem](article.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 24, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026,\n  author = {{OpenAI}},\n  title = {{Global Uniqueness for the Smooth Isotropic Elasticity Inverse Problem}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/article.pdf}{OAI:Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/article.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/article.pdf", "bytes": 426737, "sha256": "5cadfeb6eb45bea20b6ceb4879266d49938154c38dbf34a6531b47b4f3cfbff9", "base64": 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uw16TFjqPsEEID6/e3r36/dVg1gAItAZdgYhiJF9tT6/ef8xWO/iIgEeAYX91XU8EbbPVcfXrq//0OcYsIIS5ghD8sleg9XvHzXeLLmkSOi7cmVB50gHu6O6flhknlLkmJF2kAvDXTdlfyOgvy54TZZRdpdNMwHJ2uQmxSP41rVXCsX+J9CwGhQZC1aTXn8CEOb+iZdcYxTlI8P+QUR5VxaoJknJE/DNUc5hrrppXHI7ztVny8yLw2TAFgZ6BDwFgAJIMs2VLKA+yCYgbDkyZEeVRaDQrdILw3fmbQjj5siyPDDuaTqZMIcOHThO8GE0HkpF6abp4L3mqzYqDR1G+Ezr83FzbDIRvHPey3chodX5lM+ylm8mXpos3A3oCaGmym2+ueAghFksKDDIqQJYSUn15XQSUMc7GXiGJAW6ADpvJnNdw+vvkX5eNR+nnvahehFqCQTjX080sliIAe4A9zOhji5HrfVCNbI2u+19u1muiuAUWaIRYRQtl2SIn//dcL9Lc5P/PuS6ucH1O35tFH7URElwLpPAbLhxuc7NAVlI/GxXVEs1ZqllcsrviK3IdOftvr2vAlaVYfhHFll2wgQ5qutaVutM83MclQqSY/dM3trQUoLLM/KGN9YfWY/39AXPGK3DBPB+tljPYLIBtA3AQ1oPUBNjPQh6Jqem585lpyCp9Lolj2j0NfvfjK/KZ4H+jZNM1y2RffRkrPzLBigp0xjoOBPau6umzb25t1xzPoWwkfWEH2n1hBxp87cZQ7cZ9aa3d7KqTr/eUR9+v6pq+bR6qre9lj2XXwwuVmPiYEyiNpcKQs5XHnW0/CC1w/Vwm57r6/Wxr2yH1uUoe2ubT0aXX7iN0jsuDiru8IJp0gNZLnFvg2mDsU0qxomkgvEUzAw5RoHQ/90RPZ4lMzy5ouj8fj/7p77ZtNvetxe1sn3zbUPfCBJJqX9DcUJGtLXtXMoOmaqjOZZPqnFIZhPox3ZeP1pUyePL2vD1WO1s6MUJqTUJrd1Vd9rZDACWHyiH332FFmENvKyrtdX5sbcOkddP7B083jS1B5k+9FzTzNT6c8dh01jf1TVik7nqgCOuE3eswmOYGjlXtvGSjICpxTiWbcg+KdrJ+vNeq2patVzfST+FIM5NSIjRVXSjv0DDPFfi/c1VGMIE1ViFcxfHoPznre3oIqm67qgtmnM/MeKrLGfiPzHh6f6o6EB/SUGS0T3joqn1d4Uoorm1Zb226fJJQQGJP8ilQ/MDwL2unFR01jraAhf/uM25TZnhWBAS3vhkYTqIpLllTJCdbdufWc9VJPBTSxz3JQqUZZqm4p53tbXuqaqQg83pQkCuA1+4BFiuPR5w8E0AupFGwVFXv/ffgBNxX8iO787Fy2shot/Cp24IYQBatfwW92Z234KcqV7N78pONu8cXT0jnNNURNnac+pRL9yMNSxX53PfXvMSgksoB7OmYmfOYTG4yAAAq6vwRNpqT0uOD01qZwxZRAU8PqBe4c/vom/e2OVksfbfINRzRuD7IRJZ0vtPURdKkvgLp+t/7/78+Hb32dD2qRkTFfx2Op7Ku/ctNHqhMOZAQMSG/ygUFqD03ZpEN/kjT1Z0mAtEQtjUdOJzIAtvgN/yZBb3Yrd3ZqT/Jk09nxx//Qvo2qg0M3Tbnh6OPhXh6EWrADZ2SjEpJR3iHFkO/7+4D5dY7Zfi4q7qHY7kdnFJHBzL/Xn4uTy58t+WSyikJeTnxwjsGLUkKSH3tG1oLXpmHVVun2jqEFvg+WpOeRBJ/buLsG5rv8NilxFmejo5qaOtsWzkGwPNNacOKqdB5TDOTV8Utcu0qsVH/jxgnpUEDr1q/i6at9qDC1eh0gZr9sfkUnrfHsjq9kEjFZwsycV0fhUYnOyPwQg/9GbyUGeSsFF/9WYHTHA1q1IJ8eh8ZtduHEOo2qQyWLeYzMxWTOyGEAf8N5HlR/4/OYRYhPCjQDoAK5xPREmyiwhBjieCZqmtw5fewge4bnCoc3EEzHgZu28rHinF7FTr2DXh0DdI6D32HAKSTe28V8H1gppjoPIB9wSiSg9HUPnDnyakE5/ZI5tiWdTdYvG8L6MOE8J1THLP+BeCqpwooIR9AmMIk3fkTWEg/wNmc4GwetpvHcsTXE3gHIsd5X+d6A/SaQBPJBKQlkk6THsAQCWWM+Nl5aGx6quxxN4XfgGM7e96Rs0E5CTBwklpA0NPzW0U3QMYpZPJQtqGzeuEkh5JOuoiFtp4jLpllKWe0rbIDXwG8ceomAebSHiKmiTHguLyibZACCja+LQLx8P6Vzm29qxvIacIhz0VhAwd9bv+hEqlg/i7B7Yo/niSIacr3efHaDDgMN5mhyloo/otJIUynCiyXp1xTVf+0mPpBypGvNgAPDKPsuSX02p0jBH4kBNudAFWlw/m8ifNGYXSqtfIye9t9ds6IF8EOsuRdeQQoSq076wPKsXkYbRzaD9UeVGwDKYLDjtAymCY8j4YBL6NhuNdB++HZPj6ULg/s/NLnDtHfZQwUuUhRDnSvYIJuEe5UYMyoc2pwFfg8uIfcuwdsu2/Lk8XgLyFJ6ps9QV9UV3do6DqBS0NqncLilwkuyBEXYJ8IRUmMvt352Hf+4+0IBNpWmDzelr4ehDhPIf+Ku7++uYjAs7h40DPAS4BeChV3/5heyAJgGcAtoYpUQZynOy8byUBTKhQGGLp/rQLzrb9n4RodvmQUP/yg0RHl5Iiww6l88B3u2+Y0jEE4gI3l+bE6Vg624BevCgi0sFCF6UBJHV1IQ4i8I//qJmr8x4eDE2hXYf6woHNSO+PxmcwUu+UBWN6O39LfpIjmui4EJnNgvom7Y5iCdOdXu3W6Hm7oiI8UtQ4O9n0Nl8gOJRFXhf+EXl2Iaeqq/g0l4q3anYwU45UggI3n7bhGeWxqG+Z1tmYhblMLxSO7psDlQ+5C7IN8PZXahNtXYJX3th3jzXECuBWFXDlWkCIpTd3/mJhKVBcKNr56oBxZv58JLmLSTsY7VqmwIDQ1dRcr3YhHHED6hXN9X563SOJhoFMmu7Iv0zgcehAoGLhZVgzXO8Y4twk3n2A4cZNoJp5QvLOP5ZZKD/PNiwEvRLGxOo6TtbYvAYC5JNpHWweisX6x7c+BhztSgHZvQ03BJfLFLFZA9ibIzH8mpZhBwzzxxTxHx1AY8QmWSX7GJAUcvePrF/x+pIBPmRlhr/9eG+D+qTw1PeVWDhkUlM5QrwHxw3OMEkwAlGZecjQ+W3YZvy9YoXRDCjaAz4nh84KlhhPWp8NpCktX79Pi/VWZx0Md1HBl5r9cuz/BJ35CAAqIxmfX7ozqW/c2GeiiLC6oUTfJMLCN3MQD//TsSgVeF/nD++YM0I/kL9m3v7KqXZzG0jolD+6fAzxBFXIEoufTQ3Bj+gVpq0E8J2IyGLtMs4a8teCpmtPt8yxDhqJ9qZuyp1DvHm1DJ79U1JGkOaukAQNBGJTDRTWCDQeotC8JOLvcBluOXu+dq+CCzf0kNmGRoAaXF5w8tmG8wOEYKyFxs6Hn/gxhFP1mkdDo23zMpbuiEpHuDzoiPpqB73jIa+L+H9/AajrHYoRytVdHJDoKpPKX6uxbmk/o5+xuaa8LQYhrvJcVrk/ZkJuEg4uZ05DJu8pilasq6xBh6uq+GfOymiLG6JLA9Tr8M63CPIVAs7Nj5Xnwx1TNeHIF5S0FBufwH8KFV9uFg5gXgMoMfEi8UWYusEY2lkGYyzyiAV6Hc3+PeQaIuMJMhJJy0KQJFMKTFvvQVtMWH+p08v3B1pRq/w3YuafIAIKME3BN5ScK4H1ry56qEV5jz76oBd+6A1Xy1bwEp0Zeg109Vt3TCXOPJ5z59UVGhJsSeao5ZbCdhSzZJcDuCqqmK8hov62HsU7U0AC4pavCUcYILjjVdrgOd5uB+ACDfYFl51tDUuTEy1/goyTkgYWZEXxZuBr2JlmeSojdUX8nXuT0zQNCIGnpgBA3FgRDm5yVBDiXaZZTeknnWS4nnRnYFrKqIeEnkYk/dvh27aiFqZEshtd9Ka35HqwefMaXteCJdY8OUV09WVHJX902+GW+sg5nQNmkkNaTVAvK1MeEeKICeMAStKZIjhUeTgVFAmV1dpgl/+aqWLwgAAlgCwyBkmV/i35Mti/u47M0x9oFXmQefuITMiUXhOClqrfH887DRR4qXRyzBQw2ftu5O1OpKKDyYLE+sT40ezSLCek087mzszknVYE8+VT5PXtFGn4AQPPej0MXjJbhL0w07Wl6dslNxCin7Xnya0Ph4Wi/0O6a9tT5/rQXk/xUHemgsu033QP+Fsdv1uWOvzVVgIyuN+3OjLs7d+ehOg2vP+HJV18FsgYXli6EeizzGkYu+G2cE6EXup8xEiyQ/BAdqY0/hdAhF8KNIFN33QXzNambqxfhvE00v54JapZuIW/plxZOyKQKlavPzZwB4lquxXjJfpYMveCQlAFE1JKyx//AkRj8gSNKSeRq2dv907qQyVXzpaNCCQ/uOIwUxNUiQF23PmkZS0qujAMf4uQUW8IQX74wfP5LFGzy7McnioaNXxoVwc3qszv/2Nu2asbfI03O6gu8nEe7RnGAc+hCWu4jq3HZoZlkh2bMDt3RUpZ8Fx/xYLJGR7KT3/qgT2o7qhZE1z+wDtP71JcSSnOZfRsHpyiqu3JNsNjZ8QiAPSWL8AvFaovccb/oUZhO+gsCTvkIofpPsPlTQzF19kugidvoppYyBF91/aYCdj73HTiOK5d0YPB4SWfpp1QQRn+svMoQeaVfc3IlxuOBwI+JVud8ahO8mAohS052V51Pr6lGOquRjI5/oSg11HHxJ0/hJ07+7eDIxDzBo1/Myun/ns5oMr+FItpCQcdV2Uhsh2nEboY9ht/S4Y8rxErylLHpLwGGrm/vXv0PyXFXvAplbmRzdHJlYW0KZW5kb2JqCjEyMyAwIG9iago8PAovTGVuZ3RoIDUzODMgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjapTzbkts2su/zFcrTampnuARAXGhXTp2K13GyldTZTVy1lbL9wJE4I8aSOCGpeCZff7rRAAVQJCXbLx6KxKW70fduOF08LNLFm6vU/f3u7dU/vtfZgqVJnuZs8fZ+wZKUwbd0wRaaL3SaJ2kGH3aLd8tdua4Ou+tbIdiyq/Fvuqw6+l2smrptrz+8/dc/vjd5tKBbjWmWDBb8Xzc+nQCAaZ3IXMeT3qeM07wY8HfL5Po2y/jy7aZ0MF5zs/x0zfXSAdvW20NX1fv2+pYtb+hdsV+70Zuyajwy3aHY0vc1jq2u2fJPXKhsHsr9qmxvaOCqrvaral06Wuz7leihfOrKBufWTYIgLwLKqlwmWc4Jp1/LFcKFY65oRPPghv7y5mrxDlYTS+4+09HZl9mybLviblu1m7K9tqO6TeWfmoIwvS/d9kAsw926xiS5UAtlWJJngsD4vnqCqRkQhf7s6/1fZWOJlwOuu8dt6QbskbSH7ZZ+/Ym/SsBz1dXNHA9wyRPFs3jX9ykfPdAQWi4NcICJJ97Q7m09uyXwkDFmakvGVMREt1ym8DllY2vCVy6CyTHjRvDmeaLkYNdvr2+5Ect0nHsdNsSP8PCpqbpyDjMBAiW0jvf4J81QIGlJrlM7AYRH8MUtS7Q2i7frCP9pFARwCBNDDObgAZyzPP98SluqNudYIOMsMcPlUeRl5kQeiNYgFxbbASmrXfFQ7YvmmYQwSXkgh8okIs9oucei6VB6tFzW97PKjGVAnDyePcXIwYaMi0SDJovmtY8Fag6trNzB3o/bYl86MNyXT5tqhQpoM8vrAlaV8eKfyxCToHOpEg50j1avHLUKgvKoMODdDuE9bLsK1EZP0xDNV8XB4QT/PAMI8peq3BX7vRv9iPICKrcArfIS3uW5Va1Ok4FdOB6izBKTOZgaWKPaV/sHUoNNSapcgAazC9bNGpihK+klacuMtCUpsqZ138jIoU4t6VVGR9NaSyOX31+DONcNDQqMC/78VHUbmoIs+US41Ptyj8h2lYfpcVO0s1IumQJFncUoliOHKpPULESSy9wfqchA+zMZHK6SwQQwIKGqixZ7t3yakMee7BKYLctlDNjNmIRlYPhhoB1AtpHny21ZrOmM4Me6aoGwq3LnyIO8sK5Ci0vjHouqGdNb7+zX96lM501BnogsjwEqgLOY0su7CV/kqNGzRGUyngw7slFtbuGBo53Vl5wnmRjQp5hSloYv+YRNMmz5w8gZcpNwKUnM9bzeD/AUYGkzOM4Iqm9HoToaojRhw4MeVZ8X4ZebU/xe0ScZ8q/gieQhx4szqGXwOoPVIzhfzB1RBsKnlBge+oxJ42ech+IcjCBV5oT6txy0Zjp7CBmoZzbkpoczOj1TEnT6UCLAbM7RxKjEiMGcu6861kn4JOOJYYO9rPIV1uZ7pzZQNlyDgVXeErRof8gScFLxqIhzUNodvQPbuypnCcty8Dc4i1d+PaqANXh8l4hbADJPJVhTEa8+62Zx4EmN9A9n/DBu5dUUPANcwcRkfBGNZOnEsaV6/NhCpMAk5OjmhyCiNrQkX1eg5lsfVlkHx9rYpiytO8d8BMdju2ACu5AHdsHa+PWhj6CG5gddNbBaZH7QomeMAhw4/ee2K3f0yhlsePqpwE0r65TBoMcGg7xH8iqZ9RHpyboOMGC1Ldq2WvkBNgy0y+zeC7APdsxhX/1xKOnDqnZOgP1zKCzg9su7qQjwKHxpAgjFSLE8jgxDAQfmlVzE42/O7pKDDWAD0hFDjO4iIT4XKCLh+A94mDrv5XRwKikgAgEd2Yq6cc6ip4U2y03ZlPR0aG1wC0+F+4YkZst1icIMLsJ+7fwG+kp+gwtHkWVaOJuioY+POLNAzpvyJPR5T0JImRidx1gcnCexPmdhNQTBksWTJz0JGxfmAG3ZgVfr6NBtCodryJz4k5gTUCjuu7IZUZDS6CTN3a57IHyxrf6yZMeNmHFREwOHFt626EVa6jNhhYYJmwiAT3fVtupQEp/pdbV2Qhq9vbfuMRPO8WOcEip4Ovf+YdWNUqyPQ4FMIoYbdcCoiWUQNxwmbBFTBo1zOjFT5f7kJvUahqCZymNYrDnKlj92jkrt4bG3MeWacH5VNFuMbEYEQWqRSDDB5Ne3HcSqXWnPXIK2eiiBbkhBsKNOQcFTQX/8QdjwCn5vqofNLUQ3NIEdE0AkJmTzYJwNkleHpiVZwwXtmYcrN41LSdnfh5aMKDx2o2ZXqtRF0BvkgWenDrmBcAq0Y1etKpuNe6Z3NVGIQrsbfDY2Tqv+Ql7jJkdAcOC9zTHhiy0Fb/2y902xK+lTtZ93T8FRzOFVD+PnGu9JfhCZSiTTx5XvAhzLpwIY2wIYmTEIYm38KDTZORwLFqHtmsCEYaAlAvLCRmAYaRNrPkDOmnprLaqCbW1wSQGn9PlHFcRY8MPGTvZ7QdqTflSdW+STjX2BhezilnPhLcp/03Y0FjgLBE9CAN9VFCpTJGx8ArOdy2AqctDjDKb07ub0rOx0FmDT1AiAyzw6tL1CaenBvr2HU2jj7KtLfUrki9RlYJ3LIUKaQej+B1lnF9NvCrenXrrRBf0BCwWcSnMineqUQ5QoKIsVLrOJkhIlEFqtKqdCXa4BTuzGJRfKbtQegcHF8AfEO0E/C/n2cdyscbAO42EcTxM4ZJAFwViwxNXrt1d/XB1TSwLVFjrkuWaL1e7q3Yd0sYZvyKwZkPOTHbkDXxJUGjxtF79e/YcqDDHQfinwfDPwOr2wKbKeGI+Di6uW7efiQd+mNDzGqMpieUx5yYktOF/+fSqE4wTrCYFEKhIGkZtMDQTDQwIJ0BmeQFwlXA8IFEEggDJagRemj7HtuN8OFJTgDILSyiZPTsD5wooAGPLK1x2dXysCzdNjAbzBwFIDKxpBnybDQkvG/qBiu58n4Eoez4mNoCRZorS5gNZIIDlLa7fUBcRW1msLsJtwIJGLt/XDlIOS9wSbJsxMAuHxxVRirjfHgFGqF5lWcFa9evN5TvpLRsD+3h/1aOOdCXhPOqrYPzg9anO38J70f1MVMNItWc/bX5t2MzIG6Ktt8DHYNHli+ADfG4IM3JbVfA2Fg9TC2tFkUkaoTP5nInoPPIAsEfHsdCIhCOCcyawInSUSKDIERp6FwogEGDme+Pf5Wk2W8Ex+Md4Zk4nQ7BLMyUFWyx8dwzkHcjK9pMA/1gOa/n6OCTKlIPKMJ/n8O5Wtb/zu5BsMqrGZ4sB8Io6KyAcSx7yR8XkjeFd8tG5AX3V1DoENZcfQ4xzV2q3AVKkk7j5TmjmRAaXhc664woFZosDagLJMpR+XjRu+xBi9uAUhyUVgJIB8DN+DB2GU+9CO7CMtQXkG5HEOxjejWhseGS6nwbi55b6d0GLAYD+P5as5BFV5hPvv4My9nCAAJnMhbrqQAubrKMBGKTBG55AA5MroUWwFjJNzyA7oK8DuhBm6b8dIwiAcAR0cgBDQJF4PnBBw926Risa5fRORMWd53nPh4tYkgmcw0YWtOPE/I/gZW+ifwW9oLQAQGacgp8xgDlH225EtgRXEION5AQNxmZhUzxxsnuhUxQf74kzeGjVKypMsdRrlB8xlzSi9HPtbdDxniqjyM4mKMjJG1ZkatQQHIouhQbchS1NSdPgAcQvoP4qFzrZ9pCwxTMQrPk64QDBgqb8ZP3lYYJQuEFEYWPczCAPixMxl7MY198BOF8dVmkg+OHUwfAqcpbeeZFtLMoqzm56KYfQHMFMi01KbgZ91bQNZfG53NaVx0IThXJ/2mLCjEpwJYJYIoqdL6kHDg7LJIQt/tS+L5pLNJYZ4cDTROhf0GNia8hDol0NjjZ0pRrqK80/lblfMN07JhI21Tj34HEZvwqsmMOKUj25dBN/nBw42oeYSz02cJhjnH5w1Zuo5EJvJKWMfr+Ks/XHg+Sh/zOza5RHydtpJrYYuTWsTbfjp3qcwooSTsB5WkfjkJkhalGcRWtqgkQKRa0xAOn/K5yQxr4e5zKZqbcYRft/X21Bc1vT2znaK4LNebuptvaubR2yMsR8hMpE2MnF5S4Twnv66rgMdNLHB23V1THLaYKelQYUDaVu35Vg2ToCLan0Bq8ZgjXId5W2oD1AuX5Ufq/c8439zcZJFpW4+uoDK+cOrer/vwYD3ffXJzTpbE8IYRugYqkGvYIgAoAbxUhaP/3BDm3/a1H2nS4EZ1q70SUNURQ+W+3dHPeX5wh6WWX5yweXr9mO1/1s7kigWmUl4qr1mKbbPLTFc6iJMtvzt2mB4+oCRz8/Vdus+Owe881WGwn84SyAuOAiaiveWJxTqA0pwzRSLR9tilkz7I6Wdu6YsKH3KXKYa3x4P9LavL8LrOyoKHPZrbESj3tKiKxzurnW08AtXf2KLkGe+/BjaaxAvoHmipAONlJg1wZb2d7DD1lZSM+3VlozlC7OUPlcKvOsywa7gJmznFlbd3OmSsPs0wB2WoWgKHHxT7+h9nPfHN8O8P473CHvul305IVTvnCUsNX2yu4X4y28pbELaQdS0Ey4EbjWWYMjBZ2XyogzDicI9dtDqgXpLdSKlK+D8n0XL0DmCCnkGm0n1GPj14CrXbsxdT5h+PHaXFW70umo/0lNX7chI9eN8s1reN6sZl8gBeaUMzfaGXm8nMsd9IA84IreHWPw20m0DgbWwHpYSKkjMjbU26L5cBq41rBmt/WKsraCHRoCjaEQ8Y6z3R9vy62nvz4AVNOAG5x2M+4YSHK/Hwwd9cQWoxxGMm4TILII4kBhve+BhV4BYP3krmQbti9hblrrqDmlBfkxCXLtSDVbltofd3nVReG1Z712vxJbcQyd/3BfIcIwrrbllBm3qPKzxcJB91AkVKXrfUe+N9n3pOuRd1Yj7Khe+e6Q+TlIBp4VObiAYUQ7LqFmSu0bLAKS2BMF38FL+xingsLlfB839fFkcHnybiMe09m4alhndVljD0UHdiAeb9gQLq+rwAavqvhBTn7g5iJtSSc7YoMUR3KVV3+JIvhPPj5/XFRBelNbF7zscyWMJRkVU7Zsg2+u+w5Cm7bfP5LW4ts9l4+tgzsGydWF7np6ICAIV9KQr6FFZC97fkXN2sEcAv13i0vJtYMy5BM9D8RBtIJ4rgOu+AA7v+gI43YtwZW4ydjuXSnOmrz9gx3CNXyOozKW+jcNhiZ+dTwow+wlnAiRM+RkWI/HTqK7J0F06KQ+cRDE9aTKdgWnQ8dKW9mz5XywFlsVHh1O1Q1E6sfNAW5HbmJnEBUhqD9iXHHmgTSZ71CXeD1DxSpep9qmutR5ABrFLNoRyVrXj9R/J83jGV6p2/hmqXeUXK/ceS86krQ5EMHsFTZlk20vhAyWrtG6P16T4aOrYqfFhYOlvKxmv0KJC8pgvzbGSqFwcXDw0peuwsQr+2HIj3D0k6sTh3jqtikNbDgaeFqPzpe9rWdeuQWj+vo0AhyIDIkfAneE6Pc91fZkD/AksUEZLz19JEaiXzWXAZBEw7FzvB0oXV/HS2KxTRgcLhAs7V/r+KXLFZ9tsJbyT8fpvzlAIq5iS8c+gEJZQjTRfc1yDRIWGYcGo30ZuHhgIPDUMlaavxftacLSnO4XFLbcq+BaQU2zmeAYOBwMFz2RwUUVqOiGwxSX9tBZJ2hZCl0xoUXKl6e//TBU1IQQDqx9vMlXU1J991wZiT4W2KVwds0xbh0XdY0MPle+ArDqfIoG39X4wzLUH4qd7QtN1EsKbIFbB74euxYuNJ7V3CS46y1C+mc3wzxTfSZPPFN/7tTya65n7nVGaMIcfMibPyQ1LlucJ2CkfmHn76RyMDBRyhbfCtpFWnk7hjQkF80pDzXClsVWJYNS+7CuLdZ9Y2znr7tqhqTd4WJYX5NUnQ/fahaIMdIZiKuxokmgOKnffSPI4D4AfW/obxPXSWRBJ7YDwx7rZ5LNI9M33rVvar+LjUe3jUdzJ8RMaOsnYBXdcYZI8SdRKTr1enhht738HJ615kiqXD6FO633Z+sQMJUb4qF+KAbf1JG0/cGyyfXYSHdawD4wf7wyPObS+pZ3iqie/k9sd/QDYqw9Rfq2dr70t/+xNfIiaNKDRTX9RcEUtd3Tnse1vPeI9h9a1trdtuUPDTTyT+agqS2NLlKUBM7TuoiTFT3VwC5gFreupK0L41G+WWhzL7fyFEZli6TvCZNKQHSeBkYAoO5p1ri0bXfoc613hpKeXZ/cCdx2r7tG0d7MbaZ2kMh9CN33b42jVwZRhYimc+OHY/D11uSfHlnEVT+PTJYDXLl4Lt5acw1w2RdGJnSX4BQp8rmjW199+OVoTnTCWnQB1gXlnGQRZymVcvC81HnsY9IpEPOOX2dDDzIceeA/2NhgH/gvWVSAufXW2uJqC6hpAf+NuoPQNrM2ZK0kSLBs4WNEir85cO2AKJAo8lmjS/MU+hq33g23uz21jVMLAL4gmzQoTMzk2REYTLrioyPBCZj7Y6MO805bCfDFghKnrabk5S1IOcWUmBiD8e8xP8BM46I0UzwAMNtczchzuAq4mJhyjXWavLfKMYpMJko5cWzRz9W97TfjseXAlbOA34C8gJD9zdREzg3k2IOO5q4s8x4beAY5USsqPOVd0HdC8/o7Ox7FxOw//Awv7f1vsq5W9r8SwV5u7Xu3c59sDSw52tdhu53VmKkEEBjL+XZ+em2G4MaUkwKmGCOJUK8Wkuc0s/XPbqCJTNxQ9MHeNfh8WKcXx/zdIT2sn78YZQU6yAd7m9LI3dn8JHdrOV5OPt4rGWBjiUY4N7WDJxRf2yZ0pnb/5otL5z2NHI1xr0HFxPnqZ/uUUcIxZ1XcC3LmO6ZG29DdoPMagzLChkkVQsi+AkoVg+s62cVpOOmpT3hg2TasvZTvrLrjjs39+xCtmQKs5MPAyUz51kUAxvLVs1/puQkpY4KUMF39xJnUEzouRx0tf/3R1gL6w3VW2vg7H+eDbF6xiQyn+o78uynMK0zhVBbnVYr8fHigMsS0Ve2qNyEf+5wvMNW6qrcv3b+qtTa3PubwgkVpFgD+duy6HN/TTeM5Rk3ITdeAc0QgiJQyTBlG9dun0Tb2rHyDMqw/tVJfusTEnuqOHTTyWumGcKKgM4QPM8RTAhOZgF/D7yG1EFdrcUwFi0y06QetLEgfMXDDbpQjWxDa9Hu1FP+b126v/B4Ox5t8KZW5kc3RyZWFtCmVuZG9iagoxMzkgMCBvYmoKPDwKL0xlbmd0aCA1MzY2ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42sU82ZLcuJHv+oraN3ZYRRMXAdqhCYdm55Bj1p6d0ZtmHthV7G5aLLJFsiS1v34zkQCLYPFQjxyxL10kiCMzkcgbnezud8nuhxeJ+3399sWfv9dyx5I4SzK2e3u3gycD35Id2xkTZyLd6SSLEwkfT7t30fdlnVfV041R0cubvWBZ1D8U+CCixyqvi+7m97d///P3JhvPydyETOs44zqc8UcakfIdfNSJHQCfU7Pbs1hrvXt7hF6/JZxTx0VwWabihJtw9rumPRF4Of083OxZ1FQ38OfUtI/49lAe6NvtuT5WxRoGgstYZeksBouACaFjk2XhoKamUYylswspHishwjHfbi2kYMfYZFB8s5dSRW96h3/e0UPd+N/630VrX2R0XzW3eeV6NpUlEZIH1t2xOEm4W0nzXQrklhmnRbri0JdN3SFPCBF9eiirgh7LvqOHD2dL+b4E0hf1DddRTx9ub7iJnuiZmAkebIdzVdFbVdauvTvful2ihcr+AZ8kMIiQtzd7GFW0RCYgyxxhDYuBuCH4387t+TLTjSkhMhNz4IhgvtLh3LflxzKv3Ba8fSg6B/ddfvB0yesjYYC4z9EZAM6koInPdfkRyVW0Xe5oc34EJrZN+M5hzbzuqKlpe2orPpxz3B+3eNMeCtfZ0/tYdnB+D8UJt8YMW1M1lqQHbHtPbc3d6gHJZAycFwD9wxYNZZLGKZ+M6hvPHnZT3fYf8ipvLT0F0pNa2+KUl3VZ31v64YHQI/qlJgaoHP0CYhmHoBkhaAJiAQ92g0xLZmUa7r/KwmWO5WmWo/ZMy1mBx7OYyYwknplKvHDhMXpcqJjxCY6vECMWzXLuO4shkdZEp/y9ZSbCtH/Ie2onKtPzAU51n1+YQkdlvcYAMtFxwlgI0uclweURkSyNjZkg4k6Gwf1tEABg/YygNZ5zzczmm9HmKxkb6eZrQEaI/bHM75uaTo+eMMBfPSUcSYrPfdGWTUtvH29UGuXVeaBX4Xjk3h3KVeXHsywWgoUg/bC0w4NIx3aRhaNekZwYdBIniTbu8ua4RPKLVpoZhidLJdF3cASqsh8Es8ZTH5ImFDMdNaIQbjvHJ8cCiHcq6/nzM1JbOok1l7tUMpCkigD5LVHJGjU1AG3YdAxXG0uBgJEyDYf9aW0dk8UZnNTpOunGOlkK2ltdocRWraMkidNMhINebSzEQHQyaZ5HPAZtIlHPph7DU6qn1NuDEchXlxOwwYI9m4gM7KYZMD0VJ5LN8i6AYrUCF+lIMNhXL9Y7egUluHpaTQIMdk0itgEzB4bRfG4TL+c1Yda8DbpUzf2SxshGtFJqrDR4LDVDpZGCmW6VBps1KiWIELEbdfstSZI5RGA5swKKSZZAESxmKg1A2TLXRZpeU/ghH8t53KeetlOTJb9mMksQaImZ0LWdARaMYxgp4gzU9hjUEF8JPMt3+1G3L9h+CcJUq/kjPK8ugQpJNhnxemsVkH4Z08879FID3lPYPm+tZFR8fXIVW98IOAMy1bMbMWNib9FUJeBzpfoZQlsxAZww0SgftpZhYHpI9TyiKjiDKhHPI6riBnjLPE87KOBuM2WUNxtnTAkTKzkh3ctrQwmObQK9uI41aEfb69ND0Rbr6goAQttzPOr1lj3DGKwF1kYwaltfqdigOh0P+ry5FOzNFCtLZbBZePSXNSeRgV0teBaO/WVGlIBjr7OxKBGzJwNmSbNAlvzX3PLvwNMxs+twBrRWMwul415WsPOZ4UP3ZXOQZeB7oR87RnmwwAdr27kGa5oz5TE3k4k+bG0WTzVwqnkeX3BgXC2yZ/IFxyiUljOMsegw5a2zersTugtZ1PQP1lRm0T9r9+lYonsBh4Z8pXLwIcG5qBr0Imkq6v3QnJr7oi6ac+ed/rHPz2TMMu1Bs0b1vmmP6LbKBNzgpw6DKX1xw6ITNsmRPTPR6Nj/QsgFkc2MjFOUmOOF10T2HlQzeEss1fN7O8KHJxnZtSFSS2bcS8CHc5A/NlyEARPEgEJU+HTxyfANfDJpfbKO3h/b5jG/z/uCqHKLYaQn+nSuyw9nIHnnut7RHBL8l2NZ5+1TYKXhLqBBDjzpXPrJ9rrwy8isxCBOXjVocOKXx7x/6Fzo51eKj+EKL2j+9t4t9MsPLyyXiSh1nyk26xqtb6mjwkeUHnxsqS3vyyP6aNoHz9xpxXjIwfvLNhzGIgfdLflrFDMT4NtTMAQwSw+lwwxAJk5JgFMkqERwf/AEgfiSMcggZzHtGeMqet2c6yOSbi+0gnWP54Nje3gleODh8eFGsOipKw+WatBivcc7H6m7igzxUDEpCZLcn9S3D21zvn9ozuhmKh9/Vga33oaKmjuMQqb68sUS6RP+aajhQD1HiFM0EmfJy7ajmTvY2O7uyc1Bk2XRg6X4Iy3VY0ivox7WRVbWAWnA91jZa+gM9vLVbkNzXiPXH2lCNIa128Qpb6INIf1hQh6sZuJ4gNS+b/ZA7IMNu5I8ktEpf6RdHpaHPdBL8hJXwwCdcAHln4rTKUdfi0c8XhKaWWKFzrfN6USrZnRwAQoXT6GGugN2GQkDPfXpQigZn/gkMvVxwFRG/aeGHh5vFI/cRkKH/Hgqe/rS4pfCfq6oBUU6vNKhgPcBKje4b9YsBZUoOBQmpNCzLIUQozHhFWh9bSZz2yidN+Aux0TynczQ6vUBece7c+drMOCUNQqDcV5TzHLuDNeOvYFrTAZzCgAE9zBYqfhYtE9oZdodwSOrQCLdwJ+mrA/l0R63ZMiTzJOfSwUUneD+FYZaGms4VGNDrd5y+FMTM5lOqcj4LFGsekNESTbKjHjRnV9AN6fWg2VJd3agmd5tJLanHsjTXVfeVo5OnuXpK0jHbiBhHgoP2ERJ7iAmmCy8tzdgySAIWkdVeOYAVwO02KMFmjrX/ucW+zf2z92VIFkS5jZUBRSSSsdp6kTXaxSvhsEZRsGH9oyVndDGgds4fbSSFRre3sAfFJHC/h7p60/lmVreLTHuxV4E0xTYIoCAmRDfseViRMxSGfb/HXcwUyR0L/p3BCapCmgoqrzrbXIRWv+7bOERej9UiGa/txZcs/9HcT7ldU0jUDLP2INSgunEvCVyiauCpQScgFkyCarbaXevkfHbY96SqYLfwXwsyV7qh4C17YWA4++tV2r0+lN++k2gv4QvJ1KZx3NVgk2jJY/+eW7pE6qW2m8fpfmAMohj6VcoT4/V07ojLWxII8B0KSQ5Ig6wcIwWSjBuNTiA5BRJrMAhuwQirUH4zY0zlnn6amNZlqBAMeFMYt2HhRFiMuKbrWUwcsqycFCybDczMFqsmZwgE5bWvmWRs1W6ErZ9YFjsc2hqsCCtvYb9HprqiAZrqjTqVNxBN8MRLSVZD30TYhn4YnkdG6bMh31crg3nyfvGzVV2W7vDMIOUbbIAG5J8JhZ4SsNxpMcugXwep7hh405HOAYLQVcVnbeWRf2ZgsEyXXcjyJ+Bmz1Bst5cipk4g/0PRv1pK+ae8Cvg5mPuwVICjI15Wi57jxLamApHLXiP22RVBlzRKaqgf7IvAETLOFH8PwSIyWJhzNL+BvnbSzZYxDoRO8lhTu3E0dutlXiC+ZhnchJPwGXn85x0pYMvSzFQ/5OVUKFxkhwCzitVbMDDd+dDBUaEVbj4neRIcQCFUv7bhzegX37fFtYJgk62IGN87iRXMdga8xy1ZGWKJSszwEVK2AAk9nhqdLjBt/gWjKUC3X3rBVVPBOmoxGKsXRPQIU67h66iFoM/qMUlOUHlCNAC1KEFvGcron8Vvetus+nwWzg3/MlNSNK4HJxOvVlTAezBkRFHcP5tQ3MI9BmMCsZcrNKZ1JnSEiyp2oFOnruM/pG/z0+I9bnN3aeqc5TxkQl685Uing6hPph4rwLTqtx5NBitAMu2pdDBy2lhz+HckrFyvNSD+IlLHxYBt/dw7jpvhLiKmtIVvPjoi22UQxmL/X23wod8yoa/x1P2cTVyQqex0M76/9XZSmBIEeM5V/dIz2Qe8myULbeWmrXd8XtDvzn91EV5/3BzMe/aBzc72WPUiRjoxXdvX3x4MbAN+EYgEIHY4OcZtjucXrz7Pdkd4SN6jwJcmE+268l5R8mu2v364n+pKjDkrGEuj+dxhZ/GbKjAK1IipI+NiXEy/QH2Q3N6zA89BeYs4p5SwBTlwdNnxogBq/rlIBVG3JViZsK5Nt1DW9bvJ5R3NNXe4iWampCmlpsaP9J53YP0RcsMjmuw1h92nscIcJWCoTiZGdwIWwsHoFgrbcCnLSw1k+hbzxZUYpZ49ukuzOjIfe5tIYxjTOd6er4T6HE7KjW1Z0QXb8jEiMgyi7n24J3ACaGaKrXAsERc44/mHMOiwsWMEpYxMiO+imGHuTyYaww7Ro0noH54GuLnIqsq6nwBWdP2RSBllM+RqHEVjKUGCKQLt10TxG6gjt5Q+LVo3QKVDWLjAl4WYkR1KMlRYWXWtVaDLrasxiLgpAsmcCgw5OulLiacjKeDvoqdBxcm1TGTKpwZUU4zX8CnKZiBx80HNDDVUrhqtLK+5KBmMxz4cStVhPpQXOHHlcthrFjGfFT6izHxKYlW0ka2Itoh4SqkYNMuQd3PqOzg7HaNx7B0WSIfyz6eD0HiyATneapVOQch7bK8QXwI8wi+sleMQpyhKuxdDMa+jJiYjKCXPmdQU+4pHxeKXuJNmAPxPa1cd4WbRb+oOxMw+LmL7n+PQp0CxKNq4A5NhOMFFipePXfTGuPHtugObelKRt2AjzdK+RSR7V0vsZFYNpHCBIsDjDsqWXq4StsJaesJJkiUqjgVNmSCBmA8c3Q5VrJrZ7V9F8SZpIsz4S8FfuCBbE5p40fvfbYIX4mh8MkWutvS60PxqbRZNTP49f6bc+9hBwKtYdc6tE3n5ipyimPRm1Ohg+MPoxFJaq2LMZaJzTBf0DQsVtyh+TNy00W9OymXDuoOBB4RMw2zWCCTgfE6+jLIad91kMmhOWoEGSEGRFDpxuKRI/PeuGzbINJh4q4r2sE8VcBDh+p8LCnVl2KxxEgJTI4lprYHX9Dvf3B47pYYUm4wJBXzu8Se3QSbPvKc7/bBuH0Yknuh2sXKEkzYezhX1G4CihdDLaR2wykkeBtcg5koVzV3GiuZDpp7ZgqMpwPv/6EpQGWL5+MxcwvmWkphpkFlwt+C+eySQ66kPxVXeSPixRSzsX1udVjq5UCKty763GeEF3UWtOlUhUtfAmJBAAK0Wyqo/s7dmPnXfFGbMFQJ6Hpd6b8wrIGJJTY37bLFL/F+hQyBRlqsIZoKVM7BkJ9mwNEAjZxFclFpawNmoAmnfrV+gQEMG6OeAcy4Yt9vjdgp6KhTV31p+3GmAqCD2QQAwnejTn8dbcl4NlgW/PmZ6RZ3RHKgrpjQwBY/OI48ofsvpXMhjHUhNqrr8A5UMF9LKfdvNsP1YGYmWTg2WQpajWSowDJ4Z3SRucQGp2ziRSRZDEwIQzJb1/5VXsQwl1s+9CImUUgs37JePZ+PKmNx1+u5vacg/7h4tt0gCZPGpp0Dujjtx7zaSkKZBB9cHVMya8WxwYpbOx6SAwmzcOE5pAA+LZ+Fk1Aq5nyCk4sV/LhRjIgBJTblkyWwlAXLuFP7ywZYEszTBM5aMHXZTSjtcx01vd7mdHXNJQUFZkCO5blbr/BEYolwoVno9lJltusej6J0mepD4T1Ir//pPo+8WJ8NFg3V66UfoSmKlyqVj63+3CKaiFTnUkEiwyKQJQvFkF9m07a+4Mc6Mf42ySMV0/jCIPT6mupsy6m+piZESZDYCti/X2dlOHcgVAMUX8+m7ScRrTQx4ahbuxqKQIq7pIyabEpdRRV9OdzYeob+cl0plFxSgf8mbGBfpNma4DI2thEKrlDP01QDkMfF44ATzZzSZQLIFOZMdEgAPKYmib77SGFuQNaW1STXbqotVWF40U97aU47Pi8zsQ7qPFPPITUAoWbvPoSQg28p1Pjuw3ysnVb6aa70Nom5zOZWmrsvMOo1B3YKc81CHeodrBcOLmzgnT7QHsl8UQmVny1SUC9wNdaGGudDKAowKAowYGGHda8U3v3Et3NtI1NYJtL2z9sqhpUmTM5dDtnaqzBY9eV7BpokE2Juxf/InvH/7z1bsKf3TCc2E7YHk09z80U46Q2c+NhdeLVs2cxvPUwQLDN/SLG+38jxQmvy4PWi9cUpATrNg4CNLikQqb42DUJTKTAj5FUWZJph/sssrgBmYi+C0iY54fymduWn7oiVh3OVty+X2WOO3BomFLOsenUfAW9rzV8Im24t50tby5T8QgEcrHVh3uR6b1dKMuS8IpDgrqVfiLP+sktwiPOX1IaEV+DQhTN/RCPltpyumBe/ZNTjB0w94Je2L/DXNZczHC9hNcPQSxCgbb8ukTLMBZ4beH5XLtC0vnFSPYjmnh1PBYTqjxQQJnpSQcgYvxTm/9pUH4db7INFzkYxYxbU3jlf57FtbitbaQjm+nJImK1G4NiQm2FY0n5Y91GUjBMZAv9lsnnpVqdXXnotrpAIFkQp/jZ3P2ivsrV07rADAnU5+O0BEi+dc+mpfV6PH0iexngS/zAdlsGTktnMYTD3yKudHBO8EYmQAGsqpb/umPi5/MobCfILyCYFW0WFIFuvl0X/k/fEteP/bnDxzTK8yOpGWO5DQy5Jhn9HQG95vV4nhncRdTDXj7N3ZjMV3D+jRN8eb0mD9NX2vqX9UNENqcPW5W7QoQZgGi+8eQ8wBXaWIbBbd2WZRrlztc5S1i5JbE6nqI7rtVccdjuY81mW4+KFTY43V8FwG8/sEvyULE1Y1BZUF+FvdNDtHleTKXxNJjbYPMonvEzd0BcsOsqi4X9aQMvgb9OIocRpYB8qcTJDiVNQ6mlLffFOx9NMFssoik149nTLSOUzOMUhtyk8bPHlqvA4EdbYFNzyUi7PI5W+Sj5Bky2hpl5YlwbUM1xiXpHZvKL9QD+U5HKZJAtE0Q0L2kq3ni7VTRDzdSwuwWlrfAT3tQH6UhvAXWn0Gktn+M8vhilfb8RyGTCGkpf+L4P0IqPKtSJ/T6tPqGP/4U9h/x8WdHWZaGCl2/MA7+V/MfFLkrP0cSV3c4dT4cld07pVXQFgWEY/JN6FraOQPAYRR1DLoCvI5P8DKebmuAplbmRzdHJlYW0KZW5kb2JqCjE1MCAwIG9iago8PAovTGVuZ3RoIDM1ODQgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnja5Rtrc9vG8bt+BT5CU/OCewPxJFMnbVJn/CFN1Gk7imcMkZAEmyBoAJSs/PruvUgceAAfstPO9IsI4O72dvf2faskuouS6MeLxP5+d3Xx1Q+SRThBWZLh6Oo2wihJOAwmEY4kRiKRkUwylDAYraLr+LHs7i9nlOK4u8879ZTEt3Vz+fbqp69+SDMPlAWDqUQJYT6cjVnBeSRRJhO9QCDCeDSDXamIrhYwC5tZ4yhilsIb9mGjyxljJL66Lwx+8/pyRtK4+C2hYl4Wq0t46VpDRb5amIdF2a6X+byo/PH5JY7rcnU5M4/lojDf65Xjgt2k+NQVTVk3L8z3x/uisSN2Co7bvHKfirZTpG15JEkkMo5YRgwJ89pg0ZQ3G5isVjEHiMb1eq0pqtuys59KM79oboEGO71oAONKc4PHfytWczt3C+duuSkWZjLwhhXLhfne5l3Z6i9t6GCv9Yo3gROkjCM1qX+GJASCIUlJ1Jt1nDw4WEkflkRS0D6sb4A5Mo2TkPBcawIfFbOK/MPyyVAP3B6hk8bfhcHwBORMH2FPHNMUccYikWKUMWqO8spxW0nIalEs3BkqLOOiyTtQH3OGrWV/ZQS2NqpGnZDCSNfUqzuDNQBcwvkuy3VXzvUhi/g1SIGMjSCqOQTmmAlmRVPcbZZ5U3aK/qfLlMYvDAPy9XpZGtRInC/b2pFGegIqUkQzZqjK503dtpNqnzCUpsxf9ufQAfa2wRgjkmJ/0W8JJsFDiK2y3ZUPiu5iGh9OESXCB31A7KQndhgLX+5SALc/bShEJGXx92ZI9PfBKQgJiyjKeOqZu3HugImgYJEH3OHJJN1ZBigOzuG7AzuRJEWMD5j10izK0tA2BAN7gT5vxfcB9gLbZNanmh7ChXBYLPaoxmGZsKqwtf6LEiwZBWts7V9i9QzHv4P6FE1tDbqapSBe/PXq4uOFI4sKDFIDZEkJrKfRvLq4fptECxj8KUoAnzR61FMrS1gSLaNfL/5ufKtP0xaWI2QxIdw9BlCRIZIMTtCKft4amnLnbfLl0oyUnR1agDl4uOQ8Bqvu9MQ5pqZwlqNr9xwSBwVmdrt243xO03lGqeeW9ufUt2aKxYXCYTxo09vc7dyRRV0BUmMbO1W7QDBmeblyNrNcBQ5IpggTZTEokpw974AcLEe5f0AYy4Fmg5e5GVF6GLJKJkjfQyZIZJnn1JrQ8TPEgZjeLLRPOqcEjKXYojtBegIoUMEs6T4IliqRJODGsikQAlyb2HIvAIKD75/GYhwECDg9nQ59hDgBI8wQZcBnZV5AcEmCUi6ddZlhTHj8SgVsEpRgc6dCPBW6abGCT+1T2xWVHV4t3EO+fDKOE94gXiurvHMexj8reMoyP2bmGdAKYYGNALaevZh3pQkdtYCDP9/MnW7kds48Bxc9e5OrcLTM7WSLto7x+hFE6kUQ20Ah39dIt8yR4mLB3OrqYwmWQ5kAICfdWQGeEjDVYhCW1mBkSEZ3xhVM6TZIVvZFDSrtV79dk69aNXirI9JGYaK+r7W+P7UlUOzA+UH4bwnh2mRkO5OhV+Zl0yrLRdP4lZq5eA9Berm6sxveFwF4WdyF6APjyuDENH175kktqs1vfaNMkXnOzc9WcODZJkbw1D85eF035WperhWJ+jVvbNp0Z9OS1gzc5HO19wd/139epiB9c4W9HbnWhsDg39xZQn758WJruhnkRBROzKMMZ3aZyfz6TGActA88tzf/7UsnsY4N9/Yhn8/rTY+dfpDIwQpo3VNAtPkHx/O6yrUt3zyUy1o9PJjvRlzh4d+XKYSdVV7Vmm74cpBKQhhEKIMNceJT2Y8iKMROAvvzlRMVJP51q5awM34LgsUJthEEfIGkz7iisiuteAN/wfLrAN5Q9Gim3lkBMm+dg3CrpL/W6ZjmwzLf7bdptWYCQOPe9NFkPY4yApFHahDurClJQaxsknCr9IATJSlMbwyDKndr2s686GSnbj7YN5vAw6eVnW28dOocedpPQEyukXrJSxrOqC1eSjXN421TV+bpTVFV+diJXuspBGH/7GyIk2opGYQmnKTg7LNdaNIUbTsINdrNTTtvIPnREQ6DQ+6CYTLO4JlEMwIeEORDO40qFJIap/7NuL8HayVAbQgWApQ1CIHiZByCVBD4yNaExn8aWUiIt/W7c5Ef3ZrA1vonnOHo3c/adIRLhLMpYquvx8pCwvfCLGUmO+vl4ZASr8C+P9lUt2031VopY2s+lNUaRH4inQIEwLr6oCtlCbL425FEZosWYUKXeLzFyVhqO4UET5GQ2AekFBzE61uDTPXNQWwyhkg2AEInEm3iVHEMLUokkhn1Ib47kjkQwGlF9Bazye04HEU2OIqDZFMhESP0KLK16aDxD6pS4ioqJiIDSQFzaCOw1aJnAklsXMAcojKlVCaeG1owBiKapHZ3ZTPHC1D/GDElqXSVrrESwHW8AaOgbMIiCGMii7Z1oYUy8QwcRQA6ZKNJmkYgdChxufwvYVw/C4p+HkWRIMzLo8oQAFA4TPzi4Kg1qsYwSPAI6iHEYEeSHUYMopBE9BGbMHg68nO1M283MEYSZDm0mweHIpp6bFhM2fr9og0EW2C8Ikwge/WqNkJG4D8FEWrWDDJACRIBGUNqBeJfAaRnCcIQgYG3goTcZtnvA5tezzh4j3BRjCEJ6RXgg0GZbJ4+JmEhxsEEEGyPce/DheoB46pxMT0NA0IhDuVnYBCSuv1S+bOFLsg1gdTTUeKGT8f5fQjnPfq1RehHJSfyXSJxDgmjAVLPRA33X+iSaaQCGggugVgqESfcAPw1rIL8ucbyUND17lQztzg2LhwUyLelrSHAD+HpOu7TaGigzXnY0FOxqcaw0aKV4PH9DnqGwyzzrayyZ5idYmazCTMLvkFy38yWL081tLAtM3aWPtfOyqMclJ0VAsYhusQerAMWUx602SdQkoHdoMdRgvu7H7B9csL2CbDXsg+rZ/HGhe95lipDWrZShJ0P/zkszAzLqVx2Kprqp7NBvTtXVacWB7NGyhME2EbKMCfpTsIpwo5ZXsHQJJaUw7joJ5YqxdlF+qN3cSpeA33y1i9Gasrptn8CI8axv0hXa/WuxcdNvgyqrlsvIVmEs/Q3LR/CR4cldxI7ihFJIHUSwoeoukA0Qqqsm+3Kuoox9XKjc+wXqiyL45tNZwY+6MpusTaVW/hQ2oFwcqVG7lQJzmRYrfliqr1SVaWqdd6VN+XSXXAPki5KlQ7bUlrp4LrJOuFZFcX2iv7GIKAKfGn8iLzyFHDHuwxKQGUSxKQNFkzBa8YSHgOLhksxGTCdcQlJJiiNTjLBGBcPRfNkHlUxjmPbDADz1uptlzAOs8Ijwgd5ONcaYhguR2QMMYgnTenMhuFvNmdUguSZiWNwjSZwXLzZiHOeMI4vx7w5JtM7nZ0OH3NEI0YwGQ/p/1sR54gDwTTTGx9zWCdTSw9z8CgHcT0TEnPrE8iohsxAF7JM+mXk1yujwkZlm66cq+6bFyOlHtDtaeXpdzXtmRC1jypelqM9W/x/R8sOCcxZggp+4Txc+TG4ais1wnxdJxSJuzFK41XdVPmy/L1QB78w38wNbrf7JNUNLlctKFo8OtdFORB1DHKXyQwkC36JjfffhHkwWSrcTyRoAul8Gqmeq4xvs9wgfznKVLzam1kFWpoyxCF4ZBBKbZsnnIb6CUeMw3cxiGKgtAfgSxYU9828DgHeBQhjDKVUnEKZx2hLGIVwngyXD3MXDoHVLEU0EV+2THCUWQ3cQpHJ0NzjmoAJDJ/CtaHs7wvE85L4gFP8kj7aEwLghipW9o7WIWZPvZfPgxoGC/MppGWZr4fGM9Fxz8SYRHAKyn4QF6Xd674x7u584QHC9zAPMYflEus6gmtae3PQTSnSXi8CR80wpAxe5svCTb2qROxlvvuwsECYfbZyyVlVad9+CcQJ9aolrwI7qdQaH07+VRmD9lkwLpCfPkeBA/IXjvFZRYlR5XoVOrVs1yNuISRh+l2P+PPpH8Z0mKieeKM6bFR1VMpLdpm/6rXDUuqcp//PEIKbJI3YJA3eq7xryk/mea5H1J9tv0Snm5ckjr+HpLhQQx838HX59MIsMVoJD66/Sq9eaUh2RDdt7KaKfsxBdIAheC/m2H5SMQeJDUQVc+y1fWJBdw0yty4h3dqJ7rE2wctcZ6U6gNmS1Q93TRsITNzamloHOovCgszt9KrWnSoqHlrZTWxjUBrcxELTvW2Ks03XD7NCPaKMg+kDCZAESbCzz2kRdaAckwYdovuBodfTApEYVstNrcAu/7nZEqmaeQZVhtGqUMKRVHrAVMGx3+KARa//B0u/0JHpQof62pqp5mDhg+3pqZtFuTLtgTBafFrnq9b0KsEcELqJQhNVhSqBPZQmw27Xcj0E2COT6p5QD+ShhneQAp2K9ddsDm0jOJwLGe5zoMVc9ebVhlG2b7C8ffLYKmLd+KjaI+e1FmrF+kBxa7lXrAJZgpgT9zrpdv+6w2zzaG7/7eeFbeFs1P8Djf7HzsNYTxJ1Xvv0qqBlBov/sm2zN7KWd7qoR5nYHliw+UvVx4aNXybvUii7fro2hHqWIaruFsBju0vgSQJGIj35zEjvUDJ7clvBw1hXVPqH18wPoxKqvIw2vz3jkpH2TunEe8Z97KcuH+gYtGOuVc7jcpB2IRFlKmyFeMuJ93nXredecNLPfsGZqv4B8SXuN+nz7zfJl77fxMfcb5L/t/tN8cfcb349GVjNMqoLPDOaIUwsMtxbAhHlfwB8yz4hCmVuZHN0cmVhbQplbmRvYmoKMiAwIG9iago8PAovVHlwZSAvT2JqU3RtCi9OIDEwMAovRmlyc3QgODMyCi9MZW5ndGggMjc4MSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrNWl2v2zYSffev4GP7sDQ55PADCAokabObRVsEyS3abpIHx1ESI7526o+gd3/9npFs2bIt6d5cJdgHi7JMkWeGw5kzQ1tlFCtnVFDWWJWVdRZ3yrHHrXIJjVeBjLJRBUaTVEYXQt9gR0TKWhcUMdqAFsM4XEgGClYGtkxROYc2kHIYLjq0EW3A86RsTpguKzKWRt5g5BSUxwwOk3pS5AHFO0yRovJeUZTnrCjJc5lKnmMoi3l9Upielc+YyvgRk0hCiiEKJ6sEQrBB4WtgVoIgZMiH1yKwRCCJ6AY5XSKnslXeOFbRAQnbURQkCf0wA5NXETMGTBeBIGHYmDAzOses2EKLkAzdgpIhvSGVgISBMTnFIfMI2uXE+A4oxngFyYOFbJAoOPmeVPDQRXbQPQYXqAELkfF7CEbljBYXa7CAEGCUpQUai4UJGchSVtF6WSVSkeQXrGr0WFOLT2SAtVYEMElWEBJ4eRIhAsBbrHayNo/wAXiWgYHKZQwIC0Bf3DjBD4ks1iVFaNpi1VOCTGJL2YiNoF+2LEYBpZIghiVk7+LIQr25fB1mlCNGtTCCnNAZcibpKgrIYimQI2eRQczHGKC0XoQnA7MT3MZ5sVd8jE80ElVaE+QXL/YdRSQW+85WxJUbaN8KZpOz3IlejLzAYtEmlXaLOwsjtiI/dAN5vLxONo4ePFDjF2r8z+XVUo1/VN+ti+lmtlxo+7364YfRd69cDPjEV8aYp3JZyGUjl5VclnJ5K5etXKb1r7P618X3rZPQ+SSP6ve29XTl+JN6zhu5vDLe1E+KW6A4fmnSHHr/+JNcPtRTrOsRpvVL8+P+BzUcBixfeleDWrUL786Ff3gZaSnUe7lc1wMf1qE4k2PdFOHQ6/oWSjg8Pgh801To9Lh/0ZxnVs8zac69bteEP9fEszsuxbpe6HmtsUsWsL5sO4cBy/7/re8Wter3L83rAe9qFZ/qN1d1v/1L75ovXd9GbXyutl+aip/WWmwRZlN3KM5kPCjmbd8It5W2Q5hwLszzGsW0HuvzYWeduYJlrcge8Q7DFfUr75oWNruw0S6h376pBbjkMq8uA3hzwb/Nz3qd+60WeRb1cPMza5w2R6ok7BGEWjxTw1hnzTEPG/0f9deb2g4acLfNN/+qnx30fXAq68t2eX1wMz3CXHCzj2udXNcID0N3BY6vbWLnnqnd9M/kfgkSatRzNX4y27wWoR88GI2vbj4Vavxs8r4YjR8vF5tisVmD20nH0fh5sV5uV9NiLYSxfPJL8XY2ebT8W72UB0FIV6bXIwywwptgYFW3h4vFEuO8BLssp/S2aqhqXNX4quGqCVUTqyZVTYX49aiBthx9NH6xfbMpv/88W3wcjR8tV2+LVYnLvB7/a/x0/PilLb+IINONehmtFhLqvddRqA4nzcKp2WusPro9VN10Z1AUNmkLyuvQxggeTlmDtXWioMFRuOw0Fs25pA1YojesPedOEG54ECFrkmQqOA1LcUke204QfngQsIpkkIaFoDPyIsdBs3edKHh4FGS0k+TPZY1kzDnSJvlOEGE4EClqJymjSTDFiIzU6oysRUzUUwuK8wg3JBSCfUqCSQF7VhRisEqR+6HQ8FBi1iELJEBAbknZauTi/VAG3DBIvjVJXkdOG6iFrdNlYuhYZ3/ZeRR/bSeVD2uuz1FA+OPP/yCLlmxZp0BqsZ3PX7d1kzTb9HXxCc4NZtzTjYLRUq3o6kbYAg75L3JgnbE/u/oi09eMbN/CkcB+O7vGoD1kTWLqzZ5PEAZLLT4RDftdzEQoYt7dI0XnsL/3iuPuHhk2p929lED28TZlFcz+XsLm7h65uwpu/wWKCPvZpHaznw03YT9bkLLH/j7Kosk9gI+frZbTFwVsBNH8xydqfFX8vTkNnCdhPtNpmM9fGOZjJUSs4McKeaxAx0o7sVJMrNCnSh2pwp8qXpCqUVI1SqpGSdUoqRolpSEJgQ1WJ2iBozACbKMoG4CxiNi1iS5up82HYrkqrk930/2AELMOBAvDhmBYESEcMMyEo9N8p119VxxG2eMo5LXJjO0LGNZhebQUF33KrQRpOtsU+vFkjoGXCyl1DgeGxdfDJvZo2EYdYRy9cF7czD8X602x+u3D/HqyEFhxOFguCF0EaxSywgo5gyZxeYHQxnZUv04+Tq63q0kNKvOj5XbxdrK6GVBnxBW73KFjF+E1+RbofgMgPxwQCwBSXQaPSfBeFqwqwY14D7ZLqQsHGeMGVAhCQknnImnMzwic3oF1w6Zyyw4vcfy0/jhbPJ/M15vlAojop/lkvZnhp0EXK2sPg95jc4hwzt4Z25Cr5hGQhQF6mFFZqkYcB9dxWM3UwkO/MiTRRnBCu4xOxGXGwlhByqBjqcOin2KXbT/P5svPf06uJ9fLzZKM5a+xjg5eOiOOU+kKABHhIyGUE1rrOowdE+y9ArDFIV0nXKQgCJLegcUQtj8CGxyV5tSB6Gqy+Hm2JUMDbkFvxZ7B4GDfgVKZ+Xrn5OBL+9wRVT4Ui3/PJov3QHQ1wepRGGb1Tjg1gh6BBJDDKhq4LBgangEeVtF8QxJgJa5J8JcckC0YL2tRE3ARu7ty+2xvxe2b3UrSEYQFONvdlUqbkhOjnm4EUzQm93WzhL2SYxsTP2bZx2z6mA+3Euhjyn1M0Y9pvAX7ypcYN3rnmn07ldN9GDfytlPKLeeMX8S5UyW3HHxWrdu1ftfyrg27Nu7atGsHra1ZLLMcbAdkxz7LiSI2O6w3gsQhoH+9staJr5EkXU7ykRiDbWS4O8gZYKomuHZX83sx/WhzyAMCQaYuBKwCwnCBTmD1AbkUtIx/PFnNC/wyoNOz4GXYI+wdfIyc/iN0YfMwe4Qy+lYFQO8z2Fk4wICX8qCrt4ExYAlQjDcj0XGJdZSzd7DoJNUmWcSWEs8+BPCQ+ZeDfYBNKIc8MHJWDgwxUVkghel0hMri42yKAMnDQSGHmBLABKXihCCUdDCxrIqa2MdNYbN2yKJXQu6QlE0E14Ik2SA7x+pYuBpL8fYVWkQd+d9Ib1jsDzuNIs5x2GnUZ46iyHHYadR27lvDsVVRqxFRXPyyiCJ/cClDg9u3dtfuQozbhRi3CzFuF2JcGDKUgEJrRGbsPQb7SVLhhOeU+NJzMhGGJIXIxQirD7vPoAYeWYeIy2w0c/yGpNAkzd5WRQ/5Lw8hvsJDsBFG/w1LQ1KSIrgkDw8QYLOUvPgHHxjL5L52RnGyOiRVBEYmgeCK8CEnegEb1Vcksk8rbsjsFFpgSfnETriMGAakUtrYlZ0OmPqd7B4n7B1IGLwb3pIkxEpKj9Sv7VRtKIs5MV2EjnJmV85cVuXl/25y0pZbEquOhEb+h3ebjKbZD9wLYYNsfzdkXQgyfd3KdCakwfKUxpHBUVRpOwBoHBm0JzD3TlT2B/5HYaU6k/+CsLI777e7A3+7O/G3/p5h44Rxw9xg9NBIklNkDwpFZQm9rM9+S+5PUmTkisWmYMvCh2NZK6nG/B+wfzbYXjBPdgiqSMQZyVqGUUt0SzZ3Rjca0kU4iwRN/uIqjlvoJRy7T1ZYjRQNul2EO3cR/nYHms1+2JpSmQq5u5/URk1K8kfpkqP3DSpuL2TbM2gISHjkiF2qnD19wYvkz9lyFh5cD1oiOdgEf0Ki592d6ypt5ZMm2W0huFJqsEyXXN+x9zpw31ZP1u29/gfPPb2jCmVuZHN0cmVhbQplbmRvYmoKMTY2IDAgb2JqCjw8Ci9MZW5ndGggNDA0MyAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrNXG2PG7cR/n6/QsgX78EWw3dyY7io48ZJCxdIbQco6gTonrR3t7ZeLtLKPvfXd4bkSssVubpzr4G/nKVdvswMhzPzzIxMJ1cTOvnxjIZ/v3979u1LIyeMkpKWbPL2cmL4xNCSUAnf5pN3xZvdRbupZm2zujr/7e3fvn1py/7wd1MhRPHGv4pXglclK6rV/BzGyGK3HV3iQ3hFB68YLZ6lZ3Fjil8pV+mJnIvicWYi58UyQzFQctV8POe2qLc5YmU3e7AnUEPZr1TR5MTio3/MmB6QoyzOpHli57ntFDt3Ax7nloZ3mzQ1y+wU4fg4jyYf7/yHMpk6q4Lg4wkjlElQZpg2sZaUQk90qYgs+eTtEka9PLe8WG9AB+FM2+saP5TFzaZZzZqbauGf31Sb1r9oVn7VsCKHHbWV8ZId92d+zOYqDH794xm8FUSEl/6O9XiIiOVCEEZtvPSTjs7zKStq/2W9Wnz2n7b1bO2uExA6rzegp0oVVYv6alBf/fDLlIw7hoTVhFsW77pLCbhPqyg1YaqMZ4Go3IZNu/UkbWfVogqSXiFJu2W9qdpO+LNqNasXJJIvWBttGSml8Gv+fb1BruEW1rc3i2bWtMC7FU4u8LBJ86Y0oVZMphxX8mbrhR+owZqR0lBv2ogpzWTKiBbGj0reY5iA6x9G5bV9d0pno/2ZJEZE+zep+ZpIxvv7B/unVH8tTYyaME506QeJsKGZlKTUXOOgqSLUWJCL48gN+2eCrimFwzVOfBYIdOPeJ/Z8N4VL/IylaEbWqER6GBzpCan9OSUbSqhQkXDep2ZLImwknGX+CO5HARfEqi+hYJdYDC6ZVKePWhFLVX+tEY+VlBpoAXxK7ROtIL6M5vcpmo/4P2j7UMxPU3ZlKqgkWkrQtxKoV34RsMwrf8fb62brI4YVuuGeDUEDkHTKYPcVOBxUYFXqELfkHJGlfUdEjwXNRx2m7t8IoZx3kERR2QmjWzq+OgVL0sMYEaycTHsr3Ntfc++vs4rzOsOq1fnIyhb/zoc4aQLneYk+Pg9kKsrTbOS4/pANJ+SXneIH9EXJs2QSzgL88R94mHm/kT/msZgueQRTRikxksP10GDfeWdo+lKK2INhaIofyLdwYsBRed8ShNI8va93KdGvO+civti5yLuZSdY3banFFEQR/GEd1T04KYlm4gs4uZfBH2gfqA3VscV/IPvJFRws/8rtZyqUFHC8A9llY0n7ILEk7GUMwRCJW9hbRvFCdDP99AmMonr8vEBqGtxmPDKjjQeknRPhw6iEhDCMs69DJcZs7UEekfAlJ5TyyR1Eeiz8+XepeAk+2Q4tgSJRM1EcpKCsR0xvfbjEENOuzwGF9ZIXe5jFQGcMYKxo5ikYK7MwtkcSU4wwPaCJnE+l1EBa7Snb7m5uQJiATR3MhifVdltv2ma98l8v14uFI/5T4OVys17iJ+Dk9121aFpk6rN/BwDXvWm75evbtl7N67l/PFvDVvDwVyr0rKnPmY8j27DyGp57gP7D27PfzzoJKQrBKNCvBFg7bSez5dm73+hkDi+RXwEMf3JDl6CC1pTwaTF5c/YPn0IbRPXdWp1MvBowfuJ4FTNgKPRQlse0Cg4Y/jBuhFYK1AotA63xEtIq+FcRxsXYEpooqffsJpYA3WLjVOSXKAkX9+cjkbZkBG5dlAVSVBHZ5RW8KgoDGjivN9P1yn21xbxBdag39WrWDbj0/7bdjBYzGZ/wz9o/qHZXy9rlN1qnczjJq7dHKlv/rNlGC1lQ8s0MU6D+ab1Zhk9h2Qu/hP9Sb9tmWeH6g2yJLCGQwiQHclVdwjIeGN1cI0Gft83M5bQQR22q1faye++SNYCp3tQzd+1y994NVsObH5I22gl2ygRRAba9bG79+lWAbOvVf2qwQ9k0L5jNDAQAXKg7RwEnf5TqfZHwASXEDwyCnVLFkWqsG2Bi4UZPe8M+Ne31F9HIFR2NFvqTj1LZVAHgcuCKpjOaIcu1T5l/2jRtnQS8Ls0y5ejjgn35SyJKKSHsdaGqMfvwI02dIYYBMYeBI8jwLuIZ85xP4S24jOKHVEhqiealj6v2/lPIdO6EGR+V6lOh1e1o/hxYGuG2zkd/KH3VI3IK8uhJR8foBnzSQXOi5Q4ExmlNDfaV+0AmbNOJ7joTasE1GT24TpIRaQquM2UxN+MR14DOXsAVFvguXV0py5AFgB0Pca0AH6SsjNX5JWZ915v8Jd3mVBAYzVoR2yVDMjcv2GpYvV42swVEKQd7+mbtLfR6UX/sjJ234UDoKr0jvnudgtiAsQ3I7C6Wq4wsV5Pme2oBtCMs5eaQw/1wvxKN0N2MYaZZwRIA93mwDTyhwxQjWa7Czj+FEeDbYYTR3sQoM+EQhobr6iPUKfWAFlC1DMqTVG7r3D8gVq4iAxUjH0YUUxPwT5jod8P+tT+YPoJXDkdxsB1B3bpDsj2CgSSlgeKObZGGUFqWGOAYdrBCcVUuhSmvU0pRwj3SfeTDM3BcKRbhm/c5iCJo5qwhOk/lYPAqg2iOz3rItEG/0huWqXhIwiEC61G6zXDEBU9xFJ0uIBJAShT+hk0vUmyDQgk4v2lv3MeMP9YjLuMgu+E0qUfydEmpgqJJc1KoIUrpDZuPkIZkfJcp2wHf+3hRGEGYlPtaqPC1UEEDTpxhwPrEP3FBbiiQbndL/zCYRAq4aolRLgS7qxD9+pFoBbcO8TEMs7dhuHvsP1ZdzbJaLPyTn5pFCHe7uuv2ppqFmdX8/bo5YDZ4EsYiRLRApXu229YdFi37/KqSqFJ5fvfm/CeItpu2qUIQvG03u1m729TB7H+6BgRQhaoDwmjcbg5DtsEb7Ga487UfUG1HC6xgLzkoYETIWMgk7WhEyXqTj5DjnnPAF7Abi3fd1MuqCVUVPL1Ffes5uGgWzaquNu7UVPFivdq2VU/ismhWB4cYCte8J2QJ3gbtOO4SsDtgJA9uFN+jF1xMiWJZdegdvsxrr0W1UwrlfKeb5A9LCQ/gzR7Ae11zOqrKbhR3wcxtHda4AM0KA1DZRo5HYgaVy5iFnIR7XEsOF1PaeF7Y8mLXeppWWLVfhy+eL1SeAyMg4MXnIaQTCKTRX+GSi2pzVY9Bky6EG8ZWjImQK+hwG4wxw4J+/6ZQMK2W+W1/3iCEFUD9tglpGcsLQXimTcZFk4q+8YkdhNUeC8NNflFtFqB4oTmgU4VeG4dJtHH0iGZ8wLeUZfHqHJBF3Y5ePMmJhWgp4uv7cOviNfuSEEoRDXY6mnbhtvPnWAUVO1cgnd1qXuO7uX/nxmGAaHSxwNukJBhZ+JKOXPdKqCH8A5lGe/6SC8uszeRPQfDlPjsdlSUpxCOyH12ydNnfwOZTTbQKMdksWX3pSUtRDUHKgPKcF92nebCJhqdPZmQruKlSDQ7maSrO3u8DAEIMZ6TSBhgj27545ElihMY45IhvltavQ76WQdTKS0ByInTNeGeo7F5t/LeDlWhdtw8+WrohBoak7YFyLglB359yeHCfahjeKrBdifhZWcJKLGtIQg8pA6a8p8XgpMw9tvHjIbk+5qaE61CA/CGhtwD+7b2qQUN9drkDQzhlR7mDIWQtwZhjhTOw80tCHNbhAxirS/G/yWJQQFRYjASdMDqCSxH6MA5S9PASS2Aqh55kCj0NzIiHTyXE7YdEkMxnCl6A3rFuqUgmmOwt9YMI5etSkOMq0kBDXqWj8ZTiQAjIYOf/i+aolOZoPQTaAv4cVf8iBRvXKES0Azwe3yBwtsL0NSqT82EQ4OqQ9NGdxcy7+6mgBihwzUH7hoGfN875uj+XR0FOLmRj4KsYqAuX6OhCOeXn3WgQAdSA1VfxnEcZ77DfSGB3JIsnnXKKDAZLU8aTbk/tJLVL1A53YhB6iLLLiQxVuZuswaGao8m5ui3mGV8fRf+s5C5cuB+v4ABLK4aT8pUANoqJSnZSUJxZwME2IahMK/HUCF08BzFCMNsVMdyXPXByb1xXK6JY/ObbbmeuOQ2/XyOEPFoAkGUYj13GTzBI5MV2PRowcbiFgie1MKvuWI9TgznNNoHeOAcDKMIIiG4lZUjxDFAg4pcOCeILXnig6ActGgTRCPLw2yHOx3GfXDahuboOmQF8drkOEx1oww+vqptFNQMcDgLXmu6bs8diZc7BOMmY6kfJ7l7milOHgsA3J5RECqwUDgTybJQYheV9liQmv43mhLEBB49HtzECHLqJZ1w/SiXsBJE8qoDxdK+BESLKIz87RXSJzSYqJqHXwJdRXDDeHAKWaNapY1DgSwDbHm3Fsql6yWnxLlfCPFwi47pCo2WZjkub0f2xIMsBx0+GYJ2DxDU2vOHbV/VyWflECRzxb09C1Q6Li8mSAZZh5/XtyaJGKo5/NlLvyBrxgo2WO0Z/6cKwfs6ly5Pzrovh+rgbgYGySghcmEVQO1rFB1jBjI1bJ4bOar+aJGBD/K43iV0tWDtA+zgOzMBgUwkmoNtUEWrNoF0jZrRbykDQo21kO47CaAb3zhBp/9cSy7CNW1lXod6XN3KQwCECdQIRiDsjgl5B5RQiSGABiJEZ82GuvFM8ew8sIPtYoE4Gw3Gz7qOENGAhZlEmZRn7hEGVglI16Q16lgx5Q+tlb8tUFQd/XqMSRZxkBSVGMpnOuzrZrAw3KQ2EBqXllFiwkxY4fkC5xLx8TCgLdso/iKrksLQaR0TMQSL1h0KirlyfNQdM5FN7NpnaUxKMcXTyY6m9HtJNpvZKH2YcQG6+IPZ92jW5ho97/f7j3dT47n1EhCbVUwhulgGf6DlChWE0QrSGWIDq0ZTrUW8pRgzdN9mZJtuK0u+HxF/16ZiYvBPmpc72Ih1gm/Uesb/kixOthMxSd8WjSRh8hEY1+NRF+WCuXNMb/h6kX6soXa3CvcZM5GI09x46EqPtvjlBoxAQYhoRT8IkulDFX7EQ5bZuq7ZeRsQ6mIXUVv77lStyHdhQRevrhBh5+Qfhx5Im/FjyqD8WNQ5wk5VlP0mLbXWhSrlsusoYNpR64Lf57Mc4gHcqpEsgTiy9vd03nDRXoeTmGxDloXi5B037n0D5Et0S6yqbputPwRrmvk9PYbiPfXoyxPvPb25cXlmyk32/Jtn36/bs1YD9D1RZEUrBKPBmWzu2JAJpHI73ZbNtp67pMhQU665nxpUY70COzJGDlUKvF5vmYtfmoljufqIVNRd9NeEEv1M4keoJOcZ38VrPE4RZlw67/y9Xcr+CYXdbS/TXSgUz+Eui8msPZo7/YwI59h8TXD9P9w3qzG+HTv7EMuOWrzM/CxVfctCjjTLsbr/+SDU7JWVhiOF3lIW4myzu+NOvu8niRIrJjKSYELmWd/uFLKYFQiNPNqU9ZRTUEQLVqcb/jMAvGecxABr/F4PdcjIKZW5kc3RyZWFtCmVuZG9iagoxODQgMCBvYmoKPDwKL0xlbmd0aCA0ODAxICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42uVcWXPcxhF+16/Y+MVghTuZ+7CjVCyXFTvFVCW2XHmg87DcBblw9qAXoGjp16fnABaDnQF4SIoreZFA7Bw9PT3dXx8DPLuZ4dlfXuDw/6s3L/7wWvEZwchgQ2ZvrmfwpOE3PCMzRWcKG4Q5/LCdXRbfNfXZv9789Q+vten3IKG5VkjzQZdvfQcpZgoZhV17g4zgM46E1rM3K2j0E8bYtxP9dpcFCb1p7+0cI63lbA5vhPH9175Zdh2EUKTogLLd/rA9mzOmiqq2/+vi6mxOdbG/263KVfuGmuLd2JopvMNmMPTXicUopIyazQmSTHqqSWpcjhSjs16rsDZCZL/Z5ZxIVfw7NcJl8TbThcm2R8RQypGEFcwZolT4ScOuCT0T0ExJvwChZsBHLiLyJeu1gb2RhM3m4thsam+oMEhiHjPw3LP/blddwy5t3vl9ul+Xu7G9YJwiQ0w81PrlZxMUMMkQEyzu5mVCFfV2sdmgszkXrHizLv3L9+Vh36zn+8OqPHhKG3ggxTYI0npxBoL01gpPaSd3s7HjoZKGwQZrP1PjRzVFvdjaJ457ghhm/nq9h1em2NflGAOIoYjAUYvG/wx6Kl38KXTEQy6wTpIJ0iTui1Osu/TU7j2tjkF+Ac160fiXsKY6rAo449gCbxeH8G5xVe8PfpHupLklwx/v2nFCu015HQasq1V4t792HG2XzC25ysRk/4SFo/yFb3O4CY2//8sL+JUhFX70OtB3ICcbJYhEBCQzGvo8rGB3NifFKiLFbqwGbQB7027szrZmxfKMqsJtocptoWUqTx+WSzdGffcTZnJZwZjAqcafCX5kPysO5WbRVG/tz6V/0+x9o2btxZnBQhn/tVzlKGBF8rBcFlYOuSj+eaa5H5wX+6tmUYXzKJVV6pI6JUAEQYwR0HUcYaVaFU+E57DVDib3WsevhzQKJBiHTaKgwFzDMqHPBGyBtS6wda7N5wlFBQMRbdWVMUHPfpYYCejHYtZr9HKdosp4pdeb8scEWzQIkQJTBMqWPY8ng9UKJmZzibiS48qbOO0tJ7Q399p71PpoxCSHpSnW0UZ5Wqas6vk6QTYxiBHq7aGKdmBoDzk2s16rdZK1BLMPwtpTcRO/AXEbsN/KG43k7SINBFJiaJUazPrp5FDKEzlk8K88gX59eR0XUHnsnzyQAPc0U30B/SKl1gjCMFrArhoJDrqeUtgE6hW4t/egTq8Xy2Z/GLW83E5p4v4ZaQFrxY7Y9fPE6q24GDitmoQdoklpAUU7m/davUxiaGsMU/07GTxtT6a4RcDOSZCTaLnOxgC3dntrgu79H8HgWPsBwOi7xr9d7Fb+oWpCp2oXIJMqykNdnvu39TYYzWbtDB682vg3y8Wmel+GURYHi5WcUf/mzYtfXnTgQMIZMbCrDHwGQWfL7YvLf+HZCn60pp4BBLx3TbegQjQ4CHi2mf3w4h/eLRooonasdsmW46+mGMUVQxxOb8So85bqIF5Xfo3e73CIAmHaAxVYIwCBvu/Wcuhu01S3mwoYBQMI6Lobl01LQzzMt4mtt26Z6IvmuPdF6Jj31VsCERpcJBUTYHkgRYTwYSHX3SnDA2gCP+JRPwwOoNSDSf5o+WuyLhRjuG+5cIZ+qqTzHqOhSRonhWVd2V0Ka/JoFh5uD150V3fLxr853G1KOBkCXoKuCe29WMDDwB2d3GeBYZ8tXk1stDzd6I/tf/c4KCiAHzHgYOBRi7qP8g6nDAbhvtXqDg56450DLRwOBZT6fbl1SuZttbuxXMIB4tqnoKyB06a4CXC4bhuV/sG7WvaprJtqu2jCX/dVsx73MCXSnMUkfj7lXwoJzBusC/Sdm/F2s1iGyb1nk5uaYwX+uU5OHe0PnEwmo0DDlAPMiUZKDVYVOP2mZdnWMrJTPP7dcr+rm4V3SOqhLyQ0uAEgT15tLTr/jher8tYdhNJZAetK7B7tkHzp/Rnnhu3Pgod0VZe/3AUHyY/sPM3bptoHT6xnZvYb8Ni9wQnOEYjHFrUKGKsjMjDASSEV0lj65byuduB2gUQy7xCC5ByqZtw3ByngcOCicQBL5bRPNz+REgAojzu+jLhNlHLoI2ry3TbNVKK41Xo0c2aP8xqA1QARo0GdH0iKvwVBWC5a1nYe/JiONhSkdEBmmVBQDlJbQ3SinwYIpjoyMBrgci6I7UWSvX59uZ5aPIgDwnyw+HpdXTu8wnAbowBVUm6r5WYBslRbWayBJxtvdq4PThqXozzhcObYYHPXAJrwFIUCGyTAKkU9j1EUT2FQrqZ3KgVHVHTi56MBLqjlNeUR6A7MZSdrgKUkPhkI47QLctygE/VjOnwiQHpZPKITNHMMud076qqb9VHZJFACNCSgWigvfj+6CGq3Vw6m/Dm9gt5unCAILttekeEkCoHIgRRTFeH3LAtsEBSLAT2L3eq5y4QjDHM+d5VU6vwOAxm0t8snnQVLsogppB7DIca5jU7GDDoE4Vjut7eLw+Jq4/wGJn1wkqku8KVOzJd9570QmwBIWC+OkYLD1hprbzW0c06CaTmEKFsUpWYnWYRx85bCZwScBsXpAKHJxyE09QCERhEFzcEQA5/cNXOnjhc/VLvlSJTymwTNcIYxj7yHx4JKCf4EePZSdJEBlraN3MUiAO4bFfm9qdBTygUnEhy5iNTP03E9IpCN6/Uajhor8ymNFQNwJWlE3HmLYrzMc+dwdHIb3k2FxsGOyVR0HJ1614KB/IAn0B6VEe8aw5JsyNB71/EQXAsYAoyYGnXQYdO47Bz01BCWOeppQxhE2ePXESdR1VBz9V0aYrFUcP+/3h/2m83iYPUFuDRw/FDEcVCidCDKXOji9ZkC0GpVF/wRnMqQNBrNT4LLBUo2oiCELQbT9GmmIF9EDAg/GqT0VIIihkXc54g0x2YTYA3g56jnuV+pV9KwUgurgAMb//qYDvGpEGgALD2Dk3Qzyg+mGaLcDIlsVc0IkUyDlFgbnSCyfFsexmEeFYiawbT3ubgEFq09PNVpqgunR+YUu6DTdPAGNGdfyS4zWcGjbyjB4Oohu8RoLIZLjQRQk5K5kZnAiWHW0er3+tL3gkapeTRFRA2IS+fedRx0ZZPEACxX+kRM2sjoyQH1R7GpaitKZZ2Ku7eyTjkCG+uiNYDhOz/wY6QrUoZaI6lpP1/xJDutADmQEzsdSwNBhkd2+v6jpm4mshEXyTi7EjbOTqWJUGjEiC7O3rU6yv/AVr9K8XEObkxPcgZIXSINWEQ+NpXWZyEFRhPyKdJf/315Un15urhPZp/BLdb/RxJ1glmlZn2B+iJdU0AoZsJNzJDOarY5IwwRGHBODSjEsEV/2ztLW9p/9mD6zo/R+3FzeOl+vs7ZPT1i93TH78hVAm1qgWNPt9OUbrdlVTGUzxmxjKUCkI7T1siXYqXMDth8Zcik3UnSljc0DvwF/ju2l79Wdbowzzd/n2M3OO6PZLchYNj/v5jtrPr1u2p3E6R8Bdw+VFd3NvLZYtJ356nVzYkCzaDtUbf6UT9EiYpnKlH6WKN8kdyAkR37Jl3NoWiUc8gRAC41aMpew/dpAeDcb4wTxREH/zqzdQTeFNUufypenfWE7DTkRQnJlVfmzhOQnCqvJBjgJxVRfeVvGoz06HwQGDllHtM53uVUP8E6yTzwzBn7TfIuaXZ7ZGbMriYumGqtrmkHy3v+IKmgsGyxT1+D/N1p/73753oYOzjN8nV5eAxsApTDwaRjEjyPr25vnf/MMQy7v/Wxy7pyaZ1cmMi1hqUO4kS+8nOfi1mbSb+RghfLNI8JvHepwi/To1pjsKre5hNd97mq1y65L62DN5gzVwLjA6SCFa/eRfFiapQrqhoOMhppo6NFqC19DFSr0DIe+txvmC+esYW1dzdbm/a01bahnhaw2NIlx13D2let2jB11+Cw305VEwg1YEyLuPNCJqgtMiRxt0wmoxduZ8YAsA4pf9jQuR6xQBf3aWkAORnfOdDQ313n49qvM6aIyDELlLTnsBrDP1I0fMqg36c1D3CnixCn6+glQQyofgRa+fAuXywgDCkp+4t7tmwEPOECaSOaQ+SEgUnSJt3SiTBYCYCPfor91D4K3Iv3p4Z57fTwFxNlNQpgFFYzJjDigkc1ixKG0LTY3x18tgterEpL3K4KGXt4s7/2/zdtH3slpvaPrnLGlUaMJhiVlQIak5Ax/X9OYSOCKI+LVX5OVwEzsK69Vq9zOAK0XBKEwVEEywI4Qmr+kPJU8czyVDFWP20L47kBJ70jZhxorZNJQqQxeYyKGRIBjj1TkSuU2blHMxu0HzNPBm1ZeWcaOtuEZV/aXJ2SKH6wxRfHalEvxEv/d3m8OaFcYRD8FOeD6/Nh/tcVgITiJVdyqnslp7bwqFwcTlNihGOkiZ4xTgBKm2cVnHZjBUqy9abOWz5vk9JnpL2gMZGXZiN5aZ7OSwvmqpdtXlo9tXLwcXlpGuWlvy27vHQU85vb1AxI0RxgOKPq48RAyadw308M4vDQHoOg8on++0cA1APoFe0NyIwGHOjisfx/5r5BWqHbCwfqYZ7zXJGkWqcSpF4/RpYyar03wm9DrQ+wJuJSW8+hBbhJsAOqmrj6eRt+1irIzxtbd7n4t3dvPJ71D6uqdvW026Oqt7fv9tvg1+56taFO/YXbcaF7uG5oS0nDdUPuMBIndNKbSxZN5Mow2ORwOluD0TeIoTSVStAHbQngRdmMlqHCOwJKOOrzVWK3wS3WLqSnjueT0olSeUK1cyGi0V9OXZwBoYo65FXsf8ehSkY8BSgelQl55q9CwI5hkPtouVYOuSDgu9eNfaLF9d1uaWF67X+oduNVtXBE6ICFr6agFAaTJkjcCaysJrj44e6qbqrmrrEHbKyUQik02Or7CflgBiAAF4+QD4459CBpAYnl1RZQPikE/XaCWZz6+viIBr8pRzo56F0+OFdPOuXDQ84VdvVq0cgPc8+4MbDWAVEf5qMBtohO6ycbg5MbjQYeetYgZz7hPBgkCBh+sHOCh0GfEIj+Kv0hBMyyOm/gkFrXrdfwsewjYKXZB3KRgBjRZ15Ay6+tmQQHYBHuh9fl5nq+WP28r7x13JV1sEb7kaDYRf5K+vF2TfIOJgXjZCPXgGtJwPFfJTU7OYkmsQTL8AAhp42RQgp6Pbs4NCLHq5HY06OaIo6VqySnatTT47BAGXt6sdi1Q1nsx81wfYNaUYZHakUzBk3j+Hr6RaqOFbwu2yAZ1hvMk2YJI8pd7/gALOmGehBLqMmyZE46pgyLiO2Vgz5TRgQlJbaAhqVxClANfOEBV2xpPzhrcM7d9e7ncKUdioCxO0aoxjTUkbpcMBGzOJpIjHC3HXvRREbbb5PA0/HCnP3Ll3wcMTgtdj6wsvF/rapDuWyTSfkwooQFcR7PnWanvYwpLNrB/qs1I+wEhEb5WISlHaubNuZnnlVUCxcCieht3NWUu6BS1/vNqj7yaAxGCQ7Wz8SD5RVlfC0qqygBiT98PUxCb8JiEpwVYcWPddjYcJ8oC9RsClTQeIyLZDGm3ZbeMibJ4wwQLh+QV+12/h4HDRdrdXuxFt5sKhefc88DoKaARFc32o7T9D6Ncqjbe4OHm7tt8BaDOQ1ngXfoHOxqih/2ZgsdZFHG7Z54iN2jU8fdfjeqZ/eygITwQrmzZWM9v8uFc8ZyLG/tJWd7A5NI2Wb0TwJJWPcjQlHkxiJ6yyBXSj+NfdRDUvrqg2f0AZ7DfvWIhAVTy7M8Z9lYPVWqbDvcZuoJwUjZdk8KkmXbBqm4PvLTVV31FpCtuqJRTCrH9q7kO5/1ggMMCIO0t8Bschv8X3tDEByizWYeEl02ym3fuwsE9mG9sC0BP/kvv7nfGv//dl+PRk2sA2Ws09if+BOUv1BbOvQREimYgCzxeDmg5hTo+29+bcqWY1VgT/s5A/vsDL19+Hax3oE2Eq8Wu4X/wJV/D3pyfyi3R+a3JQ29+zDEoqf2dt2hvD2UdS80p9zE7obeyEXPo3TnbqpyjSQc3miyfN7VinjQae9zVSPdBwUl2C9yMnRS3XXk2EoTcMIJYFvKWWwgH7+dETnKX7iIyHGlKthux+4hTMxfH1IsHjhnzIbJyBFsYqaNmS2mIp0t6wyOmt4cyq0FFonNAZBAXPJfk+mV27CLGIhqRMj1JCFauU/GZKTk1KVuS6jhoQy3TcOnD31s0LUI6GM8aMZcpdeA9gkp4tggLlTc67z99GK1tASt+1/MbL9twk6KYoeAC0uERfiCXvnL3SL6IMOccTIZKjPpgHiMzNpPejJf2nB0S3isZLrMQe/Ou2vV+15I1HtR1+UBfB77yT/3ov+FxuzFyHbZz7gYSYjV/c+6GGmpoOI5FyMfvY6Rr8sybIMDbMYBgrdRofhzjMDK/wAuU6AdCmVuZHN0cmVhbQplbmRvYmoKMTk2IDAgb2JqCjw8Ci9MZW5ndGggNDkzMyAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrdPNuS27ix7/4KPVIVi8Ed4HF8quJdO7spV2WzdtWeKu9WhTPijLjWZZaUvB5/fboBUCI4ACmNHScnLzMiiEuj791okMxuZ2T2lydk8P/F2yd/fKX1jJK8IAWdvb2Z0ZwweEdmdKbZTJMiJwJebGbvstfVZlPOF0LwjOdi/svbv8Jg0R/8biEYy34mkryZL2h2PadZua6W8wVXOqu3c6azfdXUu8a1VO2+3pT7CgZQP10Ay7ssx+YjuH98RVkxWE+T7PVckqzauxlM0IH6rbCC5oqZcD8v3AglZzovNLEDTG4Ym/G8kMXs7RI6EdeJ0gAylUs9W/S6XcVWf7fgQnXLDEDv4Zkzk1PCQ+Dy+UIqlr1dVY3dHmDMAMY+1u2+HdupINimw8lWfqest1OVMyNnC5obztweOnyomYReWrle2sxYzlSIjnBtBXgwswXJBdeu2//6biSxZcF5TqE1gJJEEQVINCZrD9crh4L9qtw/hZ+Fzn5fVdvqAyAottiJiVUhc1GwYJEhoZg22Z/m9t8qNh320Nnz9OCfCRcRfhIK8Cv7DPUzIR4G2e/4LqNRsoqciyJgtRDMgKZU58aRlE+TVIcMHmJd58SogKTldpngcWmyT6gWsmcg9EplN53IqCEdVcbSGHwdwYoEeVS0jz4Wo47INex4EWBZxindoWRIXpCxZ+5NYcI3FDTVNxHYmMx1EcDGz4aNplgdcNmW+7q9uU8j+2WEzyQBoheX8FnAOEBoBbAsVK6k6sZ3DD3gSJoXAhlIFZ4zXkcxOkKClzHWLXIQVeBdxScAkDkFRu51/BSnKDCkxbRlrhHJvUnQQiiZLUHdNvXVYV/vtuV6vgAdsL4H5SOtOUtS6EViSmCypxGaLKjkuSKojU0uqAw4XelZkReKWdldyJxooBPLC2Nct/+Lk5OigWKgCsBC236/xphhAcA+j1KJgoljfEYZYNvbh7QVEbkkMlgmgBo6QW+dU3PkTSqxH2joBSomE2/WYfMDlSJzycWMgHWirmcTgRAMDPR+CGG4XVCzBqS01+1TZCOwoOQoAaoQn7eTAES3DxA+oVUgUmBVT3ob1A3Ids8WswhFBRCeIX8ce53EMFgzbhsAI0TziMEPNV+ujBguQqMkUtafApPDjyhgIi2L38Q1A6fssZ5KQD9wOoDKYCT1UEcOwAY+gMkXJufEd30fV2WfErYOlM/72F6AxtAA1oAx+QVITS8i9UD2RdGj28B7ApFFa3KE8g8JmoG7v4pMDo4IBRHpiRNLsIfEPfT6JfCc8ikoMXFE61ww8Z+I6IAhKQf/RqJGkR1vA0/SuIYasuT/xEMwQxmxEAK3k6SvAXaK5BxtCQfBEn5j39+kjdqYnazbufNdNru5pNluv3rqGvbgoqem1EfpeTAlYZEp3XM9Mt+L5HwAvo06YYIKpyuv/XToZXy0Tcv0vCdX5MHUlIpBlAoGSRbIL/C/s+U/NHYP9s/NMKh9EEMrgXEfwMd5dgf+i8l2bb2vP8yZybrmpt5e13fok3AususdujkZRNJcXdeVi7RdT9j3xv3apYgLE7yO8xIOq+PxJgeXEVgXVACwmTd7m5SUggv9a1ywAWKS0qEq6rTAghqEBTyhLoRJ6xbrMHXdEhB8SIUr8eVBVgFLD1cPFah2Dliv24gGfZ9CW2EegTbQUkkl4ta7cMcCnFPBY1suIrq81w3iQYakHwGWM97NNoy70B/4MRYTgl4DLwxthgjjrmIYdzFnWsQRHIrgJLdPR2GJxYACXCHQnZfDEtXd8Mt0aQvwMUHPSwXOCup7TF28XaECOKBepCSr9/YHzar1ur7b19f1nILMQ4971177jodt7XUAPOys9nzy8u2T354ck2MQyXI5kxqjPz273jx59wuZLeEd+K85B5h+tz03PjtGZuvZmyd/d9nDcKPdVB3YPWctuVXGhY0xgr3mmFAQ2Te77a+H2xLDLwf/uipxi6AKi6zyCNhbXQkv+0oR2u/KZt+h4BpHrcrtrc1GQhOmMxyatjjXvkENWmTLw3Vl9d0gESqlyBm4Dha4qLYFYAQvUMna/239qRrL01ElcxVO+7eJpBnV0BmcmmBQKso+rqMNyOVgA6tIBsFFcpckqpJwFgokVD2AkyZSxoCtRHLpxKEGPHd2GboYh+CDqMvQxQTNKRdfEl1sCk5pQD2YS9AFPOuYrVlWTcd422p+xCUID8k+VQ2ohF2OaQue/c2PKd2/q3K9Hs0jC+BP8HcDqF5MpXWhs4DWi1AuMMlHB/v/GEEloJzLIEVE0sq21+vZFNQgV2pIczYKsiEQ35gol6SXMRD8QfiZoPPAHB2XKiBaYQ9G8UQiVYDxjyrcHiCSQhAhBlOCvy7ANrbj5yioCQXJNSkCMoV77ul2QyFokuGY52MLFGBQKY0uciYvpEChBIO9ASx/GFWdGAULHY5Y3U+YMwrxlDaDdZy1AfuyQWNzWO/rO5tHRMtjrReaMxDW3w54blaiJXcmD9qvnE0fU1rgLRTDJVdxrBkVyaanbbPhOSE8nBltsyQ+RAHYmqrFHW1vHbw7F7JUTbm3IRc2bV3P415d1LV0rZOqSBe5kAOmeDFBbmFMzvQAclRF47qo0DmmBoJRzyaWkgSYFu1yfxBLa3BEwKps3eZLlybseX4gYgzCB+Xm8d4bcgvTImvBUdneuid+dP3cu577Yx/B/XmKpMCDsnpduSHWbcTX1okxfSfGZO4dB3ZFNly6ByBa3bgxYG0gCoWAuOyiUT8gHpQfsaoK8AqLYF9TYiTAbyFMBmPKpnJwnJASQR7lVoXYEVcuej6g+FErYfYHcjD4bH9uE0d9ik37VRydYRUuN6Gp9FlW6xQ8YbCymlJrCh4g4ArgSPMebq1BNxgJpzAl4bxplNdYZMByiYoF/gv1mZGBn6qDczl6Ao/5RzY4gQ/sI8+lYJgzP/YDB4xNIIsJ4fzIPrLezRcyG3cKJbOxbTAspM8wRYA+ZIzmScgUnvwMyPhLlIZoqvEwGuSyRXkAigYqt3VtLubDX6X7h9UYu82dNTDXNla0zZhfw8jHJtiQJa7ds1cM0NHrBj9tuam93IFLAeFsGK8SkhPlwf+9tjk7biHgHGfebKwp4L2odY/R071rPImrjf9YlwZj2dqFXNcu4cVQuZWbat/UH/10vpTEv3XxWdPaagnbBHNWjetbupZ2g3bHAxbRhZEoEF04SX0lQbfI/tBc2Qj1qcvFtbveppk1ddu6g8wSAXf8YW4Hu9bb2j22wchT4I4Dd9t2XzrIrAFm2fe+oAamtDU1IxzM0RqKEP7vIpxbgGsgJh2EvlNLFNg+Gs7cVLeHddn0SMt8+hV+DI5Ux51NoVleKO7rjyIAuzT/GQAT2bmkYOGNCGdud2sLT+vYAWN4/O9Ew/jcs2VawHVncm2P+3Zfbfyw2v+/9bZx2zH1icHf+dQvAtLceoh+/MuTE6FokVPwOQPwRJhf7u+GM4hdCA37P3WLgfDsmmrjHqjMqWd/64vCjx+rTdm8x98EX7NfQqGD3YGIRSyskOCPUeGlIBBE4N3m9rA5agxs6dQAz0BAj/3a+w328RK/bl2PDnWAZt+yDzRAgRqg/mQ9R2gLpd6zGO9TBQWnAQf1zum47dK5qdC+3zkB7+0LQl48jbH72ncJ+BZWLBsn2j80Oyf1Nh/vVJlA3Kljht22YI2SH707NNeVz2b95DEhTrP3ZJyfZNylk7Dp10O7d0N2V/uy3lbLp+5F7Re4r6v1so1IBkg8AUOywJogUpxXUlCcWVLAv05JgTijpOCyigJzdkUBO6eiQJxXUSD+BRUF5tyKAvnVKwqip5/xER8jUFED3hNH74mS0HsKBrtT2V6nZ6to+g194XSWRedcmMvrE/okZODygA+KB7Pq/LIC9V9eVnAhvcUj6D2WbR2sew5rhL45ULXQ6suUQIgzSyDEf3sJxP8/pgirNYylla3WoGdXa6iRE78wvOCG5Zr4ZMeruYHADh0PI32sMpaOgPAaottwitXEkpTBhng4xpZWFxgR9YKUiXMswQawX1JZn4bO0Bzc4XDmozONSDm4Uz33VG+3lccWJhTRfzKicz6hsZdIs32cH4dh4dK1dDvGOHpd3vnGG/ffOWwwoc0f7Q5X7loDNNjF8i5YpD1/kqvC+kwW8De/HSAics5nd/oIP1qc7rDZHN/cNV1KxsaDfReVoU4Sg3k7qYoFFe8yLAEKg4dTcigAlwmkDw3ntm4rzb7fY2Kh8hDXzrfvrnFAyykLiU+D4M/wLhyxpBg9B6Lg+fHh9rhI8Ak9pRcxBWYeDvzovWWP1Y66PkxDkg4iAC7BlSenQxaxdfkGnpVt22UQG/T884fJMsmZS6/7SUayZQTkgivhs2XhFAKPFRWwPMAzMgWYDaGOCbfIFAAFnqY/aooiBz/94n3YrB8FSbWXFtCDvrFIhZBBUR8yCKzBYDL7YTUHEty3tcvoaBnE4PDYVBjoObGAR4z+MFHT9d435RaDOozW7Sk/tN1gJNpOF1ZAKDZjhXZlw0jpn+Yg4ZhKAyu1teL3Oz4YG5Ah2fd4kHrorjTtXc/qY3nd/VyXrc9l2fILbOvtBx+dIsJfMKmvOFhbJYJt66p0kSk+XLn826rEbh+smGHzTffjqpevE3io4jjTPixrC/Dd2qb4qk06l4VHO7rL2Dh9JPzxAP6AvR0ssgVzOs9H5s1ttXUBLei+n9BGVW6AU6jVLZ5Nu2evqWGC+ph+d8FtfXPvKCu6AB9+VKAhOwaAx52f8N5qwuVxUlff4ga2fugRfba9ajY9hSx7W9YQxHZJklc2E3E6gDMOtoPNCSLi7Xav97tJ86cLHU4NMUQqDXUEB4s5QFkE426a3SbU+ASGwNvB7OMaX44qfHk0Jhz0hAmnfuq27k8rQQwaTFyOnjxixnCw/W8TB+GntQuWczbY/OixMCfgOGgeXWcYm3NbK65PEfI0MVBFcTbYBt4uc/iwEtvZ5G1X8IMywE32pgZ5GLVr3OREpjjkYfmeJKPle7w3OI1izNEZKocotqdnJHms4DfrSq5AngbmkUnlNHmQIBsePtjslFUCdNo7ifLqKRWofSowggs0UjYmYSfv/2XU71SG9cOwR12M0jbfdOHFqB/jx4vmwqtRDCtteXCcNH03qru154+poleuADXKnD2v7s97lbzzNXXhSj8GLd02xpeFSHwkSLd7A++DdwAEyABryJkI+YQlLhva2y9Bz8+BOzrQlc6q+F0/cHP5v4ej3yQuT5rLb/uhInkU631BllZfhqVNEi/6q/H0mxhPQ0AFZvZMnub/ep6O2B286wGSV4QXCcD2NnGjSlRuBCaGwNpxGejZcO8QmJh/x9b9RXHLFvEivWzzrbWYZaL6xJLckzcFiZthcykJRpe82OFA3h+BEY8WWQJX/SUfukDwrkmgLjmET1x6KB/FrsBoLCeE2au5pDsgiwmbtsmq/0yG+4djl6tJhmNTHPc+PoWtc0ghcnpZEafq5UzAJpjgPMHj43hILG4mwE1z3iM2UkbTyRDD4PWLIud91hL5MaENbIdfpLAHetoW6dterzBNdzw9D6seeD8StifR5eZuXe8Py6pN3SfjHYrDExGJVWT8URXcVgqik4I7Q8NK4Gjpm/sMQ6/X06C+QFhT2rT+VH3vkkCIBX9gj0mF/iE/d1U68KapuyThsm4x8VL16x/EqaYhstNFIf3xas/4fe0oJgwiea6V6vt8y/pD7CxE5AWgsncd9QTR1MXVl1F7TSRPff+BxB1CNak+4wxDSDFdOu7Eo9dryg17SDSq8uIyT/2B/402pW8qYjuSlmIRCYh9iCTEW+yUS+RMsAFhaRxDEnikT9ip7+NYT/Uzch8sun+ucmnMeRTVEYqGfG37J/iajPP1UVKGkFMto5BjTSyjX5oXvzB6GSBHFzH+GrANKm14FnycbUAoigBtX0WwhkqOQtjXF6wvQTt6ljGbPAimIFdAOQbRW3fs+efR+npT2C8RBSOmbscVWG88WAUVKR25t8BJL9OOlyV9is6dCRyz6U11s2uq7jTwt0PdVO34x9l0LowewjLCxuPekYjr/SKn9AxZCz7MJgBJPATsuXO4R5KosG1/4cKel7pSPnIqJEQsVa7mj2Tl2tYLd1lmnWGV3yi6JNX2Ts5XQBeb9qvC0lBKtL1D3D9UItm2cofHeLq2d0142LMe+24bHqwwWoQzfkEFm76/VYCXOtyKrbjmx4NC3EFTIaXctny9p+gRGX7bKz68O2CChmtbfo9O5bZ3Lpeu2QaxNgMMXE1Azwv8luAAeuuxwvre1y3652Ukuyvrxu/v28oeelen7oMTgEI4N7V3wt6VrkJQUH10Hm8YMEBEcVdeJ1QAGBdMwAX+73fxcx2X19OnbymxxJcXNAXsnDo+T8nF6dtfCb8xOiT5hYD4V+IEB4sY/ULAEGoV/UJA3Jf6fBP/MJNjuiRC4lsN2e1oWu8hKP389SDV7j3wRxMzhpXv4kcn0ojEUgOIteXAoCclCWIDHr+JYyPxUZ6iENIH48ebgadzr4UBZYefvwNxMN2H3UzQ5+XbJ/8EhGGuFgplbmRzdHJlYW0KZW5kb2JqCjIxMSAwIG9iago8PAovTGVuZ3RoIDQ1MzcgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnja3Vxbk9w2dn7Xr+i8ccpqLu4Ad+NUHNnyatdV8doqb1VkV4XTzdHQ6su4myNp9Ovz4UI2wQbYM5ZTlcpTD0lcDg7O5TsHB0MWbxdk8e0zEn7/4/WzP73UYkFJWZGKLl7fLDQtFdELTaqSCLxZL94Ur2+bqyXnvOgO9e54d7VkptgfOvtOFM0VLX67v1rSou7a/c43xO8vr//2p5eUqvHgb9zHb/w3xRa6rDRxXyhXJTVssaSl1tpP+zNhzDeNSTQlZ4tRu/boW5lq3GpJ0U6bxZKJUviGPyQmRiNlFqKURExnNWQ8niiVEYtl1FKS1Mxvivov4IfSxXVqINeRgolaF1/aH1NkhsEozA7zY4JurUut1P9ZwlPd/WIuTa5mJ09LlSWFeYqSwkU5RIZmhCsmw3Nr1PDPKRF8s6yk8WsRJe8pW0C8q5LxSOhe7dZNs37u9eLuvuva3dvk+p02fZ8SUVlqtOFlJav5rYZMUGz1qGXYJ5bm+PbrzEZJUnyBnozNbNfXzg5sL23WhHFF4MSHK3RuvAm5re3De/8mMZdRJVNjnp7Wfy4KIP1nQmiaxXZFKSvAYPU0ddokH61N8g/UpjS12LmUQAhVGqH+aGo/i6n1JTGYzpZRKyqkIEGvxKBXtCQMDguD9/5JUVoKwRavt84/XTFd3CfdAA39KCEllzTumHMI2imbmXdDI6Io1SWMcTR2V7+zVDVHyLhhkG4pinpz75950e5mqa1AgIgH/GuCWDhpeC9n1x5PLcOolaji0T/cNrMUMUFLKVncqb40k0QnPdmt9d4KPRhTXi0VEcV/prFCPzEntDTYi2iMlJWHlZRP5gWnrGTVhBfWRFW66Bzswd6tAsU/E65WLfiEh85v49ZJ3qZr7zYPLZDQzgKht3OMFOCJqSaMTNrhEZlC0ZKxCSNzqjzMpCS2bCJFszZpPKMhYDs9mzFt1XuRduwKfANctMxxhn7v1bjXYbaQlXZc8KpyBit50fx27/Dk0buIHuNBNauyUnAHltxKQqe5dXgSwhP2XJC0azTGSgcpRZCOnE9MW7JX65TbkCXhDrQqExxur6Uc8lhp7ehUMCbathJEzdtrapsKWB9SBXtNPLAQosSuYQwsNXwRaWNdHGbXddZ8ZNcnKIDCXua49N+J7XCCLWApwBRP4r+kOlelhjNawmQyCEIeYy2ZqkpFrePH5tEAx14F+Wh7wdidsMOha683TQAYx71v0GvJMy97h7dBCH/49pllYsnDRx8RDW75NAeHmXMjHSGShxAJQWLfLbvbQxOejw/Hrtn6dh/a7jbAvUO7W7V39SY81pDuLPT7Oq1ZWFg5dYLGlBVXC8lh84A2nRK9vILq7Q+YCdq92tSHtrNMebgy1Bk0rYoPeNfMuh0toewiHjgrqyP/ZwA0+KTfl/MOTpcEkjyZifILMzGiAIZ53O+LWceFOBBRRdyjxVTikvPirNQwFlFHt7dzs0lTCsUT61qKIsQ3p7nPw+NqwOqVmXwSqscr4NsIDMIzVHocHfBLPs9iK9jeiEY4YyllCPVB5M47tk2Qp/1u3YbwHh+Ptf36cJx13AL8k3TCil9z2LPf+TOWiKFP1lWJCm6gSkpfjjb4U20eS1putyS/TJs2zkU9XjNERYBgznSQI77lQhb/dmFCCUAjOI27k4zTxs7Wu/UcNdZVM1ZS69628xvFmLoQJJxYeUY+PIafsFKIxCaTfumjpPQqrOQKwYvvmi7txjhnF6ESwo5S0Cqe9iGhbQAcwoFMxYNDouksguZ8MWoVUFdqSHSDNYzGZLkxWWLM7hJLqa5KBv852cgslAO/rh0Yc76NFQGZHWEAvJ/dPPgP+0MPhpt1/6a73e/2h613eQE6W1i3bnd154IfvG1DmtAjRbxYt4dm5eHerGpIBL46IZGwEl86HUlbvoEVsEeAKzQe4fncnJIRcFydzSmurPo/blIOn6B1YtJIzYgplQggfbceGNQePKpYHfbHYw8rPFfX96vOSb+wdnv33De82R9yIIMXnzI6Au2cSUIkxbZiFvkldWGCvKlUY7n9Im8h2tRM2AFiYeC5gsQ8P1O65MYuJbOx+iiblEmCcaLyPNHFaecTSY9MiuXfEzEEr1yQNl5dzwQziiGA/43jASXzzCZgigUu4jKzU+SA2QK+9Onk5PJPohqT0+e8zIXMpOWuI+/ZN6+f/fZsSAZIhBtoK4wuOcL21fbZm1/IYo2Pf1sQvDKLD67p1koDOEEWm8WPz/7hTzpivg9DAaboELB8yhheK2iQGj0brlS85PAhI8n65xWi4BAhrJvdvmsiuyeKu029qw+hQXt8N3NqcpNm1EOuCxU5JGnZnkKSVNkALko057LMikdZ5j/P5OV/fSLdKtPDKd2/Xo2pP+ts+tTLuU/j8GlssGQz8VfEEi1c0vCcI5EoLSF5sPZLfToy+yFFB4J7RMoI7k2Ie3OBnRAKoJ5OAztEAE29ssu49U/+7/2x2flnl0h579KOh2PjAWNIXvUeeDZ4sYdKAArR9F2CLeAHj20EuRRMGVVyY+KhrcOSxGaIdBGo/dC6kAN/HZsNIIH/u/bLO27DWlx8jfc3Ntt2uKJFvW18k/1N5FeFBK7jMp7292UEJjslDCIIPEUjZ1zvCb+Y0iiV5G+WcxIRrxITzu2aenYmyWkJp/JHbST+AkIdoIpwkTGNsIqwRm3n/zo09ab9FGxd2x377yFbAiyz39xv+9Re0EtvD2/dw7FdeQApiiOaOmDogI4qvqkPm9aeOQdY5BMvx37c7d2m+egf3jb7bdMd2pV/3N917crBS7scM1oNrQDAQyoSge6xO9yvhtQjZGvjU0msuHc6hVfnSSf//mTO6SmpWfVJTVrcHCCoAQO/yYng4OsEKzkzMX06ls3RSriA8hoeNw+5sB89uPZE0F8sKyXtj/UppOlj54naNNtt7V8CkMNmBB5YhzXBq4JIyCX382w8IF2NMl1Yadf0rq3xMUSzgxdcBcEYBKfd3e63+7fNrtnfH/vk3eb9lYTiX7ebPo/ljw1Dbk0Mts70ts52u7+bpJF3TbNuwjy3zaEpI/5BzvVEzkeCwQ1H3BRC6O88ZwQlsBDZJHjljMv3EM9BEsxo1224bU3W2j+cLdxMUuHHUZSmp1FvvBDKJi5NKFF8dyVZ0XRzlsIukgCfR4t9lXPOUo1w7wyumFAz4qugEqFRFc937ei0uSXmOMQVh766l+Gp3UGY3jvxcm3W/vVmfnF2E20SWYV5vpqtH8iGPy+Cz48sJ4ISaNkIGSTxOPy5TdtHZ/f5A/UEJuPuAO0JACTCH5OV2jyZwx+0p4WwfHzwKh0X6aqv75gKgE1uvkjFFFXJ4PtHi9glODomJ+o+NJ+kOBACIJSf8JYmBdBniRQBmNLagylp8xuwgvXq1j9BsMVH92qdAYlySLY8VYJepala4tNzP7sPCXqiwuFXshgCGMLWspyijEScxABcK8RcXJhSwId8Rpx0GoqXirCnxEk/pQVIEjKXbPjqqVH1J4sM+uR699RChJ8yhQjKbrcvRIA5l1nRsthoVJxgz0o5gmijQqLntramXVEHYvFjQSwMm8ewymKJ9ycMgedtDczyMbQN4Mc/7ecP6LUsDcLPaPKUSYERcFZgKHB4mkXJrx5I05VNRRQ4vZNW70w4naJFgxU/zDolUZWgIxroh4TJOKvVSEoI+MKDnQjNJmFkfkncaJfIiyi5kDIXxLijrajPk8D3xLrYeLtaXEoPzxma8ZKEPRhgkyU99xsDI3QYXDEk9mBtUf+w6+U1eOILfpdolw0Z44n43KrUlfSrV5PV6/Nsw6jZ9dPNrOA2VpZWhbq63eUr8KwhTqUBTMmkShIbT2Vcg1Ezj1QwbO1/Iu3Hc6T99nuv8Vn6fsqsURpSzPVLoosKTsTVGzwSXlAzKaVVUZmDYHrBSqaqOXGWroALz1I9AYXEdEOEiXjEfpxSwmqccVlygiCJK19PIQK537bvHdx03jhdVDy/BZbc56Pt1oVToHrd3s+MljJsFEGcIBHuI0nbZkrKWISBYtsWj1qVxkSYNc015lVz1K5f1W6dX8fL3AEuVU8F1xIYJ0a9WXCtPhdcm/9H0j9Nvk+EPwvbdRK2U8gB0wnYHk8j3QSPhOEJtMvm0O6ysufn7CLcNaXkAr8Mv5+HdsNISjsmPwbtBu6+zxwjUTF3jPTV+1wJdjV3dvMyh1oVoQG1qjnIamLEygRAvqZjxCp92lWKic+SY1SKr/sLhazAhPYIaDzBH4BKH6eKOjadZ7gKFDxOExGxZDRRTI5AySWOMw5OyAnLHUpWxaub+doi6ACWH/V8mRKf8XSKAWaKuNPJTGbqY5iCbaXTTrnaDmtA5sKvMTn2tE2oM3LsoaDh7lDQiAwT3zjpu0E0Np9m50wDrpl4DlevW2XKgt4oSud81N8vbSoUAa5ksqznHgN8GED1HM3QE0Mn2/T3JCC2pWDVI1BwfIYO/TLqaZIq0EmzCU1tsA2A07zYb12Srl51Ya3BrssIx07sDYUEc5gdFkZcuSGCNRkygXaK/mRLDjWVeBumgFnyhz7SpoL9EMf3IRVsCbCpYEuiywTjxZ1rFLKK9syos5nYhxCfvr5t+p718Wghuc//u6J9l//Hl9v9Zj0i50IRl4E7jBb69SWGSwL7ZybsaQMH7jb1ahCjU6QFfgpb5KiBmELSf3+TB2kJ52mwyxLmklFXzPwZzrMfiaqS9ZXOn3K4PHaeI4Dp/gha7mOi4YgHD/trG8GFNJ17c/3gf7d9GX4+utOZPbCpezUMVO9GsVkvKLa4/3pj/2q8qDuvSE+RnM/bxGl5wBYLSKj7FSwUbn/vogLf++bsQOLs4O10gwVIC2PYqnchtN/rf14Z4SsMXEWKOBzdwYfqD7IswSu/BttkfBRxOrPx9Vy6qEPf+jTexyacVQwnOu4crrKn0sLvkO3of3zV2P5+t+573dzvVjloN+gKXIIC1IrWdZM7hjxpGNCpijuNj3+a9fTAihpb3mBGWnUq7Xa1F/7m6k1f4I2ozXLvmC+m+plQlTsJ6o/euv7u7Iv6fmUPum9Hp1nutOdmf0jeOaBCuRqXJcyw5NP45qyO/3XGIcB03GTq+4lxrncus5rsKERUx2P5DP9HtLUF3IwCtilKtxuAiERKd79hxtKoUtHY0MSkDUPBNRkz1BZxmriWUEFruL3hx/pjj/8a4qBxYacE/rR6qvolvEhKLcah1F7UYCwMN8/eDGs/ZGC/HqKnCfMq5fJpj2AeU9CTWe71Y4259yl3suEq1tPZBx+LJJdiz8eg1LoU/QTr2UKgGFfbqzLV5dLCs5LY381vh2GfQCF00qRrEv+3KJyvErH2jfFS9PbtiIj6aA14M38rklq9reK+CYxg81EGEJBCtpT8rPOk01BQYc7mhO8MJTzVxo2vqFhZFyZe6fwVFenLdqMeN5dugqrKJc+iTtbZCFflDJfcXt8HcCk4HeoBZoMxzdzVxmjMd5d5cabK1iK9S53PKoByY1M4rMco3yXCanvBshpnuNLX8SmG0ZMUFxNpaoW0J8ZLTUe+9NwCEdmTPdWZp64ULlWK373Q/I0alzXi8Q4BKGlJfJWN3f1N3bmCGLvtzbFrt7Yaz3252W8AxuxdySAV1+56TV9yRvlIyUnl7u72cuWK798eoO2Ty6cZWaLMlFTqeJxfZ73/7/cIiVPyfq64lsKWoIDrhA1Gsh9dxhUCuY2gp7sOypebj9fneBtKhQC8WBVQGV5MKoZsUT2goMW4vCpe3IZ6QxcaolsdOm3DrQaH/S2Gvu/2MLZ8/ljQZm9ETBk66UtLErDRZrKiCdJ1RDhIOyLyPrquvHmY4mGAgHDd4LitN5tREbKDxMMdBHsTN3ztwp3K/a4Z/s+Lbxzqr+xAzaie2QUL0l7xDiW4cYDAT6fd9uJCCBaOU+jczCSIqa0El1E9xGzZgc+QpC7zQgoxWZRW36VPHQjA6ajVF9kLUWzwWOTcSs/9Jwauv8qsgjF+Qoi5sBFBJzMucLCXnh2RVdQBzv1/ABlCf0UKZW5kc3RyZWFtCmVuZG9iagoyMjUgMCBvYmoKPDwKL0xlbmd0aCA1OTE0ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42q1cW3fbOJJ+z6/QPK18NuYQd2Czc85OXyd9+kxmpjPdD+5+oGXGZkcWPZSUdPLrtwoFkAQFUnaSF1uicCkU6vJVocBydbsqV98/K8P/r14/+/N3Rq5YWbjSsdXrNytWlKWCH8sVWxm+MqUrSgm/3K+u1s3+4lIIvj7c1Tv8JNb7dvvugtt1fUPfr/HLB2pV0aNNu8OH0Mys6wu27m6hM3w+UKu/18f7aheHq7um3hcXl1LK9U93XbN72+xu6TeYlHrcNPu39OlIAzXbnixq2j5cXOJsXXVoO3q0a7t7ahUXsa33+4vfXv+AK+bDgrVThXScFny4q3bY5s/f2XLMpKtLocyahZ9cwr8wFCtFYdxkvL/kBhvNz4DbXOq0k6BO6TYRBbcNsXVPTRjTWVKkLgSbjPo2R/zV+ufsSFd+Q34tyzLPDc7F+uUN/aZBaApnSv+TMIUyenXJCi306vUNzLDLzqB8g1Gzt5nRgHylVpei4FxRqx9DKw2/OQMz+VZGuRUvuHbUKL9PrLBOruCBCjP+75nN4VwWhomUjYsywEVZcGOeJgNcwAIESzvxrAysn4Moa7UGddyg6INENF4lSCe7Q3PdbJsDPvlwYfkaFEvB59d3oTFo0qFrro+Hpt1VW3pW/+dY4Xf6Vu1uxuMeQD9BozJaY1nhpCBi6+22eTg0G+jp1Lqrb4/bqotk+GdvaJBZ3TG2EFyko/4f9nj27etn/3nWt7OyYExBO1WAvq0298+ufitXN/DjD7CzwtnVe9/0fgVbB8wrV9vVT8/+SXYvEa1hKNyyIBEfc1wfq6sDUbQypXN/1+JK39NKwYIcFkUEdVNOhvj5zLxcuUJxm3bydg1m3N+33vq1hzt64PfS6WAefYtqu607+oLWFORCyxLkIo7x0LVBjLxx8c+2NOzGC8pkHi8QI2nQsH+gXERXNP9BIrfeSEtyHUqvqyCj6DesABmdSJe1hQOjoJUsrAxjvnyzbHqhu+Vpl58z9gRYr2VinJrcuKIQlo1t04uLS6ZNdkgwK5zxZMzfz0mRYAWTNiW36oBNkoMAkTABe1p6sD9u8NEdfRvvCn5/01X39f45elBHfnpJ+BwvlBXpzBk94w72E+jXygI6kJ+jZ8NQogA1T/Rs1iQK8KUnHPq1VOXS4kRpCml0XgrUiCRbGGuBcGUDOYOfU+N2vbFPFnTJC+D76tIU2okFIWIAB0BVYSFl8F2Pk8jfz/FGoK085Q0DERAavA38U+syjyFQYtA8eEG7q5uOPo6UvwPEtq0ATzH0HoY79B6x3aGrdvvGe4uMS5CgCk4RQW+Ouw22GwBkV4MTiIiu/rUUclePcCNb37Xb9r7tHu68I4HnRNAmQDq+vj7ubrahT7so5+DAilK4lKRvMlvsCiEU7JFTATrw3I6DxIOzgR0HVfCt/nVGw4XhhXM6nf85AdP3d+2+jnD2pu62HwLi5RNLPqw4w2tuQLr0yBGAvQDX/q6ptoimhV2/PITHKV/hgTcZ+NEMLMV2VeiAe3MdAHVWGOMqQcRVqVNivv8xZyQV2ACbQ4ULYg4KOlnoYAHAAOUIAtWwaDbGnb4+NxE4ZMPNyUwsj8ACdwlSAbNCcAKf4jb8dKibHbGX0BQ88zGRQf3ZeI/4nB7v29zeMkAmzgS3gICP9f3Jtx5RO9vjnn7b1xvQ2QDh4Hv7hv4fyLvDp7jh8LHfcPhc/wFwEIMv1S8Inr56G+Z8gGhs0zzE1m+8GtJ8UbNHs42lDNcEPLNytKiyLDRsTeAuCRgCBBR0i2ThmoC0PT1435AOIOvugYY/6MsdzAHhZE2LF4OsOrJcexzlOf3kNwH+hyjR4gru6ZMnWrj1lXd9RGR3G6j91/fPBokCRZbapuTb0ItC6mSlwoDQI5Adtw/0AIfBAAYKAAE8JxoeivCkZPa30HR/fHjYNpEXVWSocwNDlQVz5MIME9Mp4h69IOQVJAGej/h3Od0FiNyrfQiob1v6IXS7rjwEeRsC85b+f99VR0B49OUsKyXT3iYmhLsTVvZrlBxYr1ja/jefKlDrl7skRyDWbdfcNhTT4PLvif4MVFUGjJZ0Idwni9l8jBEQ9G3CgohlMmY1xkPS75PAaxsoua9g1D+GbAlZd6l765Vj0tUarF3Ki+jUaQO79nh71x6zocWVnyvn3oQE3n0p/wbbxzWMVQI29q2KXtEH5K4AkShdEntfX1jEFmDqwGFfHyoyi5wwrAuRBXx/qNBAHbwYckQHDyR94B4hzvW2Uq83F96X0SZ4OYWmHhtbxMb4raJ/b9AIeJO3BBFMWTDAcT2xP2f4d4rOsvmQEQsgcCg4Z8OwZ1GrRd/ohg4fz83g4IvgyQxsJmOEPFmMRiWEL9KOBjvkpAjmczkupPONaJQcmKtGkuDV1qz/6rfm4LOCuFeAhjqAk6AhSzQ6tMHuZKdmOaRKCVG6GTr8ZWl4xTAVJc4LgsgKQjImBEOwM6NWv5xhFHheUJkRqc9P7BXAEzSDIT0ZjMwoaQPmIVjxC8QJ9zE3mFIGYik05uVcoWL+K5dgUQ42zgJdJRgO9VkJljgUV6ARLg38plwzArmmU65Nk46SmfxWXgFWteuv8r1UOauDSA/IJYX3Osr+ZICRfk17/4IeADpz7P2Z05vF6f0CF9aek1kGxp8Lfd58nUjtEsH5nZEjLumUCAjEfLAdfcvZYNtAVOY01yG7IpgDC2RLMe0+FSKF8g2xcWmo5V9n+fm5u5HPmHOwtnPU0a9fhKKEawZwJKz3k7Y4x28wnJKvRkyEBbE5dpuE3U8zmyfUiS8ngNM+/3MmWkcrCy5D9xHjXQjDEWVIgoAS4fHx0AJoFoupSOXD1mQ86GPmTlmUi5BsKjD409cZjRK80ODcRpiO5TGdAZx2qQsdzf3m3FmU0wUDQzyhfRm+cAh0VNrly+dZ5o9OGDpHe0LyDB6SwXNuaVsxjsFH7a6mD7u66k59omAW+AL4g4lCukWfSMtMfOIksxzHigTfLDBMgv5wPBCaY1iJCuisS5iVHI0Be7gdH42V5zJXwgAsnggBJgKVWr/aEd8azCx5hYDQvNoc6AvGrATOu8PzRTxnYFl8oiNfndlpCTC5xNgiL5wzB6HSgX20btorb9KuvJVdwgIjenBECzbzCZJ3gHhmP4V3SGEZkz3HXYM5iu2HcZwt1x/rLkTc1X4+/jvMuSWAB3+aRw45r8K4HAy3WRCcwXCbGBL6g+NXuzR2PoIJ1Jum9tmrA61P0rHUEExbPG18dwGCBgo6u8yXOXo1B6PLc/QyZsZjmMLohODrec68zG8l/BIyuT7ZFOshcuNcMm4KMAgJ6l4EtzCxmTlYP/Wl2RSqLUqdnF399wJceT1zuq/kggtGdzYHdtm8Tl55vXqE8plPAeJZcphPEOZ1EstezEgPFQNLGvR4KGphJMLluGqlDFUr9DkmKllfcVJSpYn/tFBPAr8u1xLYspDSpZTN1RL0S+FgJx24z6SXmE9lSxZPgYGeobrEf63CcvOZrhKzW8cYhJZnTmQkIFUDupjQtYwU7GOQArNy2fsZ9H5TmzCpRFFA86guBGSUz4F4ljdAymOys/ZyvFMIJ5D6hCP9lvSHPomQCltIocfH7If6tqOsZ0lVUHZUBVWOU2lhU0d5TLLD53aOa1YoMZn8cG51ADh81nZK8hyhg/KcOdkzmJhg6bg/ZmCygb2XGZQ8T7KwIC9ystAXQQX8YRooeVLcsA+KcDPOloPebPdt2Mi/hUId/3Tb7+mo3g6oLKwNAGCcCPXHpF1DDvHQXLBRdQYegC5XRACskE6mo/8yxwHVg3kN/JrQ5E+JQiXdsqBI0AKddp7bGfWonenpAv/uN308tOewWH/TYEhWdwFbNNWhP04Nm8aTbBkPh3L44eFhdPiaFDxRt1jWdHpGh3lTIcKhxEnBkywxMb0BLxAM6jY8HRtYWabnveBAyG3AD7+3gZrYsZnzIpKdzzDjQcqE5POpjiQm0ACbWDrC/AkpoiMYPDhGIJ7OkttwxBeW6HcD/vcxBC5lf7ze1/5IEoIEsILIg+N+aXGwF8IBqrNuITeMWbjyqekz7grtsjZ9LudrFnK+APAMD0fs/kAnJHI326q5j8W0ifrLNR5opqJlZTh0uWSgRQ7hoCxUjCO/wwOwrS+qCgh1emYyi6oXUhTIHz7PulyKgpVYZqQelaM4n6JwhZHoxXE3zmSHcoACixSMeXzu4SyiyMXTKcXo2HmKKGaPaIaUhAgpCRFTEmIhJWEg6gQGOzCMjn9mSiKMZUXBpVhOSagvk5IwCQuzUElBuIOoDPk9Di2pTgFY5U/zsRhiW2VjLyMKzUQae72bsaJMzlsHM1sijUXQnxxB5QrTlPHVeZgsV49Llp+UOGO2/HI0wHdzkZGbFUo8hB9VboAtL5gIZcl9+X+ayECFt8DkpO25826dPe+OpQ6RAsZBnkqbDk2uXEUzKkcVrqKvcMUDx15afOVaLAABQLU5BEvc7ggGq4BO0Tajnwr9HrbVrgrjUvVq11ShgMfXtIa+h9DcHz2j3d5jLqMHhURqwBFsVMYByNDvOXE3XGPYIKaRdagdCok1g4k1KitaxmEMILJRLB16DrD3tDADO+ImvQqqKfymedfcEFDilk7ZzXr/YX/w1SxYrkq+HougYO27UTVhDBfhS0CzMkGz9NMmAOiQJopjhOVeR1yFAA/Lq4c+pu9DHU6OT9G6AT4MxVS74GiPPnVDtMKH27NVFScWo1SUrZnzjajjPHfA6lNVEJjgCbn7vAr2OBRW12mzUMFOI40OWAM8GKq+h1KfHpn4Ck3kTiyqqfZ73M92V5wuCwLEgkMQG9m9sKwSloBVFbSsdAiJoIFj+ZtdGkJ7zFPOD8Hw5FN82hAOuj59HTMXq3Ra084dbFWs33+NQK2lsHJTdV2IxkM17LaugsqJoYDPZ5S61tf/3d7RgwePkj/sm03MA5Ctq7vn1Pl9VDQMs2vCmize0jp07ZYeUE0ci2g9ZrWCPDC0qmAMN/V9iLR+LbnKREbcssLEqwA3Id7BK2A+DtYOQC5WE0McH9QRHlGgZsok4A2REpYnarn+ukVCsTQZ+DTnVPwYohATx+LnGIdehrhmB66ZMlxPUP31BDI5ps/wwRjVcorLghM0KQN+zJ3ZTuNffuZMRjoHrs+mI9f7Q3NfHXLFwVxbQHIq5lwoB2SonCqaVSfPZRCk85VeyWBoE85dblAK+qm0H943ERYiI8u9JDoMD33uHz7dV2+jhLoYGcGHTdt1VMVI3+NpgVOwEqyGo8fVG4qXsGsVLg86OnmAfycC6OTgTpz0C+r2h2khGzJRoT8MWSBQlv2hO25CboE7coJUnFr1Ps6t6z8esELe08xDnSk8hr67TUxB+COyG8IYCBaWQncsD1I2JebvM3BUWLOOSrlUP6ZFUU7HlPMZYlUOxc7oMLByj9Zl16HyFvWyRX16T8ulWr/9CC64VOEyMivxToyOCZV9c3OMF9T8ZIC89s3Hellira81SYZ6de7QW0H4qSfzn8uoMI0IddonqsakAgLvz2QC4RTODzg/cfH9Zs/Tb1VhAa5N6Wfzd0jDFT/td0YhkNr9frwFUxIen+gMPEuU8Ql7IrnwJ1FP2hOJYEI/bUsApvr7PJ+0J/zL7onUWFTJHrkndElTxUuaGqX/uI1FkR6jj80ShxgxnheDTWrJIJl4Q2CydRgsRKtPQcWenpKZ5aa3gF5X7+ub5nj/X6HNAEH8+Nvjfa/Tu5vnU2wO0/7RZ0/x7sNtVxNY8Li8nzVzrsGxSixWy49GERgl+PPgY70fo5RyHKRFwNNb1G7AOJ5mHwvyGAsyulxXN7d3dIBys2iBJew87uWYxFfnTuEEWhUAf0mvs3lasCrW2GmnKMILs2kNNoCfzDabcmJToGXX9QD+LKZqT1HifoIHg+Bc5nxnWRYAWkKs3t1QYO4wZbMJ5fBlcBK0VW505Aq+1UfRQ27exUnd2rtNyvjjTgcpx9HejFvCA0Bwrr9Gib/4WKbtAEVATzBbN9SuOt5GRBtpSY6nQrr60A7jT0JMZvHUZNgu0iYMrybIXHjsWyToFLbHTJMvI81ggKKUDtW//+iQMjz7CXfwvMuXBZ/ZZyu8zP0jhbpARldP7jwQ9wQV5tt139ArVl/VQ8/owulIvMzUnKXrY/yksM2sv7swmM9AgweMflf74Ac+7trdR5AWxfyuQcPdEUiBtlv6/V2Nv/krgUsl3Xh3zOiUewCNAqMmFCX5LrDYENN5EMv4OGugy0DnEi6AoN6BF0m6P6mifgLqTAzcYzM+A+6GdJnEm4ciJeFsnZQvFWbTTmV5djbooN1kwXwZOgGHsSZn3KNDAX1xdjKDmSn5lMmM9jH4ZLJz8+AVjlMOsqzwhFcyeLsIckvhTbxbR2JTxXt5D/738K3ZYbVTvtJpOLkjcJeQ8sMc/DfKJ53OLQ89mlJPFBChIPTU5ukSgk6NK/6ETRMaZMqIT5EQYAEALvaUySzIlOZPlBC8s2OseayI0A1O3Px2hz7v3H0XCe5c20dZkezlpAX7JpXzRUU5A4cyHERYEbTSdESN1nh6kY7hkTLwupBa9EcDR7oXOvP+HLtwIlLlXv6Cr0UZw3Sw4CbDhhKPEPEFKvNntMofyHGwpjx5ocN1ZlpXqE+ftVycddGKzHKNLRzMAnFy/uBqIP3UWkg+7lyedhYL9k7ZPns0RR1KsLMnP2Lu5CenOxht7mjOKoAExO7XXiy7OxJP/HPTh6an56XopwQavbIQbPG81BaW88Xz0jhWFP6boXxZpa+ZsJOaVJUNJ7vsyq2vaRh1DnwP2UgV7zXDit+0ASO17wkxK8zUeL48eFXuDv4lWxGoT160gkWqjoW0YrgGrka2QKzDIzJhMQzUMl6ohp+q8MD3CZfNVS8jM06FQ5SE71kZz/9t9i1QRvmXBxjTv7BjHsv1y+MI5pRJx/enR0AaYjmIAz7QN++ZW3DJtIzlzKhQvBBY9jwe9/GljH2pAZqP+YP0QS4mzMP3zcjUqsyXK/IeLszzCS+UYuokWQ/GzNohTreE04Ettd/czd2y86IXniSDjezQPBlSiaJ0E7YG1+RLwrHyekuUDAXlWCE1cUwQnAKXpD+c8YNsK68Mt3P1L8qOwuwTA8SYiLo3xnjKv/BhdsBjZkstlctnfAt5E636U/+emnTzLey98KUwk3jgsYU5YJNsJhs5LSF3eJUuoXPOcX+VvT5QKJZeSlu0fNPZo+HrX76UeeUH1Yt/nZ8dX+cxol7M3zmbLHLeCQXDO7vfP+Y2XCpgBFvacD3d8Ckv4n4HJxDOnMsFOvdHvAfc+4jqEB12cKIPx8PcjWsa4N+5F7jgrTOdXwrsMxadPGI5gD1LnlnOvHjluMo5BHXiaVzNosGejBczRTLa+GR4/rqCkXnqTGGl+Yw9P6HuEagxrXzigkEAIX3pU0QP32bfg6VhpqdczZ1UzGmg59IUvGTLDGf41rqkhC8nY0DO9KbwSMLs3FQTE4kyxlL2waJwK/80h3WfGpgIWDlTOUpPY4QRyeUjZXExMmGfPO2nCtmlY2Km4iiYvlxoiuFlQiufv5Z8mSzpMz2N/AxPM3/B6EXmrjSol/PXyS5tMdTrMRV1HUsebf6xSR+fCoXCms2Ca/YY7VWfqb38jEqdqm+X51LWcxhwxy6lM6PWJzQkrNZgxzBLCZEa+zxGTwIL5DMYZRlfABpl2S69XpZnuCwh1GD4bsC+1SDJE1n8KjOHg81kniHxOn1kgxi16hk/qSexDG/8sfJEl1N5h2iLsxWGHb37nUWPr2aMpV0ua53JJyCGfuL9fM5twQCGmmL0TgCwF3GYIX1wyRm9huBSlrC2IAmsTFp9+/rZ/wPpHh7HCmVuZHN0cmVhbQplbmRvYmoKMjQzIDAgb2JqCjw8Ci9MZW5ndGggNTAxNCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrtPNuW20Zy7/oK5g1zomn3/WId7TkbR9q1N/FxLOXsg+wHDokZQuJlDICWla9PVXeDRIMNkDMarRMnLzME0Jfq6rpXddPZ3YzO/vKMDv7/y9tnX71m3M0YJY46Nnt7OzN8ZqgjVMLTcvau+KG+UqzY+T+35Ornt98dun/12sh+V0Yohw90xuCtIpqr41AbGOrvV5YX5dW1kLxY7LZNW+8Xbfe4uZ+31c06fr7d1Zv5Ovyeb+7XVbtflk183i7Dj3ZVbrvudV12Y8Hr+KPebxfztozNl1Vzv54vyg304qZocS19mGHl2ikiHQ/g7ptqewddnYxDOlXcr664LT411cJDB29291fXMFpZz9tdTa6utbDF6+q30G++Df+rMGN5V9Y461evbYJzFiHgjhItWArG96EHY7rf5d21oqz4iXIVB6QjOyGYOR1Thk7p9r2LIC/DwuorVszhNyuqagpoYTRR2qQT/Bh6KKAA4gz1HSzhVsyuGYCjA23R3LhAMIzPeq3+hPDw7JAcFipdMia7cMw8UnGinyhlsGxthr8yA4ceKYSa9yBUlFjOPYTQ08/9fRabnAhpZ71mfzqzsdI5YpVL8a6mdkoBU0o+oPI6Rwq9aRS81nKwvZHO3wJbIBNBR9tjIsuIkyK0XOyAPWxR/kSFXlTIebZokZGFLuY1cpVQRYNUtgktd+0qfN1tY6v1OrTa3QZQYa4c73BBjHbp7DmaMcQaNxPEqYhokcNAb0lcaCJsOjAgQCpe/Pn+fv0pgPlv5WYzj+IRe9V3sfuPf3mGZAJNJGGJ/Iyv211YXxAy8KKebz9ct6u6jM/Np6YtN0FcdZuPdGV0CtRPVNFRCGB2MZgdO7DhBkojiEjHfY5gmKJp53UbROJhe1QxT+DC7deWgCAJXUHmfsB2AnrX1aIN2JLFel6jLPRf2hWIyTxvwdd/zWwh44o4xvwm2rCJPMN715QIChLHgJAIrTp6sDMYwAD+oBVsrrHIoIyaRCylBCGBCxz8ZTbOiCQgZfFtG1bU7IEWKq+jAOp5fOmJ2nREHRSBLX7FP2Xd6Ttof4sc4CXupsyKJpDiIPBn17ByLlSA4HVOQry7du5ACScoLRD/ungRpFqbHSCQBYBti69zHPcOPpnim8y+SAuNDCBJdQgX2cUY4FSQib12IF/pFYL0T2HiV5nt5LBTWuJOGWO6bpxn5ZciyolZr+XugSTGJZFW95cyTWGcskeQ2AleKKxDENtDC8/DzTkvvs2xhiKGyqARzYSWlcSIoBFjqxdn9ICBtgC9Zo4woAfP3R9XZV1OaRxGAYF80CsHNoDjVM42YMykipzxRJHfnNGSTDACC00AOHLHkMkOnVTQeWkvSs9NJkE+DGfjkwiSjoBmTXvUKB5enJtLQQ8tHjKXBixzPZzrzDTagZBlJwhkeQtSUoqmvbd259W2mbR2QWMzLtOh2wxxgJgXLkcco8TKYdfNEK1otChdfLOKMrkB2YvwopGO/zsjvf71SoEdvJ40eoWFCdLxvzsDlACdLQbL7eEKNevEjBKMN2YvQpZ8EK4kA31NB3B9rEBjobXw6u2zX54dYAD6FiBXNbegUs1ssXn27mc6W8JHtCEE9P7om25QlHmTYj178+w/gqM5EEDdWN20yyMKT8xy6XWaGJGFkuZlIYeR1cMIRxr0UgbIAPsHkOTphBXLQCwL5NFVNFmbSSGINhG6w2A+RO4RZsz9AJ9uZJH4KepcPVgkl7pvz7K8ojIWzAdNgAVCs8U5zofXnJ3AnjcsDjKGgcAHZk06jQl8nVVTo/CASqboZw7gGZFEQrKiBIPqlz3uWogl0GBrw5fdtgwv8nbBQUQx1B4DHJzjcw4MJTlLO6G1CFbTv+/XbRV8Bpi8nC+Q+1cBpsVuvd9sI1i3kwpKgNpQUqVTvD4nfzQnHOySpNMNAvBp2r8Hc825MRoeDzxY6UNAD6IfYcGEVAOEt+dmcpxYxy+nDHrkZb8Nv7VliCmxomrDu4gX//u/yjqSTTVJLYpz7yhmYB93rwUFq3CAXaDS09iUglXGQNruBhVG3voXyl3ApQy8iGTIeS52AWa67jtZYG6bDC9TlCtGTFibqLr4jIM5zjtr0zsgN5lZgbIFe+ys42agBhIR/RVPEAigcFouMA3vlOkPl3MiHAEP+Qt7qScuhACZjW6qG7oQJ14ceK8/npNlFDaNJgsFSabAaXpTbReTao9LGAdUVYIjVJcXyA4O+LUq6fsSOJDrvAIPIQp0aFclWnW4gUGaNuHBB5jaCtSBDyZpWjS7dfDC07AKNcSCSZqSySODKn2zAricS5tB49tV+SnAGKJh+CP8u60xFnABMaJsALMF0HUBIboeIR5I78hHOQI9OAlDZQRChAnP1ewSd/W7PLc5HQJMXegZoych5CeK1RzFcNyoECyq59smxk/8VsfQSzDE6ja0qrZdaCk2AZqTv5VxeJ9sWMSmTdmORQZk8bcRGSsd0iMGKvzc2f4hbHw09cxpDOVmBFkwbs5mEuCDW6vPG7To3Iu+l0yGBAlqFjSV0opQOszOeOY5ZmeU7ShykPxAvgImaysfCoRmt7s6UvC2057wNpha+OM2fD0wqU2ZNGy/8jhFfr2vy2bSgZRagK4YLOOoLcaNfPBpNRhoST/Pj6z4a3W3KuvrXb0MqRoMjTrT4zTFiFQxDO6JcUB65W/38BJwEiOBASlIaD6K28UHI8WihTqvU6J91Xzofh444sf5uml38e27MXl0NNkkcWDuJ8CaVFD11yUsJRIERNL+eZjsTbmIOwwPPL4sf9nP2+MaUUaCbYmSDzNS4VHi488kDQ+HIJISQNQ0RpffBh7lRV0u9nUT5+JoFQcLvo6sv0Hq2zdt+LyYg/5Zh9+RzUWUFIs24ByHbKrlfh6bfVxV6zjXZv4hRrIPQsLnJN/v72LCEJ5BRsS+olhWUejclVHvDc0zcE+EimHvg9wyQW7ZYl3Ol3FGhzEGWNdm2sIAuWrTUW+yfpS2POdHjadmDeggJ9OhvXvCim9DasYGhblA4lwH8nSHPQ8NAIF+dU1c0SqucwvGdHi17VK4wkZ5Mo8RlvA9SflCGx9yOAwWEUz1kXIMqB/MRvnoKAKd7jY7QPgCH2kHE/PCfxtAoV2qOC6EwrTr+OUmcqR/2MyXsfduG5IWNDIoDhxI0z8MhCJ0SPLMNiyXHoDzaQMOzpoVIDSydtBxyRL2WjoDbltU7mwkcIAqA1hPjikUU7wfCzkIfabn93m9DV5Lp7g/9hR0U5ZdxifN6MmQCzQnucC8ivVKeyyLkfNWmHM+WN/Xju/zoW9hkxjyGWckOyKdHHHCtwDEBNJDu2WxABUXJUugMkQbmD9dbizk35sPUdBCDx93Cv3b1Txru1wrG8LpwOFa8MSpSFYHRoBO0jXHfMpwgdrKQcLmQXtDgZXd028Ne+zeeIZ5OclMNL8+1mFySLH2gTiRweHPLCCJ772D3exBkwj/kWoHpgin4jMp/BFA0GkgjkwxRM+L4I1gWtc63FNLaOe/vsnAjE6u/d9Mt5eRrXhSsn2TJ1v1YLoFpP4/1faolgpiJCYvtfdGBgR5uk2KTpQToaOc126EaneebgyRaaXThdRyWgJA0eTNe7ZG5ilDE6Pl02zLafmD5ibdlxFK9yjM0YwvCmBfjGr4ZVTzddY8B6ZSyO+GKNOTVJLYfjiJDZLxkoGf1wWN367q3f5utdu3nZVaodXA3dGxeh6eP9ZVOx26Exz8R56O/0tux4k1ifPxfiSwd4Ccg9rjgO9k6JeJk8iVJnY4+wQxqiwxck4Uz8E2oCyYC5yMXrPHEZZ1ublOnLADHgR1xLIBHoK7z2O8En3fdjJZIjHR5AZjZEg6BPKvs4u8iL77gGtLlJSZDRzP6VDCNEt73GTAPPVlPwNMhwlc/gAwpXctXZ7o1RTRPx5KjAtTbodQgnCWY5Fu7z/SGDUBUlnPfUSE9xz0QUGepOBIhqG9ByvZ2aC2zQa1Q5SqGYRWh+4uRoAG0ZIYdT3EAKJTDFokBs0O4bZ8gdb7vC8FcnoksCmsIVYN4prOx2Nl7fHlurByXd1+Cs+xyFsfIhPVumq7QCZ89/FL+NzFL32XUKQJb3shMeZLTaQqvokBpXOlHVgKAY5YH+ofzsUwlSHcuaTPXfVrjMlMTWZBABuWdHyVoXLrmbGXMjhH5cOklgbJ1K83QNxnAcNKUzvjRLvITz8ACoXL1gCijWV4osN749LRJCsjAky0/ppfnkmpYAxVdMHhH0aCFJoV/zyuKzxk14yJrn+yFKwqNirFcL6aMXoxvZax9vQbJNQ6hhXlodb6yoe8YojFxHJrcagpyHHYEYknFV3SnKZT0kgpCIVlZH5kGBvy5vyMlBGEj0uZT1W5XjbPkxA6iIv6pgIRUn8aixmJKV8mdSeyRa8jucWcf2Z8hek1h12JhDhpouTCL+DuMs68H6su9mPVWT/2gnV68pyk3M9wXx4TH7l0b07B8dBOWWxvsn49UB9zvw/qjckLn+CTjYgaFIjjEz+Fu/kIQskLNqcJB+/80bi9xNR/BB2cpa2LPLV315pZG50zlxygkD3lgbXqNibS3hzS001MdRyyJffgmpVdXsh2jhpocdfljZJMm/WJJdtLiBuftYDW/iQcHtLo6hqWyRGVvkkonCCMDYA8ZxG6ybMj8mBDY1HyYOhQzZQT1/ZspZIELYnEkQw4XquUccGeMCxlHhCWMk8aYEhwbDlAZE62j437C4phvkke80358jUmTnE9njZSo6cP+9AqAcSmB9DyCUWb2GCMEys6MuqSvAnTyEEWsJczP8lhyiM3HSyL9XqH/T96wzl2vDnYMOxxNkxMi91VxxKjIY4wvQteNKZoWFd88zgDgl8aCBdfJBD+hw/7PpHJARttnfx9kxZ/5L16SGpkaFtpfRHvfW7GT0vC+ecGoPn/hI3hD9qYaV/jqfJSQvqaov9j2A0pKKyO4SJNQT0qTdjFXnLBftDkwrovhuAkf5K1vRF0wg1qTcxPRBPr7YhdcKw/ysceO+2cxjPBbAhlt1hfUdabq17FRi8AsoNfXV1ftSy/zq7KMh+e9zGCLnLzu7l36VYyYr+IoXpqLX9m3joQpM1nnyjRmn/mMvhlRv/XI/FYsJcP5iqnWBGtutM4/QKvrMHtmIc/6TaWQDvMg7c+MJZ2ehmiCmy8Nl6wWN4Wi7E41qlifW3nWvLuNgbeHXfxXWL0+1hjyYrVbrO7K7flbh879kobeeqvhrtaptw7jGKDGZys5svkXA4IlEYTCYPnMDiVc+HFt7EytYyZg09Df4VZAzuq49HOeVMepEZXqt5GZwM2In4DGdPEoOqhojXWAkv6OLc8uh+wLz5YMJZ9MHixh0pCmP/wuF9eNmn7h/XkLxCQE1HB1KMwRNCx8j037VE8JJn/JHjnZ4UpKkFOZQytMZo7R4OnA8FEAG0J3nPks/Q8d7h7Q4p4w9S8qziWaXX45JnPcDogmWSbM+eJknbkOooB+8CIRvWvoxg5kgXyMHuxBqeESX720prkBJXy9ZHJKrpT3eP3TzG8CCbpMyESBOdTIsHJLNo4WEXMjeBt/IwnSCm8HCOB7SXKZTeh9yQeXsF4kBw/6XMMiwKFugFxzTObEUKNyjOfB/8+s0o6u7a+FGuyLIb3zp1honSwuqkjwBLwiPnJwWaNmVE5hmdg0Cv1jwkUPJSBJg7vwIMbrPv59DEhRzh+6vcYO0vBnnhjLYgynSfbcWPDYNz+tj2eQOrdrsW4Jgwz5ThSs78Bt2bR3crUL/q6x8uaFvsY+qTwXNXhy2292yQnJGixLJuqxrzxcixUT88f3QaprekAwLFQfb5qbKSm9vHa+jQufRAnFiw1kQI7lQLu2cCAwv32wzbEjbsjIXgcxkVnkoKmAUE+X++75+6w9hgv43ktoI4Emld51ClzEcf0b/SyQNKg2pPRfVqAFq8O5npiylJiOAzHOFiI9rIEkbzkcjGnYYtEOnB0EXqH08GukexhszN6yfRMWmLAEUiGblYxA+DPF/szFfFXGX70DpHBU0jOxXQDPDe7fb2ILeMJovVue9fEQXbTVytwEDYuheevWfqmOkpKe/G+C6uh2wCRJMiecNALwV/E7MhgKUGMqMLfnrEFFLQ1Hp9etJhYHEol0OjgxIFnSrsrU/5+ZRVmKLkWwbHEHxgzKW934RH9xts2fAhHv3zbbfhfbRM3E1818SYPPDoXB4wX4/nfu2kWE6A2ONcJkE/GYVgrJGFn+oMf13S4880d73zD98uyroKgaLtEkX+fvTKRZFQB8LXqigb/E61fsN8C2eKP/XaBpcD4Mxwln7zxCnQkGEvJkK/PrNrHt+Btv483thIwhjsJr1A14S9/zYl/c+byRGySuTzRz7MLI4S8t0+b+1njyqdPzkuJRrJJVnD58flL73EANZO5x8GHHsbLkoEO0bXjR8987Gg9iPX8TXCgjtX5s+KJogCB4VyCDpQXwhR/xkOSAc9eRpwSo+nStBjQeI4UzGHvF+t9iA1xHcse8EffNgnPVfw1B+burmbhoOyrtgkfIrNoZBZoE0O3cZ60KsJP5e9S9CM1kfOa+CneFuAwOIcF1v7l9MUuUnFCmTys8W/nEKmhvT40f4HT8ePyD6fry2VK0kdTivrrfST3hRrh5GdK/q/ePvtvcDmjwwplbmRzdHJlYW0KZW5kb2JqCjI1NyAwIG9iago8PAovTGVuZ3RoIDM1MjcgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnja7VxZb9xGEn7Xr+AjhXiYvo8EAdZxDmxgZHcdLbCAkgd6hpJoz2WSY1v767f6IIfNac4lKRYWedKI7KOurvqqupsouU1Q8vMF8n+/v7r4+ifJEowyjTROrm4SnCFE4CVKcCJJIpHOEIM3i+Q6rVfzTVOulpcTSllaFZc4bfJyWbv/m7u8Mb9oCg9wulmvLydEpquqce/z5cy9Lj43xXLme9WLFTRT6aq5m9+7928v4f979/q/RbVyT8vl5R9Xv3z9k9IBuZ5UKikQLkN6G9djnEEqRabZoFd2OWFMpFd3Ze1mXgMJwMhHQ1bhnzV3hSOwXM42UyMU3+3lTVNUZt6ONJCi0Dxjmrjxf0eULcumaNld5EszvP+vbop1/cIL6qN5YcRceXFMV1amBYwhpmVhO3qht9T2BOoerKKCu7bj/eBecZ7ITEtk3xCOMo5lQjPNdXI1A5KJayZIr9kEZRTRZCJBcq7VG9+KJhxaSWFaiYxKlUxwRn2jX10jjEWfGp0pJhKWMYJds99BSa6lViHZhJB2plCx1+kLJ6S7YjktOt7t39y9WRYlCPP2zojtrZNSdedE6oQ2c+1WN2aCix+vLj5ctGpkWgMvxlYYMIOT6eLi+g+UzODlLwnIAuj8ZJsuEpUpYkxsnvx28S+3yELht2N1dmFY/j6iCzcSiE9Q4VrxiCqu0yomEJVJM+22b2btcsKIyhDHMCzNOPMq/q1oouuLCGgDbQnKMPFt8wilPJNCggZ5aw3XMUInrDWAoPP1BCOR/uEJQAEBPFMURNAb+bvIyNAMKQrkZkIHhgZE6UwLYq1xQjPLS6aVb/WfuGVjLqEZzpSkrt27KNVcpd+hqNhAaqA4IAdL0lo0ZX6y/iAC3BHryw0sH0V1/C46EfwEjvviySO9jXZEYEfR0cCyFelbzLewKrAwi2WXfWoWtf7CKifPS+X0z1K5CsTzNtKbZUKRM1T+TcyVTKiRjGLwA1xdy9/VnXezhtGq9nG++LDJW5RAjSudMGyacMvjhfOm1a13q29+voCRWKb8SwdMrFA49rG2mBf1dLW2HroLwKuobCgEclD8hBjpKEfmjxEdKCudqA5Chac4bi4CnCHEPoLwUNcDijAgDJUYI5VBmAxt/nqiOhHthOqOgz4N4JAFhFqrYLmfBg4mKZJewyd24IMIkikUrmbwKsapxLiC1hrho7mSfa5iTooN9fxYPmqXK2ewRKrWWw0Bl3k14hLAXKUmR/mEX6NCBj6B9D45Z5hZLHJgBevdhQ4VutmwN0AKcEq9Vl7JMdekM05xdEi0d0gr4SjlUadl1EeEtgyzDOO2u/FmJGNKhm6i82bLVbXI5y3gXtZNBUmOAf9NHXNIHnYX67zKLapXNPUA/rb8WC5vowrj4PKk/rKOKrRoajFCz1HNyo+Xp7slmIqeuYDH3JJ4Rgs45mQIYIwnwr7EBLPeyF+NUAZJ0YhrAVyiMX1U1/IhCjfMejrGS+j+kh5buIChaLtwSbdwIXfv1SY4tdjOJtX/rs1CmzCOXWpufswKV4EoljOXETKOfGo3VkbAkJhpHYz8ZqSI0BJCYOFQwIz9Pgb8GApK4zHMj3qaz/PKUZBXt5uFT93BTzBFDMm1p6+c+VdlUxa+N7gRVwiwLTr+1jaPva/LqfFW5klV1OVsA/8N6g8cQ4ItHG3F5/W8nJbN/P6bOF6hmZAuN9ReSTfRjMIkwX14OeZYFPgeCX4FYd7TOTyw5QMDJ3UA8mOrLuYZTUaq5ZfBcK+jwfVEVwlKkViPyXDEVYrn4yptKJUgP2VgqDqoxDEHJTOwz1McVJDgef9ELY2D2tEw1cGm6gGABokvhpH0CRjpXIhkw8GeSPE+WoO7BljUFpKGXMc8PgW9icMkwk+maZ/Gry59vGplPCRkjIrfEeGjCw+fQPl24R0mnYSk7w+/ELgoiNdIEoglFiBGKaCAkmIEBKbt7MVEJN5ZNj3OsqNxVaMMaTMBU53HsxGWbtczY5DomwwMEn1GfCnk5XzuAO3UlUl79ecWFbOu1tq0OLoqTaVVpW39v+pVCz4Xs2i5mqU3m6Wtp9e2oM7SV/Cz+LDxUXF+idP7MfOF3u/jVhCLYJSJjPAR9zsYfMf/vo+GRMZNhcSCtn6Y4KpXEQcvzTXEEiKD2voga7X63DbarpWBR/w+MoMGq7KhGaNB0bhfme9WWTCzzBQGGLTt2ltZwdoQmSI46fG6x/H/I+5myT4/u1VFWHQEvABIprc3gSMh5QiMGy50lSkgYNIbdsv3cJehq6LTDFHjRQAKId/r1apazQHp3Y+WuqzRA2IclLus8Vqcty1x5e2aq6rCLonxfZwqWh8FB03USCo2MHAH0notybg6X0f0AkFcErdnpMIBUOioJKVOzmpo2UMLiO2HABQxW1N9lvZuiAyjZbshItv4G91iMjH5VXz2AZN0jEkyZDJuTM4F5k1Z39yPVAommHBgGh9XKsAPw8PqeDwsnwQPk/MqB1U0I9VktBCB9g63UwnYiTUOz6I4dzgeb0BemJ1XsP12jAxjqiMxr4pjNsTocS5hRyqPEPOEPiboCf2wqCfPj3ryOUe93tJ+zKgXyeuOrLBOKIM0nhHvm7za/h6cSGC7JxLK0UhG47vgMAnl526Dg9dmgPN6nfsHTF4Xi0V+KFqz4eaUx6ZX7SmQOl94cGvHe3H45EU0umkMiD/gkz1gu39bhG6PkdSRSScYHBM3W3sEgesPo3e4lcozZA5znLKXyh68fe5TkLvITIDfw1XxLkI1NIIueJsgY2s7oMmJdQvxxyp8HHGOHCwSBCbIeATSmWm9S19sB7a/Csei2VhwDPj1sQMgJn0YxwM/b/idiIzJ55LbyHCFPLqXFwDj1F9e3pXjNbgsXyxgvX00BCAX7BHiwHaF/VDYE27eH5ZteWC6Wr7b3OZNm/ivbW2grV6bFjOf/lS32wNc3lVH9yXAL5tkog+LZ1HTFVKdUKnuNXxOFemn3qw7tQJ9AHKLYyG32IXc9o9hKCp9LOP5LkMZE+yo2WUm+nMfVdWz9bwRFM6R0TQeG4bGsTjIgKrzhD9+SIfTwbZ2i8ZuVvO5Pcf6yZcXZqUpK7SnUG+q1QJ+MXzwpA5me47qmDPAMSGZ47naOAoKlPEgnxgaamwNQ8aJKXmaWh1/hLyFPCxvefqABoli0mN2j8XfjRi5zTSr41VGNfgWcbzKxDEq44+nskdNNR9FZwOhbHPNrvpP2HhAenU5kXgUWlDguH9U8rHw/aMdj30ovtdHodoRDLwH3uM/Hd6Lh8H7Yzl+CLx/xmvrWHxPLL6XXxbfk2eC7xmksS285x28hxZa++CvFAB9AJywcqXk7iTJrysDzj858L7eNHsP1oAHkqZm3B9gE1/zjB8F4vrkYabABehw+O/2EsR1JtGAoxhGNncl6OkESbIrsDGL6WiS4MhAN0Gn8bO6vd2qRzrpEXCgQaRioLGv9pFPzC0qADNBj1Od3ThBBGsgaFeko9t0E8a4h7+sux6VR2uSLC0+51Nzn82/Dm667T8zRhUDtgdaex2zbXO0SuiTGWcIHB8b2rb1TmhsV4mNFB+720wMiIDoHQwav4FE5Nml14ALrqzDDWZ8Mbga2FULgurA4DQbQb4Ab0ZY52V1ud1GM/Is6vimlXAAsn/r6cueuN3ZRhsPKVFzgoRXqQdtKA3DmgH5exPvTbyKoJg6eqdN7N1pC892mZq2SnrnWsb23EBd5tRir+WfekLupIO68c36TJ17bDo8RRbQbE+R/XUw+bxTaqMMRSsUkNIoeWyB3v7POgQfLx0ZmxaHT3X1nayE6U0JUwCM5v6+sd+k4j7myTbm8e4mchsB1/nSXt0uVxAJl+ZXHfhnvr32XBWLvFzOiso9L+qmXORN4duXi7WrK/V69O6I8TSv66IaBthtcFKQj4BqAzYOlaVktCo1xLEcgcpUOLJFCtLI6X4YajAXkE5Qj8Tmtb+LXt+5Stq26OX32sqb9i68r1/P87ppy2312oh0nruStql0t6VwH8O2KGVWLFddpfytux4+duiNpi+jThkSIcFHDDK0oB2P8qI7ybeMTjsxcF6Y0fuVxvcxOmCVjpPx/3bi4IlKd7x/4uDs0l1MO8zx/pd2HrBTSB54HkRavvCJSTsybmzrpyBFoC0k/q1cTgtYwkST9GZV7c04AWERFnZ/N5KPtNOZTwJQCHVBJ5OPKJrivZMxaguMQcdvD83GQC1EhJ3I/swe9DGUycFpIDvBgz507CsT+yaHwMK5DgcazzKCA6dnZBnCQqBjD+t5G/1b/AMBOn5bfFRiWtvDnAGnhyocJnVT8CgqnmOrLoc2D15Gx5MYH5t5tywSaj4Wo3ZYxM7Y99aXCNP2wxlB35MkP2Bz56L+47EpAT2xgRfYX+lRDNJbMhTM7lbpzibe4MD9E7E66iqpvdY28CbeVVrMRhRK62K6MoCXKNwHqubVEKiaL6lIHF8DZwFV1VVLKCQkg5H9Fq5KP9nLEYDQ3tgrjjot8qZsMfvtEMRqnElT/7efRGqBqbvY7D5d5C9erKrBBY7mriq6zxyVTZl3t0Dmm8Xy8NeRaPrPyt+SXtWl+0jSznd1OCV2m8dTueezOgi0TwXzn9UJR4CUIdEKPB3ZN4L5lo3oPswTGQF8JfiSs0Yw250nM7HnQ1wUIZiLG4iGsd8kwySwHZDj/wAnGirpCmVuZHN0cmVhbQplbmRvYmoKMjcxIDAgb2JqCjw8Ci9MZW5ndGggMzg3NSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrNXOtv4zYS/56/Qv2moGuW78cVe8D1tnttcQXaa4B+SAus42gTYf1ILSfb/e9vhpQcUSYlJ04X/RJbEkXOizO/GY5Di5uCFv85o+3nNxdnX71lVBdMEiE1Ly7eF4YXhjpCJSsurovLUp3PGOOq/HG+W5wLVt7W65vzmTCq3N1W4cuyml8/3t3O180dfGflZrtrzn+/+OGrt0YWjBJHHcMlGKHUAQG0YLictpJo64qLFSz303YDL3Nbbpp6V2/W+P5ZGLq9ad/533/OisuZ4K5URLQDAkPt7cVm3QDZQMs5K+/x2wK+ADEzIWhLNwy7X9d/+Kft9e1muVlttne39eK8nWd1N9/WDZDh39y8D/fDDDjVOVD68ZybchOePIoCh6EocH7kyHhxkPOZlLT89dxyWBVIR2HwniwMI0wGUfxGhdw2O5hL6/K62tYPuFoVruf48ShtFNh2F+6tN9vVfBmGvYfv4e58fR1u3W03SHBisub+LqK2ug73q2ZXr+a7aj9luA2ima+qXbXtKAwvVuvrar2oyAF3jGhqCq0MUWgHyOLF7XZzf3O7uUfSBZpU3YRvTbVA9b/CK+MJhJvXm3bcVVhph4x48Veg32odHqLWltWf4eLBP13sNtt2YiBus6uabr3q4B2Js7OyhhmX9bqab8OIb+8Xy/q6mq8DXz2VSRl2CzKEwkVlXN8vdiQyTdgEZrAJ+nrnkjjTKv6/1WoFHEvKwMBZageBmRsHBqLoBVpSZwSmNQKwzL0RwHc0AhjbzRSRcVkOyWTcxUtJ6cq357C1veKVKDdrlJqSsKsUbAr8U4Gx6kVdrXdhyB3enNfbV2FgA6ryi9to7k6KkgKfnMVSuGnfoBmpSSaIpYOXXoeXGNPJdbgiSsZvbFPLXM6YNuVyc5OiGh6C8FftSrG0+vRpRhwfrIb7Xzmw+06Ct/P1TdWK9X1sWoxwB/uFMQJ0h9cfwBvNr5ZVk5SmNATMt5gxRZziwX3fpdnjxmZkxSkBv1NYIgzrTXH27cXZH2cdbRyWMkoXmoLtQ9xYrM4uf6fFNTz8oaBECl189ENXhSPKWPi2LH45+zlEnJjsbi7lFJFGhDXBaaEG5l+DVXPGyuapbOAzAzuE0pSoLCeGygLkZcN6Hw55FBwED3FKOUsOOBTO7jkE/TLQc8xiROh+KmuIc2FFniQbRKAU0GVBBTnhgzcC/SogTIPjOU34+7l6lK2CGXJw04AFiLStTq5ygua8L+h4vzoC+/pRzizBjkb9m2k5B9mMibmb6VDMMdeaaNXn7CbHmbLIGUt7AeT7Lu2dGVNUeP8Mju3R9Q6cxUxQQRgrZlyAOZpAiw+AsO48fDQr8K+83Oxuw3W9fqi2uxp8QLheze/Cl+A9Ekzgw2/DM436dIa27sISDbYyY8SYdvHfKOdJUi0BoRa9kbtNS8+uqZbv0a1pWn6/CzcBdykGHLBy3WDwaTIxzGkvoyzGK+UBviuz0rxs5QOUJdQ1UxAtLDhHDlbGVGDiTUIsAH0FD0KxQ6FoA4+d5hoHImiVaOBUdeNk0tnAbgbZzRyxru+UwboZ3gfsbXW725vEOoBxKXpc7yT9qC+SOw2+MgvTGcI7L/o6Y7jgNX9MsM45oc5mDOLleben8J5apc96FzhSfApwE0Jl+IylCiMBjvcGvk5QCRFagUn1l+/JI9aSIJAZwNLgp20bX2kGZGhD93SB/RIp0S4FIJjWt/2c4A32NM+xNnANqERRRCNzrs4xUV6kfIgjQj/BYgB/2c7RfZHcxQA/rBroMmSBh1Thfv8uZce42HEyMIRRG8uAtfqAnXDg7v+dJASMLenfjPQaxCDAR4IA5A8gI9iVj5r9eFt5F1olcZ7ggAh1a0ThhZRyNEC406w85ThSPhOUpqQ5ailNFCDk3kAE2WDV+0BwKN0Pvpjw+l0SAvbiQVIrxUzClkPvAN7NCDG+c+QJ4kLEZNgE2OECRMV4IS1kImCKp8Cd/VwG3aSK8E609yyRsB8Agxr9GNJkj7uBeRGD8tJwR/YQ4YARBdzCKkcwMp0BKNgg4B0iRry6gXQO3qAH1SZC9nOsL5vifcjCQpkFvEKDn3beCFge8kKGpsDYXsIK9nMlrGAc9WadInJIJ4DvTSoOWgjzBouJ3O5xARepSRxhivv4vx86AcKTzgEcD+OiL+2EqQpDsbr5IqYqDAfEIv4aU3XD6Bw5mZskPoKseShwlYZI1OhY4P9I1mYBzAipgJues8AIJrIRLK7xYZ4rFZAIy/maxfdYlrO0LQlB7rK4X859YciatgAHT698KWm326xmy+o95hEWK3q77afwvF4nqd17IzAoMVh4PLUAl3N0atHjEKRHjBPxSj5ns/SRm1AYe6jwA2uPY8UvoSmhIOtoxrtcMid4ab7IwCF4lgpsABe41pmU5gAPARwpopFZLyDlnsx8EYwhnhswB9miEq78BpPasaqghM0ELETvPjVlG6vPqeAW+tNjlXyMJAuwlA7eSaUY8K4CP/BUmhTVxMBVND8W1rHEGkjr1weFQClpjwb9UCxY+9R7u/zUlhF82R++PB5unONxCA5ans+MKbuRYyg7S/slFrvjCjK4VK0xxQG16NZ3/eTR7Mb/eX9QFx/AZqllWyAVAvc9rPDQ1fi3TRVq9HgUIxmf3OQsucn9FHWTQ9caXSWHeCnaqDKfLD+moHJbIvZRdw+VMRUG5646bPUY2lR/XIsbBma1R+aDsWkg5wKQ6y12h2kxFlavcgyZkXqqzaIeDTasRQ/6nlDd5D68gefQ5DBYP6242U1FrU/zsgABIBQsB+MEEKifXOsdLCoBbptCOEG4VL3y8iFaoqAeD8snsADW9UUbvONqyYECAZp9GdyEI1qKqdIryFAcw/cRtVc/VcT156m9DoWKGu9L6hgIKscQUXRQJ4zsQRvAKTJ4Ev8ZjiKXy3B1jYdico2nqDKcKvqjyPl9U43FGE453NPxSquvw4EI5MLSsfKfE2djXDJiYXNEc9BklaJ8hbVbFgIF0rlCJu6Xu/puWS/m/gy+pR8c8afR8zUswCvA48ICSmY9JDME5GAEkEELK4Bkd9oe7+bqON3v8RE9CocXA2Xebaum2oZI04yCAAH2JmT89ndpXJKtQ2Vpk0oA8h+oHxCTUQbbBiAE1rtR+5EOMArsrmiCNxMGowD242FA9NLrsWUUB/jEkqscnfFkhQDwEC7UUAiDk3eh8WjUhKe/3F81u3p3HwzWx3cPHfxBBQCJXYcocFPiAT/2hoQukXC/+uN+3r3sccdMSDZ5PpHFF6K8aRs2kq7aOQCSYByQwQrT1YFbQOE/0hGUoyOePJ2FdM2gM5YYA8xpp7P7uUA9ykSns298q8PdRCRKO+9chcYDyqla4NXkksO0BqKSclNiswgT+MuIbT/XodhOjnhPLIEeImwPAW+xSyezFsA8r11zRP9ACvl67aImm2RIRQK8ePrm/6Z+qNveqcftiq0ji43PYQ626BW66k/t8LYRy/pGLHht3r1z3+CcuWxaltu05FaTADmJ+G8yJ53alZ/qanndpA+FQAqcp51Bdq13QUHZvSBgL7RbLbebwgb+kJ5BsBHDGt2B/iNTWpyQd6K5QExsisDEfJKgMTFk1rZPs45+ASnTJDHCxzy9VVLG8V21rZKGxMN5VnS8+GKK4CcpYiqyaSQ9RDaed9G+7CrgE5IndVp2uJ8LMhep9Qu56CgmjrbKAMgBjKMnOBYCciX+Igx3U8X8ZrvfJvfLwUtOjpynWDq9a4YLnRApv06tBckeVrcRjmrIVnv17b+1Y3hq75uWRBs3tZXADKy08IndC/a0rdTNpSB15TYyrWGnhHAGKaNmWP6K+ZNEYc06Gvl3sccDTYA5fjnlElLHN7CpHQDSWBxpQ9HY0TZLjkzafzqYCEoJ08q3V+iu2WBfa91V2KrlURFgLp88zev1Hj4dAAQhnqMUdVRf7WdxEhmwhvzPARwvfbc+FqNRFveZcjGkFsxF4fld0PgL11gv/0ogl8NdIVJZrA9OZU/OEAHWDS8S6U70J91cTOK9vD8JM+Gu0NPuxIT9047keWm+xMH4gRL0iBKaCfAqJlRvjlr1yaFHJEuskNlSrQdCZ+kj7L0P1yMFVtj3lAvTnjPLjuBB4RW8OYTs9ldCb+rfqBCAgteYA+7q8OMUZ9u0Eb60xde6TRfdY4NspuqK+1fFqzQTVSruD3MGL4XEE9ZruoJUFdWtBPCIpa3opalTLJk7qo6qmoD+5WBiLO4y/SiW6k8sczZ7obwfP5uWmFAoE895NVXBFIxYGJwiZOp8FX8zwQbrjZSZ+qtauHAyfvX1eKFUQsohhproKhdTJUoKKEuy+G0IGBo8xK/1+Mk2mrOF292P3hbPO17sWuIOA5jICbqL2kzlYrrML2leOKgfVUU60Q8fVmLKV4MKExaCq8b/EHDZ1oIzR8NUePg6w4ZkJ7sun561PIlBk9H7XwCqjuxkS9Zpc6ainh3RvN9ieCw9cT7JLYSdgimAyVyc1isZZpKWGMbznZL+cJJhh88+svE0nGCKHZmdLJ6bnpyotJlSe9sbGBh/NpqmeaywiJDIIO1TEFzFINERmURHGf0CmY6iRAvf9Wu7Y8G39Xq+XH46t6LzAU2drYHj43fPclsfclmiyGeJIn1miXEzdXQtCMMuf/BD9tEPjVrZdIs1H2+xnip9f07XhE2K1uqe8xhNgHCqyfw81eNsDKGM+eWwV2DEAUnIp6kcz2ksUYgXIAV3kvc6Rw9aKWDqfodEGsQMm81TfS2SEQV0H0H/dPEQf+Ws7RHkYx+sjfuMX7pw83zP+NlKRZOtJOHX8NiSLF33Y3jfR+po/BP99l4L4cOvuUOLxhz/nYDmqq3gWFfu8J8n4KAwwcf21fmH/kwP5yCm+fK+u67Xo+2p2F+odETpizU7CA0oQMaTH9snI6kmAoB3/90xxOhGD5K0yh5JR6kNJczFa77G/3thct01rYb+hU1BkRqv6y02NC66f3LRTw6wbNt1fqJvav+lQohZ13Vzt5yHZhwU1HWvYaEPa4OdWG8ne1Trn4R/a0AOHYbCVmH0q6O+gqLX0bL1FfHb0qrCOMLg8cgM2iud5mfgXsrPmsER/EHAExiI/hlKrD4slypIqSXQ08FGFv8iFeT3fzCtqbUKZW5kc3RyZWFtCmVuZG9iagoxNzMgMCBvYmoKPDwKL1R5cGUgL09ialN0bQovTiAxMDAKL0ZpcnN0IDg5OAovTGVuZ3RoIDI3MjAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjavVpNbxy5Eb3Pr+AxuXDIquIXYCywu442ARJgYfuQxPBB8Q4SYR1NIo2D3fz6vMdu2d2yZnosdXSwyWmx2cVi1atXRcacXHDRiovJ8K86afidmjOpLubgUla00ZWQ0Iqrxt/FtRrRYkwQdho6OW9iwduRQ4ugk/ArY97Y8FLFPwsZT9BJBZ8sHBf5H57WhMd82ihBgSRROKY5kZw2sQYnFjArPizJAt9wkvlGzU5KZKegk9jB67UPFnS4vJacBqwnYnbFN9CJTtXCJjZxaoVP1GmOkKUVp4XytIoOhccLWhUTYpqKb0rANC2yg2kaFBObOQtYn8QA7altJKgzhYYl4E8JvwSasWyRa3NWKGXEmCrsYEyF1gSfsD4zJLGW2CkuBaxGQkPH8kbEXJLCCbNL3CoJGJP6k+pSifwEOtWgvoi3GnUkweUoSoW6LNhUgYazBnYqOlCG4M9Zq0HX4nLmPNjfXCGdCJ60hCdKc6iYGQopnExUXVF+Xc2VBAWIJleyQTDNrpQKbWhxpQlfr64Gmpo2V6l07KurisnEDAaWMaElVxOVaRkdfhSbWFPjk+hq12HCn6rGjeAztS8Qq219gTC8JthtwZtNsZ2SxLUEWQTb2jJFhQQwYs6Dtyo/gRda5SegmtYMM2NfW+t2BhvAFmDNKbFXoA/aeuA3hb4TDLsstPaQsDDYLF2CpoL1xFBjt1b0IAZ6lX7S4ubFi832za//2rntj5d/32223++vD7vrwy3cJMM1X222r3a3+48373e33Zf6oz/tfrq6/G7/i3sbXN9TavbdBlPc4F3syTDs2+vrPWZ62x0cT7qDD20b2hzGNo5tf9O923zzzUSyPs9m+/rj3w799x+vrn/ebL/b3/y0u+kShHfb32//sP3+bew/KPP7g3sL4/bY3tyiL1iwSPMVXyjBfC4Y9a178cJtX7vtD/s3e7d96X6z+/fHy8PV/tqr1986CLGKHFGjx8bCZczTMKEOT0tMWnzUvCSJrSeJaPCBaFCrL4WOU7zB9ODe3kJ6UJJbvElB0lPEgKlOxEiSPRwCruvp57BXb/Bu6MbDjh+U4v3VYedfX37Yv/nv/iPQr624PdiWBPSE5/rEUGAB0qHV7AGSS9tT1pMEGOQZRO4kUasewetcSWaG8hIL69H1ldv++S9/dQWgmUp3gOuPHz68OzYOAcUrwMxyQZtOD4anK2XVhTnheQqfRyj0Asg7ORih20tlQCs+z4deAJy6Ai6Af2nAlO0FPGvEEfQJR2M/IoANiIUfmcFj7ANC7z54AUgsdy/g4QB66DdGr7vngOy7PlH7bgzANd31I7C5fZo0k1j0H5B8++PN/v3rHfYXIPvywm3f7H453Me4++hb7Qv0rXIW+sba7sMvFtDhtaSxzWM7wnIZYbmMsFxHWK5xTTi2lgF70HFtviGgpyi+YhdLQBvzM/qZWYPNJgAPJIKmLScPeweVQYSoaUmSvCL2NPMFzMNC89I6efNGTiZAgvZwkDr8Y7e/2f0TgsiK0NMjQP0kiMbsQaZOyTHRyJqCIBjQeQQwYNCMJgYFUJaavKo+495oq55bMgpi0Xxtcq4gdU1zxV4gQIEywjxhHFm8wpGQdHhQ22eUJCJAgcuTd0IHCJkIUMxTYgKxWfTgdj8+VT0vPs3GkS9g3RZPj0OO4g18PgVsV06nBwNbvQCKwIV8ACp+ZdSZBo5ZcJkEnVk0mkaRaXSZhq9ZZPocUE6Enc8x67FRp33J+Zs9Nuq0MYq0kdw3GVt9WlSZc0mN4EhIWeELyKtARxIsFDtZg6/SjnPJl79eHj7s/yNBVoQMGh2Tf2VAUWaWkAZWo6XCXxYDSwwrioIsR5lUgjoyH5TQubYUUDGLS5KkFTGDAG5MeBHmG+0YLsZkNPnBgk6r5D5mtDM5bZvT1CJErtPDQL09OQ+yZuSNeXFOhqpkC+NArnqKJSF7VpRODib5zspKAyZuR2FoAgBTSJqBwTF8iixUJXkAoWYgM0WiKaTNYGxGkidwNYW3GPCCPgWVmDHcQyVWoh6HSiyKURbWxIY2rclxI0Jzhh4SHZ/FGOx+xicTQjdY5mlrtzWLDoYvZhizGQwfm2PFfCazTOS67f8qyT2ENmSUnUABqduQ1ZbCEm/xIdXjEP2725+vrl9dfrg97K+R8t9Pb1noPAcKZuOYVRbgYm4U5vRYSSwcwfiRFmiLpwcTD5DglFwWJs3gt+BMSjIX68JgcM9C1AY/aXIOLTma9B7Lkmc0Y0JFpr49BYDzEuDjgPPk1FgkfQEHoo+GgzzCQBnbYc2sXg/toD/WrtckLQiCRibbms8IDuQHLXKHwKejHneJi/3N7eF6d3P1/qk1sLlABrNJFREqCA8PwJyxtchNWz5Rjvvh5vLj7uaACLZiiqHSfAtAUPhJFaJF8hVUKsEFipQl3FqTuIRBH2pDahxBIQKSHk0KZrUkyJqsEphVEfGEwQQ+p8hEA6xFWjxarvhcJNA16S33RFxUgmjr9Lb0g7vgs8WviikvO1s+D8Bn4/qpmn+gijgbBl7vrQqwCtDdzkroJqA4BcujiDrjS8dSwGMUaY6KR4H04UTxschp+gVyWnwsckockXFYBE/lhvbu90i0dCRamsZ2RFwdEVdHpNURaS2sScg0AsyKgtNXTzAnvESeP7buzCd9x+bZx1P5GKbjEeYoiJUA9mP9dCpKfE5mGJmBwSyRk5Y80DDAXEbuIUuHDOsSw6Kep5sJ8GGwf62xV9pSAt+KjySGT9MNdBISmVwB0vLcVIcCcQzI4xd1s2YYRArIM3kt/cvOsDvRqCPac3vOgiwhFDikCH8ZrUXxgChVZjhLGmlrFoaLVwCxBngwoiHDcK9UMyzHZ1QIEM/z9gl2opGba+0cHamNr2bPqBG6D0/0wR0R+niqwisnEsmblsSwNc9PYaK1fhIkIt9rxc6VZM2iF+zDl8aLHj40mEolsCCaHmUnR6CebMLkPHIyHcfzdaOPMMl7KL2cDo4ZrIlgk9XXh9LL2cxI47PxrlLxvANyhNDMyMOxcvWM0Rwr9EyZziwXnNZ25qndhAI9nasMks+4SkqP5So2cgwbOUYKYztylnENvJyzZjEIe5V5xQkRtiChjE09Yht0j42XZwwmvD/A8w4zcI+auWcwT+sHSTHEZ0wgIhIIkB5Qcl/7/RMkELxsAEqSwlImsyZmIdAbL/kBxHnTSQLvBiWkVNmHvBjo44qi8C5OvyuYE+Cz8oYdkquhdFh1SZI1T8U1gnYxoMReszQD1MBjYCnYHPsqSQhfdxnNEoBOx/H8OyNzyIgpVnVhsGQMyrxv2I/vTw6OIFQ84oelyUNgO58YIb3fuATm2sK8yIFD69fsfFP72qPDY5A7LcUdrbkfreNNk9FPeP1YJC5flt/zeeV3KeU+EqcxG8x37ZgVjvLyiuLQ1lWRGHGZbDojw0isIFj2XFcGIstJ1Elrwp8yiAdYQkaaYUMNmJdCE3hKW6aOawIxrxAg9ULa5Rn9WFqi73fikhYkSWuy6ciD2tZ61TFrHmi0Rt5chm7KkiQrZui8XpGQGCuAqvAybbF+dMoULIfFs5u45mUcxGmaaYNueOWbF0cpEdLREhd18ik4/Q8xQFLjCmVuZHN0cmVhbQplbmRvYmoKMjg1IDAgb2JqCjw8Ci9MZW5ndGggNDE4OSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNq1W91z2zYSf/dfoXs6ehqx+AbZzM1cmzbpdZrpXeNMppP2gZZom4lEuSRVJ/3rbxcAKYICKH+0L7YI4mOxu9j97WJJFtcLsnh1Rtz/by7OvnypxYKSNCc5XVxcLWhKGLwjC7rIsjTnaqFJnhIBL7eL98nFTXm+FEwnq932tmiqdlfjs0rK3/dFV9kneFu4H/XunGXJnX24PF/CXzfBumxXTeWa1ratcqO68pwmzba1T7sr12rXVrD2Zr+t27616Cbvb8+XNLnBhT+31arYnP928cMCtpi5jWm2ULlMRc7spla7uu2a/crSz6lOtjh433b2qd2bGW83n88zmaSwiJDJy+oTzvvlyyz3+OfWYFSmmRL+Qr8Sxuwgn+kj2hgTqcr8YUW9toQU+C9LVp83Vb0um9n1c5FKPZnoWztCyoVOc03MgDzlXC54mst8cbGGTobCxZKknIjFUqWaj9q/fKnYaPD7xBFBqfKoIKlmcsFSrakd/SshLETv+yXVOvnhBFc4p2kmub8bIwiVvNnfgnhAXrsWpVRaVl3tGvvDaIWR6W5b1ijXrn8BijPDQaHzNMsmqvJLgIMKOMgXS5oqOC1ms9TNSyLbETnweyqcr+wg6BQiJs+PR7wIEKPTTOdjcfKggHSqQZDLUb9/4EnMku8CUqZ5qnKNO9Ra9+IMa/LIfOApy2iaC26prcyBZajEgtGk3e6s1Lob2wCHHw7xH/inbLrqclPa9m3RNXDWzFhrChiYlWZbbKo/reGgSdcUdeu0oOls28hKMKsO+MMZCQo74KIxGuP6b8t1td8+s0pljpyZ+YSW4C5VlvJc3EtBtHeWfAVBmfDFqFdQI94bMYVET0Ga3Mo+OyV7bmWfPUj22bzsgQtsMdKRmwLYCztPNlbSYInBggZtAPb6McQ5mgomjqxTYGWVe9o8CB9nbsEztVco6s8VOJYaf13bV9Y6YJ8SvACaWS4O2qStNmHbwb9h78r9X+NUFTgPnPcSH/bYqf0qtE3OUgHmcAkWnmpnKb4NsBzcLTAyfNr8GdFViDHLfwlMd6x9H8LaJygba9+/wqJCRXkdWIaxlOS5R/YHYMrzGO3wU8jH0z7hA8882p+D8KjS4Z2iuknYHmxFD6ba3yVMEByIc4bdWH9Y4+YQyCN6oRgBNDUCU6C5I21VmdUtaCyLFdrCmznrA74zFZNZT/BReXyMkktzljLGvZmLxtHrzC+I11hgbLpEWj/bHVhIhq3Wpv6ztU8WC7rt2c0V9XVpB6Fpx/Y/zqWAlWDuqjAnagM/y9aAkrFr1kJ4xP1KJMFOZ7ZLc+36/vzqDN7KlLqXFvnaAdTOeti1JCylCL9GEwPMkIqisNpy2h+tP1HgkR08PmzKbBk2ct2UZoM82e27tlqbKc6+uzj7/WwQInhJhYsCZBQgr9X27P1vZLGGlwhbOdj/O9N167w7WWwWb87+Z+G7r6LDXD1V1nzQU86aZnkqhfZ3A76Qa57c3ZRW8MKZS9hM+akrAYKubfNqZ2E8+FS1qkrjyLsxB5CH4FUvbvDN3r3BOZrK4LS+3wihQZNVCbvqWPzAdQnqmYsBvzdN2bv/el3VzrjfuhjAGOkVqBGEAsaYb8ri0MtzFdeVBSBt3E+FPDxjoGCM3s/H39fKhhaioAEi99ahcY846tWhEFML7ZlALQdwD4hBCucx35qIwjnFqnVe7yAiaPdE9Mx5QBS6qMuglVIslZQ7j+cIeRW2rLjhB/KC5eBBlGfVIl5Sc8tz5Xua8WRZqgENQcyUDSiHkMCijt+wJ4NMljoF4+AJYhoKQQQFIRSo6ySQCnHgu3rd27Gjid4H8RmHIEVkMbRAZtFCbwHv6cz8CFpCSEYAbpoT+FMdRnb9ic0zsGzKH/OgUCpKCQUdJpT6U1v1pUkDqK1qjJWCp25n/18aQ1Hah8po8x/4x0UdGFZKkrzeb7rqdlOtBtiHI9GeuIGdW8QLZI6mBKszhKWcOMoYWCB4cV11FUQ3jhIbscIPA5fzOFzumSpZlhJgn7f1H4Mx4X1A9FIoDgAVjQsoNck8IO1OezFA5B433zZ29+v9qrMtzX5T2k4DSjYI+dJC43vYWQrel4OSenYjhpTp45Dy48zQ6wB3OU81UR4V4VRJHArTlEFgcEzd1AIsGUSwB8N0RCG8fRUiUYCCaJ9R9BEkjinsEXaUj9oi7Cz5zzqkbYClYR9xUCRgRQ0BAiIyxKFPAUX9XJh24ZTPgyJQz9z09uK0PvBzei+GHJxJQonkBWg2GBvnG+NhrkjehRkmqJpTvHsqxFQKjEfZb8LYJwfoDFw8o2KcdDgVobtuzpTwIbvax9vlerAex1rBQNgMJKnB9An1NK3o51J5Srme1wrLgbsKoFHdW8DCGbyDKkzyzC6BLqQEWKGtfX6JKWRr6aXJ5/b5Bucv2tK+Wjmn0gBQRRcxl+pFc8km63T3yDMzQEDeIGPlFbhNtNerzjP1A0kjlJ9ZlI+ddrbPqHMIk8vkdlPUrsvgTPb9pgv777Joq3aK+AWAVsIzSyhEBuHzgPIIprGylGbsXinMaRoL8e2ncj0kfqIn+1NQcRJnIX5yeoMMKByghhBqt901t4C3t0sIfIEDm32/1L7+6G5PasdkH2tP9q50yiEk9nzmfwOc0AbiHjECEcfzLpKoIbnnO6MmTMdNmA46cC7hzKp7e3AeoOJYBRQ94SODXlxClCD/Wi8e4RMS+EWcundgDXkeJFKA8CT/W/z4VUg6cOLIKV3x0w8cwmBPVx4IpgJZxJNimAZcPFX5JM55qDYwmgJY+EsZ/VXYrSilhMldgRkeMlJLrilEztrHIBdDrjriMT3T4uWrx4GQ6CONUfZ7fJtrfG8bZC0j3LjKRxsZiID+TiOThdUYZiXyadREpEdBesqJT40TilQePBcXYOW4y6m9xtuslUkPYqrQZtbAyG+LPraEf8NFOT40JlQsGpttg4bCDbYR1KrbNa7B5MvQKd+UVWOb6qp20zh/3VRlix6JZwhFmIUi0LHYbHfmvtuExrS/h5sNQIUSYCCov8EukmkcOCLAUwFzvUHPBsJn1wP8rkTmD318wDsmSpI8Fdyf2R2n4+IBvI3WmUs+9mBpOGtGCuMQ+RDPz6fysczCn/vdqeRHLmCn3B/U5+7a4V58hKnN9dcRrGYoEo4yAbsHoHAGVlMQudITXO1taJisJ2p9ovKASha39KYOASyxNHlP0605BW0zsKVK+VxxSZ9VsW8D0SbH/C2Kn4DvYE8KK/qpxrs/RBXHARwYLUB1LG7SvgmZNKQz9xJlp7jCFWwkm+iKyXXR3rOIIWPuZctNeHLVJ4cH1H5nApbi43JTbatuMHxidE4AdTPO+8uj6/3WXQ0crJw1NnBCoPkOm3MIsjYb2wE1ePPZttrThI39RS073JvNHCtOVCqBVR4t90s9Ro1Gv0VOdQoOwJ/bGdc3m2plgx5LvDXImZcLMwzGbfT1PNb69p6752ju3yByYpNZZrUDs6q66ioTwJtHk2BD7o7DKCphl2hAx1OcujhTcxdnA3EQXaXgm/2pd3VPjoUc6LzGfsbFvLNOhkEUnfHJxFEnkw/lT1h7pPxRTjTvzjObjzU1YzY5i7lioNHpWF32vtZ6YpvaxZKwuvp934+oy7ad7G5ic49dB8spWAfpORgT87agykN+dIiNVR8Sw2HLkwEiYKDtAu7bwhABPa+a3bYPwqt+GsPt5cDo/irPBOhe0dtoyklCW9oqB9zxrXFkePFXr4a7UKJH29OI/GQvfZvPLgcsYR4BaBbbsnOpM9YbHwb8/NS5DHi53Ra2EfPLmE2v1qXLt3fmIta8vCpWfRbeLsbG0TpNvq82zsg0bub2tgDw9cw+3N1U7ordPtqzBJMUm3Y3zT8wBDtoGowx22z6wkY/G47ZDZdUs+wUeKENYoTdZzxJvbMECqw9aw3AmoElcX7uR8sFQSScwlhWCibFA/zG1J7czoYEfpHU4fLHJ+L9EZGUTbySkBpgF/iNsovF4NDjLd4e65FzC9Ux/RyqJFFwdoUe4zeXw5xQYu/sxmU/l4YqWFjCHm/NQ43CBnI2ZRdJ3UDfetY7Mxm/LKBByiyihVXtOZbOMgA9u9aRdygsm6y6pIBCmKAPiLIGlzVCEpNr50zJ8RXoTAgFYCWyXymTw+3k9MowtrT1F/Fx/w5sDGBoznx443wEyFtCNwyJMSVEMxPXUaJnCi91SuDYoE651P9M8qUKkcOwVOl+5CiPnNg1LCDZETnmJJrVZwj7OsrbCF9NLQ6mdzC7My+cWAkULEzpaGzweDB7y/M2THqe96VUkxPiV0YxYByF6E70xaLoqM4lSWbjQUp4yv1xB+WNr0chhqEQ+XkDTXUqJcb5wbq2KhWebSEo/Kh39Z9IExzhw5s53CJBXbAqbbzK16doQy8jWIS2IkziyjxtbzflbEU6xzJVXHg8ddTwKTVTso1v61NbEQTMGPqr8XpDMS/QbT4WOGyrLqvrm0uzF7w1v7GbxD9OAjbp7wduGhlM4ODN3hBi1Qyjc0ErzoP13ljGeype1d5N1xChPrJSHpSf2vIQNqqUN2f+bdi34M2pjdzy5MJ86lDW1ttYHCIRkAKSAee72d3ZIAQ9UFd0WL1jEBN0vsE+NFkfYRL/OjQ3dVYeMEGjU/WGIw4URsgX6z/AglIJAZOU/VkwZB0FREiqA8OW0Mhlz+H6KzMlb97kJ9ylPnKXk7uGjMtx9dS7EwX9TEpTe+3RgPffAM9IRIoGo9Tz33BAqC6VP2tMM0XkG44HaGZUfJzCEYaD7RFydzNgGQtumKuWBpGZ+ANeFPbfyrzcmj6FuVc0g7Bmui3do0mbyaF41GrGR/trchsI2oQZDCrAjhOHyHvbfEKbaZ5Kkht1Blw9Uud76PN7+93PudY2ayoxgrVhDdJYdyXu7zr8XY6V94fotaU8gTPntGgAmUgOMPVznIDYpRmm1WdyUN+HUBGFwBz89uhO/0OoYG4Jy34RBGYUwhrif4kQxzPfhr94kFofFR88VPF9qlC5DD7rDfLzcPiCuOpF6KoZhjKPqDq0jDRgcrJ3OiPlIYJoUNFosnZHLVgmAdMq0HxOTc7vSfncYTKG9yh03j8Kk8+l98vnZl7mcrJpaaJtZr4+Md2ehaMlJjQ++NHSx7AOxbQf71s/hr9p0MxEmIw54N+fBD4C/uBYNFDK/E8UvNjAOwVTHaZCYT0p09MzMFHXbwMT42cUWplwQk2OAEP9koC9/HymR/rA/elHZJTinEeKOak5AFjFFiPugA0TcSvyIvx9Eacs9GnC0VpeDe/HkIfHWlDBYxHxkYsX4wkfqhuAKZk2xsvkF+d0A1nJVB63kXDACDMqoPKn6YA80gH5VB0gdL5aGAwZIIQDF57HatEgljQ2LPwVKFjTOefUy3Fi6LBEQUIMSEAaRD3R0LnJcpAZE/e5t3qUncuOzJwcm7lYnQDPcnfRrKOOYslFlkqFtYvysIeLPipYGfdRQyDggoAMP7QBzOLeb3bYwfQqXPzgwM2+rq5MVLbtwU4cYozyBdOk2Dw4YwSiIDTk+N2t4xkV3hAQ/v8B3i+nCQplbmRzdHJlYW0KZW5kb2JqCjI5OSAwIG9iago8PAovTGVuZ3RoIDUzNDMgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnja1VxZc+NGkn7vX0HPExVuwXUf7ZiInfW6x55wxHrsnvCEe+cBIiEJ3SQhA2S3Nb9+M+sAUWABpFq2N/ZFAgp1ZuXxZVYWyeJuQRZ/fUFG///zzYsvXmuxoKSwxNLFm9uFVgUTbKGJLYiAkvXi7fJ/iCR1XcM/evWvN3/rm3/xmjI7bEwLQhV8Igu6sKbQTB072kJHPx4eHq4kWTZddXXNJV3u78s9dvnFa5N2FDqhihdC0rSX29CCTA1NlS2sGo2Ni5gdSouCaZ02eoRpquWXV9dUKeiBs7NDW1JIIk+Gpr7hiF5vHRHqzhOj2zaOOPt7/97s/P/S/9tV9d39javR3vuaV1C8xq9s2TiqvPj6zYtfXsQlQTlsJtCB2oLzxWr74u2/yGIN3/62IAW3ZvHR1dzC4gvDsNlm8eOLv3u+SMnU9zVkjP/yq5Kw3sJq4iriUHLBCy2Nr+RotriGAQlfXKtCczr48MVrxQat3y7bQCkg6ZDGtKAwIQYVeWRKwq6uGSPLf2RpO9wVbmnBFEt3pbi6FkC4b289Aff3gScrpOovh3JfwwZk5xJ6FZoXytq01+8zi9KFNGZxTQvFVZx8z0rJpEVhgHiDij+dYzhJoHzEtX+e43NJWaHYU0VKAgtRrqdEaoJCkoH4ipNWhOYm+NbLmb5IziRXWRHPyxk8mYFaonKhrCyEZb7dfYkSCIJe7mY1BAfKqVHb7zIioAvBBIiAlTbh9OlZUWEKQWzadddsDo4J3dw6pzs5CP8eOJQ5uYfiencq9wx6M9C3JrIAtTYr+KIgSs8Jfuysn9o5wadyRr6jGpCFEUE/tOeIw5QqpOQpcW6bFgkgnXYEOqyrnTMqSKlq7z81t77gAelVtki9clvtq7ab22ZBQAqlSIc78uT0PAUUCztq+BKmINDQVWEf673/H5hOwsau7gP7hYK476g346ysBesENDC0sIIPaSDNsvpQtY9ZmYLPenryXgkqsnwDkre8r7xZ1v0MOt89kg71ons57OpfDqFeuVv7QpjJNtT1/5wto9GW6eXtYbcKvAxfm6y+eeu+TdnpC43xnLWF1SJJcctsIKsmBTNAVkULDRvpyPrGWQIFBhm4xT+tkIOa3Wpz6AJZoLB8eNg8+sd94/9vy31b/3r9odwceiGF4sPu/a75uOtehoa3wIOh4X1Vh8dqB20r1zcZkBy+tNiV6++udM/rKlp+7C1Ox7f84L6t9k1bJGCt5yTNFkqAiZGBi753Q/ntui1GAC8Fh/Bke6RDFJjkUV+vr4xYJpIpcafEr9Xavzwgo8EiaJDEl7P2g1FVEFAy6XznLaweW9jpJTBQ6JSatPs6iGW12dQP+3rl3+qd/+9RAj5cMbP8iH8aX9BW5SascVPuykCDD1cSCNHW5c2m6l6BsAm9rGH1+2BxHtp6t6ofYtPucYtd3gCwgY5DYT2rrnA7mSko4VFTUZkTjIGi0qZgqODSZvPw2LBC0JOh8jI4GMoY2MCTZpKeG86qgoNdStqBA8JJxvCYQgKkHMIrmuUqBXaMLhIURvKq5ppaVETZ0SgodwAQw+HOEYFyUNpcn1Ihx6DeZHCy/HgPzAdsgBqUac+X8Lprdv+u2sa/oAWYoSLjtjCanuxaHrWB/mUiLgYmn3xi2ix/yJCDqUIYlQM7OeJfD+qBRCGZb88QjxlgBURswzXMw04GcB+wYdLibkIZ9MNYDbpgNAxaR2GX/73z1K7K4YZ0B6CkWtVg2KFw70wBQ8tXbja+xrru3rvNpMvvqu22DLoVh2vvwrg/gGZ+62qLIvWuXbFZ3tUfsP8KlEbggDIBBij/gOIkCLRHjWB5nfZwppdzVF3wCp3oZdXua1BEvtzbqgnUAN9fZ7nzWmgGhnFfd7eP9e5uipG4yOppdI8V4r9P8YVe50GDoBo8HmBQHdliLFBuGyV6elOz5dnZSgGV1MWTFWcdt8FkzdRkX/rdcYAx08U1KG+l7OKaIY/zY4Amj5n+I7cJpACTnFjLAB7BZZdQTSu/JGrc4inRiVpNJ6QLAriUFVoIX+vzPEsxxpZ1bjoMUDPohKdPJ6t2RaGEHU5nztr0PKUSDa+dowkyRcTYUCS670gRO4pVcEAV14MOJriBcuvtgGOIOb5A35cfJV5qWjCmg1106smiugGJvvHIHXkeev/WAQ34uGq2D9g/oFhUWHoZP5Rt5R+A49oKDEp4vW82zbZpH+4dBIKCCOFDux7D550oCl6j0elEHzM0lAUXNme8T33/uH4B5pRDcdL357OzEcy51ynZHrNOLLJXxrhPb4eQGDI1aeeB/H+JtNw2d9Wuag5d74GYwW5KXShtTr1+4wOk7gmQJAYo6u7e+QhQEuuU7j/sLG6wXVbhNbjBcy0rb1rax1B8Bc1D0Qa8G9Tv7sNt6z0heESb5p9usOJjnGb4POSauNLBQgVIMThlnv7rYDnBI4ZxoScG84SegAW3SD+mll81u1212qPxW6PnsKu6ztd7aJuH8q7cV11sV4en1ZUPnVL3BB4bPjqCMokAHl/RAWkO+HR33xz28+EABSqFpnOfi3+kQCgJfF4Q95wEKIKJQqjRPByZJLKZWznIeLlySARfXcQIOQJDRusxaJCcFDLDczz4LEd+4UB278+IuNE8uiiH3br07MNDuGVQp8c/HLcLlc4Hz1JC+TMF1H3eJweBYkeXgYM0oRbmIbD305Vh3hmWHg/p+ObHgoeq29eAaMKbW4pSx89HJ1TJSBgdCONqdkA5ZDOgqARP+Otf99Vu3QOcKdYASwpAczTXvL5gvWYEmyZGC7zBJT362QV0j4trcli8H13ZQlqWdvRDNhxptHWMaeZVWj9FgNxsvC7nf8Ck7st2u0ExHPMT4eAJ9OtB7qhW5aELvFDvAy85cRxsgOeZTR15LRfQpBaWKiRMiBdUPTOg2XcWJ3w+oMkuC2jaJKCZklcCJRXgEmUDenGgVIRgE1DAh04ObV0FUcvEAvjy3aELFHNqbuviKYeoILE8+gtdFuyCYvDu6xA+vs9jo58m4DKilvc5IAeoC+wf+HiMBeP5TajGB0BOF2ApgRIsEOJdBuy9vZZm+Xnejaegj4G2UCB7UM7EBOgEPPVVmINe4KkgUwFzEhAd0DTkJBYwGg5QHIjC9aDqBLmmAi+wFjWD0OcDmU9D9hy4UJmnQGl1BtkLB6X5H4Xs1UXI/uj10N+Ic2khUSh4oYyY51xkXGUTzk2nqQrD0G1FKRtS7WQySLbfSPI4dxQeSt53GULLQks7kDyWkQxPZeuOS6Ng0LzHK6HhUC5enTErYCSEIqBOQ6Timwp9DcDl/hwBH26cVag2Dix89GXo3uB/j/DwCcGEsx638UvoZ9e021BnVYZm1S8H0IhSLMtNcHwQHw0GC32U7x2sCK2a2ZNAwXxAL1nOH4UJh4aaW3fOnszjJboeZNnVu1U1os/RhQuYqz9Pxb0RoFiJ9J0ArnN2yHmOuw6welsGhIgRqOgkniApZnt31GOvGBGLvSEmDM7Rj9uwjS5SBd+cvTts9vXDpl71wzn2eBl80Pl4p7B4Qpuu5N1k0Ae1MpPnT74ZuKgj8sxEbsPKR+hIgIgStFfBYUe/p2ofgm0vj+C7x7HTgbXfSGlgVNpxIWoN+WStNxpHF+CWX26U/zQ1WYDiv9H6BBgUwNmfuLxpODJiFQV40wwXPmMlv8ptgyn0KOTwp4kIOir3QbXfEraldJo1HsrOeBGmYEYOqdGHr7NolAIcZUT8fnDUXARHn7T/03BUPwuO8k+Fo9lAO6gbUFr5E9GpOLtOgpWXKxLtUhMcerpA0PT/c/A04v8J9MTPoyd1Hj3BrKm01LkPgO0j4kUDRXrTDVsHCoQbwAIxLhS8SgLG23viyVFVF775wCGJ3nooPez9QwNteHh0obI9wgBfUPp/LiVx4KRiXuK1PxLH46/Q+NYfge3jnI54AF5iPJHEjB4SgB42QR+33KwO3kA6j5lhMIgvq3HUKFJBY1aCJ8LH2h3EYVLKpmtC9koX0lg2fo6rMlRpqzsYpsVYpI/E+KyW0H4WDTIhC5WM/JSksElwxzAnRST9DiP8ceKD2B28fax9xo0ZnQPiCsNe7cN32EwMdgHBv90luTsD0pyGcLkCiBVjQj4uPJcyR1CvpI1enwl0UiimiqaNEFkR6aNIzGpE9QE0Masc+wHSXzVbFw62LpUKq3WwCAxdYtFX5SEc32LcGqHfD3W1LXehj2GPOh6XmHBcYkK+R43sg7U9nd0QQzpjgaczoMCeemJAPmELYmKCn98CzEL98XCzb0sA5zHkXvp/vncbT3PD5pjAt6DRHsoWk1To0ono5spl17gvkTFehg67UAyQfVP7fJw0iH/ujBoan55RL0P3m6oMx8wu9pQ9DBjgYXCLCskDHU6J3YUj2zcxlNwAVWvP8xhDHMSrRcjY4ckBFlbKHISfxqdz4WcpQOzQxTMhD+ANxp+dBhGgKLY+lH0VdKaMPcLDpnThOSyq8Ixh61/qNJePMeGyzpIxYqQnS/wlKP8x4YfGoNcZ4KQgmkx6fukTwzrHIdFf6/qp+wz8fnIcwAij+mmT05dMjsPkpB71fFvWm85PxZ10YY6mfz2G7OEF2NwnjKHKogoZow0r+Ii70Rw2JyccnBhwSoJXHBMw+dDa+bEw1grSX6G/PJkN8VP2IF14DypJEGA6H//FZLlBxXAwEjxo03vQXORzeiVzPXDwFxDaPC+nN3YWKfQ75fSehsDlMATek2CFW3gfpTheCRk7C5iILGjwFcQlvkK6W4jg9MRmjcY62a0ccJSFkPIP8C9H0e6QJjCZQz8BmJ1nnc1ywRHss64njNNLpoXiWXT+lOBsNkwxcDCco4W3Dj7Lx+4HGRljUr+aAjL8iEmlVQDkpE9aQT30l5D760x319XuoPrOF7gsYvhfBkvtznZuom566UvDIa8/CccCh68FiWYNcNDg3KharwOksDHs1MwBNm79NZ9kzk+Bs6d5fHyQVy+AMZKuz+WBCtBQUo8a/X6R1pnZMwVKVpzMPh8JdPQ+nsT1uwrbBAi+mT9WloQBx1+4B/KsSzFchaSY+svSrjf1tt6fSTFmEng5pgucO+I2vDDgsiRtwG4rWP0b79tZGUZ1j7HoJJpsZd4WMjxCdw4Se+7tltBVnOofYggTwIZCMaKvv1Zh3U0Qn+zQncl3tS4PIenkqWnylwWFTuLkPbhjuhjNAAwVN2oiz9GtL/VnN48IswOjfI/rLtvAIX22iH+NOSDdvBhlSHtuGRLQr6T8CQsJl0qGssJNQUiIxHzdu5V4DejTQL7PwWn8jQN/+N94Xd6W63q1P/o/oSoajsb5H+Wm/nd5dJXySXvTCJeBD661HLHMOYQ7znDwDprwSUCJjyjC3eLjMn26Ww/MB5dH+NAJcLodN4kwTP3nsFkxSdjwcANlQJG+pe8VDC+mQjf+Rg6euvxyqB+SNqOh0epuql/9yzf1JljXNszWZQ/luZHSQnvQKmnQHf/MUBokmKei+S5/Ji+AOwe1/jyF9/Qxtf7CAKkAv4pNB66/yUEzVhCdpMq+y+XJzsBbjP+zJFd2OuUhp5hx67UZzmBg+MXiWkP/9MmW3xTa3TbuXZUv87ciEDhmD3X82cNgVtlkbumA72jxIRv4lf/nbGL+jgoA+umtPN76G90JN+BFoVbEa8D8ecaz7wsAixVnrKd4xtXQsdMwSqW6m3GFQPIIgZXiWQhecg90/jlrHfGu8tBvOSOCKhHBS2UGNAEMI9zPBEyPIgpuglzIT5aLZJgUED9XLtSny0Uyq0m50OPF0ylwK9IjAKrEMewT4uE+RdndtOR6eegOPpJoMAcFYGhdhoToH5uYbld98HXrnbOmmGDX+jo++8Qs14eVj1NKFuOUeCnoxrltLpt0zs8ywt1qSiZ7xi6kTDlNB0FoAZWTrusQkl1tmq5au1nTOGsDMOFhPq0CU45HlB1cKs7ZHv2Z1185GMpJQe3UDyJMr0ty4B54Gy8shtkHEIxKUhgjszeibPApJuE0Zcw5t0kn3005iMdW1inBpNXZHx3hQFmatnkSF4xRO0/DKeEXFX7OiyKz8ry6S9YIlGfkdI10+t6V5JHN7DBHGw8kZn1OJlFA2HioyCWjOyRKJAshU6zU967Mae8PAdN14JV0PacMIzL58067DArhlV9vedM17c38T4dwPAAesdj7yTTQifua+St22qVg5IXrJBrGpD25EesO17/Pp6oIqye6HqV0cp5e1iL/FymdKoka/pyJGspCe8FiVJ5Rr8cfNaGFGeuYYWxt6AtSjswZK0VPbVPdhizueKTOlx/7A4reA2kiR+Kp3r4JzsvO/9ZC/sIFntlXrTvK8GdeMesez9z7Q7VYenTI9DLm36cHjBx/7wF/3aHe9yeFwosFOOlH563/ZQWb3OJAVUqs6m9xiGVbT+S29ToGuUzQtOn3mWt6UM0k0S9gXp7HlQa5VhVKnsa3x7rODjSzAv2YTOPVrC4XBJiJpS2ehDNPY9XUDKXzs2mg+c98AhQaz0uwQ79qo9z93GQNITFDRz7TJw6wDtdn3GO5ftcEcfAFEYPpZVt1gaVX4ZsPebuuu9B+4HcbhF9VGP0Ud7nMhdMUBYoxYxGCm/58HFM7nKvP+uwSsoz/d80+5pmG7JZ0BSSYAXJMYTmGA6DZTb2p4w0pAgPWbQi5E0w3WZ9kt4Q+dpgkcHApKvDmgyL4sx1xbX0Oz8bdy/vo7+WNFQxAMxt/uOR4J2tw4B2iTsMYS719ON638RfiJs7c0rvRqX/BXWTomfJ34topPvTspoKYeiqIOfLUjEqCmK+eJkAUPE1rc0GZjKQmoa9PkFStcwONsysJtcNxoiKPaV7tqu7zEjCItjueKPeBN/8bAMEWfNXs/PlS/A2DzLyvhQbcjTEs5nlt7VIiTpehCq5PkceXWVhJAfgSmrrRjj4XwJ3k9ycAkQJ6SC9rz7HhwPGdY0NmL+HDyV91I7PHpkeYk6xFuJ9efMK989MfyePAH8N40rThfCbFxnEBJROCzUjaz/l0VUnMhTaRnSDW4fntpSJnAZddYBypAKYfjYi5mPbk1zAxxuvzOcElAGQYBMVfCutrff3mxf8Csf5oigplbmRzdHJlYW0KZW5kb2JqCjMxNSAwIG9iago8PAovTGVuZ3RoIDQ1ODYgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnja1Vvdk9s2kn+fv0KPnDoLS3yDceWqNk7sy5a3KreZy26dkwda4ng4kcQJSdkz+9dfNwBSBAVQY3vr6u7BHgrER3ej0f3rbjBffVjlqzdXuf/73c3Vn15rsaI5KfKCrm5uV/Bk4F2+oivNVjovSC7gxX71Lqu76zVTeVa6P+/hj8ma42FbbV3Lrrrt3VN9uIZ3H69ZkVVtV5HrtWA6+9G/3VYPdmx12Pop4Q/N9tc0a+B3kTX93e7JvWkO17/d/OVPr00xpZJ6EnlOCfwL6fw158wNSrLGc0UKMRv3AlbUKuvqw6bynG7vG8dKP/B+2J44LJBDgxzWQKYj9zYlnEAkff1+VyGNM3mrQhJRMEdP46XUln3TwvqcqmzbuL+d/7trbJ9NuQOBGZmhpIXKbu5qP6BsPxz3lSW2dy3HrhrejXPAFJ8cZa7pvduGI3KLv2+b1kmUUhXdB8WJMjP6f3JDFKgRKXRuB2gijVmtKVFcrW62z9wtrQjNRTj7S6CMiaz2XLXVH8e6HTg7DGI6tvfVpq8/Qjdg6Mm1Nm5LmyXNkkwTTk245n9HORJCBxzdX2BH8oJQGBNMTQZtKLTvZgwpYEplKKgqd71eXxuRNbhJXGZVuUE+7pbYoJzBaBnOkiJwXJpyQ3LgMhj1AlcVWVf1iwsqSjSdDf17RG4wlufcCU4HgjN5ki5QBA6/gsl/zWW+SJHJiWJngwatW1jNKMLU+WoU7USRfbu0KMslyWkRjr2JiAEY0jKQApL28j46OSW5BrpOfW8v8cCYQcVLCCxxmhnnhNH5oJzGaLIHkSr9LIkyoYhmRUSiEYV8NxizPy+6ADDZRKkZi2/dCCnDg8rEipNCFk547NIxYAUjQs0E0R0fnF1u2h5tuz0Tze6IHqyv0YUd8NGflk931aHyXsK1WIOFp/ex7vrOd7PWGhq7vbPn4ADdi/c49MnaBtj8nE1chTJwFoR3zc6iVW3d+GWq3Q6JeXBkbeARTaC2JpBz58ewH9CH3g7bnM+qds3hQ+da+sZP5jl4WtwIkBb64oCwf0RUHlwu17GDH+7ElF8Ox0mz2dxePX7sHbF7ZO/Y+V/gDsrdInSATRW5Caf8GkMV0Fsokhc8nPySoRI5rCTEfFD8WE1XEyCdwsiz1eLHyu/1SRf6u8ptM+K39YhTLLDxb+pt5TQMlQgV0nDvsQbiNYgnB1spBTHCk3Bzh52PnZvDL8MDJY9ZFNsp5mrxOILFuuhrBeGARSa91h+vpcxAH2hmz2kFD17/ATLU21FVZgYRd1Gy1ZoJIkFV7Fyfi2pC5kA6XE5Ju6RxakHjzvhMaVhajaZ6MmP+3ZoxhnY/PqV9G/U/79Y0N2kv8xW+JK3VcBzYSRM1rKjgUBhmnCbC/guweXV3ZxEiaC9aaW4GrYSGsusAKG9dK6h759u7QWF6tNWF8aYTOm0QR2o4E/ZoHE/2Fd7ty9/tCercT2v0YbJ/Vm3jWm6toYYmtK36km0VAnYZIeOUqRRynsgCoClhoJrBOLCbKlfZfx3qP47gnTovkRolUlC7cfUg51l8IuEIAvbxTmc0CkCJqMJjLq1T7CEw8u23A2zdYndetUNQsuRSAIeCtw5WvYSumeBEgyUKBqGrkCz7ee5et8PmPtiABwLSwR2C665dL4nao8F6HCvvpa2knusWhZCwBSqk5z+ibrGQAT65j4CYd2tpsn+jF2QgdA5RCQ/XtFBDgU//UMEetRMmAzsOm6wN0R7t/Nz4SHYHzDrHukemPVbYbuvDB+8iDoE34dnDrjyUfhmQIIaidQlhr8M8PPtQO80H6XsVGYNt7ydsUgB3TOOOeU11S0XNDg5Km0A8merLLYzHG2KIrZFDJLC1WQsg8eN44D0ZdW2PkfWTVz/cXP1xNQaXEJVZnXCS3uyv3v2Wr7bwDg8cGJnVJ9tzD/8gDFVitVv9fPWfsxkEuHxp4GCDy1+YQhEpFDwlpwAiCgDlXzRFQRj/bDYiaScf7EoJIRcVTv/fDpHmPEwBjKGNAP/IgS5/WF6lVEINgdq5e9Mm4cCyd3GXJ2FfWVxv8Dj8llxID8EGyOSMhjjpA9AIjr+CnZBqhXBEBEFMOFgTDaZlPen2TcKHazNh6ZxwmaedtYcHKXnwHCVveU/hgA8pM6Ymxogx8HzOGHmjgyfMDHbGZA+tSxreVxjl9LU7h+6d88y7nfsFDrveWFdA4RT7Lui7/MRttxwWS9B2EZL0Q8SQQ6giLRrUegzpWSo2HnllmgFuEcHsyykGIwilNBgQ9yu5SpEz41QTmsNa0540TygubP6rS/vHGbPJoCmJgAP2TvKl+3PX7Jp90z7cQZRqG7rj+/doWY+HrUuSJncE/RyMiO1ImihhcgcQJoMwbRsoVd/WH2sXFQTYEnjBlHHhU8bvL9NIbX8ZjnuFyUv0EnnKLig5iDcwAAwAES2mKEFcgIBUQFgO0DFYHyEgrP/62jCXTizyrHos9w+7CryzyIXDx4VN9nNTZBv0cHdl27ufnwBAVMuJeUMUE+GqJ72bnRfKWBBf1wntlHqa/FKJuHgMwhklHKYIaACTxLUaTNLcxVvmpVcD4N7Zko3Nvy9G7pJIPlupirKam1gm4YxTFnA6CcJSzAoGxgnMzkzgFzIOHDyoUc/eJRrNghTxmNT3+jYxH4yRWsW2Pc2j1CQXZ+RKusijAi3Q4ll7A1A5Qk86ulIFhPOzqV8sEqM1EZyGI+4T3heCvos6LowC6KXnOr5EQlGQuZ5cYlTmHOxsMWfUnpURxlvDUM6DCVHA0RC+yDEz9Ty7bcu9D7ea23QyKObWKAV4Y5hzbGbu2OYJIQV4cdLxpc+C2vreGMKcMlSbcudCFw6gYe/zvYfqFJ0Ix/cJ8MPbkI+z7cReP8QzciA4MuZ4TVj/EZh8HqpEr6rf618Bg+BSDPFM17fHTd+536X707floRtz1EPX/QPEX5016/DbR+PA3pixPnj81PhA7m0JEdymLg9+9v6u9JPZ3OrYWrkHlw3QpxShaz0MfFE5UQplbHxqeXIz2Gppb8NSlOojPtLMpbOxwBnbBfhjgfqVm7j94Ff425urE0piEAqDtw+WZH6Uq4IH1DEGAZWakfjCLQaYsWmrvfvBicAH7QJfZk58lO0/6o/ucVIf/s1PcuycvsHjrUOmlr3DAeuUzWHdPXU9rDFow/QoYW61CLAwVmjL1st6W/bloIwTheAjmLFtJ5fms/4WwoyxbRXXl7E24ErFdmTtVaB535f1wdVC8Eg3e9f+cGcTYl29GdaudmWHxYi5jRBgooX06oDae1u1szrFJ0t2+btr3tX7uvfZgGaeeHDHYiRJBBDfZhtSB1RE8byrWukEhE5ZGt+RBKoG3XX6toWghhjm0zU/tVj/x83san8ohcok4YmkvmDW3f+17F1V2GoZK4y7klB4/WSFdhLGEz/aiG6S9NDzpEdIP2Uz+wzuDHCkNg5HMjWcA3j41LgHPM2HbXUtmc26Q8sGnxv8r4LNUZu6OvTuzQM2lrW97wA/LTCHv29t3/2+TJ33d7YbIzQ82t4fT8sUeaEtYBvSoUAXzdCi1T0eJOkkxqX2nEDDwx1qMfazmgwt27pD64hN1R6IX/fNum9tj43fLZhgXz50rn8N0Lqu/A9vSe1TZQdVrn/tO5Tuz9FmaN0zFk2go8tcSpvAEtVuG/X0XFotDMoWb5LZrkS67PFlIrev9JemR5ZKDacUydlI0K0fDtuFekLs0BpGpBLPisLPzmwKUiJuiJPIKP3fFeU1XSzcgMgWEk5/iwSWhkOkzqeZJZ5KEUAUNk0t2SB2mg8446UY5DYzHmtOwYsi6J5qalf2dXf7BPbrhVN9h1VA56uPVeuP6KE5/NMdncY1uGP04P50cCJhlo9VAlLa8zPmQOYWDeQbB/EgIwGIbM0galLUUft9osDNUqpXLKrem/QZ+DadwvtrZEM5lkFUQEUsDAOdThFHqQtD58R9dp2QZ29iFAq8zhLKiX4lhVbPYEYG4I0h8wri8v9rFtAaO5saNdmP25QS5kJmeIMjGU6A0t8nCE/Yyfu0Bv17Wu9YfJEWUcY3MTDCtITIha8MYZKdCtOS0Hxis0KGp0CI2tIBKyDuUeO9twFgUAw1ESJs0BFSl7999Nhi8SKcgfMgwmkfL9FChQBWinDUC4f7e3/H0oy+2P5wjdpdrpxnN6Ghs7QOIAE6NtEaBJ2kUOEYBwSAN5zXZjkWiHMZ9rt40QtAN9HQO5w9lakeBklGRKHOlqIJTUamG7Dcy5SAbWVyRv+rS9vDTWEvPwSjbIJfZD968d4CIPOh1y3Guq3bhrQS2VHburWI87RXQxh70lIK+AIOCBFUTCNZye10rb2JBD9gjhzx5QALeXZXdu6h9F32zdDZpRGA/BDv8bGa7n59ums633Eop1vHd9iMaFJYdltc3AZkkp/ow1i993B3JEdkdjAG2g7GzoGzYCumIARVPlLrXMe+HkCxvRqHU7R9GDbAnmFFBFRZUn9V5SfvvPG/W3IWj4/Za8ysAkJgNos9hEYejucOjhtMeVhShsARWvwtDZp9svK3gSM0u8CRxGMoyVX2un7EOrFLfMMfBzQa/8NeMbNBu7LJYe0j6cXcsADDJ0MOUuHjNLOH1U2uw3HlYbucz5WAp3g4po+BA2vVpheM4obCzG8YsYspWjCzVM+5vWCHhOIE/N181OWkN2ilZDPtYJcSsHgxLxhhXdnLZ2RapfqcpQqIRpmYL3VhlULbznP5pa8koNL++eFh9xS5roO3f800nYDhvk8npOPo3GSCzFJk2KzdfdB8CIxzMzF00Lpxl2vGiH5I08GrB4jobYqJu+ua0ITGwofEdsa28nNWj2h7XJ/gvg68m94pgvfLH4fgXit4NZHCd7HIBzCkDM6CjKLQ9tKlm0IQLfl0OfQ7dLxJ5AUX2SYqwfn5tPoptTUYMY4RTV/vy77qwqzqeFEdMYII5xkOXXSTYYP1PFESu/IFKJQIFOF05hdBag6pHKmyl5DR7bgLP7vl+jXMyXg4918u3e4qMD1rwkEb/0FH6XL5S2VJoYmhM0l9loFMf6Sicgj7aDj1kD79hPCPFdndmDSvW/e4q8rhDhUFRnbH/ZBvHwrlzBdVZjqTQ9iT+7LAWEYpLhwJiqZ5OvL7CO8F4VyeXZRfrf2tVCw40aWAMoH0ADAVYOIhWGOTxEEiZKQXNYFKkPOcHYv9ePZ3/BTKC8Tf7GsHcRZDorgACe98QtvvQHEqqfiMaz5USorT5za+I27VC9fH6vzerdS0ZVvvnlwvNHJt1XVeLadJb2oModJjVn/i8RLmtnoca0GgqUEtiGoBKN+P+e7JodqfxltozzXw0ImdGXgX1hSnMsbJtkPrCD8Xv8TICRU8pPIYu8VEmAgtbjLu56nrDaNoEPtrM1vVQVpWWBTHjHck2jsS5NJXGVpbI2DmWQxi/Rm19wsYZF/OoNAFhBizVS8Qiq6I0Zm+/Kt935RISXEXZLiiD7uGO6YpalGiCiaT4l8q0RnGAokA7ptMsJBPi1FA8VLdVyktYPPp+s2x7yAQO78dSgEN47UCqgqIahfvdoI3g+Aon96rnLE9zuUF7G91scUrrt8jhhNDRft0o9d+z3Eq6ImsPH7Ye6A31P/GS73JWzNAigAHGSR9f4l/GqfmX0HQhR0Gj6VnO+wvOKYyfMdYtQ+wVL68bKjF8CiCGGkhbbitP8a1Ds5NnBrYYWE+Qwhn1CwVM9YMibpPXZ2VwwXOz6uG4JyJpGE0XwiKIIkWszLAzaB1p+8FNql8PmjiL7Ev/gD/5+rzrMYctUDwbnQoz6UPYn6JWw6m+VeaDl4UUyqGK+hnHwx6VO7urUAgtfMfecU+W8T08uHc/rhcHJwkhspkvsr+jHNROFbczO1PtHKV+nbKvf0uIjqeE1WEp6SNV+elDL6+8vbOQikQyNsLFWaQI09UmM94WSPAU6A7gVbHP0yVTMS0dF6m1Jw/72uvqBbm/28Pw8zEUZuznps4XyObWfyzGkhsA0B9pBQxmqLFzzn/ltfYvNQQwfmX7Sz9qp29LNJiWaTfxDOkYBKYGao5dCDzdBjW2tgyBERhgFm9zlMVdAJb8z/bQOHSCmVuZHN0cmVhbQplbmRvYmoKMzI2IDAgb2JqCjw8Ci9MZW5ndGggNDA4NiAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrtXFuT27YVft9fob5xJxZD3AF7+hAncdtM0ovtaaazzgMtcXeZSKJCUtlsfn3PAUCKoEBJu+skbqZPkkBcD87lOxcqm93MstlfLjL/+fLtxaevFJ+RLDWZIbO31zP4puFZNiMzRWcqM2nG4cF6dpW8rNrbyznLVLKp6nW+Kn8plu53c9+0xbpxP6APSfJLapKfLqlOCte6rcvNotzmK/8zr9vL795+9ekrnQ2Xv7JP32UZwaewCbcVamgqhAn387elm0GImUqNyuwEJjXGzOYklUzO3i6hG3e9Jo8JC6YUWoO508s55zR5aw9TXJKkrHHjOlmW7zLGihqais2icI0l/Grc1/a2qIvrqvbHzjfus9pezpEYdd5WtW+69p/1sluktofGvZn9HUgjUm6o21fewiCmknXV+G/Vpnjmvt3dVg0uy2SyqNxysFe5KIsNXkTbuG653Rt8aXZbt6uqbu1NQlu5wS1cfPn24scL0hOIptwyA095RmeL9cXVd9lsCQ9xswwoeWe7ruEetMKtr2ZvLv7l2Cu8326u/lB4Q+8yQqcuqSMEg3YmSEgNe0sGbsmf6M4yXHlza8/bnWlZ1OVPl0IC8fCieq6ER+/d+Xeb4Pg923ENWyAiXPRdJjJLJNenvvGdX//lArktVf6hEzE3gBxcrMgk8CwPpy7X21VZNI4WUiEzSyqRFN2OCAfOhiapdCqU7HZEhFtgNhcwq+7JeqwZrsYMqS1SwQTskUp/LV/6fQzFS6dSA11SkfFutiyLiOFVQvxoOmidZ7B5PZuDwGGDG894TBEQkhquZjSVRnU9hV+JEBnojOTryA5gAgUkHGoCEh2uUiKgYd/NHmlOKY3Pq1POeDAvjR0AGIHRcFpBYnS/Sv4dWwZIlcnY9gOKXiUv9hQMOQbPz2aEpYydxQ+TbBIs6LhkLkGKhevYEUnPcLSSrpcCbU2Bl0xAogPSS1AXoFVkvyjlURrNqdLJ55FjUuATgnKRUt2zJLXLwfQMbwpMh3kaBQ4FhZ8jKPRpgmKmBMUcCIoeCkqMnWTKJH8qN9GPjJuCE3IQY4KX3ffaK4zRIV9G1jBWsQGBSKYC3AAytO915Uz0aGmwesiC+6EDUQ90gkwpCIAEzeA7fhIjte2PFJeGf2i+/T0V/Ej4DzV8/QD1CFYTGj5K9cjPYWj5G/Dzh2fngIQsVQBcQAFntL9rymJ8P6cEYB8HhcYA9Wjf+8/Tmv4fcd8AsNqeCGM2mUISV3NC2BHr+zwGPfFQHQQHHgNKSGJSTYVDXB5xiuR6t1m0ZbVxv7rPtnu8Kq5b961s3Gfxc1tslg6aCsCfVCX37vsvRV35adDF8MNaP25Rrbf5wk/mkTv6Dr7l7rbwa+fuA120flP2gADThwfsIa5JFdxIcLzXca4AW81SI7wEsBN0Q4RLaDhxR4VdUyzTAGoDlYUB3QiDqPK9P9tuV5Y4LPln7T2oqiktvacAOPTmCUvpCITbSdrKTeavh/fOnPfk4FGJVOTZCZAvUkJiKN/6IyJ5U+J8UahqF/khzoivJoYQZrohgeBTDWoGbmPOAEa5O/mr7yYHQq7AUYB7s97CSKHrQBeQiIZAhU6sQhOdQruNa3OeZuAopXKvrQDKWWH+QMdl0ElwPC2ljwALIZ9qgIsSHTLhqZJvlnF1hBd2O6GO4HBHlNjA8gSKXacZl1aSzHkmdiRiJGUEVel+gmeOee9Qm3hGrt63ebmJaO05yzKw7mg7UaTjRk79wT3Ih3pgKmClaYhxKBQESDyYYX/YA44BfzO6DTiqYsE2TiKdA0JYqDPcRdSggw/FmQOe7Gl8MYWJZKCmArlFNUXgthQf4bmYOgIh50fVkbb+jtkj7VOe5VyRbqrQv9QpAbv0IYjySGH5tbzI7LgX+YRwizov3KLODrewmBQehlsmePyRwv6h/Qn1RAdZ/t4mHixz5rwKBts5U6yIjANsSmUKR54RZDmyZzmAVjQImIYoXFCTEt6jcAzl7ppoWMSPM8A/YOmDcdNm6QFGOWqWAP8IeUa8Rj08XnPS9kwmOABQWT4LaGCROBGDCLj1SaClQw1xelIE6LC1YLKvo/4CB5g2IGhUbAfbpCjq4EkGM0+Z7H47lALbmnDQy8h2APApHssLjQhan9olEFNyerDLKFTzGQqRfHbdFrUj7zZvmvymcD+sR0K4ddoI+nXvm+LHHbojCOloliyQwzGvs3Edm3JteX7V5pui2jXWP4KR15WfHn2bsejAbgGJcO1zW+1tXeD6ABYX1Wq33jR2MQPOZte62afv6puicc132FjkP9g14bfdPHzi5qn03ReY34on9qgahjBi2Q1qhI0I43Y1XLjdbhG7fxA41E7wwTOvQfMX8WAa0RrYkHZQNzodTEOVdS26bu9P8AHmo7QwIWVP8EGXqQJCNG1dbW7i5HbOKFDUZgiR0N0oFPa68beUY7Ko3S1dvmjvSMNtA8I3rMsYutzWwOvFEERdWoaB5vwGuGHtU4Ted7h2Hf0Ilmytor1vyoXN38KjBhgH/fDGdbgpfVYtBi1olmZgauaUgWvGDmJZIQJlYLEGl/qhLl+ed/nyyOX76/Ven/fwphyKPKKCGJrNMDcdHX2QOXqBZlR1uxqluwWLzjgBjg5yUSNGnRMwe0m+6vLLLCk8X97fYWbb3X61mfaVY8oXgYMg6nz1exgqpiQ4QCRNzTF/D+PA3KcZ7Owpaep+LgVwVZlxmvqsEOLVXAupO1DDelAzCJFpnQrOZ1wAZ1DZhxZtFl9m3p/Pf3C/VuXaRQMlSZq8LZvre/fAiSm0giIBvQJuvzXl+KTON02X478kVr6hnzMWw5FNAWP9oG1e1qCuBFPJ20tNk8o1N4Xv295a7ABNy7IGfQ/mAHo9G0fzEK1xMJYagS8e7K4u22LCNDB2WpbCYBB8JTpgqCnABcRXalqcgmkPxGnKX8JJ5YFEnYoIxYAnVQim/zjAcxBt8BNY28e7mDnaHx/IBuXy4y4fmBFvqwaWZ+PQjrc6yKRlF24/sDqhEgUkAXSeU25xqN3I68hJMFCgQg+bxtIxQOhM88CXnvJDpT4v6BTja+iQZSbwQ4/xdeBIn+BrMjVrdnRWz9jgnn8yHbiaCHWCxQdz/oDwxQHH2kjnYILXcWivz4wQ0NHJfr2bUQ+4mSddzB6NTBzGIiNYAtwrYkMc2ppHO8ebyI4UEF2fJxHj6BKNb5yB7f4YJeJsuquPVyDeRHW41Ob/AjElEGckfMFeAcRgcEDmwMu3Lr/KwRkrGldZyCX4TgC2LFDmzg3mzkf7uevgs7bWY+YqyNu6LtC2rlyTLa6Fhi6ljE65oN4UwlzgtNkSW+iyRmC4W7Ullgv6pXwq+UisRCD30dHBJhgVIDRw2ONzVANq0hkTJuWC++hDYY288MEPwW3i+zlgBEPB8Vi5Nji1rbiFbr58U2D55k8WETfuwcIW10Ln95aAhZ8VMe91UdfFaJn21hbPYkvR+G9DWgO0vbstV900t/7L2M0GwsjB0QBnmMwfrS7WuU0gMzKOq0HL8bgakTIlQNpgwqm4Go/E1Q4uoN8mUQByyGivvsD520vNvIvBiItE6MRRuY9EwAMMWlmyM9JhM+KPW9owBrZ7/mTUJ9oHXa3vYpckyTdVXVS4gncsn+37juIYMFUKV+A2fBiDWJajkAnm75c+klG2truPVuRubPQCFOhBTsbhCUon0tNUZH19elz3R5Ul+JPSnOVaAJzJAn/l4SlmosDdNg+yM9kJOzNtG95HUbWhfOK0hzWgGTkov43n24G4eOlRchAl4p4i+NZP8emmMdVY5kDgQcD0KNLVVNOxkrPZLExJylQDonpMlOxYHcPQZI4jGbjZZdm0dfnehv1AT9+j5vCundMiTgJvc69CfJE9T7Z1tW+KVlkYkA5bqTug25Szpn8bZ+3XDkuas8KS8lFhyUlHYE7BzERBv6S26PRM1K/PQ/30TNKa/5WIrzrqY0UEC0HuxMEBExluPm6mmlBNQA7qyPFltCzNJnZB46p9zpvSeKRbgpgOOj4/DSYpAweVe/T6dwQanEUwomsHoFeu89b/snEj3mVC8EuAFaFhD/aeOaTtoeU9zukipG58WR8DczyjKdA32OqTsFx3fg7Ou2YjIrg9ItT0m7bxWY7BM/9eWbHsDuhCv/YM5UHeiKL+NR4g7vOUfeXkIC7nAZ1HxGiNhgFtCrunPKyVzSyCUwMEl/X0d79u88Y3D96hw3G1e5ttuVs4LN0Frlm2D1yzUbS7dW2DBCf8AuPlwGYFRLHUcfWlWdIt7d4gy3+I5FCJAb/a+Ci9C8aj+RMEzZ9wxo4mm+LnNqjAs625+/D+n9m/cUedi1iXTRfUrLohfQ6G7nMwHl7jQX1T49fdrvJNEURRKVbZupbRm36jayeapYT7g+02JSagi6YJb97VLXuQ3Y6eDbfH3FZSrzyomUigAqsYAFpEgsNvfIrgM+RNniwuBdBhvb0E97cnDee9/Pak4bi2W5eH644BDAcP2ZUTh6+VaqeBhps4Wfp79P2+Lj2McWczmtll+7t8LpF9Jh7aVkW+dK4U6dPyrpNP7WMmyFUDuGZM07h2X7pqBXF4rQKceK7d0uAWVVYNAvP5dLIBB67BIILLQ+tknW9RJBr363Q8gRKRaqwqHy70nxi4cO9hDZD191GvY7B/SrHMgodzPz9WyE6ZSBUb7ebzo4XsJihkP/BL8IWnYZXtny6DJM7IvUOgE7d5B9p8f0pl3wQJtow6hZnklU29HbUyLGO2YCYYfpKyDJw7IUd0suhGxM2PKy92fNN4zlmA/rQ8OmAY914xlpFsyra0HoL9uXLxrYVtORaeygCckNGVP6SyaJrOglBbXRpM/aI/1f2BRuQUpNfXn+xzrbaIwtnUWPbqiIM0eCPg0A/kyVdTLwsojt4qnXLb1LEqzUk/OpnIYtco8RNIFv2GyWFH3tUh0xw1LElZ3K8ubYQMiLss6qElZBiV6xXfEIPsq2FwhpAjfYElYYBHPJdc+zdimLPGPLmu83VnIPGOcb5iIkaEARXp5vPJxC+i1Rio6Lgtr99zJoJ3kFXnM9FjSdWj1Q+BL5XR6fjTmdwUCSe9jL2ukKWaoktl/xsBV99G/iKAKFDXYoZJJnn4DwEgqF3pBU8pvjw7LL0Ii2T6mUiqifd4dIxac7iVKYqBf8OwIFb3/zBwxrHDkhWTKvOAekEThvAUNuzHPp+IF4Et4mg20Nj5u+2LWkNm74GpbUb4ifUcTZ8XB9WzW3hQCRzuw8/jUIz/7wd0mJDbN0VfG1ZXuxur26pdh+5cnQd8A+ksN0sfqeWJKwSKiYlBMRFhHOeLaI4H7uNxHuIUvmCn8QXYjbCI6Ehg9ZuYY0vTzOhg29/DJbyY2jt8RUZ+7N5HdGB6VBM2RKEHrMVAFYGuAV8j1dpLEQn/mQIE+L/qijv0CmVuZHN0cmVhbQplbmRvYmoKMzM5IDAgb2JqCjw8Ci9MZW5ndGggNTExNCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrFXFlv3EiSfvevqMcSVuLknUl7vFhMH4aNWWCmW4CxcM8DVUVJ7KlDTbJ8zK/fiDxIJotJltzTnhcVi5VnZBxfHCmyeliR1ZsXxH/+5fbFn37UYkVJlpOcrm7vV/BkOPxIVnSl2UqTPCMCftmvPqyLw/bqH7fv/vSjyYddqG+esyynPO7yf66DlCud5ZrY9irjnK9uaKa4Wt1uoRV1rdIroURnisp47Kq5uuE8Xzf749UNM+tj+wgvBLHrtL9Uhyt4//GK5euybqu7XZld3QjJ1rePpWuxwQaPxeHBfz/eu8+PV1Kti7oqoI+fpnioy/B8f6zdQxsGanGgT/jn6F48FVXtWx9PbVNtS9zkix9uX/z2IlBM5CITHAhNgTqSrjb7Fx/+QVZb+PEd7J/nZvXJNt0D+YzO4Wm3+vnF393BGTIkVzdWIBES9hdC2RJtJWHZsB+S1pPp+/IXwsWhnDt1pnXGJVspozNtpOv/xveIVvjhhmmzfj01mPtpillYngmpI25hUyOITHO2GrTyg6nhYCbTMJbIpDGBPoRMTOo5cnXDMsHE6kZnMubUESl4pgVfAfuDxNhmL1NED1TTsFKiV0qpTHJHM8eTwL6bYrc57Yq2Oh7ci6e63JRXdA1cTYGnHxxX/bXc7wvLU27I+sGP/dObF0BQ7Cgz5hs4efevD0fHrE5Ynp52Vdm8nDtjTkVGgLzD5b5Z2iJnKmNCRZ2syOKk7mPnBBc2vPsyO78hMH800l8njk3b0+IgSnnEKTNrzGVmWDxyedge98f66bFq9m6Z4Rza7oS+7KrDtqyvLZfQjLBeYyrJ7ARurN9OxQ72LIRwvYXga1AFoJf0uq1a/Pji3iaVBAPB5CCgStEMyPN7dEQ3VFjjnI4YbIuBfoK+8d6uYd2aO2WLG2iAYxvUF+V4w/iyblr3FkniWNvu+t6RsDsPlSkRz/MLkSTJ5sDilIx4HHvQ8ckIq0hGQ2+rpq2ruxOux/KgYaj6tFHr9xVYkxmWxJPmudW4dqj3UxpvML9RWZ6ruM/rWWtqgDNN3MHLHKVqpDxBiHpVdqZ1gcnebheOmHKSGTla4DVqGmDUR2dqhTe5Mpg92UmC+/YR9UpZN05zwYvR8TLDMwnCGU2ydL7qkuPlRMKxinhka8VyUKyoN0u3g+MTqBwNiyxasOGzOodmOViPaMTvvVFhA62TW9UIBkprHSSKLUkUz2FUMiL3tgJJ4WUd1EPRlp7uxwMwZ0z5p11xKOpAB2p6w6KBPRQDxWaYGxfAjOzADBKFm/Ut8nrZtM6cwLG5gWHewZHiaXbiit8+VRZjwdNTfbSE3J42beNeWewErwrfwi3QDV6i+OPT/emw6ccrwKod8HS2Ude2Lg7Nxyt7Tk05pWQpsyDSbs8PDnsMgyPfKq+a8D3sDScESHdq3IaRIO5jczw5ciN1XCPQz00Zft3tyrBgKd0epew3B81x/sZOydYPlVt2E/G9UBywlYyX/W/he2GAf9CADUeGxc4wtgRRoWxEw+8nzCnaCnlmTlc3YHTAGN8oQFyRmY2k4sO6XhABCfjAoEocruPeCSUa6Xxw3hK0oQbxdm7Ibn+0Jw5SUHqVMwsfKJegdlQ8SpvSpQBEWQqj6vW7BKToVktlnuVMx5Oh0Amz/vHKgCzXnSQ388gaUFcuppednp9RMByAw6Ne17PzgK6QjMY9Ju1ZNA8H3M/zuFfg6TO6dnMJmkmhx73IJK7+sH4FukHpsOmZxUiecSXPFjPpWH6w5L+z0LPcHQ8Pwagd5zEwTEFMPEUKhcoJFJpePYcDEBOrJ7PrAUCjQAKjTn+cGM+sXsCOc/Ec2nsHXYLnbWmv1s3p6ck58bWX7Oow4S8LkwkB+IHAp1JzWBg9FqVHYDj2GP1g3bq3CwSkkqYJFcgJZld457JeElWhWCapmlQVb8EEl9vreUVuwC9Vz5RbyXgm6Oi4XiPtyZqk/KXYa5UGHEvuXf1PaFvnRB6QjjSjTr9Oi/uXhE4GBvk1HTb47yurmeuFxVNO4QhHC7keRYwccM3XVes91brcF9Whc7o9n+aBT/P1Hg3QaddW4EZvAlSC93f4ft6rBYdOA89EC5oPW+jfF7ZI0obDEsT4ZEN8zXrpuffS/d6sqJ4cdCs97U6HCsz3PjSq5lEI2GQ2nrFdWKYEtEzFqFMWQa3AovgZolG31tLCogASNg1GAo8HD6w3xcH90Pmo+PbO+Qjuy77Y+s5H33Zz3D8VG9+2Od25MGNb1i5quAug/X6aZ3GId9PKUfCOF3lArfBUfn4sTg0Su/VxIDu+D24i/knNJFIz5coRDqSNxQcEZh3e3IC5k8JbjB8+g6NcHjZX0vlR0jhawGf5ESanGMTAbwiLyyyxNyFBq3Vb8ySt28ejZSPgnGIX6OtNwbY6BB/IHp0PF6Fj5GnkHZJ+SGFP8nO5TdM+5Z59wMGnNa4iGUHtzmjGQOSGpiLlC5qxLxirZA1uGszeN3w9g0d/nXbsqVLrn8rpjSJs6uc+HzU1aBJ3fFj/z8SGBSh36aKzXjd5lScBKUErrVxwlhrbihIdx1FHNCFwIizTwlvj/0oofcbW1dRqGMnA4b9oOSpazuQRiUyBAAyW04OaMWleJnwdKkdqiYJfxzq1hK7iKXYVwVoqIuKGS56inPMUuyWAzwfOVx4Pfe39cO/CF94vPzow5qMk7pX/aV+0dfXZBwec4qur0vq/YCQew0g+nGDQo9sP4wNmMXANTc4D12sft3gbog0bF3/ADIXaVD5k0m1j26/Cu5MAdXp3kgApkBeQBM3xVG9mHTFKwBGTIu52P8F/MuOCnoeC0Hq+sto7Zd9Ed0Yu0B7NVNTlMNGV+0TX0MSSsZbLF/0HmgPy13k8Fapx5bwulfS6utUySgH0qniIJPJ34TjR596ash0k3TDPUdTFvgQ7Os6yfXqsXKLOZ+Jc86HPYO0BvjvuTj0G8+5FDroOqH9q2iF8KR1utTKiet4QBlA1+O8uknLXAvorfXTKM7de3592O/e0L4vmVPvI2TziYRTQPoh2NMG7lOJQXS9UCDzude2jZYewMLDNYQ1+iTaGZWVGr/+GslFYwMq0ZYyqquw52Rchbtc+Fr7JzkqofSw/g+HfNuGLwwF+Lucwj0JzQgtUtD7/4YN48+EZAspRjXqmwjOoZBLhGRT4d4thbtD6YHijuXycEE6z+GcfvUsu14DgMBoP8X4pg0Lw8E3cycmz9vIsdVqepV6UZyZza6+iGZblebhG6ElBjKMRZsVZMgUK2UVBAfYex1S0X50lgBYbfPt4rCz7wA/gL/x2Km3gFGD0MWoN7lzZgFfVuK739XEfw3xgF3CfBDPnoXNQ+w4SVs4oDHD5V+FxzPsPHQCb2apq/2oAwnfFU5ONRQLkNudAVOGyc3a1P1eHBYtDwefLRdyrnXD5eAathslxcoEA5IA5o5E/WQWBaTuCiYK7qq0LkFlDbZKPSZ8Bwl834Dq1tQ36zzGjAcsA3mo0y5slGcEKBj3qZG2AIGqNjk+sTRLxBi4wqiLjYVLZsWFKRtK4D5t13qUC73p0QrhUsC9/hg/KkAsFpfBtfl5BSKakecbcQI4MMPB47nnqwlIy6BB3AhlWmoF/h0p+dkqFAHa0yKWcJxxmltPRlLNJT5FDd0Un+WbCM1JzWU/4dTHrKZkB2o8oeeey8gEb5AOVw0UWMhX/KjED5s0+fKo+g9ZnvnSce/P2ts/0J+VHwAnLeMJvmKLp9swU4NHRxi2oUADjqmYAEXzOt0MgalSWBS887NoUp/m8B0CWTEsVTfp+aaVC8oyRuFNYYLGry2L7xa2iP7f7+QQwbhscSh2KBbzC/3MKmBD17LCmTIU1cbAlvUEBGXLw46JFzuoN9C6MMeNtLRcGUMrjTsgDRoaSJT7MEsO3x+POIkZ4LDb1sfHPvowAmweOseHXAGSFWFft2Qj3AFwfbTbaFcm4Bx+hW+QlSbh13aLVL7huw8gNnMT61RJwkuBZazZiFwyp53kypJ4PiiwF0RkLRQC4WQvcRb5+ez9r6QDIZgaxxbB/ggcTeHoxrp6n0xhdcaYQmRrt4tqldXxQToVIQVX77zZQ6B73VdM4xD2qe1IMYA0cHVUZFb+vOLIbK6xxoTiy2xvTBMDE9Oaa2Xwhy0kmYJyo53ODBsnEJ+/qXoytUolmmcvm+ARTjk6h8U4hV84p9C4hfHVlsGoxVANNpmoMbc2QC14rVxJhFpwqoFQGujzaxvulzWMCDMsqLt+8XZorj7CRjIHfymEFKlTMeFcYy3hdRNuWwzqY1OWCfIbHPnurgsEJ5wf58MVsLlzKTAA8imZ+tvhaUQWOkaloTe/e5cAqo42yeRQPvElN3COZZhvCeAo/R71CLslqNyBMeVa67Wk8KJEwXeFZ3gX17ZctqJFNH+WxVddliPhgsbdjuLHDiFWkNGCDzo8JmYTCuXYPu+NdyEPECCYkfuqqCX7gp75IMLYN+AyQ3uULvGeG4ap0cP3NUjT+7OgXqyZSqQcb/e/9+olqQjZbTcgAV891t/zImePM++dmGWwmabbwwwcl7PAvL8iaPCfJbDPJQDiRNos1MterxCCSkK8X4dlJU/tcP1z1JD+vmED7IPIVV4CpmZ6tmNDgevHZiokwmDRgcL2x+suEW2IypsVUzX6itiSaBcsS8mEl/8tpVa61zeXZcjbRZTyAeUQmiIxzdbch32jvMzj5FqgDLCrpE/wALIvdbljrilGkbenTIYdtFF9yVlL02cxOMaWiQBzj+9qroB+xBNOlV9R6V7SlfwRIe+2fbITsUDbepQqwGB59ZNW7ntW9+yz8YFcUTPtDGLDPV7Ou0kb7Cgbs7IeEnR9xUBsNKrfWzwsYH37uQ2wb/yZU94IbNkiucDglyUJtph/QXYthWECBofgumo91BZ1StQS3MfyAGOx371eDcj/ujw9AjuOpcS+GxamhdoMMagLt6w5qhtR6n6bKh6SBtdkswYA8EyEAlitgYDHC6oAqboP3319quLnHfMY4Vm+T3XhC7paBvdOxga83lh2x7td+9DvD2P3Dae9TXC6xVflfFnNpeiqXFrJ+9uqHX1dfkuvyB1Xzz7EBxfJtETKH58IjOtjEo9zMtXtlw8LhVCOhifgK3gQw1Qdcy6LP//CoymBcvXAmckzloKx8cupnsOiSeIMOQAMlG75bwe6qGxCAHNynWyQ2rGr3M7TuJfxcKUnwTR0nKO7u7Xi8qHgPOdy5RtXCrl7ZvXVSH/p0iGN8GFKB+x8yCSXmztrS1d7wkDkTkSzA+8Px0BPXsdnOgx3XcJOoir+gcsL6FbLzWaf6b5ZrbsEgndXcfpcEKrgJi0gSMCOFnR6mXaPrQeFNAiQATX+YsGWcKMDyeRRA2KQAGKZNDb20+GNqOryPo3mi0CTeFfjmJio08ZtMXOa8oRqsN7yLsOtl9x7mN8xWg6YztwA3U5Mx0GLKJG5ZjG8Chg37hoAlGYLJ/506NyCkjIb9FVTxq/RONIANc+lOpmbEqvfAKMMpU9uhWNMynPDlZUWajHKYSA7uFjJBOovZuK8LIVAKWMqAAxiNlWK0YamlYpkhKu63cHuYGpbJXMR9NpdMlksb7Y4nq/2GUemWCKLQL2xRP+LbKgC5aoNgEF8V9xaAMetffqxcPBt/uKCQE4MXZkTyzVLJZQ48QUa0RR3KyPo7h0pu4mpT5mosAyL04GSYe2eEgJh4W3dR3MUWAdO445tEuLMPCRO88pvHvRbrTUAJqNFMn72blxa34awcmIqbs1m9J/h6npFhci2fu09FbBbmeftUIpPIjlMbvWifBiEnndjnnM1yQacBrLI3MXc+uBHu7/WZEKydtOwR3J2tv+u9jWtd3T0vcKbRsfnYRVq4v4yMtZ/O5QkzNINwCnR3INBXyFpnoGrOIjO2pIJ6LHNf7Cu8isnXKS+KapA35TP/3z36Igpb4mvcvS5AbDv32so3frfgPiq4gJd1CRit8ci6aD0cm7/uDvobeCJaw8yxdlExUG/Qd9xvoaYD6ILFP1Gnj0sz5TCT4Wczpe5jGOEcWYNpfk9F0buytrIvxxosgOjzaTtXXz+12HRWWlCbXYo61fj/GBqPxK9sEtXm/d0bW6MIiw03Oc+QMd7AUtQnc5vTna8z411B9hhjIt/+NBHGkDzT0tj0qpm5UO7+9cDNoNkAYjmhSMQTe1fRw3bvLw6KxycQKA+QcHyUJg8gvPvHDelqaxDcfQiE9Nh/0lgwjLAAIASHw3hAWE6RKyNm6t96jKtqR/+oIR0K/JiohlJjtb/iYEMFroX7E7isLPq8ZuDrV3MWerO4BLEil1k+e1dJMOB9EUfexjmDMBichZYBNiaqwvXM9tViWPirt39pdlFwix50JgzvEDrFZU9xFSPW6k/9A5D/AF+93ScIq8UfR9hRXhTzK+hNXcJXgDJn+aob7DK++obbl1/DVzJnz+Kr+B/L8BRf8X8zX01fLZ5aJTAgRsO/oVadzQmx9IWMydWDS6e/qexefjvj3GOmBKAd6T1m9BWlreDCCKz/NqjB33156Qv9rHdta1atNyl9uBcfqn3xUB267k9FjTWt+IigyzZ1H11ozn5xlwp3YQUY67ahOdejLqM+18haYh3ubHTJigr/jVD4JzU9PgJv1QZ2+tLnQS7D/scaj07Kw4PLy07d3Dq7pJW4N6WFimNJn7/qf0N9mRJhlVE2LRz5rAgn0IvOvy18AamZueH0ZVqgjPia/4Z1EV5j/9kNt5MqRGWSXqim/0AV8ipKoYx1yY2isEqEyLktsHPsY6I+YMP/H8T7hQ4KZW5kc3RyZWFtCmVuZG9iagozNTEgMCBvYmoKPDwKL0xlbmd0aCA0NTUxICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42sVb3XPkxo1/379i8hSqIrXZ32xv5eqSzXqdVGznNqq6q5L3gZqhJNozpExytFr/9QEaTQ6bQ86s4tj3oiGb/YEG0MAPQCtd3a/S1btXafj98/WrL76yasVT5lLHV9d3K3jKDHxMV3xlxcqmjqUKvuxWN0lebS6upLRJ91DgQ5asL4RLHur24oonxQVPKmq+3VebbUF975p8V1xefLj+G8xI8wqumBaTyb9PdYq9XlGf5j50fv/uFXzVzISPRDkN4H7aMclCpUzDjqKpy5ZIyYm6dldfXIksqbuHQGK+K7ef6Lm+o071o+9TNHlXN238LWwfBtbNjp7Kin7/Xux2+eI2/GjNxHQrlzT4YwkUeUaxlIuDEIzTTDlBm3nMkaFdAURB1y++ytKx+HCJdOAlfHXjr70AuJRMicnE3WuQorHJ09y8Y4q4siyDiaPRvTSm+kQE1Z49aYIcR00JVEr1XGzoZVO2P54iWUjHrJ0s+hcaofXKMmdTP8AxKfVKMqfd6noDnTy3V1cpk6lcXRlm5aj9i6+MGA2+SZq5TYx3L7RlJiaDXVwpJZKvLjKZ1A3uh07D04WwoEGfaIdPF1on+XYfdk/KxFGZ2tA0Eq1/z+mnrbf7riQW8qTdP6JqgjY2HTAvqEs6OrMm48wpGXS/8sr29vrVT696XmaK2SyDPXBmhF6td69uPqSrDXzDwyRdtvroe+5A1Cw1Fh63q3+++h+yF7F8+rn6NTcnxKJALFzzZfb3QtIsUxl1W5LHsGGeCpCEjXf9kHsWCuB3U+SbT/Ry6/lWFBW9ruuq7Zr9GtjoJaiSazrYIpgI25sIkWyLu46egKFgF4JoQXJjwyaNBU2RMS3nDJtbNGzjbUonWZrFM4PqNAXYoEB08fzYFG1bhK13D2V42lflT/uit5VOjVTFZCBvRdON9cwkxEFNSmimRlMnd/tqfegN2rxgjfRZaySkYJnJYmJQZCYJJolMk5k3TeMt4eF0QsVTLZomOrg2eV90+6ZCy1SSD+PJPW2yq+n3EdseUPCf2nKdb6l1HVjSbMoq74rAsPuSlAOmaok5P9SkNN0RKyuQV6RBKBOtQP3V+PjO8VUu8vUmeT5lym+Qsad4ElSvN3hZxjSQYxRnwmki69rrloLdwvFq66qs7vHVJHuvfvjBO0l8aAu0a114C0b/auPZ3QCTiqBGOLx4LtuuqNZFP3m733ZhQo8+wswwkJbzmo1cBFFaw5O/VoOa6xFLhWVWmcGBduV6v82bSxJYVVfFoMUkdrIDOinyZlsWDb18RBkW+Y8EFVD8FQr6yTc394R/oKGjz+3+ti2QunWvGcEqmUGtbgljhOlwHsBSRThSbbnDFmBAXhX1vu2XhQMfqwxnloNHAlvNwQ76PYKvgRPcfPJyAcML7rUhPreMxM6Fm1jUkbydhPnSlBm0wjjf1/W23tXN40O5BsanItkUjxc6TYpqA/vzT9Sed/jLg5jxw4Y+PD7gycEGPD3Y4l9q/6fxHf3hw3PE5oGESWXyJ9hOBmO3tXea8Ag2b10AFT3noenOO2FwxsEEYq/etQ6I8aAdOgOr7cJO8+Z+vyuG48oloUeukjfFj+X3Qgl6u1ky6QPGMoJJkcWzT5DfiA44ruBIJ8SggnKHbgmM/I4WBuRGdOXeUG3ohXCn7wBqQA8ek8IvaTOOaP4PbJNvC0rbgkZ8CKt8fCCeQkdv7nI8nIFpfkzEmomRQDZaCYbCEeV39XZbo0g+4ulNXfK2/bGsfh9e1vXucVs8Xx2k4pvh8YH87oYazjJZOID7mYrX1jGTR1QKZ1gqXdz9A5gOAyryLdFLK2+L7pTLklowx7N4oqcFqCIGdADwR00GBUxCq+YRewIlxJE1HRtHTscOToca6+AmOBiCGXI1eFijdLzymzPkakDcqZgMYnPnx6Rhxofg2EiPKODiBozayHpgA1jcn4umppemQNgS9Crv0Hu6pDgJGDID3NeHhb9PhViABtkgejiOKPrDmDOoBHQFceUw4OnMChKAKLcmWmEpJIJtUzB9gkk+cKbHHl0tiFeBa87caG9v55YdkapAfRGnDgM8jIOFgsGEJ/JG4Co9RjLk8XvXKkfCV8AmHQLtnmTwUuggvHOrNsH71d5e9V6s19iFADWTTADcjWZ/WtjVQA4H15Vlk1EB4/01eNUeFesxsYO/3+VdU6K/PnX2BQRFU9q+mYlmONmmK8Eg1PbBzA9gUV8flDUKkG6uQO0PSjkJS59mR0RA7hgVy8FaodmzMcXn9F+alOHpH495PruSgd6ZPlppMTUAsK4JvI8PAGytPKkiChwmgpiXqYiy0FtNtLZuerRHWn8RwhZKT5ygAfUfTKtLRcSfRZNqMyakjsd8CfrpZFBBzBDs0A3dlgcMAlDOJ5o8GMzjeIE7xTQEOtGU5yJOcSriHHwlx1jWTWcWOk7lCQFKLl+2vvys9RXgUBNPTLqip7oCDad1RSrFnD6isj+Hyx5bp0xaG49DuGREsN8gm8e8bfP7QJhH9pqiZj0YdTj2FEH25IYE6STy09wxQBYTWyoT7x8bgmwt4fnuIe/6j0OqsvMBKmah5oI4BPU61SxLQ/rg/eB4bXIS7XDwLxrZMB797gzvOISxRop4EEJSxP4UHAKeb/0msSnsFxsp0sMoo4RDKIsm0FgGvDMaj4e0aTtqhWgrD7EkfCKnebAXMmMcncOYnnOaytPPUVVlwdIjvBpPfYskfzppwQDGWOBfNOy/Zwy9YRzQzhUHfhpyJKC7Zs5PaCvOwlAANUxNl72cjQpvrpTgqMQbDCRPG0IFAEtoFRnCCV4BF2ezPvEHYOTo0/uZzUPoDv57nNCVs7SOY6kUhvMJSX1EClp1IhmUJX/zyRrnLa+QIVnjhmSNb/xh34bPRdvlt9uyffB57PAJtVYVoW8AHPA0aDMlCjKv0KCuYeF/lj7vQWPCwwEbux4bZ8mR3VDgV2yqoyJNPwnYipElCc5WTp2tOhomD8NUKKBAKFyNYN3BQUGPIf/njvNUz/P8vpyQ2YSMGceUR1gvrFs85+tuO3+YhIGzZwBloYm2UQI6OhsQzIA1GoGx3waHcc1MthqvupS0mzv50EFq+9KjH88MAAEQ2WiGd7NZwdEOjo+tEPA5nSfcf/1mhniJtRYzZvl8yeXXQ8QRoaAgwpiRKP5jpvbf4rdn2x8uAm9fohQSN/KbK8WUsjmJC8kU1m8OPOYvlHhUvPw8ievFsycA6kIEf6DmjxfezSzw+sul4MEcMq0GTIzCeoXjfSY8mK9N0ZSUdh3MtG/HGs3YiiqqqCiKNZ/Dpyr6dGSdoedTmW/Ln73DuOxnJofhbecoh9IvE+YEUx0ma3dDdXkUwStEnSqgTp8Bx0mGQi28EEjDJ3RsIWndnExJcAg5tdPx5G9nxKqZRQP3GWb5F6rGcUV3SBuA+0TfENGKTlm6IF7Y77re7XyWBPmx92h+KMNSK/GlBmQf1wQBwBip4tnP4k51CnfyQ2DvoKuM5y53j9uS0EfWxwkIHwIOyXpN1UFTbS9m+u6SkJqDllBqWJQyag+gSOsCivxhoRw171YWuvsj+l9nkm0uY5ni8eriZAyTKoD/Jh7RnMmT8dQxxSfLoGYAb97Uu0dAJVehHu9IEYSRo6IUviuf+MJfn15wmF7At8dtjmUnHHBf9rHdyRgWrNmU4f+vLmyc+zQWIkQXE4fWFmLRdBluw6b9AUN+RAVmbIB3LLT7EhZyaR2A+FBtPcpGknmWiPr7+inV7PoU0x6PbUtXTvrirjlgeGqeVGub4h5rhSUF16/7LGJfIBxCTmz2q/WF83HwSlifFjagEH0WNL+j6opfB4vQoWB3IG6UhZrUWySYLdMXZB8ffLjZF6d7dh3qBFgg1Uol/3uRkV33Xar1dr8Jb95UnEqIG8cMGJto3XcLl4WGLA6mvCa0ns28ZwIgvY0HPb9ewKZY5wZsys8R4gwzhh9RspSZNCH6MNNYCRrOVFskLCWNjpc6V21RKaDlTMWDKDMJKxbDVaJT+QQtWWanfDu3rknBXkwkhADDilCH6kgnDYVqk9AT9gkeqFfCJhzhO3Z86QhzaAIMcD/ixK2jFIyUNCpcOoqnwOwy0MYkPzUDxYTp8gxAhDxNxPIUjgn58m34u1M8NXinSiowxCAHZKDkvsrmzawB+8SFTt4XcHolXiDDm2T+ApnVhM1sX62wdP8EO4CNMGtMGON1svaMT+utJOibsXIMYinVM1J1kXoVAOPXEvThyV1T7+jLPyjvCAa5Lb0FXL70CL2Pk75+OoIcabiKlZcVpVE4Zs8yfwdIpH02hCf3Rb0ruiY0E1SFObowSSAHjTXh5Dm0K5xkXATvQGnYAPXAMgqCQu6QVvT37x68J/rY46i86y8z9ogp5pIbkNQIRUNjnzFBV4KpQVjukj5R8sUPKMIUDarA4XZJv2LRL0hl40HymB4Ni3oRTY6psA78sx6ujYXUS5NX7WO4w9QdopILYOp6uIbhdZYzp7XX2RTAOmbmwzU+g7cNvNJe92o53P61VPSLdNYr6X677TW4Ak07UzQB8mXqaxGe/L+fLs07INXFI75ecB1g85LfodLYs+aZC0CCNovnHWr3o0vBbhq8jW9DZ3TF1z/1oZQdQqkF3Mfh7KRidkfTBJdxiPustdNQarnCAftKtYpnR61UvE8JZofLEEsOz0qI4kw8ybd0pRK2KRZgPrD963PeiQuQpv0ctldUH9vS2xZ1uBouG2PMdjsuuozUi+yh4AYEHEAjCUmZmXg3TlsP0YWg+yDjWd7MB0OfIRQuDYAmHs2GRsrhDVU86vv2dPBt8aKJica/PYOPfMU9ZsMfT8ZTDtSSq2jA4jnjCNF4CowEZPvdmd0LCJgzG/MSNVLz5KNPppzausCqsIvJOrtgBsoldTQGbHVNNzpVj/bxgdIw9OJt+1x9D75tyyoMGQ6/Mmdv6UAbd2ZOiZYv6UBfbc2cqnxXndJYtKopKK0Ieax/zEaSxkSVl1mQrZjFxPqo2wHgT4o8w5lYzDIuFAmWLNCN91nfLSRY7YiSo+TDKJ89oYafurCggmpMiiVdvu9AF+77y8GqVwH4drD/VNp8+Uayf28j4tRGZABOSHxRtXQHAsJwCGibgkVgDYbbJTQpwHKAy1G9b/bXEIXLEBhMZzmqLuIdTUoAWGITxKh41/604REsBZcwXvbrcxVBkSqALCYa5G/jCghzqpoIoFtpWCGs6cP9tr7FVxQpdog8O3ZofT2R7tVe4rb5QcALMRpmAwGfxNRfCdNb2yPxfntub0prOLEqmrIMWwOKvZVCqi/IPoX9WJdQQW18j5hDtMJNBlYoWIVg8NCimDT5Bi/qkkfVGUR7OFeBLeuyLbafLqkdxOjxqu6dMzwg9qWyKBHQo/O5cwDd31Du4/fLleI5cyXw4rww3mBlZwyWJIOV/WKDNdXpvpIei/+KpwAzMhuXKRf9pXawd7/PX8+0mTHrUIWwYM50qnqW8HQuqb6QZeUABgB8jiZ4vVhL5NyDQwgZzmzyl5u9Kfh6PSs0oAmveshYOuGCT9b/v4HUNvlpX3cQbnX0ofXK76/4j85FOfuPeRyeswxWsN5P4wLpiUsSfneY2vrdr82iSLwWkBAEx4ChUj49GpOSy89heLZC8wMQBIczgScLDEka7zEaq/w/gY06vZ6bCyAwUDuea47go7lGCi5HswG4dXIlWMbD/6hdz/IMoCpEA6DIDs7DcBKAi5Kn9CCE7B/E0oMEDf915LZ0fiG8t9Dns88vOESWAUpm2tkjOcf9D8R8hgJwCRx2c1L7j2hA+jINOBIugAA85ZF6o+CsORLlspSh8yDcK5nq/qxi84Kxti8qOd9cZVzRzQcAUYuuBkBDxiQqa+RTiuCj5//zC+zTzXw4rgeBRncNFBPKRdWkWdW0zMJ5G/X6crm2N7cOOBBOpmOY4YTvHvX6sHBxDXeaH+CKBSi0q/H1vqi82a73Ldlrb7v9p9ojmsM/88HHuprHIPhtDoNwA5YDOv+mQdMRBjmC7yo+B+CC8CqU87flicj4v1DfXr/6F5FSF+cKZW5kc3RyZWFtCmVuZG9iagozNjcgMCBvYmoKPDwKL0xlbmd0aCA0NjY1ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42r1cS5fbxrHe61cwO8yJCPf7IR8tEseyfU8SJ/ack4XiBYeEZmCT4Bggrcevv1XdDRANNgCSlrOQhgD7UV1dXfXVo0kWjwuy+OYFGfz96/2LL95QZheU5JZYurh/t9BsoYnNiYCnzeJt9q/6TtJs7/57l9/9dP9/Xfcv3mjR7/p2KRTP/nK35JxnT/vd/rGoiv2x8S+e75Y0W9V38N+uONTlp9Wh3Ff4nYCRfZvDU+E/rPdVufbflQ1O+sUbE5FJBcm1YIslE7kkxtP6X8JYaExiwpiBLyVJjfQ2++RfS7nQudUkjA/DL5Y0V1z5wZOdoRVni16rL2GVSieHtLllLBqSpog9GxIIp3dLpk322v+5biXM5NKIBbJJ+AEdjxZLkguzWOqcERqtkFIVCYSjhOXK6pYekp5+yRhLM1PlQrE0CdpGNNDUyDJnlPVJAC5LQrIyudnX8oflhJuLiCOp+c748+fPxxyT5A2Z5I0Tl+Tqv0yfDeQku4JfPGdS3XA0UoPJnGh+65lIkvzKvx5qJkos6CaUDJWztvcCWlgLugwmQ8WnFMkJk4v7HQz03cGronJTVHegPw7lfwkXRdBG1kTaKIwBujSXWsYD/SuxbpUrpRY8t9JOLLtPHio8C7yKhj5J+gg9wotH1Omr2akkz/H1cCqaZi0y6X15eEpqj44SI3NtaJKSeMCIEstyqga9Xt0thZCZtx09m4HbUzeH1oDcodLf15uyWh1aq4L7+LSq2509NMHEuA3+Df8r6iY0HrE7gTJB8HgOdvo6CZ8+La+TuoLkkpnk2RvdTeEMwICHryfXBqJGjBnuf2tbB8ujjEYE8cuWN0u2lDm3akzeRyiXFqYQN1JOR+wfnKJY94zbP5qVI9NRm2sdbx2b5YEF23DpOcxeerldVZsAqiqn42hPvwkYTys/UHdumgLQVuhTHqaE35/1lGAaaCQDuNEXGQV9oZjrCf2oc614NNrIvjjQlN4W5fajP9WoACenGsUeAzGakJsx2sDScsJTbDgXUbDIvWbTEG1KRKF9fz42bY3PIMdQJnPU1SK797ImMrCl+x1q5H0d3IL1qqr2B//tA+LbbA+mxD3+dicViHPZhOdVEM5V51CghvfK+9A5Eknmuw5fpe2XNFnuERdVwHGDgsdz4Ixb21f7qgEIUEfGRsB8MOl7/G/fruwRfZuiWO6r0Ga93x53VdqMMJoTyWP35SkJFBhYmVmAdDoJnX69xVUgueH0dq+nTB5mnitz2Zj8Ck+KflbUCJMxnC21BRyOl5Xzmnt+D65QCcntsTmn6gYscd320M/v6JI/AtQvObhuGly36BD9rayLdQsCV9v1ceviDME4OpzqD/Ap1LB73hYf/MNDuS2rYlX7Ns/13mmkzRFjF2s43oeX/pvH0qPF5OG26LONhibOVBOTBAWBjivsp7SylsbcpBkusJGfiUhOc0r47UeHTJ1WlZxSjCqHhNHUA1zHrtwD1AzipuWN7oE3zmjZ2lWOR7U4UVNDpc6mZrkg+gZ1/LuMUwtP1G0REplbc4sPNUazSm7Z9FgjcQVlNQthBd4PK5gWdcMQwG/JRW5AVBzyvr8z0sEGRoLrCQ4qzernujj4t6CcnBsKH+M4Krx4t6932AcUW3jj/lKL8Ilmx2qzLfzzbuVirg3qLAa6ry5hniknnWtw/AHGR9R+P+KltCvkhuVExH1GTV43k1FwdPSwFyFzk1kB5mpAIT0ZjlhuOl9d5BQEVXKWKx06vfUdFOt1eLsUSQGE9yz7aYY0Aa6EZjwm7fVUdAY9c6Js3OOrSTxwFirpZhcC9F48FEjKpLOsQDvzwfSf5lapwElgbLhK0Fd83js3MmdMJieciZn8ryBjb6WSWBA1eSbYU56Odp7OwOuWgPW5CO77qmnKx2oXQoopEqi0OePeBxG2Q0ajriMnyeUSKpMO9LQ7/glX0fJxZGxQx7TNjegZFcrjwZNWDCY8sfUceIAh004zIEl/mgIvSWq5ATAmbmDFjPHQFzkeem7/ZnjibOKEuRxbNMupuGWLZqz8tVGB2xY9rengRAkDiEpTf6JKF0hgYEk3hQ8JFBjNAlccbOopNsBCkpG1yJ9ldQEWtwlHEbwED+ldeNnil9vVuqwepzQasyyXwMGIoDlVzYnXQlGnB5z749Rc3CWKRNxtPTsZJ7B9Ju7l11ZMBri5JrmWA16fxC2ym7D1Og7+/TxHluG5APh9LQ+sccIY8yBQJGOKQN338isnbBFb9rQyoKCxqQUPsz/A6NL5VUsXTOdkIDIYs50MyOvckMHWPyWoOT+qP8/Zc2lyoelwm2cMuQIgxWxS7CeQgwEUdiZQ41kldoFACAtSSgeUrFOsgS0lRF3FGwT1lOjreCPhHVPyOt5IsKmGmgt547CGpNl3h6bLYvbwhtACYBbzg2x9Hmy92qLOU23QdPsR47Dty4FLAW9WdeE/PO234IDUz0/l2iEck/1YVuvwZdCkKit3q8cidN2W7ctq+igD+mQmpvbbsYRgH8prKuJeIePRo9oHcbYfW0J6lqGHvNwOsB7jFAH/oB3UWQ2bFWAV4P/6o3+Mg84uGmRC1sQGU2SBoPC+CbxrdpPeEBPcGc+IgFG/q6WYSZYPqZ51vJhUuQEMM+g17nh1symZc1QU/Y49z0ulPC+mETuDslPgtvFOrmlaFadjT1QAeo518QQKHJOgbh0ccDXlAwa4A8Wyv5e/OAjwvmyKl5PCq0CHA6QfcBEsxFKo9rSrQUrLMB1bJMp+Hxv0HBpGYsaiR32eoOtqYNQRURpRvZjCYANGfpqdiqlc6vOpRq1Ae6pCtqXYbv3TpsB8f1Vs/JF78Od7vTo2xRTVeGDAmlLwsAbJ4VHlrHUumBh2m2aOobk0g6nWc1YANKKR6mwi6r3ryZQ9hXccvoonTIVHh8Bowi+I4c8snygD917p6xiFFTOCybjTHKMouORWmwSnRhPjggQzAR/SFT1jChOAgxwsai4kxhjJpTRjnBibiaFtO9v/2ZgY49zViEX92MSZAhbM1uswhP9qIPX/nIG2DMSeQ+Ook9OtPLt/8j6Zm94dXYc+uu2wsB0Hj/f827h6gRt9vsiWpS98m/oxNP7hmxfwrcp5VK7ak5E4bgfsU4OzesrvOGI80AmE+xojeHDx1QStEtSptOY6WukltEpuQeeeMXjQyphcgvcvQC5kG+70GXiqs+IDOr1Nua+m67UUAGV4FQ1yXbrSJdHtDWH7kaBGz9W6MIRHQaPC9saL+POkQ4++Ocwa9fidIcrb1sbmzhpoCy4Ha3OmEjZ5tW32/tOxWuPZekJISrNHjIls2m+6KEnt3xxaGWlWu/ApESKxWdsKnIqyenTZBZb5CVUoqoOvnedhnefhDkdOaD+Ew3o1Tc++7qPnlMCjd0p0l+dQWUcvnkL87Aot4IuV79HzVfwLf0ChwWHvX/gV4oe6/C3M5L0F//rBZ0+mDgYCGEFkvIDvU5vVX7HgWM3G416oGcE9uW+Jqve43vcjuT6m5k2IAIFXePRZVDF29ak1N8RLLysI0BMFAX2GSUyeY9yKXVaDCozzNWtuFNkTNNKreUNnDlQgFup8fP/Uib/jv82aY/1zsW7jgC/9S48amAmCDR9+8WNUxTZ80fiWQbiM94b9x7Ye6Xw37Sw0wtCdsCJewYwuuqr27qodo2M71vKaSwBlqJD65Pb8Q5bwDznmOgDxwJCCd9nRkbPUTQSeqFEDvrizZOAslWGXnv1pOpnx/g79etwfStRq2gWH4c2qaYraF4oEISL6JETcMmBImKqs1tvjBiPDLkjlAwU0eCCheI35AmTdFSAv1zhZW3tMMRIRhxIalEYXYuGh9Jx2oe2gzt2oVVM2h6If125rWrrINsWIx3HtXSMW1bQwp1ZfDVNUXFtw7vVk2vBtKLpL369xt1Ouzc0I6i49fL7E/oXFCPrCYoTxBBjmoK+vIbiM7KTlNzlXtj9WWfWKIYW3trqN84nsXQ02PBnDMCLHTGi/OOl5LPfmeDBV9DMpDp+3+k1SX1X1P4aTf0Dxyh+DFC8urhsMxwQ/u3yUJm7Za/ZqREuzkw9iYWq0YVyFwN8b0ITb7cc7w3ytusZAKQjtRy+yzh7vt2CQPYrDx/YSYIN22d/4kCejmobwisOqeTz1tzPkMq1zTlnc6clHw+UAWGIlskOmDz0TAq2wkNDr/Cq8b7pCfNcgqGffcrXuYtCAC0+aGENxNMTF3C3Ilz6I7S0AQth9E6LcYUITJjRhwi4Orl3J4t4h/+aw8k1yn9788egvw4QOoTlYl7ad/8Kbz/VhX4dQeuNb7hFCHfyOPe6rNpHgcDb8LcJdmhCAr3xyYev7nsbMYzvkS6U4QX+c991WUHMeuLkSTCwdH7FPYv4K6HV347D+jkxFlm4hRDtCyJWE6H60PXUHaySJ+IfxZZIcOLLPqypV/RTV4sf1zRQzoz46aaK7GPH4QRv1moXq3mY/qO3tqQ0eZFdkn4p67y8yf33/4tcXXdSGM1fd0crfevfi7U9ksYEv8ZRyWMV713QH/0BbAALdLn588e/BEHgpjCOeZXpqCJVLoeDT6BBABV4LvWkIV0B09TqmboX3VCUcUxyDGbyAHSDjD3eSZMVuVf+SlgcBKh+ATsABzA7TmTO3yo0MisCCavmwcuXdVsLu/nosMBnZ1yKMa/CaYvJuj731TYQSzkREI2887HIOBhDnb7Igac/bMnzsGQ+0fq/ullJ72Na2O3hfAh7fo5bbH7cb/+iikNrFW+Bp1dmLXhyFKaxBNp6aal+hcHsj87jdP7RosGld3YutKLXcxeWj8UfTs11QCmQOfIm4V8gbf19Fh1Oiq+Sv1UgMT21eDozkpgTmrHxEGA3Gxr/uc2x2FVhhqcCFvm4VQoAmAKMc9QL3v9i+C6ztRYfhEdiOsbBVfVb8C/sjAY1gGZ8DFDGOUOgFdhniYLtnwA2jrvIiGvbbuVAhx2wUjzuFlOd9m8Kvig8Bx6zqx+Ou6KOAY9Mm97vdOdSrqnl2Eb59HTrCcex+0+GEBeqiOdTl+tBur8MMzVmVJMNLpzYQF/HlTD3o4cb18BPaEPy5A3/zut67ACDMVwbCjMW8wHDIoUoC1pjAGmkBReGPXwDWAn6t6rJxA8H7PpX+jSsxg7/hRzNcj3buMf3jOsizXIV7PXMHGbAiEYM1fzN2qZT36o9QCqJer6PtEGBBKJFxk+82SfUdDS0S/VDQFHf36ArkB+rsw/YjHndF2mBDHwQSq0GRCIACso3aIZJMQxvYpw8TlaMsjUAA7oPNSviYIBskSo/y5KJBqwBx/fyoDxMCNZ5Y99GrYycD4cUR9DFMvfWPvxUoJQ5cjyxO96/kDGUUzme6SIERV4geufj/GLlm611TvJncd4HjqvjsyzH0iEUJ0Kg3wEQQ6B8jP75hbUQCmyNhwAgYQrCIhpHUlpeWrxPDY4G2jojoLXjgCCuQ617DsZ+bENbqcC9EtGhiXOEsEVQwKuI9u+Knd3o4hWLVmgHfX0oXTw+u94fpLDyASW7jPh9mfGYKUFXjt/1OcNqlstkPxbYrqAVh1+i2TeebCah/ZeLBvh6JOXeQDF4rOaDg9bT95O4HJaIe347eGRa+WGnJ4Ax/PxdCAOVB7IAY1HKmDcwCF47PoRCtXtbl41NrYzXLHtoIHiK+X3xrZ06Qd233GEBoGpug6bIXKih4FHbCSmDQuytgAd2AhrbfabboReSGibjPhy9HmOsvGBA6R4jFQJI5I2SiarSLmvtY+AkHsyEAY30Hkc1BLw3OEyiviJKxuFJHPh5sTmzc62UgsNp0cfu6AEsY6tJP996LsJSHtjACPv+92O1WE2iCs5RH02UWwoggbf4Cu45lsUWwPQhLATDYEJF6aOtJ71wiLFQ5hmCefxzw2GQBG7s2745Vx3CTtX8DqtRdYMy9PUW/XKBw6TPX8PBUhArU09yl//68zNNHq96FmjF3NximPws8CQE7iNasLb79z53hWYgi7FYd+/EXAFxSOvx6Qe9O3UBqlATPA+8mE/ejCk6hfzObPFEaU28KU03u9x+wyAPAFNO8tUsieQR1bgxaMPxRmvBrUW9GIjeU0HQEmoRrPb17R3+5W1pA+O9SxMFh5wL/ED5RlWlzTTC90VtDe9d+LDUK3v4EihjpRYmeuHDyYfTHOqYunI9a2yXLMQAlQW7aGzEk6vD1/Yv/B7slKhYKZW5kc3RyZWFtCmVuZG9iagozODMgMCBvYmoKPDwKL0xlbmd0aCAzNzY2ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42s1bW3PbNhZ+969Q36hpxBJ3sJ3MNGmTtjud3W7rzu5M0gdaoi1uJNEWqTj+9/sBICWCBinJ8W77kJgXEDj3c74DKJncTJLJDxdJ8/f15cVXbxWfkCROk5RMLq8nisQilROVpHHC8WQxeRe9Wq2mM8ZYVC/zKjeXPLpaldMZVdF8iv8+VO59ts3dRbU2b3VU1kv3oNhM/7j821dvddpd7J2d6pN75dPxLoqnM855dDnVmPhDsbnZ0+Ao2N3e2jXyKYm2s21xszSk1G6UI08b8nT0wT0rrzEjYdH7RCRmSYghmZDJ1sjCXPz6wwWWFTFp3zoh4Rm+IC8aFjeLZvnKkcTl0QnpyHw3xUdDYl418km6QqAJi6kSkxllsdTcaSMxctfRy7BAzav3CaVWOdduDCGyL/UEg5JkYApKo7fTGWFpO5OOvg7pCFdp2vCq6ERKEkv8vVyDyp8aRVyXK6gCM9xXTm71MqtD65JmIsLBsz/X2yE25DE2rkNSfTcjiYp+Wrh3kk5UnKrEvmI6ZpJNZlhcayfuH49xTlIQLLlP8sepkLCVooLHOL7LTZCLlm0qSEwT4c/y92NrU4xWINj7yjqOiC6XReOWy3JVrsvt7bKYN96xg3qjzWLVuKt7Wa3d3bzcVPndLt9Yf1rBvR4cC9fZvC631lINFbqjeq5jQplbv15uyx28cUzJhMQ6Sf3vfoSlydau+8oelEVLBWEqJor4UxpRMBq9fjDuoKKf8/U6G/RUDJGRjEnfW18YD0ijoq7cLE4MUzu8qNzfzL3aC3JM1fC+mFHtU/oPiDiR0RdDXq0GDbEVAJMyptyftSH93mgePrh0xK7N9a6q3d3BVBtpXS7d+zEF8hQGJ5S/2tuQt3UoFCackR7jL8eWEYTHwth394tBp9ZhpyYyFkQ4p07HnXpPKUtjwlXfmIzht6RpHQsOcTNQiHnsiMtl41D73LSdrfLrNicZpzukzSYv8SYv0SfmJTfHxsVYt9A2zxbBfEKURJjw00m76iOji74PydIkJG5kqZRuJ6A0NAG3a3QGjujNMBLUHU1iBTML6A5WITBMSTNMxqkbxRP5iCpvPRUrnk5YnKTCDTyeR3X0Kpj+QLpQyukNJmaV0Q+NoDtRE6GVDe4HI6EKarIli45qd1sVi+bFMquaEdnmg7vK7CCKAFMZK2rukFNwYwog6+jKODpi+ohDUaljBUo8in4MCB7ln9ROz6ov0UG3oVrETPVm33OzKNb5pioM0ea2dhZL06i0gYeYwNOws82vy+0oI5wKMJL6S30fZoTRAUaGDFaNGGyHXY7okPa0+9IogkXJcE2LCPsGLN7ZHJzVhSsMOiFPx1pQf9bx0ADjC0YGO22c0EOeFgqeL2Sbp5HiETCEK0LVoQgN52wKASEmenO8CoaZw5qEyTgxaup+ZGUkh2RkC2MFd9jMRy2AQECCCn/qg24fV4skjeQxclPkf3oWuUalqENfVU6UG5s7HWASkROrq5rsg+o2a+pAWFGoNoCQjb16BHznvhCiY9Yq1oirDHGvCYssRF6HNQZNSM78mRtZ31sym2x0VWfFeK3KUuQ82iNyG04i10ckDkAWs0Q9QeKXLRDMP9X5tjAVmZEwoqgrXIva/HnwnAvxWIEnIRNkU9Ut1Ve7fJ+NB5Aqi34IpwHz2fuE8U9hB3LfDmQ/poVlF+mGjPgD0kM5jKB/CM/tYKItHtvkGgTZIvptd1VBYDsbjVrADmxLPqMoYdHttjQqaAGufXYskvHQdLbyunhzeXF3sQ+TjMawkb0q5+uLd38kkwVemgzM4F73duga/xRQHZ+sJr9d/LM3BdcIIBKRFshhZApEWC5xNTwFxqP0eNIUaYwK82w+bO+GYE1AqVQgYZigAJ9kxrma2g6AAEmGUIE4PUNitla6Kep8Vk+Rbx9QpDJiHR/vdpvCgL68qtz9qgVLI0EF/sRRBAu85Qib1p/+NdXUzonMfl+4SJhE1dLawr17bpsA9gpAM1u1rmqfmHLYjXHVgL3Z5lW9LeZ1vnD3V7aKLuu6XLcVtvkEBZOtj27d623z/HGzCg9XedZYp3LWiWctVSizakd35h5U82yVbWeBlMqRMkyvwLD+KPa4lpHFW1fFza59DAG9cNGqzcGADFXuNbhMxAAn5coAcCe7fQ8sh26albJts4pTl5uhbHHAujyw2Ph1bVxe8OinzaKYt15ZbHrNNbNoq/9D9AS3GuGakSbkZ5ubltZtuR4IcJiNDMVNS+tgyNyEY9Y3jsSs7UpWu7Xff+TR/bao61ay+afbVTEv6hUEzx2K2wcYzK761V2nr8EliUXS9FVc9wBQjZg41Z/HlA0eB1yy6OepgC+M97sEoKbyV/p9qITRBpQwGqogTLhPo19DTS0YbMptrdAgsTa9+yR3eSdpGmvSE8CV5QZCBWfzqYBZbja5eQa/NH8W7pVrkeKivLUfbF642/uiHm0KwR9s6PLW3AyIwjCLYk8Mlej7rohARSf8OWmQfZdpu/SvxjXHEw6R9mQEmkz2k8ZKiVTR3VFRc5R8SvemMTSMLc2BfpCyvG9eh/A6jRVq5I7unUvPeMwFmcwQuzDevij+c5xUZEpwPWwVpu+Pu7JeWllyVIX2Jf7LVrN+KUa44URZIdqpbA0ndGNc69tV/mn20ZZmuDdKESq63m3mpkyp3NByaGMB734P6xmeCXgB5FVdP5iQGpKzQl4nbDIzrboGDX4bEC+0gA9Md4I1fYciNB1ipoZPHUYNzUW9uf4TRqkMxtIZZS0uHHoJb9sbflNF2KwN25CpFxCkAlxOJbU9FZiIuZpRGEAz7N+ByWZI/ShKZhSldbthlAUAC6p6EhZ1DJBoiEmaqmXEjoE72lEdO57MDFQmvhn7q6TgWE0Mw2dpMwtrIE3laRqgsMIvh7cn3idEhvQDd019qwoyBa6lR8idjTxDzSqmtGqaVbLtD4Tcfb+lk6DCZhMgFBSobN+82gwYmySdXuIAciQJVKB6cw4gx4FI+ijU7/dhqIol3Mybe5BPR7CNAqYotoWQpLbcw/Nl6QLZTb6xsafcVdOGQ8a3VT0rbWBb5Fv3WfVQ1fnaDbFpzs1uJiHNJiju5zY2mkc55pHzIt/UlUWzphxzM9mUmW3rYr5DwfnCfVhcjzbJGY8JQkePcRpmHKvRWKemH84ASRrP/njYq3JB1f7NXMjdlJt8fVs/NC9vLQfNkArIMa+bN0PQGXMMxWKSpDbv4vP6YFuBYH5SmAvtm9yNfWUxcjJkIaP5ZZwnvzyEnaqE2SCpaSPyX7bWOOx/14+q0iHEBcQXEyQAxlGgiaY9+MtuvMSkGgZC/G8+BgMg1doFHuUHwMGGGQf67E/9crzehbXK3hdH8qE6KRqrfjQeFqJy2woeDXBEAZt4O9WkKUSY7UVnFjWOVq4UGRUO7c9XHSGCorZAZep9Yz0hdTh16TBraXEhaIETuotis8g/jRbSSCBC9dgrjmjS7EpqcY4mGWf4nJy5DJexaVx2vxnagtovJIjtjYTEO7wQCn/ap+6QDh5hUXECQEkTU6SE+H0M1ZDej3WbWapihKEzRM4TFDKan2do3GyipjpoaS5ZmQRXO+MCmF67PoMxOdO4cN2D7XgGQuSQYYMbJMs0vhIq+k7YaziYtjVrt0He3O2ypkV5tDWJIme4M9mc4jFnHdZrw9+uzg69T9uBst2QYtuc9kGmL1yjuC46/Uy+P78TFE+CQg6hDrkmFuycyr8KRzpo0i/3npIPvw0jAtEvOQOQQCCNYEZtZmyDLU/C5XqKAITqXGj2vBDjiQhDWYTB0lGIwQGSDcQQPYhB4HIWYzRQuSrCGIPoLsQ4kmF9iOHJpclpHTl/ES6rgU6JsRzTwGatRsYORH0bPh+hQtbnkYRgyujJqtd/JdXLz1M981Rf/Fmq970ciIi4o4HIjZwQ7yxDX7sSXBmwr09E+/pZ0b6f/QQsVnfR/vD5k6CtIgMz4VWFRzou6iS18yG1E9/j+w5oznfwruY/x/3UScH/GEPpAEPcb5MEC7W+HR/qpb5yPgaPWgh2HD2omFNPNV+GOBH2lOOf1qJ6stG+HtAwrk5Ry2mZ5TS4dJpjnIUDe+ZvVOlDrzHjfx1udTGpz7PYZxDNaS72GRA5gEqHPOnr43us5gwZZdou3bbh7FaXIPYEKhPUnAC0902lKppK1W382R7DKCpPUdvx1F/l7hiCTXiMosD7xrWOSLOTCLo6p9zMrdu4zeeZ2+/sjizfJ4zNFkV2U26ylXvZ7HJuH0ZREZF2f8Mj5DkN4Tn97YT2BAdGTgT3+Sn8Y2KI8whKwh/zNETU29PmWsSS96RpDvGNAcAE36S0T02wu81jzX0Pqo41DQQiNwC1P/1Lc3hh5KRdD0hSs83Gmm8P513cljpvTySY7eIDyLS/X9i2A5qt7h4UVPujLWMnhz4OdB1UEj0UZq99tQgLWJkzH+SkjainQMizClVfPxr68XYfBqpPIvXz5fHEDqOo6ptxV+egAYAYSWyVxPQ5ZeHVUE4KewWKd6CyU+HA/rjpVUjK5tiQ6B5KPX87CeWLEj2FjtD9aD/pG8d9avRltkQhGPY/yLZ3Z25iclvr/mUt67uwZVHCgpaFHOYXgmEFGcn//w1r6FcVWj1Jz3ujepQDZeqCikTWZIdSG/XzPlPNzIkBajZ9ubZlvR113xRE3TNM7keIqv0RIo/m5f4Hg+3+m60u+r9e7BwYCunanINGQdYNyc+k7KSHLYX0tD3yU4UgsFT2ZO1JJQo9HytfHcMwj5tViqafj5UT8pxo+TSWwmj5MUd/QSjrrWKRbGp+VcO4HFeC+eEV6NH2XOvTWihPgbantTWGE/Lz2N4gVFXPCFXPtkkWrD08BTcpHLVzcugHk9P02+R5arrK+ANXSxsaFsE0I5UOemK/28M9TxwJYgNST5EkOgIpzow7qfWoz1e9p5XqPNVruwHXoSEI+qk51YeUYbrWqeymP92mvwN4g560i+/cqMKNpv6gN5cX/wUEEYkLCmVuZHN0cmVhbQplbmRvYmoKMjc5IDAgb2JqCjw8Ci9UeXBlIC9PYmpTdG0KL04gMTAwCi9GaXJzdCA4ODMKL0xlbmd0aCAyNTA2ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42r1awW4cuRG9z1fwmFw4LFaxSALGArveOAmQAIu1D5sYPjjeQSLsRkpkGdj8fd7jtOweyzM9kto6SOzpriaLZNWrV8XONYcUpAfD/1xD9ZCrBhG2FsQFbQk5GVrcsx6y46/jd7NgTdGm4Mk3uUmoxt8eGu7lVkPPbFvojj5aD5IENzrGSoUXgovOi4whlbIcmwP2FkRd0SselfEWHrn5GFsqB+4FmnOEjrup1KAJPwS3NUHvrLzIuKiCC7ygnFavuPC0UcEcijU84uTwXBO1NF5QDnPQhE6hEC4glws77EEV+rIvLbnhVTwqjn+U89TQM354QT+CP+/j1aANU1bMRDtWAr0Gk4w+JAfLTmHsgkFfxT5YUcigdyuVF3jk2TaasebuVAGPWkVnmH9JhglmCUWoS86hDF2yhmLFIAyZgsVWhUxFr6qQadgUVcXmpbxRGIALB0VfLhxUa/CMARV6uza+ZcFL5VsleBV26MEb19BywEQxHSxfHfMyCzW7o+cCu+DyYvNqwThqNDSqijdrk8KFDLXjH1SuHauLJWgJSipWsSXuH+bRhEPCBPEK+nXYmXLXCy6wBoE/mnOA0kKrlRsE4Y6Nxr6Engpex0Z14WSwij1zLbCKnUavGKZj7zYKp+jqGB13+9gSLFU3qK0wx+6Z2wrhNh7hTucGYA8lJa6cw0JT5ubThxIXULFskrBlm2fPNtvvw2tYHDzvx7D96W9/x4phZWps6Ovyw6+/vtl8882X5bBM2PAlGexWLFDypFyWFh0G4anHlBcGlmoQdphRjbLQb5JoWIuSSoSbLwlbbLAWbEQ07NqB8Iury5vw7FnYvhA4JLocr73AmmKjp+sasPv7a4GXwPf3P+Binm6vadq3QnBbbM/+h9Nob68rd2F/DY+FLtN9mPrtCPB/3w8ADbc/XF+9e7m7Ca/D9ofvX4Ttq91vN+Gj8q/+958dHrz9526zfY6J7C5v3gNdxuub7Y+791cfrt/t3g/IG7f+uvv54u13V7+F19TbhR6R32CYt9d4Fx5R93LfXl5eoavXA3ipC4GX7WdDD7nN9uWHf9yM33+5uPxls/3u6vrn3fUYIr3Z/mn75+3z1zJ+UKl3mI21HDtWTzwSRay3CD8BFPQIk4HYt2NPXobtH69eXQVs6e92//3w9ubi6jJix3/PtVlFESk9FoA9UD0C4REWYNQETKWjLCnic0Vods3O87e5nHiJDbBlCpsWOS2MgBkrUE+bRkajk8LwUoSTSExckpOaowPIFuW0R6Lq6Rn1FCUz3EpMVY853NzH5q70yQMOvefAreb+dsyVjjpuIVu4fQHw2x7lb73f8bdeH+xvee9vXafWprZMra/qh9gpRivE/YhoX5IDpmVgOyjOF83/5l+7q+vdv6Ot6YYGx++MalbghuAfaJ0BFA5Uuj6dIuBxAABSpxYZMkwRZwapieCpS3jQ1lMExGpEuEkR0MGoJJ3nKVJXRMico5ESq0WGFoFtKBAgG6J0q/daEgLF5GyLEHkgd4SSzGXAySP5p8GUuy10CJYO+yKli8X6OQB1VuA/C7nmaHUARDPk+gRKc9LwQIBSuUMImDQ8DKCYyFAX5jH71qe2Tm2b2v44oEL+NvdL7C6Do3mN5Mg5e2xYPatgEKl/0QrfXdzs4vPdLxfvcsplPY8A6wVKAqSE5BN8Fb6JRBCW1yK4+UmwyquSFwzIwJsz0RKZAdCqK1PPClolC6iZV1QEfsRkMyPgF6RVpOeZaTp8EdB+GiNsVbRydIclkJowMlL/lgAvSJDAl0TKwt6suCTWwOaQekoCvoGFWK+MsETTYvmkHnpoI8AulhHOAc1DOeT8mgdWn5SjtZgzg6+Ivb4gjEDdZBRZItPQe1K7o5nRMRSdg+UROvdQVMx+BxWzPRQVZT9DOsG+zVPb102bQHbGskmsTr6WYgJTrMhwvT6pm2WkHyUUJG7I7eFusB3E05HQeV0w7xXdDFsRXUBgE/yKtZ3egcEGRQpyfV3MJFdEY4VLjPohSA/sH3nt8FGpLdZa7pXT0utyOc/l53LCtBomV2pHllgWhDv2sBKZUmS97hxXPsZ1ZnWNubsfUJpjfOjLydhnWR1Tc1nP9/VOysZK5AN9P0++nyffz5Pv530Kx8rovp2e6/RcdU1syKoD692ROEDrbPAEYw3Vo+fTZKCs6ZIKF2zEAiiQYToIwzGzYAjMkNIXHSGtiA4NLBFE1RUpEavrQAmyAzfAltyruvNI4HaPPInwxIEDy19kjNiXWOQpFRGsAJI4DBp7YoUG4NRyKCBqLS3C5Yok2sCLav+kiWVyVz+pyVcxVw5sysMpYCdsxMjmK9g88LtqeUJFYKRG8twVW8TjGeSoeZwYRWv3UoRQr+fl2QdyI34UHlfkocojCnfzUDEPA0cZ4NEC3ac48FC0L3IH7a2fhfZayudor1P+q1P+q1P+axOq24T6NsnZJGe3clOebG1V9Ke1ghHWxEysBUYDBmUkiNKfEuiQ5lSe8U56FNGRXJzS5OtEISSAucA46xg4KEKBEIFBnSUvQm5/jCaHpQtxidNZKIIbMNdJCEHSurHW+ZVLF5+VUVqPpTUe0IIu83i7xgRLLsxWj6THQ5c/vP/l4jKntGb1AmM3EyB+jzxTzjCVWnmcDF66eAa0Zg6RwdiBPh8VMWZXcqYiuqLzYGADvpTEgXkaD7144g5DafKUxAmcFRxuVLiczlPAV7yOCldZPBUTW3FvkKQY8jpNCoN1lhfHga+y9tZsAU/086BYzjsPP5TLBO/YtC7KZWetfFFMOuy39dNyjMXJeLTPU2u/b3p21oHaPOQerVEfnovPajAHp9Zrnqip1zsB28tDA/akr07LpNPE+NnGaKdlerNW9pNGgdp4cgRS6UjKQC4Ly9QLLuxrYkkRZGFmkx6FCRBz/fP0WPNAzRJoNL/0qqNonqGBg6gwEvVcnlATbYWRH3HGQEmwFgnJF2CWRwk5+X2g5JEwX9L4GEYUXIkH/85DzxYE991OF8/8bqHo1geXMG0ulwWJ8P5zKkSaBWFxZEQgLiQNvchp4QqyA+/PIDc8qz4tm8dBnKguaAubqXBjQWbEitWxD3jmcHYsRzlWwjoKfwff7xz5uODxR3S3H+jMEK/JQxFvmiC/Gtu3U0oyzY8fjO3bKTWpE0LWCSFrWxcRQR7EPqYEBp7nSc5NTtakNTwtdOzaR1WgGunwmarkdcmvNVaiAEaD+sr47vEoOh8BgEeCc2JtEAOT7pXxgRw2xhEy6hk0L615pAqyM7wO0Yr1KIUmSAU6wlXTpSCxYlmKvJJFy1LGyEzvgcnQhBjYyvlr8n8ierFJCmVuZHN0cmVhbQplbmRvYmoKNDA0IDAgb2JqCjw8Ci9MZW5ndGggNDMyMyAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrFXElz20YWvutX8AhVrE7vS1KeqSzOxJM5JLF8slM1MAVJSEiCBiEv8+vn9QIQDXYDsqI4B9sk2Mvr12/53gLj1c0Kr/51hsO/316effmD4iuCkcGGrC6vV4ogidVKYYMwhydXq1fF5W19OL9gjBX+X150t5V/sN6U9ba68l9um21zU+2q5i4MO3w8dNXW/3jdtOe/Xf77yx+0Ge/3yg1853+KSYGfpCnK3VVuHive5uYVyP6ygocaTopXZKU1MkyupBGIG7q63MLBnl+nliZhhjGIYx7PeI0pCXPweM5oI4I1opzF856eX1DNCpyhl7Ci2QGfCIXznvvvezulqMLXw92bQ9U9sV+0Zf9ujnLKKdJSxCS8W6CbCoKEEp9KN9DSVoEue1f+EJ7oq7q8aXblxj8EmuFAXfvRf20c8weaOeEIaz1lt3Bbn/kx7U0Y/Ou/zuBXiWT40Yuzn0CmN8+ZQVpMLqTcHBpP5E397pyaojrMcZRrhfBUfN4ucJTDYEbVgzjq5ZcgjEVYDTRTGKBAE2RAvNxi32ya3Y3VLyc3wN2P/su+rtbVOSneW4UlhVNWUhy2jZOpprv1D/al/0SLekGeNNJcxXu/TJ1gTDAVCnEt4llP/H7P3t6VXd3sIhFgBI6oWTxhSQRUVgTGtDCmkOQ0XrqtDl1br7uDp6lrPFdK/3VizUixqXdV2fofm/aq3pUgykHWTH9JsIXUiBnut7iqX2PG4D697NdOF5goqv787tu1vaTBPuKpnRMDE3IXRAw8oyTe+925NYRfw9pSZWW1J5xiYOVkgZ6TJ2IK1vWCc1283NVv77yAkWLXf3Aid/Ane197CRPF/6q28Z/qXd0zQhZXZVf6x/u22Zc3ZVeFqbW/FwnnEBLEuz7c1l7YBWhv03+0rqhXeD26BcFBZMNBrJxbkgUpvnEs15F5ha/rZrer1p33ZDpY2zAyOAlQ66RqWCaaeL9f/QxJVwoZhb1jRVqZFUNGGO9VdynWjk9BhUZEyHhp74H1oLoj0p8E2ps5SWESe9cwXjTrGvQwy3jNHM96OruP9uoXzXi7uI82iE1nOYtJsxbTXtGs9eKUI6UmfHy5xH3OGAKfFM8KQvQD6P5m8/Fcc7Bns14Dg0+bCOMRQsxsDnwQjKavHi780Hlz30X204o9J4ga4Sek2eKR08s0Nwk23kTz4Mwt6OsC+gNFtKp3F7Cft14jMDhS7Bi/MIdfnFnwWtUjSebMgvN1Z88uz96e9UeBwyOq5XCe9fbs1W94dQU/WlVnoIzv3dAt/FGISb7arF6c/TJZwrkfcF5czq0gkYABeGYFAv6bP2wJgyj79GM4ZE6wXIFLNEJQZ0HgegkFgWLegEjEgKWEiuK7ZrvfVMGfKGFNlvvX3wx8WDusU1TgjOS6hjuBL53/qa3gR/j6zv5VeY82uFOggUbCg1cXFAMOEZ6En9tzUIfG/WX3FDr8qyBoqNxjNwKCgJwLd5MIIlM3jiaEnOAM2vs+TZHCeiUM3BJokBP9F3d7K3+qaA7AAk4l8KLs+k/9o3MY8N7+1fgHV/VhvynX1da6a1V0F11z0bXl2nPWjtiW+4P/VN60lcW9XGCvKvbh3XG3wSsZdlRPocAhMONpXDfbbY8Aqg9d1dZNe/y2O8CmwRVe37XW/XnvJIo3A9ITxX+q7bacYS4MoSnmOv/9cxu0tDnUndtudiFAs5OFvDP2EVp+nkzPc8Bbe3DqEdGm7kZHc5I08vAjMyckRz0b4YJ2h3CMNliXbWmBXXUYDJUzdoTI09DzWcJXg1K5a7og4Di0F3Uw2zRlMwEv0dVo3LW/RFZU76wMVS0goo/eUO6aXY+CWOHs991mkw+Kczu+skd1N8iL58G87su2q9d3m7IN1nseNp/epAd7bq3+XpIBEcAdRShYAY4EDkjm+wQLDYRLxDNQTRkY+38wndhzMAzcNADtIA7Kgd5XxR/+sRCjDYEgjN2OEuI0txBJHgApd2HDqKf/TaxGcHIlPLuSZyFVOg2NXrmfUsyiAA7p38ws+mjMovdnVpLgr3LoiI4MAAERViGOe1Hv1s6KmKNWuW8+sF93TXvwDw77cjeH5p0vAZgeLf9d4sgnaJ4tEU1sMmCy8hNPVQBQZhQt9ufxNtANOYRHcONlC2HVNpzptgwfetuii5u2vKpDyOkgKyued2GU9S1gknpnY0J4FVCdscBsamwxAfEMINTjNWd7fnRkZs1XSu6kQByrz6mkCinJopXyqpmimChEAPr9pZpy4lCkGa/UJEXWw/hfU8rNwPYuyqYlmK0uRsOeePMfcP8I11/f7dYDPJge/IKBRxDWIxggOTjBdTqVkTWNgCm5s4j9AtvEuSAkAid7cuNx4EDhwqRaccWRIWwOcBOAEIQMmN0h7glVQBaBIIQD2lA4oO7UtXKH/hdlEbA80AXnIMTMCCOADwWELTPDnArThBkQ+hjyWNwQQD7j9+OGWNYYxw0x5sY/8lKT8W1fg7slUgIi9k41IgMUicTy8hpjljMSsfJ6N26da2JfkHgh1OiCUoZDILkorEwANuf0ce5nWGx8P5+gR1OmgOgIHQvr4zMlIbTMGBf6Pw5T+sU+nSlJ40ywcWmaEVss8pLIDGln8I0S6WARqQirlW0wpu8dtvYIhRdXNiTnu76CNthen6pXNlXvvK0ApH9V2RxfNqOzTQUyGshVAegFxPh72n0x7TVAxe4rXg4nV5roEicsWsmLDcnEYJRSq5c445bh1z8SlDDhoNVfeq6pv5ysNHKXSxgoJp2BouiHkJ68YQHOnj38WhbjhjhQsS5AfU5xOlkpHzc8TboPd8JHFj59RLF/OxdOSzPeCGGwxMwCVytuftKzSYqFh/CCHaHem1Ag7Jrtxaa67vywNxtvjdZ26h+huD+TVWHprIoGu6gzYW4mqRLGbatyQJ8RYy6EBsOtQj5CHy1yWkR+SWFyWytXEVkkQT2AjSO1kzMDjyUECdHB5oQqSYbN+LOIDLpExgm8pGDrIyrEguSTPJH7OQwfQNkFhXPO8Huf2x1m01wgpoofUwklgoS8V46EA8jgaTbcK9VAECbHmrvthmEau9y8i4Avva7YrO+uvq03ZecjaTxUCjM1WmnjMh4v9mM6dSb1vU46opQogzSwOVr96XzNGIz5dMZxJxnjNk75OKT85wLjKJgfAYOjxa97RnU9C3cNoBpbZG/Lrufq1hqZN3VIwcNDauDrsfiO7Tpb/6k+zGVzOKAEoifX911aEnKWaHQmbgtJkyM5cIaPGShcVKWzkrdzrBeAIrUW8VK/JERBI0l5JAq/n5OxBcgSa2tXwrB4i9PcDpNgk0moFx55zDINIYuJadVXD10TS1zqK8fQduhCYdGN6yK4HJfB/5BHuz+lrZMiM9boQ3oOyXvTULoMNxpcpa1jll3yfkHgFGWxN0oZ+9N7/dtcDv9TLgeAyZiGmeTa413YjD3PsgtuEsv+dNNEmiVv0Syc+EFM59DzDCVU4AXXm9+VYTF43090a7a4jzEL8SrBqVY5axGIQsqE2toP51oUfVXSRqofqqtZn0KUq/tEi3zIpcb7SZQhCu4mmvTE9/4ET8FddxCc29gaX217Ci2ONX2p8Lpcdz2ZddxURm3OWcp49aWmMoLv01hIDcT9oO7R0nWo2QZrd9tsmm3T7m/rdV/L3a2PnV8ZEH30YNzlJaIdvlvgJ7ctSWIyyZUBaHHpLjQY4qrqC6wD0cH6Vr4CeOxv06MGN4Y5osFlhLpr7U136EDy5jJYeV+20J4fqtj4x2vfJqKn/NFFW+3b6hB8Qdn1NeK5ViNs5SciK+ehxidhAPwZ1dG8kH8J1NpWm2044a46NtxkCTEAegB6pimJbCoAExqnZcqkIGhQJzMO/GSuhcoMfbQYKR4TMdtBZZOo9qxB1fRImfQAsE6bEtViUyI3GlBOfDE/nV+ovH0bn0NY3yKm1ypSfnLAPASCQWEFgiJJQo2tTEfmxqgoMs/cE8yxPdGJi8oLlu35sdnBMeVDOy9hcWhBDVzXEFg4ZbFdAx+sEhycmfC9lPDQdZz4j858WLG0Mhn6DDblfu8bJH2krorbsnVVP9p3ZttF2rCE69vceVUtdzcuA2lhWJ8doFYVD6Cnw5peQNxzAKF7O982N3hlVb2yAinDYUcFWwjrEIG78RYM1AqEbAdH9Ga+j5wyZpAIiiioarxIBgEPBVcJ+s1ZPGlb/uFIPBxbSxwBR5mXwDtSBMYPbXbuh3oXelbrtrKDunA/1rZK2geGwlWZd6FXVfaey16Razj6OBzZDXa8CxVr2+tqrZ2vWE+gOoX4UeLQRe7eADh2a6hc+zrniLvSFEOCh9gth8ToA5GYC9lnkB+oFs+n5j55v+dXc7Ug9djbRQ0dMXxmDNC+AWNC+364ZI0HIwImhIIMgyefqWbAOoDGo2JGtN+wEpewI4lqu9NkL+Xg2FxVy43qMu/eWH59FmCeRaT6iEjJsYRCtc04MNdsiGlgbqrXaS5G5fmQrg9JR7FoMFrSvSUU6cuLKun1mb0ZO1S6fH6fqCEZh0m4B+4qV0ADbmicrE1xgTQ1kRNKp/WU6xeP0sRDTIhPw4xPJoRJxHTsDReaIcKoXjff5ZjDeg1LpgrbbIw1y+2sRbpJheQEguH7MlllmJyI5VJ7ESvt9E/w8TUmPAfM8ENYmSqgSwjsWQxSRwV0c9pMkiaUZQRQk/QVcQOqL0eF5PX9ijcJAf+0QlGiFm1LuRzANLWIks9ab4jUONFztWgubRM4sMlQV0U7Hs72vyjHe943d3+dMl4XFtxzwDIXEDKTvlD93qXaspmym1QFS0KMYO5XNYwrWPcQrWl6Tcp7WZqIQA4fH1CTDZ1p31br0rZi57t+H0prqhyoKbJjHszMGQsVxyL2Da17XJpEGkde4Iu5TBPJuEnB+gr3kIMZRyULr/SyPuF46n25x9EcP7xHmbs6JYBeiM+rdF1QQ+RsU6zjPmU6WxSMec0RVmYu8z5JhDJQ3UQmVKqVQUaCPfMFOkGELW7iBf8MREuAFtHIHBb7bOYxj+5S0so1su+vjDtqskXyqEHzJpkuEYZbruCABS89TrQmGDQP8C4mMxphPcoKLLiRiwqxTm0P5lkzfr+zTJqce/WK1rPWV54c5+QODYgwyOBo3DzUyEhCb+ymHeCKkjENuX5zEVGQt47KJC05Fa7NL8G7SDc864x/OTccNdMaLGy1PlIN0EmL4r9ayAhAEE2oRFKa/iWhN+7tHmuN1NBiDR/rq/593a6uDtYMCloMDiXzjjQBKaQk3uEm2QNHMUvhzAXsl9ML4eqyOhKkaMNeS0Dq2BywNcBZvbIN8HRR2pjMAVsqH4BrH/FsuUJ5LwTgXZGYCMJsndxm6GwO8C+72HfJw8vwfkF8+OyxmH2vAHxGROUXs9lqGGkrxNEM/854En3Y7hL+4PvIKiWzKaqpZgaV81lLUMhyv998jP+7CAwe2hbyxtMWyzpk7j8LoEPRhcB5VLy0RZZGFt/7fk2f3ON9fQooHDVsThN3BMCB6xa169iMatku/o8mMwFtEntnHX0O9Nj8yNN1uiDwZNypGl6he9OVdaZWJWxMEte822SHrZ2QcDgJ7Yh93oOi/LnYk8/AqZt03kMTfb/XViIsk1ftU+c/hSxT3z8DWvJaSykiAK9Od0tqrcVRdBYNGDDJFkip6I03IGGRugwUYhhh2PNR2ZHTIVnkcsdfzb5sfCEMhFSA/2w2wPTBBI3mPLs8+z+F78myCmVuZHN0cmVhbQplbmRvYmoKNDI4IDAgb2JqCjw8Ci9MZW5ndGggMzkyMiAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrFWmtv20bW/u5foU8LGhvRc59higJvkk3TBEnbjb3YBk6xYCTGZiOJKkm59b/f58wMJVKhZCUt9kUu0gzP3M7lOc8Zik1uJmzy4ozFz6dXZxffWTXhLM1YxidXHydWTCzLUqbQmk+uk+e/bcq7c62TfHHOk2J1LmzSLu7PnUoenf9y9eriO5f1x3Nl6PtkKniaqTDH/wVBg6nTzDIvp1ObicmUp0aaIFWOTSdT6fikJ3XaXL+OzaUwl+jP9Z4JHgVZX/B6KrlKvg2PtO6tJHTqMjPhIjVZmEPG/dhJlmZGGJKa6pRZBxWkmYtiP49se8pSZZTXlI2aykfWvE6+5aOatimXfi9MxlWejqyC50o7HF6z/iKTqUq5k5OpTQWLti5H1ZZBbkIHtsctwAcWyMctkJH2TrGA0C75+9gceCQEBnIzsg8hscLQq0bPhEObwUZ+O59Kk3wTNMOZwxOyjIHW9FfqVo3rdnBOTCB4X7nfHjiydXRkecBb8Uyz0ZHJzZgX46sLWorLjhpBpVbKSU8Kq/ARLahUKDGY7oFQ3k43akOVOpUNpvv1fApvyEcVyFMJ3feE/z4SkGHAxEF263iMjXuoxsjpQPKQ2tUXqp2bEO+9zYq/Vu+jPmZSx+yIhg6ElRrzcwtAy8aWGqiZAFZSW8mtlvkhLZt9LR/yhaGnlvl4PDvXP+LjIDRMbRzoIAkxIc23O9TMpFx0Kp5AHLHI8IdPLGaE6ozMUovMcbXEgKvbAlAhdTKrzqfCJcV7Js2spMTokrYZBeo4HVeUV/VwviOwAizT2Rhkw6JGDmFleNL+IbhlKYefDFbN63iKZhmOUbW3oaNahQkBECNHEIpD02442dsRX4eK4TC9E8iH9im0ShXAezB1ej5VypHS78P28ppYSPgOzas/inlofNysZm1ZrZrQnBdtUS/LVff4A5knztF2Jmyp83diNFW0KSYfM+vWM1znGdAnkoNAnPp9rvOyjitvVvOipq8KLrJc53XZVKvdObZPltC0H1D8ga2WVR1b2AI66BwcOMqTTuwGNIw2dtTFpBWpFtlwc4eSbP9AgOZU2r2B30IVNkvYmOmuO28JZgnHWlGb+zMs1/St3apd7Z2zWgc9rzrd9lVL/Ar502+iKVqvPZW8LpZLHwhnQbK+iUPevjjznE0mQNgoEAguxj/CejxL8vV6UXpvgFhb+VW3Xs0Y3NUN1+2wfXSxhDBjf6U+hnSnEcKlVtvh1H5L0EtZLOZHzSkMGJP5bGMHzbld1UqAvhmOI2s6edCaYLztbV1tbm6rTXsMAyRgTLm9Tf0pDNg6oQGXJQjozxyNf3VLrrQ57v2ZwTg+HL8c2VmWansCAeptTXGVMiH2NXpkMwq1gjT2tM1kY6zgoJ4UINLs+2vUE+q1nHBw4OAqk6lS/MscPDvFvzXPwGD39AKAHa6PmNYZ6gipg8BRLLv2aPFpRFMSeRH5+EG72dQa0acDR1j1mEVQkLFMja6T7RO1wTrFaGnEQip3QeZDlDF4kllrAjU28K0p8+VRx5zk+NGcMr6yxCJ/0dnEnzlbjPAHDidPPpw+9XBjDkIlcaYfjqXPDkeILGWSr+YhV8GD6+JjVRdj60+xjJB0xUGFn9tWsmZkRyggrfxrPfbAStzB0bKvMOw3yAvcWJpXjwWdTNmJJ5AnnuBE08V1ODfDhbg2w+povJQLFc1oDDAkMPv/cibxPzyT+LNnOuARPCT2L/e00aqM2CASpYnhFKnC26Jp6xKMfnUTQjNQtpG7D0llOBeHOKpM1nVFyabLOF18x2lvC0T5MvW57vnV2W9nXc7SyDbCAcg06ifuJrPl2fUvbDLHQ8qAEinwdy+6xD+LE6jJYnJ59s+9KZTTmAKAgcLtyBTACeA6OzIF5I/v4vAUSNHyy8/hL4c5WCSoj1SEyP52WJNXW9mZ6SOAcjULmXxLFrwh+vcCGVgZ3XM6Ea/DrvkvQ3rR4xQZEoAWIC3wCx0LwSeLD6FeqFtfq/Hkp3NniPTQ92f5AkXXe2mkr7SkSX70hQkDogeJEoVcFn2gborwkKbMkmqzQr0yz+v7IHp3rm2SLzZRCO7zYUEegnlt8jKWxxxF1u6A117yskDBmVN9o02ojPD5w2aJqmd2rgXdoPuuJ9RY5Yv7pmxCT8g7+PIy3CAgaODSO3UIhIVyEsayUR3n0iVU0WBiHLIrfLVKvHrw+axaIXg2m2Vo/nSL1WbNNkp6ew/Jjye+1IU8ymFSjE1CGcsSBJf/LKNOn1WL4r1UFv9kFbqe+NcCefMo6iy/CZHGEqMJRVCPhEUutxU2iut5Po9KflrnTbkoyjoP7Xkx4I+dR1gOGhs94k3eFktsgbflLI/5+21Zhfied7cEKnmVrzBvFSV45lh6uIZUSiZVXd7AjoswoJnlq30inA47ppzIDve8IJMxMq7FnoPDpGLvOFoGwPPHKW9zr/VfFziDMiZ5Vnwq3wsloDkHI/d8PDyPjhkaH6mupi/NfQO9NKFxV+bhC91ELIo/4sC8zheLYhFabZ2vmrVfuqrbdNy9aQOXL5+8wRCbJa+qTe0VRI29UgNnkxoVh+Kp1mprqttiCS+Ft/IYBo7CgG/D4KBfykRbqlQUVR+PgQeK/AmxwuJjgeYDFp1X5WNO6CfthVBvuDHaqp/3rfrNkasF+FFe/1zePRaSXo5lUrk7eYJbSLvnFfJh2BNEmHnQ3OsNDslk8gwVlb81YMm7c6cDrDE4d5mvbuKD76vVzX2Uf11ufC+P0EJd/nERGld+koqCMhMBLJmMs0oPlj5hZh1YojPgL8AyIiWTOw+Mg/xnscgbAiEMnt+v8iWhDg/vCvtOYkEqFQc5QOb3R4UdzQN2jCYwMIFgXKo7PnbZA1txG3zjslgjGOgwXfbY+owZsZalImxgLvV5EDM9tJdEbW27G7PLsNq9v1OMqecfvpW3i0BDfJ0uktfFrN2EW1hEQdUGvBQhkOkL+DzX+vF58GCosCRG9GHjsf5RkCkWi3JNYeVbdXGzgdwir8uWlrw/dzKJkrGy4eEebLbNGHgUEqpIfvrH82bUTHQ3aK3rm8lvqncEGY4Q1d+l2TKoANsHVs+j6sPHj7O2+twm34Tx+QyEoqFbxFiOyeODjsQtnGbT3lb19LZqtnsI6n44ej/3B/0gqAOffRXrlfUCJqkoWpTSyXOKg08lMRQL6vJiUX3wGKrozrj8bVOscOhwiUOxRP2eptKXbUTaGJHUSfPBknSrSiyZunY5AY2PlGvrvZkuZzTTLSUSM8e4IgpU65jz8xZjIgdhPWzSGTxBg8LFhPV7GV4Z8HCxGxOmxydytxuC6TflYhF5wDq+Zog36mW+aA7kGqWIYiyXUMoeu9EBZPA5llH0KSQH5RNlE+mzif8UWkvePWWMH8SgPTIiQauz7kKryzCM2YuGMUETASmE+CrqYEaSxL6bCQHziD03k2CFz5tPgai5LuiRsPMlRVMTWm+h+za+k8giU3ZdSZSNUWX3GfpL8kZirpEnu14yQCOimEsW5arI4/eyqdq6Wge4yvxbAsoV5ayMrwmQk0Z9oqd5BVqMZJcyFZnwy9Vd3CUoxU81uYPfx1FKwd3OCTJmyQkywbeEgomjiUjtzO3cBRDITLXh/IK7C3khhTrB5kru29w+iCwCn5yrfWTBjqPJFUEnmZz6yORN6NvZ22pJFcwelEAkEP6teWkCmFdF8yq5zfVxykA3iR5v6SZ6q49RNq8pwEPnnF5ivWdSFvW0w5X9k4GTcx1xJf7EKIxn8MF4oQ24mBrraP/oz5J35Wb67JZkNx79IAoGBBsqeO+bvMlvwfqjqIekZb6s2ioKvCrjNMSsbmJnUB4merVZFVM/avMhiL0uiihUzEsgZPNo1FFRtmU9Z8VBhXEgK9u3JMCzgv7zuFWsWvJbUITirlhU62XsiKUWHlxBd7d+AKoSTxHQ+bcgEyvLw44uzLYQw1fOfCUGEkysD2r7N52vqhfz8Pxy+7azpBers0dB6m28qq9D8/n8pohz//AqfkHAyFNRU2iTCpUNUZMrcZFZlzkuLFOZUP9hjGVfxM87coiccHvBFIOC7Fdhr3s4DqVLJY+s9TtSIiooYBpqlNCEd7QrmAbFG+k6Mzz58VMeBJb5qvxYLeaHkh8JI/mtPR/UXEWXWc035P+aS5/63sMNKPuBNByyvlYuoRodOMc9wHnM49B3bDLBohjsl51sP7A/afhe1uPm4td0Vi/z1M/FLNHkr1J/9mB9JJhLlYhs9PvcMwPw6Rf0w4B8U9T+bbG2yRMqLduyCXyHEoTiZB/UPU1RAzOKVeh7L60KN0HeydFzWy2qZVWvb6fe9yuQmPa9VGrlsSwOFMlb6kMQrg4RGexiS1Samd+D6a5/sLtidcB4exxcoY7mmUmVjVUGlxpVkOcuwsrtzYZ2p6UtnMETlaffMXpHj9m4Yw9Ws3TBrNWeuTh70F7curS7CPhxUdxQptGhit2kofESUeEvn+5KKpKIfPh+j8fCGoJzsm5AdOoIRXBAdIIzZZJneY38RCUsDS3AK6D2IrR8uvIzho+m9SnF5zpq95gJNV/nS4qwKOsz3IHMxY1JtYskMNAtS5fK8foJ3/v11gE/0ZbvX+gh1VJpLslZ2IOXJE4lTu4YDWAnuAaLDxGU6ohr0Po7RsMuAL7SOM7As0BjDWDfipPBeA8vuFapiW5brmCU1hew3e3aui7WdRl/ZON7/nX15jIUfZRWPFJlJ7gmrbXnmiM3zFm2tztpwk8vd85JK7+jwnmThsaecxJ1CkVq965wxzXody3v6JfRO89UalfpkXCs9Mg/mviDocDW1a7G2/Fu9TnvVkPerXbEbKRm82cUKrXdpWnk5SyyfYTaDzlRqLuyqEnXl231afeehO3IVxCOZ2a7CKFGjJDdHTEk/QXE7x49441uCaBsjkSBkq5P5tWJZJ4lku9cn/I+U6O3GJzj09nuFoOfemk45PgSfy/CKid4pfVvTgde+fC1cKZS1wHmu3L6fbMhUFPQ52tPWkUs7KjnRREf/ZB/yv3V/abOiejDMZ7u6DyGdD+IIxxt+78to4eeuBPr9rcE6Njal3r9TwRtMt8sytAR2L9wnbztAejItb0Tqcs6dj+s+MIVu/efg7cBqu8W9uQaT3pEDL/sfMxF8IsILDDJ6cYHSPCpUYJd5HmmxPy43fffQQqQIbo6QRUR3M+/Lh1m2+dXZ/8FqbW50wplbmRzdHJlYW0KZW5kb2JqCjQzOSAwIG9iago8PAovTGVuZ3RoIDEzNjIgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjanZdLc9s2EMfv+RQ8QjMljffDPSW143FqZzqxOu3UzoGWIJtjkUxIMR3303fBBfWgZNmTg0ViARDA7g//XdPkIaHJxTs6en6Yvjv5aGTiMqe5TqaLxPDEUJdRyZLpPLklt0x8nXydflpPSVhGObzRhCWOZ05tTShhwoWvJqkwjHzOn/Jywg3pmhwteTXHl4uuCh2rR9+g4c/HZZlXVTZJlTbkYlnf50vs6arie+cr37bYXtTN8DF8Fv2nfoQf37QejfeTFNp1V83z5hlN35r6funLOLkp2qJ6CAcL56F290DamYxajQcqwkLcEb/M21UxK1ZhqeeJFSQL808+Mu62HHibSmHI5SRlpPrhq1VRw+bhC0qQ6xxOXObwkYmiJPc4fcf9t+QXXI0xeyqVuaNcSSODUVDCnJNZjEbYbvMQ9/0F4oILz+vilNGMUWpOPnykjAumJNuN4C35Fb/Xeo+rwb7Q4psmX3UTRkr0zcgvVmVMxEAHH1vwSj2Bx7/Z7hIpp/2MlJnMCTGwJEcswSA7gklrkzFnt2ESboDJRZjAgjDBSw+TG2ACwwYmCW6+rML+foQhkQ8YEmEIcRGW5Cu0Ri+4eLQNPmBC7sLguKOIAzZKPwefFV35EhKKkZvL99djp3LpEq0UPAUe+FPdNVXPvoINVPjc5oaToff9BD4KY5/bon2RJCEI13dUUQ4/7JRrEYjixvWdMhCljhElxZooJszJDaVCM8moExywdHIM1gEMwhlHGKhXJUULk4ldRRF7iiIGRZEIgV0rithWFOg+77EuT6EFozb6IlFfIHZriZGDxIgoMeKQxIh9iREwQkkIT9df7MC206NTcZUJpfBYG0ESai1I0NCoOGDcUhx7XHHkIcXRdEzOy4oDqzIVGKEMQaGqB4Wa2Msh8kdBsTvS01LKOU/hNz0oJExkEt1AudEpewNHB+REH5CT3YUU4C21waWui6en4MoajsQsucmX8Q0xYo5c+WiZTqwi/9VdEBFmyG95A6HqeYBOD1EBp/ZO3sy2PSe91NiBEzBuSQ20kC14OSuafIaL1t9QSQHRumlfCjHsYh3Ndvbo96SEukRZkSnG8bSoDyAQcH2OyIOQchN2plkIO7NyEA8Kuf1Y2OVe2KmFsMOfoCx1b9EHuqcP5lV9UFplyrhBNB/7m6rJzfMSr2m7QiHQg0TocdGhtyXCcnBXb3xYa4Me1R46TK0bLCH0oBI6qoQ+pBJ6XyX0WiWC6By6G0qJTHO5IxIv3nuxCXMbk8ZinDSOZgfG1Vb4VZ8emHZDr7PmbemBCwg/cwa2+JOX2b4edKEy7WJW+LvI63ItmXBfY0kohpDDC2SO7wVaDbkq+tusBTnzQAfMXc++yss7oRg2ZjVeR4jPHWS7WeGRmxa777H86z8ZCwa9XQzocLXhFUY9Lv0qXdXpZ98F0A4Gm4vMiFhplvk3dHuf+CE759j8UgTtqeJJBIyrikW9nB+h4hIyAUIIKPzRBEaiDB2TAhdQcD0K1ClKNyrAxVtVwNoTBjVCqiWnJ/nMa+l+jge3zwNVI+9RlVkaS6fPdRNL0gZCt8Kd/xWypp8FSX4K0ZfhoocK4k4YE0JcNTkEp1s85HFy72MeczAnc6wqOZmGy194bLSrPKTZgAjoZzOMucGw9wUxwNVVD5vPPdZlDc1haCAmbBO0vJ8D9gN4SCcyK2Mx/Pud0LoZksWR4O/miR6Bf3yxglZTLFZH1YCxU2H7/zqEs2sR0K/lgPOgZmfXV4ht42d1Mz8e9X4HAIyj4UIvEqg0MuVEEkplvBGBAr5bYJ5P3/0Pj6uh7AplbmRzdHJlYW0KZW5kb2JqCjM5MCAwIG9iago8PAovVHlwZSAvT2JqU3RtCi9OIDEwMAovRmlyc3QgODk4Ci9MZW5ndGggNTE3MCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNrNXFtvW0eSftevOI+TB5/Tt+rLwhggmcC7A+wAQZwFZtcwFhqZk3gmtrySnMvL/vb9vqom2RQpURGJ2QHMPs0+1d3Vda9qyrHkyU2xlMnHimedAkZiTVPyCU+ZkuDpp9QavuZJIp9lksJnnXIgWJtybhex+qlgWnJxavhEwHjXsH51kw+hTLF5bJQFnTB5cQGdOPnMvVuafClYrcnkm8PyLU/BpXIRW5mCx+axAb8YInaQKaSM6XhymeQAK4kdwAoQTA6wOXIkoIMTJiAQKo/Ds3gnF8kBt5BwOuenyNMn7isex/YkCw/jPQ/c0OFaiSNAILimJEoR50ucIIItfJ5SCe0ieXxpjq/qJC5zVpskxMypk3DDFPyESRwJoKdgQRxNWuTyoKjnuaJDh+cCD7Kv5cJjZiYtfSD1MzrAMEeeGxNyTHyF6eSWB1lyCuwUdDKIxFdCanFlSXiF11lqxspYMCsw+ZmJGF4VACcwKFdKBdhdnOC4QaYScKYEEhclAuhYJBPVOJVCykcAQxAuEthUHdiecP6qREhuqsppl6bK9x4CU1OBfIC3VXAmD2GoIhwJ6FB0wJSaQQmP3WsuHiuXqRYAp4TphRwEh5ojxXC25oSI1allboovrWDDJJA+VyFWSSB+nrtRzn3wlBMcD8TNwFtAjJgqGALmekgA51b0QDD0IN4SIJMpQ76lgY0pe/ZAW0qnzw4UTBl7lFAJRxmvjj3sUX2LFy9fXizf/fppNS1ffvx4fXexvP78lzv9/u/vP/79Yvnq+ubd6uaNg5q6t8u/LX9c/vDG65eL5dvV1d30Bto1Uz1F8hyBK/RmprhIbjPkHXBfTi9fTsvrafnX6++up+Xr6Xer//l8eff++uMM0C+m3//+Av9OxwQ6PpPsMYZZaqBNmalVMckMKh7DJJ8PE1B/FvAixIQnuFYqaBRhNMoMNfhNmHxNEgcs/+20/Pk//wsmDnJe5goGf/z8449vH4ILWeYAEwETCX60x4ELTEAKc4D0HoNLRWYHtXh88xRnEdonP/t4FLjNkZJb21wgrY8Ce4gXjwVDNMf7K7+6/ninhH0F5YXO6KxX9A9u3adB633vaZ76F5Azt3UfDqT2Pjqy7kMzoTh9Mm1b7F+gtpLXL2Di2hoK9rHYdsBw+ebm+ur1CvIxLd98/Wpavlv9cje93RW5by6/X10sf8BBVh/vbmnyOZ2CdXv9+eZqdaseRYf+tHr3/vKr618mFcUMB1FagGx9c3mDuXAkYnAqxbfYlR6RuNAh6rP5/gz9Gfsz9af0Z+7P0p9Gkbdn0hZfwErQtnnwv9KCzwH2H7ZyzuUfqLbRp7nSQ0Fd6VxidEDIK2IwzscwKWc0ZQKDAbculSZMndEMbwL33ObW2kFM7n5YXd+sPszA9YwkcWUusKlSSBrEIgGKjUALHntO8ige4ax4tACrEzd4INCaKwPB43jIHM9o2SES2W3pAX8Dn/MUNPI50UCIPDOMkgx0GCo5OOHIuBjsyfVxST0rQUKuc0FISt1xBfFwc7NjhBvy3MpR7fXujLwJsB+FMU6cNWqE6OLpq8wth38gTSicMTGgC3NLjFZBI8aNUWYEmUdp4u/5f2Y0T/H/u3BwaFBWpjKPwvlW5sCYEGQqsMKPAgeaA6QJ8C1zhct7HNhnjceQB0FnwimuevTCo6fe8cI7nnfHuY+OG52cz+aeQ91zzyE/1z03OxbzQnv6/uzjvo/7Pu5Df8b+TP0p/Zn7s/Rn7c++Xujrhb5e6OuFeE73DrbMBXzJrkIPkb0gGk9MHgO09ZjNPGN6gGR+ZgrV8QhBZmEy66lLR3UynNE8IITN8F0dEfFhzmDJEzFpDyJyaGdkhpPfkgDeikFegtEW6Hx0jLTwvSACd4Trh9GzLF++fKk7LF9ecfPl9fIf3/6Rn9/9cHf36fZfluX2/d3qdv5weffD/PPl7Q/vP35/ByRX7z4v/3uz+rx8uvy0urldblZ/XUHmr1bL1eWPQBEgn9799YvnH6OBcEx8keC5zLoHswwmx/A9Ifz2Y7y7fj9f33y/eAfjG8sS0p98zvDxf34+ktDCmeWONZLikF+xbPNcJC9vfnn/k6J5+ZfbJUTX4Ghjqj/F5yPpfZ0rq0aOHirAvsMJQCZhImYJcjKSGUgGB9785E+gJGIuulPk8nCf8BaKdGXhb86x/HYkVV4/vL8zQX336+Xdj9c/LVjViyz4vABiEOuTZBQpQxPWIrJauwARjQnxc0Jg7+JpIupcWW4dhMg59CWEE1QpwwrC60eXETjS1VjmA2KIlxPRrHVxSOReSPYe5F3iEkM6maRB4DgQBIRIv4LoIiEALScqfQpLK7VVH5B1tpD+2znXTiBrikhPtrgivZ0R5Dwb1121ovwuDmIkrpxOTvjhihgsIEj1SPxZs0ISfCLrfV7+Nl/dfLiE+tNQlRmCeoKdKghSI4IHpIGNdEW+hQyDBXpE2P50dfrqFawUDIs7wZjGCjfEsjfiXceIFQIqrNdqxexUna9ugUzGXL2LQBkUjqGUcDL/vfg5Mx4SWAAnwD7MqT6D/z///PP84Xb+/OLu+u+/Xs+XV/PfPi2fblafbt5/vFtgTBeIQnwBz3KSYV27LA+xihNIMLP47hE8BTmrwYr4t1DH3Ak2i7VnXiAgwNIbHAZaJU+gfczldGx9zP5FRiC/XF62FN59sZs3hvK0vHGEC6Avk8as8lEfB2bdOPKEchwOOIeWj8KlDDn0T4CDZ3UlHYeDZ3sCGLiCjDYch4N55BXNUTjH0n85CheENcZ2HC44eBR/FM43h9g4HodL8J7uOF08axmu/NbkfUzSx0R+JxPfZN/3EnwodzlYpH8o3X9u9h7bXvYeyzOzd95NWvbcs+/Ys+nUs+vYs/TY4eIaLj+SbT/BwsBuBzA8tzhLa6o/dk3KG5jzeMYQvaQTIvgIT1jBxQxfUxhmVgg9r3sjwiF/er72mo7RJ7gEBJgltXRKssG6pfRsvPKuHPpEsUNEF84VuIcXaF+4UPILf7IHh3Ayp9Byo4dzyTAVJzuX82IKAZoL9FUqk1+kGxBSJ/jOI9TzoOoqUMUnOv+inSargohCYJghW/wNBKI5+C/ERa6dVlxA0l4W3wpM1yn5MFS7CQwv+BwSiBgri/54ssKQzhpVXK1yOomYmvDwxyhz489UHES1+Ukc5ME9Q0ZXn999+FGRfXd9tcBNBB/vBT2xPi3o2YGDeQ/uKEyu0LB0fK2cDt9k78GB4qzmHoOTUmcXj+8rCTncgTv8PTjEeEGe5NSf7GXftKy/uZozf+7CHwDN7V7voY9nDho64HHwR5boj8Oj4/q7b4ZpuyOFhdLePjglZplLH3h78aYgJYFJeyOQ+ajuGN44EKTAt1AgnXoXAZHhY/AOASrQapDWzFEKGUSt8V1iv2QkOVNF5oSVQOGm942FK3Fjh8WaLpAqtgYgMrrD6PaHscnakSqntOOahwQgSwWyLSQcqOHIeao1cqRVkKk2PS4cb5PAefgOUqRC0lQ9j1MaVpawJ0SFXvcpXC1yzYIwszF3wOpKNFES8QcoUwUGGAnEzXtQjb8MhAlKSnyQlleFMErMKMd2l2rUGgQGvIrvmIk0sgpMItsY/pIhmZdkneUJ+R9L/MQ2Z26cnJ2m8fCI/DnBc4G+TOES0rmMHAKS1JBi8ceGlJbkeSzrZ9a9n9Jijewjj5X1ICYmICvo2ZDPKIJei4BF75Ix7gmD5Lf3StZ9O0TVg7C1mePbSIPb+/b2lLavqa1qVt/L+uPuJfGExcWZmiTUW2YbheKVIVQUrwjSG1yshJBg2s3blwLK1DaspKva/L5DoAYaDfsOBqktBbntjBjNOyX4Y0/ryXbmSCX+jI+f7bdO7c3YSAuOBl2pc8o4O1BO1+OVeOcUfxbHdH09O2dyPmXSKyk1OkeBaShr7hu2vtw/Wz//fTobtZTORtGx3Zk/0NjoOlJ3bHd22lvBaDy2ldm3cdfo0Nw+XXbovP4YPa2N/j42qZWNvHe6DtQdW6P0SO9O9YHKOzoEi192ZIF2wR+i115bq0q2UmfkwdgaNfZb8m2HDw+0rcqujj3QGpe9D1uT8nhraA8sgKFKQpR7q8aut0WZsNdKVcOm7Tgeo46L2l8TZ2v5e4fedoesrQR9a+2w5tjSdY8f7xKP2h+Fv1/r7TjrIY9ZHXHbb71pX39UpVRv4R/5yWaaHm1r1T2e0Hb4vvZmj8GJVKWl9yb7RnYlbB/yxs5Hvzmd0EAp6UvYqg+1iGOTRl3GtZ3tHnm0A7Mc7FhfTkc0OMs8BLw4pcapSjeVRSprVHWn/2ZZMKiDpnlhjII4izND0OCuaAwP6UmMQej5KX8W3DGyyFE9v6ibb8SAUYAlRIy9GKlUDfhshWgcaUHFij8jPvwIwM73VrBHPEM7rlk1LrLxsS96kgIYSKZFUFLU6Krsl6ouk9jXQHMJYzzrL9RVJxzHG1wwqKcRnQ6YHXNKaPUcNWqkhziEqZQxoNJzs/Sv/Uqm6i6F7it70UCaf/CgvBvaXbqx+ibm+CSZ7VE5UAvDyyWtUwNGNdH4jmxYjX1T5801o7e4U+UBfKPZjuQ5787Af12/qDyx0g+uRg3zRF00g/mi2EiranlE5S1p+mDUfpwL1n9K2489svnpk5+0dH88/Gbc+6AsH5rEDDr29pFJAeqyYXFxGnxoa2bAWtYnLBmz1iAMIUuteoI1HMF2rok8pN4WDQL45yNq3SE1kDf1UfyDkOaSeiJ1qoH2oDpdN2qCxmtf/sUIfXpNfhOD1sxEoUHyIcIYsd+RRv4ZDAWLhV3azcQJommkUznPGsYOp91vR/o96DkHxowas29VbJ3RSlScNTHZVcqZZRxprO97azNiUCo+aoFoV9oOdiNnbKRpHuldUac1KEtH24XN9l3eDZUBcjzsqBMdUUkHiXC6Ud1Rauc1wmZrI3b4ImPGr0GSGkuW980RS4eMleOSNAcIPCPr6RD6QjNmMH3lYKKrfU2muyKY8Nu+2iIt9TsDxvmRtCNhDnghK3oYB4f+Dvd1fEcq1D8MkrWWpS38urDSU5OySVDu957SakQg/fP0aU9YVJvDo+v9tiMD8Pb7GHcdBA1FOuQD0Z7aO4t7tTUIPaoVtawdkLTdStP6BiwN42BWUkqhh7eYmM4XqzRWP6rFUtrmnpQHLVnR2jmnkudUUhla56bBrYbxFSEXfwuq6KWkeaIoCrR5rdpWGjZnNWcqIEcC3oFkSUPx/XZNfQuwrB2y3A3ht9mvhaEM87XHGkrgvRA0LPIM1oq6npioYfpnXzvj1mc4Hb2FH2XTGsT2Xdz07M0pbV9RW6vwaFhvtR4oVbDv6t5Ka1rvUdhMFS9IT+hstCJSk0KzdKZ/RFqpolrmKmYxctkAUC5aL5dav2q0TKKVqvFaTgrHjb2H0Fi3O9paNljLlhoqxAbRR5J6TCyqfOPTKi0pdkdtfOpUsFGtt9mIcUXnbWDtfRIN2nRG2kLpO6OQ9XvrmLB2OTFF6iF+r87uqtpevyeJg6qOCZBJYTRNGezHxm7pH5eqIrrdnfi9uaBmxWs5bLCc1sIq9220xDccoW87qIkMpkffDegdQuyhT9jDoqxx2cQknTwknUUzvQyvUX7VnMlKeTV1EjM69yoA63OqivoNC2xM5/bVLd9Xgvf1N/l82Gwva4uwJdj2yOMh1lWyPA0pZu8P3NPRHZ6G7Rp1LFZsoA0iakEhqmR1hmzf0ipFTUyyFZqLFp3V7jkNpszitKzJDmtaUUgq0cS2FyicWq+W1papsDpW9H7CxoLeg9MVixGY+i98y5/xi95yRL3NZaSlSQ3mxx7HJo1j913fHhWfKUyHggGJTqMpp2E664dJ40UmpbRvQSNuXhtk1TgNgmNTTUzdIsUuNq1YSK+xl50p8fcgKlLSeuUxaL0+6SXhuoTUlBOkiGj6m/Vt3lEva7dUsXKXBGa8TDr5UyddUT1qUj6nqvaYf3eqfIxWVvG+w0HSWlWrX/VSAmuowoiLc/+jNdg6+gH+KUBdK1FRXppqeVEp8So9dpMlepPAP+zW0H8IJkY/PAYW9w2IwVlaNlqsnZn9Hm0TK7muAjvK86jp3ApILHGrXv8vlm4/UPwnt3RD/d8mivtnsHRrnzmUabfe7z5+fuuB5V7YOxLYSKUEXsd2GqqMZd6BkCM5x3Zn/WHu/m1ctatSY6RRr25jUzvPPm3vn3MtnHvYuNzFfE3JgZ5jO4r8OH+g6j4uYRTee4Xw8WORR7cLA53H1iiw3xp3Bro/mGsNWnAICbKxV4QfUlLDcP+0Y7sfstyXtIMr34fZb8NWvyyK3PnxgxaWrNU4mJFepSXejlnPInreVRFjLT5rK5to395FNZfW57unfvpsbXseMbSiHrSPaGxR+Uc0kHKn1/teeWkawpKVlcn0/6Ux2VVpbFpG1j/+1/qDlsT0f1rh9EpA61cL1vvdidPUpPSNvbcE2LDQtqgcbs/ce+YsLBLqI2u6201F74eitSBNgMt6204Pi3xc6d9bLwTqvA0s3yetZyfRVjmzfWdUYo8f6ff0gbeXVo3ij5fNz5/QBo1bafe07ji0zFbTeiQmrTCts76S1t462C9TctXrMoseA+MInRnUrsGq9+u09Xo2s6+t8Qhy/fXahIos+Lq8+SIarhBRM3PrCya+2xKCiyT9McS2F5qGSJv3emAdE59VYQNLDzZjA2Xv+H/XsNUQin1N8jU4dFqGC1qObao8EIjc73aKXQ4XCBJtTbML8aZJqRLK+nrfwfsE3rGVXsRbr9c0zBxHJFOd+L+hWLWm96yGoxA2wrvR3Nflr9vTTj8UhugjjM3q46ozNlKK3jvZ3HFNhWEZlApgKUPaQpq71PNYX9u3F/8Hx5Nx0AplbmRzdHJlYW0KZW5kb2JqCjQ2NiAwIG9iago8PAovTGVuZ3RoMSAyMjMyCi9MZW5ndGgyIDI2MjM5Ci9MZW5ndGgzIDAKL0xlbmd0aCAyNzQ3MiAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42rS6c3Td2/o9HNtqnN00tm3bVqMd27Zt22nYxrZt22zSuNGbnvv73nPuHfffd2Ts7MyHc831rPVJRjbpZwVlWkFjW0OgmK2NEy0jHQMXQEZWydbawIaRgVbI1soYwETHwMACR0oq7AA0cDK3tRExcAJyAdidzADyRk4fiQ4AJgYGTjhSgDjQBujw4TQGGLoDZIFOBirudkBGAIXBX0DB1tGJ1tDA8cMNtDE1twFSfqQI29q5O5ibmjn9qcFMS/un0p9sITqAlIGRpa2ro6U5wMDGGCBFJ0sHkLN1/TCaAyhsbQCGQDMDKxOArQlABagBUFUWVVIGiCvJqyooU9J9FFZ2trOzdfh/XISVVVTFaQAignIqogCgGg1AXFVZ5c93FaDNB39TGoCcyof/T5+PwD/psqIqgiqaCqKM9H/WAGAEuAAdHM3/tP0vbmQfzAB/U/tINXGwtf6rAYDCzMnJjoue3tXVlc7U2dGJztbBlM7O6i9+KmbmjgBXWwdLwMe7A9AK+JcwzjbGH3I6mQH/VeDPlgBkzI2ANo7AP0litv9yWn9I+ZH0YXf6N7EPIZz+1LT6VzjAEQj8jzZmBo5/5cooKMgArA3MbZyANgY2Rh+BTgZOzo4A/b9sHy+gMfm/CAIBws4ODn96yP6fy+Hfbf6PupDtx8p0rDy9DVz/e8cMbJwdPf6hzX8u28jWxtHc0cnxXxWBABNzK+Af9o5/9szc5i+brKCcpJiosgqtzMfg2dDK2n6oY0Pn5Ob0V/SfeoIiMlwADgY2ACMnC4DhY0hFbYyFba2tP1g7wv2RT8T8QycnWwd3+v+aaksbW1cbz/+2mpjbGJv8Ud3Y2Y5e1cbc3hkoKfL/Yj9McH/bTIFOAAYA0B4AdDMyo//T6q9J+WNm/GP+kMDb087WDmBiYOUI9DY3AX68wXk6GrgAAU4OzkBvz386/hPBMbIDjM2NnD6G/OOgwP1VXdLGxBbA+S/zB5P/c/2/7af465BSfpxQY1sbK3eAMdAEjl7O1uljGCj+/zlj/9VLzNnKSs7AGkjxn4L+d5SBtbmV+3/G/VeIOvAPVYr/kWzuKGbuBjRWMHcyMvuXqv+ySzoZfAy9oI2pFfBjR/4yqf45R1YfA/tx6Zj/ubMAtIwsbP/l+5hFI0sboKMjgO1facAPDf6L74fwf9gC6JUltaTUpaj/a1z+ChK1MbI1NrcxBTCxsgEMHBwM3OEYPmaAiZUV4Mn4McrGQLe/hgRAT2dj6/SRArBzdvIGmNg6wP3ZSDZWAL3gH9O/EBuAXuhvxA6gF/43YmcC0Ev8jZgB9JJ/o488mb/RR57svxEHA4Be4W/0UUXpb8QCoFf5N+L8yDP4G3EA6A3/RpwAeqN/I0aGj6LG/4CMAHrgv+GHEPR/7eM/Aj7amvwNmT6qmZj/ncD8B7r8o8KfcFtnh38U+Agx/Qf8IG72D/ihovk/4Ad1q3/Aj27Wf0PGD+42f7f6WLTNxzz8w/+xGNt/Q5aPZNv/cH9ws/vb/bFWO6CDue0/1GD8IPcP6owf5Bz/7vfhdDR3+4f7o8LfSrF+NHcycwD+Q4sPuk6utv9I+GDs/A/4sViXf0j7UcD9H/CDrcdf8D9nW+HPpf7XncXw97D/v6fdX1jZycHWEqhubvzxpP9HiKyBk4O5mzbDx4XD+GH/+Pq/n3T/owHp33flP7KFhGzdPGlZONgAtEycrABGNgb2jwUwM3v/R67Rvx48f112H6fy//CfWx8ABLoBjeBWFm2NuIMsUhtDyn1EC6e/QZJy0p1XYvFpSMVDrGRMt+Nhi+TtEgP5i/yb/TLJimxlJLh0fZL9bUo0SIMwrd42W5Kqpm6NFQX2DHxkffCQRAXHctXoVAMyZZf9vnUSU55I5RZolrLMZrbGtxICVMdOhTnbu56imSbfUX+lEOt8a13Ph3QtnmdswnCwQnNbRsHtwFue7gB1en/CiI0y6BVcoZrTLwjBGpOCsuvpQtGiTi1Q+VpfvXhsDBVV04OPZ61MYYOg6uZfc4+ZuQxeJUnIkuDQL4RIR+w0ED4tMMEUXJYp80ila7VWV2jmAyU8W6Dm7FVkejZPENgPFflVHW9DOcbTk4Kc54yj05Ao4NPRtOpXcLnKcc61W/jX3EKH2thUAOhikBw6FdaNdKQDy8LlkzpsbpGe8CeEbzYPYII7pYAeJqpioMHoSNQJtEm4R7cBhjFWZLHwgImQUFcRHq9LGC8RyolXheJtJ8vEiAxSJbvFrp8DAvOqgwF/FG0AV7U65J3EGu/jgl9KifYcqAJZ8B3XFjsrHBMfGc8TByXVZoicECxObw1DGbll+/5UnZnMq/AAZUEfgA1rHGe5R+Q+OWykor8+KbUgl84p2itWjX1lZOlKvR3OYyq+1Q0WQtaLoqCvuy77lYOwPpICtfBHOJt0xnIk5LkFtfujYJtoeoFL2RzeOxP+xj0q9VVvzwFwOLwoZ83NsSAvBx7Aenywd0LyZJlanxjw8zP1rBYS/tI1U/1ihvt08Hxd/UOFf/qSykCvYz7O82v5E/ou6sbUZDJiFf5MANRSMeTM/VVHW9+gOU3mUZ5sSWh7CpbtmwTyRk/V4xZT1NjOdxTybU31U8H+q+57ScHDPChhbrcZb2U7CAidCeKnFmt/wNQ+QlPJa+wKceIcR65QQa68Qdl8aUC+ILgm6/TiGnW1hpbKRBgohd1L6t5rFZ4iCIROp0MTXUDJrjV9L5na/qbCF57u9qFi6VxT52AjMrvEIY+Qqmdmff66Mpchu2F4MS3lX78W/NFedzJaiqBzEOKCdRRJcmmIBRrP8fQYEHw5gqD2B0WT4vbvR1sbGM8ZlQiMtDEwOa4T5ghJTmbDbrlSw2GFsz0wLYZvvMCJ1drSU9xSD9jmfQpNnttp4iGbhAUEoF0NS9tVVD9BUhAVrp3SOnnenhPtKIMb4qJRRNDoo2jKhLBw9UVGP6yG56zHOauNITGqfoFBsqBqiWoJglEPW0jVUOJ7i5OAIqOHyDEXtmH1NvvOIDDgd6KogL5fnUE5ZdTDLTD5GXh5lWTffFh4EB4yIO3FUyBtz1Au8zOReAthMnZbQFeJexmkbhMNoYtm/QCppqU+2TOIusMuVqkw7yR2CL8qvPXUbzZMTZX0u4j0r+pfuMJrvY5reDs9dzvFwgaiXUHxLhZzj6CqJ4BtPYtpLoSoRsrBSnGp3mtsGNxfXSD8DOnQorfhEvbuBSeEcvFETmzmgce7xgjQMkr4N+5KtnnPzuMEcUnQI2wonI2zqSssQ1f4UoGWgvv8VvSo7QWBKHf5I5Hv+I6kKeZ71WScnXCT1go1p65K/pLtrz4+cF+GNMnrf58cht6nqjN/nWXfvzcqX9afCxLOI5PpJdmKi9g7aduvhFvRaz3go7j4YRrFx1KP3mwT4g0G3SHx5meKFSWC9uI30Wr4+D21J72iALditB8tBSF61Mz3BxM+zU0CvM2VXdr8ie9Z2aCCP9KXRVZ8H5NNM6DnnnObW4+gsu53N/nPZN2gvaxCYsX57dKXfazFQzFxO58736L4PlHeGyAj48YhA2OABbGjQCg6u1s77t/XdmoIcHWZr18iYDRb1SHSR78J5qra1hQKF13tgDCWzaQfSGbsQtMF2f+UeAS+PJ/Y9Dt6PWbO+wP3dQftG0i/fp7Ad/fw9+SHXhrrhVbtLHN0/zWL+qvSWEKdSREEicJNdn4v6uhkv1bA18JuM1WEeiQkfBqss6GEU+sWfSSYKXoBXvQlxIuMUm5PwHCvLmF2glHJRK+kUg0LImUHrP936GWAe9Y90o1onUvhLDzqCczKII5uNF/m5rMJL7hOE4dZmbn1kqMDgzBIC895VzneBqonV04lDutjioTrKfHKwTypLSNL002h/zhma57gT7+Bjh66njs3y3pcrc8IGGs6eEOJvsEc4JFRcu5m03M6wUbbt8MUnJ5mT8+edgOkjV0x/s+aDZoSXTbxfsSEG3DjxW4JYZhzODX2rCkCffIxxtzK7HTyoek0XCNf6M48dBTdnntxuV/kvoA7z/uKxW+f7KkwnjPVrROYq9Y+blRGoklxyZlYhdj1N46HxQ53S8bNsTuFlOzOKu2MVjwRz60929CIj7+aVzDhKHgSHmJCighu1tj8pBzYZrNR82jPV1PpAq+LNcaRzLSAVfU5MgmmWPtskIzBF3WDr4vCk39o8z/27STZLp3q262GyfIiO1v+kaSVS7W7wt3dsrlDBrG91Fck73qePTTMvM3KtrhLpZmUy/dfK4fNk1FPNxQmVaPBv8fiNh7xM20allb9n9CU0SDRQQxKNAgW3YohSpqVInOpo/14BP3ptJQR4MePaJZuBrr5djAsiAQdHMrohiA1dHJ7450p8vefkDqlNDZBJwJqm2d6zdMaUz8DJC6uRdynYSoO9lnCfWhIR2zoc1XDvI4pc1UsV309LuzvGybIv1M9gsmJfh5SnZOWBMqUVoKeQyrAyIvD/tS/NYhad5WpNOaBIEFEoeqENxJWUvfI7c4sW9+WPJDwHHdlyEMlnhfchg/HPZUPufh+Zis2kkkFv+U4clZRdJJatOJkGX9UE9kAQWzceD5wyiV6S0D31jO8+tP97vp4Qyw6PxxWqlhPyce45Xdo41hLVcoU4rl+HZIgHoJW9qp2pRtoF6zkfkHrnN5sbDcJYYA3FExJCxLM2K0Ug0nG7S7CwlC5b2OSSDyetKwVDWJ4aGgRD55QZIwh5ZdNltJurbfx0JBocZ2vfpi1Os3jVCQbRJUbv5eVNrHq839BfzHIK1Zi7I+sj8JF+/q1WPet+kmKLAqZIcS1uOPuWW9Rve6bThLddLfBuA+4Ik/NM+mF8YKFUpshIiUmF67SKJCBq/4LJN7boAkPuSZLF7Mp3oJOYKxUB6vcj7SmCNZeowbWOoN8wIRMxcN+pOxNJlUWlEFGyXg77zIa3KGFhUWY4RjKKBljaNei0jqbGgF5ObN2e36gZPV1WscraeRwYhPTxHZHn1A5+fsjO60coe7LJceI3Q9qG8UzdjWmQYwgiNkECHeMoeTaoUL4HOxlG8L08w4Fvzt8VEltWJHVBmUHD4AMMD8C7jcNbOtXjgpysoiWlCjXqW8KRF9qxj7vuuJFD0lBjQo91tF9MjlC+wbKguY5PWdCB9n6k346NLFmZkHLTViQg5EEB2guge0wa+TrJQCF8Wkqbg/mrboshRyX27ruaLY6OAIv3Y9dZT9bpEm67jOA2ehHTesLY0YK7+XhidYEFR4Zk3YjIpstkZ2mzV6OAJwtDnFj59iEIJW3BXJisRfnbhdcxgkiOOsPLSqbbTwtOFvKKpMSTyKJxV9obCh4mHGW82RkfeT9EEh8+euPEKy8CqQ5dlo+cO6WHAlFsSOCeoHRDq+8rRAT5BtEW2ok3nPXk1I1XDx8mj9mzDVtdEA7ZAZS65K3YrF0u649WsoaImezQhVZ+SX593sd9kNPppfwqKGWhHi7bqllVCvmbpl251bKDQIO4QcdIFVs0fHsAxGDPtF5LXVrkwYX83jVzt0qWCVnY9t3Dji3OQ3XwZpL4JzmznxDR7V8iY5IGJq3YYwSuxUHbxTdtZABP6JEV9/vfypxUBK/qfwRBynjjrPY51372yww2J/kzYoqTzHmcRPGWKdXiPWXgXKsflRi1ZVIX+ZssX6TUj0fiqdKfbmhNNEO+IpWGJIL9OMjt+tMchIWWcBTkg8urA8bm2e2EUYgmtgL1o/WeGpMTZVu3tSipGKzQY/z67joWY4fdkB11sYw7xgoPvTBzQCqTlVZbcNkeTogC/2mcqjR/sZSC6iejD8cruVCF1GM4ZzfUFDWFpQk9bnbiAfT1xJO3MooF6FV6J819WLfV3gTowtV4onLbBNHjYn4aUYYJaWqMQMHCa+YxfzHaUgnLl4jh0Nnrt6/7MhNfVnOxqdH/t3ugrr/6Nz6Xdrfa7cKFaLt3UujeWFoyc8UY5yXnK0FDtjh3kWiecPN853NGR2kIjdRkVmKSmQU+zvCRvYFU7n1DdPAPjlCllXSogjFgf4dZUMecSUb5SMZsgQRoJ2Npm56tHj7ul5uZInX+z2RyCKuSUAtOvbKxQrsvRsG4yF1jpm5AcDoZ/WATxIB1mBFCkzjXP+NTbDAOQbWoPGcb6RcMKFp++vTybrX/QVpagRnMWyj7c/6/mYAaFn90Rbo6H72iUjv122Ek3zG6wzPEbRT78oYVnn80yyXGWR1Ap4p0CR8crNlaOZ9tQCiJ387ctFlvIhk3EsKv1pgyzPUIDqFARR1MWxWBFRzEAqSE86B9LpxP8pZ0SvxoLcX1wPm3QKJcCIYKo900Gnn/ZompfCN4RnbDwX5MvgfBriLYsiOJLxNRptrty43ikCkNjDu8ICe5Z0nH/HQNsbthbPiIQyXgDb7W8OKLDPldtykZGSsmxtrJMh6sfUCMlAssn05HzI5Aman/aZWaJX6CKX2wmWyPF5URfEFS9KhZ3RUKVpwCXxp3/oOWLG8h3XYriMlIDmhTa4CjU+416RkI8mTcBba72frxL6Ut9jx+QhG+7ngX42BAnnUR95f4yAtdjmqk7AZQz/BUiVhiyh8JulMDbCEb+gQbTYk/tWCHN+jrjS66zQaBNaRplMh2ZZeDGQ/xe7FuTx9xjHT7lKGfJHKO8rQd6J4IFoyOHLkO3oi1+Wx1yzhUNzy80tmhtcd05RIK6wWNahZJUKXggk+LQUZHthw3ViBXIevp2tV5DSNPKBOPp8Cy7DELkabQziDt9jye21alasFLf1yqekohX2GNuGlslb+2O9ErTT4pemW7nxuQbrJjHZL1rqFyUIbLCzqs7iMfSJP3FRCq5VW6/cQ2eBvF2kE1oNKbqxBsR22syuwqW4B5fnHSM9b4WHwQcVMd/EDjcm9YMvE2Ypp4LitvM2uMHNgBkV6y0Ne+kuv6Lamgr4bjCyWn/cCk1xvK76raV/BOlK9b9A/wPSUEDCHblmED1CDxoidHTaKR/LtsOstutHy7NrZfVuFKoO9TwNtWXBVWRQuO5kmhxAavuUi9nnJuQjG7BioWNUDxAcTuxbD90QmfrmUCUGGhmpkEWurVSm+SAS7sQiw6e0lwnqCxnaYQbyOnUSrOMnPWEJqCBVY729Nk2FAKQtJmP9F98LO0ElWl2WVlSD2uPU76tS9PKPWkDx5e/L5UVmHRT7BpX0GewzGrnad9NeeJOzRsPN4XsED76NG40TBNPXv5kBoEzCnw9300O0iQ02pZ3SqJ10a0Yyu81e7xJLzHyTXrjRZKUzFn6B55WLwAaphHdNksXt26pvwAz/cM6UsDctH2qaIusk5q/FXNhW0Wc0Ve3jz4fiukoLg3pS90RXl7HwEby+cic0ZjZfLgnnMdUy+ilpzkKCMcWcyUHZjW4XQHE5fcB8shBC9qh8tR8uRH1w8T/aQTThQVZWshTK5yIjd9RWqAUkzRMC+SUjj8BHd0Z97uQ3wgYJC6pK3iCemoFo16bKzBtHb2Fn8VJbZ0Lwn3txltUwioiQhHeSBXdaSQypye/fIertSXKSp1K+ebVeekXTWYA/Np5mfd0P5nvduMTjBIF8vGPtdLrfYwtzqtgrCcXTu5k0gfTr3LeVqjcWRlrCkjpih7tJzkPAX9BqNRPjQTR5TYCfTyIPb15g0IYAId5DmGfzPFFVqeepkpWEo+rka64ZDaaS0/UA+k6eHcuGkV0V3R+hY8kVzreR6Rj7eH6SiAw8r68X0ervtgvuYY+eiYlGNbiAKKNgBMkgIrxpPHsm+T9Haq3x8ICRKnfQ+fZhECm3ZOogWU62/0aV+Ylpef/WXpqRLL2qsSvwmZzuLNdRUhpJ1rCWOPbuezYQvPLDjAwyXTHqPWTS1SVBLvJez/i0t2rhPx2gI+LmdLqrEJKop3t52PBWCI6V2GGjign2pJk/cu0TlztsEbPaHTEGak0uGv0SnD8XxuyvG+t4+Gbacsam0fQpnk8lxgH6BxSQ0nH/oIohrUBCiJ+PRTOMJDHLNjqx1dyqYdaiJN0J6dOP+3aVuI4DNaI7YhlzbzotyjcRYl7V9eshEFlrC54ZtYc18D4/cwD4PSkb9iaOhlbiEqaui1x+X3JNo13lE77IbS8eevhh2HULZjLpOMQFywbMyJhqFdQg8WAVzSGFgEmcNckopQ7d2q+i6mpHo1yU6Jui4trSLWcsSKuXLAknNzUv+ECNlq98LUb3F8HhZ7gWk5Ultf4rUsT/HGOeZp6qAI/1uK5HFGlXygvH3DAbWXV+T3zYC7ji/L8bjWQdiYx3nvYVl+4u2eyMWbYYHqMSWBTCaw3zVbbJ1cg30hhmKeKQdMNNzmvyFeigGxwhtnM/Gvu8byNDy9LEuSwK6mc8aHt4LC3G1CMF3Hs1qoLQ3Gz2QdezaF1R6uVRTYloJpA2Fy+vTN79AmGan+DX1B5Smp6C2ofkOHzXdZpOPOa3R9BoJcoTN9DejEfYOzBf/+GQN1eM7jmKu5BviJ1vgvDz4GHz6uWPykG0hGQN18Nbqhgh8TtEjhSEHbHrNV5Vh1pWeTqOgza3Fn9xj+dlg99qz8U4eWQxW+OGG3HISZegoZ8uNyjSYSA+O+J7f+YksESzgGuzQVurk1BcVf1UxhoCkzZfXgit+hYuZKBNT2IWnFqaUG1oxMqesECzauzzN0/qMVW20kUevwQBbqWbWRaXI9J2nMOxYAFt2kUPgSMO9U+6KO+Oss64BtzTVzF504gjbNElql/X20eGQ7Zd0Sczo2wyb2eeIeC7kU+pI7tLu8PHfzie8aKyppPJR040T+LXXNH1GVK/l0FbUsgilyzmz78dEtSkZejaJFMHpSD6HIw/jV6f+EvbchKOAambeQne6w6fyNsUmEvEn/huySdnh+N3Ai1R4dAQ4CAXaQKoDBpC1RtoZZgc13gprFv9yqsB6M19W2lY/HyK7T2db725nK74C13VDQwGfa2EIJreBjHb7IHyevnuIjA54cFuqxEk31TVWWdSPTGiWb2HfMs+AUuzBh8xG9UTo3ZgScd1FcA31zhE3dMfhOFK1gy+dIEXHWlvhzyhDnQSqRhviRX4SFbCb9jDpndg4yTHpb+smggqLVi4Y31ZOwWocUWMis1F9Ikk87xQ7cnsV2yqf6hWIe9GIE3JllhZ9mHVm5sfIcHihHpRfuFdyC93bH3OsxgkEHiIozkcSgnNhtCDJ6HoQMEu5de4//yzEiL+zBybnBapd2XE6jMTMO+qPfg9MA7PuKisoqh9awOpbNnJQWh+ndlPFZL3ULab2uANHjtiR+IJunJnNcU22ixpBPoYb5u6TvVMoZ1PZBvuJN7RHlQsSPf6KxNT8Z3pJRn1ct/vpz0St7U3aME52C7cl5CJ5HKeKIvRFQ53rDUqcBPaW6CKw75/hYJ196L7ZGoX3qd/DI+o9gNnVI8q9fhMiEtDsd7oyooKcB59QO9dlsx+ZhMVJnguSxAPuN0mK8BxlnpV77PtXSX1fD1nuaM6uKoGkBSvh6rNjHL00vPFROXNZWSnlu6GSbYdrwbl5fbnzZGh1ih4ZsHNpnXC3L9P3QmDkMfmJrzaBC4EqVceFr4HGlNFeVZMF7EYPqQkkNatRTwvfH9zgitiAhEWkml/F8wrOFyTWbiQvWUSG67zeG3+i/yRiBHY51yW0YAaqJSvok43vNVAHtaHIb/6ap0D7kKCBGTxfY3kqPh3wU0K4XU5q47kmahSyKI4T58xKjZ+/C/09g7iYIuELTDfCxNmk/nLicGjrIX7xebsOy0tHrMtALBoOseI5NVEyBk4etEuplOsMghXBS/ymYU815JS+OY1fyr30ScbCjIJSpyDoz7K9VJFeRt1Xdv2x1uWpe7zGJ6hPX/ieJ6cMJG1Dk8V8D1jnsC7Qt8BHUtVHi5R+71tP1ER6PdmRiN5jT52vi5atWHsGQvMchcb3DV8fCO1FGs87FhUIIhzirk5M0pRu/Ay1CehsVNcT3rhXgWfo8P9pZxcdoaDuE8GqiXBqgXWSSIZn6dhucJ411HJ3u7B54n2vDYUW7MsGn39lcZV9MPr6ZdEhDSnbA1JsTz7ynvle7Y3p+l2UicnAY4hdDRRdo9/0Eryc8sbR3GsO+xJx0biL8z1RvDZ+b8Md6VKd/dCj/eB44sfvlTSXjMlcte8G1reN5+JkcLgBF311cMnHER7ML7lvcKsdjeiYJ1BR4HT5OIjR7s6OZd6htT0U8COBLYeU2tUciy1YzCjtS2akknIDP7QKhFM/QfSXMYB0CbSA1DdxG3bdBIq7gEygZcVNrNN6YN1bxDvnO1yjOHqq3SlY4VTx+QeearZKtvDOSPXDyQmR3Z3PrZ6CjpFBQJAO7xAmWhicMlaWyBdZd4GPVzhLubwmmPoLPP30AGuhcy5OddPunA92iFtHT0HVEorK55+whAr5XihP/Mmvnb/zZY+Kjr+FRNmMI/386Jx36662ebyLN+td7LZ2/2oyP6FQprrfg72qc4jz1VBev4oxBLSNNV1TYQyk+Dz5RcqFGMxhQWJ26LzH6jTP9kJBbxDZSR/Mqh5HXRibypqogtmLr+brJVU5/yyJHeWCbm4x5dMvCaJBtefRp4VbZqMe4fMM5/Tzz/rTzZ766wZUjyE+VzeSmG4K3R6tdUFifekV5UaVqdhLZ7PXrbTMRhe/iV49B1W53vg8B2+N2qrCkAG0s3ivXiMHRz+YJpLtNudpa/r35n3lhb/CRrMY6c/6NaOXdRZYnp1vF4iQq9Nd0hUTP7VlwOcHVvs0YGtPMsgd4hKc35vNNXjbXJdNqLTudInWE47GOvEKxhCk+NYEINyVrJ7gionHsRm8iubvaQh27ztKViIa/Ajp5uYfGTZW4au2peZmsdJ1pH1ysCJD4gzjS1bXinSEU3cgPFl0K9Us++raNSt8oBoEiBcADXamC1tzhbR93p4RNgmNz8PbS6DHb9pUdakhQdKL2xR8axX+XKxokR/LOjSe4RVtnxM7SSd1fdMkSGaoPTibTrQ83q31lfjttVFOqIkr0x/k6pOrp6XAXLm9a4tXRLwW0xsYZQUNs2gVqZx/hAPXlNeFmOT6zBqkTHQ7fAZOYnldQtGzy2wWJHDbzoBNviHfGqfcmkDiUkqWlFHKYujR7RWl90KaClQwq1cUdAkZXPG2cNpvM3YLFlKL2g6JvDKAsF5RL6kMWglbfyBLsmqdpOHnNM9Vts47eHIc88usKum76JGlPtWtoBSDfgLDZF0sKe+PyzeZ4XEUTK731vdNb6aHWSqJJAkIhjp/X5mQu2fbHvCKOK6LuPwHSfaWcHl/vMGlhTT26WTojhtRALpLza7c8rHq8DmcPKq2QC/b2lmPf/J8Kz9tFl557tRQzzdI1h7FQ/vII0XcM6J0xG6sDYKXFGkZkuU6axPaIBLkDolVCb6GT9EemiotTu+Jcf/j10stF96TMYvoszpYH6sYKL3AC7lSgmuP1JCRYE1B3UgrISgg1fGV+JdSJjBajrKHt3EhmN/Y4y28W+xB0yrfnnQHYvYffaVpf8I2Zk1nY6wvYIsYs04Rl1VGDjCpc4wzWd5z5zPjZRFkvWWG6sUm0QKNvklIAA0tYi107XWbb2634xdxVeGoXOxLRjzkf/SOyoXfgzA5oUbDRxMiolUO0G8crJMn2XkP0RXaGutJKu6VphUthNViwbGSxDWOb1d7o8mril/KaNNnGqkE/3T7Pv1As/dmI66nC4UKmcgm9rIkDROPLErjO2vf09I6qoQjO9pRTyGX1fEbUvDXkdjeXelVmcwXlt7pQ9G4Qh/48bFS/iMnlFKzRGtu3jbRBxTBwm/QYhJJfN/0ipvMh2FmXOidvqZu47GWjmwNloclC+C9vQsTPag3jpoA9ZVpmEA8vcIZ7Up3fFq/KG1SSvvx4pp/urd6oNblX5ZzRFJDXNyktLE0eHnloc7HL3ejzwTv5dFk0PGgyuakKshNP/HEZeKO4c1Dmnh66xSSidb3uW9ttcYIOOlPt+mHpOVNKZxku2oCOTOF2I+c287bySoYVy5rvUkLrCZDj28zccVZeUIU/SQ+OO2wi4gEgtgpBaXW6QtG0Gx0z3tIuGK5coKJJ20naXxgrienmBmbPmkOpGD/Xyd1a2megRMNq+cWYjtceyk5PISIO0C+egLRW1L8UQEPCt1eAW7oNxlD53geYxijrXSmWd23mJ2EFSYkVz8LHEeE1DuZvF3tN8fdL/FVBIjnBPMnuBe+O9wH2RM9Qu6AyDEnovG4Hcwnd0irb/VdEMrvbqm9S6BZ2d71298v4M2QayE2RsgZoJsuoDLQkSR7om6nnmxl1VefsSjtswURUysIbYgvtTSG6hqKLEKQC2rAv6o1zIRIffKEJnnUHzIBZU0iOyqdCYPy7BYa8yakKuG3WQCkAIHluHpK7ubO48WIWAk1u4mOhEeiOuNy0xU/rRwbV6cvWPzg25KC8QSAXkKKu/5Ot2U4mkO/DkAPunmA5ZQas9RGd4LFw+leWl73voaNlVaZcY9wGlk0MZYlkRFJ4O8hQ/7jmFGEYfxXeA2GXR5RM4MTAm11WmpB4WRO03qk4KzbOhvJe50vgnuB4tXz/b4wl7PvFO8SX4SA9c6ZCrPSS6irAwqwdxnXz9qezo7+RmEqhCvx65oh6UUMQfyrbjHiIP6w81GZKr28tt4H3mlK7VpwSrzB0p+arQeDP2ticSv5VpKmUPrMkBSpSHp4gTvGg+DLWBrE17FVymt/AyP4H3/qo94GsEbORSjdSH45GuTLatkNglx15CltHOQ/avm01MceyLvAHSLD4UHK1MdI/JMBYH+QtnG8CNA/j2imuBzM5SVJU+C6Qqrh2m6GUlUdTkgx9QGb9/4hK2mKGSuoEtSVbUF1P1JanH1lCUYUnI8Kmq3gwRMxtvTGeYc6uQgah+V5IKR99qIBAXr0BYXaDEASiRG4rlrrHDWMvn0xjPIYIS0zgh4yiPhFmUN6GBWDDTJ2pFQ3Q8ESZg4fRVy9GoG+F/10SEuhEbe5ZjMiOFhKkd49XlvoqZbmTNGt8Kb3adZkgvZaL6vPI3pdWENlKlc1ucEijgOBKdOJaNo3sF3y/H/8x2dcgKT9jYI07LQd4nYfTgjc1ZEl+4BhFp9plmD7y89zvgoh8ujyvfjTT9ws1mszBR5jerOx+Scv3ycDk8jYf5R0WD0SHWyQtgJQ76Lyfa/dmiBP0RpUwUJeoMZrGKd4GqZLecYtBScDUt4REkbdpV6tyd+P3epmvQUMH50Gf10aVWumNJ9OlV96pSLW2S9kQF5ixEo/sQprVX2SCKMuDwyMX2qFK5zTJg9gfX8qhT5eDRTGPDOuzYmPV013jyP3EdQey0L8xUJtl/4w44FtUI141v75IHqJNSLWML3hwWfPjY1no0sGg2jqpLxgiRd3bu7ZJf0eDA710RLYhtJImrhZbN11FX2P5zdFmDO+QA/qzL+u197M4YeR5P079GLcahSy6RffKasbVOH0EhRzSFhchPBlpb48nBcogvLnTK+vrxK0lztezeUVfNnJzJ6T2yR9NSaI3wYF1drrIPzPomEBh8+PFZHHTQ1PyxeD7YIgbUd2wXQRLkVcc9xSmLtlRblCEcuEh1EMvyTBDX8/3ULFsJtYY2luHF+avDAsITEnspw+wjDeK8nT1KuJHKIUdtO6+Gm0UqdemAagLhILbu2axYqJk3fib+XefckyZI71abXSGVE3OFkFg30ObWpKj2e/3OSgXCW7RfiCAf5VqQdYFA9YYKZCNzb44UNgZFl5rHy7LD7pSUG76fOwbkvy6u2P/aucXTNhRbelXvu30xMbLiKwg/wzRaVT12CijFDod1819BoLmM9T/t+4V/jxtmPwGmiWv9BRN88j7RKdu7hSQ0VuG7EjZSXPPih0TI1eH9okTyi+8pS680ddUk37HY+x7xGVlAFsLTKocvDfrwHK8t4xmfNk2O8uTUviXjCuWaB+2yzlCI0tqBCCqMuMqfjShXy5s1ClLT7mIjwpaHIbdDY399jCLhmDUu+kP4Dzit+f+UtIMFGOm6n0dI6VZipgkGP4U4O4P1EhOkb+wtRsBczuJAaMx209kx762jbpe+R8n5t1JDuH9MQOXH+B1ZlETADbSYagyDSldZGlV/zBybVuX2jR/Y5jFlFxlU1xpQjwz9WW9sr6gXx+S17HY5LjeiTxbJ8E66KvAAv6QwPtoUAZWvmrYrvuQPxQPvKSB8pVxMb3HEkZhwtxgiv5qM86qShzmtJKX+QMuVS01jMxEmfUJ7CujCXeUJYcd6BA/IHTuSAem6UA/A3XqSO9K3offsXFo0W4LCHscCgL4grw9I5vmBoowaK5uwpt7bKJYBpCFgdX5xQJJZRinFkHcaLAxuMOt4wrlvolEpY8cbtyrNTn49Vv9FS/0RGXBsWJKpFRIOze6l2Al7NOIQjcj37r9FZ+k/3lVMM5yIDlz9ekG5nCKb/MpG9T+HsiQYXd5DWYw4M8gTMYo+/IU6YaWpZju7SFGl999DXA5oDkbV7sGhTa4QKPmTq+rHDVFyv+97GYlTu9dkRWaDevqsIM+M5TdnZdJIrW2HHDakb0RY1L8HHvcIfYbeMRLj4lb7B8nDwxk3WXlKtuYnjNDZcI1FXFcBQsVNR2iYUPjPLyQJtU4ZiL6+8rA3MCn/Jpo/gJXuHjqoQjiO98DppEmDtW8EqRglA1Kdtg5gFfWmT0YIp+QHhbYGjGMuBZ+KSy8Y01Tew7t5nCwlcNl1ZPvxeew3CymL8enhYeyJlc/D7HcJ/gAusEH1Ct9YsWaFGePVcLNZ1dUXKS0Dr3NPpewgwtKQk9B4k8sqD8mTdBgUl1TOyxa0spw195uNzIfUR1L0WpN1fT0B2TMv5t0rxnpjZ/0XCmLFUO/bpBW10hLueg3IgQ4t6b8VRX+t5qOKWW8vCMGUCNp5yldT6V2D6FFsvylbu9wKYOVHCWNQqBX7zmgWUBPe7IvJt5xbjIgqn25IBgNIEmQxmcONbsy0L7uEZN/+E9F0lDwUT9TwGm9eJxa0LUDjhQ/AIptVX80oiIRUzUx3XTyFvewAkESK/bie/++AifVRgMAfmfqYnuiFdVH46R+HOPJCvAcwvigQ1wqybf4SIFCyJIMuMQoy9YmkvBTmwmpihaT+T3s8pJgpnBJ4Sh19hEspE1zUYlfhbNhNeIXjiCiEifiB6T/bA+1GNwfjZQjBURE5dabUQPHHOJIXSMbcRp+7nVBfsr+PIdcepqVA2aBdgH6/8Z1lBINrM7bpWavNjlPC2hhBuzvqonmRnSzxNivmdqNrZckrFHwtoSSqym8/gTzXzMa6TjpJ1fvtYgMdt7jgR5iyfTthtRj+5c38Jtv3NPqGvPWqw24thzVkRymhak/aGHUl+GTEf06pKyqpKICpZ3hhjheKMBaR5xukon7dNoAhJ+DvmbVbZsELrmCs9IeqMfzZnS962QQipYscIOwpj5JqHTOHU6k8YJudohA+ZhB2OHM7SMkvRpkYDLdkb2GBhhUULZNIGa0EeQucZtkogBoqWEAy1O3auTtJwOmWOO8XPcc+bSoGWB3YS4PLdhrSAQFtZy1DTjiCR84xkaeluar3Jv/d1j+/jVoJQnN2PZBEzXYGqsgUWZWRWKA18mZ0JGPodGs7Vbz3k3luFbzZ1N4uAhY6fe6pla02KdSHJRLvnVgxrA0jYPdM3lPMWqvzK8oqsnpSK89JUiLlmFUDA8ZpOEx3jk2oA3Hro8YmG58ekHlhvlQCDCujzBO3upnbraTkIlvHoz09bD+v7cB5tbQzLwRENour5qCkLTM5Kxf5TKPDmh9uYSqEcXwWy219mvkmy4hHvjNjb7Blox/PM1E94W58mTUg//Dq8KlKRnJOWK1e3dJvGXXV+KlZcsiCF/hBdl1fMliPC8iZBybbaCIAk5VpAaUaoQB9LxFgiZCZNY68uC+3eSjYnKoL63t7kzo4oQ8J6qwtINhyLLVZoty4IdIsw5kPKjtZj8pFUNY4yYfLvGlR4BvnKZzJRxRbOKqtjN8nJqAfM51gMYudgY2goa0nmD3bWachOU0zODxulfnZb2aRUlXOFpGbixvzhaOTdsUCaq+lnds1U8Z2unP2WkHPe0nT0f84aGnRrHs4QpYPZuy9qui/FgogwFvCZTHtCUMNTiIl25EKGHRQhDmbiG3Kf4Ml+/2aTxNJgs8qaiZtXJaYfG0UHsxHX09d+qMBzslDaEv1Y54SdWrwW4qz+HFZ8fAUvu+rK0yPNNCLJwSD4fZejATpam3gr694RfrAvZyiYjoNfhN9W799DBbb8TGBRyUqP3thb9OhuCfCWfsAaQ/WZ20e6vLU/pwfEUmx67lG9dQ4kIDYiF0M8UdjUK6OdINKwufZMDN/IUzfIdrGvcA9qiMi9UhjQfEp8j9FtDuGZzFFUymANI1QNYcQaJE3ANdOTR1Jo8RGbaPHb1spxM7T+diUHgvpPuX5kgmit9bd4+nDq02qnvB23c6cpJhEHx4UbBfJZZTwb75EaUZTOPYCiet+3ahsHDAE64Ec/VuRv5m3Db35/oFHev95vYQHU7edSBGxBb3hMs+TToyxADHp4q9jiTYTztctWgeCvVIo4dIpazgB5+Buvyd8HEmJ+kBjkV6AL2KeodGu3KM6JHQs3JppPQM/KG8HElvuTcjCgGXbQ8e+u8V58y5pzbT2/S7MGuRpZZALPG4qmyGkeit+BoLYQmd1iB2j5bYK3HTQJAC7BTBi+WgHTw5+s52Wgj9kLar1CjqoFzVVQvNeHQ/bxs64dQy8u51UL6zHZRN+zPafCBHZlF9cAvuH6fDEFOLIMLFSmcbNw40a/rSm4fNt3Sm65hQtcy4+lP6sywSyDyppgHkhaK60IK6sxBNLI3ZvubJwfl8REMCkcl8A+WpKPV7em0wrR+3YUAINIwN7DPp9zpggoa4phv8LxhiKw/oaM/UTFY43ovvBRk7X5Bj0w77MO4QsA2WQy0VVW/DVpx5D4lbZ3p6V7GsWrGaaAOhmQMrraaMRNt9RqIQDgkGNeaxyPanOc03Ta1KubKGtixckV8z31Na08HI6EHN+qILUxzqotQdzx0d4ko9vTp+WqjXRJyFpBcEGCnfS0oVeaDmj6l9eXk0R4zk55nNUVl9uWllFzBGlRUEt9YO19zRA+TAXnH7A2kdq4Ay/pbqsdUUiht3JTxXpcgKI/bbSlY19dNrY5I+yOeGWkA4q3kczbs06eDUTsen2TXxTU/uVQuclV7cjgDCrLaQzTlx42bl4ZzmjQFaIeZJuogfKz4bs8zEooXozhbre+AUd+a61O36cVXNCd+y95Uw7STERIJJTJ71RNNZfI9iVOGHqT+N1Ut5jT3MqHVidxxn/hKrAWA5OswEr+mSJHPhk2Wp24M1pm8tv8GMY3ldXbM1cj93ZGVeuM3X23upzrMkX6LwmYeIz4BBWThhhiX/JKMQN1WmgQsqrUdsPEmKPUbXnsQx9DBVFBsFqOvIbA6t5Xr/eMBZqiNTtsAhp7JZe3Oo9ECwSIwnUEjpjMPnfN4F8ZxdmeNPRc1mRUnaB0sx8k7axLxGBGhljuZxmiK317U3FeoQo8cv1xMMoqXcPvHlCjLKFV5HFjtl7/taeyctxrklvRFZ5Mz+FZiCGU0/874WhQN4tGsv8N4kXFSrRiyS/xTeYC5hpvMpdKw6sftm2/6DAsieb94FobUa5+PGPksl4EhRXrJRH5jq+uUKOkJCvsNwp2JiKeCbVAsUA23w8jOVMf7XjqTng1gfXpHhVaXt1Vt2SQk5VIfF7RadJDueBwvM0f0JbXDUq7IRQYhqys4bA/0bTpmyugotxQ0zak/UwmEKFg1pGV5+ShBliFYp4nPwyMNP+vpkzDxODEbbaIw56FOwMZSsWiRpKWaBCwOUN6Zg9iE+9ANP7AAN9DkLMuI7df7rboQ3WSnacJDbJph8iT2Zy8WDYc4QZ5QcDO7DJWhK1X50w3PH2ZCeBUx6yQEfWwwIlVo39/RW2BFWyY8sYdjcBi/T4uSTPRvraAp4TLVTypNMoSlXrZ3kBisfDker84etaoo6qtzpmnMZseiKaT2TBtESmLVkPo+ZkntRslJjMiKf/wp51ChdmUAEsquCkyUnutnR+1mPucC57WYrsLAzJb9kLn3j7AFbfKWJIUYVzGZCsKFxyGq5c3ZjOhwztVKKbqBftF4/NcWqfMBWLE9zcj5+WxbNBJLD0UBxcNex4FaEcjPn15bGLDfsIIzBKqMy4bNBzDzzC1i+LAyz0VDhms+59tcOWBP+y0B97cyFEvxBb+33wvxDXbzS9nS+7faSINjetzSr2FDnE0ygwGXj860yQuE1IpvPoOFdnJC7Vo/+ZfWTrarFXU1ZCV9zerRSTGfhVjpEmdkv8xwP+fXCDUctE7Tb5z/7cNVRjj65Wqb6klui/lw3I7H4XIrUbdhQhEpFy2OA/Y48jqI5iRnsjnAFANI4oXoeXCkxEgFmJgj8ESaPrOAqzgHwX4gDOf51bIg9Eyc162r4Y8nYccefoDN/A7mR2ElZj7mYM6+71daykanMBXVzaTT8BzfQKfv4NfwijnnTgGesaUdzNzd1j/PrswgcUqJBn0Yd6kbAxNGpmnPaEnqzSybb9cJfJ5SNMAIxxJgZaMO5nhlr3piU6sUMROUIZixLc1REExDLecwOjG3s9+RZTItfdUSmmZv14FVNtaUtLOStCiOEa+XyrrDgDU73Oz/1Q7upncidciderUnhJ5fW6ygUtd8akI68c6Eu7WA8aMVz+Roai12hNsimt1OTuOoUSf6ckGq8XM717xNmW1htx8i8NesrgNJTLpourPLyqZ0qrMlmapIpW0OFcwWkMyUvx1+Tp1Vno5DEsvJAlMwr3GETaEXGpfCLFcXET+VxQKC9dIhezJyUW3TPZhTXJoU/yg4jLz93JmE3nHs2W/6mmEF7Il8Ml5g14RU0rQmr8C0tYhATvO4KA9cKQMWeeVFwPdwo9VVhh2aBpGdYuSkFwuppN6aFT77heAcv9geIW3NDktBK/rhBvqo9PhQQhCLq25HU32V58yNErwqRycItKkCxb7osfRhENNnQcanl47N8SivfCk0LayIxp6MHq/IaqQdg4fD0COWUDEin+OCAlYPxj0VLWC5NPZ+rE0TASYcM6glLL3Psb9xF5b92oyiPXqPcM1G4FuRaqKrACI9yA+0DDcmn960hoLkwOCXa2Gm+OX3hfdnLNmwB6TfP4c0gSK2LTtwqQSImD7sJWee9YpiIZafv0GApQ7DcgbBRWX5WGp57F85hei65pg/XITIShCx6E3Jq/24Y0gx/QTlD8/0HKwrZO7YIfK98z1J6GBYCLmaTv10WFraUE6t683vF9/XAgvrAzYKBvJjsoCf6THRTHQtHXTfPGiIWUg3BK2jWlM49oNAYd1NCu9vwgiG+tvzQUxn83zWSjdafpkXu++2M1Zd41iwglOCueZzojQK7EXVl15MSI9suAYHjTmbhXkolFl0TKVW9jSnLZNDU6pwlWxbNwyGr+1lP+77JTwtrCUm48gzLX6zB3lnNFgZejxtqFTfPeOiLZtT+IKlaD/Ndrn8Bu3bX+aHFLCbwH644qQeGTshoQvFiMpBUkaqjWQcl8WnTPg8l6tQ9Sh6k2zrTPyuoH5hQ85Vj7jQAA9D18cV08lNZ3ftSvrqh2cBgS2v3E7+9b74ibn0Ezv3skspNfKmG7pfVrE7+egxR6ue54/1dFQrNJJL5VLlYmnB3xrvyv4GOYxX0FKJsjOfS/UMA+MX9sE3OrJfmJ4dggaKDedFXdSTHC06Yn1098+nn6G8OR9skNKDftlS83SpwJpVUePkZdnbKt4hqxBM3aYPPtkGxPUv7nlVm4KeFnVvMvx2orHrJbLGVgpdFXvAUtB2Fq5IJct6X4S8PqaKu0jQCNNSPmBqaBX3RsfAiS/GViOkovw0hcdvKgWxUBtWh4t81uH8c2zINuNgB1agEEiqb6B6QUOXzBkFf9LuPRS0X9nOJYWcN7lwVSWG6bkHF6TXKW2pzpyXWYZc75bxI3mTy1jImCdK/yH958s2/aM0ewkGHGgdrI61+UwfjRhgcvm7YfVuSH04wlbavgJ47jemcxIZiwTMR2oy0oXH5tOT6OfA8vsGKEo1ijTrXwiLuP7Joi4NZCzAEE+qrK29J+yCrcIu/blU+q6KNQPfONtMK+WAL6atM92liyhxcG6rd5wEfhdbn/gn33itL4ppBxPgnDZSxvEw/ET7nHUD6jlzMKBkzFc4qgPV3p3AoJNkwPRXJNQszL/vSOmSbYKZ/LxnEGedanFR4oMnHbbVVrarjbpmwuQ0oeyXz4AiCI6So25SyevOVjhKP1H/Jm6nTC7p5L0kMXzSmYoaJquSmqWzWrD6Ne9dOYnZrFxVWv9buBP9Y8DOIi3RrfCa6VkyE95My0MofqK/lbBQTjDUyttBn8tqfW0XJd82FOq+rWiRyW1PytcV5k66gJhI0AGIa/QsBnfoIsWN3M1bmoiQlIKVpJffC2X1oo1Gx+OLmbptLMoE/VLQJYNME1XvNV6xNPQhb5eo3yAGy3fGToN/4DaAOPudBIf1Smui/DLK4UZe4k9kqeAxpzER/81LRl04ea104o92qfR+Pdzc4d9EwXraBroP47zu5oqEU6T9cFDr0odtcGV6gjRP0UkVj1fayTj2qH3+CXZRONZX2+WnXIT+ibq/sCCzo0gpKuf9IS3eIHScnVo/TZst3hEt5TPmgFhb7BHsSWxht6fg9YgtYY79MfNW+7F1jkU3S3jQWo6a427BcUy4yOeVx1zB+MBJu00LIQyHJMObYyGhTl72z5sKsAc5LJ/eZpTXRimih0yVvV6cNfs07VaMTYVcCJtAXC6Mo8+6vR2aqnPz/U4VH77MWamJJhpRegRJTWFA4EVu2FSStdZcVcBD2lkkWXFOiUBRTq/fOD7/qpLO0UQ5rW2SoCe41T4YynjlCsWtvN4bjLmz6FTFiLwpAvFYbvDqwzLTWu/mdk1BPRrm1vxNJYW9JI3tHFqx6cETLmnCIswzEv6WKJZl5+3vN3/l1uzOULLQfYhY56eKKslnNmQ4zGVdwmt1aogf+5ODoS5efr9mRi9JtiM95c0nez8ylzOnI/JFFn/HV5Q7YsL0RHaYjEbuXVJRIUCGn+kTfUbJ3TkcsI/LDVvfV98TtDZXG0yLO510LsY4JyepZII9nUlPJfnu+fMoihqJqntfKvOw7lefe8IEJtnjJnTj2IxKL3RdE54ramPJL8upl9KqS83K9Z597nIDxoLqWJ4dDtE5zLAUPQfPUIkGWNP7a4+gExVAWrM48NGWb3hu8NqPC9BHxcMgAb6i8HF+oL5Zjop8BbI4QMrnaYW3DUn+5WyCUZD6Amt+UxgHZyxmrfasyGWix3Fqw7D+eIyH9a0+4PIWo2eeLeim6zXPZrREMWfGZqXhXgzPyZHVndtDQh//banZKZeeKDD1WsdNclTugK13HIfnUBU6rWJEMQPvgliUuJitdJIrIHVja32UkUK6tH/VXF/M2TrN0G3BivgSjEbOdIJ4b7/sCCTgqju4Bnw5x0VOzBUpyDcRMZsXeUFfrQKVjFldBVgUDqZUYeE0QRybzi8Xhpg1oW6/nITi4sDwmi4ZTWgEwT1onOUj5N/g9BP7rns0CxZbaQ4d94AbajsVwcVbEFUPjcEAqa8iR/esvv8rS+dKMYrsNE1Gsx42aNMxRQoXvIl1ypjDZ4gKfIK+HEJaZ7wiT2ry3/YggXrzbxbpKueCx3PXSJGJl5MVy13Npqypcs3TlayoJfqPSdZw6HeW3/N2w4fK04uYEeZHo79pywD/12dQGkCIWcS+u3Y9r7TI0IDEPkgB0CPPaGEC5VsD7R1T7wwL1pWyPfLSJzO69INyOS52WxvG4hlLrU97TEr7JYslTxVRrw0ob8UeFkgLjlPffF+6otLNTp9sHIVftgN3QgCp7XZQ/gCfxH3tPr4VYjpn+eXccbpaiIFNqGEz8w7szuwS7OEJnUMYW98YzLG5b+cUK9GVybVJW5uKI+AjqbVTMoBaJeDeLeUq9w8/CfWhwrELFzUB6dEkGfmv7zH812xsT1wwNSfVub08yd/23uw8FtKRAy+cNUSh28ATRaovurjvsz3psfb5dV3vklqDhV+Mf1g+7Uvms4fA8tcqlOIRJb+1sVNqE+JztmtIbCNcojuGt2H/5je1faHCcJsYP4HU/kSLakwNgpUbOKJdUaRihdhnh/nGZSqN5l+MThlAKNRWv1aA9wzE2lwmTwtc1s49KrRaUntGypEE95fh+V5IQwuObGLX33t9kY9i7E0ca7fEoZsXldXbG3tvy88ZzYcxX2D7/7VxTs2VMAASDW4mycS2bfvGycS2bdu2bTuZ2LZtZ2Lbxm7V1r59v6JPn+rq1ni8uwDCdjsx4KvgZ5xxsQO9oB1WmzXl9DkgXs+7NYX2hlomihCMHa8nd2a+ICx1SitaNduTaZq5SoXuPlXHcry/3rsQlWlIgwc7prJ9Z8lkSlQkeLqMrRVUhYRLcIfxtxdfurrKtyImOoicqxKGXrRANb8/mMPH1PrO3+Yz/njuuEtQrCssA/hiBjczHEameGchsHuPl6diGj2dF9vZ/+jZWscJY/yulwvhNAmfL8fJnedg4tG9iAXA84rX3Vrql1GZ1kwa0WIfQaxFo7qqy5HtkJA7jlgrAR1fxpuc5vQihHczUt1t/y6Bv4w8xzYe0SKMUPu1OOfnBTX+vgXntUJRj3vguolHO8KVQf2UghlUH7oCnMW8nGLNQvhlnR61iP2kwliX6iegupUUNhMMsIsD1bRnYsfJ+nvp/eVKS0ICxrHrCufeO2kSF9rXwnIIYHDgvFwHd5jdHPtuSnOgday5ACUf3X2iaP/hWNYdvnyHKO9YOhOkOg2EzQR3jN4sBJo9wrq+ZyOTrG7EZMKR8Ou1CNG8UIS0DsOOrlPrB2T78G/urWFCAACnR/0F0Da1Z+hkazsH3tAQ3Vq5e3/63YlvhtIe5vqmU988JsJpprvj5nUu+4Y5Y9fVr3XGjhysWDbCe9pnDWX7+XtVLRMSmVvu/PAuhgnahP1qYXueNOO3TeJ8aEWFsR/rXZSWkYY9nj0NrtjzqGGZBUhbiwgG9Dh6R5Oz3dv+sFqVKOCAPZebvwKFPXnD7l0CfvN6xBA9VeV5NYDsWjlrUEbSAVjJ0UePDM5VwL3vRdHbVIqGtAZ7iwbVnsz6hk4r+xT4WpL/rwC2+NQobg6dnqSGIbsPtAWeywkB33ognfDuY671GBZcd85tOt1rHYTGTwNOV6xE3Ty1rkKHF8W6UyQrdqmcyGsrZMToE4i09AckkMw/7megU0u7B38fnI3J7KZRTkRDRe+IzX0bn5Sn8CimSgEvby0u2nLeqG9HF6YHPBdiP4jF3ZUMUBdzbXb9fXiKj8aQi4FMPJ2Wzm9snLp/4w4N+4BiU7bzxR4ZVqyQJgV1sHoiTZIVaSh3ykTcahwjPed97JeQDiB4nCCXRQ1bAfX/siY75nvl+WIzQPkQk+u7m073kfeaIxWEbxtXa/m9G1PK9b5C4RWz4iXw5w8eFWEmurgTxRawSgxmkNuMd0uEXnPkAlfAz1E+dXtMWm2IZEmrVO0QSRN1V74r/0OzVCe5CYuEM1SlLSCto4+cUlV2aKqzciErk+E6HG/uvcd52QDAjXIl1EbtQln+UtqfbPSvYtC1a4HmLSckg+F8eSmeATUESuyNxldOZ5Ah0HnUXAINoacl8CG4tPHK08bnZbX4V+jCZMhQT4xnM3JEiNB50+JBEoS/yw7zl3YANj3ilLa+V/1KbPmN3ExVlUipiakr5QCZZ5lNq6ySIDMCzgjaITXiV4cCVww7nJWo59OAxJwEHcynwNqOVY5UYEYDV7xoaWPCLCE+t+RXq3wmlou5STqeNWJOjowLZFqaPy/ay4CDjzuNZFxErHb3SX0PTG4JW+ngn/yYHpP6PuU+dc8PNkSYoUEPnuLRPqFTkoWHVsVVl3sGEvMWB4ko5Ow30jOrroG9qR3kV8XewsEaycADXKza6/x2mpH5EIIEFkjcH7fn1k6xpyey+gCQnmMhygx4DV+iT6POU3M9JnzhVTAI/3XgzVWY+UZvnLwDnvzKBZYRy1+BwYOeYg9sA3uH3eWTigHpiNo9tOY004S973mfsX/NczvCxVYeg5JFihS7A0F/Bdc9BnlMdrvJwtWD2ITiq7FHk7opP1Va0Bum//Dz352dLZABPxrgfntVYd+SE55ZJeq7SCHfcyxrzvzQp/LTGgKgx//sX9A+4sYpMov/kDV1n51IMAyJaSOdecxAUpGuYGJ1haCl1NDzOmDLOknJGoY5z6msBuJwQNSjfz35Kt515VN5g7cm7mpponGaS0cnavNDPPSlT/Br5IhI07xBDWMApE8h2DIVlVqVuShOC+iTTR5Ac36M4sfEm1spCtslr9qIv3bE9i61W9w3mDyLoBigUy0q85pNMl/QFqHK6TKmvk5QrNMKKysCeitTO+s7JZlB2hkeZoZLKvrBWXhnPCpKrFa2eefh6MfHtQCmPUuz/SUP4DYfhm6aTSK3vwtR7FrzE+Bu/Sshpwv5Kqh81xGyc8lRSe8scm8NAWmxoPwYAypAzO9nxp+6USVaC4jUjSUI0IL8BC4/P96fUS/cQMqkF4L2mDhtr6k9rDWGDTG5umgoUy2xqmGD7GhreffcGzJnpUDgiC+7I/559OaA7S6v2BoGNVcjZH1qkjFU1GGplA/rYzL6+tV6T42h+q/7akxNKtLAm/t1Rn1PnPhllZcLP5JOBPBqYo0eEWY5YkvwwSS4TB024qPwEmlDu+rvrOrstAiT4PB9f8cZfiERm9dxD4d2XEVcoMwb23NkIsdNloJ2aNIHyL00Q4K0L8OTzUVEh63Q8E5+fAS37M+XUmWt7zqAWouc7JXOSpHRRbPNkjs/fY2FIGzt2cGVR13HPNM8qu7T17X33HJ37gzpDzxU8UUXgzVpzgYse6AYnlSSvxVCPe2dE74aWNpJsZotOKv2dfJeawk70Dx9r/pnXtF0dxhMbxy//THwsogcYatU7NgqgCDwNuZ9Ly03czODVm0FM20hoBZmGL1Gx3poz1VZUiw00/iYT/mZO20/4CDXvA2CtdcBUycoVlUyp2T2qYsxuPrl+HVWCK6YKrzeKagfwSaJMMl974PdpdjLbV2Jasry2T8vhUpDu5POZRLEqI+3UbzyJJFGAuz5WlBvLA7UIEA7aoCGpEHX2MVeuih/+1P8MsbmqBU+4zn7mzOXPgHB9gs0PQKNLLJkZmIVNPYzetTQkrnlEFPiQDnA+VGfAqKN9ayOCx/ZOqVPNNETI/1lclcNVgP9/jxjjxNzAWgA4FEP6KkYwUgI9lTDbtn3N/xlQPO0CdM3xKMjw/2d88hYJrG2sjRi0WAYJN9epMmWiEZUfryUqEDOmrNri4hp3rNG04yfgCkPEcZPkw+AwoAmodskeGIEI01V1Msd96upCoFiBeu1bpIiF74xNpbW2yycKF+kX293uLrjfUYceThPKy6K46ek4H38atB+70R1uRQcP76Ho/d3UMFMve/9hz95G9JQAyDZZsxkR3iGLacOVOhRdfmWJunJGPehAkvcYKv3by6+8ztvgYoKlDez4OmVTLKDUnc+evvVC6Jkos36EZCA0U43YltGwAvkjfniWPu04v8OPhS78GRYOokIiAt2rmLMgwSFrzu+/iEFIJqi1ADTaeWl4rEhLHQweYzezyEiK+psIxGtHvTi9VFlA6iC+vDopvOZdxHm6WzYat0wQM3JVzKU4bfeoJSqjn6hMq2DCoJNJNJKbAdTvYOZpV6gwGiLYCvk4eoky2YsfA3H2ea5jeVLKWYPv9a7wIm2iudwnzP1Mh0w5YD6K2V7y1C5XTSHXGq3fiUkfXOWY0khWbU7u9HtU9iHgeUuw28YNLY9XEtg0XRFvj4c1M9xgFuEJu+Hr9Sk2s+Vl2IXcnLgjcNTRLjaDOU02tyZnBGomFk7fJXs5zyOJhe/B9CgVDM3RI3QqQax4SEKaKs832BcywRjReYi7iEi/XoXQWNRZk+qi1XXI2sttXvVbdN3MftXznghh+ScRNqvJFECYiZZJLT7i+RK/ALswLHgy+ufmicF91d/EeaLJ9NiKMUfGG+1zbYg8iPkpKpW+oA93LIUZpyhpy5eGuaeLu0IahAZWJ8q6wf9SWb2vmv2IxSCigHJ/qOBzANGbhe3N1zlB0T7PmFvIq9Kre2nRb/fEH92//LkCcyuOq1NRC1XFuCLwFtoUJ+YRmgzeWqqpFgultmxJUQqkjJJuCGsY/Bvg+MXTJwYHWI/+DuQ1Ux/0mCT3Na67PdNzdoiUOw1nwSVKIHOarydrJ+KR3j1epEaVCqGu/k0u8xhl8P2UluU4gzyh+GZLkGkHd7DVsMO4NFyIwXCsSj2r2+gsY6D013bO69O8x/YzRtCHfjrRhcMFaYdutPEc+QZi3bdiVRsamAR5dtMQZdxGryDUGHGG48e4rMEzSu0tcQ7gnOW6oiBee1kuY/eXelD5YGyzbWqrq13iEYiXHVHWE+BQHandL4GeTyi9OiPTJDVRJNesI+XfYqhDEKzy47r7OfZ2BKcDoofHdvmKqtovx8Hs2ccDnLJVbN6dPy0BLE244bec5k1lwk2KL4can+4esnEfyqdBSiigX8U24YkhMinJgqoIzrUktCgciR3TYa60zzb9qUTD+WXEJCwjENYnwpkRSjzOqkGd/BseDC7EdtabkxwBQJ0mPySFV0mJf8kcvGrYg9y6dlkRR6+f3Mpv08j/59VdeqIzobmf7AGiWLf+FaJbqPdUffVJS95XZ0l8UzxXc2m23cpCj1CLZ8J1dD3xAs0VfpruNbCC8M2zQoOyF1YLAL0+LzUmnaqQ35Txkwc3PzKcDuNcOiz/6ihJStDFI244QzAjxTgBVTV+RJQnHXBStPg4YkVZcVw23rjHKGLRVD0qqyt9JO0QzzrmkRZPgb63USvH5EGrsiwyJt5QwcfxO0Pr2WLIdIKqWVZfriXgXYsPytmcflDfo9+Nch9OdrWXr3w8CFvAHVPU2jWxbehnujbmrQGAygtMrp4cEcWPerUoTuD5l3qN+IHxGLj6jCHo+QGls6GJPbEhmnJ4Kr7AEWEro8DnqOPXCsctswANYCVjn6SOx4m1r+WodNB1rDvsWfdVpXDWvIA+vS80KEBLMu7ocbxUR0wJ/F884RumGThB1kn+KSPQs1DrEeLRFGnZTnYueioJ+4meHBEbk2s7M8/E7ypPTkvcl/XsXtXQ1hyiMhrnSlRFgLm2Ofyg89GPG33h+A3F/Q8q5SGOHlbX8cJXZGkYJNnJUk6X2hP/QErISxCX5lV0LvwKfY1Qm/8bjymmju3KEshXchHuk/zjfHLX5kmca8s2X1QOONfBpW4IhBxoV6N4Tr827utXp5rVdS+ib2DrJso48RKHoOKMZfQWfneA5EG2xvpGrtoWg8/X/KQIbl2/z45sdXIUnikIrh8U86oTfGa6ZWyV+KbcIxK+/g6rEB/Ly0/DDddkEcyhZufif32vwOvoJr3rCTtj2/iJkVz/nBwt7CWuwuhwXUSq6NbhxsrnOxpGyjYMmLQgJLWd1W0hUYOyCffk9aNkw5j3IzhU1Tyj4HSg2IILpI3SrcUahauOuS8J7nbsg5MlzOXqyKT6U0Ge7fwlaqKDwA8IW9AHm5QipLSz2JoB95HKjCUKZUJxVFO2I1pdLaJgLsqOwcWdNqTIWstdVhqOEqXG+gvh1achcjJj67TfNgJhlkZdY2oEU+kRFdWc/NxSlkkXYTG7RoAocU+b7s7EFDMFw8ilCly9TrNvYPG8o0U8sXW5vpKzcXXi6C2eeMGLJPCXPc/7ZN4c9tMt6NbCa3SNVBJwFIndKTSYTLvx9oMr9ncVWqhmltsxrrbVGjysa859wi1IL0ZaI3NQdz5IEkIuxzbuH4pBlfuoMEKERbQjU0YqYTwA5WLS8pJyiE7nGgu88+d9m7u1Qxiexyu/vQh+QeUtd+tscMxJZN1yD+PWTPOgs8aB+6hwmoqQiuRBY7gXpQYVV9Wx9ldM23CQGA6t2PQcw1+V/ok9GgXrCPbWCmS8oTlcs0sTFFevn+0YkZDmj4+ygcdyS4CL9inwjbBSc7Fkt9w24G9eJj4Yks14LJVhHu6opB56GE86lyDgRyCVHiTGu+s1DQ3LpiVkhXVwNOAULINzPsgaQps1rHTP37z9SykocZKmgYNVDO2HkjxnX2tRv3fRUIqATCBAVXKdQMWm2H5Sb+UdWqEOgpYb+eX4dKH6n2pG7FwEfReePhlWVQw7yT9h9Z+L/yBqH7uf7oUoUflwhHFiNkg0fKErLQtM2SGyJolU92PpLtp4UAlnp5zNALRXrbP6UuD6+HB7yPP3MqymG7pD1c8/wsTOoYHfFTc9gMIXngmfGL3AdpK54NqMHWJOGhTh/BZCp7tkgioM+UXQjwjl2dmic2Ikoku7FXOu51t+8XR0kmOvTCNgSb3KWscuw3Hwg9sXE+gwYbJsoj+htyJinr8pdZx7PfvovKv+IIrR37+4ThcmspiozzSlFQskPf0DdEJmsYkwtyYIHP/i71Pd31ayByjc/FPV9i8G1SbAVtU7mSZy0pRc0F2mFdwJ7d4WfPirxSJ9nkJeOlTqbNJbRh/2IblEG8OsY2AHiH8rDTkUsOjgxO4ZcpvWNbV/a7ynWW7rXz5ENeqj/OsAHUeGGhPZUlUy+3iEhXiezgKltffBUlx4EqGBHT6UNvt1MFmaDs2lRxjxD+fUNMMRv2eaL5BzGGEtaJna5FqCb0Wfg5pHHYzrLzQwHOZ3ed6vFIUtapbIG812P2RDlVQZ3vPIVwBYnXRj58HmiPgwFO6cxJ/ODrKRnVZAMhJR+WCIgDpIUdc6NWL6G57RFAJj6yhWIwGbkVpCS3uq5xolheNoQSy24aIq4frqig+8GrUqclB46T6BB13fky1vdrl89q4R+mUT1IdYpLzjJc5RAazG9LVuGKiFrvZP/sbglEwm6F5/JqbjhIJPmahCcW1Eb2DdENe4C8x0lRbfG52zCVl6bu/UJPBhzW3ktt4Z2Ls5qvUwH2W78yMoalrVnvxG+ZT0Z1P0e/vVuLvYXvcRgtswFyEHbatT89/7G3PIo/ebrii7aHF37c9baVI2M7AF6dWpJ+S1e9vlPBIw8BNiiLRxDZ+mcqZM3UWWrtqvFWaQzjCoR+2yUj1xqY3r/MLzgr3iftzbSZ+vU3r7qI12WN4rBGxD9IWutMIhrgb0luyoEx6tDjbhtiuK0LOFj3QOTzInMA9jEdMPij9LHW0iPpOBUy2Kfe+4OsO79ouctO8uVID8hrZo0UvY5CKd+/nPEHzOVbKd4U+9r/Iob3ns741uPHY1qTrJccerE0oWaZnofy51bGnvLMDl3vVImF5/dAJV7BCjEQrhuvwjEA1/dkqevGKXpmRZM0ijEiKjLvaWq3Qcp9ToGBpuZU1nuieSHz3CfXCaD6YEVn90VmJV96myuBr+hc9DUE6UaH5uG1V4Nw85qHN4i98IhL2FzpliWpF4TBIGTWg+HUhj6VetmaD3+pkoM4+PPXtF0s3cadXjZ3HJzLhS6LYRmU3BVYSjaVb3iYXV8A9Q4OU4vXdqd+HjeJOdPqaXNua5fRIM5ceWcOe8TiQ32eGDZXw1AA9x35mGuLvLICZc+LryD6+mLgMiXAlOV0Txp+oYVOMAZvNQPyf295k7LDIpMBX3RK0FZPnni8xv8GFOQnPuDvyaoTo6O+//tRlqgsph4CGRqZpWJ/8xmjaq4VUH2lIs/yf7YVBBYIaRISSvyRT/1wBcZ1ZUaFeN1joBsc6HoUSf3mK7LCJP3IkY9v+QJWcaZIllXkONsWIHBbZymr2uiot7SVM8OgZcot0B+1fivTspcxGUD/2XluLLjVZG5H30COuGguwXPzHacXKdljag2CgZndwhv9FxzMGVpf8TlXnCJD6qWu4WStxnSkzTF+2di09I9Be6f31GucNB3wsXbzl1wGBkmc99PMWCDTGm9zWH3g2BjCjy8M61FDEQ/8PdBTFDo3wS/vTTC9ScErlRjm9HxziIEM8D0i/Whj9djHGnsPPc8x0ODimCGmgTKf62mgQz1eaO1lGr1LJWbhbTaccDYR7Rug1lx/Y5HXX0vbUc4AoAswjhuDwPy+IP4jxiWjIL9tJLH7eCBdDW6FZ6FRbbLZsUYItCPFqL5RK3G9b8jn29zgPURtzOCPGi9pnZM+3IhAaWsvl+XBTC1mlRSymuXh5orkFbxtruJlTmfWZvw5Moq8a2HAlJytBfV6MMJkxRSioCPFJBkN6DI8vt/kOw/YPp8qofpKHcJ+bwm/tH0OR6Xcjxe1FDQxEo6yTCvSpKl06n3/S4J6xmusHrBUY1k4qEfVgfQnpXCZs6EgzqDqk6a5o/rpKCm61zkHo//Lyobq2V1qUHMmm62z/AalxfBOCbI6quL/mumpToNmWX9hU0i4G602IUH8poKqjttP4NM/ucLLAZuV91QoAnDbIPEa3hZe0CpTMSqslJURzQjD/qjT5R+AjxNhAIdYIcwcHSpUtinu8CYzNvZL5B352nj/vbKCGzH9xvIprwj2ANUumMWGG1m9Nq5hTSK2Z9/vZSoRVDu85gCiVfPqkr34i+9Dzl8vm/SkAkhYUUq1ObI3ARitBc6+sAclxjp7XfV9JIacvGQlcViLulJ5gASt4QWWjEm1BCUEecGtUbc+x05PYlypa+brJAlIz3w4Op+iCUUYaFF+Iwm9S95dfQUbgPL7mRO3XzLcc1it7dojha1sMV9IOuCjpNnJr/53jQdiZNwyDIOwSg8igt+8OLHvW/vTEQE6tQ47MXtijXmQikT4CItORktLGBSrfijkIH1NqQR1WMziOLIh3NjUuRGj9I1cRDSo4UrwsUmrZhpTO+pUtayAl96S8JZ058pKZHQ/GkbOMYiopmCybqt3tjcPK7+6UgEeuwe0lMy71paW7SeDSSo1KyVDkveUYeGpZODTTRmjn+zsKFS4lE91W60+jzuAxjswVmwNZrO9AvD9kkNdo1t+iAKgjKgju8AnUHii7GFlmEqnUXo4Ol2O0TPV2nkmmvL5vuszoe9StkFajQeVTUPf0vTLvifQVGhyi6CgOTOF/T1sdU7TdfmmYs/ndduxoLcyqsfCljuEvtch1KhDwxfTPH086kQh26wfwPADY4wUmsPgFYW2xllq84h3gQaBlqHiZTNA8fZOmeiRHam31ZWz6aixoG95BWZzdXW+Kby2OBUqVCYNEmtbfyyp4n8Aq7681wc8MLZWIBmjn7DUNoz/pKmjOERNCaRIT4WCVsn+vbKtcvlx40NxNViMtW32u+hsUujF7ylAoXtNVshUOfHxLIm+xzz2l9yFqpRGW9ZaM9luyj7rSy2SSeNfomVHlqhDgWe/mLotJxiCepcSaVz3Kwwq0QgFFxFJDwq31LLmg94T4/5UDPXYf0A+6+UKNzQrPPnIBnwiQpnup/ecRh2HSiIpzF69ShufYBxUgYVkcOp9Bd4Kto8ahYP8MB2l3RP7FCsNNunLu8c21fIJR/YobgMJ/4/+ChzWRyU1z9IBi6yWF+3EufFH+nl+v/t+KDGh1t6aof1sdGmLUwqIja5pqr5hTDLheyPPzSeWIsl2BKLFZfWT0seqfHwgqFtM+fyVJUIgYyHl9RBzpHoAZUzpQW3F28O1x2i+h+Pk3ELYcnClSHtT8BHow26BMAsGEN1wAt/z4sYgpIPb6RAMZdiFl3z6B6lUuKNPECnKBo4NSGAj2taUc6J3H61wE1PXtSDXN8b+244OI155QaDCGfFO49il9s794VjPBjWd1eUUu7rc0z/CW40VRLzL5A6NZjlqo/aS+uR1AjpyaprVP5Ftn9LfghHEs3fahyP3ZCHezR/wP6VQGW+nhYkvpAa/jqvjRK4I61KlhLNw/bWsGkd4gehnlcJhackkRpnABtaqXdVsUrlP7EnIDJ1uSz+sGU59JgSax+d6+DQoyDI3Y8xlFdJ0YwFUKBVE4g5R98ntx1JHKJNzYieESVYs0O5y669VaCLms8SIeq/YEH/BAGSogHVaOpZgFlmVOG+FKfRf3pYDi8S2HV6Ro5OWFviSrpq/Y0eBttQxzVitiRl4z6q7bUUBDLHizuHAePO++BvNXj9PJxtpU66FyXXggdh7yYqqwoQtP6yK0ZcPzHqzWn9XfYY7KWPz5g2N7mfsDvEcPQda7qC/ArqzXpXI+SzfZCSMzYniwBjr7KwZDqoumL9gN9xGVE0TwfirOv36YohlCLQv00qJbkwrm3le2jFUBb/OqJZbcVX5h9V+hhCWlKSEMDC+483YllHyYnwnRhIDbZVkHP74el2FYPV8mAi3wFB2lVUjV2mVpuIdoXgNITPPiGSCcrsSBjLDGtFg+iH6cRwJqjw47Ww4seD28g6YfvX/+20JpronVku+u0GOXDxWuyt/eov7YIXlefpUiDhHnu6UeR4e7zSDsFXS86RW/D+dRJyNnqknRC75Bqpw74+gtAjz3Bp2xlRFsRI+C79SjoxV0hQIVuQ46UB8CzLrHiek/7woYBY3MQU5Zf2qWYXdmHf9s4g+wivEqODeb9wg5XSTwd4oixkxMRchKP4T/a4WDAHaAaUEhoFJ0yJDBLTz4EdiNWVO8t/AhuEBK/cBXK69QuxTuM74Vyr9oj0aw8SZHG22wF6m5Ie71st4WojlZS7saanWKBdNXraNHGQK4nvQmnFBavE/eiR88dvgxxz9kiTKenoHQJvvr1LBht2oO/jH/IXBHA8+Ne0NhZoflVH3qXXIwo05Ce0p98R+yF1SzgE1fDWW/yKadZmhN8Qziw5ZWGDZssoRix7w7gaoM328NLLjZUIRgrvL0NZiY4RInUMXHebSafFB/lyo6JwP4/pXPsp2ER6D/rcUKYiw5CtKCW6qf5P4NFKzC7SzrXtQz6lckTBC1sHSKnFleuw+yvxmzmROcepXjglImDhFk3YGOUAcIMhtq3755IyWmWYmlVbITfpuqQyPSlGExzv7y1nRfilwqAG+HyKDMziyJFSN/A9dPFI9acnX+eEvoX8E04CSIVpX6FQ36caFpDZ4p9BzjNc0/K7Q/w1EddmJ6ZtKD7SSdtFS+H16lNkI/8xxJyPne5oX0AqyvkZEvzD/xkQqtGd9pXWDgF4QUiZb3eT4I6wcuz7nZFvx2a0q0UC2dOvmIFLeN6o5gOClq/RzKR6Q2gNI3/mCYyLZmNFS9p4t6z5ShyZNRBIZuHPozIbhOBus2GV9z46UQaIIyC+oVhVp/tHPR/8Z57+rb4tHFr50LA7Z0oQiK9dpW41SkJVFEoxUof/PX3ZVJ9qnFl8U36OPk4u0Nw+ICI8BzVNDjyl9jojFm0j2V63tk/EUFXfLwe/lAKEdrCFpLCw0Waa/29jU8oywaPiDgavp874JswmLWoZ2ZOEllXHQR43Q3roe++9qtQIqG8hqP15fHJnliAoZDjW1gBVP+TQgbOofLxHIAiv27qlVkS6MXKOxIKBpw+Ea27/mqjf7J4OjROHWrsOewmLrTPMBSUMK1D/dr2TzVHJ0H9/f+8/pOaj+gsmDju3pZHxCqaOEqJSfXSUVOgJOrDMMgr2xh7bbAuAnYJtqJCg6WSYAK2otEzpbCTGe8jkGVExsyLn3j2pMO7nHxPIZCk+TqXJiGuZ9y+cZE6rZHJlHb5cYMhCoMs5dHb/aeW4oKnkyFXNweaHX9rRrRTLqDtOmO5Q1tADdFDbrWfg/bT6YTmCb5PsL1ivVzKvGpndSFBQpxXJ9oPu69suIXQmvKqAhKuNCEnjnp5QfN3shCLNqsfewJ51QtFxS3OKb4N86m80Afw4nSBXkHBUTNCWIqYf2rYAhysrbqkSb1dFcYIz+nU7XQL8nI7kUcS1M9LZCJf08+Okz0d0p4pE+uCIXIs9hv8YZh56DQmPjwsrYpz7MY1emDVBa7RsVeJQZIiOkYJAVgEPXtGZb+woko1ZFFbxcfmlUk5foADLD4XUvYzfCG2duOO0CM9aV1YPuZPNxDKoMu8LJhO1WT8/HrSoUMJt8WZtxCnmL3rtXPBBzXRy48nCU6LODJA6tb9OKnO4H+C8i2mlqdwyYO5bhkYWPs8/tZJAW+oswi2JX51xEno/stpy3t/3ONEEdlxmn8hQn5epx0KS6NCZxnpMDsVOICplZLBIqpmTpbojtk829PNQfm4dLG0TlYGHOnMUtUlM/99vjA+0pycI6gNBlFwF9Mft9IO3DXlbQdLqj9duszicQsW8kgNNDPvILwnfCjXEI/+lvFfySdkePKL9EdHJLxY9CufRKAEQj1xecMckd6Ee2GHQmEvf3QXM17IPOLpE/azPop7TuFDBVf0qkAO1NoRkCvnBHGinzsLJz3XfNEAPjGNXJySEtcKEPuoqV4cyL5anBQWpQuWXg5PkEAddjdO+QLmlDU+othcbQ5mr8Y05zqrJVBBhpJvFklnD1IlA8B9KjLYCYoxOH0+FuHvYmNKKp43iMfoDP4iJY1fJQ2nnPT+7sL8VoGQIo6y6O7SZjCxqcyBNqc0KlseMRN1dZgnSGNZieAcpCpafzPUVrmEKJtRMeYVgQdeDUizM/ds+PekvV7JK3zSITBAPLXtZn7UrmW1a02O49i3fTpbcQKvXpL4sy6uBir/sWdpaqGEwhqJcNDekuY7KHyUCuOza59X52ToXDVfHvFws4NVjgMiY/eDhJohmlR+o3vCPbpHFkMaFzybuAebDGsjJt/8Oc690zA1avDIA++NuGopf4+LgbiSf4cqEiTGPsADKppiyJWOBSV7bkbyr4jbH7C5X2DbC9JctP8oGh/hD0oxSMTjQm5UZi6XAvO8IwaULpVg2tvR5E8QGX3d8A/qry1e0+zl/TjxYpOxO9DZeweNHrqM+/xGRJgW15W54V2fY/zTApx35xxKTdeNw3cwW1d0pJAEg0Bjfha0+RhDHGxWeRetg8/N55VQ1NV9rwmUw0e6Ogs+ZDy0SmI3q5TgEksUSoX82OjtqVn3a6ttLqC4s0d9r6vtYdeFlkfp8x8fho6wxtMxvpoZUQ8eGPjJTTFUZG3SN2KK4CErGk2lNhlBjbEFrwue0e2QDgL9jE6xacjwTZfXx4vFpm7dLeJ+CIjL7UebCgTmPiZJysJJKTmCIb+Mz8w14xt2WCTvlgKrwzwKsUU/UiHBVYKEnlNICLNt+ucyQQ3d0x8KfnT63IzhQZgeUC6Xp1HUYd25LsdkRnD3iOJHfFG9ceX/pufwfcpzVGAqoTU78t6IFoc2au3jxB7VcLYO30aho/iPkL1UB5JndySLF4RQOY+JdJxH5vwX/0T1kTu8PrLNPmXxO+THZG/8n+1suRoLGONEWcAE/KCPFyuhgUEOzWHlqkShIfX5Yyr8lnmDTANlV6TokTkDe3NI9Sh/EdnquN2ckIZsoT+6VFAywui83wke9vXUDbddCyqDz0pzEkj3OPWR6aiF5iOuJdfOjpKya5I/IEdUvF1E8Kwd5vXmzhTUzTtNxPvh/vql/yKrcIaAZkiSvjwqtBEIAQ4sNHrfy8DfYfDhTMaXdUq11+kE0vkgxu/KKhOjAtLnIzpwCrm3xlgBfz+EMfro5wNXUw9mf+I/3mRh5LYGtlR5ZC4Uw+EgXhb5YUnX45wenHtJILk9WQCbh09SCimgGMjAtWd8ExLXmTr9Zd2Rzh0hwmEvvvWsoO5ZLeKk161gjYN/BK+34iqBhfRrbm2WoTDvtZpaI0HexWW5q1gih3iFKR5nqXxFMbDyovcZLTZ+Qnxoo1TYqcT+qmDbP1LhUkBkqvWlpXbnZOIcSh3d1zfATVFwM5+yXissmjh9DJ20o8nMsdrJ9+e1OB4fvLBJaMXh66/H+MPG/H+3Uw1RKcqObF/i5cYEYznRSdFKjyUFfOuVHjZ77NLeY0W3Oa2kyJwcSDC5dcBGSedrXLNA9thxj9l0N85Syrw/DfFRhlD9PiD8mm4JzobQH3sgW/cGChXY6XXYw41C+cEVUFfM0ZytYe2vXWSefYRz79Q3RD/DkffgqBgZjjgz/piSQii4X3gUqe2kY5m4dY4EsoHVKcwoHxvlQ4W+uUpRM9Jx6rl3JPFEa9UXI3/ceUD7namINwHiWBF3yDRIABNll7ryW7rnGJSYcf6J2MaT5TV4cXvwNA6ZtDiw7OqS4KoMgODj+ZDi2edRTBHSrxtQgbJ1me49VTjb8Pez6eTtgPyqmw5i5i4jQpwhE/nhuPGVR2HWbQp3GtxTyCRcfRtkEevK3QlpvcTOkjESWMcP+0d6EIgrFTC6CG5hqKS1/7Omg6B3/6I0p24+2wTCMR6AciUEFXxQZ4dpxmfEZW3p1oWJsVoaJvXHW4PACj0RUafiWTSVKkI6gc+1KAe22JcSawGHU1kZA9VIGWNJ6+a5AZmT6fKCa+7zh3kuIbBldOu1k5IoOEe0uixxFIRWfkEqn1Px4vIZxMo3twc9OhKnE+1HDWoRsq+g8vrl5mm8GuO8gs0Nbr6Y8bE2It8E25kgYNm2vbR4SLACKmVLxPajcrifwDHDLX4CmVuZHN0cmVhbQplbmRvYmoKNDY5IDAgb2JqCjw8Ci9MZW5ndGgxIDIyMDMKL0xlbmd0aDIgMjUxMzkKL0xlbmd0aDMgMAovTGVuZ3RoIDI2NDU0ICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjatHlzdBzc1nfMxlYzsW3bamxn4kxsO21jm43RmA0bq3Fj2za+9Lnve5/n3vX++61Zg9/mb++zzzlrZihIPqkyiJjZmwAl7UEuDCyMzLwAeQUVeztjEAsrg6i9rRmAlZGZmR2BgkLMCWjsYmUPEjd2AfICuFwsAUqmLu+OTgBWZmYeBAqAFBAEdHpXmgFMPAEKQBdjNU8HIAuA2vgv8Mne2YXBxNj5XQ0EWViBgDTvLmL2Dp5OVhaWLn9isDEw/In0x1uUESBrbGpj7+5sYwUwBpkBZBkVGAGK9u7vQisAtT0IYAK0NLY1B9ibA9SAWgB1VQkVVYCUipL6J1UaxvfAqq4ODvZO/8NFTFVNXYoeIC6iqCYBAGrQA6TUVdX+vKoBQe/8LegBimrv+j953g3/uCtIqImoaX+SYGH6UwOABeAGdHK2+pP2v7hRvjMD/E3t3dXcyd7urwQAaksXFwdeJiZ3d3dGC1dnF0Z7JwtGB9u/+KlZWjkD3O2dbADv705AW+BfjXEFmb2308US+K8Af5YEIG9lCgQ5A/84Sdr/S2n33sp3p3e5y7+JvTfC5U9M23+ZA5yBwP9IY2ns/Jev/KdP8gA7YyuQCxBkDDJ9N3QxdnF1Bhj9JXt/As2o/kUQCBBzdXL6k0Phf1VO/07zv9RF7d8r07P19jV2/+8VMwa5Onv9ozf/WbapPcjZytnF+V8RgQBzK1vgH/bOf9bMCvSXTEFEUUZSQlWNQf598EAMCvbv3QExuni4/GX9J56IuDwvgJuZE8DCww5gfh9SCZCZmL2d3TtrZ4Q/7RO3eu+Ti72TJ9N/TbUNyN4d5P3fUnMrkJn5n66buTowqYOsHF2BMuL/Y/suQvhbZgF0ATADgI4AoIepJdOfVH9Nyh8xyx/xewt8vR3sHQDmxrbOQF8rc+D7G4K3s7EbEODi5Ar09f6n4j8RAgsXwMzK1OV9yN83CsJf0WVA5vYAnn+J35n8r+p/lp/6r01K875DzexBtp4AM6A5ApOivcv7MFD//9lj/5VL0tXWVtHYDkj9nw39bytjOytbz/+0+y8TTeAfqtT/h7OVs6SVB9Dsk5WLqeW/uvovuYyL8fvQi4AsbIHvK/KXSP3PPrJ9H9j3Q8fqz5kFYGDh4vgv3fssmtqAgM7OAE7Ov1TA9x78F9/3xv9hC2ASl1NWVVWh+69x+ctIAmRqb2YFsgCwcnACjJ2cjD0RmN9ngJWDA+DN8j7KZkCPv4YEwMQIsnd5dwE4uLr4AsztnRD+LCQnB4BJ5I/oX4gTwCT6N+ICMIn9G3GxAZhk/kbvOoV/I25mANOnvxErgEnlb8QOYFL7N+J59zP+G3EDmEz+RjwAJtN/Ixbm96Bm/4AsACbgP+B7EvO/Ieu7r7nVvzEH2x/o9rc9xx9ze1enfwR4N7H4B3ynaflvyP7eFktPB0sg6B8W7zKrf8B37rb/gO8E7P6GLO/k/+HL8k7e/u/o77b2oH8Uw/LOzuFv9fsqOACdrOz/UT3LO1vHf8B3tv+oheWdmvPftb4rna08/qF+D+jyt/qdi4ulE/AfzXkn6+Ju/w+H91Vy/Qd8L9XtH71+D+D5D/hO3usv+J/j++nPuf3XscT89zz/z4X2F1Z1cbK3AWpamb1f5v8wUTB2cbLy0GV+P1NY3uXvj//9pP8fCSj+Pg7/4S0qau/hzcDOxQlgYOXmAbBwvE/qe/e4fP/D1/Rfd8tf59n7xvtf/OdgBwCBHkBThIU5e1O+EOuUxrBSP4mCiTJoCh7G4wpsQS3ZOKiF9IkfBLjiuZukQKFvgc0BGZTf7OWlefX9kgJBRVoUIVi2r6stiZW/rs2UhbeM/RT8CJAlREZyNBjVgzIU5gPKOkhpDmRz8rWL2acyWuNaPwLURw7FeH50PkSxjr+hXSaT6pW1LudBuxfOsDRhOtmie8yj4rcTzE+0g7u8PWDGRBr3iCzQThvlh2GPyMI4dHeiahNdkcWRES9Unqf0+p43+3SMEwRSivtVYZl1d5cwnTUSGmJV1n4i14RVGF9PUstZmP+h5erP97jUEKaTI2+jyFWFwBW7lyHL3+uOiaT7BqTT3gYj5Q+eWf9CDhOkFMGr0r/odragObtYfaRXg0YQXHSiMb9OLeFA8Nx0oPamNw3f7wlheP5KfkGBuYZyFnYM+J5A6XMeYuzKLXa9VcEvbWe9FwMZmxX1SjCmIaFtLW2LZfswK5P2yby4ypu0D+94S7qTg2bIgOHjSDA9+fONfIhj5ImxWWUAx9jHhK/ulCOcI53sq0PuMzRq+58pjmPZTjFb+x/mGhRDuw55znjNP243M9hHqWSJD6o3iq5uV1rAkZfYw1D7boXH+4edx6cmk79pbgWJ5mDG8nH7pbd4+Cx+O+KDuZLiNa0jxLGFvB9iKWxq6PVZ24QJcDpTQeJQZzZCoobSVojOfbPDgGBpgyP1lq900/Qk/qyWEz/QNeGHDiV3tXAkcuALozAQ2Lfz295QraOqhqxPQMSc7wNFIocSz/RswQaP/9Q2koGxqjZtwfVJSdqPEnsajqeuo6ggC8wliYIE+jju2GjRVJgCWpdeM+xkigso7js478uwrs+H/JbmlddymN5u05fJ0SlQg99zgsiWMcN5p8jHic2z7NbK5/JtFjfrsFZLP0owLE7D2AceSmrRs8oDMZJl4aQh8g1IUOH3FZ2Y3owHrPoQp2mav+hqPdHzOOXzFGii59pfFN4O2uw7bpEF704mWwrRbyYNIgslSaSylTHIF8Ho0WnJyY/HLIvNwVKHccwSMQkprPAj/+Y8aZDYoRlGy07wUlRyb4Dnji54ycmDTWnIX3nCkftIvOKgc07JXc3y9ZC1ega71jsJavBz9zNzgfVlrTuBpa6C30uamegvKfXo8x3zRaRTdJ/s1OWDQb5t29uZmsX8qh3GIyXYe8iwQacGjTpTZTc4USqiwR+DxVUTbwXHcDw1UHXTPFn8JE7TPdf5RSuQAtZINpyM1wmF/PwEXOw5ym0GzaxcA98iuPFxtio3r9qq2KG+Cxc2htdIOuChtCUpok6lCyFYdvdI0yKOvS0Y83b12gtA+zdc+zGsPDTYcuK85u+9QKsgp9kiCgmdVgn8NINBagjcUxHNSDUkO8odCteZUJ+vuRWeRGq+RuyEYZIyjzspI4Lu0/540t2kotx5r2lPmNnXtUPCQ8uEZBiW8+WbSV0ErwVviin1xvliPl3AiDvXKgXXXoOSAbbYFpjkvs9VVLpEqbOrd7dtHzQUJAvFVNEXwpRheD2Nujmq4GTYgDrsBG1Lq8cbK9qSR3Ox5983Bk6JUQx9Os+fsJ/opb9nheKRJfQHNfEciQ3P/3wRVq2d5X9A42CkK/EC3idmIHriHjr5budGrXs7curGsKCyaN/qMxcX4HZD29xyjwHaNObtSU8EK3rtXVGEDb8nU0YHfIMf+7J+ZWNZFmk4tb7vuDRl8uUYzvr70Ln8ELvwRkl1RfdyZZNHsLMRWzpFVzTfhz1LchTkyIO3hGw5fFOguWy5lRuBV6Z3UjtNdUYCIlLQ5c+cjK4WRY3eqBHS+fNhW1O+3Wht9yOBCFRBUeb+3K/1Tx9Wi2ckOVEZpMpjW5i+kYolCtbt+dn+jDppY2jucPdIsHF43GCs2Yzjc7PNb/35qK7Xung5ctuUj/2cIAWtRh+CJfqDeIlyaevz+GTC5he88cxWs+cjqd6Fgc84Iut5ZE8dKR8XhDx9IA6DYTxYXFSkiZuiXl9qdTglKnk8p67LcPrJh+t0Q5P57U4oaU/0D0g9rggYNYXmzZMXIsDEhVh/Wel2QxZUFIU/1V3TPgzGkQ3ggWmJa4VZ/Fyk/l5I1nq45n1CzKevrX/jqIYEp/7I5ftoGXBDER5lLYAXMDAJrS47qg9bcfgRicpcye4J9BYehWBpJAGc6HR9+bpO9sg5adbUQfFwj/bNfByqZ1zHXXGL+gu5plXTCfPzIyoZwtzQPjw7wc2OgdYVdYxT6hwgPp4JFsJZ5YdPMvqvEQfcOh7Zu1MHNx4Di5+/Mhw7YNJ3+3vR761lQD7uOLwfFhFHJS35yVLAflFk3lq23AzFrrIkN8vtxd98xNESmilh8UhN5i7wSClzy3JZr8tPW0msw3xofXTCqXCYX5igEC+UuI1APmHnSQ9Wxxxt8eS+Wkf/rmDZLdw/ToaKtcB/1kf6S+Ok6ArTm9s4Vqnz66ld3dQepucYL0QHZJ96dUCUcIvq1LFGuJFbHvapX2CwuRuZf0Qf3v3SVfZPw20ij1/YwK23iUyPt2vqCqof5yxRcDIJWqRW2Sw3aAYeavlMkkn4ypcSlHd4iLh1GMtaZYzfTfGly7cSzSPtCQ+qKp800OyfuH9GPBiLtYZDc41+FTRkeB+3BORRUyndtkjDdAmSqHU9RXsKFJCaghHarcF1QJQAuBa8zxKIGuEbBGm6jcduhbGKlvnXF3arMXOO+E50zJ0zQjRbhXrvAAMmI3c0y1+ev1CXGUouOxLD0gsd1mgLQ0kw+gifQ7u3sbDHRejrUdshTFg2aTMnbs1+23HrdeNAudV4j33TM/ugJYS9CHCg/qCliZBsGlyW47OT4KqiajVsrZ5xhgx9QVg6QEsVCaVLROF0DLeKST4liqQO7u6pVLfa0ZEtAl02rXSBV04ePJrLXqpzXisXgINal7pwUKrMHZoeGIsmytsQT7R/2Ge9pPWE2+MlMv6J/zujX9Gt84kh+0X3FKJROhbj6q98S6cf/JEBtFg4C/0vBVIScRdZmoLVyjfKaUiQLLWrnQUEn1CFTFlpkFXosfoTVNAVUZWLsXgy2vHtuKB+tG283Wdr/MoUvIX+HAw/8HMK6ekFdc2wAEU43a0qgARKvyszaahKTvGOo1BEIdXQ3hMV2UGihZnSH/kwYx+wwRqHQXuAhrvtHcLajv+7dCyOie4tOL/XQYfyuc0/WmKEaaGMsTSaGx+8VV6RxcWR7BIBwQnH/GGhyC3nHlEY8ePSc4XpOBa1oNOxuHflbLtWtWoewW8HnmyWJz6BUPsft2S6lcRI3Ju2HLivZmiXhS/S8F2hFQxGjyxEQ5lKEy/VHN4NZEICKCbo5xiwDO1jtZdI/d8CxtIeKXICA4UQY8YYx0Q7R7WV7B7lJBq3GVmOvV7D1yT62ZCz0OXfVGLwLy0ncDhd4bH8FOLgv+EecMGKSVQkmNo4Lqw07/CipGJQRINBrzlzhNtrTtFuevq9wsc2tYUrkNV0d924Xpd6vkFd1HV7mD0NttC5L3CjTF/Eav5YDj+5J2qbpJKMMOhq4dfqv1Ytr3T2KBk2irs59PlYJIOtmIo25zucdigf6At0q47P8pX7al1IvZHyAuFHgKgzBGtLMkWPm9cnIHvop/J51aWDk4UCav7utgKacOMkgqFOEW+w8PI8X+gXahfjfDv+eXrvfm478FdvgtgIljIO4beNk0F9AaXjB2EKLK+tjmlQbR9dXvQNyyp8A51Dwu/Qa3ZW+t5fflHfvmyg4EmKOh9XWNtWsy2jYUG5dFDBwJlf7XxjcVgoh5plUYMaZTNuiOGu6IiwHKUUgE14UUY8m2iJZvadQhPjD3KjEar64ZFBbHdNjtYJCMIEUOia1UZkeiD16nusYIhqBmorOqrAms69ANLJLuUchWIh5HtVZnRx+HBHfKK+6U/cOvgOeG9HFFirLUxdKjCbta6OkcLFGaLqZvC9Wkbxk4zxsigR356q9F1gs0D3wxGnAHd4vtf+ULlPyfTSK5xlyquPjL0QbmE/0lKxp4XElA0vlz+RDfm8LtsDTK8XZaSjOsR7ucm3rO8MAD8PDbyMcMjh6RR35V9uyhXRS7EV7t9UILPtceEMtkP7vOArep9KPrbX/vhhPXTg18xwPKH6irtzf11J+WA5C8j+boDxZZMpeN/H2ecjqC8OnPJrMBNfi4nMq0YZwiZQrlICpKxydANzPgsZF8z3g5S5uaOMt0tX2YzoFOwtBtsDS8NUKjflOjAkdxi1hmeSGxqvwdjGQZE0brIuMCc6H8tnnFip0zalqvlZH45kBhYF2NGu3mFbxAv++kZyUKWau8+mKKRKvqrgv1TBn2ldBPSd+85tEvD00SfYmLwWBy/BQf2ntmoFoLHqCrfmhiB/PrcwrQjqAqEw09/yzQTXoJ5jxCy7tD51k08DbW6G75YFEtz3l0actAt4/BpDXu197stQX7muldydchm2XNRbzlZoZHSUH/GESKyzrturdp86WcXFVmKBdsDovYJllvDn/hqpBdeedKkoPVrQOoEOgj2NQRG93Mvzmt75RyYaYqoPX+P5bF/ycrqjz69+yJhI3Uszl2i5MOYkBctTbQO3hiavHqXNI1RB+bc/kMBizCcGbGiFlQjdC/N+45BRXhF+x7gPhhWPbkn62ZbxcmG7nUpUn8FVTpyS+xByLdzS5oW2gGRrFiKuujmajeJBU6k6DWpnXrd6Vn3QRX1aBEfX8rD+fD4TjQNQ1gwSxzLUFwfpaIsh5YwDszIRA2YUO9qheQIwEvqhq/AYqFFrrWAOdn+QZikTsyqyJhm7tMdiLmqUCA191078UK4RkA+CIWIyimMVUFXzobQfDTeD0GlnJaYQCXAxNnR2Mg7zlLUxBZiSA9gtsTWnUru5BVe4o0acYpnt6YQ3o8Y6cffocOZ6rsUunVpW6R4FOmWs05aH1Dp4drBgaxjHFqhPT3z6cGPzuLCaGkabqY/gFkpVo4nl1OolGqJ0MyoQqYGzsoVoqfTNsAQUIebSsh5IipGMPnAkyHoFoULwKszUqNrBmK/3uIa06HvSHDcfyvKK1pDY7UO43OhJfsORk82AmsHvP5HLWIrjyvALQnjBFcIEXITTfTCLxCOWqRY3UIhz2sCOmdeCmSuqdMMtFegt0RN2K9UWP3GJwHE8kZ5YVkjE7vX5kLvZ34L16Lmzd0ouoEEs3bpVFdubvreJ8+NEKR4aLOASuUx/z3hfABVWLDO47VuxVU07y/CgeH8C4IOSXvd6/xb178sHlZRgxPVUm3mbEmDbBo3ShaxhBfrOQrUjwzaPQ7/QtRHKWhr/kecuFN+x6qdspGpsz+RFRWbEBDodCFCMXIqTtAsJ09giFO1srgP9hw6DNZuJwnpfT5qbIRa3gDwuY3JHCXZJIYrfI9QbP3csJ/ZvVogUqpYtvVFP8y+mrjl4J70hacBAstfWIVEM9Rw9lv1tgOn8iPWPdHHO6t4TOTMNcZSta+U02eXuo8gNfMsPz25euDXSwi1c7lGEFwE2ROuBU0MtSZ0ft0IY31q3ILs366VEmAQY565Z12RHXWqHYfszwdhxiWDbsOz2HGlFLITEFxk/+oosmtcm++Vu90Vco0gG99AYdvVFacjSyu6uXFDkcNrXEKRnGXaa96VGxccFBcDgc7R+/2h7EyMCVj7oxn52lJdRrkd+NtyoLd0Jigsg/biCMFroER+BNY333bnMSZsqTzJSJL4qKjytFD+7JjtXQDr05yOjtsHHzZO7drjLs9lMvzxih0cDozQzDs9SPK5HQl8dkOQAH7KUyeHmwRnl5IiKqzckER7Fep7aHfISQb/dD+mdfMCA9YSogpH7bC9W1etBRLFGYWppDbOdTI7RXn+XwkE3DrueorJLFK+eMjAMv4UORT9raKKUIOEECMY30Ttn9MUcLjENUVd+7Kw1Jm9aMgwMfw1utE48aB+CsAM++xcbCiTvT5IZ03w31gpdmdLVA39GHL4VBEnWtnnFZSHF7JXko9e1jj6rhXtI6N3unn72fT6gw1FfrY6Zgcn9wpqg7BMrJ/5wDV/F9DZwIrFR7lSKAGkpfTd6WF9Fw1Snc5soDQyjoocSDwrnNXNvN+tOrr5f4mHvChrxJyBj7HItQk3rGVTlPZI/d3F/WpXSpfdpH5XwjMOTAJu5eVRqDjPtlZG74wmWIOdVwNXlqY6+pPx7jMmO7qvzJllcJRHSMdHMAf6J/wbfTvadbhKR034a8cy5lxaL55EABvyUlDGvJXozUwz/nI2NibTCPF6u+NUWlgBBYyOJaoS/5emEwEYM5+MTz3JLOvTRPRfbmY1j7gERT4nGBcEmMYtd4vGPyozf7swx9CRNI6vji1+llWYW0QnBEYTz5zwj0aMqUmKlT8qa9EdKgwWIOoL9yy0Hii0+hqlksD0/wKjM1L28KKVoBXnj5Ux4NEoujPnU4TZG18IwYIDAG2YmMVk2RSZOhvCoP8wvkqnmBrwqpvwuKBnVFpSi05xN9p8HF/nVn2FSemjDjAS3H5/lgAkf1rbjoqhhSnyke1Ix8cHsp/iyRZEknvsUFvjGqXhq031Vp2URmJU8uYZFr50mVKj9/Lx+a2ziBvTwB9vbbtKe4zZH/LEjxJikgrS16X56qLL6GJv5wXbTefEO91jPg0OlXcUS/7lduVYLekFryvqLWCn2CZZkrzMDIpGSpcvZjha4Ued9KKFNC2LagKZ2EhtWSzLXNQvkElDJ15FrtWmNdDCEbMyka6bMBk/LIrwfokLuR0XNywe9iuGRN5s9BWxf0H2cTy/4o4wgQ0CFwVxDgWSQ1BAlguZbTedf5oo+VqWcks/2a3sDYT96Ss7jcsrMbF+w0hTk5WHG5fPQGxyiXFNPieX9ggJX2LLiZrKjqVwKyKdo6n6JhiNuXcRqdpw/rUAux1CYC1r+kfON08YJq2nxGzXQVY60lNlaROxBABQwSBBnjiQrXYMzO1OTaCGaxWqUfdFb9Dv8C5pmKIp04fpl2kqsR5h1b4U7vt/GWHgv/2y0SCzQ3puDkfwztNELAFL/Ub9zxi5Js4XfyWESY6XYV7IxU+bnp1Tbhh9Jtdu5gmIHDH5MVMVo2tSk5jCGi/zNheWwof0eMK+1Wd8uCR4EK+ukCR2l2UAE1TltePgQAU2fmfxqtDMbBLbefiM8j8/AWMlPlF4MSKQonU1lm0oZb/sRMiRsHwh1B7fQxx/VDM06tl05HlD5qBcrYVRjiEkpFTQDu4KLCB313N0Uv0QIpT1MVW4lI3402VzSt/2GpgMRzPtNkPTWf3Tzh0p5Zlan4ndaclmByOMD8wibaEisGzKb7a+q/narH1R1SZOFQpwTOEQ2Zp4DzMU+85oRyiTGtHq0eH6EFelR5hLKx7dhyWPpaipuCLWQMNlhSuwxjx3ursEg82oYhXwmh5hjgSKyLtxUmIJdzbDBLp7dG5NE6oYDE76ZaaSgtERK0TDcntzELh2w/KZm5lvnOeBBlO2loVhYndaVuGhBnwUFUH4ygOHbcOAfYd7Sx0oimaOIN/GmUMrTZtN//gbSR+cpfqEfCodbl9VpwqxBmy95DZnT1Sxm5T3omQyWXgXTwHFfIayjUe5Cv8md5XYkB1qyhDGdE46E+UP4JZ0PMHUxre77qSZqYTflbTe6yaKEhwSwDbTuMZItJeYugOB0xyp6R8uGVoXMs/tIEJcnCQOkXLfrU1JGHWG4Ma0g2CwBT/TyatH84OgaFJDBtDWweZ4jzFcve02yubL65EPYVNfZwVRHrdlw7fzFS56HtQlCJuhEjVNJF5M06bM/1z9XeyuKiB9H8jipVocSIawWjbNLU0L/Ej1roy252uhE9ONYvdOeQxgPBjLfImlS8FkMCbQfH7C6Fix3VKqxf1kJ1AhfaclajLjY7Itwhx1TEqBMDaEmPY0bNlRbql8kn2RPOdplA2dofxoSmiGXqs90EhK7KtnAOPHa9pz8FJcWiyIO30+UrbKlJD2cc5L0m9PqYyMWmqQ1vMrXIqoggP9AaUSvMmi+vp5/jFwOIQLIpPOxT81+ZrWMNigfflNUkLiZ/rdOGnX3d3YaymWSOha0vIjCx9wpZfRM6UEh3MbQAnn+0PHF8oUfuz3zZqQ8SlANRBFgbG96r0fo9U7HvB5ZOFPpCmYSIQP1YuyOhWxRzH0iBh/LqD9p042PWru4PK/4DAr9CrXrjIWFOjImXFkKyJgYENz0QJH59EtvP1i0jAGbZy52c1eiFbPlIfHDJCbgaWib//tccJQKEpqSyFMuB/EuDwv/3LUcuw5xKnaeB+Qv6VP2ZZ9jnl89Y2UZSirp4Ay4L15Oupw/XXtsNSQs5dqafhg/OYp3HrM2I4dO61SwrojvVjkwLWcJCn349nC3IbgDFqzvOYtE/WH9lnlJO+H2Kyx2NX8HyWfkh0Mczw798Vhcjvj8/s23uu/XGR0y6XZpkklBjz2xBCaZ2xjOlCUxIKJjc+a0HvFG8UO/52o6ZdoUZFfWLxZevuP32QgvMR0rBoNxrkgOD5mUEmF6kBrTvSz9gDuMUCtBG9fOLjETaDJ0s8PqQaFmjy8ZZINdpwaEqx8SYI31GbPDY5q5PEQPkQxstUExw2jiHCq9cZQ43S4D/J9xTMHMTZFCk755YkxJfhYUwA4K5sC1uFK+i6tRCL5YS9JMbseaZ+/jMHHT9nB3k5GfeONAIjyrUM1HS1d7RCSWxyZVLeXEB4eVeoZSbBGytm/b+jTKzs/qcXs4vA80Bek9unoVro+Xk8tK1AY6pxZeeFCFwNFat/XfMh38gPXtMzyVNte5vU1nPR0EIDSKf++j0wmDWiXzle9wrLAzb4V3jNfTsgiJyWAN64JwGXT0ywPoefTh0bEKYmIpzD5RIf8W270zhvpt5dFCUnaxQ1qNIUElapUy66qG2XSN6rB8K9l3MB1OOJ+B/RFcf/oVs695R9q3R/23mfGPUsjPkEy7lozV0mMCw1W6tkd0tPy0n0LbkMPXJloQunFo/ArqPJfqjtU37jTGuFs9IVKDDdhSihFtTjycKq1GG3rgk2WpsyNLn7oSqBSn+oYnurGNzM5XOUjrz87j17osTJNnqibnGnqOqSNlMYT8tlMaD8dxlO2GV2JVDD9JMsc2JKcIVquxK5mgaZtu3eLjU7nsBikZCCLN3c5cP3NVJT2m7oRNIHvQfi61dqhYnRjpdHOyzm/ZC5hLUnwvPdJYNKlAnmytvr6GxjatbCO3CYczJvNy/4pyVbUHsSOPwJXkcepQtRym15i9OpUPHIeygQzJv+rxnGFwuyfIzq1qNEVS25evUFq+vwd8Lfz8AnogNWgOGyslGJl4YXoTNDZrcEjZwX4hCRtIu8mIg8k+5/kLu41I+8MO7e2WTsgdtyW6ynOhcosiZZCb/26lhGD0qHcLeozw2oWWxtFkGvd1UgUsXk6JikURye54N/Czb1RPPB4Ve2LJJ9x8eqFXU+ag+me1nLJuZ0/GCY5pFV8LNwDVmwq87ZDItW8qsPXty/Zd2XbppHu6i1+rYaIB9GTRAkbP2cRSZfehsOLj968bym7brgf6D1y5PtOuvTRlrWF9mwqTBWkDOKEjEdlUF6B7XbopHzY9M0O3iXBZ2OWy+EPTntxOtEdKiAFM8Du3DqR0jl1/q0qLRDHB7zpy/JsFfm0wDlAu198eBEMd3LUPGXoLvnXU+KBJ8vIIXj50yTugtNGsMdVKr7IeBkQqzBCiHPIoH9FH4fRklEqGXZDNi5x7oNqpFQilvE0jc+5mCImxqbDPRIRLEl1mkH3HU4xJ5T3d8IIC2UEpZU3IXeQGNh/CU8iF1EntQWlWpxPrh5+/vXyCDc22X3nmxtekSOKT+8zjF5c09FZ7S74dq5SydisM2zI5AqC5PMpKiqNv+tTBiAkaWcHO01ISzC8XSOcKZ7j34ulVpEhQP4BlB4cL8KhBOPIrB4BzVSMajR7B6nqFpFhufY/GPWY5Q4/4PEfAtV+aXajLFVhcRLVe7WCx0vIBLySPURYy7xLPS5HoDujSMiKoU5EDq0I+RtxEbJKx7q6pj/Hbm9mw3S+zKnsftX4glm+1B3xLxLOhMnDAkRFK4ZZLZoSzfFwpuiq3Zk4eCDr8ImpG2KXn8hBfHA4Jh+NUYxRX90QPZzA83t0tYYBb8ImQ7Tvm1eQt77xlMMX++O0KLFu3dEOuCp51dVjAzXGvXFXts+QBmqe+3bhsp5EFl+upzUfpZSDv5yMC9odjqAKiNwpb49QSvFk9zqk+dTvZI/rcfuEZr3srafGoCTCeJZGDvg58P5v6aGrq7Osvwk6CPE3HnpfTQfT2EkWV+uAoFSnxocSEvVCBpvLuDrg9O/fHN6A5bHwNdtw8AcOzfSWJSvVswxorgjsipdh0tBLyntaAFWpR2yaTVizn3Z8bL7skwstgL4b2OBRV2DNyMfvygugMCg9mZV9GMyhpzDX3zqU6d3cwtkyYCyfluwPNL8ulH3FVTZOwOm9O1i20owkiP0LMHvOWoK8nwqgjsrl6sCWxic0ueeE8YCbVwhAbym01DZljOiJsUIt9easnRGHWis6GfJxbAF/Ob2wd0jiKnBq9KM6YUsPWWZZPRWFPo4qXI+9GA6SAU8CepmkaXB6XDZjFJYQIWCnaFibgdqHvw/vcgG+wJytfEw0Y7PZdLEU61xJHnlqnMqr2lSGTqGreLQf2SyWViB6EGCTGZXg3FiHNrLzN57/gxTbu/wL7CFOxYF3Rp0C498VBtLy4s1eJiMqiHSmkomNt+epH7EgJ7peo2s7V5Wy8zBEcxKZlHT7+3RwXG2RJyCNXtwGaZOSP+M+OjXNba3iHNVVwsj+313O2NfB7L7/Qe5/8xq7jC0IsFlOeTgUHQdWbCK4ksL6kTAiBm4x+XUK5zew1RBAfVzXccGTF30PdNsP6EN6+Aimzl4vYSxevSURYmwS9LvKkj7UXA6cEtmoVI29faO69M2X4xmgVTy9jW3+2gztoz3kAKWgGsxsrn6CwhkoxMbCMrnHpvFYoGjnvRDHwRv0yptScmquSW/Pt8ej/igseNm4+BN8EBc3dsLca0LgKrd8TSSBYBzVGtUK8pkHuO30xLvudl19Qu3bS5E6ROEXtIat14PMH/gRO/sbAhtRLG/qxc0Qqub7z9pnR5kZqcwOjRXMfZrgl8GoephIldLUQnwayw7A4xINV7jkwx9o7Wq760nl3WWawNxFqZW6Lkzzn/L3bbYSy4nnf6AqrrcEmXw/kq8/NnH7dsyRLV4bR6NTHMB0q/Xc8kfWA1X4JiAfnaFKaByU+TiRiX0SfXfyTsrPUMAWzTxCCXc8dQGkReDJo4nBN3Mduy+1Nhyo91J86SY3MGrknAquTg27i7resMUWFoqfyuRsfI/tgYGzWtp3wrBNQ/D4FivCeOrXrXZuGzaXoOmyBQYxXYyrR9jWteXPuAz09PFf0Q2WhjoR0bvbgrwetOmiUKjfark8e+zc9wJPl195GiiTThYbcbTPZPwWZhiiCzQPSkXxWJ9OvevvaUgOBLJOpvGx5Hc6J9Uy/6a5BYlm45AeVNjluipRRKBCDIWT3DI5fh2Y5u59eNTB5u3hRJgPiIcTlfUv7xX5qjJjWGWcIK6urLgk5nh9SkiRkq2+i8CwRRBJCqlfGKCgeZ3K4U3z4vBbp4rhwYmnBwe1qiKHHxhc+zbPfF9CrOFSc903JarWQm+uX6EP0qLQ7XdpTa5blldm1lSfGQPJwV5IFiXhE82itE57Tr9SOtjI/lYtW7TIs3rNi2vuvHT2bkrIS7F9xDx/j1mwDHeDnjAPnDOTAXgAC3+FWb+6844PcMhTOsti/RiL0Slniom/VsiF7tWy8QqR/Db2H4T/2iCxbADpSMVGMmrmWjkb2U3rZflVXppCs2BVzF143oGcD7a3wvKq4+L9qQWh7+9lA1TLEtbq4qt/3khp7b2YSgXnMJjJkz0GEvNGZrO/yVEsXRCHZZH7w2Sc0h0TBZUPH3+rqY5QPjSMxsnX62bJ2mcyDdE3N/4VAlNuvvpgKtiVfUxC/8sqlEn1AYqIPtA1nyXkjGjK3JNmaqpFn4zLVoMBVAhTycwAe4g8jkl6jrctue8ffjxQK0SB4J2h0b5x8pYdk7xDQpCUUNiAOM8op/NYcNN495K2VPuJclkXPndmJPlC3LIdbIa4fsQDMCMzz5KG60maJt5rI+SJI1JeHNHv9NI0rLHV/QwcbfiTn0A5JeXiloAxhcN84Kk27+PN1Xj60fGjS90x6itHx/MYv1nrrwhCG/lug3FnBi4qvIPSxhnQhM0tfW2ClZgnQ9vvG12ODw/mgme6IfChmcoqwS7FDNUoIEvQeZqbDO5WYYFZuDsvLLT1wHx007Q8PX/ygvn7ufGpIOk2671CCjvzFkZ9gkUb/nAM1hLdoEmmti6JkAvrMUUUjxqn3yRBmrZ1CUCm6NaYognm3Pc3zRXmQw6B+gyHb282MYc6bDJX1lGppr1RrrCVMgtw1PUuLcRQLryqdbJYW1KJsXEd+2ZzBcVdekvIJFS+BFkDTkeVChCsMK/p2SDb+kXXaufaRhyq3LeiO1zoNIlv7HtiSX2WTjf8mXoWmCIUjVuS8ckYh4encjCawEbIbPAVZhZQnktfxbXdrz7enXyAlp1Eej7ylxGptoZg+eGpVlFdP1v1E6bp6p3UJpHFXX9eizBfzI5pXVcZ7IlqjJRyGrsGJq60O2przwI8n3cO2HF1I6c4IuS9qoEK2duuYUbMNJ7NeAH4hd+/jk4OrJwLvRzRpBs1k+Uh2vcZNzQgmj1Cf8B2XLVjU1hSW+Uuv3RxbPozeQHCKdSFQC0EhqgavzKigXBmPrTy4ry0TldcbcsJgb8GWqOEtXQeWYTQXyZmBXHnpgCmj9Wmw89czzlQ6b3vf2H0qK6V5hjetusuQKAmdxccYF4fnjaXLE8zvVfIGgmNJq8lW3xmMdr44JUIvbK7GYfofECuPigigelRKFu41tSoqies0z/FuXu/O8dbQ5mFyQR+zUn5FO5oLe1zx9eqMQ972jFL1CxXtS5vZ7VRxdPvY61R1pQ72ocDh0d+AHcNn9NBEwxcbCAiRHM6t9F+QPJU44/ckgMZW4R+WRS1tUhb9dg+i2unUBH+Ymd/GAfsaXXpupYmVrpFjFt529IHI5Bc50zCa44K+XR0U4evogKXW1+kYlM0L6Lbx1OvlGQserZmZixH+mbkq9cLeL5fWg7A7YaGJT7Az9OaU11Wr6ZX04djj6XU7anr3blmgoQngYFSrGe1UVPhFxOEUcOD3E2/Ouvvl7gy33O4PHkXHvqXAbXoFXIvy9ZbUgdZgjJWtNyc6QSQMc02ZTin2oiwNKpS2MTZYTMcKjMzYcf8opEW34CFhStuWxKMUR/ROKVYvBx4hHuvUsUHFcDlsFBGqyRRD+l/+CE4O+PwBdTILobMqdzvLZBxMCxCd9rp6o6GvET4S1GHUDvOZoQ8notO3xPsyi6OtnRwJtV9rWM5NEutVwRx0PWy6JoA8bP2qIfdY0V0j2xJD6H6UeQK80ak+EgSt3B+mVs3Ntky1CcMeIsicOsHoYU3plFMoV8eVSdm/VVKeuRFKspU5SN0IGMIAOcPbLx8m191irJ5NXAvrg076Y5dg/daQSFmXuLedDfoTIiKbtOHNc5vKSxW+NcEz/bz3qIHDleREo0iYrC4wiLoCEs5CgO9cRy3D7jCGwjodkUC4jdXVLEAsaTJs8eYWb3+7lQXp8XBTgF9y+fVtNPQW+Z22F10ZuYOppXrNGRbKEsyFP+glRhwJwn+RPbwaJ+7WJt3+GeQYH/WdYhkW9GB6yIy1tkSr0VqHb/Bt/HtUVaZ7kIRJ7j4I7ylKxsxanq/bgAsvqeMxxynuiBorourOP49ksl+R16aQph9vi+c3mq5w1trrpusrAiMEGcuFhGswTK6cdlA9/HU8axE+UGbB5ekxgjMLueXs3NOwEv75AK6wtQs8Vp64fqLAE8h2idCj5tidPV07aZZAIwR/yEIsQ6P2s2ZOaM0Rg/hNaltVoMh2Ic4MEsfCSvtWwN2HlH1t1zlkd8drECAkiq5D+akNDSYtAnOYkoraJsnIrZ0AbbIxWpBaFgriO6sUiV1M8tH85DHuTMwRzYHlDyMenNS8IloE++gg5BHD7Yqibwm4yIg6lIpIdMMFeAAqgj1ZONoR9Z9gmctvbvlINUOCIkveL2D1y3pbHDqfKkfaJZ9mybHDoSPYFbyu5Hd/mLl4DFxJQviWCtEno43LC5FguKMCUmF+/9xzzjk3siKATwvT07qSMMwDnzuWRdGqN2cULqhI6iFhT1GkWpjP/BFAJ5sVwDfH0aXl+1wG78n/xWWRgsf2B2bIkoYGbBxmtrBJ5+mTSIFYpXmZAlN/FVWtF04f6qnA//XL1DMxKepQbzzQtS0j8vJnTkTqSyBpWoiTPTzyQfAUT/5pYW87tKx1doFVtJCjGcLaTtve6MDtHDx70hKyndEcHQqTXS8/aQzmF5wzWfkpzBqyWjRSCDfizugFBOHymdlkHzIW3lmeChk26L6PFLmzBoVlRVImpisjR9QVXtRiLyec4HHjg7xbeZyo2769qT+FuNbhkFqMHNryW0NcXfsNsHiZrYJDsrRuidpTlR0juXoecyWaHsG/QKq3hrt72ErvESEo9pMiry9P+8cFewfRdcXle6LfIq0zwt+e/Hahd+6AnRAoUzzQ5+BfnTKoumMxKUqxivlM0cFyd/C1OJJOxchfZ47ZolNKjPZBd3/vk2yeGoM32bStTVkwdwVD6qqawE8C12x1V+g8TtBhNyYjSGyvD2niO4d8xQoa0eP5F0u0rP5M2gSjYWhYFpFxkghBjiS4yMPOGi5TBdp9K3nVjqMVTHQbaYpfpt18wuMkQBBX7nNjsHVZl/f4Z163Z+K84VYV0K6hGc/Doz3VnampvDktGAGDaFbsZTFKTeT3UFcqAeyjnzviriImJi5ev14ysdFzqiNWRlDhEA6ziiE+Hy8x25MCPcMJ+508Sw3Vo6tbIdXdYJnu7pwY66NjHAjR3iabLk0rDmVaa/D6oX07e6rh9AJjVopdjt4vanWwCEELPT3qpTRy2mDmg5Wq6YglL0SlXP6QYc5bCC/KN1GC553F2nlVN//9YpEAbir/SuTYSOm85pfU7UPOCa/wh7munWl8upYaGVkNvwZwUIGtxDlMp/fxgv9nREspLkTdoUPFYpdqbf3wnpvXpQawE1rcsIBtwlN1hhsdMoPiPe6Dzm6kmZ7G8rXwxPgMyy51jxzBTFZcRM2q6GF5uyK1MhHyWfffLvk4M5+9KFFx0Hx99NTLzlgbgl7Vzlt4Q8/XunssPGM0cd+/JnfmaVf7zQ4JpNgOT8HEVTutyEwnG/7OCpYe5yf+asottWoEHW32SWubAsjtkXfxoXtQgc5qA8NarIWLLGInbmWLbSP0elMQwXxlUnd18vFnQAtx1Pq9KyTdYF7Ch2JV3Ksgnmzqcpjncxj9Lfu4+qULJlsOpsJzGTrn0QWwsJcyzKoPz1SiRaDLtsbaLGxpH/5H+8+0N11ang2kCnxN09iBv1XVRP2wOaMJHshLcl2+CqtjFEm4ZUhf1/VSMmm2WZPpb9Bxzy62za+g2OdM+S/yYSHApyU02AQ7JAn32ixaV8BbJobfrB9BEFaWGN3zOXO7wmY/aXxpx8hx6D6MsDImylLQ+zEXn/olNRM7zgBDVupbHk6hrnnlzY5QUF5cSEymKYrKkYzf54YLiQkiDSsRmAw3D3CzvrxzZq7MoT3TX8qRD5srUoKXk+atn7w+LmFgY5/9iiO3H9Dfmv8+4pPXNBxyev54JHle3xwY32Zd4/rVXtiWwx+6qPOwqkKKq1wKhgyVaVYKCAiizDry+dU9KFyOO5JJ5Vw+tKKLbxj83Tq2i0WoK5NJJu5k4eWlyToHTtvk1R9deU6le8tx1luZQOXbUozVb1u+NnEHl0lUZs7GpJMaBk2TeVhDqzyJoFQzo5j63xFq6HJo3kwceZt35C3xUaxON9S13Bxp4smM8GrEbV5gSXb4w76RZJ/q1foBNbuzzdSRxbB7j/RWAFlJOJ6IQgqpTm/+z7hyctZFeHNGNcKMWwkXlKiDfG9xMf1wYpOuRBNPFm+oK+FbuN7S+iF6jDCFM3zSM0gCWzxWdqKNUdEOIkbYdc/beNsZ5Lmqr4DRyaVl4U81MsAw2zAzdysyQWVYEwRlhnDWU3Oe5u5mzK/BXZNtCA24ZjZFSSGfqAf5XsjYYrSu8ZDfNhYfcFktnd805OB9f5+Y9pdzZR+im/l40XlbWia4idTpQFdfuUy30hXRtq+Dnfnh+r2RLK3lFclblcOncoV52cfVnROg8TqZyNT2EEp17Qz/IvwRYkRFPMrxfIqeSgoRYHwzu70jkwCGCvcBfdTlKFIlYsNyIzIssYjbc5esrvmASir1irFBQsfoS3KqtJZ/RfJ5x+fX+mb5roLbDoLkaHucT/lcqZIWqifbEitob+SIeLdPtsoKZMXfbiTUv/ZI6PsgTbch1Mjn9OtoybfzA92oO1B4RlikpT5BiNDz1QZsvj1JtT49AHJZI/jR+Bm9os7LMzM49ggQNwqKoeV9O/+PP11+I7N7OHiD/2hraQLUL2mjq1UpSdTDIJuyu5GVDTuuVgU2iHFckq4JZHyBpFID55IyBiEGTSYKnX8SsYzZUnGKCyNOy2iSUjBf1n5GVer3xz2q2US0oHyiBzNKQDfOnp43NDQqL0tipne6SyLU/EW3DmfNGu3TSCMEZ3tikf1zRoST8RfRqEs3Mr2fkGV7C8SJrlaAVeCALqaVefmPXMlnzOMjqwllPcBDg/7ejoTbdtyzM3MtPNHVfM+NFJaw/W9fQq+GRjqbKEgWSMIwk4kqFOPLSKqGBinbnjlhZMvyzofLdNXzLY8Msp9M9B7SgVn7+5XcC6b09gnS0IbpX8HqUnPAzw1rf994UifUqwQad2+KgQFR71w+VEnavhJC+Zjtv6z+2CUuEAyttu0Ia5SNv4MP/1Eny/7bFAtT6SROTr44YIcRiMrzZKhXmKM7vxSVCaGbLAhhAcnEIw5uQG3HPbfOuuLCa6xNWL8R+EWp42tJGr0TdPLjNsLrAEmF0FGlvNNtbFEze1oVRLSFn6c3yedy+/yTgE48/hnklFHb6M5DOO7BBbCdXZyOROeMPRoN14CC60BCBlfrHOBDvx36hgZESPntILUKfMn85yP9wAMck1MwalCtnh8acRQe9RSPicdFWP9uYJXAnDRFApbbyqNRp4SSScxiUs6rErTyoxjjru9nPaOvKPaw4ePCmvIoeQg1g8pM+pcM12QneTcklTrgswlxWTGXTMhQrrud3ghmlmEK2uKc17xeviN8Y7vjmwqBTpgGdzVQwtt2b2r6psNOx2IRWb9B2jBpP2ggBjkXLcA1GcIIvRjMoI5e8J4nTFsurar6j4cm00dVdc3LkdWOFaRNBiuJ4JJj+OckGgqvR89dhQghYBo+QGcRxjRgeCdnICJGi8GECbWFu3rUvP5YZPD/QIaCc3+YJr9fDIsD0jvPWpu9Ehuii1gPcav/vb+9kZxJ1KZJsr56dTxOG/S4Nqp/D83NN8txyjlH/1Xflh2x+ONqldFOtM/sIuMYBkJQmv2yMXUn/bNtQ8hzT6Kt8LZULvlIcJvQR4yJKhWZbta0RAE3BkB5b2ViU7waBBSbIjM4Svuo8BJ0dbUhJeD3kHUrIcPqghhJ768LzI7Cvib9N0/TEbI3FMSnCDKfBzCrNi1fwQ84KtumghhTosNp4odyQQtJePOMshzeQ6rg1qgIma1FUOhzzY+cvmi9URVTLWN5PxYjBBfcILBQfoq3PK4Xy3TJFSetmXweKpFBuo6N00R73jX8qJOftRdbXuwkM6fBUFvDKX6Kht+HQ1510O5ELF0JZiWQ/cIIjtoovBVZX3wyJov7udGz8jZLfTOGkKM6pIyMbrVYDY0MrFO70dNyVljzA555ohhKugC+ojJyjSY5/TIJBZUMu3nZl6trZtkct8ce/ZaVMNMV/fwUTA8hnW0siK2H0Z9AUqgyqQi1+r03NWsyGAPDSW9x8EYjn5W9j6mWg9Jw14vseTLel9OSr6a5mJNlu8KTZ5kxImP5kb+eeMKiCBGqfF3paL5sdaGLKMU/JKByRhEoKIf72OcrAtsYywrLb706toounvXFQ3eyrmL07c/jN2RivpRZStZlHKU/ZMtWJvUOUBm2tikHX+x4tS/o3rebmpucJJAhbMpiZQ2igtYWhWS3K+qUqNicjO1p9gCRYEF94KESH2xuh0TVZvX23B81EJiU43oGSIYTyShxcS/P0mX0lLTL38T2lkFbnaJyU+2ZHMjgYpXogQqvQw6tOM42c0QcXlYiiIL8neKC0struVMn/vPXBryjudItUJI5diPaGFtxqj1GKs4+a8JTsG33nJ44WGuDrl+uA4zy4yucA/kJc5CnzMhLttr29zGU/BMKql2z+Z0MP7MvcJCnUSccg4tHITVSSg1zicBx7SXTdwky2/AHHF/pkkzXp+iu4afBc7yML8gHNgLlJFgeKT7aTBj+trVMGru94pCV5igLLIOkTKLT4qZtGmfY7gGEbTvmN1zM+BIj9vxw/2AS9EEhnclQKjpaeMDgfM5GHjYx40TNunWSIHiT+StDA72j25CKgk9+uXemaebCaADtyePqjPCtfoKWRH8/IrjdhYZVrRgzyrH/78xHpZuR8AL3yk4AFW7LtjdRU4iYrt9qHm03lsSCJUQPgbrxUncf37GD1fC8sRnJdTPXwnaL9Op2Nl99fYOz3GEPrk5qmOhKGheWAOUB9fYshfaT9oZ1930VX3gRuL2PHbpeEhgVky+49UWB2F6PrEuCOj6s9vck6d7Zu6nS3X4gFmBh1FqlaewVNv9R4MyOyckStXAoJkkImtBmp7QZSRhiP8ZAPIhsC1WMHXerzL1zMSd+2Quq/rhJZ8hRq3gIti9Lit9FfZXRtfLUk8F3oxGy0x0Ugq/lnjufuoM0IltBloQjQyBCXbWq8k2YI8zJEGgbmpeAQbDOW5I2LOEztndV8r2hc4S4G80Es3uhyi2dBcW/P1m8PsW5r+zQ0D8r+5J1qYW/vPiXmJJYqnhl+V5EUV95e+B4qSSBdcuiMq9K8KL91Fn5HvZenXnl3dmsXel5dh+5ss0xRaOR+o6dbEzdcX1HTPMUUaIAG9P+ZKdV9YbHx2WPAyGz7jpklggbb7Z0I95U2zMdgQ3xGbnGGOJ2NPP5E6pHzCj4E/qUBi7u+toTjaP36+LCO+v6CtXmhaI9qe6++1t2LJI4NgeuBPlm30dH1KURKVHXgowgPPSvOEhNvq+PS8FLNg36wm5AAMDdYNA8aOdE51PvS2z0FfDk3It3YF825EejgzEW4J5OrUYjHr/EdtoyaXjNgU3p9ne95K33HcLRlQ7tHpvrJKFwd9tE0fTFs0Is0C7f56emP1WIQd7x2vOb1wvsa0djNDX+ZGoQ0aJLrlj4tmo0I7n/no2im63x79Iu2xIMHD/F4CeII/zuGKetKH94+EdkG6fTulLkROPukkQbj3WtGfaohkqzbP6hyXDwgxwu6zl2BpMng13U6DSoCv34kxIqrLE8o3Ezd9tPe2o/EHRonWW+/1pzZSe4CyWZkLOlWO570yP1+JnDJy+fFtoVIS6jtHzGiULvsnni3f+6891rIUPk9aUFGXWw5Bue8NY6ioS7QA9UwOBcm6ur/UuMnm8kUyOS0uFyCCjgABEcS5Whg8T+Zs0DZADSD2rLhuZBOh2w6uexuEvQnqxNYjb83MWJY1kgRldkecOMpT7in7I9/z3zLs1yWm1rGhHPAGyedZWE8uXZvCXm9Vys0KLZIEfaPGSwEY1b5FHRlUJBI7K1ep/GorU7MqRyiV6Z0TZsqzSz2eRUkiK6yxw8KpfDECRFKbgZZuY3RnjI05PJ82Ry1rMsTf/ZqqcWQ121Q2AOYSXG1ULfYrBi4bp55KfLafQ0aX8JS2kQNnkp/W0ym3hPLq7LUTeguZj6GAytcTOLWcipzt+ur4VvkUVexKJHA9KJ4f4ih0Axk5DkTnzcn9eiAiYLLK2wrf6CaSHLf/gk/EivH0+RXoo4wq2b/9VyNTxNbxnX4eRQtDBpXA44aBvL68fvu0oews8R3j3qotYVVM3zna+zbYjtRJ3gypdX11m2Rbq55sSqLy2KcPL34HGuStf9DOXPo00d3J8/AytY8zyLXtSNdzb7ItsevoiRj7qtC7o2xF2LglG5WbqChVkb72Qb8ZjMCg7GsnzRcYx3f7xTOJ1Wa538doEXRQh//eDSEnI5uoNdCm7Bz7sBOzwGpmbApWyypuHjcLm8uZPfdzlwZMfUdKHCwraIxTWQvct3OFJtahl265m1cG1nqdi3p4x/XMjt7ryV5GNS++KRQrhwB2E7APxNanBAdkHy4bHLdwsm742SWNu3rj7wicY0CMnadslPv45Iso5mfnjuQEz9zEGzkBymt4QfSnP/iZlo0EVdO9Wy4iVRPq/TgGSydOh8JFDnkhTYmq5hUJLMq1yQM8RgLdxbqlNkc/DZ4oY2VL+aXiYGtR8XwhNPzL1SyvL/Bc8lMNr7698/jxfN4vhMWmQV9D3w2C/SxWRhEKmUOk75m/iH1oCmLnZYas2vaOCC7dnlKNouVDc6zDlJx28LSpu8ymdsESAA8+xjt7K5Abes6yyNF8KbqUaBi+agCPMWdZek6f2F4Tz+vnjQV8P0p4ezcCgZjHdMnEOTuV4fckkGgzyZqu7zM+Uxu4NE8upXy29nyZ6gqDmlQtxi3W6orL9tGsu8DjPCnW0LLYCylg0Orvt3EFAC6j2qf3PZAnKvCWloq3Xz8euzC13dZnFDgD4nmgk86jLzDYuXFcCRyvjQuYWwvK64Ifz0ftHZAt8AVXD6RwcPx52CsY0Hn3kUKdhfaQne+EotwFeREALiINXXpT1sGQ9cjhtR6hp7t7tufxXz5O55Zze3yoUChLhn+m51t0M2MDMbJDDbWimcaDahe/cFPRUVIuXUJW8xNLfzpXjt6LRtBRz90M6/SwwFflyw/t0GLzr6/iCEA9yRRhMl9hLPFCHzCZzNeZwgSTQr7j7bSuqkVqzPPykhFi77EhcM1xeBCLyz4RwwCFt3tBHjemh2sFHDw/o5+/pWBJmRk4aMDpD8LvJW6fooaA5dNItsDBelYMhUjd+C9/949wfIdGK2dQ02Rsof/AMm7a6QaBVSF2Bpi3dj4Yhrf5en5Q55uUKZoOcOZV1W8IJMJpVo7LyCun/OQTbrIqYLpdbLJhBCQDNn8oBvYb/SmKsk8sCEZG7uiPkgnsb8jgUPrnWj3PCLbY5sHxIr2VlLOfOZ3VwTEnbLFR35dnXjLW80ZFF+0gEY3R0ibIkOVmq7egmiYuUW0Qjd7dbX3zcAugoD1qBu2YkBBNoqV16kXhbaLh/fb9ys/o1zAhYSn9qZcZPneSk6HJEaqJ7lscAt4yORf1ogYEbNq9nVTqQT3khDodAQfm4twf7V7ej8BcSnFOv+qAnT48qEkedVO8gEwcgFmSjTJVHmM+Ru2Fz1T3ChYA/j8vkD+ZlKuEcCMtiOIPzAzvaBBRNxDruv+9/01AzCnCyVRpWZR/4K1+eqbSmf7hVmrtGEiULxIwrcYeCkZmGketD6snW2mGijFb/79+wDavTqlTM7VETn+PAR7YbYlsq1w5GTB8Za+V0Ruk3rUAsy7T7WP0NZSjamK37PAYo46k/KpJMtnp9kAHNnR0Ewp1PTfbbRPcNBkwp6fFNmgIF/o3A8vu0Ug1jNwUKasCFCOmLqp+Gusa+kPjETgsgL8nODJciasapuJZGZyI33zJp0NeqF7Q4w12qgXS5dRqvL/+TMyLLLuHGG52fRQQrOAd7lhhXAaxoke2YLSrPOBMWU13woLZy+Ac/zgSBBn0TMj7fqy7R0z8NJkGkvAlwnpzPQUOCM1/Pa1Gemdp6NrQaGoSb4d3LJLLgPoheon00so93HMtMS5IMBGk8wWrF5a/WWsnIpZpVLmm+mVOVuZruILmdjL3jk6mWfXNnsMejQ2S/ZazZ8NbNQVBRZwO4UugqQm45RIDaphjh9jpZonpEhSaNCl09Vk0tT+SVNnQYHbqeyELGIa/VkvdiI85TbTvTR+HJ7fwIbzj+2LPPYVf8l9eCjQ3uKb1it24fdDYg69gRVdThQpjdWWmYWH+jpuc3BtPJ72tKgTq/JDZ3udsW+986XteaLlRbEyZ9dg+s1R11C8l0kCizQHNLOktFmU8kwVMj/o/vqIc0S2myRb8ZF39W4Dm6OXjdSY7o75Czi8fE955cM3rV71L73deA1+fR1pemvvqqtyw63QfYuU7QoIvrILDdKNUG113moh6FbhgSjIkvEQ7exM54B8E4/BQ8JB+O2G53SUvyLFbVteu5fzH0KPzsS2J+MZVhiLWeAUMJdQxj5YgAkMIy2Im7xueYROhwxAQpLvRBMflrAUBPPrQotmVF6NbxFGJ3jzqrPT5NI6k6VrEQnVy7uLqUi+buaC6GDrn61GFvJy0gdivZnwhMLacaY4UVLDcTbL5c+JzbJ2sTCj9cTaD3sHaEGIn16NrWqT3NT/VdULWHMDclRDJUYPi1d7wY0VI6HLczDkeUj7AtI+xKpjUFHpK+Pghv7oPr8gLeBhtfzkc2RtiIuiMC8/2YgiBSWQSTNGqZCyr0zS1/OhEH1bi/MPurnfl8L3qEwu7LVj6xISSJ/Ixvv7Ta9zSP2pJV53fSI5YUMjdTrdooQf9EdbARlElybgz9B2kDRdlO4BzBJWAMGH+GSWR5UA/ZTqokr2aetFLg8+xRSjSse1EKSz9XOAAmZwvb9vLeF0+OBeE3MKBdExFNJEdPSi9ULnDYQuDj9Gm8lSdBg6i6z7TgFBpnEeWbXDAm+Fr+FkmUKKGYo/nDgdBT0EcCS8d229tarKf0wMDJzdIryr9l+B6nCqW9O+HVJg/5iltHIbhU9d0ubadWz5k1KnpSR0NZB9XjXHgoWvJdNjwjOuqFXgEMva/XUhZb6YMhsYBhKLLOuzXaXcVIrx9J/5rx9hyq8TL+n5bdhH4DBkQ5ZaXF6n/mtMZk/M6x9YQZqmpbeVOm1eTlnO7O40XDwmt7mkpEyTAogvb9FggGdTwITA7N86C32dR4GVVxBpcaBttnZ4H1pNDPhTYSJvbw4uuAaafDyYGyz+YYWgNPeM/ujfQ6ZlTptS12DMabdVXVAkGcpX/Y0WTnzeMpjUvMpo8007G8l6+3k5xct+HYny6ldAQa54QamN2wIDpa06EiVYnt4hvV3c8ICY3hLZuTdOzW/mYk3iQY797AdXxhrLVgb8zG3yCwXwoBhBO9sI/4RR0frPPaBi9uRWH1sIxWLDWobKBe7FyiZfX/K+GRr2a5014+6DETbR2fZeqO+tTjOdk4zvG0Ht6ljYsp3Hj253FORJGzG5fheBF/nUNpKm0A/0pPMQ23wVgw9qY1fOVq/Ieg+JZRL9rF1RrrKscJfuQfFD9vEuRmvnc/nyCIwH51Meks8GsgvxGJ21Odq2eTF3MyHTKGRKLQ36FBVwHdVpvzNX39vvOo+iaNr1dKCbr+NgIUBVpWksgbZT5++5G9rYzjdbZE88G58KqOj+Z6emEiZTu5lLjzjjkjK1n2DotnnjlTSZnrel6YUYIz24GIC76XmdhLADILuAxqPI0uefl8I8u7Eyx/nY6fF+3KAoZHlqRGQdk8aWXLj8uFladLkK6E1H6N/9pMnHuV2RUqGoMIWsiTg3oHWas5CwoRszXR/8wEVCsUNzKOxZ444IWNdimr52tXUpUecd3QwROwGAMrmaRPTqJIRd/zIvYxPC2qzy0LWMZCR+xlEJ5waJlmwaRSGgzdeAq6EQgv8xT/ogTSs2SzcKiBlDWUT6w36V3+krPAhDOgpLtjotCwuihsjs+8vuA4pQFpjKpUUmSNuBvfT4hdBEPNZnY5suRm5akRlvHYshZ2vG3eqxm5ujVkPFyiU8TukGs/tOPhVaucnQ5Eu/iobRCJJMtJeDrfp0IHASGxX5S3UEvtm9AYQwimfbGAApEcjGMLIipVaYFTckBNYTgZszJOlixJpW8bSmR/YzMBaXyL+/bZOQrc6m4Vew7ME5ae3cc2vVYFSeaO4MG2SP9l7KMJpU9rtegBhpUDyLLgbw4FuYmfq8xL7B2f8+xus5YubdyJaE0+FL27DsaSc+y/h9DjoW44SJ/vQLC5QHdcvDrScv2nx/RfJ9PvysnsqyOFXDGlNHsGRkhyao+jURBXoPNa3keKMtUs3ZVlAoHj8LtJiLDpt7h+aVN5R1Awin8msrl7TmRt3Mh+SOkW7yVWjxINlQiVdQbz3FIEguXVbPc3pzO/zg1fI6VxFb7ZAgAw9pbGFSUFAigP/pma3N29T0FJJYrShnlqBAvg+04iEbf+3vcuWpOBwbpEWita4y+gUCgUe3NSb8RmGwX9BKbd7XCETr2XeKVKiVEUcNSr1gqDDLA1juk5Auo5cCm1R7W680RtQaMrLhwR7qXy7IwEP4zWpVcb1Dv4CqUGYp5JcgykIk8CzWeoN/a8J5CN2HnCs9Aq/YGAj4voHUZpVJveHtY5bfXR0MY3NjSe8HqZaAUmZ05aKlki0T9XIHVauBYBM7HI5+X8/5wk6P5BmyFQ/uiW5WLyHhwtQ1cCOQsJ8mMssomzryDNpLUoT4lknNLV5NFAjcTvt4s+li8MVWjlRC4MONCNP+MBvU+G4XG0zoqwAgcHuzJ1Jxfi6kY2wUyb+ZuzMzCLtSBYoqrASUmfZKc6L56X5KdEfmqkQv0x7eRoeIPKkBLL0VHHWOxY6IrRJ2CAZCrBn0Frn24LF5pL6IXBaLMKaauXnj7SyrkZL7PWGXN0xgrfSGW8mjqqSZsrRX0H3qgrtwRWvV6YyDI80qAWwTgJRMlqgRDi8FNR801SpgKU5phepJf9vwQOrkDrtkKH3STe5iU+BG8bX0Ud5etAMYmIXpFpLgUwBSW14z4iBrED9ix3jb9HQ1ahZD6ZvEdJhIkuu7TyrjZ0EKd1xY8tSHFM/LgCzm7bRJwvc4K5z74qfgZHkqXGFC3v5CNV+C+mQUlBWvi9+wxUfULudXA1qz7r6E0rvFWyhu+z/u8WBUa8FrjNSAHEe2UXhi0u7NWkJajnmIQrCXgdmnFuwPv0VVPuBAo6j/n7EhLhN1N3YvyxBYuKXRaaedxdnS54ikpE8GyY1hAgIBc5MqbpqXazTnBe8EvAzodXSDr+FMOcSnQqQrT6h5vAlXuNXX5/U6mRk/c0xlJbBWFnNx8XFNipbJTfrlr4m4DQuQWXTAghEjJMTOrmK8hZ1l6Tp/YXhvI/NWM7dW23mMDJoW2VKu19YgjaYY7jsEbPqvudrjxbOnkeIIe2p/eGANtOdX0n+QzPKCL0+4StbDUj7dc1yutEX786K3xam7nkjOA5olrl0fE6eFOxyAQ1JErE9AJxiXNBnHJOqOpGzmTe+b3V0lEAUSdaQEdE8C9lIg0MLTsZ/puymW710JdxT/GmbvcTy0tsEoVqZnVPgWc+WVKbtGIaDQ6za0lRW57/NMq9DJUeQa2h1WRTcHji4VHKMj5UiOw3ApnRBTegmerEj3YWJR2+z5MygrByfxH7FABRNOV84pfNBN1WPu07TZLfQE5037qRfcDvQnx1uS4+SXlQ4aRgPE3T4sjOj2FV1LS5mbkRTtcFIjyqk6YSmE/rPONQv6W8PrFm0EcpyZjoESoU+uU1VPVN5yJZCKVFlIMchUgWApXRcjl8epuVmcxYuDgVlm6NIlL9Rk2zDw/yyl//HpWZ+QaieoNjXeG6VlU7x2WvbbFb9gIbQW01ciNiL7D7RMFNUvTCX/TrANFjZvVUt6biwJu3omaM6hGvzYMa0x9ZoYUCvsqr0ZuCx9l/JPThqFI/YA8X8UOd3HV8il6mcgG3X98EC7WM8QhqE2uqeFHJH0+ur4rtY63REFLylZL9vF5vMg2GwZga8XrUo4z0kOt1Q5UYdo/O8iFaV2oYl5paLcOohjW0jQ9prunREh/25SuzFCUbjW3miYUu5qd0K8YosKDUGpiUkwxE8753mFWql588XTrPXSGBpeyVccoRvFXDEkWXIxSpG2EAUuIh/V26dXJ9hXsgwowqy26QQdZPtu+/zsUC3z1G9UbCZBF8cM8CETMLWIFI/4gMooMI4MFRNSP0/nEWGZezoEcpmW3Fc3LrvwAn6Ld3600Z93MU1fmrfmtjDXmpDtoI8Tj0uAad4E3iYqYpwbiowqNBPc8b6ha0wk/LLIVDOhqpi86RVwgsWhQmuBIXSB0AoRJqO1bf6m1vWcOVUJVOxKUoXTipFmaSp6kXxtcymJQDvwv2UbVwfNnRT9VVZ9AkOXzkoJcaM1xQdlB/X4LZeMqjupmx7ZS+Aptf+BJTW80tgrclkwtGt3+4RyaZupdbrx8zMiKje6sC+NbTXlypxNfBoAHt+h72dQF8uKb9mBnRFenD9Ff/apBEQ4k54oY4oUQfTroz/d8DIhFCYsy8gmtvIGj8lXRmaNU9NKNxc1Ry9N5oDOyYMCMXitxrkG30VE+wQWln22aksSYTqPSdum4USx/SO+GguBN3+/HgZdI8ILBnm5tdKLQxICV/VwPEbDqZgHYPPohg3d6AOvP23fRfQ2aYRK/DRSE6nQdhFh/1mbSJZmlE6Wl+PObuo0IrrZGWh+xJYm5GmVzUK4Vq3sSzQGbSvdYJZDHSY5bhkHYwSWV41ZxlprTa0CAuJVpgAwe5XA7RrKAhmoObpEjRIN4aKJXFz7Q0w77WdyFAAhP11yzbyPAvqXFWtJ4yo1VL0tcg0U+HA9tUlaK3EFhpZxCL/MCBN2onZLDkZdVxItPcRNyrjt7sQkOnubk0PHvtBLBTrsIVIwJuFlPon4U3fiHJKud7r6NBqVVtkLWhssPDdPKQZvC03fVa5diOeSFlCpVzkh+CmBZT1Kej/Uk2h1LsGKwaQ/uYNRRfLjsanbrkPdg/UXiHOH5tsMSBY3unUi/DFmJugOuhmdkDWVksMLztZXJ+LijEGrIDbhc5mMhYd3ojDZWp9kJWQWuieCQSslG78LaIaJvcjziWBTjCHZHDxbF6Ph/dM+4nPiRwdYdeXFknclghELSNOh7HDnXtyN5cLsgDvr+00NPoBe1hvzFVrA1SrVU7xJID6yXk0xFV5zBDygS8oYm3jvThRDjLHg4Xvyimqr5oiVCSoif25cb24wSQde/Ku6GZNatIp0xtudxNgkiLsjOFIil9sYGNyq0ShCHLL0W+BIqYk2NcUeMpeF4JNaptX0d8c7HcS+l5uKO7bso0+ZddHGtFvmuoHwrPTnEVybwI+oCXgAJAhO/8ttlmt0emZPiUIrUiQKo7UDSfqFTBE0j5bp/A52HaA3BbBOHKKhQ5FN6hiLoADKQQr8543l38K7UAunfwARf/TTzNMpfM2s7rJevyVmaoPRS0Cj0/OFMt06niyaesoSkIjQDjxLleVaD8n0bI2HjrBlQGG4Nt2tNZks5y1YAe/fMMtj3gYh1FAL1XDjgygbkGx6k0dJAlrMC0R/aTIGLCjgStxTZAlgUNp/nHDlVSUKRo1taHqpYyOE1xdvhns14UQNc8BQlktHvc/ggsA4Yj16Yosd+TrvOQUrZ3PEUXZd3PnXUA+Z4k6dmrDGcr4TVIquYwZJ2oQTIdw0+YsfuqXWdM3rnpxaq3Rbt87txTFoDf1jfQ6Od9PSURdu/C02iqBTRTcE2HMlVTbLkaxYeThcrdEhFDSy77NsszlvhjoQ6whNH2JeyQLv1ECyo3nLmLpjBGGRPu7SHAdYJKrvYVr7Q/WlHXXyM7hJVzLNt2sm5dNPmQZprhJNv6h+XNMCP4LhBCdhkN9CW3CV258PmIn4dA7mMQjg78xto9qYwnJvAEnFeZycQ3vQIaUQClglH2jtr9U2oL/7x+I/aRZiUHn24171BINtSgtS+jq70S6xUsL+ktQKIEd6u+b3Sy+osweQFZRQKMOetXDxdWxuqujVPky54Ax0pHV8wfzdQ8aMt1rOPdrv6qaueP9vjtlcpmRhpdCR6gpnY9L9CLvxInGolb8X7LggOGVymxLUGg4LHZkRNEDBmXT554gRNEXXvFj4nemFvEioA4WPp7gb6YP9LmLW7kXEsknT3GGMr/JP2lbUkpsB2Yf5pU7tMDu+AX3oCRc3dqfmqxYHcHwQLryJPtcD6UsC4n9eKI5hMnzEQPc822JTZ9O3j8fZ+Z4hMDLRzY4+Fv726SNITPNHD3jK4KjipuprVhdBc+1O/chLikz3QUPsIIK+hsqu3v8hvBvql6YEkiBzUd7MItbc0FxTrgBfHVUO7VDxCD3rL3jiq9NV5t9xPrUfthS///48OhNpetexz3QmXASt9o9PDXrb3mzEOTrvhiH7gH7c2IGOKBfmaRAf29HrQ+ZJEU0QrPj9haY7XKSS8wzr4gQ2hNgb4am2zDpeVxYTUwOjEz3MHti9KtxKR7V8XIqcH7X7VAJDst13OOEGu1EhEkVMuynmrw+5gwAMSZlqN8kC8nXZbP6bv/0A2HsxFxNOL6JEcUPRoIwNF87xakMznlXLUSgfbUII8AeL1bqeH6UUxFpzXQ2ph0J2y9dPo02QOXBC8C5QYwQlMurlu5AXc9Y46Vp3wcJJmPIRlMgsvzGylENFpesH/ChnDcqlzFKvLYwiOn5tyFneoyUJATNhUHKGFwvPCFlOv0LDlGG9pSacMQLPmqrWwRA2YjXZZgLWqj8mSjiul90V2ka1bqwaNrTguv0TLlTaKEgJMC7Q36XoOu0EhrzZt2RoEETIE5iA7hTfb0N1ihiLAcXX8BoXtWx9jWJfekTaibXfPzf+bS3pg0mKS+nDuHQanaBiF6ByzPmaqyykMJjCDSv+xFiXON9iscRUZDLJMbA1JumYw50gvnEFDMfepG090iY4vrRRXb4Gx1ojdw9XCSvLWzbQpdX0sJMk0TNKNpxWI5VAf1JwsEjJVi+k8/VF0nivyNXC9F6ayWmBTr5uaB/jzzwaJfiXgm3WU3l+GAJr8TbwrL3L20Z+gxO/k5z/pZp81qt3yJncG5Ry5boVhxkeCN0tbsRO4YLWKndN4cSQe96GV1HR8n/3VgVN2ELlbNv9FFHGMgBsj7p0V7D2hMV/WqVw/RkcJ5HiRUyrVJRkMpo1XOc8NqqNgYdWJx2ZrlJblH4IfuOzeMaEhrabvWkLiDoLOY6eR4nyxPY360OGgL123DgEY6jtrWy9pXWYDSab5cu4fiooSqdF5qIm52AvqViZXNQZD61kTZpUNygZZaxaclmVRFQeV/fi2NQZhGJ9+uAgqvUNkp7FjDKC9AsYXVfNcVkEvHW3OArx0db1Zq31se9Gxi+fe2G5mLXA/MUPY2RLhOGi+1UFqIPECyCvMy1kggcxxxrrW6Mei2AfNBpJgE+QkSLLn+by1SFiI4/dDYuQIxfhOPneBtdBoLF9tj+QZT+tsgLAstT4SzhSAd9rQXt+e64A/KZ7BwZz9gxz3xVTAE2F10F+RE14XuMmCuDlbH47UNb4H5c/Em9Wb3N4Z6LZ7XcgpJ4sRIKOl03po5ZnybpGIPDbntEi3xOBlXI+iUVsDgRhEWcpcoHYQnyOB4kw4EYXN6eIxp4wjkbbqxISLK/fsua5Ff9dxLjG/Up35RledTkXl8Yvu/VFFVmHme+xnW7S1dU1q//B38nWk/pN1XImwHOLxr0bv29yStHSLRNzSpdHOPF3PNqXEMcT+43IhnnDqzOdcL1dANuE/wcpZ2wLnMdGAnzyr99NOpe7hS3OfYNRdECcGyZ2S4cpQGKAj+CQPjzcy3f6AuPSmYXl1bGZo4TcKdUEdUi3X+1xbxvH5FmDimnYUHXoB5oSLjSgz85NG+RVoJn78e+3NuXP8FtY/lnFW9uMLSOiJ9n/ishb8271JcZB0fRmMHTtFDYZLazSFAdMHPEKsHutO0jMgCGnTcVMkgHl+XGj97N4bjgdJdw2LwrTzZo6YyRuCSW58HyB8qh15WTKyAOkWhJeRVNmEN9a75Mjux0ttm7fWi8tzxTulw1ZcIkBIbadeTJSd4EjRzsKn7vzg7sdkFBFgJrxvwBzHq1QehbOjsoH7Tnps6J6nyS4DbHA61ceHw3pEJILWn8K0oTs7SJH5Tkgp47Tpn90gGZq5Rwn2b+iwutpItQ5H7Artc/ptzIg7jTWxJIHN3R/npz3E0YHFkWiGwo7z4KUL4tB0kzAqVJK+oByyI/6jtspNCnm/ZgQcsa8wgEo8w0wGxvuortWCIVdKzFDLjA1LZKZ5nIRJ6X4TygqqsZbo+5zd13Bpq+xvH/lVyfMT9F8CP/ciCEq+3mNkx905L5bU/gRQezd/RnnmLZSVULnbkn2syB07Cwu90IH+xl3f9+Zfa1A9IhuyaK2WGZ9A3BrrgkBdfdheHVppJC/pvbAn2zmCcjXZYMEYUIMeEp8Nvde1yRI/mQsypb7NEgP98XPVf7YOk00MJo4g9wmBqw6yEOQc1Vm1Ma8VYKgz49OqAvyax7tGpUGlxGm2wjW2deBzPZmHzY0B33gfI1c8MRNX/QVJf/KPs5hsJDj0i67Ae4eje4hIzrTgyDG3CUjs63n9osxGkwyPZH5Agwk9vMA/wKAq1gaYR/zlry9zHh8LpX0d4+k14jGrbubCQGspnfIB+2nA8DO6b6F331rlZxoGYHG+edRc/kpAaCNu/6loAosDOBJUE3/7L594A5PKeB/dVMuaFtMrYlLNCLlfzdE4KehcOqtM0SFksdDecQflwT2w6ZR++GisSAf3pUV2Zl/g8PvyI/165nOUpXjvX0P9i9fg1LaAb1zwbVcAnanKX6niryxC23Nm4miK1ZrWBHU+DVuiNOHkCMeH+cJM9+kvTtjQGiQ+KV2K3H3XdKIBZMSMH7lIxfWx0eGWkwnG5MdPsb49HqvHc2SeRveLtqyB1SaFe3dxfCXM6k92SsZE3ym/cU5L/g16E1l4qNXDF660NnzbKv26xoUP+xzp9GCCVm0tVD75RR1uik7Fhwv4u3mw0x3tiU4Q/C8h71ZETCLjpy8Pugs4Ndm33mwESxRyxQNffrAmPvMjHVW440GfBV6qa0qoRrDtV1557jhfM04arUiIUdNLbuzHfRwXCIb0xo6bP1mhFPZzJqzIgEY8giS9jIoH6/sjVbCJu7HN4Eam8egfXiK8wyA8HHH7Qzps0yVfXyc4+Af6nNo0EX3m2Vy7TSKjL72Dp0fcgmQgjq3t6rYBN/Bz7ooAVIST+4XhRiF7CIdCpxM7N1tboElldaoAMA2UQQd7mykU2XZ7g5FTriFXgzZVGmg2wEMKnkQV5lONLSMJKvDoPbHYjolWjvEERDdHNjUZJ+uDWIK2HXD4cjLgK9+myDOhgfIQ8/3ZGAfJWkN8ndW3FN53J/yMfUFvynHVDZluyoFeJIpcjxXOPJZ9b6zVzRwokQU5KGUaVy2oVfynCb9FhOK63l/WwmM8Sw42iM3t+j3ZajEUzGTLjHP0gbxYHv2m5n5ZL+0zQcwUl5zi/m74yzMwitk1OLhC//Gp9kP38FSIunNLVyc9POd8a97xG1WiUNXwCpbUaOwaPtzcn06PEA+zrnEFazKIihf7Jn0lHNt9DvITyTb25/Mq/GDNEciR9yYDQNN2jz/Xh7ZZXlbWuxuDxbPRepZQ8sTN7P/5ptpn37cXr7NILCWTQQZB4+aD549ToOKKZvDX2FkLfxM8xEioElLG5d1ZnzLt488djANG1FOBa4sgAHQ0I/X0bqbLe+27EjUlJpp3PQZkXiks5KRvLD4pTFWK5UnRAz4HQm/cdSTuVTkwGeT2BL/k5uf+8JDPupQ8jT4xGu7zeVxp3ld3jq8eHn/ARX6U0MUywNgnJYTS06cpuD9JGyeV2d2TLWzLb+GQeqol6BPHPwXDcWhqHLT60LOWNguYxLc54RSyxJ1sW6EyqG1NUcH3Q/SjAMqTqDyGvJzaT1+Gjymxd8SXRw6w3VC+JyqNF3CKvtxV0b6A0bqKO2FoVl1I06lBOQZPG3k4Xx8CFPddU8ATpDVexUQIs+VHSqhqms5FNj74lC3IJZqvg7OOFJgl/P/Sg4C0+pKFPidbFDOWPwi8rVZVEdMgNkq5NM5cGWkz9aUkG29X4lebwd9tBcB1twor1n7+UhCXmhj6osAFs2J75DlA1g9iuRhdl4tBTO7+MKAI8lTtGpH3AxXxjyuxF4SwPb8X6jukVXODOFH0CgPzxHhnWEkINVdciXgSj5ZbXWeum6YU6Mn/hsM5Pl7KozrR+LMWztG/RA0wCg2UmZcvbJEwzRg7kXEnXpyGuAVYD06/+Huf4xY0RKXzLyOUbf7XzLHuTbmtlS8AqRD6Aspdp4ieIQjD79ls9mLE4VLLrJonuUlMm88/YhXcFMCDZ1MTah2lmkz4I1CoiaGoao4XWvf9dud2EKWuCePYnKenvesRvIoEISaEa9N/ttV9KW+83gzhcV/IAcVO/dfAerZw+mp9W4LYbJ747L73TjXeoIA6o7Vn4c/jqD/tK+Dq1XYnZiTksoznsxQL2Ke8yT9457Yk+6xyAmXAR9y4hda76hm40kZDGuT1KKesii4RXtyfhWr6GdnJ5lV9DO1J35qybsipnPNxwaB6lWgQCYHhbUQnuC+X8lccRtXmhJbNHbsFpdP69w9fYqg6rDrkOk8OEAMajRI8VvIj9ZEWQQB/c5K5sJEH5jQsCFpjhALDR2/CNX6dnWSNm3MX8aDaoxAG4QZM+9fto3/wrnSzTbhjjAnOi+gygtdNA5PM4VJTJrMnskb0YYwRe8bx4/VRap0ENcIKNPeDpC2DEntj2vWUxC+eZeUJbHXXkgZ+DBSU4BOtiLivj42aqhPLuTh0QzAQWJZJ1/0NNWOE5ZtMB7pYgqSnAJDYzzQBeiHYbXtHd0sljKrea08868fs4o23Q7ySsuw77+Ux7IC3DQW05VanQJkAf7pKOs4VpYBYREGkdffKiVyA0Mpq8vp65K7tDNnRILFbs30GS+WgFDV0mpfc6IjdYaCcgRZzpf9NpBIK5rt0Li9vfpO1jL5mxYCvNfNdVk64+JciEvs9yAWILoE4eYDbKWWP5DDCFd03zyeJ1ZjiwkmnYkbzPhTKn0yRT43HdFS9MWzl/vedhIiHyR5JEiYqEryql7OTVao6eT/lBrFemywiynKgAHqqY7wbufhqY8hwb7CK2E6B1EyhAbq8gPxnJjPIxnmiMm84r40RLpGYc6lco8I7rmol8K0hZtVYi4efKnk271rg1sFgoj7kK1T3c4BE1wtTZjBBHeCCU3rlwO84SmvNEJRbskr2EWyYJjkDZv61az0XO14q31OVib78SulyA8RCTTGWYEByDtprCVnW7RqayDl69EfpVSMvtrQkZkc6hJRDvc+SUd8zLi1Y3dvS4cbrKFtcOizhYRmuRPE9LPeByWI02+kjCBLFCtXFQtYYOdYAplbmRzdHJlYW0KZW5kb2JqCjQ3MSAwIG9iago8PAovTGVuZ3RoMSAyMTI2Ci9MZW5ndGgyIDM5MDEKL0xlbmd0aDMgMAovTGVuZ3RoIDUxMzAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjatVZ5PFT921YUjZKdUA4iu5mxhZB9F2ZsLTJmzpgxGzNjHURChCcplBZbslSWbAmVKKVFUXZZUikUIcryntHzPC2/9/f+9358xjj3dS/X976v733Iyzih1ExwNB/QkkZlqiHU4fqAvYMDhkmwCGGCVAaRRkXA1VxA30Ayhg4g1JFwOExe3owOYpgQZI5hgvoAQodJAFCgPxOk+IB0AHLRg8kDViAVpEM4DvAJBRxAJgYd6g8iAEXM2oMTjcFU88EwIBik+hKpoBIUYkbzD6UTfQlMdg5NNTV2Jna0qTpgi8GSaMEMEhHAUHGArbqDOuBIC4aMRECRRgV8QAKGjAdoeAANegCuKAsXFGDlss/VCaWkDiVGBfr70+h/czFDoV2tVAFzE0e0BQC6qQJWrig0+zcapEL8fVUBRzSEs+tAjuxwBwu0CdrTyQKhwT4DgACCQDq7M//BTQFiBvykBoXi6TTKWgFAkcBk+utraAQHB6v7BjKY6jS6r7o/eY0fmkBkAME0OgmAvukgGVxrTCAVB7WTSQB/JGAPCLAnYqGpgOwgS9oPkAK1EgqC7Mx/iUGNYLJzkn+4AwwQ/K0MAcNYi7V3crIHKBgiFZo1hoqFHJkYZiAD8F6zQR8Qt+sHQRAwC6TT2TUc/oHo/5b5h7opDTrZQTIrAhP858Qw1EBG2C+9+f3YWBokNQaT8SMjCOCJZJDNnsGeGZG6ZnMwcbSxtECh1ewh7VHVHGhQd6jqzBDmmjc7n4m5vT6go6cFIKAPW6cWVJwZjUKBWDNg7PaZE6E+MWn0UI3/rnESlRZMZf0fDngiFYdnzwIX6K/hSiUGBII25n+HQSbYT5svyATgABgAgCFYggabwJp+2GYE2ww1JoLlT/MH8BgyA4wg4kHoC8ZiYIJAgEkPBCNYvwK/P8EQugCOiGVC0oeuD2wtuw0VTwP0fpghJv9Af4tCEakO3Sol6OriaFRyKIAD8TANRxoTkoji/8/N+6OWZSCZ7IihgIr/tbd/BmAoRHLo/x7yh6s7yGav6EijUzDkPzAiw5IYAuKciEws4Uerf9htmBjofphQfckgNKY1kyv7ypEhbUP7icjecIAaQkvnDwySLZZEBRkMQOtHGAg15g/m0DTYvAENT7SrpwlK5b/Lac3fgoql4YhUXwCprQNg6HRMKAwOaQSprQ2wENAFwIEhayICNNSpNCYUAvgHMiMAPI0OYw9aUxvQ8KFDMwGZZBDP9CH6svE1SFvzT+gnpqnzL7Y2/N/itP4D+wkikVAkAcMMJuLAn0bEbkCDvUl86RgyjsjwJ2NC/wW19AANfwy0P9gkTH+po6P9O/ALO81fkN+oIX4HfkHgfyNrlH8ro/MH8ksdrV+h3woh/0B+6QACSsgIpFDW3oN/nhaJhI7LJJJx4G8dgpQEXUa2BnA4WqAP+Qfyu3Kc2Nt1bU3Af0rp79fO2jOKSaeRQHciDnrp/uICyYtODDkAh+44ArJDP//8dei3AvI/19Mv0aamtBCWGlILUEPq6UDK04LGr6uLjPgtFPvjBbC2XqAj/PPM3r4ACIaAWFjPKxrW4JhfRnVcUaRFXnvxBnk99YmrokYetqlcPefa6yXFzbNHZEHj/OjaqCyFfJq9tf6hyDPR1AIP+WMi5JXBm6evPZvFOe8dxUQ6REryWZi0XXJTdz2a5dAdVdwoqzRueynX84rWi6y61LodgGvbBzO9+tuLycinqwIz6bIHi+v6czYEX+5E1AjTyYIh3fwSDZLd7Q3rmKuLwn8lYZpMepQ7vHPjRNtsN/rfvc1/4JuUXXE661zEwnqGlc6XV/yM5ZRXKq9LgUh+QtinNGnza66L9/rELHXzHOLo2zKSL0guPtCMcsgl2ffOaOl8Xvggx9M2it//utgzdy8nHtszedorSfaGj5B34/q+gccGKzyq83Jbk2K+2WHdCSphEV8Oi/otGx+/EKvjujJ+rAo4eSuZFLnQ96W8Xbbaw0f7huLZDQbc4sOkqUhhPyvUq9Flsh2pp3hJedLdejjtRdJ8oZ6wsEnGDpPmaGzy7duDCrurD/GvHH6s/bEFGHXZCuNrXS5EeR7Zo67hXfu6E9F3QCydwRoPbZWZqjrxWLFI7RXfp6DI95igQzysR30KFxjMd/jOJmRKgLRcje+jQ1Y7+fW+GL3f8rBddbtDajGfZzpwcu4Yj4PSiLfbNT4V6ea78hGfOcuYyVMjebAx/Q87l5v155XxdYO82u6Ls4+WwfOLmlbKZQmyXNsPbVky8TYaxi9fDU/oTcZOSLWPjBhZtLRKuGHH7p9RU6yIzblno5t8iLvqdleXaf9Qxm6jBTKeVe2cGe1hVTl1y0uK8+A7h1uGxg/bt3S8SL4Ta9j7+saDt6bWgFVx/Cc3vd6DJY1TbTqCemdqEyyfd5Vad7VSA1IEUAv5m4xDLYI57o+FkWo35O726+3hU/uUhm4NOSTQ6+Rg3maAEX/gKBkmZpeLd6TJTyTWiotmSchvyZD3DacnUOw7cw7c/Vh0okj8+bVVi5L3d316NBGr8O1i/QmZcrxJoeI85/2fg3PheTr84qunBpe8NxpJEBVqQmCBzwKG+6aenihXOrzTMayWZ2vHlsZHC4MNzTqVpZm5yBORNy3x0WKPd1Pl/WPC+QaVNeanIqYCCJU62Zo7z8cozE1Mkc+oL1F2CYWUXjmZd9VB9eyMfntSBqoLpWYnmScGW5QbeJlSn5L4TLTp3iFVhERv7JcAwJR+MldVPFU7MUVe7F3At7hmmc2Buo352s/M+GczmQQtPiAkIU1z1CKwnClh2CMHsw4Ud8o8PGxM261C8Rv6ygk7eRZ7Il/ps9ns87B0IQN1Ew6VdWDHrWe6XAVmojvbYfWyyxvOW3sshoe2Hzt1dbTYaM/Fmckc71URtWvXYmzNBg5W8c+ORO8mfKXIt8anwEaeqFYXznVGxacUeJXe6KxuGZ1Z6nZTfjzg31YhNyl9QulUZYNYL1ZbqTX8hn5k1M3TTNErie/Md2CWPCfer8aNbUwpf7hewJcGxn/0HBu+uV5N3YpjbjaBlPjNqpJL89LrGIRFsXcn1w3xiIqq06oX5c72utrSwprKU0ixZraF6y0I8daOG/mfwM90CpnvEmC4pNGv9hNY/FO50y9mtfWfsziVOKi2s37HkgK+bQs8Q95ruL2q4UW/yFYAXyz8ZLki2tgoGj6qh1nxjDks0y9YJxMfh9K6XXn+KQ5j5MLsSdmnItdvVU8dFyhqL1l6IDDPVXExaN0n7e+0ADjvTUnO5LzY8sgnXO+2o9LLRd98VQ+8rts30DkuUT86hkw2jo4YNmhwdy5vvVRgyMVDmWjC52NmK2sExks9yq4+jQsUPo3kstngNXU58UCqxhP4UB6lMZOrFWdiN2FaCGNZILptxemKb3Uvk3XqL2iGFX8eUm6DWXjn6U2i6ia37S2RudvlnC0qk/i4OrrMRDwl6Di17ovpaIDQq7Km6c+bch+AR/tNpIpqnE3zF6i7wOv7nw3S5cSDRxTBPoWKJ7MeMhlvq8QNOE15Ss481tz7oL5XdJ9gkEleVq6/o8mrHJspvj1m85W9afiWjaLvz1zuWEcIxdtsrfKUlib0HRldHjtY1siNfDTTNS2j6bBfpzemcd2VT+hqzfUtOediomLye2wS3zxKVRZJW1BNbw6PsfjoJhE6x01FPngzKTqrwuG/bmn/I8fIpQunHo4XYu44viLt6HZPMEiK0G7jPcRRxmUUean7suoAVZM6PMbvdG+2sj12utI6dixVplIwAFnaJKALbjLqv7ZjSUJIvO8yXNxvXelLTvXG9Ndpz85m1Nxj9JXFDpo/73y4yyGmfpOCotqQyLEs7qmnh2NXtxaa7w7BfM7eK437xLOk/3A7KpPjirtxRqMivvixbJvwZrc6zUdfx88YXonYvK3HuUbFkyd2/q+M/EoXvroOi0/Bl2+J9XiNX6h/Mj2yi7GykKPR7dIctEFgLLh2UWmOX94mNqaoxyptU2S93wozX+52GDhzDlZryKEVDvrH4GruLCSxWBKbg08e9gx3uNo0Wk5emaF8qKif3pwrLi5NLamnd5ue4U6wSbG9tENbpMHl/Lln38SMpEunVh5PCSsdBE433dytahxVY5tmO5RctPAx0KaugXMqPsT0pDr6y5JmeTCvkJm+louytYiBqn/mtonovRlpzbKiaFIsypV53sJnfsGw743dwj4f5v6DO95eukDQ8UzFTts/M2pe3N9x0rBHayX2L07xoUxGi/M8mTu7G69zRJoYtM+nYfnu1hnd0ROtw74HdaLoxRYCI5T5vaQD9+Q5E/WGbrhxVN7tgHN37jP5/JWbO2m4VPR1tB4J890OTs8J8hJsbEjNWj25n+PcoCLcm0pOfUf9rLWxPPpIK/UCXrYhMaFhS7Su4UxhvLemdeb5hKSdCovco8E58l5FQ5v5SYaJ4ajZ4I6uju+M7t5+/W2FfkWm5cGnWVetOzWENJNCjhCoFW46Snz6EYd6dW0DnjRd9xrX2nISLhgWwvq+TTlYixx3+fnEZFrpUwefbwOzkgVYxzom+sNAWriwfMVf63hX+fx3qiztRWUtZtVyR8bARuJQm9sd6vaoms6cM/MnLfPUNn/buZ6YOYo+SH3gKvFXnWuqJhX79VyuWSP4gVfncw7q8yeW3t11Ny9+8Mo2TXhaH3GTEDLs231IIeVaXXqW2tXIrrbk9zJYedIz8s45Pj3REtaRKeMkwRWURtsS2k64H5zWv3lJ6P2HkF43ku+Ih/iGWKFcoxzE42bLwqhyjAdjPZdPdbnudhHhr03928pyd2KmJ+o+nepOT2yp1QibI8e71hS8pZwoO16j9LlUF37BxHPKqdyOg/fUtZcpd4WStlesVniM9TmnFXFVp1DWayw40AX0ByvVmO4XLupFH8PcSk8Wmd4jCUdVqPqGNBz1GhK9MfPVScRpU9e097jt4jXgTttDZP5A+UP91DQrgbiXI+U9reGvRXk3Xt6ul3QunTAr+Dap+jgy67sSIuXDfIRu4zpk+VLWrVuftEOOAoTIYl6bvJYQ33OT+vmdfD3al8lblO0ndGq17sgUFbJgds9HmG8tXkrlyGU8Keibad8xGfDiAt+d0eLaM9dnxYbIA9/KvvPzRpSek3M0klD03NC3ovvuQFRzvGqi3OhKqwr5RYy3Ab09npXaSkMKH0a0XpQKnW2yXPjmQdm6ueV+NOpdv+XVhMdxL92fC88bFZxGHJeLie3aPMd1HvV++Ujt/jfw7as7jmD9Hy19izS9H5MlPJjklajVSHIDyChDvltCElnz0w63xIbt4NXvpKt4yKrnP0vtMrMJaZhrIatwztfJko+zXgcpZ2dN3NjU8rna5VXFLWQeIL6rOb++K2oQ/hyjNxB+2P14HKtkjnJpo9edDt4J0U0KFh0+zpruy0laMlLesHteYRVi4fCqUEDOQXL6xe4NbenA0ZT73VoFzB3G0D8PCk8izo/KOvq5JxvcCjAKlhpJ5FDIP6BwuScp3ccqqC+uw87qJE/Drr0f7nmOJCxRzDIIy6+z7uOOa1a/wbUO2hXcKMBzH83n/po3LzsZl4fu0mzIXObQiaomJbkraStTjmjvdGk7+4D5Pnj+M9pF+9Htca5xQtq9BD3hixKv23f7Ht0HsqSSPc6TOg5iP74/HUBp2hfEWqnpHBNpbKz7gtpVJeSPuvy0JFzaU2hyW4pYx3A3en+kMzHzalWrUMv1ExW8vcl12Rl8WZj3ZOVDxhzyO/Rztl6Zfmu29Y34WErq06ZTdLd5v0j4YHiptvzXe220kPC58/gS59WSnvLyIXsXPY/lGluiMzzNSiwra8ktxQmXeMw4BXFNtepds/++qS+IpsS3breZ7Wd7zsaiq1518jfYD/x1xvwbT9nkJ9MppQKVVOGHIrsMS9smlPD1NtwadyQrWJ6eQjDM9qnTr1RdPZiUHUhZ0Gk54ylr4pJPFH9LVvR9wQhPsyTT71KrbXfgXpO8UUOKbaS7lFNSJR1IhRCD62JodLcCV/ansx1SE56sM1YrNnwLb+VFTtgZnpYTzBIfqiAs+6pKrYZdr7Kys7bn0H1cucdB8jQX3D0ApeKV3TYqGtX4IYVpmR1ryxtEpsvBby528W/1fRdas774+x6+07NZX6i7UsJzBXXnOs+5HEky+C59YoiYeUTeN1buq9BdeOBOwQvIZLcKAgnvWI6/8tyP+zmv5o6YYEXP48+25wisi1MKYcToHMXZeE4PSgXtVC33pw48dimoE0ELGsymyZVy1J94ZC8DutmrPuTkBeZkGJG0cacvpRutlHqddc9uEk49rjA8qeBr9kHCcQPR5ILUd2X8HTczQkLRNck4KfORvcc0K2ERh3XwvbNmqxM0Eupd6xHJMeb3QWfBFku1l3aHN52KHjyAuvj2ONK6+Xusu3os3/pkraiMjBztVExGPgeuK6uET8tsy9MwBGGdpGS0c9zXl1d5Lvk9MH6N1bHMyd+tamDhgxPbrcx5TGiLLcsdxns9YGxvGmcejMO1axvWPbfQoNWmZ8myIGrQr8OppOOLzYhRpp/HeHhYg8ddB+BJtv3RGZI9mLJn9qZHlFDCA1Rc0qw1oQmtKlcmcWWBprrvbcd6BJG5LnxM2D3u3srF604Eq3h30tStbGmZkcRCsTiS+vp7TVMnvzfZPEG1c3kqB6sdFU39ELjgP7Fd8u1TLYpsf/HVjbAyG/1tsWbxA7H8tYUF2Va5ukkyNwXjZy5yTSCSPtaPENM534dg95G6ff3Ur795mri1qKGq1/NUlh16rLTtPr8X3TBlJNFaJvhbhQCh431e7/8AMfxMEAplbmRzdHJlYW0KZW5kb2JqCjQ3MyAwIG9iago8PAovTGVuZ3RoMSAyODQ3Ci9MZW5ndGgyIDE2Njc0Ci9MZW5ndGgzIDAKL0xlbmd0aCAxODI5OCAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42rS3dViU3do+THeDdAzd3d2d0iANQ+cwlIB0SjcS0iWNNEh3SUlIg5SAdMc3Pvt9t+59/N4/v4MDhvPKc53rWuu+h5ZSQ4tV0srFAijn4gxm5WTjEAKoqKqag20VweaOdpacHKyaQBsPR3MQgJONi4MDhZZWGgQ0B9u5OMuYg4FCAE4+sC1AC+gKBjpZAEEASIggCi1AHugMBEH8VgALH4AqEGyu7eMK5AQwmP8DNFzcwawW5u4QN9DZxs4ZyAhJkXZx9QHZ2diCf9fgZmX9Xel3thQbQMnc0sHFy93BDmDubAVQYlNlA6i5eEGMdgAGF2eABdDW3NEa4GIN0AbqA3S0ZDW1APKa6joaWoxskMJaHq6uLqD/4SKtpa0jzwKQkVTTlgUAdVkA8jpa2r//agOdIfxtWABq2hD/7z6QwN/pqrLaktoGGrKc7L/XAOAEeAJB7na/2/4XNzoIM8AfapBUa5CL0z8NAAy2YLCrEDu7l5cXm42HO5jNBWTD5ur4Dz9tWzt3gJcLyAEA+QQBHYH/COPhbAWRE2wL/FeB37sDULGzBDq7A38nybn8y+kEkRKSBLGD/00MIgT4d03Hf4UD3IHA/2hja+7+T66KhoYKwMnczhkMdDZ3toQEgs3BHu4As39skF+gFf2/CAIB0h4g0O8eqv/rAv27zf9Sl3KBrMzI0dff3Ou/d8zc2cP97V/a/OeyLV2c3e3cwe7/qggEWNs5An+zd/+9Z3bO/9hUJdUU5WS1tFlVILPnzKrqAlHHmQ3sDf4n+nc9SRkVIQCfIA+AE/L7e05lna2kXZycIKzdUX7LJ2MH0QnsAvJh/z8G3MHZxcvZ9//yWts5W1n/3gUrD1d2HWc7Nw+gosz/5EBMKH9sNkAwgAMAdAMAvS1t2X+3/mdyfps5f5shkvj7urq4AqzNHd2B/nbWQMgHiq+7uScQAAZ5AP19/3b8J0Lh5AdY2VmCIUMPOTgo/1RXdLZ2AQj+ywxh8r+u/xkHBi42yHlihBxaKxdnRx+AFdAahV3NBQwZDob/f87cf/WS83B0VDN3AjL8v4X972hzJztHn/9H/H/F6QF/82ZQcwE5mTv+l8/OXc7OG2ilYQe2tP2XyP+y/6uUpLONIxDAysnDxsHNx/Uvj87v0+YIGWvI1WT3+3L77ef7Lx9kYi0dnIHu7gAejn9cQIgy/8Uesh2/uQPYJeUk1V7LMf8fw/RPsKyzpYuVnbMNgIuXD2AOApn7oHBAJoSLlxfgywkZfCug9z8jBGBnc3YBQ1IArh5gf4C1Cwjl9zbz8UK6/Db9C/EB2KX+IH4Au/QfJABgl/mDBAHssv9G/BwAdrk/iBPALv8HcQHYFf4gbgC74h/EA2BX+oMgXJT/IAgXlT8IwkX1D4JwUfuDIFzU/40EIFw0/iAIl9d/EISL5h8E4aL1B0G4aP9BEC46fxCEi+4fBOGi9wdBuOj/QRAuBv9GghAuhn8QJM/834iLA5Jo7uhq+8ckCLFY/BUAqWUBedL98UMMln/8XJBwS1u7fxt4ICuwhFxWfxI4OSAErP5kcELUsAI6gv+OgJiAfyK4f0NXdztHyPT+SYMUBv5HEkRH678gREibv8IhTW3M/+YBOSHsNr+f+JAb/08WpKrtXxCiuN1fECK5/V8Qop3DXy1+Q3NX17/k5ISo4WjuZGH1V1sIEcffR+3fQbwQIo7uliA7V/BftSGyOv1V6Df0+OP+vZy/xOD6Df92Q0q6/nFDDjy7qzkIbGfuaGVn/Uekf+xAkJ3LX9vB/dtma8f5V/Hfxf5SgROirNtfECIZ6K9oiBdk6/KXH6LhX6vlgoS729n8tRE8EN3cHc3d/9KdE0IC/FcOpATY3OMvOX4bbP9j+3+r/7cCEOU9/4IQ/bz+wN96ef9VHzJg3n+t8Df2+QtCBHj7D/zPO1Hj9yvDP09Ajj+X5P+8S/2DtcAgFwegnp0V5E3yrxDIxQmy837DAXl8cULskJ///c/4PxrQ/nny/pUtJeXi7cvKzQVg5eLl+D2xApBLisP/PzIt//VS88+DE3KX/y/+/UYBAAK9gZYoS99cLIVD7dObwsvfyRZ+rYCnFWQ7qsQX01dKhFv68LWDhFAmb4sKKF4U1BKYRVfkoqIgZPwuNci5RJ829JXj81prStXUpdVriW3zd6rvSDBkJcc+6rLpBGepLgZWfKFiPFD6WGBQyjOT1ZbYRg7QGTuUFuzouovlmnzBPk+jMqpoW8mH9yqe42zGAznieC9iEXeSLH7thAa/3OHFx5j3Si4xzZoVhOOPKSG49nTh7kDHFIs/vmfYyeLYh4mxNYp/6RXBYK5xY/2gvI/qLDByVaS4yjqhKIKCCwV9SlbLtvVFI+ai5wQpw5ooUO+9dNoDp88FtCc2FDUZHeao5Z7ayuQw0k4nSpP269QL9kjSzGcBr3XhETm95a1OgOfbg2V/faZ1WhclzYGAHKr3YqQDK8lPa3E+ZHW794QbrfVSArY+TMwjqN5Ld1bwNEP6X58f3IQ+oFvzoi3ExI/IlojGIA6/GFtvLixZNusG0GO2eM68kHuePRIvwsjt4M7ylxLafdw7QoJTeJxQNlyU5YpYdBii75Eoqe+5phEUtD8eTp+XG7ylwaYKpoh8zsrnYYZTOigU2Un7NL9w40DaMcuZF+M0x18a90pWaLvuk7XVVGdSawabI6ZhFZ03nJThkaYQoUKmECuMwa56oiqv2DTDoR740CVi7Wm6o9BQUqx4wURqu9FaKwuMV4N8LkJW/ZTITd80duJraqE/1fqeIZoJQDQrc8BtWjwdGYAi5jjHUQHcLs4SdIE1ehcaYWpowq/g8WzKrKj71jkmjPj1Ypu6/UVk28ttLEyd9UUA2ubcHIB4yxnONXTrWIzPogkqalun+rkhTV5h4Javn79bqkl5u8ZnzaNSDaQz2X2O4ZQCNZM1vNTBfrYRGweDyd15iFQYbZIwMSMQ7mjd9YtE2PxcV2ViEfWrgr41uFUq8TvIdsrA1nnJgP7hdoKLRo/Z9h20D4XEPDE16kecX4v2AQl56THngHtOpc+c6c/OfupU/V0qqjeVQ7/2Bus0M5Q3XlYJ8D61ToYoG7QY3CZxwpDzbH25HorVTd8yalxgZTINTH4VSXhkRi26k0IVNy06/Mk6rJibWrID5hPAqgLqYcvhiZ5OBE4Ol/6hgjWyaVre+9UlYVTg0SXabBU543qhvN2nkeS16JpoDHqFd8XB86b+rdEb1xPvwFovKdVmqi1UIS4V0PNo7VnpfVkbHOhqs6k0IRFrDfdVaSJRGADZrEQznMnRLkVktm/J9SQN6tYWMlWV7ESIteVl2CUz38Kp5RWqEsdev1htTqsIMRibjrXDLfj3p5qE4sd/GDxkox0e8No4PNQPgUfHbIx+ny7tl525CoN0gLy3VOmPHpax4Aub3CGUz5XRIO9RLM5m3VvavLKjqvwhR5MfcUgJbqIB/+WHvc5mHGqs8Of0mI0zseyzBXj80QgGUOQX59kCgVQjzg3RZXDxB7YYjM+CjNrzcFcaBvcZk97322/fUAv54SI+Ndv67dI0XdAquAUVhub+tJhUDOK4uPvuYHaFraoFLTGiXpmSQxZwWK3ZQXbcq+0oYX/FujqU0NaSmd0PHYFzMaPnls2QTvf5GIvZr/KD61sOUSBHQ6u8XFEoU35AK2GS20bBnVNdPVkwXqb9o7NaMe08/e6nbz0mzwXeP7J+zEvzamNle5UvkDLehXG5/byU8tjt0fKZlo+M1haJf5U9D3fxXp2+jSftfer7QtcWBUkGWxonJUM/YZXLOLJGqnfl0J9ZJYbtq2MrZhsyH4XD+o2vLphK6RRCkU+cl6OjknTx7Cky4Zs3sn7tc3BOSuk+h18D9QQazgjLW6vjT5UGAm86vV8MDynhI+vphRInq1fYNtz4Ix9gSGDYtWhcetGaWOCbbW98Jb+PH+zEI5dz+QmTpoUuhhk6d30XfZVOH0clIuDjhMo98u21tbcKmlnndb9A4ffPkyMNCva+BujJT4akwlUFedw/XpV8aIfHskqRJ6x65riBPds+0D8rNXeWxm+rF39WF48D5jtGD8cwssyj07208IFlmxJoTIKqUQTsxGEJS4gxORwm5UI27JRjFQHEdT8rOxtqrRPao97ECuV24JCzHIeJscgxd/xErmtv6op5/1RRlk1hJORuoUR82yCnaUz5bRwZ6K7YkfhRP+4kq9cQSb8OvVt6PENHhja9TRPD1rE6bsD8Qmz2wxsvUZ+o85CZ1MILiubgWtyZGQ1h5F4AfAAfBfyWC0fOiRcr2v4eXWCnep2R6UIYaGMgyEazeft7xx1r4eKPJxfAqpWP/hVz7k+AELJgpS6ZyVfagi89fWuajHIlTr3g63MJbB504iOXNa+cNy2vQle+OBy1SIZnlGKcoBS5be2si5P5oejF4v6UPqRCrMA9MXdzwJ9H2+fN552Wo3+FJU34yjYfVcvFVpRMaePDCb69zcHbSxgYl9y1FyUBYXlkGzFjeF7mqlkoV3nmKnLQBmmaEW+DJzMOyq1Izl3FwthECt6OSdKQbZ/KVA09zijQq1g7evPQ7XpjzB8hQQqIaHRCfBWp2Yg5S8Az7Hok3ePHXL3peXYiqr5IoHrgMK+XHAGF36usenCTMWGseNoT/S7a/gxhITjC8Y09GcNFObRXIFTGlFE1pxafnv4VPM9uzhAXG10J8QaCCHz0Q8X6tgql7iU8F8kkc91tY3ARnewWKGbfwhzRnlauDW3LoXYf/tHLoedxkfyuIKve2j94YllR/24kioV47wyUUd53SL4wq4TrVeq29Z4fCbdHmWt+y9zFdtAtXOAb7N4VIwezKQbSeIV0F+RA7dYvDNgqscH/BA3cfVbswLiHNSad238nTQzc8b4ins1+MIb61gDrxpN7jLtpF3PliY0u/kAVPD73FDQYi5iiDv3sY866o3J6/1Zi1imHRzyLPT1RAEUOqCnwK74ET+vdLXWq3Whs+5gy3rEkvf0LRvL3BRjw/tUte5jFNXZJV148TvE3Tuuq70Z1Gsm9GbwJ4PtKnbW9Z64ygJScomnbU4LVp8w3ZhmSCBqJyvRz4gg1S8LHdX0rUiDDscCoxszXN5gce2cCB5HfTb88c3m8MTqZhduN5xfUFsmCxdVD7m6q+MZ5z1476OdSrPMcYXHjynumUrEutYNXs3DGSCfTVC5el9RwZ2+J6k7lKK7HoPI9iArZrjKNvzBUa9fvWHamfjWnsQYGZ6qU/O36+lkmzo+76OcO3MRyCnvMYVoy8jJrxPF3eyNLylY5ov4+myjJas1e/BG4dO/OucXHHTiZFwhfshxHOE2Bahklcdy1UMsWNAX6SsGuwewdzMyiznNH1lFM6JcUR2+LCCaVwc2vyL2d3x/PA9wngwzwb/ry0xyZVz9Gmw8Hl5wP4Sx4nJGGMGw2Xd0WRWdnNKj6DJNJ6EkS4Lns1JExarXTtp3Yrm5cEtdMz+eo0AvFTaamS1PD0s1/tsa/ArcsUz3kNGncoPEkiTF9Ta5FOX01/rU7T464eFrW0Br6wV4os7kPSD9DzZF13vmswLyVtLpgwpAfZxCbpUuv9CaDIadkNGdk61em+Z4T2Z5bw8vXpPa+Zzqe8Btb7iQ3a6ufmXaWfo7h49VQxVja30iu+0lUvcJzpl9HdLvqtzjf37PUCXoyF83AOVKvnNpRETM0JMwLdh0eaqvXdPeBzJOiz4eHd7cYy+CumxKM0cgLG709kMnxnoeCz5BVYt9R6uDellGpIsdzlJIrLcyMnnlppH+q2djZ3KFroi6X1uzQtqQxfm3zY1ShO9Qt3QjH81VJQXanKuck/8UobPcbUmPJGM8YIzGBqvDkvl/qe6ZsbdcE4fi1DFWZ3vGerVq4j4nBo/nDIrPicqMm1GZKpySmckOtl1wBG23X250nRgLpiIF537yROyw2alGSH28nazaSjoDtNMwp468rhLHVWMbFYGpZhVhM3yRPoCvGJTx1flSyRNErZFJrWuSOKDQySuWu9bcM9at9NQJb6BfzmE9qqSWwk3wCTpI13C6r7ErKLx873yk/znt/m3rhlXN9xYZLxertfw2Li4U6hsxan2YjbMfwPWU0fIbo1uDDW/wKW648UbLcp/NJsvGkpi4GuLqCsoxYfm0aQlgf32zkTzT1KbLlOLqUjM+9My5cXdk5qg6wn1lLYweO4kmRjNlab985Obx8bQufqaxt9TmkdaQL2tf8UsEoV90mPCP7zOdqQnavEAN1UCxYZlDX36HPNjq7Vicyo111yYyhD7CvLTyU1JNnH+KO6hJ33nGVtvwocr+9NlWUGkHQT7tf1bMcYiTHFFFyyiXyHYnJ1L7lVQuTOvEu7L3BZ221Vlj8T3o2yd3HHmUqR/cCxYjUAqpirVEt1iiHIo1fHJr51l7u1x2wN/a5FM6YHvEim417OqmBoaJlfYpK7Oh6eTTGZPEKA5Lx+Zp8YAtjR/sWuSSCYtjKjHeKp/geTS1qiQR3N/q2MLZxZMQuN1DP/rPNS4TEXWL9i9GFj0UFm0p1LMNUqWwU67KpweSPslTwwZZry6Tivk683tcZzoSjZrW0NxYNbVEbRMpcgPLNB1svIQ2bPtZsd7zZxwI1tD3RHxZE5o9su+uv8x3Wt1jlgUcVADkj7X5zoeIvCnxSHx40PWuq0Bu3tMXXZNu+b6GtU3BIUyq0ooUZHg3lwRIvYeNHOz3imSi/d1LYBlo4aelcnI0cMcylRKq8B0qXCW4Kn3Ocd9x4WqBIOqDmTuU4JifMG92EgjuXsl8NXhN/yhIqDLlusXv9Edori52k0TuqtG2kuBfvUFvOUSpjBKPaOUjaObvUYHiQyNIyY+yHGBZUjMhQLaJBLvkVw2sXJDK3jr2cgqw8i1kWUtJ6daTkMt7A7HZPuFJtp+xBmWBXALNF+dHak42fdgKsdgK7ct4CzRhCSpnwayJxg227Gd3TNLb1mvjnwpJLAc7ULJPiPUlPUb3Y1uPHBRkU+cYSnQPpJjlyL08FAw8YQtQtwzwujuL9TDwGENsqmp8lUE2SF97OTdmPHqhgH5Z/iHzafKYaWN3Y4lrUfK0Ki85J78+DcG+olSK+vvRraconfKhuzJXPj0w/AZXwRHPRROrNiHZARPVUI7KbKGYOFvtLZirJooxAKHvmLyiub7BmTxMDNkFdRQL1OjPQrMhcalnTFo2k+uxexTnatchf17kfso9SdsAMUcqq7IAlvdp7gm7cGebO/t7SXJXOSFXFlFL1+bHaZdXW7eUu6SQFpat4ORlx0Tnm9rureYse3tt95gnftQWuudk3FprQ72GQRqAEvf2LXvX3mptuUfhUcudt7xwR2MWhAnoJm0bGCkeSvMPwhi5I4aBh0K+igF8lXjfsHSYdnTaeXSd4Ye14vQHuxU4UziFsoq6D2ze6vgbViZ8dpn8lREXSKYOK2jV5N2UfqPE9rWH5AvkcYZMwo/WWlr35ybDQ7W3wlvn4k2t+zcBOiRkIpg2qVqSDLbWbjhF1i3kk/DHYXmE2hYNlX/2rTGzmKMdxMpe8gRF91NblE/MFvbQccZJY45uLapjCZ5NX1A2AJKUD00ibnYSe1uCy7Gox4V86sXq8LUn519myOqEk6QoCmnorBJS8ebjunpS83+p0pkmq+jYrMsrTXgb76UqyWePhEvMlVuLUygXKc2/FWjcQhqWJ+tRJKR+Jjw80J3h1LYQ4raZef4XB6lQZmtEvsqitMfvpOVC7Pmn7MLUww4nOR69007I7Jz7xvgctkrLBN37EoHhR24RPt6YDPUkPKYdCU8lQekjcI+vpPSB6/6b6IY7hBW3igHGHuo4B0YrNiI3mQs1fyewLkeWh5lAurzlbc5gbsGLjoBEUidnQmUsT2jn+c7gy3OWXVzcnrvbt7DxOLseu55o9qaakkUXwLSLhhVd2rPTe5+tgbKe0RSYbWBT/j3uJ9S9ELikRmsdEiS+J8lZSU/ClNLWVaZGTGwpae5ie9qo1l2FwjS7ksFOLa243Yt3QQ+LBMPDAbapCYUlvGMTk1izTMASgIxG+CuEm3/1LHLCkwiNE0DQr5xOdNPde2o4suucygYyG/9T5axjxLsAphYGvtNX4dudAd084i5KFqGCfI3lQiopeZoV3N7pQiTFqEmyKclLYKP94Cy4F1i3vA9pppSFThD3UroegpRHzh+9LZySNXGsFyzViS3XO2dH4Uw9iggc+5nb486EiYX7DoDohwe/rmL3jnR+Pz4UyHdasd2wxYSq0rernEw5mBDAVxgA/dXvZHQhaUQZ6l4II9oefqZuhfb43SUUcB7bcYDOfyQftWnF2Q8PUr+v94ARw3jtTyy13cC9o2T4cIRk4CEdpH09P2qg2Gu08hC2gC2qBePXBvgW3FfQPkgmAkmTi2WM2tzYuqJlxXwIJ33QeE5N9fJGM5CiaOzM7zi9VETsSVNP9Fvt05HBOOarEOOl83AOz4Zl766UKtQBEBabtr5Z8nZU4n1ESJla2Lnlks9s1TB3h0wjmd3ViK0eoirOLDUkjfKkNCATSWprlNBL5A1QriMQ/rrUlaEYWn7TH3uphoC8ITdYbd4n3kMfJc0J5xAw4QGGdoDXdlabw1F7+qGTdTzQw90n015Za76JC7HJdsdN0qlzFCRl9Ya0NJw+28uEtqaVjKNe3PC/NPeb6WYQGbjgiBmFtHzELkWdIcBCX35AFarAuDpCP5/Aqrnc1bHLtyN0bjOoPZgb7UQ4ZhVheMe86ceqQ4LN44X7q+CVRJ4YuQlasiB/X1+pHjbWmc/NlcJKV3yi0dQdzVE4rjVthW2tLkPxysXWj4jJ/fjHTi+TEnwb3SrKrDUtY7JyW6E71hk9ok8T1IH8qjHUTXSkACq3os/Fk0Gf+zrmvs5Gpq79ap8dceviWQXgv3RMh2iyCDB9KLDfttQTW3ZwVMEpHotJJZDQ8wG1LGIYwb4yrbK5WSzBkM2oZWc/ySF5WRB4kfshpCJCnLcm/8+uPmhMFdvKvU5k6j+o7zir3RzAQBEYqGH2UwjOkpP65iIW93c4Y0eRKV1Jom4tNMwClLAdiKNIHViCfwvjXPQBniCPS454SXrUkE9XyvowSCJuaHFi1HM9TP7DceSSLrtpMofh7qfzEL30XX+4jO7neyi+4j4hpc7GBTZK+b6iX88bnRpjogmjElFP2Vr/76CWRhbHkllYJz934AY0Qxa/NmJzewZGi0IYGQ8ndxnPtGD8+/ydyc/O39JJx3ju22HMfbKYUqrw6gsdF8/GaHBgsl2zYPe3kNBjMVosl0Dtat8mfa4HbDTHoc277FKYvfROsDRYdHX4zumdrlmB8FbgCEz2MMtMcqh11sINXQ8VJwaGNcj3IkfoVaNMk8JM2s+xHMcOmRpo91oNZ92PR0QjOWqVYYnbdyue9oEBPTKU+aOeovZ9DA2GzOQ/ls+Ln1o/0E6YgQvsuvHzm6LbVhjnnXOig6cCq6pCWJ0tyJt1f+Bve277cCEtVVRWMIRg/3/JAK2/ZDCc3VZC6KGAzhyTKFaop1mfPwsyssoORxxzxVbr08aTcok7FN8fYIracK8Rrj7LqSIVcfBvmjnhnUjWm/bqW+FJDjrXyS6XorzLlm6zK8HiTD4pZLgfWUu3yzyrW+F8PPsSxM5gJonwuxxh9l4++RvbAsdHAjrY4r3dk9/IqIYHP/sTeoL3NQQi4E+/NlV7C2b9g74cpeFMywrpTmjhLffKO1Tag2KuTJAvNcyGRcDdeQkt2QIFRYrSs1T0fYbCaz2SX/CymeQz8BNC0FELED6PerEFTsM+eHphQMAN/DG6JQ/p6H+ZpRZOQ0U3tuRzmT6SSv85voo7Ima3fSC9c+kjZ/vJjfR68T6X0frfqKZ6s9ZNnOnSKpbpAa9n0WcPJ7nrnSbBIDSfRoYqmdYq6mft3MHE6qqtmZR7gC2B4iD+KNyqqZQPslsJYJdpB+aDrbbk0mydd+SF3YnS9er9yIT+TrXrotvZVuF0C0ty5m4Opwjp74HTN6sOziqFGNof/SI/G4wSxe5t/YcWN4az4DYzM19f4sJ+h81RaLjAdv6CVJTBV/Oqf/oGvECnZ1XxCMUF9awaSIjfzEBYtIitlPB3mNh5I/IJo9cVcaqCkID9N5Gq2NO7IN5dXg0nMto9fh+wQEcGBD+u+U+1MW+ljbG9u6BpF5943RyK6hJtfDSG3tSXPwAa3tifxkQH0Qk7xAKt2mdV20VUfig6vMz593YE022CjlCwYy6HzePatpgIpn8GKO/nK70b7r86z7FdYYOfsbufWSFLdBqcXyu+DOulwuY2F8GhDPz8hLWADMDWmM97ODoS1jSogweDNaixRcnisN1U08JZIl0jRZbKZSEOPkZuqDHOQ2wi4JUbKY6RaVw18NDhS09/ivP/Sg/v+pcksV/GlpZ93Ide3UJGOy/xWKzFMqAGfYExRLJvM+OD9vvTPVvykEx3UB2bzEoxDz9IE6TCNc/SkriShmmwWYVLTiwByKbKlOLkaNvGC5xPBKBg7X8FZLtDIJd/M28e5PJ8fltclKDVwj7hzjaQq397kGzjec/iHNi9ibByJFD74P1hlZ8r1i0prU8YGH41mrYliTSFX5cVXR+TAdkenEWF99/Ob/9wflV4nYlRyn5u4WBCTlVw4TQdD9/4uWRn3ZgH1a1QxDcmz1phzIye7d7+/j/uEVQmBOcaOWLdqILRJGXW7dJFzkNn5jALSvmnN183G7Q78z4h+jblJpR7waAjd9R13GN6GDuH3MBmGvXEmGWQD6cvm6uU4tg+wnPJh9wspLURVd0VROOZRlAyGnoAFOM1dQmsupdcvFVJL+zg0TzgTET7YOp1yZSm/vIk/vdCfCMvUJgxx/mzhBFfMizWX5ak3LW/a7MpVUwCyd/qkBZJIT9MuTLVFwnvBL/oeaHGk5X54sSwStfjng/Fve6lFUR/WWta0tX/ltxrFmXq/avvi0egObllqY9ZlluJVdOcBVSxRbXbwySiY/pwSFyyWszgnFVDUFhXxQJL5MuLa1r91HyeWU0iXtjAXZ1rwRuSHYWxKMUPOt3aibkKPsqGZx9Zp8ZLRRGu8yfc5rMWLShY4ouYKZl7D+9gJ9k4rRAZuyHDqBeRPgAyst98W1cdDs38KqatkNpE3EudMrdYDhmYUc01JluZWY0srbJ2yNbWfxUIpo15FT12Yt+HDafXXo885M3UtEGewdMX2zlLlo7WpIYwncEZdVqSfNwOmVt4Z6ThvFQXTT514TRCOPJTnUDRXQfdyBiy8ESabYD7POiCOeyFbm6ehT8NZNtxgfiVzLivgqBNPtwKN4S6JiyU6TMJVCuJ/DZJksrUOQmBLhz+HpX9UiTzglRCqSFX2isLYD39EujasqblbllFRmtMItAtnlokJoJGet8w1jFlhemPKbCjMBOr0MFSmVTM1Lyak0LiN8s+l1VdPrm7dRBFmjPLdb2cfGWfR+lk2AVVhla3bG5iSofPUd3dyyAPH30hG1JkPlPgmO/1t5v3oGw2tu5OElf53RAcrVHEaUEWdO+kKTzQomiArgiieuwwOdp/yLHy8VCJ4yQ/HtXsHZU8Ygzq4Ul8+Zte3198byumtsjbSeV+oOUF9mg4Jb9vfyrtf/roUgBojsX9f3e77rWoKfuGJ/SK/01PvSPlQ9eDFaFjaej6RZchZ5eFSdY6qMhCHIbx4DKl/xEV6Sx6X9iIp9iMBG5EyN61E5tpnsqjIdqeMtBgKhT6wMo9tKUztmD73KtGGTnNEys3lz8pGXAssll0eHnCH7fJ0pWJFyXRZjyisOhteY1YZ9II2w0V88/dfFXYTaMVzUX38Z4lVSJfaLOnVEV92ni8mF+VjDmTC6RsyENpFoQA6No94tXeH2CqyLFun278oWrL44QaoVb/yDodDF7vw2E69hrnaaFF+4DG6vOvMeQhZWNI+1n62uxqHP1xNGvN01jQyKKwUJsDir2pbqqv8jFiPvcxPXbG/mOm3BsdNZAhPrJZ+YywvSJCPXXYef6w7YPqj0uD+4ysZ7OmjIvj5r6AiT+PrPR1ppi/Qa+QsMk/s4MKXoV4leP7Z/hT3RQq862ZtXq2IOdbv9U3PKG9LB/HnkOkb+xZ/yMA4JeCzOKewxUaeu1N/IoPHQOQNO4jwSA5HU9B+6ohhhF8e/hU1MSwdGFW03l9zLC2Mr8g2yPB91+F4Old7xdur/6FdvUTUB4fvlSqrzhtcWscPoodZYQpNF53Vp/Yf5MKGkuAdQvLay+eU02IxCNEkg/jY5W0ekL3Rm6VTEAOXNLVjljIqSH6+TVwhP0/qm0tzFX+VT6nYvmQ65HhOrF5LwUCIjNqR9I7bvh/xq5i8eUd+9sjCstCHF+cpC3pcAhmsAt/iJPfmay9iFm74tQ8wZY22Z6fL9tVySCDjpg1TqVC3FZwhYkWjd6aN8U/dGeXMWp683gf5+Ul3WvZYkxObfQ8dqNsEgYMMlSy1bdqJcIZcb7vUIn2r7nga03ZhlpXr9Bv58xsy3+N3qRxRjIbpgFG6lXSEHY4KmVcNbgQNYZUC0PPnKDuJd1EUCAKXK1KdbC3X7c7mH6u3PBFFfGnTEpLe+u0pPGcd5KXOhqPxVk7CIZwknwr/SqocQPzeH5MwVoVgwY0T+JFQqVzZZLn/oHV9DEXzAtwr7Cow0tDy5oxdWzjhWnArEiDBLA0VRD7wscC/cSJExAR+fpOkeNln4ZNDK3EtiAFjwKQI+d0Vc4zQ99xsGFLkqFRa+kPazD5Y+rEsnF4FVRNWfqclgef3GVFKG1W1GYZui1STrG7h4ry71kLgV+nhpP0FQQhmz6e3bW5CLJol4fRseKvdw2E8+6iaGnuWVycOhfi3+RYTRQmBg2TJF1IiKRV6+CRXq/y8J7ucVOu69J94b6d2fPXZc9RgpOg0gb6MzK9JDWqE2zaYot+0Ea9wBzf2xUeg2oMr4xJ+oBzx4TFMfZwvWfK39ppBILXpeJXo2Q75MsqghqwXc/IL6zkRH1Ez5GY9O6ZqcP69Er6O2nZET+KablORUzA4NBirdSc+iaG5g40XHFg4u2Z135zjh2S7Sjk1xpf982tqzcmtJxtIXj6k5ZNktHpgW1/hXOGK8o+TMmTqhTfzPC7SAq993zh8l7mPhTn0h/FMdlHmtOlukH0rW3CZxzmqh2uXd/QBPeUzDGs0Cy/q+2icGxX/xK8PGHuTC5oIk1yLoHLCKSmV+AzCqwgP7c7wm86Q+p/CY/jfHkLrienzAltKipSS7ibztNsnyJvj2Jrxv5Al3HPSb5N06HG+H7OuoB19EqZmohVZ/BWMk0wNg1li7vgmTPCp28OnAZuJ+tQB53mpcCJriNJL3jbk/PjL263XXTnf3nsFcbbY8zwTwY7SnRvhRtKwWlwef7/LNecJ1Iyj/uknBLvnVUWBOqV1th3fqXRjeTJeEEv3ljMUvte5v4k0Rg8V/RrOrqWMIRqBg0GGczbBRN3pw9GQhyVcQNJlGtKx95T8Z9VjEg8lOO5CAQPd3Huy5RRafpDSwdF0a+wkNUoR07cRbDQM5RShUi4iPziV9TZ8LvJmHzdm+NX4uXtbZR9piSW9d/NNJ8mhpjUV91YtuCwt7HeFibLpeA6tMgmLNtROHd7ffwl93PA6350TUBY3qz8SwN6r9Rl1ZJyonaCsEGT5mq4tR5pa4AKNw+OoItA8tGzEBfPDZojlGx6bh7pu+Hjy4oLcStflWn+D2aeokTaDEoX4e7iFIRE0mmAnWnNSqyBQudTguPwYpTY/2xKqniWUVyzCl75jJP55Jp5XLnl6ntf1KB9+SVfYShIR91bJZnR0XCQJNHqtBLuiwCDNNI7oki/TAxR157k38myaMZJccKOgvJLALW3qlRgB8oIqutTLVGi8LjmnCy0i7titJgu640w+rSsC9tG5WmLIjPAtL6vHJVPIFmZL8dO3iRyUgfxVTUV3yyTLjmgraxyIMmq+xc33BYbTosXw7OKt1U1vwtJCoEJ8vMfXb9HOieHOx8z2td5KRiATz4xuJfkLEuoYbwShJKfwVsZxoGePu610TYcb4xvF8nyTDCVeWspBc038Sl2RKEla728sYSxUw4BQ02ppsCgnFfGoRHJU5f3IhFHDWV6pkkwwHrmDjDV6P5hUP9u25UVuFpXk4Z/EAQ318lWUfD+tfe9TMLJac0eyKY3nDNSLdft9ArF7vP/PUUA7hR30nGpnRga+yNa6XFn/6zs3hznLL7V0QrtbZ8hr10g8LnWuCFnuxGzQmwBF4EBw7ih/EeXMt67rQ9zsrx5f2T5ZA8ZdCX8gFb5iwmkWLcXmhWsCZ8/Ot6twm8HwnYlEbw0vBJxzeDu/MOMNqyAsIugTfzGIzsuVUcUcmVdBmPh5YZhvMgv1BlpWbMhPpHpqdO6lOpMvs8twoqDsiLZQ/9hT9i40TjfrGfKFG1o3fRdzv3WvrUJN6ACpRRCElFxQYn3QH5fUGpGhSz9JwIuHNtohQxpWFw+4tqZ8qneKYxU34sJoC/fjnZXpmS961eCoMlHTxUlu3M0FBgzzHtpZj4XXcWXVu6foDHtA+XeW003qq3wY3P31ucxMJNIiSbwF5CIiCUMDso5y15WzOgHFQXWu5NPSdKa5Y+/kVl3dfyWwYsPcnVMMcrpFrAbJpcl6COJ5H2GNTRInKdS+JlAcKrBr6+YUoUJ9WihE1y4tb+RRGRLHQAgzAlVqStgeROPOTLWbaeIIvEvZ99P+ha4C05dAZ7udnakZv9TkoR+Y5hPfFhcvMsSqVMg/hpvac9xI17T+SppsQFNdTaGlwXi5nnb5IyXsSx/7sHVjsWpyEVnmQXyAhlDzsseb1i7/Uy8P49GzWdLhtS++QR1lOEKDF3aCFhOCYlYT1dDuPE3R4jZUMye+eU2A1NeDFxLV6ws/umcdI8jyuN53MAOu2RNEKVQ0U6vkPRAc1yy/fsZn23fZQBvmhDeLDb+nxucMO0X/MuA44Z2vtoLpvyinnVXdGKf0E79ux7OrDWw5GbFyW/+9UTBjizTmCHH80YMjvdF7jxXk5x2SJv+VUFnBPMWXUoLve1rHaokHZT/9HIqVTcw7Cb+uTxgz72FILdTMHOXzjtzm8HPHt1IN2gNvM3Cyt3RwDhvi49BNam2xOjm8LBEFq8x2yrRWeAldwsaKKRKwx9GHGe5mL7/4CGbz5dDWYwE7Jzo6lejkvikrTU7uUCKc0rqTYrwKc81aZ5Le05lBYd1e/YhPxMV474KHe7UwZhmcGDG2KymlmAfNg2hUXK9DS9p6Ce2Ks0u3na8kHlaWgeT0BT8VE63LZ8URcA38papd2mWusWbCjY63uIziauGnYjtzPVddFxN1PVd5e81IueuS3WmZF1Esv2hr26ZEsjBlkkfQ5q/VVVXUFUgftsTNWVw89Pb5ipv6qXTQV23y7JPKqoXOR+QQSdh0W4c8qGrTz77G8DrfzQl6IqZofyirlbfQ80xfv9RKgX+4t+iQFAAD9TbXpzcctj6ldRpQ8MWx4zJjwzLeRbIK2THBVk4QTGYxR0pkfRqz6AuSfqQaUZx+fxRCt9vbNlTH+zQ70rt40DxYIGKCyqd86hANv40g+eKXOqSFnNfXTHglsUpnsmDYxxia9+MpG50etyRhbRsYCnVfVVwx0bBkcoSmkx6U3YcoyG798EiNpMmcJp66kza8M2d8hy+8CSVP51SBFI3rMBltEdMjFLQzbAjTio2I5L5qvwBTJwJXnb02ed+AtsLyARz9VgPUTalAp2WjzOF1LMOlvOsZwctZKL47QI57D30LezpMxQ7bfEnuYkn9604lSaUsIJ/QH2UqHoQbitXjGTTJ70c/hYWH1Mb7MKU4k6vuPeOwjmk55N+RPp6UeBvQt1Jk0KZ8sRwzBrCZkWzOyNRC6qAebBhKqh3ArWJQ5OpYfMdYUbhB++2yoix65vWluWknusWScwEgp43El/LsiV1OE4D1LToOsTalAOedI193pLyBbwu8jEz30f4l6nf31lMffMfr2fJwoLp/Q7GszgsDtthMnC4ofzxP/5rU43s1y5qzA1/hDv9h76GCIHUHvpJkgcG7UHaVkG+WySaZwFLTCHXMaHhdfQus0x4qgVCzFxkeKO4Jh+6meO43pQCM/Lwyo6PJXxKCoPde0B8ICMnFeXBhnGFUgoR0VQZHXsK24pSu+qGfrLZPkOuSkc4sOCcOgribXXMqJuZv1/EZ6n4G2BwEE1nSvkRWsHls+7NNSwoEhFPy3t1JtnZJugv1pKKfMqib3cct8k2SwbZHLgjEPmsQzDPxb73bCNdjRyyUANj9uiwpZxshmWapoWL9eTKZcyTOax9xus5dA0az/fRqRPm98aD6QVylbL1IQ2K03eKGHxypHbh8KpoH9Fq2dz9sYwU6MaJuJ3jwMweJVHiMjmc887UinU44uvqvMKmF4NzAEEQGr61C/BBPDv+ps+NBq7ke/l3xKayAwVxLnRvL3SxC1Ov5zkCFo1LDBTUkcr3nFUHGFYHGeN8FbAcDD+W9XwMufbpoPs3HynYCE35Os9yS8T30MjjijHIfvfBcattyK6WLL7TgwhAoUY3gKG64kByoYT+3mDQdCyBo57LdjJ0VSkB5Eij4wztwPgqzV6B4YIhspfup6z8k354wrdX5qToY1/HWqBAHKiUufjaUbhFT5YSCdkRHKOazdBT/QguWUSCxu5I0oMtZqHIXOF3i8mDdFtM6TRT0V0436CGzLmyMFPTijvq6Mzc1hRxrHFEbQLJBfbrk/p20mAgrumeTxODwzmF9W7UUqecHs/vHcBiOll2VSVE9kc1YqYuPGp30H0hJBXt2EsiKCdQOFhOsEKeSBl2zK8/f5iXK3EFrawaVKQYl1LSL1Ix4FLtz8uYMIzN7Kw+amos7CFVUgwS+MsysxX6Md8CpyiUTlzt8/ZNOWyHQbOo4NmJOFQHs0ZJzwIpf9dVSQvjW5wlhnPddTVWoNHzUkZP53oM3t8+OJS45XBO9HEj09l14dGeWCbHCxCxJiaFwaPUakBa0tsn9NQoxvELPyLRiSQj6OMvM+MOvCwK8MeRTxy8BuYWLuzG10jMFHN2Is2y/ArcwO2oeJkW+ixQcZmnughtTe2OMAJe+Q8ft2rcc2kICG1EbKqYwulnXLjuzL9+SPzIPsiHvSuTvqbriZZJ0o75XCvUZN3dRSsyPZmEnukz3ASaTucGrSO2vB3KVNUn2B6UG5yh5WMyTPQQQm5ss5FOrx4acHL/1tg4xZfBn3Yjf73CfqUL4Scn69KtmMz4K9QvaT1fnmG9wHpRGwEdzkSiawQLDhlUN2aJFyty827TpgbbXCqqkZHbVwbiJXKJXbXuEyRx0LX7BE7EJfoC93NQQbZ037bsHbIlLKegx+fKOou9xERZ/GLquv7KOu++pc79NJqxV/AR0u6gtLXm9t7Vq+WzyhihQ0bUa8wnvE9TqTwo9t7DTLpDKgH1jXixsWSMytd54LnH8OXt3KzmrBi6JvuGXsv6v5eRFgyjOSbA0OGcGe35vvY+zl+qWtPLqL61miPmdHsOM3s4TN0vhya3dSrO5uN1JSHUFCA47bL3tT5eBOSR6lwjtMyccSf6oRZVhNxk87CxgVVvq8CbazKXetKq0f1bqC3Nz17qRZ+QKh9m4SsnclE+vEdHTwkoZYU02Pq1qxaivlJpLbaj99zMhYhYk+GDXi09vFnzBOChTEUpJlfasYo6MoWvp3ct56+5Vl+JSxCPU8M4tDgIx0K7v9uL9rxLbRx3UxtUwdrEJOD8t2CU1dD69XrdjawF98VaxtO7P/yl4ZbMDgBmIUOIayLLwJVMKsQOgvJYL5VikIQ5K8N62ecP0Y1ieqTjq+WLOh438oVlwWMMot2a7O1kv4ZnUUXkU3Skp1PB4XnTbQdY36UZg6QzqVLC7nm/vW3xMu52K5WHXjWObXzphCYAotp1X5TBh7JpNVyTjJHnwJW3B8gV79BFJvwC6ezTjdsOmz5Tso7eaTu/L3Cijp3tcVBcR6YpMFKd5TtEXiyYm7NMR7EvXCMNd09NowzM8QnUMvNRdG/tmWE+7ZiEiDJSFpxnxb09JOEYJ8BGu/PrXjnahnd60X4p4ZntLSHVS4gRqcJ5h6qlUEVnhn8AZdAzKxi1bw4SPe/i18MBwlNMCk4rNmHQuMTKlRQEYWBfx3FFCMjxbhyZ12FdpP7RPnJyPbJ9zehbnu/3x25TkvKycuvDJQac1KuX2ngg1SnLqxX5CAYpGFyeZWMLuWarCmsZjjm6bZkxqHnPv60OjM1qQt6m2VhA0zn3kCMGM5VISDv2dVXjsH+siD6z9XjNM5ZdNcemzqklonm3SpGvF0ClmFN1mqHuo2evhdY1pvJ/cEqQOJ4CttErBI5LOMdaCiaxhcHemR6anSYksYIppj810qw5trE4m5cAGAXMNhNlKY+CcSur7sb8R4AWqSKyNjrVZO/LX1908jXo6xZ9U2DlXaMRC8wpfxwZg4gekX1zZ8OXmLvAVVrT6rs44vhP9ETzg8AIbNI/eeidenLpt9EAeR9O1q+/e9pTqb5LlNxVyYHgtwEdxn7UxUUFYacZOBAKfoo9ILqfJfU1WpRvYxpyOWOO8csjukJMZrpusouLZzZNj/siZCNoO5CHFyZfCW073GsPMZmcvN/RS3Zc5kDHfhDN6vakOh6LFS7uUFyOCnDMkDSQ0IL3wXwnWcbqqzprqPjYTbCrtuvh0ozPL0/MQszc49cqLfAF0/4Jdydx0v5LPzHtG8KaJ7UU7p/dbn6fc5KrNZiHhinR4WRB2VEVhF7upROseYcAJnyq8zLzO+BFo9p0fmjNrxhqPI5bBpvhVsecHDEyZgxgh5Abenm05fKLA74RJqiGcGUhseuatdpaKXDT0xEosux27WKhYvsqxtcao5yJyiLI+RhNYNb8+xugdRpGKxJl4T0DTMbkRSPWxB6KGPIaKGAfcFO4HhHzkevm+OnsMdlkYzpn/Aitb9laWEWfG+LQqcs3ZycQ/V8H5rsUbkc1ljwz93fqPQ5YX3DiiPmq0T58w5xMY6ZM4fJYoacTRPOifqx9X6mfE/L9FVtQb1qQn/NQisJEQm3z7GjfqzQ+NTxNeq8Li2K/o56+uAY3DBPYfIx1HnlpiXtcidwX0dKt4Fvt5iESYrQImA4Qf5BZKYTeLgnZrOREeTm47CwgXc8cXAnlB37IXAW3fvZN02jAzFde7Lp2h0IpAZ85C3irJrwRL3vqwFqb42HHNrLpLUEdlW/oNO72SvvPwxlDGqQ2gceVJPAl//0OjxvjmGMk+FwnXh+TuNCujogP/CdGQhK07r2ZYPZSG6DSO0DC57GlCr62S4ky/ijCwFhrHuwV9mJMt4y36064gXmVWBmpDevtaWA2N7Mpmgc3NJc9i+W2G82fMa9SYJ5FlhduHbm4R2sIwxfaOdHtPPiXn7XOCGRvc4x9yQme0D0nGbucEjovpfuQELB3I+j6nqijyFS3fdPm0u33Emr24T3W/I56tsPdpPFBsfapyhJHEdRPEN+5eiohLwjWq6lvVL75HDeRvDGgb5lWp5JRU1WvFHv55u0pY7jkd8bYdjSlrnA8ZyH7WiBGvQTYgtz9Y2TOlXmhbtYHwOrIPVc05GtVi34kCJheXkKr3usb+m+j4bkAFBof7thSlDaafSqXx1xYqgvRqL99COpkYHKuIOlhngIdwhP0DzSdR0/08UNRGDQ7fcg2Sja1r2bA2VOiwbBpaLRHhflvET0rh1CJqrM9nOpeAsPfF8EE2AqAO79LLjMAi1s4fSnOxNMtxga7jfL2wabxddGcph3yu78jCrpebJUI8g7jHJM9VmaB/WOc84FIdEiXrq3/ehVfuYz5RbOODv7lCpRBwLqxXFybF6DlpGLkLpns8p03GTHTe4VSXgZ+Dgn+zf3tcxYdWwCoT4ZojM7Co7V7MWDdgRTk5/Mr0WvUuuDl2JV1S+yr629V9RAgFKDyu0rWttyGstVgueNxE3Xr05aCos/vySv+XJkzQMI4uf6b8Z4XXIu9C9uxTvNdLWwuMizA7bzMiHDqgIxNFCYm/UjqzkBW3MwlL6eY8tj9RhYxSFJ0muchxNJPuMDSjJ/ZiJkCpns6BhCsX6usjjftmpbFpH9v4f9F5TDPoBGQZtd1kyxcmrL48hdK8C9rpJi2KE29kJiHv+2Y/9gPZWZ3GruLBpDfm5wC+i/QZQtQdGjlLFlrtzqQK2/PdjV9B9aOsjcOhjbnk4ESMzlGYQqv4CVIxEbC8oluA1+iNmPTdY9QpMxOh/XRsHGtu1bklTfIoPvRNvubTk56PD/NbD4VEvi/nD2f2FM2uYZkImNfy+ns2iW5yP0U5bnI1JLz233ZuFhj1Gi6L0V05/mjc4lqkkHf5Jra2te71i+HCCrXFalqx7gPzftcrtYXGcHFiJOg9V80Np+MtnG9Us0dvBSzRvOFoDccNdhVi7K67E+5f83DqzadkVH7xHWidXY/rrdmqzWxYfFPZkBRk6O8tLPZLpKzQjVlbL5rcYWca7Gnm0QZH+wFYPqjYWWge0sWsZNR8kK0UjtgVKOFx36xZ8MHjU3MCqB6daa/L9JO+DV0FZig8/p0p6uTUvLepIrve5sQEigydGN5AV0iTjseUWzYbyliUtWfGuZaJ+7hISHLGJyZPWs9eKejQGDJ+QmnjxB5oHCgCAuJ1I26kQsfzTs9AKwr8N+isFMDzM6/iqUUp1li/aD/HXTfSa7h7TS7h6JHFzzrQJOLcyIe1ilgWu12rXNGpvduO6cn6EoFsp1L2grpZqT65L/Z1yZeXFBko/Bk0jvunnwnikFhYPtv5yfhn/ehK5uv+mEqWU5qw3CD2tgd7TO9GSkE/u3rHVKdi+GWOqw7bpOuda62DlkT4yFU62137mrwGIthDNiLd2k4MQ5AvqRV4KOA66SI3ZHjbYTrxYI9aEXYrPcDPKlNlqOfkwkQlzSH3NadLkN/2LvDx2fDXM/7S1Kt2SeHXrLOCnWFNQ7Z+pViRsamD/PvRGxO8tQKBhsZ7ZsonXLYJaXSs1hjB7UjdqIlcVFoB/BHlP6zch8V8Y+LoUqny3lslY0qcE7oszuvLtMIPji6OCr4ppCSf8DJeDnA0b9T9UsbLcX7f+d1RY50mDOkF7IWtp7EsEOLh7fzK+ZgF6rNSpOyNdNyaZLVGO7elDIOgbzGGZ1pCDodI61vB5RyOAmLl0QzcSPwRKDY8Kncf0AQPo2tjFGnt+/y+tOYlHPr6vdUzw5NtAyKEdmV2cSylYQFB8vhReuPRwM7PpeGbFvF1lLIzNMOF+H5bE6rFb7Mvs2H5GhRtg2h2DT8lTFqSTp979s/O4JItU/lNwCvPg12flbT96FzGI6xgDFfXuHKSF6ujan58kN1t6Huc08lEMOn9Qnl52CWJrL92fdzRhWjPSWEd23nmaaoLm0jgcV9PCZSyW400RTIhdbJM50bLi2MrF9CevDkqYoWLDrk1nUa3a8I+kUuKemheUDaixu+JxpqPBY82DrVgWymwmX0bn7iRIAye4apo7TkdHScoLJ+hIkrdO55H2/+pj+MQ+VUOvC7+eM6OISBAy5NiK39D/eCDvBSsibfHFFkBaoCXYuCZzSSfDDC7lkXTFTWKR3Lh0wEO99M6YH1r7ctzKondjBFa8ci/8EjJBu2SlMtXqViziMepF/Bvtjz1cJwdjb/nteYojaXMaC9HpcDJrwjAcVaEfyyPz019IlK/MdA0ZEpg8d/QfnL2XlFYfTLC+qjx0zrKLTP4sKPp08trzGUGq5sq7roa4mjjwbnwcrfqvVay4WZuhxPz+OchgPIkD0bngh/JdxiXmdS7SFg9Yfto/kBGrvIKbwvjJIrrno61zojyvY3507UxQNcJ00iukIvqCk+rPb/PS+KuFIlMb3pdsmHmkpAalr4IvvXomMl5tFybo1YPbef7t2O0yi+7jwxtq+rpXz4FwVufX8y3/6jzddbWHfOCYreY/0T5nhaDiedo1TGMsMw1xzeHQey0pJ9jrUP5h2FPHS0ZH5bUEj9Gk/qAY50x75DAnPcWDCHWhZhB6nvudBKbBb67oIGiLqZrbNQhmJxBaReRq2yxp3CKje7WWoz3eryKJcgWfF6RFA4tbAwmm57uBcg1G5mNmYzYc8rZE5UfRejXhRlNn1jrNvc4Er/oDFAomTHaC/AfmomFzlsZCFCd4Euo+Qs7AqRCo9VrvHDPnAhjYTDwIoMoIyuKSeFvbSUqGSqyX+ItOIh8CbgfuYlm2/oYMSkndCd+Rim39WhXz3KgaAzComBWc+5zDD8fHzsTvJMCgJWbGYrfnC1Iep8s53ndz9T1zRhtvsgtKFaKd2EI+6RNZyQmndou1HVytlbw3hOUVGtV6JDRa+wktIN3AjvTi4mvgTsUciyiWp2mrQWrJtxb5O3Q/BkHcOQPz5up91Vl91A/vd0E+2sxYWj/vwCWBWn6Rxpgc58egJrNj8stfRlyVTsoxFV6WDMZZQ2fI8OhDdX0+9MYQOS/vETblJDBKDjLoMRAJXhaXUr7NLjDaN2g/rYc6JkNrqPMe1vS96lg9O/gRL3rQV7UJpBLEIR3INhjoH0xvSWpWlSlhg4bgREvV/BT/SRvcv5ZLJb19hHmmO8X5jVPPF39hwjwkBMXWXl1DthtBqQ0UCm3szdcNrTfae7ORR/cexFa/u0gj9xc9AooNdgnJBPMcso0+mXS1yrGa1swsD358a9R0wje8u2/a7wwnqHJA47QR5vIeArSi5Z//WNeeURsQ+qb2fKPK7Iy2JciMcL8gFmlnmhBj4t3zelPSjoYpr8Fj6iDlrKaKncR8fdDCZoUaqWrTl4L4IdxiR+asglwt0v8dGg/L5cgTchVHAPIZEUzHJ24k8ySnzwo/prQsED142GQXxh+8OeUi6QUY4v47sDdwTqeFzicCWSZl69x8QlqPIgbIQoFlqoOxrORD72imuediDPf1L7MZBeoHvCg5afT7q4m4W/YE/2uI8BpBljMu7r0w07F0sFdECAPHX0T9lcMFQ02+AWBPgLeONG2pxwSwDi/8oUkTjFfectsJ2Fy4JrhdMziizZ/X+GDup5Je8y4WoqHZyMd8FpMYKQ2Hm+/SUVwLl9v6f7SQVS9BNpT6RjgeVp/Ze0cqZkM3A+AqRuvdf8djw0G8kQ3jyNc0DVGd+g1wsswYpLyckwApKim8WJNH29asc67f3RpFHQ4QTvAHOKPmp0KCZ3O5ymANTpxXi3CQfuCuoyq+WCTB+n+vEPjKhnP9wlrC5JlJ0toZ/Ci2+PhNU6q7FkaE8HN+scsgr070HGAUfBtNwGOQwtkO27TbcoD7GY+PgFRfMm8l2veLwSDORyMRHPewmVlZEUcpqEqroeo79oFbGgoYosbqkW7X9FRfLylxwtZO8o5mZx5ei9BUHJbkYFX1CBNwbRxwKJ+8QrizympvmJCt/0mXIaBcd/maJ9OPtzi0TVbwd3M1a7pQGic5W5DwTvTUZug+2xACzk0u2y5RmNcCfN2CbytMD3LvgSB+c4s3aWaW0YIkQwRhLK97Qh2St827b2sRn5cZspGKQDIJtdQA6oZMKL6uPm/NUOec8S7XivhTKNBNOWe8h6UhXDUhRp7SLBRF0nwsR05NlqRKLUj4t/kDATBaC90patYOu2UE52h+tyDNYBSz7M6Sz0ysQaW8x7Mc/X2sBwQ6rPKThr+q9tWUolxep+H5kUkhS8P3XySISAZ5ZZK2KRQjq/eK+qHL3NovgEprhzPU6D2TEUDCNSyctnOGf0x69mqgtz7RtVXAuMmafZd1WGeOIZuM1Th9/euXca56Unis0dJzudCs/f+WRL2LSP7Pyy01x6nEK6yG/0GeznfDshhcVN0J9W/xeGiLgExZgBWiBzGY7Y7Kq7SV2HUripe1uLBwVwfLhRGyA3X5E1vQoMuvqJuBBkVUbDFnJ9sxxuYgs9m6KjurbwdVqMZSLA1Gnjd92hn1G5BbjfyglzOuVOWHrbKqEN/g16GpDmg8sNRDzfPLJ7S+cDvr81EoTxOPUkeE3ncFdrLRMYsccm+2Fe+P1ILOQvkDPKdgMs63hLMFHihjUpC0Rn1/a+dFY+PZnPnO2sun3D3NfQzaZJiTcJE5KcE2PsTNwWwYbVKicE0c0iPLvZa2Jor/uMI4kvG/zYU0cpRyLJkQN+6LG56exQSSSVypWReJvZGsLDclIKW/SVtbZFkkL2sruLM+bx+7SpWFwO/kswwK7sJmeYAC1j6OljhVhQAWefcOCiEubJ6BojFWWdztaUvlpodat5sTk47u/Zeo8O7WM2TwdOqnR5weIYbSsYbtZNwcE35ZmnmylcogA7dqXYl6vExk/gdTUAhk8Syb6E7rAaQCmVuZHN0cmVhbQplbmRvYmoKNDc1IDAgb2JqCjw8Ci9MZW5ndGgxIDE3OTkKL0xlbmd0aDIgMzU1MwovTGVuZ3RoMyAwCi9MZW5ndGggNDY4MSAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNq1VXk8VHvYz5bIvhWiQ4gwYwbZpjTWrI21hBjmDMOYGTPHHkUIWaON5CoqjJS41lLKFhLZSgspSxSKYrr0ntFdur33/fP9zOfMmWf//r7P8/xGWQHnpIklUH1ACyoF0kQhtAwBWzs7PORvBeHJJN/dmo6gXwgZTwdQCLSWFr+ysikdxEMkKsUMD4GGAGo35A84gTQIDPIB6QDsYsCvDFiCFJAO2wmATwRgB0J45wgaiAJU8esCjsqANH3wDNgMUvxIFFANDjGl0iLoJD9/iJ1DW1OTnYkdbYIArPG+gdQwRiAJwFMIgDXCDgHYU8NgJQlQpVIAH9AfTyYCVCLgDB4CXJzMHZ0AS8cDLjgnNQSc2CmERqPS/8Ri6uTsYqkBmGHtnc0B0FUDsHRxcmZ/O4MUGL+fBmDvDNvZdWBHdriduTPW2Q1njkKyzwCggFCQziCxy/6CTQVGBvwDDQ4l0qlB6wUAVX8IohkikWFhYQi/EAaEoNL9EDTyOj5nfxIDCKPSAwH4TQfJ4DoxIRQCTCfkD/5IwG4OYEvyBSkMkB1kQf1hDIKphINgPfQ3MJgIiJ2T/MMdYIDgv8r44xnrsbY4nC0QhCdRIJCCp/jCjhAeCmEA3us6+AEJO38ABAHTEDqdXcPuLxP97zJ/QTehwifzIEdF48N+7RieEsKI/Imbfx/bl0phkBgQ40dGECCSyCAbPYPdMxJlXWeHtbeyMHdy1rSFZ4+iaUeF2aEgoHBo3ZudD2tmawjooPQAFPyw59ScQjClBgXBqBn8bPrMSDBPEJUegfzv+Q6kUMMoUf+HkUiiEIjsHhBCaEgXCik4BLQy+zMEVvH/o/MDIUALAIMBMNzXH8kuvD43bDWKrYYJiY6iUWkAEU9mgNEkIgi/+KMY+FAQgOghYHTUz4Z/S/zw0QgkXwgeeXht+NezW1GIVMDghxpG8pfpz2FQRSPgbVKDV5ZApZAjAAJI5EfaUyF4NFT/fzbul1oWIWSyPT4IVP1PXn91xgeRyBH/2/0Xt4MgG7WqPZUehCf/YiMxLEjhIAFHgnz9f1D8Q/8jE5biRwYBTZQOQkt7N/qHxYW9aWR4pOFricS+2ABNff1fTPCw+gZSQAYD0Eatm0CYll+ww71gIweQpqbWLqaH1P97kNZ9zSm+VAKJ4gegdXcDeDodH8GvBU8HWlcXiELBI08Aw9fHB0AiKFQIDgFoIVA0QKTS+dkt1tMHkPZs1bqkrwMgnf+W0DBAJEhjkMjwQf5SorRgH/+fxN0AMuAn0QBABv0jomBn+k+iLoBk/JOfLcIbCeHXVf/mAMe+HdbHXesfUv68NtdlJ4hODQQPkgjwn8ZPLjBRdFK4uxY8qyhYD3/++uX5rwLK/6zZT9EmJtTwKC1AE60Lk4jWQQF6ulrR/4rz/XF7re8I3Lm/ZPbVAYBgOOjL/2yQ6msUH3Du98SSGPMrT0p5lA0Qs0ypvYess7if5T5plN1q9tsbRdC4KLb2eJ5KEdV2v6FnzJlYytVDyvGS5LVXdTnlPYsEh33j+Bi7GFkhc2xngSvCJS7Pbvh46V1FtWnrgstu13T68uqz6uUBl873pgaNTStp6MffRT+dVfQorX9RyBNW3I+qkaCTxcKHRWTuyA4/ucMBfV+RyEjFN2Of7XrqfTlRqtN6I+1+k/gk84i/mHE+q1dyk3SwhR+fYaP8jV0jiSWIkSTverBDZXfUMqu6u++kgPUNdQOnONOXG8uYxnJl6o3d2vUlwg9QWmN03TNPNbpFc4weTEp/R48kaxdhKNpS6uA+Q2ORwp6Gi5S1/rXuK4SFkHODwmoJ4VcWtwHRaOSna+68b4I9P42fvRvZKz5dRIs0A/glF0y3XyJQoWLORMWFriq6UqQm5nTvRf7lXedKs002KzreS75QcHK67uK0dNbhcOzbY1vf71Qb+ZYmrOIWzsvY2Z90WKouU8Vw/lI91yD4GahRIvgohWU48krHvOoOvKJQFXAxSnZUoq2UPlkOvVrsWpXm+aRXQffUl8w0GLdsICFuNpg8064qWhLV97uQa/SS4soV1E+NKs8xH50hDTYum0HbnlpFaBoHRGAJOjYMjod3UnECpxXsy2T7+dYE8ZZCpNZLxDTGAjYlOftS7vCNgr01Wwv1sEPIiKp3cnOVnAVWEaNtXwtiw8uj/+BySSY0HbuupbiPJXuUyaHnpvRQjnNDK7fjDYzGwNeZQJalL90hduvz8vuDo07vdE4Izer9JsWvxAqtDTIecj83tcdGXkyqgwNKR1UzHXrd22tntg5jxl2UdxQICC8piY6mHcq+l+XT9Nt7qzE1juzerINX+L0UIoI3HlfR3S7dNHu1ihsvYR/3m8PaSM+1m2fHrWNmxVeMIFATsXhtkyOiLlLyfcOur7aTxu7Jul88O/ZethfLW0jzOmWfvDlWxHMjg8eTuTMEZX3vqNwFk+OhXO5Cl/n11fHXq5xP9XwR5t8/6jZpRfdGBwznPot1356325S/1jEa3zJXW+oeeEId5zTUjEue609BcGIUsg6ZCIY1TXZGuKomHpRkTXyoTV7cMV8XXcNU27kS85DZPL3Y+JYyEah3eYE0jgr7YLbJK3t1mo8Vlz96wzO/6ohv8fn8iNfMqivot6vDXaePBJrhBiV1zh26rPndPupe3dhLmfO3Xp0eThuK4eh1FHE96TZcI0lAsB4qOtxxfvU4lPXh6a3Oc36tnW82DY0haxU2bl/djRnDXL8g2JM/lnZxMO9lFe1YlEZciNT9q1XZjiMfg6wT98SKnzyzpNBa4jkr0XD77dzGC2FTycH3aN9uaChfEzFPntOP5Wfmor8lZex4cBzj67ltkrv5lcX1iPp3NvZe8b2xlm5CPDa08c8C9Z5FB3K/3b6sX/M8U+B13C5LPOH8hhzmKWNrFeP9KpidFUcEOaMslslGtwI0b4/ZadUYd3wL152RV7JcWMovioiTKKrtCOdKkcPZTGXn3U2sGFL+tIdcUyojODenVTmZ1dx5wXtE/J7MNPVWPb9flGZ2qFgp78EL1dmKqXa7eD8tG6arFB7RXYY49PQTv+et4VINQ0QOoshrC7XR75h8qzyFoeq7WZz9GpKLLlLR7/FP+ZfHuD5zlBcsf++xSXv2mB5TN7ILAAVB8RlB1sFjRcx5BYEpNcRrmw3vH3Xa29wfalZKEXaynH0T3eIvkY4LlLuVNl8ut+tRcrV1xZNSroYT+6pTC4T3DQq2PH1OGp4jEu/78ugXiOnWD6OAia0Nv7V94ngwEae+EqugKUF8Y5BT64DS2VuWGil6PSG01nSzdeQW3T6/Xl0nRfSaF+fnj9XP022M88uOfzR0dTo53LeFmHBLsb5/KTzGzd0NikyIiG3alC4NtRr0jmJuuFiPf2/M9j6qLtIw09PUnarG2VuBbpaK3icsqfyVu1FWLePjKFamLNe7eXXhaUhu4/P8qu9VPbSDRXMLrYES6VwB347N8y6klNmWany4qSifqmrr2l/ad/WhRtKkJtrN+/jhw+FVCmSM7v6wWZG0tKnV1zwZ+Od7+Fqlpyl4ns3nv3GbuIktxi/V5F3k1V3Z0zKDADtfy3+jRTZ7Hb30cK5Y9FIQ721HRvdjnEPAwYUszniD2XT32B2kgWfeEzl+sj6456z6Nr2vBI9mtAcqik+SI+uUpwnNMqXjIBA3+IycOMUxcczxo7MGUhKtyq99X//sAwvz+9a9BV2uXIeuCRRayZ7O0lJ6NlcZQKCVNLRdLP00/lUlrk1E+TRGuJnnSiQ3Nke/eQALRj8Y7mTkPfK8sGPb2/2s96YFlGELhy+egj2jGSdoXvv6TEL0Hn5bTblh8Eim4OlH1oC/fd681AhfAj5if+6T+xwipYJAVXGNcFN+SvUBb8jdTeVz7Cy27OFJ8XeH386lcG1ut3HLs88ZvnR28lTVFXX5MKWxtOLEObWOwc9b6y4UhFur2wwwF+u6RnffbnH7aPLu6/Qm3y9WPEnCEuNN9259l+A7MNOslLMx+oNyLYMZaqtLc+sTCDKbaHS5W853JgSRWtVq0bZ1PHXnK3GNAfQDQcsAj9faoTN1hplbHptDyy+uBk4x9qlVgBlhTZFazf3Fjw+YbL57APMeSRZzDE5/Eem1yzlWSBZZTfMIrekSUXw+SX/ulnI4rS5r1yODuyVEOauSKzL33rV+mkhIKe+5/0C8oXtHVc6kXzZ9rVOImz58Q3/eRU0cqyHCd9y2On1ZVbdum3qCDuvQw6GvfpfKPVqlVKLxenYtLO2I9pm6nCXJzfjJbFE7ibcvx9eyinLO4jb4Tbi5MMuD/0hMXp5PnXvOeV1aNDU7/Qxf9wgTQUXVYhZw8fvKCR8a3J8ocg1H3ZgX3jthKy18gkMVbHTxj6MKShv+nvuIPl0Vo4i+z9rwCpOcU2FWOlIw/1EFvYRKkzO145fZIt7cADnXQlfKH0+EfNrWfLFpcXpnQH8FwnnF22iB/seeF++CYgV6ukvEa0LvyfZhKiOxR+oeDGHCbm7iE60impMNmeHy5X+gg5dT7lsWTKSpcxaWYAmA39QiJ7W9LLV0q6Djkqzdtzsebc+yfZv3cOekJU1VHnG4ZnXpRcLNXq3JCkbw40Cc8qP2Tb30SwaZ/RdieWWL2pZXuTZDUlkT8TNvOgS37qU5Dw/o5PjqbhAVMZlu7Hh7OWmDvRK6LoHTObAlpim747UEbY03tSg86ShxOO3C+UJMzgmJbjeb8Ambhaj0DfvbiZlk3vg3e2ZtkLqdCOs+j0xxzL1o8Y8yuaIqwqfE07OcFuzXjJWIjfGyfe2Zkfvdl8SeBTVlvsNaYPZFGshrKnfsxu6YtY3aU3q6qpgb8J/t9ys4IRj8lCkbbL+jxtU9KcDSTiawpPGz7pYvkUNvI3ly7bqmWzzHoxV4u1QrM05dtj8u0TEesJza8TL5Mo9989VPRp9Ne2v/wLbGNBllDVBHa6HMVQ+Rz3yiQN2HHWWVG5hGqJTr2584Zbu9Wbx9dHpIfkPoWprMxbKFG3xl289rnTTyeSuscvfGB9NXZ2l81ob9DnEmd+QoSrpJvMiQc160bLHMK4O3/aX36ldWy/YLyOdjPbwqjfU43vsVB8/JL5Wla7MOLAStCMyyNG4ZhKoX9XHPn8AG3bnI2W9s8ujcXtXQBxCrO8p19M6RQylnefplbLb0Fc35+wZPd2mdQ/QuJnQzd0Ton401KXubPXFasfEMGWm16mcy07LmrCezbXFFA5fUlZPS3XdHo+32CZMYDlf91zNmM9m3sw5KZH7vEHi1Z4EroSHDiNV2k8UpGtkut0pAmx7WRylEHapv5zC/e2SHUEWb64ltm0huN8uk0GX5Xzt4DDvV76rI0YTvFpVkO6hMIk4Gci+PWUUpGwl1TUe8OHzaMN+iZEmf1PTQ2suY5w1H1q0tt9C0LXdXunOSKjl/B+OCmq63h1UtuyHVIx8f5tnyWZMixLdduyO7WeKz4u2OLe4KnV6mPbEbN8leOxOVcftJ+rmS5KldRMmEVh/3xSc6p4rVKiZGB66d1pleRoy9pcW86IicUPs6kq2QQd8ppA8QmxOPhodjxwufjlJWaqelTYT3Sn/6uvPA6Ahw+GFu+x75FX/pFhfDRpp1MOmRpavHouiGvb7v7dPiv02doQdjiyyvnpiFQhqVAoSdedpuMe6c3Tg0b0L8zrrJfxqvl5+/q6z65XD4XmPIva5vE02+4u78lIpbNN2/cb9LgdDv6Q7yw9W/j7yMjV/cPqZS9JxFFmZ0xeJKrMy5zHwQlovKFQ5TNU/TcLeSDBudaEItUr3bzhyXV6LMVJntLJup6CLJ1xUXFfu8CvdgeoR+UCRjHM368QMWxfhjGd4HCfUvP0cj+9oPjAVuMeKu5i7i2uZwSfJwC5Hz+dCHZy8wUEispuHNdFTzl1d1+glXxJxiW7mYAYDil8qWmlShDyNpYq2XV4uXCmvHGxtQjfFmSRgHuzcccpVuGt8zA5U5zvQYaOLBPe1z8wceZ7xA9DA9qSmV9LO3fQa0qekG+H3ujgYsL0zUzauYc8JtfkUDxiu488dJM8cCc1iSCEeSsUCea0CIUyjYovik3cT3A8nKO1KsisjrfZOZcX+/WXcZs7qDXiG91fVwobGFXvkOvs75M0S5mm2FxvjQ8tDVk24eb8zxDQYNzAy1SGUS1xLvmZBLIycXzB6V6tXZXxwsvKCT/Vvi1UEpZeuMsqKVB7O4+LqNcwO+V/kq0/4HPYIyvAplbmRzdHJlYW0KZW5kb2JqCjQ3NyAwIG9iago8PAovTGVuZ3RoMSAyMjc1Ci9MZW5ndGgyIDkzMDEKL0xlbmd0aDMgMAovTGVuZ3RoIDEwNjc2ICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjatXZ1XNTf1i4hIN2NMHR3g3RKd4MMMMDAMEMMLUhKS4eAhEh3SoqANEiDgjSSonQLd/D3nqPn3Pf+eT/zmXhWPOvZa6+9v8NMr6PPLWcHswEpw6Bwbn4ePgmAhqYmEO74BA6EgG3FuPVADp4QoDuAn0eAjw+LmVnBHQSEg2FQRSAcJAHgF4E7AvRBrnCQiw3IHYAIEcdiBqiAoCB3hN8OYOML0ATBgQa+riB+ABvwN9CBecC5bYAeCDcI6gCGgtgRKQowV193sIMj/J5DkJv7nuk+W54HoAa0dYZ5eziDAUCoHUCNR5MHoAXzRhjBADYYFGADcgRC7AEwe4AByARgqK+kpw9Q0dM21NFn50EQ63u6usLc/0eLgr6BoQoXQFFOy0AJADLiAqgY6hvcfxqAoAj9DlwALQOE/74OIvA+XVPJQM7AVEeJn/d+DQB+gBfI3QN8X/a/tLEglAH+SEOk2rvDXH4XALA5wuGuEry83t7ePA6eHnAemLsDjyvktz4DR7AHwBvm7gxAfLuDIKDfjfGE2iHaCXcE/UNwvzkADbAtCOoBuk9Shv3jdEG0EpGEsMP/LQzRCPg9J+SfcIAHCPQfZRyBHr9zNXR0NAAuQDAUDoICobaIQDgQ7ukBsP5tQ7xBdqz/CAQBFDzd3e9raP7L5f7vMv+SLg9DrMwC4h8A9P7vHQNCPT38/urNfy7bFgb1AHvAPf5hBAHswRDQvXqP+z0DQ3/bNOW0nigr6RtwayBmD8qtCUN0B8oD94H/jr7nk1PUkAAIC4sA+BHv+zlVgtopwFxcEKo9sO7bpwhG9AkOc/fl/d/n2xkK84b6/z+c9mConf39Hth5uvIaQsFunqAniv+TgjBh/bE5gOAAPgDIDQDysXXkvS/8e27uzfz3ZkRDAvxdYa4AeyDEAxQAtgchvrD8PYBeIADc3RMU4P+34z8RFr8owA5sC0eMPOLYYP1mfwK1hwHE/zEjlPzL9T/DwCbAgzhN7IgjaweDQnwBdiB7LF4tGBwxGmz/f07cf9VS9oRAtIAuILb/ta//HQx0AUN8/+/w/wozBt2rZtOCubsAIf/lA3sog31AdjpguK3jPy3+x/4PkxzUAQICcPML8fAJigj84zG8P2kQxEgjriXw/cWG8N/3+T98iGm1dYaCPDwAgiK/XSBEX/5LPGIz7qUDeBUNNdW0FTn/90n6HasEtYXZgaEOAAHEsALd3YG+WHyI8RAQFgb48yNm3g7k83t+ALw8UBgckQJw9YQHAOxh7lj3eywiAuCVvzf9g0QBvAp/kBhCwL+RqACAV/UPQuRp/EGISK1/IzFEpN4fJATgNfg3EucD8Jr9QYh6wD8IwWLzbySAGCFeG8Qd/8ePMNj+GwkheG0Rp/KPn58PQW73h0CQH8ALcvUAQxAb8ScGIc7+LygI4HX4CyJIHf+CwgBe8F8QsWinP/z8CL0QoIuN3d8SEBpd/gq5h55/3PwIhX/ECAjcwz9uASFEAVegOxwMhNiB7e3/Wsq9wxHM/xcTQqn7X0yIdbg7wv7yI6R7/GkWotEeEKDHX2vjR1DC/yJAxMOBf2m5J0Dcl3/1n/9+wV5/4L16n78got2+f0FEo/1+w/8cbp37e//3Rcb3Z9r/54H4G+vD3WHOIGOwHeLvwF8hiBPgDvYx50PcQvwIO+L1r1+W/1GA+c8F+le2vDzMx59bQAjALSCMOB/3GyEqzBfwH5m2/zyZft9/iEP5L3z/WACAQD4gW6wvszBbyTCn9KYXpYFKBeNlaMziPN8ryKRN1BIffMkcb6emUMxbYwDJvA1uDspieQvTUJWwDEwNhhaZMIeRQm6XWlIqx07sdGXXgYGagdR4SnLDuUY8hiFZmp+Dyt4zsO+o5b4xLRaazGpNbKUFGA7vKoi3d17GCXy6IzxKY7Aoa/2aj+ZdOM3/jsQdQuTzmYCqg/rzeAcy/O6SJD4W2C33hWPK+s0LsmE1dNeuTuKtCitHIpnXVxOkDyndlB0wJdppqzgWXpTyLERat4IGWUT8L64aRycjcNSqOMX1QxQW0csrZB6Vc7aPCraW4vfw8626C6dOcY0Spkj2bFHeCSxECb59DBUk4wTJSsgQ5I+1ZUNvp29HC+wOPdNn8dnDfQpOaAABArxHxeYYa26WR+tp7/0miIQd3IssSKZfo/ZPG32jYu3eDzbnW3Os4a2d3AKj+80fPWWzQ8VYL8nFfnCiRkuc9FY9ujLk04R+llP23oncXrC3ReDGCBG/DP+tG+40uzql4TLneclL2u5HbzhsO223sxOlP9CuYqEjz14zvj59zfXDy+FkRL3aHpvpTPgy2DZednvk4Dn9aeOtpAWhJQESnjrH/Hltr7jwHpxE/lFDpukFd8Z4qO4w9sEHl22RsRn52Tjdsmn0DLcj43JvwYvdDg+6y5r1Sjsk7NG2vTVY5KADX4MFiVK8fFIgE390ekOI8LdbAG4DBvpOA9X5kGCvQ6e3fYe4NXqviCathzMzSOzbhO1osYW2XBpg5r3lUu35XKG52ac3dXvqPq88MvqnYqT8jeFfgj5EMDqNeCifUnhh5OeED0hzQaePr2JJm572TJC3W8ncCha+K8p97mY6ZB+Q1jgurWFleFN7skYSox5X4umQEY8xREFXVNDTgyUYVAHTSkp4rNKpmsnoVYFSgx07/mglaNBJiVuvoRGWCmjWsTm0UGULlhJJaVOf0mWWGwCgDZZZoBt4kURrqSxMm+DUVk6iT+QEgKRZEn6p1ul1fl/gshuEwb3rvu2iJZeGhdGmXfIfUHMCu6FhOjNH9f6630XUu/MZZSYeLt/iI1UZK5stICfWx5FspxDfDHuX3lRhnmbKWAgzu56LY+88Ig8xXMVBGXvX5Dt/Aa3X7dhrLp5eaOkzJs0Xfjow2WPYpsStWL0cyOwAk4I5L2bwKwtKzbMvBM8bnGiO6duFB77BbUxAP4/J857MfO+nH8BoUvuVuCp9P+F8ooo1f9iw+1RorVG51ZC/KjIMpwxOZbxPpxRRzAOvzn9K2pegXFlb39STVP3ZyA2W1qwzewck0XbK0R2hjn5RoBMxXfyy/YHsabJa2zS77VsMXnx33ehd1cTNyhPshFiglm/gV6Sz5cRBDhpc3uQXzxazqOlkY+4CFTmDfL1nqLq7515MK+VS3RwFrx5sbLvhKe5SvKzWcGmxJKV19K/Uo/R3wTsl0LpbM/mgWylF9+A6gp0jK/FtXdEptvL2CqYA0onUlYgdSbcFq1aTcakZCvdbf4254b73lS0breNKb071fqkCcyemTXW7sMuOhU84f3xFjfqQI1hX96tOQ/oFp1+JlTXSL+wtpcOuQLHYL8e2U9xxt0+un29yz3YaDpg8mq82auOGPH5HaTWhOVQLjBu6G0oSU43qKcu5PfbeyPIjVliOQTNqyyqyaJ9tlGQOLUBKDa2YYiV6qWtDMqoSD/Dyralvpp/XUcrEuzi9yb/zf8V0/oToOQeGBR5k/jHPJptTIM/3Q5nW1ueCWl7eqH18UUJPI4pVsLlxdocDbhdoJJczoYu9guxnjD91Bms5JCU1iBkh7f3R8ckjnpc4iVHs/j82pLruBHKGq1Xs6/21zGJV7iDhweREciUBn/Weenhrsb9wwNkK0jlTUGm/fsyPefD0IISMqe4cLCi2UvQmYJecLlhJqOIrWtzAp0P0jNqqTVhMseq39ZIlSVSNBbK8z+OuuRdhRWG3N7jf1Zoud5olxNLM9Q+MkPDykT0wTc691ONPWpb4Mmn3nxt+E1B9hhtUstlptgWyTGM2KXqmz9/jidK9vvDKYWO/Gi2I4sHDtqoOs9HG5SHt95J+Gih65kK4XQN0+4sEXlfzwIMgKohlfCrT0JhGHhaOg+liHP3V/Hs4j0WMuFFxwousqu+dhz2anE3dQleXncE2QeCeJMZpENL8BYZZeBL2DPr5FwnX6Izn8PybSVrjqNK+TKy3sV1P+KWiqNmy8RreMbuOlnGucPTkpVQQY2RrdQfKmJTG70maVTLn1IXTCd1er31meK2bgUrVKvVe/Jfih0C7LJIkD9wu2vNq0Rx0v/M3lANLNY9uHBkOVTP6Br/PU42tdprWb+vyDgykTz1HZr4I0O4rZgKoh5cAx7fZKljbD/jjHmqwElqw2fv0Poh8qOB05hUarGIlNxd76M4k5R/LyXoraIC7l0vdafBcX6bDR5e2DnWYSk26gYJqDjs7Ud5H7WcH83hOBY6gusCEdL7Q7ZSYRMERfVAgyOBhUEBfopeoxkQ/RiA7ylMSzbm9WbZG+uD+65ATqvnp93GFjHEF6rnVRA2Zy5lIXgYPMzLdep8OdsZ9tSQvU5v2x5NXKGW3iLvj5MUpIMZjyIIXjCWumWbxwhSlZJa3qTJpVGJlvUIa4rqIytovBwGMstLvngWgLUms2+FNNPKiAVqzNnTM4heDRGzJAQyRp/JmmWV2MbwYNXK+ulyyWtPxhxxawd8fzzARM2scIU+/lqY2HN2ISJ8gmjWh/lGpcY4cYC0sWxgbo8pjrV+OKQYbhxHxZkRt31pHrmtab0ViqsxfCoUrSqn75Eqa0s0Wbay9IuZqzEAKy5V+eXPqDRFhNrq1QDWfipVLTmGZKK2wC3+fU08qdtRX+UWzSy19h5fBhLeQqneFP5HJ59AZTy5ggX6MQ9ZyXYufb3aZcNk2UfMlBzJK8vq4etH4nfGJJQcVKW91l7aYrEqhfnfbcJ/VZUJok9XK14NQHJT3b4bWHl1VN4p+/nI8lFfmHO6e2D6f5YB2UGNT/PIGtBZQ/FKjWXNA3ohYzVA1gO2YQ8UFLHMsnUAsO7VUXNsX4IIredreLaUImc+Q9nvhIJILKmR8cQPe2++7jWug7qdpdrKfZKnSnHKWP07Ary4MsWHxnMmejy9iV4tk4SZ4vUf5wDk+EJWVU7eNgv12qcCYKNBDskmmtsfNT0biiFQsoHPva/5SGa4HSgd9A8nu5ONyF091kjG1DBoNnic3P8t3uL9a4RpcX24tP7vgqgu3OF+msDXynXeKORiQ6mDP2gRkao5kxm+7nKtJuJSImIhX777LF054Xxc5ZVVLzlcjf91zZatMO2fXyDc52Fx2vc3AY8+us8tEltF09yhvL1emDm0a+dArCNtm3ebAZkY1ga/rNslbuUio1OygTj2InKA+48tOqa5YeGZw2shuHM2e4+PUHun10BH0IHKHFr87rcPG5FA/+UMULNN6kc7qORbjjtCJsrwhE0cfCR0DZ6LxRz86sY9bexrkgD5+1RgwOv1PmGwhSNh7mDWW9RdLq16LTxioNc4+PEEZIq2FNECcyGEQ1b3U1vb2TzUe+XLNal4alfLdLVmPsHokeVVP3jZJrihmlpiG0h014Mrt8X4ZNHpHZ4VunLEjgfHlMJqeinMa1fFpOalraXy1CnceRwHhqftqxOYoJTVcImlXmUjs7sKQPURlv9+EDrWt+UN2umZb9P7gQUX72Ya56c4NsSiUEmsy9GpauEdSv0w/Ddd2hiVoN6rrWFZOMdlewYKGskw8BZfsO9wBz5J5MVWv5vTWiZmG7Fsvx8e3z0I7LKUXdjhoN5aQX0VLwSHaI4zS57U/tw8EC+J1j0sA1uVf932o8i5+tMwpdNA86epQvI0Hx14poJ6xpuRfx2u9bs1eZjGqPR45bi4n18vSICLDRVrSUJUbkJo9Jz7SrUEttP/KyMqLMV4aubJjBKCwiWLlj8oteD347ohXjnEmTOOLzjKbHSfZJeW2Nqu4DhsFo7Gusd5q70NDN8MkV8lmAu2OjY2SL5E/H3LLqPSXaCMflEnqPknocfTynCm8mX5dgxzxDhOG8WKeV+JZ3XD1y2mePZ/Yi3zJldTNXALHz9y/nJyqgpRF/B4DptxVnxzJPC1oM+fEZVT9yt0TaVucsF0iq4Wi9GBVcYwUUxHPSMGqpPp8lrZkWoJuxbWCZ7vPlohKuYnCn+jiBFp49XpYb81yxVdgqcm1+Tk7h9mmZF2rD2lRln+QW0Zo6uS555oJDgXdSjVJRiULxrnF+UTfSAmlQNoVEJ++rXmFi87i5fmEdH1WrSMlszHrO2nu3YDOL+bqtaXxw8GYU6Yf66MPy1oI0hl2NxWfhJ1AotVLW/rr2B/Pisf2504EIH+zuG2CK3DoxKhZtUSQ4lJy7+sFTtY5YsnhC9a9rT/tAbsZ1HFzzrJ+Wvo2eosFNdk/NcBsa/oOsr5pnQ/0tKGdtA8Mnsp7/E32J9JpC53QgzOkFYbEVeWOG36X9FmXGhhoy9kAl2I995GBhQ1z9wRmt+06MrnvriGKE9IDbV9fmQg1NssMSSN+9RFTF4FsgPYgJAmu8N7bczJNGFO3/aAiZ6YhWCzZz49kqNecERakbASJoLDVB59kUBgbKrJhrbG9XY01cDNmWFYOMb+U+Dn/dWPiQJlJ0dn+Z0LhM216V7huaMzFXiGnYanmuE5Yv/8AccXqo+8mi6SwfB7hSPFZKbqi8ULscBSGeXJ9FxaNHwDK1qnvQn2kGmBDJAkMvdUg3y4Bnv0iJ+3k8VOdHC0VTQOJrwW2MgkML1+kOhn+wh2GfBoeaMyR3x6yJZBD2tB9FmWvYAYbwwnUeEDtJwY1yxnVe6GjQj6U/zpUOcSO/SF1pFWkR8wNPmIeLruAj0O7Jt1+fsL3PJyfriP+ipVaKeNqMvxiCsutfeCqvIaGu7ihGumFBfquXzynin1XCk6E6HvH1GPydoHGSSITqeBFCzN4bbEyFIfOzBx5zdrt5JI1X96EO+INKZrngUmM8JPG8e2lgRbSmz4ypdnps0zG9l64xJBKu+zQEpFkjifkVvDhEo5jNldZciOBwsQKt1pN1pqSOGf2IdP0bN3lrYtzoQB67eGIwwB/tvQzrv4sAJZih9OT9HFqErpQZNwEVZ6oGkb/d4u4Xo1qKbQpmAZzbMgEX9JiNulvwkTIMPKqiFpYSTb9rLtPZodfq3+zf5+aiEeyfYLrjT7xw58r1f+cUOBsje1uoqzrWhXDX5G5M9uKFTqC8us66FxtP1d1afDRJCljkSWhUVQuE1t2OJ9el0GBoIUasIjts72F0U1Qx7lLsRXvRxrRq7Kuql/C5NwyhpD2+GerWlKNV0+fvyJnx/7EirbZJG4Z9eUrRMV+FUVDB5YFPRGRO/KFYHZ/OXcLSf8VMYnGQ9Evghuf2U6ZiR01oAafxUsQ0IVU07gwYCx0zqRGLZgm5TgaBKagi2JheF8vQWDYe5Rbanou+EjosPfquyJ2aJL7oPqQCaIyEfbPX0IvV6zPz3bx3k+/ft2+l62W+RiNPot9fERJhPf2UuuSwm7pZO9X3tNP8bYhqOY+foE/qOSa+lDCpHwEXdeF9OZ0yR9xZ9MrS6dQNSzKa2+w679w7Y6qWspPPb31YVyq779eOv3Vtf/4c7vY9pSxbr2uBUi+11PgzE7o1o1Jyg+t2xMs/TXbTPttMS1perGKlsvt1zIsgbnhehtC/B1hbmK3ZQ+g08wLZ4rvH7G52dIs0m8/f0waxjhnUXS3m1CHGT008uS0nWEo/F4o+APe+VN4zW+NRd6yTgEstQxPF9u+Vj72PsxSXpfBknWn9WPX+mIRQ8KKwvNeP8xH4InUMICNh+NZhHDdK5a55wPdWO9Ocl9mX9hRPTBHA7/IOI4CvLMIR6Y49NucrHOiIrLcSxfM6+RVJcNuIOxc2h/Zd9fvXDHIpefIYnzFlGTjAokLvOBOsgxexydS3Nc6d/fbB+T9LHBHIWNuGQPXlcdsPnh7hl38YPkVxwLOZNJZeD6fkmZNe8ZypLHRxYOYHYNXisixF8chF4B8fXsmA+PPzfWrgTjfnnXr/OxwVhALDTz0o/GvnemtmGQc6raZcKq5KGr9KheH+UrS+UEwGhvenKbFSAWWkjRVz4VZ59Vj4fH6lc5Gdrx27smBMf9FKoWnD3ZfiWmnHdR49W+xy6dw8e5LCpn9usxoG5ePOhTGRIukmr2r71Yk/VbWMiOENCaFafX2pcwi40XRD+/Cg4C3LwhdWSqSxkaESpDAHJiQN0UrOL708EmwzzchiStZK04A4a/u3fymTbot7cTB5+9NP6LWiGGhobKYPzCtyOJ0WHkq87XvgiWM5d1GdC0dsY2rInyXirBCiRFOl75yMsHsbENR8fpiRmEJA6IX85H7zSMlSzNHfdP6l/a3kfxwvc9iuCu/xD/rHeskPu5a4VKjL0gY3HMtvTY//SgaP0Us7d/7KAlSYmOrHTDkbGCkHUyGWmWlHZkqQpKSN6SYNbblVC0BsT/KchoPZVVaIKS3gunja+3OSLugoBIalQWYfpSqusKf6/KaEt/I2t2MTCQRNbWmSL7ppwjO1yLAVOfFKOhg3XSfkPiQXJPt/3ybgP2iQxtNDh18cvdBd2jLgzQyN+/4NdCp1G7s9OVzJqHDxxrT5+PyH+e5aUIGDsTo029IDgZ5H7qiqU5B4c7GN+QA/B/QGpPA3DGnheEAGZfi93Ee1Q0kveGbZ8BKZOpiTCmeLZdhm0Q3V+q+elhrXM2v6kcBV5DmVptAF5ktjMmG1YWp9jRwR4hIXt95GvYoK+Ht/kb0K9zYhaSLsAWJmR/6G8JjHlOknNfOsqU8702Qwx+ezbQmta4wtHja5hOQ/zTlQcYXMGtsi2ZLwqlt7Ot9L7MYUni9OvwLrz1eDk6BemFxxXFDyXQX3olt/SCcaXA8wPlGRJJKBAmF0TgeL9sOpvtF4C7ZxXp1iML0pDsbk1NW/BvVJCDfTb1XtlGoY9FIaFCevHWmvZ9hk8M+Gb+dbOMoImxwv+kbxfaAJDrL0KHRHWmrNODuDbDkK2miBbHuQedKCX+9N+kxjZUlUbcPvNYgXx8rOLjLLRgkq7PFMExpE2X8IEbwQJo48te23VJ2RYKQx1X3wIy4M9hAh8zuBgMHPhak5/PQBt50tghOCBaVME5R2+bY2Obo/zL58bE6hlph1Z6Uc7/+Z+uW/AHXVo+80+iHUXAkEda+17ZCohm8LTtkT08MUH9NVeaLhhELdDuV1O1bYBw363rjyW2Gz0pI7z/3YMvn/qBEZ/tqHwJeZbUn3WL90X8dN5z0Drderxhe13NIF91GxqK5+Pq8L5YDG+vgtKyifO6tSThmeooOd51L+4uEkKX2ttQwXf2GBcs+PIepnLRSndcX4ui5k1MvLddhafRHaiZYfMOU3i34rjGTU32hFZ32zSX6kawclF42jK87J7WadGucUTae1qEHCh99/xxpr5bGTUbS/uIrawU0BYlWYOTNx1hF/k8Xzp8f75oKbP4g2yHHqFyoDbKPGoZk/sCVcIkhruRRkJFv2SGc8x+QGxNgfcLoWzXE8fZFG35j/2jMVb4qcEg03michMTLwVnnS5V6AuU++kPbDo4eJYpjf9vHHh9bD25Gywd+mIs3u3wYcUpB7VP3jcCt+OVFGeQ6jMNVK8PgSJMdsNt2TLNx3B3G6WvtC1bCFKM6KCDf5B6frKl/iqqNfOGZhE16HD7YUTzrnfuwmRHXGzcrh4MGTxVN0A5UeV6hKfHBgazxfPch7Aipne2gvO/tqO2bDqGFius1Dq4qoGlNmh2Oa72iinxTSmfgZ3m7gGHCd32nt3VPJMcbQeNJDEEofO6AMzesQ75OMGd2xHfB0II8G0idG1bMJ/22VXzyOzuUvMI3nqE/G4wigiSCaRy0/c2v4MH1Zasec3M+gGHjBZFrQxqrORRn3h8kErQ2bIqHhBT1t0EMnJ9cY3uzyxnpWadpk1+2KJtYTkdrtrUKRIzzR0DCFUk3S01fq+sUDbqzj5yUK7IcQfP03ydcPj4t5jLomhtwSjuzHH9JI0yf8u1XY9HPWBKZzGRyLMAzA1l7W2fs5mAy6sKtym7oiF5NruMFpA11qT55Ws7fi9SA4ODqNqxeR2552NJ2MKzcWSzyhoQU2hZnbq2MWe4159OQ09JKb6rrwCH+XA8pzE1kVfDlG6/TVJspSYgeQf5sqbn+cKAXRtK58QT5IkQ0oUzXI0wl9HZN+EVvHhQFs0G2Q/+1FSB4+oBfI/qb9NwqwecbuIX+erdOWNYZu6oQyGsrSUErM8Gi5VWvRwakBNbeRF66hQZjXsIqK1JMKooaHnrns2P4Gv+jIwfpTXAPr8S6BbWj/FfCQGIzIPS08op6lGsUhvrDQA+7wm7gVXodRdJyXK+I+3GYQscZss/3xgiJl+/i83oM3V+ltX+9JNravq1M79J+1+43GcUlGzXVZRTNOJGvlhvOidbWGcRhgcP4FfnnKCHhrUyHpA9eQWAk8vz6eGh9iDARUMiDh/6DUijB1bIRj0mC+iRflU3aF8YlJ1XKY2u1ScnbwDfLbo2Yz4carhZnnVwhvUtyHw1TmrD40mz5CNpDA1qs27ckjrkyP5q/lSAfyLuqfDdqzjmReHFumLtIJ2Xojamb8eh1gMP1Skq8Zmd0afCnVec3jAJOAmAO6ZrLmRekA0yUB1mk7zX3a5+nPyhn+8EsnXZoOuvKXH6o73QEMD0M0WJKevVTDT55sVE89nTpjFqoaD3inI3PUuM7FcNs1fkzoZqWRyrEsmaD5QAVBkFVy5MxrSlNBf9v3xMs0nBKx66JwEwmE6ia7jdCP09Wmva+EfNPj8NvC5kEnJ+BrNgivQU1Ejv4TPQqrFN7c9m853qrfY0XbmR64cGGS4f7GBgSJQ4WijtjbuXyqpuHx5Vn/eSStfwpqavL7modpEJ6dLqVytmzKUFqvlfWE4ufw58IuGHH1/YHNtPwrsqBDPKQxxyh8S59v8bHvKGsN6lrHiThCddNTwsJlC5EnmBUStYQJMRY9S8N36ETSuoPm5JMsp11pQbcYZU8qOOagA4JIjGNOmKzdWt4QUdz3DGNH74zt5Dks/q2uExIbDZvxLMbt1dBPXGDh+o2mbOBV9FIjZUZpp2+twVzPSx9+r63jFTiLH5LbPMiR6FWlbzIzqragRD2afYpFIPLbjSotv6WpeChQ03SFWoGGiMwpHNF57mDr4MjiqomnmODvdnXE5aqJpNeHAe3pJV6gpQroUJLTtv3DKmMLD8fYUenAfRsJU/oJetAH4t/1k3Q3Z1l/Mrz1b4TlZmiMo1pkdIsW2+KGhumLaDL4AtA3kV6qoNqc3hMz0ZXT8OesFRlVjwiJ/Ps7mH7T/X5IqaQ1mSMMD4aJfzB6WNiWnO+oab3dsu9sx9N80oNp08ea1zGxI53iqpS2GT0DawZpygTsUWkf8nIpFnbFKbv/8wxJU5Nhvxu18cymdNznt6NqAdv7aomGg9STFp/ZHWc8eH6CoN8roaiOE5YjqlRIoP0ZxqW2uKmjlTZ5tKo2KlRzj4Dyi8k2dmWW+b0pjV8R2Znozfs8MsGwzpekVjNCiGZpOqzPK0aIdgC+kLq1oZBr7SsqkKgNftLy0GHLVQghNeQep9Mdj7/W6RHIJPPSTdqmcJ3SOKvzycxL5of4kkPRNC1XtAag+jiUoiaatQKh6t4OcN6BfYSuB7u/8hCwQlpzhdaDnnsi1UFQTkixN1ZNXmSao6xb6bQuFUfwaJLH1WOvIxT4gEEaiqPJVvbRxmv6wr4eIs6+TSEv4TrRx2pq5182AHpurHHF9oHbr8NWstGDqWQtO9oPF4ULQKolol6jdmxi3+akt4x+2ne3khmRZzO7Y3T+2TNvVsVAlsz7DcOOksXmA7t6neNPsdFMr+5CZlokZdSbW2HHD+oKNYfJ/NUS/d5S0ASf0RfQGnKk5BM+LYI9wmWlD62Qv7XzHZNDA8k2Wbs1UuMiVj6eWTL6gm7mY9v+UjmSM3ggeC2oGPLpGXq117yY89Y2t2fS2RjZgGm+TGRPwZ+VIYwJ1eLpbq4pi+BIGtvxutbMi9j6bwb81TVDWPIA5WoxYGMGHTqVcHoPgCU8ZFZ9rQJpbL1j8QSR039vK1Yah9SlsN3Q+xjithZn4nkJ7TLye+9eJVlnJwQLW30s8OyKED2td9pFZu1QQQ4/VCHBPtD/gY31q8lCqb8CV6hwOYdUR71g1hUi1yFbSe+najxJhykYlp73C9bXVsaU9jfPI468R/LssNmW/eJyZlfaj8qYXWUwlCybg6pOjc4X8skDW8xj4tiDIg1mMrLiVovS50UjJAe2VnMj9aassbi8jTFc1LB4ZtYU0N6dNieQ2A8cbO7i45qj8ukfpZavP0hR98CRPGFEU/V+o2VHbp20JBKgJeUU2Kk2XXcabTqTyKmkAsfve5jTXS4N6ljQMyZ9egnTfJQtbUfn9XEXJmM1n982mX90JcgFfk6VTM5Vm9M8BPxhZSA45X1G0cz/FyrrC8K0HwhT+y058tGvp3MaCWtIqeFwr6Ya/3R6d4dHbxWTqX94GhaMfEVY8z848JhyZdZQR1IPUdy28UNZk/AL1ZW1oJmynkcJkhyyx09qeoeh3EiTyNpBS1Nj2ruFH8UCbgroT1bFbm+se1TW4kMwrQRSkMNVXQ39WaRX7/xbf6mV8fxJo/rpzurMk3dMmX5kEDZDwpSqvbOn6PW1Hk8Dr/smp8Nsl801hXm6aw/9XTRRsern2yyie8z4BeCu4wlIWGO6zzk7R2WPcCriCrpNErIlg1iMXZ4+Co56DuxAqS+BEut5yIx68Yvm773lvVgwfNy4ZXFKIk/XaEC+8PEc1tSwMER5GSHZR+f5nKmnuu17fti2mQR5EPitVUHf/fSN8H4TuvpC+moRtXAWvbFYOoPbNjxRYV4Fx66pT+xCJ8fRw1bkHYzWPqpN7ln1uq1AK4I+YyM9kgn6kue+39ITkx8XiC3bHoDF8hkqnqpIZPwLJhw1K1SVHNaw/pr5oha+rcm93namxMrhnrUSyd9DSH/tms8olFVOnF8Ea8fpDfDVFcigtFA1BDR1t7hZrwT1wij5U+bBLnuDZNCVe+FXPNj6JLURhsKsDX2y4+K79JFOzPs4SQWvSiVsHxuz4ipIhZi7tFCi59h77oVdzW9O2bwf7AKw+T0HRRE3ZCjyrVM14y59AuHY0t1rZuR3cgS+MLE0Z+4noPjLD7IZc9Qe4fHzIa1U5CzjVy4qKZu5LR1ac21u82Cy2djKaBuXiJL2iQLRpc4hTt1AfppNNVbEBpr5wUW0bo0xTA2Tcue3pEidDFtnUl9zjAS4EZ78OFF4PYsdaCWKzJSGuoJ0Ww6Ei8+aci117KmXWJJXSptZ/2Ymz6psvrPusrE9JdU+sWXebef7djcbHI6aaZSsQ+6589XfCbmU959liRCPZpb9W/MyZIJ2KMHC7aNahXorE6JoAh/1egmMX+ckz1Q4LdpvmKzGD9KzxoEQmUIfibqkKKIJvJmBp2nLpiZNl1RuUQnk7+LdrgyBNvnLIT/1PERGypA9DZcLlfbLjfvE6f0noMhJSmDDte7wdZ1maTRmAF0hgKpxQn0hxwPklXTdFcr6x6aP9Xod1I/jBQ6Sj/bf5IrV9kK3fvVL/wISGgqUKAllMXqG9L1anp4yU3k7TURs9RxAn3ppww2AjmdDM6PtBFBQ2vrsVw7uxwfd9U6dZ+Pm0tY1IBDrqVJverxkee1zFOiad8qFYLITSuYp+PF2JZePppczGs+IIv0rWBIePJ96IsbMhL1IcVIPrqtkVA0GiaouVLMcd1o4ztrDiDydiVMaqvSlGQH4P5q7iWxNItba1x/NxH6Ut93Zby0fkEI9q/Bt1g+jiTfz8z80pDpt1vloL7z/Z2LW8n+VEG+4qtUaaDrLvqn9qERHwI6lMWqilp5sJ5G1S+AvXgBc5gOeLmF2m9vDzw7A7/NV7/J8oC6kOYm0xsKl/q6rW4GET66ON1tXDGt23a0D5PWyI2KHCLXIEX5wlCbxy/xaE/hQUqHQLK2pWy5EyPTIyWUuq+702IlSby9wlnV9DJJoInorflassj3R7U7ygcvKz4+VWzuLxzLs3zwmO0x3mMCP7XvfsWlz28Fz2nZPJ7ah39X3XtOKN6EkrAtT3QYYaBGsMMxb0tC6G9TxHL84DnlqN+As2TuQspNC4PWeVPMqY1iIuPT61OBvhcmx0QKtgO+zUvO1hezlwT2qkiuhRuDggMhb/Lly5iGSpaSJikb0wdnK7p9mU1Cxm7sw2XcUxoktRvi92M7xWow50iY+Q5PO3NwVAOtsgQygV8YCr1eCdmC6hwGHaqWUp+EjX/wucUAh0RO3RXifzwKbgEyFrpLAB+XtC99p+yrtmSjhNaRF/VZCkQYjL8iTmY2O64Gd9z4ItdK/h/Yz+4JCmVuZHN0cmVhbQplbmRvYmoKNDc5IDAgb2JqCjw8Ci9MZW5ndGgxIDMwNDcKL0xlbmd0aDIgMzUxNjgKL0xlbmd0aDMgMAovTGVuZ3RoIDM2ODQzICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjatHl1WNTb2jYhnQpI4yAt3d0p3V0DDA1DDF2CII20dEtId3d3h4BIt3Qj8I17n3N0n/P++11cMHM/64l73etZ67dmoCJXVmMUNQObgKTA9hBGViYWPoC8girYDmjPysKoCrJwsQU6AdiYWFg4UKmoxJ1AQIgV2F4CCAHxAbghlgAlUwg0FurBwsKLSgWQBtmDnKCDZgATD4ACCAJU93AAsQJogX8BZbAzhNEE6AwdBtlbWNmD6KAh4mAHDycrC0vIrxzsjIy/Mv2KFmMCyAJNbcBuzjZWAKC9GUCWSYEJoAh2gxqtALRge4AJyBJoaw4AmwPUQdoADTVJVTWAtKqShrIaHRM0sZqLgwPY6V9cxNXUNaQZABKiiuqSAJAmA0BaQ0391191kD2UvwUDQFEdOv6rDtTxV7iCpLqouo6yJCvzrzkAWAGuICdnq19l/4sbNZQZ4Dc1aKi5E9jurwIAWksIxIGPmdnNzY3JwsUZwgR2smBysP2Ln7qllTPADexkA4C+OoFsQX8J42JvBpUTYgn6O8GvVQHIW5mC7J1Bv4KkwH8P2kGlhAZB7ZD/EIMKAfmV0/Zvd4AzCPSPMpZA579i5ZWV5QF2QCt7CMgeaG8KdYQAIS7OAOO/bNBfkBnN3wRBAHEXJ6dfNRT+PeT0nzL/pi4Ghs5M39bLB+j23ysGtHdx9vxDm39O2xRs72zlDHH+OyMIYG5lC/rF3vnXmlnZ/2VTEFV8KyWpps4oD208e0YFMFQdeyaIO+Qv71/5RCXk+QA8LFwAVl4OAAu0SSXtzcTBdnZQ1s6ov+STsILqBAE7eTD/b2Pb2IPd7L3+jwFzK3sz81/am7k4MGvYWzm6gN5K/MsdakL9bbMAQQAsAJAjAORuasn8q+Bf/fLLzPrLDBXCx8sB7AAwB9o6g3yszEHQF1QvZ6ArCABxcgH5eP058E+EysoNMLMyhUBbHbpdUP/K/tbeHAzg/dsMZfLvoX81Ae1fW5UOuk/NwPa2HgAzkDkqsyIYAm0J2v8/O+2/akm52NoqAu1AtP+j6X87Au2sbD3+6fpfLlqgX2xpFcFOdkDb/xqzcpaycgeZKVtBTC3/lvZv+1sIENr/ovYWtiDosvxl0vi1pWyhvQs9f6x+HV8ARlYOrv8ag7alqY09yNkZwPF3GAgqxH8xhqr/iy+AWUlORVRHm/5/2+YvP0l7U7CZlb0FgI2TCwB0cgJ6oLJAe4GNkxPgxQptbDOQ+1/NAmBmsgdDoCEABxeID8Ac7IT6a0G5OAHMor9MfyMuALPYb8QNYBb/jXgAzBK/ES+AWfI/iJsFwCz1G7ECmKV/IzYAs8xvxA5gfvsbcQCYZX8jKBe53wjKRf43gnJR+I2gXBR/IygXpf8gHigX5d8IWl31N4JWV/uNoNXVfyNodY3fCFpP6zeC1tP+jaD1dP6DeKGewP8gNjZoGiDQ1AUC+sMGDQeaWYGcQNAj6A/zr0iIla3Zb1deqKfJbwQV0sQJujFAEFuQOeS3nf0/9r+32H8GoMxMfxdgh0ph+l9c2KE5TU1BZla2tr9Zc0LLmoJtoc36bwsHxy+Lnd1vH1YWaDazPyA00Z95oZxA/6wFbUhm0D/4sXJCc0B7Hehs+buTfuVxdAHa/pEaumjmvyEbdFbmVr/Tsv+Crn/U+eUOdnH6IwHUxeIPoX9BEHRf25uZ2Dr/4Qad5G8iHNCVs/RwsATZ/+EBtVn9AaEzsv4DQhfQ5g8IVfHPWUBp2/2GrNCp/87MCQ21h54Ef4xDdQD/5swB5Qz+r7WDniPM4P/pIw5oHfA/MkH1cPg9DK3rAIQ+V//RQhys/7L+s4F+VXAAOVmBfy/0LyIOti5/yMYKtTj+hr9iHF3AENA/U7GyQtX9Y01YoVL+TsIJJe0MsrP6Z9dx/vIBuf6xApzQJM5W7r/ZQHVztv2zf1hZofV/l+WETgxi6QT6oz2gAkDcwH8EQHO4/Fb1V/u4/I+qvFAqf12PnE3BTn+KC11k1z8gdB5uf7QqtJb7HxBKxuMPCK3k+Xsq0EyeIKe/if3z8Ff+dQH668nO8vtp8K+b4V9YDeIEtgFpWZlBb8V/uCgAoQeCux4L9LHMCrVDf/79zuAfBah+3yj+iBYTA7t7MXJATw1GNl7oQ4QDKhS0mbh9/hFr+vcl7a8rAXQr/xv/uiEBQCB3kCnq1zmwKX+g9ae6D0W+krkTXxCoeJkOS14KacvGPPuaMtFCTCCRtf4aJJzn3/AulToPLC/DZ+Cb4G+fr00ViGf7uNIYXzp+YaYisgH0VfAlxpQUHc7UZNIISFVYePel7TXdnmxmjk4Bx1RqU0wTGUBjeF+ct6X9NpJt7On5WeJr/S9Ny9kIbp9nWOtxnWxfuC9gE7USL0y0wkKebnE/RgC7RL++mTbO+fByWBbRobMdZxM24rPwz3DazVS+GxxJ2lvHlUWNPSS9UOdWNBIYUj9Kki8W0gCaOH3Ul24p3Qr4CfAh45iwMXnObS7+OW8nxGky3u8T1j6fXLjjZT/31hFRXwr4JHm41LX7yYaSL9lO2lM19YNFs3C9uyCaG3/CG0lAdGr2gWzVnQLPaCrDpcvN6kPVrq6dzjDq4zryTe1XY+SweDL71IN2Jrjh3A8PR8yyqJJ8iRjvBmHFqpVWia+ZLbAN4Z8rEJBa80bD63KdlhxL7QYWErAAxELlxBDTZ0P0PlVITyu1O0muXCPXbsHsvCrJv9Th6Rxc5YI9xW5stG8kwyTRno2veOP2KvCdFHgZwiMuhcb3RGDB5WOmjL9eMGiD1VxEkJ5sUIxAXnpCedfCbkm/9AEBz9CAAGedXzYpekF16U0/o4v4j0iOgfWUEJeok1oueTxrQXL+0WjjUAZu9dWBrcOFUV1tzCxNoiGy6BIDN7rvBZXjI4ORDXSdVftUWIq18t8rwzW/hv2cjr1YfPuYmfYzHseb8y7IWSw6alwBhhCEaKZE/fxJ0aIilhLfkXpzVWe+q0g0i3jB5bGU9oOIDOmKZr7jfbcKkcfWsTCeELXRgoi0nHkDy5AuiqR+dnlbJeUTO6g0QH5pDRW/IopPmpZhGxcNxXzmIGBYD16hb9nAts2eJPklWuVFoYvk5tz0oS9Lu+rNdUow5OVVarNhHpIahvL+Y2K7dceZW2GulisB12byGXn+t70SVPjSieMY1ip7cTa2V0qzaSq1eGpo+0TVEI9z4gz7+C1pVLTzZ/APtaRtET4IgsRG11GFQ7DnY/ZkUVrChrtdAWzEb+sF04XSerphDA7kc3MCwhnDR0veXpTdOcIiGAJGk2oAkgqoDN5yvOwt/iVYJySkKlMmcw26840OLugTc2fKmEvFVN1St9bGtNnYdoz5Qp+RuibLTEXY3tI7UMQrTN4+oK9fX1jkkc3nYe1/aJ5FM9Mr9BVMTcpFgWN3fnJ5iy9uJoHT9SMdWSbvjp1RmzH86XxiUmh1MwbucH2OMuvz0TUMgo68IRaSwqUhvMYX9r6Os9lLzrI4S6YgPeJvtnsaJk6BLSkxyachDwy1bUm833jttkQn/WHt5k40+aiDiGY3VtHmgPZZeS+jGflqDz/53KcYjhxuGP+crsxSehkAJqYOOg5YjQ3nMeng9C/z07+3p12bHLBLM/8B26DB8WZzCesdw3M6a7TWbg6uXJ2EWnSx/eloOn/58DHCFVL78lVp1/x1HpM9coZXMCKZzwPLOCPB+upsS0VfN/TRI7G/pkrwkzTSKS3IU+vFySJrlW5G43rrNhwMOo9G8kptYahpbXkvXHOGrsIerzeoQbzzx6Mx2Cjp/IDouedVJhnrTt8TWHKN+nBV2bo9ZtUShMlZBHNHHYuUHYoexUuHpqzlER1R3FEDQ5TvU/nhgvrvlmhzHUkQafg97qfFw9MIti92VbrO4Pke7fJwsoMFENngVi4wVgNqJWrKHWn27fQ+XmY5qyT5qlHtR9IFlXtl4u4zeKugmPvXOzLE0rzyD3/4GXaDktbybfRpzXAts9kDO0zHKlRTvQPViOXzDeyS5Cd2bgJuPv6PieG4In0xlXeOrqtjsTQYwW/0QOufRcTpP436WV1b5HueN60yxOXNZXoiv2RauLfVUSS2xW47W1YP8E7heo/ojsbqIditHYaPqQuoQvP29YLMelKw2S6ss792PX/9fhzL0epItdXVnaNeMCh28HUDrbytmaqefPVBqce4/gxRhuqG8KSUQx8YCSPiesfYS+qK2rFDv9sRLxMDWL/GdZuu9GE1f/hxL/xsguTdGdUR7vnDR7eJUoymcdJB4uhOIjvQiy4vspWHUiEhCw76Z2yDiT+3nfm5ic+NOTj11nd2HlPJOTaO2zptPleg+DhFuzmrT4pw4/Sm87Wth9+Rrfo7VXbtmVEXebhJz6xv7XByh2HfCzxa6XunuEVWfqNXN7pojItk2oNLnQNRD+egwVNQG1YKDyQWOQu0Zgbn3PdTmrzqTBM2ERXzb6kVyLP2B85ydU3AEjEsys3PE6m8XEPTG3dh4Epb5a+GAD2n4GBMI683dDJNKmit3z9+8NllX+QBx5UyUxLrF9bqnUW2vnRCq+xhSPnUpq3OIHmiJVOZnc1+6PM0QmWIcADchuGMt7VngTMDj8I6h5U0lqq2TMyNpe8MThT0MdqSPYkULNNsdrwWESIoTcui13ar5ZaeZqWe2aR58Gx61HUku37TQ2P0bZ9LDjNyLH/rqx46fwYr7pchRvRWsQMvbLRkdkEYUY3QMMWZHuO1s/ci37m/uCwBD97SFZsEM5bnzHskvXS6nKcJJJwQiq0lMxkT6TJwxzJeCjikVOu3y9o4fk22JoA9JoUdXIGgZTsXTOOo2LJYuWp1r0FP+ZZ8Oac454URlxi3JuaQDhV/9tdMdgp3y0ylSshGH4d6eRVuYbzc6+R5I41VKUWxyoUsuweH0aU4cGH9qZ6lJXXtkYKe8fOZx1DimMgODj5YZASZd681719o4YZiOetlSYWqq5bvzAt+0vXgVxV1C4uRGbKW6f90Lh87PLMLz+rTxTw9uPDq1cPIN3xUWytUl2zQfTp67IoJZKeRg5hPvRCogWeH9+qL9JzuLCec6+gz9xF8z0etST0SSl+/Pv7Rp2BZF0elacqHBXNRxdmTbwxPUnZfYt6nw0fND7dZxTVDNIa55aVY7kW5Z41ckUMI373pOFyRig5q6c6PV0jZtvnWx36M2g4intZFcbYZGsqiWT2h653nZjp7ewdn0SmCk8GWO8BMFECs3aZmEQSh4tT40pemZLRIPrneJCzg89ZJoFo5ky46BSbVnGpULSN+Ug+nBTLaZv/TPLbyEJfpg+2O0avW+AkixtdkgmYTMmMcmmjtsD3A9bxmPu8UOcqOt5mVQBzAWyKZlSl4xzsbuM8wMY/lavKfabcWoh6CzEOFNatIjBDPMddPgipsHvlWqbLUmTH8LIlP7ngRz57jemVLzUeY1DG35WZJvj/vkYxYHPfL7nsKu2LDcIFjrVW9jz59XdHFg49+5Br+ua51blIF897mmJJwkJjiKjreoGQo9igaeNdduNbgZmv1mAzwbbRmsn396eCD5lZGwELDwtfu3a7JwG2Q7aPdGyLL/ghPjxy4Zt1neE6J8yy3j8z8cS9/iroZXJyF9BOreI0G0LZnlGshv9Jaup1X9GbBNyMwTRTILTbAGUaAwbbJmJ20R5N8GLZwGfig9sX/eyjpRXRjaV2ZsiMjsggHSCoy26yBUUC43Y7EK7Gemi3jGdciFqVT+KmR+8Bu8dBBhVEwp5hSP9LyNoZKSx4DDT+T+ALccxzTkLE3S8TO0Q0wvSuSuSqZjlyFAY7syQOsPErZ1Ck4RiA+6kCGJDY3BryDgF0Ljqc06hiv08m7q9jb8byIz6/waw654FyIC7+FrpNGPeDr9i1GwQuff8da9lOyMPyBWVUPirwKHixw+bov4GQhq0nI+8A8afrThw3F1tpXEZkU+M1T+Cu3cIqf3OLPWb3WtkMKDUSj8sdd2/ACppm9MXNJPMXOajTWUAs38NRnlPfDq9ao5EvJjK3+RCiSXh0Y3tSWa+bseDUskwMYjLw3UcEzGiIJOVRhI3wumPHjs11TxXOci36P+K1Oqu/HYAUyUNL4Zo2bBwveKFdncjmIN0XoJQbC5o76f7IjFynpuxM2NtwYkMUuqlcRy7uxp9lq14K9nVnYxEe9fmgXQHnJFKbUNyrdqVzYz92WjNeO/Jnr4QqxgYzFOuv6Y8bge9nItFRuu1S11B/3m+3J596yHPnpmjRYzaPsSLiOJThp0WN+kUh8EdIz7o/4IeWuH/ce+SAcW8K0HaK+aFPtOj1Owk3MndnlRWyVMrp4wVw/Dfns837IKnDrMYotTdZjeBrC1/xMOlA7dz6mLGRa/jGmnB5rM7vRc7XddXRJL6W0raZ6ae8+G366GsvcOj3fYglHfTr9gYzUI5HW3zrYFtz05YYGEx43aE2oh1iURI1Pv2+9VrOaS0aTDwP4gmIwuCudODWkT6f96wj/bS+xiLsYCiZHcnyU+NpARy23rREXciiFwwoSH36X1Vu4yaeabvq4artupafesG929xwI52YoNW4CyfuUOUSiOavpwu+N7mzMCo7TZREnFisNSyC7XvWbEcqfHg59XeNxKHd2hUFi/TMZiFjEcd2D3cvyegjMwmKE2oyS8akRfB5IBVTV9f6mBnPKF8+acLv3pmfjXJO0+O/65DrQO06B3w6xAeQidOpRw/BnrdJnMpI7nIIpK32hw6Xf0b9nVyor0Z6tIxO+VwEUsJ2+oQYWv+oiacNZ9QncJXnfYSVnpHTx5NQzW+04pF66HqHaP07+QtiHSISpbqtLySBrTuvFwOf9hciZioRAdfFMhddGLjACcrMcbj8JpmrkzUw7CJY9usjQyjd+BDe15ouQh/XrVYxn6310uSEsDM5H+HYGlnUVvsh30nT/KGL/wO62jl+AnnIiar5uqUKKCbKSZ2QiwayWcvriU54WeFDNpPx4ftkLFNmt6e2u7b8LjJj0J/9Ml1q3SzT2GGYQzkIBdmS/Vls5k6kRFniFczHVruZRf2OZY7RowrAgHKbINnjUV9+6JkFSe0fQii9pH3Y0P87Wf6JjxX/ZPYDolxtIjEEMudBiW6T9jtbACgiVoDQW0BX90ZyIylqLncs/gttnWSpwTZj06uvuwZZFPiyGD1Jha+OTRFUJICE3DUIgLLoJZtBsqU3bLrvee6nzFWfBOJC+rPdAn9yvL6jDVndTejC2X4h3aaJ3LG+ceMUQ7HyIVL2zAvPjoCLKe9kzUZH28v3R7bMDK53K6dxP5pos6lVTyobv8iJFo81HG0LY31gkcVok791PMY7YlqZbs+vRj+dPwBT0euzb37LsyJGNN37xysRUtJWy6bjkEPQ4MYxPbpIDSIfvRaLPifChHwXwEhUw73bAC7RLvDd7d9nxhl+/4+GjDlvfouyQs0WEXPGsJlwOxVvxZRKg77WmBFYwRl7VNOnAEbC+XXGcSsV9aqqn/jrI10/DlcG37RQzn5RW2khFD6OR15MiHm+fwmYkmu3i2Kb3SI/QYRq5WSBu5Fp5SU/nlVaVCnceCrsCcn7aLGp/JiguFSkHWLfzSqiFOVJk2UhXhn2qKyhDQOJ6iyJ06G7/iOTDl4TvHF6oSc0f3SaBV1OYVS008/sxOOhrK2i5AVHjtHHveFj90e1TEal7dIULn9fr3PYBLegI8zZUP3P7pdpmosfUqliHBVc8oeb1EFwYMcaO75t5gpALf6QiLt7rKGKNmNBXmOdajVBCZu2LJj72cyXTZIx/5K0T92AVxi3XiKhEde19lhO/PdyEcAFI0XNSI6iJFayI5cWUzbIDkM6SingzKuRG5bGd2Do50rNiiDbj7NDlAlflgAkpuimoOsKQ4GFetDXTzRsGGEI88G21wMdlmjnfn68Hv7W/TTzkPCJlpKaJxAUnfsDV9mn+qW4fmhY+UHR1x8R3IF2Ux6+/djxrV3SS0UupkTVeJhR1YzM7/BX1i3GYMKjj/sWU/NyIqiSdRG27cCJ2XZTWNLt7ScMPn7Ua3HGti2ALU4e4rFyc1lPPlb7aKU8DoTUeJF2Sa+WSUSeZWKSvA6QLJq5RxXpg8M4B7aTIAN/aaSLjSEqa5l1jp9lOxybqsALN9iBHDp5B104i69vMCr1q58Jg4KzPVsnh3d75KPuahjqby0K/0RO7PnGbttieQptwzKuF5e6HFpb9y0vrN+GCcxH6HylLzkxyrQMQ+sKyGmLXWx8zg6Kv6NirmAZGZRZIOggHazaJ3vk7mX8dvDZqii2e8TQ8jeATb6zPnTzG2fdmhWXWftmz7hXWYXl76ttWEJcQrG+TNR8antkIKs+hqRZtw7miHW5Dqm3bxMjGLs198vPAc3q24SnfPEzW5SclKKPbGOSvunH34lXqt2f1+2EwVdnphDfoHhSD3w9ICBSyOnhtqssCpSaf6hqOI4pSEbzLbluUT46sTxvZlK3C8nWFkPg99EWvooySblHIRzbcqDFPrDl+fo/qtWk7a8Qv3N32ZBPTY1uC0+8zl7r6HinAVIo2W/ppS78EG3XLVFOpc8+4SyhMOAkb1dJ6CYIuOTH80usgROtcMr0DsxhiI2T9PBXTuyxGI73vc3VTJRwLIbwTF+GIbKWDT1VPkrydUg0rCtuBwkKWLk5KV1efjB21MUfoy3ZLTvf3qlrySoDG6es3qZhIOVd6yRRbwXnFIEeKM0zl5S/YpF9sdhBY1b9RpcNebbng4Y6u0u0X8FxW8kx0jH9PaMSQ3H9t0vWd8xg9D1eWvAeLwrww2c2c2UD0OMpR9DG2QNdpoIKS2zqQLiOOlnQDhnaTLTFL/aOFqC9ns02GCTdwSh4Nbv9mASGKsVueZzGY6/G1FIj7ShIJi6ihrmu8ITx9WazfP5GHpcm3BJHepLBHBeP2xc/3ikMdnUoloITzxUKPCAAA0OITG8ne63Xz3s1zUszRjks+17WUBstd0E68BAYYBmnZ715vniEL6FyvJIhMupppgmknGx86VyZ0C42ToH3hVXrdGI2tT/qDjIA10LNLKlIbpSCiR//5J8OFFM/ieOtrHHrgK8n0Ii8JysFCy2hcB2YpY5ZDQ0LdU8X7lZycLqTFchVrlI4QdMZj5PzKoDPIUa3Wdxz5HKWZ0A66WL2tTcGMT6xfJrzgMASGf9IYOjtrXWmTxjW/1gysxeo1j2nmI31tywSvc0nOtMKK+IFZPXdSpjVETEv6YEHuim+SIhNLdBfMqHiQHAT47BE0dixhejRlmDzPiPpsA1UlSXwcDhER8H5/4BVgb2RrYmmHB1k6oSkujPYe/95k/AeyQG1k2kA/2ydTv4KXpzoWGmFkjOPuTpydx4LFLdn2OuDiK6udlszaIPjIAd7DNzUZ0iRymwzeJeePfAv61lrEi913I/giYQVhKaQkYXJtkuXKUifO32lxOZYRdMhgMUseHLjWe+vbzfbeAB4QQd/6mXe59WPuv8Jri7o6aTeLGx+U/bAvcXlw4cfDcL3LO+Bwjv6Gwf1/XakuXpIu6SJFpDBNp99Ohgt91asX9w78siJL48/OcDrncdkSwoS3z4gPaFHVFhMo4xQDs75Yg7gaMwt66eZb97/QYsG2d/M7WELa5/PRjujAD0XR+84v6QAikBhhjQ3xtrhapXftJ7UMBdzEumEeUEuXHUxG0H8bDjOKo0h6BqzUPlcUs/9gMNCUxUWPcUf9XcYrGcMJ3eJWF5jkAOOEPrhc4BN30CHnYdhyw5LwmJe8zPZOvR1plCQbL8lpL5cpbQTr+HHtpTeyDr3SyI7FBXL7S3Odd3qBhtS+7iwLKObku0QY5Cay+FkiOFGRNCnJizSp4bXWL2YWX5DAoorkzLna7Cy9Z7pF1aqPQxO0qPRKZS0Jbpkub3JZPHshxSTksC63MC15GdatXPsQNc8Pi9q4V1NKv5fiiaw9P2VdGTvy6NHh7Tv78H2r7dsnCfcPj5SKm1w7pV0f7PTXmt2GadtkBGGf89PPl0ayMbIdLQo+MWUCuB8GT0parjb1dPb0v3wySgd9epD9FJX/NWAGd114qribwKlCH5/E8cNlE0VmBrmqX8zlGYaGfQKYkAZVQmOB1EIoN5Z360ZqfJfw+aQ/Y32fh4V7RUzE65ghsyaFPMoXBvico+MhQU7dM+snFMgM7yNsVI/pwHGZgFZ5hZ3o0eW6iD63KvGgqb1eIBG+3JRxvlriKEGxXe6lyHuD+XtzkZXWkh4DaZ/gkOP+FLhPV7ODg18KVam+37JOBKTFBfXNXoR3GqTPdzVniLKtt/tt2V4pC5WE7NZcLG+rqEoe6UC4DM7sYyjQCPnl/J/U0n/UYRS8MuZ7Psu8dJLG4cb4OFqp2eKUUTxSQUUafonfN1DPQukTyoFLYuCv0iv4WvCwT8d364Ew8ebLTswT6F7tOgJsmqlch75u4h2G5nepxlOgkNXHiVz+85bdYJqKRLxRZoPRBYh0ODeBMIMS9ZltzRNPIhZN82DK+0fTYJNjDHn4TxWBuP4DKtoRtBd9qa+/lYWg5CRWYs0Yt8e9kuAhgKtTLCN+Nk+e3ys5v20P9/NEDdOSL7FsKYsUPNzyWinwI83aVTPsSS945K2Sm1Du2DdHL8/a4qGYjbWnicpg7e8pKxTErrhwDidZBmp3CGF3PejfN5PX5Zpnw/lRWXLw7whkO6RIJPLsSj2eCVk+Nb7O6FtRnC1c9Ell7n+93Nc4VMnfgpMRH3zOI2p4x0AlRGSddXU7Tz3MsED8tujC+8WTK60C/zM9R+cfjI8aTIn7O0KeC5jsoscUXxXGTzthTDWT3e58vJlMdPQukXxFdjNjM71D5yfo360VSrraLul4l7SvOsDDi0TXotwZ+YA7t75sDbBvNwCuEdvrxeA1qk+kqT9UwVQJj1l3Cpch2GdXP+u9c+XthAEH+V4QAq0xmATNgOLiJKwhlA+75OQwpWVI7z+qVdT3ibNm87xkE9gLi/Z5dvwisd64S/1idtDvpkWYPiUWdbBQ6Bme55zeGLuo1Ig9RBK/vYdKAu2YCjNWoafBlCD2uVX/6YvkV4umBHji8wrc2OwnH2Qc47G9TduzLLdkmexdpY6p4nyHccWHXsrXLCcIHndRVTzoGRhpOjBklM2NraNZVlBVMeu8hl1/UYzam3kjf9VQrtuFmViB+Sru676skQpwbxEO5T64XsLswce9TuS5cdNP1pREwaPtw41nEr19A3xB75Ec+MAeJfYTRl963ZWOnd8+p//+9UPu9dEnIw9no9ZmdkEc8BS19GEPWos1QF5OaG/mgutV5O1aZ2m7Wlyn2YvTueVV+v6aoBHsCFPyg9NeA/klWL/t3lADGx5aHvfYKKuJmvqv15HpOyZZUqrltst+MCoGuSOoGhvFSJlPCO9iQoYCXhWr1EjgSfN8llOPk0SJh7msLLlBDJ7jEfjsjudM+wU2Rerl2kAqE4URE5EuV4NLtFuAbTdV5frPwJjO0ePRshwV3DHKQCldzfxAWIBa9YQ77vuddL1ReaUG55ctbJ5NBkZGp3J3xcsor8wrJ+s+BhAtPglYNLunWgkv31ygK9AG7HRjz8RJY0u8kmJlKkk24GRT+HEbQdYNYDGxJL3noz+4jBH8FsSws0Wt8q64nHpg7jmqBSJXK7OePU++jDYBotm7NWR9LIWL3prPL1qa58zTAq2TjHZ/+o2fGsGVI3Rgs5zYU9pswlw69y3GjXa6hTaurxit4haKu0lif2BXVe/kIM1BF0Sh3x3jxkdgkY1W9oQxBVSz5u+T15IVC7vEVGQek4XAVqX2ciR0KZXB04Vxtc9+myJDuqS+77rZYAh0iZDEi7GtUipHllhw+PQ+rQl08riJNoCJ0F6jv/KxTQfxLNJxllv5B5YXy5llE5VOR5XWB+0duv75U59Eyuhy/+iWlRmZk1bPF5sKCsYmC5ErZbdOO/UPySFpjcujwv14JiGMfWp69G0WWo1VKYaTxybiO4nq92YeQXUnM9NUC2fjPtZRfo8aZk47K43cdDrWnd86ySi0mMOY9Yv1C309zeNehaaqLXYT3QCk+3y5fChc6wW29mIliDGOCDe3gL0v3TZrXxZk/rRuyIr1wltcwBiGQ7ftE42EMK+YxNbaFO5J+MKEtdqmfSKCpO4JSLpdzoVwvWtuwnYxdubcZxlk3zhanLEu9VCFrCz7lVpl1R7TWMqazt8n7ksuJKQZWGNTjdpzVL2DW/aFdJHHEjAY8G/DdlTp0+syTJn2u+FuXcCiLIiosJLZlHBbA0uORUZYnV7WXbGWBgwl+PnmlSMRI6ArN/bNn+iSjGWwGZ13K9kqD+xxcAkvE6FkiKYmN1e79d7ghpPMIYqmi08yoWY6D9oQpLHoW65LdYwv4rXeM333sWnaNhma7qCJHZbIQaOKmnOML95bYvRYWHLpofvS9KF3XWEyN7kfP2g4JIPm1Lhi8fzQ/A7Nya+vDEce1j919wUPKyxl4bNoWedBhkHXcsT5WlQURd1hyqVlM8PLqfCbeHYx9dcZTm2K6n7S1nM7QRiYIrrbqoIdw9XGUsO+Rd7NFLBYG5xFGn2XmI9wRaJJBYnRwnmRU/o5DVnnBPXsCndlQadR+crvCXzsSpRCPDIaJ+rWSHYYfMY83VmB3BVYy1NhuqiyOWDCGw6fpnK0Xk0qNlkrSGcKnSSjsvczVWKRfTQW/ZVPgd+XcXXbzoMTeuV947Y3BcVFsW5k2ZcjEXV0TxjXishPBTZcH2abB6f87IK8OR80I+TIkXZrigPr2xtN+AV9dRKwpFPFquFZj/bk4TH7b3qTpqhf+xV2undiROecWZumnZCSnDm9CTObBaeDNbgOS/ZPeY9Sfu4/F/rZwP1tzv6bPw6TxIuotTYToW8G5equRWViC10LqwkGMMkJ7IIt7xHW56sUQHRMX78mKDiWo8ULCdi8b2Dqo29l0aL9OVVo5OjKEg3Bcj8Tg03jSAV1ZSHU8dI1FczB0ADKYUKvKEwzp1VeLcx2CzzNEqvHLr1tD8EuR1tWbyZFhiATnmSSqPByLe0+k260831/uZ9uVE+VFKk2VuoSMP8cRR49XJjk39+X2VCpURa75uDWwF6tfWfCskS3+7mCundLKbWthOh8P3oZXko4OF40Szv5zIFJdolMTMjWgC20UnWM8KFW9zkt89HT+QYzZkd4D8zFCMt3wLUDizt+R7rxpUM8/EonOWeVOjlt8xjcES9czoedH/wa3J5XB0xkku+wbzd4ytVf2tO4Y6iXPrci7DER5pkN81aV3woiGRBsQ46ZuE59vug5EaXZGIxIX+vE3VyNYM2158ub4m7dX5m2GNUz9QLV5eZ+u2JOwQPFhN1eZYXhK7M9dvBnZ/cW/cYNecycsoDpJexXiBYJRg4qJy/rqyDl9WSdl6aBZ3HofVuZB3IzT7kj4/W5rOG74ke99eRr8si82Gq74VrGyRmnNHtWRmMd0jxsQWKjtyjo/dkx79HINxL03o4dWnOS9JRToZSx4mHMFe3tR4xOY394XPVPE7L+8Yz/ILAxdkaX+l0TZ+7MMrdpsE9BJeiShljb4OI0YEDZ8sgOpS64oKnAb83tUGPXKbiM89VyeLRnVvJYSrtxYPIzsyy1FHjdDUhyzEoe7/0pb7a1BgcDxnnrG8SfADZJSpstCPLP/mpt62MjuaX5l0SaHATZgkbHu0qSpRoZRpVWxFfmAc1LXMQlgEJDslR/0yoOEUmvENPnH6vhSVrt3NexpxEsvFKkefs8xwO+dA8e2uQb9GtQ9h8w1ydo2vp6YL0ez4L17nmw2ID4bUnMkY1vpfeEjj9nMOfTlY4EGNNUAODkG/2tBu/O0FznRieWitue4ZhTPdLIW/cj1r4cPpVXh0Hrqq+zwsp7yi6GTRxHIz8JwvbPsbLa0TuheD5IFFN0uG7tqbyyWZLN8s013wOwVJ/elnHBHO7t1RCh3FeLZHb39AG9UEDem//KG35D8AH/ChkNRjdW7AO7R3DISCP+z+CWxsdNJO+OntRE0DtuyFvnpFmEJPMSvdNVUq2Lu0QrlgnHfI8fY+56dtTyyBflm8CtnGN4GCkHNGlDmBmC7yXZJQbdzwtMGuqDuFKdJAElCtYwkcuFG00WcUQ6KAhUMtOfE/P7+ordhmw9y0xToveY4E1Pagvy4xmCVC9Ko4cGpxPfFiTet9XF7+J0u4g05oyWmCw7Zx4aFteQu8AVXOOdIu8Na8kH70jpS2bc+5aPPpTgZsRHX9JjPCoTte43skWGv7h7ItxZy1LbP4mRG1TV84G83rg55slatUqTSQwfUroqCszaz1YKfmscILNn9kgqMi+W+mmS+BEoxvcYhRLWC+Mb6VRyNs0cdfMxDdGRoNuz6lE8LViq5GQ536NG4rK8hKQDy7ZRce8Fo8feRsLlUEONqTvvJstrhr4RZv7h5wBJPyflxJUBea0aYmc2P/h+rnIBaQU99Jf8BvIkswtA/jYK6qkdTfctn7XrnfNXDilyjnjWO6XLzz/xx9UvdQpFmSfJUJ+y4MA8cvfLb6JNumTjO8zcTVY2zExcNT8wOLpKjf+Iwbu7KxZbqE29lvApEi1X/NxzbBr/cXKl+P1F2gn3XInZu+Qy8qPuZ1rVi6sbRF6N7QY83Vv7Dbl7N1Zeot8zgF/H2REdXFkv+3DSt+0LjdXX6fzUcTsjmGlftJgUza/1WBNSIZ3Uy3Ey5aeLpJ6zeaXcDt0PwRB7w9Nu9i+982rNFpcBvBXXUzWiVvFoYyPTr2HtaH6T5tauFb2pGyoNDCsjcdcZq9Ry1Stnk/80wYYAa1ogbJ9LuylKxcJBayYUNUW0SIVh5xt8EKgBFi9b0UIdPGIijiHMTkpQajX0bOn3JvGXWRs0Z0cVzDVlJ16SJXd2R5joHERkfmCtRHPzsYOHX7BgkhDVkMRmHzsA7bmdKT/bab6cfBvwFfLd8k2vIokoeITghI8j5RjYk76jqPlE4eMJrF7FR+gBGRYwdr15w8itlYl6hSbGhlqlH+kmNp0stJ6j62ze1nnvCsCgYXDkz63DQb9RG/mckAjaxNqgoLLmspYM9r4VGabuakVCjVndeLjSTuG40UkwvyJVmw8GPOH3OEYgSD+kEu4oYBJcRUdJ0iIa2YRd6qjKmGB2SxS9KyRUHEjwvoHhNBaV+yIfI/nsDV+JS2hCAaeRx4SVGaL31KZlVgXFyhphe4KLiljRK2f4nGOJSHtm02bD5auBoKfC6s3lFfboZ3UjjvNvcK1ja3+8uCYD7hiAZDYys/ErPAZY6LWU3D99PfHstz+E792J1U4faMR/g6Q6s3ocHmFKl37WLyLlPc3XWrALfw0xlCdJCqCenD/+aPlAA6ndxDzae5rWfykw8u0TanfMznMJMBIH04j5dniSUCJ4vF7SQ1+B4X2QNIJxSp59aLLNxUF9IP1H7dF6VfJDAprWqSNBwIf6j0VRYjdnd35AGKYcyC70g+aDR0js7gfDdlXX2QQZM4hFpu0kVCP7YideUVYOif0A629GC3cSDZcOfGXKvved5ns88ucg+CFOFo2Z8N1m2zhkwWIuo4kmTZpTa+TVyZz2+cyPwjJRvkbvPmMhsRezmdWWNvoLkMe6VJ9Ljd/pQzAmhRzy6pt9BBliNKtRjTXf+k1zknpjgWlxjQvMg/iuQ+MOdLTVo/oYmNuW6lUsvjf73nfV7ZH6vGOuMxIxr+NBAFt5FaYptP+c8SokGm+KudPezNTitiUFwlufZZHfWOO8jRqD0xjoeWES3Pr6sd7yNdFscsxJ7VD167fBBI6tGlt41z8zFXb0GLrv93LffiM06XcTK+x4PmgS/iPNc8HME695uPNNxcsRIh4Vjhlu9ah/HeWmailm74tabiwr3U71cbM4dtB18yxdSWB9/V518KiNGeYt5TuFSnVythfoZtyt+vD88PWPlmbhTHdCBkROWZV2LgM9wxOHrr7oNZUS1xlyuPmdDoVIHVN0R50VfzJ6lj+sswRHpredydz4NGNzPtLDmkLWWyjcxN1zlYsEz3mlhyDma8eCJupzycBOHMgg00n2gfMweZy1z6GEzSyufSdMTpAAaTg7FitiXpIG3YT37fMQ+UeptDyKhxMFXeqaVPJWhVkIbgLAOKBOui4tDTX+uoscYcev9+LdwDn8x50lomDExiACMVLlnxy7ZjKWYnU/88jj4+Hr8NP5YFGPhTpSyrFqcj+QWORpMACffZoR29XZ0id1SSNlKEVJn0MtaM/F5fym1SUYXSRDddk4DG9zlBxk2J53LOf1POVemU6oeUQDM428xTENYyz0voqfqqHVxLW0cHuO3ScA+LCLno61/BP78Jw48t78zr9e8fOXReYF7ASyd3UjLeNr57DVDp/FIjxKVt5H3do9E8I8JE4PQL8VNupoerEe9Aj6wnKsbEQJJp8uJTPnu7wDy1uOc8UvtpoTszKGktOGZqn1vcmuVuMnxmz36czKNhh9S0yfh8KbL71Kko/Lq5wf2XdITy5blIEvekXNHONlEpuwyOSf3wja/2YP7559PZmjfvf1lKpKJ1Wn8D4vyM+zxq0DrjtjQbjG4RE+Mr5Z3vdn6+bQYbYLNVz0QsV9/orPN3EzwDx5SMgCR/ZuseTTBoca85P9hgoVLWCdBw7FqjY4tbV7kzXXyOVLJlNVoeNlyOxNcKKN0ErGd7gIGMLDgaHPqxBtbdZnJswdAhPwlpixJ5NYmjY+wR1ks9ubSR9UTFZfV+rcOUmXi2pHP/dkSEm2kIk1fXoWXI7CT80refx6+0Nvc9XDvWMXxhZ1yT73M2uxEhhtvMzlBjXTQVxM+ZqWHd9t0MWOGE1tLm0b8sIr+8q8YxlFBNkcC3ASOV6kZgiN1Ua5ly3WxyDrmv0g5reOXEQ0mWq7p0zHvvjPgZB6qTj994rnls2vaRi54Cw8CQ3n6zlo/B33hQ+NldocQqj6m5JfcfUGd2MIJLbfIqSf1qaFZUzDvvhyTJtfasT9RILm9dIhwURfCGKj46hcXpOsGI62CuoRcQUFROrIGIrkPzBuVcV+9YHnbO+LblkmoVHlGIGleN3+od9AnfFs/fADzftd8VsQ0alJ97dOQVC0bXG1SxokeU4dWBf4uXmynCk7Tjs9tTDjho4+tFiMG25ZQ7ricyb8HS++r9GTbCzCOop6tPWYOfF57VLRYdMCLL4X5p3Wm3bF7bN0mG6q+27q5Vlgnw/j91pJXJXzLl+ZmtHZs/BtdhsnvPrFPFqQi9zrIhZrUfFbQfsgjo1O3mRy9Jl5KyXnMzuLN17rCDAsMpUUG+/j3S6KKzX1TlCc3zx9Y75G7sy9HqjxNeyVHX+NsvUWQE325HLxdlr1xQt0puXK0VnjbMU4WIfkKSlTU37E5m2NN4ejeibuvNgGNLlMdWqttPXzrKos/V/5Yq98gxir77iOclM/nngf2hFXypPHhj1KWn04syxt21bs2Q8iuhgmIh/dWJnIceQBzOE1hW3ajY7GtRMwY7jQHwVOUWngfdddbZ3/qo2KTi5kTy6gPl1tupVhzGsyKzQQzRqm6xjrdnetcDSVTyEz6mWlOlPwufXxCrjHhopPXQA2D0/1wm/tVuJYjGH3VbeqZsiYTEKXrzvx8lBE0PoKE7RQ3nEIFq/GeQgXdpjeKf/oc/x051on8hMh653Uy5gPDm8smWHViyyss2ri5melbmfA2s4Xm7AeTtzej+o0edsO2uEOfNEw50emBq/NKOPKC3R+yMmWDdMWG5L0PPW8jvGlFJDAKq2ojtooSm9bIlkL8xD/xHPYQOkouIMYnuK+yXow3Ar7toVONoXODBly2sIpD4felOnnDRPqHyUccCe2J83XxpyBvocG4in++MVDc1p+uJRzqabsDEGCfE9NjzQvJPm2iscmr/S5iPq9pL4oBKPjFUZCOUfMiOW84xtxXExGxg3njbNwJpm6iIxN7E53IkXGvuaIvEEbQif6OT1DnfhPOimliltqcbAZR8BjOe7EqzMszV3kg+Vb6zikduGyS0dQQtZ7zRMHXreRVyrz0ysC+qZexfjq9Abf8vSAqly2vZHGJ9hNiFjbJnFWfkj5lATfWDv8p0uWLGC1GD+QeDKaPTt4IPw5Ydp4ZlV+1knBOlehEHhI8ybo1odCu3IZp+zTbgaNHhxJjXXd+41c+7qlIZV2NWEl8z0H2R1XcZMcwJf8KIajdfj+aMrQmYvuhyTAmPEUQWGy/egykEc6nO5UO2rUvHinb14xqPzSBiuR5UlmTjO9t9O8tFuj+EkdNdgp7vzJ/uuqpige5WkJg1CTNrEh4pR1apvq2k2JjsKrRu8+cPDnB79bsPcDl81tpchaPCbOvITQz6O5l9iEgXLYRb0PA7CVEuUBzSPd2/xwnvKsFFpq2+CT+Y2AA/aKc26qM0Etg4Z+80QtH5dK6QHlEAcByw6ChJesZ/vvvtNe0CYzzHdy2Ku/ZgfeHSlxXs8ze6Nw4GAkNmMZ8DPdv1XewScek21vz9ap8J0dFPxkOzSFGFPh9E2uFbNj9Bor0B1Yrrs/a6tdiMnPB1R2WGiw4d3pDT3WsotL+tg/PPYxan9ayvVd5uISOzzDq+3uiw/YAaF7p7XrGbrb/t7f2YsFJUEK6gPcPyrMeC7U2CT97QMaA07Xhye4jr91pVw2wY+thtXsJEU+H3sJ5u2L+EiVmJyAfhWOYKz4gNFq6Dav72NLgReOYU2MYNloXpEUp+GSzpFoaMuKEyTVHPn5fQNn68dIOrSFhAlrIrbSF2hyrbQGm1a2FcK+XMG911KYjwxpYR+Ldm4xaDYiC5o2eGHLtfqXEp/xRDzX3NsL0cBd/4zCE/BO1yKcdPXY/Cl7vOkFGA2gwkpYKLFdeK/zcUvwWR8lYUFgwAiDzRgubBIO9QuUqja98qpb9kpjIo6l7VGOKM3LlNhyzaGCgOr6Is5PKAqm+2TB+0q6nzezBpKGQ1UZT+dEzm9uGbMqq3MVUxtbyjBDkZkmg5x/Nm0glVvopicbGbcbUeSSg7RicZ/NHGf/H/8ponIZIe2wkuT4suSdPYjUvf/Vo8nr4VCMJKvc1MSjsILSBVdcfdnIlDacBMTgUbGkWMcySwdEkWCPAlhidJ4OoDfb/4gt1JzAsX3ZhZ0WbOJJ8piT7+Bjg1JEvUTVoPeAdNtSNN/z4pLTxejOhrXlwJ2wL2NOAaSjoFfPUghICqJ/+jSEyZqZUFO3/vaF5tLMJ9piZjyNZ0yqs+1f2CKOeB4TPx+ZcnQyaVQp6ueWYrr3Vq2v58tYNV780OzrHc8P95eLr29DNaKBoaefEYhE8xUrSbbyktP7oqjBf0+eMmCwqP1eTcHrw7uJacQ3J7tELRqlL8QvndhATZoHFhajXXTe91MpOJfnOv7U6rhVShKoIvmZJdQ4G+yE+z7jC7sf3mfCv1gx0G6K41iHrNumshB/OQqD425ptbt0ooXtkXO+poZVNHo31oPzRE4qoIpAR2XoUhS1fl14CRCc1WqwvKmVSqdbCdIMpFiT7Kv6xmOKdRVSlynj/5Vk+WGwwiZAeWyWRMdAPw09ewzxsTa+plcfE2Nz2oSi+BNNDPKhN87GvBJhlZCVrQTLLnL/ts1u/IAULD9+akEN3DbavtnA92tyRscsm3kU/JYQQ61V75OsRPvx+DMrwMeGEZMgBylWiEPbCdDAs5chzmH7yY2MCY41NeeRBZbZ9C2TxBQvWas5VdmPKkqxkYNABNB6hLJ4/SVR2kY+Xf7tz4wZEliGlTtx7U+WkgKCHYJHtFRB/O045RM6UlG8SVNRnRbVxfuO4deIP9jx1N4fm8GMbkmBpgNA7Zwmr7vV789dDrW79u5UnpFuwHV/8K+ntW7FCm1t7AGh7rPwjSVBKnvlkypo65E7cOPHq8GqvFNcW8Z9INTFtCQ3zqB9QwVW6/2BYdGAwuBjOWYffmdtnoJHZDg9/VzTeJX2kpoSq+pvRU83r5MVyL/W7O/fkPDt9pgVZrJvTrPHkb+Htz16hwHCWh9lgnTwmBsOWaLhJj/AP2Gn2qjnHPAJEqxGdF5P2716xhY/+LI9xWYxR2FdwXZOm7VhC53QALFz95HNXD5UhSXU/FLMzyWGRC/6jotUWtjdIYQakVzbrxPs+mLd2PJZ9GPeZzQc63zDjWy6sPDa7O0f+qqGX0ijhWvdNADzKB1cBGE3VrvzMY+C491rWb4K64TUFHaBIWOKPUMiapal5TDNDAlNMB8H7GrgmbI6DuubOyQWKvpMsirTNiXldkcLa0JqwijtEWWPyww4DOMBxij9GZh6y6zRPUP9F3aQ8FcNjoOe7o94Q3neVbvlgtWbaHvrjM5Snwn66+qlLVXfEus+l8qwUltwwtaWDcUBtig9vc0kzxhIMgsyXuC+t5i+H/KMK0Rlk8GjLCxLFaaEWFCllp4A240cNpvQ56de+xlyvGvJeLCOCcgPrhHUIag5eEOS0I5MKxIIswJnIqg6/VM8Rdj2IFbiTdHYGMdLK/6lcTr+N8H0ARNI96GNdyRNTp8+e49vpBTxWHBJg/XRpieIxoeaOVs5rIS1Y3ToLyWVdh3HxeFONbA06wJwJLiBaw9FCoiHakx+Tmg/1qdii71g9xyZaNKEw4R+nj1ip7AsQHCLcyxs4KsRMePsHiTsyuJky0jCA/kn3DuEPv/EBcyThs3D96lwNn2RysHYp9to6EewbNNjEptR2eCXJLwY1SbUTuKlR6veUhOP2FgPiWbC151+EPwxM2C4Re7ssSSqeC7sO/hWWD+fk2xYOMhhdkWoC9BPWMcXL1A5YJYIbttNSuebU/ji9f2HGjP2C3e95zARju9kI5mpat+1F2cyJqYdbmgYClyxWt5uH79kpa8L/iJUDicZoq9/3KHI4p8s/Fryo+uPWJOoKw30uvqr5sU8uVQIFTPSnZwyrT/p/jGxgYf5l8QobfrskINZ5LRzlcKvx0PUX4H1/vgwP7fGTlNf9ZE973/M/2AcJbnm2fxcHNgZUpIcdXnfsBJXcsfGhkXaNjp9cDCdgej7GGcvfDzf1QDpYZOoYs1grn5/SGl83nckhoHPI/W9GbgRfyW+oCW/PgYj5xGt1E/muMCpQW/x3EJ5HLV8actiVKBBVrrsMk8zms6t7NnMkfnG8ZZObO6IQAk+4XVdBgxWOInOtqmXm9fLjvOQ+Q8Jzt42kZm5ow9xJCNW79SEmvwQBpxs3s121+LrDq7WunR9MKy3xXCLzGBE+obuj9recHNLTDJbaCAoH56xfRPX9YyGuL4QmacgeTbQRF6T5hvS3I/8ySTHx49pVeBTIczpsoSKN+LKBDh+738IVLGXNnV/sR3d2z5IxLDjmrp6gw5bUT8BSs8zCnA2fs0CA8QQze+8rgtM+8SDoh5Fo0H9VQYxnU9XpC4HzsOpHbK6eIRRVCpIA8+ZNHCSUCuahqAjSN3aIGPIO5I0fPbyUD/2OdGEVSBXB3enVUiJwTeNlSXQV6MjjOndTwZJEhhvu/z6VRDLHOw+9K4ULyaY9uFiny/7qVmP6KVlc+IGfsRv70rPLiw6/VJxPNil4LLPl4sbhuVkOKZbhqYi5nZj+aRd4JQVG248gNuThcK/iitMIUADftfrk76vW2+LmRzuYK1sqfahINeWLtVM9ZVOpKcbMuaQCvJDh/gGorAF9lUj7kfM3ODJoETTcqMiZ0FdDrRdK4d3y95yOLR9keThihc0TGkU+1+Nz1DBkwgM74dnguaxUx7YfAUdaQ6Qy+T1UbsxbXklxV4JZLR2CXqAxmyL9h1bzz46PderyjEqKZC2DSEsUc0uK3YlynqkbEPXpGrnggygcTrMg80mIQEg+fOX9WABtMVPSC5i9g1XfEQ7EvS7/fLPwqT4vYA2lfjA74vEpgTSgzKewzd4k5dH/gJau6MxLWeRa3UBAfO9Jbz8GvxC/hOnxRKzxl5Dz/koXBAkbbGs7JDHplCIPcjgrcbV7+762L6n4XrE0K22LjPVBvjpoJ1IT/tFLy5jstMwLV6uA/t9RFnahCdM3hH/OCAutLl5QD+qt2SAZ6UkDX5bDfJtYrjDC3tEV3ISMGJXV/aNxT+7leKBdx9OnfLZEakkkxgflesfl5V5hRxzPlxsez510jUbC8ajc4upACuiPR5lNQYio5sS733JqziZH6bTszkasqmUJ5T222hF9Y9cTWXQTutbAX3yXFPdLxKTfb6D1hQv6cNfsN+xqUe16EDdBspxK/q29/5rPjz2gfEdvsrNlMSSX7PXMKdwpMwmGm5Jm38ZBcT+XXgalm2FxOHLpj4lJKqydP7rTykYZTPbUxjBLZ1Wo3NVESVGKmVjgb2rcjYAObINsrcKPm/2IyU1Jc+uWDpqonTEA7ddBVHlFjsQ916bOwRYc7NM1+Gta4GplP14Xq3xDQVVAgOat3v6WiKoLXnq6nh4qnQYxdc+bwjwXwGRIWVvamXjcxe7YYJVJ0dlcklU/HCF8V6YPteW8CaFZLmW6/C1lMFag2TjH2dgkYoRXstXF04JD91JBWknXG3WZaey6D2SUcoedSIc/j+2vqlHGEBZcmzbtm3btm3btm3btm3b/Ma2Z+5Jds/dl/0JnVR1dSrpqgMadbss1KbPu98ZvQhWtFlVbMZFA3eDNqrS4mXNwYGDTwVZyaE/ad8vBDei6rcGYbFfepQu1m67FMZ7iuS+3jZVwgVAECB13fq/YB5ZzcEUXNOOjcSK1Uwh3bB81wq6RAhK8cPRdiP9op7Uai0l2FDTEUzZH6qzKW/Db9CXhzXGmYn559bG2MfaB2rBpdyU+7q59MAFvNbsk9XlSwEotstEuaOzISYdeoBpvEEqHRL/XU5IyPwR3nplchaZz1+ItMqWAQKjfmhU1urlCwMULLml1NsAjTR6ttr5B8e9L/3eqwcrSyeNEI/+FHx8h7R4eG2pnm9jD2A0v7JDIP/3breL0sT0mVGsy1Fo3uTxPlQBF9AQ7uWkGLHE5dO1WUDWEcIkp5M6Y2WQkB2yW4EikVh5e2xQ2BApSA9D5bjEITX7V6OIkOuDVfrMju+oVGckwiaxtBgUWHpUPD/R2CSPxA68uT4KKRBPLfcySiMF/H3UGT7zo00ZbQNGfHx2fcw3xfgshxJbY++9A9fL7IXmMlwrwKywCWcyJ696EWdlCkvyKKlgbKo3q8v3rAInJ3NzI/Q765HyJWYEdRLleVP5irI6IWYTfCg4LevEZ9vv1orvHDDcLCUBuLQkSmhceK/fweeYYl3bkpmJQTj0CRu+BxcpjQN5QhkTYuVc6tphQNkP7Vs4w0aiHqTgTcgWwCZGcQoL5JNFFsa+Ayzx5jczrbsCvZeErbLdeiE8WSMUNEyL12iol+XfFwD7KxXypqrmopHR16KLmj1uFmC/6oNFLj7S5JZXk1nK1r0P5k8GvP4Ag3b8flGLyqTOGGhXMawDILJWI9lh1zPY2FONYzt8B04gzKw/x0e789mzFui6SYVo2pj3G3tHlVFwRYQ7iprbJhUL6f1JCfyVIbjZ3lk4HmFLTZ2hFW06sMuCO7xIxSa6f6nl1OazedaI6892Dy5KhafTHAfKn2GT+9JVhlT3PfGxcYbW0A6cgkVeRnMcRRHDJPi/XMAnBYFLunV3fcZrEAlWOXhQYJXIvi3o69Uhrj03ycL3fJ62VOSeUR0kcwqNmgYT73FYvnGyvd5xaeGoWFc99Pk+QsIr0EXD5X8ORa9Y3pkEZxulKHIdFHNQJ3Wz6ZbufKq4XVpKPuUqH0jNoxyw88k6y/SgLZeqlmqQ6ZrXU2kfbYk3zKciNYJeuPgitfTnNgW4QTni0qAolMksyrATy8UPFR6ejK9YDvosbVq+6B19yeSQH+GgTly6kguSzwRk9sDSTrfcDl7vmRQsQibJ0JDYahZK/izhF80i741GsYbzw+vZB04ohGbdkfzZ2gtp9jMBrnpTEAtbffpRV55WCBxUciQML98+eHoZ5bWVBPdwjS38ck0KJQIculI2+JdAmTiuZSplTfIR0cohIsI4fGTZWJK0aPMkB4zbDJhQFpu3GwZPYNukRDVVKE3dRly73D6g//xkt6QZRSXlo1b433/46jlEePsI88NK9nZMTWut2ElLmGksYwUeqUQCntFWq4jBsWoxiKfoYTQOUR7xGjGfMi5IWLBhOaqYAc5be3upH4m9zGskSoyapAJEAZ/2++8IRGpTS7wufhv2zW9fA3HcLQs3JdsqxtlvcjF6XCMmJzEoC97ayz285zrwqr5IUdrWpWz0hMxSDRatMLSlJ2Rb9ljyV1cCiB4Pp3yz4dKBsEVt0g89WppL5xvIuJ9eytJuw8IVMpqpXxBjW4MVDfVIrz+29TeJ6PgaGq6aY0gVTKxwvgcbkfYIXFKHLz4ejQeker1jkBDkMB9XK4ZbqmS0+vnG/UbznSw/Sqzq80WjQpJ4jZn8kQSLdiSLIaNOvSCidAIxr60vJk8Gh2Oyij1/Fwuyppetk1cbQKiMdP1T1Tol2GmbSvmwbcQjP4BghnjKkLNU4iuzLCoYIAhr3OEU6xzH8azd09pa5cl/Gze/nQcaJ8nUOD2nVpg7kQU9vSZWLkKoyINUh1+k2twVPcgMJGQf34nFTBGqv/XVVF0ccowhFrESuHfF6kcU5LT9KdMZs4pC+TzBDk8UWNbnQd4gJuyYIyTgTcMJX8G9XDwLbgzVXfo042TFm6gQ3tzBXAUgg6IdmENO3Yt7hLrZOW9C59U38ejwC/OfggF89bZEW6iYqLms6PfAwFA8yu8XOPof28X0sG8xXew+FDoxspw+cwNJaZyGTpL2DJ+OgN/NNDDpCzNYojQkFbwtJXu2M6vTKW+lULFU2/14GqaQAn8LNFfZyv/FowTHxXrDkRy1YNAZm5W8mhHpannuAvAlVAw0vLxZtqEBHeNDkhuDUoKcuzXoG9kgQtaSVKBxK6KKob/YRba+5tWQsy2kqrtuVttiLGDAFsjl88Kcaql9U1BEnCzcikNnIBY82+QanuJL6Tqvi8YuXocl6hHGwM2Pj3t6J6F4z6ZqWHaAhrtY0ai2cZidKH2r2wYwuGzYfeL6zBKYcbzmfyNnt9OaKLoAiEhfiJ6TNdme6jG4fhkqxouICZaGFuMcEE2X5FcvtfVDfrdM0QZWCg11+IZo3qmwEbPsTUl4DW/rX7mH5Q5XRuKkQ9ex1bEmFL+hbbR1a/gd9SXmt/B6Gyj+MGPKyUAVikN2UKo+pyS/nAxdDc8XCnb+ByHBFFbW4cAZFl8UcTCghV2fCRV74p0dGIbf00e3+FtREAeqIZHXLZLfJTAv6pncH8sH4XHVZ/FEePxTIEP/GG/5DcPe1Zu+6AW4r19IW2kkil8d5kEdmJaBq1rPebf6+Z1jPEcbC7HY5vZ0x1PMiM5s42AaF3bQIGkRXii+2/C89IvSEg7FXHwjcR7gR/h30s3yrbaaBngovrlODaW3m3pDTDhNOxbSbnkUlneQ3gqv5NNeR9H9vrPQMLLspbTM081WJJePr5Ksx+6+9IVOVaaRR/XZgPk697cl+Kfj0p+yReqnkKQoKYczEsHUhxXEnzD3VWzDpEtFYqKQ446bWOrfum3+xclS7JMWYJSFoqpuQH7UHE6geX+Pe50L6sAfhcZ3QcaaqH1qf4MDOq/X0//tf09TaZSnacZiF3eHzURSiT7J90Rb9rc333zZsgIvDnF00FHkJzVHwjFjBXDqJQ8m7RhOue9v0HDTSbiQpvBiIvzcAcmOFUUmm+qhD2JR8V8T+de/6KJCjwYXtz2oDWAJOb2rof2CWzKQPcwk24hBPhI5w4wZz+bjJLCqAcEhRDNTQ4rHOOUTxSu6H9L/vMrcNF24Cv6ChYmE6KfJeyYVFHwvlvQGafbvQo9OjS5sHhpq5wsDjYaqFc02z5fN9J8xcBuIe7v4fOQBcD6Ft+aFrS+ULlMrF3E9wM4lZ2LSLGDi3Kbtu93/isDGI06vqIt4WOPMG1X8UaDIwIFqJ491+k9ILoasDWik4htgKmkpg79AioEOnyswTYi2NO6ZUIrTEp/OLKYdApphc1Qd4h/AVQsn/cvA2b5tn4QB8PIRh0QnWfi5oyP0SN2oOoPAtEOircLLGCTHzAq5N0WX960vUjtK7lMvocyAygZ3W0xwMWXU7urfelo3CJ/kr3Lgu36gcWG8YjuKzDaYqiE0lqNkLPhX3TUjiWSdIs+b9DHf7+oKRcgEsj3WFrGZF204zKhprwpWzdUZwWwImxCyEY57p9QNsqJ9UiTg8khHt9j212eIriNcOCkhGrNKzrjFTZAwKaSH4EhLHlQkdrJGRUiwFqqDQP4DcZ2jAO8qnoqAwtnjlIKWbv1aFC0/0JUb7OA+qjpBmZXO9qad2KPIiFO9OEve84xaBRqlEj0QJlPguCRK90FdWx8w1PvcK+krJ2b8T6+s0YCHQLWh35odCTB1xG2dIdk7HpVO5UE98Ur+DVoQ7bc/QBCUzqUqLI9CK89nD0osfH9zC4qdusGvsOHngoo0x1lu4OsKaWCHq42e4psm5o8wM77h/oeGM8a0edesjsqL2JZwRd5acDg2yfIAnUfDC6mUxm7Rl4d0+SKVK9jg7xwZN6NjkQ8bTuxR01AUupB6yaJ3S56D0/iGLvw8hS58pcdrS3YaxgFAMFjTQ+QZfmn+fp5CaEHG/AUGGamShbef0yPsY6B6GhL91mKQr0MogDcF6MlJP5w4lrHmXrRJFYHCgSaGiOhca7MEHL6uFt7Sko8NLVn3WrVETsfQD0MYvJa7wbpVKxpiHWYj6lEwQpGZlYvNAvcZy8+GiiIKkCxoXAeNh19WQyDbUo6bQNvT+PQ60XvslxzvdIi/X5lq77SegizuVsfdFHz6byN0EPKKaU7RyAFX/TcWGbOpFJeyuN4DH5W+fc02Y7KRMkJtRmXLjSSVdR4o+P5GOuiwbPu2Gpj/yJzIG+SxcoqzeU5j4QDegF0FZ+YvB8hIMHPQppCCgOlDc8P3xGpgwmKvTIWapGuH/lvK2ayAhFkSvgdFnJ6ynAp6d9YN/o47N7wxkRz5BnB2ASk86zSaVaXkW67KsA2M53Luna/O0n7xUROrLM07TXbmUZcwQUEL2ICVZoz2+YqiC8Sp9ocPnt0s3n3cNydSbK8gsObE0Q4Z4qN7xX4+3w/ukOpaxzbMsF2KtQP1FGRy8sMh517MDTeyg524yWCVvTJske26ixr0g6HYaRIiDL7vqVak38BdzoHMf4rU2ZKOy46ImBjaZwmgsScPBLZy3/J5cy6gtKuBpLMahG/+Gie1bEC1gGqSZ8t5c7OOJ8GeqB1HafJoW5lHbrC3zhDvhj5CnPM99CcSWySlszAaYW47mqzReDsSw2P9WDbGfuxbsO+lpV7fu9dUmz1jMR1BOHrGlzmAeJDjXa4OyAGFpGwlT4ZX/YKq/HgtrdPXJhbLL1+maS4iKCIzpPIeUNpIgqmXucmcXK2yyzjRSRMJ4zxsz03Ho9rJOLDkWMoku7DJFXpXSYeVcVW+EP94K+MZX4tKB9BMc7niGA7cOlR4NhG0V7WfzRRN2m99JnR1quaqIIzmE+keGLEMj37B+FjbdqIbAR7EkQYtawyz6OjK5gYt5Y6huxh/kQ4le+N1ChKq8R+Ukl0/J3iUd9PSWSZ2tp+7ydBY9USSj04eTsXSRiPKrzoe4eSR+i937DjGLDv7zcqSpLicRL1/rsRyGNBGWUzbc6n10BgnnbSwQh7yceUG2xN0anI2iW7wnrRxcDiJhX2GnxKl2v46N5LCS2SOeGa6QXmzNPGk78NabGvFSKRu3i8iJP4pa9H36YVTAV5UGWX9QYOammKQ5mr5VAlgFhTCAZcLEna9aGi3Jiy4TKMifURiANbsDsP6LnZXK1D0scNMnYry32NDFMEbNoDZES71R6p9HTLfQbaOImBtwAcKsQOw0yx9T+zdpRDs+GJyu5IYSp9PKvMXcivV+Nkq1/ECv06aE6Gfk5TmKRk4dGT0DvxkkILGqaCpB5nCzpghMsUbyRvUrg+UV5/c4NbgqgSprfikrUoxev2I51bhbkzQSFce/2S00RjnbI9JT5MOdpb/t3tZYdz2Iyd2lwblU6ef3uRz8i38EV5inN372d18nK5sl+o8dKY1muTtumvhQS6vnLENmehFhepPFoU3BySO+Xr0QLH6Y193JwgJZinifJNT/UrNHEPYDew6T1owhKBkfv44GGV2cY3aOEpcPCqRJRL1CiUGjh5p/GYpSY2d1NDHv9K7oi9l03nahhvU7HGfKafYNRaEwTOepevMMWxzaNaYaIxfgPuIXuIjfnuAf5mpT909ILrXkZcD/yibLzyAHeAUEuAVF9TGq3G0gLTVHdzbK1llu29TQ2Eve1AKYZtK5bmdS3WmsMk+9BPfDpXUjhi1SFnUWEe2AiXJb5eEri7FewwfYk26MklxC7P+rDNM4LAPFpC0hzhrqD+ZYz3HtW5ndaK0B0Phz6Mw4mVEoLVrMmD+zPpyi3qxzQAQZuw9LE5y47o53uFd2voYmDpIuJ+D/sHujCjW4MvbsQsVcVv2zmTFv9qYyvu7PioFkRnCl0Rkyf6AIJ2YFDiNowjbfVFpLRt3EgDXj3l7qDNY2ZPRZd8GZzzlOEueu3FsOKHsRfBze8WMFRFwkqePl9vtJesvlz4hATAzg8e5p5EPPsEvzt6afNui9hkCODSNzPdh2ba1B7O8Qy2tCANK6kbjofd6hDkhKOzH3t1xduQiHiFazPeFcWhc32jsmihOaZAdvLDfekFBrrZrNuaBFzY+0UxFYf2t0V8sDoKmR+IotInn5H7xqFNz5Bh5WZj2guyyo7WjTMrCJ7ude9w48yXc8Z0SOqI2yfgHUXqXK8OIUOsRPfL4TLz9MzOo0P+xvRE8qBW0hCcKs4c7WU5w/l7j1HdblWhZwb+qp8LD9n3v1RZnlcCj3qamyQzskJNl4FQaBlz5DjW3DVKGqahj+v+ErpRe/+JHt1mQkRGvlcGr8JZ+E3xtJ47VaQMNgIsCRZbJc3U2BMBYe4eD+sahbHLbzsWXj6jvFVkbgs39M4OvG8INux4O7dnke9Y3mToYRI8gTRUCroClbvVLglyGqP1Eh9hmpq2HE7rSRl2mzKWQin1TQIIwhsyxc3WPYss5qwetnXi+Sk+3e6JYOx9/vWEuGHeveKwmZCcE/omivWtjRyGaXXAyBtBZdcgdjVvoH3xweXlJ6bGyO/Wb4oPcTv5M9uGuEHQKtvf7kr1V3pIkmjDDPlVnOR2pjTuvkUsOPz6MeHxh2j60EPOZtuZWgr6Xu0IPh4MegpO9/5nEPVX9faw0+ShLbz13fIahLY43SbDsi3uPbuF7AC6nbFQbrfsE6OFWDRDq2X18FKwm80gBC0LALXt4V+IjgC9tE7mc3PaduvQa3ycEiBTyGQhdi5GsZ58KIkCaSewJHrOaOyfIMYuwIZMIElo3erO2QDPJxRLZHvwmQSvBrLoM+5SQtqNs1J0/iGZ1GeS+YYKilBSkGwNKSdCa0xyW5G+D5ktkWTCqTGTs/qCS1ZhIRPTPxyB7qTh9z000WLr3dgdE7ltEBKxVtWkpQdzEIOxrzlSjsMHaYrVASypKRZ4fyGNK45fxn5UhQUpv6QyCOwMulFbGVaIP5tYi54e32jidb1gyw6hk1F0J+uY3tbR79finXI4Xd0RvB3fxF6peUE9+7oPgMVH21D/oQCPVufIYs/LtsYOG2uTDCVfeocyRE5vCXpoWMPdKK3IXHfHb2pPQKzZ/TVh+/S+aE7V/3+4DgOBHVYtQh3CHLhSsXgammhxn6LqzV/SzANBrkANbABxJ52bb+FNYWyEGvo3bPg2ilbBIRV8/OodQSr1vd7bc+j6aVrvY59hPYzb7XSDL71XjcaWPZqykbdBtp76pISwklFzirqZbxqpbHNvpFpi817QCE59ARkNXSGztZquDSzaGmY6oiiLyoQfobFzdYw/FVO/TPXwkNqXxwNRHJq967Pj7OhezCD3wy4sp7X4/mqmK55zRbM52OwdwMCrlt483tIP25ld2kLg/gb8dqy2+Vo7Reo0CZIptxTA3ImTCqkAZyfuxARWeHo7VhaAAGfVRWm58dedU0jaQ8omE++IgQ8xktqIllkgEwdjufjGVEQZdapUxsWn9Kdpa000zCO+3H6f7E/F923fiXASuk97RmBizSXm7mTbkGrkEl3zlaO0Igca/kaHDhCCteSk+hxCo0DXTONC5RIXH9hGLKJ/TqxnIhvnxndFV6EFWx4C668sfBh4PKAae5j8C4YlYAQkeXsEFGSQh0G7wN3b9rrNoKK3h0wtuL/P0W1iQPd8c7b5rdYRG+GGwPf13yjvTN4XUwXlN0//nP1mD/Io0+XsXygsI2od2SxdTPA0KI07Rtom3FvaJDRRz6YxHw8oihEq0vzc0eWR0suy0J/8Xj0iMpGUmA8vgL4yRD64CX9O7vyApRNgBg5qb1dSFnU445a+KrJxaVyXdSbkizpI7fHUBuQSUnj9e1gNZ5kr+CZMlZYfLJb15337QFCJ+Jsgmx6jnhU2imD4lF688zdyf1Tz72E2I3tO/zR/JNuM6c0sZl1aarsoO52JMMFWVO6s7B1DyBhG0+o/aQ4ikpIPRDYV4PrGSXFdbBmJI1eaz6gFL91IMlHCkRG/rdqRqt4ZTsEzfuW5TQbGXmScjhjS0exEwg9wpQiiPda0Xn7m2nuyBZqfoCGIvUk3TVc5psubtDMUiIraeygsX0A2Xy+J18Tf37Tz7Um4zzAb8kpa8Ii76D1asA/axF8UhLAJnc5JZQ+3lIfW7vocDWW08DnxIDQJfO56NbeagKPkDNF0Kmh0AnLOQFgjtnWi6bsnUCxP5eKAIf8wc2fWf65r0Gh8pgPgFwouGh3hny2uPcMCdYuNPeCPmiTiIv/DFzB4OfpjoFvCFjOyCiuClBe+DMtuCF0MCtkYJKlSLROmT3QoWjVKuP0RzWzn/DmsChIwyej/sdJmMKtKU54+68Tl5Yh6rFk9YH+gNUj3PDOtxuRTcSEsbWGsUGAM84qKQBJCa17O5Ebs2d1jlOj9o/zzkCfO8abNaxSZQX0qJ5OhVhEajbP2PnykIB0WA5RrF2pgwNW8zc9EZhOJ2ctYMQ4pjUySxY7ZGoE4jBvTW+g/oH0PvRfq2Rc65vjFSWaU8dPCU0aRMmz1/6dvVQvneVjIWP1dTKdXu6w39x3EF4uOat2eSwtQDIkE4Q0Yrcoh4dn6mSYhI8P3BGKBrAJtq5i4D4bD9lh+XJ6lZpmny5KSNUCXojfC8OkUwCtPBKmS5JWLcOj0s+Hit1LqjlrPA/aCTBLIUsHDg+GjhYLeWsCwaXCntndHFjF/8dJDSqTNU7AWWSLP+2n/G/Jq8Jc+vysK9phReUTAgcysI4sUH326l/ZODP1rML08LM42Mr3PjGTwOr5pgmvBg1+UoyvL4LJ7HGBAwzJEo/ybZj42OzdecL0+D0Ddmj2UDNDkQYHmU9QkZ6LLnR5UGWsAe16masJ5wtlxVEIfDDgePrIMnFr0+lEDdWc1O6AQQq6rt5hKveXaCMZwiuTuvj09Qq0mRdbdarEagyC9toac+XkCqxnYqKc3qrlwXmWm2hmkjxOYed/bky6LySI9ifGdLq6OoQ/gpcfHlDeqMdGyncX/BASihwffaftH5LAiny/qB7fBMeWe58viqAM94Ozz2CEz5MNJ8L/Bb852L1poCuJSBHwCfmVEU/PEw+L64H1/0GdSwbL47liUA7G7SeA36FvEXk55TpzhGeUZpJsQ0xHmUCQ4RjuywxT2zePR1fpbOnemr26p7e9uXmWrK0ia7QHMQne4I9pBA00GVcIHRlO5G9c4SSvROEbNkaAWWE4F6lEhOT4DAoRU32O9Lyr+uW+qJjXnGHVuG4Q4XAaFwrRUWCUvx30qpVOsVM/WZS1lGfMHqzAVr5DusSOqRt+9SfXm922WB8WTT1Rw07ft1bMDSTdrtNmWZvhRlQYCOpTbrNnPH3PyAFUX2JZl7ZI8h8cocFNVcXtWM0oNbBLaN2wlw91OLuGg4Ix8EL+mgN+XQRSbisrYPNp61XuTyAdiLpPosqcll5sj/UetE6BpyXdxfsaWWFYFAkvDgt+PNhqcUlm3CGcgr+XykUoBRFsp78Gsu3axNUY1rhRj4GM9IAwHmrY/EZ7c+YJg2gCOsDf53FCJ5DS/zpRYAS1UF+Te4ko0iL9HpVjvsJx3lKEUW6S99wvXfIz9smdQaxN5PNYdq1gKjVLuW/3L7BJZGGDIi87RMYjJMRVzbqB7tUpfNe0RvU7NWO722JHWI4XVInl9vt0oAFCoxfh0AiR0A1exfJGf+P2sGXThB/T1q743gCAg6lb0CUxGOJ2P1c9qFdxZdA/Bj7/19LwY43y7jgK6O2RRz+bawcfFGu8iwZUOZ7ilfrZCi1CVI/ZW5bAhPOdbP3e24rppKFlamIMna3KiPmpd2eVkI9Yx/mq1iQbGWdOpcsUGMKoY7wm1xBYCWpZyh+3R+iMynhvK/WiTr9pA98Lmrfqc/KVTobbdV73Z6tEP0qu04U3ol2rWihInTQxZj3QTLRRE46d+gbriHWrRHLCfXWY2y2Y0bNcP4GL5AVyjagdmtJZJarVYiMPrecnH+7OTgoXclKHUHTqZdxwmbAZgpMCj76rNtiKxz6vkH0HysUvGJ+yL3nyJKypbJ95xUCvQ826nHdv48DQ8U5LzAjdLdmJYdnWeHw1/ducELCLPL8uGgSoYj8fV1+1hUvKndxsR5qG7fBywOBdt3y4CX9OuEfIkzqIk4NHxyccPEO7C8eBP4rPUaibdfN3+jeVs1Bm9Pk6Gd531tGO/oarbids8NiBBz0dLhSReEgj+yZ+C6Tk6JN8TqRVL9k2Okt8bTGtor5OVih+qSN+yfonIzom9uPLbxjn1tM0pBbcvU86UuugbWfS60s2PpoP93A6sHOPC3LGc1pvapu11NesgooZATwamxJ6fdTFdV9bMcRQYXZfPVn2yEECFpwih+ePbQcT3VxC+PpUdsiy8pk8d49AcpRL8XR09IKnzOi59hJBL4ItxVVekIRERhIuVmu5cZTwkqG4HCTWf5cHVU21h6rYboamwlDXWKcUWWglSrD7t3g9EAEelOAZ9K8dTmhov0kSxQ/ak1xt7uvVgWjFeNXjd0Ykg9Yj8UrXLy9oK13Dwgny7XjoUq6dOTNxDj76/zHDWNowSEImqYXbDARxmqzADU//YP4wyrqJNJ63CKrlK7DBlL+ypJHXDwGlwSaLa+9a/qJbBAC4l07apvaYKx0IL83gL3/vgxigMld0no8VpDBINrG0frrvLevEcQygiBmvedhnIMdUwboFpUT+8GOvCMM5IN8OVtPmq6qnM6g1q46Zn2sYrRX4pBY3p5oJy6X75M9tJqD6YgvgwW+1w2tAQvEXEKZTYdoxWbBGBBPhaWIpkLoarbab+bxn71KNH0dxbviruIQ9pHl3EW708FCK3Pd2N88XltWz8ZRCN+9gKAFTQCbmZiuum+4Omfs9l8IePibAKmvmVBMbwMH3/NKZ8QTNrAIYReC54jQrRRniMYzCXUPv1eqBuwGJ2moBUnyHH7Izgzk1aAtIBanRg8KGq4Becl4jwdzIdJLIk4l7f1XzByv3/JqUorzKLz5fISGVZl8ztTWqbOAmcM6ylsX4pr4zmH754+VGecLRZWgbRRa5CuLv2epAzU74/mIAK3NXRkOM/wM1uCEj2extZxWFSU3mRbovSbix7kcht7wM+rhX2x2XGtEm996Vo8CuYuEQf5dRoM4P5o+zCX7clm4V7kUkFxa13NFHY+rr5UZotXCKB/FWSjpaTJcdD5MH7kW8sUW4RE/eO4Am6+ohsnYh12TrL0+tBveajfmHkmUn1iBf9zKlRAifZsNCdb7cW2DD2yXoXic+pcboosjfPB2xsQ2RQ6UItwtyHabzGIyaq5ARW5NLKHfQpVaDQQ99ljZwru6INnOkmkS3T37KmiqdPjkw/M8bEc3GmBsMdYKIb8tbeQ34SQGvJ8MXRMwHvbWQ4LOLQhy295ynJDIRTKDnF4q2ttwT+pOMnnBtThMMt+Oznbdhjg/KoSJBnHIJ58p8/PyZO0IMy1uw3hzkdCC+9XRDAE0yBbluEYzdgwsv5dnzanR6YD079cC9+LsuJJxwJm8r9WfnjmRKag9V0PUtLzxnMhDMcPkxgMPxEq0dncR0IEf2I6GnUP+AhU0fsPES6Mlg6nQYutFzTlUmgSNr8BUTe01zt2jxwLFhmEtr8sk/LXKoT+rOeG6DZV1ODfCtPxOH+Ui2XbXwd/FF1erj0Wm9gzMSaApSE04tdQninhh8y1gy/mXi9Tf8y0NfGQ/u0Wmn11cHwQp/Jw8okS1bqcoYgZZM6c11/I4UjgP70dTGvwhIF+4d4C/YUTB7diUdRTX5U4V4sSYHSdX682FWsysRycoES8YOZF1INenFra/jvOKdCKT8ySxayAG26AKw/QEo6qFmozRUdmaBqghDRXW9VsZrxTRcaUMVLuYjBJCfOPlwPx7Nioq5clzXYvgI5WzOEIQI4gGqbZrKAh9gSj6TC1+WdRgrI4kn2/wHMagtiWTEknfeQvir+jLmd/RqfIWtj/aismC8frrFFWAD1XTpIheE/N+RzsWr6+vdNsaeoAEhd2bE6atcGskXJpDV5/F9NMrjdfmiwSfc+CVK8MFRJPFwPlfvZ7LBRAGvccwsN5syar+ZNymrFQpgBamkPikWratlKXpaZYKLAkANQlvMmExeWFMFPRWhKgSZHgvy+d1E+su4699oqlQSvK0D495OKUg0IFtigAPOZYL00TppH+KsFY1pIk1/K0kqUZ9rn5tmIDNmWgkZy+QOuPBST3vBjrygO03V7HV6CwpdebYSXVevvtHQTqSiw/TPk227xxkx/r2CAUc/KuuEvEERHPXXIy2EcC5/mqUH2AoI3OkxanUpvr1RjnCGFQAbjJuuUIYxqYxmbBwjhQbTTYsFtBQln0litr/cF7rN+Gv19aMh4nZGtvsf+P3q1FjxJmPEb+8wWKymwzNT/jrIt4xXU9Y2JvofiR0SpPN/lG2zTOdIopcewMUx/AhuVcWTNF4tI9g2bvsQGrgzOXesUM/MRcw5nw0hDl5uMHHkZFrFg+rj7qR2Qewp5f2/8j2/G1Wy4U9SUvZ/ODSdVnhr6OGhlIqmfPulcTg9mV4n5hnCvwG6lceBy92WqBeK0Xh6Y8p9Yf/qLr/Q0hcvhDvnggh9/9f7+omVOpLLtL3/PH0vK0zmNQ8VYfBWSg0ubpo8p1wFvVgXtxkJoiNBJ1J0CHWn1lsEhQjiIl4RrrkDxfgjZv+gCTQCmZW7uvCRVeZCS9jaT2ka/3OsdeK9TTFZc26+yajjT4b40FX4mcbrIw4loUO95JqhvwcsyZVQ2rPN0df7i2fG+h3BtiPY0dbDuqW7VMzdSRhoP4Nhe//6wV78pWXU5ye2sCsKxEYSV57uFAVGjPOVFE1WkoBD5+1UvwxJ/9snpTpqCl6f20uNlIixIWoRwlGgSjK07m8kyusv3m74gUSHPaXqVoWiA7NdQcmc138k3Qsfs03kA6Zg6NgmG4+FXUQs+L/KbJ9/Bwo1pFbgLgIBzZ/qlAguZRjd+m6KqiSi0DgxIrSyLRxTcsOmo28jiWz7++8mEL+dUuVN89S2JkqkAzPEaHgCny+OC2fncmF0dAh+N6epYSugz1HfpyYFSdt105TITlsIw2wVw7gMYy0vKRFbOgJGn6rsojMFAhwigHd6WshRN6wIc/nMhy/70I2uJlQZDmTISa61CJLHxYtnDDs+Fv7ajMYkU7elk636PtTPYlOoLeewQ0MNTiZf/pC1yvO08pqd5I82g2VXj+hh5lTCx5qEsq49XGwmSyqslLr89jSNgxj9WiCEk414gkZhWk4WIElIMEpRa51+IKEHX3FVRK8N0sRke7gDEeG1TZduGpZOKNJ5GTGsCU5rgL5kwGtOy+MDDDkiUE5w5UHT5W8hHm9HvmLA0iWGfnJj3f9en/O7ruSDxf43wsYyvkYOKRYwL4Y0qbNvUNNPjaaRgdXZd7+qhHDRwZOor3DHP4iDXgASgbm18FbDoKy2IeyD48PgUdGyVBao+3h4BvMriqCdZXZcCbIAyvePYkGJNhN69k/YITWQCPDzoLubvMNrZXfsNt1zVATNI2MqtYiPRpMAuBNXhQHdesDmVK8yh/LXMIH/VWxayMAHFWFO8zm/0jkN6Lvfn2Qc7VJ8PA0/Oc1i9NCMNXoAgk6It5lLlzWG97zAo+J0y+Y/mDKJM3UTWEU4+2/QfRnhmaZTooqC6o4Mzx+jREWIRf5JGSROEhgYnZ4uqtSjCHtpdQhOIHgxq2DUUhaNxTBmTN8Bo/S+TIcvYmXvf7hDGHIAnxIgRQhH15e2/KJDmzi+dC0vtl3Zrojl4wLWQFWfUC8IG2IAJy94XFkcVCTC6F4evmtxeqrQS1X+Bb0zei3Q9D+uJY1ILlL5QEC85ztxm2hhaQaZvCWciVPTUWW6xrQF8nX/zLnIINVZqWTjCnch9IFD674r+XaZ/LHUVH3kEMbe4PsMJzuTVpSEW79vHXlMv2e56gPV7IRcoaj+3dTT67mamiE0fJDoIExGHbr+ocOpcS1BZ6xNCoWpZpNyU2STNqggXUGOhaJLv1o8745wcvo6NPEO5waUpBj70zIqODIpUFvVTppA3iTwiCZG9izLcj+Ae6pU62Vy8JHKyPyuggttBCy/56cK/M6D3krRvrnE7DRhRxbo8Ojnizebwija74vzp+LeFNhMwljDwam0/ZSl7SyaOTaaubvq4YQA9Av2iSpg4p5NZbDeMKDAXLuy/BY+JvKUxwur1pcOAY5TewkPulljSE/dVKRYMynxcTprY7jMfDMMP21Iv00xvE7GmVoQNZ8BgTtQw2G05XWyXqnz6X09vRLM/XGcJBgbOIFXFhiEmwUzNuyUz+Bkp6ivMaRUKHZY2g89dDt/2Yx7mZPJQlTPMlEfKUZ0/EEGs2d7Dm8gYHpTuYmC+P2HFDOTQJXdHA2xRpFd2IfLVgCN9ixFPCS6Iaxlg+kvILetIwZ7c0s0/9VCoiZBVwJWDqpD5G0Hi62kERUw5+9kljSSAlBsEZzWhHFKV5O73ONpq29s0kLytx5iHuWNBZTnVzZgVV+tgkerj+zR/DAddHWq7GmUfmR1L8xw5317MqwEkQt77FZYOByvasYnlb0SArRmJjOHaSKuYbtxwzJglMtCSVCQX0FRvL/Llwd5E15DbIFXbHewXFGsE/ieC1McZ83UM33qov5o/UbdjLfMnH+2ckxbdCdhnU2S422e/rSowbKxUa3hb3rt/ZllRQlLSebRUwBi9xpXUwozNB1tBr2HR8TQsfOUBaXxm1R6aaCZw7/+7A4LgE8hFFtL6v9RayBck+LhDsYlNZoer7rc5FNquE5BcFQUd4sdqWON4//xeKzVsqM/jZ12kka3oPSw3hAws4TtXkOeqqkbDQxAYQ5kdAcauWkpeoHThqswqqs/5C1qRT7/4+HFMhFmdo7ng6Ccnrx+LZsMVhub8fsI39csbNXJTtLeJB0BD7c5iNi1oXIRpL4xxw21Zp6454Il5aPTfzE8O7ntk6O6WlSg4S7nqF2gFjum3idXur84YZxNu6/leysDLXwCioJluWXc9bb2wjMrGC9drBpTTWQbDRjTNa+US7MMKGTR0PFt9ZIpMhoCNfPsZoHHXbyncxJN1rMspNERwEONHqUgGEd1LnCAxkolimNb3zHfX1fKvnRWP034DY9l6OhWqr26ptpz2PWMJLtCXxteUSFbXvFDrGoCzLoxaqm2ifdEpz/dw7Lg8tqdkHqsdS1nS867FLmWLLc2GkoNrFJqNcl4SObUAjW63zqY8PvTpbZ5YePYrFkNPKhgnjOjekULHzPG4N1RCUCGsgccEb4RvqWYzt3spdL97oXgsYbE2CNS72BwzjsOj7k6ARy/d4I/WWej1jnz9AkzZEp9cz42bi3OcNun+bbl1ZCQ9Ka3GuNpRFUh5Z4QWblo3vcvlNN/0tHaGsJC+xcnidcVcH3GzWicO+AwQRPYMfhdD7ob0iU9g8Ub5uSiq+Q06WgwkyDbOABsjWtwwFbWqNCMcKFydrVTj2gbwWDCdOlmi8zvApil/vi+Rsvt2sra76/XKtnRcAYWJ8nQicxvdJYIrVIrb/Os3qXQ9/5sBEPeYovLM60gDQUWZF2V/O5TonU5ChEktT8F8K4aqi9I9m5U9VxSA6BGDYFQDwt30lECCs6ZOZ8MYFZeRUKw3p9T+1xOUWqjsNX42y8+uGKNQp7NfF7mJpiIM+ssbhMiDJfncPCpbq5tqQ1g+FDCyemyn8jxJ2yyxGwzw1bYrFA0hwTgUzOgTXg+SC48UhzZhFrFLuyh5jKINWExHEUjxrFXRLAUTvIsIKYTzPsNvIsquHBeUQ+XyUSDZ1F1Tags9CxKk11Q6baS4w60V6PIur4vcNMVi0qS7p1Mi3iWNkZdb7ulz6em2leqnc56/ym/JXZZaQsQ/HR3g6l/UCZKBrkKJ9ZQ2HqsIh+WETI1gh1DoEOUndAtxKkH9naWookSLz/Slwg8dN+SRizWOQdVQuUum5+/7VbYeNCewxfVk36M/19dWJlQDv/oVYLScuRyiKizmTaT0r0zAbaKHbp8JAFEZaWgWY2nPsIKR+8D9cIUyGfmqeQuPPe8BwxRGcdmgRGLmwxS95QOfeUwjAOlo5yoZ5VSAQCKeG/+Z/D32YZBwY2pjLlKbBd/WWp3aiKnsCA/dQs4zhV9ATrnsA168p1JzN78/TpMGMrx5h7AJpPFjypfdGBpfKKSWbxKgkp54L5D+Csj0IbNjhzvRiISNANaKLFYHywejZxY0vbrZK6XDgi/3prFby4b3SXSo2AS43Sj+7q5NB3PDFsLTjkZ6gj0GPdE2/XOAmAjqKcaYCPlmAXSZtA8ZCfc+MbqN8H9fOuLd78pMWMvWS++g+tTSSawXkwF6GybSw8kiWiiNGuRCgC1E7Pjx++8nztIHCiOWjuxL7a02xtu1ep7sgpB1coXZeYHURDv4MGe+Zf9R8JGvtUlR8XS58CrPLUcRO8tYRLDR0L9A5wBeUE7rE/TGZ2RLzQ29Tr7jphwhGsUG9c+pUn6fCdv/X0g87KrQikWTq2fCjrCpWqe4rQhKGkJtlxcRGMHIYf3bTxBfvETAmm83zPHttjqUia6hvesspzjVkhSUT1usEuD6y0XqArbPirNxVmQkkIZ8QzKgTP/k4CHCmJ+LpZnx36zsAYKBkLdXrP5xLFHKq+POEX9FUC4dq00pfsrt7jn/bwBU7BACQKjH56PEJYSwkZWsx8ULrbapBH1uL5i2EuO9aPv4MfXiXu4CZ2mgQXG+LL0/F6O3oTiE7uUVrbYDb9rVxe5AP+7P0la/uhuVzjVu863W2NuaJ6udAR4ugaO4bYuHS8wKbRFRO3aFPukw9uT4piFtZ84FhZWp1DFb6Nycaxn2NrRdTrQyCj5r2Kmf7KS3a9o+O0Yjg7iBTVIlJVAGEaFDAdAFZyJeCGlwRhssqNb11LDcA0F1UN2aKCR5FcvlgNABv/99E95EEqeAZqp5Dz8u4BjgGR73CiADar9H5kXRjha/Hkmt/xPrh+x/7NlzbKEZE3uFJiiXbiu/smSZpY9fYABCZajmCjTLD2nLmiGuz13alSvUHc8Fd2qCtaAu5mO40itkavQjbtfEHGn9pqwdPVOD3GNDMT7PbHjOeNsGMYgGoizc/9CYUo56V2kbciTL4wp/eLCg7L89wtmdKFQgrUGwUwhoGybrBY4ex0zWbkGyUa2A1K+/xOYLCgRHypCSmyiKrv+F4WUJyKUdfTXefTQMir521YfDAE+QJZs+iTBW88fNJH3phg+zNvg1700ZpJtS2db92Xgt5OvaNeDoUsrv2oUKtFrf77SE50YcjfVbEdgUzZKCJXYZ+pUWY/j+18iwOI3bRb+9xj2Y3XSGcRBW2FqryB1/mRxYhUYdRAkeP7nhUR6VuNgHEso/P1CPP4liI89dw80nEpZQWL9ce3Me0E8xEgBhgRWn4eBnQzU9hHfs1nRm24VX5ZIDG0iZDmIIOGr2IRxvhvn8JJKzIVpehgZqFAEXm1hW4KhLlgaV++2WidNBVoJjTJrunFOTh5c8S6+EslFS0q8E/nlpLP6B63lw46velaP1QRR3wpIjcEbJFi5p2KN/jv9RKEWDPu/+Wv8+9zaXRZcfm8f4f00i3lS22fQyIekaLyzTzQgR22DeUmQOyRibeR757DuR5TC8TI1IFxXxFsfSKkRk3AQWBppJoJPdCKD+BzNOukKF1pNgVvmqWNOx5HUVo+xiESvz6pAv/mvymb9mz6pD0kLiYazxEKiRXqgBau7tvnkiKUgrex6YrGL+/ELADlpzLgbe9cbp3SLtR2sbxBcyZz9rAHqLbne6c/wleTREofLXIvw85faMkP1TmbtPh/3pZULYTSaYKA01aquE4yLPvaZsBt3Gecga7gFbwQzrA3kQcvDlZ5u83bs4lTvD0bpMWLJZEXkE3Altu5On5vF+MVp/NvWINRYP089bE73E5thJmOzgsrPP0OgbuHJt9PL0JhyhS9afmkRfgdjl229eC+pXoUNmH2JzqalhuLRr5sxKzIAcM4bsk9r2qdH2bZr7/SKmrMhVBs9e0W07+8Yau1WpPosT9CegqcW/AuP+JoIzzAk9acBONBOfbfhJjYCn42z+ZV42IGx4mO/XNWY+a3oitpAEyU59EvCwGboLp+T4brc7BYjRO47Y3pxHOaDKfSy5OZgy7aquWLBcEQ1PfQOJ88Yy0rJYUKVQB0fZiN108BwNbDhqyIzD1SQJxe7iIVsMZf7oPageGRbWxBLL0Z4ANSuk6Kb3LkkZ2znZibc84ClFVPUuwbOlzzySctwJxpjrgVzDbdMEH4IcZvZ89DoFbp816gx7PzkH7+XUzBZ4fGhZlZH1wpb7Pc7UwA84pj17lmPFJBlrLToVWv81TlZQPR7fvHaiRRhWlBvCMVBrgr3KrbdyI1TtigF1Z3sXkvCrnJ5Kbn0QVRZ2SGcn21hfTBUXB7ObMJqgq8U2Mh6LT/zhBt/1NTQqBtCdwxRVr/TN8mC2fFmVLXcnMR2S6YFlGvfuV1VXH83ELx04bbhENE3RzKRQuyq1Z0QCk3yAOZcSk6xf/Hgm43ZHWKYBZj859sG4WdukNkPCP6CvIPxVc9BPKpiqOniKRa2kLXW5wouNsRSFNQQBlrQpFrYmnBmboIuMODc7vyPxfe648bDZJJ4GBf2/rWEodLXU1DGvJixza9loAJ8BxWrOhkhyR4l4za9dYPbM/rfikR950Gi5o4slRX8F6VgY0IrIwQs+TpjpyhzLumXjps5qIUnJaCa4RbCpVBJB5YZgso20A9Kp+k/wxCN/u23MdldWj6fBRfWFma3geT3d8QB9HIL0+ihDQ75EZgmyIpkSNUCqstMozZaD7C6SqinB8i8tQCHWxBhqxLKDHWq/D9aBstYHMm340mMDHUdXt4GmqaKyoF7jQe6Hqc8/7olJI95YQKzexVPHXwBwVy6TKrUZa2ltLmklq1Lc8dYWpfOAMtimQv65s6Gb5o/4oYy7zKbanDbEdD8F+uhQkFtQRWBb/NFwCe/4oFE256BRmJywaJajPXdeR7jj+q6F6fG5oFdUmXsCQdmQZtqjhpoxZ71M2Iku1e4javyvkrI25BkaOTMQyfK4wRb6j43TAX0L7HZVxl3XhUZL+EU+uifJLB1vtR8WRyi/kog74a+t+JbRr74ZMAUUhmfq8++5hUguP0GRy+lWtyNldc4Fro66IudjDa9MJA99tfotxdkBLGG9T0YdOuClpj30OAnI/fkwP1Jsdbu4cOo/GxdQMPstHReQ95QRo7FBkT5rHjEA+t4nD6dupUXMNdH2SdVnSDvJI6gBFb8Nqyifj8t/J+erayjJeMLojL4DMHVQqWZgkRuEYTKzGL1XTYo7toa/+xatT/RdopUljXDjKRuHsNyCMcdqWjyFvEea5Bb8M7TZM1tbG4GYSOar5PqHmai76XMBpAJ7db5M33RaZ96KFbP7LFqpuyqH6NzkW9ATG2uHJAxMZw4HA1zoXOlS5I/IG8o5UyHUH8HIVeHWD9EH5nQayUlwszRcJmfUu1Ohnq8lth4as1UTEj6Z5Y/6uoDOdliak+oyaGQ2if6Gtv3Fa6c/pMnw2hDih9ZjvVLZU0ssw0GsMlOQ1aVA3AnJmNERZlP7XYAA1jEkMZiFWaQSdw5Yy7mT6L2iWpp66Hxh0hYZwI8g25z29xQ2z5uhCMtYUu9lpq6LASajPZp9ToAjT4jp0MsRkuiApRlZE79P6oqAV9pGHjgptfGbYumB0thjwEXiTevKKjvyqK62Ge336IlVeF9bJ5gS7Jeij7PFSqdx4wmxQIC3wSuAJUVcwanlblVXT/jja4TpdAw3n8CIqc+LNCt304EhQNUxjvc+r7xQhqGBuXd2jWEY7KQ1RXyIRmpqtA093hd67p/GEM9z8jQn5Fvo8huJ2rS6LNCofo06YauAzLREzNICh8AVQ8DgmlUeowmAC3D1fUUjuE8hiYusovG1qCAmiLGt0GkLrqhI3J190YRn3V9yguo8JGnwb+KIrh8NXcXG3M64WwNgil3zHKNXFmFj0d6aAw/50UGb6TdYwNNtk2SW477t6OIxI7BbuzA0UWCagrqNb4RbS6EMUgnf9q0+1CQygt7uqRFxrEAd2Yd/lOiJQNdYgZAqb10FgPR3FLvKcQ9ZE7BtSiR9PL9a2cLodL2cZcWJ5WFihrJDyQqqDdMu/RqwhPw2HdlBghk+R+iZsRGdF1UFYupIF0SM8N9kiq7ADfkAYwlyid0hEXkODcSHb6hS0lYVImV2lEYdmvh3gOOOk/nhtCU8Tfh4RLqKjyYHiWl/KOMpv0KEp1x755h9I+HeXeK7ZmhejOIRCRK1m0HaM+3WJ0DgpfEkzI7qAO+HSqFO8qdNTZg49CLC+40bPtbazOHF8eSW/JuYrJdHJxQ6oC6HemPT3s2eUdAK+2r080zg+jLl1jQANb6ZQ9a9H71/3DzpxGINbT1z6HMFeYvOppiyvYo3s9TSUrXs37qqpz1vewtZ0EQRDO/VWP0F3FQHhRHQjmzC2oNTlEs5FFkuaDadGjXudgjxboeHtDgKZOYH3mPx83bLJ5ndrWbgsRWXvqojTfIfmKaHmtLga9ZSkLFYMAF6EwOWd3OKNoeVKILhbowrZmSoGPn5dZ+4/zwOJKc0+0j8L0deiTf8Gmg23hora+CScV+VY7KaUOvs66zlE7LQPxfqZCzVAhAD/7xZd6dMKV6WZJ1o4E959UpdBztOQTTjZ5mQp4bdk7nBhNNeSZQfl+eshJd93fJUabmuc6gM3Zt6abqX9Jt5YUwkyUXSU4uCYSBAtRDF+MLRWp8pIqQu5MVR69ja3pbAIkoyiBUMogDA42Gkq069+YezuKr0wUPcveAKl5Tj3jcWW7cd9yeG5EJ8uROv6dqoG4p5kVohVwmznv/buwJle9/Lx3Nu8B8PYKFI8jhj3ieYKNE1j+qeqFjF/ScgQh43V1ivHmb+gsXzV95bhiLegZnZnz9mSKiqMtYcVABhNdFoMxKIktbOjNLuSOEJ/i1SEC6cvKPiY4QjEbHB8a9bHaZqgEc4nmsmHK51MJpYZxaXvylbSdAEyRSbTgY1PCkUHrulytvJCPFuvT0ZAT2dXmJaM8qxmLPhTusi5xFIDYkmy322+aipPC4Ztd/pVGIN71hFcxz/3zSyM7uvPv0Bbd/1wf6jT9kkVqTRMMvEu3/yRaqIE0SSOXsC9mT1avUwNn3x7+6uDfwVEBYxdQ7J4qnMTE9tirljt6Nv58g457qArN6z+l1Sl943PpRtCVyz+UdMYkDfZBfq4oQ2AvAv4Xm67KiTth4h6pcxj0osfz9jJ8c3ml45ER/+njy+/8O1Eo6I6oNcu1WD9u9hE09+8hESeqQY0sO2cN81pvq8tzZuEua3X3Sj2AAzbGT3jtUASXk/rKtGr37ZfuiElvbgT4uqiCEMId2FtzDUnUE+O4nftbxvdk2EhUWqLYmnTstFFYIo0rZ6vlx5kLAlVDPqpwrK3nCVWCxLCyKmQQFLSCnp969p8XJBuH6iJKosSv5k24PbU7gssaZP4Kx9Q44w9aSlncSIThyXOMSwxzhKhcwtaCZMqGRe7nkpuEEAcocj/ri3/mH7IJJWIBZ4jOIaNkFEfNB0GHIohDAV45vlMmlL72EYEPfsantxdeFSG7atzIyBOiYjDcMHe425tJJv/s6anfuNfr60sssxlHrvK0Tk+AIcB5gtXoGAWpr0sIlyVELKVpSqMjvNGZR1Ghp+38DNkyxWfjaY4xslbhm9yXUFKfAbGS7ijVgrzpfJFYMYP7EhWOFVN3bhXfcgc1eg4aVx3U+/PzDQXhI7Wh4GdXC1t2f8EiA138nbMRs+Vu1GOKgwjscmP1hLU5W3aljF5JxMBLGU+D48he35LK8ccR0uvuz/ZFD40B0p1cINkYcwxMqQQTKJDIxh//4DxHamJqiKRdJyJR5mzD6tkuAgWb2/+pdFtSCxmnGCI91rBloyoHBjUimBIUh+TekWK2FjDghaNlKFL4ZlTXmuW1osqrFr/duhha4ccevrNWFmG7FlHgL7sf9OPwr3Kk4WWO04ZXgMv8hdAk95Fo+WFUFItNODqG4JT2JhMMUfVZI1DpWSip23iL8H8jE8G8w6P4C6OX8RKFYLbYOVjZ/hd8LerG8jc5oL4QuNY+dPBU76pZ72F3Zr0Xste3dZSD42pgrNOHG206sX6zcC+r0PxTO0ZFCdkbfgbsdQqZnCWnHtfrgExt5YmpzSo3QKsn7KIrXfM1uv19VtMDDSy3GjLDwOQmqTdhIP1hl7Rwusz80+vIq1JYMo8LGECnJ+1DOVLRY3YGMlVZCUmIdQezL9bSzkn57B5EzKviQzUjW73kgmzD+NHGBfsjBAZAe8+LJ5nJ3jJBt+o1sZFdX47g7zRbDY02WxC98nAjIKWXPMMLtX50kfw4zGGPJkMV1qjb4Hts2ZtsM46VcdSSOTbdE/LGLiaqyUEWQszqH7Xiw9fpJzytENyiL5EIjhwDlZ8g85iKWfIbnRE/YtG58BUYePPSPZEriMFZOIVIL8kq3sIGVRJor9wEexwErDZyybSKPPQVEyUlUpuSeHYM0PcIDDbdMej2GBLvRwAdWW2X1s7UFTel6tWaGaBtqw0Y8EX9cUDVOCNn8GoY1mbKmP3MANQBY3rdM4zhAWDD0k9sAiI2SPgAYjD309v6R2XNfs1G72NC/vKNu+EoyvLlIjRj45kjl5e5KzyiMHH6pdMrmPzer3VMt8o5lMVogcOBnokA0qig+TvpXueppQti8X0ChB4Y3cq2NjwOm+e7sXTPrvJWQbDZeCbTrNmrL0/EhyNKB2/dgqjpwm4WmNI8e/SqvlSanBQvCTN6g26foR+c9ium4GrtnbkVvKMS96P8UTjviFsZBeeqJYfO+PIP8tadloMpvMh1bKjM0byVwA8U++qd8xRu8gs5r6Z4KmKiSqLxvEwdN16hYUO5EU7RWdKVcqpdKFsOlVZzM3p3vGToNwOCnsL0Da0SKJGkTqeUsYL76hlMwNIa2SPz2TQ7Ra+zLzqIgsYoLE+EkaaIM49BMkSi5ww7tIqTNHRYEXJtHYv4fIl4ZbPzjXC56hif06PHtMNc2dYxCQhqHC6/g+d85bmrZiYS18mLw0CkQvoahL3vk4jbjZEAU5Xm6z9EeYfROnJrR06/N0midJS3g8a5MOv2a8+miqqW1ApCzSE1aurGHL/qiyh7FBOqLneCJ/SPzihYZHhp8Gh87+D9pyDMu5HRrfSuIXMtw1OMufsIr53njNzu4Ol/bCx2s8ktf7OSI2HtQgs9r/WVrxNtGfLMBab8LsuOuglyhV9+9c6CiUdFynXd/fTDwxIyHaxaw7GUTW6qkaiDQBXh5MNtnRS+CQukZUOg8Be47vFOD7SvDnc+7izaxC5YZhzCYy4AdK4Z6RB8KJ37XeU2eEHAiodCL44Dki44o/bLnLZ06WlW0jWQn3saQNuO7cato+tTjFreg+eMH7eEQMI6ca1revnRMf9HObKOG5k04mr7E10TKX+87lSO8WpDM55Vy2MURr+TAk0qvLx394fRjg2botwwTGHXlZxwHXdU6WoiB5qqo0dfUlhmXEy/Z3ORXfv81cwQvUK0Z7HLZGsk2sOA/MVn5yhFG5RgxbdV5pz0oKj88WkG/JyONJe3DQdhPR2TvU7DBYXJcSd1Pn6wL+PSnJZpSUYxsCYIxMHXp3GcTgSSDkkW6Vp0PMQTlwtsBA57XvITToFr2ZBcOYTTdbh1WZPlzpoBxmwop1fif4k0uC/26weng+FbGDOdJUPAdQojQfLJ2I/CYiXM4VGhK1pXRhbYbWRqq1088HwKCpUwEVDEcNlCA5RWeUeZobq7oj5uy+3oZ2kibH8ei/DhRx2NK+HfZId7fL/lnmONabTgU5di9OHy7KY2I2xs/yX1k7/xBsqniX81VyzXG4lUlglQAlQJerIr+JdX6KvfjQXQUT3io/WABCHklZXoXCM08q24sUjLxCE/SbvWVRHaaGouhQCsgND3Xswc2oY8E5UcpJJB2YK7OZaBt0w7aYvpJvHq50vgHV8oRIzo2jG86OF+PuoN2HM1x+O9+5Fhu6JLmrIIt648UApFOetcxh3bBO9zATQo38AtD2be2wuJ//eW7w6+LxQTgqwG6iEzniUkx/jsTCTQ5EOETZGKZieV6FEUU9zA4KY6lcf1Zf4+1qgtNfy/k77Ik9Gwnnjp0oBvc14xYZ6sedmbqxUDNSHuTaLl8jiT8mxvYvrhbtPi53iYS2sLUSeIFAwbjD4bE+kFiPlSv+ai8+tvcziCAiodHC3Cj5WGbeAqeomqPEM+8b7DRQhfrnyyyFQRi4TmQvbI8CscT4ZEltgKLrfrxa2xUh3zfxk77NElk6S1NiMUc3FwMGGLFLkp7Xep+0bLo15mykjSu7OuT5BpM0aywQ2xSoV728t6E8+PWpqqqMjxYFX36l3YTHq5nSQN8vpYHmY/+EhIdhPDK2vqbjHh+xUyfazhqjNyxbUrUjWYal9niPJOK+8uf+S1ghEjlxrTphibaW6tFXFn5/diy7QQ6rLxcNmGUkCKujwLF6VxTBGzIdTVKD8PIqsFvephb0MHfpbZh8C9kLkuW3p1o5KvJZmtPq7WE/ipnJE0cwfOHd+CmPV4eUH3fEzpX645BW6oCe+p+51VCQXOBCEDzn6VCNq1ORCMY16zmXpezNv74TRYI6FxUxDhn2h4ejWL2zq+qkP3ueMOBprv7+JVhvvFR8CvzuqCT3NgQiYGDh79lWmQR/HlDH8fYOnqn/nc102QP8SaEd6GUSUgs3B/bFy9hOnKLdWp9prv1uetCeXG3GbEuqW9TG1G2X2l1kcK7FCNN94vQWiKrVA44q/kBhNvRj82Ff9sMrP9b+6hxoXFMFDXiI5zxzZ0Li8K/wc44Yl4S5kHuXv4JcLvgT7xwl6jYXzwh5qmNkRnPjRZxdbuP9WLvBF0ReRvzGqfEkzZ4tcc5XyHvF18BS0Gk/M3M9gaVvgJotymq4o0mEgUTPrypEgFH4+dM8b1pUyWYcBUo9X/YHfAKXyeie9URqoWu4rhh/Uk6dEM0rcfYMTmU0SMJsQfsI6pPRDMBXEajw7JBGX8P7I9BQbiYjMUAMBSwS64b6fMLuVsuSgPkPpLeM4A4YOzkSKrEtKtia1ZlMYdLzqAC0u8okqWEbJTEKx9w6OiYYmbBL84SCMJxOtZ6NnTK8VWVi9rUENEzENM42f0HN3f2bjUpvSuQCKffipOlgqvFX8Aly9G21fHM81DjmJk3/DWw1fGr1JIgZA4q6+EB31GlGxkC9lqk8jrxNIJnyyHqBLbc2ZwhkuO/PxjS/Tj5JSo9kFXInS/8Vk+0zr/frNvEVfUAcDYtZDaTG+jzZ5UhIhRbc4xOMzLHXpWXDhkSWURISxfNlxXgmUOkKK7BjWR6banRxLkyhXIyxaz3V1ia4Q/KYWgPzg4rYW1QL4HyeM8xvkms+lTEx0oCYnilj6WVUndAEdZNBXw9qDF07vPzx23MSkdzYobduf/L56O6i4mJnUSY424ptxvbzcpcGsQrdb/HZ+TCd8q6Owf4ElGCPvWL4WrOz4ztbBoC1YtiaHHL198AcoPRGH4un+c24kjnIwqmoYHUtxuD/IsZhUE8IznKTC4qOyJUbwSdY+fk1nKhlOmd4oOHHl5sZ3YRLXlT53wuClblohpKSZ+ADStQ81hW9KWZGHu4ylftXD2Eg0x6S4uVbmXxgMsA5wI8XkzCdZeEqkm2RiyORNEQaYzZFAmtdFIoz+dkYcRn6xgKMu/sTmRDCTJHYkynC1C7F0HIf9GkQdmIs3MDjUgYzjvJ9Ld+v1y1mGIeF5xLJV3ud0dXR1PWergEAIaKjwm5EouNa8vaF7sxXchsx/2NvmIEuLnIViRcAsA2VCBWbPZEjq4O5qF2eNUAoU4Xl3dxDgYK91wKEQ+COk9R4aNHT/ZH/tCv8fM/u1gXe53bGOkR02sqnNnSnsryt6y/MY91fLzFDXlUfP/Z6Zpi8aPAqOXDmYM0+QHN//idtcs1vDO4VxWtik1ngyxj+j4cYBLWn5F76LYqOiW8Cxu3VzoTaHby+5OlvdYWSLLfWlWv9F5PmUXqZhhCCqUpSrqwifCrKSxWu59QKWpHC5aCzmDheUftLSv8ygjgCJrkQURs1/uNQB4brCMv7qOqrXitMK+GO4ZvX6h5QO4qFht/9cIubGZOcle3WgEznxRJar1SrCquA7csb1nRq9F10s5kTGWy2EmFCQyhbtSEA1xMvOIO4UqPX3AHK0GOcYHXH1+np0YuPC+17jYW9oERNTp2o3hADnz2/2k//Dcm/js5nXLHZVNw+ZeqD5r9hHLckF6klZeJQTDHpFg8EPXThbVO+Pi2HVSBwwyJ7XxbKQZi/GwEnn4UULE+QthbkivmFVawM9Sl13S2ta37WftM4R064weoiySAFfXAplbmRzdHJlYW0KZW5kb2JqCjQ4MSAwIG9iago8PAovTGVuZ3RoMSAxODcxCi9MZW5ndGgyIDIwOTM5Ci9MZW5ndGgzIDAKL0xlbmd0aCAyMjEwOSAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42rR5ZVhbW7c17u5OcHd3d3d3gkNwp3ixluLa4lDcirtDi7u7u0vRj55zX7337/fkSXamjzXWnCvJDhWZqgaTmCXIHCgNcnJnYmNm5QcoKqmDHM2c2NiZ1IHWHg5mrgB2ZlZWTgQqKglXoJm7LchJ0swdyA/gcbcBqFi4v8e+e7Cy8iFQAWSATkDXd6MlwNwHoAR0N9P0cQayAWjN/hJUQW7uTOZmbu9moJO1rROQ7j1EAuTs42prbeP+JwcHE9OfTH+ixZkB8mYW9iAvN3tbgJmTJUCeWYkZoAzyelfaAmhBTgBzoI2ZgxUAZAXQBOoCtDSk1DUAMuoqWqoadMzviTU8nJ1Brv+DRUJDU0uGESAppqwpBQBqMwJktDQ0/7xqAp3e8VszApQ13+1/6rw7/glXktIU09RTlWJj+bMGABvAE+jqZvun7H9ho35HBvgXtPdQK1eQ418FALQ27u7O/CwsXl5ezNYebu7MIFdrZmeHv/Bp2ti6AbxArvaA96sr0AH4FzEeTpbvdLrbAP9O8GdXAIq2FkAnN+CfIGnQ30bHdyrfg9717v8E9k6E+5+cDn+7A9yAwP8oY2Pm9lesoqqqIsDRzNbJHehk5mTx7uhu5u7hBjD9S/f+BFrS/A0QCJDwcHX9U0PpHybXf5b5B3Rx0PvKDB38Asy8/nvHzJw83Hz/jZv/XLYFyMnN1s3d7e+MQICVrQPwD3q3P3tm6/SXTklMWU5aSkOTSfG98ZyYlEDv7Dgxu3u7/+X9J5+YpCI/gJeVG8DGxwlgfW9SKSdLCZCj4ztqN4Q/9EnavvPkDnL1YfnfjW3vBPJy8vs/DFa2TpZWf7i39HBm0XKydfEAykn+j/u7CuFfOmugO4AVAHQBAL0tbFj+FPyrX/6o2f6o34kI8HMGOQOszBzcgAG2VsD3C4Kfm5knEODu6gEM8Pt3w39KCGw8AEtbC/f3Vn8fF4S/sss5WYEAfH+r35H8w/Q/TUD716jSvc+pJcjJwQdgCbRCYFEGub+3BO3/n0n7r1rSHg4OymaOQNr/xel/O5o52jr4/Kfrf7noAP+gpVUGuTqaOfyXzdZN2tYbaKlq625h8ze1f+vl3M3e+1/MydoB+L4tf6m0/oyUw3vvvp8/tn+OLwATGw/Xf9ne29LC3gno5gbg5PzLBHwn4r8Qv7P/By+ARVVTX0ZNieF/t81fflJOFiBLWydrADsXN8DM1dXMB4H1vRfYubgAfmzvjW0J9P6rWQAszE4g9/cQgLOHewDACuSK8GdDubkALGJ/VH9JPBwAFrl/SXwAFpV/SrzvNo1/Sny8ABbzf0rvq2CxeB8Ds39q2FjZACzAf4pc7AAWK5CH67/Z35M7/ktkYwWwOP2b+O7v/G/ie/5/xXK9S2623v9m5gawuP/L/J7K3Qv0L3DvUH2Brn8r/pNk1T8HzV8TxPov1v/nBP5L1nB3BdkDdWwt3z99/s1Fyczd1dbbgPW9/dne9e+Pf7wz+o8CVP+a3H+LFhcHefsxcbKzA5jYed83i+P9LHlfMU/Af8Ra/H0Y/jV67+3xD/nPSQQAAr2BFgiLcyALgTC71IaPJR+k8idKoan4mE/KcYR15eOhFjMm2gjxJHO2yIEiBcFNQZnUBSBFWX6jD8nBTkW6VGHYDq9rzUkV4zeWaqLbZh+UPhCiSIkNZ2sza4VkKi0ElXaQ0x3KZ+fpfeecymyJbyEBaA0fSfC1df7+zD72hn6VQm5Y2rKSC+1VOMPWiOXqgOG9gEbQTrgw0Q7u/vYb68snsx6xRfpp07yPOMPyMM7dnZj75cY2GCLfHiexUV8x9aJ6GMQYLDp+Yi6ZSWpmQ2PhDOJDbrLzeBMYzaOEcjwgZ6PIcUckFQuwQGVheZK8SuiFDtLHs+MOJTkpHZOXLLPMxWg010F44rpv7GMey6v87I/Ip/qJk1jRZr4OpCM2bmwPhZfr5VqHo/0Wx4KNwahKjXLGILMhUZEzSzmo7GKeLqZBA+eREpZHkLCVizb0qEkDXMGQXFKeeoMsQ/HmvowA96Wvz5Qw4wYwT81eR8Pga1mKqGVqz8KR/gin7oMnbaIWP6bvwY0XKcQnwQqRo0nNyWCU1VtJgPVttqMc1JvKZfp0y289nV3Og5SfPJ/kmTa+FdJeSm3wyWoaH55fwc6eYjpTJxPi5bj6sc/dUbyJ4GfkpooxzrRl1eSrscF+ZvKd3MQ2hJCaPvs8d43wbGGSmTPhWeaclFLsSra6CGrWQg8tn+7bAZtH2xy/92x9qa+eXijVD+y789jZnjgR2NQrg7MUmqNGOuGEJHPpT8e8qtsXF4yOMcNQn753xT4tgeglNnjclhzOmn9h6shROQIQrpYIBkdIIyfvt558Bdj8JqxrMO0qwgXq2uve9Kyjkg53AC4sRJiRh7ZdgxXsao2rifAjlBjgp7jigyNaLSFSSvIh6K9nmveqB7pHcap69RlN2CJrbmSVrOiQb+Q1vVVHnB9rwec2xfGggq+Coy4LjU6XGT6l0yrMuGJ5ELZY4CVlL3YG1gEz1sTvcSrURbLkLj7FgAwKRE5G8OcS40fv65Xsj+Oowq0Lps+mdMYtG8nf5H1ESmGpwCmKih2LkoYvL0Qb0BvwWwMEO5h0xzEohm9z5jOC/dGeaD5+kJ5tIRSutdrI6H9cnnhATqbwNeHWRDcfhD1GeZ5fzjk0f3FA69nUsuG2SqYscOPjdZuF+AX8ln4D9ctP0BGMwLTmw1Rthazq6XA6OOc12eWLVcqdQi44pA/ez+tjRjPbiLFS6bsu9WORu6unDFCqHHYTgxXcaPi9CYyltmThdmQEJny22yFqW0pwkQSTrWRRsEeR9iR1WcraSWrAU4bxyMlejL+zHfZR4u2jNxgusSeM9SaH4+opZyLaRKtNE/B6R1DcY30BwO1BjXn97MqkTYRoUPU9isq6owAENx+LZlIDob4SiyputLxmHr2rKZvKKH+Jdc/qymg84BmESiGo5ZE4QzECFt3+vaQegPr9OMjf5iDJ9ieCFI58lIoWgVNt46IfbLYC6VZxxjwTMcFUZ8zADxL59e6TvI7IheARyISEdPTAkFeVz+4u8I8rgKG5vRqa/KDsq1lbQWZfKdxX+ABGEXpl1XQ2GNcL16Y+zwFguF6WWfEpvE5truBITOKbX/+zvy9PJTTF6bXYY12ziKk8H9qHjIbzns/qLfukaeknKhA8l1Ekzs3EjiVMMd8MhjKXCsK/xcrI80lxoU6FXii7VjoVIEy/HIImNAoHuU5/eoz1IUjfVmnY8U8b2xBVRvKetxfO11P1CDN640vK8JDGcawL9XCjv/mBXlqJ7V0HfaxLoR+dMMzoa/JexxeqqXpGycWSfxPgNTVtsB8yX4X1LG3Ecls2zpL2yqbV8rIQ3GPlx4fce2mO38USV7goX5ypZONEXEOZOUIqiARV0M/ObC7teUN87aJdVgw0f/CeYTE2CqwMjhcu/ODgjd5YzNuK7qJb8FQOJwNXNNsyTb+P+ynDKY9/wyRUTUJctjWMqvD3oxzeN6k+yiAoye4ugKjAqMBQAnyX2ltmh/X3L2WbtOKPJPxxKXAHff0OeWeyo5uIgRyMiM/XMz44vuyfdLPjbW3MT2H6ckYQIjj5G4rzArzPH3KmAs85GyAons2cb1etuqUZET6tRrPlwCP4o87B9Rq3tkXdWquDflhHG9hLW/9EuDoZr3QHH1avr+vkNxvRAv3yuedJpHqiWtwjtxk/VERAFbRaPXY70Hz8nhtcgsmQQo7a08wXKjSRVSdeWVx8dclJI5Hu0iQe/apXE+7Vk2OgZPOWg4Z4a/ljma1uRtLo7QWugzPsyxEY9ghtSyJXYSK6pVkqv/S9a/2u7ZfB5Sq1FcbOMdjg+Czol/UvblBVqw9fuDZvaJf3lZ3FPS0UozMbYJvmtqwphV/YUZjVcYXVBBZLLgoFDYses+IX8j65ZCfKqn5FwZ3j2TmHwkqoqCf5afSwoXG13KTZ18ldhJYWTiQh24v2DcYlGRs6Xej5vJRuedISBovCgslyLTFbmzuBQtpk/ZHBDvDtIOynfTNFzUkIXHvAWBlqDx03Y3WAZx2n6hWL6Ki/SNL40cccZKogxzyCvRqC2vka+HjRsdB2doxu7Zxu3tXYbusfZUcuMfcwpxzYGqHnlmCjO3FKw/d6P99wig8Se2Jv5X4JovoHO/UzuuxqPAooHnw5+D0cIgL/WaaijJNt2876xaVv5fR6vKHUoGpw5ddRAWoxvBKrXwasUq4q84QJVMq3UHwxyyFDeApyCL1IMD6+uk/F67g8rOALdMdXcLeiR96jxHnPhqq1KlSCZgEQO26zEciHyG0LZXyuY7dtqkzNMV+Dc+35+8FuGOwO8yWy3XZ8KPPk8IpPNha7BJipD+1B0Noz6RRzbG59wHkqcuHj300y2iVLOriWrSR085UhuOXhdcU0GZAICWOD15e1oLutSR9/3nXW1A/S95N3LVESRd2IWBib2U7RDAFPA4pim4n1l4Zfq/ivO6MWFakEV3YTaJjvswhWwZ4lWpYy9qIIPYxieg0D9Wn3zVbc56yF8gM+J8Rv6V/5hGR/lqn/7pWG6/TM+OuYaQNyJ0TfJgu7BaHPg4XLZ0P152vx5rkKu/oLBzKO4MXFsH1ALE/Asp1UUqHtMNinfJQor+BvkQo7Cl+ME6YePg+1149r7CiGMmTeK0CtCkZypGyMsrS1DEZg5aBE/eT3sFgcrYQXcC/QvCT4XUatKVVT+zlBxKWhqrI1+ng+/WLOXIT+kh77giTeEN/h+oezpX8ZHqeiW5z05UUk+oD9HBZR81cbXezEorGW14x47t7SDZdqOKM4HrAd7buTnSljzNxXY4ywdatuUlYzP67DqzAl3NYVXAQ2w4yYr497XdKYiRZ9ck6RQBKTCaJ9tiMeKtfaL2KV/eD4RpzSiIp1ojfTLOtr+yeiOWHfCavgZHWuh3Cey3sbyjCSRjgLDrZ/UibI+QP1nMMN5WI2udJTpzUilrd+3RXyQhkR3WGVJs5mI5wuq4823v5UziZM/5GwrwEhC2KaU8IYXGxVW4PBRvL3DTGCa7rOKSzGm4iSCwcZZ+bEptLoF83NOsQgRhD7+BSSufd0bAbw4pUeNBw7D85Mw0Um9+a15BNpllPYy2Wc/7rfIiN9y9nID6Sk/3jUGVzKk8C2bCTIQSHHFdTunS5SotD4q+8LlpW8co/JxKFqbHKF8a97yW2QR6DtgG3/iaigv26iwW4JmiGbZZ0rRXHiim6eEvAJh0BQ6kEtrDGWGg8xf7la2kHVzfZ3aV6QxZHrskMs+yNnkll3cZK994aWYh1mmGql4FdCPKXbHRs0/LzLXlGLwerWNBaP8ms2+IdK3zU5zYmazSDGkzZ7bhk1zV+jVDn+7q+ldLRB1OhtFFONstQJB32yH0hvbwwylBFUC1OlEqO4Jy44mfdZjriwTO0EIIs7UL0c8DY7HNVmgOkLPauvSXloJj0Laes6jPGmpHmQlZzYlUc8B7bwoy/ir02QI0ht/citdSchYRZYaDPYDuLIsPwbWNl8FMZgH5md5w85Wc/Pbn5hiQ2IYGXyHIdVz9EFFsK8TKUkZX857HH8hJcItrNwgtlVL76smk5U6yi5npPschC6OR2QOSmTN981h8fu/RqtKOBiN1aQ1pRvJrYDr8k33/o7aTa0yGRBXsGEDjsIb5Go+NxI3RW2DBd+M/9RkSAshuxGH5be0fD3Vbwmtnzgqlh5JYakV0IO4XYCC1HjmpZnFSWKcdya2GuLaHqTsg4v3ieV5zmn8XmuEptvYHIcZbv7d2QG3jLRb+a925wsPXalujP2J2+qXXglzulMdrpfOC1+xFyHzyB0CFoSfI0y8fvELHHgAqQmR9qjk2JS9YdSJ5RA3iBANNzpulUMO7p05mxLSoGa4+oysmb8esOKVne4n2aaIXTwkXwqU2H+TVCcO4Uexf239BF+jz2DdmsVsavfkaMTt62eYV5uLOqiO6YJ76wouBLvaWq5UkjR+V2QFyZJ76G+rUhgCfhBii/W3XURvo0gc7QeqroJrLV3kh6qps/FgsQH5N2oy9wqKUytL9JtYjssJGyA3FSbSjlg+sF0bDKWidn3XglTfcZNy1dqTvD5KP/VSvXJRW+igZIjlKi3K5asE6FzHp6GelVbS8mIxo9FpbJMUfjx0W8R2GzEVGPEOB9NBH3H706Vi9aXcDlRUspFIrxJeZzbiHXKlOQ/8qoQBsglD1bx45qpdmZo3/l+12iNeGQvcwTi9CpqtrNSyPZKKJSPuYc1P+xj0RZJEfsNxzivYbXGGCRgd1S5XohdBmjVZQZfT9J8wzdznsjpl02T7X8+AyKsaA1CHpZBdndST9WjZRnQEQKadOJffSBG6h5fwraw8KVsOnjtlPoEX68zgdYsX2deHkMjV0r2x3J1D7N4drwEtIkMp87QghV7BqklNtrtNuPimcrNWMbIFRzAHm7u8AVh7mDafiprZuke6Vl9asuxXK0RsJn2XztAVcqe6G8+vM5VyXwqQirSsodyW4EQyCne+/T2PWj9hJBp60PLZEsZjJBsHw6a46EMKmTgKvYeOj8e8bAalE6gtV4wQTMDqtG3nxMlhImnQDCBib4502gzaOCVpLP8VjWQKuuA7FeFDQMV8a15EgSW9LLdEJQG+QhBakl1Bhs8nbiLX6a4WvYKO6hOuYnINvfzcZ3tDDLfd6qD+scayMDqdD9u99Cpoa4ZZTP0sa3Dcnt9MOr4uhBTLinl5i/fAt1mj5ZCgBYmxDS7+f6S1XOeUHFaV44wNu7QchDfSEq5sNPOTf02iU1FEmpNA8R6IeLYvhmZb5DX6D26ylSJ6cah+zJmpFvLpjp9hZCQHwshpuaGyXdvkAffMvzq5zIv9Tt1f9ShQuWtIQMcbVCazUSEbzAAOF2TIwoZdCRQYAplrr41VopHxSZLVGIDUGsj29otHUHKQ79FwITVb/t9I/pLwgqSdnccHePtSgPGmPk3euEtURApiyQ9B9edrmK6qOl0KBg0LBPpWmlmVeAbFLm8psVGASGLBW9w+Lcp/7paIYZuyGoxNZNDEiyOQjsFIhdf3vGvkcMum9kGi9YH+CZDFJkelAKiv3XateAJE5pgNZ9yLTTyRvVAV7iHK/53p1Sp0XyFyGGysIJFCC3YJvKQha/9xZvK+rhv5mZsJVB8MO5MgfWmYm6UJBuDA8XCxgWdhoEyLxmLkhPSHa83dNW5GnOet7NVMBmdQOHqN7x8zzfQmqav4vTgZGgbjQndHjtxHpIQPMPBWB/GvGQM2ydxtAj96Gb1Sg+kFziwU+ZS4puJ7fn3H+jY9GOWKDH82TKIBgwvseudi0HxSOL0NVwb6FnMe69fjeKPZ12W1x7C67PAQ0MJUjG+SDyob8CSL3PVjP08JbdI/R27zV5wQM4WIYK9c5TwIzBO6tiFUI7vuMs/iCaIH9siesf52aqmyuWb0mbEjcknj4Xjp743dVV/Trfsgf006cBG4nLjpyqO55mvGy69bh8kmzeQJQKFVh5v2BJJbg9n4hjJ8WVJmZll8J7zcpfyLL+dHJMdAR4seF3ct2saVlf6+szzogy/PrExsO0iXLeu24yIcirJfn2KnzyciSegjRZ5Ex8zQiijncosYmTYYBn8+fyTtjZjsBzUSCT6cDaaz02VmTOBTTwswjB83fMW/hCD9FM1+lHBAUJ9vF27Kqk53Dr3AKtYk67Tc/P7RHRbgSR/5yWKZzA0Jo9l0zQ04b5MNyef7+8v1JX2hzYINHVWcEUdYiGfBK5eQC+c/F0NM9zgU9q1c7Bw4DDOHOoY56Qjwn0BAa0XK6bfq2ME6W1jVZKXdvnqdyBzvLDInjqFcjMfLg9EZ22NnFcUUyPlQ2S1plx+c/MGg47xjU1q8UaasSQzJZ8SBvaESLK/UqwJ3ZIC7eMiITkiobGT1DU64PRjWaNwMD6NdUZJpPhyPGgXIBb+gPAmp3kTdlI3/rghVrS22EjWPrdFzibVqjPqb4tnD69W5YCaElonLiRXvhPd4JBUfsulRCUBJxBS7FBD+oruMAA6/hoYAFXk6cWftpFlF+w4TxzUu4DQVg3DxSEsCI081dIjWIJAKCJ73wKRVwEvNCNNa9D/Nbtrb+z43kulWg124UyanLaUjIqDzxNBJfcBNTeVbQnsjtF1iExXq9zBtb4+S8xuTnMf2i/RH4TNdj12whDlLRWSR9gZdJJ4//wacrO0YgorfU/KBl8h7DuZjuRMLpnp6Ut3fgK9OGE2A66lk2xeVe6Kc0HATrm0HMYpKJAfM7uFImAVQtY7RQhLr0Hw+hvOnL19PMOr2Rr6C68lz0nWaZUT3UZUwWpnlS/SF4EtgwKSQ/SkIWpHmYK7OP/H4WAvd+PVe/hMO2q+6TjVPhb7+wJ1QjIt0bSVhfslKjlyLjjFbChYlweHw6ikTUz3bIBMTzQ8DGaGDCrr/cG9P73kR6PUs+bzmTJ/MUg9YfnSC5smJHD64DXGyeSLAvl8dz9fWZPPxWn1IzzxiOK+6Ig3osPxwnxDem1w3Ls5aT7kG+PlxJEQnGR2xSSyrW/izlMQYBaf77f1ss2rae1CXz8GHHAs8Y9MzY++ENuf19ekYClDgOad7nMQrcM4C3ac4kWZjD/pId1mry+0EvWXXDPNnYZZY5XPTrWmVaMDEemNYiKJbTSXOMubno2gX42Dvx+H4XfnYGahH6gkWPxuyzRsyNCnz6/E8j1ebTkh9167+5GnlbhmDdPfZC6o6x2OR+epPOp/dvhW+CuVoDQsEr30onaq8ympCO2ZbRIJDrvPBMZ2bzSvzw7bPT/TrPB4Yha/ojAIDIaqFhvmbYB7AS9rVPqx090B67tMaHcZ1YCfX8UqnGK79oEbhChnJT1NaEn9o+gd88+xEngmjuzbIVjHKc6Ue7dMHzxsBGpcuNLfVGIbmLAiw8H6DKHjH1gOdWWj8bZ5daJohgdBMLaZ1WEXctuM+Au7nRy9F8EXm0ZG1DRi38WGemPmCLSlj2JP0rfgVbCdG/mQX2wzQvAS/cHIMYT1oLTXAtvbwzd6c37F2Qidp8kHfWYkM5UXUgh3jIA/ValbtfX7tohvsCDVF48m4MCkiMUnkDzJ8vNmGqTNV15DxvWFy3UAc2SgUcKhTtIQDSrBmUEEciIiZwnqhrPiwZwcBuH8zbizvZm8sAXg1hlgvPtUJXi9j5dRnxmspAr80rU89chFKU9w1ukOqijukqhmLH69E9XSy4zUc+IkqgvUCGGz9+D1UfomptR4xzHdN7jPlV8+bC+6lZeApgQE2eOsf780ve+8d8ETYGW51bbmQ7jaxmqLRXmUXgZ79SWLmNJmhuNlSUoV3rm/G47j/b7vA/ZVgTdxXZmqkSg3QNwVf2Prpq0oj/1Ggcwewd8+KvbsiLmSbbmUk0v03k5/gnxXaNgjeAmagr6peUmcfQM/3Xy/QOXD/q+vjE3IWZdFFSJjGnybzi4/ENplYFV331zrUHEphdfY0o28KITOeSHyxGNaQuBnUS29aG+jnwy3Hw74KK9DWG/NPmOmQE/YBzDHpqdWLrlNqafYkv5Iv58pLJGIL5Ng/RHQ/sMnWqWGbuZoWs5bh4Uolu9o/lMCdZTurZ1gES8n6d0zY6BXBoT/qYM/D/OCF1y/mJDlnBqq623dRcNzHNn6U4Pd6FLch/GVmmnkH1kH3MRL7Plx2zk/vzuwI6+wf39ji9VbSO8yFheEy7oZVJXwTTH9hHZ9g9CtumPO2Nhe49GkQr+HgdI+n+CZLETWSYREPMP+pM4DDgMLcEPJVmu0wKTd41D7sJuYC3W9ERhE47dzUL6OC3eKRY8mU38w1nirHKsE2Amvn3W9/LVLlGzU1XgGQXqO08Ax73tzEjxCP6G/kZH7ckYulVWGx69+zV0eUU5XIQp9S1lgl6qjUako9YnRvG9lvppbcdbXYmOhLRrxhtJswTz5AcLHVQfQXOHzqOCuXjs4A9cEmZAnh+z33dSCOpJI3+FXOfmslAIjSU3vR1Sp+hoZiRl4bsdMGJ1oIoq66LhNHkz3gvAOwU6WEHQdM/MFmIfb3O5JT5prhd2OOkQfu2mQSjGIg0ShWzG5L9w0Sa+GbFMRjg9N48CrxjyRi8SWJl710Ac+E6G5vQmf3KXOewyRFxtDYjFl9GxomHmGC8fqN1J37ShYjJ6Ym5KotFS0S29MfdlSNYoO+RbDOC/DQ8KgdZRKip62eCz9YVntKUUiZOmEGnDyioEQEqxoCZrX/miEnZbDoMEbnQdGajY/ikyQ1aPXa7p/kNrvfFOtn95U3MVzHuLm15OavgfNque26pXTUZPx9/Iqd5+7gOKwyzDWepfJNd1nEHl+/0z6cvaBeDPuV3uYXp4OJHotaue6bKcK2eV83uqMOzhbeXajF04x5tcqnHA0duFfhB9Rz6mx1wl/SYBnm5eDWqYKroaEEeMyZj+fOIhk47gZC6wjLex31LOp1SZzG2R+c02iFH4CzCc82xf7LkYjVv6cvvQ9HAXn4TGqtzbWHHT0YHuYQaOVYI4frKizqzUGynJfRvMTNRVPi4uH5kQedmNQQ8H/HzeTe1f7iZRF3lJ9xCQvODlsSnARTw4CZ3Fyc9S062eyKE4gbbYv7A/ybv98BLOW99ziVUDOUcpsmZiU2cTxf/32AO92pYO49PmSfwaOMI0Sj2QWWTrjNK4FbuE7XW9v183JYxzsS8xNdJ97gB1s7pn9UW8RNXhWFWdeDTfnrXGKSow/2SIPG4wkDX6uXBPqBte3ZfuVqYQzSEBNQ5UmVDhoYcGoJS5pE/oXksNdN3nPSasLPmFUeWxomlO9nSXpmNKPj5v27PRVUF8WcT3R0GlynUJfpoqxZwgPFHB/iXkhWfvN/YQpS/G4dkmFlx11jUzfDMz5DdHV2HIJrWg4YLAP7kqx1hzURLdnPR2YMJ6HzrGbJI0TXsLEw6Q8/XvZ2/GDm2eyN25nJirDJqC6684CmNmOlgQY+NIYw3IW4TSYTGZOgddqa3qvG2uR8IwXmG9zKuY9grka0fGJ7Jg/oxPzvqTPXruPrL4iiQyXHS2c5UqDWclQkv9z0ZQDkxeYC+Fm0rK17KtKWtWEZEQgeyD5xlZnphcXD+lu3q00JZte8HKWHhk/1gfmviuztWEiBeUzzYGSSaWfCHw/tnuFWb0IRWciYR4YFPSL9H0zndJfZ9WPzHpcWnhRTsAVj/uePFb2PJcGQ9xw5viL5XkomQua0DPY9DZAUkOimnssHrRp/dm2dYLjxLa3RtodvEhcMT6KzHs47dXV5K7se687ZpgKa8i/i2XkEEPO6NbMcZWUOuMyaXUrKLiRbFCSDmriarqIzw6X4dw4cdyJvHoR+Eqagvek5GVtVH9YJGac90IKRP7gb/DXTJ8qQtQgy7eWrm82Vu2mJXm7xkG6EQej8bTxAAt26li9stUub7DmTaLV64MkulKu4qb7grRxn9VC1fort+/yq7DRcWgwptZwWv4YCre92TmepXLQbsBPI3411RgjZoWvRs4iSdQ9Yez2OTOLBj0GlIxuRWz5aZFlmhFe3CTZjmZpjzlR33kyFzHonJwOxlcwzLbk51vuxhlp4YeZYnMTbW7buT4tcOYelEm9bXNqsLw5batR0QK2hDnsbcFmF15IdnNdJ3xo0cfNfriIenVc8ZTX+N7dJGxJW2yIM5Ct0oMbl33EoH+xM1CrHAvr31CwN27eUr3XQO/ozYG4xMHMUVsO173aI2reehsw+KXeEHdX92Ml8zAmWhXzd9oCh1xDltJGfrHZjCfrh7bI3mszsjV4nQoctMG3UxYd2oWB1OrfE9UkA9/H1RmNnLcam9g18rjzGSG+KPWD85PQgp2hs7S2bg5prFKtYVr/bGicvQJ2tP9S1F33wBY25b/cndgjX8zKLd/J3NP/WAs8Gra50f2C4GGuUwn7PMBpdC1fqtNzp0J/ZD9gw240wd0DZ4EIr7POLLfbCpN7HR0+WqRm3wvadCoP4E8qh+NSKI1Qp+Wfn5d3ioY+KVoKLLWZLxT9gJhnMfbrWuX2y0iF6Ge7hzdR6+NFmpPp+g12JGsa7IbnMS1FstgvmYlGk+6EcOs+4ZQHSxGlGp8hk4EcML1AOBQ1vV0SK2WGy9D0ExtUIWxKKNpRPCHnABEU4QO2uw+FjDGYmbh9hvg5EeQB3vyIBcRziaXqnjIGEQ5jonwmWVEL5Xhxu7pg7oePChGmpCcETnrsgcYTXSUXanDKUoywaqzmn/M8XlRU/ITgztEw8ASlVxYBnSsoEpXbZ4KVmAd+ur0MHwZIqHKUUhLngy3ayRyFx6rVIk/dKgF8v33HnrLAJX3jOys52ZybZcn805MHLjqPRQy6XqUaggcFVVsyJFSKWC6Dwo+OdkmU/AKs6mTxKBpQowJQUmOQ9fZGqPzrzfE5VvTo7ihGyJxn0UtVe9mUHG4f2yIYGocrVlN4MqrCPz5/w0U87jYhP986C2bk7t6qC6fKyvHSfuJA6BW3W4z46E/lijukN/MJB9INDhKCaPVBRT9pD6n0aXMqUAalIsNf0JVrp18RWmudiYl/WdG3I/hM04CvC7TqX3tfuNajrfqhWgfGyEv6y5FygRXQEZVqjyP7iFInlCChpM631J4jSvnGAT5s1BQKEARZYpTLQ8K8oz1iy3TfSFJONHVqEVe2OdzwpRhZYEcC5Wv2m47M3Dch3obmwvTP9td6+MvUNATOL1qkSyZDUOs1d/RaDhUgVAnF8ntuoqqmDQzH+YbxTBHyixGUWiBIbvH6g16HLwMrmqz7Qcr+E3o19qdd9OAsHT3C9c5ZpAvUBXx8qI+043U4Q/5srovfGn4XWVkO0PmnZvNy73gRXC99+sHQCSx5uQuQUyU/VbLLZ232e5K3K7IL0h/XE1wAqcGq2rbSa11A+ImXpu70OyeTNKFJLt0KZvAQ9+ciz948TeOJyjzcMsBGHCs4t0P4CDGecveKkxUjmA3GizdrmFLTPmKm8sURQhB56LOJyYb2fLXppYbmCjWsTMpR0P8IrtVsrzgZQNV9F/8RZ0zGAW2tFgsytMi6lUj9LV8KRX7SSrEAGyc6OkJ1uFgqXdLOozP2jokKE2fMfuKMs206ui07NsKo0VWfu4+mH4kjSQBENrX3oBldX4Zl5syIs2yfPVjUBl/kQfPmYSlp5p+mGR3bNYIVku1vnZ7AOQ3xGDUk+syVjKbnzz+6uxWSPS81pBBHVymZr5OSMJjMR4baAY19jVC8PGShfaE+6Yj5GXGPe3QtUpioruK3gtLbJsQygqRY7EtgoySzrdb2DzNw8EN7U/emPCrqEyIcqXInT8s+zjLk/CWa/aJnzL4TItiVKVLy63Lf3dlUfUGRLWT7CTZIihXPY6+6veEgGHdEhHp5r7Dp0xkw8IqRKvGOl4me1QUAS9zbgl4auImCFYetpLik7XVckEIc3xYpwXNi0BXQZbu2b+aJIs7Gas01Mfee+MGjFT+78LOkbPb6llXkWn5tLvGFpn4FpmRSVcfLN7Xd4NiRNxdaf7wRdeOhef7LQOtK9bZQHFrA2b7lYtW3eU/cr6RmCMkDLfdLv9bSGc8r9vF1q7UhUEtBT/urM8tdRw9cQZp9LXbs0OXMfe58wclDdVp2Ma1QIUieVq2jHwkKH+AQNZw0SjGVMgN58u1kcrVoywQzLBBjC8MKsHNZpka38n68enhkOa53Kg5sgAKVyQlyAz2mQS5JvTYylibIzogHRtamanGxWsWWTzwH9/DI8N4JFvAUo6YkKfvky4Q77Gm+Zs+oXUu5nO6RrhIpbjBKKpMthmL59IwzG1beHlQNUcpsNq423w6cgdQd13FC25Ja+G4b0YEiMf27oZiQkv5JkFRbfZDIdq2w07dZ56QFjCsnW84pTVT6BaR9zZk6mbo4m59IxBRG0akiXE9/ZnS89dsc0f+oYNB3iciRr5JEXrhA6VtSUHhDkmmy00mINWqsi5lCJp81+HZ2oJe327iQyn7pwljTBo28lbPpL4GmLsA6UiRDwm3oxnXZADEebWT/YwvTlsaBkADfqqwVMVFqqZdTlaRAuUWKDuBuMX3IAd4QaqxNOtT4WuY28Pst79dHf8Z6xHGOoDhRHm8C/0ffr3UcRja+und4YE/Si5NBXYvLrOwqXNs3pIHDJ7ZfKRJGxJFcUkaPM/tjVZ6uQofMtL25uRzab/G6ol70I9wef5edYsLc908rMwToZ6ncuhYJK7c532E3gVCThsnFm3NzbRcfrqKnBNuwr1r9U6huqjOWEUfNzLtaMAGT33l09F2Om5IV+ePOeiz30PVUn+Mn1hKvWquInc9abE6Uwiqgz0pdoqY3fz4O+My8HssgD1Nk/Z7yIlmlV1glf2AeuRfhG3Qt8yJwP07cXhqMIUhmGBa0KBnWVLBxzH96Bs6AM4gcVVtZLHomZciT+u7+5FrOFjKMXjFAcfKQHeDYGrvMDiJo5OpS3b6gT+E+yLpPdpOXSJJ3pYnNtCSi5jmW2/STRJDKiLJkIZUqEf4YWPd/3OU+hl6F3s9QRLZ32YnInRsLUVGzJ7L5tK1zZyfJ7zd+3Xe0En8IQ5rV8NBDQPURNjYceKIvjDCKWR0ghtZ/Ds2soY34w/48QOSS6inByOUK97s8Ys9ot18nYfZ5uwgo/MMNvpkdMrOQpZmEBBFbJOUTCu0XKWX4z31noYtn+uTfCpsDLz7iSUpqcyX6IvLUX7bxEmxrph/pf23RHamgT4JW3wvdzg6zhLVbKBDrvG82eVj2PEjTEltoCPEyHbvrdpzmpbOO8HtETIbYa0RR10fr/7KfLk1WEkSQeDl1ZMD7ZGhbZFzueeovfvOlW/KMJyAY8XscwwcUSO0T6YJ9DyWB5lqD5hI6Wm4ySBPyB1RFkuX6dcTyhZ+8+ngAZu/F/bLyEwz1TEuvHHkE78Ta/en6IxR2ZcpDxhhtLDzTHr4XbrkZLlPdGweu7peq773hcpE17HoofhiOPUhbjsNmmDPY3dqIdd7xn6mHXB79OC4abKsOjH8gy4VIGA3bOedm25iQxmUcR452mfgf820O7srVhXEZSHTZ40d+qKec2zPUoT7+5prNAWL4HjYPDBiilYzEOsZOV8XT7+BzLEdT/784Y5BQEVu5ijk9LSZQusqaHxp8iRMoL+nRYytPaHUJ3Q3hnvFJz4Vx/EyTp0uq96nNqeWlw5IxhRPd+YPYkg4DNbim71UD0/fqBLxZxvJKAiiudPz5gtBHCP627EseCJsyUpQsse79JrLEnNtxtSK367s997c2r0o4OQTGZpn8Ht077ChofM3Lw0kkCxhbkX3CChp3++oYcvBYTxpaGEV1FAwC9/Ov7Ma6gOmf5yEOjGUH7uxHxrnLpBUuA9mGht/QBHqg8XTE+X1rpxmFtD5M3V2FDY9HatfmMbt29VPpnIKAtESOFLPoV93E6yKlFpYD7EOTir3X8fLP0Run07Xf04seIY3SfiRwBhEKiIBzIGtZauPzI3sEBwxLPmR1venpOVJ6uXmDrnMzjWpav7nVQs9l/XqyLflaZYe3kim+TWWUbWCf1kEXzZe+iG/+o3NtJQv/6zAuYuOKvoDgXra7PYoY3r1RIiIcLNfWAhmeTVtLf+ngqQjal5FIWwfnbr9HuMAz81+o8xywh4vonC2CLh/iMj1IFVeAMr/naaH3CNqfiXQQ9InSFdkKlVaSfhYfmGvpdOZ0U3QtD87g126BwAQiFp9W8qM/o/vn4rddcBCTAyqp6aUufywIYgtmFi5+Gnr/jjn8w18YSp8ktjLFz5rXx/f5/oOhPHvCaAW9aQ1tDYcg/9AAzvR2Dg5/i+ZgIQEiObLUZ8KLkRjy6w/JPYGMoj+QtFh+R9xHjFn9hG+Eguat318LaliDNur5RMinfJU5WywqHjJfZUOTpOOWwrFs2Fq0s92UiyBhBncx1+T3Jr2/mbqgSezavKfBMrLk/8BYG254vz5lFBNw7h/EgwiB+dDJXQOnVh3JT0YSIIPDzr7/faO7DHXZG96W6Wa9M/8055//9rNgXmxVD6TfYgIoxCvTcxPWWaKFZ34hTPrStCc+IQvBP9SQPG1cjIc57Easvx7w5ffJoTBItsM9nEsMmUR3NowU/oRtoeiwbJutB3iITSvByaISEOWCmFk+X9hpvwR9nDBQMoxciDkRnmTV0GxnTSYZZziLSuwb3s7uCG13jeWhTEeM07ZclMeh/NCwQn3dXD8WjKoEvs26RCdrb0qaZIX0wot4sCT1/YVT/Rn3EoEtW8wOKjV1rESTNkx+ZkdV3Ddh+cnU7KEORKGbH+Fq5/7i3qR41F5iWOoTLrrebh1gySZ+GyQh3U0txrWpo10nFLel/kDLR9x9JM5j0KSt6ReyAmYt82qUEq7KjD8wQuYPvGXcKrX8zUmVyySS/qh9RrRpRUVQtYoQhm1gLoZcuDiJQPYrxpOa34GfeOSGIirP9ElhncWgZXleGg6OnkU5PzaAPRBVET2QswzXDNUo0LMH4q5Jp4saKq7ctKvcZdDjTaqqH8gnxwAdgWdFrleGNMLKB54HO8RGNU1T65nKHOc2fPGiqEtHs3cTrLYP2BYslu6fZo9EnnKWlPYSXI1RuCj+dI7TOP3opz9EhyxMGl0Io4q+Ml04Xn9qVKW4zyKRAQ3RswceSlrqIGBe8zqmmlJ2AHui64HH7WCWMAGZZlDmJLeUQkZ8/yuVhKOGmc6hVwpr0dBFxpA0tllP284j3OqiSDBq50HMFfxiavS5qhzG076+4ojUqdmJpGGdeNpKvL64Bc2vMq8RzrFn56P1TeB0j6nyLTnatcRg4traEwIDvpbQoMBAWccSOF4KHLUSDL6XmB+SW3o1JLknslSj7Df+I7ZOtm22b+BBGl/9lRhm+Dr1k2M4Ugmt57mV+OFT4Wi0otK8s9MGCHuG+kM2hfgsFK49w2VFPk7euH4q5OUowrDkwBkWR7hepruAa+HOO4ftNHpF8tiz2o1CV42LcseBEUJBMPqiov5lMikcu52Bv3xgZtW6bW4bJswmJ1GZefl2rV1MhMIwWbDH2IPGNQ3F+hdHRBqYq76M2u80fUWvdoSUQoOezAHT7w21aN+5ZHoHtJLWxnhRUZgNCcHhqyWPuxc/jUE7oho/z+owvnRwYVBhTEq0XvJ2nVsDkgnwdtE6jb1dDnZCpgaDE8UR9wSvIszBUbYm+Z9qAgkVGBz2VK1SIz9jc5cJVW5YRWjk3ZfL8GFIdY2ctHIbItgJ7puRi18OsJzhF6AdYWt3h8milIwPrrOInoMfjD1/p7xnM8EsGXLDQfqRJjURzFIO6GVclogl177yTTojONAqNWc+pcRckXn0jJIhahC+JD9mMoxNhocJutz4KV+v+nUywWfMl2IQ3+Laiau3NJlWcl3urp99hm53CBQ1uZf6fIpDcBghsB3Euu1AOQYmTM6CSEFUrKnbPNNbPAhSAbmmoTsF3kWHyvJ+Y1Vno12OYi5WURJj8Dno+F7sfhzRKmkg1mds6LvsX8re+c2QzmcGfmcupmH9FzjrAuf6RhMsTxPlUDkFT2zOukeHdpXMGwvS/3GX9BCKek8kGMWOFewgBcWlUTgOksaJ/FGK0sq2XbitlAGUoD8APsX1mcV0X2yuuwcW51tmtDLH6aeDE/7FiDFs9ei13MGwXxdDU95KWdH52/kSUeIdAnfrQtRvoyMB0eXy7i6Gz541nptoiru00sgTzCBxVuIxS8stFd4UxMBbL+kU/rlfinQzM14M/Udu/SLCJQNapuPWllKTHWK/rZ3uHHBkVb7CtE3kdVz6L2UdcC/CBwh7Oi2aRm6UfwoGseGxelOW0dAqIXFr7azd80//VHsQOF6oOYuYprE9RcL5gQQhjjbhOpEpTmqo4E6lFopgIIn3s+b6i1QTEdyFUaFrN6b0Dy9twfjOMwOFH85oh5GL0L0yOPshPuw/XlAxZHt7bzl7f7GycGX30TbLM2bhXjGrUCESkM++GUW8aFc4PCYeyzRN9Q7SPiJCtrVZY0WdaWSr4jAbKSJ1CGlk9T3N5dWeh/INpjhdyGq/cT8ntZCwIOjD3APodFXx1HzptUQl1723jCBmmx3Ax3zelmXIfX9BZONVmh1dAJkgY/kSR7f8IpPv4DNbLzInyd5uDneqpwEXT/Nf3XvtlhJnmkYE2kKyi8bCoT3tEvKRx2kLWtkikbv0fOiXQtBF+6p8lGJWcejTdRoF0SSgd7udqfsgoKb4x+rcBgw9P9M+7zVsoVckcuBulU7aC1AOwFGNW3oMKJbaxI2WgwCnjgo0d4icbRn5TbySIwgvPwQtA1JXJwXEvlyKfhTogSxLW0UVqlsfYY2J3SANIZc/hbiv4o3Ja7MZz9SJCSriS1yVW0s8HGWv4dlTNKwTckxvT0NNzhquu15nr9TmCLopqNh1p9sRxRBSd3vRKlq6CiY/GR4JWsRpZt5qKe9pUys+xbrPfRSPtDeuVoT89t0AuYgH942q+hueGzzWLDIfQexppu51hCn8iFGQAjYfDg/B+kdIDFVIct7eX3kudq7eSyiJeaGH0/wjYRc7SXmaDlABTG88tbaPL2nDrp7wZT+znDygbQmr2c9RRZkX8F0On10znkm8ogXBKScDDI3M0JY88zrYR3RnUAV/ES5qF9jDmtkYKjTGc7JFvOTtkezwtyS56Y2FcsT22OhXNXu0SC8HGmiIg1OuVbJNVvaDXcquijJP+MIeY9A+Tfix6+EjuVlzeLqa7KH7MpRZVjsWGjOuigeOA0zCelLgwzjnuAN9IDpMxpCop7KJL5WfUpDDOs/zqvnHST0kkYPd1wd+f01QB8PST3CvuGEp+76U7s3dVnQ+sT3NgRE2RjZPOf9h3frSCXyA9dv0jv/lo+R5y3ftaubQDDNoyGE/O7fUYpdV9Edi7937zzeH1Y+PLtGkoZeRfeTYtIo89OE0aUSIidMRmdjqfITNBZk9GDUfHVBFPQohp+1EfpCAj9bVXEA8NMjkTnfDYXWKgYi72MvhtxMW7CNF0rAv9iKC8nQE4zfY+tn5fgNeN9YmbMx8bY046p8CjZ8oWIa3e/dJSW7luaLK5IRw3Gry3DzicnIDjHipQ3bCpCF8Pdx3oX5/bp5lCmNVnvISWiokKRQy89CY7fevcaXSOEwtO9Dn+BjidENY7KcM92s3KxSyHwg/ttObzLhwFPtTrsOt8nrf4Z7S4rG/1Jcm/bJXOY2zkxL5zT3vXvN1crHi1BQOqtHbgWpZKlZFGNciBixSXlqoC+5CBuVtfr5DRJORTCcz327gZMVE4Tf3iROrRj7GM1vFDKLojx/mpQJen2QOdHZ6yi2M3nKuRM2hMZwUV3qE6/rYnh2IW+/TeVss1itOcAXMI8VjfBWDzLC3PlzhPJ/EHrMf63nYspAJM2xv+gidjBrFi7qjrPw6Oj7YZR1ZJCu/qJovcaVCZS5EoddKQ7nMh5I3zbgpNIXeNmJxEJX+8H665XsWefy4OjT4fC8Kl1uh1kq63qIvDKSVL6igP14hLIx6yWGp0qNK6hE1w/fHrSQ2nuVbA82OamJgQ/HBodYFSqZmOqvARoHxtqKhULBXUyuaBfjRDMkx+dli1wTXylK41vagLCRvySfZf/ySUcO3bXkFWc7ocgHB8eFzYkCPseiD4rRIRWiSNTmfClQ9cSQYx5vh6zFGnesJv3dGJPRbgc4lpaK9sThviptzYeSO8PdgH6N73thHTMm8ftnAoG7aBtQ88ZSxn0QnZSldGxxjOnuyFhuXpzJUZ24YHXVIK481CxLH6HiXpp5Zi2EKMsV5I3DBRIqu+7WnNQqQ+WNgRRGITdLzs8MkKtdjULwoympwhDphHxpLvnrifvgRMEfI215H9hxMxN7A7QAOJP6q0i7hR+uA5WAL1+zSlKuwoGuAKUkyYHPsHbGVU605+oOkqOUDMwRJcZDmx4BZOfDfoIqGg6qDyDXkGvpzk6mW5EmD2PGuWQb5GII8nZ61ZpssADzVgkkep3C19t0Nr4hXSvLPEts13Y6fRU/9nTxpjEGwY+GHssJzTgZ1BJ3Y8+u/K1/vmww9iDVB7tzyseXifswhT96gA+ekdCqjOnh9QqdLKwKl736uwmeVsb/NtkcbIL/oSiKH4Csug+eN96FzDGTM60kvP3fnu0L6RsFroxhzRITBHeXjzjDcFX7AUepS0A64BvvkD3ZqgLvG9S03IhiFxlCzfzxjInmjPZl1FhsjW+9ZDcFpPsQzmmKU0IjpVJCtMr4jlla4B8rFfWXTCO80ml5bxKa75wIfxFpVqONTFMTMgCZmZ3vCmTq1oIB/F9vpQvUEkQBlVeoxpKrdS9mSNxj/9QO6Fd2hxQVq02ccaOUtg3YUj3tEVJhbMf/CYWG/KVW2Rnr9jxkhnJd+2DSyoh2n44eZ8jdCZMS+nSHBZXh8X/tyvyRJHdmu/6YDv17HQIQ5tYfaRu8vczl5ZBMDd0KMSIuybhsPooALoXitWkSCTN75XKMiwcNdiS/BgNUcvoBTS+ppFgiRp/OGQQse7XhLPTtgl41e2eWLhuQ/DqO/jtvFoojc0yA1ilrrqzGq2V/PkGhNxYQ/y/Q5PTkDvl/sXmtOSVglGjp/46o9Dn1V8ZTLg8UEE3Z17Dv1mUTVIvZb5rMK1iejxaPu8rHEeLicXo0MfkgFl0prI97xsUBIn1e0z2A5PbysmkqyL4KIwc9XP9x8zdl3vIXriiu1m4TnrTB9+1KRC3xgGTNpvAxmvsJnE6e4r1law60/DmGpHBXq3VTRRBWiWPA4o5dMntFBqlrk2tslh7PFPZZBeEaql/wk+i69Taq9wKVIu54sVjvqBZfHVmmc4hgbNnC8bMCYCv0hTQ0TET1gg85RxZG/O6y9KS5nVcnfvMaoi5O5/6j8a7qd9YS0XJYMkWJmlwykOVREGULhThWtm2o1j7nZr0pKEDstfZamRGeqmnmEL8IHvPW1dcQAFw/bk1N1UrojLizDKhw49Vh3UX69wsvXgPGPi6dVtoeXuLhFrK1Q5OIYcdzwYX3psz3XopviPTn50XoR5Tc+G81W3lTjewphvPJ4t/BQzN9x9ShI3dpFu2Uz2jjO+/zsZnhYzzwuxyTdcByg1jlVsvnj3jrzo0hwyR53aiJV+oa7jfb3La1J9E2EVda++XJMnoHc39rndNW4TQcPlTAXD2702APjiTixEcPLb6gZMAOyUKs0vkQKh0QEWGBcH1wFlH71MPD4ldgB9WlT/EDuDMbllKHvcpMBZ+lLkdStw1U3odovtvnS/CTnBLL5mKLwLx2CgrvOMIswMPK4aeElAtD4Wy4cmWeWrR3kVVXq3sOywNG78Eq4bRt4JO2RbL1THX4N1KO5SfLGpNttyEheeJrdq+vkZLwyJdVM9bMAZUq7O+M3tH6zZRffPhI61y4CxSYciSeWsnobtKZZcxbl83ChYklD33b0hqeLCKWEJmu82wB8WgmGQgafhgqunaoGC7Qb0kVdAF3QHF1fSnqHhjdniP5+U4G0l2x0RR9T1AjzND5/tbeDBytputiGPfviZHp8ZKWY7pZEYR/ul8/rH4GmRRm1/Hv2pCHw+T+jKP0gdpc9amzYIFkN9SVOXRgbk3ALxq5hOAfRAn+H+Vso25gREWmW4TuCqbROGJeZiszYwucvJDzwk1v2Pc9UkKOlM39CN0mwb2qfEIU6LW/aLvx2cOV0JOU8hPDANDQyeDJeBLYZXuuOpqSg9rgjSEl9Ifw5mqd8tOS6s7M+4AxJFalruJE8GFWKgSPV7Zkxxux3iK1wbz5vewfRlzpt8R/9AcK6KMqk5FoEZ6F4e6vNwX3GmG4UNCIHBeNN+i/Sp9IjFWHgMLAqtpX+rE6XCLFR7YBv1SOrveIjQhKh2nS2nBMCOcDQkYYgp1mBzO8OIV5HEOkzb4FtjzazKv01pMZGvmL5q0SnhFBwzs0GlSalxlHdjQ5muiycJDrIrdsKW5j7q/3U95e5P2PeiNyoUmy3K6n5OhDGHFWrzprJpovNuycRzwP3HUY4TByXK0ozxUsDSVWVELFjGBYkIjFRq7hvurdkSPBZW5SEiwQlG2mqNPUFTuObWjnEYml5Gu9LoPl/N+cEfhG8sEndMoZv1FPafTDfLld1jteiXdcONYP+WNxRkHpgrUuP2/zal0YLqCHJfauwhLnNoKLDSOi8Oxh61suD1sMQYCmagpYlMp6xaBxytMn0zNd3qRGL7iZhK5svu8/GGxG1mFCyOrmAjUcz88HEyPpYZeq7WcrcrzTwUz5a1GrxpP5LUPL0/wv9FALrO4OWrpsmNCzB0qKHVgOT1v0XKfashjFFyzh2ojnpK298P7TYc1CBnRT+PtIia3HjerTshOxMnBOLLmiaQAyEIskzvzOo33AtFgw0UNLpK+bmfq5zy04Cv2D1RjtBgkp2AoIBQDAawz+HHWbUBZfk8gytDMIdLDoRgg0wMIfFcJESk4rPm7LgEYf8A1zqD9XGBF08hBAGCNAkv3OrZU41h2yb1nARH+BiASNV+9aC/tycvox5/iIW1cU6nWFYXC6PKvO7MYKV8IfHsfoRRsFpYUryjBniEDgtsDIdCM0SjRrdB1sc54GJ5S/3kJHq9NQs9qjdFS88qQhfV5SIVDZbB0A6rJVcpYTso+CnAANqvkAlhsQ2LygIUlVZExQBPq6dnhhucVfeC8WyeZtKlxuc8pPxolB2mwyP3s43j711thqyVu/W3e4ouDhvMqBwDVt8XZyrb4kYb4vyFkLB4nkCTmmWz6ThJPLHJdEXkB0Bfgo5OXww/G3srbVL2tL9kOkhceSEMrurkbWtt0kFVsWf8dkGbTi6gZBwdyXBO4RGnAME2uNK10xPbuTYj4FJE8MsVs7kvhMJzwWNuDq/aRm+3n8Y5rYiRDob4N1fpcLNtTenD5Q5tegQxfrOrNZCrj5AkXcA9uP/IeuMVB4M7fUdskOE2EwJX6/EtyEUtTBhpuA+AoQkL9j1eZ1N55WIos5YLjxoFpr4GEttX4mPyviVzQFiwT7lW+Qq0wPwTdE8fCtl9vvrRv0ug1J0g4SoUd8ueI2L8VjmLyezds+BnfEKblmVzPoG7XGot/VLt2VdCctQtLS+KB7Uwr+Ni8Mj2q6bJ07lt+wkoBjb/B/LGM0pl5SeB7EEVABxRC3rBTj3sCquS1AoxPEd13Hvse1RniJSqBEFR/13U1SbLHF3WlPRlvt22Tw3RxH6wrMdU6epAj6S7VGE16ThVfgAKNkfunFasylRkLOIuhw6pxs4LCph7kB0xXO+Y2Y76xUGIYbIptVZo2Rq8rg1kLLxrDo/HJ6NRlGDEq6JueTNPxnNFtgLHDJ2ejNRPEL3NcH4Qj15w4OrsAGZCgf4sAj8/vVcK5bghheHMk43B55rRKV6OSHFIYLmf5dBEtkO/2FMdBYQ21oOPbOkunZt1N8f0EwexxyGImUa5yMK8vtbQV7XfGKRsY0hGgBlP87XAvoRsmLs49TbUvOAmKA7Ee+jblGVbQlX6yB0xfZMcnMxH170FU6GwZ6B31xveOQo+AYXdOmZYSc+ueTOqNMBnsjGpeNSxggcdJnGKxuSSA5ZYwVVuZmsHFW1B6axMO5Ss1WhVQ+KzEvkiyuONs1QQDBwPeUHdYWhGxcQxs+GLY93y7Bpyua8XxF4Za8zifmySh0hqc/JC1S56YVVUH+UIt+6wuqHTVWLD0a7OrUumoIDURDth9xYXWGX+Px2KIC1ilfwOF20SJ6jmxlFM24JieOTASM02xA+c0MFyN6J0PIt/xCZer6y/YjGmsXmo1uZ0L2H9tu1zznJgRM8zlZaRyRJS8nJGpAol+l0BMOfrGCIdCEzn21FGlUz2GSm75eqiKGvMyZ3U3nTuOaU3LLJ2cvddL/SxF/RowZ10R6PKBfjYfksTBCiQUWFZBO4a0tvY9A6jKMrxGswoD0CEupFB4yaLtOd+dGLPtlbt0N8hKrZXzVGUVse4xZgt2IZ7kwviR4uoBKzNMiNFvjb/vIN/EFbqIqK8MXVsY4zP5dFZtrxaLjRgfAsf/KXsgrc+jfBPu53zcZvY/UYGcQgbLRdUuiOscBC4opt+Y9waCbPsB8iqUTvIfqmwS07rJLlMcv/nqqyfgyFPr0jf1/btYL75gPVTEqmeKKICueEBx5rPiEB8LZj3hTbGr+LezEkk0tCg8IzyHRP1eyEFEspZvcv2ic5/3Y2WG41eASSrErM6oQ+4lod3DqvLRwf6B0ARLWjhTfqlbZ3HAc2CnUI45cHJXOz966JQMV4uwef6zRuybzEPwPGTCn8olZ9pNAC7gcoda2rutUuQfp0AI55GfFxC5EMFgbd4xGdwosFDh8h3mZBU7SNi+SLLcCfv+NmqBZ9dB4SjRXjGs8zDLR6Uj5dmkmcEEfEhvlSvqWoYS7PlSNB5JmADPhbCOOdMmpOeEXYtZv3RH2pQVxNT5LI9qvctABso/O3kSSp5H9PWooV1pffflSDeQpWc/Jn5RhZ9KRVSCuXnb3hNUS4Vq0QxznPmMjK4fUKjYHyRKAUmLWarQbgnli3cR/WQvYaUzaYgD37KiAiblgu/zrX1RcEQJCsseOk1pFoQHr8pOv6IbvIXYHHaHnZHa3qol+FAIO+bMJq50N2ZKxqlyDP4StmKRqJfNQRVgJHtNBBkC/oMxSHZevNC9HGLpX2WdaPDWm4W6GIz4YhPAvkZW41+gL69RAeGua3yyhsnb21nPO8AlwBLoWyscHVFZAliD4xMMZUEcUP6AE5K+Ccj+6uGAPuq+77Grwr/KnIaJ+5DsQ5DIgXG8IVdKXY9Hd03joIrOXznn2V2p3o/lFpI9jMgQ4wal1c+pMfMZOBD5fncgLxtThx8JTNrx16rbU+7hCJiL6H4wlXcmPnswYbtlmd3jT9O2z0ZcjmvBGchCnuMZseCeQET4svWfyCbmlkobPJstgoRKOa/18V5g4PhPgyTDHmry6v5HYLDHp4IwgeQFhDe870iCRXtvDqKZ0M+qgai+hF8Az5yW5Jy8mG7HKj+Bx+8Tl0VUs5XlAod5hWeuIpsYX3EUQUce2XICK02EzwB8HZsdOdjeRo4cmiO9Bg7mdEN+/pLOStom0oTFUK4NpsAhs216/fPi4jEHEyE3oxTu6SS/3V7m3v5gVupKDjmjm3/BptoGBDfq8Kn5ZuuSYRcO0E90M6kodW/JvnpgnfB6rr62IrLhylz1/MALc1+LZBKcdrFT9iJXuiD4XjQbENkQ8New18am7Z8Kr5hzpXQ800QkzhNSm8Ax18iyOsjvcxESEghVhhaYEmqnHzUTGT9Bz8Qs4iHQSZ6bBeBsw2XF6YIujIqxEZCEStNcWzqZBFuR5TIaQTdphoKOedXGlyu5MbhRqVetTm9ECKtxF4S/Xoduew8ZT9vcZzwm08c/4a4TIr0gVKYb4893NontRQabQ2rY6UevGZzEirV1PsjX3dQlD71qxN6oIcxf54zGPXIMt3K4QLH1LL2zm26eA3cFYI1watwcrxSg8BKAX6pN4uk34DdEcy4/3VXaBakB2LmRUozB7TswzeI6B/+7cXhtWaHiWpXpeJutxpZ/luVQkD35uBkk/YZZmMU/KEJuXk4u9wZl+nURN2g9ShTB7soQdYz59c+xsxVgZM7saTwWsHL2XwFECJuaaDvsRixNf1L8OyeFcf6lan9K/bu991xg0oCOutCaZB2T7jjSJfBjjSZkhW1i4oOW6ErVjBkPYPuTbUUrn1PumsDt1JQIfb7fPpxGeOaVLjIkkqdi9koRkAZk7U5tTa/fDGT5RGw1RFEj/cvryDP2iVxdqexMG/KYlFJoFDmpDz7sl9d9qg0trCmgM5pC9qNC+vofYhsKLBHIDPCYtv8L32RT0zmV9OVLypiQ2vUQxe+D0cCC8YaUKbCiYmtjIe0HQA+4pgDwiEFNiZLs2grMOashCSHQsA7S7Pwi2GtC/5H85+BkNbspQCMoWp5sp7LO/YvEMiaKwtrcKV7E0RVEJqDICFl7Pp9tJPLETGXGIPyAG5oDdVY2KwxvgTZ72R4H1JyCJENfiPkb8TcHey94WbdJTiZYLGPaRqz/XVykG6+YKppvFUJ/ISrvcQySG0iW0E5huRO2xBcMHBkmnJWsRziHb2BHaKi94K43rmsbsvsIdCLDzzrcOhvwbx54fe9CphDnUpwafQEH5uLcH+1e3o/AXEpxTr/qgJ0QnFhY//S1X7MZ/e1I/3ysrzOPRqGXdLXaHvhLj+6kJeIZE8ujHgJnwr1GNqC1EUsbjcFAWzI5CFLDREkOBmtmXtUQYH/NogNKblz69TyvHDHqq+RLTmWsSWGqwGA3Xlf2bwbQ+Uz6pdKztExJaLOpwtJUt58IekQ9qHR1ZWhj+R4ZcsfvrAd+94Sy/+vdizxi5koAWf0k6eVNQQSC2hICc2ogl0HJd2m+r0nRnwhB1ejsfM6qTSjds8o4vvHSwLGWCyhsl+tDvflxCmn+YK1F8JqaqLbneFajxqezeznpK7pcQhgtcu1DDNuFjzTUpZ42NdFj3n11tTCy+I67SzUfxQ7ejRilbYpwOGtkV5A29FNtlaZnGjCWUVkMcCeR/ZM4bCVM689J6HMLC7mC91rPMxCfiuet5JVNCx4YAjoNcKn8H12uluGnqcYCPeBSKOwUCvtBuXc8vw/UxYKSfm9IFR5yDjPKhGxdCqgRNJJZCRVcYK06d67IQ9x1yX6svNKk+n4oOCkynhHk6xYgaZ9DY5fUUMzwiIgBxZWVj2uOItgNEUi0Sc8d+ONeE5RekoI0jjBpE5tBT7QXHqZFUW/bHoGSK4JZMsJ3nDsAivRq/3R4HwRePT9IToATt9mEs6GOZ1ZV9MqsVPH5M94mxRDIpk29EdelL9/+iiq5K+2IawUzx8gl2vxDOm5dQsqyzSqwi6SuJP7ytnyGcjtUe+J/b4H/V+RNJ0oWYHHYXSHzD4rjoLM+xojVSA/JKMrSbl8CASXi+QuRY050BA5ufnl+GK9XlB3ffeIHKtqbkfUs6dhyzUbVNOqvosvEYnMGxYJX6aJ2L0xtVOa+ThzkMDcunsf8jWK2Ltr8FQUv5qwhKiDgK4a+VZ6asWsy0N8kd9jcmfCjP+g8lz5llxWW3HQjyvOp4pUDWQgdtVhj4RohRVrg2qXA/q/kgo/GWZvDscxtN5sFgJu4PRdbHakMJHj7xZUl12sD5Xir6SO9k26LNN6qdMDfXCkwCnvKZjxDKJZ8l3tDC2PObN/lCgu4ZXbcRhNueJ37tVGuLCg4mCMDMYv1RUP2Ce4Hc/VZoswAlFocATRJT7FNHW2dg65RLJzuDBxgKQ42FqPwIkM8P3psKw1QV3aiPR/5vBJU5nYIH40jherijHItzGucqxO7wTnpSH8zhBXvosJTuIdvAuzQrO6lEbINTV9yVlxD8CKYne9Bx/bHoQTTQIc1u4q639T1gHXy5Kcqq0T0lUq6wX6LDa/sv6I2EbezVK4ylGjG4F/Q6usCa+tbGy7WlYlYekiwdU6dfUkMv0agm65a8jNNX3ZvUmJrYyHtYRylOpA0mZY4xl9FsPV9dgAhXo/CYPsFlLhcHyBLZQnVIBU0lU6vUPuhIrCUBKqR69hKOU9HiD0ENmOG2SlFk13Ow94seu/nJv0zAxifuyOyW4vWJ2rat0/IuoCAgB+bx2Edz51qpKhlEXfLhVHUTe7WgRjzNSZZKXzqm/X+fAIg2ET+FByummj2TSz5tO6VZywT9NcJwonlQ5/6Sp2Kn5JAKO13k3tHdmLMzne62uTxsO5RDBzUvT9wvTjXN0qYf+4QJoE8VQ3HJMkrF0Pldf+XmRi2yU7ZDTj8JVYtf9WbofkQLVoTSSx2MFDY4+1k5fwnR7ZBKjQsz1iiqjLbMxUiuYzcmQLSyDV2MZro/XLiiKuJ9KvQZv+gwedG0gwUS2ROp+/LArUSqXDnUyimd6fc79g03q0NcUEvj6vlok3hVgy5SddGtcNsJBmaVm9uEHj3ufNIlU1ZoTuuz63fJWhz6bX4HO0wcSj39SMUmyRGCAnnpEJ9LtNsNTChvlGCtPlV8KTS8MC5VWWUoeaRcTqVP/oJfSRrwVwPTkZSa3uZ37A5CvAMGfq4RPbwGq4rrbPCkKX4OAafBy1UelrXocO627jLvyVaLy+yQ/sqQXfV27Zdu+m/qlhuBfHCOql4pmadHZfgF8J5hs0F9DnOgKnf3f+i/xmoKJbwIKAFhshpnwwSdgr5DIoXu7RhPDCvicVH6NhQN7ME1znsQcuIWkqZ2B3Vmy+VzCszNZFZLr5goNDPzZ6sL9qKDdfxzGb/KT9/tzgkv5APcYzvDi6kay4aIidRJB0ZvvMjmV2pUalIMR97HMtRwllCamYdZlBQa0PLE2EA1HLKMsLy6itoU/VG4P3frevlPiELyNkima06QAKiTjE+MM3692kTyQbLSxYFiD2h2UZkl9TFsHesZd4dZh1YWFP+FXaeo9K7Qjk44Uat3qWXDzO9kPTcYySQUTBsjsLkDga+9crb7OtA9y5xHwmIUJbhz6kNMtSmauES/Py9bZjTsA3bxTiAN1seVjiBAOz4UIdLuhEnB5bT0mNTi1yX+6i31VDqJwSaODCJq2GifwCULQ4Tmb5ZAJJoH2Pa43ab/qEedhNqi+40J1l08Sl0u9yF2JeZ1rqKoJKT0yCfCcHDQVC4Ws9CaPtXYUuZnGnP5VEfIuz0qWtKZML0kQDeG3kC4HC+EPX4QA2r1vJD1dyLHk+pPy5uWjn2tba+eneLu8w4Id73L+PeYqgTMlKTK754iaKhmR3GWdgyafGc8/zn/Dahnllo9SbQb7u12rOt1frHV6m2MKqtbxB/+n2VgnxJjLe0lOU3sOUV5gyUBygv1XDXbQEgnyL+GoxRvlD/fqanSpdOpN7QglKA8wBXPupbrPOU16Mwzqakth5DXfvd1+zBWpcvOge5fIh95H1KtTej8eOs1xZrIn8tAT7QtSRyvTOdiy9tBkNGDslSbJhpm6jK1Ewh2NXExUZfooJ5vCK7hpCpajqSII47PA31e4N0FmMOj7J5aEXRis334tNEK9dEpnSu7faspgx1Lm2V+HYoDkc1wSOPtoUgrpy3aRd80xy2hTXZWWV/YksCa9hI8+EesUW06amb9fH3PFuWv50YTeR2oKNcyjlaH3o1RUKGRt7ve9gp8Ypzuk9isVTiyE8l4KsWWRFd/5ZpjSBHvx9zTx4/Til18BZR7eiG9Vr14x7yKBkyuW0HFEP1N27BPUgkw46LbyS4wIjNgLiU1unVW0Jr0IES8e+qgcj3nz3HxGj8yhXcFFMDQaHEAokIyrvr4cCcnHr0+GDZXfpvsMmFEar5vHLRhQ02vNsTutKOJ/V75I5OCUTQ4mV3J4lePlkGVGAfE/3hGyc8GKqMIVKL9X3frwDnixhlGSpL55Y1d+l+BWdOhQ9g8sBOuZRI4RFcWDIKZCe3f4VgplbmRzdHJlYW0KZW5kb2JqCjQ4MyAwIG9iago8PAovTGVuZ3RoMSAxOTkzCi9MZW5ndGgyIDIzODI2Ci9MZW5ndGgzIDAKL0xlbmd0aCAyNTA0OCAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42rS4ZVQcyrY1imvwQHAad3d3d3encWiCuxMILsHdCe7u7u4SXAPBLdgj+9xz9z7nfn/f6NEyl81Zq1ZVj25yYiVVemEzkAlQAmTvTM/MwMQDkJNXAdkZ2zNz0qsALVxsjR0BLAxMTGwI5OSijkBjZyuQvZixM5AHwOlsCVA0dX7PfY9gYuJGIAdIAu2Bju9OM4CJB0Ae6Gys5uEAZAZQGf8FlEBOzvQmxk7vbqC9hZU9kPo9RRTk4OFoZWHp/KcGKz39n0p/skUYADLGpjYgNycbK4CxvRlAhkGeAaAAcns3WgGoQPYAE6Clsa05AGQOUANqAdRVxVVUAZIqiupKqtQM74VVXRwcQI7/o0VUVU1dkg4gJqygJg4AatABJNVV1f68qgHt3/Vb0AEU1N79f3jeA/+ky4urCatpK4kzM/5ZA4AZ4Ap0dLL6Q/tf2ijelQH+lvaeau4IsvuLAEBl6ezswMPI6ObmxmDh4uTMAHK0YHCw/UufmqWVE8AN5GgDeH93BNoC/2qMi73ZezudLYH/KvBnVwByVqZAeyfgnyQJ0L+cdu+tfE96tzv/r7D3Rjj/qWn7r3CAExD4HzSWxk5/5copKckB7Iyt7J2B9sb2pu+BzsbOLk4Ao79s70+gGeW/BAIBoi6Ojn845P/tcvxfmn9LFwG9r0zP1svH2O2/d8zY3sXJ8x+9+c9lm4LsnaycnJ3+VREIMLeyBf5R7/Rnz6zs/7LJCytIS4irqtHLvQ+ePb086L079gzO7s5/Rf+pJywmxwPgYmIHMHOzA5jeh1Tc3kwUZGf3rtoJ4U/7xKze++QMcvRg/L+DbWMPcrP3+n84zK3szcz/9N7MxYFR3d7qswtQWux/wt9NCH/bLIDOACYA8DMA6G5qyfiH8K95+WNm/mN+b4SPlwPIAWBubOsE9LEyB76/IXg5GbsCAc6OLkAfr386/hMhMHMCzKxMnd9H/f24IPxVXdreHATg/pf5Xcm/Xf8zBFR/HVXq93NqBrK39QCYAc0RGBVAzu8jQfX/z0n7Ly4JF1tbBWM7INX/6el/BxrbWdl6/Gfof4VoAv+opVIAOdoZ2/6Xz8pJwsodaKZk5Wxq+a/W/ssu7Wz8Pv/C9ha2wPdt+cuk/udI2b7P7vv9Y/Xn+gLQs7Cz/JfvfSxNbeyBTk4Ajn+lAd8b8V+K37v/Ry+AUURUWVJWivb/js1fceL2piAzK3sLAAs7B8DY0dHYA4HpfRZY2NkBXszvg20GdP9rWACMDPYg5/cUgIOLsw/AHOSI8GdDObgBjOJ/TH8hTmYAo+TfiBXAKP2/iIsJwKj0N3r3qf6N2AGM6v+LuDkBjMZ/Iy4Ao8nf6J3P9H8RM9M7IfAfkAXAaP4PyAZgtPwHfGex+gd8L2z7D/he2e5vyPwu1/4f8J0I9A/4TuTwD/i+ms//gO+8jv+A77xO/4AcAEbnf8D31br8A76rcv0bsrzzevwF/3N7lf5ccX+dXaa/9/t/7v6/sKqzI8gGqGll9v69948QeWNnRyt3Xab3g8f8bn9//PuT/n8QkP99Z/wjW0QE5O5Fz8bE9D6UXBwAZlZWrj+9YPf5j1zTf13Dfx3698H8N/5zBwKAQHegKcLKIsiUN9g6ufHLd1/x/OlSaHJuhtNyLAEtmTiolbTpdjxssZwdEqBgQUCzfzpFAUhOikffNzHAvkiLPBjT9vVHy7eKqRszZaFdY195XzxkceGxbA0G9cB0+WX/0k4S6mOZ7DztYrbZ9Na4VkKA+tiJKHd712MUy+Qb2lUSiV5p63outFvhPHPTR0dbdPdlVNwOvOXpDnDnt8ePMZHGvcIrNHNGeV+wxmRgHHq6UPU1MFLUizcnKeYVRfb4J2Th22u7awTHb/TNHeUV02cq+fTL80kWLmEo5LFrFAoT2QSnrrbRWVnu5hMw8duEN6fVdyak7UgYbvDEmYumvi22WZL3EpVJg0heeCVrjilaqvVaD0QY2iZ7pJ8/ONw9WKMEfFhFeBlTule1LNeqJBMl/2mbb78L1hq0DDt6hm7Ai6WR5YOb+4UNpGiy3OWuRrTyIxKUhGuh6q5NenNFDATtqGktIKTAGfMQDn5jSE+uiUEmVQ1zttusllFhqyjNptXOq4/8AK9YAqy8MTs/+LTmmdCmx6g55ESRT13PUtTGs/NthcKH8Dr0eAPqrQflh5h+f8Ur5mw74gtbrJ0kj7aRYP1H/zktq9klwppHL71hvInesno1zvYagVejcIIu8i7IRj7KKYsIni491bUnPwGZe+hr5RgvjsCcAURFCDueZezTn6inJ7GGMzL6SPBNdeEsXKkfViIgNGYIBmPdNcH8uKszIrJ+qenAMu7nAUfO7ifnU830g8OItaWxb25tMjFlmIEWHWlkJtwxsD0TKkTZ/MPejrgWt3ZWkiizGfd0t2tDVYeDEBU+lOPzltmkFJoS3h8L3/x88Cp/KTAx4wyg5tS6TV1YNt00npoVrlilMkscAnJPoMkWgi0vMRYP9LGfWW76Yjn4s/coBci00tZCd+ElyetBjk5d5jncKR1IH7et8dyTJBXeijXpMxmQ6xIgGAWChF9S1yEDh5IZUI3SzSQiQ+TPgmw4o3vpJBdFv55yFzyqZ/zc/f3I27xn1f2RLc+qI750zJZsYISPAzrVudgQf50t4/mjQTtGiPdB2vylYhd7JTL5bm6jtuQSymuzXrwqrK4+FITOFpvCveW3Rq0i+RrqRPjcw7s7LO0bmAFaMfLslpq+WgkgWKzw1IHwN9CIIDrMLgqt3QDx2MpHNKXSLJNhDlElvd0lRaeSGd4EZJPYjDNktkuGClkKu2UE7sxtjYlB6WmOVa5MydtnRDtNSCGGFzol2jjUBSZzLIcKRCpCYppvI7vXZJ14/ZXT666WqLluaavxmdSESo1XNyQAth17kcoZGWrz1sK5xQ0ELk3Fn6DXsJvxOT0MfjAoSYxlN9ik++16c9UNSvdLbAI3+XC5DQTN0BHjV1K1HMK22Vf5PsPv4jXwW7Qj06MxjGp9MOU046Wm5gdXMkY8fTsNqf0+xwMPQs1Gnu0CHcAazLHIPlUyjmAr4iSZ2k6doKddjuXeGRYDSAHb4mq88il/ptFZU9Tf+PdWN3lFG5D81kg2lJtN+79UMfuweAyAkUdi8xcbTTN+PffqcbFq67EwIZNOJQtOkbR9haMVpnmb+z+ID5jQdbZ42op1+VQcF9oWLGjLPstw0I+eVlHpCUb9QlwKXMs5+rIQvLdlM3UP17hOvbW1xWbc7XQSwT/PvY+aIaKxLYXh4J3UZ/UQVLSV3gHHLrwOeycDD5/RMlzykwjKCuD6k1nERKiN5rQq/qPb82xq7f6J53aq+yPI+4XD5rFGaPsbMsaSmMDzr0UsLDauM4cjkDMdr/434p2qGDL0w8gS2hpvwjrUSNX9byusJz8oTE8nAiKXfOK7i1G08pN8dkAJP71RcZpzo4TZS29Xl7Q7uIkxhbqp/Xo6sltmG8LjNgQwRUzqvGasLzXQz9L6I+4+vIX8lPWqpDkhRrsw5ej7VuRb76BvkFpQRfKjNpwhWp2mYFQwGJmLjyEyd/zTgPi1RMC225Ds6uRn0+r2Rb0IahXhsHEcRnqxoIMdvrJswV9uOyCMIxMaFtKHI3WQw0fG2ttGKKYpwuCanvOcEURyOSJqyF3gUwBgttersM0TogBL8NrY3l6ToC+UiHd+mB2wj3D6fBBqr+D2YSni4tM35WHFnXHdUqpGm/Fnd3UJDMf6KTWwm6b2i02Jw3xFL4nYuHpsWtYugqLnZkwTOh9rdM+QuTkX9p9QL8O3KzcDFAG6sy18H/IYyQX3dmf27UUZgTqRpDJMWMYpEMIYCZ9u9QpjypHyOL7fiVVi/SDoSpymVgtF6PP6shrAWZLnervgKWlTYdcszfAloqNshpw4bq0sYFG8w6i+62poHx9CrajZQZW17IO9oKL4bYo9Ef+mzgnpZ0nf4HpjnAqHHValHh6luxlUFt5o0iMr63ONxzjStUAm2UGT5XkbNgZVdDQB2jPhyajCAeSszSYwycbZqYLTE6jIrctDEiBHqcvXA7QIVlovXpEwKlYe6yzRHGZIrPtAtIpS3O+JcpHCBZxJwuI009RJh3GTXwJSM70jD+HwzZWmMrvGkxow9VxSFpfQPs4Yb7P0Ez6ldwnqYvsEEefkEZB8Hvq9KHH6aWEkSXRE/4DDHiLAIbATtFUtIl0QjFI4y9KVAKTsuyMn2MaLMAxQYqncvNPjkUmoUocIWY3D5lN0kWPynoS2m2oLAB8vPIRnszTYGC5ju+lGaSbk6lEx5Acx4+ORqms1TV4LjXd8bjV4yxEyX7ZpF7/HTcv4IWo2YbUAczg3glWuMfPN2ig6D2jAQBM13qnGLshxcyCuKJprvrPPetmtNKe3V3lMx/6L+6Niq4oR/ucdkciq2VlwT9jDNTOUrdkGeV/JMWZWe7DRn3djvC2kW9YjuaWjBk9nOHoWwahg7JkMSy82fCRYsLBhYKsC8ekqV7BcpGY1gzRHzPiinAy26WQff83tQ4k3HTUHUBjcoKGOQRxGlY1VuSGLzJEdjbobC7KfmJWG9fUhc+UYphYI+YEkK1BSycTZROp27z5JLQzKkCUZeBUNFQxkdn9+OOAlGStsFlOyqtaunPGcEyFlgmZ9yf3E1OFn00kNgdoSHA1qwQ+Q9uspTVqsD3CVFjlY4NmdFtRLciw9pnILo2TJTEk2G3bhEPEcvm+QL/tmT81p7BNPd5TB1Mbqo5DJNScgsXufTTtCH4A/uFrDoYaDnBmb9DlJZzg4nTdM16fUoWKNMu88eDLxEAfVXrufeElVQaob+/xLFh/VMWIjDeS9r3inWuFNlrfuaydu5gtSqWH+NwoEtqT+PgPDyrOD/E1ipta1449g4m4n+nljSwayjdNCDOyTQR7f++awbLnXG0U5l3KFVdJRX6pi/b1o0swo7sARUZUGbj98Zy6pnI4lj4PWJ8JbhxlO870sgxreClVi2dY+Pf8pGeF5iCsoemz00EAjGxYYlbz+sKVAoI05irRrlbOEVuZviW79tdAwtI5H9oOn2XzesM0RB5ZDJESmHdzk9xTu7TbpaOFmCRjPGxcCmILy7aAoQuew79gUmIlWEaRJcsWLYlDuGIrl7Wb3ZL9j8jxg5IC+NLFPNYsKUAt1qDQ9+Ut1gJqBp4hUEZy9Pp37LuY7UwJWvV5ESN/6EV/u2eSsPkuor6r6StdDvNqxheyCOzlavwCuQ95TkbTUsaHjOpCJLdqJZbLx7gcqvddgGYEcVQ5smFoOzn6jaEpvZk046ogtInQ6mHgllgYFCL7CKALXcM0elK5nu7iIx3pDgW/FflRkB9C4fqG4EpIeVwY89gVnMvKcuvQiazNNSJ4GkPUvkuysJcm9Uibn7OGcOOYaqGr3Uw0JloOCwZXwb3oeEHlceRSwl1pve5j9tT5DGQYpHVw1in5Dm2V1XRrxH1XUKecq0TpUnqlhn3286L04lRWi3aZaDYbHE1y6HgO3cyyPBwlLXG9ei+kTAx7lV2VVw6BeosbwEXbmTtY7oYjGh4rmzHne6HetSaS7ZjclwI3nTvTQKh7YOVHvEAoZvwpmWTrfldxGYFJ8Sa1rwGzsTHFU+OqBBlGGOTGIYv8E+QAeIXgPpH/SDbL5HasERU08RPulVQSPTr2Aj99vmdlZFDaax2/NJlG7obkV/dWfNZiQz4XPPmHMn7YHUAhrgjLG1yig7iRrIaC6qVhwNR168ABiJhRooee7UjQRbRc5rLm8dZkP9UQj6BYIF0xBRbC0XnP+ID49huX1M0zzWpTUJvkqNqB4a3NShJK7eyBCZuNLl4ak5d7qcrzAjp8CjP9iSLAWRVed3hqdt6l8++f4MjbwqYP82IFDK4WwTnEljEhITWhjFNNvdaCGKDuaXPmAm/hCK+XPyapugh1vvUi/IdZz/DQbLrEUy0jy06+lfSRmv0yet33e93M4Ppv3+EE8Z2ky17GLbEDQEzQqiWs6cKMDQX4Wq5+ZWH9ZeTxbdEpLc7kkzPz8o6KiyzENXDjuxOmFzcr5VZtdZyzVpsp7Kjr2c8NhpkSp6ZuPiPs4JVk6BjWTZlhRVZdtcUeypkGXjd/H5jA/TftMvWxjg2GmAt44BJc8VGro5vHaO3T1KvGOY6F1t2BaqR7HBBASCKm8DImLlvwjC25fsumjedQb40lc/0j5b+K+0ay6x3FicD6Ep/3PFLT7kz5Uh7ToWd88FxLqI/in9ZE37JFeirtSBkxpmVeU0m4Txsa8EClwZx/4DumremEf6CYClW5aoRaQ0u08m8LVf8ZRe4cPGXyseWMTy5Ym8CDSDw4YbL2oW+uzwc133y6OGRqAba1T16VQd8s46bvkJnBcg6se0clA6IKJx6bqmWHpyhI/seS/VOWY9uyrvxfbypfGOJMr90GcJbs4QAGMYnFAyFWntFKS8kD5+CBOmZodRdpAqzay+pfxjRSBkK6KMf1Kg30K0c7bvAh/w3Tu1X+xnTDX9j0TwyxVWc+momztmZU3WkjJ5uj0I95zHF/dr+QSbYO9JKBxenakXRgwh7T9PcDtR6PMsnaFkm57ekjvEb/u1X87Df7A0NWpNbWRt1FI25U6S3EbmJnmzQD+TPHhuJTyy756wXnQ3Abgjb1shQGj4sGwuJHmRYjYSec4LRXR0dvWAXAp7EJi8JofD1ErSL+rJEcDE4frV2dv0IwEPyT1LJqTfTx9SaH+aaU8R8W6FDho+zx4FsgtnDptFWW6Kk4YFivTTsPxe36a23k78xdjD/bEF/sZ3SnGVqkZ1X3775FhGcE3fLf3Ox1WA23+wekhzwNBZzZDokNlyOFjjb026ASnmh2VW0Rjd8/+2ZeBi1+DsBuzrplyXJXY5KvECwJAb5i3QqvXbeudB3WgldkWDCxdU3o71SDk4XKszaL5YCdje6QINlkWyP1pwugPIujnGp4QHBZRxju6Ti+4WWqtP2JC/L9tuhjScRQTBTl7sV/E/9aqDXV7S3I7/0gZKqZXSzajOVKehwGHiFcyG2bUN/552wpBpyEl4AMnJ8iu1AQZDHvvToYqV65W3T80pVHqnEHHj+sCPcUUFI0r4qN4Gg8omTBhTP/gsWeqgzpVl7WjVIGyB7V0E6oduT3gWh4iIjDoUbWaeC6IEgeiw7vIqU1/Al+b+jg/avhVQuBuemL6GNWRT94pqx+/OaSAGoqReJu5vZPUmAmuw0vyOnUrU2HB2olhrR/zhvPyrRicbhjLOsGA/mXANkTXzesRwQWWFaLtCA4+55htURCH/zVkhX0vtBexgT908nLA7BjlxEh/sLKH93ujGnM72NQuyMfVxGQVzjFDYliZrGSkt8XSo9wi/ghdxJp1wr1EDB1R1wLLMDIXDZ1ITXW1vWTmldU/0G1Fctq/TV/Lrk9me9pO+clC9qLMv0o17bv493AfzTYCz1orgymZ8Sp3ZOXQqM98MQWci0KyRb6TV4H7R3KGumSVEUMsntUW+CjNI/CIScY4RhjJrtj944oJoKCAA5cN30/WlU9ybQfJmDXyGm4INOGYpxz8lSP0Q0ftzxa9fbwfjBnzeAqlLTEsverRLKKv2d2+3NkcUmnJOuqc9YRiLfBMWUc4py1Clgh0d6WU+nFb6vZh/lIxRm+gUWneee1GO9aPaq+kw6nDrzjjT23AmjbFFCMMmnBJTtliv/6108bhfHFNyQVHRRPnwAmxNG2TDrT3vC0zfTakYqzlslihylUcEbNEjzIVlr0H0yIK8NhrwqYbtXyoM9QsdRtxPJ5pj/6Rk9E6fIaGsk9KTciPGGe3JuLRNlTkvahTyG+iujpxwprIif0tGtJqbi65XeaWw4R4M+w6Vz8JZ+nTzwkxSW5qiKFN6YB1BPG68p4YS2DbLzqlNdEGuD3I+1Q04PpoNb/OJMwnhg0yj45mwlDWPEqVkNVbI2dcB1q0SThpDGqQNNLCRyMOdjSoRJk2inQ0X26egV0qoHwlvcXnPHTzZu+gkHhRR+fSfERM9t2HMVXhkANrHizCM6LUZmnr7KlyDGn6VwEwp9+f8jZOmb3294WTuV0qg7XMU9gZC28qdfKEEqX6fluhKlsmC08JyICf+5u520TaLJZuiOtqSkHr93OYvxXN3vPt6zKGYyNFnf6W59FK/dndkr3P/0OE881XmmeVVgsvt5RmQWX3qD39GZSVWPmQpLTDYUs4g3LVA9L8FYvqkEO2mLJ0pfXq0rrLIRCfiLnmsr9PkEJ3RpF3pisYa+8cbAqzz8CiADYHVhyRIrPhPydll3V4xz1c642/CYVmWKDt7ouyF98k8vHnnQ3MohA4PWr0BCs12ueQq/UJjnUhejlWDd/r/i1FTpeSlXlrpR6K1RUDFTqgvkrdb51Gr8XIZEowS9d1i/sjabTNuMHrWaK20mmUjdWulVQ/7nQNOLOLO/+Qrr31i+FD3ZITVmM/wvDxJsgNho+//Kx/+NKbTsYxwHJ2ht92Jb+94FRVf9dRE2/wR0Lc4vwKXzmIoibHL9TE7KXk7nIAftDdjvcrurv6406jrh5vdXGDyDR2MdMbvaS4PeuYnbGtyYxmaDW3nl/sB5qhjvOYlnlNiZT2KAVOWa6Z5PN4GPUWgbZK2g7IGGIoHzCcU+KaAJgFN/IZaHzfILEGUNDTtmu/5QFXkmGfCEUKbLZ3NHxsJBcbSnggh1/V9bW/0vl3LX5VjhS0EV6OQbsUo8b5374CRzD8xKabdniKnj+r5gspSGOQ35K4SvoqkyYgip8e0mhf86E7tYinmlczT9PtfiH3icdTZQUMCSyln+He7eXpjt+CCXGzcFrpxg9gTA+5IycTcKuYReyW19Oe2unq54xeZqfI4QRE9Nw9rwV97DYjRTZsR7et7AK5OeMa2BHd4IPB6hKdcfmkTPnWYDhPmsQM83bCiNDjw+oHq+8u9FXk9X02R/3BxbKUJ+dOvw7PpL19mTlEZfMjWCLs5Scm/b//3xF72dAO9CSna8bGB3NhnUeu/mnldNSq28ShGgI4pT+rI8RF/l2Ad1F85UNDKUsAlKMIOrrYNNkPKAUTJhDVris3wEXPZNvbXqP5MeMMgMJ7WNaG3bwKzpLj8zI5UI3lsigTKt1vjlv1g/3WUpVQRlPyxD+mdymEygEKEs1opWVbQUeR/4RCFATW0BUlu4eg1ulRC+iPzpxP5CFT0DnkTXkA8NLsPQk4hk6dLekqCEMfFllsX/IfEPG3shGUklmiVOrywEi3UZw+593ImSC+NNHP9VD9Wj6MqXISLSG8dghByRqLYXAPE567kmKlrUA6zYnMT6f3Vi5BHGIlZ04Nd7sW19kg6WbO+/SxSHb96yW4Il6X3dMcHRrrw7aaqk6FHC1HRkT9Mc0hnXmYRmvs3eNcIZyJLWOgxT6+OBn0UbauB1xi/ZX7ZCPW+e3dSKIKE5CiCsf/7JEwsmiDxrkIoanp62rlofBP1Wieod0IVj7vNbE+WbVSXnRh6oLYL65uKWM8LEEIFgIEZkUu8zQ+tK5mlwhtFSE5J5a2o2th3D4NusSJoWPFRGZZdb1KXQ7aJDnWoxy3BGY4BJ4iuFZyaBeoA+fQDKoaiLWNXxKCgVNcgooyB6uwzg3H6Aw7xcXyeBaW/im7M7aIKitZ4w/Je+asnefMvzI89i6VNo1nuNDlRUJhIGcrktBFLLfHiTuySPH0dvGYnPn5gfHdHl0dt7Y3UQ5PKX1nQfe9FdX48SAi2+nv1MqQzGExpjMYj5p0UBgPQ7Ed+kelGcLjmkZg8awvW3WR86cEsN8fPlV04OZl/9Qs5e98sMAt71MUkIvwgy07wavywXBrPZi2qSlDOPnaaCnw5CR6oj7BMUgjSiN1/SoqB+aG//6zLzqkLbn4Uq2y0Lsxiag/s9XcaQi2/1IgSCBUVW8yhNgAnfu3mJOJ5FjpQrPGUoe9Abb1ZLeT8rp4rmYUncRXUYqmkoInXUS+717UF31zKTmPbrNZE7U2x5nmSIFW6hxmixaZFQpnb/kDWLybIMSa+OG2YCbBXjt5gTzYxm6vJ1IPXG0yOLYQqSKuy9C+plLawi+a2GHY6xw4aP7yqfV8bOXJg7bENxJZF7p88fBFO3h4EJ4a8MXPWuu9F4iGpGlg8VdoSjQ3lNwRWWbKwoMjL3L3Ex/nYTlsjM+xzRT8931G9HmUlSL0GWQzNNXTGfqQN9qooksMDoiMWdnCKQQi82TOdzNoyvTbbeRJFLs7IOv3MMFRce8E0MTZL17e4MBd+4jvnd23T7yjwsYM9lfIJGXG1eOOTypVTE9mAewzeviUKxT4fszSeyD3NuthgiP4rvaHQqup3R4gVLa+IEy6e5CSCbrIcs0Oh4l1ZVusq0Y+JdKrEm7HSQuLL7WF0MVGukiBlksn0ahpIIltfIuqz0P/tVNQLK0CljnQDOxNeFaD/TKRONxflkK5UMhjSgpC3jivInYr4fNQHv9mZ6f++S9ZybAlIzRXBmeLQpV7qJ8nj49V1maZ0/f706j4lWrpC0q3RgglkDaD8DIL00OkvRgEMz4so70LUj5r65TrpU4emQlwQ2wKYyx+sVWWqwMN9HUd39y+7qGkO49vL0ziav1MMA6ZnAlEHB6WeQUfQzIxSyaTaOTSgiBU9DRr8s1XTA3m4wpfOI9PoGtwoLNpWLuYs4ppogIy8gA/T/4u9d3PKkeCQYwcI73JGA9UdpmkEZhzwyEFMynLLdfvQys2aW4K4Uh3FAeUy1uDRWJ20apvmWCMSOOozZQoJaqfLV5XFbu6NqCK1Noyd2UJ3SI7Xy7YMvkx0mQ/VLEwEOon27IW9LQscCk6lA0/3iuMmLxgoCC0e4vB2YzARQXf+yDm3+WL/6p/pkRZKhGRa8M0mvDY7MRYSC54w50dFf26NFOe6m7xE+ITcx0cvDNASDQh2jmWTf7GslMEf4D5ZK9ugsxpxGMGH63j6TyDwyoBh4l1Jd6+oNnMbeq8WZUDyGkzwjIbwiS+kBe0lDc0WvSBByvrEfbBN4AUCSXcwiY+PCwYDifp9XvluQiiZS16miv2kJ/tWTZCkDU28o32jIp10Ut9/j2N0kO/aBvbj2BGgY5dTbOIel/GCZEswbNt5J8IBYfuvye4n/legExf0df5Y0EbmBUFWMAYkdJSguf+kTd8K0xWjqRqaYhiGO+UHEWoT0wIqNHKIRiw1Bli1g8yVigaOWnBP2smF5T1OK58VnPHxuKP6alwCtAg2CIfFeHFvySYzWc2U31lfjHVAYqIXq3RR7xdT88IbO19sbk5ivIm4unlLlufI/48xOnn20G+Rzc895kmTBuZow4KEmEViWaCVrNsZXff6NnkYXLp0EBGzhEu3NPjza5r3Hv1I0/gc07kp4p8lqRnSwd8kSPVH1QgWUL5Xs0eu91yoqgQCS9liSbGed/uvi5VaE4Gl6o97JPHCU0C7ZxMl8sFwVcCCnQiXMg9WcatiLED70ZsCgIK6pwuJmzSMaGZ1d9By98aNWPD0WjjuvcodrG6YtSbjHTqvnCewe7qPvyWoWSXIzDw5TyMQ1wEUyiDPsToG6SFU+XC08AjuDRgJ1MQmiT1SHBqknQ0JLna9WP19CpuTOuxSxhl/ykeWYHbwLhD23yaS+w2NBGHD3BVAGkmQs4ILHIqPeBcKfiAVL7w5EbNf/I3OZppeAt2FxQMcWPceFRwpLi5v0T24iWfl/T6zAuuwwQiPG8E2dno1ABG/aouT9ynF1/QnQVtepNNsmK4aahnvvXYIG2kJaai1Bvw0sNKtYN1PX5V5ZdjAA5NqJtcvxyulaeuY7SC+OkQdwykSzCN0xStDj7SinmAvdSsgk0+mVOq3JtLl4aHKfs6wfHoRRszsEr2cpXjKeQRwPYvteWX4dQofozdP18PpRUibCeqmK6umuwcjfP9pOA9XzwXdA1pajck3DgHv0TJRxVmWznYeksA/jPWmsVUgfcOFqmiKGqBeo8sVulQURx/3iNu47fqJWU2j5id/H7zNfyLD2Ek1CXAuqrCS0b3Ggs6HCl69rQJReyFQrSVK3kUbo2GtSEgNTBfTjof4zUeDegjWwvz3SUkXEz8TFz+ohr+deRh1PXBUqpva3x/7LEnQA1CdNeulEiY4JNaMDJLm1eD3M669qy3ww/5aHL8urrC6u/4KBN+bSdLJei32PEbD/jp9g1LqwGP6Kro0BhgxkVauFKChGGdmxFqVfPuK8fc0/OJlhJGati3mFTljosclUnxGutx34SVMPlSn97Y1RQv8Et3Ycv6gzmhoE0zMN35m82YeNSubFq4Xh8E4R9DmNn50qK7Dtcy0jCTf3w4nkFE748PH+PqOElimv5WYgmrG6fuuQI9vY+gosc8qTtR6eILzViPx+JLRUhSdhYMhSUwZxZ3Je/NbypL3jvxI3EH89NJ5nKxi1c+fs6KT0xJQE/YIIKVUq2bUrLAJ2XjjYXCIUv3FATUZrysBb9Sy6VCIV/EPNPJ0WvF25jxCqEo67cC9480Q8pi+KvVj6Cnt2/GFStmEh8C6y41Bsi29Xz1pwmCV/pearHJYGLiDkZOvlOTUbVwBAafTRw2xoNK7KUsINC4fl6UEdHqJRfuLaI7opeRGFJpLvJu7cBWQz5EWWJky6pAa0d1lL686bgu3JnhOXeFmY1TLZUunwi2tQn5IoyrGozPFePlzd2Epyq/2FCRuCcE36/88r+Sr6A/xQufsize+65COoenTzvzwVHOfqktUWQhb1cqtR/ev05cpVw+ykVeZOlMvfqcF6dtFQFGbHVXByqD1uwzBSv/8XW5WfZ0Tnc2znE3E+DzfsrG0SLA1JHbiupRahfv62TOvCxh9tzx0VQ7m8yIFrQRhhnLk1lmyyYav5bppLAoteacuD0gBuAmLp03v3pVpjOgD4lLw1k3cptH5z8ZG5qFDnO3TA5WvydpfUjpO6NbQKTVTpFeHql3+qF+Lp1bXjd1HjihjSumInbNNR2L4Rh8s/FBsBStR+3rWLVHhza1cPC52icyyXLNksZ0+dLhmw5N3vVJ5nytBD7uBsofx0U1PAKqM1e1nFvhUWQena6SvsoJ30RVPey+lBrQGBHlcFSMBwgugjaeIYBYL3la1Yd1pGpG9Aeo3WaP6lADuoisrfEEAmCqzp9d0R4HRXmYWiJVWGcbfvUMoYCo3rhSNQ0Bzadk1df1+IERSd9fT6lvY3+FeDLlBDcK5Ix5J3/p68zmgnt2absCz4uHKy6V3ZzACbOzoRUh8ob2qUGBEl9fPEGanani9kPm1Uu5F3q02oUMbD7yhrW7JjTTCfnAXmvx4EaBqaOg1Ffdd4oLD3FuPnDFP/3BVqryzpUUQbiVetMb2Yu66tfZ73JIgyXWtjXYryp78hZuK+BX4dth6rOMeQ2SUf7PbdJc9i18Ddboq0c93KnEH+aXrBSdruwsaLx2oMGYFDGmuwTxl7o1eEUsYxI24W4gxJeoAjvSHlU6C0uMGaYSSYX3Uv0/POjC7WZyKtvcOlKB98s63VOAKxj6T/ZjvBET8FyI0N16fJo16JLWWQl4wB/pJ/yJJwHL1BAqQsd3XdGX2wVzeRj644bGGNmFnshOikwBeF+o8PGE/5cE8S7DzmwAhzNZlMYKYmzf9SBwD79tpZOEgxqDPtPtMIxPXqIPbt7SnaRcqPOHeVKLj+/X6t5N92bjzAKfYnXk0dIY08HeYhmcLG5DTbx2I+Xci/3zk5Kh2eUfh1VW9rfNTQ4VrIDz+tkN550nBt0vD3FGY2LhJpmD2BGeTD/Eebi6dn/9mrz9eZqwmimQ9dEAFkanTO+5vJnivJ4A1xOkvR7+SJHT3jNxY0VvhPtDPwhD4KJw+vUpA76erPYJpt5oaN7cHQbzuvmCXzm1GPXzEaCxBZXceUeCgLES2MSCD5aQly+d/PXsSXPg+63Z4BEsK/mULTzBL4PX8QgM/p0BN//HADWDEBXDY5PZ7THofJv5hLQxluDy9a03kO9EYxmT1VEUvBM9/zIRS/kmQs4H4ka1Gjv7wG4QF89o9DQfhDEfS7VavNO629fD+Av0cVQpW+ccs2NwLdcoqeuOrye5eAZfKe9hB80rAZCc2qbYdUuo7IRdJ+GTxQFe2UpUOg27LZ/DkZXYDF1lOZ6MeiBMJksTGuCiyow24Zz8hCuitpFEmkriYGAckHCbD6lf65FVSQtdtmGD7rNjT+loRQ1nY0nWiawwyoGM0sm6paflyD6B5d4vy8IPIr9qemvFJ0xoZL9pPH9NYVqE/tBH6tHbTFl6JWJGfAHPZ8S7yEW5E/Npwfts24lmd51CLl3rGNrbm/l0KshDuHtF79KsmIZveNi8yY/EoFo0NiTizYEJEe62Pay1gr/deYrOr+uD6++Cn2xOsRso3wYLn3Xlf42+oGF9pPDyFLPkh93129vDqjo70QqCRkJpCP+QSm6koMs0/DM6vmz/OssncTXz6FXZinR5PACZyVpfW+dw1wMC3JfGZlI0qlfqI0q3ufsjs3H/ZxnEJ3MD9V0c5/WMiJCgp191mPc4hnDKP0aWiYhexjc+hX942Trcs31Mx5i3pK+XjkRe/X6VcEghQ4JL3KOz5cxXvUEFZYys6IRUxUiv02TRBxgkF92xSLemaZx6YyKZeRshAtHZUPlH/kxfPLn2AqO2ZIHduwuZDt6sWw+O8y75wDzIsqudCyGYHsWXplo/5ZoBPksfloNsr2y6PeDejhdmCwsjYtFTiy6YJ5csVVxccY19of+jay/9YFzUn6NyhT/tFRXv4Oua2Y7BJECDHXBmEYP4kmTnnZ1rKywe/Yyot21m4VuTlOX/LBSu9bGfoxl6z8NivPJC6ZR90H5tQkqkW3UJPV+1kH8KDAPGabH+6JNyRRG2LGOBGYpqO/kO9Sqb6CnVXCoA8RnsBYLj6id4Gayq7O6RY0eMXYXD5UfuT2upcBQOyCspt+yMn3dTCqI1jVcQeiq/7CEyOZQ4E8j/WqAMn+wvNMSFo1f14iM8ii16YRhWsroDYOVfWbW8bFBM3QdLY9GFu6d720e/prM4Iie8HqGMahUUq3yva+zjDT6F7X8ad6N1OxHxYOxnlB/jrJIs+fZrwV7idIDy65ChB9jPvRJ/vC62GUQssusVePhRPokzguUnFkT+M/bfESQHEW38CqonjJFxuit6jT5Z5I/9va5htW9XQ6a9KdRfe7MnJR8Vl8fGH8VI0HRc7ORO0zcLlByqSFEvEGgkZD80N5xsmCQizIskhY0VpqgKGDFwepw7f9ujHLiN+YCO2BjS+xEqhdoMwg1piILu86+urCMLNxCNj9v3WG6+wrHRVJudfWyT+EogsW20DhPDVEvD5aPjpc2xgTGrCoB4UOaBc+S5Mfg+ml7dwtpTgeAe6NwyxlVMkcIqSpEVPmH3+TZj5aQ6j6pz0YKU804bZdLDNLgAyT9fIWCZhh363MYHA1JYo75oH+WHGRxP8QRCDB/yib1dGqQ0oo2Ja+jcL5lGmSJ7by+/ncNhcmp6x5FkSCQJyW2OQGrkt/t7hsL0yu6Czy10ubc3VUG6TPov/qmVJomTZirPKTKfnYrpBT7LVv3w7GhJ0sIJX+uTwTBY+bjAgOXkzDbQsDqqEZIRx24teQxeYTgn2rOOOimP50m3fwviSKtlVycTQ7jNavMAL1DwBM66n28NugbadfVaSip8h6enEPQawq4TDcSdhXZp6vmJPXnwk88d/RZFyMRhKpQl9jiNdwGmRDPI9T6Kk+PYIvne82dQXrwz6Ub3D5k4TOsCAOqBGoNu/TKgZk2BBKJEE5sSDLtEZa61ZPJb2LB6gv2l+npsDmAylyTf7vOQnWSUFW7iG1pDzOJD22Z5mgBe7nym9cTvmbHoCrLahx+MapBfOZ7ok7TnMQ4Ktdrd8pFZKxNYijMyW5BBdsGwNitdx9JhkJfh5FZ3Xg+RtqVM1jd1om2o0woO28VapyiRU01bRs+OsuCxInJe3GvIv7v7nxQjFvuUHTxVhEPP5KyDSKstXDhJ1WpSfXiQztqay6tlrgW+8+EWS03VzkOOQGlNmr4q7z7DARN9WIsNV/ICo3GRAXSCKnPSkltJ8sjRX++g+JConSDz0/iBJZNQ21os1EF2SMuj3B9eMATt7blxGt16tdNLKZI2LhApV7ZL6bMJemdfT011sgktFvyOcVZRokT5Kn/EN1YvodL9RnudRPXqCP+R9Bl83vWOOZEwA4gwVyHYLHBWtGFnUmJEk5wARyLxHUlmYQvqme2jEuNye6q3jU4DIoqR6TWi3cqRR4iXDffhOlFRqujKgkVZp4OoDl8w/BztCS11By7ccYthkuIds4JP8DLJD/4ieIMQC52AnKMmfXyWbQ+qWTMV74/9FHcP8s0yavFoPZofNs63IMJ813TRef06yDZ3fZkssaetERWuHkuqZK3mIqfXTNmfZWwoD4IKmpmxZpOHTNZBkkNS6Ib4U+zI3EwVlSrSdQujO/gLFgvNYJaeG52dE5P6ymLGnCljd6GXv0Pjk2dGIISy6LkJGZfvzXoHv2F4rmXvwhRx2sIhJ9UfSSWaeLl/uXVz6oXEz3jWFgQzk7dIz/70diZ2Ixyvoo9TAYtbe3bk34d8YIx6PT3ghKUZAFH1oQHe8o5ybnyOQdbY4Ufftcgkx6gVlsxQgMcTbHPaAAnyKhYPeYe5a86iXUes5NDbKbjdJtp9PFuJ//e0pm130WBaWvfw8Rvtrc0kXJWA8n1VSbGF9e8al9Mvqk0SyKx6y3F2AoJu0HBMUnx3s6cJAZgXpsSFc4Wx9sRwmwZczysDhjcfHUXGdwsmOfWviOdpyW+/j4ZF/iCRq0EPSyhvDCluAoAN1a6+mXBMuLF+2TU59aARwX2KW1ewoMod9Soob7Z8HBfFj2/H8uVdQvEngDgzbaDLaTYhuWpBietRsfuqsqDgzBJP1j1TBb0XeKbY1j6jzEWNfynLsxAGvQ4GWE3MuiVr7pS8tEUhjEWFfoRh2VeMHDVXzujgCYFNCNVIAEQzORgf5qbx3GC4f03h0LwP1Jx0uzvtC5827ls3ArffvgmOzC1R5T2cd1zUDs4e8IC+tHPVnZcerRdXUUlUF2nAb+qwPIKvJlV7gw2ZMgxb6XHHFPjw6kql2RGS70XEb3r6nBGmbxqkvmSfgwFmsaLtVSkvCVYoSbECQyEXeFS5tFceePYWUdZTKVf9wsVA0YkQhJVuy4mjNMNiByKSWRQE10HzIUH/VlL6q/9TqbU8ma72S6e1AfQ1xLZePmE2hzlC/uWj6qt2Jc7w12/cxCJmrNYKwg1iFuHpEtx7EFVgTGdIsFZvuXHoIw6uXY9KolGDUW2jkKWGDngg/7aJq37KfgRlbT9EQqSI327TKBrCD+eK+lAev/Md22VDEtOWixpyvmgy93xdLq3uWTxT0oEOiIcFpUoLWss34ey9rhU/BELL5hw0UN0Qsw9UwFn2JYKVFt9YHahyB0209LjR5eJs4Y3h1YbvdEVnU/iYEDpp5VeF68lFW19ABTjd0tX9GnWxlSd7NlJbIblNx4I9FXugJ4JNgas7kixxGo5zPZSCDQ87hkwCX8xdKgX9THF2GPtlgzV2f1TTuRh4sA6/eTAT8WAey89npdCCW6qUd+bfIiJrH+tuy8SvDStpFH49sViIp/FpuFYr1DmJyMaHmTcEoaDOci+qqlQjyZCx+AsKMXO0lTvJ2yOhgfkIeuN2ANSpSdXGWJyjh4ms/9NaWskjMOlgbpj1QRNxAC4g0JrJnuDFMz52YlQG6+0oKjb0ZJDyrKKBcET/YUv1aq1Zrb+LowgvZ5/YG8VqfrqfPgRG0OOXa2OMKVXRc/pFu7Jy7RN88i4yag0+wzzs2s0RD+Bl5cpmlVci0yIGvG0R/MZEvLry8Zx6CFxSqe88D1zpsQpBdzxqZWhDVVSRfdDiJ8oXVUI3fOcNTnDhLF24M5be2DytzqsH9o0Vl3PLegcDfJcHjaVeq3F0Ic7WhCTtmYHmiSGrlR2enHCfUz40pzU17p1ZiIM8NLrmwrDJrxLD+u4JAbdwL9/Z4zXfAh43SAK+to2GpnGCAxJ8ow5drAXaN+ilNHIVmeUoJCAWO4Tt0zs5YKPVY+Nas5gIqBKQIizu6le0sjXKPlFMZbROwVFUtoQ1B/omCwxR8QbTP+ZHGabfCQqjfLoGeKW0PYHDJszRNRQA8y/ytyFdD0/RXgtJ6i4NiRxsZciXBSL7Qx26CkAmr9bma0LcrynuZYCRlNtBUJZQs7pmaL+0DnUp7l0uFSZs4hRtcZQMhIEjaq5jFKzIKUUX2qPbRQmVFRuBTd/vQ95Aqr2ZjU0p/dYW+FPU66MbLxItPE4GVrUKKVwMMZ5WQR+it/nR8Dbf8+khIR+5F4/bjtR4E9JvDwacfYSQfkXj16pmeeKxPluAr7uZCWmXNHi60HKQwWcPLh0bOjFckF4Nwg11mKJhAwe3SO3pHop14NDRnmntp06psvNcBbcvzoBSGYIKZpJ80eOudOZDce7uJnzTcmnI+SFVWPWtoDAjlWQdbacUPaehftuTR7mbVkoYkAiN9Hmw6kk7wCwwnETtViM8Kgue1qK/jiLkK7X650ftnGJirrAUlfI9wKlAWMxLtTgSZnkOP+zRsC0PWWLdApcTPTrYeEFfz86hniAHCuf4sp+0doncJEK6wPYXJEO0POH+sOzh7ju20dY2v+xnIQ3cuLZvjPfq3bnV8qQ3l7VbWIxd92M8vK4k+8VQQozatee1ZKhekza/VHDGK85AmNrlblmQ8SsEPAhi5XHk83rbGItkSUtalV0d0hKkbf4tH6AkMczZLue20xT1HTiZe3y19ZGt/E5t08jbqk1torWVXoEuykplzyh6MhYk0OnarEIGg3LKXt++JobEfRj5uokcdNInu9c/fwnej8zuIjWCkmv7XKiuBMqiN0/dOV212KYV3kBY67tmAzHm35viOYwTwwSPqEdBkD0qwVUxpsMGmWccEEo0fvPqJHE3hK1Ub1c6JF9Bgf2o8f2pp3UXcpZbosQ0ykrJWoH1kbivFltaJpMnhnxog/rbrBTnxUi3q3qSkliR0ceHx2YKN9Os/8c3a7kK2/dOYjolhdxHWvmktZ4ZVgo7f2fNw8yvgHXcTG8rrZz87wn1nbhnfQZ9Kk7Ogd4n4y1wxdZtLQnXH9yeYhh3R8IOfqMnQKqluTVPttwnLA+eZriNWlxc3HejjnKUzyEQjMGmGn1X8E1DZDfe5CdERLMl8QxJ6rnNffGYW7NKmjgYeKiRTSRhry5Sir1M1Ng3sWD/lSLvstgdxspv+PtQTKoBnSsK/x5Yvl6YiGTEvkEP//W3sYW5L4Gca9WHk/AEUMY1de0NOyJSPNbV7HOErVz5vGecV+RMoAdJl251MjJDMH+iDnsTPNjJgi88SXQ8T1ztvSdkSwThOS3cAsvNbQIPyV1v47d7Z548qgp7gPLMnMOW+i7MAbrcqfByxmfMnC199D0r9fuoEnj3DhqNoUqrZ2EXTg4bb9gknJCE860PAapqW0oR7RtKhXv5tezbkdO9tmldvMdTRM3Y0WlT3ZpTl8IDo6K4gOwp+4g+ex7oLDGcyK5u3i6w7cIXRW7vJcpkcCepM4aNcyiuoL5Xl8yeAyHkH9tLsQESDJzQdFm/Ap5TKuYlhNhi9LzjCqSlrVmvytwMmL/ruZRy5mFi6CXZGscm4740u1rAcz26zdDoee3wNZ5t25JvrfyEJU3rHTpCMPegqUDSDlSYyUlYsfVXhESEWi8ZvxhbVnjJ6pGqEhHvOvTf4a0jXlUJNI8dn9/Fw2lFeDM7Hk5v7ZL+VVKQYTprP0MK39XWDNA2nHsYrKULnUuMy1ty0v1OsYqfEbksp0gKtTL0DR3ogC+zmlG5PIhbQe7KotBqkAyFgBUt9yTA+8ZCyuVB54WyGjCo2txFeoyOn/J7vCU0h8824Ct95bVKHLIbyHhUId/QMknjZzJvWy82L99y2w8UkyzOb776qvuuNruFqWL7vyP4YmSWfnBRcVdGuqeZct/P2VVtmwZKJa1TESM1X8JYloyPZ9ZO2f46rF3ckdkQDCstw1GWOg+pjEi1n5V0Jr4rocs9xOdf+rocsqjw+ltZKLt/AX0AcK7Cvn2ymvh92wpIlb0T8KXqV7/ew20ZpMKmx6ayaqFDhn/Kh4Q5nqnY8nPT70EmGDxPqYp4eTKQGI4IkyyhIeHkjqrFMPEkziX831vv7boa3Pnruu8Wkdk6tWWBS3ZfMWebFRlOglMgT/UUOPoJt/tpdXcSctSQG4QGSXHFRI3dwAJ3Iy05PXbOT0SPbc0Tb6eGTjTws5W1R/h5tOC8uzfzRZsTwDYNEb2hBEXV3s67THl/NaVM+epB9TMQTsQFkitgY2YF3jAfYlfm/Oq9T49+5CHLFM+T+HQ/REAv/IRaxAxVYZjxGDveBj542hmMQGprFJssh3KxlDEYU1P4w/dB2o+W9dIsqPNEmZZowADDh/TVuGkxk3AvyBQVbWWTimnvqtxrJEeZTj1lwjpEq7k/2ADD90e1hXZAISgn/m1p6+xLEbrHglApHykZSASZZlCL4Fu8hh0unhytQyJolzgPehxTibeP8M7IUHFsJG3BX8sxhi6zIPds0xqcxD39DgheI+Gf3wJEsvGNzNPN3pXD/B7f4ajxmE++diaHjI6v9Tm/cBXgqHNVM8kj79W3bvyYvV25hBKcRBodslemPQ8mySQqjLMNFFJDAuEwsPZgT6CJdvfSGrh4ftbdyVqilIpwTWZR6wudFUhAgpwZWcTqvTQ+2VZ28GLX8zIjajiQOSeR54M+sEPsQqMt1j1R3faAWta/KF7ZB2/Ikg3wMWe5dDOPVEPsn1c2zJGC8kYMx14SmjpIwW+urrFhKLE+lyesSMmrUCvc/nC4aWTxc16NSCPKchN/1GzLagZp361/TxBZth16EHVJeUN4WUjEa148wJspnK/94vlZFZuXP8QH0UgshE9VQhy0JK2S1fME1hGd8RvCb0stAE8g/3aWNx5QpL0n+ekGKewyxMw16l4XleINSkfPbnVEUqkR8mrn+Q4yv6IFJ9lOvgvcwDuvFTdc3XB/qRH4O6gqH8seIhvh05eMCymQgaPHBnQuC6npAubyKzvDyUvu9eG5mOpcu/GRy5J0wys4N3/6frf7Jh853Eey6oEvqrg0mIZaLqmhZ2/0ysl13mkO/p9dOW/uaFwLMeFOOeHcGDnJJmBxl7dLMad+VZLEbCu5TyOtJoj7sRFbJs6vdSuDZb6Kcx3YhQXjhqUcS6OGC83vYWeQjpTi4QD7BuNlfMeQ8vMrDwlCk5MFH5KkuDkR9VDFWDMAnFOWbD9Fkj71cZjnoXxTfcPTbCK0wGgJdX3UZ6Ziy9jFuTyOaOk0YcmiKQsLxLnD8DSoANazosvml/j3XyQo1zaFIhzeuinMf6J7ZkVG/BH2eUh52LeGoiScMt0px9gwGAYu1yuPfjgFaLsV3dbdH8xssBdS8lmhESwtA+bHYVIk+1i2bhrFOqQIlly1CNbLGquzQ4Z8MktvK0jqPLbzVPUopmKNy9L+RkdE7YW8f2Pg83NdBJi1BcoM91QWkHSLmteOFYUJM8xyxIvHGcnFtW3aPoAP2W84BdqitoR/Urc3tkBSVb9m8Kb33nV/ggWEFn3k6M6CQW7AcvAlNwXpMNFniRWezom3nYSpubOUZ5wyCmnuUiQWUvW8r3ihlbDmFnczJE/EnrWEFLoJtiUdhVUUzPZpG4mbJdj84jj6nKg6T0YsnYAAUXJ6+KLuYaqD0J1x5OllS2UnCS3x7iAGKaiIRjn5p3MMo+xw8o9m6zqlfkeTN9do9nydaNRtD4xvDinfBaYjJnhHK9ijnRbham0dbBwbmjJZWWr46VM/ifyupTe/xQwYCdMrf0SOQTNRMfiyJVz0Y4XTkJd6k4BvhmKkfX93u230TmsCas3Xc5FD0PuTwFq1LsSenb/9Dg9KElL7yXhZMzNKFvjJA33pJbe92AxhFM531XgBmumEaoQLDC0ICdGbJuumLuGJ6ryp2qn7/C0ScuuInbx7WkUb5ewm3rxi7B7OT1Jkt9knO34dCT0Pnkc756dG0p0VFLEd9QZY45caM9gl7iU1QsRYHp+le1HmbJi1eMi33Qgv1JUJKF5TBu0/lZLGe04VICBRzpl/gEXsyudntriqFtjwoqEh40IAwykK8+Tx9nlU98cgTu/JTkjRXZb1jEyYaPRcp+X7JjgltP6Q8iooQyaLwJBl+b1FYlTFRA9M2+2ZnOoBE/sj6pOxxU6izlDK/9fGPbVVwjBcAM62bdt2M9l2k9vZxmR7qtnZdpNt27bb2fzek+/s+RHrYF3rvlYHr4BjimjBuUPqd8tBYQuLlpQpiM27tKNMLM8HzHUkJt/lfYk3KUuqvDgFfxSC+gN+vakbxopOicnvYZIxdADhgdCpmnS5UJH3BAr+87nz+9O6pZbIf4PPZL/Kcrh+fh0itRLT4ris6xXeyNNPeeaMG8N1cD5MdewQe343KXQkGNdCBJzUYBt95pwOfiR17fIXjuGx6uUZP6uXNebOVMcCxVJSI343H1/wdFykKDXe3IB69GGqjle5ZZTa7KDIlh3aU2KoiZ9hT0oyFPH/E6ixtJep5Q4wFK0AcLSuM9DrisTVN5O7wRMfC4a9WSP94TfP3KLXyZGjbdcPD6w5SQRohDQINnhNdS+qpCIsTU2XVUIXEq5EWJyiozDt+KElj4y3nwa4nYVMS8WQnghkLG6quVBCfqtvLHZu2ZFeWaoTib+xyBs+2spNNLb8wVv5vQCZkaRfWyCT+SiFCrmVjrq7CCmUX6cxUd/L6sigSl6gVCJYg1C8PrE21SsQh0iWQcli5JEkh1j4g9hfQblWc091IPc6RvpQQwTelYIT9mAjpgichjoAugFZVnmcMcIYwjE0yZiTwGDou55GU6Q9tApqjDaYhLQtM6SHe3Sg90Z4dZ8cPrZTBHrQTdr7nG7u7ixNkxSQL1ZiWh67cOSlgMTiwZyLDDc4HNN/9X4bYct3GoK4ZZDWNldaMc/OQIeKBPu/tsu5yNcVD7I936K0l0FOVsm/2h6SQ3bfHVX/HY8iRZv0SKOevMthXkOxSuKeJ3cc8qP8VoXmXAbyR5dJ4sva0n7MfgJJvpYyyfDDDapS+TTJL+D6gkQXqyye1A+nA7Uptsi3okoDz6bvMWtLc2KwukqIoJEp5dYye97DMASuZVeXwM++YWWbnTopN1bXLE1hBnvzOaB2SMAbEb8rtLWHkUU3VxqvicuT/uLi7YTQ4nKNZvFg9J3NKMThPFTUhSjz/FlAenfzUP5JQ6ODVFDM5ZXRh8AksmrEuhmz/KTql84HzVvTpvbz7/21oBvmUwzeoAESTX+tYuUFDVtthKjF9bGWunuuaIK3GpnuyN0923hOZsWOljB0n+irvsRc2VHGas1qXEW+vM8rNyLxvK5/hu6kluKf1b8sCi2xSkLdHGgXRdghpod8o3XsRV9MgdlkU7ZtfmTolAmU2OVnSnHsISTDRwDuhOxp6pw/qpFieme7Z+LU1N4h2FafS43tAK652jeUjylQhFBa5ipbYpwjd68NGKDNT/MfC3rGg5RYyBSiZmvn+GQc5K/vi1tnNpaIIj0KELBORn3ZQY+iydH76waQwoZSjpQZg543eEPoxxAWDyZ6yx+3PL3ekL5uQX/jfN6VoFyhyN/mvUJ8NXFAI0789ZLwY7SKUfPkoEdmQDiDW1mRbtNgiah1xqpdHvSiyxRhEzIjc1jwfEatcxSQatp0fuojHygjp/ldTK3Yq9Dfhga1N2TsHf5a8Eqk4cQVZNR1PXP3eZ7mJ+tZXIyUywf24xdxwHhQTaIn0fcwMY/iBvK44aoyhDjiHEwNunbROcLM9monHwbPYIB04UWc0kadeYeRI07BEF/z7uzVILNa3tsN8fh1REEAJOushbcE+1U2Cs4bzw01R4JDE50w7NKTT7aIQrpBn9mzronXU3ai/ByWg3MgiLD5Xwz4pVAYgA/51gF+PxeNyyTbs8winnPa8qKcK/efoRNY++d+0qzqF/mEY2NVtm7gz4AnQNgsHMLR+wvZxVAuww51xeOASFOuregOGxX4MEGR4IUJi+PA5iPFleTv5gf3DC2Rdwd3sY6lKkLKgQcHz7PKuKio2wsNMAPr4D7O4w8o/AMthUXI64rj92bDpe4GGHhr1cPpACiyrxGZzdXfUlNy5oJTxnqtf55WyoydPdgS3VAj1b3SxXyx4nszFwVs8eP+qi1Zdh0q0HgvBrzN5hzSscmkMWH1SEntITQMAR3JqZZqy7sIfeGuZ43DkFrgNK2eqkXlecOwLZczSmRffVXsK/HHxWHjjNTgYDVHViqFznz2HzYxR937H90gHTA/iIqz61hcifHg78e7lc0Lg09UsBn+5OWtd6b/ayj/gRmrgbPJ5ryFNo3hoKThlZ/JsvQ+J11XFazGDXnYcEVCX8QH06vYtJn3aNMmImD6Ti2ScdV3rjJE0VTEJAes8waB49P1j9dOSJ/ADMGFGDRVPSGWkt6Lp/BUWMaEcA11Gnaw5f5pvUI0rjIi4on9FNiWNLRC9RnyT1zYkwG6dlPPeOLqwm+YMAaza83ScYBBP0pZfx/6WFRZiyISqvBSZPmNkIV02dbtA6RX7VvIfT6eKcueyevMUsieKehv9kDJGqrCZMGa/ljYO/AfWamjFwN5rDaztKccW6SMlqvKQj69gMIeVvhkJP7kthqOXjs3wiiPP0b4NRGmGiW9Vvl9sJe2CO9yquEGm5zArU2IOHbthT38P259Etqnu/SNEbQRgA4AedfKScRWyAYo9cU2fe9sFTkHiKt+1CT+HwPijYO+ZSk56pclLgiCVsJeH1ON33dIHgM0oaIKRwSwlb8DU8d8Wl5Sj+gVAWGS+UPqM8dnx0LX43r9SSiXpQuRvxJAVNRPJS0fxtUIfwFWelkkAVC//tV1w0FxzA6lm5ONOI5A1/tonJEjXV6jWvp9eRwb3U6AaY9nH9x10qgQw/lFBS/X28YwJ2kNd5M4o7CMedVCOQdLCrNWRP7mkVJPKd1uWYdrBGcAvrOOPH37HLkX1kBHAhDa4g0dUPoecqDMn7NVdX64Sl+tcuYfDla1e6bUv1FxWw/s4B7eqcMsUKD86mFTNhdyaCFnkEC5AkyZA3EfFFlMhqYw7HhNIJnwL11EB/2ADgdq0Vz/prJOu0PomN1mw8fuQ3KU81jCLtgc7xJxyBSyHTd9bvrKu4v05oGxACoOjGJ8QkVvuDd3YaA7+Dk2nkgjVTvvV2/e8Iy3aZktzX2lU9m8Jk37LLJi5TW14rsw5k0uNB89JAf25b1a/r4Q/Hv4Ag9eCplI5BStJAIQotyIGv2V7KJAgiGotQlnhjk9lAMOa/ANWD10mUb9F9Y+CePpZ77wLslAGHGXK5RUvDGLX67TcEPs7+6IVxbeMf7a+3Pd7jHvcPfoGuTmaNjtZ+ZTezHx/Fqojv3be1KjzXyYNxieOvpbFmv4qRC4fFW6S622ER1/JpmKKBiGu0MAongbl4iLfqNzJkcD+Xx9Vt5Ayz3Hloz8PSnt6WT+7B+kBdXoexYDDqimrGmiOjOzC57jfu2Hbm8L0pPcXotLLCqbXROt1p1XBcqlljnZ6obPeHZm5ywV/3XCm6m/MWbA9AVsa6R7DLBGgj2pIx5OtYLF2tr/ZcjzuMX63sjd8lSnYVIpe6FnxERWa9TWUfLc/HWv+oFHxP6OsSAnPJBTT1EQdXIAP2b7S8sOhOd1itnaEBu5JVUuoeQnv2neK2wZ0xjU2vAF9IKJbYhnpLxQyFUzZceHQCkTzOn64hLb4chsnZeHW0z9u0HrNX4mOe2QHGlkfpSrYRSufqatWsDcdwc7nr+SmsLghObCj4scnH+pTwVothX/tc7FiNXNMmXnjSMXUakAkguNwlTHJD+cvJdIgd4kPJEjoN/EWfnAqAB09Itm2clTHd/LrJiU3PjJE8Lfe5C0H5bpXFKux9YIr8YL2pIshDjbyV1e8pvp0D/Dou1kpVsjiboFCdhWKPvdTWqXKbyXOmtdfjwjTGD6h3eyV6xmjdzE9fQtldyAlvFyM8l8R7JpmX+7TS2HiC2xYPoSN3/jNrWL5BsmXb2Dvody1qHypd9VKXz06oaZ1merAIXyxhq/dPA+uDQz5JbjqySpwhGFIzt6Crfo2t+uxVDJC7WsNk2hIVOpDww60Rd3Tq/bj8UKHtqUqRR59J2/nartWMxvnEsrW/RccvG8nTw4Z97qeiXycSQpR2U6U5hdKu0f+JVU8zsdn+dPnsN/3+n0X+gKoI4H4OzsBWBIeg2E4P9I8yVdgXs993rX7DXK9guGfRM66E7RhngpTDKoToaB8OQfEfqa8yIvQQJZ/rmh/poHDo681Hn2byPKB7u7m+yxJSEY7tWWhQpKmLmjPzroreMyToxW1BN0I8s/02hnKFTK1hV8cc7kohJkgmUHUTO59B8lb3XjyDK1Jisx+MRI1DFRj3+xNJ7Rs4oGnyq4KqRo/vyXlV9l7255fdS9KN3QmOYWMExQTL+bTcMQSjTWN0MtLrAZeMpwT67vJR39bTpwwMXab1Ohs2h7+a31jFN8JnXwV6vOP2iHGemvFeNaVpKY7jWd2h8oSAEFdpuO8PIVNe9nGCov9SBvobDY/eD52X+sMnABqCoTAwk61mProcfLy+oGIhFMPmnUrHCKti0KNWQ5TNBqL+97gYoRzj3DaDn2TkBVgNJ8VY8n5kFUCFcXcKbbcvCza60ND4aiUi5UAYiJWmR65nWNlwoabMkpeXUh8cvQcuMbnnTMSKT6kykNYqnFZnUMtL0xchE7iNJFJmxbZbaL0yPo4W7SMYOk3V407RuOLf20ILi1ipJPl4QyquU+ul9WjJXmem7p336iodMPzpcstdehUft5fKt+Nfg5Jox6NUI1VtsXZF3jj1QI7EXFM69ebIq61sU1H98mduNVnH3UoIfMCzMci1eIdAA42BJJiFocQyr79FAlV1F7iFZgfmDYTAIGgABDmi5OP52sO6x0HqZeHoCeIOHkB6hcO/Bf2r2ToRjfLzZaW6y5TtNxTSp5mzXnPMo3acMrdAC73drX6R05+qQB4RamtDgrhnerjsu96bugECEWFaPU314KtIy3a5vYCvDczwyyDJPwiC+CAp36DROCOtF6qV3i18TR77lD5s/NNxNPQzb219ROfcPt0r5lSU539Hl1/2s0epvKEqTi/B2Xz8JrwcNbATLSSMoUGT/6HwsaeGdn6wPS2Zzw2wBM7w6Fq8r5bWiYTXX1+7nleKz8bX13ZRoRreW6UTuwpPtAROEiBbmB1HF5b27+MSW8JB8U9VzieX2idO3i9jPMZBFwjNDTN+yA7FORmGrTDVxESDAX1Vgiobg5tL4ksO09y0/aPptOHQtO07u+9X9OZztDj1A/oL5kmNnnkUsVRPvRzTw9c0VblSbOhO4Y4WsX5ym/M6CXnG7cnW97ROmnbmhAlzC95JIK/Z8DesqbkQcWD4jZ2lZwDVZNk3wD1fQZ13JhCAmajDn9Jy4kz10oTKnhJrPEvkrPuoTGaIEhBE+/wR2bJbn/lHsaKdJ0o607CD+DLc4LFUcoZBE5aT1EzApdE0lizMQ8kkwFIRtQZeHPgrkeNYHfyXP+DaHcSUdGDv4NfWig1FfFFwdNrkYGNxkg0MbM8cODZcGtwp9tOL7CHx9+KUS0Ms7SWUvZ2xYWgZHr1iGZ8P7z49GXrd3Nv7X30orh5LSn3ypc1DizCWIN7wKNDXG4NLPKKgzSUKFCC08L6f7Yx9r/MYe63qf+svwyxQjF/BE3hLcfNoDazIDv1Tjm7hcMjjS8CMK4RBtORMKXhz7tMAjoqPxcDuIuLs8SZBbVerVD/T77lfNbfrU5PuvHi3jqpDSZSXaUNiSD/c8uOEVLr1T/J3509Vf+r6L6B7g5eoHp0DCG+5AV81FExlSiKbrqXoJE99knrMhqN3hPuO8jfCo1HjC6jNzS2ep/9Rmpkwxf1g6aq/HS1H6ku6tKrwZh6y1FPYqYh1khMhWJE5vCNCy0QAfcXy2tPPtreU3ZI+wMQnPwHB/et9Zvh6sdRKS3LPpfSz/jtLZCLwG6Y/IdCZUHNdrriVH6Se5kG0kSo8CaD/v4QOsjRxMPb8dX5Gx5hTG+hd9yoov2dr+rzS1zwolSfvi+QnwqkpResDZFWZiJyXIJLYpQJAgt29qayijqpC/tESq9N/+c5tG1GlMw9xquhjgb3CHb8we/EeeWhgjcDP4PbLKKsGTVqJl8ImRSsEmKOQXPk6ixqZUqIG1AHWuTdvEWXgJ4cm6AtlWDmaCLqvTLaRpa3ki+69VRZllWO5wRCuBt2PAJXMyZLwKDOhlOymLU4Woia+8ATd8CuvBw/CApHpkEk/tzE2ZVMFGfR1YPOZLerF6f4jx4d+CDvQwrW63lNKmH5/mUXeN8hL7oI6o/Hi+aISkCC5yYtT1PdRlO4U+ib+iBMk0Y3MX0Ho5nGrsrzdPqg8WRO6KCez9ZKZE3eTc7zFuv/3bVXY5BZ9ffyKTugCGxBi0ml3tnUjYRBXnH05iNhOA5WnYijGI74xedF7oyZ61HFzQPjR6em1zeuqdXvSccedPR+Bt1C68eoiSknHa4TXC7/nbrbPs6hPXrHcy9sDYr4NPJgGyIgSykmnGk9T2oSqhrYyhxTQe16pa/FmgXWs7QRdPeM3b8Q3YYqZWgjJqeIdHkIO1Yoe6AS/ZNsUFfhSCHLyHCfnBcsHFeS/S0F3XV859vKNSbZFPQI+IGxdk2GYoaj3teNuKyqqb9MDdGdkI8NbT6pZoYr1lm/s57+owsiTl9sr4Dk4mDko371fb78e5tG4a5y2+u7LFweeTax3dz6648Ra8Ffdvc151gve8mF/9layj32Bel9ef+vgjO7zroRUqZpE4130hrCfH4/AjGPsArQxRMALkqdq2CrekQ6zH3ysbedrsUZD3CrXsSS64goQyFFG2LBwUOZGMG+3ZzjGEoFG2Htm5mAYrnM1WWu6iUE6K0EdgiEUYMxrO5qe3FKw8L9TxDls003sDf2CiUzQM2Oqt5CF/8BLTKLI7aY1kWSaUyUzCap/gtdqkbAJwqcBLHmhTEMFva3T8G4+oMVb63E4Su3TAJtr0tX7DPLfrS9COO6HLC7pFAZx1PfJCXYWVD0Dh0Hl4aPsznoNCuHZo7s4KcR9x428tMTgEq8jMxSdizWTzwKhYyC2N18hGnQt/Npp2Xe/EnEBqWDqq2KHfeRSRxz6OYO/w/6BoDT2o8KM+aypEnIDLECMcm8Z2AL4OLks6nZLL+Gwte+aKfSlsOm96eeydpXmoytI/dhBDlrDzkbXMAovng8/TewCTjzpBvypLWuyfZteVBpiXb4xpl6mlN0PJ6FjGxsIjTAMBTGExMDaXegnvZojBdLa6v/zrphRzYQ96PbBSzCloAx+e0I7Owda3Pv6TfrxKM8YFiq3ntyf7oWcpzpvfmt1EoQytRSTKcjj9qzzqx0r1mETDg0AZba3DgVpk5Y6mvbt1PamPkcVJjMyGw+UtcN7ysktWlyH748yVqKj0/Xg5DK7MuQ3vW8JDOKfaHneqDaWcdAB9IzvtkaECAcOSkYCp2twMOH0Rai+S59R1g8dQEOU8uX+Msh+C4Udcg7JPAaICtatnOs1/yFrGJ2OJqSt0Sd6HdROlaEuukx3BBiS+hU2ihFZI6bP7E2EJHuU3qZ/kqXxhzduGMhCSukxq77BQgfDyW40sxjOXFFFkzyWlVJukIaejtbABwylzqbYPjjsJMyKWh4WyNbbeo3Nha3aCQRuq93EzTy7mIowOEUH4AM65xPAZQizhXCS5XOThM8OcOuyzic4xvQwDYaedXXe8yrZ+vOG3HZ7K0EK0fmSAMNWQgJ1RZUf4SH7ef0jhL5RyIsXmS2VBckFcANBLdOw3Eu4qPk++65uk81A5bAUPxXysokiDOObBVRvCs6EYGO7l6V3ojISlbE1JmO4f37VW5g/q1CE+oSqDkPrG4zikyoIsYYiM9Hb5e0okYQa7ojoUzdqHFajGw4SPXnE3T1BOX2Lk5QhLOfQmcYR92quIwT+k5iZYXxkMa/7LwpSL44oZk583qwTgZrXMAc1p3gI+ko0deGUJOVUMWtc9TBUKHWw2kXrY6ba5mzor1ypIskiUwBFCiTyVubu3Q6VoTu4hgTgbuo3b0p9en6AkYnimFB31YS0SHpcjf27goBorLyDYLi+fK/+yAxIqge/VMgSNkhKel+kDFyD5xobTQ6r7qrtMyiCGJ2nhn9IMF7Uxa6A8Z0Dnpoct+9DbFU1ZI9UDUSlbyoarnDiRss9+VpjOkV1UOsWTXS7UXluUx+qkjgtiiB3nj+KJV3jgqVlp9Obivx7vkVeDxkQkp6//jBiV6791S03AxrcoUvmcpqkVjWXpRg5suA92pSOz+b2BPr3XmYa9YwF/8L+sPJjGQjw8Seu6AkaQeADZDalRe6FG+opnShOucKcNaT0GRrxZXrUTDlzXQsxSf0HBkCD/GeDU56iwXdntc3yWXb+OlaM3fK1SfjJL//mPFOUk2UzXYnTssFmWG34nz8C9Ppll9I31kbfuX1ChnbqvOA2hLqJPoiysDBG711awrctkJdE7pHs/ntMThmMYeXw4ojW45hcRpAFAIVwtZu8aNVh4hBTQepbDHbHgApiLEwLOQmlO927XzsMWlRK91IKBNiwNRlA0biPX202V8iiSprGeAyskTS1iSIE3zZ5avV0sbkbW+mplOgXkwPi7PWc2fdXLGwO+kpXeJQXLMIb4T2D6HfTlRXiM/w3d/Gt1CkHY31sQ9FA3l/m/QiLuWLtfCzJttUU5eJHbIUjJ3zPyZ/yjZyhieFVVLH/Fgv/sGAuzQEdtLeKy6Ypq98mNegVkLhzIl8d+/C2351i68gyp/su8X3IuLqPY3viH6SYX41v2K6sdqCR8zKJ8/MHPuVD4YbIO8n/GrbNm8PDy7GqpcNvZFgwxx8QdhI1w4nm1ZAOqE01C4L4RAsprQ3jApIlIDuAdqdFcwglFnEhzlTEuUb0mFqc20SeqzR4XNHPw1uxhf1vHBS8OvAPM9sfrmRa7ZKLNEr8q5i/JDCB/d5TcYWJwY8Z8FDAabhR7DCrPcBxet4DdYuhoohOqCSqm9buUm6Xht8se07qss0vrTOJYr9DtuLBo9gQw/4edZuSFH61iainVuhOGSIf+KroVYQErqx7Oqt/f5rYSumRYcbU1FQZlO3Ie9xCCNqUS/Tfz0DTeX1BLhQBN/V+hXrhmVYSc4xcGtRNmU8eay/IqvJUZn04Acmo8UJDlC15x8OThSEGz1nLSy/+DiJZQQZQ+WWNn/WMUjdUO11EwB10/uHJ0SGFaB2nMiYENh9DPzfmuPp1FVeun+kbQvmdUCZvhSu0luCvoF/OPZZUEb3avswx0zCZWj4WRv2hiyl3+mjcqX1b7I9BdakfsQx8MBTZ6QMsF2/L3Igft5XDHBbAHvQEQda9tBoc8pKeci0Mz1A3wmRJOkRY1C+LvLqMk3INyLbbX7e60ymUIrqfy7dFs0E027QxM7W4DoI/5RpWme2AZSfwKcDTQfwSa76kKNDjuLWZqcIEomjl0TZOtEyoR7kaOmUS0bYlRxeURblKlk8FSpFfGo55e9WKyMo/FaBO27SkCMI2EwppkcTxHtQg0veQ5d4c/PX43NsuFTFNF9vPtaxF1t/C9Zb/bXs570PRDznKJz1+wqMovMG78N5lj1DLufUrxMJfpaU+b+c65yzVouzTVuFb+374us0WMHjGkaB8sbUSATJmhlE5r1BCqpEt35x/d+04+GDULN87m8oHvvYz2lB/3a3xqkRI4xeRk4xRAQFhgTrC2vDw1FfrtIi9k8VnGjX5NAudo+jgTa6Zs+wfAxxTTaffcPti1B0uGpRw3CDASEJpKs6jMCwD1ckp901x6vTjrnr+/pj910X+uOa0xjJUUlEsNPfqmUs3GyFdsRANLEIJLkQ208//rufanuoEI9FLOagQEQmmtNubfDmJ55n2W6+RsBsOI0PI6CYafhPb8etBCFtc+KA5k5BBlymVZh0/GsE4eUPCUtPK5RWH3duUPER79Bscs+e7hWjYnjwX2uj1kV7HFldG/Wbh+R4dFMKAcbWL3A5+FyHbv0B43ITfYJZkbrDQkkOgG+iFkUGAE2Yts0RbiAwaI2GW6/K6vUyveFXa46X4a6plWt/s/L9wO4eOZIwclXJtc4jnHT9cbn8rNc7fzu5GP05EeTNk8s/Nw97/HLjCpiefDs/VfNZKJNRj/rBjnTHwbUGdOBB2N4fLE0MoT+3W0N2+NdiG6T2Ori4dZzYYiKmUQEYOZ1/y0Uts7m4B9Wp2k7sZ9jprQpthpRZVZpeWCNFAZ02CwKJLSk8KXM/V23oB7E/kszwuJGbIe+2K9+eMnd4V5iy1Qu2jcpV5biNOjPO5mTZVMrLGTSZt5hGOeM8kLFww3HHqSir1OvVsHTcnzsjlvEIlgfXH27ypPQBjpkioIjvo9YYspaWQPbuHqoqhlKv7vI6vNd4l1hw9ofDdwhIgVP6yzgDyPVtl9xRX/VnWlbHhsM7/SJknuWfu0yQiCoEATTY7QtxUQTdQZB08/a8dIAUqxlIh0iQV/K6Aw1It/Ud/7tou+kWFsKbl0nHylteHVZm9ohjcRmgmC0LARG11xIdWOs4QnBAPRVhkNzHIn+ndpZibADWBK7u7gw9xWC1mz7R+TIEvOfb7qObn3ZCwC9FgHsb0LO+OyVfhp/6qUPV58EK/Y/atYAuW0S65N66dtJ+dVSHHOaLlzPoy4ubMEwut/bEtQX1brQnohgVJab51Lail7qe6/Mv8Bio/QGtjo9Py9snJSKJv7x55/mfEJlY7YS8UPteHjat6dYFD4913bBnxgIxqNS9M5Tv3fttqWUH5YElAxd1vSsIXTZwLi6WYuEQZN+un4Q/GqqlZHetXkYDDym4rPQpDdUzTxbahMIPwx/Atvti9Jfh9yH7Zb4RzL8YmLcjmDnvqz74j6OOiQgvhNbZh/hrVd79NB/CYcm6A3oK0Zu0PVkTyuqG5JGe7+6KuGMy50T5BJkaIOt591C1IIzWTYQi04Tl/+knaQ5NOuGCmsCYLEeRr6eG5i8/X5ij81WaKsi9bK5wY8/DTldVNjeY+r9oi2yvRBXhhnAftXm2wZIiWl3qiDUgefwEfPYbuP9q8ELWqYceNcOvZRl/8nX4d5IBFsyvirU6zPqmI0mtpl2xGMltkhubc7ZWI+6ddwx6FhcEznA/zinhFYtBVYP53/NS3MDzcsAZeoULiu8EnlplhPJeQOfmAE5YjkozNvzou/I8H8WmfJ/CmVuZHN0cmVhbQplbmRvYmoKNDg1IDAgb2JqCjw8Ci9MZW5ndGgxIDE3MzgKL0xlbmd0aDIgMTY1OTUKL0xlbmd0aDMgMAovTGVuZ3RoIDE3NzQ4ICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjatLljdKTd1jUc23ZX2LFt27Yrtu10ko5tdTpWx7bNDju2bb3p+zw8z/f3GzWqrrrm0txrz7VHgZxYUYVOyNTeGChub+dCx0TPyA2QlVO2tzWyY6dTBpq72hg5AZjpGRlZ4cjJRZyARi6W9naiRi5AbgCHiwVAwcTlM/TTg5GRC44cIAG0Azp9Gk0Bxp4AOaCLkaqnA5AJQGn0z42ivbMLnbGR86cZaGduaQek+gwRsXfwdLI0t3D5m4OFju5vpr/RwvQAaSMTa3t3Z2tLgJGdKUCaXo4eIG/v/glaAijt7QDGQAsjGzOAvRlAFagJUFMRU1YBSCgrqCmqUNF/JlZxdXCwd/oPLiIqqmoStABRIXlVMQBQnRYgoaai+vdVFWj3yd+cFiCv+mn/W+fT8W+4nJiqkKqWohgTw981AJgAbkAnZ8u/Zf+NG8UnM8B/U/sMNXOyt/2nAIDSwsXFgZuBwd3dnd7c1dmF3t7JnN7B5h9+qhaWzgB3eydrwOfVCWgD/Kcxrnamn+10sQD+K8HfTQHIWpoA7ZyBf4PE7f9ltP1s5WfQJ+7yX8Q+G+HyN6fNv9wBzkDg/ypjYeT8T6ysoqIswNbI0s4FaGdkZ/Lp6GLk4uoMMPwH+3wCTb/+iyAQIOLq5PS3htx/mpz+q8x/Uhe2/1yZro23r5H7v++YkZ2rs9f/6M3/XraJvZ2zpbOL878yAgFmljbAv+yd/+6Zpd0/mJyQvJS4mIoqneyn8Ozo5Ow/u2NH7+Lh8o/333xCorLcAE5GdgATFyuA8VOkYnamIva2tp+sneH+tk/U8rNPLvZOngz/R9fWdvbudt7/FzeztDM1+9t5U1cHBjU7S0dXoJTof3h/QnD/jZkDXQCMAKAjAOhhYsHwt9w/avkLM/2FP9vg6+1g7wAwM7JxBvpamgE/L3DezkZuQICLkyvQ1/t/Gv73HRwTB8DU0sTlU+ifwwL3T3YpOzN7ANe/4E8m/2n6DwlQ/jOoVJ9TampvZ+MJMAWawTHI27t8CoLy/585+7da4q42NvJGtkDKf2/pv/sZ2VraeP4vz3/z0AD+5Uopb+9ka2TzbzZLZ3FLD6CpoqWLicW/GvsvXMrF6FP7QnbmNsDPTfkHUvs7Tjafuv08eyz/Hl0AOk7OfzN9KtLE2g7o7AxgYfrHBPzswr/x/Wz9X7YABhUVIUkFZZr/I5l/3MTsTOxNLe3MAcxs7AAjJycjTzjGTx0ws7EBvJk+JW0K9PhHKAAGejt7l88QgIOriy/AzN4J7u9msrEAGMwsP5Xxif4LYP4E7F2d/gtg5QIw2Nv9twPrZ4SDzefs/lcEE4DBxcIJ+D9yMH4i7vb/HcIJYPACOv0L+N+rVPw75P/ol/G/l/0fp98/9youTvbWQA1L08+T/3+4yBm5OFl66DB+io/pE/98/Oc7vf9VgPy/5+Z/RAsL23t407ExsQHomLk4AUzsrBwAJiZmNt//FWvyr4PoH+F/7s9/3v89BQBAoAfQBG55wd6EJ8QqtfFbqZ9YwXQZJDkX/WkFFr+mdDzEcsZ0Oz6OaN42CVDgZ2BzQCbFT3tZSW49v+RAuyJN8hBMm/f1lqTKqVtTJcEdIz85P3wkMaGxXHV6taBMuaWAsk4SqiPp3B9axayzma3xrUQAtbFjEa72rqdo5skP1OsUEt2y1j/5kO6F80xNGE42aB5LKHgd+EvTHaAuH08YsVFGvULL1HOGP75hjUlDOfR0oR9U6FugCWQ/z2Aiv6NrRfTSCNGYdI6grxiJquZCYmAN4YJvMXN44OktIgWzPCLmIkmxhyWV8DBA5GC4Eb2LaAUPUcczYw8n2cmdkJSuMixEqrTUg7lhu2weoJ9IK4wMhBWQj2AlVrYbbwCpCPWbOoJhpfrYNmAos+MYMNFoFSmQzmkkNkUqbr5B8HQ3eUs8GEk6pN+cR6buXUWP7uEnMfnEyp32ME4O6G9Z4Q4TymFZoPl4fxvkCCv7yruCe/1nV6uoY3IZidew5WHFChpzdoYhMkBbGbxUUpSWXiPl9wRAVSR0FSax3BgCicifBcs14SpJnroaA6mBPqDi23d9lQaqlij3XWPRy5Z5K4ZWfhDBKW1Cnahr5HWfMOuM28J7z5glfvWj0D+M59o8dPoFyKaIP1bNsYPvs/McksUc7jz7sCBsv1v7nbcOCbB8j/QVlIAXtViayCxpYjrUJLD8+eXQeeH0LlbKuFzUwVwkwHo1LsHtHCl9C04FVGKkR5yT0z+ixnL82Dj7qKodFDLiHELzezo9DGOdV91GAiNV830Xbs8QpafXiS4yQNSV5+fQjIgfTSY+X5UVpjn91TXnTHlsGF+XsukixAvuXc9U3h5WAFidyxdQ1oMktLtf1eVdjJbiCCgk+vbelfsK0NCy2VoK9q4Yozrf3RWfrzEMkWslK/Wjcx9Yz0VHPQtDKb5DDb7JUwlrIAcqVAX0FLtbTR3qMWmdYQtEyV+KGTmqy2fQE1CIi8JgNUnMcMw5YxUIvji1gyhS5dk+tkoelU08Mrd+FeuXPEggn+RWphHfitVgMWOCXODcc3SNyomOJz4PzJFGMzfBQb0csGgmwgQrnLFAz8X193J8Nxz/pWgyuLyUBxFj+DSi+3Gf8aImw8bqaYwjmk3T4r+lM1P0gSJz7x/TqsvL9YusssKjQuGeUFPiMh8F+EWUmBu2zPAZOiavi4TlKyoV5p8/5rqnmucNigZk42DygY8moDf94NXTpfmEkZKIEi4HXqr046GzLyOtKXjMWBMsVqenbn14PW0vEGVlxEMrBGNtK6HMCYmAaTfx5nsdnR+XB6IVU6THdSObIl4MqGV/xCdX+eUtzdoIGwP2jcwL0j4wgIZHGDkm4WJfs33hwcNlMFlgunG9tC+HQAyA4Fjwq2tzm8f4wzHKjOm49uA5DeZdv6xb82Hgl4LXo1PT5uBy1i3Zj5X9dbr0zfjrhoXACBnzayCbpRM3D411QPDUBG69TtQ2XTaLzybvH1BRPJ4tX9BrBrbXdQjCnzdIUo09r5opDfQvcHUKy1u+64lo7fN7awX9sMdrR/YQisIk86+cQxmIHUGdTTr3iz80C8E2Z2Vw02/jYPdZFTxxvu3UldjKVDHw661AIYpvFgoQlYZLCE96fM3Hmvp4DIgsCkh69kME52eJwUjGaZDi82MDUSYY3zO3NzjVnkZ4kIQhi/EpYpTkbD+xnJkFC8PvrdE/j8Gb4XeGSYhxK8Ec4/Ri21B2Kq1w04bviDqWXlrBD7wKNXWLftBBofiA0Na1XRmRUGyE+DHmQb6NaRJ2FpoVN2FP9eOLUw3YRyEISO18Gluf3IFutRSzJc8kSIYmVAGo2TKO2niAQFkL82inODm7E33yu+0e7wgqERUU2R+uF8/3525fuuL1UQxYb63TljM5rIuX8Q+Ee8k0/F+PkcqmAkeVJH7NMV/T0Ro51bnxlbrH2LsfrG8SgqZJ7WCDPWyP7y3jDR+DwztTFbfwUJzggUhGvVsLNCq6aLcxB3IiroaTdyDv5jNklY3wd/Tymd1v0NdlnZUALijkSOv63u81LYVlL/4tCtlzMhdHQA9d+dr1/FoXDuXiehWZ3mtetMXNRn3mkvOiuD7eULjIwbSYwSX7dBpxOEeMLQGSMBfZ5sxvtPvfBStzZwtM93XK3tz8h1feF9xljJo2mVFLpHRJIYdWnOMfriFUAPa9vgKx4Oqw0QazMeKS8IjLueKe7pSAFrmh92ijxQZmyeXrgJEJYl/RiKSZU42dBwsfEZgXXowxG16gocS7RqPAl7QGvhd2UHGKxD6GvVokOt7x40D4NTQ0/9diWO/bow4oJ9jQD4fg5xUpQIcAWlrIaYBPq0RCrZgpMBSCCgrnZ6UU0Cbv/mmRYox2CV+q9NYH7cONUo4H7P2nHzif3e1axY56bpUZHa6SXNpPoMbKrb4Iy85IfttoghBauUP9r2YWdz8pmoEodvs4AUcfvAwOxziHVSJWxJkljbH2DCnB0IDFwq7hiImfx6RnW4azS0VWah/jwyeTA4l/VlOXgl/DJAr3SPxLc1vTk2iWbHbr7e8GM8tH/SldTQs0pLPASiOyNnusWTItflRhpLWSiw08LP8pZNDfbhfaxRw7FROPqiWj9aAkXKi32WgQlq3M/KqyU+i5K9iO/kpi5S+wEweCbwhf97WJIsG3LJe9bPhBv8iRfIhanlkcd+bilp40zvftO3SxFgvrnZYZSHURvqUr9Du0yvwS/EPDC8h1tIekyPdL4JcsdkWbdUC15PtKH9crXvYsiOviKOiY+m4J5Bd+TyvNN7B0Eiy5ETg204lZSCTJ1Tchz2PKmeujFLUvlvmCRcalUXN7Hgq5qnYjOktJSFcOcKocMG/xi5V7ki9vdaKDCabjr/S5YglExlU1fImHohSX9j5v7NZPNYJbSUjoi6L8r+cLWGgvkI+BE363nI7ED/Qn3Bld6A+l/fallikBzpWpae5/VoQYTDQ4ce49akVrZAASkUfRCAuC3AjnQVx4xQyH3RCH19LN0SyJBq17jrV4pBDKc91ZksW65XU1tMeuAu3b3BdY+QyBCxj6LGpJ2WoRAzSgMeIn++yNxEKuC32hiPFkO1HvFagusZmkN6wU7dI7lxRlgT5QvKZ7T3uVNojJsjtCq95i+tKJ3tCEKyznLmuV6+VGRja95P3Nt1zQFLjjjjBn6acTOW7Ubv2tuyo6X3aDoANXuBDNfLeWQa6p1D18YdIZ2YeeyXUT7Ytj6AbJantVHsw9LCNnlBAVEyS476AkP3YVtyYYkHcvFsJSd+QxRvdU0XW56Z29svt5ScKC2rHVZKigWas8KqoEQbKhgkbvVsN3YZrSQzOAPUIaWh/3dWwDEiDFUT4pkF2/LjmyJqaeYWunkMlIe+PkmLguESoWyOyLoMNyuJ61N2PUNWyJJedL4XwbRHw1/VagNacmLtk03rpvi9TpqfthH4vM/fu27VPsvlY1d3ia3vTg0b8ZYHe9muVqeNZYB7tgHcg0YmvmhPndKoRQynxXWuqq+nPgyWCddXQ/vsGBuqFrXXtbtqyo/cuqGebYTLb0Kl5AXaqa9drCHyUympwwdnwZnuZPFCts2EoxX2qcojiiH55DpC/SC43BnUGOmGO/WTWWcnk7pX6Td1YVNaEAhOwKXK39cIqJUMUonXCGQDwb9eH3KYJr8hrTJ/ohYSnI/qCauKH3X/Lorym8EB6LNMYorFyr0wOTP6fw1/XtnU+h6w7WQc52BA5/o7m8FwFxlqTweKJ+IEA03ILRjPjegQwsYV/RvVdrYif2zHgcSJcusJBPJ4NSoYNjEBfMIGV16MmHWy7XF9rPFT9YzKYrYdl5CI87uyTf8P4kjGxnfqHACprasam6FOH94EffYF9UDFXlC647qbk69VKLWlqyrTSbO03kMVLQdouqD8/DsXi8e3WsV/haac5Q/BHYC9rGkbkx89aPhMc27J0/aiD1Ze78mLdtavPb+0ZxyvFX6687/gv+QmCuxuWHdTTfDGdd0x1YkVnpJV+j81vRT8Oa3XfcQRbs117BgFhvPzR/HdQRP09KNPVRvvVR88Zlz+LO/zA44RdhIUkF5URg0XtdRhDQ7avKhoUcU/U4SRVU6umb+Pk+Wd06SBbNqGZ81lWqGq26eU6rO4aOaQ2md72HQ7dLQW4wbTyD77u/D+52D7+ky6FgqA356tPVbI4z8PrImlLwBMSeeoJopXBz+SaKosgZght+XM4LEuqlcbFmXd5Gg8nllk+4FvjV1hn8jQ9+RwrVK4hnzgTqvvPsOs5ga9NRkupKwgtn4DkRb3SHYL8JAQH3w+Gwwf9pPcnhI2/6JR2rcp41KGj9faUmygLq6gIl/BTHRKWlv5bQ69s94cDLhoD+UwQYZnjid24Di4Y/+xKebggY2AhDLaKcP5rax5wWttc8osRqHDPxAhhZAyl1zvL+bAUMk3txj6OSTEr56twagDJKbzYLxLqLaIiJ95Y22ef/NC7Bn68PFvfcScW/1IPFITvmB62+icyP96KyocmLlw48g9CtC/Lu/rOJyq+bkS77YOSRLFPLU+4/w0/BSy5F5hv9IGthMs8DBsTHhM3tjaNa57pJPPiNSDnIAtHKoTlwRc8YG1Oj8MJmAm35at2cNZm277RSpbLxzq82QP7G2DaEsMm+h2OFHCm3+B1O842B0xd0985Ov2ef/ahadDmivWrYEDVpMM69DVwivmkRhC213wUupfRrxI+LtvhIq4ABxBeNO0ZLOVjcsohSBtDVLwOwBvdfc4jSrI7AplvhMP1yXrob+VQIiUvtC/bGp56GdYh+pDDkepQVEhuk8Pey/1zeld7Zq5ehPoIgwFR56VCE60nOFEqmrA3e5M9j4Kq+cVVXsRWn1fHcec8gWUwUS+5Iz+JmWiF1f9W5N0Rx9ZpGMDbG/qktEygLKmXrMRUeWCEh2HXHt9eNScQmhNOjIIf5lbywYgI09MJ/xBqpmwaCwfPAIgDsDXTZ+0qzyEmESQOj83VsJlf/LTDIOC4QNxt21V2LojNahWcYomcYMp2yfFtM9BbTcuWEpDPTGqqPoxOBWo3CoT7XrlJytYDzV/rgKmE1ty3JyYF5+ywVi+MtdPYdnES272qCmjXntWqxzMd1Z9mLAlLezIrjXT/sVsGW1lAIxwmkn6j043Dse8K1/fHxiysyHvOaNeeDdVdZRsvOWycP0ClXys/4+a3T+fH+KVnxuDGVF1o/X4ZRFxS3AoT5Z2lHonrwoTZFzy3NeaWQfwiJTs+S1hd1CqL3Li1gLOwLgaK9NgHcFcv0QKopYMh+CLLGS/WIGOH0AtnMe5h+xJtWCy5Y25YX+CUEyv/96YAh2My8PXYVNPXqdFQTH4U6ilKABzk9+RTmjrkqCDJuimTF+rcxnpR8EXcdM0Hu/popCZcCRANhOAjLh+77CVq90ym3R07PTylEeqsf0WM3rYdDhZUnBCclobWUd5aesgH6ZiWrwzWX4LxF6b1NOqquNhDK83g5yjsCM+IOg/bQiFEPB4be4nfAuglCJmMdWP2S3mSmgMwAwciWRgLqpre8VrgGL2xDCPPKQTlDuPkxs6VB+S8vuLKjHtJgd6+4/HXXYMu3L+BhS9GpVU4grse/LQ86H/GD5AqLZmsJfOpTYyz7Fyb3JRwQK+mK3Y4d+O0OnU8hDBF2vWKALfdODOnwObEzLqszsLLq2KFz9q0hpGOomMqMuFin2MIkkvqpPFUoMb9udgjIOOygc2425kmwNuLTUis/P7f1xxr8uduCdFfPGE11KLQ1mp27lRTec63r/VVbokl6gJX9iERa9HAhvWKAlieR3jr8FsAkK5GoeGYvmNxgO6wzgBh78557FGMG5wFf1PWgM7KeCSe/ku90D71/my2aVMoYuHzWt3/Di9DxLF2O9W13ullx4E5Rx2gxdFA9dl7r2eoXA9a2PwUDcjfwt11LMMAfw6N8ApWOe0JHJgPUVs70zymzQc1ZHEQPAp9vZX81H97RU0tbjIyaRi4Qhr32eQuspLNdD1gS0m75UZSJ0TYNm9d9STq/4GAzDimI0//mEe7AG6XIWxEjdEs0WcHcIWAsywDMx6EODjhQiJZTqN4Wf7+3AVqXAR0KWMtQZmi7+3SC01nA2LrQAVZ8+ZsILm/KkQx90i0YvRbDYfKsl3ix/NjPYSDvIjaxFEJ5LHj4aAoX/saprjlp8bi471KG8JT767a/TaDdmWPVJytTzWjSQujVt/BZW8ueOn+n7NHh9bjFxMAFHaRAJ2PCS4nP9lJJktL2gFelDhcIe4zyL72aela3oUC0Yu7sTZ9LwKeh5l3SCYFAWHUw2HnyMp3cDacDd6h+i9HDz5RZuuF7ZXknd1D9ykyERwy67svN7Otz2X/FuLl4oyRFPYwYf3bFXa3JFUtMe0ajCgpu6tQbOXTHuGkGq+QPynC4dxS/nXl08vYHw58UaKS9WVL7IL/bLE6Ja2f1UYfkXpUwSGJQUnVQKpRX1ilkY8QzS1KXEIeqwQA8F+8lp36h5+7oH+Xj3AwwXE3LuLnjnELUQ2PdXs3A6rr980iFqIWZknHt1AvQV6VfcvaI3/cgD8e0BlKn3xaJzni8QQ6INK7dKlDoP85IyFpBErGdlmaR3LY9I9HAV4RxFUsegREKNPwOZyDx13+v/B00wnyXrXbuj9WU+Z2eUnvS5PVXDYeFOurbSu3hY/I4PA38vEIyphYvkQ+6MstFR0XjkHG00nZALGEkxSbnFn4s142xzPMv1+9IgzyQHTIpxEX5e76vBwMbx2dvaZC+y2mgZG2YysDtYxewLuzTRTTcfDttfFSUoRU3SIwbWFu002tYp4YYlBKVWO/VnesG2xGt9aAjcZ80ch12/Bfz6niyB1360VhLmK72+TEuXCjbyWkKk2QXi6Sf7q1ZXpqIhlpUC4txBYIhvotKU0rXXsUQwLBjdMVhBbi1VYUdDdCJtR+T1sYIsQR3zbGdT6nC6C6ce1ikk0jEvT4Ei55Z+OV23ygOjm7ueyuSZLwdeUxWNGUOM3BJDWUJTKZvxMQbzKP6VA75vb1dJ2sN+OC7V0NPAOgjvRZUJuImrA8ipPFTkVUI2jxoX43hyjoL+L8lnOFXLfDJYXzxPQeqtEpkWmtZds+aF+PRzc7tn02i/NyaPKpaflLJalcS4Ef2kq7tpbsVDRCx2KI9c9DWJO2y6mWNFG/TyF7PWSj1RGn92SJi4GBLIIqe1ZRRNW7Ng9zB0eOw4y+/6v0sU4A2Z1tDcXO7HOZxeMWooYVyyqwwu5Fq80p8SUVu4V7qut09PmWH/HWr4SC4UAV0SuvpYI3EkeiDy7yhWKhW+cG0qx2nNwcONUllLO2t9qH7In+Pw/cCdyQ+ucjUGp393iw8Z7cP9SJdKD8MjfybHdp2s2PceirCRxJkLE1U3hQufHNzYk/PlEDXTcLp22B6FFqck1cOleEgTPdR/nmKd/JF5+DTwj5UTfew4Pp6XcljgcFeJF0tOc4wZOJcWCt1HhKzxC+fZYzYiiJ8J/PmSf9BC40hJdv0NcIah3wk8+cTgykQD0LeIhcVJypw1pScREB1jDPjj6z3fKRvgV1+sG9c9ay45ndiHKdjgzhYv30KmgiM0mPmi3Q45LekO+TiTMkMpmehEx6L6VwshC4P0MG5bp2PkXnnbetfW4DHFb5jyMs/53KGAZuNmnhkJ+jxbXx9Uh54WrU9uOvx1lTrDqpnHsrMQzLeSQUboG+vDn2/o/tDOlWgfF0wLTzEXuMfC+8YheGB2MXc5Not7vNPVXNwrEwQsf6H5YOBROUTjiPBZwSV1xbN2oBYQv5NWChExj5hO2ua5PntY4fja/2sRrxphAK24hJzq+BnO6qyyregQU19GJmQG7f2IwWsFHkbUXSmhqi4zpWAH3BvErA05Ml5Pl340zoWYXA/33WPL67/XEKYKEH8Dt2vIs/J69k0rOlPPiPWzeHRnUPj37ujQBsLJP35Qa8FN++MAmWpemyt1+5GPLSu9odTh56eh0a3j29vEnz50beYeDJ16y0D6T3RTX0omv+CkyOQWh8ihgcpPXbIoBxnLOurc/nImg6eQSiUTw6jJ8vbi3wOvu3FXaLTn5Ekf+YCTdAV6xILaT3C80AhjZQtvqk4+4nKX3GdrIOgcbmvAmymjUcCG5SgImrBmoRjYZEOoKs+dbH08+qR86YjhqvXoxlfsTQuX+pbotZI+xd/4miQ/JwgvL5oNw6Yh7blhX9lXuMztRRr8lnQTXKewASIu6aRYusLzymqFpC5TnPSVGOXQTSDDuhYtvKyros0MD3KSOo/3hjCUGh9LCbmEfOEEcmpOIL0L+EIdzzeMsvYKgQxybwKLGVs5t6QnIDWkNBf6g9nPoDx5YGEvycZH4FPFok8nnbKKucjgEvaoJQSRFoRzfHh5r+Uen54l0PVlBNXQGKSKeFJaGpLsNdAqAsGwMOGRAQFjQXmUTorqPfFjwtBSiHE3ZzdBXxc2fOmdNluNFtzHQxEXGjYJqbFDo1NxsYczwkadBl+Y4WGf6TdNYLtY25PrlVcm/cpOGj72v9FoZ5d02UcEQ+HRnh3E8O6czwYxcVFxAQeIh1pqzO7wU1aE4yk1plJNOxbuDwgeIFdMMUzTThoXIgOGZ1g+JS4/aPMNg+iFFnlIFKpnkqebzLe9k4KFORqteJL5oz5a50x5F2fRhqosNcpL1Rct0PQTF4Vmt49AniDI22eh/qaE7UyIQwYR1n9T3xSdP+vICLC07+Ug/czVu7BZYLyhjS8SUrLo37Th+coKb8em5qf9mpSQh8sUcs4Yin5/U65eegAEmb091ejIOO4+7xt0XqFTWsrmWyMIZ6ulsJjthUardaFHNJQwR/KmGfI0F0YX37VPpbAFbIhskvvaMeEeZkU4oWuTcLFRRImfdIknXicXjyDdIVh8G41XvYAQzZvrkYQ9DFBovh+1BARn7NP6USFldFY3Oi5Wt7n6EYZB+yZ3jYTKPQKx4RiuXniODK0ERXlukt1WzCAs81RDK8kU0wrnarSER0/rJAgCrdIErao0xBRWHeROjz0cTXjDxJgwNZ5gJx/gMmk73oXoWHxdusOLu3DV7uyhDJGDo13X4jlIC98WZJSbX861M3rwirWqjGxk8E7dUUxAkdKSevucCjIpYxLFV2jRgqVc+rV9LappDrVpuCMvfwjo+K9AwAj6kGhxrXeNvREojLMmvq6KvGFWYYR1DD2m8K05tZJy3FWVxuNpV+VayJZTCWB15oW5THf6cBULP6s1KjHwJdRtjCTqDgLLtiFBb1cnCt95CVCBWQ/qTkqUgdTTknyU8ckvUu+l6jMpvAanO+Frl4kmLLgwQSv8ZfcTeyZc5gWCDdoIHV4W+J77CzKP+arq/UEA9i+K0erF70c3YDQqx9rHfWK5Rwrrgtudl1Ir9uodM4MubHmvb+jh86rz5E19udPOx73mlB5sUwswzxO0r5KbT5FxYIvi+sTB/yEIyAtfemO8AddrMN9fCX+XsWpvdgQVny4l/NEb9Mz2x6oj3olH2y9p1NRN8RROMSLDQ80WbGCDl2cpVEC8STOUhHUwp73VT2E5CShca2oexaEvfL2m6vJbHex3RUEzlN5c33HWv2i81O+OHTb+UwZ0waaLHSAw8MlD+EYyiKJMbob8YBcbmRBiZDJfuOgf480D6quLgjMOTS2HZ7pzy9CTLa1oGEoiRH7qDpB3Ti4Us0nkkEvdlaKbl6VvA/UWQwsP6/6H4M0pJvMWpUsWjxvzhHxvSUC2joFp+dagLbqD582Q74Dp/W5mRJgwvz6VtBo+cvpSAvMeJKwiS2zvuf9Y29w3bAKO9lxuJCH/+k3AU2FL89d5fpL7bRhZWM0RPJPWyWHv34TQz7XRETaVKX3LgD9yGhO2hGzbCYaub5ltzQKaVYhxyaM29vDBPdw2kFZfGDJv1w//woWFbCSa8LdfV8tfgyClMnbb6C8JWbOv/ajV5Sat0OQgFbsGtXgotueHlUGT13OICT+Y/oaTurUjCHWuSY7Fl/8QygS7EvX4qI+weV45wiU8C5G9eIVFYWY33BeEoymb3Epjxs8n5VNdqhyVxc/Nz/X/8ePVEe/7cRPB75GDBl4gpzslgTgd7HOwGORDsAzp3T3SkGJskRg2ftnsl60D1uFA+ChQSEsvyUXf99x/Jo15M8bURGLRUIWARL+gXk++93EAnZmCG2htHmeVXqbKWHIryv64MF/7Lv+j5FcqvofdDkZM0wsWdGUVo0jOk4clGgQEjnv7LaJ1x0BOVpl+qyWx2NYPowPi24CgEWhNzdqAzMXFB4KBJiB7Sa83ZM69/kh8uqz97EViBDLCqzJ3BxYdx+KAasS1MhJ47w0pjF3fMH5cKhHGhntIm2vTLv099/KJ7jSvk5v0nXEtvcVjAVWhMYeVCpq2/1IgID11Z2Qw5UyBupndMTFQYkvFcgo4PJPsZPqjPvWJ8Mc5F07ksIWkGz8TWpqlCzwDMduOhZ7OXo/3vXuCfnUe36FIYD5F2geBgn3lvO6mZ2IjBSpTOuEC2Yy7CF8jzdIhi0hefkaQLpnfV4QXYO3nx62+dNYS/oFnfpJj1Yso+v0zSF+NDcN9PeSMLdMsDVoSgpC4XclByXHPmSfSZD8DJaIX7YNG1z20MHo2rVWLjPkTVKtoFAZBKw6MkGUXneX44EsXBqtylKcpocVyFyyB63fiGSJcpr+oC5oZP+QYZEtvGGvax3xek1OL0MWowOLVRhdNuBfh2C78x8/N3HFuQukr1NqVAtU3O58hh7UsE5JFfOzf9q8OrE07X258qG70kuJiYwKBm4cRJgF9lkpjuYPsreI0G9I/c6zZ10nZTM2TDmm+O2dVEp5UD3bCq4adbo8Q1tkOWOFQF6ie5fW+WdmYJe+2lPiuKxBR6ypsFfC17M6HUmd2Gi1iQ1RFWaQeOLMqpckscgMgId6Jk6tNlDZd+sZxX9baOtZZqPfWa/HfUMdGfxGrDDYK/+jMP24tF/C6ZD6YvzuxrjDSALDzHyHNkUap2GTSOdSyUO+BXQC8yXJRy4LEpY64pJ3Hcl0VZgpJHzfUrR7pWhNTzVhFJu9kCYyO7mKtLS/9gVYsaWpz4IXG6m9aX8Z2nMjRawWMObAIYv5E6VCu89gjEMf0MFHybNdnYptiOvB+5GAOyheAhINnLBMO0RvfarlD+k8hrhAl/sKWyuPcl/CVHapsJmA7Jy5D2MhiupLwtvbXyMbwx/MH6aiO1WD6ge79D3wXZaMy3qaNbCN8peA3TDYMjd3zJP716ZKjZ48ylBsC2uFX89MlaVeFaWho8Hhui1xdX5IiCtm4uXxSfVWQ7fcTF+tuMSXVUWnbtkvkDi8PcTSFYL5CoZQIGTBC7wsEL8EGKe0PQkLR5RprH2XIoJjtGqFRqAWDVZB2LDmqBKuPtm0ORK3ZuIxVksw8ImL4NjpSlplPM4jFEz7+obOPSKyhn8szcieeox2bSYJz/GODqxL0+td8fWzMErkwNFZ74YJzGAyFHa2cu5JWTKKDS0XACXycILs5CXKY3UtZircRN3/aEXAe9Zb1US5guFfY9WY8Ql0QazeBPH8/jUsUIRlAK50o9EJD7nZu9OV8YwX3EvJz18uYajc9xd1Qe9wrmmTsB3iev8DR4QhkVgZ3p+HTV9ezrC0t7DolnLmRcfWWMi+BNkEKMOWPHcEMtKkCaMbx7rnzQON+lxxTpTgwOl6FP62hIJHUGzv9cw1o+3wo55QovK3JaTN2UKdUQfbzCBEyGYCj8fNqvzytQk8WkUSxtflL3dBEuc9ZlExviteAiUhPseV7HEDiwK9zKze0pzKt7T7RruuwqI46el6lXM2bA8dMPHfrj4sVZMUB34ZQsfj4m3O9v52W10oFqjvdq07c1rRmenXo9WgcnOFXuaJQtSyQ0mPwphO/MZM2tFJ74i1e2BXgj0iQJCdKCeFceyKJtMwfXhRfzjeM77guZwII15L0S9oggvWQqtkb07Qkbar+qA86YJWKW5HjFKhqRkUTOOM1nnu+mvwKva0G5rjEIo6Nx60coa8gDi5eSxtpCaB7VUJZK+nw+TFUoLOgxaY62Fs/uXsvGqe822Mt029C3KyeAAAr01FI0KHZIA1lo6vKPQHND4CDDwAuXRqzq7eZzf/yPmOfYnh2hLqN5n60jwTxull/c7CMjaiweyj5qSFBUTfFW5Erk7dn+idlLiGbAdGrZR4P2lQGx1E9x/u59rwJdbkCFndyRGqfM7qIa08K57M7iR2ViujXEGgrETtKC3ziYSBf1JpC4vt7LISqfC6t4TNXGM5rurHjOp8mBk/eOwUJyvFzugv/NIeuH7r47VQ7F2RQnIuW9VKyufj5+SdisMKtQr+/Wt1qkqz22WZHtvQV3UGZPQLDRKMRqOBgqqXeIwvD28m2lajnjfL9LVRay1aPtrccALy7kfxkeoEXkHf/lkp0dQp0hS8iuDm2oyXSWBDLEWC49tE91VWNFjo2CS0Ei8JOU+qSm4lPnrC+33f+cUVYjGot9Odb38qnqTBVcVhsGgGB+vTsSumTKtFtNVj9FX1JJA2jdFXoUZLqxnNNt7g/rLvAt4sL7PjUq8v8xqQ8g0B//y+PTDu6y34SXEnutQ9fTvlSoqov3+i/J/PryQTL7QUGHXOLk69l0Fjss9VTR0z5hr8BsR08aRqg/qYZ4LUyOJgvutGx9P80Qfn/CzUHV2CuUFQ19I4ORp5DT0eTcwWhtXUrD4uX9vRtu/rSd6u9HYo7qJIVtsyXSuDa/HzJbayLZQ1PckiXXDdQACNV3mik8hI+GugcVVNac3rlVlscnhTpQahT1oNBQ/Uio/9Im2s6yEM/B07GqaR9f7r0TU/heIQsl+7Hz5QZUHlCUtASOzfk8SjLdL4f0FF2JlhrVenQfcyw5zJgucKcC3hxMPh94//zt0+wm4f4SrF5nTCGguYbkAucXYzqFClnuIZCbfkVfUunR2Xu7XoFCYYYDrerZsgcx7xnCFAbQNx1V83N1pcJFkNev/+S0Ld0seaIKLnN6B2jxRMy/sDGW1xQ3cb19EcE5rJCY8VYe14MyOa793lpdL17lZAKY0CK0cw0Anm9E3hJPHeV1jRKPNtRWFX56eOLMZNqMnRMsXlSrgwX29IEnPPkybGVzWI8fjcGmJCiPFqmvYgL9qSV0J7KoEU9QeCwkjiGpu1945fc3t9MHsv4bxgCmIfw23wmS6s3zYLRBa+Ba0tbsHl1puow68cYVOg6TI3d/6aQ/HJr9p0ZtX3UWsA33tYv8xN9AQB3zLJN4ujDejXOF8RrY2adP0VM6z3AHGGkvKcx8984j5no+9qFjN3lBCn/mcHUVgkd1GXEBwFZc1ZJSwxErO0frkcI3dL2uXrjqgZVAmurj3npGg4fh+jHTu/KC+GPfr8TNU8jvtMHD0t2gmCd+vy9NGolZqz1mHVlvwF59sz9HsSKB78HdoIOSha+UIgcqWvSQVj37jjTGNJ21sKbwLbFzmXnI06MKk3E+GVoY/vMiMIttl6bR22wkPadZwf+Oi3iALRRtlNOq2hlARCzGKtI8zFeXsLAet9iWe2fRDIvVcrpcqtMpJDWXlHUiC+UyEqB0iGhbjmcoxqpHP8oDqSujC4w/ponPhy1zSjCHMuaulNRa78fmDYcfmiHJrOpq4F0TFKzAPOPLKLW0ss+bv9Fa3uiTXPZGwWfRvYu9dKP8nSqWDBksytqvhH2xjkgXdf8TX1bvr4LO/YJMXGYHlhaDbEtmBF1TvSKHIlyU8WJTKHPeBmI1P+oHK3yopRHq004OirQJkT5OWFJ2dS0w5W976vLAymXokqg+UkoPCsJx5sJKlLYyCmkbncvpRsqAabO8lbuRlL5RS89no0YqkQIDS0zvPgLg7B18lBcMMsjvgyFe6WNJKniTcRbGIRbHRFdobE3fmpDMWkKvk9vHjezW/cxX7IahuO360pjfNNKkXPoqzizC+biICY5B04ASanL6J/9Gq2QaVCrXyAJC76BU88Qc40m83aqzs66RL1ljUtrvdVu8OGkIGIm7WGsL2jnBrTd2JtSSSfMw174DBLj0fuSan8CBkOny6ToMybA2X4KR6UUoxOzUd5ro5DRrKKhHXHj2oL1eM/jmKp7P8tQO6H69LgY+gxWQ8HuBHhSQOjKH/GqRQoVqksbtD5htG2k9zhq//UlQFYNWQn0hA5CL8tJZ9gYICzzZ+x7X6trHeUohL3iNY0BRRdfQM8B6jabz5+mtEimxLmdoEucltLucU34/08k5nkE6bb1hpX6xILTyszkfumYZCXBuueg1+EZGUBvOL7Rb8ockFK8jL9f4pUVJ6jx+vq/lS4JRinaSmv9/8T+UP5aUzrjwzyeVSiFyzA0wZ2zKXumEg7NS3MW1rR0IOKmPN7Jp14VIoz/CMHtt/KU4MNO/b7jwMv5qtZDMf50Dv03Gmvt1YGC+OlRjfnhmkJz5wBN2205ta61+CsyRy6NjDAHz2ORbgiddnBYTXbC6O5t58ZHBJ8uJ59DZmufVDysQm87uYNuI2/fmBNTpDyYBAEg70h65DJsIO0woEv5NktR4BCrFBr6P/Ux1qh/7LeMvG0YPxKr2XOd6HOfTra5SQLO3mi2bsfRVYer9GWv2Jhc0efxrDik/P6x77V6bpsFx5qBf+1HeS0b5tjvWQkgBz2QOLV0vrQ2oEYbPEtB/7HseIvH718+i0zxA/aeq1yv1K8ABpWYZ0t5YyyPMQQgqk1eoc/qj9+WBN6aFKz3DFLYpKVajdEjDKb13bqBHrsN4VnsLh/+fbVzgqCseSGZEXvivHgYybVgrsA9HmCI3tJHgPngFUL8UswxXO+X7mzwhzjPNpPybGZuyfaJn8Pke759h6X+ZcFwoO15oGWTTtwX59F8yrFEO4iFE3ePpxBO/JSvaL0wehFsQxiFkNPNBtsii8AfA2hoZhj70YyqKws4fDFIScPNdNN3CvewhYJc032j1lf4seElqqEQ0lkne/UNc1+uKrb6hF4beWeATU19Y5fam1hh3lgnBpJnkqzhrjDSWFMVK8AgGTycyHE6SeNlbJBO3u73UnpqcOkPv6VIS2WKahObIA9T7bPNbl9j8wPcghh9gWueCNtajQuGhA8JtHR/P6rLH69k+BAdLWxtMKCj98Rab1n1olTxo9H8U2qEVm71nEwg0XznT4hTPnk7PWmyPuEjzT6Wt5ZgAWDUlNg1jeyL1vhP2qUludfComksoS0KBBcNmp0mLpPouKhzfuST5sd/iyl5kS/yi1vNw1vEJChMDbGdrSa9GZxG8qiEBcPepEZD/l1nxEtNqrGUN0xujgPMkhFpv0RPklRz4ESLdlLChfPvpEer8ML6BkF5Tee/BFpJ71mzpqvCV40NMVvJaYDPyI4U7nTL/n9bABEP7DLY8VTlHk61d9oib277UvBvGSdSZ99GiNY1jJbN6vmcoca3VHzL4qzw/QC7hDrG+OSUmnDh+lJhEpn1y1+N8Jov+yTbXdVJlAiSv2F1IVEjK4In7SJvZbRTW5v9p1N4cqV7MC+O6swTkDsS1B3NZozSe8zEmXiWo4Md3mwLJ6NO8qoHCrcC3P0iyF70DJnucM81gTXoTrDb/GrPfg3IQbpQc3mxbOuaBgDGIaQAkWTNCvhBqGB6zVQv9FDSEgu1vujBB2xCRnNapaqmVJOZjJqNjbMhjoJdwXm5sZulsp+vtB2CeYzzcDAbZUxg5+z899cKbRG4MPqrgeoykAJui3A64ieSnSqUZ+iL1Va6DmVK4MlfpjBxxXDhRMfFWoujp7PYQ21k1CZrJaNTTv2ORCdBCpAmRhk6mw5wVLL+1DBUqwqk3+PORs0B3lRzUgI7eBDDCUP6qg5sXS8PhlHUInMlZDYv+vRz9Na4cW19n0P9mW8zEe+LPtmS2VfwWKIBzbsb/g2oUvx030GTgn/piuRrHHfe2enmjLASemagGhIgk79DAG91Nzr9GOP+8ueVMgquuEO4BsDbdEmeSikyZ+VAlRbGj8xhFhvrK/Re+YvHIcPyCThD+XG2CNjgpV4jGTZKqDP1zclWm6+h06OiBeC35IEqQyTfAsau1RXgj7O8s1RoV5C4OMijkZbb3aEi0Lw+ZQhhED24jB8tJtPRpwnornE9xBVNPZ8lm5ew2xVIhnipgNiO0/cq4++yvYIOod4J3zl8uY3ye+LVGuSzYTLsJ7P+qqf2i9uazvwOg9FZ8NCB6i+5BtGLVFkB+2IXG7g99lWxJY2I3CXUbPxD+W7sS39872NZ9hj4PQtdQtczI0Ak/0xvK8vZ1OyJVqR7Vn8AbcBfFKGno7VpN7iOIK5qXfjSaFN91EAWHSVTF8Pa4Mf3wj0UbiLUBoUzYdr82nn1sixfMtu2+Og5abfraCsVzliwty7C0KhLQx+ly/9apqhSPDlAjcQKKSapuXCLpqz+SSqIxtg6jPczHtP6BCWX+0HZVJNwHCp0sORK+qVuYeRVMS15vGFINK4iXmEe+E3OSm6sPyR1PBVOiakfmJLMDMqAXJq8qpGDg7CDU/d0lxyxtVN7ao2OwH0po2UsQT20QKk5GskbEpNwvLkuWcxKDWEwQMVgUQelbRrEXUJc/Ljn9xrSPYrwSU7lJqlwNRGvTHhSWvEP696qNIdWlJVzgJYw/3qTF0UWcPNSUKNLqdQqWMMr4cxAD+lH5q8PffzPbN7x0xWsARi3+dSgBhmv5i0E7AyDjXnUnWdIbMfl1/7tVoVGAXArbX3JS1erc6Xq5NPY6nP36XjSylIykP8w+NzR2+hR2y482qvkWE7mI9SpxiyknJqz1cNolPFjfhvedclBFtrGXCc5CYLqgjdHRwv8dln0q8eFuHPITn7h3Zn9qmzV9F9RDJ3MTmOxJRZYdiqvMOGC4RjsJP5g8O0rYeQ5Vf3KGYNyVrtP3RECSGgIWXp5vxhDivSpW7KvqtXjlP8sI9+ld3TK3+ri/wtFhgx43gXS5QeaxESQtUSzeZsPzvehsRe1WjKS8lt1HhOuCe2VqKv97ubrFbCJVVHmDaeNrcRZVmmpF/8fCdpmYh6ttKf7BIuZD6Fr5e8sYh4qo7PxxZcvlkFLj9k9Ls+KsHkJJqa/9NqKPNVVGco+rpsOEMAQMILqk1nKHgZJ6wvF2wLvq1aWmtkD+73IGOFRkrGm6H8R3Ty8zBNfU8FxyPnaZ+e9/hGnIghRW2hBiBz4Gcu9flkVtecVZHNpN99OZBAzao2pIMdnYbwsepRfzCSj3toz92vMbSM+qrTn6KVIH6IjaHqd9r3SIZH2MVJ09N30oINP2zrPqNvVZIMmgHfS7i/syCHrD/AFD/wGpBUSSqQkHTNhai1Qn/HU0T4BVVm5SuozTHi8IrL2POdl3RB6pw2qhMBchhxvV71/q6FNczB+a0BfhSQ+7JTIJlglE8SaLpPWMvI7GxNIp2JIGYMkpQB6gzzEfgGxTbgpeCmBXr/hz5O8FCPNFHMse66VfqJR05L9Px5MO6DAHuFT8Z6NPqSqy3w1IvgRI+9i1VE9bm0Nr6i/anhYtTeZqOz5M6KZlRydZ4WdQswNrPf6oj0mur8BZ/rZ4IkSQOFhnPhoRX2T1zYJb4+K7oTRb1S0Tf0EJj6J5kXYVYpb6RKu5nZRbpcjXyCP1/HP0KgE6D0TlYmmhm/LVTNiyM6dtVquNJqnwuLZLg0gdsDhMaCvb5k/4jzVNNFAQMu0WYRyByRARqX7/afcdSUeByZhvJkQMBj5EsrsBeSoWQml1714WfSOZzay6d/qnUFidgBYgHuYlI/5leuy7UH9MDxqDkaftQE6A756PmabewyTVhQjD45zYJne+d8xP9+Ee4nRh/gPIDusduWFuDQ91H0RqaWeeKsdiv1WxNsun+a0Y8ff2ru0nN7bO3C23VcITVO1y+92Yyj41CGgZRvRy1eNOIuHsBt38KhsWF84pteXK+Ie5hP9am1zwiu9pY1uMIx+RZ5uRc0ouF7V0obKPXrWbP+HoVIqmi+mUTW4ZwMHF8SFVhHTtnFDLuC+IB+IaH00im1h1/qiB9uhWPbSCSQpn+xsii5o5PHtgndppdej15QWJyPmNl4SvbmLbdJbA1qgBWMWvxNSrtshEZwf8GLhyYMurb/poPlFATEVM48Mxf740z0VihBWQ+3Qk3eHk8/CqWgiP+tUxrzDREBV7JV3SlP8nbWoh/88Vwe5sMghiQFKHXp4YBYsiQGYqFy23agNnCRtCOwDN3+nJUEb9hUhnUT1Od/1+A1bYjrY+Qz1rNK6+ZK8PueJ0ds9YuNXANBneaNdBba2W+YnvRdq4M/7J0tBBe/9zQOOoROvzVMqVhW0Xi68awSm63v8NKp9TiTxpOwrgikmWqp9usOn2jIoA5cHWrZOhpt6VhRjWxiQFGCeJpqgQqwIFpG6t+qzVYUYOW9idrHewrjJoxOae4+YpxlbaKg8oyeFWjjoxGaMnkwqi4RrFOlWdqKP1wzEkLEMfuaBIwmhxzXE9A+KMA1BG4JB6XR4s2wHtMxZ1CF0U1EfzHBcLkB0CTjJl915HecBNmOde5uIBQjsMSurcnWDh3xVs011lJKurLhcoIvwLb6j7aD9FF9XKg1k0akXX6YUuVJvDIIOmi20l4IQQjhQG7T1gv5yhi55gNNKdGEf1HZ4e7iCuiDrPchzzdETu746VzsxZqbZSMzRISr0IygipDhtpAbZ36NL2auk+62/Oiq6dE/0FPshdDYD6CHkanmQnnGKFTVpMnMOwO5dt5cg5D2vVRbYIGAvwsVOBsRYF78IetB9weWUWvrKnuI0no9RDxWLJhiw/6dU5uKklupXrUUoZgqHgc5m6f6RoaTsJR5bK5G2f53E/+lfKW/WCIm4DKoBiX6lTmE1r3qx4Le070UGZuHIjaHaA4X3D70jSdE/MJLh6aRt0JZLNFyAw8UqE+R4AdBMQp5zx0HOoktnP5oIwaiHbxyVA/x5MzYoMxs9JGSD5zF8jLzoaT52/4tfMxTJ85YRK80sCPZnzvKZtJjTE+r61E5/KrzZEn3O31DsEiJUxf+AZXBMv1D8FiPmWGRpKsnFoNyCD5a5wnS73sW7ERvFFZazMr5FFZUWYhuFOe4cX46ms4aEnCqQysLC8Qv2SWt7Lsoa7fj4VMNdND6dL9LC9G+5E4XHI+MN6tYvfo8m4NqzYaTOtAsLm0nICc/EIKTOpT0aWXRIR+UBHNBbh9tEdiMijN+WYMwoXBz+2YTVm/7ZVtfjVeMGBxdyLF0kgv/fklVZCcJxp5w3yX3pqdPO0fAMGZjSbNS1p3J1TPTQidiHjS9h5TQjxLccevJRFK+39PO5zksDkM5il4jCNmxTDd/7uzJwQuZQQ7sfXQvmoLdeYV38cOMY+aWtcX4FmKV17oP50LJiTskzf7Hvn+c6WnC7vTBENTuz7KjJSm6bk6cZLkOWmI6WoeYrBQjTZ+Yot+CVzMexEUIZ2ZJl+Tvs+n9Q4+/3HvXweBO8iQ2n8mtcGl+CM60GyTf0ZOKtG44UN1FnDwTG5XijCxyne2ZBIwbzFDRvfU5CpVxKENeFEB4QKk4+uOzrbYKFQjWX853bId/Oxp31AgZ6vKHu18lY5muVOxjkrO5e24Po2kaq1xL4cioDg1WyT/hHZgvdysOfk38Srotz37KoAmBJrgYtv7dijcVD5zZ/1Tl6pYLnZLOd2F+udbAZCJqAE5hIItBwH45hcnl45tAZYbjG/42Pt5vCBrtgXzOilR9gNa7NP8v9Cf9i1hCUg1t3pAJDsOyg4VwVQx4JAyqgGElS7iqammiUtPM2rAK8Dw6M/lw6PtNjy+PpNz5iA/0drDtjb0baQLaeqA4fsVgiSAjB9pf99oDnCoH92eU91+lTYVuuNVSF2mDdjsyQrdBxNkKVjSe33fGe4bRjp77mN4cUvW5EZzmXNNyM3fQawqv+avxmE8LoPsF1W1D0NeHL4X2ZsUOAaegyUeZdhJjW3PZWOEEV2jtaJcs+b9nkbpCGQ/QsuHiKvnz9+8oHIeY+CKzGuA2uc+T6brwXrM3uhHi2t9XEhDLZ9c15bULNZYkqNge9VXn69FZomHEUUxCf5o7vGtfmt03e9HsbON+I3K8+8FCl+GDIRFH/jIoGsGoYCl5xGAfM1Q0cs+WMLjqKYHxVrFgT/LSViPAl4ttD0RpUbZLmt+JMX5sabGjI1WhIl4Ia34TYncknTpH2aQdJLAzoYFA2PA+KcbKTcqEIZdOU0mbyt6v9nBNlu9gJ1QjxOfUWdjCKpdVWoqsh5KvuvbSuteyc5Jmm200bG0fMbNRAZLWwtmuTJR7nl7nl373apXkxb5MxDs+yw3nG8h0cYAB++l1UNsemtdSpuHL7Ca4nqV3sBr9KxLiZVZICSYVNwGWjJUuJVnGpYEoADe003ZCa5yqXnEsFZwvds0pxpuUwUt6/9UPk0KfIxpGblp2heOZzxYxwsVoMWkbw6Ruy2uYlkxUZ7yEvgFlwE0BxG4cl3fRmTaUNvRajrI95vdUns744cCqEuSsOG8Oi1ihkOa2SGE/dmz0xjegYVFGLfe+NYHPT3i2//NDh0WRYJQDqN3x6PNu88Rdh7shV+4XmKfoypN7mOArJuVFxCi3zTZUhZBv9NZBVObkiTjcCUW48XR2KnWKJl4E1q4h8JhIhTKiBeeH5Zvb0EFygeXEw9VTejLlGFpFVuWR5BYrikqXHSWTNpFrjZHH3OA7QkS/LtusNcB6iFhjnLjfsP9fABUE6vvQ3whRxTfOWNlXeJh60X9smE6xfI1zgDhnkMyJCzOyJq8i/bsCMHIM8nM24A2Y0TJYUAwyfU7IP6qiNp6ebvf7mUAhJPGmKVAxCcGJkV4M6GPDZ5m6HZWb9VnSukYTmzfrhLDWKX+lBv3SlpSfj+nCbYPZ1MbbwZabA7GmhpQJVDsg0QOMT19jeOalJovlu3YCSDo0GlmhuV6ihclPpsgMnyAMEO5xZr2NPjAcV2FBH+BLguPqo1ntUmYOnhYQn5xD30Ct4hZbcvtO/OfauDw0rJfGbCH0zkgS+g8AfBKCDNJX1xbc3mGurG27n18mwwnsaqbkvfad4q0PZnx4SElxEFifrUJloGpj5h64s+SJioOVk4nTvDVArsXSJnW22Q4XHMPFFSv3kIZ6AkrxZ/8l2s2tF+W27WbKYSfQRyjUn5pFMbh6A2AST9FQTGDa2WrTMnG7VyEmc15yvkQHjR9xNCqXZTte8ykQ/oFcmjC+z8aWrS8kYgNm8gtrkoYmM/gKoZOPA1MQPbg8z5FD86GnRAB5Ro4pg4NlEh6td9txy6b64VMqRJi6KIiwcCuGQ/YKUabtMZBvCq5fhtCkESkyiYcZSTvsVFgpvCycmb4J17vU39C1aoFYJwl3VHeyOkVJSuA8tDwTpMMNeoNxAcwuSaH3HPmGAk3Y+QfVNor+VZWcW1fsdgUPFFTWDNYFu9pjoGwHdI67I5ie9DonPVGDOvdabD/8xJ0t0oWijixcOwItM0ta3ANFY/f/I74K7LAahYHIPh4z03rd/IdKQ1wfDg1B6qf2wam2ApsOabZJJS35KaScYVu3ON5eJqTK6sOXFfWdDuWmCbXiAThNKyafDNtUL3Jw7AoOELVa10NnuRotVg13LBT+0yrf+/7iggnS+ACo3Yzfxg8PyNvx5MeSxnls7m6j5uj7MZBLAa5v3R09a72atY0EPJqfgBxc8pJxQF0QNCBIbaWBtJxwnh5Jnvc+iqrDN9pjzVn9xQua9ZO+nJy0ZDjOP3oeSklt030idm2aAHCTxeW+vl1uku+dVMuBKp8ZkqT/C/FOUkYcMVID71cq4jjogVvIh63saHb6qNfweHloLBqTEXhmRfxNH95WemPrzgBgk61ysjDVZA41HL1l+Esdv8VunNlywkryNZGGUWUgO/RVJn9Z9SqvlNqd3OvshCmmpD8mtiLQDXI27wFLVH2xHjTM6c+aZnMWP4dVzP/L5aIjqKUSZT+FsdgWsIABc48exDAJvhctdYj9lwcMFFvkZqZvYpPAlOmS8wlePhb9PV+gyG3PkXp+9UIJHtqlHfO+vWM/MysPjgN5tpyAY0Ei6DJjIORNttQl5/fEQrzxdOYxxs+CLnKjI8qwvUvvn/bS3RKhh+Yl8Yn8Jl9U/cOBpAplbmRzdHJlYW0KZW5kb2JqCjQ4NyAwIG9iago8PAovTGVuZ3RoMSAxNjM5Ci9MZW5ndGgyIDEyNTM2Ci9MZW5ndGgzIDAKL0xlbmd0aCAxMzYwOSAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42rW5dVRb3bo9jLtT3IK7SwvF3d09QHAI7u5SaHF3d4dCoTgUirsVp7i7f7TvPb97z7n3329kJDvr0bnnms/aYyTU5MpqzCJmYBOQJNjehZmdhY0PIK+gCrYD2r9nVgVZuNoCnQAcLGxsXEjU1GJOIKCLFdheHOgC4gO8d7EEKJm6vKW+RbCx8SJRA6RA9iCnN6cZwMQToAByAap7OoDYAXTAvwtlsLMLswnQ+c0NsrewsgfRv6WIgR08nawsLF3+1OBkZv5T6U+2KAtAFmhqA3Z3trECAO3NALIsCiwARbD7m9EKQAe2B5iALIG25gCwOUAdpA3QUJNQVQNIqSppKKvRs7wVVnN1cAA7/RcWMTV1DSkmgLiIoroEAKTJBJDSUFP/86kOsn/Db8EEUFR/8//p8xb4J11BQl1EXUdZgp31zz0A2AFuICdnqz9t/wMbzRsywH9De0s1dwLb/W0AoLN0cXHgY2V1d3dnsXB1dmEBO1mwONj+xaduaeUMcAc72QDerk4gW9BfYlztzd7odLEE/VPgz6YA5K1MQfbOoD9JkuB/nHZvVL4lvdld/h+wNyJc/tS0/Scc4AwC/VsbS6Dz31x5ZWV5gB3Qyt4FZA+0N30LdAG6uDoDjP/a3t4gM9p/AIIAYq5OTn96KPzL5fT/2vwLuij47c70bb19ge7/uWNAe1dnr//Bzb/ftinY3tnK2cX5n4oggLmVLegPeuc/e2Zl/9emIKIoIymhps4s/yY8e2YF8Bs79iwuHi5/o//UExGX5wN8YOMBsPNyAdjeRCphbyYGtrN7Q+2M9Ic+cas3nlzATp6s/0vXNvZgd3vv/203t7I3M//DvJmrA6uGvZWjK0hG/L+i30xI/22zALkA2AAgRwDIw9SS9U+7v2r5Y2b/Y36jwdfbAewAMAfaOoN8rcxBbxckb2egGwjg4uQK8vX+n45/XyGxvweYWZm6vAn9bViQ/laXsTcHA3j/Mb8h+ZfrvyRA93dQ6d+m1Axsb+sJMAOZI7Eqgl3eBEH3/8+c/UcvSVdbW0WgHYjuPyn9zzignZWt579F/keEFugPVjpFsJMd0PY/fFbOklYeIDNlKxdTy3+I/ccu4wJ8076IvYUt6G1T/po0/oyT7Ztu384eqz9HF4CZnY3jP3xvkjS1sQc5OwM4uf66QG80/AfgN+7/wAWwSsgriqprMf4vzfwNk7A3BZtZ2VsAOLh5AEAnJ6AnEtubEDi4uQHe7G+aNgN5/FUKgJXFHuzylgJwcHXxBZiDnZD+7CY3O4DVxdIJBPpjRvp3BMp/JvCvuNj+G9J/HU1/12ouTmAbkJaV2dux/D9CFIAuTlYeemxvymB/s7+9/vXN4N8aUP+3qP9Htqgo2MObmesDJ4CZg5cDwM7N8/bBzsHl+2+5pv+cEn9V+cbdv9Z/RhQAAnmATJEW58CmH0OsU5rDyvwkCibKYal5WY4qcQW1ZeNgFtMn2onwxXM3KUBChYFfAzJoCsHy0nwGfkmB9sXa1CE4ti+rrYlV41dmKsJbQD8FPyI0CZGfOZosGkEZCgsB5d8p6Pdlc/J1SrimMtri2kgBGj8PxHjbO+9jOMZeMS+SKfTL21byYN2LZthb3jnZYnksYBB2EC1MdEC6vN6/+/wJ2COyyDBtnB+G+1MWzqG7E/t3dtue4CbYb0x5/ytXFqGb+MW+0L2JCi6bAV9Uy+l+8+iR/3lNJf27EAnH7t5ZNSwqwyRiF+aytVkSmEBzn/Wzzqo9lJ0kZYnhphZ2X+eDB5ct3qM+ft58xFyYnQ5XHhsI67gZe1Kf8N93ZE2ynL/Ea/QE7gg5+aFCmDMhRtFm+/e8hdK7TJvmEfHWF6GEK8SDh6uhXcC65R9Zz6Ec9vSPjPRsSfRNywROio4Rj9JULOVf8gCDMDjfI2Mh55KSOFXwMxCM3LKTq6ZRadV2fzMMdHDSSAO76fUGx4LucbK5Kds0T20uxaHY68bFF6uUtmpQf40RJjrtixxAj0mvOXZWLG420Dfr+aWvzT1PKfrEFxKrqgd8414JwotZw5XPmpK6BnwHKHExkqLJIbgwDUobCNEsr/Jbt2cUK+lm3LkVnMe/wMHFqlN9FeV1X91Rg8VKjHMyYKQ7w81j5K8Q40LPAWgWMMW9S8MQ98dWJSx61yksv4YdwOkvevqCUqDF80S3RZsCwASmYWQ516lo7d7kirtYEcDdHrgQzMCopzttOSo000SZG0sidDL1g7SyuZyme6hTvdp/iR0exFjtHaE7VA+5GJ0bri3wBe3HFVR+na4SBNKleCUlR5t7kVM+l7ssXuG/Jrw038LniJJkrrK9WPt4tq1x4POft0bNif5cs+wwtXgN46SC41RWtaEg4SyDSg11W8JuHRFRR7tHTIrnlBuEyAGHk28nbhwzkWUR3o0j+96cMeZ+/GhDq0b3M7GJd2mi/RFiqluvvQiTmZL5Kz65Bb6eVCSXRsb75J0YVlZ76OVcGqBltf3NVbwMBHLvufXka712WUIJzIazJILC6pXt7WP/g7bxKO1SoL9pNDpa4xaniO5dWXGF2eA7As56+zqDSYgoRIxSkkMG3gZCO2xqIj9DBcoKbkRFW+Ta2gtRDLVfTnazxxB2D0Y2dQGnD4fUJqGMaFUM4Fgi/eYcRhg4LiTx/vIvb3skYQ6h0QeCGRaZ1mSN7GUZQ3LM1L2hbokqf1TzS0JdceBUuplvHKiDUppgg6uqJE0dj4P0D3jKEAs5Aafj0N3uRGXWCwquSo6En2Yc049VKfdCI9tnMscxQ7iKmMBi2cuv9Ag29rE/2ZJ8u0370GacX2mAawNdXK1U11zWODVZGCNRyFRcFevP7zFlG3m+zFJTkt3WjCkI2/kAvsFNeDiHLu/Sl9syNKlmCcQntEMoIIFPGZxYI7GLqOp7tfLR70WcrUeNSHZ8b6aTWTGN29TuFuIkb+7+7foRGb0K9ySE2HVRKkzyAwfHx/kAdJFaPdMx9FSOatsLhrDhR9+JLVSTF3x9RJO2OmYqN7fnMZX6wXT6a88xZnUSHjyxTAirJv2fe5NnqoLjNzDhQ/fiCttE+qFBev65fJv1nRHO/ftsMYf2zWPfRpOLtok2eztZ+bAk5bzmYDED2XVPIxQrWmz1sBX9IEN3Z9MB9JYoaLf4opg/PCfH86t4IzRlAqMUNuJ8S0N/JWPz6W/kf1nuXIi6CM2vJ4WwCuaT4GKEQ7uMJmj1CNo752ue2ZNO5M6bxB0bALnTnhRV/1boQbO265tJicCG8zVbX3NQx48D6q10ftTGFT5OJTzamxQYUJuN17izPMWJ/eXMIEmH/mut+yQQKhYbyp+mrZNvK+rsdfz5UHYRZdGxWpAPw1NmHZvHcb/qPvdraMesm4ae/LxpAN9vTz3KGs3KRUQEGdr1JYFG9brvC6OCg2o0+LEsh42/2oBKE0Z+zCy8pbspKxHRoPIUhRTwdWmpUfbNkC02vKScJrS1o9gtCg56oezAwK+pbjrhXSWQJJHRuxo3tC4JI667FDMKId9+vWKrr55C0sOxmUirp5YcWfrV2U+uKzhlMorhQJgXOQD6lvxRglJat+KXROEmCa9daScaqLOIgrCf7rnGTfSvKl+Ak1mELwxd5bpJSyx9pz5i7trrDla1UYrL8PCd3925prwecilbfXh8TmltnfScbzYZounX7Gii5bVGVGZQt+TfJ9BXNVg8KX3H5C265KRJc+QjQaPpjadA4HiEvNshRiHrJLOlRCFOS5E/EjCi3f9Wz0Jv2yvxblHfUUqfLLZVR9PaNdT8LBcP4htX6IfybL+8wpMpB94+1GzMAJLMZ/pBlMjsdwg+I6nXSRcDsr2gPtVvSuyYSjbNh0JzsoWopJGuqMQC99cELz94r2bCeVL89chvkomj0sdFEd1qR7PtmsEynnmDgpnbe/XHaYeAJaYM4+8CeCys0Ndm6SBzLvXSh8ru4FiBySmHWQm7WYBYVVfk0PlI8m99MeHoTfdULWu6fNIF8wfXUd4qFy+yo1UC2EuO4KFB4VOhJnoKD0kpHrmncmFGk2Gag0iHlcJLfezMtg6lp/NNOrNFH0lmJoxpCyUnRJRu3aoUItPCMPneF+jBRedY6MWm1XgKrt6KaOaRrPhx5dyjDjwxVyIGLclJ3qK4cv5Fr6nUQXR/HHl0iuKmNGSH2NhgHAjKg0KtZADJtLloU2kbhnd1fguCXc92OF0jlzCAhSFrqWd1aVTOmOWnrvzI4EDhzy6VMM5Q6FbMNM/04Z90xQDixDClrzZMUBFmLyo8NMFsWPDtWpg1Tmm3U/c2LUqGQpB9zzErMYeUUdTqBUIxyKAvw2gbrWT5Ajkz9Izyzijq493PKU6mtTJybdB1PtEj3/OsjasuKwuMNc+t7RZWNvzr+jBY5XdB08en4r3TKyStIlCPETfs5Dha04W36bFGawSRHUAF31dlJuFoRWr2CVqpu+/mViCx09GRym0Tn5eAPXJPrYwXNr30IvmF4uSSFU6KWfeQzjLoFd2grGaTrqL27tErK2ZjwlWDYGzBs6KJl8dMlNn8p4OnxmbukayIhVO4rpAfqwV0J8/GkqEWtq838jk0bMOdrMGT76dovqpymPJqeF7UTwsiYduKpmcF2DLdVee5FSM8qbhiwnxLPrhjV6Sk6NM6Mp5aeac+A6y3JKzEtRK6Yc6GruDbtFYb4oS7Kk3/sa8DKhjMDIoMxhnbtrZ+XCPE56tLxHdZrq0I5EbPoQguYo2F752HBJ45CWvsRqT9umzvVXhHYF84zm9TvJ8gz4vJbgI4XU+TUOWQMPLLR3jeo4nBUQMREouJBmuhtmtdMV7JF6zo/uDsq0XECDGHdZl8e/BMf2XTO5qwk7qp1TX0klI19ZiJWGVLRqBXnCh6q9oAOX3Fctz8fUr+QP0ivxYwNZ5jlEP20g75g79NyLY+m/RMLGbFsI7h6OGu7+nYk2FhsHVUSm3IyZvDa2zywCnn2+ViT1BGTlE+pjOVWDPEvvlJ4/Q9YMcIzWaGqJL284bIw1VOtXKxsiuWFSqzCkxDGkMdyUtkHJb5Jt6I5AsCpK1APy6RyyrMkgve4MEn2rrd79OhbaijKNZjOet8dB6oH8RHle6g5OluCBlO32/f6Gw3VPMvH2L5cwgZBylg3yar1hGcaAC8BbrTVS6sOHigM8LQFBLU6UfSmvNgSUxveiCfrWyE4CEzwrGQSzYfvdF1aCwIymyGMb60Ma2FomcT6mtrzpB8bmFbF7tf0/fgwo7C3b8zHMJyZTuPObceDshf8MSM3r6Yum6cmy1Dxg+FxVOOuEM65RKUPsApnqnSgVQEUmcae43znuy6Dduyk4TMTDh2S+onretN3jRD51pavph9Pr/2rcx+XCpGJhyRpN2AGY4NtaDCvUQu/KBseDA93Rm1aSt97644r4Tnl3vYfbCWPFp8Iq/m8N5UWd+S7J2kpSO3wyebdccmslVRXaQiJ5mwoSZOLOse+7KQQnN0iukvOjqYZRAvM6hcdFM/k8fJHZ+3rkmGm298VFvoPHF/1fwc+RJWoYC4pdNIfCt7rOfahRF6UOUmua3Iqu+e4MQ1OVczbJqoILSDZe3iTJe4sqEYk/ODHEgBt+bmoMXGkPRKJRo6pW/XuxxgGgYXv1CILrKP9WF7akybqN1YJe9s5/SgdHBqYXW3xsr+uo1FkHO4x3LgG2A6P2KdlDHOWcN7ImemWXqLVBwzGZLEOIOjv1wKoTJKU0p24n3Q6VMXxiw/fPUXVWP2RjiCximvA819EkIeFZ/ldLKHr7hjcRhx3XsqDGtLUEi9Wg3KM+aNU2gbHC+40b9JlO5VOAa8DvsKBkM2Kg3AQz02968pUv6fyImZcgpzbQemRTPrN8NaPnynJdjktZzR6Vc2aJY3mV8scXswavj4Kym7PI+4VkgVRUfZ5XXi/bqBfhFd0C1AMeuOiOkC6z05WaCwLO9zvXOz4lzoN1bY32qM7QHqlIRS3ai/kdSy1FL5pV0nNI8ABjLlFEaJiC4ybpWVkmZNrYU2Qj4/lTYDZPLpdPJvlKv2bK+OEl6Kkfx1U53LvebF9W99+jxddEidIyq+n7Wp9wffSFkwmLcFHuPWW4xrpYj5oyHxz1nOOwjBt0Q6oZd1ZLaTcTDwng5fh2J4zxZ1x1RovyPUHmGQpPjMakYOeV3Pv3HM5/mgmRymLk83FC00sMRsrUEthuLkohKo3jtrYBnOBljZO949JsCnFzbRZn6MrXccaBUVdeT/tLXoCuUB7/bz1pmQWyiOg7CJNe6KG35U3DUQhQn+A57SChzrpdlB4chr4F7TZ1b6jRxfR91Zi2vNThkTP8vopI7SK8h2hgY5xk+DpG17N1Pj5R++scNdyx8mCaHgLp6PXTm59FmhF8REmW2nbcMMLmv5vOg9Eq13VRrDhHE2vhMj24rrtjeuVhvCISlpYeiIy9TbPLv0HCDp/93hhVy4Z7bEHsy01Yhr9qkxIkVSaH1dmi+czVE1kUvrppqeVvCUZxpiqAzYJn5RW/TkMzxkkNDhxYRi+wI5yEBm8nGSSk2rXjPp1zXbZ319ftfE2t1+misnZzBjBvxEKQhkI5O/bRdYn+3CzDKBmiaUSXUT72SdfNL2MplsigHNSdNNTTnt9L04OSKfxL7GmHQmELWMcMcZbbmkUa0wzzA68L2MEfXy95Vb9qYZyLhX0VPVCe2jdub3LaUTeiQQTRe1Kxn70VHK2PepC5wyzdkK6wWlwLl5HZmG58D91VA0Mr/boc6JAsDsHfy8JbGPwRphuE0moidX6apdILWKHF7p3BC5AwnWiuuAWAePuED3xmQVgjeLGJwOAV8U5MYDtQBCvJijEflhMiS5r/t+rb1G+oakI3XLIX/2qSYGJCwhzSWBrZjC/zWxP7kkxrsFuOyOFfVF2uiOkZmVfDUY0fBmwn/NwAST0IRILauMdpO1q7+jWUMSmGfcTYizb00I66e0rG8G7hnVTHyxx1KBh9wACVxa0//opVYQZoYUvn49zlO2giAOKBpno4aYvhSNiRDAtaSvp0xB93LSadM+x8ljQg0O+4L9ueaW62KrcfdQ6r7q05WyvkRBDMIyS34WE0Qrb711W6VxhX8AweLgmFz0WtvvBHg2Ypafk/W0Kcma5yUhBViwu0/zo9WWyYNVm3TJOzucBcgpLdx1ZWSDBIhTgME45yhLVQpVvwftRI2WLleprbZy2ZLRKMrgZKSvwijvlSHIln2lMRBsWBM4SAdFJI466Les6VvgiwitvBSieFCF8Qj6n2eQQ5ZTGAjiVkBZwTRCHy8kcWa8Iv0OoEuAL00wWNiENANI1noYysfmFlMzSNatqnRVV/eVbuQQ6fmO641EtsR2hZT9Jp9aS5gkEQRmaOl7fUSWnaUmbTJFCsfuiwF5MMG1mQUYCPwDNVndEaFWNFKYpfJrHbhH5c1Lmwtf3BypQq1glSQgsooZvz+3r0N5SokgVlq0TORycTOB7++Yzfjv1R7X7iuUbpfqCYsbYKXeGW6H1BX17AtyKF2rG6DspTBRsYuQOaNHIZ5AGADMIyZWwvj25xy/lUMrfuKAixNREt7PRKcTiWaix/SfOieH9sbE5f35WEskz9vru2OTb85CzqXoPCgi3X1UDOCt7hvxJ/2l33iXYly/VYUfUUMuEdDip5WVF1csVCHqJTGm0PQYwI36rQ7GdBCzf8Xg2IXPb4Y0lLlPbxjSZgHbNUSnMzGOo25gkplviAAGTSdCzQmVu/ApCHzg+8EJzIAfx4V8vpQjc1yEPXXp+roYE0qcTN/q8YAxxtAlqu/Bm0baqTUer2aXPXTZic6FzO/SQeGLiCVkhfpSpJayOUK7QTKeLydFzQddlmD6y5eCVWmYVl6L9aVHrgDtfhkeVgbUQQEv5aV0ZREE2JO2hk1JQgU3jxmre83iXjETtG3c77I3629um63IOleHYdyl1kFyBqYJUUh8djuON4m90UBpbrn5tpTrm48ei3nLHOdPRzemZpA/caZ6fC5Hlabb8F5qKvQyJLr1GX2Slb3LrKCvVjzg1slZguusqv/gFxYo472Di567Qni6u/oLiutv851ITOUvkS64cAJoSAafZfVG0SSaK/1FTpZEbKCtdL1gZouI6/MtoI61a41K5BKfEeSbPrMfPhu2mwnL04aY7aooiZ9Y3L/zD8H0lO4bKHVcYGDZUUs85EohQzA0cZ3HBDd4sx+6cTHQFgxgy52tnu8pdcUc8c7Ct+x1+DPQQdN9bLJ4bxTUU49qF8lm3PoT7u4er/mgAFu0Lhzjxt3W9RlwQHBWJdUTVOWkIbBx/FDxApf9nmz5ayzVb9LzQlGj9JJsTwRd5tDLc8hK0hcLdwERNbQiNMG9JWw0x6LwggSDuM3z42sFtwRyNmAU0/TsjbOyxULXJbGETYMBe2usQu7IDmlXKgjuqPv7F/pkmUCvllXYiB8aUyerCZXq4lA/UAfnQBvLyWY+yepTyINteb7vsfVf21mWL7J5vx/hDGdtOJEogrv9JoQo9WF/+5x9NUmuX42AIIPeKH9yc/g/fmmYcfMQRpPYc97lDaCu81TBF1aBcuwtmRRf1knUHR2JKUFV4x0iWExS2ieFzxox+NJqKdyP1Zn+CBU1X5T6GP/K8lkmusphzoyj2qBNXXfUNSE2WL427hUxKtIUY5XqOwT0l9i4tQsYQTUqRpJYO0r1TDruNm9eS5yxkbxAySELxPE7kz0NejFEmZvTkCYeLQxG9MTHX/Baeh+MoENSxKe64/LsF+zhF7aNGiiekQWorZgZkKhYGlK4PsGLTVxwBWVEZSLzHicrC7r+oDdOVKGOXnU2hjM1WaaSNHysWk1CplQnGUBuyKg1PjZLqk698QzAuAV/zfyKzeBu3M1HPXP9uasH+dhrq+bHyudveXGPz524H1Wm4yzKBIHPGSKwAs0eNnXcqbysGJxwt+zV9gwnBebJ63irZDtznOMf0x61iq2w6EnKEXF254QxjHjHLWJvfqJAZJRs/QgJQUpqFc2SDhL1xxXUsQiKMR75hIIXP3YPMVUB6dgXzIKjZe2hKZ/RcmM+Xk38wtLuEYCtZLdaB/tdiOSSeE52Cjei/uHjmiHPeH9ofJwdSjY73EDQ+mLBt5olHQYwDxDlfIDdawG+43ssdhI5zNwJPE1jfuodwVXThDGeKDCF9ee9ojKfsYZ6YEQpD7znkbUD8lSnTp+TUBtrd15NGUedtC5OuIAuzX3lFGwuqr4JXTmJ2PnA2f4BZzRVhiZMYSMrb2GCIcY5cnmnUXm5VLEBh6GE/C6UV2JvoAoWCIEKHEB6z0+HNme+bTAcNwq9S1N3QJI7NZ3bp7ucK8mlrfaLqpO/6sTvU5KZOgh9YPHolaUKMANJQ7UWAF1mkPeelGVbc8SK+baFtGoUEkRegD9ttfUIhCjw3u6PCTZLsPoc7+CjvbNetMEF58AbTPBSuP8KWc+EXOZb89Ve6VfXJVm8jtGOnVginoS+wa1DS9LenIk+UJzZa0tdSLC/V2YKVb7hg2wJBrBq7siUCwXYr8M84bf1unISgNUJ01WN3ngm9k4+7VKXXE/tF8NnSfWmH7P3bJcN10OJ0+SyzAgqZ7z7Rj9JNcl8H3p7TcLF0S4TW7IVgm/r7a96CdQzwa7vZFLgLqGNbdp+bTtT54xcP4UmQF90L4t2WUXy2bKfFUpjHUIHGKeJvYqF031DX02WfBQ7Qq6rqxOImnjUGszBO/ymuZAhoTVIwDRKXkRN82FbfqOci96jj+YWQxMp42tMR5zkefGLYPWKgYP+lC/BhTd++sgDsa0b6vfH67xY7c7Sd/jb3iEcHYTzZaNxrIyvwfl9DroURR/DvSCyNdh5WlePMfdhPovMg6hPrZdtP62G93Mf1kNO51mNZo3I81NbI5oCTT8WScsP9ECuhZ+dww6mBs3h4qQEo+G7INoGEF5OeE2jp7HnZRLHTpnAG36zWFLDqk48jfPNqtNM3nDgjsxZhj5WliARV47RCuLq6AMu3MbaQTDAp5qkaVSgeWS1Bf5UWPWVTUFiN1mEPt6MQKFowH5Npyiii6dE6EIZPRwzXkj4Ed62OfRtKF4vdSTO43xAfAZl3UvRocHLroA7zttu8KZ6kxk/fpxZ7b02IWy9t8xdcF3xV5AKQnItNn9WZuoe9nqdKJSno8YAOQfWBm2r5dBEnBuQf7nT4RdXItl0aq92TKgZeZUEEPbq6lXKPV2D8lpVG+4M4cgpksou/OOsmKVCvI+OJ/7dJQ5O0zcmmaOYkmabh98vdD9Iqkpg7LU1P3dqUM7H0++3cbOIJBQq7krcGvN8mhohZItbaM13UN7pacFbE9ebLNTYed8uiRiG6Vk9+4mRxr4A/CCqF7DSQc1Ew93qkVMqSLb04H8ecauEYGKcsKhgZEznvUUFzUNcwqPIe/Hei/bHE33y4hDVZbxCVctqHu8s77mkgXL/5JrjIJjpN/xPxU8dRKRnGsst8dnJ5te88KmS+oagjXyaGTby0zJcAu0MAXKBQWm6Yen0C531SD3xa0xpPbZSKURYJd5ZarwPCKJMw4f2hCWRzQVVyilaFr4JVRuST4I4ZF9aPpGjLaYwIoV0iOcLWVjwhQOMsMYcwbaV1FFTKJKZ9Z8OzxuN560+vzOdDOnDzoDE7IzY0VdUPftwfYj14Tm4X0I/xVZ4YFOBMmd8f7jEdBQHVcTnoikzKnsaEqv8lK64yuj9KzGyN66bhFhkMhv4zllcjRkaSzacj9YXZa8/km4Dbs4rPeg7Q/F8j8Igws8qvIqFu6Aj4hAtODlLJWLQlMfjKGSxPJHeL61UwQJtvULXBXk6u9Dm0bNL6bJQIeStr1uDzZiCWZZ0UVEcslXVAeWTBlPaRNjajfsKnWtRHsk5l12hJqvCWG2nnCsOvEmy6dzxbsLUNuxYKLeYq1TDTfwmCm3g19GSqk4E+1Lu+/T1alESZG3+TXsXByMuNzGteGi+Vioau3AuNqIiJpW4HsPfd+h0SZwbneVTwbHmkwsO4zxOMU51cpoD4u5TdPjEYXwK0u1XhYuQP4bPPGS25d8lQzTC0M8VBxPYvsuq7jNmcJZIxboV0Oj0y27kRBD/aX1pwFCGaQ9oDSr7jEOcH5GS9cWRZqC4fc13jnKtU12at5qLnCtTJjyWV2KVDhJjGpd4uKWi+tf3juKvpxyxJTu/b7QXYjSu6ssEv58ypfwcy/9K9/vari7pQGoJQ2XjCDOo7HmpW+jg2gnviBJlmI0sMg/SdjoA/Q4yWugWxPyoF2zz8EUZhp58kPGT6ru9Vf8fx/NpFFpRiEaOp0qtu+05w2w/TicRaH2IbNX9uRoRWaB8luNE72CfZltfGFNRi79F2SLa4/lPuhJ0GU2nDCaRsScEoqfsOkGdN31wPEscrSHxKpVZAqUi65Y+pdFtthm5aFkRzrbVWq5ipmrlRDepoofgxxXyk3gmrGccwxgcfeTvvK6UyC6aPvP9kplOtqkHxZytt0lAX0N2AyHqgzmnN7eybRzQNCGyWer9bPajnJ+ceYxZMGGOJCBJkqXX4dhYYGzvNuejSp8bnUdKm81WdTzcuYq7M9OA/3iFTdSPxOlVXXhxQs8kOsnaqmykEA3DclssmnwNqdIxL02TYxMFaZcMz8M0k35MrpZTmkm0dc3QAzn2HV5b0WD8suEDIo1mU54ygDJ+NfEEdp5JtOm6Zh3w3UvWI3yzas686tRr4d420W8KwvH4U8aUvitXQWoi3ogJAF/tkmQkB5JcAEarv/dWvoOwRuL0ui1eozvBpkVM3EGputS5CO1Ki4wKV38ItS8NUuvvLtgZuUk6ZgjoSZnrPPJk/Sdvpma0KppelbxjCAj6XO/SIXrvu9pa3OPhO+Q4d80keU4WD6zInpAON8FpyN4SufTUL14ReueQ7O2RViU7jE8pvd0jOsfCZQ7rr0abpgo1JPetUEp9HrKNMWnhsegHiL+RdXB/67aINz9TZ/6YiQm9sWJFjLD7pAIQU1rHy/4CgKOhWIVT7BaV5VH14dgpDncQ2MvdfPy9GoAMaRiwzJR5Oobhdp7Rc9QzY+JChJyLAgKuyclbavm/gxbs4oRR+cS9owGlbm9iyVegyQNSlxNOVHX9KSsZzyukSdjLa2SXEbX9uit2Nc/F5Y+5kq/M2t7JgUuiXmhS9jWE/BCeRGszwTExfNxA5fLuwfJzXuvFJw8l2ut6rLQs8il+ZUv/6BguNlNIWbclPtPdmPhm1L7vuMOzfc3Zaut5OsXiPtEiaV2buMCMMTif7raNH+uuqlmNsW020pWlya5hTyrcn6WazIFrIU8LlYCTkLJYzxdc22DBt6f7d7XaSHOLI8+Z/snQpagADSFY1pTUGDlaIEZ7Qc6Ae/G7eTyiGie9VFp5knOjwLWpFrRqKDJXl7haaXufQtpQT8mpy4lNFshP5/SFlgjutEsq42dbdqxgcf1mk47oEflfFuFZQYVYIHTlbf96ozADgePdn34mFIwigFY7Zbbd8GQSpwkmAn3zbE8m1Gv8Q0+Pi5pP7n5ft9upNX72r7ZJ/NLsqCH98XWrjhaF6JBYWPRb7rQItbt8pVPdt3Cp+dSK9fxPQgHcdGYFd8gNKxVfIOidFdTqHeea/NVmb74aCD8o6mCrRo5qk40u8BMmOv+6oZY3gRSahOZUYRC+PocJDOwPM7NDEUkyuOHQnH3Pal26xRlg3+RmLV5ysD2b7NdrBlb3IWpaUR82NSG5BnXF9jeu8ld9UxuxDhPgxIGTHMZdmT2GO1IyjIq7p55romtHT3JgFGkG4vgj0qOyOdM7GPv4Ast/X/DjBgM2s6TAU0+aT8vF7t7snY1UoqRzMB7EHKYtGKyniD0GIVm+1XFQuvJ2H5Yx8A7317ruyWaZVkse7oGwVIJr4o2wzEyy5OwLcgbO1uPJNxZhjN1tZ+uca5PlukYB9S/5mqfmEZ96h8kXv2q0iiTAP3KXpB0UJrsjTSIA8lWOvfNq+rogbZoFsbvlejsj5CDch4YVhllbBg+/B1aCaN0oGkAGDLM4xPMM2jzD4rHY2Xev80/QTkIjHXnEOjG/OX2QMic70BPfZegSj/jQ72doinTyBWIwyzRV1KrACshQQ599eFpl8LRPxTM+mZeKiP38sMWDmVVYlHC37m/1xd5vZFgOai9batXB8FV3ZgJdys5G/WIWQvt2AvqhoPcuCBHRc1v8kdPuUm+mgSLy9uWTeXEps6+r8dRdXmYn1Nd+3kfAWZDWDHok+uCWYVCqI9FR4UM0qqphSLbObovhB6XuI19/TKhPtTWlSSMDBlrmsZdhCkx99vDhOiLftDVrn9xoZli8QZVeK5IQMUn8CxCO5PZdwUt1lodN26hhuLGvt5IMnQ6XGHOP0YN51etFNnsIBm6Tho2m5vYIEGDqVWXEnWwuvJdJteVhuphBCzWfJ+f7s0MWUokAjPutDzXquPa0HqjqVZhWpGNuWj7kcTFfFC5sKS7G4OJ0Dwzi6edzc9DpOUaM37ULI+o6bKnpupU0/wTLcE0Knjh2eEQX84btY0CUJZTf2DjLbRB1Cj9EyXoHA8jhCK4e5L9CvNeW9jOX/T/+v5diWcRu45w30z8+bd1snLSGVZ4AHrcDv362RPP5suKIIoM4C8Hz7dORlrkoPunr7vvMfNS0I5aoPEkeePmF3t85us9qdKpUC6gVffM8IgSi30rHEXD8yef4sbYwA3ymMGqmusjZqWMqyoknAmmLPtykB+4xlOa0IkYgpy0O+z+cC/ozTj8gwyDr1ZREUlt8LwQjzMdiGNVBqa7EIpnAuKz2ceUkkzGS2A/daiCXBybaaIk7PcKEekz3t8Eir38cHuMw8niipExYQj6/mf9dtmfetq5+faLyg74HnBFRiedw4+TwyQTh4sSAhoLqPcHe3ZxULslEYzux5ViMf9Sp9hGboMlXxVKMp8P6FKLu6lQzdzcsBhpeu1SFqs1I9YM82IcgTcsLkawpyiC/VuMukmc9H87iVYVsfX4MrmpdGcfrAzcb+x1dKI2Dx+mfGfwZ24EYK25g6OURljOSU8V5vihW6Ef4M0yzM6r1Rci5q/b3d9r3fXUZ6RPliUZfzNHMCw3aiU4YbFck95Vk5X8oWBs277q8BMw6iCJ8oMQelGE9lILcPcJ4SDUS9WY/j7JgZl/ifygwqGdYxcKg+Wp5foDdkxIY3TVOmHi+WDipE5o8sETRdxkn+xS1fjxdX5JW/ABtkNoQzxVAoGVYkctq/CE78c66/KJlfwunTuuuE8eVuPvQT8XAb6quAEmASD6UDnSFd4T4y6ywRYl8eeRzspXX8KAeqdX3DHdcVjY0ncGYoUPZPQHi5u01dXB7nCz4mlz06SPwASCOuZyQ3fkQMfmN3e+xmV2Sca5s03GNE6CDhW82xJcT5tHu795xN6DkJQIzqcSnI9ElixHPkC8TQgtWAIw0fTo8CZpOvihooLULwT1gXhN39LvhRVmjaK+stKGTPAHA8l4LBQvB0/rlPKMrmYlj9xxnIUhTsVZHVVfiKWJz8xNUa98IaGa0hErAErN2fSUeNI8d6N1XE85t1WwdCgcG9nd+rBp/Vev5iMJzv1B21LYAieeN9qDF0Kn4+yILopf6sVvphklCS1HdFod6vdEYQRjpY9HcfCc+pJqYkaRadFEvCr7BMlx+SLky1zDrhYxikFKMsjXt93pPLadVxcgMdN6Si4ZHjDmaMkp9tyc2jEi97ZprFR1/ZFIY4akU32q30wZLD4QltAwYSpN6v3E47A+CrEWsoJ/xekv6qfufvIs2DTdU77RthDINviuHH8ZRrFISpA8yypDdSQGTAtQ5tOTT9cyXqpASUe7LTEFtxSwUpLf6nRJZE4Yn+cJnFlFgw/1Bt0q/jR282HBuSi6b2bK4CeTqzYtSFmaJGd1j2Qg4l6/N5eub9cgeV6vYEcwXbA6dlSOtUt/Cb8UXQTJ1OokBz3S+X+dFp8nuvjlZv2OJ/H3w5YmwwZzBA2X0DkPBppAEK2ZAdHeprFKkqqd2JcYwWj+9eOxOVhX90UTsIxFPQwHkEtyLOaHcRIbOfQomZKD+yRd8N6OYu6PaXObsYwLKtEBj1SOxoUed4Qyoivc/XeILoBl70g5nWb91zatXaT/e09CvRJjnWsU6HW5eZs2jsAkk2r7DhODxtBxvZx7/Qse8O3L7UbDvw/RaCxGkdXRzA86qU+h5d48g84wBmh2324hi2NmLMyVKJGKdjk9gDjc+3elPyAbdpIl9eklCWUjzhmo4Vybz38/Sn5kCf30jnZgUmmm7sMXRHKaQrVZIucKTYS73DSP98CWMjypnXDHiAmbI0/Sde8LxXgAnPulQ73nEquEpojB5zygQnaR+Opz7HSI5sGzo2smBkD+cCONXD8cSGxhM38fl9DxCXG/nrUANrFtM2cLhlvZUiOcoB/Rk2F5+/qHJcpBcx3PhSL+zL7gCR8L1GZcJ+vvKGiwg1BOtfuR0xaXyF5IO6yPzuQWlaOun2Oh78MDLfiyUTX9DP2l83YAdu2y67EuqGn+ACg813yptVEu9HtI0YXUXj6uNL7sF+kso88Y++ksC9aFgTqgqIj77Kn6bwdQHhXjKk5cZlntUYjQkLCnM4d0CI8ac51m5X5/8Q6SF2V+fkPYSGMzUQ7osig7yI2bLp+mrfLFV1yED+wYNfHlbt+BY7DFmlTUoOD99rtyGflJiIxg0zsgc7EKymncYGEZrkn4glUqCcIurydtJLeCcQpWfwA/tqSHL85ldsf2wvLmNxEw/Rq5w1xvpOGsoX7WV5MXqfYpEeqqncGtGWbLs1elIVDs4CB3ysfG5jSv8MXSy/Aigt9or6q3Wo0e0+q4yX0/ajYf6CbwXv90jZYE3UOKc8kiOGjZkg3YUfuSBddGm5wcq/VmDRXXJiKbITisoXakhbRz583z3NJO8QcKLRcCM6Bmm8zA1Hnth/elLPQFFu/Cq3UjfB6R7ep0QBqjcodtsyN/pxtSRxHQJSYtTYWF2MPBrs5pb5VsDo4Antq2oasnZX3KvsNmpbqS12arlXoQ+OCew/ObVq3yVVC+Cgr1qHGubtSgH6kdcKLTIZ6SrxOVJn3sPPXmkTirUCN1W1+KfympUxpFmFGa7P5+EqGllbwrhhWKysA+sLezvM57xTGwhw/OQUn7u87tMjdcQgKRVL0lthag3sacdy3avOnrx4q6UauXutIE67Oz8tejzHntqypW0fdlGIUE5xDV7VZk2lmdUWZjNQWAH3jU0jwgPY2A8jNX+AD/osyr6B+XjLvuoHRZga/tCBITJl77vflpL8xkrymBzNoPjUKIfN7ilK7jvheDyUtlhnmGDbxpq9wzNfwR1ldflGCZGUUQwVgqrhAO85tO6aeDob4MSXcgvkKOlc7OpTAsituZloqBZOW0Teyk62KAUxzOyaXuKfFR8dzgxBgLauwbn4ZdC1PfmvhQNiOpKw2wv0kEaMWGVSS/NmH9lFVIbi7MBydjdvwgSy+e5KHUVTzzwdBkYpqhIxcPtVLiyWOQ4ZW3ntUbpN9SQZZCm4L70QfD3UHQj2z1Vb/nGbxexq21Q7B/5fFwMb5fblRqL95JEjrMadsNM/41hCmdcDvT/rXeFdSzrBoO8P0WD/5IZlcKnBJ7mqEitQJ7ZgqvUxxQfvNungZhoK6Ot6A4u+kBNukFAGcjJdD7ned0ewYJzwIwHaFfVFuV9x+FLn8yipi+CcZsN/SN09Ya7Gp5oteVbWiWxRhN8cR3biWqDRp4GetSI3G87HCfByYP7r1FzZSNNN7esXUB5aqxlP8JVKsfBw13xIgPQ5SO/O81NbMn9QeUBGXNydm3LhtVEwRHVkIRM+GLp647h8K09Q93n8XmQ9LPp1wqRyyOZUMpASOk4ClpXNtQ1f0nBYrSV3+S4R6NSFKm5AvBUK0mC+Hs/uA3MG5hvP9oUji5jTOESIt6R5NhL4iAC1nHs1hUR5NpGA6UCqGOvQvh5j+cUdJ4Tf6dOFCvDFovHLkH3//i4ge3fTK+oJVXjlbyrrt6y1aWOKbvknBKbjPuC+MXchNBiV5Rj5MxXxaehtJ+DhL9zB2SAMuPAeTOlIqTYVCWOP0T98Mv3wsurulr1aWsJH7KvN2Dyy1iFlo/iLeOqY0T1kaCk9R50w/ks9YFJJTtW0+cKiWI4dR3CvjppUYOfiVc9n/eQ5+FeUPUnoCaAjNY7c+VHPuVFX4REaxo30pMGF1uIfU1dAC0opPXM5RYrrSuJbKQF8h+cSvIk8BXdgOOkdh9KI9WxBMf5ZHj9qMhkHlMuP34s1pciQs9F45fzDA7bT/lm/TmBBJKxZ5y3TS/0hE3Vs7JaziVWB1wl17pRvfcQdJ07dwKobqNQlLXJfUkKJL3TXvFpPcNKU6jPl7Fis0vHNjZYa92rLWVV5BoZAoOpDeFX56NUM/w5baiiSWyqsAtRzkQR4LOyKELuiXLd+Ok6AhyHPNNu6U04n1bH2qV64UiuBlEO9YVMVK6jG92U3nXa5MB60RvySqUvHlYWiS3UxRYtKuMjH+JR80ATjH60DYiuz1xPy0TA5d8vcUgf8hmFIOHwEF7DigRj0dKq8nVDYKEcy5xp9JY/vGdnzRhRUnh2TmlbgKu4lyzaJclkbjfvTxjYC3FTjZV7ZbghF0hXcAlQqBWBqUUX1YwvABFZCXdmQgQv6vBLJ30MFdmquE+VpRVaQVgNcudJn72/tMwWTuoIjjuk1V1COTQPvSSYf9AmyI622weCmDQ7Eaqe0rcMOE3y1aQhXGEcme4qP/MpfpmZxTgisbYi35AU5Y44pDdHygDcR9Qkfwq8UIFUnLr+6FJeN5uSyBg0CvXYqLv9kWQD8dL9+mq4eUIFU313B6avlI3hqL+r8eZMVA36eQOldeF87wnHuhr53N24SakD48OkyO9e+r0sfng/y7BLgqnxRvUJG7y9fhM3C8c9FIKTd5LmF0PCp+ms673FUJDoPeT+3hn4Wh9t3aHiktJyMHjoCZe6fcb5KwdYr7xJ06iPopR/OEyU6K69xLrheBrh1DQatirCxmDnmvjIhFuiEP3gRP2mmQBISQ8+mR1evDKImIXFXUWLz0bNk7R9mleLimEsFWt7fIeY1/ST1UELci+8N3vs/wPn9FoHCmVuZHN0cmVhbQplbmRvYmoKNDg5IDAgb2JqCjw8Ci9MZW5ndGgxIDE5MjgKL0xlbmd0aDIgMjI3NzgKL0xlbmd0aDMgMAovTGVuZ3RoIDIzOTk3ICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjatHljeJ3rtnbQ2HYyk8a2nca2jSYNZmw0RuM0tm3btm2jQWN/6Vrn7L3XPr+/K1fy5rmH7vceYzzzxyQjVlCmEzIBGpmKAW0c6ZjoGbkBMrJKQGtDG046JdOvTlaG9gBmekZGVlgyMhF7U0NHC6DNF0NHU24Ah6M5QN7Y8SP0w4ORkQuWDCBuamNq/2E0ARi5AWRNHQ1V3GxNmQCUhn8dFIAOjnRGhg4fZlObrxY2plQfISJAWzd7i6/mjn9ysNDR/cn0J1qYHiBlaGwJdHGwtAAY2pgApOhl6QFyQJcP0AJACbQBGJmaG1qZAYBmABVTDYCqsqiSMkBcSV5VQZmK/iOxspOtLdD+f7iIKKuoitMCvgjJqYgCTNVoAeKqyip//qqY2nzw/0oLkFP5sP+p8+H4J1xWVEVIRVNBlInhzzsAmADOpvYOFn/K/hc38g9mgH9T+wg1swda/1UAQGnu6GjLzcDg4uJC/9XJwZEeaP+V3tbqL34q5hYOABegvSXg42lvamX6lzBONiYfcjqam/6d4E9TADIWxqY2DqZ/gsSAfxutP6T8CPrAHf9F7EMIxz85rf52BziYmv6jjLmhw1+xMgoKMgBrQwsbR1MbQxvjD0dHQ0cnB4DBX9jHr6kJxd8ETQEiTvb2f2rI/q/J/l9l/pe6MPDjzXSsPLwMXf67Y4Y2Tg7u/6HNP1/bGGjjYOHg6PB3RlOAmYWV6R/2Dn96ZmHzFyYrJCcpJqqsQifzMXg2dLLAD3Vs6B1dHf/y/pNP6IsMN4CTkR3AxMUKYPwYUlEbExGgtfUHawfYP/J9sfjQyRFo78bwf+ba0gboYuPxf3EzCxsTsz/KmzjZMqjaWNg5mUp++R/vDwj239hXU0cAI8DUDmDqamzO8KfcX9PyB2b6A3/I4OVhC7QFmBlaOZh6WZiZfjxgPRwMnU0BjvZOpl4e/2n45wmWiQNgYmHs+DHoH8sC+1d2SRszIIDrb/iDyf+a/mcEKP9aVKqPLTUB2li5AUxMzWAZ5ICOHwNB+f9nz/6rlpiTlZWcobUp5X9L+t9+htYWVm7/8PwvD3XTP1wp5YD21oZW/2WzcBCzcDU1UbBwNDb/W9i/cUlHw4/ZF7L5amX60ZS/INU/62T1Mbcfd4/Fn6sLQMf0R9Z/2D5G0tjSxtTBAcDC/pfJ9EOG/yL8of0fugAGISF5RXlJmv8zM3+5idoYA00sbL4CmNnYAYb29oZusIwfg8DMxgbwYPqYaRNT178mBcBAbwN0/AgB2Do5egHMgPawf7rJxQRgMLL/EN7U0crUzPGP8W+c5V/43y38l4ELwGD8r9NHTQbTf9jZPxKa2jkZWv3b5yOVmcXH9P0LYP4AgE72/wKYGDkBDP8OYGL6yAH815H1oyLQxvQ/zB/xtv82M36cDD+ujH/wZ2X6H/Sf7Fk/uNhafdw8/+Ly4edobm/6H+w+8jm6/Ef5D27upvZ/A/9skcKfK+qv7WP8d8/+5+7+66zsaA+0NFW3MPn43PoPF1nDD1FdtRk/VofpA//4+d//dP9RgOzfW/8f0cLCQFcPOtYP6emYuZgBTKxcHH80YfP6R6zx39foX2v7MVz/e/5zhwFMTV1NjWGXF4DGPAHfEhuCir1Fc6dKIMi46M/KMPk1pGI+LadMteFhf8naITEVyPNt+p5KngeUkeDW9Y73tSnQIAvAsHrbaP5ZPnljoii4a+gt642HKCo0mqlGr+qXKrv0vaSDhOpYKjNHs5B1JrUlpoUQoDp6IsLV1vkYwTzxjvI7gUSnpGUtG8Ilf46pEd3eCtV1CRm3HW9pqh3U8f0RPSrcsEdomXrWICcIc1QK0ra7E+2wTM8cVSD9aRoD6Q1NM7SHRojGuGMYbcXwi0omBDrmIA74NjOHK67uIqI/ywNCJqIke/DPIh6GTxnozoRvIpr+g9QxzFhDP21kT0mKVxkWfig314E5YzluHaKdSskP9wfnkg1jxpW3GW2aUhHoNbb7w0j2sm1CU6ZHM2Cg0iqQI57TiG+JlD2eaY3Dq47Oj2i2OTKl4IXppgO0G8sn4EDcgGgIu7FBm77xl7lgrw5p32Tn4lF8bpHnZvOpglbaELU3xsX3NBlIiWSJzk+XBLpoVd6f1XNBDxC1yapqSiP0JXWyNAvO0N73/KsF2Krjl3JDIFs+jYENQToId4gA8ugsNFRWmSXEFyNOjns5Hsy31ogoCWQaOBaqrTX3VRxM2ngV5zuLytzHOAx+TkF6JTAZVHqwar0JRBVlmBDZAy+sBHV3VBcPmrnNh/kIv3usgFUxBuF2uhLJodY6/M4xH0ReU0tzSO0p02BUHiU/kmBwk081JsB1mY6Ie+fju+9asymIpY7LPCwRaWIMZUdHALMuhrFxS05jr9rweWpx0CBhm5AslK5tyciiaVCy0PqZGQkRPN/OuU+4KZPks1+R8k1/bJt18Y6sxYzbsKIvLrwwXNJIYOt8li9nKnwwKqgI6jCmLe3lVCfiBjmMgeknojawuHbxUfY55/AAHs9QF95FHotlEA9+GLEg3Us6JhbWWRBQGO2ek4aJnKX/uxFdHTLxJ2zcTK3QYZrC/cxwbnd7IXGTGU4uNxLh3XY4m+HPCMxQ7BP9x9UK0oOuTsI5tRhEh5tuOKrfDvJsW/JEqvBQlyhHO3zUd5eL9bT0+gCCb8lIA+4uArfbpGYWDNN1psVIKOBkOKWDXYyRFEAcJO81SZ7sV97Rji6F+9ZSG9/GZ4DK0VrsrVh8t0bYyCGZKyBkl3IQogMrKFTFO5RYA8s/orNpPTV2bB46K8auDXWoLJfh7BTgWYJP6/sLSo+/8BB1WX8AdTLxWpBc4xiGdFAnx5WUV2lui96P9CpxXxWdT40T5xR3nUSiOxJsO2p37Y5013s5jterFCI+w0JQsPFCP3YJ9qRnUuMMtN1wcTSBVq7Atb/LSJNv7/M27iMM9uTA9tHjirBfwjt87Iib1PMOpKcYudx5VSlSJ/0KbgDgwlSKQA6WSpfgbXQ3PfMedUjH0R9leDQo3g5o/yr//dDr1Rv0+Dm8+nd9Io7guZwHur0HNJBij9+Gv+AC4SASa7jrUgFaybG83rLkKo6YUGK6iWFPSpM6U7+av+xUpQTMEy+YEynjIl0BoZfClgbtM9s7ZdimkXoU4/jYpJ+NjEy9h662ssfMeyi05VTfyL76LzNG6+vblnRkqCGLI01ZIU5MFCNzarvMl+piv/yJYNMdc0KTpiUpz0UFOY1R36kL8NzzYi8RRyKVJPcF/XwpvAW/+4tfmLO64PK4+eNyDZRsAkRv7fCn0frI3f6k+ngOvFizTSrzPAVBk4IhbVZI5Y8FXdq7kvFfjxnvkc1pQlWQwxqryZfI9HGR8KZ8NR4FXaZj7ugWOTm3xtuSAVIF5WgwbWrmOfmkA9MDP+SELEnDgDspX9dutkdfDsDJYSncT32cjBxY+AOuhYzd8Vap6MunVIfdUSfhYvS4yLmLL3mIu290gnvGqmk0IS2tc0ZItwBZr2tdkhGOmlK7ZVWnMof23rkkJFBHJoPXiQ7GkTwzrysogzEu5bCfJxvvmLbgh93KhZB8mXC6VPhhCCUA3Qx73kwqrhlgb+y8TDFcbEig8keFgbl3wKempeHDHRvJDv19DUkCXPVqhfzaoPuzMcSjXcz4XPa2wdZMnxrr3C/CnGMsO274pd1rU529HPQEh3RxldNDO8ZaesJhtfykR2hi09xxLqcXAwBrDnKnSZHP6iT18K7hUmBBnubtkfrHeWrKrZXUuiNqCPUdhxDyTVXZ3PVJrX2+uaSfFKZFufG7IaQgm40ad8IcOuAo0DZn6U5aqPjPS4q6BONNWgnHUSU1wrGnCMLmti7BGZZQyq+1g1JrkPek1LFC4ipJYs3XlJ0ucRucD0D8BM4oexFIoIvMzxBAqyVEHYDp52BTZqwqLlRf6Mf74aOT0oZ8e5eOtJ2fq0EEL1c29svaW0UY3Rlfz/fvvY3w0BCl/WR2mEDaoNdM23PFGL155hIXr3Zu8VR9Tr/l0gfc7uvpoBbhOVzbihBxCmdXjbzKTfggDO0ULHwZCWbLOzYRbUk7baCwiwqM6FLc4zIuWzwMCi/5EnLuqyu4E54v40+CjawCBIF5DGRi402J7DxcTUtBhaeWY/iFaPwIF3GnQG7rMZsqkaEirKW3VkTXO1H0Y0h/goCAFW9gWlENvRN1pe7saEkCoCYxegTo80pSNJrnQt4gKIAweJB0KBK35zFUfHTn4XHpV52rtOao/dT/ZR36W2RVK8fYlE/za8Ti9BXTLGrPpKMX23PgioMKDIkCtlDe/QX2SFA5oNyZWRP9skFOWYgZCW8AY48a+bVFkSuCHcFS1G3x87ToMQ9qYXLzFDGiEIbJRcihttuPu/HB0WHo3NFXrpaxGsCaOBLMF2WmjcOYAbk82V1QI6ZG0q/+nvfPFUSD97CYdJX7Wou+7gUUDhOYSMZICwKKhKOeSo0Nc7bCNZY6Hl6oG8ZEh8+0tUlTiYsv/ieXP5+hs73ZIVBco6RfUUt25/SQ2x0sjXnDOjmxMEMo20tDmQusXfw4CPgkAuoRUeP7sV7U2lVt+58InLT69JahHzGYKWFxnjlVTWMWW+83M381O+ryBspJtB41phMkWjyZ5xzi0JUgADMITyKclou5OXQglt3yUJiHm6LjXYVkI3KIcivqKnM2fBqr68oTB/ECM7U8ppLZ1U7TXJTSKPtQFOV2aJ79B+0M0fW79l4w1FmuwDe1ok55kZq6EiA3FSThPSsnAoTE85we4NwEiiJ3V11Cnmsivhmx+cmu5VG4VFvKcLFYaHAlYDpO0GhdkFceeIk0F51/UtjrEyuFsMEdOfukopgEv6BLeGzZDZmoZVg0kF0G7XpG39jduKfKyy4DId3pw3bZQLHe/0ZdLFHh8OPa+503OH2jOZ5tnzFgo07ZOEhTzqVdkUlNHXBHdFvm50kNrezExDcJgXmj2xX/kgOFdzTg/SgKFTG+DAol5ebzbdx/zn6kwllC+LruQDP5t2G8niBr98t4b6KDHFnzGpXScDUoBfCO52sFmJ7JUb+rTzqzHhbBXGc/Pz070ggpdG1G9Ou3WbrnuaOhlMx8F5rFvt5kRiT+MN5ayajaFLoRwgd37MDpO7Hrm2EFJsl9RemYVHivcETHkdeXJ0z5t/zwig5PUbgWHAZzuE3JbxE8/SMpwBVdrXvIy1zYqgsRX8XB2QS/CcrFK4oFq5kcTBkqjK9TvnP3FvXjXFs7OTf2LTU3FePWOJEt5Ixq9RW/Xvf2cu5p5C0VMxt5cgqxuzmwJEhvMw/VWvicri6eURhgZTP7GwoTLyy63fIaYUEIZUy8GWxmaNRFxzsdAByqxwJ0h+hxy9emr1Ox5tE2j6+oy5KDJBh4x+xBi1icb/H6iwM+KMz8EKnj8GLbM8TpUXkplhhk5XREagkRaU++W7oVjVLAM24Q7mVv52RhyljxakOuYRTI9yOo4p5ioNyDk2jdkFGQ7ENQBy3xu8dEGMdttZY8cFPu/I3lu5/LsvhUCKuYCsJLLsSWRcYMiO4+g7XXo2VSkDhIAeItxBPis1Hp0+t0McyUTQH4isYGhKUHEtQP/DfOczkY1dH+jRbRdbXG3/Wxmwo4J5eSUi1DVVPvmXcd1WKxuPxP6iIRp1B38u0eDstpJXOxL5a/y+tzfYJBBi3rx/HsDRW0PutVTp+9lUl/WmvFJ7qi136bJ2NS2y20FJ2ICyJZyAWXrqz9OmGPW4iK/31aZSte9wp3b+7VWTZCKRvU70ro8qU7j3R/EQ7bWZmCCaWh/okVOfwAgwAjrHDhZvVA7sp+PqzDhOTzErWpvSiyunpHQH3ygAAt08u0W05nasGoBvYi7Xr0Pt3TqEm8ivsswdJYZGxTDYoAdvv5SpcyeXuQf0O06009XKFf7JlUcE/ldCYRjUvFaBtn8YaesCfdQ+ZLFItn+JQiEmQBm9neztkGxv1clnn63fekNWrVhNGpoQbitfU14rITjk/fhMtANDAy15qUjYfREWWq0R7knggJi5Z55RlPYDy6FH5/MpiDQUVBJSQucsj7VNa615EMqe5iQCFdl6Peix8K/xXbagTCubsN1zoEr8pqahfUuCcxlEPR6w2Na29gAuHUaHqlXGOSPzOUvu8yrSTX89fvSNzHB8uDbLPPyu32d0XavHxvlGUir9iWlqWF74XO/sS0ESMbN6qb9RnKKD7NhuVw67bhrAdN8SWtK+KSh1gUrRbTqXXFrsj48NWrvbuFKePGKEHmassTMAKm+KO6QdlGp98B6lTku35o3jnAUyhABPPKphAXQBlaHl2oQKQPNLqr/dPcXeiPVOTuHwbCrU2/PYCV0U1zagLMXJk8n5HmONgWE8rT2SSgGfEnE7bBHYT6B8WY9AW4Br1M59FsTIqitL4qt6YGepCeoVRO88WOe0UUkyBL55zZX/bQgrH3ro1NDYEke45IJ+GEQfBlstNFkKFFixO1c6IouQqAkAjRR891fJona3mdpzBvvypiAP0Rn0rY7VKXUzGbFYPaxgg58uK2wugr7vpV17coqX6MIwZO2B0F7kZwNIafa0izDbwnIJ7hc6B6/ieRr31zqiKswobfZBRT31rtv6HThx6cRL/g1ppRu8KPPyBLGHl6x8/ZbMQ+FCEz1cGVYjj2mIYYhbxjj/vWpiQsvh5GcYt5K3t7AnXRqzIhy0ltpHdYk8wP7ekqtYY+bxLnn7fQoOfsv0qWuqk2Zg27QAeveGihMnOp6YdX80a54WFWbEbLeInEdxJjiIuaJbzwsxkp1ElCBUvhUVp6TSDIaisGSbNih5hHxyZf99seijxXZ8oH4HAZpHLzUT2X1PRUF1cWD298oXSeht95P1Xy9vG5Rvl8uc3VAl5vKU8CYTBh36ra1AXlYb/clBrKTCxGmXa9jhSMoARsd0r09GC+Q569vIWC/8KhFw8942++6MnnyGWrIdgwlBNYowW5JPbiFo360ywAz4WfohU3m8b3UAZ6jG6Xn5qU1MuVVV4NS80aC2dpgjlsCe/ZHHPNtT5MJZ1ZMU03EROzou1aAIp2HIq7W0ei//JkIjOxPo3VsXmf55YJ7ZveZkxsgtZI0pYxZpeS3uFyIPPnkU0CV1xvWahVPnnMCjLQDnEWolWZK8PP5JY/A3hFg+6rYmRuMF/xgw1qVq88qegcP2eN8E16delxOhFbWKp+QzEwSVRn/cUfBM4oU0kz6dgAAk1+IOCL+I0R5CgB0a6RPxqcwobkSRBAqpHrXsa9j38xN0IuMLlchyt9tYjuYFQKYUVR5HlsuLVeWoApWUIonf3Z+7FqqVcdv0gexStQo0ztPeZENytMkL5vbt/v7LN48fI861chE6WTTadBHzxB166qqgSnt4xu8ywewvqXuyqsUgHP77C+B0Zz7mVz5S73hKreGWCkLxx1Ndien380Qvyufc0M8e16UQ+6AWUT5ghlnfgqGg/bDRsuFZxW6/095S6EyFFvEDVpTqWcqTQ5SoO488e2hkxASup1aB34OPD2gR2Yzv7sXIirugTzWLY9hOnxMuh+JKSDtBMY1YKwBNcznp9ixWSN92JnamlhMJt09HyMPhW2IGnRIQS6uZ5P8kJN4sKtKLHpWpUb0bK9bEckJxiyIGZp6sRLZsd7sxC2DkravJ6QzbZBWkcIdZqA9GzJaTMw2mBBX3y6t04vNcKqXQdaWE1xF0WFfjgVL2ImGg26oNe022wf20OpuKVVgscD6UbGtDBg8jDmDMLLhShjZu2r9pR7yw9Ck+M2AClp+GNnuXob4awYBsnsMvTQyAs0O3URym9ELGmA682mi/KW0Vcjhci7uPYXNa+DQPOcvq+T50vkQku9WcZptsE7Bl15ub+UyLpOPfLNji+n+fXnoZ1svPaglNkdNFRl1zQQU1xwZoQoMM7oeZgaDeVSJLKXgtYgRNuEgJOvukKzoxTrQ73y0SFD76AGY3xN8pVs42EwLB2x4SBbZlnc6r55WNzDvEhplhCz8UqOSYX15HTAn/qL0HlVUhkkt6xeB5fd0q0OBUSe5lrcKYrBrI97i0egZd6J05Zw4II55lXrNdrTAa9u/Z4ZxT+PHunCvH4T9eB3TEG24tqU9TzCF22HtL9YLh0JnlU7O9MfeVcJ2p1+N2ziB9dxE2rnp5bz+8ISJ+2pYZlkuZA5m2GSZaaI3uCAt9XkJ0ubrm3VeVUMLi/ICb09iCpFRdWbFAOp3qsgfo2zqWIdJJ3tKW9DIdATXmzU0gx7ZnTVq6hppN5hZZe5UQ6FptNq20TAN8/q3+H5VGAK3RgXLTviLFjNA6+uTC6Rvyt0sOtcwB+aI9x4/aMTEl4Uf5XJqQ8eAnvaOG+GbOKyqcdganfBp/M3dB22O6M4w0oobo+j5IOJx46iw3IXQO6gVTACRJ5sJHImQ1wLWlf2V/aBXBSMCCZL7352d8vkY1HKzlMCx7p50p1rQLvdCaToQwbq6Ca9iF8OXuf3s7j7lze/m5U1AyixeyIm6IcNt/fREjPIo9iaPVws7vzohmF6gNRDYt0MvtZ+eEdc2249yXOv8uef54LKiz9iqYuV/elg5CwOBrUdbop/oxOP1XB6FLo1Crpwx+O1xE1F9rBSIVOclpTcNPWqOGduNBvzsXzPvzC3G/O5KVqXWEVwzVAjZzFiRWoJz0tNmUHeqVu0071tBhwSb61qVHxnXRrUdYo0CwhsBzdDyCMJCn8ZAotH+6wDFtRomLCrIBkJ30JFwEEizAqX9SV9m5XbEBLincNTX+ugZFMZUd/IS4+cZIbG6Q1UATfSzZdUq4GtW+aY4+hgeVZFq3a9WAAL9MKqb9PIQvq1jB801ys7+d4Ymxn7CwUVGcSq5xhirPf9kvLcRwC3ZJn6bFiolBw522L2ISa9Au7Fq0jFeUpvWDmJwvGVCYES7wFgW3O6GE/ucvVpOMHbyiQBYRo2s26xXfOYCuVs/gUhTgQKrje2J5vqDT2Nf8sar8Gff0/RY9H0PDXHhbJygQLwtV+U1eP22mgO/BEve78zKXGP7fVZaeUz3WSDvlnNu2EdJ0JcWWvGmRj7IV3Jp4t9pIqScI/YmKOxsDIbqdO75+w0u4RzVZNIE+KA9se44Yz7btglIC1Oq7H6gVSnCvErSxRWvdGxat0pJY1r7D55u2gxcd/C1I0IsZLDIz7fkB2ggdQE8jAAitCSnxgUpKAlPt3Ct47h18E3dgcrVh4CL3mLt+ozCtyxVWMsinyyK1vhDPGwF70Nkd9Z+eYhBKkhYsbfuEtOmB3FZSijHvGxOAKiIZMPRjy+0gF+EC3wgNAbdSRBRBT8qMn1p7sf6zZadfzWhbbUtFp5J9UArSaCJB/ru83cQdPfeuwsIhYf3AortS4vLLq21CFo6C08n5J/LaTeIxkLhbEJ41Pmo5VkHetieR9d1j7/A5WG+7PbOaIpk3wcmM7jd0ZSGlZzcz9DNOz9SqEf39aZ8DO2avxKFk4LMwKtspQPEXNfYrFLUgpOqHnHAxXHcRtOxh4Y7daihKmOMGQX2ksJbseeRlVhF6UK0JQ/c++Rr6mxnrXDgnKu9+uGBn2psq85CnURrnUAE+OcjKtZt8ErPxN53gvA+WJXZHgnk1m3wwYtQnEGJidYfH5vonUaZxg4Me0HNzQk9QY6imBkkkgqVgXlArO+c0iPOQQ/3HX4E+kp8XdLhXvGmE906g8OOUmnY77MWbon2COx5OmA/CACwhdMzGqDmF9lqikkSFTQsktWm5t41aFNRBF4WUlQwmaCssyjx9tNJHDr9s8QggthP34YZkaedHNZmlmwxdzBigCvr3ob44TGIY/9CyeiYQppbLoI3Utw9yBcNta9i1ri+9xY4TKwAyIzqIYet8u0K3NZaL0V1ny+SM7LkaV2uRRaIGoY6uwyjHZX0RdYFvD3sw7AGV7VoW1TR7el2vcu9qeiADGZ43stnxD9x5rj0BBRbPAX6kN+1P3aOo2J3hSA2lkgO3roZHS6aelC83jL5M6cS7QtcLWarxKsBSe8bQmNNubCvs1BGfuJXix9mMHgtUZ8cN02tkvKwnJJ02DTSEEmkKonZKVMJQSHbbaYT6N1X0O/KYvWnfWAqbdRpOv0tYTSH1U51t9E9vzLkTIN7RzqkaDucRJ6uRRUEtdnkze4i/fQfM2UwiCoVD6aOZTn1LwB4t8cpIs8yG73TyDd9HdxY3EV0Tyf1mwnqsl3fVLN14jVpagwVxeWXsAYZ4K1dq7xlOqzP2NVS9yi3+zLJXev+pWRaiuyHuJjYXmzaOEvsHYFjq9ab7EQk8AEk38V5iFZDiVwxGcKRVF3Tvtx4oUxHKzc7fRthrlfDSZIsWZqVAiyGqKJzJ4aF6klkthz9Wmxx3CHT0Yll2z8cfCLvEkaus42D3FOpf5S87XnQMNBbbzJrH+EiLpzyDviRWhEGDx6AQbk94sATR9ClaBXtzuDvnVcNg3uOlZMPJllTi2tJ1BlkoTX7NUz/ExHT+3TOwuGplwirQ627KTyKlUdefO34Pa7Kv/1I+ca/UwN05LaTrwo2zoPucKvonanTfEy3NHnPSYHKJoKLzFTm/LNr/s8PEwkxmQ5ngovCuY6IrJyV43TvffmPB0NxYlklVWTgeyv2QO6e6xlknxANQTykDNiTbRkIKaGdE3aiZEmywWUlVB9Tbnllny2xXcwypbdg7lPsPh0BXmtHvwDP+Jof4Vjwcc1KIQ/Vua2iG19k//2yf0zwpcJI+Qdp76SwTg0qiuDNZj8fWk5/YBpX3FNRAhp291r+Bb15HFTYxh9tWY+tU0LE5CyKXHTNjcOvem4g9aDacSbOElO75wkAMJ2kl99LuTSkT5IPA8qER0VPEij8aLA47RJLtQ40MwB3H1tGjEKxDfyKGzmJGmh8ercCw7BXHk1NOlxdEucwQT/zgzWeZhq3zLd2FwvNO/nNZ3rV2mU4GKeLxfEYJXRWSvCJt2N6TQm68hURNtAtm4vktWAZhfnYAvC6e/rcQc6/FIMjHHw1I0o/E2lHtQXGo4xDYiUrDGZSqQl1QKEWK1fgv08158v3JLjk/YdTY7osGXuLQ/GVGwiIudhK8wQsChHxAWZ/Tvn6TZ6FldVrg+TSGb2NVxQDCa9tGUIrm6Qq+va2iAcK8F4vnIY48YUQHzx6ZNeVbSyotex+z4iip26lFvN9marULAetzAKPOtXRdzh0Ly5G3KYoOMECbiw4iWhl+7Pp2r3kfKzIpxmvIo9nfezlhuk0FaQ/56SU8eYvqho7lsyHbJEPNPT3z7R5Z6wJoJOBAaSeaEmB313TVXtfcxD5CO+WQDJgnPiNbl0FBQgxpuATIWE5nf8O4bbkSFspjJayBat6H7nc2nUhtZ1FQUeqHi+5wxW2NXXQntNsEfxif3LDAC/CQIKvko9sRqra6Hyp+vWJfO8OqWnSbLv9vcsK7hngxQYxdFoJ4w5BQwIAlv8B/guslzbv3NnHUvJanmneEQ1VkQLkKAOPMITeQ4WpUgHKlEHLFIpiaE0cVQbw+HnN8Znnq5idXi2Bzw3Dphf5qgmA+TTFhC2JIxvfNzZoRIy5n6FSe9JR+nFzjxEDLXXTyrvyfjTpF67/5QB8RSSXI9IwGFf5tY2pu4iQNiMJSYI0XhNFg9+ZSCar7PUL3OZYJtUZ8JnsNIcbYnzUfyG58TsftUsARbqBpkAkm9IRL8WyZBwaiYbPx0fIUytcWB+MrkzbfUkqyfFKfgze0kqKQdvIe1a8jwwipopkKdzyXMrFTsk+LMdRz8bw4shjAoIZjiVoqn8PnSwg1lTJrCcpiwv/npr9tPQMsv4KiUSxDqGN0fBqUedouCUmvlv1ncraiYBWmXyyfRRYiSJ8Tk7FDFHaySiC9GftCesXmT8yA681OVjNeSLWWV677Wo1vvwtpC61MLssrrYzT0HFvll2vqT3MTIxNlazOEYBKpJ893JP0XDuJarRJZJ1oORIANJ1Eu+EASsWh/aJo75yas+hu/3u+Yz3NhukzZkpV8nkvXaxGfi3guYUKhmeEiWdp4VMvJAaqk0/EQ9BbXgbaFHlJL7Ia9aGWfCWTuUzCx+OLo2Hr0NCWLGg4mLsaoyPbgxubT2QNDgWEnezhL6UDZ+tba7jHH5Km9GLvOcZWBaSiJuqYDVz4e9srbE4V4bMyV9leXbdAJDJh1QK374Sb0qhUhnikNXhYr0plNbnpV8YGCbHhqNa7xdeNAzoWXE0CSBlzs78r6CZmmLPNIgPgg7S3k6gQ8b8SoiumhECcNljRP4VuMIlyZBpFTh6ooxejmmRu5YSy57+wIfYn2s8wm+9WcgnqCpp3DOa2+JJvGJwzZHJvuqksJolcJz1wBQsK9Yq8DyOPTnJV1yTdYSL0cJ9wTZWB9bvODgfFXJgf7jsFi6e7eytlkposqZLCcGu3lY49BBg5d41sTm7WyQiZpy15P3+8Xib+35xEgndT+pcvYG1szy7Ow2EAQj4jA77hhho+0ju5BUvc2id7p0XaY1IySjJndomwKLEc/hrtVN5JVLwlFpaNDQUfuhYDhOieeqLh8Gs8fMiyG9H5IoTUP0WW5GPNkZtNMpY1hSGu69p5deWHLG5viJOHtJA9Q+l3CgmxzON3okJLydAXmvxtjhgk5doF/3gkgj79nisi4C1YMRgmJFzSte+VDzkIXUiiSUN/bQ44X1YYu3LtHFRKMXUpG1LtFoB5dIYPINsnuiEeIGl0RY1wcaGm/x0wxPacT6zPtEnhVIoX8x2+WcPg/RrLne+X4VFCXzuGGLERcIFNjNyePx7MVa3lPdkNZWgQJ6HyiRwOvMwKprxXhmuHKt1q00Ux+S9JJD6kncnPl00qNoXCqrob9vjtwiOXP/TJoWFq2YIPl6Eex9cdpdPUyuxuU2MsfSjCbk5j40dxxATXwG0ScPVzqAZD9B2/hzfskG9xPOsnpm1tWy8DUVv9s4aExFBBOMVF6hrE0F3zXGZ2JOQ9vvtYMlHVqsdepU431hTo5S96+wt2iCunbZAEv6aJsrmPTsTl3D13qsNhMAIMCAQ7x2E8Pw0C1elxb9cORE44xIDOfOzkC/bL6hMiXEnXg+TjFzxal4IDhkorXFJa8ItXPEoWh3hL2uzmGfbRbzF9om+HCi+kie0tOe9XhlWJ2BdiLKpEGWsMu5N2JDujMXTcCkEIpwKQ4dJXKNBeTxQRtJuiIRsxxzvKEzwZlHjbVKYcY9tjrVczwmpbgREpvu29LJzyMTE7tk1jpW2qBfHiUFlqIeMhxF5TeecTbfaEUBcavCTNtVel6lX4SMr+1hKZUFdq5Eo40sW1FYfTQwtt7vO29BcHLHWMdp5D/XEUpUIPb+kK4erTF/pwswZCPaaiDYFwlQJO+xwQd3JkgvMy5G5U20sg4WuY8YnApy6w339uRKDbaiKfm61rz2k5EwV4bTvjBbFFvO2XDYqHmpiAiicxRBtltf4nqoCeUShbFtFtSm/iLaz1a/uW1MxnkUtMhliJT+fAVJpYT+d+fuvo/C+hZy+9CP24MN0GFA1xrF5810xkQpwWB6LJsuiM8ckOihHiAO6ufqF5rQpGiO1DLTra4Y1howBVHknxSw5mn7R5LfoTNxNk6LP4WWT8VI/F4X0UcJd0tjrYcW3NqgbkUoITU+84lOA7WWNVk7Y9av/hX9qRuez6u8r1LgPaqrzDdcNihmK6BJ6KBILW9tgiQxgbHUMaKrxZwRjKlYCcM9HOmXMJk7xRMb2p1BgBhn85kS+ukPeZ5oeFYc/p88ogzCwd/EDVYwZ2ekBRmM2iycGv2wrymHMLSaFoZd12yp7V1DwVHvdmmULdOSvO0Mzm1yD46emaIbtztlaGbXwoIduIfet4Vxs65AWyhloVK8IJcWdxDhiXOjV3BTMFdCMfwLVZuDUcXRxu2sY0xzOXGm1HnKEryq1AK3d8BxvXMbi+0O6u8Uw/pAKJZSoPKb94Z8joID2dqWvq3ZGlSeTNjPeTmB8OjOY49Ea0+oa/TG3++0HWlmsW1CgzA69SNnhkZ4j/cZKPKypHQSSj3E4dxV1V9T+qPLqgVGNE6nP83S+Afzt4V/rry54k64Xr5ypu0xFcO2DQzGG59pM5buBok4bxYY8gUxd5vLHGps5h23lieFAkksT034hhMn3xUEoeFajAeYi0mM7eIt5n5JsmLt8tcHyYozXoBBFxkpEqtrSihjoWvTURj2MXNaiQSTtYv2Nag+9zSJIv1W9GwwGDa+4ZF/q2WlB4A7DoKyxyG7bDPoxDzs06GliGOwTWZL8nf+GU3YcnImhEjDDPkdahIS1zh06Eter3BmTusU8tZL3i+wPOyi8PtU6UTqg3tm/UQW2/7QBpsLUWObb2fsvVFv+2GeN6jDrpotdBapZjI489LnIATfRFJHpXlc48saK72H6PrrshCLAL/7cs6ottjzdZMq5BlckvaECGnMiNK/Oik146bdy1Pj6FWcr2AOB3/fSyBPi37P8dRSVOS71SiqSx5swAh/YTeuX0LeiFI30sBZsnY/Yl1E02O8RGQcSZm4R3JRE0xlI8HQNM9tHvQB0G8kINfxZ7fBYpQC4dEsOxa2ACMKZ32FYUspTTkPBCnpPCV21LBqy8aWRr7cm68OgVJ6iofsSs1gJvUID2Mk+5j9Izq/SAq+CL1bSBZVYClT8vw+rioy5Kq4pTlvyKLoKDx3UiH1FZyaaLgoGIcQccJdMaAB01nE8an5tUMC1pTtzDiH5x2+tHDegZkFCvP0Schrya6F72EbbBXh7IfoXNXqdd3vTAjeLxD86L9gdg+znsY9zG/qH4yi1p5oe52/0Lke3HjWup6LUClHtrZFcFwsro8ikWY+2tCiIVcFfJUCtQeRoaXjF5DO5iLVi28EssdX1VxK4s1EhLSBpYO3nSnVjRjcyE83eanuRedKWJIotqhzPdZQHXelPQpHuhvjxZIfJxGzz2C04/wiJ6n6OZmpvE1Clox0A0pWCKUTNP4tQmZFsQTDKT+1Mmg/uYePYxxMjXSwGbP4xyvroS8GuRrRiV/IWkg/H7Vj40JfXKuS1ESgENSt6u3t79TTsJAONzQFerWAok5Bppp3iEnGt9Rmc8F+1M6UZ7Cwxfyk59h3+ijJCGjt1WbbqxNKwwoFW6klfCLhyqKeZLffrcI4hbq2Y6qubJ+gtPTh3uYN8GpTnVukw4Z0vH29WloQXa8tVI5jSYIgvf3D+UcMjLjvlo/5COLOwsPpI9IhT9aTv1ehzQkmavrjHcT19a8qQsyI8WYwnbp7ioYEErlUJ50GQXBvI59xAISdMVGwhVUxte+VYi5sHENTaouXSCfU4aCGXavv0FYXZfdKAyJ0wpTCY4dItJak/BSq38JmwXfk+faCHhlWcFxZsiZDSsHgbKwAJlEJLjm2OF/etvaJ5jvqQdVqXDjwZ59gxKx+L1bHH0nTZd+O4QwrRcJeh+RwzevDKeF5F87nJgafTw0KUErYekenOhHJrJmKPy5SQhzgtr/gq8NqdwvKXrrLbnmBH6Q+D5lCK5YYl0lpwttUlj1tsJJqgOqrJ3FPhqLWj8wgDpHVDsjDRh2HLUslVqxsGqGZr7t9/xWskm2JT7zpvnOhoIOEn7p+5eiZ82gbTRylc64iV3jo8CndeEB7ASky796/pWAUhaYlasIRytBlHktADcq8tshZHq6tlMSc/dwdDV+fhdO3myJWnI1U8l3EwQJEIqmmnCA3Vw+cJpIAYNICR38OknWmutRBNwgWgzWTeZmbkJdXKl0z0bs+7t6rV6yUM+GEd+vxFYm8xxnTLH2h81xn6va7Y8/95AKjkdEE+DM2/tUvbtg+va82d+Jn5PQyGC3CR6o7kgWcpGBs31tzkpqhpdvgq/c34uX2fwV2xmZEE8SUo1rs+u741dGRdOxz1SjdR4gUxRpdZM1eX+m51L6DjyQPfZ0ZjuPezRhGYtZNq5YWotON1o/n50JYZJOj1vqm9BOi94anFgiGr+S3lT0ZV56xf0jYOkNveB4a5h8fFzrbxiZY0T9D7tSwjIhFWJhT00XHDumKatkV9A5854iELYHCzkVEWd5Q0GazUOzmy4blv/wZAPum7IX5GCJAB0haFk46b+ePO5J4PVVi5zB6zowg7D4u3LoecxgJ5sDdpBcCaBHiweR/vwZdTSrGB7KWqPNsivulwE11v2t4iFOqMuybEAmICYhUNOrkm4Sh9LHAv/gugolq7J7aX+IJfhFQ96BNzr/RRdzvDPehBFEWT+H2Y13mYeRf7UhLdNBdru0PBnMm13HTUa2Qs4IK1Erteb7yMKkjeBGa/abmguTtEgMNRsj9pu6v/vvitoE8SaVVgIKIbulbuU07TRcq8f3BVrVyB9bcb63ms3beUbncHCNlMfA5QoKGrVlSigTUFa0tmnKTt0lO7ireaZSTiakuPfso7yXvvID8hIICOhs/JUbFr5WTEdtx43rCzulEQY8UzU7USfSRX+Jdkk/gpKSOrsrOpllSBLoeTAF41m5OdLk9Q5kmjcI2CBt99xyhBoSiJbirPVWqV0qRR0bvWLniyNC8QPufwxSGPEkagUjAOHllMGGD+3ZMwmjNb+RO3U+YfvhkFex6hXmiyl1ZDOLehhZsel51ULur1ikF5qkdVOyYBViWPxbaTN1QooZAvSTFHxTwINHsFGAHn8gZO8aymCKPv5360OpLoNWK89Q7K75UpNRn+rWRJDd0O0stLwdv5CmbQ1EojEwNl/be/CyGrePyI52HQ8GJnr4RsQpCKIzCVRxFD6ofG5t/JZJ73ZOZIUFfx5dmP54xDg7uRSxueyg9d6F5KAUJMK5KlVlg7UUi6v0ed5E+D8mQq7fCahNrb2foNQKEjKkM9ibupbHl2sa3tE9qFoMnhxm1y5D5zfliff59k10lCb9kxUldmmZjRg0raDdnOseJygn90KgLfzkOKrm9hBeCzsXrDPsk7cUGEb2i1vEILOmylA7TKuO88/9+ddfi10amR+i90EXunAhflL6QynftvYRzDw1DpXAXl0fMjk7nku1ISBk/Snt3J3ygj9jaKHvzAisJV96l9b0z9bZPnkPWoodqfESRxi/YswptEo3ANZUGq0GWwwXTMJCxZVIcU9moe+uumXeiHUJ0nc1xD0NJ7oU7uvVWluPxEy9q/g0nC2q2QErJji+hZBp4hIpqQJGtciT1cW64khMZBrJ2l6fBm0lScVWHfgzcwjM5cyZhcxzwYcgso2YjoLEBS9PMwweotO9qCQjfu39I5ZmH3rWrKfwRGkJACExZd9rkvciPUe5b7CO/nO3k3NtzXpH25AkncrJBnWT1Fl1WT9Nko0ZFumS4lcGdVnBoOQ5Wn2d+mS/nGRhGqNyMfGg9IdKSzYtgj08TryL2j/0tE7f4+BJhsqy4OxpeqW2ldx1h1L48aWv/204uUVymz44Hyp717FfIe/6v8E+5Ljp4L+yG32u/+KdSQJSatmm9ZvLtm1lPswSDyWB5evb9qrh73udxn3DaAU3MgeBvj8KcCd0tzMluEibEd7YvVUv/gnMImZLhjRd9NODEQbPuUFWdOi0ykDgNXoOUkMwdHForiKSOUNX49eaYhY4Q4cycR1UFFF8WzlN9tcmffB81rqyR3ncbvzCeGcfYCobHNWujGSW16g4DUYMfth6fLu182rCtJmm23m5i4NCuigQl2znYfIGeKTaDXpHcdvkB4/cPfZgyzvva68TxbPcwpXIyJ3DookvW8OlfUGTlihbRTdoaMgTRuVzkAW6q1LryYDqfxIqDBmTf+61PpGtCutbdzyK+sSr9Jp7CwF2V7eltJ/huu/NONuAkaby9Pi71RQv6rNqpbdR6imSHCFjEHfCshzctphdwprInlvPCn62EbXOtq9maKPn9YUo8lK3fpWTiswpEGjzxdz/pyvY9jzqgyq/FxeeDaop+p7GsnHu+B42G8ZwpmqcmfygzMNsS3tRxkE+ZHnHdeFsVlDX3Epm1cchLtmYo1YP1CatNlSZnGPnKPa2jA3qPiSavflHdCjs/V8AefxvGD+IQlmk3eL+VZKU+2qPrfCPrVZ2kAIKU6+vJnUSQcbum1YMzfJkmsG8BdQsF33wyuCvDctWiZk8nVXXWfl/6nI5Y6+7/Sa8giFia4KocTzaoO+Rq53QQhyj8zsijF1LmINqfk+xJhYROQG09Jg/mtgvEpbD+sq869kXvicYmCyW3y+AS8iQv/MdLXYvUUcSgQ+9ZKOBiG7g/WEiuXoSE/0wZknO0LXxAeT818tuCo+6J+7Md6CmKlN2n1wTlo08kt/X1QGv69e7CQZt7Txvbib7o3cL+N9JPi47fnS69XiUm7rAzURutw3yh2TvND68682J5lrL97IYeEM4weqsntt3Uk32epU1qahsMM9echFR93O2yUVh9qVWLdoch6ZUK1lxasO0bPt2R946S0gKbyejy0QuTh8teo4vM/OFKHVCzU5iMvm5XdYsy4Fj/ANjMmPXk4cJh9trnM2FZ12ciK7uLYxl7NmtSMWKOJNAhv6hM6WQaSHd1pvG8sITHGRDx9YEd7ZZMrxuurLsTLTHA0zvK9DboQMPu/6w2pT0S5cO+x+kCM55fZJUgYF1yv9ZZ9IMjIdpqxIXPDwCTSRB10ozpj6oohXFijoEZyKeVVaT57kQC0pIDEOl/C0FogWpizkILwImE/JGqA0lrtatSiaTnmzx13uxIX3XXQVCITdH2qaLaclBpBWVbF03PUzNOnD4SD9RWk6qaMsd7gFvmwJyY/pkuKYj0ZA07X4WkhLiMpMdxgMVVnbcwXBVO9JwDQI5fyrYtHN7bicnl/3WdARcnc4U3Kh+WDUClw+gCloCHsz5rLK8B4c1ZQxTqSFWlRwGh789Wym34zBvMJBrEc4anF16q/Ak15qHGP8AEH6qf990R64mIs7uCS0xU4MEnqBSlo2lvNZziBMxPyeckwdQgM8CvSfR5P6jjR+dR/S1HetQkm61cUu66o/09snMik4o2497nzyxTS4qtqkXuOg9i8/YZUHEINXrHFB0wL4NmePEIsOfJhFuY+JudPzoCom+9lQcjExW9759deyd22QxxpvfaTSMz6gdEEqblVIhWqMvYeVDmvVJuEdvuMeg5MBSROg2uvipeFrxQ+Lmggl2UzXJiLbacesTrlIZYd3krHHtMkSbcMVS5+FrY4wlgoVc3kEfgNlEpH58wYdS+ZHs60qVfIcxI1UVVbTJGB5bMDfo1hrFNRHTAUsGVQwdOXwvRdYim8Q6mbM9Ugpd36hppPVHF4n7d6dltgjK8UIsHIewVt+MKSk6xvPkSz+7JOPKyg+/46ye33C94PJ5ZUrdRGfSSWUyrpCttcPdB70bQgnhlVvHNcLYNiqsn5gBsq4GWb0LIWjcdBltvOoRfRMN8TsAddZgYTlJwByl3i1weJO067y7w9VwKOjMyMtF5R726fChDXm8R/DUt6WclB3CtuEDNBFM1Q17HGoLIJjx/GDl62wHSqx0vioczRpVPbGSskOX0640068Nk2UhuF51fQvCU2gmohK40te/eqRFNnvX1wDIKk/NXveL9Kb+1lbz+5qMH/0S0VBvprdq/o1RxnzIrPc2R80Llk9RheXJ4tXRbpKW7fYUpwbM8A5Z0Acp/sPAFg71m4DoVoXAXNZGg+mbRIHhYoMSW/+ypxcLDr2NfpyzzGk20MWVXmjORi0YUPKECaN9RG93gs9STtjy6YaNLLWVonzZQTJ4MS3Ej7vDIveX9bq5NXVOqqi7HkfW+q1itymIUOliMdLJLMs0kOii/BGWHkz1IanPYcXd2r/VczXM1PZ82oIaACW4bSXtbbQ7wpSpQ1POTI2JH9Z4ES+hiaaLjNcapByexI6OM0mnYN82PEdGirdeBt6CDE2lQyOx94lJMfuOUdmTED2ArkEkRtV4g141AwOh8FSK92+eeMH/AITdZWXFEGFioEgJ9uI9tjag3YEQ5K/TnR+SPdw0wC4xOEu3Jaj/6ZLxY674R3MVNNz0hOzE7IMyrc63m27WlYV4MFYE5JBDQps84hYZT4wB/W9xTNkBeS1eNjkDZ95uNii5UHK34BqPwvtdkrwIneb5Jsjq3Jv3XtUVbvrF7rICe+arat9yo/Q55F4gD2sPvl/SjFYlBVHEKdArnHDtlPOBG2LI/qrfvtN1mcZWKZ+3xKCB2P/+qi/dJoSBiPCQs2rlBSIZFxyObVJwdZ4qC1EqPf3mbLmPlZdp4RdG+QoALLQG4hIow/SLrtFDd5suq29wrzWzj6NIRzqlkPC9UX5Hycv0ETMAp34u19gXEHtPlTclCx1d5mM2zSgYVim11JdqlkHTezuKnWSHdAlP5rhI4mFwHv87TWu3NFIWwThRBw0fnzodMcfpNIKzwLCVi2Ngn6chZzhwOFFv3pG6YhORXRp/R9UoY/kAOFYLge1K8zItn8dTc+iJ1JpHLNKfw/Y2Hb5OK1lZcniDF/yb6TsPJmdLuU/TcSeWQI/My4NckUQCGw5EiBbbozL05M2eoydngeS3WLd31KkJhB5fv+pLGACxV46HE+kSN1QATBhZTIXiGKns+b2GaCm+AzuqC5BkqEaKh++yPI8v2YnEmYgc88bKdN+ADD7gLVjDHpPpIAy4R9ZoJNXiGemDvaZLW17E0B/MIaa9pvRI8bnDNuiyTXkUJWwKD2NL2QAtodRk5vcJ0MZSS80IaN3ySbD96cyAXUsdSv+j1g4pTP94OWaIpFnp33isDXewCxX33zrLHkgdgkW0CmRbJoHhO+uVRtJEEkPwaxnGxEsY7qVaecXy7BiaixK+TEFW4FIp55JbD3BjPZeYxEDpKE+T2CCyJi0j4uP0Ci4aQHL7NYqKyU2ictHTyLfIkRHiDKaN0ZBPNTqWKQhT/GuCaWRIqdi4ew0WtWECvGYeJVyG8CtshAyklO7Rte9H2J3+FbI/imfUuQLw6XHFSmJ1AGLLONtGOMR6wK9qyex0mv21dOxRk92F6mqW8y9YGldhSQlPDp1/OoZlwxnwyRJSO/WTAZBXyblf9iRENCo2VEMnaB9tz4nOfyGRO9NTN+5ngnBB+ghHyPcqheA2tnkPtD3u3x+l4cOFbDxxIWASqMpFv1+Oiolkx7msW6PqnVoFHrVf+5MoUUBHBr5bs55sEecm+R5FBOg1gS7wza8GIuPn8HKgD/reWlChCOveefW6OmoQOIaUdly0q/f534saHJlQ6ia/3MYlCHrGoQ1bPLe8oGY/rkXYYU4diTmmLTj9ru4O3f3I6N63h3PojZQLgkc/tzNoj+Bnx1Q2FiLak/baki2NhuwD4xsvoCof1N12QDqD+LLKm+LBzrBB1UXvXkqr1xKupKUg/v0JJbD1I6zezczqEA4thlktFkdU0SIS8Tqalb9QH/cjrG67hnmeWEnQHeXJ+i6OcB/JDUi05hphVa4BEeGhVSqy/FHDeeO/O+vt0fB9jVvSaCQFqr3AXiCcICyPrHvSIbZvmW0WJoEPbtSkFCd1cGaEFclV8l7vcSQPVN+/IP88bnU/sGip1mUwbgaKllnNY67Ucd5g2dvdp6rDhIYIN06TySlO8Vs7CRkzdPrAXpZ8TH/6JLmKNsSvSQAhWVF5diuNUwgErOcDzXV2UpH3vV9Flv8+JarDrRYATBkJUpCqrfBvWUuC358WJkQChgPHo1uPD7+EvXqzcPivQkKFmc64uixue0HdmIHLij5aGLUghx/FftmFiwqraRxKVkVAe9cM23u2x+FP0qWvuaIW9tdeQJNSqUlcLL3d4o/SUbKe46mDmPwG4hY2NLqyD8OhKE6JVAzPWmg6EWLMM+UV0t7NgBKuM5JR+Y8PYikveLRHy+KWjsMF8g+YPjG0DleCL2IdL+TYEDKaeHHE2CJzXS+FXYDCE/awSi8QiCCeZoVpI1jj+Cw92bxlcSTbfP71mAyqMY2MMXX073ktvlGdbEPmO4IHSeVCiL5t6BI1aymIlGyFlsNnK131Wqb6Py9X+FyHsb8iF6FG43bpEcVYHHBeufR2zvcYhjYLvgB4m2Uwb0m1dJCKV4GQ8Gjl/fbhim3YOFoI6yB/H6YzyftWWbIlHFao1W0js6v6f7vGJcl43QnbjxDa/gFsjIqt5WMlpmd9u2r0YZKJUKgUpnfIhrM5YyCtdGgAuR/PVrr9LjRQH2u3KbpTx3hjZFkYwCSQzPJUyfvS8DK2/ctL1dF37yAzzjfHz+MPvXYAWiizIi8w9nn03HjWGvb6V9nfYO8svrPIEd77ECeRjHPrznpKyy1DbB2sV9uk4BY+/6Bd09vH/X9tW+U4H4HB1MzGxiatd3d1xMXV1c+V0d3cN0830FZucZrq7mzF9dTfv+374fXmf3/9wnlPPORZkvi4IXomh9yStTURbfkYvdHfojOeQmL7zRgES7XtHn84gKVeRtPGK8I3z3F0OYFu2MpmFxnpyxo5LcTPCLVIBaQDOb2XSc/nhF3BhzHKFWbUeyLFnCyCN85dyj5varIc1VItTPkOKSnDDsDkZWQe2pQvBsYz/t2K/u8POtNye90tlQlOQ1ruYDW9yFPYcoGTK0F/k+EvuWUbsF6lKETlq/1J78c9PdeCPiXvn62EwymTKIv8/NMXIbi7cmBKS2va30K93CymYP7io8UlpWwOaIx3j9phDpmRWdWo0XyLngKoYpLA8TqCq0hUB4MJ25CfXt5N6GN6BKV955865k1SGCubz2QdjyItGst1STlqDZthBH4yfpaEW8jMKY46BbnAftefMSMAVDmk3ItZMYQHn0bD7dhz8KHaWAv5K6tY10J5TLFhAsf9cAX6VOhFDq+YIZ58SxlGxqasqUML8E9ABK0Vssjdr6PxOY5Rsu04i2hIyBNL+jsTcQ9/TtC68lWF4k9o+biYZC2dtsoT9EsJ4VWX3WTeuBxPJ26DJMF06L3ecQmUyWBxJnbSvFb89nDv10IvjUR7p1MoCr64xt09vdjyoREJmiuJjAb02YJBWYA+w/4UZy8LJx11fUos97R7EN4z4imNVV4C6M3KTQAGVY86SR5lmB2aG4EtAgq86vpkSlkx2jAWSrm8PTWgmBrHNj1HkWcWOyKpDjhW3fLruZKqbWhqotsEZwjJmBudaNItnOYOONznFV0cDYas6jAPeA4BYoe+3+QgZnkZiygcArD5M85EbnwgOGxlhYgdPS0Zvf5vYath95Qy9yI9GI0P1CrV2lYmKzAP3pxpLqQK/YnS4FpQbXO/qQSxAAIVMnLtsULzLGCdm50Z36uKLX6y7vSKVgjwkd89T6oYJke1RNm7QZYPQ5IAC0Efg5dDnEvQ7o3NQjsY+etWqgjmxouGw3deLsT2p3LKMO/RX1IMuG8mvQwGwoomgyOBZOV4CX3KSvfb7gwLirAZ3TutmYoU8llchlFmi2/QlWajFJXRkkacz1hgli4ujdtrH6634Ss2mzp9h3Pwt2oVdS9HsPXqfiHKQeKA02qFKpW9ZFPNSWwmuH9lGHMhfoByTYQHMVAh4Gpt7A5YnijSw1Z7UloWGI9fJoaruvbs2fMhYVCO/NZx7310ryHWqUOFMqkPWlIKbFsMQ+zwoQ+KHyVUa+tL3Rvpvit74ood5X7SQJYDzaYpHvO4LIi6R78iJcIvQsaTFGj4OFBryCTTvY+UXTws6xDspIbVqlO4ax48k7ieaxhivjxahCZ/86Jgutndsc5ertXVRDRspVcAuzbtGaJEdceAobqaNJkVgjGTQDNMS/Yb6ZlJ/aKS/ikfXCGt98kndcQwSqb0EMihrZSMiOUP2DGwnFSH3vtjWlZ1C2oCcT0e7N5Yewl+xYawwCTKUuYVDsi8uVId5VsBoE13gz4S5HKh1bL3ulKyXfoUuea8fLM/tWXIEa/JL+1xNoKN1mUc9sc6Mf2P6NwFwOqh1JJUAI6k0FXBbiFP5DfJvArxv4HsvWPf2Z+UQJXHJZ6+ipJZUhB9H/noYgHQA9OJsRo3oFV059MB/itHsq9qC3AjHKAarAYMFATEnX4yHLwyMUYbaLlWLGbDfVD//gbVyBTJaSVxs5EiVGCot/pdr2xnj1d94EbYqzIWqjP8NLLgYe+ba4J4zGzgHjBABayLY/disVdAEboLkV6iem+KYXdCg4UTjM7akdEhsFlPhvbzqvNZ6DpGUqIYErBxfj2prIV/76oOL9lfVVVGmOIAPS9YEJLvdhJrJc/8rEb3CC1IRxvN748jyWpfdIfB7F7GZmsRnv9iYX3lDRHZdSdVuRjq4Kr+d4AddMMc5LH7HOAKr3WJmTaZQHYO5cdrlauokkf/h4HElxS314NrwU4/0pRa+bxOxK5r26kwuwuoZKBEoj4yLgmWoyT/NWQ9NAf0jM7PPkCLvl0F1e4p+Oiwy63y9fAzs8xMvMTQtyNJ7vfQ6f4Y/xO5NiOLT+vPt0sXZrYf/HL4Qj7NuldNRIq8PkwnrNymtl8qez5k3f8dqLWHP7pJ/tZkF3lOfECxOiMqP982PNmpwafKfA2ji4gjUrJnGrtQnxjRP207h/y6EHF3fxQ/eKKuK0AiktQ4bxMcXZnT3OSjz1oCD0Dzmy9zSihcj6fw33Bsui8RuIi42+qJ2Twu69/N/eVr71v2g/yLNePDIY8DsG1pIFreIF51dj6BnkZAn5AKzn7G74iXdhpqpphHWvZfb2P8jqA31Ho7tJvoFX8laBNY1dvGriKzGYHnlDhG0XI4dQaRvlsqHl9amAl2MK7trZqtf2208JVxYaeaL2Fcc7hIUPU/cEcjdplZDzu353a55y8bRmTZX8V3Smpm2vmELhkbWPyeqjvJ4WqnVaul8JkxCtbFKGFhh3BxwpntgOat5f+rqAfmmuTRi76cwwoOpvrU+lM0bQyb8A4zArDYy0BAPqLaaDN2Rw4rzMrn807PUOuOncSL/kt59NyRMH/RK+kN8YdbLhB8jYQrxuTn8DjkaOafPu11Z166QYaL1aNVNs/CuTxJXkkVM6JcIVhP2ciP2qnN8gfyrpt9zxWC/kSIzCDL7QznHcypzfDSoIS4y2tjtDbETq3vb9VO2HzoU7s3jC2FbZG+Ez5RSSbVwopaMpQgxcALtuDg0XLX0SqYBfgzDX1j97K5DhW3zyhDgM/WB9/4TNoaobp0HNea2aUY/C0fpXj4m/jm6HcHR4alacWR0rHuw+HReRnRocPwzS+Axmyb1qpE74Xf2z6DavUNyoX74Xk1HjZW5vzFO9c2fKDjz9kCtrELij0L+a92PtWjsp68uMTdF181JtBW7QWD9b4SLGX8DNcN8MHTopP20fy5Xh01Y8xVBaYupgILCebLC5EEf0lu6fcLey9/6SXWz4cM4Z40N0Cvsk1VaSSqNzfaKVSWAqmQpNKe/auQsos0m4zJzR6iRWZLSC4fDnzCYMkfaaOy8bbSwDN2WK6pJxNHEJawnrEHEt2nOC0uw3U2MMI5NDa7AyKviYdyWUEeNpmFeN2FTjy1sM77LHT4wU+uPfOnqFBbRztDhPTl4tRfVzOeVE1Wl5F1b6A/2vgNmGAH+WIv+BtVCWRXlfMICNsW/ircHzQkzAXxbTC4xxyjLOZAzvZJqJhHWoEwiUM486A8beYEhUz6CtMJkKDk9H0qxe5MC/D/DXD+XmYqHU5+zDae5JlPRCCNj4kUEbIZ1jvX/6r8KR3lECj4cLUpNzHM0iAmOudrVTtD5SB6aq9ulcjpqlylvK+B8q3IxX1xpDhVeA0fy5ADcgwg0D3Ao5lzDNxaNQ4HHhwr5B8OKT0gy3DxXVdE/FMRosDGy7VBtqnREdBpYS6k8QeZ3PwJU6JE9qXtJCdxLUZjrJhp/Qc4Wnm3+VY4PjzUjn14vSntQR+j2cORgcHsCEq4LCb0xlUDy7px5ecp2zQjUknw6eMITGWqGcOzVipE5dMqC3CJyBXxEu5S6tQ/jFnSDp6LqbvI6SlMp0lWraIROdft4QfVkTHr93zPmXBGIsQ+2/Wq/MzvexroDLIxOkXSrPcLFTvyMoJABPA7g7Shh77vhh49CtzRQBIQGFGbDb2ChWxFXq9jfqNNE2hp++q2DmlKqK1wfz5Bj1vjFlf/oHrJmBee6jMgee51q2U8TeZTOQ4bbC9RYVKE7izThjyc9VjmWH3tQl35DCbWVS8u1G8pRnuNehwIKhBUXqTBjpbIbRi3tXRV4Z5tM2jqEvFNrO1DFZ2zjfGhPKrjSCE6gdONkkL6a+iRos76eKE5q/rQ9DutAa55Ufzr8tKnFlMf9B2uAtiWA2QYjFZQuzuOAYpVpnyUf/BYTAJbGxLp/teheK7CoejnCMz7txDueoD6hegnv/X+eZ8h9fG7ar9ahCJguSJU/k+SZw+tNJd0ulh/HO5ZvkE2/jihFpFQrRfS6xu3uGhDG2Cnv/DxxG1v1aWSqYgE4g+TMpoK7xHnZhST3nthZxTSbxXc0OSRYKEHreMM0lk5b3RyqbIBxBFylkHC7kvjwq5KDgd3AFNdKEiDBkLBNCn1rS1E1bvRN+G904RT6Qe7kvFTcScjCqkeJOYyQKncQbh0bUdEnvnyCrCPqUASJQ8XiTGjtXl5W34E6Jqzj0SeOTkPTl/QBRHmVUXfbzGSjJaBeOCBfXHkyvNx7fxBm7yErjLnPyHMFebJxcCU4ViS8CgkK3BpSvqeE77lI3RzfyJeubImUmD7i8bXEISuUHZD0xxfOLxOAmC+EIO3kJpU2dfW7N+u/FX4v/r09Xh2lzMLfVJWfFsXexSS2aVBLx3yM8xgPfanraqEvB5M7xGwyHxllD35GDw9DZgQJS63Qd6Y5I1ANCgbyvS34f/3oKX1br4kGhvMCRfMmdQo6pWCvUfGHPXKua/Oc08gG5KDZucDMnc6H0I+vxXe2DCqEkXpdakP/hr3uzFeZpjH9Z4atDhqzhZIs3zmYr9i9fK1puTlzvXRSCHsVdvJHGD5F/r7OjtY0vyjThTg476ClUfjn8yutOw8zFn0sd+bzH2w5y9EZb+ijiDrxHzo5F6I9Xxay3wtxx2bmj2ERVe5w1AnvS41KZU12cgV1Msm1Ehr6jyHM9jpgVYu02icdnT1NQPrj93rbHoVqCmu3pwiDZvMpRpNUNbrYTTcTFHOzNVpZ4+eqzURkTPuPFY20dwhOvGo/qaW22thLOrAALJR0Qyu+wDSB8q5mAqkzgaxNJWU5jLv6y0quDPnj5GRdSVuemSuwJIsLPFAn7KtZFI+FnkqXbsHvb4TmnGeV2Jjd25a7bV/vwxW82k6izlU1lqMXe+CbRio71nMPU1yj1/ofv5oO488R4L/+wbGZqOaYP3aF+hs1MMErrZ1WEQw8RA4G5vWKzOkIRats1ggKgmKu+ekQFsP+oPBNpEvJy4BHlTfLXytaQh8dxs5TR6fubZTYTmWykqWsOLZLuOM+XkZxNqUHI8xaoJaav7p9WXCVzI3kCRiAmjRoa7XxgZIHaM2HIhdQTBCJG+4uHuE1fDvF8RpvO4JtJe8vcb2AXRTZFnHvGVJuM99Iy4ZKI/XVB27myL40E20Tv0OlQlOLZZhUWb76VEOCsZStX+wN4xHChbj7KeSG7qBUOKubjLMJsySp7kqhKowYqcZ7MkPnO+WLotSe/TQ23KNoA2T27wk/pzTFOTeK8sl4r/DvOA0HKU/gSco8s7J8xxCAle3/NBnRQUMys/DdPT4Ha+vHKF3bi9OjmtKRs+hp30N3comc0q0zEFNrIlAC9k+fYB5Ws471EikdcwoNHBWl9lBD4N9KUa6d8x+lq/Tf3ThLVZQqnxkL7kLa/aV+El2vc+gK0gg5+LyTsCSpJtH8tpvkHnwsu8ZkdB210IvbpY75tddiGmzi6HPDbjOD0Mrkmn8lrgFHnvz3OiJ9YL6sCnGjzl/2NoCybpUz8qlWr4NsS6xVlQQYiPitD9IywIrrkTGiuNm4EaUtBLPUPzkZfzqjc7fMaiimQZv/oiflJoPEESqFq0GhPX6Z7UrxxTiIPjPnjisSt3opHmMYqu5YKKuSRsgBJfDWzF2Rpi73e3ssoaJgzc51SXh2IjWvsSkHqRkILG1Innt66fHrgLZqScUYFxdRP15+38exC2SbD/xOQv86zOJcl+GFOek9L3N3+hiO8X/Soj6loZutvmg/Dz/X6WcTV28hmJcIIH/siZtJfzVOYSfN3WW81tpL/rkkj4ZQX/QZZouJWLP8K6i4VX2Z1vhlCgmIJhTvwiv7109fAPCw3WNDWcrUT7EfTaL49lWye0554D7omD9NWbUOpy/COF1Kv/QSq/BcFu3NQrnipMnoaFmDgD+SEDBtJ+NGU/EdKWSNOVjcInu0i9lIEKqJpkpCt/GTW+kQybWxVZovx8VYR1EZ08CviZjfvHd/xu+sJ6HdMQVJqT3sXL6rNtz8PcZqRy1uPalM1CROwGdUteRqUru6DUX2wTfrCRwBnXGQzaacFEWCL6uzvGUg1TV6r8EH8V68vqPBe/ZR3bt7b8P846PHNC+LvbJzr4XBfVA6ZsHKGmdQS8FexlPDvn8v5D/1ABzbWjPuUM7vlny0FP4Lj3F+EEa2KQMgoGlv6WocHMXyCH/cO70RHc068Zzb+klcIR9B1aFSMNr+l3LK4n6o2CcvpONyTblLSYb15i9qPAPhdAH0GK37Y9SDn938RYQljJBSPl4qO12kWEsxTrnRYEtvf/JkZ3dX+VP9L5eKgbPPV4Tp9PtwkD/JIptMAS9ZiJVWFKjEizlCsYKvrnPve6dpkiRClYp+9+Zl7DkeX03jDAHJAY8eNiCM/qkw8KcbeI5jHr9E5u6zGWsqIZ5K/fWLyvP33xQOkIG4quHIyumDFc+bR5t0kJ062Fc0/bHUfK50T+03b++CfMDaOsN6pZtPXgraEI9Kz8BD1WG9FM1tI4JGeMN/zjJm97Bgkm3HSU5JY0pcLuH7ViVZLN5jn9LfoWu7bph8FS2JKJm4Me+FHDwtU5exkbqEYkqs6F70us6wbsJidBvkKFv0SBFf+eGLyDCbYhY3S1tQC48HIwJZjs+2TUSewbghZti69n497/G1p9/J5aQDUQdu63wKech6BktpmaNjbgrj525YwmEe0+tBMGut8j2wxsgGj/YlE0Xv8zVDcVDEon8TO/uQMznvlO+04vVoDjoC97Pc8rIh1OmswTwr3cf2S8uZCRdDhgpaBswgXZCOKMcgW9u6Xrd/cAkuMw7iz9oYy4WUPvK78GJWWfPTVrNgEqhNOupOREKLsNm1mT/lC+cOGvFKZLhBXz2yhWAgR4Y4wciIlL2isys+sSfRPg8tw1NR3o8fgBgLxIyOc1mu0NL37ZsDodqVV7OZLu+7h/Dyc9TkQXXf4vQboBzTkkBr9WixoqxaQViN2gTq9UUpt+HgzL+OLQQfOUn0X1bN0wU8vCxfO98whW4DflTLQjrvnA7padZOQ3gXNjoAlix4Mh8QZDi54tNJXbs0eaOxce9Grte843/uIF6R4TY9HM2sjOhiirH5Qp7uhydF2sn3CgsNP0I9zyUu6l5TTD1mAffBXlt4sZgZCL9O85ouCe0gO1GYy87X5hLLkgEM5aJw7kkDUqoQ4YWVh/UHRmw9wvTsK9deSmNNtmU376R9F6XJkixBFtfpoid1PrqJOdzIt62QXF5WTzPCQIfUcZ6Sw50R2uQv/pZ6zhEa6Dkbz/OTG1ma2h4H0p+9UtKRImCNAdAWvcas/4U+k2tNwXX+k1QHboaP+FrIIhdsYgTLuxAUFkob6Vjaw5iXSsmR1cPdPcCYYdP1TUQOFlys0UwROlMq+pLoUdcmQer/ddMylU/CABCZvH041mdOE8tF/I+ekop4qlP03+DaM2a9QR1343KuNU9cj3tBkg+gDEQ5bKoMrsPX+XITds3Yz91qNJiMGlFMr8eKah4wgkrJIZoDcSsK1720MqFG3bkg1iCPr6HYVthgc7y+I5H2zB5UeKert5xXq3vmCSmwRHqVjzmDcMeLyHM23Ye3haa+NU9LetWwY2JQTd+3KocYmdI1zExM9XAzpxWi7Q9uq2ZUdWmOR4k+oYmz4FRXDTRv2nauomrij+9bgeJTNQdMe66gCv+mkc24IrRetswINM/DnaEXZvJszxuU+eHHQd09cyw7pKEUmqT2pgPyWIUh9K/9qNgr05hH4x9fJUb1v/ZhxV2le7jjeg8KcILSkdKhQ0gF+pBQtPOFnMgPJO6DXBm1IvlBnvLnqi9MOFQ2mTqJqg9/6APHQLqyzCwohVPSz7FB3YKxxpvm+GlCyG6IvZoP+XIe2N4+B1GO4CtlHBQHl/qt8nhSyZi70NGdH+IHrx1HAvGeDzXtLBsMSLbhYPLxTaZNjcwvrbCjEWAC+vBBvSnmHlFpP+IoQdVL0o/8332z+5QMc4wAbxEa4vPjbeuU8Wdv1MSyoxK0DoqPPuIp2laeVgKdtQ59OMuHDNVjeGwIoG7romQN2JSLg80RvaU2f9pVnzFyJnPjuT7ZunMowZz3304imayppa2+jcAOO+HogG3Xdza8v6tvajgsH4F0wurdYXTtttZL/M+r5QspoL6wrcXOoubnGJfXABnzk59/66QjVjwPLrPPUdyNNgJDrzcr8A49vN7sjU0TzYC985jcPMCHz+fZlGTNIBJJ5IuNl9BN+BhXS0NRq0y52oyQ2gzmerg2wzUIOKnmn8nj3m0JRvzVrAVDKy3CSBxJSCza6ayvBRvgxgcI85DjqJVbe+bw+nIquJMttjhJeYpv9bfNHZD948OGn326GYZib9yQrjg/CTXmqqyHz/Awj5drigoTloXwFCtdPU2v3TJY5qUc+4ph1kHIcgRDnC+V0+MFRbcJgxlOiuSIQXcoQn2vrrmiCoJAnqbFBE5+sZve/HUkmmobatonU7ubiaJxQYykDy6KKx3XMfqklRtAK6dQjaH4LXGLy9bsgfBZxW1Kg5HmH7re0ixHoF2u/i9D9Av1RA1hXvIOGaZTRFLa8Y5sEbF0B/E1yJSk4+pT/HZ1gFl9CH68oY/KHZFuOsUArrDqee+8H4lb8c8kwdXhyieTgkqOBYn2gpkm9t7cYr4bheCN3tvolga6Eq6fOzaJOki+U+nOLpnzPCv5MHo0gL6OcbCWNve8X9j86S2fOeVG5tZMeYpFc6UXeg1zFjZiIT37B9KqmTGpqlQW6nRK545UgTM35yaJKtTSf2saXG3N0sj+BZL99NGfbHAoKYPuDybN9jcQ1u829h03ftMutTUROwxGoaBniM96gQM3vjIY5wHVGX7I35vo+baHLJts0ZIuGq1SEzQDqz7Jy7rFuUdLb00rzV/G+LftJ+sSmjeTLJVYZOdsh3hy1Wxi/Fm+RPM0r9crkvzr5L7TYc95ap+Z9tYAUur+7IH4KN5u/ZcBstyo7UvWfqffJlyrXaJqVMG6ZDeeDc1bt8B7n964Q1j6MfbRYCOL6w1qBsBOpv2GldrqTTXzOV2rOLHAxNgFm9R178BdjuISr3WNMp4A85aom6VDJ4MUlOqghdD21jsiKVdbRq7t4lgqun2rdkPyxxwmFcmPzOWYmIhfGWXi4JyYnLphWdVyQR4UII/6gu8FChjpsjG2eLnjshC9JgQLH9TRGi7K1HsOzkpmdNjRknRR1OClt9/sFryyLg5JDyZbjL+w5YPYqFhZakqv/HabiU9YszjBLQbst7Y1RZpoa4SqydLx+/ofsL0c5gARJaGCjCHKy5d+p+QKJJyOQdtJDAYC/rNI6wCHqKXsSuQykQ8XPUuopoDBkwPLQ9W/20+fuATqbQMGnTMozbfYtOn+3Xy7+YE/92OtgGMuuW5VeJMnilbRMaJAX0/50GTtzTbggD4BuXH3sdVx3cp8/oA+nddyf8Iznq+EionJwG6ww+AdXJTv9rX3bzpXIIZveTGZ65a4H4vDttz3RT9F80S+40/+AnBVy17h02REWqv3cjhY3YUB/AUOG8cBbuR9+mZFOd18wEkvj1idzxaj5hp0QvJ77nC7Zh1Xv7CwI4eCFIHfPjWfucsdfetzhJBpk/8PvDuwbwplbmRzdHJlYW0KZW5kb2JqCjQ5MSAwIG9iago8PAovTGVuZ3RoMSAyNDM0Ci9MZW5ndGgyIDIyMjcyCi9MZW5ndGgzIDAKL0xlbmd0aCAyMzY4NiAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42rS5ZVBc3bY1jLu7p3F3d3d3hyCNu3tCcHd3dw0WCO4S3AnuENz9I8+59yTn3PfvVxQ0Y9oYa6651t5VTU6spEovbGpvDJSwt3OhZ2Zg4gHIyavY2xrZMTPRS7sY2ViaAFgYmJjY4MjJRZ2ARi6W9nZiRi5AHgCniwVA0cTlPdUJwMLExA1HDpAE2gGd3p2mAGNPgDzQxUjN0wHIDKAy+gco2Tu70BsbOb+7gXbmlnZA6vcUUXsHTydLcwuX3zVY6el/V/qdLcIAkDEysbZ3d7a2BBjZmQJkGOQZAAr27u9GSwCVvR3AGGhhZGMGsDcDqAG1AOqq4iqqAEkVRXUlVWqG98Kqrg4O9k7/o0VUVU1dkg4gJqygJg4AatABJNVV1X7/VQPaves3pwMoqL37f/O8B/5OlxdXE1bTVhJnZvy9BgAzwA3o5Gz5m/a/tFG8KwP8kfaeauZkb/sPAYDKwsXFgYeR0d3dncHc1dmFwd7JnMHB5h99ahaWzgB3eydrwPunE9AG+E9jXO1M39vpYgH8V4HfmwKQszQB2jkDfydJ2P/Lafveyvekd7vLv4W9N8Lld02bf4UDnIHA/6CxMHL+J1dOSUkOYGtkaecCtDOyM3kPdDFycXUGfPzH9v4LNKX8l0AgQNTVyek3h/z/upz+TfO/0kXs31emZ+Pta+T+3ztmZOfq7PVXb/5z2Sb2ds6Wzi7O/6oIBJhZ2gB/q3f+vWeWdv/Y5IUVpCXEVdXo5d4Hz45e3v69O3YMLh4u/0T/ricsJscD4GLiADBzswGY3odU3M5U1N7W9l21M9zv9olZvvfJxd7Jk/H/zLW1nb27nff/tZtZ2pma/e68qasDo7qdpaMrUFrsf6LfTXB/bOZAFwATAOgIAHqYWDD+pvtnWn6bmX+b39vg6+1g7wAwM7JxBvpamgHfP+C8nY3cgAAXJ1egr/ffjv9EcMycAFNLE5f3QX8/LHD/VJe2M7MHcP/L/K7kf13/MwJU/xxU6vdTampvZ+MJMAWawTEq2Lu8DwTV/z/n7L+4JFxtbBSMbIFU/93S/44zsrW08fzPyP8K0QT+FkulYO9ka2TzXz5LZwlLD6CpkqWLicW/Ovsv+7+4hO3MbYAAemY2BiZWDpZ/edR/Hyub9/l9v4Msf19hv/0c/+V7H00TazugszOAjekfF/C9Hf8l/H0PfssGMAoriChL69D+n9n5J0zczsTe1NLOHMDCzgEwcnIy8oRjeh8IFnZ2gDfz+2ybAj3+mRgAI4Odvct7CsDB1cUXYGbvBPd7VznY3+v/Nv0LcQAYRf4gTgCj6B/EBWAU+4O4AYzi/0acTABGiT+IGcAo+QexABil/iBWAKP0H8QGYJT5g97Z5f6gd3b5P+idXeHfiOudT+kPemdQ+YPeGVT/oHcGtX8j7vc8nT/oncHo34j1ncHI1uF9vH+P5r9j3q3Gf9D7qk3+jdjea5u8n/8/NZiZ3glM/4LvnQD+G7KwvksDGpm4ugD/CnkXb/YHsrwzmFn+G7Oz/oZuf8e/W8z/gu8aLP4oet9NC08HC6DdXxHvNsu/4Puirf+C7+uz+Qu+09v+gczvy/mrFPP7cuz/kL3H2tv9JY35fSkOf9zvu/neTUv7v/rB/C7e8S/4Lt7pL/iu1Pkv+F7A5U8n3rldLJyAf/jY38W5uNv/lfC+NNe/4PvS3P6C73Ld/2r0e7bHX/C9vOdf8H0pXv/A/zyUSr8fTv/cu0x/Tun/PLX/waouTvbWQE1L0/c3lr9C5I1cnCw9dJneL03md/v7z//+p/8fBOR/7vu/skVE7D286dnYuQD0LO8jzMzKxfG72+y+/5Fr8q8H6D8X9vt18r/499MLAAR6AE3glubtTXgDrVKagsv8xAsmyyHJuRl+VWIJaMnEQSylT37HxxHL3SIBChb6t3zOoCi0l5Pi0fdL8rcr1iIPxLR5XfuWWDVxbaostG3kJ++HjyQuPJqjwaD+JUN+8XN5Bwn1oUxOvnYJ23RGa1wrEUB99EiU+3vnQxTL+BvqZTKJXnnrzzxI96JZ5mYMJxs0j0UUvHb8xcl2UJe3B4yYSKMe4SWamY/5wVijMlAO3Z3oO6CRRYLPEVQ7GUwHIDQY8PVM64ngWL2lUa2vl9KPRo26shr9qbMUdmh3tedQGILq12b+ufv2mzg87gCsGhMrWia2TVq0tmM+Et/xtoBUKxCFjjD6ODbu801QkyLNETPNBvZdCtQDIWFqLl48C81fstIUTwP5+5JfHtgLnpBXIEdxXiImYvBqKpy6WIWONNadVrQv8VF4fvZ7hzB4dXkmy590TbZCMXDDDi4cBUL3LwGUj5LdqpH9w+z4hYImJIUNuv1VK2Kt3/psT54JfjL8KJj0cEy5bShsSdCvliGbkMw2TCicGkLoNKqsqUy5HpVQwndlEhMh4g8N/GDEL2LE6OwPKpGJsjDHWTs7E59cViGITl9W0KBde6+05Y9nFZDoPReIAIVhUP/EqPk5Gw0+lVdYhDV1J4UteXSmqqSxGlY6MgQ8+fHpAW+jqsVSH4TV7EZp9+YNuD+Jkjk20jL8hZZOovcLAdTxzQt4N/7+YM9a8QwzOtTU1Tpzp2zZlLhY5A+9XWJ3Jzk+L3zQmVSp1qdZpRTGRJRMaG8h62jWepeqnPGOc3+aPOmZmU9gPZ9BAOYiO/wiKecpkupiAknfkn0ZBfRe7/zw999YXz61tGAAIX17qMNmKq/x6l9yX1vL13pynaHi6yhUetpy7JI/4uN5ZGwIGwaNhgP5VirIyFAWq7DcxKR9luAnk+mNDSm8tu6jbuTJzGyRUPMy4LGy8dGhPObLkwV/VqnGRulKWg8VWTRa5Xk5xqAPEjpcaUs+UETfLGZTBCHLDn+nqesMhp0plicQbasLeeZsWP8Ij0pxk0Vhz6151cnJJjUlR9YG97ZE6/ajgwjl6zO4q8qJC4QeTAihQ1cfvQwKBQhRB3w7YpZgF5zh8vdz6kI9UmZ0rvxOqE5dFwPUT3CH2WQ1+9LIZ71PLLjJGyNxGbGrbd+hw1Ly6bg6VB8julsArgNwZd9ju4eBaTwHjXuObKVn0/psXF/6ARpCNi7jmDQHL4TYOsydFJlDLYnCHHnC1u6aAYas7ceVhJ2ItSMLtPv3WR9KfY0oWzHli4qn6wl8GpIEEiifgh1/5uDbjTIMnkqxM2+vZ4v6rQ7Js4IAGCuoIshzzI4RwCN8Iyiv73r9VZZRg1PrrqiQY/Mf7+mFyY8ECzXz63r4G5VGRL/mTIwzPTGjtrRyK5zMNjnhKkZtmQ4PZo6V5qDB8x1q0iRpujmvfsonJR/UHfZzmOg0p1ReLNbqM4D+ph7tbpf4CzbuKyqhLdQB1x2ZLrpkPGFUAjB7qFM1nUC1I2OVJ6vuY3xjWWMbn0ajWqk8qzK+5NemlzqUzpD7qvwI7LglEW5j0kbJDsrb+w83fmLTLnlm0IRB0x844hDO7z44vamUFg7hTuGq40VQj73mePxcTjG9setO+HLZYjWvPEILuhKWHZoH5NZut8cpSri7vKNs3fL1MgNQ8+glx5QshmEdeOazNCdxm3kPhI0ydpsfIwerErkTuKxyggpn68KcsPQkPrVkwbVfIElMOHbp+MShkS98EqIgcYq4MPQYOqgYOa41DGEXURyE/rkHf4lXDL7iA4SwednBZlBma3vQ30/UE1yhtxEwngWzenwNehwa4DCloEW8zihmdK0ceGVgHnJNQmuKuyrqUP5aSEh2WGWd46ZAEYUMNhRIek/vGDE8CxVuujJF6KXy8naQXxHfsRhcq8rn+Qnd2nzrYbTTp21nrPKne47FZT4O1xWMeOsB6AoH6kLiEx5hSMRLm/RLtH+KiGQpdVaM7DXJWV/YQL7Gs2H9bpQTcll75vcPLDTcZyM3QSjec0Xd0UrBsTcsfFKQCpLbt4QzWgOp7AW42GTlTFZhTUigQ4vltDToF97RLVy90QEX7O3R0oQxjgiGzBQIOgtGe+t8LyXu1lFQ5DkW30J8SuZxhl2Q4kGDIGMskHpyc68P03HF6j2pxntq/R7YdTwGEeI+QgZEQ96DAuqY2AUqs0aSyXUYFx1xOEkKoe/6VKTVKLMp9dsirOy4cZvce7QzyimSV8/hKZc2y+lT65hTRKzwgg8xEXmI+u7j88/jC3Z0IeLSUSInqKqW6rXobnuxQ0B2bTHrRs3bOvu27ZU2PGJu6YiQYLApqZ6Q41X7azIaHGaYayniiD5W2KIKzXZ9n8xS3McAPQ0MNYnykeJP5ZWulU0cqkHW78iASaBlHfu0ebgnyXOIAf/L4P0qRIdrVsUNfemHEjNK4ymh0qRsStnPwse0UzK3WiHWlPtJXLjNf5mDmzsRralOHqa9xEH2tpk2k85fYlCbCOb6RZJdy6Jd+IcvTr5SwgG8tdCnL1cia9VbRnOCdBSPgJMdEf3rKn0lIXmmM+g3h6MVfQ4kldqQbMErv+AN9V4eNqzmJekQ6C6FOMxZ1BeXKnd8Q9KJIkoQvfmXPYS+DDffmU7rUpte+1ZCevKr1eQGNTWv4u1sBymROj+iSOAsam1xdLd8u6blqbsnfdOva4PD4Ao67BtLiGwZ1GSWPrcAWC3xcRV/ZXhGs84IqjGMLyqHyHauHDsMGvSfWQVNcL4Hz5w203X8EBGok3zQ1Zk4Q5stpXyBzLqlH7xPhNwEFjwdhohgtpzmsdyYMaa09wh6et6DzUmRjJ19agYXIPAXtv2qWvhLICSQO3NjyPpULBwxfdm6Qyi1TTJMDOy8jgjt1T7SVzx2VWp7+ex62w0uAokQSnjQLbI5FYnIu9q+rsCRxNxrXR67xCIHrCEGL9FKXb1mUHsnrqzRknMioiQQT2vhqjWcT3HE20LvW9s9zpTQ4PyAkDkVaEGRZU8OXqQPjQ0wR8CMnPSkvQJ8NdtN6gNvolXzrCbKiWh8aGk2Y7nGrv0X83BR7UGGUBRYzOS2JMnXs+wDFidXCfmn3XLfKOGYEg4ePWR1jOrCMfwTaUkxNP3BT23mYDnMoBfU4NvAJ3/A9KBZKyf7rHgaJDQVRE1hzrSTQOymWyHMVWg+95whvAq+X8lcQUrI6eSgIA0AtwjECiJn5quSy6LucrqRvh6LKBTfsHPjNLCN8FBCGCs9LXg3JME2Ulovpn4H7NhvWkCaj1R8yc2H22wXkvfc8Z5Y6lfS9a7gNJIOjZjnXLpz2ZwdtqQ26SyLp4euhUq2WeRznSMye2pw15pUVwaLvenZcbXkt44Zyk+Ys48fEDxnkGq5QxtqhTwJ1X0L8CReDGk27BM5h8+QvgWg6S3Wobom772tK5BwfpDnCRcc8M3hyDj5OLBNXKT3zb47tGAv3JcfioXQtgVh3m15pI4Zr5wGYoApa2bH5+JR7Ky1RKOWISDdCBIUeP1r9l5O7ETu7SBM2xTNZWK1WfHXGxrWhFwcjJCj6FHiMvwgJygnb5uJ65FX/YlRzEPE4sjjw1mZj2EpNliXFFcLDjUzDWrwByulBVD8kaUW6ydzp5HUkaS3SaK+PauczFKB6XKhfDKkIi6+H/3gr+ZVy0LJUxWBoSUePlNwbekcPq2RJ7RV9fBaczGaF9ag4FNYE+3KX3AC5ygO5jGZog1lXfa91BjGgqa9ibyp4g/FMCrqCDLpfMlsrNsGPe9lbZ829pw/yhPnKWoxK396YEM9jzDUwe7eVbv1CxOuaOaypE46hYrN94jzgj/opIwwogl8qhos8mxwKNsWKk5VDnuLsHrAKK7dMa9HUn8agbJ8FbAsBwkBXYCj0NLwX2LTnPLxd0Gs2QDxDKcM8Rdl7YcrII5CHA5Bi2u34kRvjNfdeCblpA494alXPs6ZVzvzC+fiWrifwviSu16Wo9nt+NTnmQruXrRLX3HC0RLXTsteYFZeH8svNrMfy6UFniO+6Ofh2/WL3fig9MEwnpHnkDK8FJZMf/c5IbR+jWQnUM/kc9ZU1GCLZc9rC80kz7n5s+1HaLabNG5Skci5QE2YD0iXF42lX36w+21toGDYtLssf5nxkPsyj+MfFIL8tglSlf31M8bXNjUjMhSwMZFbxHIj+K+JIiRdH+ArmiyK1uZIlmkM1oXSBY412Y383EtKPD6HWLTyz6hHYB3Kz4/20hJisXBEwZZ9pPmIGMFdq2aMpmB/zDURzr3RgSFr08yClYslrlINbsuUoC29EkZ9uR2GJtMcmiIhuLEhxRPC5KiSyKZ5W01NKXDGTrymBLubzYb9OqW6MkIVNWiu6vPsCFoQVIogBmf/Mwv3YOcuchFSO3wp7DNmT8QwPSJFydqy/UaOb29ODv2elRn+5ifcRPoORPzBSsUJAmhsYZKdqpuGjaJNdiEXaDlyjeBmV1g4rNsEm3S4Ab3tcuM8kbDOLU0Np01mGGJM3S9CyrGwCGMNn9Z71F3Ik3mL2QEKYfcIFTzqbIQ1noVfiKQ+MyodDfCnYXcjkX1jkiOh355sUeq/UdI1Wgga0IiZ1X60qmXE2g1gVKCTU1MUw1uUaVyzqIaGFXTwMfGHHo7XLMtN5118RZCHhtJYDvtgbUM/LcrOQRvE9cJZ9cChUSlmISxHOGVfIKY8K3XIYA6Oj/Pco+5OUQzX3VJ9s1n7wBABHFbc8OZVlKdSxa89ku/CxYH3WmoSEYPVpux3eowdkItwyNT1Ew0uGXDXIWqRre/n77QU2klTh+rFZj0nGJfzcgoNO1VP7jKAFdBTsR7kg9nZL0QgO1NKrdRmUtM1Kc/+MG1PAWY9Vc/var233HIm/tVeIv0arbvIu6dWvDJ2+FTsCWTg12CWiff25BL8uOGSDHGaSHGCKQ2EvPCBbw7E9aOHOq+cF/ze+peeX/nfpJFY7m6H7Qfnp61ZKH7qr3XOW4ClTgsxKFY1NVYscz6kq1yjpBqsP3rQtFqRuV2OFUndoH9gyOZmFGET1RkguACN0GSB8Yi6yjFh41krWyQu1gKxs5reX7SxsdNnrvKwJpZARookYGy0bBdSYfHvHBZN5xTGk7C69VN1egXx+Wk7mVFTb3ez6/GLiUbP5EbyuwTuLgRBrIB6KVrhTZGuIJC4CuumUBpoVNNbGg15olDw/3gDgrY8C8ZO9qSTJXzcA/1aEnqszj0MWsuDBX+nmNdMZ9/XjXS6XdAUXF3jxqSttIUrCdZHxzTp65Qze/ncW/zN74Phc/80apriYWqqAcyFi4SiyYNKWqTAYjAgueAmKq0MXnQ0ElMyIfvyCl1qNlpqmRMnWo1nRhPlbEJOz+b78XLgbPURIXoqvqvNirEyooLmM76/DHiQmMAt6f4MhBNLoncKhzkOZCj7+Ar7irzvjVCuvzvhzTqRWi/hUkrkuVyAOsGW4XNlv8jYmjIK9r7vNpJUSNd+y2XuYuB9V7qWv6D5Wsk5FgnBV5nCY4A6SeJVdY1NJsWt8l1cnXzAxXqSZnI75iJbP7uxmw69/qfzgYUnhhpym6NfQBR7MzuzThx+/XZnXx+B/hqdfrSUA5eygij+KmWiO1QQ1atFZGkOfSWoLzknJqNJjvW4X/1LD8d2TEhIgewSSqHn/Fuh+OMjCh7sXS2j5dxO3ysqoDB9O+vCeVzsC1AYqpiAXo5PxniLWfvl5lph8ml9Hdl2O3X59QohoSXllDOpHdm9Vbtk+vOUxjHnuD86PkUk63wA3LdgPDzXmOCZgVu4QzsSuYlqO+gDlqCefbIpsm6qxEw+a/u6IZq0TWhPqyyF5UvvjWbP3hUufvbJMLjibv4ls8j0WYu0mQtCNg9Mfxw2485vK6l7Tu5F6VCDcK8Wgx0vqmIvhyLZ3z7wpox8k+1gZ8FO4DH/Ortm9S5txVeEs52hKtFwoGJC4EcgCqvO3ki5ons8E98C0s7bbVJCuQsFDkW+YlgCQjZFqTsfe0qvZsW0vnXUqMfBh8aWuUEwlBUgBkGEfkHC6qaPB1VcD7WvLjENs6ObvXfONIefeHnI1/0p5RopkMjavHsSkOYi77GqsHRm6Bsz1Flc1Zm3drzo59Th4P3XCqG7yAb6eh6BYyKkilrYYBzLdfdorS1vUmkL8tr67XTKtOLP04KEZwqWbAka+eqiI5jDfncQIwhdkfNR4V5fM5ClssTvHwlcbPO/LsKGpaOarTVphajaJjV3FCdb6OuwGtApuD6SF+iJYsWz1ghE+/HgxhG5EQp6ycRcCe6mdbS5zhnn5W8buBphgP1K2m2krIpmtix/YlMvGvdZtIxTRSEzpHfbEEsbL1HbfVQxGBtkY5/g0aF85MgkfhFSPSv8bqXZlasxr0os0kW2NdzDQVIEdYMWJXJPGcqvxeehPha69znANYrlJgJbNJpr/qMJEoudA/bhA4SgOZ5hrft3SXpFGqGRwcZVM6feB8garhF39ussEWzbIwUUpIKw6+VCVJQtBY+8aCpl6JnqyDf3rqGA6v1MFkJyapgRZsOkvNI8UDfVNej4RYqfDsKc60i+4b2hqMakgyw9sZIZWnPUh5fCcJ89B4b8BMY6MCjQB1laNiYmRYcjiwzzoWaiBe+gRl3d7x5WH3dnEnouKAPt+r4lQ9OR8s+UDi7LdqGrQ6gjTxx6tLdGecKL7d/0H6PXCZjJqxCx0g5UgkVXD2LbTAtXq+tmYZvqfUtkWLv+hTyKl3BG8ev4V0OBE07iuOeP5gjxeg910LuwyLFrHRSJeUmoGrDttk+L0QrtKYXuxE4HhpNXCZQ8Invqr4UzmgZXpV8IzxekuDMq2E9/mFAeyIdH8sd9jSyRCD1IcABYsg5Ug7iKDwk8643JeS8+DyRKl9YLg7LPEq1gsXG7BLn5qJqUpmy9LHzl3J7Qq1UQfutc+7XielZGWIylWM7Zz9sX295XT54Tqv/p4TKMKGIAd42TCmSCcVY5iIaYToe/7pPu27ZrMRumridcvCYUE9pZSVNq8Ju2SRqTSczlwyP5F1Bjunrzzit9SmaIcTD3XTWQi/IupyQGlxaFy/nWS21glSiWiHp5ppb7Ih2S3eQwL+mF4Wyjp9pxrv7xzkJCfKEf1UZCuTDLUPg9tDZJ78kHUwx0u270SyRsLPpf/rJNZkzTcQ8050brkS33tPqZUHdOgszid3CQ43InLXawJoHtKIOuJ0l+q4OasOWhtGMmoCq7HxsM4RwIPTphJF87+dl3e+6YDMxC9ncD9mgU6nDEXL8dWOT+CErRKvQV2khai05jbSdmyYA6aUSboDl4aFkYaRqOjkJhHwQPUsMcKFFRzP8uqY5NNIDFFaLyBNwat41L/Ea3qtrWIbLv4QGMLRxVl22hrnNOszZXGj73Sk6vHDa9nF2X3ZENOxZBmWcL7gp08sHEpdGf8GmoPVv8oGFnDnruiYkNEHIOXlpQeOwNFtaQQx+xSxNUSG/KNmKiQDDBnZ+JbgUbMPAP/QG+kJyC9lZLN8+l9JiV6k2hIkvXYzBAkTNXkvjjlWNXJxOtp3DGODH4TKIAHf/IW4KXa0Sfuir3x+PBHZpYiEKbnuN5StMNR58AUn9M1Z3Coda9S/eL+FX4ILUMbJl00ZVn75KsJ+AXt3AspKPdbYw3wWbabqTJXXpkwvdmmeQ4dxt0Lnn+Mg+NQrn2q5An8MeiKjS6hjJkyfRjltohNr0MA8j7E3IWVYvZzk24cdFUgvGcSh1q5g1B78fYp0PSGaeBfSCJVLLs486t0qCI1+mn04mGDGzYMVB1EGnouexCBm0/CGewRwy3JhLRn79gA5W4N110Z0nC8Jsc9Dqg2vOE4PNBIW6ZjBIaP3xZx6uIcoh3EJZsVdbvwoX69c1JXhh03sq9p/pFkhLNP254gj4U8ikyar5ow1Kqn0RqCicytU/5BnwSq0duXWYk46F3SeeRBv5sTF1CQTlNQhyeUij2cBveMsizYwk7S6AlDU/IY6k7N7FjKw+JlLorgpSIfyYaYTxbvJJ1gGMORJQaCVO1ZTunne6cE418t3w0mblhi/Uomsp/KmFA0UzfzI73bg+lLT0Dz+SROqh4TJeLWdGq9CvQmouSirovw31ZY7FXPP0sQSMxez6suzzJ1T9rHUzw3MqQF0+5Wkpp4GUuzHcOutPNI5M9bXbuw4Prj/CVnU9WTQRGgqM9jkHBIINLP9UkjkYP3H7kD0aWmhtIunnY49fWtldd5ldz+OQJLjS4Hxqsqfoe9oh/5CDes0FM97jsY6l8iXUq1fPJpQl0glU9YXBZYpNIGse39FnVvE4MpdeDrNJTxjPm83hz4af7hEOMkBV3lPON3w0hAYaw4LMrC9gQ/YerUEYLb9ENzdhZdafiBZTWs7DodhGvYt2Qg2ExDJaBrbaqNtz52+HnuWLfXs9zTuGh7fHUu6+h9ccp1ciImiblQo0FShHO3UyrcfS9jk6cLz/O+ewHvSLl84nFYNmAiMQBNz99CXTYzYE9SGuTSLMYMXnlzF9iiwSnDV0EX0pLR4oJUWBrhiQx7ZOXfnCVmE4eOeweNvJIyG0oskHlbRLGjlZOgxxr4Tu+kCHhv5jT8l9PyghSosPXDg3lIOepcRQS7Un7kFqx3gLjCjiGnVfUWV4bQfEc9Fmf+5Bw6RdlMGSfL5o/vkCyhzLTc8u5/ZhN89DvnT/z73nTee4E6ZkzwVHsBw3AXZ+RLXx706wPFiXSt6MsCA8KzinmHD1M57zTQgCzeEBDHrh2Y8Nvgk64E99mvIs0tqlVMSavJnh4SEzT5KRmKOCG3zw7CYZaUTm2lrk6DH4zo23c2Mt0Z0tb2aUlJCSjx4w7Xz7ltfJoEh/Q3u29+L4RvnWk459wLHYbttWZxMnGZCkcTJZsjCJuj/48peLSJA8DKY8a2vrjTnEf9zHjMqjX065pLyk3AuF7ysBufF8JD4NtHvoHlzgfw6JyGnY1Hz1+l6qs6KcxERXXi/xTCNl7aBm1Zuz8EX5tO29+9HHiWxLzjwF4ZOZKNd99uroo2Lyixv1ZZMAurb7iZce5lzzWQkAWXrXLrW9+70k+R701yQBr0X4QqIxhlMqJusE50+SSLp3VnDLqblOpdpfxquaY5KhsKk8ak239QsL4HRrxeNAdKvbx5XQXfGq/P+JOIakWW4uKLR+Jt9Ky7LhnnVuFIt8KAcG7CLy2PDaj63KCoUq8OP+ytmG66p7mjIPgiRDEqtPUlryv7Bvn49wYkw4MR38f+6/bDveqKpK3gPbgDz+gN5norXi6oaoCXc7xVf77HS6TTPPtroZv+efWq62unwZ9+wT6+UfuWPwqVvC713sIggDNzn4ssVwoHb4lLao7/+GlSY0tvUNT0jYvg40ZgxJ+sUJ/5bnMjpnk1Z8cPpzP2ZpKfl8HvZHpg+mbH+tLNbP0+p6IZEYGIG3E+IUL6dskbgv6hBYQrko9vXSJJ5jnqUT2FbcoLpq7Cv11cu9vi9uROR85PH8Nj40vW89FyIq/CkTLzwY0uiIYU3ckZhUomry+on13cPV+sOAhLer+Ma3O5TtwLIJgI80QScXdrxMS7AtHkC2hlv8ZA3TcLgl8QEOH0NuFnJnueFzllZcPSv9GncAxoij3rCamWC/rnGuX9osbP5+/+JWNPQYo6G7x+EKSfjX1taWyIx27LFGTz1tDMSXxtokAf3dwRW5MNfKv42wlflOZinSnM6h5eWrDlsxiBmJZWd2zlW00BeyPi99/whLuBdHXNEhnCkto6N2N/2wgwL7ow6VC1CJMn8OxOcr7EiF1w5TfI+iIrG/doiqsNvykNhZjHJZw5DzA/6M9ZQ8k41KVYyEG8ADyPF1yPaKC+2IIs0Cn4zBNdpKnRT3Kyg0XgrSVsBWOO8XucZI4vYV2IMURl8w007xpdO75NZW0dcPVh5+iRo7Hn9xkjxZuSAeGi8yBfkpN2BiTt6mzPdF5ePoJpVmIvSpqRpWMxDAh6Aq3IIepOEh8GqSwcYE1PLM9MshHmLS17wxVpwnV8id6Fo5JmKEIJDmqifmAQVtRIIPfFftrND23L5LNJqv6bIas2ufSQyBF0zMTP2ESVoQZNyiAF/b8JTKtKTuMs/qr1AG4emuN3hB+FBU09gXaw2wfcGP4pyMLQofGpiYiL5kytIKejvPobPg2vF5tWDckVJ3/Xo/kvWTYcZlsEwnZedWWY1PtQZrt5tS5mnshvhPRUy5j07FmYnTx4QR2bsETlZgbBUgUaDGikFLicnHilLYEBAixd2aTsbqQfoY7D8ktd9LaxOR56YxGMidq6OSmF1PRuTgloUtDkLN1kMM+JNnDXTVMErpekU0t3irWzsONQ+SFl8Z4+NL6TBlQNIYiiQUburrN029c3//0x8eiYK/6QezC8XVAioGrDeLWuhJn7RzyMJxEmq90DCiNOYy1XCmrgSnrV5vTywBlyHmRxnxrX0AM7/RLey8EpvJ17ZmGg0QYIk22SdlRKrkswXfeOg2mbIm1hS5F8nPeTZYfyDhUBXFqSVXxQgZ+pamm1S9oORBzGtENMShKjTY293hMS5vbzhMWTYeoFts+GvF22pABXzGuHVGoqme6JfzBEm8vUs1tLbS380hUlO3sDHpX7GNiButi1SakU6Rkpdm04is3iTi4rw9uKoZeOS46ggSGYRg8lwWj5LuE1e+XlmUbHtdtQQPatb9dPk49ZjF110++0Rro6tZ2Yi8HPXsI9GWwfP3IBGbMc4XSpDSC8nVJDnhuuUW6vd0XJ6A+f9oLtqhCTAbd09KGl/qJ0qDo8mP/rAZKooeiUcqvyUr0xuvRhtGaPv5PzeYJuh/ehhVvpLxRg6XYy/dNXXJ00gOYnfmWhYNw0T210UB5QT8Ib8MI/LTgPDTa5SpaIVz5koGDqPJTVPFyoWm84dKs1Vxu8Fn9G8b0Oj9z7Gl8gS/fT7Xb2YIdL1US4kxMFhpuYuan5G650T0D7TZ50ntGGk2UYlfhhmsUh5+3Ev2HqscKXcY4nVqoX5v6H0aNOyVFpvXOjIiU8rM+zXrFrVwq4acqgRchuGnK8vY6PL9kfIKkO6/vtoj0O2rxHHQVdWsYgm/E2ZBhpVZ0e7YVJ9v2l4/Yz5l3/kLOGpVHNfLRRP8GWtMvPLzte1yWbOlOd5iWpEv9lm0sTNiCY9blI4K8/9X4fvuSPvnsp0LtJ1rYcLgT4lXEWSsCB3OdVm9XnI/roqS3hGcbIL1MTM3CnMh9SxplajoHRn4jy5dJmgod81BBF0jVRLNYfWzA64vTyDKxwKFJewSDYq3NLb/PqB5P4vy4mijoFda0T/EwiczjGwmE/IwtpoRMxvIg3uWsGQsumFiZJr8qrz8dh//EYxYnG3kdealiHxEoht+0VEWDXBJK16NooM8tkKR0+cp2vLbB8NhVDSR9tsbdXLhF7fp6QWJ2zl4JI5STbNy57igqpsteaZyTgsvKwBoTv07c556NWN/dx270DUYwgqAwGviiu86fMjKh96H8h9TpI2Fv5Ss5YVkSlWd2aA3yTH6dDB57kmr32EiTOV+BwXGX7nmQ69y1BrvEOugkRyG39PNI568ObUSEj5+rbo8r7D9XXxztD7bOeIhFJSY0H6X4rAHgkyqqwBwWPHIoqvgFCLLr9BxYn5jLVhD5Yl9PMI4zia46UIEkV1kq1ZaCo+Ds5bcXVLSe3wXzmMLTYxsarOOO+LFvEaKyU5NDND71BYVRiT240HdzBpJ3FXz/4T9tBTX/ZGJU7QYXMAOZ5fLD98jpGcKzCfb0Qs4qLJJVzJJ8aqMovL89BmWVGFP+LFh6n0IIcQTXQ5K5et/WM4R4jA+Nw5NmZXqssfc675nUbDOlvLMaAlfTThg6eXFFxvHeAyVX+fOhkl4xFoAALkhU7j6U4aLhCG8d8VYhmq3lpCrW4BTOFEk9ijrdhb/I2tT5FI4tU/TAsIb0KwVvdrVvHnCyssOjwyWAN5WJALzy1p7Rc9VYd+DjgoOEqaVOgAUY8QC3H8frnOfcEdh+GxLCI9ETrWLGrxaqTqMao1E9QtZdt8OeunjfhtlQGYP0sTbOp1x0O4ISZhjbq8+FqGGiJS2Z9+tzZ8+XwCOdEJh4tbB9Pu0bnDVD59pvaqqjVPx0Dfe35dKez248TWcfwvnz5TEV6Wnzf4AL1CJL8hbxKqlCPOWizF+3X0T7XRzonECJ6RG8ZkUrXG3VVqLrc0SPsMdk2SB/7aG7xc8eLsppiE4X+obkRGJu3BU2jsbM0maq2vaBzLugCYaUkBpQ17Kvvsuc5Gr8wyof6sbsUPX+M9J8jYxJfgSG/x1fkystrj3PLLzqEyA8wWV/F87cFKyhYaCeTEGf6v5zeARWtYpTDRLV8qTThTcpgNPqqC72K+Tpfs1a8Ngol43hsJPwSt2eIguKUMURFOd4+mtAu0XyShx9zxqDo/q8Y/HatQNIiXucpi7GqKGIwBXRJP3BQjKoRJamKHIfRCw59WqovCSZE9u1nfMVDnXKL7dRjaS2WibMxYwlhthVeNYRl+oPwmcsOevGD05Ks00Tp+kl2y22PPcKacXuT9LBB4g8NaAu6T/fpsl5Yrw2NI0aWRUGE1slt5HiRyjAqTNqrHH9GCylZFFQj3xooZX1iM9QGsO94eEB1775VnpZOXeyFGJtFIRCExS4TZNFMLfPOA/dOsM6iI+U0CBFqNJS60aNlmt1rSUXPDRdaReniTDKDjSN5EGysAKf2DGEDbNrbvPdrbrFpoKF6Y16qM9wVRTtiWILzZeDb3svml04ZbIAhMKRlcS2LKnq3D/VbJabW1DdRATSKxD2pZWZqXFE6Me3kWKlBOSTcH98ToMod1z9SoIjxONOi6HhkXu25Lf7Sak/H0HMg/LYRPfJVi/SrHcq0EEumI2nN4xYFEFtNksFChVLp4YTtRayiv9p135O2BOcvLgj6DMHp42idnUpNgwShATXMmfWLqMvsTOVz0gV6oPGUOBsT3Oisycoo+vJ8W5a6+pBjmBaCBdclnfzVCDVRT51ibOFSMujapFtolL3XvjLJx4dHEThGA5I+vm1W/edGdyWgyyZDfpoPwzPdiUVKnxdMFO7tRKDO+oJ9p852The/BSvbP0/r6BJQEpLMaZtFzkYS4IxO1M6Siwvpe36m/H9MJi8sU9jcHXZM3CAuZ4/uzAcAodHZPzaz/ObLQ7Vu9JSHwUEhYW4EcwNg1vpbJpYDpkZ2xdUQw9HFOLNwx6fngYEveNsf845b8TQWGfij1L46Oo9PwqHJbxB08UIgKJXMBWAF2qGQ+N7BPC8mHxR12sez91XNg3QGliqI2tqaFDoVRHacF+Go4pToqRuqIehh6f/cO8uEX9GBq07zxwiXCIf25OZjt59HyXJXMlB6pOMAvEdG8qQJOxIlw4Q1Xfj6WzMrKXDm15Slhrq2egZrToQlzC937FwiQ5C91U9amsQeseVWxnvWznkF1wqgiq9wXny+Vo1MQhktYDWrfoq63EFi8ZxTNK8Fz/5C6wtBXg6/srLNnlF1P5XEdH9jKmn6/3nptPVlakSWtrQGL9MoW+R7LF3PrlDD3K6w23HLA1wIVUuZyjGmReOBG5CG8sHR3tgOOyKnQ1h0qLldDwbukISc9tPG1RzGENeFnBeqoxIqlVIEYRfSxrzIi1W0aa6oWT86GhJH4dLoEpTQBgJSRXiCNnbUVK/wHgYFPP58z1+E6IemsNk00N7cU75KR3jq8BlUsgrw6aGp60b/6qHfh4gRg/ZiGb1fBoEBun3Dapoycz4VkuCIyZbbkp4j8PbISErwSwyBGwMHzT9J7c4vCAq2Uo5f+RWwlUoRoLuq+lkti+9N2Cboq3VoGgYmmEWW1ejVJfejchBiZxHv8JbihJTTzFqi/bGiJSSoO18s2Exu2p5g6XmcxTTRqTMbZ+qLUHOyqk3PjVrVMtPn9j9wlhet+y2IYn623CtYRX6U+AkkRBvore1qhXg83d6Y91wLsEtLfGLvmeplT4F+lxqq4O3Smfql0nnzuTEE/AxoI3O2icNPRulpSRYG3j3wEeiBic5n4pds2OY/C9qqYXpu4b+XSGmyxkga8mMHThYMxJVJCIirLf+/V12xH54QT2VbihKyDSxHFjsk14i8t9BFnCRbWUvnWWE0RXsWsXDSPIGBFWljRA1m8O1Ziyp8XpB+V8KKRknUGOFYzq+vU6cvVW177ktRc0DZAWqlMB6ZWZlQcLBQ4ChMyorzeu9T7xyRpYOmFjpJnxFnvddp+JJKN8GLdw1pb7Hu83/IOehJyINRxB3Jj7ip2A52EG6RAyzNkL19qdFFfbB+WU18BqBQrYOKQGmU2acJUAV5VrsRlGi/H0NvrIwzJVaKTWEpk6k38SSY6neGl0GnBxGFv5RS4/2WYqov20tHF5ueFe2PFYKh9pth2Rjjc3qQUdza/c4m2KBOPG16V6+kYWcp8ScNxURm1nrQBjPtX+wsSjwY3zBGVFDOvilys4Enx9n/cqnj8vMd9UftKo/6HWR+ItksLpBpA65HrKHQKQJ81K8RPmo0xk2zjsscUrji6fNs7iOPfT4fZqHFOESJc2TcQjAkzvWPizJF5EhFXM+AOW93RSj90WTWG1EyQW9ezbQQTnTpTU+g87VUqB3Kevscr4uJJBkAH75woOknko94fVxo2ZoPNZXX4mSKaHJishPL3NMxigwlEOop1czO0SmqVSs7uB0MmguiwAjj5LqZXOtrBiU9iNE+i4hoJUPK7luqQ8QsZbt+VD8+vXgLMZXLZu4kLn1qrwNZbTEIa0goyPBBtZsPLldrR/x7daw7EsSyWymnNRILA7OB2z08anVNQdbHFF467lxZAQsVbWKdeaAGY4eNZFV24LXoNmxyhNF9I0Rr7qfAX2al0L5Iyzgk8ptr3ovSxluVxJ97PFR0EEIvRJPfh+Cv58ILp/zElriNs9v1cSROFejRm2Te59chLh8KAOjraGeXONt8UxBcuxnaPnu+rKKK7RXDmNNz6u7dJoCx5i3pBqwfgwwqkidrE9attjBjp1o/SgKjPxUK2ESz5+3SU1iXBd0qUx8zN3DOptSyssC61PRqPARYwwvExYtdB/L0lrTr/9D9jICjOElHE4Apdy1m3QiUaBPJKJgSZN8vz7NXmLSk1apc4XwsuMhenlls9yop+RmUPKnoakXiy/2LpgBXd+ojcAFKImm7DOvqOuv2eHhoKERbxhdafCY6REDjYe5shCdXKdb72clcVyOpWhVs5EqkUkK8qQx+31WGOO2i7vOm8D08pVaWAVAPZAdmtXo/BOhPOPYcHkLhiaol3qW5PYDnp9X8XWN5NFS5gHXVxGS8EiGsScEujKlWQ/YrDSzU21HuNYuAX6eaV9LR5XLGd9ktz6jz3K0OzjPCyqsnPg7w5ocOOnpl03NSUBkVqF7kxctiptNQgcTBSvI6Iu2RAyoe5wZfmFbazbIh7b+9f/4XtwNf4Vr98EYXzjfp+X14K07/1nqeFqLVnip+9qJUuXOTLo8PFT5FQP/uRS15JiMnYawJrPOup/1IkgXADTmv/TS8jpTEkGAJhBO5ZYIpCWnkzhjNPlcjkEAVEBXKM+heRabcuMEGfky0N+yJXsnPTJkqsVGuEUUnUul59SrdqhJX+lnDyluoBQNdYaEiHk2d3KaS1fy/ZKgAur+orBph77AXRj0PnoODZh8Dtiq0a2/ta+k69fFuddJAPAeW0sr5Kbz4QSzJXqmf0OGR74vjB0PnBdmdm/YXtw5XKzPe9IKJaWY9pdG6zMeD8dhgoNf57tCc/ZGNwWo+toUswklBWk0lxX6CYajWe84EXb1OjYbWCSDJzvDxKlTcPnl4z7g+znJW2dFS9zgx3XirvZ4TVxeQ88HLemJHA3MfNY+FrIHKpwehDAbCji3yqiYqXwj72aFlTYF2dvN4wivRLOV+7owbd+G2InMbLM4VvNhWUY2ok9B8ePFBksvXzTpgIeroXFBg2kuEyT6ihS7Lj+Ow+qs9HWBn4K188dA9BaOhFOKJwFPuAVzXXq9DKUZZMkEwS0bIZuktyjWwgRCD7sbWMksbYvRSNjkC1aTKZ+8c7VZEQi49vMAc/4c4MbFDw40VPr+U0u96i4M2roWCp4C+emHyYTo1LJRA64e4OB2/CRMHpYpsx9udDsqXkIvFEakH4OUslRaIq/aW0O+K6sWF8WYIu/VONmDykedaHr5gLPldelCEHFtNVEms0i8J8ZVGafT08h8bAh6MPd2pE+sdPDPV7OedqMs5IRVuHZNRXeGmJve0ESmtTgVkGVKrJJPqiCZeTiMOhUi5434dBOw5e2Q4MWff93dgUlk1ydw1HezyPF//REUL9TOwEtal8PIZL2uv/SLSBZbcPJoGlguHVWzS7Yt5HAR5PD2lH6WSQ9yc3h/i5D2rRknprkxdSyFHVca+lfJc+H8We3eIxRlOaMzNNHl4BhxHg918tdE3MErHme5sp+wMi1E27P/+56x7QybKBKPFc+WZyEb4rMam23uouTR8gsLskytLisqrBzK63CCdz5pUaIdTEa0BbLw8M5jnwsOPvd4M7IRSkmt6Saa2UimioSfgGJXtPHWuFjYUxdaqusABWOjt6No0qNnZpAJnSV/4ZcExq4XMSUBVc0ZuFAF2UmbXKypm2mjSQtJXnUNKzhRi3TtJ033WpNC2DRd25wpywQ3pq7CAXNPI9SNm9OuVWxDUFNOTx88Yla6B9FAdDWMTGszm0Ju434VrzFml7Znb/pvHz9iiPQ4vV2m4lmJQ/cspEBjf0OuSZ75eqWGSydlAPGNo4+kZTS/YsWqU4JgUxgTcmdf03RHCz6tNBxUM1OTdB2X/TTtNDFjRTUFq91+82cwyPJHA9/PDtxMnXiug+Hu47QN7TXOiEKuixk+TYwgTXi/QFl2s97YpyYCDc3xXbWMdjvHyVqU4GlT5Mi2UGNDbH9E5cCoJlBkDcqpNUZZkS9K90IkndAtZvLdZCdqqomDigFV0cXh7yxuRAPiNgPGK+CaGSuD5TUZUSsQFvkYbj3dej45af7kfdjIzR/lbKvBXWPw5sORkLIL/DJv5R+0FgH9YyKU8hcEtjtaSfoJxouVljZCKUOxYIgKmoVWBeLBT0l6IyZOFZZEabr62s0ef8boKo7CcVdjKGFqP0Nezz0/mM1GWtWozHGsqJEJFOCotijvi0QemizTFvtCACAD0+24m28W/hxYb5VnoLqgaG0C3wMARJ/UeJPQnVXMOLCYBxjyI2+b+9aq7rCTudILZ0qEIV7EbqMEUsREB3O23Hx4+jWG9LK/CRzf9PFgJ81cJInM6oK7DteomBHmkMyKT4R8NX8le7noXr4mIzCqDEXTOLi6E3MWsO+FmLXP59r0Q+9zxAiE1qki64ZxGdK4finMNENTfgV/dVXjYywuHLKWwZOsz1Yur4liNY9cC+lnP64Hncmz/JE1JsdHbgVrYvQx1q8QoAPfK6qenBpFYC5kNcMpOh1Pbdt+JvVzT1BQynI5y33kWL47AxDE+z1m/V7hopbz9iCgEEu8e2dClVttUQRvT8PTP4Gy3JN5SIO6F8a9AdwDU4bjWMgAhA/7ldPb9UdpKnH4j6iu2xBiXSWmNzDuZZwkCTADlKxJt3uB9Fia+eMtjqsABoL8gtEiFAdpcttugeUo2W46MJq4xTjH6+ufIjMWlrf0Zcdh8djQLH4V4W7bYojMsxLmKsQ/0vW0Fra2LtlWcQ8/LfdsG4cT2eX/YjxCerDcmZ2k2NtDmIJT7yI/uh/G8DjuzF2T6oqTUslp1SyI+5j7ozn1c4Yhidoixade059bcQdVXrRa2U2RPpgmTRkPmcGxhU65bjDV/h+9e37hfqcVzO9JRI97eJPuVH/cywFGfS4R/BaxNK6wYKsNtjzLBfflxasZXZvPkd0VlOh70XZigw7ckGIqJFyjayKD6mGgyVV5tcj6BfuI/8OA+RvZGsL3OY+y0h3wLRqirM+OEJ4CQdd0uo1uFvDr0lZRvP1dFaMAMgfajYVMf21f6U4wGvogPMn8ySwv62CVSQZxWNRvLjSjdyFaqd+pldqgSpB8UjMHJt031j9Xtzn6C4cElarNrUhdlxSqjYBicgpdyD9lixVcX+lEyRqB2VQxRrSUwM3IkBcNz5VcuxWD8sWUdORmUQTWeaPXMeqJz7KH30uTck6oVkZa0lEynrnjEaXnfDl80Bw4D0GD23Vmk23BcIXLLj71xqrnh6wdx0B3Ku4qxFhDI5wGbPB4ysHVtS2eYDKOf09mVnejQcF9tUo0X/FTM70dijF/PfCsWVLh0ZWUEOgtjyv/OI5HCi56vSM4YQ4JT6NW8tGmdwidGlRvFgPWD1VehBgSYodcdlAZQXZ9XfWY31BtcphDQj3wPPzzwFHA7Dw71+rhaVpWchShPyRAr4KVxiSxuDqgg5VBxLTxpUY6hlrHNiw0TFccjNxkHU4pbcbxw86JNQ5Ycjlhxr2DFssnRn9B52959SitfHf9cwHdx3T3JLhDnIjK+ZgCUftNBRXWUE4VVN/IYWS0aFYv+LYm/ekHq7mVmnniqU00p6Vrvt8qdxBNWXiBUsb4Vy9CV5/Y4qYRc97q6+HB7TrIGQtdroPC+uO1yDSgyntVWvWHICKv+GZwYPFMMFI6S2GO33flArnGp71qNlPKkn3CvtiMboozbXMTL+sLMogFLkaeCY0z84WotWj7A+dTC8AWveMawogdv2690J4sesKUMraKCnvcRxV2oxt+H5PYZRDkE/mRaqErTZ9LYzYwyAB1PSk9fE7lLr1GEUFecII7XQy9OK4Ajf/mwDpz3mUQfUzDIB9ixJUaJvj5tEG/d81JA4a5CIftiLsOnY7SvZB03eetwHZTqWjoi1EAffMLortkaO2blq4G/szpUe9BW1I7ATD5u0FEbbL3qm6t04HbysI2yjYhX//+xxbi1GCdnlIeY1Mha45CO9/J86GWI2+cx/Py4wB2/j7IQrNYdAxg45S3Evs2sR3TjtVDyTft0rVSWsEa/si8lGOU+tibslHbp8w7v3MfsdvrHYORsF6Gof1+XuqzYQiClmrWw6GphPEHxS46z6+HRzVfLduTxU5/mXIvVMGgNAr5oQTVzaHIizzEAs7VRIV6G7olC0asEzJkcQ/w6vzi1ln1EQvfVoKOAejDPRbUJhImbHey9VfozPT7aw/XWmlSqeUvFJ/6GnliyUEPnPxuj9K9Oe8eEPD1ZuA6UcpXTPdqU2L8M4xkR2TqOg49qif8eogbsQg9O3c6ufLwFUnWRLj66BZReSRIdZGQiCHIP6ANax3bOQwfG6hvYTDmD0uIIKqyQ/0wBZxSZLRg7mNe2y2+WHfow8IoJ48Cpjl/2Ain7xuIEMFW69lRVTuHTX+cACerbnTg9GK3/WV1gfKIPEwpbTQIzuumMIrNobrpzC30o2O4wc083PsHglXlt+/I6+ANuh/b8G2Nl531iy0y6cAvkssCdk7GUQo3xw+rlx5UM78rCwoge8nUjzSBIGw26jLqghsswGAfDKdV31MnFLlB9PRWoVeXcZLLUK4s5SPkYj9ax6V8yGTtYCsQL4XEkULMtW0fF/CEJPlsQUSaRzw9c+Tc2ij4wj7gNdz7jfChcNrU2Yjfstlwjt7/tOlZFa907zw+9USm/XkLOUA/n03Iu3XMkJSxQyPjifdCIR4JMds2lNiNffIb16EfhpvNeD2yeAYJWrKILF/ZVKy4tEDP+KUMOIFqXIau7H0sJAUMPIVOfK/Vnj1Tfns5eumD8V1IFhRvxOc3fxSOsgSHHlkMavHXqULQk5qOcMGcEHTAolBd52pgm7dGASXiqxJe+9E3Fj9qc6Hz1QyRQi2PHou+qTJPUdZTUrKeJo1RZcvjyVifzoGuq4Dt3KjpBnm/6A+qiV9Gy2OK8kW3LFM5d6CqPr0NnNplsxYQdATqu7NKkkTcqEPhZbqooPSYlVY0VYvvaa16GMeihEA7mXsKgShB4ZRjnJhQ6TYtF4mhSARWw4oa7RjK7zPexpW6p2de8Sr2FpPTcpfXOIi35O6mnMsHBXl/+XoNisfCeLtpfElTzdfsC8FIF6QTT1R3Kj9VDr4+RG/lu5omx/T2K52Kumi+b3p2sqgLQ1h70oB0MgFaKFPDzf6nBrKhFeWdXyYUKbWInEhyUqtFzHLAQ5/3pZ5KPQgK4fYgBLEMv20DWobx3FFa2Zi6MTV5GldI32MjKSYywcxZ+MJqGN51LnkDZpFBau2MNGleBrboeArc6YvClGU5Qp2YKtg3kmWPIXhEWqXK5QAl6dsvdcS0R3Fj+7Gw04pD+PhVcFlkdGg3WuUfwyzUMTCCT1Yttk4WVCGNkrm9GFjhjz4sdydq7NwljFGQ0cfFQNLkn9tcYkel+ciWmQlmSVEztv9Sr2YSbK66r8k1tRJiPonRGjyms713xhYVH8kgv47jxENXCtKv0+0VR4ZwnkvlC3b8jtR3I5Ph9UllMLsWhoMr4qfXry4U44rg2TOjI7kK0izyLe8msv8v2hol5QacnEKI18dyeFVk4BnxPKW+SBHJzN+a28gaPyVdGZo1T00o3FzVGS7UfH4sXrX2g3eoLs8XjPxR911IFbrEqJ8ChsZPMoM57tIdg2BLyxYJp5Jlot0lC5taKUdebYx5QaB5sf2Jug3w5JcWPHw7s5+yT5wWr5xO/uUe8pi19gKiCMu8WX8vnJWbaZSGdPoTP3eTXyTvVddpPmTFFPLrMD1c7pDCKplwddTxl778VDrPtl2xoQZG0HYrQAtJKIxIA1DEA224w4r/q1y+vHVnoDMOaNuszh26Z7hPKXWFhxvN0rxjS4uoTdQoREhfIf31HIsrAImr+I4puH2er4QwIEb9gyg/r6z0WTm8OGyP1AWa3LDDKmd2qdAlP/d54sf3z9akmsqUIh9ZOcmhrYRWdjBFh7zVfpRJMNxFt/59dhxA2+KprYH3ACon8u3jRZ5JwnY2W6dJ6rfG9tdt2Wx3lZM8t/PnwaWeX03Ert2UthiJinvOdW2HT5xQW+7f8Hj47yAELCPxFAA15Qt7nINkHH4NcYZorOz9LrZsrVML5eBs+uD9nmT3CipBx9l24GwLpQF2Vnc+enjGFKaS3nbrvagKwOjEuHsbh3UlZCgCgglFYJhbN215BfcwLDb5f19QLy8cOMUKEpE2y5kk8A/PAixr185YGWLJ3jbvjINLESp2T7S3aUvlugjWyg6JDPQnwN9AqvGEREqIebPiXQ834wAgTxImffCk2d+frT4J+o13W0vk3I7lz8V7pAd5J3L2XEr2DMofFon1L3fTMgornbmzEaCchdInFMGGO5Z39BreBKmeWfgWHg6CGHCCZ1+Rz3u4d7wb2XWC9EEdRYXk4Q8h5RBihfT7bTqrzobG7caTt1DcRl06ADzrAKByQLlvR/HlXgXdCvdUJ6TmcFiLcDqQAPPuY10iZCSTqN2ACbXRtFIP8Y1iaiBvH2Fd8FXP4VyK+UtJ5Pffy+qRUMkUnBPaflfjqdAM+DnbU8TCTzQIwZgX8MoQL1KolLVZ2cZsq+jBO/P+7xZd6MPpEJlJqPxgyxtGsj84hmP96SeXuxbPEqRN1pIuA4UGwoELgO0R5Z1wA0ilhdSYJ1Nenv6g4BCGZxUtRgg5S4LoOHfpbV/398NO2OW/StdJaIInP7Vp+CVNVW8duBE4TeBPJlQCaUhCQajWH6/MsTo3Fbqu5wNF9QyDQU3vXnAb3HbnuXAOu7VN6vADm/lffTyZ3EDFO7Dwpox8ScEoUH9kgiGzxC6rW/2YrPgNWSegfaijqph6iZ4o7Wv7ROXQjX5hGZiaWMBmr1NUzRgxRKmTSN+VuwgBreg99zikhCPaXcHB9Q4Y+F6fWXjdFNNpuhcHFl0wvecEifazJufivjm5/zveqN3RP5YmVHkNWpqa08Tqc3b/fIBjRAD1+7RoSjN4+7rAZJRuokH68e31AJIgOmOO2rB4TQd4pCRMq5XvL/wz04skeUSmqUgruYrmKSCGTZ0/3cxS2IkcE8EMRO2xcr5Bb+oNNZy+BMgyML8/GsCWnci7cYoOUDRfdks2+2DBAmLOqpO1CI2phx3uJzsufS3ySHjKo06tgb3HYZffFCuPWjB93D5ZXlJXT7ptrgT4Cp/cTni14YUXvui9B1bVGp/ZGMdNE8NJ9BretsvH0eIBvM2XJ57powKRtFmfsTxFbdAOUuCM+/BS0dtCLSRSAfvWRj8x0Zm9B6J/hmycCv9cNsLvHvEfPS7lH5kgFbhYovH75WgKQBN2cBpAVm3pGRDw2Ewj6C0J47Jf8OHb4ozOoTv0BqEy5v39xSgpCMoxX5WhqCFF13T7NQjQHztw0HKqy/iuhmwFBNyuLqCvY9RdgPPA6M+uP8K0/uzH+PY/uuBkiDOoqBTlNqYfK9l9RiYBUok9xwlBqwMRBx5lK9dZMHw2BqdS+G1aiy/+vug6zLxzxIHWKGLWnW1b+LJK84JIQzft5thGiG7x/Mp3hsf2F/uCT8BysgP09ZvhrZNmcBoiepgnzpXm3KJP51534omxcq+b+/6uOF+lctP+Eon7tMhd33L19rAeMLLZ+m/aKLSaWCjdHiyfQ8rFp3SzF00mb0kVa0Ul0Tqy2x8OaJfm94/2Y18XywMJlmzaJvjSekbDBZorCYEbo1/qWvZTD+2ui7yekvgLdEdhKQ6O0OSUZt7oh+GLktpJZh7dYAE7OzlCg4V2MqNzfFMhmIXZXsCDJbtqaNc3Nu4odOvUJuN7KN6/X1nb25eFSJ4fqdlsV9xLdgMDjWy6ufF7IyphF3f8MZGT2iFiIfhC/Q8+EoIBPf6VAwoLljKtgJJ83Jxp14/x+fW/xvzKo9rDI8vlvb/3aMlmGgJ+B4+oJaLuAofMmzHV3cp0rsOwlRvWizSPcl+KV/X60nF7B+xexRLXMq4rzvIbpoS9SbxzF6wiLWXFkt6YEOxWJ5RPVvJgsGi1iLa7SRZVZEanhrZtww+7V6SMtcbGopc/kbw5hQcO2QStM7f2qQ+q0jklt+djOQ6x+IS3k5tEuSXLjXE8GmCJo3Dgh5U8KNFmwJavvXUDMCJA/vIvTYJGjzUq9p/vnScANflvqcjMCBvrcGM1fbngZQlruv+5Bc1DIZf8zlIXyNE42eB6M1MfrbNdmj/e+FbEPSgyRxYsOS0j4MM276Zzo/+z1HjvMrKG0XOOYMB0xsDS8HOGAL/64+Rnvqm75iWKINnjLhdAGioJxNVNZrXd5rZKJ1kObMI0xPZr/B9iUjnN/YXEQcDXoXTEDyNmqJl3Zi33QRjdCHdo7ESok6fuy22Vw1u4faKz8a6+4FXbuvq8d5B4bkzGRf/VYG0sn/0ekNFRJMQAzb9zGUV/dSO3IbK5wMbDW2A1SnOfbdu3SNC4byH5SXEhiH/EJlRqeo2qz+LZQYcv89Vp6hJ+/65JY0oPHwKgDIeCTJMJFokKf4fGj9x5QOGYHuiuPY0KYm5uYYQLdH9pEznUf4UnRSPAPboz1EYCKk+bmkHkXAaDAIM+J0oZLQThwFLpE8myBSBY477rBaVZV5G/I67O0Z+OkNDwgcWCIs8cTckFs0O7ckFldCQOMccGnJJz9b3mQBD0Q+jP1CNoZjiIWTvqMMN+6pd7WPtvpH1v+9ewx44bnAMIw6DII4Z4LU8n2PMDrImEgyBahBzJu9tTYzHVa8OzWgX0+/lcD1g97g3oKNLfoH0UQrDr8qkIRjRzaiESiEPfSchLFhp75v2VyCm6GSRzOmyBrgvo+xpp5LeS4wte0Yk51eH4Kl13MH1KXyTmHdLCV40C5HF1neQGwBj57gIKX2wCF231v0RamPL/kc7GalCpNJ+zscKmlZMKGYWAvgRyPtC2TOwzc57ah+1oJomoFzjN0AN/UQU9XSCjArUcuqqYpM9jQM0eTQ43q27/RyfIaCaJDqsOK4MSnlmSuitTTduG2uosoM9C6DdyMB5fWdC6JC9f6E6kiglhje7eDeipGrZv30MUTcTNmRpz9/j4M44YGA/+WwurKESs0Iv354D1jD9ka0qHf0w1KT26rSetkcRMKPHVeZidjDtdatEWQIysv3DrairJjXbrkP2qU64vlEq0HqcnDenOVb66LaS9W/I49ODAXleE+JHScImSOLoZF1b3fjcUOy5YIl2Ek0FBoMqJaSGw64DqA/MVuhTIDoAkiBm8z6qQfasnGQomi2ohOJzP3F/Drx6d4QbjzHr2r3a871+S2Arqws9Y7AG9YdfLlixOpnjUMB8TwDrYBap09lVmlXaAYYySO6u1bXe2ri2ic/WDdilyKJUG2QqzQGPlCVH6MYgdhcgi2Qcwq73cDyz1OsGJyjZuix8lrH7joMy4ZJ9glXJJ7dleIN8La+YQdhSeWaQC6JQ2sYDgqi/Z7KUS/FpVXz+mbI828vpFUPgeQdblf4CIGzvNesJ6/KUDHOS8UywITLOztMEUoZiuR7T0us697vhxYd5Ja2NN+ppo//IKvuIAQ+dViqohwEbbyK5MVd1R648rmFoQarhLfH8hdiI4uG5bfbI7rJ3alX62WsGOZVnuRH6ZA+yuyT1enT4Y9KBycGyNLjtxxelisXA2VAnBjQAZRTtAUXUEKvIJuM8IwrrUfKSEdpG3BiGwt4KBk1kSDH2ClIoB5TrhJrJfz5DT5nIHe1rUEBYPhHG9kOh/QFD6HG+mHJv0CUADn+wFqWs9oC7K7pBz3ODZ+oUWGzPZNJt8IfhoJLF4XVEnvNxFPAGHgvQWw0ONKHCY8equXN0svz6vdJILr7hV0JB7IdnhGAKlQk0xw5OmvHWFAzlUUpRf063AwXD7u0z8WAXah4FqoRObtmBC/hPIPzs2d3f0owyXqauUlc7fmf2KN+/aisDVL7jFfHoEa2SvfyvVDqznlEuJW8NdbgnS3GKfiQfC1Rn88NMk7cB5iP1bWID/lPSVGjQ8AzEFVFVkSEGJyBgjwnS+jFK0lFO4jibZm6d0fA/eGfybCnBgBDZxLpYo7Sfk/k0XWPX5wIZji5gzd10njEW29wgujZVaeEdhpnGksfWidn8yHypVopWbHtF9GP4hPMt5IIFIV2Mcje3ukX7T6gkyRFQEDLI4qomTVoUfZlRhkszP1rNnZDlPt1Kf+vrCWBJsKWJbn+3WvRRJf4LGUUEyXJt1DEAzvT6iUGUvaEuKpkvVfQw7keDr8FNY2QxUgMuJyF3/DA/AOf14Rv10ipjfd+T1ff6gcRFrn+BK8NtxbjonXck2RLX7GzOiX0vMkHGIkA5sbLMo0ksUWZHdhNppvh/ExH0ZxHNBD5a1axFLJHseMEgju5RWc8H8AO1/5L6PY8eFntW2clVKLQm7r4eOsBRIxuaSp1D8zq88uzBfGthPx53iPte68jSX5KNtND9PVD7Dxa+U5NvGe/ciBCgU9YppqGQ66sLajoXrcaqL/YaOivzUwujbjiW8LZUzb8B0YC5piMT5OocvlJD66K1Fb3AYUpuoCOPtmnoP+fPTDXvlmbbls0Id6gWYi2ruCS4Yqo312ZGbMatolGu4/WB3FpTIz6X1L7ZzVEAbAK/fshtNpxDcSW5DIb2/EtzUQIF5cmrl2pDlHgZHacEVZz5NcPhtBHAHzUNrui1WA7mszxcqL7w76NyKfFoPoRzj4EuLGUbHyu43Gq6Jz830Osd+BrdNW+lHQIo789TRxtx3gZzDLoGuadAZ9aRuJlHsFixEVpH7qfBlVT2SMTw5fwogjg9q1tTnV+ag/OuxHeYri2GwKjd1jkPEwj2Drgm1fh+EpEEJu03ikiGkC4lM+LOZ5it70CbNu844Yqm4dQKzDxAywGzOfZY5V+tdUQuTUIM8PxsiJcXRA6tgwwYfawdGxoeYTZTmnDktFxf7609dvkuUf0RhzLAxjLRVoXrXmker3bvOuX2e8d1z1YDD0gJYkCWrG7GBD2vyLjHFPNv3dULzCF9/nfIHWEFc9wI0Ywbo+fVgfGKJxXAh1rAu7ue5ew77i7mQf/8ZNj1sWIgpR/D3kwwtFoNzMmIKDChLUIzLDYyfX6x7dAsapNyV6d9WjnnCFubwkK4T+apqB/acttNxOm86b+9Du/3El0/Laqmnlzy+vTb0cQFXoEFudD0cDU/Yg2loe/x2apIqhFcKXhXZrj/FO6pTeOvBmwgJgDjF9R7RzrzBWZddqi+rS69ZMv1wBl/undbtybfOJ6RLv5xWVxfcl7sw+w6yMVYOKV5TxT/Milk3hDT4BIBK9/FRBL/kS7X8gpdUSjsBfGxaZbfq/Cbj3maBiUJ+cldPT6XBvuMaajOhf3F/gxFKl1yIt+IoeefUF+0bOTJS2LmoJLRhDrVcFmOO6aG6XH5ZIAJLlypj0lp/DuH+UeOfJ7bJ3uVtjP21roM86TJdB9bRj/BU72peZMn9A6qXS3x6Ta1SKMXuDfebE+tdaLRbpneFMdUVGorvrxQ+kVrJVtOnnkgqhKojitcLrI9vNMNzPzRR9k8jDO8vCXQvj+O7wO6dqrNBK20uA7P26hPFxxqMLNUT09sNvEAQyBnv+XT0QoGUfoDlBPqe6FnkGDsUytiftoirXcK7c4bTj8sxMZu7w44NRKl89pSq1WkzzQCA/vGmC0/pkKJRkEgBLl91HccS3iT+SglJbvY4rVqTSOhyfNcVHMRKvsr4oRoNnCDsMC0phTLWNFAZOb4zY/wp4BxSl1sN5c2GdXnc6Q38aiiI0D9X5VovEPsxyg1/Xq5XNzSuOS9IEV9F/Om9LxU4d51PisTUTBfnocTmqfTOBQZyZed83bDKSKD11yTQ98gb7sTmX6EDdtP+EN5K03d0OU+19Ld5IfyBFin8/wkAHlW0GR9DBMLQApZAc5MCDNOjgXYrJc3BZmURDcyBzLW1ob51o0dmVMQCkPxr1+atENxD3/C8XSkPFpqy1MnFAc6xPkqDnhZ+L0Fj/4ivXt+ub/uJwQb2zmHV7efKa2CZwJoygEg79jw5dMi3sLI/x+8TfwSPmQ10m7zj9XlIyz2tXiD2gyPQvVDgOD1JyiTUaZA0iR36w41PpInqBcfmIX6kp1ihEfL8YS+4OLbX8i8OizBzH89a4UhTu/viGpN5ZmMdRFUj2D+vccWNjR6Omg9VNb70WXX3OUd84HtTlm8KXYUR87e4/Ecb4fOFydaeR0LWTq3cVmR3e9iGTWco08ItRNbgI/NSuV0tPnT2v0+/okgA8lKxVFw6qqm0QfVBipYWM6nXmc0C6QAWA1Q7fZL5G9GTo7x7bsZJe3msxwfEa0eQTljSm59o5x4itnG5GSkqmA3zeIKytSN/1h0PS5IoEgYTYBC4lO5qcM8tUIMbh01rnnOGvjMwzRpe7jj76XlI2MHw/Mpb8DH6QU0mzWgH1aM4vCEGIYUQAbZr+s0a04fU6JQCanPLP7MGkOjl5LaIo9Y3F0Jgot9YTfqVztC7XXcXLOgSkRHoUNXq4b7V3BEShjJULNiJ8Ptr+YN0jISQJ1j0o31QMlAcosnGgR5tyYa8ryflbg/KkHrddvVqY8ygoxG9UvwpB/x9maW7GZunHB0k4eQZwZu5wZOXqOWw+8rUUYqmkRZ1W4UwxZB7UZQdc0b2pcbYiRJRhKzb0OmKwiIANMb5ebVmAaki5iv5W7YFulJ1WuIpps4p/sTH/bJVLJe/vRxswnxL0/gZDpmXAggn5sj3ukHFSpqb3rx52pjmXAFd0kTYT005HUvfGo9gwzRlfayqpE4suQ8f+3lfxGjTnLgPTiYmmYKz3BMBxnlUB1C+wluWqbO04HbZn4YyI72+LnEWGOYBLNMBMgzXj4jvRoMP4sW19/RyBEVTDOo/EMCzjw/CGRRhFcoQBf7vkYD9mB0MjvJ999WsS+EKHQORF5/ZlXd2aOkSMvodvX2kyc1YNXGL09Y0fmjK9ke0AYJx6SV6bNiGAfv2jIYzafGKjPSH8SaLh1Bk0RHM2SRVgZB2Tt/7fZL8RlHHsXdZsmPGalK7ZeLpq8AG0hwIZWI9Ld9OghRFKFyRZxUcHZH9behAIslWIixv9wvvF/fBDVh4jhbbAuHudFZK8nV++gdH/vmmhgdAeJZE/LlHfm9kwKoMo7R/wgJQqHFbpWx5PyJl5vjxrNQvupYaK3ksjQYT/fGfhN5V66ukBLTkZrlNb7NEbRBzc7pCSn8O3irPC6XRKY9Q1JF5JTROs7pHOh6cvVOW+AWg7dorlJXzkoBLuuxk77ud44gmNvbfJJkE32JC0uTnX0J82Lz6t+OLW5EWFyosnEJzLjhSxpU7URmltFPqmYmU61zzkgeGfmwHzDcnXWrv3EcYXf2Q5WtacXE6rmSQtQs3XSjB8jdP5TcaLMg1SRcBSJzv5EqqBPYwGE8GHb3sDHXvfkPglXDzGyK6T0JPa+LPJd4pykG0urmVL78SLZ3tIOh6azfXlVEOW/pHiw9D6wEkxA7SPW8GFfI3vZn3rVCi5RLkfl2M31hP23IreK1C1a3Lz7qA8JwgWljCJTJAZ6oyMA6+X/sX0wlIb8y6LRent9gKzTotnQGgT4xpN6Hq0PQyg7ylnxCeawEn5YxQsUDy02EXACxWGMEj9sIB9ltLMH471JfX8eIYR1uJYGo2igegj5IU+qfN4FyI9nSRbz5Fj5MSOidvNIOrkDb/jJxSliGreDJ89y/x4biAh+BDCIW4BZZzim5js85pRBBiPOicAcWbBAcz4LMUQzntO65l6AlEO7B5ycK9U2/UI/O4YcSXHQ16yh+1GxXTSmGUbIKJmrITCsUyR4zqBl4M4erAbMInnKr/clmI48rpOlHTeoE9xXYWFXacPp4NS08PtXmv3Fy3n3MZmcNtqTDBELb07TP68hODFSJ3n25elP5SboTUKgV7A+iZE+oxXyqGjz04RXAlUbU0CCWgYN4ZTgROyl1KIA91b/v1P9LRUGplLPVV96/eivuGDqEcyNEcLTJdK2GB1oUZ2AfsgCOUGeKa9EvwiwL0VhfYUR+UwZ1tClUeX66GDdw7M31jcLuA4YDUkZ36YcP8YTMLWJ2v5LyLE3LDwbWsKGyeIbR4CBtPLTMlTJT3Zp/T1khXC3lewivxd7zJRWJYSIdHiByHj2VcWA0S7BnBrHvXxaQzZu8rv7vQDRJ3FRggTInhD9NCYCSe9fcFLAwpjy+1YcnorDh2QKyt0i30n1u2RtPucrxF4aaKXqjUhix7prH8BZ+VUg7t6p4TEVL7i8Jw4Zo+Ngv/Y74+Zv0SiNpLf2GL5mA5z0dlwy+AxvmPiZLaQpO5zIPuMJBtElPyTjZFmbztHWjJtDREMtovbNxolRRzk+iAZuYxMZdAtrPX45CetW8+OyT4QKo2PIzT/KiEzUDwLo58UAYsyADLO1BX/OSqYvYMrnYhpbmFIqEqGIp4Y0WlV+G0GBRFIWwmKGoUiaepqeMjV/r0J5f3pCsGb3x/+f3QSSDZ39TqLaVTFuiP19AVhCeaTKrRlRbLebQ02/csjALS7GgzBmASThznj4d+qCpeW4jBYWC/d9yx8hvqCEfde480b6vlrJvwoIwXraMylvyUasMbuYHE9WMVvc08nVoPuFehEO0BbFOO25PEpnf6DCTiaQmCf+rytMG/T1+J11tGjpDLnEDhIUlFOsJBEBIWhvlRHODovlPRC7/u68jPXW5NjyXejoWwUheNaETpIfdEJfjoYRxQutFQyblzJTf9CmVuZHN0cmVhbQplbmRvYmoKNDkzIDAgb2JqCjw8Ci9MZW5ndGgxIDIzMzYKL0xlbmd0aDIgNjc1NQovTGVuZ3RoMyAwCi9MZW5ndGggODE2OCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNq1dnk81Pv3vwyylK3s29tOjLFFtrIr+75kHQyGMcPM2HcJici1C9kjZCtZQtYQQkjZZY9skf076t5bt8fv8+fv4THGeZ7t+Tqvc84LN7uuAVjBHmULU0UhsWARIWFpQFNLC4p1MvBxtUUhMCLCYH2YowcCigZEhESFhUm5uZXQMCgWjkIqQ7EwaUBEAusEGMDcsDBXWxgawJlIkXIDajAkDI3T2wO2PoAWDAs19HGDiQB80B+CLgqDBdtCMTg1DOkIR8L4cS5KKDcfNNzRCXsWQwwMPot05q0oBKhD7VxQXhgXOABF2gPqQlpCgDbKCwfCAT4UErCFOUERDgDKATCEmQJGBir6BoCavo6RrgG/EC6wgYebGwr9NxclA0MjNUFAWUHbUAWAGQsCakYGhme/DWFIHH9HQUDbEKc/y4MzPHPXUjFUMDTTVRGBnJ0BEAE8YWgM/CztH9x4cMyAX9Rwrg5olOuPBACfExbrJg2BeHl5CTl6YLBCKLSjkBviBz9DJzgG8EKhXQDcNxqGgP0ojAfSHldOrBPsZ4Cz6wE04XYwJAZ25qSK+ql0xZUS54TDsf8SwxUCexYT8dMcwMBg/0njBMX88NXU1dUEXKFwJBaGhCLtcIZYKNYDA9j8wHAfmD3vT4IwQMkDjT7LofWPCv1vmn+oK6JwJ7NA+AVAvf68MSjSA+P7W23+e2w7FBIDx2AxPyPCAAc4AnbGHnN2Z3DkD0xLQfuWqoqBIVgT13tIsBYKVx2kENYb+8P6LJ6CsqY0ICElDojgPmd9qoK0V0K5uuJYY0jPyqcMx9UJi0L7QP5Xh7sgUV5Iv/+pdoAj7R3O7sHeww1ihIS7e8BuKf/thINIf2GOMCwgDMDcAZi3nRPkLPmP3jmDRc5gXFEC/NxQboADFIGBBcAdYLgvUj8M1BMGYNEesAC/3xX/lUhFJAF7uB0W1/a40SH9Ef0W0gEFSP2EcUz+Uf3dEHyiQriJ4seNrT0KifAB7GEOpBBtFBbXHnz/f6buj1yqHgiENtQVxvc/KvunOdQVjvD5fzn8YWgCO2POp41Cu0IRf+jgGFW4N8xeF461c/pZ5p/4LSwUNxcKSEcEDACLiAsJi0mI/tQYnU0cAtfauPUEP1twZ3qJP3S4rrVzQcIwGEBc+IcKhqvNH/RxF3JGHoAoaxsYausI/K9++mGtgrRD2cORjoDoVQkAikZDfUiFcU0ievUq4CeC6357mPePLgIgQkgUFucCuHlgAwAHFJr07KYlrgIQhTPopyQBQBR/SZIAROmXJAVAVP6VJIUBiOovCeen+Uu6BkC0f0k4P51/pWs4P91fkjgAMfwl4bgY/SuJiokBECjS0RaNaxkYFgFzwP6mE/9d97MB/1GKSOFy4CqB8vovLiUKQGxx9hgEFOP0y1pU/AxG/xb76g/A3hbxmxGOy1ky2H9oiIhe/Rv+g8FV3Jnt4Gg7BMwNgVuI/4YWxlUUF+Jsp/wylsQFcTx7E2FomLsH9LeskjhquFWG+QM+q9MZ7Oph9+scoiK447lC3TBY1G8BcLRd4cjfKYjgnF09EFi4G67f/kXFRQAIEmqLgP5meAbBHH881X+U7BourBvuzCj7s3cDR9v+Nx3OzQ2NwqkxHrYY2O93hisKGorbMb+fRQrXORg4bl6hvx3+v/Oge/Zk/Nh/wr8G5O+39IdsgEWjXGAmcHvcfxK/meCGBg33NhfGLS8RHI77+ecvy/8k4P61d3/zVlREefuBRaUAsJQEbqBERCQAScmrAf/xtPv5qP1Ym7g5/kc+e1EAGMwbZkc6NoKykwlzTn4RXhSokvuumJBbSuhLCe11U/WHBGNp7xqY6JUfz3LAbuSFvAxO58lDad6UtgxMDEEWmHKH0SBOJmsTSvt37PXk56CBWoFM5CoKPVnGQkah6VofgosbOfiX1bNyzArFB9PrHtaxAkY9K0pSDU37MaJ9p1RbSRwWxXXj2YRe+e9Fai6jEdTeHygZXzF9ePfqHPZ0/3JsNLRFYezKkE1OOG2POpHb6yZK8wNmjeIkl8XYwV36lvtXZ4uePut/TXLhRnmcvn4bY9lQsOGA17l47m/OumTzNpGZqdTnpnXSbSwucnhmjhvOP5xDSs4fL55/pdjczKWilbMrtXclzVjAgkW0Yyeo2YUqIC6c1oDxjs/4jBtbEP1NMyg4K2yffI7xq08KddFDtSe9wsPfv2+IjXj1m2E8ObwnF6M9OAa5Qa/eh8l0Dgqgb8QeZpYxSlgP58F0GQ93MkD1FmXPW5ovxo8mXGhPtUxoKiKK6ELn2hY3sLMG9i0FdrgGFbdf7jWh4uvQyGWSc2WdTTMl0X3tOLIbQ83CtazYyj1xZ66LzvmjeTW/mOV9+/WYgglehBAF4zf+2YC/bNdRjSjxua8Ysumuu66cYPzT+41+j6RAYk7nHDTYiG4mjRfnFQtlpTtPXBAXmub2MsPmZMNjaBmus0trprVuDe8YXMeuDZgkfN9SQQ2OD59PGt4vPA6HjD1X0ECQp7hHrQgSYbLGGKQGebmAwMsarr5zoEtU7r7lpMNysum+luSomVE6RXa1CXewLcpW535+lZqquYGobzzXUSMYlkUfwZmdSdU2fTHe7flFOUNleXViRWbmYULvCx/0c0zl+z51zbz9NlF4yyvTl0Uq3r92hY6XlfFQkEHyHmusOphBdwQscFNHnjDSxDbJJAt+NZGXSwUcwoRNW2C2hbmzbuBrOldanbBGHn2PeoxOjPbYpR1z/7xElSK201j0gbmzEgXeox96UQh65b3V40ZkQ5Q5mXx/b8KbteMGiVC1HPioP8xnhCvAl2At4kmiPleKVAycXSWAaDA2euI7e04i5OvbMconIYK3hZg/ngenBXRstyalgOezORUSn8GCfNrkDOVamZYFZt1y2tlF7njWBmMxJCjHh2+MaEwjiGSYeCeXvFimDzeKtCowbc7tcnob0d4yV+aJVC8PySykIhHmSmBKJ1TjhfXSmtDTIn7X6c1lswvRav66zgZynJ5T1+fbJjul29aj1M+Zfiwl5rDktaSx6UnIwLpYiBVj7yFkkIOpQG0w+VYxWVDktz3qHf3n/AcRRm7gfR3x9+dZYRWplxnELL4N3RNAk0SEbU5fdpw0o4uQVs5Un4RkHyguBHLo+w34X+Jg9e1VF35ye8xGj21nd9bt/fe3GWH6rDM29NKtxSd3ILTHJpTkgY9HE6GabBMljcMxnzKvgFqCCuG7RUyIjdh88B3a1+7ufA5DV1ec/Zrp14XXmAiI3WrNyyzO0xNXhrm/yFyTiPYN9K9J2UUQAuejawJf8vHvqkvP8M/bzou7+OSPEMFbT/2ZuSnd9lN2Glt8aV57acslXUhwkObJ5wBbs5KFSAST5vZmpghbcm7nX6dnkoh+fEf+9FKV7GoRBWAQtqyi+3nD4SaiWpbVa1gt5ZRCGPsELI69e5FQ61Xm2CWH8oxvmHR1SsWbNXR3iUWrQgQJQN8O5WbCQAXLpxGOuR37U88FS2bFy8zVHPtB37Q5ZisUhqeYtUaB7LXcUBXhRnR7iBg+d1uCrrj7aKHx9uVHrwNAlmRyQffVbnGI8sWk1LR4kCuOkC3Ms4ouvuXbjd7L23d49alZNyBPvOj2RqVGMB1lVcrYcpHetbtpIQkGnIvc71kErKZbN6RtpGnpLrE773srur7dGuW4HOetKLKC2LoXmqdG6m7IzB/6indvyHnbNAVhffdr62oVzGqBiJqkhiwfUf/me0K584u0cD9mEMd2v0bdljTBZmKcIUGCZeBA1IT948JinSMkD2+nYU2W6rlJJlTzyCfP+FzihwK6kwcgh7vCB4OF69uvGZ55aBcHHU5cxR7yV/siPrmXcc/hS2zozAbrhD7SWlp/iqWIKevfWU1ORm7zjfhfOSAjNMkemTviV72QWxz+OkyEanx/bR/pPe339s3jvvdf5QulOkZARIT9ocxksp8O9Qursbr3E/ZiddYud/vLTeVXPOCiSTvBF+BeCjxHvdoiJXLhwOiZz6EVm2KbgbDxjYRngyqvyVuU6qahoPseic/pKg2OXFg+bEDC36m7mg02XfQ8VRP9pCyNXIfw803seV7LLHmrUmtb4CXS6dwV7v1cGylNef3VxFaqZmuTH7/1yzm7tgAmefP4y5OJvEvCoUMEARM5moN05u2mibeMHD1sLB+HLqg3EC/S9ohOsb69MUdH1aqg1270oRzverK3jEPLQITB8Uwz0wlB31+Ki4MnNgOWGcDoHl+WXGi7Ac93hoEvPut0TewEls2PEruEts6xeq5mRnV5QXWoqLMyC3KieIj+ykoVo7QARSdnx3+2LYsfYf4Y3Kivs1qNCHD87EeSDMZEjEyKyNFudoEfwvWO6qsuPlhXD7UHCxMlm1x75+0b+c5JB2sohFDnCxotpW5kWzl88V3Jx009kWAYv6emAs8qqHbnu7lfzq5MYC40gxXPhv6YSkKsMo45GlwksG7HY7dBPTE7JE/nKchJm14Ztp/J5UrQUop2k7lZD1pWUPG+YKm8JX9XITHVZlR7DRRxMCBtdKM36Xyf9RZnYWbZQpNlWW1OP3dcdR59NHdYLRYAbX2mWVVn5Y4atzCMh5y8O1VOez8TO9KrrZvqLRwcXpunmVUsMJKGGJLlbokeGx/gozs+1MpgzL6fd3spjoAyhmeUc4rKkZ35hsKThmT1fJE8hmvZhjGCqd2inP4NO0idx7dIhWSXCs89YfCypzPsF/QDhrHaOl+nrRuM6gW4allpOgm3VgXNBvuAHWpj3kzO6s83KAI7nrAK6aex+CJgN8qiFe37y+a3O4t2G07KG/nsv4SV4mtY5FCVcO6npilgbHR14uM0d9dnUfNW0dwtUMKRx8ixe+cIPp4OxD69FEqipFgTwk53g5yh1OTlB5RNWZ9C9sbuOhyIYNZpEs8M6ctSW6YJed9HUelY3WxvrNKx4QKv7/lGqMg6BU/jvg7tNDPxf7XwybigQqCE4I1tcqRg1r1Huu4NSD2T2DQWkqU0ttqWlb1hb8dnqEcRXVZrkTlMr99fX4oSm1jt9SQ+Se7LGD2ld4rS40HUzjpfnfecHREXDfoy8/LRstBtFXUyhImjHmlpnoQmXYFUXugJQ7+uNePJR5FoP5Wp482pkb8sqesZTMmqv9JNVZU9j+/MoCjWKdREUxO2iRK9mGN36qZLGFHiMXnWK7/pdyCRRTHKtCRwK3wNxMPtQp6KZsbWfzTQ8DTQyS0mu5B5j+WJSv/hW8JDoVTbKIKVAWPrERDTDepET+uxt7RugY3JUFYVKoKdq+RO+atlGZGsjI1hH2RP3Pnnl6+PE7EcO9umNmXbKpfteGn2QTTIdSaHJ+HJItOyUfL+dgkvjgkg930OZX3uB3B4I+/wsoXcPz6K+k5iaBDtEDhx+gJBS8aiRBIqmE5nQVaXoHjoFtt4WxnU4NCgegX20f9iwJv69tjwZbPqoAarix/gFvPHPKOfyR8+fQX5rrl2NDies3essUfysCzZIFN9oEQCPbB9Xr6J6dk0+jSgcuVS/iNJHf3bz8iq4tYZMUeOt9aIRuyLnJr4btuLxUnzI+yZ36yLNF+szVfK4mZLIZfipmu+mV6TEh4+45tV/XXEzLbr0taTAPejUrL5hIanoO5rMfIvMpUPQ3qNwOK6FkKTS48hXY7seOwUw03uQZ1D7S20gYxSYgTr40vvQ98u173mUnIkEBypNQouuPt6n/FDnF9ftGPHuH4JfsvQy6SRMCE61Db8phCmJKS94Xx3kZnlM9aY4eD8+ya7l1qEU0/GXMPvdbtdLqprzOadq5P01inKCBx0gVzadH1j00EU4GQtttaO1+eEMR5cryKbL3sgz9G71tBBeJ87i+HUJDCJLWU4p2T0mGbIzIUzxGT01tduF23V8y/cFuef6fK7SXfO5SkvL4YUhJa87rz/4uIjtWcjZimEd02jOhfooMHXmukv61WzSr5P0kdUmCtzcBrEjgqf7GYUKa+8XToUe58MimJTzD//l5fwBKUK12rYOGd5x84hn722Jccotz/F+Eyxqoz+7el0DVQMgyfohRbHkYtZ4ObDyXKj7LmPNdfNJBKCo+QXjg6Aru9yFV8ORda/hCXaPIjk8S89NvOjv1QW6mv9LQg/ozqqhCLQPdKB582dyLs7NslzLwbvaDzLkHnNyShswtzw3GGHED4fECVBXB5fvDPHkG3U6VPQs0q/18qSvtq2qVwhAMn6PObGl0LoLr4nOMhKkn9tMNUR7YdPwXNZSaX4XZAG9Yeaq1ZN0u9p0iZ53EO7I+Y87948SXWyz25eNUiagohKTUU+pLgUyLhVYUaQfo3289wuTZ6Vv9y3xfKW/uQaj5Mn4YrVLz3T6NXcI7V2mhLJmWJLO1jnc4y0qx4K5/MPrxXQdxcFH8Q98OMujW0KHSIWHGE21juQyomDZEf48V5fbu99RlUBUrfgqVZL97W5qbRyqvh9Zruj/XYtXqUfwSl3JzzkY2GxYRdvBfSxg4bB/v5z44Y4PQFNFWQzvvbKHKFoWUrGshJnRbJo7L6XR0RJj1kaSPWijsbUcT4ro8wBwQYJpdBgSoCCyl6+feKLHVW8BUEVRjNmg/Pgh/S6SuT92dlawZ/6uCZNhzD87rrZr2plrJrbRZzFFl6QjCUuALoAn3qdaMfrZUMaPvwPgxmB02+R9z5B128NtAha9V+UtuwvS0mKvTdgxV3FPBvX4RiceXXb8womlQ4MUs2Uy/nSh9bjxysatjn3+AW6VXlu7tqK3WBMtG0J4aF6Eatzf2KYrtZwoMXMyDlNOheiky+SIO7eLFLn+qWJ9xtx96pSCGf30oYEitY+uqxRzLi8YNK6i+77iP9yCZbEUjG7Q+h0ZBBFnqNhJMAmiI1Oo4antHTccjU0/cxwM1nTq78qrHU+ilH2FYJx1k9wbOxw0a7GLhtzX5iE9iNms8aEODfGdRhj3a5M2dexVK5Z/UBcXGVyc8+Uocorsvv9PRIvh+SsU909/qcBGUguHzPpNwz379jmUILfdz7W7ZVZ0uG+A0QtMs1f0o6JL+VeLVa9cHAjLa6wPKCxuG6s94lOV0PQDkvmJDaGelrL1mZlIS3RjBcc6NfZ+aTC6TRm5As2ceG1HdC1Mz05GrRyeD4I39XfKkQ8mj1ENe4QufEWo1SKlaXI2q27ei8uXeLcxkxL7KMW4+vBb/MiTz/fG8SCAj5pH0Qesn8MtkyzStzekhSjSLkZUxg3nbkWuHtdYBQucd5KgZjCn+vbilZ8G11TCQyPVn1Wu+q0Y5wzdkiIf8KUgG6zlb2WwxV/zcy/kEcbf6p0bZZug+3T7CfS4YAgsc9UPZIx9FZT8hcjPc61X/Lds/wqP1SyzLCmVMNmPr9ItD3f3THcuoqSSjJszbxt0krDcM5c73tSzDLa/+UGI2lRhVLQl4QHIuVaaXdFamPAxJBVRPZRfUng0+MeTc2y+OaIWi7dOBJFRtGXrXSp1Qvuud9pRUW8OccERiglPzQ5P1no+Nh8TPaV3KKcdeZ8lruFrD7vwfOwHtmih1ceVeVbF32trrMVLXu0rcFZRlctznjH1Hh7SM2s+5MqE7L4sYIDZcE4wR3tL9aJbUf9Lu49bvP6fu7iCyflrTfhPW14CDayget6dB1zeHju3fbLUUV5zWoait6kxRaHw1loj4ynjQ2uOpZ/BS4WH8ud1h5F6dK2h4GVY+yVK8fsCnZT0Yt8RlNZ3Dxoyun7W1yWHQ5CHEwvwxttF93LV0JotRQhwobD7xaHjnhP11jijvevdG1dJepkqrznRi5LBtUIsizsvYaSWWiiZ+mNiunxZdnwHninlra3ROUuCmkkrjHpiikZMOntkuMkeXU6SvzyfKf7OL7cnIvI4dVLeXdz66YJhuRJVHWrU3ySapi/lF052A1qsi/qSWDLjyEK5hQEmz9iyyiXiAzvL5skNFWVG6RMkLRpZPDp6GuVp3B+yxDRMpv4Id7K6ZsW9SNBcvP69noWsjQz9fnsuT0nO8gV0rc0or3EW/zmURPS9CBqO9VM4zWwVQjfZPlRbeXS6wC8xpkX3NsaDzmrOXylNnQzXw5RmBKOk57oZxryhO2x413uhH2ilSct/oju2roMo45pf5rdXZrXXqhOE/4hSzyKNhpmZDrcqy3tYx9aS24dzNTGcudJhZnwdesCCqRN6PejN1HgD58hs90SAeSdPK/Dc6nrDI3Ew0BCNeONDk94yFcci7wIQtrJIhxYHbY3ycJrZLSpmELuo0dY5PivlPKKf5XX9GrcFDLZXJucH9qYcte73WYusVolqMB304rtWCtf3UdWL1Y/R85Gz+jLQqlRBNRDSeLAl6LeuE8z9dCkYN1SikX9AmL4sK7i2bW150tfjX1AOldepjINKYWXQl0nbOt1mTqnuAIXdUnjhhzasg336kwWr7tMmrDVrV6IQc2vydsEBcV5bXlCTJ0kHOQI+dKcZl30lStCD2/j7Vbt31PtKGhGO8R7etXQWjKWSua8wpTXNTmfQCmqOIQ21JxrnuZkDwi0M2V3S3J+e6bWHWHncB5xfrUXtb428USdWG13WtHfsblnS+rGgitX2WJ8aHuUS8Kavm/4Ro809D5pO8Pu4Prlz4T6QepXaL12qx69Fwv5elQLfe0lnJBb7zfF5T+HCnMriIRNaxl/Sk2iLquMoDIkXqW7Bcq9V6MSiqim0P58lyQ03CREdsxpVKblPfdl0uTNmza3Vb7nmY8VUM5oCC7xAshmnVOxIbI3ogbMj7eqaK9sZ9mR9IrORRt3W73f7st1kbuXfO7D0KulDR5J7vOU8LUmVYT0krWn+ULht3r3iURqkO2KdNnTnQdoIfnT56KgaQ3yl10mL0inY0YFiMoNkvAO6rkGZt3vbFfDwYZfx787xTIY6HqpaVdG1kwp9LI67d0B9b7U88Z779O22Teg/CrvXFKYR9Ft+eS0o3l381L6XYHw9FW9lzIFBAK9eaWNxZmbe5/1LtJkEijK9a6XrB57ateW0Z8E1gksjplHkqR/8I+2XvOY4cHuvt1de9LNQRY/5md94tRt5rWDSpew21Xrjtl+bnj30dOLVTIeF/FCeR7Ia24EioEhy9/Nnsrsn8o/TIlv7a6p9/LliTKtkbmtsd6Qy7i5dsOWO4W9XLFBVy741DlGKoTIAFGuH61Kjze3sPyJcJjIydGkU3n0+3AYH1lqRjf7qw6B+b6wGUTVWD5S+op9ZpbwFYZ3NAzPeF/TTLnoWFo6+MqDXAU/bJK+C56hMjtI+dpG0X+dyDdV7/0ppenTC9HsVQRqmKXJMt81PG+RvcbapyeTdVZGpsbjzCudlYauTxvHVFyDz30A/Fga6FNNK29Vz0vONGcFVuhngjgr+oVLOWomkc0gs4cBtxgzLkgac0RM2sJO7jprb0zXuVgkgOTWic6rXuMQmjGaiqK3PIBr46GPKgsiaCx3bW/xEudh/H1ityU9MvJEBjgwRcnk+3+tvg1ZlvdS29Xw58hA7vvXlK6eRIWmX0ruynXluAn/Wjrlpnvvgr6OsHF774eH4Tc4QfSnXNcYoq8/ZvAisH1+uSVd8ro7WEYHKmn88a/EZeHCFvaDu5QKdRR3tpQrrvs2Rgbu5pCvCHRocmM9hKOMnF6MSMhe4CsrzB5KCq47ZxxxJVpkgzQqsV99k7NBoSzktZssgkHR4rYJ6Fq+TaXjjO43g1K7zWnjXIHn25LNFqR3ac/nc+9iL7wmLCpQy0z5Ci9Y7759Ijs0m/OXg6F01XKUQmVAQVVjMk2IIgQSuUBCJ1YRQgvnZzFpnA6ADN6iDdDc5F174Tmn+WxOIntivjn0Vqg8p0/Dx/O52zbSKKruAzekghzkYnIw1vPhIadVbv1cvLJqqXFGOzk8G7+qKeCrQrdMhrdFlJ68UGnPVPAO3qbf8MzjJttF1xLNoocMLpcIMYA8baTULuRQ2YAl3AdcUM/XAvHtbw0gtX7oFalB1RA1JCVOLJg8ZfOWhXYx/jMrikYjV74VHx3PlGmTlN3mszip76Sje1U8NMKV59WwO5opMiX3EZb+hsJn1Dt62cX47pUg146tkdusEq+Glm7p3KFq+FZC8Vl/vOBT2Taf7PUmK4wqdL1zIbEebwBUhkl+LCKjz/osnoVb3DpVsJJkX43D2jWR4VasS/Dq5nMGhbJz+E5tmHcEL80lCRgFBuXwOyaEeNYbOSLsb6ZXjuQ07dyP9yl4pkt284HjyVNskuMG8i/a2qk8wCK85/mSlzEPU7vEGwiN/VG4S/FKlj9GA7rMZlQeRCk33mP+wND0CFnm15MzUhsLXx9SSBpWinhW9PhJhk1ZJEjRAUj+VtN+pCX5WUNrG8yTQT87M3Kzzwqebh+YbGxRbcdpv0JcqDj4gMLLgrqLKZQk2+XI3rENtO9xssJ+egeQiJq4q2KnfKCdkRKmsHnlqEVqJoQmtA31pfuk1OfSOmHS8OpLKp4kHsOlgmMKHqNrzYxzXs9fTUIjCLkw0SusK26VBY45uPVKo4HB9hoJtM2F4qNveoUZXRPBHqQbCsj0NdtfqVJHNBPOxasQ8eGbyc4T92fHkpheuvo8XM9UUdGdn4Cp+7s927xVkY18jRMLFcDcxvnWvvd5h1xuEX9TivpTSRVdLkayFWEww+NpyyDP0cNzaroVirXcZaUWxPcsHXo+cZGCnOXoEhecRnk/n6jtbBgJp12jVp2gUp5hU37OJ5q0+3jzjZwLt3sMwV9R5dHOYkNs4lnym/4lTd+Gtlw+F+1IRw2WsMR1yR1fLo9tIB/qtuq6x79VL3PN+oq6OgmPTkB8Cl7vaEhDvfQ+z54mw9VAWVB+06msXHbi0lcXw5r2sRCu2UFAriWmJsFJ5MH/Ae6bIRcKZW5kc3RyZWFtCmVuZG9iago0OTUgMCBvYmoKPDwKL0xlbmd0aDEgMTc0OQovTGVuZ3RoMiAyMDg3Ci9MZW5ndGgzIDAKL0xlbmd0aCAzMTgxICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42rVUezxU6RvfFDJZ3STR5aVYqpkxNG6lMi4hIzEupeSYOTOOmTlnds7RjEQq6WLbsLqxSkVKSLmmSBeE3CWKFFvaSqntvuF3Zmy71Wf3z99nPmfOeZ/vc/m+z/t9XiMDT2+qPQ8Lhp0xlKAyaGa2wJ3NhogQ73BxMCbCLalesCBMBEkBg2ZuZkYxMnKQwhCBYKgjRMC2gGFJhABvWELA4mBYCkgXG4oRWAGjsJTEeSA4HLBhAuKES2AGMIGUC08MJ6jBEE7CMCpAUNiUDHHAJOFSRBBCKHJYUKmKTIpoFg24QVwhJsOFCIBQHnCjsWnAA5ORRgSYYCgIhkMgER9gfMCB/YGPt5OXN1jhtcrH09uURib2DpNIMOlfXBy8OT4rFgJHew+OE4B9F4IVPt4cxT8HRkn+goXAg0PiijqkoyKc7cSx56zxdGLQFXsADLARluKIouw33IxJZuAfamQoX4qJlQWASQhBSGzpdJlMRhOE4QQNkwpoEpGSHycEwYEMkwoB+ZbCIljZmDCUR7aTCIFHEyhOB7gjXBjFYUWQMzYKislWkkGknfibGNkIQpFTNOoOcBj+qkwIhCtj3T093YEYQlACRiGUSzoSEBGGgyCljXxg3g+jBGHgECaVKmqwP0PSv8t8ps7CyJ2tE0VEQrJvTwxCw/BNX/Tm621zMRRHcAIfzQgDPiKCFexxxZkhqNLGtvdwdXby5lDdSe2hVDZGdgelEXJC6a3IZ+/obgsWMawAg3wUOnVCeQ6YWEyyximK9jkiZJ8ITBpO/w+BC1FMhkb8F8pHUB5fcQq8MAndB0V+DINdHf+KIU2Uf2wCmABmAP4RwHJuCF1RWqkchZmhMJMtiYyQYBLAh0Q4HInwYfJFicChjTAgpGFwZMSXwNcrCrk5HsIlSNGTg0NRZndF+RiwGTWTTD5Df8nBxJxGzpMpObQ8DBWFAx7Mp9A9MIIUh8n/Z+a+qeUcJhJ5QGLY5N8b+603JEZE4f/i/42fH6zgbeKBScWQ6BsMwZ0ROczzRAhuyGiTR+2uBETOhD0qEMGAylhEM7OwNB9FfBTTJiJlTV5NiOJyA1Rr628gUrBcIQrjOLBgKCGYbMw35MnTUFAHdJbjqrWeTgv+Q0tKZyeUi/EQVADMmZYAkkqhcIoZKRBzJhNEMEjd82C5UkGATkMxggwBkjAiEvAxKUVxygwyig6RMyJFcKGYrKKAlYi5GYkgKClYhAj/28qwsgB0MUJO4heOTECXSBGSsML09V48FZOuFK7ZP5v76wpUrr0JKSaE/RAeWfoLF3LDUkQeYEaqjkHayd/nr/VfFTD6Z2C+iGaxMHkEdRGg2iyyBgwLcxtgZW0Z+VUgd/QqUsqdPILPa8U9AGBYDnMpne0Yd3FM6MGi2NNRTieaslSNbGjPsnWW+rsljOtMbrqkr+uY1msIL0vfWhKdYpyOubvYro/avxU96W8UM000fO9CUk7ja97q5X1QFDtKX8vJvu6oL81nWwq7Izqr3ND0d7ejx9dkLmpJKU0onQ186p442Fy6/GGvecPI5FcHDNdllXYdU5VltDGKtaWiKfKOSXpl+h1NZWOIkQ/a+36Crtp3zm8NOh6rU+emJrlyeVLAx5krsw4I+/e1vNW9GsfsPX3mbOMVDc1lefFeXtf1clujOc2yMYlGb0I9JzwM2nXk8JQx91elBK373nDjkS7Ow4Q+1OrhUL96GauiYp4T+/hbm3fzk30XrJtlXvV6S4VwcmR8rI633vbwrgeSOVt0XdZA1KMxH7T69F6EH5pyOmHFqXqzW+/fv+xj85IucgUfF0wHk13QNzGrAnbfbZpj/dh4d51O70XnjL54h35d64WyQk7DxIA7ToU+bxwK0/nxCT3+xcklGSu51ztFKtfzgcHy9bbjxXvUaHh6ulz+K7q6wn/o4yGtOvoBjxM7sj8lT7MXDll3H97LNOJol178Vdw5t+Qx/6khrh8cWMxf8Si6+Eg3r7CoMy/n8eC88w2y4dSdV46ZoC27Wj+tbujEgjTeR3mujUtl7XK7MHXldqPyN5eDmClNIqCLZdIGYoLncfJi4ordVlfa//7eefv4Xi22/bBc3e3iixKtiAB/3hy+ZsPwWsvqB11XYxd4qvoFvGzLjNlwUJzsldl7gum/3xZuKArSj3dj9QckNjI6C+Ws2uCbfMF3NUR7dclPHnZ6+tp2ubOpPVI//95HT5mEWo8l+wfQh3c9cKi9PXxlK21g5F4uuO1ps2PCWO2q3YyTuqZ6rRTTo0OHTPwNAn89v2E4OTQ6cLg3Tz94Twg1r2Iy5OM1aFegOjV+5sPkm0MiKqbaW3Qjq/r5wY7ocr2XifdIlWgOH7j9ymTiy9tnTLgBy4YPbujimt679t0c33QW+4kbr8Mnf8aZpjj88SpvYbvFdLkE2Xn7zLRuvZW3r0oL0vK3V52MLjqTcJayiJW1d46OuUE8deeHxGWZURbOg6m9S4zaatdoXPD9qGOYUndtxPIFYvGHndevr2KiTramb6vNsMnOGsgzjTfKV6+1n1gq3laAW3HvqN8y6GM3w3MTtjgI5TmWE7LKXtVLdHaJTF7YUFaP900YObb8U1XlrKi0iP53s8/62iWqYu0v5wfZHu8RjK+2M7HrTsxzOHPZrqXXjb1hsNvj3GRINMnY6qnrgwhHSiXboKf4vL3+AmkjczqtzXN9c9gpy4rCw/bruDE/9g0WXDIzFYxrm4G/3tl2pZASs4/2E0XW/2lqnuPDVstr+mkvd1/DUl1qy4tjh9OeuA927z90eqyQiHc1PKxS5Dw86TI7K8kw+oRx3nWmUMbO9WvRiD+wx3VyqMbFLeYTNesPvXrXY/Wz/plLJ72m1bw2qnDJvVJjOnTGasPiFc8Yfi4/Ny59l6925FVgfQMzsqru5uz7lzZd21e6VT0o7mzN3dhCyVD/o3lx6F2PVv9b19eOzLftPtd7wPy7V+4grbn2cFyLf3NVklpKWWrHOP7wG43DvxSi0xyPPx+fBJnynY0Hzx6N4mChw8F3+7tvtXaZn9gc0PR9k4467XnaoVPCzKLso3MbfRJVVZouRf15q05bS6Xy1E3jmWqZL5bkaOSsvQdn6IcmrMsc3y6yOJJ90+lC8EkZozq0JlZe6IHaTrLbNih5HTuFdn+Pi8QJgpziRL8cC1poIXZduCp0I7MksFHyws9i4LyA0bzSzKN0kLun6o3NVW3LzBYffMQwrSmtzev4MmbRruJUX2w6MnPptJzcCea3EnfOax173HqGi1uQWYB/yW7QkIbEVHlLCgeEW7QqKPPiC/ZGPrc1tp0cuFyokbxuQvVp/3UNOffd4tpaQtX77iy77LpkR1rF+5pUi4KP6Hmr9wNJdM1yNHte+PLpsgGHYzAdGbcSr1lYs/Gs2m2ruPYHxk+eRqtwfWMqfppDCzFmVner3HhDf3aHo93hmhiacePyp7RY/avmH4oapq/OiGcYD21xepCj2zAwnJQxfeCu2anzRb/sPuuQfu5m3Z7NRZWFpjGP3pu7qCa93Dr7YOAVj7n1rHP6q72CekrvzYvfPjSgJ7xh90tFzb6qmP1/lF3oT5u5kj2rNMd+RsdYCx+LZRq7ah83q6gVbGpuZI0trHpZsWoX+8HcR2+8nQySodxzmwrGtf8h2su1jNJ83DG13Xn9b6LdouPWsujq6oL9lWN+/Jn1dNbDaSUZgrFuhW0fb+UzxcKlBUkjC8rlyzI2cjXtwkaOtFkudaiP3t/ISvVY/CLHe5t4szrB2nDbLaHmxnkR9jbiYP2iQwGLtX57vOimw+XlPXhqs65zX+SHCT/vN1DD17q8tbl2tauuvLwnu9wRvaa9A9hairH0PRt2wSM71m//pPJkzQ8lR3Pmf5f2vrm7vme6YdX3MYcPWJeJTWoiyhaw54g2p0zpqS2hr9YWlqa+vZZdpk8zjJogWLI3UPwcq7rCPGiVr1HptqOFs+TctM26ki1NPc1ZRTPaNvKHzaJeZafnn5Bb7OA9vpHbqJm/eKTI4vtJwSacC6sE+3VzUVxfk7nQd2yCgenUm3eDfXN2djuUZIw0yTvu/DFp7rOrghP32b+bEsckHR9e6zxrMax8t0mz+9JwZENeB2vtctd+rSrVylTj07+de1d5PtTzicrcnpPl7KWdKUcO+1BUEzKSxVP8TvrdbZ0NnRs5OOO1a6Dr8wMbXrTqFC/d3qBuWOQzgM/v2zerd8HYevXNM/6819XW/j8qXGQJCmVuZHN0cmVhbQplbmRvYmoKNDk3IDAgb2JqCjw8Ci9MZW5ndGgxIDE5MjQKL0xlbmd0aDIgMzEzNgovTGVuZ3RoMyAwCi9MZW5ndGggNDMyOCAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNq1VHk8lHv7RkpZI+QgnmRsMYy1sZUYsgwyQ1nqGDMP8zCbWYwhS4rUUZYsWcpSyZYUhaSUOBxCySlarBXKSZYsbe8zOp1T/d73z99nPjPPPPd1L9f3uu/7i9jsjtOzIdEDQHs6ja2HQhqYAy5YLIFNxvGoAXQKa5ueBxjEoRCYAAppaGAgikDYMkECG6LT7Ahs0BxAmbLJAA5ksEFqAMgEYBe0KAJwAGkgE8ZJQAAPwIJsAp7HAFGAFmHlxZ3OYusFEFgwDNKCIBqoDYfY0hk8JhREZvNzGOnp8TPxo3ciAScCMYTOZYVAAIFGApyQWCTgSufCRgjQotOAAJBMoAQC9EAAD+4FPHEYDxzg4OHm6Y7TRsKJcRwGg878m4stDu/poAvY2bjiMQDopQs4eOLw/F88SIP5B+kCrngY59eBHfnhWAzeBu/tjkHp888AoIAwkMmC+GV/4qYBMwP+pQaHBjLp1JUCgBaZzWaY6+tzuVxkEIfFRtKZQUgGZYUfngyxAC6dGQLATyZIAVeE4dBIsJxsMvg1Ab87gAtEBGkskB9kT/8KUmEp4SDYzv6HGCwEm5+T8tUdYIHgD2XIBNZKrIu7uwtAJUA0Nkgj0IiwI5vA5rAA/xUb/AVJml8JgoAth8nk18B+g5j/lPlGfScdPpkfJTKKwP25YwQahxXxnTY/HptIp7EgFpv1NSMIBEIUkM+exe8ZRFuxYW1cHe0xOLyeCzx7ND0sHVaHhmSHs1e8+fls7FzMARMTUwAFf/lziqGRbOlUKsyaJcqXzw6CdWLTmTz9/zHgITQ6lxb5v9BAiEYK5HeBxGHoe9KgUA7oaPd3DGwS/dcWBLIBAwAMBcBwIlmfX3plcvhmFN8MSxIVyaAzgEAChQVGQYEg/BCNZBHCQIDN5IBRkd8DP76JoswAEkRkw0MPL47oSnZHWiAdQH81w0y+QX+Pg5YhEt4nbXhpSXQahQeQwEBRfVc6Gx4Orf+fnfuplj2HQnElUEGt/y7sz94EKkTh/Rf/n/z2gHzeWq50JpVA+QmDWPZQOEhyh9hE8leRv9od2QR4J2xoQRQQ0EMZIw2MTA2/Ip78baPAYw1fTRD/coNxvtI/YPDEEkNoIIsFGJmuQCCszE/s4XbwuQP6zlhn152Yrf9jmFacMTQinQTRggBDeGIJTCaBJ2oAT4ihiQkQiYIHnwSGr4wQoI+k0dlwCMDgsKOAQDpTlN9m/pzrE+AlYUKsECpchQ9/ReBj6RMhJpECUjkUNsSA6f2DmaC/YQwKvOzf7IYGcDaIBk85xP7O28wI0KdCtO8dUSaw6f+k3QY7MmAqdBL/LgKZ8Or+E2FosIIxYL0g4sr5/81vBkMwj59qGMA1GEwIFvIfkxHMm0mAExAoK8YfhXfn30sra2bwbyf+vrBX3nFsJj0E3AORYJ2+c4G7w4TCfQ3gHUHBdvjz7d++Hwog/l3v76J37qSHR+oZGQB6aLhpKNQ2E8DMDB31QyTx6825sp2wAN/e+dcWAILhIFG0/0860eJwcNa1hNJozNmestUINPJNhbz1XqdU4f6cnhtKCnYFI2rg9nMH62JzNc7RXXaZ74vOOEgr3os4LEf5/Lw+/WL3HGn3jlFCNDZaSRJj05HvhfSMy8U+ji27qaY94ZRf5H3B+EHu9dTrKoBnx6Qt+satpeOGXV+kZzLV/MquPy1czT3/EFUry6TIhD9er9io9LinUZD9ZUk2OYlwx6Zfp9e/KEG+w2kN4/at9b7Lys5lmSGvkh+8V7jzm8lIafml7tvrxLdXpXh43FWs7I3F3+cKpiHmg93FXvgnnsmWERxyy/X3k1ALO/MU/yJ1lGb24tMrkcadTU3qGGzRe/SCTo7XVr9Nhq1zMU0h0lEpCfI4xUO8p8MM1RiFXd4EvfzDS5Kjim95p2RKUx1K7hn0LS5Om3A4/kPe/iPNmkJSk6Mb1e8erhDvvV2eN+spH6AjpEeps3p9UGNQOEw3RX5jNT7tlFysY1/rvnGCbe0s1XSLubSf6aB/b8te+StV1TbrdaZTHj0cEg2T1tC9Z0o/IiU5vtErUUNug0iIf1K0vGgOr3Fr4MhISd4t6sZV8Y5y5ZPL60KTR7elOfu8+/AOyEqorBLQaD6mGw2coBiElE2bDVbNakcPl18H1Q8s6EmFl95QX3U0sEvGOX0XM+7a6ZCmq8NsmwFPG18cNLZ7pOrR6o+XzCuoNxuOVaEsXQDV+E31lNLXjiYHLBVrt4sXSYtn28f6tE0/8m/aHtqu+wapaF63a0Pv0Zufo8W1PoZ9vCAifAMrd2a8JqXer+p0j6KNWnHCpbr61pMH25pXLZ29lrElyaKmw6664YXk1koiL+TqpnQ81MBpzY/gHIytPVD2oLsvQ3HLXlErzFrLQ2S8VW9ETdbqenSsi8zDxmXWWszCmA4lp3r6bNgc9k4W2iJ+vDAJ057m3+h4+kV2p0TY67GyKEa5cXLJgdJCgbWyd5eza143+widKtP2slglIjI72f9l4VadhfEBJ4Fb1WmvcnlzG15GsD5UrXldklnsd0VpYkdB+podxkPJi2sKZS/2ftrZgHYL6+Q+S8izf/4XcCgvR3/B4rCYaPeW40Zmv8ynRz1szj2xXURyakAR3aeBOqW1nO8wbXVMfd+Jzb5mGRmBD6j9B+PVrN3eCgYFur921Wg6h3h5x9LdudS57cRaggPVvFrZe3hD2aQh28E1s8Ki+MaHz437jIwC8QrI/LvJ1pBHU+DUqZwL88qTDQ9Fw+fqIrV1ZxJx+8V11pjGHaxu9+tCqBZ38HwarMXYj09b7n7VEMyZsJIxyUK4nTi/vajnoPmVbaYRstNTKhUaY3bXGtRcdyacfv5qr+D8LG+90tBoqELLHe7VLQN+mfRHakpKMWdruC39fwhcHMvLK7xX2Sn1QdcCOY2m3KRhFJSrspRNPj9QllKYvJiqR5dv83EVlidbBTgY4ayB279v0NyXNOzgaZj5+HIqpWho1ebRHadH69OVQ8HKQfmhDWLX4u/ICp/SJglxJX7X0A3969aZKfrpZpFhrQshiFBi4h0ndMtwdauLZMEJp7s1+gefsFKdVHUWw2myxa1m6ROFBBt8eNZ+xP0JrZaECMdfE718C7Rj8uPWzDgpnNlTcPV2eK0h2f+SjumjBaPlX9Tj8aun1TY0vM2B9p8hv3Ng30oizR53yFy48VQwwkBc/Qnv2lzTFC8y+F33VddjC9YD0+iB866fr+XV3t/9uO3Z6Qblwu09AZHhCfNc1p01RefTDidKYZi3MZfi2qbmNAbfX3FYqJI+q/rhGKqcqFXW1kEKNjqmhp45IFlmXZFBy71xUdN9ydsu7aRzXqWGtRQj4qjJNv96FZZ/wM6BWcwDcnOlgq3w/tkbxMW4yj5OCeboTV4xjaJd8UIOCm2WVAuxvEnpLfHDPbXOQbQP1LfLx3hJaUdSdtlKuLr0fDGRUH5bsRyz9mMu1cnIbzR5PJIg/0bpJF22KZd5/6NwF5bbnyPvda+Wktl/SNlrUsg+FWsNCM6+VflsoA+54ApSx4r1fN5fy2nM+/jHKIErFSrTi0dhOojVa1TsMM98E9EM1wn7cc31w6+zu4u0YhYJ6com4o5mtrOXJeUWcZ0JA60eGQq73GmGeUCPte8M4tSlU6EHMqsjd589O0cXwClf9rSenyj3U5cYVMNdr361l1UZR54Izr1ie9WgrTOMbv4w1GfLEVqRSu/FXf1kKT/9+HXrmUeeqxgJjvMQ/VqvJrTCabFyqZe5jAHsxBNdm/vOwR/Ih/a+y2jEemiOhk1PFHwuuxXbUVxrnZ27K77cuoeglpl083NJOvKxw/O23Qc/abge8RBTjvCfmNFKmTrh8bGypo8dAwjXXe5Z8hzFLBRc6Rtoxs97lb6o+RiNG1sz0zmCOlCrs2HHzcr2c9xNq/drW12PG7aSZqeFJAq4tGT0NbVHhbnsOvlSvDaxYlfWKzN/w+RVMUpTbxKOliYYtXfuLLquliqTicjswnj4Fr+8rbE72fKe+rDSXiXqW9uKG/WPzk44sxKDlJj39ty9+KtFg97zSa4Qa7JoBpGqE5/kgDZMG8xPy1lEzitdDPqrgJdP9JFNEwmYVlCrd1Lf5hAc299aUJrffUR7fPLyQC/qlti1BmLr4DHRaETP+fMhRtER1byumg1JzTM7n0hlWmSkaRUiHeLWIZxN2y4NWR6noCf6N/yirOMxUXOuQe5IfEmLAQ/fcWgm4dghZIMKUuXO7raOEjWEh5S+tAV46Hfj3PvzL20HN05iME9PPnzTKnL/ScpjN5Yb+PEPT1kQgfmTfsHKQQpblbDWW8RKWjKJhOCZTPk6Hyks1CQfjxWXA/XSgwU2bnMWssDn4t80pxsL087d0FZ1m2tz3+NXUXK1aPP18c1oz7Uq7xdWc1IfS2VYcNWvVbc4YZ54cw087nmVzjdvS15UzVnlbxi1+KbDSj9zk2SywiAUNoF/drUY/QkbF6EUx9B+ITWGRm21FO2+dGWAOrj9o5K7SpolCT21ZWJTkIBEgcbSIVy6ngSvvh2X0RqMzNc9YQstCLz/Vf5LtafmyXWxf83EzJp3ZAvGyM5lzWK9TJ4U0ds1rZZ9HJeyRNgKSVahFmRJ8ObzvvYlbRm1V85PfUU6C/MLBi8U8dTvb5zziyu7Lan44IhXwcYnZUvnUvY8NYifsjb5zWGV0aPnF2zcZ2rGE+1764NGZlsoblV66OyFrYtz2x+DqZXd246nq7Gi/jzXZZCEM5fPUf1NaEe0xDFF0r1+xSwFoh+JXHOil9Is/C7wSmbhu7el4dM1huZFA+MTooPuKps2rQ7Z/yL1U9qaOJ1ygprUTVGbro0txuietCtv63ynMpn6i9LZqlWI3zEplc45f1y3R/qkNWA1R8llpiGCoe3CU4UBBfGPWRafAPFP92f2hb6X3yCNZ19siRp5tbputIC5bQTjefyOT6+ZNA662v3grkqVxWCX+9PDkqZONJ6O0lzWa15zVwtPoNlp+bjuA8r5oaiB3RrWGkMSvC8VJRtnAppeWxAVq9jC7/27CuT6nk1//OvPs9E5kcsFnYM+Yzb7nKaWlwW7R8T2hgVdEZPnaF/bb9C3HGs76OMyjCmcXUoVGRu4R4uZo1eRf7neOSFu6Qu+Of1sdnjCHLyUPSdYXb5H33G2zc95/5Brboc7Y31y+UTxGYLjM9kKxhdNrLHH1s1iMjH5H5id/qq9Wr+aebUTUlrVe+K84/EWW4OlrX/TKYVeyJ80k9dc9B4Seht4DJTYX2KfuNSFnV+/bnOXdNERyycT58+90ySgM/zcxq7ajK/i1JnoZVaVX04qB1IWBi+3VGMEobWoBnzjLQ6K4vxoRsS1jcxzG33dOHNQLyNFch3AyLmS3vPm1PkvL19Eha37kHcSl7dxJAdn/BLbNJxtfIMpQyxNF2jLWHtBfy/1r3T/c3cfDBgHzSdmAMxDH055X76esq7kC+1AlonYUev3OuXZywEczKnRhGHE5Mz0VB1hCFUlLtD6qnrVncppqbTbA0Wf+vLX2v3OCLSszXo+KjRvFg/ZRgRL2B2fPlorN6q5qOSGWBaT8JYp5XYGPmhFyawvyuPkpHabAaqNahdGFycv1LSucuhqpuZXDQuxX2ImIj5BV8XNFvaI7dfcZR7ybr9b26uOi+J7qHXY3f6vm2+5JRBtJ1Fcj61dyupF5ulz+W//GIZcTIc2JUm4uxv7/7IlvmpsoXS1kFX9klfw+A5Vl/X/AeVlkv0KZW5kc3RyZWFtCmVuZG9iago0OTkgMCBvYmoKPDwKL0xlbmd0aDEgMTMxMwovTGVuZ3RoMiA2MDA4Ci9MZW5ndGgzIDAKL0xlbmd0aCA2OTExICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42o10BzSc7da2Er1FiRLCEEQ3eie66IwSLTLMYJQZZkYniDZ69ChRowTRe08I0XuPRCdKBCFa4pO8ec857/n/tb5vPWvN89x7X/u6d7n2cLDqg/gVIQgbqBoCjuYXEgBKA3RAijpCQAAQKCIABAoTc3AYwdDO0L/txBwmUCQKhoBL/wdCGQkFo69tKmD0LyACDtB0dwYIiQCExKWFJKSBQIAwECj1NxCBlAaogD1gEICOAEATAYeiiDmUEa7eSJi9A1oa8K9PAJctN0BISkqC73c4QNEFioTZguEAHTDaAepyfaMt2BkAQtjCoGjvf1BwyTqg0a7SgoKenp4CYBeUAAJpL8/NB/CEoR0AhlAUFOkBhQB+lQzQBbtA/5QmQMwBMHKAof5ygBB2aE8wEgq4NjjDbKFw1HWIOxwCRQKubweAHmgD9Fyh8L/A2n8B+AB/mgMQEhD6F92f6F9EMPjvYLCtLcLFFQz3hsHtAXYwZyhAT01bAO2F5gOA4ZBfQLAzCnEdD/YAw5zBNteA36mDAWqKBgDwdYV/6kPZImGuaJQACub8q0bBXzTXbVaFQ5QRLi5QOBpF/Cs/FRgSanvdd2/BP8N1giM84b5/n+xgcIjdrzIg7q6CxnCYmzv0gcofzLWJ+N82eygaIAYUEZKSFAJA3QBQL1sHwV8XGHm7Qn87f5uva/D3dUW4Auyuy4D6w+yg1y9iXxTYAwpAI92h/r7/6fjniVhICACB2aIBNlB7GJz43+zXZqjdX+fr+SNhXgAL4LX8hADAX8+/vqyuFQZBwJ29/w3/PWJBLZCOmqEW75+S/+VUUkJ4AXwlAfyi4tdyFRERAghd6xrg/08afTDsTxr/EfoAbocASPyV7XWb/s7Y448EuP7sBzfgn1y6iGvhQgFc/9a5JVAMaHv9I/R/VvvvkP+fyH+x/K86/++M1NydnX/7uf4C/D9+sAvM2fsvhKq7MxT53wBT6F/7qgOFwNxd/tv7AA2+XgFFuL3zv9oHQ6nBvKAQfRja1uEvlfy2Q6/F/89YVbgtAvJrS4TFxAFgJBLsTQy8loKwmBjAV+h6nSBQr98qBAgKwBHo6xCAqzvaH2CHQBL/GomUJEAQ5W6DgqIhNs6/XMT/oLd1RyKvl+T3BK/v/vv8eyOhUC+oLfHcNMJWJsSxOqT1tFKR0ZN/fVhugmPd9Dk3v+8css39nJwgmbsi8+kn5Ilict/bmx9WVbmO78+z/PDdaaolCG9ONGi58Lu0jjccW28hnh2l7Rl5uaNY8+4OERO/0f0Nvx9ufiZBTrhN2B2aHDlu7pLk+nnUp57d6l4170oWBsOm1w02KsS1SC5LxvljjKMtg4omOXJtXkzRs+Gj+e8Q8lB99aKYPD6ZoMoeuWLRjOcl9t+NESnwNV8Sjj2b8lksMxJGdTKwM5jT38E9phoc4/RV2krRpJvxLS740DPr1YhybCWdqwLkz6V5vN4KTutL9bCJioQmm/vF7pNFr+Bs1o0GY5PJI4fUsVGle+9PdTmGIQqZi+eZnXnnVFZThCTimvldZRVDENZCrdmG0J6TtgSysno0UNxtPHzC+xI6q7yVP6sUyI2j4wGcmOf3eRbPLG2hmcV4AYjvM+vwEnA/UpCozJnynH/am9pyYvIizVSyT8vfBYgIcFndth98fvNZNHic5dZtEopFPaOXpGOvCb4gkvlVuXBqGg2EqxVZKnBfW0uHV9p8yk0/qsLorKOXwvaXRPRLd6SpTX8e8WiHn2FL0w9N3zHwbyA9gPEGtFgMqtyLN+C8R/wOL+Et6JLQL6SjrVxv8Oh2wYoj1pcqi/6+nWVJYccBiLl60qrdfWIZQRwahsUA+tvsXVdA68VORZabDd9aJfrB8yw0cozkgcni628yYpACpcuulMpzTlHsb6Jkgxb4lMqAcQwrmAL7e7vvP/fq68OkHC0KiwKxcymaBA0GDATDZp/xBvngaKxHmt2lvDHBk/lzHoQxJKPQkW6/yY3H5Oxw7yY5u9W0b134JmdWUNS2H82oeuc9krIX9PREH0EEtVj4CJEDqeblRnbIfVWVRcVCus9FTP1pPVwgSp0rfoZTqoP0jbdrDDNt+gPxZt/lVfU4P3BjMQw/IsWb4mc5qUQcS3BJ7NylsLWY78UkF5xVtXJe2R9NWqb0BbHWEMnV68Uyn5s7fpHMQpb61LeukFaEoYvzg9WEmnQVnpvu0VN6ULjGq80z4UqwID6Vs5bG0RL9gGcQL44NDny4a5Q6sgUVvSH540ZhPDmOtuhKg2jK4CqiP4sVn4WSd5MZQluc3NVMfTceXZ/pDf8xJxpSpKaQumonSbiLvoG//Gazk8sIRDGW5GPLkpt2BqplH7Q/qi/K39K6sojP0Fni1JNZx6zy3gu43zp9SHslFssTpyKMJ4gcJtLy4T2Ysma5FRb8bcL1tgNR3aP8xw5zQz+/8FQwk79f1kUtjGD3JD+oYcYomGy47z3FPLnN1RukmN21g4p+5zNnIb8Tx7fxHbRx5ykZ2Pwod1FPT/zBDJLZcZHErYqoSH64AfvKLqERo77MHowtXOYd+yTPoHJJK2Ch0XCJ/3MLhTvXGf875o416ZNaYpYNbA8QkYAtoLlhf8u7JpFujhAUiJ+Wg0U73OTxRpJhzn/fovIsqePm7DGFZasp4VumFRUMlnmar4855HDUoAlrzrm6BBnuF8A9GPRcNH26BnarGdP6Rl0sNVx9QjYl4qFDipZFpaatShWRJZ5QxvdxVjWB73gW4VK45sM8KkSqEDIHQs/KgvB049hOfURRDbO/t3k6EKBvsWsUi5SMIu4Roe6M09SxUQBxEzq/3X/ts54oHKqS4kupqkz7lnS4qavtA76N/RiyEkRs+ZEuFUMeL3qrqY5vbZCApuPTU/wQT/cwezr3lru1Uqu4XFpQsrVIAnxpPC83GPfkxFJA7pRaP+0jpQaC+FP9Iy8oP1ksLty34iJKTXnDdubxbVNQiFjRz29YYQZdKT8Shh7ckVhSr4nKnBriLvDGxk1QWkttUCcQ1vWMsyAOZBedyNSGnWZbMgRdErF0MZ8t5Udkx5bM+aPOH/Tw5iyGhqU//kEdmhR/hSp4dRlBoRkOnnHmk8r3bCGSlK7IoKnNvvLVpzBhVGp8WVzXMZ14iw+ZeM55BzvgQuCRjXWyfNdjc5lvBmGGmT/ISi5j6HAcGw8JqpluU1ZKdgxHmknvkUsaoKiQF1WnxIfi8wezpX2b5gZvX6xo5A84HeuHix3V5MjwuO6oz2acLrfEHebC09IqckX4+NQ5F/a/jd9SwyqOY+qeXyzG2+vwJrLWM6MWNSivZjhrQXt/4FzN45dHHVoEHVmMKi75sLtmGO/HFW6Umek08fPi0l9ewicgyQR3QLWIyojSnS3t5efWGo0tHIESzBlPuFQaTWdWdAcvVQKtBYni7rPSKNHJgRzw89jer3aiKeICCxSbuDyHPuG+mjxyyca3fGl/9aKE+lFKv09qZ5reh/TyUNnX6uLRTcgyrAONjDEiPVNrR1dJdS1C/0OD6SheCV3peMELCKFXTB2ZfZHowgd7SQObEmh/OgtL5bn+kx83zle5cDyKzVMb1XoiLjqy6Qc4eU2NXL74qKlM5nrEbgXI8L8Si9f6CJfwmycmLPfvz1ONK4rm9meJ6NObsPugg8v01YcskwF3Z43sQd53nvv0vuYzQmAr14akvRvPVRj23p6++igXdYvyRUKb8fu7kAlLhWCOw3aw6aFsTswjH12DrTjZN3BSX/ohK3zYpjbzOhdjG2b3i1dJUEcHEjiy9hMfUJqza22DHYpy9tfXuPiR7vzjC2lKPstiSJ296ysjRan6KknKjpyW/SjfTzp459beywC5UKdUoM0z3tF0RH73gvPloOM+hJAlULd06egecHaXvc9E2kn+MePmeoyC1jGvnAg1dLIAQFliixdKjAjrnzr2VuykfSYnby/QVP7thzw6qoiH/1WsoXF73bBo54aGA+b2WY1TFisWUfWQXugNDyYaDb7ZpdFTlh6cfFfG3sAYcrO6qiI7YxY6gnK7RW5io4Sn4q2Uei+dgtz5FSYwXOMRKHOxentaKTRVY02i/UlCH59NZ5HkhX7D/J2g7apcB7sDs0/TLKd5aS3nKVt7nrkNKdH5o1LVR1Zdt0mh1iJfN1o+R1OwtHy87K93h3i1x1Bli1RW36R8iMH1nPOD7Q72AEbV4kRUM42ZvgyL+5/vEHbdpAr35tY8Nk0CpD5IPmcxHA0ISU6QNbaEVVyx8IoegDel2/evvoELY5VWBERygCHnn3dcHtX7mUTV5CiTHp/MwbtHLfZIngvBF7HkW8uPkMqhuu2N62rvwQtFa9X3q5FKrBuzAQDgSQVODv55EF6DoNG6sLdw6wF720ScyyMXitcfv6Li7XDiDlcMFshc+7Eg0pr3492OUfHdTg2nkndP5g7avAqNZdI5RQ3LyL7tRsD0S4ES0mVYuwQvWTjz/DPcdbcLdLPbNFZSZqV5QaQquauGirS5xoYITqmFy5NcsitMd8gm2qlSFhmAf/7FbPqErSSuwUP5m3d5t8q6RLi+hcptKuyNqHLi8a62FjUcCvtM5s0oMt7V2sjhZ29dC0W3ht0zlkMIL4S/qiiF2WVZTd4r7EY7E7TVhjMfSY8I7c7gr8qHGQKVxm393nVl77zkOXmZdnieKF1N0cdG+HZKJkKt8nyZaDx9O7KemGIwQzACQ7zxzuiDKw++XQdO9aZruJ+u4UP6qI+EmlJ0CqQYIquvwhjDd9Vq9LhGg6OvNw42X7PzPj1iZXntxLJHTRoTVl+z+Uw7YVjO+5SOj99RGGfSoQ2PwHWqbgwjbiydL6EWtv3tnmIaZCf5uGFyZCX5HAam32/JMZ9Jw2rSXfZPBQ7bfExdprHwzPpYYlKw16KB/hZZYxlO8cWKQyM9J2K7a/uJe1tm8D1rX+mMt9mNCZSB28oySfYTTqHdrADm+bkMCad4GLPhTsT6jU9OscXyCjCiRTJuSQ7683uGbwy4hX3Kham1yMjOXzoagl6suRQsBdaR5SutufFvDie1kuH/zLQzpX2sIfNjnmiax1x7Mhtt+eiBk73REp2pTniDB2E/k45g/m7c2AK94r5pTXyov/oF+PSpkP1+uI6q6fEejH0qMnWCND/LCL4oXcnf2lY0wBSE8tSU3p28ZKJqGKkRyYnfgqVJsNtiTqecP1o4p0ADZUdjdyM2fszb+znXPeXWCSFRm9K/P1PJFDQnWV4X8Yqpdk7ToKk+oFswKlWTgXAVQvGVXKEyyHSplCOx4n0cj5Kx2KSKtxfNp7Jy5+Hm48lTL8pjdx7aD71c0LBUFBmF5J6yGGWFmr00SZ9rVTftTwWN4NJi7WfHmyKiGZ9q9A4fKnd+yEPfoFOJGBmqvjdNF2SovrJReyOB6uULPGw5aTtWbzMg7LUFjmIXHtPg1zqriVo3EHZlDQkoHe4jTpTBffGlzgY4WGjl3/NmKuRpScA6V2J0u9hmuL58mrSaVgEzmFyTyYukHIPllDq76ZdGjX9zYLCPLjC/eBQzs18xEPS0YHWYukFdZSR6jaKI+e56lGBE0q540TvJoZspAnm1K7Kz9tkOW6sLbuRkcSvvoYkt1LEzN18utQrb0TCPvDWon7xIwQVlU/w0oIZdrOPJ1ayxOjy5GHSMEjkWjSGxHBF5s72IUSRfGFQfK4LJuoSUcdknHoFQrzvPRdm3X9ZNv7ozHCvHHift91GzthoEBrro1ZaNGmw8Tn3f+44ooT7dNgPN0Zb0bYptkwLP/EHmptjIMuhmr8edASgzvmWB4toZac0nPEuOpJ+LLk7HWPo2qYEOL2itTgOXII9qIi04tsS+5uBEbwvc035eOFjp44e4jff55PwcGKoO5svhNNVff1sI2mf85p2EG0YYSXfsthPJKmDOWZqFV0SCsjHwcv0QqvjZGIXDTWHohJGhjZ3ZABscUJS5vETjrwD7lnz4zSB8L04F2k/KBa0iLlU1y0nH2k/cQgnUU+ZPVMt8N26REZGeU06r3ilVlxXWubPOvn7hUmZN8tn0jbWGce97AYaRjIYG41z/mDzCmxVxIs+CHIGJBm37XvosheQMs7VwRgm8XXZF4b7F7K61pXhHYtVbzWbdJ/wTSV+sqGcPyttL2Fzi81DsUcMJ29pRrqjxUTZ2dG/p87OOZzH6yw68hZq17fWJb7Yv2uujEnEnGR2J1i1eR1lbjEfzSMz1+5Js+UGjj4mbh+MyQt9tdNzSjIkPgZelPOLgSJaXiaiPT3j7mtm6XBjLYDvGZyLa3SO0GK6g9K6Hhxv7lbB24Sv8sR/mXSLtMDbqWDcCC2swx1SJ0Le6nBcvrOZyt5NZgbiSsbz5WVmWEi8N6Iq/8twhlCJ61oW9uiLm01QnQGJ6TK3rhbwwfSAlNlXRcYmHPrtyl8WiTmYc7/S34XCV+/EkbgB/Rrgp5ON9B0if9PbVs/6zMpZE3LT2yAcaEjwdb4q7cFSMqAT5NfeHM65evRAoyZk1SFwndOC/tyUR5NQd8ygsh8hkZiNgNCvs5syFwhPy0oS7VtsEZPtyqGDZ16hDZexi/IIlYGEsxKgG8ZjV9rIGtZ/K9p2+Tj5p7Wm49mysn+VZVlem8DmuMqeS53njZzLdNTjOzEa0oPQALmfA+3l9ve7Jp2cES0fLn1LbOvSm+dYiRB3HD4zap4QogqkyoZEHBkO4WVbqCNz76dYvhyfDCGnGhgmGHHv7nKpaSwuVtTo+8GDRFiOdE7vvfu4a0KgZcBq7dW5942faTc2DMpsPBM1STNGTRlRuI1YLNeupdHgDFIPaRmyM956TL1TML0w+/VYe5MgQwXMa4yLz6YwEvFkxjlc0KepOnfywfWiQvLTyvRwRXGZohChms6ALezQzdPFhrl/nhHUJg6nJvH/F02kc60INmioqL8r9Khlg61aePKsCz2mgsKKmDvvW/kKkc6oTByNCCT0owcgGp6/+qOoyEVKRF8DHo67Ti5bRDoxoR9xSsgjzdQ4LU37mqQ1ptoE7QYTONE0VSc1fBzSf08y+stROOnvW1tgBzemcfFSOe7dcG4Qt/YW9DPnwo0lE/SrNl+EUQirVbbSHekKSYrkK5YC6U1LSXcTn9DwWgVpHjxHz1TqehUeiZEnVYjrVo1WcYVqnxqG6LR+f6V1lJkQtN23jc8gPue9wopcxaw6Wqh4RHyeLhZUEXDO+DnszJnfvdyVB3kf04o6DTbwnZh47xHV4GFkW73oMdWHhrQVJeqa1aw1I42JK57NlrVvhkjizn8soxkWnVSi27V9nyLFGZhd2ZN08c6U3W5852sV3chfutOxst5MMImvAerGpx27WQjDW2wDwSn5fEO5KzZWTNoWLaT5jHFTpSSqy3fmIaaK2VuU4PbSHk3aWOLnZStkypN1qx3b3n1/8Gkd3e+SuTY/VxYKo5w3Xd0YeXMeylX166tmk4GJ75HH88H1s823iDS4cFVSkW4tUdpe732MQr2LAbIh6WFooVA2/SWEkO1pC+Jjx1n5pevfYhNrPZvMLkoCvgpY9u0dpit8DxkpqWdMz3vs0bmz5UIkI3nWlHxtEJc3kkSl/P7hpOSgWnR+ogssdtq70oAyfWhi9sPmuMr0uyFUr65nHwA4m254pTRRtcUVT54MU82KOmcrCzvEjdCBV7azONI7eRtw9xiT6eJG/wGZIEOhXxfT95BEW36v/Ht5ahWJLDmgJxmuMSA5gCQwOFfpwGK8X7xtjmpU8/uielk1tsO/lJwFS8pqFdDO+F2x4NUWrdY5ZBfDvdjWYuAJzmFMXZILHtOxM0dJfw1kFNLTHfPp9Z5z7iKvz8cLNz8VaSuEfv4pDu1ecJ43o+t32+dH8IPw3JPXRoU3ytZTzh3GTCwq6kXbRssPmpOIOnjrlcrQaIQ7cybp7t/MGY6H3V/ooGm9qOXlCYZ6zJwTWRTee2Jg3c7h54/R4JB5rO006hnMM+WFi2Yg6YkSINm2yetUcdO/A8pAnQf1stCnoBw/tXcru1GWNCUQ3MW7ZBJsxqvu5eQ7U3mmfyil4BTchfcb1/pRzlbDdQ1m5FEBd/ND4023LUOe+Pj32+r6KUK38GzZXFw1t9NOkhRg+PePuVCotF90wihTyWSexxrhk1kWJojP33Ds3endNiQiIieO4w29VfkKW6SF3HZ16KwWmcwwpMPMlRLcM9oJ3o3UiHaLk88CY4m1ZqXuv2d58La9SneinSgGR+dfRPPgBbz5U2iF8zOMCFMVdZ4ju6aZNN3/fwhdZUNLh9pQi3mlUNEpsBdugdY+dblvEoDdPP20+iwNnpHQExNws32VkiQ5DwiZbF5vfDXQ7/7Sd/SKGdmjJGI0Y7gTlVtuK7PObl/C8SCTJrFbNC3qnsuFLpUJ4N3QHMPAQl6mq2TTgKD4qkYbQSbVTIGigwPn5pJCB79DrnXh7s1jGeEtFDljmpwBLSbsE3H3KJuriFTGWqs3e63+9U6fy3j0BVXTgUSmfyNcHBXE1d524Ru7RLtaAZovM5p8dNiVL9MNwOfnIhsQ1q+vhWkDRzHCWbojPukC5oASzB7vODIPtWuIxhUxjTcucX7+2JUBMauy4QTDkjR/MRrnq8RW+Cs1JKOXumGYXW114NYmHjb2/XykbroosOflLuYiy2b3t4sgHs3UZ31X8Xyl+YI5qIU3zmmg6l9jCi1Xgsz4ILRKMYp3WcJ68HdiDEWjl3MO8uq39rG0IRHQVi26+0dTWVLjqHkjkOgMTzZOs+GLhoewCzdAejDRJeqEV/JqSisUINN9abICYzjMIHNk2y3zYaMv6OKypnI/6KaGl7IahdocJm2hbYixDyVeXdNIqAv2tYK4cE7aHdZTufCY7whYaLkVui4+T/WM4PZ78XP4pJS80GHti/R0kXG2E/RI6O3pnT/xxRaxxorxS0gY6X3yVUKxFwdzPKYoIm/B7umJyCMSsh6vBgayotNBWhoVWbfTSGPowUYvVkBsD57uUPZx7qfYmXfy55sNHPWWLc3q9CoN7cvM/XgF2fOlDn8SfvzQB9gXnquAKONI0OptkJOhwObfxZVryrzfqBS4s4psqLTM44RckTP6MyjkSGaL2jFGMpQ7E+YmZkn9+MF7qqBb2xZpaiFXJB188mkBGM1juhYW4yrl67eeQR1RScuOmayK4PjQKF2TLS1kdJ49C4E6AmCrcud3agf8BbNE49wplbmRzdHJlYW0KZW5kb2JqCjUwMSAwIG9iago8PAovTGVuZ3RoMSAxMzM0Ci9MZW5ndGgyIDE4MDcKL0xlbmd0aDMgMAovTGVuZ3RoIDI2NjYgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjajVMLOJT5Gi82OVOqk1Kb8N8iKs0VhUHul9xiFMquz8w35svM982VmVyK1ClhtXaFHPaRS2IVEpVLVKQkKymVSoTQKpcuRPsNqm3PeZ5znnmeb/7v+/7e++/V+c7dc5MlCwuA7TBUvIlCJJsAF08rFwoZkMk0IplMJejoMBAxF/6kJ+jsgIUiBENN/oKwFsKQGNfZQGI5EEOBk4QLKDRAMTKhbDYhkwGVTDb+BMSEJsAGCkZYwIUInDAUFhF0rDG+TIgEcsQm4PMT6DHXA4qx8Wb9aXdgyYOFCBNCgQsk5sA8PCMT4gJPjInAYtlXIfToHLGYb0IihYSEECGeiIgJA83X64MQRMwBHrAIFgbDLCBvGbhCPHi2NSJBBzA4iGjG4ImxxSGQEAa4goswYVSEu0hQFiwEeHbg6egM3PgwOgN2ngHog9nhAAqR8jncrLc8EIJOO0NMJsbjQ6gMQQMBG+HCwM3OmSiWivUBhLLkQIgrwnB/KBhCuFAADpguHQJ2ltsBhHc425+IKUT4YhFRhHDlPZLkYfAx26Isa4zHg1GxiCCvzwYRwkx87jLS7HKDUCwEDf0ksRGUxZa3wZLwSV4oIpDAjjazGFxF+KILhMXAkEyjGG+hAlgAYCmTQ5InYMj48LSRIlfjPYSH8jE+YONtwOEIG8b/CKEiKBgGYqEEDg/9q+FriUChABbCFIMAOBBBCV+i42qYPSPj+xciUrCLjNOPAsjy3+eXH84wFoZyZV/g0ysmebv6+Hh7bZxt+bPRygqTgtBNhoZgkwEV5ynNgAaM8Uf413HcIWS2jr/4OqJsDGyeKRef06eSg2c5oDd7IOvB17FcMZy5MND7QvTdZEMyE/9Q/m+6T7v8N5bLo/xPov+9IjsJlztt15sB/Icd4iFc2QzCVsKFhX8H7IRnDtYFZiES3t+tjmIIvwFLNJD7eXyIyA6Rwix3RMzkzNBkWg/j7P/a1xZlYiz5mVANjQAkFEIyAhnnAhVfWCgFvycWLJ2mISARUUyMuwC+RBwO2JiQIF+J0WZAsparpqUtZEBy/yJRAcljWvoqJVMiFOKXM71VvJ5P8vSZwrAUZhLa2zCmafSe4uiKd2ct1UM2PW+iGqjUDceMKu/stv02uHd7lDX9/Qjt5NH7lpdbe46+f7p0gHrqY1mQtxo56PsnYSlHTdTaz1XMYwd06Yc1LzUbHxA5S6g926qrff1pKCtQbajc43rI6NY761foC14/v5v2wp6+rrzw7W+cofm65weJCxWa4o7OX7llC22hQnSlZVploVWR5Z3WKkdt0fDWppeJGnYv3qdcu0k1fFb34LT+XKQzRb2/X4W/bTSt3zy5r9Zo1Tb/ZnBHp7vWduVEgl7UaEV0pF6AqNHTzP4PQUl135V2yd3xO5GCroD2rKmVUdljPk4qv7lqupqpmdMEd4J0De7Pf+i9b0/i6XstyxrPNe02WJj3yN4/Kfhe/D5VayyaGHTPmlIWkHKi4ATn1LESu49KHfoT1zYqpxCPGiq7FxHt6LaNq2hO7XtiOpsb3rE1fv7F+NUxu76ORyL2k6nIVYpGyN5DzkOGD0/90KxIqA1yaN9/TnakdqQyr/NiffvVhkTNIl27xptZ0eL1P/W4RTg8ePJmp7+iGPv28rvdp9K5rX5YbYBXpIvxaLZR02Cm4DolIvP+xyi7lR5J+R2jUFHsheurfk05/9pwouBFuXIpMcJXa+zQ87GwXMYN6/11HVmvNA27yqxC0f7xEBPihLdpsodP04Kaif2/W2r4YjmVi7QN78xRptOTit85xKYF/yo9fimeJuBg2YaVnKQpv47i9FJJ1Hra4CL3NEXT8QvuS9Y45ly6use/TiHm9w5/lfBhjw12R7Y77j0lHlH3T7qReobZu5B0KL/n47ulgO9Ro1p78Aa95eDLqq6zO1QexSmSsmLsV8aN0R/69C9WYSU4/DJS+Kxn9cUqD0DVZhTq/rhssykxUTF6wKM+p6+zgEdWyO9sDJHdfdZcuODy2tQjDnNft5hTbeeteeuhuuBxZtXr1YR69u1/uCf6Mg8lrGJuKNE4UvE6ZK6RT+EP7psi7oW9lOlb1XQEJXaX71LwyFb92SDofiCZfv2bF5WWkTLlVllMqUE4//n8QLhmQxdbc7fPt8n5NL+tzY288rjBbtNiUWhET9Th6vKP7a9SVYkTx9akXmM0BpHC81tsxjuXO019aHvgVUfdedqeVpxwvmDZNq+B8jHocIu5hFx7adcZ2fIgxm1CofqWzAMRWnGCyG29U91EnmYZy3lJrnLq3Q760w0NN2+dUM9zVtM62B9uHr/1u+PHHsfy30YcRS/Vq2ksvx3RS9N3nhfVkjvksXTOnGMlp88KVC7HZVjEjDHmCbWv2MwfAf+Omp+0++2BoVuJlWNV6YIfJrQ06OQVsvAs77snzvV3254W7xt4Wx2dk66lxEM+BCV3f7MoxTZJ49yLNffit8TrnY+NaH6sPFRab9rxkC3ztGpLzsvLsC5ST20xXV4DijY7OjnNMdDcgc2//SgvgvNPvWXauZ5Yx8pl50dzhvaxcp/tOtu74hdtks7i6sGlqbqH1wSZR/I5ZdDzoDcZ53gbu6VFXr6/mul0vdDqqd6etrbEddiC1rm7+74CJe9uwJPKzp5XtEQfs/rhcFTnjK1pZmQyuWHywpmBEqWGvB2Rt9tuSEbzI2898xpmTNoHM+rIucvpRsyysQpBO+NipAOFMaRa+VpRrWg4jbPvVGpfVY3xxag/SrrqnbIYhKvSzKb08Cmr08jPBulvd6Ddph+WMOhGzre10mLA5JEjO0/uz3muWruwrNBpAd1vanthlxI1Oftfwm9GHFHzJkO/rCpbpsO6gfSVMSlbL07GxXvcSqE7NOsc17x84GNN4x/bdNygda6xJw2jrQino8ygQYx7lrnh92WQRlx+WelImM5kwrjxfhui+vjtpJHvFz9r8c0IP+vKPl8w1CE4kNL4ptPtzdRA3QOlqrz7AwltDmMXriVr+G2rbrORZIr2rm0ZwBb73mv40ed6w6Gq6ljNg+SU4hazoRUl4zop/B+7lpjmOdVKQWTnerO0g/AqN12r5JIPB6DSKN2nxxX7JlZL3THiHuoc4O0ZvIw+uPC5y8be14T9mQNal+bsDXKOji1b3nphLrbrg4DgHdp0TIt/Yq51zYGGlEm3pslKJU2TQs0nwlbjxMlb3pnepgp1rwiIlzCF0LJz3Uu1p+mbFb2YF/esenoYNq6L96Jtnduh/iqjG4a7CrUT+n96t8a1mP7UL8AjZljtbpeFadvDw6XKFvqyvvsKe586RmTm/laqfdN6RC+26aSNJLfi5MTLmxOxd9ZZDa91K38z5B/jatYaKRp2b8s3NaroJCvuFxS8cBIVqqn1SgNU3jcN9WP7nOujyWHfq12RnAzjFuhLEwadHxelpaonGu964tWhTeIVWI807q0wysnSFVGKQzeZ8KfUolwsErItlFoduQ/Ci25l/JStOiw+IM2uzlikHGZrBfleUYpePcfi6u7S2gtHr5LtbVHHPwGd0khXCmVuZHN0cmVhbQplbmRvYmoKNTAzIDAgb2JqCjw8Ci9MZW5ndGggODU5ICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVU1v4jAQvedXeA+V2gPFTshXhZCchEgcdluVarVXSEwXCRIUQNr++/WbmbS7VQ+B5/Gb8ZsXx7759rSe2Lbfukl0r9WzO/fXoXGT8vvmFNzcVH1zPbru8sO51rXj7PlBPQ19s3YXdVuuqlW3v9x58qprDtfWjayvSYV73XcfFKyjbl/cr4lrJofj9o/R/g8DDfbL/nLwrK8JykfVp6iitJ9uOO/77kGZe621Dyy7tuyP6OQcTEWNmo76dvuuHUSS2kJgYELV7puLjOi3OXpLkLx+O1/ccdXt+mA+V9NnP3m+DG+k8i6YPg6tG/bdq7r9pM3Pra+n08FBh9LBYqFat/MlvQc/Nkenpl+3+U56eTs5FdLYsLKmb935tGncsOleXTDXeqHmdb0IXNd+mjMRp2x3I3fpubr2P6GO8kUwN0g2IQVMiUCMQMKByAdCA4yA1h77QOZxXHMg84EEjJQq6wSMHIw8R8CkYJRYpeIaHgfzCoyKUyowltSEpsASjBpFa06pUbRGSl1yACl1hQDX8Bjtj33ms7Hv5vdmEIu8OBTWhnAxA0bjOiwT4IhwBRP0jDgWy+iY4xVwwtgCp5ybAmccJ37OuTWw5Tic1gWvS5yS4wVwxV6jpok4F3HDGgrCKXE0vSD2NYInhutH0GC4fpLgwbj8GNObrj7GVGf5D3/k1P/HwAvhXTgLaS+wDgNuohmjn1R2AzzKyGvjrfXYMIbXWcgY2rKIMepnM8ZLYPLakBdZwhj1s5QxcTLuHxoy8YLWJS/MDL5kBWNozkrG8CirGFN96t/E2BNZzRg6c9YfY92c9cfg56yf9lDO+mPoyVl/QrmsP0HvOetPic/6E+KzzhSac9aZ4h3nrDOiXNYZUS7rNPRhsc8WvVjxGT1a8TkGFp+JIz5jLSs+Y19a8RnrWvEZflrxmTjiM3q34jP0W/EZOq34jN6t+AzfrPhM9cVn6LfiM3QW4jPWLcRn8AvxGfxCfIaeQnymXPEZvRfiM/HFZ+JnfDIQlrMIvRTiP3opxH/sw0L8p5r8rViqyd9JQXXEf3AqXiuGDxXH4wgPjiaZI8xzVS0nFJ1IOKpxtbxfA811GPwNQfcPnfs48fede7+iTv0JWfTQ3Tbepxg91sFf4zjfTgplbmRzdHJlYW0KZW5kb2JqCjUwNCAwIG9iago8PAovTGVuZ3RoIDg1OSAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVVNb+IwEL3nV3gPldoDxU7IV4WQnIRIHHZblWq1V0hMFwkSFEDa/vv1m5m0u1UPgefxm/GbF8e++fa0nti237pJdK/Vszv316Fxk/L75hTc3FR9cz267vLDuda14+z5QT0NfbN2F3VbrqpVt7/cefKqaw7X1o2sr0mFe913HxSso25f3K+JayaH4/aPCf0fBhrsl/3l4FlfE5SPqk9RRWk/3XDe992DMvdaax9Ydm3ZH9HJOZiKGjUd9e32XTuIJLWFwMCEqt03FxnRb3P0liB5/Xa+uOOq2/XBfK6mz37yfBneSOVdMH0cWjfsu1d1+0mbn1tfT6eDgw6lg8VCtW7nS3oPfmyOTk2/bvOd9PJ2ciqksWFlTd+682nTuGHTvbpgrvVCzet6Ebiu/TRnIk7Z7kbu0nN17X9CHeWLYG6QbEIKmBKBGIGEA5EPhAYYAa099oHM47jmQOYDCRgpVdYJGDkYeY6AScEosUrFNTwO5hUYFadUYCypCU2BJRg1itacUqNojZS65ABS6goBruEx2h/7zGdj383vzSAWeXEorA3hYgaMxnVYJsAR4Qom6BlxLJbRMccr4ISxBU45NwXOOE78nHNrYMtxOK0LXpc4JccL4Iq9Rk0TcS7ihjUUhFPiaHpB7GsETwzXj6DBcP0kwYNx+TGmN119jKnO8h/+yKn/j4EXwrtwFtJeYB0G3EQzRj+p7AZ4lJHXxlvrsWEMr7OQMbRlEWPUz2aMl8DktSEvsoQx6mcpY+Jk3D80ZOIFrUtemBl8yQrG0JyVjOFRVjGm+tS/ibEnspoxdOasP8a6OeuPwc9ZP+2hnPXH0JOz/oRyWX+C3nPWnxKf9SfEZ50pNOesM8U7zllnRLmsM6Jc1mnow2KfLXqx4jN6tOJzDCw+E0d8xlpWfMa+tOIz1rXiM/y04jNxxGf0bsVn6LfiM3Ra8Rm9W/EZvlnxmeqLz9BvxWfoLMRnrFuIz+AX4jP4hfgMPYX4TLniM3ovxGfii8/Ez/hkICxnEXopxH/0Uoj/2IeF+E81+VuxVJO/k4LqiP/gVLxWDB8qjscRHhxNMkeY56paTig6kXBU42p5vwaa6zD4G4LuHzr3ceLvO/d+RZ36E7LoobttvE8xeqyDvyd/31gKZW5kc3RyZWFtCmVuZG9iago1MDUgMCBvYmoKPDwKL0xlbmd0aCA4NTcgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42m1VTW+jMBC98yu8h0rtIY0N4auKIhkIUg7bVm212msKThcpgYgkh/779ZsZ2u2qB9Dz+M34zcPYVz8en2e2HV7dLLrV6smdhsvYuFn5c3sMrq6qobkcXH++d6517TR7ulOP49A8u7O6LjfVpu/ON5686Zv9pXUT63tS4d66/pOCddT1i/s9c81sfxiN9m9gDfJLd9570rfzygfV16CipF9uPHVDf6fMrdbaB9Z9Ww4HtHEK5iJFzSdxu65vR9GjXqEuMKFqu+YsI3o3B+8Hkp/fT2d32PS7IVgu1fzJT57O4ztpvAnmD2Prxq5/U9dfpfmp58vxuHeQoXSwWqnW7XxF3//99uDU/NsePzgv70enQhob1tUMrTsdt40bt/2bC5Zar9SyrleB69v/5kzEKa+7ibv2XF37V6ijfBUsDZJNSAFTIhAjkHAg8oHQACOgtcc+kHkc1xzIfCABI6XKOgEjByPPETApGCVWqbiGx8GyAqPilAqMNTWhKbAGo0bRmlNqFK2RUpccQEpdIcA1PEb7U5/5Yuq7+bMdxSIvDoW1IVwsgNG4DssEOCJcwQS9II7FMjrmeAWcMLbAKeemwBnHiZ9zbg1sOQ6ndcHrEqfkeAFcsdeoaSLORdywhoJwShxNH4h9jeCJ4foRNBiunyR4MC4/x/Slq88x1Vn/w5849dcYeCG8Cxch7QXWYcBNNGP0k8pugEcZeW28tR4bxvA6CxlDWxYxRv1swXgNTF4b8iJLGKN+ljImTsb9Q0MmXtC65IVZwJesYAzNWckYHmUVY6pP/ZsYeyKrGUNnzvpjrJuz/hj8nPXTHspZfww9OetPKJf1J+g9Z/0p8Vl/QnzWmUJzzjpTfOOcdUaUyzojymWdhn4s9tmiFys+o0crPsfA4jNxxGesZcVn7EsrPmNdKz7DTys+E0d8Ru9WfIZ+Kz5DpxWf0bsVn+GbFZ+pvvgM/VZ8hs5CfMa6hfgMfiE+g1+Iz9BTiM+UKz6j90J8Jr74TPyMTwbCchahl0L8Ry+F+I99WIj/VJP/FUs1+T8pqI74D07Fa8XwoeJ4HOHB0SRzhHmuquWEohMJRzUulo9boLmMo78g6Pahcx8nfte7jwvqOByRRQ/dbNNVitFDHfwFapDdRgplbmRzdHJlYW0KZW5kb2JqCjUwNiAwIG9iago8PAovTGVuZ3RoIDg1NyAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVVNb6MwEL3zK7yHSu0hjQ3hq4oiGQhSDttWbbXaawpOFymBiCSH/vv1mxna7aoH0PP4zfjNw9hXPx6fZ7YdXt0sutXqyZ2Gy9i4WflzewyurqqhuRxcf753rnXtNHu6U4/j0Dy7s7ouN9Wm7843nrzpm/2ldRPre1Lh3rr+k4J11PWL+z1zzWx/GE3o38Aa5JfuvPekb+eVD6qvQUVJv9x46ob+TplbrbUPrPu2HA5o4xTMRYqaT+J2Xd+Ooke9Ql1gQtV2zVlG9G4O3g8kP7+fzu6w6XdDsFyq+ZOfPJ3Hd9J4E8wfxtaNXf+mrr9K81PPl+Nx7yBD6WC1Uq3b+Yq+//vtwan5tz1+cF7ej06FNDasqxladzpuGzdu+zcXLLVeqWVdrwLXt//NmYhTXncTd+25uvavUEf5KlgaJJuQAqZEIEYg4UDkA6EBRkBrj30g8ziuOZD5QAJGSpV1AkYORp4jYFIwSqxScQ2Pg2UFRsUpFRhrakJTYA1GjaI1p9QoWiOlLjmAlLpCgGt4jPanPvPF1HfzZzuKRV4cCmtDuFgAo3EdlglwRLiCCXpBHItldMzxCjhhbIFTzk2BM44TP+fcGthyHE7rgtclTsnxArhir1HTRJyLuGENBeGUOJo+EPsawRPD9SNoMFw/SfBgXH6O6UtXn2Oqs/6HP3HqrzHwQngXLkLaC6zDgJtoxugnld0AjzLy2nhrPTaM4XUWMoa2LGKM+tmC8RqYvDbkRZYwRv0sZUycjPuHhky8oHXJC7OAL1nBGJqzkjE8yirGVJ/6NzH2RFYzhs6c9cdYN2f9Mfg566c9lLP+GHpy1p9QLutP0HvO+lPis/6E+Kwzheacdab4xjnrjCiXdUaUyzoN/Vjss0UvVnxGj1Z8joHFZ+KIz1jLis/Yl1Z8xrpWfIafVnwmjviM3q34DP1WfIZOKz6jdys+wzcrPlN98Rn6rfgMnYX4jHUL8Rn8QnwGvxCfoacQnylXfEbvhfhMfPGZ+BmfDITlLEIvhfiPXgrxH/uwEP+pJv8rlmryf1JQHfEfnIrXiuFDxfE4woOjSeYI81xVywlFJxKOalwsH7dAcxlHf0HQ7UPnPk78rncfF9RxOCKLHrrZpqsUo4c6+AuutN1QCmVuZHN0cmVhbQplbmRvYmoKNTA3IDAgb2JqCjw8Ci9MZW5ndGggODU3ICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVU1vozAQvfMrvIdK7SGNDeGriiIZCFIO21ZttdprCk4XKYGIJIf++/WbGdrtqgfQ8/jN+M3D2Fc/Hp9nth1e3Sy61erJnYbL2LhZ+XN7DK6uqqG5HFx/vneude00e7pTj+PQPLuzui431abvzjeevOmb/aV1E+t7UuHeuv6TgnXU9Yv7PXPNbH8YTerfwBrkl+6896Rv55UPqq9BRUm/3Hjqhv5OmVuttQ+s+7YcDmjjFMxFippP4nZd346iR71CXWBC1XbNWUb0bg7eDyQ/v5/O7rDpd0OwXKr5k588ncd30ngTzB/G1o1d/6auv0rzU8+X43HvIEPpYLVSrdv5ir7/++3Bqfm3PX5wXt6PToU0NqyrGVp3Om4bN277NxcstV6pZV2vAte3/82ZiFNedxN37bm69q9QR/kqWBokm5ACpkQgRiDhQOQDoQFGQGuPfSDzOK45kPlAAkZKlXUCRg5GniNgUjBKrFJxDY+DZQVGxSkVGGtqQlNgDUaNojWn1ChaI6UuOYCUukKAa3iM9qc+88XUd/NnO4pFXhwKa0O4WACjcR2WCXBEuIIJekEci2V0zPEKOGFsgVPOTYEzjhM/59wa2HIcTuuC1yVOyfECuGKvUdNEnIu4YQ0F4ZQ4mj4Q+xrBE8P1I2gwXD9J8GBcfo7pS1efY6qz/oc/ceqvMfBCeBcuQtoLrMOAm2jG6CeV3QCPMvLaeGs9NozhdRYyhrYsYoz62YLxGpi8NuRFljBG/SxlTJyM+4eGTLygdckLs4AvWcEYmrOSMTzKKsZUn/o3MfZEVjOGzpz1x1g3Z/0x+Dnrpz2Us/4YenLWn1Au60/Qe876U+Kz/oT4rDOF5px1pvjGOeuMKJd1RpTLOg39WOyzRS9WfEaPVnyOgcVn4ojPWMuKz9iXVnzGulZ8hp9WfCaO+IzerfgM/VZ8hk4rPqN3Kz7DNys+U33xGfqt+AydhfiMdQvxGfxCfAa/EJ+hpxCfKVd8Ru+F+Ex88Zn4GZ8MhOUsQi+F+I9eCvEf+7AQ/6km/yuWavJ/UlAd8R+citeK4UPF8TjCg6NJ5gjzXFXLCUUnEo5qXCwft0BzGUd/QdDtQ+c+Tvyudx8X1HE4IoseutmmqxSjhzr4C1kd3WkKZW5kc3RyZWFtCmVuZG9iago1MDggMCBvYmoKPDwKL0xlbmd0aCA4NTggICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42m1VTW+jMBC98yu8h0rtIY0N4auKIhkIUg7bVm212msKThcpgYgkh/779ZsZ2t2qB5Ln8Zvxm4exr348Ps9sO7y6WXSr1ZM7DZexcbPy5/YYXF1VQ3M5uP5871zr2mn2dKcex6F5dmd1XW6qTd+dbzx50zf7S+sm1vekwr11/ScF66jrF/d75prZ/jB2Rvs/DDTYL91571nfE5SPqi9RRWm/3Hjqhv5OmVuttQ+s+7YcDujkFMxFjZpP+nZd344iSb1CYGBC1XbNWUb02xy8JUh+fj+d3WHT74ZguVTzJz95Oo/vpPImmD+MrRu7/k1df9Hm554vx+PeQYfSwWqlWrfzJb0H99uDU/Pv2/wgvbwfnQppbFhZM7TudNw2btz2by5Yar1Sy7peBa5vv8yZiFNedxN37bm69j+hjvJVsDRINiEFTIlAjEDCgcgHQgOMgNYe+0DmcVxzIPOBBIyUKusEjByMPEfApGCUWKXiGh4HywqMilMqMNbUhKbAGowaRWtOqVG0RkpdcgApdYUA1/AY7U995oup7+bPdhSLvDgU1oZwsQBG4zosE+CIcAUT9II4FsvomOMVcMLYAqecmwJnHCd+zrk1sOU4nNYFr0uckuMFcMVeo6aJOBdxwxoKwilxNL0g9jWCJ4brR9BguH6S4MG4/BzTm64+x1Rn/Q9/4tT/x8AL4V24CGkvsA4DbqIZo59UdgM8yshr46312DCG11nIGNqyiDHqZwvGa2Dy2pAXWcIY9bOUMXEy7h8aMvGC1iUvzAK+ZAVjaM5KxvAoqxhTferfxNgTWc0YOnPWH2PdnPXH4Oesn/ZQzvpj6MlZf0K5rD9B7znrT4nP+hPis84UmnPWmeId56wzolzWGVEu6zT0YbHPFr1Y8Rk9WvE5BhafiSM+Yy0rPmNfWvEZ61rxGX5a8Zk44jN6t+Iz9FvxGTqt+IzerfgM36z4TPXFZ+i34jN0FuIz1i3EZ/AL8Rn8QnyGnkJ8plzxGb0X4jPxxWfiZ3wyEJazCL0U4j96KcR/7MNC/Kea/K1YqsnfSUF1xH9wKl4rhg8Vx+MID44mmSPMc1UtJxSdSDiqcbV8XAPNZRz9DUH3D537OPG73n1cUcfhiCx66G6b7lOMHurgLwW931MKZW5kc3RyZWFtCmVuZG9iago1MDkgMCBvYmoKPDwKL0xlbmd0aCA0MzAgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42nWSzW6rMBCF936KuYtI6YJiCLdNK4SUkCBl0R81UdUtsScpUrCRAal5+3psSKWoXQCfZ87g4/FM/r1ug4XUewxmtxzesNW9ERjkT2XDJpOVFn2NqntGlCjHbPsIr0aLLXYwzTerjaq6GyveKHHqJY6q30VLPFbqR0L7wHSHH8Gpxq+I209Ql90nfgWc5LuqO1nZHwqwYbgOgyt8R9NWWj1CdMs5t4G1krmu6TAtCwdDEI4WD5WSZnAFe/LIohhkJbph5d6itl2h4u257bDeqINmaQrhm022nTk7nzcsfDESTaWOML02Z5PbvmlOSEaAsywDiQf7T9uH57JGCP846UW1OzcIsVtH3pvQEtumFGhKdUSWcp5BWhQZQyWvctxX7A9+aQUjxpeU+CwNS2fuJ/P1MrMcOS7uiGMfXxPPfPyBOPHMif97drV3Xl8Q3/v4gnjuOSJ+8JwTLzzPiJee3V6555h45XlFvPacEBd+L/KZDP5Jkwz+58SDf9orGfzfZ74T7uTUNLrmy42I3hh7WW4W3A1Q7yuFl3FpdENV7nFzNo43rV4K9g2cXgGECmVuZHN0cmVhbQplbmRvYmoKNTEwIDAgb2JqCjw8Ci9MZW5ndGggNjAwICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNp1VMtu4kAQvPsrZg9I5ECYB8YhQkjYxhKHTaKAVns19kAs4Ydsc+Dvd6obEwltDpiamuru6jbN6NfHbrLO64OdmGcpPm1XX9rMTqLfaeONRnGdXUpb9W/W5jYfbrtX8dHW2c72Yhxt421V9E9OvK2y8yW3g+r/otCeiupbgjpivLd/J+eyLJR0X5My7b+KfiIh3xf92cl+UAhHi0daUOAf23ZFXb0K9SyldMSmyqO6RDOdN70ZEtPB4rGo8vbmShzg0VNa5EXW3070zEo3FQTvrl1vy211rL3lUkw/3WXXt1fy+eRN39vctkV1EuNHc+5yd2mas4URIb3VSuT26HK6ObylpRXTHzq9q/bXxgpNZ8Xesjq3XZNmtk2rk/WWUq7EMklWnq3yhztlOORwHLSB08oXPMzaXzkiBN4QESpHKGRTPhNzEAhRHBLGIBYg1kREEgRyqJgJAyJxWCsmAkdohOsFsWEEAuE6JIJyGJQ1UEhpQMwQ7lMVOYMxH2ofVfR8Dh9zKAJWOIz2hz7NbOg7+0rb24ikWcCcRJjWEr1JzfwLsGEcAs8Yb4B5FmuYckGEKc+CMcVSO0pTzoT4BNNVpJchYR5yDKy5LvGa68boUXPdGHk0102I57qRBg4YkwY5tR/SjGnofgS94alo+DchY4zdRDxgwjHzCfCGedJTHk35Z5Jnhbo+1TIavfsJY/Bz0igFPqBaysBDQLW0gbcgZg1mFVAeGQe3N0ZvCD9dLNt9L7JL27qVoY2kPcAGFJW9L21TN4iiD2378CeD03vi/QOo2Ew6CmVuZHN0cmVhbQplbmRvYmoKNTExIDAgb2JqCjw8Ci9MZW5ndGggNTk4ICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVE2PokAQvfMreg8mzsGxP0TGiTERkMTDzkxGs9krQuuQyEcAD/777VclbmI8SF6/flX1qrAY/fraTdZ5fbAT8yrFt+3qS5vZSfQ7bbzRKK6zS2mr/sPa3ObDbfcuvto629lejKNtvK2K/sWJt1V2vuR2UD0XhfZUVP8lqCPGe/t3ci7LYu6ekzLtf4p+IqHeF/3ZqZ4LhGPFAyso7I9tu6Ku3oV6lVI6YlPlUV2ik86b3tyI6eDvWFR5e7MkDjDoKS3yIutvJ3pmpRsJgnfXrrfltjrW3nIppt/usuvbK7l88aafbW7bojqJ8YM3d7e7NM3ZwoeQ3molcnt0Kd0MPtLSiunzNu+i/bWxQtNZsbOszm3XpJlt0+pkvaWUK7FMkpVnq/zhThkOORwHbeC08g0Ps/ZXjgiBN0SEyhEK2ZTPxBwEQhSHhDGIBYg1EZEEgRwqZsKASBzWionAERrhekFsGIFAuA6JoBwGZQ0UUhoQM4T7VEXOYMyH2kcVPZ/DxxyKgBUOo/2hTzMb+s5+0vY2ImkWMCcRprVEb1Iz/wZsGIfAM8YbYJ7FGqZcEGHKs2BMsdSO0pQzIT7BdBXpZUiYhxwDa65LvOa6MXrUXDdGHs11E+K5bqSBA8akQU7thzRjGrofQW94Khr+TcgYYzcRD5hwzHwCvGGe9JRHU/6Z5Fmhrk+1jEbvfsIY/Jw0SoEPqJYy8BBQLW3gLYhZg1kFlEfGwe2N0RvCXxerdl+L7NK2bmNoH2kPsAFFZe8r29QNouhHuz58X3D6TLx/v3pLYwplbmRzdHJlYW0KZW5kb2JqCjUxMiAwIG9iago8PAovTGVuZ3RoIDU5OSAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVRNj6JAEL3zK3oPJs7BsT9EdGJMBCTxsDOT0Wz2itA6JPIRwIP/fvtVyWxiPEhev35V9aqwGP363E82eX20E/MqxZft6mub2Un0O2280Sius2tpq/7d2tzmw233Jj7bOtvbXoyjXbyriv7FiXdVdrnmdlA9F4X2XFT/Jagjxgf7d3Ipy2LhnpMy7b+LfiKhPhT9xameC4RjxQMrKOyPbbuirt6EepVSOmJb5VFdopPOm97diOng71RUeXu3JI4w6Ckt8iLr7yd6ZqUbCYL3t6635a461d5qJaZf7rLr2xu5fPGmH21u26I6i/GDN3e3vzbNxcKHkN56LXJ7cindDN7T0orp8zZ/RIdbY4Wms2JnWZ3brkkz26bV2XorKddilSRrz1b5w50yHHI8DdrAaeUCD7Px144IgbdEhMoRCtmUz8QcBEIUh4QxiCWIDRGRBIEcKmbCgEgc1oqJwBEa4XpJbBiBQLgOiaAcBmUNFFIaEDOE+1RFzmDMh9pHFT2fw8ccioAVDqP9oU8zG/rOvtP2PiJpljAnEaa1RG9SM78ANoxD4BnjLTDPYgNTLogw5VkyplhqR2nKmRCfYLqK9DIkzEOOgTXXJV5z3Rg9aq4bI4/mugnxXDfSwAFj0iCn9kOaMQ3dj6A3PBUN/yZkjLGbiAdMOGY+Ad4yT3rKoyn/TPKsUNenWkajdz9hDH5OGqXAB1RLGXgIqJY28BbErMGsAsoj4+D+xugN4a+LVftZi+zatm5jaB9pD7ABRWV/VrapG0TRj3Z9+L7g9JF4/wDmekttCmVuZHN0cmVhbQplbmRvYmoKNTEzIDAgb2JqCjw8Ci9MZW5ndGggNzkwICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNp1VU1v4jAQvedXeA+V2gPFH4kNFUIKhEgctq1KtdorTUwbCRKUhAP/fv1mklaqtgfg5eV55nlmbG5+Pe8madm8+Ym5l+LFd82lLfxk/Xt/jm5usqa4nHzdP3pf+nJ82z2I57Ypdr4Xt+tttq2r/i6It3VxvJR+VP1ftPLvVf0lQR5x++r/To6n7qpk+Jmc9v1Hd51IyF+r/hhkPyhEoMV3WtDCP77tqqZ+EOpeShmITV2umxM200XTwZCYjhYPVV22gyvxBo+R0qKsin54ou/iFKqCxbtr1/vTtj400WIhpi/hZde3V/J5F02f2tK3Vf0ubr+bCy93l/P56GFEyGi5FKU/hJihDo/7kxfTH3b6qXq9nr3Q9KzYW9GUvjvvC9/u63cfLaRcikWeLyNfl9/eaclL3g6jdha0ch2+tJ4ny2ihdMDKEDGzIGIQCRE2BmFBOBBpmoNIQax4iQaBeCrjJSmIDYicCAdCUwJE1mpuQFACy4QDQfHImMqgMIhhOEYKH7EKOEFuKQOOFgkUCSs0YlgUwipeAmMWm7OU20j4sLBgEyZmIFAPOwfhNiAcPDnHBUIWh5cOabXUMhApTKa029RlVPWxvFaP5S4+9u3QGa0VvEhFzlcwKjXhjDDXXhGOic8JcwvW2Lq0vBZ+JHtTqJEkFzLLgFENnWzQQ5mzXWoOe4ihUVwbizhqRtisgeesQQG05KZgq1oxRhytGWOtZg8xdXLGPSP9nDHpU8akz3gtvOkN4zkwN0/Bm6G80mCtYZ9hjAKmvJq6ZbhWcgVM05SsaFYSxhSHamUM6XmPEt4M71GSnqcoxd4ND7LEYTA8f4p49qzgP+aaGGgS1muMU8IHSWMvCc//DJ4t98vBp3WM4c1yXof4doiDmJbz0oG0nDcjvCF9QnquVYy9uGGu4MFRrVKDk+mGWmF+HNdKw4OLeZ4xS47nik6hm3EdSL/iGUOPHPmxljQZY/TFbRjTucgZy+EE0MTjBsKd+Xm9FZe2DTcfXax0neEiq2r/efeemzNW0Ycu7fG/Ak9PefQP4h2nLwplbmRzdHJlYW0KZW5kb2JqCjUxNCAwIG9iago8PAovTGVuZ3RoIDc4OCAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVVNb+IwEL3nV3gPldoDxR+JDRVCCoRIHLatSrXaK01MGwkSlIQD/379ZpKuVHEgenl5nnmeGZu7X6+7SVo2H35iHqV4811zaQs/Wf/en6O7u6wpLidf98/el74cv3ZP4rVtip3vxf16m23rqn8I4m1dHC+lH1W3RSv/WdX/Jcgj7t/938nx1F1teE5O+/6ru04k1O9Vfwyq2wIRWPGDFbTsj2+7qqmfhHqUUgZiU5fr5oSddNF0cCOmo79DVZftYEl8wGCktCiroh/e6FmcQkmweHften/a1ocmWizE9C187Pr2Si4foulLW/q2qj/F/Q9v4dvucj4fPXwIGS2XovSHEDLU4Hl/8mJ6e5vfovfr2QtN74qdFU3pu/O+8O2+/vTRQsqlWOT5MvJ1+eOblrzk4zBqZ0Er1+Gh9TxZRgulA1aGiJkFEYNIiLAxCAvCgUjTHEQKYsVLNAjEUxkvSUFsQOREOBCaEiCyVnMDghJYJhwIikfGVAaFQQzDMVL4iFXACXJLGXC0SKBIWKERw6IQVvESGLPYnKXcRsKHhQWbMDEDgXrYOQi3AeHgyTkuELI4fHRIq6WWgUhhMqXdpi6jqo/ltXosd/G1b4fOaK3gRSpyvoJRqQlnhLn2inBMfE6YW7DG1qXltfAj2ZtCjSS5kFkGjGroZIMeypztUnPYQwyN4tpYxFEzwmYNPGcNCqAlNwVb1Yox4mjNGGs1e4ipkzPuGennjEmfMiZ9xmvhTW8Yz4G5eQreDOWVBmsN+wxjFDDl1dQtw7WSK2CapmRFs5IwpjhUK2NIz3uU8GZ4j5L0PEUp9m54kCUOg+H5U8SzZwX/MdfEQJOwXmOcEj5IGntJeP5n8Gy5Xw4+rWMMb5bzOsS3QxzEtJyXDqTlvBnhDekT0nOtYuzFDXMFD45qlRqcTDfUCvPjuFYaHlzM84xZcjxXdArdjOtA+hXPGHrkyI+1pMkYoy9uw5jORc5YDieAJh43EG7M79utuLRtuPjoWqXrDBdZVfvvm/fcnLGKfnRlj38TeHvJo394r6ZYCmVuZHN0cmVhbQplbmRvYmoKNTE1IDAgb2JqCjw8Ci9MZW5ndGggNzg5ICAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVU1v4jAQvedXeA+V2gPFH4kdKoQUCJE4bFuVarVXmpg2EiQoCQf+/frNhK5UcSB6eXmeeZ4Zm7tfr9tJVrUffmIepXjzfXvuSj9Z/d6doru7vC3PR98Mz95Xvrp+7Z/Ea9eWWz+I+9Um3zT18BDEm6Y8nCt/Vd0WLf1n3fyXII+4f/d/J4djf0nDc3LcDV/9ZSKhfq+HQ1DdFojAih+soGV/fNfXbfMk1KOUMhDrplq1R+ykj6ajGzG9+tvXTdWNlsQHDEZKi6ouh/GNnuUxlASLt5d+8MdNs2+j+VxM38LHfugu5PIhmr50le/q5lPc//AWvm3Pp9PBw4eQ0WIhKr8PIUMNnndHL6a3t/kter+cvND0rthZ2Va+P+1K3+2aTx/NpVyIeVEsIt9UP75pyUs+9ldtGrRyFR5az5JFNFc6YGWISC2IGERChI1BWBAORJYVIDIQS16iQSCeynlJBmINoiDCgdCUAJG1mhkQlMAy4UBQPDKmcigMYhiOkcFHrAJOkFvKgKN5AkXCCo0YFoWwipfAmMXmLOU2Ej4sLNiEiRQE6mFnINwahIMn57hAyOLw0SGtlloGIoPJjHabuZyqfi2v1ddyl1+7buyM1gpepCLnSxiVmnBOmGuvCMfEF4S5BStsXVpeCz+SvSnUSJILmefAqIZO1uihLNguNYc9xNAoro1FHJUSNivgGWtQAC25KdiqVowRR2vGWKvZQ0ydTLlnpJ8xJn3GmPQ5r4U3vWY8A+bmKXgzlFcarDXsM4xRwJRXU7cM10ougWmakiXNSsKY4lCtjCE971HCm+E9StLzFGXYu+FBljgMhudPEc+eFfzHXBMDTcJ6jXFK+CBp7CXh+U/h2XK/HHxaxxjeLOd1iG/HOIhpOS8dSMt5c8Jr0iek51rF2Isb5woeHNUqMziZbqwV5sdxrTQ8uJjnGbPkeK7oFLqU60D6Jc8YeuTIj7WkyRmjL27NmM5FwViOJ4AmHjcQbszv2608d124+OhapesMF1nd+O+b99SesIp+dGVf/ybw9lJE/wCxdaZiCmVuZHN0cmVhbQplbmRvYmoKNTE2IDAgb2JqCjw8Ci9MZW5ndGggMTAyMiAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNptVk1v2zgQvetXaA8B0oNrkuKHVBgGSMkGcti2aILFXh2JSQ3EsiHbh/z78s3Y5maRg42n0Xy8GY6edPfXz8eZH/bPcVZ9FeWveNyfpz7O2r83h+Lurtv3510cT99jHOJwvXv8Vv6c9v1jPJX37UP3MG5PX5Lzw9i/nYd49frcKcTX7ZhdUKe8f4r/znbHzU6K2fN5+3bajjMB56ft6S05fXq/TMbyo7GkoH/idNzux2+l/CqESIbVOLT7Hdo4FvMLlXJ+JfeyHYfpwqd8BrtCqnLY9qfLFf33uzQPBD++H09x9zC+7IvFopz/SjePp+mdOH4p5j+mIU7b8bW8/0gt3Xo8Hw5vETRKUSyX5RBfUsbU//fNLpbzT3u8+Ty9H2Kp6Foyr34/xONh08dpM77GYiHEslys18sijsP/7inBIc8vV98u+YpV+lPKN8tiIYHlGgYZRDIoBVyRwQcYGmBPIZVOhgr1KknWxsCAkIpCVLdKBg0PTR6qTsQWBtgo9vAwwNtoylGjikEVw1UCGVoYmKkCMYtwe6lChhoGYqdahFiE20BJWxCzCLfUnLIo60DMEbFKtjAgh2vYAOoOA3JctkFIQEiQeWItcKtuE0tTv45Xitu8+9+b6XI0KU6mOME5GlQRPAqPNgS1ZNhHMya7Bbap7YQdYwsMysqtK+CGMfnQ8GSLtkRgjKmIluviHATPwtc4ecFnjFqSuEniJrm1DkcteQ865Jeac1KsYYwBScujoFjHdvKv2Y66smE7OEvm6QkzT0/+LfugR9kxBgfFPLsOmHl6wpr7Qr+K+KRBAFvGyK8cbxhyqpqXC/wV1VUGM1c8H0N5Oo5FL4qXwIGbWnMe2nau6+BfcV06l4rqegdulWMfslNdUVEsL7gBt+rCAfkr5sCxzME64BXnBJ+Kz87BrumMTCDM+9OiF83cAmpp5hYwQ80zodlq5mbBX9ech3DDmHw858Ru6I7t4KxXjMFZrxmDs+E9N4g1qCuEBwfDu9HRA827wY8/77BETsMPrEGPhnuUyGkpZ6XgY/mRVahruceO7Nwj7ae99IgdsNxji5yWZ6vJzrPV9KQr5kmYZqhpt90lP+bmOL8kzPkrcHOX/IRpVo40yvGZCpyF47qBdIf7ajCf+j/77AVxoFgvs1Z4lbXCV1krvM5a4U3WCu+yVvg6a4VvslZ4n7XCd1kr/OqmFSRlpFyy/ihkfp1FJKgsIqHKIhJ0FpFgsogEm0UkuCwioc4iEposIsFnEQkhi0hos4iELotIWGURCessIq3IItJWeeitvh1GbhjvTrzpb6/l/jxN6Y1NnwP0IsYreDvG2xfDYX9AFP3oU+P6bYOrH+viD90ONwwKZW5kc3RyZWFtCmVuZG9iago1MTcgMCBvYmoKPDwKL0xlbmd0aCA4MzMgICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42m1VTW/iMBC951d4D5XaA4vthDhUCMnOh9TDfmhbrfYKiduNVBIU4NB/v34zhMCqB5D9PPP8Zvzs3H35+TyzTb/1s/irFL/8oT8NtZ/l3zb76O6u6OvTznfH7943vhlXD4/i59DXz/4o7vOn4qlrjw8h+Kmr30+NH6M+D3L+re2mEOwj7l/8n9nusN0pOdue2vdj280kgl/a43sI+nRdBFDcgoKSfvvh0Pbdo1BfpZQBKLsm73co4xDNz1LEfBT32nbNcNYjtlAXKS2atj6eZ/Rf70I/kPz8cTj63VP32kerlZj/CouH4/BBGh+i+Y+h8UPbvYn7W2lh6fm03797yBAyWq9F418DY6j/+2bnxfzTGi8xLx97LzTNFeuq+8Yf9pvaD5vuzUcrKddiVVXryHfNf2ux5pTt63WsVOFP6zRbB0ADiAkwEkACYMERJYAUgCEgo4gMwPKKIwdQALCZCYDCLop3KRGhwKGIw7oFAHCoJQNLABaAYw4CSgAVgGUFDg1SzaSZBQClmpTa3AHIKPoqAqTaMQCOGDpirqVERIyUmFLUEkCCDRbIkzKMo1WK4lIqThcpAAhLSZjOodSAwzCHQ4QFh9VTgyz6a5OpyRaqbTo12UKTzaYmW/BZe8WBHZ2cmuxA6pKpyQ4pzk5Ndqjc5VOTHcpw5dTkHEpzfWlycNBolYUZrVP/3QzhkJnYFDKO6dD5rEw5zpnWVuO84n3lea5YaZqO87PFzDgnnZp6rrjdCcrUmg94cY7TMc9HHs15Go3XnBeMEU6WWhUCMWbXWHQ65nOxBcbs+9DaMD53knDWVlIu1a1ywh2PKT7nMXHSvor5S3YGtMfsEonDSFiPhLaUrRykhDFpU+w1coTSyE0NebCCGVI2RkLxbLSYrEg+XRSEszbqieEaFWIM1xijFsM1JhTDNSYwvWEnGtRl+HYY1GLAX2RmUVR0wYyjx8JRDl16mRLO9UvKL6/GFcVX0JHJ6V5aeoWkJqMXt76y5a2vnLr1ldO3vnLm1lcuu/WVqyZf5XLyVR5PevLkopNuADkeryne/stDXZ+GIbzh9IGgpxmPctv5yzdk3++RRT/6+IxfO8x+VNE/bRvRFgplbmRzdHJlYW0KZW5kb2JqCjUxOCAwIG9iago8PAovTGVuZ3RoIDk3NSAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVbLbttIELzzK7gHA85B0QwpvgJBwPAF+LBJEBuLvcrk2CvAogRKOvjvd6qrZceBDyKKzeqe6uohRzd//bxfuPHw6BfpVxP/8qfDZR78ovl7e4xubtrDcNn76fzd+9GP16enb/HP+TDc+3N829y1d9Pu/CWQ76bh5TL6K+tzUu2fd9M7BevEtw/+38W8X7zsZ2vCFdiA/LA7vwTSp8/jEIw/BmNJ+sfPp91h+hbbr8aYEOimsTns0cYpWqqUeHkV97Sbxln1xI9QF9kkHnfDWe/kOuyDH0i+fz2d/f5uejpE63W8/BUens7zq2j8Ei1/zKOfd9NzfPtRWnh0fzkeXzxkxCbabOLRP4WKof/v272Pl5/2+MZ5eD36OJF7S13DYfSn43bw83Z69tHamE287vtN5Kfxj2c2Y8rj05VbBK4pcUldtonWSRpwkiNgAg4BPExrBsoQSPuAMwYCjta5DbhwEgg4Whd4WDQoZCxqVKhRVWDYAjUaSGwySQk4BKCj5bIBR+sW7LaTQMDRukNKx5QOKR1S+pQBpPQrBFgj4BBAjb5hADX6FgEWDRgGXZ2wxlytGf7bzuqiSSvoN+gwSUwOnDCONkxKXAOviDvgjIZCpsmJpU5FLLkwLLGJ1KSbeY4f7pv3e+g27ft9g/vuN/6V03+MgWelB5tiKBY9hJHCKZuQC32WDtYwzEoPiQzASg9JC62WpjpM2xaMO+CSucKpOCvhcDN06MVKbzZD/1ZnIXz21AinIycBZh+ybmK57aA/SbifWuCCGH2nrOnASVkzBSelRzX0ZLoX4UlGTgZO1rFfaMh69oWauSEfGoqWcfAL8lvUKahTZlnSZwP9pSWGn2VCjPplSozcckWMvVKKz9bCzzInlpoFsXBKzhHaSr5G0mMpPtsVei/p8wo+lw0x5li2xFJffcbcy54YOivqz7BuRf0Z+BX1yz6pqD+Dnor6c8ml/hy9V9RfCJ/6c+FTZwHNFXUWeGcq6kwllzpTyaVOi74q9Rm9OPUZPTr1GR8tpz4LR33GWk59xh5w6jPWdeoz/HTqs3DUZ/Tu1Gfod+ozdDr1Gb079Rm+OfVZ6qvP0O/UZ+is1WesW6vP4NfqM/i1+gw9tfosueozeq/VZ+Grz8Ln+9gL1s+t08+cfNXCefbhI1frYNBkrYPBBq11MLIYX5ga5tZ8qRIYUetgwGn0A4Fm2oJ8waW8SDLUlvwsxQ/fb+ZIo33PF677XSyOLxy2byfjcJnncGjKiSxnIU7B3eTfDu3j4Ygs+clpf/17gbsfffQ/sZ0ZdgplbmRzdHJlYW0KZW5kb2JqCjUxOSAwIG9iago8PAovTGVuZ3RoIDk3NSAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVbLbttIELzzK7gHA85B0QwpvgJBwPAF+LBJEBuLvcrk2CvAogRKOvjvd6qrZceBDyKKzeqe6uohRzd//bxfuPHw6BfpVxP/8qfDZR78ovl7e4xubtrDcNn76fzd+9GP16enb/HP+TDc+3N829y1d9Pu/CWQ76bh5TL6K+tzUu2fd9M7BevEtw/+38W8X7zs5zxcAA24D7vzS+B89jgOsfhDLJaUf/x82h2mb7H9aowJgW4am8MePZyipeqIl1dlT7tpnFVM/AhpkU3icTec9U6uwz6YgeT719PZ7++mp0O0XsfLX+Hh6Ty/isIv0fLHPPp5Nz3Htx+UhSf3l+PxxUNFbKLNJh79UygYev++3ft4+VmDb5SH16OPE7m3VDUcRn86bgc/b6dnH62N2cTrvt9Efhr/eGYzpjw+XblF4JoSl9Rlm2idpAEnOQIm4BDAw7RmoAyBtA84YyDgaJ3bgAsngYCjdYGHRYNCxqJGhRpVBYYtUKOBxCaTlIBDADpaLhtwtG7BbjsJBBytO6R0TOmQ0iGlTxlASr9CgDUCDgHU6BsGUKNvEWDRgGHQ1QlrzNWa4b/trC6atIJ+gw6TxOTACeNow6TENfCKuAPOaChkmpxY6lTEkgvDEptITbqZ5/jhvnm/h27Tvt83uO9+4185/ccYeFZ6sCmGYtFDGCmcsgm50GfpYA3DrPSQyACs9JC00GppqsO0bcG4Ay6ZK5yKsxION0OHXqz0ZjP0b3UWwmdPjXA6chJg9iHrJpbbDvqThPupBS6I0XfKmg6clDVTcFJ6VENPpnsRnmTkZOBkHfuFhqxnX6iZG/KhoWgZB78gv0WdgjplliV9NtBfWmL4WSbEqF+mxMgtV8TYK6X4bC38LHNiqVkQC6fkHKGt5GskPZbis12h95I+r+Bz2RBjjmVLLPXVZ8y97Imhs6L+DOtW1J+BX1G/7JOK+jPoqag/l1zqz9F7Rf2F8Kk/Fz51FtBcUWeBd6aizlRyqTOVXOq06KtSn9GLU5/Ro1Of8dFy6rNw1Ges5dRn7AGnPmNdpz7DT6c+C0d9Ru9OfYZ+pz5Dp1Of0btTn+GbU5+lvvoM/U59hs5afca6tfoMfq0+g1+rz9BTq8+Sqz6j91p9Fr76LHy+j71g/dw6/czJVy2cZh8+crUOBk3WOhhs0FoHI4vxhalhbs2XKoERtQ4GnEY/EGimLcgXXMqLJENtyc9S/PD9Zo402vd84brfxeL4wlH7djAOl3kOZ6acx3IW4hTcTf7tyD4ejsiSn5z1138WuPvRR/8DdiYYnwplbmRzdHJlYW0KZW5kb2JqCjUyMCAwIG9iago8PAovTGVuZ3RoIDk3NSAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVbLbttIELzzK7gHA85B0QwpvgJBwPAF+LBJEBuLvcrk2CvAogRKOvjvd6qrZceBDyKKzeqe6uohRzd//bxfuPHw6BfpVxP/8qfDZR78ovl7e4xubtrDcNn76fzd+9GP16enb/HP+TDc+3N829y1d9Pu/CWQ76bh5TL6K+tzUu2fd9M7BevEtw/+38W8X7zs5yJcAA24D7vzS+B89jgOsfhDLJaUf/x82h2mb7H9aowJgW4am8MePZyipeqIl1dlT7tpnFVM/AhpkU3icTec9U6uwz6YgeT719PZ7++mp0O0XsfLX+Hh6Ty/isIv0fLHPPp5Nz3Htx+UhSf3l+PxxUNFbKLNJh79UygYev++3ft4+VmDb5SH16OPE7m3VDUcRn86bgc/b6dnH62N2cTrvt9Efhr/eGYzpjw+XblF4JoSl9Rlm2idpAEnOQIm4BDAw7RmoAyBtA84YyDgaJ3bgAsngYCjdYGHRYNCxqJGhRpVBYYtUKOBxCaTlIBDADpaLhtwtG7BbjsJBBytO6R0TOmQ0iGlTxlASr9CgDUCDgHU6BsGUKNvEWDRgGHQ1QlrzNWa4b/trC6atIJ+gw6TxOTACeNow6TENfCKuAPOaChkmpxY6lTEkgvDEptITbqZ5/jhvnm/h27Tvt83uO9+4185/ccYeFZ6sCmGYtFDGCmcsgm50GfpYA3DrPSQyACs9JC00GppqsO0bcG4Ay6ZK5yKsxION0OHXqz0ZjP0b3UWwmdPjXA6chJg9iHrJpbbDvqThPupBS6I0XfKmg6clDVTcFJ6VENPpnsRnmTkZOBkHfuFhqxnX6iZG/KhoWgZB78gv0WdgjplliV9NtBfWmL4WSbEqF+mxMgtV8TYK6X4bC38LHNiqVkQC6fkHKGt5GskPZbis12h95I+r+Bz2RBjjmVLLPXVZ8y97Imhs6L+DOtW1J+BX1G/7JOK+jPoqag/l1zqz9F7Rf2F8Kk/Fz51FtBcUWeBd6aizlRyqTOVXOq06KtSn9GLU5/Ro1Of8dFy6rNw1Ges5dRn7AGnPmNdpz7DT6c+C0d9Ru9OfYZ+pz5Dp1Of0btTn+GbU5+lvvoM/U59hs5afca6tfoMfq0+g1+rz9BTq8+Sqz6j91p9Fr76LHy+j71g/dw6/czJVy2cZh8+crUOBk3WOhhs0FoHI4vxhalhbs2XKoERtQ4GnEY/EGimLcgXXMqLJENtyc9S/PD9Zo402vd84brfxeL4wlH7djAOl3kOZ6acx3IW4hTcTf7tyD4ejsiSn5z1138WuPvRR/8DnZIYpAplbmRzdHJlYW0KZW5kb2JqCjUyMSAwIG9iago8PAovTGVuZ3RoIDk3NSAgICAgICAKL0ZpbHRlciAvRmxhdGVEZWNvZGUKPj4Kc3RyZWFtCnjabVZNb9s4EL3rV3APAdKDa5KyJKoQDFBfQA7bFk2w2KsjMVkDsWTI9iH/fvlmxklT5GDhafRm+OYNJfrmr5/3Kz/Oj2GVftXqVzjNl2UIq+bv3TG5uWnn4XII0/l7CGMYr09P39TPZR7uw1ndNnft3bQ/f4nku2l4uYzhyvqcVIfn/fROwTrq9iH8u1oOq5fD4uIFUIP7sD+/RM5nj1WMqQ8xRSn/hOW0n6dvynzVWsdAN43NfEAPp2QtOtT6quxpP42LiFGPkJYYq8b9cJY7ug6HaAaS719P53C4m57mpKrU+ld8eDovr6TwS7L+sYxh2U/P6vaDsvjk/nI8vgSoUDrZbtUYnmLB2Pv33SGo9WcNvlEeXo9BWbo3rGqYx3A67oaw7KbnkFRab1XV99skTOMfz0zGKY9PV24Rudrhkvpsm1Q2jdjmCOiIYwAP05oDLgbSPuKMAxEnVW4iLjwFIk6qAg+LBoW0QY0SNcoSDFOgRgOJTUYpEccAdLS8bMRJ1YLddhSIOKk6pHSc0iGlQ0qfcgAp/QYBrhFxDKBG33AANfoWAS4aMQy6OmG0vloz/LdbxEWdltCv0aG1Oge2HEcbOmVcA28Yd8AZGwqZOmdMdUrGlAvDrLFUk93Mc/xw37zfQ7du3+8b3He/8a+c/mMMPEM9mBRDMeghjhROGctc6DPsYA3DDPVgaQCGerAttBo21WPapuC4B3acS5ySZ0Uc3gwdejHUm8nQv5FZEJ97aojTMccCcx+0rjW87aDfWt5PLXDBGH2nXNODk3LNFJyUPaqhJ5O9CE8y5mTgZB33Cw1Zz32hZq6ZDw1Fy3HwC+a3qFOwTpqlY5819DvDGH46yxj1XcoYuW7DGHvFkc/GwE+XM6aaBWPiOJ4jtDl+jahHRz6bDXp37PMGPruGMeboWsZUX3zG3F3PGDpL1p9h3ZL1Z+CXrJ/2Scn6M+gpWX9Ouaw/R+8l6y+Iz/pz4rPOAppL1lngnSlZZ0q5rDOlXNZp0FcpPqMXLz6jRy8+46PlxWfiiM9Yy4vP2ANefMa6XnyGn158Jo74jN69+Az9XnyGTi8+o3cvPsM3Lz5TffEZ+r34DJ21+Ix1a/EZ/Fp8Br8Wn6GnFp8pV3xG77X4THzxmfj8PvaE5XPr5TNHX7V4mn34yNUyGDRZy2CwQWsZDC3GL0wNc2t+qSyMqGUw4DTygUAzbcF8wo5eJBpqy/wsxQ/fb86hRvueX7jud7E4vnDUvh2Mw2VZ4plJ5zGdhTgF91N4O7KP8xFZ9KOz/vrPAnc/+uR/xP4YqQplbmRzdHJlYW0KZW5kb2JqCjU1MyAwIG9iago8PAovUHJvZHVjZXIgKHBkZlRlWC0xLjQwLjI5KQovQXV0aG9yKFwzNzZcMzc3XDAwME9cMDAwcFwwMDBlXDAwMG5cMDAwQVwwMDBJKS9UaXRsZShcMzc2XDM3N1wwMDBHXDAwMGxcMDAwb1wwMDBiXDAwMGFcMDAwbFwwMDBcMDQwXDAwMFVcMDAwblwwMDBpXDAwMHFcMDAwdVwwMDBlXDAwMG5cMDAwZVwwMDBzXDAwMHNcMDAwXDA0MFwwMDBmXDAwMG9cMDAwclwwMDBcMDQwXDAwMHRcMDAwaFwwMDBlXDAwMFwwNDBcMDAwU1wwMDBtXDAwMG9cMDAwb1wwMDB0XDAwMGhcMDAwXDA0MFwwMDBJXDAwMHNcMDAwb1wwMDB0XDAwMHJcMDAwb1wwMDBwXDAwMGlcMDAwY1wwMDBcMDQwXDAwMEVcMDAwbFwwMDBhXDAwMHNcMDAwdFwwMDBpXDAwMGNcMDAwaVwwMDB0XDAwMHlcMDAwXDA0MFwwMDBJXDAwMG5cMDAwdlwwMDBlXDAwMHJcMDAwc1wwMDBlXDAwMFwwNDBcMDAwUFwwMDByXDAwMG9cMDAwYlwwMDBsXDAwMGVcMDAwbSkvU3ViamVjdCgpL0NyZWF0b3IoTGFUZVggd2l0aCBoeXBlcnJlZikvS2V5d29yZHMoKQovVHJhcHBlZCAvRmFsc2UKPj4KZW5kb2JqCjQ2OCAwIG9iago8PAovVHlwZSAvT2JqU3RtCi9OIDg2Ci9GaXJzdCA3OTAKL0xlbmd0aCA0NjA3ICAgICAgCi9GaWx0ZXIgL0ZsYXRlRGVjb2RlCj4+CnN0cmVhbQp42r1cWXPcuBF+16/A4yopGcTFoyqVKh0+ZFuSbcnxkfIDNUPNMJ5rORzb2l+fbgDkgATJmVFcqcpmyCYIfP11o7vR1K4MIxIQGQVE8Ah+OQllDL+SMBagICRMhXgRExYzRmQcEM4UhwtOeBQKuJBEBBwvQpgljo5kHBOhhCQygXkjBY8SeCGIErgAqZQhXOA/DMfERLEwJCoIiFIBgwtOVBQADJkQlTB4VTESigRmloqEYQLzSE6iAB/JiESSwxwhiSIQAKIoBkgqInEAQqVIzAXMKkksg5AkcK9iRiJG4kipozAgcSIiAjASxiISJiThAC6UJJGRIgwGJqHCdWGMEASVZwGurYCTgMFCIYML4AAGwgWQdqQSuAgBfwgzBDEHtPhWggPiBMkFIngUwRWDKwHzM8bhSsFQxoS+grWYDDkR8BJTLAF28SKKjkBfxtBSHA0VgVo8gIsYIDAcHKMBcXACVMNYHoBmKArARgpf4gwYVByWBXKAZg6v8TCQRwr8gHGgCGTwpmCoKAfIQgQASsA6QiYRXIHSIgSFFMIScQIcIz0yiPEprAFmwTdgDbASymANGUawBvDNZKzwKawBlsensIZiuK6ENWAhvII1lAQTKUALjoh2lmiCGFRWEtZQCRhUgVuwENSHK1gjFFIcKXAMoAi1BJ5YiKMVGARcDWYAR4OrCPRX4FssjBEBGjaMeXz0j38c0bvHVUboi+WivMjWoyJflcviSN9fp3N4cnv59fWn139/e/VhOU8XLDg5W87GMGCWTtZEmpFnZ8tf5N8nuDFOeIIYA7S4EN+O6Ol6lC1KcLfkiJ6nq1dZPpnaW1wQn52A+Y7oZZnO8tHpYjLLSHBEb8ts/i90jCP62b4EGwrmmKbFbVaSP+gpPaPn9BW9pG/pFX1HP9A7mtJ7OqJjmlH9Cn2gDw851f/7kdGH5aagEzqlOZ3ROV3QRb7I6JIu4f9XdJUV+XJMC7qm6/wXLWk5LbKMlj+XdEN/0Ef617FR90UOECXsk4B8OPrnP/ei8eLN+9vbDzWNfIjGCGnE3aP0xhGRQ2MsGzTi7T40BskOGi87KOwhb/q4mmaLmsMWfX/uT2ByCIFf7j5+Ob0FAq/Scvr8V5kt1vkS/fFDNtnM0qKbSwgB4JGwsZiEXRhFfEulDFwmA4fHMAh6eAxdGgVzabwv0tH3rJxlD+V9Pmnd1vcFvuo8r+6B17T8mY8zmi/KbFKks3G+Xs3SR7pKi2yB05zBW+6Nc3efN2/snZ68fq26c2/v89bdhK4383laArUVgDKfjTMN7UeGtI/Hy839LGuYEnLHAaY8fXF6/f6FNaXheZcdIfSecIxlAQS5SAVuYGntCDG0I04wnlhbBr22NFvigj6nL+hLHWFe0zc6ylzTG9gm72Gj3MJW+Uj/RT/Rz/QL/QrbJp2tprh57rMyhR00muZ0tAQyYS+NsxnIICit1vlsuaA44gE20yTF55MiS8us0HHpP/Q7/Z6uVimdpfP7Mfxk6zWdGRphv803GLY2uN/SoszRTx4eqr23muaMrnK9B4vpUu/DCSywnqXrKe7HdAN7EhfH/fiT/qK/cm9bRuIQW56fv/54/rlhy3DQlIE1JIec93sMKaJeQ16DkSrOp8DtXAcnTUFTZ3VQLP949frmoqFzvEcYQq2hIPpNWke8V2vjvK/AYa91RP9KXbe0LlnR8mAT4n8qh3N8zPUwdK3aqzx/+gG+5DlSdAipN2/en3757NQZg4RKgX6UYFxnmCN5I0c2S414v1JjILZX8eBCb+N2VHhreDPB4WaeTVKdSU2AMOEBgkM62pQZTcd5VmTrfE1THVfRLttM0cgKaCvz0miUFmCp0Sgb57MZGHGJhqsNSTMzzFQ8EKDRONmfm3SmM3hXEp9kBZA8vp+t3XyOsadZF5mJlzVom+mrVOPkjjoEzTZriD9/bpYQ0fQDXQ5k89ygXmc/YC0sD2oncoqETb3SZjHOivVoWWQ2UoF30b+yYtl0sYNKiHd3X1++v3JqsGEX45hzYjwHJPI3uZgacLFL8J5brL20Xa2p0BgrpNDUU0CSx0F8UO49O3//8s2rmoNoBwcBbjMo66GWx6MYVy4HYihyqb5SNHY5CFwOnsO2uoS9cwv7xlShWINOnUpzZcvL0hSUTR4Oylu3t6evbup6fDhlncBxDaMNHq9k5NHwJFdAtnpcYbtR9XbDDbXdI775D0pdz99en919qtTeZX2gFNQ2J1JUW/7Pakf9QVbr2FTtoARyenrz/uayUm1HQpYqtKrhGf+3WDQKe1UzSWEg0NvQbWJ2bX7w+nSEkX85GHh3OchBMfL0+uz95VcnDRtd+1iMbRYWGCP+BxadwkaFB9XlpiqvEq6uw+eQi9aQ3nQI8dKklxMbZ9rvQ6fa9on2Z0fVkxwUji+ub++ub2wpefs4v1/O1jvPtAk50UdaxrAX6HAOleXTikm56yyEjL+1px8sa9LFxHXn7a3xybQolj/N5T2ITaq/Twv8B6oO7f5ZvRFsoTDKi9Es0+4Mz+YAujoZmY2BJ6Ht1XwzmsL+WK3LJZ3nC3hpvpmV+Wr2SBfpPVRJC9hzeI41ixs7IhMwHZQpxRKdZHO/zqBCgapsBNOu83kOrOs1mjY9KLWcXdx8ffe8adMdCQaOBwl27wTYNorD32BR1X88SNdYmq2/wzEfcuviIV/k5aPlcFXk82YQTg7KL2+u3lyftXTfEYqxkk90XxhSWRQlv8Odw/2UNx5X+43jgC1a6iFtN8qKFRTc+QjV05HYobHyqyadB+W0N7dXLz68+fvV7ekVCzrpgwAsQ4H1Gfbmg0bHlbvsKeW0uPr6WwNxwOwV2LxNdQ5KLp+vv3z5/BHUOetT5wQcASrvgHAhBUm4c1KWoexNKHvpEwpXn3PMGa4u+EnG0+X5YgSnn8UEloJTMNh8McrW5N+c2XMW4ZFOJzkRASYVImIn/zinoK7UTaS06cmmH5tsTMTCRK6TEGYbk3d0VaBzlj5A6aOUKR30ga06YdmzFhw3dnWySMyrkyqJo7qX5YR2kohGsUIS5ZzNSBI1+sWTuofVXbHbhAl1NG8ebgnnyp6R4TKqTsf1cRn8gdUHYLgRNpsTLoU9psJluD2qEq749jgJd8o59H7by8hBR4O13b9ttXurx7Afm91S0Lijk9ts0HY0b93WcV8reUIE72oM1/1eEsdeV5ckQbvTTJK4akLj575t33c/thirGqDYDzKNTd32JCzS/U7dKCI8aLY3sde0wTbkKjfNJd2wLNMNQYNPYUPJqlVFRKLbUETF1U4xu0e3SM2msQUDCWWje/WEhi5Jwrrt2uni+FEFnRzr3m4339fNbM5oppY65bgZi4TbJENiJ2EBmFY2Q6a3FZNbSBEeNuofwuO6oiKws7bFGzLuFkVwmLCprc6PtlIjSrYKLlOakVA0k2QdlCA/oTng2ETfQs1TxSOpYxELeHeNuKPu3JaY+Ol5W3tqG9l0jN8UTYm4p2Obth8RttGHVOsDHQQqe0CDPNMT4IW2F/I2FM7hzENtlo79KM7MwVATRiJGX5JI0EtDXBzYIE4SNhy2E+PB+AGfQnwKFM3xi/02UGteah/+5ud0SKqb+1LfohCqg7N0neGT/q/EjVIAUrCpfuiLvFiXmIvB946AV3vDOJQsn/JxOV1jujZjK7PoP5PQkrvlxwWQM8ac3VOZD2Ht+xTbwhoF+2NV0T5Y5eFYB7riLbixD5c5cCEBOtTyfeCqw+EOdFjbcHkbLn6V3rLLQgeu3AdueDjcgWZoG65sw8XPBY4zMAeu2gdudDjc/t5MC23ikcsj1xeEcFw32AdtfDjaPT7Yt3ecBztwOQ4cjpXnwYr5qJMncLzj23QbsucXjPUGicRjWnmYWXA45h3fYNuQQw9yI64xJ1DIcB/I7AkxePgTahty7EHmvSyLfSDzJ0De1aprb0I56M3Yragx+zRzH/MTUt2uVlQbcjgEWSaOY4h9ED8h4e1qILURx8Mku44h94H8hKTXbtI0IeKfe7YgJu5+w5uaVekDekJaa7dZ2oC8MBu62SF2azA/qLLo/1vFNMIpc8hSsWfPyEf7hMTV/2mwDdbbLlI4aJWTZeVeYJ+Qr/o/6LXBejtFudQqt4AJ9gDLn5Co+j/Rtbe17wb9+1rtg7YjR71LJ9n6iJ4vN9gtPYL7Qp9kud0fb/IxHP+ENgoxn3Uw1ZhfEZtfUyzh393i77enrMDMl2vCTKsd6vbA/HJpfk2cghI+fPIaPLJzxnZOEziJMDGaCG7WFkavJ60hTClJhPnLWCJMjQ+HY6Of5JH53b0Gl9WkkeXbfMXUf0qu51ZdfN9sylm+wGm04xAbTtFvCLc3lRLVizmcl4mwHnNKhB1mVazeeldkP/DP0l0nMm+K+s2g/01uN9919qvEP3P3puFJNQ0PO6cxb3YA4Kp+kzfexL+rdxC4O6rGo5mxeCwzJ6I1P6vmt7G0e36bpwxOLn2crKaYyaF5uDtP0DFPTTgLBuZxCWcdhNd8hwOTuNyzDu5r6ln3JPrFRgbC7yDg1X9cLEcnt2ValMfEFKzkj8symz9jx4Sb4GcFHAXMEYhj8DQrGOVl9uw8nY2zAk5WSRwc4ynJfZZ9z0c84OoY/10Hs2Po23yely0Q7eHf2oDN82m2eJ2ni8nbfHOXLmFk+HwGLpTD08dj/CDtrH3xmJaz5Q8cBDqYosM+er7+ni94EDCElbQffEhn63KJz3ljdnPC7x4p8XnkPH+xBD9fZAXqEyQAwKzjqr+PSp2TdZPzskg3WVFCFI9hORE4YC4h221+5MDGl3SezpflEiGfp8UsgydIQrhjNFNNKmJn/Nt8cZ1+T+ebIsV//wUfu5xef2RJovmRDSlAEChVnbw0tGm80q19heDjdAbwwRkTdQbxZJwWGq9wlr5NZ8u7v5YbYxgWubBuH2c/Mmx41/PEWiHXe+7SBRgLLIXwmcvzp2z0nSUhzmrz2R/YOsWm9DOGe8v0Hz1lB8E3p/C0d57CXg1jb1lAaZPvVioQDKt2vivGLZwknpzjcOWJkYGqKtjq1ITUHN+PXzxDJ1GRtwgGD08v8SxE2/mKobls0nbF4EUs9udGU8VxrwIaU3N8vwJSR8+gDUlqUrmMfDmOV4kvx6AbdswjUM58uUQ579VCA2u9MKiGwvmEtw7CCvzlERXzRyMoW925YpybhZ4YYzSLhlRQx60XhlSIMOLG3ioYGaXyxBifZRuT0uaMhC8GcwoW+XKG8nhAh+i4/UK/Dkq7gfCUUNoNBE98OTAuJPPkaLZIemI0W6Q8MZot6t/SBlbzhSEl0NyxzxWaO/ZVQ7Ml3BPHWHP44gTFbfuEaDZhmi09GjQ8KRwMqqG2tgwifxWG8tiXAz1SBJ4cjRkmnhhtGfmj0ZTG9Tp1MLCaLwwpARqLWHmrhCj2VYtQ7GuGVoh9DRLkwW6eFRyikH/FB6Aj/Xagh9nIMV7yxpS4FaV0JejVKnYl2qG5K0FfjhvzIBEmQrvg6lXrUT3AkDDWmBC5sudKK0GaZOBK0E+VCwywJ8K9x4gSxt2wwuPtqG5YHD0+dtnh6ISBciXAjqyChpEAO7IBFLM5d6fBhCy72cI1q0HdqDBtK+bMhgk7dKnCVB27IDFJJ+4IDMzMMrfORuhAf9N1FO8EhZ7lDPNw1c/QAFFrWtC+KnxqmS5bg5ZQ4QZnDaGOOU0JLOGlM3d9Z2AfTm2Q1kK6RmqKkOgmcB1eKzz4NxajbWisnLUhx+DEetAiivZoH7H7HGOaxVhOs2WB50pcOpFNIddVaJWxtlIsNm3PqZbailW2pTg29I5WbTiNFzzw24dIeBUwtlLk3Paraqkp9phqSzEsVRRXUl1JMF+KY2MPewNNY3QvcJvMWxBtJhRtKRo7Ym2pTkS8LcU8FId9EFUDYiMJme6ZsuFE2eOQslFdWTKULW+Ufzp2mwPdBbhdwuZaZbekMk0q/d9IML8WglD7FshdiggLXFjgtv+qbHtW2UCqJNuR+Tq2vV3CurySFrjNd0qqoZ05xL6taZU9eyhpQctBtjtnvIBT8Vr/5xy8PvZ5WsKJenJkWqjbrmzVDXX6UdpvlarbVZPsajnO6Md1tm2d3qyyxanWj4i6d/VfvbAQ4gplbmRzdHJlYW0KZW5kb2JqCjU1NCAwIG9iago8PAovVHlwZSAvWFJlZgovSW5kZXggWzAgNTU1XQovU2l6ZSA1NTUKL1cgWzEgMyAxXQovUm9vdCA1NTIgMCBSCi9JbmZvIDU1MyAwIFIKCi9MZW5ndGggMTMyNiAgICAgIAovRmlsdGVyIC9GbGF0ZURlY29kZQo+PgpzdHJlYW0KeNollklsVlUUx+/5Jlq+TlDopRNTactUKJ2AMrQMpZSpLdAWCoWWukDjQrcOITHmLEw0caNNXOjOqDkrV0YTlY3GlcbL2qgLdyq6I0a8v+Pml3Pf97373rv/3z3vhRDC00IIhSBhvjlQ7ch1OkIloACucewwVRGUwBLHRqjKoJJhXRwbZrgO1ORhFI4NMawF6zl2kWODDKugjmOXODbAsB405KGWOXaIYSNo4ljkWD/DDWAjaAabwGbQAiLYAlpBG2gHHaATdEsIjf7QW8E2Zm4HbcAv1Aq6wW6wFfTw5+1UW4Av2B7QxQ87gS/EdqpdVJ1gL8NesBvsAX5sH9gP+sABcBD4U/rj+7r4gvlK+hL72p8Ex0GPhPUf+MN4UEe4ZAfwSx6l2kY1Co6xkgPgMrgApgDJ6GHABHocnABXgF98DIyDU+A0OAPOgglwDlwA58EJCZ1dfmuTzDINuINYoBoE/jAXwSVwGVwB02AGzIJ5MCVh4GWf7yq4DubADXATLIJbOOkWr4IFCZMP/bTbrMYuqjvgLlgGK+Aep+G9Ybthu+G4ueMNoA7kPbNyl/kM260G4LjheMIDq0r4u8ruUh7LsNiaAO4a7hruGu4a7hqeWr2EF/0BDZUNlQ2VzXXEF8Niw2LDXSNaQ0xrl/D6m34u/tkO4PfC8xoqK/oYEhoSGhIachn5mv+KddYrYc1zM78kihqKGooaihqKGuEZ1hl9w3DSsMQQ0wYkfNznsxwF+GfuH06qD7HJsMmwybDJsMmwybDJJgE22RTADctefb3mM+OaoY/5OqOPIY0hjWGJXQOoYqhiBG+3wbSERw98FuSyBYBIhkiGSOYiYYlhiWGJuSWrWOzpL0n49V2Sjo4iKAF6WKyQ236qHpG3XvC/1AJvh5vARlAj4d8O/7UKvDHWA9phbAQ0wbgBbAYtgFYVaUaRThObRRq+81nwJdLNIn0t0g8ibS7SviKdK+4E9KtIq4r7gN9pH6Cvxa0iXU98PvSJvYB2GA+AITAMRgDNI9Ja4l6RkWE/7SDoB4cArSWSfhwF3grGwSlwGpwEgyLnXvEJ6EORPhTPgLNgApwDdJV4HtDDIv0ljoksfuPn0twibS7Sw5I/1iy4Cni1xetgDtwBd8EyWAF0izgj8vxPPt88WAA3wE2wCG6BewAt1Lc9bz/1NkeWSpbqmS+JPHjW/4IqiiqKJboO8ObUWpEvu/0v/jIkffVXIOkrvmgzQB9FBiV95R2lhKxkrlWRtU98FlRRLFG0ULRQPFA8UDxQPFA8UG8UPYC4lbgVGXQvwBLFEmU5FQ+UBJUElfC0Q+QjXzAlfSV9JX0lXyVBJUElQfX3wgzIuX32mp9GoErcyZ+NyJTIkj+vf5L4ExFe8tUgweRLQkZKRkpGSkZKRkpGSqDK94sStxK3ErcSt5Kgror8+C23kYJUnns/SGHsbR+KFD5c9ip/H33+i1dFKRYfeVWSYss7XpWlOP7Qq4oUf+jxap2Uev/xqkZKb1z2qlZK37d5tV7K5S+8qkp59S+v6qT81WOv6qX886xXDVL+fcSrRik//f9qTVJpr/dqg1S6/8j3vOe9jL7JjP7HGUMMj0xkHPsz4wR/GV/LODOWMfFbxtSVjEufZsy0Zly/n7HwJON2W8byaMYzi34N4k6kmvyzgk2c2MSJbZrYpgkF0hhAi4QWieCTB0r6ifQT6SfST8Sd2L+JDZv8E9G/Cdm6ybcuGzthSUKG5DK4B67AnFTuv5Rv8tWG8B8ESmrLCmVuZHN0cmVhbQplbmRvYmoKc3RhcnR4cmVmCjQyNTIzMAolJUVPRgo="}, {"path": "preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/main.tex", "bytes": 2107, "sha256": "6bcef41f845106bd10219b920717b7f99d10a45a22376e893b96482cfd743514", "content": "\\newcommand{\\DoNotLoadEpstopdf}{}\n\\documentclass[11pt]{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{lmodern}\n\\usepackage[margin=1in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage{enumitem}\n\\usepackage{microtype}\n\\usepackage[colorlinks=true,linkcolor=blue,citecolor=blue,urlcolor=blue]{hyperref}\n\\pdfinfoomitdate=1\n\\pdftrailerid{}\n\\pdfsuppressptexinfo=15\n\n\\newcommand{\\R}{\\mathbb R}\n\\newcommand{\\C}{\\mathbb C}\n\\newcommand{\\N}{\\mathbb N}\n\\newcommand{\\dd}{\\mathrm d}\n\\newcommand{\\Id}{\\mathrm{Id}}\n\\DeclareMathOperator{\\supp}{supp}\n\\DeclareMathOperator{\\tr}{tr}\n\\DeclareMathOperator{\\rank}{rank}\n\\DeclareMathOperator{\\diverg}{div}\n\\newcommand{\\norm}[1]{\\left\\lVert #1\\right\\rVert}\n\\newcommand{\\abs}[1]{\\left\\lvert #1\\right\\rvert}\n\\newcommand{\\ip}[2]{#1\\cdot #2}\n\\newcommand{\\Lcal}{\\mathcal L}\n\\newcommand{\\Ecal}{\\mathcal E}\n\\newcommand{\\Pcal}{\\mathcal P}\n\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\numberwithin{equation}{section}\n\n\\title{Global Uniqueness for the Smooth Isotropic Elasticity Inverse Problem}\n\\author{OpenAI}\n\\date{September 24, 2026}\n\\hypersetup{pdftitle={Global Uniqueness for the Smooth Isotropic Elasticity Inverse Problem},pdfauthor={OpenAI}}\n\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe prove that the full static displacement-to-traction map uniquely determines both real smooth Lam\\'e moduli on every bounded connected smooth domain in $\\R^3$, provided $\\mu>0$ and $3\\lambda+2\\mu>0$ on the closure. This resolves the smooth three-dimensional isotropic elastic Calder\\'on uniqueness problem under these positivity assumptions.\n\\end{abstract}\n\n\\tableofcontents\n\\medskip\n\\input{sections/introduction}\n\\input{sections/boundary-reduction}\n\\input{sections/analytic-estimates}\n\\input{sections/physical-amplitudes}\n\\input{sections/transport-matching}\n\\input{sections/coefficient-recovery}\n\n\\begingroup\n\\small\n\\bibliographystyle{plain}\n\\bibliography{references}\n\\endgroup\n\\end{document}\n"}, {"path": "preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/references.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/references.bib", "bytes": 8573, "sha256": "07b5cfbc1b62153b976f32fb8f2025c8538efada142bec78e6470f6e9b37eb02", "content": "@article{NU1994,\n  author  = {Nakamura, Gen and Uhlmann, Gunther},\n  title   = {Global uniqueness for an inverse boundary problem arising in elasticity},\n  journal = {Inventiones Mathematicae},\n  volume  = {118},\n  year    = {1994},\n  pages   = {457--474},\n  doi     = {10.1007/BF01231541},\n  note    = {\\href{https://doi.org/10.1007/BF01231541}{doi:10.1007/BF01231541}; see the erratum below}\n}\n\n@article{NU2003,\n  author  = {Nakamura, Gen and Uhlmann, Gunther},\n  title   = {Erratum: {Global} uniqueness for an inverse boundary value problem arising in elasticity},\n  journal = {Inventiones Mathematicae},\n  volume  = {152},\n  number  = {1},\n  year    = {2003},\n  pages   = {205--207},\n  doi     = {10.1007/s00222-002-0276-1},\n  note    = {\\href{https://doi.org/10.1007/s00222-002-0276-1}{doi:10.1007/s00222-002-0276-1}}\n}\n\n@article{LinNakamura2017,\n  author  = {Lin, Yi-Hsuan and Nakamura, Gen},\n  title   = {Boundary determination of the {Lam\\'{e}} moduli for the isotropic elasticity system},\n  journal = {Inverse Problems},\n  volume  = {33},\n  number  = {12},\n  year    = {2017},\n  pages   = {125004},\n  doi     = {10.1088/1361-6420/aa942d},\n  note    = {\\href{https://doi.org/10.1088/1361-6420/aa942d}{doi:10.1088/1361-6420/aa942d}}\n}\n\n@article{TanLiu2023,\n  author  = {Tan, Xiaoming and Liu, Genqian},\n  title   = {Determining {Lam\\'{e}} coefficients by the elastic {Dirichlet-to-Neumann} map on a {Riemannian} manifold},\n  journal = {Inverse Problems},\n  volume  = {39},\n  number  = {9},\n  year    = {2023},\n  pages   = {095004},\n  doi     = {10.1088/1361-6420/ace649},\n  note    = {\\href{https://doi.org/10.1088/1361-6420/ace649}{doi:10.1088/1361-6420/ace649}}\n}\n\n@article{SaloTzou2009,\n  author  = {Salo, Mikko and Tzou, Leo},\n  title   = {Carleman estimates and inverse problems for {Dirac} operators},\n  journal = {Mathematische Annalen},\n  volume  = {344},\n  number  = {1},\n  year    = {2009},\n  pages   = {161--184},\n  doi     = {10.1007/s00208-008-0301-9},\n  note    = {\\href{https://doi.org/10.1007/s00208-008-0301-9}{doi:10.1007/s00208-008-0301-9}}\n}\n\n@incollection{EskinRalston2004,\n  author    = {Eskin, Gregory and Ralston, James},\n  title     = {Inverse boundary value problems for systems of partial differential equations},\n  booktitle = {Recent Development in Theories \\& Numerics},\n  editor    = {Hon, Yiu-Chung and Yamamoto, Masahiro and Cheng, Jin and Lee, June-Yub},\n  publisher = {World Scientific},\n  address   = {River Edge, NJ},\n  year      = {2003},\n  pages     = {105--113},\n  doi       = {10.1142/9789812704924_0009},\n  note      = {\\href{https://doi.org/10.1142/9789812704924_0009}{doi:10.1142/9789812704924\\_0009}; 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Math\\'{e}matique},\n  volume  = {347},\n  number  = {17--18},\n  year    = {2009},\n  pages   = {1017--1020},\n  doi     = {10.1016/j.crma.2009.07.005},\n  note    = {\\href{https://doi.org/10.1016/j.crma.2009.07.005}{doi:10.1016/j.crma.2009.07.005}}\n}\n\n@article{Weck1969,\n  author  = {Weck, Norbert},\n  title   = {Au{\\ss}enraumaufgaben in der {Theorie} station{\\\"a}rer {Schwingungen} inhomogener elastischer {K{\\\"o}rper}},\n  journal = {Mathematische Zeitschrift},\n  volume  = {111},\n  year    = {1969},\n  pages   = {387--398},\n  url     = {https://eudml.org/doc/171213},\n  note    = {\\href{https://eudml.org/doc/171213}{EuDML record}}\n}\n\n@article{ImanuvilovYamamoto2004Carleman,\n  author  = {Imanuvilov, Oleg Yu. and Yamamoto, Masahiro},\n  title   = {Carleman estimate for a stationary isotropic {Lam{\\'e}} system and the applications},\n  journal = {Applicable Analysis},\n  volume  = {83},\n  number  = {3},\n  year    = {2004},\n  pages   = {243--270},\n  doi     = {10.1080/00036810310001632772},\n  url     = {https://www.ms.u-tokyo.ac.jp/preprint/pdf/2003-19.pdf},\n  note    = {\\href{https://doi.org/10.1080/00036810310001632772}{doi:10.1080/00036810310001632772}; \\href{https://www.ms.u-tokyo.ac.jp/preprint/pdf/2003-19.pdf}{institutional preprint UTMS 2003--19}}\n}\n\n@book{McLean2000,\n  author    = {McLean, William},\n  title     = {Strongly Elliptic Systems and Boundary Integral Equations},\n  publisher = {Cambridge University Press},\n  address   = {Cambridge},\n  year      = {2000},\n  isbn      = {9780521663328}\n}\n\n@incollection{Calderon1980,\n  author    = {Calder\\'{o}n, Alberto P.},\n  title     = {On an inverse boundary value problem},\n  booktitle = {Seminar on Numerical Analysis and Its Applications to Continuum Physics},\n  series    = {Cole\\c{c}\\~{a}o Atas},\n  number    = {12},\n  publisher = {Sociedade Brasileira de Matem\\'{a}tica},\n  address   = {Rio de Janeiro},\n  year      = {1980},\n  pages     = {65--73},\n  note      = {\\href{https://sites.math.washington.edu/~reu/papers/reference/calderon.pdf}{original scan}}\n}\n\n@article{SylvesterUhlmann1987,\n  author  = {Sylvester, John and Uhlmann, Gunther},\n  title   = {A global uniqueness theorem for an inverse boundary value problem},\n  journal = {Annals of Mathematics},\n  volume  = {125},\n  number  = {1},\n  year    = {1987},\n  pages   = {153--169},\n  doi     = {10.2307/1971291},\n  note    = {\\href{https://doi.org/10.2307/1971291}{doi:10.2307/1971291}}\n}\n\n@article{NakamuraUhlmann1995Boundary,\n  author  = {Nakamura, Gen and Uhlmann, Gunther},\n  title   = {Inverse problems at the boundary for an elastic medium},\n  journal = {SIAM Journal on Mathematical Analysis},\n  volume  = {26},\n  number  = {2},\n  year    = {1995},\n  pages   = {263--279},\n  doi     = {10.1137/S0036141093247494},\n  note    = {\\href{https://doi.org/10.1137/S0036141093247494}{doi:10.1137/S0036141093247494}}\n}\n\n@article{EskinRalston2002Elasticity,\n  author  = {Eskin, Gregory and Ralston, James},\n  title   = {On the inverse boundary value problem for linear isotropic elasticity},\n  journal = {Inverse Problems},\n  volume  = {18},\n  number  = {3},\n  year    = {2002},\n  pages   = {907--921},\n  doi     = {10.1088/0266-5611/18/3/324},\n  note    = {\\href{https://doi.org/10.1088/0266-5611/18/3/324}{doi:10.1088/0266-5611/18/3/324}}\n}\n\n@article{ImanuvilovYamamoto2015Elasticity,\n  author  = {Imanuvilov, Oleg Yu. and Yamamoto, Masahiro},\n  title   = {Global uniqueness in inverse boundary value problems for the {Navier--Stokes} equations and {Lam\\'{e}} system in two dimensions},\n  journal = {Inverse Problems},\n  volume  = {31},\n  number  = {3},\n  year    = {2015},\n  pages   = {035004},\n  doi     = {10.1088/0266-5611/31/3/035004},\n  note    = {\\href{https://doi.org/10.1088/0266-5611/31/3/035004}{doi:10.1088/0266-5611/31/3/035004}}\n}\n\n@misc{ChenJiangLiuTao2026Elasticity,\n  author = {Chen, Lu and Jiang, Yan and Liu, Hongyu and Tao, Longyue},\n  title  = {On an inverse boundary problem in elastodynamics},\n  year   = {2026},\n  note   = {\\href{https://arxiv.org/abs/2609.20134v1}{arXiv:2609.20134v1}, 17 September 2026}\n}\n\n@misc{Dyatlov2026,\n  author = {Dyatlov, Semyon},\n  title  = {Lecture notes for 18.155: distributions, elliptic regularity, and applications to {PDE}s},\n  year   = {2026},\n  note   = {Lecture notes, version dated 2 October 2026; accessed 3 October 2026; \\href{https://math.mit.edu/~dyatlov/18.155/155-notes.pdf}{author-hosted notes}}\n}\n"}, {"path": "preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/sections/analytic-estimates.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/sections/analytic-estimates.tex", "bytes": 11309, "sha256": "260ae799948c889ae125b81b756216f3d085cc8c66a5954f575a54055e2c2134", "content": "\\section{An augmented system and analytic estimates}\n\\label{sec:analytic-estimates}\n\nThis section constructs a scalar-Laplacian augmented operator and a\nsupported estimate that will control differences of transferred physical\ndisplacement--divergence pairs.\nAdjoining the displacement divergence to obtain a system with Laplacian\nprincipal part goes back to Weck \\cite{Weck1969}; see the account of\nImanuvilov and Yamamoto \\cite[Section~1]{ImanuvilovYamamoto2004Carleman}.\nThe identities below give the formulation used in this proof.\nWe first work with one of the smoothly extended coefficient pairs from\nLemma~\\ref{lem:common-extension}, and suppress its subscript. Set\n\\[\n m=\\mu,\\qquad k=\\lambda+\\mu,\\qquad \\ell=\\lambda+2\\mu=k+m.\n\\]\nThe energy assumptions imply $m>0$, $k>m/3$, and $\\ell>4m/3$.\nFor an independent vector--scalar pair $U=(u,d)$, define\n\\begin{equation}\n\\label{eq:augmented-operators}\n\\begin{aligned}\n R(u,d)_i\n  & =m\\Delta u_i+k\\partial_i d\n   +\\sum_{j=1}^3(\\partial_jm)(\\partial_ju_i+\\partial_iu_j)\n   +(\\partial_i\\lambda)d,\\\\\n S(u,d)\n  & =\\ell\\Delta d+2\\nabla k\\cdot\\nabla d\n   +2\\nabla m\\cdot\\Delta u\n   +2\\sum_{i,j=1}^3(\\partial_i\\partial_jm)\\partial_iu_j\n   +(\\Delta\\lambda)d,\\\\\n P&=m\\Delta+\\nabla m\\cdot\\nabla.\n\\end{aligned}\n\\end{equation}\nThe scalar $d$ will equal $\\diverg u$ for physical solutions, but keeping\nit independent gives the compatibility identity needed below.\n\n\\begin{lemma}\n\\label{lem:augmented-system}\nFor every smooth pair $(u,d)$,\n\\begin{equation}\n\\label{eq:augmented-identity}\n Lu=R(u,\\diverg u),\\qquad\n \\diverg R(u,d)-S(u,d)=P(\\diverg u-d).\n\\end{equation}\nIn particular, $Lu=0$ implies $R(u,\\diverg u)=S(u,\\diverg u)=0$.\nThe normalized augmented operator\n\\begin{equation}\n\\label{eq:normalized-augmented}\n \\Lcal U=\n \\left(m^{-1}R(u,d),\\,\n \\ell^{-1}\\bigl(S(u,d)-2m^{-1}\\nabla m\\cdot R(u,d)\\bigr)\\right)\n\\end{equation}\nhas the form\n\\begin{equation}\n\\label{eq:laplace-principal-form}\n \\Lcal=\\Delta\\Id_4+\\sum_{j=1}^3 A_j(x)\\partial_j+A_0(x)\n\\end{equation}\nwith smooth matrix coefficients. Consequently, the difference of the\nnormalized augmented operators for the two coefficient pairs has order\nat most one and coefficients supported in $\\overline\\Omega$.\n\\end{lemma}\n\n\\begin{proof}\nThe first identity is the coordinate expansion of $\\diverg\\sigma(u)$.\nTo verify the second independently of the constraint, write\n$v=\\diverg u$. Differentiating \\eqref{eq:augmented-operators} gives\n\\[\n\\begin{aligned}\n \\diverg R(u,d)\n ={}&m\\Delta v+\\nabla m\\cdot\\nabla v+k\\Delta d\n       +(\\nabla k+\\nabla\\lambda)\\cdot\\nabla d\\\\\n    &+2\\nabla m\\cdot\\Delta u\n       +2\\sum_{i,j=1}^3(\\partial_i\\partial_jm)\\partial_iu_j\n       +(\\Delta\\lambda)d.\n\\end{aligned}\n\\]\nSubtracting $S$ and using $k=\\lambda+m$ gives\n$m\\Delta(v-d)+\\nabla m\\cdot\\nabla(v-d)$.\n\nFor the principal part in \\eqref{eq:normalized-augmented}, the only\nsecond derivatives of $u$ in its scalar numerator cancel. More\nexplicitly, if\n\\[\n C_m(u)_i=\\sum_{j=1}^3(\\partial_jm)\n                   (\\partial_ju_i+\\partial_iu_j),\n\\]\nthen this numerator is\n\\[\n\\begin{aligned}\n S-2m^{-1}\\nabla m\\cdot R\n ={}&\\ell\\Delta d+\n   (2\\nabla k-2km^{-1}\\nabla m)\\cdot\\nabla d\\\\\n &+2\\sum_{i,j=1}^3(\\partial_i\\partial_jm)\\partial_iu_j\n   -2m^{-1}\\nabla m\\cdot C_m(u)\\\\\n &+\\bigl(\\Delta\\lambda-2m^{-1}\\nabla m\\cdot\\nabla\\lambda\\bigr)d.\n\\end{aligned}\n\\]\nThis proves \\eqref{eq:laplace-principal-form}. The support assertion follows\nfrom equality of the extended coefficients off $\\overline\\Omega$.\n\\end{proof}\n\nThe order-one difference of the two augmented operators is the forcing\nterm to be estimated after physical transfer in\nSection~\\ref{sec:transport-matching}.\n\nFix a nonzero $\\theta\\in\\C^3$ with $\\theta\\cdot\\theta=0$, and write\n\\begin{equation}\n\\label{eq:phase-notation}\n D_\\theta=\\theta\\cdot\\nabla,\\qquad\n E_\\tau(x)=e^{\\tau\\theta\\cdot x},\\qquad h=\\tau^{-1}.\n\\end{equation}\nFor $s\\in\\R$, the semiclassical Sobolev norm on $\\R^3$ is\n\\[\n \\norm{v}_{H_h^s}^2\n =\\int_{\\R^3}(1+h^2\\abs{\\xi}^2)^s\n                    \\abs{\\widehat v(\\xi)}^2\\,\\dd\\xi.\n\\]\nFor vectors, we sum the component norms. These norms and all Hilbert\nspace adjoints below use the Hermitian structure, whereas products\nsuch as $\\theta\\cdot\\theta$ remain complex bilinear. Constants in the\nfollowing estimates may depend on the coefficients, the fixed ball,\nand $\\theta$, but not on sufficiently large $\\tau$.\n\n\\begin{proposition}[Supported Carleman estimate]\n\\label{prop:carleman}\nLet $B$ be a bounded ball. For $U\\in C_c^\\infty(B;\\C^4)$ and all\nsufficiently small $h>0$,\n\\begin{equation}\n\\label{eq:augmented-carleman}\n \\norm{E_\\tau^{-1}U}_{H_h^1}\n \\le Ch\\norm{E_\\tau^{-1}\\Lcal U}_{H_h^{-1}}.\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nPut $\\varphi(x)=-\\operatorname{Re}(\\theta\\cdot x)$. A nonzero complex\nnull vector has nonzero real part, so $\\varphi$ is a nonconstant linear\nlimiting Carleman weight for the Laplacian. For\n$\\varphi_\\varepsilon=\\varphi+h\\varphi^2/(2\\varepsilon)$,\n\\cite[Lemma~2.1]{SaloTzou2009}, with Sobolev index $s=-1$, gives\n\\begin{equation}\n\\label{eq:convexified-carleman}\n \\frac{h}{\\sqrt\\varepsilon}\\norm{v}_{H_h^1}\n \\le C\\norm{e^{\\varphi_\\varepsilon/h}h^2\\Delta\n                 e^{-\\varphi_\\varepsilon/h}v}_{H_h^{-1}},\n \\qquad v\\in C_c^\\infty(B),\n\\end{equation}\nfor $h\\ll\\varepsilon\\ll1$, with $C$ independent of sufficiently small\n$\\varepsilon$. Its statement has a gain of two derivatives and permits\nevery real $s$. The sign of the Laplacian is immaterial here.\n\nApply \\eqref{eq:convexified-carleman} componentwise. A first-order term\nin \\eqref{eq:laplace-principal-form} contributes\n\\[\n e^{\\varphi_\\varepsilon/h}h^2A_j\\partial_j\n       e^{-\\varphi_\\varepsilon/h}v\n =hA_j(h\\partial_jv)-hA_j(\\partial_j\\varphi_\\varepsilon)v.\n\\]\nIts $H_h^{-1}$ norm is bounded by $C_1h\\norm{v}_{H_h^1}$,\nuniformly when $h/\\varepsilon$ is small. The zeroth-order terms have\nthe same bound. Choose $\\varepsilon>0$ so small that these terms are\nabsorbed by the left side of \\eqref{eq:convexified-carleman}, and then\nchoose $h$ sufficiently small relative to this fixed $\\varepsilon$.\nWe obtain\n\\[\n \\norm{e^{\\varphi_\\varepsilon/h}U}_{H_h^1}\n \\le C_\\varepsilon h\n       \\norm{e^{\\varphi_\\varepsilon/h}\\Lcal U}_{H_h^{-1}}.\n\\]\n\nThe factor $e^{\\varphi^2/(2\\varepsilon)}$ is now fixed. It and its\ninverse, smoothly localized around $\\overline B$, are bounded\nmultipliers on $H_h^1$, uniformly for $0<h\\le1$, by the product rule.\nThey are bounded on $H_h^{-1}$ by duality. Removing this factor gives\nthe same estimate with $\\varphi$ in place of $\\varphi_\\varepsilon$.\nThe multiplier constants may depend on $\\varepsilon$; no subsequent\nabsorption involves them.\n\nFinally, write $\\beta=\\operatorname{Im}\\theta$.\nMultiplication by $e^{-i\\beta\\cdot x/h}$ shifts the semiclassical\nfrequency $h\\xi$ by the fixed vector $-\\beta$. The weights\n$1+\\abs{h\\xi}^2$ and $1+\\abs{h\\xi-\\beta}^2$ are comparable,\nso this multiplier and its inverse are uniformly bounded on\n$H_h^{\\pm1}$. Since\n$E_\\tau^{-1}=e^{\\varphi/h}e^{-i\\beta\\cdot x/h}$, this proves\n\\eqref{eq:augmented-carleman}.\n\\end{proof}\n\n\\begin{corollary}\n\\label{cor:physical-solvability}\nFor the same $B$ and $\\theta$, and all sufficiently large $\\tau$,\nevery $w\\in C_c^\\infty(B;\\C^3)$\nsatisfies\n\\begin{equation}\n\\label{eq:physical-test-estimate}\n \\norm{E_\\tau^{-1}w}_{L^2(B)}\n \\le C\\norm{E_\\tau^{-1}Lw}_{L^2(B)}.\n\\end{equation}\nMoreover, for every $f\\in L^2(B;\\C^3)$ there exists\n$z\\in L^2(B;\\C^3)$ satisfying, distributionally,\n\\begin{equation}\n\\label{eq:physical-solvability}\n E_\\tau^{-1}L(E_\\tau z)=f\\quad\\text{in }B,\n \\qquad \\norm{z}_{L^2(B)}\\le C\\norm{f}_{L^2(B)}.\n\\end{equation}\n\\end{corollary}\n\n\\begin{proof}\nApply Proposition~\\ref{prop:carleman} to $(w,\\diverg w)$.\nBy \\eqref{eq:augmented-identity}, its augmented forcing is formed\nfrom $Lw$ and $\\diverg(Lw)$. If $F=E_\\tau^{-1}Lw$, then\n\\[\n E_\\tau^{-1}\\diverg(Lw)=\\diverg F+\\tau\\theta\\cdot F.\n\\]\nThe Fourier definition of the norms gives\n$\\norm{\\partial_jF}_{H_h^{-1}}\\le h^{-1}\\norm{F}_{L^2}$.\nSmooth coefficient multipliers, localized near $\\overline B$, are\nuniformly bounded on $H_h^{-1}$. Hence\n\\[\n \\norm{E_\\tau^{-1}\\Lcal(w,\\diverg w)}_{H_h^{-1}}\n \\le Ch^{-1}\\norm{F}_{L^2}.\n\\]\nTaking the displacement component on the left side of\n\\eqref{eq:augmented-carleman} proves\n\\eqref{eq:physical-test-estimate}.\n\nLet $A_\\theta=E_\\tau^{-1}LE_\\tau$ on test functions in $B$.\nSubstituting $w=E_\\tau v$ in\n\\eqref{eq:physical-test-estimate} gives\n$\\norm{v}_{L^2}\\le C\\norm{A_\\theta v}_{L^2}$.\nFormal self-adjointness of $L$ gives\n\\[\n A_\\theta^*\n =e^{\\tau\\overline\\theta\\cdot x}L\n                      e^{-\\tau\\overline\\theta\\cdot x}\n =A_{-\\overline\\theta}.\n\\]\nThe same estimate for the null direction $-\\overline\\theta$ thus\nholds for $A_\\theta^*$. Use the $L^2$ inner product linear in its\nfirst argument. The functional\n\\[\n A_\\theta^*v\\longmapsto (v,f)_{L^2(B)},\n \\qquad v\\in C_c^\\infty(B;\\C^3),\n\\]\nis well-defined and has norm at most $C\\norm{f}_{L^2}$. Extend it by\nthe Hahn--Banach Theorem and represent it as $(\\,\\cdot\\,,z)_{L^2}$.\nThen $(A_\\theta^*v,z)=(v,f)$ for every test function $v$, which is\nthe distributional equation in \\eqref{eq:physical-solvability}.\nThe norm of the representing vector gives the asserted bound.\n\\end{proof}\n\n\\begin{lemma}[Scaled interior estimate]\n\\label{lem:scaled-interior}\nLet $B'\\Subset B$. There exists $h_{B'}>0$ such that, whenever\n$0<h=\\tau^{-1}<h_{B'}$ and $z,f\\in L^2(B;\\C^3)$ satisfy\n$E_\\tau^{-1}L(E_\\tau z)=f$ distributionally in $B$,\n\\begin{equation}\n\\label{eq:scaled-interior}\n \\sum_{j=0}^2 h^j\\norm{\\nabla^jz}_{L^2(B')}\n \\le C_{B'}\\bigl(\\norm{z}_{L^2(B)}\n                         +h^2\\norm{f}_{L^2(B)}\\bigr).\n\\end{equation}\nIf $f$ is smooth, then $z$ is smooth in $B$ for each fixed $\\tau$.\n\\end{lemma}\n\n\\begin{proof}\nThe positive principal coefficient form of $L$ is\n\\[\n m\\abs{\\xi}^2\\abs{v}^2+k\\abs{\\xi\\cdot v}^2,\n \\qquad \\xi\\in\\R^3,\\quad v\\in\\C^3.\n\\]\nThus its ellipticity is uniform on $\\overline B$. Conjugation leaves\nthe principal part unchanged and introduces coefficients of size\n$O(h^{-1})$ and $O(h^{-2})$ in orders one and zero. On a ball\n$B(x_0,2h)\\subset B$, set $x=x_0+hy$ and multiply the equation by\n$h^2$. The resulting operator on the fixed ball $B(0,2)$ has a\nuniformly strongly elliptic principal part, while its coefficients\nand their derivatives in $y$ are uniformly bounded. As\n$(x_0,h)$ ranges over $\\overline B'\\times[0,h_0]$, these operators\nform a compact smooth coefficient family with a common ellipticity\nbound. The local parametrix estimate persists under a small\ncoefficient perturbation, so a finite cover gives a uniform constant.\nInterior $H^2$ regularity for distributional $L^2$ solutions of smooth\nelliptic systems is given by\n\\cite[Theorem~15.1 and Remark~15.2]{Dyatlov2026}. The same parametrix\nargument with matrix symbols gives the localized estimate for systems\ncorresponding to the scalar Proposition~15.6 of that source. With the\nuniform constants just obtained, it yields\n\\[\n \\sum_{j=0}^2 h^j\\norm{\\nabla^jz}_{L^2(B(x_0,h))}\n \\le C\\bigl(\\norm{z}_{L^2(B(x_0,2h))}\n                      +h^2\\norm{f}_{L^2(B(x_0,2h))}\\bigr).\n\\]\nFor small $h$, cover $B'$ by such inner balls with uniformly bounded\noverlap of the doubled balls. Squaring and summing proves\n\\eqref{eq:scaled-interior}. Iterated interior elliptic regularity,\nwith $\\tau$ fixed, proves the final assertion.\n\\end{proof}\n"}, {"path": "preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/sections/boundary-reduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/sections/boundary-reduction.tex", "bytes": 3747, "sha256": "dcf42332efe94212ad4dffbddbf8cf5264020d79d3cd14da719f1511bc812528", "content": "\\section{Boundary reduction and physical transfer}\\label{sec:boundary-reduction}\n\nThroughout the proof, the two coefficient pairs satisfy the hypotheses\nof Theorem~\\ref{thm:main} and have equal displacement-to-traction maps.\n\n\\begin{lemma}[Common exterior extension]\\label{lem:common-extension}\nThe two pairs admit real smooth extensions to $\\R^3$ that satisfy\n\\eqref{eq:energy-positivity} everywhere, coincide on\n$\\R^3\\setminus\\Omega$, and equal a common constant admissible pair\noutside a ball.\n\\end{lemma}\n\n\\begin{proof}\nBy Theorem~1.2 of Tan and Liu \\cite{TanLiu2023}, equality of the elastic\nDirichlet-to-Neumann maps determines all boundary partial derivatives\nof both Lam\\'e moduli. Our inequalities imply\n$\\lambda+\\mu>\\mu/3>0$, so their positivity condition holds.\nTheir definition of the boundary operator is\n$\\lambda(\\diverg u)n+\\mu(\\nabla u+(\\nabla u)^T)n$, so its Euclidean\nspecialization agrees with \\eqref{eq:physical-dn}.\nConsequently the two pairs have identical jets at every point of\n$\\partial\\Omega$. Lin and Nakamura also give local boundary\nreconstruction, with curved boundaries discussed in Section~4 of\n\\cite{LinNakamura2017}.\n\nSmoothly extend the first pair to a neighborhood of\n$\\overline\\Omega$. By compactness and strict positivity, shrink the\nneighborhood so that \\eqref{eq:energy-positivity} still holds there.\nChoose a smooth cutoff equal to one on a smaller neighborhood of\n$\\overline\\Omega$ and supported in the first neighborhood. Interpolate\nwith any constant pair satisfying \\eqref{eq:energy-positivity}.\nThe admissible set in the $(\\lambda,\\mu)$ plane is convex, so this\nproduces a smooth admissible global extension of the first pair, constant\noutside a compact set.\n\nFor the second pair use its prescribed values in $\\Omega$ and the first\nextension on the complement. Equality of all jets makes this piecewise\ndefinition smooth across each boundary component. Positivity holds on\nboth sides and on the boundary. This proves the assertion, including\nwhen the complement of $\\Omega$ has bounded components.\n\\end{proof}\n\nFix these extensions and retain the notation $\\lambda_j,\\mu_j$ and\n$L_j=L_{\\lambda_j,\\mu_j}$ for them. Choose $r>0$ so that\n$\\overline\\Omega\\subset B_r$ and the extensions are constant outside\n$B_r$. Here $B_R$ is the open ball of radius $R$ centered at the origin.\n\n\\begin{proposition}[Transfer of physical solutions]\\label{prop:physical-transfer}\nLet $B$ be a ball containing $\\overline B_r$. Every smooth solution\n$u_1$ of $L_1u_1=0$ on $B$ has a smooth counterpart $u_2$ satisfying\n$L_2u_2=0$ on $B$ and\n\\[\n u_2=u_1\\quad\\text{on }B\\setminus\\overline\\Omega.\n\\]\nIn particular, $u_2-u_1$ and\n$\\diverg u_2-\\diverg u_1$ are smooth and supported in\n$\\overline\\Omega$.\n\\end{proposition}\n\n\\begin{proof}\nSolve the second Dirichlet problem in $\\Omega$ with trace\n$u_1|_{\\partial\\Omega}$, and use $u_1$ outside $\\overline\\Omega$.\nMatching traces give an $H^1_{\\mathrm{loc}}(B)$ field $u_2$.\nThe restrictions of the two interior solutions have identical boundary\ntractions because their Dirichlet values and boundary maps agree.\nFor a smooth test vector compactly supported in $B$, the weak boundary\nterm contributed by the interior therefore agrees with that for $u_1$.\nThe coefficients and displacements coincide on the exterior, where the\nsame test contributes the opposite interface term. Hence the glued\nfield satisfies $L_2u_2=0$ weakly on $B$.\n\nThe extended operator is smooth and strongly elliptic. Interior\nelliptic regularity, applied also across $\\partial\\Omega$, gives\n$u_2\\in C^\\infty(B;\\C^3)$. The difference is zero off\n$\\overline\\Omega$, as are all its derivatives there. Its support and\nthe support of its divergence are thus contained in\n$\\overline\\Omega\\Subset B_r$.\n\\end{proof}\n"}, {"path": "preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/sections/coefficient-recovery.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/sections/coefficient-recovery.tex", "bytes": 12002, "sha256": "443d34bfc0b3998bedb4ea1fdc714ac0e66c74c51b090905f68e0479f1033016", "content": "\\section{Recovery of the coefficients}\\label{sec:coefficient-recovery}\n\nThe holomorphic comparison from Proposition~\\ref{prop:transport-matching}\nis constrained by the geometry of its polarization bundle.  We first show\nthat this comparison is the identity, and then recover the coefficients from\nthe transport matrices.\n\n\\subsection{The bundle on the null conic}\n\nLet $H\\to\\mathcal C$ be the holomorphic bundle with fiber $H_\\theta$, and\nlet $\\mathcal N\\subset H$ be the null line subbundle with fiber\n$\\C\\theta$.  Thus $\\Ecal=H\\oplus\\mathcal O$, where $\\mathcal O$ denotes\nthe trivial holomorphic line bundle on $\\mathcal C$.\nOn $\\mathbb P^1(\\C)$, let $\\mathcal O(-1)$ denote the tautological line\nbundle and $\\mathcal O(-2)$ its tensor square.\n\n\\begin{lemma}\\label{lem:conic-bundle}\nThe bundle $H$ has no nonzero global holomorphic sections, and\n$H/\\mathcal N$ is holomorphically trivial.  More precisely, under the\nidentification $\\mathcal C\\simeq\\mathbb P^1(\\C)$,\n\\[\n H\\simeq\\mathcal O(-1)^{\\oplus2},\\qquad\n \\mathcal N\\simeq\\mathcal O(-2),\n\\]\nand their quotient sequence is\n\\begin{equation}\\label{eq:conic-quotient}\n 0\\longrightarrow\\mathcal O(-2)\n \\xrightarrow{\\ (z_0,z_1)^T\\ }\n \\mathcal O(-1)^{\\oplus2}\n \\xrightarrow{\\ (-z_1,z_0)\\ }\n \\mathcal O\\longrightarrow0.\n\\end{equation}\nHere $[z_0:z_1]$ are homogeneous coordinates on $\\mathbb P^1(\\C)$.\n\\end{lemma}\n\n\\begin{proof}\nA homogeneous parametrization of the conic is\n\\begin{equation}\\label{eq:conic-parametrization}\n \\theta(z_0,z_1)\n   =(z_0^2-z_1^2,\\ i(z_0^2+z_1^2),\\ 2z_0z_1).\n\\end{equation}\nIt identifies $\\mathbb P^1(\\C)$ with $\\mathcal C$: on the first coordinate\nchart its inverse is\n$z_1/z_0=\\theta_3/(\\theta_1-i\\theta_2)$, and on the second it is\n$z_0/z_1=\\theta_3/(-\\theta_1-i\\theta_2)$.  The denominators cannot both\nvanish at a point of $\\mathcal C$.\n\nConsider the two degree-one columns\n\\[\n h_1=(z_0,iz_0,z_1),\\qquad\n h_2=(-z_1,iz_1,z_0).\n\\]\nDirect calculation, with the complex bilinear product, gives\n\\begin{equation}\\label{eq:conic-frame}\n \\theta\\cdot h_1=\\theta\\cdot h_2=0,\\qquad\n h_1\\times h_2=i\\theta,\\qquad\n \\theta=z_0h_1+z_1h_2.\n\\end{equation}\nTo interpret these homogeneous formulas as bundle maps, write\n$\\mathcal O(-1)_{[z]}=\\C z$ for $z=(z_0,z_1)$.\nThe assignment\n\\[\n (\\xi_1z,\\xi_2z)\\longmapsto \\xi_1h_1(z)+\\xi_2h_2(z)\n\\]\nis independent of the representative: replacing $z$ by $cz$ replaces\n$\\xi_j$ by $c^{-1}\\xi_j$ and $h_j(z)$ by $ch_j(z)$.\nIts local polynomial formulas are holomorphic. Since the images lie in\n$H$ and are linearly independent at every point, it is an isomorphism\n$\\mathcal O(-1)^{\\oplus2}\\to H$. Likewise,\n$\\xi z^{\\otimes2}\\mapsto\\xi\\theta(z)$ is well defined because\n$\\theta(cz)=c^2\\theta(z)$, and identifies $\\mathcal O(-2)$ with\n$\\mathcal N$. The final identity in \\eqref{eq:conic-frame} gives the\nfirst map in \\eqref{eq:conic-quotient}.\n\nThe expression $-z_1\\xi_1+z_0\\xi_2$ is also unchanged under the same\nrepresentative scaling, so its local polynomial formulas define a\nholomorphic map to the trivial line bundle $\\mathcal O$. The row\n$(-z_1,z_0)$ is everywhere surjective, and its kernel is the image of\n$(z_0,z_1)^T$.\nThis proves the quotient assertion, including at both coordinate-chart\nendpoints.  It is also consistent with the induced bilinear form: for\n$p=\\xi_1 h_1+\\xi_2 h_2$ in a local frame,\n\\[\n p\\cdot p=(-z_1\\xi_1+z_0\\xi_2)^2.\n\\]\n\nFinally, any holomorphic section of $H$ has holomorphic ambient components\non the compact conic, so those components are constant.  Such a constant\nvector is orthogonal to every null vector.  The vectors\n$\\theta(1,0)$, $\\theta(0,1)$ and $\\theta(1,1)$ span $\\C^3$, so the\nsection is zero.\n\\end{proof}\n\n\\begin{remark}\nThe exact sequence \\eqref{eq:conic-quotient} does not split holomorphically:\na splitting would give a nonzero global section of $H$.  On the other\nhand, the displayed splitting of $H$ itself gives nonscalar holomorphic\nendomorphisms of $H$.  The next argument uses the transport equation to\nrestrict those endomorphisms.\n\\end{remark}\n\n\\begin{proposition}\\label{prop:comparison-rigidity}\nThe comparison endomorphism of\nProposition~\\ref{prop:transport-matching} is $G=\\Id$.  Consequently, for\nevery $x\\in\\R^3$ and every nonzero null vector $\\theta$,\n\\begin{equation}\\label{eq:equal-restricted-transports}\n M_{1,\\theta}=M_{2,\\theta}\\quad\\text{on }\\Ecal_\\theta.\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nFix $x$.  Relative to $\\Ecal=H\\oplus\\mathcal O$, the upper-right block\nof the holomorphic endomorphism $G(x,\\cdot)$ is a global holomorphic\nsection of $H$, and therefore vanishes by Lemma~\\ref{lem:conic-bundle}.\nIts lower-right block is a holomorphic scalar function on the compact\nconic, and hence is independent of direction.  We may thus write\n\\[\n G=\\begin{pmatrix}F&0\\\\ A&f\\end{pmatrix},\\qquad f=f(x).\n\\]\nAll these blocks are smooth in $x$.  Taking the upper-right block of\n\\eqref{eq:comparison-transport}, and using\n\\eqref{eq:transport-matrix}, gives\n\\[\n 0=\\theta f-F\\theta.\n\\]\nIt follows that $F-f\\Id_H$ vanishes on $\\mathcal N$.  This holomorphic\nbundle morphism consequently factors through $H/\\mathcal N$.  By\nLemma~\\ref{lem:conic-bundle}, its factor is a morphism\n$\\mathcal O\\to H$, which must vanish.  Thus $F=f\\Id_H$.\n\nThe upper-left block of \\eqref{eq:comparison-transport} now reads\n\\begin{equation}\\label{eq:comparison-rank}\n (D_\\theta f)\\Id_{H_\\theta}=\\theta A.\n\\end{equation}\nThe right side has rank at most one, while $H_\\theta$ has dimension two.\nTherefore $D_\\theta f=0$.  Equation~\\eqref{eq:comparison-rank} then gives\n$A=0$, since $\\theta\\ne0$.  As null vectors span $\\C^3$, we obtain\n$\\nabla f=0$.  The exterior identity value of $G$ fixes $f=1$, so $G=\\Id$.\nSubstitution in \\eqref{eq:comparison-transport} proves\n\\eqref{eq:equal-restricted-transports}.\n\\end{proof}\n\n\\subsection{A finite-type uniqueness lemma}\n\nWe will show that equality of the restricted bottom-left transport blocks\nleaves at most a scalar-identity ambiguity, then use the following\nelementary lemma to remove it. Indices\nin the lemma range from $1$ to $n$; all sums are written explicitly.\n\n\\begin{lemma}\\label{lem:finite-type}\nLet $U\\subset\\R^n$ be connected and open, with $n\\ge2$, and let\n$\\eta,q$ and $B_{ij}^{\\ a}$ be smooth, real- or complex-valued functions\non $U$ satisfying\n\\begin{equation}\\label{eq:finite-type-hypothesis}\n \\partial_i\\partial_j\\eta\n    =\\sum_{a=1}^n B_{ij}^{\\ a}\\partial_a\\eta+\\delta_{ij}q.\n\\end{equation}\nThen $(\\nabla\\eta,q)$ satisfies a homogeneous first-order system with\nsmooth coefficients.  In particular, if $\\eta$ vanishes on a nonempty\nopen subset of $U$, then $\\eta=q=0$ on $U$.\n\\end{lemma}\n\n\\begin{proof}\nPut $v_a=\\partial_a\\eta$.  For each $s$, choose one index $i=i(s)$ with\n$i\\ne s$, without summing over $i$.  Equation~\\eqref{eq:finite-type-hypothesis}\nand commutation of third derivatives give\n\\begin{align*}\n \\partial_s q\n &=\\partial_i\\left(\\sum_{a=1}^n B_{si}^{\\ a}v_a\\right)\n       -\\partial_s\\left(\\sum_{a=1}^n B_{ii}^{\\ a}v_a\\right)\\\\\n &=\\sum_{a=1}^n\n       (\\partial_i B_{si}^{\\ a}-\\partial_s B_{ii}^{\\ a})v_a\n    +\\sum_{a=1}^n\n       (B_{si}^{\\ a}\\partial_i v_a-B_{ii}^{\\ a}\\partial_s v_a).\n\\end{align*}\nThere is no derivative of $q$ on the right because the off-diagonal\nentry $\\partial_s\\partial_i\\eta$ in\n\\eqref{eq:finite-type-hypothesis} has $\\delta_{si}=0$.\nSubstituting that equation for the derivatives of $v$ yields\n\\begin{equation}\\label{eq:finite-type-system}\n \\begin{aligned}\n  \\partial_s v_a&=\\sum_{b=1}^n B_{as}^{\\ b}v_b+\\delta_{as}q,\\\\\n  \\partial_s q&=\\sum_{b=1}^n C_s^{\\ b}v_b+d_s q,\n \\end{aligned}\n\\end{equation}\nwhere the smooth coefficients are explicitly\n\\begin{equation}\\label{eq:finite-type-coefficients}\n \\begin{aligned}\n C_s^{\\ b}\n   &=\\partial_i B_{si}^{\\ b}-\\partial_s B_{ii}^{\\ b}\n     +\\sum_{a=1}^n\n       \\bigl(B_{si}^{\\ a}B_{ai}^{\\ b}-B_{ii}^{\\ a}B_{as}^{\\ b}\\bigr),\\\\\n d_s&=B_{si}^{\\ i}-B_{ii}^{\\ s}.\n \\end{aligned}\n\\end{equation}\nThis is the claimed homogeneous system for $v$ and $q$.\n\nIf $\\eta=0$ on an open subset, then $v=0$ there, and a diagonal entry of\n\\eqref{eq:finite-type-hypothesis} also gives $q=0$ there.  Along any\npiecewise smooth path in $U$, Equation~\\eqref{eq:finite-type-system}\nrestricts to a homogeneous linear ordinary differential equation for\n$(v,q)$.  Uniqueness with zero initial data propagates its vanishing along\nthe path.  An open connected subset of $\\R^n$ is path connected, so\n$v=q=0$ on $U$.  Finally, $\\eta$ is constant on $U$, and its value on the\ninitial open subset is zero.\n\\end{proof}\n\n\\subsection{Completion of the coefficient recovery}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\nSuppose that the two displacement-to-traction maps agree, and use the\ncommon exterior extensions furnished by Lemma~\\ref{lem:common-extension}.\nPropositions~\\ref{prop:transport-matching} and\n\\ref{prop:comparison-rigidity} give equality of the transport matrices\non $\\Ecal_\\theta$ for every nonzero null $\\theta$.  In particular,\n\\eqref{eq:transport-coefficients} gives\n\\[\n D_\\theta\\log(k_1/\\ell_1)\n   =D_\\theta\\log(k_2/\\ell_2).\n\\]\nSince null vectors span $\\C^3$, the difference of these logarithms has\nzero gradient.  Its exterior value is zero.  Hence $k_1/\\ell_1=k_2/\\ell_2$\non $\\R^3$, and the functions\n\\begin{equation}\\label{eq:common-ratios}\n c=\\frac{m_1}{\\ell_1}=\\frac{m_2}{\\ell_2}>0,\n \\qquad b_* =\\log\\frac{k_1}{m_1}=\\log\\frac{k_2}{m_2}\n\\end{equation}\nare well defined and smooth.  Indeed, $m_j/\\ell_j=1-k_j/\\ell_j$, and\n$k_j/m_j=(k_j/\\ell_j)/(1-k_j/\\ell_j)$.\n\nEquality of the bottom-left blocks on $\\Ecal_\\theta$ means\n\\[\n (Q_{1,\\theta}-Q_{2,\\theta})\\cdot p=0\n \\qquad(p\\in H_\\theta).\n\\]\nThe annihilator of $H_\\theta=\\theta^\\perp$ for the nondegenerate\nambient bilinear form is $\\C\\theta$.  Since each $Q_{j,\\theta}$ is\nlinear in $\\theta$, there is a smooth ambient matrix $K(x)$ such that\n\\begin{equation}\\label{eq:null-line-preservation}\n Q_{1,\\theta}-Q_{2,\\theta}=K(x)\\theta\\in\\C\\theta\n \\qquad(\\theta\\cdot\\theta=0).\n\\end{equation}\nFor fixed $x$, the proportionality factor in\n\\eqref{eq:null-line-preservation} is a holomorphic function on $\\mathcal C$.\nTo see this at every point, choose a local holomorphic representative\n$\\theta$ and a component $\\theta_a\\ne0$; the factor is\n$(K\\theta)_a/\\theta_a$.  These expressions agree on overlapping charts\nand are unchanged by rescaling the representative.  Compactness of\n$\\mathcal C$ makes the factor constant in direction.  The spanning\nproperty of null vectors then gives\n\\begin{equation}\\label{eq:scalar-ambient-difference}\n K(x)=\\kappa(x)\\Id,\\qquad \\kappa(x)=\\tfrac13\\tr K(x).\n\\end{equation}\nIn particular, $\\kappa$ is smooth.\n\nSet\n\\[\n \\eta=\\log m_1-\\log m_2,\\qquad v=\\nabla\\eta=g_1-g_2,\n \\qquad \\beta=\\nabla b_*,\\qquad \\alpha=\\frac{c-1/2}{c},\n\\]\nwhere $g_j=\\nabla\\log m_j$.  Because\n$\\nabla\\log k_j=g_j+\\beta$, the expression for $Q$ in\n\\eqref{eq:transport-coefficients} becomes\n\\[\n 2Q_{j,\\theta}\n   =\\bigl((c-1/2)g_jg_j^T+c g_j\\beta^T\n                 -c\\nabla^2\\log m_j\\bigr)\\theta.\n\\]\nSubtract these identities, use\n$g_1g_1^T-g_2g_2^T=g_1v^T+vg_2^T$, and apply\n\\eqref{eq:scalar-ambient-difference}.  Defining the smooth scalar\n$q=-2\\kappa/c$, we obtain\n\\[\n \\nabla^2\\eta\n    =\\alpha(g_1v^T+vg_2^T)+v\\beta^T+q\\Id.\n\\]\nEquivalently,\n\\begin{equation}\\label{eq:coefficient-hessian}\n \\begin{aligned}\n \\partial_i\\partial_j\\eta\n   &=\\sum_{a=1}^3 B_{ij}^{\\ a}\\partial_a\\eta+\\delta_{ij}q,\\\\\n B_{ij}^{\\ a}\n   &=\\alpha(g_1)_i\\delta_{ja}\n        +\\bigl(\\alpha(g_2)_j+\\beta_j\\bigr)\\delta_{ia}.\n \\end{aligned}\n\\end{equation}\nThe coefficients $B_{ij}^{\\ a}$ are smooth on $\\R^3$.  They are fixed\nfunctions determined by the two coefficient pairs under comparison.\nThe common exterior extension gives $\\eta=0$ on a nonempty exterior open\nset.  Lemma~\\ref{lem:finite-type}, applied to\n\\eqref{eq:coefficient-hessian}, yields $\\eta=0$ throughout $\\R^3$.\nThus $m_1=m_2$.  Equation~\\eqref{eq:common-ratios} then gives\n$k_1=m_1e^{b_*}=m_2e^{b_*}=k_2$, and therefore\n\\[\n \\mu_1=\\mu_2,\\qquad\n \\lambda_1=k_1-m_1=k_2-m_2=\\lambda_2.\n\\]\nRestricting to $\\Omega$ proves the theorem.\n\\end{proof}\n"}, {"path": "preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/sections/introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/sections/introduction.tex", "bytes": 10882, "sha256": "1472cc03ac1ed0546c0bacec93e0c7820bf15d5e1928af2909515c30f8156687", "content": "\\section{Introduction}\\label{sec:introduction}\n\nLet $\\Omega\\subset\\R^3$ be a bounded connected domain with smooth boundary.\nFor real functions $\\lambda,\\mu\\in C^\\infty(\\overline\\Omega)$ satisfying\n\\begin{equation}\\label{eq:energy-positivity}\n  \\mu>0,\\qquad 3\\lambda+2\\mu>0\n  \\quad\\text{on }\\overline\\Omega,\n\\end{equation}\ndefine the strain, stress, and static elasticity operator by\n\\begin{equation}\\label{eq:physical-operator}\n e(u)=\\frac{\\nabla u+(\\nabla u)^T}{2},\\qquad\n \\sigma_{\\lambda,\\mu}(u)=\\lambda(\\diverg u)I+2\\mu e(u),\\qquad\n L_{\\lambda,\\mu}u=\\diverg\\sigma_{\\lambda,\\mu}(u).\n\\end{equation}\nHere $\\mu$ is the shear modulus and $\\lambda+2\\mu/3$ is the bulk modulus.\nThe inequalities in \\eqref{eq:energy-positivity} give uniformly positive\nelastic energy. For each $f\\in H^{1/2}(\\partial\\Omega;\\R^3)$ there is a\nunique $u_f\\in H^1(\\Omega;\\R^3)$ with $L_{\\lambda,\\mu}u_f=0$ and trace $f$.\nThe displacement-to-traction map is defined weakly by\n\\begin{equation}\\label{eq:physical-dn}\n \\langle\\Lambda_{\\lambda,\\mu}f,g\\rangle\n =\\int_\\Omega\\left[\n \\lambda(\\diverg u_f)(\\diverg v_g)+2\\mu e(u_f):e(v_g)\n \\right]\\dd x,\n\\end{equation}\nwhere $v_g\\in H^1(\\Omega;\\R^3)$ has trace $g$, and $A:B=\\tr(A^TB)$.\nThe weak equation makes this expression independent of the extension.\nFor smooth boundary data it is the traction\n$\\sigma_{\\lambda,\\mu}(u_f)n$, with $n$ the outward normal.\n\n\\begin{theorem}\\label{thm:main}\nLet $\\Omega\\subset\\R^3$ be any bounded connected domain with\n$C^\\infty$ boundary. For $j=1,2$, let\n$\\lambda_j,\\mu_j\\in C^\\infty(\\overline\\Omega;\\R)$ satisfy\n$\\mu_j>0$ and $3\\lambda_j+2\\mu_j>0$ on $\\overline\\Omega$.\nIf\n\\[\n \\Lambda_{\\lambda_1,\\mu_1}\n =\\Lambda_{\\lambda_2,\\mu_2}\n \\colon H^{1/2}(\\partial\\Omega;\\R^3)\n       \\longrightarrow H^{-1/2}(\\partial\\Omega;\\R^3),\n\\]\nthen $\\lambda_1=\\lambda_2$ and $\\mu_1=\\mu_2$ throughout $\\Omega$.\n\\end{theorem}\n\nThus Theorem~\\ref{thm:main} gives a positive resolution of the smooth\nthree-dimensional isotropic elastic Calder\\'on uniqueness problem under\n\\eqref{eq:energy-positivity}. It uses the full zero-frequency boundary\noperator in fixed Euclidean coordinates. The coefficients need not be\nanalytic or close to constants, and their agreement near the boundary is\nnot an additional hypothesis.\n\n\\paragraph{History and significance.}\nThe inverse problem asks whether static boundary measurements determine\nboth spatially varying elastic moduli.\nThe scalar conductivity problem posed by Calder\\'on\n\\cite{Calderon1980} and the complex geometric optics uniqueness theorem\nof Sylvester and Uhlmann \\cite{SylvesterUhlmann1987} are important\nantecedents, but elasticity couples two moduli through vector\ndisplacements. Nakamura and Uhlmann recovered the elastic boundary\nTaylor series \\cite{NakamuraUhlmann1995Boundary}. Their original global\nclaim \\cite{NU1994} was corrected in \\cite{NU2003}. The erratum identifies\ntwo defects: the prescribed initial-value problem for a planar matrix\ntransport need not be solvable, and substitution of the corrected complex\ngeometric optics solutions yields a pseudodifferential equation where a\npartial differential equation had been asserted. Its corrected theorem\nproves uniqueness when both $\\|\\nabla\\mu_j\\|_{C^m}$ are sufficiently\nsmall.\n\nEskin and Ralston developed higher-order matrix complex geometric optics\nexpansions using invertible planar transport frames, together with\nrestricted elasticity uniqueness results\n\\cite{EskinRalston2002Elasticity,EskinRalston2004}. A direct inspection of\nthe differential map from their auxiliary vector--scalar fields to\nphysical displacement \\cite[Section~3]{EskinRalston2004} shows that it is\nnoninjective. The reduction alone therefore does not transfer equality\nof the physical boundary maps to equality of unrestricted auxiliary\nCauchy data. The proof here transfers exact physical solutions while\nretaining their actual divergence.\n\nIn two dimensions, Imanuvilov and Yamamoto proved global uniqueness for\nsmooth coefficients satisfying $\\mu>0$ and $\\lambda+\\mu>0$, without a\nsmallness assumption \\cite{ImanuvilovYamamoto2015Elasticity}.\nIn three dimensions, Lin and Nakamura gave local boundary reconstruction\nat finite regularity \\cite{LinNakamura2017}; Tan and Liu obtained boundary\njets on smooth Riemannian manifolds and global recovery under their\nanalytic hypotheses\n\\cite{TanLiu2023}. A recent preprint of Chen, Jiang, Liu, and Tao treats\nstructured shear moduli under axisymmetry, separation, or directional\nquasianalyticity in the specified geometries\n\\cite{ChenJiangLiuTao2026Elasticity}.\nTheorem~\\ref{thm:main} treats the full smooth class under\n\\eqref{eq:energy-positivity}.\n\n\\paragraph{Conventions.}\nAll differential identities are complexified by linearity. Dot products\nof complex vectors, including the null condition and orthogonality, use\nthe complex bilinear extension of the Euclidean product. Sobolev norms\nand Hilbert-space adjoints use the usual Hermitian structure.\nEquality of the real boundary maps extends complex linearly to the\ncomplex boundary data used in the proof.\n\n\\paragraph{Proof strategy.}\nThe proof converts equality of the boundary maps into an exact exterior\ncomparison of physical solutions. Boundary determination first gives\nmatching boundary jets, from which we construct common smooth extensions\nof the coefficients outside $\\Omega$.\nGiven a solution in the first medium, equality of displacement and\ntraction then lets us glue a solution in the second medium to it across\n$\\partial\\Omega$. The two solutions, and their actual divergences,\ncoincide in the exterior. Section~\\ref{sec:boundary-reduction} establishes\nthis transfer.\n\nFix a nonzero complex null vector $\\theta$, so\n$\\theta\\cdot\\theta=0$, and write $D_\\theta=\\theta\\cdot\\nabla$.\nThe real and imaginary parts of $\\theta$ span a plane on which\n$D_\\theta$ is a nonzero multiple of a Cauchy--Riemann operator; the\nremaining real coordinate is transverse to these planes.\nFor solutions with exponential phase $e^{\\tau\\theta\\cdot x}$,\nthe leading displacement--divergence pair $(a,b)$ has\n$a\\in H_\\theta=\\{a\\in\\C^3:\\theta\\cdot a=0\\}$ and $b\\in\\C$.\nThe resulting transport space\n$\\Ecal_\\theta=H_\\theta\\oplus\\C$ has dimension three.\nThe displacement--divergence reduction to a system with Laplacian\nprincipal part is classical in Lam\\'e unique continuation\n\\cite{Weck1969,ImanuvilovYamamoto2004Carleman}. The formulation here uses\nan independent vector--scalar pair $(u,d)$, retains that principal part\nafter normalization, and satisfies a compatibility identity for the\ndefect $\\diverg u-d$.\nIts supported Carleman estimate, together with a compatible high-order\ntransport recursion and a correction using the physical elasticity\noperator, realizes a full local frame in $\\Ecal_\\theta$ by exact\ndisplacements. The construction controls both the leading pair and its\nweighted first derivatives. Sections~\\ref{sec:analytic-estimates}\nand~\\ref{sec:physical-amplitudes} prove these facts.\n\nThe leading equations have a useful normalization. For each coefficient\npair, set\n\\[\n p=\\sqrt\\mu\\,a,\\qquad\n s=-\\frac{\\lambda+\\mu}{2\\sqrt\\mu}\\,b\n       -\\frac12\\nabla\\log\\mu\\cdot p.\n\\]\nThis is an invertible change of variables on $\\Ecal_\\theta$, since\n$\\mu>0$ and $\\lambda+\\mu>0$. In the $j$th medium, the normalized\ntransport takes the form\n\\[\n D_\\theta\\binom{p}{s}\n   =M_{j,\\theta}\\binom{p}{s},\\qquad\n M_{j,\\theta}=\n \\begin{pmatrix}0&\\theta\\\\Q_{j,\\theta}\\cdot&T_{j,\\theta}\\end{pmatrix}.\n\\]\nHere $Q_{j,\\theta}\\cdot$ is the covector\n$p\\mapsto Q_{j,\\theta}\\cdot p$. The lower coefficients are smooth in $x$\nand linear in $\\theta$;\nLemma~\\ref{lem:transport-normal-form} gives their formulas.\nThe upper equation $D_\\theta p=\\theta s$ is the same for both media.\n\nTransport comparison followed by holomorphic variation of the complex\ndirection has a close precedent in Ceki\\'c's work on connection Laplacians\n\\cite{Cekic2025}, whose parameter argument follows Eskin's analysis of\nYang--Mills potentials \\cite{Eskin2001}. Ceki\\'c treats full\nconnection-system boundary data on a trivial vector bundle. The comparison\nneeded here must be obtained from physical elasticity data on the\nconstrained fibers $\\Ecal_\\theta$.\n\nOn a cylinder given by a planar disk times a transverse interval, let\n$Y_1:\\C^3\\to\\Ecal_\\theta$ be the matrix of normalized columns of one\nlocal frame realized in the first medium. Transfer its exact physical\nsolutions to the second\nmedium. The two augmented operators have the same principal part, so the\nforcing for the difference of the physical pairs has only first order.\nThe derivative bound and the supported Carleman estimate make the\ntransferred leading pairs bounded in $L^2$. Weak limits supply a matrix\n$Y_2:\\C^3\\to\\Ecal_\\theta$ of three second-medium transport columns.\nThe two normalizations agree in\nthe exterior because the coefficients do, so $Y_2=Y_1$ there.\nThe local comparison $G=Y_2Y_1^{-1}$ is therefore an endomorphism of\n$\\Ecal_\\theta$ equal to the identity on the part of that cylinder\noutside $\\overline\\Omega$.\nOnly the original columns $Y_1$ need to form an invertible frame.\n\nThe initial comparison is obtained in a weak sense on transverse\ncylinders. Section~\\ref{sec:transport-matching} proves uniqueness for\nsupported planar transport equations and an estimate on fixed supported\nSobolev spaces. These results assemble the local comparisons into a\nunique smooth field $G(x,[\\theta])\\in\\operatorname{End}(\\Ecal_\\theta)$\non $\\R^3\\times\\mathcal C$, where\n$\\mathcal C=\\{[\\theta]\\in\\C\\mathrm P^2:\\theta\\cdot\\theta=0\\}$ is the\nprojective null conic. For a fixed ball $\\mathcal B\\subset\\R^3$\nindependent of $[\\theta]$, it satisfies\n\\[\n D_\\theta G=M_{2,\\theta}G-GM_{1,\\theta},\n \\qquad G(x,[\\theta])=\\Id\\quad(x\\notin\\mathcal B).\n\\]\nDifferentiating this equation in a conjugate conic coordinate while\nholding $x$ fixed gives a supported homogeneous transport equation.\nIts uniqueness makes $G(x,\\cdot)$ holomorphic.\n\nFinally, the planes $H_\\theta$ form a holomorphic bundle $H$ on\n$\\mathcal C$. It has no nonzero global holomorphic sections, while its\nquotient by the null line subbundle with fiber $\\C\\theta$ is trivial.\nThese facts and the universal upper transport equation force the\ndisplacement block of $G$ to be\nscalar. The remaining upper block equation uses $\\dim H_\\theta=2$ to\nmake that scalar constant in $x$ and remove the remaining off-diagonal\nblock; the exterior value then gives $G=\\Id$.\nEquality of the transports first determines\n$(\\lambda+\\mu)/(\\lambda+2\\mu)$. The remaining equations for\n$\\eta=\\log\\mu_1-\\log\\mu_2$ have the form\n$\\nabla^2\\eta=B(x)\\nabla\\eta+q(x)I$, where\n$B(x):\\R^3\\to\\R^{3\\times3}$ and the scalar $q(x)$ are smooth.\nOne differentiation closes a homogeneous first-order system for\n$(\\nabla\\eta,q)$, whose zero exterior values propagate by uniqueness\nfor ordinary differential equations along paths.\nSection~\\ref{sec:coefficient-recovery} gives this rigidity and recovers\nboth coefficients.\n"}, {"path": "preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/sections/physical-amplitudes.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/sections/physical-amplitudes.tex", "bytes": 16197, "sha256": "325b113beab400718495b72f8f00f9d1525b82384e6dcf1cf6cd8a4e5a63749a", "content": "\\section{Physical solutions realizing local transport frames}\n\\label{sec:physical-amplitudes}\n\nWe now construct exact elasticity solutions with controlled leading\nbehavior for both the displacement and its actual divergence. We begin\nby identifying the equations obeyed by these leading terms.\nFix the null vector $\\theta$ from \\eqref{eq:phase-notation}, and\nwrite $D=D_\\theta$ when convenient. Since\n$\\theta\\cdot\\theta=0$, conjugating the operators in\n\\eqref{eq:augmented-operators} gives\n\\[\n E_\\tau^{-1}R(E_\\tau a,E_\\tau b)=R(a,b)+\\tau R^\\theta(a,b),\n \\qquad\n E_\\tau^{-1}S(E_\\tau a,E_\\tau b)=S(a,b)+\\tau S^\\theta(a,b),\n\\]\nwhere\n\\begin{equation}\n\\label{eq:leading-operators}\n\\begin{aligned}\n R^\\theta(a,b)\n   &=2mDa+(Dm)a+\\theta(kb+\\nabla m\\cdot a),\\\\\n S^\\theta(a,b)\n   &=2\\ell Db+2(Dk)b+4\\nabla m\\cdot Da\n                       +2(D\\nabla m)\\cdot a.\n\\end{aligned}\n\\end{equation}\nFor smooth vector amplitudes $a_0,a_1$, the first two terms of a trial\ndisplacement give\n\\[\n E_\\tau^{-1}\\diverg\\bigl(E_\\tau(a_0+\\tau^{-1}a_1)\\bigr)\n =\\tau\\theta\\cdot a_0+\n   \\bigl(\\diverg a_0+\\theta\\cdot a_1\\bigr)\n   +\\tau^{-1}\\diverg a_1.\n\\]\nAn $O(1)$ leading divergence therefore requires $\\theta\\cdot a_0=0$,\nwhile its scalar term also contains $\\theta\\cdot a_1$ and need not equal\n$\\diverg a_0$. We retain that scalar as the component $b$ of the leading\npair.\nDefine the fixed complex vector spaces\n\\begin{equation}\n\\label{eq:constrained-fibers}\n H_\\theta=\\{a\\in\\C^3:\\theta\\cdot a=0\\},\n \\qquad \\Ecal_\\theta=H_\\theta\\oplus\\C.\n\\end{equation}\nThe transport equation on $\\Ecal_\\theta$ is\n\\begin{equation}\n\\label{eq:constrained-transport}\n R^\\theta(a,b)=0,\\qquad S^\\theta(a,b)=0,\n \\qquad (a,b)\\in\\Ecal_\\theta.\n\\end{equation}\nIndeed, putting $P^\\theta=2mD+(Dm)$, we have\n\\begin{equation}\n\\label{eq:normal-transport-component}\n \\theta\\cdot R^\\theta(a,b)=P^\\theta(\\theta\\cdot a).\n\\end{equation}\nThus $R^\\theta$ takes values in $H_\\theta$ when $a$ does. On\n$\\Ecal_\\theta$, the coefficient multiplying $D(a,b)$ in the two\ntransport equations is\n\\[\n \\begin{pmatrix}\n  2m\\Id_{H_\\theta}&0\\\\\n  4\\nabla m\\cdot&2\\ell\n \\end{pmatrix}.\n\\]\nIt is invertible, so \\eqref{eq:constrained-transport} is a square\nrank-three system with principal part $D$.\n\nFor clarity, write $\\theta=\\alpha+i\\beta$ with\n$\\alpha,\\beta\\in\\R^3$. The null condition says\n$\\abs\\alpha=\\abs\\beta=\\rho>0$ and $\\alpha\\cdot\\beta=0$.\nLet $(y_1,y_2,t)$ be positively oriented orthonormal coordinates in\nthe directions $\\alpha/\\rho$, $\\beta/\\rho$, and their cross product.\nThen, for $z=y_1+iy_2$,\n\\[\n D=\\rho(\\partial_{y_1}+i\\partial_{y_2})\n    =2\\rho\\partial_{\\overline z}.\n\\]\nWe denote the planar disk $\\{y\\in\\R^2:\\abs y<R\\}$ by $D_R^2$.\n\nFor each chosen transverse coordinate $t_0$, we will select a smooth\nframe of \\eqref{eq:constrained-transport} for $t$ near $t_0$ and then\nrealize its three columns by physical solutions.\nEarlier systems complex geometric optics constructions also use\ninvertible planar transport frames \\cite[Section~1]{EskinRalston2004}.\nThe next lemma records the local parameter dependence and inhomogeneous\nsolvability with transverse support needed here.\n\n\\begin{lemma}[Planar frames and inhomogeneous transports]\n\\label{lem:planar-frames}\nLet $I\\subset\\R$ be an open interval and let\n$A\\in C^\\infty(D_R^2\\times I;\\C^{n\\times n})$.\nFor each fixed $t\\in I$, the equation\n\\begin{equation}\n\\label{eq:planar-frame-equation}\n \\partial_{\\overline z}V=A(z,t)V\n\\end{equation}\nhas a smooth invertible matrix solution on $D_R^2$.\nFor every $R'<R$ and $t_0\\in I$, there are an interval\n$I_0\\Subset I$ containing $t_0$ and a smooth invertible solution\n$V$ on $D_{R'}^2\\times I_0$.\n\nGiven this $V$, a radius $R''<R'$, and\n$F\\in C^\\infty(D_{R'}^2\\times I_0;\\C^n)$, the equation\n\\begin{equation}\n\\label{eq:planar-inhomogeneous}\n \\partial_{\\overline z}v=Av+F\n\\end{equation}\nhas a smooth solution on $D_{R''}^2\\times I_0$. If\n$F(\\,\\cdot\\,,t)=0$ for $t\\notin K$, where $K\\Subset I_0$ is\ncompact, the solution can be chosen with the same transverse\nsupport property. These assertions also hold with $D$ in place of\n$\\partial_{\\overline z}$, and for systems obtained by multiplying\n$D$ by an invertible smooth matrix.\n\\end{lemma}\n\n\\begin{proof}\nWe first construct frames locally at a fixed parameter. For a\nbounded function $f$ supported in a disk of radius $\\delta$, the\nCauchy transform\n\\[\n (\\mathcal Tf)(z)=\\frac1\\pi\n       \\int_{\\C}\\frac{f(w)}{z-w}\\,\\dd y_1(w)\\,\\dd y_2(w)\n\\]\nsatisfies $\\partial_{\\overline z}\\mathcal Tf=f$ in distributions\nand $\\norm{\\mathcal Tf}_{L^\\infty}\\le C\\delta\\norm f_{L^\\infty}$.\nThe latter estimate follows by integrating $\\abs{z-w}^{-1}$ over\nthe support disk. Choose a smooth cutoff $\\chi$ supported in a\nsufficiently small disk and equal to one on a smaller disk. On\nbounded matrix functions, the equation\n\\[\n V=\\Id_n+\\mathcal T(\\chi A V)\n\\]\nis then solved by a convergent Neumann series. Shrinking the disk\nuntil the operator norm is less than $1/3$ gives\n$\\norm{V-\\Id_n}_{L^\\infty}<1/2$, hence invertibility. The\ndistributional equation and interior elliptic regularity for\n$\\partial_{\\overline z}$ show that $V$ is smooth on the smaller\ndisk. This proves local smooth invertible solvability.\n\nIf $V_i,V_j$ are two such local frames, then\n$\\partial_{\\overline z}(V_i^{-1}V_j)=0$ on their overlap.\nTheir transition functions therefore define a holomorphic vector\nbundle on $D_R^2$, whose underlying smooth bundle is trivial.\nIts holomorphic frame bundle has fiber $\\mathrm{GL}_n(\\C)$.\nThe disk is Stein and contractible, so a continuous section of\nthis frame bundle exists. The Oka principle for sections of holomorphic\nfiber bundles with complex homogeneous fibers, in the form of\n\\cite[Theorem~1.1, p.~1018]{Forstneric2009}, supplies a holomorphic\nsection; this homogeneous-fiber case goes back to Grauert\n\\cite{Grauert1958}. In the original smooth trivialization it is\na smooth invertible matrix solving\n\\eqref{eq:planar-frame-equation} throughout $D_R^2$.\n\nTo obtain local parameter dependence, choose such a frame $V_0(z)$\nat $t_0$. After writing $V=V_0W$, the equation becomes\n\\[\n \\partial_{\\overline z}W=B(z,t)W,\n \\qquad\n B(z,t)=V_0(z)^{-1}\\bigl(A(z,t)-A(z,t_0)\\bigr)V_0(z).\n\\]\nChoose a cutoff $\\chi\\in C_c^\\infty(D_R^2)$ equal to one near\n$\\overline D_{R'}^2$. On its compact support, $B(\\,\\cdot\\,,t)$\ntends uniformly to zero as $t\\to t_0$. On a sufficiently small\ninterval $I_0\\Subset I$, solve\n\\[\n W=\\Id_n+\\mathcal T(\\chi B(\\,\\cdot\\,,t)W)\n\\]\nby a Neumann series with norm less than $1/3$. This gives an\ninvertible solution on $D_{R'}^2\\times I_0$. The integral operator\ndepends smoothly on $t$ in operator norm on $L^\\infty$; its\ninverse does also. Hence all parameter derivatives of $W$ exist\nin $L^\\infty$. Differentiating its equation and applying interior\nelliptic regularity successively gives smoothness jointly in\n$(z,t)$, with bounds on compact subsets. Thus $V=V_0W$ has the\nclaimed parameter regularity.\n\nFinally, choose $\\chi\\in C_c^\\infty(D_{R'}^2)$ equal to one near\n$\\overline D_{R''}^2$. The formula\n\\[\n v=V\\mathcal T(\\chi V^{-1}F)\n\\]\nsolves \\eqref{eq:planar-inhomogeneous} on the smaller disk.\nThe transform acts only in the planar variable, so it preserves\nthe specified support in $t$. Dividing a system with invertible\nderivative coefficient by that coefficient, and using\n$D=2\\rho\\partial_{\\overline z}$, proves the final assertion.\n\\end{proof}\n\nTo carry a leading frame through physical transfer, we need control of\nboth the displacement--divergence pair and its derivatives.\nCorollary~\\ref{cor:physical-solvability} gives physical solvability with\nan $L^2$ estimate independent of $\\tau$. We will make the\ncorrection small enough after taking a divergence by first constructing\na compatible expansion of truncation order $N\\ge4$. The proof below\nobtains a physical residual of size $O(\\tau^{1-N})$ and a conjugated\ndivergence correction of size $O(\\tau^{2-N})$. The resulting strong\nconvergence identifies the first medium's leading columns and, by exterior\nagreement, the exterior values of the transferred columns. The weighted\n$O(\\tau)$ derivative bound controls the first-order forcing obtained by\napplying the difference of the two normalized augmented operators to\nthe first physical pair.\n\n\\begin{proposition}[Physical realization of local transport frames]\n\\label{prop:physical-amplitudes}\nFor every nonzero null vector $\\theta$ and every\n$t_0\\in(-2r,2r)$, there exist an open interval\n$J\\subset(-2r,2r)$ containing $t_0$ and three smooth solutions\n$(a_0^{\\nu},b_0^{\\nu})$, $1\\le\\nu\\le3$, of\n\\eqref{eq:constrained-transport} on a neighborhood of\n$\\overline B_{5r}$, with the following properties.\n\nThe three columns form a frame of $\\Ecal_\\theta$ at every point\nof $D_{2r}^2\\times J$. For each $\\nu$ and all sufficiently large\n$\\tau$, there is $u_\\tau^{\\nu}\\in C^\\infty(B_{5r};\\C^3)$ with\n$Lu_\\tau^{\\nu}=0$ such that, on putting\n$U_\\tau^{\\nu}=(u_\\tau^{\\nu},\\diverg u_\\tau^{\\nu})$,\n\\begin{equation}\n\\label{eq:physical-leading-limit}\n\\begin{aligned}\n E_\\tau^{-1}U_\\tau^{\\nu}\n   &\\longrightarrow (a_0^{\\nu},b_0^{\\nu})\n                  &&\\text{in }L^2(B_{4r}),\\\\\n \\norm{E_\\tau^{-1}\\nabla U_\\tau^{\\nu}}_{L^2(B_{4r})}\n   &=O(\\tau).\n\\end{aligned}\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nWe construct compatible formal amplitudes and then correct the\ntruncated displacement using the physical operator.\nFix an integer $N\\ge4$ and radii\n$R_0>R_1>\\cdots>R_N>5r$. The coefficients are smooth on all of\n$\\R^3$. Apply Lemma~\\ref{lem:planar-frames} to the rank-three\nsystem \\eqref{eq:constrained-transport}, starting on a disk\nstrictly larger than $D_{R_0}^2$. It supplies a smooth\ninvertible frame\n\\[\n \\mathcal F(y,t):\\C^3\\longrightarrow\\Ecal_\\theta\n \\quad\\text{on }D_{R_0}^2\\times I_0,\n\\]\nwhere $I_0\\Subset(-2r,2r)$ contains $t_0$. Choose an interval\n$J$ containing $t_0$ with $\\overline J\\subset I_0$, and choose\n$\\chi\\in C_c^\\infty(I_0)$ equal to one on $J$. Multiply each\ncolumn of $\\mathcal F$ by $\\chi(t)$ and extend it by zero in\n$t$ to obtain $(a_0^{\\nu},b_0^{\\nu})$ on\n$D_{R_0}^2\\times\\R$. Since $D\\chi=0$, these columns still\nsolve \\eqref{eq:constrained-transport}. They are a frame on\n$D_{2r}^2\\times J$, and all have transverse support in the\nfixed compact set $K=\\supp\\chi\\Subset I_0$.\n\nWe construct a physical solution for any one of these columns\nand suppress $\\nu$. Higher-order transport expansions for systems appear\nin Eskin and Ralston\n\\cite[Section~2, equations~(2.2)--(2.4)]{EskinRalston2004}.\nThe recursion here must cancel the vector residual\nwhile making the conjugated actual divergence have leading term $b_0$.\nIts scalar equation makes the next normal constraint compatible with\nthe vector equation;\nthe final correction will be made only in the physical displacement\nequation. For $1\\le j\\le N$, we seek smooth coefficients $(a_j,b_j)$\non successively smaller disks such that\n\\begin{equation}\n\\label{eq:physical-recursion}\n\\begin{aligned}\n R^\\theta(a_j,b_j)&=-R(a_{j-1},b_{j-1}),\\\\\n S^\\theta(a_j,b_j)&=-S(a_{j-1},b_{j-1}),\\\\\n \\theta\\cdot a_j&=-(\\diverg a_{j-1}-b_{j-1}).\n\\end{aligned}\n\\end{equation}\nThroughout this recursion, write $q_j=\\diverg a_j-b_j$ and set\n$a_{-1}=b_{-1}=q_{-1}=0$.\nThe last equation in \\eqref{eq:physical-recursion} is the\ndisplacement--divergence constraint at order $j$.\n\nWe first verify the compatibility of these three equations.\nConjugating $P$ gives $E_\\tau^{-1}PE_\\tau=P+\\tau P^\\theta$.\nComparing coefficients of $\\tau$ in the conjugated identity\n\\eqref{eq:augmented-identity} yields, for arbitrary $(a,b)$,\n\\begin{equation}\n\\label{eq:transport-compatibility}\n \\diverg R^\\theta(a,b)+\\theta\\cdot R(a,b)-S^\\theta(a,b)\n =P(\\theta\\cdot a)+P^\\theta(\\diverg a-b).\n\\end{equation}\nSuppose the preceding recursive equations have been solved.\nApply \\eqref{eq:transport-compatibility} to $(a_{j-1},b_{j-1})$.\nFor $j\\ge2$, the preceding vector and scalar equations, followed by\n\\eqref{eq:augmented-identity}, give\n\\[\n \\begin{aligned}\n \\diverg R^\\theta(a_{j-1},b_{j-1})\n       -S^\\theta(a_{j-1},b_{j-1})\n   &=-\\diverg R(a_{j-2},b_{j-2})+S(a_{j-2},b_{j-2})\\\\\n   &=-Pq_{j-2}.\n \\end{aligned}\n\\]\nThe preceding normal constraint gives the same term on the other side:\n\\[\n P(\\theta\\cdot a_{j-1})=-Pq_{j-2}.\n\\]\nFor $j=1$, both expressions are zero by the homogeneous leading\nequations and $q_{-1}=0$. In every case these two terms cancel in\n\\eqref{eq:transport-compatibility}, leaving\n\\begin{equation}\n\\label{eq:recursion-normal-compatibility}\n \\theta\\cdot R(a_{j-1},b_{j-1})=P^\\theta q_{j-1}.\n\\end{equation}\n\nChoose a constant vector $n_\\theta\\in\\C^3$ with\n$\\theta\\cdot n_\\theta=1$, and set\n$a_j^{\\mathrm p}=-q_{j-1}n_\\theta$, $b_j^{\\mathrm p}=0$.\nAfter subtracting this particular pair from the desired\n$(a_j,b_j)$, the unknown takes values in $\\Ecal_\\theta$.\nEquations \\eqref{eq:normal-transport-component} and\n\\eqref{eq:recursion-normal-compatibility} show that the residual\nvector source belongs to $H_\\theta$. Its scalar source is\nunrestricted. We are therefore left with an inhomogeneous\nsquare system on $\\Ecal_\\theta$ with invertible derivative\ncoefficient. Use the uncut frame $\\mathcal F$ and the\ninhomogeneous part of Lemma~\\ref{lem:planar-frames} to solve it\non $D_{R_j}^2\\times I_0$. All source terms, including the\nparticular pair and its derivatives, have transverse support\nin $K$; the constructed solution retains that support and\nextends smoothly by zero in $t$. This proves the induction.\nAfter finitely many steps, every coefficient is smooth on\n$D_{R_N}^2\\times\\R$, hence on a neighborhood of\n$\\overline B_{5r}$.\n\nSet\n\\[\n a^{[N]}=\\sum_{j=0}^N\\tau^{-j}a_j,\n \\qquad b^{[N]}=\\sum_{j=0}^N\\tau^{-j}b_j.\n\\]\nThe first equation of \\eqref{eq:physical-recursion} telescopes\nto\n\\begin{equation}\n\\label{eq:truncated-vector-residual}\n E_\\tau^{-1}R(E_\\tau a^{[N]},E_\\tau b^{[N]})\n       =\\tau^{-N}R(a_N,b_N).\n\\end{equation}\nThe normal constraints telescope separately, giving\n\\begin{equation}\n\\label{eq:truncated-divergence}\n E_\\tau^{-1}\\diverg(E_\\tau a^{[N]})\n       =b^{[N]}+\\tau^{-N}q_N.\n\\end{equation}\nUsing the dependence of $R$ on its scalar argument, these\nidentities give the physical residual explicitly:\n\\begin{equation}\n\\label{eq:physical-truncated-residual}\n\\begin{aligned}\n f_\\tau\n &\\coloneqq E_\\tau^{-1}L(E_\\tau a^{[N]})\\\\\n &=\\tau^{-N}\\bigl[R(a_N,b_N)+k\\nabla q_N\n           +(\\nabla\\lambda)q_N+\\tau k\\theta q_N\\bigr].\n\\end{aligned}\n\\end{equation}\nAll coefficients on the right are fixed smooth functions.\nConsequently $\\norm{f_\\tau}_{L^2(B_{5r})}=O(\\tau^{1-N})$.\n\nCorollary~\\ref{cor:physical-solvability} gives a correction\n$r_\\tau\\in L^2(B_{5r};\\C^3)$ satisfying\n\\[\n E_\\tau^{-1}L(E_\\tau r_\\tau)=-f_\\tau,\n \\qquad \\norm{r_\\tau}_{L^2(B_{5r})}=O(\\tau^{1-N}).\n\\]\nIt is smooth in $B_{5r}$ by Lemma~\\ref{lem:scaled-interior}.\nThe same Lemma, on $B_{4r}$, gives\n\\begin{equation}\n\\label{eq:physical-remainder-two-derivatives}\n \\sum_{j=0}^2h^j\\norm{\\nabla^jr_\\tau}_{L^2(B_{4r})}\n                      =O(\\tau^{1-N}).\n\\end{equation}\nDefine its conjugated physical divergence by\n\\[\n d_\\tau=E_\\tau^{-1}\\diverg(E_\\tau r_\\tau)\n             =\\diverg r_\\tau+\\tau\\theta\\cdot r_\\tau.\n\\]\nIt follows directly from\n\\eqref{eq:physical-remainder-two-derivatives} that\n\\begin{equation}\n\\label{eq:physical-divergence-remainder}\n \\norm{d_\\tau}_{L^2(B_{4r})}\n    +h\\norm{\\nabla d_\\tau}_{L^2(B_{4r})}\n \\le C\\tau\\sum_{j=0}^2h^j\n                    \\norm{\\nabla^jr_\\tau}_{L^2(B_{4r})}\n =O(\\tau^{2-N}).\n\\end{equation}\n\nNow put $u_\\tau=E_\\tau(a^{[N]}+r_\\tau)$. It is a smooth exact\nsolution of $Lu_\\tau=0$ on $B_{5r}$, and its physical pair\nsatisfies\n\\[\n E_\\tau^{-1}(u_\\tau,\\diverg u_\\tau)\n  =\\bigl(a^{[N]}+r_\\tau,\n         b^{[N]}+\\tau^{-N}q_N+d_\\tau\\bigr).\n\\]\nThe smooth finite expansions and the remainder estimates imply\nthe first assertion of \\eqref{eq:physical-leading-limit}.\nThey also show that, if the last displayed pair is denoted\nby $\\mathcal A_\\tau$, then\n\\[\n \\norm{\\mathcal A_\\tau}_{L^2(B_{4r})}\n       +h\\norm{\\nabla\\mathcal A_\\tau}_{L^2(B_{4r})}=O(1).\n\\]\nSince, for $j=1,2,3$,\n$E_\\tau^{-1}\\partial_j(E_\\tau\\mathcal A_\\tau)\n=\\partial_j\\mathcal A_\\tau+\\tau\\theta_j\\mathcal A_\\tau$,\nthe second assertion of \\eqref{eq:physical-leading-limit}\nfollows. Repeating the construction for the three initial\ncolumns proves the Proposition.\n\\end{proof}\n"}, {"path": "preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/sections/transport-matching.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Global-Uniqueness-for-the-Smooth-Isotropic-Elasticity-Inverse-Problem-September-24-2026/build/sections/transport-matching.tex", "bytes": 24369, "sha256": "f608d14a65e98f1ede20fd677e291fc8f28e69f39fc4e11e04f5d3011be7ab2a", "content": "\\section{Matching the leading transports}\\label{sec:transport-matching}\n\nProposition~\\ref{prop:transport-matching} constructs the unique holomorphic\ncomparison of the two leading transports. We first derive a transport normal\nform and prove a supported estimate for parameter dependence.\nThroughout this section, a dot\nbetween complex vectors denotes the complex bilinear Euclidean product.\n\n\\begin{lemma}[Transport normal form]\\label{lem:transport-normal-form}\nFor one coefficient pair, set $g=\\nabla\\log m$. The change of variables\n\\begin{equation}\\label{eq:transport-change}\n p=\\sqrt m\\,a,\n \\qquad\n s=-\\frac{k}{2\\sqrt m}\\,b-\\frac12 g\\cdot p\n\\end{equation}\nis a smooth invertible map of $\\Ecal_\\theta$ to itself. It transforms\n\\eqref{eq:constrained-transport} into\n\\begin{equation}\\label{eq:transport-matrix}\n D_\\theta\\binom{p}{s}\n   =M_\\theta\\binom{p}{s},\n \\qquad\n M_\\theta=\n \\begin{pmatrix}\n  0&\\theta\\\\\n  Q_\\theta\\cdot&T_\\theta\n \\end{pmatrix}\n \\quad\\text{on }H_\\theta\\oplus\\C,\n\\end{equation}\nwhere\n\\begin{equation}\\label{eq:transport-coefficients}\n \\begin{aligned}\n  T_\\theta&=D_\\theta\\log(k/\\ell),\\\\\n  Q_\\theta&=\\frac12\\left[\n   \\left(\\frac m\\ell D_\\theta\\log k\n          -\\frac12\\theta\\cdot g\\right)g\n         -\\frac m\\ell D_\\theta g\\right].\n \\end{aligned}\n\\end{equation}\nIn particular, the bottom-left entry in \\eqref{eq:transport-matrix}\nis the covector $p\\mapsto Q_\\theta\\cdot p$. Both $D_\\theta$ and\n$M_\\theta$ depend linearly and holomorphically on $\\theta$.\n\\end{lemma}\n\n\\begin{proof}\nThe inverse of \\eqref{eq:transport-change} is\n\\[\n a=m^{-1/2}p,\n \\qquad\n b=-\\frac{2\\sqrt m}{k}\\left(s+\\frac12g\\cdot p\\right).\n\\]\nIt is well defined because $m,k>0$, and multiplication by $\\sqrt m$\npreserves $H_\\theta$. Write $D=D_\\theta$. Substitution into the first\nequation in \\eqref{eq:leading-operators} gives\n\\[\n 0=2\\sqrt m\\,Dp+\\theta(kb+\\sqrt m\\,g\\cdot p),\n \\qquad\\text{hence}\\qquad Dp=\\theta s.\n\\]\nDividing the second equation by two and using\n$\\nabla m=mg$ yields\n\\[\n 0=\\ell Db+(Dk)b+2\\nabla m\\cdot Da+(D\\nabla m)\\cdot a.\n\\]\nHere\n\\[\n \\begin{aligned}\n 2\\nabla m\\cdot Da\n   &=2\\sqrt m\\,g\\cdot Dp\n     -\\sqrt m\\,(D\\log m)g\\cdot p,\\\\\n (D\\nabla m)\\cdot a\n   &=\\sqrt m\\,\\bigl((D\\log m)g+Dg\\bigr)\\cdot p.\n \\end{aligned}\n\\]\nThe terms containing $(D\\log m)g\\cdot p$ cancel. Thus\n\\begin{equation}\\label{eq:transformed-divergence-transport}\n \\ell Db=-(Dk)b\n   -\\sqrt m\\bigl(2(\\theta\\cdot g)s+(Dg)\\cdot p\\bigr).\n\\end{equation}\nDifferentiate the definition of $s$ and substitute\n\\eqref{eq:transformed-divergence-transport}, the expression for $b$,\nand $Dp=\\theta s$. With $c=m/\\ell$ and $\\gamma=D\\log m=\\theta\\cdot g$,\nthe result is\n\\[\n Ds=c(D\\log k-\\gamma)s\n   +\\frac12\\bigl[(cD\\log k-\\gamma/2)g-cDg\\bigr]\\cdot p.\n\\]\nFinally, since $\\ell=k+m$,\n\\[\n D\\log(k/\\ell)\n =D\\log k-\\frac{Dk+Dm}{\\ell}\n =\\frac m\\ell(D\\log k-D\\log m).\n\\]\nThis proves the formulas. The top row takes values in $H_\\theta$\nbecause $\\theta\\cdot\\theta=0$. All the direction dependence displayed\nin the formulas is linear.\n\\end{proof}\n\nThe comparison equation can now be described in terms of the columns\nthat the physical construction must supply. Fix $\\theta$ and a cylinder\n$D_{2r}^2\\times J$. Suppose for the moment that\n$Y_1:\\C^3\\to\\Ecal_\\theta$ is a smooth invertible matrix of normalized\ntransport columns for the first medium, and that\n$Y_2:\\C^3\\to\\Ecal_\\theta$ has locally $L^2$ columns satisfying the\nsecond transport equation in distributions:\n\\[\n D_\\theta Y_j=M_{j,\\theta}Y_j,\\qquad j=1,2.\n\\]\nThe columns in each $Y_j$ are normalized by that medium's own change\nof variables \\eqref{eq:transport-change}. These changes agree outside\n$\\overline\\Omega$, where the extended coefficients agree. Thus exterior\nagreement of the corresponding physical leading columns gives\n$Y_2=Y_1$ there.\n\nUnder this exterior agreement, define\n\\[\n G=Y_2Y_1^{-1}\\in\\operatorname{End}(\\Ecal_\\theta).\n\\]\nOnly $Y_1$ is required to be invertible. Multiplication by its smooth\ninverse is legitimate for locally $L^2$ columns, and the product rule\nin distributions gives\n\\[\n D_\\theta G=M_{2,\\theta}G-GM_{1,\\theta},\n \\qquad G=\\Id\\quad\\text{outside }\\overline\\Omega\n\\]\non the cylinder. Consequently $W=G-\\Id$ is locally $L^2$ and supported\nin $\\overline\\Omega$ within that cylinder.\n\nFor a transverse coordinate $t$, restrict the coefficients to the\ncorresponding plane and use a basis of $\\Ecal_\\theta$ fixed in $x$.\nOn planar endomorphism-valued unknowns define\n\\begin{equation}\\label{eq:planar-comparison-operator}\n \\Pcal_{\\theta,t}W\n      =D_\\theta W-M_{2,\\theta}W+WM_{1,\\theta},\n \\qquad\n f_{\\theta,t}=M_{2,\\theta}-M_{1,\\theta}.\n\\end{equation}\nThe supported planar equation required for the comparison is\n\\begin{equation}\\label{eq:inhomogeneous-comparison}\n \\Pcal_{\\theta,t}W=f_{\\theta,t}.\n\\end{equation}\nMatrices here may be regarded as vectors of their nine entries.\nFor almost every $t$, the $L^2$ planar restriction of the local $W$\nis supported in $\\overline{D_r^2}$ because $\\overline\\Omega\\subset B_r$.\nThe physical transfer and weak-limit argument below will supply the\nsecond columns $Y_2$. Slicing their distributional cylinder equation\nwill initially give \\eqref{eq:inhomogeneous-comparison} only for almost\nevery $t$. We therefore need both uniqueness for supported planar\nsolutions and a way to pass from this almost-everywhere construction\nto smooth dependence on every parameter. The next lemma provides these\nfacts on fixed Hilbert spaces, which will also allow the planes to vary.\n\n\\begin{lemma}[Supported planar transport]\\label{lem:supported-transport}\nLet $\\mathcal U\\subset\\R^d$ be open, let $n\\geq1$, and suppose that\n\\[\n \\Pcal_\\rho\n   =\\gamma(\\rho)(\\partial_{y_1}+i\\partial_{y_2})\n       +A(y,\\rho),\n \\qquad \\rho\\in\\mathcal U,\n\\]\nwhere $\\gamma$ is smooth and nonzero and $A$ is a smooth complex\n$n\\times n$ matrix on a neighborhood of\n$\\overline{D_{2r}^2}\\times\\mathcal U$. Then the following statements hold.\n\\begin{enumerate}[label=\\textup{(\\roman*)}]\n\\item A distributional solution of $\\Pcal_\\rho W=0$ on $D_{2r}^2$\nwhose support is a compact subset of that disk is zero.\n\\item For every integer $j\\geq0$ and every\n$W\\in H^{j+1}(D_{2r}^2;\\C^n)$ supported in $\\overline{D_r^2}$,\n\\begin{equation}\\label{eq:supported-transport-estimate}\n \\norm{W}_{H^{j+1}(D_{2r}^2)}\n \\leq C_j\\norm{\\Pcal_\\rho W}_{H^j(D_{2r}^2)},\n \\qquad\n \\supp W\\subset\\overline{D_r^2}.\n\\end{equation}\nThe constants are locally uniform in $\\rho$.\n\\item Suppose that $f(y,\\rho)$ is smooth on a neighborhood of\n$\\overline{D_{2r}^2}\\times\\mathcal U$. If the equation\n$\\Pcal_\\rho W=f(\\cdot,\\rho)$ has an $L^2$ solution supported in\n$\\overline{D_r^2}$ for a dense set of parameters $\\rho$, then it has\nsuch a solution for every $\\rho$. These solutions are unique and form\na smooth function of $(y,\\rho)$.\n\\end{enumerate}\nThe same conclusions apply to matrix-valued unknowns, after their\nentries are regarded as a vector.\n\\end{lemma}\n\n\\begin{proof}\nFor a fixed parameter, $\\Pcal_\\rho$ is elliptic in the two real planar\nvariables: its principal symbol is\n$\\gamma(\\rho)(i\\xi_1-\\xi_2)$, which is nonzero for\n$\\xi\\in\\R^2\\setminus\\{0\\}$. On each sufficiently small disk,\nLemma~\\ref{lem:planar-frames} gives a smooth invertible matrix $F$\nsatisfying $\\Pcal_\\rho F=0$. If $\\Pcal_\\rho W=0$, then\n\\[\n (\\partial_{y_1}+i\\partial_{y_2})(F^{-1}W)=0\n\\]\nin distributions. Its components are therefore holomorphic functions\nof $y_1+iy_2$. A homogeneous solution that vanishes on an open set\nvanishes on every overlapping frame disk by the holomorphic identity\ntheorem. Connectedness propagates this conclusion throughout\n$D_{2r}^2$. A compactly supported solution vanishes near the boundary\nof the disk, proving \\textup{(i)}.\n\nWe give the estimate on the fixed supported space. Extending $W$ by\nzero to $\\R^2$ is harmless because its support lies in $\\overline{D_r^2}$.\nThe Fourier symbol just computed gives\n\\[\n \\norm{W}_{H^{j+1}}\n \\leq C\\bigl(\n    \\norm{\\gamma(\\rho)(\\partial_{y_1}+i\\partial_{y_2})W}_{H^j}\n       +\\norm{W}_{L^2}\\bigr).\n\\]\nHere and below in this proof the norms can equivalently be taken on\n$D_{2r}^2$, since the functions under consideration are supported in\nthe smaller disk. Smooth multiplication and, for $j\\geq1$, the\ninterpolation estimate\n$\\norm{W}_{H^j}\\leq\\varepsilon\\norm{W}_{H^{j+1}}\n +C_\\varepsilon\\norm{W}_{L^2}$ give\n\\begin{equation}\\label{eq:supported-elliptic-preestimate}\n \\norm{W}_{H^{j+1}}\n \\leq C\\bigl(\\norm{\\Pcal_\\rho W}_{H^j}\n                  +\\norm{W}_{L^2}\\bigr).\n\\end{equation}\nThe coefficients can be cut off outside a neighborhood of the smaller\ndisk for this calculation. We will also use local regularity for an\n$L^2$ distributional solution with smooth right side. In a local frame\n$F$, its equation becomes a scalar Cauchy--Riemann equation componentwise\nwith smooth right side. Subtracting a smooth local particular solution,\nas supplied by Lemma~\\ref{lem:planar-frames}, leaves holomorphic\ncomponents. The original solution is therefore smooth in the disk.\n\nTo remove the last term in \\eqref{eq:supported-elliptic-preestimate},\nsuppose that \\eqref{eq:supported-transport-estimate} fails at a fixed\nparameter. There would then be a sequence $W_\\nu$, supported in\n$\\overline{D_r^2}$, such that\n\\[\n \\norm{W_\\nu}_{H^{j+1}}=1,\n \\qquad\n \\norm{\\Pcal_\\rho W_\\nu}_{H^j}\\longrightarrow0.\n\\]\nAfter passing to a subsequence, compactness of the embedding into\n$L^2(D_{2r}^2)$ gives a strong $L^2$ limit $W$. This limit is supported\nin $\\overline{D_r^2}$ and solves $\\Pcal_\\rho W=0$ distributionally.\nPart~\\textup{(i)} gives $W=0$. Equation\n\\eqref{eq:supported-elliptic-preestimate} now contradicts the\nnormalization of $W_\\nu$. This proves \\textup{(ii)} at a fixed parameter.\n\nFor the parameter assertions, equip the fixed complex Hilbert spaces\n\\[\n \\begin{aligned}\n X_j&=\\{W\\in H^{j+1}(D_{2r}^2;\\C^n):\n                  \\supp W\\subset\\overline{D_r^2}\\},\\\\\n Z_j&=H^j(D_{2r}^2;\\C^n)\n \\end{aligned}\n\\]\nwith their usual Hermitian Sobolev inner products. The subspace $X_j$\nis closed. The map $\\rho\\mapsto\\Pcal_\\rho$ is smooth in\n$\\mathcal L(X_j,Z_j)$. The estimate at $\\rho_0$ persists in a\nneighborhood: absorb\n$\\norm{(\\Pcal_\\rho-\\Pcal_{\\rho_0})W}_{Z_j}$ into its left side when\nthe operator norm of the difference is small. This proves local\nuniformity in \\textup{(ii)}.\n\nWrite $\\Pcal_\\rho^*:Z_j\\to X_j$ for the Hilbert space adjoint with\nrespect to these fixed inner products. In particular, this is not an\nadjoint in the complex bilinear pairing used for the null vectors.\nThe lower bound in \\textup{(ii)} implies that\n$\\Pcal_\\rho^*\\Pcal_\\rho:X_j\\to X_j$ is coercive and invertible.\nConsequently\n\\begin{equation}\\label{eq:supported-left-inverse}\n T_{\\rho,j}\n   =(\\Pcal_\\rho^*\\Pcal_\\rho)^{-1}\\Pcal_\\rho^*\n       :Z_j\\longrightarrow X_j\n\\end{equation}\nis a bounded left inverse. It depends smoothly on $\\rho$, since\nadjoints and inversion of bounded invertible operators do so locally.\nThis argument uses a lower bound for $\\Pcal_\\rho$; it requires no\nsurjectivity onto $Z_j$.\n\nFor each $j$, set $W_j(\\rho)=T_{\\rho,j}f(\\cdot,\\rho)$. An $L^2$\nsupported solution, whenever it exists, is smooth by interior\nellipticity and hence belongs to every $X_j$. It must equal\n$W_j(\\rho)$ by the left-inverse identity. Thus the smooth\n$Z_j$-valued residual\n\\[\n \\Pcal_\\rho W_j(\\rho)-f(\\cdot,\\rho)\n\\]\nvanishes on the assumed dense set, and continuity makes it zero for\nevery $\\rho$. Uniqueness in \\textup{(i)} identifies the solutions for\ndifferent $j$. Smooth dependence with values in every $H^{j+1}$,\ntogether with Sobolev embedding in the planar variables, gives joint\nsmoothness in $(y,\\rho)$. This proves \\textup{(iii)}.\n\\end{proof}\n\nLet\n\\[\n \\mathcal C=\\{[\\theta]\\in\\C\\mathrm P^2:\n                         \\theta\\cdot\\theta=0\\}\n\\]\nbe the projective null conic. The fibers\n$\\Ecal_\\theta=H_\\theta\\oplus\\C$ form a holomorphic subbundle\n$\\Ecal$ of the trivial bundle $\\mathcal C\\times\\C^4$. For example,\non a chart where $\\theta_i\\ne0$, the vectors\n$e_j-(\\theta_j/\\theta_i)e_i$, $j\\ne i$, give a holomorphic frame\nof $H_\\theta$; adjoining the scalar component gives one of $\\Ecal$.\n\nCeki\\'c constructs a transport comparison for connection Laplacians\nthat equals the identity in the outer exterior component\n\\cite[Theorem~3.4 in the arXiv version]{Cekic2025}, using full\nconnection-system boundary data on a trivial vector bundle. The comparison\nbelow is obtained from physical elastic transfer and weak limits on the\nconstrained fibers $\\Ecal_\\theta$.\n\n\\begin{proposition}[Matching of the transports]\\label{prop:transport-matching}\nFor the two extended coefficient pairs of\nLemma~\\ref{lem:common-extension}, equality of the physical\ndisplacement-to-traction maps implies that there is a unique smooth\nfield\n\\[\n G(x,[\\theta])\\in\\operatorname{End}(\\Ecal_\\theta),\n \\qquad (x,[\\theta])\\in\\R^3\\times\\mathcal C,\n\\]\nsatisfying, for every nonzero representative $\\theta$,\n\\begin{equation}\\label{eq:comparison-transport}\n \\begin{aligned}\n D_\\theta G&=M_{2,\\theta}G-GM_{1,\\theta},\\\\\n G(x,[\\theta])&=\\Id\\qquad\\text{if }|x|>2r.\n \\end{aligned}\n\\end{equation}\nFor each fixed $x$, this field is a holomorphic section of\n$\\operatorname{End}(\\Ecal)$ over $\\mathcal C$. In fact, for each\nfixed direction the first equation has at most one smooth solution whose\ndifference from the identity has compact spatial support.\n\\end{proposition}\n\n\\begin{proof}\n\\emph{Physical transfer and weak limits.}\nFix a nonzero null vector $\\theta$ and $t_0\\in(-2r,2r)$. Apply\nProposition~\\ref{prop:physical-amplitudes} to the first coefficient\npair. It supplies three exact smooth solutions on $B_{5r}$, with the\nlimits and estimates in \\eqref{eq:physical-leading-limit}, and an\ninterval $J$ containing $t_0$ on which their leading columns form a\nframe on $D_{2r}^2\\times J$. We perform the following construction\nfor each column, temporarily suppressing its index.\n\nBy Proposition~\\ref{prop:physical-transfer}, the first solution\n$u_{1,\\tau}$ has a smooth transferred solution $u_{2,\\tau}$ on\n$B_{5r}$, with\n$u_{2,\\tau}=u_{1,\\tau}$ outside $\\overline\\Omega$. Define the\nphysical augmented vectors\n\\[\n V_{j,\\tau}=(u_{j,\\tau},\\diverg u_{j,\\tau}),\\qquad j=1,2.\n\\]\nThe difference $V_{2,\\tau}-V_{1,\\tau}$ is smooth and compactly\nsupported in $\\overline\\Omega\\subset B_r$. By\nLemma~\\ref{lem:augmented-system},\n\\begin{equation}\\label{eq:physical-transfer-forcing}\n \\Lcal_2(V_{2,\\tau}-V_{1,\\tau})\n       =(\\Lcal_1-\\Lcal_2)V_{1,\\tau}.\n\\end{equation}\nBoth normalized systems have principal part $\\Delta\\Id_4$.\nTheir difference is therefore an operator of order at most one,\nwhose coefficients are supported in $\\overline\\Omega$.\nThe weighted derivative bound in\n\\eqref{eq:physical-leading-limit} implies\n\\[\n \\begin{aligned}\n \\norm{E_\\tau^{-1}(\\Lcal_1-\\Lcal_2)V_{1,\\tau}}_{L^2}\n &\\leq C\\left(\n     \\norm{E_\\tau^{-1}V_{1,\\tau}}_{L^2(B_{4r})}\n    +\\norm{E_\\tau^{-1}\\nabla V_{1,\\tau}}_{L^2(B_{4r})}\n          \\right)\\\\\n &=O(\\tau).\n \\end{aligned}\n\\]\nThe function on the left is extended by zero off its compact support\nwhen a norm on $\\R^3$ is used. Apply\nProposition~\\ref{prop:carleman} to the difference in\n\\eqref{eq:physical-transfer-forcing}. Since\n$\\norm{F}_{H_h^{-1}}\\leq\\norm{F}_{L^2}$ and $h=\\tau^{-1}$, we obtain\n\\begin{equation}\\label{eq:bounded-transferred-amplitudes}\n \\begin{aligned}\n \\norm{E_\\tau^{-1}(V_{2,\\tau}-V_{1,\\tau})}_{H_h^1}\n &\\leq Ch\\norm{E_\\tau^{-1}(\\Lcal_1-\\Lcal_2)V_{1,\\tau}}_{H_h^{-1}}\\\\\n &\\leq C.\n \\end{aligned}\n\\end{equation}\nThus $E_\\tau^{-1}V_{2,\\tau}$ is bounded in $L^2(B_{4r})$.\nAfter passage to a subsequence, chosen simultaneously for the three\ncolumns, it converges weakly to a vector $(\\widetilde a,\\widetilde b)$.\nThe strong convergence of the first amplitudes and the exterior\nagreement of the physical solutions give\n\\begin{equation}\\label{eq:exterior-leading-agreement}\n (\\widetilde a,\\widetilde b)=(a_0,b_0)\n \\quad\\text{almost everywhere on }\n B_{4r}\\setminus\\overline\\Omega.\n\\end{equation}\n\nThese weak limits satisfy the constrained transport for the second\npair. To see this directly, write\n$(a_\\tau,b_\\tau)=E_\\tau^{-1}V_{2,\\tau}$. The exact equations and\nthe null condition give\n\\[\n \\begin{aligned}\n R_2^\\theta(a_\\tau,b_\\tau)\n       +\\tau^{-1}R_2(a_\\tau,b_\\tau)&=0,\\\\\n S_2^\\theta(a_\\tau,b_\\tau)\n       +\\tau^{-1}S_2(a_\\tau,b_\\tau)&=0.\n \\end{aligned}\n\\]\nWhen tested against a fixed compactly supported smooth function,\nthe terms multiplied by $\\tau^{-1}$ tend to zero: all their\nderivatives can be transferred to that test function, while the\namplitudes remain bounded in $L^2$. Weak convergence passes the\nremaining terms to the limit. Moreover, the physical divergence\nidentity says\n\\[\n \\theta\\cdot a_\\tau\n   =\\tau^{-1}(b_\\tau-\\diverg a_\\tau),\n\\]\nso $\\theta\\cdot\\widetilde a=0$ distributionally. We have proved\n\\[\n R_2^\\theta(\\widetilde a,\\widetilde b)=0,\n \\qquad S_2^\\theta(\\widetilde a,\\widetilde b)=0,\n \\qquad (\\widetilde a,\\widetilde b)\\in\\Ecal_\\theta.\n\\]\nNo derivative estimate on the transferred amplitudes, beyond their\n$L^2$ bound, is needed for this passage to the limit.\n\nThe physical transfer has therefore produced the second transport\ncolumns, but only as weak limits. We next obtain a supported comparison\non almost every transverse plane and use supported uniqueness to extend\nit to every plane.\n\n\\emph{A comparison on almost every plane.}\nApply \\eqref{eq:transport-change} to the three leading columns for\neach pair and denote the resulting maps by\n$Y_j:\\C^3\\to\\Ecal_\\theta$. For $j=2$, these columns are initially\nlocally $L^2$; they satisfy the equations distributionally. Since\n$J\\subset(-2r,2r)$, the cylinder on which the first columns form a frame\nsatisfies\n\\[\n D_{2r}^2\\times J\\subset B_{\\sqrt8\\,r}\\subset B_{4r}.\n\\]\nThus the weak limits constructed above are defined throughout this\ncylinder, where\n\\[\n D_\\theta Y_j=M_{j,\\theta}Y_j,\n\\]\nand $Y_1$ is smooth and invertible. The change of variables agrees\nfor the two pairs outside $\\overline\\Omega$. Define\n\\[\n G=Y_2Y_1^{-1}.\n\\]\nThe calculation preceding Lemma~\\ref{lem:supported-transport} now applies:\n$G$ is a locally $L^2$ endomorphism on the cylinder, equals the identity\noutside $\\overline\\Omega$, and satisfies the first equation of\n\\eqref{eq:comparison-transport} distributionally. With $W=G-\\Id$, this\nis the cylinder version of \\eqref{eq:inhomogeneous-comparison}.\nThe operator $D_\\theta$ differentiates only the planar variables.\nTesting the cylinder equation with products of a planar test\nfunction and a transverse test function, and then using a countable\ndense collection of planar tests, gives\n\\eqref{eq:inhomogeneous-comparison} on $D_{2r}^2$ for almost every\n$t\\in J$. For these $t$, $W(\\cdot,t)$ belongs to $L^2(D_{2r}^2)$\nand has support in $\\overline{D_r^2}$. Indeed, $W=0$ when $|y|>r$,\nand on its remaining support multiplication by $Y_1^{-1}$ is locally\nbounded uniformly in $t$. These assertions can first be made on\ncompact subintervals of $J$ and then exhausted over $J$.\n\n\\emph{Existence on every plane.}\nIn the orthonormal coordinates associated with the fixed $\\theta$,\n\\[\n D_\\theta=|\\operatorname{Re}\\theta|\n                 (\\partial_{y_1}+i\\partial_{y_2}).\n\\]\nThus \\eqref{eq:planar-comparison-operator}, as an operator on matrix\nentries, has the form of Lemma~\\ref{lem:supported-transport}.\nIts coefficients and the source $f_{\\theta,t}$ are smooth in $(y,t)$.\nThe set of parameters for which a supported solution has just been\nobtained has full measure in $J$, and hence is dense. Part~\\textup{(iii)}\nof that lemma extends existence to every $t\\in J$ and makes $W$\nsmooth in $(y,t)$. It also makes the choice unique, so the results\nfrom different intervals $J$ agree on their overlaps.\n\nSince $t_0$ was arbitrary, this constructs $G$ for every\n$-2r<t<2r$. Extend $W=G-\\Id$ by zero in the planar variables\noutside $D_{2r}^2$. This extension is smooth because $W$ is already\nzero for $r<|y|<2r$. The equation holds across this extension, and\nit holds farther out because $f_{\\theta,t}=0$ there. If $|t|>r$,\nthe entire plane misses $\\overline\\Omega$, so $f_{\\theta,t}=0$.\nPart~\\textup{(i)} of Lemma~\\ref{lem:supported-transport} then gives\n$W=0$ on that plane. Extending by zero also for $|t|\\geq2r$ is\ntherefore smooth. For this fixed direction we have constructed a\nglobal smooth comparison with\n\\begin{equation}\\label{eq:comparison-uniform-support}\n \\supp(G(\\cdot,\\theta)-\\Id)\n \\subset\\{(y,t):|y|\\leq r,\\ |t|\\leq r\\}\n \\subset\\overline{B_{2r}}.\n\\end{equation}\nThe final containing ball is independent of the direction.\n\nFor later use, uniqueness holds even if a larger compact support is\nallowed. The difference of two such comparisons solves the\nhomogeneous equation on every plane and has compact support there.\nThe local-frame and holomorphic-continuation argument in\nLemma~\\ref{lem:supported-transport}, applied on a disk containing\nthat support, makes the difference zero on each plane.\n\n\\emph{Smooth dependence on the direction.}\nThe preceding construction gives a unique comparison separately for\nevery nonzero null vector $\\theta$. If $c\\in\\C\\setminus\\{0\\}$,\nthen $\\Ecal_{c\\theta}=\\Ecal_\\theta$, and\n\\[\n D_{c\\theta}=cD_\\theta,\n \\qquad M_{j,c\\theta}=cM_{j,\\theta}.\n\\]\nThe equations for $\\theta$ and $c\\theta$ are consequently identical\nafter division by $c$. Compact-support uniqueness gives\n$G(x,c\\theta)=G(x,\\theta)$, so the field is already well defined\non projective directions as a pointwise family.\n\nChoose a local smooth representative $\\theta(v)$ of those directions,\nwhere $v$ ranges over an open subset of $\\R^2$, and a smooth local\nframe of $\\Ecal$. Define the orthonormal vectors\n\\[\n e_1(v)=\\frac{\\operatorname{Re}\\theta(v)}\n                   {|\\operatorname{Re}\\theta(v)|},\n \\quad\n e_2(v)=\\frac{\\operatorname{Im}\\theta(v)}\n                   {|\\operatorname{Im}\\theta(v)|},\n \\quad\n e_3(v)=e_1(v)\\times e_2(v).\n\\]\nThey vary smoothly: the real and imaginary parts of a nonzero null\nvector are nonzero, perpendicular, and of equal length. In the\ncoordinates\n\\[\n x=y_1e_1(v)+y_2e_2(v)+te_3(v),\n\\]\nand the chosen bundle frame, \\eqref{eq:inhomogeneous-comparison}\nis a smooth family of operators of the form in\nLemma~\\ref{lem:supported-transport}, with parameters $(t,v)$ on\nthe fixed disk $D_{2r}^2$. For every value of these parameters a\nsolution supported in $\\overline{D_r^2}$ has already been\nconstructed. The smooth left inverse\n\\eqref{eq:supported-left-inverse} therefore expresses this unique\nsolution as a smooth function of $(y,t,v)$. Returning to physical\ncoordinates gives joint smoothness in $(x,v)$.\n\nThis reasoning uses the separate fixed-direction existence results\nand their uniqueness. In particular, none of the earlier weakly\nconvergent subsequences has to be chosen simultaneously for varying\ndirections.\n\n\\emph{Holomorphic dependence at fixed physical coordinates.}\nA close precedent for this parameter argument is Ceki\\'c\n\\cite[Theorem~3.4 and Lemma~4.1 in the arXiv version]{Cekic2025}, whose\nparameter argument follows Eskin's complex-parameter method \\cite{Eskin2001}.\nNow let $v$ be a complex local coordinate on $\\mathcal C$. Choose\na holomorphic nonzero representative $\\theta(v)$ and a holomorphic\nframe of $\\Ecal$ on this chart. This frame depends only on $v$.\nIn this frame the matrices $M_{j,\\theta(v)}(x)$ are holomorphic in\n$v$ for each fixed $x$: the ambient formulas\n\\eqref{eq:transport-matrix}--\\eqref{eq:transport-coefficients} are\nholomorphic in $\\theta$, and passage to a holomorphic bundle frame\npreserves that property.\n\nRepresent $G$ in the same frame and differentiate the first equation\nof \\eqref{eq:comparison-transport} by $\\partial_{\\bar v}$,\n\\emph{holding $x\\in\\R^3$ fixed}. Joint smoothness just established\njustifies this differentiation. Since the representative and the\nframe are holomorphic and the frame is independent of $x$, the\nresult is exactly\n\\[\n D_{\\theta(v)}(\\partial_{\\bar v}G)\n  -M_{2,\\theta(v)}\\partial_{\\bar v}G\n  +(\\partial_{\\bar v}G)M_{1,\\theta(v)}=0.\n\\]\nThe derivative here is a matrix in a holomorphic trivialization,\nhence represents an endomorphism of the same fiber $\\Ecal_{\\theta(v)}$.\nThe common support bound \\eqref{eq:comparison-uniform-support}\nimplies that this derivative is zero for $|x|>2r$. Compact-support\nuniqueness on each plane gives $\\partial_{\\bar v}G=0$. The smooth\nmoving coordinates were used to establish joint regularity; the\nequation was differentiated only after returning to fixed physical\ncoordinates. We conclude that $G(x,\\cdot)$ is holomorphic on\n$\\mathcal C$ for every $x$, completing the proof.\n\\end{proof}\n"}, {"path": "preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/README.md", "bytes": 675, "sha256": "b9311932ec47de2daecb5360d726dc359411a457855687cf45ee031f54bb3a77", "content": "# [Monochromatic finite sums and products in the positive integers](paper.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 23, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026,\n  author = {{OpenAI}},\n  title = {{Monochromatic finite sums and products in the positive integers}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/paper.pdf}{OAI:Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/main.tex", "bytes": 2812, "sha256": "b0ac7ff727b0062e71b986aed1607ab258b49e1f8d8f103123a0f8ffc4a41ac7", "content": "\\newcommand{\\DoNotLoadEpstopdf}{}\n\\documentclass[11pt]{article}\n\\usepackage[a4paper,margin=29mm]{geometry}\n\\usepackage[T1]{fontenc}\n\\usepackage[utf8]{inputenc}\n\\usepackage{lmodern}\n\\input{glyphtounicode-cmex}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage{microtype}\n\\usepackage{enumitem}\n\\usepackage{booktabs,longtable,array}\n\\usepackage{xcolor}\n\\usepackage{flafter}\n\\usepackage{tikz}\n\\usetikzlibrary{arrows.meta,positioning}\n\\usepackage{hyperref}\n\\hypersetup{colorlinks=true,linkcolor=blue!45!black,citecolor=blue!45!black,\n urlcolor=blue!45!black,\n pdftitle={Monochromatic finite sums and products in the positive integers},\n pdfauthor={OpenAI},\n pdfsubject={Hindman's finite sums and products conjecture}}\n\\urlstyle{same}\n\\setlength{\\emergencystretch}{2em}\n\\setlist[enumerate]{leftmargin=*,itemsep=3pt,topsep=5pt}\n\\setlist[itemize]{leftmargin=*,itemsep=3pt,topsep=5pt}\n\\numberwithin{equation}{section}\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\newtheorem{principle}[theorem]{Principle}\n\\theoremstyle{definition}\n\\newtheorem{definition}[theorem]{Definition}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\newtheorem{example}[theorem]{Example}\n\\newcommand{\\N}{\\mathbb N}\n\\newcommand{\\Z}{\\mathbb Z}\n\\newcommand{\\Q}{\\mathbb Q}\n\\newcommand{\\R}{\\mathbb R}\n\\newcommand{\\C}{\\mathbb C}\n\\newcommand{\\E}{\\mathbb E}\n\\newcommand{\\PP}{\\mathbb P}\n\\newcommand{\\1}{\\mathbf 1}\n\\newcommand{\\eps}{\\varepsilon}\n\\DeclarePairedDelimiter{\\abs}{\\lvert}{\\rvert}\n\\DeclarePairedDelimiter{\\norm}{\\lVert}{\\rVert}\n\\DeclarePairedDelimiter{\\ceil}{\\lceil}{\\rceil}\n\\DeclarePairedDelimiter{\\floor}{\\lfloor}{\\rfloor}\n\\DeclareMathOperator{\\FS}{FS}\n\\DeclareMathOperator{\\FP}{FP}\n\\DeclareMathOperator{\\HK}{HK}\n\\DeclareMathOperator{\\Lip}{Lip}\n\\DeclareMathOperator{\\TV}{TV}\n\\DeclareMathOperator{\\supp}{supp}\n\\DeclareMathOperator{\\dist}{dist}\n\\DeclareMathOperator{\\lcm}{lcm}\n\\DeclareMathOperator{\\rank}{rank}\n\\DeclareMathOperator{\\Ad}{Ad}\n\\DeclareMathOperator{\\Span}{span}\n\\title{Monochromatic finite sums and products\\\\in the positive integers}\n\\author{OpenAI}\n\\date{September 23, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe prove Hindman's finite sums and products conjecture: for every finite\ncoloring of the positive integers and every positive integer $k$, there is a\n$k$-element set whose nonempty subset sums and nonempty subset products all\nhave the same color.\n\\end{abstract}\n\\tableofcontents\n\\input{sections/01_introduction}\n\\input{sections/02_framework}\n\\input{sections/03_arithmetic}\n\\input{sections/04_correlation}\n\\input{sections/05_prediction}\n\\input{sections/06_rough_progressions}\n\\input{sections/07_cube_limits}\n\\input{sections/08_alignment}\n\\input{references}\n\\end{document}\n"}, {"path": "preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/references.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/references.tex", "bytes": 8480, "sha256": "df3775aace692844c40dde71645b87959a10930610ce63b75f916afd957e4c91", "content": "\\begin{thebibliography}{99}\n\\addcontentsline{toc}{section}{References}\n\n\\bibitem{Alweiss}\nR.~Alweiss,\n\\emph{Monochromatic sums and products 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Math.-Ver. \\textbf{25} (1916), 114--117.\n\n\\bibitem{SelbergAP}\nA.~Selberg,\n\\emph{An elementary proof of the prime-number theorem for arithmetic progressions},\nCanad. J. Math. \\textbf{2} (1950), 66--78.\n\\url{https://doi.org/10.4153/CJM-1950-007-5}.\n\n\\bibitem{Szemeredi}\nE.~Szemer\\'edi,\n\\emph{On sets of integers containing no $k$ elements in arithmetic progression},\nActa Arith. \\textbf{27} (1975), 199--245.\n\\url{https://doi.org/10.4064/aa-27-1-199-245}.\n\n\\bibitem{TaoZiegler}\nT.~Tao and T.~Ziegler,\n\\emph{Concatenation theorems for anti-Gowers-uniform functions and Host-Kra characteristic factors},\nDiscrete Anal. \\textbf{2016}, Paper No.~13, 61~pp.\n\\url{https://doi.org/10.19086/da.850}.\nRevised version: arXiv:1603.07815v4, 17 January 2017,\n\\url{https://arxiv.org/abs/1603.07815v4}.\n\n\\bibitem{vanderWaerden}\nB.~L.~van der Waerden,\n\\emph{Beweis einer Baudetschen Vermutung},\nNieuw Arch. Wiskd. (2) \\textbf{15} (1927), 212--216.\n\n\\bibitem{Yamada}\nT.~Yamada,\n\\emph{Explicit improvements of the Brun--Titchmarsh theorem for arbitrary intervals},\narXiv:2312.16090v1, 26 December 2023.\n\\url{https://arxiv.org/abs/2312.16090v1}.\n\n\\bibitem{ZorinKranich}\nP.~Zorin-Kranich,\n\\emph{A nilpotent IP polynomial multiple recurrence theorem},\nJ. Anal. Math. \\textbf{123} (2014), 183--225.\n\\url{https://doi.org/10.1007/s11854-014-0018-5}.\nRevised version: arXiv:1206.0287v4, 4 June 2018,\n\\url{https://arxiv.org/abs/1206.0287v4}.\n\n\\end{thebibliography}\n"}, {"path": "preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/01_introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/01_introduction.tex", "bytes": 11236, "sha256": "4598c20bd1300e28661319cc656e3f4937a3a0f44aebd50f1f3786887d0adb54", "content": "\\section{Introduction}\\label{sec:introduction}\n\nWrite $[n]=\\{1,\\ldots,n\\}$ for a positive integer $n$.\nFor a finite set $A\\subset\\N=\\{1,2,\\ldots\\}$, write\n\\[\n \\FS(A)=\\left\\{\\sum_{a\\in B}a:\\varnothing\\ne B\\subseteq A\\right\\},\n \\qquad\n \\FP(A)=\\left\\{\\prod_{a\\in B}a:\\varnothing\\ne B\\subseteq A\\right\\}.\n\\]\nThus each element of $A$ may occur at most once in an individual sum\nor product, and singleton subsets are included. We prove the following.\n\n\\begin{theorem}\\label{thm:main}\nLet $r,m\\ge1$ be integers, and fix real numbers $R\\ge2$ and $D\\ge1$.\nFor every coloring $\\chi:\\N\\to[r]$,\nthere are distinct positive integers $a_1<\\cdots<a_m$ and a color\n$c\\in[r]$ such that\n\\[\n \\chi\\left(\\sum_{j\\in J}a_j\\right)\n =\\chi\\left(\\prod_{j\\in J}a_j\\right)=c\n \\quad\\text{for every }\\varnothing\\ne J\\subseteq[m].\n\\]\nEquivalently, $\\FS(A)\\cup\\FP(A)$ is monochromatic for a set\n$A\\subset\\N$ of cardinality $m$.\nThe elements can additionally be chosen to satisfy\n\\begin{equation}\\label{eq:separated-elements}\n a_1>R,\\qquad\n a_d>R\\left(\\sum_{k<d}a_k+\\prod_{k<d}a_k\\right)^D\n \\quad(2\\le d\\le m).\n\\end{equation}\n\\end{theorem}\n\nTheorem~\\ref{thm:main} resolves Hindman's finite sums and products\nconjecture positively. The same assertion for colorings of\n$\\N_0=\\{0,1,2,\\ldots\\}$ follows by restriction to $\\N$.\nThe separation in \\eqref{eq:separated-elements} also eliminates every\ncollision between expressions except the shared singleton values.\n\n\\begin{corollary}\\label{cor:distinct-expressions}\nFor every finite coloring of $\\N$ and every $m\\ge1$, there is an\n$m$-element set $A\\subset\\N$ for which $\\FS(A)\\cup\\FP(A)$ is\nmonochromatic and\n\\[\n |\\FS(A)|=|\\FP(A)|=2^m-1,\\qquad \\FS(A)\\cap\\FP(A)=A.\n\\]\nIn particular, the union has $2(2^m-1)-m$ distinct elements.\n\\end{corollary}\n\nThe proof of the corollary is elementary and appears at the end of\nSection~\\ref{sec:framework}. Both conclusions concern prescribed\nfinite sets; the separation does not assert the existence of an\ninfinite simultaneous sequence.\n\n\\subsection{History and significance}\n\nThe separate additive and multiplicative problems have classical answers.\nSchur's theorem guarantees a monochromatic triple $\\{x,y,x+y\\}$ in every\nfinite coloring of $\\N$ \\cite{Schur}.\nThe finite sums theorem of Folkman--Rado--Sanders extends this to the\nnonempty subset sums of arbitrarily large finite sets; Sanders's original\nargument appears in \\cite[Theorem~2 and Corollary~1.1]{Sanders}.\nHindman proved the infinite finite sums theorem \\cite{Hindman}.\nApplying an additive theorem to the coloring $n\\mapsto\\chi(2^n)$ gives\nits multiplicative counterpart. The simultaneous question is different:\nHindman constructed a finite coloring of $\\N$ with no infinite set whose terms,\npairwise sums and pairwise products all have one color\n\\cite{Hindman1980}.\nHis finite conjecture asks for arbitrarily large finite sets instead\n\\cite{Hindman1979}; see also\n\\cite[Question~17.18]{HindmanStrauss}.\n\nFor two variables, computer-assisted arguments of Graham and Hindman\nalready established the existence of a monochromatic quartet\n$\\{x,y,x+y,xy\\}$ in every two-coloring of $\\N$; see the historical\naccount in \\cite[p.~82]{HindmanPhulara}.\nBowen gave a proof in which $x$ and $y$ are distinct and exceed any\nprescribed bound, and more generally controlled the individual variables, their prefix\nproducts and their total sum \\cite[Theorem~1.1]{BowenTwoColors}.\nFor arbitrary finite colorings, Moreira proved the existence of\nmonochromatic triples $\\{x,x+y,xy\\}$ as part of a wider theorem on\nprefix products and polynomial shifts \\cite[Theorem~1.4 and\nCorollary~1.5]{Moreira}.\nAlweiss gave a polynomial proof of the mixed triple and related\npolynomial-shift patterns \\cite[Theorem~1.2]{AlweissPolynomials}.\nMore recently, Alweiss, Bowen and Sabok proved that every two-coloring\ncontains $\\{x,y,xy,x+iy:1\\le i\\le k\\}$ in one color for every prescribed\n$k$ \\cite[Theorem~1.2]{AlweissBowenSabok}.\n\nResults over fields developed in parallel. Green and Sanders proved\nthe four-term result in sufficiently large prime fields\n\\cite[Theorem~1.2]{GreenSanders}, while Bergelson and Moreira developed\naffine ergodic methods for mixed additive--multiplicative patterns\n\\cite{BergelsonMoreiraField,BergelsonMoreiraAffine}.\nBowen and Sabok established $\\{x,y,x+y,xy\\}$ over $\\Q$\n\\cite[Theorem~1.1]{BowenSabok}, and Alweiss proved the full finite sums\nand products theorem over $\\Q$ \\cite[Theorem~1.3]{Alweiss}.\nRichter obtained integer density theorems for the mixed\npair $\\{x+Q(y),xy\\}$ using Fourier analysis, ergodic theory and\nlogarithmic averages over products of primes \\cite[Theorems~1.5 and\n1.7]{Richter}.\n\nThe passage from rational to integer configurations requires more than\nclearing denominators. A common dilation scales a sum by one power of\nthe dilation and a product of $j$ entries by its $j$th power; an\narbitrary coloring need not respect these different changes.\nThe ordered-block selection in our proof follows the Ramsey and\nfinite-sums argument in \\cite[Section~5]{Alweiss}.\nOur Alignment argument also adapts the compensating scale updates in\n\\cite[Section~4]{Alweiss}, which fix the current target product while\nretaining the requirements secured at earlier targets.\nThe additional task is to control the additive shifts arithmetically\nwhile preserving all the required multiplicative colors.\n\n\\subsection{Proof strategy}\n\nThe finite combinatorial part of the argument uses ordered blocks of\nwidely separated integer variables. A Ramsey argument and the finite\nsums theorem select block products whose nonempty products already\nhave one color. The remaining task is to ensure that their sums have\nthat same color. We do this by constructing bounded predictions for\nthe color indicators and then arranging that those predictions remain\npositive at the required sums.\n\nThe proof separates these tasks into a Prediction Principle and an\nAlignment Principle. Prediction replaces the color functions inside\nthe relevant counts by piecewise nilsequences (Lipschitz functions\nevaluated along nilpotent orbits, separately on intervals and residue\nclasses), while retaining the multiplicative color masks.\nIt also ensures that a color occurring at\na center is unlikely to have a small predicted value there. Alignment\nselects from a fixed finite list of rational scale vectors so that a\npositive prediction at every center remains positive after all the\nrequired additive shifts. Its success probability is bounded below\nindependently of the complexity of the nilsequence models. This\nuniformity permits the final choice of small calibration errors.\n\nThree constructions provide the analytic and arithmetic inputs.\n\\begin{enumerate}\n\\item \\emph{Weights for the distribution of a block product.}\nEach chosen element is a product of several raw variables, so its\ndistribution differs from that of a single variable at the same largest\nscale. We encode divisibility by the earlier variables in nonnegative\nweights and attach the same weight to every sum ending at that block.\nPrime substitutions and weighted Cauchy--Schwarz remove the product-color\nconditions and reduce the counting error to one-variable additive tests.\n\n\\item \\emph{Predictions at two additive scales.}\nThe counting estimate tests short additive shifts, whereas Alignment\nneeds one model valid across a larger interval. We construct nilsequence\nmodels on nested intervals and use Ramsey selection to make their\nprojections close. This supplies a common model for both requirements,\nwith nilpotence step depending only on the requested configuration size.\n\n\\item \\emph{Realizing shifts through nilsequence states.}\nAdding an earlier block product may require a noninteger change in the\nlargest-scale variable of that block. We first make an integer residue correction and then\nrealize the remaining displacement as a transformation of the\nnilsequence model. Two separate arguments justify this transformation:\ncomparison of averages on arithmetic progressions, and a lifting theorem\nthat makes transformations for different models act on one common state.\nThese transformations generate a nilpotent group whose step is independent\nof model complexity. Finite polynomial recurrence in this group gives\nthe required uniform alignment probability.\n\\end{enumerate}\n\nThe rough-step comparison and the passage from marginal cube symmetries\nto joint lifts are stated separately in\nPropositions~\\ref{prop:rough-step} and~\\ref{prop:face-lift}. Their\nhypotheses make explicit the joint polynomial dependence and the\ncube information needed to control otherwise unavailable shifts.\nThe proof uses the inverse theorem of Green, Tao and Ziegler\n\\cite{GTZ,GTZErratum}, the concatenation theorem of Tao and Ziegler\n\\cite{TaoZiegler}, quantitative polynomial equidistribution\n\\cite{GreenTao,GreenTaoErratum}, and nilpotent polynomial recurrence\n\\cite{ZorinKranich}. We state the required forms when they are used.\nThe intervening transference, progression-comparison and lifting\narguments are proved below.\n\nSection~\\ref{sec:framework} gives the two principles and derives\nTheorem~\\ref{thm:main} from them, including integrality and separation.\nSections~\\ref{sec:arithmetic}--\\ref{sec:prediction} develop Prediction;\nSections~\\ref{sec:rough-progressions}--\\ref{sec:alignment} develop Alignment.\nThe missing-corner Lemma~\\ref{lem:cube-corner}, proved independently in\nSection~\\ref{sec:cube-limits}, is also used in Prediction. All parameters\nare qualitative: their finiteness and order of choice are essential,\nbut no numerical bound for the smallest configuration is claimed.\n\n\\begin{figure}[htbp]\n\\centering\n\\begin{tikzpicture}[\n node distance=8mm and 7mm,\n box/.style={draw=black!45,rounded corners=2pt,align=center,\n text width=5.65cm,minimum height=12mm,inner sep=5pt,font=\\small},\n result/.style={box,text width=9.8cm},\n arr/.style={-{Stealth[length=2mm]},semithick,draw=black!60}]\n \\node[box] (weights) {Arithmetic scales and divisor weights\\\\\n   Section~\\ref{sec:arithmetic}};\n \\node[box,right=of weights] (geometry) {Rough progressions and cube lifts\\\\\n   Sections~\\ref{sec:rough-progressions}--\\ref{sec:cube-limits}};\n \\node[box,below=of weights] (prediction) {Weighted counts and bounded models\\\\\n   $\\Downarrow$\\\\Prediction, Sections~\\ref{sec:correlation}--\\ref{sec:prediction}};\n \\node[box,below=of geometry] (alignment) {Finite polynomial recurrence\\\\\n   $\\Downarrow$\\\\Alignment, Section~\\ref{sec:alignment}};\n \\path (prediction.south) -- (alignment.south)\n   node[midway,below=13mm,result] (deduction)\n   {Product-chain selection and a positive weighted count\\\\\n    Theorem~\\ref{thm:main} and Corollary~\\ref{cor:distinct-expressions}};\n \\draw[arr] (weights) -- (prediction);\n \\draw[arr] (geometry) -- (alignment);\n \\draw[arr] (geometry.south west) -- (prediction.north east);\n \\draw[arr] (prediction.south) -- (deduction.north west);\n \\draw[arr] (alignment.south) -- (deduction.north east);\n\\end{tikzpicture}\n\\caption{The main analytic routes meet in the combinatorial deduction.\nThe diagonal arrow records the shared missing-corner reconstruction\nfrom Lemma~\\ref{lem:cube-corner}.\nAlignment fixes a positive mass before Prediction fixes model complexity;\nthis order permits the counting error to be made small enough.}\n\\label{fig:proof-map}\n\\end{figure}\n"}, {"path": "preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/02_framework.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/02_framework.tex", "bytes": 19733, "sha256": "62d170d52d3e1197c09270100c1a97e215d123b7187e54a0a20c4a86f03273dc", "content": "\\section{Two principles and the combinatorial deduction}\n\\label{sec:framework}\n\nWe first state the two analytic principles in the precise forms used to\nprove Theorem~\\ref{thm:main}. This also fixes the order of all parameters.\nThe proof of the Prediction Principle occupies\nSections~\\ref{sec:arithmetic}--\\ref{sec:prediction}, with the independent\nmissing-corner Lemma~\\ref{lem:cube-corner} proved in\nSection~\\ref{sec:cube-limits}.\nSections~\\ref{sec:rough-progressions} and~\\ref{sec:cube-limits} develop the\nnilsequence tools for the Alignment Principle, proved in\nSection~\\ref{sec:alignment}.\n\n\\subsection{Ordered blocks and admissible scales}\n\nFor nonempty subsets $A,B\\subseteq[n]$,\n$A<B$ means $\\max A<\\min B$; we write $A<i$ for $A<\\{i\\}$.\nFor a list of multiplicative quantities $u_1,\\ldots,u_n$, put\n$u_A=\\prod_{j\\in A}u_j$. In particular this convention applies to\n$t_A,h_A,b_A$, and $x_A$. Empty products, when they occur, equal one.\n\nA \\emph{block} is a set $B=T\\cup\\{i\\}$ with $\\varnothing\\ne T<i$.\nWe call $i$ its \\emph{pivot} and $T$ its \\emph{tail}. For such a block let\n\\begin{equation}\\label{eq:adding-blocks}\n \\mathcal E_B=\n \\bigl\\{P\\cup\\{j\\}:\\varnothing\\ne P<T<j<i\\bigr\\}.\n\\end{equation}\nThese are the blocks whose values will be added at the anchor $B$.\nA \\emph{chain of length $m$} is a list\n$B_d=T_d\\cup\\{i_d\\}$, $d\\in[m]$, satisfying\n\\begin{equation}\\label{eq:block-chain}\n \\varnothing\\ne T_1<T_2<\\cdots<T_m<i_1<\\cdots<i_m.\n\\end{equation}\nAll blocks in a chain are disjoint. Moreover, if $k<d$, then\n$B_k\\in\\mathcal E_{B_d}$. This last property is why we use this particular\nordering of tails and pivots.\n\nAll asymptotic parameters below are indexed by positive integers\n$w\\to\\infty$, and\n\\[\n W=W(w)=\\prod_{p\\le w}p,\n\\]\nwhere the product is over primes. An integer is \\emph{$w$-smooth} if all\nits prime factors are at most $w$. We say that a positive quantity $A$\n\\emph{dominates powers} of a quantity $B\\ge2$ if\n$A/B^C\\to\\infty$ for every fixed $C>0$. Every finite index set,\ncomplexity bound and tolerance is held fixed in this convention unless\nadditional uniformity is stated. Sampling an interval always means\nsampling its integer points.\n\nFix once and for all a nonprincipal ultrafilter $\\mathcal U$ on the\npositive integers $w$, and write $\\lim_{\\mathcal U}$ for its limit.\nEvery bounded real sequence has such a limit, and it agrees with the\nordinary limit when that exists. Every set belonging to $\\mathcal U$\nmeets every sufficiently late range of $w$. These are the limit and\nextraction properties used below.\n\n\\begin{definition}[Admissible parameters]\\label{def:admissible}\nFor a fixed $n$, an admissible family consists of positive integers\n$M,h_1,\\ldots,h_n,H_1,\\ldots,H_n$, positive powers of two\n$X_1,\\ldots,X_n$, and independent random variables $t_1,\\ldots,t_n$,\nall depending on $w$, with the following properties.\n\\begin{enumerate}\n\\item $M$ and each $h_i$ are $w$-smooth multiples of $W^w$.\nFor every block $B$, and also for every singleton $\\{i\\}$,\n$h_B\\le M$. For every $A\\in\\mathcal E_B$, the ratio $h_A/h_B$\nis an integer multiple of $W^w$.\n\\item Each $H_i$ is a multiple of $M$ and dominates powers of\n$2+M+\\prod_{\\ell<i}X_\\ell$. The quantity $\\log X_i$ dominates powers\nof $H_i$.\n\\item The law of $t_i$ is the harmonic $W$-unit probability measure\n\\begin{equation}\\label{eq:harmonic-law}\n \\mu_i(y)=\\frac{\\1_{X_i\\le y<X_i^2}\\1_{(y,W)=1}}{Z_i y},\n \\qquad\n Z_i=\\sum_{\\substack{X_i\\le y<X_i^2\\\\(y,W)=1}}\\frac1y.\n\\end{equation}\n\\end{enumerate}\n\\end{definition}\n\nFixed positive rational factors have only finitely many denominator\nprimes and fixed denominator valuations. Consequently they can be\nabsorbed by $W^w$ for all sufficiently large $w$. In particular, every\ncolor argument used below is eventually a positive integer. For a chain\nand fixed positive rational $a_1,\\ldots,a_m$, the ratios\n$h_{B_k}a_k/(h_{B_d}a_d)$, $k<d$, are eventually positive integers:\nthe pair belongs to \\eqref{eq:adding-blocks}. These facts allow us to\nuse rational scale lists without ever evaluating the coloring on a\nnoninteger.\n\n\\subsection{Piecewise nilsequence models}\n\nA \\emph{nilsequence of step at most $s$} is a sequence\n\\begin{equation}\\label{eq:nilsequence-definition}\n k\\longmapsto F(g^k x),\\qquad k\\in\\Z,\n\\end{equation}\nwhere $G$ is a connected simply connected nilpotent Lie group of step\nat most $s$, $\\Gamma\\subset G$ is a lattice, $x\\in G/\\Gamma$, $g\\in G$,\nand $F$ is a Lipschitz function on $G/\\Gamma$.\nThroughout this paper a family has \\emph{bounded complexity} if its\nnilmanifolds belong to a fixed finite list and its observables have\nuniformly bounded supremum and Lipschitz norms for fixed smooth metrics.\nNo bound is imposed on the translating elements $g$ or the points $x$.\nConstants are included. Products and Lipschitz combinations of any\nfixed finite number of such families remain bounded-complexity\nnilsequences of the same maximal step, by taking product nilmanifolds.\n\n\\begin{definition}[Piecewise models]\\label{def:piecewise-model}\nFix finite sets $\\mathcal A\\subset\\Q_{>0}$ and $[r]$, and an admissible\nfamily. A system of step-at-most-$s$ models consists of functions\n$S_{B,a,c}:\\Z\\to[0,1]$, one for each block $B$, $a\\in\\mathcal A$,\nand $c\\in[r]$. If $i=\\max B$, then on each interval\n$[kH_i,(k+1)H_i)$ and each residue class modulo $M$, the function is a\nnilsequence in the progression index. The complexity is bounded\nuniformly over $w$, all pieces, and all model indices. The representing\nobservables may be, and are, chosen $[0,1]$-valued on their entire\nnilmanifolds.\n\\end{definition}\n\nThe sequence and its nilmanifold may change from piece to piece within\nthe fixed finite list. This freedom is necessary in the inverse-theorem\nargument. Real parts followed by clipping to $[0,1]$ justify the final\nsentence of Definition~\\ref{def:piecewise-model} without increasing the\nnilpotence step.\n\nFor a block $B=T\\cup\\{i\\}$ define its divisor weight on all of $\\Z$ by\n\\begin{equation}\\label{eq:divisor-weight}\n \\nu_B(y)=\\E_{\\sigma=t_T}\\sigma\\1_{\\sigma\\mid y}.\n\\end{equation}\nThe expectation uses the raw laws of the tail variables only.\nFor a fixed tail product $\\sigma$, we have $(\\sigma,W)=1$, so\nthe conditional mass of $\\sigma t_i$ at $y$ is\n\\[\n \\frac{\\sigma\\1_{\\sigma\\mid y}\\1_{(y,W)=1}}{Z_i y}\n \\quad\\text{on }[\\sigma X_i,\\sigma X_i^2).\n\\]\nThus $\\nu_B$ averages the divisibility factor inserted by multiplication\nby the tail. Corollary~\\ref{cor:product-law} controls the change of\nendpoints back to $[X_i,X_i^2)$ and shows that the sum of the absolute\ndifferences between the masses of $\\operatorname{Law}(t_B)$ and\n$\\nu_B\\mu_i$ tends to zero.\nWhenever several copies of a weight are expanded, they receive\nindependent divisor samples. In particular, two occurrences of\n$\\nu_B$ do not share a sample unless this is expressly stated.\n\n\\subsection{Prediction and alignment}\n\nWe use finite lists $\\mathcal B\\subset\\Q_{>0}^n$ and\n$\\mathcal A\\subset\\Q_{>0}$ satisfying\n\\begin{equation}\\label{eq:scale-list-closure}\n b_B\\in\\mathcal A\\quad\\text{for all }b\\in\\mathcal B\n \\text{ and all blocks }B.\n\\end{equation}\nThe coloring is a map $\\chi:\\N\\to[r]$.\n\n\\begin{principle}[Prediction]\\label{pr:prediction}\nFor every $m\\ge2$ there is an integer $s=s(m)$ with the following\nproperty. Given $n,r,\\chi$, finite lists satisfying\n\\eqref{eq:scale-list-closure}, and tolerances\n$0<\\tau<1/4$, $\\eta>0$, there are admissible parameters and\nstep-at-most-$s$ models such that the following conclusions hold along\nthe fixed ultrafilter $\\mathcal U$ in $w$.\n\\begin{enumerate}\n\\item For each $B,a,c$,\n\\begin{equation}\\label{eq:prediction-calibration}\n \\lim_{\\mathcal U}\\PP\\bigl(\n \\chi(h_Bat_B)=c,\\ S_{B,a,c}(t_B)\\le2\\tau\\bigr)\n \\le3\\tau+\\eta.\n\\end{equation}\n\\item Fix a chain $B_1,\\ldots,B_m$, $b\\in\\mathcal B$ and a color\n$c\\in[r]$. Put\n\\[\n c_d=h_{B_d}b_{B_d},\\qquad\n f_d(y)=\\1_{\\chi(c_dy)=c},\\qquad\n \\nu_d=\\nu_{B_d},\\qquad S_d=S_{B_d,b_{B_d},c}.\n\\]\nHere $f_d$ is defined on positive integers and may be extended by zero\nelsewhere. For $\\varnothing\\ne J\\subseteq[m]$ write $d(J)=\\max J$ and\n\\begin{align}\n L_J(z)&=\\sum_{k\\in J}\\frac{c_k}{c_{d(J)}}z_k,\n \\label{eq:sum-forms}\\\\\n U(z)&=\\prod_{\\varnothing\\ne J\\subseteq[m]}\n \\1_{\\chi(\\prod_{k\\in J}c_kz_k)=c}.\n \\label{eq:product-mask}\n\\end{align}\nThen the nonnegative weighted count\n\\begin{equation}\\label{eq:weighted-count}\n \\mathcal I_{b,c,\\mathbf B}=\n \\E_{z\\sim\\bigotimes_{d=1}^m\\mu_{i_d}}\n U(z)\\prod_{d=1}^m\\nu_d(z_d)\n \\prod_{\\substack{J\\subseteq[m]\\\\|J|\\ge2}}\n (f_{d(J)}\\nu_{d(J)})(L_J(z))\n\\end{equation}\nsatisfies, for $z(t)_d=t_{B_d}$,\n\\begin{equation}\\label{eq:prediction-counting}\n \\lim_{\\mathcal U}\n \\abs*{\\mathcal I_{b,c,\\mathbf B}\n -\\E_t U(z(t))\n \\prod_{\\substack{J\\subseteq[m]\\\\|J|\\ge2}}\n S_{d(J)}(L_J(z(t)))}\\le\\eta.\n\\end{equation}\n\\end{enumerate}\n\\end{principle}\n\nThe step bound depends only on the requested chain length $m$.\nThe complexity bound may depend on all the fixed data and tolerances.\nThe extra weights on the sum forms in \\eqref{eq:weighted-count} are\ndeliberate: they put the function at each sum under the same majorant\nas the function at its last summand. Positivity of this weighted count\nstill gives an actual monochromatic configuration.\n\n\\begin{principle}[Alignment]\\label{pr:alignment}\nFor every $n,r,s$ there are finite lists $\\mathcal B,\\mathcal A$\nsatisfying \\eqref{eq:scale-list-closure} and a constant $\\delta>0$,\ndepending only on $n,r,s$, such that the following holds.\nFor any admissible family, any system of step-at-most-$s$ models, and\nany fixed $\\tau>0$, define $\\mathsf A_w$ to be the event that some\n$b\\in\\mathcal B$ satisfies, simultaneously for every block $B$, color\n$c\\in[r]$, and subset $D\\subseteq\\mathcal E_B$,\n\\begin{equation}\\label{eq:alignment-implication}\n \\begin{split}\n S_{B,b_B,c}(t_B)>2\\tau\\quad\\Longrightarrow\\quad\n S_{B,b_B,c}\\left(\n t_B+\\sum_{A\\in D}\\frac{h_Ab_A}{h_Bb_B}t_A\\right)>\\tau.\n \\end{split}\n\\end{equation}\nThen\n\\begin{equation}\\label{eq:alignment-probability}\n \\liminf_{w\\to\\infty}\\PP(\\mathsf A_w)\\ge\\delta.\n\\end{equation}\nIn particular, $\\delta$ is independent of $\\tau$ and of the model\ncomplexity bound.\n\\end{principle}\n\nThere is no assertion of a convergence rate uniform over all\ncomplexities in \\eqref{eq:alignment-probability}. Its positive lower\nbound is uniform. This distinction allows us to choose the small\ncalibration tolerance before constructing the models.\n\n\\subsection{A finite combinatorial selection}\n\nWe use the finite sums theorem in its following finite form: for every\n$m,r$ there is $F=F(m,r)$ such that every coloring of $[F]$ with $r$\ncolors contains positive integers $u_1,\\ldots,u_m$ whose nonempty subset\nsums all lie in $[F]$ and have one color. This is the finite sums theorem\nof Folkman--Rado--Sanders \\cite[Theorem~2 and Corollary~1.1]{Sanders};\nit also follows from Hindman's finite sums theorem and compactness\n\\cite{Hindman,HindmanStrauss}. Indeed, if the finite form failed,\nthe finitely branching tree of bad finite colorings, ordered by restriction,\nwould have an infinite branch. Its limiting coloring of $\\N$ would\ncontradict the finite sums theorem. We require no distinctness of\nthe auxiliary $u_d$ here.\n\nThe following selection uses the Ramsey-by-cardinality and finite-sums\nargument of \\cite[Section~5, Steps~II--IV]{Alweiss}; the ordered tails\nand pivots are chosen to match the additive shifts in\n\\eqref{eq:adding-blocks}.\n\n\\begin{lemma}[Selection of a product chain]\\label{lem:chain-selection}\nFor every $m,r$ there is $n=n(m,r)$ such that, for every coloring\n$\\chi:\\N\\to[r]$ and every list $x_1,\\ldots,x_n\\in\\N$, a chain\n$B_1,\\ldots,B_m$ has all nonempty products of\n$x_{B_1},\\ldots,x_{B_m}$ in a single color class.\n\\end{lemma}\n\n\\begin{proof}\nChoose $F$ as above and put $n_0=2F$. Repeated finite Ramsey gives\nan $n$ such that every coloring of the nonempty index subsets of\n$[n]$ has an $n_0$-element subset on which the color depends only on\ncardinality, when the number of colors is $r$. More precisely, apply\nthe finite Ramsey theorem successively to the subset sizes\n$1,\\ldots,n_0$, choosing the successive ambient bounds backward.\nApply this to the coloring $A\\mapsto\\chi(x_A)$, and call the\nresulting cardinality color $\\kappa(q)$, $1\\le q\\le n_0$.\n\nColor $a\\in[F]$ by $\\kappa(2a)$. Choose $u_1,\\ldots,u_m$ by the\nfinite sums theorem and put $q_d=2u_d$. Then $q_d\\ge2$,\n$\\sum_dq_d\\le n_0$, and all nonempty subset sums of the $q_d$ have\nthe same $\\kappa$-color. In the homogeneous ordered index set,\nchoose successive groups of $q_1-1,\\ldots,q_m-1$ indices for\n$T_1,\\ldots,T_m$, followed by $m$ indices for the pivots\n$i_1<\\cdots<i_m$. This uses $\\sum_dq_d$ indices and satisfies\n\\eqref{eq:block-chain}. A product of the corresponding block\nproducts is $x_{\\bigcup_{d\\in J}B_d}$, whose index-set size is\n$\\sum_{d\\in J}q_d$. All these sizes have the chosen color.\n\\end{proof}\n\n\\subsection{Deduction of the main theorem}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}, assuming the two principles]\nThe case $m=1$ follows by choosing any integer $a_1>R$.\nFor $m\\ge2$, fix $s=s(m)$ from\nPrinciple~\\ref{pr:prediction} and $n=n(m,r)$ from\nLemma~\\ref{lem:chain-selection}. Apply Principle~\\ref{pr:alignment}\nto fix $\\mathcal B,\\mathcal A$ and $\\delta>0$.\nLet $C$ be the number of triples $(B,a,c)$ indexing calibration\nevents, and let $K$ be the number of triples consisting of a scale\nvector, a chain of length $m$, and a color. These are fixed finite\nnumbers depending on data already chosen.\n\nChoose $0<\\tau<1/4$ and then $\\eta>0$ so that\n\\begin{equation}\\label{eq:outer-tolerances}\n 3C\\tau<\\frac\\delta4,\\qquad\n C\\eta<\\frac\\delta4,\\qquad\n K\\eta<\\frac{\\delta\\tau^{2^m}}4.\n\\end{equation}\nNow apply Principle~\\ref{pr:prediction}. Define $\\mathsf G_w$ to\nbe the event that none of its calibration failures occurs. The\nunion bound and \\eqref{eq:prediction-calibration} give\n\\[\n \\lim_{\\mathcal U}\\PP(\\mathsf G_w^c)\n \\le C(3\\tau+\\eta)<\\frac\\delta2.\n\\]\nSince an ordinary lower limit bounds every ultrafilter limit,\n\\eqref{eq:alignment-probability} yields\n\\begin{equation}\\label{eq:good-alignment-mass}\n \\lim_{\\mathcal U}\\PP(\\mathsf A_w\\cap\\mathsf G_w)\n \\ge\\frac\\delta2.\n\\end{equation}\n\nOn this intersection choose a witnessing $b\\in\\mathcal B$ and\napply Lemma~\\ref{lem:chain-selection} to\n$x_i=h_ib_it_i$, which are positive integers for all sufficiently\nlarge $w$. It supplies a chain and a color $c$ for which\n$U(z(t))=1$. In particular, the singleton product masks say\n$\\chi(h_{B_d}b_{B_d}t_{B_d})=c$ for every $d$. Because\n$\\mathsf G_w$ holds, all centers $S_d(t_{B_d})$ exceed $2\\tau$.\n\nFor a nonsingleton $J\\subseteq[m]$, let $d=\\max J$. Each\n$B_k$, $k\\in J\\setminus\\{d\\}$, belongs to $\\mathcal E_{B_d}$.\nThe corresponding instance of \\eqref{eq:alignment-implication}\ntherefore gives\n\\[\n S_d\\bigl(L_J(z(t))\\bigr)>\\tau.\n\\]\nThere are $q=2^m-m-1$ such factors. Thus at least one of the $K$\nnonnegative model integrands in \\eqref{eq:prediction-counting}\nis greater than $\\tau^q\\ge\\tau^{2^m}$ on\n$\\mathsf A_w\\cap\\mathsf G_w$. Summing expectations and using\n\\eqref{eq:good-alignment-mass} shows that their sum has ultrafilter\nlimit at least $\\delta\\tau^{2^m}/2$. It follows from\n\\eqref{eq:prediction-counting} and \\eqref{eq:outer-tolerances} that\n\\begin{equation}\\label{eq:positive-total-count}\n \\lim_{\\mathcal U}\n \\sum_{b,c,\\mathbf B}\\mathcal I_{b,c,\\mathbf B}>0.\n\\end{equation}\nIn particular, the finite sum is bounded below by a positive number\non a set belonging to $\\mathcal U$. We did not need a fixed\nchoice of witnessing chain across different samples or values of $w$.\n\nFor some sufficiently large $w$, one weighted count is positive.\nSince it is a finite nonnegative average, there is an actual tuple\n$z$ in its support for which every factor is positive. Put\n$a_d=c_dz_d$. The product mask gives every nonempty product of\nthe $a_d$ in color $c$, including their singleton values.\nFor every nonsingleton $J$, positivity of its color factor gives\n\\[\n \\chi\\left(c_{d(J)}L_J(z)\\right)\n =\\chi\\left(\\sum_{k\\in J}a_k\\right)=c.\n\\]\nThe extra divisor factors cannot create a false positive; they only\nrestrict which tuples can contribute to the count.\n\nIt remains to obtain the prescribed separation. The finite rational scale lists\ngive a constant $C_0$ such that $c_d\\le C_0M$ for all these counts,\nand $c_d\\ge1$ once it is a positive integer. If $d<e$, then\n\\[\n a_d<C_0M X_{i_d}^2,\\qquad a_e\\ge X_{i_e}.\n\\]\nFor $e\\ge2$, put $V_e=2+M+\\prod_{j<i_e}X_j$.\nThere is a constant $C_1$, depending only on the fixed finite data,\nsuch that every support tuple satisfies\n\\[\n \\sum_{d<e}a_d+\\prod_{d<e}a_d\\le C_1 V_e^{3m}.\n\\]\nIndeed each preceding $a_d$ is at most $C_0V_e^3$, and there are\nat most $m-1$ such terms. Admissibility makes $X_{i_e}$ dominate\nevery fixed power of $V_e$. Therefore, for the prescribed $R,D$,\nall support tuples at sufficiently large $w$ satisfy\n\\eqref{eq:separated-elements}; the first element exceeds $R$ since\n$a_1\\ge X_{i_1}\\to\\infty$. The positive-count set in\n\\eqref{eq:positive-total-count} belongs to the nonprincipal\nultrafilter and hence meets every sufficiently late range of $w$.\nChoose $w$ in that intersection. This proves both monochromaticity\nand separation, and completes Theorem~\\ref{thm:main}.\n\\end{proof}\n\n\\begin{proof}[Proof of Corollary~\\ref{cor:distinct-expressions}]\nApply Theorem~\\ref{thm:main} with $R=2$ and $D=1$.\nEvery $a_j$ exceeds the sum and the product of all its predecessors,\nand every element is at least two. If two subset sums were equal,\ncancel their common terms. The greatest remaining index on either\nside would exceed the sum on the other side, a contradiction.\nFor products, cancel common factors and compare the greatest\nremaining factor with the product of all preceding factors.\nAn empty side after cancellation equals one, which is also smaller\nthan every remaining factor. Thus both subset maps are injective.\n\nTo compare a subset sum with a subset product, first compare their\ngreatest indices. If these differ, the expression with the larger\nindex exceeds the other expression. If both have greatest index $j$,\nthe sum lies in $[a_j,2a_j)$, whereas a nonsingleton product is at\nleast $2a_j$. A singleton product equals $a_j$, which equals a sum\nwith greatest index $j$ only for the singleton sum. Hence the\nintersection is exactly $A$, giving the stated cardinalities.\n\\end{proof}\n\n\\begin{corollary}[Finite interval form with prescribed divisibility]\n\\label{cor:finite-interval}\nFix integers $r,m,q\\ge1$ and real numbers $R\\ge2$, $D\\ge1$.\nThere exists $N$ such that every $r$-coloring of $[N]$ admits\n$A=\\{a_1<\\cdots<a_m\\}\\subset q\\N$ with\n$\\FS(A)\\cup\\FP(A)\\subseteq[N]$ monochromatic and\n\\[\n a_1>R,\\qquad\n a_d>R\\left(\\sum_{k<d}a_k+\\prod_{k<d}a_k\\right)^D\n \\quad(2\\le d\\le m).\n\\]\nThe subset sums and products have the distinctness properties of\nCorollary~\\ref{cor:distinct-expressions}.\n\\end{corollary}\n\n\\begin{proof}\nFirst color all of $\\N$, refining the given color by the residue\nmodulo $q$. Apply Theorem~\\ref{thm:main} to this finite coloring\nwith $\\max(m,2)$ elements. Their common residue $v$ also equals\nthe residue of the sum of the first two elements, so $v=2v$\nmodulo $q$ and $v=0$. Retain the first $m$ elements. The\nseparation and monochromaticity persist, and the preceding proof\ngives the distinctness assertions.\n\nIf no $N$ worked, the finite colorings avoiding such a configuration\nwould form a finitely branching tree, closed under restriction and\nwith a node at every depth. An infinite branch would color $\\N$\nwithout the configuration just proved to exist. Indeed each finite\nconfiguration and all its sums and products lie in some initial\ninterval. This contradiction proves the finite interval assertion.\n\\end{proof}\n\nThis compactness argument gives a finite bound, but the proof does\nnot supply a numerical estimate for $N$.\n"}, {"path": "preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/03_arithmetic.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/03_arithmetic.tex", "bytes": 33626, "sha256": "b8575d77d1652ebe55e7db0f4a6fdbcd3709b70cf8b253d9d8825c0fd80b141e", "content": "\\section{Arithmetic scales and divisor weights}\\label{sec:arithmetic}\n\nPrediction compares a count sampled at the pivots with a count sampled\nat the block products.  For a block \\(B=T\\cup\\{i\\}\\), conditioning on\n\\(\\sigma=t_T\\) changes the harmonic density of \\(t_i\\) to\n\\(\\sigma\\1_{\\sigma\\mid y}/(Z_i y)\\) on\n\\([\\sigma X_i,\\sigma X_i^2)\\), with the same \\(W\\)-unit restriction.\nAveraging the divisibility factor gives \\(\\nu_B\\).  We first show that\nreturning the endpoints to \\([X_i,X_i^2)\\) has negligible total mass.\n\nWe then construct scales and prime parameters for the correlation\nargument.  The prime laws have enough harmonic mass to permit prime\ninsertion, and their residue uniformity makes products of divisor\nweights have mean one on the required linear systems.  A separate\ncoprimality estimate for independent polynomial values will allow\nSection~\\ref{sec:prediction} to combine cube tests with different\nprime-dependent step sizes.\n\nWe use \\emph{rough} to mean coprime to \\(W=\\prod_{p\\le w}p\\), and put\n\\[\n |a|_{>w}=\\prod_{p>w}p^{v_p(a)}\\qquad(a\\in\\Z\\setminus\\{0\\}).\n\\]\nAll finite templates in this Section are fixed before the limit\n\\(w\\to\\infty\\).  A template specifies numbers of variables and rows,\nblock sizes, and integer polynomials, but no color functions.\n\n\\subsection{Harmonic sampling identities}\n\nWe give explicit total-mass bounds to distinguish multiplication of\nan argument from conditioning on divisibility.  For a signed measure\non \\(\\Z\\), write \\(\\|\\xi\\|_1=\\sum_{n\\in\\Z}|\\xi(n)|\\).\nThus probability total variation is one half of this norm.\n\n\\begin{lemma}[Sampling and changes of variables]\\label{lem:sampling}\nLet \\(X\\ge2\\) be an integer, set\n\\[\n \\vartheta_W=\\frac{\\phi(W)}W,\\quad L_X=\\log X,\\quad\n Z_X=\\sum_{\\substack{X\\le n<X^2\\\\(n,W)=1}}\\frac1n,\\quad\n \\mu_X(n)=\\frac{\\1_{[X,X^2)}(n)\\1_{(n,W)=1}}{nZ_X},\n\\]\nand suppose \\(L_X>W/X\\).  Let \\(Y\\) have law \\(\\mu_X\\).\n\\begin{enumerate}\n\\item For \\((k,W)=1\\), every residue \\(a\\pmod k\\), and\n\\(0<A<B\\),\n\\begin{equation}\\label{eq:periodic-harmonic}\n \\left|\\sum_{\\substack{A\\le n<B\\\\(n,W)=1\\\\n\\equiv a\\ (k)}}\\frac1n\n  -\\frac{\\vartheta_W}{k}\\log\\frac BA\\right|\n \\le\\frac{\\phi(W)}A.\n\\end{equation}\nIn particular \\(|Z_X-\\vartheta_WL_X|\\le\\phi(W)/X\\), and\n\\begin{equation}\\label{eq:harmonic-residue-error}\n \\left|k\\PP(Y\\equiv a\\pmod k)-1\\right|\n \\le E_X(k):=\\frac{W(k+1)}{X(L_X-W/X)}.\n\\end{equation}\nThe total-mass distance of the residue law from uniform measure is\nalso at most \\(E_X(k)\\).\n\\item If \\(h\\in W\\Z\\), then\n\\begin{equation}\\label{eq:harmonic-translation}\n \\|\\operatorname{Law}(Y+h)-\\mu_X\\|_1\n \\le \\min\\left(2,\\frac{2|h|}{X(L_X-W/X)}\\right).\n\\end{equation}\n\\item If \\(1\\le k\\le X\\) and \\((k,W)=1\\), let\n\\(\\eta_k(n)=k\\1_{k\\mid n}\\mu_X(n)\\), an unnormalized positive\nmeasure.  Then\n\\begin{equation}\\label{eq:harmonic-dilation}\n \\|\\operatorname{Law}(kY)-\\eta_k\\|_1\n \\le\\frac{2\\log k+(Wk/X)(1+1/X)}{L_X-W/X},\n\\end{equation}\nand \\(|\\eta_k(\\Z)-1|\\le E_X(k)\\).\n\\end{enumerate}\nFor the asymptotic conclusions, let \\(K,H,V\\ge1\\) be auxiliary\nquantities depending on \\(w\\).  Each residue and translation error is\n\\(o(V^{-C})\\), for every fixed \\(C\\), uniformly for\n\\(k\\le K\\) and \\(|h|\\le H\\), if \\(X\\) dominates every fixed\npower of \\(2+W+K+H+V\\).  The dilation error has the same conclusion\nif \\(\\log X\\) dominates every fixed power of \\(2+W+K+V\\).\nThese conclusions still hold in expectations of functions bounded\nby any fixed power of \\(V\\).\n\\end{lemma}\n\n\\begin{proof}\nThe indicator in \\eqref{eq:periodic-harmonic} is periodic modulo\n\\(Wk\\) and occupies exactly \\(\\phi(W)\\) residue classes.  On any\ninterval, its counting function differs from\n\\(\\vartheta_W/k\\) times length by at most \\(\\phi(W)\\): apply\nthe discrepancy bound of one to each occupied class.  Partial\nsummation against \\(1/t\\) gives \\eqref{eq:periodic-harmonic}.\nApply it with \\(k=1\\) to obtain the bound on \\(Z_X\\).\nFor a general class the error in its unnormalized mass is at most\n\\(\\phi(W)/X\\); subtracting \\(Z_X/k\\) and using\n\\(Z_X\\ge\\vartheta_W(L_X-W/X)\\) proves\n\\eqref{eq:harmonic-residue-error}.  Sum the absolute class errors\nto obtain the total-mass assertion.\n\nFor translation put \\(H=|h|\\).  Translation invariance of the norm\nreduces to \\(h=H\\ge0\\).  On the overlap of the supports,\n\\(1/(n-H)\\ge1/n\\), and \\(W\\mid H\\) preserves the unit\ncondition.  The negative part of\n\\(\\operatorname{Law}(Y+H)-\\mu_X\\) is therefore precisely the\noriginal mass on \\([X,\\min(X+H,X^2))\\).  Both measures have\nmass one, so the norm is twice that mass.  If\n\\(H\\le X^2-X\\), the interval \\([X,X+H)\\) has exactly\n\\(\\vartheta_WH\\) units modulo \\(W\\), since its integer length\nis a multiple of \\(W\\); its harmonic mass is at most\n\\(\\vartheta_WH/X\\).  If \\(H>X^2-X\\), the norm is at most two,\nand the second bound in \\eqref{eq:harmonic-translation} is already\nat least two.  This proves the translation estimate.\n\nFor dilation, at a multiple \\(n\\) of \\(k\\) the pushed-forward\nmass is \\(k/(nZ_X)\\), supported on \\([kX,kX^2)\\) and on\n\\((n,W)=1\\).  Thus it agrees exactly with \\(\\eta_k\\) on the\noverlap.  Changing variables \\(n=ku\\) in the two boundary pieces,\nand using \\(k\\le X\\), gives the exact formula\n\\begin{align*}\n \\|\\operatorname{Law}(kY)-\\eta_k\\|_1\n =\\frac1{Z_X}\\bigg(\n  \\sum_{\\substack{X/k\\le u<X\\\\(u,W)=1}}\\frac1u\n  +\\sum_{\\substack{X^2/k\\le u<X^2\\\\(u,W)=1}}\\frac1u\n \\bigg).\n\\end{align*}\nEach main term in \\eqref{eq:periodic-harmonic} is\n\\(\\vartheta_W\\log k\\), and the two errors are at most\n\\(\\phi(W)k/X\\) and \\(\\phi(W)k/X^2\\), respectively.\nDividing by the lower bound for \\(Z_X\\) proves\n\\eqref{eq:harmonic-dilation}.  Its mass assertion follows from\n\\eqref{eq:harmonic-residue-error} with \\(a=0\\).\nFinally, integration against a function of supremum norm \\(B\\)\ncosts at most \\(B\\) times the total-mass error.  The stated growth\nconditions absorb every fixed choice of \\(B=V^{O(1)}\\).\n\\end{proof}\n\nFor later use, uniform sampling on an integer interval of length\n\\(T\\) has residue law modulo \\(k\\) at total-mass distance at most\n\\(2k/T\\) from uniform measure.  Each residue occurs either\n\\(\\lfloor T/k\\rfloor\\) or \\(\\lceil T/k\\rceil\\) times, up to\nthe harmless endpoint convention.  The same bound is uniform in\nthe interval's location.  Translating the interval by an integer\n\\(u\\) changes its probability law in total-mass norm by at most\n\\(2\\min(1,|u|/T)\\).  Product laws incur the sum of the coordinate\nerrors, by telescoping their tensor products.  These facts and\nLemma~\\ref{lem:sampling} apply conditionally when the interval\nendpoints, dilations, or translations have been fixed by outside\nvariables and the displayed bounds hold uniformly in those variables.\n\n\\subsection{Recovering the product sampling law}\n\nThe next Corollary identifies the center weights in\n\\eqref{eq:weighted-count} with the actual block-product sampling.\nOnce the shifted factors have bounded models, this identity will remove\nthe remaining weights from the count.\n\n\\begin{corollary}[Product law]\\label{cor:product-law}\nLet \\(B=T\\cup\\{i\\}\\) be a block, and let \\(t_j\\) be the\nindependent harmonic samples at its raw cutoffs.  Let \\(V=V(w)\\ge1\\)\nbe an auxiliary scale, and put\n\\(K_T=\\prod_{j\\in T}X_j^2\\).  If \\(\\log X_i\\) dominates\nevery fixed power of \\(2+W+K_T+V\\), then\n\\begin{equation}\\label{eq:block-product-law}\n \\left\\|\\operatorname{Law}(t_B)-\n                  \\nu_B\\mu_i\\right\\|_1=o(V^{-C})\n \\qquad\\text{for every fixed }C>0.\n\\end{equation}\nFor any fixed family of disjoint blocks the corresponding joint\nlaw differs in the same sense from the product of their weighted\npivot measures, when these hypotheses hold at each pivot.\n\\end{corollary}\n\n\\begin{proof}\nCondition on \\(\\sigma=t_T\\).  It is coprime to \\(W\\),\nindependent of \\(t_i\\), and bounded by \\(K_T\\).  Apply\n\\eqref{eq:harmonic-dilation} with \\(k=\\sigma\\), uniformly in\nthis range, and average.  The comparison measure is exactly\n\\[\n \\mu_i(y)\\E_{\\sigma}\\sigma\\1_{\\sigma\\mid y}\n     =\\mu_i(y)\\nu_B(y),\n\\]\nproving \\eqref{eq:block-product-law}.  In particular its mass is\n\\(1+o(V^{-C})\\).  Disjoint blocks use disjoint raw variables, so\ntheir original laws are independent.  Telescoping a fixed tensor\nproduct bounds its total-mass error by the sum of the individual\nerrors times the masses of the other factors, all of which are\n\\(1+o(1)\\).  This proves the joint assertion.\n\\end{proof}\n\n\\subsection{A sequential construction of the master scales}\n\nWe now work on a master index set \\([N]\\). Here \\(R_l\\) are auxiliary gap\nlengths and \\(X_j\\) are cutoffs for the raw variables.\nSection~\\ref{sec:prediction} will select the final \\(n\\) indices and choose\neach \\(H_i\\) from these gap lengths.\nThe smooth factors \\(h_j\\) make all required shifts integral.  The prime\npools supply the harmonic mass and residue uniformity described above;\nthe gap lengths absorb the finitely many polynomial divisibilities\nneeded by the subsequent changes of variables.  The raw cutoffs are\nchosen last in each gap so that the preceding sampling estimates apply.\n\n\\begin{lemma}[Master scales]\\label{lem:master-scales}\nFix \\(N\\), a bound on block sizes, a finite set\n\\(\\mathcal A\\subset\\Q_{>0}\\), and a finite list \\(\\mathcal D\\) of\nnonzero integer polynomials in finitely many prime-parameter slots.\nThere are choices of \\(h_j,M,X_j,R_l,P_l^-,P_l^+\\), for\n\\(1\\le j,l\\le N\\), with the following properties.\n\\begin{enumerate}\n\\item\n\\(h_j=W^{w2^{N-j}}\\), and \\(M\\) is a power of \\(W\\), divisible by\n\\(W^w\\), with \\(h_j\\le M\\) for every index and \\(h_B\\le M\\)\nfor every block under consideration.\nEvery permitted adding pair satisfies\n\\(h_A/h_B\\in W^w\\N\\).  For a master chain and\n\\(c_d=h_{B_d}a_d\\), \\(a_d\\in\\mathcal A\\), all \\(c_d\\) are integers\nand \\(c_u/c_d\\in W\\N\\) when \\(u<d\\), for sufficiently large \\(w\\).\n\\item\nThere is an integer \\(e_0=e_0(w)\\) such that, for independent uniform\nunits in the slots modulo \\(W^{e_0}\\),\n\\begin{equation}\\label{eq:small-prime-exception}\n \\PP\\bigl(p^{e_0}\\mid D(\\boldsymbol u)\n       \\text{ for some }p\\le w,\\ D\\in\\mathcal D\\bigr)=o(1).\n\\end{equation}\nMoreover \\(W^{e_0+1}c_d\\mid M\\) for every possible \\(c_d\\).\n\\item\nWriting\n\\[\n V_l=2+M+\\prod_{j<l}X_j^2,\n \\qquad\n Q_l=W^{e_0}\\prod_{w<p\\le V_l}p,\n\\]\nthe pool \\(\\mathcal P_l\\) consists of all primes in a finite union of\nconsecutive complete dyadic intervals\n\\([P_l^-,P_l^+)\\).  Its law and harmonic mass are\n\\begin{equation}\\label{eq:prime-pool-law}\n H_l^{\\rm pr}=\\sum_{p\\in\\mathcal P_l}\\frac1p,\n \\qquad \\lambda_l(p)=\\frac1{pH_l^{\\rm pr}}.\n\\end{equation}\nBoth \\(P_l^-\\) and \\(H_l^{\\rm pr}\\) dominate every fixed power of\n\\(V_l\\).  The residue law of a sample modulo \\(Q_l\\) differs from\nuniform measure on \\((\\Z/Q_l\\Z)^\\times\\) by\n\\(o(V_l^{-C})\\) in total variation, for every fixed \\(C\\).\nAll slots are sampled independently, including slots in different gaps.\n\\item\n\\(R_l\\) dominates every fixed power of \\(P_l^++V_l\\).  It is divisible\nby \\(M\\), by every earlier \\(R_j\\), and by\n\\(M|D(\\boldsymbol p)|\\) for all the nonzero values required for\ndivisibility in gap \\(l\\), with these polynomial slots drawn from\nthat gap's pool.  The integer \\(X_l\\) is a power of two, and \\(\\log X_l\\)\ndominates every fixed power of \\(R_l\\).\n\\end{enumerate}\nOne can include any prescribed finite family of polynomial nonvanishing\nconditions in \\(\\mathcal D\\).  Repeated entries and zero values then\nhave probability \\(o(V_l^{-C})\\) for every fixed \\(C\\), whereas the\nsmall-prime exceptions in \\eqref{eq:small-prime-exception} have\nprobability \\(o(1)\\).\n\\end{lemma}\n\n\\begin{proof}\nSet \\(e_j=2^{N-j}\\).  The strict inequality\n\\(e_j>\\sum_{k>j}e_k\\) shows that the sign of\n\\(\\sum_{j\\in A}e_j-\\sum_{j\\in B}e_j\\), for distinct subsets, is\ndetermined by the smallest index in their symmetric difference.\nFor an adding pair that index belongs to \\(A\\); for two chained\nblocks it belongs to the earlier block.  The positive difference is\nan integer, so the corresponding ratio of \\(h\\)-products is divisible\nby \\(W^w\\).  Fixed rational multipliers have bounded valuations and\nonly finitely many denominator primes.  Consequently they are absorbed\nby these powers for all sufficiently large \\(w\\), leaving at least\none factor of every prime dividing \\(W\\) in each required ratio of\nthe \\(c_d\\)'s.\n\nFor completeness, a nonzero polynomial over \\(\\Q_p\\) has a null zero\nset in a product of \\(p\\)-adic unit groups.  In one variable it has\nonly finitely many roots.  In several variables, expand in a variable\non which it depends and choose a nonzero coefficient polynomial.\nBy induction, the set on which that coefficient vanishes has measure\nzero; off that set, each fiber has finitely many roots.  Fubini's\nTheorem proves the assertion.  The decreasing events\n\\(p^e\\mid D\\) therefore have probabilities tending to zero as\n\\(e\\to\\infty\\).  At any fixed \\(w\\), the list of polynomials and\nprimes is finite.  Choose a common \\(e_0\\) making their union\nprobability at most \\(1/w\\).  Reduction of uniform units modulo\n\\(W^{e_0}\\) has exactly the required distribution modulo\n\\(p^{e_0}\\).  We can now choose the power \\(M\\) large enough for\nall the finitely many stated smooth divisibilities and bounds.\n\nProceed in increasing order of \\(l\\).  At this stage \\(Q_l\\) is a\nfixed integer.  The prime number theorem in each reduced arithmetic\nprogression modulo \\(Q_l\\) \\cite[Equation~(1.1)]{SelbergAP}, followed by partial summation, says that\nthe harmonic prime law on \\([Y,2Y)\\) tends to the uniform law on\nits unit classes as \\(Y\\to\\infty\\).  Since there are finitely many\nclasses, choose a dyadic lower endpoint so large that the total\nvariation error is at most \\(V_l^{-w}\\) on every subsequent dyadic\ninterval, and also \\(P_l^-\\ge wV_l^w\\).  Increasing the upper\nendpoint gives \\(H_l^{\\rm pr}\\ge wV_l^w\\), since the harmonic prime\nseries diverges.  A mixture of measures having the same error bound\nhas that error bound.  Thus this finite pool has the asserted\nresidue distribution, however long the union of intervals is.\n\nOnly finitely many polynomial values occur in a finite pool.  Take\ntheir nonzero absolute values, the earlier gap lengths, and \\(M\\),\nform the required least common multiple, and choose a multiple\n\\(R_l\\) at least \\(w(P_l^++V_l)^w\\).  Finally choose a power of two\n\\(X_l\\) with \\(\\log X_l\\ge wR_l^w\\).  There is no upper bound in\nany of these choices, and no later requirement changes an earlier\nchoice.\n\nHere is the elementary zero-value estimate used for the final\nassertion.  If independent variables each have maximum atom at most\n\\(a\\), a nonzero polynomial of total degree \\(d\\) vanishes with\nprobability at most \\(da\\).  Expand in its last relevant variable:\noutside the zero set of its leading coefficient there are at most\nas many roots as its degree in that variable, and induction bounds\nthe leading-coefficient exception by its degree times \\(a\\).\nFor our pool, \\(a\\le1/(P_l^-H_l^{\\rm pr})\\); the same estimate for\n\\(x_i-x_j\\) handles repeats.  These bounds are smaller than every\nfixed inverse power of \\(V_l\\). Finally, the residue-law approximation\nmodulo \\(Q_l\\), together with the Chinese remainder theorem (CRT),\ntransfers \\eqref{eq:small-prime-exception} to the actual prime slots.\n\\end{proof}\n\nFor a block \\(B=T\\cup\\{i\\}\\) at these scales, its tail bound\n\\(K_T\\) and \\(W\\) are at most \\(V_i\\).  Since \\(\\log X_i\\)\ndominates every fixed power of \\(V_i\\),\nCorollary~\\ref{cor:product-law} applies with \\(V=V_i\\).\nIn particular, the joint law comparison holds for every chain and\nhence for bounded expectations of all its block variables.\n\n\\begin{remark}[Countably many fixed tests]\\label{rem:arithmetic-diagonal}\nLater, a fixed test may be raised to any fixed moment or combined\nwith any fixed number of other tests.  This does not require a\nmoment order growing inside an estimate.  Enumerate the resulting\ninteger-polynomial templates and positive integer auxiliary parameters.\nAt stage \\(j\\) impose the requirements for the first \\(j\\) templates.\nEach stage has finitely many requirements, so the preceding proof\napplies.  Choose increasing thresholds \\(w_j\\) so that the finitely\nmany error bounds at that stage, including constants in the estimates\nbelow, are at most \\(1/j\\) once \\(w\\ge w_j\\).  At a given \\(w\\)\nuse stage \\(\\max\\{j:w_j\\le w\\}\\), with the growth exponents also\ntending to infinity.  Every fixed finite test family then satisfies\nall the conclusions eventually.  This diagonal choice can be made\nbefore the functions to be tested are chosen, because its templates\ndepend only on their algebraic forms.  All moment limits below mean\nfirst \\(w\\to\\infty\\) at a fixed moment order.\n\\end{remark}\n\n\\subsection{Coprimality beyond the CRT cutoff}\n\nThe next estimate treats prime factors larger than \\(V_l\\), as well\nas those controlled by the residue distribution of a pool.  The\nlinear-forms estimate below uses residue uniformity separately.\nCoprimality has a different purpose: in a cyclic group, the subgroups\nof multiples of two coprime integers together generate the whole group.\nSection~\\ref{sec:prediction} applies this fact to combine cube tests\nwith different step sizes.\n\n\\begin{lemma}[Rough coprimality]\\label{lem:rough-coprimality}\nLet \\(F\\) and \\(G\\) be fixed nonzero integer polynomials in disjoint\ntuples of independent prime variables.  Each variable is sampled with\nprobability proportional to \\(1/p\\) in a dyadic interval \\([Y,2Y)\\).\nLet \\(L\\) be the smallest of these lower endpoints.  For all\nsufficiently large \\(w,L\\),\n\\begin{align}\n \\PP(FG=0)&\\ll_{F,G}\\frac{\\log L}{L},\\label{eq:polynomial-zero-bound}\\\\\n \\PP\\bigl(FG\\ne0,\\ \\gcd(|F|_{>w},|G|_{>w})>1\\bigr)\n &\\ll_{F,G}\\frac1w+\\frac{\\log L}{\\sqrt L}.\n \\label{eq:rough-coprimality-bound}\n\\end{align}\nThe constants are independent of the ratios between the dyadic\nendpoints.  Consequently two independent tuples from any of the\nconstructed pools have coprime rough polynomial values with\nprobability \\(1-o(1)\\).  The same assertion holds after restricting\neach tuple to a prime-only event of probability \\(1-o(1)\\).\n\\end{lemma}\n\n\\begin{proof}\nThe prime number theorem gives a maximum atom\n\\(O(\\log Y/Y)\\) in the harmonic prime law on \\([Y,2Y)\\).\nThe polynomial zero estimate in the proof of\nLemma~\\ref{lem:master-scales} proves\n\\eqref{eq:polynomial-zero-bound}, uniformly in the endpoints.\n\nWe shall also use the following consequence of the interval\nBrun--Titchmarsh inequality.  If \\(p\\le\\sqrt Y\\) is prime, a\nharmonically sampled prime in \\([Y,2Y)\\) belongs to any prescribed\nnonzero class modulo \\(p\\) with probability \\(O(1/p)\\).\nIndeed Theorem~2 of \\cite{Yamada}, together with\n\\(\\log(Y/p)\\ge\\tfrac12\\log Y\\), bounds the number of primes in a\nreduced class in that interval by \\(O(Y/(p\\log Y))\\).\nDivide by the prime count \\(\\gg Y/\\log Y\\).\nHarmonic and uniform sampling on that interval\nhave densities within a factor of two.  The zero class contains no\nprime in \\([Y,2Y)\\) when \\(p\\le\\sqrt Y\\).\n\nRemove variables on which the polynomials do not depend, and argue\nby induction on the total number of remaining variables.  A fixed\nnonzero constant has no prime factors larger than \\(w\\) once \\(w\\)\nis large, so it gives the base case.  In each nonconstant polynomial\nchoose as its main variable one whose interval has largest lower\nendpoint in that tuple.  Denote those endpoints by \\(Y_F,Y_G\\),\nand the corresponding nonzero leading-coefficient polynomials by\n\\(A,B\\).  The events \\(A=0\\) or \\(B=0\\) cost\n\\(O_{F,G}(\\log L/L)\\).  If a common prime divisor of \\(F,G\\)\nalso divides \\(A\\), apply the induction hypothesis to \\(A,G\\);\nif it divides \\(B\\), apply it to \\(F,B\\).  The two tuples in each\napplication remain disjoint, and their total number of variables\nstrictly decreases.  It remains to consider common primes dividing\nneither leading coefficient.\n\nPut \\(Y_0=\\min(Y_F,Y_G)\\).  For \\(w<p\\le\\sqrt{Y_0}\\), condition\non all variables except the two main variables.  When the respective\nleading coefficient is nonzero modulo \\(p\\), each polynomial has at\nmost its fixed degree many roots modulo \\(p\\).  The preceding\nBrun--Titchmarsh consequence bounds the two independent tests by\n\\(O_{F,G}(p^{-2})\\).  Their sum is \\(O_{F,G}(1/w)\\).\n\nFor the remaining primes, expose the entire tuple whose main endpoint\nis \\(Y_0\\), say the \\(F\\)-tuple.  Every variable in that tuple is\nat most \\(2Y_0\\), so, when \\(F\\ne0\\),\n\\(1\\le |F|\\le C_FY_0^{\\deg F}\\).  It follows that \\(F\\) has\nonly \\(O_F(1)\\) distinct prime divisors exceeding \\(\\sqrt{Y_0}\\).\nTest each such prime \\(p\\) against the independent \\(G\\)-tuple,\nwhose main endpoint \\(Y=Y_G\\) satisfies \\(Y\\ge Y_0\\).  If\n\\(p\\le\\sqrt Y\\), the cost is \\(O_G(1/p)=O_G(Y_0^{-1/2})\\),\nagain excluding the already treated event \\(p\\mid B\\).  If\n\\(p>\\sqrt Y\\), each of the boundedly many root classes contains\nat most \\(Y/p+1\\) integers of the interval.  Dividing by\n\\(\\gg Y/\\log Y\\) gives\n\\[\n O_G\\left(\\frac{\\log Y}{\\sqrt Y}\\right)\n =O_G\\left(\\frac{\\log Y_0}{\\sqrt{Y_0}}\\right).\n\\]\nThe last inequality holds for all sufficiently large \\(Y_0\\), since\n\\(\\log Y/\\sqrt Y\\) is then decreasing.  Summing over the bounded\nnumber of large prime factors proves the induction and\n\\eqref{eq:rough-coprimality-bound}.\n\nConditioning pool samples on their individual dyadic intervals leaves\nindependent harmonic prime laws.  The established bounds are uniform\nover these choices, so averaging proves the pool assertion.\nRestricting to events of probabilities \\(1-o(1)\\) changes any\nprobability by \\(o(1)\\).\n\\end{proof}\n\nThe prime pools now have both residue uniformity and rough coprimality.\nWe turn to the mean-one estimate that permits repeated\nCauchy--Schwarz with divisor weights.\n\n\\subsection{The weighted linear-forms estimate}\n\nRecall that for a block \\(B=T\\cup\\{i\\}\\) and independent tail\nproduct \\(\\sigma=t_T\\), the divisor weight is\n\\begin{equation}\\label{eq:arithmetic-divisor-weight}\n \\nu_B(y)=\\E_{\\sigma}\\sigma\\1_{\\sigma\\mid y}.\n\\end{equation}\nIn expanding a product of weights, every occurrence of a weight\nreceives a fresh independent draw, even when its block label repeats.\nThe next Proposition records precisely the row hypotheses needed\nbelow.  In particular, restrictions on prime parameters are permitted.\n\nThe mean-one linear-forms estimate below plays the role of the\npseudorandomness conditions in Green and Tao's transference method\n\\cite[Definition~3.1 and Section~5]{GreenTaoPrimes}.\nWe prove the estimate for the present divisor weights and\nprime-dependent forms.\n\n\\begin{proposition}[Weighted linear forms]\\label{prop:linear-forms}\nFix the number \\(q\\) of rows, the number \\(d\\) of base variables,\nand a bound \\(b\\) on the number of raw factors in each divisor.\nFor each \\(u\\in[q]\\), let \\(\\sigma_u\\) be a product of at most\n\\(b\\) independent harmonic \\(W\\)-unit variables at the master\ncutoffs of Lemma~\\ref{lem:master-scales}, and write\n\\(\\nu_u(y)=\\E\\sigma_u\\1_{\\sigma_u\\mid y}\\).\nAll these raw draws are independent across occurrences.  Suppose\n\\(\\sigma_u\\le V\\) surely, where \\(V\\ge M\\to\\infty\\).\nLet \\(\\boldsymbol p\\) consist of independent prime slots from\nfinitely many gaps, each having \\(V_l\\ge V\\).  These slots are\nindependent of all divisor draws.\n\nLet \\(\\mathcal G\\) be a domain depending only on\n\\(\\boldsymbol p\\), and let\n\\(\\ell_u(\\boldsymbol x;\\boldsymbol p)\\) be homogeneous linear\nrows.  On \\(\\mathcal G\\) their arguments are integers on the\nbase sampling support.  Assume the following conditions.\n\\begin{enumerate}\n\\item Given \\(\\boldsymbol p\\) and any possible divisor draws,\nthe joint residue law of \\(\\boldsymbol x\\) modulo\n\\(K=\\prod_{u=1}^q\\sigma_u\\) differs in total variation by at\nmost \\(\\epsilon_{\\rm base}\\) from the uniform law on\n\\((\\Z/K\\Z)^d\\).  The bound is uniform on \\(\\mathcal G\\).\n\\item For every \\(w<p\\le V\\), each row is nonzero modulo\n\\(p\\) on \\(\\mathcal G\\).  There is a fixed finite list of\nnonzero integer polynomials in the prime slots such that, if all\ntheir values are nonzero modulo \\(p\\), every pair of rows is\nlinearly independent over \\(\\mathbb F_p\\).\n\\end{enumerate}\nThe CRT law of all prime slots at the primes \\(w<p\\le V\\)\nis within total variation \\(\\epsilon_{\\rm CRT}\\) of independent\nuniform units, with independence also across these primes.\nThen, uniformly in every further prime-only event\n\\(\\mathcal E\\subseteq\\mathcal G\\),\n\\begin{equation}\\label{eq:restricted-linear-forms}\n \\E_{\\boldsymbol p,\\boldsymbol x}\n   \\1_{\\mathcal E}(\\boldsymbol p)\n   \\prod_{u=1}^q\\nu_u(\\ell_u(\\boldsymbol x;\\boldsymbol p))\n =\\PP(\\mathcal E)+\n O\\left(\\frac1w+V^q(\\epsilon_{\\rm base}+\n                              \\epsilon_{\\rm CRT})\\right).\n\\end{equation}\nThe error is absolute, so the statement also applies when\n\\(\\PP(\\mathcal E)\\) tends to zero.\nThe implied constant depends only on the fixed row and divisor\ntemplates and polynomial tests.  In particular the error is \\(o(1)\\)\nwith the scales of Lemma~\\ref{lem:master-scales} and base residue\nerrors smaller than every fixed inverse power of \\(V\\).\n\nRows may have rational coefficients if their denominators are units\nmodulo every possible divisor: interpret their residues by inverting\nthose denominators.  The integer-value and row hypotheses must still\nhold.  Clearing such denominators leaves the congruence calculation\nunchanged.  In particular this permits smooth denominators and prime\npool denominators larger than \\(V\\).\n\\end{proposition}\n\n\\begin{proof}\nWe first express the normalized divisibility count as a product of local\nkernel counts.  Their excess over one is nonnegative; this lets us bound\nit without retaining the prime-only event.  We then dominate the divisor\ndraws by laws with independent prime valuations and sum the local excesses.\n\n\\paragraph{Local kernel counts.}\nExpand each weight with its independent divisor draw.  Replacing the\nbase residue law by exact uniform measure modulo \\(K\\) costs at\nmost \\(2\\epsilon_{\\rm base}\\prod_u\\sigma_u\n\\le2V^q\\epsilon_{\\rm base}\\).  The CRT theorem now factors the\nnormalized divisibility count as\n\\[\n \\prod_{w<p\\le V}\\alpha_p,\n \\qquad\n \\alpha_p=p^{\\sum_u a_u}\n  \\PP_{\\boldsymbol x\\bmod p^{A}}\n       (p^{a_u}\\mid\\ell_u(\\boldsymbol x)\\text{ for every }u),\n\\]\nwhere \\(a_u=v_p(\\sigma_u)\\) and \\(A=\\max_u a_u\\).\nPrimes with all \\(a_u=0\\) contribute one.  Homogeneity makes the\ndivisibility conditions the kernel of a homomorphism into\n\\(\\prod_u\\Z/p^{a_u}\\Z\\).  Its image has size at most\n\\(p^{\\sum a_u}\\), so \\(\\alpha_p\\ge1\\).  With a single\npositive valuation, the corresponding primitive row is surjective\nmodulo every power of \\(p\\), giving \\(\\alpha_p=1\\).\nIn general one primitive row with valuation \\(A\\) bounds the\nkernel probability by \\(p^{-A}\\).  If the two rows having the\nlargest valuations \\(A\\ge B>0\\) are independent modulo \\(p\\),\none of their two-column minors is a unit.  Fixing the other\ncoordinates, the two selected coordinates are uniformly and\nbijectively mapped to the pair of row values modulo \\(p^A\\).\nTheir required divisibilities consequently have probability\n\\(p^{-A-B}\\).  Additional rows can only reduce the probability.\n\nWe bound the nonnegative excess uniformly, so that an arbitrary\nprime-only domain can subsequently be retained.  Let \\(\\mathcal T_p\\)\nbe the test that at least one of the prescribed polynomial values\nvanishes modulo \\(p\\).  Set \\(\\beta_p=0\\) when fewer than two\nvaluations are positive; otherwise set\n\\[\n \\beta_p=\n \\begin{cases}\n p^{\\sum a_u-A-B}-1,&\\mathcal T_p\\text{ fails},\\\\\n p^{\\sum a_u-A}-1,&\\mathcal T_p\\text{ holds}.\n \\end{cases}\n\\]\nOn \\(\\mathcal G\\), the preceding kernel bounds give\n\\(0\\le\\alpha_p-1\\le\\beta_p\\).  For every choice of the\nparameters and the true, bounded divisor draws,\n\\begin{equation}\\label{eq:local-excess-domination}\n 0\\le\\1_{\\mathcal E}\n       \\left(\\prod_p\\alpha_p-1\\right)\n \\le\\prod_{w<p\\le V}(1+\\beta_p)-1\n \\le\\prod_u\\sigma_u\\le V^q.\n\\end{equation}\nIn particular, we can first replace the prime residues by independent\nCRT-uniform units at cost at most \\(2V^q\\epsilon_{\\rm CRT}\\).\nThis replacement is made while all divisor draws are still bounded.\n\n\\paragraph{Independent comparison laws.}\nWe next compare those draws with laws whose prime valuations are\nindependent.  For a raw factor at cutoff \\(X_j\\), put\n\\(\\epsilon_j=1/\\log X_j\\) and define\n\\[\n \\widetilde\\mu_j(n)\n  =\\frac{\\1_{(n,W)=1}n^{-1-\\epsilon_j}}{\n    \\zeta(1+\\epsilon_j)\\prod_{p\\le w}(1-p^{-1-\\epsilon_j})}\n \\qquad(n\\ge1).\n\\]\nIts denominator is \\((1+o(1))\\vartheta_W\\log X_j\\).\nIndeed \\(\\epsilon\\zeta(1+\\epsilon)\\to1\\), and\n\\[\n 0\\le\\log\\prod_{p\\le w}\n        \\frac{1-p^{-1-\\epsilon}}{1-p^{-1}}\n \\le\\epsilon\\sum_{p\\le w}\\frac{\\log p}{p-1}\n \\le\\epsilon\\log W=o(1).\n\\]\nLemma~\\ref{lem:sampling} gives the same asymptotic normalization\nfor the original harmonic law on \\([X_j,X_j^2)\\), and\n\\(n^{\\epsilon_j}\\le e^2\\) on that interval.  Hence\n\\(\\mu_j(n)\\le C_0\\widetilde\\mu_j(n)\\) pointwise, with an\nabsolute constant \\(C_0\\) for sufficiently large \\(w\\).\nFor at most \\(bq\\) independent raw draws this incurs one overall\nconstant \\(C_0^{bq}\\), rather than a separate constant for each\nprime.\n\nThe Euler product defining \\(\\widetilde\\mu_j\\) shows that its\nvaluations at different primes are independent, with\n\\[\n \\widetilde\\PP(v_p(n)=a)\n    =(1-p^{-1-\\epsilon_j})p^{-a(1+\\epsilon_j)}\n    \\le p^{-a}.\n\\]\nThus a product of at most \\(b\\) raw factors has valuation mass\nat \\(a\\) at most \\((a+1)^b p^{-a}\\).  This bound is uniform\nin the individual cutoffs.  Divisors remain independent of each\nother and of all prime parameters.\n\n\\paragraph{Summing the local excesses.}\nFor sufficiently large \\(w\\), none of the fixed nonzero test\npolynomials is identically zero modulo any prime \\(p>w\\).\nThe elementary polynomial root bound on the product grid\n\\((\\mathbb F_p^\\times)^k\\) gives\n\\(\\PP(\\mathcal T_p)=O(1/p)\\).  Under the CRT-uniform law the\ntests at different primes are independent.  Combining the valuation\nbounds with the definition of \\(\\beta_p\\), the contribution when\n\\(\\mathcal T_p\\) fails is at most a constant times\n\\[\n \\sum_{A\\ge B\\ge1}\n  (A+1)^{C_1}(B+1)^{C_1}p^{-A-B}\n \\ll p^{-2}.\n\\]\nTo see this sum explicitly, choose the rows with the two largest\nvaluations; all other valuations lie between zero and \\(B\\),\nand summing their polynomial factors is bounded by a fixed power\nof \\(B+1\\).  The factors \\(p^{-\\sum a_u}\\) cancel the\nnormalizing power in \\(\\beta_p\\), leaving \\(p^{-A-B}\\).\nOn \\(\\mathcal T_p\\) the same argument, with all lesser\nvaluations bounded by \\(A\\), gives\n\\[\n \\sum_{A\\ge1}(A+1)^{C_2}p^{-A}\\ll p^{-1}.\n\\]\nIts additional \\(O(1/p)\\) test probability again gives\n\\(\\E\\beta_p\\ll p^{-2}\\).  The two displayed series bounds\nfollow by factoring out respectively \\(p^{-2}\\) and \\(p^{-1}\\)\nand bounding the remaining geometrically convergent series using\n\\(p\\ge2\\).\n\nWe have now obtained independence across primes for every quantity\nin the nonnegative right side of\n\\eqref{eq:local-excess-domination}.  Consequently its expectation,\nafter the single global divisor-domination constant, is at most\n\\[\n C_0^{bq}\\left\\{\\prod_{w<p\\le V}(1+C_3p^{-2})-1\\right\\}\n \\ll\\sum_{p>w}p^{-2}\\ll\\frac1w.\n\\]\nThe original local product has lower bound one on\n\\(\\mathcal E\\).  Integrating that lower bound and the estimated\nexcess, and restoring the two residue-approximation errors, proves\n\\eqref{eq:restricted-linear-forms}.  Rational denominators which\nare units modulo the divisors induce the same homomorphisms after\ninversion, so the proof covers the stated rational-row extension.\n\\end{proof}\n\nSeveral consequences of Proposition~\\ref{prop:linear-forms} will be\nused without altering its hypotheses.  A fixed product of factors\n\\(1+\\nu_u\\) has main term \\(2^q\\PP(\\mathcal E)\\), by expansion\nover subsets of rows.  If at least one factor is \\(\\nu_u-1\\),\nand the remaining factors are each \\(\\nu_v\\), \\(1+\\nu_v\\),\nor \\(\\nu_v-1\\), its average is \\(o(1)\\), provided every\nsubsystem in that expansion meets the same row conditions.  This\nis cancellation of the constant main terms.  In particular a\nprime-only exceptional event of probability \\(o(1)\\), contained\nin a domain of primitive rows, has weighted mass \\(o(1)\\).\nOne may also condition on a prime-only event whose probability is\nbounded below, dividing \\eqref{eq:restricted-linear-forms} by that\nprobability.\n\nThese statements include systems assembled from different gaps.\nUse the common divisor bound \\(V\\); every relevant pool has\n\\(V_l\\ge V\\), so projecting its CRT law supplies the required\naccuracy.  Independent replicas give independent slots.  For\ncoefficients involving\n\\(M(\\boldsymbol p)=M|D(\\boldsymbol p)|_{>w}\\), a prime\n\\(w<p\\le V\\) dividing such a coefficient must divide\n\\(D(\\boldsymbol p)\\), so the indicated polynomial tests cover\nthis possible loss of a minor.  Fixed integer contents are\n\\(w\\)-smooth eventually.  Primitivity and separation of each\nactual row system still have to be checked; those checks are given\nin Sections~\\ref{sec:correlation} and \\ref{sec:prediction}.\n\nTo verify the base-law hypothesis in those applications, every\ndivisor product \\(K\\) is at most \\(V^q\\).  Uniform auxiliary\nintervals of lengths dominating all powers of \\(V\\), and harmonic\npivot variables whose cutoffs dominate all powers of \\(W+V\\),\nare therefore jointly uniform modulo \\(K\\), with error smaller\nthan every fixed inverse power of \\(V\\).  Their conditional\ninterval locations do not matter.  A short translation changes\nsuch a box by the sum of the displacement-to-side-length ratios;\nthe translation and dilation bounds above cover harmonic variables.\nFor example,\n\\(R_l/(J_0M(\\boldsymbol p))\\) dominates all powers of the common\ntail bound for each fixed \\(J_0\\), since\n\\(M(\\boldsymbol p)\\) has a fixed-power bound in \\(P_l^++V_l\\).\nTaking square roots of these lengths preserves that domination and\ngives negligible relative translations.  These observations supply\nuniform bounds for the shifted boxes used later, as well as for\nunshifted boxes.\n"}, {"path": "preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/04_correlation.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/04_correlation.tex", "bytes": 32343, "sha256": "b1c1e08bb4465bd6c69300df7760b5f3165781f49d9bb9a2cd9153515478cc62", "content": "\\section{Removing multiplicative masks and detecting a shifted error}\n\\label{sec:correlation}\n\nThe purpose of this Section is to turn a correlation containing arbitrary\nmultiplicative masks into a test of one function on an additive cube.\nThe number of Cauchy--Schwarz steps will depend only on the number of\nblocks in the chain.  This independence from the master index count is\nneeded when the analytic tolerances are chosen before the Ramsey bound.\n\nFix a master chain\n\\[\n B_k=T_k\\cup\\{i_k\\},\\qquad\n T_1<\\cdots<T_m<l<i_1<\\cdots<i_m,\n\\]\nwhere each tail is nonempty, and fix positive scales\n$c_k=h_{B_k}a_k$ from the finite list allowed in\nSection~\\ref{sec:arithmetic}.  Write $V=V_l$, $R=R_l$, and let\n$\\mathcal P_l$ be the prime pool in this gap, with law\n\\[\n \\lambda(p)=\\frac{1}{pS_l},\\qquad\n S_l=\\sum_{p\\in\\mathcal P_l}\\frac1p.\n\\]\nIn this Section $z_k$ has law $\\mu_{i_k}$, independently for different\n$k$.  We abbreviate $\\nu_k=\\nu_{B_k}$, so that $0\\le\\nu_k\\le V$ on\nall integers.  For $\\emptyset\\ne J\\subseteq[m]$ put\n\\[\n a(J)=\\max J,\\qquad\n L_J(z)=\\sum_{k\\in J}\\frac{c_k}{c_{a(J)}}z_k.\n\\]\nFor sufficiently large $w$, all the displayed coefficients are integers,\nand every coefficient other than the anchor coefficient is divisible by\n$W$.  The scales $c_k$ are positive integers with no prime factor greater\nthan $w$.  We consider\n\\begin{equation}\n \\mathcal C=\n \\E_z\\left[\n   \\prod_{\\emptyset\\ne U\\subseteq[m]}b_U(z_U)\n   \\prod_{\\emptyset\\ne J\\subseteq[m]}g_J(L_J(z))\n \\right],\n \\qquad z_U=\\prod_{k\\in U}z_k,\n \\label{eq:correlation-initial}\n\\end{equation}\nwhere the functions are real, $|b_U|\\le1$, and\n$|g_J(y)|\\le1+\\nu_{a(J)}(y)$ for every integer $y$.\nFix $J_*$ with $|J_*|\\ge2$, and write $g_*=g_{J_*}$ and\n$a_* = a(J_*)$.  Bounded extensions outside the positive integers are\npermitted whenever needed.\n\nWe will keep one copy of $g_*$ throughout: first remove the product\nmasks, then eliminate the other linear factors, and finally identify\nthe sampling law of the surviving cube root.\n\nAll estimates below are uniform over these functions.  An $o(1)$ may\ndepend on the fixed master data and on a fixed positive integer $J_0$,\nbut not on the functions.  Uniformity also holds over any fixed finite\nset of $J_0$'s.  In particular, it does not assert a convergence rate\nuniform in a growing master index count.\n\n\\subsection{Prime insertion and weighted mask removal}\n\n\\begin{lemma}[Prime insertion]\\label{lem:prime-insertion}\nLet $i>l$ and let $Y$ have law $\\mu_i$.  For every fixed $A>0$, uniformly\nover functions $F$ with $|F|\\le V^A$,\n\\begin{equation}\n \\E_Y F(Y)=\\E_{p,Y}F(pY)+o(1).\n \\label{eq:prime-average-insertion}\n\\end{equation}\nFor a fixed positive integer $k$, coprime to $W$ and bounded by a fixed\npower of $P_l^++V$, one also has\n\\begin{equation}\n \\E_Y F(kY)=\\E_Y k\\1_{k\\mid Y}F(Y)+o(1),\n \\label{eq:prime-fixed-dilation}\n\\end{equation}\nuniformly in $k$.  The total-mass error before multiplication by $F$ is\nsmaller than every fixed negative power of $P_l^++V$.\nThese assertions remain valid with other independent variables as\nparameters.\n\\end{lemma}\n\n\\begin{proof}\nThe dilation assertion is Lemma~\\ref{lem:sampling}, applied at a cutoff\nwhose logarithm dominates all fixed powers of $P_l^++V$.  The weighted\nlaw on the right has the same harmonic density as the law on the left;\nonly the endpoints $[X_i,X_i^2)$ and $[kX_i,kX_i^2)$ differ.\nThe logarithmic boundary mass is $O(\\log k/\\log X_i)$, with the residue\ncounting error from that Lemma.  The scale separation makes their sum\nsmaller than any prescribed negative power of $P_l^++V$.\n\nAveraging the multiplier in \\eqref{eq:prime-fixed-dilation} gives\n\\[\n A_l(Y)=\\E_p p\\1_{p\\mid Y}\n       =\\frac1{S_l}\\sum_{p\\in\\mathcal P_l}\\1_{p\\mid Y}.\n\\]\nPeriodic counting for the moduli $p$ and $pq$, including the $W$-unit\nrestriction, gives their reciprocal probabilities with uniformly\nnegligible relative errors.  Therefore\n\\begin{align*}\n \\E A_l&=1+o(V^{-C}),\\\\\n \\E A_l^2\n &=\\frac1{S_l^2}\n   \\left(\\sum_p\\frac1p+\\sum_{p\\ne q}\\frac1{pq}\\right)\n   +o(V^{-C})\\\\\n &=1+\\frac1{S_l}-\\frac1{S_l^2}\\sum_p\\frac1{p^2}+o(V^{-C})\n\\end{align*}\nfor every fixed $C$.  Thus\n$\\|A_l-1\\|_{L^2(\\mu_i)}\\le S_l^{-1/2}+o(V^{-C})$.\nCauchy--Schwarz bounds the error in replacing $A_l$ by $1$ by\n$V^A(S_l^{-1/2}+o(V^{-C}))=o(1)$, since $S_l$ dominates every fixed\npower of $V$.  The estimates are uniform in the parameters of $F$, so\none may integrate over those parameters afterwards.\n\\end{proof}\n\nA row template will mean a vector $A_R=(A_{R,1},\\ldots,A_{R,m})$ whose\nnonzero entries are monomials in finitely many formal prime variables.\nNo prime variable occurs in two different columns of a single row.\nFor $a(R)=\\max\\{k:A_{R,k}\\ne0\\}$, its associated linear form and weight\nare\n\\begin{equation}\n \\ell_R(z)=\\sum_k\\frac{c_k}{c_{a(R)}}A_{R,k}z_k,\n \\qquad W_R(y)=1+\\nu_{a(R)}(y).\n \\label{eq:correlation-row-template}\n\\end{equation}\nParallelity of templates always means parallelity over the rational\nfunction field in the formal prime variables.  This qualification is\nessential: accidental equalities after numerical substitution are\nexceptional events, not identities of templates.\n\nReciprocal dilations preserving a product, followed by\nCauchy--Schwarz to remove its bounded factor, also occur in the proof\nof \\cite[Proposition~2.4]{GreenSanders} over finite fields.\nHere the divisibility weights allow the corresponding substitutions\non integer variables, and we carry out the elimination for the full\nfamily of product masks.\n\n\\begin{lemma}[Weighted removal of multiplicative masks]\n\\label{lem:mask-removal}\nPut $q_{\\mathrm{mask}}=2^m-1$ and $K_m=q_{\\mathrm{mask}}2^{q_{\\mathrm{mask}}}$.  Starting from\n\\eqref{eq:correlation-initial}, one obtains a family $\\mathcal R$ of at\nmost $K_m$ pairwise nonparallel row templates, with one distinguished\nrow $*$, and functions $f_R$ that may depend on the introduced primes,\nsuch that\n\\begin{equation}\n |\\mathcal C|^{2^{q_{\\mathrm{mask}}}}\n \\le C_m\\left|\\E_{\\boldsymbol p,z}\n                 \\prod_{R\\in\\mathcal R}f_R(\\ell_R(z))\\right|+o(1).\n \\label{eq:mask-removal-output}\n\\end{equation}\nHere $|f_R|\\le W_R$, the distinguished function is exactly $f_*=g_*$,\nits support is $J_*$, and every prime parameter has the law $\\lambda$,\nindependently before deletion of exceptional tuples.\nThe row templates, the number of parameters, and $C_m$ depend only on\n$m$ and the choice of $J_*$.  They are independent of the scales and\nfunctions.\n\\end{lemma}\n\n\\begin{proof}\nInitially the rows are the indicator vectors of the nonempty subsets\n$J$ of $[m]$.  They are pairwise nonparallel.  We remove the masks in a\nfixed order, maintaining \\eqref{eq:correlation-row-template}, the weight\nbounds, and a distinguished copy of $g_*$.\n\n\\paragraph{A substitution fixing the mask.}\nConsider the mask with support $U$.  If the distinguished support has\nan index $u\\notin U$, use the change $D(p)z$ given by\n$z_u\\mapsto pz_u$.  Otherwise choose distinct $u,v$ in the distinguished\nsupport, necessarily both in $U$, and use\n\\begin{equation}\n z_u\\mapsto z_u/p,\\qquad z_v\\mapsto pz_v,\n \\qquad \\eta_p(z)=p\\1_{p\\mid z_u}.\n \\label{eq:balanced-prime-substitution}\n\\end{equation}\nThe first case has $\\eta_p=1$.\nLemma~\\ref{lem:prime-insertion} justifies the first case directly.\nFor the second, first insert $p$ in coordinate $v$ and then use\n\\eqref{eq:prime-fixed-dilation} in coordinate $u$ with the function\nread at $z_u/p$.  Thus division by $p$ is used only on the support of\nthe displayed indicator, and its multiplier is retained.  At this\nstage all unweighted functions and all products of row weights are\nbounded by a fixed power of $V$; even the intermediate fixed-prime\nmultipliers are absorbed by the stronger endpoint error in\n\\eqref{eq:prime-fixed-dilation}.  Both substitutions therefore have\n$o(1)$ error.  In either case $z_U$ is unchanged.\n\nCall a row invariant at this step when the coefficient vector of\n$\\ell_R(D(p)z)$ is a scalar multiple of that of $\\ell_R(z)$.\nThe scalar is $1,p$, or $p^{-1}$.  All divisors in $\\nu_{a(R)}$ are\nsmaller than the pool primes, so multiplication or admissible division\nby $p$ preserves their divisibility conditions.  Consequently\n\\[\n W_R(\\ell_R(D(p)z))=W_R(\\ell_R(z))\n\\]\non the support in use.  Put\n$\\Omega(z)=\\prod_{R\\text{ invariant}}W_R(\\ell_R(z))$.\nDivide each invariant function inside the fresh prime average by its\ncorresponding weight, and call the remaining integrand $H_p$.\nLet $\\boldsymbol r$ denote all prime slots introduced at earlier steps.\nThe current mask and row functions may depend on $\\boldsymbol r$.\nAll these previous slots are integrated together with $z$ in the outer\nexpectation. Thus the substituted correlation is\n\\[\n I=\\E_{\\boldsymbol r,z}\n b_U(\\boldsymbol r;z_U)\\Omega(\\boldsymbol r,z)\n       \\E_p\\eta_p(z)H_p(\\boldsymbol r,z).\n\\]\nHere the new slot $p$ is independent of $(\\boldsymbol r,z)$.\nWeighted Cauchy--Schwarz on the joint outer probability space gives\n\\begin{equation}\n |I|^2\\le\n \\bigl(\\E_{\\boldsymbol r,z}\\Omega(\\boldsymbol r,z)\\bigr)\n \\E_{\\boldsymbol r,z,p,q}\n \\Omega(\\boldsymbol r,z)\\eta_p(z)\\eta_q(z)\n H_p(\\boldsymbol r,z)H_q(\\boldsymbol r,z).\n \\label{eq:mask-weighted-cs}\n\\end{equation}\nThe old tuple $\\boldsymbol r$ is shared by the two branches, while $p$\nand $q$ are fresh independent slots. This inequality applies to the\nfull prime average.\n\nIf there are $s_{\\rm inv}$ invariant rows, the first factor equals\n$2^{s_{\\rm inv}}+o(1)$, and hence is bounded in terms of the number of\nrows alone. Indeed, expand $\\Omega$ as a sum of products of $\\nu$'s\nand apply Proposition~\\ref{prop:linear-forms} with $\\boldsymbol r$\namong its prime parameters. Every anchor coefficient is a monomial\nof pool primes, hence a unit at every prime $w<\\pi\\le V$. A nonzero\nminor of two templates is multiplied in the actual rows by smooth\nrow and column factors, also units at such $\\pi$, so its possible\nvanishing is covered by a fixed polynomial test. This bound uses\nthe average over $\\boldsymbol r$.\n\n\\paragraph{The two prime branches.}\nIn the one-coordinate case the two branches have multipliers $p$ and\n$q$ on $u$.  In the two-coordinate case, first discard $p=q$.\nSince all remaining factors are bounded by $V^{O_m(1)}$, its contribution\nis at most\n\\[\n V^{O_m(1)}\\sum_p\\lambda(p)^2p^2\\PP(p\\mid z_u)\n \\le (1+o(1))V^{O_m(1)}\\sum_p p\\lambda(p)^2\n =\\frac{V^{O_m(1)}}{S_l}+o(1)=o(1).\n\\]\nFor $p\\ne q$, the multiplier is $pq\\1_{pq\\mid z_u}$.\nAbsorb it by \\eqref{eq:prime-fixed-dilation}, replacing $z_u$ by\n$pqz_u$.  The two branch multipliers on $(u,v)$ are then\n\\begin{equation}\n (q,p)\\quad\\hbox{and}\\quad(p,q).\n \\label{eq:prime-two-branches}\n\\end{equation}\nEvery surviving multiplicative mask is a bounded function of a scalar\nmultiple of its original monomial; its two copies can be combined into\none mask on that same support.\n\nFor an invariant row the two scalar-changed arguments are\n$\\alpha_p\\ell_R(z)$ and $\\alpha_q\\ell_R(z)$, after absorption if\nnecessary.  Its new function is\n\\[\n t\\longmapsto\n \\frac{f_R(\\alpha_p t)f_R(\\alpha_q t)}{W_R(t)},\n\\]\non the arguments actually used, and it is bounded by $W_R(t)$.\nThis formula records why only one weight survives.  It is extended\nelsewhere with the same bound.  In particular, in the two-coordinate\ncase an invariant singleton on $u$ acquires arguments $qt$ and $pt$;\nthe weight originally evaluated at $pqt$ equals $W_R(t)$.\nEvery noninvariant row instead gives two functions, each with its\noriginal weight bound, on the two substituted rows.\n\n\\paragraph{Preserving the row structure.}\nThe new templates are pairwise nonparallel.  For descendants of\ndifferent old rows, a purported symbolic parallelity specializes at\n$p=q=1$ to parallelity of the old rows, a contradiction.  For the two\ndescendants of one row, use the exponent description of the\nsubstitution: a column is multiplied by $p^{e_k}$, with\n$e_k\\in\\{0,1\\}$ or $\\{-1,0,1\\}$.  The $p$ and $q$ branches are\nparallel precisely when all $e_k$ on the row support are equal.\nThat is exactly the definition of an invariant row.  The common\ncolumn multiplication by $pq$ in \\eqref{eq:prime-two-branches} is\ninvertible over the rational function field and does not affect this\nargument.  The distinguished row is noninvariant: it contains both an\naffected and a differently affected coordinate.  Track a fixed one\nof its two copies.  Its function is still $g_*$ and its support is\nunchanged.\n\nThe new slots occur in at most one column per row, as is explicit in\n\\eqref{eq:prime-two-branches}.  Thus the template invariant persists.\nRepeated prime entries and exact polynomial coincidences have\nprobability smaller than every fixed negative power of $V$.\nAfter absorption the integrands are bounded by $V^{O_m(1)}$, so these\nevents can be deleted or reinstated at $o(1)$ cost.  The reinstated\nsubstitution-form terms on $p=q$ have this same harmless bound; the\nlarger, pre-absorption diagonal was estimated separately above.\nConsequently fresh slots can always be sampled independently.\nAt the next step the entire existing tuple, including this step's\n$p$ and $q$, is the shared outer tuple $\\boldsymbol r$. The deletion\nand reinstatement argument restores its independent product law\nbefore that next step. Any temporarily retained prime-only domain\nis covered by the restricted form of\nProposition~\\ref{prop:linear-forms}; the base variables and the\nindependently expanded divisor draws retain their original laws.\n\nEach step removes one mask, uses one square, and at most doubles the\nnumber of rows.  There are $q_{\\mathrm{mask}}$ masks and initially $q_{\\mathrm{mask}}$ rows, giving\nat most $K_m$ rows.  Iterating \\eqref{eq:mask-weighted-cs}, with its\nbounded prefactors and uniform errors, proves\n\\eqref{eq:mask-removal-output}.  Fixing the order of masks, the choices\nof coordinates, and the tracked branch makes every template depend\nonly on $m$ and $J_*$.  The original and intermediate correlations are\nbounded by constants, by the same linear-forms estimates, so raising\nthe inequalities to these fixed powers preserves $o(1)$ errors.\n\\end{proof}\n\n\\subsection{Directions with one vanishing row response}\n\nThe product masks have been removed.  To eliminate a nontarget row by\nCauchy--Schwarz, we need a translation that fixes that row and moves\nevery other row, including the target.  We choose these translations so\nthat all target responses are equal.\n\nLet $\\mathcal R$ now denote the output family of\nLemma~\\ref{lem:mask-removal}, and let $d=|\\mathcal R|-1$.\nFor a nonzero integer $n$, write $|n|_{>w}$ and $|n|_{\\le w}$ for its\nrough and smooth parts, including multiplicity.\n\n\\begin{lemma}[Polynomial directions and integer translations]\n\\label{lem:row-directions}\nFor each $R\\ne *$ there is an integer polynomial vector $w_R$ such that\n\\[\n A_Rw_R=0,\\qquad A_Iw_R\\ne0\\quad(I\\ne R).\n\\]\nThere is also an integer polynomial vector $w_0$ with\n$A_*w_0=0$ and $A_Iw_0\\ne0$ for $I\\ne *$.\nThese choices can be made from a finite list determined solely by the\nrow templates.  Set\n\\begin{equation}\n d_R=A_*w_R,\\qquad D=\\prod_{R\\ne *}d_R,\\qquad\n M(\\boldsymbol p)=M|D(\\boldsymbol p)|_{>w}.\n \\label{eq:correlation-modulus}\n\\end{equation}\nOn the good prime tuples planned in Lemma~\\ref{lem:master-scales},\nthe vectors\n\\begin{equation}\n v_{R,k}=M(\\boldsymbol p)\\frac{c_{a_*}}{c_k}\n                  \\frac{w_{R,k}}{d_R},\\qquad\n v_{0,k}=M\\frac{c_{a_*}}{c_k}w_{0,k}\n \\label{eq:correlation-integer-directions}\n\\end{equation}\nbelong to $W\\Z^m$ and have sizes bounded by a fixed power of\n$P_l^++V$.  Their responses satisfy\n\\begin{equation}\n \\ell_*(v_R)=M(\\boldsymbol p),\\qquad\n \\ell_R(v_R)=0,\\qquad \\ell_*(v_0)=0.\n \\label{eq:correlation-direction-responses}\n\\end{equation}\nEvery other response is nonzero.  At primes $w<\\pi\\le V$ it is a unit\nunless a member of a fixed list of nonzero integer polynomials in the\nprime slots vanishes modulo $\\pi$.\n\\end{lemma}\n\n\\begin{proof}\nHere is an explicit finite choice of a kernel vector.  For a row $A_R$,\nchoose a column $j$ with $A_{R,j}\\ne0$.  The polynomial vectors\n\\[\n b_k=A_{R,j}e_k-A_{R,k}e_j\\qquad(k\\ne j)\n\\]\nspan its kernel over the rational function field.  Enumerate them as\n$b_0,\\ldots,b_{m-2}$ and set $w_R(t)=\\sum_{h=0}^{m-2}t^hb_h$.\nFor $I\\ne R$, the polynomial $A_Iw_R(t)$ is not identically zero:\notherwise $A_I$ would vanish on the kernel of $A_R$ and hence be\nparallel to it.  The product of these polynomials has degree at most\n$(|\\mathcal R|-1)(m-2)$ in $t$.  At least one of the first\n$(|\\mathcal R|-1)(m-2)+1$ positive integers is therefore not a root\nover this field.  Use the first such integer.  This is a deterministic\nchoice, gives integer polynomial coordinates, and has every asserted\nnonzero response.  Applying the same construction to $A_*$ gives\n$w_0$.\n\nInclude $D$, all nonzero response polynomials, and the required row\nminors in the finite polynomial list used to choose $M$ and the gap\nscales.  There is no dependence on the functions in this list.\nDelete tuples with a zero polynomial value or a repeated prime entry,\nand tuples for which $\\pi^{e_0}\\mid D$ for some $\\pi\\le w$.\nThe deleted probability is $o(1)$; the first two parts even have\nsuperpolynomially small probability in $V$.\nDeletion at this point remains legitimate for weighted expectations:\nfor any product of distinct row weights its integral over a prime-only\ndomain $E$ equals $2^t\\PP(E)+o(1)$, where $t$ is the number of weights,\nby expansion and the restricted-domain assertion of\nProposition~\\ref{prop:linear-forms}.  A domain of probability $o(1)$\ntherefore has $o(1)$ weighted mass.  We henceforth normalize the prime\nlaw on the remaining good tuples, whose probability tends to one.\n\nFor integrality, write $D=\\epsilon S Q$, where $\\epsilon\\in\\{-1,1\\}$,\n$S=|D|_{\\le w}$, and $Q=|D|_{>w}$.  On the good tuples\n$S\\mid W^{e_0-1}$.  Since $M/c_k$ is an integer divisible by\n$W^{e_0+1}$, the expression in \\eqref{eq:correlation-integer-directions}\ncan be written\n\\[\n v_{R,k}=\n \\epsilon\\frac{M/c_k}{S}\\,c_{a_*}w_{R,k}\\frac{D}{d_R}.\n\\]\nAll its factors are integers, and $(M/c_k)/S$ is divisible by $W$.\nThe assertion for $v_0$ follows directly from its formula.\nThe degree and coefficient bounds for the polynomial vectors give\n$|v_{R,k}|+|v_{0,k}|\\le(P_l^++V)^{O_m(1)}$; all the scales $c_k$\nare at most $M\\le V$.\n\nDirect substitution proves \\eqref{eq:correlation-direction-responses}.\nMore generally,\n\\[\n \\ell_I(v_R)=M(\\boldsymbol p)\\frac{c_{a_*}}{c_{a(I)}}\n                   \\frac{A_Iw_R}{d_R},\\qquad\n \\ell_I(v_0)=M\\frac{c_{a_*}}{c_{a(I)}}A_Iw_0.\n\\]\nOutside primes dividing $D(A_Iw_R)$, respectively $A_Iw_0$, the first,\nrespectively second, expression is a unit at $w<\\pi\\le V$.\nThe smooth factors are units there.  This proves both the response\nclaim and the finite-polynomial nature of all exceptional tests.\n\\end{proof}\n\n\\subsection{Additive elimination and removal of the remaining weights}\n\n\\begin{lemma}[Weighted additive elimination]\n\\label{lem:additive-elimination}\nWith the rows, good-tuple law, and modulus of\nLemma~\\ref{lem:row-directions}, fix $J_0\\in\\N$ and put\n\\begin{equation}\n L(\\boldsymbol p)=\\left\\lfloor\\frac{R}{J_0M(\\boldsymbol p)}\\right\\rfloor.\n \\label{eq:correlation-shift-length}\n\\end{equation}\nIndependently conditional on the primes, let every $u_R^0,u_R^1$ be\nuniform on $[0,L(\\boldsymbol p))\\cap\\Z$, for $R\\ne *$.\nThen\n\\begin{equation}\n \\left|\\E_{\\boldsymbol p,z}\\prod_{I\\in\\mathcal R}f_I(\\ell_I(z))\n \\right|^{2^d}\n \\le C_m\\left|\\E_{\\boldsymbol p,z,u}\n       \\prod_{\\omega\\in\\{0,1\\}^d}\n       g_*\\left(\\ell_*(z)+M(\\boldsymbol p)\n                    \\sum_{R\\ne *}u_R^{\\omega_R}\\right)\\right|+o(1).\n \\label{eq:additive-elimination-output}\n\\end{equation}\nThe constants are independent of $J_0$; the error is for fixed $J_0$.\n\\end{lemma}\n\n\\begin{proof}\nWeighted Cauchy--Schwarz will leave a cube of $g_*$ multiplied by\nretained row weights.  We first form that cube, then use the direction\n$v_0$ to remove the retained weights in a weighted second moment.\n\nEvery interval in \\eqref{eq:correlation-shift-length} has length\ndominating all fixed powers of $V$, uniformly over the prime tuples.\nIndeed $M(\\boldsymbol p)$ is bounded by a fixed power of $P_l^++V$,\nwhereas $R$ dominates all such powers.  Insert independent uniform\n$u_R$ on these intervals and translate\n\\[\n z\\longmapsto z+\\sum_{R\\ne *}v_Ru_R.\n\\]\nThe translations are in $W\\Z^m$.  Their sizes are at most\n$R(P_l^++V)^{O_m(1)}$, and hence are negligible at every pivot cutoff,\neven after multiplying the total-variation errors by $V^{O_m(1)}$.\nLemma~\\ref{lem:sampling} therefore changes the average by $o(1)$.\n\nEliminate the nondistinguished rows in a fixed order.  At the step\nassigned to row $R$, all its current copies are independent of $u_R$,\nbecause $\\ell_R(v_R)=0$.  Let $H_{\\mathrm{out}}$ be their product,\nand let $\\Omega_R$ be the product of their bounds $W_R$.\nAll other factors, including weights retained at earlier steps, belong\nto $H_{\\mathrm{in}}$.  With all variables other than $u_R$ outside,\nthe exact inequality is\n\\begin{align}\n &\\left|\\E_{\\mathrm{out}}H_{\\mathrm{out}}\n                   \\E_{u_R}H_{\\mathrm{in}}\\right|^2\\notag\\\\\n &\\hspace{6mm}\\le\n   (\\E_{\\mathrm{out}}\\Omega_R)\n   \\E_{\\mathrm{out},u_R^0,u_R^1}\n       \\Omega_R H_{\\mathrm{in}}(u_R^0)H_{\\mathrm{in}}(u_R^1).\n \\label{eq:additive-weighted-cs}\n\\end{align}\nIt follows by applying Cauchy--Schwarz with density $\\Omega_R$ and\nusing $|H_{\\mathrm{out}}|\\le\\Omega_R$.  In particular, the assigned\nfunctions disappear and their weights occur once, while every factor\ninside the average is duplicated.  All prime parameters remain\noutside throughout this procedure.\n\nWe verify every use of Proposition~\\ref{prop:linear-forms} in\n\\eqref{eq:additive-weighted-cs}.  A current copy of a base row $I$ has\nthe form\n\\[\n \\ell_I(z)+\\sum_Q\\ell_I(v_Q)u_Q^{\\eta_Q},\n\\]\nwhere $\\eta_Q$ is one of the already duplicated choices or the single\ncurrent variable.  Distinct base rows are distinguished by their\n$z$-coefficient vectors.  For two copies of the same row, some\nduplicated coordinate $Q\\ne I$ has different choices, and its response\n$\\ell_I(v_Q)$ is nonzero.  Their difference therefore has a nonzero\ncoefficient on an independent variable $u_Q^0$ or $u_Q^1$.\nTaking a minor with an anchor $z$-column proves pairwise independence\nat every relevant prime outside the response-polynomial tests of\nLemma~\\ref{lem:row-directions}.  A row's anchor coefficient is still\na monomial of pool primes, so each row is primitive at all\n$w<\\pi\\le V$.  Finally, conditional on the primes the base variables\nare independent harmonic variables or independent interval variables\nwhose lengths dominate powers of $V$.  Their joint residues modulo\nthe product of any fixed number of sampled divisors are uniform with\nerror smaller than every fixed negative power of $V$.\nThese facts check primitivity, pair independence with polynomial\nexceptions, and the sampling hypotheses.  Expanding the weights now\nshows that every prefactor in \\eqref{eq:additive-weighted-cs} is bounded\nby a constant depending only on the number of row copies.\n\nAfter all $d$ steps, the nondistinguished functions have disappeared.\nThe remaining target factor is\n\\[\n G(z,u)=\\prod_{\\omega\\in\\{0,1\\}^d}\n g_*\\left(\\ell_*(z)+M(\\boldsymbol p)\n                   \\sum_Ru_R^{\\omega_R}\\right).\n\\]\nFor each eliminated row $I$, there is one retained weight for each\nchoice of the duplicated variables in the directions $R\\ne I$.\nThus the retained product is\n\\begin{equation}\n \\Psi(z,u)=\n \\prod_{I\\ne *}\\ \\prod_{\\eta\\in\\{0,1\\}^{[d]\\setminus\\{I\\}}}\n W_I\\left(\\ell_I(z)+\\sum_{R\\ne I}\\ell_I(v_R)u_R^{\\eta_R}\\right),\n \\label{eq:correlation-retained-weights}\n\\end{equation}\nafter identifying the nondistinguished rows with $[d]$.\nThere are $t=d2^{d-1}$ weight factors.  Iteration has proved the left\nside of \\eqref{eq:additive-elimination-output} is bounded by a constant\ntimes $|\\E G\\Psi|+o(1)$.\n\nIt remains to justify replacing $\\Psi$ by $2^t$ in this correlation;\nits mean alone would not justify that replacement.  Translate $z$\nonce more by $v_0u_0$, with $u_0$ uniform on $[0,R)\\cap\\Z$.\nThe sampling error is again $o(1)$, and the target $G$ is unchanged\nbecause $\\ell_*(v_0)=0$.  Put\n\\[\n B(z,u)=\\prod_{\\omega\\in\\{0,1\\}^d}\n  \\left(1+\\nu_{a_*}\\left(\\ell_*(z)+M(\\boldsymbol p)\n                 \\sum_Ru_R^{\\omega_R}\\right)\\right),\n \\qquad H(z,u)=\\E_{u_0}\\Psi(z+v_0u_0,u).\n\\]\nThen $|G|\\le B$.  The same row check gives\n\\begin{equation}\n \\E B=2^{2^d}+o(1),\\qquad\n \\E BH=2^{2^d+t}+o(1),\\qquad\n \\E BH^2=2^{2^d+2t}+o(1).\n \\label{eq:auxiliary-weight-moments}\n\\end{equation}\nFor completeness, in the last expression duplicate $u_0$ independently\nas $u_0^0,u_0^1$.  Copies from distinct base rows are distinguished on\n$z$; copies from one base row and different old shift choices are\ndistinguished on an old $u_R^\\eta$; copies with identical old choices\nbut different $u_0$ branches are distinguished by the nonzero response\n$\\ell_I(v_0)$.  Target rows have distinct cube shift choices and no\n$u_0$ response.  All forms in every expanded moment are thus distinct\nand satisfy exactly the primitivity and polynomial-minor conditions\nchecked above.  Expanding each $1+\\nu$ and replacing every product of\n$\\nu$'s by its main term $1$ gives all three powers of $2$ in\n\\eqref{eq:auxiliary-weight-moments}.\n\nConsequently\n\\[\n \\E B(H-2^t)^2=o(1).\n\\]\nCauchy--Schwarz with density $B$ now yields\n\\[\n |\\E G(H-2^t)|\n \\le (\\E B)^{1/2}\\bigl(\\E B(H-2^t)^2\\bigr)^{1/2}=o(1).\n\\]\nThis is the required weighted replacement.  Combining it with the\n$d$ inequalities \\eqref{eq:additive-weighted-cs} proves\n\\eqref{eq:additive-elimination-output}.\n\\end{proof}\n\n\\subsection{A one-variable cube estimate}\n\n\\begin{proposition}[Uniform correlation test]\\label{prop:correlation-test}\nFor every $m\\ge2$ and every nonsingleton target support $J_*$, the\nconstruction above determines a finite family of integer polynomial\ntemplates $D$, with good prime-tuple laws, such that the following holds\nfor every fixed master chain and valid gap $l$.\nThere are an integer $1\\le d\\le K_m-1$, a constant $C_m$, and\n$\\theta>0$, all bounded in terms of $m$ alone, for which\n\\begin{equation}\n \\boxed{\\quad\n |\\mathcal C|\n \\le o(1)+C_m\\left|\n  \\E_{\\boldsymbol p,y,u}\n  \\prod_{\\omega\\subseteq[d]}\n  g_*\\left(y+M(\\boldsymbol p)\n                  \\sum_{R\\in\\omega}(u_R^1-u_R^0)\\right)\n                       \\right|^{\\theta}.\n \\quad}\n \\label{eq:correlation-test}\n\\end{equation}\nHere $y$ has law $\\mu_{i_{a_*}}$, independently of the primes and\nshifts; the primes have the normalized good-tuple law of\nLemma~\\ref{lem:row-directions}; $M(\\boldsymbol p)$ is given by\n\\eqref{eq:correlation-modulus}; and the shifts have the conditional laws\nin \\eqref{eq:correlation-shift-length}.  One may take\n$\\theta=2^{-(q_{\\mathrm{mask}}+d)}$.  The gap length $R_l$ is divisible by every\n$M(\\boldsymbol p)$ that occurs.\n\nThe estimate is uniform over all the masks and functions in\n\\eqref{eq:correlation-initial}, including $g_*$, and over each fixed\nfinite set of positive integers $J_0$.  Neither $d$, $C_m$, nor\n$\\theta^{-1}$ depends on $N$, on the scales, or on the functions.\nThe polynomial templates and direction choices are fixed before $M$\nand the prime pools are chosen.  In particular, the same tests can be\nused both for a bounded error and for an error bounded by\n$1+\\nu_{a_*}$.\n\\end{proposition}\n\n\\begin{proof}\nOnly the pushforward of the cube root remains after\nLemmas~\\ref{lem:mask-removal} and \\ref{lem:additive-elimination}.\nFor fixed primes and shifts, write\n\\begin{equation}\n Y_* = \\ell_*(z)+M(\\boldsymbol p)\\sum_Ru_R^0\n      = kz_{a_*}+h,\n \\qquad k=A_{*,a_*}.\n \\label{eq:correlation-root}\n\\end{equation}\nThe target support remains $J_*$, so it contains some $j<a_*$.\nThe coefficient\n$b=(c_j/c_{a_*})A_{*,j}$ is coprime to $k$:\nthe prime slots in different columns are disjoint, their numerical\nentries are distinct on the good tuples, and all scale ratios are\n$w$-smooth whereas all pool primes exceed $V\\ge w$.\nAlso $k$ is coprime to $W$ and $W\\mid h$.\n\nCondition first on every $z$-variable except $z_{a_*}$.  Put\n$X=X_{i_{a_*}}$ and $\\delta_W=\\phi(W)/W$.  Uniformly in the variables\nbeing conditioned on, one may take\n\\[\n 0\\le h\\le H:=M(P_l^+)^{B_m}\\sum_{j<a_*}X_{i_j}^2+dR/J_0\n\\]\nfor a fixed $B_m$.  The logarithm of $X$ dominates every fixed power\nof $H+k+W$, and in particular $H<X/2$ eventually.\nThe dilation and translation calculation of Lemma~\\ref{lem:sampling}\ngives\n\\begin{equation}\n \\mathcal L(kz_{a_*}+h)\n   = k\\1_{y\\equiv h\\pmod{k}}\\mu_{i_{a_*}}(dy)+\\mathcal E_h,\n \\qquad \\|\\mathcal E_h\\|_{\\mathrm{TV}}=o(V^{-C})\n \\label{eq:correlation-root-progression}\n\\end{equation}\nfor every fixed $C$.  More explicitly, the total-mass error is at most\n\\begin{equation}\n O\\left(\\frac{\\log(2k)}{\\log X}+\\frac H X+\n                  \\frac{Wk^2}{\\delta_W X\\log X}\\right).\n \\label{eq:correlation-root-tv-bound}\n\\end{equation}\nTo see this directly, let $Z_i$ be the normalizing constant of $\\mu_i$,\nso $Z_i\\asymp\\delta_W\\log X$.  The exact image density is\n$k/(Z_i(y-h))$ on $[kX+h,kX^2+h)$ in the class $h\\pmod k$,\nwith $(y,W)=1$.  The comparison density is $k/(Z_i y)$ on\n$[X,X^2)$ in the same class.  The $W$-unit restriction agrees because\n$W\\mid h$ and $(k,W)=1$.  On the intersection of the intervals the\nrelative discrepancy is at most $H/X$.  Harmonic progression counting\non the two remaining boundary intervals gives a logarithmic main\nterm $O(\\delta_W\\log(2k))$ and error $O(Wk^2/X)$ before normalization.\nThis proves \\eqref{eq:correlation-root-tv-bound}.  All three terms\nare smaller than every fixed negative power of $V$ by scale separation;\nthe crude bound $\\delta_W^{-1}\\le W$ already suffices here.\n\nNow condition on all remaining variables except $z_j$.\nSince $i_j>l$, Lemma~\\ref{lem:sampling} makes $z_j$ uniform modulo $k$;\nits relative error is at most\n\\[\n O\\left(\\frac{Wk^2}{\\delta_W X_{i_j}\\log X_{i_j}}\\right)=o(V^{-C})\n\\]\nfor every fixed $C$.\nThe same holds for $h=bz_j+h_0$ modulo $k$, because $(b,k)=1$.\nAveraging the density in \\eqref{eq:correlation-root-progression}\ntherefore gives\n\\[\n \\E_{z_j} k\\1_{y\\equiv h\\pmod{k}}=1+o(V^{-C})\n\\]\nuniformly in $y$ and in the conditioned primes and shifts.\nThus the conditional law of $Y_*$, after integrating all $z$'s, differs\nfrom $\\mu_{i_{a_*}}$ by $o(V^{-C})$ in total mass, uniformly in the\nprimes and shifts.  This argument also covers $k=1$, when there is no\nresidue condition to average.\n\nEvery remaining cube argument can be expressed using this root as\n\\[\n Y_*+M(\\boldsymbol p)\n                 \\sum_{R\\in\\omega}(u_R^1-u_R^0).\n\\]\nThere is no other $z$-dependent factor left.  The product of target\nbounds is at most $(1+V)^{2^d}$, so the total-mass error just proved is\nstill $o(1)$ in the cube expectation.  Substitution in\n\\eqref{eq:additive-elimination-output} and then\n\\eqref{eq:mask-removal-output} gives\n\\eqref{eq:correlation-test} with the stated exponent.  All constants\narise from fixed counts of row weights and squares, hence depend only\non $m$.  At least one other row exists, so $d\\ge1$.\n\nFinally, $M(\\boldsymbol p)$ divides $M|D(\\boldsymbol p)|$, whose\ndivisibility into $R_l$ was arranged in\nLemma~\\ref{lem:master-scales}.  The finite template assertion follows\nfrom the deterministic mask and kernel-vector choices.  Every\nsampling estimate and every restricted linear-forms estimate used\nabove was uniform over the functions; taking the maximum of the errors\nover finitely many $J_0$'s proves the last uniformity assertion.\n\\end{proof}\n\n\\begin{remark}[Tests used in the next section]\n\\label{rem:correlation-dual-interface}\nThe base vertex in \\eqref{eq:correlation-test} is $g_*(y)$.\nAll its other vertices may therefore be regarded as inputs in a dual\ntest\n\\[\n \\mathcal D(y)=\\E_{\\boldsymbol p,u}e(\\boldsymbol p)\n  \\prod_{\\emptyset\\ne\\omega\\subseteq[d]}\n  h_{\\omega,\\boldsymbol p}\\left(y+M(\\boldsymbol p)\n              \\sum_{R\\in\\omega}(u_R^1-u_R^0)\\right),\n \\quad |e|\\le1,\\quad |h_{\\omega,\\boldsymbol p}|\\le1+\\nu_{a_*}.\n\\]\nIn particular, choosing every $h_{\\omega,\\boldsymbol p}=g_*$ and\n$e=1$ recovers its cube expectation as $\\E_{\\mu_{i_{a_*}}}g_*\\mathcal D$.\nThe enlarged test class, including independently chosen inputs and\nbounded prime-dependent signs, is fixed without reference to a dense\nmodel.  Proposition~\\ref{prop:correlation-test} itself asserts the\ninequality for the repeated target function; the pseudorandomness of\nthe enlarged test class is proved in Section~\\ref{sec:prediction}.\n\\end{remark}\n"}, {"path": "preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/05_prediction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/05_prediction.tex", "bytes": 37841, "sha256": "bb74c0d64b7fdf4f4e19dd974c44d6e1cb8ebc48d2902c61722d82a37b4b343e", "content": "\\section{Dense models and prediction}\\label{sec:prediction}\n\nWe prove Principle~\\ref{pr:prediction}.  The correlation estimate of\nSection~\\ref{sec:correlation} first allows the unbounded color weights to be\nreplaced by bounded functions.  We then choose nilsequence approximations at\ncoarse scales.  A Ramsey argument compares their projection energies with\nthose at the finer scales required by the correlation estimate.  All analytic\nmoduli in this last comparison will be independent of the number of master\nindices.\n\nFix $m$.  Choose an integer $s\\geq3$ such that\n\\begin{equation}\\label{eq:prediction-step-choice}\n s+1\\geq 2(2^d)-1\n\\end{equation}\nfor every cube order $d$ occurring in Proposition~\\ref{prop:correlation-test}.\nThe order bound in that Proposition makes this a choice depending only on\n$m$.  The later Cauchy--Schwarz argument will bound a $d$-cube test\nby a subgroup norm of order $k=2^d$; concatenation then uses order\n$2k-1$, whose inverse theorem gives\nnilsequences of step $2k-2$.\nFix also the data $n,r,\\chi,\\mathcal B,\\mathcal A$ in\nPrinciple~\\ref{pr:prediction}.  For the moment fix a master index count\n$N$ and the parameters of Lemma~\\ref{lem:master-scales}; only blocks\nwith at most $n$ members are used.\nFor $B=T\\cup\\{i\\}$, $a\\in\\mathcal A$, and $c\\in[r]$, write\n\\[\n \\nu=\\nu_B,\\qquad f(y)=\\1_{\\chi(h_Bay)=c},\\qquad \\rho(y)=\\nu(y)f(y).\n\\]\nHere $f$ is zero on nonpositive integers.  All sufficiently large $w$ make\nits positive color arguments integral.  We make two replacements,\n$\\rho\\to F\\to S$.  The bounded function\n$F$ must approximate $\\rho$ against the cube tests from\nSection~\\ref{sec:correlation};\nthe piecewise nilsequence $S$ must additionally admit alignment at the\ncoarse scale.  The gap-energy comparison will make these two demands\ncompatible.\n\n\\subsection{An algebra of dual tests}\n\nA cube type at $B$ means one of the types supplied by\nProposition~\\ref{prop:correlation-test}, at a gap valid for its chain.  We\nalso include, at every gap $\\max T<l<i$, the type of dimension $s+1$ with\nmodulus $M$ and no prime variables.  Fix an integer $J_0\\geq1$.  For any\none of these types put $M_{\\boldsymbol p}=M(\\boldsymbol p)$ and\n\\[\n L_{\\boldsymbol p}=\\floor{R_l/(J_0M_{\\boldsymbol p})}.\n\\]\nPrimes have the good-tuple law of\nProposition~\\ref{prop:correlation-test}; the variables $u_j^0,u_j^1$ are\nindependent and uniform in $[0,L_{\\boldsymbol p})\\cap\\Z$.  Its dual tests\nare the real functions\n\\begin{equation}\\label{eq:prediction-dual-test}\n \\mathcal D(y)=\\E_{\\boldsymbol p,u}e(\\boldsymbol p)\n \\prod_{\\varnothing\\ne\\omega\\subset[d]}\n g_{\\omega,\\boldsymbol p}\n \\left(y+M_{\\boldsymbol p}\\sum_{j\\in\\omega}(u_j^1-u_j^0)\\right),\n \\qquad |e|\\leq1,\\quad |g_{\\omega,\\boldsymbol p}|\\leq1+\\nu.\n\\end{equation}\nThe functions, as well as the bounded prime factor, may vary arbitrarily\nwith $w$.  The finitely many cube templates and any fixed finite set of\n$J_0$ values are included in each uniform assertion below.\n\nTo construct $F$, we need more than a mean-one estimate for $\\nu$.\nThe next Lemma shows that $\\nu-1$ is asymptotically orthogonal to the\nalgebra generated by the dual tests, and controls the tests sufficiently\nto replace them by bounded ones.\n\n\\begin{lemma}[Dual-test pseudorandomness]\\label{lem:dual-pseudorandomness}\nFor every fixed $b\\geq0$ and dual tests\n$\\mathcal D_1,\\ldots,\\mathcal D_b$ at the same block,\n\\begin{equation}\\label{eq:prediction-dual-products}\n \\E_{\\mu_i}(\\nu-1)\\prod_{q=1}^b\\mathcal D_q=o(1)\n\\end{equation}\nuniformly over their inputs.  The tests may use different permissible\ngaps and different cube types.  If $d_*$ bounds the dimensions of these\ntypes and $A_*=2^{2^{d_*}-1}$, then for every fixed positive integer $b$,\n\\begin{equation}\\label{eq:prediction-dual-moments}\n \\E_{\\mu_i}(1+\\nu)|\\mathcal D|^b\\leq2A_*^b+o(1).\n\\end{equation}\nFor any fixed $K>A_*$, replacing every test by its clipping to $[-K,K]$\nchanges it by $o(1)$ in every fixed $L^p((1+\\nu)\\mu_i)$ norm, and\n\\eqref{eq:prediction-dual-products} remains true for the clipped tests.\n\\end{lemma}\n\n\\begin{proof}\nExpand the product using independent prime and shift replicas.  In replica\n$q$, of dimension $d_q$ and modulus $M_q$, the nonroot rows are\n\\[\n Y_{q,\\omega}=y+M_q\\sum_{j\\in\\omega}(u_{q,j}^1-u_{q,j}^0),\n \\qquad\\varnothing\\ne\\omega\\subset[d_q].\n\\]\nThe root row is $y$.  For a fixed nonempty $\\omega$, choose integers\n$a_0,a_1,\\ldots,a_{d_q}$ such that\n\\[\n a_0\\ne0,\\qquad a_0+\\sum_{j\\in\\omega}a_j=0,\\qquad\n a_0+\\sum_{j\\in\\omega'}a_j\\ne0\\quad(\\omega'\\ne\\omega).\n\\]\nIndeed, the hyperplane defined by the equality is not contained in any\nof the finitely many hyperplanes excluded by the inequalities: their\nnormal vectors $(1,\\1_{\\omega'})$ are pairwise nonparallel.  A rational\npoint avoiding their intersections exists, and clearing denominators\ngives the indicated integers.  Insert a translation parameter for this\nrow, acting by\n\\[\n y\\longmapsto y+a_0M_qv,\\qquad\n u_{q,j}^1\\longmapsto u_{q,j}^1+a_jv,\n\\]\nand leaving all other shift variables unchanged.  It fixes the chosen\nrow.  Every other row in its replica has a nonzero response, the root\nhas response $a_0M_q$, and a row in another replica has the same nonzero\nresponse $a_0M_q$.\n\nThese directions are compatible even when the gaps differ.  Put\n$V_B=2+M+\\prod_{b\\in T}X_b^2$.  The shortest shift interval in each\nreplica dominates every fixed power of $V_B$.  The translation parameters\nfor replica $q$ may, for example, have lengths comparable to the square\nroot of its shortest shift length.  Their lengths still dominate powers\nof $V_B$, whereas the resulting displacements of its shifts are\nnegligible relative to their intervals, even after multiplication by any\nfixed power of $V_B$.  The sum of the root displacements is likewise\nnegligible at $X_i$.  The parameters depend on the primes only through\nthe lengths, which is permitted by the uniform box version of\nProposition~\\ref{prop:linear-forms}.  Thus inserting all these averaged\ntranslations changes the original expression by $o(1)$: use the crude\nbound $1+\\nu\\leq1+\\prod_{b\\in T}X_b^2$ and the translation estimates of\nLemma~\\ref{lem:sampling}.  Fixed direction constants do not affect these\nseparations.\n\nEliminate the nonroot rows one at a time by the weighted\nCauchy--Schwarz procedure of Lemma~\\ref{lem:additive-elimination}.\nAt the step assigned to a row, all its current copies are independent\nof that row's translation parameter.  Bound those copies by their\n$1+\\nu$ weights and use their product as the outside density. Squaring\nduplicates that row's assigned translation parameter; all other\nvariables remain in the outside expectation, as in that procedure.\nPrime-only bounded factors can be discarded outside the\ninner average.  Every prefactor is bounded, and one weight for each\neliminated copy is retained in the final expression.  The root, which\nis never eliminated, contributes copies of $(\\nu-1)$ indexed by all\nchoices of the duplicated translation parameters.\n\nWe verify the linear-forms hypotheses at every such application.\nEvery form has coefficient $1$ at $y$.  Different base rows have distinct\ncoefficients on the original shifts.  Copies of one base row are\ndistinguished by a duplicated direction with nonzero response; for an\neliminated row these are precisely directions other than its own.\nRoot copies are distinguished because every inserted direction has\nnonzero root response.  A distinguishing minor is a fixed nonzero\ninteger times a single replica modulus $M_q$; no difference of two\nmoduli is required.  At a rough prime, its possible vanishing\nis therefore included among the polynomial exceptional tests defining\nthat modulus.  Small-prime factors and fixed nonzero integer factors\nare covered by the smooth modulus and, eventually, by $W$.\nAll replicas use gaps after the common tail $T$, so the multi-gap form\nof Proposition~\\ref{prop:linear-forms} applies with $V_B$.\nThe arbitrary prime-only restrictions in that Proposition include the\nseparate good-tuple restrictions in each replica.\n\nExpand the retained $1+\\nu$ weights and every root difference.  Each\nresulting product has the main term obtained by replacing each $\\nu$\nby $1$, with a uniform $o(1)$ error.  In that substitution at least one\nroot difference becomes zero.  Thus the final Cauchy--Schwarz expression\nis $o(1)$, proving \\eqref{eq:prediction-dual-products}.  When $b=0$ this\nis simply $\\E\\nu=1+o(1)$.\n\nFor the moment bound, Jensen's inequality and independent replicas give\n\\[\n |\\mathcal D(y)|^b\\leq\n \\E\\prod_{q=1}^b\\prod_{\\varnothing\\ne\\omega\\subset[d]}\n (1+\\nu)(Y_{q,\\omega}).\n\\]\nInclude the root weight $1+\\nu(y)$ and expand.  These rows are already\ndistinct without the additional translations.  Proposition~\\ref{prop:linear-forms}\ngives $2^{1+b(2^d-1)}+o(1)$, proving\n\\eqref{eq:prediction-dual-moments}.\n\nLet $\\mathcal D^{[K]}=\\max(-K,\\min(K,\\mathcal D))$.  For integers $b>p$,\n\\[\n \\limsup_{w\\to\\infty}\n \\E(1+\\nu)|\\mathcal D-\\mathcal D^{[K]}|^p\n \\leq 2K^{p-b}A_*^b.\n\\]\nThis estimate holds for every fixed $b$; sending $b$ to infinity after\nthe limit proves that the left side is zero.  Telescoping a fixed\nproduct and applying H\\\"older's inequality with respect to\n$(1+\\nu)\\mu_i$ now transfers\n\\eqref{eq:prediction-dual-products} to clipped tests, since\n$|\\nu-1|\\leq1+\\nu$ and all required fixed moments are bounded.  No\nmoment order growing with $w$ has been used.\n\\end{proof}\n\nWe use the separation and polynomial-approximation proof of the dense\nmodel theorem developed independently by Gowers\n\\cite[Section~4]{GowersDense} and Reingold, Trevisan, Tulsiani and\nVadhan \\cite[Section~2]{RTTVDense}.\nThe preceding Lemma supplies the pseudorandomness needed for our\nparticular weighted test class.\n\n\\begin{proposition}[Bounded dense models]\\label{prop:dense-model}\nThere are functions $F_{B,a,c}:\\Z\\to[0,1]$ such that, for every fixed\n$J_0$ and every dual test at $B$,\n\\begin{equation}\\label{eq:prediction-dense-approximation}\n \\E_{\\mu_i}(\\rho-F_{B,a,c})\\mathcal D=o(1)\n\\end{equation}\nuniformly over its inputs and over the finitely many master blocks,\nscale labels, and colors.\n\\end{proposition}\n\n\\begin{proof}\nFirst fix a finite collection of $J_0$ values, a positive desired error,\nand a common clipping bound $K$ from\nLemma~\\ref{lem:dual-pseudorandomness}.  The support of $\\mu_i$ is finite.\nLet $\\mathcal C$ be the closed convex hull of the signed clipped tests,\nviewed as vectors on that support.  It is compact and lies in $[-K,K]$\ncoordinatewise.  The candidate models form the compact convex cube\n$\\mathcal F=[0,1]^{\\supp\\mu_i}$.  Finite-dimensional minimax for the\nbilinear pairing gives\n\\[\n \\inf_{F\\in\\mathcal F}\\sup_{G\\in\\mathcal C}\\langle\\rho-F,G\\rangle\n =\\sup_{G\\in\\mathcal C}\n \\bigl(\\langle\\rho,G\\rangle-\\langle1,G_+\\rangle\\bigr),\n \\qquad G_+=\\max(G,0),\n\\]\nbecause $\\sup_{F\\in\\mathcal F}\\langle F,G\\rangle=\\langle1,G_+\\rangle$.\nSince $0\\leq\\rho\\leq\\nu$, the right-hand side is at most\n$\\sup_G\\langle\\nu-1,G_+\\rangle$.\n\nTo bound it uniformly, approximate $t\\mapsto\\max(t,0)$ on $[-K,K]$\nwithin $\\delta$ by a polynomial $P(t)$.  Every fixed power of a convex\ncombination of signed tests is a convex combination of their signed\nproducts.  Lemma~\\ref{lem:dual-pseudorandomness}, followed by continuity\nfor the closed convex hull, therefore gives\n$\\langle\\nu-1,P(G)\\rangle=o(1)$ uniformly in $G\\in\\mathcal C$.  The\npolynomial approximation costs at most\n$\\delta\\E(1+\\nu)=2\\delta+o(1)$.  First take $w\\to\\infty$ and then\n$\\delta\\to0$.  Minimax produces models with the desired error against\nthe clipped tests; the clipping estimate gives the same conclusion for\nthe original tests, since $|\\rho-F|\\leq1+\\nu$.\n\nApply this argument successively to errors $1/q$ and $J_0\\leq q$.\nFor each fixed $q$ it works for all sufficiently large $w$, uniformly\nover the finite master data.  Choosing the stage $q=q(w)$ to increase\nsufficiently slowly gives \\eqref{eq:prediction-dense-approximation}\nfor every fixed $J_0$.  This diagonal choice is made together with the\nfixed-template diagonal in Lemma~\\ref{lem:master-scales}; it asserts no\nestimate for an arbitrary growing moment order.  Extend the models\noutside the support of $\\mu_i$ with values in $[0,1]$.\n\\end{proof}\n\nHere is the first consequence for a chain with a valid gap.  In its\nweighted count, replace one factor\n$\\rho_{B_d,a_d,c}(L_J)$, $|J|\\geq2$, by $F_{B_d,a_d,c}(L_J)$.\nThe difference has target $h=\\rho-F$, with $|h|\\leq1+\\nu_B$;\nall other linear factors still satisfy the bounds required by\nProposition~\\ref{prop:correlation-test}.  The product masks and the\ncenter weights are retained.  In the resulting cube, its nonroot\ncopies of $h$ are admissible inputs in\n\\eqref{eq:prediction-dual-test}; its root pairing is therefore $o(1)$\nby \\eqref{eq:prediction-dense-approximation}.  Equation~\\ref{eq:correlation-test}\nand a finite telescoping sum show that all nonsingleton color weights\ncan be replaced by their bounded models with total error $o(1)$.\n\n\\subsection{Nilsequence tests and nested projections}\n\nThe bounded functions $F$ now replace the weighted color factors in the\ncounts.  Calibration requires another comparison: $\\rho$ and $F$ must\nagree against bounded Lipschitz functions of the eventual prediction\n$S$, so that small prediction values cannot carry much actual color\nmass.  We first establish this nilsequence testing property, using\nmissing-corner reconstruction to express the tests through the dual\ntests already controlled.  We then choose coarse models whose\ndifferences from $F$ have small projections at the finer chain scales.\nThe next subsection turns those projection bounds into counting estimates.\n\n\\begin{lemma}[Testing piecewise nilsequences]\\label{lem:nilsequence-testing}\nFix $B=T\\cup\\{i\\}$ and a gap $\\max T<l<i$.  Let $S'$ be any family\nof bounded real piecewise nilsequences on intervals of length $R_l$\nand residues modulo $M$, of step at most $s$ and bounded complexity\nin the sense of Definition~\\ref{def:piecewise-model}.  Then\n\\begin{equation}\\label{eq:prediction-nilsequence-testing}\n \\E_{\\mu_i}(\\rho-F_{B,a,c})S'=o(1).\n\\end{equation}\nThe assertion is uniform for each fixed finite list of nilmanifolds\nand fixed observable bounds.\n\\end{lemma}\n\n\\begin{proof}\nLemma~\\ref{lem:cube-corner}, proved independently in\nSection~\\ref{sec:cube-limits}, supplies a compact set of nilmanifold\ncubes containing every linear orbit cube\n\\[\n (x_\\omega)_{\\omega\\subset[s+1]}\n   =\\left(g^{k+\\sum_{j\\in\\omega}v_j}x\\right)_{\\omega\\subset[s+1]},\n \\qquad k,v_1,\\ldots,v_{s+1}\\in\\Z.\n\\]\nOn this set the nonroot vertices determine $x_\\varnothing$\ncontinuously.  Its Stone--Weierstrass consequence gives the following\nconcrete input.  For any accuracy $\\delta>0$, the observable at the\nroot is uniformly approximated by a finite sum\n\\[\n \\sum_{t=1}^{q}\\lambda_t\n \\prod_{\\varnothing\\ne\\omega\\subset[s+1]}\\phi_{t,\\omega}(x_\\omega)\n\\]\nwith $|\\phi_{t,\\omega}|\\leq1$.  The same Lemma gives finitely many\nsuch recipes, with fixed total coefficient bounds, for our finite\nnilmanifold list and uniformly bounded Lipschitz observables.\nThe recipes are valid for every $g$ and $x$, so they can be used on\nall pieces even though the translating elements and base points are\nunrestricted.  It remains to realize these recipes as our dual tests\nand control the cubes that cross piece boundaries.\n\nUse the extra dual test of dimension $s+1$, modulus $M$, and a fixed\nlarge $J_0$.  If $y$ is farther than $(s+1)R_l/J_0$ from the endpoints\nof its $R_l$ interval, all its cube vertices lie in that interval and\nhave the same residue modulo $M$.  The recipe for that piece then\nreconstructs $S'(y)$.  Recipes can be selected through the input\nfunctions of the dual test: for a term belonging to recipe $r$, put\nin every nonroot input the indicator that its piece selected $r$,\nmultiplied by its single-vertex factor evaluated on that piece's\norbit.  Sum over the finite recipes and terms.  On cubes staying in\none piece this selects exactly its recipe.  Each resulting input is\nbounded by $1$, and hence is allowed in\n\\eqref{eq:prediction-dual-test}.\n\nWe record the weighted boundary estimate needed for the discarded\nroots.  If $E_l$ is the union of these boundary strips, then\n\\begin{equation}\\label{eq:prediction-weighted-boundary}\n \\E_{\\mu_i}(1+\\nu_B)\\1_{E_l}\n \\leq O_s(J_0^{-1})+o(1).\n\\end{equation}\nFor a fixed divisor draw $\\sigma=t_T$, divisibility by $\\sigma$\nand coprimality to $W$ are periodic with period $\\sigma W$.\nThis period is negligible compared with $R_l$, uniformly in\n$\\sigma\\leq\\prod_{b\\in T}X_b^2$.  Counting in each full $R_l$ interval\nshows that a union of end strips of relative length $O_s(J_0^{-1})$\nhas that proportion of its weighted mass, with relative error\n$O(\\sigma W/R_l)$.  The harmonic factor varies by $o(1)$ within a\ncell, since $R_l/X_i=o(1)$; the two partial cutoff cells have $o(1)$\nmass by the same counting estimate.  This proves the assertion for\n$\\sigma\\1_{\\sigma\\mid y}\\mu_i$, uniformly in $\\sigma$.\nAverage over $\\sigma$ and also take $\\sigma=1$ to obtain\n\\eqref{eq:prediction-weighted-boundary}.\n\nThe recipe approximation costs $O(\\delta)$ against\n$|\\rho-F|\\mu_i\\leq(1+\\nu_B)\\mu_i$.  For fixed recipes and $J_0$,\nProposition~\\ref{prop:dense-model} makes every dual pairing $o(1)$.\nThe terms outside the good roots are bounded by a fixed constant\n(depending on the recipes) times the quantity in\n\\eqref{eq:prediction-weighted-boundary}.  First fix sufficiently\naccurate recipes, then take $J_0$ sufficiently large for those\nrecipes, and finally take $w\\to\\infty$.  Since $\\delta$ was arbitrary,\nthis proves \\eqref{eq:prediction-nilsequence-testing}.\n\\end{proof}\n\nUse the fixed nonprincipal ultrafilter $\\mathcal U$ on the positive\nintegers $w$. For each pivot $i$, form the real Hilbert space of families of\nvectors in $L^2(\\mu_i)$ with uniformly bounded norms, with pairing\n$\\lim_{\\mathcal U}\\langle\\cdot,\\cdot\\rangle$, after quotienting out\nzero-norm families and completing.  For $l<i$, let $\\mathcal N_{i,l}$\nbe the closed span in this space of bounded-complexity piecewise\nstep-at-most-$s$ nilsequence families on the $R_l$ intervals and\nresidues modulo $M$.  Complexity is allowed to vary from one family\nto another but is bounded within a family.  Denote the orthogonal\nprojection by $P_{i,l}$.\n\nWhen $l<l'<i$, the divisibility $R_l\\mid R_{l'}$ aligns the smaller\nintervals with the larger ones.  Restricting an orbit to a smaller\ninterval only changes its base point.  Consequently\n\\begin{equation}\\label{eq:prediction-nested-spaces}\n \\mathcal N_{i,l'}\\subseteq\\mathcal N_{i,l},\\qquad\n \\|P_{i,l}v-P_{i,l'}v\\|_2^2\n =\\|P_{i,l}v\\|_2^2-\\|P_{i,l'}v\\|_2^2.\n\\end{equation}\nProducts and Lipschitz real combinations of finitely many representing\nfamilies remain representing families, using product nilmanifolds.\nThis includes clipping to $[0,1]$.\n\n\\begin{lemma}[Ramsey selection of gap energies]\\label{lem:energy-selection}\nFor every $\\eps>0$, a master count $N$ depending only on\n$n,r,|\\mathcal A|,\\eps$ can be chosen so that there are indices\n\\[\n k_1<j_1<k_2<j_2<\\cdots<k_n<j_n\n\\]\nand $[0,1]$-valued piecewise models $S_{B,a,c}$ at pivot $i=j_u$\non intervals of length $R_{k_u}$ such that\n\\begin{equation}\\label{eq:prediction-coarse-approximation}\n \\|S_{B,a,c}-P_{j_u,k_u}F_{B,a,c}\\|_2\\leq\\eps.\n\\end{equation}\nFor a chain on the principal indices $j_1,\\ldots,j_n$, let $l$ be the\npadding index immediately before its first pivot.  At every block\n$B=T\\cup\\{j_u\\}$ of the chain,\n\\begin{equation}\\label{eq:prediction-fine-projection}\n \\|P_{j_u,l}(F_{B,a,c}-S_{B,a,c})\\|_2\\leq2\\eps.\n\\end{equation}\nThe families $S$ have bounded complexity after $N$ and $\\eps$ are fixed.\n\\end{lemma}\n\n\\begin{proof}\nThe energies $\\|P_{i,l}F_{T\\cup\\{i\\},a,c}\\|_2^2$ lie in $[0,1]$.\nColor each ordered tuple consisting of the increasing members of\n$T$, followed by $l$ and $i$, by their bins of width $\\eps^2$,\njointly for all $a,c$.  There are finitely many colors, with a bound\nindependent of $N$.  Apply finite Ramsey successively for all tuple\nlengths $3,\\ldots,n+1$ to obtain a homogeneous set of size $2n$.\nLabel it alternately by padding and principal indices as displayed.\n\nAt each selected block approximate its coarse projection by a finite\nlinear combination $T'$ of representing families, to accuracy $\\eps$.\nIts clipping $S$ to $[0,1]$ is still a representing family.  Pointwise\nclipping decreases the distance to $F\\in[0,1]$.  Since both $T'$ and\n$S$ lie in the coarse subspace, orthogonality gives\n\\[\n \\|F-S\\|_2^2=\\|F-PF\\|_2^2+\\|PF-S\\|_2^2,\n\\]\nand the analogous identity for $T'$.  Thus clipping also decreases\nthe distance to $PF$, proving\n\\eqref{eq:prediction-coarse-approximation}.  There are only finitely\nmany selected blocks and labels, so their representing families\nshare a finite nilmanifold list and uniform observable bounds.\n\nAll tails of the chain precede its first pivot and hence precede\n$l$; also $l\\leq k_u<j_u$.  If $l<k_u$, the tuples $(T,l,j_u)$ and\n$(T,k_u,j_u)$ lie in the homogeneous set and have the same color.\nTheir energies differ by at most $\\eps^2$.\nEquation~\\eqref{eq:prediction-nested-spaces} bounds the distance\nbetween the two projections by $\\eps$.  As $S$ belongs to the coarse\nand hence the fine space, \\eqref{eq:prediction-fine-projection}\nfollows by the triangle inequality.  The case $l=k_u$ follows\ndirectly from \\eqref{eq:prediction-coarse-approximation}.\n\\end{proof}\n\n\\subsection{Subgroup cubes and the inverse theorem}\n\nThe energy selection makes $F-S$ have small projection onto the fine\nnilsequence space.  We must turn that conclusion into a small cube test\nin Proposition~\\ref{prop:correlation-test}, with a threshold independent\nof the master count $N$.  Finite subgroup norms supply this link.\n\nFor a finite subgroup $Q$ acting by measure-preserving transformations\n$T^q$ on a probability space $\\mathcal X$, and a real bounded function\n$h$, use the convention\n\\[\n \\|h\\|_{U^t_Q(\\mathcal X)}^{2^t}\n =\\E_{x\\in\\mathcal X}\\E_{v_1,\\ldots,v_t\\in Q}\n \\prod_{\\omega\\subset[t]}h\\left(T^{\\sum_{j\\in\\omega}v_j}x\\right).\n\\]\nThis is the usual subgroup Gowers seminorm; for complex functions the\nusual alternating conjugations are inserted.\n\nWe use exactly two external inverse statements.  First, the subgroup\ncase of the finitary qualitative Bessel inequality\n\\cite[Theorem~1.23]{TaoZiegler} says that for each $k$ there is a\nfunction $b_k(\\delta)\\to0$ as $\\delta\\downarrow0$ such that\n\\begin{equation}\\label{eq:prediction-bessel}\n \\E_{\\alpha,\\alpha'}\n \\|h\\|_{U^{2k-1}_{Q_\\alpha+Q_{\\alpha'}}(\\mathcal X)}\\leq\\delta\n \\quad\\Longrightarrow\\quad\n \\E_\\alpha\\|h\\|_{U^k_{Q_\\alpha}(\\mathcal X)}\\leq b_k(\\delta),\n \\qquad |h|\\leq1.\n\\end{equation}\nThe bound is independent of the finite family, the group, and the\nprobability system.  Finite subgroups are rank-zero coset\nprogressions in that Theorem.  Its dilations leave them unchanged,\nand its multiset sum of two subgroups induces uniform measure on\ntheir subgroup sum.  The statement consequently has exactly the\nform \\eqref{eq:prediction-bessel}.  Rational probability weights on\nindices are implemented by repetition of indices; arbitrary finite\nweights follow by approximation.  We choose $b_k$ nondecreasing and\nenlarge its argument by a factor of two to absorb the approximation\nslack; the resulting modulus, still denoted $b_k$, tends to zero.\n\nSecond, the Green--Tao--Ziegler inverse theorem, in its finite-list\nlinear-nilsequence formulation\n\\cite[Conjecture~1.2 and Theorem~1.3]{GTZ}, with the correction\n\\cite{GTZErratum}, gives the following statement.  For each integer\n$t\\geq2$ and $\\delta>0$, a $1$-bounded function on an integer interval\nwith $U^t$ norm at least $\\delta$ correlates by at least\n$c_t(\\delta)>0$ with a sequence $\\Phi(g^kx)$ on a nilmanifold of\nstep at most $t-1$.  There is a finite list of such nilmanifolds,\nwith connected simply connected groups, from which the manifold is\nchosen; $\\|\\Phi\\|_\\infty\\leq1$ and its Lipschitz bound depend only on\n$t,\\delta$.  The cases $t=2,3$ are the established lower-order\ncases recalled in the Introduction of \\cite{GTZ}; Theorem~1.3 supplies\nthe higher orders.  No bound on $g$ is asserted or needed.\nWe use only this inverse conclusion, not the original auxiliary\nProposition~8.3 addressed by the erratum.  The interval norm here is\nthe normalized cube mean with all vertices in the interval, equivalently\nthe zero-extension norm divided by the norm of the interval indicator\nin a sufficiently large auxiliary cyclic group.\n\n\\begin{lemma}[From subgroup cubes to a fine nilsequence projection]\n\\label{lem:subgroup-inverse}\nFix a cube type of dimension $d$, a gap $l<i$, and $J_0\\geq1$.\nAssume $2(2^d)-2\\leq s$.  For every $\\gamma>0$ there is\n$\\kappa=\\kappa(d,J_0,\\gamma)>0$, independent of the master count,\nthe gap, the cutoffs, and the cell probabilities, with the following\nproperty.  If $h:\\Z\\to[-1,1]$ is a family with\n$\\|P_{i,l}h\\|_2<\\kappa$, then\n\\begin{equation}\\label{eq:prediction-subgroup-conclusion}\n \\lim_{\\mathcal U}\\left|\n \\E_{\\boldsymbol p,y,u}\n \\prod_{\\omega\\subset[d]}\n h\\left(y+M_{\\boldsymbol p}\n       \\sum_{j\\in\\omega}(u_j^1-u_j^0)\\right)\\right|\n \\leq\\gamma+O_d(J_0^{-1}).\n\\end{equation}\nHere $y\\sim\\mu_i$, and primes and shifts have the laws in\n\\eqref{eq:prediction-dual-test}.  The constant in the last term\ndepends only on $d$.\n\\end{lemma}\n\n\\begin{proof}\nWe pass from the short increments to subgroup norms, combine the\nsubgroups using rough coprimality, and apply the inverse theorem in\neach cyclic cell.  We keep the cell probabilities throughout, so every\nthreshold is independent of the number of cells and the master count.\n\n\\paragraph{From short increments to subgroup norms.}\nDiscard the two partial $R_l$ intervals at the ends of\n$[X_i,X_i^2)$; their $\\mu_i$ mass is $o(1)$.  Within every remaining\ninterval and every residue modulo $M$, harmonic measure differs\nfrom conditional uniform measure by relative $o(1)$, uniformly in\nthe cell, because $R_l/X_i=o(1)$.  Only residues coprime to $W$\noccur, and translation by $M$ preserves them.\n\nWrite $q_l=R_l/M$.  Each cell has exactly $q_l$ points.  Give it its\noriginal probability, normalized after discarding the partial\ncells, and identify its progression indices with\n$G_l=\\Z/q_l\\Z$.  Their disjoint union, with these probabilities and\nconditional Haar measure in each cell, is a finite probability\nsystem $\\mathcal X_l$.  Translation by $1\\in G_l$ means translation\nby $M$ with wrapping inside that cell.  Replacing the original cube\nby this periodized cube costs $O_d(J_0^{-1})+o(1)$: every vertex\nmoves by at most $dR_l/J_0$ from its root, so only roots within that\ndistance of an interval endpoint can wrap.  All functions in this\nargument are bounded by $1$.\n\nFor a prime tuple put\n\\[\n q_{\\boldsymbol p}=M_{\\boldsymbol p}/M,\n \\qquad Q_{\\boldsymbol p}=q_{\\boldsymbol p}G_l.\n\\]\nThe divisibility provisions in Lemma~\\ref{lem:master-scales} give\n$q_{\\boldsymbol p}\\mid q_l$.  Its subgroup has order\n$q_l/q_{\\boldsymbol p}$, while\n$L_{\\boldsymbol p}=\\floor{q_l/(J_0q_{\\boldsymbol p})}$ tends to\ninfinity uniformly over the pool.  The pushforward of a uniform\n$u\\in[0,L_{\\boldsymbol p})$ to $q_{\\boldsymbol p}u\\in Q_{\\boldsymbol p}$\nhas density at most $2J_0$ relative to subgroup Haar measure.\nThe difference of two independent such variables has no larger\ndensity.  Thus the $d$ independent increments of the periodized\ncube have joint density at most $(2J_0)^d$ on\n$Q_{\\boldsymbol p}^d$.\n\nFor completeness, this density can be removed with a bounded number\nof Cauchy--Schwarz steps.  Regard it as one factor, independent of\nthe root, on Haar variables $(x,v_1,\\ldots,v_d)$, where\n$x\\in\\mathcal X_l$ and $v_j\\in Q_{\\boldsymbol p}$.\nInsert one uniform subgroup direction translating the root alone;\nthe density is invariant in that direction.  For each nonempty\n$\\omega\\subset[d]$, choose $j\\in\\omega$ and insert a direction\n\\[\n x\\longmapsto T^a x,\\qquad v_j\\longmapsto v_j-a,\n \\qquad a\\in Q_{\\boldsymbol p},\n\\]\nwhich leaves that vertex fixed.  These invariant changes of Haar\nvariables commute.  Successive Cauchy--Schwarz inequalities outside\nthe assigned direction discard first the density and then each\nnonroot vertex function, including its copies from earlier steps.\nThe root has response $a$ in every direction.  After\n$k=1+(2^d-1)=2^d$ steps its retained copies are a Haar subgroup\n$k$-cube.  Taking the $2^k$-th root proves\n\\begin{equation}\\label{eq:prediction-cube-subgroup-bound}\n |\\text{periodized cube at }\\boldsymbol p|\n \\leq C_{d,J_0}\\|h\\|_{U^k_{Q_{\\boldsymbol p}}(\\mathcal X_l)}.\n\\end{equation}\nThe argument acts inside each cell and integrates its probability\nthroughout; its constant is independent of the number of cells.\n\n\\paragraph{Combining the subgroups.}\nFor independent tuples, Lemma~\\ref{lem:rough-coprimality}, also after\nthe good-tuple restrictions, gives\n$\\gcd(q_{\\boldsymbol p},q_{\\boldsymbol p'})=1$ with probability\n$1-o(1)$.  On this event B\\'ezout's identity implies\n$Q_{\\boldsymbol p}+Q_{\\boldsymbol p'}=G_l$.  Consequently\n\\[\n \\E_{\\boldsymbol p,\\boldsymbol p'}\n \\|h\\|_{U^{2k-1}_{Q_{\\boldsymbol p}+Q_{\\boldsymbol p'}}(\\mathcal X_l)}\n \\leq \\|h\\|_{U^{2k-1}_{G_l}(\\mathcal X_l)}+o(1).\n\\]\nTogether with \\eqref{eq:prediction-bessel} and\n\\eqref{eq:prediction-cube-subgroup-bound}, this says: for every\n$\\gamma>0$ a bound on the global $U^t$ norm, $t=2k-1$, sufficiently\nsmall in terms of $d,J_0,\\gamma$ makes the periodized cube mean at\nmost $\\gamma$.  This assertion is uniform in all finite system data.\n\n\\paragraph{From the global norm to a nilsequence projection.}\nWe next show that a fixed positive global norm forces a fixed\npositive fine projection.  We first justify use of the interval\ninverse theorem on one cyclic cell, without requiring the resulting\nnilsequence to be periodic.  Let $v$ be $1$-bounded on $\\Z/q\\Z$ and\nextend it periodically to $[0,Kq)$.  For $L=Kq$ define the integer\ncube domain\n\\[\n A_L=\\{(x,a_1,\\ldots,a_t)\\in\\Z^{t+1}:\n               0\\leq x+\\textstyle\\sum_{j\\in\\omega}a_j<L\n               \\text{ for every }\\omega\\subset[t]\\}.\n\\]\nThe $2^t$-th power of the interval norm is the mean of the periodic\ncube product over $A_L$.  Averaging a parameter translate\n$r\\in\\{0,\\ldots,q-1\\}^{t+1}$ gives exactly the cyclic cube mean\nat every parameter point.  The discrepancy is therefore bounded\nby $\\E_r|(A_L+r)\\mathbin\\triangle A_L|/|A_L|$.\nFor a vertex $\\omega$, its displacement is\n$r_0+\\sum_{j\\in\\omega}r_j\\leq(t+1)(q-1)$.\nThe boundary discrepancy at that vertex has size at most this\nquantity times $2L^t$: once that vertex and its $t$ neighboring\nvertices are specified the integer cube is determined, and each\nneighbor has at most $L$ choices on the relevant side of the\nsymmetric difference.  Summing over vertices bounds the numerator\nby $O_t(qL^t)$.  Nonnegative increments and root with total less\nthan $L$ already give\n$|A_L|\\geq\\binom{L+t}{t+1}\\geq L^{t+1}/(t+1)!$.\nThus, uniformly in $q,K,v$,\n\\begin{equation}\\label{eq:prediction-cyclic-interval}\n \\|v^{\\mathrm{per}}\\|_{U^t[Kq]}^{2^t}\n =\\|v\\|_{U^t(\\Z/q\\Z)}^{2^t}+O_t(K^{-1}).\n\\end{equation}\nThis is an estimate for the powers of the norms.\n\nIf the cyclic norm is at least $\\delta$, choose a sufficiently\nlarge fixed $K=K(t,\\delta)$ in\n\\eqref{eq:prediction-cyclic-interval}.  The interval norm is then\nat least $\\delta/2$.  The inverse theorem gives a uniformly bounded\ncomplexity correlator on those $K$ periods.  Splitting the\ncorrelation into its $K$ period averages shows that one period has\ncorrelation of at least the same modulus.  Translating that period\nback to $[0,q)$ only replaces the nilsequence base point by a\ntranslate $g^{jq}x$.  It leaves its manifold, step and observable\nbounds unchanged.  We have proved a cyclic-to-interval correlation\nstatement on fixed representatives of $\\Z/q\\Z$; the correlator\nitself need not have period $q$.\n\nNow write the cell weights of $\\mathcal X_l$ as $\\alpha_C$.\nThe exact identity\n\\[\n \\|h\\|_{U^t_{G_l}(\\mathcal X_l)}^{2^t}\n =\\sum_C\\alpha_C\\|h_C\\|_{U^t(G_l)}^{2^t}\n\\]\nshows that, if the left-hand norm is at least $\\delta$, cells with\n$\\|h_C\\|_{U^t(G_l)}\\geq\\delta/2$ have total probability at least\n$\\delta^{2^t}/2$.  On each such cell choose its inverse-theorem\ncorrelator, rotate its complex phase and take the real part so\nthat its pairing with the real function $h_C$ is positive.\nPut zero on the remaining cells.  Every chosen sequence comes\nfrom the same finite nilmanifold list and has the same uniform\nobservable bounds.  They form one permitted real piecewise\nnilsequence family $V$ at gap $l$, with $|V|\\leq1$.  Translating\nfrom conditional uniform measure back to $\\mu_i$ costs $o(1)$.\nThus along any set of $w$ on which the global norm is at~least~$\\delta$,\n\\[\n \\langle h,V\\rangle\\geq\n \\tfrac12\\delta^{2^t}c_t(\\delta/4)+o(1).\n\\]\nThe harmless smaller inverse threshold allows slack in all these\ninequalities.  The step is at most $t-1=2(2^d)-2\\leq s$, so\n$V$ represents an element of $\\mathcal N_{i,l}$.  If the stated\nnorm bound holds on a set belonging to $\\mathcal U$, select the\ncorrelators there and set the family to zero on its complement.\nOrthogonality and $\\|V\\|_2\\leq1$ give the positive lower bound\n\\[\n \\|P_{i,l}h\\|_2\\geq\n \\tfrac12\\delta^{2^t}c_t(\\delta/4).\n\\]\n\nChoose $\\delta$ small enough that Bessel and\n\\eqref{eq:prediction-cube-subgroup-bound} give the prescribed\n$\\gamma$, and choose $\\kappa$ smaller than the displayed positive\nprojection bound.  If the projection is less than $\\kappa$, the\nglobal norm cannot exceed that threshold along $\\mathcal U$.\nRestoring the periodization error proves\n\\eqref{eq:prediction-subgroup-conclusion}.\n\\end{proof}\n\n\\subsection{Completion of the Prediction Principle}\n\n\\begin{proof}[Proof of Principle~\\ref{pr:prediction}]\nFix the data $n,r,\\chi,\\mathcal B,\\mathcal A,\\tau,\\eta$ of the\nPrinciple, with $s=s(m)$ as in\n\\eqref{eq:prediction-step-choice}.  There are\n$q_m=2^m-m-1$ nonsingleton factors in each count.  We spell out the\norder of choices before selecting master indices.\n\nChoose a positive target cube error $\\zeta$ so small that\n\\[\n C_m(2\\zeta)^\\theta<\\eta/(2q_m)\n\\]\nfor every exponent and constant in\nProposition~\\ref{prop:correlation-test}.  There are only finitely\nmany orders and exponent choices, all controlled by $m$; increasing\nthe uniform constant if necessary makes this one finite set of\nrequirements.  Choose a fixed $J_0$ so large that every\nperiodization term $O_d(J_0^{-1})$ in\nLemma~\\ref{lem:subgroup-inverse} is smaller than $\\zeta$.\nFor the finitely many correlation-test types, apply that Lemma with\n$\\gamma=\\zeta$, and choose\n\\[\n 0<\\eps<\\eta,\n \\qquad 2\\eps<\\min_d\\kappa(d,J_0,\\zeta).\n\\]\nThese choices depend on the requested tolerances and on $m$,\nand are independent of $N$.  Now choose $N$ by\nLemma~\\ref{lem:energy-selection}, construct its arithmetic scales\nand dense models, and carry out that Lemma.  Restrict $h_i,X_i,t_i$\nto the principal indices $j_1,\\ldots,j_n$, relabel them by $[n]$,\nand set $H_u=R_{k_u}$.  The intervening padding gaps and\nLemma~\\ref{lem:master-scales} give all conditions in\nDefinition~\\ref{def:admissible}; the models $S$ have exactly the\npieces required by Definition~\\ref{def:piecewise-model}.\n\nFor calibration at a selected block choose a Lipschitz function\n$\\psi:[0,1]\\to[0,1]$ equal to $1$ on $[0,2\\tau]$ and to $0$ on\n$[3\\tau,1]$.  The bounded distribution replacement in\nCorollary~\\ref{cor:product-law} gives\n\\[\n \\Pr\\bigl(\\chi(h_Bat_B)=c,\\ S_{B,a,c}(t_B)\\leq2\\tau\\bigr)\n \\leq\\E_{\\mu_i}\\rho\\,\\psi(S_{B,a,c})+o(1).\n\\]\nThe function $\\psi(S)$ is a bounded-complexity coarse piecewise\nnilsequence.  Lemma~\\ref{lem:nilsequence-testing} replaces $\\rho$\nby $F$.  In the Hilbert limit it also lies in the coarse subspace,\nso, writing $P=P_{i,k_u}$ and using\n\\eqref{eq:prediction-coarse-approximation},\n\\[\n \\langle F,\\psi(S)\\rangle\n =\\langle PF,\\psi(S)\\rangle\n \\leq\\langle S,\\psi(S)\\rangle+\\eps\n \\leq3\\tau+\\eps.\n\\]\nThis is the first assertion of the Principle, since $\\eps<\\eta$.\n\nFor the count comparison, fix a chain on the principal indices,\n$b\\in\\mathcal B$, and a color.  Its block scale $b_{B_d}$ belongs\nto $\\mathcal A$, so all its models have already been constructed.\nAs shown after Proposition~\\ref{prop:dense-model}, telescope the\nnonsingleton color weights $\\rho_d(L_J)$ to $F_d(L_J)$ at error\n$o(1)$, retaining the center weights $\\nu_d(z_d)$ and every product\nmask, including the singleton masks.\n\nNext telescope $F_d(L_J)$ to $S_d(L_J)$.  Choose as fine gap the\npadding index immediately before the chain's first pivot.  It is\nvalid for every block of the chain.  Each individual difference\nhas bounded target $h=F_d-S_d\\in[-1,1]$, and\n\\eqref{eq:prediction-fine-projection} gives\n$\\|P_{i_d,l}h\\|_2\\leq2\\eps$.  All other factors, including the\nunbounded center weights, still satisfy the bounds of\nProposition~\\ref{prop:correlation-test}.  The parameter choices and\nLemma~\\ref{lem:subgroup-inverse} bound its target cube mean by\n$2\\zeta$ in the ultrafilter limit.  Equation~\\ref{eq:correlation-test}\ntherefore bounds each telescoping error by less than\n$\\eta/(2q_m)$.  Summing the finitely many differences gives a\nlimit error at most $\\eta/2$ between the original count and\n\\[\n \\E_{z\\sim\\bigotimes_d\\mu_{i_d}}\n U(z)\\prod_d\\nu_d(z_d)\n       \\prod_{|J|\\geq2}S_{d(J)}(L_J(z)).\n\\]\nAll factors except the center weights are now bounded by $1$.\nThe blocks of the chain are disjoint.  Corollary~\\ref{cor:product-law}\nthus replaces this last expectation, with error $o(1)$, by\n\\[\n \\E_t U\\bigl(z(t)\\bigr)\n       \\prod_{|J|\\geq2}S_{d(J)}\\bigl(L_J(z(t))\\bigr),\n \\qquad z(t)_d=t_{B_d}.\n\\]\nThis proves the required count comparison with the stated tolerance\n$\\eta$.  Every limit in this construction may be taken along the\nsame fixed ultrafilter $\\mathcal U$, as allowed in\nPrinciple~\\ref{pr:prediction}.  In particular the auxiliary\n$\\eps$ was chosen before the master Ramsey count $N$, whereas the\nresulting model complexity was allowed to depend on all those\nfixed choices.  The proof of the Principle is complete.\n\\end{proof}\n"}, {"path": "preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/06_rough_progressions.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/06_rough_progressions.tex", "bytes": 31145, "sha256": "cc96f98af453ced164b8ba55f450dcdc7ff0bbf5ba4a9c575f41bd6b7610f18c", "content": "\\section{Removing rough progression steps}\n\\label{sec:rough-progressions}\n\nAt an alignment pivot $i$, the input of a model has the form $t_Tt_i$, where\nthe earlier variables in $t_T$ are temporarily fixed.  Consequently,\naveraging $t_i$ samples only one progression of step $t_T$ in the\nmodel input.  We will need to remove the factors of this step while\nretaining polynomial dependence on the factor being removed.  The\ncomparison proved here permits the sampling box, residue and observable\nto depend arbitrarily on that factor.  This last uniformity is necessary\nbecause the actual pivot cell is determined only after sampling.\n\nAll nilmanifolds in this Section are quotients $G/\\Gamma$, with $G$\nconnected, simply connected and nilpotent and $\\Gamma$ a lattice.  We fix\nrational coordinates on $\\mathfrak g=\\log G$ and a smooth metric on the\ncompact quotient.  ``Polynomial in logarithmic coordinates'' means that\nthe coordinates of $\\log P$ are ordinary real polynomials.  Their degrees,\nbut not their coefficients, will be bounded.  For a scalar $a$, write\n$\\norm{a}_{\\R/\\Z}=\\inf_{k\\in\\Z}\\abs{a-k}$.\n\nThe orbit theory underlying this comparison begins with Leibman's\nqualitative equidistribution theorem \\cite{LeibmanOrbits}.\nWe use the quantitative form of Green and Tao, with its multiparameter\ncorrection, in the precise version stated next.\n\n\\subsection{The corrected quantitative equidistribution input}\n\nA rational filtration is a finite decreasing sequence of connected\nrational subgroups $G_\\bullet=(G_j)_{j\\geq0}$ with\n$G_0=G_1=G$ and $[G_i,G_j]\\subseteq G_{i+j}$.  A map on $\\Z^d$ is\npolynomial for this filtration if every $k$-fold group difference takes\nvalues in $G_k$.  Fix a rational Mal'cev basis adapted to the filtration.\nA horizontal character is a continuous homomorphism\n$\\xi:G\\longrightarrow\\R$ such that $\\xi(\\Gamma)\\subseteq\\Z$; its\nsize is the norm of its integer coefficient vector on the horizontal\ntorus.  In particular, characters of bounded size form a finite set.\n\n\\begin{theorem}[Quantitative Leibman theorem, corrected box form]\n\\label{thm:quantitative-leibman}\nFix a rational filtered nilmanifold as above, a number $d$ of variables,\nand $0<\\delta<1$.  There is a constant $A$, depending only on these data,\nwith the following property.  If $P:\\Z^d\\to G$ is polynomial for this\nfiltration and $(P(v)\\Gamma)_{v\\in\\{0,\\ldots,N-1\\}^d}$ is not\n$\\delta$-equidistributed, then there is a nonzero horizontal character\n$\\xi$ of size at most $A$ such that, on writing\n\\[\n \\xi(P(v))=\\sum_I\\alpha_I\\binom vI,\n \\qquad \\binom vI=\\prod_{j=1}^d\\binom{v_j}{I_j},\n\\]\none has\n\\begin{equation}\n N^{\\abs I}\\norm{\\alpha_I}_{\\R/\\Z}\\leq A\n \\qquad (\\abs I>0).\n \\label{eq:rough-leibman-binomial}\n\\end{equation}\nHere $\\delta$-equidistribution means comparison with Haar probability to\nerror at most $\\delta$ against every observable of Lipschitz norm at most\none.  The constant $A$ is independent of the coefficients of $P$ and of\n$N$.\n\nThe following consequence will be used.  Fix also $c,C,B,\\eta>0$.  Let\n$\\mathcal Q\\subseteq[-CZ,CZ]^d$ be an axis-parallel box with each side\nat least $cZ$, sampled at its integer points.  If\n\\begin{equation}\n \\abs{\\E_{x\\in\\mathcal Q\\cap\\Z^d}F(P(x)\\Gamma)\n       -\\int_{G/\\Gamma}F}\\geq\\eta,\n \\qquad \\norm F_{\\Lip}\\leq B,\n \\label{eq:rough-haar-obstruction}\n\\end{equation}\nthen, for sufficiently large $Z$, there is a nonzero horizontal\ncharacter in a fixed finite list such that, writing\n$\\xi(P(x))=\\sum_I\\theta_Ix^I$ in ordinary monomials,\n\\begin{equation}\n \\norm{\\theta_I}_{\\R/\\Z}\\leq A'Z^{-\\abs I}\n \\qquad (\\abs I>0).\n \\label{eq:rough-leibman-monomial}\n\\end{equation}\nThe list and $A'$ depend only on the fixed filtered nilmanifold,\n$d,c,C,B,\\eta$.  The same assertions hold with the nilmanifold and its\nfiltration chosen from a fixed finite list.\n\\end{theorem}\n\nThe equal-side statement is the corrected version of\n\\cite[Theorem~8.6]{GreenTao}; see the expanded erratum\n\\cite[p.~3 and Section~3, arXiv:1311.6170v3]{GreenTaoErratum}.\nThe one-variable case is\n\\cite[Theorem~2.9]{GreenTao}, which is unaffected by the correction.\nThe published quantitative statement bounds the rationality of the\nadapted basis by the reciprocal accuracy.  Here the basis is fixed;\ndecreasing the accuracy below the reciprocal of its fixed rationality\nbound absorbs that hypothesis into $A$.\nWe use the equal-side case of Theorem~\\ref{thm:quantitative-leibman}\nand now verify the stated box consequence;\nno assertion about unrestricted unequal side lengths is needed.\n\n\\begin{proof}[Derivation of the box consequence]\nNormalize the observable by its Lipschitz bound.  Choose a small positive\n$\\lambda$, depending only on $d,c,C,B,\\eta$, and tile the integer box by\ncubes of side $\\floor{\\lambda Z}$, leaving strips along its boundary.\nThe strips contain at most an $O_{d,c}(\\lambda+Z^{-1})$ fraction of its\ninteger points.  Choose $\\lambda$ so that their contribution to\n\\eqref{eq:rough-haar-obstruction} is less than $\\eta/4$.  Expressing\nthe remaining average as a convex combination shows that one cube has\nHaar discrepancy at least $\\eta/2$. Apply the equal-side case of\nTheorem~\\ref{thm:quantitative-leibman} to\n$P(a+v)$ on this cube.  Integer translations preserve filtered\npolynomiality because a group difference of a translated map is the\ncorresponding translate of its group difference.\n\nThere is a fixed bound $D_0$ on the scalar degree.  Multiply the resulting\ncharacter by $(D_0!)^d$.  Indeed, on expanding the products\n$\\binom vI$, this multiplication clears every denominator, so each\nordinary monomial coefficient is an integer plus an error\n$O(Z^{-\\abs I})$: a coefficient of degree $I$ receives contributions\nonly from binomial degrees $J\\geq I$, and\n$Z^{-\\abs J}\\leq Z^{-\\abs I}$.  Finally expand $(x-a)^J$.\nThe translation has integer entries and $\\abs a=O_{C,d}(Z)$; hence\nits integer coefficient contributions stay integral, while its errors\nat degree $I$ are bounded by\n\\[\n \\sum_{J\\geq I}O(Z^{\\abs J-\\abs I})O(Z^{-\\abs J})\n =O(Z^{-\\abs I}).\n\\]\nThis proves \\eqref{eq:rough-leibman-monomial}.  The bounded multiplication\nof the character still leaves a finite list.  The bound on the location\nof $\\mathcal Q$ is used precisely in this last translation estimate.\n\\end{proof}\n\nWe explain why Theorem~\\ref{thm:quantitative-leibman} applies to the logarithmic polynomials used\nbelow.  Let $G^{(j)}$ be the lower central series, with $G^{(1)}=G$, and\nsuppose $G$ has step at most $s$.  For an ordinary degree bound $D\\geq1$,\nput\n\\begin{equation}\n G_0=G,\\qquad G_i=G^{(\\ceil{i/D})}\\quad(i\\geq1).\n \\label{eq:rough-stretched-filtration}\n\\end{equation}\nThese are connected rational subgroups, the filtration ends after $sD$,\nand\n$\\ceil{i/D}+\\ceil{j/D}\\geq\\ceil{(i+j)/D}$ gives the bracket\ncondition; the condition with $i=0$ follows because the lower central\nsubgroups are normal.  If $\\log P$ has ordinary total degree at most\n$D$, its coefficient at a monomial of degree $j$ belongs to\n$\\mathfrak g_j$, since $\\mathfrak g_j=\\mathfrak g$ for $j\\leq D$.\n\nHere is a direct verification of polynomiality, including the effect\nof noncommutativity.  Suppose the coefficient at degree $j$ in\n$A(x)=\\log Q(x)$ lies in $\\mathfrak g_{j+k}$.  An ordinary difference\n$A(x+h)-A(x)$ has its degree-$j$ coefficient in\n$\\mathfrak g_{j+k+1}$, because it arises from degrees at least $j+1$.\nUsing the Baker--Campbell--Hausdorff (BCH) formula, expand\n\\[\n \\log\\bigl(Q(x+h)Q(x)^{-1}\\bigr)\n   =\\operatorname{BCH}\\bigl(A(x)+[A(x+h)-A(x)],-A(x)\\bigr).\n\\]\nThe terms containing no occurrence of $A(x+h)-A(x)$ cancel, since\n$\\operatorname{BCH}(A,-A)=0$.  Every remaining bracket contains at\nleast one difference.  Filtration indices add in brackets, so its\ncoefficient at degree $j$ belongs to $\\mathfrak g_{j+k+1}$.\nThe BCH series is finite.  Induction starting at $k=0$ proves that the\n$k$-fold group difference takes values in $G_k$.  Its degree is bounded\nin terms of $s,D$ throughout.  Thus arbitrary coefficients in a\nbounded-degree logarithmic polynomial are allowed in\nTheorem~\\ref{thm:quantitative-leibman}, using the fixed filtration\n\\eqref{eq:rough-stretched-filtration}.  The same argument works with\nany fixed number of ordinary variables.\n\n\\subsection{An interpolation fact with smooth denominators}\n\nThe arithmetic reason that a rough step can be removed is the following\nfact.  Its analytic constants do not depend on the length of the\nprogression.  Rational denominators may depend on that length, which is\nalways fixed before taking the asymptotic limit.\n\n\\begin{lemma}[Exact division after interpolation]\n\\label{lem:rough-interpolation}\nFix $e\\geq1$, $q\\geq e$, and $A>0$.  For a fixed integer\n$H\\geq2q+2$, let\n\\[\n t_z=t_0+mz\\in[S,2S),\\qquad 0\\leq z<H,\n\\]\nwhere $m$ is a positive integer with all prime factors at most $w$, and\n$\\gcd(t_z,W)=1$ for every $z$, with $W=\\prod_{p\\leq w}p$.\nSuppose $w,S\\to\\infty$ and $Z/S^a\\to\\infty$ for every fixed $a>0$.\nLet $\\theta(z)$ be a real polynomial such that\n$\\deg(t_z^e\\theta(z))\\leq q$ and\n\\begin{equation}\n \\norm{t_z^e\\theta(z)}_{\\R/\\Z}\\leq A(S/Z)^e\n \\qquad(0\\leq z<H).\n \\label{eq:rough-interpolation-input}\n\\end{equation}\nFor all sufficiently large $w$ there is a rational polynomial $M(z)$,\nof degree at most $q-e$, integral at every index $0\\leq z<H$, such that\n\\begin{equation}\n \\abs{\\theta(z)-M(z)}\\leq C_{q}A Z^{-e}\n \\qquad(0\\leq z<H).\n \\label{eq:rough-interpolation-output}\n\\end{equation}\nIts coefficient denominators have only prime factors at most $w$.\nThe constant in \\eqref{eq:rough-interpolation-output} is independent of\n$H$.  The threshold for $w$ is permitted to depend on $H$.\n\\end{lemma}\n\n\\begin{proof}\nWrite $T(z)=(t_0+mz)^e\\theta(z)$ and\n$\\epsilon=A(S/Z)^e$.  Choose $q+1$ integer nodes\n$z_0,\\ldots,z_q$ in $[0,H-1]$ with pairwise successive gaps at least\n$(H-1)/(2q)$, for example the nearest integers to $j(H-1)/q$.\nLet $n_j$ be a nearest integer to $T(z_j)$, and let $R$ be their\ndegree-at-most-$q$ Lagrange interpolant.  On rescaling the index interval\nto $[0,1]$, the interpolation nodes remain separated by $1/(2q)$.\nThe Lagrange basis polynomials and all their coefficients in the\nrescaled variable are therefore bounded in terms of $q$ alone.  Since\n$T$ already has degree at most $q$, interpolation of the errors gives\n\\begin{equation}\n \\sup_{0\\leq z\\leq H-1}\\abs{R(z)-T(z)}\\leq C_q\\epsilon.\n \\label{eq:rough-interpolation-uniform}\n\\end{equation}\nThere is a positive integer $D_H$, depending only on the chosen nodes,\nsuch that $D_HR\\in\\Z[z]$.  One may take the product of the nonzero\nnode differences occurring in the Lagrange denominators.  In particular,\n$D_H$ is independent of $w,t_0,m$ and the polynomial coefficients.\n\nLet $a=-t_0/m$.  The polynomial $T$ has a zero of multiplicity at least\n$e$ at $a$.  The coefficient bounds in the rescaled Lagrange formula\ngive, for $0\\leq j<e$,\n\\[\n \\abs{R^{(j)}(a)}\n  =\\abs{(R-T)^{(j)}(a)}\n  \\leq C_q\\epsilon(H-1)^{-j}\n          \\left(1+\\frac{\\abs a}{H-1}\\right)^q\n  \\leq C_q\\epsilon(1+2S)^q.\n\\]\nOn the other hand $R^{(j)}(a)$ is rational with denominator dividing\n$D_Hm^q$.  We have $m\\leq S$ because the progression lies in $[S,2S)$\nand has at least two terms.  Consequently a nonzero such rational has\nabsolute value at least $D_H^{-1}S^{-q}$.  For fixed $H$, the displayed\nupper bound is smaller than this number for large $w$, by the assumed\ndomination of every power of $S$ by $Z$.  Thus\n$R^{(j)}(a)=0$ for all $j<e$, exactly.  Polynomial division now gives\n\\begin{equation}\n R(z)=(t_0+mz)^e M(z),\\qquad M\\in\\Q[z],\\qquad\\deg M\\leq q-e.\n \\label{eq:rough-exact-divisibility}\n\\end{equation}\nIn division by $(t_0+mz)^e$, the only denominators introduced, besides\nthose already dividing $D_H$, come from its leading coefficient $m^e$.\nAll their prime factors are therefore at most $w$ once $w$ exceeds the\nprime factors of the fixed integer $D_H$.\n\nFor every integer $z\\in[0,H-1]$, \\eqref{eq:rough-interpolation-input}\nand \\eqref{eq:rough-interpolation-uniform} place $R(z)$ within\n$(C_q+1)\\epsilon$ of an integer.  Since $R(z)\\in D_H^{-1}\\Z$, it is\nitself an integer for large $w$.  Write $M(z)=a_z/b_z$ in lowest terms.\nEvery prime factor of $b_z$ is at most $w$, whereas\n$\\gcd(t_z,W)=1$.  The integrality of $t_z^eM(z)=R(z)$ then forces\n$b_z=1$.  Finally divide \\eqref{eq:rough-interpolation-uniform} by\n$t_z^e\\geq S^e$.  This yields\n\\eqref{eq:rough-interpolation-output} with a constant depending on $q$\nalone.  Only the rational exactness steps, not this constant, required\na threshold depending on $H$.\n\\end{proof}\n\n\\subsection{Uniform progression comparison}\n\nCall an integer $w$-smooth if all its prime factors are at most $w$.\nFor a nonempty finite set $A$ we use $\\E_A$ for normalized counting.\nThe next Proposition compares two sampling laws for the same polynomial\nfamily.  Its joint polynomial dependence on $t$ and $x$ is essential;\nthe box, residue class, and observable may be chosen separately for\neach $t$.\n\n\\begin{proposition}[Removal of a rough step]\n\\label{prop:rough-step}\nFix $G/\\Gamma$, a number $d\\geq1$ of spatial variables, a degree bound\n$D$, and constants $c,C,B>0$.  Along an arbitrary sequence $w\\to\\infty$,\nlet $L,S,Z$ satisfy\n\\begin{equation}\n W\\mid L,\\quad L\\text{ is }w\\text{-smooth},\\quad\n S\\to\\infty,\\quad S/L\\to\\infty,\\quad\n Z/S^a\\to\\infty\\quad\\text{for every fixed }a>0.\n \\label{eq:rough-scales}\n\\end{equation}\nFix a residue $r$ modulo $L$ with $\\gcd(r,W)=1$, and put\n$\\mathcal T=[S,2S)\\cap(r+L\\Z)$.\nLet $P_t(x)$ be $G$-valued, with logarithmic coordinates polynomial\njointly in $(t,x)\\in\\R\\times\\R^d$ of total degree at most $D$.\nThese polynomials and their coefficients may change with $w$.\n\nFor every $\\eta>0$, the proportion of $t\\in\\mathcal T$ for which\nthere exist a box $\\mathcal Q\\subseteq[-CZ,CZ]^d$ with all sides at\nleast $cZ$, a vector $b\\in\\Z^d$, and an observable\n$F:G/\\Gamma\\to\\C$ with $\\norm F_{\\Lip}\\leq B$, such that\n\\begin{equation}\n \\abs{\\E_{x\\in\\mathcal Q\\cap\\Z^d}F(P_t(x)\\Gamma)\n       -\\E_{x\\in\\mathcal Q\\cap(b+t\\Z^d)}F(P_t(x)\\Gamma)}\n       \\geq\\eta\n \\label{eq:rough-discrepancy}\n\\end{equation}\ntends to zero.\n\nThis conclusion holds for every sequence of the allowed data.\nIn particular the exceptional proportion is uniform over the\npolynomial coefficients, the residue $r$, and all the boxes, residue\nvectors and observables in \\eqref{eq:rough-discrepancy}.  The latter\nthree may be chosen separately for every $t$ without any polynomial\ndependence.  The same conclusion holds for a fixed finite list of\nnilmanifolds and fixed complexity bounds.\n\\end{proposition}\n\n\\begin{proof}\nThe induction will turn a discrepancy on $G/\\Gamma$ into one on the\nkernel of a horizontal character.  To preserve polynomial dependence\nduring this reduction, we work on long progressions of exceptional\nvalues of $t$ with smooth difference.  Interpolation on such a\nprogression will split the horizontal polynomial into an integer part\nand a slowly varying error.  We first specify the stronger progression\nassertion to which this dimension induction applies.\n\nA \\emph{bad progression family} consists of fixed\nbounds $d,D,c,C,B,\\eta>0$ and a fixed nilmanifold such that, for every\nprescribed integer $H$, there are sequences with $w,S\\to\\infty$ and\n$Z/S^a\\to\\infty$ for every fixed $a$, and progressions\n\\begin{equation}\n t_z=t_0+mz\\in[S,2S),\\quad 0\\leq z<H,\\quad\n m\\text{ is }w\\text{-smooth},\\quad \\gcd(t_z,W)=1,\n \\label{eq:rough-bad-progression}\n\\end{equation}\non which every index has a discrepancy at least $\\eta$ as in\n\\eqref{eq:rough-discrepancy}.  Here the logarithms of $P_z(x)$ need\nonly be jointly polynomial in $(z,x)$ with the fixed degree bound;\nthe boxes, classes and tests may vary with $z$.  The sequences may\ndepend on $H$.  It is equivalent to ask for arbitrarily large prescribed\nlengths: a longer progression can be restricted.  We will prove that\nno such family exists, by induction on $\\dim G$.\n\nFirst, failure of the Proposition would produce such a family.  Indeed,\nsuppose the proportion of bad values is at least $\\alpha>0$ along a\nsubsequence.  In the index coordinate $t=r+Lu$, the set $\\mathcal T$\nis an integer interval of length tending to infinity.  For prescribed\n$H$, finite Szemer\\'edi's Theorem \\cite{Szemeredi} supplies a fixed\ninteger $K=K(H,\\alpha)$ such that every subset of $[K]$ of density\nat least $\\alpha/2$ contains an $H$-term progression.  Partition the\nindex interval into blocks of length $K$ and discard its final\nincomplete block.  Some full block has bad density at least\n$\\alpha/2$ for large $w$.  Its bad progression has step $jL$ with\n$1\\leq j\\leq K$.  This step is $w$-smooth once $w\\geq K$.\nJoint polynomiality is preserved by the affine substitution for $t$.\nThus \\eqref{eq:rough-bad-progression} holds for each fixed $H$.\n\nWe shall repeatedly thin a bad progression to one color in a finite\ncoloring.  Finite van der Waerden's Theorem \\cite{vanderWaerden} makes\nthis legitimate: to obtain a length $H$ of one color, start with the\nfixed length prescribed by that Theorem for $H$ and the number of\ncolors.  Under $z=a+bu$, the new step is $bm$; $b$ is bounded in terms\nof the initial fixed length, so it remains $w$-smooth for large $w$.\nThe new step is still at most $S$, and all scale and degree hypotheses\nremain valid.  A finite choice of group data can also be fixed: first\npass to a subsequence for each length, then choose one of the finitely\nmany possibilities occurring for unbounded lengths.  Bounds in all\nthese colorings will be independent of $H$.\n\nFor dimension zero the quotient is a point and every discrepancy is\nzero.  Assume the assertion has been proved in smaller dimension, and\nsuppose that a bad progression family on $G/\\Gamma$ exists.\nFor each $z$, at least one of its two samples has Haar discrepancy at\nleast $\\eta/2$.  Write its step as $p_z\\in\\{1,t_z\\}$ and its\nresidue representative as $b_z\\in\\{0,\\ldots,p_z-1\\}^d$.\nSubstitute $x=p_zv+b_z$.  The resulting box lies in a fixed multiple\nof $[-Z/p_z,Z/p_z]^d$ and has comparable sides.  Its common scale\ntends to infinity.  Theorem~\\ref{thm:quantitative-leibman}, with the\nfixed stretched filtration \\eqref{eq:rough-stretched-filtration},\ntherefore gives a character from a fixed finite list.  Thin to a common\nnonzero character $\\xi$, including the fixed denominator multiplication\nin that choice.  Write\n\\begin{equation}\n \\xi(P_z(x))=\\sum_I\\theta_I(z)x^I.\n \\label{eq:rough-character-polynomial}\n\\end{equation}\nThe coefficients $\\theta_I$ are polynomials of bounded degree in $z$.\nThe coefficient obstruction in the $v$ variables is\n\\begin{equation}\n \\left\\|p_z^{\\abs I}\n       \\sum_{J\\geq I}\\binom JI b_z^{J-I}\\theta_J(z)\n       \\right\\|_{\\R/\\Z}\n       \\leq A_0(p_z/Z)^{\\abs I}\n \\qquad(\\abs I>0),\n \\label{eq:rough-substituted-obstruction}\n\\end{equation}\nwhere $A_0$ is independent of $H,z$ and of every polynomial coefficient.\n\nWe claim, descending through the positive spatial degrees, that there\nare rational polynomials $m_I(z)$ of bounded degree such that\n\\begin{equation}\n m_I(z)\\in\\Z,\\qquad\n \\abs{\\theta_I(z)-m_I(z)}\\leq A_1 Z^{-\\abs I}\n \\quad(0\\leq z<H,\\ \\abs I>0),\n \\label{eq:rough-integer-coefficients}\n\\end{equation}\nwith smooth coefficient denominators and a constant $A_1$ independent\nof $H$.  Take $H$ larger than the finitely many degree requirements of\nLemma~\\ref{lem:rough-interpolation}; this does not restrict the\narbitrarily long progression assertion.  Suppose higher degrees have\nalready been treated and put $e=\\abs I$.  Their integral parts in\n\\eqref{eq:rough-substituted-obstruction} contribute integers.  Their\nerror contribution has size at most\n\\[\n C\\sum_{J>I}p_z^e p_z^{\\abs J-e}Z^{-\\abs J}\n \\leq C'(p_z/Z)^e,\n\\]\nsince $p_z\\leq2S=o(Z)$; here $J>I$ means $J\\geq I$ and $J\\ne I$.\nIt follows that\n$\\norm{p_z^e\\theta_I(z)}_{\\R/\\Z}\\leq C'(p_z/Z)^e$.\nIf $p_z=t_z$ this is already the needed bound.  If $p_z=1$,\nmultiplication by the integer $t_z^e$ gives the same conclusion:\n\\[\n \\norm{t_z^e\\theta_I(z)}_{\\R/\\Z}\\leq C''(S/Z)^e.\n\\]\nLemma~\\ref{lem:rough-interpolation} gives $m_I$ and the claimed bound.\nThere are only boundedly many spatial degrees and coefficients, so\nthe descending induction terminates with $A_1$ depending only on the\nfixed data.  The length-dependent thresholds for rational exactness\ncan be met simultaneously.\n\nThe positive-degree coefficients now have the required integer parts\nand small errors.  To obtain a bounded horizontal error on the whole\nbox, we must also split the spatially constant coefficient.  This\nrequires a finite coloring argument.  Let $q_0$ bound\n$\\deg\\theta_0$.  Color $z$ by\nthe interval containing $\\{\\theta_0(z)\\}$ in a partition of $[0,1)$\ninto intervals of length less than $2^{-q_0-2}$.  Thin to a long\nmonochromatic progression and reparametrize it.  On each consecutive\n$(q_0+2)$-term segment of the new progression,\n\\[\n \\Delta^{q_0+1}\\floor{\\theta_0(z)}\n       =-\\Delta^{q_0+1}\\{\\theta_0(z)\\}\n\\]\nis an integer of absolute value less than one: the constant part of\nthe common fractional-part interval cancels, and the sum of the\nabsolute difference coefficients is $2^{q_0+1}$.  It is therefore zero.\nNewton interpolation, or induction using this difference identity,\nshows that a rational polynomial $m_0(z)$ of degree at most $q_0$\nagrees with $\\floor{\\theta_0(z)}$ on the whole new progression.\nIts Newton coefficients are integer differences; hence its ordinary\ncoefficient denominators divide $q_0!$.  All previously constructed\n$m_I$ retain \\eqref{eq:rough-integer-coefficients} after the same\naffine reparametrization.\n\nSet\n\\begin{equation}\n m_z(x)=\\sum_I m_I(z)x^I,\n \\qquad a_z(x)=\\xi(P_z(x))-m_z(x).\n \\label{eq:rough-horizontal-splitting}\n\\end{equation}\nFor integer $z$ in the progression, $m_z$ has integer coefficients\nas a polynomial in $x$.  By \\eqref{eq:rough-integer-coefficients},\nits error satisfies, throughout $[-CZ,CZ]^d$,\n\\begin{equation}\n \\abs{a_z(x)}\\leq C_1,\n \\abs{\\partial_{x_j}a_z(x)}\\leq C_1/Z\n \\quad(1\\leq j\\leq d),\n \\label{eq:rough-slow-factor}\n\\end{equation}\nwhere $C_1$ is independent of $H$.  To see this explicitly, the\nconstant term lies in $[0,1)$, and an error coefficient at degree\n$e$ contributes at most $A_1Z^{-e}(CZ)^e$ to the first bound and at\nmost $eA_1Z^{-e}(CZ)^{e-1}$ to a derivative.  Only boundedly many\nmonomials occur.\n\nChoose a rational vector $V\\in\\mathfrak g$ with\n$\\xi(\\exp V)=1$.  Such a vector exists because a nonzero horizontal\ncharacter has nonzero rational differential.  There is a fixed\npositive integer $K$ with $\\exp(KV)\\in\\Gamma$.  For completeness,\nthe Mal'cev coordinates of $\\exp(tV)$ are rational polynomials in\n$t$ with zero constant term, by the finite BCH formula.  Taking $K$\ndivisible by their finitely many denominators makes these coordinates\nintegral, which places $\\exp(KV)$ in the lattice.  All choices depend\nonly on the fixed character and rational group data.\n\nPut $H_\\xi=\\ker\\xi$ and\n\\begin{equation}\n Q_z(x)=\\exp(-a_z(x)V)P_z(x)\\exp(-m_z(x)V).\n \\label{eq:rough-kernel-polynomial}\n\\end{equation}\nThen $\\xi(Q_z(x))=0$ identically, for real $(z,x)$, and\n\\begin{equation}\n P_z(x)=\\exp(a_z(x)V)Q_z(x)\\exp(m_z(x)V).\n \\label{eq:rough-kernel-factorization}\n\\end{equation}\nThe group $H_\\xi$ is connected and simply connected: the exponential\nmap identifies it with the kernel of the linear differential of\n$\\xi$.  That kernel is rational, so $H_\\xi$ is a rational subgroup\nof dimension $\\dim G-1$.  Formula~\\eqref{eq:rough-kernel-polynomial}\nand the finite BCH formula show that $\\log Q_z(x)$ is jointly\npolynomial in $(z,x)$ with a degree bound depending only on the old\ndegree and nilpotence step.  No bound on its coefficients is needed.\n\nThe polynomial now takes values in a smaller group.  To apply the\ninduction hypothesis, we must also express both sampling laws and\ntheir test functions on a fixed compact quotient of $H_\\xi$.\nPartition each test box into a bounded number of rectangular\nsubboxes, each of side between $\\lambda cZ/2$ and $2\\lambda CZ$,\nwhere the sufficiently small constant $\\lambda>0$ is chosen below.\nFor example divide each side into the same sufficiently large fixed\nnumber of intervals.  On a subbox choose a point $x_*$ and replace the\nleft factor $\\exp(a_z(x)V)$ in\n\\eqref{eq:rough-kernel-factorization} by\n$\\ell_{z,*}=\\exp(a_z(x_*)V)$.  The error in the test is at most\n$C_2\\lambda$: the derivative estimate\n\\eqref{eq:rough-slow-factor} bounds the change in $a_z$, and the action\nmap on the product of a fixed compact subset of $G$ and $G/\\Gamma$\nhas bounded derivative.  The constant $C_2$ depends on the original\nLipschitz bound, but not on $H,z$ or the choice of subbox.  Choose\n$\\lambda$ so that twice this error is less than $\\eta/8$.\n\nSplit each subbox further according to $x\\bmod K$.  At a fixed $z$\nand residue vector $u$, the integer polynomial $m_z(x)$ has a fixed\nvalue $j\\bmod K$, and hence\n\\[\n \\exp(m_z(x)V)\\Gamma=\\sigma_j\\Gamma,\n \\qquad\\sigma_j=\\exp(jV),\\quad0\\leq j<K.\n\\]\nThe resulting test on the smaller group is\n\\begin{equation}\n h\\bigl(H_\\xi\\cap\\sigma_j\\Gamma\\sigma_j^{-1}\\bigr)\n       \\longmapsto F_z(\\ell_{z,*}h\\sigma_j\\Gamma).\n \\label{eq:rough-kernel-test}\n\\end{equation}\nThe intersection is a lattice in $H_\\xi$: conjugation by rational\n$\\sigma_j$ preserves the rational structure, and a connected rational\nsubgroup meets a rational lattice in a lattice.  This can also be\nseen by taking a rational Mal'cev basis of the subgroup and clearing\nthe finite coordinate denominators.  The displayed map from its\ncompact quotient to $G/\\Gamma$ is smooth.  Since there are only\nfinitely many $j$ and $\\ell_{z,*}$ lies in a fixed compact set,\nall the tests \\eqref{eq:rough-kernel-test} have one uniform Lipschitz\nbound.\n\nWe justify the sampling weights in this splitting.  For an interval\nof length comparable to $Z$ and any residue modulo $q$, its count is\nits length divided by $q$, with error at most two.  Multiplying this\nformula over the fixed number of coordinates shows that the normalized\nweights of the boundedly many subboxes under the unrestricted and\nstep-$t_z$ samples differ in total by $O(t_z/Z)$.  For $w\\geq K$,\n$\\gcd(t_z,K)=1$.  Thus both samples assign weight\n$K^{-d}+O(K^dt_z/Z)$ to each $K$-residue vector inside a subbox:\nin the step-$t_z$ coordinate, reduction modulo $K$ is a bijection.\nThe total difference of the weights of all the pieces is consequently\n$o(1)$, uniformly in $z$ and in the box endpoints.\n\nAfter freezing, the original discrepancy is still at least\n$7\\eta/8$.  Write the two averages as convex combinations over these\npieces and replace one set of combination weights by the other.\nThe error is $o(1)$ times the uniform supremum bound on the tests.\nIt follows from the triangle inequality that at least one piece has\na conditional discrepancy at least $\\eta/2$, for large $w$.\nThis lower bound does not depend on the number of pieces: it follows\nfrom a convex combination, not from an unnormalized sum.\n\nFor each $z$ choose such a piece.  Its subbox and frozen observable may\nvary arbitrarily with $z$, as allowed in the induction assertion.\nThin once more to make its residue vector $u\\bmod K$ and its coset\nindex $j$ common.  In this subbox substitute\n$x=u+Ky$, taking $u\\in\\{0,\\ldots,K-1\\}^d$.  The unrestricted\nsample becomes all integer $y$ in a box of side comparable to $Z/K$,\nwithin a fixed multiple of that scale.  The step-$t_z$ sample becomes\none residue vector modulo $t_z$, because $K$ is invertible modulo\n$t_z$.  The new map $Q_z(u+Ky)$ is still jointly logarithmically\npolynomial, and $Z/K$ still dominates every fixed power of $S$.\nWe have therefore obtained a bad progression family on\n\\[\n H_\\xi/\\bigl(H_\\xi\\cap\\sigma_j\\Gamma\\sigma_j^{-1}\\bigr)\n\\]\nof strictly smaller dimension.\n\nAll the new degree, box, observable and discrepancy bounds are\nindependent of the progression length.  Characters, rational\ndirections, residue vectors and cosets range over fixed finite lists;\nas explained at the start, one such quotient works for unbounded\nlengths.  The sole length-dependent quantities were interpolation\ndenominators and asymptotic thresholds.  Those denominators enter only\nthe unrestricted coefficients of the kernel polynomial.  At every\nretained integer index its spatial integer part has integer\ncoefficients, so the fixed residue splitting and the finite list of\nquotient lattices are unchanged.  This contradicts the\ninduction hypothesis and proves that bad progression families do not\nexist.  Failure of the Proposition produced precisely such a family,\nso the Proposition follows, with the uniformity asserted in its\nstatement.\n\\end{proof}\n\n\\subsection{Residual steps and conditional sampling}\n\nWe record explicitly the form needed when the step being removed is\none factor of a larger product.  This separates the polynomial\nrequirement from the arbitrary choice of actual sampling box.\n\n\\begin{corollary}[Removing one factor while retaining the others]\n\\label{cor:rough-residual-step}\nIn Proposition~\\ref{prop:rough-step}, let $q$ be another positive\ninteger, fixed while $t$ varies but permitted to change with $w$.\nLet $a\\in\\{0,\\ldots,q-1\\}^d$ also be independent of $t$.\nSuppose the spatial boxes have common scale $Z_0$, with\n$Z_0/q$ dominating every fixed power of $S$.  Suppose\n$\\log P_t(a+qy)$ is polynomial jointly in $(t,y)$ with fixed degree\nbound, and the normalized boxes lie in fixed multiples of\n$[-Z_0/q,Z_0/q]^d$ with comparable sides.  Then, outside an exceptional\nproportion tending to zero, averaging $P_t$ over the class\n$a+q\\Z^d$ agrees to any prescribed accuracy with averaging over any\ncompatible class modulo $qt$, with the same uniformities in boxes and\ntests.  No coprimality between $q$ and $t$ is assumed.\n\\end{corollary}\n\n\\begin{proof}\nWrite the finer class as $b+qt\\Z^d$ with $b\\equiv a\\pmod q$.\nUnder $x=a+qy$, its condition is exactly\n\\[\n y\\equiv(b-a)/q\\pmod t.\n\\]\nThis identity does not use invertibility of either factor.  Apply\nProposition~\\ref{prop:rough-step} to the normalized polynomial and\nthe scale $Z_0/q$.  Its uniformity permits arbitrary compatible $b$\nand arbitrary endpoints of the normalized boxes.\n\\end{proof}\n\nIn applying Corollary~\\ref{cor:rough-residual-step}, the retained residue\nclass must be chosen independently of the factor being removed.\nSection~\\ref{sec:alignment} verifies this condition for each factor of\nthe tail product.\n\nFinally, uniformity over additional conditioned parameters has a\nprecise probabilistic consequence.  Suppose $k$ factors range in fixed\ndyadic intervals and smooth residue classes, and let $\\nu$ be the\nproduct of their normalized uniform laws.  Suppose a conditional law\n$\\mu$ satisfies $\\mu\\leq C_0\\nu$, with fixed $C_0$.  If, for every\nchoice of the other $k-1$ factors, the exceptional proportion for\nremoving the remaining factor is at most $\\epsilon_w\\to0$, then\nFubini's Theorem gives $\\nu(E_\\ell)\\leq\\epsilon_w$ and hence\n\\[\n \\mu\\left(\\bigcup_{\\ell=1}^k E_\\ell\\right)\n       \\leq C_0k\\epsilon_w=o(1).\n\\]\nThe uniform bound $\\epsilon_w$ follows from\nProposition~\\ref{prop:rough-step}: otherwise choices of the additional\nparameters with exceptional proportion bounded below would themselves\nform a violating sequence of permitted data.  This gives the conditional\nform of the progression comparison used in Section~\\ref{sec:alignment}.\n"}, {"path": "preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/07_cube_limits.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/07_cube_limits.tex", "bytes": 39210, "sha256": "cb447cb94564bd4f69e1175c424af0f6cb03d765f6a925a0dbf19564c37750d9", "content": "\\section{Local cube laws and lifts of face symmetries}\n\\label{sec:cube-limits}\n\nThe comparison from Section~\\ref{sec:rough-progressions} will give symmetries\nof individual marginals of a joint nilmanifold-valued state.  We need to act on\nthe joint state even when those symmetries do not preserve its other\nmarginals.  The purpose of this Section is to construct such actions on a\nuniversal cover.  Their nilpotence class, rather than the dimension of the\ncover, is what will control the finite recurrence argument.\nFor the cube formalism in higher-order ergodic theory, see \\cite{HostKra}.\nWe prove the algebraic and lifting statements in the precise forms used here.\n\nThroughout this Section, a rational subgroup of a connected simply connected\nnilpotent Lie group means a connected subgroup whose Lie algebra is rational\nfor the rational structure determined by the specified lattice.  We use the\nbasic rational Lie theory of nilmanifolds: such a subgroup is closed, its\nlattice intersection is cocompact, and its orbit in the nilmanifold is an\nembedded compact homogeneous space.  A rational element is an element with\nrational logarithm in this structure.  Conjugation by a rational element\npreserves the rational structure.  For these rational-coordinate facts\nand the periodicity of rational polynomial sequences, see\n\\cite[Section~2 and Appendix~A]{GreenTao}.\nAll filtrations below have connected\nrational terms and satisfy\n\\begin{equation}\n H_0=H_1=H\\supseteq H_2\\supseteq\\cdots\\supseteq H_s\n \\supseteq H_{s+1}=\\{1\\},\\qquad\n [H_i,H_j]\\subseteq H_{i+j}.\n \\label{eq:cube-filtration}\n\\end{equation}\nTerms beyond $s$ are trivial.  We write $\\mathfrak h_i=\\log H_i$.\nIn particular $H$ has nilpotence class at most $s$.\n\n\\subsection{Upper-face groups and the missing corner}\n\nFor $q\\geq0$, identify the vertices of a $q$-cube with subsets of $[q]$.\nFor $D\\subseteq[q]$, let $g_D$ be the group-valued array equal to $g$ at\nvertices containing $D$, and equal to the identity elsewhere.  The case\n$D=\\varnothing$ is the constant array.  Define\n\\[\n C^q(H_\\bullet)=\\HK^q(H_\\bullet)\n =\\left\\langle g_D:\\ D\\subseteq[q],\\quad g\\in H_{|D|}\\right\\rangle\n \\subseteq H^{\\{0,1\\}^q}.\n\\]\nFor $q=0$ this is $H$.\n\n\\begin{lemma}[Upper-face coordinates]\\label{lem:upper-face-coordinates}\nThe group $C^q(H_\\bullet)$ is connected, simply connected, and rational.\nOrder the subsets of $[q]$ by increasing cardinality, breaking ties in any\nfixed way.  Every element of $C^q(H_\\bullet)$ has a unique expression\n\\begin{equation}\n \\prod_{D\\subseteq[q]}(g_D)_D,\\qquad g_D\\in H_{|D|},\n \\label{eq:ordered-face-product}\n\\end{equation}\nin this order; the map from the face coefficients to the array is a\ndiffeomorphism.  An array in this group which is the identity except possibly\nat the all-ones vertex has its remaining entry in $H_q$.\nThe group is invariant under permutations and reflections of cube\ncoordinates.  If $\\Gamma$ is a lattice in $H$, its image in\n$(H/\\Gamma)^{\\{0,1\\}^q}$ is compact.\n\\end{lemma}\n\n\\begin{proof}\nFor $X\\in\\mathfrak h_{|D|}$ write $X_D$ for the corresponding face array.\nThe Boolean monomials $\\prod_{j\\in D}e_j$ are linearly independent, so\n\\[\n \\mathfrak c^q\n =\\bigoplus_{D\\subseteq[q]}\n   \\mathfrak h_{|D|}\\prod_{j\\in D}e_j\n\\]\nis a direct sum of vector spaces.  The entrywise Lie bracket satisfies\n$[X_D,Y_E]=[X,Y]_{D\\cup E}$.  The filtration places this bracket in\nthe summand indexed by $D\\cup E$.  This remains true when a set is empty,\nbecause $H_0=H_1$ and the filtration is decreasing.  Thus\n$\\mathfrak c^q$ is a rational Lie subalgebra.\n\nEvery suffix in the chosen order of summands is an ideal: a bracket with a\nsummand indexed by $E$ stays at $E$ or moves to a strictly larger set.\nSuccessively passing to the quotients by these suffix ideals gives\n\\eqref{eq:ordered-face-product}.  Equivalently, the coefficient at $E$ is\nrecovered from the entry at vertex $E$ after the coefficients at proper\nsubsets of $E$ have been removed.  This also proves uniqueness and smoothness\nof the inverse.  The connected subgroup with Lie algebra $\\mathfrak c^q$\nis closed and simply connected, as follows from the exponential\ndiffeomorphism for a simply connected nilpotent group.  It is exactly the\ngroup generated by the faces.\n\nThe same recovery at successive vertices proves the assertion about an array\nsupported at the top vertex.  Reflecting a coordinate replaces $e_j$ by\n$1-e_j$.  The resulting face indicator is a linear combination of upper-face\nindicators of no greater cardinality; the decreasing filtration therefore\npreserves $\\mathfrak c^q$, and hence its exponential group.  Permutations are\nimmediate.  Finally rationality gives a cocompact lattice\n$C^q(H_\\bullet)\\cap\\Gamma^{\\{0,1\\}^q}$, so the indicated image is compact.\n\\end{proof}\n\nWe will also use the following explicit rational filtration on the cube\ngroup:\n\\begin{equation}\n \\mathfrak c^q_i\n =\\bigoplus_{D\\subseteq[q]}\n   \\mathfrak h_{\\max(i,|D|)}\\prod_{j\\in D}e_j,\n \\qquad i\\geq0.\n \\label{eq:cube-group-filtration}\n\\end{equation}\nIt has $C^q_0=C^q_1=C^q(H_\\bullet)$ and degree at most $s$.\nIndeed the bracket of its $D$ and $E$ summands lies at filtration level at\nleast both $i+j$ and $|D\\cup E|$.  Thus it satisfies the required bracket\ninclusions.\n\nThe following missing-corner constraint and its coordinate-product\nconsequence appear in \\cite[Proposition~11.5, proof of\nProposition~11.2, and Appendix~E]{GreenTaoLinear}.  We give the\nface-coordinate proof, including the uniform approximation over\nbounded observable families needed in Section~\\ref{sec:prediction}.\n\n\\begin{lemma}[Continuous missing-corner reconstruction]\n\\label{lem:cube-corner}\nLet $G$ be a connected simply connected nilpotent Lie group of step at most\n$s$, and let $\\Gamma$ be a lattice in $G$. Equip $G$ with its lower central\nfiltration, with $G_0=G_1=G$.\nIn dimension $s+1$, any $2^{s+1}-1$ vertices of a cube in the image of\n$C^{s+1}(G_\\bullet)$ determine the last vertex uniquely and continuously\non the set of admissible partial cubes.  Linear orbit cubes\n\\[\n \\bigl(g^{b_0+\\sum_{j=1}^{s+1}e_jb_j}x\\bigr)_e\n\\]\nbelong to this compact cube set.\n\nConsequently, for every $\\eps>0$ and every bounded Lipschitz observable $F$\non $G/\\Gamma$, its value at the missing vertex is uniformly within $\\eps$\nof a finite sum of products of bounded continuous functions of the other\nindividual vertices.  Those individual functions may be taken Lipschitz\nand bounded by one, with scalar coefficients outside the products.  For a\nfixed finite list of nilmanifolds and fixed bounds on $\\norm{F}_\\infty$ and\n$\\Lip(F)$, finitely many such approximation recipes suffice at each\naccuracy.\n\\end{lemma}\n\n\\begin{proof}\nReflect the missing vertex to the all-ones vertex.  If two group cubes\n$a,b\\in C^{s+1}(G_\\bullet)$ agree modulo $\\Gamma$ at all other vertices,\nthen $z=a^{-1}b$ is a group cube whose entries at all proper subsets of\n$[s+1]$ belong to $\\Gamma$.  Recover its face coefficients by\n\\eqref{eq:ordered-face-product}.  Inductively the coefficient at a proper\nsubset $E$ lies in $\\Gamma$, because its value is the entry at $E$\nmultiplied by inverses of previously recovered lattice elements.  The top\ncoefficient belongs to $G_{s+1}=\\{1\\}$.  The top entry of $z$ is therefore\nalso a product of lattice elements.  The original two top vertices agree\nmodulo $\\Gamma$.\n\nThe deletion map from the compact cube set to the space of partial cubes\nis now a continuous injection into a Hausdorff space.  It is a homeomorphism\nonto its image, proving continuity of reconstruction.  For a representative\n$x_0\\in G$ of $x$, the displayed linear cube is represented by the product\nof the constant arrays $g^{b_0}$ and $x_0$, with the codimension-one\narrays $g^{b_j}$ between them.  It therefore lies in the cube group.\n\nOn the compact image of the deletion map, products of continuous functions\nof individual coordinates form an algebra containing constants and\nseparating points.  Stone--Weierstrass approximates the reconstructed\nobservable by finite sums from this algebra.  Lipschitz functions are dense\nin the continuous functions on each compact nilmanifold, so the factors\ncan be made Lipschitz and then normalized to have sup norm at most one.\nFinally the family of observables with the prescribed sup and Lipschitz\nbounds is compact in the uniform norm.  A finite uniform net, followed by\nthe construction for each member of that net, proves the last assertion.\n\\end{proof}\n\n\\subsection{Adapted factorization on a sequence of scales}\n\nThe lifting argument will use face information in every dimension\nthrough $s+1$.  We therefore need one rational filtration whose cube\ngroups describe all these local laws, together with the point law.\nTo obtain it, we first factor the orbit.  The left factor will vary\non the scale of the full interval, the right factor will be periodic\nmodulo the lattice, and the remaining factor will lie in a fixed\nrational subgroup.  Its Taylor coefficients will satisfy the\nirrationality conditions that give equidistribution in all the\nassociated cube groups.\nThe smooth--equidistributed--periodic decomposition of Green and Tao\n\\cite[Theorem~1.19]{GreenTao} was refined using Taylor-coefficient\nirrationality and filtration descent in\n\\cite[Definitions~A.5--A.6 and Lemma~2.9]{GreenTaoRegularity}.\nWe prove the sequential limiting form needed for the joint cube laws.\n\nA polynomial $P:\\R\\to H$ is \\emph{adapted} to $H_\\bullet$ if\n\\[\n \\log P(b)=\\sum_{j=0}^s b^j U_j,\\qquad U_j\\in\\mathfrak h_j.\n\\]\nThis definition is unchanged on passing from monomials to binomial\npolynomials.  Products and inverses of adapted polynomials are adapted:\nin the finite Baker--Campbell--Hausdorff expansion, a term of polynomial\ndegree $j$ belongs to $\\mathfrak h_j$.  Terms of degree above $s$ vanish.\nConstant factors cause no difficulty, since $H_0$ normalizes every level.\nThis definition implies polynomiality by group differences, as required\nin Theorem~\\ref{thm:quantitative-leibman}.  More explicitly, suppose the\ncoefficient of degree $j$ in $\\log R(b)$ lies in\n$\\mathfrak h_{j+v}$.  Give a formal shift $t$ degree one as well.\nBCH shows that every coefficient of total degree $j+k$ in\n$\\log(R(b+t)R(b)^{-1})$ lies in $\\mathfrak h_{j+k+v}$; brackets of\ntwo terms can only increase this filtration index.  The expression is\nzero at $t=0$, so every surviving monomial uses at least one power of\n$t$.  For each fixed $t$, its degree-$j$ coefficient in $b$ therefore\nbelongs to $\\mathfrak h_{j+v+1}$.  Iterating from $v=0$ puts every\n$k$-fold difference in $H_k$.  The same argument works with total\ndegree in several variables.\n\n\\begin{lemma}[Taylor coordinates and finite descent]\n\\label{lem:adapted-factorization}\nFix a connected simply connected nilpotent group $K$, a lattice $\\Lambda$,\nand a rational filtration $K_\\bullet$ satisfying\n\\eqref{eq:cube-filtration}.  Let $L\\to\\infty$ along any sequence, and let\n$P_L$ be arbitrary adapted polynomials.  After passing to a subsequence\nthere exist a fixed rational filtration $H_\\bullet$ with $H_i\\subseteq K_i$,\na positive integer $d_0$, rational elements $\\sigma_0,\\ldots,\\sigma_{d_0-1}$\nof $K$, and factorizations\n\\begin{equation}\n P_L(b)\\Lambda\n =h_L(b)Q_L(b)\\gamma_L(b)\\Lambda,\n \\qquad\n Q_L(b)=\\prod_{j=1}^s a_{j,L}^{\\binom bj},\\quad a_{j,L}\\in H_j,\n \\label{eq:adapted-factorization}\n\\end{equation}\nsuch that the following hold.\n\\begin{enumerate}\n\\item The maps $\\beta\\mapsto h_L(L\\beta)$ converge locally uniformly on\n$\\R$ to a smooth map $h:\\R\\to K$.  They and their first derivatives are\nbounded on every fixed compact interval, uniformly in $L$.\n\\item For integer $b$,\n$\\gamma_L(b)\\Lambda=\\sigma_{b\\bmod d_0}\\Lambda$.\n\\item If $1\\leq j\\leq s$ and $\\xi:H_j\\to\\R$ is a nonzero rational\nhomomorphism annihilating $H_{j+1}$ and every $[H_i,H_{j-i}]$,\n$1\\leq i<j$, then\n\\begin{equation}\n L^j\\norm{\\xi(a_{j,L})}_{\\R/\\Z}\\longrightarrow\\infty.\n \\label{eq:level-irrationality}\n\\end{equation}\nHere rational homomorphisms include all nonzero rational multiples of one\nanother.  No boundedness of the Taylor coefficients $a_{j,L}$ is asserted\nor required.\n\\end{enumerate}\n\\end{lemma}\n\n\\begin{proof}\nWe first justify Taylor coordinates.  For any adapted $R$ write\n$a_0=R(0)$ and remove this constant on the left.  Suppose that, after\nremoving the factors of orders below $j$, the remaining adapted polynomial\n$R_j$ is the identity at $0,\\ldots,j-1$.  The $j$th forward difference of\n$\\log R_j$ at zero equals $\\log R_j(j)$: all its other evaluated terms\nvanish.  Terms of degree less than $j$ in the logarithm make no\ncontribution, and all terms of degree at least $j$ have coefficients in\n$\\mathfrak h_j$.  Thus $a_j=R_j(j)\\in H_j$.  Multiplication on the left by\n$a_j^{-\\binom bj}$ leaves an adapted polynomial vanishing at the first\n$j+1$ integers.  After order $s$, its logarithm has degree at most $s$\nand $s+1$ zeros, so it is identically zero.  This proves the ordered Taylor\nrepresentation.\n\nChoose $\\lambda_L\\in\\Lambda$ so that\n$c_L=P_L(0)\\lambda_L$ lies in a fixed compact set of representatives.\nNormalize by\n$R_L(b)=c_L^{-1}P_L(b)\\lambda_L$.\nIt is adapted, has constant term the identity, and\n$P_L(b)\\Lambda=c_LR_L(b)\\Lambda$.  Adaptation is preserved here either\nby the preceding BCH observation, or by noting that conjugation by\n$\\lambda_L$ preserves all terms of $K_\\bullet$.\n\nWe describe one descent step for a fixed current rational filtration\n$H_\\bullet$ and a normalized adapted polynomial with Taylor coefficients\n$a_{j,L}$.  If \\eqref{eq:level-irrationality} fails, fix one of its\ncharacters $\\xi$ and pass to a subsequence on which\n\\[\n \\xi(a_{j,L})=m_L+\\lambda'_L,\\qquad\n m_L\\in\\Z,\\qquad \\abs{\\lambda'_L}\\leq C L^{-j}.\n\\]\nChoose a fixed rational $V\\in\\mathfrak h_j$ with $\\xi(\\exp V)=1$.\nReplace the polynomial $R_L$ by\n\\begin{equation}\n R'_L(b)\n =\\exp\\bigl(-\\lambda'_L\\tbinom bj V\\bigr)\n   R_L(b)\n   \\exp\\bigl(-m_L\\tbinom bj V\\bigr).\n \\label{eq:filtration-descent}\n\\end{equation}\nIt remains adapted to the old filtration.  Its Taylor coefficients of\norders less than $j$ are unchanged, since both multipliers are the\nidentity at $0,\\ldots,j-1$.  Modulo $H_{j+1}$, the order-$j$ coefficient\nis the old coefficient with the two displayed corrections.  To verify this\neven in the presence of lower Taylor factors, commute those factors past\nthe left correction at $b=j$; the commutators lie in\n$[H,H_j]\\subseteq H_{j+1}$.  Its $\\xi$-value is consequently\n$-\\lambda'_L+\\xi(a_{j,L})-m_L=0$.\n\nReplace $H_j$ by $H_j\\cap\\ker\\xi$ and leave all other positive levels\nunchanged.  When $j=1$ replace $H_0$ by the same kernel.  The kernel is a\nconnected rational subgroup, since in exponential coordinates it is the\nkernel of a rational linear form.  Nesting is preserved because $\\xi$\nkills $H_{j+1}$.  A bracket inclusion whose target is the changed level\nis preserved because $\\xi$ kills each $[H_i,H_{j-i}]$.  Inclusions whose\ntarget has larger index already land in $H_{j+1}$, and the other\ninclusions are unchanged or have a smaller source.  Thus this is again a\nfiltration.  The Taylor calculation shows that $R'_L$ is adapted to it.\nIn the case $j=1$, all higher coefficients already belong to $H_2$,\nso its entire image lies in the new $H$.\n\nThe removed factors give\n\\[\n R_L(b)=\n \\exp\\bigl(\\lambda'_L\\tbinom bj V\\bigr)\n R'_L(b)\n \\exp\\bigl(m_L\\tbinom bj V\\bigr).\n\\]\nThe left factor is bounded and has derivative $O(L^{-1})$ on every\ninterval $\\abs b\\leq A L$, with constants allowed to depend on $A,C,V$.\nThe right factor is an integer power of the fixed rational element\n$\\exp V$ at integer inputs.  Repeating the procedure strictly reduces\n$\\sum_{i=1}^s\\dim H_i$ at every step.  It therefore terminates after\nfinitely many steps.  If any fixed rational level character still failed\n\\eqref{eq:level-irrationality} on the final subsequence, the same operation\nwould give another strict reduction.  Hence all the asserted character\nlimits hold.\n\nThere is no conjugation of accumulated smooth factors by rational factors\nin this procedure: from\n$P=\\epsilon_1R_1\\gamma_1$ and\n$R_1=\\epsilon_2R_2\\gamma_2$ one gets\n$P=(\\epsilon_1\\epsilon_2)R_2(\\gamma_2\\gamma_1)$.\nThus $h_L$ is the product of $c_L$ and finitely many of the bounded smooth\nfactors just described.  Pass to a subsequence on which $c_L$ converges\nand every bounded scalar $L^j\\lambda'_L$ converges.  Since\n$L^{-j}\\binom{L\\beta}{j}$ converges with all derivatives on compact sets\nto $\\beta^j/j!$, this gives the first conclusion.\n\nFor completeness, the right factors have a common bounded period modulo\n$\\Lambda$, even though the integers $m_L$ need not be bounded.  Finitely\nmany fixed rational elements, together with a finite generating set of\n$\\Lambda$, generate a discrete group $\\Lambda'$ containing $\\Lambda$.\nHere is the denominator argument.  Collect a word into integer powers of\nthe finitely many generator and iterated-commutator types of length at most\nthe nilpotence class.  Collection terminates because a commutation error\nhas strictly greater commutator length.  The logarithms of these finitely\nmany types have rational coordinates.  Applying the finite BCH formula to\ntheir ordered powers bounds every coordinate denominator by one fixed\ninteger, independent of the word and its integer exponents.  This proves\ndiscreteness.  A compact fundamental set for $\\Lambda$ then shows that\n$[\\Lambda':\\Lambda]<\\infty$.\n\nThe normal core $\\Lambda''=\\bigcap_{g\\in\\Lambda'}g\\Lambda g^{-1}$ has\nfinite index in $\\Lambda'$, so $\\Lambda'/\\Lambda''$ is a finite group.\nLet $a$ be a common multiple of its element orders.  Each\n$\\binom bj$ modulo $a$ has a fixed period, for example $a s!$ for\n$0\\leq j\\leq s$: Vandermonde's identity shows that\n$\\binom{b+a s!}{j}-\\binom bj$ is divisible by $a$.\nConsequently every accumulated right factor is periodic in this finite\nquotient with one common period $d_0$.  There are only finitely many\npossible maps from its residue classes to $\\Lambda'/\\Lambda''$.\nPass to a subsequence on which this map is fixed and choose rational\nrepresentatives $\\sigma_r$.  This gives the second conclusion and\ncompletes the proof.\n\\end{proof}\n\n\\subsection{The joint local point and cube laws}\n\nThe factorization has fixed a subgroup, a filtration, and a finite\nperiod.  We now identify the distribution of the remaining factor\nand its cubes.  The key step is to detect any horizontal obstruction\nin a cube group in one of the Taylor coefficients controlled above.\n\nFor a rational element $\\sigma\\in K$, put\n\\[\n \\Gamma_\\sigma=H\\cap\\sigma\\Lambda\\sigma^{-1},\\qquad\n \\Gamma^{[q]}_\\sigma\n =C^q(H_\\bullet)\\cap\n     (\\sigma\\Lambda\\sigma^{-1})^{\\{0,1\\}^q}.\n\\]\nThese are lattices in their respective groups.  By Haar measure on\n$hH\\sigma\\Lambda/\\Lambda$ we mean the image of invariant probability on\n$H/\\Gamma_\\sigma$ under $y\\mapsto hy\\sigma\\Lambda$.  Use the analogous\nconvention for cube cosets.\n\n\\begin{proposition}[Simultaneous local cube law]\n\\label{prop:local-cube-law}\nUnder the hypotheses of Lemma~\\ref{lem:adapted-factorization}, retain the\nsubsequence and its data.  Define probability measures\n\\begin{equation}\n m_{\\beta,r}^{[q]}\n =\\text{Haar image on }\n h(\\beta)^{\\mathrm{diag}} C^q(H_\\bullet)\n \\sigma_r^{\\mathrm{diag}}\\Lambda^{\\{0,1\\}^q}/\n              \\Lambda^{\\{0,1\\}^q}.\n \\label{eq:local-cube-haar}\n\\end{equation}\nThe empirical point laws satisfy\n\\begin{equation}\n \\frac1{\\lfloor L\\rfloor}\\sum_{0\\leq b<\\lfloor L\\rfloor}\n       \\delta_{P_L(b)\\Lambda}\n \\ \\Longrightarrow\n \\frac1{d_0}\\sum_{r=0}^{d_0-1}\\int_0^1 m_{\\beta,r}^{[0]}\\,d\\beta.\n \\label{eq:joint-point-law}\n\\end{equation}\nFor every fixed cube dimension $q$, every $\\beta\\in(0,1)$, every residue\n$r\\bmod d_0$, and every bounded Lipschitz test $F$ on\n$(K/\\Lambda)^{\\{0,1\\}^q}$,\n\\begin{equation}\n \\lim_{\\alpha\\downarrow0}\\limsup_{L\\to\\infty}\n \\left|\n \\E_{\\substack{b_0/L\\in[\\beta,\\beta+\\alpha),\\ b_0\\equiv r\\ (d_0)\\\\\n                 b_j/L\\in[0,\\alpha),\\ b_j\\equiv0\\ (d_0),\\ 1\\leq j\\leq q}}\n F\\bigl((P_L(b_0+\\sum_{j=1}^q e_jb_j)\\Lambda)_e\\bigr)\n -\\int F\\,dm_{\\beta,r}^{[q]}\n \\right|=0.\n \\label{eq:local-cube-limit-order}\n\\end{equation}\nThe inputs in this average are independent and uniform on the specified\ninteger progressions.  The same assertion holds for boxes with side\nlengths between $c\\alpha L$ and $C\\alpha L$, whose base coordinates\nare within $C\\alpha L$ of $\\beta L$ and whose increments have\nabsolute value at most $C\\alpha L$, where $c,C>0$ are fixed before\neither limit.\nFor fixed bounds the assertions are uniform over the tests and these\nboxes.  In particular, arbitrary fixed-modulus progression restrictions\nare permitted if the base has residue $r$ and all increments have residue\nzero modulo $d_0$.  Every progression modulus is fixed before the\nlength limit, which always precedes the locality limit.\nThe same factorization supplies these conclusions for every fixed $q$.\n\\end{proposition}\n\n\\begin{proof}\nWe first prove equidistribution before inserting the smooth factor.  Fix\n$q,r$ and abbreviate $C=C^q(H_\\bullet)$.  The cube polynomial\n\\[\n \\mathbf Q_L(\\mathbf b)\n =\\bigl(Q_L(b_0+\\textstyle\\sum_{j=1}^q e_jb_j)\\bigr)_e\n\\]\ntakes values in $C$.  To see this and record the needed coefficient\ninformation, expand\n\\begin{equation}\n \\binom{b_0+\\sum_{j=1}^q e_jb_j}{\\ell}\n =\\sum_{D\\subseteq[q]}\n      \\Bigl(\\prod_{j\\in D}e_j\\Bigr)F_{\\ell,D}(\\mathbf b).\n \\label{eq:boolean-binomial-expansion}\n\\end{equation}\nEach $F_{\\ell,D}$ has degree at most $\\ell$, and every one of its\nmonomials uses exactly the increment variables indexed by $D$, each with\npositive exponent.  In particular it vanishes for $|D|>\\ell$.\nThus the $\\ell$th Taylor factor on the cube is the product of the face\narrays $(a_{\\ell,L})_D^{F_{\\ell,D}(\\mathbf b)}$ with $|D|\\leq\\ell$.\nThese are in $C$, because $H_\\ell\\subseteq H_{|D|}$.  This expansion\nalso shows adaptation to \\eqref{eq:cube-group-filtration}: a coefficient\nof total polynomial degree $k$ in a factor of order $\\ell\\geq k$ lies\nin $\\mathfrak h_\\ell\\subseteq\\mathfrak h_{\\max(k,|D|)}$.\nBCH preserves this assertion on multiplying the Taylor factors.\n\nWe claim that $\\mathbf Q_L$ is equidistributed on\n$C/\\Gamma^{[q]}_{\\sigma_r}$ on every box whose sides are fixed positive\nfractions of $L$, whose origin is $O(L)$, and with any fixed progression\nrestrictions.  The error tends to zero uniformly for fixed bounds on\nthese data and on the Lipschitz tests.  Otherwise\nTheorem~\\ref{thm:quantitative-leibman}, applied to the fixed rational\nnilmanifold and the filtration \\eqref{eq:cube-group-filtration}, supplies\nalong a subsequence a nonzero horizontal character from a fixed finite\nlist.  Pass to a further subsequence on which this character, denoted\n$\\chi:C\\to\\R$, is fixed.  It annihilates commutators and takes integer\nvalues on $\\Gamma^{[q]}_{\\sigma_r}$.\n\nFor a face $D$, write $\\chi_D(y)=\\chi(y_D)$ wherever this is defined.\nChoose the largest $j\\geq1$ such that for some $|D|\\leq j$ the\nrestriction\n$\\xi=\\chi_D|_{H_j}$ is nonzero.  Such a pair exists because the face\nsubgroups generate $C$, including the empty face and $H_0=H_1$.\nThe restriction is a rational homomorphism: it is integer-valued on the\nlattice $H_j\\cap\\sigma_r\\Lambda\\sigma_r^{-1}$, and conjugation by\n$\\sigma_r$ preserves rationality.  Maximality makes it vanish on\n$H_{j+1}$.  For $1\\leq i<j$, choose $A,B\\subseteq D$ with\n$A\\cup B=D$, $|A|\\leq i$, and $|B|\\leq j-i$; one or both sets may\nbe empty.  If $u\\in H_i$ and $v\\in H_{j-i}$, their face arrays on\n$A,B$ lie in $C$ and\n$[u_A,v_B]=[u,v]_D$.  Since $\\chi$ kills this commutator, $\\xi$ kills\n$[H_i,H_{j-i}]$.  Thus $\\xi$ is exactly a character covered by\n\\eqref{eq:level-irrationality}.\n\nApplying $\\chi$ to \\eqref{eq:boolean-binomial-expansion} gives the\nscalar polynomial\n\\[\n \\chi(\\mathbf Q_L(\\mathbf b))\n =\\sum_{\\ell,D}\\chi_D(a_{\\ell,L})F_{\\ell,D}(\\mathbf b).\n\\]\nAll terms with $\\ell>j$ vanish by maximality.  In its degree-$j$ part,\nthe coefficient of\n\\begin{equation}\n b_0^{j-|D|}\\prod_{k\\in D}b_k\n \\quad\\hbox{is}\\quad\n \\frac{\\xi(a_{j,L})}{(j-|D|)!}.\n \\label{eq:isolated-horizontal-coefficient}\n\\end{equation}\nIndeed the top part of the binomial polynomial is the $j$th power\ndivided by $j!$.  No other face can contribute to this monomial: its\nincrement support must be exactly $D$.  No lower Taylor order has\ndegree $j$.  This proves both the value and the absence of cancellation\nin \\eqref{eq:isolated-horizontal-coefficient}.\n\nOn a fixed residue progression, substitute\n$b_\\nu=r_\\nu+d t_\\nu$ with fixed $d$.  Its top-degree coefficient is\nmultiplied by $d^j$; translations of the box origin do not alter it.\nThe bounded scalar coefficient obstruction in\nTheorem~\\ref{thm:quantitative-leibman}, clearing the fixed\nbinomial-to-monomial denominators if necessary, would therefore give\n\\[\n L^j\\norm{a\\,\\xi(a_{j,L})}_{\\R/\\Z}=O(1)\n\\]\nfor a fixed nonzero rational number $a$.  This contradicts\n\\eqref{eq:level-irrationality} for the rational character $a\\xi$.\nThe claim follows.  The argument also covers $q=0$, when only the\nempty face occurs.  If $C$ is trivial, the claim is immediate.\n\nIn the averages in \\eqref{eq:local-cube-limit-order}, all vertex inputs\nhave the same residue $r\\bmod d_0$.  Consequently their right factors\ncan be replaced exactly by $\\sigma_r\\Lambda$.  For fixed $\\alpha$,\nthe preceding claim gives the Haar law for the $Q_L$ cube.  At every\nvertex, $b/L=\\beta+O_q(\\alpha)$; Lemma~\\ref{lem:adapted-factorization}\ntherefore gives\n\\[\n h_L(b)=h(\\beta)+O(\\alpha)+o_{L\\to\\infty}(1)\n\\]\nin any fixed local metric, uniformly on the sampled box.  Multiplication\nby these bounded elements and passage to the compact nilmanifold changes\na bounded Lipschitz test by $O_F(\\alpha)+o(1)$, uniformly over the\nremaining entries.  The latter uniformity follows from smoothness of\nthe action on the compact nilmanifold.  First letting $L\\to\\infty$\nand then $\\alpha\\downarrow0$ proves\n\\eqref{eq:local-cube-limit-order}, including its stated uniform versions.\n\nFinally partition $[0,1]$ into finitely many intervals of length at most\n$\\alpha$.  On each interval and each residue class, apply the same\nargument with $q=0$ and freeze $h$ at an endpoint.  The proportions of\nthe residue classes tend to $1/d_0$, and the interval proportions tend\nto their lengths.  Letting $\\alpha\\downarrow0$ gives the Riemann integral\nin \\eqref{eq:joint-point-law}.  This integral is well defined because\nthe Haar images depend continuously on $\\beta$.  No further subsequence\ndepending on $q$ was used: \\eqref{eq:level-irrationality} proves the\nequidistribution assertion for each fixed dimension.\n\\end{proof}\n\n\\subsection{Lifting a symmetry of one marginal}\n\nThe local cube law describes the joint state as a mixture of Haar\nmeasures on compact orbits, with one filtration describing the cubes\non each orbit.  In Section~\\ref{sec:alignment}, the progression\ncomparison will show that an automorphism of one component preserves\nthe projections of these cube cosets when applied on a face.  The\nnext proposition converts precisely that information into a\ntransformation of the joint cover.  We will then show that any family\nof these lifts generates a group of nilpotence class at most $s$, as\nrequired for recurrence.\n\nChoose linear coordinates on $\\mathfrak h$ by choosing, for every $j$,\na complement of $\\mathfrak h_{j+1}$ in $\\mathfrak h_j$.\nAssign weight $j$ to coordinates on that complement.  The weighted degree\nof a monomial is the sum of its variable weights, with multiplicity;\nconstants have weight zero.  A \\emph{polynomial shear} will mean a map\n\\begin{equation}\n T(u)_{j,k}=u_{j,k}+p_{j,k}(u),\\qquad\n \\deg_{\\mathrm{wt}}p_{j,k}<j.\n \\label{eq:weighted-shear}\n\\end{equation}\nIn particular the displacement at weight $j$ uses only coordinates of\nsmaller weight.\n\n\\begin{proposition}[A marginal face symmetry has a joint lift]\n\\label{prop:face-lift}\nLet $K=K'\\times K''$ be a connected simply connected nilpotent Lie\ngroup with lattice $\\Lambda=\\Lambda'\\times\\Lambda''$, and let $\\pi$\ndenote the first group projection and its induced map of nilmanifolds.\nLet $H\\subseteq K$ be a connected rational subgroup with a connected\nrational filtration $H_\\bullet$ satisfying \\eqref{eq:cube-filtration}.\nLet $h\\in K$, and\nlet $\\sigma\\in K$ be rational.  Let $S$ be a fixed automorphism of $K'$\npreserving $\\Lambda'$.  Suppose that, for every $1\\leq q\\leq s+1$,\napplying $S$ to the entries on any upper codimension-one face preserves\nsetwise\nthe projected cube coset\n\\begin{equation}\n \\pi\\left(h^{\\mathrm{diag}}C^q(H_\\bullet)\n                \\sigma^{\\mathrm{diag}}\\Lambda^{\\{0,1\\}^q}/\n                   \\Lambda^{\\{0,1\\}^q}\\right).\n \\label{eq:projected-face-coset}\n\\end{equation}\nThen there is a polynomial shear $T:\\mathfrak h\\to\\mathfrak h$ such\nthat, for every $u\\in\\mathfrak h$,\n\\begin{equation}\n \\pi\\bigl(h\\exp(Tu)\\sigma\\Lambda\\bigr)\n =S\\bigl(\\pi(h\\exp(u)\\sigma\\Lambda)\\bigr).\n \\label{eq:joint-lift-identity}\n\\end{equation}\nThe lift is a bijection of the cover.  Its action on the other component\nis unrestricted.  This conclusion applies separately to every point\ncoset from Proposition~\\ref{prop:local-cube-law}; no measurable choice\nof lifts as a function of $\\beta,r$ is required.\n\\end{proposition}\n\n\\begin{proof}\nPut $J_i=\\pi(H_i)$ and $J=J_1$.  These are connected rational groups;\ntheir Lie algebras are rational images of those of $H_i$.  Their\nfiltration satisfies \\eqref{eq:cube-filtration}, and projection of the\nface generators shows\n$\\pi(C^q(H_\\bullet))=C^q(J_\\bullet)$.\nIn the remainder of the proof use $h,\\sigma,\\Lambda$ for the projected\nelements and lattice.  The projected point coset\n$hJ\\sigma\\Lambda/\\Lambda$ is preserved by $S$: each of its points can\noccur at a vertex on the specified face of a constant cube, and face\npreservation, also for the inverse map, gives equality of the point\nsets.  Choose $c\\in J$ such that\n\\begin{equation}\n S(h\\sigma)\\Lambda=hc\\sigma\\Lambda.\n \\label{eq:affine-basepoint}\n\\end{equation}\nThe ambient automorphism\n\\[\n \\Psi(y)=h^{-1}S(h)S(y)S(h)^{-1}h\n\\]\nsatisfies\n$S(hy\\sigma)\\Lambda=h\\Psi(y)c\\sigma\\Lambda$ for $y\\in J$.\nWe next verify on the group cover that $\\Psi(J)=J$.\n\nThe map $y\\mapsto y\\sigma\\Lambda$ on $J$ has stabilizer\n$\\Gamma_J=J\\cap\\sigma\\Lambda\\sigma^{-1}$.  Rationality makes\n$J/\\Gamma_J$ a compact embedded orbit.  Given a path $y(t)$ in $J$\nfrom the identity to $y$, the path\n$\\Psi(y(t))c\\sigma\\Lambda$ stays in this orbit.  Lift it there starting\nat $c\\in J$.  It is also a lift under the ambient covering\n$K'\\to K'/\\Lambda$ after multiplying on the right by $\\sigma$.\nUniqueness of lifts under this covering forces the ambient lift\n$\\Psi(y(t))c$ to lie in $J$.  Thus $\\Psi(J)\\subseteq J$; equality\nfollows because its differential is injective and both connected groups\nhave the same dimension.  Consequently\n\\[\n \\Phi(y)=c^{-1}\\Psi(y)c\n\\]\nis an automorphism of $J$, and the action on the point coset is represented\nby the affine map $y\\mapsto c\\Phi(y)$.\n\nWe claim that its linear part is the identity on each associated graded\nspace:\n\\begin{equation}\n (\\Phi_*-\\mathrm{id})\\mathfrak j_i\\subseteq\\mathfrak j_{i+1},\n \\qquad 1\\leq i\\leq s.\n \\label{eq:face-graded-identity}\n\\end{equation}\nFix $U\\in\\mathfrak j_i$ and work in dimension $i+1$.  Put\n$\\exp(tU)$ on the face with its first $i$ coordinates equal to one,\nand the identity elsewhere.  This is a path $z(t)$ in\n$C^{i+1}(J_\\bullet)$.  Apply the affine map $c\\Phi$ on the upper face\nof the last coordinate.  The resulting array $z'(t)$ has its image\nin the same cube coset by hypothesis.  At $t=0$ it is the constant-$c$\nupper-face array, which itself belongs to $C^{i+1}(J_\\bullet)$.\n\nThis quotient assertion lifts to\n$z'(t)\\in C^{i+1}(J_\\bullet)$ for every $t$.  Indeed this cube group\nis rational with lattice\n$C^{i+1}(J_\\bullet)\\cap\\Gamma_J^{\\{0,1\\}^{i+1}}$, so its quotient is\nan embedded compact subspace of the product point orbit.  Lift the path\nthrough that quotient beginning at $z'(0)$ and use uniqueness in the\nambient product covering, exactly as in the preceding paragraph.\n\nMultiply $z'(t)$ on the left by the inverse constant-$c$ last-face\narray, and then on the left by $z(t)^{-1}$.  The resulting group cube\nis the identity except at the top vertex, whose value is\n\\[\n \\exp(-tU)\\Phi(\\exp(tU)).\n\\]\nLemma~\\ref{lem:upper-face-coordinates} puts this element in $J_{i+1}$.\nDifferentiating at $t=0$ proves \\eqref{eq:face-graded-identity}.\nIn particular $\\Phi$ preserves every filtration level.\n\nChoose filtration-adapted coordinates on $\\mathfrak j$.  The polynomial\n\\begin{equation}\n D(Y)=\\log\\bigl(c\\Phi(\\exp Y)\\bigr)-Y\n \\label{eq:affine-log-displacement}\n\\end{equation}\nhas weighted degree strictly less than the output weight in each\ncoordinate.  For its linear part this follows directly from\n\\eqref{eq:face-graded-identity}: an input from level $i$ moves only to\nlevels greater than $i$.  For the remaining terms use BCH with\n$\\log c$ and $\\Phi_*Y$.  Every nonconstant bracket correction contains\nat least one copy of the fixed vector $\\log c$, which spends at least\none unit of filtration weight but contributes no polynomial degree in\n$Y$.  A bracket contributing to output weight $j$ therefore has input\nweighted degree at most $j-1$.  The constant term also has the required\ndegree.\n\nThe linear projection\n$p=\\pi_*:\\mathfrak h\\to\\mathfrak j$ has a filtration-preserving linear\nright inverse $R$.  To construct one, choose an adapted basis downstairs\nand lift each basis vector of weight $i$ to $\\mathfrak h_i$, which is\npossible because $p(\\mathfrak h_i)=\\mathfrak j_i$.  Set\n\\begin{equation}\n T(u)=u+R D(pu).\n \\label{eq:cover-shear-construction}\n\\end{equation}\nThis is a shear of the form \\eqref{eq:weighted-shear}.  Explicitly, a\nfiltration-preserving linear map can send an input coordinate of weight\n$i$ only to an output of weight at least $i$.  Substitution in $D$ and\nthen application of $R$ thus preserve the strict inequality between\ninput degree and output weight.  Moreover\n$p(Tu)=pu+D(pu)=\\log(c\\Phi(\\exp(pu)))$, proving\n\\eqref{eq:joint-lift-identity}.  Finally a map of\n\\eqref{eq:weighted-shear} is inverted successively in increasing\nweight, so it is a polynomial bijection of the cover.\n\\end{proof}\n\n\\begin{remark}\nInvariance of the point marginal alone would not suffice.  On\n$\\R^2/\\Z^2$, the automorphism $S(x,y)=(x+y,y)$ preserves Haar measure\nbut is not a translation.  For $a=(0,1/3)$, applying $S$ to the upper\nsecond face of the additive square $(0,a,0,a)$ gives $(0,a,0,Sa)$,\nwhose alternating corner sum is $Sa-a=(1/3,0)\\ne0$ in the torus.\nIt therefore fails the two-dimensional face condition.  The analogous\ntest in dimension $i+1$ forces the graded identity\n\\eqref{eq:face-graded-identity} in the proof above.\n\\end{remark}\n\n\\subsection{Bounded-step shears and Haar approximation}\n\nThe lifts of different marginal symmetries may move the same coordinates.\nThe next Lemma controls the group they generate and gives every fixed\nword in these lifts the same limiting distribution on the joint orbit.\n\n\\begin{lemma}[Nilpotence and expanding weighted boxes]\n\\label{lem:shear-haar}\nOn any finite-dimensional real vector space with coordinate weights in\n$\\{1,\\ldots,s\\}$, all the shears \\eqref{eq:weighted-shear} form a group\nof nilpotence class at most $s$.  This bound does not depend on the\ndimension or the coefficients of the shears.  A direct product with any\ngroup of ordinary translations has the same bound.\n\nLet $\\mathfrak h$ carry the adapted coordinates above, and let $\\lambda_E$\nbe normalized Lebesgue measure on\n\\begin{equation}\n B_E=\\{u:\\ \\abs{u_{j,k}}\\leq E^j\\text{ for all }j,k\\},\n \\qquad E\\longrightarrow\\infty.\n \\label{eq:weighted-haar-boxes}\n\\end{equation}\nFor every fixed finite set $\\mathcal T$ of shears,\n\\begin{equation}\n \\max_{T\\in\\mathcal T}\n   \\norm{T_*\\lambda_E-\\lambda_E}_{\\TV}\\longrightarrow0.\n \\label{eq:fixed-shear-tv}\n\\end{equation}\nFor every fixed $h\\in K$ and rational $\\sigma\\in K$, the images of\n$\\lambda_E$ under $u\\mapsto h\\exp(u)\\sigma\\Lambda$ converge weakly to\nthe Haar image on $hH\\sigma\\Lambda/\\Lambda$.  The same limit holds\nafter any fixed shear, and hence after each word in any fixed finite\nlist of words in the lifts from Proposition~\\ref{prop:face-lift}.\n\\end{lemma}\n\n\\begin{proof}\nComposition preserves \\eqref{eq:weighted-shear}, and inversion does so\nby solving one weight at a time.  Let $V_d$ be the space of polynomials\nof weighted degree at most $d$, including the constants, and put\n$V_{-1}=0$.  Substitution by a shear preserves $V_s$, and substitution\nminus the identity maps $V_d$ into $V_{d-1}$.  Indeed every term in the\ndifference of a substituted monomial uses at least one strict\nlower-degree displacement in place of its coordinate.  Use inverse\nsubstitution if necessary to obtain a group representation rather than\nan opposite representation.  It is faithful because all coordinate\nfunctions belong to $V_s$.\n\nLet $\\mathcal I$ denote the algebra of linear operators on $V_s$ which\nsend each $V_d$ into $V_{d-1}$.  Then $\\mathcal I^{s+1}=0$, and the\nrepresentation lies in $1+\\mathcal I$.  The elementary identities for\ninverses in a nilpotent algebra give\n$[1+\\mathcal I^a,1+\\mathcal I^b]\\subseteq1+\\mathcal I^{a+b}$.\nThus every $(s+1)$-fold group commutator is the identity.  Extra\ntranslation coordinates can all be given weight one, which proves the\ndirect-product assertion as well.\n\nFor a fixed shear $T$, its displacement in a weight-$j$ coordinate is\n$O_T(E^{j-1})$ on $B_E$.  Its derivative matrix is block triangular by\nweight with identity diagonal, so its Jacobian determinant is one.\nThe image $T(B_E)$ is contained in the box with side bounds\n$E^j+C_T E^{j-1}$.  The latter box exceeds $B_E$ in relative volume\n$O_T(E^{-1})$.  Since $T$ preserves Lebesgue measure, the symmetric\ndifference of $T(B_E)$ and $B_E$ has the same bound.  This proves\n\\eqref{eq:fixed-shear-tv}, uniformly over a fixed finite list by taking\nthe largest of its constants.  The assertion is deliberately for fixed\nshears; no uniform coefficient bound over all shears is needed.\n\nFor $a\\in H$ fixed, left multiplication in logarithmic coordinates is\n\\[\n u\\longmapsto\\log(a\\exp u).\n\\]\nBCH makes this another shear: every displacement bracket uses the fixed\nvector $\\log a$ and hence has input weighted degree strictly below its\noutput weight.  Equation~\\eqref{eq:fixed-shear-tv} therefore implies\nthat every weak subsequential limit of the projected boxes on\n$H/(H\\cap\\sigma\\Lambda\\sigma^{-1})$ is invariant under all left\ntranslations by $H$.  This compact homogeneous space has a unique\nsuch probability measure, its Haar probability.  Compactness supplies\nsubsequential limits, and uniqueness shows that the entire sequence\nconverges.  Translating on the left by $h$ and on the right by\n$\\sigma\\Lambda$ gives the stated point-coset Haar law.  Applying\n\\eqref{eq:fixed-shear-tv} once more proves the identical limit after\neach fixed shear or word.\n\\end{proof}\n\nThe locality and finiteness qualifications in this Section are useful in\nSection~\\ref{sec:alignment}.  There the marginal face symmetries will be\nproved for every fixed residue modulus before $d_0$ is selected.  The\nfinite recurrence argument will then use only finitely many fixed words\non each limiting coset.  Lemma~\\ref{lem:shear-haar} supplies their common\nHaar limit without imposing either a dimension bound on those covers or\nmeasurable dependence of the lifts on the limiting coset.\n"}, {"path": "preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/08_alignment.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/build/sections/08_alignment.tex", "bytes": 44453, "sha256": "b71e3a8db5e74a24244436b2c7e025c18b8729efe3dfed722dc698266243ee81", "content": "\\section{Finite alignment of the models}\\label{sec:alignment}\n\nWe prove Principle~\\ref{pr:alignment}.  The argument separates a finite\ncoloring construction from its realization by integer variables.  The finite\nconstruction fixes all its choices before the threshold and the model\ncomplexity are known.  The integer realization uses the marginal cube\nsymmetries from the preceding two Sections; its passage from existence to\npositive probability loses only the number of those finite choices.\n\nThroughout this Section, a target is a block $B=T\\cup\\{i\\}$ with\n$\\mathcal E_B\\ne\\varnothing$.  A pair at pivot $i$ means\n$e=(B,A)$, where\n\\[\n B=T\\cup\\{i\\},\\qquad A=P\\cup\\{j\\},\\qquad\n \\varnothing\\ne P<T<\\{j\\}<\\{i\\}.\n\\]\nAll lists below are finite.  Their sizes may depend on $n,r,s$, but never\non $w$, $\\tau$, or the complexity of the models.\n\nThe integer meaning of one transformation is elementary.  At a center\n$tp$, where $t$ is a tail product and $p$ is the pivot variable, an\ninteger displacement $q$ need not be divisible by $t$.  Since\n$(t,M)=1$, choose an integer $\\rho$ with $t\\rho\\equiv q\\pmod M$ and put\n$\\Delta=(q-t\\rho)/M$.  Then\n\\[\n t(p+m\\rho)+mM\\Delta=tp+mq\\qquad(m\\in\\Z).\n\\]\nThe change $p\\mapsto p+m\\rho$ supplies the correct residue modulo $M$;\nthe remaining change $m\\Delta$ is in the progression index of the\nmodel on that residue class.  We will realize these two changes by\na transformation of the model state.  A transformation chosen for\none target can move the states for other targets as well.  This is\nwhy the finite construction uses words in possibly noncommuting\ntransformations and protects the comparisons already obtained for\nearlier targets.  A general word need not translate the integer\nvariable $p$.  The precise residue choices and the states on which\nthese transformations act are constructed after the finite plan.\n\n\\subsection{A finite plan for words and rational scales}\n\nPut $p_\\ell=2^{n-\\ell}$ and $p_I=\\sum_{\\ell\\in I}p_\\ell$.\nAt an update targeting $B_0=T_0\\cup\\{i\\}$, define the multiplier tuple\n$\\kappa_{i,T_0}(k)$, for $k\\in\\N$, by\n\\begin{equation}\\label{eq:alignment-scale-update}\n \\kappa_\\ell(k)=\n \\begin{cases}\n k^{p_\\ell},&\\ell<i,\\\\\n k^{-p_{T_0}},&\\ell=i,\\\\\n 1,&\\ell>i.\n \\end{cases}\n \\qquad b'=b\\kappa(k).\n\\end{equation}\nHere multiplication of tuples is coordinatewise.  Thus $b'_{B_0}=b_{B_0}$.\nFor every pair $e=(U,A)$ at this pivot, with $U=C\\cup\\{i\\}$,\n\\begin{equation}\\label{eq:alignment-positive-exponents}\n \\frac{b'_A}{b'_U}=\\frac{b_A}{b_U}k^{\\lambda_e},\n \\qquad \\lambda_e=p_A-p_C+p_{T_0}>0.\n\\end{equation}\nIndeed, if $a=\\min P$, then\n$p_a>\\sum_{\\ell>a}p_\\ell\\ge p_C$, so already $p_P>p_C$.\nThis proves positivity for every pair at the pivot, including those with\n$C\\ne T_0$.\nThese compensating rescalings follow the mechanism in\n\\cite[proof of Proposition~4.1]{Alweiss}.  Here the transformations\nfor different targets may not commute, so the comparisons that must\nsurvive an update will be indexed by words.\n\nFor each pivot $i$, let a group $G_i$ of nilpotence class at most $s$\nact on a set $\\mathcal X_i$.  Associate to every pair $e=(B,A)$ at\nthat pivot an element $L_e\\in G_i$.  For every target $B$ at that\npivot and every positive rational $a$, let\n$C_{B,a}:\\mathcal X_i\\to\\{0,1\\}^r$ be an arbitrary map, called its\npalette.  The finite choices below must work for every such collection\nof actions and maps.\n\nA word template is a word in letters $(e,\\gamma)$ and their inverses,\nwhere $\\gamma\\in\\Q$.  At scale $b$ it is interpreted as a group word by\n\\begin{equation}\\label{eq:alignment-letter}\n (e,\\gamma)[b]=L_e^{q_0\\gamma b_A/b_U},\\qquad e=(U,A).\n\\end{equation}\nThe integer $q_0$ is chosen after the finite plan; all exponents actually\nused will then be integers.  Words act on the left, so $wJx$ means first\napply $J$, then $w$.  The empty word is allowed.  At a center\n$x\\in\\mathcal X_i$, a requirement for a block $B$,\nleading-multiplier list $\\mathcal L$, and word list $\\mathcal W$ is\n\\begin{equation}\\label{eq:alignment-word-requirement}\n C_{B,a b_B}(x)=C_{B,a b_B}(w[b]x)\n \\quad(a\\in\\mathcal L,\\ w\\in\\mathcal W).\n\\end{equation}\n\nNilpotent polynomial recurrence was developed by Leibman\n\\cite{Leibman1994} and by Bergelson and Leibman through polynomial\nHales--Jewett theorems \\cite{BergelsonLeibman1999,BergelsonLeibman2003}.\nWe use the finite-coloring consequence of nilpotent polynomial recurrence\nin \\cite[Corollary~3.7]{ZorinKranich}.  In the form needed\nhere, for finitely many ordered words\n\\[\n w_j(k)=a_{j,1}^{P_{j,1}(k)}\\cdots a_{j,l_j}^{P_{j,l_j}(k)},\n \\qquad P_{j,l}\\in\\Z[k],\\quad P_{j,l}(0)=0,\n\\]\nin a nilpotent group of bounded step, every finite coloring admits $k>0$\nand $J$ for which $J,w_1(k)J,\\ldots,w_q(k)J$ have the same color.\nOnly the subgroup generated by the finitely many letters is involved;\nit is countable, as required by that Corollary.  The recurrence statement\npermits these noncommuting ordered products.\nFor the handedness, apply its left-translate formulation to the inverse\nwords and to the coloring $g\\mapsto C(g^{-1})$, and then invert the\nresulting pattern.  To check the polynomial hypothesis of that\nspecialization, let $D\\ge1$ bound the degrees of the exponents and\nstretch the lower central series to the filtration\n$H_0=G$, $H_j=\\gamma_{\\lceil j/D\\rceil}(G)$ for $j\\ge1$.\nThe map $\\alpha\\mapsto a^{P(|\\alpha|)}$ is an IP polynomial for\nthis filtration, since repeated derivatives are ordinary finite\ndifferences of its exponent.  Product and inverse closure, as in\n\\cite[Theorem~2.5]{ZorinKranich}, includes the required ordered\nwords.  Include the identity word in the recurrence family.\nHere $k=|\\alpha|$ for a nonempty finite index set $\\alpha$, so the\nparameter is positive.  Stretching the filtration verifies the\nrecurrence hypothesis; the acting group still has nilpotence\nclass at most $s$.\n\nThere is a useful uniform finite version of this statement.  Give every\nletter type its own generator in the free nilpotent group of step $s$ on\nthese finitely many generators.  On the compact space of its colorings\nwith a fixed finite palette, each successful pair $(k,J)$ defines an open\ncylinder set. The preceding recurrence statement shows that these cylinders cover the\nspace.  A finite subcover gives finitely many pairs $(k,J)$.  Pulling a\ncoloring back by the homomorphism that interprets the generators proves\nthe same assertion in every nilpotent group of step at most $s$.  The\nfinite alternatives depend on the word templates, polynomial exponents,\npalette size, and $s$, and on no numerical values assigned to the\ngenerators.\n\n\\begin{lemma}[Finite word plan]\\label{lem:finite-word-plan}\nFor given $n,r,s$, there are a positive integer $q_0$ and finite\nlists of scale updates and word jumps with the following property,\nuniformly over the actions and palette maps just specified and the\ninitial centers at all pivots.  Start the scale tuple at $(1,\\ldots,1)$,\nprocess the pivots increasingly, and fix an order of their targets.\nAt each pivot one can choose updates\n\\eqref{eq:alignment-scale-update} and jumps from these lists so that,\nat the resulting scale $b$ and center, the terminal palette comparisons\nhold simultaneously for all its targets.  These comparisons are\n\\eqref{eq:alignment-word-requirement} with leading multiplier $d_B$\nand word\n\\begin{equation}\\label{eq:alignment-terminal-word}\n \\prod_{A\\in D}\\bigl((B,A),d_A/d_B\\bigr)[b],\n\\end{equation}\nin a fixed order, for every target $B$, every $D\\subset\\mathcal E_B$,\nand every multiplier tuple $d$ obtainable from later prescribed updates.\nWhen later pivots are processed, the center at this pivot is left fixed:\nthese terminal requirements already include all the later scale changes.\n\nAll these lists depend only on $n,r,s$.  There are finite lists\n$\\mathcal B\\subset\\Q_{>0}^n$ and $\\mathcal A\\subset\\Q_{>0}$\ncontaining, respectively, the final scale tuples and every palette index\nconsulted during the construction.  All intermediate scales belong to\nfinite lists as well.  The list $\\mathcal A$ also contains every required\nproduct of entries of a tuple in $\\mathcal B$.  All powers used in\n\\eqref{eq:alignment-letter} are integral.  If each $L_e$ also translates\nan auxiliary integer coordinate $v_e$ by $1$, all auxiliary indices\nvisited from $v=0$ lie in a prescribed finite slot list.\n\\end{lemma}\n\n\\begin{proof}\nWe give the backward recursion, including the requirements protecting\nan earlier target.  At a fixed pivot let its ordered targets be\n$B_1,\\ldots,B_q$.  Suppose the required lists after update $j$ have\nalready been specified for $B_1,\\ldots,B_j$; denote them by\n$\\mathcal L_{j,B}$ and $\\mathcal W_{j,B}$.  At $j=q$ these are the\nterminal lists in the statement.  Taking a full product of the leading\nand word lists only strengthens the requirements.\n\nTarget $B_j$ at update $j$.  Its leading product does not change.\nFor a word $w$, let $I_k(w)$ denote the old-scale template obtained by\nreplacing each letter $(e,\\gamma)$ by\n$(e,\\gamma k^{\\lambda_e})$.  Equation~\\eqref{eq:alignment-positive-exponents}\ngives the exact identity\n\\begin{equation}\\label{eq:alignment-word-inflation}\n w[b\\kappa(k)]=I_k(w)[b].\n\\end{equation}\nFor $B_j$ and a fixed current center $x$, color the acting group by\n$g\\mapsto(C_{B_j,a b_{B_j}}(g x))_{a\\in\\mathcal L_{j,B_j}}$.\nIts size is at most $2^{r|\\mathcal L_{j,B_j}|}$.\nRegard each rational old-scale letter needed in\n$\\mathcal W_{j,B_j}$ as a separate abstract generator.  The words\n$I_k(w)$ are ordered products of these generators to powers\n$\\pm k^{\\lambda_e}$, with all exponents vanishing at zero.\nThe finite version of recurrence therefore supplies a finite set\n$\\Omega_j$ of alternatives $(k,J)$ satisfying the required equalities\nfor $B_j$ at $J[b]x$ and scale $b\\kappa(k)$.  This set is independent\nof the incoming numerical scale.\n\nFor each previously processed $B=B_l$, $l<j$, define predecessor lists\nby including, for every $(k,J)\\in\\Omega_j$,\n\\begin{align}\n \\mathcal L_{j-1,B}&\\supset\n   \\{a\\kappa_B(k):a\\in\\mathcal L_{j,B}\\},\n       \\label{eq:alignment-leading-budget}\\\\\n \\mathcal W_{j-1,B}&\\supset\n   \\{J\\}\\cup\\{I_k(w)J:w\\in\\mathcal W_{j,B}\\}.\n       \\label{eq:alignment-word-budget}\n\\end{align}\nThe unions over all alternatives are finite.  If these predecessor\nrequirements hold at $(x,b)$, then for each new leading index\n$a b_B\\kappa_B(k)$ they compare the same old center $x$ both with\n$J[b]x$ and with $I_k(w)[b]J[b]x$.  Equality through the old center and\n\\eqref{eq:alignment-word-inflation} give\n\\[\n C_{B,a b'_B}(J[b]x)\n   =C_{B,a b'_B}(w[b']J[b]x).\n\\]\nThese are exactly the requirements after the update.  Thus\n\\eqref{eq:alignment-leading-budget}--\\eqref{eq:alignment-word-budget}\nprove preservation, with no assumption that the jump fixes earlier\ntargets.  At $j=1$ there is no earlier target to protect.  Backward\ninduction defines the whole plan, and forward induction executes it.\n\nNext plan the pivots in decreasing order.  Once the plans at pivots\nlarger than $i$ are fixed, take all coordinatewise products of their\npossible multiplier tuples; call this finite list $\\mathcal D_i$.\nUse $\\mathcal D_i$ as the list of $d$'s in the terminal requirements\nat pivot $i$.  The preceding construction returns multiplier options\ndepending only on that list and the fixed data, so there is no\ndependence on an unknown incoming scale.  The last pivot starts with\nthe singleton future list $\\{(1,\\ldots,1)\\}$.\n\nStarting all scales at $1$, enumerate all intermediate and terminal\npaths through these finite alternatives.  This gives finite scale\nlists and finitely many rational palette indices $a b_B$ at every\ncheckpoint.  Define $\\mathcal B$ to contain the final scales and\n$\\mathcal A$ to contain all these indices and all needed subset\nproducts of final scales.  Now choose $q_0$ divisible by the\ndenominators of every rational number $\\gamma b_A/b_U$ used in any\nevaluated word on any enumerated path.  This does not affect the\nabstract recurrence choices: $q_0$ merely changes the homomorphism\ninterpreting each old-scale generator.\n\nFinally enumerate all prefixes of all words, their cumulative jumps,\nand the terminal tests, with the now integral exponents.  Their sums\nin the auxiliary translation coordinates give a finite slot list,\nwhich we enlarge to contain $0$.  Intermediate word evaluations use\nonly this list.  One may assign arbitrary palettes at other slots\nwhen defining the coloring on the whole acting group.  Every step\nof the construction used only $n,r,s$.\n\\end{proof}\n\n\\subsection{Integer residue shifts and polynomial arrays}\n\nFix a pivot $i$ having targets, and condition initially on the earlier\nraw variables.  Write $p=t_i$, set $L=MW^w$, and, for every pair\n$e=(U,A)$ at this pivot, write $U=C\\cup\\{i\\}$ and define\n\\begin{equation}\\label{eq:alignment-residue-data}\n q_e=\\frac{h_A}{q_0h_U}t_A,\\qquad\n t_Cr_e\\equiv q_e\\pmod L,\\quad 0\\le r_e<L,\n \\qquad \\Delta_e=\\frac{q_e-t_Cr_e}{M}.\n\\end{equation}\nThese are integers for all sufficiently large $w$.  In fact\n$h_A/h_U$ is a multiple of $W^w$, which is eventually divisible by\n$q_0W$, and $t_C$ is a unit modulo the smooth modulus $L$.\nConsequently\n\\begin{equation}\\label{eq:alignment-residue-divisibility}\n W\\mid q_e,\\qquad W\\mid r_e,\\qquad W^w\\mid\\Delta_e.\n\\end{equation}\nThe last assertion follows from the full modulus $MW^w$ in the\ncongruence.  In particular every fixed positive integer eventually\ndivides $\\Delta_e$.\nFor an integer slot vector $v$ put $s(v)=\\sum_e v_er_e$.\nThe identity realizing one own-target letter is\n\\begin{equation}\\label{eq:alignment-integer-identity}\n t_C(p+m r_e)+mM\\Delta_e=t_Cp+m q_e\\qquad(m\\in\\Z).\n\\end{equation}\nIt uses no divisibility of $q_e$ by $t_C$.\n\nLet $D_i^-=\\prod_{\\ell<i}X_\\ell^2$ and use microcells in the pivot\nvariable of length\n\\begin{equation}\\label{eq:alignment-microcell}\n R=M\\left\\lfloor H_i^{1/2}/M\\right\\rfloor,\n \\qquad N_{\\rm loc}=R/M.\n\\end{equation}\nAdmissibility implies that $R$ dominates every fixed power of\n$M+D_i^-$, that $D_i^-R/H_i\\to0$, and that $R/X_i\\to0$.\nSince $W^w\\le M$, all slot shifts and all bounded array evaluations\nbelow have size at most a fixed power of $M+D_i^-$ and are $o(R)$\nin the pivot variable.\n\nAfter discarding partial endpoint cells, condition on a microcell and\non $r_0=p\\bmod M$.  Its first point of that residue is $p_*$, and\n\\[\n p=p_*+M\\ell,\\qquad 0\\le\\ell<N_{\\rm loc}.\n\\]\nBecause $r_0$ is a $W$-unit, every point of this progression is a\n$W$-unit.  Its harmonic weights have ratio $1+O(R/X_i)$, so the\nconditional law of $\\ell$ differs in total variation by $o(1)$ from\nthe uniform law, uniformly in the earlier variables.\n\nFor $B=T\\cup\\{i\\}$, put $t=t_T$.  For every prescribed slot and\nevery prescribed bounded integer array index $u=(u_e)_{e=(B,A)}$,\nthe numerical arguments we will use are\n\\begin{equation}\\label{eq:alignment-array-argument}\n t(p+s(v))+M\\sum_{e=(B,A)}u_e\\Delta_e.\n\\end{equation}\nExcept on a set of conditionings of probability $o(1)$, all these\narguments, for the whole microcell, lie in one $H_i$-interval for\neach $B$.  Here and below the constants may depend on the fixed\nfinite word lists.  To verify the boundary assertion uniformly, in\na dyadic pivot range $[Y,2Y)$ the preimages of $H_i$ boundaries have\n$O(Yt/H_i+1)$ neighborhoods of length $O(R)$.  An interval of length\n$a$ has $a\\phi(W)/W+O(\\phi(W))$ $W$-units.  Relative to the\n$W$-units in $[Y,2Y)$, these neighborhoods therefore cost\n\\[\n O\\left(\\frac{t(R+W)}{H_i}+\\frac{R+W}{Y}\\right)=o(1).\n\\]\nHarmonic weights change this bound by at most a constant on each\ndyadic range.  Averaging the ranges and using $t\\le D_i^-$ proves\nthe assertion.  Endpoint cells have still smaller cost.\n\nOn a good conditioning choose the nilsequence formula for\n$S_{B,a,c}$ at the common interval and the residue\n$r_v=t(p+s(v))\\bmod M$.  For each $v,a,c$, write that formula as\n$F(g^k x\\Gamma)$, using a group representative $x$ and absorbing\nthe fixed origin of $k$ into $x$.  Retain the entire group-valued\npolynomial array\n\\begin{equation}\\label{eq:alignment-polynomial-array}\n u\\longmapsto\n g^{(t(p+s(v))-r_v)/M+\\sum_{e=(B,A)}u_e\\Delta_e}x.\n\\end{equation}\nOnly finitely many evaluations must agree with the model; the array\nitself is defined for every $u$.\n\nWe record why these arrays lie in fixed compact nilmanifolds.  If\n$G$ has lower central Lie algebra series $\\mathfrak g_j$, let\n$\\mathfrak a_q(G)$ consist of polynomials in $q$ real variables\nwhose constant coefficient is in $\\mathfrak g$ and whose coefficient\nof total degree $j>0$ is in $\\mathfrak g_j$.\nPointwise brackets preserve this space: a bracket of degrees $j,k$\nlies in $\\mathfrak g_{j+k}$ when both are positive, and a bracket\nwith a constant coefficient lies in a still deeper allowed level.\nIt is a finite-dimensional rational nilpotent Lie algebra of step\nat most $s$.  Its simply connected group $\\mathcal P_q(G)$ consists\nof the corresponding arrays, with pointwise multiplication.\n\\[\n \\mathcal P_q(\\Gamma)\n   =\\{a\\in\\mathcal P_q(G):a(z)\\in\\Gamma\\text{ for every }z\\in\\Z^q\\}\n\\]\nis a lattice.  For discreteness, sufficiently many integer evaluations\ndetermine every coefficient of the logarithm; a convergent sequence\nof lattice-valued arrays is eventually constant at those evaluations\nand hence constant.  For cocompactness, choose a rational basis\nrespecting the lower central series.  The change between logarithmic\ncoordinates and ordered Mal'cev coordinates is a rational polynomial\nwith zero constant term.  Clearing its finitely many denominators\nshows that the exponential of a sufficiently divisible integral\ncoefficient vector is lattice-valued at every integer input.\nIn a rational Mal'cev basis for $\\mathfrak a_q(G)$, fixed integer\nmultiples of the basis vectors therefore exponentiate into\n$\\mathcal P_q(\\Gamma)$.  Successively reducing the ordered\ncoordinates modulo these elements, starting with the quotient by\nthe next ideal in the Mal'cev flag, places every coset in a bounded\ncoordinate box.  This proves cocompactness.\n\nInteger input shifts $u\\mapsto u+\\mathbf e$ preserve the Lie algebra\nand this lattice, and evaluation at a fixed integer input induces\na smooth map of the quotients.  Formula~\\eqref{eq:alignment-polynomial-array}\nbelongs to this group: in its BCH logarithm a term of degree $j$\nin $u$ contains at least $j$ occurrences of $\\log g$, and hence\nlies in $\\mathfrak g_j$.\n\nFor each target take the product over all $v,a,c$ of these array\nquotients, denoting it by $K_B/\\Lambda_B$, and put\n\\[\n K/\\Lambda=\\prod_B K_B/\\Lambda_B.\n\\]\nThe choices range over a fixed finite list because the original\nmodel complexity is bounded.  Increasing $\\ell$ by $1$ multiplies each\narray on the left by the constant array $g^t$.  Thus the joint state\nis a linear orbit on $K/\\Lambda$, adapted to its lower central\nfiltration and of step at most $s$.  Denote its state at $\\ell$ by\n$Y(\\ell)$.  For an own pair $e=(B,A)$ write $\\mathcal S_e$ for the\nlattice-preserving automorphism of the whole component $K_B$\nshifting the corresponding input coordinate~by~$1$.\n\n\\subsection{Typical marginal cube invariance}\n\nThe state $Y(\\ell)$ now stores every model reading needed at this pivot.\nWe must show that, for typical conditionings, its marginal cube laws\npermit the array shifts $\\mathcal S_e$.  Removing the rough tail factors\nfrom their sampling steps will supply these symmetries.\n\n\\begin{lemma}[Conditional face invariance]\\label{lem:conditional-face-invariance}\nFix the bounded-complexity model family and the finite lists above.\nFor a good conditioning on the earlier variables, the pivot microcell,\nand its residue, form the joint orbit $Y(\\ell)$ just constructed.\nFix a cube dimension $a\\le s+1$, a positive integer $d$, constants\n$0<c<C$, and a bound for the supremum and Lipschitz norms of tests.\nUniformly over boxes for $(\\ell_0,\\ldots,\\ell_a)$ in\n$[-CN_{\\rm loc},CN_{\\rm loc}]^{a+1}$ with sides at least\n$cN_{\\rm loc}$, all fixed residue vectors modulo $d$, all such tests\nof one marginal cube, and every own-pair automorphism, the difference\nbetween the marginal cube average and its image under that\nautomorphism on an upper codimension-one face tends to zero in\nprobability over the conditioning.\n\nConsequently, from any sets of conditionings of probability bounded\naway from zero one can select a sequence on which, simultaneously\nfor all these fixed data, the marginal face invariances hold.  On\na further subsequence to which Proposition~\\ref{prop:local-cube-law}\napplies, every projected local cube coset has the corresponding face\nsymmetry in dimensions through $s+1$.  The modulus in this last\nassertion may be the period selected by that Proposition.\n\\end{lemma}\n\n\\begin{proof}\nFix a target $B=T\\cup\\{i\\}$ and write $t=t_T$.  Formulae outside\nthe conditioned cell mean the extensions of the already selected\nnilsequence formulae, so all bounded-multiple boxes in the statement\nare defined.  We will compare them with averages whose step has no\nfactor $t$.\n\n\\paragraph{An exposure retaining polynomial dependence.}\nWe prove the assertion in probability by changing the order of exposure.\nWe must freeze the target's model formula while leaving its tail\nvariables available for progression comparison.  A fixed pivot microcell\nalone does not do this as the tail varies.  Instead we condition on a\nshort bin for $tp$;\nthe eventual microcell origin will enter the base coordinate $x_0$,\nnot a coefficient of the polynomial family.\nExpose the earlier variables outside $T$, the residues modulo\n$L=MW^w$ of those inside $T$, dyadic bins $[S_\\ell,2S_\\ell)$ for\n$\\ell\\in T$, the pivot residue $r_0\\bmod M$, and a bin for $tp$ of\nwidth $t_{\\rm lo}R$, where $t_{\\rm lo}=\\prod_{\\ell\\in T}S_\\ell$.\nDo not expose the exact pivot microcell at this stage.\nThen $t_{\\rm lo}\\le t\\le2^{|T|}t_{\\rm lo}$.  Every $r_e$ is\nfixed: earlier residues determine $q_e\\bmod L$ and $t_C\\bmod L$.\nFor own pairs $e=(B,A)$ the full integer $q_e$ is fixed, because\n$A\\cap T=\\varnothing$.  Hence all slot shifts $s(v)$ are fixed.\nSo are\n\\[\n r_*=tr_0\\bmod M,\\qquad r_v=t(r_0+s(v))\\bmod M.\n\\]\n\nWe may discard exposed bins whose entire preimage in $p$, for every\n$t$ in the indicated product of dyadics, is not inside\n$[X_i+2R,X_i^2-2R]$.  Such a discard forces the actual $p$ into a\nfixed multiplicative neighborhood of a cutoff endpoint, up to an\n$O(R)$ enlargement.  Its harmonic probability is\n$O_n(1/\\log X_i)+o(1)$.  Also discard $tp$ bins within a sufficiently\nlarge fixed multiple of $t_{\\rm lo}R$ of an $H_i$ boundary.  The\nboundary count preceding the Lemma gives probability $o(1)$ for\nthis discard.  The constant is chosen to cover the given\nbounded-multiple boxes, all slots, and all required bounded array\nindices.  On the remaining bins the interval and residue selecting\nevery formula for this target are fixed throughout the exposure.\nThus all its parameters $F,g,x$ are fixed as the variables in $T$\nvary.\n\nFor later use, the conditional law of those variables is dominated\nby a constant, depending only on $n$, times the product of uniform\nlaws on their dyadic bins and prescribed $L$ residues.  Before\nconditioning on the $tp$ bin, their harmonic densities in each\ndyadic differ from uniform by at most a factor $2$ per variable.\nIf the exposed bin is $[Q,Q+t_{\\rm lo}R)$, its preimage at a given\n$t$ has length $t_{\\rm lo}R/t$, between $2^{-|T|}R$ and $R$.\nIt lies fully inside the pivot cutoff.  On the residue $r_0\\bmod M$\nthe unnormalized pivot mass there is\n\\[\n (1+o(1))\\frac1M\\int_{Q/t}^{(Q+t_{\\rm lo}R)/t}\\frac{du}{u}\n = (1+o(1))\\frac1M\\log\\left(1+\\frac{t_{\\rm lo}R}{Q}\\right),\n\\]\nuniformly in $t$.  The discretization error is relatively\n$O(M/R)$, and the common normalization cancels.  In particular\nconditioning on this bin changes the preceding product comparison\nby a bounded factor.  This is the required domination; no exact\nmicrocell has been fixed in deriving it.\n\n\\paragraph{Putting the tail product in the sampling step.}\nLet $p_*$ be the first point of $r_0\\bmod M$ in the actual\nmicrocell eventually selected.  Put $Y_*=\\lfloor Q/M\\rfloor$ and\nchange the base and increment coordinates by\n\\begin{equation}\\label{eq:alignment-cube-coordinates}\n x_0=\\frac{t(p_*+M\\ell_0)-r_*}{M}-Y_*,\n \\qquad x_j=t\\ell_j\\quad(1\\le j\\le a).\n\\end{equation}\nThe unknown cell origin has entered $x_0$ rather than a polynomial\ncoefficient.  Since $p_*$ is within $R$ of the actual pivot, these\ncoordinates lie in boxes in a fixed multiple of\n\\[\n Z=\\frac{t_{\\rm lo}R}{M},\n\\]\nwith all sides comparable to $Z$.  The original residue restrictions\non $\\ell_0,\\ldots,\\ell_a$ become one residue vector modulo $dt$ in $x$.\nChanging an endpoint by less than $dt$ has vanishing relative\ncounting cost, so these are precisely the permitted normalized\nbox averages.\n\nAt vertex $\\omega\\in\\{0,1\\}^a$, set\n$X=x_0+\\sum_{j=1}^a\\omega_jx_j$.  Its array exponent is\n\\begin{equation}\\label{eq:alignment-joint-polynomial-exponent}\n X+Y_*+\\frac{r_*+t s(v)-r_v}{M}\n       +\\sum_{e=(B,A)}u_e\\frac{q_e-tr_e}{M}.\n\\end{equation}\nEvery parameter here except $X$ and $t$ was fixed by the exposure.\nThe displayed exponent is a polynomial of degree at most two,\nwith its only possible quadratic terms of the form $tu_e$.\nBCH then shows that the logarithms of the arrays,\nviewed in the fixed array group, are ordinary polynomials jointly\nin $x$ and the variables $t_\\ell$, $\\ell\\in T$, of bounded degree.\nThere is no bound required on their coefficients.  This joint\npolynomiality is the hypothesis that permits Proposition~\\ref{prop:rough-step}.\n\n\\paragraph{Removing each factor of the step.}\nWe compare the sampling in a class modulo $dt$ with the sampling\nin the same class modulo $d$, removing the factors of $t$ in a\nfixed order.  Consider removal of $t_\\ell$, and condition on all\nother factor values.  Let $w'$ be the product of the factors still\nin the step after this removal.  The current step is $dw't_\\ell$.\nThere is a representative $a_0$ for its base coordinate modulo\n$dw'$ which can be chosen independently of $t_\\ell$, after\nallowing all the finitely many residues modulo $d$.\nIndeed the base congruence modulo $w'$ is\n\\begin{equation}\\label{eq:alignment-origin-congruence}\n M(x_0+Y_*)+r_*\\equiv0\\pmod {w'},\n\\end{equation}\nand the increment congruences are $x_j\\equiv0\\pmod {w'}$.\nSince $w'$ is a product of $W$-units,\n$(M,w')=1$, and also $(d,w')=1$ for large $w$.\nThe Chinese remainder theorem therefore gives representatives\n$a_j\\in[0,dw')$ determined by these congruences and the chosen\n$d$ residues, independently of $t_\\ell$.\n\nSubstitute $x_j=a_j+dw'y_j$.  The current restriction becomes a\nsingle class vector modulo $t_\\ell$ in $y$, while removing it gives\nunrestricted integer $y$.  No coprimality of $w'$ and $t_\\ell$ is\nused: both original classes are nested congruences, and division\nof $x_j-a_j$ by the integer $dw'$ is exact.\nThe new common box scale is $Z/(dw')$.  It dominates every fixed\npower of $S_\\ell$, uniformly over the other factor values, by\nthe choice of $R$.  The substitution has coefficients independent\nof $t_\\ell$, so joint log-polynomiality in $(y,t_\\ell)$ is retained.\nThe variable $t_\\ell$ lies in a fixed residue modulo $L=MW^w$\nin $[S_\\ell,2S_\\ell)$, with\n\\[\n S_\\ell/L\\longrightarrow\\infty.\n\\]\nAll hypotheses of Proposition~\\ref{prop:rough-step} hold.\n\nThat Proposition bounds the exceptional proportion uniformly in\nthe polynomial coefficients, all permitted boxes, class vectors,\nand tests.  Its uniformity in endpoints is essential: the exact\nmicrocell and hence the endpoints may depend on the pivot sampled\nafter the exposure.  For each choice of other factors the\nexceptional proportion in $t_\\ell$ is $o(1)$ with a uniform bound.\nIntegrate first against the independent uniform dyadic/residue\nlaws and then use the conditional domination already proved.\nEach removal therefore fails with probability $o(1)$.  A union\nbound over the finitely many factors and the triangle inequality\ngive agreement, to error tending to zero in probability, between\nthe original marginal cube law and the law with only its\n$d$-residue restrictions.  The same reasoning applies to a test\ncomposed with the fixed face automorphism; its Lipschitz bound is\nstill fixed.\n\nIn this latter law, applying $\\mathcal S_e$ on the upper face\n$\\omega_j=1$ replaces $x_j$ by $x_j+\\Delta_e$ in\n\\eqref{eq:alignment-joint-polynomial-exponent}, at all slots and\nfor all observables of this component simultaneously.\nBy \\eqref{eq:alignment-residue-divisibility}, $d\\mid\\Delta_e$\neventually.  Moreover $|\\Delta_e|/Z\\to0$ uniformly, since its\nsize is bounded by a fixed power of $M+D_i^-$ whereas $R$\ndominates all those powers.  Translation of the corresponding\nbox changes normalized counting averages by\n$O(|\\Delta_e|/Z+d/Z)=o(1)$.  Comparing on both sides establishes\nthe asserted face invariance.  Taking the finite union over\ntargets and own pairs is harmless.  This argument proves marginal\ninvariance; it makes no assertion about the other components.\n\n\\paragraph{The countable diagonal and the period.}\nEnumerate the conditions consisting of a positive integer modulus,\na cube dimension at most $s+1$, bounds $C\\in\\N$, lower side\nbounds $c=1/j$, Lipschitz bounds in $\\N$, and tolerances $1/j$.\nThe assertion just proved holds uniformly over the uncountably\nmany endpoints and tests within each such condition.  Each of\nthe first finitely many conditions fails with probability tending\nto zero.  Given events of probability at least $\\epsilon>0$ along\na subsequence, choose an increasing number of initial conditions\nwhose union of failures has probability less than $\\epsilon/2$,\nand select one conditioning from the event outside that union.\nBy making the initial list and the accuracy increase successively,\nwe obtain a sequence satisfying every fixed condition with error\ntending to zero.\n\nNow fix the finite choices of ambient groups along a subsequence\nand apply Proposition~\\ref{prop:local-cube-law}.  Its rational\nfiltration, period $d_0$, and rational representatives may depend\non this sequence.  The face invariances have already been secured\nfor every fixed positive integer modulus, so they hold for $d_0$.\nFor any fixed small relative box size $\\alpha>0$, they hold for\nbase boxes near a given macroscopic point $\\beta$ and increment\nboxes near zero, with the residue restrictions specified in that\nProposition.  First take the sequence limit and then let\n$\\alpha\\downarrow0$.  The projected local cube Haar measure is\ntherefore invariant under the face automorphism.  Since it has\nfull support on its compact cube coset and the automorphism is a\nhomeomorphism, that coset is preserved.  This proves the last\nassertion with the required order of limits.\n\\end{proof}\n\n\\subsection{From the word plan to positive conditional mass}\n\nFix one of the finitely many incoming scale tuples at pivot $i$.\nLemma~\\ref{lem:finite-word-plan} gives finitely many complete\noptions, each recording a cumulative jump word, a resulting scale\ntuple, and its slot.  Enlarge their number to one common bound\n$Q_i\\ge1$, and let $V_i\\ge1$ bound the number of possible slots,\nuniformly over incoming scales.  These constants depend only on\n$n,r,s$.\n\nFor given earlier variables and an integer pivot value $x\\in\\N$, let\n$\\mathcal P_i(b;x)$ be the following event: some allowed outcome\n$b'$ from the incoming scale $b$ satisfies, for every target\n$B=T\\cup\\{i\\}$, every $d\\in\\mathcal D_i$, color $c$, and\n$D\\subset\\mathcal E_B$,\n\\begin{equation}\\label{eq:alignment-pivot-property}\n S_{B,b'_Bd_B,c}(t_Tx)>2\\tau\n \\ \\Longrightarrow\\ %\n S_{B,b'_Bd_B,c}\\left(t_Tx+\n       \\sum_{A\\in D}\\frac{h_A b'_A d_A}{h_B b'_B d_B}t_A\\right)>\\tau.\n\\end{equation}\nFor the large $w$ under consideration, every displayed offset is\nan integer.  This follows either from admissibility and the finite\nrational lists, or directly from the integer powers and\n\\eqref{eq:alignment-residue-data}.\n\n\\begin{lemma}[Conditional alignment mass]\\label{lem:alignment-mass}\nFor every fixed $\\tau>0$ and every permitted bounded-complexity\nfamily of models, outside a set of earlier-variable/microcell/residue\nconditionings of probability tending to zero, the conditional\nuniform pivot probability that\n\\[\n \\mathcal P_i(b;p+s(v))\\quad\\text{holds for some prescribed slot }v\n\\]\nis at least $1/(2Q_i)$.  The same statement holds for the conditional\nharmonic probability, after replacing the constant by $1/(3Q_i)$\nif necessary.  The constants are independent of the threshold and\nmodel complexity.\n\\end{lemma}\n\n\\begin{proof}\nSuppose the uniform assertion fails on sets of conditionings of\nprobability bounded below along a subsequence.  By\nLemma~\\ref{lem:conditional-face-invariance}, choose a sequence in\nthose sets with all its empirical face invariances.  Pass to a\nsubsequence fixing the ambient array nilmanifolds and using\nProposition~\\ref{prop:local-cube-law}.  There are only finitely many\nobservable formulae in a conditioning.  Their common supremum\nand Lipschitz bounds give, by compactness, a further subsequence\non which all of them converge uniformly.  Denote the limiting\nobservables by $F_{B,a,c,v}$.\n\nFor each stored factor and integer input $u$, define the reading of a\njoint state $y\\in K/\\Lambda$ by\n\\begin{equation}\\label{eq:alignment-intrinsic-reading}\n \\mathcal R_{B,a,c;v,u}(y)\n =F_{B,a,c,v}\\bigl(\\operatorname{ev}_u\\pi_{B,a,c,v}y\\bigr).\n\\end{equation}\nHere $\\pi_{B,a,c,v}$ selects the indicated array factor and\n$\\operatorname{ev}_u$ evaluates it at the integer input $u$.\nThe coordinates of $u$ are indexed by the own pairs $(B,A)$;\nwhen adding $u$ to a slot vector, insert zeros at all other pairs.\nThese readings are continuous functions of $y$ and are defined\nbefore choosing any lifts on a point-law coset.\n\nConsider a point-law coset of that Proposition,\n\\[\n h(\\beta)H\\sigma_r\\Lambda/\\Lambda,\n\\]\nwith its Haar image measure $\\mu_{\\beta,r}$.  The preceding Lemma\nand Proposition~\\ref{prop:face-lift} lift each own-pair array\nshift to a polynomial shear of the joint cover coordinates\n$z\\in\\mathfrak h$.  Such a lift may move other target components.\nAdjoin its translation $v\\mapsto v+\\mathbf e_e$ and call the\nresult $L_e$.  Lemma~\\ref{lem:shear-haar} puts these transformations\nin a nilpotent group of step at most $s$, including the ordinary\nslot translations.  Write $\\Theta(z)=h(\\beta)\\exp(z)\\sigma_r\\Lambda$.\nAt slot $v$, color a state by the palette\n\\begin{equation}\\label{eq:alignment-limit-palette}\n C_{B,a}(z,v)=\n \\bigl(\\1_{\\{\\mathcal R_{B,a,c;v,0}(\\Theta(z))>3\\tau/2\\}}\\bigr)_{c\\in[r]}.\n\\end{equation}\nThese palettes have at most $2^r$ values, as required by the finite plan.\n\nApply that plan pointwise starting at any $(z,0)$.  It returns\none of the $Q_i$ options, a jumped center, and terminal palette\nequalities there.  Only own letters occur in a terminal word for\n$B$.  Write $m=(m_e)_e$ for their integer powers.  Their projection on\nthis entire component is exactly the commuting array shift\n$u\\mapsto u+(m_e)_e$, while their slot effect is\n$v\\mapsto v+m$, with the zero extension just specified.\nConsequently at a center state $y$ and slot $v$ the two readings\nare $\\mathcal R_{B,a,c;v,0}(y)$ and\n$\\mathcal R_{B,a,c;v+m,m}(y)$.  This statement holds even if earlier\njumps involved letters belonging to other targets.\n\nTo check the numerical meaning, use the corresponding prelimit\nobservables and evaluate the same readings on an actual array state\n$Y(\\ell)$, with $p=p_*+M\\ell$.  The center value is the\nmodel at $t(p+s(v))$.  The translated value is the model at\n\\begin{align}\n t\\left(p+s(v)+\\sum_e m_er_e\\right)\n       +M\\sum_e m_e\\Delta_e\n &=t(p+s(v))+\\sum_e m_eq_e.\n       \\label{eq:alignment-own-letter-reading}\n\\end{align}\nEach occurrence uses the correct residue-specific observable at\nits new slot.  For the terminal word at outcome $b'$ and future\nmultiplier $d$, the powers are\n\\[\n m_e=q_0\\frac{b'_A}{b'_B}\\frac{d_A}{d_B}\\quad(e=(B,A),\\ A\\in D),\n \\qquad m_e=0\\quad(A\\notin D).\n\\]\nThey are integers by the plan, and\n$m_eq_e=(h_A b'_A d_A/(h_B b'_B d_B))t_A$.\nThe leading index is $b'_Bd_B$.  Therefore\n\\eqref{eq:alignment-own-letter-reading} gives exactly\n\\eqref{eq:alignment-pivot-property} with $x=p+s(v)$.\nThis numerical interpretation is used for terminal own-letter\nwords.  An arbitrary cumulative jump need not be a translation\nof the actual pivot orbit.\n\nFor an option $o$ with scale outcome $b'$ and slot $v$, let\n$F_o\\subset K/\\Lambda$ consist of the states $y$ satisfying every\nrequired comparison.  Explicitly, for each $B,d,c,D$, put\n$a=b'_Bd_B$ and take $m$ as above, and impose\n\\begin{equation}\\label{eq:alignment-closed-comparison}\n \\mathcal R_{B,a,c;v,0}(y)\\le3\\tau/2\\quad\\text{or}\\quad\n \\mathcal R_{B,a,c;v+m,m}(y)\\ge3\\tau/2.\n\\end{equation}\nThese finitely many conditions are closed by continuity of the\nreadings.  They depend only on the state,\nthe option's finite data, and the limiting formulae.  In\nparticular they do not depend on the choice of lifts.\nFor every starting cover coordinate, the pointwise palette\nconstruction puts the $K/\\Lambda$-valued coordinate of the jumped\nstate into $F_o$ for at least one option $o$. Equality of palettes implies\n\\eqref{eq:alignment-closed-comparison}, including at equality\nwith the threshold.\n\nSample $z$ from the expanding weighted boxes of\nLemma~\\ref{lem:shear-haar}. For each fixed option $o$, let $Y_o$ be the\n$K/\\Lambda$-valued coordinate of the state after its cumulative jump.\nThe jump acts on the cover coordinate $z$ by a fixed shear, so the law\nof $Y_o$ converges to $\\mu_{\\beta,r}$. Pointwise coverage gives\n\\[\n 1\\le\\sum_o\\Pr\\{Y_o\\in F_o\\}.\n\\]\nUsing the closed-set direction of Portmanteau separately for the\nfinitely many summands yields\n\\begin{equation}\\label{eq:alignment-haar-positive-mass}\n 1\\le\\sum_o\\mu_{\\beta,r}(F_o),\\qquad\n \\mu_{\\beta,r}\\left(\\bigcup_o F_o\\right)\\ge\\frac1{Q_i}.\n\\end{equation}\nThe last implication also follows by summing the indicator\ninequality $\\sum_o\\1_{F_o}\\le Q_i\\1_{\\cup_oF_o}$.\nThis is the only quantitative loss in passing from pointwise\nrecurrence to the point-law measure.  Although the shears may\ndepend on the coset, the sets $F_o$ do not.  Therefore\n\\eqref{eq:alignment-haar-positive-mass} integrates over the\nmacroscopic-position/residue mixture without a measurable choice\nof lifts.\n\nReplace each comparison \\eqref{eq:alignment-closed-comparison}\nby the open comparison\n\\begin{equation}\\label{eq:alignment-open-comparison}\n \\text{center}<7\\tau/4\\quad\\text{or}\\quad\n \\text{translated value}>5\\tau/4,\n\\end{equation}\nand take the same finite intersections and union.  This defines\nan open set containing $\\bigcup_oF_o$, so it has mixture measure\nat least $1/Q_i$.  Proposition~\\ref{prop:local-cube-law} gives\nweak convergence of the actual uniform conditional state laws\nto that mixture.  Open-set Portmanteau therefore gives lower\nlimit at least $1/Q_i$ for this open event.\nFor sufficiently late terms, uniform convergence of all\nobservables has error less than $\\tau/4$.  If the actual center\nis greater than $2\\tau$, its limiting reading is greater than\n$7\\tau/4$, forcing the second alternative in\n\\eqref{eq:alignment-open-comparison}; the actual translated\nreading is then greater than $\\tau$.  The good boundary condition\nensures that these actual readings use exactly the selected\nmodel formulae.  By \\eqref{eq:alignment-own-letter-reading} the\nopen event implies success at some $p+s(v)$.\n\nThis contradicts the selected conditional probabilities being\nless than $1/(2Q_i)$.  Hence the exceptional sets have probability\ntending to zero.  The uniform-to-harmonic total variation estimate\nfollowing \\eqref{eq:alignment-microcell} proves the last statement.\nThe compactness subsequence and how late the estimates hold may\ndepend on $\\tau$ and the fixed model complexity.  The constants\n$Q_i$ and the displayed lower bounds do not.\n\\end{proof}\n\n\\subsection{Returning to the raw variables and successive pivots}\n\nThe conditional conclusion permits a bounded list of shifts of\nthe pivot.  We now remove those shifts before proceeding to the\nnext pivot.  This step conditions only on earlier raw variables;\ntranslation invariance is not claimed inside a fixed microcell.\n\nLet $T_a$ denote translation by $a$.  For the raw pivot law $\\mu_i$,\nput $X=X_i$.  Lemma~\\ref{lem:sampling}(2) gives, for $a\\in W\\Z$\nwith $|a|=o(X)$,\n\\begin{equation}\\label{eq:alignment-raw-translation}\n \\norm{T_a\\mu_i-\\mu_i}_{\\TV}\n \\le\\frac{|a|}{X(\\log X-W/X)}\n \\le (1+o(1))\\frac{|a|}{X\\log X}.\n\\end{equation}\nHere probability total variation is one half of the total-mass norm\nused in Lemma~\\ref{lem:sampling}.\n\nIn our application each prescribed slot satisfies\n\\[\n W\\mid s(v),\\qquad |s(v)|\\le C MW^w\\le CM^2=o(X_i),\n\\]\nuniformly in all earlier raw variables.  Thus\n\\eqref{eq:alignment-raw-translation} gives $o(1)$ uniformly for\nevery such slot.  In particular it remains valid after integrating\nagainst an arbitrary event depending on earlier variables.\n\n\\begin{proof}[Proof of Principle~\\ref{pr:alignment}]\nFor the given $n,r,s$ take the lists and all budgets from\nLemma~\\ref{lem:finite-word-plan}.  Let $I$ be the set of pivots\nhaving targets and define\n\\begin{equation}\\label{eq:alignment-delta}\n \\delta=\\prod_{i\\in I}\\frac1{3Q_iV_i}>0,\n\\end{equation}\nwith the empty product equal to $1$.  This definition precedes\nthe choice of admissible parameters, models, and $\\tau$.\nNow fix any such parameters, any family of models of step at most\n$s$ and bounded complexity in the sense of\nDefinition~\\ref{def:piecewise-model}, and any fixed $\\tau>0$.\n\nSuppose $E$ is an event of the earlier raw variables on a given\nfinite path, with incoming scale $b$ and all the earlier pivot\nrequirements secured.  Lemma~\\ref{lem:alignment-mass}, integrated\nover the pivot microcell and residue, implies\n\\begin{equation}\\label{eq:alignment-extension-shifted}\n \\frac1{3Q_i}\\PP(E)-o(1)\n \\le\\sum_v\\PP\\bigl(E,\\mathcal P_i(b;p+s(v))\\bigr).\n\\end{equation}\nThe exceptional set of conditionings has probability $o(1)$\nunconditionally, so its intersection with $E$ also costs $o(1)$.\nConditional on the earlier variables each $s(v)$ is fixed.\nEquation~\\eqref{eq:alignment-raw-translation} therefore changes\neach summand by $o(1)$ when $p+s(v)$ is replaced by $p$.  There\nare at most $V_i$ slots, and consequently\n\\begin{equation}\\label{eq:alignment-extension}\n \\PP\\bigl(E,\\mathcal P_i(b;p)\\bigr)\n \\ge\\frac1{3Q_iV_i}\\PP(E)-o(1).\n\\end{equation}\nThis estimate is uniform over the finite incoming-scale list and\nthe finite path events.  No conditional density assumption on\n$E$ has been used.\n\nOn the successful extension choose the first successful scale\noutcome in a fixed enumeration.  This is a measurable choice\nfrom a finite list and partitions that event into finitely many\nnew path events.  They depend only on raw variables through the\ncurrent pivot.  Apply \\eqref{eq:alignment-extension} to each path\nat the next pivot.  Pivots without targets impose no condition\nand can be skipped.  Summing the finitely many path estimates\nand iterating yields total success probability at least\n$\\delta-o(1)$.\n\nFor completeness, consider explicitly a requirement secured at\npivot $i$ when its resulting scale was $b^{(i)}$.  Every later\nupdate multiplies the scale by one of the finite tuples used to\nconstruct $\\mathcal D_i$.  On any ensuing path, their complete\nproduct is some $d\\in\\mathcal D_i$.  Requirement\n\\eqref{eq:alignment-pivot-property} at pivot $i$ was required\nfor this very $d$, at the original earlier raw variables and\nthe original raw pivot.  Later steps change none of those raw\nvariables.  Therefore at the final scale\n$b^{\\rm fin}=b^{(i)}d$ its leading index is exactly\n$b^{\\rm fin}_B$, and every offset ratio is exactly\n$h_A b^{\\rm fin}_A/(h_B b^{\\rm fin}_B)$.\nThis verifies preservation between pivots in addition to the\nwithin-pivot preservation proved in\n\\eqref{eq:alignment-leading-budget}--\\eqref{eq:alignment-word-budget}.\n\nEvery successful final path thus gives $b^{\\rm fin}\\in\\mathcal B$\nsuch that, simultaneously for every block $B$, color $c$, and\n$D\\subset\\mathcal E_B$,\n\\[\n S_{B,b^{\\rm fin}_B,c}(t_B)>2\\tau\n \\quad\\Longrightarrow\\quad\n S_{B,b^{\\rm fin}_B,c}\\left(t_B+\n       \\sum_{A\\in D}\\frac{h_A b^{\\rm fin}_A}\n                             {h_B b^{\\rm fin}_B}t_A\\right)>\\tau.\n\\]\nFor blocks with no adding pairs the sole comparison is the\nidentity, which holds automatically.  Taking the ordinary lower\nlimit as $w\\to\\infty$ gives probability at least $\\delta$.\nThe lists and $\\delta$ depend only on $n,r,s$, whereas the rate\nof convergence may depend on the fixed threshold and model\nfamily.  These are exactly the quantifiers of\nPrinciple~\\ref{pr:alignment}.\n\\end{proof}\n"}, {"path": "preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/paper.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Monochromatic-finite-sums-and-products-in-the-positive-integers-September-23-2026/paper.pdf", "bytes": 775466, "sha256": "ab835f6a98c10d6d4498b19e7446facd7b481fef85fee54232c76b45bc9e0888", "base64": 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"}, {"path": "preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/README.md", "bytes": 698, "sha256": "87b726bfeeb54facd9befded62bf27425887a6290fe8bc4e464ae7e8803568bb", "content": "# [Pointwise Multiple Ergodic Averages for Mixing Transformations](multiple-ergodic-averages.pdf)\n\n**Author:** OpenAI\n\n**Date:** October 4, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026,\n  author = {{OpenAI}},\n  title = {{Pointwise Multiple Ergodic Averages for Mixing Transformations}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/multiple-ergodic-averages.pdf}{OAI:Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/figures/square.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/figures/square.tex", "bytes": 1185, "sha256": "612ec646d065435d11af1592aa005bd457031d26fc4db90d3bfec65fd96025d5", "content": "\\begin{figure}[tbp]\n\\centering\n\\begingroup\n\\setlength{\\arraycolsep}{10pt}\n\\renewcommand{\\arraystretch}{1.45}\n\\[\n\\begin{array}{c|ccccc|l}\n &1&\\cdots&b&\\cdots&n&\\text{row parameter}\\\\ \\hline\n 1&y_{11}&\\cdots&\\colorbox{blue!10}{$y_{1b}$}&\\cdots&y_{1n}&E_1\\\\\n \\vdots&\\vdots&&\\colorbox{blue!10}{$\\vdots$}&&\\vdots&\\vdots\\\\\n b&\\colorbox{green!12}{$y_{b1}$}&\n   \\colorbox{green!12}{$\\cdots$}&\n   \\fcolorbox{black}{yellow!25}{$y_{bb}$}&\n   \\colorbox{green!12}{$\\cdots$}&\n   \\colorbox{green!12}{$y_{bn}$}&E_b\\\\\n \\vdots&\\vdots&&\\colorbox{blue!10}{$\\vdots$}&&\\vdots&\\vdots\\\\\n n&y_{n1}&\\cdots&\\colorbox{blue!10}{$y_{nb}$}&\\cdots&y_{nn}&E_n\n\\end{array}\n\\]\n\\[\n \\underbrace{\\mathbf z_b=(y_{bj})_{j\\in I}}_{\\text{row }b:\n       \\text{ old parameters }R(E_b)}\n \\qquad\n \\underbrace{(y_{ib})_{i\\in I}}_{\\text{column }b:\n       \\text{ old parameters }p}\n \\qquad\n w=p_b=R(E_b)_b.\n\\]\n\\endgroup\n\\caption{The ordered square under $\\lambda_Z$. The highlighted row\nand column meet at $y_{bb}$, whose old distal parameter is $w$.\nBoth have law $\\lambda_Y$: the row by the single-slot marginal,\nthe column by the factor map $Z\\to Y$. No invariance under\ntransposition is asserted.}\n\\label{fig:square}\n\\end{figure}\n"}, {"path": "preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/main.tex", "bytes": 2511, "sha256": "b5845110548f538c476d0d8d981cc3deab5b0e8df294125f763de2c62296eb65", "content": "\\newcommand{\\DoNotLoadEpstopdf}{}\n\\documentclass[11pt]{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{lmodern}\n\\usepackage[margin=1in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage{microtype}\n\\usepackage{enumitem}\n\\usepackage{booktabs,array}\n\\usepackage{xcolor}\n\\usepackage{tikz}\n\\usetikzlibrary{matrix,fit,positioning,backgrounds}\n\\usepackage{needspace}\n\\usepackage[hidelinks,unicode]{hyperref}\n\\input{glyphtounicode}\n\\input{glyphtounicode-cmex}\n\\pdfgentounicode=1\n\\pdftrailerid{}\n\\hypersetup{pdftitle={Pointwise Multiple Ergodic Averages for Mixing Transformations},pdfauthor={OpenAI}}\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\\newtheorem{definition}[theorem]{Definition}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\newcommand{\\R}{\\mathbb R}\n\\newcommand{\\Z}{\\mathbb Z}\n\\newcommand{\\N}{\\mathbb N}\n\\newcommand{\\C}{\\mathbb C}\n\\newcommand{\\T}{\\mathbb T}\n\\newcommand{\\E}{\\mathbb E}\n\\newcommand{\\dd}{\\,d}\n\\newcommand{\\law}{\\mathcal L}\n\\newcommand{\\norm}[1]{\\lVert#1\\rVert}\n\\newcommand{\\abs}[1]{\\lvert#1\\rvert}\n\\newcommand{\\ip}[2]{\\langle#1,#2\\rangle}\n\\DeclareMathOperator{\\tr}{tr}\n\\DeclareMathOperator{\\Ran}{Ran}\n\\DeclareMathOperator{\\supp}{supp}\n\\DeclareMathOperator{\\rank}{rank}\n\\DeclareMathOperator{\\spanop}{span}\n\\newcommand{\\articleid}[1]{\\begingroup\\hyphenchar\\font=-1\\relax\\textnormal{#1}\\endgroup}\n\\setlist[enumerate]{itemsep=3pt,topsep=5pt}\n\\setlist[itemize]{itemsep=3pt,topsep=5pt}\n\\numberwithin{equation}{section}\n\\title{Pointwise Multiple Ergodic Averages\\\\for Mixing Transformations}\n\\author{OpenAI}\n\\date{October 4, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nLet $T$ be an invertible mixing probability-preserving transformation.\nFor every integer $n\\ge2$ and every fixed tuple of bounded measurable\nfunctions, we prove that the consecutive multiple ergodic averages of\nlength $n$ converge almost everywhere to the product of the integrals,\nas the averaging length tends to infinity through all positive integers.\nThe probability space need not be standard, and no rate of mixing is\nrequired.\n\\end{abstract}\n\\input{sections/introduction}\n\\input{sections/inputs}\n\\input{sections/selected}\n\\input{sections/channels}\n\\input{sections/correlations}\n\\input{sections/square}\n\\appendix\n\\input{sections/packing}\n\\bibliographystyle{amsplain}\n\\begingroup\\raggedright\n\\bibliography{references}\n\\endgroup\n\\end{document}\n"}, {"path": "preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/references.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/references.bib", "bytes": 5535, "sha256": "7d50d2fc26c0a6065336850fe6ed176f99be0a58d24b353ba100b3d6bd2693b3", "content": "@misc{MixingCompanion,\n  author = {{OpenAI}},\n  title = {{Rokhlin's multiple-mixing problem for one transformation}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Rokhlins-multiple-mixing-problem-for-one-transformation-September-23-2026/paper.pdf}{OAI:Rokhlins-multiple-mixing-problem-for-one-transformation-September-23-2026}},\n  year = {2026}\n}\n\n@misc{FourfoldCompanion,\n  author = {{OpenAI}},\n  title = {{Pointwise convergence of fourfold ergodic averages for mixing transformations}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Pointwise-convergence-of-fourfold-ergodic-averages-for-mixing-transformations-October-4-2026/fourfold-ergodic-averages.pdf}{OAI:Pointwise-convergence-of-fourfold-ergodic-averages-for-mixing-transformations-October-4-2026}},\n  year = {2026}\n}\n\n@misc{SlopesCompanion,\n  author = {{OpenAI}},\n  title = {{Triple ergodic averages with distinct integer slopes}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Triple-ergodic-averages-with-distinct-integer-slopes-October-4-2026/triple-ergodic-distinct-slopes.pdf}{OAI:Triple-ergodic-averages-with-distinct-integer-slopes-October-4-2026}},\n  year = {2026}\n}\n\n@article{Furstenberg1977,\n  author = {Furstenberg, H.},\n  title = {Ergodic behavior of diagonal measures and a theorem of {Szemer{\\'e}di} on arithmetic progressions},\n  journal = {J. 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Ser.)},\n  volume = {34},\n  number = {1},\n  year = {2018},\n  pages = {79--90},\n  doi = {10.1007/s10114-017-6366-1}\n}\n\n@article{HuangShaoYe2019,\n  author = {Huang, W. and Shao, S. and Ye, X.},\n  title = {Pointwise convergence of multiple ergodic averages and strictly ergodic models},\n  journal = {J. Anal. Math.},\n  volume = {139},\n  number = {1},\n  year = {2019},\n  pages = {265--305},\n  doi = {10.1007/s11854-019-0061-3}\n}\n\n@article{Austin2015,\n  author = {Austin, T.},\n  title = {Pleasant extensions retaining algebraic structure, {I}},\n  journal = {J. Anal. Math.},\n  volume = {125},\n  year = {2015},\n  pages = {1--36},\n  doi = {10.1007/s11854-015-0001-9}\n}\n\n@article{Zimmer1976,\n  author = {Zimmer, R. J.},\n  title = {Extensions of ergodic group actions},\n  journal = {Illinois J. Math.},\n  volume = {20},\n  number = {3},\n  year = {1976},\n  pages = {373--409},\n  doi = {10.1215/ijm/1256049780}\n}\n\n@article{Jamneshan2023,\n  author = {Jamneshan, A.},\n  title = {An uncountable {Furstenberg--Zimmer} structure theory},\n  journal = {Ergodic Theory Dynam. Systems},\n  volume = {43},\n  number = {7},\n  year = {2023},\n  pages = {2404--2436},\n  doi = {10.1017/etds.2022.43},\n  note = {Corrected version: arXiv:2103.17167v4}\n}\n\n@article{JamneshanCorrigendum2026,\n  author = {Jamneshan, A.},\n  title = {An uncountable {Furstenberg--Zimmer} structure theory---{Corrigendum}},\n  journal = {Ergodic Theory Dynam. Systems},\n  volume = {46},\n  number = {1},\n  year = {2026},\n  pages = {211--217},\n  doi = {10.1017/etds.2025.10214}\n}\n\n@article{Fernique1974,\n  author = {Fernique, X.},\n  title = {Minorations des fonctions al{\\'e}atoires gaussiennes},\n  journal = {Ann. Inst. Fourier (Grenoble)},\n  volume = {24},\n  number = {2},\n  year = {1974},\n  pages = {61--66},\n  doi = {10.5802/aif.506}\n}\n\n@article{Sudakov1971,\n  author = {Sudakov, V. N.},\n  title = {{Gaussian} random processes and measures of solid angles in {Hilbert} space},\n  journal = {Dokl. Akad. Nauk SSSR},\n  volume = {197},\n  number = {1},\n  year = {1971},\n  pages = {43--45},\n  note = {Russian}\n}\n\n@article{Tropp2018,\n  author = {Tropp, J. A.},\n  title = {Second-order matrix concentration inequalities},\n  journal = {Appl. Comput. Harmon. Anal.},\n  volume = {44},\n  number = {3},\n  year = {2018},\n  pages = {700--736},\n  doi = {10.1016/j.acha.2016.07.005}\n}\n"}, {"path": "preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/channels.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/channels.tex", "bytes": 14073, "sha256": "0256d0d6f7966b95b96a17f84462891f345ed503b239070046d830304a233032", "content": "\\section{Channels and closure under selected laws}\\label{sec:channels}\n\nThe selected length is determined by the original point in the suspension.\nIt can therefore change the joint law of other coordinates, even when\ntheir unconditioned law is a product.  We organize those coordinates in\nthree groups.  Independence of the original point from any two groups\nwill control the effect of selecting a length on all three groups.\nThis channel construction and its closure argument adapt\n\\cite[Section~5]{FourfoldCompanion}; the proof below permits every fixed\nfinite set of slots $I=\\{1,\\ldots,n\\}$.\n\n\\begin{definition}[Channel]\\label{def:channel}\nA \\emph{channel} is a flow $Y$ over the suspension $C$, with factor map\n$\\pi_Y:Y\\to C$, together with a finite list of probability-preserving\nmaps $D_\\gamma:Y\\to C$, called \\emph{outputs}, and positive rational\nnumbers $q_\\gamma$, such that\n\\[\n D_\\gamma(S_Y^t y)=S_C^{q_\\gamma t}D_\\gamma(y)\n\\]\nfor each fixed $t$, modulo null sets.  Partition the output indices into\nthree groups, numbered $1,2,3$; empty groups are allowed.  Set\n\\[\n x_0=\\xi\\circ\\pi_Y,\\qquad x_\\gamma=\\xi\\circ D_\\gamma,\n \\qquad d_Y=(x_\\gamma)_\\gamma,\n\\]\nand let $d_{Y,H}$ be the sublist from groups in\n$H\\subseteq\\{1,2,3\\}$.  Let $V$ collect the phase coordinates of\n$\\pi_Y$ and all the $D_\\gamma$.  Under the measure $m_Y$ require\n\\begin{equation}\\label{eq:channel-laws}\n \\begin{split}\n  \\law(d_Y\\mid V)&=\\bigotimes_\\gamma\\mu,\\\\\n  \\law(x_0,d_{Y,H}\\mid V)&=\n       \\mu\\otimes\\bigotimes_{\\gamma\\text{ in groups }H}\\mu,\n       \\qquad H\\subsetneq\\{1,2,3\\}.\n \\end{split}\n\\end{equation}\nAn empty spatial list has the one-point law.\n\\end{definition}\n\nThus the spatial outputs have a product law independent of the phases.\nThe original spatial coordinate has a product law with the outputs in\nany proper collection of groups, but no independence from the complete\noutput list is required.  The suspension $C$, with no outputs, is a\nchannel.\n\n\\begin{lemma}[Conditioning and extensions]\\label{lem:channel-conditioning}\nFor a channel $Y$, both identities in \\eqref{eq:channel-laws} hold with\nconditioning on its maximal measure-distal factor $W_Y$ in place of\n$V$.  Every flow extension of $Y$ over $C$ is a channel with the pulled\nback output data, and the same strengthening holds there.\n\\end{lemma}\n\n\\begin{proof}\nChoose a positive integer $h$ such that $hq_\\gamma$ is an integer for\neach output.  The phase vector $V$ is a factor fixed by time $h$, and\nits time-one action is distal, so $V$ belongs to $W_Y$.  For any spatial\ntuple appearing in \\eqref{eq:channel-laws}, its joint factor with $V$\nhas product law over $V$.  At time $h$ the spatial coordinates evolve\nby a product of positive integer powers of $T$.  This product is mixing:\nthe assertion follows first for product tests from mixing of each\npower, and then for all bounded tests by approximation.  Consequently\nthis factor is relatively weakly mixing over $V$ for time $h$.\n\nOn the other hand, $W_Y$ is distal for time $h$ and relatively distal\nover $V$.  Relative disjointness, in the form of\n\\cite[Proposition~2.2]{FourfoldCompanion}, makes the spatial factor and\n$W_Y$ relatively independent over $V$.  Its conditional spatial law\ngiven $W_Y$ is therefore the product law in\n\\eqref{eq:channel-laws}.  Pullback to an extension preserves the joint\nlaws of the original data, proving the channel identities there; the\nsame argument gives their stronger conditioning.\n\\end{proof}\n\nWe now prove the closure property that will permit repeated extensions.\nRecall from Lemma~\\ref{lem:normalized-joining} that\n$\\mathsf M_b(Y)$ has state space $Y^I$, measure $\\lambda_Y$, and flow\n$(y_i)_i\\mapsto(S_Y^{it/b}y_i)_i$.\n\n\\begin{proposition}[Channel closure]\\label{prop:channel-closure}\nLet $Y$ be a channel and $b\\in I$.  On $\\mathsf M_b(Y)$ take\n$\\pi_Y(y_b)$ as original factor and take every $D_\\gamma(y_i)$,\n$i\\in I$, as an output, in the old group of $\\gamma$.\nThese data make $\\mathsf M_b(Y)$ a channel.\n\\end{proposition}\n\n\\begin{proof}\nEach new output is probability-preserving and has positive rational\nspeed $iq_\\gamma/b$.  It remains to prove the two conditional laws.\nTest them with products of bounded spatial functions and products of\nbounded phase functions.  Such tests determine the laws.  Expanding\neach spatial function into its mean and centered part, and scaling,\nreduces the proof to real spatial functions that are either $1$ or\ncentered and bounded in absolute value by $1$.  We may also bound every\nphase function by $1$.  Whenever at least one spatial factor is\ncentered, the tested integral must be zero.  A test using the new\noriginal coordinate uses outputs from only a proper set of groups.\n\n\\paragraph{Time intervals and offsets.}\nUse the selected-law formula \\eqref{eq:selected-law} and condition on\nthe old phase vector $V$ under $m_Y$.  Fix an integer $h>0$ clearing\nall output-speed denominators and write\n$t=hl+s$, where $l\\ge0$ is an integer and $0\\le s<h$.\nFor fixed $V,s$, all phase weights in the test are independent of $l$.\nThe spatial function for output $\\gamma$ at slot $i$ is evaluated at\n\\begin{equation}\\label{eq:channel-times}\n T^{e_\\gamma i l+a_{\\gamma,i}}x_\\gamma,\n \\qquad e_\\gamma=hq_\\gamma\\in\\N.\n\\end{equation}\nThe offsets $a_{\\gamma,i}$ depend on $V,s$ and range over a fixed\nfinite set, by the suspension formula.  If the original-factor test\nis present, its argument is $T^{hbl+a_0}x_0$, with the same finite-offset\nproperty.  Equivariance and Fubini justify these formulas in the time\nintegrals.\n\nOn an event where the selected length is $N=c2^k$, $c\\in F$ and\n$k\\ge a$, put $m=\\lfloor c2^k/h\\rfloor$.  Replacing the normalized\ntime integral by the average over the $m$ complete intervals, followed\nby integration against $ds/h$, has error at most $2h2^{-a}$.\nFor all sufficiently large $a$ we have $m\\ge1$.\n\n\\paragraph{Tests using a proper set of groups.}\nGiven $V$, the relevant old output coordinates have product law and\nare independent of $x_0$, by \\eqref{eq:channel-laws}.  The selector is\ndetermined by $V,x_0$.  Suppose first that some output occurrence is\ncentered.  For fixed $V,s,x_0$, integrate the spatial output product\nat index $l$.  The expectation factors over the old output indices\n$\\gamma$.  For an index having a centered occurrence, the times\n\\eqref{eq:channel-times} in distinct slots separate as $l\\to\\infty$.\nMixing of all orders makes its integral tend to the product of the\nmeans, hence to zero.  A single centered occurrence already has zero\nintegral.  The convergence is uniform over the finitely many offset\npatterns.  All other factors have absolute value at most $1$.\nThe Ces\\`aro averages of these absolute expectations consequently tend\nto zero uniformly over all sufficiently large lengths.  This applies\nto the selected lengths as well.\n\nIf every output test is $1$, the centered factor must be the original\none.  Birkhoff's theorem for the ergodic power $T^{hb}$ gives convergence\nto zero for $\\mu$-almost every $x_0$, simultaneously for all the\nfinitely many offsets $a_0$.  Since the selected lengths tend to\ninfinity pointwise, they have the same limit.  The conditional law of\n$x_0$ given $V$ is $\\mu$, so bounded convergence completes this case.\n\n\\paragraph{Three groups and almost orthogonal vectors.}\nIt remains to treat the full output list, with no original-factor\nspatial test.  If one group has no centered occurrence, every spatial\ntest in that group is $1$, and the preceding argument applies; the\nphase tests of that group were allowed throughout.  Thus suppose each\ngroup contains a centered occurrence.  For fixed $V,s$, write its\nspatial product at index $l$ as\n\\[\n u_l\\in L^2(\\Omega_1),\\qquad\n v_l\\in L^2(\\Omega_2),\\qquad\n w_l\\in L^2(\\Omega_3),\n\\]\nwhere $\\Omega_j$ carries the product measure with one copy of $\\mu$\nfor every old output in group $j$.  These are real unit-ball vectors,\nindeed functions bounded by $1$.  Given $V$, the old spatial output\nlaw is the product of these three spaces.  The vectors depend on\n$V,s$ only through the finite offset patterns.\n\nFor every $\\eta>0$, after omitting finitely many initial indices, the\nfamilies $(u_l)$ and $(v_l)$ each have absolute pair inner products at\nmost $\\eta$ outside a graph of bounded maximum degree $L_0$.\nAll these bounds are uniform in the offset patterns.  To verify this,\nin each of the two groups choose a centered occurrence at some output\nindex $\\gamma$.  Its coordinate integral in a pair inner product is\nsmall by mixing of all orders whenever the times in the two copies of\n\\eqref{eq:channel-times} are sufficiently separated.  The times within\neach copy separate once $l,l'$ exceed a fixed threshold.  Failure of\nseparation between copies then requires one of finitely many inequalities\n\\[\n \\abs{e_\\gamma i l-e_\\gamma i'l'+a'}\\le R.\n\\]\nFor fixed $l$, each inequality permits only a bounded number of\nintegers $l'$, because $e_\\gamma,i'>0$; the same holds with $l,l'$\ninterchanged.  Their union is the required bounded-degree graph.\nThe coordinate integrals for other output indices are bounded by $1$.\nThe separation threshold and $L_0$ may depend on $\\eta$, but no rate\nof mixing is needed.\n\n\\paragraph{Conditioning on a selected length.}\nFor fixed selector index $a$, partition into the events\n$N_a=c2^k$, $c\\in F$, $k\\ge a$, choosing one representation when a\nlength has more than one.  Given $V$, write their probabilities as\n$p_{k,c}(V)$.  On one such event of probability $p>0$, the conditional\nspatial law of any two groups, and of each single group, is unchanged.\nIndeed the event is determined by $V,x_0$, which is independent of any\nproper set of groups by \\eqref{eq:channel-laws}.  The full three-group\nlaw may change.  Its density $\\rho$ relative to the product law satisfies\n\\begin{equation}\\label{eq:channel-density}\n 0\\le p\\rho\\le1,\\qquad\n \\int p\\rho=p,\\qquad \\norm{p\\rho}_2\\le\\sqrt p.\n\\end{equation}\nThe reason is that the joint measure of the outputs and the event is\ndominated by the unconditioned product measure.  These statements hold\nfor almost every $V$; the length events are countable.\n\nWe choose the rank bound to meet two requirements: the packing\nbound must be $o(m)$, and the reciprocal bounds must be summable over\ndyadic length scales. For $m=\\lfloor c2^k/h\\rfloor$ set\n\\begin{equation}\\label{eq:channel-dimension}\n d_m=\\left\\lceil(1+\\log m)^{3/2}\\right\\rceil,\n \\qquad z_l=u_l\\otimes v_l\\quad(0\\le l<m).\n\\end{equation}\nLet $\\Pi_m$ be the orthogonal projection onto up to $d_m$ leading\neigenvectors of the positive covariance operator\n$\\sum_{l<m}z_lz_l^*$, taking its full range if its rank is smaller.\nFix $0<\\varepsilon\\le1$ and then fix\n$0<\\eta\\le\\varepsilon^2/10^4$ in the preceding graph bound.\nTheorem~\\ref{thm:packing}, proved in Appendix~\\ref{sec:packing} from\n\\cite[Theorem~4.1]{FourfoldCompanion}, gives\n\\begin{equation}\\label{eq:channel-projected-average}\n \\frac1m\\sum_{l<m}\\norm{\\Pi_m z_l}\n       \\le\\varepsilon+o(1).\n\\end{equation}\nHere the error tends to zero uniformly over the offset patterns.\nIndeed, after the fixed initial indices are removed, the number of\nprojection norms at least $\\varepsilon$ is at most\n\\[\n C(1+L_0)\\varepsilon^{-2}\n \\exp\\!\\bigl(C\\varepsilon^{-2}\\sqrt{d_m}\\log(2+d_m)\\bigr)=o(m).\n\\]\nThe removed indices contribute $o(1)$ to the average.  Importantly,\n$\\varepsilon,\\eta$, and the resulting exceptional degree are fixed\nbefore $m$ tends to infinity.\n\nFor the projected terms, Cauchy--Schwarz under the conditional event\nlaw uses its unchanged two-group and one-group marginals.  Term by\nterm, including the event probability, it gives\n\\[\n \\left|p\\int (\\Pi_mz_l)(\\omega_1,\\omega_2)\n                   w_l(\\omega_3)\\rho(\\omega)\\,d\\omega\\right|\n \\le p\\norm{\\Pi_mz_l}_2\\norm{w_l}_2\n \\le p\\norm{\\Pi_mz_l}_2.\n\\]\nThus their average contributes at most $p(\\varepsilon+o(1))$.\n\nFor the residual vectors $z_l'=(1-\\Pi_m)z_l$, let\n$A=(\\langle z_l',z_{l'}'\\rangle)_{l,l'<m}$ and\n$B=(\\langle w_l,w_{l'}\\rangle)_{l,l'<m}$.  The covariance trace is\nat most $m$, so its remaining eigenvalues, and hence the nonzero\neigenvalues of $A$, are at most $m/d_m$.  If its rank was at most\n$d_m$, the residual is zero.  Also $B$ is positive semidefinite with\ntrace at most $m$.  Consequently\n\\[\n \\left\\|\\frac1m\\sum_{l<m}z_l'\\otimes w_l\\right\\|^2\n   =\\frac{\\operatorname{tr}(AB)}{m^2}\\le\\frac1{d_m}.\n\\]\nBy \\eqref{eq:channel-density}, the residual contribution, including\nthe event probability, is at most $\\sqrt{p/d_m}$.\nMultiplication by the fixed phase weight does not increase either bound.\n\n\\paragraph{Summing over lengths.}\nWrite $m(k,c)=\\lfloor c2^k/h\\rfloor$.  For fixed $h$ this is comparable\nto $2^k$ for all sufficiently large $k$, uniformly in the finite set\n$F$.  Since $\\sum_{k,c}p_{k,c}(V)=1$, Cauchy--Schwarz yields\n\\begin{equation}\\label{eq:channel-residual-sum}\n \\sum_{k\\ge a,\\ c\\in F}\\sqrt{\\frac{p_{k,c}(V)}{d_{m(k,c)}}}\n \\le\n \\left(\\sum_{k\\ge a,\\ c\\in F}\\frac1{d_{m(k,c)}}\\right)^{1/2}\n \\longrightarrow0.\n\\end{equation}\nThe series converges because its summands are $O(k^{-3/2})$.\nThe projected contributions have total limit superior at most\n$\\varepsilon$, by \\eqref{eq:channel-projected-average} and the same\nprobability identity.  These estimates are uniform in $V,s$ and may\nbe integrated.  There is no measurable-choice issue for the\nprojections: choose one for each of the countably many sizes and\nfinitely many offset patterns.  Letting $a$ tend to infinity in the\nselected-law formula and then letting $\\varepsilon$ tend to zero\nproves the required vanishing.  Both laws in\n\\eqref{eq:channel-laws} follow.\n\\end{proof}\n\n\\begin{corollary}\\label{cor:channel-copied-law}\nFor every channel $Y$, the copied spatial list\n$(d_Y(y_i))_{i\\in I}$ under $\\lambda_Y$ has its full product law\nconditional on $P=(\\omega_Y(y_i))_{i\\in I}$.\n\\end{corollary}\n\n\\begin{proof}\nApply Proposition~\\ref{prop:channel-closure} and\nLemma~\\ref{lem:channel-conditioning} to $\\mathsf M_b(Y)$.\nBy Lemma~\\ref{lem:normalized-joining}, the old parameter array $P$\nbelongs to its maximal distal factor.  Conditioning the resulting\nconstant product law down to $P$ proves the assertion.\n\\end{proof}\n"}, {"path": "preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/correlations.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/correlations.tex", "bytes": 10385, "sha256": "e58e451b3290966757a6263ad2c4e8f1e2cf5eb48139b61b7ebf74000eedb7a2", "content": "\\section{Minimal correlations and saturated extensions}\n\\label{sec:correlations}\n\nThe positive correlation in \\eqref{eq:positive-witness} records the\nassumed pointwise failure. We continue the channel-and-saturation\nargument of \\cite[Sections~5--6]{FourfoldCompanion}, with an additional\nminimum-support choice. We choose such a correlation with the\nfewest possible spatial slots. This choice supplies the product laws\nfor proper subcollections that a pointwise theorem of greater length\nwould otherwise have to provide. Among correlations of this minimum\nsize, we maximize the number of slots that use only outputs. The final\nsection will increase that number and obtain a contradiction.\n\n\\subsection{Spatial roles and minimum support}\n\nLet $Y$ be any channel. At a slot $i\\in I$, we allow either of the\nfollowing spatial lists:\n\\begin{align*}\n L_i(y_i)&=d_Y(y_i)\n   &&\\text{(an output role)},\\\\\n L_i(y_i)&=(x_0(y_i),d_{Y,H_i}(y_i)),\\qquad\n   H_i\\subsetneq\\{1,2,3\\}\n   &&\\text{(an input role)}.\n\\end{align*}\nAn input role may therefore read the original coordinate jointly with\noutputs from any proper subset of the three groups. This allowance is\nimportant: later, regrouping outputs will produce precisely these\njoint lists. A test may ignore some entries of its designated list.\n\nFor a nonempty set $J\\subseteq I$, designate one role at each $i\\in J$.\nChoose bounded real functions $h_i$ of the corresponding lists, centered\nfor their product spatial laws. By Lemma~\\ref{lem:channel-conditioning},\n$h_i(L_i)$ also has conditional mean zero over $W_Y$. We consider\ncorrelations\n\\begin{equation}\n \\E_{\\lambda_Y}\\left[\\Phi(P)\n          \\prod_{i\\in J}h_i(L_i(y_i))\\right],\n \\label{eq:role-correlation}\n\\end{equation}\nwhere $\\Phi$ is any bounded real function of the full distal parameter\narray $P=(\\omega_Y(y_i))_{i\\in I}$. The channel $C$, with no outputs,\nhas a nonzero correlation of this form by\nLemma~\\ref{lem:positive-witness}.\n\nLet $k$ be the minimum of $|J|$ among all nonzero correlations\n\\eqref{eq:role-correlation}, over all channels and all such data.\nThe small-slot law \\eqref{eq:small-conditional-law} gives $k\\geq4$:\nwhen $|J|\\leq3$, the conditional expectation of the product given $P$\nis the product of the conditional means, all zero. Thus $n<4$ already\ncontradicts the assumed failure. In what follows $4\\leq k\\leq n$.\n\n\\begin{lemma}[Product laws below the minimum support]\n\\label{lem:minimum-product}\nOn any channel, choose fewer than $k$ distinct slots and designate\none allowed spatial list at each. Under its selected law, these lists,\njointly, have their product spatial law conditional on the full distal\nparameter array.\n\\end{lemma}\n\n\\begin{proof}\nTest the conditional law by a product of bounded real functions, one\nfor each designated list, and by a bounded real function of the full\nparameter array. Write each spatial test as its product-law mean plus\na centered function. Every nonconstant term in the expansion is a\ncorrelation of the form \\eqref{eq:role-correlation} with fewer than $k$\nslots, and hence vanishes. Only the product of the means remains.\nProduct tests determine the asserted conditional law on these finite\nproducts of standard spaces.\n\\end{proof}\n\nAmong the nonzero correlations with support size $k$, let $r$ be the\nlargest number of output roles. Corollary~\\ref{cor:channel-copied-law}\nimplies $r<k$, because the full copied output collection has product\nspatial law given $P$. Choose a channel and a correlation attaining\nthese two extremal values. We next pass to an extension on which\nconditioning on the parameters, or on the parameters and one slot,\ncannot be improved by any further extension.\n\n\\subsection{Saturating the parameter and single-slot data}\n\nFor any flow $Y$ over $C$, define sub-$\\sigma$-algebras of the selected\nlaw space $(Y^I,\\lambda_Y)$ by\n\\begin{equation}\n \\mathcal D_0(Y)=\\sigma(P),\\qquad\n \\mathcal D_i(Y)=\\sigma(P,y_i)\\quad(i\\in I).\n \\label{eq:data-algebras}\n\\end{equation}\nIf $\\rho:Y'\\to Y$ is a flow extension over $C$, functoriality gives\n$(\\rho^I)_*\\lambda_{Y'}=\\lambda_Y$. The pullback of $W_Y$ is a distal\nfactor of $Y'$, so is contained in $W_{Y'}$. Consequently the pullback\nof each $\\mathcal D_l(Y)$ is contained in $\\mathcal D_l(Y')$.\n\n\\begin{lemma}[Simultaneous saturation]\n\\label{lem:saturation}\nEvery standard flow $Y$ over $C$ has a standard flow extension $Y_*\\to Y$\nover $C$ with the following property. For every further standard flow\nextension $\\rho:Y'\\to Y_*$ over $C$, every $f\\in L^2(\\lambda_{Y_*})$,\nand every $l\\in\\{0\\}\\cup I$,\n\\begin{equation}\n \\E_{\\lambda_{Y'}}[f\\circ\\rho^I\\mid\\mathcal D_l(Y')]\n =\\E_{\\lambda_{Y_*}}[f\\mid\\mathcal D_l(Y_*)]\\circ\\rho^I.\n \\label{eq:saturation}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nWe use a countable bounded-energy construction of the kind underlying\nAustin's sated extensions \\cite[Theorem~3.11]{Austin2015}; the argument\nbelow proves the assertion for the particular data\n\\eqref{eq:data-algebras}. In a tower of flow extensions,\nfor a fixed test $f$ introduced at one stage, the quantity\n\\[\n  \\left\\|\\E[f\\mid\\mathcal D_l]\\right\\|_2^2\n\\]\nat later stages is nondecreasing, by inclusion of the pulled-back\ndata, and is bounded by $\\|f\\|_2^2$. At each stage introduce a countable\ndense set in the $L^2$ space of its selected law, retaining all\npreviously introduced tests. Schedule all pairs consisting of a retained\ntest and a datum index so that each pair is treated infinitely often,\nwith positive tolerances tending to zero. A diagonal enumeration gives\nsuch a schedule even though new tests are introduced at every stage.\n\nAt a scheduled step, extend the current flow so that the corresponding\nenergy is within the prescribed tolerance of its supremum over all\nstandard flow extensions of that stage over $C$. The supremum is\nfinite. It can be taken over a set: standard Borel spaces, their\nprobability laws, Borel actions, and factor maps admit codes on fixed\nstandard spaces. The identity extension is among the competitors.\nLet $Y_*$ be the countable inverse limit of the resulting tower,\nwhich exists in our category by Proposition~\\ref{prop:selected-law}.\n\nFix a retained test, an index $l$, and a further extension $Y'\\to Y_*$.\nAt every scheduled optimization of this pair, $Y'$ is also an extension\nof the stage just before the optimization. Its energy is therefore at\nmost the energy achieved immediately after that optimization plus its\ntolerance. The latter achieved energy is at most the energy at $Y_*$.\nAs the tolerances tend to zero, the energy at $Y'$ is at most that at\n$Y_*$. The reverse inequality follows from monotonicity, so the two\nenergies are equal.\n\nIdentify $L^2(\\lambda_{Y_*})$ with its pullback in\n$L^2(\\lambda_{Y'})$. The two conditional expectations are orthogonal\nprojections onto nested closed subspaces. Equality of their squared\nnorms forces equality of the projections, proving\n\\eqref{eq:saturation} for each retained test. Stage functions have\ndense span in $L^2(\\lambda_{Y_*})$ by\nProposition~\\ref{prop:selected-law}; the retained dense sets and the\ncontraction property of conditional expectation extend the identity\nto every $f$ in that space.\n\\end{proof}\n\nApply Lemma~\\ref{lem:saturation} to the chosen channel. Pulling back\nits data preserves the channel laws by\nLemma~\\ref{lem:channel-conditioning}, and preserves its nonzero\ncorrelation by functoriality. Its old parameter test is a function of\nthe enlarged distal parameters. We henceforth call this saturated\nchannel $Y$. Write $J$ for its support, $A\\subset J$ for its output\nroles, and choose\n\\[\n  b\\in J\\setminus A,\n  \\qquad |J|=k,\\quad |A|=r.\n\\]\nThus the role at $b$ is an input role. The purpose of the remaining\nconstruction is to turn it into an output role while keeping a\nnonzero correlation on the same $k$ slots.\n\n\\subsection{Regrouping the other slots as outputs}\n\nSet $Z=\\mathsf M_b(Y)$. Its state is $(y_j)_{j\\in I}$, its law is\n$m_Z=\\lambda_Y$, and projection to $y_b$ is a flow factor $Z\\to Y$.\nWe give $Z$ a channel structure different from the full-copy structure\nof Proposition~\\ref{prop:channel-closure}. Retain all old outputs from\nslot $b$ with their original group numbers. For each $j\\in J\\setminus\\{b\\}$,\nalso include the $C$-valued factors whose spatial coordinates form\n\\[\n Q_j=L_j(y_j).\n\\]\nThese are old output factors and, when the role at $j$ is an input\nrole, the original factor $\\pi_Y(y_j)$. Choose a surjection\n\\[\n \\tau:J\\setminus\\{b\\}\\longrightarrow\\{1,2,3\\},\n\\]\npossible because $k\\geq4$, and put the entire list $Q_j$ in new group\n$\\tau(j)$. Every new output has the correct marginal and a positive\nrational speed: the old speed at slot $j$ is multiplied by $j/b$.\n\n\\begin{lemma}[The regrouped channel]\n\\label{lem:regrouped-channel}\nThe preceding output lists and groups make $Z$ a channel over its\noriginal factor $\\pi_Y(y_b)$.\n\\end{lemma}\n\n\\begin{proof}\nWe prove both channel laws conditional on the old full parameter\narray $P$ on $(Y^I,\\lambda_Y)$. Every phase in the new channel is a\nfunction of $P$, so conditioning down then proves\n\\eqref{eq:channel-laws} for $Z$.\n\nFor the full output collection, its spatial lists are\n\\[\n  d_Y(y_b),\\qquad Q_j\\quad(j\\in J\\setminus\\{b\\}).\n\\]\nThese are allowed lists at $k$ distinct old slots. Expand a product\nof tests on these lists into constant and centered parts. Every term\nwith fewer than $k$ centered factors has the product-law value by\nLemma~\\ref{lem:minimum-product}. The all-centered term also vanishes\nagainst every bounded parameter test. Otherwise it would give a\nnonzero correlation with support $k$ and $r+1$ output roles: slot $b$\nnow has an output role, and the other roles are unchanged. This\ncontradicts the definition of $r$. If the output list at $b$ is empty,\nits centered test is identically zero, with the same conclusion.\nThus the full output collection has product spatial law given $P$.\n\nFor the second channel law, choose a proper subset\n$H\\subsetneq\\{1,2,3\\}$ of the new groups and include $x_0(y_b)$.\nAt slot $b$ the resulting list is\n$(x_0(y_b),d_{Y,H}(y_b))$, an allowed input list. At any other retained\nslot $j$ it is exactly $Q_j$, and such a slot occurs only when\n$\\tau(j)\\in H$. Surjectivity of $\\tau$ omits at least one of the\n$k-1$ other slots. Fewer than $k$ old slots therefore occur, so\nLemma~\\ref{lem:minimum-product} gives the required product law\nconditional on $P$. This proves the second channel law as well.\n\\end{proof}\n"}, {"path": "preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/inputs.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/inputs.tex", "bytes": 10571, "sha256": "c734c3336f55582556863e419983983405ef90b99218f70c4ab7477d93cdeb91", "content": "\\section{Dynamical inputs and standard models}\n\\label{sec:inputs}\n\nFix an integer $n\\ge2$ and put $I=\\{1,\\ldots,n\\}$. We first state\nthe dynamical results used below. The distinction between norm and\npointwise convergence matters: norm convergence is available for any\nfinite number of slots, whereas the pointwise input on arbitrary\nsystems will be used only for at most three slots.\n\n\\subsection{Standard models and the two companion inputs}\n\nFor the fixed tuple in Theorem~\\ref{thm:main}, record all integer\ntranslates of bounded measurable representatives of the functions.\nThis gives a measurable map into a countable product of compact scalar\ndiscs, intertwining $T$ with the invertible shift. The pushforward\nmeasure defines a standard Borel probability system, and mixing passes\nto this factor. The original functions are its coordinate functions.\nChanging representatives affects at most a countable union of translated\nnull sets, so almost-everywhere convergence on the factor pulls back.\nWe may therefore work on standard probability spaces.\n\nThroughout the proof, factors and conditional laws are understood\nmodulo null sets. We use Borel versions of measurable functions.\nSub-$\\sigma$-algebras on a standard probability space have standard\nmodels modulo null sets. A flow is a jointly Borel action\n$(S_Y^t)_{t\\in\\R}$ on a standard Borel probability space $(Y,m_Y)$,\npreserving $m_Y$. Factor maps commute with every fixed time modulo\nnull sets. Joint measurability permits Fubini in all time integrals.\nFor a preserved factor, induced transformations at fixed times can be\nmodeled as invertible maps; we take time integrals on the original flow.\n\nWe use the following statement from\n\\cite[Theorem~1.1]{MixingCompanion}.\n\\begin{theorem}[Higher-order mixing, companion input]\n\\label{thm:multiple-mixing}\nLet $T$ be an invertible mixing probability-preserving transformation.\nFor any fixed $s\\ge2$, $g_1,\\ldots,g_s\\in L^\\infty(\\mu)$,\nand integer times $t_1,\\ldots,t_s$,\n\\[\n \\int_X\\prod_{j=1}^s g_j(T^{t_j}x)\\dd\\mu(x)\n \\longrightarrow\\prod_{j=1}^s\\int_Xg_j\\dd\\mu\n \\quad\\text{as}\\quad\n \\min_{i\\ne j}|t_i-t_j|\\longrightarrow\\infty.\n\\]\nFor $s=1$ the equality holds for every time.\n\\end{theorem}\nThe cited theorem is stated for sets and ordered times. Simple-function\napproximation, invariance, and the finitely many orderings of the times\ngive this formulation. No quantitative mixing rate is asserted or used.\n\nOur second companion input is the smooth-average convergence consequence\nof \\cite[Proposition~3.5 and Lemma~3.6]{SlopesCompanion}.\nAn \\emph{averaging profile} there is a real compactly supported smooth\nfunction $\\varphi$ of mass one with moments of orders $1,2,3$ equal to\nzero, and, for some $G\\ge1$,\n\\[\n \\norm{\\varphi^{(r)}}_\\infty\n \\le \\bigl(G(r+2)\\bigr)^{G(r+2)}\\qquad(r\\ge0).\n\\]\nThe constant $G$ may depend on the profile. We need the following\ntwo precise conclusions.\n\\begin{proposition}[Smooth triple averages, companion input]\n\\label{prop:smooth-input}\nLet $R$ be any invertible probability-preserving transformation, and\nlet $b_1,b_2,b_3$ be distinct nonzero integers with greatest common\ndivisor one. For bounded $g_1,g_2,g_3$ and every averaging profile,\n\\[\n V_k^\\varphi(y)=\\sum_{l\\in\\Z}2^{-k}\\varphi(l/2^k)\n              \\prod_{j=1}^3g_j(R^{b_jl}y)\n\\]\nconverges almost everywhere as $k\\to\\infty$. Moreover, for each\n$c\\in[1,2]$ and $\\epsilon>0$ there is such a profile with\n\\[\n \\norm{\\varphi-c^{-1}\\mathbf1_{(0,c]}}_{L^1(\\R)}<\\epsilon.\n\\]\n\\end{proposition}\nThe approximation is \\cite[Lemma~3.9]{SlopesCompanion}. Neither part\nrequires mixing or ergodicity. We will not assume pointwise convergence\nof longer averages on arbitrary systems.\n\n\\subsection{Relative weak mixing and distal factors}\n\nWe recall the structure facts in the form needed for the auxiliary\nsystems. An extension of an invertible system $R$ over a factor $B$\nis \\emph{relatively weakly mixing} if, for bounded $f,g$ with\n$\\E[f\\mid B]=0$,\n\\begin{equation}\\label{eq:rwm}\n \\frac1H\\sum_{h=1}^H\n \\norm{\\E[\\overline f(g\\circ R^h)\\mid B]}_2^2\\longrightarrow0.\n\\end{equation}\nAn extension is \\emph{relatively compact} if the invariant finitely\ngenerated $L^\\infty(B)$-modules span a dense subspace of its $L^2$\nspace. A module consists of finite sums $\\sum_j a_jF_j$, with\n$a_j\\in L^\\infty(B)$ and fixed $F_j\\in L^2$; invariance is under\ncomposition with both $R$ and $R^{-1}$. Individual modules need not be\nclosed; see the corrected module characterization in\n\\cite[Theorem~2.5(ii)$'$]{JamneshanCorrigendum2026}.\nA \\emph{relatively distal} extension is a tower of relatively\ncompact extensions and inverse limits. A system is \\emph{distal} if\nit is distal over the trivial factor. A joining is an invariant\nprobability law on a product with the prescribed marginals.\n\n\\begin{proposition}[Relative structure]\\label{prop:structure}\nThe following statements hold for standard probability-preserving\nsystems, without an ergodicity assumption.\n\\begin{enumerate}\n\\item Every system $Y$ has a maximal measure-distal factor $W_Y$,\nand $Y\\to W_Y$ is relatively weakly mixing. Every distal factor\npulls into the maximal distal factor of an extension. Automorphisms\ncommuting with the transformation preserve $W_Y$.\n\\item Factors and joinings of distal systems are distal. A distal\nsystem is relatively distal over each of its factors. Distality is\nequivalent for a transformation and any of its nonzero integer powers.\n\\item Relative weak mixing passes to nonzero integer powers. The\nconditional product of relatively weakly mixing fibre laws over an\ninvariant joining of their base laws is relatively weakly mixing\nover that joining.\n\\item A relatively weakly mixing extension and a relatively distal\nextension of the same base are relatively disjoint: every invariant\njoining identifying their bases is the conditional product over\nthat base.\n\\item For a distal transformation $R$, every $d\\ge1$, and bounded\n$g_1,\\ldots,g_d$, the averages\n\\[\n \\frac1N\\sum_{l=1}^N\\prod_{i=1}^d g_i\\circ R^{il}\n\\]\nconverge almost everywhere.\n\\end{enumerate}\nFor a flow, the maximal distal factor of time one is preserved by\nthe flow, and its action at every nonzero rational time is distal.\n\\end{proposition}\n\nThese are the conclusions of\n\\cite[Proposition~2.2]{FourfoldCompanion}. They include the\nnonergodic form of the Furstenberg--Zimmer structure theorem\n\\cite{Zimmer1976,Jamneshan2023} and the pointwise theorem for\ndistal systems \\cite[Theorem~C]{HuangShaoYe2019}. The cited proposition\ngives the component-disintegration argument needed for the latter\nin the nonergodic setting. In the flow assertion, invariance of\n$W_Y$ follows because each time commutes with time one. If\n$r=p/q\\ne0$ is rational, the $q$th power of its action is the\ndistal action at integer time $p$, so the power assertion applies.\n\nThe following short argument, from\n\\cite[Lemma~2.3]{FourfoldCompanion}, will also fix the norm-convergence\ninput explicitly.\n\\begin{lemma}[Relative norm convergence]\\label{lem:relative-norm}\nSuppose $Y\\to B$ is relatively weakly mixing for $R$. For distinct\nnonzero integers $a_1,\\ldots,a_d$ and bounded $G_1,\\ldots,G_d$,\n\\begin{equation}\\label{eq:relative-norm}\n \\left\\|\\frac1N\\sum_{l=1}^N\n \\left(\\prod_{i=1}^dG_i\\circ R^{a_il}\n       -\\prod_{i=1}^d\\E[G_i\\mid B]\\circ R^{a_il}\\right)\\right\\|_2\n \\longrightarrow0.\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nBy telescoping and scaling, it suffices to show that the average of\n$v_l=\\prod_i G_i\\circ R^{a_il}$ tends to zero when all functions\nare bounded by one and one of them, $G_s$, is centered over $B$.\nInduct on $d$. For fixed $h\\ge1$ put\n$F_{i,h}=\\overline{G_i}(G_i\\circ R^{a_ih})$. Invariance gives\n\\[\n \\ip{v_l}{v_{l+h}}\n =\\int_Y F_{s,h}\\prod_{i\\ne s}\n                  F_{i,h}\\circ R^{(a_i-a_s)l}\\dd m_Y.\n\\]\nThe induction hypothesis replaces the factors indexed by $i\\ne s$\nin their average by their conditional expectations over $B$. The\nremaining product is $B$-measurable and bounded by one; hence\n\\[\n \\limsup_{N\\to\\infty}\n \\left|\\frac1N\\sum_{l=1}^N\\ip{v_l}{v_{l+h}}\\right|\n \\le\\norm{\\E[F_{s,h}\\mid B]}_1.\n\\]\nFor $d=1$ this bound holds directly. The Hilbert-space van der\nCorput inequality now bounds the limiting squared norm by a constant\ntimes\n\\[\n \\frac1H+\\frac1H\\sum_{h=1}^H\\norm{\\E[F_{s,h}\\mid B]}_1.\n\\]\nRelative weak mixing for $R^{a_s}$ and Cauchy--Schwarz make this\nexpression tend to zero as $H\\to\\infty$.\n\\end{proof}\n\nFor positive slopes, inserting functions $1$ in omitted positions\nand using Proposition~\\ref{prop:structure}(5) shows that the\ndistal-factor averages have an $L^2$ limit. Thus\nLemma~\\ref{lem:relative-norm} gives an $L^2$ limit for arbitrary\nbounded tuples with distinct positive slopes on every standard\nsystem. On the original mixing system, the trivial factor already\nsatisfies \\eqref{eq:rwm}; the limit is the product of the means.\n\n\\subsection{Triple convergence along all lengths}\n\n\\begin{lemma}[Triple pointwise convergence]\\label{lem:triple-convergence}\nOn any standard invertible probability-preserving system, averages\nof at most three bounded functions at distinct positive integer\nslopes converge almost everywhere along all positive integer lengths.\n\\end{lemma}\n\\begin{proof}\nAdd constant-one functions if necessary to have three distinct\npositive slopes, write them as $gb_1,gb_2,gb_3$ with\n$\\gcd(b_1,b_2,b_3)=1$, and apply\nProposition~\\ref{prop:smooth-input} to $S=R^g$. By scaling, suppose\nthe three functions are bounded by one, and put\n$F_l=\\prod_{j=1}^3g_j\\circ S^{b_jl}$.\n\nFor each rational $c\\in[1,2]$, choose a sequence of profiles tending\nin $L^1$ to $h_c=c^{-1}\\mathbf1_{(0,c]}$. On a common conull set\nall the corresponding smooth dyadic averages converge. For\n$N_k=\\lfloor c2^k\\rfloor$, the difference between the ordinary\naverage at $N_k$ and $V_k^\\varphi$ is at most\n\\[\n \\frac1{c2^k}\n +2^{-k}\\sum_{l\\in\\Z}|h_c(l/2^k)-\\varphi(l/2^k)|.\n\\]\nThe last sum tends to $\\norm{h_c-\\varphi}_1$ by Riemann sums.\nArbitrarily close profiles therefore give the Cauchy property on\neach of these scales. The $L^2$ limit established above identifies\nall their almost-everywhere limits with the same function.\n\nFor any bounded sequence $|F_l|\\le1$, changing the averaging length\nfrom $N$ to $M$ changes the average by at most\n\\begin{equation}\\label{eq:length-change}\n                 \\frac{2|M-N|}{\\max(M,N)}.\n\\end{equation}\nFor $2^k\\le N<2^{k+1}$, compare $N$ with\n$\\lfloor c2^k\\rfloor$ on a finite rational grid in $[1,2]$.\nEquation~\\eqref{eq:length-change} makes the limiting error\narbitrarily small as the grid mesh tends to zero. This proves\nconvergence along all integer lengths.\n\\end{proof}\n"}, {"path": "preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/introduction.tex", "bytes": 5908, "sha256": "143b7c0839372544863dab5b70c1b0bca66f6b4876d73c56c945be7bb12eb244", "content": "\\section{Introduction}\n\\label{sec:introduction}\n\nLet $(X,\\mathcal F,\\mu)$ be a probability space and let $T:X\\to X$\nbe invertible, bimeasurable, and measure preserving. We say that $T$\nis \\emph{mixing} if\n\\begin{equation}\\label{eq:mixing}\n \\mu(A\\cap T^{-r}B)\\longrightarrow\\mu(A)\\mu(B)\n \\qquad (|r|\\to\\infty)\n\\end{equation}\nfor every $A,B\\in\\mathcal F$. For a fixed tuple of bounded measurable\nfunctions, the consecutive multiple ergodic averages are\n\\[\n A_N(f_1,\\ldots,f_n)(x)\n =\\frac1N\\sum_{k=1}^N\\prod_{j=1}^n f_j(T^{jk}x).\n\\]\nThe problem is to determine their almost-everywhere behavior, not merely\ntheir limit in norm. We prove the following result.\n\n\\begin{theorem}\\label{thm:main}\nSuppose that $T$ satisfies \\eqref{eq:mixing}. For every integer\n$n\\ge2$ and every fixed $f_1,\\ldots,f_n\\in L^\\infty(\\mu)$,\n\\[\n A_N(f_1,\\ldots,f_n)(x)\n \\longrightarrow\\prod_{j=1}^n\\int_X f_j\\dd\\mu\n \\qquad\\text{for $\\mu$-almost every }x,\n\\]\nas $N$ tends to infinity through all positive integers. The probability\nspace need not be standard, and no rate of mixing is required.\n\\end{theorem}\n\nThe exceptional null set may depend on the system and the fixed tuple.\nThe theorem concerns the times $k,2k,\\ldots,nk$ and does not require\na common exceptional set for all bounded functions.\n\n\\subsection{Context and prior work}\n\nArithmetic-progression averages became central to ergodic Ramsey theory\nthrough Furstenberg's proof of Szemer\\'edi's theorem\n\\cite{Furstenberg1977}. The distinction between norm and pointwise\nconvergence is substantial. Host and Kra proved $L^2$ convergence of\nthese averages for every finite length on arbitrary\nprobability-preserving systems \\cite[Theorem~1.1]{HostKra2005}.\nZiegler obtained another proof through universal characteristic\nfactors \\cite[Corollary~1.8]{Ziegler2007}.\nFor pointwise convergence, Bourgain's double recurrence theorem treats\ntwo factors \\cite{Bourgain1990}. Higher-length results have been\nobtained under additional structural assumptions: K-systems\n\\cite[Theorem~4.2]{DerrienLesigne1996}, weakly mixing systems with\nsingular spectrum on their Pinsker factor \\cite{Assani1998}, and\nweakly mixing systems whose pairwise independent self-joinings are\nindependent \\cite[Theorem~3.4]{GutmanHuangShaoYe2018}. Huang, Shao,\nand Ye proved pointwise convergence for every finite length on\nergodic measure-distal systems \\cite[Theorem~C]{HuangShaoYe2019}.\n\nThe present proof extends the fourfold mixing theorem of\n\\cite[Theorem~1.1]{FourfoldCompanion}. It uses two substantial\ncompanion results. The first proves that ordinary mixing implies\nmixing of every finite order, the assertion of Rokhlin's\nmultiple-mixing problem \\cite[Theorem~1.1]{MixingCompanion}.\nThe second gives an oscillation estimate for triple averages at\ndistinct integer slopes on arbitrary invertible probability-preserving\nsystems \\cite[Proposition~3.5]{SlopesCompanion}; together with its\nprofile approximation, this yields pointwise triple convergence\nat distinct positive integer slopes without a mixing assumption.\nSection~\\ref{sec:inputs} states their exact forms and derives the\nrequired triple pointwise theorem along all lengths. These companion\nproofs are not reproduced here. The selected-law construction,\narbitrary-length extension argument, and tensor estimate used below\nare proved in full.\n\n\\subsection{The arbitrary-length step}\n\nWe briefly describe what must change when the number of factors\nincreases. A failure of pointwise convergence allows an averaging\nlength to be chosen measurably at each starting point so that a\ncentered average stays positive. After passing to a suspension flow,\nthese choices define a probability law on $n$ output coordinates.\nThe law has the correct individual marginals and is invariant under\ndifferent speeds in the different coordinates. The same construction\nworks on extensions of the flow.\n\nThe method of \\cite[Sections~3--6]{FourfoldCompanion} organizes\nadditional outputs into three groups. Their spatial coordinates are\nindependent, and the original spatial coordinate is independent of\nany two groups; independence from all three groups is not imposed.\nWe retain this three-group structure even when there are $n$ slots.\nHigher-order mixing and a tensor-packing estimate show that these\nindependence properties survive the selected joining construction.\n\nTwo changes make this approach work for arbitrary $n$. First, we\nminimize the number of slots supporting a nonzero centered correlation.\nThis gives independence for every smaller collection of the specific\nspatial lists being tested. A slot may read both the original spatial\ncoordinate and the outputs in any proper subset of the three groups.\nThat flexibility permits the outputs to be regrouped without imposing\nindependence on arbitrary collections of whole states.\n\nSecond, an extension is chosen so that conditioning on all distal\nparameters together with a single state cannot improve under any\nfurther extension. This is a conditional-expectation energy\nconstruction, related to the sated-extension method\n\\cite{Austin2015} and adapted from\n\\cite[Section~6]{FourfoldCompanion}. Applying the joining construction\nagain gives a square array. The single-state conditioning identity\ncontrols its distinguished row and column. Their parameter laws and\na Hilbert-space argument then produce a new nonzero correlation with\nthe same number of slots but one more output-only slot. Maximizing\nthat number gives the contradiction. Only the pointwise theorem for\nat most three slots on arbitrary systems is used.\n\nSection~\\ref{sec:inputs} records the dynamical inputs.\nSection~\\ref{sec:selected} constructs the selected laws and their\nconditional marginals. Section~\\ref{sec:channels} proves preservation\nof the three-group structure. Sections~\\ref{sec:correlations}\nand~\\ref{sec:square} carry out the minimal-correlation and square\narguments. Appendix~\\ref{sec:packing} proves the tensor estimate.\n"}, {"path": "preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/packing.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/packing.tex", "bytes": 9110, "sha256": "bd1f4856c207aff455046be19bc1f19e5bf77deb4203f2a768364f41637d33bb", "content": "\\section{Tensor packing}\\label{sec:packing}\n\nThe closure proof uses a deterministic fact: a subspace of moderately\ngrowing dimension captures a substantial part of only a small fraction\nof an almost orthogonal tensor family.  We reproduce the argument of\n\\cite[Theorem~4.1]{FourfoldCompanion}, with the same quantitative\nestimate.  Throughout this appendix, Hilbert spaces are real, tensor\nproducts are Hilbert tensor products, and logarithms are natural.\n\n\\begin{theorem}[Tensor packing]\\label{thm:packing}\nThere is a universal constant $C$ with the following property.  Let\n$0<\\varepsilon\\le1$, and let $d\\ge1$ and $L\\ge0$ be integers.\nLet $x_1,\\ldots,x_m$ and $y_1,\\ldots,y_m$ lie in the unit balls of\nreal Hilbert spaces $H_1,H_2$.  For each family suppose that a graph\non $\\{1,\\ldots,m\\}$ of maximum degree at most $L$ contains every\noff-diagonal pair whose absolute inner product exceeds $\\eta$, where\n\\[\n 0\\le\\eta\\le\\varepsilon^2/10^4.\n\\]\nThe two exceptional graphs may differ.  For every orthogonal projection\n$P$ on $H_1\\otimes H_2$ of rank at most $d$,\n\\begin{equation}\\label{eq:packing-quantitative}\n \\#\\{j:\\norm{P(x_j\\otimes y_j)}\\ge\\varepsilon\\}\n \\le C(1+L)\\varepsilon^{-2}\n       \\exp\\!\\left(C\\varepsilon^{-2}\\sqrt d\\log(2+d)\\right).\n\\end{equation}\nFor $d=d_m$ as defined in \\eqref{eq:channel-dimension}, this is\n$o_{\\varepsilon,L}(m)$, uniformly over the Hilbert spaces, vectors,\nprojections, and allowed values of $\\eta$.\n\\end{theorem}\n\nThe proof separates directions on which the testing space has large\nvariance and applies Gaussian comparison to the remaining directions.\nThe comparison is the finite Sudakov--Fernique inequality; see\n\\cite[Section~1, Equation~(1.2)]{Fernique1974} and\n\\cite{Sudakov1971}.  The following matrix estimate uses the Gaussian\nintegration-by-parts moment argument behind matrix Khintchine\ninequalities; compare \\cite[Lemma~6.1 and Proposition~7.3]{Tropp2018}.\nWe include its proof to retain dependence on the covariance trace,\nrather than on the ambient dimensions.\n\n\\begin{lemma}[Gaussian matrix estimate]\\label{lem:packing-gaussian}\nLet $B_1,\\ldots,B_r$ be matrices between two finite-dimensional real\nHilbert spaces, and put\n\\[\n H_j=\\begin{pmatrix}0&B_j\\\\B_j^*&0\\end{pmatrix},\n \\qquad S=\\sum_{j=1}^rH_j^2.\n\\]\nIf $\\norm{S}_{\\rm op}\\le\\sqrt d$ and $\\tr S\\le2d$ for some\n$d\\ge1$, then for independent standard normal variables $g_j$,\n\\begin{equation}\\label{eq:packing-gaussian-bound}\n \\E\\left\\|\\sum_{j=1}^rg_jB_j\\right\\|_{\\rm op}\n       \\le C d^{1/4}\\sqrt{\\log(2+d)}.\n\\end{equation}\nThe constant is independent of the dimensions of the two spaces.\n\\end{lemma}\n\n\\begin{proof}\nWrite $H=\\sum_jg_jH_j$.  Gaussian integration by parts, followed by\ndifferentiation of the matrix power, gives for every integer $q\\ge2$\n\\[\n \\E\\tr H^{2q}\n =\\sum_{j=1}^r\\sum_{a=0}^{2q-2}\n      \\E\\tr(H_jH^aH_jH^{2q-2-a}).\n\\]\nFor a fixed self-adjoint $H$ with eigenvalues $\\lambda_u$ and a\nself-adjoint matrix $K$, the absolute value of such a trace is at most\n\\[\n \\sum_{u,v}|K_{uv}|^2\n       |\\lambda_v|^a|\\lambda_u|^{2q-2-a}\n \\le\\tr(K^2|H|^{2q-2}).\n\\]\nThe inequality follows from weighted arithmetic--geometric mean and\nthe symmetry $|K_{uv}|=|K_{vu}|$.  Summing over $j$ gives\n\\begin{equation}\\label{eq:packing-moment-recursion}\n \\E\\tr H^{2q}\n \\le(2q-1)\\norm{S}_{\\rm op}\\E\\tr|H|^{2q-2}.\n\\end{equation}\nSince $\\E\\tr H^2=\\tr S$, iteration yields\n\\[\n \\E\\tr H^{2q}\n \\le(2q-1)!!\\,\\norm{S}_{\\rm op}^{q-1}\\tr S.\n\\]\nThe operator norm of $H$ equals that of $\\sum_jg_jB_j$.\nTaking a $2q$-th root and using the hypotheses bounds its expectation by\n\\[\n C\\sqrt q\\,d^{1/4}(2\\sqrt d)^{1/(2q)}.\n\\]\nChoose an integer $q\\ge2$ comparable to $\\log(2+d)$ to obtain\n\\eqref{eq:packing-gaussian-bound}.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:packing}]\nWe first record an elementary consequence of the exceptional-graph\nhypothesis.  If unit-ball vectors $v_j$ have absolute inner products\nat most $\\eta$ outside a graph of degree $L$, then for every nonempty\nset $A$ of indices,\n\\begin{equation}\\label{eq:packing-cluster}\n \\left\\|\\frac1{|A|}\\sum_{j\\in A}v_j\\right\\|^2\n       \\le\\frac{1+L}{|A|}+\\eta.\n\\end{equation}\nIndeed the expansion contains at most $(1+L)|A|$ ordered diagonal or\nexceptional pairs.  A lower bound on the norm of an average therefore\nbounds the number of vectors in that average.\n\nIt suffices to work in the finite spans of the $x_j$ and $y_j$.\nTo see this, let $Q$ project onto their tensor product and replace the\ntesting subspace $\\operatorname{Ran}P$ by $Q(\\operatorname{Ran}P)$.\nFor any vector $z$ in that tensor product, the norm of its projection\nonto the new subspace is at least $\\norm{Pz}$: in the supremum defining\n$\\norm{Pz}$, replace each unit testing vector by its $Q$-image, whose\nnorm is at most one and whose pairing with $z$ is unchanged.  The\ndimension does not increase.\n\nIdentify tensors with matrices from $H_2$ to $H_1$.  Choose a\nHilbert--Schmidt orthonormal basis $A_1,\\ldots,A_r$ of the testing\nspace, where $r\\le d$.  The matrices\n\\[\n R_1=\\sum_jA_jA_j^*,\\qquad R_2=\\sum_jA_j^*A_j\n\\]\nhave trace $r$.  Let $E_1,E_2$ be their spectral subspaces for\neigenvalues strictly greater than $\\sqrt d$; each has dimension at most\n$\\sqrt d$.\n\nDiscard indices for which the projection of $x_j$ onto $E_1$ has norm\ngreater than $\\varepsilon/10$, and do the same for $y_j,E_2$.\nCover either exceptional subspace's unit ball by at most\n$(1+80/\\varepsilon)^{\\sqrt d}$ balls of radius $\\varepsilon/40$.\nIf one ball contains $s$ of the discarded projections, their average\nhas norm at least $\\varepsilon/20$.  Applying\n\\eqref{eq:packing-cluster} to the original vectors, with\n$\\eta\\le\\varepsilon^2/10^4$, gives\n$s\\le C(1+L)\\varepsilon^{-2}$.  The number of discarded indices is\ntherefore at most\n\\begin{equation}\\label{eq:packing-discarded}\n C(1+L)\\varepsilon^{-2}\n   \\exp\\!\\bigl(\\sqrt d\\log(1+80/\\varepsilon)\\bigr).\n\\end{equation}\n\nLet $Q_1,Q_2$ project onto $E_1^\\perp,E_2^\\perp$, and put\n$B_j=Q_1A_jQ_2$.  The coefficient map\n$z\\mapsto(\\ip{A_j}{z}_{\\rm HS})_{j=1}^r$ is a contraction.\nFor a remaining index,\n\\[\n \\norm{x_j\\otimes y_j-Q_1x_j\\otimes Q_2y_j}\n \\le\\norm{(1-Q_1)x_j}+\\norm{(1-Q_2)y_j}\n \\le\\varepsilon/5.\n\\]\nThus each remaining index counted in\n\\eqref{eq:packing-quantitative} has coefficient vector\n\\[\n v_j=(\\ip{x_j}{B_ay_j})_{a=1}^r,\n \\qquad\\norm{v_j}\\ge\\varepsilon/2.\n\\]\nThe map from $x_j\\otimes y_j$ to $v_j$ remains contractive.\nOutside the union of the two exceptional graphs, the tensor inner\nproducts have absolute value at most $\\eta^2$, and that union has\ndegree at most $2L$.  A collection of these coefficient vectors in a\nball of radius $\\varepsilon/8$ centered at one of them has average\nnorm at least $3\\varepsilon/8$.  Applying\n\\eqref{eq:packing-cluster} to its preimage tensors bounds its size by\n$C(1+L)\\varepsilon^{-2}$.  A maximal $\\varepsilon/8$-separated subset\ntherefore has cardinality $l$ such that the number of indices still\nto be counted is at most $C(1+L)\\varepsilon^{-2}l$.\n\nWe now bound this separated set.  For $G=\\sum_ag_aB_a$, the centered\nGaussian variables $Z_j=\\ip{x_j}{Gy_j}$ satisfy\n\\[\n \\E|Z_j-Z_{j'}|^2=\\norm{v_j-v_{j'}}^2.\n\\]\nGaussian comparison gives, when $l\\ge2$,\n\\begin{equation}\\label{eq:packing-comparison}\n c\\varepsilon\\sqrt{\\log l}\n       \\le\\E\\max_j Z_j\\le\\E\\norm{G}_{\\rm op}.\n\\end{equation}\nFor completeness, the comparison of finite centered Gaussian families\nfollows by applying Gaussian integration by parts to\n$\\beta^{-1}\\log\\sum_j e^{\\beta z_j}$ along their independent Gaussian\ninterpolation.  Its derivative is\n\\[\n \\frac\\beta4\\E\\sum_{j,j'}p_jp_{j'}\n \\left(\\E|Z_j-Z_{j'}|^2-\\E|Z'_j-Z'_{j'}|^2\\right),\n \\qquad p_j=\\frac{e^{\\beta z_j}}{\\sum_a e^{\\beta z_a}}.\n\\]\nIt is nonnegative when the first increment variances dominate.\nLetting $\\beta\\to\\infty$ compares the expected maxima.  For\n\\eqref{eq:packing-comparison}, take the $Z'_j$ to be independent\nnormals of variance $\\varepsilon^2/128$ and use\n$\\E\\max_{j\\le l}g_j\\ge c\\sqrt{\\log l}$.\n\nCompression gives\n\\[\n \\sum_jB_jB_j^*\\le Q_1R_1Q_1,\\qquad\n \\sum_jB_j^*B_j\\le Q_2R_2Q_2.\n\\]\nThese operators have norms at most $\\sqrt d$ and traces at most $d$.\nLemma~\\ref{lem:packing-gaussian} therefore applies.  Together with\n\\eqref{eq:packing-comparison} it yields\n\\[\n \\log l\\le C\\varepsilon^{-2}\\sqrt d\\log(2+d).\n\\]\nThe resulting bound holds for $l\\le1$ as well.  Combine it with\n\\eqref{eq:packing-discarded} and absorb\n$\\log(1+80/\\varepsilon)\\le C\\varepsilon^{-2}$ to prove\n\\eqref{eq:packing-quantitative}.\n\nFor $d=d_m$, the exponent is\n$O_\\varepsilon((\\log m)^{3/4}\\log\\log m)=o(\\log m)$,\nwhich proves the asserted $o(m)$ bound.\n\\end{proof}\n\nSince projection norms are at most one, the estimate also gives\n\\begin{equation}\\label{eq:packing-average}\n \\frac1m\\sum_{j=1}^m\\norm{P(x_j\\otimes y_j)}\n \\le\\varepsilon+\n \\frac{C(1+L)}{m\\varepsilon^2}\n \\exp\\!\\left(C\\varepsilon^{-2}\\sqrt{d_m}\\log(2+d_m)\\right)\n\\end{equation}\nwhen $\\operatorname{rank}P\\le d_m$.  Here $\\varepsilon$ and then\n$\\eta$ are fixed before $m$ increases; $L$ may depend on that chosen\n$\\eta$.  This is exactly the order of quantifiers supplied by mixing\nin Proposition~\\ref{prop:channel-closure}.\n"}, {"path": "preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/selected.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/selected.tex", "bytes": 16137, "sha256": "2a0338f808b7d93c98f4d2beb400082e83628f584a5c60a1e3e0d9ec921daeba", "content": "\\section{Selected lengths and their joining laws}\\label{sec:selected}\n\nWe assume that the asserted pointwise convergence fails for the fixed\nset of slopes $I=\\{1,\\ldots,n\\}$.  This section records that failure in\na probability law and constructs compatible laws on all flow extensions.\nThe construction retains a positive correlation while forcing every\none-, two-, or three-slot marginal to have its ordinary conditional law.\nWe follow the selector and joining construction in\n\\cite[Sections~3--6]{FourfoldCompanion}, keeping the extension and\nconditioning properties explicit for the arbitrary number of slots.\n\n\\subsection{A positive witness at selected lengths}\n\nExpanding each function into its mean and centered part, and then into\nreal and imaginary parts, gives a nonempty $J_0\\subseteq I$ and real\nfunctions $f_i$, $i\\in J_0$, such that\n\\[\n  \\int_X f_i\\,\\dd\\mu=0,\\qquad \\norm{f_i}_\\infty\\le1,\n  \\qquad\n  B_L(x)=\\frac1L\\sum_{l=1}^L\\prod_{i\\in J_0}f_i(T^{il}x)\n\\]\ndoes not tend to zero on a set of positive measure.  Here and below\n$B_L$ has an integer subscript.  Lemma~\\ref{lem:relative-norm}, applied\nover the trivial factor, gives $B_L\\to0$ in $L^2(\\mu)$.  After changing\nthe sign of one $f_i$, there are $\\delta>0$ and a measurable set\n$E_*\\subseteq X$ of positive measure such that\n\\begin{equation}\\label{eq:failure-witness}\n  B_L(x)>2\\delta\\quad\\text{for arbitrarily large integers }L,\n  \\qquad x\\in E_*.\n\\end{equation}\n\nWe use the roof-one suspension\n\\begin{equation}\\label{eq:suspension}\n  C=X\\times[0,1),\\qquad m_C=\\mu\\otimes\\dd u,\n  \\qquad\n  S_C^t(x,u)=\\bigl(T^{\\lfloor u+t\\rfloor}x,\\{u+t\\}\\bigr).\n\\end{equation}\nWrite $\\xi(x,u)=x$ and $\\upsilon(x,u)=u$, regarding $\\upsilon$ also as\na coordinate in the circle $\\R/\\Z$.\n\n\\begin{lemma}[Selected positive witness]\\label{lem:selected-witness}\nThere are a finite set $F\\subseteq[1,2]$ containing $1$ and measurable\nfunctions $N_a:C\\to[2^a,\\infty)$, $a\\in\\N$, independent of the phase,\nwith\n\\begin{equation}\\label{eq:selector-grid}\n  N_a(x,u)\\in\\{c2^k:c\\in F,\\ k\\ge a\\},\\qquad\n  N_a(x,u)=2^a\\quad(x\\notin E_*),\n\\end{equation}\nsuch that $B_{\\lfloor N_a(x,u)\\rfloor}(x)>\\delta$ on $E_*$.\nMoreover, there is a continuous nonnegative function $\\Psi$ on\n$(\\R/\\Z)^I$ for which\n\\begin{equation}\\label{eq:selected-positive-limit}\n \\liminf_{a\\to\\infty}\\int_C\\frac1{N_a(z)}\n \\int_0^{N_a(z)}\n \\Psi\\bigl((\\upsilon(S_C^{it}z))_{i\\in I}\\bigr)\n \\prod_{i\\in J_0}f_i\\bigl(\\xi(S_C^{it}z)\\bigr)\n \\,\\dd t\\,\\dd m_C(z)>0.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nBy~\\eqref{eq:length-change}, changing the length from $L$ to $M$\nchanges an average of a sequence bounded by one by at most\n$2\\abs{M-L}/\\max(M,L)$.  Choose $F$ to be a sufficiently fine finite\ngrid in $[1,2]$.  For every sufficiently large integer $L$, some\n$c\\in F$ and $k=\\lfloor\\log_2L\\rfloor$ satisfy\n\\[\n  \\abs{B_L(x)-B_{\\lfloor c2^k\\rfloor}(x)}<\\delta\n  \\qquad\\text{for every }x.\n\\]\nEquation~\\eqref{eq:failure-witness} therefore supplies, for each\n$x\\in E_*$ and $a$, a pair $(c,k)$ with $k\\ge a$ at which the latter\naverage exceeds $\\delta$.  Choose the first such pair in a fixed\nenumeration.  This is a measurable choice from a countable set and\ndefines $N_a$ on $E_*$; use $2^a$ elsewhere.\n\nChoose continuous nonnegative circle functions $\\alpha,\\beta$, neither\nidentically zero, supported respectively inside $(1/4,1/2)$ and\n$(0,1/(4n))$.  Set\n\\begin{equation}\\label{eq:phase-witness}\n \\Psi(v)=\\alpha(2v_1-v_2)\\beta(v_2-v_1),\n \\qquad v=(v_i)_{i\\in I}\\in(\\R/\\Z)^I.\n\\end{equation}\nFor $z=(x,u)$ and $t=l+s$, where $l\\in\\Z$ and $0\\le s<1$, the\nphase factor is $\\alpha(u)\\beta(s)$.  If it is nonzero, then\n$0<u+is<1$ for every $i\\in I$, so\n$\\xi(S_C^{it}z)=T^{il}x$.  Summing complete unit intervals gives,\nuniformly in $z$ and real $L\\ge2$,\n\\begin{align}\n &\\frac1L\\int_0^L\n \\Psi\\bigl((\\upsilon(S_C^{it}z))_i\\bigr)\n \\prod_{i\\in J_0}f_i\\bigl(\\xi(S_C^{it}z)\\bigr)\\,\\dd t\n \\notag\\\\\n &\\hspace{18mm}\n =\\alpha(u)\\left(\\int_0^1\\beta(s)\\,\\dd s\\right)\n B_{\\lfloor L\\rfloor}(x)+O(L^{-1}).\n \\label{eq:suspension-witness}\n\\end{align}\nThe error includes the incomplete last interval, the change from\nindices $0,\\ldots,\\lfloor L\\rfloor-1$ to $1,\\ldots,\\lfloor L\\rfloor$,\nand the normalization.  On $E_*$ the main term at $L=N_a(z)$ has\nintegral at least\n\\[\n \\delta\\mu(E_*)\n \\left(\\int_0^1\\alpha(u)\\,\\dd u\\right)\n \\left(\\int_0^1\\beta(s)\\,\\dd s\\right)>0.\n\\]\nOn $X\\setminus E_*$ its integral tends to zero because $N_a=2^a$\nthere and $B_{2^a}\\to0$ in $L^2(\\mu)$.  The uniform error is\n$O(2^{-a})$, proving~\\eqref{eq:selected-positive-limit}.\n\\end{proof}\n\n\\subsection{Selected laws on all extensions}\n\nFix these selectors and a free ultrafilter $\\mathcal U$ on $\\N$.\nA \\emph{flow over $C$} is a standard probability space $(Y,m_Y)$\nwith a jointly Borel probability-preserving action $S_Y^t$, $t\\in\\R$,\nand a probability-preserving factor map $\\pi_Y:Y\\to C$ satisfying\n$\\pi_Y S_Y^t=S_C^t\\pi_Y$ modulo null sets for each fixed $t$.\nAn extension $\\rho:Y'\\to Y$ of such flows is a probability-preserving\nfactor map, equivariant in the same sense, with\n$\\pi_{Y'}=\\pi_Y\\rho$ modulo null sets.  We do not require a common\nconull set on which every real-time factor identity holds.  Joint\nmeasurability and Fubini justify their uses inside time integrals.\n\n\\begin{proposition}[Selected joining laws]\\label{prop:selected-law}\nEvery flow $Y$ over $C$ has a unique probability law $\\lambda_Y$ on\n$Y^I$ satisfying, for all bounded measurable functions $G_i$ on $Y$,\n\\begin{equation}\\label{eq:selected-law}\n \\int_{Y^I}\\prod_{i\\in I}G_i(y_i)\\,\\dd\\lambda_Y\n =\\lim_{a\\to\\mathcal U}\\int_Y\\frac1{N_a(\\pi_Yy)}\n   \\int_0^{N_a(\\pi_Yy)}\\prod_{i\\in I}G_i(S_Y^{it}y)\n   \\,\\dd t\\,\\dd m_Y(y).\n\\end{equation}\nIts marginals are $m_Y$, and it is invariant under\n$(y_i)_i\\mapsto(S_Y^{it}y_i)_i$, $t\\in\\R$.\nIf $\\rho:Y'\\to Y$ is an extension over $C$, then\n$(\\rho^I)_*\\lambda_{Y'}=\\lambda_Y$.\n\nCountable inverse limits of extensions over $C$ exist in this\ncategory.  The selected law at such a limit projects to the selected\nlaw at every stage.  Pullbacks of the stage selected-law spaces have\ndense union in $L^2$ of the limit selected law.\n\\end{proposition}\n\n\\begin{proof}\nFor each $a$ the expression before the ultralimit defines a genuine\nprobability measure $\\lambda_{Y,a}$ on $Y^I$, by integrating the\nmeasurable orbit map over the indicated finite time interval.  We\nfirst show that each of its marginals converges against every bounded\nmeasurable test $G$ to $m_Y$.  The one-slot continuous-time averages\n\\[\n \\frac1L\\int_0^L G(S_Y^{it}y)\\,\\dd t\n\\]\nconverge almost surely as $L\\to\\infty$.  For example, apply Birkhoff's\ntheorem for $S_Y^i$ to the function\n$y\\mapsto\\int_0^1G(S_Y^{is}y)\\,\\dd s$ and discard the incomplete\nunit interval.  Their almost-sure limit has integral $\\int G\\,\\dd m_Y$,\nsince every deterministic average has that integral.  As\n$N_a(\\pi_Yy)\\ge2^a$, bounded convergence gives the same integral\nlimit at the selected lengths.\n\nEmbed $Y$ as a Borel subset of a compact metrizable space $K$.\nUltralimits of the $\\lambda_{Y,a}$ against continuous functions on\n$K^I$ give a positive normalized functional, hence a probability\nmeasure on $K^I$.  Its marginals are $m_Y$, by the preceding paragraph,\nso it is concentrated on $Y^I$.  Call the resulting law $\\lambda_Y$.\nTo obtain~\\eqref{eq:selected-law} for arbitrary bounded slot functions,\nnormalize their bounds to one and approximate each in $L^1(m_Y)$ by\ncontinuous functions on $K$ with the same bound.  The difference\nbetween the two product integrals is bounded by the sum of the\nsingle-slot errors.  Under $\\lambda_Y$ these errors are the\n$L^1(m_Y)$ errors, while under $\\lambda_{Y,a}$ they converge to those\nerrors by the marginal convergence already proved.  Letting the\nerrors tend to zero proves the formula.  Product tests determine a\nprobability measure on $Y^I$, giving uniqueness.  In particular, all\nthese identities depend only on the almost-everywhere classes of\ntheir slot functions.\n\nApplying the product action at a fixed time $t_0$ shifts the time\ninterval in~\\eqref{eq:selected-law} by $t_0$ without changing its\nlength or its initial-point selector.  For slot functions bounded by\none the error is at most $2\\abs{t_0}2^{-a}$, proving invariance.\nFor an extension $\\rho$, substitute $G_i\\circ\\rho$ in the formula.\nEquivariance, the equality of the selectors, and Fubini identify its\nright-hand side with that for $Y$, proving functoriality.\n\nFor a tower $Y_0\\leftarrow Y_1\\leftarrow\\cdots$, take the full\ncountable product of the underlying standard spaces, with the\nconsistent inverse-limit probability measure and coordinatewise\nflow.  This is a jointly Borel action.  A cylinder through stage $r$\nhas probability computed from $m_{Y_r}$ by the finitely many bonding\nmaps.  At any fixed time their equivariance and preservation of\n$m_{Y_r}$ leave that probability unchanged.  Cylinders determine the\nmeasure, so every fixed time preserves it.  The measure is\nconcentrated on compatible sequences; using\nthe full product avoids any need for one compatibility set invariant\nat all real times.  The stage projections are factor maps modulo\nnull sets, and projection through $Y_0$ gives the factor onto $C$.\nFunctoriality now gives the asserted selected-law projections.\nFinally, the stage coordinates generate the inverse-limit\nsigma-algebra.  The compatibility relations hold under the selected\nlaw slot by slot because its marginals are the inverse-limit\nprobability measure.  Thus the pulled-back stage sigma-algebras on\nthe selected-law space are increasing and generate it modulo null\nsets.  The usual $L^2$ approximation by an increasing sequence of\nsigma-algebras proves density.\n\\end{proof}\n\n\\subsection{Distal parameters and small marginals}\n\nFor a flow $Y$ over $C$, let\n$\\omega_Y:Y\\to W_Y$ be its maximal measure-distal factor for time one,\nand let $\\nu_Y=(\\omega_Y)_*m_Y$.  Write $m_{Y,w}$ for its conditional\nprobability measures, so that\n\\[\n m_Y=\\int_{W_Y}m_{Y,w}\\,\\dd\\nu_Y(w).\n\\]\nUnder $\\lambda_Y$ put\n\\begin{equation}\\label{eq:distal-parameters}\n P_i=\\omega_Y(y_i),\\qquad P=(P_i)_{i\\in I}.\n\\end{equation}\nProposition~\\ref{prop:structure} ensures that $W_Y$ is preserved by\nevery flow time.  Such statements about factor sigma-algebras are\nunderstood modulo null sets; all time integrals below are evaluated\non the original jointly Borel flow.  If $\\rho:Y'\\to Y$ is an extension,\nthe pullback of $W_Y$ is distal for time one and hence is contained\nin $W_{Y'}$.  Consequently the old parameter array is a measurable\nfunction of the new parameter array on selected-law spaces.\n\n\\begin{proposition}[Parameter and small-marginal laws]\n\\label{prop:selected-parameters}\nFor every flow $Y$ over $C$ and bounded measurable $H_i$ on $W_Y$,\n\\begin{equation}\\label{eq:deterministic-parameters}\n \\E_{\\lambda_Y}\\prod_{i\\in I}H_i(P_i)\n =\\lim_{L\\to\\infty}\\int_Y\\frac1L\\int_0^L\n   \\prod_{i\\in I}H_i\\bigl(\\omega_Y(S_Y^{it}y)\\bigr)\n   \\,\\dd t\\,\\dd m_Y(y).\n\\end{equation}\nFor every nonempty $J\\subseteq I$ with $\\abs{J}\\le3$,\n\\begin{equation}\\label{eq:small-conditional-law}\n \\law_{\\lambda_Y}\\bigl((y_i)_{i\\in J}\\mid P\\bigr)\n   =\\bigotimes_{i\\in J}m_{Y,P_i}.\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nWe first explain the passage from discrete to continuous time used\nin both assertions.  Write $t=l+s$, with $l\\ge0$ an integer and\n$0\\le s<1$.  For each fixed $s$, a continuous-time product becomes\nthe discrete product at slopes $i$ for the shifted slot functions\n$G_i\\circ S_Y^{is}$.  Whenever the corresponding discrete averages\nconverge almost surely, Fubini and bounded convergence show that\ntheir integrals in $s$ converge almost surely.  Starting the discrete\naverage at $l=0$ rather than $l=1$, and dropping an incomplete final\nunit interval, each has an error tending to zero.  The resulting\ncontinuous-time convergence holds along all real lengths $L$.\n\nApply this observation to functions from $W_Y$.  Their shifts still\nbelong to $W_Y$, and pointwise convergence on the distal factor is\npart of Proposition~\\ref{prop:structure}.  Thus the time averages\nin~\\eqref{eq:deterministic-parameters} converge almost surely along\nall lengths, and therefore at the selected lengths as well.  Bounded\nconvergence and~\\eqref{eq:selected-law} prove that equation.\n\nFor $\\abs{J}\\le3$, Lemma~\\ref{lem:triple-convergence} and the same\ncontinuous-time argument show that the selected $J$-marginal equals\nits deterministic-length integrated law.  The extension\n$Y\\to W_Y$ is relatively weakly mixing.  Apply\nLemma~\\ref{lem:relative-norm} to the distinct integer slopes\n$i\\in J$ and the shifted functions $G_i\\circ S_Y^{is}$, for each\nfixed $s$.  Conditional expectation commutes with these shifts\nbecause $W_Y$ is preserved.  Integration in $s$, using the uniform\nnorm bound and bounded convergence, gives\n\\begin{equation}\\label{eq:small-replacement}\n \\E_{\\lambda_Y}\\prod_{i\\in J}G_i(y_i)\n =\\E_{\\lambda_Y}\\prod_{i\\in J}\n    \\E_{m_Y}[G_i\\mid W_Y](y_i).\n\\end{equation}\nIndeed the difference of the deterministic averages tends to zero\nin $L^2(m_Y)$, so also in their integrated values.  Applying this\nidentity to $G_i$ multiplied by arbitrary bounded functions of\n$P_i$ yields\n\\begin{equation}\\label{eq:small-own-parameters}\n \\law_{\\lambda_Y}\\bigl((y_i)_{i\\in J}\\mid(P_i)_{i\\in J}\\bigr)\n =\\bigotimes_{i\\in J}m_{Y,P_i}.\n\\end{equation}\n\nConditioning on the full parameter array requires one more step;\nit does not follow from~\\eqref{eq:small-own-parameters} alone.\nUse the invariant transformation with speed $i$ in slot $i$.\nBy the stability under nonzero powers and conditional products in\nProposition~\\ref{prop:structure}, the $J$-marginal system\nin~\\eqref{eq:small-own-parameters} is relatively weakly mixing over\nits parameter law.  The full $P$-system is a joining of the distal\nsystems $W_Y$ at these integer powers.  It is distal, and is\ntherefore relatively distal over the subarray $(P_i)_{i\\in J}$.\nTheir joint law under $\\lambda_Y$ is an invariant joining\nidentifying that common subarray.  Relative disjointness in\nProposition~\\ref{prop:structure} makes this joining conditionally\nindependent over the subarray, which is precisely\n\\eqref{eq:small-conditional-law}.\n\\end{proof}\n\nThe full parameter law is thus unaffected by the selectors, although\nthe full state law need not be.  We now turn that state law into a new\nflow while preserving one chosen slot as the original system.\n\n\\begin{lemma}[Normalized joining flow]\\label{lem:normalized-joining}\nFor $b\\in I$, define\n\\begin{equation}\\label{eq:normalized-joining}\n \\mathsf M_b(Y)=\n \\left(Y^I,\\lambda_Y,\n   S_{\\mathsf M_b(Y)}^t(y_i)_i=(S_Y^{it/b}y_i)_i\\right).\n\\end{equation}\nThis is a standard probability-preserving flow over $C$ via\n$(y_i)_i\\mapsto\\pi_Y(y_b)$, and projection onto slot $b$ is an\nextension onto $Y$.  The array $P$ belongs to the maximal\nmeasure-distal factor of $\\mathsf M_b(Y)$ for time one.\n\\end{lemma}\n\n\\begin{proof}\nInvariance follows from Proposition~\\ref{prop:selected-law}, after\nrescaling the time parameter by $1/b$.  Slot $b$ has speed one and\nmarginal $m_Y$, giving the stated factor maps.  In slot $i$ the\n$W_Y$-factor is distal for time $i/b$, by\nProposition~\\ref{prop:structure}.  The law of $P$ is an invariant\njoining of these rational-time distal systems, hence is distal.\nMaximality of the distal factor gives the last assertion.\n\\end{proof}\n\n\\begin{lemma}[The retained positive correlation]\\label{lem:positive-witness}\nWith the functions and phase test of Lemma~\\ref{lem:selected-witness},\n\\begin{equation}\\label{eq:positive-witness}\n \\int_{C^I}\\Psi\\bigl((\\upsilon(z_i))_i\\bigr)\n    \\prod_{i\\in J_0}f_i\\bigl(\\xi(z_i)\\bigr)\\,\\dd\\lambda_C>0.\n\\end{equation}\nThe phase array in this integral is measurable in the distal\nparameter array of $C^I$.\n\\end{lemma}\n\n\\begin{proof}\nContinuous functions on the compact product of circles are uniformly\napproximable by finite sums of products of continuous one-coordinate\nfunctions.  Apply this to $\\Psi$, use~\\eqref{eq:selected-law} for\neach resulting product, and then use the strictly positive lower\nlimit in~\\eqref{eq:selected-positive-limit}.  Finally, time one fixes\nthe phase on $C$, so the phase factor is distal and lies in $W_C$.\n\\end{proof}\n"}, {"path": "preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/square.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/build/sections/square.tex", "bytes": 11913, "sha256": "068d972333248fc07c4595c65dbdee0b89c7a8428800a62bfcf52fa9cddb74d0", "content": "\\section{The square argument}\n\\label{sec:square}\n\nWe now use the saturated channel $Y$, its nonzero correlation\n\\eqref{eq:role-correlation}, and the regrouped channel\n$Z=\\mathsf M_b(Y)$ from Section~\\ref{sec:correlations}. The selected\nlaw $\\lambda_Z$ is a law on an ordered square of $Y$-states. Its\n$b$th row and $b$th column both have law $\\lambda_Y$, but for different\nreasons. Saturation will relate their correlations without requiring\nany symmetry of the square or any pointwise theorem for many slots\non an arbitrary extension.\n\n\\subsection{A common corner and two conditional expectations}\n\nOn $(Y^I,\\lambda_Y)$, put\n\\begin{equation}\n q((y_i)_{i\\in I})=\n    \\prod_{j\\in J\\setminus\\{b\\}}h_j(L_j(y_j)),\n \\qquad\n g(P,y_b)=\\E_{\\lambda_Y}[q\\mid P,y_b].\n \\label{eq:q-and-g}\n\\end{equation}\nThe function $g$ is not zero in $L^2(\\lambda_Y)$: otherwise the\nnonzero correlation would vanish after conditioning on $(P,y_b)$.\nOn the same state space regarded as $Z$, the function $q$ reads only\nthe newly added outputs. Write $e=\\omega_Z(z)$ for the new distal\nparameter and define\n\\begin{equation}\n u(e,y_b)=\\E_{m_Z}[q\\mid e,y_b].\n \\label{eq:u}\n\\end{equation}\nAll these functions have bounded real Borel versions.\n\nBy Lemma~\\ref{lem:normalized-joining}, the old array $P$ is a factor\nof the new distal parameter. Write\n\\[\n R(e)=P,\n \\qquad \\kappa(e)=R(e)_b.\n\\]\nThus $\\kappa\\circ\\omega_Z$ is the old distal parameter of the\nslot-$b$ factor $Z\\to Y$.\n\nA point of the selected law space $(Z^I,\\lambda_Z)$ has the form\n\\[\n \\mathbf z_i=(y_{ij})_{j\\in I},\\qquad\n E_i=\\omega_Z(\\mathbf z_i),\\qquad E=(E_i)_{i\\in I}.\n\\]\nThe first index specifies the row. Define the column states and their\nparameters by\n\\begin{equation}\n y_i=y_{ib},\\qquad\n p=(\\kappa(E_i))_{i\\in I},\\qquad\n w=\\kappa(E_b).\n \\label{eq:square-parameters}\n\\end{equation}\nIn particular $y_b=y_{bb}$ is the common corner. The column\n$(y_i)_{i\\in I}$ has law $\\lambda_Y$, by functoriality for $Z\\to Y$.\nThe row $\\mathbf z_b$ also has law $\\lambda_Y$, because the one-slot\nmarginal of $\\lambda_Z$ is $m_Z=\\lambda_Y$.\nFigure~\\ref{fig:square} records these distinct identifications.\n\n\\input{figures/square}\n\n\\begin{lemma}[The row--column identity]\n\\label{lem:row-column}\nUnder $\\lambda_Z$,\n\\begin{equation}\n \\E\\big[q((y_i)_i)q(\\mathbf z_b)\\mid E\\big]\n   =\\int_Y g(p,y)u(E_b,y)\\,\\dd m_{Y,w}(y).\n \\label{eq:row-column}\n\\end{equation}\nMoreover, in a single $Z$-state with law $m_Z$, the conditional law\nof its slot-$b$ state given $e$ is $m_{Y,\\kappa(e)}$.\n\\end{lemma}\n\n\\begin{proof}\nApply saturation \\eqref{eq:saturation} with datum index $0$ to the\nextension $Z\\to Y$. It says that the conditional law of the entire\ncolumn given $E$ is the law of $(Y^I,\\lambda_Y)$ given $p$.\nThis assertion follows first for $L^2$ tests, and then as an identity\nof conditional kernels by a countable determining class. By the\nsingleton case of \\eqref{eq:small-conditional-law}, the conditional\nlaw of $y_b$ given $E$ is consequently $m_{Y,w}$. Conditioning down\nto $E_b$ gives the same law for the slot-$b$ state of row $b$ given\n$E_b$. Since row $b$ has marginal $m_Z$, this proves the last\nassertion of the lemma.\n\nSaturation with datum index $b$ gives the stronger identity\n\\begin{equation}\n \\E_{\\lambda_Z}[q((y_i)_i)\\mid E,\\mathbf z_b]=g(p,y_b).\n \\label{eq:column-single-row}\n\\end{equation}\nIndeed the old datum is $(P,y_b)$ and the new datum is\n$(E,\\mathbf z_b)$. Multiply \\eqref{eq:column-single-row} by\n$q(\\mathbf z_b)$ and condition on $E$. The singleton case of\n\\eqref{eq:small-conditional-law}, now for $Z$, states that the law\nof $\\mathbf z_b$ given $E$ is the $m_Z$-fibre law over $E_b$.\nWithin that fibre, \\eqref{eq:u} conditions $q(\\mathbf z_b)$ on its\nslot-$b$ state, whose law is $m_{Y,w}$ by the first part of the proof.\nThis gives \\eqref{eq:row-column}.\n\\end{proof}\n\n\\subsection{Independence of the two parameter descriptions}\n\nThe integral in \\eqref{eq:row-column} pairs a column function with\na row function. Its nonvanishing will follow from the fact that,\napart from the parameter at their common corner, the two parameter\ndescriptions are independent.\n\n\\begin{lemma}[Parameter independence]\n\\label{lem:parameter-independence}\nUnder $\\lambda_Z$, the random variables $p$ and $E_b$ are\nconditionally independent given $w$.\n\\end{lemma}\n\n\\begin{proof}\nLet $F_0$ be a bounded real test on $W_Z$. In the single-state\nparameter law $(\\omega_Z)_*m_Z$, let $\\overline F_0$ satisfy\n\\[\n \\overline F_0(\\kappa(e))=\\E[F_0(e)\\mid\\kappa(e)].\n\\]\nWe prove that $F_0(E_b)$ can be replaced by\n$\\overline F_0(w)$ when tested against any product\n$\\prod_i H_i(p_i)$ of bounded real coordinate tests. All the\nvariables are functions of $E$, so the deterministic parameter-law\nidentity \\eqref{eq:deterministic-parameters} on $Z$ computes their\njoint integral. At each fixed averaging time $t$, its state integral\nis\n\\[\n \\int_Z F_0(\\omega_Z(S_Z^{bt}z))\n    \\prod_{i\\in I}H_i\\big(\\kappa(\\omega_Z(S_Z^{it}z))\\big)\n    \\,\\dd m_Z(z).\n\\]\nMake the measure-preserving change of variable $z'=S_Z^{bt}z$.\nThe product of the $H_i$ becomes\n\\[\n \\prod_{i\\in I}H_i\\big(\n    \\kappa(\\omega_Z(S_Z^{(i-b)t}z'))\\big).\n\\]\nFor each fixed $t$, this is measurable with respect to\n$\\kappa(\\omega_Z(z'))$. To see this, the slot-$b$ map $Z\\to Y$\nis a flow factor, and the old factor $W_Y$ is preserved by every\nflow time. Hence the old parameter at time $(i-b)t$ is a measurable\nfunction of the old parameter at time zero.\n\nConditional expectation in this single-state integral therefore\nreplaces the first factor by\n\\[\n \\overline F_0\\bigl(\\kappa(\\omega_Z(z'))\\bigr).\n\\]\nReversing the change of\nvariable and taking the deterministic averaging limit gives\n\\[\n \\E_{\\lambda_Z}\\left[F_0(E_b)\\prod_i H_i(p_i)\\right]\n =\\E_{\\lambda_Z}\\left[\\overline F_0(w)\\prod_i H_i(p_i)\\right].\n\\]\nThe fixed-time identities suffice under the time integral by\nmeasurability and Fubini; no simultaneous pointwise model for the\nfactor action is needed. The $E_b$-marginal is the single-state\nparameter law, so $\\overline F_0(w)=\\E[F_0(E_b)\\mid w]$.\nProducts of the $H_i$ determine the $p$-law, and $w=p_b$ is itself\na coordinate of $p$. The displayed identity is exactly conditional\nindependence given $w$.\n\\end{proof}\n\n\\subsection{Why the pairing cannot vanish}\n\nLet $\\alpha_w$ be the conditional law of the column parameter\narray $p$ over its $b$th parameter $w$. Let $\\beta_w$ be the\nconditional law of $E_b$ over $w$. These are regular conditional\nlaws on standard spaces. Lemma~\\ref{lem:parameter-independence}\ngives\n\\begin{equation}\n \\law(p,E_b\\mid w)=\\alpha_w\\otimes\\beta_w,\n \\qquad R_*\\beta_w=\\alpha_w.\n \\label{eq:parameter-disintegrations}\n\\end{equation}\nThe second identity uses the row marginal $m_Z=\\lambda_Y$:\nits old full parameter array $R(e)$ has the same law as the column\nparameter array, with the same distinguished $b$th coordinate.\n\nFor almost every $w$, work in the separable real Hilbert space\n\\[\n  \\mathcal H_w=L^2(m_{Y,w};\\R).\n\\]\nThe bounded functions $g(p,\\cdot)$ and $u(e,\\cdot)$ define\nsquare-integrable random vectors in this space, with laws of their\nparameters respectively $\\alpha_w$ and $\\beta_w$. There is no\nmeasurability ambiguity in this fibrewise assertion. Bounded Borel\nversions of $g$ and $u$ are fixed; disintegration and Fubini yield\ntheir sections for almost every $w$. A countable generating algebra\nof the standard Borel space $Y$ has rational simple span dense in\nevery $L^2(m_{Y,w})$. Tests against that span give weak measurability\nof the sections, and separability gives strong measurability.\n\nThe conditional mean of the second random vector recovers the first:\n\\begin{equation}\n \\E_{\\beta_w}[u(e,\\cdot)\\mid R(e)]\n     =g(R(e),\\cdot)\n \\quad\\text{in }\\mathcal H_w.\n \\label{eq:fibre-tower}\n\\end{equation}\nHere is the conditional-law justification. In a single $Z$-state,\nwrite $y$ for its slot-$b$ state. Lemma~\\ref{lem:row-column} gives,\nconditional on $w$,\n\\[\n  \\law(e,y\\mid w)=\\beta_w(\\dd e)m_{Y,w}(\\dd y).\n\\]\nConditioning \\eqref{eq:u} down on $(R(e),y)$ gives\n$g(R(e),y)$ by \\eqref{eq:q-and-g}, because $m_Z=\\lambda_Y$.\nUnder the preceding product law, conditioning $e$ on $(R(e),y)$\nis the same as conditioning it on $R(e)$. This proves\n\\eqref{eq:fibre-tower}. One can impose the scalar conditional\nidentities first for countably many generating tests and then use\nFubini and density to obtain the stated Hilbert-space identity for\nalmost every $w$.\n\nSuppose that the right side of \\eqref{eq:row-column} vanished\nalmost surely. By \\eqref{eq:parameter-disintegrations}, for almost\nevery $w$ this would give\n\\[\n  \\langle g(p,\\cdot),u(e,\\cdot)\\rangle_{\\mathcal H_w}=0\n  \\quad(\\alpha_w\\otimes\\beta_w)\\text{-almost surely}.\n\\]\nCondition on $R(e)=p'$ and use \\eqref{eq:fibre-tower}. We obtain\n\\begin{equation}\n \\langle g(p,\\cdot),g(p',\\cdot)\\rangle_{\\mathcal H_w}=0\n \\quad(\\alpha_w\\otimes\\alpha_w)\\text{-almost surely}.\n \\label{eq:independent-orthogonality}\n\\end{equation}\nA square-integrable random vector in a separable real Hilbert\nspace cannot be orthogonal almost surely to an independent copy\nunless it vanishes almost surely. Indeed, otherwise its norm is at\nleast some $\\epsilon>0$ with positive probability. By a countable\ncover with balls of radius $\\epsilon/4$, this event has positive\nprobability in one such ball. Two independent copies then have\npositive probability of both lying in that ball and having norm\nat least $\\epsilon$. Their distance is less than $\\epsilon/2$, so\n\\[\n \\langle v,v'\\rangle\n =\\tfrac12(\\|v\\|^2+\\|v'\\|^2-\\|v-v'\\|^2)>0,\n\\]\na contradiction. Apply this observation to\n\\eqref{eq:independent-orthogonality}. It forces $g(p,\\cdot)=0$\nfor $\\alpha_w$-almost every $p$, for almost every $w$.\nThe singleton conditional law for the column then implies\n$g(P,y_b)=0$ in $L^2(\\lambda_Y)$, contrary to\n\\eqref{eq:q-and-g}. Thus \\eqref{eq:row-column} does not vanish\nalmost surely.\n\n\\subsection{Increasing the number of output roles}\n\nChoose a bounded real function $\\Theta(E)$ that detects the\nnonzero conditional expectation in \\eqref{eq:row-column}; its sign\nis one such choice. Then\n\\begin{equation}\n \\E_{\\lambda_Z}\\left[\n   \\Theta(E)q((y_i)_i)q(\\mathbf z_b)\\right]\\ne0.\n \\label{eq:improved-correlation}\n\\end{equation}\nWe interpret this as a correlation on the regrouped channel $Z$.\nFor $i\\in J\\setminus\\{b\\}$, the factor\n$h_i(L_i(y_{ib}))$ retains its old role. An old output role uses\nonly the retained outputs from slot $b$. An old input role uses\nthe new original coordinate and retained outputs from its old\nproper subset $H_i$ of group numbers. It may ignore the additional\noutputs in those groups. Each such test is centered for its new\nproduct spatial law, since its marginal on the coordinates it\nactually uses is the old product law.\n\nAt row $b$, the factor $q(\\mathbf z_b)$ reads only the added outputs\nwith spatial lists $Q_j$, $j\\in J\\setminus\\{b\\}$, and thus has an\noutput role. It is centered: under the product spatial law of all\nnew outputs these lists are independent product lists, so the mean\nof this product of centered tests is zero. Hence\n\\eqref{eq:improved-correlation} is a nonzero correlation of support\nsize $k$ with $r+1$ output roles. This contradicts the maximal\nchoice of $r$.\n\n\\begin{proof}[Completion of the proof of Theorem~\\ref{thm:main}]\nIf the required pointwise limit failed for a fixed tuple and a\nfixed $n\\geq2$, the reduction and selectors of\nSection~\\ref{sec:selected} would give the positive correlation\n\\eqref{eq:positive-witness}. The minimum-support argument rules this\nout immediately when $n<4$, and the saturated, regrouped square\nargument rules it out when $n\\geq4$. Therefore the averages converge\nalmost everywhere to the product of the means, along all positive\ninteger lengths. Finally, the countable-coordinate coding reduction\npulls this conclusion back to the original, possibly nonstandard,\nprobability space. The tuple and $n$ were fixed throughout, as allowed\nin the statement of the theorem.\n\\end{proof}\n"}, {"path": "preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/multiple-ergodic-averages.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Pointwise-Multiple-Ergodic-Averages-for-Mixing-Transformations-October-4-2026/multiple-ergodic-averages.pdf", "bytes": 435847, "sha256": "b9d8fcffac455475a2d846d1e28904f920e10b73dff498b718783023524f7d77", "base64": 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"}, {"path": "preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/README.md", "bytes": 680, "sha256": "88ba083a2d303d8daeb9754894e82d2ec6641d74eba82b5bfa65726e344f0e41", "content": "# [Relative generation and the generator problem for finite factors](paper.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 23, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026,\n  author = {{OpenAI}},\n  title = {{Relative generation and the generator problem for finite factors}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/paper.pdf}{OAI:Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/main.tex", "bytes": 830, "sha256": "6f11a0a517df4ae39e1e9951a9162fb0e23c3c77fb4e84b77806cfa5579ade22", "content": "\\documentclass[11pt,letterpaper]{article}\n\\input{preamble}\n\\title{Relative generation and the generator problem\\\\for finite factors}\n\\author{OpenAI}\n\\date{September 23, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe prove that every type $\\mathrm{II}_1$ factor with separable predual is\ngenerated by one operator, equivalently by two self-adjoint operators.\nCombined with the established direct-integral reduction to this case,\nthis gives an affirmative solution of the generator problem for von\nNeumann algebras with separable predual.\n\\end{abstract}\n\\input{sections/introduction}\n\\input{sections/independence}\n\\input{sections/perturbation}\n\\input{sections/generation}\n\\input{sections/consequences}\n\\clearpage\n\\bibliographystyle{abbrvnat}\n\\begingroup\n\\small\n\\raggedright\n\\bibliography{references}\n\\endgroup\n\\end{document}\n"}, {"path": "preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/preamble.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/preamble.tex", "bytes": 1216, "sha256": "ee41ebb1183872849fc1858a7f8d4794d732ac3a101616018e6fda17120f8d6c", "content": "\\usepackage[T1]{fontenc}\n\\usepackage[utf8]{inputenc}\n\\usepackage{mathpazo}\n\\usepackage[margin=1.05in]{geometry}\n\\usepackage{microtype}\n\\usepackage{amsmath,amssymb,amsthm,mathtools,mathrsfs}\n\\usepackage{booktabs,array,graphicx}\n\\usepackage[numbers,sort&compress]{natbib}\n\\usepackage{xcolor}\n\\definecolor{linkblue}{RGB}{25,65,112}\n\\usepackage[colorlinks=true,linkcolor=linkblue,citecolor=linkblue,urlcolor=linkblue]{hyperref}\n\\urlstyle{same}\n\\hypersetup{pdftitle={Relative generation and the generator problem for finite factors},pdfauthor={OpenAI},pdfsubject={An affirmative solution of the generator problem for separable-predual type II1 factors},pdflang={en-US}}\n\\pdfinfoomitdate=1\n\\pdftrailerid{}\n\\numberwithin{equation}{section}\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\\newtheorem{definition}[theorem]{Definition}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\newcommand{\\norm}[1]{\\lVert #1\\rVert}\n\\newcommand{\\abs}[1]{\\lvert #1\\rvert}\n\\newcommand{\\R}{\\mathbb R}\n\\newcommand{\\N}{\\mathbb N}\n\\setlength{\\emergencystretch}{2em}\n\\allowdisplaybreaks[2]\n"}, {"path": "preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/references.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/references.bib", "bytes": 7988, "sha256": "47c3c5418074e753946cd68f3d1c608785263745724e6f19941b82a12fe83343", "content": "@article{Popa2014,\n  author = {Popa, Sorin},\n  title = {Independence properties in subalgebras of ultraproduct {$\\mathrm{II}_1$} factors},\n  journal = {Journal of Functional Analysis},\n  volume = {266},\n  number = {9},\n  pages = {5818--5846},\n  year = {2014},\n  doi = {10.1016/j.jfa.2014.02.004},\n  eprint = {1308.3982},\n  archivePrefix = {arXiv},\n  primaryClass = {math.OA},\n  url = {https://arxiv.org/abs/1308.3982}\n}\n\n@article{Popa1981,\n  author = {Popa, Sorin},\n  title = {On a problem of {R. V. Kadison} on maximal abelian {$*$}-subalgebras in factors},\n  journal = {Inventiones Mathematicae},\n  volume = {65},\n  number = {2},\n  pages = {269--281},\n  year = {1981},\n  doi = {10.1007/BF01389015},\n  url = {https://doi.org/10.1007/BF01389015}\n}\n\n@article{Sherman2012,\n  author = {Sherman, David},\n  title = {On cardinal invariants and generators for von {Neumann} algebras},\n  journal = {Canadian Journal of Mathematics},\n  volume = {64},\n  number = {2},\n  pages = {455--480},\n  year = {2012},\n  doi = {10.4153/CJM-2011-048-2},\n  eprint = {0908.4565},\n  archivePrefix = {arXiv},\n  primaryClass = {math.OA},\n  url = {https://arxiv.org/abs/0908.4565}\n}\n\n@article{Willig1974,\n  author = {Willig, Paul},\n  title = {Generators and direct integral decompositions of {$W^*$}-algebras},\n  journal = {Tohoku Mathematical Journal, Second Series},\n  volume = {26},\n  number = {1},\n  pages = {35--37},\n  year = {1974},\n  doi = {10.2748/tmj/1178241231},\n  url = {https://www.jstage.jst.go.jp/article/tmj1949/26/1/26_1_35/_article/-char/en}\n}\n\n@article{Popa2021Ergodic,\n  author = {Popa, Sorin},\n  title = {On ergodic embeddings of factors},\n  journal = {Communications in Mathematical Physics},\n  volume = {384},\n  number = {2},\n  pages = {971--996},\n  year = {2021},\n  doi = {10.1007/s00220-020-03865-3},\n  eprint = {1910.06923},\n  archivePrefix = {arXiv},\n  primaryClass = {math.OA},\n  url = {https://arxiv.org/abs/1910.06923}\n}\n\n@article{Popa2021Tight,\n  author = {Popa, Sorin},\n  title = {Tight decomposition of factors and the single generation problem},\n  journal = {Journal of Operator Theory},\n  volume = {85},\n  number = {1},\n  pages = {277--301},\n  year = {2021},\n  doi = {10.7900/jot.2019nov01.2279},\n  eprint = {1910.14653},\n  archivePrefix = {arXiv},\n  primaryClass = {math.OA},\n  url = {https://arxiv.org/abs/1910.14653}\n}\n\n@incollection{Popa1995,\n  author = {Popa, Sorin},\n  title = {Free-independent sequences in type {$\\mathrm{II}_1$} factors and related problems},\n  booktitle = {Recent advances in operator algebras---Orl{\\'e}ans, 1992},\n  series = {Ast{\\'e}risque},\n  number = {232},\n  publisher = {Soci{\\'e}t{\\'e} math{\\'e}matique de France},\n  pages = {187--202},\n  year = {1995},\n  url = {https://www.numdam.org/item/AST_1995__232__187_0/}\n}\n\n@article{Haagerup1979,\n  author = {Haagerup, Uffe},\n  title = {An example of a non nuclear {$C^*$}-algebra, which has the metric approximation property},\n  journal = {Inventiones Mathematicae},\n  volume = {50},\n  number = {3},\n  pages = {279--293},\n  year = {1979},\n  doi = {10.1007/BF01410082},\n  url = {https://doi.org/10.1007/BF01410082}\n}\n\n@article{Wogen1969,\n  author = {Wogen, Warren},\n  title = {On generators for von {Neumann} algebras},\n  journal = {Bulletin of the American Mathematical Society},\n  volume = {75}, number = {1}, pages = {95--99}, year = {1969},\n  doi = {10.1090/S0002-9904-1969-12157-9}\n}\n@article{DSSW2008,\n  author = {Dykema, Ken and Sinclair, Allan and Smith, Roger and White, Stuart},\n  title = {Generators of {$\\mathrm{II}_1$} factors},\n  journal = {Operators and Matrices},\n  volume = {2}, number = {4}, pages = {555--582}, year = {2008},\n  doi = {10.7153/oam-02-35}, eprint = {0706.1953}, archivePrefix = {arXiv},\n  url = {https://files.ele-math.com/articles/oam-02-35.pdf}\n}\n@article{Shen2009,\n  author = {Shen, Junhao},\n  title = {Type {$\\mathrm{II}_1$} factors with a single generator},\n  journal = {Journal of Operator Theory},\n  volume = {62}, number = {2}, pages = {421--438}, year = {2009},\n  eprint = {math/0511327}, archivePrefix = {arXiv},\n  url = {https://jot.theta.ro/jot/archive/2009-062-002/2009-062-002-010.html}\n}\n@article{Popa1985Cartan,\n  author = {Popa, Sorin},\n  title = {Notes on {Cartan} subalgebras in type {$\\mathrm{II}_1$} factors},\n  journal = {Mathematica Scandinavica},\n  volume = {57}, pages = {171--188}, year = {1985},\n  doi = {10.7146/math.scand.a-12110}\n}\n@article{GePopa1998,\n  author = {Ge, Liming and Popa, Sorin},\n  title = {On some decomposition properties for factors of type {$\\mathrm{II}_1$}},\n  journal = {Duke Mathematical Journal},\n  volume = {94}, number = {1}, pages = {79--101}, year = {1998},\n  doi = {10.1215/S0012-7094-98-09405-4}\n}\n@article{GeShen2002,\n  author = {Ge, Liming and Shen, Junhao},\n  title = {Generator problem for certain property {T} factors},\n  journal = {Proceedings of the National Academy of Sciences of the United States of America},\n  volume = {99}, number = {2}, pages = {565--567}, year = {2002},\n  doi = {10.1073/pnas.022593699}\n}\n@article{GaoEtAl2025,\n  author = {Gao, David and Kunnawalkam Elayavalli, Srivatsav and Patchell, Gregory and Tan, Hui},\n  title = {Internal sequential commutation and single generation},\n  journal = {International Mathematics Research Notices},\n  volume = {2025}, number = {8}, pages = {rnaf103}, year = {2025},\n  doi = {10.1093/imrn/rnaf103}, eprint = {2404.12380}, archivePrefix = {arXiv}\n}\n@article{Pearcy1962,\n  author = {Pearcy, Carl}, title = {{$W^*$}-algebras with a single generator},\n  journal = {Proceedings of the American Mathematical Society},\n  volume = {13}, number = {6}, pages = {831--832}, year = {1962},\n  doi = {10.2307/2034069}\n}\n@article{SuzukiSaito1963,\n  author = {Suzuki, Noboru and Sait{\\^o}, Teishir{\\^o}},\n  title = {On the operators which generate continuous von {Neumann} algebras},\n  journal = {Tohoku Mathematical Journal, Second Series},\n  volume = {15}, number = {3}, pages = {277--280}, year = {1963},\n  doi = {10.2748/tmj/1178243811}\n}\n@article{DouglasPearcy1969,\n  author = {Douglas, Ronald G. and Pearcy, Carl},\n  title = {Von {Neumann} algebras with a single generator},\n  journal = {Michigan Mathematical Journal},\n  volume = {16}, number = {1}, pages = {21--26}, year = {1969},\n  doi = {10.1307/mmj/1029000161}\n}\n\n@article{Voiculescu2002Entropy,\n  author = {Voiculescu, Dan},\n  title = {Free entropy},\n  journal = {Bulletin of the London Mathematical Society},\n  volume = {34}, number = {3}, pages = {257--278}, year = {2002},\n  doi = {10.1112/S0024609301008992},\n  note = {Numbered references use the preprint version},\n  url = {https://arxiv.org/abs/math/0103168v1}\n}\n@article{BrannanEtAl2023,\n  author = {Brannan, Michael and Elzinga, Floris and Harris, Samuel J. and Yamashita, Makoto},\n  title = {Crossed product equivalence of quantum automorphism groups of finite dimensional {$C^*$}-algebras},\n  journal = {International Mathematics Research Notices},\n  volume = {2023}, number = {20}, pages = {17749--17787}, year = {2023},\n  doi = {10.1093/imrn/rnad060},\n  url = {https://doi.org/10.1093/imrn/rnad060}\n}\n@misc{OpenAIFreeGroupFactors2026,\n  author = {{OpenAI}},\n  title = {{An isomorphism of the free group factors}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/An-isomorphism-of-the-free-group-factors-September-23-2026/An-isomorphism-of-the-free-group-factors-September-23-2026.pdf}{OAI:An-isomorphism-of-the-free-group-factors-September-23-2026}},\n  year = {2026}\n}\n\n@article{Behncke1972,\n  author = {Behncke, Horst},\n  title = {Generators of finite {$W^*$}-algebras},\n  journal = {Tohoku Mathematical Journal, Second Series},\n  volume = {24}, number = {3}, pages = {401--408}, year = {1972},\n  doi = {10.2748/tmj/1178241478}\n}\n@misc{Hayes2026Bundles,\n  author = {Hayes, Ben},\n  title = {On Generators for {$W^*$}-bundles},\n  year = {2026},\n  eprint = {2609.16428}, archivePrefix = {arXiv}, primaryClass = {math.OA},\n  url = {https://arxiv.org/abs/2609.16428}\n}\n"}, {"path": "preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/sections/consequences.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/sections/consequences.tex", "bytes": 3877, "sha256": "1a8e55fd20aabfa98bc4ea54b0b45207c8b023bcbd4a454776080c17c149eef4", "content": "\\section{Consequences for generation and free entropy}\n\\label{sec:consequences}\n\nThe universal statement of Theorem~\\ref{thm:main} has consequences\nbeyond the relative generation theorem. We first apply classical\nreductions, then compare two generating tuples of a free group factor.\n\n\\subsection{General von Neumann algebras and generator invariants}\n\nWillig's direct-integral reduction~\\cite[Corollary~2]{Willig1974},\ntogether with the established type~I and properly infinite cases,\nreduces the generator problem for von Neumann algebras with separable\npredual to the $\\mathrm{II}_1$ factor case. Theorem~\\ref{thm:main}\ntherefore gives single generation for every von Neumann algebra with\nseparable predual.\n\nLet $G$ and $G_{\\mathrm{sa}}$ be the generator invariants defined in\n\\cite[Definition~2.7]{DSSW2008}. They are nonnegative infima over\nfinite generating sets and finite projection partitions;\n$G_{\\mathrm{sa}}$ restricts the generating sets to self-adjoint\nelements. For every complex $\\mathrm{II}_1$ factor $M$ with separable\npredual and normalized trace, we have\n\\[\n G(M)=G_{\\mathrm{sa}}(M)=0.\n\\]\nIndeed, by \\cite[Corollary~5.1]{DSSW2008}, a positive $G$-value for\none such factor would force a nonsingly generated factor in the same\nclass, contrary to the universal assertion of\nTheorem~\\ref{thm:main}. The cited generation convention need not\ninclude the unit. To pass to that convention, replace a generator\n$x$ by $y=x+\\lambda1$ with $\\lambda>\\norm{x}$. The invertible $y$\nhas support projection $1$ in its possibly nonunital von Neumann\nclosure; that closure then contains $x$ and equals $M$.\nNonnegativity gives $G(M)=0$, and\n\\cite[Theorem~5.5]{DSSW2008} gives\n$G_{\\mathrm{sa}}(M)=2G(M)=0$. These values are the defining infima;\nno generating set or projection partition attaining zero is asserted.\n\n\\subsection{Dependence of free entropy dimensions on generators}\n\nFor a bounded finite self-adjoint tuple $Z$ in a tracial von Neumann\nalgebra, let $\\delta(Z)$ and $\\delta_0(Z)$ denote Voiculescu's\nmicrostates free entropy dimension and its modified version,\nrespectively~\\cite[Section~2.4]{Voiculescu2002Entropy}.\nThe $W^*$-invariance question asks whether either number is the same\nfor any two finite self-adjoint tuples generating the same tracial\nvon Neumann algebra; see~\\cite[Section~2.6]{Voiculescu2002Entropy}.\n\n\\begin{corollary}\\label{cor:entropy-generators}\nFor every integer $n\\geq3$, the factor $L(\\mathbb F_n)$ with its\ncanonical trace has two finite self-adjoint generating tuples on\nwhich both $\\delta$ and $\\delta_0$ take different values. In\nparticular, neither dimension is determined by the generated\ntracial von Neumann algebra.\n\\end{corollary}\n\n\\begin{proof}\nFor a bounded self-adjoint $d$-tuple $Z$, one has\n$\\delta(Z)\\leq d$ and $\\delta_0(Z)\\leq d$. Both dimensions equal\n$n$ for a freely independent variance-one semicircular $n$-tuple;\nsee~\\cite[Section~2.4(a), (c), and (d)]{Voiculescu2002Entropy}\nfor $\\delta$ and~\\cite[Section~5]{BrannanEtAl2023} for $\\delta_0$.\nThe standard semicircular $n$-tuple generates $L(\\mathbb F_n)$ as\na von Neumann algebra. A self-adjoint pair supplied by\nTheorem~\\ref{thm:main} generates the same factor and has each\ndimension at most $2<n$. These two tuples give the assertion.\n\\end{proof}\n\nA separate isomorphism argument in\n\\emph{An isomorphism of the free group factors}\n\\cite[Corollary~7.1]{OpenAIFreeGroupFactors2026} gives a stronger\nfixed-factor conclusion: each of $\\delta,\\delta_0$ and the\nnonmicrostates dimensions $\\delta^*,\\delta^\\star$ attains every\ninteger value $n\\geq2$ on finite self-adjoint $W^*$-generating tuples\nof $L(\\mathbb F_2)$. That conclusion uses the trace-preserving\nisomorphisms between free group factors established in the cited\npaper. Corollary~\\ref{cor:entropy-generators} uses only the\nsingle-generation theorem proved here and the standard dimension\nbounds above.\n"}, {"path": "preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/sections/generation.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/sections/generation.tex", "bytes": 13690, "sha256": "19a74014b332050fe0f62761bbaaf1e8d30222b0ed530c840e5baef2b413226d", "content": "\\section{Relative generation by continuity}\n\\label{sec:relative-generation}\n\nWe now convert the correlation supplied by Proposition~\\ref{prop:perturbation}\ninto generation. Throughout this section, $P\\subset M$ is an irreducible\ninclusion of $\\mathrm{II}_1$ factors and $M$ has separable predual.\n\n\\begin{lemma}\n\\label{lem:unitary-completeness}\nThe Hilbert space $L^2(M,\\tau)$ is separable, and\n$\\mathcal U(M)$ is complete for the metric $d(u,v)=\\norm{u-v}_2$.\n\\end{lemma}\n\n\\begin{proof}\nThe unit ball of $M$ is compact and metrizable in the weak-star topology,\nso it has a countable weak-star dense subset $S$. For each\n$\\xi\\in L^2(M,\\tau)$, the functional\n$a\\mapsto\\langle a,\\xi\\rangle$ on $M$ is normal. Indeed, normal\nfunctionals form a norm-closed subspace of $M^*$, and approximation\nof $\\xi$ by elements of $M$ in $L^2$ gives norm approximation of\nthis functional by normal functionals, since\n\\[\n \\sup_{\\norm{a}\\leq1}\n |\\langle a,\\xi-\\eta\\rangle|\n \\leq\\norm{\\xi-\\eta}_2.\n\\]\nIf $\\xi$ is orthogonal to $S$, normality implies that it is orthogonal to\nthe unit ball, and hence to $M$. Thus $S$ has dense linear span in\n$L^2(M,\\tau)$, proving separability.\n\nLet $(u_n)$ be a Cauchy sequence in $\\mathcal U(M)$ for $d$.\nThe adjoints are also Cauchy, because\n$\\norm{u_n^*-u_m^*}_2=\\norm{u_n-u_m}_2$.\nFor $a\\in M$,\n\\[\n \\norm{(u_n-u_m)a}_2\\leq\\norm{a}\\,\\norm{u_n-u_m}_2.\n\\]\nThe left multiplication operators associated with $u_n$ therefore\nconverge strongly on the dense subspace $M\\subset L^2(M,\\tau)$.\nTheir uniform operator norm bound extends this convergence to the whole\nHilbert space. The same holds for the left multipliers associated with\n$u_n^*$. Strong closedness of the left regular representation of $M$\ngives limits $u,v\\in M$, and passage to inner products gives $v=u^*$.\nMultiplication is jointly strongly continuous on bounded sets, so\n$uv=vu=1$. Finally, convergence on the vector $1$ gives\n$\\norm{u_n-u}_2\\to0$.\n\\end{proof}\n\nFor $u\\in\\mathcal U(M)$, write\n\\[\n N_u=W^*(P,u),\\qquad\n e_u:L^2(M,\\tau)\\longrightarrow L^2(N_u,\\tau)\n\\]\nfor the orthogonal projection. For $\\xi\\in L^2(M,\\tau)$, put\n$f_\\xi(u)=\\norm{e_u\\xi}_2$.\n\nIf $N_u$ is proper, there is a nonzero bounded vector orthogonal to\n$L^2(N_u,\\tau)$. The perturbation proposition will keep its projection\nnorm bounded away from zero along a sequence of unitaries converging to\n$u$, so we seek points at which every $f_\\xi$ is continuous simultaneously.\n\n\\begin{lemma}\n\\label{lem:projection-continuity}\nEach $f_\\xi$ is bounded and lower semicontinuous on\n$(\\mathcal U(M),d)$. There is a dense $G_\\delta$ subset\n$\\mathcal C\\subset\\mathcal U(M)$ such that every $f_\\xi$,\n$\\xi\\in L^2(M,\\tau)$, is continuous at every point of $\\mathcal C$.\n\\end{lemma}\n\n\\begin{proof}\nLet $\\mathcal P_P$ denote the family of finite $*$-polynomials in a\nunitary variable with coefficients in $P$. For each fixed $u$, the\nvalues $b(u)$, $b\\in\\mathcal P_P$, are dense in $L^2(N_u,\\tau)$.\nTo see this, take the operator norm closure of the polynomial algebra.\nIts von Neumann closure is $N_u$, and Kaplansky density approximates\neach element of $N_u$ strongly by bounded elements of this\n$C^*$-algebra. Evaluation on $1\\in L^2(M,\\tau)$ gives $2$-norm\napproximation, which can then be followed by operator norm\napproximation by polynomials. The density of $N_u$ in $L^2(N_u,\\tau)$\nfinishes the assertion.\n\nFor fixed $b\\in\\mathcal P_P$, the map $u\\mapsto b(u)$ is continuous\nin $2$-norm. This follows by telescoping products, using the fixed\noperator norm bounds of the coefficients, the fact that unitaries\nhave norm one, and the identity\n$\\norm{u^*-v^*}_2=\\norm{u-v}_2$. Consequently\n\\[\n d_\\xi(u):=\\operatorname{dist}\\bigl(\\xi,L^2(N_u,\\tau)\\bigr)\n =\\inf_{b\\in\\mathcal P_P}\\norm{\\xi-b(u)}_2\n\\]\nis upper semicontinuous: an infimum of any family of continuous\nreal functions is upper semicontinuous. Orthogonality gives\n\\[\n f_\\xi(u)^2=\\norm{\\xi}_2^2-d_\\xi(u)^2,\n\\]\nso $f_\\xi$ is lower semicontinuous and\n$0\\leq f_\\xi(u)\\leq\\norm{\\xi}_2$.\n\nFor completeness, a bounded lower semicontinuous real function $f$\non a complete metric space is continuous on a dense $G_\\delta$ set.\nFor $\\varepsilon>0$, let $G_\\varepsilon$ be the union of all open\nsets on which the oscillation of $f$ is less than $\\varepsilon$.\nThis union is dense. Indeed, in any nonempty open set $O$, write\n$s=\\sup_O f$ and choose $x\\in O$ with\n$f(x)>s-\\varepsilon/3$. Lower semicontinuity gives an open\nneighborhood $V\\subset O$ of $x$ on which\n$f>f(x)-\\varepsilon/3$. The oscillation on $V$ is less than\n$\\varepsilon$. Baire's theorem now shows that\n$\\bigcap_{n\\geq1}G_{1/n}$ is dense, and $f$ is continuous at each\nof its points.\n\nApply this construction to $f_\\eta$ for $\\eta$ in a countable dense\nsubset of $L^2(M,\\tau)$ and intersect the resulting dense\n$G_\\delta$ sets. By Baire's theorem, their intersection\n$\\mathcal C$ is again a dense $G_\\delta$ set.\nFor arbitrary $\\xi,\\eta\\in L^2(M,\\tau)$, contractivity of $e_u$ gives\n\\begin{equation}\n \\label{eq:projection-vector-lipschitz}\n |f_\\xi(u)-f_\\eta(u)|\\leq\\norm{\\xi-\\eta}_2\n \\qquad(u\\in\\mathcal U(M)).\n\\end{equation}\nThus, for $u\\in\\mathcal C$ and $v\\in\\mathcal U(M)$,\n\\[\n |f_\\xi(v)-f_\\xi(u)|\n \\leq2\\norm{\\xi-\\eta}_2+|f_\\eta(v)-f_\\eta(u)|.\n\\]\nApproximating $\\xi$ by an $\\eta$ from the countable dense subset,\nand then letting $v\\to u$, proves continuity of every $f_\\xi$ at $u$.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:relative-generation}]\nTake $u\\in\\mathcal C$ from Lemma~\\ref{lem:projection-continuity},\nand suppose that $N_u\\ne M$. There is a unitary $z\\in M\\setminus N_u$,\nsince the unitaries linearly span $M$. Let $E_{N_u}:M\\to N_u$ be the\ntrace-preserving conditional expectation, and put\n\\[\n \\xi=z-E_{N_u}(z)\\in M,\\qquad s=\\norm{\\xi}_2>0.\n\\]\nThe conditional expectation is the restriction to $M$ of $e_u$.\nTherefore\n\\[\n e_u\\xi=0,\\qquad f_\\xi(u)=0,\\qquad\n \\tau(\\xi^*z)=s^2.\n\\]\nFix\n\\begin{equation}\n \\label{eq:fixed-alpha-generation}\n 0<\\alpha<\\min\\{1/4,s/32\\}.\n\\end{equation}\nFor every integer $\\ell\\geq1$, Proposition~\\ref{prop:perturbation}\nprovides a unitary $u_\\ell\\in M$ and unitaries\n$t_{0,\\ell},\\ldots,t_{\\ell,\\ell}\\in P$ such that, with\n\\[\n W_\\ell=t_{0,\\ell}\\prod_{k=1}^\\ell(u_\\ell t_{k,\\ell})\\in\\mathcal U(N_{u_\\ell}),\n\\]\none has\n\\begin{align}\n \\norm{u_\\ell-u}_2\n &\\leq\\frac{\\alpha}{\\sqrt{2\\ell}}+\\frac1\\ell,\n \\label{eq:generation-perturbation-size}\\\\\n \\left|\\tau(\\xi^*W_\\ell)-\\frac{\\alpha s^2}{2}\\right|\n &\\leq s\\frac{4\\alpha^2}{1-2\\alpha}+\\frac1\\ell.\n \\label{eq:generation-correlation}\n\\end{align}\nThe fixed choice~\\eqref{eq:fixed-alpha-generation} gives\n\\[\n s\\frac{4\\alpha^2}{1-2\\alpha}\n <8s\\alpha^2<\\frac{\\alpha s^2}{4}.\n\\]\nIt follows from~\\eqref{eq:generation-correlation} that\n\\[\n |\\tau(\\xi^*W_\\ell)|\\geq\\frac{\\alpha s^2}{4}-\\frac1\\ell\n \\geq\\frac{\\alpha s^2}{8}\n\\]\nfor all sufficiently large $\\ell$. On the other hand,\n$\\norm{W_\\ell}_2=1$ and $W_\\ell\\in L^2(N_{u_\\ell},\\tau)$, so\n\\[\n |\\tau(\\xi^*W_\\ell)|\n =|\\langle W_\\ell,e_{u_\\ell}\\xi\\rangle|\n \\leq f_\\xi(u_\\ell).\n\\]\nBy~\\eqref{eq:generation-perturbation-size}, $u_\\ell\\to u$ in\n$2$-norm. Continuity of $f_\\xi$ at $u$ gives\n$f_\\xi(u_\\ell)\\to f_\\xi(u)=0$, a contradiction. Thus\n$M=W^*(P,u)$ for every $u\\in\\mathcal C$.\n\nTo see that the full set of relative generators is $G_\\delta$, choose\n$(\\eta_j)_{j\\geq1}$ dense in $L^2(M,\\tau)$. With $d_{\\eta_j}$ as in\nLemma~\\ref{lem:projection-continuity}, this set is\n\\[\n \\bigcap_{j,n\\geq1}\\{v\\in\\mathcal U(M):d_{\\eta_j}(v)<1/n\\}.\n\\]\nEach set in the intersection is open by upper semicontinuity.\nVanishing of every distance is equivalent to\n$L^2(N_v,\\tau)=L^2(M,\\tau)$, hence to $N_v=M$ by the\ntrace-preserving conditional expectation. The set is dense because\nit contains $\\mathcal C$.\n\\end{proof}\n\n\\section{Two self-adjoint generators}\n\\label{sec:two-generators}\n\nRelative generation over an irreducible hyperfinite subfactor will first\ngive four self-adjoint generators for every separable $\\mathrm{II}_1$\nfactor. We then apply this\nuniform bound to a half-trace corner $N$ of $M$: the identification\n$M\\cong M_2(N)$ allows the four generators of $N$ to be encoded by two\nself-adjoint matrices.\n\nWe use Popa's irreducible hyperfinite embedding theorem in the\nfollowing form.\n\n\\begin{theorem}[Popa]\n\\label{thm:irreducible-hyperfinite}\nLet $N\\subset M$ be an irreducible inclusion of $\\mathrm{II}_1$\nfactors with separable preduals. There is a hyperfinite\n$\\mathrm{II}_1$ subfactor $R\\subset N$ such that\n$R'\\cap M=\\mathbb C1$.\n\\end{theorem}\n\nThe special case $N=M$ is\n\\cite[Corollary~4.1]{Popa1981}; it is also used explicitly in\n\\cite[proof of Theorem~3.8]{Sherman2012}. The relative form above is\nrecalled in~\\cite[Introduction]{Popa2021Ergodic}.\n\nThe two-generator result for the hyperfinite factor is classical\n\\cite{SuzukiSaito1963}. We give a tensor-model proof for the later\ncount of generators.\n\n\\begin{lemma}\n\\label{lem:hyperfinite-generators}\nThe hyperfinite $\\mathrm{II}_1$ factor with separable predual is\ngenerated by two self-adjoint elements.\n\\end{lemma}\n\n\\begin{proof}\nBy uniqueness of the hyperfinite $\\mathrm{II}_1$ factor with separable\npredual \\cite[Section~1.3]{Popa2014}, we may use its standard tensor model\n\\[\n R=\\overline{\\bigotimes}_{m\\geq1}\n \\bigl(M_2(\\mathbb C),\\operatorname{tr}_2\\bigr).\n\\]\nOn the $m$th tensor site, let\n\\[\n d_m=\\begin{pmatrix}1&0\\\\0&0\\end{pmatrix},\\qquad\n q_m=\\frac12\\begin{pmatrix}1&1\\\\1&1\\end{pmatrix},\n\\]\nwith the identity on the other sites. On that site,\n$2d_mq_m(1-d_m)$ is the matrix unit $e_{12}$, so $d_m,q_m$\ngenerate the full matrix algebra. Each of the two families\n$(d_m)$ and $(q_m)$ consists of commuting projections.\n\nFor any countable family of commuting projections $(e_m)$, the\noperator norm convergent series\n\\[\n b=\\sum_{m\\geq1}2\\,3^{-m}e_m\n\\]\nrecovers every $e_m$ by spectral calculus. Inductively, after\nrecovering $e_1,\\ldots,e_{m-1}$, form\n\\[\n r_m=b-\\sum_{j<m}2\\,3^{-j}e_j.\n\\]\nThe restriction of $r_m$ to $1-e_m$ lies between $0$ and $3^{-m}$,\nwhereas its restriction to $e_m$ lies between $2\\,3^{-m}$ and\n$3\\,3^{-m}$. Since $e_m$ commutes with $r_m$,\n\\[\n e_m=1_{((3/2)3^{-m},\\,\\infty)}(r_m)\\in W^*(b).\n\\]\nApplying this encoding separately to $(d_m)$ and $(q_m)$ gives\ntwo self-adjoint elements whose generated von Neumann algebra\ncontains every tensor site, and hence is all of $R$.\n\\end{proof}\n\n\\begin{proposition}\n\\label{prop:four-generators}\nEvery $\\mathrm{II}_1$ factor with separable predual is generated\nby four self-adjoint elements.\n\\end{proposition}\n\n\\begin{proof}\nLet $M$ be such a factor. By\nTheorem~\\ref{thm:irreducible-hyperfinite}, applied with $N=M$, it contains a hyperfinite\n$\\mathrm{II}_1$ subfactor $P$ with $P'\\cap M=\\mathbb C1$.\nThe trace-preserving conditional expectation onto $P$ embeds\n$P_*$ isometrically into $M_*$ by precomposition, so $P$ has\nseparable predual. Lemma~\\ref{lem:hyperfinite-generators} gives\nself-adjoint $a,b\\in P$ with $P=W^*(a,b)$, and\nTheorem~\\ref{thm:relative-generation} gives $u\\in\\mathcal U(M)$\nwith $M=W^*(P,u)$. Hence\n\\[\n M=W^*\\bigl(a,b,\\operatorname{Re}u,\\operatorname{Im}u\\bigr).\\qedhere\n\\]\n\\end{proof}\n\nThe uniform bound in Proposition~\\ref{prop:four-generators} also applies\nto half-trace corners. The following matrix construction reduces their\nfour generators to two after amplification. It is the von Neumann algebra\nform of the $2\\times2$ encoding in~\\cite[Proposition~1.1]{DSSW2008},\nwith the off-diagonal entry fixed to the identity. We include the proof\nto make the recovery of the generators and matrix units explicit.\n\n\\begin{lemma}\n\\label{lem:matrix-encoding}\nIf a unital von Neumann algebra $N$ is generated by four\nself-adjoint elements, then $M_2(N)$ is generated by two\nself-adjoint elements.\n\\end{lemma}\n\n\\begin{proof}\nWrite $N=W^*(a_1,a_2,a_3,a_4)$ with $a_j=a_j^*$, and choose\n$C>\\norm{a_1}+\\norm{a_2}$. Put\n\\[\n A=\\begin{pmatrix}a_1&0\\\\0&a_2+C1_N\\end{pmatrix},\\qquad\n B=\\begin{pmatrix}a_3&1_N\\\\1_N&a_4\\end{pmatrix},\\qquad\n Q=W^*(A,B).\n\\]\nLet $E_{ij}$ be the standard matrix units of $M_2(N)$.\nThe first diagonal block of $A$ has spectrum in\n$[-\\norm{a_1},\\norm{a_1}]$, and the second has spectrum in\n$[C-\\norm{a_2},C+\\norm{a_2}]$. For\n$\\norm{a_1}<\\lambda<C-\\norm{a_2}$, spectral calculus therefore gives\n\\[\n E_{11}=1_{(-\\infty,\\lambda)}(A)\\in Q,\n \\qquad E_{22}=1-E_{11}\\in Q.\n\\]\nThe off-diagonal cuts of $B$ give\n$E_{12}=E_{11}BE_{22}\\in Q$ and $E_{21}=E_{12}^*\\in Q$.\nThe first corner then contains\n\\begin{align*}\n a_1E_{11}&=E_{11}AE_{11},&\n a_2E_{11}&=E_{12}AE_{21}-CE_{11},\\\\\n a_3E_{11}&=E_{11}BE_{11},&\n a_4E_{11}&=E_{12}BE_{21}.\n\\end{align*}\nThus $NE_{11}\\subset Q$. Multiplying this corner by the matrix\nunits yields $NE_{ij}\\subset Q$ for every $i,j$, and hence\n$Q=M_2(N)$.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\nLet $M$ be a $\\mathrm{II}_1$ factor with separable predual and\nnormalized trace $\\tau$. Choose a projection $p\\in M$ with\n$\\tau(p)=1/2$. Set $N=pMp$, with normalized trace\n$\\tau_N(a)=2\\tau(a)$. This corner is a $\\mathrm{II}_1$ factor\nwith separable predual: precomposition with the normal compression\n$a\\mapsto pap$ embeds $N_*$ isometrically into $M_*$.\nSince $p$ and $1-p$ have the same trace,\nthere is a partial isometry $v\\in M$ with $v^*v=p$ and\n$vv^*=1-p$. The corresponding matrix units give a normal\nunital $*$-isomorphism\n\\[\n \\Phi:M_2(N)\\longrightarrow M,\\qquad\n \\Phi\\begin{pmatrix}b_{11}&b_{12}\\\\b_{21}&b_{22}\\end{pmatrix}\n =b_{11}+b_{12}v^*+vb_{21}+vb_{22}v^*.\n\\]\nProposition~\\ref{prop:four-generators}, applied to $N$, and\nLemma~\\ref{lem:matrix-encoding} provide self-adjoint $A,B\\in M_2(N)$\nwith $W^*(A,B)=M_2(N)$. Define\n$x=\\Phi(A+iB)\\in M$. Since the real and imaginary parts of $x$\nare $\\Phi(A)$ and $\\Phi(B)$, respectively,\n\\[\n W^*(x)=W^*(\\Phi(A),\\Phi(B))=\\Phi(M_2(N))=M.\\qedhere\n\\]\n\\end{proof}\n"}, {"path": "preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/sections/independence.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/sections/independence.tex", "bytes": 10394, "sha256": "3d94150c7a5ef2ee5f8df9cdcddc896c29206842ec8ccb48cefa377b726a4e07", "content": "\\section{Free Haar unitaries in an ultrapower}\n\\label{sec:independence}\n\nThroughout this section, $P\\subset M$ is an irreducible inclusion of\n$\\mathrm{II}_1$ factors.  Fix a free ultrafilter $\\omega$ on\n$\\mathbb N$.  We use the tracial\nultrapower\n\\[\n  \\mathbf M=M^\\omega\n  =\\ell^\\infty(\\mathbb N,M)/\\mathcal I_\\omega,\n  \\qquad\n  \\mathcal I_\\omega\n  =\\left\\{(b_n)\\in\\ell^\\infty(\\mathbb N,M):\n       \\lim_{n\\to\\omega}\\|b_n\\|_2=0\\right\\},\n\\]\nand its von Neumann subalgebra $\\mathbf Q=P^\\omega$.  The trace of an\nelement represented by $(b_n)$ is $\\lim_{n\\to\\omega}\\tau(b_n)$; we\ndenote this trace, and its associated $2$-norm, by the same symbols as\non $M$.  We identify $M$ with its constant copy in $\\mathbf M$.\n\nA unitary $t$ in a tracial algebra is called \\emph{Haar} if\n$\\tau(t^n)=0$ for every nonzero integer $n$.  Two unital subalgebras\nare \\emph{free} if every alternating product of centered elements\nfrom the two subalgebras has trace zero.  We say that a unitary is\nfree from a unital algebra $D$ when $C^*(t)$ and $D$ are free.\n\nFor the perturbation in Section~\\ref{sec:perturbation}, we will choose\nsuccessive Haar unitaries in $\\mathbf Q$, each free from the algebra\ncontaining the prescribed data and the earlier choices.  We will also\nneed this freeness to survive multiplication on either side by unitaries\nfrom that algebra.  Popa's theorem supplies independence relative to\n$\\mathbf Q'\\cap\\mathbf M$, so we first identify that commutant.\n\n\\begin{lemma}\n\\label{lem:ultrapower-commutant}\nThe inclusion $\\mathbf Q\\subset\\mathbf M$ is irreducible:\n\\[\n  \\mathbf Q'\\cap\\mathbf M=\\mathbb C1.\n\\]\n\\end{lemma}\n\n\\begin{proof}\nLet $b\\in\\mathbf Q'\\cap\\mathbf M$ have a uniformly bounded\nrepresentative $(b_n)$, and put\n\\[\n  \\delta_n=\\sup_{w\\in\\mathcal U(P)}\n       \\|wb_nw^*-b_n\\|_2.\n\\]\nThen $\\lim_{n\\to\\omega}\\delta_n=0$.  Otherwise there would be an\n$\\varepsilon>0$ and a set belonging to $\\omega$ on which\n$\\delta_n>\\varepsilon$.  Choosing on this set a unitary\n$w_n\\in P$ with $\\|w_nb_nw_n^*-b_n\\|_2>\\varepsilon$, and setting\n$w_n=1$ elsewhere, would give a unitary in $\\mathbf Q$ that does not\ncommute with $b$.\n\nFor each $n$, let $K_n$ be the closed convex hull, in $L^2(M,\\tau)$,\nof the elements $wb_nw^*$, $w\\in\\mathcal U(P)$.  The unique element\n$c_n\\in K_n$ of smallest $2$-norm is fixed by conjugation by every\nunitary of $P$: each such conjugation preserves $K_n$ and its Hilbert\nspace norm.  Moreover, $c_n$ belongs to $M$ and\n$\\|c_n\\|\\leq\\|b_n\\|$.  Indeed, convex combinations from this orbit\nhave this operator norm bound, and an operator-norm-bounded\n$2$-norm limit lies in the same bounded ball of $M$.  To see the latter\nfact, left multiplication by such a convergent sequence converges\nstrongly on the dense subspace $M\\subset L^2(M)$, since\n\\[\n  \\|(x_r-x_s)a\\|_2\\leq\\|x_r-x_s\\|_2\\|a\\|\n  \\qquad(a\\in M),\n\\]\nand hence converges strongly on all of $L^2(M)$ by the uniform\noperator norm bound.  The strong limit belongs to $M$, and its value\nat $1$ is the prescribed $2$-norm limit.\n\nThus $c_n\\in P'\\cap M=\\mathbb C1$.  The trace is constant on $K_n$\nand continuous in $2$-norm, so $c_n=\\tau(b_n)1$.  Every element of\n$K_n$ is at $2$-distance at most $\\delta_n$ from $b_n$.  Consequently\n\\[\n  \\|b_n-\\tau(b_n)1\\|_2\\leq\\delta_n\\longrightarrow_{n\\to\\omega}0.\n\\]\nThe bounded scalar sequence $(\\tau(b_n))$ has a scalar ultralimit,\nwhich represents $b$.  This proves the assertion.\n\\end{proof}\n\nWe use the following special case of Popa's independence theorem.\nThe atomic-relative-commutant case needed here already appears in\n\\cite[Lemma~1.4 and Theorem~2.1]{Popa1995}; we state the later\nformulation from~\\cite[Theorem~0.1(a)]{Popa2014}.\n\n\\begin{theorem}[Popa's independence theorem]\n\\label{thm:popa-independence}\nLet $P\\subset M$ be an irreducible inclusion of $\\mathrm{II}_1$\nfactors, and put $\\mathbf Q=P^\\omega\\subset M^\\omega=\\mathbf M$.\nFor every linear subspace $X\\subset\\mathbf M$ that is separable in\n$2$-norm and satisfies $\\tau(x)=0$ for all $x\\in X$, there is a\ndiffuse abelian von Neumann algebra $A\\subset\\mathbf Q$ such that\n\\begin{equation}\n\\label{eq:popa-independence}\n  \\tau(x_0a_1x_1\\cdots a_mx_m)=0\n\\end{equation}\nwhenever $m\\geq1$, $x_0\\in X\\cup\\{1\\}$,\n$x_1,\\ldots,x_m\\in X$, and $a_1,\\ldots,a_m\\in A$ have trace zero.\n\\end{theorem}\n\nThis is the constant-inclusion case of\n\\cite[Theorem~0.1(a)]{Popa2014}.  The hypothesis there is that no\nnonzero corner of the coordinate algebra $P$ intertwines into\n$P'\\cap M$ inside $M$.  It holds here because $P'\\cap M=\\mathbb C1$:\na nonzero corner of the diffuse factor $P$ cannot embed normally\ninto a finite matrix amplification of the scalars.  The conclusion\nof that theorem is independence relative to\n$\\mathbf Q'\\cap\\mathbf M$.  Lemma~\\ref{lem:ultrapower-commutant}\nidentifies this commutant with the scalars, giving\n\\eqref{eq:popa-independence}.\n\n\\begin{lemma}\n\\label{lem:free-haar-extension}\nLet $D\\subset\\mathbf M$ be a norm-separable unital $C^*$-algebra.\nThere is a Haar unitary $t\\in\\mathbf Q$ free from $D$.  In\nparticular,\n\\begin{equation}\n\\label{eq:free-endpoints}\n  \\tau(d_0t^{n_1}d_1\\cdots t^{n_j}d_j)=0\n\\end{equation}\nfor every $j\\geq1$, every $n_1,\\ldots,n_j\\in\\mathbb Z\\setminus\\{0\\}$,\nand all $d_0,\\ldots,d_j\\in D$ such that\n$\\tau(d_i)=0$ for $1\\leq i\\leq j-1$.  The two endpoints\n$d_0,d_j$ are arbitrary.\n\\end{lemma}\n\n\\begin{proof}\nThe subspace $X=D\\cap\\ker\\tau$ is norm separable, hence $2$-norm\nseparable.  For example, applying $d\\mapsto d-\\tau(d)1$ to a\ncountable norm-dense subset of $D$ gives a countable norm-dense\nsubset of $X$.  Apply Theorem~\\ref{thm:popa-independence} to obtain\na diffuse abelian algebra $A\\subset\\mathbf Q$.\n\nWe first choose a Haar unitary in $A$.  Diffuseness gives arbitrarily\nsmall nonzero subprojections of every nonzero projection: repeatedly\nsplit and retain a piece of at most half the trace.  Normality of the\ntrace and maximality under inclusion then give subprojections of every\nprescribed smaller trace.  We may therefore choose recursively\npartitions of $1$ by projections\n\\[\n  \\{p_{m,k}:0\\leq k<2^m\\}\\subset A,\n  \\qquad \\tau(p_{m,k})=2^{-m},\n\\]\nwith $p_{m,k}=p_{m+1,2k}+p_{m+1,2k+1}$.  The self-adjoint\nelements\n\\[\n  h_m=\\sum_{k=0}^{2^m-1}\\frac{k}{2^m}p_{m,k}\n\\]\nsatisfy $\\|h_{m+1}-h_m\\|\\leq2^{-(m+1)}$ and converge in norm to\nan element $h\\in A$.  For each continuous function $f$ on $[0,1]$,\n\\[\n  \\tau(f(h))\n  =\\lim_{m\\to\\infty}2^{-m}\n        \\sum_{k=0}^{2^m-1}f(k2^{-m})\n  =\\int_0^1 f(s)\\,ds.\n\\]\nIt follows that $t=\\exp(2\\pi i h)$ is Haar.\n\nWe next remove the centering requirement at the two endpoints, so\nthat \\eqref{eq:free-endpoints} can be applied to words with arbitrary\noutside coefficients.  Expand each endpoint into its scalar and\ncentered parts.  A term with a centered last endpoint\nvanishes by \\eqref{eq:popa-independence}, since every nonzero power\nof $t$ belongs to $A$ and is centered.  A term with a centered first\nendpoint and scalar last endpoint vanishes after moving the first\nendpoint to the end by traciality.  It remains to consider the word\nwith both endpoints omitted.  When $j=1$ this is the Haar property.\nWhen $j\\geq2$, rotate the final power $t^{n_j}$ next to the first\npower $t^{n_1}$.  If $n_j+n_1\\ne0$, the resulting word is covered\nby \\eqref{eq:popa-independence}.  If $n_j+n_1=0$, the two powers\ncancel and leave a word starting and ending with centered elements\nof $D$.  This word is again covered by\n\\eqref{eq:popa-independence}, except when $j=2$, in which case it\nis simply the centered element $d_1$.  This proves\n\\eqref{eq:free-endpoints}.\n\nLaurent polynomials in $t$ are norm dense in $C^*(t)$.  As $t$ is\nHaar, the centered Laurent polynomials are precisely the linear\ncombinations of its nonzero powers.  Expanding an alternating\ncentered word, and using \\eqref{eq:free-endpoints} followed by norm\napproximation, proves that $t$ is free from $D$.\n\\end{proof}\n\n\\begin{lemma}[Absorption of base-algebra unitaries]\n\\label{lem:haar-absorption}\nLet $D$ be a unital $C^*$-subalgebra of a finite tracial von Neumann\nalgebra, and let $t$ be a Haar unitary free from $D$.  If\n$a,b\\in\\mathcal U(D)$, then both $atb$ and $t^*$ are Haar unitaries\nfree from $D$.  Both satisfy \\eqref{eq:free-endpoints} in place of $t$.\n\\end{lemma}\n\n\\begin{proof}\nFor any Haar unitary free from $D$, formula\n\\eqref{eq:free-endpoints} follows immediately by expanding the\nendpoints into scalar and centered parts and applying freeness to\neach resulting alternating word.  Replacing $t$ by $t^*$ merely\nnegates the exponents, so we concentrate on $v=atb$.\n\nConsider a word\n\\[\n  d_0v^{n_1}d_1\\cdots v^{n_j}d_j\n\\]\nwith nonzero integer exponents and centered internal factors\n$d_1,\\ldots,d_{j-1}$.  Expand each power of $v$ into individual\nletters $t$ and $t^{-1}$, with elements of $D$ between them.  Within\none power of $v$ all these letters have the same sign.  A gap\nbetween opposite signs can therefore occur only between two\nsuccessive powers of $v$.  Such a gap equals\n\\[\n  b d_i b^* \\quad\\text{for a transition from $t$ to $t^{-1}$},\n  \\qquad\n  a^*d_i a \\quad\\text{for a transition from $t^{-1}$ to $t$}.\n\\]\nIt is centered by traciality.  Every other gap joins equal signs.\nExpand each of these latter gaps into its scalar and centered\nparts.  In a resulting term, removing a scalar gap only joins\nletters of equal sign.  Thus each block joined across scalar gaps\nis a nonzero power of $t$, and consecutive blocks are separated by\ncentered elements of $D$.  The endpoint factors remain arbitrary\nelements of $D$.  Formula~\\eqref{eq:free-endpoints} shows that every\nresulting term has trace zero.\n\nWe have proved \\eqref{eq:free-endpoints} for $v$.  Taking $j=1$\nand $d_0=d_1=1$ proves that $v$ is Haar.  The general formula and\nnorm approximation by Laurent polynomials then prove its freeness\nfrom $D$.\n\\end{proof}\n\nWe may apply Lemma~\\ref{lem:free-haar-extension} successively.\nSpecifically, given a norm-separable unital $C^*$-algebra $D_0\\subset\n\\mathbf M$ and an integer $r\\geq0$, choose $t_j\\in\\mathbf Q$ Haar\nand free from $D_j$, and set\n\\[\n  D_{j+1}=C^*(D_j,t_j),\\qquad 0\\leq j\\leq r.\n\\]\nEach $D_{j+1}$ remains norm separable: polynomials in a countable\nnorm-dense subset of $D_j$, the two elements $t_j,t_j^*$, and\nrational complex coefficients form a countable dense set.  Thus\nevery application of the lemma has the required separability\nhypothesis, even though the previously chosen unitaries already\nbelong to $\\mathbf Q$.\n"}, {"path": "preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/sections/introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/sections/introduction.tex", "bytes": 8560, "sha256": "9cd53130619572d62963eb847e204ee838205a21f7325041b4bb8bb5d19b0c69", "content": "\\section{Introduction}\n\\label{sec:introduction}\n\nFor a subset $S$ of a von Neumann algebra $M$, write $W^*(S)$ for the\nsmallest unital von Neumann subalgebra containing $S$. Thus adjoints\nare included and generation is taken in the weak operator topology.\nThe generator problem asks whether every von Neumann algebra with\nseparable predual has the form $W^*(x)$ for one bounded operator $x$.\nEquivalently, it asks for two self-adjoint generators: their sum\n$a+ib$ recovers $a$ and $b$ as its real and imaginary parts.\n\nWe prove this assertion for type $\\mathrm{II}_1$ factors by first\nestablishing a relative generation theorem. For a finite factor with\nnormalized trace $\\tau$, put $\\norm{x}_2=\\tau(x^*x)^{1/2}$.\nAn inclusion $P\\subset M$ is \\emph{irreducible} when\n$P'\\cap M=\\mathbb C1$. Write $\\mathcal U(M)$ for the unitary group\nof $M$.\n\n\\begin{theorem}\\label{thm:relative-generation}\nLet $P\\subset M$ be an irreducible inclusion of type $\\mathrm{II}_1$\nfactors, with $M$ of separable predual. The set\n\\[\n \\{u\\in\\mathcal U(M):W^*(P,u)=M\\}\n\\]\nis a dense $G_\\delta$ subset of the unitary group $\\mathcal U(M)$\nfor the trace $2$-norm topology.\n\\end{theorem}\n\n\\begin{theorem}\\label{thm:main}\nEvery type $\\mathrm{II}_1$ factor $M$ with separable predual is\ngenerated by two self-adjoint elements. Equivalently, there is an\n$x\\in M$ such that $M=W^*(x)$.\n\\end{theorem}\n\nThroughout the paper,\n\\emph{separable} for a finite von Neumann algebra means that its\npredual is separable. This ensures separability of $L^2(M,\\tau)$;\nit does not require operator-norm separability of $M$.\n\n\\paragraph{Earlier work.}\nThe type~I, hyperfinite, and properly infinite cases of the generator\nproblem were established by Pearcy, Suzuki and Sait\\^o, and Wogen,\nrespectively~\\cite{Pearcy1962,SuzukiSaito1963,Wogen1969};\nsee also the historical discussion in~\\cite[Section~3]{Sherman2012}.\nWillig's direct-integral theorem reduces the general question to\ntype~$\\mathrm{II}_1$ factors~\\cite[Corollary~2]{Willig1974}.\nConsequently, Theorem~\\ref{thm:main} also gives single generation of\nevery von Neumann algebra with separable predual. This last consequence\nuses the established direct-integral reduction, whereas our proof\nbelow concerns finite factors.\n\nMuch of the progress in the finite case has used additional structure.\nBehncke established single generation for tensor products of two\n$\\mathrm{II}_1$ factors~\\cite{Behncke1972}.\nPopa treated factors with Cartan subalgebras~\\cite{Popa1985Cartan};\nGe and Popa treated factors with property~$\\Gamma$ and recovered the\ntensor-product case~\\cite{GePopa1998}; and Ge and Shen obtained\narithmetic group examples with a small-support\nrefinement~\\cite{GeShen2002}.\nShen's generator invariant unified these and other positive\nclasses~\\cite{Shen2009}. Dykema, Sinclair, Smith, and White established\nits exact amplification formula and sharpened its connection with the\nnumber of self-adjoint generators~\\cite{DSSW2008}.\n\nSherman showed, in particular, that finite generation of every\ncountably generated $\\mathrm{II}_1$ factor would suffice for a positive\nanswer~\\cite[Theorem~3.8(3)]{Sherman2012}.\nMore recently, Gao, Kunnawalkam Elayavalli, Patchell, and Tan proved\nsingle generation when a countable unitary generating family lies in\none sequential-commutation orbit in an ultrapower: successive\nmembers can be joined there by finite chains with Haar intermediate\nunitaries and commuting adjacent terms~\\cite[Theorem~A]{GaoEtAl2025}. Popa's work on tight\ndecompositions studies stable single generation, which requires every\namplification of a factor to be singly generated~\\cite{Popa2021Tight}.\n\nRequiring generators to vary continuously with a family of factors\nleads to a different problem. Hayes constructed a $W^*$-bundle---a\ncontinuous family of tracial von Neumann algebras---over a compact\nmetrizable base, with separable-predual $\\mathrm{II}_1$ fibers, for\nwhich no finite family of continuous sections generates every\nfiber~\\cite[Theorem~1.1]{Hayes2026Bundles}.\n\nRelative generation by one unitary is already available for finite-index\ninclusions, by the downward basic construction used in\n\\cite[Lemma~6.2]{DSSW2008}: the associated Jones projection $e$\ngenerates the extension over the subfactor, and $1-2e$ is unitary.\nTheorem~\\ref{thm:relative-generation} applies to every irreducible\ninclusion and also establishes genericity of the relative generators.\n\nThe universal conclusion of Theorem~\\ref{thm:main} also makes the\ngenerator invariants $G$ and $G_{\\mathrm{sa}}$ vanish for every\nseparable-predual $\\mathrm{II}_1$ factor. It implies that Voiculescu's\nmicrostates free entropy dimensions $\\delta$ and $\\delta_0$ can change\nunder replacement of a finite self-adjoint von Neumann generating\ntuple. Section~\\ref{sec:consequences} gives these deductions and their\nprecise scope.\n\n\\paragraph{The main estimate and the proof.}\nOur argument combines Popa's free-independence construction in tracial\nultraproducts~\\cite{Popa1995,Popa2014} with a perturbation whose effect\naccumulates along a long word. Given a unitary $u$ and a target unitary\n$z$, we coordinate a perturbation of $u$ across its repeated occurrences\nin a word with coefficients from $P$. With a small parameter $\\alpha$\nfixed, the perturbation has $2$-norm of order $\\alpha/\\sqrt\\ell$,\nwhere $\\ell$ is the number of occurrences. The resulting word has\ncorrelation with a test vector $\\xi$ equal to\n$(\\alpha/2)\\tau(\\xi^*z)$ up to an error of order\n$\\alpha^2\\norm{\\xi}_2+1/\\ell$.\n\nThe essential estimate is uniform in $\\ell$. An operator-norm bound\non each perturbation separately would grow with the length of the\nproduct. We instead dominate all coefficients of the nonlinear terms\nby a free-group convolution operator with nonnegative coefficients. The length-one case of\nHaagerup's inequality~\\cite[Lemma~1.4]{Haagerup1979} bounds its norm\nindependently of $\\ell$. This controls the full exponential remainder,\nincluding cancellations between words. Proposition~\\ref{prop:perturbation}\nstates the precise bounds. The witness word need only retain a fixed\npositive correlation as $\\ell\\to\\infty$.\nThe free variables and perturbation are first constructed in a tracial\nultrapower. For each fixed word length, choosing one coordinate of\ntheir representing sequences gives the required unitaries in $M$ and\n$P$, with an arbitrarily small additional error.\n\nFor a fixed vector $\\xi\\in L^2(M,\\tau)$, the norm of its projection\nonto $L^2(W^*(P,u),\\tau)$ is a bounded lower semicontinuous function\nof $u$. Baire's theorem supplies common continuity points for a\ncountable dense family of vectors, and contractivity extends continuity\nto every vector. If $W^*(P,u)$ were proper at such a point, a nonzero\nbounded vector orthogonal to it would have projection norm zero.\nThe perturbation estimate gives nearby unitaries for which that\nprojection norm has a fixed positive lower bound, a contradiction.\n\nFinally, Popa's irreducible hyperfinite embedding theorem\n\\cite{Popa1981,Popa2021Ergodic} lets us choose $P$ generated by two\nself-adjoint elements. Relative generation then gives four\nself-adjoint generators for every separable-predual $\\mathrm{II}_1$\nfactor. Applying this uniform bound to a half-trace corner, and using\nan explicit two-by-two matrix encoding, gives\nTheorem~\\ref{thm:main}. This encoding belongs to the established\nmatrix-generation techniques of Douglas--Pearcy and Wogen\n\\cite{DouglasPearcy1969,Wogen1969}; we use the von Neumann\nalgebra form of the matrix encoding\nin~\\cite[Proposition~1.1]{DSSW2008}.\n\nSection~\\ref{sec:independence} states the exact independence input and\nconstructs the free Haar unitaries required in the ultrapower.\nSection~\\ref{sec:perturbation} proves the uniform perturbation estimate.\nSection~\\ref{sec:relative-generation} carries out the continuity\nargument and identifies the full relative-generator locus as\n$G_\\delta$. Section~\\ref{sec:two-generators} gives the corner and\nmatrix construction proving single generation.\nSection~\\ref{sec:consequences} treats the general von Neumann\nalgebra, generator-invariant, and free-entropy consequences.\n\n\\paragraph{Notation.}\nWe write $\\norm{x}$ for the operator norm and use the inner product\n$\\langle a,b\\rangle=\\tau(b^*a)$ on $L^2(M,\\tau)$, linear in the\nfirst variable. The same trace notation is used on tracial ultrapowers.\nAll subalgebras are unital unless a corner unit is specified.\nFor a von Neumann subalgebra $N\\subset M$, $E_N$ denotes the\ntrace-preserving conditional expectation and $e_N$ the orthogonal\nprojection from $L^2(M,\\tau)$ onto $L^2(N,\\tau)$.\n"}, {"path": "preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/sections/perturbation.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/build/sections/perturbation.tex", "bytes": 13516, "sha256": "dd6e233551e3e2710439212556cb294474b57d6037cfb45e10806a3cc092d083", "content": "\\section{A perturbation with uniform correlation}\n\\label{sec:perturbation}\n\nWe seek perturbations that tend to zero in $2$-norm while producing\na fixed first-order correlation along words of increasing length.\nThe nonlinear error must remain bounded independently of that length.\nFor this purpose we first record the length-one case of Haagerup's\ninequality \\cite[Lemma~1.4]{Haagerup1979}, together with its elementary\nproof.\n\n\\begin{lemma}\\label{lem:length-one-convolution}\nLet $F$ be a free group with a specified free basis, let $\\mathcal L$\nbe the set of basis elements and their inverses, and let $\\lambda$\nbe the left regular representation on $\\ell^2(F)$.\nFor every finitely supported family $(b_\\gamma)_{\\gamma\\in\\mathcal L}$,\n\\[\n  \\norm{\\sum_{\\gamma\\in\\mathcal L}b_\\gamma\\lambda(\\gamma)}\n  \\le 2\\left(\\sum_{\\gamma\\in\\mathcal L}|b_\\gamma|^2\\right)^{1/2}.\n\\]\n\\end{lemma}\n\n\\begin{proof}\nFor a letter $a$, let $B_a$ be the orthogonal projection onto the\nclosed span of reduced words beginning with $a$.  Split\n\\[\n  \\lambda(\\gamma)\n  =\\lambda(\\gamma)(1-B_{\\gamma^{-1}})\n   +\\lambda(\\gamma)B_{\\gamma^{-1}}.\n\\]\nThe first summand has range in the range of $B_\\gamma$.\nThese ranges are mutually orthogonal, so for $\\eta\\in\\ell^2(F)$,\n\\begin{align*}\n  \\norm{\\sum_\\gamma b_\\gamma\\lambda(\\gamma)\n                (1-B_{\\gamma^{-1}})\\eta}^2\n  &=\\sum_\\gamma |b_\\gamma|^2\n                 \\norm{(1-B_{\\gamma^{-1}})\\eta}^2\\\\\n  &\\le\\left(\\sum_\\gamma |b_\\gamma|^2\\right)\\norm{\\eta}^2.\n\\end{align*}\nFor the second summands, the triangle inequality and\nCauchy--Schwarz give\n\\begin{align*}\n  \\norm{\\sum_\\gamma b_\\gamma\\lambda(\\gamma)\n                    B_{\\gamma^{-1}}\\eta}\n  &\\le\\sum_\\gamma |b_\\gamma|\\norm{B_{\\gamma^{-1}}\\eta}\\\\\n  &\\le\\left(\\sum_\\gamma|b_\\gamma|^2\\right)^{1/2}\\norm{\\eta},\n\\end{align*}\nbecause the projections $B_{\\gamma^{-1}}$ are mutually orthogonal.\nAdding the two estimates proves the lemma.\n\\end{proof}\n\n\\begin{proposition}\\label{prop:perturbation}\nLet $P\\subset M$ be an irreducible inclusion of $\\mathrm{II}_1$ factors.\nGiven $u,z\\in\\mathcal U(M)$, $\\xi\\in M$, and $0<\\alpha<1/2$,\nfor every integer $\\ell\\ge1$ there are\n$\\widetilde u\\in\\mathcal U(M)$ and\n$\\widetilde t_0,\\ldots,\\widetilde t_\\ell\\in\\mathcal U(P)$ such that,\nwith\n\\[\n  \\widetilde W=\\widetilde t_0\n     \\prod_{k=1}^{\\ell}(\\widetilde u\\,\\widetilde t_k),\n\\]\none has\n\\begin{equation}\\label{eq:perturbation-bounds}\n\\begin{aligned}\n  \\norm{\\widetilde u-u}_2\n    &\\le \\frac{\\alpha}{\\sqrt{2\\ell}}+\\frac1\\ell,\\\\\n  \\left|\\tau(\\xi^*\\widetilde W)\n           -\\frac\\alpha2\\tau(\\xi^*z)\\right|\n    &\\le \\norm{\\xi}_2\\frac{4\\alpha^2}{1-2\\alpha}+\\frac1\\ell.\n\\end{aligned}\n\\end{equation}\nAll products indexed by $k$ are taken in increasing order from left\nto right.  The unitaries may depend on $\\ell$.\n\\end{proposition}\n\n\\begin{proof}\nFix $\\ell$ and work first in $\\mathbf M=M^\\omega$, with\n$\\mathbf Q=P^\\omega$.  We use $\\tau$ also for the ultrapower trace.\n\n\\smallskip\\noindent\n\\emph{Free variables and prefixes.}\nSet $D_0=C^*(1,u,z,\\xi)$.  By\nLemma~\\ref{lem:free-haar-extension}, choose successively\n$t_j\\in\\mathcal U(\\mathbf Q)$ Haar and free from $D_j$, and put\n$D_{j+1}=C^*(D_j,t_j)$, for $0\\le j\\le\\ell$.\nEach of these $C^*$-algebras is norm separable.\nDefine\n\\[\n  s_1=t_0,\\qquad s_{j+1}=u t_j\\quad(1\\le j\\le\\ell),\\qquad\n  g_k=s_1\\cdots s_k\\quad(1\\le k\\le\\ell),\n\\]\nand\n\\[\n  W=g_\\ell s_{\\ell+1}\n    =t_0\\prod_{k=1}^{\\ell}(u t_k).\n\\]\nThus $g_k$ is the prefix immediately preceding the $k$th occurrence\nof $u$ in $W$.  Since\n$D_j=C^*(D_0,s_1,\\ldots,s_j)$, Lemma~\\ref{lem:haar-absorption}\nshows that $s_{j+1}$ is Haar and free from $D_j$.\nIn particular, $s_1,\\ldots,s_\\ell$ are free Haar unitaries.\nEvery nonidentity reduced word in them has trace zero, so the group\nthey generate can be identified with the free group $G$ on these\ngenerators.\nSince $W=(g_\\ell u)t_\\ell$ and $g_\\ell u\\in\\mathcal U(D_\\ell)$,\nthe same lemma shows that $W$ is Haar and free from $D_\\ell$.\nApplying it to $W^*$ and $z\\in\\mathcal U(D_\\ell)$ also shows that\n\\[\n  y=zW^*\n\\]\nis Haar and free from $D_\\ell$.\n\nFor $p\\in G$ set $Y_p=pyp^{-1}$.  These form a free family of\nHaar unitaries.  Indeed, if $a_1,\\ldots,a_r$ are nonzero integers\nand successive labels $p_1,\\ldots,p_r$ are distinct, then\n\\[\n  Y_{p_1}^{a_1}\\cdots Y_{p_r}^{a_r}\n   =p_1y^{a_1}(p_1^{-1}p_2)y^{a_2}\\cdots\n      (p_{r-1}^{-1}p_r)y^{a_r}p_r^{-1}.\n\\]\nEach internal ratio is a nonidentity element of $G$ and hence\nis centered in $D_\\ell$.  Equation~\\eqref{eq:free-endpoints}\ngives zero trace.  It follows, by applying this to the reduction\nof $v^*w$, that distinct reduced words in the $Y_p$ are orthonormal\nin $L^2(\\mathbf M,\\tau)$, including the empty word.\n\nA left perturbation of $u$ at its $k$th occurrence in $W$ is\nconjugated by $g_k$ when moved to the front of the word.  We therefore\nuse the conjugates $g_j^{-1}yg_j$: conjugating the $j$th one by\n$g_j$ returns $y$.  Taking imaginary parts will give a self-adjoint\nperturbation, while freeness makes their average small in $2$-norm.\nPut $\\operatorname{Im}(b)=(b-b^*)/(2i)$ and define\n\\[\n  h=\\frac\\alpha\\ell\\sum_{j=1}^{\\ell}\n                \\operatorname{Im}(g_j^{-1}yg_j),\\qquad\n  u'=e^{ih}u,\\qquad\n  W'=t_0\\prod_{k=1}^{\\ell}(u't_k).\n\\]\nThe $g_j^{-1}$ are distinct in $G$, so the summands defining $h$\nare orthogonal centered imaginary parts of free Haar unitaries.\nEach has squared $2$-norm $1/2$.  Consequently,\n\\[\n  h=h^*,\\qquad \\norm{h}\\le\\alpha,\\qquad\n  \\norm{h}_2=\\frac\\alpha{\\sqrt{2\\ell}}.\n\\]\nWrite $H_k=g_khg_k^{-1}$, $p_{kj}=g_kg_j^{-1}$,\nand $c=\\alpha/(2\\ell)$.  Cancelling consecutive prefixes gives\n\\begin{equation}\\label{eq:perturbation-prefix}\n  W'=\\left(\\prod_{k=1}^{\\ell}e^{iH_k}\\right)W,\n  \\qquad\n  iH_k=c\\sum_{j=1}^{\\ell}\n                  (Y_{p_{kj}}-Y_{p_{kj}}^{-1}).\n\\end{equation}\nFor the first identity, use\n$e^{iH_k}=g_ke^{ih}g_k^{-1}$ and\n$g_k^{-1}g_{k+1}=ut_k$ for $1\\le k<\\ell$; the product order is\ntherefore essential.\n\nLet $S=\\{p_{kj}:1\\le k,j\\le\\ell\\}$ and let $n_p$ count the\nordered pairs $(k,j)$ with $p_{kj}=p$.\nFor the identity $e$ of $G$, one has $n_e=\\ell$.\nIf $k\\ne j$, the word\n\\[\n  p_{kj}=s_1\\cdots s_k s_j^{-1}\\cdots s_1^{-1}\n\\]\nis reduced, since its joining letters have different indices.\nIts initial positive segment and its final negative segment\ndetermine $k$ and $j$.  All off-diagonal pairs therefore give\ndistinct nonidentity words, and\n\\begin{equation}\\label{eq:perturbation-multiplicity}\n  \\sum_{p\\in S}n_p^2=\\ell^2+\\ell(\\ell-1)=2\\ell^2-\\ell.\n\\end{equation}\n\n\\smallskip\\noindent\n\\emph{The first-order correlation.}\nPut $C_1=\\sum_{k=1}^{\\ell}iH_k$.  Each diagonal term\n$cY_{p_{kk}}W$ equals $cz$, because $p_{kk}=e$ and $Y_eW=z$.\nWe show that all other linear terms vanish when paired with $\\xi$.\nEquation~\\eqref{eq:free-endpoints} for $W$ and $D_\\ell$ gives\n$\\tau(\\xi^*W)=0$.  For $p\\ne e$, the element $p^{-1}$ is\ncentered in $D_\\ell$, so\n\\[\n  \\tau(\\xi^*Y_pW)\n   =\\tau(\\xi^*p z W^*p^{-1}W)=0.\n\\]\nFor every $p\\in G$, one also has\n\\[\n  \\tau(\\xi^*Y_p^{-1}W)\n   =\\tau(\\xi^*p W z^*p^{-1}W)=0.\n\\]\nIndeed, split the internal factor $z^*p^{-1}\\in D_\\ell$ into\nits centered and scalar parts.  The first contribution vanishes\nby \\eqref{eq:free-endpoints}; the second is a scalar multiple of\n$\\tau(\\xi^*pW^2)$ and vanishes by the same formula.\nIt follows that\n\\[\n  \\tau\\bigl(\\xi^*(1+C_1)W\\bigr)\n   =c n_e\\tau(\\xi^*z)=\\frac\\alpha2\\tau(\\xi^*z).\n\\]\n\n\\smallskip\\noindent\n\\emph{The uniform remainder estimate.}\nIt remains to bound the contribution of\n$\\prod_k e^{iH_k}-1-C_1$ uniformly in $\\ell$.\nGroup the ordered product of exponentials by its original degree:\n\\[\n  C_d=\\sum_{\\substack{m_1+\\cdots+m_\\ell=d\\\\m_k\\ge0}}\n       \\frac{(iH_1)^{m_1}\\cdots(iH_\\ell)^{m_\\ell}}\n            {m_1!\\cdots m_\\ell!}.\n\\]\nThe term $C_0$ is $1$, and the degree-one term is the $C_1$ above.\nFor fixed $\\ell$, the expansion is absolutely convergent in\noperator norm, because\n\\[\n  \\sum_{d\\ge0}\\norm{C_d}\n  \\le\\prod_{k=1}^{\\ell}\n          \\sum_{m\\ge0}\\frac{\\norm{H_k}^m}{m!}\n  =\\exp\\left(\\sum_{k=1}^{\\ell}\\norm{H_k}\\right)\n  \\le e^{\\ell\\alpha}.\n\\]\nThe Cauchy product may thus be grouped in this way, and its sum is\n$\\prod_k e^{iH_k}$.\nWe will prove the bound\n\\begin{equation}\\label{eq:perturbation-remainder}\n  \\norm{\\prod_{k=1}^{\\ell}e^{iH_k}-1-C_1}_2\n  \\le\\frac{4\\alpha^2}{1-2\\alpha},\n\\end{equation}\nwhich, unlike the preceding norm-convergence bound, is uniform\nin $\\ell$.\n\nLet $F_S$ be the formal free group with generators $T_p$, $p\\in S$.\nIn particular $T_e$ is a generator, not the identity of $F_S$.\nLet $\\Phi:\\mathbb C[F_S]\\to\\mathbf M$ be the homomorphism\n$\\Phi(T_p)=Y_p$.\nBy the orthonormality proved above, for every finite group-ring sum\n$f=\\sum_{w\\in F_S}f(w)w$ one has\n\\begin{equation}\\label{eq:perturbation-coefficient-isometry}\n  \\norm{\\Phi(f)}_2^2=\\sum_{w\\in F_S}|f(w)|^2.\n\\end{equation}\nDefine\n\\[\n  B_k=c\\sum_{j=1}^{\\ell}\n                (T_{p_{kj}}-T_{p_{kj}}^{-1}),\\qquad\n  A_k=c\\sum_{j=1}^{\\ell}\n                (T_{p_{kj}}+T_{p_{kj}}^{-1}),\n\\]\nand\n\\[\n  A=\\sum_{k=1}^{\\ell}A_k\n    =c\\sum_{p\\in S}n_p(T_p+T_p^{-1}).\n\\]\nFor a group-ring element $f$, write $|f|$ for its coefficientwise\nabsolute value, and compare nonnegative coefficient functions\ncoefficientwise.  Group multiplication gives\n\\[\n  |fg|(w)=\\left|\\sum_{ab=w}f(a)g(b)\\right|\n      \\le\\sum_{ab=w}|f(a)||g(b)|=(|f|\\,|g|)(w).\n\\]\nThis comparison already includes all free reductions and all\ncontributions collected at the same resulting word.\nThus the formal element\n\\[\n  \\mathcal C_d=\n     \\sum_{\\substack{m_1+\\cdots+m_\\ell=d\\\\m_k\\ge0}}\n        \\frac{B_1^{m_1}\\cdots B_\\ell^{m_\\ell}}\n             {m_1!\\cdots m_\\ell!},\n  \\qquad \\Phi(\\mathcal C_d)=C_d,\n\\]\nsatisfies\n\\begin{equation}\\label{eq:perturbation-majorization}\n  |\\mathcal C_d|\n   \\le\\sum_{m_1+\\cdots+m_\\ell=d}\n            \\frac{A_1^{m_1}\\cdots A_\\ell^{m_\\ell}}\n                 {m_1!\\cdots m_\\ell!}\n   \\le A^d.\n\\end{equation}\nFor the last inequality, each tuple $(m_1,\\ldots,m_\\ell)$\ncorresponds to one nondecreasing sequence of row indices in the\ndistributive expansion of $(A_1+\\cdots+A_\\ell)^d$.\nIts factorial weight is at most one, and every other summand has\nnonnegative coefficients.  No commutation of the $A_k$ is used.\nThe index $d$ records the degree before reduction; resulting words\nmay have shorter length, including zero.\n\nLet $\\lambda$ now denote the left regular representation of $F_S$,\nand let $\\delta_{\\mathrm{id}}$ be the unit vector at its identity.\nBy \\eqref{eq:perturbation-coefficient-isometry} and\n\\eqref{eq:perturbation-majorization},\n\\[\n  \\norm{C_d}_2\n   \\le\\norm{A^d}_{\\ell^2(F_S)}\n   =\\norm{\\lambda(A)^d\\delta_{\\mathrm{id}}}_{\\ell^2(F_S)}\n   \\le\\norm{\\lambda(A)}^d.\n\\]\nThis uses only the coefficient $2$-norm identity\n\\eqref{eq:perturbation-coefficient-isometry}, with no comparison\nof operator norms between the two representations.\nLemma~\\ref{lem:length-one-convolution} and\n\\eqref{eq:perturbation-multiplicity} give\n\\[\n  \\norm{\\lambda(A)}\n    \\le 2\\left(2c^2\\sum_{p\\in S}n_p^2\\right)^{1/2}\n    =2\\alpha\\left(1-\\frac1{2\\ell}\\right)^{1/2}\n    \\le2\\alpha<1.\n\\]\nConsequently $\\norm{C_d}_2\\le(2\\alpha)^d$.\nThe operator-norm convergence established above implies convergence\nof the same partial sums in $2$-norm.  Taking their tails and using\nthe triangle inequality therefore yields\n\\[\n  \\norm{\\prod_{k=1}^{\\ell}e^{iH_k}-1-C_1}_2\n  \\le\\sum_{d\\ge2}(2\\alpha)^d\n  =\\frac{4\\alpha^2}{1-2\\alpha}.\n\\]\nIn particular, orthogonality between different degrees is not\nrequired.  This proves \\eqref{eq:perturbation-remainder}.\n\nCombining the first-order correlation with\n\\eqref{eq:perturbation-prefix}, \\eqref{eq:perturbation-remainder},\nand Cauchy--Schwarz gives\n\\begin{equation}\\label{eq:perturbation-ultrapower-correlation}\n  \\left|\\tau(\\xi^*W')-\\frac\\alpha2\\tau(\\xi^*z)\\right|\n  \\le\\norm{\\xi}_2\\frac{4\\alpha^2}{1-2\\alpha}.\n\\end{equation}\n\n\\smallskip\\noindent\n\\emph{Lifting for the fixed word length.}\nEach $t_j$ has a representative consisting of unitaries in $P$.\nTo see this, start with a bounded representative $(b_n)$ of any\nunitary in $P^\\omega$.  The polar part of $b_n$ extends to a\nunitary $w_n\\in P$, since its initial and final complementary\nprojections have equal trace in the finite factor $P$.\nThen $b_n=w_n|b_n|$ and functional calculus gives\n\\[\n  \\norm{b_n-w_n}_2=\\norm{|b_n|-1}_2\n     \\le\\norm{b_n^*b_n-1}_2\\longrightarrow_\\omega0.\n\\]\nHere the inequality follows from\n$|\\sqrt t-1|\\le|t-1|$ for $t\\ge0$.\n\nChoose unitary representatives $t_{j,n}\\in P$ for the finitely\nmany $t_j$.  Using the constant representatives of $u,z$, form\n$g_{k,n},W_n,y_n,h_n,u_n',W_n'$ by exactly the formulas above.\nFor this fixed $\\ell$ these are bounded sequences, with\n$h_n=h_n^*$ and $\\norm{h_n}\\le\\alpha$.\nBounded algebraic operations and continuous functional calculus\nin the quotient show that they represent the corresponding\nultrapower elements.  In particular,\n\\[\n  \\lim_{n\\to\\omega}\\norm{h_n}_2\n       =\\frac\\alpha{\\sqrt{2\\ell}},\\qquad\n  \\lim_{n\\to\\omega}\\tau(\\xi^*W_n')=\\tau(\\xi^*W').\n\\]\nMoreover $\\norm{u_n'-u}_2\\le\\norm{h_n}_2$, by the scalar\ninequality $|e^{it}-1|\\le|t|$ and functional calculus.\nChoose one index $n$ for which\n\\[\n  \\norm{h_n}_2<\\frac\\alpha{\\sqrt{2\\ell}}+\\frac1\\ell,\n  \\qquad\n  |\\tau(\\xi^*W_n')-\\tau(\\xi^*W')|<\\frac1\\ell.\n\\]\nBoth conditions hold on sets belonging to $\\omega$, so their\nintersection is nonempty.  Set $\\widetilde u=u_n'$ and\n$\\widetilde t_j=t_{j,n}$.  Equation\n\\eqref{eq:perturbation-ultrapower-correlation} proves\n\\eqref{eq:perturbation-bounds}.  This choice is made separately\nfor each $\\ell$, as the proposition permits.\n\\end{proof}\n"}, {"path": "preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/paper.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Relative-generation-and-the-generator-problem-for-finite-factors-September-23-2026/paper.pdf", "bytes": 205670, "sha256": "c5838d4e6aba1dc13c368ca778839478d1a7a6429ee068101b01c33dc97b6e89", "base64": 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"}, {"path": "preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/README.md", "bytes": 848, "sha256": "f14dc2f210e823d82d1d25ae46443394d1c45d8c3ec4de95633454c6c3a266f6", "content": "# [Strict hot spots and absence of interior critical points on smooth simply connected planar domains](main.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 24, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026,\n  author = {{OpenAI}},\n  title = {{Strict hot spots and absence of interior critical points on smooth simply connected planar domains}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/main.pdf}{OAI:Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/figures/boundary-signs.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/figures/boundary-signs.tex", "bytes": 1611, "sha256": "dea9d720b6956cfcf4e8969095a826f2f98e7c6b2ae1b5ebed1199faf65cf09e", "content": "\\begin{figure}[htbp]\n  \\centering\n  \\begin{minipage}{.84\\textwidth}\n  \\centering\\small\n  \\begin{tikzpicture}[x=1cm,y=1cm,font=\\small,\n      line cap=round,line join=round]\n    \\draw[black!65,line width=.65pt] (0,0) circle[radius=1.25];\n    \\node at (-.67,-.69) {$\\D$};\n\n    \\draw[line width=1.1pt]\n      (-1.25,0) .. controls (-.8,.38) and (-.4,.22) .. (0,.22)\n      .. controls (.48,.22) and (.78,.18) .. (1.25,0);\n\n    \\draw[line width=.9pt,dash pattern=on 3pt off 2.2pt]\n      (0,1.25) .. controls (-.26,.91) and (-.20,.55) .. (0,.22)\n      .. controls (.35,-.20) and (.30,-.90) .. (0,-1.25);\n    \\fill (0,.22) circle[radius=1.7pt];\n\n    \\foreach \\x/\\y in {-1.25/0,1.25/0,0/1.25,0/-1.25}\n      \\fill (\\x,\\y) circle[radius=1.6pt];\n    \\node[font=\\large] at (-1.57,0) {$+$};\n    \\node[font=\\large] at (1.57,0) {$+$};\n    \\node[font=\\large] at (0,1.55) {$-$};\n    \\node[font=\\large] at (0,-1.55) {$-$};\n\n    \\draw[line width=1.1pt] (2.10,.74) -- (2.72,.74);\n    \\node[anchor=west] at (2.89,.74) {Positive crosscut};\n    \\draw[line width=.9pt,dash pattern=on 3pt off 2.2pt]\n      (2.10,.10) -- (2.72,.10);\n    \\node[anchor=west] at (2.89,.10) {Putative negative path};\n    \\fill (2.41,-.54) circle[radius=1.7pt];\n    \\node[anchor=west] at (2.89,-.54) {Intersection forced};\n  \\end{tikzpicture}\n  \\caption{Connectedness of the positive set gives a crosscut joining the\n  $+$ points. It separates the two $-$ points, forcing any negative connecting\n  path to meet it. The dashed path illustrates this contradiction; all four\n  boundary signs are strict.}\n  \\label{fig:boundary-signs}\n  \\end{minipage}\n\\end{figure}\n"}, {"path": "preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/main.tex", "bytes": 2748, "sha256": "cf375b0720950a8e3d8ea8fec26a482d99a9597f93b1890762caa45ec3cb5f33", "content": "\\documentclass[11pt]{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{lmodern}\n\\input{glyphtounicode}\n\\input{glyphtounicode-cmex}\n\\pdfgentounicode=1\n\\pdfglyphtounicode{negationslash}{0338}\n\\usepackage[margin=1.08in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage{microtype}\n\\usepackage{enumitem}\n\\usepackage{needspace}\n\\usepackage{xcolor}\n\\usepackage{tikz}\n\\usetikzlibrary{arrows.meta}\n\\usepackage[colorlinks=true,linkcolor=blue!45!black,citecolor=blue!45!black,urlcolor=blue!45!black]{hyperref}\n\\usepackage[nameinlink,capitalise,noabbrev]{cleveref}\n\\numberwithin{equation}{section}\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\AddToHook{env/theorem/before}{\\Needspace{5\\baselineskip}}\n\\AddToHook{env/lemma/before}{\\Needspace{5\\baselineskip}}\n\\AddToHook{env/proposition/before}{\\Needspace{5\\baselineskip}}\n\\AddToHook{env/corollary/before}{\\Needspace{5\\baselineskip}}\n\\AddToHook{env/remark/before}{\\Needspace{5\\baselineskip}}\n\\newcommand{\\R}{\\mathbb R}\n\\newcommand{\\C}{\\mathbb C}\n\\newcommand{\\D}{\\mathbb D}\n\\newcommand{\\Sone}{\\mathbb S^1}\n\\newcommand{\\V}{\\mathcal V}\n\\newcommand{\\HH}{\\mathcal H}\n\\newcommand{\\Q}{\\mathcal Q}\n\\newcommand{\\dd}{\\,\\mathrm d}\n\\newcommand{\\grad}{\\nabla}\n\\newcommand{\\curl}{\\operatorname{curl}}\n\\newcommand{\\diver}{\\operatorname{div}}\n\\newcommand{\\tr}{\\operatorname{tr}}\n\\newcommand{\\ip}[2]{\\langle #1,#2\\rangle}\n\\newcommand{\\norm}[1]{\\lVert #1\\rVert}\n\\newcommand{\\one}{\\mathbf 1}\n\\newcommand{\\supp}{\\operatorname{supp}}\n\\newcommand{\\PSD}{\\succeq0}\n\\DeclareMathOperator{\\spanop}{span}\n\\setlist{topsep=5pt,itemsep=3pt}\n\\setlength{\\emergencystretch}{2em}\n\\title{Strict hot spots and absence of interior critical points on smooth simply connected planar domains}\n\\author{OpenAI}\n\\date{September 24, 2026}\n\\hypersetup{\n  pdftitle={Strict hot spots and absence of interior critical points on smooth simply connected planar domains},\n  pdfauthor={OpenAI}\n}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe prove the strict hot spots conjecture for smooth bounded simply connected planar domains. More precisely, every nonzero eigenfunction for the first positive Neumann eigenvalue has nonvanishing gradient in the interior, so all its global maxima and minima lie on the boundary. This holds even when the eigenvalue is multiple.\n\\end{abstract}\n\\input{sections/01-introduction}\n\\input{sections/02-preliminaries}\n\\input{sections/03-vector-form}\n\\input{sections/04-boundary-kernels}\n\\input{sections/05-reciprocal-kernels}\n\\input{sections/06-conclusion}\n\\bibliographystyle{alphaurl}\n\\bibliography{references/references}\n\\end{document}\n"}, {"path": "preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/references/references.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/references/references.bib", "bytes": 11347, "sha256": "fd9465feeebc402ced4c3cff58eb6ee1816dfcb8e3038dcb8b1f4d12e264575b", "content": "@incollection{Rauch1975,\n  author    = {Rauch, Jeffrey},\n  title     = {Lecture \\#1. {Five} problems: An introduction to the qualitative theory of partial differential equations},\n  editor    = {Goldstein, Jerome A.},\n  booktitle = {Partial Differential Equations and Related Topics},\n  series    = {Lecture Notes in Mathematics},\n  volume    = {446},\n  pages     = {355--369},\n  publisher = {Springer},\n  address   = {Berlin, Heidelberg},\n  year      = {1975},\n  doi       = {10.1007/BFb0070610},\n  url       = {https://doi.org/10.1007/BFb0070610}\n}\n\n@article{BanuelosBurdzy1999,\n  author  = {Ba{\\~n}uelos, Rodrigo and Burdzy, Krzysztof},\n  title   = {On the ``hot spots'' conjecture of {J. Rauch}},\n  journal = {Journal of Functional Analysis},\n  volume  = {164},\n  number  = {1},\n  pages   = {1--33},\n  year    = {1999},\n  doi     = {10.1006/jfan.1999.3397},\n  url     = {https://doi.org/10.1006/jfan.1999.3397}\n}\n\n@article{BurdzyWerner1999,\n  author  = {Burdzy, Krzysztof and Werner, Wendelin},\n  title   = {A counterexample to the ``hot spots'' conjecture},\n  journal = {Annals of Mathematics},\n  series  = {2},\n  volume  = {149},\n  number  = {1},\n  pages   = {309--317},\n  year    = {1999},\n  doi     = {10.2307/121027},\n  url     = {https://doi.org/10.2307/121027}\n}\n\n@misc{Rohleder2021,\n  author        = {Rohleder, Jonathan},\n  title         = {A new approach to the hot spots conjecture},\n  year          = {2021},\n  eprint        = {2106.05224},\n  archivePrefix = {arXiv},\n  primaryClass  = {math.SP},\n  note          = {arXiv:2106.05224v4, revised 14 March 2023},\n  doi           = {10.48550/arXiv.2106.05224},\n  url           = {https://arxiv.org/abs/2106.05224v4}\n}\n\n@incollection{Rohleder2024,\n  author    = {Rohleder, Jonathan},\n  title     = {A variational approach to the hot spots conjecture},\n  editor    = {Cardona, Duv{\\'a}n and Restrepo, Joel and Ruzhansky, Michael},\n  booktitle = {Extended Abstracts 2021/2022},\n  series    = {Trends in Mathematics},\n  volume    = {3},\n  pages     = {37--45},\n  publisher = {Birkh{\\\"a}user},\n  address   = {Cham},\n  year      = {2024},\n  doi       = {10.1007/978-3-031-48579-4_4},\n  url       = {https://doi.org/10.1007/978-3-031-48579-4_4},\n  note      = {Author version: arXiv:2404.01890v1}\n}\n\n@article{AglerMcCarthy2000,\n  author  = {Agler, Jim and McCarthy, John E.},\n  title   = {Complete {Nevanlinna--Pick} kernels},\n  journal = {Journal of Functional Analysis},\n  volume  = {175},\n  number  = {1},\n  pages   = {111--124},\n  year    = {2000},\n  doi     = {10.1006/jfan.2000.3599},\n  url     = {https://doi.org/10.1006/jfan.2000.3599}\n}\n\n@misc{DengJiangYang2026,\n  author        = {Deng, Haiyun and Jiang, Xuyong and Yang, Xiaoping},\n  title         = {Quantitative characterization of deviations from the hot spots conjecture on convex domains in two-dimensional space forms},\n  year          = {2026},\n  eprint        = {2607.17882},\n  archivePrefix = {arXiv},\n  primaryClass  = {math.AP},\n  note          = {arXiv:2607.17882v2, revised 31 August 2026},\n  doi           = {10.48550/arXiv.2607.17882},\n  url           = {https://arxiv.org/abs/2607.17882v2}\n}\n\n@article{Schoenberg1938,\n  author  = {Schoenberg, I. J.},\n  title   = {Metric spaces and positive definite functions},\n  journal = {Transactions of the American Mathematical Society},\n  volume  = {44},\n  number  = {3},\n  pages   = {522--536},\n  year    = {1938},\n  doi     = {10.1090/S0002-9947-1938-1501980-0},\n  url     = {https://doi.org/10.1090/S0002-9947-1938-1501980-0}\n}\n\n@article{QiZhangLi2014,\n  author        = {Qi, Feng and Zhang, Xiao-Jing and Li, Wen-Hui},\n  title         = {{L{\\'e}vy--Khintchine} representations of the weighted geometric mean and the logarithmic mean},\n  journal       = {Mediterranean Journal of Mathematics},\n  volume        = {11},\n  number        = {2},\n  pages         = {315--327},\n  year          = {2014},\n  doi           = {10.1007/s00009-013-0311-z},\n  url           = {https://doi.org/10.1007/s00009-013-0311-z},\n  note          = {Related author preprint: arXiv:1303.3122v1, titled Integral representations of the weighted geometric mean and the logarithmic mean}\n}\n\n@book{Showalter1994,\n  author = {Showalter, R. E.},\n  title = {Hilbert Space Methods for Partial Differential Equations},\n  series = {Electronic Journal of Differential Equations Monographs},\n  number = {1},\n  year = {1994},\n  publisher = {Electronic Journal of Differential Equations},\n  note = {Electronic reprint of the 1977 original},\n  doi = {10.58997/ejde.mon.01},\n  url = {https://ejde.math.txstate.edu/Monographs/01/abstr.html}\n}\n\n@article{Filonov2005,\n  author = {Filonov, N.},\n  title = {On an inequality between {Dirichlet} and {Neumann} eigenvalues for the {Laplace} operator},\n  journal = {St. Petersburg Mathematical Journal},\n  volume = {16}, number = {2}, pages = {413--416}, year = {2005},\n  doi = {10.1090/S1061-0022-05-00857-5},\n  url = {https://doi.org/10.1090/S1061-0022-05-00857-5},\n  note = {Russian original: Algebra i Analiz 16 (2004), no. 2, 172--176}\n}\n\n@article{Friedlander1991,\n  author = {Friedlander, Leonid},\n  title = {Some inequalities between {Dirichlet} and {Neumann} eigenvalues},\n  journal = {Archive for Rational Mechanics and Analysis},\n  volume = {116}, number = {2}, pages = {153--160}, year = {1991},\n  doi = {10.1007/BF00375590},\n  url = {https://doi.org/10.1007/BF00375590}\n}\n\n@book{Pommerenke1992,\n  author = {Pommerenke, Christian},\n  title = {Boundary Behaviour of Conformal Maps},\n  series = {Grundlehren der mathematischen Wissenschaften},\n  volume = {299}, publisher = {Springer}, address = {Berlin, Heidelberg},\n  year = {1992}, doi = {10.1007/978-3-662-02770-7},\n  url = {https://doi.org/10.1007/978-3-662-02770-7}\n}\n\n@article{BassBurdzy2000,\n  author = {Bass, Richard F. and Burdzy, Krzysztof},\n  title = {Fiber {Brownian} motion and the ``hot spots'' problem},\n  journal = {Duke Mathematical Journal}, volume = {105}, number = {1},\n  pages = {25--58}, year = {2000}, doi = {10.1215/S0012-7094-00-10512-1}\n}\n\n@article{Burdzy2005,\n  author = {Burdzy, Krzysztof},\n  title = {The hot spots problem in planar domains with one hole},\n  journal = {Duke Mathematical Journal},\n  volume = {129}, number = {3}, pages = {481--502}, year = {2005},\n  doi = {10.1215/S0012-7094-05-12932-5},\n  eprint = {math/0405472}, archivePrefix = {arXiv}\n}\n\n@article{JerisonNadirashvili2000,\n  author = {Jerison, David and Nadirashvili, Nikolai},\n  title = {The ``hot spots'' conjecture for domains with two axes of symmetry},\n  journal = {Journal of the American Mathematical Society},\n  volume = {13}, number = {4}, pages = {741--772}, year = {2000},\n  doi = {10.1090/S0894-0347-00-00346-5}\n}\n\n@article{Pascu2002,\n  author = {Pascu, Mihai N.},\n  title = {Scaling coupling of reflecting {Brownian} motions and the hot spots problem},\n  journal = {Transactions of the American Mathematical Society},\n  volume = {354}, number = {11}, pages = {4681--4702}, year = {2002},\n  doi = {10.1090/S0002-9947-02-03020-9}\n}\n\n@article{AtarBurdzy2004,\n  author = {Atar, Rami and Burdzy, Krzysztof},\n  title = {On {Neumann} eigenfunctions in lip domains},\n  journal = {Journal of the American Mathematical Society},\n  volume = {17}, number = {2}, pages = {243--265}, year = {2004},\n  doi = {10.1090/S0894-0347-04-00453-9}\n}\n\n@article{Siudeja2015,\n  author = {Bart{\\l}omiej Siudeja},\n  title = {Hot spots conjecture for a class of acute triangles},\n  journal = {Mathematische Zeitschrift},\n  volume = {280},\n  number = {3--4},\n  pages = {783--806},\n  year = {2015},\n  doi = {10.1007/s00209-015-1448-1},\n}\n\n@article{JudgeMondal2020,\n  author = {Chris Judge and Sugata Mondal},\n  title = {{Euclidean} triangles have no hot spots},\n  journal = {Annals of Mathematics (2)},\n  volume = {191},\n  number = {1},\n  pages = {167--211},\n  year = {2020},\n  doi = {10.4007/annals.2020.191.1.3},\n}\n\n@article{JudgeMondal2022,\n  author = {Chris Judge and Sugata Mondal},\n  title = {Erratum: {Euclidean} triangles have no hot spots},\n  journal = {Annals of Mathematics (2)},\n  volume = {195},\n  number = {1},\n  pages = {337--362},\n  year = {2022},\n  doi = {10.4007/annals.2022.195.1.5},\n}\n\n@article{ChenGuiYao2026,\n  author = {Hongbin Chen and Changfeng Gui and Ruofei Yao},\n  title = {Uniqueness of critical points of the second {Neumann} eigenfunctions on triangles},\n  journal = {Inventiones mathematicae},\n  volume = {244},\n  pages = {299--353},\n  year = {2026},\n  doi = {10.1007/s00222-025-01398-x},\n}\n\n@article{McCullough1992,\n  author = {McCullough, Scott},\n  title = {Carath{\\'e}odory interpolation kernels},\n  journal = {Integral Equations and Operator Theory},\n  volume = {15}, number = {1}, pages = {43--71}, year = {1992},\n  doi = {10.1007/BF01193766}, url = {https://doi.org/10.1007/BF01193766}\n}\n\n@incollection{McCullough1994,\n  author = {McCullough, Scott},\n  title = {The local {de Branges--Rovnyak} construction and complete {Nevanlinna--Pick} kernels},\n  booktitle = {Algebraic Methods in Operator Theory},\n  editor = {Curto, Ra{\\'u}l E. and J{\\o}rgensen, Palle E. T.},\n  publisher = {Birkh{\\\"a}user}, address = {Boston},\n  pages = {15--24}, year = {1994},\n  doi = {10.1007/978-1-4612-0255-4_3},\n  url = {https://doi.org/10.1007/978-1-4612-0255-4_3}\n}\n\n@article{Quiggin1993,\n  author = {Quiggin, Peter},\n  title = {For which reproducing kernel {Hilbert} spaces is {Pick's} theorem true?},\n  journal = {Integral Equations and Operator Theory},\n  volume = {16}, number = {2}, pages = {244--266}, year = {1993},\n  doi = {10.1007/BF01358955}, url = {https://doi.org/10.1007/BF01358955}\n}\n\n@phdthesis{Quiggin1994,\n  author = {Quiggin, Peter Philip},\n  title = {Generalisations of {Pick's} Theorem to Reproducing Kernel {Hilbert} Spaces},\n  school = {Lancaster University}, year = {1994}, month = aug,\n  url = {https://eprints.lancs.ac.uk/id/eprint/61962/1/Quiggin.pdf}\n}\n\n@misc{FriesGoffengMiranda2026,\n  author = {Fries, Magnus and Goffeng, Magnus and Miranda, Germ{\\'a}n},\n  title = {{Friedlander's} inequality and the {de Rham} complex},\n  year = {2026}, eprint = {2412.03369}, archivePrefix = {arXiv}, primaryClass = {math.SP},\n  note = {arXiv:2412.03369v4, revised 25 March 2026},\n  doi = {10.48550/arXiv.2412.03369}, url = {https://arxiv.org/abs/2412.03369v4}\n}\n\n@misc{deDiosPontHsuTaylor2025,\n  author = {de Dios Pont, Jaume and Hsu, Alexander W. and Taylor, Mitchell A.},\n  title = {Sharp bounds on the failure of the hot spots conjecture},\n  year = {2025},\n  eprint = {2508.16321},\n  archivePrefix = {arXiv},\n  primaryClass = {math.SP},\n  note = {arXiv:2508.16321v1, submitted 22 August 2025},\n  doi = {10.48550/arXiv.2508.16321},\n  url = {https://arxiv.org/abs/2508.16321v1}\n}\n\n@misc{Miyamoto2007,\n  author = {Miyamoto, Yasuhito},\n  title = {The ``hot spots'' conjecture for nearly circular planar convex domains},\n  year = {2007},\n  howpublished = {RIMS Preprint 1591},\n  note = {Revised 30 May 2007},\n  url = {https://www.kurims.kyoto-u.ac.jp/preprint/file/RIMS1591.pdf}\n}\n\n@article{Nadirashvili1988,\n  author = {Nadirashvili, N. S.},\n  title = {Multiple eigenvalues of the {Laplace} operator},\n  journal = {Mathematics of the USSR-Sbornik},\n  volume = {61},\n  number = {1},\n  year = {1988},\n  pages = {225--238},\n  doi = {10.1070/SM1988v061n01ABEH003204},\n  url = {https://www.mathnet.ru/eng/sm2554},\n  note = {English translation of Mat. Sb. (N.S.) 133(175), no. 2(6) (1987), 223--237}\n}\n"}, {"path": "preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/sections/01-introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/sections/01-introduction.tex", "bytes": 9603, "sha256": "15461d58f1bc1b0e6cc0367542a42e712e9dec6779132a291dfa69c8183afc91", "content": "\\section{Introduction}\n\nThe hot-spots problem arose from Rauch's discussion of the long-time behavior of heat flow with insulating boundary conditions \\cite{Rauch1975}. Its spectral formulation asks whether the extrema of a first-positive Neumann eigenfunction occur on the boundary. Bañuelos and Burdzy distinguished strict and nonstrict formulations, and assertions about every eigenfunction from the existence of one favorable eigenfunction \\cite{BanuelosBurdzy1999}. These distinctions matter when the eigenvalue is multiple.\n\nLet $\\Omega\\subset\\R^2$ be a nonempty bounded simply connected open set with $C^\\infty$ boundary. In particular, $\\Omega$ is connected. Set\n\\begin{equation}\\label{eq:mu}\n\\mu=\\mu_1(\\Omega)\n=\\inf_{\\substack{0\\ne v\\in H^1(\\Omega)\\\\ \\int_\\Omega v=0}}\n\\frac{\\int_\\Omega|\\grad v|^2}{\\int_\\Omega|v|^2},\n\\end{equation}\nand let $\\V$ be the real Neumann eigenspace for $\\mu$. We count only positive eigenvalues in this notation; sources that count the constant eigenfunction first write $\\mu_2$ for this same eigenvalue.\n\n\\begin{theorem}\\label{thm:main}\nFor every $0\\ne u\\in\\V$,\n\\[\n\\grad u(x)\\ne0\\qquad(x\\in\\Omega).\n\\]\nIn particular,\n\\begin{equation}\\label{eq:hotspots}\n\\min_{y\\in\\partial\\Omega}u(y)<u(x)<\\max_{y\\in\\partial\\Omega}u(y)\n\\qquad(x\\in\\Omega).\n\\end{equation}\n\\end{theorem}\n\nThe theorem imposes no convexity or symmetry condition. It applies to every member of the first positive eigenspace, including when the eigenvalue is multiple. The nonvanishing-gradient conclusion is stronger than \\eqref{eq:hotspots}, since it also rules out interior saddle points.\n\n\\paragraph{Background and methods.}\nTopology matters in the hot-spots problem. Burdzy and Werner constructed a planar counterexample with two holes and a simple first-positive eigenvalue \\cite{BurdzyWerner1999}. Bass and Burdzy obtained examples with both extrema strictly in the interior \\cite{BassBurdzy2000}, and Burdzy subsequently obtained this behavior with just one hole \\cite{Burdzy2005}. The simply connected planar conjecture is stated explicitly in \\cite[Conjecture~1.2(ii)]{Burdzy2005}.\n\nImportant geometric special cases already establish strong gradient conclusions. Jerison and Nadirashvili proved a positive directional derivative for every first-positive eigenfunction on convex planar domains with two orthogonal reflection symmetries, allowing multiple eigenvalues \\cite[Theorem~1.4]{JerisonNadirashvili2000}. Probabilistic coupling methods developed by Bañuelos and Burdzy \\cite{BanuelosBurdzy1999} led to further results. Pascu proved boundary-only extrema for antisymmetric eigenfunctions on $C^{1,\\alpha}$ convex planar domains with one reflection symmetry, where $0<\\alpha<1$ \\cite{Pascu2002}. Atar and Burdzy treated every first-positive eigenfunction on lip domains, a class of Lipschitz domains bounded between graphs of functions with Lipschitz constants at most one \\cite{AtarBurdzy2004}. Miyamoto obtained interior critical-point exclusion under a spectral-diameter condition, giving nonsymmetric nearly circular convex examples \\cite[Lemma~1.2 and Theorem~A]{Miyamoto2007}.\n\nThe polygonal problem has a complementary development. Siudeja proved hot-spots results for a class of acute triangles \\cite{Siudeja2015}. Judge and Mondal, with their subsequent erratum, excluded interior critical points for every first-positive Neumann eigenfunction on every Euclidean triangle \\cite{JudgeMondal2020,JudgeMondal2022}. Chen, Gui and Yao established the finer classification of nonvertex critical points \\cite{ChenGuiYao2026}. These results concern domains with corners; \\Cref{thm:main} concerns the full smooth simply connected planar class.\n\nRecent quantitative work measures how far the hot-spots conclusion can fail. For general bounded connected Lipschitz domains, de Dios Pont, Hsu and Taylor determined the sharp dimension-dependent upper bound for the ratio of a first-positive Neumann eigenfunction's supremum over the domain to its maximum on the boundary \\cite[Definition~1 and Theorem~4]{deDiosPontHsuTaylor2025}. Deng, Jiang and Yang obtained quantitative bounds for convex domains in two-dimensional space forms \\cite{DengJiangYang2026}. Such ratios concern extrema; excluding every interior critical point also requires excluding saddle points.\n\nOur starting point is Rohleder's variational principle for tangent vector fields \\cite{Rohleder2021,Rohleder2024}. In its lip-domain application, taking componentwise absolute values after a suitable rotation preserves the required tangent boundary condition and yields directional monotonicity. We use scalar boundary multipliers to keep tangency on an arbitrary smooth boundary, and construct those multipliers through a reciprocal Green-kernel theorem.\n\nThe difficulty is to control the gradient without a preferred direction in the domain. Each Cartesian derivative of an eigenfunction solves the same Helmholtz equation, but its boundary values need not have a fixed sign. The Neumann condition instead constrains the two derivatives together: their vector is tangent to the boundary. We use that constraint through a nonnegative quadratic form whose nullspace consists exactly of first-positive eigenfunction gradients. The remaining task is to construct enough scalar boundary multipliers that preserve this nullspace if an interior critical point exists.\n\n\\paragraph{The mechanism.}\nWrite $\\D=\\{z\\in\\C:|z|<1\\}$ and $\\Sone=\\partial\\D$, and let $\\Phi:\\D\\to\\Omega$ be a smooth conformal parametrization. The proof begins with the variational principle for tangent vector fields associated with Neumann gradients. For a tangent vector field $X$, its divergence--curl energy is at least $\\mu\\norm{X}_2^2$, with equality exactly for gradients of functions in $\\V$. After this conformal change of variables, solving the scalar Helmholtz equation in each Cartesian component gives a nonnegative quadratic form $E$ on boundary vector data. If $g$ is the boundary gradient of an eigenfunction, then $E(g)=0$, and a scalar multiplier $b$ satisfies\n\\[\nE(bg)=\\frac12\\iint_{\\Sone\\times\\Sone}\nN(s,t)(b(s)-b(t))^2g(s)\\cdot g(t)\\dd s\\dd t.\n\\]\nHere $N$ is a positive boundary kernel, and the corresponding interior evaluation kernel is denoted by $K$.\n\nThe sign of $g(s)\\cdot g(t)$ prevents a direct positivity argument in this identity. The main kernel result supplies a family of multipliers whose squared differences remove the factor $N$. For every interior point $p$,\n\\[\nD_p(s,t)=\\frac{K(p,s)K(p,t)}{N(s,t)}\\quad(s\\ne t),\n\\qquad D_p(s,s)=0,\n\\]\nis conditionally negative semidefinite. It is therefore the squared distance of an injective Lipschitz map from the circle to a real Hilbert space. Applying the preceding energy identity to its coordinates gives\n\\[\n\\sum_j E(b_jg)=\\frac12|\\grad u(\\Phi(p))|^2.\n\\]\nA critical point would thus produce too many eigenfunction gradients, contradicting the planar multiplicity bound or boundary uniqueness.\n\nThe construction of $D_p$ is the central analytic step. We first regularize the disk Green function so that the entrywise reciprocal of every finite kernel matrix has at most one positive eigenvalue. This property survives successive rank-one updates along columns of the current kernel matrix, which sum the Green resolvent over a finite set of integration nodes. Positive quadrature and monotone limits then pass from those matrices to the singular Green kernel and finally to its boundary kernels. Taking the boundary limit before removing a compact-support cutoff avoids treating a Poisson kernel as an $L^2$ function when it need not be one.\n\nThe kernel theorem, \\Cref{thm:negative-kernel}, holds for every bounded nonnegative disk potential satisfying the subcritical Dirichlet inequality \\eqref{eq:subcritical}, independently of the conformal origin of that potential. The reciprocal-matrix condition comes from the McCullough--Quiggin characterization of complete Nevanlinna--Pick kernels \\cite{McCullough1992,McCullough1994,Quiggin1993,Quiggin1994}; see also \\cite[Corollary~1.12]{AglerMcCarthy2000}. Schoenberg's squared-distance correspondence supplies the Hilbert-space realization \\cite{Schoenberg1938}. The analytic work here concerns the singular Green kernel, its positive-potential resolvent, and its boundary limit; the resulting theorem can be used independently of the eigenfunction argument.\n\n\\paragraph{Organization and background.}\n\\Cref{sec:preliminaries} proves the spectral gap, the smooth conformal reduction, and the multiplicity bound. \\Cref{sec:vector} proves the vector variational principle. \\Cref{sec:boundary} constructs the boundary kernels and derives the multiplier identity. \\Cref{sec:reciprocal} proves their conditional negativity, and \\Cref{sec:conclusion} completes the argument.\nWe use standard Sobolev compactness, Poincar\\'e and trace inequalities on smooth bounded domains, Poisson solvability and elliptic boundary regularity, the maximum and boundary point principles, elementary complex analysis, and planar separation. All specialized ingredients used in the proof are established below. The smooth-boundary regularity input is \\cite[Chapter~III, Theorems~6.1, 6.4 and~6.5]{Showalter1994}: $L^2$ Poisson data give $H^2$ solutions under homogeneous Dirichlet or Neumann conditions, and higher regularity follows with smoother data. Subtracting a smooth extension handles the inhomogeneous Dirichlet data used below. Bootstrap and Sobolev embedding make weak Neumann eigenfunctions smooth up to the boundary. Interior solutions of $(\\Delta+\\mu)v=0$ are analytic; alternatively, $e^{\\sqrt\\mu t}v(x)$ is harmonic in one additional variable.\n"}, {"path": "preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/sections/02-preliminaries.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/sections/02-preliminaries.tex", "bytes": 7682, "sha256": "f36349de99fd9c3ff1be367545d23a31364dd921672fb0a3b368a1f3d3021e49", "content": "\\section{Spectral and planar preliminaries}\\label{sec:preliminaries}\n\nWrite $\\lambda_D$ for the first Dirichlet eigenvalue of $\\Omega$. Both $\\mu$ and $\\lambda_D$ are positive by the respective Poincar\\'e inequalities. Their Rayleigh infima are attained by compactness, and the eigenspaces are finite-dimensional.\n\nThe strict separation below is the first-mode case of the Friedlander--Filonov comparison \\cite{Friedlander1991,Filonov2005}. We give the plane-wave argument of Filonov in the form needed here, including the strictness that identifies the equality space of the later vector variational principle.\n\n\\begin{lemma}\\label{lem:gap}\nOne has $\\mu<\\lambda_D$.\n\\end{lemma}\n\\begin{proof}\nChoose a nonzero nonnegative Dirichlet minimizer $\\phi$, using absolute values in the Rayleigh quotient. Then $\\int_\\Omega\\phi>0$. Work temporarily over $\\C$. For $k\\in\\R^2$ with $|k|^2=\\lambda_D$, put\n\\[\ne_k(x)=e^{ik\\cdot x},\\qquad\nw_k=e_k-\\frac{\\int_\\Omega e_k}{\\int_\\Omega\\phi}\\phi.\n\\]\nEach $w_k$ has mean zero. The form $\\int(|\\grad v|^2-\\lambda_D|v|^2)$ vanishes on $e_k$ and $\\phi$; its cross term vanishes because $-\\Delta e_k=\\lambda_De_k$ and $\\phi$ has zero trace. Thus every nonzero $w_k$ has Rayleigh quotient $\\lambda_D$, proving $\\mu\\le\\lambda_D$.\n\nIf equality held, each such $w_k$ would be a Neumann eigenfunction by variational equality. The Euler equation first holds against mean-zero tests and then against all tests, since $w_k$ has mean zero. But distinct plane waves are linearly independent: restrict any finite family to a line segment in an interior ball whose direction gives distinct frequencies. Subtracting multiples of one fixed function leaves their span infinite-dimensional. This contradicts finite eigenspace dimension. The real Neumann inequality and its equality case apply in the complexified space by separating real and imaginary parts.\n\\end{proof}\n\nWe also include the smooth conformal parametrization used throughout the proof; see \\cite{Pommerenke1992} for the classical boundary-regularity theory of conformal maps.\n\n\\begin{lemma}\\label{lem:conformal}\nThere is a conformal bijection $\\Phi:\\D\\to\\Omega$ extending to a smooth diffeomorphism of closures, with $\\Phi'\\ne0$ on $\\overline\\D$.\n\\end{lemma}\n\\begin{proof}\nIdentify the plane with $\\C$ and fix $a\\in\\Omega$. Let $l$ solve the harmonic Dirichlet problem with boundary values $\\log|z-a|$. It is smooth up to the boundary. Simple connectivity gives a harmonic conjugate $\\widetilde l$; its gradient extends smoothly, so the conjugate also extends smoothly in boundary charts. The holomorphic function\n\\[\nm(z)=(z-a)\\exp(-l(z)-i\\widetilde l(z))\n\\]\nhas modulus one on the boundary, modulus less than one inside, and precisely one zero, of order one. Near the boundary, $\\log|m|$ is harmonic, negative inside, and zero on the boundary. The boundary point lemma gives a nonzero normal derivative, so $m'$ does not vanish there.\n\nFor any $w\\in\\D$, take a smooth inner approximation of $\\Omega$ so close to its boundary that $|m|>|w|$ throughout the omitted collar. Rouch\\'e's theorem gives exactly one zero of $m-w$ in that approximation, counting multiplicity, because $m$ has exactly one. There are none in the collar. Thus $m$ is a biholomorphism in the interior. It maps the boundary onto the circle by compactness. Its local boundary inverses imply injectivity there: two boundary preimages would give two nearby interior preimages. The inverse map is the required $\\Phi$.\n\\end{proof}\n\nThroughout, $\\Sone=\\partial\\D$ carries arclength $\\dd s$, of total mass $2\\pi$.\n\nTo bound the dimension of $\\V$, we transfer its mean-zero condition to the boundary and use connectedness of the positive and negative sets to control boundary signs.\n\n\\begin{lemma}\\label{lem:boundary-weight}\nThere is a fixed smooth positive function $\\beta$ on $\\Sone$ such that every $v\\in\\V$ has boundary trace $h_v=v\\circ\\Phi$ satisfying\n\\[\n\\int_{\\Sone}\\beta h_v\\dd s=0.\n\\]\nIf $v\\ne0$, its boundary trace is nonzero and takes both signs.\n\\end{lemma}\n\\begin{proof}\nBy \\Cref{lem:gap}, the Dirichlet problem\n\\[\n(-\\Delta-\\mu)L=1,\\qquad L|_{\\partial\\Omega}=0\n\\]\nis coercive and has a smooth solution. Testing by its negative part gives $L\\ge0$. Hence $-\\Delta L=1+\\mu L>0$, and the boundary point lemma gives $-\\partial_\\nu L>0$. Green's identity and the Neumann equation yield\n\\[\n0=\\int_\\Omega v=-\\int_{\\partial\\Omega}v\\,\\partial_\\nu L\n=\\int_{\\Sone}\\beta(s)h_v(s)\\dd s,\n\\quad\n\\beta=|\\Phi'|\\,(-\\partial_\\nu L)\\circ\\Phi.\n\\]\nIf $h_v=0$, then $v\\in H^1_0(\\Omega)$, and the Dirichlet inequality and $\\mu<\\lambda_D$ force $v=0$. Positivity of $\\beta$ gives the last assertion.\n\\end{proof}\n\n\\begin{lemma}\\label{lem:nodal}\nFor $0\\ne v\\in\\V$, the sets $\\{v>0\\}$ and $\\{v<0\\}$ are connected. Its boundary trace cannot have four cyclically ordered points with strictly alternating signs.\n\\end{lemma}\n\\begin{proof}\nFor each component $U$ of a sign set, the function $v_U=v\\one_U$ belongs to $H^1(\\Omega)$ and has weak gradient $\\one_U\\grad v$. Indeed it is locally Lipschitz in interior balls: any transition between $U$ and its complement crosses a zero of $v$, so the local Lipschitz bound for $v$ also bounds the zeroed function. This gives the local weak-gradient identity, and the global $L^2$ bounds give global $H^1$ membership. Testing the eigenfunction equation by $v_U$ shows\n\\[\n\\int_\\Omega|\\grad v_U|^2=\\mu\\int_\\Omega|v_U|^2.\n\\]\nIf at least three sign components existed, a nonzero linear combination of two restrictions could be chosen mean zero. Disjoint supports show that it attains the quotient $\\mu$. It is therefore an eigenfunction, but vanishes on the omitted open component, contradicting interior analyticity. Both sign sets are nonempty, so each is connected.\n\nFour alternating strict signs on the boundary would give a simple arc joining the two positive points, otherwise in the positive interior set, and another joining the negative points in the negative interior set. Such arcs exist by connectedness of open planar sign sets, using short interior end segments and then polygonal paths with loops removed. After applying $\\Phi^{-1}$, their endpoints alternate on a circle. Planar separation forces the two arcs to intersect, contrary to their signs; see \\Cref{fig:boundary-signs}.\n\\end{proof}\n\n\\input{figures/boundary-signs}\n\n\\Needspace{7\\baselineskip}\nThe following multiplicity bound is due to Nadirashvili \\cite[Theorem~3, p.~227]{Nadirashvili1988}; see also \\cite[Proposition~2.5]{BanuelosBurdzy1999}. The boundary-sign proof is included for completeness.\n\n\\begin{proposition}\\label{prop:multiplicity}\nThe real eigenspace $\\V$ has dimension at most two.\n\\end{proposition}\n\\begin{proof}\nSuppose its dimension is at least three. Since the trace map is injective, choose $0\\ne v\\in\\V$ whose trace $h=h_v$ satisfies\n\\[\n\\int_{\\Sone}\\beta h\\cos\\theta\\dd s\n=\\int_{\\Sone}\\beta h\\sin\\theta\\dd s=0,\n\\qquad s=e^{i\\theta}.\n\\]\nIt is already orthogonal to $1$ by \\Cref{lem:boundary-weight}. Cut the circle at a strictly negative point and write its angles as $(\\theta_0,\\theta_0+2\\pi)$. If $a$ and $b$ are the infimum and supremum of the positive angles, then\n\\[\n\\theta_0<a<b<\\theta_0+2\\pi.\n\\]\nThere are no positive values outside $[a,b]$. Nor is there a negative value between $a$ and $b$: positive values on either side of it, together with the cut point, would violate \\Cref{lem:nodal}. The function\n\\[\nF(\\theta)=\\cos\\!\\left(\\theta-\\frac{a+b}{2}\\right)\n-\\cos\\!\\left(\\frac{b-a}{2}\\right)\n\\]\nis strictly positive on $(a,b)$ and negative outside $[a,b]$ in the cut interval. Consequently $Fh\\ge0$ everywhere and $Fh>0$ on a nonempty open set. Thus $\\int\\beta Fh>0$, contradicting the three orthogonalities.\n\\end{proof}\n"}, {"path": "preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/sections/03-vector-form.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/sections/03-vector-form.tex", "bytes": 3926, "sha256": "f5c9ee53e8760474cbca784973258d624e70188fc005cb89deaac41dde60a555", "content": "\\section{A variational principle for tangent vector fields}\\label{sec:vector}\n\nFor a real vector field $X\\in H^1(\\Omega;\\R^2)$ with tangent trace, meaning $\\tr X\\cdot\\nu=0$, set\n\\[\n\\Q(X)=\\int_\\Omega\\bigl(|\\diver X|^2+|\\curl X|^2\\bigr),\n\\qquad \\curl X=\\partial_1X_2-\\partial_2X_1.\n\\]\nThe following is Rohleder's vector variational principle \\cite[Theorem~1.1]{Rohleder2021}; see also \\cite{Rohleder2024}. We include its derivation, in particular its equality case. Our curvature convention below is the opposite of his, so the corresponding boundary term has the opposite sign.\n\n\\begin{proposition}\\label{prop:vector}\nFor every such $X$,\n\\begin{equation}\\label{eq:vector-inequality}\n\\Q(X)\\ge\\mu\\int_\\Omega|X|^2,\n\\end{equation}\nwith equality exactly when $X=\\grad v$ for some $v\\in\\V$.\n\\end{proposition}\n\\begin{proof}\nSolve\n\\[\n\\Delta v=\\diver X,\\quad \\partial_\\nu v=0,\\quad\\int_\\Omega v=0,\n\\qquad\n\\Delta j=\\curl X,\\quad j|_{\\partial\\Omega}=0.\n\\]\nThe Neumann compatibility follows from the tangent trace. The solutions are in $H^2(\\Omega)$ by smooth-boundary regularity \\cite[Chapter~III, Theorems~6.1 and~6.4]{Showalter1994}. The rotated gradient $\\grad^\\perp j=(-\\partial_2j,\\partial_1j)$ is tangent. Thus\n\\[\nZ=X-\\grad v-\\grad^\\perp j\n\\]\nis tangent, curl-free, and divergence-free. Its components are harmonic in the interior, so $Z$ is smooth there. Simple connectivity gives an interior potential $r$ with $Z=\\grad r$.\n\nHere $r\\in H^1(\\Omega)$, as can be seen without assuming its global integrability in advance. For $M>0$, its bounded truncation $r_M=\\max(-M,\\min(r,M))$ has local weak gradient of norm at most $|Z|$, hence belongs globally to $H^1(\\Omega)$. Fix an interior ball $B$. Its averages $(r_M)_B$ are bounded by $\\sup_B|r|$. The Poincar\\'e inequality, with this fixed-ball average as anchor, gives\n\\[\n\\norm{r_M}_{L^2(\\Omega)}\n\\le C\\norm{\\grad r_M}_{L^2(\\Omega)}+C|(r_M)_B|\n\\le C\\norm Z_2+C\\sup_B|r|.\n\\]\nFatou gives $r\\in L^2$, and its weak gradient is $Z$. Testing $\\diver Z=0$ against $r$, with its zero normal trace, yields $\\int|\\grad r|^2=0$. Thus\n\\[\nX=\\grad v+\\grad^\\perp j.\n\\]\nThe summands are orthogonal in $L^2$, since the second is divergence-free and tangent. Moreover,\n\\[\n\\Q(X)=\\norm{\\Delta v}_2^2+\\norm{\\Delta j}_2^2.\n\\]\nIntegration by parts and the two Poincar\\'e inequalities show\n\\[\n\\norm{\\grad v}_2^2\n\\le\\norm v_2\\norm{\\Delta v}_2\n\\le\\mu^{-1/2}\\norm{\\grad v}_2\\norm{\\Delta v}_2,\n\\]\nand likewise $\\norm{\\Delta j}_2^2\\ge\\lambda_D\\norm{\\grad j}_2^2$. Hence\n\\[\n\\Q(X)\\ge\\mu\\norm{\\grad v}_2^2+\\lambda_D\\norm{\\grad j}_2^2\n=\\mu\\norm X_2^2+(\\lambda_D-\\mu)\\norm{\\grad j}_2^2.\n\\]\nBy \\Cref{lem:gap}, this proves \\eqref{eq:vector-inequality}. Equality forces $j=0$ and equality in the Rayleigh inequality for $v$, so $v\\in\\V$. Conversely, gradients of functions in $\\V$ give equality directly.\n\\end{proof}\n\nFor a differential-form interpretation of this divergence--curl energy, see \\cite[Section~5.1]{FriesGoffengMiranda2026}. We will use the following boundary identity in the fixed Cartesian coordinates of the plane.\n\nLet $\\tau$ be the counterclockwise unit tangent to $\\partial\\Omega$, and put\n\\[\n\\kappa=\\det(\\tau,\\partial_\\tau\\tau).\n\\]\nThus $\\kappa$ is positive on a convex circle.\n\n\\begin{lemma}\\label{lem:curvature}\nFor tangent $X\\in H^1(\\Omega;\\R^2)$,\n\\begin{equation}\\label{eq:curvature}\n\\Q(X)=\\int_\\Omega|\\grad X|^2\n+\\int_{\\partial\\Omega}\\kappa|X|^2.\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nFor smooth fields, the difference of the bulk integrands is $2\\det\\grad X$. Its integral is the boundary pairing\n\\[\n\\int_{\\partial\\Omega}\n(X_1\\partial_\\tau X_2-X_2\\partial_\\tau X_1).\n\\]\nThis identity extends to $H^1$ fields by smooth approximation, since traces are in $H^{1/2}$ and their tangential derivatives are in $H^{-1/2}$. If the trace is $a\\tau$, the pairing is $\\int\\kappa a^2$. This follows first for smooth $a$ and then by approximation in $H^{1/2}$. It gives \\eqref{eq:curvature}.\n\\end{proof}\n"}, {"path": "preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/sections/04-boundary-kernels.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/sections/04-boundary-kernels.tex", "bytes": 9388, "sha256": "ecec2c27220f8c9a6a0663637fca26e6ea0ed5012f271cf388110e35fa9c0675", "content": "\\section{Boundary kernels and the multiplier identity}\\label{sec:boundary}\n\nOur objective is a boundary formula for the nonnegative vector energy from \\Cref{prop:vector}. We first construct scalar solution and interaction kernels for a general nonnegative subcritical potential on the disk. We then return to the conformal potential and derive the multiplier identity that will connect these kernels to an eigenfunction gradient.\n\n\\subsection{A subcritical disk potential}\n\nThe conformal map of \\Cref{lem:conformal} defines a measure\n\\begin{equation}\\label{eq:q}\n\\dd q(x)=\\mu|\\Phi'(x)|^2\\dd x.\n\\end{equation}\nIt has a bounded positive density. A subscript $q$ on an inner product or norm denotes $L^2(q)$. Conformal invariance of the Dirichlet integral and the Dirichlet inequality on $\\Omega$ give\n\\begin{equation}\\label{eq:subcritical}\n\\norm\\psi_{L^2(q)}^2\\le d\\int_\\D|\\grad\\psi|^2,\n\\qquad \\psi\\in H^1_0(\\D),\\qquad d=\\frac\\mu{\\lambda_D}<1.\n\\end{equation}\nUntil the vector-form application in \\Cref{subsec:energy}, only boundedness and nonnegativity of the density and \\eqref{eq:subcritical} are needed. In particular, the kernel constructions apply to any measure $q$ with those properties.\nFor such a general measure we fix a constant $0<d<1$ satisfying the same inequality; the zero measure also admits this choice.\n\nThe Dirichlet Green function and Poisson kernel of the disk are\n\\begin{align}\nG(x,y)&=\\frac1{2\\pi}\\log\\left|\\frac{1-x\\overline y}{x-y}\\right|\n=\\frac1{4\\pi}\\log\\left(1+\\frac{(1-|x|^2)(1-|y|^2)}{|x-y|^2}\\right),\\label{eq:green}\\\\\nP_s(x)&=\\frac{1-|x|^2}{2\\pi|s-x|^2},\\qquad s\\in\\Sone.\\label{eq:poisson}\n\\end{align}\nThe Green function is positive and is assigned value $+\\infty$ on the diagonal. Its logarithmic pole, harmonic correction, and zero boundary data give the Green identity. Also $\\int_{\\Sone}P_s(x)\\dd s=1$.\n\n\\begin{lemma}\\label{lem:green-operator}\nThe operator\n\\[\nTf(x)=\\int_\\D G(x,y)f(y)\\dd q(y)\n\\]\nis a bounded self-adjoint operator on $L^2(q)$ with $\\norm T\\le d<1$. The same statement holds with $q$ replaced by any measure $0\\le\\rho\\le q$.\n\\end{lemma}\n\\begin{proof}\nWrite $\\dd q=a\\dd x$, with $a$ bounded. For $f\\in L^2(q)$, the source $af$ lies in $L^2(\\dd x)$, since $\\int|af|^2\\le\\norm a_\\infty\\int|f|^2\\dd q$. Its Dirichlet solution $\\psi=Tf$ satisfies\n\\[\n\\int_\\D|\\grad\\psi|^2=\\ip{\\psi}{f}_q.\n\\]\nCombining this with \\eqref{eq:subcritical} gives\n\\[\n\\norm\\psi_q^2\\le d\\norm\\psi_q\\norm f_q,\n\\]\nhence the norm bound. Symmetry follows from the Green kernel, or by testing the two Poisson equations against each other. The Green formula can first be proved for smooth compactly supported sources and then passed to $L^2(\\dd x)$ by approximation; $G(x,\\cdot)\\in L^2(\\dd x)$ gives pointwise evaluation. These sections vary continuously in $L^2$ on compact interior sets, so this is the continuous interior representative. For $\\rho\\le q$, its density remains bounded and its weighted Dirichlet inequality follows from \\eqref{eq:subcritical}, proving the same result.\n\\end{proof}\n\nFor Lipschitz scalar or vector boundary data $h$, let\n\\[\nAh(x)=\\int_{\\Sone}P_s(x)h(s)\\dd s.\n\\]\nThis harmonic extension belongs to $H^1(\\D)$. The unique weak solution with trace $h$ of\n\\[\n-\\Delta W_h\\dd x=W_h\\dd q\n\\]\nis\n\\begin{equation}\\label{eq:extension}\nW_h=Ah+T(I-T)^{-1}Ah.\n\\end{equation}\nIndeed the correction has zero trace and solves the required Poisson equation. Uniqueness follows from \\eqref{eq:subcritical}. The construction is componentwise for vectors.\n\n\\subsection{Positive kernels and their estimates}\n\nFor $p\\in\\D$ and distinct $s,t\\in\\Sone$, define\n\\begin{align}\nK(p,s)&=P_s(p)+\\sum_{n\\ge1}(T^nP_s)(p),\\label{eq:K}\\\\\nR(s,t)&=\\sum_{n\\ge0}\\int_\\D P_s\\,T^nP_t\\dd q,\\label{eq:R}\\\\\nN(s,t)&=N_0(s,t)+R(s,t),\\qquad N_0(s,t)=\\frac1{\\pi|s-t|^2}.\\label{eq:N}\n\\end{align}\nPowers applied to $P_s$ mean iterated nonnegative kernel integrals, not an a priori application of an $L^2$ operator to $P_s$.\n\n\\begin{lemma}\\label{lem:kernel-estimates}\nThe kernels above are finite at the stated points. For fixed $p$, $K(p,\\cdot)$ is positive and bounded. The kernel $R$ is symmetric, nonnegative, and integrable on $\\Sone\\times\\Sone$, with\n\\begin{equation}\\label{eq:R-integral}\n\\iint R(s,t)\\dd s\\dd t\\le\\frac{q(\\D)}{1-d}.\n\\end{equation}\nFor Lipschitz boundary data,\n\\begin{align}\nW_h(p)&=\\int_{\\Sone}K(p,s)h(s)\\dd s,\\label{eq:K-representation}\\\\\n\\ip{Ah}{(I-T)^{-1}Ak}_q\n&=\\iint R(s,t)h(s)\\cdot k(t)\\dd s\\dd t.\\label{eq:R-representation}\n\\end{align}\n\\end{lemma}\n\\begin{proof}\nThe elementary estimates\n\\[\nP_s(y)\\le\\frac C{|y-s|},\\qquad\n0\\le G(x,y)\\le C\\log\\frac2{|x-y|}\n\\]\ngive uniform bounds for $P_s$ in $L^{3/2}(\\dd x)$ and for $G(x,\\cdot)$ in $L^3(\\dd x)$. H\\\"older and the bounded density imply\n\\[\n\\sup_{s\\in\\Sone}\\norm{TP_s}_{L^\\infty(\\D)}<\\infty.\n\\]\nThe section $P_s$ need not belong to $L^2(q)$, but one Green integration gives $F_s=TP_s\\in L^\\infty(\\D)\\subset L^2(q)$. We can now apply the $L^2(q)$ contraction estimate to the remaining iterates. For $n\\ge2$,\n\\begin{equation}\\label{eq:iterate-bound}\n|(T^nP_s)(p)|\n=|\\ip{G(p,\\cdot)}{T^{n-2}F_s}_q|\n\\le\\norm{G(p,\\cdot)}_q\\,d^{n-2}\\norm{F_s}_q\n\\le C d^{n-2}.\n\\end{equation}\nThe constants can be chosen uniformly in $p$ and $s$. This proves the assertion about $K$.\n\nFor distinct $s,t$, the product $P_sP_t$ is integrable: near either boundary pole, the other factor is bounded, and $|y-s|^{-1}$ is locally integrable in two dimensions. The terms with $n\\ge1$ in \\eqref{eq:R} are summable by the first smoothing estimate and \\eqref{eq:iterate-bound}, since $\\int P_s\\dd q$ is uniformly bounded. Reversing the nonnegative chains proves symmetry. Tonelli's theorem and $\\int P_s\\dd s=1$ give\n\\[\n\\iint R(s,t)\\dd s\\dd t\n=\\sum_{n\\ge0}\\ip\\one{T^n\\one}_q\n\\le\\sum_{n\\ge0}d^n\\norm\\one_q^2,\n\\]\nwhich is \\eqref{eq:R-integral}. Diagonal values of $R$ play no role in these boundary integrals. Expanding the Neumann series in \\eqref{eq:extension} and using the positive kernels gives \\eqref{eq:K-representation} and \\eqref{eq:R-representation}; the same bounds justify the exchanges for signed data by absolute domination.\n\\end{proof}\n\n\\subsection{The boundary energy}\\label{subsec:energy}\n\nReturn now to $q$ in \\eqref{eq:q}. A vector boundary function $h$ is called tangent if $h(s)$ is tangent to $\\partial\\Omega$ at $\\Phi(s)$. Extend its fixed Cartesian components by \\eqref{eq:extension}, and set\n\\[\nX_h=W_h\\circ\\Phi^{-1},\\qquad\nE(h)=\\Q(X_h)-\\mu\\norm{X_h}_{L^2(\\Omega)}^2.\n\\]\nWe write $E(h,k)$ for its bilinear form. \\Cref{prop:vector} gives $E\\ge0$ on tangent Lipschitz boundary data, and\n\\begin{equation}\\label{eq:E-nullspace}\nE(h)=0\\quad\\Longleftrightarrow\\quad\nX_h=\\grad v\\text{ for some }v\\in\\V.\n\\end{equation}\n\n\\begin{lemma}\\label{lem:boundary-energy}\nFor tangent Lipschitz data $h,k$,\n\\begin{align}\nE(h,k)={}&\\frac12\\iint N_0(s,t)\n(h(s)-h(t))\\cdot(k(s)-k(t))\\dd s\\dd t\\notag\\\\\n&-\\iint R(s,t)h(s)\\cdot k(t)\\dd s\\dd t\n+\\int_{\\Sone}|\\Phi'|(\\kappa\\circ\\Phi)h\\cdot k\\dd s.\n\\label{eq:E-boundary}\n\\end{align}\n\\end{lemma}\n\\begin{proof}\nBy \\Cref{lem:curvature} and conformal invariance of the scalar Dirichlet integral applied to each Cartesian component, the bulk terms of $E$ are\n\\[\n\\int_\\D\\grad W_h:\\grad W_k-\\ip{W_h}{W_k}_q.\n\\]\nThe equation for $W_k$ permits replacing $W_h$ here by $Ah$, since their difference has zero trace. Harmonicity of $Ah$ then replaces $W_k$ by $Ak$ in the gradient term. Since $W_k=(I-T)^{-1}Ak$ in $L^2(q)$, this gives\n\\[\n\\int_\\D\\grad Ah:\\grad Ak-\\ip{Ah}{(I-T)^{-1}Ak}_q.\n\\]\nThe boundary curvature term changes by the factor $|\\Phi'|$, and \\eqref{eq:R-representation} identifies the second term.\n\nFor the harmonic energy,\n\\begin{equation}\\label{eq:harmonic-energy}\n\\int_\\D\\grad Ah:\\grad Ak\n=\\frac12\\iint N_0(s,t)(h(s)-h(t))\\cdot(k(s)-k(t))\\dd s\\dd t.\n\\end{equation}\nIndeed both sides diagonalize in Fourier modes. The Hermitian energy of $s^m$ is $2\\pi|m|$, while\n\\[\n\\iint\\frac{|s^m-t^m|^2}{|s-t|^2}\\dd s\\dd t=(2\\pi)^2|m|\n\\]\nby a geometric sum, verifying the normalization. Smooth convolution approximation, with uniformly bounded Lipschitz constants and convergence in $H^{1/2}$, extends the identity to the stated data. This proves \\eqref{eq:E-boundary}.\n\\end{proof}\n\nFix $0\\ne u\\in\\V$ and let\n\\begin{equation}\\label{eq:g}\ng(s)=(\\grad u)(\\Phi(s)),\\qquad s\\in\\Sone.\n\\end{equation}\nIt is tangent by the Neumann condition. Differentiation of the Euclidean Helmholtz equation commutes with its Cartesian derivatives, so uniqueness gives\n\\begin{equation}\\label{eq:gradient-extension}\nW_g=(\\grad u)\\circ\\Phi,\n\\qquad E(g)=0.\n\\end{equation}\n\n\\begin{proposition}[Multiplier identity]\\label{prop:multiplier}\nFor every real Lipschitz function $b$ on $\\Sone$,\n\\begin{equation}\\label{eq:multiplier}\nE(bg)=\\frac12\\iint N(s,t)(b(s)-b(t))^2\ng(s)\\cdot g(t)\\dd s\\dd t.\n\\end{equation}\n\\end{proposition}\n\\begin{proof}\nSince $E$ is positive semidefinite and $E(g)=0$, its bilinear form annihilates $g$: this follows by considering $E(g+th)$ for real $t$. In particular, $E(g,b^2g)=0$. Subtract \\eqref{eq:E-boundary} for this pair from the formula for $E(bg,bg)$. The curvature terms cancel. The harmonic term gives\n\\[\n\\frac12N_0(s,t)(b(s)-b(t))^2g(s)\\cdot g(t)\n\\]\nin the double integral. Symmetry of $R$ gives the same expression with $R$ in place of $N_0$. Their sum is \\eqref{eq:multiplier}. All terms are integrable: the squared Lipschitz difference cancels the singularity of $N_0$, and $R$ is integrable.\n\\end{proof}\n"}, {"path": "preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/sections/05-reciprocal-kernels.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/sections/05-reciprocal-kernels.tex", "bytes": 16766, "sha256": "3bd38fa245f3a112d2107d9de5475045ed6834ee209f0bbf58fb30ebe9fde716", "content": "\\section{Reciprocal Green functions and a squared-distance kernel}\\label{sec:reciprocal}\n\nIn this section we prove the reciprocal-kernel theorem for a general disk potential. The kernels $K,N$ are those of \\eqref{eq:K}--\\eqref{eq:N}; their construction and estimates require only the measure hypotheses restated in the theorem below.\n\nA real symmetric kernel $D$ with zero diagonal is \\emph{conditionally negative semidefinite} if, for every finite family of points and real coefficients with $\\sum_i a_i=0$,\n\\[\n\\sum_{i,j}a_i a_jD(s_i,s_j)\\le0.\n\\]\n\n\\begin{theorem}\\label{thm:negative-kernel}\nLet $\\dd q=a\\dd x$ on $\\D$, where $a\\in L^\\infty(\\D)$ is nonnegative, and suppose there is a constant $0<d<1$ such that\n\\[\n\\int_\\D|\\psi|^2\\dd q\\le d\\int_\\D|\\grad\\psi|^2\n\\qquad(\\psi\\in H^1_0(\\D)).\n\\]\nFor the associated kernels $K,N$ and every $p\\in\\D$, the kernel\n\\begin{equation}\\label{eq:D}\nD_p(s,t)=\\frac{K(p,s)K(p,t)}{N(s,t)}\\quad(s\\ne t),\n\\qquad D_p(s,s)=0,\n\\end{equation}\nis conditionally negative semidefinite on $\\Sone$.\n\\end{theorem}\n\n\\begin{remark}\\label{rem:zero-potential}\nFor $q=0$, this kernel is a rescaled squared chordal distance. Indeed, with the disk automorphism $\\varphi_p(z)=(z-p)/(1-\\overline pz)$,\n\\[\nD_p(s,t)=\\pi P_s(p)P_t(p)|s-t|^2\n=\\frac1{4\\pi}|\\varphi_p(s)-\\varphi_p(t)|^2.\n\\]\n\\Cref{thm:negative-kernel} supplies a Hilbert-space squared-distance interpretation after a nonnegative subcritical potential is added.\n\\end{remark}\n\nWe prove the theorem through reciprocal matrices. The reciprocal inertia condition comes from the McCullough--Quiggin theory \\cite{McCullough1994,Quiggin1994}. For finite-valued positive-definite kernels whose entries never vanish, it characterizes the complete Nevanlinna--Pick property \\cite[Corollary~1.12]{AglerMcCarthy2000}. Quiggin established the reciprocal condition for a class of weighted one-dimensional Sobolev kernels, represented by Green functions of regular Sturm--Liouville problems \\cite[Section~2.2, Corollary~2.2.2 and Theorem~2.2.3]{Quiggin1994}. Here this condition serves as an algebraic model; all the matrix and analytic arguments needed for the singular Green kernel will be given below.\n\nSay that a real symmetric matrix has property \\textup{(P)} if it has at most one positive eigenvalue, counted with multiplicity. This property is preserved by positive diagonal congruence, subtraction of a positive semidefinite matrix, passage to principal submatrices, and limits of matrices of fixed size. Reciprocals below are always \\emph{entrywise}, never matrix inverses.\n\nThe fixed interior point $p$ connects \\textup{(P)} to the desired conditional negativity. Adjoining it to boundary points will give a reciprocal matrix with zero diagonal, boundary block $B_{ij}=1/N(s_i,s_j)$ for $i\\ne j$, and border $\\ell_i=1/K(p,s_i)$. Property \\textup{(P)} for this bordered matrix forces $x^TBx\\le0$ on $\\ell^Tx=0$; the substitution $x_i=K(p,s_i)a_i$ turns this into the required inequality for $\\sum_i a_i=0$. We prove this last algebraic step at the end of the section.\n\nWe first construct bounded regularizations of $G$ whose reciprocals have \\textup{(P)}, and prove that updates along a column of the current kernel matrix preserve this condition. The resolvent construction then uses four limits, in order: refine a positive quadrature at fixed $\\epsilon>0$ and compactly supported $0\\le\\rho\\le q$; let $\\epsilon\\downarrow0$; send the moving evaluation points radially to the circle while fixing $p$; and finally increase $\\rho$ to $q$. Compact support keeps the Poisson sections bounded during the boundary step.\n\n\\subsection{A bounded regularization of the Green kernel}\n\nDirect quadrature of $G$ would encounter the infinite value $G(z,z)$ at every integration node. We therefore need a bounded replacement with finite positive diagonal that retains the reciprocal condition \\textup{(P)}.\n\nThe scalar integral used below is an equivalent form of the logarithmic-mean representations in \\cite{QiZhangLi2014}. We derive it and then establish the positive-semidefinite disk-kernel property that we need.\n\n\\begin{lemma}\\label{lem:regularization}\nThere are bounded continuous strictly positive kernels $C_\\epsilon$ on $\\D\\times\\D$, $0<\\epsilon\\le1$, such that $C_\\epsilon\\uparrow G$ as $\\epsilon\\downarrow0$, including on the diagonal, and every finite matrix $(1/C_\\epsilon(x_i,x_j))_{i,j}$ has \\textup{(P)}.\n\\end{lemma}\n\\begin{proof}\nPut\n\\[\nh(x,y)=\\frac{|x-y|^2}{(1-|x|^2)(1-|y|^2)},\\qquad\nL(h)=\\frac1{\\log(1+1/h)}\\ (h>0),\\quad L(0)=0,\n\\]\nand $f(h)=h+\\tfrac12-L(h)$. After multiplying the $x$- and $y$-entries by $1-|x|^2$ and $1-|y|^2$, respectively, the kernel $h+\\tfrac12$ becomes\n\\begin{equation}\\label{eq:lorentz}\n\\tfrac12(1+|x|^2)(1+|y|^2)-2x\\cdot y.\n\\end{equation}\nThis is a positive rank-one kernel minus a positive semidefinite kernel, so $h+\\tfrac12$ has \\textup{(P)} on finite sets.\n\nSince $1/G=4\\pi L(h)$, the identity $L=(h+\\tfrac12)-f$ will be useful once we prove that $f(h(x,y))$ is positive semidefinite. For $h>0$,\n\\begin{equation}\\label{eq:log-mean}\nL(h)=\\int_0^1h^{1-\\alpha}(h+1)^\\alpha\\dd\\alpha.\n\\end{equation}\nFor $0<\\alpha<1$, let $c_\\alpha$ normalize $v^{\\alpha-1}(1+v)^{-1}\\dd v$ to a probability measure on $(0,\\infty)$, and set $r=(1+v)^{-1}$. Integration by parts gives\n\\[\n\\mathbb E_\\alpha(1-r)\n=c_\\alpha\\int_0^\\infty\\frac{v^\\alpha}{(1+v)^2}\\dd v\n=\\alpha.\n\\]\nFor $h>0$, scaling $v$ in the normalized integral yields\n\\begin{align}\nh^{1-\\alpha}(h+1)^\\alpha\n&=c_\\alpha\\int_0^\\infty\n\\frac{h(h+1)}{(h+1)+vh}v^{\\alpha-1}\\dd v\\notag\\\\\n&=\\mathbb E_\\alpha\\frac{h(h+1)}{h+r}\n=\\mathbb E_\\alpha\\left[h+(1-r)-\\frac{r(1-r)}{h+r}\\right].\\label{eq:mixture-algebra}\n\\end{align}\nThe last equality remains true at $h=0$. Integrating in $\\alpha$ therefore gives\n\\begin{equation}\\label{eq:f-mixture}\nf(h)=\\int_0^1\\mathbb E_\\alpha\\frac{r(1-r)}{h+r}\\dd\\alpha,\n\\qquad 0\\le f(h)\\le f(0)=\\tfrac12.\n\\end{equation}\n\nNow\n\\[\nk(x,y)=\\frac1{1+h(x,y)}\n=\\frac{(1-|x|^2)(1-|y|^2)}{|1-x\\overline y|^2}\n\\]\nis positive semidefinite. Indeed $(1-x\\overline y)^{-1}$ is a Gram kernel by its power series; multiplying it by its complex conjugate and the positive diagonal factors preserves positive semidefiniteness. For fixed $0<r<1$,\n\\begin{equation}\\label{eq:schur-geometric}\n\\frac1{h+r}=\\sum_{n\\ge0}(1-r)^n k^{n+1},\n\\end{equation}\nwhere powers are entrywise. This series converges even when $h=0$. Each power is positive semidefinite, as follows by taking tensor products of Gram vectors. Thus $1/(h+r)$ is positive semidefinite, and the positive mixture \\eqref{eq:f-mixture} proves the claim. The mixture has finite entries by the displayed bound, including on the diagonal.\n\nDefine\n\\begin{equation}\\label{eq:C-epsilon}\n\\frac1{C_\\epsilon(x,y)}\n=4\\pi\\bigl[h+\\tfrac12-(1-\\epsilon)f(h)\\bigr]\n=4\\pi\\bigl[L(h)+\\epsilon f(h)\\bigr].\n\\end{equation}\nIts reciprocal matrix has \\textup{(P)} by \\eqref{eq:lorentz} and the positive semidefiniteness just proved. Its denominator is at least $2\\pi\\epsilon$, because\n\\[\nL+\\epsilon f=\\epsilon(h+\\tfrac12)+(1-\\epsilon)L\\ge\\epsilon/2.\n\\]\nIt is positive and continuous, so $C_\\epsilon$ is bounded, continuous, and strictly positive. Since $f\\ge0$, these kernels increase to $G$ by \\eqref{eq:green}, and $0<C_\\epsilon\\le G$. On the diagonal $C_\\epsilon(x,x)=1/(2\\pi\\epsilon)$, as required.\n\\end{proof}\n\n\\subsection{A finite resolvent update}\n\n\\begin{lemma}\\label{lem:update}\nLet $M$ be a real symmetric finite matrix with strictly positive entries such that its reciprocal has \\textup{(P)}. For any index $z$ and any $c\\ge0$, the matrix\n\\[\n\\widetilde M_{ij}=M_{ij}+cM_{iz}M_{zj}\n\\]\nalso has a reciprocal with \\textup{(P)}.\n\\end{lemma}\n\\begin{proof}\nPositive diagonal congruence transforms the reciprocal of $M$ into\n\\[\nH_{ij}=\\frac{M_{iz}M_{zj}}{M_{ij}}.\n\\]\nWriting $a=M_{zz}>0$, its $z$-row and column are identically $a$. Put $z$ last, and let $H_0$ denote the remaining principal block. Subtracting the last row from every other row, and doing the same to the columns, gives the congruence\n\\[\nH\\ \\sim\\\n\\begin{pmatrix}H_0-a\\one\\one^T&0\\\\0&a\\end{pmatrix}.\n\\]\nBecause $a>0$ and $H$ has \\textup{(P)}, the first block is nonpositive. Hence\n\\[\nU=a\\one\\one^T-H\\PSD.\n\\]\nSince $H_{ii}>0$, one has $0\\le U_{ii}<a$, and positive-semidefinite Cauchy--Schwarz gives $|U_{ij}|<a$.\n\nUsing the same diagonal normalization, the new reciprocal has entries $H_{ij}/(1+cH_{ij})$. The difference from the constant rank-one kernel is\n\\begin{align}\n\\frac a{1+ca}-\\frac{H_{ij}}{1+cH_{ij}}\n&=\\frac{U_{ij}}{(1+ca)(1+ca-cU_{ij})}\\notag\\\\\n&=\\sum_{n\\ge0}\\frac{c^nU_{ij}^{n+1}}{(1+ca)^{n+2}}.\\label{eq:update-series}\n\\end{align}\nThe series converges absolutely because $|cU_{ij}|<1+ca$. Its coefficients are nonnegative and every entrywise power of $U$ is positive semidefinite, even if $U$ has negative entries. Thus the normalized reciprocal of $\\widetilde M$ is a positive rank-one kernel minus a positive semidefinite matrix, proving \\textup{(P)}. For $c=0$ the identity reduces directly to $U$.\n\\end{proof}\n\n\\subsection{The interior resolvent kernel}\n\nFor $0\\le\\rho\\le q$ supported on a compact subset of $\\D$, let $T_\\rho$ be the Green operator on $L^2(\\rho)$ and define\n\\begin{equation}\\label{eq:M-rho}\nM^\\rho(x,y)=G(x,y)\n+\\ip{G(x,\\cdot)}{(I-T_\\rho)^{-1}G(y,\\cdot)}_\\rho.\n\\end{equation}\nIt is finite for distinct interior points, since its Green sections belong to $L^2(\\rho)$, and has infinite diagonal.\n\n\\begin{proposition}\\label{prop:interior-reciprocal}\nFor every finite set of interior points, the reciprocal matrix of $M^\\rho$, with diagonal zero, has \\textup{(P)}.\n\\end{proposition}\n\\begin{proof}\nFirst fix $\\epsilon>0$ and replace $G$ throughout \\eqref{eq:M-rho} by $C=C_\\epsilon$. Its integral operator $T_C$ on $L^2(\\rho)$ satisfies\n\\[\n|T_Cf|\\le T_\\rho|f|,\\qquad \\norm{T_C}\\le d<1.\n\\]\nThe absolute pointwise domination proves the norm bound without requiring a separate positivity assertion about its quadratic form.\n\nPartition a compact integration set into finitely many measurable cells with uniformly vanishing diameters. For each cell of positive $\\rho$-mass, choose a representative, and let $\\pi$ send that cell to the representative. Use its mass as the quadrature weight. Uniform continuity gives uniform convergence of\n\\[\nC(\\pi(\\cdot),\\pi(\\cdot))\\longrightarrow C\n\\]\non the integration set up to null sets. The associated operators $T_\\pi$ on $L^2(\\rho)$ converge to $T_C$ in operator norm; for example their norm difference is at most $\\rho(\\D)$ times the uniform kernel error. Likewise $C(x,\\pi(\\cdot))\\to C(x,\\cdot)$ in $L^2(\\rho)$ for each fixed evaluation point $x$. In particular, sufficiently fine quadratures have $\\norm{T_\\pi}<1$, and\n\\begin{equation}\\label{eq:quadrature-resolvent}\nC(x,y)+\\ip{C(x,\\pi(\\cdot))}{(I-T_\\pi)^{-1}C(y,\\pi(\\cdot))}_\\rho\n\\end{equation}\nconverges to the regularized continuum resolvent evaluation. This follows from convergence of the source vectors and of the inverses in operator norm.\n\nFor clarity, if the nodes and weights are $z_\\alpha,w_\\alpha$, set\n\\[\n\\mathsf T_{\\alpha\\beta}\n=\\sqrt{w_\\alpha}\\,C(z_\\alpha,z_\\beta)\\sqrt{w_\\beta},\n\\qquad\n(\\xi_x)_\\alpha=\\sqrt{w_\\alpha}\\,C(x,z_\\alpha).\n\\]\nThe normalized cell indicators form an orthonormal basis for the step functions. On this subspace $T_\\pi$ is represented by $\\mathsf T$, and it vanishes on the orthogonal complement. Thus $\\norm{\\mathsf T}<1$, and the correction in \\eqref{eq:quadrature-resolvent} is $\\xi_x^T(I-\\mathsf T)^{-1}\\xi_y$. Evaluation points enter only these finite vectors, including when they coincide with nodes.\n\nWe next prove \\textup{(P)} for each sufficiently fine quadrature. On the union of the evaluation points and quadrature nodes, start with $M_{ij}=C(x_i,x_j)$. By \\Cref{lem:regularization}, its reciprocal has \\textup{(P)}. Expanding the finite resolvent sums all chains, multiplying kernel entries along each chain and the quadrature weight at every intermediate occurrence. These nonnegative sums are finite by the preceding matrix formula. Restricting the allowed intermediate nodes gives a subseries and hence remains finite.\n\nSuppose some nodes have been allowed and their chain sum is $M$. Admit one more node $z$ of weight $w>0$. Chains with exactly $k\\ge1$ internal occurrences of $z$ have total weight\n\\[\nw^kM_{iz}M_{zz}^{k-1}M_{zj}.\n\\]\nAll factors are positive. Finiteness of the full series forces $wM_{zz}<1$, and summing over $k$ updates the matrix to\n\\begin{equation}\\label{eq:elimination}\nM_{ij}+\\frac{w}{1-wM_{zz}}M_{iz}M_{zj}.\n\\end{equation}\n\\Cref{lem:update} therefore propagates \\textup{(P)} one node at a time. This counting remains valid when an endpoint is a quadrature node, since only internal occurrences receive weights. Coincident node locations may be merged by adding their weights. Thus the discrete kernel \\eqref{eq:quadrature-resolvent} has the required reciprocal property. The quadrature limit gives it for the regularized continuum kernel. If $\\rho=0$, this conclusion follows directly from \\Cref{lem:regularization}.\n\nFinally let $\\epsilon\\downarrow0$. Every chain integral increases to its counterpart with $G$ by monotone convergence, as does the sum of these nonnegative integrals. Off the diagonal, its limit is \\eqref{eq:M-rho}, and the correction is finite because\n\\[\n\\big|\\ip{G(x,\\cdot)}{(I-T_\\rho)^{-1}G(y,\\cdot)}_\\rho\\big|\n\\le\\frac{\\norm{G(x,\\cdot)}_\\rho\\norm{G(y,\\cdot)}_\\rho}{1-d}.\n\\]\nThe direct diagonal term tends to infinity, so its reciprocal tends to zero. Finite entrywise limits preserve \\textup{(P)}, completing the proof.\n\\end{proof}\n\n\\subsection{The boundary limit}\n\nFor compactly supported $\\rho$, write $K^\\rho,R^\\rho,N^\\rho$ for the kernels in \\eqref{eq:K}--\\eqref{eq:N} with $q$ replaced by $\\rho$. The boundary limit will be taken with this compact support fixed, before increasing $\\rho$ to $q$.\n\n\\begin{lemma}\\label{lem:bordered}\nFor distinct $s_1,\\ldots,s_m\\in\\Sone$ and $p\\in\\D$, the matrix\n\\begin{equation}\\label{eq:bordered}\n\\begin{pmatrix}B&\\ell\\\\\\ell^T&0\\end{pmatrix},\n\\qquad\nB_{ij}=\\begin{cases}1/N(s_i,s_j),&i\\ne j,\\\\0,&i=j,\\end{cases}\n\\qquad \\ell_i=1/K(p,s_i),\n\\end{equation}\nhas \\textup{(P)}.\n\\end{lemma}\n\\begin{proof}\nFix compactly supported $0\\le\\rho\\le q$. Apply \\Cref{prop:interior-reciprocal} to the points $r s_1,\\ldots,r s_m,p$, with $r\\uparrow1$ and $r>|p|$. The explicit Green formula gives\n\\begin{align}\n\\frac{G(rs,x)}{1-r}&\\longrightarrow P_s(x),\\label{eq:radial-one}\\\\\n\\frac{G(rs,rt)}{(1-r)^2}&\\longrightarrow N_0(s,t)\\qquad(s\\ne t).\\label{eq:radial-two}\n\\end{align}\nThe first convergence is uniform on compact interior sets. For the second, one may use\n\\[\nG(rs,rt)=\\frac1{4\\pi}\\log\\left(1+\\frac{(1-r^2)^2}{r^2|s-t|^2}\\right).\n\\]\nThe bounded resolvent in \\eqref{eq:M-rho} and the uniform convergence on $\\supp\\rho$ therefore imply\n\\[\n\\frac{M^\\rho(rs_i,rs_j)}{(1-r)^2}\\longrightarrow N^\\rho(s_i,s_j)\n\\quad(i\\ne j),\\qquad\n\\frac{M^\\rho(p,rs_i)}{1-r}\\longrightarrow K^\\rho(p,s_i).\n\\]\nHere the first resolvent correction tends to\n$\\ip{P_{s_i}}{(I-T_\\rho)^{-1}P_{s_j}}_\\rho=R^\\rho(s_i,s_j)$.\nThe second tends to\n$P_{s_i}(p)+\\ip{G(p,\\cdot)}{(I-T_\\rho)^{-1}P_{s_i}}_\\rho=K^\\rho(p,s_i)$.\nThese pairings are legitimate on the compact support, where the Poisson kernels are bounded.\n\nScale the reciprocal matrix by positive diagonal congruence with factors $1-r$ at the moving points and factor $1$ at $p$. Its limit is \\eqref{eq:bordered} with $K^\\rho,N^\\rho$, so it has \\textup{(P)}. The zero diagonal stays zero throughout.\n\nNow choose increasing disk cutoffs $\\dd\\rho_n=\\one_{\\{|x|\\le r_n\\}}\\dd q$, with $r_n\\uparrow1$. Every term of $K^{\\rho_n}$ and $R^{\\rho_n}$ is a nonnegative chain integral. Monotone convergence in each finite product integral and then in the series gives\n\\[\nK^{\\rho_n}(p,s)\\uparrow K(p,s),\\qquad\nN^{\\rho_n}(s,t)\\uparrow N(s,t)\\quad(s\\ne t).\n\\]\nThese limits are finite by \\Cref{lem:kernel-estimates}. Taking the reciprocals and the finite matrix limit proves \\eqref{eq:bordered}. No $L^2(q)$ boundary limit of an individual Poisson kernel has been used.\n\\end{proof}\n\n\\begin{proof}[Proof of \\Cref{thm:negative-kernel}]\nUse the matrix of \\Cref{lem:bordered}. We first show that $x^TBx\\le0$ whenever $\\ell^Tx=0$. If instead $x^TBx>0$, choose $y$ with $\\ell^Ty\\ne0$ and replace it by\n\\[\ny-\\frac{x^TBy}{x^TBx}x.\n\\]\nThen $x^TBy=0$ and still $\\ell^Ty\\ne0$. In the bordered form, $(x,0)$ and $(y,c)$ are orthogonal, and $c$ can be chosen so that\n\\[\n(y,c)^T\\begin{pmatrix}B&\\ell\\\\\\ell^T&0\\end{pmatrix}(y,c)\n=y^TBy+2c\\ell^Ty>0.\n\\]\nThey would span a two-dimensional positive subspace, contradicting \\textup{(P)}.\n\nFinally, for coefficients with $\\sum_i a_i=0$, put $x_i=K(p,s_i)a_i$. Then $\\ell^Tx=0$ and\n\\[\n\\sum_{i,j}a_i a_jD_p(s_i,s_j)=x^TBx\\le0.\n\\]\nThis proves the assertion on distinct points; repeated points are handled by combining their coefficients.\n\\end{proof}\n"}, {"path": "preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/sections/06-conclusion.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/build/sections/06-conclusion.tex", "bytes": 5690, "sha256": "432eaebd0c9864ded73e200afe6a54421b4b45d035e3f45700ce9368301cb17a", "content": "\\section{Hilbert multipliers and the absence of critical points}\\label{sec:conclusion}\n\nWe now use the conditional negativity proved in \\Cref{thm:negative-kernel} to construct all the scalar multipliers needed in \\Cref{prop:multiplier} at once. The Hilbert-space construction is the classical squared-distance correspondence of Schoenberg \\cite{Schoenberg1938}; we give the short Gram-kernel proof.\n\n\\begin{lemma}\\label{lem:hilbert}\nFor every $p\\in\\D$ there is an injective Lipschitz map $b:\\Sone\\to\\HH$, where $\\HH$ is a separable real Hilbert space, such that\n\\begin{equation}\\label{eq:hilbert-distance}\n\\norm{b(s)-b(t)}_\\HH^2=D_p(s,t).\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nFix $s_0\\in\\Sone$. Conditional negativity makes the anchored kernel\n\\[\n\\Gamma(s,t)=\\tfrac12\\bigl(D_p(s,s_0)+D_p(t,s_0)-D_p(s,t)\\bigr)\n\\]\npositive semidefinite: append to any finite family of coefficients the coefficient at $s_0$ that makes their sum zero. Give formal finite linear combinations of symbols $b(s)$ the bilinear form with Gram kernel $\\Gamma$, quotient by its nullspace, and complete. Since $D_p(s,s)=0$, this realizes \\eqref{eq:hilbert-distance} and $b(s_0)=0$.\n\nPositivity of $K$ and finiteness and positivity of $N$ off the diagonal give $D_p(s,t)>0$ for $s\\ne t$, so $b$ is injective. Also $N\\ge N_0$ gives\n\\[\n\\norm{b(s)-b(t)}_\\HH^2\n\\le\\pi\\norm{K(p,\\cdot)}_\\infty^2|s-t|^2.\n\\]\nThus $b$ is Lipschitz. Replace $\\HH$ by the closed span of $b(\\Sone)$; it is separable by continuity and separability of the circle.\n\\end{proof}\n\n\\begin{proof}[Proof of \\Cref{thm:main}]\nTake an arbitrary $0\\ne u\\in\\V$, with $g$ defined by \\eqref{eq:g}. Suppose $\\Phi(p)$ is a critical point. Choose $b$ from \\Cref{lem:hilbert}, and let $b_j$ be its coordinates in a finite or countable orthonormal basis. Each $b_j$ is real Lipschitz, so \\Cref{prop:multiplier} applies. For partial sums, the absolute integrand is bounded by\n\\begin{align*}\nN(s,t)\\sum_{j\\le m}|b_j(s)-b_j(t)|^2|g(s)||g(t)|\n&\\le N(s,t)D_p(s,t)|g(s)||g(t)|\\\\\n&=K(p,s)K(p,t)|g(s)||g(t)|.\n\\end{align*}\nThe right side is bounded and integrable. Dominated convergence therefore gives\n\\begin{align}\n\\sum_j E(b_jg)\n&=\\frac12\\iint K(p,s)K(p,t)g(s)\\cdot g(t)\\dd s\\dd t\\notag\\\\\n&=\\frac12\\left|\\int_{\\Sone}K(p,s)g(s)\\dd s\\right|^2\n=\\frac12|W_g(p)|^2=0.\\label{eq:sum-energy}\n\\end{align}\nEvery summand is nonnegative. By \\eqref{eq:E-nullspace}, for every $j$ there is $v_j\\in\\V$ whose boundary gradient, pulled back by $\\Phi$, is $b_jg$. The trace equality holds pointwise because both sides are continuous. The boundary gradient of $u$ is $g$.\n\nSet $S_g=\\{s\\in\\Sone:g(s)\\ne0\\}$. This is a nonempty open set. If it were empty, each Cartesian derivative of $u$ would solve the Helmholtz equation with zero Dirichlet trace, contradicting $\\mu<\\lambda_D$ unless both vanished. Then $u$ would be a constant eigenfunction for a positive eigenvalue, hence zero.\n\nThe two possibilities for $S_g$ lead respectively to multiplicity and boundary-uniqueness contradictions.\n\n\\smallskip\\noindent\n\\emph{Case 1: $S_g$ is dense.}\nThe set $b(S_g)$ cannot lie in an affine line. Otherwise continuity would place $b(\\Sone)$ in that closed line, but a continuous injective circle cannot map into a line. Indeed its compact connected image would be an interval; deleting an interior point disconnects an interval but not a circle with one point removed.\n\nChoose three points of $S_g$ whose images are affinely independent. Their two difference vectors are linearly independent in $\\HH$, so some two coordinates $j,k$ have a nonzero $2\\times2$ minor. To see this, if all such minors vanished, a nonzero coordinate of the first difference vector would force every coordinate of the second to be proportional to it. Thus the functions $1,b_j,b_k$ are linearly independent on those three points. Since $g\\ne0$ there, the vector traces $g,b_jg,b_kg$ are linearly independent. They are gradient traces of three functions in $\\V$, contradicting \\Cref{prop:multiplicity}.\n\n\\smallskip\\noindent\n\\emph{Case 2: $S_g$ is not dense.}\nThere is a nonempty open arc $I$ on which $g=0$. Since $S_g$ is nonempty and open and $b$ is injective, some coordinate $b_j$ is nonconstant on $S_g$. Consequently $g$ and $b_jg$ are linearly independent, and so are their eigenfunctions $u$ and $v_j$. Their gradients vanish on the boundary arc $\\Phi(I)$, so each has a constant trace there. Choose a nonzero linear combination $w$ of them whose constant trace on that arc is zero. It also has zero normal derivative there.\n\nTake a small neighborhood of an interior point of the arc that meets no other boundary portion, and extend $w$ by zero to the exterior of $\\Omega$ in that neighborhood. This is a local version of the zero-extension argument in \\cite[Lemma, p.~414]{Filonov2005}. Integration by parts against compactly supported tests shows that this extension solves $(\\Delta+\\mu)w=0$ distributionally: both its Dirichlet and normal traces on the intervening boundary vanish. Interior elliptic regularity and analyticity make the extension analytic. Since it is zero on an exterior open set, it is zero throughout a smaller neighborhood. Analytic continuation inside the connected domain gives $w=0$ on $\\Omega$, contradicting independence of $u$ and $v_j$. No analyticity of the boundary is used.\n\nBoth cases contradict the assumed critical point. Thus $\\grad u$ is nowhere zero in $\\Omega$. The continuous function $u$ attains both extrema on the compact closure. Any interior extremum would be critical, so both are attained on the boundary. An interior value equal to either boundary extremum would itself be a global extremum. This proves both strict inequalities in \\eqref{eq:hotspots}.\n\\end{proof}\n"}, {"path": "preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/main.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Strict-hot-spots-and-absence-of-interior-critical-points-on-smooth-simply-connected-planar-domains-September-24-2026/main.pdf", "bytes": 390272, "sha256": "7a65d7bc83270c2b1eccc49a3921fd61bc281283b7d0c3a5059df0a2f8778413", "base64": 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{"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/README.md", "bytes": 727, "sha256": "6b32afbadf435843c325fd3be749f009a45fd764387f9f2d7293bc0c9f5cb9b8", "content": "# [The canonical massive continuum limit of the two-dimensional O(3) model](massive-continuum-o3.pdf)\n\n**Author:** OpenAI\n\n**Date:** October 4, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026,\n  author = {{OpenAI}},\n  title = {{The canonical massive continuum limit of the two-dimensional O(3) model}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/massive-continuum-o3.pdf}{OAI:The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/figures/editable.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/figures/editable.tex", "bytes": 1991, "sha256": "c8f6be7062406f52c835498cceeb9c86cfe355cf29046c405cab39f96498e530", "content": "\\begin{figure}[htbp]\n\\centering\n\\begin{tikzpicture}[x=0.65cm,y=0.65cm,font=\\small]\n  \\draw[step=1,black!12,very thin] (0,0) grid (15,7);\n  \\fill[blue!12] (1,1) -- (6,1) -- (6,2) -- (7,2) -- (7,3) -- (8,3) -- (8,6)\n      -- (4,6) -- (4,5) -- (1,5) -- cycle;\n  \\draw[blue!65!black,thick] (1,1) -- (6,1) -- (6,2) -- (7,2) -- (7,3) -- (8,3)\n      -- (8,6) -- (4,6) -- (4,5) -- (1,5) -- cycle;\n  \\fill[blue!35] (2,2) rectangle (5,4);\n  \\fill[blue!35] (5,3) rectangle (6,5);\n  \\fill[blue!35] (6,4) rectangle (7,5);\n  \\draw[blue!65!black,dashed] (2,2) rectangle (5,4);\n  \\draw[blue!65!black,dashed] (5,3) rectangle (6,5);\n  \\draw[blue!65!black,dashed] (6,4) rectangle (7,5);\n  \\fill[blue!12] (10,1) rectangle (14,5);\n  \\draw[blue!65!black,thick] (10,1) rectangle (14,5);\n  \\fill[blue!35] (11,2) rectangle (13,4);\n  \\draw[blue!65!black,dashed] (11,2) rectangle (13,4);\n  \\draw[red!75!black,line width=2pt] (3,3) -- (4,3);\n  \\draw[red!75!black,line width=2pt] (11.5,3) -- (12.5,3);\n  \\node at (2.3,1.45) {$E_C$};\n  \\node at (12,1.45) {$E_{C'}$};\n  \\node at (3.5,3.6) {$U\\cap E_C$};\n  \\draw[<->] (4.6,1.08) -- (4.6,1.92);\n  \\node[fill=white,inner sep=1pt] at (4.6,1.5) {$d$};\n  \\node[anchor=west,align=left] at (0,7.8)\n      {Marked boxes form $U$; a $d$-collar separates $U$ from the unchanged exterior.};\n  \\node[anchor=west] at (0,-0.7)\n      {\\textcolor{red!75!black}{Thick bonds}: true bad bonds \\quad\n       \\textcolor{blue!65!black}{Outer boundaries}: retained components of the dilation.};\n\\end{tikzpicture}\n\\caption{Schematic editable regions. Each dark region is a union of\nmarked boxes, and each light region is the complete connected component\nof its $d$-dilation. The replacement uses all sites of a selected light\nregion. Its outer collar lies outside $U$, so it joins the unchanged\nconfiguration without creating a bad bond. Distinct retained components\nhave nonadjacent sites. The drawing suppresses lattice boundary\nconventions and is not to scale.}\n\\label{fig:editable}\n\\end{figure}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/figures/orientations.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/figures/orientations.tex", "bytes": 1644, "sha256": "9a895a897fd15efb719ee663e9cb5f2d14da30ca7a0d59ec42ef93f3aca8c8bd", "content": "\\begin{figure}[tb]\n\\centering\n\\begin{tikzpicture}[scale=0.9,>=Latex,every node/.style={font=\\small}]\n\\begin{scope}[shift={(-4.8,0)}]\n  \\draw[step=.4,gray!35,very thin] (-1.25,-1.25) grid (1.25,1.25);\n  \\draw[->,thick] (0,0)--(1.6,0) node[right] {$e_1$};\n  \\draw[->,thick] (0,0)--(0,1.6) node[above] {$e_2$};\n  \\node[align=center] at (0,-1.9) {First regulator\\\\orientation $0$};\n\\end{scope}\n\\begin{scope}[rotate=53.130102]\n  \\draw[step=.65,MidnightBlue!65,thin] (-1.3,-1.3) grid (1.3,1.3);\n  \\draw[->,MidnightBlue,very thick] (0,0)--(1.6,0);\n  \\draw[->,MidnightBlue,very thick] (0,0)--(0,1.6);\n\\end{scope}\n\\node[align=center,text=MidnightBlue] at (0,-2.2)\n  {Common coarse frame\\\\orientation $\\theta$};\n\\begin{scope}[shift={(4.8,0)},rotate=106.260204]\n  \\draw[step=.4,gray!35,very thin] (-1.25,-1.25) grid (1.25,1.25);\n  \\draw[->,thick] (0,0)--(1.6,0);\n  \\draw[->,thick] (0,0)--(0,1.6);\n\\end{scope}\n\\node[align=center] at (4.8,-1.9) {Second regulator\\\\orientation $2\\theta$};\n\\draw[->,thick] (-2.9,.25)--(-1.85,.25)\n  node[midway,above=5pt,align=center] {$+\\theta$};\n\\draw[->,thick] (2.9,.25)--(1.85,.25)\n  node[midway,above=5pt,align=center] {$-\\theta$};\n\\node at (0,2.15) {$\\cos\\theta=3/5,\\qquad\\sin\\theta=4/5$};\n\\end{tikzpicture}\n\\caption{The two inclined first blockings produce the same physical\nframe. Their relative inclinations are reflections of one another, so\ntheir scalar field normalizations agree. After the first step, common\nregular blockings erase the difference of their remaining data. The\ndrawing shows orientations only; the first coarse spacing is $5L$ times\nthe microscopic spacing.}\n\\label{fig:orientations}\n\\end{figure}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/main.tex", "bytes": 3215, "sha256": "a84181e9ba763ce1db080258b487fede39b8617fc657a4c181d3ec733061a207", "content": "\\documentclass[11pt]{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{lmodern,microtype}\n\\usepackage[margin=1in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage{enumitem,booktabs,longtable,array}\n\\usepackage{graphicx,tikz,placeins}\n\\usetikzlibrary{arrows.meta,positioning,calc,fit,patterns}\n\\usepackage[dvipsnames]{xcolor}\n\\usepackage[colorlinks=true,linkcolor=MidnightBlue,citecolor=MidnightBlue,urlcolor=MidnightBlue]{hyperref}\n\\hypersetup{pdftitle={The canonical massive continuum limit of the two-dimensional O(3) model},pdfauthor={OpenAI},pdfsubject={Canonical scaling along all diverging couplings, continuum reconstruction, and a full mass gap}}\n\\numberwithin{equation}{section}\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\\newtheorem{definition}[theorem]{Definition}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\newcommand{\\E}{\\mathbb E}\n\\newcommand{\\R}{\\mathbb R}\n\\newcommand{\\Z}{\\mathbb Z}\n\\newcommand{\\C}{\\mathbb C}\n\\newcommand{\\Tr}{\\operatorname{Tr}}\n\\newcommand{\\Cov}{\\operatorname{Cov}}\n\\newcommand{\\supp}{\\operatorname{supp}}\n\\newcommand{\\spec}{\\operatorname{spec}}\n\\newcommand{\\dd}{\\,\\mathrm d}\n\\newcommand{\\norm}[1]{\\left\\lVert#1\\right\\rVert}\n\\newcommand{\\inner}[2]{\\langle#1,#2\\rangle}\n\\newcommand{\\Id}{\\mathbf 1}\n\\newcommand{\\id}{\\mathrm{id}}\n\\newcommand{\\abs}[1]{\\left|#1\\right|}\n\\newcommand{\\Sph}{\\mathbb S}\n\\newcommand{\\Sch}{\\mathcal S}\n\\newcommand{\\Rref}[1]{\\ifcsname Rloc@#1\\endcsname\\csname Rloc@#1\\endcsname\\else\\textbf{[unresolved R locator: #1]}\\fi}\n\\input{reference-locators}\n\\setlist{itemsep=3pt,topsep=5pt}\n\\setlength{\\emergencystretch}{2em}\n\\setcounter{tocdepth}{2}\n\\allowdisplaybreaks[2]\n\\title{The canonical massive continuum limit\\\\of the two-dimensional $O(3)$ model}\n\\author{OpenAI}\n\\date{October 4, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe construct a canonical interacting massive continuum limit of the\ntwo-dimensional nearest-neighbor $O(3)$ model with unit-length spins,\nno external field, and no topological term. Normalized by susceptibility\nand second-moment correlation length, the limit exists as the bare coupling\ntends to infinity through all positive real values, without selecting\nsubsequences. The limiting fields satisfy the Osterwalder--Schrader axioms\nand have a nonzero connected four-point correlation on separated time\nsupports. Their reconstructed theory has a unique vacuum, a nonzero vacuum\ncomplement, and a positive Hamiltonian gap on that entire complement.\n\\end{abstract}\n\\tableofcontents\n\\newpage\n\\input{sections/introduction}\n\\part{Construction at a fixed physical scale}\n\\input{sections/preliminary}\n\\input{sections/setup}\n\\input{sections/free}\n\\input{sections/rg}\n\\input{sections/shooting}\n\\input{sections/comparison}\n\\input{sections/trace}\n\\input{sections/sources}\n\\input{sections/continuum}\n\\input{sections/os}\n\\part{Canonical scaling along all diverging couplings}\n\\input{sections/trajectories}\n\\input{sections/volume}\n\\input{sections/fields}\n\\input{sections/uniqueness}\n\\FloatBarrier\n\\bibliographystyle{plain}\n\\bibliography{references}\n\\end{document}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/reference-locators.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/reference-locators.tex", "bytes": 31433, "sha256": "284d69d9b584606a2b4becc85ed391f6a4fd86510b5ba81d9a8405c2872199b0", "content": "\\expandafter\\def\\csname Rloc@sec:introduction\\endcsname{\\cite[Section~1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:measure\\endcsname{\\cite[Equation~(1.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:mass-definition\\endcsname{\\cite[Equation~(1.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@thm:main\\endcsname{\\cite[Theorem~1.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:target\\endcsname{\\cite[Equation~(1.3)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:depth-bounds\\endcsname{\\cite[Equation~(1.4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cor:all-temperature\\endcsname{\\cite[Corollary~1.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:physical-main\\endcsname{\\cite[Equation~(1.5)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@intro:Delta\\endcsname{\\cite[Equation~(1.6)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@intro:Xi\\endcsname{\\cite[Equation~(1.7)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@intro:preliminary\\endcsname{\\cite[Equation~(1.8)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@intro:calibration\\endcsname{\\cite[Equation~(1.9)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@intro:DN\\endcsname{\\cite[Equation~(1.10)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@intro:comparison\\endcsname{\\cite[Equation~(1.11)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@sec:finite-volume\\endcsname{\\cite[Section~2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:transfer-kernel\\endcsname{\\cite[Equation~(2.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:trace\\endcsname{\\cite[Equation~(2.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:p\\endcsname{\\cite[Equation~(2.3)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:q\\endcsname{\\cite[Equation~(2.4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:purity\\endcsname{\\cite[Equation~(2.5)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@lem:squaring\\endcsname{\\cite[Lemma~2.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:squaring\\endcsname{\\cite[Equation~(2.6)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@fig:rectangle-doubling\\endcsname{\\cite[Figure~1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:rectangle\\endcsname{\\cite[Equation~(2.7)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:Delta\\endcsname{\\cite[Equation~(2.8)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@prop:criterion\\endcsname{\\cite[Proposition~2.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:small-test\\endcsname{\\cite[Equation~(2.9)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:width-gap\\endcsname{\\cite[Equation~(2.10)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:criterion-gap\\endcsname{\\cite[Equation~(2.11)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:quadratic-recursion\\endcsname{\\cite[Equation~(2.12)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:slab-bound\\endcsname{\\cite[Equation~(2.13)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:mixing\\endcsname{\\cite[Section~3]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:section\\endcsname{\\cite[Section~3]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@thm:preliminary\\endcsname{\\cite[Theorem~3.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:Xi\\endcsname{\\cite[Equation~(3.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:theorem-mixing\\endcsname{\\cite[Equation~(3.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:theorem-delta\\endcsname{\\cite[Equation~(3.3)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:local\\endcsname{\\cite[Section~3.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:1-1\\endcsname{\\cite[Equation~(3.4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:1-2\\endcsname{\\cite[Equation~(3.5)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:1-3\\endcsname{\\cite[Equation~(3.6)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:1-4\\endcsname{\\cite[Equation~(3.7)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:1-5\\endcsname{\\cite[Equation~(3.8)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:moments\\endcsname{\\cite[Section~3.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:2-1\\endcsname{\\cite[Equation~(3.9)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:current-moments\\endcsname{\\cite[Lemma~3.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:2-2\\endcsname{\\cite[Equation~(3.10)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-2-1\\endcsname{\\cite[Section~3.2.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:2-3\\endcsname{\\cite[Equation~(3.11)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-2-2\\endcsname{\\cite[Section~3.2.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:2-4\\endcsname{\\cite[Equation~(3.12)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-2-3\\endcsname{\\cite[Section~3.2.3]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:cells\\endcsname{\\cite[Section~3.3]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:3-1\\endcsname{\\cite[Equation~(3.13)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:3-2\\endcsname{\\cite[Equation~(3.14)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:3-3\\endcsname{\\cite[Equation~(3.15)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:3-4\\endcsname{\\cite[Equation~(3.16)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:gaussian\\endcsname{\\cite[Section~3.4]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:4-1\\endcsname{\\cite[Equation~(3.17)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:4-2\\endcsname{\\cite[Equation~(3.18)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:4-3\\endcsname{\\cite[Equation~(3.19)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:4-4\\endcsname{\\cite[Equation~(3.20)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:4-5\\endcsname{\\cite[Equation~(3.21)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:bands\\endcsname{\\cite[Section~3.5]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-5-1\\endcsname{\\cite[Section~3.5.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-1\\endcsname{\\cite[Equation~(3.22)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-2\\endcsname{\\cite[Equation~(3.23)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-3\\endcsname{\\cite[Equation~(3.24)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-5-2\\endcsname{\\cite[Section~3.5.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-4\\endcsname{\\cite[Equation~(3.25)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-5\\endcsname{\\cite[Equation~(3.26)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-5-3\\endcsname{\\cite[Section~3.5.3]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-6\\endcsname{\\cite[Equation~(3.27)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-7\\endcsname{\\cite[Equation~(3.28)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-8\\endcsname{\\cite[Equation~(3.29)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-9\\endcsname{\\cite[Equation~(3.30)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-10\\endcsname{\\cite[Equation~(3.31)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-11\\endcsname{\\cite[Equation~(3.32)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-12\\endcsname{\\cite[Equation~(3.33)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-5-4\\endcsname{\\cite[Section~3.5.4]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-13\\endcsname{\\cite[Equation~(3.34)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:5-14\\endcsname{\\cite[Equation~(3.35)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:weighted\\endcsname{\\cite[Section~3.6]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:6-1\\endcsname{\\cite[Equation~(3.36)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:6-2\\endcsname{\\cite[Equation~(3.37)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-6-1\\endcsname{\\cite[Section~3.6.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:6-3\\endcsname{\\cite[Equation~(3.38)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-6-2\\endcsname{\\cite[Section~3.6.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:6-4\\endcsname{\\cite[Equation~(3.39)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:6-5\\endcsname{\\cite[Equation~(3.40)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:6-6\\endcsname{\\cite[Equation~(3.41)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-6-3\\endcsname{\\cite[Section~3.6.3]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:6-7\\endcsname{\\cite[Equation~(3.42)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:variance\\endcsname{\\cite[Section~3.7]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:7-1\\endcsname{\\cite[Equation~(3.43)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:7-2\\endcsname{\\cite[Equation~(3.44)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-7-1\\endcsname{\\cite[Section~3.7.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:7-3\\endcsname{\\cite[Equation~(3.45)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:7-4\\endcsname{\\cite[Equation~(3.46)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:7-5\\endcsname{\\cite[Equation~(3.47)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:7-6\\endcsname{\\cite[Equation~(3.48)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:7-7\\endcsname{\\cite[Equation~(3.49)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-7-2\\endcsname{\\cite[Section~3.7.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:7-8\\endcsname{\\cite[Equation~(3.50)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:7-9\\endcsname{\\cite[Equation~(3.51)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:7-10\\endcsname{\\cite[Equation~(3.52)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:iteration\\endcsname{\\cite[Section~3.8]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:8-1\\endcsname{\\cite[Equation~(3.53)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:8-2\\endcsname{\\cite[Equation~(3.54)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:rotations\\endcsname{\\cite[Section~3.9]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:9-1\\endcsname{\\cite[Equation~(3.55)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:9-2\\endcsname{\\cite[Equation~(3.56)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:9-3\\endcsname{\\cite[Equation~(3.57)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-9-1\\endcsname{\\cite[Section~3.9.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:9-4\\endcsname{\\cite[Equation~(3.58)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:orientations\\endcsname{\\cite[Section~3.10]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-10-1\\endcsname{\\cite[Section~3.10.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:10-1\\endcsname{\\cite[Equation~(3.59)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-10-2\\endcsname{\\cite[Section~3.10.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:10-2\\endcsname{\\cite[Equation~(3.60)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:10-3\\endcsname{\\cite[Equation~(3.61)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:10-4\\endcsname{\\cite[Equation~(3.62)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:neutral\\endcsname{\\cite[Section~3.11]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-11-1\\endcsname{\\cite[Section~3.11.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:11-1\\endcsname{\\cite[Equation~(3.63)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-11-2\\endcsname{\\cite[Section~3.11.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:11-2\\endcsname{\\cite[Equation~(3.64)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:11-3\\endcsname{\\cite[Equation~(3.65)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-11-3\\endcsname{\\cite[Section~3.11.3]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:11-4\\endcsname{\\cite[Equation~(3.66)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:11-5\\endcsname{\\cite[Equation~(3.67)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:conclusion\\endcsname{\\cite[Section~3.12]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:12-1\\endcsname{\\cite[Equation~(3.68)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-12-1\\endcsname{\\cite[Section~3.12.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-12-2\\endcsname{\\cite[Section~3.12.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@pre:part-12-3\\endcsname{\\cite[Section~3.12.3]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:12-2\\endcsname{\\cite[Equation~(3.69)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:pre:12-3\\endcsname{\\cite[Equation~(3.70)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@free:section\\endcsname{\\cite[Section~4]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@free:recursion\\endcsname{\\cite[Equation~(4.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@free:bounds\\endcsname{\\cite[Lemma~4.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@free:P\\endcsname{\\cite[Equation~(4.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@free:derivative\\endcsname{\\cite[Equation~(4.3)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@free:C\\endcsname{\\cite[Equation~(4.4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@free:explicit\\endcsname{\\cite[Equation~(4.5)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@lem:free-stack\\endcsname{\\cite[Lemma~4.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@free:isometry\\endcsname{\\cite[Equation~(4.6)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@free:ambient\\endcsname{\\cite[Equation~(4.7)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@free:harmonic\\endcsname{\\cite[Equation~(4.8)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@free:null\\endcsname{\\cite[Equation~(4.9)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@free:Tidentities\\endcsname{\\cite[Equation~(4.10)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:section\\endcsname{\\cite[Section~5]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:class\\endcsname{\\cite[Section~5.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:scales\\endcsname{\\cite[Equation~(5.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:observation\\endcsname{\\cite[Equation~(5.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:kinetic\\endcsname{\\cite[Equation~(5.3)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:projection\\endcsname{\\cite[Equation~(5.4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:canonical-norm\\endcsname{\\cite[Equation~(5.5)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:slots\\endcsname{\\cite[Equation~(5.6)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:directions\\endcsname{\\cite[Equation~(5.7)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:error-norm\\endcsname{\\cite[Equation~(5.8)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:covering-gas\\endcsname{\\cite[Equation~(5.9)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:density\\endcsname{\\cite[Equation~(5.10)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:closure\\endcsname{\\cite[Theorem~5.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@thm:rg-closure\\endcsname{\\cite[Theorem~5.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:coupling-step\\endcsname{\\cite[Equation~(5.11)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:shape-comparison\\endcsname{\\cite[Equation~(5.12)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:coupling-comparison\\endcsname{\\cite[Equation~(5.13)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:gaussian\\endcsname{\\cite[Section~5.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:stack-sizes\\endcsname{\\cite[Equation~(5.14)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:ledger\\endcsname{\\cite[Equation~(5.15)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:ledger-errors\\endcsname{\\cite[Equation~(5.16)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:core-reserve\\endcsname{\\cite[Lemma~5.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:reserve\\endcsname{\\cite[Equation~(5.17)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:window-bounds\\endcsname{\\cite[Equation~(5.18)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:gaussian-ratio\\endcsname{\\cite[Equation~(5.19)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:ratio-series\\endcsname{\\cite[Equation~(5.20)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:chain-price\\endcsname{\\cite[Equation~(5.21)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:prediction\\endcsname{\\cite[Equation~(5.22)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:prepared\\endcsname{\\cite[Section~5.3]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:core-bound\\endcsname{\\cite[Equation~(5.23)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:old-polynomial-bound\\endcsname{\\cite[Equation~(5.24)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:ratio-envelope\\endcsname{\\cite[Equation~(5.25)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:joint\\endcsname{\\cite[Lemma~5.3]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:joint-product\\endcsname{\\cite[Equation~(5.26)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:joint-rarity\\endcsname{\\cite[Equation~(5.27)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:transport\\endcsname{\\cite[Equation~(5.28)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:connected\\endcsname{\\cite[Section~5.4]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:tree-condition\\endcsname{\\cite[Equation~(5.29)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:preliminary-output\\endcsname{\\cite[Equation~(5.30)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:canonical\\endcsname{\\cite[Section~5.5]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:canonical-vertices\\endcsname{\\cite[Equation~(5.31)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:canonical-map\\endcsname{\\cite[Equation~(5.32)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:canonical-contraction\\endcsname{\\cite[Lemma~5.4]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:row-difference\\endcsname{\\cite[Equation~(5.33)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:error\\endcsname{\\cite[Section~5.6]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:error-transfer\\endcsname{\\cite[Equation~(5.34)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:error-contraction\\endcsname{\\cite[Lemma~5.5]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:error-contraction-bound\\endcsname{\\cite[Equation~(5.35)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:fluctuation-omission\\endcsname{\\cite[Equation~(5.36)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:affine-prediction\\endcsname{\\cite[Equation~(5.37)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:high-order-remainder\\endcsname{\\cite[Equation~(5.38)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:normalization\\endcsname{\\cite[Section~5.7]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:raw-error\\endcsname{\\cite[Equation~(5.39)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:normalization-identity\\endcsname{\\cite[Equation~(5.40)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:kinetic-reset\\endcsname{\\cite[Equation~(5.41)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:closed-error\\endcsname{\\cite[Equation~(5.42)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:finite-choices\\endcsname{\\cite[Equation~(5.43)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:section\\endcsname{\\cite[Section~6]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@thm:calibration\\endcsname{\\cite[Theorem~6.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:sums\\endcsname{\\cite[Equation~(6.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:kicks\\endcsname{\\cite[Equation~(6.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:L1\\endcsname{\\cite[Equation~(6.3)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:L2\\endcsname{\\cite[Equation~(6.4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:A\\endcsname{\\cite[Equation~(6.5)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:EL2\\endcsname{\\cite[Equation~(6.6)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:chaos2\\endcsname{\\cite[Equation~(6.7)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:chaos4\\endcsname{\\cite[Equation~(6.8)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:GP\\endcsname{\\cite[Equation~(6.9)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:T1\\endcsname{\\cite[Equation~(6.10)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:T2\\endcsname{\\cite[Equation~(6.11)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:W\\endcsname{\\cite[Equation~(6.12)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:Z2\\endcsname{\\cite[Equation~(6.13)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:green-decay\\endcsname{\\cite[Equation~(6.14)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:green-comparison\\endcsname{\\cite[Equation~(6.15)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:green-quadratic\\endcsname{\\cite[Equation~(6.16)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:j-asymptotic\\endcsname{\\cite[Equation~(6.17)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:direct1\\endcsname{\\cite[Equation~(6.18)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:direct2\\endcsname{\\cite[Equation~(6.19)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:block1\\endcsname{\\cite[Equation~(6.20)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cal:block2\\endcsname{\\cite[Equation~(6.21)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@prop:calibration\\endcsname{\\cite[Proposition~6.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:calibration\\endcsname{\\cite[Equation~(6.22)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@sec:trajectories\\endcsname{\\cite[Section~7]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:trajectories\\endcsname{\\cite[Section~7.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:calibrated-kicks\\endcsname{\\cite[Equation~(7.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:admission\\endcsname{\\cite[Proposition~7.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:scale-calibration\\endcsname{\\cite[Equation~(7.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:first-exit-bound\\endcsname{\\cite[Equation~(7.3)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:barrier-bound\\endcsname{\\cite[Equation~(7.4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@fig:matched-trajectories\\endcsname{\\cite[Figure~2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:terminal\\endcsname{\\cite[Section~7.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:matching\\endcsname{\\cite[Theorem~7.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@thm:matched-endpoint\\endcsname{\\cite[Theorem~7.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:terminal-difference\\endcsname{\\cite[Equation~(7.5)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@rg:two-end-bound\\endcsname{\\cite[Equation~(7.6)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:section\\endcsname{\\cite[Section~8]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:cover-definition\\endcsname{\\cite[Definition~8.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:cover\\endcsname{\\cite[Equation~(8.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:densities\\endcsname{\\cite[Equation~(8.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:activity\\endcsname{\\cite[Equation~(8.3)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:activity-difference\\endcsname{\\cite[Equation~(8.4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:density-finite-energy\\endcsname{\\cite[Equation~(8.5)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:exponent-finite-energy\\endcsname{\\cite[Equation~(8.6)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:exponent-difference\\endcsname{\\cite[Equation~(8.7)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:stability\\endcsname{\\cite[Equation~(8.8)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:cap\\endcsname{\\cite[Equation~(8.9)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:thresholds\\endcsname{\\cite[Section~8.1, condition~(C4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@thm:relative-comparison\\endcsname{\\cite[Theorem~8.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:bad-probability\\endcsname{\\cite[Equation~(8.10)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:relative-cover\\endcsname{\\cite[Equation~(8.11)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:relative-density\\endcsname{\\cite[Equation~(8.12)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:partition-conclusion\\endcsname{\\cite[Equation~(8.13)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:wider-flags\\endcsname{\\cite[Equation~(8.14)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:rarity\\endcsname{\\cite[Lemma~8.3]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:joint-rarity\\endcsname{\\cite[Equation~(8.15)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:absolute-cover\\endcsname{\\cite[Equation~(8.16)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:denominator-cap\\endcsname{\\cite[Equation~(8.17)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:disseminated\\endcsname{\\cite[Equation~(8.18)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:filling\\endcsname{\\cite[Lemma~8.4]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:editing\\endcsname{\\cite[Lemma~8.5]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:edit-size\\endcsname{\\cite[Equation~(8.19)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:inventory-after-edit\\endcsname{\\cite[Equation~(8.20)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:complete-ratio\\endcsname{\\cite[Equation~(8.21)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:macro-support\\endcsname{\\cite[Equation~(8.22)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:macro-weight\\endcsname{\\cite[Section~8.4, display following Equation~(8.22)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:removal-identities\\endcsname{\\cite[Lemma~8.6]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:background-identity\\endcsname{\\cite[Equation~(8.23)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:IE\\endcsname{\\cite[Equation~(8.24)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:difference-identity\\endcsname{\\cite[Equation~(8.25)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:ratio-recursion\\endcsname{\\cite[Equation~(8.26)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:marked-macro\\endcsname{\\cite[Equation~(8.27)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:good-set\\endcsname{\\cite[Equation~(8.28)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:exceptional\\endcsname{\\cite[Lemma~8.7]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:exceptional-bound\\endcsname{\\cite[Equation~(8.29)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:flag-entropy\\endcsname{\\cite[Equation~(8.30)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:forest\\endcsname{\\cite[Lemma~8.8]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:forest-conclusion\\endcsname{\\cite[Equation~(8.31)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:tree-charge\\endcsname{\\cite[Equation~(8.32)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:C4\\endcsname{\\cite[Equation~(8.33)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:edit-hit\\endcsname{\\cite[Equation~(8.34)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:gas-hit\\endcsname{\\cite[Equation~(8.35)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:root-hit\\endcsname{\\cite[Equation~(8.36)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:normalization\\endcsname{\\cite[Lemma~8.9]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:normalization-bound\\endcsname{\\cite[Equation~(8.37)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:RG-density\\endcsname{\\cite[Equation~(8.38)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:kinetic-form\\endcsname{\\cite[Equation~(8.39)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:kernel-ratio\\endcsname{\\cite[Equation~(8.40)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@lem:alignment\\endcsname{\\cite[Lemma~8.10]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:alignment\\endcsname{\\cite[Equation~(8.41)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@cmp:block-correlation\\endcsname{\\cite[Equation~(8.42)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@sec:assembly\\endcsname{\\cite[Section~9]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:blocked-partition\\endcsname{\\cite[Equation~(9.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@prop:cutoff\\endcsname{\\cite[Proposition~9.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:cutoff-comparison\\endcsname{\\cite[Equation~(9.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:K-threshold\\endcsname{\\cite[Equation~(9.3)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:beta-depth\\endcsname{\\cite[Equation~(9.4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@thm:lower\\endcsname{\\cite[Theorem~9.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:lower-depth\\endcsname{\\cite[Equation~(9.5)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@fig:fixed-reference-box\\endcsname{\\cite[Figure~3]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@sec:upper\\endcsname{\\cite[Section~10]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:block-increment\\endcsname{\\cite[Equation~(10.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:actual-slab\\endcsname{\\cite[Equation~(10.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@prop:upper\\endcsname{\\cite[Proposition~10.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:upper-depth\\endcsname{\\cite[Equation~(10.3)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:block-observable\\endcsname{\\cite[Equation~(10.4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@fig:block-supports\\endcsname{\\cite[Figure~4]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:block-correlation\\endcsname{\\cite[Equation~(10.5)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@sec:physical\\endcsname{\\cite[Section~11]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@eq:physical-bounds\\endcsname{\\cite[Equation~(11.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@prop:continuum\\endcsname{\\cite[Proposition~11.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:analytic-block\\endcsname{\\cite[Appendix~A]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:free-strip\\endcsname{\\cite[Lemma~A.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:analytic-1\\endcsname{\\cite[Equation~(A.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:analytic-2\\endcsname{\\cite[Equation~(A.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:analytic-3\\endcsname{\\cite[Equation~(A.3)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:analytic-4\\endcsname{\\cite[Equation~(A.4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:alias-division\\endcsname{\\cite[Lemma~A.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:analytic-5\\endcsname{\\cite[Equation~(A.5)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:canonical-fixed-norm\\endcsname{\\cite[Appendix~B]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:canonical-path\\endcsname{\\cite[Equation~(B.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:quadratic-packet\\endcsname{\\cite[Equation~(B.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:quadratic-affine-cancellation\\endcsname{\\cite[Equation~(B.3)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:quadratic-packet-norm\\endcsname{\\cite[Equation~(B.4)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:fixed-norm-kernel-assumption\\endcsname{\\cite[Equation~(B.5)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:fixed-norm-affine-assumption\\endcsname{\\cite[Equation~(B.6)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:fixed-norm-substitution\\endcsname{\\cite[Equation~(B.7)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:one-norm-contraction\\endcsname{\\cite[Lemma~B.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:one-norm-conclusion\\endcsname{\\cite[Equation~(B.8)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:row-first\\endcsname{\\cite[Equation~(B.9)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:row-second\\endcsname{\\cite[Equation~(B.10)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:row-to-path\\endcsname{\\cite[Equation~(B.11)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:degree-n-before-anchor\\endcsname{\\cite[Equation~(B.12)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:spare-width-fixed-space\\endcsname{\\cite[Equation~(B.13)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:quadratic-before-anchor\\endcsname{\\cite[Equation~(B.14)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:output-comp-cost\\endcsname{\\cite[Equation~(B.15)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:rg-geometry\\endcsname{\\cite[Appendix~C]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:factor-ownership\\endcsname{\\cite[Section~C.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:principal-log\\endcsname{\\cite[Lemma~C.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:explicit-core\\endcsname{\\cite[Equation~(C.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:read-sets\\endcsname{\\cite[Section~C.4]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:local-marginals\\endcsname{\\cite[Lemma~C.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:joint-majorants\\endcsname{\\cite[Appendix~D]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:gaussian-product\\endcsname{\\cite[Lemma~D.1]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:endpoint-load\\endcsname{\\cite[Equation~(D.1)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:ratio-size\\endcsname{\\cite[Equation~(D.2)]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@supp:product-reserve\\endcsname{\\cite[Corollary~D.2]{OpenAI-O4}}\n\\expandafter\\def\\csname Rloc@app:joint-application\\endcsname{\\cite[Section~D.1]{OpenAI-O4}}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/references.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/references.bib", "bytes": 11178, "sha256": "1fe67062dcc7a81a43cee482c59f5f03a567acd6945087e8c2eb553f8a4ce387", "content": "@article{MW,\n author={Mermin, N. 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General theory and long range lattice models},\n  journal = {Communications in Mathematical Physics},\n  volume = {62},\n  number = {1},\n  pages = {1--34},\n  year = {1978},\n  doi = {10.1007/BF01940327}\n}\n@article{FILS80,\n  author = {Fr{\\\"o}hlich, J{\\\"u}rg and Israel, Robert B. and Lieb, Elliott H. and Simon, Barry},\n  title = {Phase transitions and reflection positivity. {II}. Lattice systems with short-range and {Coulomb} interactions},\n  journal = {Journal of Statistical Physics},\n  volume = {22},\n  number = {3},\n  pages = {297--347},\n  year = {1980},\n  doi = {10.1007/BF01014646}\n}\n@article{FortuinKasteleynGinibre1971,\n  author = {Fortuin, C. M. and Kasteleyn, P. 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Generation of effective actions in a small field approximation and a coupling constant renormalization in four dimensions},\n  journal = {Communications in Mathematical Physics},\n  volume = {109}, pages = {249--301}, year = {1987},\n  doi = {10.1007/BF01215223}\n}\n\n@article{BalabanGaugeVariational,\n  author = {Ba{\\l}aban, Tadeusz},\n  title = {The variational problem and background fields in renormalization group method for lattice gauge theories},\n  journal = {Communications in Mathematical Physics},\n  volume = {102}, pages = {277--309}, year = {1985},\n  doi = {10.1007/BF01229381}\n}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/comparison.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/comparison.tex", "bytes": 29051, "sha256": "87c5e048af7f787217cd6eaa120d8fff516d6ae52e7e17fe62be8b9e95c335d9", "content": "\\section{Integrated comparison of endpoint densities}\n\\label{sec:comparison}\n\nProposition~\\ref{prop:endpoint-matching} compares coefficients and gas\nactivities. To compare partition functions in volumes growing almost\nas fast as $L^{2K}$, we need an estimate with the same small parameter\n$\\delta_K$. A pointwise relative estimate is unsuitable on configurations\nwhere the signed covering sum is very small. We instead integrate the\ncomparison before estimating it. The energy released by smoothing a\nrough region then pays for its replacement and for the combinatorics\nof the covering sum.\n\nThroughout this section $H$ is fixed and large, and\n\\[\n t=t_0=H^{-1/2}p,\\qquad p=p_0=(\\log H)^{P_0},\\qquad Ht^2=p^2.\n\\]\nWe work on standard axis tori of bounded aspect ratio, whose shortest\nperiod exceeds a constant depending on $H$. The periods will also be\nrequired to be multiples of a fixed dyadic integer depending on $H$.\nAll constants below are independent of the cutoff depth and the periods.\n\n\\subsection{The comparison to be proved}\n\nLet $e^X\\Xi$ be one of the positive endpoint densities from\nSection~\\ref{sec:shooting}, with its bulk scalar removed, and write\n\\[\n Z=\\int e^{X(q)}\\Xi(q)\\,\\dd q,\n \\qquad \\dd\\mu(q)=Z^{-1}e^{X(q)}\\Xi(q)\\,\\dd q.\n\\]\nHere $\\dd q$ is product normalized area measure on $S^2$. Recall that\n$\\Xi$ is a mandatory covering sum. For\n\\[\n D(q)=\\{e:|q_x-q_y|>t\\},\\qquad e=\\langle x,y\\rangle,\n\\]\neach label $\\lambda$ has a fixed nonempty inventory\n$J_\\lambda$ of bonds, a complete site support $P_\\lambda$, and a load\n$s_\\lambda\\ge1$. Its weight vanishes unless\n$J_\\lambda\\subset D(q)$, and its support contains every argument and\nevery positive or negative eligibility test. A compatible family has\npairwise disjoint supports. Thus\n\\begin{equation}\n \\Xi(q)=\\sum_{\\substack{\\Gamma\\ \\mathrm{compatible}\\\\\n             \\bigsqcup_{\\lambda\\in\\Gamma}J_\\lambda=D(q)}}\n              \\prod_{\\lambda\\in\\Gamma}k_\\lambda(q).\n \\label{eq:comparison-mandatory-cover}\n\\end{equation}\nIn particular $\\Xi=1$ if $D(q)$ is empty. This inventory constraint\nwill be retained in every identity below.\n\nConsider another covering sum $\\widetilde\\Xi$ on the same inventory\nrules, with signed or complex weights allowed. Put unused weights\nequal to zero in a common label list, and define\n\\[\n d_\\lambda=\\norm{\\widetilde k_\\lambda-k_\\lambda}_\\infty,\n \\qquad\n p_\\lambda=\\norm{k_\\lambda}_\\infty+\n                         \\norm{\\widetilde k_\\lambda}_\\infty.\n\\]\nThe symbol $p_\\lambda$ is an activity envelope; the unsubscripted $p$\ncontinues to denote $(\\log H)^{P_0}$.\n\n\\begin{proposition}[Integrated covering comparison]\n\\label{prop:integrated-comparison}\nFix any constant multiplier of the covering cap in\nSection~\\ref{sec:setup}. For sufficiently large $H$, let $e^X\\Xi$ be\na standard endpoint density on an axis torus as above, and suppose\n\\[\n \\sup_x\\sum_{\\lambda:x\\in P_\\lambda}\n                     e^{As_\\lambda}p_\\lambda\n\\]\nis at most that multiplier times the covering cap. The exponent $A$\nis the fixed support exponent of the endpoint class. Then\n\\begin{equation}\n \\frac1Z\\int e^X|\\widetilde\\Xi-\\Xi|\\,\\dd q\n \\le \\exp\\left(\\sum_\\lambda d_\\lambda e^{2s_\\lambda}\\right)-1.\n \\label{eq:orig-16}\n\\end{equation}\nThe statement is uniform in cutoff depth and in the admitted periods.\nOnly the base density $e^X\\Xi$ is required to be positive and reflection\npositive.\n\\end{proposition}\n\nThe proof has three parts. First, reflection positivity controls the\nexponential moment of kinetic energy in any specified set of rows.\nSecond, rough configurations admit local replacements with a definite\nintegrated energy gain. Third, exact covering identities organize the\ndifference into forests to which that gain can be applied.\n\n\\subsection{Kinetic energy and a chessboard estimate}\n\nChoose $D_1$ to be an integer comparable with $(\\log H)^2$, with a\nsufficiently large fixed multiplier. In the kinetic part of $X$,\ntruncate the exponentially localized kernel $\\ell$ at radius $D_1$\nusing a symmetric truncation. Group the resulting positive square\nand the direct term $HS_t$ at each unoriented bond; denote their sum,\nincluding the precise coupling, by $Y_e$. Enlarge the fixed multiple\nof $D_1$ when necessary so that it includes every mask read of these\nrows. Each $Y_e$ is nonnegative and reads only this finite stencil.\n\n\\begin{lemma}[Energy remainder and normalization]\n\\label{lem:comparison-remainder}\nOn a terminal torus of volume $v$,\n\\begin{equation}\n X=-\\sum_eY_e+X_{\\mathrm{rem}},\\qquad\n |X_{\\mathrm{rem}}|\\le C_Lv,\n \\qquad\n |X_{\\mathrm{rem}}(q)-X_{\\mathrm{rem}}(q')|\\le C_Ln\n \\label{eq:orig-17}\n\\end{equation}\nwhenever $q,q'$ differ at at most $n$ sites. Moreover,\n\\begin{equation}\n Z\\ge e^{-C_Lv\\log H},\\qquad |\\Xi(q)|\\le e^v.\n \\label{eq:orig-18}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nExponential row and column moments bound the omitted kinetic tails.\nA change at $n$ sites affects at most $C_LD_1^2n$ mask centers, and\nthe factor $e^{-cD_1}$ pays for these fixed powers and the coupling.\nFor the remaining terms, the endpoint bounds in the verification of\n\\Rref{cmp:RG-density} give canonical size\n$C_L(Ht^4+t^2)$ per anchor. Converting the anchor sum to a support-hit\nsum costs only a fixed power of $\\mathfrak m_0$: complete records\ninclude all graph distances, compensation tags and masks, with the\nrequired load and path moments. The regular errors obey the\ncorresponding support-hit estimate. Since\n$Ht^4+t^2=H^{-1}(p^4+p^2)$, these quantities, even with their fixed\npolylogarithmic factors, tend to zero as $H$ increases. This proves\nboth remainder bounds.\n\nIf all spins lie in one cap of radius $cH^{-1/2}$, there are no bad\nbonds and $\\Xi=1$. The exponent on this cap is bounded below by a\nconstant times $-v$, while normalized $S^2$ area gives cap measure\nat least $c'H^{-1}$ per site. This proves the lower bound for $Z$.\nFor the other bound, discard compatibility and inventory constraints\nonly after taking absolute values:\n\\[\n |\\Xi|\\le\\prod_\\lambda(1+\\norm{k_\\lambda}_\\infty)\n \\le\\exp\\left(\\sum_\\lambda\\norm{k_\\lambda}_\\infty\\right)\n \\le e^v.\n\\]\nThe sums converge by the support-hit cap.\n\\end{proof}\n\nThe chessboard estimate uses the retained seam reflection positivity\nfrom Section~\\ref{sec:shooting}; see also the general reflection\npositivity framework of \\cite{FILS78,FILS80}. We include the finite-tile\nargument because later rectangular periods require arbitrary even\ntile counts, not only powers of two.\n\n\\begin{lemma}[Exponential moments of selected rows]\n\\label{lem:row-exponential-moment}\nLet $B_T$ be a sufficiently large dyadic integer comparable with a\nfixed power of $\\log H$. On tori whose periods are multiples of\n$2B_T$, every set $I$ of unoriented rows satisfies\n\\begin{equation}\n \\E_\\mu\\exp\\left(\\theta\\sum_{e\\in I}Y_e\\right)\n \\le\\exp(C_L|I|B_T^2\\log H),\n \\qquad \\theta=1-C_LD_1/B_T.\n \\label{eq:orig-19}\n\\end{equation}\nIn particular $B_T$ may be chosen so that $\\theta>0$ and\n$1-\\theta$ is smaller than any prescribed fixed inverse power of\n$\\log H$.\n\\end{lemma}\n\n\\begin{proof}\nFor a tiling into $B_T$-squares, let $f_z$ be bounded nonnegative\nfunctions of the sites in tile $z$, with reflected-coordinate\nplacement denoted by $\\theta_zf_z$. The chessboard inequality is\n\\begin{equation}\n \\E_\\mu\\prod_z\\theta_zf_z\n \\le\\prod_z\n \\left(\\E_\\mu\\prod_y\\theta_y f_z\\right)^{1/N_T},\n \\label{eq:comparison-chessboard}\n\\end{equation}\nwhere $N_T$ is the number of tiles. To prove it for arbitrary even\ntile counts, first make the finite collection of tests strictly\npositive and normalize each test by its fully disseminated\nexpectation to the power $1/N_T$. Among all arrays formed from these\nnormalized tests, choose one maximizing the expectation. Reflection\npositivity and Cauchy--Schwarz across any tile seam bound this maximum\nby the geometric mean of the expectations of the two reflected\nhalf-arrays. Both half-arrays must therefore also be maximizers.\n\nIn one coordinate regard each transverse slab pattern as a letter.\nIf a longest consecutive run of a letter has length at most half\nthe cycle, reflect a half containing that run with a seam at one end;\nthe run grows. If its length exceeds half the cycle, reflect a half\nlying within the run and obtain a constant cycle. Repetition makes\nthe array constant in that coordinate. Repeat in the other\ncoordinate, preserving the constancy already obtained. Even tile\ncounts ensure that reflection interchanges the placement conventions\nat every seam. A homogeneous normalized array has expectation one,\nso the maximum was one. Limits removing the added constants prove\nEquation~\\eqref{eq:comparison-chessboard} for bounded nonnegative tests.\n\nFor a fixed tile grid, retain those rows of $I$ whose complete stencils\nlie in a single tile at distance at least $C_LD_1$ from its boundary.\nIn each occupied tile use the exponential of the sum of its retained\nrows as the test. Dissemination gives a subset of the positive rows,\nwithout repetitions, because the stencils remain inside disjoint tiles\nand the rows transform covariantly under reflection. If $J$ is such\na disseminated set, Lemma~\\ref{lem:comparison-remainder} gives\n\\[\n \\E_\\mu e^{\\sum_{e\\in J}Y_e}\n =Z^{-1}\\int\n e^{-\\sum_{e\\notin J}Y_e+X_{\\mathrm{rem}}}\\Xi\\,\\dd q\n \\le e^{C_Lv\\log H}.\n\\]\nThe chessboard norm of an occupied tile consequently costs at most\n$e^{C_LB_T^2\\log H}$. There are at most $|I|$ occupied tiles.\n\nFinally average over all $B_T^2$ translations of the tile grid.\nEvery row is retained for at least a fraction\n$1-C_LD_1/B_T$ of these translations. Since $Y_e\\ge0$, generalized\nHölder applied to the corresponding grid estimates yields\nEquation~\\eqref{eq:orig-19}.\n\\end{proof}\n\n\\subsection{Regions that can be replaced at an energy gain}\n\nUse max distance on the torus, put\n\\[\n f(x)=\\max_{e\\ni x}|q_x-q_y|,\n \\qquad a=\\varepsilon t,\n\\]\nand choose $\\varepsilon>0$ sufficiently small, independently of $H$.\nMark every site of every integer square box of radius at most one\nfourth of the shortest period, including radius zero, on which the\naverage of $f$ exceeds $a$. Let $U$ be the union of all marked boxes.\nDilate $U$ by $d=CD_1$, take its max-neighbor connected components,\nand retain the complete components containing a true bad bond\n$|q_x-q_y|>t$. Denote them by $E_C$. Distinct retained components have\nnonadjacent sites, and every true bad bond lies deep inside exactly\none retained component. The construction is illustrated in\nFigure~\\ref{fig:editable}.\n\n\\input{figures/editable}\n\\FloatBarrier\n\n\\begin{lemma}[Replacement and its energy bounds]\n\\label{lem:editable-regions}\nThe constants $\\varepsilon$ and $C$ can be chosen so that the\nfollowing statements hold for all sufficiently large $H$ and\nsufficiently large admitted periods.\n\\begin{enumerate}[label=\\textup{(\\roman*)}]\n\\item The unchanged values on $U^c$ have a measurable extension to all\nsites in $S^2$ with nearest-neighbor chords at most $C'a$. For each\nregion $E_C$, independently sampling every spin in a cap of radius\n$c't$ about this extension removes all its true bad bonds and creates\nnone. Any subset of the regions may be replaced in this way. The\nconditional sampling density for $n$ replaced sites is at most\n$\\exp(Cn\\log H)$ with respect to product normalized area measure.\n\\item For a union $E$ of selected regions, let $n=|E|$. There is a set\n$I_E$ of rows, consisting of all rows within distance $C'D_1$ of $E$,\nwhich includes every changed kinetic stencil and satisfies\n$|I_E|\\le Cd^2n$. Its total stencil enlargement is less than $d/2$.\nFor the original configuration and every sampled replacement $q'$,\n\\begin{align}\n \\sum_{e\\in I_E}Y_e(q)&\\ge cp^2n/d^2,\n \\label{eq:orig-20}\\\\\n \\sum_{e\\in I_E}Y_e(q')&\\le C_Ld^2p^2n.\n \\label{eq:orig-21}\n\\end{align}\n\\end{enumerate}\n\\end{lemma}\n\n\\begin{proof}\nWe first establish the exterior Lipschitz estimate. At a point of\n$U^c$, every allowed box containing that point has average gradient\nat most $a$. Compare the spin at the point with averages over\nsuccessive concentric doubled boxes. The discrete $L^1$ Poincar\\'e\ninequality, obtained by summing coordinate paths, bounds the change\nat scale $R$ by $CR$ times the average gradient on a fixed enlargement.\nFor two points, stop at the scale of their separation and compare the\ntwo averages through an enclosing box of comparable size. Summing\nthe geometric scales gives\n\\[\n |q_x-q_y|\\le Ca\\,\\operatorname{dist}(x,y),\\qquad x,y\\in U^c.\n\\]\nThe radius-one case uses the same coordinate-path estimate. If the\nrequired scale exceeds the allowed box size, the sphere's bounded\ndiameter proves the estimate once the shortest period is much larger\nthan $a^{-1}$.\n\nExtend these exterior data componentwise to an $O(a)$-Lipschitz\nEuclidean-vector-valued function on the continuous torus, using\ndistance cones. Within distance $c/a$ of the prescribed data the\nextension has norm bounded away from zero; normalization therefore\ngives an $S^2$-valued extension there with Lipschitz constant $O(a)$.\nChoose a torus mesh of width comparable with $c'/a$, where $c'$ is\nsufficiently smaller than $c$. Preserve this extension in every mesh\nbox meeting the prescribed data. Choose a common arbitrary spin at\nall remaining undetermined vertices, and use measurably selected\nshortest arcs on the undetermined mesh edges.\n\nEach remaining box now has a boundary map into $S^2$ with uniformly\nbounded Lipschitz constant after rescaling. To fill it, cover the\nantipodal image curve by $O(1/\\tau)$ spherical balls of radius $\\tau$.\nTheir total area is $O(\\tau)$, so for a sufficiently small fixed $\\tau$\nthere is a point $p_*$ uniformly separated from the antipodal curve.\nIf the boundary map in disk coordinates is $g(\\theta)$, the normalized\ncone\n\\[\n F(r,\\theta)=\n \\frac{(1-r)p_*+rg(\\theta)}{|(1-r)p_*+rg(\\theta)|}\n\\]\nhas a uniform Lipschitz bound. A fixed bi-Lipschitz change between a\ndisk and a square preserves that bound. This is the filling argument\nof \\Rref{cmp:filling}, with the area of $S^2$ replacing the volume of\n$S^3$. Adjacent fillings agree on their already chosen edges. Fixed\nordered nets for $p_*$ and measurable arc choices make the construction\nmeasurable. The same construction works if there are no exterior data.\nRescaling gives the claimed $C'a$ bound.\n\nChoose $\\varepsilon$ so that this bound is a sufficiently small\nfraction of $t$. Choose the sampling cap radius $c't$ smaller still.\nAll sampled adjacent spins then have chords below $t$. A boundary\nedge of a retained region lies in the collar outside $U$, where the\nextension equals the unchanged data; thus crossing edges also remain\nbelow $t$. Every originally true bad bond belongs to a retained\nregion. Consequently replacing any selected regions removes exactly\ntheir bad-bond inventories. A cap of radius $c't$ on $S^2$ has area\nat least $ct^2$, so its normalized sampling density is at most\n$C/t^2\\le H^C$. Taking products proves the density bound.\n\nFor the lower energy estimate, select disjoint generating marked\nboxes in each component, in decreasing order of size. Fixed\nenlargements of the selected boxes cover all generating boxes.\nDilation by $d$ enlarges the area of a unit or larger box by at most\n$Cd^2$. The total selected area in $E_C$ is therefore at least\n$c|E_C|/d^2$. On each selected box the average of $f$ exceeds $a$.\nIf this average is supplied mainly by chords greater than $t$, the\npositive linear branch of $S_t$ gives an energy lower bound\n$cHa^2$ times the box area. Otherwise Cauchy--Schwarz in the quadratic\nbranch gives the same lower bound. Bonds incident to disjoint selected\nboxes are counted at most a bounded number of times. Since\n$Ha^2=\\varepsilon^2p^2$, summation proves\nEquation~\\eqref{eq:orig-20}.\n\nChoose the fixed multiple in $d=CD_1$ after the stencil constants, so\nthat all stencils read by rows of $I_E$ stay within a $d/2$ enlargement\nof $E$. On the replaced sites all chords are below $t$. Any\nunselected component of the full dilated construction has its\noriginal marked part at distance at least $d$ from $E$; elsewhere\nthe unchanged chords are bounded by the exterior estimate. Thus\nevery chord read by these rows is at most $t$. Uniform summability of\nthe row weights gives $Y_e(q')\\le C_LHt^2=C_Lp^2$.\nThe support count $|I_E|\\le Cd^2n$ proves\nEquation~\\eqref{eq:orig-21}.\n\\end{proof}\n\nThe lower bound is proportional to the number of replaced sites,\nwith only the polylogarithmic loss $d^2$. This is what allows an\nintegrated estimate uniform in the total volume. Fix $B_T$ in\nLemma~\\ref{lem:row-exponential-moment} so large that\n\\begin{equation}\n (1-\\theta)C_Ld^2<\\frac{c}{4d^2},\n \\label{eq:comparison-theta-choice}\n\\end{equation}\nwith the constants of Lemma~\\ref{lem:editable-regions}. Next take\n$P_0$ sufficiently large that\n\\begin{equation}\n p^2/d^2\\gg d^2B_T^2\\log H+\\log H+1.\n \\label{eq:comparison-p-choice}\n\\end{equation}\nAll powers on the right were fixed independently of $P_0$. These\nrequirements can therefore be included among the finite choices\nbefore taking $H$ large.\n\n\\subsection{Exact covering identities and integration}\n\nWe now keep the geometry of the original configuration fixed while\nremoving selected inventories. This distinction is important: no\nmodified configuration is reclustered.\n\nLet $\\mathcal I$ be the family of retained regions of a configuration\n$q$, and let $D_C$ be the original true bad bonds assigned to $E_C$.\nChoose filled values at every region as in\nLemma~\\ref{lem:editable-regions}. For $I\\subset\\mathcal I$, let $q(I)$\nbe the configuration in which precisely the regions outside $I$ have\nbeen replaced. Then\n\\begin{equation}\n D(q(I))=\\bigsqcup_{C\\in I}D_C.\n \\label{eq:comparison-inventory-edit}\n\\end{equation}\nWrite $\\Xi(I)=\\Xi(q(I))$; these are complete positive sums.\n\nAt a fixed active set $I$, join two labels of a full covering family\nif their supports meet the same active region. Call a connected group\na macrolabel $U$. Its enlarged support and weight are\n\\[\n S_U=\\bigcup_{\\lambda\\in U}P_\\lambda\n \\ \\cup\\!\n \\bigcup_{\\substack{C\\in I:\\ E_C\\cap P_\\lambda\\ne\\varnothing\\\\\n                                  \\text{for some }\\lambda\\in U}}E_C,\n \\qquad\n w(U;q(I))=\\prod_{\\lambda\\in U}k_\\lambda(q(I)).\n\\]\nAll labels supplying the inventory of a touched region belong to the\nsame group, so each macrolabel covers every active region it meets\nin full. Distinct macrolabels have disjoint enlarged supports.\nConversely, compatible macrolabels covering the active inventories\nrecover a full original covering family.\n\nIf a site set $S$ is disjoint from the active regions, denote by\n$\\Xi(I;S)$ the complete-inventory sum restricted to macrolabels\nwhose enlarged supports avoid $S$. This restricted sum may be signed.\nFor a selected compatible macrolabel family $\\mathcal A$, let\n$S_{\\mathcal A}$ be its combined support and $B_{\\mathcal A}$ the\nactive regions it covers. The exact identities from\n\\Rref{cmp:background-identity}, \\Rref{cmp:IE}, and\n\\Rref{cmp:difference-identity} become\n\\begin{align}\n \\Xi(I;S)\n &=\\sum_{\\substack{\\mathcal A\\ \\mathrm{compatible}\\\\\n          S_U\\cap S\\ne\\varnothing\\ (U\\in\\mathcal A)}}\n (-1)^{|\\mathcal A|}\n \\prod_{U\\in\\mathcal A}w(U;q(I))\\,\n \\Xi(I\\setminus B_{\\mathcal A};S_{\\mathcal A}),\n \\label{eq:comparison-inclusion-exclusion}\\\\\n \\widetilde\\Xi(I)-\\Xi(I)\n &=\\sum_{\\substack{\\mathcal A\\ \\mathrm{compatible}\\\\\n                              \\mathcal A\\ne\\varnothing}}\n \\prod_{U\\in\\mathcal A}\n       [\\widetilde w(U;q(I))-w(U;q(I))]\\,\n \\Xi(I\\setminus B_{\\mathcal A};S_{\\mathcal A}).\n \\label{eq:comparison-difference-identity}\n\\end{align}\nHere and below macrolabels in a displayed sum satisfy the inventory\nconditions just described.\n\nTo verify the identities, first select a compatible family. Every\nremaining support avoids $S_{\\mathcal A}$ and hence every changed\nsite. Its values and all eligibility indicators are unchanged by\nremoving $B_{\\mathcal A}$. Its inventory loses exactly the removed\ninventories in Equation~\\eqref{eq:comparison-inventory-edit}. This\ngives the background sum\n$\\Xi(I\\setminus B_{\\mathcal A};S_{\\mathcal A})$.\nExpand the indicator of avoiding $S$ as a product of\n$1-\\mathbf1_{\\{S_U\\cap S\\ne\\varnothing\\}}$ to obtain the first\nidentity. Expand $\\widetilde w=w+(\\widetilde w-w)$ in a full family\nand subtract the all-$w$ term to obtain the second. With finite label\nlists these are finite algebraic identities. A full family contains\nat most $|D(q(I))|$ labels, and the absolute activity bounds give\nconvergence for countable lists, so the identities pass to that\nlimit before any division is performed.\n\nStarting with Equation~\\eqref{eq:comparison-difference-identity},\niterate Equation~\\eqref{eq:comparison-inclusion-exclusion} until\neach term ends in a complete base sum $\\Xi(q')$. Every nonempty\ngeneration consumes an active region, so this recursion terminates.\nFix an ordering of the common label list. Take absolute values and\ntelescope each initial macrolabel difference in this order:\n\\begin{equation}\n |\\widetilde w(U)-w(U)|\n \\le\\sum_{\\lambda\\in U}d_\\lambda\n             \\prod_{\\kappa\\in U\\setminus\\{\\lambda\\}}p_\\kappa.\n \\label{eq:comparison-marked-product}\n\\end{equation}\nThus each initial macrolabel has one marked original label, carrying\n$d_\\lambda$; the other labels carry $p_\\lambda$. The final complete\n$\\Xi(q')$ is positive.\n\nWe record explicitly the gain after integrating such a term. A term\nwill be encoded below by its original labels and the exact site sets\nof its consumed regions. Fix these sets, let their union be $E$, and\nput $n=|E|$. Let $\\mathcal Q$ be the measurable set of original\nconfigurations whose region construction and original inventories admit\nthis encoded term. Label lists include zero-valued labels: every\nspin-dependent eligibility test remains in the evaluated weight. Thus\n$\\mathcal Q$ is defined before sampling, even when a later evaluated\nweight vanishes for some filled values. Average over the sampled\nfilled values; only consumed regions need to be sampled. Then\n\\begin{equation}\n \\frac1Z\\int_{\\mathcal Q} e^{X(q)}\n           \\E_{\\mathrm{fill}\\mid q}[\\Xi(q')]\\,\\dd q\n \\le e^{-c'p^2n/d^2}.\n \\label{eq:orig-22}\n\\end{equation}\nIndeed Equations~\\eqref{eq:orig-17} and~\\eqref{eq:orig-20} imply\n\\[\n X(q)-X(q')\\le C_Ln-cp^2n/d^2+\\sum_{e\\in I_E}Y_e(q').\n\\]\nBy Equation~\\eqref{eq:orig-21} and the choice\n\\eqref{eq:comparison-theta-choice}, replacing the last coefficient\none by $\\theta$ costs at most $cp^2n/(4d^2)$.\n\nThere is no change-of-variables invertibility assumption here.\nWrite $q=(q_E,q_{E^c})$ and let\n$h(q'_E\\mid q_E,q_{E^c})$ be the conditional fill density. Its\nuniform bound is $e^{Cn\\log H}$. After using that bound, positivity\nof $\\Xi(q')$ allows the restrictions defining $\\mathcal Q$ to be\ndropped. Integration over the original $q_E$ contributes at most\none, since it uses product probability measure. The remaining\nintegral over $(q'_E,q_{E^c})$ is bounded by\n\\[\n \\exp\\left(C_Ln+Cn\\log H-\\frac{3cp^2n}{4d^2}\\right)\n \\E_\\mu\\exp\\left(\\theta\\sum_{e\\in I_E}Y_e\\right).\n\\]\nEquation~\\eqref{eq:orig-19}, $|I_E|\\le Cd^2n$, and\nEquation~\\eqref{eq:comparison-p-choice} prove\nEquation~\\eqref{eq:orig-22}. This is the only integration estimate\nneeded in the covering recursion; no restricted signed sum appears\nin a denominator.\n\n\\subsection{Summing the forests}\n\nWe complete the proof of Proposition~\\ref{prop:integrated-comparison}\nby explaining the counting in \\Rref{cmp:forest} for these regions.\nInside each macrolabel choose a spanning tree on its original label\nnodes and the active-region nodes it meets. Make the choice\ndeterministically using a fixed ordering. Join each later macrolabel,\nagain deterministically, to one preceding-generation macrolabel whose\nsupport it meets. Choose a deterministic intersecting pair of their\nunderlying gas or region supports as the endpoints of this edge. Use\ntwo edge colors to distinguish edges internal\nto a macrolabel from edges joining generations, and root each tree\nat the marked label of its initial macrolabel.\n\nNo region node is repeated: a region is consumed on first appearance.\nNo original label is repeated either. Its inventory is fixed and\nnonempty, and the regions containing it have already been removed\nafter its first appearance. It cannot be eligible in a subsequent\ngeneration. Within a generation the supports are disjoint.\n\nThis forest retains the entire term. Contracting internal edges\nrecovers the macrolabels; depths in the contracted rooted forest\nrecover generations; marked roots recover the telescoping choices.\nThe exact region site sets recover the consumed active sets and\ntherefore every evaluation configuration. Thus distinct terms do\nnot acquire the same forest. For a fixed original configuration the\nunconsumed geometry and inventories are already determined; there\nis no further multiplicity to count.\n\nAfter integration, Equation~\\eqref{eq:orig-22} assigns an edit node\n$E_C$ the weight\n\\[\n w(E_C)=e^{-c'p^2|E_C|/d^2}.\n\\]\nSince the consumed sets are disjoint, their product is exactly the\nweight for their union. We may now enlarge the possible geometries\nto all connected site animals. On a bounded-degree lattice the number\nof animals of size $n$ containing a fixed site is at most $C^n$;\nthe same bound follows by a spanning-tree traversal on a torus.\nConsequently, with $R=C_L\\mathfrak m_0^2$ large enough that\n$|P_\\lambda|\\le Rs_\\lambda$,\n\\[\n \\sup_x\\sum_{E_C:x\\in E_C}w(E_C)e^{2|E_C|}\n \\le\\sum_{n\\ge1}C^ne^{-(c'p^2/d^2-2)n}\n <\\frac1{16R}\n\\]\nfor sufficiently large $H$. The activity cap likewise gives\n\\[\n \\sup_x\\sum_{\\lambda:x\\in P_\\lambda}\n                    p_\\lambda e^{2s_\\lambda}<\\frac1{16R}.\n\\]\nIndeed $A=64\\ge2$ and $Rw_0\\to0$, even after multiplying the cap by\nany fixed constant, by Equation~\\eqref{eq:setup-covering-norm}.\nGive a gas node size $s_\\lambda$ and an edit node size $|E_C|$.\nEvery node of size $b$ has support at most $Rb$. Summing over an\nintersection site and the two edge colors bounds the one-child sum,\nwith an exponential allowance for descendants, by $b/4$.\n\nMore explicitly, induction on maximum tree depth bounds the total\ndescendant weight below a root of size $b$ by $e^b$. For the induction\nstep, a child of size $a$ contributes at most its weight times $e^a$.\nThe sum over unordered distinct children is bounded by the\nexponential of the one-child sum, because ordered $r$-tuples with\nfactor $1/r!$ only enlarge it after distinctness is dropped. Hence\nthe descendant sum is at most $e^{b/4}\\le e^b$. Monotonicity of the\npositive majorants allows arbitrary depth.\n\nA marked gas root therefore costs at most\n$d_\\lambda e^{s_\\lambda}\\le d_\\lambda e^{2s_\\lambda}$.\nActual forest components have distinct marked roots. Dropping all\ndisjointness conditions between their descendant trees and summing\nunordered nonempty root sets gives\n\\[\n \\sum_{r\\ge1}\\frac1{r!}\n \\left(\\sum_\\lambda d_\\lambda e^{2s_\\lambda}\\right)^r.\n\\]\nThis is the right-hand side of Equation~\\eqref{eq:orig-16}, proving\nProposition~\\ref{prop:integrated-comparison}.\n\n\\subsection{Partition functions and ordinary alignment}\n\nApply Proposition~\\ref{prop:integrated-comparison} to the two endpoint\ngases in Equation~\\eqref{eq:orig-15}. Summing their support-hit bound\nover sites gives\n\\[\n \\sum_\\lambda d_\\lambda e^{2s_\\lambda}\\le D_Hv\\delta_K.\n\\]\nThe exponent comparison in that equation then shows that the ratio\nof the scalar-stripped partition functions tends to one whenever\n$v\\delta_K\\to0$. For example, with $\\varepsilon_K=D_Hv\\delta_K$,\n\\[\n \\left|\\frac{\\int e^{X_1}\\Xi_2}{\\int e^{X_1}\\Xi_1}-1\\right|\n \\le e^{\\varepsilon_K}-1,\n \\qquad\n e^{-\\varepsilon_K}\\le e^{X_2-X_1}\\le e^{\\varepsilon_K}.\n\\]\nFor small $\\varepsilon_K$ these inequalities bound the logarithm of\nthe partition-function ratio by $C\\varepsilon_K$. The constants\ndepend on the fixed $H$, but not on depth or period. The same\nintegrated estimate will also be used when activity differences\nare localized at specified source sites, in which case their sum\ndoes not carry a factor $v$.\n\nWe also need a local consequence of reflection positivity.\n\n\\begin{corollary}[Terminal alignment]\n\\label{cor:terminal-alignment}\nOn every standard terminal axis torus considered in this section,\nfor each fixed $C>0$ there is $c>0$ such that every bond satisfies\n\\begin{equation}\n \\mu\\{|q_x-q_y|>t/C\\}\\le e^{-cHt^2}.\n \\label{eq:orig-23}\n\\end{equation}\nThe constants are uniform in the cutoff depth and the admitted periods.\n\\end{corollary}\n\n\\begin{proof}\nChoose a translated $2$-by-$2$ tile containing the specified bond and\ndisseminate its event by tile reflections. The periods are divisible\nby $2B_T$, hence by $4$, so these tiles have even counts in both\ndirections. A fixed positive fraction of all bonds then have chord\ngreater than $t/C$.\nThe direct kinetic energy on that event is at least $cHt^2v$.\nEquations~\\eqref{eq:orig-17} and~\\eqref{eq:orig-18} bound its\nprobability by\n\\[\n \\exp[-v(cHt^2-C_L-C_L\\log H)]\\le e^{-c'vHt^2}.\n\\]\nThe chessboard inequality gives the single-event bound after taking\nthe corresponding tile root. This is the argument of\n\\Rref{cmp:rarity}, whose hypotheses have now all been checked for\nthe $S^2$ endpoint law. No such estimate is used in oblique periods.\n\\end{proof}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/continuum.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/continuum.tex", "bytes": 24782, "sha256": "a8252a916c90d2b6d383f3767d9d5304866f49b58800a43b08ab0f1b2b8058a4", "content": "\\section{Continuum and infinite-volume limits}\n\\label{sec:continuum}\n\nThe source estimates provide convergence on descendants of fixed coarse\ncells.  We first obtain bounds that allow these cells to approximate\narbitrary test functions.  The trace estimates then identify the\ninfinite-volume limit, and comparison of the two inclined blockings\nsupplies the rotations absent from the microscopic lattice.\n\nWrite $a_N=L^{-N}$ and let $B_N>0$ be the field normalization in\nEquation~\\eqref{eq:field-normalization}.  For a compactly supported\nfunction $f:\\R^2\\to\\R^3$, define\n\\begin{equation}\n \\phi_N(f)=B_Na_N^2\\sum_{x\\in\\Lambda_N}\n                  f(x)\\cdot q_x,\n \\label{eq:smeared-lattice-field}\n\\end{equation}\nwhere $\\Lambda_N$ is the physical lattice, with its specified origin and\norientation.  On a torus the sum uses its periodic lattice and a periodic\ntest function.  Scalar tests of a specified component have the same\nmeaning.  For component indices\n$\\boldsymbol\\alpha=(\\alpha_1,\\ldots,\\alpha_n)\\in\\{1,2,3\\}^n$, introduce\nthe lattice moment measure\n\\begin{equation}\n S_{n,N}^{\\boldsymbol\\alpha}\n   =(B_Na_N^2)^n\\sum_{x_1,\\ldots,x_n\\in\\Lambda_N}\n       \\E\\!\\left[\\prod_{i=1}^n q_{x_i}^{\\alpha_i}\\right]\n       \\delta_{(x_1,\\ldots,x_n)}.\n \\label{eq:lattice-moment-measure}\n\\end{equation}\nThe expectation will be taken either on one of the physical tori of\nSection~\\ref{sec:trace} or in the periodic infinite-plane state at the\nsame cutoff.  These choices will always be indicated when they matter.\n\n\\subsection{Local moment bounds}\n\nA terminal cell is the set of microscopic descendants of one layer-zero\nsite in a standard blocking.  In physical coordinates it is a unit\nsquare, up to the choice of lattice boundary convention.  We use the\nterm unit box for any translate of a unit square in the regulator axes.\nOn a torus, boxes are interpreted in a periodic coordinate chart;\nbounded-multiplicity coverings give the same estimates when a chart\ncrosses a period.\n\n\\begin{proposition}[Moment majorants]\n\\label{prop:moment-majorants}\nFor every $n\\ge1$ and every component array $\\boldsymbol\\alpha$, the\nmeasure $S_{n,N}^{\\boldsymbol\\alpha}$ is nonnegative.  There is a constant\n$C$, independent of $N\\ge K_0$, the axis torus in the exhaustion of\nSection~\\ref{sec:trace}, and the positions of the unit boxes, such that\n\\begin{equation}\n S_{n,N}^{\\boldsymbol\\alpha}(Q_1\\times\\cdots\\times Q_n)\n       \\le C^n n!\n \\label{eq:unit-box-majorant}\n\\end{equation}\nfor all unit boxes $Q_1,\\ldots,Q_n$.  The same bound holds in the\nperiodic infinite-plane state.\n\nFor each fixed number $D$ of terminal cells, their component field sums\nhave joint exponential moments in a neighborhood of the origin,\nuniformly in these cutoffs and axis tori.  If $\\mu$ is the source-free\nterminal probability law and $z_Y\\in\\C^3$ is supported on at most $D$\nterminal sites with $|z_Y^\\alpha|\\le1$, their generating function differs\nfrom\n\\begin{equation}\n             \\E_\\mu\\exp\\!\\left(\\sum_Y z_Y\\cdot V_Y\\right)\n \\label{eq:orig-34}\n\\end{equation}\nby $o_H(1)$, uniformly for fixed $D$.\n\nOn each fixed tilted physical torus of Section~\\ref{sec:trace}, the\nexponential-moment bounds and Equation~\\eqref{eq:unit-box-majorant}\nhold with constants that may depend on that torus and on $H$, but remain\nindependent of $N$.\n\\end{proposition}\n\n\\begin{proof}\nFor finite volume, expand each interaction factor as\n\\[\n e^{\\beta_N q_x\\cdot q_y}\n   =\\prod_{\\alpha=1}^3\\sum_{m\\ge0}\n       \\frac{\\beta_N^m}{m!}(q_x^\\alpha q_y^\\alpha)^m.\n\\]\nThe expansion is absolutely convergent.  After multiplication by any\nspecified spin components, each single-site integral is zero if one\ncoordinate has odd degree, and is nonnegative otherwise.  Since\n$\\beta_N\\ge0$, every surviving term is nonnegative.  This proves the\nfirst assertion, including repeated insertion sites and oblique\nperiods.  Positivity passes to the fixed-cutoff periodic plane limit.\n\nLet $J$ be a set of at most $D$ terminal sites, and let $C_Y$ be the\nmicroscopic descendants of $Y\\in J$.  Iterating the exact source\nsubstitution of Proposition~\\ref{prop:source-step} identifies $z_Y$ with\nthe coupling to the vector\n\\[\n       B_Na_N^2\\sum_{x\\in C_Y}q_x.\n\\]\nAt the terminal layer, write the source-dependent density, after its\ncommon bulk scalar has been removed, as\n\\[\n e^{X(V)}\n e^{\\sum_{Y\\in J}z_Y\\cdot V_Y+G(V;z)}\\Xi_z(V),\n\\]\nwhere $e^X\\Xi_0$ has normalizer $Z$ and law $\\mu$.  The source-anchor\nconvention and the norm bound of Proposition~\\ref{prop:source-step}\ngive\n\\[\n       \\sup_V|G(V;z)|\\le DR_0.\n\\]\nIndeed, a monomial that survives setting all sources outside $J$ to\nzero has its designated source anchor in $J$.  The same specialization\nhas a useful consequence for the gas: every surviving changed label\nhas complete support meeting $J$.  Its weighted activity difference\ntherefore satisfies\n\\[\n \\sum_\\lambda e^{2s_\\lambda}\n            \\norm{k_\\lambda(z)-k_\\lambda(0)}_\\infty\n       \\le Dw_0.\n\\]\nThe original and source-dependent activities satisfy the combined\nsite-hit hypothesis of Equation~\\eqref{eq:orig-16}.  Consequently\n\\[\n \\frac1Z\\int e^X|\\Xi_z-\\Xi_0|\n      \\le e^{Dw_0}-1.\n\\]\nSince $|\\sum_{J}z_Y\\cdot V_Y|\\le3D$, subtracting\nEquation~\\eqref{eq:orig-34} from the normalized source integral gives\n\\begin{equation}\n \\left|\\E e^{\\sum_{Y\\in J}z_Y\\cdot\\phi_N(C_Y)}\n       -\\E_\\mu e^{\\sum_{Y\\in J}z_Y\\cdot V_Y}\\right|\n \\le e^{3D}(e^{DR_0}-1)\n      +e^{3D+DR_0}(e^{Dw_0}-1),\n \\label{eq:localized-source-comparison}\n\\end{equation}\nwhere $\\phi_N(C_Y)=B_Na_N^2\\sum_{x\\in C_Y}q_x$ is vector-valued.\nThe right side is $o_H(1)$ for fixed $D$.  In particular the generating\nfunctions are bounded on a fixed complex polydisc, independently of\nvolume and cutoff.  This is a localized use of the integrated gas\ncomparison: its bound depends on $D$, rather than on the torus volume.\n\nFor one real component cell sum $X$, the bounds for $\\E e^X$ and\n$\\E e^{-X}$ imply $\\E e^{|X|}\\le C_0$.  Thus\n$\\E|X|^n\\le C_0n!$.  If $X_i$ are component sums in possibly different\nterminal cells, H\\\"older's inequality yields\n\\[\n  \\left|\\E\\prod_{i=1}^n X_i\\right|\n      \\le\\prod_{i=1}^n(\\E|X_i|^n)^{1/n}\n      \\le C^n n!.\n\\]\nThe left side without absolute values is the mass of the corresponding\nproduct of cells.  An arbitrary unit box is covered by a fixed number\nof terminal cells.  Nonnegativity of the moment measure therefore\nextends the estimate to arbitrary translated products of unit boxes,\nwith a fixed enlargement of $C$.  This also shows that the estimates\nare unchanged by translating the microscopic origin.  At a fixed\ncutoff, the cell sums are bounded local observables, so the estimates\npass to the periodic plane state.\n\nFor a fixed tilted torus, use standard blocking in the regulator axes.\nIts period lattice is divisible at every step, and its terminal volume\nis fixed.  Absolute bounds on the regular exponent and on the covering\nactivities from Sections~\\ref{sec:rg} and~\\ref{sec:sources} bound the\nsource numerator in that volume.  The small-cap lower bound for the\nsource-free normalizer bounds the denominator away from zero after\nremoval of the bulk scalar.  This argument uses no reflection\npositivity of the tilted torus.  It gives a finite bound depending on\nits fixed terminal volume and on $H$, uniformly in depth.  The preceding\nexponential-moment and covering arguments now apply there as well.\n\\end{proof}\n\nWe record a direct consequence that will be used repeatedly.  If real\ncomponent tests $f_1,\\ldots,f_r$ have support in a fixed bounded region,\nthen every real linear combination of their smeared fields has moments\nbounded by $C(f)^n n!$, uniformly over the cutoffs and axis tori under\nconsideration.  To see this, first bound an even moment by integrating\nthe absolute test coefficients against the nonnegative component\nmeasures and applying Equation~\\eqref{eq:unit-box-majorant}.  Odd\nabsolute moments follow by Cauchy--Schwarz from even ones, with an\nexponential change of the constant.  These bounds imply exponential\ntails at a positive radius.  On a fixed tilted torus the same conclusion\nholds with a torus-dependent constant.\n\n\\subsection{Convergence and comparison of orientations}\n\n\\begin{proposition}[Continuum limit]\n\\label{prop:continuum-limit}\nOn every fixed axis or tilted physical torus used in\nSection~\\ref{sec:trace}, all joint moments of the fields\n$\\phi_N(f)$ converge for continuous tests.  Their finite-dimensional\nlaws converge, and the limiting laws are determined by their moments.\nThe moment measures converge on arbitrary continuous compactly\nsupported tests in the insertion coordinates.\n\nIn the periodic plane state these assertions hold for compactly\nsupported tests.  The same plane limit is obtained by taking the\ncontinuum limit on the axis tori and then their exhaustion, or by taking\nany simultaneous sequence of cutoffs and axis tori for which both the\ncutoff depth and the doubling index tend to infinity.\n\nFinally, let $O_+$ be the rotation with cosine $3/5$ and sine $4/5$.\nThe plane limits obtained with regulator orientations $\\Id$ and\n$O_+^2$ have identical finite-dimensional laws when tested in the same\nphysical coordinates.\n\\end{proposition}\n\n\\begin{proof}\n\\emph{Fixed tori and fixed coarse cells.}\nFix a physical torus and a layer $j$.  A test that is constant on the\nmicroscopic descendants of its layer-$j$ cells has an exact source\nrepresentation at that layer.  The coefficient multiplying each test\nvalue in the layer-$j$ source is\n\\begin{equation}\n             L^{-2j}\\prod_{i=1}^j c_{i,N}^{-1}.\n \\label{eq:regular-cell-source-multiplier}\n\\end{equation}\nIndeed, its microscopic coefficient is $B_NL^{-2N}$, while applying the\nsource substitution through the first $N-j$ steps multiplies this by\n$L^{2(N-j)}\\prod_{i=j+1}^N c_{i,N}$.  The definition\n$B_N=\\prod_{i=1}^Nc_{i,N}^{-1}$ gives\nEquation~\\eqref{eq:regular-cell-source-multiplier}.\n\nCorollary~\\ref{cor:source-endpoints} and the density comparison\nEquation~\\eqref{eq:orig-14} imply convergence of the normalized source\nintegrals near the source origin.  The multiplier in\nEquation~\\eqref{eq:regular-cell-source-multiplier} also converges,\nbecause it contains a fixed number of factors.  To justify taking\nratios here, observe that at layer $j$ the torus has fixed volume.\nAbsolute gas bounds and regular-exponent bounds control the numerator,\nand the small-cap lower bound controls the source-free denominator,\nuniformly in the original depth $N$.  The common bulk scalar cancels\nfrom numerator and denominator.  Thus source generating functions\nconverge uniformly on a sufficiently small complex polydisc.  Cauchy's\nformula gives convergence of every joint moment of these cell tests.\n\nThe layer-$j$ grids can be aligned across depths.  One may choose the\nphysical origins of the approximating lattices, or translate successive\nblock partitions: their available shifts fill one ancestral block\nmodulo the microscopic spacing.  The period identifications are\ncompatible with these choices.  An origin error of size $O_L(a_N)$\nhas vanishing effect on any smooth test by the moment majorants and\nuniform continuity.  More generally, the grids used in a Cauchy\ncomparison may be chosen separately, since the dilation coefficients\n$c_{i,N}$ and $B_N$ are independent of translations of the blocking.\n\n\\emph{Approximation of continuous tests.}\nEvery microscopic descendant of a layer-$j$ cell is within\n$CL^{-j}$ of its center.  Replace each continuous test by its value at\nthese cell centers.  Its error on a fixed compact set is bounded by\nits modulus of continuity at $CL^{-j}$.  To estimate a mixed moment,\nexpand the difference of the two products by replacing one factor at\na time.  Nonnegativity of the component moment measures and\nEquation~\\eqref{eq:unit-box-majorant} bound each resulting term by that\nmodulus of continuity times a constant independent of $N$.  First let\n$N\\to\\infty$ with $j$ fixed, and then let $j\\to\\infty$.  This proves\nconvergence for continuous tests.  Finite sums of product tests are\nuniformly dense in the continuous functions on a product of compact\nsets, and the total moment mass there is uniformly bounded.  Hence the\nmoment measures converge against every continuous compact test in the\ninsertion coordinates.\n\nThe factorial moment bounds give tightness of each finite list of\nsmeared real fields.  They also give uniform integrability of every\nfixed polynomial in that list.  Any subsequential law consequently\nhas the moments just obtained and an exponential moment in a\nneighborhood of zero.  Such a law is determined by its moments: its\nmoment generating function is analytic in that neighborhood, and its\nTaylor coefficients are those moments.  Thus all subsequential laws\ncoincide and the finite-dimensional laws converge.\n\n\\input{figures/orientations}\n\n\\emph{The two inclined blockings.}\nConsider the fixed tilted square torus of side $5M$ and axes $O_+$\nfrom Section~\\ref{sec:trace}.  Put one microscopic regulator in the\nstandard axes and the other in axes $O_+^2$.  In their respective axes\nuse the inclined first steps $O_+$ and $O_-=O_+^{-1}$.  Their frames\nthen agree, because $O_+^2O_-=O_+$;\nFigure~\\ref{fig:orientations} shows these orientations.  Both regulators have the same\n$\\beta_N,a_N,B_N$ and compatible periods.\n\nStop at a fixed layer $j<N$.  If $B_N^*$ and $c_{i,N}^*$ denote the\ninclined source normalizations, the exact source multiplier is now\n\\begin{equation}\n       25L^{-2j}\\frac{B_N}{B_N^*}\n                    \\prod_{i=1}^j(c_{i,N}^*)^{-1}.\n \\label{eq:inclined-cell-source-multiplier}\n\\end{equation}\nThe factor $25$ comes from the area of the first block: the product of\nthe area factors through layer $j$ is $25L^{2(N-j)}$.  Reflection\ncovariance of the two inclined prescriptions makes the multipliers\nin Equation~\\eqref{eq:inclined-cell-source-multiplier} identical.\nThey are bounded at each fixed $j$ by\nProposition~\\ref{prop:source-products} and the bounds on the individual\n$c_{i,N}^*$.  Convergence of the ratio $B_N/B_N^*$ is not required.\n\nApply Equation~\\eqref{eq:orig-14} and\nCorollary~\\ref{cor:source-endpoints} after the first step.  At the fixed\nbottom layer, their generating functions differ by a quantity tending\nto zero uniformly near the source origin, also when evaluated at the\ncommon bounded multiplier\n\\eqref{eq:inclined-cell-source-multiplier}.  Explicitly, if $r_j>0$\nis a common coarse-source comparison radius and $M_j$ bounds that\nmultiplier, restrict the physical cell sources to radius\n$r_j/(2\\max\\{1,M_j\\})$.  Their substituted arguments then lie in\nradius $r_j/2$ for every $N$, so uniform convergence and Cauchy's\nformula apply on one fixed polydisc without convergence of the\nmultiplier itself.  The common cell centers can\nbe chosen identical by translating the microscopic lattices.  Their\nphysical mesh can be fixed independently of $N$ at the chosen layer.\nEvery assigned microscopic point lies in its inclined first cell, so\nthe final descendants are within $C\\,5L^{-j}$ of the common centers.\nThe same continuous-test approximation therefore proves equality of\nthe limiting moments, and hence of the finite-dimensional laws, on\nthis fixed tilted torus.  This step uses only the moment bounds for a\nfixed tilted size.\n\n\\emph{Exchange of the cutoff and volume limits.}\nFor real $t_1,\\ldots,t_r$ and compactly supported real tests\n$f_1,\\ldots,f_r$, set\n\\[\n       F_N=\\exp\\!\\left(i\\sum_{\\nu=1}^rt_\\nu\\phi_N(f_\\nu)\\right).\n\\]\nIt is a bounded local microscopic observable with $|F_N|=1$,\nregardless of the size of $B_N$.  Once its support is contained in the\nprescribed interior portion of an axis torus at doubling index $k$,\nSection~\\ref{sec:trace} gives\n\\begin{equation}\n \\left|\\E_{N,\\mathrm{plane}}F_N\n          -\\E_{N,\\mathrm{torus}(k)}F_N\\right|\n       \\le C e^{-c2^k},\n \\label{eq:characteristic-volume-comparison}\n\\end{equation}\nuniformly in $N\\ge K_0$.  The fixed-torus continuum limit has already\nbeen proved.  The uniform moment bounds make the plane laws of this finite list tight.\nFor any subsequential plane limit, taking $N\\to\\infty$ in\nEquation~\\eqref{eq:characteristic-volume-comparison} bounds its\ncharacteristic function within $Ce^{-c2^k}$ of the same fixed-torus\ncharacteristic function.  Sending $k\\to\\infty$ identifies every\nsubsequential plane law.  This proves plane convergence and identifies\nit with the successive torus and cutoff limit.  The same inequality\napplied with $k=k(N)\\to\\infty$ gives every simultaneous axis exhaustion\nclaimed in the statement.  Uniform factorial moments give uniform\nintegrability, so moments converge along these limits as well.\n\nFor either regulator orientation, the trace-with-shift estimate of\nSection~\\ref{sec:trace} gives\nEquation~\\eqref{eq:characteristic-volume-comparison} also for the tilted\ntori.  First take their fixed-size continuum limit, where the two\norientations have just been compared, and then send the doubling index\nto infinity.  The resulting plane characteristic functions agree.\nBecause the comparison uses bounded characteristic functions, it\nrequires no moment bound uniform in the increasing tilted physical\nsize.\n\\end{proof}\n\n\\subsection{Euclidean symmetry and regularity}\n\nWrite $S_n^{\\boldsymbol\\alpha}$ for the plane moment measures just\nconstructed, and $S_n$ for the finite collection of their components.\nThere is no pointwise prescription for these correlation functions;\nthe following bounds specify their distributional meaning.\n\n\\begin{proposition}[Euclidean regularity]\n\\label{prop:euclidean-regularity}\nThe plane correlation functions are nonnegative Radon measures\ncomponentwise, and for every scalar Schwartz test\n$F\\in\\mathcal S((\\R^2)^n)$ they satisfy\n\\begin{equation}\n |S_n^{\\boldsymbol\\alpha}(F)|\n   \\le C^n n!\\,\n       \\sup_{x\\in(\\R^2)^n}\n         \\bigl[(1+|x|^2)^{2n}|F(x)|\\bigr].\n \\label{eq:orig-35}\n\\end{equation}\nThe lattice moment measures converge on Schwartz tests, with the same\nbound.  Finite component norms can be incorporated by changing $C$.\nThe limiting correlations have permutation symmetry, internal $O(3)$\ncovariance, and invariance under all Euclidean isometries of $\\R^2$.\nBounds for products of separately translated tests are uniform in\ntheir separate translation parameters when the decay weights are\ncentered at those parameters.\n\\end{proposition}\n\n\\begin{proof}\nThe compact-test convergence and positivity from\nProposition~\\ref{prop:continuum-limit} give nonnegative locally finite\nmeasures.  Partition $\\R^2$ into unit boxes indexed by $u\\in\\Z^2$.\nOn each such box the weights $(1+|x|^2)^{-2}$ and\n$(1+|u|^2)^{-2}$ are comparable by a fixed constant.  Therefore\nEquation~\\eqref{eq:unit-box-majorant}, followed by summation of these\nweights, gives\n\\[\n |S_n^{\\boldsymbol\\alpha}(F)|\n  \\le C^n n!\\,\n       \\sup_{x_1,\\ldots,x_n}\n          \\left[\\prod_{i=1}^n(1+|x_i|^2)^2\n                          |F(x_1,\\ldots,x_n)|\\right].\n\\]\nHere the sum over product boxes factors into $n$ convergent sums, so\nits cost is exponential in $n$.  Since\n$\\prod_i(1+|x_i|^2)^2\\le(1+\\sum_i|x_i|^2)^{2n}$, this proves\nEquation~\\eqref{eq:orig-35}.  The proof applies uniformly to the lattice\nmeasures.  Cutting off a Schwartz test outside a large compact set\nthen gives convergence on Schwartz space: the weighted supremum of\nthe discarded tail tends to zero.  Replacing each weight by\n$(1+|x_i-y_i|^2)^{-2}$ proves the assertion concerning separate\ntranslations, with constants independent of $y_1,\\ldots,y_n$.\n\nPermutation symmetry and internal $O(3)$ covariance pass directly\nfrom the lattice.  For a fixed physical translation, approximate its\ndisplacement by lattice displacements.  The error on a smooth test\ntends to zero by the majorants and test-space continuity.  Thus\ntranslation invariance also passes to the limit.  The microscopic\nreflections and quarter turns similarly give reflection and square\nsymmetry.\n\nLet $\\theta$ be the angle of $O_+$, so\n$\\cos\\theta=3/5$.  Rotating the entire regulator rotates the arguments\nof its moment functions.  The last assertion of\nProposition~\\ref{prop:continuum-limit} therefore gives invariance under\nrotation by $2\\theta$.  This angle generates a dense subgroup of the\ncircle.  Indeed, if $\\theta/\\pi$ were rational, then\n$e^{i\\theta}$ would be a root of unity and\n$2\\cos\\theta=6/5$ would be a rational algebraic integer, hence an\ninteger, a contradiction.  The same irrationality holds for\n$2\\theta/(2\\pi)$.  Rotations act continuously on Schwartz test space;\nEquation~\\eqref{eq:orig-35} consequently extends this dense-subgroup\ninvariance to all rotations.  Together with reflections and\ntranslations this is Euclidean invariance.\n\\end{proof}\n\nThe fourth-cumulant test in Section~\\ref{sec:os} uses cell indicators before\napproximating them by smooth functions.  The next estimate justifies\nthat approximation and also allows the microscopic cell boundaries to\nmove slightly with $N$.\n\n\\begin{lemma}[Boundary strips]\n\\label{lem:boundary-strips}\nFix an order $n$, a bounded region $K\\subset\\R^2$, and a bounded axis\nrectangle $A$.  Let\n$E_\\varepsilon=K\\cap\\{x:\\operatorname{dist}(x,\\partial A)<\\varepsilon\\}$.\nIn the plane, uniformly in $N\\ge K_0$ and in the microscopic origin,\n\\begin{equation}\n S_{n,N}^{\\boldsymbol\\alpha}\n       (E_\\varepsilon\\times K^{n-1})\n       \\le C_{n,K,A}(\\varepsilon+a_N).\n \\label{eq:boundary-strip-bound}\n\\end{equation}\nThe same estimate holds on the axis tori, with the sets interpreted in\nfixed bounded periodic charts.  It also holds with any one insertion\nin the strip.  Consequently continuum convergence extends to products\nof bounded axis-box indicators, to smooth approximations of these\nindicators, and to cell boxes whose boundaries converge to the given\nbox boundaries.  More generally the same conclusion holds for bounded\nJordan measurable sets.\n\\end{lemma}\n\n\\begin{proof}\nChoose a fixed bounded relative-coordinate box $D$ containing $K-K$.\nUse coordinates in the regulator axes, so the full displacement lattice\nis $a_N\\Z^2$, regardless of the microscopic origin.  On a torus evaluate\ntranslated spins periodically.  For a lattice site $x$, define\n\\[\n A_N(x)=(B_Na_N^2)^n\n     \\sum_{y_2,\\ldots,y_n\\in a_N\\Z^2\\cap D}\n       \\E\\!\\left[q_x^{\\alpha_1}\n                    \\prod_{i=2}^n q_{x+y_i}^{\\alpha_i}\\right].\n\\]\nFor $n=1$ the sum and product over the other insertions are empty.\nEvery term is nonnegative.  Microscopic translation invariance makes\n$A_N(x)$ independent of $x$; denote its value by $A_N$.  Notice that\nall $n$ field-normalization factors and all $n$ lattice area factors\nare included in $A_N$.\n\nLet $Q$ be a fixed unit box.  Summing the first insertion over its\nlattice sites and using positivity gives\n\\[\n  \\#(Q\\cap\\Lambda_N) A_N\n     \\le S_{n,N}^{\\boldsymbol\\alpha}\n                  (Q\\times(Q+D)^{n-1})\n     \\le C_{n,K}.\n\\]\nThe last inequality follows by a fixed unit-box covering and\nEquation~\\eqref{eq:unit-box-majorant}.  Since\n$\\#(Q\\cap\\Lambda_N)\\ge c a_N^{-2}$, with a constant uniform over the\nlattice origin, we obtain $A_N\\le C_{n,K}a_N^2$.  If the first insertion\nis in $E\\subset K$, every choice of the other insertions in $K$ is\nincluded in this relative-coordinate sum.  Hence\n\\begin{equation}\n S_{n,N}^{\\boldsymbol\\alpha}(E\\times K^{n-1})\n       \\le C_{n,K}a_N^2\\#(E\\cap\\Lambda_N).\n \\label{eq:first-insertion-count}\n\\end{equation}\nFor a fixed rectangle boundary, elementary lattice counting gives\n$a_N^2\\#(E_\\varepsilon\\cap\\Lambda_N)\n \\le C_{K,A}(\\varepsilon+a_N)$, proving\nEquation~\\eqref{eq:boundary-strip-bound}.\n\nOn a torus use the same displacement box $D$, with periodic evaluation\nof $x+y_i$.  A fixed bounded displacement box can represent a periodic\nsite more than once, but its multiplicity is bounded at the fixed\nphysical scale under consideration.  The unit-box argument and the\nmoment bound therefore give the same estimate, with this fixed\nmultiplicity included in the constant.  Permutation symmetry treats\nany insertion coordinate.\n\nFor completeness, if $A$ is merely bounded and Jordan measurable,\nthe lattice squares centered at points of\n$\\{\\operatorname{dist}(x,\\partial A)<\\varepsilon\\}$ are contained in the\n$(\\varepsilon+Ca_N)$-neighborhood of $\\partial A$.  Thus the counting\nterm in Equation~\\eqref{eq:first-insertion-count} is bounded by a\nconstant times the area of that neighborhood, which tends to zero as\n$\\varepsilon\\downarrow0$ and $N\\to\\infty$.  Continuous inner and outer\napproximations to the indicator now have arbitrarily small error in\nevery fixed-order correlation.  The error for a product of indicators\nis bounded by the sum of the errors with one argument in a boundary\nneighborhood.  Compact-test convergence applies to the continuous\napproximants, and then the neighborhood widths tend to zero.  The same\nargument covers moving boxes, since their symmetric differences lie\nin shrinking neighborhoods of the limiting boundaries.\n\\end{proof}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/fields.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/fields.tex", "bytes": 37815, "sha256": "f244f83621cfc0b4c8ea67ed83f97a4a0e8c0e7eba2f9c5ee16f6298414a86ac", "content": "\\section{Continuum fields at a fixed bare-coupling offset}\n\\label{sec:fields}\n\nThe density comparison of Proposition~\\ref{prop:matching} leaves one\nreal parameter, the limiting offset of the bare coupling.  We now\nconstruct the field hierarchy at each offset and compare the three\nblocking prescriptions in common physical coordinates.  The last part\nof the section controls the full two-point measure, including its tails.\nThis is what permits normalization by susceptibility and second-moment\nlength; convergence on compact tests alone would not suffice.\n\nThroughout, the parameters have the choices of\nPropositions~\\ref{prop:admission} and~\\ref{prop:uniform-volume}.\nWrite $I=(-R,R)$, and let a run of type $P\\in\\{r,+,-\\}$, depth $N$,\nand offset $s$ have bare coupling $\\beta=\\vartheta_N+s$.\nRecall the matching condition\n\\begin{equation}\n (s_2+A_{P_2})-(s_1+A_{P_1})\\longrightarrow0,\n \\label{eq:field-offset-matching}\n\\end{equation}\nfor two sequences of runs whose depths tend to infinity.  The\ndiscrepancies $u_j$ and $\\lambda_j$ are the source-free density and\nprecise-coupling discrepancies at layer $j$.  By\nProposition~\\ref{prop:matching}, both tend to zero at each fixed layer\nunder \\eqref{eq:field-offset-matching}.  All finite-period comparisons\nbelow use the same reference thresholds and the common record lists,\nwith zero padding, prescribed in Section~\\ref{sec:setup}.\n\n\\subsection{Local sources and their normalization}\n\nWe use the exact source representation of Section~\\ref{sec:sources}.\nIts additional regular records have positive source degree and are\nanchored at a source site; their complete supports include every source\nand spin argument.  The covering records retain their mandatory\ninventories and complete supports.  All norms sum absolute power-series\ncoefficients at source radius one.  In particular, identifying source\nvariables or setting some of them to zero respects the bounds.\nThe leading linear spin source is kept separately from these records.\nSpin-independent terms of source degree at least two stay in their\nsupported regular records.  There is no source-dependent volume scalar\nwhose removal could change a moment generating function.\n\nTo distinguish a source cap from the offset radius $R$, write\n\\[\n R_j^{\\mathrm{src}}=H_j^{-1/10},\\qquad\n w_j=\\exp(-p_j^{1/4}).\n\\]\nThese are the caps denoted by $R_j,w_j$ in\nEquation~\\eqref{eq:orig-27}.  In each bulk or finite-period\nrepresentation, take the positively weighted source discrepancy of\nProposition~\\ref{prop:source-step}, divided by these two caps, with\nregular differences measured through seven derivatives and absent\nrecords padded by zero.  Write these quantities as\n$v_j^{\\mathrm{src,bulk}}$ and $v_j^{\\mathrm{src},T}$, where $T$ denotes\na common terminal period followed through the layers.  In parallel\nwith the density discrepancy \\eqref{eq:offset-family-discrepancy}, use\n\\begin{equation}\n v_j^{\\mathrm{src}}\n =\\max\\bigl\\{v_j^{\\mathrm{src,bulk}},\\ \n                  \\sup_T v_j^{\\mathrm{src},T}\\bigr\\}.\n \\label{eq:offset-family-source-discrepancy}\n\\end{equation}\nFor a bulk-only comparison the supremum is omitted.  Otherwise it\nranges over the periods being compared, or a compatible admitted\nfamily, with the same source weights and caps.  This simultaneous\ndiscrepancy is uniformly bounded by the caps.  It always includes the\nbulk data that determine $c_j$; a discrepancy on an isolated torus\nis not used to control that scalar.\n\n\\begin{proposition}[Source transport along offset trajectories]\n\\label{prop:source-input}\nEvery admitted run above carries the microscopic source\n$\\exp(\\sum_x z_x\\cdot q_x)$ exactly through its block integrations.\nThe additional source lists begin at zero and satisfy their caps at\neach layer.  A step of side $l_j$, from layer $j$ to layer $j-1$, has a\nbulk coefficient $c_j>0$ with\n\\begin{equation}\n z_x=\\frac{z'_{[x]}}{l_j^2c_j},\\qquad\n |c_j-1|\\le C_LR_j^{\\mathrm{src}}.\n \\label{eq:offset-source-substitution}\n\\end{equation}\nFor a comparison on a common ordinary step,\n\\begin{align}\n v_{j-1}^{\\mathrm{src}}\n &\\le q_s v_j^{\\mathrm{src}}\n +C_L(\\log H_j)^D\n        (u_j+\\epsilon_h+|\\lambda_j|/H_j),\\notag\\\\\n |c_{2,j}-c_{1,j}|\n &\\le C_LR_j^{\\mathrm{src}}\n \\bigl[v_j^{\\mathrm{src}}+(\\log H_j)^D\n        (u_j+\\epsilon_h+|\\lambda_j|/H_j)\\bigr],\n \\qquad q_s<1.\n \\label{eq:offset-source-comparison}\n\\end{align}\nThe statements hold in bulk and on every compatible admitted period.\nThe coefficient $c_j$ is the same bulk coefficient in all these periods.\n\\end{proposition}\n\n\\begin{proof}\nThe initial source has zero additional regular and covering lists and\nhas all support, normalization, reality, and internal covariance\nproperties required in Section~\\ref{sec:sources}.\nProposition~\\ref{prop:admission} supplies the coupling bands for every\nstep, including the inclined first step.  Hence\nProposition~\\ref{prop:source-step} applies inductively and renews the\ncaps.  Equation~\\eqref{eq:offset-source-substitution} is\nEquation~\\eqref{eq:orig-28}.  We spell out the simultaneous use of\nthe comparison proof, since the scalar is extracted in bulk and then\nused on every period.\n\nBefore the final scalar division, the one-difference estimates in\nthe proof of Proposition~\\ref{prop:source-step} give an isolated\ndegree-one transfer factor $C/L+o_H(1)$ and a higher-degree factor\n$C/L^2+o_H(1)$; these are the discrepancy versions of\nEquations~\\eqref{eq:orig-32} and~\\eqref{eq:orig-31}.\nThe remaining regular terms and covering transfers have the smaller\nallowances specified there.  Apply these bounds to the bulk and to\neach period, using the dominating discrepancies $u_j$ and\n$v_j^{\\mathrm{src}}$.  Extract $c_{2,j}-c_{1,j}$ from the bulk\ndegree-one output coefficients.  Its source-discrepancy contribution\nretains that degree-one transfer gain; its remaining contribution is\nbounded by\n$C_LR_j^{\\mathrm{src}}(\\log H_j)^D\n(u_j+\\epsilon_h+|\\lambda_j|/H_j)$.\n\nNow perform the alignment subtraction and the scalar division on\neach period with these bulk coefficients.  The extraction paragraph\nof the source proof controls the subtraction, off-mask corrections,\nand winding compensations by fixed multiples of their coefficient\nbounds, with spare support exponents paying for the declared winding\nrecords.  Changing the scalar argument in an already small\npositive-degree remainder has the additional source cap and is\ncontrolled by the spare analytic radius.  Thus these operations\npreserve the preceding contraction gains up to multipliers fixed\nbefore the choice of $L$, using the already reserved support exponents.\nAs in the closure of that proof, choose $L$ sufficiently large for\nthese multipliers and then $H$ sufficiently large for the remaining\nallowances.  The resulting common contraction is still $q_s<1$.\nThe precise-coupling discrepancy and history forcing are shared\nacross the family.  Taking its supremum after these normalization\nestimates proves the first line of\n\\eqref{eq:offset-source-comparison}; the bulk extraction estimate\nproves the second line.  This is the simultaneous interpretation of\nEquation~\\eqref{eq:orig-29}, rather than an inference from a\nfinite-period discrepancy alone.\nThe source-free output and the source-independent bulk scalar are\nunchanged.  No terminal-coupling equality is a hypothesis of that\nproposition.\n\\end{proof}\n\nFor each run define the positive field factor\n\\begin{equation}\n B_{P,N,s}=\\prod_{j=1}^N c_{P,N,s,j}^{-1}.\n \\label{eq:offset-field-factor}\n\\end{equation}\nWe suppress some subscripts when a run has already been specified.\n\n\\begin{lemma}[Fixed-layer sources and comparison of products]\n\\label{lem:source-comparison}\nUnder \\eqref{eq:field-offset-matching}, the source discrepancy tends to\nzero at each fixed layer, and $c_{2,j}-c_{1,j}\\to0$ at every fixed step\n$j\\ge1$.  In addition, for the ordinary and either inclined run at the\nsame $N$ and $\\beta=\\vartheta_N+s$, with $|s|\\le R$, one has\n\\begin{equation}\n C_H^{-1}\\le \\frac{B_{\\pm,N,s}}{B_{r,N,s}}\\le C_H,\n \\label{eq:offset-product-ratio}\n\\end{equation}\nuniformly in $N$.  The products for the two mirror inclinations agree.\n\\end{lemma}\n\n\\begin{proof}\nThe normalized discrepancies are uniformly bounded by the caps.\nFor a fixed $j$, Proposition~\\ref{prop:matching} makes the source-free\nforcing in \\eqref{eq:offset-source-comparison} tend to zero; the shared\nordinary history also gives $\\epsilon_h\\to0$.  Therefore the upper\nlimits $V_j=\\limsup v_j^{\\mathrm{src}}$ satisfy\n$V_{j-1}\\le q_sV_j$.  For every $m$, $V_j\\le q_s^mV_{j+m}$;\nboundedness and $q_s<1$ imply $V_j=0$.  The second line of\n\\eqref{eq:offset-source-comparison} proves the scalar assertion.\n\nFor the product estimate, compare the ordinary and inclined runs after\ntheir first steps, identifying their output coordinates.  Admission\ngives $|\\lambda_j|\\le C_{L,R'}$.  If $x=N-1-j$, the common-tail history\nforcing is bounded by $C_LH_N^D r_0^x$, with $r_0<1$.  Iteration of the\nshape recurrence used in Proposition~\\ref{prop:matching} gives, after\nenlarging a fixed exponent $D$ and choosing $r_1\\in(0,1)$,\n\\begin{equation}\n u_j\\le \\min\\{C,C_LH_N^D r_1^x\\}\n             +C_L\\frac{(\\log H_j)^D}{H_j}.\n \\label{eq:offset-clipped-comparison}\n\\end{equation}\nHere one first sums the geometric convolution and then uses the uniform\ncap.  The logarithmic ratio is eventually decreasing as its argument\nincreases, so its convolution has the displayed bound.  Finitely many\nsmaller arguments are absorbed in $C_L$.\nApplying \\eqref{eq:offset-source-comparison} gives the same bound for\n$v_j^{\\mathrm{src}}$ and\n$|c_{\\pm,j}-c_{r,j}|/R_j^{\\mathrm{src}}$, with possibly larger $D$ and\n$r_1<1$.  The latter quantity can be clipped because each coefficient\nsatisfies \\eqref{eq:offset-source-substitution}.\n\nThe second term is summable after multiplication by the source cap:\n\\[\n \\sum_{j\\ge1}R_j^{\\mathrm{src}}\n               \\frac{(\\log H_j)^D}{H_j}\n =\\sum_{j\\ge1}\\frac{(\\log H_j)^D}{H_j^{11/10}}<\\infty.\n\\]\nFor the first term, split the steps at\n$J_N=\\lceil K\\log H_N\\rceil$.  On the first $J_N$ steps from the top,\nthe clipped estimate costs at most\n$C_H H_N^{-1/10}\\log H_N$ for large $N$.  The remaining steps cost at\nmost\n\\[\n C_H H_N^D\\sum_{x\\ge J_N}r_1^x\n \\le C_H H_N^{D+K\\log r_1}.\n\\]\nChoose $K$ so that the exponent is negative.  The first step itself is\ncontrolled by the individual scalar bounds, and finitely many small\ndepths have a uniform bound.  Thus\n$\\sum_{j=1}^N|c_{\\pm,j}-c_{r,j}|\\le C_H$.\nAll coefficients lie in a fixed positive neighborhood of one, so the\nsum of the logarithmic differences is bounded as well.  Exponentiation\nproves \\eqref{eq:offset-product-ratio}.  This is the argument of\nProposition~\\ref{prop:source-products}, with bounded-offset admission\nsupplying its bound on $\\lambda_j$.  Reflection covariance of the\nbulk source rule gives equality for the mirror inclinations.\n\\end{proof}\n\n\\subsection{Physical embeddings and moment bounds}\n\nWe give the terminal lattice spacing the physical value one.  Set\n\\begin{equation}\n a_{r,N}=L^{-N},\\quad U_r=\\mathrm{Id},\\qquad\n a_{\\pm,N}=\\tfrac15L^{-N},\\quad U_\\pm=O_\\pm^{-1}.\n \\label{eq:offset-embeddings}\n\\end{equation}\nThus an inclined first step brings the coarse frame to the standard\nframe.  With $a=a_{P,N}$, $U=U_P$, and $B=B_{P,N,s}$, define\n\\begin{equation}\n \\phi_{P,N,s}(f)=Ba^2\\sum_{x\\in\\Z^2} f(aUx)\\cdot q_x.\n \\label{eq:offset-fields}\n\\end{equation}\nOn a torus the sum is over its periodic microscopic lattice.  For\ncomponents $\\boldsymbol\\alpha=(\\alpha_1,\\ldots,\\alpha_n)$, let\n\\begin{equation}\n S_{n,P,N,s}^{\\boldsymbol\\alpha,\\mathrm{cut}}\n =(Ba^2)^n\\sum_{x_1,\\ldots,x_n}\n       \\E\\!\\left[\\prod_{i=1}^nq_{x_i}^{\\alpha_i}\\right]\n       \\delta_{(aUx_1,\\ldots,aUx_n)}.\n \\label{eq:offset-moment-measures}\n\\end{equation}\nThe expectation is taken in the periodic plane state unless a torus is\nspecified.  We use the common physical square tori of side\n$M_k=2^kM_0$ supplied by Lemma~\\ref{lem:inclined-periods}.\n\n\\begin{lemma}[Uniform moment bounds]\n\\label{lem:moment-bound}\nFor $N\\ge K_0$, $|s|\\le R$, and every type $P$, the component moment\nmeasures in the plane are nonnegative and satisfy\n\\begin{equation}\n S_{n,P,N,s}^{\\boldsymbol\\alpha,\\mathrm{cut}}\n      (Q_1\\times\\cdots\\times Q_n)\\le C^n n!\n \\label{eq:offset-unit-box-bound}\n\\end{equation}\nfor arbitrary physical unit boxes.  The ordinary torus estimates are\nuniform in $k$; on each fixed inclined physical torus the same assertion\nholds with a constant depending on that torus.\n\nEvery fixed finite list of real bounded compactly supported vector\ntests consequently has a joint exponential moment in a neighborhood\nof zero, uniformly in these cutoffs.  The same is true of continuous\nperiodic tests on each fixed torus.\n\\end{lemma}\n\n\\begin{proof}\nComponent positivity is Lemma~\\ref{lem:finite-beta}; its finite-volume\nexpansion also applies to the compatible oblique periods.\nFor an ordinary run, choose at most $J$ terminal sites and denote their\ndescendant-cell vector field sums by $X_Y$.  Iterating\n\\eqref{eq:offset-source-substitution} identifies $z_Y$ with the source\ncoupled to $X_Y$.  After removal of its source-independent scalar, the\nterminal source density is\n\\[\n e^{X(V)}e^{\\sum_Yz_Y\\cdot V_Y+G(V;z)}\\Xi_z(V).\n\\]\nLet $Z$ be its normalizer at $z=0$, and let $\\mu_0$ be that source-free\nterminal probability law.  At $|z_Y^\\alpha|\\le1$, source anchoring gives\n$\\sup|G|\\le JR_0^{\\mathrm{src}}$.  Every changed covering label has\nsupport meeting one of the selected sites, whence\n\\[\n \\sum_\\lambda e^{2s_\\lambda}\n       \\norm{k_\\lambda(z)-k_\\lambda(0)}_\\infty\\le Jw_0.\n\\]\nThe two activity lists obey the combined cap with a fixed multiplier.\nThe base law is an ordinary positive endpoint law, reflection positive\nat the block seams, with $b_0\\in[H/2,2H]$.  Its periods have all the\nrequired divisibilities.  Proposition~\\ref{prop:integrated-comparison},\nwith the band verification in Lemma~\\ref{lem:band-endpoints},\ntherefore gives\n$Z^{-1}\\int e^X|\\Xi_z-\\Xi_0|\\le e^{Jw_0}-1$.\nAs in the proof of Proposition~\\ref{prop:moment-majorants}, subtraction\nof the endpoint-spin generating function yields the explicit bound\n\\begin{align}\n \\left|\\E e^{\\sum_Yz_Y\\cdot X_Y}\n       -\\E_{\\mu_0}e^{\\sum_Yz_Y\\cdot V_Y}\\right|\n &\\le e^{3J}(e^{JR_0^{\\mathrm{src}}}-1)\n       +e^{3J+JR_0^{\\mathrm{src}}}(e^{Jw_0}-1)\\notag\\\\\n &=o_H(1)\n \\label{eq:offset-local-source-bound}\n\\end{align}\nfor fixed $J$, uniformly in depth, offset, and ordinary torus size.\n\nFor one real component cell sum $X$, the bounds at sources $1$ and $-1$\ngive $\\E e^{|X|}\\le C_0$ and $\\E|X|^n\\le C_0n!$.  H\\\"older's\ninequality bounds a product of $n$ such sums by $C^nn!$.  Every unit box\nis covered by a bounded number of terminal cells.  Positivity of the\ncomponent measures extends this estimate to the product of any $n$\nunit boxes, at an exponential cost in $n$.  Periodic coverings handle\nboxes crossing a period.  At fixed cutoff the cell sums are bounded\nlocal observables, so the estimates pass to the plane.\n\nFor an inclined field in the plane, use the ordinary field at the same\nbare coupling and depth.  For either sign, the exact relation is\n\\[\n \\phi_{\\pm,N,s}(f)\n =\\frac{B_{\\pm,N,s}}{25B_{r,N,s}}\n       \\phi_{r,N,s}\\bigl(f(O_\\pm^{-1}\\,\\cdot/5)\\bigr).\n\\]\nLemma~\\ref{lem:source-comparison} and a bounded covering of the dilated,\nrotated boxes prove \\eqref{eq:offset-unit-box-bound} in the plane.\n\nOn a fixed inclined physical torus, the terminal volume is fixed.\nAbsolute coefficient norms and support-hit bounds bound the entire\nsource numerator in that volume.  The source-free normalizer is\nuniformly bounded below after its bulk scalar has been removed:\nintegrate all spins in one sufficiently small spherical cap.  There\nare then no bad bonds, the mandatory gas has empty inventory and\n$\\Xi=1$, and the regular and kinetic exponents are bounded.  Positivity\nof the complete source-free density permits this restriction of the\nintegral.  The resulting bound can depend on the fixed terminal volume\nand on $H$, but not on $N$ or $s$.  The inclined descendants of a\nterminal site remain within a fixed distance of its center, and every\nphysical unit box meets descendants of only boundedly many sites.\nThe preceding one-cell and covering argument now proves the asserted\nfixed-torus estimate.  Reflection positivity of inclined steps is not\nused.\n\nFinally consider a real linear combination $Y$ of a finite list of\nfield smearings.  For even $n$, expand components and integrate the\nabsolute test coefficients against the nonnegative measures in\n\\eqref{eq:offset-unit-box-bound}.  A fixed bounded support can be\ncovered by finitely many unit boxes, so\n$\\E|Y|^n\\le C_Y^n n!$.  Odd absolute moments follow by Cauchy--Schwarz\nfrom the adjacent even moments, since\n$\\sqrt{(n-1)!(n+1)!}\\le\\sqrt2\\,n!$ for odd $n\\ge1$.\nIncreasing $C_Y$ gives the estimate at every order.  The exponential\nseries converges for a sufficiently small positive argument.\nH\\\"older's inequality gives the same conclusion for the sum of the\nabsolute values of the finite list.  This proves the assertions for\nsigned as well as nonnegative tests.\n\\end{proof}\n\n\\subsection{Convergence at a limiting offset}\n\nThe bounds just established allow us to pass from descendants of fixed\ncoarse cells to arbitrary compact tests.  We first work on a fixed\nphysical torus, where the density and source comparisons control\nnormalized integrals directly.  The uniform volume estimate then\nidentifies the plane limit.\n\n\\begin{proposition}[Limits and comparison at fixed offsets]\n\\label{prop:offset-limits}\nLet $N\\to\\infty$ and $s\\to s_*$ with $|s|\\le R$, for a fixed type\n$P$.  The fields \\eqref{eq:offset-fields} converge jointly in law and\nin all joint moments for every finite list of continuous compactly\nsupported vector tests in the plane.  The analogous assertions hold\nfor continuous periodic tests on each fixed common physical torus.\nThe limiting laws are determined by their moments.\n\nTwo such sequences satisfying \\eqref{eq:field-offset-matching} have\nthe same limits in their specified embeddings.  At every order and\ncomponent choice, the plane measures\n\\eqref{eq:offset-moment-measures} converge on continuous compact tests\nand in the strong topology of $\\mathcal S'((\\R^2)^n)$.\n\nDenote the ordinary limiting hierarchy at offset $s\\in I$ by $F_s$,\nincluding its moment-determined joint laws.  For either inclined type,\nthe limit at offset $s$ is\n\\begin{equation}\n F_{s+d},\\qquad s,s+d\\in I,\\qquad d=A_+-A_r=A_--A_r.\n \\label{eq:inclined-offset-limit}\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nFix a common physical torus and a layer $j$ below the initial inclined\nstep, if present.  Its grid has mesh $L^{-j}$.  Align the grids across\nthe runs and use the same ordinary coordinate prescriptions on their\ncommon tail.  These alignments are allowed by the translation covariance\nof the blocking.  Translating a microscopic origin has, by microscopic\ntranslation invariance, the same effect on the moment measures as a\ntranslation of size $O(a)$.  Lemma~\\ref{lem:moment-bound} and uniform\ncontinuity make this residual error vanish on continuous tests.\n\nAssign a test's value at a layer-$j$ center to every microscopic\ndescendant of that center.  The exact source multiplier for these cell\ntests is\n\\begin{equation}\n L^{-2j}\\prod_{i=1}^j c_i^{-1}.\n \\label{eq:offset-cell-multiplier}\n\\end{equation}\nIndeed its microscopic coefficient is $Ba^2$, and each traversed step\nmultiplies it by $l_i^2c_i$.  For the traversed steps in either geometry,\n\\[\n a^2\\prod_{i=j+1}^N l_i^2=L^{-2j},\\qquad\n B\\prod_{i=j+1}^N c_i=\\prod_{i=1}^j c_i^{-1}.\n\\]\nTheir product is \\eqref{eq:offset-cell-multiplier}; the area factor of\nthe inclined first step is already included in $a^2$.\nOnly a fixed number of scalar factors occur, so this multiplier is\nbounded, bounded away from zero, and compares between matched runs by\nLemma~\\ref{lem:source-comparison}.\n\nAt this fixed layer the period and its number of sites are fixed.\nThe coefficient norms for canonical records sum their path\ncoefficients; the masked regular norms bound the sums of their\nsuprema; and the covering norms bound the activity sums at a support\nhit.  Proposition~\\ref{prop:matching} therefore makes the differences\nof the regular exponents and the absolutely convergent covering series\ntend to zero.  The free-history bounds give the same assertion for\nthe kinetic kernels.  Their masks and reference thresholds are common,\nand $b_j$ remains in a fixed band.  Thus the source-free exponents\nare bounded and compare uniformly after their volume scalars have\nbeen discarded.  The source-list assertion in\nLemma~\\ref{lem:source-comparison} gives the corresponding comparison\nof the positive-degree terms in absolute coefficient norms.\n\nFor a finite list of cell tests choose one sufficiently small complex\npolydisc in their source variables so that all substituted arguments\nlie in a smaller source polydisc at layer $j$.  The absolute series\nbounds control perturbations of these arguments as the multiplier\n\\eqref{eq:offset-cell-multiplier} varies.  The source-free denominators\nhave a uniform positive lower bound by the same small-cap argument\nas in Lemma~\\ref{lem:moment-bound}, now at layer $j$.  Since the removed\nvolume scalars are source independent, they cancel in the ratios.\nThe normalized generating functions of the cell tests consequently\ndiffer by a quantity tending uniformly to zero on this polydisc.\nCauchy's formula proves comparison of all fixed joint moments.\n\nEvery microscopic descendant lies within $CL^{-j}$ of its layer-$j$\ncenter, for both cell geometries in the embeddings\n\\eqref{eq:offset-embeddings}.  On the fixed torus, replacing a continuous\ntest by these cell values therefore has an error bounded by its modulus\nof continuity at $CL^{-j}$.  Telescoping a product of tests one factor\nat a time and using \\eqref{eq:offset-unit-box-bound} bounds the moment\nerror by a constant times these moduli, uniformly in depth.  First let\nthe depths tend to infinity at fixed $j$, and then let $j\\to\\infty$.\nThis proves moment comparison for continuous tests.  Comparing two\narbitrary subsequences of one sequence proves the Cauchy criterion,\nhence moment convergence.\n\nThe factorial bounds of Lemma~\\ref{lem:moment-bound} imply tightness for\neach finite list of real field variables and uniform integrability of\nevery polynomial in them.  Each subsequential law has the moments just\nconstructed and an exponential moment in a neighborhood of the origin.\nIts moment generating function is analytic there and is determined by\nthose moments; this determines the law.  All subsequential laws thus\ncoincide, proving joint convergence in law on the fixed torus.\n\nFor the plane, let\n$\\exp(i\\sum_{\\nu=1}^r t_\\nu\\phi_{P,N,s}(f_\\nu))$ be a characteristic\nfunction observable.  Its modulus is one regardless of $B$, and its\nmicroscopic support is the support of the smearings.  For sufficiently\nlarge $k$ this support satisfies the lift and half-size conditions in\nProposition~\\ref{prop:uniform-volume}, including the shifted-period\ndescription in Lemma~\\ref{lem:inclined-periods}.  That proposition gives\n\\begin{equation}\n \\left|\\E_{\\mathrm{plane}}e^{i\\sum_\\nu t_\\nu\\phi(f_\\nu)}\n -\\E_{\\mathrm{torus}(k)}e^{i\\sum_\\nu t_\\nu\\phi(f_\\nu)}\\right|\n \\le Ce^{-c2^k},\n \\label{eq:offset-characteristic-comparison}\n\\end{equation}\nuniformly in the cutoff and offset.  Plane tightness follows from the\nplane moment bound.  Every subsequential plane characteristic function\nis, for each fixed $k$, within $Ce^{-c2^k}$ of the already constructed\nfixed-torus limit.  Letting $k\\to\\infty$ identifies all subsequential\nplane laws and gives their equality for matched runs.  Plane uniform\nintegrability then gives convergence of all joint moments.  Only bounded\nobservables enter \\eqref{eq:offset-characteristic-comparison}; no bound\nuniform in increasing inclined torus size is needed.\n\nFinite sums of product tests are uniformly dense on a product of\ncompact insertion-coordinate sets.  The component measures have\nuniformly bounded mass there by \\eqref{eq:offset-unit-box-bound}.\nThe joint moment convergence therefore gives convergence against every\ncontinuous compact test to a nonnegative Radon measure.  Summing unit\nboxes with the integrable weights $(1+|y_i|^2)^{-2}$ gives, at fixed\norder $n$, the common bound\n\\begin{equation}\n |S_n^{\\boldsymbol\\alpha,\\mathrm{cut}}(f)|,\n \\ |F_s^{\\boldsymbol\\alpha}(f)|\n \\le C^n n!\\,p_n(f),\\qquad\n p_n(f)=\\sup_{y_1,\\ldots,y_n}|f(y)|\n                    \\prod_{i=1}^n(1+|y_i|^2)^2.\n \\label{eq:offset-schwartz-bound}\n\\end{equation}\nThis proves temperedness and convergence on Schwartz tests by cutting\noff the tails.\n\nFor completeness, this convergence is strong: it is uniform on each\nbounded subset $\\mathcal B$ of Schwartz space.  The higher weighted\nseminorms bounded on $\\mathcal B$ make the discarded tails tend to zero\nuniformly in $p_n$.  On a fixed compact set, restrictions of\n$\\mathcal B$ are uniformly bounded and equicontinuous, hence have\nfinite uniform-norm nets.  Convergence for the finitely many net\nelements and the common compact mass bound make the error uniform on\n$\\mathcal B$.  Combining these two statements proves the strong\ntopology assertion.\n\nFinally, a type $\\pm$ sequence with offset tending to $s$ and an\nordinary sequence with offset tending to $s+d$ satisfy\n\\eqref{eq:field-offset-matching}.  This proves\n\\eqref{eq:inclined-offset-limit}.\n\\end{proof}\n\n\\subsection{The relative two-point measure}\n\nWe next pass from local convergence to susceptibility and second-moment\nlength.  Write $S_2^{\\mathrm{cut}}$ for the first-component two-point\nmeasure of a run.  In coordinates $(y_1,z)=(y_1,y_2-y_1)$, microscopic\ntranslation invariance gives the exact factorization\n\\begin{equation}\n S_2^{\\mathrm{cut}}=\\alpha_a\\otimes\\nu^{\\mathrm{cut}},\\qquad\n \\alpha_a=a^2\\sum_{x\\in\\Z^2}\\delta_{aUx},\\qquad\n \\nu^{\\mathrm{cut}}=B^2a^2\\sum_{x\\in\\Z^2}\n                          C_\\beta(x)\\delta_{aUx}.\n \\label{eq:relative-factorization}\n\\end{equation}\nThe first factor tends vaguely to Lebesgue measure.  The second carries\nthe susceptibility and length, so we must control its mass at large\nrelative separation.\n\n\\begin{lemma}[Uniform two-point tails]\n\\label{lem:two-point-tails}\nThere are constants $C,c>0$, uniform over $N\\ge K_0$, $|s|\\le R$, and\nthe three types, such that for physical unit boxes with center\nseparation $T$,\n\\begin{equation}\n S_2^{\\mathrm{cut}}(Q\\times Q')\\le Ce^{-cT}.\n \\label{eq:offset-two-point-decay}\n\\end{equation}\nConsequently, for a unit box $Q_u$ centered at $u$,\n\\begin{equation}\n \\nu^{\\mathrm{cut}}(Q_u)\\le Ce^{-c|u|}.\n \\label{eq:relative-unit-tail}\n\\end{equation}\nAlong each convergent-offset sequence of\nProposition~\\ref{prop:offset-limits}, the measures\n$\\nu^{\\mathrm{cut}}$ converge vaguely, in total mass, and in second\nmoment to a finite nonnegative measure $\\nu$.  Its tensor product\nwith Lebesgue measure is the limiting first-component two-point\nmeasure in the relative coordinates.\n\\end{lemma}\n\n\\begin{proof}\nLet $X_Q$ and $X_{Q'}$ be the first-component field sums over the two\nboxes.  Internal symmetry gives zero means; their fourth moments are\nuniformly bounded by Lemma~\\ref{lem:moment-bound}.  Write\n$T_D(x)=\\max\\{-D,\\min\\{x,D\\}\\}$ for odd truncation.  Then\n\\[\n \\norm{X_Q-T_D(X_Q)}_{L^2}^2\n \\le D^{-2}\\E|X_Q|^4\\le C D^{-2}.\n\\]\nThe second moments of the other factors are bounded, so Cauchy--Schwarz\nshows that replacing both variables by their truncations changes their\ncovariance by at most $C/D$.\n\nFor sufficiently large $T$, at least one microscopic axial direction\nseparates the supports by $(c_1T-C_1)/a$ lattice steps.  Apply the slab\ncovariance bound in Proposition~\\ref{prop:uniform-volume} to the\ntruncated variables.  Since $aL^N$ equals either $1$ or $1/5$, it gives\n\\[\n |\\operatorname{Cov}(T_D(X_Q),T_D(X_{Q'}))|\n       \\le CD^2e^{-c_2T}.\n\\]\nChoosing $D=e^{c_2T/4}$ proves\n\\eqref{eq:offset-two-point-decay}; a larger constant covers bounded\nseparations.\n\nFix a unit box $Q$ near the origin.  For small $a$, $\\alpha_a(Q)$ is\nbounded below by a positive constant.  Positivity and\n\\eqref{eq:relative-factorization} imply\n\\[\n \\alpha_a(Q)\\nu^{\\mathrm{cut}}(Q_u)\n       \\le S_2^{\\mathrm{cut}}\\bigl(Q\\times(Q+Q_u)\\bigr).\n\\]\nThe Minkowski sum on the right has a bounded unit-box covering, whose\ncenters lie at bounded distance from $u$.  Thus\n\\eqref{eq:offset-two-point-decay} proves\n\\eqref{eq:relative-unit-tail}.  Enlarge $K_0$ if necessary to have this\nuniform lower bound for $\\alpha_a(Q)$ throughout.\n\nTo identify the vague limit, choose a continuous compactly supported\nfunction $h$ with $\\int h=1$.  For every continuous compactly supported\n$g$ of the relative coordinate,\n\\[\n S_2^{\\mathrm{cut}}\\bigl(h(y_1)g(y_2-y_1)\\bigr)\n       =\\alpha_a(h)\\nu^{\\mathrm{cut}}(g),\\qquad\n \\alpha_a(h)\\longrightarrow1.\n\\]\nProposition~\\ref{prop:offset-limits} supplies the limit of the left\nside.  The local bounds in \\eqref{eq:relative-unit-tail} then identify\na unique nonnegative Radon measure $\\nu$.  Product tests in relative\ncoordinates identify the limiting two-point measure with\n$\\dd y_1\\otimes\\nu$.  Finally \\eqref{eq:relative-unit-tail}, summed\nover boxes, makes both $1$ and $|z|^2$ uniformly integrable outside\nlarge balls.  Vague convergence with this tail control proves\nconvergence of mass and second moment.\n\\end{proof}\n\nFor the ordinary hierarchy $F_s$, denote this relative measure by\n$\\nu_s$.  Its candidate susceptibility and length are\n\\begin{equation}\n m(s)=\\nu_s(\\R^2),\\qquad\n \\ell(s)^2=\\frac{1}{4m(s)}\\int |z|^2\\,\\dd\\nu_s(z),\n \\label{eq:offset-mass-length}\n\\end{equation}\nwhere the second definition is made after the positivity established\nbelow.  Finiteness already follows from Lemma~\\ref{lem:two-point-tails}.\n\n\\subsection{Positive susceptibility and length}\n\n\\begin{proposition}[Canonical normalization at a fixed offset]\n\\label{prop:normalization}\nFor every $s\\in I$, the quantities $m(s)$ and $\\ell(s)$ in\n\\eqref{eq:offset-mass-length} are finite and strictly positive.\nFor an ordinary sequence with $N\\to\\infty$ and offset tending to $s$,\n\\begin{equation}\n B^2a^2\\chi_\\beta\\longrightarrow m(s),\\qquad\n a\\xi_\\beta\\longrightarrow\\ell(s).\n \\label{eq:offset-cutoff-normalization}\n\\end{equation}\nFor an inclined sequence with limiting offset $s$ and $s,s+d\\in I$,\nthe limits are $m(s+d)$ and $\\ell(s+d)$.\n\nDefine $G_s$ by applying to $F_s$ the change of smeared fields\n\\begin{equation}\n \\phi(f)\\longmapsto\\frac{1}{\\ell(s)\\sqrt{m(s)}}\n                     \\phi\\bigl(f(\\,\\cdot/\\ell(s))\\bigr).\n \\label{eq:canonical-hierarchy-normalization}\n\\end{equation}\nThis is the unique positive constant length and field rescaling that\nsets both susceptibility and second-moment length to one.\nAlong the ordinary sequences in\n\\eqref{eq:offset-cutoff-normalization}, the canonical cutoff fields\n$\\Psi_\\beta$ converge to $G_s$ in joint law and all joint moments, and\ntheir component Schwinger measures converge strongly in $\\mathcal S'$\nat every order.  The analogous normalized inclined fields converge to\n$G_{s+d}$.\n\nThe functions $m,\\ell$ are continuous on $I$.  At every order and\ncomponent choice, evaluation of $F_s$ and of $G_s$ on any fixed\nSchwartz test is continuous in $s$.\n\\end{proposition}\n\n\\begin{proof}\nChoose two ordinary terminal cells in a fixed bounded region, with\npositive distance between their closures.  Their two terminal sites\ncan be joined by a path of fixed length $r$.  On an admitted axis torus,\nCorollary~\\ref{cor:terminal-alignment} applies to the source-free\nendpoint law in the full band $[H/2,2H]$ by\nLemma~\\ref{lem:band-endpoints}.  Its hypotheses are precisely\nthe ordinary positive, seam-reflection-positive endpoint hypotheses\nused above for \\eqref{eq:offset-local-source-bound}.  Since\n$t_0\\to0$ and $Ht_0^2=p_0^2\\to\\infty$ as $H\\to\\infty$,\n\\[\n \\E_{\\mu_0}|V_{Y_1}-V_{Y_2}|^2\n \\le r^2\\bigl(t_0^2+4e^{-cHt_0^2}\\bigr)=o_H(1).\n\\]\nInternal $O(3)$ invariance gives\n\\[\n \\E_{\\mu_0}[V_{Y_1}^1V_{Y_2}^1]\n   =\\frac13\\E_{\\mu_0}[V_{Y_1}\\cdot V_{Y_2}]\n   =\\frac13+o_H(1).\n\\]\nCauchy's formula applied to\n\\eqref{eq:offset-local-source-bound} transfers this estimate to the\ncutoff cell sums $X_1,X_2$:\n\\begin{equation}\n \\E[X_1X_2]=\\frac13+o_H(1),\n \\label{eq:offset-separated-cell-lower}\n\\end{equation}\nuniformly in depth, offset, and admitted axis torus.  The large fixed\nchoice of $H$ makes the right side at least a constant $c_0>0$.\nPassing to the plane at each fixed cutoff preserves this bound.\n\nChoose nonnegative continuous compact tests that dominate the indicators\nof the two cells and whose supports still have positive separation.\nThe cell descendants lie in fixed such boxes for all sufficiently large\ndepths.  Positivity transfers \\eqref{eq:offset-separated-cell-lower}\nto these continuous tests, and compact-test convergence passes it to\nthe limiting two-point measure.  In relative coordinates this gives\nnonzero $\\nu_s$-mass a positive distance from the origin.  Hence both\n$m(s)>0$ and $\\int|z|^2\\,\\dd\\nu_s(z)>0$.  This also explains why a\nlower bound only at coincident points would not suffice for the length.\n\nAt the cutoff, \\eqref{eq:relative-factorization} has total relative\nmass and length\n\\begin{equation}\n m^{\\mathrm{cut}}=B^2a^2\\chi_\\beta,\\qquad\n \\ell^{\\mathrm{cut}}=a\\xi_\\beta.\n \\label{eq:cutoff-mass-length-identity}\n\\end{equation}\nLemma~\\ref{lem:two-point-tails} gives convergence of their numerators\nand denominators, with the latter limit positive.  This proves\n\\eqref{eq:offset-cutoff-normalization}.  The inclined conclusion follows\nfrom \\eqref{eq:inclined-offset-limit} and the same tail lemma.\n\nTo check normalization and uniqueness, let a translation-invariant\nhierarchy have relative two-point measure $\\nu$, susceptibility $m>0$,\nand length $\\ell>0$.  For positive constants $b,l$, the change\n$\\phi'(f)=b\\phi(f(\\cdot/l))$ sends its relative measure to\n\\[\n \\nu'=b^2l^2(D_{1/l})_*\\nu,\\qquad D_{1/l}(z)=z/l.\n\\]\nThe factor $l^2$ is the Jacobian of the first insertion coordinate.\nThus $m'=b^2l^2m$ and $\\ell'=\\ell/l$.  Requiring $m'=\\ell'=1$\nforces $l=\\ell$ and $b=(\\ell\\sqrt m)^{-1}$, which proves\n\\eqref{eq:canonical-hierarchy-normalization} and uniqueness among\npositive constants.  An overall field sign, if permitted, gives the\nsame hierarchy by internal symmetry.\n\nApply this calculation before taking the limit, using\n\\eqref{eq:cutoff-mass-length-identity}.  The normalized field is exactly\n\\begin{equation}\n \\frac{1}{\\ell^{\\mathrm{cut}}\\sqrt{m^{\\mathrm{cut}}}}\n       \\phi_{P,N,s}\\bigl(f(\\,\\cdot/\\ell^{\\mathrm{cut}})\\bigr)\n  =\\frac{1}{\\xi_\\beta\\sqrt{\\chi_\\beta}}\n       \\sum_x f(U_Px/\\xi_\\beta)\\cdot q_x.\n \\label{eq:canonical-cutoff-identity}\n\\end{equation}\nFor $P=r$ this is $\\Psi_\\beta$; for $P=\\pm$ it is the corresponding\nspatial rotation of the same canonical field.\n\nThe limits of $m^{\\mathrm{cut}}$ and $\\ell^{\\mathrm{cut}}$ are positive\nand finite.  The scalar multipliers in\n\\eqref{eq:canonical-cutoff-identity} are therefore bounded, and the\nvarying dilations of a continuous compact test converge uniformly\nwith supports in a common compact set.  The moment bound proves that\nthis varying-test error tends to zero.  It also gives uniform box\nbounds for the normalized measures: the inverse dilation of a unit box\nis covered by boundedly many old unit boxes.  Factorial moments then\ngive joint-law convergence and uniform integrability just as in\nProposition~\\ref{prop:offset-limits}.  The common weighted bound and\nfinite-net argument in that proposition give strong Schwartz\nconvergence for the normalized measures as well.\n\nFinally let $s_i\\to s$ in $I$.  For any one of the scalar quantities\n$m,\\ell$ or a fixed component Schwinger evaluation of $F$ or $G$, choose\na depth $N_i\\ge i$ at offset $s_i$ for which the corresponding cutoff\nquantity approximates its limit at $s_i$ within $1/i$.  The convergence\nstatements above apply to this sequence because its offsets tend to\n$s$; they identify its limit with the quantity at $s$.  This proves\nall the asserted continuities without assuming uniform convergence in\nthe offset in advance.\n\\end{proof}\n\n\\begin{lemma}[Normalization of the shooting hierarchy]\n\\label{lem:shooting-normalization}\nThe shooting hierarchy constructed in\nProposition~\\ref{prop:continuum-limit} has finite positive susceptibility\nand finite positive second-moment length.  Applying the unique positive\nrescaling \\eqref{eq:canonical-hierarchy-normalization} to it is the\nlimit of applying canonical susceptibility and length normalization to\nits cutoff fields.  These assertions also hold when its sufficiently\nlarge auxiliary parameters are fixed independently of those used for\nthe offset trajectories.\n\\end{lemma}\n\n\\begin{proof}\nUse the shooting prescription's own constants and physical mesh\n$a_N=L^{-N}$.  Theorem~\\ref{thm:trace-uniform} supplies its slab\ncovariance estimate at scale $L^N$, and\nProposition~\\ref{prop:moment-majorants} supplies uniform fourth moments\nand all-order factorial bounds.  The factorization\n\\eqref{eq:relative-factorization} is an exact lattice identity, so the\nproof of Lemma~\\ref{lem:two-point-tails} applies with these constants.\nProposition~\\ref{prop:continuum-limit} then identifies the relative\nmeasure and gives convergence of its mass and second moment.\nEquation~\\eqref{eq:terminal-cell-moments} in the proof of\nProposition~\\ref{prop:non-gaussian} gives a uniform positive two-point\nexpectation on two separated terminal cells.  The continuous majorants\nused in the proof of Proposition~\\ref{prop:normalization} show that the\nlimiting relative measure has positive mass away from the origin.\nIts susceptibility and length are therefore strictly positive.\nThe varying-test argument and the exact cutoff identity\n\\eqref{eq:canonical-cutoff-identity} now prove the normalization claim.\nAll these inputs concern this shooting prescription itself, so no\nidentification of its auxiliary parameters with the offset construction\nis required.\n\\end{proof}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/free.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/free.tex", "bytes": 32912, "sha256": "a47bebe47fa4e62bf6181ebe7da695a90d087dd101485a28849b4d65328de716", "content": "\\section{Block observations and the free kernels}\n\\label{sec:free}\n\nThe exact integration in Section~\\ref{sec:rg} uses a positive decomposition\nof the quadratic energy and exponentially localized Gaussian kernels.\nWe construct these objects for ordinary square blocks and for one inclined\ninitial block.  The inclined construction is needed for the rotational\ncomparison in Section~\\ref{sec:continuum}.  Its geometry differs only at the\nfirst step; after sufficiently many common ordinary steps, the two free\nprecisions approach one another at a rate of order $L^{-2}$ per step.\nThe linear construction and positive decomposition are those of\n\\Rref{free:section}.  We prove the changes required by the inclined geometry\nand retain the stronger history estimate implicit in the proof of\n\\Rref{app:free-strip}.\n\nFix a sufficiently large dyadic integer $L$ and put $L_*=5L$.\nWe use the scales in Equation~\\eqref{setup:scale-definitions}, in\nparticular $\\gamma=(\\log L)/(2\\pi)$, and the history convention of\nSection~\\ref{setup:scales}.  Constants without an $L$ subscript\nin this section are uniform for all sufficiently large $L$ and all\nhistories.  Constants denoted by $C_L$ may depend on the fixed $L$.\nOnly finitely many derivative and exponential-moment orders are required\nin any application; these orders are fixed before $L$ is chosen.\n\n\\subsection{Cells, weighted means, and the spherical observation}\n\nUse the ordinary weights and spherical observation defined in\nSection~\\ref{setup:scales}.  Write $\\chi$ for their fixed even smooth\none-dimensional bump and $\\rho=0.14$ for its support radius after\nrescaling a block to side one.  The same bump is used in all the\nblockings below.  Coordinates in each lattice plane may be translated.\n\nFor the two inclined blockings, let\n\\[\n O_+=\\frac15\\begin{pmatrix}3&-4\\\\4&3\\end{pmatrix},\n \\qquad O_-=O_+^T,\n \\qquad c=\\left(\\frac12,\\frac12\\right).\n\\]\nFor $O\\in\\{O_+,O_-\\}$ and $Y\\in\\Z^2$, define\n\\[\n \\mathcal B_Y^O\n =\\left(c+L_*OY+O[-L_*/2,L_*/2)^2\\right)\\cap\\Z^2.\n\\]\nThe coarse site $Y$ is embedded at the center $c+L_*OY$.\nSample the same product bump in the $O$ coordinates of this cell and\nnormalize it.  If $w_Y(x)$ denotes either an ordinary or an inclined\nweight, the averaging operator is\n\\[\n (Qu)(Y)=\\sum_{x\\in\\mathcal B_Y}w_Y(x)u_x,\n \\qquad w_Y(x)\\ge0,\\qquad \\sum_xw_Y(x)=1.\n\\]\nFor a cell of side $l\\in\\{L,L_*\\}$, smooth sampling gives\n$\\max_xw_Y(x)\\le C l^{-2}$.  The centered first moment vanishes exactly.\nFor an ordinary cell this follows from coordinate reflections; for an\ninclined cell it follows from the quarter-turn symmetry proved below.\n\nFor inclined cells, define $m_Y$, $\\widehat m_Y$, and the independent\nconstituent tags by Equation~\\eqref{setup:unit-mean}, using these weights.\nThe normalized spherical observation is Equation~\\eqref{setup:observation}\nwith this $Q$.  It is again a probability kernel, since its normalizing\nintegral depends only on the length of the unit vector $\\widehat m_Y$.\nThe scalar free observation uses the same $Q$ and independent Gaussian\nnoise of variance one.\n\n\\begin{lemma}[Geometry and mean error for the inclined cells]\n\\label{lem:free-inclined-geometry}\nFor either choice of $O$, the cells $\\mathcal B_Y^O$ partition $\\Z^2$,\nhave exactly $L_*^2$ sites, and are connected by nearest-neighbor edges.\nThere are no fine sites on their boundaries.  Quarter turns about cell\ncenters preserve the blocking.  Reflection in a fine coordinate axis\ninterchanges the two inclined types, with the reflected choice of centers.\nEvery site of a cell can be joined to its central region by a path in the\ncell of length $O(L_*)$.  Moreover, for every spin field, every cell, and\nevery value of its tag,\n\\begin{equation}\n |m_Y-\\widehat m_Y|\n \\le \\frac{C}{L_*}\\sum_{e\\subset\\mathcal B_Y^O}r_e,\n \\qquad r_{\\{x,y\\}}=|q_x-q_y|.\n \\label{eq:orig-3}\n\\end{equation}\nThe ordinary cells satisfy the corresponding estimate with $L$ in place\nof $L_*$.\n\\end{lemma}\n\n\\begin{proof}\nThe two block-translation vectors are integral and their determinant is\n$L_*^2$.  For example, for $O_+$ they are $(3L,4L)$ and $(-4L,3L)$.\nMultiplying a boundary equation by $5$ gives an integral right-hand side\nand a half-integral left-hand side, because the center is $(1/2,1/2)$\nand each relevant signed sum of $3$ and $4$ is odd.  Thus no boundary\ncontains a fine site.  A half-open cell is a fundamental region for its\nblock-translation lattice, and its fine sites give one representative\nof each coset.  The number of sites is therefore its index $L_*^2$.\nA quarter turn about $c$ preserves $\\Z^2$, the rotated square, and its\nproduct weights.  The same holds about every translated center.\nCoordinate reflection changes $O_+$ into $O_-$ and transports the\ncenter and weights accordingly.\n\nFor connectivity, use the rotated coordinates relative to the cell\ncenter.  If both coordinates have large absolute value, one of the\nfour fine coordinate steps decreases both absolute values, each by\nat least $3/5$.  If only one coordinate is near a cell boundary, choose\na fine step decreasing that coordinate; the other coordinate has room\nfor its change, whose absolute value is at most $4/5$.  Repeating these\nmoves reaches a fixed smaller interior rectangle in $O(L_*)$ steps.\nThe same moves reach a bounded neighborhood of the center; paths of\nbounded length within the interior then join the central fine sites.\nAll moves remain in the cell.  This also supplies the cell paths used\nin the block alignment and coercivity estimates.\n\nIt remains to prove the quantitative mean estimate.  In fine coordinates,\nthe support of the weights lies in the square\n\\[\n A=\\{x\\in\\Z^2: |x_i-c_i|\\le (7/5)\\rho L_*,\\ i=1,2\\}\n\\]\ntranslated to the cell in question.  This square lies strictly within\nthe inclined cell: its rotated coordinate radius is at most\n$(49/25)\\rho L_*=0.2744L_*<L_*/2$.\nFor each ordered pair $x,y\\in A$, join $x$ to $y$ by first moving\nhorizontally and then vertically inside $A$.  In the weighted average\nof these paths, a horizontal edge in row $r$ has total load at most\n$\\sum_{x:x_2=r}w_Y(x)\\le C/L_*$.  A vertical edge in column $s$ has\nload at most $\\sum_{y:y_1=s}w_Y(y)\\le C/L_*$.  Hence\n\\[\n D_Y:=\\sum_{x,y}w_Y(x)w_Y(y)|q_x-q_y|\n \\le \\frac{C}{L_*}\\sum_{e\\subset A}r_e.\n\\]\nSince the spins have unit length,\n\\[\n 1-|m_Y|^2\n =\\frac12\\sum_{x,y}w_Y(x)w_Y(y)|q_x-q_y|^2\n \\le D_Y.\n\\]\nOn the normalized-mean branch,\n$|m_Y-\\widehat m_Y|=1-|m_Y|\\le1-|m_Y|^2$.\nOn the fallback branch, $|m_Y|<1/2$ and every possible constituent\nspin satisfies\n\\[\n |m_Y-q_{\\tau_Y}|\\le1+|m_Y|\n \\le2(1-|m_Y|^2)\\le2D_Y.\n\\]\nThis proves Equation~\\eqref{eq:orig-3} uniformly in the tag.\nThe coordinate-path argument on an ordinary square is identical.\n\\end{proof}\n\n\n\\begin{corollary}[Quadratic mean error under small chords]\n\\label{cor:free-small-chord-mean}\nFor either block geometry, suppose that $r_e\\le T_j$ on the within-cell\ncoordinate paths joining weighted sites in the proof of\nLemma~\\ref{lem:free-inclined-geometry}; in particular, this holds if all\nnearest-neighbor chords in the cell are at most $T_j$.\nFor $H\\ge H_0(L)$ the normalized-mean branch is forced and\n\\[\n |m_Y-\\widehat m_Y|\\le C_L T_j^2.\n\\]\nThe bound is uniform in the layer $j$ and the cell.\n\\end{corollary}\n\n\\begin{proof}\nFor either geometry these paths have length at most $Cl$, where\n$l\\in\\{L,L_*\\}$.  Thus any two sites of positive weight satisfy\n$|q_x-q_y|\\le ClT_j$, and the squared pair identity gives\n\\[\n 1-|m_Y|^2\n =\\frac12\\sum_{x,y}w_Y(x)w_Y(y)|q_x-q_y|^2\n \\le C l^2T_j^2\\le C_L T_j^2.\n\\]\nSince $T_j\\le H^{-49/100}$, taking $H\\ge H_0(L)$ makes the right-hand\nside less than $3/4$, uniformly in $j$.  Therefore $|m_Y|>1/2$ and\n$|m_Y-\\widehat m_Y|=1-|m_Y|\\le1-|m_Y|^2$, proving the assertion.\n\\end{proof}\n\n\\subsection{Scalar precisions and their dependence on the history}\n\nWe first specify the operator spaces and Fourier convention.  On a\nlattice $\\Lambda$ identified with $\\Z^2$ in its own frame, scalar fields\nbelong to $\\ell^2(\\Lambda;\\R)$ and oriented edge fields to\n$\\ell^2(\\Lambda;\\R^2)$.  The forward gradient and its symbol are\n\\[\n (d_\\mu u)(x)=u(x+e_\\mu)-u(x),\\qquad\n d_\\mu(k)=e^{ik_\\mu}-1,\n \\qquad D(k)=d(k)^*d(k)=4\\sum_{\\mu=1}^2\\sin^2(k_\\mu/2).\n\\]\nAdjoints use counting measure.  At a fixed coarse momentum, write\n$\\alpha$ for the fine alias index.  The one-cell fine norm is\n$l^2\\sum_\\alpha|u_\\alpha|^2$, while the coarse norm is $|V|^2$.\nConsequently, if $Qu=\\sum_\\alpha Q_\\alpha u_\\alpha$, then\n\\[\n (Q^*V)_\\alpha=l^{-2}Q_\\alpha^*V.\n\\]\nConversely, for a coarse-to-fine map with $(TV)_\\alpha=T_\\alpha V$,\nits adjoint is $T^*u=l^2\\sum_\\alpha T_\\alpha^*u_\\alpha$.\nThe adjoints of the finite matrices $Q_\\alpha$ and $T_\\alpha$ in these\nformulas use the ordinary Euclidean inner product.  Stars at complex\nmomentum mean analytic continuation of the adjoint on real momentum.\n\nA history $h$ is a finite sequence of ordinary observations,\nor an inclined initial observation followed by ordinary observations.\nThe empty history has $K_{\\varnothing}=K_0=D$.  If $Q$ is the next\nobservation from the fine lattice $\\Lambda$ to the coarse lattice $\\Lambda'$, its output precision, conditional mean, and conditional\ncovariance are defined by\n\\begin{equation}\n (K_{h}')^{-1}\n =\\Id+QK_{h}^{-1}Q^*,\\qquad\n P_{h}=K_{h}^{-1}Q^*K_{h}',\\qquad\n C_{h}=(K_{h}+Q^*Q)^{-1}.\n \\label{eq:free-operators}\n\\end{equation}\nThese formulas at the massless zero mode are understood by continuation\nof the Fourier expressions below.  Equivalently, the first formula\ncomputes the covariance of $Qu$ plus the observation noise.  We write\n$K_h,P_h,C_h$ when the particular history is understood.  A prime always\nrefers to the next lattice, not to differentiation.\n\nTo make the localization assertions precise, an operator on one lattice\nhas an exponential kernel bound if, for some $c>0$,\n$\\sup_x\\sum_y e^{c|x-y|}|F(x,y)|<\\infty$.\nFor a map between the fine and coarse lattices, use their physical\npositions measured in coarse lattice units.  The analogous supremum\nover input sites is its column bound.  Polynomial factors of any fixed\norder may be included by reducing $c$.  Fine differences mean\nnearest-neighbor differences in the original fine coordinate directions.\n\n\\begin{proposition}[Uniform free bounds and history contraction]\n\\label{prop:free-bounds}\nThere are $L_0,A_0,c,C>0$ such that, for every dyadic $L\\ge L_0$ and\nevery admissible history, $K_{h}$ is analytic and bounded on a\ncommon complex neighborhood of the momentum torus and\n\\[\n cD(p)\\le K_{h}(p)\\le CD(p)\\quad(p\\in\\R^2),\n \\qquad K_{h}(p)-D(p)=O(|p|^4).\n\\]\nThere is a real, self-adjoint analytic edge lift $B_{h}$ with\n\\[\n K_{h}=d^*B_{h}d,\n \\qquad c\\Id\\le B_{h}\\le C\\Id,\n \\qquad B_{h}(0)=\\Id.\n\\]\nThe lifts and their inverses have uniform exponential kernel bounds.\nFor a step of side $l=L$ or $l=L_*$, the operators in\nEquation~\\eqref{eq:free-operators} satisfy\n\\[\n KP=Q^*K',\\qquad \\Id-QP=K',\n \\qquad\n \\sup_x\\sum_Y e^{c\\operatorname{dist}(x,Y)/l}\n       |\\nabla_f^sP(x,Y)|\\le C_s l^{-s}\n\\]\nfor every fixed nonnegative integer $s$.  Here the distance is between\nthe physical fine site and coarse center, in fine units.\nThe mean $P$ preserves constants and centered affine fields, with the\nrotation and scale of the output frame included.  The covariance $C$\nhas exponential kernel bounds in coarse units with constants $C_L$.\n\nSuppose two histories share their last $r\\ge0$ ordinary observation\nsteps, using the same ordinary $Q$ in these steps and identifying their\noutput coordinates.  Then on a common fixed smaller complex strip,\n\\begin{equation}\n \\norm{K_{h_1}-K_{h_2}}_{\\rm an}\n \\le C\\left(\\frac{A_0}{L^2}\\right)^r.\n \\label{eq:free-history}\n\\end{equation}\nHere $\\norm{\\cdot}_{\\rm an}$ is the supremum norm on that strip.\nThe difference vanishes through degree three at the origin, and the\nsame estimate holds for any prescribed finite set of analytic derivative\nand exponential kernel norms, after decreasing the strip or exponential\nweight.  The corresponding differences of lifts and their inverses\nobey this bound.  When the two histories are followed by a common next\nordinary observation, their $P$ and $C$ differences obey the same bound\nwith a permitted prefactor $C_L$.\n\\end{proposition}\n\n\\begin{proof}\nWe give the analytic estimates with the geometry visible.  This also\nidentifies the hypotheses of \\Rref{free:bounds} that are retained.\nIn an inclined step of side $l=L_*$, the fine aliases above coarse\nmomentum $p$ are\n\\[\n k_\\alpha=O(p+2\\pi\\alpha)/l,\n \\qquad \\alpha\\in\\Z^2/(lO^T\\Z^2).\n\\]\nChoose representatives in a centered rotated square.  For an ordinary\nstep use $O=\\Id$ and $l=L$.  Write\n$b_Q(k)=\\sum_xw_0(x)e^{ik\\cdot x}$, with integer origins when aliases\nare glued.  Centered origins only change this symbol by a translation\nphase.  On\n\\[\n \\Omega_\\delta=\\{p\\in\\C^2:\n       |\\operatorname{Re}p_\\mu|<\\pi+\\delta,       |\\operatorname{Im}p_\\mu|<\\delta,\\ \\mu=1,2\\},\n\\]\ndiscrete summation by parts in the fine coordinate directions gives,\nfor any fixed $J$ and multi-index $\\beta$,\n\\begin{equation}\n |\\partial_p^\\beta b_Q(k_\\alpha)|\n \\le C_{J,\\beta}(1+|\\alpha|)^{-J}.\n \\label{eq:free-bump-decay}\n\\end{equation}\nIndeed, the sum of absolute $J$th differences of the sampled normalized\nweights is $O(l^{-J})$, while at least one fine difference multiplier\non a nonprincipal alias has size $c(1+|\\alpha|)/l$.  The weights vanish\nnear the cell boundary, so the summation produces no boundary term.\nComplex tilting and $p$ derivatives insert bounded rescaled coordinates.\nThis is the proof of \\Rref{app:analytic-1}, without any restriction to\nan axis-aligned support.\n\nThe centered principal bump is uniformly nonzero on the real principal\ncube.  For ordinary blocks use its product structure and the small\nsupport.  For inclined sampling, its Riemann sum converges uniformly on\nthat cube to the product Fourier integral in the rotated coordinates;\nthat integral is nonzero by the same support bound.  Increasing $L_0$\nand decreasing the complex strip preserve the lower bound.\nThe exact first moment is zero, so the principal product has the\nquadratic Taylor bound used in \\Rref{app:free-strip}.\nRepresentatives relabel on overlaps of momentum cubes; the centered\nrepresentatives there have comparable alias weights.  We use these\noverlaps also at the faces of the rotated alias box.\n\nFor a compound history let $m$ be the product of its side factors\nand let $O_h$ map the output coordinate frame to the original fine\nframe.  For the coarse site at the origin, let $v_h(x)$ be the product\nof observation weights along the unique ancestry of its fine\ndescendant $x$.  These weights sum to one.  Write\n$c_h=\\sum_xv_h(x)x$ for their center in the original fine coordinates.\nThe transform in centered output coordinates is\n\\[\n W_h(\\xi)=\\sum_xv_h(x)\n   \\exp\\left\\{i\\xi\\cdot O_h^T(x-c_h)/m\\right\\},\n \\qquad D_{m,O_h}(\\xi)=m^2D(O_h\\xi/m).\n\\]\nUsing integer origins instead multiplies $W_h$ by a translation phase,\nwhich cancels in $W_hW_h^*$.  This is the origin convention used when\ngluing aliases.\nSeparating the principal alias in the covariance gives precisely the\nformula of \\Rref{free:explicit}:\n\\begin{equation}\n K_{h}(p)=\n \\frac{D_{m,O_{h}}(p)}\n {W_{h}(p)W_{h}(p)^*\n       +D_{m,O_{h}}(p)R_{h}(p)}.\n \\label{eq:free-explicit}\n\\end{equation}\nHere $R_{h}$ is the sum over nonprincipal aliases of\n$W_{h}W_{h}^*/D_{m,O_{h}}$, together with\nthe independent noise variances.  The latter form a sum of products\nof squared-weight sums, starting with the last observation.  This\norder matters when the first observation is inclined.\nFor the empty history, take $W=1$ and $R=0$.\n\nFor every nonprincipal centered representative, on a fixed sufficiently\nsmall complex domain,\n\\begin{equation}\n |D_{m,O_{h}}(p+2\\pi\\alpha)|\n \\ge c(1+|\\alpha|)^2.\n \\label{eq:free-alias-denominator}\n\\end{equation}\nTo verify this, use $D(z)=4\\sum_\\mu\\sin^2(z_\\mu/2)$ in the fine\ncoordinates.  On the centered fine box its real part is bounded below\nby a fixed multiple of the squared real argument minus a fixed\nmultiple of the squared imaginary argument.  A nonprincipal alias\nhas real distance at least $c(1+|\\alpha|)$ from the principal origin.\nThe same conclusion holds for representatives slightly across a face,\nby periodicity.  Thus the proof of \\Rref{app:analytic-2} applies with\nthe indicated rotation of the argument.\n\nThe remaining issue is a bound independent of history length.  In a\nsequence of $i$ ordinary observations, each descendant has a unique\nancestral position and the squared weights sum to at most\n$(A/L^2)^i$, for a fixed $A$.  Descendant positions divided by their\ntotal side remain in a bounded set.  Parseval at opposite imaginary\ntilts, followed by Cauchy--Schwarz, therefore gives the estimate of\n\\Rref{app:analytic-3}:\n\\[\n \\sum_{\\alpha\\bmod L^i}\n   |W_i(p+2\\pi\\alpha)W_i(p+2\\pi\\alpha)^*|\n \\le C A^i.\n\\]\nFor a ball of radius $C_1L^i$, the same bound holds with a changed\nfixed constant, since that ball meets only a bounded number of alias\nboxes.  Earlier factors have absolute product bounded by a fixed\nconstant: their real arguments cost at most one, and their complex\ntilts have a summable geometric bound.  Split the aliases into shells\nbetween successive radii $c_1L^i$.  By\nEquation~\\eqref{eq:free-alias-denominator}, their contributions have\nsuccessive ratio at most $A/L^2$, after changing $A$ once.  If the\nhistory begins with an inclined observation, use Parseval also on the\nfinal shell ending at its full rotated box.  Its side factor $5L$\nchanges only fixed constants.  This proves the shell bound of\n\\Rref{app:analytic-4}, uniformly for both kinds of history.\nThe noise sum is bounded by the same geometric series.\n\nConsequently $R_{h}$ and its required derivatives are\nuniformly bounded on a fixed strip.  On the real principal cube,\n$W_{h}W_{h}^*$ is uniformly bounded below: its first\nfactor has the lower bound already proved, and the departures of\nsubsequent factors from one have a summable quadratic bound.\nThe denominator in Equation~\\eqref{eq:free-explicit} is therefore\npositive and uniformly bounded below on the real cube.  Its uniformly\nbounded derivatives keep it nonzero on a smaller fixed complex strip.\nThis proves analyticity and ellipticity.  At the origin its denominator\nis $1+O(|p|^2)$, while its numerator has quadratic term $|p|^2$.\nReality and inversion symmetry remove odd terms.  Every precision\ntherefore has the same jet through degree three.\n\nWe now compare two histories with $r$ common final ordinary steps.\nFor a sufficiently small fixed $c_2$, aliases with\n$|\\alpha|<c_2L^r$ can be identified in both histories.  Put\n$\\xi=p+2\\pi\\alpha$ and denote the common product of the last $r$\nfactors by $W_r$.  Taylor expansion of all factors preceding these\nsteps, with the linear terms canceled in each $bb^*$, gives\n\\[\n |W_{h_1}W_{h_1}^*\n       -W_{h_2}W_{h_2}^*|\n \\le C|\\xi|^2L^{-2r}|W_rW_r^*|.\n\\]\nNo division by a bump factor is used.  On the common nonprincipal\naliases, the inverse denominators differ by at most $CL^{-2r}$.\nFor the rotated denominator this follows from the same Taylor\nestimate, since its leading quadratic form is still $|\\xi|^2$.\nParseval now bounds the change of the common nonprincipal sum by\n$CA^rL^{-2r}$.  The shells outside this common region cost at most\n$CA^{r+1}L^{-2r}$, and the part of the noise sum preceding the common\nsteps has the same bound.  On the principal term apply Taylor's\nestimate directly to the numerator and product in\nEquation~\\eqref{eq:free-explicit}.  Its denominator remains nonzero\non the common strip, giving Equation~\\eqref{eq:free-history} after\nincreasing $A_0$.  This argument also covers $r=0$, when only a uniform\nbound is asserted.  Cauchy estimates on nested fixed strips give all\nprescribed derivatives, and Fourier inversion gives the exponential\nkernel estimates and their polynomial moments.\n\nFor completeness, the edge lift is obtained by applying the bounded\ncoordinate divisions of \\Rref{app:free-strip} to $K-D$, which vanishes\nto order four.  They give a Hermitian lift\n$B^{\\rm raw}=\\Id+O(|p|^2)$ with bounded analytic coefficients.\nIf $\\mathsf c=(-d_2,d_1)$, then $\\mathsf c d=0$, so addition of a\nfixed sufficiently large multiple of $\\mathsf c^*\\mathsf c$ leaves\n$d^*Bd$ unchanged.  The longitudinal form is bounded below by $K/D$;\na two-by-two Schur complement makes the resulting $B$ uniformly\npositive away from zero, and $B^{\\rm raw}$ is already positive near\nzero.  All divisions are bounded linear operations on fixed nested\nstrips.  The common added term cancels in differences, and the inverse\nidentity preserves Equation~\\eqref{eq:free-history}.  At empty history\none may keep $B=\\Id$.\n\nIn aliases the conditional mean is\n\\[\n P_\\alpha=l^{-2}K(k_\\alpha)^{-1}b_Q(k_\\alpha)^*K'(p).\n\\]\nOn nonprincipal aliases, Equations~\\eqref{eq:free-bump-decay}\nand~\\eqref{eq:free-alias-denominator} give arbitrary fixed inverse-power\nalias decay.  The principal pole is removable by the covariance\nidentity; its zero and first jets prove preservation of constants and\ncentered affine fields.  In inclined coordinates an affine function\nis evaluated at $x=c+lOY$, which is the rotation and rescaling in\n\\Rref{supp:fixed-norm-affine-assumption}.  Each fine difference\ncontributes at most $C(1+|\\alpha|)/l$.  Taking sufficiently many bump\ndifferences and Fourier inverting on a smaller strip proves the\nclaimed bounds for every fixed order $s$, including differences\nacross cell boundaries.  Finally, the nonprincipal block of\n$K+Q^*Q$ is inverted first; its rank-one update and principal Schur\ncomplement have the positive denominators used in \\Rref{free:bounds}.\nThis proves the covariance bound, allowing constants depending on $L$.\nAll these operations preserve the history discrepancy for a common\nnext observation.  For occurrences of $K^{-1}$ on nonprincipal aliases,\nuse the inverse difference identity and the same denominator bounds.\n\\end{proof}\n\n\\subsection{The positive kinetic identity and symmetry of its construction}\n\nThe scalar estimates do not by themselves provide the positive local\nenergy used in the nonlinear integration.  We next lift them to edge\noperators.  Write\n$\\mathcal X_\\Lambda=\\ell^2(\\Lambda;\\R^2)\\oplus\n\\ell^2(\\Lambda;\\R^2)$ for a pair of edge fields.\nThe following identity applies componentwise also to ambient\n$\\R^3$-valued fields; no constraint on their values is needed.\n\n\\begin{proposition}[Positive completion for both block geometries]\n\\label{prop:free-positive}\nA common $c_0>0$ and real analytic edge operators $\\ell_{h}$\ncan be chosen so that\n\\[\n \\mathsf A_{h}\n   =\\binom{\\sqrt{c_0}\\Id}{\\ell_{h}},\n \\qquad X_{h}=\\mathsf A_{h}d,\n \\qquad X_{h}^*X_{h}=K_{h}.\n\\]\nThere are real maps\n\\[\n \\mathcal M:\\mathcal X_{\\rm coarse}\n        \\longrightarrow\\mathcal X_{\\rm fine}\\oplus\\ell^2(\\Lambda';\\R),\n \\qquad\n \\mathcal N:\\mathcal X_{\\rm coarse}\n        \\longrightarrow\\mathcal X_{\\rm coarse}\\oplus\\ell^2(\\Lambda';\\R)\n\\]\nwith\n\\begin{align}\n \\mathcal M^*\\mathcal M+\\mathcal N^*\\mathcal N&=\\Id,\n &\\mathcal N X'&=0,\\label{eq:free-positive-isometry}\\\\\n \\mathcal M^*(Xu,V-Qu)&=X'V,\n &\\mathcal M X'&=(XP,\\Id-QP).\n \\label{eq:free-positive-ambient}\n\\end{align}\nTheir kernels have exponential moments.  For a block side\n$l\\in\\{L,L_*\\}$, the fine-output part of $\\mathcal M$ has row bound\n$C/l$, column bound $Cl$, and first and second fine-difference row\nbounds $C/l^2$ and $C/l^3$.  The coarse slots have bounded row and\ncolumn norms.  For a common next ordinary step, every history difference\nhas the factor $(A_0/L^2)^r$ of Equation~\\eqref{eq:free-history},\nwith prefactor $C_L$ allowed.  At empty history one may take\n$\\ell_{\\varnothing}=\\sqrt{1-c_0}\\Id$.\n\nThe construction commutes with simultaneous square symmetries of its\ngeometry and its input data.  An individual inclined history has C4\ncovariance; reflection transports it to the reflected history.  Ordinary\nhistories, including empty history, have the full square symmetries.\n\\end{proposition}\n\n\\begin{proof}\nThe construction in \\Rref{lem:free-stack} begins with an operator $T$\nfrom coarse edges to fine edges satisfying\n\\begin{equation}\n T^*d=d'Q,\\qquad TB'd'=BdP.\n \\label{eq:free-edge-identities}\n\\end{equation}\nWe verify both analytic divisions in that construction for the inclined\nstep.  For each block-translation vector $lOe_\\mu\\in\\Z^2$, choose a\nfine coordinate path from $0$ to that vector.  Its edge Fourier row\n$\\omega_\\mu(k)$ has length $O(l)$ and satisfies\n\\[\n \\omega_\\mu(k)d(k)=e^{ik\\cdot lOe_\\mu}-1=d'_\\mu(p).\n\\]\nCollect these rows in $\\omega$ and set\n$T_{0,\\alpha}=l^{-2}\\omega(k_\\alpha)^*b_Q(k_\\alpha)^*$.\nThe fine adjoint normalization gives $T_0^*d=d'Q$.\nFor an ordinary step this is the coordinate-path formula in\n\\Rref{lem:free-stack}.\n\nThe remainder\n$R_\\alpha=B_\\alpha d_\\alpha P_\\alpha-T_{0,\\alpha}B'd'$\nis divergence free, by $KP=Q^*K'$.\nThe principal values of the path rows are the translation vectors,\nwhile $d_0P_0$ has linear term $iOp/l$.  These linear terms cancel\nin $R_0$, so it vanishes through first order.  On every nonprincipal\nalias $R_\\alpha(0)=0$.  Its norm is $O(l^{-1})$ with arbitrary\nprescribed inverse-power decay in the alias index.\nThe first division of \\Rref{app:alias-division} therefore gives\n\\[\n R_\\alpha=\\mathsf c(k_\\alpha)^*\\gamma_\\alpha,\n \\qquad \\gamma_\\alpha(0)=0.\n\\]\nAt the principal origin use local coordinates along the fine axes;\nthe rotation changes constants by a fixed factor.  On a nonprincipal\noverlap divide by a component of $d(k_\\alpha)$ of size at least\n$c(1+|\\alpha|)/l$.  The resulting scalar quotients agree on overlaps\nby the divergence-free identity.  Thus $\\gamma$ has the same arbitrary\nfixed alias decay, without the factor $l^{-1}$.\n\nThe second division must produce a row $H$ with $Hd'=\\gamma$ while\npreserving this decay.  In the inclined case the true momentum-period\nlattice, in the $p$ axes and with the factor $2\\pi$ omitted, is\n\\[\n lO^T\\Z^2\n =L\\begin{pmatrix}3&\\phantom{-}4\\\\-4&3\\end{pmatrix}\\Z^2\n\\]\nfor $O_+$, and its reflected version for $O_-$.\nThe rectangular lattice $(25L)\\Z^2$ is a sublattice of index $25$.\nWork first on this rectangular covering torus.  Its alias decay weight\nis the sum of the $25$ translated weights centered at the copies of\nthe principal alias.  Apply the sinc-power interpolation operator\n$\\Pi$ of \\Rref{app:analytic-5}, with $25L$ in place of the rectangular\nside.  For each translated weight its cyclic convolution bound is\nunchanged.  The two rows\n\\[\n H=\\left(\\frac{\\gamma-\\Pi\\gamma}{d'_1},\n          \\frac{\\Pi\\gamma}{d'_2}\\right)\n\\]\nare analytic: the first numerator vanishes on each $d'_1=0$ plane;\nthe second vanishes on each $d'_2=0$ plane because $\\gamma$ vanishes\nat every sampling intersection.  Cauchy estimates in the $p$ variables\ngive bounded removable quotients, with no extra power of $l$.\nAverage over the finite deck group to recover the true period lattice.\nInteger fine-site phases make this descent agree with alias gluing.\nWe have therefore obtained the second division with constants uniform\nin $L$.  Setting\n\\[\n T_\\alpha=T_{0,\\alpha}\n       +\\mathsf c(k_\\alpha)^*H_\\alpha(B')^{-1}\n\\]\nproves Equation~\\eqref{eq:free-edge-identities}, with alias bound\n$C_Jl^{-1}(1+|\\alpha|)^{-J}$.\nFourier inversion gives its row, column, and fine-difference bounds.\nThe divisions and the inverse identity preserve history differences.\n\nWe describe the remaining positive completion to specify the role of\nthe normalization.  On real momentum put\n\\[\n U=(B')^{-1}-T^*B^{-1}T-d'd'^*.\n\\]\nThe edge identities and $\\Id-QP=K'$ give\n\\[\n UB'd'=d'-T^*dP-d'K'=d'(\\Id-QP-K')=0.\n\\]\nCoarse coordinate-plane division on both sides factors $B'UB'$\nthrough $\\mathsf c'^*\\mathsf c'$ with a bounded analytic scalar\ncoefficient.  Also $\\mathsf cB^{-1}TB'd'=0$.\nBefore the last division its aliases have bound\n$Cl^{-2}(1+|\\alpha|)^{-J}$.  Squaring and summing with the fine norm\n$l^2\\sum_\\alpha$ gives exactly the estimates used in\n\\Rref{lem:free-stack}:\n\\[\n |z^*Uz|\\le C|\\mathsf c'(B')^{-1}z|^2,\n \\qquad\n \\norm{\\mathsf cB^{-1}Tz}^2\n \\le Cl^{-2}|\\mathsf c'(B')^{-1}z|^2.\n\\]\nIn particular the inclined step has the correct normalization\n$l^2=L_*^2$, rather than the area of an enclosing axis square.\n\nFor one fixed sufficiently large $\\lambda$, replace the inverse lift by\n\\[\n \\widetilde B^{-1}\n =B^{-1}+\\lambda B^{-1}\\mathsf c^*\\mathsf cB^{-1}\n\\]\nand make the same replacement at the next scale.\nIt leaves $\\widetilde Bd=Bd$ and hence the precision unchanged.\nDefine the modified defect by\n\\[\n U_*=(\\widetilde B')^{-1}\n          -T^*\\widetilde B^{-1}T-d'd'^*.\n\\]\nExpanding the two modified inverse lifts gives\n\\[\n U_*=U+\\lambda\\bigl[\n (B')^{-1}\\mathsf c'^*\\mathsf c'(B')^{-1}\n -T^*B^{-1}\\mathsf c^*\\mathsf cB^{-1}T\\bigr].\n\\]\nThe preceding two bounds therefore show that $U_*$ is bounded below by\n\\[\n [\\lambda(1-C/l^2)-C]\n (B')^{-1}\\mathsf c'^*\\mathsf c'(B')^{-1}.\n\\]\nChoose $\\lambda$ first and then $L$ sufficiently large, so this\ncoefficient is at least one.  The defect has a factorization\n$\\widetilde B'U_*\\widetilde B'=\\mathsf c'^*B_2\\mathsf c'$ with\n$B_2$ a uniformly positive analytic scalar.  Positivity through zero\nfollows from\n\\[\n \\mathsf c'(B')^{-1}\\widetilde B'\n =\\bigl[1+\\lambda\\mathsf c'(B')^{-1}\\mathsf c'^*\\bigr]^{-1}\n   \\mathsf c'.\n\\]\nChoose $c_0$ below the common lower bound for $\\widetilde B$ and put\n$\\ell=(\\widetilde B-c_0\\Id)^{1/2}$.\nFor $S'=\\mathsf A'(\\widetilde B')^{-1}(\\mathsf A')^*$ define\n\\begin{align*}\n \\mathcal Mz\n &=\\left(\\mathsf A\\widetilde B^{-1}T(\\mathsf A')^*z,\n                       d'^*(\\mathsf A')^*z\\right),\\\\\n \\mathcal Nz\n &=\\left((\\Id-S')z,\n       B_2^{1/2}\\mathsf c'(\\widetilde B')^{-1}(\\mathsf A')^*z\\right).\n\\end{align*}\nThe identities $\\mathsf A^*\\mathsf A=\\widetilde B$ and\n$(\\mathsf A')^*\\mathsf A'=\\widetilde B'$ make $S'$ an orthogonal\nprojection.  The definition and factorization of $U_*$ then give\n\\[\n \\mathcal M^*\\mathcal M\n   =S'-\\mathsf A'U_*(\\mathsf A')^*,\\qquad\n \\mathcal N^*\\mathcal N\n   =\\Id-S'+\\mathsf A'U_*(\\mathsf A')^*.\n\\]\nTheir sum proves the isometry in\nEquation~\\eqref{eq:free-positive-isometry}.  Substitution of the two\nedge identities proves Equation~\\eqref{eq:free-positive-ambient},\nand $\\mathsf c'd'=0$ proves the null identity.\nAll square roots and inverses are uniformly analytic on a smaller\nstrip.  Their exponential kernel bounds preserve those of $T$.\nAt empty history retain $B=\\widetilde B=\\Id$ on the fine side;\nthis removes a nonnegative subtraction from the defect and improves\nits positivity.  It gives the asserted value of $\\ell_{\\varnothing}$.\n\nFinally the choices of lift and intertwiner must be the same rules\nfor all data, including data with different individual reflection\nsymmetries.  Given a particular construction $F$ and input data\n$\\mathcal D$, replace it by the finite average\n\\[\n \\frac1{|G|}\\sum_{R\\in G}R^{-1}F(R\\mathcal D),\n\\]\nwhere $G$ is the square symmetry group and the actions include the\ninduced edge and plaquette permutations, signs, and integer\ntranslations restoring the chosen origins.  First make this average\nfor the lift; then make it for $T$ with that lift fixed.\nEach defining identity is linear in the output being averaged, and\npositivity and lower bounds of lifts survive the average.\nFor inclined data include both reflected types in the input family.\nThe subsequent factorization and positive square roots commute with\nthese actions.  Thus the rules are covariant even when an individual\nprecision has only C4 symmetry.  The averaging never replaces that\nprecision by a mirror average.  Being fixed finite averages, these\noperations also preserve all discrepancy estimates.  This common\nchoice is used in the nonlinear comparisons\n\\eqref{eq:orig-5} and~\\eqref{eq:orig-29}.\n\\end{proof}\n\n\\subsection{Finite periods and unfolded symmetry}\n\nAll kernels above are chosen once on the infinite lattice and then\nperiodized.  The permitted finite tori are quotients by square,\nrectangular, or oblique sublattices of translations, with the bounded\nshape ratios used below.  Their period lattices must be divisible by\nevery applied blocking: after each block map they become translation\nperiods of the next lattice.  We require only that the shortest period,\nmeasured in layer-$0$ sites, exceed a fixed $m_{\\min}(L,H)$.\n\nPeriodization folds the absolutely summable bulk kernels and their\nidentities.  For expressions carrying connecting paths, retain the\nlifted path before folding, as in \\Rref{free:section}; its length\nrecords a contribution that crosses a period.  Locality tests at any\nlayer use the shortest period in that layer.  Enlarging\n$m_{\\min}(L,H)$ enforces simultaneously all scale-neighborhood\nconditions following \\Rref{rg:scales}, including those for the inclined\ninitial step.  Calculations on short symmetry orbits use C4, or the\nfull square group when available, on the unfolded bulk labels.\nThey therefore remain valid on asymmetric finite tori.  Terms whose\nrecords cross a period keep their separate long-path bounds; no\nsymmetry of such terms, or of the finite period lattice itself, is\nassumed.\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/history.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/history.tex", "bytes": 5754, "sha256": "e71c0c32bc8de1c412a924de8889f03975e8c44e9b519df0450482bc94b1d283", "content": "\\subsection{Historical context}\n\\label{subsec:history}\n\nThe nonordering theorem of Mermin and Wagner, and Mermin's classical\nversion, exclude spontaneous magnetization in the corresponding\nfinite-range two-dimensional systems~\\cite{MW,Mermin1967}.\nMcBryan and Spencer obtained algebraic upper bounds on spin correlations\nby complex rotations~\\cite{McBryanSpencer}.  Such an upper bound does not\ndistinguish algebraic decay from exponential decay.  Local Ward\nidentities gave further correlation inequalities and high-temperature\nmass-gap criteria in the work of Aizenman and\nSimon~\\cite{AizenmanSimonWard}.  A continuum mass theorem additionally\nrequires control as the bare inverse coupling diverges and the lattice\nspacing vanishes.\n\nFor more than two spin components, perturbative renormalization\npredicts that weak microscopic coupling produces an exponentially\nlarge correlation length.  Polyakov identified the renormalization-group\nmechanism for two-dimensional Goldstone fields~\\cite{Polyakov}.\nBr\\'ezin and Zinn-Justin developed the expansion near two dimensions,\nand Br\\'ezin, Zinn-Justin and Le Guillou analyzed the renormalization of\nthe model and its composite operators~\\cite{BrezinZinnJustin,BrezinZinnJustinLeGuillou}.\nThese works explain why weak microscopic coupling can coexist with a\nfinite long-distance mass scale.  They also identify the perturbative\ncoefficients against which a constructive renormalization should be\nchecked.  Establishing those coefficients is logically different from\nbounding the nonperturbative remainder throughout a trajectory whose\nlength diverges with the cutoff.\n\nIntegrability gives a complementary and much more detailed picture of\nthe expected continuum theory.  Zamolodchikov and Zamolodchikov solved\nthe factorization, crossing and unitarity equations for\n$O(N)$-invariant scattering and identified the sigma-model\nsolution~\\cite{ZamolodchikovZamolodchikov}.  Hasenfratz, Maggiore and\nNiedermayer obtained the exact mass-to-renormalization-scale relation\nfor $O(3)$ and $O(4)$ by matching the Bethe ansatz with perturbation\ntheory~\\cite{HasenfratzMaggioreNiedermayer}; Hasenfratz and Niedermayer\nextended the calculation to arbitrary $N\\ge3$~\\cite{HasenfratzNiedermayer}.\nThose calculations determine a mass ratio within the integrable\ncontinuum description.  The additional problem addressed here is to\nconstruct the field distributions from the specified nearest-neighbor\nmeasure and to establish their spectral properties after removal of\nthe cutoff.  No factorized scattering matrix is used as a premise of\nthe construction.\n\nSusceptibility and second-moment correlation length provide intrinsic\nfield and length units.  This is the familiar zero-momentum normalization,\nused explicitly by Campostrini, Pelissetto, Rossi and\nVicari~\\cite{CampostriniPelissettoRossiVicari1997}.\nTheir strong-coupling calculations and the lattice--bootstrap comparisons\nof Balog and collaborators~\\cite{BalogEtAl1999} studied normalized\namplitudes and supplied evidence for continuum universality.\nHere the question is convergence of the entire canonically normalized\nSchwinger hierarchy for the specified nearest-neighbor model and periodic\nthermodynamic state, as the bare coupling tends to infinity without\nrestriction to a selected sequence.  Comparing this limit with a\nterminal-coupling construction requires control of both the field\ndistributions and the normalizing correlation moments.\n\nRigorous work has supplied several distinct parts of this program.\nKupiainen proved asymptotic validity of the $1/N$ expansion for lattice\nmodels above the spherical-model critical temperature and obtained a\nmass gap for sufficiently large $N$ at each such\ntemperature~\\cite{Kupiainen}.\nKopper later proved mass generation at sufficiently large finite $N$\nwith a suitable ultraviolet cutoff~\\cite{Kopper}.  These large-$N$\nresults do not specialize to a cutoff-removed construction at $N=3$.\nGaw\\k{e}dzki and Kupiainen constructed a continuum limit with asymptotic\nfreedom for a sigma model with a \\emph{hierarchical} kinetic\nterm~\\cite{GawedzkiKupiainen}.  In that setting the hierarchical covariance reduces the flow to\na single-spin recursion.  For the nearest-neighbor model, integration\ngenerates interactions between blocks whose spatial decay must be\ncontrolled.\nMitter and Ramadas constructed the Wilson renormalized trajectory in\nperturbation theory in the effective charge and developed the\ncorresponding effective-action analysis~\\cite{MitterRamadas}.\n\nThe organization by successive block integrations belongs to the\nrenormalization-group tradition of Kadanoff and\nWilson~\\cite{Kadanoff,Wilson}.  Ba\\l aban developed localized effective actions and constrained\nvariational methods in lattice gauge theory~\\cite{BalabanGaugeFlow,BalabanGaugeVariational}\nand in the low-temperature analysis of classical $N$-vector\nmodels~\\cite{BalabanFlow,BalabanVariational}.\nGoswami studied the constrained background-field problem for\ntwo-dimensional $SU(n)$ chiral models, including the $O(4)$\ncase~\\cite{Goswami2024}.\nFor the two-dimensional $O(4)$ model, Dybalski, Stottmeister and\nTanimoto proved existence and uniqueness for the constrained\nsmall-field variational problem associated with one block-averaging\noperation~\\cite{DybalskiStottmeisterTanimotoVariational}.\nAru, Garban and Sep\\'ulveda proved that, after fixing one spin\ndirection, rescaled transverse fluctuations converge locally as the\ninverse coupling tends to infinity to a rooted vector-valued lattice\nGaussian free field~\\cite[Theorem~1.3]{AGS}.  For $O(3)$ this field has\ntwo components.  Their result identifies the local Gaussian\napproximation; the construction below also requires control at\nspatial scales that grow with the inverse coupling.\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/introduction.tex", "bytes": 13861, "sha256": "905d9d766b31b9640339685a1143afde70413e630cc08058b6eabe1a48024d8f", "content": "\\section{Introduction}\\label{sec:introduction}\n\nThe two-dimensional nonlinear sigma model asks how a field constrained to\na curved compact target can produce a relativistic quantum field theory\nat distances much larger than a lattice spacing. For the sphere $S^2$, the\nbasic lattice model has a particularly simple definition. On a finite\nperiodic square lattice $\\Lambda$, let $q_x\\in S^2\\subset\\R^3$ and let\n$\\sigma$ be normalized area measure on $S^2$. Its Gibbs probability is\n\\begin{equation}\\label{eq:lattice-model}\n \\dd\\mu_{\\beta,\\Lambda}(q)\n =\\frac{1}{Z_{\\beta,\\Lambda}}\n   \\exp\\!\\left(\\beta\\sum_{\\{x,y\\}\\in E(\\Lambda)}q_x\\cdot q_y\\right)\n   \\prod_{x\\in\\Lambda}\\dd\\sigma(q_x),\n \\qquad \\beta>0,\n\\end{equation}\nwhere each nearest-neighbor bond occurs once. We consider no topological\nterm and no external field. The continuum problem is to let the coupling\n$\\beta$ diverge and the lattice spacing vanish while preserving a\nnontrivial physical scale. A mass estimate at fixed lattice spacing does\nnot settle this problem: one must construct the same limiting fields on\nwhich the limiting gap acts.\n\n\\input{sections/history}\n\n\\subsection{Canonical normalization and the main result}\n\nFor each $\\beta$ used below, write $\\mu_\\beta$ for the local limit of\nperiodic square tori as their side lengths tend to infinity. Existence and\nuniqueness of this periodic limit are part of the preliminary estimates.\nGiven a lattice spacing $a>0$, a positive field normalization $B$, and\n$f\\in C_c^\\infty(\\R^2)$, define the three-component random variable\n\\[\n \\phi_{a,B}(f)=Ba^2\\sum_{x\\in\\Z^2}f(ax)q_x.\n\\]\nFor component indices $i_1,\\ldots,i_n\\in\\{1,2,3\\}$, the corresponding\nSchwinger distribution is the expectation of the product of these\nsmeared fields. Its extension to general tests on $(\\R^2)^n$ is equivalently\nthe discrete moment measure with weight $(Ba^2)^n$.\n\nThe lattice itself supplies two normalization constants.  Put\n\\begin{equation}\\label{eq:canonical-lattice-scales}\n C_\\beta(x)=\\E_{\\mu_\\beta}[q_0^1q_x^1],\\qquad\n \\chi_\\beta=\\sum_{x\\in\\Z^2}C_\\beta(x),\\qquad\n \\xi_\\beta^2=\\frac{1}{4\\chi_\\beta}\n                 \\sum_{x\\in\\Z^2}|x|^2 C_\\beta(x).\n\\end{equation}\nHere $\\chi_\\beta$ is the susceptibility of one spin component, and\n$\\xi_\\beta$ is the positive second-moment correlation length.  For a\nvector test $f\\in C_c^\\infty(\\R^2;\\R^3)$ define\n\\begin{equation}\\label{eq:canonical-field}\n \\Psi_\\beta(f)=\\frac{1}{\\xi_\\beta\\sqrt{\\chi_\\beta}}\n           \\sum_{x\\in\\Z^2} f(x/\\xi_\\beta)\\cdot q_x.\n\\end{equation}\nThe factor $4$ in \\eqref{eq:canonical-lattice-scales} is twice the Euclidean\ndimension.  For a translation-invariant continuum two-point measure\nwritten in relative coordinates as $\\dd y\\,\\nu(\\dd z)$, with\n$z=y_2-y_1$, its susceptibility and squared second-moment length are\n$\\nu(\\R^2)$ and $\\int|z|^2\\nu(\\dd z)/(4\\nu(\\R^2))$, respectively,\nwhenever these quantities are finite and positive.\n\n\\begin{theorem}[Canonical continuum limit]\\label{thm:canonical}\nFor the nearest-neighbor probability \\eqref{eq:lattice-model}, the\nperiodic thermodynamic limit $\\mu_\\beta$ exists for all sufficiently\nlarge $\\beta$.  Both sums in \\eqref{eq:canonical-lattice-scales} are\nfinite, $\\chi_\\beta,\\xi_\\beta>0$, and $\\xi_\\beta\\to\\infty$ as\n$\\beta\\to\\infty$.  The following limits hold through all real values\nof $\\beta$ tending to infinity.\n\\begin{enumerate}[label=\\textup{(\\roman*)}]\n\\item For every finite list of vector tests in\n$C_c^\\infty(\\R^2;\\R^3)$, the fields \\eqref{eq:canonical-field} converge\njointly in law and in all joint moments to one common limiting hierarchy.\n\\item For every order and every choice of spin components, the associated\nSchwinger distributions converge in the strong topology of\n$\\mathcal S'((\\R^2)^n)$, that is, uniformly on bounded sets of Schwartz\ntests.\n\\item The limiting hierarchy has susceptibility and second-moment length\nequal to one.  It is the hierarchy constructed in Theorem~\\ref{thm:main},\nafter the unique positive constant length and field rescalings imposing\nthese two normalizations.\n\\end{enumerate}\n\\end{theorem}\n\nThe assertion concerns the one lattice action and periodic thermodynamic\nstate specified above.  The normalization removes the freedom to choose\nconstant physical length and field units.  It does not introduce a\ncomparison with other lattice actions, external fields, or topological\nterms.\n\n\\subsection{Construction and physical properties}\n\nThe proof first constructs the fields at a prescribed physical block\nscale.  This gives the following more detailed existence statement,\nincluding the physical properties that will pass to the canonical limit.\n\n\\begin{theorem}\\label{thm:main}\nThere are a dyadic integer $L>1$, a constant $H>0$, bare couplings\n$\\beta_N\\to\\infty$, and field normalizations $B_N>0$, such that\n\\begin{equation}\\label{eq:main-prescription}\n a_N=L^{-N},\\qquad\n \\beta_N=H+\\frac{\\log L}{2\\pi}N+O(\\log(N+1)),\n\\end{equation}\nand the following assertions hold for the model\n\\eqref{eq:lattice-model}.\n\\begin{enumerate}[label=\\textup{(\\roman*)}]\n\\item Every finite list of the variables\n$\\phi_N(f)=\\phi_{a_N,B_N}(f)$, under $\\mu_{\\beta_N}$, converges jointly\nin law and in all moments. The component Schwinger distributions converge\nin $\\mathcal S'((\\R^2)^n)$ at every order. The same limits are obtained\nwith suitable periodic physical tori tending to the plane as $N\\to\\infty$.\n\\item The limiting Schwinger distributions are Euclidean invariant,\ninternally $O(3)$ invariant, reflection positive, symmetric and clustering.\nFor every component distribution and $F\\in\\mathcal S((\\R^2)^n)$,\n\\[\n |S_n(F)|\\le C^n n!\\sup_{x\\in(\\R^2)^n}\n       (1+|x|^2)^{2n}|F(x)|,\n\\]\nwith a constant $C$ independent of $n$. They reconstruct a local, unitary,\nrelativistic theory in $1+1$ dimensions.\n\\item The reconstructed theory has a unique vacuum $\\Omega$, a nonzero\nvacuum complement, and a Hamiltonian $H_{\\mathrm{phys}}$ satisfying\n\\[\n H_{\\mathrm{phys}}\\big|_{\\Omega^\\perp}\\ge m\\Id\n \\quad\\text{for some }m>0.\n\\]\n\\item A connected four-point distribution is nonzero on smooth tests\nwith strictly separated time supports. In particular, the limiting\nfield correlations do not satisfy Wick factorization.\n\\end{enumerate}\nThe trajectory is selected by fixing its terminal kinetic coupling to\n$H$ at a fixed physical block length.\n\\end{theorem}\n\nTheorem~\\ref{thm:main} constructs a continuum limit along a prescribed\ntrajectory.  Theorem~\\ref{thm:canonical} identifies its canonical\nnormalization with the limit along every diverging bare coupling.\nInteraction is expressed by the fourth assertion of\nTheorem~\\ref{thm:main}: a connected four-point correlation of the\nconstructed field is nonzero.  The mass-gap estimate and this criterion\nuse the same fields and the same choice of physical units.\n\n\\begin{corollary}\\label{cor:canonical-physics}\nThe canonical limiting hierarchy is Euclidean invariant, internally\n$O(3)$ invariant, reflection positive, symmetric, and clustering.  It\nsatisfies factorial tempered-distribution bounds and reconstructs a local,\nunitary relativistic theory in $1+1$ dimensions.  The reconstructed\nHamiltonian has a unique vacuum, a nonzero vacuum complement, and a\nstrictly positive lower bound on that entire complement.  A connected\nfour-point distribution is nonzero on smooth tests with strictly separated\ntime supports.\n\\end{corollary}\n\n\\subsection{Proof strategy and new estimates}\n\nThe starting analytic tools are the local angular-integration and exact\nblocking constructions in \\cite{OpenAI-O4}, denoted by R when discussing\ntheir adaptation. We use their stated intermediate estimates with the\nhypotheses specified below. We do not transfer the $O(4)$ mass theorem to\n$O(3)$. The difference in target dimension changes the current identities,\nthe Gaussian normalization and the coefficient of the coupling drift.\nSections~\\ref{sec:preliminary} and~\\ref{sec:rg} establish these changes\ndirectly. Section~\\ref{sec:setup} fixes the imported definitions and\nidentifies the dimension-independent inputs.\n\nAn exact blocking partitions the lattice into squares, introduces one\nretained spin in $S^2$ per square through a normalized averaging kernel,\nand integrates the fine spins. This preserves the partition function and,\nwhen sources are retained, the generating function of the original spins.\nOne carries those sources and all the corrections they generate through\nsuccessive integrations. At a step with side factor $l$, rescale the\nsource at each fine site in a block to $l^{-2}$ times its retained source\n$z$. On a constant retained-spin configuration $V$, the resulting\nlinear term is $c_{j,N}z\\cdot V$. Normalize its coefficient to one.\nSection~\\ref{sec:sources} constructs these positive coefficients and\nproves that the accumulated field normalization is\n$B_N=\\prod_{j=1}^N c_{j,N}^{-1}$.\n\nThe proof must connect a rough large-distance estimate with a precise\ncomparison at a fixed physical scale. Four additional estimates make that\nconnection possible.\n\nFirst, write $X(\\beta)=C\\exp((4\\pi-\\eta)\\beta)$ for a preliminary\nupper bound on the mixing length, where $\\eta>0$ is fixed.\nThe strict saving below $4\\pi$ comes from the transverse fluctuations in\nthe first coarse tile. The pinned-cell inputs are uniform over boundary\npins. The resulting mixing estimate is uniform under cuts and weaker bonds.\n\nSecond, spatial inversion and tangent reflection give contraction of\nthe terms remaining after extraction of the kinetic coupling and its\nspecified low-order corrections. The contraction can be made arbitrarily\nclose to $L^{-2}$ per step. The\naffine quadratic contribution has already been removed by the kinetic\nnormalization; inversion cancels the next spatial cross term. This\nadditional cancellation allows the coarse comparison box to grow while\nthe total error in its density still tends to zero.\n\nThird, an integrated comparison controls changes in the gas of\nlarge-gradient regions. We fill selected regions with small-gradient\nspins, retain the complete positive density at the filled configuration,\nand pay the filling entropy with the original gradient energy. The\ncomparison loses only a volume factor. The strict saving in the\npreliminary length permits a reference box larger than that mixing length,\nwhile the nearly $L^{-2}$ contraction keeps the error over its coarse\nvolume small. Together these estimates transfer a finite-box criterion\nfor exponential decay from one reference cutoff to every finer cutoff\nat one fixed physical box. The transfer-operator criterion then gives a\ncommon physical decay rate. More concretely, at a reference depth $K$,\nthe preliminary mixing length in physical units is\n\\[\n a_KX(\\beta_K)=L^{(1-\\eta/(2\\pi))K+o(K)}.\n\\]\nMatching the effective densities to finer cutoffs is controlled by the\nerror parameter $C_H M^2L^{-(2-\\upsilon)K}$ in a physical square of side\n$M$, where $\\upsilon>0$ can be chosen arbitrarily small. Thus $M$ can be\nmuch larger than the mixing length while this error tends to zero.\nFixing one sufficiently large $K$ fixes a successful physical square\nonce and for all; its decay criterion passes to every finer cutoff.\n\nFourth, independent local sources are carried through the exact\ntransformation. Their linear response defines $B_N$; the remaining source\nterms contract. This gives convergence of the actual smeared lattice\nspins, local exponential moments, and a fourth cumulant that survives the\nlimit. Two reflected inclined first blockings compare regulator\norientations through a common coarse frame. The resulting rotation has\nangle equal to an irrational multiple of $\\pi$. Together with continuity,\nit gives full Euclidean rotation invariance.\n\nThe resulting architecture separates the long-distance estimate from\nthe source construction. Sections~\\ref{sec:free}--\\ref{sec:shooting}\nconstruct and match the exact trajectories.\nSections~\\ref{sec:comparison}--\\ref{sec:trace} obtain the estimate uniform\nin the cutoff. Sections~\\ref{sec:sources}--\\ref{sec:continuum} construct\nthe Schwinger distributions and prove their symmetries.\nSection~\\ref{sec:os} reconstructs the quantum theory and proves both the\nfull spectral gap and non-Wick factorization.\n\nThe final step is to make the physical normalization intrinsic to the\nlattice.  An arbitrary large bare coupling can be written as a reference\ncoupling at an integer depth plus a bounded real offset.  The effective\nhierarchy initially may depend on that offset.  A summable sensitivity\nestimate for the noncanonical kinetic increment, proved in\nLemma~\\ref{lem:sensitivity}, shows that trajectories with the same\ncorrected offset agree at each fixed coarse scale.  This is stronger\nthan a bound on the size of the increment: it controls its change under\nperturbations of the trajectory.  The resulting argument in\nSection~\\ref{sec:trajectories} is useful whenever a marginal coupling\nmust be matched over arbitrarily many renormalization steps.\n\nSection~\\ref{sec:volume} extends the fixed-scale volume comparison to\nthese bounded offsets.  Section~\\ref{sec:fields} then constructs their\ncontinuum hierarchies and proves uniform two-point tails.  The tails\npermit passage of the full susceptibility and second moment to the\nlimit, so the normalization in \\eqref{eq:canonical-field} can be applied\nafter taking the continuum limit.  Finally,\nSection~\\ref{sec:uniqueness} compares two descriptions of the same\nmicroscopic field: adjacent ordinary depths, and an ordinary versus an\ninclined first blocking.  Besides rotating the lattice, the inclined\nstep contributes a length factor $5$, multiplicatively independent of\nthe dyadic factor $L$.  The two reflected inclinations remove the\nrotation; continuity and the two resulting incommensurable offset\nperiods remove the remaining offset dependence.  Thus the same exact\nblocking geometry serves both Euclidean symmetry and canonical\nuniqueness.\n\n\\input{sections/method-history}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/method-history.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/method-history.tex", "bytes": 1165, "sha256": "8f12262406d7c021011b102f4a9892f6e0147d244a25e6912c03237e78b00e5d", "content": "The conditional comparisons in the preliminary mixing argument use\nGinibre's formulation of ferromagnetic correlation inequalities and\nthe positive-association theorem of Fortuin, Kasteleyn and Ginibre~\\cite{Ginibre1970,FortuinKasteleynGinibre1971}.\nThe passage from local disagreement estimates to loss of boundary\ninfluence is related to disagreement percolation~\\cite{VanDenBergMaes1994}.\nIn exact blocking, the connected polymer sums use the classical\ntree-graph approach to cluster expansions; a useful formulation of\nthe Penrose identity is given by Fern\\'andez and\nProcacci~\\cite{FernandezProcacci}.\n\nThe finite-volume comparison uses reflection positivity and the\nchessboard method systematized by Fr\\\"ohlich, Israel, Lieb and\nSimon~\\cite{FILS78,FILS80}.  For the final passage from Euclidean\ndistributions to a local relativistic field, we use the reconstruction\nframework of Osterwalder and Schrader~\\cite{OS73} in the corrected\nform with the linear growth condition of \\cite[Section~IV.1]{OS75}.\nReflection positivity, Euclidean covariance, clustering and the\nhierarchy of distributional bounds are consequently separate parts\nof the continuum argument.\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/os.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/os.tex", "bytes": 23235, "sha256": "b1768511a3ee947f42d934a0372a48864b7866cf13fbbd32dedb1a6ed9bd1efc", "content": "\\section{Reconstruction, mass gap, and non-Gaussian correlations}\n\\label{sec:os}\n\nSection~\\ref{sec:continuum} constructed the plane Schwinger functions\n$S_n$ and proved their Euclidean covariance and growth bounds.  We now\nverify the remaining reconstruction hypotheses and transfer the uniform\nlattice decay estimate to the entire reconstructed Hilbert space.  A\nseparated four-point test will then show that the limiting field does not\nsatisfy Wick's rule.\n\nWrite $x=(x^0,x^1)$, with $x^0$ the Euclidean time coordinate, and put\n$\\theta(x^0,x^1)=(-x^0,x^1)$.  We use $\\phi^a(f)$, $a\\in\\{1,2,3\\}$, for\nthe limiting smeared fields supplied by\nProposition~\\ref{prop:continuum-limit}.  Expectations of polynomials in\nthese variables are the corresponding pairings with the $S_n$.\nFor $s\\in\\R$, set\n\\[\n (\\tau_s f)(x^0,x^1)=f(x^0-s,x^1),\n \\qquad (\\theta f)(x)=f(\\theta x).\n\\]\nThe same notation $\\tau_s$ denotes the induced translation of a field\npolynomial.  Define $\\Theta\\phi^a(f)=\\phi^a(\\overline{\\theta f})$ and\nextend $\\Theta$ multiplicatively and antilinearly to field polynomials.\n\n\\subsection{Reflection positivity and exponential clustering}\n\n\\begin{lemma}[Reflection positivity]\n\\label{lem:reflection-positivity}\nLet $F$ be a polynomial in finitely many component fields\n$\\phi^a(f)$, where the smooth compactly supported tests $f$ have support\nin $\\{x^0>0\\}$.  Then\n\\begin{equation}\n \\E\\bigl[(\\Theta F)F\\bigr]\\ge0.\n \\label{eq:continuum-reflection-positivity}\n\\end{equation}\nThis holds jointly for all three components.  The Schwinger functions\nalso satisfy the reality relations for a Hermitian multiplet and\n$S_0=1$.\n\\end{lemma}\n\n\\begin{proof}\nAt every cutoff the nearest-neighbour measure is reflection positive\nacross microscopic site and link seams.  Since the union of the supports\nappearing in $F$ is a compact subset of the open positive half-plane, a\nmicroscopic seam within $O(a_N)$ of $\\{x^0=0\\}$ separates these supports\nfrom their reflections for all sufficiently large $N$.  Evaluate $F$ on\nthe renormalized lattice fields and reflect about this seam.  Its\nreflection-positive quadratic form is nonnegative.  The moment\nconvergence of Proposition~\\ref{prop:continuum-limit} applies to each of\nthe finitely many terms in that form.  Replacing the microscopic\nreflection by $\\theta$ changes the tests by a translation of size\n$O(a_N)$; Proposition~\\ref{prop:moment-majorants} bounds the resulting\nmoment errors, which tend to zero.  Taking the limit proves\n\\eqref{eq:continuum-reflection-positivity}.  This argument permits\narbitrary component indices and polynomial coefficients, so it proves\njoint positivity rather than separate componentwise positivity.\nReality and normalization pass through the same moment limits.\n\\end{proof}\n\nThe next estimate retains an exponent independent of the polynomial.\nThat uniformity will exclude low-energy states throughout the Hilbert\nspace, even though the constants multiplying the exponential depend on\nthe observables.\n\n\\begin{proposition}[Polynomial slab clustering]\n\\label{prop:slab-clustering}\nThere is $m>0$ with the following property.  Let $F$ and $G$ be fixed\npolynomials in finitely many smooth compactly supported smeared\ncomponent fields.  If time translation separates the slabs containing\ntheir supports by a distance $R\\ge0$, then\n\\begin{equation}\n \\bigl|\\Cov(F,\\tau_sG)\\bigr|\n       \\le C_{F,G}e^{-mR}.\n \\label{eq:polynomial-slab-clustering}\n\\end{equation}\nThe exponent $m$ is common to all such polynomials.  The Schwinger\nfunctions satisfy the Euclidean clustering axiom also for Schwartz\ntests.\n\\end{proposition}\n\n\\begin{proof}\nLet $X_{i,N}$ denote the real smeared lattice fields entering the two\npolynomials, with the translations needed to place their slabs.  Complex\ntests may first be split into real and imaginary parts.  The factorial\nmoment bounds of Proposition~\\ref{prop:moment-majorants}, uniformly in\n$N$ and in translations of each fixed test, imply that for some\n$\\varepsilon>0$\n\\begin{equation}\n \\sup_N \\E\\exp\\Bigl(\\varepsilon\\sum_i|X_{i,N}|\\Bigr)<\\infty.\n \\label{eq:polynomial-exponential-tails}\n\\end{equation}\nIndeed, the even moment bounds give factorial bounds on all absolute\nmoments by Cauchy--Schwarz.  Summing the exponential series at a\nsufficiently small radius gives an exponential moment for each variable;\nH\\\"older's inequality combines the finitely many variables.  Neither\nradius depends on the translation of a test.\n\nFor $M\\ge1$, let $T_M(u)=\\max(-M,\\min(u,M))$ and form $F_{N,M}$ and\n$G_{N,M}$ by replacing every real smeared field by its truncation\n$T_M(X_{i,N})$.  If the polynomial degrees are $d_F,d_G$, then\n\\[\n \\norm{F_{N,M}}_\\infty\\le C_F(1+M)^{d_F},\n \\qquad\n \\norm{G_{N,M}}_\\infty\\le C_G(1+M)^{d_G}.\n\\]\nEquation~\\eqref{eq:polynomial-exponential-tails} also gives\n\\begin{equation}\n \\norm{F_N-F_{N,M}}_{L^2}\n +\\norm{G_N-G_{N,M}}_{L^2}\n \\le C_{F,G}e^{-c_{F,G}M},\n \\label{eq:polynomial-truncation}\n\\end{equation}\nwith uniformly bounded $L^2$ norms for $F_N,G_N$.  To obtain this bound,\nexpand the polynomial difference into terms supported on\n$\\{\\max_i|X_{i,N}|>M\\}$.  Each term is bounded by a fixed polynomial in\n$\\sum_i|X_{i,N}|$; the exponential moment absorbs its square and the tail\nindicator.  Cauchy--Schwarz then shows that replacing the covariance by\nthat of the truncated variables costs at most\n$C_{F,G}e^{-c_{F,G}M}$.\n\nThe truncated variables remain bounded local lattice observables in the\nsame slabs.  Equation~\\eqref{eq:orig-26} supplies a fixed physical decay\nrate $\\mu>0$.  The number of microscopic edges between the slabs,\nmultiplied by $a_N$, differs from $R$ by at most $O(a_N)$; this rounding\ncost is bounded uniformly.  Consequently\n\\[\n |\\Cov(F_N,\\tau_sG_N)|\n \\le C_{F,G}(1+M)^{d_F+d_G}e^{-\\mu R}\n       +C_{F,G}e^{-c_{F,G}M}.\n\\]\nSet $M=1+R^2$.  The polynomial prefactor in the first term is absorbed\nby $e^{\\mu R/2}$, with a constant depending on the degrees.  The second\nterm is also at most a test-dependent constant times $e^{-\\mu R/2}$.\nThus the estimate holds with the common choice $m=\\mu/2$.  Moment\nconvergence passes it to the limiting fields.\n\nEuclidean rotation invariance, proved in\nProposition~\\ref{prop:euclidean-regularity}, gives clustering when one\ncompact collection of insertions is translated to infinity in any\ndirection.  The constants can be kept uniform as that direction varies:\nthe rotated tests have uniformly bounded suprema and supports in a\ncommon bounded set, so the unit-box moment bounds give a common radius\nin \\eqref{eq:polynomial-exponential-tails}.  Their polynomial degrees\nand coefficients are unchanged.  To extend this assertion to Schwartz tests, first approximate\nthem by compactly supported product tests.  At every fixed order, the\nproduct-of-unit-box majorants used to prove\nEquation~\\eqref{eq:orig-35} bound the approximation errors uniformly in\nthe translation: place the polynomial weights separately around the\norigins of the two translated collections.  Finite sums of product\ntests are dense in the corresponding Schwartz test spaces.  The same\nuniform bounds therefore allow approximation first and translation to\ninfinity second, proving the full clustering axiom.\n\\end{proof}\n\n\\subsection{Osterwalder--Schrader reconstruction and the full gap}\n\n\\begin{proposition}[Relativistic reconstruction]\n\\label{prop:os-reconstruction}\nThe Schwinger functions $S_n$ reconstruct a tempered Wightman theory in\n$1+1$ dimensions with a three-component Hermitian scalar field.  Its\nHilbert space $\\mathcal H$ is positive, its fields are local, and its\nunitary representation of the Poincar\\'e group satisfies the spectrum\ncondition.  The vacuum $\\Omega$ is unique and normalized.  In the\npositive-time reflection realization of $\\mathcal H$, Euclidean time\ntranslations induce a strongly continuous semigroup\n$e^{-sH_{\\rm phys}}$, $s\\ge0$, with $H_{\\rm phys}\\ge0$ and\n$H_{\\rm phys}\\Omega=0$.\n\\end{proposition}\n\n\\begin{proof}\nWe apply the Osterwalder--Schrader reconstruction theorem under the\nlinear growth condition~\\cite[Section~IV.1]{OS75}.  We specify its regularity\nhypothesis to make clear that the distributional construction in\nSection~\\ref{sec:continuum} suffices.  For $F$ on $(\\R^2)^n$, use the\nSchwartz seminorm\n\\[\n |F|_p=\\max_{|\\alpha|\\le p}\\sup_x\n       (1+|x|^2)^{p/2}|\\partial^\\alpha F(x)|.\n\\]\nThe linear growth condition requires a bound\n$|S_n(F)|\\le \\sigma_n|F|_{sn}$ with fixed $s$ and\n$\\sigma_n\\le A(n!)^D$ for fixed $A,D$.  Equation~\\eqref{eq:orig-35}\ngives this with $s=4$: its weighted supremum is the zero-derivative part\nof $|F|_{4n}$, and $C^n n!\\le A(n!)^D$ after increasing $A,D$.\n\nRestricting to Schwartz tests flat on the coincidence diagonals\n(all derivatives vanish whenever $x_i=x_j$) gives precisely the test\nclass defined in \\cite[Section~II, following Equation~(2.1), p.~284]{OS75}.\nNormalization and reality follow\nfrom Lemma~\\ref{lem:reflection-positivity}; permutation symmetry and\nEuclidean invariance follow from\nProposition~\\ref{prop:euclidean-regularity}; joint positive-time\nreflection positivity follows from the same lemma, extended by\ntest-space continuity; and clustering is\nProposition~\\ref{prop:slab-clustering}.  These are the normalized\nEuclidean axioms with the required linear growth bound.\n\nThe theorem applies to a finite Hermitian multiplet with\nscalar spacetime transformation law; see \\cite[Section~I, Remark~5]{OS75}.\nMore explicitly, the test\nfunctions carry indices $a_1,\\ldots,a_n\\in\\{1,2,3\\}$; reflection\npositivity holds for arbitrary combinations of these indices, while\nsumming the component bounds costs at most an exponential factor in\n$n$, already allowed by the preceding factorial estimate.  Thus the\nmultilinear reconstruction has a single positive Hilbert space and the\nasserted three-component field.  Clustering gives uniqueness of the\nvacuum.  The positive-time reflection realization and its translation\nsemigroup are part of this reconstruction.  This application uses\nsmeared distributions throughout and requires neither Euclidean point\nfields nor a prescription for equal-time products.\n\\end{proof}\n\nThe uniform exponent in Proposition~\\ref{prop:slab-clustering} now\ncontrols every state generated by time-ordered fields.  Positivity of\nspectral measures extends the resulting exclusion of low energies to\nthe Hilbert-space closure.  This is the same spectral implication as\nin \\Rref{prop:continuum}; we give the argument for the present\nreconstructed states.\n\n\\begin{proposition}[Gap above the vacuum]\n\\label{prop:continuum-gap}\nFor the Hamiltonian in Proposition~\\ref{prop:os-reconstruction}, the\nvacuum complement obeys\n\\begin{equation}\n H_{\\rm phys}\\big|_{\\Omega^\\perp}\\ge m>0,\n \\label{eq:orig-36}\n\\end{equation}\nwhere $m$ is the common exponent of\nProposition~\\ref{prop:slab-clustering}.\n\\end{proposition}\n\n\\begin{proof}\nWe first justify the density of states to which the slab estimate\napplies.  Coincidence-flatness does not imply flatness on a surface\nwhere two times agree but the spatial coordinates differ.  The positive\nposition measures and Euclidean rotations nevertheless allow us to\nremove these surfaces in the reflection norm.\n\nSpacetime rotations act only on the insertion coordinates, since\nthe three internal components are spacetime scalars.  Thus each\nfixed-index component measure of $S_k$ is separately rotation\ninvariant.  For any such measure $\\mu_k$ and $i\\ne j$, set\n\\[\n A_{ij}=\\{x:x_i^0=x_j^0,\\ x_i\\ne x_j\\}.\n\\]\nLet $B$ be a ball centred at the origin in $(\\R^2)^k$.  It has finite\n$\\mu_k$-mass by Proposition~\\ref{prop:moment-majorants} and is\ninvariant under simultaneous spacetime rotations.  For each\nconfiguration with $x_i\\ne x_j$, only finitely many rotation angles\nmake the time component of $x_i-x_j$ vanish.  Rotation invariance and\nTonelli's theorem therefore give\n\\[\n \\mu_k(A_{ij}\\cap B)\n  =\\frac1{2\\pi}\\int_B\\int_0^{2\\pi}\n       \\mathbf 1_{A_{ij}}(O_\\alpha x)\\,\\dd\\alpha\\,\\dd\\mu_k(x)=0.\n\\]\nHere $O_\\alpha$ acts on every insertion coordinate.  Increasing the\nball shows that each equal-time surface has measure only on its full\ncoincidence diagonal.\n\nThe OS construction uses ordered positive-time tests; see\n\\cite[Section~V, following Equation~(5.2)]{OS75}.  One may also begin\nwith all coincidence-flat tests supported in positive time.  We show\nthat their reflection completion is the same.  Truncation at infinity\nand away from the time-zero boundary reduces this comparison to\ncompactly supported tests in the latter class;\nthe smooth positive-time support condition and rapid decrease justify\nthese cutoffs in Schwartz topology, and the growth bounds imply\ncontinuity of the reflection form.  Let $f_n$ be one of these compact\ntests, with the coincidence-flatness used in reconstruction, and write\n$[f_n]$ for its class for the reflection form.  Choose\na smooth even function $\\chi_\\varepsilon$ that is zero on\n$[-\\varepsilon,\\varepsilon]$ and one outside\n$[-2\\varepsilon,2\\varepsilon]$, with values in $[0,1]$, and put\n\\[\n f_{n,\\varepsilon}(x)\n   =f_n(x)\\prod_{i<j}\\chi_\\varepsilon(x_i^0-x_j^0).\n\\]\nThe difference $f_n-f_{n,\\varepsilon}$ tends to zero off the internal\nequal-time surfaces.  Those surfaces have zero componentwise\n$S_{2n}$-mass away from full coincidences, as just proved, and $f_n$\nvanishes on the latter.  In the reflection form the positive and\nreflected negative time arguments are separated from one another.\nThe integrands defining the squared reflection norm of this difference\nthus tend to zero almost everywhere for every component measure of\n$S_{2n}$.  They are bounded by a fixed constant on a common compact\nset.  Dominated convergence proves\n\\[\n \\norm{[f_n]-[f_{n,\\varepsilon}]}\\longrightarrow0.\n\\]\nThis approximation is in the reflection norm; it does not assert\nSchwartz density after cutting equal-time surfaces.\n\nThe support of $f_{n,\\varepsilon}$ lies in finitely many strict time\nchambers.  Split it according to the ordering and use permutation\nsymmetry, permuting component indices with insertion coordinates, to\nput each term in $0<x_1^0<\\cdots<x_n^0$.  A compact subset\nof this chamber is covered by finitely many product boxes whose time\nintervals are disjoint and ordered.  A partition of unity and\nproduct-test approximation in these boxes give finite sums of products\nof smooth compactly supported one-field tests, converging in Schwartz\ntopology and hence in reflection norm.  Finite component sums obey the\nsame argument.  These compact time-ordered product states are dense in\nthe OS space $\\mathcal H$, and the approximation also identifies the\ncompletion of the larger coincidence-flat positive-time test class isometrically with\n$\\mathcal H$.\n\nLet $F$ be a finite sum of products of smeared fields, with each product\nsupported in strictly ordered, mutually disjoint positive-time\nintervals, as constructed above.  Its test kernel is coincidence-flat.\nWrite $[F]$ for its reflection-space class in $\\mathcal H$ and set\n\\[\n v=[F]-\\E[F]\\Omega.\n\\]\nThese centered vectors are dense in $\\Omega^\\perp$, and their semigroup\nmatrix elements are\n\\[\n \\inner{v}{e^{-sH_{\\rm phys}}v}\n   =\\E\\bigl[(\\Theta F)(\\tau_sF)\\bigr]\n       -\\overline{\\E[F]}\\E[F].\n\\]\nFor all sufficiently large $s$, the reflected and translated supports\nlie in slabs separated by $s$ plus a fixed constant depending on $F$.\nApply Proposition~\\ref{prop:slab-clustering} with first observable\n$\\overline{\\Theta F}$ and second observable $F$, using the\nsesquilinear covariance convention of Section~\\ref{sec:trace}.  It implies\n\\begin{equation}\n 0\\le\\inner{v}{e^{-sH_{\\rm phys}}v}\\le C_v e^{-ms}.\n \\label{eq:os-vector-decay}\n\\end{equation}\nFor a finite sum of product states one applies the same estimate to\neach of the finitely many cross terms; the exponent remains $m$.\n\nLet $E_H$ be the spectral resolution of $H_{\\rm phys}$ and\n$\\nu_v(B)=\\inner{v}{E_H(B)v}$ its finite positive spectral measure.\nFor $0<m'<m$, positive measure in $[0,m')$ would give\n\\[\n \\inner{v}{e^{-sH_{\\rm phys}}v}\n    =\\int_{[0,\\infty)}e^{-s\\lambda}\\,\\dd\\nu_v(\\lambda)\n    \\ge e^{-sm'}\\nu_v([0,m')),\n\\]\ncontradicting \\eqref{eq:os-vector-decay} as $s\\to\\infty$.\nHence $E_H([0,m'))v=0$ for every centered vector in the dense set.\nSince a spectral projection is bounded, it vanishes on all of\n$\\Omega^\\perp$.  Taking $m'\\uparrow m$ proves\n\\eqref{eq:orig-36}.\n\\end{proof}\n\n\\subsection{Surviving fields and a separated fourth cumulant}\n\nIt remains to verify that the vacuum complement is nonzero and that\nthe limiting field is non-Gaussian.  Both follow from observables at\nthe fixed terminal block scale.  This uses the same field\nrenormalization as the construction and the spectral estimate.\n\n\\begin{proposition}[Nontriviality and failure of Wick factorization]\n\\label{prop:non-gaussian}\nFor a sufficiently large fixed terminal coupling $H$, there exist four\nsmooth compactly supported real tests $f_1,\\ldots,f_4$ with strictly\ndisjoint ordered time ranges for which\n\\begin{align}\n &\\E\\prod_{i=1}^4\\phi^1(f_i)\n -\\E[\\phi^1(f_1)\\phi^1(f_2)]\\E[\\phi^1(f_3)\\phi^1(f_4)] \\notag\\\\\n &\\quad\n -\\E[\\phi^1(f_1)\\phi^1(f_3)]\\E[\\phi^1(f_2)\\phi^1(f_4)]\n -\\E[\\phi^1(f_1)\\phi^1(f_4)]\\E[\\phi^1(f_2)\\phi^1(f_3)]\n \\ne0.\n \\label{eq:nonzero-fourth-cumulant}\n\\end{align}\nThe reconstructed Hilbert space satisfies $\\Omega^\\perp\\ne\\{0\\}$.\nConsequently the spectral bottom on $\\Omega^\\perp$ is both positive\nand finite.\n\\end{proposition}\n\n\\begin{proof}\nChoose four distinct unit terminal cells at fixed bounded distances,\nwith gaps between their closed time intervals.  Their number,\nplacement, and separations are fixed independently of $H$ and of the\ncutoff.  Translations of the block partitions, as allowed in\nSection~\\ref{sec:continuum}, realize this common physical placement.\nLet $X_{i,N}$ be the first component of the renormalized field sum over\nthe descendants of cell $i$.\n\nThe local source estimate of Proposition~\\ref{prop:source-step} and\nthe comparison in Equation~\\eqref{eq:orig-34} give, on a fixed complex\nneighbourhood of the origin,\n\\begin{equation}\n \\E\\exp\\Bigl(\\sum_{i=1}^4 z_iX_{i,N}\\Bigr)\n   =\\E_\\mu\\exp\\Bigl(\\sum_{i=1}^4 z_iV_{Y_i}^1\\Bigr)+o_H(1),\n \\label{eq:cell-generating-comparison}\n\\end{equation}\nuniformly in the cutoff and in the admitted axis tori.  Here $\\mu$ is\nthe corresponding terminal spin law and $Y_i$ labels cell $i$.\nCauchy's formula transfers the uniform analytic error to each of the\nfixed moments of order at most four.\n\nFor completeness, the alignment estimate\n\\eqref{eq:orig-23} controls the spins at these finitely many sites in\nmean square.  If a nearest-neighbour path of length $\\ell$ joins $Y_i$\nto $Y_1$, then\n\\[\n \\E_\\mu|V_{Y_i}-V_{Y_1}|^2\n \\le \\ell\\sum_{e\\text{ on the path}}\\E_\\mu r_e^2\n \\le \\ell^2\\bigl(Ct^2+4e^{-cHt^2}\\bigr)=o_H(1).\n\\]\nThe path lengths are fixed; $t=t_0$ tends to zero and $Ht^2=p_0^2$\ntends to infinity as $H\\to\\infty$.  Internal $O(3)$ invariance makes\neach $V_Y$ uniformly distributed on $S^2$.  If $U$ has this law, then\n\\[\n \\E U^1=0,\\qquad \\E(U^1)^2=\\frac13,\n \\qquad \\E(U^1)^4=\\frac15.\n\\]\nThe coordinates have absolute value at most one.  Telescoping a\nproduct and using the preceding mean-square estimate therefore gives,\nfor every pair of distinct chosen cells and for the four-cell product,\n\\begin{equation}\n \\E[X_{i,N}X_{j,N}]=\\frac13+o_H(1),\n \\qquad\n \\E\\prod_{i=1}^4X_{i,N}=\\frac15+o_H(1).\n \\label{eq:terminal-cell-moments}\n\\end{equation}\nThe first moments vanish exactly by internal symmetry.\n\nThe estimates are uniform on axis tori.  We may thus first take the\nperiodic infinite-plane limit at fixed cutoff and then the continuum\nlimit.  The descendants in a standard blocking are axis boxes;\nLemma~\\ref{lem:boundary-strips} identifies their limits with the fixed\nphysical cells and justifies any converging displacement of their\nboundaries.  Denote the resulting cell variables by $X_i$.  Their\nfourth cumulant is\n\\begin{equation}\n \\E\\prod_{i=1}^4X_i\n   -\\sum_{\\{\\{i,j\\},\\{k,l\\}\\}}\\E[X_iX_j]\\E[X_kX_l]\n   =\\frac15-3\\left(\\frac13\\right)^2+o_H(1)\n   =-\\frac{2}{15}+o_H(1),\n \\label{eq:cell-fourth-cumulant}\n\\end{equation}\nwhere the sum runs over the three pairings of $\\{1,2,3,4\\}$.\nChoose $H$ sufficiently large that this quantity is nonzero.  This\nchoice depends only on the fixed cells and the earlier RG constants,\nand can be made before the later choices of $M_0$ and $K_0$ in\nSection~\\ref{sec:trace}.\n\nApproximate each cell indicator by bounded smooth tests whose\ndifferences from the indicator are supported in shrinking boundary\nstrips.  The approximations can preserve the strict gaps between the\ntime ranges.  Lemma~\\ref{lem:boundary-strips} makes every moment in\n\\eqref{eq:cell-fourth-cumulant} converge under this approximation.\nThus suitable smooth tests satisfy\n\\eqref{eq:nonzero-fourth-cumulant}.  Since they have separated ordered\ntimes, these are Schwinger correlations in the domains used by\nreconstruction.  Wick factorization of the reconstructed Wightman\nfunctions would, by their Euclidean continuation, give Wick\nfactorization for these Schwinger correlations as well.  Equation\n\\eqref{eq:nonzero-fourth-cumulant} excludes it.\n\nThe same two-cell calculation can be made for unit cells reflected\nacross $x^0=0$, with both cells a positive distance from the seam.\nTheir first-component correlation is $1/3+o_H(1)>0$.  Apply reflected\nsmooth approximations to this pair.  For a suitable real test $f$\nsupported strictly in positive time,\n\\[\n \\norm{[\\phi^1(f)]}^2\n   =\\E\\bigl[\\phi^1(\\theta f)\\phi^1(f)\\bigr]>0,\n \\qquad\n \\inner{\\Omega}{[\\phi^1(f)]}=\\E\\phi^1(f)=0.\n\\]\nHence $\\Omega^\\perp$ contains a nonzero vector $v$.  Its spectral\nmeasure has total mass $\\norm v^2>0$.  Since $[0,\\infty)$ is the\nincreasing union of bounded intervals, some finite $E$ satisfies\n$\\nu_v([0,E])>0$.  The spectral bottom on $\\Omega^\\perp$ is therefore\nfinite, and Proposition~\\ref{prop:continuum-gap} makes it positive.\n\\end{proof}\n\n\\begin{proof}[Completion of the proof of Theorem~\\ref{thm:main}]\nThe shooting construction gives bare couplings $\\beta_N\\to\\infty$.\nThe prescribed lattice spacings satisfy $a_N=L^{-N}\\to0$, and the\nsource construction in Section~\\ref{sec:sources} supplies the positive\nfield renormalizations $B_N$ for the standard nearest-neighbour $O(3)$\nmodel.  The terminal kinetic coupling is the fixed number $H$ at unit\nphysical block length.\nProposition~\\ref{prop:continuum-limit} establishes convergence of the\nrenormalized correlations and finite lists of smeared fields, with\ninfinite volume taken first or simultaneously along the specified torus\nexhaustion.  Proposition~\\ref{prop:euclidean-regularity} gives their\ntemperedness and Euclidean symmetries.\n\nProposition~\\ref{prop:os-reconstruction} reconstructs the local,\nunitary relativistic theory.  Its full Hamiltonian gap is\nProposition~\\ref{prop:continuum-gap}, and\nProposition~\\ref{prop:non-gaussian} proves that a nonzero vacuum\ncomplement and a nonvanishing connected fourth correlation survive in\nthis same continuum limit.  In particular the physical gap is positive\nand finite at the fixed scale of the construction.  Every limiting\nobservable uses the field renormalizations already prescribed from the\nnearest-neighbour model; the blocking observations are integrated\nexactly and introduce no additional microscopic physical fields.  The\nconstruction adds no symmetry-breaking mass term.\n\\end{proof}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/preliminary.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/preliminary.tex", "bytes": 42604, "sha256": "66773d15559a7e115bb9e328f622012f29fd3e1498cfd33d956b8e9cc48d05c2", "content": "\\section{A preliminary correlation length}\\label{sec:preliminary}\n\nThe first input is a mixing estimate that is uniform when bonds are\nweakened or deleted. Its exponential scale need not be sharp. The strict\ninequality in the exponent below is, however, essential to the later\ncomparison of effective densities. We adapt the pinned-cell argument of\n\\cite{OpenAI-O4}, retaining its current estimates and modifying the\nrotation algebra for spins on $S^2$.\n\nFor a bounded local Lipschitz observable $F$ with finite support $A$, set\n\\[\n \\mathcal L(F)=\\norm{F}_\\infty+\\max_{x\\in A}\\operatorname{Lip}_x(F),\n\\]\nwhere the individual Lipschitz constant uses round distance on $S^2$.\nDistances between supports are measured in the maximum norm, with the\nperiodic distance on a torus. A finite free subgraph carries no boundary\npins; bonds outside the subgraph are absent. Write $Z_b(n,w)$ for the\nhomogeneous partition function on the rectangular $n$-by-$w$ torus,\nwith normalized area measure at every site.\n\n\\begin{proposition}[Preliminary mixing and rectangular doubling]\n\\label{prop:preliminary}\nThere are constants $b_0,C,c,p>0$ and $\\eta>0$ such that, with\n\\begin{equation}\n X(b)=C\\exp\\bigl((4\\pi-\\eta)b\\bigr),\\qquad b\\ge b_0,\n \\label{eq:orig-1}\n\\end{equation}\nthe following statements hold uniformly for all bond strengths\n$0\\le\\beta_e\\le b$, including zero.\n\\begin{enumerate}[label=\\textup{(\\roman*)}]\n\\item On every unpinned rectangular torus whose sides are at least\n$CX(b)$, and on every finite free subgraph of the square lattice, local\nLipschitz observables $F,G$ supported on $A,A'$ at distance $d$ satisfy\n\\begin{equation}\n |\\Cov(F,G)|\\le\n C(1+b+|A|+|A'|+d)^p\\mathcal L(F)\\mathcal L(G)\n e^{-cd/X(b)}.\n \\label{eq:prelim-mixing}\n\\end{equation}\nIn the homogeneous model the periodic infinite-plane limit exists and\nis unique.\n\\item For each fixed aspect bound $\\kappa\\ge1$, the constants may be\nchosen so that, for even $n,w$ with\n$\\kappa^{-1}\\le n/w\\le\\kappa$ and $\\min(n,w)\\ge CX(b)$,\n\\begin{equation}\n 0\\le4\\log Z_b(n,w)-\\log Z_b(2n,2w)\n \\le C(1+b+n+w)^p e^{-c\\min(n,w)/X(b)}.\n \\label{eq:prelim-doubling}\n\\end{equation}\n\\end{enumerate}\n\\end{proposition}\n\nThe proof has three stages. We first obtain a uniform upper bound on the\nresponse of a pinned square to slowly varying rotations. A one-site\nestimate supplies a strict saving in the initial bound. Finally, local\nrotation estimates give decay for centered one-site functions, and two\nconditional ferromagnetic decompositions extend that decay to arbitrary\nlocal observables.\n\n\\subsection{Pinned squares, currents, and rotation identities}\n\nLet $Q(S)=\\{0,\\ldots,S\\}^2$, with arbitrary spins fixed on its boundary.\nOrient every bond in a positive coordinate direction. Its coefficient is\n$w_e=\\beta_e$, except that a bond tangent to the boundary has coefficient\n$\\beta_e/2$. This allocation makes the actions of adjacent cells add to\nthe action of their union. For $e=x\\to y$, define\n\\begin{equation}\n s_e=q_x\\times q_y,\\qquad\n E_e=(q_x\\cdot q_y)\\Id-\\frac12(q_xq_y^T+q_yq_x^T).\n \\label{eq:prelim-contact}\n\\end{equation}\nIf $F=(F_1,F_2)$ is a smooth $\\C^3$-valued test field on the unit square,\n$z_e$ denotes the rescaled bond midpoint, and $\\mu(e)$ is the bond\ndirection, put\n\\begin{align}\n X_Q(F)&=S^{-1}\\sum_e w_es_e\\cdot F_{\\mu(e)}(z_e),\n \\label{eq:prelim-current}\\\\\n \\mathcal H_Q(F,G)&=\n S^{-2}\\E\\sum_e w_e\\overline{F_{\\mu(e)}(z_e)}\\cdot\n E_eG_{\\mu(e)}(z_e)-\\Cov(X_Q(F),X_Q(G)).\n \\label{eq:prelim-stiffness}\n\\end{align}\nThe covariance is sesquilinear, with conjugation in the first entry.\nThe restriction of $\\mathcal H_Q$ to constant $3$-by-$2$ matrices is\nrepresented by a real symmetric $6$-by-$6$ matrix $H_Q$. We call this\nmatrix the stiffness of the pinned square. No lower bound on $H_Q$ is\nassumed. For a unit vector $a$,\n\\[\n a^TE_ea=q_x\\cdot q_y-(a\\cdot q_x)(a\\cdot q_y)\n =q_x^{\\perp a}\\cdot q_y^{\\perp a},\n\\]\nso $|a^TE_ea|\\le1$. The half weights give\n$\\sum_{e\\parallel\\mu}w_e\\le bS^2$, and consequently\n\\begin{equation}\n H_Q\\le b\\Id.\n \\label{eq:prelim-initial-stiffness}\n\\end{equation}\n\nLet $R_a(t)$ be ordinary rotation through angle $t$ about the unit axis\n$a$. Under $q_x\\mapsto R_a(tf_x)q_x$, the action\n$\\mathcal A=-\\sum_e w_eq_x\\cdot q_y$ satisfies\n\\begin{equation}\n D_f\\mathcal A=\\sum_e w_e(a\\cdot s_e)(f_y-f_x),\\qquad\n D_f^2\\mathcal A=\\sum_e w_e(a^TE_ea)(f_y-f_x)^2.\n \\label{eq:prelim-score}\n\\end{equation}\nFor a general infinitesimal profile $u_x\\in\\R^3$, direct differentiation\ngives\n\\begin{equation}\n D_us_e=E_e(u_y-u_x)+\\frac12(u_y+u_x)\\times s_e.\n \\label{eq:prelim-bond-derivative}\n\\end{equation}\nThus, if $u$ vanishes on the pinned boundary and\n$(d_Su)_e=S(u_y-u_x)$, integration by parts yields\n\\begin{equation}\n \\E\\bigl[\\overline{X_Q(d_Su)}X_Q(F)\\bigr]\n =S^{-2}\\E\\sum_e w_e\\overline{(d_Su)_e}\\cdot E_eF_e\n +\\frac12\\E X_Q\\bigl(F_e\\times(\\overline{u_y}+\\overline{u_x})\\bigr).\n \\label{eq:prelim-ward}\n\\end{equation}\nHere and below the cross product is extended complex bilinearly. These\nare the replacements for \\Rref{eq:pre:1-1}, \\Rref{eq:pre:1-3}, and\n\\Rref{eq:pre:1-5}. The current method belongs to the Ward-identity approach to spin\ncorrelations~\\cite{AizenmanSimonWard}. The factor $1/2$ in\nEquation~\\eqref{eq:prelim-ward} determines the variance gain below.\n\nWe use an auxiliary parameter $B\\ge b$ and impose\n\\begin{equation}\n \\log(2+S)\\le16B.\n \\label{eq:prelim-size-restriction}\n\\end{equation}\nFor every fixed moment order $r<\\infty$, the current estimate is\n\\begin{equation}\n \\norm{X_Q(F)}_{L^r}\\le\n C_r(1+b)\\log(2+S)\\norm{F}_{C^1}.\n \\label{eq:prelim-current-moment}\n\\end{equation}\nWe verify the change in the proof of \\Rref{eq:pre:2-2}, because its\nuniformity in the pins will be used repeatedly. For a one-axis profile\n$f$ that vanishes on the boundary, Equation~\\eqref{eq:prelim-score}\nand invariance of the product area measure give\n\\begin{equation}\n \\E e^{tD_f\\mathcal A}\n \\le\\exp\\left(\\frac12bt^2\\sum_e(f_y-f_x)^2\\right).\n \\label{eq:prelim-score-mgf}\n\\end{equation}\nThe same Taylor estimate bounds fixed moments of the likelihood ratio\nof a profile rotation by $\\exp(C_rb\\sum_e(f_y-f_x)^2)$.\n\nTo extract a transverse current, take a patch of side $d$ with a\nproportional buffer, a scalar test $h$ of patch-coordinate $C^1$ norm\n$H_{\\rm test}$, and a compact score whose potential is affine with slope\n$d^{-1}$ in the required spatial direction on the support of $h$.\nChoose its color and the rotation axis perpendicular to the desired\ncurrent color. In the two opposite rotations use\n\\[\n f=\\arcsin(h/N),\\qquad N=C\\sqrt{1+b}\\,H_{\\rm test},\n\\]\nin place of the half angle in \\Rref{pre:part-2-2}. Rotation of a bond\ncurrent differs from rigid rotation through its midpoint angle by\n$O(\\sup|df|)$. After multiplication by $N$, the resulting test error is\n$O(H_{\\rm test}/d)$ per bond. There are $O(d^2)$ bonds and the score\nnormalization is $d^{-1}$, so its deterministic cost is\n$CbH_{\\rm test}$. Equation~\\eqref{eq:prelim-score-mgf} bounds the moments\nof the two rotated scores, including their likelihood ratios, by\n$C_r(1+b)H_{\\rm test}$. Hence\n\\[\n \\left\\|d^{-1}\\sum_{e\\parallel\\mu}w_e(s_e)_k h_e\\right\\|_{L^r}\n \\le C_r(1+b)H_{\\rm test}.\n\\]\nA Whitney decomposition of the square has $O(S/d)$ patches at each\nboundary-distance scale $d$. Multiplying the patch bound by $d/S$ and\nsumming the $O(\\log(2+S))$ scales proves\nEquation~\\eqref{eq:prelim-current-moment}; the bounded-width boundary\nlayer is estimated directly. This is the argument of\n\\Rref{pre:part-2-3}, with constants independent of the pins and of the\nindividual strengths.\n\n\\subsection{A uniform decrease of stiffness}\n\nThe next lemma turns the local identities into a bound at a larger\nscale. The enlargement factor is denoted by $\\ell$; it is unrelated to\nthe fixed renormalization factor $L$ used later.\n\n\\begin{lemma}[Stiffness iteration]\\label{lem:prelim-iteration}\nFix a sufficiently large $D$. Suppose that, on squares of side $R$,\n$H_R\\le h\\Id$ uniformly over all boundary pins and all strengths in\n$[0,b]$, where $B^{-D}\\le h\\le B$ and $B\\ge b$ is sufficiently large.\nThe pinned-cell iteration produces a common side $R_*$ with\n\\begin{equation}\n H_{R_*}\\le B^{-D}\\Id,\\qquad\n \\log(R_*/R)\\le4\\pi h+C_D\\sqrt{B\\log B},\n \\label{eq:prelim-iteration-conclusion}\n\\end{equation}\nprovided Equation~\\eqref{eq:prelim-size-restriction} holds throughout\nthe iteration, including each proposed next square. All stiffness\nbounds remain uniform in the original pin and strength class.\n\\end{lemma}\n\n\\begin{proof}\nWe give the changes to \\Rref{pre:cells} through\n\\Rref{pre:iteration}, including the estimates that determine the\ncoefficient $4\\pi$.\n\n\\smallskip\\noindent\n\\emph{The conditional potential and its derivatives.}\nTile a square of side $S=\\ell R$ into $\\ell^2$ cells and condition on\ntheir complete boundary rings $\\omega$.\nTheir interiors are independent. If $Z_c(\\omega)$ is the pinned\npartition function in cell $c$, the ring action and conditional current\nare\n\\[\n S_g(\\omega)=-\\sum_c\\log Z_c(\\omega),\\qquad\n j(F)=\\E[X_Q(F)\\mid\\omega]\n     =\\ell^{-1}\\sum_c\\E_cX_c(F).\n\\]\nFor the lower-left rescaled anchor $z_c$, put\n\\[\n N_c(F)=\\sup_{c^+}(|F|+\\ell^{-1}|\\nabla F|),\\qquad\n D_c(F)=\\ell^{-1}N_c(\\nabla F),\n\\]\nwhere $c^+$ is a fixed enlargement of $c$. Let $P(B)$ denote a\npolynomial whose coefficients and degree may depend on fixed moment\norders or requested accuracy, but not on $R,S,\\ell$.\nEquation~\\eqref{eq:prelim-current-moment} gives\n\\begin{align}\n |\\E_cX_c(F)|&\\le P(B)N_c(F),\\qquad\n |\\mathcal H_c(F,G)|\\le P(B)N_c(F)N_c(G),\n \\label{eq:prelim-cell-bounds}\\\\\n |\\mathcal H_c(F,G)-\\overline{F_c}H_cG_c|\n &\\le P(B)\\{D_c(F)N_c(G)+N_c(F)D_c(G)\\}.\n \\label{eq:prelim-cell-replacement}\n\\end{align}\nThese are \\Rref{eq:pre:3-1} and \\Rref{eq:pre:3-2}, obtained by\nsubtracting anchor values in the bilinear form.\n\nFor a scalar ring profile $u$, let $H_c^u$ be the cell stiffness with\nits pins rotated by $R_a(u)$, and put $O_c(u)=R_a(u(z_c))$, acting on\nthe color index. Rigid covariance and\nEquation~\\eqref{eq:prelim-current-moment} give\n\\begin{equation}\n \\|O_c(u)^TH_c^uO_c(u)-H_c\\|\\le P(B)D_c(u),\n \\label{eq:prelim-anchor}\n\\end{equation}\nwith the same estimate for the derivative at zero. Indeed, remove the\nconstant rotation at the anchor, rotate the interior variables by the\nremaining profile, and differentiate the contact and covariance in\nEquation~\\eqref{eq:prelim-stiffness}. The residual score is controlled\nby the current estimate; $E_e$ and its first derivative are bounded.\nRepeating this calculation at the current pins gives the estimate\nalong an arbitrary-amplitude path. Each cell law in this calculation\nstill has the original untwisted interaction. Furthermore,\n\\begin{equation}\n D_u^2S_g=\\ell^{-2}\\sum_c\n \\mathcal H_c(a\\,d_Su,a\\,d_Su).\n \\label{eq:prelim-ring-hessian}\n\\end{equation}\nThese are the required versions of \\Rref{eq:pre:3-3} and\n\\Rref{eq:pre:3-4}.\n\nTo exploit the variance in the stiffness, choose $D'>D$ and\n\\[\n h<v\\le2B,\\qquad c_0\\ge B^{-D'},\\qquad\n C_c=v\\Id-H_c-c_0P_a\\ge B^{-D'}\\Id,\n\\]\nwhere $P_a$ projects onto the two spatial components with color $a$.\nAdjoin independent standard Gaussian vectors $n_c\\in\\R^6$ and\nGaussian vectors $n_{0,c}\\in\\R^2$ of covariance $c_0\\Id$, all independent\nof the ring data, and define\n\\begin{equation}\n J(F)=j(F)+\\ell^{-1}\\sum_c\n       (\\sqrt{C_c}\\,n_c+a\\,n_{0,c})\\cdot F_c.\n \\label{eq:prelim-augmented-current}\n\\end{equation}\nTotal variance gives, for every constant real matrix $A$,\n\\begin{equation}\n H_Q(A,A)=v|A|^2-\\operatorname{Var}J(A).\n \\label{eq:prelim-variance-identity}\n\\end{equation}\nThis is \\Rref{eq:pre:4-3}; it uses no positivity of $H_c$.\n\nHere are the commuting derivatives used to bound the variance. Let\n$\\psi$ be a nonzero real trigonometric cutoff vanishing on the boundary, and\nlet $f_i=\\psi\\varphi_i$, where $\\varphi_i$ are real orthonormal Fourier\nmodes of frequencies at most $4K$, including the constant mode.\nFor each nonzero pair $\\{k,-k\\}$, choose one representative $k$ and use\nboth modes $\\sqrt2\\cos(k\\cdot z)$ and $\\sqrt2\\sin(k\\cdot z)$.\nTheir respective quadrature modes are\n$\\sqrt2\\sin(k\\cdot z)$ and $-\\sqrt2\\cos(k\\cdot z)$.\nFrequency sums over both signs therefore agree with sums over these\ntwo normalized real modes. The cutoff has band $O(m)$ and\n$K+m\\ll\\ell$. With $f=(f_i)_i$, define\n\\[\n G_0=\\ell^{-2}\\sum_c\\nabla f(z_c)\\nabla f(z_c)^T,\n \\qquad\n p=(c_0G_0)^{-1}\\ell^{-1}\\sum_c\\nabla f(z_c)n_{0,c},\n \\qquad \\theta=f\\cdot p.\n\\]\nExact quadrature identifies $G_0$ with the continuum gradient Gram\nmatrix. It is positive definite: a combination of the $f_i$ with zero\ngradient is constant and vanishes on the boundary; division by $\\psi$\non an open set where it is nonzero then annihilates the Fourier\npolynomial, hence every coefficient. The base variables are\n\\[\n \\omega^0=R_a(-\\theta)\\omega,\\quad\n n_{0,c}^0=n_{0,c}-c_0\\nabla\\theta(z_c)/\\ell,\\quad\n n_c^0=O_c(\\theta)^Tn_c-\\sqrt{C_c^0}\\,a\\nabla\\theta(z_c)/\\ell,\n\\]\nwhere $C_c^0$ is evaluated at $\\omega^0$. Conditional on these base\nvariables, the density of $p$ is proportional to $e^{-V(p)}$, with\n\\begin{equation}\n V(p)=\\tfrac12c_0p^TG_0p+S_g(R_a(\\theta)\\omega^0)\n +\\tfrac12\\sum_c|n_c^0+\\sqrt{C_c^0}\\,a\\nabla\\theta(z_c)/\\ell|^2.\n \\label{eq:prelim-conditional-potential}\n\\end{equation}\nThe successive changes of variables preserve product area measure on\nthe rings and have constant Gaussian Jacobians. Thus the derivation in\n\\Rref{pre:gaussian} is unchanged. Differentiation in deterministic\npotential directions\n$u_i\\in\\operatorname{span}\\{\\psi\\varphi_j:|k_j|\\le4K\\}$ at fixed base\nvariables gives commuting operators $\\partial_i$. Their adjoints satisfy\n\\begin{equation}\n \\E\\left(\\sum_i\\partial_i^*Y_i\\right)^2\n =\\E\\sum_{ij}(\\partial_jY_i)(\\partial_iY_j)\n  +\\E\\sum_{ij}Y_i(\\partial_i\\partial_jV)Y_j.\n \\label{eq:prelim-ibp-array}\n\\end{equation}\nThe strictly positive Gaussian quadratic form in\nEquation~\\eqref{eq:prelim-conditional-potential} justifies integration\nby parts, as in \\Rref{eq:pre:4-5}.\n\nWe now state the band estimates with their accuracy parameters. Fix any\nrequired inverse power $B^{-N}$, and then choose a fixed exponent $J$\nsufficiently large. The coefficients and degrees of all polynomial\nbounds are fixed before this final enlargement of $J$. Assume $K/\\ell\\le B^{-J}$, $Bm\\le K$, and\nEquation~\\eqref{eq:prelim-size-restriction}. For fields of band $O(K)$,\n\\Rref{eq:pre:5-1} and \\Rref{eq:pre:5-2} give\n\\[\n \\|N(F)\\|_\\square\\le C\\|F\\|_2,\\quad\n \\|D(F)\\|_\\square\\le C(K/\\ell)\\|F\\|_2,\\quad\n \\|d_Su-\\nabla u\\|_2\\le C(K/S)^2\\|\\nabla u\\|_2,\n\\]\nwhere $\\|a\\|_\\square^2=\\ell^{-2}\\sum_c|a_c|^2$.\nThe Gaussian field $\\nabla\\theta$ has covariance $c_0^{-1}$ times the\northogonal projection onto its gradient subspace. Consequently, for\nany fixed $n$, outside an event of probability at most $e^{-B^3}$,\n\\[\n \\rho:=\\ell^{-1}\\|\\nabla\\theta\\|_\\infty+\n       \\ell^{-2}\\|\\nabla^2\\theta\\|_\\infty\\le B^{-n}.\n\\]\nThe evaluation bounds for band-limited fields and the grid argument in\n\\Rref{pre:part-5-1} are scalar spatial estimates and apply without\nchange. Equations~\\eqref{eq:prelim-cell-replacement}--\\eqref{eq:prelim-ring-hessian}\nthen give, simultaneously for all potential directions,\n\\begin{equation}\n \\partial_u^2V\\le(v+B^{-N})\\|\\nabla u\\|_2^2\n \\label{eq:prelim-band-hessian}\n\\end{equation}\non this event. The error before choosing $J,n$ is bounded by\n$P(B)[K/\\ell+(K/S)^2+\\rho]\\|\\nabla u\\|_2^2$. Everywhere the same\nupper estimate holds with $P(B)$ replacing $v+B^{-N}$, and\n\\begin{equation}\n \\|J(F)\\|_{L^r}\\le C_rP(B)\\ell\\|F\\|_2.\n \\label{eq:prelim-crude-current}\n\\end{equation}\nThus exceptional events remain negligible even after the fixed powers\nof $\\ell$ and mode counts used below, since $\\log\\ell\\le16B$.\n\nFor real deterministic $F$ of band $O(K)$ perpendicular in color to\n$a$, put $F'=-a\\times F$.\nIf at most $CK^2$ real directions in this potential span satisfy\n\\[\n \\left\\|\\nabla\\sum_i d_iu_i\\right\\|_2\\le C|d|,\n \\qquad N_c(\\nabla u_i)\\le C,\n\\]\nthe modified nonabelian derivative estimate is\n\\begin{equation}\n \\E\\sum_i|\\partial_iJ(F)-J(u_iF')|^2\n \\le B^{-N}\\|F\\|_2^2.\n \\label{eq:prelim-nonabelian}\n\\end{equation}\nFor completeness, if\n$r_u(z)=u(z+e_\\mu/(2S))+u(z-e_\\mu/(2S))-2u(z)$ on direction $\\mu$,\nthe derivative of the physical conditional mean is\n\\[\n \\partial_u j(F)=j(uF')+\\tfrac12j(r_uF')\n   +\\ell^{-2}\\sum_c\\mathcal H_c(a\\,d_Su,F).\n\\]\nThis replaces the coefficients $2,1$ in \\Rref{eq:pre:5-8} by $1,1/2$.\nThe Taylor remainder is bounded by\n$P(B)\\ell KS^{-2}\\|\\nabla u\\|_2\\|F\\|_2$.\nThe anchor rotation of the noise differentiates with coefficient one.\nIts deterministic drift combines with the last displayed contact to\nleave $(H_c+C_c)a=(v\\Id-c_0P_a)a$, whose pairing with $F$ is zero.\nThe remaining scalar linear-functional error is bounded by\n$P(B)[K/\\ell+(K/S)^2+\\rho]\\|\\nabla u\\|_2\\|F\\|_2$.\nTaking its operator norm in $(d_i)$ gives the sum of squared errors\nwithout a mode-count factor. The non-anchor derivative of\n$\\sqrt{C_c}$ contributes at most\n$P(B)K^2\\ell^{-2}\\|F\\|_2^2$ after summing over directions, because the\noriginal noises are independent of the ring data. These are exactly\nthe estimates \\Rref{eq:pre:5-9}--\\Rref{eq:pre:5-12}, proving\nEquation~\\eqref{eq:prelim-nonabelian}.\n\nThe other required identity is the uncentered Ward estimate. Define\n\\[\n \\mathcal D(F,G)=v\\ell^{-2}\\sum_c\\overline{F_c}\\cdot G_c\n                -\\E\\overline{J(F)}J(G).\n\\]\nFor $\\zeta=r\\xi(z)e^{ik\\cdot z}/(i|k|)$ vanishing on the boundary,\nwith $|r|\\le1$, $m\\le|k|\\le CK$, cutoff amplitude $\\xi$ of band\n$O(m)$ and bounded fixed-order polynomial derivative norms, and for a\nsame-frequency exact gradient or bounded constant-matrix test $G$,\nassume also that each amplitude's first derivative divided by $|k|$\nis bounded, as in \\Rref{pre:part-5-4}. Then\n\\begin{equation}\n |\\mathcal D(d_S\\zeta,G)|\\le P(B)/|k|+B^{-N}.\n \\label{eq:prelim-frequency-ward}\n\\end{equation}\nApply Equation~\\eqref{eq:prelim-ward} before replacing the gradient by\nits leading Fourier symbol: its contact cancels the raw defect. The\nremaining phases cancel in the bracket, whose test has $C^1$ norm\n$P(B)/|k|$. Equations~\\eqref{eq:prelim-current-moment} and\n\\eqref{eq:prelim-cell-replacement} finish the estimate. The changed\nbracket coefficient affects only an absolute constant.\n\n\\smallskip\\noindent\n\\emph{Auxiliary weighted-current control.}\nTo lower-bound $\\operatorname{Var}J(A)$ for a constant matrix $A$, we\nwill pair $J(A)-\\E J(A)$ with the mean-zero divergence\n$\\sum_i\\partial_i^*Y_i$ of a suitable test array $Y=(Y_i)$.\nEquation~\\eqref{eq:prelim-ibp-array} splits the squared norm of this\ndivergence into a Hessian contribution and a crossed derivative\ncontribution. Equation~\\eqref{eq:prelim-band-hessian} already controls\nthe Hessian. Before choosing the main test array, we prove a weighted\ncurrent bound that will control its crossed derivative contribution,\nwith constants independent of the number of frequencies. Set\n\\[\n M=B^J,\\quad K=\\ell/B^J,\\quad\\ell\\ge B^{10J},\\quad\n m=\\lceil B^4\\rceil,\\quad\n \\psi(z)=\\prod_{\\mu=1}^2[1-\\cos^{2m}(\\pi z_\\mu)],\\quad\\chi=\\psi^2.\n\\]\nFor a long step put $K_1=\\ell/B^{4J}$ and $K_2=\\ell/B^{3J}$; for a\nshort step put $K_1=8M$ and $K_2=256M$. Frequencies belong to\n$2\\pi\\Z^2$, and\n\\[\n I_\\chi=\\int\\chi^2\\ge1-CB^{-2},\\qquad\n T=\\sum_{M\\le|k|\\le K_1}|k|^{-2}\n   =\\frac1{2\\pi}\\log(K_1/M)+O(M^{-1}).\n\\]\nDefine\n\\[\n w(k)=\\sum_{\\substack{H\\text{ dyadic}\\\\M\\le H\\le K_2}}\n H^{-2}e^{-\\sqrt{|k|/H}},\\qquad T_w=\\sum_k w(k).\n\\]\nThe scalar convolution bounds in \\Rref{eq:pre:6-3} are\n\\[\n T_w\\asymp1+\\log(K_2/M),\\quad w*w\\le CT_ww,\\quad\n (m+|k|)^2w(k)\\le C,\n\\]\nand $w(k)\\ge c(M+|k|)^{-2}$ for $|k|\\le K_2/2$.\nShifts of size $O(m)$ change these weights by a bounded factor; their\ntails beyond $K-O(m)$ are smaller than any required inverse power of\n$B$, including the polynomial factors in $\\ell$.\n\nFix a unit color $r$ perpendicular to $a$, and put $r'=-a\\times r$;\nthen $r'$ is also unit. Use the annular exact\ngradients\n$G_p^r=r\\,d_S(\\psi\\widetilde\\varphi_p/|k_p|)$,\n$2M\\le|k_p|\\le4M$, where $\\widetilde\\varphi_p$ is the quadrature\nFourier mode defined above, and directions\n$u_i=\\sqrt{w(k_i)}\\psi\\varphi_i$ with $|k_i|\\le K$.\nWriting $b_p=|k_p|^{-2}$ and\n$U^r=\\sum_{pi}b_p\\E J(G_p^ru_i)^2$, the same-frequency Ward bound gives\n$\\sum_pb_p\\E J(G_p^r)^2\\le Cv$. In the integration-by-parts proof of\n\\Rref{eq:pre:6-5}, Equation~\\eqref{eq:prelim-nonabelian} supplies signal\n$U^r$ rather than $2U^r$. The Hessian contribution is at most $CvU^r$,\nand convolution bounds the squared differentiated arrays by\n$CT_wU^{r'}$, up to arbitrary inverse powers of $B$.\nThus, for $U=\\max(U^r,U^{r'})$,\n\\[\n U^2\\le Cv(v+T_w)U+CvB^{-N_*},\\qquad\n U^r+U^{r'}\\le Cv(v+T_w),\n\\]\nwhere $N_*>100(1+D+D')$ is fixed first. The derivative errors cost\n$B^{-N}T_w$, not $B^{-N}K^2$. Taking common positive lower weights,\nthen pairing opposite frequencies and coordinate reflections, proves\n\\begin{equation}\n \\sum_{|n|\\le4K_1}(M+|n|)^{-2}\n \\sum_{\\mu=1}^2\\E|J(r e_\\mu\\psi^2e^{in\\cdot z})|^2\n \\le Cv(v+T_w),\n \\label{eq:prelim-weighted-current}\n\\end{equation}\nas in \\Rref{eq:pre:6-6} and \\Rref{eq:pre:6-7}.\n\n\\smallskip\\noindent\n\\emph{The main divergence test and its variance gain.}\nFor a constant real $3$-by-$2$ matrix $A$ with $|A|=1$, choose a unit\n$a$ normal to both columns, and put $A'=-a\\times A$. On the main\nannulus take\n\\[\n u_i=\\chi\\varphi_i/|k_i|,\\quad\n G_i^{A'}=d_S\\bigl(\\chi(A'\\widehat k_i)\n                        \\widetilde\\varphi_i/|k_i|\\bigr),\\quad\n Y_i=J(G_i^{A'})/|k_i|.\n\\]\nThe band separation ensures that these $u_i$ belong to the potential\nspan. Their gradient Gram operator satisfies\n\\[\n \\left\\|\\nabla\\sum_i d_i u_i\\right\\|_2^2\n \\le(1+Cm/M)^2\\sum_i|d_i|^2,\n\\]\nbecause $\\|\\chi\\|_\\infty\\le1$, $\\|\\nabla\\chi\\|_\\infty\\le Cm$, and\nall main-annulus frequencies are at least $M$.\nIn each current pairing, first combine the normalized cosine and sine\nmodes into the equivalent Hermitian pairing of the two complex\nfrequencies. Apply Equation~\\eqref{eq:prelim-frequency-ward} to the exact\ndiscrete gradient before making the leading-symbol replacement in the\nremaining contact pairing. Exact quadrature and\n$\\sum|k|^{-2}\\widehat k\\widehat k^T=(T/2)\\Id$ then give the first two\nlines below. The Gram bound and\nEquation~\\eqref{eq:prelim-band-hessian}, with the negligible exceptional\nevent estimated by Equation~\\eqref{eq:prelim-crude-current}, give the\nthird:\n\\begin{align*}\n \\sum_i\\E Y_i^2&=vI_\\chi T/2+O(B^{-N_*}),\\\\\n \\sum_i\\E(\\partial_iJ(A))Y_i&=vI_\\chi T/2+O(B^{-N_*}),\\\\\n \\E Y^TV''Y&\\le v^2I_\\chi T/2+O(B^{-N_*}).\n\\end{align*}\nOnly the second line changes from \\Rref{eq:pre:7-1}.\nThe crossed-array bound remains\n\\[\n \\left|\\sum_{ij}\\E(\\partial_jY_i)(\\partial_iY_j)\\right|\n \\le C\\{vT^2+v(v+T_w)\\}.\n\\]\nIndeed, the two differentiated arrays each acquire the new constant\none. Their complex pairing is still the sesquilinear pairing at total\nfrequency $n=k+l$. The convolution\n$\\sum_{k+l=n}|k|^{-2}|l|^{-2}$ is bounded by\n$CT(M+|n|)^{-2}$. The longitudinal part is bounded by\nEquation~\\eqref{eq:prelim-frequency-ward}; for the transverse part,\n\\[\n |\\sin\\angle(k,n)|\\,|\\sin\\angle(l,n)|\n \\le\\min\\{1,\\min(|k|,|l|)/|n|\\}\n\\]\nremoves the convolution logarithm. Equation~\\eqref{eq:prelim-weighted-current}\nthen gives the second term. The cutoff replacements are those of\n\\Rref{pre:part-7-1}: terms divisible by $\\psi^2$ use the weighted bound;\nterms not so divisible have an $S^{-2}$ Taylor factor and use\nEquation~\\eqref{eq:prelim-crude-current}, with $\\ell/S^2\\le1/\\ell$.\nNo regularity of the random ring data is required.\n\nPair the mean-zero divergence in Equation~\\eqref{eq:prelim-ibp-array}\nwith $J(A)-\\E J(A)$. Cauchy--Schwarz gives the modified form of\n\\Rref{eq:pre:7-8}:\n\\begin{equation}\n \\operatorname{Var}J(A)\\ge\n \\frac{(vI_\\chi T/2+O(B^{-N_*}))^2}\n {v^2I_\\chi T/2+C\\{vT^2+v(v+T_w)\\}+O(B^{-N_*})}.\n \\label{eq:prelim-variance-gain}\n\\end{equation}\nFor a long step, $v\\ge h\\ge1$ and $\\log\\ell\\le h$, this yields\n\\begin{equation}\n \\operatorname{Var}J(A)\\ge\\frac{\\log\\ell}{4\\pi}\n -C_J\\left(\\log B+\\frac{(\\log\\ell)^2}{v}\\right).\n \\label{eq:prelim-long-gain}\n\\end{equation}\nFor a short step $T,T_w$ stay between positive absolute constants, so\n\\begin{equation}\n \\operatorname{Var}J(A)\\ge c_{\\rm sh}\\min(v,1).\n \\label{eq:prelim-short-gain}\n\\end{equation}\nThe constant $c_{\\rm sh}>0$ is chosen before the final increase of $J$\nand $B$; those increases suppress errors, while all leading constants\nin the weighted and crossed estimates are absolute.\n\n\\smallskip\\noindent\n\\emph{Summing the gains.}\nFor $h\\ge A_0\\log B$, take\n$v=h+1$, $c_0=1/2$, and\n$\\ell=\\lceil\\exp\\sqrt{h\\log B}\\rceil$, where $A_0$ is fixed large\nenough for the band conditions and the error in\nEquation~\\eqref{eq:prelim-long-gain}. Equations~\\eqref{eq:prelim-variance-identity}\nand \\eqref{eq:prelim-long-gain} decrease $h$ by $\\delta h$ with\n\\[\n \\delta h\\ge\\frac{\\log\\ell}{4\\pi}-C\\log B\n \\ge c\\sqrt{h\\log B}.\n\\]\nThere are at most $C\\sqrt{B/\\log B}$ such steps. Summing\n$\\log\\ell\\le4\\pi\\delta h+C\\log B$ bounds their total logarithmic\nlength by $4\\pi h+C_D\\sqrt{B\\log B}$. Below $C\\log B$, choose a\nfixed $0<\\delta<c_{\\rm sh}/4$ and take\n\\[\n v=h+\\delta\\min(h,1),\\quad c_0=\\tfrac12\\delta\\min(h,1),\\quad\n \\ell=\\lceil B^{11J}\\rceil.\n\\]\nEquation~\\eqref{eq:prelim-short-gain} decreases $h$ by at least\n$c\\min(h,1)$. The $O_D(\\log B)$ remaining steps cost\n$O_D((\\log B)^2)$ in logarithmic length and reach $B^{-D}$.\nThis proves Equation~\\eqref{eq:prelim-iteration-conclusion}. At every\nstage the only conditioned cell laws are the original nearest-neighbor\nlaws with new boundary pins, so the uniformity class is preserved.\n\\end{proof}\n\n\\subsection{A strict saving on the first square}\n\nStarting Lemma~\\ref{lem:prelim-iteration} with\nEquation~\\eqref{eq:prelim-initial-stiffness} would give logarithmic\nlength $4\\pi b+o(b)$. The next argument saves a fixed multiple of $b$\nbefore the iteration begins.\n\n\\begin{lemma}[Initial reduction]\\label{lem:prelim-initial-reduction}\nThere is a fixed sufficiently small $u>0$ such that, for all sufficiently\nlarge $b$, the square of side $S=\\lfloor e^{2\\pi bu}\\rfloor$ satisfies\n\\[\n H_S\\le b(1-3u/4)\\Id\n\\]\nuniformly in all its boundary pins and all strengths $0\\le\\beta_e\\le b$.\n\\end{lemma}\n\n\\begin{proof}\nAt a site at distance at least $S/b$ from the boundary, write its\none-site density relative to normalized area as $e^{-F}$.\nFor each fixed $u>0$,\n\\begin{equation}\n \\nabla^2F\\le(1+o_b(1))u^{-1}g_{S^2},\n \\label{eq:orig-2}\n\\end{equation}\nuniformly in the pins, the strengths, and the point of $S^2$.\nTo prove this, follow any unit-speed great circle through the specified\nspin and rotate the other spins about its axis with a radial cutoff.\nThe cutoff equals one within radius $2$, vanishes at radius $S/(2b)$,\nand is linear in the logarithm of the radius between them. The second\nderivative of the negative logarithm of the marginal integral is the\nexpected action second derivative minus a variance. By\nEquation~\\eqref{eq:prelim-score}, its upper bound is $b$ times the\ncutoff Dirichlet energy. Taylor expansion on each bond gives\n$\\log|x+e_\\mu|-\\log|x|=e_\\mu\\cdot\\nabla\\log|x|+O(|x|^{-2})$.\nThe summed square error and the integral-comparison error are $O(1)$\nbefore division by the square of the logarithmic radius. Hence the\ncutoff energy is\n\\[\n \\frac{2\\pi+o_b(1)}{\\log S}\n =\\frac{1+o_b(1)}{bu},\n\\]\nwhich proves Equation~\\eqref{eq:orig-2}. No positive lower bound on any\ncoupling entered this calculation.\n\nFix a unit vector $v$ and put $z=v\\cdot q$, $Y=\\nabla z$, and\n$M=\\E z^2$. On $S^2$,\n$|Y|^2=1-z^2$, $\\operatorname{div}Y=-2z$, and\n$\\nabla_YY=-zY$. Integration by parts against $e^{-F}$ gives\n\\begin{equation}\n \\E(YF)^2=\\E\\{\\nabla^2F(Y,Y)-3zYF\\},\\qquad\n \\E zYF=1-3M.\n \\label{eq:prelim-fisher-identities}\n\\end{equation}\nWriting $a=(1+o_b(1))/u$, the first identity gives\n$\\E(YF)^2\\le a+6$. Cauchy--Schwarz therefore yields\n$(1-3M)^2\\le M(a+6)$. If $M\\le2u$, then\n\\[\n M\\ge\\frac{(1-6u)^2}{a+6}\n   =u(1-O(u)-o_b(1));\n\\]\nif $M>2u$, the same lower bound is immediate. First choosing $u$ small\nand then $b$ large makes $M\\ge(7/8)u$ at every interior site.\n\nFor an edge with both endpoints in this interior and any unit $v$,\nCauchy--Schwarz gives\n\\[\n \\E[v^TE_ev]\n =\\E(q_x^{\\perp v}\\cdot q_y^{\\perp v})\n \\le\\sqrt{(1-\\E(v\\cdot q_x)^2)(1-\\E(v\\cdot q_y)^2)}\n \\le1-7u/8.\n\\]\nThe excluded boundary strips contain an $O(1/b)$ fraction of the\nbonds. Since $0\\le w_e\\le b$, their contact contribution is bounded\ndirectly. Summing the contacts separately in the two spatial directions\nand discarding the nonpositive covariance in\nEquation~\\eqref{eq:prelim-stiffness} gives\n$H_S\\le b(1-3u/4)\\Id$ for sufficiently large $b$.\n\\end{proof}\n\nTake $B=b$ and apply Lemma~\\ref{lem:prelim-iteration} after this first\nsquare. The total logarithmic side is at most\n\\begin{equation}\n 2\\pi bu+4\\pi b(1-3u/4)+C_D\\sqrt{b\\log b}\n =(4\\pi-\\pi u)b+o(b).\n \\label{eq:prelim-total-length}\n\\end{equation}\nThe size premise is valid before each application: accumulated\nlogarithmic length is at most $(4\\pi-\\pi u)b+o(b)$, and a proposed next\nstep adds at most $b$. Since $4\\pi+1<16$, the premise holds for all\nlarge $b$. Any fixed polynomial enlargement can be absorbed by taking,\nfor example, $\\eta=\\pi u/2$ and increasing the threshold for $b$.\nThus there is a common side $R_*$ with\n$H_{R_*}\\le b^{-D}\\Id$ and every later polynomial enlargement bounded\nby the length in Equation~\\eqref{eq:orig-1}.\n\n\\subsection{From small stiffness to local decorrelation}\n\nWe next prove that the small-stiffness square controls rotations under\narbitrary conditioning outside a collar. This supplies the centered\none-site bound needed for the final ferromagnetic decomposition.\nChoose coarse cells with sides comparable to\n$s=R_*\\lceil b^{D+C_0}\\rceil$. Rectangles with sides in $[s,2s]$\npermit tilings of all sufficiently large rectangular tori. Profiles\nare supported on a fixed number of cells, vanish at the outer boundary\nof a fixed collar, and have uniformly bounded cellwise $C^2$\nextensions. All pins lie outside this collar.\n\nFor the effective ring action $F_{\\rm ring}$, subdivision into\n$R_*$-squares and leftover strips gives\n\\begin{equation}\n D_f^2F_{\\rm ring}\\le Cb^{-D}\n \\label{eq:prelim-profile-hessian}\n\\end{equation}\nfor each bounded fixed-axis profile. Indeed, putting\n$M_*=\\lceil b^{D+C_0}\\rceil$, each complete small square costs\n$CM_*^{-2}[b^{-D}+P(b)/M_*]$ by its stiffness bound and\nEquation~\\eqref{eq:prelim-cell-replacement}. The strips have total\narea $O(sR_*)$ and cost at most $Cb/M_*$ by the contact bound.\nChoose $C_0$ above the degree of the fixed polynomial in the cell\nreplacement estimate. This is \\Rref{eq:pre:9-1}.\n\nIntegration by parts gives\n$\\E(D_fF_{\\rm ring})^2\\le Cb^{-D}$. The square-root density relative\nto product area therefore has rotation derivative of squared\n$L^2$ norm at most $Cb^{-D}$. Rotation pullbacks are unitary, so\nintegration along a fixed-axis path and telescoping finitely many\nfactors give\n\\begin{equation}\n \\E\\left(e^{-[F_{\\rm ring}(\\phi\\omega)-F_{\\rm ring}(\\omega)]/2}-1\\right)^2\n \\le Cb^{-D}.\n \\label{eq:prelim-profile-displacement}\n\\end{equation}\nFor a fixed-dimensional compact family of smooth profiles with bounded\nparameter derivatives and a bounded smooth homotopy to identity,\n\\Rref{pre:part-9-1} upgrades this to\n\\begin{equation}\n \\Pr\\!\\left(\\sup_\\lambda\n |F_{\\rm ring}(\\phi_\\lambda\\omega)-F_{\\rm ring}(\\omega)|>\\epsilon\\right)\n \\le\\zeta(b),\\qquad\\zeta(b)\\longrightarrow0.\n \\label{eq:prelim-profile-uniform}\n\\end{equation}\nThe input is Equation~\\eqref{eq:prelim-score-mgf}: compact Lie-algebra\nscores have Gaussian tails with variance bound $Cb$, and rotated\nscores have fixed moments bounded polynomially in $b$. For parameter\ndimension $d_0$, Morrey's estimate and a mesh of spacing $b^{-6}$\ntherefore reduce the supremum to $O(b^{6d_0})$ uses of\nEquation~\\eqref{eq:prelim-profile-displacement}. Taking\n$D>10d_0+10$ makes the error tend to zero. These estimates are uniform\nunder conditioning outside the collar.\n\nTo obtain a finite-range orientation law, adjoin independent Haar\nvariables $g_X\\in S^3$ at free coarse vertices, with identity values at\npinned vertices. The group acts on $S^2$ by the covering homomorphism\n$\\rho:S^3\\to SO(3)$. On a coarse edge $X\\to Y$, interpolate by\n\\[\n \\Phi(g;t)=g_X\\exp\\{\\vartheta(t)\\ell_0(g_X^{-1}g_Y)\\},\n\\]\nwhere $\\vartheta$ is fixed smooth and constant near the endpoints, and\n$\\ell_0$ is a measurable logarithm of norm at most $\\pi$. Write the\nretained ring variables as $\\omega=\\rho(\\Phi(g))\\widetilde\\omega$.\nFor fixed $g$ this preserves product area measure, and the conditional\nlaw of $g$ has finite-range cell potentials. Common left multiplication\nsatisfies $\\Phi(kg)=k\\Phi(g)$; each unpinned cell potential is therefore\ninvariant under this multiplication.\n\nThe profile construction of \\Rref{pre:part-10-1} takes place in $S^3$\nitself. Fix a coarse vertex and compare two trial assignments differing\nonly at that vertex, relative to the actual group data. Their relative\nprofiles agree on the perimeter of the incident cells, which we call\nthe star. Enlarge the parameter set by treating each vertex quaternion\nand each edge logarithm of norm at most $\\pi$ as independent variables.\nThe endpoint equations and agreement on unaffected edges define a\ncompact subset of valid assignments, containing every chosen measurable\nlogarithm branch. At each valid assignment, the finitely many relative\nedge arcs together with the identity omit some point of $S^3$.\nTheir distance from this point stays positive on a parameter\nneighborhood. In the resulting stereographic chart, correct endpoints\nsmoothly off the valid subset, with zero correction on that subset.\nThe interpolation of \\Rref{pre:part-10-1} then gives cell extensions\nand homotopies to identity, chosen to agree outside the star for the\ntwo trial assignments. A finite cover by such parameter neighborhoods\nsupplies uniformly bounded smooth families. The supremum over these\nfamilies controls every measurable branch, without differentiating\nthe branch selection.\n\nCompose these $S^3$-valued families with the fixed smooth action\n$(g,q)\\mapsto\\rho(g)q$ on $S^2$. Compactness bounds its required\nderivatives, and composition preserves the homotopies and agreement\noutside the star. Thus Equation~\\eqref{eq:prelim-profile-uniform} applies\nto the whole star test: call a coarse vertex bad when varying its group value,\nfor some neighboring values, changes its incident-cell action by more\nthan $\\epsilon$. A finite coloring of overlapping collars gives, for\neach finite vertex set $E$,\n\\[\n \\Pr(E\\text{ consists of bad vertices})\\le\\zeta(b)^{|E|/C}.\n\\]\nAt a good vertex each conditional orientation density minorizes Haar\nmeasure by $e^{-\\epsilon}$. The disagreement exploration in\n\\Rref{pre:part-10-2}, with failure probability\n$q_0=1-e^{-\\epsilon}$, consequently assigns a fixed path of $r$ vertices\nprobability at most $(q_0+\\zeta(b)^{1/C})^r$. Choose $\\epsilon$ small\nand then $b$ large enough to beat the bounded-degree path count.\nThis proves exponential decay from the pinned boundary on scale $s$.\nThe exploration samples original conditional marginals at every reveal,\nso its adaptive order preserves both marginal laws.\n\nAveraging the comparison over common left rotations, and using the\ntransitivity of Haar measure on $S^2$, now gives\n\\begin{equation}\n |\\E O(q_x)O'(q_y)|\\le\n C\\norm{O}_\\infty\\norm{O'}_\\infty e^{-cd(x,y)/s}\n \\label{eq:prelim-one-site}\n\\end{equation}\nfor every bounded Haar-centered one-site function $O$ and every bounded\none-site $O'$. To see the passage explicitly, condition on the boundary\nof a coarse region around $x$ of radius one third of $d(x,y)$, use the\nrotation comparison inside it, and then multiply by $O'(q_y)$.\nSmall distances use the trivial bound. This is the $S^2$ version of\n\\Rref{eq:pre:10-4}; it applies also to merely measurable coordinates.\nFinite free subgraphs are covered by padding with zero couplings.\n\n\\subsection{Neutral observables and completion of the proposition}\n\nThe preceding estimate does not require a local observable to be\nsmooth, but it concerns a centered one-site function. To control all\nlocal observables, write\n\\[\n q_i=(\\cos\\alpha_i\\,z_i,\\sin\\alpha_i\\,w_i),\\qquad\n 0\\le\\alpha_i\\le\\pi/2,\\quad z_i\\in S^1,\\quad w_i\\in\\{-1,1\\}.\n\\]\nNormalized area measure is the product of uniform circle measure, a\nfair sign, and $\\cos\\alpha\\,\\dd\\alpha$. In particular the Haar mean of\n$\\alpha$ is $\\pi/2-1$. Conditional on $\\alpha$, the $z$ and $w$\nvariables form independent zero-field ferromagnetic XY and Ising\nsystems with couplings\n\\[\n J_e^1=\\beta_e\\cos\\alpha_x\\cos\\alpha_y,\n \\qquad J_e^2=\\beta_e\\sin\\alpha_x\\sin\\alpha_y.\n\\]\nTheir partition functions multiply to give the density of $\\alpha$\nrelative to its product one-site measure.\n\nThe correlation inequalities used here are the plane-rotator form of\nGinibre's inequality~\\cite{Ginibre1970} and the ferromagnetic Ising\ninequalities. The XY and Ising energy means and energy covariances are nonnegative,\nand their two-point functions are nondecreasing in every coupling;\nthese inequalities, including zero couplings, are proved in\n\\Rref{pre:part-11-1}. In each sector all first derivatives of couplings\nwith respect to the angles have the same sign, and mixed endpoint\nderivatives of a single coupling are nonnegative. The mixed\nlogarithmic derivatives of the angle density are therefore\nnonnegative. Its log lattice condition gives association by the\nFortuin--Kasteleyn--Ginibre argument~\\cite{FortuinKasteleynGinibre1971}. We use the\nassociated-variable inequality\n\\begin{equation}\n |\\Cov(f,g)|\\le\\sum_{ij}\n \\operatorname{Lip}_i(f)\\operatorname{Lip}_j(g)\n \\Cov(\\alpha_i,\\alpha_j),\n \\label{eq:prelim-association}\n\\end{equation}\nproved in \\Rref{eq:pre:11-1} by comparing $f,g$ with the increasing\nlinear functions having their individual Lipschitz constants.\n\nHere is why the quantitative localization in\n\\Rref{eq:pre:11-3} remains valid with one sign sector. Splitting the XY\nangle into an angle in $[0,\\pi/2]$ and two signs produces two independent\nconditional ferromagnetic Ising systems. The additional sector $w$\nis a third such system and has no further angle variable.\nIts sign covariance is controlled directly by the associated-variable\ninequality, with sign dependence bounded by a constant times the\nsupremum norm. For the two XY signs, the truncation and angle\nlocalization in \\Rref{pre:part-11-2} give the fractional two-point\nbound with exponent $1/3$. For the third sign correlation\n$0\\le c^2_{ij}\\le1$, one has $c^2_{ij}\\le(c^2_{ij})^{1/3}$.\nThus the same sum of conditional two-point functions to the power\n$1/3$, with polynomial support and boundary factors, controls arbitrary\nconditional covariances. Deleting conditional Ising bonds gives the\nsame boundary sums, by coupling monotonicity.\n\nThe normalized circle components, $w=\\operatorname{sign}(q_3)$, and\n$\\alpha-(\\pi/2-1)$ are bounded Haar-centered functions of one spin.\nEquation~\\eqref{eq:prelim-one-site} consequently implies, for the full\nconditional-sector two-point functions $c^1_{ij},c^2_{ij}$,\n\\begin{equation}\n 0\\le\\E_\\alpha c^t_{ij}\\le Ce^{-cd(i,j)/s}\\quad(t=1,2),\n \\qquad |\\Cov(\\alpha_i,\\alpha_j)|\\le Ce^{-cd(i,j)/s}.\n \\label{eq:prelim-sector-correlation}\n\\end{equation}\nThis is the replacement for \\Rref{eq:pre:11-4}.\n\nLet $F,G$ have supports $A,A'$ at distance $d$, and take neighborhoods\nof radius $\\lfloor d/10\\rfloor$. For the conditional covariance given\n$\\alpha$, apply the preceding arbitrary-observable bound and average.\nJensen's inequality and Equation~\\eqref{eq:prelim-sector-correlation}\ngive $\\E_\\alpha(c^t_{ij})^{1/3}\\le(\\E_\\alpha c^t_{ij})^{1/3}$,\nso this contribution is a polynomial times $e^{-c'd/s}$.\n\nFor the covariance of the conditional means, delete the conditional\nXY and Ising bonds crossing the chosen neighborhoods, keeping the\noriginal full angle marginal fixed. Each cut mean depends only on its\nlocal angle set. Differentiation with respect to one angle differentiates\nat most its incident couplings, each with derivative bounded by $b$;\nedge observables have absolute value at most one. Hence the individual\nangle Lipschitz constants are at most\n$C(1+b)\\mathcal L(F)$ and $C(1+b)\\mathcal L(G)$.\nEquations~\\eqref{eq:prelim-association} and\n\\eqref{eq:prelim-sector-correlation} bound their covariance.\nTo restore a deleted bond, interpolate its strength. The derivative\nof the corresponding mean is a conditional covariance with the bond\nobservable. Apply the conditional arbitrary-observable bound at radius\n$\\lfloor d/100\\rfloor$ and dominate all weakened-system two-points by\nthe full conditional two-points. Averaging and Jensen again give the\nsame exponential scale. These are $L^1$ replacement bounds, which\nsuffice because the conditional means are bounded.\nThe neighborhood volumes and crossing-edge lists have polynomial\nsize, and small $d$ is covered by the trivial estimate. This proves\nEquation~\\eqref{eq:prelim-mixing}, with possibly enlarged $C,p$, because\n$s\\le X(b)$ by Equation~\\eqref{eq:prelim-total-length}.\n\nFor completeness, the volume and partition-function conclusions use\nthe same uniformity under bond deletion. Cutting a collar around a\nfixed support and integrating the coupling derivatives compares any\ntwo large periodic volumes by a polynomial times the exponentially\nsmall boundary error. The local periodic expectations are therefore\nCauchy, independently of the exhaustion, giving the periodic limit and\nits uniqueness as in \\Rref{pre:conclusion}.\nFor homogeneous rectangles, apply the common-cutout comparison of\n\\Rref{pre:part-12-3} once to horizontal bond expectations and once to\nvertical bond expectations. The difference between the normalized\nbond sums on the $n$-by-$w$ and $2n$-by-$2w$ tori is bounded by a\npolynomial in $b,n,w$ times $e^{-c\\min(n,w)/X(b)}$.\nIntegration of the coupling from zero to $b$ gives the upper bound in\nEquation~\\eqref{eq:prelim-doubling}; the comparison is uniform at every\nintermediate coupling because all strengths remain in $[0,b]$.\nFinally, the row and column transfer operators are nonnegative:\n$e^{bq\\cdot q'}$ is a positive kernel by its tensor-power expansion.\nTwice applying $\\Tr T^{2k}\\le(\\Tr T^k)^2$, in the two coordinate\ndirections, gives $Z_b(2n,2w)\\le Z_b(n,w)^4$. This proves the lower\nbound and completes Proposition~\\ref{prop:preliminary}.\n\nFrom Section~\\ref{sec:free} onward, $L$ denotes one fixed sufficiently\nlarge dyadic renormalization factor. Constants chosen before $L$ are\nuniform for all sufficiently large $L$; constants denoted $C_L$ may\nchange after $L$ is fixed. The same convention applies after choosing\nthe terminal inverse coupling $H$. Fixed constants in positive-power\nerror estimates are understood in this order of choices.\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/rg.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/rg.tex", "bytes": 65945, "sha256": "3a57d39839a067d8ad6bf2c8f9a26a841f6008d6fe1d0c90f4ad300408a66d08", "content": "\\section{An exact renormalization step with almost dimension-two \\texorpdfstring{\\mbox{contraction}}{contraction}}\n\\label{sec:rg}\n\nThe linear operators of Section~\\ref{sec:free} identify the quadratic\npart of one block integration.  We now control the nonlinear remainder,\nincluding configurations with large spin differences.  Two features of\n$S^2$ matter.  Its tangent planes do not have a globally preferred frame;\nwe therefore perform Gaussian completion in the fixed ambient space\n$\\R^3$.  In addition, the full stabilizer $O(2)$ contains tangent\ninversion.  Together with spatial inversion, this removes the cubic\nterms that would otherwise limit the contraction of the normalized\nremainder to order $L^{-1}$.\n\nThroughout this section a step goes from layer $j$ to layer $j-1$.\nWrite $l=L$ for a regular step and $l=L_*=5L$ for an inclined\nfirst step.  Put $t=t_j$, $t'=t_{j-1}$, $p=p_j$,\n$M=\\mathfrak m_j$, $M'=\\mathfrak m_{j-1}$, and\n$g=b^{-1/2}$, where $b\\in[H_j/2,2H_j]$.  Thus $t$ is comparable to\n$gp$.  All geometric cutoffs use the fixed reference scales $H_j,t_j$,\nincluding when two precise couplings are compared.  A history records\nthe linear observations already performed.  Histories may start with\none inclined observation and then use regular observations.\n\n\\subsection{The retained class and the step theorem}\n\nWe use the retained densities and norms of Section~\\ref{sec:setup}.  The tangent coordinates at an anchor $o$ are\n\\[\n y_o(x)=\\operatorname{proj}_{q_o^\\perp}q_x.\n\\]\nCoefficient tensors are invariant under the full orthogonal group of\n$q_o^\\perp$.  Their value is consequently independent of an\northonormal basis of that plane.  The odd canonical slots $P_5,I_3$\nvanish; it is convenient to retain their zero entries in the canonical\nvector $\\mathbf P$.  Quadratic packets are averaged over spatial\nquarter turns and compensated to have zero affine Hessian.  The two\nfactors of a quadratic polynomial are symmetrized before compensation.\nIndeed an $O(2)$-invariant color contraction is the dot product, and\nquarter-turn averaging of its symmetric spatial quadratic form gives\na scalar multiple of the identity.\n\nThe same quarter-turn convention is imposed on all histories.  Every\nchoice also commutes with simultaneous reflection of the geometry and\nthe input data, as in Section~\\ref{sec:free}; a precision lacking a\nreflection symmetry is not itself averaged over that reflection.\nPaths can be chosen by retaining the finitely many coordinate-order\npaths together, or by taking their union.  A connected expression keeps\nits individual joining paths before its output mask is enlarged.\nEach bulk regular-error packet is invariant under simultaneous spin\ntransformations and under spatial inversion about its anchor.  Its\nmask is strengthened to a common invariant graph.  As in\n\\Rref{rg:class}, the exact off-mask correction is retained in the\ncovering gas.\n\nThe error norm uses actual classical derivatives on the open graph\ndomain, with $k=8$.  At a spin $q_x$, the rotation directions are\n$v_x\\times q_x$, with the spatial weights specified in the common\nsetup.  At a single site they give uniformly equivalent tangent norms, and\nfixed small spherical charts have uniformly bounded coordinate\nderivatives.  For graph norms, chart chain rules also multiply spatial\nweights; the resulting mesoscopic and load factors are tracked in\nLemma~\\ref{lem:rg-error-contraction}.  No smoothness is required of an error away from its graph domain.\nIntegration factors with Gaussian color indices retain their full\ncontracted tensor.  Thus internal invariance holds for each integrated\npacket, although it need not hold for a component before its indices\nare contracted.  The same convention applies to factors carrying\nexternal vector indices in Section~\\ref{sec:sources}.\n\nOn a torus, a calculation is declared winding when its complete lifted\nrecord crosses the prescribed shortest-period threshold.  Short orbit\ncalculations use the bulk symmetry of the lifted labels, even when the\ntorus itself is not invariant under those symmetries.  Winding terms\nretain their winding price and need not have the bulk normalization.\nThe initial nearest-neighbor representation has these conventions:\nall witnesses of a deleted masked row are joined in its component.\nFixed enlargements of graphs, including projection to an inclined\ncoarse grid, change only the constant in the load-projection estimate.\nThe short-record threshold can be decreased and the minimum admitted\nperiod increased to accommodate them.\n\n\\begin{theorem}[Exact step and comparison]\n\\label{thm:rg-step}\nFix $\\upsilon>0$ sufficiently small.  There are choices of $L$, the\ncanonical caps and comparison weights, $P_0$, and finally $H$ such that\nintegration against one normalized block observation sends every\nadmitted retained density at $b\\in[H_j/2,2H_j]$ to an exact\nrepresentation of the same form in layer $j-1$, with all renewed\nshape caps.  The representation may be iterated when its new coupling\nbelongs to $[H_{j-1}/2,2H_{j-1}]$; that admission is established\nseparately by shooting in Section~\\ref{sec:shooting}.  Its precise\ncoupling is\n\\begin{equation}\n b'=b+\\alpha_h(\\mathbf P)+\\frac{\\kappa_h(\\mathbf P)}b+\\Delta,\n \\qquad |\\Delta|\\le C_LH_j^{-1.05}.\n \\label{eq:orig-4}\n\\end{equation}\nHere $h$ is the free history, and the bulk scalar, canonical slots,\nand kicks use the prescriptions of \\Rref{rg:canonical-map} adapted\nbelow to $S^2$.  All retained norm caps are renewed.\n\nFor two regular steps with common geometric scales, let\n$\\lambda=b_2-b_1$, and let $u$ be the fixed weighted shape discrepancy:\ncanonical coefficient differences, regular-error difference divided by\nits cap, and covering-gas difference divided by its cap.  The regular\nerror difference is measured through order $k-1$.  Then\n\\begin{align}\n u'&\\le q u+C_L\\operatorname{poly}(H_j)\\epsilon_h\n       +C_L(\\log H_j)^C|\\lambda|/H_j,\\nonumber\\\\\n |\\lambda'-\\lambda|&\\le C_Lu+C_L\\operatorname{poly}(H_j)\\epsilon_h\n       +C_L(\\log H_j)^C|\\lambda|/H_j,\n \\qquad q<L^{-2+\\upsilon}.\n \\label{eq:orig-5}\n\\end{align}\nFor identical histories $\\epsilon_h=0$.  If the histories share their\nlast $r$ regular observations, one may take\n$\\epsilon_h=(A_0/L^2)^r$, with the constant $A_0$ of\nSection~\\ref{sec:free}.  The same-step Lipschitz bounds with no history\nchange hold also for an inclined first step.  The representation classes and constants\nare common to all the admitted histories and periods.  Signed covering\nactivities are allowed: the logarithm used in constructing the regular\nexponent is the convergent connected logarithm of the small background\nfactor, based at one.  The mandatory-cover factor is retained as a sum,\nand no logarithm of the full signed density is taken.\n\\end{theorem}\n\nWe prove the theorem in five stages: a sector decomposition isolates\nlarge differences; ambient Gaussian completion treats the remaining\nspins; joint estimates control products of the resulting factors;\ncanonical extraction identifies the finite Taylor terms; and\nnormalization returns the remainder to the retained class.  The last\nstage uses the strengthened contraction proved below, rather than the\n$L^{-1}$ contraction of \\Rref{rg:error-contraction}.\n\n\\subsection{Sector geometry and the energy identity}\n\\label{subsec:rg-sectors}\n\nWe first specify the geometric data that remain fixed during each\nGaussian integral.  All distances are measured in fine-lattice units,\nwith a coarse position embedded at its block anchor.  Primitive free\nkernels are truncated at a radius $c_LM$, and inverse windows are\nchosen on the same scale.  The constant is reduced so that the sum of\nradii in each of the finitely many local compositions, including block\ndiameters, reference displacements and kinetic-mask reads, is less\nthan $M/100$.  This is possible because $L$ is fixed and the mask\nradius is $O_L(\\log H_j)=o(M)$.  Omitted paths have weighted sums\n$C_Le^{-c_LM}$ and are kept as separate factors.  Arbitrarily long\ninverse and determinant chains keep all their successive windows and\nendpoints in their records.\n\nA smooth edge profile is one for $|d_eq|\\le t/4$ and zero for\n$|d_eq|\\ge t/2$.  A smooth observation profile is one for\n$|V_Y-\\widehat m_Y|\\le Wt$ and zero for\n$|V_Y-\\widehat m_Y|\\ge2Wt$, with $W$ fixed sufficiently large.\nExpanding each profile plus its complement gives an exact partition.\nA chosen complement is called an input or observation \\emph{seed}.\nEvery true output bad edge is also a mandatory seed.  Join seeds whose\nradius-$50M$ neighborhoods intersect.  A resulting component, together\nwith its assigned factors, is called a \\emph{core}.\n\nThe assignments are those of \\Rref{app:factor-ownership}: spins within\n$3M$ of the seeds are frozen and retain Haar integration; numerical\nenergy rows within $5M$ are assigned to the core; stationary squares\nwithin $8M$ and default profiles within $14M$ have the same owner.\nLeading determinant changes have an owner within $3M$ plus the inverse\nwindow radius.  The larger collections do not duplicate the numerical\nrows.  Distinct $50M$ footprints are disjoint, so every assignment is\nunique.  Maximality of a component is imposed by compatibility of its\nfootprints in the pattern sum, not by an extra distant predicate in a\nsingle core factor.  A \\emph{complete support} records every numerical\nargument, mask, eligibility test, present or absent status query,\nwindow and joining path used by the factor.  This convention is\nessential for factorization later.\n\nHere are the energy rows whose ownership has just been fixed.  Let\n$d,d'$ denote fine and coarse differences, let $\\ell_s,\\ell'_s$ be the\ntruncated free edge operators, and put\n\\[\n X=(\\sqrt{c_0}\\,dq,\\ell_s dq),\\qquad\n Y=\\sqrt a(V-\\widehat m),\\qquad\n X'=(\\sqrt{c_0}\\,d'V,\\ell'_s d'V),\\qquad a=1.\n\\]\nWrite $X_2,X'_2$ for the second components.  For a vector $z$,\n$\\operatorname{clip}_t z$ has length $\\min(|z|,t)$ and the same\ndirection, with value zero at $z=0$.  With the output kinetic mask $m'$,\ndefine\n\\[\n \\zeta=(\\sqrt{c_0}\\operatorname{clip}_{t'}d'V,\n                         m'\\ell'_s d'V),\n \\qquad (\\nu,w)=\\mathcal M_s\\zeta.\n\\]\nThe free bounds imply\n$|\\zeta|+|w|\\le Ct$ and $|\\nu|\\le Ct/L$ entrywise.  The input kinetic\nenergy plus observation energy minus output kinetic energy equals the\nsum of\n\\begin{align*}\n \\mathcal F_e&=S_t(r_e)+\\tfrac12m_e|X_{2,e}|^2\n                      -\\nu_e\\cdot X_e+\\tfrac12|\\nu_e|^2,&\n \\mathcal B_Y&=\\tfrac12|Y_Y-w_Y|^2,\\\\\n \\mathcal C_E&=\\zeta_E\\cdot X'_E-\\tfrac12|\\zeta_E|^2\n           -S_{t'}(|d'_EV|)-\\tfrac12m'_E|X'_{2,E}|^2,\\\\\n \\mathcal U_E&=\\zeta_E\\cdot[\\mathcal M_s^*(X,Y)-X']_E,&\n \\mathcal N_i&=\\tfrac12| (\\mathcal N_s\\zeta)_i|^2,\\\\\n \\mathcal Z_E&=\\tfrac12\\zeta_E\\cdot\n       [(\\Id-\\mathcal M_s^*\\mathcal M_s-\\mathcal N_s^*\\mathcal N_s)\n                                                     \\zeta]_E.\n\\end{align*}\nThis is the identity \\Rref{rg:ledger}, applied to real ambient fields.\nThe cross terms cancel because $(\\nu,w)=\\mathcal M_s\\zeta$; the last\nrow accounts exactly for truncation of the free isometry.  In\nparticular \\Rref{rg:ledger-errors} gives\n\\[\n |\\mathcal Z_E|\\le C_Le^{-c_LM}t^2,\n \\qquad\n |\\mathcal U_E|\\le C_Lte^{-c_LM}\n       +Ct\\sum_Ye^{-c\\operatorname{dist}_f(E,Y)/l}|m_Y-\\widehat m_Y|.\n\\]\nHere $\\operatorname{dist}_f$ denotes the fine-coordinate distance\nbetween the embedded coarse sites; dividing by $l$ measures the kernel\ndecay in coarse units.  Thus its sum over $Y$ is bounded independently\nof $L$.  These formulas define the rows before any small-angle\nexpansion.\n\nSelect the $\\mathcal F,\\mathcal B$ rows farther than $2.5M$ from every\nseed.  Each row incident on a nonfrozen column is selected.  The\nretained defaults, including their closed derivative supports, imply\nthat all spins read by a selected assigned row are within $C_Lt$ of\ntheir block reference.  For regular cells this is the coordinate-path\nproof of \\Rref{app:principal-log}; for inclined cells it uses the paths\nand mean estimate of Section~\\ref{sec:free}.\n\nThe support prescriptions of \\Rref{app:rg-geometry} are retained in\nfull.  In particular an optional factor $e^V$ with merely measurable\nspin dependence is prepared as\n$1+\\chi_{\\mathrm{att}}(e^V-1)$, not by opening\n$\\chi_{\\mathrm{att}}e^V$.  Here $\\chi_{\\mathrm{att}}$ is the product\nof its attached chart protectors and alignment profiles.  It is one\non the original sector, remains attached to the selected letter, and\ndoes not create an order-one failure outside its plateau.  A separate\nglobal chart profile removes nonprincipal exponential preimages.\n\nFor a calculation with empty output inventory, all queried output\nstatuses are fixed to no flag, clipping is replaced by the identity,\nand good output masks by one.  The resulting analytic formula is\nextended through graph gradients $<4t'$ and cut off only on a larger\ngraph domain.  It agrees with the physical formula on the outgoing\nregular mask.  If output inventory is nonempty, its statuses stay\nfixed and no field derivative is taken.  In neither case is a\ndiscontinuous output predicate differentiated across its jump.\n\n\\subsection{Ambient Gaussian completion}\n\\label{subsec:rg-ambient}\n\nFor a nonfrozen site put $T_x=V_{[x]}$ and use the principal intrinsic\ncoordinate\n\\[\n u_x=\\operatorname{Log}_{T_x}q_x\\in T_x^\\perp,\n \\qquad q_x=\\operatorname{Exp}_{T_x}u_x.\n\\]\nThe sector bounds give $|u_x|\\le C_Lt$.  Frozen coordinates read by\nselected rows use the same logarithm multiplied by a smooth radial\ncutoff inside its injectivity radius $\\pi$, and extended by zero.\nThis is a globally smooth function of the pair of spins.  Protectors\nare supported strictly inside that chart and equal one on the\nrequired plateaus.  Unlike a tangent frame, this construction is\nintrinsic.\n\n\\begin{lemma}[A normalized ambient completion]\n\\label{lem:rg-ambient}\nFix a primary pattern, the frozen spins, the tags in the observation,\nand the output data with their statuses.  Let $D$ be the scalar column\nmatrix, on the nonfrozen sites, of the selected rows of\n\\[\n D_0=(\\mathsf A_s d,-\\sqrt a Q),\\qquad A=D^*D.\n\\]\nAdjoining the normalized scalar Gaussian with negative log density\n$b|Dv|^2/2$ and setting\n\\[\n \\xi_x=u_x+T_xv_x\n\\]\ngives an exact integration in $\\R^3$ at every nonfrozen site, with\nLebesgue Jacobian one for this change of variables.  The matrix $A$\nsatisfies $c_L\\Id\\le A\\le C_L\\Id$, uniformly in the pattern and\nperiod.  No cutoff on $v$ is required.\n\nFor a selected row with reference $T_i=V_{o(i)}$, let its affine\nconstant be the projection onto $T_i^\\perp$ of the physical row\n$R_i(0,V)$, where $R=(X-\\nu,Y-w)$.  Insert the smoothly extended\nfrozen coordinates in $D_0$ and denote the resulting affine row by\n$D_i\\xi+F_i$.  On the selected-row and smooth angular-cutoff supports,\nthe additional error caused by the scalar coordinate in subtracting\nthe affine square is bounded in absolute value by\n\\begin{equation}\n C_LM^D\\left(t^2\\sum_x|v_x|+t\\sum_x|v_x|^2\\right).\n \\label{eq:orig-6}\n\\end{equation}\nThe sums run over that row's complete coordinate reads.  The same\nestimate holds on the enlarged no-output-flag graph domains.\n\\end{lemma}\n\n\\begin{proof}\nBecause every row incident on a nonfrozen column is selected, $A$ is\nthe corresponding principal restriction of\n$d^*\\mathsf A_s^*\\mathsf A_s d+aQ^*Q$.  Block Poincar\\'e, with vectors\nextended by zero into frozen sites, gives\n\\[\n \\norm{v}_2^2\\le C_LL^2\\bigl(\\norm{dv}_2^2+\\norm{Qv}_2^2\\bigr).\n\\]\nThe same argument uses the cell paths for an inclined step.  Together\nwith the fixed positive direct-edge coefficient it proves the stated\nellipticity, as in the paragraph preceding \\Rref{rg:window-bounds}.\nThe empty exterior causes no exception: the Gaussian integral is then\none.\n\nAt fixed $T_x$, the orthogonal sum\n$T_x^\\perp\\oplus\\R T_x=\\R^3$ is an isometry.  Hence $(u_x,v_x)\\mapsto\n\\xi_x$ has Jacobian one.  The original spin measure contributes only the dimension-two spherical\nJacobian\n\\[\n J(u)=\\frac1{4\\pi}\\frac{\\sin|u|}{|u|},\\qquad J(0)=\\frac1{4\\pi},\n\\]\nwhere the value at zero is taken by continuity.  The scalar integral\nhas been normalized, so no degree\nof freedom has been added to the physical measure.\n\nAll reference spins in a selected row's reads differ by at most\n$C_LM^Dt$, while physical angles are at most $C_Lt$.  At zero angle,\nthe normal component of $R_i(0,V)$ is\n$O(C_LM^D(t^2+e^{-c_LM}t))$.  Taylor expansion of the stack gradients\nand chord mean proves this bound; scalar kernels commute with a\nconstant reference rotation.  The linear variation of the physical\nrow is $D_0u$, with remainder $O(C_LM^Dt^2)$.  Meanwhile\n\\[\n D_0(Tv)-T_iDv=O(C_LM^Dt)\\max_x|v_x|.\n\\]\nThe product of $T_iDv$ and the leading physical tangent row is zero.\nThe remaining cross products give the first term of\nEquation~\\eqref{eq:orig-6}, and the scalar-square mismatch gives its\nsecond term.  Arbitrarily accurate truncation errors are included.\nFor a symmetry tie between finitely many references we use the average\nof the projected affine row and retain every reference read.  The\nsame estimate can be made with any one of those references.\n\nThe enlarged no-flag domains merely replace the fixed small-angle\nconstant by a larger one.  No small-angle bound has been asserted for\na nonselected core row, whose physical energy remains unexpanded.\n\\end{proof}\n\nWe next make Gaussian localization explicit.  For a nonfrozen site $x$\nlet $\\Omega_x$ be its inverse window, restricted to nonfrozen sites.\nFor $a_0\\ge0$ define the column-window inverse and its residual by\n\\[\n (\\Pi_{a_0})_{yx}=\\mathbf1_{y\\in\\Omega_x}\n               (A_{\\Omega_x}+a_0)^{-1}_{yx},\\qquad\n E_{a_0}=(A+a_0)\\Pi_{a_0}-\\Id.\n\\]\nWe use $a_0$ here to distinguish the resolvent parameter from the\nobservation precision $a=1$.  The estimates \\Rref{rg:window-bounds}\ngive weighted row and column bounds $C_L/(1+a_0)$ for the inverses\nand $C_Le^{-c_LM}/(1+a_0)$ for the residuals, including their history\ndifferences.  Define\n\\[\n C_s=\\tfrac12(\\Pi_0+\\Pi_0^*),\\qquad\n \\mu=-\\Pi_0^*D^*F,\\qquad r_s=D\\mu+F.\n\\]\nFor large $H$, $C_s$ and every principal marginal have fixed-$L$\nupper and lower spectral bounds.  Use one common ambient Gaussian\n$Z\\sim N(0,C_s\\otimes\\Id_{\\R^3})$ and substitute $\\xi=\\mu+gZ$.\nExpanding the affine squares gives the exact density-ratio identity\n\\Rref{rg:gaussian-ratio}; its remaining exponent is\n\\[\n \\tfrac12Z^*(C_s^{-1}-A)Z\n             -g^{-1}(A\\mu+D^*F)^*Z.\n\\]\nWith $E_s=AC_s-\\Id$, the corrections are the convergent series\n\\[\n C_s^{-1}-A=\\sum_{r\\ge1}(-E_s)^rA,\n \\qquad\n \\log\\det C_s=-\\Tr\\log A+\n       \\sum_{r\\ge1}\\frac{(-1)^{r+1}}r\\Tr E_s^r,\n\\]\ntogether with the resolvent expansion for $\\log A$ in\n\\Rref{rg:ratio-series}.  Each residual path contains at least one\nexponentially small residual hop.  For every fixed required support\nexponent its summed bound is the chain bound \\Rref{rg:chain-price},\nwith the same bound for one history difference.\n\nThe scalar normalization in Lemma~\\ref{lem:rg-ambient} cancels one\ncolor in the leading determinant.  Its coefficient is therefore two,\nalthough the Gaussian used for all estimates has three ambient\ncolors.  In the explicit local determinant compensation of\n\\Rref{app:explicit-core}, replace the coefficient $3/2$ by $1$ and\nuse $\\kappa_g=(2\\pi g^2)J(0)$.  For example the leading window scalar\nat $x$ is\n\\[\n h_x=-\\int_0^\\infty\n    \\bigl((1+a_0)^{-1}-(A_{\\Omega_x}+a_0)^{-1}_{xx}\\bigr)\\,\\dd a_0.\n\\]\nIts empty-pattern value retains the site's phase modulo the block\nlattice.  The frozen-site compensations and changes near a hole are\nassigned as above.  Residual scalar-determinant terms are the same\nexceptional paths with fixed coefficients.  Thus their complete\nsupports and locality are unchanged.\n\n\\begin{lemma}[Prediction of a spin]\n\\label{lem:rg-prediction}\nOn a stencil with the margins of \\Rref{app:read-sets}, in the empty\npattern or away from nearby cores, put\n\\[\n q_x^0=\\operatorname{Exp}_{T_x}\n                     (\\operatorname{proj}_{T_x^\\perp}\\mu_x).\n\\]\nThen the three estimates \\Rref{rg:prediction} hold with this spin:\n\\[\n r_s=O\\bigl(C_LM^D(t^2+te^{-c_LM})\\bigr),\\qquad\n |dq^0|\\le Ct/L+C_LM^Dt^2,\\qquad\n |\\mu|\\le Ct+C_LM^Dt^2.\n\\]\nThey hold with the fixed normalized derivatives and with a single\nhistory or precise-coupling difference.  The leading constants are\nuniform before $L$ is chosen.  Everywhere, including on extensions\nnear cores, $|\\mu|\\le C_LM^Dt$.\n\\end{lemma}\n\n\\begin{proof}\nUse a tangent chart at a coarse reference spin $e_0$ and denote the\nfirst tangent data of $V$ by $w_0$.  At angle zero the fine spin\nprescription has tangent $w_0([x])$.  Its variation in $u$ adds\n$D_0u$ to the rows.  With full kernels, the choice\n\\[\n u(x)=(P_hw_0)(x)-w_0([x])\n\\]\nsolves the linearized affine equations with zero residual, by the\nambient and harmonic free identities.  The normalized mean has\ntangent differential the identity.  Projecting the constant row does\nnot alter its first tangent.  Invertibility of $D^*D$ therefore makes\nthis the exact first tangent of the affine Gaussian mean, with zero\nnormal component.  Only linear data are used in this identity.\n\nWindow and kernel truncation change the comparison by\n$C_Le^{-c_LM}$ in all required weighted kernel moments.  On the\nsupplied output graph the stencil lies in an $O_L(Mt)$ chart.  Missing\npositions outside it may be filled by zero in that chart; the retained\nmargins make their contribution exponentially small.  Based at a\nblock spin, output chord bounds give coordinate size\n$Ct(1+|z-[x]|)$.  The summable weighted rows of $P_h$ therefore give\n$Ct/L$ for a one-fine-edge difference, and $Ct$ for the mean with its\nconstant part subtracted.  Products and remainders of the smooth spin\nand projection maps cost $C_LM^D$ times the indicated products of\nsizes, yielding the quadratic errors above.\n\nEach normalized derivative contributes $t$ times the order-eight\nspatial direction weight.  The leading row moments sum that weight;\nother contributions cost a fixed polynomial in the stencil size.\nChanging the direction anchor inserts the corresponding distance\nweight in each slot, paid by the complete record and its load.  Every\nmap derivative beyond first order has at least two factors of $t$.\nStationary rows have zero first tangent except for the exponentially\nsmall window discrepancy.  This also proves the needed mean and\nstationary-row differences by the free-kernel comparison bounds.\nFrozen-angle extensions outside these local prediction regions only\nneed the fixed polynomial bounds.  A cutoff of their defining data\non a larger graph domain gives the global bound for $\\mu$, without\nrequiring a global spherical logarithm.  This verifies, in particular,\nthe empty-pattern predictions at every center used in a mask transfer.\n\\end{proof}\n\n\\subsection{Products of factors and large-gradient reserves}\n\\label{subsec:rg-products}\n\nThe completion has reduced each sector to one Gaussian expectation,\nfrozen Haar integrals and finite tag averages.  Factors in this common\nexpectation are generally correlated.  We establish product estimates\nbefore expanding their connected contributions.\n\nFor a core $c$, let $s_c$ be its number of seeds and $I_c$ its Gaussian\nread set.  In its energy sum subtract each selected assigned affine\nsquare exactly once, and include the assigned stationary squares,\nprofiles, local inventories, attached protectors and normalization\ncompensations.  Denote the resulting factor by $B_c$.  Thus it is the\nfactor \\Rref{app:explicit-core}, with the scalar modification in\nLemma~\\ref{lem:rg-ambient}.  An input primary that belongs to a true\nbad-bond inventory is supplied once by its old covering label; every\nother selected input primary retains $\\mathbf1_{r_e\\le t}$.  Nonprimary\nedges already satisfy $r_e\\le t/2$.  These local inventory rules are\nexactly the original covering constraint.\n\n\\begin{lemma}[Ambient core and tail estimates]\n\\label{lem:rg-reserves}\nThe core factor retains the reserve $e^{-c_Lp^2s_c}$ of\n\\Rref{app:explicit-core}, apart from the additional envelope\n\\begin{equation}\n \\exp\\left\\{e_0\\left(M^Ds_c+\n                         \\sum_{x\\in I_c}|Z_x|^2\\right)\\right\\},\n \\qquad e_0=C_Lg\\operatorname{poly}(M,p).\n \\label{eq:orig-7}\n\\end{equation}\nThe bounds hold on every attached profile's closed derivative support.\nOn a selected unassigned row the exponent to be opened has, on its\nangular-cutoff support, the envelope\n\\begin{equation}\n C_Lg\\operatorname{poly}(M,p)\n                 \\left(1+\\sum_{x\\in I_i}|Z_x|^2\\right),\n \\label{eq:orig-8}\n\\end{equation}\nwhere $I_i$ is its Gaussian read set.  Such a row is the sum of an\nordinary letter supported where $\\max_{I_i}|Z_x|\\le2p$ and a tail\nletter requiring $\\max_{I_i}|Z_x|\\ge p$.  The former has ordinary\nmajorant $C_Lg\\operatorname{poly}(M,p)$; the latter has an exceptional\nGaussian reserve.  Both assertions hold with every fixed required\nnormalized derivative and a single marked discrepancy.\n\\end{lemma}\n\n\\begin{proof}\nThe charging proof following \\Rref{app:explicit-core} uses direct\npositive squares, failed-mask witnesses, output reserves, and the\nmean-error bound.  These estimates are independent of the dimension\nof the sphere.  Lemma~\\ref{lem:free-inclined-geometry} supplies its mean bound,\nand Corollary~\\ref{cor:free-small-chord-mean} supplies the stronger\n$C_LT_j^2$ bound when all relevant chords are at most $T_j$.  The numerical-square comparisons are the ones in\nLemma~\\ref{lem:rg-ambient}.  Consequently the lower bound for the core\nenergy, including its profile derivative supports, is unchanged\nexcept for Equation~\\eqref{eq:orig-6} summed over selected assigned\nrows.  No direct square is charged twice.\n\nNow $v_x=T_x\\cdot(\\mu_x+gZ_x)$, and\n$|\\mu_x|\\le C_LM^Dt$.  Multiplication of\nEquation~\\eqref{eq:orig-6} by $b=g^{-2}$, with $t\\asymp gp$, bounds\nthe added exponent by a fixed polynomial times\n$g(1+\\sum|Z_x|^2)$.  Row counts and local overlaps are polynomial in\n$M$ and independent of $s_c$ at a fixed site.  Incorporating them into\n$e_0$ gives Equation~\\eqref{eq:orig-7}, and the single-row version is\nEquation~\\eqref{eq:orig-8}.  The remaining core factors have the bounds\nin \\Rref{app:explicit-core}, including $e^{-c_Lp^2s_c}$.\n\nFor an unassigned row insert smooth products of radial profiles in\n$Z$, equal to one through $p$ and zero beyond $2p$.  On the ordinary\npart the exponent and its fixed derivatives are\n$C_Lg\\operatorname{poly}(M,p)$.  A derivative in a reference spin\neither preserves its small difference from the other references or\nreplaces that difference by $t$ times a direction weight.\nDifferentiating $v$ likewise inserts only a fixed polynomial on these\nsupports.  The tail part has the same exponential envelope as a core\nand forces a Gaussian coordinate of size at least $p$.  Derivatives\nof its profiles have polynomial cost.  This splitting changes no\ngeometric statuses or row ownership.\n\nAll extended chart factors agree with their physical values on the\noriginal sector; the global profile suppresses other preimages, and\nthe protectors remain attached.  Thus the estimate has not enlarged\nthe integration by including an uncontrolled branch.  The marked\ndiscrepancy assertion is proved below with the product comparison.\n\\end{proof}\n\nFor clarity, we specify the remaining factors to which the joint\nestimate will be applied.  This is the eight-class preparation of\n\\Rref{rg:prepared}, with the row split just described.\n\\begin{enumerate}[label=(\\roman*)]\n\\item Selected unassigned row corrections and the smooth mean-defect\nrow $\\mathcal U$ are opened with angular plateaus containing their\nfull-sector values.  Their ordinary order is $g$.\n\\item Unassigned stationary squares have order\n$g^2\\operatorname{poly}(M,p)$.  Unassigned $\\mathcal C$ rows vanish;\n$\\mathcal N,\\mathcal Z$ rows have exponentially accurate exceptional\nbounds, using the free null identity.\n\\item An old canonical polynomial or regular error is called remote\nwhen its entire projected graph is farther than $14M$ from all seeds.\nThere its graph mask is replaced by a smooth product equal to one\nthrough $t/2$ and zero from $2t$.  The error is defined through $4t$,\nso the resulting function is smooth.  A nonremote term retains its\nactual mask and attached protectors and meets a core.  Path estimates\ngive \\Rref{rg:old-polynomial-bound}; old errors retain their actual\nnorm caps.  A common chart on an arbitrarily large graph is not used.\n\\item Kinetic truncation tails retain both paths and all mask reads.\nThey have arbitrary-power accuracy with exponential path weights.\nTheir input evaluations use the same smooth or protected prescription;\noutput evaluations use the fixed-status convention.\n\\item Inverse and determinant chains are kept literally.  A ratio\nexponent $Q_\\alpha$ has at most two Gaussian endpoints and satisfies\n$|Q_\\alpha|\\le e_\\alpha(1+\\sum_{I_\\alpha}|Z_x|^2)$.  Even the eighth\nroots of $e_\\alpha$ are summable with every fixed support exponent\nrequired here, by \\Rref{rg:chain-price}.\n\\item The spherical Jacobian is opened as a smoothly cut-off ratio\n$J(u)/J(0)-1$.  It starts quadratically and has order\n$g^2\\operatorname{poly}(M,p)$.\n\\item Remaining edge, observation and global-chart defaults are\nopened as one plus signed failures.  In a remote observation default\nthe mean is normalized only on the smooth branch bounded away from\nzero.  Its failure and all its nonzero derivative supports force a\nlarge Gaussian coordinate, by Lemma~\\ref{lem:rg-prediction}.  Projecting\n$\\xi$ to the tangent plane cannot invalidate that implication.\n\\item An old covering weight keeps its supremum envelope and its\nprotectors.  Its nonempty inventory meets a core.  Its merely\nmeasurable physical-spin function is never differentiated.\n\\end{enumerate}\nThe three-color Gaussian scalar powers, the corrected two-color\nleading determinant, the Haar constant and the observation denominator\nhave already been extracted.  There is no ordinary factor of order\nzero left unaccounted for.\n\n\\begin{proposition}[Joint integration and comparison]\n\\label{prop:rg-joint}\nFor every finite admissible selection $\\mathcal A$ of prepared\nfactors in a fixed pattern, including the compulsory cores, there\nare nonnegative majorants $a_\\alpha$ such that\n\\[\n \\left|D^r\\E\\prod_{\\alpha\\in\\mathcal A}F_\\alpha\\right|\n \\le C_k\\left(1+\\sum_{\\alpha\\in\\mathcal A}s_\\alpha\\right)^{d_k}\n                        \\prod_{\\alpha\\in\\mathcal A}a_\\alpha,\n \\qquad r\\le k.\n\\]\nThe expectation includes the Gaussian, frozen spins and tags.\nFor nonempty output inventory this asserts only the undifferentiated\nand parameter-comparison estimates.  A single marked discrepancy\nretains its size in this product bound.  An ordinary primitive of\norder $r_0>0$ has summed majorant\n$C_Lg^{r_0}\\operatorname{poly}(M,p)$ with the prescribed support\nweights.  Exceptional factors have arbitrary fixed power accuracy,\nand seeded connected sums retain an $e^{-p^{3/10}}$ reserve after\nall decorations.  Disjoint complete supports factor exactly.\n\\end{proposition}\n\n\\begin{proof}\nThe unmodified factors have the concrete envelopes of\n\\Rref{supp:joint-majorants}.  Their proofs use the prediction lemma,\npath bounds, and the graph $C^k$ domains just verified.  Rotation\nfields realize tangent derivatives with uniformly smooth bounded\ncoefficients.  Projection derivatives outside a smaller plateau\ninsert fixed Gaussian polynomials, already allowed by those estimates.\n\nInclude the new tail-row failures in the simultaneous-failure bound\n\\Rref{rg:joint-rarity}.  Distinct rows have polynomially many overlaps\nand each forces a coordinate at least $c_Lp$.  Their Gaussian rarity\ntherefore has the same form as the previous failures.  For a product\nof the extra envelopes in Equations~\\eqref{eq:orig-7} and\n\\eqref{eq:orig-8}, core components are disjoint and the number of\nrow-tail reads at one coordinate is $O_L(M^D)$.  The diagonal charge\nin the combined quadratic exponential has operator norm\n$C_Lg\\operatorname{poly}(M,p)=o_H(1)$.  Its trace and constant terms\ncost at most $C_Le_0M^{D'}$ times the sum of core loads and tail-row\ncounts.  The determinant estimate \\Rref{supp:gaussian-product}, at\nany fixed larger moment exponent, thus applies.  Increasing the\nfixed H\\\"older exponent if necessary, these costs are paid by the\nexceptional reserves.  The summable endpoint charges of ratio letters\nand the deterministic ordinary envelopes give precisely\n\\Rref{rg:joint-product}.\n\nComplete supports include the windows determining $C_s$ and all\nstatus reads.  Its finite range then gives zero covariance between\nGaussian read sets whose complete supports are disjoint.  The frozen\nHaar and tag measures are product measures.  Hence\n\\Rref{app:local-marginals} applies to \\emph{marginals}; it does not\nassert independence of conditional Gaussians on overlapping supports.\n\nIt remains to justify derivatives of hard spin evaluations and the\nmarked comparison.  These are addressed in the next two paragraphs\nas part of the proof, because separate pointwise Taylor accuracy\nwould not imply the asserted joint estimate.\n\n\\emph{Transport at a hard read.}\nAt fixed $T_x$ the map\n\\[\n \\xi_x\\longmapsto\n \\left(\\operatorname{Exp}_{T_x}\n             (\\operatorname{proj}_{T_x^\\perp}\\xi_x),\n                              T_x\\cdot\\xi_x\\right)\n\\]\nhas inverse\n$\\operatorname{Log}_{T_x}q_x+T_xv_x$ on the protector chart times\nthe entire real normal line.  Its fixed chart derivatives have only\npolynomial growth in $|v_x|$.  Holding $(q_x,v_x)$ fixed while a\nparameter varies is achieved by the Gaussian transport\n\\[\n W_x=g^{-1}\\left\\{\n    \\partial(\\operatorname{Log}_{T_x}q_x+T_xv_x)\n                 -\\partial\\mu_x-(\\partial g)Z_x\\right\\}.\n\\]\nHere $\\partial\\mu$ is evaluated at fixed frozen variables and tags;\noutput spins may move only in the no-flag calculations.  Extend this\nvector field by a smooth cutoff beyond the protector support but\nstrictly inside the chart.  It gives the transport and divergence\nbounds of \\Rref{rg:transport}, with the allowed powers of $g^{-1}$\nand fixed polynomials in $Z,M,p$ and directional distances.\nDifferent sites can use different charts simultaneously.\n\nFor merely measurable input functions the same assertion follows by\nchanging variables to $(q_x,v_x)$ on the protected reads, with\nprotectors attached, and differentiating while those arguments are\nfixed.  This is the change-of-variables version of integration by\nparts.  At every finite cutoff and for every finite product, the\npositive quadratic charges above remain strictly below the Gaussian\nquadratic decay.  The chart changes have fixed margins; the unbounded\nnormal coordinate changes orthogonally, with a scaling and shift.\nDifferentiation under these integrals is therefore justified by\nGaussian domination.  Pullback to the common Gaussian marginal gives\nthe same score estimates.  The inverse covariance on any union of\nreads has norm $O_L(1)$ by ellipticity, so its density scores have\nfixed polynomial moment costs as in \\Rref{supp:product-reserve}.\nNo discontinuous predicate in a moving angle is tested.\n\n\\emph{One parameter or history difference.}\nFix the geometry, output data, tags and frozen arguments, enlarge\nsupports to contain both endpoints, and use the same cutoffs.\nInterpolate $C_s$, $g$ and $\\mu$ linearly.  Evaluate $\\xi,u,v,q$ at\nthese common interpolated arguments.  Pure window data, determinant\nchains, Gaussian-ratio exponents and their recorded paths can use the\nlinear interpolation of their endpoint formulas at common $Z$.\nFor residual row and core exponents interpolate \\emph{after} each\nendpoint construction: regard them as functions of common\n$(\\xi,V)$ and fixed statuses, and interpolate those functions.\nNo energy identity is assumed for a mixture of endpoint kernels.\n\nBoth endpoint core budgets hold on these common physical/chart tests.\nThroughout the interpolation, $|v|/g$ is bounded by $\\max|Z|$ plus a\nfixed polynomial in $M,p$.  The first tangent prediction of the\ninterpolated mean is the convex combination of the endpoint first\ntangents.  Passing to its spin adds only\n$O_L(M^Dt^2)$ and arbitrarily accurate window errors.  Thus the\nsmall-$Z$ contradiction that proves every failure implication still\nworks, and the global chart failure uses the same bound on $\\mu$.\nAll H\\\"older estimates therefore hold along the interpolation.\n\nLet $\\varepsilon=\\epsilon_h+|\\lambda|/H_j$.  On aligned supports,\n\\[\n |\\mu_2-\\mu_1|\\le C_LM^Dt\\varepsilon,\n\\]\nwith its normalized smooth derivatives.  The corresponding stationary\nrows have the same discrepancy factor because their first tangent\nvanishes up to window accuracy.  The projected affine maps have\nbounded cutoff extensions and exponentially summed windows, so these\nbounds remain valid with the polynomial hard-transport losses.  In a\nrow-square difference, the leading physical term $D_0u$ cancels\nseparately at each endpoint; its remainder difference has the same\nsecond-order small factors times $\\varepsilon$.  In\n$D_0(Tv)-T_iDv$, a reference difference, or its derivative, multiplies\nthe kernel discrepancy; frozen normal columns are zero.  The mean\ndefect in $\\mathcal U$ is already quadratic, and its full-kernel\ncancellation holds separately at the two endpoints.  Kernel-tail\ndifferences retain their exponential factor.\n\nConsequently an ordinary letter's parameter derivative is bounded by\nits majorant times $\\varepsilon$, with fixed polynomial losses in\n$p,M,1+s_{\\mathrm{tot}}$ and fixed Gaussian polynomials.  A normalized\ndirection supplies $t$ times its spatial weight; the $t^{-1}$ cost of\nan angular cutoff is paired with that factor or with the mean/window\ndiscrepancy.  Unbounded tail letters retain the same discrepancy in\nthe exceptional envelope and polynomial scores.  Hard uses retain it\nby the displayed inverse transport, attached profiles and endpoint\nexponents evaluated at common physical arguments.  An input-list\ndifference, handled separately by zero padding if needed, keeps its\nsupremum difference at a measurable read.\n\nOnly a fixed number of scores or derivative slots occur.  At fixed\nH\\\"older order their Gaussian moments grow polynomially in the\nnumber of reads, hence in $M$ and the total load, even when reads\noverlap.  This proves the marked discrepancy estimate with $k-1$\nadditional derivatives on a regular output.  A derivative placed on\na changed regular input uses at most $k-1$ of that difference; a map\nchange acting on an unchanged input uses at most its $k$ derivatives.\nOrdinary derivative costs have remained of ordinary order.  The\nlarger hard-transport costs are paid only by exceptional reserves.\n\nFinally row shifts, chart reads, field rank and the new profile types\nhave bounded stencil complexity and polynomial overlap.  Count and\nsite-hit bounds therefore use the same loads, with enlarged fixed\npolynomial exponents.  Their spare support exponents absorb the\nload polynomial in the displayed product estimate.  The order and\nseeded bounds now follow from the same summations as\n\\Rref{rg:joint}; this completes the proof, including the discrepancy\npart of Lemma~\\ref{lem:rg-reserves}.\n\\end{proof}\n\nThe hypotheses of the exact connected reassembly\n\\Rref{rg:connected} are now available: joint products, site-hit bounds,\nthe tree smallness condition, and complete support and inventory\nrules.  Its Mayer expansion is used only for the background without\nmandatory output flags.  Components with flags retain their complete\ncovering weights.  We obtain \\Rref{rg:preliminary-output}, with the\nexact mask-strengthening corrections described there.  For countable\nfactor lists, first perform the algebra on finite selections; the\nvolume-exponential absolute majorant from the joint and tree bounds\npermits passage to the full expression, including its derivatives.\nPositivity of a truncated signed gas is not needed.  Thus the output\nrepresentation accounts for all mandatory covers, not only the\nsector with no seeds.\n\n\\subsection{Canonical extraction and its diagonal contraction}\n\\label{subsec:rg-canonical}\n\nThe connected representation just obtained is exact.  We next separate\nits finite-order Taylor polynomial from a smaller regular remainder.\nThe coefficient calculation uses full bulk kernels and aligned charts,\nso it is independent of periods, masks and chart cutoffs.\n\nIn the empty pattern let $R=(X-\\nu,Y-w)$ with\n$(\\nu,w)=\\mathcal M X'$ and full kernels.  The mean-normalization\ndefect is\n\\[\n U_E=X'_E\\cdot\\bigl[\\mathcal M^*\n                    (0,\\sqrt a(m-m/|m|))\\bigr]_E.\n\\]\nLet $R^{\\mathrm{aff}}$ be the affine row constructed above with full\nkernels, $\\mu_f$ its completed mean, and\n$R_{\\mathrm{stat}}=D_f\\mu_f+F_f$.  The negative-log vertices are\n\\[\n \\frac b2\\bigl(|R_i|^2+|D_iv|^2-|R_i^{\\mathrm{aff}}|^2\\bigr),\n \\quad bU_E,\\quad\\frac b2|R_{\\mathrm{stat},i}|^2,\n \\quad\\hbox{the old canonical slots},\\quad\n -\\log\\frac{J(u_x)}{J(0)},\n\\]\nwith the normalized scalar determinant understood.  This is\n\\Rref{rg:canonical-vertices} with the physical and added scalar\nsquares paired before their affine subtraction.  Substitute\n$\\xi=\\mu_f+gZ$.  Assign order $g$ to a relative output tangent and to\n$gZ$, and retain total order at most four in the negative logarithm\nof the Gaussian expectation, understood here as its formal connected\nseries at the constant term one.  Equivalently, for each ordered multiset\nof at most four vertices, use its connected Wick expectation with\ncoefficient $(-1)^{v+1}/v!$; internal as well as intervertex pairs are\nincluded.\n\nAn anchor is removed by a simultaneous orthogonal transformation.\nA smooth local choice of that transformation exists on each fixed\nsmall chart, for example the rotation from its fixed center.  The\nresulting tensors are equivariant and do not depend on that local\nchoice.  At first tangent, the sum of the physical and added scalar\nsquares agrees with the affine square.  Harmonic completion gives\nzero first tangent for the stationary residual.  Hence the row\ncorrection begins with a cubic containing a fluctuation.  The\npossible cubic of $U$ pairs a normal quadratic defect with a tangent\nfirst-order row and is zero.  Connected contractions require the\nsame number of pairs as in \\Rref{rg:canonical-contraction}; the\nambient Gaussian has three colors and at most four vertices contribute\nthrough order four.  Odd output invariants vanish by $O(2)$ symmetry.\nThe kinetic Taylor slots have even parity and unit affine Hessian.\n\nFor precision, write the retained polynomial as\n$\\sum_{r,d}g^{-2d}F_{r,d}(y)$, where $F_{r,d}$ is homogeneous of field\ndegree $r+2d$ and $r\\le4$.  Scalar terms are extracted per unit\nvolume.  Let $S_{K',o}^{[n]}$ denote the homogeneous degree-$n$ part\nof the unmasked output kinetic density, assigned to its anchor by\nsplitting edge and row terms between their endpoints.  The triangular\nprescription is\n\\begin{align*}\n P'_4&=F_{2,1},&\n \\alpha&=\\text{affine Hessian of }F_{2,0},&\n I'_2&=\\operatorname{Comp}\\bigl(F_{2,0}-\\alpha S_{K'}^{[2]}\\bigr),\\\\\n P'_5&=0,& I'_3&=0,\\\\\n P'_6&=F_{4,1},&\n I'_4&=F_{4,0}-\\alpha P'_4-\\alpha S_{K'}^{[4]},&\n \\kappa&=\\text{affine Hessian of }F_{4,-1},\\\\\n J'_2&=\\operatorname{Comp}\\bigl(F_{4,-1}-\\kappa S_{K'}^{[2]}\\bigr).\n\\end{align*}\nHere the affine Hessian is the bulk sum per anchor, and\n$\\operatorname{Comp}$ is the quadratic packet compensation of\nSection~\\ref{sec:setup}.  Once the total affine coefficient is\nremoved, the individual compensation coefficients sum to zero, so\nthis operation preserves the polynomial.  These are the formulas\n\\Rref{rg:canonical-map} with the odd slots set to zero.  They define a map\n$\\mathbf P'=\\mathcal C_h(\\mathbf P)$ that depends only on the free\nhistory and the incoming canonical coefficients.  After the displayed\npowers of $g$ have been sorted, neither this map nor its kicks depend\non the precise coupling, cutoffs, regular error, covering gas, or\nperiod.  In the exact output representation we assign the canonical\nslots to this finite coefficient operation; its difference from the\nactual integral is retained in the error and gas, with the further\naffine correction included in $\\Delta$.  Thus a run initialized with\n$\\mathbf P=0$ has exactly the deterministic canonical orbit of these\nmaps, while its precise coupling obeys Equation~\\eqref{eq:orig-4}.\nThis is the distinction used in the shooting argument.\n\nCoefficient sums are absolutely convergent in the single fixed\ncoefficient norm.  The inputs to the summability proof in\n\\Rref{rg:canonical-contraction} and\n\\Rref{supp:canonical-fixed-norm} are exponential free localization\nwith its fixed moments, finite-degree tensor contractions and old\nexponential path norms.  Our chart and projection vertices have\nexactly these properties.  In an off-diagonal contraction, an old\npath may be rescaled by the step size and joined by free-kernel\npaths.  Only a fixed number of paths occurs per vertex at this order;\ntheir polynomial weights are paid by the old spare exponential or\nby kernel moments.  Choose the coefficient exponent $\\sigma$ small\nfor this finite set of moments before choosing $L$.  These estimates\ngive the asserted $C_L$ bounds and their history and coupling\nLipschitz versions.  Folding and affine compensation use this same\nnorm, not a succession of norms with smaller exponential exponents.\n\n\\begin{lemma}[Dimension-two coefficient contraction]\n\\label{lem:rg-canonical-contraction}\nFor the diagonal response of each nonzero canonical slot, the\ncoefficient norm of the output is at most $CL^{-2}$ times the input\nnorm, where $C$ is independent of $L$ and the history before the\nblock size is chosen.  The corresponding quadratic transfer has\nzero affine Hessian.  The same conclusion holds for an inclined\nstep in its rotated physical coordinates.\n\\end{lemma}\n\n\\begin{proof}\nFor degree $n\\ge4$, \\Rref{supp:one-norm-contraction} gives\n$CL^{2-n}$ and hence the stated bound.  Only the compensated quadratic\nslots require an improvement.\n\nAt an old anchor $o$, expand the conditional-mean row in a displacement\n$r$ through spatial degree two:\n\\[\n P(o+r,\\cdot)-P(o,\\cdot)\n       =A_o(r)+B_o(r,r)+E_o(r).\n\\]\nHere $A_o$ is homogeneous linear and $B_o$ homogeneous quadratic in\n$r$.  In the exponentially weighted row norm of\n\\Rref{supp:row-second}, their coefficients have bounds $C/L,C/L^2$,\nand\n\\[\n \\norm{E_o(r)}\\le\n C(1+|r|)^3L^{-3}e^{C\\sigma(1+|r|/L)}.\n\\]\nOne obtains this formula by a forward Newton polynomial of degree\ntwo, then telescoping its remainder along shortest paths.  The\nlinear coefficient includes the second-difference adjustment from\nrewriting that Newton polynomial in homogeneous powers.  Telescoping\nthe second and then first differences uses only the third fine\ndifferences supplied by the free estimate.  Thus no continuous\nextension of the lattice kernel has been assumed.\n\nInsert the expansion into a compensated inversion-invariant quadratic\npacket.  The two-linear-row terms cancel as coefficient arrays by\nits zero affine Hessian.  For quarter turns, this uses the symmetric\nquadratic polarization fixed above.  The linear--quadratic terms are\nodd under $r,s\\mapsto-r,-s$ and cancel by the packet's spatial\ninversion.  Every remaining term has bound $CL^{-4}$ times a fixed\npolynomial in the path length, with its rescaled exponential weight.\nThe spare old exponential pays that polynomial exactly as in\n\\Rref{supp:quadratic-before-anchor}.  Output compensation costs a\nfixed multiplier.  Summing the $L^2$ old anchors over one new anchor\nleaves $CL^{-2}$.\n\nThe constant and affine identities for $P$ show that an affine\noutput tangent transfers to an affine input tangent of slope divided\nby the step size.  Each old compensated packet is zero on that\nfield.  The aggregate transferred affine Hessian is therefore exactly\nzero.  Inclined coordinates merely rotate and rescale this argument;\nthe same row estimates hold in their physical units.\n\\end{proof}\n\nThe canonical calculation has now identified the coefficients and\ncontrolled their own variation.  The remaining obstruction to\nTheorem~\\ref{thm:rg-step} is an old regular error appearing once,\nwithout another small factor.  Its contraction is the subject of the\nnext subsection.\n\n\\subsection{Contraction of the regular error}\n\\label{subsec:rg-error}\n\nIn the empty primary pattern, the isolated response of an old error\nis the operator\n\\[\n \\mathcal T f_X(V)=\\E_{C_s}\\,[\\chi_X(q)f_X(q)],\\qquad\n q_x=\\operatorname{Exp}_{T_x}\n                \\bigl(\\operatorname{proj}_{T_x^\\perp}(\\mu_x+gZ_x)\\bigr).\n\\]\nHere $\\chi_X$ is the prepared smooth graph cutoff, and the output\nanchor is the projection of the old anchor.  This is\n\\Rref{rg:error-transfer} for the intrinsic spin prescription.  For\nload $s_X\\le L^{1/4}$ its output graph is enlarged to a complete\navailable good-output ball and all required stencils; the exact\ncovering correction is retained.  After projection the radius is a\nfixed multiple of $M'$.  The entire orbit packet is kept together.\n\n\\begin{lemma}[Contraction after removal of the affine Hessian]\n\\label{lem:rg-error-contraction}\nFor every fixed required output support exponent chosen before $L$,\n\\begin{equation}\n \\norm{\\mathcal Tf}_{k,j-1,B}\n   \\le\\bigl(C/L^2+o_H(1)\\bigr)\\norm{f}_{k,j,A}.\n \\label{eq:orig-9}\n\\end{equation}\nThe little-oh may depend on $L$ and is uniform for $H_j\\ge H$.\nThe same bound holds for an input difference through order $k-1$.\nA map change acting on an unchanged input satisfies the original\nmap-difference estimate of \\Rref{rg:error-contraction}, with its\npolynomial losses and one extra derivative on that input.\n\\end{lemma}\n\n\\begin{proof}\nUse an even smooth Gaussian guard on all coordinate reads,\n$\\max_x|Z_x|\\le c_Lp$, with a transition strictly inside the required\nplateaus.  The normal component is included.  The prediction lemma,\nwith output graph chords allowed through $4t'$, puts the unperturbed\nfine chords strictly inside the graph cutoff plateau for $L$ large.\nChoose $c_L$ small enough that the same holds with the guarded\nfluctuation, and on the Taylor segments below.  The guard complement\nhas an exponentially small cost times fixed polynomials in $M$ and\nthe load, by the Gaussian union tail and the joint derivative\nestimate.  These statements apply on arbitrary graphs, not only\nwithin a single chart.\n\nFirst suppose $s_X\\le L^{1/4}$.  For output derivatives of order at\nmost three, omit the guarded fluctuation by Taylor expansion twice\nin it.  Its first term integrates to zero because the guard and the\nGaussian law are even.  The argument of\n\\Rref{rg:fluctuation-omission} then gives\n\\[\n C_LM^D(1+s_X)^Dp^{-2}[f_X]_{k,j}.\n\\]\nIt requires at most five old derivatives.  The same argument applies\nto the tangent projection in the present spin formula.  Choosing\n$P_0$ after the fixed $M$ powers makes this a delayed small factor.\n\nRemove the old anchor by a simultaneous orthogonal transformation\nand use geodesic relative coordinates for the unperturbed spins.\nThe leading first tangent is the conditional-mean prediction.  Expand\nit, as above, in $x-o$ into a homogeneous linear spatial polynomial,\na homogeneous quadratic spatial polynomial, and a remainder.  In the\nold normalized direction norm, with its low output jets, their bounds\nare respectively\n\\begin{equation}\n C/L,\\qquad C/L^2,\\qquad C/L^3+o_H(1).\n \\label{eq:orig-10}\n\\end{equation}\nNonlinear chart terms and window truncation belong to the final\nremainder.\n\nHere is the weighted-norm verification of this assertion.  If\n$|x-o|\\le L$, coarse exponential row moments sum both the output\nchord variation and the output test-direction weights.  Fine\ndifferences through order three give the stated powers of $L$ with\nuniform leading constants.  If $|x-o|>L$, an output direction weight\nbased at $[x]$ is bounded in the row sum by\n$C(1+|x-o|/L)^8$.  Dividing by the old weight\n$(1+|x-o|)^8$ gives $O(L^{-8})$; the polynomial predictions based at\n$o$ obey the remainder estimate with this same division.  Chord\ncoordinates have at most linear growth from the anchor in the ball,\nso their values satisfy the same argument.  Nonlinear terms in each\nfixed jet have an extra $t$ with at worst $C_LM^D$ cost.  Paths outside\nthe supplied stencils have exponentially small weighted sums.\nThe ball lies in a common chart because its output radius is $O(M)$\nand $M^Dt\\to0$.\n\nFor completeness, the chain rules can be interpreted entirely in the\nnorms already defined.  First tangent directions at a spin are tested\nin great-circle coordinates, with rotation axes perpendicular to that\nspin.  Their symmetric jets are covered by the rotation-derivative\nnorm.  To differentiate a different chart or a composition, use\nnormal coordinates at the point of evaluation and the usual chain\nrule.  Chart changes on these fixed small charts have bounded fixed\nderivatives.  Products of direction weights can occur in one input\nslot, including for an unrestricted output rotation axis.  After\none slot's weight is divided out, each additional direction factor\nis bounded by a power of\n\\[\n t\\bigl(1+C\\mathfrak m_j(1+s_X)\\bigr)^8.\n\\]\nOn bounded loads this tends to zero with $H$.  At arbitrary load it\nhas only a fixed polynomial load cost, and each additional mesoscopic\npower accompanies an extra $t$.  Also $t'/t$ is uniformly bounded.\nThus the higher map jets asserted to be $o_H(1)$ have that meaning\nin the actual graph norm; no uniform bound on all test directions\non arbitrarily large graphs has been assumed.\n\nExpand the invariant error at alignment.  Its constant and first\nterms vanish, and its affine Hessian is zero.  Tangent inversion in\nthe anchor stabilizer makes its cubic term zero.  In the quadratic\nterm, the two linear spatial predictions vanish by affine\nnormalization, and the linear--quadratic predictions cancel under\nposition inversion of the packet.  Equation~\\eqref{eq:orig-10} then\ngives $CL^{-4}+o_H(1)$ for every surviving quadratic term.  The\nquartic Taylor remainder has the same gain.  For the derivatives\nthrough order three, Taylor-expand the required derivative with the\ncorresponding reduction of order; fourth and, where needed, higher\navailable old derivatives suffice.  The Taylor segments stay in the\nold graph domain: anchor alignment is an isometry and small-log\ninterpolation distorts chords by a bounded factor tending to one.\nThe guard leaves a strict margin to the domain boundary.\n\nFor output orders four through $k$, retain the fluctuation and apply\nthe chain rule directly.  Each first derivative of the relative map\ncosts $C/L+o_H(1)$; differentiating a moving tangent projection of the\nfluctuation has an extra $t$.  Every higher map derivative is\n$o_H(1)$ in the same norm.  A chain-rule term either has at least four\nfirst-map derivatives or has a higher derivative.  It therefore\nagain gains $CL^{-4}+o_H(1)$, without requiring an old derivative\nabove the requested order.  Summing $L^2$ old anchors and the fixed\noutput support price proves Equation~\\eqref{eq:orig-9} on these\nshort labels.\n\nIf $s_X>L^{1/4}$, use the direct composition estimate at the end of\n\\Rref{rg:error-contraction}.  On the guard, its absolute first jet is\nthe bounded row of $P_h$, with no bare leading $M$ loss.  Higher jets,\nincluding moving tangent projections, have extra $t$ and fixed\npolynomials in $M,1+s_X$.  This statement is local at each halo and\ndoes not require a chart for the whole graph.  Direction-anchor\ntranslations have polynomial load cost; for lifted torus paths the\nphysical distance obeys the same scaled upper bound.  The spare old\nsupport exponential gives $e^{-cL^{1/4}}$, paying the anchor sum and,\nif desired, $L^{-4}$ before it.  The guard-complement estimate has a\nbare $M$ power only together with exponential accuracy.  This proves\nthe estimate also for long and winding labels.\n\nAn input difference uses the identical proof in order $k-1$.  For a\nchanged map, the verified composition and window estimates act on\none additional derivative of the unchanged input, at most $k$.\nThus no derivative is lost from one iteration to the next.  All\narguments retain the graph masks; the strengthened orbit masks and\nshort-load balls are accompanied by their exact covering corrections.\n\\end{proof}\n\n\\subsection{Taylor comparison, normalization and closure}\n\\label{subsec:rg-closure}\n\nWe now compare the exact connected expression with the canonical\npolynomial and close the retained class.  The comparison is an\nestimate on the actual integrals; it is not an assumption that a\nformal coefficient computation describes them.\n\nAn ordinary primitive of order $r>0$ has the majorant established in\nProposition~\\ref{prop:rg-joint}.  Inflate it by $g^{-.96r}$ in the\ntree condition \\Rref{rg:tree-condition}.  The remaining $g^{.04r}$\nbeats every fixed logarithmic loss, including the $M^2$ site-hit cost.\nThus connected terms of total order at least five retain $g^{4.8}$;\na smaller exponent pays the finite derivative and logarithmic costs.\nOld errors count as order four with their stronger actual cap, so\nonly their isolated response needs Lemma~\\ref{lem:rg-error-contraction}.\nAll other such terms have order at least five.  The resulting\nremainder and discrepancy bounds are \\Rref{rg:high-order-remainder},\nnamely $C_Lg^{4.6}$ and $C_Lg^{4.5}$ times the normalized discrepancy.\nExceptional terms, including the new normal-coordinate tail letters,\nand canonical paths beyond the mesoscopic cutoff have arbitrary-power\naccuracy.\n\nFor the terms through order four, group their at most four origins at\none anchor and strengthen their masks to a common good-output ball\ncontaining all reads.  Choose its fixed radius multiplier and then\n$L$ large: the old canonical balls shrink by $L$, whereas the fixed\nnumber of new stencils requires only fixed multiples of $M$.\nOff-mask terms are retained in the gas.  On this ball and the even\nGaussian guard, prepared factors have their smooth vacuum formulas.\nFor factors negligible at this accuracy, such as tail letters, that\ncoincidence is not needed.  Choose angular cutoffs with a large enough\nfixed $C_L$ for these plateaus.  The old graph-edge cutoff through\n$t/2$ uses the fine-edge prediction, first choosing $L$ large and then\nthe Gaussian guard constant small.\n\nIn these local charts $u,v$ and output coordinates $w_0$ have size\n$g\\operatorname{poly}(M,p)$.  Every row formula is a composition of\nbounded multilinear kernel maps, finitely many uniformly smooth\npointwise functions and their truncated windows; its positional\nsummations have fixed polynomial weights.  Taylor expansion in\n$w_0,gZ$ through field degree six for a vertex with prefactor $g^{-2}$\nleaves at least seven small fields.  It therefore costs\n$C_Lg^5\\operatorname{poly}(M,p)$.  Vertices with other prefactors are\nexpanded to the corresponding degree.  A normalized output derivative\nsupplies its factor $t$ and spatial weight.  Taylor expansion with\nits degree reduced by the number of derivatives, or a direct smooth\nbound once those factors are already supplied, retains the same\n$g^5$ estimate through every required order.\n\nThe quadratic and harmonic cancellations were established above.\nEvery expanded exponent starts at its indicated positive order on\nthe guard, so exponentials minus one and Jacobian logarithms have the\nsame Taylor control.  Truncated first-jet errors retain their\nexponential factor.  The same proof with one marked discrepancy uses\nthe kernel differences and Proposition~\\ref{prop:rg-joint}.  Removing\nthe guard, restoring full Gaussian polynomial moments, and replacing\nshort kernels and covariance by full ones have arbitrary-power\naccuracy with every fixed positional moment.  Any connection created\nonly by that replacement contains a long kernel path and has the same\nprice.\n\nOn a fixed origin multiset, the finite product and Mayer-log\nidentities are identities of these finite polynomials.  They give\nexactly the connected Wick prescription above, including repeated\norigins and the Jacobian logarithm, as in the proof following\n\\Rref{rg:high-order-remainder}.  This proves the exact Taylor comparison\nand yields \\Rref{rg:raw-error} with\n$q_1=C/L^2+o_H(1)$.\n\nIt remains to return the raw error's constant and affine-Hessian\ncomponents to the bulk scalar and kinetic coefficient.  Put\n$b^-=b+\\alpha+\\kappa/b$, and let $R_{\\rm raw}$ denote the raw\nregular norm after the canonical prefactors have been expressed using\n$b^-$.  The preceding estimates give\n\\[\n R_{\\rm raw}\\le q_1\\delta_j+C_Lg^{4.6},\n \\qquad q_1=C/L^2+o_H(1).\n\\]\nFor a nonwinding raw label $f_i$ at $o$, fix $n\\in S^2$ and set\n$v_i=f_i((n)_x)$, its value on the constant spin configuration.\nThis value is independent of $n$ by $O(3)$ invariance.  Let $b_i$ be\nits unit-affine Hessian at that configuration.  The graph norm gives\n\\[\n |v_i|\\le[f_i]_{k,j-1},\\qquad\n |b_i|\\le C(t')^{-2}[f_i]_{k,j-1}.\n\\]\nThere is no support-size loss: a unit affine direction at displacement\n$r$ is bounded by the permitted polynomial direction weight.  With\nthe exact off-mask correction retained, subtract $v_i+b_iS_o$ as in\n\\Rref{rg:normalization-identity}.  The normalized function has zero\nvalue and affine Hessian and costs only a fixed multiplier in norm.\nSumming the bulk coefficients gives\n\\[\n \\Delta=\\sum_i b_i,\n \\qquad |\\Delta|\\le C(t')^{-2}R_{\\rm raw},\n \\qquad b'=b^-+\\Delta.\n\\]\nThe corresponding kinetic correction is\n$-\\Delta(\\mathcal E'-\\sum_oS_o)$.  Allocate $S_o$ to the kinetic\npaths in proportion to their affine Hessians, whose sum is one.\nEach resulting path expression has zero affine Hessian on its mask,\nand \\Rref{rg:kinetic-reset} gives total regular norm\n$C(t')^2|\\Delta|\\le CR_{\\rm raw}$.  The constant is fixed before\n$L$, using uniform free-row moments and the fixed derivative order.\nActive $\\ell$ rows obey their masked bounds; inactive rows retain\ntheir specified convention.  Mask complements are covering\ncorrections with the same site-hit estimates.\n\nThe last coupling change does not alter the assigned canonical\ncoefficient lists.  Indeed, retaining their masks, the negative-log\ncorrection for their changed displayed prefactors is exactly\n\\[\n -\\Delta P'_4-\\Delta P'_6\n       +\\bigl((b^-)^{-1}-(b')^{-1}\\bigr)J'_2.\n\\]\nHere each polynomial denotes its summed masked list; $I'_2,I'_4$\nhave no coupling prefactor.  Conversion of the fixed coefficient norm\nto $\\|\\cdot\\|_{\\rm graph}:=\\|\\cdot\\|_{k,j-1,A}$ on these\nregular lists, using its spare path exponential, gives\n\\[\n \\|P'_4\\|_{\\rm graph}\\le C_L(t')^4\\operatorname{poly}(M'),\n \\quad\n \\|P'_6\\|_{\\rm graph}\\le C_L(t')^6\\operatorname{poly}(M'),\n \\quad\n \\|J'_2\\|_{\\rm graph}\\le C_L(t')^2\\operatorname{poly}(M').\n\\]\nThese bounds hold through order $k$ on the respective graph domains;\npath-length and direction-weight powers are summed with the\ncoefficient exponential.  Since $b^-,b'$ are comparable to $H_j$,\nthe combined correction has norm at most\n\\[\n C_LR_{\\rm raw}\\operatorname{poly}(M')\n           \\bigl((t')^2+(t')^4+H_j^{-2}\\bigr)\n       =o_H(1)R_{\\rm raw}.\n\\]\nIt already has zero value and affine Hessian: $P'_4,P'_6$ have degree\nat least four at alignment, and $J'_2$ is compensated.  Thus it can\nbe retained directly as a regular error on the same masks, without a\nfurther coupling extraction or a change of canonical coordinates.\nThe marked versions of these products give the same discrepancy\nestimates, using $|b_1^{-1}-b_2^{-1}|\\le C|b_1-b_2|H_j^{-2}$\nfor comparable couplings.  This proves quantitatively that the\nexact coefficient lists follow $\\mathcal C_h$ while the remaining\nfeedback enters $\\Delta$ and the renewed errors.\n\nConsequently \\Rref{rg:closed-error} holds with the improved $q_1$,\nand affine extraction gives Equation~\\eqref{eq:orig-4}.\n\nWe give the period argument explicitly because asymmetrical tori are\nneeded later.  All orbit regroupings use fixed graph enlargements.\nA short complete calculation is the corresponding bulk calculation\nwith its finitely many lifted spin positions refolded.  Its symmetry\nis the symmetry of those labels, not a symmetry assertion about the\nfolded spin configuration.  If a fixed enlargement crosses the\nshortest-period threshold, declare the result winding before the\norbit manipulation.  The spare support exponent pays its winding\nrecord, and the lower load retained in graph projection preserves\nany winding price already present.  Fold the bulk coefficients and\nsubtractions with their off-mask corrections.  Missing bulk constants\nor affine corrections on a given period are subtracted with exactly\nthe winding charges of their long derivations.  Actual winding terms\nneed no bulk constant or Hessian normalization.  Thus the scalar and\ncoupling increments are independent of period shape, while all\nperiod disagreements retain their stated price.  This is the\nmechanism of \\Rref{rg:normalization}, with shortest-period length\nreplacing a square side.\n\nFinally choose the constants.  All support enlargements, derivative\norders and polynomial exponents above are fixed before the scale\nchoices.  Take $L$ sufficiently large that the fixed multipliers of\n$L^{-2}$, including error reset and support dilation, leave a strict\nmargin below $L^{-2+\\upsilon}$.  Choose successive canonical caps and\ntriangular comparison weights using\nLemma~\\ref{lem:rg-canonical-contraction}.  Apply the raw-error\ndiscrepancy and the stronger-than-cap gas discrepancy with their\nrelative normalizations; error and gas comparison weights may be\nreduced if needed.  The input coupling parameter in the cited\ncomparison estimates is $O_L(|\\lambda|/H_j)$.  In the normalized\nregular error it incurs only powers of $\\log H_j$; affine extraction\nmultiplies the raw cap by $(t')^{-2}$, a bounded factor after the cap\nis inserted.  The high-order and gas discrepancy reserves pay the\nremaining powers.  These statements give\nEquation~\\eqref{eq:orig-5}.\n\nAs in \\Rref{rg:finite-choices}, choose $P_0$ next, larger than all the\nfixed logarithmic requirements, and then $H$.  Any strictly positive\npower of $g$ in Equations~\\eqref{eq:orig-7} and \\eqref{eq:orig-8},\nand in the other estimates above, beats the now fixed logarithmic\npowers at this last choice.  The little-oh terms are uniform for\n$H_j\\ge H$.  This completes the proof of\nTheorem~\\ref{thm:rg-step}.\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/setup.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/setup.tex", "bytes": 31120, "sha256": "ee1d5807355a60d546174713e5f6a1f87c80266756ed51517acc80f183163652", "content": "\\section{The retained densities and their locality norms}\n\\label{sec:setup}\n\nThe block transformation will retain a coupling, finitely many polynomial\ncoefficient lists, a regular local remainder, and a separate contribution\nfrom configurations with large nearest-neighbour differences.  This section\ndefines these objects.  In particular, all later comparisons concern\ncomplete densities, even though some terms in their representation may\nhave either sign.\n\nWe use the scalar block construction and the local representation\nconventions of~\\cite{OpenAI-O4}.  The definitions are given here for\nspins on \\(S^2\\).  Their preservation under integration is the\n\\(O(3)\\) assertion proved in Section~\\ref{sec:rg}.  Neither that assertion\nnor the continuum construction is an imported \\(O(4)\\) theorem.\n\n\\subsection{Lattices, reference scales, and the observation}\n\\label{setup:scales}\n\nA lattice at one layer is a square grid with its current orthonormal\ncoordinate frame and unit nearest-neighbour spacing.  A finite lattice\nis its quotient by a rank-two lattice of translations; the corresponding\ninfinite-grid formula will be called the \\emph{bulk formula}.  At each step the period lattice is a sublattice of the lattice of block\ntranslations, so the observation descends to the quotient.  The\nshortest nonzero period, measured in the current lattice units, is denoted\nby \\(\\mathcal L_j\\).  We use rectangular periods and the bounded-aspect\noblique periods specified in Section~\\ref{sec:free}.  Local distances may\nbe measured in the maximum or \\(\\ell^1\\) norm; we specify \\(\\ell^1\\) for\nthe coefficient norm below and otherwise use the maximum norm.\n\nChoose a dyadic integer \\(L\\), eventually sufficiently large, and a\nterminal reference coupling \\(H\\).  For integers \\(j\\ge0\\), set\n\\begin{equation}\n \\begin{aligned}\n \\gamma&=\\frac{\\log L}{2\\pi},&\n H_j&=H+\\gamma j,&\n \\mathfrak m_j&=\\left\\lceil(\\log H_j)^2\\right\\rceil,\\\\\n p_j&=(\\log H_j)^{P_0},&\n t_j&=H_j^{-1/2}p_j,&\n T_j&=H_j^{-49/100}.\n \\end{aligned}\n \\label{setup:scale-definitions}\n\\end{equation}\nThese are the scales of \\Rref{rg:scales}, with the drift normalization\nappropriate to \\(S^2\\).  A run of depth \\(N\\) visits the layers\n\\(N,N-1,\\ldots,0\\).  In a step from \\(j\\) to \\(j-1\\), the precise\ncoupling \\(b\\) lies in \\([H_j/2,2H_j]\\), and\n\\(g=b^{-1/2}\\).  We abbreviate \\(M=\\mathfrak m_j\\),\n\\(p=p_j\\), and \\(t=t_j\\) within a step.  Thus \\(t\\asymp gp\\).\nThe thresholds \\(H_j,p_j,t_j,T_j\\) remain fixed when two precise\ncouplings are compared.\n\nA \\emph{free history} records the scalar observations already performed,\nincluding their coordinate frames.  Its length is \\(N-j\\) on layer\n\\(j\\).  For regular observations the history can be identified with its\nlength; after an inclined first observation the record, and not only\nits length, matters.  We write \\(h\\) for this history.  The allowed\ninclinations and their common analytic estimates are established in\nSection~\\ref{sec:free}.\n\nConstants written \\(C\\) when choosing \\(L\\) are uniform for all\nsufficiently large \\(L\\).  Constants \\(C_L,c_L>0\\) may depend on the\nsubsequently fixed \\(L\\); after the stated choices they are independent\nof the layer, free history, period, and varying precise coupling.  All requested derivative orders, support\nexponents, and polynomial powers in the geometric estimates are fixed\nbefore \\(P_0\\) and \\(H\\); we choose \\(P_0\\) sufficiently large and then\n\\(H\\) sufficiently large.  Fixed powers of \\(M\\) and \\(p\\) therefore\nremain powers of \\(\\log H_j\\).  Admitted periods satisfy \\(\\mathcal L_j\\ge C_{\\rm adm}\\mathfrak m_j\\),\nwith a fixed constant large enough for the prescribed local neighbourhoods.\nThe minimum terminal period \\(m_{\\min}(L,H)\\) is enlarged accordingly.  Since \\(\\mathfrak m_j/\\mathfrak m_{j-1}\\le2\\) for\nlarge \\(H\\), the same choice works at all layers of a run.\n\nFor a regular step, partition the fine grid into \\(L\\)-by-\\(L\\) blocks\n\\(Y\\).  Let \\(w_Y(x)\\ge0\\), \\(x\\in Y\\), be the sampled and normalized\nproduct of a fixed even smooth bump, centred at the block centre and\nsupported in the coordinate square of radius \\(0.14L\\).  It can be\nchosen positive in the square of radius \\(0.1L\\).  Thus\n\\[\n \\sum_{x\\in Y}w_Y(x)=1,\\qquad\n \\sum_{x\\in Y}w_Y(x)(x-x_Y)=0.\n\\]\nDefine the scalar averaging map and the spin mean by\n\\[\n (Q\\phi)_Y=\\sum_{x\\in Y}w_Y(x)\\phi_x,\n \\qquad m_Y(q)=\\sum_{x\\in Y}w_Y(x)q_x.\n\\]\nIndependently for each block, let a tag \\(\\tau_Y\\) select a site\n\\(x\\in Y\\) with probability \\(w_Y(x)\\), and put\n\\begin{equation}\n \\widehat m_Y(q,\\tau_Y)=\n \\begin{cases}\n m_Y(q)/|m_Y(q)|,& |m_Y(q)|\\ge\\tfrac12,\\\\\n q_{\\tau_Y},& |m_Y(q)|<\\tfrac12.\n \\end{cases}\n \\label{setup:unit-mean}\n\\end{equation}\nTags may also be sampled where their values are unused.  With\n\\(\\dd\\omega\\) denoting normalized area measure on \\(S^2\\), the exact\nobservation is the probability kernel\n\\begin{equation}\n \\mathcal K_b(\\dd V\\mid q)\n =\\E_{\\tau}\\prod_Y\n \\frac{\\exp\\{-b|V_Y-\\widehat m_Y(q,\\tau_Y)|^2/2\\}}\n      {z_2(b)}\\,\\dd\\omega(V_Y),\n \\qquad\n z_2(b)=\\int_{S^2}e^{-b|V-e|^2/2}\\,\\dd\\omega(V).\n \\label{setup:observation}\n\\end{equation}\nHere \\(e\\) is any unit vector.  Rotation invariance makes the denominator\nindependent of \\(e\\); explicitly \\(z_2(b)=(1-e^{-2b})/(2b)\\) for\n\\(b>0\\).  Thus integrating the density against\n\\(\\mathcal K_b\\) preserves its partition function exactly.  This is\n\\Rref{rg:observation} with observation precision one.  Its piecewise\nmean is measurable everywhere; the proof will differentiate only\nspecified smooth extensions on graphs with small chords.\n\n\\subsection{The scalar quadratic carried by a history}\n\\label{setup:scalar-data}\n\nThe quadratic data in a history concern real scalar fields and are\nindependent of the spin dimension.  Choose one orientation for each nearest-neighbour bond, using the positive\ncoordinate directions.  Site and edge scalar products use counting measure,\nand adjoints refer to those scalar products.  Start with the nearest-neighbour\nprecision \\(K_0=d^*d\\), where \\((d\\phi)_{xy}=\\phi_y-\\phi_x\\).\nIf \\(Q\\) is the next averaging map, integrating the fine scalar field\nin\n\\[\n \\exp\\left\\{-\\tfrac12\\inner{\\phi}{K_h\\phi}\n                   -\\tfrac12\\norm{V-Q\\phi}_2^2\\right\\}\n\\]\ngives the next precision\n\\begin{equation}\n K_{h+1}=\\Id-Q(K_h+Q^*Q)^{-1}Q^*.\n \\label{setup:scalar-schur}\n\\end{equation}\nThe inverse exists on each finite connected torus.  Inductively,\n\\(K_h\\ge0\\) has only constant null vectors, while \\(Q\\) preserves\nconstants; hence \\(K_h+Q^*Q\\) is positive definite.  The Schur complement\nin \\eqref{setup:scalar-schur} has only constant null vectors again, as is\nseen by minimizing the displayed nonnegative quadratic form at fixed \\(V\\).  The formula defines the quadratic after extraction\nof its scalar Gaussian normalization.  Bulk kernels are the common\ntranslation-covariant kernels whose periodizations give these finite\noperators.\n\nFor regular histories the scalar construction of \\Rref{free:bounds}\nand \\Rref{lem:free-stack} supplies a fixed \\(0<c_0<1\\) and a real edge\noperator \\(\\ell_h\\) satisfying\n\\begin{equation}\n K_h=c_0d^*d+d^*\\ell_h^*\\ell_hd,\n \\qquad \\ell_0=\\sqrt{1-c_0}\\,\\Id.\n \\label{setup:edge-lift}\n\\end{equation}\nIts kernels have exponential row and column moments, uniformly in the\nregular history, with the constants and derivative bounds specified in\nSection~\\ref{sec:free}.  These are scalar statements: the operators act\ncomponentwise on ambient vectors.  Section~\\ref{sec:free} gives the\ncorresponding choices for the additional histories used here and proves\nthe stronger history comparison required later.  A retained density\nalways carries the particular history and edge operator chosen by these\ncommon rules; it does not choose a new lift on each finite torus.\n\n\\subsection{The class of effective densities}\n\\label{subsec:setup-density}\n\nAt each layer, the exact integration retains a kinetic energy, finitely\nmany polynomial slots with summable coefficient lists, a differentiable\nerror, and a sum of\nweights covering the edges with large spin differences.  We specify\nthese four parts and their norms here.  This is the density class of\n\\Rref{rg:class}, with the tangent coordinates and spatial symmetry\nadapted to $S^2$.\n\nFix a finite layer torus $\\Lambda_j$ and free history $h$.\nWrite $M=\\mathfrak m_j$ and $t=t_j$.  Bulk formulas below are the\ncorresponding lifted formulas on the infinite lattice; the density\nitself is defined on the finite torus.  All distances below are in the\ncurrent lattice units.  For an\nedge $e=\\langle x,y\\rangle$, put\n\\[\n d_eq=q_y-q_x,\\qquad r_e(q)=|d_eq|,\n \\qquad D(q)=\\{e:r_e(q)>t_j\\}.\n\\]\nThe orientation chosen for $d_eq$ does not affect $r_e$.\nFix $0<d_0<c_0/16$ and choose $R_*$ sufficiently large compared with the\nexponential localization length of the free edge operator $\\ell_h$,\nuniformly in the history.  Define\n\\begin{align}\n S_t(r)&=\n \\begin{cases}\n  c_0r^2/2,&0\\le r\\le t,\\\\\n  d_0tr,&r>t,\n \\end{cases}\n \\label{eq:setup-clipped-energy}\\\\\n m_e(q)&=\\mathbf 1\\!\\left\\{\n    r_f(q)\\le t\\exp\\bigl(\\operatorname{dist}(e,f)/R_*\\bigr)\n                     \\text{ for every edge }f\\right\\},\n \\label{eq:setup-kinetic-mask}\\\\\n \\mathcal E_{h,t}(q)&=\n       \\sum_e S_t(r_e(q))\n       +\\frac12\\sum_e m_e(q)| (\\ell_hdq)_e|^2.\n \\label{eq:setup-effective-energy}\n\\end{align}\nThese are the exact prescriptions of \\Rref{rg:kinetic}; in particular,\n$S_t$ is not replaced by a continuous interpolation at $r=t$.\nSince $r_f\\le2$, a mask $m_e$ queries only edges within distance\n$O(\\log(1/t))$ of $e$.  Its defining bound and the exponential row\nmoments give $| (\\ell_hdq)_e|\\le Ct$ whenever $m_e=1$.\nThe functions $S_t$ and $m_e$ can be\ndiscontinuous.  The differentiable norms below apply to the retained\nlocal functions on their stated open domains, rather than to these\nindicators as functions of a moving output configuration.\n\n\\subsubsection{Supports, records, and graph masks}\n\nA label includes its formula, its anchor when one is assigned, its\ncomplete support, and a connected record covering that support.\nPermissible anchors are chosen in the complete support and its record;\nevery anchored graph contains its anchor.  The complete support contains every spin argument and every edge or site\nqueried by a mask, an eligibility test, a test for the absence of another\nobject, or a window used to determine the formula.  A record consists of\ncubes of radius $\\mathfrak m_j$ together with the paths joining them.\nIt has an integer load $s\\ge1$, chosen according to the conventions\nfollowing \\Rref{rg:kinetic}, so that\n\\begin{equation}\n |\\text{complete support}|\\le C\\mathfrak m_j^2s,\n \\qquad\n \\operatorname{length}(\\text{record})\\le C\\mathfrak m_js.\n \\label{eq:setup-record-size}\n\\end{equation}\nThe support cardinality here counts sites, including the endpoints of\nqueried edges.  Intersecting records can be joined with load at most a\nfixed multiple of the sum of their loads.  The record retains all paths\nused in a connected calculation, including multiple occurrences of a\npath.\n\nUpon projection to the next lattice and enlargement by the fixed\nneighborhoods used in the integration, the load is chosen to satisfy\n\\begin{equation}\n       \\frac{4s}{L}\\le s'\\le D_*(1+s/L).\n \\label{eq:setup-record-projection}\n\\end{equation}\nThe upper bound expresses the shortening of the joining paths under\nblocking.  The lower bound is a convention: unused load is retained\nwhen necessary.  The constant $D_*$ accommodates either the regular\nblocking or the inclined first blocking.  This is the convention of\n\\Rref{rg:projection}.\n\nOn a torus, the record retains its lifted displacements before positions\nare reduced modulo the period lattice.  Recall that $\\mathcal L_j$ is\nthe shortest period length in the current lattice units.  A complete record is called\n\\emph{short} if\n\\begin{equation}\n                         s<c_*\\mathcal L_j/\\mathfrak m_j.\n \\label{eq:setup-short-record}\n\\end{equation}\nThe fixed constant $c_*>0$ is sufficiently small that a short record has\nan injective lift and remains within the required bulk comparison after\nthe fixed geometric enlargements.  A short formula must agree with the\ncorresponding formula on the infinite lattice.  All other records are\ncalled \\emph{winding}.  This classification concerns the complete\ncalculation: a constant, or a function with small displayed support, is\nstill assigned a winding record if its construction used a long record\nor a period-dependent discrepancy.  The lower bound in\n\\eqref{eq:setup-record-projection} retains the required load when an\nalready winding term is transferred.\n\nFor a connected graph $X$, define its mask and its open graph domain\nby\n\\begin{equation}\n \\begin{aligned}\n 1_X(q)&=\\mathbf 1\\{r_e(q)\\le t_j\\text{ for every }e\\in X\\},\\\\\n \\mathcal U_{X,j}&=\\{q:r_e(q)<4t_j\\text{ for every }e\\in X\\}.\n \\end{aligned}\n \\label{eq:setup-graph-domain}\n\\end{equation}\nA graph may consist of a single site with no edges; in that case both\nedge conditions in \\eqref{eq:setup-graph-domain} are vacuous.\nA graph used for a local function contains that function's spin\narguments and is included in its complete record.  A masked local\nfunction is defined to be zero off its mask; no value of an undefined\nextension is used there.\n\n\\subsubsection{Tangent polynomials and their coefficient norms}\n\nFor an anchor $o$, define the relative tangent coordinates and a\nnearest-neighbor quadratic function by\n\\begin{equation}\n y_o(x)=q_x-(q_o\\cdot q_x)q_o\\in q_o^\\perp,\n \\qquad\n S_o(q)=\\frac14\\sum_{|e|_1=1}|y_o(o+e)|^2.\n \\label{eq:setup-tangent-coordinates}\n\\end{equation}\nHere $|\\cdot|_1$ is the lattice $\\ell^1$ distance in the current square\nframe.  For $q_\\circ\\in S^2$, the spherical exponential is\n$\\operatorname{Exp}_{q_\\circ}(u)=\\cos|u|\\,q_\\circ+(\\sin|u|/|u|)u$\nfor $u\\in q_\\circ^\\perp$, with its continuous value at $u=0$.\nIts restriction to $|u|<\\pi$ is one-to-one onto\n$S^2\\setminus\\{-q_\\circ\\}$; the inverse is denoted by\n$\\operatorname{Log}_{q_\\circ}$.  The coordinate $y_o(x)$ is a globally\ndefined tangent projection, whereas $\\operatorname{Log}_{q_o}q_x$ is\nthe principal geodesic coordinate on this domain.\nFor each required degree $n$, choose a fixed basis of\n$((\\R^2)^{\\otimes n})^{O(2)}$.  A basis tensor is evaluated in\n$q_o^\\perp$ through any orthonormal identification of this plane with\n$\\R^2$.  Its $O(2)$ invariance makes the result independent of that\nidentification.  A degree-$n$ label is a coefficient $a$ multiplying\n\\[\n T\\bigl(y_o(o+r_1),\\ldots,y_o(o+r_n)\\bigr),\n \\qquad r_i\\in\\Z^2,\n\\]\ntogether with its paths, mask, and complete record.  A label containing\na zero displacement is discarded, since $y_o(o)=0$.  The full $O(2)$\ninvariance forces every\nodd-degree tensor to vanish.\n\nThe five potentially nonzero slots retain the seven-slot indexing of\n\\Rref{rg:slots}:\n\\begin{equation}\n (bP_4,\\ I_2,\\ bP_5,\\ I_3,\\ bP_6,\\ I_4,\\ b^{-1}J_2),\n \\qquad P_5=I_3=0,\n \\label{eq:setup-slots}\n\\end{equation}\nand\n$\\mathbf P=(P_4,I_2,0,0,P_6,I_4,J_2)$ denotes the coefficient lists\nwithout the displayed powers of $b$.  A subscript denotes homogeneous\ndegree in the relative tangent coordinates, rather than order in $g$.\n\nFor a position list, set\n\\begin{equation}\n u(r_1,\\ldots,r_n)=1+\\sum_{i=1}^n|r_i|_1,\n \\qquad W_\\sigma(u)=e^{\\sigma u}(1+u)^2.\n \\label{eq:setup-path-weight}\n\\end{equation}\nThe paths from an anchor to its arguments are counted with multiplicity.\nPath rules are chosen equivariantly; when a symmetry exchanges tied\ncoordinate-order paths, the finite collection of choices is retained\ntogether.  The complete record contains those choices.  The coefficient\npath parameter in \\eqref{eq:setup-path-weight} is the fixed convention\nof \\Rref{supp:canonical-path}.\nFor degree four or six, the norm of a coefficient list $A_n$ is\n\\begin{equation}\n \\|A_n\\|_\\sigma\n   =\\sup_o\\sum_{\\text{labels at }o}|a|W_\\sigma(u).\n \\label{eq:setup-canonical-norm}\n\\end{equation}\nThe same fixed $\\sigma>0$ is used at every layer; it is chosen sufficiently\nsmall for the required free-kernel exponential moments, before choosing\n$L$.\n\nThe quadratic norm also records the subtraction that removes the affine\nHessian.  Let $\\mathcal C_4$ be the four quarter-turn rotations of the\ncurrent square frame and put\n\\begin{equation}\n M_{r,s}(q)=\\frac14\\sum_{\\rho\\in\\mathcal C_4}\n       y_o(o+\\rho r)\\cdot y_o(o+\\rho s),\n \\qquad\n Q_{r,s}(q)=M_{r,s}(q)-(r\\cdot s)S_o(q).\n \\label{eq:setup-quadratic-packet}\n\\end{equation}\nThe two factors of a quadratic monomial are symmetrized before taking\ncoefficient cancellations.  The spatial identity used for this\nsymmetrization is\n\\[\n \\frac18\\sum_{\\rho\\in\\mathcal C_4}\n \\bigl[(\\rho r)_i(\\rho s)_j+(\\rho s)_i(\\rho r)_j\\bigr]\n                 =\\frac{r\\cdot s}{2}\\delta_{ij}.\n\\]\nIf $y_o(o+r)=Ar$ for a linear map\n$A:\\R^2\\to q_o^\\perp$, then\n\\[\n M_{r,s}=\\frac{r\\cdot s}{2}\\sum_{i=1}^2|Ae_i|^2,\n \\qquad\n S_o=\\frac12\\sum_{i=1}^2|Ae_i|^2,\n\\]\nso $Q_{r,s}$ vanishes on every affine tangent field.  This uses the\nsymmetric part of the quarter-turn average; full dihedral symmetry is\nnot required.\n\nA quadratic label is the entire compensated packet $aQ_{r,s}$, with\nits compensating nearest-neighbor coefficients tagged to that packet.\nIts norm contribution is\n\\begin{equation}\n |a|\\left\\{W_\\sigma(1+|r|_1+|s|_1)\n                  +|r\\cdot s|W_\\sigma(3)\\right\\}.\n \\label{eq:setup-quadratic-norm}\n\\end{equation}\nThe norm of a quadratic list is the supremum over anchors of the sum of\nthese contributions.  These norms are norms on the retained tagged\ncoefficient lists, not on the resulting polynomial after all\ncancellations: different retained lists can represent the same\npolynomial.  Thus a compensating coefficient uses its own\nnearest-neighbor path weight, while its tag retains the original\npacket's cancellation and support information.  This convention never\nshortens a mask or a complete record.  Equations\n\\eqref{eq:setup-canonical-norm} and \\eqref{eq:setup-quadratic-norm} are the\nfixed norms of \\Rref{rg:canonical-norm} and\n\\Rref{supp:quadratic-packet-norm}, with $\\mathcal C_4$ replacing the\ndihedral group.\n\nThe retained coefficient bounds are\n\\begin{equation}\n \\|P_4\\|_\\sigma\\le B_1,\\quad\n \\|I_2\\|_\\sigma\\le B_2,\\quad\n \\|P_6\\|_\\sigma\\le B_5,\\quad\n \\|I_4\\|_\\sigma\\le B_6,\\quad\n \\|J_2\\|_\\sigma\\le B_7.\n \\label{eq:setup-canonical-caps}\n\\end{equation}\nThe caps are fixed successively in the indicated slot order.  Polynomial\nformulas and their affine compensation are formed in the bulk before\nfolding onto a torus.  A label with $u\\le\\mathfrak m_j$ may use a common\nmask ball of radius $C_\\chi\\mathfrak m_j$ about its anchor.  Longer paths\nare padded by fixed multiples of $\\mathfrak m_j$.  The resulting load is\nat most $C(1+u/\\mathfrak m_j)$, with the full compensated packet retaining\nthe required graph.\n\n\\subsubsection{Differentiable errors}\n\nThe coefficient norms control the polynomial part of the density.  The\nremaining regular functions are controlled on the graph domains\n\\eqref{eq:setup-graph-domain}, including enough derivatives to compare\ntwo successive exact integrations.\n\nA regular-error label at $o$ consists of a function\n$f_X\\in C^8(\\mathcal U_{X,j})$, a graph $X$, and its complete record of\nload $s_X$.  Write $v\\times$ for the skew-symmetric linear map\n$w\\mapsto v\\times w$ on $\\R^3$.  For $1\\le r\\le8$, define the rotation\njet\n\\begin{equation}\n D_{v_1,\\ldots,v_r}f_X(q)\n =\\left.\n  \\partial_{\\theta_1}\\cdots\\partial_{\\theta_r}\n  f_X\\left(\\left(\n    \\exp\\!\\left[\\left(\\sum_{a=1}^r\\theta_av_{a,x}\\right)\\times\\right]\n                     q_x\\right)_x\\right)\n \\right|_{\\theta=0}.\n \\label{eq:setup-error-jets}\n\\end{equation}\nIn each derivative slot independently, take all vector fields satisfying\n\\begin{equation}\n |v_{a,x}|\\le t_j(1+\\operatorname{dist}(x,o))^8.\n \\label{eq:setup-error-directions}\n\\end{equation}\nDistances in this bound are physical torus distances on a finite torus.\nFor $0\\le k\\le8$, let\n\\begin{equation}\n [f_X]_{k,j}\n =\\sum_{r=0}^k\n   \\sup_{q\\in\\mathcal U_{X,j}}\n   \\sup_{v_1,\\ldots,v_r}\n       |D_{v_1,\\ldots,v_r}f_X(q)|,\n \\label{eq:setup-error-seminorm}\n\\end{equation}\nwhere the $r=0$ term is $\\sup_{\\mathcal U_{X,j}}|f_X|$.\nThese are classical derivatives on the open graph domain.  Values away\nfrom that domain require no differentiability.  This makes explicit the\nrotation version of \\Rref{rg:directions}.\n\nSet $A=64$.  The error-list norm and its cap are\n\\begin{equation}\n \\|f\\|_{k,j,A}\n   =\\sup_o\\sum_{X\\text{ anchored at }o}e^{As_X}[f_X]_{k,j},\n \\qquad\n \\|f\\|_{8,j,A}\\le\\delta_j:=H_j^{-2.05}.\n \\label{eq:setup-error-list-norm}\n\\end{equation}\nEach regular function is invariant under simultaneous $O(3)$\ntransformations of its spin arguments.  Each bulk packet is also\naveraged under the quarter turns about its anchor, including position\ninversion $x-o\\mapsto-(x-o)$, after its masks have been strengthened to\na common invariant graph.  Short folded copies inherit this inversion\nparity from their lifted bulk packets.\n\nFor a smooth local scalar $F$ with these spin and spatial symmetries,\nits \\emph{affine coefficient} at $o$ is\n\\begin{equation}\n \\mathfrak h_o(F)=\n \\left.\\frac{d^2}{d\\theta^2}\n F\\left(\\left(\\operatorname{Exp}_n\n       \\bigl(\\theta v\\,\\widehat e\\cdot(x-o)\\bigr)\\right)_x\\right)\n \\right|_{\\theta=0},\n \\label{eq:setup-affine-coefficient}\n\\end{equation}\nwhere $n\\in S^2$, $v\\in n^\\perp$ is a unit vector, and $\\widehat e$ is\na coordinate unit vector in the current lattice frame.  Symmetry makes\nthis number independent of these choices.  It is an ordinary second\nderivative, so $\\mathfrak h_o(S_o)=1$.  Polarization gives the mixed\naffine Hessian\n$\\mathfrak h_o(F)\\operatorname{Tr}(A_1^*A_2)$ for tangent maps\n$A_1,A_2:\\R^2\\to n^\\perp$.  The affine coefficient of a bulk list means\nthe sum of these numbers per anchor.\n\nA bulk or short error has zero value at every aligned configuration and\nzero affine Hessian there.  More precisely, fix $n\\in S^2$ and linear\nmaps $A_1,A_2:\\R^2\\to n^\\perp$.  With the anchor kept at $n$, its\nnormalizations are\n\\begin{equation}\n f_X((n)_x)=0,\n \\qquad\n \\left.\\partial_s\\partial_t\n f_X\\left(\\left(\n   \\operatorname{Exp}_{n}\\bigl(sA_1(x-o)+tA_2(x-o)\\bigr)\n                            \\right)_x\\right)\n \\right|_{s=t=0}=0.\n \\label{eq:setup-error-normalization}\n\\end{equation}\nThese affine tests are made on the lifted bulk formula and do not have\nto be periodic.  The first derivative at alignment vanishes by spin\ninvariance.  Winding errors are allowed to have nonzero values and\naffine Hessians at alignment.  These are the normalization conventions\nof \\Rref{rg:error-norm} and \\Rref{rg:normalization}.\n\n\\subsubsection{The mandatory covering sum}\n\nA covering label $\\ell$ consists of a bounded measurable function\n$k_\\ell(q)$, a complete support $P_\\ell$, a record of load $s_\\ell$, and\na nonempty inventory $J_\\ell$ of edges whose endpoints lie in that\nsupport.  It is required that\n\\[\n k_\\ell(q)=0\\quad\\text{unless}\\quad J_\\ell\\subset D(q).\n\\]\nThus $J_\\ell$ is the inventory denoted by $D_\\ell$ in\n\\Rref{rg:covering-gas}.\nNo sign or differentiability condition is imposed on $k_\\ell$.\nDefine\n\\begin{equation}\n \\Xi(q)=\n  \\sum_{\\substack{\\Gamma\\text{ a finite family of labels}\\\\\n                   P_\\ell\\cap P_{\\ell'}=\\varnothing\\ (\\ell\\ne\\ell')\\\\\n                   \\bigsqcup_{\\ell\\in\\Gamma}J_\\ell=D(q)}}\n                       \\prod_{\\ell\\in\\Gamma}k_\\ell(q).\n \\label{eq:setup-covering-sum}\n\\end{equation}\nThe empty family has weight one.  In particular $\\Xi(q)=1$ when\n$D(q)=\\varnothing$.  If $D(q)$ is nonempty, every one of its edges must\nbe supplied exactly once by the inventories in the family; inventories\nare not optional decorations.  The covering norm is\n\\begin{equation}\n \\|k\\|_{j,A}\n   =\\sup_x\\sum_{\\ell:x\\in P_\\ell}\n                e^{As_\\ell}\\|k_\\ell\\|_\\infty,\n \\qquad\n \\|k\\|_{j,A}\\le w_j:=\\exp(-p_j^{1/4}).\n \\label{eq:setup-covering-norm}\n\\end{equation}\nThe supremum norm is over the spin arguments of the weight, with all\nof its recorded tests included.  This is \\Rref{rg:covering-gas}.\nThe covering sum is absolutely convergent even for a countable label\nlist.  Indeed, dropping compatibility and inventory constraints and\nsumming over all finite subsets of the label list gives the bound\n\\begin{equation}\n \\sum_{\\Gamma}\\prod_{\\ell\\in\\Gamma}\\|k_\\ell\\|_\\infty\n \\le\\exp\\!\\left(\\sum_\\ell\\|k_\\ell\\|_\\infty\\right)\n \\le\\exp(|\\Lambda_j|w_j).\n \\label{eq:setup-covering-convergence}\n\\end{equation}\nFor the second inequality, each support is nonempty, so\n\\[\n \\sum_\\ell\\|k_\\ell\\|_\\infty\n \\le\\sum_{x\\in\\Lambda_j}\\sum_{\\ell:x\\in P_\\ell}\\|k_\\ell\\|_\\infty\n \\le|\\Lambda_j|w_j.\n\\]\nIn particular $|\\Xi(q)|\\le\\exp(|\\Lambda_j|w_j)$.\n\nAll lists are covariant under simultaneous $O(3)$ transformations of\nthe spin arguments, with constituent choices and tags retained together.\nThe individual regular functions have the invariance already required\nabove.  Anchored lists are translation covariant in the bulk.  Their\nspatial rules commute with quarter turns, and with simultaneous\nreflection of the blocking geometry and its data.  In particular a\nreflection can exchange the two inclined blocking types.  On an\nasymmetric torus, the symmetry operations concern lifted bulk labels\nand their folded copies; they do not require the torus itself to have a\nquarter-turn symmetry.  Any operation encountering a long record or a\nperiod disagreement carries the winding load.  Tags and full invariant\ntensors are retained together under these operations.\n\nAnchors are assigned equivariantly by distributing a term equally among\nits permitted anchors while retaining identical complete supports.\nEligibility and internal compatibility tests remain part of their\nlabels.  When masks in a spatial orbit differ, they are first replaced\nby a common stronger graph mask.  The difference is retained exactly\nas a covering correction, since it can be nonzero only when that graph\ncontains an edge in $D(q)$.  The same convention applies to all later\nmask strengthening; its algebraic reassembly is the one in\n\\Rref{rg:connected}.\n\nWith these definitions, an effective density relative to product\nnormalized area measure on $(S^2)^{\\Lambda_j}$ has the form\n\\begin{equation}\n \\begin{aligned}\n \\rho_j(q)={}&\\exp\\{\\mathfrak v|\\Lambda_j|-b\\mathcal E_{h,t_j}(q)\\}\\Xi(q)\\\\\n &\\quad\\times\n \\exp\\left\\{-\\sum_{\\text{canonical labels }\\alpha}\n                         1_{X_\\alpha}(q)\\,\\mathcal P_\\alpha(q)\n       -\\sum_X1_X(q)f_X(q)\\right\\}.\n \\end{aligned}\n \\label{eq:setup-effective-density}\n\\end{equation}\nHere $\\mathcal P_\\alpha$ includes the power of $b$ specified by its\nslot in \\eqref{eq:setup-slots}.  The bulk scalar $\\mathfrak v$ is the same throughout the compatible\nfamily of representations over admitted periods.  Constants caused\nby a period-dependent discrepancy remain in winding errors.  This is\nthe $S^2$ version of \\Rref{rg:density}.  Estimates for the representation\nalso permit signed densities; densities obtained by the actual\nobservation kernel are positive.  We call a representation \\emph{admitted}\nwhen these support, symmetry, period, and norm conditions hold and\n$b\\in[H_j/2,2H_j]$.\n\n\\subsubsection{Comparing two densities}\n\nTwo inputs are compared on a common reference layer, with the same\n$H_j,t_j$ and geometric thresholds.  First use the prescribed common\nrefinements of their labels, masks, and complete records; any mask\nstrengthening retains its exact covering correction as above.  Then\nretain the union of these refined label lists and assign coefficient or\nweight zero when a label is absent.  Corresponding functions now have\ncommon graph domains and complete records.  Write $b_1,b_2$ for the precise\ncouplings and set $\\lambda=b_2-b_1$.  For fixed positive weights\n$\\omega_1,\\ldots,\\omega_7,\\omega_f,\\omega_k$, define\n\\begin{equation}\n u=\\sum_{a=1}^7\\omega_a\n          \\|\\mathbf P_{2,a}-\\mathbf P_{1,a}\\|_\\sigma\n    +\\omega_f\\delta_j^{-1}\\|f_2-f_1\\|_{7,j,A}\n    +\\omega_kw_j^{-1}\\|k_2-k_1\\|_{j,A}.\n \\label{eq:setup-density-discrepancy}\n\\end{equation}\nThe third and fourth coefficient discrepancies are zero.  Polynomial\ncoefficients are compared without their powers of $b$; the change in\nthe precise coupling is recorded by $\\lambda$.  The volume scalar is\ntracked separately and is not a component of $u$.\n\nThe weights are chosen after the successive canonical caps so that the\ntriangular coefficient estimate and the error and covering estimates\nhold together.  They remain fixed across layers and admitted periods.\nEach individual error is controlled through eight derivatives, whereas\nan error discrepancy is measured through seven.  Thus a difference of\nintegration maps acting on an unchanged error can use its eighth\nderivative without reducing the regularity of the next individual\ndensity.  This is the comparison convention of \\Rref{rg:closure}, with\nthe stronger contraction proved in Section~\\ref{sec:rg}.\n\n\n\\subsection{Endpoint laws and the scope of the imported estimates}\n\\label{setup:endpoints}\n\nAn \\emph{endpoint density} is a layer-zero representation satisfying the\npreceding caps, with \\(b\\in[H/2,2H]\\), and with the bulk scalar removed.\nWe denote its remaining exponent by \\(X\\).  For an actual observed\nrepresentation, \\(e^X\\Xi>0\\); its partition function and probability\nlaw are therefore well defined by\n\\begin{equation}\n \\begin{split}\n X(q)&=-b\\mathcal E_{h,t_0}(q)\n       -\\sum_\\alpha1_{X_\\alpha}(q)\\mathcal P_\\alpha(q)\n       -\\sum_Y1_Y(q)f_Y(q),\\\\\n Z&=\\int e^{X(q)}\\Xi(q)\\,\\dd\\omega^{\\otimes\\Lambda_0}(q),\n \\qquad\n \\dd\\mu(q)=Z^{-1}e^{X(q)}\\Xi(q)\\,\\dd\\omega^{\\otimes\\Lambda_0}(q).\n \\end{split}\n \\label{setup:endpoint-law}\n\\end{equation}\nThe scalar removed from the\npartition function is exactly \\(e^{\\mathfrak v|\\Lambda_0|}\\).\nFor a fixed endpoint reference scale, \\(\\delta_0=H^{-2.05}\\) and\n\\(w_0=\\exp[-(\\log H)^{P_0/4}]\\); with \\(P_0>4\\), the latter tends\nto zero faster than every fixed inverse power of \\(H\\).\n\nThe definitions distinguish three kinds of input to the exact blocking\nconstruction.\nThe scalar identities and localization estimates of\n\\Rref{free:bounds} and \\Rref{lem:free-stack} have no spin-dimension\nhypothesis.  Their extension to the extra histories is proved in\nSection~\\ref{sec:free}.  The Gaussian product and connected-expansion\nestimates of \\Rref{supp:gaussian-product} and \\Rref{rg:tree-condition}\napply to finite-dimensional Gaussian integrations and complete support\nrecords satisfying their stated envelope and support-hit bounds.\nSection~\\ref{sec:rg} verifies those bounds for the actual \\(S^2\\)\nfactors, including derivatives and the auxiliary normal coordinate.\nFinally, the improved coefficient, error, endpoint, and source\ncomparisons are conclusions of this paper.  They are not included in\nthe definition of an endpoint law.  The separate adaptations of the\npreliminary mixing estimates and the finite-volume Laplace coefficients\nare stated at their points of use in Sections~\\ref{sec:preliminary}\nand~\\ref{sec:shooting}.\n\nThis separation is useful in two places.  Signed covering weights can\nbe estimated algebraically without assuming positivity of a truncated\ncovering sum.  Reflection positivity and the continuum spectral\nconclusion, by contrast, concern the complete positive laws and are\nestablished only after the corresponding observation and limit\narguments.\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/shooting.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/shooting.tex", "bytes": 15934, "sha256": "eb8ef31df0fde4f7abfde382f8701fc5cca6dff8ba95d4e119a07a69dfe4651d", "content": "\\section{Prescribing the terminal coupling}\n\\label{sec:shooting}\n\nThe exact transformation of Section~\\ref{sec:rg} allows the number of\nblocking steps to vary. We now choose the microscopic coupling so that\nevery trajectory ends at the same coupling $H$. This gives a common\nphysical normalization and permits comparison of different ultraviolet\ncutoffs. The first task is to identify the limiting kinetic increment;\nthe second is a two-endpoint estimate, using the fixed terminal coupling\nat one end and contraction of the remaining data at the other.\n\n\\subsection{Initialization and the kinetic increment}\n\nAt history zero the unmasked quadratic energy is exactly the\nnearest-neighbor energy. We initialize all canonical coefficients and\nregular errors at zero. The covering representation is obtained as in\n\\Rref{rg:trajectories}: join true bad bonds together with the witnesses\nfor all deleted mask rows, and assign each resulting component the\nexponential of minus its energy difference. The difference between the\noriginal energy and the clipped energy is nonnegative. Each true bad\nbond contributes at least $cp_j^2$, while every remaining deleted row\nis a positive square. The direct-energy reserve therefore pays for the\nconnected support count and gives the covering cap. The initial bulk\nscalar includes the constant converting the chord-square action into\nthe nearest-neighbor Gibbs weight. This construction uses only chord\nbounds and positive squares, and hence applies to $S^2$.\n\nLet $\\mathcal C_h$ be the canonical coefficient map constructed in\nSection~\\ref{subsec:rg-canonical}, where $h$ counts completed blocking\nsteps. Define its orbit by\n\\[\n \\mathbf P^{(0)}=0,\\qquad\n \\mathbf P^{(h+1)}=\\mathcal C_h(\\mathbf P^{(h)}),\n\\]\nand denote the two kinetic increments evaluated on this orbit by\n$\\alpha_h$ and $\\kappa_h$. The map $\\mathcal C_h$ is the finite formal\ncoefficient operation, independent of the precise coupling, regular\nerrors and covering gas. The exact output representation assigns its\ncanonical slots to this operation; the discrepancy from the actual\nintegral remains in the error and gas, with its additional affine\ncorrection assigned to $\\Delta$. Thus the canonical slots of an exact\ntrajectory initialized above follow precisely this orbit. Its actual\ncoupling recursion is\n\\begin{equation}\n b_{j-1}=b_j+\\alpha_{N-j}+\\kappa_{N-j}/b_j+\\Delta_j,\n \\qquad |\\Delta_j|\\le C_LH_j^{-1.05},\n \\label{eq:shooting-actual-flow}\n\\end{equation}\nwhich is Equation~\\eqref{eq:orig-4} along that trajectory.\nThe triangular coefficient contraction and the free-history\nestimates imply exponential convergence of both increment sequences. The same\nlimits are obtained if the first step is inclined: after that step the\nregular transformations are the same, and their histories converge by\nthe second comparison in Section~\\ref{sec:free}. Denote the limits by\n$\\alpha_\\infty$ and $\\kappa_\\infty$.\n\n\\begin{lemma}[Limiting kinetic increment]\n\\label{lem:kinetic-increment}\nFor the nearest-neighbor action and the normalized observations used\nhere,\n\\begin{equation}\n \\alpha_\\infty=-\\gamma,\n \\qquad \\gamma=\\frac{\\log L}{2\\pi}.\n \\label{eq:orig-11}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nWe compare a fixed finite-volume Laplace coefficient before and after\nblocking. Only the first coefficient is needed, so the second limiting\nincrement need not be evaluated.\n\nFor spins on $S^D$, let $A_D(n)$ be the coefficient of $b^{-1}$ in\n$\\log Z_b(n,n)$, with $n$ fixed during the expansion. The calculation\n\\Rref{cal:direct1}, which is stated for general tangent dimension $D$,\ngives\n\\begin{equation}\n 4A_D(n)-A_D(2n)\n   =-\\frac{3D(D-1)}{4\\pi}\\log n+O(1).\n \\label{eq:shooting-bare-coefficient}\n\\end{equation}\nIts action normalization is exactly the nearest-neighbor normalization\nof the present model.\n\nFix a sufficiently large terminal side $m$, put $n=mL^s$, and set\n\\[\n B_h=\\sum_{r<h}\\alpha_r,\\qquad C_h=\\sum_{r<h}\\kappa_r,\n \\qquad \\widehat b_h=b+B_h+C_h/b.\n\\]\nInsert $s$ regular normalized observations with these formal parameters\non the tori of sides $n$ and $2n$. For fixed $s$ they are positive when\n$b$ is sufficiently large.\nPin one spin on the final layer. Transitivity of the target sphere\nidentifies this with integration over the global orientation, up to a\ncoupling-independent constant. For each fixed $s$ the remaining\nintegration is finite-dimensional. The aligned saddle has a positive\ntransverse quadratic form. Outside an aligned neighborhood the action\nhas a strictly positive excess: small microscopic nearest-neighbor\nenergy forces alignment, and small observation energy propagates that\nalignment through the successive layers. In this neighborhood all\nmeans lie in the normalized-mean branch, so the discontinuous fallback\nbranch introduces no saddle contribution.\n\nSuccessive Gaussian completion therefore computes genuine Laplace\ncoefficients. The computations are precisely the canonical vertex\ncalculations of Section~\\ref{sec:rg}, with cutoffs removed on their\nplateaus. The auxiliary normal scalar is integrated with its\nnormalizing determinant, so each Gaussian pinning factor counts $D$\nphysical tangent components. Thus the remaining pinning power in the\ndoubling difference has coefficient $D_0=3D/2$.\n\nFor completeness, the uniformity in $s$ needed here is uniformity of\ncoefficients, not of a bare Laplace remainder. At an output period\n$\\bar m$, a finite-period coefficient differs from its folded bulk\ncoefficient only when a complete contraction crosses a period. The\nanalytic kernel and Wick-coefficient bounds give an exponentially\nsmall error in $\\bar m$, with fixed polynomial position factors.\nPropagation through one subsequent coefficient calculation costs at\nmost $C_L$: only finitely many orders occur, and their position sums\nare absolutely convergent. The earlier periods are $mL^r$, and\n\\[\n \\sum_{r\\ge0} C_L^{r+1}(1+r+mL^r)^C e^{-cmL^r}<\\infty.\n\\]\nThis is the uniform coefficient estimate in the proof of\n\\Rref{cal:block1}. It applies here because the projected chart vertices\nhave the same exponential kernel and finite-contraction bounds, the\ncanonical slots and increments are bounded, and the last pinned free\noperator is uniformly elliptic on the fixed periods.\n\nAll extracted bulk scalars cancel in the doubling difference. The\nremaining Gaussian power is $D_0\\log\\widehat b_s$, whose coefficient of\n$b^{-1}$ is $D_0B_s$. The bounded final coefficients and the preceding\nwinding errors consequently give\n\\[\n 4A_D(mL^s)-A_D(2mL^s)\n   =D_0\\sum_{h<s}\\alpha_h+O_{L,m}(1).\n\\]\nSet $D=2$ and compare with\nEquation~\\eqref{eq:shooting-bare-coefficient}. Division by $s$ and\nexponential convergence of $\\alpha_h$ give\n$\\alpha_\\infty=-\\log L/(2\\pi)$.\n\\end{proof}\n\n\\subsection{Shooting to a fixed terminal value}\n\nThe remaining layers are indexed by $j=N,N-1,\\ldots,0$, so that\n$b_N$ is microscopic and $b_0$ is terminal. Put\n\\[\n c_{\\log}=-\\frac{\\kappa_\\infty}{\\gamma},\n \\qquad\n \\vartheta_j=H_j+c_{\\log}\\log(H_j/H).\n\\]\nThe reference sequence satisfies\n\\begin{equation}\n \\vartheta_j-\\vartheta_{j-1}\n   =\\gamma-\\frac{\\kappa_\\infty}{H_j}+O_L(H_j^{-2}).\n \\label{eq:shooting-reference}\n\\end{equation}\n\n\\begin{proposition}[Admitted trajectories with prescribed endpoint]\n\\label{prop:shooting}\nAfter the choices of $L$ and $P_0$, take $H$ sufficiently large. For\nevery integer $N\\ge1$ there is a microscopic coupling $\\beta_N>0$\nwhose $N$ regular transformations remain strictly inside the bands\n$[H_j/2,2H_j]$ and end at $b_0=H$. Every such choice satisfies\n\\begin{equation}\n \\beta_N=H+\\gamma N+O_{L,H}(\\log(2+N)).\n \\label{eq:orig-12}\n\\end{equation}\nAlong these trajectories,\n\\begin{equation}\n b_j=\\vartheta_j+O_L(1),\\qquad 0\\le j\\le N,\n \\label{eq:orig-13}\n\\end{equation}\nuniformly in $N$, with constants bounded as $H$ is increased.\nThe trajectories starting at the same $\\beta_N$ with either inclined\nfirst step are also strictly admitted and satisfy\nEquation~\\eqref{eq:orig-13}. Their terminal couplings agree with one\nanother, although they need not equal $H$.\n\\end{proposition}\n\n\\begin{proof}\nWe give the shooting argument to specify the continuity and uniformity\nbeing used from \\Rref{rg:admission}. The exact map is continuous in\nthe precise input coupling on every finite strictly admitted segment.\nFor the initial covering representation this follows because its\ngeometric predicates use the fixed reference thresholds; for each\nsubsequent step it follows from the uniform product and integration\nbounds in Section~\\ref{sec:rg}.\n\nOn an admitted segment from layer $k$ to layer $j<k$, summing\nEquation~\\eqref{eq:orig-4}, the bounded second increments, and the\nexponentially summable errors in\n$\\alpha_h+\\gamma$ gives\n\\begin{equation}\n |(b_j-H_j)-(b_k-H_k)|\n   \\le C_L+C_L(k-j)/H.\n \\label{eq:shooting-barrier}\n\\end{equation}\nVary the initial coupling in $[.6H_N,1.4H_N]$. Call it high if a\nstrictly admitted initial segment reaches $b_k>1.25H_k$, or if a full\nstrictly admitted trajectory ends above $H$. Define low with\n$b_k<.75H_k$ or a terminal value below $H$. These are nonempty open\nsubsets of the initial interval: continuity gives openness, and the\ntwo endpoints give nonemptiness.\n\nThey are disjoint. An upper and lower mark at layers $j,k$ force a\nchange at least $(H_j+H_k)/4$ in the quantity $b-H_{\\cdot}$. An upper\nmark at layer $k$ and a terminal value at most $H$ force a change at\nleast $H_k/4$; the corresponding lower-mark statement is identical.\nThese changes contradict Equation~\\eqref{eq:shooting-barrier} for\nlarge $H$, because $H_k-H_j=\\gamma(k-j)$. Connectedness of the initial\ninterval therefore supplies an initial value in neither class.\nEvery step changes the coupling by at most $C_L$. With $H$ sufficiently\nlarge, an exit from an admitted band would have been preceded by one\nof the strict internal marks. Thus this value reaches layer zero and\nends exactly at $H$.\n\nTo estimate the resulting trajectory, set $D_j=b_j-\\vartheta_j$.\nThe denominator comparison\n\\[\n |b_j^{-1}-H_j^{-1}|\n \\le C\\frac{\\log(2+H_j/H)+|D_j|}{H_j^2}\n\\]\nand Equation~\\eqref{eq:shooting-reference} show that the difference\nequation for $D_j$ has summable inhomogeneous terms: exponential\nincrement errors, $O_L(H_j^{-1.05})$, and\n$O_L(H_j^{-2}\\log(2+H_j/H))$. Starting from $D_0=0$ and summing\nbackwards gives, on every finite run,\n\\[\n \\max_j|D_j|\n \\le C_L+C_L\\left(\\sum_{r\\ge1}H_r^{-2}\\right)\\max_j|D_j|.\n\\]\nThe coefficient sum is $O_L(H^{-1})$. Increasing $H$ absorbs it and\nproves Equation~\\eqref{eq:orig-13}, hence\nEquation~\\eqref{eq:orig-12}.\n\nFor an inclined first step, the limiting increments are unchanged\nand their transient errors still have bounded sums. Starting from\nthe same $\\beta_N$, the first-exit estimate\n\\Rref{rg:first-exit-bound} therefore gives\n\\[\n |D_j|\\le C_L+C_L\\sum_{r=j+1}^N\n \\frac{\\log(2+H_r/H)+|D_r|}{H_r^2}\n\\]\nthrough a hypothetical first exit. Its maximum is bounded by the\nsame absorption, and the resulting interval lies strictly inside the\nadmitted band. An exit is impossible. Finally, reflection interchanges\nthe two inclined prescriptions and preserves every bulk scalar in the\ncanonical and error normalization. Their running couplings therefore\nagree. This scalar equality does not identify their coefficient tensors\nin common coordinates; those tensors require the comparison below.\n\\end{proof}\n\nFix one such $\\beta_N$ for each $N$.  The construction at a prescribed\nscale uses any such choice and does not require uniqueness of the shooting\nvalue.  Sections~\\ref{sec:trajectories}--\\ref{sec:uniqueness} will remove\nthe choice of trajectory after canonical normalization.\n\n\\subsection{Matching the complete endpoint densities}\n\nFor two regular trajectories of depths $N\\ge K$, align their final\n$K$ layers and use the discrepancy variables $u_j,\\lambda_j$ of\nSection~\\ref{sec:rg}. Here $u_j$ measures the normalized coefficient,\nerror and gas differences, while $\\lambda_j$ is the precise coupling\ndifference. The upper endpoint has bounded shape data; the lower\nendpoint satisfies $\\lambda_0=0$.\n\n\\begin{proposition}[Endpoint matching]\n\\label{prop:endpoint-matching}\nFor every sufficiently small fixed $\\upsilon>0$, the parameters may be\nchosen so that some $\\rho<L^{-2+\\upsilon}$ satisfies\n\\begin{equation}\n u_j+|\\lambda_j|\n \\le C_H(1+K)^C\\rho^{K-j},\n \\qquad 0\\le j\\le K,\n \\label{eq:orig-14}\n\\end{equation}\nuniformly in $N\\ge K$. After the respective bulk scalars have been\nremoved, write the endpoint densities as $e^{X_i(q)}\\Xi_i(q)$.\nOn every common admitted terminal torus of volume $v$,\n\\begin{align}\n \\norm{X_1-X_2}_\\infty&\\le D_Hv\\delta_K,\\nonumber\\\\\n \\sup_x\\sum_{\\lambda:x\\in P_\\lambda}\n e^{As_\\lambda}\\norm{k_{1,\\lambda}-k_{2,\\lambda}}_\\infty\n &\\le D_H\\delta_K,\n \\qquad \\delta_K=L^{-(2-\\upsilon)K}.\n \\label{eq:orig-15}\n\\end{align}\nThe constants are independent of depths and periods. The two mirrored\ninclined trajectories at depth $N$ have the same endpoint estimates\nwith $K=N$, when their output lists are expressed in common coordinates.\n\\end{proposition}\n\n\\begin{proof}\nThis is the two-boundary argument behind \\Rref{rg:two-end-bound}, with\nthe improved contraction and history rate. Let $q$ be the contraction\nconstant in Equation~\\eqref{eq:orig-5}, and put $r=A_0/L^2$. Choose\n\\[\n \\max(q,r)<\\rho<L^{-2+\\upsilon}\n\\]\nwith strict spare width. The coupling coefficient in\nEquation~\\eqref{eq:orig-5} is bounded by\n\\[\n \\varepsilon_H=\\sup_{j\\ge0}\n C_L(\\log H_j)^C/H_j,\n \\qquad \\varepsilon_H\\longrightarrow0.\n\\]\nWriting $a=(1-\\varepsilon_H)^{-1}$, the step inequalities imply\n\\[\n u_{j-1}\\le q u_j+\\varepsilon_H|\\lambda_j|+f_j,\n \\qquad\n |\\lambda_j|\\le a|\\lambda_{j-1}|+aC_Lu_j+af_j,\n\\]\nwhere $f_j\\le C_L\\operatorname{poly}(H_j)r^{K-j}$.\nSet $w_j=\\rho^{K-j}$ and\n\\[\n U=\\max_{0\\le j\\le K}u_j/w_j,\n \\qquad W=\\max_{0\\le j\\le K}|\\lambda_j|/w_j.\n\\]\nThere is $F_K=C_H(1+K)^C$ such that $f_j\\le F_Kw_j$.\nIteration from $K$ for the first inequality and from $0$ for the\nsecond gives\n\\[\n U\\le u_K+\\frac{\\varepsilon_HW+F_K}{\\rho-q},\n \\qquad\n W\\le\\frac{a(C_LU+F_K)}{1-a\\rho}.\n\\]\nTake $H$ so that $a\\rho<1$ and the product of the two coefficients\ncoupling $U$ and $W$ is less than $1/2$. Since $u_K$ is bounded by\nthe common caps, these inequalities prove\nEquation~\\eqref{eq:orig-14}.\n\nAt layer zero the masked slot bounds, the load bounds, and the\nfree-history discrepancies convert this estimate to the two norms\nin Equation~\\eqref{eq:orig-15}, exactly as in\n\\Rref{rg:terminal-difference}. All sup bounds are taken on their\nmasks; no off-mask extrapolation is used. The factor $(1+K)^C$ is\nabsorbed into the strict spare width between $\\rho$ and\n$L^{-2+\\upsilon}$. For the inclined comparison, start at layer\n$N-1$, where the first-step outputs obey the common caps, and compare\nthe shared subsequent regular steps. Their terminal couplings agree\nby Proposition~\\ref{prop:shooting}. The same proof applies, and the\none-step shift is absorbed in the fixed constant.\n\nAt every step only a scalar per site has been extracted. The layer-$j$\nvolume is $vL^{2j}$, so the total scalar is exactly a constant times\n$v$, independent of the terminal period. All period-dependent\ncorrections remain in the winding lists. This proves the claimed\nuniformity of the scalar-stripped comparison.\n\\end{proof}\n\nThe endpoint densities are strictly positive: each is obtained by\nintegrating the positive microscopic density against positive\nnormalized observations. In standard axis tori their laws also retain\nlink reflection positivity at every retained block seam. Indeed the\nmicroscopic positive kernel factors across such a seam, and the\nobservations on its two sides are independent and transform into one\nanother under reflection. This is the verification following\n\\Rref{cmp:RG-density}. Translation, quarter-turn, and mirror symmetries\nare retained whenever they preserve the period. When tile reflections\nare used below, the relevant tile counts are even. No endpoint\nreflection positivity is required for oblique periods or after an\ninclined first step.\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/sources.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/sources.tex", "bytes": 28318, "sha256": "3dc63e9b9990cd2b412472a09c27bce845d88692ef7022d440018c6155f0e056", "content": "\\section{Exact renormalization of the spin sources}\n\\label{sec:sources}\n\nThe estimates obtained so far concern densities and partition functions.\nTo construct fields, we must also compare local observables at different\ncutoffs.  We do this by carrying independent sources through the exact\nblock integration.  The coefficient of the linear spin source determines\nthe field normalization.  The coupling prescription of\nSection~\\ref{sec:shooting} remains the prescription at zero source.\n\nThroughout this section a step has side factor $l=L$ or $l=L_*=5L$ and\nmaps layer $j$ to layer $j-1$.  We use the scales and graph norms of\nSection~\\ref{sec:rg}; in particular $k=8$, and\n\\[\n R_j:=H_j^{-1/10}.\n\\]\nThe estimates in this section require no information about the\ninfinite-volume state.  They apply in bulk and on every compatible\nperiod admitted in Section~\\ref{sec:free}.\n\n\\subsection{The source class and the exact step}\n\nAt each site $x$ let $z_x=(z_x^1,z_x^2,z_x^3)\\in\\C^3$ be an independent\nvariable.  For a power series $F(q;z)=\\sum_\\alpha F_\\alpha(q)z^\\alpha$,\nwhere $\\alpha$ ranges over finitely supported multi-indices in the\nsite and component variables, its coefficient norm at radius $r$ is\n\\[\n [F]_{k,j;r}:=\\sum_\\alpha r^{|\\alpha|}[F_\\alpha]_{k,j}.\n\\]\nThe same convention applies to supremum and activity norms.  A\ncoefficient of a regular function is measured on its graph domain;\na covering activity is measured by its supremum.  We omit $r$ when\n$r=1$.  Absolute coefficient sums form a product majorant: products\nare bounded by coefficient convolution before any integration or\ngraph summation.  In particular, identifying source variables does\nnot increase this norm.\n\nWe adjoin to a density in the class of Section~\\ref{sec:rg} the\nexponent\n\\[\n \\sum_x z_x\\cdot q_x+\\sum_X 1_XG_X(q;z)\n\\]\nand source-dependent covering activities $k^{\\mathrm{src}}_\\lambda(q;z)$.\nEvery $G_X$ and $k^{\\mathrm{src}}_\\lambda$ has positive source degree.\nThe graph domain of $G_X$, its load $s_X$, and its mask $1_X$ are those\nof a regular error.  Its complete support includes every source site\nappearing in it.  Covering labels retain their nonempty inventories of\nbad bonds and their complete spin and source dependencies.  All masks\ntest spin chords only; they impose no condition on a source variable.\n\nThe source lists satisfy\n\\begin{equation}\n \\sup_o\\sum_{X\\text{ anchored at }o}e^{As_X}[G_X]_{k,j}\n       \\le R_j,\n \\qquad\n \\norm{k^{\\mathrm{src}}}_{j,A}\\le w_j.\n \\label{eq:orig-27}\n\\end{equation}\nWe may separate a regular list by degree or by its multiset of source\nindices.  Each such record is anchored at one of those indices; in\ndegree one this is the unique source site.  Bulk and nonwinding\ndegree-one regular functions vanish when all their spin arguments\nare equal.  No such normalization is imposed on winding records,\nand no alignment normalization is imposed in higher source degree.\nIn particular, a spin-independent term of degree at least two stays\nin its source-supported regular record; it is not extracted as a\nvolume scalar.\nThe classifications as short or winding apply to the source indices\nand anchors as well as to the spin dependencies.\n\nThe lists inherit the spatial covariance conventions of the\nsource-free class.  In particular they are translation covariant\nas lists in independent source variables: translating a complete\nrecord, its anchor, and all of its spin and source indices leaves\nits coefficient function unchanged after the corresponding\nrelabeling of the arguments.  Thus a specialization to finitely\nmany nonzero sources need not be translation invariant, but the\nbulk rule that produces its coefficients is translation covariant.\nThe preparations, record assignments, and normalization rule are\nalso reflection covariant as a family of prescriptions.  A\nreflection preserves the standard prescription and exchanges\nthe two mirror inclined prescriptions, with their reflected\ninput lists.  The same conventions apply to regular and\ncovering source records.\n\nThe lists have real coefficients and are covariant under simultaneous\ninternal $O(3)$ transformations of spins and sources.  They are\norganized by retaining the full vector or tensor polynomial associated\nwith a multiset of source sites, including all its component\ncoefficients.  Thus covariance is imposed on a tensor contribution,\nnot on each scalar component separately.  No tensor norm with an\nuncontrolled dependence on the source degree is used.  At the initial\nlayer the source factor is simply $\\exp(\\sum_x z_x\\cdot q_x)$, so the\ntwo additional lists are zero and all these conditions hold.\n\n\\begin{proposition}[Exact source step]\n\\label{prop:source-step}\nConsider an admitted source-free step of Section~\\ref{sec:rg} from\nlayer $j$ to layer $j-1$, with precise coupling\n$b\\in[H_j/2,2H_j]$, together with source lists satisfying\nEquation~\\eqref{eq:orig-27} and the normalization and support\nconditions above.  There is a real number $c_j>0$, determined by the\nbulk linear response, such that\n\\begin{equation}\n z_x=\\frac{z'_{[x]}}{l^2c_j},\n \\qquad |c_j-1|\\le C_LR_j,\n \\label{eq:orig-28}\n\\end{equation}\ngives an exact output representation with leading exponent\n$\\sum_Yz'_Y\\cdot V_Y$ and renewed source bounds\nEquation~\\eqref{eq:orig-27} at layer $j-1$.  The source-free output\nis exactly the output at zero source.  No further source-dependent\nvolume scalar is extracted.\n\nFor two compatible inputs to a regular step, let $u,\\lambda,\n\\epsilon_h$ be the source-free discrepancies of\nEquation~\\eqref{eq:orig-5}.  Let $u_s$ be a fixed positively weighted\nsum of the regular and covering source discrepancies divided by\n$R_j$ and $w_j$, respectively; the regular discrepancy is measured\nthrough order $k-1$, with absent records padded by zero.  The weights\ncan be chosen so that, for some $q_s<1$,\n\\begin{equation}\n \\begin{split}\n u_s'&\\le q_su_s+\n   C_L(\\log H_j)^D\n          \\bigl(u+\\epsilon_h+|\\lambda|/H_j\\bigr),\\\\\n |c_{2,j}-c_{1,j}|&\\le C_LR_j\n    \\left[u_s+(\\log H_j)^D\n          \\bigl(u+\\epsilon_h+|\\lambda|/H_j\\bigr)\\right].\n \\end{split}\n \\label{eq:orig-29}\n\\end{equation}\nThese estimates hold in bulk and simultaneously on the compatible\nadmitted periods.  The fixed exponent $D$ may depend on $P_0$.\nThe order of choices is a sufficiently large $L$, then $P_0$ large\nenough for the fixed polynomial losses, and finally sufficiently\nlarge $H$.\n\\end{proposition}\n\n\\begin{proof}\nWe first integrate after the substitution $z_x=z'_{[x]}/l^2$, leaving\nthe final scalar division for the end of the proof.  We estimate the\noutput at source radius $2$; any fixed finite collection of such\nradii can be used by increasing $L$.  A coefficient of degree $a$\nthen gains $(2/l^2)^a$.  Since each block has $l^2$ sites and\n$T_x=V_{[x]}$, subtracting the coarse leading exponent gives the\nidentity\n\\[\n \\frac1{l^2}\\sum_x z'_{[x]}\\cdot q_x\n =\\sum_Yz'_Y\\cdot V_Y+\n       \\frac1{l^2}\\sum_xz'_{[x]}\\cdot(q_x-T_x).\n\\]\nIt remains to integrate the second term together with the old\nsource lists.\n\n\\paragraph{Preparation of the source factors.}\nWrite $M=\\mathfrak m_j$, $p=p_j$, and $g=b^{-1/2}$.  We use the\nsector integration of Section~\\ref{sec:rg}.  A core is one of its\nconnected exceptional regions, with load $s_c$ and the Gaussian\nread set appearing in Equation~\\eqref{eq:orig-7}.  A prepared\nletter is the exponential-minus-one factor used in that integration.\nThere are three additional sorts of factor.\n\nFirst consider the term containing $q_x-T_x$.  All such factors\nwhose sites lie within distance $12M$ of a core's seeds are included\nin that core factor.  If these neighborhoods overlap, each site is\nassigned once, using the ownership convention of\nSection~\\ref{sec:rg}.  The attached profiles are extended to any\nnew exterior variables read by these factors.  The absolute source\nseries contributes at most $\\exp(C_LM^Ds_c)$ to the core envelope;\nwithout spin derivatives the bound is\n$\\exp(C_LM^2s_c)$.  This is paid by the exceptional reserve\n$\\exp(-c_Lp^2s_c)$ in Equation~\\eqref{eq:orig-7}, after the prescribed\nchoice of $P_0$ and $H$.  These core factors may depend on the actual\nspin variables.\n\nAway from those neighborhoods, choose a smooth angular plateau\ncontaining every full-sector value of $q_x-T_x$ and write its\nexponential as one plus a letter.  The letter vanishes at angle\nzero, and the plateau gives $|q_x-T_x|\\le C_Lt_j$.  Consequently\nits majorant, through the required normalized spin derivatives, is\n\\begin{equation}\n C_Lg\\operatorname{poly}(M,p).\n \\label{eq:orig-30}\n\\end{equation}\nThe mean estimates in Section~\\ref{sec:rg} give the same order for\nthose derivatives.  Gaussian polynomials produced by a fixed number\nof differentiations are covered by its joint estimates.  The new\nreads are the site, its block reference, and the attached profiles;\nin the remote preparation they also include the stencil deciding\nthe selection status.  These are already contained in the halos,\ncore footprints, and status conventions of the sector construction.\nWe use its local extension for a term with no output bad-bond flag.\n\nSecond, prepare a masked $G_X$ as an old regular error, with the\ngraph cutoff in the remote case and with exponential-minus-one\nexpansion.  The preparations in \\Rref{rg:prepared} use its small\nnorm, its graph domain, and its complete supports; they do not use\nthe affine-Hessian normalization of a source-free error.  In a\nlinear use, the coefficient majorant retains the substitution\nfactor $(2/l^2)^a$.  Nonlinear powers are bounded by products.\nThe summed majorants at a support hit, including each of the fixed\nlarger output load exponents required below, are at most\n$C_L\\operatorname{poly}(M,p)R_j$.  A nonremote measurable evaluation\nstill meets its core.  Changing the anchor of a spin direction to\nthe chosen source anchor costs only fixed powers of $M$ and the\ntotal load, also in a discrepancy estimate.  No source integration\ndomain is being translated.\n\nThird, the positive-degree parts of old covering labels are\nprepared by the same activity estimates.  The substitution and\nEquation~\\eqref{eq:orig-27} satisfy those activity hypotheses with\nat most a fixed combined factor.  Every such use still meets a\nprimary exceptional object.  Terms with a nonempty output\ninventory require positive-degree supremum bounds only, without\nspin derivatives.\n\n\\paragraph{Absolute coefficient sums and exact reassembly.}\nFor clarity, all product estimates are applied after taking\nabsolute coefficient majorants and performing coefficient\nconvolution.  The majorant of an exponential is the exponential\nseries of the majorant of its exponent.  For nonlinear powers of\na masked $G_X$, its undifferentiated envelope is small before\ncutoff derivatives are charged.  At most $k$ factors can receive\nderivatives.  Their Leibniz placements cost a polynomial in the\nnumber of factors, so the one-factor bounds above remain valid\nwith an additional\n$C_L\\operatorname{poly}(M,p,1+s_X)$ multiplier where required.\nThe passage from anchor sums to support-hit sums uses the fixed\nprojection powers and spare support exponent exactly as for old\nerrors.\n\nRepeated source variables cause no change in this argument.\nFactorization of separate contributions is determined by their\ncomplete supports, not by disjointness of their source monomials.\nInside a core, coefficient supremum sums of exponent powers have\nthe core envelope already obtained; differentiation through the\ntransport produces only its fixed polynomial score costs.\nThus the hypotheses of \\Rref{rg:joint-product} and\n\\Rref{rg:tree-condition} hold in the coefficient norm.  Each\nargument mark is retained in the output graph.  A singleton\ntransfer inherits its source anchor.  Any other connected term is\nanchored at one of its source marks.  If covariance requires\nseveral anchor choices, split the term with nonnegative\ncoefficients summing to one and identical complete records, or\nretain its ordered marks.  This operation incurs no loss growing\nwith source degree.\n\nApply the exact connected reassembly \\Rref{rg:connected} to these\nlists.  Keep every zero-degree prescription fixed.  In particular,\nthe scalar extraction, canonical normalization, and error reset\nare first performed at degree zero, including their exact gas\nrecombinations.  Their output is the source-free output.  The\nremaining exact algebra produces regular and covering lists of\npositive source degree.  Connected source terms with an output\nbad-bond flag retain the stronger activity bound $o(w_{j-1})$.\n\nWe next separate the part that needs a contraction estimate from\nthe terms that are small by order.  A regular output term using\nEquation~\\eqref{eq:orig-30} is bounded by\n$C_Lg\\operatorname{poly}(M,p)$.  A term containing a new core\ndecoration or an old positive-degree covering activity has\narbitrary-power accuracy in $g$.  A term using two or more\n$G_X$ factors is bounded by\n$C_L\\operatorname{poly}(M,p)R_j^2$.  The same bound applies when\none $G_X$ is accompanied by an ordinary source-free correction\nor failure factor, since its order in $g$ is smaller than $R_j$\nat large $H_j$.\n\nHere these order statements concern summed lists, not just an\nindividual term.  Outside cores and exceptional terms, the\nintegrated and Mayer-log expansions have summed source-free\nenvelopes $O_L(g\\operatorname{poly}(M,p))$, together with errors\nof still smaller cap.  Inflate each marked majorant by the\ninverse of its claimed order in $g$ or $R_j$, divided by\n$C_LM^{D'}p^{D'}$ with a sufficiently large fixed $D'$.  The\nsummed hit bound, including reserved output exponents, remains\n$o_L(M^{-2})$.  The tree estimate therefore preserves the marked\npowers.  This also treats quadratic and higher terms from the\nexponential of a single regular source function.  In a compulsory\ncore the source series is charged inside the core factor, where\nits exceptional reserve remains available; it is not treated as\na small optional envelope.  The same estimate applies to the\nsubsequent list operations and mask regroupings, using their\nspare support exponents.  Those regroupings can carry source\ndegree only in covering terms, apart from the linear-source\nnormalization performed below.\n\nThe terms not covered by these small-order estimates are the\nisolated linear transfers of old $G_X$.  They are the empty-pattern\nGaussian expectations of \\Rref{rg:error-transfer}, with the\nprojected spin formula of Section~\\ref{sec:rg}, with $G_X$ in\nplace of the old error, and with each source index specialized\nto its coarse index.  Their old cutoffs, required output graph,\nand output mask are retained.\n\n\\paragraph{The isolated transfer in higher source degree.}\nFor degree at least two, the anchored norm of the isolated\ntransfer, with any fixed required larger output exponent, is\n\\begin{equation}\n \\bigl(C/L^2+o_H(1)\\bigr)R_j.\n \\label{eq:orig-31}\n\\end{equation}\nThere is no Taylor normalization in this assertion.  Each coarse\nsource anchor has $l^2$ possible old anchors, whereas a degree-$a$\ncoefficient gains $(2/l^2)^a$.  For $a\\ge2$ their product is at\nmost $4/l^2$.  It remains to check that composition with the spin\nmap has a leading bound fixed before $L$ is chosen.\n\nUse the Gaussian guard from the proof of\nEquation~\\eqref{eq:orig-9}, including the normal component.  For\n$s_X\\le L^{1/4}$, the old cutoff is on its plateau.  The absolute\nfirst jet of $q_x$, measured in the old normalized direction norm,\nis bounded by the row of $P_h$ with its weighted moments.  Its\nleading constant is independent of $L$.  Differentiating the\ntangent projection of the fluctuation gives an extra $t_j$;\nhigher derivatives of the spin map also have an extra $t_j$,\nwith fixed powers of $M$ at fixed $L$.  The value itself requires\nonly a supremum estimate.  The projected short load, including\nthe necessary output halo, is bounded by a constant independent\nof $L$, as in \\Rref{rg:error-contraction}.  Thus composition and\nthe fixed output load weight contribute a fixed multiplier.\n\nFor $s_X>L^{1/4}$, the direct composition estimate from\nEquation~\\eqref{eq:orig-9} has a fixed polynomial in $1+s_X$.\nThe unused old support exponential pays this polynomial and the\nanchor count.  At every load the guard complement costs an\nexponentially small Gaussian tail times fixed polynomial losses.\nAfter listing these losses, choose $P_0$ and then $H$ so that they\nare negligible.  Summing the old coefficients proves\nEquation~\\eqref{eq:orig-31}.\n\n\\paragraph{The isolated transfer in degree one.}\nFor degree one the corresponding bound is\n\\begin{equation}\n \\bigl(C/L+o_H(1)\\bigr)R_j.\n \\label{eq:orig-32}\n\\end{equation}\nHigh loads are again paid by their spare support exponential.\nFor a short label, write its degree-one coefficient as a vector\nfunction relative to its old anchor spin.  This vector vanishes\nat alignment.  On the same guard, the predicted relative spin\nvalues without Gaussian perturbation have old normalized size\n$C/L+o_H(1)$, by the relative-map estimate used in\nEquation~\\eqref{eq:orig-9} and \\Rref{rg:affine-prediction}.\nThe retained Gaussian perturbation adds at most\n$C_L\\max|Z|/p$, with fixed powers of $M$ independent of $P_0$.\nIts expectation is $o_H(1)$: there are $O_L(M^D)$ read\ncoordinates, so either a union estimate or the sum of their\nfixed marginal moments suffices after the choice of $P_0$.\n\nApply a mean-value estimate between the aligned configuration\nand the guarded spin configuration.  Both the guarded chords\nand this interpolation remain strictly in the graph domain\nthrough $4t_j$.  The value of the integrated vector function\ntherefore has the factor $C/L+o_H(1)$.  This estimate precedes the\nanchor summation; the degree-one substitution contributes\n$2/l^2$, canceling that summation's $l^2$ factor up to a constant.\n\nFor positive-order derivatives, each first derivative of the\nrelative map has the same $C/L+o_H(1)$ bound on the guard, and\nevery higher derivative is $o_H(1)$.  Moving the old anchor may\nalso rotate the vector as a whole.  Internal covariance expresses\nthis by a local smooth rotation of the resulting vector; every\nnormalized derivative falling on that rotation contains a\nfactor $t_j$ times fixed polynomial losses, for which the input\nsupremum is sufficient.  Thus no derivative above order $k$ is\nneeded.  This proves Equation~\\eqref{eq:orig-32}.\n\nThe same two singleton arguments apply to input differences\nthrough order $k-1$.  A change of Gaussian map or kinetic history\nacting on an unchanged input uses one additional input derivative,\nat most order $k$, and costs\n$C_L\\operatorname{poly}(M,p)R_j$ times\n$u+\\epsilon_h+|\\lambda|/H_j$.  The estimates therefore retain\ntheir input contraction and the required map forcing.\n\n\\paragraph{Difference estimates for the other terms.}\nThe one-difference joint estimates of Section~\\ref{sec:rg} apply\nto the prepared source letters as well.  They preserve the order\nin Equation~\\eqref{eq:orig-30}, with an additional\n$C_L\\operatorname{poly}(M,p)$ factor multiplying\n$u+\\epsilon_h+|\\lambda|/H_j$.  In particular, changing $g$ costs\n$O_L(g|\\lambda|/H_j)$ in that angular factor.  For a masked\nsource function, the same assertion uses its old graph norm\nand the composition estimate just described.  Powers of $g^{-1}$\nfrom hard transports are charged only against exceptional\nreserves.  Consequently exceptional terms retain arbitrary-power\naccuracy or their stronger output-inventory price.\n\nA difference in one of two or more regular source factors gives\nthe nonlinear bound\n$C_L\\operatorname{poly}(M,p)R_j^2u_s$.  Marking the relevant\nfactor in the inflated-majorant argument above proves the same\nbound for the entire connected list.  At most $k-1$ derivatives\nfall on a changed input, and at most $k$ on an unchanged input\nwhen its map changes.  These are precisely the differentiated\njoint and tree hypotheses; no higher derivative is invoked.\n\n\\paragraph{Extraction of the linear coefficient.}\nEach bulk or corresponding short degree-one output term is\n$z'_Y\\cdot F_X(V)$, anchored at its source site $Y$.  If all spin\narguments equal $v\\in S^2$, covariance gives\n$F_X(v,\\ldots,v)=a_Xv$ for a real scalar $a_X$.  Indeed the\nstabilizer of $v$ fixes only its span, and transitivity makes the\nscalar independent of $v$.  Use the exact identity\n\\[\n 1_Xz'_Y\\cdot F_X\n =a_Xz'_Y\\cdot V_Y\n  +1_Xz'_Y\\cdot(F_X-a_XV_Y)\n  -(1-1_X)a_Xz'_Y\\cdot V_Y.\n\\]\nThe middle term vanishes at alignment.  Its norm is at most a\nfixed multiple of the original norm, since the anchored\nderivatives of $V_Y$ are bounded.  The final term is an off-mask\ncorrection and is put in the covering gas by the exact\nnormalization operation of \\Rref{rg:normalization}.\n\nSum the extracted coefficients per bulk anchor.  Spatial\ntranslation covariance of the source lists makes this sum\nindependent of the anchor.  Together with\nthe previous coefficient $1$ of the leading exponent, this gives\n$c_j$.  Use that same bulk sum on every period.  Actual winding\nterms are left unnormalized.  When a bulk coefficient has been\nextracted without a corresponding short term on the period,\nretain its compensating one-site term as a winding entry.  A\none-site graph is allowed; alternatively it may be enlarged by\nnearby bonds with the corresponding off-mask correction.  A\nmissing bulk coefficient has complete length at least the\nwinding threshold, so its spare reserved exponent pays this\ndeclared record.  The classification includes the changes to\nthe records themselves, exactly as in the source-free\nnormalization.  Winding spin functions are not required to\nvanish at alignment.\n\nOff-mask source corrections have the stated summed bounds with\ntheir complete support prices; passing from an anchor to a\nsupport hit costs only fixed powers of $M$.  Contributions\ncarrying an output bad-bond seed retain their $o(w_{j-1})$\nactivity under opening and regrouping.  At zero source every\noperation in this paragraph is the identity, so it changes no\nsource-free label.  Equations~\\eqref{eq:orig-30}--\\eqref{eq:orig-32}\nand their difference versions give $|c_j-1|\\le C_LR_j$ and the\nsecond estimate of Equation~\\eqref{eq:orig-29}.  In particular\n$c_j>0$ for sufficiently large $H$.\n\nFinally divide all output source variables by $c_j$.  The radius-$2$\nbound controls this substitution at radius $1$, its differences,\nand the sum over all positive degrees.  For a common scalar\ndilation, the extra degree factor in a difference is paid by the\nspare analytic radius.  The leading exponent is now\n$\\sum_Yz'_Y\\cdot V_Y$.\n\nTo close the caps and discrepancies, divide the output bounds\nby $R_{j-1}$ and $w_{j-1}$.  The isolated regular multipliers,\nincluding alignment subtraction and the fixed normalization\ncosts, can be made strictly smaller than one by choosing $L$.\nMoreover,\n\\[\n \\frac{R_j}{R_{j-1}}\\le1,\n \\qquad\n \\frac{g}{R_{j-1}}\\operatorname{poly}(M,p)\\longrightarrow0,\n \\qquad\n \\frac{R_j^2}{R_{j-1}}\\operatorname{poly}(M,p)\\longrightarrow0\n \\quad(H_j\\to\\infty).\n\\]\nThe normalized gas discrepancy has its $o(w_{j-1})$ allowance.\nChanging the dilation on an already small positive-degree\nremainder gains the additional factor $R_j$ from the estimate\non $c_j$.  These observations give the first estimate of\nEquation~\\eqref{eq:orig-29} and renew\nEquation~\\eqref{eq:orig-27}.  All fixed caps and constants can\nbe enlarged in the stipulated order; their finite sizes do not\nalter the leading constants in\nEquations~\\eqref{eq:orig-31} and~\\eqref{eq:orig-32}.\n\\end{proof}\n\n\\subsection{Endpoint source convergence and the normalization product}\n\nThe source step determines the field at every cutoff.  Two\nconsequences will be used separately: convergence at a fixed\nbottom layer, and a uniform comparison of the normalizations\nfor the ordinary and inclined first steps.\n\n\\begin{corollary}[Sources at a fixed bottom layer]\n\\label{cor:source-endpoints}\nFor the trajectory comparisons in Equation~\\eqref{eq:orig-14},\nthe source discrepancy $u_s$ tends to zero at each fixed bottom\nlayer, and the difference of the step coefficients $c_j$ tends\nto zero at each fixed bottom step.  In every fixed compatible\nfinite period, the resulting source exponents and covering\nactivities converge in their coefficient norms, with the step\ndilations above that layer included.\n\\end{corollary}\n\n\\begin{proof}\nAt the top of a comparison, Equation~\\eqref{eq:orig-27} bounds\nthe normalized source discrepancy by a fixed constant.\nIterating Equation~\\eqref{eq:orig-29} gives a geometric factor\n$q_s$ on this initial discrepancy and a geometric convolution\nof the forcing from Equation~\\eqref{eq:orig-14}.  Fixed powers\nof the scale indices do not prevent that forcing from tending\nexponentially to zero at any fixed bottom layer.  The second\nline of Equation~\\eqref{eq:orig-29} gives the assertion for\neach fixed step coefficient.  On a fixed finite period,\ncovering expansions converge absolutely, while masked regular\nfunctions are controlled by their supremum bounds.  Thus their\nintegrated source functions have the asserted comparison.\nNo claim of uniformity in a growing volume is needed here.\n\\end{proof}\n\nWrite $c_{j,N}$ for the coefficient of the step from layer $j$\nin a standard run of depth $N$, and define\n\\begin{equation}\n B_N:=\\prod_{j=1}^Nc_{j,N}^{-1}.\n \\label{eq:field-normalization}\n\\end{equation}\nThis is the field normalization; the area factor $a_N^2$ will\nbe included separately when fields are smeared.  The product\nis positive and finite.  Since $H_j$ grows linearly in $j$,\nEquation~\\eqref{eq:orig-28} gives\n$|\\log B_N|=O_{L,H}(1+N^{9/10})$, hence subexponential growth\nor decay in depth.  Let $B_N^*$ denote the analogous product\nfor an inclined run.  The two mirror inclinations have identical\nstep coefficients and identical $B_N^*$ by the reflection\ncovariance of the bulk prescription.\n\n\\begin{proposition}[Comparison of normalization products]\n\\label{prop:source-products}\nFor the same bare coupling $\\beta_N$ in the standard run and\neither inclined run, there is a constant $C_{L,H}<\\infty$,\nindependent of depth, such that\n\\begin{equation}\n C_{L,H}^{-1}\\le\\frac{B_N^*}{B_N}\\le C_{L,H}.\n \\label{eq:orig-33}\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nCompare the bulk histories after their first steps.  Their\nprecise couplings satisfy $|\\lambda_j|\\le C_L$ by\nEquation~\\eqref{eq:orig-13}.  Put $x=N-1-j$.  The history\nforcing in Equation~\\eqref{eq:orig-5} is bounded by\n$C_LH_N^D(A_0/L^2)^x$, after enlarging a fixed exponent $D$.\nThe coupling forcing is at most\n$C_L(\\log H_j)^D/H_j$.  Iteration of the shape inequality,\ntogether with its fixed cap, gives\n\\begin{equation}\n u_j\\le\n \\min\\{C,C_LH_N^D\\rho_1^x\\}\n       +C_L\\frac{(\\log H_j)^D}{H_j}\n \\label{eq:source-product-shape}\n\\end{equation}\nfor some $\\rho_1<1$.  A slightly larger geometric base absorbs\ngeometric convolution factors.  The second forcing has this\nsame form after convolution: along the iteration the scale\n$H_j$ decreases, and its logarithmic ratio is eventually\nmonotone, with finitely many initial values absorbed into the\nconstant.\n\nApply Equation~\\eqref{eq:orig-29} and clip $u_s$ by its cap\nin the same way.  Enlarging $C_L,D$ and $\\rho_1<1$ if needed,\nEquation~\\eqref{eq:source-product-shape} also bounds $u_s$\nand $|c_{j,N}^*-c_{j,N}|/R_j$.\nThe contribution from the second term is summable uniformly:\n\\[\n \\sum_{j\\ge0}R_j\\frac{(\\log H_j)^D}{H_j}\n =\\sum_{j\\ge0}\\frac{(\\log H_j)^D}{H_j^{11/10}}\n <\\infty.\n\\]\nFor the clipped first term, split the iteration after\n$J_N=\\lceil K\\log H_N\\rceil$ steps, with $K$ fixed and large.\nOn the first $J_N$ steps, $H_j$ is comparable with $H_N$\nfor all sufficiently large $N$, so their total contribution\nis at most $C_{L,H}H_N^{-1/10}\\log H_N$.  Thereafter use\nthe unclipped geometric bound.  Since $R_j\\le H^{-1/10}$,\nthis remaining sum is bounded by\n\\[\n C_{L,H}H_N^D\\sum_{x\\ge J_N}\\rho_1^x\n \\le C_{L,H}H_N^{D+K\\log\\rho_1}.\n\\]\nChoose $K$ so that the last exponent is negative.  Finitely\nmany small depths cause no problem.  The single initial step\nis controlled directly by Equation~\\eqref{eq:orig-28}.\nWe have proved\n\\[\n \\sum_{j=1}^N|c_{j,N}^*-c_{j,N}|\\le C_{L,H}.\n\\]\nAll coefficients lie in a fixed positive neighborhood of one,\nso the same estimate holds for the sum of their logarithmic\ndifferences.  Exponentiating proves\nEquation~\\eqref{eq:orig-33}.  The proof does not require the\ninclined terminal coupling to equal the terminal coupling\nof the standard run.\n\\end{proof}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/trace.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/trace.tex", "bytes": 19042, "sha256": "847a3baefeee82e3190d62d2a7970df4cab93a81f134452cc3a3d16512e64249", "content": "\\section{Trace estimates uniform in the cutoff}\n\\label{sec:trace}\n\nThe endpoint comparison now produces a box of fixed physical size in\nwhich the excited transfer trace is small at every sufficiently fine\ncutoff.  Positivity then propagates this information to arbitrarily\nlarge boxes.  This section proves both consequences needed below: a\nuniform comparison between finite tori and the plane, and exponential\ndecorrelation at a fixed positive rate in physical units.  The transfer\nargument follows \\cite{OpenAI-O4}, \\Rref{sec:finite-volume}; we give its\nproof, including the rectangular and sheared periods used here.\n\nWrite $Z_\\beta(n,w)$ for the nearest-neighbour partition function on the\ntorus with time period $n$ and spatial period $w$, using normalized area\nmeasure at every spin.  All geometric periods below, including the\nauxiliary half-periods, are at least four.  Define\n\\begin{equation}\n \\Delta_\\beta(n,w)\n   =4\\log Z_\\beta(n,w)-\\log Z_\\beta(2n,2w).\n \\label{eq:trace-doubling-definition}\n\\end{equation}\nThe coefficients cancel every contribution to $\\log Z$ proportional to\nthe area.  In particular, the bulk scalars extracted by the exact\nrenormalization do not contribute to $\\Delta_\\beta$.\n\n\\subsection{A fixed physical box}\n\nThe gain in the preliminary length estimate and the contraction of\nendpoint discrepancies have complementary roles.  The former makes a\nreference box large enough to mix; the latter allows that box to be\ncompared with every finer cutoff.\n\n\\begin{proposition}[A uniform reference-box test]\n\\label{prop:trace-small-box}\nLet $\\mathcal U\\subset(\\mathbb Q_{>0})^2$ be finite.  Assume the\npreliminary estimate of Section~\\ref{sec:preliminary}, the shooting\nasymptotics \\eqref{eq:orig-12}, the endpoint discrepancy estimate\n\\eqref{eq:orig-15}, and the integrated comparison\n\\eqref{eq:orig-16}.  With the contraction slack $\\upsilon>0$ chosen\nsufficiently small, there exist an integer $K_0$ and a positive integer\n$M_0$, independent of $N$, such that, for every $N\\ge K_0$ and every\n$(u_1,u_2)\\in\\mathcal U$,\n\\begin{equation}\n \\Delta_{\\beta_N}(n,w)\\le 2^{-10},\n \\qquad (n,w)=(u_1,u_2)M_0L^N.\n \\label{eq:orig-24}\n\\end{equation}\nThe number $M_0$ may be required to exceed any prescribed fixed constant\nand to be divisible by any prescribed fixed integer.  In particular,\nall periods and tile counts needed in\nSection~\\ref{sec:comparison} may be taken to be even integers.\n\\end{proposition}\n\n\\begin{proof}\nLet $\\eta>0$ be the gain in \\eqref{eq:orig-1}.  Choose\n\\[\n 0<\\frac{\\upsilon}{2}<\\chi<\\min\\left\\{1,\\frac{\\eta}{2\\pi}\\right\\}.\n\\]\nTake $m_K$ to be a fixed common integer multiple of a dyadic integer,\nrounded so that\n\\begin{equation}\n \\log_L m_K=(1-\\chi)K+O(1),\\qquad\n 2(1-\\chi)<2-\\upsilon,\\qquad\n 2-\\chi>\\frac{4\\pi-\\eta}{2\\pi}.\n \\label{eq:trace-scale-choice}\n\\end{equation}\nThe fixed multiple clears the denominators in $\\mathcal U$ and imposes\nthe divisibility conditions of the proposition.\n\nAt depth $K$, Equation~\\eqref{eq:orig-12} and\n$\\gamma=(\\log L)/(2\\pi)$ give\n\\[\n X(\\beta_K)\n   \\le C_H K^{C_H} L^{(2-\\eta/(2\\pi))K}.\n\\]\nThe shorter side of a reference rectangle\n$(u_1,u_2)m_KL^K$ is a fixed positive multiple of\n$L^{(2-\\chi)K}$.  Its ratio to $X(\\beta_K)$ therefore grows\nexponentially in $K$.  The preliminary doubling estimate consequently\nimplies\n\\[\n \\max_{(u_1,u_2)\\in\\mathcal U}\n \\left|\\Delta_{\\beta_K}(u_1m_KL^K,u_2m_KL^K)\\right|\n     \\longrightarrow0.\n\\]\nIndeed its exponentially decaying factor dominates every polynomial\nprefactor in the microscopic periods and in $\\beta_K$.\n\nFor $N\\ge K$, run both cutoffs to the same terminal rectangle with\nperiods $(u_1,u_2)m_K$.  Its volume is\n$v=u_1u_2m_K^2$.  Equations~\\eqref{eq:orig-15} and\n\\eqref{eq:orig-16}, with the exponent discrepancy included, imply\nthat the logarithms of the two scalar-stripped partition functions\ndiffer by at most $C_Hv\\delta_K$ whenever $v\\delta_K$ is sufficiently\nsmall.  The estimate is uniform in $N\\ge K$.  Apply it also to the\ndoubled rectangle, whose terminal volume is $4v$.  Cancellation of the\nbulk scalars gives\n\\begin{align*}\n &\\left|\\Delta_{\\beta_N}(u_1m_KL^N,u_2m_KL^N)\n       -\\Delta_{\\beta_K}(u_1m_KL^K,u_2m_KL^K)\\right|\\\\\n &\\hspace{35mm}\\le C_Hm_K^2\\delta_K\n       \\longrightarrow0,\n\\end{align*}\nbecause $\\delta_K=L^{-(2-\\upsilon)K}$ and\n$2(1-\\chi)<2-\\upsilon$.  Constants may depend on the finite list\n$\\mathcal U$, but not on $N$ or $K$.  Choose one sufficiently large\n$K_0$ and put $M_0=m_{K_0}$.\n\\end{proof}\n\n\\subsection{Positive transfer and repeated doubling}\n\nWe next isolate the spectral content of the partition-function test.\nFor a fixed spatial circumference $w$, a time slice is\n$s=(s_1,\\ldots,s_w)\\in(S^2)^w$.  Put\n$V_w(s)=\\sum_{i=1}^w s_i\\cdot s_{i+1}$, with periodic indices.\nOn $L^2((S^2)^w)$ define the transfer operator $K_w$ by the kernel\n\\begin{equation}\n K_w(s,s')=\n \\exp\\left\\{\\frac\\beta2V_w(s)\n       +\\beta\\sum_{i=1}^w s_i\\cdot s_i'\n       +\\frac\\beta2V_w(s')\\right\\}.\n \\label{eq:trace-kernel}\n\\end{equation}\nThe coupling $\\beta\\ge0$ is fixed in this subsection and suppressed\nfrom the notation.  All Hilbert spaces use product probability measure.\n\nThe kernel is continuous, symmetric, and strictly positive.  Its\npositive semidefiniteness is a separate property: the expansion\n\\[\n e^{\\beta s\\cdot s'}\n   =\\sum_{j=0}^{\\infty}\\frac{\\beta^j}{j!}\n       \\inner{s^{\\otimes j}}{(s')^{\\otimes j}}\n\\]\nis a sum of positive semidefinite kernels.  Tensor products over sites\nand multiplication on both sides by $e^{\\beta V_w/2}$ preserve that\nproperty.  The sum of their diagonal integrals is finite, so the same\nexpansion proves that $K_w$ is trace class.  Strict positivity of the\nkernel gives a simple largest eigenvalue with a strictly positive\nnormalized eigenfunction $\\Omega_w$.  Write\n\\[\n \\lambda_1(w)>\\lambda_2(w)\\ge\\lambda_3(w)\\ge\\cdots\\ge0,\n \\qquad\n Z_\\beta(n,w)=\\Tr K_w^n=\\sum_i\\lambda_i(w)^n.\n\\]\nIf only one eigenvalue is nonzero, all subsequent statements are read\nwith $\\lambda_2=0$.  These observations also verify directly the\ntransfer hypotheses of \\Rref{prop:criterion} for $S^2$ spins.\n\nDefine the two one-direction defects\n\\begin{align}\n p(n,w)&=2\\log Z_\\beta(n,w)-\\log Z_\\beta(2n,w),\\notag\\\\\n q(n,w)&=2\\log Z_\\beta(n,w)-\\log Z_\\beta(n,2w).\n \\label{eq:trace-one-direction}\n\\end{align}\nBoth are nonnegative, using transfer positivity in the respective\ncoordinate directions.  The excited trace relative to the largest\neigenvalue is\n\\[\n s_n(w)=\\sum_{i\\ge2}\n       \\left(\\frac{\\lambda_i(w)}{\\lambda_1(w)}\\right)^n.\n\\]\n\n\\begin{lemma}[Concentration of the transfer trace]\n\\label{lem:trace-concentration}\nFor every integer $n\\ge4$,\n$s_n(w)\\le e^{p(n,w)}-1$.  If $p(n,w)\\le1/4$, then\n\\begin{equation}\n \\left(\\frac{\\lambda_2(w)}{\\lambda_1(w)}\\right)^n\n       \\le2p(n,w),\\qquad\n p(2n,w)\\le4p(n,w)^2.\n \\label{eq:trace-squaring}\n\\end{equation}\nThe corresponding conclusions hold for $q$ with the coordinate\ndirections interchanged.\n\\end{lemma}\n\n\\begin{proof}\nSet $t_i=\\lambda_i(w)^n/\\sum_j\\lambda_j(w)^n$.  Then\n\\[\n e^{-p(n,w)}=\\sum_i t_i^2\\le t_1=(1+s_n(w))^{-1}.\n\\]\nThis gives the first assertion and, when $p\\le1/4$, the bound\n$s_n\\le pe^p\\le2p$.  Moreover $s_{2n}\\le s_n^2$, and cancellation\nof the largest eigenvalue yields\n\\[\n p(2n,w)=2\\log(1+s_{2n})-\\log(1+s_{4n})\n     \\le2s_n^2\\le2p(n,w)^2e^{2p(n,w)}\n     \\le4p(n,w)^2.\n\\]\nThis is the proof of \\Rref{lem:squaring}, with no dependence on the\ndimension of the spin sphere.\n\\end{proof}\n\nSmall excited weight at one circumference alone would leave open the\nappearance of low-energy states at larger circumferences.  The second\ndirection in $\\Delta$ supplies the missing control.  Direct substitution\nin \\eqref{eq:trace-one-direction} gives\n\\begin{align}\n \\Delta_\\beta(n,w)\n   &=2p(n,w)+q(2n,w)\n     =2q(n,w)+p(n,2w),\\notag\\\\\n p(n,2w)&=2p(n,w)-2q(n,w)+q(2n,w).\n \\label{eq:trace-rectangle-identities}\n\\end{align}\nIn particular, $\\Delta_\\beta(n,w)\\ge0$.\n\n\\begin{proposition}[Rectangular doubling criterion]\n\\label{prop:trace-doubling}\nLet $\\beta\\ge0$ and let $n,w\\ge4$ be integers.  If\n$\\Delta_\\beta(n,w)\\le2^{-10}$, then, for every $k\\ge0$,\n\\begin{equation}\n \\Delta_\\beta(2^kn,2^kw)\\le64^{-1}2^{-2^k}.\n \\label{eq:orig-25}\n\\end{equation}\nFor a square with $n=w$, the transfer operators at circumferences\n$2^kn$ also satisfy\n\\begin{equation}\n \\frac{\\lambda_2(2^kn)}{\\lambda_1(2^kn)}\n       \\le e^{-(\\log2)/n}.\n \\label{eq:trace-width-gap}\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nWrite $\\Delta=\\Delta_\\beta(n,w)$.  The two expressions in\n\\eqref{eq:trace-rectangle-identities} imply\n$q(2n,w)\\le\\Delta$ and $p(n,2w)\\le\\Delta$.  Consequently\n\\[\n p(2n,2w)\\le4\\Delta^2.\n\\]\nThe interchanged rectangle identity then gives\n\\[\n q(4n,w)=2q(2n,w)-2p(2n,w)+p(2n,2w)\n       \\le2\\Delta+4\\Delta^2\\le3\\Delta.\n\\]\nSquaring in the spatial direction gives\n$q(4n,2w)\\le36\\Delta^2$, and hence\n\\[\n \\Delta_\\beta(2n,2w)\n   =2p(2n,2w)+q(4n,2w)\n   \\le44\\Delta^2\\le64\\Delta^2.\n\\]\nEvery application of Lemma~\\ref{lem:trace-concentration} is permitted\nby $3\\cdot2^{-10}<1/4$, and the smallness condition is preserved.\nIteration yields\n\\[\n \\Delta_\\beta(2^kn,2^kw)\n       \\le64^{-1}(64\\cdot2^{-10})^{2^k}\n       \\le64^{-1}2^{-2^k}.\n\\]\nFor a square, put $n_k=2^kn$.  The same lemma gives\n\\[\n \\left(\\frac{\\lambda_2(n_k)}{\\lambda_1(n_k)}\\right)^{n_k}\n       \\le2p(n_k,n_k)\\le\\Delta_\\beta(n_k,n_k).\n\\]\nTaking the $n_k$-th root proves\n\\eqref{eq:trace-width-gap}.  Thus the square proof of\n\\Rref{prop:criterion} also proves the asserted rectangular recursion;\nno equality of the original periods entered that recursion.\n\\end{proof}\n\n\\subsection{Local insertions and the volume limit}\n\nTo use the trace bound for correlations, one needs control of an\ninsertion that may occupy an entire time slab.  The following estimate\nhas no cost depending on the number of spins read by the insertion.\nLet $F$ be a bounded local observable supported in a slab of $r$\ntransfer steps.  The kernel $K_{w,F}$ obtained by inserting $F$ in that\nslab satisfies\n\\[\n |K_{w,F}(s,s')|\\le\\norm{F}_\\infty K_w^r(s,s').\n\\]\nWith $T_w=K_w/\\lambda_1(w)$ and\n$A_F=K_{w,F}/\\lambda_1(w)^r$, positivity of $K_w^r$ implies\n$\\norm{A_F}\\le\\norm{F}_\\infty$: apply the pointwise inequality to\n$|f|$ and use $\\norm{T_w^r}=1$.  When $r=0$, $A_F$ is multiplication\nby $F$.  The infinite-time cylinder expectation is therefore\n\\[\n \\omega_w(F)=\\inner{\\Omega_w}{A_F\\Omega_w}.\n\\]\n\nLet $P_w$ be the orthogonal projection onto $\\Omega_w$.  Since\n$T_w-P_w$ is positive semidefinite,\n$\\Tr(T_w^j-P_w)=s_j(w)$.  For $r\\le n/2$, splitting off $P_w$ in\n\\[\n \\mu_{\\beta;n,w}(F)\n    =\\frac{\\Tr(A_FT_w^{n-r})}{\\Tr T_w^n}\n\\]\ngives\n\\begin{equation}\n |\\mu_{\\beta;n,w}(F)-\\omega_w(F)|\n      \\le2\\norm{F}_\\infty s_{n/2}(w),\n \\label{eq:trace-torus-cylinder}\n\\end{equation}\nwhenever $n$ is even.  Here $\\mu_{\\beta;n,w}$ denotes the probability\nlaw on the rectangular torus.  The same calculation applies after\ninterchanging space and time.\n\nWe state the resulting volume comparison with the auxiliary aspect\nratios specified explicitly.  This makes the finite amount of\ninformation required from Proposition~\\ref{prop:trace-small-box}\nindependent of all later limits.  For complex observables the covariance\nis sesquilinear:\n$\\Cov_\\omega(F,G)=\\omega(\\overline F G)\n -\\omega(\\overline F)\\omega(G)$.\n\n\\begin{theorem}[Uniform volume comparison and slab decorrelation]\n\\label{thm:trace-uniform}\nLet $\\mathcal A\\subset(\\mathbb Q_{>0})^2$ be a finite list containing\n$(1,1)$ and $(1,25)$, and put\n\\[\n \\mathcal U=\\mathcal A\n    \\cup\\{(u_1/2,u_2):(u_1,u_2)\\in\\mathcal A\\}\n    \\cup\\{(2u_1,u_2/2):(u_1,u_2)\\in\\mathcal A\\}.\n\\]\nChoose $K_0,M_0$ by Proposition~\\ref{prop:trace-small-box} for\n$\\mathcal U$, with all displayed periods even and at least four.  Let\n$\\omega_{\\beta_N}$ be the periodic infinite-plane state supplied by\nSection~\\ref{sec:preliminary}.  There are constants $C,c>0$, independent\nof $N\\ge K_0$ and $k\\ge0$, with the following properties.\n\nFor $(u_1,u_2)\\in\\mathcal A$, set\n$(n,w)=(u_1,u_2)2^kM_0L^N$.  If a bounded local observable $F$ can be\nplaced in a rectangle of time extent at most $n/2$ and spatial extent\nat most $w/2$, then\n\\begin{equation}\n |\\mu_{\\beta_N;n,w}(F)-\\omega_{\\beta_N}(F)|\n       \\le C\\norm{F}_\\infty e^{-c2^k}.\n \\label{eq:trace-uniform-volume}\n\\end{equation}\nThe same estimate holds for a torus obtained by closing time with any\ninteger spatial translation, provided the support has a lift of time\nextent at most $n/2$ and spatial extent at most $w/2$.\n\nFor bounded local observables $F,G$ on the plane whose supporting time\nslabs are separated by $d\\ge0$ transfer edges,\n\\begin{equation}\n |\\Cov_{\\omega_{\\beta_N}}(F,G)|\n    \\le\\norm{F}_\\infty\\norm{G}_\\infty\n       e^{-c d/(M_0L^N)}.\n \\label{eq:orig-26}\n\\end{equation}\nThe constant in this last estimate may be taken to be $c=\\log2$.\n\\end{theorem}\n\n\\begin{proof}\nFirst compare $(n,w)$ with $(2n,w)$.  Both torus expectations are\nwithin $2\\norm{F}_\\infty s_{n/2}(w)$ of the cylinder expectation at\ncircumference $w$, by \\eqref{eq:trace-torus-cylinder}.  The auxiliary\naspect $(u_1/2,u_2)$ and Proposition~\\ref{prop:trace-doubling} give\n\\[\n s_{n/2}(w)\n    \\le e^{p(n/2,w)}-1\n    \\le e^{\\Delta_{\\beta_N}(n/2,w)}-1\n    \\le C e^{-c2^k}.\n\\]\nNext compare $(2n,w)$ with $(2n,2w)$, applying the same argument in\nthe spatial direction.  The needed excited trace has running length\n$w/2$ and transverse circumference $2n$; it is bounded using\n$q(2n,w/2)\\le\\Delta_{\\beta_N}(2n,w/2)$ and the auxiliary aspect\n$(2u_1,u_2/2)$.  Thus consecutive simultaneous doublings differ by\nat most $C\\norm{F}_\\infty e^{-c2^k}$.  Sum this estimate over all\nsubsequent doublings.  At fixed $N$, the limit is the periodic\ninfinite-plane state from Section~\\ref{sec:preliminary}, giving\n\\eqref{eq:trace-uniform-volume}.\n\nFor completeness, this identification also holds for bounded\nmeasurable local observables.  At fixed cutoff and fixed finite\nsupport, nearest-neighbour conditional densities give a uniform upper\nbound, independent of the enclosing torus, for the marginal density\nrelative to product area measure.  Approximation in that reference\nmeasure extends the identity from continuous to bounded measurable\nobservables.  The direct transfer estimates above already have the\nsame supremum-norm bound for such observables.\n\nNow close time with a spatial shift $s\\in\\mathbb Z/w\\mathbb Z$.\nLet $U_s$ be its unitary action on $L^2((S^2)^w)$.  Translation\ninvariance gives $U_sT_w=T_wU_s$.  Simplicity and positivity of the\nground state give $U_s\\Omega_w=\\Omega_w$.  Choosing the closing seam\noutside the supporting slab, the normalized partition function and\nthe inserted numerator are\n\\[\n \\Tr(T_w^nU_s),\\qquad \\Tr(A_FT_w^{n-r}U_s).\n\\]\nTheir ground-state contributions are $1$ and $\\omega_w(F)$,\nrespectively, and their remaining contributions have absolute values\nat most $s_n(w)$ and $\\norm{F}_\\infty s_{n-r}(w)$.  The half-period\nbound just proved ensures $s_n(w)<1/2$.  Consequently\n\\[\n |\\mu_{\\beta_N;n,w}^{(s)}(F)-\\omega_w(F)|\n     \\le4\\norm{F}_\\infty s_{n/2}(w).\n\\]\nComparison with the unshifted torus, followed by\n\\eqref{eq:trace-uniform-volume}, proves the assertion for shifted\nclosing conditions, uniformly in $s$.\n\nFinally put $n_0=M_0L^N$.  The square reference-box test and\n\\eqref{eq:trace-width-gap} imply, at every circumference\n$w=2^kn_0$,\n\\[\n \\norm{T_w^d-P_w}\\le e^{-(\\log2)d/n_0}.\n\\]\nFor two ordered slab insertions on the cylinder,\n\\[\n \\Cov_{\\omega_w}(F,G)\n   =\\inner{\\Omega_w}{A_{\\overline F}(T_w^d-P_w)A_G\\Omega_w}.\n\\]\nThe insertion norm estimate therefore proves\n\\eqref{eq:orig-26} on these cylinders with $c=\\log2$.\nEquation~\\eqref{eq:trace-torus-cylinder}, using the half-period\ntrace bound, identifies their local limits with the square-torus\nlimit.  Passage to that limit proves the assertion on the plane.\n\\end{proof}\n\n\\subsection{A uniform physical gap}\n\nThe slab estimate controls the whole transfer spectrum, because it\napplies to all bounded local observables.  Its spectral consequence is\ntherefore stronger than a bound on the spin two-point function alone.\n\n\\begin{corollary}[A uniform physical gap]\n\\label{cor:trace-physical-gap}\nFor $N\\ge K_0$, let $\\mathcal T_N$ be the one-step transfer contraction\nin the reflection-positive Hilbert space of $\\omega_{\\beta_N}$, and\nlet $\\Omega_N$ be its vacuum vector.  Then\n\\[\n \\spec\\bigl(\\mathcal T_N|_{\\Omega_N^\\perp}\\bigr)\n    \\subset\\left[0,e^{-(\\log2)/(M_0L^N)}\\right].\n\\]\nAt lattice spacing $a_N=L^{-N}$, the lower energy scale in physical\nunits is consequently\n\\begin{equation}\n \\frac{1}{a_N}\\frac{\\log2}{M_0L^N}\n       =\\frac{\\log2}{M_0}>0.\n \\label{eq:trace-physical-scale}\n\\end{equation}\n\\end{corollary}\n\n\\begin{proof}\nLink reflection positivity follows from positive semidefiniteness of\nthe crossing-bond kernel $e^{\\beta s\\cdot s'}$; site reflection\npositivity follows by conditional factorization across the fixed row.\nBoth pass to the periodic plane limit and make $\\mathcal T_N$ a\npositive semidefinite self-adjoint contraction.\nA centered bounded local observable creates a vector whose transfer\nautocorrelation is the covariance of two reflected slabs.\nEquation~\\eqref{eq:orig-26} bounds this autocorrelation by\n$C_\\psi e^{-(\\log2)j/(M_0L^N)}$, with $C_\\psi$ depending on the vector\nand its slab thickness.  The spectral measure is positive.  Positive\nmass on any interval $[r,1]$ with\n$r>e^{-(\\log2)/(M_0L^N)}$ would give a lower bound proportional to\n$r^j$, contradicting that decay.  Centered bounded local vectors are\ndense in the vacuum orthogonal subspace.  Boundedness of spectral\nprojections therefore gives the asserted exclusion for the whole\nsubspace, as in \\Rref{prop:criterion}.  Dividing the negative logarithm\nof a nonzero transfer spectral value by $a_N$ gives the physical\nenergy bound \\eqref{eq:trace-physical-scale}.\n\\end{proof}\n\nThe constants and the physical volume threshold have been fixed before\nremoving the cutoff.  Continuum convergence is established in\nSection~\\ref{sec:continuum}; Section~\\ref{sec:os} then transfers this\ncutoff-independent positive scale to the continuum Hamiltonian.\n\n\\subsection{Tilted periods}\n\nThe only nonrectangular periods needed in the construction are covered\nby the shifted closing condition in Theorem~\\ref{thm:trace-uniform}.\nLet $M=2^kM_0$.  A square of physical side $5M$ tilted by $O_+$ has\nperiod vectors\n\\[\n a=M(3,4),\\qquad b=M(-4,3)\n\\]\nin the regulator axes, with coordinate pairs in this paragraph written\nas $(\\mathrm{space},\\mathrm{time})$.  The integer change of basis\n\\[\n 3a-4b=(25M,0),\\qquad a-b=(7M,M)\n\\]\nhas determinant one.  The same torus therefore has spatial\ncircumference $25M$, time height $M$, and a spatial shift $7M$ upon\nclosing time.  Dividing by $a_N=L^{-N}$ makes all periods and shifts\nintegers.  Reflected versions, including $O_-$ in the other regulator\nframe, have the same circumference and height, with a possibly\ndifferent shift.  A fixed compact physical support has a lift satisfying\nboth extent bounds for all sufficiently large $k$, uniformly in $N$.\nThus the finite list containing\n$(1,25)$ and its auxiliary aspects supplies all the tilted-torus\ncomparisons needed later.\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/trajectories.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/trajectories.tex", "bytes": 25643, "sha256": "f3c140eb094aa9ae96568e5ddffce6cb48e35d2a6b4a339fd5c1cc5b07146c49", "content": "\\section{Trajectories with a bounded bare offset}\n\\label{sec:trajectories}\n\nThe construction above chooses the microscopic coupling by prescribing\nits value after the final blocking step.  To treat every sufficiently\nlarge microscopic coupling, we instead allow a bounded displacement\nfrom an explicit reference sequence.  We shall prove that all these\ntrajectories remain admitted.  We shall also identify which displacements\ngive the same effective density at every fixed layer as the microscopic\ndepth tends to infinity.  This second conclusion requires a summable\nsensitivity estimate for the error in the kinetic increment.\n\n\\subsection{Common reference scales and canonical increments}\n\nWe keep the observations and representations of\nSections~\\ref{sec:setup}--\\ref{sec:rg}.  In particular, $L$ is a fixed\nsufficiently large power of two, and\n\\[\n \\gamma=\\frac{\\log L}{2\\pi},\\qquad\n H_j=H+\\gamma j,\\qquad\n \\mathfrak m_j=\\lceil(\\log H_j)^2\\rceil,\\qquad\n p_j=(\\log H_j)^{P_0},\\qquad t_j=H_j^{-1/2}p_j.\n\\]\nA depth-$N$ trajectory starts at $b_N=\\beta$ and runs through layers\n$N,N-1,\\ldots,0$.  Its precise coupling is admitted at layer $j$ when\n$b_j\\in[H_j/2,2H_j]$, and strict admission means that both inequalities\nare strict.  The reference scales, rather than $b_j$, determine the\ngeometric thresholds and masks throughout a comparison.\n\nThere are three trajectory types, denoted by $P\\in\\{r,+,-\\}$.\nType $r$ uses only ordinary observations of side $L$.  Type $+$ or $-$\nuses first the inclined observation of side $5L$ in\nSection~\\ref{sec:free}, with respective rotation\n\\[\n O_+=\\frac15\\begin{pmatrix}3&-4\\\\4&3\\end{pmatrix},\n \\qquad O_-=O_+^{-1},\n\\]\nand then uses ordinary observations.  A history records the observations\nalready made, including their coordinate frames.  In a comparison we\nidentify the frames of the layer under consideration and use the same\nordinary observation in the remaining common steps.\n\nWe use the bulk formulas and their compatible finite-period\nrepresentations.  Compatibility means that every observation descends\nto the corresponding quotient lattice, while admission of a period\nrequires its shortest nonzero translation to satisfy the lower bound\nin Section~\\ref{setup:scales}.  The comparisons below use common\nreference scales and the common refinements and zero padding of\nSection~\\ref{sec:setup}.  They apply to bulk lists and simultaneously\nto the finite-period lists whenever the periods being compared are\ncompatible.  In particular, one may fix a common terminal period and\ncompare the resulting common periods at all fixed bottom layers.\n\nRecall briefly what the discrepancy measures.  A retained density has\nthe form \\eqref{eq:setup-effective-density}: it contains the precise\nkinetic coupling, a finite vector of canonical polynomial coefficient\nlists, regular local errors, and a covering sum for configurations with\nlarge bond differences.  The regular errors are bounded with eight\nderivatives on their graph domains; the covering activities are bounded\nby a sum over supports meeting a site.  Their caps are\n\\[\n \\delta_j=H_j^{-2.05},\\qquad w_j=\\exp(-p_j^{1/4});\n\\]\nthe complete definitions are\n\\eqref{eq:setup-error-list-norm} and\n\\eqref{eq:setup-covering-norm}.  For two bulk representations, $u_j$ denotes\nthe positively weighted list discrepancy\n\\eqref{eq:setup-density-discrepancy}.  The regular-error difference\nuses seven derivatives and is divided by $\\delta_j$; the covering\ndifference is divided by $w_j$.  The canonical coefficients are\ncompared without their displayed powers of the precise coupling.\nWe put $\\lambda_j=b_{2,j}-b_{1,j}$, where each precise coupling is\ndefined by its bulk extraction and is shared by the corresponding\ncompatible finite-period representations.\n\nFor a comparison that also involves finite periods, choose the common\nterminal periods to be compared and follow each of them through the\nlayers.  Write $u_j^{\\rm bulk}$ and $u_j^T$ for\n\\eqref{eq:setup-density-discrepancy} in the bulk and on the layer-$j$\nperiod corresponding to terminal period $T$, respectively.  In this case we use\n\\begin{equation}\n u_j=\\max\\bigl\\{u_j^{\\rm bulk},\\ \\sup_Tu_j^T\\bigr\\}.\n \\label{eq:offset-family-discrepancy}\n\\end{equation}\nThe supremum may be restricted to the finitely many periods in the\ncomparison, or taken over a compatible family obeying the common\nadmission bounds.  The estimates are uniform in periods, and\n$\\lambda_j$ and the history error are common to this whole family.\nThe cap bounds are preserved under this maximum; the step estimates\nfor this convention are justified below.  In particular a finite-period comparison always\nretains control of the bulk data that determine the kinetic increment;\nno norm on an isolated finite torus is used to bound a bulk extraction.\nFixed positive comparison weights give\n\\begin{equation}\n \\|f_{2,j}-f_{1,j}\\|_{7,j,A}\\le C_L\\delta_j u_j,\n \\qquad u_j\\le C_L\n \\quad\\hbox{for two representations obeying the common caps.}\n \\label{eq:offset-discrepancy-caps}\n\\end{equation}\nThe volume scalar is omitted from $u_j$, as it cancels from normalized\nexpectations.\n\nThe order of parameters will matter.  Choose the contraction slack\n$\\upsilon>0$ small enough for Theorem~\\ref{thm:rg-step} and so that\n\\begin{equation}\n \\frac{\\upsilon}{2}<\\min\\{1,\\eta/(2\\pi)\\},\n \\label{eq:offset-slack}\n\\end{equation}\nwhere $\\eta$ is the gain in Proposition~\\ref{prop:preliminary}.\nChoose the support and derivative requirements, $L$, the canonical\ncaps and comparison weights, and $P_0$ in the order specified in\nSection~\\ref{subsec:rg-closure}.  All logarithmic powers in the\nestimates below are then fixed.  Two bounds on the bare displacement,\n$R$ and $R'$, will be chosen below, before increasing $H$.\nConstants denoted by $C_L$ may depend on these already fixed\nrepresentation parameters, but not on $H$, the depth, the layer,\nthe period, or the precise couplings in their bands.  Once $H$ has\nbeen fixed, we allow constants denoted by $C_H$ to depend on it.\n\n\\begin{proposition}[Canonical increments and comparison estimates]\n\\label{prop:rg-input}\nFor every type $P\\in\\{r,+,-\\}$, initialize the nearest-neighbor model\nas in Section~\\ref{sec:shooting}.  As long as the input couplings lie\nin their admitted bands, the exact observations produce representations\nwith all renewed shape caps and the recursion\n\\begin{equation}\n b_{j-1}=b_j+\\alpha^P_{N-j}\n              +\\frac{\\kappa^P_{N-j}}{b_j}+\\Delta_j,\n \\qquad |\\Delta_j|\\le C_LH_j^{-1.05}.\n \\label{eq:offset-flow}\n\\end{equation}\nThere are $C_L<\\infty$, $0<\\sigma_1<1$, and a number\n$\\kappa_\\infty$ such that\n\\begin{equation}\n |\\alpha_h^P+\\gamma|+|\\kappa_h^P-\\kappa_\\infty|\n       \\le C_L\\sigma_1^h \\qquad(h\\ge0).\n \\label{eq:offset-increment-convergence}\n\\end{equation}\nThese sequences are independent of the precise running couplings,\nand remain fixed when $H$ is increased.  The two inclined types have\nthe same scalar sequences.\n\nOn common ordinary steps, with discrepancy variables defined above,\n\\begin{align}\n u_{j-1}&\\le q u_j+C_L\\operatorname{poly}(H_j)\\epsilon_h\n                        +e_j|\\lambda_j|,\\nonumber\\\\\n |\\lambda_{j-1}-\\lambda_j|\n     &\\le C_Lu_j+C_L\\operatorname{poly}(H_j)\\epsilon_h\n                        +e_j|\\lambda_j|,\n & e_j&=C_L(\\log H_j)^C/H_j.\n \\label{eq:offset-weak-comparison}\n\\end{align}\nIf the histories share their last $m$ ordinary observations,\n$\\epsilon_h$ may be taken to be $r_0^m$, where $r_0=A_0/L^2$.\nThe choices can be made with\n\\begin{equation}\n \\max\\{q,r_0\\}<\\rho<L^{-2+\\upsilon}<1\n \\label{eq:offset-contraction-width}\n\\end{equation}\nand strict spare width.  Here $q$ and $\\rho$ are fixed bounds valid\nfor every subsequent sufficiently large $H$.  The corresponding free kernels and their\nlifts compare in the exponential kernel norms of\nProposition~\\ref{prop:free-bounds} with the same history bound.\n\\end{proposition}\n\n\\begin{proof}\nThe initialization in Section~\\ref{sec:shooting} has zero canonical\ncoefficients and zero regular errors; its covering construction obeys\nthe covering cap.  Theorem~\\ref{thm:rg-step} renews these caps and\ngives the coupling recursion whenever its input is admitted.  It\nproduces the output representation before the next coupling-band\ncondition is imposed, a fact that will permit a first-exit argument.\n\nThe prescription in Section~\\ref{subsec:rg-canonical} assigns the\ncanonical coefficients to the finite triangular operation\n$\\mathbf P\\mapsto\\mathcal C_h(\\mathbf P)$.  After the powers of\n$b^{-1/2}$ have been sorted, that operation depends only on the\nincoming canonical coefficients and the free history.  The remaining\ndifference from the exact integral is retained in the regular errors\nand covering activities, with its affine correction in $\\Delta$.\nThe orbit starting from zero therefore fixes the sequences\n$\\alpha_h^P,\\kappa_h^P$ independently of the precise coupling and\nof $H$.  Lemma~\\ref{lem:rg-canonical-contraction} and the history\ncontraction in Proposition~\\ref{prop:free-bounds} give their\nexponential convergence, as in Section~\\ref{sec:shooting};\nLemma~\\ref{lem:kinetic-increment} identifies their common first\nlimit as $-\\gamma$.  Reflection interchanges the two inclined\nprescriptions and preserves these scalar extractions, so their\nscalar sequences agree.  This does not identify their coefficient\ntensors in a fixed coordinate frame.\n\nEquation~\\eqref{eq:offset-weak-comparison} follows from\n\\eqref{eq:orig-5} with the simultaneous bulk and period convention.\nTo verify this convention through the normalization, first compare\nthe raw lists in the bulk and on each selected period using the\ndominating discrepancy \\eqref{eq:offset-family-discrepancy}.\nExtract the difference of the kinetic corrections from the bulk raw\nlists, with the factor $Ct_{j-1}^{-2}$ in\nSection~\\ref{subsec:rg-closure}.  The same bulk corrections are then\nused to normalize every finite-period output.  Their regular reset\ncosts $Ct_{j-1}^{2}$ times this difference, cancelling the extraction\nfactor; the other reset discrepancies are the history and coupling\nterms already present in \\eqref{eq:orig-5}.  Missing bulk corrections\non a given period keep their complete winding records and the spare\nsupport exponent, as in the period argument of\nSection~\\ref{subsec:rg-closure}; their off-mask parts retain the\ncovering estimates.  These operations introduce only the fixed\nnormalization multipliers already allowed before choosing $L$.\nThus each output discrepancy is bounded by\n$qu_j+C_L\\operatorname{poly}(H_j)\\epsilon_h+e_j|\\lambda_j|$,\nwith a common contraction coefficient $q$ below $\\rho$ after the\ntriangular weights and large parameters are chosen with spare width.\nTaking their maximum proves the first family inequality;\nthe coupling inequality uses the bulk term directly.\nThe history and kernel conclusions are\nProposition~\\ref{prop:free-bounds}.  The spare width in\n\\eqref{eq:offset-contraction-width} is the choice made in\nProposition~\\ref{prop:endpoint-matching}.  The uniform $o_H(1)$\nterms in the step estimates can be absorbed into a fixed upper\ncontraction bound $q$ strictly below $\\rho$; increasing $H$ later\ndoes not change either chosen rate.\n\\end{proof}\n\n\\subsection{Summable sensitivity of the kinetic error}\n\nThe coefficient $e_j$ in\n\\eqref{eq:offset-weak-comparison} tends to zero but is not summable\nover layers.  For matching trajectories from their behavior at large\n$j$, we need finer information about the noncanonical increment\n$\\Delta_j$.  The raw-error estimate used in the proof of the exact\nstep supplies it.\n\n\\begin{lemma}[Sensitivity of the extracted kinetic error]\n\\label{lem:sensitivity}\nFor two admitted compatible representation families, including their\nprescribed bulk lists, on a common ordinary step with common reference\nscales, the discrepancies in\nProposition~\\ref{prop:rg-input} satisfy\n\\begin{equation}\n |\\Delta_{2,j}-\\Delta_{1,j}|\n \\le C_L H_j^{-1.01}(u_j+|\\lambda_j|)\n             +C_L\\operatorname{poly}(H_j)\\epsilon_h.\n \\label{eq:offset-sensitivity}\n\\end{equation}\nThe constant is uniform over the bulk prescriptions and their\ncompatible admitted finite-period representations, once $H$ is\nsufficiently large.\n\\end{lemma}\n\n\\begin{proof}\nBefore the constant and affine terms of the regular remainder are\nextracted, let $R_j^\\Delta$ denote the layer-$(j-1)$, seven-derivative\nnorm of the difference of its two bulk raw lists.  The Taylor comparison proved in\nSection~\\ref{subsec:rg-closure} gives the estimate\n\\cite[Equation~(5.39)]{OpenAI-O4}, with the improved coefficient\n$q_1=C/L^2+o_H(1)$ established there for $S^2$:\n\\begin{equation}\n R_j^\\Delta\\le q_1\\delta_j^\\Delta\n   +C_L\\operatorname{poly}(\\mathfrak m_j,p_j)\\delta_j\n                      (\\epsilon_h+\\lambda_g)\n   +C_Lg^{4.5}(u_j+\\epsilon_h+\\lambda_g).\n \\label{eq:offset-raw-discrepancy}\n\\end{equation}\nHere $\\delta_j^\\Delta$ is the unnormalized bulk input regular-error\ndiscrepancy; $g$ may be taken comparable to either $b_{i,j}^{-1/2}$;\nand $\\lambda_g\\le C_L|\\lambda_j|/H_j$.  By\n\\eqref{eq:offset-discrepancy-caps},\n$\\delta_j^\\Delta\\le C_LH_j^{-2.05}u_j$, while\n$g\\le C_LH_j^{-1/2}$ in the common bands.\n\nThe affine coefficient is the linear second-derivative functional\n\\eqref{eq:setup-affine-coefficient}, evaluated in the bulk.  Once\nthe coordinate frames and reference domains have been identified,\nthis is the same functional on both raw lists; it does not change\nwith the free kinetic kernel.  The extraction estimate in\nSection~\\ref{subsec:rg-closure} therefore gives\n\\begin{equation}\n |\\Delta_{2,j}-\\Delta_{1,j}|\n       \\le C t_{j-1}^{-2}R_j^\\Delta\n       \\le C_L H_j R_j^\\Delta.\n \\label{eq:offset-affine-extraction}\n\\end{equation}\nThere is no support-cardinality loss in this estimate: a unit affine\ndirection is bounded by the polynomially weighted directions in\n\\eqref{eq:setup-error-directions}, so its two derivative slots cost\nonly $t_{j-1}^{-2}$.  The seven derivatives in the discrepancy norm\nare more than sufficient.  The final reset of the canonical\nprefactors has zero affine Hessian, as verified in\nSection~\\ref{subsec:rg-closure}, and hence contributes no additional\nkinetic extraction.  Bulk extraction also explains the uniformity\nin the period: missing or folded corrections remain in the winding\nlists instead of changing $\\Delta_j$.  Equation~\\eqref{eq:offset-affine-extraction}\nuses the bulk raw-list discrepancy, which is bounded using\n$u_j^{\\rm bulk}\\le u_j$ from\n\\eqref{eq:offset-family-discrepancy}.  The inclusion of this bulk\nterm is essential when some labels wind or disappear on a finite period.\n\nMultiplying \\eqref{eq:offset-raw-discrepancy} by $C_LH_j$ bounds its\nnonhistory terms by\n\\[\n C_L(H_j^{-1.05}+H_j^{-1.25})u_j\n +C_L\\bigl((\\log H_j)^C H_j^{-2.05}\n                         +H_j^{-2.25}\\bigr)|\\lambda_j|.\n\\]\nAny remaining fixed logarithmic powers are absorbed by the strict\npower margins when $H$ is increased.  In particular this is bounded\nby $C_LH_j^{-1.01}(u_j+|\\lambda_j|)$.  The history terms can be\nbounded by $C_L\\operatorname{poly}(H_j)\\epsilon_h$, proving\n\\eqref{eq:offset-sensitivity}.  The argument uses the raw-error\ncomparison for $S^2$ from Section~\\ref{subsec:rg-closure}, rather\nthan an application of an $O(4)$ step theorem to the present model.\n\\end{proof}\n\n\\subsection{Admission and retuning}\n\nThe exponentially decaying canonical transients define the finite\nconstants\n\\begin{equation}\n A_P=\\sum_{h=0}^{\\infty}(\\alpha_h^P+\\gamma),\\qquad\n A_{\\rm abs}=\\max_{P\\in\\{r,+,-\\}}\n                  \\sum_{h=0}^{\\infty}|\\alpha_h^P+\\gamma|,\n \\qquad d=A_+-A_r=A_--A_r.\n \\label{eq:offset-transients}\n\\end{equation}\nThese constants are fixed before $H$ is increased.  Recall the\nreference sequence from Section~\\ref{sec:shooting}:\n\\begin{equation}\n \\vartheta_j=H_j-\\frac{\\kappa_\\infty}{\\gamma}\\log(H_j/H),\n \\qquad\n \\vartheta_j-\\vartheta_{j-1}\n       =\\gamma-\\frac{\\kappa_\\infty}{H_j}+O_L(H_j^{-2}).\n \\label{eq:offset-profile}\n\\end{equation}\nThe bare offset of a depth-$N$ trajectory is\n$s=\\beta-\\vartheta_N$.  Choose once and for all\n\\begin{equation}\n R>4(|d|+\\gamma+1),\\qquad\n R'>R+2A_{\\rm abs}+2,\\qquad I=(-R,R).\n \\label{eq:offset-range}\n\\end{equation}\nWe shall use $|s|\\le R$ for the limiting comparisons and the larger\nrange $|s|\\le R'$ for retuning.  Both bounds have now been chosen\nindependently of $H$.\n\n\\begin{proposition}[Uniform admission for bounded offsets]\n\\label{prop:admission}\nFor $H$ sufficiently large, every depth $N\\ge1$, every type\n$P\\in\\{r,+,-\\}$, and every bare coupling\n$\\beta=\\vartheta_N+s$ with $|s|\\le R'$ give a strictly admitted\ntrajectory.  Uniformly over these choices,\n\\begin{equation}\n b_j=\\vartheta_j+D_j,\\qquad\n \\max_{0\\le j\\le N}|D_j|\\le C_{L,R'}.\n \\label{eq:offset-admission}\n\\end{equation}\nMoreover,\n\\begin{align}\n D_{j-1}-D_j&=\\alpha^P_{N-j}+\\gamma+E_{j,N},\\nonumber\\\\\n |E_{j,N}|&\\le C_L\\left[\n H_j^{-1.05}\n +H_j^{-2}\\bigl(\\log(2+H_j/H)+|D_j|\\bigr)\n +H_j^{-1}\\sigma_1^{N-j}\\right],\n \\label{eq:offset-error-recursion}\n\\end{align}\nand\n\\begin{equation}\n \\sup_{N,P,\\,|s|\\le R'}\\sum_{j=1}^{N}|E_{j,N}|\n       =o_H(1).\n \\label{eq:offset-error-sum}\n\\end{equation}\nThe constants in \\eqref{eq:offset-admission} stay bounded as $H$\nis increased, with all earlier parameters fixed.\n\\end{proposition}\n\n\\begin{proof}\nThe starting coupling is strictly in band for $H$ large, uniformly\nin $N$ and $|s|\\le R'$.  Indeed, for $x\\ge1$ the ratio\n$\\log x/x$ is bounded, and hence\n$|\\vartheta_N-H_N|/H_N\\le C_L/H$.\nConsider a trajectory down to a possible first output outside its\nband.  Every step forming that output has an admitted input, so\n\\eqref{eq:offset-flow} applies.  On such an input,\n\\[\n \\left|\\frac1{b_j}-\\frac1{H_j}\\right|\n \\le C_LH_j^{-2}\n          \\bigl(\\log(2+H_j/H)+|D_j|\\bigr).\n\\]\nSubtract \\eqref{eq:offset-profile} from the coupling recursion and\nuse \\eqref{eq:offset-increment-convergence}.  This gives\n\\eqref{eq:offset-error-recursion}, including the step forming a\npossible first exit.\n\nThe sums required for this estimate are uniform in depth:\n\\begin{align*}\n \\sum_{j\\ge1}H_j^{-1.05}&\\le C_LH^{-0.05},&\n \\sum_{j\\ge1}H_j^{-2}&\\le C_LH^{-1},\\\\\n \\sum_{j\\ge1}H_j^{-2}\\log(2+H_j/H)&\\le C_LH^{-1},&\n \\sum_{j=1}^{N}H_j^{-1}\\sigma_1^{N-j}&\\le C_LH^{-1}.\n\\end{align*}\nThe first three follow by integral comparison for\n$H_j=H+\\gamma j$; the last follows from the geometric series and\n$H_j\\ge H$.  Let $M_D$ be the maximum of $|D_j|$ on the segment,\nincluding its last output.  Summing from $D_N=s$ and using\n\\eqref{eq:offset-transients} gives\n\\[\n M_D\\le R'+A_{\\rm abs}+o_H(1)+C_LH^{-1}M_D.\n\\]\nTake $H$ large enough to absorb the final term.  This bounds $M_D$\nby a constant depending only on $L,R'$ and the previously fixed\nparameters.  Since also\n\\[\n \\sup_{j\\ge0}\\frac{|\\vartheta_j-H_j|+C_{L,R'}}{H_j}\n       \\longrightarrow0\\qquad(H\\longrightarrow\\infty),\n\\]\nevery output covered by this bound lies strictly in its band.\nThere can be no first exit, which proves admission and\n\\eqref{eq:offset-admission}.  Substitution of this uniform bound\nfor $|D_j|$ in \\eqref{eq:offset-error-recursion}, followed by the\nsame four summations, proves \\eqref{eq:offset-error-sum}.\n\\end{proof}\n\n\\begin{lemma}[Retuning at an arbitrary depth]\n\\label{lem:retuning}\nAfter increasing $H$ once more, let an ordinary trajectory have any\ndepth $N\\ge1$ and bare offset $|s|\\le R$.  For every integer $K\\ge1$\nthere is $s_K\\in[-R',R']$ such that the ordinary trajectory of\ndepth $K$ with bare coupling $\\vartheta_K+s_K$ has exactly the\nsame terminal coupling $b_0$.\n\\end{lemma}\n\n\\begin{proof}\nFor every trajectory in Proposition~\\ref{prop:admission}, summing\n\\eqref{eq:offset-error-recursion} to zero gives\n\\begin{equation}\n \\left|b_0-H-s\\right|\\le A_{\\rm abs}+o_H(1).\n \\label{eq:offset-terminal-range}\n\\end{equation}\nLet $T_K(t)$ be the terminal coupling of the ordinary depth-$K$\ntrajectory started at $\\vartheta_K+t$.  Proposition~\\ref{prop:admission}\ngives strict admission for the whole interval $[-R',R']$.\nThe continuity argument in the proof of Proposition~\\ref{prop:shooting}\ntherefore makes $T_K$ continuous on this interval: the reference\nthresholds are fixed, and the exact integrations and representation\nmaps are continuous in the precise coupling on each finite admitted\nrun.  Take $H$ so that the uniform $o_H(1)$ in\n\\eqref{eq:offset-terminal-range} has absolute value less than one.\nBy \\eqref{eq:offset-range},\n\\begin{align*}\n T_K(-R')&\\le H-R'+A_{\\rm abs}+1\n                  <H-R-A_{\\rm abs}-1\\le b_0,\\\\\n T_K(R')&\\ge H+R'-A_{\\rm abs}-1\n                  >H+R+A_{\\rm abs}+1\\ge b_0.\n\\end{align*}\nThe intermediate value theorem supplies $s_K$.  No monotonicity or\nuniqueness of the shooting map is needed.\n\\end{proof}\n\n\\subsection{Matching at each fixed layer}\n\nRetuning gives an exact equality of terminal couplings at finite\ndepths.  A different comparison identifies limits without such a\nretuning.  Its hypothesis compensates the transient displacement\n$A_P$ associated with the choice of initial observation.\n\n\\begin{proposition}[Fixed-layer matching]\n\\label{prop:matching}\nFix $H$ sufficiently large as above.  For $i\\in\\{1,2\\}$, let\n$P_i\\in\\{r,+,-\\}$ be fixed and let a sequence of depth-$N_{i,n}$\ntrajectories have bare couplings\n$\\vartheta_{N_{i,n}}+s_{i,n}$, where\n$N_{i,n}\\longrightarrow\\infty$ and $|s_{i,n}|\\le R'$.\nSuppose\n\\begin{equation}\n (s_{2,n}+A_{P_2})-(s_{1,n}+A_{P_1})\\longrightarrow0.\n \\label{eq:offset-matching-hypothesis}\n\\end{equation}\nCompare their bulk representations in common layer coordinates,\nusing the same ordinary observations on their common bottom steps.\nThen, for every fixed integer $j\\ge0$,\n\\begin{equation}\n u_{j,n}\\longrightarrow0,\\qquad\n \\lambda_{j,n}\\longrightarrow0.\n \\label{eq:offset-fixed-layer-matching}\n\\end{equation}\nThe same statement holds on each fixed common compatible terminal\nperiod and its corresponding bottom-layer periods.\n\\end{proposition}\n\n\\begin{proof}\nAll the trajectories are admitted by Proposition~\\ref{prop:admission}.\nAt layer $j$, the histories share at least\n$\\min(N_{1,n},N_{2,n})-1-j$ most recent ordinary observations,\nonce the depths are sufficiently large.  Thus their history error\ntends to zero at each fixed layer.  The normalized caps and\n\\eqref{eq:offset-admission} also give constants, independent of\n$j,n$, bounding $u_{j,n}$ and $|\\lambda_{j,n}|$ whenever the layer\nis present.  Define\n\\[\n U_j=\\limsup_{n\\to\\infty}u_{j,n},\\qquad\n W_j=\\limsup_{n\\to\\infty}|\\lambda_{j,n}|.\n\\]\nBoth sequences are uniformly bounded.\n\nWe first establish the boundary condition for $W_j$ at large\nlayers.  The recursion in Proposition~\\ref{prop:admission} gives\n\\[\n D_{i,j}=s_{i,n}\n       +\\sum_{h=0}^{N_{i,n}-j-1}(\\alpha_h^{P_i}+\\gamma)\n       +\\sum_{m=j+1}^{N_{i,n}}E_{i,m,N_{i,n}}.\n\\]\nFor fixed $j$ the transient sum tends to $A_{P_i}$ as\n$n\\to\\infty$.  The error bound, with\n\\eqref{eq:offset-admission} substituted, gives\n\\begin{equation}\n W_j\\le C_{L,R'}\\sum_{m>j}\n       \\left[H_m^{-1.05}\n          +H_m^{-2}\\bigl(\\log(2+H_m/H)+1\\bigr)\\right]\n       +\\frac{C_L}{H_j}.\n \\label{eq:offset-boundary-at-infinity}\n\\end{equation}\nHere \\eqref{eq:offset-matching-hypothesis} cancels the two limiting\noffsets, and the final term bounds each transient sum by\n\\[\n \\sum_{m=j+1}^{N}H_m^{-1}\\sigma_1^{N-m}\n                 \\le \\frac1{H_j(1-\\sigma_1)}.\n\\]\nThe right side of \\eqref{eq:offset-boundary-at-infinity} tends to\nzero as $j\\to\\infty$, with $H$ fixed.  Consequently\n\\begin{equation}\n W_j\\longrightarrow0\\qquad(j\\longrightarrow\\infty).\n \\label{eq:offset-W-boundary}\n\\end{equation}\nThis argument does not require the complete error sum at fixed $H$\nto vanish as the depth increases.\n\nAt each fixed $j\\ge1$, take upper limits in the first inequality of\n\\eqref{eq:offset-weak-comparison}.  The history forcing disappears.\nFor the coupling, subtract \\eqref{eq:offset-flow} for the two\ntrajectories and use Lemma~\\ref{lem:sensitivity}.  At this fixed\nlayer the two $\\alpha$ values tend to $-\\gamma$ and the two\n$\\kappa$ values to $\\kappa_\\infty$.  The remaining denominator\nvariation is bounded by $C_LH_j^{-2}|\\lambda_{j,n}|$.\nIt follows that\n\\begin{equation}\n U_{j-1}\\le qU_j+e_jW_j,\\qquad\n W_{j-1}\\le W_j+a_j(U_j+W_j),\\qquad\n a_j=C_LH_j^{-1.01}.\n \\label{eq:offset-limsup-recursions}\n\\end{equation}\n\nPut $U=\\sup_{j\\ge0}U_j$, $W=\\sup_{j\\ge0}W_j$, and\n$e_* =\\sup_{j\\ge1}e_j$.  Iterating the first inequality through\n$m$ layers above $k$ yields\n\\[\n U_k\\le q^m U_{k+m}\n             +W\\sum_{r=1}^{m}q^{r-1}e_{k+r}.\n\\]\nBoundedness and $q<1$ allow $m\\to\\infty$, giving\n$U\\le e_*W/(1-q)$.  Next sum the second inequality of\n\\eqref{eq:offset-limsup-recursions} from $k+1$ to $k+m$.\nLetting $m\\to\\infty$ and using \\eqref{eq:offset-W-boundary}\ngives\n\\[\n W_k\\le\\sum_{j>k}a_j(U_j+W_j),\\qquad\n W\\le\\left(\\sum_{j\\ge1}a_j\\right)(U+W).\n\\]\nNo limit has been interchanged with an infinite sum: the limiting\nrecurrences were first obtained at fixed layers and then iterated.\nFinally,\n\\[\n e_*\\longrightarrow0,\\qquad\n \\sum_{j\\ge1}a_j\\le C_LH^{-0.01}\\longrightarrow0\n                  \\qquad(H\\longrightarrow\\infty).\n\\]\nWe choose $H$, once for all the trajectories, so that\n\\[\n \\left(\\sum_{j\\ge1}a_j\\right)\n                  \\left(1+\\frac{e_*}{1-q}\\right)<1.\n\\]\nThe two supremum inequalities force $W=0$ and then $U=0$,\nwhich proves \\eqref{eq:offset-fixed-layer-matching}.  The proof\nuses the bulk norms and the simultaneous finite-period norms from\nProposition~\\ref{prop:rg-input}; it therefore applies to each fixed\ncompatible terminal period as stated.\n\\end{proof}\n\nFor later use, all further requirements that $H$ be large, including\nthe local source and terminal-alignment estimates, are imposed before\nany depth or volume thresholds are selected.  The bounds $R,R'$,\nthe transient constants, and the reference prescriptions remain fixed\nthroughout these choices.\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/uniqueness.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/uniqueness.tex", "bytes": 11906, "sha256": "d91b36b86bcc41e2deb6c398519c972fdff3c7588c00275cdb2f62795840aecc", "content": "\\section{Uniqueness after canonical normalization}\\label{sec:uniqueness}\n\nWe now remove the offset parameter.  The preceding section gives a\ncontinuous family $G_s$ of canonically normalized hierarchies for\n$s\\in I=(-R,R)$, together with its positive second-moment length\n$\\ell(s)$ before normalization.  Two descriptions of the same microscopic\nmodel relate different members of this family.  An additional ordinary\nstep changes the physical mesh by $L^{-1}$; an inclined first step changes\nit by $5^{-1}$ and rotates it.  The two reflected inclinations have the\nsame offset shift.  These facts supply both rotational invariance and two\nincommensurable periods in $s$.\nFigure~\\ref{fig:two-scales} records the two descriptions and their\nphysical length factors.\n\nFor a rotation $U$ of the Euclidean plane, write $U_*G$ for the hierarchy obtained by\npushing each insertion coordinate forward by $U$.  At the field level\nthis means $(U_*\\phi)(f)=\\phi(f\\circ U)$.  Component indices are unchanged:\nthese are rotations of Euclidean coordinates, distinct from the internal $O(3)$ symmetry.\n\n\\subsection{Two descriptions of one cutoff}\n\n\\begin{proposition}[Depth and inclination relations]\\label{prop:two-scales}\nFor $s,s-\\gamma\\in I$,\n\\begin{equation}\\label{eq:uni-depth}\n G_{s-\\gamma}=G_s,\n \\qquad \\ell(s-\\gamma)=L^{-1}\\ell(s).\n\\end{equation}\nLet $d=A_+-A_{\\mathrm r}=A_--A_{\\mathrm r}$ be the offset shift\ndefined in Section~\\ref{sec:trajectories}.  For $s,s+d\\in I$,\n\\begin{equation}\\label{eq:uni-incline}\n \\ell(s+d)=5^{-1}\\ell(s),\\qquad\n G_{s+d}=(O_+^{-1})_*G_s=(O_-^{-1})_*G_s.\n\\end{equation}\n\\end{proposition}\n\n\\begin{proof}\nFix $s,s-\\gamma\\in I$ and take bare couplings\n$\\beta_N=\\vartheta_N+s$.  Regard the same microscopic law first as an\nordinary trajectory of depth $N$, and then as one of depth $N+1$.\nThe latter offset is\n\\[\n \\beta_N-\\vartheta_{N+1}\n =s-(\\vartheta_{N+1}-\\vartheta_N)\\longrightarrow s-\\gamma.\n\\]\nBoth descriptions are admitted for all sufficiently large $N$ by\nProposition~\\ref{prop:admission}.  At every cutoff, canonical\nnormalization in Proposition~\\ref{prop:normalization} yields the same\nfield $\\Psi_{\\beta_N}$.  Their limiting hierarchies are therefore equal.\nTheir cutoff second-moment lengths before normalization are respectively\n$L^{-N}\\xi_{\\beta_N}$ and $L^{-(N+1)}\\xi_{\\beta_N}$.  Taking their\npositive limits proves \\eqref{eq:uni-depth}.\n\nNext use an ordinary and an inclined trajectory of the same depth $N$\nand the same bare coupling $\\vartheta_N+s$.  By\nProposition~\\ref{prop:offset-limits}, the inclined limiting hierarchy\nis the ordinary one at offset $s+d$.  Its microscopic spacing is\n$L^{-N}/5$ and its spatial embedding is $O_\\pm^{-1}$.\nThus its second-moment length is exactly one fifth of the ordinary\none, while its canonically normalized field is the pushforward of\n$\\Psi_{\\beta_N}$ by $O_\\pm^{-1}$.  Passing to the limit gives\n\\eqref{eq:uni-incline}.  Reflection of the microscopic construction\nidentifies the scalar canonical increments of the two inclinations,\nwhich is why the same $d$ occurs in both equalities.\n\\end{proof}\n\n\\begin{figure}[tb]\n\\centering\n\\begin{tikzpicture}[>=Latex,node distance=14mm and 24mm,\n every node/.style={font=\\small,align=center},\n box/.style={draw=MidnightBlue!65,rounded corners,fill=MidnightBlue!3,\n             inner sep=6pt,text width=38mm}]\n\\node[box] (a) {Ordinary, depth $N$\\\\\n $s,\\quad a=L^{-N}$\\\\$G_s,\\quad\\ell(s)$};\n\\node[box,right=of a] (b) {Ordinary, depth $N+1$\\\\\n $s-\\gamma+o(1)$\\\\$G_{s-\\gamma},\\quad\\ell(s)/L$};\n\\node[box,below=of a] (c) {Inclined, depth $N$\\\\\n $s,\\quad a=L^{-N}/5$\\\\$G_{s+d},\\quad\\ell(s)/5$};\n\\node[right=of c,text width=38mm] (d) {All three use the same\\\\bare coupling $\\beta_N$.};\n\\draw[->,thick] (a)--node[above] {one extra step} (b);\n\\draw[->,thick] (a)--node[left,text width=22mm] {inclined\\\\first step} (c);\n\\draw[->,MidnightBlue!70] (d.north)--(b.south);\n\\draw[->,MidnightBlue!70] (d.west)--(c.east);\n\\end{tikzpicture}\n\\caption{The two scale comparisons.  The displayed offsets in the boxes\nare the bare offsets of the trajectories; the inclined hierarchy matches\nthe ordinary hierarchy at $s+d$.  Canonical normalization cancels their\ndifferent field multipliers.  The inclined comparison retains only the\nEuclidean rotation, as in \\eqref{eq:uni-incline}.}\n\\label{fig:two-scales}\n\\end{figure}\n\n\\subsection{An elementary uniqueness mechanism}\n\nWe isolate the topological part of the argument.  Its input consists only\nof continuous distributions, positive lengths, and the relations just\nproved.  In particular, it requires no monotonicity of the shooting map.\n\n\\begin{lemma}[Two scale relations force constancy]\\label{lem:dense-periods}\nLet $L>1$ be a power of two, let $\\gamma>0$, and let\n$I=(-R,R)$.  Suppose $\\ell:I\\to(0,\\infty)$ is continuous and\n$s\\mapsto G_s$ is a family of distribution hierarchies whose evaluation\non every Schwartz test is continuous.  Let $O_\\pm$ be rotations by\n$\\pm\\vartheta$, where $\\cos\\vartheta=3/5$ and\n$\\sin\\vartheta=4/5$.  Suppose \\eqref{eq:uni-depth} and\n\\eqref{eq:uni-incline} hold wherever both offsets belong to $I$, and\nsuppose $2R-|d|>\\gamma$.\nThen every $G_s$ is Euclidean rotation invariant, and $G_s$ is independent\nof $s$.\n\\end{lemma}\n\n\\begin{proof}\nFirst take $s$ in the interval $J=I\\cap(I-d)$.  Equality of the two\npushforwards in \\eqref{eq:uni-incline} implies invariance of $G_s$\nunder rotation by $2\\vartheta$.  The number $\\vartheta/\\pi$ is\nirrational.  Indeed, if it were rational, $e^{i\\vartheta}$ would be a\nroot of unity, making\n$e^{i\\vartheta}+e^{-i\\vartheta}=6/5$ an algebraic integer.  A rational\nalgebraic integer is an integer, which $6/5$ is not.  Powers of this\nrotation are consequently dense in $SO(2)$.  The action of rotations\non Schwartz tests is continuous, so every distribution in $G_s$ is\ninvariant under all Euclidean rotations.  Equation~\\eqref{eq:uni-incline}\ntherefore gives $G_{s+d}=G_s$ on $J$.\n\nTo use these local relations globally, extend the two families to\n$\\R$.  For $x\\in\\R$, choose an integer $n$ with $x+n\\gamma\\in I$ and\nset\n\\begin{equation}\\label{eq:uni-extension}\n \\widetilde G_x=G_{x+n\\gamma},\\qquad\n \\widetilde\\ell(x)=L^{-n}\\ell(x+n\\gamma).\n\\end{equation}\nSuch an $n$ exists because $|I|>\\gamma$.  These definitions do not\ndepend on the choice: two representatives can be joined by consecutive\n$\\gamma$-steps staying in the interval $I$, and\n\\eqref{eq:uni-depth} applies at every step.  On overlapping translates\nof $I$, the definitions agree; hence the extended families are continuous.\nThey satisfy\n\\[\n \\widetilde G_{x+\\gamma}=\\widetilde G_x,\n \\qquad \\widetilde\\ell(x+\\gamma)=L\\widetilde\\ell(x).\n\\]\nThe interval $J$ has length $2R-|d|>\\gamma$, so every class modulo\n$\\gamma$ has a representative in $J$.  Choosing that representative in\n\\eqref{eq:uni-extension} transports both the rotation invariance and\nthe $d$-relations to every $x\\in\\R$:\n\\begin{equation}\\label{eq:uni-global-periods}\n \\widetilde G_{x+d}=\\widetilde G_x,\n \\qquad \\widetilde\\ell(x+d)=5^{-1}\\widetilde\\ell(x).\n\\end{equation}\n\nIf $d/\\gamma=p/q$ with integers $p$ and $q>0$, then iterating the\nlength relations gives\n\\[\n 5^{-q}\\widetilde\\ell(x)\n =\\widetilde\\ell(x+qd)\n =\\widetilde\\ell(x+p\\gamma)\n =L^p\\widetilde\\ell(x).\n\\]\nSince $\\widetilde\\ell(x)>0$, this would imply $5^{-q}=L^p$,\ncontradicting unique prime factorization.  Thus $d/\\gamma$ is irrational.\nThe subgroup $\\Z\\gamma+\\Z d$ is dense in $\\R$.  Each test evaluation\nof $\\widetilde G$ is continuous and has this subgroup as periods, and\nis therefore constant.  Equality on every test proves equality of the\nhierarchies.  Restriction to $I$ proves the claim.\n\\end{proof}\n\n\\subsection{Every bare coupling and the physical conclusions}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:canonical}]\nFiniteness and strict positivity of $\\chi_\\beta$ and $\\xi_\\beta$ for\nsufficiently large $\\beta$ follow from Lemma~\\ref{lem:finite-beta}.\nProposition~\\ref{prop:two-scales} supplies the hypotheses of\nLemma~\\ref{lem:dense-periods}; our choice of $R$ in\nSection~\\ref{sec:trajectories} gives the required overlap.  Write $G$\nfor the common normalized hierarchy.\n\nThe reference values $\\vartheta_N$ tend to infinity, and\n$\\vartheta_{N+1}-\\vartheta_N\\to\\gamma>0$.  For every sufficiently large\n$\\beta$, choose $N=N(\\beta)$ so that\n\\[\n \\vartheta_N\\le\\beta<\\vartheta_{N+1}.\n\\]\nThen $N(\\beta)\\to\\infty$, and the offsets\n$s(\\beta)=\\beta-\\vartheta_{N(\\beta)}$ belong to the fixed compact interval\n$[0,2\\gamma]\\subset I$.  Any sequence $\\beta_k\\to\\infty$ has a\nsubsequence on which these offsets converge to some $s\\in I$.\nPropositions~\\ref{prop:offset-limits} and~\\ref{prop:normalization}\ngive, along that subsequence, convergence of the canonically normalized\nfields to $G$, jointly in law and in every joint moment, and strong\nconvergence of each component Schwinger distribution in $\\mathcal S'$.\nMoreover,\n\\[\n L^{-N(\\beta_k)}\\xi_{\\beta_k}\\longrightarrow\\ell(s)>0,\n\\]\nso $\\xi_{\\beta_k}\\to\\infty$ along the same subsequence.\n\nThese conclusions hold for a subsequence of every sequence tending to\ninfinity, with the same limiting hierarchy.  If any asserted convergence\nfailed, a sequence staying outside a fixed neighborhood of the claimed\nlimit would have such a subsequence, a contradiction.  This proves the\nfull $\\beta\\to\\infty$ assertions.  The same argument applies to a\nneighborhood in the strong topology of $\\mathcal S'$; it does not require\nthat topology to be metrizable.  The finite-list laws are consistent, and\ntheir local exponential moments determine them from $G$.\n\nFinally let $\\phi^{\\mathrm{sh}}$ be the hierarchy in\nTheorem~\\ref{thm:main}, obtained by fixing a terminal kinetic coupling.\nIts auxiliary parameters need not equal those used for the offset\nargument.  Lemma~\\ref{lem:shooting-normalization} gives finite positive\nsusceptibility $m_{\\mathrm{sh}}$ and second-moment length\n$\\ell_{\\mathrm{sh}}$, and convergence of the corresponding cutoff\nquantities.  Thus its normalization\n\\begin{equation}\\label{eq:uni-shooting-identification}\n \\phi^{\\mathrm{can}}(f)\n =\\frac{1}{\\ell_{\\mathrm{sh}}\\sqrt{m_{\\mathrm{sh}}}}\n    \\phi^{\\mathrm{sh}}\\bigl(f(\\,\\cdot\\,/\\ell_{\\mathrm{sh}})\\bigr)\n\\end{equation}\nis the limit of the same microscopic fields $\\Psi_{\\beta_N}$ along its\nshooting sequence.  Since $\\beta_N\\to\\infty$, this hierarchy is $G$.\nProposition~\\ref{prop:normalization} also proves uniqueness of the\npositive length and field constants fixing susceptibility and\nsecond-moment length to one.\n\\end{proof}\n\n\\begin{proof}[Proof of Corollary~\\ref{cor:canonical-physics}]\nEquation~\\eqref{eq:uni-shooting-identification} is a positive dilation\nof space-time and multiplication of the field by a nonzero constant.\nIt preserves the Euclidean and internal symmetries, reflection\npositivity, symmetry of the Schwinger functions, clustering, and their\nfactorial distributional bounds.  The reconstruction argument of\nSection~\\ref{sec:os} therefore applies.\n\nMore explicitly, substitution of the rescaled tests and field factors\ndefines an invertible map of the positive-time polynomial algebras and\npreserves their Osterwalder--Schrader inner products.  It induces a\nunitary map between the completed reconstructed spaces, taking vacuum\nto vacuum.  A time translation by $t$ in canonical coordinates\ncorresponds to one by $\\ell_{\\mathrm{sh}}t$ in shooting coordinates.\nConsequently the two Hamiltonians satisfy, under this unitary map,\n\\[\n H_{\\mathrm{can}}=\\ell_{\\mathrm{sh}}H_{\\mathrm{phys}}.\n\\]\nThe unique vacuum, the nonzero vacuum complement, and the positive\nlower bound on the full vacuum-orthogonal Hamiltonian therefore persist.\nA connected fourth distribution transforms by the nonzero fourth power\nof the field factor and the invertible dilation of its tests.  Strict\nseparation of time supports is preserved by a positive dilation.\nThe nonzero separated connected fourth correlation of\nTheorem~\\ref{thm:main} thus remains nonzero in canonical units.\n\\end{proof}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/volume.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/build/sections/volume.tex", "bytes": 22580, "sha256": "9a90d21967b8b58bc3669d1c54446de1940ac47ca44fbd0aee165a27271f9689", "content": "\\section{Volume estimates uniform in the bare offset}\n\\label{sec:volume}\n\nThe trajectories admitted in Proposition~\\ref{prop:admission} have\nterminal couplings in a fixed band, but generally do not end at $H$.\nWe now extend the finite-volume estimates of Section~\\ref{sec:trace}\nto this entire family.  The argument first matches two trajectories\nat their common terminal coupling, then transfers a rectangular\npartition-function test from a retuned reference depth to every finer\ncutoff.  The resulting bounds will allow local field comparisons on\nfixed tori to pass to the plane.\n\n\\subsection{Positivity at finite coupling}\n\\label{sec:finite-beta}\n\nWe first record the elementary positivity needed to use component\nmoments as measures.  All torus partition functions retain the\nnormalization in \\eqref{eq:lattice-model}: normalized area measure at\neach site, and each unoriented nearest-neighbor bond counted once.\n\n\\begin{lemma}[Finite-coupling moments and normalization]\n\\label{lem:finite-beta}\nFor every sufficiently large fixed $\\beta$, the periodic plane law\n$\\mu_\\beta$ exists and is translation and $O(3)$ invariant.  Every\ncomponent moment\n\\[\n \\E_{\\mu_\\beta}\\prod_{r=1}^n q_{x_r}^{i_r},\n \\qquad x_r\\in\\Z^2,\\quad i_r\\in\\{1,2,3\\},\n\\]\nis nonnegative.  If\n\\[\n C_\\beta(x)=\\E_{\\mu_\\beta}[q_0^1q_x^1],\\qquad\n \\chi_\\beta=\\sum_{x\\in\\Z^2}C_\\beta(x),\\qquad\n \\xi_\\beta^2=\\frac{\\sum_x|x|^2C_\\beta(x)}{4\\chi_\\beta},\n\\]\nthen both sums converge absolutely and\n$0<\\chi_\\beta,\\xi_\\beta<\\infty$, where $\\xi_\\beta$ is the positive\nsquare root.  More precisely, $C_\\beta(0)=1/3$ and, for each\nnearest-neighbor vector $e$,\n\\begin{equation}\n C_\\beta(e)\\ge\\frac{\\beta}{9}e^{-7\\beta}>0.\n \\label{eq:offset-edge-positive}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nProposition~\\ref{prop:preliminary} gives the periodic plane limit.\nThe symmetries of its torus approximants pass to that limit; in\nparticular each component has mean zero.  Apply\n\\eqref{eq:prelim-mixing} to $q_0^1,q_x^1$ on arbitrarily large tori\nat this fixed $\\beta$, and then pass to the plane.  It gives\n\\[\n |C_\\beta(x)|\\le\n C(1+\\beta+|x|)^p\\exp[-c|x|/X(\\beta)].\n\\]\nThe two sums in the statement therefore converge absolutely.\n\nFor positivity, expand each factor\n$\\exp(\\beta\\sum_{i=1}^3q_x^iq_y^i)$ in its componentwise power series\non a finite torus.  The series of absolute values is integrable,\nsince the spins are bounded and the torus has finitely many edges.\nEvery integrated term, including any prescribed component insertion,\nfactors into one-site monomial integrals.  Such an integral vanishes\nif one coordinate exponent is odd and is nonnegative if all are\neven.  All expansion coefficients are nonnegative.  Division by the\npositive partition function, followed by the periodic limit, proves\nthe asserted moment positivity.\n\nConsider one edge $\\{0,e\\}$ on a torus with sides at least four.\nExactly seven distinct bonds meet its two endpoints.  Delete these\nbonds and denote the resulting partition function by $Z^{\\rm del}$.\nIn the expansion of the numerator for $q_0^1q_e^1$, retain the term\n$\\beta q_0^1q_e^1$ from the chosen edge and the constant terms from\nthe other six incident bonds, leaving all nonincident expansions\nunrestricted.  The isolated endpoint integrals each equal $1/3$,\nso this contribution is $\\beta Z^{\\rm del}/9$.  Termwise\nnonnegativity bounds the full numerator from below by this value.\nSince the deleted interaction is at most $7\\beta$ pointwise,\n$Z\\le e^{7\\beta}Z^{\\rm del}$.  This proves\n\\eqref{eq:offset-edge-positive}, also after passing to the plane.\nInternal symmetry gives $C_\\beta(0)=1/3$.  Nonnegativity now gives\n$\\chi_\\beta\\ge1/3$ and a strictly positive second-moment numerator.\n\\end{proof}\n\n\\subsection{Endpoint comparisons with a varying terminal coupling}\n\nThe reference scale at the endpoint remains $H$ throughout.  Thus the\nthresholds are always $t_0=H^{-1/2}p_0$, the regular-error cap is\n$\\delta_0=H^{-2.05}$, and the covering cap is\n$w_0=\\exp(-p_0^{1/4})$.  These quantities are not replaced by\nfunctions of the precise terminal coupling.\n\n\\begin{lemma}[Ordinary endpoint laws throughout the terminal band]\n\\label{lem:band-endpoints}\nFix a finite covering-cap multiplier and $C_0>0$ before choosing $H$\nsufficiently large.  Let a trajectory obtained from the microscopic\nGibbs law by the exact ordinary observations satisfy the admission and\ncap conditions of Section~\\ref{sec:setup}, with terminal coupling\n$b_0\\in[H/2,2H]$.  On a standard terminal axis torus of bounded\naspect, with the lower bounds and divisibilities of\nSection~\\ref{sec:comparison}, its scalar-stripped endpoint density\nis an admissible base for Proposition~\\ref{prop:integrated-comparison}\nand Corollary~\\ref{cor:terminal-alignment}, with constants uniform in\nthe trajectory, its depth, its period, and $b_0$ in this band.\nIn particular, every endpoint bond obeys\n\\begin{equation}\n \\mu\\{|q_x-q_y|>t_0/C_0\\}\\le e^{-cHt_0^2}\n \\label{eq:offset-terminal-alignment}\n\\end{equation}\nfor a corresponding $c>0$.\nThe integrated comparison also permits complex comparison weights\nwhose support-hit envelope obeys the chosen multiplier of the covering\ncap, as in its original statement.\n\\end{lemma}\n\n\\begin{proof}\nThe definition in Section~\\ref{setup:endpoints}, specifically\n\\eqref{setup:endpoint-law}, already allows a precise coupling in\n$[H/2,2H]$.  An actual observed law is positive because its density\nis the integral of the positive microscopic density against positive\nnormalized observations.  The ordinary observations on the two\nsides of a retained seam are independent and are exchanged by\nreflection.  The verification at the end of\nSection~\\ref{sec:shooting} therefore supplies link reflection\npositivity without any condition $b_0=H$.\n\nThe quantitative estimates used in the integrated comparison have\nthe same uniformity.  In Lemma~\\ref{lem:comparison-remainder},\ngroup the kinetic rows with their precise coupling $b_0$.  The omitted\ntails cost at most $2H$ times the exponentially small kernel tail,\nand the canonical contributions remain bounded by\n$C_L(Ht_0^4+t_0^2)$ per anchor.  In\nLemma~\\ref{lem:editable-regions}, the lower and upper energy bounds\nuse respectively $b_0\\ge H/2$ and $b_0\\le2H$; their energy scale is\nstill $Ht_0^2=p_0^2$.  The sampling, masks, complete supports, and\ninventory constraints have the same reference thresholds and caps.\nThe chessboard argument uses only the seam reflection positivity and\nthe even tile counts, both just checked.  These are precisely the\nhypotheses of Proposition~\\ref{prop:integrated-comparison}.\nThe disseminated-event proof of\nCorollary~\\ref{cor:terminal-alignment} uses the same remainder and\npartition-function bounds and the lower kinetic bound $b_0\\ge H/2$.\nIt therefore gives \\eqref{eq:offset-terminal-alignment} uniformly.\n\\end{proof}\n\n\\begin{proposition}[Endpoint comparison at a common terminal value]\n\\label{prop:endpoint-comparison}\nFix $L,R,R'$ and choose $H$ sufficiently large as in\nProposition~\\ref{prop:admission}.  Consider two admitted ordinary\ntrajectories of depths $N\\ge K\\ge1$, with offsets bounded by $R'$,\ncommon reference scales and caps, and the same precise terminal\ncoupling $b_0$.  Align their last $K$ layers on common compatible\nperiods.  Write their scalar-stripped endpoint densities as\n$e^{X_i}\\Xi_i$.  On each common admitted terminal torus of volume\n$v$, their common refined activity lists satisfy\n\\begin{align}\n \\|X_1-X_2\\|_\\infty\n    &\\le C_Hv\\epsilon_K,\\notag\\\\\n \\sup_x\\sum_{\\lambda:x\\in P_\\lambda}\n e^{As_\\lambda}\\|k_{1,\\lambda}-k_{2,\\lambda}\\|_\\infty\n    &\\le C_H\\epsilon_K,\n \\qquad \\epsilon_K=L^{-(2-\\upsilon)K}.\n \\label{eq:offset-endpoint-comparison}\n\\end{align}\nHere $P_\\lambda$ and $s_\\lambda$ are the complete support and load\nof a covering label, and $A$ is the support exponent of the endpoint\nclass.  If the common torus is an axis torus satisfying\nLemma~\\ref{lem:band-endpoints} and $C_Hv\\epsilon_K$ is sufficiently\nsmall, its scalar-stripped partition functions satisfy\n\\begin{equation}\n |\\log\\widehat Z_1-\\log\\widehat Z_2|\n       \\le C_Hv\\epsilon_K.\n \\label{eq:offset-endpoint-logZ}\n\\end{equation}\nThe constants are independent of $N,K$, the periods, and the common\nterminal value.\n\\end{proposition}\n\n\\begin{proof}\nWe verify the uniformity in the common terminal value in the\ntwo-boundary proof of Proposition~\\ref{prop:endpoint-matching}.\nThe normalized discrepancies $u_j$ and precise-coupling differences\n$\\lambda_j$ are those of \\eqref{eq:setup-density-discrepancy}.\nThe equality of the terminal couplings is exactly $\\lambda_0=0$.\nThe shared ordinary tail gives history error at most\n$r_0^{K-j}$, by Proposition~\\ref{prop:free-bounds}, with\n$r_0=A_0/L^2$.  Thus Theorem~\\ref{thm:rg-step} gives, for\n$1\\le j\\le K$,\n\\[\n u_{j-1}\\le q u_j+e_*|\\lambda_j|+f_j,\n \\qquad\n |\\lambda_j|\\le a_*|\\lambda_{j-1}|+a_*C_Lu_j+a_*f_j,\n\\]\nwhere\n\\[\n e_* = \\sup_{j\\ge1}C_L(\\log H_j)^C/H_j=o_H(1),\n \\quad a_*=(1-e_*)^{-1},\\quad\n f_j\\le C_L\\operatorname{poly}(H_j)r_0^{K-j}.\n\\]\nChoose $\\max(q,r_0)<\\rho<L^{-2+\\upsilon}$ with strict spare width,\nand put $F_K=C_H(1+K)^C$, large enough that\n$f_j\\le F_K\\rho^{K-j}$.  For\n\\[\n U=\\max_{0\\le j\\le K}\\frac{u_j}{\\rho^{K-j}},\n \\qquad W=\\max_{0\\le j\\le K}\n                  \\frac{|\\lambda_j|}{\\rho^{K-j}},\n\\]\ndownward iteration for $u$ and upward iteration from $\\lambda_0=0$\ngive\n\\[\n U\\le u_K+\\frac{e_*W+F_K}{\\rho-q},\n \\qquad\n W\\le\\frac{a_*(C_LU+F_K)}{1-a_*\\rho}.\n\\]\nChoose $H$ so that $a_*\\rho<1$ and the product of the two\ncoefficients coupling $U,W$ is less than $1/2$.\nThe common caps bound $u_K$, so\n$u_j+|\\lambda_j|\\le C_H(1+K)^C\\rho^{K-j}$.\nThis calculation uses no specified value of $b_0$ beyond its band.\n\nAt layer zero the conversion in the proof of\nProposition~\\ref{prop:endpoint-matching} uses the coefficient norms,\nmasked regular norms, complete-support load bounds, and free-history\nkernel bounds.  These are the common reference norms just used.\nIt gives \\eqref{eq:offset-endpoint-comparison}, absorbing the factor\n$(1+K)^C$ in the spare exponential width.  All regular supremum bounds\nremain on their prescribed masks; common-list refinements keep their\nexact covering corrections.\n\nFor the partition functions, set\n$\\varepsilon=C_Hv\\epsilon_K$.  Summing the support-hit bound gives\n\\[\n \\sum_\\lambda e^{2s_\\lambda}\n       \\|k_{2,\\lambda}-k_{1,\\lambda}\\|_\\infty\\le\\varepsilon.\n\\]\nBy Lemma~\\ref{lem:band-endpoints} and\nProposition~\\ref{prop:integrated-comparison},\n\\[\n \\left|\n \\frac{\\int e^{X_1}\\Xi_2\\dd q}{\\widehat Z_1}-1\n \\right|\\le e^{\\varepsilon}-1.\n\\]\nBoth actual densities are positive, hence $\\Xi_2>0$.\nThe exponent bound therefore compares $\\widehat Z_2$ with\n$\\int e^{X_1}\\Xi_2\\dd q$ by factors between\n$e^{-\\varepsilon}$ and $e^{\\varepsilon}$.  For small $\\varepsilon$\ntaking logarithms proves \\eqref{eq:offset-endpoint-logZ}.\nEach extracted bulk scalar is exactly per volume and independent of\nthe period, by \\eqref{eq:setup-effective-density}; winding\ncorrections remain in the density.  This fact will allow the scalars\nto cancel in the doubling test.\n\\end{proof}\n\n\\subsection{One reference box for every offset}\n\nRecall the rectangular test from \\eqref{eq:trace-doubling-definition},\n\\[\n \\Delta_\\beta(n,w)=4\\log Z_\\beta(n,w)-\\log Z_\\beta(2n,2w).\n\\]\nFor later plane comparisons we use\n\\begin{equation}\n \\begin{split}\n \\mathcal A&=\\{(1,1),(1,25)\\},\\\\\n \\mathcal U&=\\mathcal A\n \\cup\\{(u_1/2,u_2):(u_1,u_2)\\in\\mathcal A\\}\n \\cup\\{(2u_1,u_2/2):(u_1,u_2)\\in\\mathcal A\\}.\n \\end{split}\n \\label{eq:offset-aspect-inventory}\n\\end{equation}\nThe auxiliary aspects are needed when one doubles the time period\nand then the spatial period.  The aspect $(1,25)$ will also cover\nthe inclined microscopic tori.\n\n\\begin{lemma}[A reference-box test uniform in the offset]\n\\label{lem:offset-box-test}\nThe contraction slack $\\upsilon>0$ and then the parameters may be\nchosen so that there exist integers $K_0,M_0\\ge1$ with\n\\begin{equation}\n 0\\le\\Delta_\\beta(u_1M_0L^N,u_2M_0L^N)\\le2^{-10}\n \\label{eq:offset-box-test}\n\\end{equation}\nwhenever $N\\ge K_0$, $\\beta=\\vartheta_N+s$, $|s|\\le R$, and\n$(u_1,u_2)\\in\\mathcal U$.  The integer $M_0$ may be required to\nexceed any fixed lower bound and to be divisible by any fixed\ninteger.  In particular all displayed half-periods and all tile\ncounts required in Section~\\ref{sec:comparison} can be even.\n\\end{lemma}\n\n\\begin{proof}\nLet $\\eta>0$ be the gain in Proposition~\\ref{prop:preliminary}.\nChoose the slack small enough to admit a number $z$ with\n\\begin{equation}\n 0<\\upsilon/2<z<\\min\\{1,\\eta/(2\\pi)\\}.\n \\label{eq:offset-box-exponents}\n\\end{equation}\nFix all large-$H$ requirements, including\nLemma~\\ref{lem:band-endpoints}, before choosing the box sizes.\nTake $m_K$ to be a fixed common integer multiple of a rounded\ndyadic integer with\n\\[\n \\log_L m_K=(1-z)K+O(1).\n\\]\nThe fixed multiple clears the denominators of $\\mathcal U$ and\nimposes the divisibilities and lower bounds in the statement.\n\nFor each depth-$N$ ordinary run with $|s|\\le R$, and each $K\\le N$,\nLemma~\\ref{lem:retuning} supplies a depth-$K$ ordinary run with\nbare coupling $\\widetilde\\beta_K=\\vartheta_K+\\widetilde s_K$,\n$|\\widetilde s_K|\\le R'$, and exactly the same terminal coupling.\nUniformly over these retunings,\n\\[\n \\widetilde\\beta_K=H+\\gamma K+O_{H,L,R'}(\\log(2+K)),\\qquad\n X(\\widetilde\\beta_K)\n \\le C_H(1+K)^C L^{(2-\\eta/(2\\pi))K}.\n\\]\nThe shorter side of $(u_1,u_2)m_KL^K$ is comparable to\n$L^{(2-z)K}$.  Its ratio to this preliminary upper length grows\nexponentially, by $z<\\eta/(2\\pi)$.  The even periods thus satisfy\nall hypotheses of \\eqref{eq:prelim-doubling}, whose exponentially\nsmall factor dominates its polynomial prefactor.  Consequently\n\\[\n \\sup_{\\substack{N\\ge K,\\ |s|\\le R\\\\(u_1,u_2)\\in\\mathcal U}}\n \\Delta_{\\widetilde\\beta_K}(u_1m_KL^K,u_2m_KL^K)\n       \\longrightarrow0.\n\\]\n\nRun both cutoffs to their common terminal rectangle\n$(u_1,u_2)m_K$, of volume $v=u_1u_2m_K^2$, and do the same on\nthe doubled rectangle.  These periods are admitted: the imposed\nterminal lower bound and the growth of the periods through earlier\nlayers give the conditions in Section~\\ref{setup:scales}.\nProposition~\\ref{prop:endpoint-comparison} applies, because the two\nterminal couplings agree.  Since\n\\[\n m_K^2\\epsilon_K\n =L^{(\\upsilon-2z)K+O(1)}\\longrightarrow0,\n\\]\nits partition-function estimates are applicable for large $K$.\nThe per-volume scalars cancel separately in each rectangular test,\ngiving\n\\begin{align*}\n &\\left|\\Delta_{\\beta}(u_1m_KL^N,u_2m_KL^N)\n       -\\Delta_{\\widetilde\\beta_K}(u_1m_KL^K,u_2m_KL^K)\\right|\\\\\n &\\hspace{35mm}\\le C_Hm_K^2\\epsilon_K\\longrightarrow0\n\\end{align*}\nuniformly over the displayed family.  Choose one sufficiently large\n$K_0$ and set $M_0=m_{K_0}$.  Nonnegativity of the tests follows\nfrom the two-direction transfer identities\n\\eqref{eq:trace-rectangle-identities}.  This proves\n\\eqref{eq:offset-box-test}.\n\\end{proof}\n\n\\subsection{Transfer bounds and the plane limit}\n\nOnly the finite list of tests in Lemma~\\ref{lem:offset-box-test}\nis needed from renormalization for the next result.  The rest is the\npositive-transfer argument of Section~\\ref{sec:trace}, now applied\nuniformly to the offset family.\n\n\\begin{proposition}[Uniform volume comparison and slab decay]\n\\label{prop:uniform-volume}\nFix $K_0,M_0$ as in Lemma~\\ref{lem:offset-box-test}.  There exist\n$C,c>0$, independent of $N\\ge K_0$, $|s|\\le R$, and $k\\ge0$,\nsuch that the following hold for $\\beta=\\vartheta_N+s$.\n\nLet $(u_1,u_2)\\in\\mathcal A$ and\n$(n,w)=(u_1,u_2)2^kM_0L^N$.  If a bounded local observable $F$\nhas support contained in a rectangle of time extent at most $n/2$\nand spatial extent at most $w/2$, then\n\\begin{equation}\n |\\mu_{\\beta;n,w}(F)-\\mu_\\beta(F)|\n       \\le C\\|F\\|_\\infty e^{-c2^k}.\n \\label{eq:offset-uniform-volume}\n\\end{equation}\nThe same bound holds when time is closed by any integer spatial\ntranslation, provided the support has a lift satisfying these two\nextent bounds.  The constant is uniform in the closing translation.\n\nIf two bounded local plane observables $F,G$ have supporting slabs\nseparated by $d\\ge0$ transfer edges, in either microscopic axial\ndirection, then\n\\begin{equation}\n |\\Cov_{\\mu_\\beta}(F,G)|\n \\le \\|F\\|_\\infty\\|G\\|_\\infty\n           e^{-(\\log2)d/(M_0L^N)}.\n \\label{eq:offset-slab-decay}\n\\end{equation}\nFor complex observables the covariance uses $\\overline F G$.\n\\end{proposition}\n\n\\begin{proof}\nFor clarity we specify which parts of the transfer proof are\nindependent of the chosen trajectory.  The nearest-neighbor operator\n$K_w$ of \\eqref{eq:trace-kernel} is positive semidefinite and has\nstrictly positive kernel for every $\\beta\\ge0$.  Its largest\neigenvalue is simple; let $T_w=K_w/\\lambda_1(w)$, let $P_w$ be the\nprojection onto its normalized positive ground state $\\Omega_w$,\nand write\n\\[\n s_j(w)=\\operatorname{Tr}(T_w^j-P_w),\\qquad j\\ge1.\n\\]\nLemma~\\ref{lem:trace-concentration} and\nProposition~\\ref{prop:trace-doubling} use only this positivity and\nthe rectangular test.  They give\n\\[\n \\Delta_\\beta(2^kn,2^kw)\\le64^{-1}2^{-2^k}\n\\]\nfrom each test in \\eqref{eq:offset-box-test}.  Applied at the\nauxiliary aspects of \\eqref{eq:offset-aspect-inventory}, this implies\n\\[\n s_{n/2}(w)\\le Ce^{-c2^k},\n \\qquad\n s_{w/2}(2n)\\le Ce^{-c2^k},\n\\]\nwhere the second expression refers to transfer in the spatial\ndirection.\n\nAn insertion on a slab of $r\\ge1$ steps has normalized operator\n$A_F$ whose kernel is bounded in absolute value by\n$\\|F\\|_\\infty K_w^r$ before normalization.  Thus\n$|A_Ff|\\le\\|F\\|_\\infty T_w^r|f|$ and\n$\\|A_F\\|\\le\\|F\\|_\\infty$, also for complex $F$.\nFor $r=0$, $A_F$ is multiplication by $F$ and obeys the same norm bound.\nFor $r\\le n/2$, separating the ground-state projection in the\ntransfer trace gives \\eqref{eq:trace-torus-cylinder}:\n\\[\n |\\mu_{\\beta;n,w}(F)-\\langle\\Omega_w,A_F\\Omega_w\\rangle|\n       \\le2\\|F\\|_\\infty s_{n/2}(w).\n\\]\nUse this first to compare $(n,w)$ with $(2n,w)$, and then, after\ninterchanging the coordinates, to compare $(2n,w)$ with $(2n,2w)$.\nThe two preceding excited-trace estimates give an error\n$C\\|F\\|_\\infty e^{-c2^k}$.  Summing over all further simultaneous\ndoublings proves \\eqref{eq:offset-uniform-volume}; the limit is\n$\\mu_\\beta$ by Proposition~\\ref{prop:preliminary}.  Bounded\nmeasurable insertions are covered as in the proof of\nTheorem~\\ref{thm:trace-uniform}: finite-support marginals have a\nuniform density bound at this fixed $\\beta$, and approximation in\nproduct area measure extends the identification of the local limit.\n\nFor a closing shift $h$, its unitary $U_h$ commutes with $T_w$ and\nfixes $\\Omega_w$.  Place the seam outside the supporting slab.\nThe denominator and numerator are respectively\n\\[\n \\operatorname{Tr}(T_w^nU_h),\\qquad\n \\operatorname{Tr}(A_FT_w^{n-r}U_h).\n\\]\nTheir remainders after the ground-state contribution have absolute\nvalues at most $s_n(w)$ and $\\|F\\|_\\infty s_{n-r}(w)$, uniformly\nin $h$.  The reference tests ensure $s_n(w)<1/2$.  Division thus\nchanges the cylinder expectation by at most\n$4\\|F\\|_\\infty s_{n/2}(w)$, proving the shifted assertion.\n\nFinally set $n_0=M_0L^N$.  The square test gives, at all\ncircumferences $w=2^kn_0$,\n\\[\n \\|T_w^d-P_w\\|\\le e^{-(\\log2)d/n_0}\n\\]\nby \\eqref{eq:trace-width-gap}.  For ordered slab insertions the\ncylinder covariance is\n\\[\n \\langle\\Omega_w,\n       A_{\\overline F}(T_w^d-P_w)A_G\\Omega_w\\rangle.\n\\]\nThe insertion norm bounds prove \\eqref{eq:offset-slab-decay} on\nthese cylinders.  Their local limit is the periodic plane law by\nthe half-period trace estimates and the torus comparison already\nproved.  Passing to that limit proves the plane estimate.\nInterchanging coordinates gives the other axial direction.\n\\end{proof}\n\n\\subsection{Common physical tori for the inclined schemes}\n\nThe shifted closing condition has a concrete role: it puts the\nordinary and inclined embeddings on the same physical square tori.\nWe spell out the integer geometry to fix both the length factor and\nthe finite list of aspects used above.\n\n\\begin{lemma}[Inclined periods]\n\\label{lem:inclined-periods}\nLet $M_k=2^kM_0$.  Embed an ordinary depth-$N$ run with spacing\n$L^{-N}$ and axes the identity, and a type $\\pm$ run with spacing\n$a=L^{-N}/5$ and axes $O_\\pm^{-1}$.  The square physical torus of\nside $M_k$ has ordinary microscopic periods $M_kL^N e_1$ and\n$M_kL^N e_2$.  For type $+$ its microscopic periods, in\n$(\\mathrm{space},\\mathrm{time})$ coordinates, are\n\\begin{equation}\n v_1=M_kL^N(3,4),\\qquad v_2=M_kL^N(-4,3).\n \\label{eq:offset-inclined-periods}\n\\end{equation}\nThey are equivalent to spatial circumference $25M_kL^N$ and time\nheight $M_kL^N$, closed by the spatial shift $7M_kL^N$.\nThe reflected type has the same circumference and height, with\nshift $-7M_kL^N$.  These periods are compatible with the initial\ninclined blocking and the subsequent ordinary blockings, and have\nterminal square periods $M_k$.\n\nLet $F_N$ be any bounded observable whose physical support in the\nchosen embedding is contained in one fixed compact set, and put\n$\\beta=\\vartheta_N+s$, $|s|\\le R$.  Its support has lifts obeying\nthe half-extent hypotheses of Proposition~\\ref{prop:uniform-volume}\nfor all sufficiently large $k$, uniformly in $N\\ge K_0$ and $s$.\nIts torus expectation therefore differs from its plane expectation\nby $C\\|F_N\\|_\\infty e^{-c2^k}$ in all three embeddings.\n\\end{lemma}\n\n\\begin{proof}\nThe columns of $O_+$ are $(3,4)/5$ and $(-4,3)/5$.\nMultiplication of \\eqref{eq:offset-inclined-periods} by\n$(L^{-N}/5)O_+^{-1}$ gives $M_ke_1,M_ke_2$.\nThe integer basis change\n\\[\n 3v_1-4v_2=(25M_kL^N,0),\\qquad\n v_1-v_2=(7M_kL^N,M_kL^N)\n\\]\nhas determinant one.  It therefore preserves the period lattice\nand proves the asserted shifted representation.  Reflection changes\nthe sign of the spatial shift and gives the type $-$ statement.\n\nThe initial coarse translation lattice of the inclined observation\nis $5L O_\\pm\\Z^2$, as in\nLemma~\\ref{lem:free-inclined-geometry}.  The periods above belong\nto it and become $M_kL^{N-1}e_1,M_kL^{N-1}e_2$ in the output\nframe.  The remaining ordinary steps yield square periods $M_k$.\nThe fixed divisibilities of $M_0$ and the terminal lower bound\nensure admissibility at every layer, by\nSection~\\ref{setup:scales}.\n\nA compact physical support of diameter $D$ has microscopic\ncoordinate extents at most $C D/a\\le C'DL^N$ in either inclined\nframe; translations of its chosen lift do not change those extents.\nFor all sufficiently large $k$, these are at most half the time\nheight $M_kL^N$ and half the spatial circumference $25M_kL^N$.\nThe same assertion for the ordinary frame uses aspect $(1,1)$.\nThus the two aspects in $\\mathcal A$ and the shifted estimate of\nProposition~\\ref{prop:uniform-volume} prove the final claim.\n\\end{proof}\n"}, {"path": "preprints/The-canonical-massive-continuum-limit-of-the-two-dimensional-O3-model-October-4-2026/massive-continuum-o3.pdf", "url": 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"}, {"path": "preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/README.md", "bytes": 718, "sha256": "d4c00a7ac0c026874a3bd0f8d21b161f1df473bf43214bb69db05f662a702553", "content": "# [The entropy-rate dimension formula for self-similar measures on the line](main.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 24, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026,\n  author = {{OpenAI}},\n  title = {{The entropy-rate dimension formula for self-similar measures on the line}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/main.pdf}{OAI:The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/main.tex", "bytes": 2261, "sha256": "4ff38d2bd7ca7f97f3cefe6550987f4da5219628df755faa9fdbd16cf6f37201", "content": "\\documentclass[11pt]{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{lmodern}\n\\input{glyphtounicode}\n\\input{glyphtounicode-cmex}\n\\pdfgentounicode=1\n\\pdfglyphtounicode{negationslash}{0338}\n\\usepackage[margin=1in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage{microtype,booktabs,enumitem,needspace,tikz}\n\\usetikzlibrary{arrows.meta,positioning,calc}\n\\usepackage[colorlinks=true,linkcolor=blue!45!black,citecolor=blue!45!black,urlcolor=blue!45!black]{hyperref}\n\\usepackage[capitalise,noabbrev]{cleveref}\n\\ifdefined\\pdfinfoomitdate\\pdfinfoomitdate=1\\fi\n\\ifdefined\\pdftrailerid\\pdftrailerid{}\\fi\n\\ifdefined\\pdfsuppressptexinfo\\pdfsuppressptexinfo=15\\fi\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\\newtheorem{definition}[theorem]{Definition}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\newtheorem{example}[theorem]{Example}\n\\newcommand{\\R}{\\mathbb R}\n\\newcommand{\\N}{\\mathbb N}\n\\newcommand{\\Z}{\\mathbb Z}\n\\newcommand{\\E}{\\mathbb E}\n\\renewcommand{\\P}{\\mathbb P}\n\\DeclareMathOperator{\\supp}{supp}\n\\DeclareMathOperator{\\Law}{Law}\n\\DeclareMathOperator{\\Var}{Var}\n\\DeclareMathOperator{\\dist}{dist}\n\\newcommand{\\eps}{\\varepsilon}\n\\numberwithin{equation}{section}\n\\setlength{\\emergencystretch}{2em}\n\\setlist{topsep=4pt,itemsep=3pt,parsep=0pt}\n\\title{The entropy-rate dimension formula for self-similar measures on the line}\n\\author{OpenAI}\n\\date{September 24, 2026}\n\\hypersetup{pdftitle={The entropy-rate dimension formula for self-similar measures on the line},pdfauthor={OpenAI}}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe prove that the Hausdorff dimension of every finite real self-similar\nmeasure equals the minimum of one and its random-walk entropy rate divided\nby its Lyapunov exponent. Exact overlaps are allowed, and the contraction\nratios may be unequal and negative. This resolves the entropy-rate dimension\nconjecture.\n\\end{abstract}\n\\tableofcontents\n\\input{sections/introduction}\n\\input{sections/entropy}\n\\input{sections/pairs}\n\\input{sections/types}\n\\input{sections/windows}\n\\input{sections/corollaries}\n\\bibliographystyle{plain}\n\\bibliography{references}\n\\end{document}\n"}, {"path": "preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/references.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/references.bib", "bytes": 10844, "sha256": "f563a1b699d0af3d877bb2f03f4399095d1deefe03da26ce0c3d25768e905cd2", "content": "@article{FengHu2009,\n  author = {Feng, De-Jun and Hu, Huyi},\n  title = {Dimension theory of iterated function systems},\n  journal = {Communications on Pure and Applied Mathematics},\n  volume = {62},\n  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{Absolute continuity in families of parametrised non-homogeneous self-similar measures},\n  journal = {Journal of Fractal Geometry},\n  volume = {10},\n  number = {1/2},\n  year = {2023},\n  pages = {169--207},\n  doi = {10.4171/JFG/127},\n  eprint = {1812.05006},\n  archivePrefix = {arXiv},\n  url = {https://ems.press/journals/jfg/articles/11876230},\n  note = {\\href{https://doi.org/10.4171/JFG/127}{doi:10.4171/JFG/127}}\n}\n"}, {"path": "preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/sections/corollaries.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/sections/corollaries.tex", "bytes": 4314, "sha256": "22b8d3d8e517a7c729fe5cbf96364b09ac7212424b4c1445fd0ee9f2f0234cef", "content": "\\section{Overlap conventions and dimension of the attractor}\n\\label{sec:corollaries}\n\nWe first reconcile the two usual word-length conventions for an exact overlap.\nThe following observation also appears in \\cite[footnote~3]{Hochman2014}.\nThe nonzero contraction hypothesis is useful here as well as in the proof of\nthe measure theorem.\n\n\\begin{lemma}\\label{lem:overlap-lengths}\nFor a finite family of maps $x\\mapsto r_i x+t_i$ with $0<|r_i|<1$,\nan equality of maps for two distinct nonempty words of arbitrary lengths\nimplies such an equality for two distinct words of the same length.\n\\end{lemma}\n\\begin{proof}\nSuppose $\\varphi_u=\\varphi_v$ and $u\\ne v$. If $u$ were a proper prefix\nof $v$, writing $v=uw$ and cancelling the invertible map $\\varphi_u$\nwould give $\\varphi_w=\\mathrm{id}$. This is impossible because a nonempty\nword has absolute contraction less than one. The same applies with the\nwords interchanged. Now\n$\\varphi_{uv}=\\varphi_u\\circ\\varphi_v\n=\\varphi_v\\circ\\varphi_u=\\varphi_{vu}$.\nThe words $uv$ and $vu$ have the same length and are distinct: if they\nwere equal, their first $\\min\\{|u|,|v|\\}$ symbols would show that either\n$u=v$ or the shorter word is a proper prefix of the longer.\n\\end{proof}\n\n\\begin{corollary}[No exact overlaps]\\label{cor:no-overlaps}\nLet $\\Phi$ be a finite nonempty indexed family of real similarities\nwith $0<|r_i|<1$ and no exact overlaps.  For every strictly positive\nprobability vector $p$,\n\\[\n \\dim_H\\mu_{\\Phi,p}=\\min\\{1,H(p)/\\chi(\\Phi,p)\\}.\n\\]\n\\end{corollary}\n\\begin{proof}\nAt every length $n$, distinct words give distinct complete maps.\nHence $G_n$ is an injective function of its symbol word, so\n$H(G_n)=nH(p)$ and $h_{\\mathrm{RW}}=H(p)$.\nApply Theorem~\\ref{thm:main}.\n\\end{proof}\n\nLet $K_\\Phi$ be the compact attractor, equivalently the set of all coding\nlimits. Its similarity dimension $s_*$ is the unique number $s_*\\ge0$\nsatisfying\n\\[\n \\sum_{i\\in\\Lambda}|r_i|^{s_*}=1.\n\\]\nFor at least two symbols, existence and uniqueness follow because this sum\nis continuous and strictly decreasing from $|\\Lambda|>1$ to zero.\nFor one symbol its unique solution is zero.\n\n\\begin{corollary}\\label{cor:set}\nFor a finite nonempty family of similarities of $\\R$ with arbitrary real\ntranslations and $0<|r_i|<1$, absence of exact overlaps implies\n\\[\n \\dim_H K_\\Phi=\\min\\{1,s_*\\}.\n\\]\n\\end{corollary}\n\\begin{proof}\nFor one map the attractor is its fixed point and $s_*=0$.\nFor at least two maps choose the strictly positive weights\n$p_i=|r_i|^{s_*}$. Then $H(p)=s_*\\chi(\\Phi,p)$.\nCorollary~\\ref{cor:no-overlaps} gives a measure supported on $K_\\Phi$ of\ndimension $\\min\\{1,s_*\\}$, which is a lower bound for its set dimension.\nFor the upper bound, if $a>s_*$, the level-$n$ cylinder sets cover\n$K_\\Phi$, their diameters tend uniformly to zero, and\n\\[\n \\sum_{u\\in\\Lambda^n}(\\operatorname{diam}\\varphi_u(K_\\Phi))^a\n = (\\operatorname{diam}K_\\Phi)^a\n   \\left(\\sum_i|r_i|^a\\right)^n\\longrightarrow0.\n\\]\nThe zero-diameter case is immediate. Thus $\\dim_H K_\\Phi\\le s_*$,\nand the ambient upper bound is one.\n\\end{proof}\n\n\\begin{corollary}\\label{cor:homogeneous}\nLet $m\\ge2$, $0<\\lambda<1$, and $t_1,\\ldots,t_m\\in\\R$.\nIf the maps $x\\mapsto\\lambda x+t_i$ have no exact overlaps, then for\nevery strictly positive probability vector $p$ their self-similar measure has\n\\[\n \\dim_H\\mu=\\min\\left\\{1,\\frac{H(p)}{\\log(1/\\lambda)}\\right\\}.\n\\]\n\\end{corollary}\n\\begin{proof}\nApply Corollary~\\ref{cor:no-overlaps} with $r_i=\\lambda$ for every $i$.\nSince the weights sum to one, $\\chi=\\log(1/\\lambda)$.\n\\end{proof}\n\n\\begin{corollary}[Three maps]\\label{cor:three-maps}\nLet $0<\\lambda<1$ and $t\\in\\R$.  If the indexed maps\n\\[\n x\\longmapsto\\lambda x,\\qquad\n x\\longmapsto\\lambda x+1,\\qquad\n x\\longmapsto\\lambda x+t\n\\]\nhave no exact overlaps, then for every $p_0,p_1,p_2>0$ with\n$p_0+p_1+p_2=1$, their self-similar measure satisfies\n\\[\n \\dim_H\\mu\n =\\min\\left\\{1,\\frac{-\\sum_{i=0}^2 p_i\\log p_i}\n                         {\\log(1/\\lambda)}\\right\\}.\n\\]\n\\end{corollary}\n\\begin{proof}\nApply Corollary~\\ref{cor:homogeneous} with translations $0,1,t$.\n\\end{proof}\n\nNo claim of absolute continuity, effective separation, or a quantitative rate\nof convergence follows from these dimension statements. In particular, the\nfinite upper endpoints of the scale ranges used in the proof have no asserted\neffective bound.\n"}, {"path": "preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/sections/entropy.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/sections/entropy.tex", "bytes": 12237, "sha256": "2e0107a5514bf09440d3809abc032c0c780add032cc760ef65d29971e5a85389", "content": "\\section{Entropy between two scales}\\label{sec:entropy}\n\nWe first establish the entropy estimates that turn a separated pair of\ntranslations into a definite gain.  The decisive property of the\nself-similar measure is uniform nonsaturation: if its dimension is less\nthan one, every one-bit scale interval leaves a positive entropy deficit.\nThe intermediate entropy identities apply to arbitrary compactly\nsupported probability measures on the line.\n\nFor a finite-valued variable $Z$, write\n$H(Z)=-\\sum_z\\P(Z=z)\\log\\P(Z=z)$, with $0\\log0=0$.\nConditional entropy is the average entropy of the conditional law, and\n$I(Z;Y)=H(Z)-H(Z\\mid Y)$; conditional mutual information is defined\nsimilarly. We use the entropy chain rule and nonnegativity of conditional\nmutual information. For a bounded real random variable $X$ and $s>0$, define\n\\[\n H_s^0(X)=H(\\lfloor X/s\\rfloor),\\qquad\n \\mathcal H_s(X)=\\int_0^1H(\\lfloor X/s+u\\rfloor)\\,du.\n\\]\nWe use the same notation for the law of $X$.  For $s,t>0$, put\n\\[\n G_{s,t}=\\mathcal H_s-\\mathcal H_t,\n \\qquad \\Delta_s=1-G_{s,2s}.\n\\]\nAs throughout the paper, all logarithms and entropies are in base~$2$.\nWrite $h(f)=-\\int f\\log f$ for differential entropy and $V_s$ for an independent uniform\nrandom variable on $[0,s]$.\n\nThe smoothing and nested-grid identities are the averaged-entropy calculus\nof \\cite[Section~2.2, Lemmas~5, 6, and~10]{VarjuAbsolute2019}; we include their proofs.\n\n\\begin{lemma}[Entropy calculus]\\label{lem:entropy-calculus}\nFor every compactly supported real law,\n\\begin{equation}\\label{eq:entropy-smoothing}\n \\mathcal H_s(X)=h(X+V_s)-\\log s,\n \\qquad\n \\mathcal H_s(aX+b)=\\mathcal H_{s/|a|}(X)\n \\quad(a\\ne0).\n\\end{equation}\nIf $t/s=N$ is a positive integer, then $G_{s,t}$ is concave in the law,\n\\begin{equation}\\label{eq:entropy-integer-window}\n 0\\le G_{s,t}\\le\\log N,\n \\qquad G_{s,t}(X+Y)\\ge G_{s,t}(X)\n\\end{equation}\nfor independent bounded $X,Y$.  For arbitrary $z\\ge s>0$,\n\\begin{equation}\\label{eq:entropy-scale-comparison}\n \\mathcal H_s-\\log(z/s+2)\\le\\mathcal H_z\\le\\mathcal H_s+1.\n\\end{equation}\nFurthermore,\n\\begin{equation}\\label{eq:entropy-grid-comparison}\n |\\mathcal H_s(X)-H_s^0(X)|\\le1,\n \\qquad |H_s^0(X+b)-H_s^0(X)|\\le1.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nThe density of $X+V_s$ is\n$f_s(z)=s^{-1}\\P(X\\in[z-s,z))$ almost everywhere.  It is bounded by\n$1/s$ and has bounded support, so its differential entropy is finite,\neven when the law of $X$ has atoms.  Set\n$p_j(u)=\\P((j-u)s\\le X<(j+1-u)s)$.  At\n$z=(j+1-u)s$ we have $p_j(u)=sf_s(z)$ almost everywhere.\nChanging variables in the sum of the integrals\n$-p_j(u)\\log p_j(u)$ therefore gives\n\\[\n \\mathcal H_s(X)=-\\int_{\\R}f_s(z)\\log(sf_s(z))\\,dz.\n\\]\nThis proves the first identity.  Translation rotates the grid shift\nmodulo~$1$, and positive scaling changes the cell length.\nReflection also preserves averaged entropy: $-X+V_s$ is a translate of\n$-(X+V_s')$, where $V_s'=s-V_s$ is independent uniform.\nThese observations prove the signed affine identity.\n\nSuppose $t=Ns$.  Let $A$ be independent uniform on $[0,t)$ and define\n\\[\n F=\\left\\lfloor\\frac{X+A}{s}\\right\\rfloor,\n \\qquad C=\\left\\lfloor\\frac{X+A}{t}\\right\\rfloor.\n\\]\nThese are jointly translated nested grids, with $C=\\lfloor F/N\\rfloor$.\nAveraging their entropies gives\n\\begin{equation}\\label{eq:nested-conditional-entropy}\n G_{s,t}(X)=H(F\\mid C,A).\n\\end{equation}\nEach coarse cell contains $N$ fine cells, proving the two bounds.\nFor a mixture of laws, conditioning additionally on its mixing variable\ncan only reduce the right-hand side; this proves concavity, including\nfor general probability mixtures.  A convolution is a mixture of\ntranslates, so translation invariance proves the convolution inequality.\n\nFinally, for any two translated grids of lengths $s\\le z$, a $z$-cell\nintersects at most $\\lfloor z/s\\rfloor+2$ fine cells, and an $s$-cell\nintersects at most two coarse cells.  The chain rule bounds the two\nconditional entropies by $\\log(z/s+2)$ and~$1$.  Averaging gives\n\\eqref{eq:entropy-scale-comparison}.  The same argument for two grids of\nequal length proves \\eqref{eq:entropy-grid-comparison}.\n\\end{proof}\n\nThe integer-ratio hypothesis is essential to our use of concavity.\nIn the argument below it is applied only with ratios $2$, a chosen\ninteger $K$, or the square of a chosen integer $M$.\n\n\\begin{lemma}[Entropy and dimension]\\label{lem:entropy-dimension}\nLet $\\nu$ be a compactly supported exact-dimensional probability measure\non $\\R$ of dimension $d$.  Then\n\\begin{equation}\\label{eq:entropy-dimension-upper}\n \\limsup_{s\\downarrow0}\n \\frac{\\max\\{\\mathcal H_s(\\nu),H_s^0(\\nu)\\}}\n {\\log(1/s)}\\le d.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nFix $\\eta>0$.  Exact dimensionality gives a measurable set $E$ of mass\nat least $1-\\eta$ and a common threshold $r_0>0$ such that\n\\[\n \\nu(B(x,r))\\ge r^{d+\\eta}\n \\qquad(x\\in E, 0<r<r_0).\n\\]\nIndeed, the sets where this eventual inequality holds below a fixed\nreciprocal-integer threshold exhaust a set of full measure; rational\nradii and monotonicity suffice to make this construction measurable.\n\nFor any translated grid of length $s<2r_0$, choose one point of $E$ in\neach cell meeting $E$.  Within each of the three residue classes of\ncell indices modulo~$3$, the chosen points have disjoint balls of radius\n$s/2$.  Each ball has mass at least $(s/2)^{d+\\eta}$.  Thus at most\n$3(2/s)^{d+\\eta}$ cells meet $E$.  Compact support bounds the total\nnumber of occupied cells by $C/s$, uniformly over grid translations\nand small $s$.  Conditioning the cell label on membership in $E$ yields\n\\[\n H(\\text{cell label})\n \\le1+\\nu(E)\\log\\bigl(3(2/s)^{d+\\eta}\\bigr)\n       +\\nu(E^c)\\log(C/s)\n \\le(d+2\\eta)\\log(1/s)+O_{\\eta,\\nu}(1).\n\\]\nThis controls both the fixed grid and its average.  Divide by\n$\\log(1/s)$ and let $\\eta\\downarrow0$.\n\\end{proof}\n\nFor Bernoulli convolutions, the following nonsaturation property appears in\nBreuillard--Varj\\'u \\cite[Lemma~13]{BreuillardVarju2019}.\nThe stopping-time argument below extends it to unequal contractions of\neither sign.\n\n\\Needspace{5\\baselineskip}\n\\begin{lemma}[Uniform nonsaturation]\\label{lem:nonsaturation}\nLet $\\mu$ be a self-similar probability measure for a finite affine\nsystem $x\\mapsto r_i x+t_i$ with $0<|r_i|<1$.  If $\\mu$ is exact\ndimensional with dimension $d<1$, then\n\\begin{equation}\\label{eq:uniform-nonsaturation}\n \\delta:=\\inf_{v>0}\\Delta_v(\\mu)>0.\n\\end{equation}\nNo separation or orientation assumption is needed.\n\\end{lemma}\n\n\\begin{proof}\nBy Lemma~\\ref{lem:entropy-calculus}, $0\\le\\Delta_v\\le1$.\nWe show that a vanishing one-bit deficit would force almost maximal\nentropy in a fixed wider scale interval.  Stopped self-similarity then\ntransfers that estimate to every smaller scale, contradicting the\nentropy bound supplied by exact dimensionality.\nSuppose there are $v_n>0$ with $\\Delta_{v_n}(\\mu)\\to0$.\nFor $X\\sim\\mu$, let $\\lambda_v$ be the law of $X+V_v$.\nAn independent fair bit $J$ gives\n\\begin{equation}\\label{eq:one-bit-information}\n \\Delta_v(\\mu)\n =h(X+V_{2v})-h(X+V_v)\n =I(J;X+V_v+vJ).\n\\end{equation}\nThus the equal mixture of $\\lambda_v$ and its translate by $v$ carries\nlittle information about its input label.\n\nHere is a quantitative form of that observation.  For two laws $P,Q$,\nwrite $\\|P-Q\\|_{\\mathrm{TV}}=\\sup_B|P(B)-Q(B)|$.\nObserving whether the mixture sample lies in $B$ bounds the information\nin \\eqref{eq:one-bit-information} below by\n\\[\n h_2\\bigl((P(B)+Q(B))/2\\bigr)\n -\\tfrac12h_2(P(B))-\\tfrac12h_2(Q(B)),\n\\]\nwhere $h_2$ is binary entropy.  Since\n$h_2''(u)=-1/(\\ln(2)u(1-u))\\le-4/\\ln2$, this expression is at least\n$(P(B)-Q(B))^2/(2\\ln2)$.  Consequently, for\n$e_v=\\|\\lambda_v-T_v\\lambda_v\\|_{\\mathrm{TV}}$, where $T_v$ denotes\ntranslation by $v$,\n\\begin{equation}\\label{eq:small-information-tv}\n e_v\\le\\sqrt{2\\ln(2)\\Delta_v(\\mu)}.\n\\end{equation}\n\nFix an integer $K\\ge2$.  The laws\n$P_i=T_{iv}\\lambda_v$, $0\\le i<K$, have densities $f_i$ and satisfy\n$\\|P_i-P_0\\|_{\\mathrm{TV}}\\le i e_v$.\nTheir common density $f_*:=\\min_{0\\le i<K}f_i$ has mass $m_v$ with\n\\begin{equation}\\label{eq:common-density-mass}\n 1-m_v\\le\\sum_{i=1}^{K-1}\\int(f_0-f_i)_+\n \\le\\frac{K(K-1)}2 e_v.\n\\end{equation}\nFor each input label, decompose its law into this same common\nsubmeasure and a residual submeasure of mass $1-m_v$.\nThe common/residual flag can be sampled independently of the input\nlabel.  On the common branch the output gives no information; on the\nresidual branch it gives at most $\\log K$.  The mutual information of\nthe uniform mixture is therefore at most $(1-m_v)\\log K$.\nThat mixture is $\\mu*\\Law(V_{Kv})$, and all its conditional\ndifferential entropies equal $h(X+V_v)$.  For this fixed $K$,\n\\begin{equation}\\label{eq:large-window-saturation}\n G_{v_n,Kv_n}(\\mu)\\longrightarrow\\log K.\n\\end{equation}\n\nWe next transfer this near-saturation to every smaller scale.\nPut $a_*:=\\min_i|r_i|>0$.  For $0<s<v$, stop the independent coding\nat the first prefix whose absolute contraction is at most $s/v$.\nThe stopping time is bounded because $\\max_i|r_i|<1$.\nThe stopped contractions have absolute values in\n$(a_*s/v,s/v]$, and the remaining coding is independent with law $\\mu$.\nThus $\\mu$ is a finite mixture of affine images of itself with these\ncontractions.  Signed scaling and integer-ratio concavity give\n\\begin{align}\n G_{s,Ks}(\\mu)\n &\\ge\\inf_{z\\in[v,v/a_*]}G_{z,Kz}(\\mu)\\notag\\\\\n &\\ge G_{v,Kv}(\\mu)-C_* ,\n \\qquad C_*:=\\log(1/a_*+2)+1.\n \\label{eq:stopped-scale-transfer}\n\\end{align}\nFor the second inequality, apply\n\\eqref{eq:entropy-scale-comparison} at $(v,z)$ and $(Kv,Kz)$.\nThe loss $C_*$ is independent of $K$ and the scales.\n\nChoose an integer $K$ with $(1-d)\\log K>C_*+2$.\nThen choose one $v=v_n$ such that\n$G_{v,Kv}(\\mu)>\\log K-1$, using\n\\eqref{eq:large-window-saturation}.  For every $0<s<v$,\n\\[\n G_{s,Ks}(\\mu)>B:=\\log K-C_*-1>d\\log K.\n\\]\nAt $s_m=vK^{-m}$, telescoping gives\n\\[\n \\mathcal H_{s_m}(\\mu)-\\mathcal H_v(\\mu)\n =\\sum_{j=1}^mG_{vK^{-j},vK^{-(j-1)}}(\\mu)>mB.\n\\]\nIts entropy ratio has lower limit at least $B/\\log K>d$, contradicting\nLemma~\\ref{lem:entropy-dimension}.  This proves the lemma.\n\\end{proof}\n\nThe next estimate explains how the deficit produces gain.  Unlike the\nconcavity statements, it does not require an integer ratio of scales.\nThe fair-pair comparison follows the method of\n\\cite[Proposition~20 and Lemma~22]{VarjuAbsolute2019}.\n\n\\begin{lemma}[Two-point gain]\\label{lem:two-point}\nLet $X$ be bounded and $J$ an independent fair bit.  For $0<s<v<t$,\n\\begin{equation}\\label{eq:two-point-gain}\n G_{s,t}(X+vJ)-G_{s,t}(X)\n \\ge\\Delta_v(X)-s/v-v/t.\n\\end{equation}\nThe same estimate holds for convolution with any fair two-point law\nwhose two points have distance $v$.\n\\end{lemma}\n\n\\begin{proof}\nPut $Y=X+vJ$.  For each $a>0$, let $U_a$ be independent uniform on\n$[0,1)$ and write $Z_a=\\lfloor Y/a+U_a\\rfloor$.\nTranslation invariance of the averaged entropy gives\n\\[\n D_a:=\\mathcal H_a(Y)-\\mathcal H_a(X)=I(J;Z_a\\mid U_a).\n\\]\nUniform smoothing also gives\n$D_v=h(X+V_{2v})-h(X+V_v)=\\Delta_v(X)$.\n\nTo compare $s$ and $v$, use independent shifts $U_s,U_v$ and the same\nsample $Y$.  The information chain rule gives\n\\[\n I(J;Z_v\\mid U_s,U_v)\n \\le I(J;Z_s\\mid U_s,U_v)\n       +H(Z_v\\mid Z_s,U_s,U_v).\n\\]\nThe first two terms are $D_v$ and $D_s$ because the unused shift is\nindependent.  For each fixed fine grid and each of its cells of positive\nprobability, the conditional sample law is independent of $U_v$.\nThat cell, of length $s<v$, contains a boundary of the $v$-grid on a\nproportion $s/v$ of its shifts.  Otherwise the coarse label is fixed;\nwhen a boundary occurs there are at most two coarse labels.  Averaging\ntherefore gives\n$H(Z_v\\mid Z_s,U_s,U_v)\\le s/v$, whence $D_s\\ge D_v-s/v$.\n\nFor the coarse-scale gain, adjoining $X$ to the observation yields\n\\[\n D_t\\le I(J;Z_t,X\\mid U_t)=I(J;Z_t\\mid X,U_t).\n\\]\nFor fixed $X=x$, the two labels at $x$ and $x+v$ differ on a proportion\n$v/t$ of shifts.  Their conditional information is then one bit and is\notherwise zero.  Thus $D_t\\le v/t$.  Subtracting proves\n\\eqref{eq:two-point-gain}.  The proof allows atoms, since boundary\ncoincidences occur on null sets of shifts.  Finally, any fair pair is a\ntranslate of $\\{0,v\\}$ after ordering its two points; translation\ninvariance proves the last assertion, including for reflected pairs.\n\\end{proof}\n"}, {"path": "preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/sections/introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/sections/introduction.tex", "bytes": 13264, "sha256": "c2c501b0e3199a6b3b53a35699a3ecb6a4d3b7035791d4c2639cd36d0d0533e0", "content": "\\section{Introduction}\\label{sec:introduction}\n\nLet $\\Lambda$ be a finite nonempty alphabet, and let\n\\[\n \\Phi=(\\varphi_i)_{i\\in\\Lambda},\\qquad\n \\varphi_i(x)=r_i x+t_i,\\qquad t_i\\in\\R,\\quad 0<|r_i|<1.\n\\]\nThe family is indexed: different symbols may specify the same map.\nGiven a probability vector $p=(p_i)_{i\\in\\Lambda}$ with $p_i>0$, its\nself-similar measure is the unique Borel probability measure satisfying\n\\[\n \\mu=\\sum_{i\\in\\Lambda}p_i(\\varphi_i)_*\\mu.\n\\]\nEquivalently, $\\mu$ is the law of\n$\\lim_{n\\to\\infty}\\varphi_{I_1}\\circ\\cdots\\circ\\varphi_{I_n}(0)$,\nwhere the symbols $I_j$ are independent with law $p$.\nThe limit exists uniformly in the address: if\n$r_{\\max}=\\max_i|r_i|$ and $T_0=\\max_i|t_i|$, all coding limits\nhave absolute value at most $T_0/(1-r_{\\max})$.\n\nFor a word $w=i_1\\cdots i_n$, write\n$\\varphi_w=\\varphi_{i_1}\\circ\\cdots\\circ\\varphi_{i_n}$ and\n$p_w=p_{i_1}\\cdots p_{i_n}$.  An \\emph{exact overlap} is an equality\n$\\varphi_u=\\varphi_v$ for distinct words of the same positive length.\nEquality here means equality of the complete affine maps, including\nboth the signed slope and the translation.  We allow all such overlaps.\n\nThe relevant entropy counts maps rather than addresses.  Set\n\\[\n G_n=\\varphi_{I_1}\\circ\\cdots\\circ\\varphi_{I_n},\\qquad\n \\P(G_n=g)=\\sum_{w\\in\\Lambda^n:\\,\\varphi_w=g}p_w.\n\\]\nAll logarithms below have base two.  For a finite-valued random variable\n$Z$, its Shannon entropy is $H(Z)=-\\sum_z\\P(Z=z)\\log\\P(Z=z)$,\nwith $0\\log0=0$.  Define the random-walk entropy rate and the Lyapunov\nexponent by\n\\begin{equation}\\label{eq:rate-and-lyapunov}\n h=h_{\\mathrm{RW}}(\\Phi,p)\n   :=\\lim_{n\\to\\infty}\\frac{H(G_n)}n\n    =\\inf_{n\\ge1}\\frac{H(G_n)}n,\n \\qquad\n \\chi=-\\sum_i p_i\\log|r_i|>0.\n\\end{equation}\nIndeed, a length-$(n+m)$ map is a function of two independent maps\nwith laws $G_n,G_m$, so $H(G_{n+m})\\le H(G_n)+H(G_m)$; subadditivity\ngives the displayed limit and infimum.  Also\n$0\\le h\\le H(p):=-\\sum_i p_i\\log p_i$.  The ratio $h/\\chi$ is\nindependent of the common logarithm base.\n\nWe use the lower Hausdorff dimension of a measure:\n\\[\n \\dim_H\\nu=\\inf\\{\\dim_H E:E\\subset\\R\\text{ Borel},\\ \\nu(E)>0\\}.\n\\]\nFeng--Hu's exact-dimensionality theorem\n\\cite[Theorem~2.8]{FengHu2009} applies to finite self-similar systems\nwithout separation.  Indeed, the maps preserve a sufficiently large\ncompact interval, extend to contracting smooth diffeomorphisms, and have\nderivative norm and least singular value both equal to $|r_i|$.\nThe system is therefore conformal, and its Bernoulli coding law is\nergodic.  These observations also cover negative ratios and repeated\nindexed maps.  Thus, for the measure\n$\\mu$ above, there is a constant $d$ such that\n\\begin{equation}\\label{eq:exact-dimensionality}\n \\lim_{s\\downarrow0}\\frac{\\log\\mu(B(x,s))}{\\log s}=d\n \\quad\\text{for $\\mu$-almost every $x$}.\n\\end{equation}\nFor an exact-dimensional measure this constant equals both $\\dim_H\\mu$\nand $\\inf\\{\\dim_H E:\\mu(E)=1\\}$.\nTo see the equivalence, restrict to countably many sets on which the\nlocal bounds $s^{d+\\varepsilon}\\le\\mu(B(x,s))\\le s^{d-\\varepsilon}$\nhold uniformly for all sufficiently small $s$.  The lower mass bound\ngives a full-measure union of sets of dimension at most $d+\\varepsilon$\nby a covering argument; the upper mass bound gives dimension at least\n$d-\\varepsilon$ for every positive-mass set.  Let\n$\\varepsilon\\downarrow0$.  Consequently either Hausdorff\nmeasure-dimension convention gives the same statement below.\n\n\\begin{theorem}\\label{thm:main}\nFor every finite nonempty indexed family\n$\\varphi_i(x)=r_i x+t_i$ on $\\R$ with $0<|r_i|<1$, and every\nstrictly positive probability vector $p$, its self-similar measure satisfies\n\\[\n \\dim_H\\mu_{\\Phi,p}\n =\\min\\left\\{1,\\frac{h_{\\mathrm{RW}}(\\Phi,p)}{\\chi(\\Phi,p)}\\right\\}.\n\\]\nNo separation assumption is required; exact overlaps and repeated\ngenerators are allowed.\n\\end{theorem}\n\nThis proves the entropy-rate dimension conjecture, formulated as\nConjecture~3 in Varj\\'u's survey \\cite{VarjuSurvey2026}.\nWhen there are no exact overlaps, $H(G_n)=nH(p)$, and the theorem\ngives the usual entropy-to-Lyapunov formula.  Section~\\ref{sec:corollaries}\nrecords this case, the attractor formula, and the homogeneous all-weight\nconsequence, including the three-map family\n$\\{\\lambda x,\\lambda x+1,\\lambda x+t\\}$.\nThe theorem includes critical and supercritical entropy\nrates.  It asserts dimension, without an assertion of absolute continuity.\n\n\\subsection{Background and proof strategy}\n\nThe dimension of a self-similar set is classical when its pieces are\nsufficiently separated.  Moran's construction \\cite{Moran1946} and\nHutchinson's iterated-function-system framework \\cite{Hutchinson1981}\ngive the similarity-dimension formula under the open set condition.\nHutchinson also established the invariant-measure construction used\nabove \\cite[Theorem~4.4(1)]{Hutchinson1981}.\nRemoving separation leads to the exact-overlap problem: can dimension\nfall below its natural upper bound when distinct words never define\nthe same map?  The set version is associated with Simon\n\\cite{Simon1996}; see also the formulation and historical discussion\nin \\cite[Section~1]{VarjuSurvey2026}.\nThe entropy-rate formulation addresses the additional loss of\ninformation when exact overlaps do occur.\n\nBernoulli convolutions provide an early and influential model for this\nquestion.  These are the laws of $\\sum_{j\\ge0}\\varepsilon_j\\lambda^j$,\nwhere $0<\\lambda<1$ and the $\\varepsilon_j$ are independent\nequiprobable signs.\nErd\\H{o}s proved singularity for reciprocal Pisot parameters\nin $(1/2,1)$ \\cite{Erdos1939}.  A Pisot number is a real algebraic\ninteger greater than one whose other conjugates have modulus less\nthan one.  Solomyak proved absolute continuity with an\n$L^2$ density for almost every parameter in $(1/2,1)$\n\\cite{Solomyak1995}; Peres--Solomyak later gave a simpler proof\n\\cite{PeresSolomyak1996}.\nGarsia introduced the discrete entropy rate in this setting and\nrelated it to singularity \\cite[Theorem~1.2]{Garsia1963}.\nThese results illustrate the sensitivity to overlaps and arithmetic;\nabsolute continuity and Hausdorff dimension are distinct questions.\n\nHochman showed that $\\dim_H\\mu<\\min\\{1,H(p)/\\chi\\}$\nforces superexponential concentration of cylinder maps\n\\cite[Theorem~1.1]{Hochman2014}, using inverse theorems for entropy\ngrowth under convolution.  For unequal ratios his entropy formulation\nalready keeps track of the full affine map, including its slope\n\\cite[Theorem~1.4]{Hochman2014}.\nThe entropy-rate formula under weak exponential separation follows\nfrom his work; the version allowing exact collisions is proved in\nB\\'ar\\'any--Verma \\cite[Theorem~3.5]{BaranyVerma2026}.\nFor Bernoulli convolutions, Breuillard--Varj\\'u developed approximation\nby algebraic parameters with controlled entropy\n\\cite{BreuillardVarju2019}.  Building on these and earlier entropy\nmethods, Varj\\'u proved full dimension for every transcendental parameter\nin $(1/2,1)$ \\cite[Theorem~3]{VarjuTranscendental2019}.\nRapaport proved the no-exact-overlap formula for algebraic\ncontraction ratios and arbitrary real translations, allowing signed\nunequal ratios \\cite[Theorem~2]{Rapaport2022}.\n\nRapaport--Varj\\'u extended entropy and approximation methods to\nhomogeneous three-map systems \\cite{RapaportVarju2024}.\nFor equal weights, their results include an exceptional parameter set\nof Hausdorff dimension zero and the no-overlap formula when $\\lambda>2^{-2/3}$\n\\cite[Corollary~1.4 and Theorem~1.8]{RapaportVarju2024}.\nRapaport--Varj\\'u proved the entropy-rate formula, including exact overlaps,\nfor rational translations and a positive common ratio\n\\cite[Theorem~A.1]{RapaportVarju2024}.  Feng--Feng proved the\nno-exact-overlap formula for algebraic translations and a signed common ratio\n\\cite[Theorem~1.2]{FengFeng2025}.\nThe distinction in Theorem~\\ref{thm:main} is that the entropy rate of\nthe full affine-map walk is sharp even when exact collisions occur,\nwith no arithmetic or separation assumption.\n\nAbsence of exact overlaps does not itself supply a quantitative\nseparation estimate.  Baker \\cite[Theorem~1.3]{Baker2021} and,\nindependently, B\\'ar\\'any--K\\\"aenm\\\"aki\n\\cite[Theorem~2.1]{BaranyKaenmaki2021} constructed systems without\nexact overlaps whose distinct cylinders approach one another at\narbitrarily prescribed superexponential rates.\nThus the entropy carried by extremely close maps must be handled\nwithout bounding the smallest positive separation.\n\nOur proof combines the entropy retained by this map walk with a\nfinite-law estimate that detects information across arbitrarily fine scales.\nIt uses translation averaging, developed by Wang\n\\cite[Section~4.1]{Wang2011}, and the entropy calculus and fair-pair\narguments of Varj\\'u\n\\cite[Section~2.2, Propositions~20--21, and Lemma~22]{VarjuAbsolute2019}.\nUniform nonsaturation has an antecedent for Bernoulli convolutions in\nBreuillard--Varj\\'u \\cite[Lemma~13]{BreuillardVarju2019}.\nWe prove the required entropy estimates here; the only external\ndimension theorem used in the proof is exact dimensionality.\n\nThe argument has three steps.\nFirst, for a finite real law $\\nu$, compare its exact entropy with\nthe entropy of its grid cell at a fixed scale.  The difference is\ninformation hidden inside those cells.  A bound for the sum of several\nindependent copies forces this information to be witnessed by fair\n(equal-weight) two-point laws at smaller scales.  The estimate is independent of the smallest\ndistance between support points.\n\nSecond, group the coding symbols into blocks and condition on their\nsymbol counts.  This is the block-type disintegration of\nGalicer--Saglietti--Shmerkin--Yavicoli\n\\cite[Section~6.4 and Lemma~6.6]{GalicerSagliettiShmerkinYavicoli2016},\nalso developed by Saglietti--Shmerkin--Solomyak\n\\cite[Lemma~6.2]{SagliettiShmerkinSolomyak2018};\nK\\\"aenm\\\"aki--Orponen \\cite[Section~2.2]{KaenmakiOrponen2023}\nexplicitly allow repeated indexed maps in this construction.\nHere the counts fix each block's signed contraction.\nFor a sequence of such blocks, its translation then identifies its\ncomplete composed map.  Conditioning costs only the entropy of the\ncounts, so the map-entropy rate gives a lower bound for the remaining\ntranslation entropy even when addresses collide.  The same fixed\ncontractions give a small sumset bound.  A hypothetical dimension\ndeficit therefore yields a linear amount of fair-pair mass across a\nfinite band of very fine scales.\n\nThird, each pair produces a positive entropy gain against the\nunobserved tail.  The bands may end at arbitrarily large depths.\nWe choose successive block lengths only after the preceding endpoints\nare known.  At each observation scale their retained blocks are\ndisjoint, so their gains telescope to a bounded total.  Averaging over\nobservation scales gives a fixed positive contribution from every\nband, a contradiction as the number of bands grows.\n\nSection~\\ref{sec:entropy} proves the entropy calculus, nonsaturation,\nand two-point gain.  Section~\\ref{sec:pairs} proves the finite-law\nestimate.  Section~\\ref{sec:types} applies it to conditional map laws,\nand Section~\\ref{sec:windows} arranges the disjoint gains and completes\nthe proof.  Section~\\ref{sec:corollaries} derives the no-overlap measure\nand attractor formulas, followed by the homogeneous and three-map\nspecializations.  No quantitative bound on the fine-scale endpoints is needed.\n\n\\subsection{The entropy-rate upper bound}\\label{sec:upper}\n\nWe prove the upper bound before turning to the contradiction argument.\nFix a positive integer $n$.  Use the finite set of distinct maps in\nthe support of $G_n$ as a new alphabet, giving each map $g$ its exact\nprobability $q_g=\\P(G_n=g)>0$.  Grouping independent coding symbols\ninto $n$-blocks shows that this new system has the same measure $\\mu$.\nIts one-symbol entropy is $H(G_n)$ and its mean contraction depth is\n$n\\chi$, since coincident maps have the same absolute slope.\n\nFor $0<\\varepsilon<n\\chi$, the strong law shows that almost every\naddress in this block system eventually has, at every length $j$,\na prefix of probability at least $2^{-j(H(G_n)+\\varepsilon)}$ and\nabsolute contraction at most $2^{-j(n\\chi-\\varepsilon)}$.\nThere are at most $2^{j(H(G_n)+\\varepsilon)}$ such prefixes.\nFor each fixed eventual starting length $J$, let $E_J$ be the closed\nset of addresses satisfying both conditions for every $j\\ge J$.\nIts compact coding image is covered by intervals of diameter at most\n$C2^{-j(n\\chi-\\varepsilon)}$, where $C$ bounds the attractor diameter.\nThe total $a$th power of these diameters tends to zero when\n$a>(H(G_n)+\\varepsilon)/(n\\chi-\\varepsilon)$.\nThe countable union over eventual starting lengths is a full-measure\nBorel set with this dimension bound.  The case of zero attractor\ndiameter is immediate.  Letting $\\varepsilon\\downarrow0$, then taking\nthe infimum over $n$, gives\n\\begin{equation}\\label{eq:upper-bound}\n d\\le\\min\\{1,h/\\chi\\}.\n\\end{equation}\nIn particular, the conclusion of Theorem~\\ref{thm:main} already holds\nwhen $h=0$, including a single-map system.  To prove equality in all\nother cases, it suffices to rule out\n\\begin{equation}\\label{eq:dimension-loss}\n d<1,\\qquad d\\chi<h.\n\\end{equation}\nThese inequalities are the contradiction hypothesis in\nSections~\\ref{sec:types} and~\\ref{sec:windows}.\n"}, {"path": "preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/sections/pairs.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/sections/pairs.tex", "bytes": 9765, "sha256": "e41d0918796a17c115e742d57abf22a39f462153f1fd250147fe0c9341b945ac", "content": "\\section{Pair mass below a fixed scale}\\label{sec:pairs}\n\nWe next consider a finite probability law without any separation assumption.\nIts exact entropy may greatly exceed its entropy at a prescribed scale.\nThe following lemma detects that excess through the mass that can be\nplaced in fair pairs at smaller scales.  An upper bound for a sumset\nwill make the lemma useful for the block laws in the next section.\nVarj\\'u's scale-local decomposition into fair two-point submeasures\n\\cite[Proposition~21]{VarjuAbsolute2019} is a predecessor of this approach.\nHere the sumset term controls the total capacity over all finer bands;\nwe prove the needed estimate by randomized nested partitions.\n\n\\Needspace{5\\baselineskip}\n\\begin{definition}[Pair capacity]\\label{def:pair-capacity}\nLet $\\nu$ be a probability law with finite support $F\\subset\\R$.\nFor $\\ell\\in\\mathbb Z$, let $\\mathcal E_\\ell(F)$ be the set of\nunordered pairs $\\{x,y\\}\\subset F$ satisfying\n$2^{-\\ell}\\le |x-y|<2^{1-\\ell}$.\nDefine $c_\\ell(\\nu)$ as the maximum of\n\\[\n \\sum_{e\\in\\mathcal E_\\ell(F)}w_e\n \\quad\\text{subject to}\\quad\n w_e\\ge0,\\qquad\n \\frac12\\sum_{e\\ni x}w_e\\le\\nu(\\{x\\})\\quad(x\\in F).\n\\]\nWhen $\\mathcal E_\\ell(F)$ is empty, this maximum is zero.\n\\end{definition}\n\nThus $c_\\ell(\\nu)$ is the largest total weight of fair two-point laws\nin the indicated distance band whose weighted sum is a submeasure of\n$\\nu$.  Summing the constraints gives $\\sum_e w_e\\le1$; the feasible\nset is compact, so the maximum exists and $0\\le c_\\ell(\\nu)\\le1$.\nFor example, if $\\nu=p\\delta_x+(1-p)\\delta_y$ with $x\\ne y$, then\nthe capacity in the band containing $|x-y|$ is $2\\min\\{p,1-p\\}$,\nand all its other capacities vanish.\n\n\\begin{lemma}[Finite-law pair bound]\\label{lem:pair-bound}\nFor every integer $k\\ge1$ there is a finite constant $C_k$ such that\nevery finitely supported probability law $\\nu$ on $\\R$ and every\n$\\rho>0$ satisfy\n\\begin{equation}\\label{eq:pair-bound}\n k\\bigl(H(\\nu)-H^0_\\rho(\\nu)\\bigr)\n \\le \\log|kF|\n   +C_k\\sum_{\\ell:\\,2^{-\\ell}\\le2\\rho}c_\\ell(\\nu),\n \\qquad F=\\supp\\nu,\n\\end{equation}\nwhere $kF=\\{x_1+\\cdots+x_k:x_i\\in F\\}$.\nThe constant is independent of the smallest positive distance in $F$.\n\\end{lemma}\n\n\\begin{proof}\nLet $X_1,\\ldots,X_k$ be independent with law $\\nu$, write\n$\\mathbf X=(X_1,\\ldots,X_k)$, and put $Y=X_1+\\cdots+X_k$.\nKnowing the initial length-$\\rho$ cells leaves\n$k(H(\\nu)-H^0_\\rho(\\nu))$ bits of uncertainty about $\\mathbf X$.\nRevealing $Y$ costs at most $\\log|kF|$ bits.  We bound the remaining\nuncertainty by successively refining those cells.  The refinement cuts\nare random, independent of the samples, to prevent atoms close to a\nfixed boundary from being charged too often.\n\n\\paragraph{The partitions.}\nSet $\\rho_h=\\rho4^{-h}$ for integers $h\\ge0$, and start with the\nhalf-open grid intervals of length $\\rho$.  Suppose a parent at level\n$h$ has length $L\\in[\\rho_h/2,2\\rho_h]$.  Let $N$ be a nearest\ninteger to $L/\\rho_{h+1}$, with ties resolved by a fixed rule.\nThen $2\\le N\\le8$.  Divide the parent into $N$ equal pieces, and\nperturb its internal cuts independently and uniformly by amounts in\n$[-\\rho_{h+1}/10,\\rho_{h+1}/10]$.\nThe unperturbed child lengths belong to\n$[0.75\\rho_{h+1},1.25\\rho_{h+1}]$; after perturbation they belong\nto $[0.55\\rho_{h+1},1.45\\rho_{h+1}]$.  Thus the cuts remain ordered\nand every child has length in $[\\rho_{h+1}/2,2\\rho_{h+1}]$.\nConditional on the previous partitions, every new cut has density\nat most\n\\begin{equation}\\label{eq:pair-cut-density}\n \\frac{5}{\\rho_{h+1}}=\\frac{20}{\\rho_h}.\n\\end{equation}\nAll cuts are sampled independently of $\\mathbf X$; a parent that\ncontains several coordinates uses the same cuts for each coordinate.\n\nIf $F$ is a singleton, the lemma is immediate.  Otherwise choose\nan integer $J$ such that\n\\[\n 2\\rho_J<\\min\\{|x-y|:x,y\\in F,\\ x\\ne y\\}.\n\\]\nEvery level-$J$ cell then contains at most one point of $F$, for\nevery realization of the partitions.  No bound on $J$ will be needed.\n\nFor a fixed partition scheme, denote by $\\mathbf C_h$ the tuple of\nlevel-$h$ cells containing the samples.  Nesting and separation at\nlevel $J$ give\n\\begin{align}\n k\\bigl(H(\\nu)-H^0_\\rho(\\nu)\\bigr)\n &=H(\\mathbf X\\mid\\mathbf C_0)\\notag\\\\\n &\\le\\log|kF|+H(\\mathbf X\\mid\\mathbf C_0,Y)\\notag\\\\\n &=\\log|kF|+\n   \\sum_{h=0}^{J-1}H(\\mathbf C_{h+1}\\mid\\mathbf C_h,Y).\n \\label{eq:pair-chain}\n\\end{align}\nThe whole partition scheme is sampled independently of $\\mathbf X$.\nWe now average this finite chain over the schemes; equivalently, the\nconditional entropies on its right also condition on that scheme.\nFor the summand at level $h$, the cell labels involved use only cuts\nthrough level $h+1$.\nConditional on those cuts, all later cuts are independent of\n$\\mathbf X$ and hence of $Y$ and these labels, so they may be\ndiscarded from the conditioning.\n\n\\paragraph{One refinement after observing the sum.}\nFix the partitions through level $h$, and condition first on a tuple\nof current cells $A_1,\\ldots,A_k$ of positive probability.\nThe coordinates remain independent, with laws $\\nu(\\cdot\\mid A_i)$.\nWrite $m_i=\\E X_i$ in this conditional law.  Each mean belongs to\nits half-open parent, since it is an average of finitely many points\nof that parent.  Fix the new cuts and let\n\\[\n a=\\min_{\\substack{1\\le i\\le k\\\\\n              c\\text{ a new internal cut of }A_i}}|m_i-c|.\n\\]\nThis minimum is positive almost surely in the new cuts.\nCall coordinate $i$ exceptional when $|X_i-m_i|\\ge a/(4k)$,\nlet $p_i$ be its probability, and put $t=\\sum_i p_i$.\nLet $E$ be the event that at least two coordinates are exceptional,\nand write $q=\\P(E)$.  Independence, before conditioning on $Y$, gives\n\\begin{equation}\\label{eq:pair-two-exceptions}\n q\\le\\sum_{i<j}p_ip_j\\le t^2,\n \\qquad q\\le t.\n\\end{equation}\nAlso put\n\\[\n B=\\left\\{\\left|Y-\\sum_i m_i\\right|\\ge a/2\\right\\}.\n\\]\nThis second event is known once $Y$ is known, and $\\P(B)\\le t$:\nif every coordinate is nonexceptional, the sum deviation is less\nthan $a/4$.\n\nOn $E^c\\cap B^c$, every child label equals the child label of its\nmean.  Indeed, a changed label forces $|X_i-m_i|\\ge a$ for some\n$i$, including a change at a half-open endpoint.  There is then at\nmost one exceptional coordinate, so\n\\[\n \\left|\\sum_j(X_j-m_j)\\right|\n \\ge a-\\frac{k-1}{4k}a>a/2,\n\\]\na contradiction.  Each coordinate has at most eight child labels.\nIn the next display, conditioning on the fixed partitions and new cuts\nis implicit.  By revealing the flag $1_E$ in addition to $Y$, we obtain\n\\begin{align}\n H(\\mathbf C_{h+1}\\mid Y,\\mathbf C_h=(A_i))\n &\\le h_2(q)+3k\\bigl(q+\\P(B)\\bigr)\\notag\\\\\n &\\le (3+6k)t\\le9k\\sum_i p_i.\n \\label{eq:pair-flag}\n\\end{align}\nHere $h_2(q)\\le3\\sqrt q$ and\n\\eqref{eq:pair-two-exceptions} were used.  The elementary binary\nentropy bound follows from\n$-(1-q)\\ln(1-q)\\le q$ and $-\\sqrt q\\ln q\\le2/e$.\nThere is no binary-entropy cost for $B$, since it is determined by\nthe conditioned observation $Y$.  This distinction is what makes\n\\eqref{eq:pair-flag} linear in the exceptional probabilities.\n\n\\paragraph{Averaging the cuts and converting to pairs.}\nFor a fixed value of $X_i$, set $u=|X_i-m_i|$.\nThe event $a\\le4ku$ requires one of at most $7k$ cuts to be within\n$4ku$ of its corresponding mean.  The density bound\n\\eqref{eq:pair-cut-density} and a union bound give\n\\[\n \\P_{\\rm cuts}(a\\le4ku)\n \\le7k\\frac{20}{\\rho_h}(8ku)\n =1120k^2\\frac{u}{\\rho_h}.\n\\]\nConsequently\n\\[\n \\E_{\\rm cuts}p_i\n \\le\\frac{1120k^2}{\\rho_h}\\E|X_i-m_i|\n \\le\\frac{1120k^2}{\\rho_h}\\E|X_i-X_i'|,\n\\]\nwhere $X_i'$ is an independent copy in the same current cell.\nThe last inequality is Jensen's inequality.  These expectations\nstill use the current-cell conditional laws.\n\nFor a fixed level-$h$ partition, integrating the cells out gives\nthe symmetric coupling\n\\[\n \\pi_h=\\sum_{A:\\,\\nu(A)>0}\\nu(A)\n \\bigl(\\nu(\\cdot\\mid A)\\otimes\\nu(\\cdot\\mid A)\\bigr).\n\\]\nBoth its marginals equal $\\nu$, and its pairs have distance at most\n$2\\rho_h$.  Each of the $k$ coordinate contributions becomes the\nsame integral against $\\pi_h$.  Thus \\eqref{eq:pair-flag}, averaged\nover the new cuts and current cells, is at most\n\\begin{equation}\\label{eq:pair-distance-cost}\n \\frac{10080k^4}{\\rho_h}\\int|x-y|\\,d\\pi_h(x,y).\n\\end{equation}\n\nFor each band $2^{-\\ell}\\le|x-y|<2^{1-\\ell}$, the corresponding\nportion of $\\pi_h$ supplies a feasible mixture in\nDefinition~\\ref{def:pair-capacity}.  Explicitly, the weight assigned\nto the unordered pair $\\{x,y\\}$ is\n$\\pi_h(x,y)+\\pi_h(y,x)=2\\pi_h(x,y)$.\nThe resulting submeasure has mass\n$\\sum_{y:\\,2^{-\\ell}\\le|x-y|<2^{1-\\ell}}\\pi_h(x,y)$ at $x$,\nwhich is at most $\\nu(\\{x\\})$.  Its total weight equals the mass of\nthat band under $\\pi_h$.  Therefore \\eqref{eq:pair-distance-cost}\nis at most\n\\begin{equation}\\label{eq:pair-one-level}\n 20160k^4\n \\sum_{\\ell:\\,2^{-\\ell}\\le2\\rho_h}\n \\frac{2^{-\\ell}}{\\rho_h}c_\\ell(\\nu).\n\\end{equation}\nThis estimate also holds after averaging the previous partitions;\nits right side refers only to the original law $\\nu$.\n\n\\paragraph{Summing all finer scales.}\nFor a fixed $\\ell$, the factors $2^{-\\ell}/\\rho_h$ grow by four\nat each step and are summed only while they are at most two.\nHence\n\\[\n \\sum_{h\\ge0:\\,2^{-\\ell}\\le2\\rho_h}\n       \\frac{2^{-\\ell}}{\\rho_h}\\le\\frac83.\n\\]\nSumming \\eqref{eq:pair-one-level} in \\eqref{eq:pair-chain} proves\n\\eqref{eq:pair-bound}; one may take $C_k=60000k^4$.\nThe separating depth $J$ disappears from the bound.  In particular,\nthe estimate includes separations smaller than any prescribed\nexponential scale.\n\\end{proof}\n\nFor a fixed finite law only finitely many pair capacities are\nnonzero.  Later we will apply Lemma~\\ref{lem:pair-bound} to a finite\nfamily of finite laws for each fixed block count.  Their nonzero\ncapacities therefore lie in a common finite range, although the\nupper endpoint need not have any effective bound.\n"}, {"path": "preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/sections/types.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/sections/types.tex", "bytes": 12424, "sha256": "01e725debeb17475acfa0c5f3b8e6937b7113e627bcedf9485a4aa28d3aebbfb", "content": "\\section{Conditioning on block types}\\label{sec:types}\n\nLet $\\Phi=(\\varphi_i)_{i\\in\\Lambda}$ be a finite affine iterated function\nsystem on $\\R$, where\n$\\varphi_i(x)=r_ix+t_i$ and $0<|r_i|<1$.\nLet $p=(p_i)_{i\\in\\Lambda}$ be a strictly positive probability vector,\nand let $\\mu$ be its self-similar measure.  Write $G_n$ for the random\ncomplete affine map obtained from $n$ independent symbols with law $p$,\nand let $h=\\lim_{n\\to\\infty}H(G_n)/n$ be its entropy rate.\nThroughout this section assume\n\\begin{equation}\\label{eq:types-loss}\n d:=\\dim_H\\mu<1,\n \\qquad d\\chi<h,\n \\qquad \\chi=-\\sum_{i\\in\\Lambda}p_i\\log|r_i|.\n\\end{equation}\nAll logarithms have base $2$.\nWe will find finite conditional coding laws whose exact entropy exceeds\ntheir entropy at a prescribed scale by a positive multiple of the number\nof blocks.  Lemma~\\ref{lem:pair-bound} will then turn this deficit into\npair mass across finite bands of arbitrarily fine scales.\n\nWe condition on the number of occurrences of each symbol in a short\nblock.  These counts fix its signed contraction.  Once all block\ncontractions are fixed, the translation determines the complete\ncomposed map.  The entropy cost of recording the counts is small, so\nthe conditional translations retain almost all the map entropy.\nThis argument allows distinct words to define the same map.\nThe block-type disintegration is due to\nGalicer--Saglietti--Shmerkin--Yavicoli\n\\cite[Section~6.4, Lemma~6.6]{GalicerSagliettiShmerkinYavicoli2016}\nand is developed further by Saglietti--Shmerkin--Solomyak\n\\cite[Lemma~6.2]{SagliettiShmerkinSolomyak2018};\nK\\\"aenm\\\"aki--Orponen explicitly allow repeated indexed maps\n\\cite[Section~2.2, Proposition~2.8]{KaenmakiOrponen2023}.\nWe retain the signed contraction and estimate the entropy of the complete\nmap after collisions have been combined.\n\n\\subsection{Parameters and conditional coding laws}\n\nWrite $m=|\\Lambda|$.  The second inequality in\n\\eqref{eq:types-loss} implies $h>0$, hence $m\\ge2$.\nChoose $\\alpha>\\chi$ such that $d\\alpha<h$.\nFor $d>0$ we may take $\\chi<\\alpha<h/d$; for $d=0$ we may take\n$\\alpha=\\chi+1$.\nChoose an integer $b\\ge1$ sufficiently large that\n\\[\n m\\log(b+1)<b\\bigl(h-d\\alpha\\bigr),\n\\]\nand set\n\\begin{equation}\\label{eq:types-parameters}\n A=b\\alpha,\n \\qquad A'=\\frac{b(\\chi+\\alpha)}2,\n \\qquad h_b=bh-m\\log(b+1).\n\\end{equation}\nThen\n\\begin{equation}\\label{eq:types-gap}\n b\\chi<A'<A,\n \\qquad \\gamma:=h_b-dA>0.\n\\end{equation}\nThese parameters remain fixed throughout the rest of the proof.\n\nGroup the iid symbols with law $p$ into words\n$W_0,W_1,\\ldots\\in\\Lambda^b$.\nFor $w\\in\\Lambda^b$ let\n$\\operatorname{type}(w)=(\\kappa_i)_{i\\in\\Lambda}$,\nwhere $\\kappa_i$ counts the occurrences of $i$ in $w$.\nThe set $\\mathcal T_b$ of types consists of the nonnegative integer vectors\nwith $\\sum_i\\kappa_i=b$, and has cardinality at most $(b+1)^m$.\nFor $\\kappa\\in\\mathcal T_b$, define\n\\[\n N_\\kappa=\\frac{b!}{\\prod_i\\kappa_i!},\n \\qquad q_\\kappa=N_\\kappa\\prod_i p_i^{\\kappa_i},\n \\qquad Q_\\kappa\n   =\\Law\\bigl(W_0\\mid\\operatorname{type}(W_0)=\\kappa\\bigr).\n\\]\nEvery word of type $\\kappa$ has probability $\\prod_i p_i^{\\kappa_i}$,\nso $Q_\\kappa$ is uniform on its $N_\\kappa$ words.\nEquivalently, we can generate the original coding by first choosing\nindependent types $\\omega_0,\\omega_1,\\ldots$ with distribution $q$,\nthen choosing the words independently with respective conditional laws\n$Q_{\\omega_0},Q_{\\omega_1},\\ldots$.\nThis follows by multiplying the probabilities of any finite sequence\nof blocks.  In particular it preserves the original, possibly unequal,\nsymbol weights $p_i$.\n\nWrite $\\omega=(\\omega_h)_{h\\ge0}$ for the type sequence and\n$\\theta\\omega=(\\omega_{h+1})_{h\\ge0}$ for its shift.\nFor $j\\ge0$, let $\\mathcal F_j$ be the sigma-algebra generated by\n$\\omega_0,\\ldots,\\omega_{j-1}$; $\\mathcal F_0$ is trivial.\nFor a type $\\kappa$ and an environment $\\omega$, define\n\\begin{equation}\\label{eq:types-products}\n r(\\kappa)=\\prod_{i\\in\\Lambda}r_i^{\\kappa_i},\n \\qquad R_j=R_j(\\omega)=\\prod_{h<j}r(\\omega_h),\n \\qquad \\sigma_j=-\\log|R_j|,\n\\end{equation}\nwith $R_0=1$ and $\\sigma_0=0$.\nThus $R_j$ is $\\mathcal F_j$-measurable, including its sign.\nLet\n\\[\n D=\\max_{\\kappa\\in\\mathcal T_b}\\bigl(-\\log|r(\\kappa)|\\bigr).\n\\]\nThe increments of $\\sigma_j$ are iid bounded positive random variables\nof mean $b\\chi$, and $0\\le\\sigma_j\\le Dj$.\n\nGiven $\\omega$, use the independent conditional block draws above to\ndefine\n\\begin{equation}\\label{eq:types-laws}\n \\nu_{\\omega,n}\n =\\Law_{\\omega}\\!\\left(\\sum_{h=0}^{n-1}\n       R_h\\varphi_{W_h}(0)\\right),\n \\qquad\n \\mu_\\omega\n =\\Law_{\\omega}\\!\\left(\\sum_{h=0}^{\\infty}\n       R_h\\varphi_{W_h}(0)\\right).\n\\end{equation}\nHere $\\Law_\\omega$ denotes the law under those conditional word draws.\nThe infinite sum converges uniformly, since the block translations form\na finite set and $\\max_i|r_i|<1$.\nThe law $\\nu_{\\omega,n}$ depends only on $\\mathcal F_n$, whereas\n$\\mu_\\omega$ can depend on the entire environment.\nAveraging over the types gives $\\mu=\\E\\mu_\\omega$.\nFor any real $u\\ne0$ and any law $\\lambda$, write\n$\\mathsf D_u\\lambda=\\Law(uX)$ when $X$ has law $\\lambda$;\nthis notation retains the sign of $u$.\n\nFor $n\\ge1$, let $\\Omega_n=(\\omega_0,\\ldots,\\omega_{n-1})$ be\nthe tuple of the first $n$ types, and put\n\\begin{equation}\\label{eq:types-scales}\n \\rho_n=2^{-\\lceil An\\rceil},\n \\qquad E_n=\\{\\omega:\\sigma_n\\le A'n\\},\n \\qquad a_n=\\lceil An\\rceil-1.\n\\end{equation}\n\n\\begin{lemma}[Entropy retained by types]\\label{lem:type-deficit}\nFor a system satisfying \\eqref{eq:types-loss},\nwith the parameters\n\\eqref{eq:types-parameters} and conditional laws\n\\eqref{eq:types-laws}, for every $n\\ge1$ and $s>0$ we have\n\\begin{align}\n \\E H(\\nu_{\\omega,n})\n &=H(G_{bn}\\mid\\Omega_n)\n \\ge nh_b,\\label{eq:types-exact-entropy}\\\\\n \\E H_s^0(\\nu_{\\omega,n})\n &\\le H_s^0(\\mu)+1.\\label{eq:types-grid-entropy}\n\\end{align}\nMoreover, there are $\\varepsilon>0$ and an integer $n_0$ such that\nfor every $n\\ge n_0$,\n\\begin{equation}\\label{eq:types-deficit}\n \\E\\!\\left[\\mathbf1_{E_n}\n    \\bigl(H(\\nu_{\\omega,n})-H_{\\rho_n}^0(\\nu_{\\omega,n})\\bigr)\n    \\right]\\ge\\varepsilon n.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nFix $\\Omega_n$.  The composition corresponding to a tuple\n$(w_0,\\ldots,w_{n-1})$ of allowed blocks is\n\\[\n \\varphi_{w_0\\cdots w_{n-1}}(x)\n   =R_nx+\\sum_{h<n}R_h\\varphi_{w_h}(0).\n\\]\nIts signed slope $R_n$ is fixed by $\\Omega_n$, and its translation\nhas law $\\nu_{\\omega,n}$.  Thus the complete map and its translation\ndetermine one another under this conditioning, even when several words\ngive the same map.  It follows that\n\\[\n \\E H(\\nu_{\\omega,n})=H(G_{bn}\\mid\\Omega_n).\n\\]\nThere are at most $(b+1)^m$ types, and the block types are independent,\nso $H(\\Omega_n)\\le nm\\log(b+1)$.  The entropy-rate definition gives\n$H(G_{bn})\\ge bnh$.  Therefore\n\\begin{align*}\n H(G_{bn}\\mid\\Omega_n)\n &\\ge H(G_{bn})-H(\\Omega_n)\\\\\n &\\ge bnh-nm\\log(b+1)=nh_b.\n\\end{align*}\nThis conditioning inequality does not require $\\Omega_n$ to be a\nfunction of $G_{bn}$; equal maps may arise from different type tuples.\nThis proves \\eqref{eq:types-exact-entropy}.\n\nWe next compare the finite sum with the original measure at a fixed\ngrid scale.  Translation changes fixed-grid entropy by at most one bit:\nfor any bounded random variable $X$ and $t\\in\\R$,\n\\begin{equation}\\label{eq:types-grid-translation}\n H_s^0(X+t)\\ge H_s^0(X)-1.\n\\end{equation}\nThis is the fixed-grid bound in Lemma~\\ref{lem:entropy-calculus},\nEquation~\\eqref{eq:entropy-grid-comparison}.\n\nCondition now only on $\\mathcal F_n$.\nThe original symbols after the first $bn$ positions are still independent\nwith law $p$, and are independent of the prefix.\nConsequently the full coding law conditional on $\\mathcal F_n$ is\n\\[\n \\lambda_\\omega\n   =\\nu_{\\omega,n}*\\mathsf D_{R_n}\\mu,\n \\qquad \\mu=\\E\\lambda_\\omega.\n\\]\nFor each fixed type prefix this convolution is a mixture of translations\nof $\\nu_{\\omega,n}$.  Applying concavity of fixed-grid entropy first to\nthese translations and then to the type mixture, and using\n\\eqref{eq:types-grid-translation}, gives\n\\[\n H_s^0(\\mu)\n \\ge\\E H_s^0(\\lambda_\\omega)\n \\ge\\E H_s^0(\\nu_{\\omega,n})-1.\n\\]\nAll entropies are finite because the laws have bounded support.\nThe sign of $R_n$ affects only the random translation in this argument.\nThis proves \\eqref{eq:types-grid-entropy}.\n\nSet\n$V_n=H(\\nu_{\\omega,n})-H_{\\rho_n}^0(\\nu_{\\omega,n})$.\nQuantization is a function of the exact finite value, so\n\\begin{equation}\\label{eq:types-deficit-bound}\n 0\\le V_n\\le H(\\nu_{\\omega,n})\\le nb\\log m.\n\\end{equation}\nBy Lemma~\\ref{lem:entropy-dimension},\n$\\limsup_{s\\downarrow0}H_s^0(\\mu)/\\log(1/s)\\le d$.\nChoose $\\eta=\\gamma/(4A)>0$.\nFor all sufficiently large $n$, the preceding estimates give\n\\begin{align*}\n \\E V_n\n &\\ge nh_b-(d+\\eta)\\lceil An\\rceil-1\\\\\n &\\ge\\tfrac34\\gamma n-(d+\\eta)-1\n \\ge\\tfrac12\\gamma n.\n\\end{align*}\nThe law of large numbers and $b\\chi<A'$ imply $\\P(E_n^c)\\to0$.\nBy \\eqref{eq:types-deficit-bound}, after increasing the threshold on $n$,\n\\[\n \\E[\\mathbf1_{E_n^c}V_n]\n \\le nb\\log m\\,\\P(E_n^c)\\le\\tfrac14\\gamma n.\n\\]\nThus \\eqref{eq:types-deficit} holds with $\\varepsilon=\\gamma/4$.\n\\end{proof}\n\n\\subsection{From the deficit to bands of pair mass}\n\nThe preceding lemma produces entropy that is invisible at scale\n$\\rho_n$.  To apply the finite-law estimate, we must also control the\nnumber of possible sums of several independent copies of a conditional\nprefix.  Fixing the types makes that count uniform in the environment.\n\n\\begin{proposition}[Pair mass in finite scale bands]\\label{prop:pair-bands}\nUnder the assumptions and notation of Lemma~\\ref{lem:type-deficit},\nthere exist $c>0$ and an integer $n_0$ such that, for each $n\\ge n_0$,\nthere is a deterministic finite integer $B_n\\ge a_n$ satisfying\n\\begin{equation}\\label{eq:types-pair-bands}\n \\E\\!\\left[\\mathbf1_{E_n}\n       \\sum_{\\ell=a_n}^{B_n}c_\\ell(\\nu_{\\omega,n})\\right]\\ge cn.\n\\end{equation}\nHere $c_\\ell(\\nu)$ is the maximum total weight of fair two-point laws\nwith separations in $[2^{-\\ell},2^{1-\\ell})$ whose weighted sum is a\nsubmeasure of $\\nu$, as in Lemma~\\ref{lem:pair-bound}.\nThe constants $b,A,A',c,n_0$ are independent of $n$ and $\\omega$;\nno growth bound on $B_n$ is asserted.\n\\end{proposition}\n\n\\begin{proof}\nLet $\\mathcal D_b=\\{\\varphi_w(0):w\\in\\Lambda^b\\}$.\nIt has at most $m^b$ elements.  For fixed types, write\n$S=\\supp\\nu_{\\omega,n}$.\nSince every $R_h$ is fixed by the types,\n\\[\n S\\subseteq\\sum_{h<n}R_h\\mathcal D_b,\n \\qquad kS\\subseteq\\sum_{h<n}R_h(k\\mathcal D_b)\n \\quad(k\\ge1).\n\\]\nA sum of $k$ elements of $\\mathcal D_b$ is specified by the number\nof occurrences of each digit.  Each count belongs to $\\{0,\\ldots,k\\}$,\nso $|k\\mathcal D_b|\\le(k+1)^{m^b}$ and hence\n\\begin{equation}\\label{eq:types-sumset}\n \\log|kS|\\le nm^b\\log(k+1).\n\\end{equation}\nThis is an upper bound regardless of any collisions among the represented\nsums and regardless of the signs of the $R_h$.\n\nWith $b$ and $\\varepsilon$ already fixed, choose an integer $k$ so large\nthat\n\\[\n m^b\\log(k+1)<\\frac{k\\varepsilon}{2}.\n\\]\nApply Lemma~\\ref{lem:pair-bound} to each $\\nu_{\\omega,n}$ at scale\n$\\rho_n$.  Its conclusion, with a positive constant $C_k$ depending\nonly on $k$, is\n\\[\n k\\bigl(H(\\nu_{\\omega,n})-H_{\\rho_n}^0(\\nu_{\\omega,n})\\bigr)\n \\le\\log|kS|+C_k\\sum_{\\ell\\ge a_n}c_\\ell(\\nu_{\\omega,n});\n\\]\nthe index condition follows from\n$2^{-\\ell}\\le2\\rho_n$ if and only if $\\ell\\ge a_n$.\nMultiply by $\\mathbf1_{E_n}$ and take expectations.\nLemma~\\ref{lem:type-deficit} and \\eqref{eq:types-sumset} give\n\\[\n C_k\\E\\!\\left[\\mathbf1_{E_n}\n       \\sum_{\\ell\\ge a_n}c_\\ell(\\nu_{\\omega,n})\\right]\n \\ge k\\varepsilon n-nm^b\\log(k+1)\n \\ge\\frac{k\\varepsilon n}{2}.\n\\]\n\nFor fixed $n$ there are finitely many type prefixes and thus finitely\nmany laws $\\nu_{\\omega,n}$, all with finite support.\nCollect the dyadic band indices of all distinct pairs of support points\nin all these laws into a finite set $J_n\\subset\\mathbb Z$, and set\n\\[\n B_n=\\max\\bigl(\\{a_n\\}\\cup J_n\\bigr).\n\\]\nFor every environment, $c_\\ell(\\nu_{\\omega,n})=0$ when $\\ell>B_n$.\nThus the infinite sum in the last inequality equals the sum from $a_n$\nto $B_n$, and \\eqref{eq:types-pair-bands} follows with\n$c=k\\varepsilon/(2C_k)$.\n\\end{proof}\n\nThe bands in Proposition~\\ref{prop:pair-bands} can extend far beyond the\nscale $2^{-An}$.  Their finiteness is enough: the next step will select\nsuccessive block lengths after the earlier upper endpoints $B_n$ are\nknown.  No quantitative separation of distinct cylinder translations\nhas been used.\n"}, {"path": "preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/sections/windows.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/build/sections/windows.tex", "bytes": 11821, "sha256": "cc737fb8e62e2c177d957eab1d130e04d814cf572d61d1eae42d83ef1e3b42d9", "content": "\\section{Disjoint windows of entropy gain}\\label{sec:windows}\n\nProposition~\\ref{prop:pair-bands} supplies a linear amount of fair-pair\nweight in a finite interval of logarithmic depths for every sufficiently long block\nsegment.  We now turn this weight into entropy gain.  The difficulty is\nthat the depth interval may extend arbitrarily far.  We choose several\nsegments whose depth intervals are separated, and show that their gains\noccupy disjoint windows of block indices.  At each physical scale the\ntotal gain is bounded, whereas every chosen interval contributes the same\npositive amount after averaging over scales.\n\nThroughout this section, retain the environment and constants from\nSection~\\ref{sec:types}.  Thus $\\sigma_j=-\\log|R_j|$ is the contraction\ndepth after $j$ blocks, $D$ bounds each block's depth, and\n$\\mathcal F_j=\\sigma(\\omega_0,\\ldots,\\omega_{j-1})$ records the first\n$j$ types.  In particular, $\\sigma_j\\le Dj$.  The logarithmic-depth bands are\n$[a_n,B_n]$, where $a_n=\\lceil An\\rceil-1$, and their expected total\nweight on $E_n=\\{\\sigma_n\\le A'n\\}$ is at least $cn$.\n\nLet $\\theta$ denote the left shift of the type sequence.  Define the\nsuffix laws, including their contraction from the initial scale, by\n\\[\n \\tau_j=\\mathsf D_{R_j}\\mu_{\\theta^j\\omega},\\qquad j\\ge0.\n\\]\nThe independent conditional block draws give the pathwise identity\n\\begin{equation}\\label{eq:suffix-convolution}\n \\tau_j=(\\mathsf D_{R_j}\\nu_{\\theta^j\\omega,n})*\\tau_{j+n},\n \\qquad n\\ge1.\n\\end{equation}\nAll these laws have compact support.  The signs of $R_j$ are retained in\nthe laws; only scale changes use $|R_j|$.\n\nBy Lemma~\\ref{lem:nonsaturation}, choose $\\delta>0$ such that\n\\[\n \\Delta_v(\\mu):=1-G_{v,2v}(\\mu)\\ge\\delta\\qquad(v>0).\n\\]\nFix an integer $M>4$ with $5/M<\\delta/2$.  For an integer target depth\n$L\\ge1$, write $r=2^{-L}$ and define\n\\begin{equation}\\label{eq:window-entropy}\n g_j=G_{r/M,Mr}(\\tau_j).\n\\end{equation}\nHere and below $g_j$ depends on $L$ and $\\omega$.  The ratio of the two\nwindow scales is the integer $M^2$.  Lemma~\\ref{lem:entropy-calculus}\nand \\eqref{eq:suffix-convolution} therefore imply, for every environment,\n\\begin{equation}\\label{eq:window-monotonicity}\n 0\\le g_j\\le2\\log M,\\qquad g_j\\ge g_{j+1}.\n\\end{equation}\n\n\\subsection{A gain after averaging the unobserved future}\n\nAn individual conditional tail need not satisfy the nonsaturation bound\nfor $\\mu$.  To use that bound, we condition only on the types through\nthe end of the finite block segment producing a fair pair.  The\nunobserved future types then average its tail law back to a scaled copy\nof $\\mu$.\n\n\\begin{lemma}[Conditional pair gain]\\label{lem:conditional-gain}\nFor all integers $j\\ge0$, $n\\ge1$, and $L\\ge1$, with $g_j$ as in\n\\eqref{eq:window-entropy},\n\\begin{equation}\\label{eq:conditional-gain}\n \\E\\bigl[g_j-g_{j+n}\\mid\\mathcal F_{j+n}\\bigr]\n \\ge \\frac{\\delta}{2}\n c_{L-\\lceil\\sigma_j\\rceil}(\\nu_{\\theta^j\\omega,n}).\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nCondition on $\\mathcal F_{j+n}$ and put\n$\\ell=L-\\lceil\\sigma_j\\rceil$.  The finite law\n$\\nu_{\\theta^j\\omega,n}$, the signed products $R_j,R_{j+n}$, and\n$\\ell$ are now fixed.  Choose a fair-pair decomposition attaining\n$c_\\ell(\\nu_{\\theta^j\\omega,n})$.  Such a choice is measurable in\n$\\mathcal F_{j+n}$: for fixed $j,n,L$ there are only finitely many\ninitial type sequences, so one may fix a maximizer for each of them.\n\nA selected pair of distance $u\\in[2^{-\\ell},2^{1-\\ell})$ has distance\n$v=|R_j|u$ after scaling.  Since $r=2^{-L}$,\n\\[\n \\frac vr\\in\n \\bigl[2^{\\lceil\\sigma_j\\rceil-\\sigma_j},\n       2^{1+\\lceil\\sigma_j\\rceil-\\sigma_j}\\bigr)\n \\subset[1,4).\n\\]\nThus $r/M<v<Mr$ and\n\\[\n \\frac{r/M}{v}+\\frac{v}{Mr}\\le\\frac5M.\n\\]\nEven if $R_j$ is negative, the scaled pair, after ordering its endpoints,\nis a translate of the fair law on $\\{0,v\\}$.  Translation invariance\nand Lemma~\\ref{lem:two-point} show that this pair increases the window\nentropy of $\\tau_{j+n}$ by at least\n\\[\n \\Delta_v(\\tau_{j+n})-\\frac5M.\n\\]\n\nThe types beginning at index $j+n$ are independent of\n$\\mathcal F_{j+n}$.  Since averaging $\\mu_\\omega$ over the environment\ngives $\\mu$, their conditional mean is the identity of measures\n\\[\n \\E[\\tau_{j+n}\\mid\\mathcal F_{j+n}]\n   =\\mathsf D_{R_{j+n}}\\mu.\n\\]\nThe functional $\\Delta_v=1-G_{v,2v}$ is convex by\nLemma~\\ref{lem:entropy-calculus}.  Each chosen distance $v$ is fixed\nunder the present conditioning.  Hence Jensen's inequality and signed\nscaling give\n\\begin{align*}\n \\E[\\Delta_v(\\tau_{j+n})\\mid\\mathcal F_{j+n}]\n &\\ge\\Delta_v(\\mathsf D_{R_{j+n}}\\mu)\\\\\n &=\\Delta_{v/|R_{j+n}|}(\\mu)\\ge\\delta.\n\\end{align*}\n\nApply concavity of $G_{r/M,Mr}$ to the selected pairs and the residual\npositive measure in the prefix law.  If the residual mass is nonzero,\nnormalize it to a probability law; convolution with that law contributes\na nonnegative gain by Lemma~\\ref{lem:entropy-calculus}, since the scale\nratio is $M^2$.  The same statement is vacuous when the residual mass is\nzero.  Summing the conditional lower bounds for all the selected pairs\ntherefore gives\n\\[\n \\E[g_j-g_{j+n}\\mid\\mathcal F_{j+n}]\n \\ge\\left(\\delta-\\frac5M\\right)\n       c_\\ell(\\nu_{\\theta^j\\omega,n})\n \\ge\\frac\\delta2c_\\ell(\\nu_{\\theta^j\\omega,n}),\n\\]\nas required.\n\\end{proof}\n\n\\subsection{Choosing disjoint block windows}\n\nFix any positive integer $q$.  Choose sufficiently large integers\n$n_1<\\cdots<n_q$ successively so that\nProposition~\\ref{prop:pair-bands} applies, $a_{n_i}\\ge1$, and\n\\begin{equation}\\label{eq:separated-bands}\n a_{n_i}-A'n_i-1>B_{n_h}\\qquad(h<i).\n\\end{equation}\nThis is possible because the previously chosen $B_{n_h}$ are finite and\n\\[\n a_n-A'n-1=\\lceil An\\rceil-A'n-2\n \\ge(A-A')n-2\\longrightarrow\\infty.\n\\]\nIn particular, the choice uses no upper bound on how fast $B_n$ grows.\nCall $[a_{n_i},B_{n_i}]$ band $i$.  Larger $i$ means finer physical\nscales, since \\eqref{eq:separated-bands} gives $a_{n_i}>B_{n_h}$ for $h<i$.\n\nChoose an even integer $T$ with\n\\begin{equation}\\label{eq:target-range}\n \\frac T2>\\max_{1\\le i\\le q}B_{n_i}.\n\\end{equation}\nFor band $i$, use the candidate starts\n\\begin{equation}\\label{eq:candidate-starts}\n J_i=\\left\\{kn_i:k\\in\\mathbb Z,\\ 0\\le k\\le\n                  \\left\\lfloor\\frac{T}{2Dn_i}\\right\\rfloor\\right\\}.\n\\end{equation}\nFor each target $L\\in\\{1,\\ldots,T\\}$, retain the window\n$[j,j+n_i)$, $j\\in J_i$, exactly on the event\n\\begin{equation}\\label{eq:retention}\n \\mathcal A_{i,j,L}=\n \\left\\{\\sigma_{j+n_i}-\\sigma_j\\le A'n_i,\\quad\n a_{n_i}\\le L-\\lceil\\sigma_j\\rceil\\le B_{n_i}\\right\\}.\n\\end{equation}\nThis event belongs to $\\mathcal F_{j+n_i}$, the same sigma-algebra as\nthe conditioning in Lemma~\\ref{lem:conditional-gain}.\n\nThe reason for the separation condition is simple.  As a block start\n$j$ moves forward, the adjusted target depth\n$L-\\lceil\\sigma_j\\rceil$ is nonincreasing.  A retained window in a finer\nband cannot decrease this depth far enough to reach any coarser band.\nThe next lemma makes this order precise for every environment.\n\n\\Needspace{5\\baselineskip}\n\\begin{lemma}[Disjoint retained windows]\\label{lem:disjoint-windows}\nFor each fixed environment and target $L\\in\\{1,\\ldots,T\\}$, all the\nretained windows in \\eqref{eq:retention} are pairwise disjoint.\nConsequently,\n\\begin{equation}\\label{eq:telescoping-windows}\n \\sum_{i=1}^q\\sum_{j\\in J_i}\n \\mathbf1_{\\mathcal A_{i,j,L}}(g_j-g_{j+n_i})\n \\le2\\log M.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nSet $x_j=L-\\lceil\\sigma_j\\rceil$; this is nonincreasing in $j$.\nIf the band-$i$ window $[j,j+n_i)$ is retained, then\n\\[\n \\lceil\\sigma_{j+n_i}\\rceil-\\lceil\\sigma_j\\rceil\n \\le\\sigma_{j+n_i}-\\sigma_j+1\\le A'n_i+1,\n\\]\nso\n\\begin{equation}\\label{eq:window-end-depth}\n x_{j+n_i}\\ge a_{n_i}-A'n_i-1.\n\\end{equation}\nIf $[k,k+n_h)$ is a retained window in a coarser band $h<i$, then\n$x_k\\le B_{n_h}$.  By \\eqref{eq:separated-bands} and\n\\eqref{eq:window-end-depth},\n\\[\n x_{j+n_i}>B_{n_h}\\ge x_k.\n\\]\nMonotonicity of $x_j$ forces $k>j+n_i$.  Thus the window from the finer band\nends before the window from the coarser band starts.  Within a single band,\ndistinct starts are distinct multiples of its length $n_i$, so its\nhalf-open windows also do not overlap.\n\nList the retained windows in increasing order as\n$[u_1,v_1),\\ldots,[u_N,v_N)$, with $v_t\\le u_{t+1}$.  By\n\\eqref{eq:window-monotonicity},\n\\[\n \\sum_{t=1}^N(g_{u_t}-g_{v_t})\n \\le g_{u_1}-g_{v_N}\\le2\\log M.\n\\]\nThe empty sum is zero.  This proves \\eqref{eq:telescoping-windows}.\n\\end{proof}\n\nFigure~\\ref{fig:disjoint-windows} shows the order of retained windows\nfrom two different bands in this proof.\n\n\\begin{figure}[t]\n\\centering\n\\begin{tikzpicture}[x=0.86cm,y=0.86cm,>=stealth]\n \\draw[->] (0,0)--(12.7,0) node[right] {block index};\n \\draw[line width=1.5pt] (1,0)--(5,0);\n \\draw[line width=1.5pt] (8,0)--(10.5,0);\n \\foreach \\x in {1,5,8,10.5}{\\draw (\\x,-0.12)--(\\x,0.12);}\n \\node[below] at (1,-0.12) {$j$};\n \\node[below] at (5,-0.12) {$j+n_i$};\n \\node[below] at (8,-0.12) {$k$};\n \\node[below] at (10.5,-0.12) {$k+n_h$};\n \\node[above] at (3,0.15) {band $i$};\n \\node[above] at (9.25,0.15) {band $h<i$};\n \\draw[<->] (5,0.8)--(8,0.8);\n \\node[above] at (6.5,0.8) {$x_{j+n_i}>B_{n_h}\\ge x_k$};\n\\end{tikzpicture}\n\\caption{At a fixed target depth, a retained window from a finer band\nends before any retained window from a coarser band begins.  Here\n$x_j=L-\\lceil\\sigma_j\\rceil$ is nonincreasing in the block index.  The\nschematic is not to scale.}\n\\label{fig:disjoint-windows}\n\\end{figure}\n\n\\subsection{Summing over target depths}\n\nThe disjointness lemma bounds the total gain at one physical scale.\nSumming over targets recovers every pair scale in each band.  Indeed,\nfor a candidate $j\\in J_i$,\n\\[\n 0\\le\\sigma_j\\le Dj\\le T/2.\n\\]\nBecause $T$ is even, this also gives\n$0\\le\\lceil\\sigma_j\\rceil\\le T/2$.  Each integer\n$\\ell\\in[a_{n_i},B_{n_i}]$ therefore occurs exactly once as\n$L-\\lceil\\sigma_j\\rceil$ for $1\\le L\\le T$: take\n$L=\\ell+\\lceil\\sigma_j\\rceil$.  Its lower bound is $1$, and its\nupper bound is at most $B_{n_i}+T/2<T$.\n\nEach band has at least $T/(2Dn_i)$ candidate starts, and each contributes\nexpected pair mass at least $cn_i$ after summing the targets.  The block\nlength cancels, so every band supplies a fixed multiple of $T$.\nTake expectations in \\eqref{eq:telescoping-windows}.  Since the retention\nindicator is $\\mathcal F_{j+n_i}$-measurable, the tower property and\nLemma~\\ref{lem:conditional-gain} apply to each summand.  Sum the result\nover $L=1,\\ldots,T$ and use the preceding exact correspondence to obtain\n\\begin{align}\n 2T\\log M\n &\\ge\\frac\\delta2\\sum_{i=1}^q\\sum_{j\\in J_i}\n \\E\\left[\n \\mathbf1_{E_{n_i}}(\\theta^j\\omega)\n \\sum_{\\ell=a_{n_i}}^{B_{n_i}}\n c_\\ell(\\nu_{\\theta^j\\omega,n_i})\\right]\n \\notag\\\\\n &\\ge\\frac{\\delta c}{2}\\sum_{i=1}^q |J_i|n_i\n \\ge\\frac{\\delta c qT}{4D}.\n \\label{eq:final-window-count}\n\\end{align}\nThe second inequality uses Proposition~\\ref{prop:pair-bands} and the\nidentical distribution of the type environment under $\\theta^j$;\nindependence between different candidate windows is not needed.  The\nlast inequality follows from the exact count\n\\[\n |J_i|=\\left\\lfloor\\frac{T}{2Dn_i}\\right\\rfloor+1\n \\ge\\frac{T}{2Dn_i}.\n\\]\nAfter canceling $T$, \\eqref{eq:final-window-count} bounds the arbitrary\ninteger $q$ by $8D\\log M/(\\delta c)$.  All the constants in this bound\nwere fixed before $q$ was chosen, so this is impossible.\n\n\\begin{proof}[Completion of the proof of Theorem~\\ref{thm:main}]\nThe standard upper bound gives\n$\\dim_H\\mu\\le\\min\\{1,h/\\chi\\}$.  If the inequality were strict,\nthen $d=\\dim_H\\mu$ would satisfy $d<1$ and $d\\chi<h$.\nSection~\\ref{sec:types} would supply the pair bands used above, and\n\\eqref{eq:final-window-count} would give a contradiction.  Therefore\n$\\dim_H\\mu=\\min\\{1,h/\\chi\\}$ for the prescribed positive vector\n$p$.  Since $p$ was arbitrary, the conclusion holds for every such\nvector, including the cases $h=\\chi$ and $h>\\chi$.\n\\end{proof}\n"}, {"path": "preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/main.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/The-entropy-rate-dimension-formula-for-self-similar-measures-on-the-line-September-24-2026/main.pdf", "bytes": 416553, "sha256": "a09f48f43c41539211024d8e079580fe0712dc3dfd8035a16f7057d9b8b5d198", "base64": 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{"path": "preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/README.md", "bytes": 749, "sha256": "a4fa1fcc486012abb41863cb4ebdb2fab2481fd29b4ad09064f2fa55e053f265", "content": "# [Unbounded Violations of the Square-Root Degree Bound](Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 26, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026,\n  author = {{OpenAI}},\n  title = {{Unbounded Violations of the Square-Root Degree Bound}},\n  howpublished = {OpenAI Math Release preprint\n                  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"}, {"path": "preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/bibliography.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/bibliography.tex", "bytes": 1957, "sha256": "2012645afaefb7cef9a4aa7fc3a6aec21d4badc9b475daffe1c0a3de7bf4f846", "content": "\\begin{thebibliography}{9}\n\\bibitem{BlaisTanWan}\nE. Blais, L.-Y. Tan, and A. Wan,\n\\emph{An inequality for the Fourier spectrum of parity decision trees},\narXiv:1506.01055v1 (2015).\n\\url{https://arxiv.org/abs/1506.01055v1}.\n\n\\bibitem{Durrett}\nR. Durrett, \\emph{Probability: Theory and Examples}, fifth edition,\nCambridge University Press, 2019.\nAuthor's Version~5, January~11, 2019,\n\\url{https://sites.math.duke.edu/~rtd/PTE/PTE5_011119.pdf}.\n\n\\bibitem{FHKL}\nY. Filmus, H. Hatami, N. Keller, and N. Lifshitz,\nOn the sum of the $L_1$ influences of bounded functions,\n\\emph{Israel Journal of Mathematics} \\textbf{214} (2016), 167--192.\n\\url{https://doi.org/10.1007/s11856-016-1355-0}.\nPreprint version: arXiv:1404.3396v3 (2015),\n\\url{https://arxiv.org/abs/1404.3396v3}.\n\n\\bibitem{Jha}\nS. K. Jha, \\emph{On the Sum of Linear Coefficients of a Boolean Valued\nFunction}, arXiv:1611.01029v2 (2016).\n\\url{https://arxiv.org/abs/1611.01029v2}.\n\n\\bibitem{KudinPasalic}\nS. Kudin and E. Pa\\v{s}ali\\'c,\nProving the conjecture of O'Donnell in certain cases and disproving\nits general validity,\n\\emph{Discrete Applied Mathematics} \\textbf{289} (2021), 345--353.\n\\url{https://doi.org/10.1016/j.dam.2020.11.005}.\n\n\\bibitem{NisanSzegedy}\nN. Nisan and M. Szegedy,\nOn the degree of Boolean functions as real polynomials,\n\\emph{Computational Complexity} \\textbf{4} (1994), 301--313.\n\\url{https://doi.org/10.1007/BF01263419}.\n\n\\bibitem{ODonnell}\nR. O'Donnell, \\emph{Open Problems in Analysis of Boolean Functions},\narXiv:1204.6447v1 (2012), p.~9.\n\\url{https://arxiv.org/abs/1204.6447v1}.\n\n\\bibitem{ODonnellServedio}\nR. O'Donnell and R. A. Servedio,\nLearning monotone decision trees in polynomial time,\n\\emph{SIAM Journal on Computing} \\textbf{37} (2007), 827--844.\n\\url{https://doi.org/10.1137/060669309}.\n\n\\bibitem{Wang}\nQ. Wang, \\emph{On a Conjecture of O'Donnell},\nCryptology ePrint Archive, Report 2020/002 (2020).\n\\url{https://eprint.iacr.org/2020/002}.\n\\end{thebibliography}\n"}, {"path": "preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/main.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/main.tex", "bytes": 1611, "sha256": "ae1bd3a69ed7430aa2c9d7ccba100281c99422a0df55eda6bab2ae6fe3535cd5", "content": "\\documentclass[11pt]{article}\n\\usepackage[margin=1in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage{booktabs,microtype}\n\\usepackage[colorlinks=true,linkcolor=blue,citecolor=blue,urlcolor=blue]{hyperref}\n\\hypersetup{pdftitle={Unbounded Violations of the Square-Root Degree Bound},pdfauthor={OpenAI}}\n\\newcommand{\\E}{\\mathbb E}\n\\newcommand{\\Pp}{\\mathbb P}\n\\newcommand{\\R}{\\mathbb R}\n\\newcommand{\\one}{\\mathbf 1}\n\\DeclareMathOperator{\\Var}{Var}\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\title{Unbounded Violations of the Square-Root Degree Bound}\n\\author{OpenAI}\n\\date{September 26, 2026}\n\\pdfinfoomitdate=1\n\\pdftrailerid{}\n\\begin{document}\n\\maketitle\n\n\\begin{abstract}\nWe disprove the Gopalan--Servedio square-root conjecture, even up to an\narbitrary constant factor. For every real \\(C>0\\), there is a nonconstant\nBoolean function \\(f:\\{-1,1\\}^n\\to\\{-1,1\\}\\) on a finite sign cube such that\n\\[\n\\sum_{i=1}^n \\widehat f(\\{i\\})>C\\sqrt{\\deg(f)}.\n\\]\nHere \\(\\widehat f(\\{i\\})\\) is the linear Fourier coefficient associated with\nthe \\(i\\)th input, and \\(\\deg(f)\\) is the degree of the real multilinear\npolynomial representing \\(f\\).\n\\end{abstract}\n\n\\input{sections/00-introduction}\n\\input{sections/01-observations}\n\\input{sections/02-reporting}\n\\input{sections/03-alternatives}\n\\input{sections/04-mean-cost}\n\\appendix\n\\input{sections/05-weighted}\n\\input{sections/06-small-width}\n\\input{bibliography}\n\\end{document}\n"}, {"path": "preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/00-introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/00-introduction.tex", "bytes": 7989, "sha256": "c1af2a21b1c43449234a2e525cfd77c6580681a7a7386356f9cf0e7036b703c3", "content": "\\section{Introduction}\n\nThe sum of the linear Fourier coefficients of a Boolean function measures\nits correlation with the sum of its inputs. For a real-valued function\n\\(f:\\{-1,1\\}^n\\to\\mathbb R\\), with \\(X\\) uniform on the sign cube,\nwrite\n\\[\n \\widehat f(S)=\\E\\!\\left[f(X)\\prod_{i\\in S}X_i\\right],\n \\qquad \\deg(f)=\\max\\{|S|:\\widehat f(S)\\ne0\\}.\n\\]\nConstants have degree zero. This is the degree of the real multilinear\npolynomial representing \\(f\\), as in the work of Nisan and\nSzegedy~\\cite{NisanSzegedy}. For Boolean \\(f\\), taking values in\n\\(\\{-1,1\\}\\), Cauchy--Schwarz gives\n\\[\n \\sum_{i=1}^n\\widehat f(\\{i\\})\n =\\E\\!\\left[f(X)\\sum_{i=1}^nX_i\\right]\\le\\sqrt n.\n\\]\nGopalan and Servedio conjectured that \\(\\sqrt{\\deg(f)}\\) could replace\n\\(\\sqrt n\\); see O'Donnell's problem list~\\cite[p.~9]{ODonnell}.\nWe show that no constant multiple of this proposed bound holds.\n\n\\begin{theorem}\\label{thm:main}\nFor every real \\(C>0\\), there are a positive integer \\(n\\) and a\nnonconstant Boolean function \\(f:\\{-1,1\\}^n\\to\\{-1,1\\}\\) such that\n\\[\n \\sum_{i=1}^n\\widehat f(\\{i\\})>C\\sqrt{\\deg(f)}.\n\\]\nConsequently,\n\\[\n \\sup_{\\substack{n\\ge1,\\ f:\\{-1,1\\}^n\\to\\{-1,1\\}\\\\\\deg(f)>0}}\n \\frac{\\sum_i|\\widehat f(\\{i\\})|}{\\sqrt{\\deg(f)}}=\\infty.\n\\]\n\\end{theorem}\n\nThe universal signed and absolute versions of the conjecture are\nequivalent. Indeed, the coordinate change\n\\(x_i\\mapsto\\operatorname{sign}(\\widehat f(\\{i\\}))x_i\\), with sign\n\\(+1\\) at zero, preserves degree and makes every linear coefficient\nnonnegative. Our construction produces a large signed sum directly.\nThe dimension and all copy counts in the\nconstruction are finite; we do not obtain useful bounds on their growth.\n\n\\subsection*{The square-root and majority bounds}\nThe conjecture is attributed to Gopalan and Servedio, circa 2009, in\nO'Donnell's problem list. It also appears as Conjecture 3.17 in the\npreprint of Filmus, Hatami, Keller and Lifshitz~\\cite{FHKL}.\nHere the singleton coefficients are summed linearly, rather than squared.\n\nA sharper proposal in the same problem list uses the majority benchmark\n\\[\n B_d=\\E|X_1+\\cdots+X_d|\\le\\sqrt d.\n\\]\nThis is the signed singleton sum of majority on \\(d\\) bits, with either\nfixed value at ties. Replacing \\(\\sqrt{\\deg(f)}\\) by\n\\(B_{\\deg(f)}\\) gives a stronger inequality, so a counterexample to\nthat inequality need not violate the square-root bound.\nJha~\\cite[Theorem 2.1]{Jha} gave discrete-derivative reformulations of\nthe majority proposal. Wang~\\cite[Theorems 2.2, 3.2 and 3.6]{Wang}\ngave another equivalent formulation and proved the cases \\(d=1\\) and\n\\(d=n-1\\). Kudin and Pa\\v{s}ali\\'c~\\cite{KudinPasalic} proved the\ncases \\(d=2,3\\) and refuted the majority bound at \\(d=4\\).\nTheorem~\\ref{thm:main} excludes every constant multiple of the weaker\nsquare-root bound. Stating both inequalities explicitly distinguishes\nthe questions despite the different names used for them in the literature.\n\nA linear degree bound does hold for every Boolean function. Let\n\\(\\operatorname{Inf}_i(f)\\) be the probability that flipping coordinate\n\\(i\\) changes its value, and let\n\\(I(f)=\\sum_i\\operatorname{Inf}_i(f)\\) be the total influence.\nThe discrete derivative in coordinate \\(i\\), defined as half the\ndifference between the values with that coordinate set to \\(+1\\) and\n\\(-1\\), takes values in \\(\\{-1,0,1\\}\\). Its mean is\n\\(\\widehat f(\\{i\\})\\), so the absolute value of this mean is at most\nits second moment, \\(\\operatorname{Inf}_i(f)\\). The Fourier formula\nfor total influence and Parseval's identity therefore give\n\\cite[Lemma~2.4 and Corollary~2.5]{NisanSzegedy}\n\\[\n \\sum_i|\\widehat f(\\{i\\})|\n \\le I(f)=\\sum_{S\\subseteq[n]}|S|\\widehat f(S)^2\n \\le\\deg(f).\n\\]\nTheorem~\\ref{thm:main} thus separates the conjectured square-root\nscale from this surviving linear bound.\n\n\\subsection*{Retaining variance with low-degree observations}\nInstead of constructing \\(f\\) immediately, we first construct a\nfinite-valued observation \\(F\\) of independent signs. If\n\\(H_N=X_1+\\cdots+X_N\\), the information that \\(F\\) retains about this\nsum is measured by\n\\[\n T_F=\\E[H_N\\mid F],\\qquad v(F)=\\E T_F^2.\n\\]\nWe call \\(T_F\\) the \\emph{score} of \\(F\\) and \\(v(F)\\) its retained\nvariance. When \\(v(F)>0\\), its standardized score is\n\\(T_F/\\sqrt{v(F)}\\). We call a positive real number \\(D\\) a \\emph{cell degree bound} if\n every output indicator \\(\\one_{\\{F=a\\}}\\) has degree at most \\(D\\). Every function of\n\\(F\\) then has degree at most \\(D\\), including\n\\(f=\\operatorname{sign}(T_F)\\). This readout has correlation\n\\(\\E[fH_N]=\\E|T_F|\\). Averaging independent copies makes its score\napproximately Gaussian, so a large ratio \\(v(F)/D\\) gives the theorem.\n\nConditional coordinate sums also appear in decision-tree inequalities.\nA subcube is obtained by fixing some coordinates. If the cells of\n\\(F\\) are subcubes, the unfixed coordinates remain independent uniform\nsigns under conditioning, so \\(v(F)\\) equals the expected number of\nfixed coordinates. O'Donnell and Servedio~\\cite[Theorem~3 and\nLemma~3]{ODonnellServedio} bound the signed singleton sum of a Boolean\nreadout by the square root of this expectation. Blais, Tan and\nWan~\\cite[Lemma~3.1 and Theorem~1]{BlaisTanWan} use the conditional\ncoordinate sum to prove square-root bounds in parity decision-tree\ndepth, where a query reveals the product of a specified set of inputs.\nOur cells need not be subcubes: their indicators are controlled by\npolynomial degree, and cancellation allows retained variance to exceed\nthat degree cost.\n\nThe main construction repeatedly increases this ratio. Revealing\n\\(m\\) independent standardized scores retains variance \\(m\\). We use\n\\(m+1\\) scores and design an observation retaining strictly more than\n\\(m\\) units of variance, although each output indicator is a linear\ncombination of functions of at most \\(m\\) inputs. Such an indicator may\ndepend on all \\(m+1\\) inputs: the degree saving comes from cancellation,\nnot from ignoring a fixed coordinate.\n\nHere is the reporting rule behind the gain. One score selects a leaf\namong the remaining \\(m\\) scores, each of which has a short prescribed\ninterval. Usually we report all but one score. On the exceptional event\nthat only the selected leaf misses its interval, we report a single\ntag. The intervals are chosen so that the sum of all the scores can be\npredicted on this event with mean squared error less than one. Every\nother report has residual error exactly one. Thus the exceptional tag\nimproves on the variance retained by revealing \\(m\\) scores.\nIts indicator admits the required cancellation identity.\n\nSection~\\ref{sec:observations} proves the finite-observation calculus\nand amplification criterion. Section~\\ref{sec:observation} then\nexplains the reports and their\nprediction error before choosing the Gaussian intervals. Once that\nchoice is fixed, the central limit theorem supplies a sufficiently large\nfinite average at each step. A finite number of steps gives any\nprescribed ratio, and a final sign readout proves the theorem. Thus\nSections~\\ref{sec:observations} and~\\ref{sec:observation} contain a\ncomplete proof of Theorem~\\ref{thm:main}.\n\nThe further constructions distinguish which features of this rule are\nneeded for a gain. In Section~\\ref{sec:alternatives}, the \\emph{coarse\nreport} shows that the all-leaves-pass values can also be replaced by\none tag; switching to an extra independent score isolates the\nconditional-variance saving on the exceptional event.\nSection~\\ref{sec:mean-cost} allows different degree costs for different\noutputs: retained variance need only exceed their \\emph{average} cost.\nReporting typical words of outputs and merging the remaining words into\none cell then gives a degree bound valid everywhere while retaining\nalmost all the variance.\nAppendix~\\ref{app:weighted-mixture} supplies an independent Gaussian\nevent for the average-cost criterion. Appendix~\\ref{app:small-width}\ngives a different Gaussian parameter schedule for the coarse report,\ntogether with the transfer argument for its law-dependent coefficients.\n"}, {"path": "preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/01-observations.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/01-observations.tex", "bytes": 6007, "sha256": "8e12855dbf6275d197ba61d4acbf0d3e1aabdd079ba83ca2d022a40475328fc0", "content": "\\section{Finite observations and amplification}\\label{sec:observations}\n\nWe first justify the degree and conditioning identities used by every\nconstruction. Throughout, \\(F\\) is a finite-valued observation,\n\\(T_F=\\E[H_N\\mid F]\\) is its centered score, and \\(D>0\\) is a cell\ndegree bound. The bound \\(D\\) may be real; it need not equal the actual\ninteger degree of any indicator.\n\nEvery real function of \\(F\\) has degree at most \\(D\\), by expansion in\nits finitely many value indicators. Unused labels have zero indicators.\nIf distinct observation labels produce the same numerical score, their\nindicators add, so score collisions preserve the same bound. A function of \\(r\\) observations on\ndisjoint blocks, with bounds \\(D_1,\\ldots,D_r\\), has degree at most\n\\(\\sum_iD_i\\). Indeed, it expands in products of their indicators, and\nthe degree of a product is at most the sum of the degrees: multiplication\nof Fourier characters takes symmetric differences of their index sets.\n\nLet \\(F_1,\\ldots,F_r\\) be independent observations on disjoint bit\nblocks, with scores \\(T_1,\\ldots,T_r\\), and let \\(H_{\\mathrm{tot}}\\)\nbe the total bit sum. Conditioning on their tuple gives\n\\(\\E[H_{\\mathrm{tot}}\\mid F_1,\\ldots,F_r]=\\sum_iT_i\\).\nFor any observation \\(Q\\) that is a function of this tuple, the tower\nproperty therefore gives\n\\[\n \\E[H_{\\mathrm{tot}}\\mid Q]=\\E\\!\\left[\\sum_iT_i\\,\\middle|\\,Q\\right].\n\\]\nThese are instances of the conditional-expectation projection identities;\nsee~\\cite[Theorems~4.1.13 and 4.1.15]{Durrett}. In particular, for\n\\(v=v(F)>0\\), the numerical observation\n\\begin{equation}\\label{eq:averaging}\n Z_M=\\frac{T_1+\\cdots+T_M}{\\sqrt{Mv}}\n\\end{equation}\nformed from \\(M\\) independent copies has cell degree bound \\(MD\\), and\nits score is exactly \\(\\sqrt{Mv}\\,Z_M\\).\nThe classical i.i.d.\\ central limit theorem~\\cite[Theorem~3.4.1]{Durrett} gives\n\\(Z_M\\Rightarrow G\\), where \\(G\\) is standard normal. For each fixed\ninitial \\(F\\), all moments are finite, and\n\\begin{equation}\\label{eq:fourth}\n \\E Z_M^4=\\frac{\\E T_F^4}{Mv^2}+3\\frac{M-1}{M}.\n\\end{equation}\nThus the fourth moments are bounded uniformly in \\(M\\) for this fixed\nobservation. The bound may change when a later amplification stage\nuses a different observation.\nAll citations to probability results refer to the January 11, 2019 author\nversion of Durrett~\\cite{Durrett}.\n\n\\begin{lemma}[Transfer of predictor errors]\\label{lem:transfer}\nLet \\(Z_j\\) be centered, variance-one random variables converging in\ndistribution to a standard normal, with \\(\\sup_j\\E Z_j^4<\\infty\\).\nFor fixed \\(r\\), let \\(\\mathbf Z_j\\) consist of \\(r\\) independent copies\nof \\(Z_j\\), and let \\(\\mathbf G\\) consist of \\(r\\) independent standard\nnormals. If \\(g:\\R^r\\to\\R\\) is measurable, its discontinuities have\nGaussian measure zero, and\n\\[\n |g(z)|\\le K\\left(1+\\sum_{i=1}^r z_i^2\\right)\n\\]\nfor some fixed \\(K\\), then \\(\\E g(\\mathbf Z_j)\\to\\E g(\\mathbf G)\\).\n\\end{lemma}\n\n\\begin{proof}\nIndependence gives joint weak convergence. Clipping \\(g\\) to a bounded\nrange gives convergence of expectations by the null-discontinuity\ncriterion for weak convergence~\\cite[Theorem~3.10.1(vi)]{Durrett}.\nThe fourth-moment bound and fixed \\(r\\)\ngive \\(\\sup_j\\E[g(\\mathbf Z_j)^2]<\\infty\\), so the clipping errors in\nfirst moment tend to zero uniformly; this is the higher-moment\ncriterion for uniform integrability~\\cite[Theorem~4.6.2]{Durrett}.\nThe Gaussian error does also.\n\\end{proof}\n\n\\begin{lemma}[Boolean readout]\\label{lem:readout}\nSuppose finite observations have positive cell degree bounds \\(D\\) and\narbitrarily large ratios \\(v(F)/D\\). Then Theorem~\\ref{thm:main} holds.\n\\end{lemma}\n\n\\begin{proof}\nFix \\(C>0\\) and choose \\(F\\) with \\(v(F)>4C^2D\\).\nFor \\(M\\) independent copies, define\n\\(f=\\operatorname{sign}(T_1+\\cdots+T_M)\\), with sign \\(1\\) at zero.\nIts degree is at most \\(MD\\). Conditioning on the tuple of observations,\n\\[\n \\sum_i\\widehat f(\\{i\\})\n =\\E[fH_{MN}]\n =\\E|T_1+\\cdots+T_M|\n =\\sqrt{Mv(F)}\\,\\E|Z_M|.\n\\]\nThe central limit theorem and the uniform second-moment bound imply\n\\(\\E|Z_M|\\to\\E|G|=\\sqrt{2/\\pi}>1/2\\).\nFor a sufficiently large finite \\(M\\), the displayed sum exceeds\n\\(C\\sqrt{MD}\\ge C\\sqrt{\\deg(f)}\\). It is positive, so \\(f\\) is\nnonconstant.\n\\end{proof}\n\n\\begin{proposition}[Amplification criterion]\\label{prop:amplification}\nLet \\(1\\le d<r\\) be integers, and let \\(\\mathcal W\\) be a measurable\nobservation rule on \\(r\\) real inputs. Suppose:\n\\begin{enumerate}\n\\item On any product of finite input alphabets, the indicator of each\noutput value is a finite linear combination of functions each depending\non at most \\(d\\) input coordinates.\n\\item There is an \\(\\eta>0\\) such that, whenever centered variance-one\nlaws \\(Z_j\\) converge weakly to a standard normal with uniformly bounded\nfourth moments, independent copies satisfy, for all sufficiently large \\(j\\),\n\\[\n \\Var\\!\\left(\\E\\!\\left[\\sum_{i=1}^r Z_{j,i}\\,\\middle|\\,\n                \\mathcal W(\\mathbf Z_j)\\right]\\right)\\ge d+\\eta.\n\\]\n\\end{enumerate}\nThen finite observations have arbitrarily large retained\nvariance-to-cell-degree ratios, and Theorem~\\ref{thm:main} follows.\n\\end{proposition}\n\n\\begin{proof}\nFix a finite observation \\(F\\) with \\(v=v(F)>0\\) and degree bound \\(D>0\\).\nForm \\(r\\) independent blocks of \\(M\\) copies of \\(F\\), and apply\n\\(\\mathcal W\\) to their normalized scores from \\eqref{eq:averaging}.\nThe new observation \\(F'\\) has finite range. Its cell degree is at most\n\\(dMD\\) by the first assumption and expansion in value indicators.\nThe second assumption applies by \\eqref{eq:fourth}. For a sufficiently\nlarge finite \\(M\\), the tower property therefore gives\n\\[\n v(F')=Mv\\,\\Var\\!\\left(\\E\\!\\left[\\sum_{i=1}^rZ_{M,i}\\mid F'\\right]\\right)\n \\ge Mv(d+\\eta).\n\\]\nThe ratio increases by at least the fixed factor \\(1+\\eta/d>1\\).\nStart with the observation of one sign, for which \\(v=D=1\\), and\niterate. Each stage is finite and has its own sufficiently large finite\ncopy count. A finite number of stages exceeds any prescribed ratio.\nApply Lemma~\\ref{lem:readout}.\n\\end{proof}\n"}, {"path": "preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/02-reporting.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/02-reporting.tex", "bytes": 10792, "sha256": "57ea4c0606a60b04dce3b5669a858dc0f9b9cbb79c90b2b5bd230a4e9cb7352b", "content": "\\section{Retaining more variance than the degree cost}\n\\label{sec:observation}\n\nWe now construct the observation rule needed for amplification.  Revealing\nany fixed set of $m$ of $m+1$ independent centered variance-one inputs retains variance\n$m$ from their sum: the unobserved input accounts for the remaining unit.\nOur rule will retain strictly more than $m$, while every output indicator\nis a linear combination of functions of at most $m$ inputs.  These are\nthe two conclusions required by Proposition~\\ref{prop:amplification},\nwith $r=m+1$ and $d=m$.\n\nThe rule usually leaves one input unobserved.  On one specially chosen\nevent, it instead reports only that the event occurred.  This improves\nthe prediction error if the sum of all the inputs is sufficiently close\nto one fixed number on that event.  We first describe the rule and\ncalculate its error for arbitrary independent inputs.  We will then\nchoose its parameters using Gaussian inputs.\n\n\\subsection{A reporting rule and its prediction error}\n\nLet $m\\ge3$, let $I_1,\\ldots,I_m$ be intervals, and let\n$\\iota:\\mathbb R\\to\\{1,\\ldots,m\\}$ be a selector with finitely many\ncut points.  All endpoint and tie conventions are part of the rule.\nWrite its inputs as $(u,z_1,\\ldots,z_m)$ and their sum as\n$L=u+\\sum_{j=1}^m z_j$.  The coordinate $u$ selects the leaf\n$z_{\\iota(u)}$.  Define\n\\[\n B=\\{z_j\\in I_j\\text{ for every }j\\},\\qquad\n P=\\{z_j\\in I_j\\text{ for every }j\\ne\\iota(u)\\},\\qquad\n A=P\\setminus B.\n\\]\nThus $A$ is the event that the selected leaf alone fails its interval\ntest.  Since $B\\subseteq P$, the sets $A$, $B$, and $P^c$ partition\nthe input space.  Define the tagged observation $\\mathcal W$ by\n\\[\n\\begin{array}{c|l}\n \\text{Event}&\\text{Information reported}\\\\ \\hline\n A&\\text{the tag }A\\text{ only}\\\\\n B&(B,z_1,\\ldots,z_m)\\\\\n P^c&(P^c,u,\\iota(u),(z_j)_{j\\ne\\iota(u)}).\n\\end{array}\n\\]\nOn $B$, the central input is omitted.  On $P^c$, the selected leaf\nis omitted.  Although the exceptional report on $A$ can depend on all\n$m+1$ inputs, its indicator has the pointwise identity\n\\begin{equation}\\label{eq:indicator}\n \\mathbf1_A=\n \\sum_{i=1}^m\\mathbf1_{\\{\\iota(u)=i\\}}\n                  \\prod_{j\\ne i}\\mathbf1_{I_j}(z_j)\n -\\prod_{j=1}^m\\mathbf1_{I_j}(z_j).\n\\end{equation}\nEach summand uses only $m$ coordinates.  This cancellation is what\nallows the exceptional report to have the same degree cost as the\nordinary reports.\n\n\\begin{lemma}\\label{lem:report}\nFor every product of finite input alphabets, the rule $\\mathcal W$\nhas finite range, and each output indicator is a finite linear\ncombination of functions of at most $m$ input coordinates.\n\nFor independent centered variance-one inputs, whose marginal laws need\nnot agree, and every fixed $b\\in\\mathbb R$, put\n\\[\n \\Delta_b=\\mathbb E[\\mathbf1_A(1-(L-b)^2)].\n\\]\nThen\n\\begin{equation}\\label{eq:retained}\n \\operatorname{Var}(\\mathbb E[L\\mid\\mathcal W])\\ge m+\\Delta_b.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nIdentity~\\eqref{eq:indicator} gives the required decomposition\nfor the $A$ report.  An attained $B$ report fixes the $m$ leaf values\nand imposes no condition on $u$.  An attained $P^c$ report fixes $u$,\nits selected index $i$, and the $m-1$ unselected leaves.  At least one\nof these reported leaves fails its test; hence the reported values\nalready force $P^c$, whatever the omitted value $z_i$ may be.  Its\nindicator therefore depends on only the $m$ reported coordinates.\nUnused output labels have zero indicators.  This proves the first\nassertion on every input, including interval endpoints.\n\nTo estimate the retained variance, predict $L$ by $b$ on $A$, by\n$\\sum_jz_j$ on $B$, and by $u+\\sum_{j\\ne\\iota(u)}z_j$ on $P^c$.\nThis predictor is a function of the reported information.  On $B$\nits error is $u$; independence gives\n$\\mathbb E[\\mathbf1_Bu^2]=\\mathbb P(B)$.\nFor the last type of report, fix an index $i$ and let\n$E_i=P^c\\cap\\{\\iota(u)=i\\}$.  This event and its reported data\ndepend only on $u$ and the leaves other than $z_i$.  Consequently\n\\[\n \\mathbb E[\\mathbf1_{E_i}z_i]=0,\n \\qquad\n \\mathbb E[\\mathbf1_{E_i}z_i^2]=\\mathbb P(E_i).\n\\]\nSumming the squared errors over the three types of report gives\n\\[\n \\mathbb P(A^c)+\\mathbb E[\\mathbf1_A(L-b)^2]=1-\\Delta_b.\n\\]\nConditional expectation has no larger mean squared error\n\\cite[Theorems~4.1.13 and 4.1.15]{Durrett}.  Since $L$ is centered and\n$\\operatorname{Var}(L)=m+1$, the conditional-variance identity gives\n\\eqref{eq:retained}.\n\\end{proof}\n\nThe remaining task is now precise: choose the intervals, selector, and\nconstant $b$ so that $\\Delta_b>0$ for Gaussian inputs.  A strict\npositive margin will survive the normal approximation used in\namplification.  The next calculation produces that margin by subtracting\nthe contribution of $B$ from that of $P$ in\n\\eqref{eq:indicator}.\n\n\\subsection{Choosing the intervals in the Gaussian model}\n\nWe will obtain a positive predictor deficit and also record the\nconditional variance on $A$.  The latter describes the event's gain\nwithout specifying a predictor: on $A$, the sum varies less than one\nunobserved variance-one input.\n\n\\begin{lemma}\\label{lem:gaussian}\nThere are a finite odd integer $m\\ge3$, closed intervals\n$I_1,\\ldots,I_m$, and a selector $\\iota$ with finitely many cut points\nsuch that the following holds for independent standard normal inputs.\nLet $\\beta_j$ be the probability of $I_j$, and let $\\mu_j,s_j^2$ be\nthe conditional mean and variance on $I_j$.  Define\n\\[\n p_* =\\prod_{j=1}^m\\beta_j,\\qquad\n S=\\sum_{j=1}^m s_j^2,\\qquad\n b=\\sum_{j=1}^m\\mu_j-1.\n\\]\nThen $p_*>0$, $S<1/4$, and\n\\begin{equation}\\label{eq:deficit}\n d_A:=\\mathbb E[\\mathbf1_A(1-(L-b)^2)]>\\frac34p_*.\n\\end{equation}\nThe boundary of $A$ lies in a finite union of coordinate hyperplanes.\nIn particular, $\\mathbb P(A)>0$ and $\\operatorname{Var}(L\\mid A)<1$.\n\\end{lemma}\n\n\\begin{proof}\nThe shift by one in $b$ determines where to place the intervals.\nConditional on $u=t$ and $P$, the mean of $L-b$ is\n$1+t-\\mu_{\\iota(t)}$, so selecting an interval near $1+t$ makes\nthis mean small.  Conditional on $B$ alone, with $u$ still random,\nits mean is instead one.  Subtracting the contribution of $B$ will\nturn that fixed squared mean into the positive term in $d_A$.\n\nWe let odd $m$ tend to infinity to choose the parameters, and then fix\none finite value.  Set\n\\[\n R=\\sqrt{8\\log m},\\qquad h=m^{-3/2},\\qquad\n a_i=1-R+\\frac{2R(i-1)}{m-1},\\qquad I_i=[a_i-h,a_i+h].\n\\]\nFor $|t|\\le R$, select a center $a_{\\iota(t)}$ nearest to $1+t$,\nresolving ties by the smaller index.  For $|t|>R$, use the middle\nindex, whose center is $1$.  These choices define the rule on the\nwhole real line.  Its finitely many interval endpoints and selector\ncut points also give the asserted boundary property.\n\nWrite $\\phi(t)=(2\\pi)^{-1/2}e^{-t^2/2}$.  Every $\\beta_i$ lies in\n$(0,1)$.  Condition first on $u=t$ and put $i=\\iota(t)$.  The event\n$P$ then has probability $p_*/\\beta_i$; it restricts the leaves\n$j\\ne i$ to their intervals and leaves $z_i$ unrestricted.  Under\nthis conditioning, $L-b$ has mean $1+t-\\mu_i$ and variance\n$1+\\sum_{j\\ne i}s_j^2$.  Thus\n\\[\n \\mathbb E[\\mathbf1_P(1-(L-b)^2)]=-p_*J,\n \\qquad\n J=\\int_{\\mathbb R}\n \\left((1+t-\\mu_{\\iota(t)})^2+\n                   \\sum_{j\\ne\\iota(t)}s_j^2\\right)\n                 \\frac{\\phi(t)}{\\beta_{\\iota(t)}}\\,dt.\n\\]\nOn $B$, with $u$ again random, $L-b$ has mean $1$ and variance\n$1+S$.  Therefore\n\\[\n \\mathbb E[\\mathbf1_B(1-(L-b)^2)]=-p_*(1+S).\n\\]\nSince $\\mathbf1_A=\\mathbf1_P-\\mathbf1_B$, we obtain the exact identity\n\\begin{equation}\\label{eq:gaussian-identity}\n d_A=p_*(1+S-J).\n\\end{equation}\nThe positive constant $1$ comes from the mean of $L-b$ on $B$.\nIt remains to make the nonnegative error $J$ small.  Nearest-center\nselection controls its central part; the fallback to the middle\ninterval controls the tails.\n\nThe elementary interval estimates are\n\\[\n |\\mu_i-a_i|\\le h,\\qquad s_i^2\\le h^2,\\qquad\n \\beta_i\\ge 2h(2\\pi)^{-1/2}\n              \\exp\\!\\left(-\\frac{(|a_i|+h)^2}{2}\\right).\n\\]\nFor $|t|\\le R$, the grid spacing and the selector give\n\\[\n |1+t-\\mu_{\\iota(t)}|\\le\\frac{R}{m-1}+h,\n \\qquad |a_{\\iota(t)}|+h\\le |t|+2\n\\]\nwhen $m$ is sufficiently large.  Hence\n$\\phi(t)/\\beta_{\\iota(t)}\\le(e^2/2h)e^{2R}$ on this range, and\n\\begin{align*}\n J_{\\mathrm{central}}\n &\\le\\frac{e^2R}{h}e^{2R}\n       \\left(\\left(\\frac{R}{m-1}+h\\right)^2+mh^2\\right)\\\\\n &=O\\!\\left(R(R^2+1)e^{2R}m^{-1/2}\\right)=o(1).\n\\end{align*}\nHere $e^{2R}=m^{o(1)}$, while $R$ is a power of $\\log m$.\n\nOn $|t|>R$, the selected interval is centered at $1$.  For $h\\le1$\nits probability is at least $2h\\phi(2)$, and the numerator in $J$\nis $O(1+t^2)$.  Integration by parts gives\n\\[\n \\int_{|t|>R}(1+t^2)\\phi(t)\\,dt\n       =O((R+1)e^{-R^2/2}).\n\\]\nConsequently\n\\[\n J_{\\mathrm{tail}}\n =O(h^{-1}(R+1)e^{-R^2/2})\n =O((R+1)m^{-5/2})=o(1).\n\\]\nAlso $S\\le mh^2=m^{-2}\\to0$.  Fix a sufficiently large finite odd\n$m$ so that $J<1/4$ and $S<1/4$.  Identity~\\eqref{eq:gaussian-identity}\nnow proves \\eqref{eq:deficit}, with $p_*>0$.\n\nFinally, write $p_A=\\mathbb P(A)$.  Since $d_A>0$, we have $p_A>0$,\nand\n\\[\n d_A=p_A\\bigl(1-\\operatorname{Var}(L\\mid A)\n                   -(\\mathbb E[L\\mid A]-b)^2\\bigr)>0.\n\\]\nThis also proves $\\operatorname{Var}(L\\mid A)<1$.\n\\end{proof}\n\n\\subsection{Transfer to finite inputs and amplification}\n\nFix all the Gaussian choices from Lemma~\\ref{lem:gaussian},\nincluding $m$, the intervals, the selector, $b$, and $p_*>0$.\nSet $\\eta=p_*/2$.  If centered variance-one laws $Z_n$ converge\nweakly to a standard normal and have uniformly bounded fourth moments,\napply Lemma~\\ref{lem:transfer} to the fixed function\n\\[\n g(u,z_1,\\ldots,z_m)=\\mathbf1_A(1-(L-b)^2).\n\\]\nIt has quadratic growth and discontinuities only on the finitely many\nhyperplanes from Lemma~\\ref{lem:gaussian}; these have Gaussian\nmeasure zero.  For independent copies of $Z_n$, therefore,\n$\\mathbb Eg\\to d_A>3p_*/4$, and eventually $\\mathbb Eg>\\eta$.\nLemma~\\ref{lem:report} then gives\n\\[\n \\operatorname{Var}\\!\\left(\n   \\mathbb E\\!\\left[\\sum_{i=1}^{m+1} Z_{n,i}\n                   \\,\\middle|\\,\\mathcal W(\\mathbf Z_n)\\right]\\right)\n >m+\\eta.\n\\]\nTogether with the pointwise output-indicator decomposition in that\nlemma, this verifies both hypotheses of\nProposition~\\ref{prop:amplification} with\n$(r,d,\\eta)=(m+1,m,p_*/2)$.\n\nThe Gaussian parameters and $\\eta$ remain fixed throughout the iteration.\nProposition~\\ref{prop:amplification} therefore increases the ratio of\nretained variance to cell degree bound by at least $1+\\eta/m>1$ at\neach stage, using a sufficiently large finite copy count chosen for the\ncurrent observation. Starting from one revealed sign, finitely many\nstages make this ratio exceed $4C^2$ for any prescribed $C>0$.\nLemma~\\ref{lem:readout} then supplies a nonconstant Boolean function\nwith signed singleton sum greater than $C\\sqrt{\\deg(f)}$, proving\nTheorem~\\ref{thm:main}.\n"}, {"path": "preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/03-alternatives.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/03-alternatives.tex", "bytes": 8976, "sha256": "4d998ab33a8064f6a3187eb64635ef183effc602229ab6fc9bc3a573cb9919ac", "content": "\\section{Two other reporting rules}\\label{sec:alternatives}\n\nThe report in Section~\\ref{sec:observation}, which we call the\n\\emph{refined report}, keeps every leaf value when all the leaves pass\ntheir tests. We now ask what happens if that information is replaced by one\nmark. The resulting loss can be computed exactly and is smaller than\nthe Gaussian gain. A second rule uses one additional independent input:\nit reveals the original inputs off the exceptional event and the extra\ninput on that event. The first modification shows that the all-pass\nleaf values are dispensable; the second isolates the conditional-variance\nsaving on the exceptional event.\n\n\\subsection{Merging the all-leaves-pass reports}\\label{subsec:coarse-mark}\n\nStart with any integer $m\\ge3$, intervals $I_1,\\ldots,I_m$, and a\nselector $\\iota:\\R\\to\\{1,\\ldots,m\\}$ with finitely many cut points.\nFix all endpoint and tie conventions. As before, on inputs\n$(u,z_1,\\ldots,z_m)$ put\n\\[\n L=u+\\sum_{j=1}^m z_j,\\qquad\n B=\\{z_j\\in I_j\\text{ for every }j\\},\n\\]\n\\[\n P=\\{z_j\\in I_j\\text{ for }j\\ne\\iota(u)\\},\\qquad\n A=P\\setminus B.\n\\]\nDefine the \\emph{coarse report} $\\mathcal O$ by\n\\[\n\\begin{array}{c|l}\n \\text{Event}&\\text{Information reported}\\\\ \\hline\n A&\\text{the mark }X\\\\\n B&\\text{the mark }Y\\\\\n P^c&(O,u,\\iota(u),(z_j)_{j\\ne\\iota(u)}).\n\\end{array}\n\\]\nThe marks $X,Y$ and the ordinary-report tag $O$ are distinct. The rule\ndiffers from the refined report only on $B$. The next lemma separates\nits pointwise cost from the choice of an input law, so that other\ninterval constructions can use the same reporting rule.\n\n\\begin{lemma}[Cost and error of the coarse report]\\label{lem:coarse-report}\nOn every product of finite input alphabets, $\\mathcal O$ has finite\nrange, and each output indicator is a finite linear combination of\nfunctions of at most $m$ input coordinates.\n\nLet the inputs be independent, centered, and of variance one; their\nmarginal laws need not agree. For any fixed constants $b_0,b_1\\in\\R$,\ndefine\n\\begin{equation}\\label{eq:coarse-deficit}\n g_{b_0,b_1}\n =\\one_A\\bigl(1-(L-b_0)^2\\bigr)\n   +\\one_B\\bigl(1-(L-b_1)^2\\bigr).\n\\end{equation}\nThen the optimal residual error\n$\\mathcal R=\\E[(L-\\E[L\\mid\\mathcal O])^2]$ satisfies\n\\begin{equation}\\label{eq:coarse-residual}\n 1-\\mathcal R\\ge\\E g_{b_0,b_1},\\qquad\n \\Var(\\E[L\\mid\\mathcal O])\\ge m+\\E g_{b_0,b_1}.\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nThe $X$ indicator has the pointwise expansion \\eqref{eq:indicator}.\nThe $Y$ indicator depends only on the $m$ leaves. An ordinary output\nspecifies $u$, its selected index $i$, and the $m-1$ unselected leaf\nvalues. If these data are attained, one of the reported leaves fails\nits interval test. They therefore force $P^c$ for every value of the\nomitted leaf $z_i$, so the indicator uses just the $m$ reported inputs.\nAn inconsistent or unattained output label has zero indicator. Thus\nthe assertion holds pointwise, including endpoints and selector ties.\n\nPredict $L$ by $b_0$ on $X$, by $b_1$ on $Y$, and by\n$u+\\sum_{j\\ne\\iota(u)}z_j$ on an ordinary report. For each fixed\n$i$, the event $E_i=P^c\\cap\\{\\iota(u)=i\\}$ and all its reported data\ndepend only on $u$ and the leaves other than $z_i$. Independence gives\n\\[\n \\E[\\one_{E_i}z_i]=0,\\qquad\n \\E[\\one_{E_i}z_i^2]=\\Pp(E_i).\n\\]\nThe ordinary reports consequently contribute exactly $\\Pp(P^c)$ to\nthe squared prediction error. The total error of this predictor is\n\\[\n \\Pp(P^c)+\\E[\\one_A(L-b_0)^2]+\\E[\\one_B(L-b_1)^2]\n =1-\\E g_{b_0,b_1}.\n\\]\nConditional expectation minimizes squared error, proving the first\ninequality in \\eqref{eq:coarse-residual}. The second follows because\n$L$ is centered with variance $m+1$.\n\\end{proof}\n\nNow fix the Gaussian intervals and selector from\nLemma~\\ref{lem:gaussian}. Write\n\\[\n M_\\mu=\\sum_{j=1}^m\\mu_j,\\qquad b=M_\\mu-1,\\qquad\n p_*=\\Pp(B)>0,\\qquad S=\\sum_{j=1}^m s_j^2.\n\\]\nThe Gaussian calculation gave\n$d_A=\\E[\\one_A(1-(L-b)^2)]=p_*(1+S-J)$ with $J<1/4$.\nIn Lemma~\\ref{lem:coarse-report} take $b_0=b$ and $b_1=M_\\mu$.\nOn $B$, the central Gaussian remains unrestricted and the leaves are\nindependently restricted to their intervals. Thus $L-M_\\mu$ has\nconditional mean zero and variance $1+S$, giving\n\\begin{equation}\\label{eq:coarse-gaussian-gain}\n \\E g_{b,M_\\mu}=d_A-p_*S=p_*(1-J)>\\frac34p_*.\n\\end{equation}\nThe term $p_*S$ is precisely the added error on $B$: the refined\npredictor had error $u$ there, while merging its reports leaves the\nconditional leaf variance $S$ unobserved. Equation\n\\eqref{eq:coarse-gaussian-gain} is an identity for this predictor's\ngain; \\eqref{eq:coarse-residual} is the resulting lower bound for\noptimal retained variance.\n\nAll these Gaussian choices are now fixed. Let centered variance-one\nlaws $Z_n$ converge weakly to a standard normal with uniformly bounded\nfourth moments, and take $m+1$ independent copies as inputs. The fixed\nfunction $g_{b,M_\\mu}$ has quadratic growth; its discontinuities lie\non the finitely many interval and selector hyperplanes.\nLemma~\\ref{lem:transfer} and \\eqref{eq:coarse-gaussian-gain} imply\nthat eventually $\\E g_{b,M_\\mu}>\\eta$, where $\\eta=p_*/2>0$.\nLemma~\\ref{lem:coarse-report} then gives retained variance greater\nthan $m+\\eta$ and the required pointwise cell cost. Therefore\nProposition~\\ref{prop:amplification} applies with\n\\[\n (r,d,\\eta)=(m+1,m,p_*/2).\n\\]\nThis criterion and Lemma~\\ref{lem:readout} give\nTheorem~\\ref{thm:main} by the coarse rule as well.\n\n\\subsection{Switching to an independent score}\\label{subsec:extra-block}\n\nKeep the fixed Gaussian event $A$ and predictor $b$ above, and put\n$q=m+1$. Add one independent centered variance-one input $w$ to the\noriginal tuple $\\mathbf z=(u,z_1,\\ldots,z_m)$. Define\n\\[\n \\mathcal O_+(\\mathbf z,w)=\n \\begin{cases}\n   (\\mathrm{off},\\mathbf z),&\\mathbf z\\notin A,\\\\\n   (\\mathrm{on},w),&\\mathbf z\\in A.\n \\end{cases}\n\\]\nOff $A$ the original sum $L$ is known and $w$ is omitted. On $A$\nthe extra input is known and the original sum must be predicted.\nThe rule gains variance whenever the conditional variance of $L$ on\n$A$ is smaller than the one unit of residual variance of $w$.\n\nThis rule has $q+1$ inputs but cell cost at most $q$. Indeed, an\noff-$A$ output fixes the first $q$ inputs and places no condition on\n$w$. The indicator of an on-$A$ output $(\\mathrm{on},w_0)$ is\n\\[\n \\one_A(\\mathbf z)\\one_{\\{w=w_0\\}}.\n\\]\nMultiplying \\eqref{eq:indicator} by the last value test expresses\nthis indicator as a sum of functions of at most $m+1=q$ inputs.\nThe tags keep the two types of report separate, and unattained labels\nhave zero indicators. These pointwise expansions also cover the\nprescribed endpoints and ties. The rule has finite range on every\nfinite product alphabet.\n\nTo establish a gain for the approximating laws, first transfer the\nunconditional deficit, before conditioning on $A$. For independent\ncopies of any centered variance-one law $Z_n$ tending to a standard\nnormal with uniformly bounded fourth moments, Lemma~\\ref{lem:transfer} gives\n\\[\n \\Delta_{b,n}:=\\E[\\one_A(1-(L-b)^2)]\n \\longrightarrow d_A>\\frac34p_*.\n\\]\nThus, for all sufficiently large $n$, $\\Delta_{b,n}>\\eta=p_*/2$.\nSince $\\Delta_{b,n}\\le\\Pp(A)$, these laws have positive probability\nof $A$. Only now fix such an $n$, write $\\Delta_b=\\Delta_{b,n}$, and define\n\\[\n p=\\Pp(A)>0,\\qquad c=\\E[L\\mid A].\n\\]\nIndependence of $w$ from the original tuple shows that\n$\\E[L+w\\mid\\mathcal O_+]$ equals $L$ off $A$ and $c+w$ on $A$.\nThe sum $L+w$ is centered, so its conditional mean is centered too.\nUsing $\\E w=0$, $\\E w^2=1$, and $\\E L^2=q$ gives the exact identity\n\\begin{align}\n \\Var(\\E[L+w\\mid\\mathcal O_+])\n &=\\E[\\one_{A^c}L^2]+p(c^2+1)\\notag\\\\\n &=q+p\\bigl(1-\\Var(L\\mid A)\\bigr).\n \\label{eq:switching-variance}\n\\end{align}\nThe same input law satisfies\n\\begin{equation}\\label{eq:switching-correction}\n p\\bigl(1-\\Var(L\\mid A)\\bigr)\n =\\Delta_b+p(c-b)^2>\\eta.\n\\end{equation}\nThe identities in \\eqref{eq:switching-variance} and the equality in\n\\eqref{eq:switching-correction} hold more generally for any independent\ncentered variance-one original inputs and an independent centered\nvariance-one $w$, whenever $p>0$, with\n$\\Delta_b=\\E[\\one_A(1-(L-b)^2)]$. Their marginal laws need not agree.\nThe nonnegative correction $p(c-b)^2$ records the improvement from\nthe fixed Gaussian predictor $b$ to the actual conditional mean $c$.\nFor the fixed approximating law, \\eqref{eq:switching-variance}\ntherefore exceeds $q+\\eta$.\nThis argument uses convergence of one fixed predictor error, not\ncontinuity of conditional variances.\n\nThe pointwise cost and the transferred gain verify\nProposition~\\ref{prop:amplification} with the distinct triple\n\\[\n (r,d,\\eta)=(m+2,m+1,p_*/2).\n\\]\nThe criterion again proves Theorem~\\ref{thm:main}, with ratio\nmultiplier at least $1+\\eta/(m+1)>1$. For both rules in this section,\nthe Gaussian parameters and $\\eta$ stay fixed throughout. Each stage\nstarts from one fixed finite observation and chooses its own finite\naveraging count; the fourth-moment bound is uniform in that count,\nnot across all stages.\n"}, {"path": "preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/04-mean-cost.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/04-mean-cost.tex", "bytes": 12998, "sha256": "0476277015aef0f1d74b4e79de49b452888f142d8ffc072df8ee1467b4700fbe", "content": "\\section{Amplifying an average degree saving}\\label{sec:mean-cost}\n\nA finite observation can have some inexpensive outputs and some expensive\nones. We now convert an average degree saving into a bound valid on every\ninput. Apply the observation independently many times, retain output\nwords whose total cost is controlled, and merge all other words into one\nlabel. The indicator of the merged cell is the complement of the retained\nindicators. Its degree therefore satisfies the same bound, even if\nindividual discarded words do not.\n\nThis operation must also retain enough variance and restore the normal\napproximation needed to repeat it. We use the score $T_F$, retained\nvariance $v(F)$, and pointwise cell-degree calculus of\nSection~\\ref{sec:observations}. The new ingredient is a separate cost for\neach output label. These costs measure degrees of indicator expansions,\nnot the number of queries required to determine an output.\n\n\\subsection{From a variance saving to an average cost saving}\n\\label{sec:refinement-first}\n\n\nLet $q\\ge2$ be an integer, and let\n$\\mathcal P:\\R^q\\to\\mathcal L$ be a measurable map to a finite label\nset.  Give label $l$ an integer cost $\\ell(l)\\in\\{1,\\ldots,q\\}$.\nThe relevant algebraic requirement is that, on every product of finite\ninput alphabets, the cell indicator for $l$ is a finite linear\ncombination of functions each depending on at most $\\ell(l)$ input\ncoordinates.  The indicator itself need not depend on only that many\ncoordinates.  When the inputs are scores of observations with cell\ndegree bound $D$, this requirement bounds the degree of the output\ncell by $D\\ell(l)$.\n\n\nSuppose now that a Gaussian construction supplies an event $A$ on which\none coordinate of degree cost can be saved, while the sum has conditional\nvariance less than one. The next lemma turns this event into an\nobservation whose retained variance exceeds its average cost. We keep\n$A$ as one cell and refine its complement finely enough that the extra\nprediction error uses less than the available variance saving.\n\n\\begin{lemma}[Refining the complement of one event]\n\\label{lem:refinement}\nLet $q\\ge2$, let $G=(G_1,\\ldots,G_q)$ have independent standard normal\ncoordinates, and put $S_G=\\sum_iG_i$.  Suppose a measurable set\n$A\\subset\\R^q$ has Gaussian-null boundary,\n\\[\n p_A:=\\Pp(G\\in A)>0,\\qquad\n r_A:=\\Var(S_G\\mid G\\in A)<1,\n\\]\nand its indicator, on every product of finite alphabets, is a finite\nlinear combination of functions each depending on at most $q-1$\ncoordinates.  Then there is a finite pointwise partition $\\mathcal P$\nwith one cell $A$ of cost $q-1$ and all other cells of cost $q$ that\nhas Gaussian-null cell boundaries and the prescribed pointwise\ncoordinate bounds. Writing\n\\[\n V=\\Var(\\E[S_G\\mid\\mathcal P(G)]),\\qquad\n c=\\E\\ell(\\mathcal P(G)),\n\\]\nwe have $V>c=q-p_A$.\n\\end{lemma}\n\n\\begin{proof}\nPartition $[-M,M)$ into finitely many half-open intervals of length\nat most $h$, and add the two tail intervals.  Approximate a real\ncoordinate by the midpoint of its bounded interval and by zero on\nthe tails.  For a standard normal coordinate, the expected squared\nerror is at most\n\\[\n h^2+\\E[G_1^2\\one_{\\{|G_1|\\ge M\\}}].\n\\]\nLet $\\widetilde S_G$ be the sum of these coordinate approximations.\nIt is constant on each box of the resulting finite rectangular grid.\nSince $(\\sum_i a_i)^2\\le q\\sum_i a_i^2$, choosing finite $M$ large\nenough and then $h>0$ small enough ensures\n\\[\n \\E(S_G-\\widetilde S_G)^2<p_A(1-r_A).\n\\]\n\nGive $A$ one label and give every intersection of $A^c$ with a grid\nbox its own label.  Retain this partition pointwise, even on cells\nof zero Gaussian probability.  Every new cell has boundary contained\nin the boundary of $A$ together with the finitely many grid\nhyperplanes, and hence has Gaussian-null boundary.\nGive $A$ cost $q-1$ and every other cell cost $q$.  The special-cell\nalgebraic requirement is assumed; the others are automatic for\nfunctions of $q$ inputs.  Conditional means minimize squared error\non every cell~\\cite[Theorem~4.1.15]{Durrett}, so\n\\[\n \\E\\Var(S_G\\mid\\mathcal P(G))\n \\le p_A r_A+\n       \\E[\\one_{A^c}(S_G-\\widetilde S_G)^2]<p_A.\n\\]\nSince $\\Var(S_G)=q$, it follows that\n$V>q-p_A=\\E\\ell(\\mathcal P(G))=c$.\n\\end{proof}\n\nThe event is fixed before the complement grid is chosen. The resulting\npartition is then fixed before any normal approximation or iteration.\nThis gives the concrete inequality $V>c$ that the amplification theorem\nbelow needs.\n\n\\subsection{Amplifying the average cost saving}\n\n\\begin{theorem}[Amplification from a mean cost advantage]\n\\label{thm:mean-cost}\nLet $q\\ge2$, let $\\mathcal P:\\R^q\\to\\mathcal L$ be a measurable map\nto a finite label set, and let $\\ell:\\mathcal L\\to\\{1,\\ldots,q\\}$.\nSuppose that, on every product of finite input alphabets, each cell\nindicator for $l$ is a finite linear combination of functions of at\nmost $\\ell(l)$ input coordinates.  Suppose also that every cell has\nboundary of standard Gaussian measure zero in $\\R^q$.  For independent standard\nnormals $G_1,\\ldots,G_q$, put\n\\[\n S_G=\\sum_{i=1}^qG_i,\\qquad\n V=\\Var(\\E[S_G\\mid\\mathcal P(G)]),\\qquad\n c=\\E\\ell(\\mathcal P(G)).\n\\]\nIf $V>c$, then finite observations on sign cubes have arbitrarily\nlarge ratios $v(F)/D$, where $D>0$ is a cell degree bound.\nThe Boolean conclusion of Theorem~\\ref{thm:main} follows by taking the\nsign of the final score, without a further averaging step.\nAll partitions and observations are defined pointwise, including\ncells of zero Gaussian probability.\n\\end{theorem}\n\nOnly restricted first moments need to pass to the Gaussian limit in\nthis criterion.  The next lemma isolates that fact; it also explains\nwhy zero limiting cell probabilities cause no difficulty.\n\n\\begin{lemma}[Transfer for a fixed finite partition]\n\\label{lem:mc-transfer}\nUnder the boundary hypothesis of Theorem~\\ref{thm:mean-cost}, let\n$Z_n$ be centered random variables of variance one converging in\ndistribution to a standard normal.  Use $q$ independent copies to set\n\\[\n S_n=\\sum_{i=1}^qZ_{n,i},\\qquad L_n=\\mathcal P(\\mathbf Z_n),\\qquad\n V_n=\\Var(\\E[S_n\\mid L_n]),\\qquad c_n=\\E\\ell(L_n).\n\\]\nThen\n\\[\n \\liminf_n V_n\\ge V,\\qquad c_n\\longrightarrow c,\n \\qquad \\E|Z_n|\\longrightarrow\\sqrt{2/\\pi}.\n\\]\nNo fourth-moment hypothesis is required.\n\\end{lemma}\n\n\\begin{proof}\nIndependence gives joint weak convergence of the input tuples.  The\nGaussian-null boundaries imply convergence of all cell probabilities\n\\cite[Theorem~3.10.1(v)]{Durrett}.\nFor a fixed cell $E$, clip the sum continuously to $[-t,t]$.\nThe clipped sum times $\\one_E$ is bounded and has Gaussian-null\ndiscontinuities, so its expectations converge\n\\cite[Theorem~3.10.1(vi)]{Durrett}.  Since $S_n$ is centered\nand has variance $q$,\n\\[\n \\E\\bigl[|S_n|\\one_{\\{|S_n|>t\\}}\\bigr]\\le q/t.\n\\]\nRemoving the clipping therefore proves\n$\\E[S_n\\one_{\\{\\mathbf Z_n\\in E\\}}]\\to\n\\E[S_G\\one_{\\{G\\in E\\}}]$.\n\nFor each $n$, the centered conditional mean satisfies\n\\[\n V_n=\\sum_{l:\\Pp(L_n=l)>0}\n \\frac{\\E[S_n\\one_{\\{L_n=l\\}}]^2}{\\Pp(L_n=l)}.\n\\]\nEvery term for a cell of positive Gaussian probability converges to\nthe corresponding Gaussian term.  All remaining terms are\nnonnegative.  This proves the lower limit inequality; it does not\nrequire deleting the cells with zero limiting probability.\nThe cost convergence follows from the finitely many convergent cell\nprobabilities.  Finally, weak convergence and the bound\n$\\E[|Z_n|\\one_{\\{|Z_n|>t\\}}]\\le1/t$ give convergence of the\nabsolute first moments.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:mean-cost}]\nThe partition and costs are fixed throughout the proof.  Choose\n\\[\n 0<\\delta<(V-c)/2,\n \\qquad 1<\\lambda<\\frac{V-\\delta}{c+\\delta}.\n\\]\nFor any centered, variance-one law $Z$, use independent copies to\ndefine\n\\[\n S_Z=\\sum_{i=1}^q Z_i,\\qquad L=\\mathcal P(\\mathbf Z),\\qquad\n V_Z=\\Var(\\E[S_Z\\mid L]),\\qquad c_Z=\\E\\ell(L).\n\\]\nFor a finite observation $F$ with $v=v(F)>0$, write $Z=T_F/\\sqrt v$.\nWe will increase $v/D$ while maintaining\n\\begin{equation}\\label{eq:invariant}\n V_Z>V-\\delta,\\qquad c_Z<c+\\delta/2,\\qquad \\E|Z|>1/2.\n\\end{equation}\nLemma~\\ref{lem:mc-transfer} shows that all three inequalities hold\neventually along every sequence of centered, variance-one laws\nconverging to a standard normal.\n\nTo initialize, observe the bit sum itself: $F=H_N$, with score\n$T_F=H_N$ and $v=D=N$.  The central limit theorem for independent\nuniform signs~\\cite[Theorem~3.4.1]{Durrett} supplies a finite $N$ for which\n\\eqref{eq:invariant} holds.  The initial ratio is $v/D=1$.\n\nNow hold one such $F$ fixed.  Apply $\\mathcal P$ to the standardized\nscores of $q$ independent copies of $F$, obtaining the group label $L$.\nThe algebraic assumption and the degree calculus give\n\\[\n \\deg(\\one_{\\{L=l\\}})\\le D\\ell(l).\n\\]\nThe tower property identifies the group score and its variance as\n\\begin{equation}\\label{eq:group}\n T_L=\\sqrt v\\,\\E[S_Z\\mid L],\\qquad\n \\tau^2:=v(L)=vV_Z>0.\n\\end{equation}\nThus this group has a variance advantage relative to its mean cost.\nWe next obtain a common degree bound without losing that advantage.\n\nTake $K$ independent copies of $L$.  Retain a label word\n$(l_1,\\ldots,l_K)$ when $\\sum_{b=1}^K\\ell(l_b)\\le K(c+\\delta)$.\nLet $F'_K$ report each retained word exactly and report one failure\nlabel for every other word.  Every retained-word indicator is a product\nwith degree at most\n\\begin{equation}\\label{eq:degree}\n D'_K=DK(c+\\delta).\n\\end{equation}\nThe failure indicator is one minus the finite sum of all retained-word\nindicators, so it satisfies the same degree bound on every input.\nThe real upper bound $D'_K$ need not be rounded to an integer.\nSince the independent costs have mean $c_Z<c+\\delta/2$, the law of\nlarge numbers~\\cite[Theorem~2.2.3]{Durrett} gives a failure probability\n$\\varepsilon_K\\to0$.\n\nWrite $U_K=\\sum_{b=1}^KT_{L_b}$ and $T'_K=T_{F'_K}$.\nThe tower property again gives $T'_K=\\E[U_K\\mid F'_K]$.\nOn a retained word, $U_K$ is known exactly.  On failure, its conditional\nmean is at least as good a predictor as zero\n\\cite[Theorem~4.1.15]{Durrett}.  Consequently\n\\begin{align}\n \\E(U_K-T'_K)^2\n &\\le\\E[U_K^2\\one_{\\{\\mathrm{failure}\\}}]\\notag\\\\\n &\\le(\\E U_K^4)^{1/2}\\varepsilon_K^{1/2}\n   =o(K\\tau^2). \\label{eq:loss}\n\\end{align}\nFor the last equality, $T_L$ has finite range, mean zero, and positive\nvariance, and independence gives\n\\[\n \\E U_K^4=K\\E T_L^4+3K(K-1)\\tau^4=O(K^2\\tau^4).\n\\]\nThe implicit constant can depend on the current observation, which\nhas been fixed before $K$ tends to infinity.\n\nThe conditional-expectation projection identity\n\\cite[Theorem~4.1.15 and its proof]{Durrett} now yields\n\\[\n v(F'_K)=K\\tau^2-\\E(U_K-T'_K)^2\\sim K\\tau^2.\n\\]\nIn particular, this variance is positive for all sufficiently large\n$K$.  The central limit theorem~\\cite[Theorem~3.4.1]{Durrett} gives\n$U_K/(\\sqrt K\\tau)\\Rightarrow N(0,1)$.  By \\eqref{eq:loss}, replacing\n$U_K$ by $T'_K$ changes the normalized variable by a term tending to\nzero in $L^2$.  Moreover,\n$\\sqrt{v(F'_K)}/(\\sqrt K\\tau)\\to1$.  The addition and rescaling forms of\nSlutsky's theorem~\\cite[Exercises~3.2.13--3.2.14]{Durrett} therefore give\n\\[\n \\frac{T'_K}{\\sqrt{v(F'_K)}}\\Rightarrow N(0,1).\n\\]\nThese new standardized scores are centered and have variance one.\nLemma~\\ref{lem:mc-transfer} restores all three inequalities in\n\\eqref{eq:invariant} for sufficiently large $K$.  At the same time,\n\\eqref{eq:group} and \\eqref{eq:degree} give\n\\[\n \\frac{v(F'_K)}{D'_K}\\longrightarrow\n \\frac vD\\frac{V_Z}{c+\\delta}>\\lambda\\frac vD.\n\\]\nOne sufficiently large finite $K$ therefore restores every maintained\ncondition and increases the ratio by a factor greater than $\\lambda$.\n\nFor any prescribed $C>0$, choose a positive integer $k$ with\n$\\lambda^k>4C^2$.  Perform $k$ such steps, choosing a finite batch\nsize separately at each step.  The final observation remains finite\nand satisfies\n\\[\n v(F)/D>4C^2,\\qquad \\E|T_F|>\\tfrac12\\sqrt{v(F)}.\n\\]\nLet $N$ now denote the total number of bits underlying the final\nobservation.  Its sign readout $f=\\operatorname{sign}(T_F)$, with sign $1$ at zero,\nhas degree at most $D$.  Conditioning the bit sum on $F$ gives the\nsigned identity\n\\[\n \\sum_i\\widehat f(\\{i\\})=\\E[fH_N]=\\E|T_F|\n >\\tfrac12\\sqrt{v(F)}>C\\sqrt D\\ge C\\sqrt{\\deg(f)}.\n\\]\nThe positive signed correlation excludes constant $f$.  Arbitrarily\nlarge target ratios follow from the same finite iteration.\n\\end{proof}\n\n\n\\subsection{Applying the criterion to the reporting event}\n\nApply the criterion first with $q=m+1$ and the reporting event $A$ of\nLemma~\\ref{lem:gaussian}. That lemma gives positive probability,\nconditional variance of the coordinate sum below one, and Gaussian-null\nboundary. Identity~\\eqref{eq:indicator} gives the required pointwise\nexpansion into functions of at most $m$ coordinates.\nLemma~\\ref{lem:refinement} therefore supplies a finite partition with costs\n$m$ and $m+1$ and with $V>c=m+1-\\Pp(A)$.\nTheorem~\\ref{thm:mean-cost} gives the signed Boolean conclusion.\nAppendix~\\ref{app:weighted-mixture} supplies a second event with these\nsame four properties and uses the same refinement and amplification.\n"}, {"path": "preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/05-weighted.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/05-weighted.tex", "bytes": 10217, "sha256": "a537b9e0c7b4ad10925bf5f951cdbc390dfe2baf857fd5c7c02ce5606f68f040", "content": "\\section{A weighted Gaussian mixture}\n\\label{app:weighted-mixture}\n\nWe give an independent Gaussian construction for the mean-cost\nargument of Section~\\ref{sec:mean-cost}. The event again requires\nexactly the selected leaf to fail its interval test, but now we first\ndivide the central coordinate into bins and choose each leaf interval\nfrom its bin's conditional mean. This lets us compute the variance on\nthe event as a finite mixture. The relevant weights change when we\ncondition on the event; controlling those weights is the main estimate.\n\nOur goal is a positive-probability event on which the sum has\nconditional variance less than one, with an indicator that saves one\ncoordinate in each summand. Lemma~\\ref{lem:refinement} will then\nproduce the required finite partition. The parameter choice below is\nindependent of the parameter choice in Lemma~\\ref{lem:gaussian}.\n\n\\subsection{The event and its conditional bin weights}\n\\label{app:weighted-identity}\n\nFor $R>0$, put\n\\begin{equation}\\label{app:weighted-schedule}\n m=1+\\lceil e^{R^2/4}\\rceil,\\qquad\n \\Delta=\\frac{2R}{m-1},\\qquad h=e^{-3R^2/8},\\qquad q=m+1.\n\\end{equation}\nLet $G=(G_0,\\ldots,G_m)$ have independent standard normal coordinates\nand set $L=\\sum_{j=0}^mG_j$. Partition $[-R,R)$ into $m-1$ half-open\nintervals $B_i$ of length $\\Delta$, and let\n$B_m=\\R\\setminus[-R,R)$. Define\n\\[\n \\pi_i=\\Pp(G_0\\in B_i),\\qquad\n v_i=\\E[G_0\\mid G_0\\in B_i],\\qquad\n u_i=\\Var(G_0\\mid G_0\\in B_i).\n\\]\nEvery $\\pi_i$ is positive. For each leaf $j\\in\\{1,\\ldots,m\\}$,\ntake the half-open interval\n\\[\n I_j=[v_j+1-h/2,v_j+1+h/2),\\qquad \\chi_j=\\one_{I_j}.\n\\]\nFor any real input $g=(g_0,\\ldots,g_m)$, define $g\\in A$ when,\nfor the unique $i$ such that $g_0\\in B_i$, all leaves other than $i$\nbelong to their intervals and leaf $i$ does not. The half-open\nconventions assign every endpoint a definite outcome.\nAll unqualified bin and leaf sums below range from $1$ to $m$.\n\n\\begin{lemma}[The weighted-mixture event]\\label{lem:weighted-event}\nFor all sufficiently large finite $R$, the event $A$ just defined\nsatisfies\n\\[\n p_A:=\\Pp(G\\in A)>0,\\qquad r_A:=\\Var(L\\mid G\\in A)<1.\n\\]\nIts boundary is contained in finitely many coordinate hyperplanes,\nand on every real input its indicator has the expansion\n\\begin{equation}\\label{app:weighted-cell}\n \\one_A(g)=\n \\sum_{i=1}^m\\one_{B_i}(g_0)\n       \\prod_{\\substack{1\\le j\\le m\\\\j\\ne i}}\\chi_j(g_j)\n       -\\prod_{j=1}^m\\chi_j(g_j).\n\\end{equation}\nEach product involves at most $m=q-1$ coordinates.\n\\end{lemma}\n\n\\begin{proof}\nExactly one bin contains $g_0$. The first term in\n\\eqref{app:weighted-cell} tests all unselected leaves; subtracting\nthe all-leaves-pass indicator requires the selected leaf to fail.\nThis proves the identity pointwise, including all endpoints.\nThe boundary assertion follows from the finite collections of bin\nand leaf-interval endpoints. It remains to prove the variance saving.\nWe let $R\\to\\infty$ for this calculation and fix a finite value only\nafter the estimates.\n\n\\paragraph{Conditional moments.}\nWrite $\\phi$ for the standard normal density. Symmetry of the two\ntails gives $v_m=0$; for $i<m$, the conditional mean lies in the\nclosure of $B_i$, so $|v_i|\\le R$ and $u_i\\le\\Delta^2$.\nIntegration by parts gives\n\\[\n \\int_R^\\infty t^2\\phi(t)\\,dt\n =R\\phi(R)+\\int_R^\\infty\\phi(t)\\,dt,\n \\qquad\n \\int_R^\\infty\\phi(t)\\,dt\\le\\frac{\\phi(R)}R.\n\\]\nThus\n\\begin{equation}\\label{app:weighted-bin-moments}\n \\pi_m u_m=O(Re^{-R^2/2}),\\qquad\n \\sum_i\\pi_i v_i=0,\\qquad\n \\sum_i\\pi_i v_i^2=1-\\sum_i\\pi_i u_i=1-o(1),\n\\end{equation}\nwhere the last equality follows from\n$\\sum_i\\pi_i u_i\\le\\Delta^2+O(Re^{-R^2/2})=o(1)$.\n\nFor the leaf intervals, write\n\\[\n \\alpha_j=\\Pp(G_j\\in I_j),\\qquad\n t_j=\\E[G_j\\mid G_j\\in I_j],\\qquad\n r_j=\\Var(G_j\\mid G_j\\in I_j).\n\\]\nThe bounded normal density and the interval lengths imply, uniformly\nin $j$ for sufficiently large $R$,\n\\begin{equation}\\label{app:weighted-rare-moments}\n 0<\\alpha_j\\le Ch<\\tfrac12,\\qquad\n |t_j-(v_j+1)|\\le h/2,\\qquad 0\\le r_j\\le h^2.\n\\end{equation}\nConstants in this section are absolute unless stated otherwise.\nLet $d_j,b_j$ be the conditional mean and variance of $G_j$ on\n$I_j^c$. Using the unconditional mean zero and variance one gives\n\\begin{equation}\\label{app:weighted-complement}\n d_j=-\\frac{\\alpha_jt_j}{1-\\alpha_j},\\qquad\n b_j=\\frac{1-\\alpha_jr_j}{1-\\alpha_j}\n       -\\frac{\\alpha_jt_j^2}{(1-\\alpha_j)^2}.\n\\end{equation}\nIndeed the complementary second moment is\n$(1-\\alpha_j(r_j+t_j^2))/(1-\\alpha_j)$, from which we subtract\n$d_j^2$.\n\n\\paragraph{The mixture identity.}\nThe event $A$ changes the distribution of the selected bin. Put\n\\[\n w_i=\\pi_i\\frac{1-\\alpha_i}{\\alpha_i},\\qquad\n W=\\sum_iw_i,\\qquad \\omega_i=\\frac{w_i}{W}.\n\\]\nIndependence gives\n\\begin{equation}\\label{app:weighted-probability}\n p_A=W\\prod_{j=1}^m\\alpha_j>0,\n \\qquad \\Pp(G_0\\in B_i\\mid G\\in A)=\\omega_i.\n\\end{equation}\nHere $W$ is finite and positive for every finite $R$. Within\n$A\\cap\\{G_0\\in B_i\\}$ the coordinates remain independent under\ntheir respective bin, interval, and complementary-interval restrictions.\nThe conditional mean and variance of $L$ are therefore\n\\[\n \\sum_{j=1}^mt_j-1+e_i,\n \\qquad u_i+\\sum_jr_j+b_i-r_i,\n \\qquad e_i=v_i+1-\\frac{t_i}{1-\\alpha_i}.\n\\]\nThe small mean error satisfies\n\\begin{equation}\\label{app:weighted-mean-error}\n |e_i|\\le h/2+\\frac{\\alpha_i|t_i|}{1-\\alpha_i}\n          =O(h(R+2)).\n\\end{equation}\nApplying the variance decomposition with the conditional weights\n$\\omega_i$, not the original weights $\\pi_i$, gives the exact identity\n\\begin{align}\n W\\bigl(1-\\Var(L\\mid G\\in A)\\bigr)\n &=\\sum_iw_i(1-b_i+r_i)-\\sum_iw_iu_i\\notag\\\\\n &\\quad-W\\sum_jr_j-W\\Var_{i\\sim\\omega}(e_i).\n \\label{app:weighted-variance-identity}\n\\end{align}\nWe next show that the first sum has lower limit at least one and\nthat each of the three subtracted terms tends to zero. This will\ngive the strict conditional variance bound despite the possibly\nlarge total weight $W$.\n\nThe schedule in \\eqref{app:weighted-schedule} makes the bin scale\n$\\Delta^2/h$, the aggregate leaf scale $mh$, and the tail scale\n$e^{-R^2/2}/h$ tend to zero even after multiplication by $e^{3R}$\nand any fixed power of $R$. The leaf intervals are narrow enough to\ncontrol their total variance, but wide enough that inverse interval\nprobabilities do not overwhelm the bin and tail estimates below.\n\n\\paragraph{The positive contribution.}\nSubstituting \\eqref{app:weighted-complement} yields\n\\begin{equation}\\label{app:weighted-positive-term}\n w_i(1-b_i+r_i)\n =\\pi_i\\left(-1+\\frac{r_i}{\\alpha_i}\n                    +\\frac{t_i^2}{1-\\alpha_i}\\right)\n \\ge\\pi_i(t_i^2-1).\n\\end{equation}\nSince $t_i=v_i+1+O(h)$ uniformly, we have\n\\[\n \\sum_i\\pi_i(t_i^2-1)\n =\\sum_i\\pi_i v_i^2+2\\sum_i\\pi_i v_i+O(h(R+2))\n =1+o(1)\n\\]\nby \\eqref{app:weighted-bin-moments}. The shift of the leaf intervals\nby one is important: it produces the term $t_i^2-1$, whose average\ntends to one.\n\n\\paragraph{The three error terms.}\nFor $i<m$, the fact that $v_i$ belongs to the closure of $B_i$\ngives the density-ratio bound\n\\begin{align}\n w_i\\le\\frac{\\pi_i}{\\alpha_i}\n &\\le\\frac{\\Delta}{h}\n       \\frac{\\phi(\\max\\{0,|v_i|-\\Delta\\})}\n            {\\phi(|v_i|+1+h)}\\notag\\\\\n &\\le\\frac{\\Delta}{h}e^{3R}\n \\label{app:weighted-density-ratio}\n\\end{align}\nfor all sufficiently large $R$. For the final inequality, put\n$x=|v_i|\\le R$. The logarithm of the density ratio is at most\n\\[\n \\frac{(x+1+h)^2-\\max\\{0,x-\\Delta\\}^2}{2}\n \\le(1+h+\\Delta)x+\\frac{(1+h)^2}{2}\\le3R.\n\\]\nThe middle inequality follows by expanding when $x\\ge\\Delta$;\nwhen $x<\\Delta$, use $x^2/2\\le\\Delta x$.\nFor the tail bin, $v_m=0$, so\n$\\alpha_m\\ge h\\phi(1+h)$ and $w_m\\le C\\pi_m/h$.\nSumming and using $(m-1)\\Delta=2R$ gives\n\\begin{equation}\\label{app:weighted-total-weight}\n W\\le\\frac{2Re^{3R}+C}{h}.\n\\end{equation}\n\nFirst, $u_i\\le\\Delta^2$ on interior bins and\n$\\pi_mu_m=O(Re^{-R^2/2})$ on the tail bin. Since\n$\\Delta^2\\le4R^2e^{-R^2/2}$, we obtain\n\\[\n \\sum_iw_iu_i\n \\le W\\Delta^2+C\\pi_mu_m/h\n =O(R^3e^{3R-R^2/8})+O(Re^{-R^2/8})=o(1).\n\\]\nSecond, $\\sum_jr_j\\le mh^2$ and $mh=O(e^{-R^2/8})$, so\n\\[\n W\\sum_jr_j\\le Wmh^2\n =O((Re^{3R}+1)e^{-R^2/8})=o(1).\n\\]\nThird, \\eqref{app:weighted-mean-error} gives\n\\[\n W\\Var_{i\\sim\\omega}(e_i)\n \\le W\\,O(h^2(R+2)^2)\n =O((Re^{3R}+1)(R+2)^2e^{-3R^2/8})=o(1).\n\\]\nEach negative quadratic exponent dominates the linear exponent and\npolynomial factors. Combining these estimates with\n\\eqref{app:weighted-variance-identity} and\n\\eqref{app:weighted-positive-term} shows that\n\\[\n W\\bigl(1-\\Var(L\\mid G\\in A)\\bigr)\\ge1+o(1)>0\n\\]\nfor all sufficiently large $R$. Since $W>0$, this proves the lemma.\n\\end{proof}\n\n\\subsection{Application to mean-cost amplification}\n\\label{app:weighted-transfer}\n\nFix one finite $R$ for which Lemma~\\ref{lem:weighted-event} holds.\nThus the integer $q=m+1$, all intervals and bins, and the positive\nnumbers\n\\begin{equation}\\label{app:weighted-saving}\n p_A=\\Pp(G\\in A)>0,\\qquad 1-r_A>0\n\\end{equation}\nare fixed before further approximation. The pointwise expansion\n\\eqref{app:weighted-cell} uses at most $q-1$ coordinates in each\nterm, and the finite hyperplane boundary is Gaussian-null. These\nare precisely the hypotheses of Lemma~\\ref{lem:refinement}.\nThat lemma refines $A^c$ into a finite collection of cells, with\ntotal squared approximation error below the actual positive quantity\n$p_A(1-r_A)$. It assigns cost $m$ to $A$ and cost $q$ to every\nother cell, giving a fixed partition $\\mathcal P$ for which\n\\begin{equation}\\label{app:weighted-mean-cost-gain}\n V:=\\Var(\\E[L\\mid\\mathcal P(G)])>\n c:=\\E\\ell(\\mathcal P(G))=q-p_A.\n\\end{equation}\nThe partition includes its Gaussian-null cells as pointwise sets;\nlater discrete input laws may give those cells positive probability.\n\nWith $R$ and this partition fixed, Lemma~\\ref{lem:mc-transfer}\nsupplies the finite-input transfer used in Theorem~\\ref{thm:mean-cost}.\nThe costs $m$ and $q$ are positive integers, and the pointwise coordinate\nbounds, Gaussian-null boundaries, and strict inequality $V>c$ verify\nall its hypotheses. The theorem therefore gives arbitrarily large\nratios of retained variance to cell degree and the signed Boolean conclusion\nof Theorem~\\ref{thm:main}. Neither the conditional variance saving nor\nthe required batch sizes are asserted to be uniform in $R$.\n"}, {"path": "preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/06-small-width.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Unbounded-Violations-of-the-Square-Root-Degree-Bound-September-26-2026/build/sections/06-small-width.tex", "bytes": 11328, "sha256": "69e971694cdd748ff681fd969254e86569395ae150b687a343d6a237139e4871", "content": "\\section{A small-width Gaussian calculation for the coarse report}\n\\label{app:small-width}\n\nThe coarse report also admits a different Gaussian parameter choice.\nWe balance the interval width against the spacing of the selector grid\nand use the same cancellation as in \\eqref{eq:coarse-gaussian-gain}\nto compute the resulting predictor error. We also show how this error\ntransfers when the two predictor constants are recalibrated to the\ninput law, using convergence of restricted moments.\n\n\\subsection{The parameters and a law-dependent error bound}\n\\label{app:small-width-rule}\n\nFor $0<w<1$, put\n\\begin{equation}\\label{app:small-width-schedule}\n h=w^{2/3},\\qquad T=\\sqrt{4\\log(1/w)}.\n\\end{equation}\nList the points of $h\\mathbb Z\\cap[-T,T]$ in increasing order as\n$t_1,\\ldots,t_m$, and let $i_*$ index zero.  Thus\n$m=2\\lfloor T/h\\rfloor+1=O(T/h)$ as $w\\downarrow0$; in particular,\n$m\\ge3$ for sufficiently small $w$.  On $|t|\\le T$, let $I(t)$\nbe the index of a nearest grid point, breaking ties by the smaller\nindex.  On $|t|>T$, set $I(t)=i_*$.  The grid endpoints need not\nequal $\\pm T$, but\n\\[\n |t-t_{I(t)}|\\le h\\qquad (|t|\\le T).\n\\]\nGive leaf $j$ the closed interval\n\\[\n A_j=[t_j+1-w/2,t_j+1+w/2].\n\\]\nThese endpoint and tie conventions define the rule on every real input.\nRestrict henceforth to sufficiently small $w$ that $m\\ge3$.\n\nThe choice $h=w^{2/3}$ balances two power scales: $h^2/w$ from\nrounding the central input to a grid point, and $w/h$ from adding\nthe leaf-interval variances. The accompanying cutoff factors grow\nmore slowly than every power of $1/w$. The choice of $T$ makes\n$e^{-T^2/2}=w^2$, so the Gaussian tail second moment is $o(w)$.\nThe estimates below retain all these factors.\n\nApply the coarse report of Lemma~\\ref{lem:coarse-report} with central\ninput $t$, leaf inputs $u_1,\\ldots,u_m$, selector $I$, and leaf\nintervals $A_j$.  Denote it by $\\mathcal O_w$.  Thus it returns $Y$\nwhen all leaves pass, $X$ when only the selected leaf fails, and\n$(O,t,I(t),(u_j)_{j\\ne I(t)})$ otherwise.  In the notation of that\nlemma, these events are $B$, $A=P\\setminus B$, and $P^c$.\nFor clarity, the special-cell identity here is\n\\begin{equation}\\label{app:small-width-cell}\n \\one_A(t,u)=\n \\sum_{i=1}^m\\one_{\\{I(t)=i\\}}\\prod_{j\\ne i}\\one_{A_j}(u_j)\n       -\\prod_{j=1}^m\\one_{A_j}(u_j).\n\\end{equation}\nThe lemma proves that every cell has coordinate cost at most $m$:\nthe displayed summands use $m$ coordinates, the $Y$ cell ignores $t$,\nand an ordinary report imposes no condition on its omitted leaf.\nThis holds pointwise on every finite product alphabet, including\nunused labels.  The boundaries of $A$, $B$, and $P$ lie in finitely\nmany coordinate hyperplanes.\n\nLet $U$ be centered with variance one, and take independent copies\n$U_0,\\ldots,U_m$.  Write\n\\[\n L=\\sum_{j=0}^mU_j,\\qquad\n \\mathcal R_w(U)=\\E[(L-\\E[L\\mid\\mathcal O_w(\\mathbf U)])^2].\n\\]\nFor the moment assume\n\\[\n q_j=\\Pp(U\\in A_j)\\in(0,1),\\qquad Q=\\prod_{j=1}^m q_j.\n\\]\nLet $\\mu_j,a_j$ be the conditional mean and variance on $A_j$,\nand put\n\\[\n M_\\mu=\\sum_j\\mu_j,\\qquad S_a=\\sum_j a_j.\n\\]\nThe interval length gives $0\\le a_j\\le w^2$.\nUse the predictor $M_\\mu-1$ on $X$, the predictor $M_\\mu$ on $Y$,\nand the sum of reported coordinates on ordinary outputs. Denote this\nfunction of the report by $\\widehat L$. We will compute its deficit\n$1-\\E[(L-\\widehat L)^2]$ exactly.\n\nThe ordinary reports contribute $\\Pp(P^c)$ to the squared error,\nby Lemma~\\ref{lem:coarse-report}. For the exceptional reports, fix\n$U_0=t$ and put $i=I(t)$. The event $P$ has conditional probability\n$Q/q_i$, restricts every unselected leaf to its interval, and leaves\n$U_i$ unrestricted. Under these restrictions, $L-(M_\\mu-1)$ has mean\n$t+1-\\mu_i$ and variance $1+S_a-a_i$. Hence\n\\[\n \\E\\bigl[\\one_P(1-(L-(M_\\mu-1))^2)\\bigr]\n =-Q\\E_t\\left[\\frac{(t+1-\\mu_i)^2+S_a-a_i}{q_i}\\right].\n\\]\nOn $B$, the central input remains unrestricted. The two respective\npredictor deficits on $B$ are therefore\n\\begin{align*}\n \\E\\bigl[\\one_B(1-(L-(M_\\mu-1))^2)\\bigr]&=-Q(1+S_a),\\\\\n \\E\\bigl[\\one_B(1-(L-M_\\mu)^2)\\bigr]&=-QS_a.\n\\end{align*}\nSubtracting the first of these from the $P$ contribution gives the\n$X$ contribution, because $A=P\\setminus B$. Adding the $Y$\ncontribution cancels $S_a$, exactly as in the earlier Gaussian\ncalculation. Define\n\\begin{equation}\\label{app:small-width-deficit}\n H_w(U)=1-\\E_t\\left[\n       \\frac{(t+1-\\mu_{I(t)})^2+S_a-a_{I(t)}}{q_{I(t)}}\\right].\n\\end{equation}\nWe have proved\n\\begin{equation}\\label{app:small-width-residual}\n 1-\\mathcal R_w(U)\\ge\n 1-\\E[(L-\\widehat L)^2]=QH_w(U).\n\\end{equation}\nThe equality is the gain of the specified predictor; the inequality\nuses optimality of conditional expectation. The calculations integrate\nagainst the law of $t$, so they require no positive probability for\nany individual central value. We next show that both subtracted\nerrors in \\eqref{app:small-width-deficit} tend to zero for Gaussian\ninputs. The product $Q$ may be extremely small; only its strict\npositivity will matter after $w$ is fixed.\n\n\\subsection{Gaussian estimates, including the rare-probability factors}\n\\label{app:small-width-estimates}\n\nIn this subsection $U=G$ is standard normal and $w\\downarrow0$.\nWrite $\\phi$ for its density.  Uniformly over the grid,\n\\[\n q_j\\le\\frac{w}{\\sqrt{2\\pi}},\\qquad\n |\\mu_j-(t_j+1)|\\le w/2,\\qquad\n \\sum_ja_j\\le mw^2=O(Tw^{4/3})\\longrightarrow0.\n\\]\nWe must control the two errors in \\eqref{app:small-width-deficit}\nafter division by the selected interval probability. A uniform lower\nbound of order $w$ for all $q_j$ is unavailable, because the grid\nextends into the Gaussian tails. Instead we compare each interval's\ndensity with the density of the central coordinate that selects it.\n\nFor $|t|\\le T$ and $z\\in A_{I(t)}$, we have\n$|z-t|\\le1+h+w/2$.  Hence, for sufficiently small $w$,\n\\[\n q_{I(t)}\\ge w\\inf_{z\\in A_{I(t)}}\\phi(z),\\qquad\n \\frac{\\phi(t)}{q_{I(t)}}\n \\le w^{-1}\\exp(C(1+T)).\n\\]\nThe constant $C$ is absolute: the logarithm of the density ratio is\n$(z^2-t^2)/2\\le |t||z-t|+|z-t|^2/2$.\nSince $T=2\\sqrt{\\log(1/w)}$, for each fixed $\\varepsilon>0$,\n\\begin{equation}\\label{app:small-width-inverse-density}\n \\frac{\\phi(t)}{q_{I(t)}}=O(w^{-1-\\varepsilon})\n \\qquad (|t|\\le T).\n\\end{equation}\nThe fallback interval is centered at $1$, so $q_{i_*}\\ge cw$ for\nan absolute $c>0$ and small $w$.  Integrating the central bound and\nusing this fallback bound on the tails gives\n\\begin{equation}\\label{app:small-width-inverse-mean}\n \\E\\frac1{q_{I(G)}}=O(Tw^{-1-\\varepsilon}).\n\\end{equation}\nTake $\\varepsilon=1/6$. Since $0\\le S_a-a_{I(G)}\\le mw^2$,\nthe variance contribution in\n\\eqref{app:small-width-deficit} is at most\n\\[\n mw^2\\E\\frac1{q_{I(G)}}\n =O(T^2w^{1/6})\\longrightarrow0.\n\\]\n\nFor the squared mismatch, first suppose $|t|\\le T$. Then\n\\[\n |t+1-\\mu_{I(t)}|\n \\le |t-t_{I(t)}|+w/2\\le h+w/2.\n\\]\nIts central contribution is therefore at most\n\\[\n O\\bigl(Tw^{-7/6}(h+w)^2\\bigr)\n =O(Tw^{1/6})\\longrightarrow0.\n\\]\nOn $|t|>T$, the fallback mean is bounded, so the contribution is\n\\[\n O\\left(w^{-1}\\int_{|t|>T}(1+t^2)\\phi(t)\\,dt\\right)\n =O\\bigl(w^{-1}(1+T)e^{-T^2/2}\\bigr)\n =O((1+T)w)\\longrightarrow0.\n\\]\nFor the integral estimate, integration by parts gives\n$\\int_T^\\infty t^2\\phi(t)\\,dt\n=T\\phi(T)+\\int_T^\\infty\\phi(t)\\,dt$, and\n$\\int_T^\\infty\\phi(t)\\,dt\\le\\phi(T)/T$ for $T>0$.\nThe last equality uses $e^{-T^2/2}=w^2$.\nBoth subtracted errors therefore vanish, proving\n\\begin{equation}\\label{app:small-width-limit}\n H_w(G)\\longrightarrow1.\n\\end{equation}\nThus the same coarse report has a strictly positive Gaussian\nimprovement for this independent choice of parameters.  To use it\nrepeatedly on finite cubes, we now fix those parameters and transfer\nthe particular lower bound just proved.\n\n\\subsection{Restricted moments and the finite-score transfer}\n\\label{app:small-width-transfer}\n\nFix a sufficiently small $w>0$ with $m\\ge3$ and $H_w(G)>1/2$.\nAll intervals, grid points, selector cells, and $m$ are fixed from\nnow on.  Put\n\\[\n Q_G=\\prod_{j=1}^m\\Pp(G\\in A_j)>0,\\qquad \\eta=Q_G/4.\n\\]\nLet $U_n$ be centered variance-one variables such that\n$U_n\\Rightarrow G$ and $\\sup_n\\E U_n^4<\\infty$.\nWrite $U_{n,0},\\ldots,U_{n,m}$ for independent copies.  Use a\nsubscript $n$ for the interval moments of $U_n$ and a superscript $G$\nfor their Gaussian values, and put $Q_n=\\prod_j q_{j,n}$.\nFor each bounded interval $A_j$, weak convergence off its Gaussian-null\nboundary gives convergence of its probability and its restricted\nfirst and second moments.  Thus\n\\[\n q_{j,n}\\to q_j^G\\in(0,1),\\qquad\n \\mu_{j,n}\\to\\mu_j^G,\\qquad a_{j,n}\\to a_j^G.\n\\]\nAll these conditional moments are defined for sufficiently large $n$.\nThere are finitely many intervals, so their probabilities are then\nsimultaneously bounded away from zero and one.  In particular, every\ncoefficient in \\eqref{app:small-width-deficit} converges, including\nthe reciprocal-probability factors.\n\nCoefficient convergence alone does not yet transfer its expectation.\nPartition the line into the fixed selector cells\n$E_i=\\{t:I(t)=i\\}$, whose boundaries are finite.  The fallback cell\nincludes both tails as well as the central cell for the grid point\nzero; it still has a finite boundary.  For $k=0,1,2$,\n\\begin{equation}\\label{app:small-width-restricted-moments}\n \\E[U_n^k\\one_{\\{U_n\\in E_i\\}}]\n \\longrightarrow\\E[G^k\\one_{\\{G\\in E_i\\}}].\n\\end{equation}\nTo see this, multiply $t^k\\one_{E_i}(t)$ by a continuous cutoff that\nis one on $[-K,K]$ and zero outside $[-K-1,K+1]$.  The resulting\nfunction is bounded and has only Gaussian-null discontinuities, so\nweak convergence applies.  The cutoff errors for $k=1,2$ vanish\nuniformly as $K\\to\\infty$, by\n\\[\n \\E[|U_n|\\one_{\\{|U_n|>K\\}}]\\le K^{-1},\\qquad\n \\E[U_n^2\\one_{\\{|U_n|>K\\}}]\n \\le K^{-2}\\sup_j\\E U_j^4.\n\\]\nThe corresponding Gaussian errors also vanish.  The case $k=0$\nfollows directly from the continuity-set criterion.  These are the\nsame weak-convergence and uniform-integrability principles used in\nLemma~\\ref{lem:transfer}; see~\\cite{Durrett}.\n\nOn a fixed selector cell $E_i$, the integrand in\n\\eqref{app:small-width-deficit} is a polynomial of degree at most two\nin $t$. Expanding that polynomial expresses its expectation as a\nfinite sum of convergent coefficients times the restricted moments\nin \\eqref{app:small-width-restricted-moments}.  Therefore\n\\[\n Q_nH_w(U_n)\\longrightarrow Q_GH_w(G)>Q_G/2.\n\\]\nThis proves convergence despite the moving predictor coefficients;\nwe have not assumed continuity of conditional variances.\nFor all sufficiently large $n$, \\eqref{app:small-width-residual}\nnow gives $1-\\mathcal R_w(U_n)>\\eta$.  Since the sum of the\n$m+1$ independent inputs is centered and has variance $m+1$,\n\\[\n \\Var\\!\\left(\\E\\!\\left[\\sum_{j=0}^mU_{n,j}\n                  \\,\\middle|\\,\\mathcal O_w(\\mathbf U_n)\\right]\\right)\n =m+1-\\mathcal R_w(U_n)>m+\\eta.\n\\]\nThe pointwise coordinate-cost bound from\nLemma~\\ref{lem:coarse-report}, with the substitution specified above,\nand this transferred gain verify both hypotheses of\nProposition~\\ref{prop:amplification} with\n$(r,d,\\eta)=(m+1,m,Q_G/4)$. The criterion gives the signed\nconclusion of Theorem~\\ref{thm:main}. Each averaging limit uses one\nfixed finite observation and fixed parameters.  At that stage, the normalized\nbatch scores have a fourth-moment bound uniform in the batch size,\nby \\eqref{eq:fourth}; no uniformity across subsequent stages is required.\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/README.md", "bytes": 639, "sha256": "be6afed57229af9beb59d1e977188777edc357b0b5a8dcfe2c91fd425c747b9e", "content": "# [Virasoro Constraints under Projectivization](virasoro-constraints-under-projectivization.pdf)\n\n**Author:** OpenAI\n\n**Date:** October 5, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:Virasoro-Constraints-under-Projectivization-October-5-2026,\n  author = {{OpenAI}},\n  title = {{Virasoro Constraints under Projectivization}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/virasoro-constraints-under-projectivization.pdf}{OAI:Virasoro-Constraints-under-Projectivization-October-5-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/macros.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/macros.tex", "bytes": 1658, "sha256": "6db4afa0b35871c71bd4316dc376bf6f5a0e64fcb10ff8b86d2f76f9ae0695c2", "content": "\\usepackage[T1]{fontenc}\n\\usepackage{lmodern}\n\\usepackage[margin=1.05in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools,mathrsfs}\n\\usepackage{microtype}\n\\usepackage{enumitem}\n\\usepackage{booktabs,array}\n\\usepackage{tikz}\n\\usepackage{xcolor}\n\\usepackage{hyperref}\n\\hypersetup{colorlinks=true,linkcolor=blue!45!black,citecolor=blue!45!black,\n urlcolor=blue!45!black,pdfauthor={OpenAI},\n pdftitle={Virasoro Constraints under Projectivization}}\n\\setlength{\\parindent}{1.25em}\n\\setlength{\\parskip}{0.15em}\n\\setlist[enumerate]{itemsep=0.3em,topsep=0.5em}\n\\setlist[itemize]{itemsep=0.2em,topsep=0.4em}\n\\numberwithin{equation}{section}\n\\theoremstyle{plain}\n\\newtheorem{theorem}{Theorem}[section]\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\theoremstyle{definition}\n\\newtheorem{definition}[theorem]{Definition}\n\\newtheorem{convention}[theorem]{Convention}\n\\theoremstyle{remark}\n\\newtheorem{remark}[theorem]{Remark}\n\\newtheorem{example}[theorem]{Example}\n\\newcommand{\\C}{\\mathbb C}\n\\newcommand{\\Q}{\\mathbb Q}\n\\newcommand{\\Z}{\\mathbb Z}\n\\newcommand{\\N}{\\mathbb N}\n\\newcommand{\\cH}{\\mathcal H}\n\\newcommand{\\V}{\\mathsf V}\n\\DeclareMathOperator{\\op}{op}\n\\DeclareMathOperator{\\id}{id}\n\\DeclareMathOperator{\\End}{End}\n\\DeclareMathOperator{\\Hom}{Hom}\n\\DeclareMathOperator{\\Res}{Res}\n\\DeclareMathOperator{\\str}{str}\n\\DeclareMathOperator{\\rank}{rank}\n\\DeclareMathOperator{\\rk}{rk}\n\\DeclareMathOperator{\\Tr}{Tr}\n\\DeclareMathOperator{\\Spec}{Spec}\n\\DeclareMathOperator{\\ch}{ch}\n\\DeclareMathOperator{\\ev}{ev}\n\\DeclareMathOperator{\\pr}{pr}\n\\newcommand{\\Tw}{\\mathrm{tw}}\n\\allowdisplaybreaks[1]\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/paper.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/paper.tex", "bytes": 1001, "sha256": "e716bb1773a9541e13263f787b355fb76ff26aa54ed17d955dea010ec99ea677", "content": "\\documentclass[11pt]{article}\n\\input{macros}\n\\title{Virasoro Constraints under Projectivization}\n\\author{OpenAI}\n\\date{October 5, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe prove that full ordinary descendant Virasoro constraints pass from a\nsmooth projective complex base to the projectivization of any algebraic\nvector bundle of rank at least two. The bundle need not split and satisfies\nno positivity requirement. Assuming the full constraints on the base, the\nconclusion includes every genus, each individual integral curve class, and\nall cohomology insertions, including primitive and odd classes. The result\nalso applies successively to towers of projective bundles.\n\\end{abstract}\n\\tableofcontents\n\\input{sections/01-introduction}\n\\input{sections/02-conventions}\n\\input{sections/03-master-space}\n\\input{sections/04-localization}\n\\input{sections/05-shift}\n\\input{sections/06-spectral}\n\\input{sections/07-fixed-block}\n\\input{sections/08-continuation}\n\\input{references}\n\\end{document}\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/references.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/references.tex", "bytes": 5149, "sha256": "315367b39e5d02abd4e0bd9d3eedf758fe724b30b59b73a278af70feed4dd1f4", "content": "\\begin{thebibliography}{99}\n\n\\bibitem{Brown}\nJ.~Brown,\n\\emph{Gromov--Witten invariants of toric fibrations},\nInt. Math. Res. Not. 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Topol. \\textbf{21} (2017), no.~1, 315--343.\n\\href{https://doi.org/10.2140/gt.2017.21.315}{doi:10.2140/gt.2017.21.315}.\n\n\\bibitem{IritaniKoto}\nH.~Iritani and Y.~Koto,\n\\emph{Quantum cohomology of projective bundles},\npreprint, 2023, revised January 31, 2026.\n\\href{https://arxiv.org/abs/2307.03696v4}{arXiv:2307.03696v4}.\n\n\\bibitem{Kontsevich}\nM.~Kontsevich,\n\\emph{Intersection theory on the moduli space of curves and the matrix\nAiry function},\nComm. Math. Phys. \\textbf{147} (1992), no.~1, 1--23.\n\\href{https://doi.org/10.1007/BF02099526}{doi:10.1007/BF02099526}.\n\n\\bibitem{KontsevichManin}\nM.~Kontsevich and Yu.~I.~Manin,\n\\emph{Relations between the correlators of the topological sigma-model\ncoupled to gravity},\nComm. Math. Phys. \\textbf{196} (1998), no.~2, 385--398.\n\\href{https://doi.org/10.1007/s002200050426}{doi:10.1007/s002200050426}.\n\n\\bibitem{Koto}\nY.~Koto,\n\\emph{A mirror theorem for non-split toric bundles},\nMath. Ann. \\textbf{393} (2025), 3337--3394.\n\\href{https://doi.org/10.1007/s00208-025-03302-7}{doi:10.1007/s00208-025-03302-7}.\n\n\\bibitem{LiuTian}\nX.~Liu and G.~Tian,\n\\emph{Virasoro constraints for quantum cohomology},\nJ. Differential Geom. \\textbf{50} (1998), no.~3, 537--590.\n\\href{https://doi.org/10.4310/jdg/1214424970}{doi:10.4310/jdg/1214424970}.\n\n\\bibitem{OkounkovPandharipande}\nA.~Okounkov and R.~Pandharipande,\n\\emph{Virasoro constraints for target curves},\nInvent. Math. \\textbf{163} (2006), no.~1, 47--108.\n\\href{https://doi.org/10.1007/s00222-005-0455-y}{doi:10.1007/s00222-005-0455-y}.\n\n\\bibitem{Teleman}\nC.~Teleman,\n\\emph{The structure of 2D semi-simple field theories},\nInvent. Math. \\textbf{188} (2012), no.~3, 525--588.\n\\href{https://doi.org/10.1007/s00222-011-0352-5}{doi:10.1007/s00222-011-0352-5}.\n\n\\bibitem{Witten}\nE.~Witten,\n\\emph{Two-dimensional gravity and intersection theory on moduli space},\nSurveys Differential Geom. \\textbf{1} (1991), 243--310.\n\\href{https://doi.org/10.4310/SDG.1990.v1.n1.a5}{doi:10.4310/SDG.1990.v1.n1.a5}.\n\n\\end{thebibliography}\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/01-introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/01-introduction.tex", "bytes": 9629, "sha256": "382baec624aa0ea2b408e030fd9d6358ce97f635c5c178b6c2d0e95dc5a8f4a0", "content": "\\section{Introduction}\n\\label{intro:section}\n\nVirasoro constraints organize the descendant Gromov--Witten\ninvariants of a smooth projective variety into differential equations\nfor a single generating function. A basic structural question is\nwhether these equations pass from a base to the total space of a\ngeometric fibration. We prove that they pass through\nprojectivization of an arbitrary algebraic vector bundle.\n\nFor a smooth connected projective complex variety $Y$, let $Z_Y$\nbe its total ordinary descendant potential: the exponential of the\nconnected stable-map potentials, with genus weight $\\hbar^{g-1}$,\nordinary cotangent-line classes at the markings, and monomials\n$Q^d$ indexed by the actual effective classes\n$d\\in H_2(Y;\\Z)$. We use all of $H^*(Y;\\C)$ with its cohomological\nparity and Poincar\\'e pairing. The Virasoro operators are the\nnormally ordered operators associated to the first Hodge grading\n\\[\n \\mu_Y|_{H^{p,q}(Y)}\n       =\\bigl(p-\\tfrac12\\dim_\\C Y\\bigr)\\id,\n \\qquad R_Y=c_1(TY)\\cup.\n\\]\nTheir zero-mode constant is\n$C_Y=\\chi(Y)/16-\\str(\\mu_Y^2)/4$, and the other modes have no\nscalar correction. Section~\\ref{conv:section} specifies the\nloop-space and quantization conventions completely. Write\n$\\V(Y)$ for the coefficientwise identities\n\\[\n                      L_k^Y Z_Y=0\\qquad(k\\geq-1).\n\\]\nThus $\\V(Y)$ includes every genus, individual integral curve\nclass, and finite list of descendants, including primitive and odd\ninsertions.\n\n\\begin{theorem}\n\\label{thm:main}\nLet $B$ be a smooth connected projective variety over $\\C$, and\nlet $E$ be an algebraic vector bundle of rank $r\\geq2$ on $B$.\nLet $X=\\mathbb P_B(E)$ parametrize one-dimensional subspaces of\nthe fibers of $E$. Then\n\\[\n                           \\V(B)\\ \\Longrightarrow\\ \\V(X).\n\\]\n\\end{theorem}\n\nThe bundle is arbitrary: it need not split or admit a filtration\nby line bundles, and it is subject to no positivity condition.\nThe base is allowed to have nonsemisimple quantum cohomology\nand arbitrary Hodge types. In particular the assertion applies\nsuccessively to towers of projective bundles once the constraints\nhold on the initial base. The conclusion concerns the ordinary\ntheory of each total space.\n\n\\subsection{Historical context and related results}\n\nThe Virasoro conjecture in Gromov--Witten theory extends the\ndifferential constraints for intersection numbers on moduli\nspaces of stable curves arising in Witten's conjecture and\nKontsevich's theorem \\cite{Witten,Kontsevich}.\nEguchi--Hori--Xiong proposed the corresponding constraints\nfor quantum cohomology \\cite{EHX}.\nThe extension to general Hodge types and odd cohomology,\nincluding Katz's correction of the grading, is described by\nEguchi--Jinzenji--Xiong \\cite{EJX}; see also\nGetzler's account \\cite{GetzlerVirasoro}.\nLiu--Tian prove the constraints in genus zero \\cite{LiuTian}.\nIn all genera, Givental's quantization formalism and Teleman's\nreconstruction theorem establish them for targets with\ngenerically semisimple quantum cohomology\n\\cite{GiventalQuant,GiventalSemisimple,Teleman}.\nOkounkov--Pandharipande prove them for smooth target curves,\nincluding odd insertions \\cite{OkounkovPandharipande}.\nTogether with Theorem~\\ref{thm:main}, this gives the full\nconstraints for every projective bundle over a smooth\nprojective curve, and for towers of such bundles.\n\nThe closest transfer theorem is due to\nCoates--Givental--Tseng \\cite{CGT}. They prove that the\nVirasoro constraints hold for the base of a toric bundle if\nand only if they hold for its total space, for toric bundles\nconstructed from sums of line bundles. Their proof combines\nancestor localization with Brown's mirror theorem\n\\cite{Brown}; split projective bundles are among its examples.\nFor arbitrary vector bundles, Fan \\cite{Fan} proves all-genus\nreconstruction and Chern-class dependence of projective-bundle\ninvariants. His argument uses the projective completion\n$\\mathbb P_B(E\\oplus\\mathcal O)$, which is also the auxiliary\nspace used below. Reconstruction of invariants by itself\ndoes not establish compatibility with the Virasoro\ndifferential operators.\n\nIritani--Koto \\cite{IritaniKoto} construct a mirror theorem\nand a decomposition of the quantum $D$-module for arbitrary\nprojective bundles. Koto \\cite{Koto} proves a mirror theorem\nfor nonsplit toric bundles. These results concern genus zero.\nIn particular, Iritani--Koto's equivalence of generic\nsemisimplicity for a projective bundle and its base,\ncombined with Givental--Teleman, already gives the conclusion\nof Theorem~\\ref{thm:main} when the base has generically\nsemisimple quantum cohomology. The transfer proved here\nalso covers bases whose quantum cohomology is not\nsemisimple.\n\nAs in Fan's reconstruction, we use a projective completion\nof the bundle as an auxiliary space.\nThe localization calculation adapts the ancestor organization\nof Coates--Givental--Tseng, including their use of the\ngenus-zero cone and ancestor identities. We give the\nadditional gluing argument required when the orbit lines\nare parametrized by a positive-dimensional variety.\nThe mechanism for passing constraints between the two fixed\ncomponents uses Iritani's equivariant shift operator\n\\cite{IritaniShift}. A local logarithm of that shift\ncancels equivariant differentiation in the grading.\nThe resulting spectral modes can be quantized and their\nequations continued between the components.\nQuantum Riemann--Roch \\cite{QRR} identifies the local\nequations with the ordinary constraints. The argument\nuses virtual localization \\cite{GP} and the\nancestor--descendant formalism\n\\cite{KontsevichManin,Getzler,GiventalQuant};\nthe necessary forms of these results are recalled at\ntheir points of use.\n\n\\subsection{The proof mechanism}\n\nTwisting $E$ by a sufficiently negative line bundle leaves\n$X$ unchanged and makes $E^*$ globally generated.\nSet\n\\[\n W=\\mathbb P_B(E\\oplus\\mathcal O).\n\\]\nLet $\\C^*$ scale the trivial summand, and let $\\lambda$\nbe its equivariant parameter. Its fixed components are\n$B$ and $X$. Write $y$ for the variable measuring\ntautological degree and retain separate variables $Q^\\beta$\nfor the full integral base classes.\nThe proof has five stages.\n\n\\begin{enumerate}\n\\item \\emph{Express the auxiliary ancestors using the two\nfixed theories.}\nLocalization factors the ancestor potential of $W$\nthrough the product of the inverse-Euler-twisted ancestor\npotentials of $B$ and $X$. The transformation is the\nquantization of an upper symplectic series $R(z)$.\nThe same $R$ factors the genus-zero fundamental solution.\nSection~\\ref{loc:section} proves both statements together,\nkeeping the families of orbit lines as evaluation\ncorrespondences. Localization provides a relation involving\nboth fixed theories; a second structure is needed to transfer\nconstraints from one to the other.\n\n\\item \\emph{Separate the shift operator into spectral blocks.}\nThe genus-zero shift operator translates $\\lambda$ by the\nloop variable $z$. Its coefficients, at each base degree,\nare polynomial in $y$. After reducing modulo $z$ and\npositive base degree, its distinct spectral values are\n$\\lambda+h$, where\n\\[\n                         h^r(h+\\lambda)=y.\n\\]\nThere are $r+1$ branches at a generic value of $y$.\nNear $y=0$, one tends to the eigenvalue zero and corresponds\nto $B$; the other $r$ tend to $\\lambda$ and together\ncorrespond to $X$. Section~\\ref{shift:section} establishes\nthis description without diagonalizing the quantum\ncohomology of $B$.\n\n\\item \\emph{Construct quantizable modes on the blocks.}\nThe equivariant grading contains\n$\\lambda\\partial_\\lambda$. On each block the logarithm\nof the shift contains $z\\partial_\\lambda$.\nSubtracting $\\lambda/z$ times this logarithm removes\ncoefficient differentiation. The remaining grading defines\nVirasoro-type loop operators.\nSection~\\ref{spec:section} constructs their projections\nand logarithms coefficientwise and compares them at\n$y=0$ with the fixed-component operators.\nSection~\\ref{fixed:section} uses quantum Riemann--Roch\nto identify the latter, up to a triangular change of modes,\nwith the ordinary modes on $B$ or $X$.\n\n\\item \\emph{Continue equations between branches.}\nFor fixed genus, base class, and insertions, the connected\nancestor invariants of $W$ are polynomial in $y$.\nMoreover, ancestor powers are bounded independently of\nfiber degree. Every coefficient of a quantized equation\ntherefore involves finitely many coefficients of the\nspectral modes and is an actual identity of germs.\nThe covering $h\\mapsto h^r(h+\\lambda)$ is connected off\nits branch values. Its monodromy transports the equations\nfrom the $B$ branch to every branch. Adding the $r$\nbranches of the $X$ cluster and returning to $y=0$\ngives the ordinary equations for $X$ up to scalar.\n\n\\item \\emph{Fix the scalar normalization.}\nQuantization is projective, so its covariance has only\ndetermined the equations up to insertion-independent\nscalars. The commutators\n$[L_{-1},L_1]=-2L_0$ and $[L_0,L_k]=-kL_k$\nremove those scalars and give exactly the prescribed\nconstant in $L_0$. Section~\\ref{cont:section} proves\nthis calculation and completes the transfer.\n\\end{enumerate}\n\nTwo features of the argument are useful beyond the\nlocalization formula itself. Subtracting the block logarithm\nof a difference operator can turn a grading that\ndifferentiates parameters into a loop operator over the\ncoefficient ring. Ancestor bounds then let identities for\nsuch operators continue coefficientwise, even when\ncontinuation of an entire quantized transformation has\nnot been defined. In the present setting these two steps\nsupply the passage from the ordinary constraints on one\nfixed component to those on the other.\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/02-conventions.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/02-conventions.tex", "bytes": 10961, "sha256": "5791916f81553732e6ff1c4c0052e2144d40108538729cfde485bb945079af3f", "content": "\\section{Descendant potentials and quantization}\\label{conv:section}\n\nWe fix the ordinary theory and its operator normalization before introducing\nthe auxiliary equivariant target.  Quantized changes of frame are naturally\ndefined up to a scalar.  We therefore record both the exact Virasoro\nconstraints and the weaker, projective form that can be transported between\nframes.  The scalar ambiguity will be removed at the end of the proof.\n\n\\subsection{Cohomology, curve classes, and descendants}\n\nLet $Y$ be a smooth connected projective complex variety of dimension $d$.\nIts state space is the full super vector space\n$H_Y=H^*(Y;\\C)$, with parity given by cohomological degree modulo two and\npairing\n\\[\n \\eta_Y(a,b)=(a,b)_Y=\\int_Y a\\cup b.\n\\]\nEvery tensor product, permutation, contraction, and derivative below uses\nthe Koszul convention.  In particular, the coevaluation tensor is the\ncategorical inverse of this pairing, without an additional parity\ninvolution.  Define even endomorphisms\n\\begin{equation}\n \\mu_Y\\big|_{H^{a,b}(Y)}=(a-d/2)\\id,\n \\qquad R_Y=c_1(TY)\\cup.\n \\label{conv:grading}\n\\end{equation}\nThus $\\mu_Y$ uses the first Hodge index.  Poincar\\'e duality and the Hodge\ntype of $c_1(TY)$ give\n$\\mu_Y^*=-\\mu_Y$, $R_Y^*=R_Y$, and $[\\mu_Y,R_Y]=R_Y$.\n\nChoose a homogeneous Hodge basis $(\\phi_a)$ with $\\phi_0=1_Y$.\nFor $j\\geq0$, let $t_j^a$ have the parity of $\\phi_a$, and put\n\\[\n t_j=\\sum_a t_j^a\\phi_a,\n \\qquad t(z)=\\sum_{j\\geq0}t_jz^j.\n\\]\nThese are formal supercommuting variables; the aggregate insertion $t_j$\nis even.  The connected, ordinary, unreduced descendant potentials are\n\\begin{equation}\n\\begin{split}\n F_g^Y(t)\n &=\\sum_{\\substack{\\beta\\ \\mathrm{effective}\\\\m\\geq0}}\n     \\frac{Q^\\beta}{m!}\n     \\int_{[\\overline{\\mathcal M}_{g,m}(Y,\\beta)]^{\\mathrm{vir}}}\n       \\prod_{i=1}^{m}\n          \\left(\\sum_{j\\geq0}\\psi_i^j\\ev_i^*t_j\\right),\\\\\n Z_Y(t)&=\\exp\\left(\\sum_{g\\geq0}\\hbar^{g-1}F_g^Y(t)\\right).\n\\end{split}\n\\label{conv:potentials}\n\\end{equation}\nOnly stable maps contribute.  Here $\\psi_i$ is the cotangent class at the\nmarked point of the stable map.  It is not an ancestor class.\nThe exponential includes disconnected domains and the empty domain.\n\nThe Novikov symbols retain the actual effective integral classes\n$\\beta\\in H_2(Y;\\Z)$, including $\\beta=0$; multiplication is\n$Q^{\\beta_1}Q^{\\beta_2}=Q^{\\beta_1+\\beta_2}$.\nFixing an ample integral class gives the Novikov completion: only finitely\nmany labels occur below each fixed ample degree.  All identities are read\ncoefficientwise in this completion, at finite order in the descendant\nvariables, and with the coefficientwise Laurent interpretation in\n$\\hbar$.  In particular, classes are not identified merely because their\ndivisor degrees agree.  The finiteness of effective homology labels of\nbounded degree follows, for example, from the finite-type Chow spaces of\ncycles of bounded degree in a projective embedding.\n\n\\subsection{The ordinary Virasoro operators}\n\nOn the loop space and its standard polarization use\n\\begin{equation}\n\\begin{split}\n \\cH_Y&=H_Y((z^{-1})),\n \\qquad\n \\Omega_Y(f,g)=\\Res_{z=0}(f(-z),g(z))_Y\\,dz,\\\\\n \\cH_Y&=H_Y[z]\\oplus z^{-1}H_Y[[z^{-1}]].\n\\end{split}\n\\label{conv:polarization}\n\\end{equation}\nThe infinitesimal symplectic operators are\n\\begin{equation}\n \\ell_{-1,Y}=z^{-1},\\qquad\n \\ell_{0,Y}=z\\partial_z+\\frac12+\\mu_Y+\\frac{R_Y}{z},\\qquad\n \\ell_{k,Y}=\\ell_{0,Y}(z\\ell_{0,Y})^k\\quad(k\\geq1).\n \\label{conv:ordinary-modes}\n\\end{equation}\nTheir infinitesimal symplectic property follows from the adjoint identities\nafter \\eqref{conv:grading}, including the sign of $z$ in the residue\npairing.\n\nFor an even infinitesimal symplectic operator $A$ on a paired loop space\nwith pairing $\\eta$, write\n\\[\n \\mathcal Q_A(f)=\\tfrac12\\Omega(f,Af).\n\\]\nUse homogeneous Darboux coordinates for the displayed polarization, with\npositive coordinates $q_j^a$ and dual negative coordinates $p_{j,a}$.\nNormal ordering quantizes a $pp$ monomial as $\\hbar$ times the\ncorresponding second derivative, a $pq$ monomial as the first-order\noperator with multiplication before differentiation, and a $qq$ monomial\nas multiplication by that monomial divided by $\\hbar$.\nDerivatives are left derivatives and all reorderings have their graded\nsigns.  We denote the resulting operator, after the dilaton translation\n\\begin{equation}\n                  q(z)=t(z)-z1_Y,\n                  \\label{conv:dilaton}\n\\end{equation}\nby $\\op_\\eta(A)$.  The negative coordinates $p_{j,a}$ in this paragraph\nare unrelated to the tautological divisor $p$ introduced below.\n\nWith this sign convention the string operator is\n\\begin{equation}\n L_{-1}^Y=\\op_{\\eta_Y}(\\ell_{-1,Y})\n   =-\\frac{\\partial}{\\partial t_0^0}\n     +\\sum_{j\\geq0,a}t_{j+1}^a\\frac{\\partial}{\\partial t_j^a}\n     +\\frac{(t_0,t_0)_Y}{2\\hbar}.\n \\label{conv:string}\n\\end{equation}\nFor all other modes set\n\\begin{equation}\n\\begin{split}\n L_k^Y&=\\op_{\\eta_Y}(\\ell_{k,Y})\\quad(k\\ne0),\\\\\n L_0^Y&=\\op_{\\eta_Y}(\\ell_{0,Y})+C_Y,\n \\qquad\n C_Y=\\frac{\\chi(Y)}{16}-\\frac14\\str(\\mu_Y^2).\n\\end{split}\n\\label{conv:normalized-modes}\n\\end{equation}\nThe supertrace uses even minus odd cohomological parity.  There are no\nscalar corrections in positive modes.  We write $\\V(Y)$ for the full set\nof identities\n\\begin{equation}\n                    L_k^Y Z_Y=0\\qquad(k\\geq-1).\n                    \\label{conv:virasoro}\n\\end{equation}\nThese equations use every genus, every effective integral class, and all\ninsertions in $H_Y$, including odd and primitive classes.\n\n\\subsection{Projective constraints and changes of frame}\n\n\\begin{definition}\\label{conv:projective}\nAn even infinitesimal symplectic operator $A$ is \\emph{satisfied\nprojectively} by a potential $Z$ if\n\\[\n                       Z^{-1}\\op_\\eta(A)Z\n\\]\nis independent of the descendant or ancestor variables.  In this\nexpression $\\op_\\eta(A)$ acts on $Z$.  Such a scalar may depend on the\nNovikov variables, equivariant parameters, and $\\hbar$.\n\\end{definition}\n\nThis formulation discards exactly the scalar ambiguity of quadratic\nquantization.  It is useful for comparing different paired state spaces.\nFor a symplectic map $M$ between them, denote its quantized action, when\ndefined in the completions used here, by $U_M$.  Our group-action\nconvention is the one in the following covariance formula.  With the\nHamiltonian sign above, its infinitesimal form uses $-\\op_\\eta(A)$ for\nthe action of $\\exp(A)$.\n\n\\begin{lemma}[Projective covariance]\\label{conv:covariance}\nSuppose $M$ and $M^{-1}$ admit quantized actions, and $A$ and $MAM^{-1}$\nadmit coefficientwise quadratic quantizations.  If $\\eta$ and $\\eta'$\nare the source and target pairings, then\n\\begin{equation}\n U_M\\op_\\eta(A)U_M^{-1}\n        =\\op_{\\eta'}(MAM^{-1})+\\text{a scalar}.\n \\label{conv:covariance-equation}\n\\end{equation}\nConsequently $A$ is satisfied projectively by $Z$ if and only if\n$MAM^{-1}$ is satisfied projectively by $U_M Z$.\nMultiplying either potential by an invertible scalar does not change this\ncondition.\n\\end{lemma}\n\n\\begin{proof}\nThe commutator of two normally ordered quadratic operators is the\nquantization of the corresponding quadratic Hamiltonian bracket, together\nwith the scalar arising from the double contractions.  In super\ncoordinates that scalar is the corresponding supertrace.  Exponentiating\nthis identity gives \\eqref{conv:covariance-equation}; equivalently one can\nuse the projective quantization formalism of \\cite{GiventalQuant}.\nThe same argument applies between paired spaces after a linear change of\nDarboux coordinates.  The last assertions follow because these operators\ndo not differentiate coefficient parameters or $\\hbar$.\n\\end{proof}\n\n\\subsection{Coefficientwise finiteness near zero curve variables}\n\nSome later mode matrices have infinitely many positive powers of $z$,\nand can also contain finite powers of $z\\partial_z$.  We interpret their\nresidue identities entrywise in the polarized mode coordinates.  The\narrays need not act on every uncompleted loop-space input; only the\ncoefficientwise actions specified here are used.  The\nfollowing elementary rule specifies the formal setting for their local\nquantizations; the continuation argument will establish its separate\nfiniteness assertion at generic fiber parameter.\n\n\\begin{lemma}\\label{conv:finite-support}\nSuppose a potential has finite descendant-index support at fixed curve\ncoefficient, $\\hbar$ power, and variable order, and has $\\hbar$ powers\nbounded below at fixed curve coefficient and variable order.  Let $A$ be\nan even infinitesimal symplectic mode operator, linear over the coefficient\nparameters and independent of $\\hbar$.  Assume its powers of $z$ have a\nfinite lower bound and its order in $z\\partial_z$ is finite at each\nfiltration coefficient.  Then $\\op_\\eta(A)$ acts coefficientwise on this\npotential.  The same holds with additional formal filtration variables.\nQuantized actions of transformations equal to the identity modulo\npositive filtration, whose logarithms satisfy these mode bounds, are\ndefined by their formal exponential series in this setting.\n\\end{lemma}\n\n\\begin{proof}\nFor the $pp$ part, only finitely many differentiated indices have nonzero\ncoefficients in the potential.  For the $pq$ part, a fixed differentiated\nindex bounds the possible multiplied index because the mode powers have\na lower bound.  The $qq$ part has only finitely many pairs of indices at\neach filtration coefficient.  Euler differentiation in $z$ multiplies\nmode coefficients by polynomials in their indices and does not change\nthese bounds.  At fixed positive filtration degree only finitely many\nterms of an exponential occur.  These observations also make the\ncross-polarization contractions in the covariance formula finite\ncoefficientwise.\n\\end{proof}\n\nOrdinary descendant potentials have this support property: for fixed\ngenus, class, and number of marks, sufficiently high powers of the\ncotangent classes vanish on the finite-dimensional stable-map space.\nIt also holds for the twisted descendants used below, where the torus\nacts trivially on the target and the cotangent classes remain ordinary\nclasses.  Ancestor potentials have the stronger bound supplied by the\ndimension of the moduli space of stable curves.\nStability ensures the stated Laurent property after exponentiation:\ndegree-zero genus-zero components consume marked points, whereas\npositive-degree components consume positive Novikov degree.\n\nThe standard splitting and cotangent-class identities used in the paper\nare tensor identities with the diagonal given by the categorical\ncoevaluation.  Their marked-point proofs therefore apply to full\ncohomology with precisely these signs.  Characteristic-class\nmultiplications are even; quantum Riemann--Roch and the two-mark shift\nidentities likewise use arbitrary evaluation classes and this diagonal.\nThroughout, a splitting adds the actual integral curve classes.  These\nconventions permit the standard formulas to be used without discarding\nodd insertions or merging Novikov labels.\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/03-master-space.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/03-master-space.tex", "bytes": 11181, "sha256": "2e4c3d953ab8461532c2dad6018cef5e7bb87be24a7fda629f4ab3e235cba6c7", "content": "\\section{The master space and its fixed theories}\\label{geo:section}\n\nWe place the base and its projectivization inside a single smooth\nprojective variety with a torus action.  Its fixed components will carry\ninverse-Euler twists of their ordinary theories.  This section specifies\ntheir curve labels and pairings, and recalls the ancestor and fundamental\nsolution conventions needed for the localization comparison.\n\n\\subsection{Geometry and integral curve labels}\n\nTensoring $E$ by a line bundle does not change the projective bundle of\none-dimensional subspaces.  Choose such a twist so that $E^*$ is globally\ngenerated, and continue to denote the resulting bundle by $E$.\nSet\n\\[\n       X=\\mathbb P_B(E),\\qquad W=\\mathbb P_B(E\\oplus\\mathcal O_B).\n\\]\nLet $T=\\C^*$ act with weight zero on $E$ and weight one on\n$\\mathcal O_B$, and let $\\lambda\\in H_T^2(\\mathrm{pt})$ be the class of\nthe weight-one representation.  The fixed locus is\n$W^T=X\\sqcup B$, where the second component is the section corresponding\nto the summand $\\mathcal O_B$.\nWrite $\\pi$ for either projective-bundle projection and put\n$p=c_1^T(\\mathcal O_W(1))$.  The ordinary normal bundles and their torus\nweights are\n\\begin{equation}\n\\begin{array}{c|c|c|c|c|c}\n F&N_F&n_F=\\rk N_F&w_F&W_F=n_Fw_F&j_F\\\\ \\hline\n X&\\mathcal O_X(1)&1&1&1&0\\\\\n B&E&r&-1&-r&1\n\\end{array}\n\\label{geo:normal-data}\n\\end{equation}\nThe restrictions of the divisor are\n\\[\n              p|_X=c_1(\\mathcal O_X(1)),\\qquad p|_B=-\\lambda.\n\\]\nHere $w_F$ is the common torus weight on the normal fibers; the integers\n$j_F$ are recorded for the fixed-section curve factors in the shift\noperator below.  The normal-bundle descriptions follow from the relative\ntangent space $\\Hom(L,V/L)$ at a line $L\\subset V$.\n\n\\begin{lemma}\\label{geo:curve-labels}\nFor $P=X$ or $W$, the map\n\\begin{equation}\n H_2(P;\\Z)\\longrightarrow H_2(B;\\Z)\\oplus\\Z,\n \\qquad d\\longmapsto\n       \\left(\\pi_*d,\\int_d c_1(\\mathcal O_P(1))\\right)\n \\label{geo:integral-splitting}\n\\end{equation}\nis an isomorphism, including torsion.  An effective class has an effective\nbase pushforward $\\beta$ and a nonnegative tautological degree $l$.\n\\end{lemma}\n\n\\begin{proof}\nThe structure group of a projective bundle acts trivially on the integral\nhomology of its fiber.  In total degree two the homology Serre spectral\nsequence therefore has only the base term $H_2(B;\\Z)$ and the fiber term\n$H_2(\\mathbb P^{s-1};\\Z)=\\Z$, where $s\\geq2$ is the bundle rank.\nThe integral class $c_1(\\mathcal O_P(1))$ pairs to one with the fiber line.\nConsequently the fiber generator survives: its image in total homology\ncannot be zero or a nontrivial multiple quotient, as either possibility\nwould contradict that pairing.  The edge filtration gives an exact\nsequence\n\\[\n 0\\longrightarrow\\Z[\\text{fiber line}]\n   \\longrightarrow H_2(P;\\Z)\n   \\xrightarrow{\\pi_*}H_2(B;\\Z)\\longrightarrow0.\n\\]\nPairing with $c_1(\\mathcal O_P(1))$ is a retraction on its first term;\ntogether with $\\pi_*$ it gives \\eqref{geo:integral-splitting}.\nThis integral argument does not discard torsion.\nFinally, the dual bundles $E^*$ and $E^*\\oplus\\mathcal O_B$ are globally\ngenerated, so the corresponding tautological line bundles are globally\ngenerated.  Their degrees on effective curves are nonnegative, and\nproper pushforward preserves effective curve classes.\n\\end{proof}\n\nWe write $Q^\\beta y^l$ for the actual class corresponding to $(\\beta,l)$.\nUnder the inclusion $X\\hookrightarrow W$ these two coordinates are\nunchanged, and under $B\\hookrightarrow W$ a class $\\beta$ becomes\n$(\\beta,0)$.  Thus the fixed theories and the master-space theory have\ncompatible full integral labels.  We may allow all pairs of an effective\nbase class and an integer $l\\geq0$ as formal labels, assigning coefficient\nzero when a pair is not effective for the target in question.\n\nChoose an ample integral class $H$ on $B$.  For a sufficiently large\ninteger $M$, the class $c_1(\\mathcal O_P(1))+M\\pi^*H$ is ample for both\nprojective bundles.  We complete in the degree\n\\[\n                         l+M\\int_\\beta H.\n\\]\nThere are finitely many effective labels of bounded degree, as in\nSection~\\ref{conv:section}.  Unless specified otherwise, positive\nNovikov filtration means positive degree for this completion, so it\nincludes positive fiber degree even when the base class is zero.\nThe separate completion by positive base degree will be used only when\nthe fiber parameter $y$ is treated as a variable on a complex domain.\n\n\\subsection{Equivariant pairings and inverse-Euler twists}\n\nThe equivariant projective-bundle formula makes $H_T^*(W)$ free over\n$\\C[\\lambda]$, with basis\n\\[\n                    \\pi^*\\phi_a\\,p^j,\\qquad 0\\leq j\\leq r,\n\\]\nfor a homogeneous Hodge basis $(\\phi_a)$ of $H_B$.\nIts equivariant integration pairing is perfect over $\\C[\\lambda]$.\nIndeed, integration over the projective fibers gives an antitriangular\nmatrix in the powers of $p$, whose antidiagonal blocks are the ordinary\nPoincar\\'e pairing of $B$.\n\nAfter extending scalars to $\\C(\\lambda)$, restriction to the fixed locus\nis an isomorphism of paired spaces\n\\begin{equation}\n\\begin{gathered}\n \\left(H_T^*(W),\\eta_W\\right)\\otimes_{\\C[\\lambda]}\\C(\\lambda)\n   \\cong\n \\bigoplus_{F=B,X}\\left(H_F\\otimes\\C(\\lambda),\\eta_F^t\\right),\\\\\n \\eta_F^t(a,b)=\\int_F\\frac{a\\cup b}{e_T(N_F)}.\n\\end{gathered}\n \\label{geo:fixed-pairing}\n\\end{equation}\nThe unit restricts to the sum of the two fixed units.  Every normal weight\nis nonzero, so the Euler classes in this formula are invertible over\n$\\C(\\lambda)$.\n\nThe corresponding twisted Gromov--Witten theory on $F$ inserts\n\\begin{equation}\n    e_T\\!\\left(R^\\bullet\\rho_*f^*N_F\\right)^{-1}\n    \\label{geo:Euler-twist}\n\\end{equation}\nin each stable-map integral.  Here $\\rho$ and $f$ are the universal curve\nand universal map, and the inverse Euler class is extended\nmultiplicatively to the indicated $K$-theory class.  The torus acts\ntrivially on $F$ and with weight $w_F$ on $N_F$.\nWe use subscripts $F,\\mathrm{tw}$ for its potentials and fundamental\nsolutions.  Its degree-zero three-point pairing is exactly $\\eta_F^t$.\n\nGive $p$ and $\\lambda$ Hodge bidegree $(1,1)$.  In a homogeneous polynomial\nbasis the first-Hodge grading acts on basis vectors, while differentiation\nin $\\lambda$ records the degree of equivariant coefficients when needed.\nThe virtual dimension identities can be expressed in this first degree:\nalgebraicity forces the sum of the Hodge-degree differences of insertions\nin a nonzero invariant to be zero.  This remains true equivariantly, by\nfinite-dimensional approximations or by fixed-locus integration.\nIt is distinct from the ordinary cohomological-degree count used to bound\nfiber degree later.\n\n\\subsection{Ancestors and the fundamental solution}\\label{geo:ancestors}\n\nWe give the same definitions for an ordinary theory, the equivariant\ntheory of $W$, or one of the twisted fixed theories.  Denote its pairing\nby $\\eta$, its Novikov monomial for a class $d$ by $\\mathsf q^d$, and\nits stable-map integrals by brackets.  Let $u$ be a formal even primary\nbackground.  For distinguished insertions $a_1,\\ldots,a_m$, set\n\\[\n \\langle a_1,\\ldots,a_m\\rangle_{g,u}\n   =\\sum_{\\substack{d\\ \\mathrm{effective}\\\\n\\geq0}}\n      \\frac{\\mathsf q^d}{n!}\n        \\langle a_1,\\ldots,a_m,\n                   \\underbrace{u,\\ldots,u}_{n}\\rangle_{g,m+n,d},\n\\]\nwhere $d$ runs over effective classes and only stable-map terms are\nincluded.  Descendant insertions in these brackets use the map cotangent\nclasses.\n\nFor $2g-2+m>0$, there is a stabilization map\n\\[\n \\operatorname{st}_{g,m}:\n \\overline{\\mathcal M}_{g,m+n}(Y,d)\n     \\longrightarrow\\overline{\\mathcal M}_{g,m}\n\\]\nwhich forgets the map and the $n$ background marks and stabilizes the\nremaining curve.  Define $\\bar\\psi_i=\\operatorname{st}_{g,m}^*\\psi_i$ for\n$1\\leq i\\leq m$.  The ancestor potential at background $u$ is\n\\begin{equation}\n\\begin{split}\n \\overline F_g(u;t)\n  &=\\sum_{\\substack{d\\ \\mathrm{effective},\\ m,n\\geq0\\\\2g-2+m>0}}\n     \\frac{\\mathsf q^d}{m!n!}\n       \\left\\langle\n        \\prod_{i=1}^m\\left(\\sum_{j\\geq0}\\bar\\psi_i^j\\ev_i^*t_j\\right)\n        \\prod_{a=m+1}^{m+n}\\ev_a^*u\n       \\right\\rangle_{g,m+n,d},\\\\\n \\mathcal A(u;t)&=\n       \\exp\\left(\\sum_{g\\geq0}\\hbar^{g-1}\\overline F_g(u;t)\\right).\n\\end{split}\n\\label{geo:ancestor-potential}\n\\end{equation}\nThe bracket in this display denotes integration of the displayed product,\nwith the twist when appropriate.  All terms with curve-unstable\ndistinguished data are omitted, even when the stable-map space itself\nexists.  The genus-one term with no distinguished marks is therefore\nomitted as well.  Since the ancestors come from\n$\\overline{\\mathcal M}_{g,m}$, their total power exceeds the dimension\n$3g-3+m$ only when the corresponding product vanishes.\n\n\\begin{definition}\\label{geo:fundamental}\nThe fundamental solution $S(u,z)=\\id+O(z^{-1})$ is defined by\n\\begin{equation}\n \\eta(b,S(u,z)a)\n    =\\eta(b,a)+\\left\\langle b,\\frac{a}{z-\\psi}\\right\\rangle_{0,u},\n \\qquad\n \\frac1{z-\\psi}=\\sum_{j\\geq0}\\psi^jz^{-j-1}.\n \\label{geo:S-definition}\n\\end{equation}\nThe pairing term supplies the unstable two-point contribution.\n\\end{definition}\n\nThe genus-zero splitting and cotangent-class identities give\n\\begin{equation}\n S(u,-z)^*S(u,z)=\\id,\n \\qquad z\\partial_v S(u,z)=(v\\star_u)S(u,z),\n \\label{geo:S-identities}\n\\end{equation}\nwhere $\\star_u$ is the quantum product and $\\partial_v$ differentiates\nthe primary background.  For clarity, the genus-zero cone $\\mathcal L$\nis the formal Lagrangian graph of $dF_0$ in the Darboux coordinates\n$(q,p)$, with $q=t-z1$.  Its point with descendant input $\\epsilon$ has\npositive coordinate $\\epsilon-z1$, and the tangent space there is the\ngraph of the Hessian of $F_0$ at $\\epsilon$.  The genus-zero string,\ndilaton, and topological\nrecursion identities say that each tangent space $T$ is tangent along\n$zT\\subset\\mathcal L$.  At primary coordinate $u$ that tangent space is\n$S(u,z)^{-1}\\cH_+$; see \\cite{GiventalCone}.\nThe ancestor--descendant correspondence in our group-action convention is\n\\begin{equation}\n                    \\mathcal A(u)=c(u)\\,U_{S(u)}Z,\n                    \\label{geo:ancestor-descendant}\n\\end{equation}\nwhere $c(u)$ is independent of ancestor variables.  These formulas follow\nfrom the splitting identity for $\\psi_i-\\bar\\psi_i$ and its genus-zero\nspecializations; see \\cite{KontsevichManin,Getzler,GiventalQuant}.\nThey hold with the super contractions of Section~\\ref{conv:section} and\nwith the inverse-Euler twists above.\n\nWe use \\eqref{geo:ancestor-descendant} only on the fixed theories, with\n$u$ of positive total Novikov filtration.  There $S(u,z)-\\id$ has positive\nfiltration, and every Novikov coefficient has only finitely many negative\npowers of $z$: only finitely many background marks occur at that\ncoefficient, and the map cotangent classes on the fixed targets are\nnilpotent.  Thus the quantized action is covered by\nLemma~\\ref{conv:finite-support}.\nFor the master space we abbreviate\n\\[\n               S_W=S_W(0,z),\\qquad\\mathcal A_W=\\mathcal A_W(0).\n\\]\nIts rational loop-variable expansions and the quantized action used to\ncompare it with the fixed theories will be established directly by\nlocalization in the next section.\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/04-localization.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/04-localization.tex", "bytes": 27583, "sha256": "43700b503d66994d32306311f50f9f78ea32b51c580d6824d15f9675691dbd52", "content": "\\section{Ancestor localization for the master space}\\label{loc:section}\n\nThe torus action on $W=\\mathbb P_B(E\\oplus\\mathcal O)$ separates its\nGromov--Witten theory into contributions from $B$ and $X$.\nWe need this separation at the level of ancestor potentials, together with\nthe corresponding factorization of the genus-zero fundamental solution.\nWe abbreviate the fixed paired space of \\eqref{geo:fixed-pairing} by\n\\[\n  H^{\\mathrm{fix}}=H_B\\oplus H_X,\n  \\qquad \\eta^{\\mathrm{fix}}=\\eta_B^t\\oplus\\eta_X^t.\n\\]\nRestriction identifies this paired space with $H_T^*(W)$ after inverting\n$\\lambda$. All series below use the full curve labels $Q^\\beta y^l$.\n\n\\begin{proposition}[Ancestor localization]\\label{loc:factorization}\nThere are classes $u_F\\in H_F$ with coefficients of positive Novikov\nfiltration, for $F=B,X$, and a symplectic power series\n$R(z)\\in\\End(H^{\\mathrm{fix}})[[z]]$, with $R-\\id$ of positive Novikov\nfiltration, such that, writing\n\\[\n S_f(z)=S_{F,\\mathrm{tw}}(u_F,z),\\qquad\n S_{\\mathrm{bl}}(z)=\\bigoplus_{F=B,X}S_f(z),\n\\]\none has\n\\begin{align}\n S_W(z)&=R(z)S_{\\mathrm{bl}}(z),\\label{loc:S-factorization}\\\\\n \\mathcal A_W(0)&=c\\,U_R\n       \\prod_{F=B,X}\\mathcal A_{F,\\mathrm{tw}}(u_F),\n       \\label{loc:A-factorization}\n\\end{align}\nwhere $c$ is an invertible scalar independent of ancestor variables.\nFor every curve coefficient, $S_W(z)$ is the expansion at infinity of a\nrational function of $z$. In \\eqref{loc:S-factorization} that rational\nfunction is expanded at $z=0$; the equality is in Laurent series with a\nfinite lower bound at each curve coefficient. Both $R$ and its inverse\nadmit quantized actions in the coefficientwise completion used here.\n\\end{proposition}\n\nThe master space and its fixed-graph geometry also occur in\nFan's reconstruction of projective-bundle invariants\n\\cite[\\S\\S3--4]{Fan}. For the ancestor factorization we adapt the\nlocalization argument of Coates--Givental--Tseng \\cite[\\S3]{CGT}.\nTheir toric-bundle proof has a\nfinite set of orbit directions in each fiber. Here the orbit lines form\nthe projective bundle $X$, so their contributions are cohomology\ncorrespondences. We first establish the geometry and gluing formula for\nthese families. After this step, the genus-zero identities determine\n$R$, and stabilization of the localization graphs gives its quantized\naction. This supplies, for the action at hand, the leg-moduli and virtual-class details\nleft open in \\cite[Remark~3.7]{CGT}.\n\n\\subsection{The moving components and their gluing}\n\nCall an irreducible component of a torus-fixed stable map a\n\\emph{moving leg} if its image is not contained in $W^T=B\\sqcup X$.\nA connected part mapping into a fixed component will instead be kept as\na stable-map factor for that component, including its internal boundary\nstrata.\n\n\\begin{lemma}\\label{loc:leg-geometry}\nA moving leg has degree $m\\geq1$ on a line joining a point\n$x\\in X$ to its image $b=\\pi(x)\\in B$. Its map from $\\mathbb P^1$ is\ntotally ramified over $x$ and $b$, and its markings and nodes lie at these\ntwo points. For any specified marking and attachment status at the\nendpoints, the leg moduli is a smooth proper Deligne--Mumford stack,\nthe $\\mu_m$-gerbe $q_m:\\mathfrak L_m\\to X$ of $m$th roots of\n$\\mathcal O_X(-1)$. Its endpoint evaluation maps are $q_m$ and\n$\\pi\\circ q_m:\\mathfrak L_m\\to B$.\n\nAt an endpoint in $F$, the tangent line of the leg has equivariant\nfirst Chern class\n\\[\n        a=\\frac{w_F\\lambda}{m}+\\nu,\n\\]\nwhere $\\nu$ is an ordinary degree-two class on the leg stack.\nEvery node involving a moving leg has nonzero smoothing character.\n\\end{lemma}\n\n\\begin{proof}\nAfter a finite cover of the torus, its action lifts to the domain of a\nfixed stable map. A nontrivial torus action on a complete irreducible\ncurve has rational normalization. Since the torus acts trivially on\n$B$, the projection of a moving component is constant. In a projective\nfiber $\\mathbb P(E_b\\oplus\\C)$, the closure of every nonfixed orbit is\nthe line joining $[0:1]$ to a unique point $[v:0]\\in\\mathbb P(E_b)$.\nAn equivariant finite map to this orbit closure is, in coordinates at\nthe endpoints, $t\\mapsto t^m$. A self-node would identify its two\nfixed points, which have distinct images. Thus the moving component\nitself is smooth, and the assertions about its special points follow.\n\nVarying the endpoint $[v]$ gives $X$ as the parameter space of orbit\nlines. On the gerbe $\\mathfrak L_m$, let $M$ be the universal line\nwith $M^{\\otimes m}\\cong q_m^*\\mathcal O_X(-1)$. The universal cover is\n\\[\n \\mathbb P(M\\oplus\\mathcal O)\\longrightarrow\n \\mathbb P(q_m^*\\mathcal O_X(-1)\\oplus\\mathcal O)\n \\longrightarrow W,\\qquad [s:t]\\longmapsto[s^m:t^m].\n\\]\nLocally every leg has this form, and its automorphisms are exactly\nthe deck group $\\mu_m$. This identifies its moduli with the stated\nroot gerbe, which is smooth and proper over the projective variety\n$X$. Its ordinary class also agrees with its fixed virtual class:\non a line\n$L$ in a fiber,\n\\[\n T_{W/B}|_L\\cong\\mathcal O(2)\\oplus\\mathcal O(1)^{\\oplus(r-1)},\n \\qquad f^*\\pi^*T_B\\cong T_{B,b}\\otimes\\mathcal O_{\\mathbb P^1}.\n\\]\nThe tangent sequence therefore gives $H^1(\\mathbb P^1,f^*T_W)=0$.\nThe ambient pointed-map space is smooth at the leg, and taking the\nfixed part of its deformation space gives the tangent space of this\nsmooth gerbe.\n\nThe universal cover gives, on $\\mathfrak L_m$, the tangent classes\n\\[\n a_X=\\frac{\\lambda+q_m^*p|_X}{m},\\qquad a_B=-a_X.\n\\]\nTheir scalar characters are $+\\lambda/m$ at $X$ and\n$-\\lambda/m$ at $B$. A node joining a leg to a fixed-map factor has\nthis nonzero character. At a node joining two legs, both branches\napproach the same fixed component, so the smoothing character is\n$w_F\\lambda(1/m+1/m')$, again nonzero.\n\\end{proof}\n\nThe geometry is illustrated in Figure~\\ref{loc:figure}. Nonsplitting of\n$E$ affects the global family of lines and its cohomology classes, but\ndoes not change the description of an individual leg or the nonzero\ncharacters in the lemma.\n\n\\begin{figure}[htbp]\n\\centering\n\\begin{tikzpicture}[every node/.style={font=\\small}]\n  \\draw[thick] (0,0) ellipse (.9 and .7);\n  \\node at (0,.3) {$B$};\n  \\draw[thick] (6.4,0) ellipse (1.2 and .9);\n  \\node at (6.4,1.15) {$X=\\mathbb P_B(E)$};\n  \\fill (.4,-.2) circle (2pt);\n  \\fill (6,-.2) circle (2pt);\n  \\draw[thick] (.4,-.2) to[bend left=22] (6,-.2);\n  \\node at (3.2,1) {degree-$m$ orbit leg};\n  \\node[below left] at (.4,-.2) {$b$};\n  \\node[below right] at (6,-.2) {$x$};\n  \\node at (3.2,-.4) {$\\pi(x)=b$};\n  \\node at (.4,-1.2) {$-\\lambda/m$};\n  \\node at (6,-1.2) {$+\\lambda/m$};\n  \\draw[densely dotted] (.4,-.35)--(.4,-.95);\n  \\draw[densely dotted] (6,-.35)--(6,-.95);\n\\end{tikzpicture}\n\\caption{The two endpoints of a moving leg. The displayed characters\nare the scalar parts of its tangent classes. Choosing $x\\in X$\ndetermines the orbit line; the multiplicity-$m$ covers retain the\nfinite stabilizer $\\mu_m$.}\\label{loc:figure}\n\\end{figure}\n\nWe now apply virtual localization \\cite{GP}. To make its use with these\nfamilies explicit, first distinguish all branches at gluing nodes and\ndivide by graph automorphisms afterward. A torus-fixed deformation\ncannot smooth any of the nodes cut in Lemma~\\ref{loc:leg-geometry}:\nthe smoothing parameter has nonzero character. The fixed loci are\ntherefore obtained by matching leg endpoints and stable maps to $B$ or\n$X$ along their evaluation maps. This description holds in families.\nIndeed, degree zero over $B$ forces the projection of a rational leg\nfamily to factor through its base, and the local monomial description\nthen applies over a trivialization of $E$. On a stable-map piece with\nimage in $F$, the lifted torus acts trivially on the domain: its\nautomorphism group as a pointed stable map is finite. Fixed deformations\nof that piece consequently remain maps into $F$.\n\nFor completeness, the compatibility of obstruction theories can be\nread directly from normalization. Denote the normalized pieces by\n$C_\\alpha$ and the cut nodes by $q$. The map deformation complexes,\nrelative to their prestable domains, fit into the triangle\n\\[\n R\\Gamma(C,f^*T_W)\\longrightarrow\n \\bigoplus_\\alpha R\\Gamma(C_\\alpha,f_\\alpha^*T_W)\n \\longrightarrow\\bigoplus_q T_{W,f(q)}.\n\\]\nCut branches are marked on the pieces. Passing to absolute map\ndeformations adds their pointed-domain deformation and automorphism\ncomplexes; releasing a cut node adds its smoothing line\n$T_{q,+}\\otimes T_{q,-}$. The ambient virtual tangent complex,\nrestricted to this fixed gluing locus, consequently has class\n\\begin{equation}\\label{loc:tangent-gluing}\n [\\mathbb T^{\\mathrm{vir}}_\\Gamma]\n =\\sum_\\alpha[\\mathbb T^{\\mathrm{vir}}_\\alpha]\n  -\\sum_q[\\ev_q^*T_W]\n  +\\sum_q[T_{q,+}\\otimes T_{q,-}].\n\\end{equation}\nHere each piece carries its pointed stable-map obstruction theory;\nfor a leg this is the ambient theory restricted to $\\mathfrak L_m$.\nThe identity comes from the displayed compatible triangle, so its\nfixed part gives the obstruction theory of diagonal matching, not\nonly an equality of virtual ranks. On fixed-map pieces the fixed\npart is their theory in $F$; on legs it is $T_{\\mathfrak L_m}$.\nThe matching terms are $T_F$, and every smoothing line is moving.\nThe fixed virtual class is therefore the diagonal virtual pullback\nof these classes, including when two legs meet or a graph has a cycle.\nThe moving part contains $R\\pi_*f^*N_F$ on each fixed-map piece,\nthe moving leg complexes, the matching terms $-N_F$, and the\nsmoothing lines. These are the factors required by virtual localization.\n\nIn particular, cutting a leg off a fixed-map vertex contributes the\nnormal matching numerator $e_T(N_F)$ and the smoothing denominator\n\\begin{equation}\\label{loc:flag-denominator}\n                  \\frac1{a-\\psi}.\n\\end{equation}\nHere $\\psi$ belongs to the fixed-map vertex and $a$ to the leg.\nThe matching numerator is exactly what makes the contraction use the\ninverse of $\\eta_F^t$. All remaining leg data are cohomology\ncorrespondences independent of the descendants on the vertex.\nThere is also an endpoint version of this rule: if an external marked\npoint replaces the attachment to a fixed-map vertex, there is no\nsmoothing denominator, its cotangent class is $-a$, and contraction\nwith $\\eta_F^t$ cancels the normal matching numerator. This convention\nincludes endpoints with no fixed-map component.\n\nThe ordinary class in $a$ need not descend along $X\\to B$.\nWe therefore expand it on the leg stack before any pushforward. If\n$a=a_0+\\nu$, with $a_0=w_F\\lambda/m\\ne0$, then\n\\begin{equation}\\label{loc:nilpotent-denominator}\n \\frac1{a-s}=\\sum_{j\\geq0}\\frac{(-\\nu)^j}{(a_0-s)^{j+1}}.\n\\end{equation}\nThe sum is finite. More explicitly, let $C$ be a correspondence\ncoefficient on a cut piece, with endpoint map $e$ to $F$, and put\n$h_j=e_*(\\nu^jC)$, with its virtual and Euler factors included.\nThe label $h$ will retain this entire finite family of moments, together\nwith the scalar $a_0$. For any function $K$ at this flag define\n\\begin{equation}\\label{loc:dummy-derivatives}\n       K(a)h:=\\sum_{j\\geq0}\\frac1{j!}\n          \\left.\\partial_\\alpha^jK(\\alpha)\\right|_{\\alpha=a_0}h_j.\n\\end{equation}\nThe symbol $\\alpha$ is a dummy scalar: the derivative acts on $K$\nalone, holding $C$ and all $h_j$ fixed, even if they depend on\n$\\lambda$. For example the later expression\n$S_f(a)h/(a+z)$ means \\eqref{loc:dummy-derivatives} with\n$K(\\alpha)=S_f(\\alpha)/(\\alpha+z)$. At several attachment flags use\nthe joint moments and independent dummy scalars. A scalar tangent\nclass and an ordinary cohomology vector are the special case in which\nonly $h_0$ is present. This convention makes the\ncorrespondence formulas precise even when the tangent class does\nnot descend to $F$.\n\nAll graph sums in this section are coefficientwise finite. Each moving\nleg has positive tautological degree, so its multiplicity and the number\nof legs are bounded at fixed ample degree. There are finitely many\nsplittings into effective full curve labels, and stability bounds the\nremaining degree-zero pieces once genus and the distinguished markings\nare fixed. The fixed-map moduli spaces bound the powers of their\ncotangent classes. These observations also justify the finite\nnilpotent expansions and all termwise pushforwards used below.\n\n\\subsection{The genus-zero factor}\n\nWe first construct $R$ from two-point invariants. Fix $F=B$ or $X$.\nAn \\emph{end} at $F$ is a genus-zero unmarked tree attached to a\nfixed-map vertex by a moving leg, with the vertex itself omitted.\nLet $\\epsilon_F(s)$ be the sum of its contributions, including the\ndenominator $(a-s)^{-1}$ at the attachment. It is a cohomology-valued\npower series in $s$, of positive Novikov filtration. Likewise a\n\\emph{tail} has one external primary insertion $b\\in H_T^*(W)$;\nwrite its contribution as $h_b/(a-s)$, with summation over tails\nunderstood. The moment data $h_b$ include the curve monomial, the stack\nand symmetry factors, and the linear dependence on $b$. These\ndefinitions include an external insertion directly at a leg endpoint.\n\nEnd denominators have infinite Taylor expansions. Accordingly, in\nidentities involving these backgrounds we use the completion\n$\\widehat{\\cH}_+=H_F[[z]]$ of the positive space, with localized\nNovikov coefficients, and $[\\,\\cdot\\,]_+$ denotes the nonnegative\npart. The usual cone identities extend to these backgrounds\ncoefficientwise: positive filtration bounds their number of insertions,\nand nilpotence of the map cotangent classes bounds the indices which\ncan contribute.\n\nIn the twisted theory of $F$, let $u_F$ be the primary parameter\nof the tangent space to the genus-zero cone at descendant background\n$\\epsilon_F$. Recall the particular genus-zero facts that we use.\nThe string, dilaton, and topological recursion relations make the cone\noverruled, and its tangent spaces are\n$S_{F,\\mathrm{tw}}(u,z)^{-1}\\widehat{\\cH}_+$ in this completion\n\\cite{GiventalCone}. The parameter $u_F$ is equivalently determined by\n\\begin{equation}\\label{loc:u-equation}\n x_F(z):=[S_f(z)(\\epsilon_F(z)-u_F)]_+\\in z\\widehat{\\cH}_+,\n \\qquad S_f(z)=S_{F,\\mathrm{tw}}(u_F,z).\n\\end{equation}\nIndeed, applying $S_f$ to the cone point makes it belong to\n$z\\widehat{\\cH}_+$, and $[S_f(z)(-z+u_F)]_+=-z$.\nThe vanishing of the constant term in \\eqref{loc:u-equation} has\nlinearization $-\\id$ with respect to $u_F$ at zero Novikov degree.\nIt therefore determines $u_F$ uniquely by the formal implicit\nequation, and $u_F$ has positive filtration.\n\nThe tangent space is the graph of the Hessian of the genus-zero\npotential. Thus its two-point function depends on $\\epsilon_F$ only\nthrough $u_F$. In the following formula the bracket sums the stable\ngenus-zero terms with any number of additional $\\epsilon_F(\\psi)$\ninsertions:\n\\begin{equation}\\label{loc:two-point}\n \\frac{\\eta_F^t(b,c)}{w+z}\n +\\left\\langle\\frac b{w-\\psi},\\frac c{z-\\psi}\n                  \\right\\rangle_{0,\\epsilon_F}^{F,\\mathrm{tw}}\n =\\frac{\\eta_F^t(S_f(w)b,S_f(z)c)}{w+z}.\n\\end{equation}\nAt primary background this is the standard two-point fundamental\nsolution identity; the Hessian description gives it at the present\nbackground. It follows alternatively by genus-zero topological\nrecursion. The metric term records the unstable two-point convention.\nEvery occurrence of this identity is rational in $w,z$ at a fixed\ncurve coefficient.\n\nFor $c$ supported on $F$, localize the matrix element\n$\\eta(b,S_W(z)c)$. If the input lies on a fixed-map vertex, all\nunmarked outgoing trees provide its background $\\epsilon_F$.\nThe insertion $b$ is either on that same vertex, as $b|_F$, or in a\ntail with attachment $h_b/(a-\\psi)$. If the input itself is a marked\nleg endpoint, the endpoint rule supplies the metric term in\n\\eqref{loc:two-point}, with denominator $a+z$.\nTaking also the coefficient at $w^{-1}$ in that identity to treat a\nprimary slot gives\n\\begin{equation}\\label{loc:S-tail}\n \\eta(b,S_W(z)c)=\\eta_F^t(b|_F,S_f(z)c)\n       +\\sum_{\\mathrm{tails}}\n          \\frac{\\eta_F^t(S_f(a)h_b,S_f(z)c)}{a+z}.\n\\end{equation}\nThis proves \\eqref{loc:S-factorization}: explicitly,\n\\begin{equation}\\label{loc:R-tail}\n       (R(z)^*b)_F=b|_F+\n              \\sum_{\\mathrm{tails}}\\frac{S_f(a)h_b}{a+z},\n\\end{equation}\nexpanded at $z=0$. The denominators are invertible there, so $R$ is\nupper triangular. The fixed-theory $S_f$ is finite in negative powers\nof $z$ at each curve coefficient, and the tails give rational functions.\nThis also proves the claimed rationality of $S_W$. Since $S_W$ and\n$S_{\\mathrm{bl}}$ are symplectic, their rational identities imply\n\\begin{equation}\\label{loc:R-symplectic}\n                         R(-z)^*R(z)=\\id.\n\\end{equation}\nEvery nonidentity term in \\eqref{loc:R-tail} has a moving leg, so\n$R-\\id$ has positive Novikov filtration.\n\nTwo further genus-zero identities identify the translation and the\nedge contraction for the quantized action. They will allow us to\nrecognize the higher-genus localization formula without computing\nthe individual leg Euler factors.\n\nFirst use the one-point function $J_W(-z)=-zS_W(z)^{-1}1$.\nThe string equation identifies it with\n\\[\n J_W(-z)=-z1+\n \\sum_{\\beta,l}Q^\\beta y^l(\\ev_1)_*\n \\left(\\frac{[\\overline{\\mathcal M}_{0,1}(W,(\\beta,l))]^{\\mathrm{vir}}}\n                  {-z-\\psi_1}\\right),\n\\]\nwhere only stable one-marked terms are included. In the expansion at\nzero, its regular part on $F$ is $-z+\\epsilon_F(z)$.\nIndeed, a marked leg endpoint supplies\nan end, whereas a marking on a fixed-map vertex contributes only\nstrictly negative powers of $z$. Hence\n\\[\n \\epsilon_F(z)=z+\n                [J_W(-z)|_F]_+.\n\\]\nApply $S_f$ and take the regular part. Because $S_f$ has only\nnonpositive powers, inserting or omitting an inner regular-part\nprojection does not alter the final regular part. The string equation\ngives $[S_f(z)z]_+=z+u_F$; using the factorization already proved yields\n\\begin{equation}\\label{loc:translation}\n                     \\bigoplus_Fx_F(z)=z(1-R(z)^{-1}1).\n\\end{equation}\n\nNext a \\emph{bridge} is an unmarked genus-zero tree with two\nattachments to fixed-map vertices, omitted from the tree, and moving\nlegs at both attachments; a single leg is allowed. Its contribution\nhas denominators $(a-s)^{-1}(a'-s')^{-1}$. Apply $S_f(a)$ and\n$S_{f'}(a')$ to its two correspondence slots, and denote the sum of\nthese dressed contributions by $D(s,s')$. A tensor is here regarded\nas an operator by the twisted pairing; the first argument belongs to\nthe output slot.\n\nLocalize the two-point function of $W$, including its metric term.\nIf no moving leg separates the marked points, the contribution is the\nblock identity \\eqref{loc:two-point}. Otherwise the path between\nthem determines a unique bridge, and \\eqref{loc:two-point} at its\ntwo ends includes the cases in which either head is an unstable\nmarked endpoint. Consequently\n\\begin{equation}\\label{loc:bridge-two-point}\n \\frac{S_W(w)^*S_W(z)}{w+z}\n =S_{\\mathrm{bl}}(w)^*\n       \\left(\\frac{\\id}{w+z}+D(-w,-z)\\right)\n       S_{\\mathrm{bl}}(z).\n\\end{equation}\nTogether with \\eqref{loc:S-factorization}, this gives\n\\begin{equation}\\label{loc:bridge-kernel}\n              D(-w,-z)=\\frac{R(w)^*R(z)-\\id}{w+z}.\n\\end{equation}\nThe numerator vanishes at $w=-z$ by\n\\eqref{loc:R-symplectic}. Thus the right side is a power series at\n$(w,z)=(0,0)$, as is the dressed bridge expression. We may first\nestablish these equalities with independent rational variables and\nthen take these Taylor expansions.\n\n\\subsection{From descendants at the flags to ancestors}\n\nWe have constructed the upper factor and identified its translation\nand bridge kernel. To obtain \\eqref{loc:A-factorization}, it remains\nto express every surviving vertex of a localization graph by its\ntwisted ancestor theory. We record the two ancestor identities needed\nfor that conversion, including their dependence on the background.\n\nFor a fixed target with twisted pairing $\\eta^t$, let\n$\\mathcal A(\\epsilon;t)$ denote the mixed potential with distinguished\ninsertions $t(\\bar\\psi)$ and additional insertions $\\epsilon(\\psi)$.\nHere the ancestors forget the additional marks. The parameter\n$\\epsilon$ is of positive filtration, and $u$ and\n$x(z)=[S(u,z)(\\epsilon(z)-u)]_+$ are specified by\n\\eqref{loc:u-equation}.\n\n\\begin{lemma}[Vertex conversion]\\label{loc:vertex-conversion}\nIn the summed vertex correlators with background $\\epsilon$,\nan insertion at a distinguished flag transforms as\n\\begin{equation}\\label{loc:flag-dressing}\n           \\frac{h}{a-\\psi}\\quad\\longmapsto\\quad\n           \\frac{S(u,a)h}{a-\\bar\\psi}.\n\\end{equation}\nThe transformation remains valid with additional powers of the ancestor\nclass at that flag and with other distinguished flags; all evaluation\nclasses at the flag are included in $h$ before applying $S(u,a)$. Moreover,\nup to a scalar independent of $t$,\n\\begin{equation}\\label{loc:mixed-potential}\n                   \\mathcal A(\\epsilon;t)=\\mathcal A(u;t+x).\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nThese are the ancestor identities of Getzler and\nKontsevich--Manin \\cite{Getzler,KontsevichManin}, in the form used in\n\\cite[\\S3.5, Proposition~3.6]{CGT}. We give the arguments because the\nbackground here has descendants.\n\nThe identity\n\\[\n \\frac1{a-\\psi}=\\frac1{a-\\bar\\psi}\n       +\\frac{\\psi-\\bar\\psi}{(a-\\psi)(a-\\bar\\psi)}\n\\]\nreduces the first assertion to the splitting divisor for\n$\\psi-\\bar\\psi$. This divisor separates a genus-zero twig carrying\nthe indicated mark, any subset of the background marks, and its\nattaching node. Splitting the virtual class contracts its two-point\nfunction with the rest of the vertex. Summing all such twigs, and\nadding the direct term, gives $S(u,a)h$ by the primary-slot instance\nof \\eqref{loc:two-point}. The ancestor classes at the original\ndistinguished marks are pulled back from the stabilized curve, so\nthey do not enter this twig calculation. This proves\n\\eqref{loc:flag-dressing}, successively at every distinguished flag.\nFor tangent classes with nilpotent part, apply the scalar identity\nand then the finite derivatives specified in\n\\eqref{loc:nilpotent-denominator}.\n\nFor the second assertion write $\\epsilon=u+(\\epsilon-u)$ and expand\nmultilinearly, distinguishing the two kinds of extra marks. First\nforget only the primary $u$ marks. The same splitting-divisor\nargument converts the remaining descendant insertions to\n$x(\\bar\\psi)=[S(u,z)(\\epsilon(z)-u)]_+|_{z=\\bar\\psi}$.\nAt this intermediate stage, the original ancestors still forget\nthe $x$ marks, whereas the new ancestors retain them.\n\nThe two choices give equal correlators. A boundary divisor in their\ncotangent-class difference has a rational tail carrying one original\nmark and $k\\geq1$ of the marks to be forgotten. Its stable-curve\nfactor is $\\overline{\\mathcal M}_{0,k+2}$, of dimension $k-1$.\nEvery $x$ insertion has a positive ancestor power, since\n$x(z)\\in z\\widehat{\\cH}_+$. Their product restricts to degree at least $k$\non this factor and therefore vanishes. This argument, applied to\neach cotangent difference, replaces the original ancestors by the\nones retaining all $x$ marks. Multilinearity now gives\n\\eqref{loc:mixed-potential}.\n\nThe assertion uses the stable-curve convention for ancestor\npotentials. Terms which become stable only after adding $x$ marks\ndo not introduce a nonconstant correction: in genus zero their\npositive ancestor powers exceed the dimension of the stable-curve\nspace, and in genus one the case without an original distinguished\nmark contributes only a scalar. This accounts for the scalar allowed\nin the statement.\n\\end{proof}\n\n\\subsection{Assembly of the stable graphs}\n\nConsider a fixed stable map contributing to an ancestor invariant of\n$W$ with stable distinguished curve data. Forget the map and\nstabilize its distinguished marked curve. Every moving leg disappears,\nbecause it has at most two special points. On the graph whose vertices\nare entire fixed-map factors, prune unmarked rational trees and\nsuppress rational chains with two remaining flags, keeping all\ndistinguished markings. A fixed-map factor which survives is retained\nwith its own stabilization at those flags and markings; its internal\nstable-map boundary remains part of its moduli space. The pieces\nremoved between surviving vertices are bridges, those ending in a\ndistinguished marking are tails, and all other removed pieces are\nends.\n\nThis decomposition identifies the restriction of the global ancestor\nclasses. At a surviving vertex they are its ancestors for the\nremaining distinguished flags and marks, with the end marks forgotten.\nFor a marking on a tail, its global ancestor is the ancestor at the\ntail's attachment after contraction. These statements follow from\nthe composition of the stabilization maps with gluing of the\nsurviving pointed curves.\n\nEnds therefore supply the background $\\epsilon_F$ at each vertex.\nLemma~\\ref{loc:vertex-conversion} converts its bridge and tail\ndenominators to ancestor denominators and inserts the factors\n$S_f(a)$. Bridges become exactly\n$D(\\bar\\psi,\\bar\\psi')$. The direct external markings together\nwith all tails become, by \\eqref{loc:R-tail},\n\\[\n                   R(-z)^*t(z)=R(z)^{-1}t(z),\n                   \\qquad z=\\bar\\psi.\n\\]\nFinally \\eqref{loc:mixed-potential} replaces the background by $u_F$\nand adds $x_F$. In view of \\eqref{loc:translation}, the complete\nvertex substitution is consequently\n\\begin{equation}\\label{loc:Wick-substitution}\n                t(z)\\longmapsto\n                  R(z)^{-1}t(z)+z(1-R(z)^{-1}1).\n\\end{equation}\n\nThe upper-triangular quantization formula\n\\cite[Proposition~7.3]{GiventalQuant} is now applicable. If\n\\[\n                   D(s,s')=\\sum_{i,j\\geq0}D_{ij}s^i(s')^j,\n\\]\nits contraction operator is $\\hbar/2$ times the second derivatives\nwith coefficient tensors $D_{ij}$, using the categorical inverse of\nthe twisted pairing. Exponentiating this operator on the product\nof the fixed ancestor potentials and then making\n\\eqref{loc:Wick-substitution} is $U_R$. Indeed its kernel is\n\\eqref{loc:bridge-kernel}; the arguments $-w,-z$ are the signs\nrequired by the negative Darboux modes $(-z)^{-i-1}$.\nThis is also \\cite[Equation~(19)]{CGT} in the present notation.\n\nThe localization graph sum has precisely this form. Labeling the\nflags first and subsequently dividing by permutations gives its\nfactorials and graph automorphism factors. A bridge joining two\nvertices, or two flags at the same vertex, contributes one factor\nof $\\hbar$ under the same contraction rule; cycles therefore have\nthe required genus power. All pairings and permutations are those\nin super vector spaces. The tensor gluing proof consequently\nincludes odd cohomology without a change of pairing convention.\nComponents with no distinguished markings can change the overall\nscalar, which is invertible as a formal exponential. We have proved\n\\eqref{loc:A-factorization}.\n\nIt remains to check the invertibility asserted in the proposition.\nThe series $R-\\id$ has positive filtration, so its inverse and\nlogarithm are defined successively in Novikov degree. The same holds\nfor the actions obtained by exponentiating the corresponding\nquadratic operators. At a fixed curve coefficient, genus power,\nand number of variables, only finitely many filtration-positive\nfactors can contribute. The remaining sums over ancestor indices\nare finite because an ancestor on a stable $n$-pointed genus-$g$\ncurve has total degree at most $3g-3+n$; in the fixed descendant\nidentities the corresponding bound is the finite dimension of the\nfixed-target stable-map space. The inverse upper action has the\nsame properties. Thus all transformations used here and their\ninverses act in the stated coefficientwise completion. This completes\nthe proof of Proposition~\\ref{loc:factorization}.\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/05-shift.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/05-shift.tex", "bytes": 18869, "sha256": "95e3d0767ca8b889e4f43e448fac18c32b616015249029e5382500ec70fd1b2d", "content": "\\section{A grading and a shift in genus zero}\n\\label{shift:section}\n\nWe now construct two commuting operators on the equivariant quantum\ncohomology of $W$.  One is the grading operator.  The other translates\n$\\lambda$ by the loop variable $z$ and is defined by genus-zero invariants\nof a bundle over $\\mathbb P^1$.  Its fixed-locus expression will connect the\nordinary theories of $B$ and $X$.  Its spectrum, computed below using only\nfiber curves, will provide the branches along which we transport the\nconstraints.\n\nThroughout this section, $\\star$ means the small equivariant quantum\nproduct of $W$, at primary background zero.  Put\n\\[\n c=c_1^T(TW),\\qquad\n \\kappa=c_1(TB)+c_1(E).\n\\]\nThe equivariant projective-bundle formula gives\n\\begin{equation}\n c=(r+1)p+\\pi^*\\kappa+\\lambda,\n \\qquad\n \\int_{(\\beta,l)}c_1(TW)=(r+1)l+\\int_\\beta\\kappa.\n \\label{shift:c1}\n\\end{equation}\nChoose a homogeneous Hodge basis of $H^*(B)$ and the resulting polynomial\nbasis $\\pi^*b\\,p^j$, $0\\leq j\\leq r$, of $H_T^*(W)$ over $\\C[\\lambda]$.\nIn this basis, $\\mu$ acts by the first Hodge degree minus\n$\\dim_\\C(W)/2$ and does not differentiate the coefficient $\\lambda$.\n\n\\subsection{The calibrated grading}\n\nDefine\n\\begin{equation}\n \\begin{split}\n G_d&=z\\partial_z+\\lambda\\partial_\\lambda+\\tfrac12+\\mu+\\frac{c\\cup}{z},\\\\\n G_a&=z\\partial_z+\\lambda\\partial_\\lambda+\\tfrac12+\\mu+\\frac{c\\star}{z}.\n \\end{split}\n \\label{shift:gradings}\n\\end{equation}\nThe subscripts distinguish the constant, or descendant, frame from the\nquantum, or ancestor, frame.  Both operators act on coefficients in\n$\\lambda$, whereas the Novikov variables are held fixed.\n\n\\begin{lemma}\n\\label{shift:grading-lemma}\nWith $S_W=S_W(0,z)$ in the convention fixed above,\n\\begin{equation}\n                 G_a=S_WG_dS_W^{-1}.\n \\label{shift:grading-calibration}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nLet $D_c$ be the derivation of the Novikov ring defined by\n\\[\n D_c(Q^\\beta y^l)\n   =\\left((r+1)l+\\int_\\beta\\kappa\\right)Q^\\beta y^l.\n\\]\nHomogeneity of the two-point invariants defining $S_W$ says\n\\begin{equation}\n (z\\partial_z+\\lambda\\partial_\\lambda+D_c)S_W\n                      =S_W\\mu-\\mu S_W.\n \\label{shift:homogeneity}\n\\end{equation}\nThis identity uses the first Hodge degree.  The evaluation maps are\nalgebraic morphisms, and the virtual class and cotangent-line classes\nare algebraic.  Their push-pull operations therefore have the Hodge\nbidegrees prescribed by virtual dimension, even when the inserted\nclasses are not algebraic.  Concretely, if the input and output basis\nvectors have first Hodge degrees $h_j$ and $h_i$, the coefficient of\n$Q^\\beta y^l z^{-k-1}$ in the corresponding entry of $S_W$ has\n$\\lambda$-degree\n\\[\n h_j+k+1-h_i-\\left((r+1)l+\\int_\\beta\\kappa\\right).\n\\]\nSince $\\lambda$ and $z$ both have bidegree $(1,1)$ for this calculation,\nthis is precisely \\eqref{shift:homogeneity}.  It applies equally to\nprimitive and odd inputs.\n\nThe divisor equation gives\n\\begin{equation}\n             zD_cS_W=(c\\star)S_W-S_W(c\\cup).\n \\label{shift:divisor}\n\\end{equation}\nThe scalar equivariant part of $c$ occurs identically in the two\nmultiplications, so cancels in their difference.  Substituting\n\\eqref{shift:divisor} into \\eqref{shift:homogeneity} gives\n$G_aS_W=S_WG_d$.  These identities first hold in the expansion at\n$z=\\infty$.  Coefficientwise rationality of $S_W$, established by the\nlocalization calculation, also gives the identities in its Laurent\nexpansion at $z=0$.\n\\end{proof}\n\n\\subsection{The shift operator and its fixed-locus formula}\n\nHere is the genus-zero input from Iritani that we use.  For a smooth\nprojective variety $Y$ with a $\\C^*$-action, form its associated bundle\n$\\widehat Y\\to\\mathbb P^1$.  Let $\\sigma_{\\min}$ be the section class\nof the fixed component with positive normal weights.  The operator\ndefined by two-fiber section invariants, with monomials\n$Q^{\\widehat d-\\sigma_{\\min}}$, is an unlocalized equivariant\ncohomology operator.  After reversing the sign of Iritani's loop\nvariable, it translates $\\lambda$ to $\\lambda+z$ and satisfies\n\\begin{equation}\n T_a=S_YT_dS_Y^{-1},\\qquad\n T_d\\big|_{F}\n =Q^{\\sigma_F-\\sigma_{\\min}}\n   \\prod_{m,x}\n   \\frac{\\prod_{a=-\\infty}^{0}(x+m\\lambda-az)}\n        {\\prod_{a=-\\infty}^{-m}(x+m\\lambda-az)}\n    e^{z\\partial_\\lambda}.\n \\label{shift:iritani-input}\n\\end{equation}\nHere $x$ runs through the ordinary Chern roots of the weight-$m$\nnormal subbundle of $F$; each quotient has only finitely many remaining\nfactors.  This is the projective specialization of\n\\cite[Proposition~2.2, Remark~3.4, Definition~3.9,\nRemark~3.10, Definition~3.13, and\nTheorem~3.14]{IritaniShift}.  The source's seminegativity condition is\nautomatic for a complete target, since its global functions are\nconstant.  Its calibration $M$ is related to ours by\n$S_Y(z)=M(-z)^{-1}$.\n\nWe describe the associated geometry in this application to fix both\nthe section classes and the signs.  For the action that has weight one\non the trivial summand of $E\\oplus\\mathcal O$, it is\n\\begin{equation}\n \\widehat W\n  =\\mathbb P_{B\\times\\mathbb P^1}\n     \\bigl(\\pr_B^*E\\oplus\\pr_{\\mathbb P^1}^*\\mathcal O(-1)\\bigr).\n \\label{shift:associated-space}\n\\end{equation}\nEquivalently it is\n$W\\times(\\C^2\\setminus\\{0\\})/\\C^*$, with\n$s\\cdot(w,v_1,v_2)=(s\\cdot w,s^{-1}v_1,s^{-1}v_2)$.\nAn additional torus scales $v_2$, and its equivariant parameter is\nIritani's loop variable before sign reversal.  The fibers over\n$[1:0]$ and $[0:1]$ carry the original action and the action composed\nwith this additional torus.  Identifying their equivariant\ncohomologies by the induced change of torus variables accounts for\nthe translation in \\eqref{shift:iritani-input}.\n\nMore explicitly, write $\\zeta$ for that parameter before sign\nreversal, and let $\\rho_0(t,u)w=t\\cdot w$ and\n$\\rho_1(t,u)w=(tu)\\cdot w$ be the actions on the two fibers.  The\nchange of torus variables induces\n$\\Phi_1:H^*_{T\\times\\C^*,\\rho_0}(W)\\to\nH^*_{T\\times\\C^*,\\rho_1}(W)$ with\n\\[\n \\Phi_1(f(\\lambda,\\zeta)a)\n       =f(\\lambda+\\zeta,\\zeta)\\Phi_1(a).\n\\]\nIf $\\iota_0,\\iota_\\infty$ denote the fiber inclusions, the section\ncorrespondence is defined by\n\\begin{equation}\n (\\widetilde T a,b)_\\infty\n  =\\sum_{\\beta,l}Q^\\beta y^l\n     \\left\\langle\\iota_{0*}a,\\iota_{\\infty*}b\n       \\right\\rangle^{\\widehat W,T\\times\\C^*}\n       _{0,2,\\sigma_{\\min}+(\\beta,l)}.\n \\label{shift:section-definition}\n\\end{equation}\nOnly effective section classes are included.  The inputs belong to\nthe equivariant cohomologies of their respective fibers, and the\npairing on the left is the one on the second fiber.  The operator\nused here is $T_a=(\\Phi_1^{-1}\\widetilde T)|_{\\zeta=-z}$.\nIts semilinearity translates $\\lambda$ to $\\lambda+z$, as asserted.\n\nIn this construction the normal weight at $X$ is positive, so $X$\ngives $\\sigma_{\\min}$.  A section obtained from a point of $X$ has\ntautological degree zero in \\eqref{shift:associated-space}; a section\nobtained from the trivial-summand fixed component $B$ is the line\n$\\mathcal O(-1)$ and has tautological degree one.  Both have base\nclass zero.  Thus\n$\\sigma_B-\\sigma_{\\min}$ is precisely the fiber-line class of $W$.\n\nThis also checks the curve labels in the shift theorem.  The integral\nprojective-bundle splitting identifies $H_2(W,\\Z)$ by\n$(\\beta,l)$.  The same splitting on \\eqref{shift:associated-space}\nidentifies a section class by $(\\beta,1,l)$.  Subtraction of\n$\\sigma_{\\min}=(0,1,0)$ leaves $(\\beta,0,l)$.  The inclusions of\nthe two fibers preserve $\\beta$ and tautological degree, hence induce\nthe same identification on integral homology.  At every splitting\nof a section stable map the labels therefore add in actual integral\nhomology.  In particular the use of \\eqref{shift:iritani-input}\ndoes not replace classes by their numerical equivalence classes.\n\n\\begin{proposition}\n\\label{shift:operator}\nThere is a shift operator\n\\[\n T_a=A(z,\\lambda,Q,y)e^{z\\partial_\\lambda}\n\\]\non the small quantum-cohomology module of $W$, whose matrix $A$ is\npolynomial in $z,\\lambda$ at every curve coefficient in the chosen\nequivariant basis.  Its calibration is\n\\begin{equation}\n                   T_a=S_WT_dS_W^{-1},\n \\label{shift:calibration}\n\\end{equation}\nwhere, writing $v=x+w_F\\lambda$ for the equivariant normal roots,\n\\begin{equation}\n T_d\\big|_F\n   =y^{j_F}\n      \\prod_x\n       \\begin{cases}\n        x+\\lambda,& F=X,\\\\[2pt]\n        (x-\\lambda-z)^{-1},& F=B\n       \\end{cases}\n       e^{z\\partial_\\lambda},\n \\qquad j_X=0,\\quad j_B=1.\n \\label{shift:fixed-formula}\n\\end{equation}\nAll operators act on the full equivariant cohomology with its super\npairing.\n\\end{proposition}\n\n\\begin{proof}\nThe target $W$ is smooth and projective, hence satisfies the\nsemiprojectivity and weight hypotheses of the cited theorem.\nIts equivariant formality is also explicit in the basis\n$\\pi^*b\\,p^j$.  Formula \\eqref{shift:associated-space} is projective,\nso its section invariants are defined by proper equivariant\npushforward.  The two fiber insertions are polynomial equivariant\nclasses, and the equivariant pairing of $W$ is perfect over\n$\\C[\\lambda]$.  Consequently the operator is polynomial in both\nequivariant parameters at each curve coefficient; changing the sign\nof the second parameter and identifying the fibers preserves this\nproperty.\n\nThe normal weights are $1$ at $X$ and $-1$ at $B$.  In\n\\eqref{shift:iritani-input} these give the factors $v$ and\n$(v-z)^{-1}$, respectively.  The section calculation above gives\n$y^{j_F}$.  Finally, in the convention for the fundamental solution\nused here, its pairing formula and symplectic identity give\n$S_W(z)=M(z)^*=M(-z)^{-1}$.  Iritani's identity\n$M T_a=T_d M$, with his $z$ replaced by $-z$, is therefore exactly\n\\eqref{shift:calibration}.\n\nThe two-marked section correspondence and its localization proof\nuse arbitrary evaluation classes and diagonal contractions.  They\nthus apply on full cohomology with the categorical inverse pairing\nand its Koszul signs.  At primary background zero no assertion\nabout a power-series extension in odd background coordinates is\nneeded.\n\\end{proof}\n\n\\begin{lemma}\n\\label{shift:polynomiality}\nFor every fixed actual base class $\\beta$, the matrix coefficients\nof $A$ and of $c\\star$ are polynomials in $y,z,\\lambda$ (with no\n$z$ dependence in $c\\star$).  In particular they are holomorphic\nfunctions of $y$ before any localization in $\\lambda$.\n\\end{lemma}\n\n\\begin{proof}\nThe dual of the vector bundle in \\eqref{shift:associated-space} is\nthe sum of the globally generated bundles $\\pr_B^*E^*$ and\n$\\pr_{\\mathbb P^1}^*\\mathcal O(1)$.  Its tautological line bundle\nis therefore globally generated.  Every effective section class has\n$l\\geq0$, and subtracting $\\sigma_{\\min}$ does not change $l$.\nThe same lower bound for $W$ was established by the master-space\nconstruction.\n\nFix basis vectors for an input and a paired output.  The section\ninvariant defining the corresponding shift entry has two fiber\nincidence insertions of fixed cohomological degrees.  The\nprojective-bundle formula on $\\widehat W$ gives\n\\begin{equation}\n \\operatorname{vdim}_{\\C}\n \\overline{\\mathcal M}_{0,2}\n       \\bigl(\\widehat W,\\sigma_{\\min}+(\\beta,l)\\bigr)\n   =\\dim_\\C W+1+(r+1)l+\\int_\\beta\\kappa.\n \\label{shift:section-dimension}\n\\end{equation}\nThus virtual dimension increases by $r+1$ when $l$ increases by\none.  Proper equivariant integration is polynomial in the\nparameters.  If virtual dimension exceeds the total degree of the\ninsertions, its result would have negative degree and is zero.\nThus only finitely many $l$ occur for this entry and $\\beta$.\nThe finite polynomial inverse-pairing matrix used to recover\noperator entries does not change this conclusion.  The identical\nargument with the three primary insertions defining a quantum\nproduct proves it for $c\\star$.\n\\end{proof}\n\nWe have now obtained operators whose coefficients can be studied\nboth as Novikov series at $y=0$ and as functions of a nonzero complex\nvariable $y$.  Before making that change of viewpoint, we verify\nthat the shift respects the grading and determine its limiting\nspectrum.\n\n\\begin{lemma}\n\\label{shift:commutation-lemma}\nThe grading and shift commute:\n\\begin{equation}\n                          [G_a,T_a]=0.\n \\label{shift:commutation}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nBy the two calibration identities it suffices to work in the\ndescendant frame.  Equivariant homogeneity of restriction identifies\n$\\lambda\\partial_\\lambda+\\mu$ on the summand for $F$ with\n$\\lambda\\partial_\\lambda+\\mu_F-n_F/2$.  Also\n\\[\n c|_F=c_1(TF)+\\sum_x(x+w_F\\lambda).\n\\]\nWrite $T_d|_F=a_F E_z$, where $E_z=e^{z\\partial_\\lambda}$ and\n$a_F$ is the multiplier in \\eqref{shift:fixed-formula}.  The operator\n$z\\partial_z+\\lambda\\partial_\\lambda$ commutes with $E_z$.\nCombining it with first Hodge degree counts $1$ for each factor\n$x+\\lambda$ and $-1$ for each factor $(x-\\lambda-z)^{-1}$.\nThe resulting commutator with $a_F$ is $W_F a_F$, where\n$W_F=n_Fw_F$.  The power $y^{j_F}$ is held fixed in this calculation.\nOn the other hand,\n\\[\n E_z(c|_F)=(c|_F+W_Fz)E_z,\n \\qquad\n [c|_F/z,a_FE_z]=-W_Fa_FE_z.\n\\]\nThe two contributions cancel.  Scalar degree-centering terms and\nthe ordinary cup multiplications in this computation introduce no\nfurther commutators.\n\\end{proof}\n\n\\subsection{The spectrum of the fiber theory}\n\nLet $Q^{>0}$ denote the ideal of positive base degree in the Novikov\nring.  Since an effective nonzero base class has positive ample\ndegree, reduction modulo this ideal retains exactly the maps with\nconstant projection to $B$.  The variable $y$ is retained.  We next\nidentify $T_a$ modulo $z$ in this fiber theory.\n\n\\begin{lemma}\n\\label{shift:fiber-operator}\nOn $H_T^*(W)$ with the fiber quantum product,\n\\begin{equation}\n               T_a\\bmod(z,Q^{>0})=(p+\\lambda)\\star.\n \\label{shift:seidel}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nThe product is linear over the classical superalgebra $H^*(B)$:\nall evaluations of a genus-zero fiber map have the same projection\nto $B$, so base classes pull out of the corresponding pushforward\nwith the usual signs.  The same reasoning applies to the two-point\noperator $S_W$ in this reduction.  Formula\n\\eqref{shift:calibration}, whose shift fixes base cohomology, then\nshows that $T_a$ is $H^*(B)$-linear.\n\nPut $D_y=y\\partial_y$.  The divisor equation gives\n\\[\n S_W(zD_y-p\\cup)S_W^{-1}=zD_y-p\\star.\n\\]\nThe operator on the left before conjugation commutes with $T_d$.\nOn $X$, the multiplier has no $y$ factor and $p|_X$ is independent\nof $\\lambda$.  On $B$, $p|_B=-\\lambda$ and\n\\[\n [zD_y,T_d|_B]=zT_d|_B,\n \\qquad [\\lambda,T_d|_B]=-zT_d|_B.\n\\]\nThus the two terms cancel there as well.  Conjugating and reducing\nmodulo $z$ proves that $T_a|_{z=0}$ commutes with $p\\star$.\n\nAssign complex cohomological degree one to $p,\\lambda,z$ and\ndegree $r+1$ to $y$ for the present fiber calculation.  Polynomiality\nin $\\lambda$ implies that the quantum powers of $p$ through $p^r$\nare the classical powers: a positive $y$ contribution has degree\nat least $r+1$.  These powers form a basis over $H^*(B)[\\lambda]$,\nso an $H^*(B)[\\lambda]$-linear operator commuting with $p\\star$ is\ndetermined by its value on $1$.\n\nIn this same grading $T_a$ has degree one.  To see this directly\nfrom the section definition, the normal bundle to a minimal\nsection has one summand $\\mathcal O(-1)$ and the other summands\nare trivial.  Hence $c_1(T\\widehat W)\\cdot\\sigma_{\\min}=1$.\nEquivalently, \\eqref{shift:section-dimension} at $\\beta=0$ has\nvirtual dimension $\\dim W+1+(r+1)l$.  Each fiber inclusion raises\ninsertion degree by one; comparing the resulting degree with the\nfiber pairing gives operator degree $1-(r+1)l$ in the coefficient\nof $y^l$.  It follows that $T_a(1)|_{z=0}$ has no\npositive $y$ coefficient.  At $y=0$ all curve classes are zero,\n$S_W=1$, and \\eqref{shift:fixed-formula} applied to $1$ has\nrestrictions $p+\\lambda$ on $X$ and zero on $B$.  These are exactly\nthe restrictions of $p+\\lambda$ on $W$.  Thus\n$T_a(1)|_{z=0}=p+\\lambda$, which proves \\eqref{shift:seidel}.\n\\end{proof}\n\nLet $I=H^{>0}(B;\\C)$.  This is a nilpotent ideal, also when\n$H^*(B)$ has odd classes.  Reducing the fiber quantum algebra\nmodulo $I$ gives\n\\begin{equation}\n \\C[\\lambda,y,p]\\big/\\bigl(p^r(p+\\lambda)-y\\bigr).\n \\label{shift:fiber-relation}\n\\end{equation}\nIndeed the classical projective-bundle relation reduces to\n$p^r(p+\\lambda)=0$.  In degree $r+1$ the only possible positive\ncurve correction is a constant multiple of $y$.  Its coefficient\nis the degree-one line invariant in a fiber $\\mathbb P^r$, hence\none.  More explicitly, one may pair the relevant structure\nconstant with a top-degree base class; the degree-zero base\nprojection reduces the computation to the fiber.  The base\ndirections contribute no obstruction because\n$H^1(C,\\mathcal O_C)=0$ for a genus-zero domain.  The coefficient\nhas degree zero, so is independent of $\\lambda$ and may equally\nbe computed at $\\lambda=0$.  It is then\n\\[\n       \\left\\langle H,H^r,H^r\\right\\rangle^{\\mathbb P^r}\n                      _{0,3,\\mathrm{line}}=1.\n\\]\nHere $H$ is the ordinary hyperplane class of the fiber.\nIndeed two general point conditions determine a unique line, and\nthe remaining marked point is its unique intersection with a\ngeneral hyperplane.  Convexity of projective space makes this the\nvirtual count as well.  This gives the coefficient of $y$ in\n$p\\star p^r$, hence the coefficient in\n\\eqref{shift:fiber-relation}.\n\n\\begin{proposition}\n\\label{shift:spectrum-proposition}\nFor generic $(y,\\lambda)$, the distinct eigenvalue branches of\n$T_a\\bmod(z,Q^{>0})$ are\n\\begin{equation}\n             \\lambda+h,\\qquad h^r(h+\\lambda)=y.\n \\label{shift:spectrum}\n\\end{equation}\nEach branch may have multiplicity and a nontrivial nilpotent part.\nFor $\\lambda\\ne0$, near $y=0$ there is one branch tending to zero\nand a cluster of $r$ branches tending to $\\lambda$.\n\\end{proposition}\n\n\\begin{proof}\nOn the quotient by $I$, \\eqref{shift:seidel} and\n\\eqref{shift:fiber-relation} give the stated eigenvalues.  The\nfinite filtration by powers of $I$ is preserved by the operator,\nand the induced operator on each successive quotient is the same\nfiber multiplication tensored with $I^m/I^{m+1}$.  Its characteristic\npolynomial therefore has the same set of roots.  Away from the\ncritical values of $h\\mapsto h^r(h+\\lambda)$ these roots are\ndistinct.  At $y=0$ the roots of that polynomial are $-\\lambda$\nand $0$, with multiplicities one and $r$, respectively; translation\nby $\\lambda$ gives the final assertion.  This argument uses\nnilpotence of positive-degree base cohomology and does not impose\nsemisimplicity on its quantum cohomology.\n\\end{proof}\n\nThe polynomial dependence of the two operators and the finite set\nof eigenvalue branches are the ingredients needed for the\nspectral construction in the next section.  The calibration\nidentities retain their full curve labels, so the later return to\n$y=0$ will still compare the individual descendant theories of\nthe two fixed manifolds.\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/06-spectral.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/06-spectral.tex", "bytes": 21283, "sha256": "2d5d2432587c52cff755ec49cecdfdd5eb5b1eec2fa166fd9d432fb743bc3d17", "content": "\\section{Spectral projections and block gradings}\n\\label{spec:section}\n\nThe shift operator $T_a$ distinguishes $r+1$ spectral blocks at generic\nfiber parameter, whereas the localization formula distinguishes the two fixed\nmanifolds $B$ and $X$.  We construct operators on the individual spectral\nblocks and compare their sum near $y=0$ with the fixed-manifold blocks.\nThe construction must also remove differentiation in the equivariant\nparameter: only after that removal can the resulting loop operators be\nquantized over the coefficient ring.  All assertions in this section are\nclassical statements about operators.\n\n\\subsection{Functional calculus for differential series}\n\nLet $V$ be the finite-dimensional vector space underlying a chosen\nequivariant basis of $H_T^*(W)$.  On a small open set $U$ in the\n$(y,\\lambda)$-plane, write $\\mathcal O_U$ for holomorphic functions and\n\\[\n \\mathscr D_\\lambda(U)\n =\\left\\{\\sum_{j=0}^{J}a_j(y,\\lambda)\\partial_\\lambda^j:\n                 J<\\infty,\\ a_j\\in\\mathcal O_U\\right\\}.\n\\]\nThe multiplication is composition, so\n$\\partial_\\lambda a=a\\partial_\\lambda+\\partial_\\lambda(a)$.\nThere is no differentiation in $y$.  Denote by $\\Lambda_B$ the base\nNovikov completion already fixed, and by $\\Lambda_{B,+}$ its positive\ndegree ideal.  We use the completed algebra\n\\begin{equation}\n \\mathscr A_U\n =\\left\\{\\sum_{\\beta,m\\geq0}Q^\\beta z^m A_{\\beta,m}:\n      A_{\\beta,m}\\in\\End(V)\\otimes\\mathscr D_\\lambda(U)\\right\\}.\n \\label{spec:algebra}\n\\end{equation}\nHere and below the notation $\\beta\\geq0$ means that $\\beta$ ranges over\nthe effective base labels, including zero.  The completion is taken with\nrespect to $z$ and ample base degree.  In particular each\n$A_{\\beta,m}$ has finite differential order, without a bound uniform in\n$\\beta,m$.  Products at a fixed coefficient are finite by Novikov\nfiniteness.  The element $z$ is central in this algebra.  We may equally\nwork with germs, and then all holomorphic assertions are coefficientwise.\nWhen a loop derivative occurs, we adjoin $z\\partial_z$ with\n\\[\n [z\\partial_z,Q^\\beta z^m A_{\\beta,m}]\n                =mQ^\\beta z^m A_{\\beta,m};\n\\]\nall expressions involving this extra derivative have finite order in it.\n\nSuppose $T\\in\\mathscr A_U$ has order-zero reduction\n\\[\n T^{(0)}=T\\bmod(z,\\Lambda_{B,+})\\in\n                \\End(V)\\otimes\\mathcal O_U.\n\\]\nAfter shrinking $U$, choose contours in the complex $\\xi$-plane that\navoid the spectrum of $T^{(0)}$ and enclose specified groups of its\neigenvalues.  The contours are held fixed while $y$ and $\\lambda$ vary\nin $U$.  Set $R_0(\\xi)=(\\xi-T^{(0)})^{-1}$ and\n$K=T-T^{(0)}$.  The formal resolvent is\n\\begin{equation}\n R_T(\\xi)=(\\xi-T)^{-1}\n    =\\sum_{n\\geq0}\\bigl(R_0(\\xi)K\\bigr)^nR_0(\\xi).\n \\label{spec:resolvent}\n\\end{equation}\nEach factor $K$ increases the joint $z$/base filtration.  Consequently\neach coefficient in this expression is a finite sum of differential\noperators, and is meromorphic in $\\xi$, with poles only at the\neigenvalues of $T^{(0)}$.\n\n\\begin{lemma}[Coefficientwise holomorphic calculus]\n\\label{spec:calculus}\nFor $T$ as above and a scalar holomorphic function $f$ on a neighborhood of\n$\\Spec(T^{(0)})$, held fixed independently of the parameters, define\n\\begin{equation}\n f(T)=\\frac{1}{2\\pi\\mathrm i}\\int_\\Gamma\n                  f(\\xi)R_T(\\xi)\\,d\\xi,\n \\label{spec:functional-calculus}\n\\end{equation}\nwhere $\\Gamma$ encloses the whole spectrum within that neighborhood.\nThe neighborhood may be disconnected.  This construction is unital,\nmultiplicative, and compatible with holomorphic composition.  In\nparticular, the functions equal to $1$ on one spectral group and $0$\non the others give mutually orthogonal idempotents whose sum is the\nidentity.  All these operators commute with $T$.\n\nIf, in addition, $[T,\\lambda]=zT$, then\n\\begin{equation}\n [f(T),\\lambda]=z\\bigl(\\xi f'(\\xi)\\bigr)(T).\n \\label{spec:functional-commutator}\n\\end{equation}\nIf a differential operator $G$ commutes with $T$, acts continuously on\nthe coefficientwise Laurent extension of \\eqref{spec:algebra}, and\ncommutes with the scalar spectral variable $\\xi$, then\n$[G,f(T)]=0$.\n\\end{lemma}\n\n\\begin{proof}\nBoth inverse identities for \\eqref{spec:resolvent} follow from the\ngeometric series.  Since $\\xi$ and a second spectral variable $\\eta$\nare central, the resolvent identity is\n\\[\n R_T(\\xi)R_T(\\eta)\n       =\\frac{R_T(\\xi)-R_T(\\eta)}{\\eta-\\xi}.\n\\]\nIntegrating on nested contours and applying the scalar Cauchy formula\nproves multiplicativity.  It also proves $1(T)=\\id$, either directly\nor coefficientwise from \\eqref{spec:resolvent}.  This gives the\nprojector assertions.  Commutation with $T$ follows from the inverse\nidentity.\n\nFor completeness, multiplicativity also gives the composition rule.\nFor $\\eta$ outside the spectrum of $f(T^{(0)})$, apply the calculus to\n$(\\eta-f(\\xi))^{-1}$.  Its product with $\\eta-f(T)$ is the identity,\nso it is the resolvent of $f(T)$.  Integrating this identity against\n$g(\\eta)$ and using the scalar Cauchy formula yields\n$g(f(T))=(g\\circ f)(T)$ whenever the functions are defined on the\nindicated neighborhoods.  All contour operations here are performed\non a fixed coefficient, where there are only finitely many\ndifferential compositions.\n\nThe commutator with $\\lambda$ follows directly from the inverse rule:\n\\begin{align*}\n [R_T(\\xi),\\lambda]\n   &=R_T(\\xi)[T,\\lambda]R_T(\\xi)\\\\\n   &=z\\bigl(\\xi R_T(\\xi)^2-R_T(\\xi)\\bigr)\n     =-z\\partial_\\xi\\bigl(\\xi R_T(\\xi)\\bigr).\n\\end{align*}\nIntegration by parts proves \\eqref{spec:functional-commutator}.\nFinally, $[G,T]=[G,\\xi]=0$ implies $[G,R_T(\\xi)]=0$ by the\ninverse identity.  Integration proves the last assertion.  Keeping\nthe contours fixed locally justifies differentiating the coefficient\ngerms under the integral.\n\\end{proof}\n\nWe will compare this calculus in frames related by matrices with\nnegative powers of $z$.  The comparison requires a larger algebra,\nbut does not require convergence in the loop variable.  Let $\\mathcal\nO$ be a ring of holomorphic parameter germs stable under\n$\\partial_\\lambda$, and let $\\mathcal M$ be either the base Novikov\nmonoid or the full monoid of labels $(\\beta,l)$, $l\\geq0$.  Write\n\\begin{equation}\n \\mathscr A^{\\mathrm{Laur}}_{\\mathcal M}(\\mathcal O)\n =\\left\\{\\sum_{d\\in\\mathcal M}q^d\n       \\sum_{m\\geq m(d)}z^m A_{d,m}:\n       A_{d,m}\\in\\End(V)\\otimes\\mathscr D_\\lambda(\\mathcal O),\n       \\ m(d)\\in\\Z\\right\\}.\n \\label{spec:laurent-algebra}\n\\end{equation}\nThe notation $q^d$ means $Q^\\beta$ or $Q^\\beta y^l$, respectively.\nThe Novikov support condition is imposed as before.  The lower\n$z$-bound can depend on $d$.  Multiplication is well-defined: a fixed\nlabel has finitely many decompositions, and for each decomposition\nthe two Laurent lower bounds leave only finitely many summands at a\nfixed power of $z$.\n\n\\begin{lemma}[Change of frame for resolvents]\n\\label{spec:gauge}\nLet $T$ and $T'$ have coefficientwise resolvents on a common contour,\nconstructed as in Lemma~\\ref{spec:calculus}.  Suppose these resolvents\nbelong to the same algebra \\eqref{spec:laurent-algebra}.  If $M$ and\n$M^{-1}$ belong to that algebra, are independent of $\\xi$, and\n$T'=MTM^{-1}$, then\n\\[\n R_{T'}(\\xi)=M R_T(\\xi)M^{-1},\\qquad\n f(T')=M f(T)M^{-1}\n\\]\nfor every function represented by that contour calculus.\n\\end{lemma}\n\n\\begin{proof}\nThe expression $M R_T(\\xi)M^{-1}$ is both a left and a right inverse\nof $\\xi-T'$ in the common algebra.  It therefore equals its other\ninverse $R_{T'}(\\xi)$.  Integrating the equality coefficientwise\ngives the second assertion.  The Laurent lower bounds ensure that\neach product used in the integration is finite at a fixed\ncoefficient.\n\\end{proof}\n\nTwo forms of $M$ will occur.  The first is a matrix equal to the\nidentity in degree zero whose coefficient at each positive Novikov\nlabel is the Laurent expansion at $z=0$ of a rational function of $z$.\nIts Novikov inverse has the same property.  The second is a matrix\nof the form $\\exp(N/z)M_+(z)$, where $N$ is a nilpotent cup-product\noperator and $M_+(z)$ and its inverse are regular at $z=0$.\nThe exponential and its inverse are then Laurent polynomials in\n$z^{-1}$.  Thus both forms satisfy the Laurent requirement of\nLemma~\\ref{spec:gauge}.\n\n\\subsection{Block operators without equivariant differentiation}\n\nWe now apply the calculus to $T=T_a$.  By\nLemma~\\ref{shift:polynomiality}, its coefficients are polynomial in\n$y,\\lambda,z$ at fixed base degree before the shift\n$\\exp(z\\partial_\\lambda)$ is expanded.  In particular it belongs\nto \\eqref{spec:algebra}.  By \\eqref{shift:spectrum}, the spectrum of\nits reduction modulo $z$ and positive base degree consists of\n\\begin{equation}\n a_i=\\lambda+h_i,\\qquad h_i^r(h_i+\\lambda)=y.\n \\label{spec:eigenvalues}\n\\end{equation}\nTake $\\lambda\\neq0$, $y\\neq0$, and exclude the values where these\nroots coincide.  On a small open set in this locus, choose their\nlabels and a logarithm near each $a_i$.  A label refers to a whole\ngeneralized eigenspace: no diagonalizability within that space is\nassumed.  Define\n\\begin{equation}\n P_i=\\frac{1}{2\\pi\\mathrm i}\\int_{\\gamma_i}R_{T_a}(\\xi)\\,d\\xi,\n \\qquad\n \\mathscr L_i=\\frac{1}{2\\pi\\mathrm i}\\int_{\\gamma_i}\n                  \\log\\xi\\,R_{T_a}(\\xi)\\,d\\xi.\n \\label{spec:projectors}\n\\end{equation}\nHere $\\gamma_i$ encloses the one spectral value $a_i$ with its full\nmultiplicity.  For a group of these values we use the sum of the\ncontours; its logarithm is specified on each component.\n\n\\begin{proposition}[Removal of coefficient differentiation]\n\\label{spec:commutators}\nThe operators in \\eqref{spec:projectors} satisfy\n\\begin{equation}\n [P_i,\\lambda]=0,\\qquad\n [\\mathscr L_i,\\lambda]=zP_i,\\qquad\n [G_a,P_i]=[G_a,\\mathscr L_i]=0.\n \\label{spec:block-commutators}\n\\end{equation}\nIn particular $P_i$ and\n$M_i=\\mathscr L_i-zP_i\\partial_\\lambda$ are matrix series,\nwithout coefficient differentiation.  The operator\n\\begin{equation}\n D_i=P_iG_a-\\frac{\\lambda}{z}\\mathscr L_i\n \\label{spec:block-grading}\n\\end{equation}\ndifferentiates only in $z$ and satisfies\n$P_iD_i=D_iP_i=D_i$.\n\\end{proposition}\n\n\\begin{proof}\nThe shift form gives $[T_a,\\lambda]=zT_a$.\nApply \\eqref{spec:functional-commutator} to the locally constant\nbranch indicator and to its supported logarithm.  These give the\nfirst two commutators.  The relation\n$[G_a,T_a]=0$ from \\eqref{shift:commutation} gives the last two.\nThe operator $G_a$ has only one negative loop power and first-order\nparameter derivatives, so its action and the commutators used here\nare defined in \\eqref{spec:laurent-algebra}.\n\nFor a finite-order differential operator $D$ in $\\lambda$,\n$[D,\\lambda]=0$ implies that $D$ has order zero: the commutator of\n$a_J\\partial_\\lambda^J$ with $\\lambda$ has leading term\n$J a_J\\partial_\\lambda^{J-1}$.  Apply this observation at each\ncoefficient of $P_i$ and $\\mathscr L_i-zP_i\\partial_\\lambda$.\nWriting out $G_a$ now gives\n\\begin{equation}\n D_i=P_i z\\partial_z\n       +P_i(\\tfrac12+\\mu)\n       +z^{-1}\\bigl(P_i(c\\star)-\\lambda M_i\\bigr).\n \\label{spec:grading-matrix-form}\n\\end{equation}\nThere are no remaining $\\lambda$ derivatives, and neither $G_a$ nor\nthe functional calculus introduced derivatives in $y$ or $Q$.\nThe support assertions follow from $[G_a,P_i]=0$,\n$P_i\\mathscr L_i=\\mathscr L_iP_i=\\mathscr L_i$, and\n$[P_i,\\lambda]=0$.\n\\end{proof}\n\nThe subtraction in \\eqref{spec:block-grading} has accomplished the\nfirst goal: it gives loop operators over the coefficient ring.\nWe record their precise bounds before making the comparison at\n$y=0$.\n\n\\begin{proposition}[Modes and their coefficient bounds]\n\\label{spec:modes}\nDefine\n\\begin{equation}\n A_{-1,i}=z^{-1}P_i,\\qquad\n A_{k,i}=z^{-1}(zD_i)^{k+1}\\quad(k\\geq0).\n \\label{spec:mode-definition}\n\\end{equation}\nEach $A_{k,i}$ is a series in powers $z^m$ with $m\\geq-1$, whose\ncoefficients are matrix differential operators in $z\\partial_z$\nof order at most $k+1$.  Its coefficients contain no derivatives\nin $y,\\lambda$ or $Q$.  Each fixed base and loop coefficient\ncontinues holomorphically along paths in the generic locus of\n\\eqref{spec:eigenvalues}, with the spectral labels and logarithms\ncontinued along the path.\n\nFor any scalar $b$ independent of $z$, replacing $D_i$ by\n$D_i+bz^{-1}P_i$ gives\n\\begin{equation}\n A_{k,i}\\longmapsto\n   \\sum_{j=0}^{k+1}\\binom{k+1}{j}b^{k+1-j}A_{j-1,i}.\n \\label{spec:binomial}\n\\end{equation}\nFor distinct branches $i\\neq j$, one has\n$(zD_i)(zD_j)=0$.  Thus, if $P=\\sum_iP_i$ and\n$D=\\sum_iD_i$ for a group of branches, then the modes formed from\n$P,D$ are the sums of their individual modes.\n\\end{proposition}\n\n\\begin{proof}\nEquation~\\eqref{spec:grading-matrix-form} says that $zD_i$ has\nnonnegative powers of $z$ and Euler differential order at most\none.  Since $z\\partial_z$ preserves each loop power, products do\nnot create negative powers.  This proves the asserted bounds.\n\nAt a fixed base and loop coefficient, the resolvent construction\nuses finitely many matrix inversions, parameter derivatives, and\ncontour residues.  Its only spectral denominators come from the\neigenvalues in \\eqref{spec:eigenvalues}.  The input coefficients\nare polynomial, so these operations continue along any path on\nwhich the eigenvalues remain distinct and nonzero, with the\nlogarithms continued.  This is a statement about individual\ncoefficients; it asserts no convergence of the $z$ or Novikov\nseries.\n\nSince $D_i$ differentiates only $z$, it commutes with $b$.\nFurthermore $P_i$ commutes with $z$ and is a two-sided identity\nfor $D_i$.  The ordinary binomial formula in this supported\nalgebra gives \\eqref{spec:binomial}, including its $j=0$ term\n$b^{k+1}z^{-1}P_i$.  Finally,\n\\[\n (zD_i)(zD_j)\n   =zD_iP_i zP_jD_j=0\n \\qquad(i\\neq j),\n\\]\nwhich proves the assertion about sums of branches.\n\\end{proof}\n\nIn particular, changing a logarithm by $2\\pi\\mathrm i n$ changes\n$D_i$ by $-2\\pi\\mathrm i n\\lambda z^{-1}P_i$.  All the modes for\none logarithm therefore span the same collection as those for any\nother logarithm.  Infinitesimal symplecticity will follow from the\nfixed-manifold calculation and continuation; it is not needed for\nthe constructions above.\n\n\\subsection{The two fixed-manifold blocks at \\texorpdfstring{$y=0$}{y=0}}\n\nFix a germ of $\\lambda\\neq0$.  At $y=0$, one eigenvalue\n$a_B=\\lambda+h_B$ tends to zero, while the other $r$ tend to\n$\\lambda$.  We call the first the $B$ branch and the second group\nthe $X$ cluster.  Choose disjoint small contours about $0$ and\n$\\lambda$ that enclose these groups for $y$ sufficiently small.\nThe projector calculus applies through $y=0$ on both contours.\nThe logarithm applies through $y=0$ on the $X$ contour, since that\ncontour bounds a neighborhood not containing zero.  Denote the\nresulting operators by $P_B,P_X,\\mathscr L_X$.\n\nFor this comparison express both frames in the fixed-cohomology\ncoordinates furnished by restriction to $B\\sqcup X$.  The change\nfrom the polynomial equivariant basis depends only on $\\lambda$\nand is invertible at $\\lambda\\neq0$; it preserves the completed\nalgebras and all assertions about holomorphic dependence on $y$.\nLet $\\Pi_B,\\Pi_X$ be the constant projections onto these two\nsummands.  In these coordinates write\n\\[\n K_F(z,\\lambda)=\n \\prod_{x}\\begin{cases}\n      x+\\lambda,&F=X,\\\\\n      (x-\\lambda-z)^{-1},&F=B,\n \\end{cases}\n \\qquad E_z=\\exp(z\\partial_\\lambda),\n\\]\nwhere the product is over the ordinary Chern roots of $N_F$.\nThus \\eqref{shift:fixed-formula} reads\n$T_d|_F=y^{j_F}K_F E_z$.\n\n\\begin{proposition}[Comparison at zero fiber degree]\n\\label{spec:zero}\nThe projectors $P_B,P_X$ and the cluster logarithm\n$\\mathscr L_X$ have holomorphic coefficient germs through $y=0$.\nTheir Taylor expansions at $y=0$ satisfy\n\\begin{equation}\n P_F=S_W\\Pi_FS_W^{-1}\\quad(F=B,X),\\qquad\n \\mathscr L_X=S_W\\mathscr L_{d,X}S_W^{-1},\n \\label{spec:zero-conjugation}\n\\end{equation}\nwhere $\\mathscr L_{d,X}$ is the logarithm of $K_XE_z$ on the $X$\nsummand, extended by zero on $B$.\n\nMoreover, $T_aP_B$ is divisible by $y$ coefficientwise in $z$ and\nbase degree.  Set\n\\begin{equation}\n \\widetilde T_B=y^{-1}T_aP_B.\n \\label{spec:divided-shift}\n\\end{equation}\nIt has holomorphic coefficient germs through $y=0$.  Its\nnonzero spectral block at $y=z=Q^{>0}=0$ has eigenvalue\n$(-\\lambda)^{-r}$, with possible nilpotent part, and its\ncomplementary block is zero.  If $\\mathscr K_B$ is its supported\nlogarithm on the nonzero block, then, on a punctured sector in\n$y$, one can choose the $B$-branch logarithm as\n\\begin{equation}\n \\mathscr L_B=(\\log y)P_B+\\mathscr K_B.\n \\label{spec:divided-log}\n\\end{equation}\nThe coefficients of $\\mathscr K_B$ are holomorphic through $y=0$.\nIts Taylor expansion, and hence \\eqref{spec:divided-log}, obeys\nthe same conjugation by $S_W$ as in\n\\eqref{spec:zero-conjugation}, with the corresponding fixed-block\noperators in the descendant frame.\n\\end{proposition}\n\n\\begin{proof}\nThe reduction $T_a^{(0)}$ is a holomorphic matrix through $y=0$.\nIts spectrum there consists of the two separated values\n$0,\\lambda$, including all their multiplicities and nilpotent\nparts.  The Neumann construction with the two fixed contours\ntherefore gives the asserted holomorphic extensions of\n$P_B,P_X,\\mathscr L_X$.\n\nTo compare frames, first expand these coefficient germs in $y$.\nTaylor expansion is a homomorphism commuting with\n$\\partial_\\lambda$; it sends the ancestor resolvent, whose loop\npowers are nonnegative, to a Laurent series in the full Novikov labels and\npreserves both of its inverse identities.\nUse \\eqref{spec:laurent-algebra} with full labels\n$d=(\\beta,l)$, $q^d=Q^\\beta y^l$, and coefficients holomorphic\nin the chosen $\\lambda$ germ.  The rationality assertion in\nProposition~\\ref{loc:factorization} puts the expansion of $S_W$ at\n$z=0$ in this algebra: a rational function has a finite-order\npole at zero.  Since its zero-curve coefficient is the identity,\nits inverse belongs to the same algebra.  The two shift\noperators, expanded using \\eqref{shift:fixed-formula}, also\nbelong to it.  Their resolvents on the two contours do as well.\nFor the descendant resolvent one can use the joint filtration\nby $z$ and full Novikov degree: its initial $B$ block is zero,\nand its initial $X$ block is multiplication by $\\lambda+p$,\nwhose nilpotent part is the ordinary class $p|_X$.\n\nEquation~\\eqref{shift:calibration} and\nLemma~\\ref{spec:gauge} now identify the actual resolvents in\nthe two frames.  In the descendant frame the contour about\nzero selects exactly the whole $B$ summand, and the contour\nabout $\\lambda$ selects exactly the whole $X$ summand.\nIndeed the reduced spectra have this property; within each\nsummand the contour function is identically $1$ or $0$, so\nLemma~\\ref{spec:calculus} gives respectively the identity or\nzero also for the formal perturbation.  Integration proves\n\\eqref{spec:zero-conjugation}, including the logarithm on $X$.\n\nOn the $B$ summand, $T_d\\Pi_B=yK_BE_z\\Pi_B$.  The parameter\n$y$ is central throughout the algebra.  Hence\n\\[\n T_aP_B\n   =yS_WK_BE_z\\Pi_BS_W^{-1}\n\\]\nin the Laurent algebra completed in full Novikov degree.  Neither $S_W$ nor its\ninverse has a negative $y$ power.  The constant Taylor\ncoefficient in $y$ of every base and loop coefficient on the\nleft therefore vanishes.  Those coefficients were already\nholomorphic through $y=0$, so division by $y$ gives\nholomorphic coefficients and\n\\begin{equation}\n \\widetilde T_B=S_WK_BE_z\\Pi_BS_W^{-1}\n \\label{spec:divided-conjugation}\n\\end{equation}\nafter Taylor expansion.\n\nAt $y=z=Q^{>0}=0$, the calibration is the identity and the\nnonzero block of \\eqref{spec:divided-conjugation} is\n$\\prod_x(x-\\lambda)^{-1}$.  The ordinary positive-degree\nclasses are nilpotent, so its sole spectral value is\n$(-\\lambda)^{-r}\\neq0$.  Choose a contour enclosing this\nvalue and avoiding zero, and choose a logarithm there.\nLemma~\\ref{spec:calculus} defines $\\mathscr K_B$ with\nholomorphic coefficients through $y=0$.  The projector for\nthis nonzero block is $P_B$, by\n\\eqref{spec:divided-conjugation} and\nLemma~\\ref{spec:gauge}.  The same lemma identifies\n$\\mathscr K_B$ with the conjugate of the supported logarithm\nof $K_BE_z\\Pi_B$.\n\nFinally restrict to a punctured sector and choose $\\log y$.\nIn the algebra supported on $P_B$ one has\n$T_a=y\\widetilde T_B$.  Rescaling the spectral contour by\nthe central scalar $y$ and choosing\n$\\log(y\\xi)=\\log y+\\log\\xi$ shows that its logarithm is\nexactly \\eqref{spec:divided-log}.  This is a choice of the\noriginal contour logarithm in \\eqref{spec:projectors}.\nThe conjugation assertion follows because multiplication\nby $\\log y$ commutes with the entire algebra.\n\\end{proof}\n\nWe have thus constructed the same block operators in two useful\nforms.  At generic $y$, their individual coefficients continue\nwith the roots in \\eqref{spec:eigenvalues}.  Near $y=0$, they\nare conjugates of operators on the two fixed manifolds.\nThe coefficients of the $B$-branch modes are finite polynomials\nin $\\log y$ with holomorphic coefficients.  Indeed\n$D_B=D_B^{\\mathrm{reg}}-\\lambda(\\log y)z^{-1}P_B$, where\n$D_B^{\\mathrm{reg}}=P_BG_a-(\\lambda/z)\\mathscr K_B$\nhas holomorphic coefficients; apply \\eqref{spec:binomial} with\n$b=-\\lambda\\log y$.  For $A_{k,B}$ the logarithmic degree\nis therefore at most $k+1$.  The $X$-cluster modes are\nholomorphic through zero by Proposition~\\ref{spec:zero}.\nThese statements require neither convergence in $z$ nor an\nanalytic interpretation of the Novikov series.\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/07-fixed-block.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/07-fixed-block.tex", "bytes": 19345, "sha256": "f5da25823a50a44b3e91fa4818c215f75c9499b1796a4d019ee0abc5d0c29251", "content": "\\section{The ordinary theory on a fixed component}\n\\label{fixed:section}\n\nThe operators of Section~\\ref{spec:section} were constructed from the\nequivariant theory of $W$.  We now identify what their equations mean near\n$y=0$.  On a fixed component, quantum Riemann--Roch removes the inverse\nEuler twist.  The same transformation turns the logarithm of the shift\noperator into a translation generator in $\\lambda$.  Its derivative term\ncancels the derivative term of the equivariant grading, leaving the\nordinary grading and a scalar multiple of $z^{-1}$.\n\n\\begin{proposition}\\label{fixed:equivalence}\nFix a germ at $\\lambda\\ne0$.  Near $Q=y=0$, use the full Novikov\ncompletion with a formal $\\log y$ adjoined, allowing polynomial\ndependence on $\\log y$ in each coefficient.\nLet $F=B$ denote the branch near the eigenvalue zero, or let $F=X$ denote\nthe whole cluster near the eigenvalue $\\lambda$.  Use the projectors and\nlogarithms near $y=0$ of Proposition~\\ref{spec:zero}, and let $A_{k,F}$ be\ntheir modes.  Then, as identities over this coefficient ring,\n\\[\n \\mathcal A_W\\text{ satisfies every }A_{k,F}\\text{ projectively}\n \\quad\\Longleftrightarrow\\quad\n Z_F\\text{ satisfies every }\\ell_{k,F}\\text{ projectively},\n \\qquad k\\geq-1.\n\\]\nThe operators on the left are infinitesimal symplectic for the equivariant\npairing of $W$.  On the right, $Z_F$ is the ordinary descendant potential\nwith its full cohomology and actual integral curve labels.\n\\end{proposition}\n\nWe first prove the identity of classical operators.  We then explain its\nquantization; this second step requires separating the rank factor of the\nEuler class before applying quantum Riemann--Roch.\n\n\\subsection{Removing the normal bundle from the grading}\n\nFix $F$ and abbreviate $n=n_F$, $w=w_F$, and $j=j_F$.  Put\n\\[\n a=w\\lambda,\\qquad c_N=c_1(N_F)_{\\mathrm{ord}},\\qquad W_F=nw.\n\\]\nAfter restriction to the descendant space of $F$, the grading and shift\nfrom Section~\\ref{shift:section} are\n\\begin{align}\n G_F&=z\\partial_z+\\lambda\\partial_\\lambda+\n        \\frac12+\\mu_F-\\frac n2+\n        \\frac{R_F+c_N\\cup+nw\\lambda}{z},\n       \\label{fixed:restricted-grading}\\\\\n T_F&=y^j\\prod_x\n       \\begin{cases}\n        x+\\lambda,&w=1,\\\\\n        (x-\\lambda-z)^{-1},&w=-1\n       \\end{cases}\n       e^{z\\partial_\\lambda}.\n       \\label{fixed:restricted-shift}\n\\end{align}\nHere $x$ runs over the ordinary Chern roots of $N_F$, and multiplication\nby a class is understood in the second formula.  Symmetric expressions\nin the roots are interpreted by the splitting principle.  All expansions\nin $x$ are finite after evaluation in the cohomology of $F$.\n\nChoose a germ of $\\log a$ at a nonzero value of $\\lambda$.  Define a\nmultiplier $\\Delta_F$ by\n\\begin{equation}\\label{fixed:Delta}\n \\log\\Delta_F=\n \\sum_x\\left\\{\n   \\frac12\\log(a+x)\n   +\\frac1z\\int_0^x-\\log(a+t)\\,dt\n   +\\sum_{m\\geq1}\\frac{B_{2m}}{(2m)!}z^{2m-1}\n       \\partial_x^{2m-1}\\bigl(-\\log(a+x)\\bigr)\n          \\right\\}.\n\\end{equation}\nThe $B_{2m}$ are the Bernoulli numbers.  The coefficient of $z^{-1}$ has\npositive ordinary cohomological degree, hence is nilpotent as a\nmultiplication operator.  Both $\\Delta_F$ and its inverse consequently\nhave a finite lower bound on their powers of $z$.  Their positive tails\nare interpreted as formal series at $z=0$.\n\nThe multiplier maps the ordinary paired loop space of $F$ to the space\nwith pairing $\\eta_F^t$.  Indeed, all terms in\n\\eqref{fixed:Delta} except the half logarithm are odd in $z$, so that\n\\[\n \\Delta_F(-z)\\Delta_F(z)=\\prod_x(a+x)=e_T(N_F).\n\\]\nThe inverse Euler factor in $\\eta_F^t$ therefore cancels this product.\nThis also fixes the direction of the square-root factor in\n\\eqref{fixed:Delta}.\n\nFor the scalar part of the shift calculation, put\n\\begin{equation}\\label{fixed:primitive}\n L(v)=v-v\\log v,\\qquad\n \\Theta_F(\\lambda)=nL(w\\lambda).\n\\end{equation}\n\n\\begin{lemma}\\label{fixed:classical}\nThe following identities hold in the formal Laurent calculus of\nLemma~\\ref{spec:gauge}:\n\\begin{align}\n \\Delta_F^{-1}G_F\\Delta_F\n  &=\\ell_{0,F}+\\lambda\\partial_\\lambda+\\frac{nw\\lambda}{z},\n       \\label{fixed:grading-conjugation}\\\\\n \\Delta_F^{-1}T_F\\Delta_F\n  &=y^j\\exp\\left(\n       \\frac{\\Theta_F(\\lambda)-\\Theta_F(\\lambda+z)}{z}\n                  \\right)e^{z\\partial_\\lambda}.\n       \\label{fixed:shift-conjugation}\n\\end{align}\nUnder the same change of space, the logarithm prescribed near $y=0$\nbecomes\n\\begin{equation}\\label{fixed:log-conjugation}\n z\\partial_\\lambda+j\\log y-\\Theta_F'(\\lambda)\n   =z\\partial_\\lambda+j\\log y+nw\\log(w\\lambda),\n\\end{equation}\nup to a scalar constant determined by the logarithm branch.\n\\end{lemma}\n\n\\begin{proof}\nThe cohomological part of the commutator with $\\mu_F$ differentiates a\nfunction of a Chern root by $x\\partial_x$, because $x$ has Hodge type\n$(1,1)$.  Apply\n\\[\n z\\partial_z+\\lambda\\partial_\\lambda+x\\partial_x\n\\]\nto one summand in \\eqref{fixed:Delta}.  The half logarithm contributes\n$1/2$.  If $I(a,x)=\\int_0^x-\\log(a+t)\\,dt$, then\n$(\\lambda\\partial_\\lambda+x\\partial_x)I=I-x$; thus its contribution is\n$-x/z$.  Each Bernoulli term has total degree zero.  Summing over the\nroots gives $n/2-c_N/z$, which cancels the two corresponding terms in\n\\eqref{fixed:restricted-grading}.  This proves\n\\eqref{fixed:grading-conjugation}.\n\nFor the shift, add $L(a)/z$ to the summand indexed by $x$ in\n\\eqref{fixed:Delta}.  The result depends only on $v=a+x$ and equals\n\\[\n \\Phi(v)=\\left((z\\partial_v)^{-1}-\\frac12+\n          \\sum_{m\\geq1}\\frac{B_{2m}}{(2m)!}\n                         (z\\partial_v)^{2m-1}\\right)(-\\log v),\n\\]\nwhere the antiderivative in the first term is $L(v)/z$.  The generating\nseries for the Bernoulli numbers gives\n\\begin{equation}\\label{fixed:Bernoulli-difference}\n \\Phi(v+z)-\\Phi(v)=-\\log v.\n\\end{equation}\nWhen $w=1$, this difference cancels the factor $v$ in\n\\eqref{fixed:restricted-shift}.  When $w=-1$, use instead\n$\\Phi(v-z)-\\Phi(v)=\\log(v-z)$, which cancels the factor $(v-z)^{-1}$.\nThe terms $L(a)/z$ that were added account for the exponential in\n\\eqref{fixed:shift-conjugation}.\n\nIt remains to identify the functional logarithm, rather than just one\noperator whose exponential has the required form.  Set\n\\[\n H=z\\partial_\\lambda-\\Theta_F'(\\lambda).\n\\]\nSolving its evolution equation gives\n\\begin{equation}\\label{fixed:log-flow}\n e^{tH}=\n  \\exp\\left(\n     \\frac{\\Theta_F(\\lambda)-\\Theta_F(\\lambda+tz)}{z}\n       \\right)e^{tz\\partial_\\lambda}.\n\\end{equation}\nFor example, differentiating the right side in $t$ gives $H$ times that\nside and its value at $t=0$ is the identity.  This is also the exponential\ndefined by the holomorphic functional calculus of\nLemma~\\ref{spec:calculus}: coefficientwise differentiation of its contour\nformula gives the same evolution equation.  The constant term of $H$ in\n$z$ is the scalar $-\\Theta_F'(\\lambda)$.  Locally choose a logarithm\nwhich inverts the exponential at that scalar.  The composition rule in\nLemma~\\ref{spec:calculus} then identifies $\\log(e^H)$ with $H$.\n\nConjugation of the resolvents by $\\Delta_F$ is allowed by\nLemma~\\ref{spec:gauge}.  For $F=B$, apply this argument to the divided\nshift $y^{-1}T_F$ and then restore $\\log y$; for $F=X$, apply it directly\nto $T_F$.  These are exactly the logarithm prescriptions of\nProposition~\\ref{spec:zero}.  This proves\n\\eqref{fixed:log-conjugation}, with the stated freedom in its scalar\nconstant.\n\\end{proof}\n\nSubtracting $\\lambda/z$ times \\eqref{fixed:log-conjugation} from\n\\eqref{fixed:grading-conjugation} now removes\n$\\lambda\\partial_\\lambda$.  Thus the operator $D_F$ on its fixed block,\nafter the descendant change of space and then $\\Delta_F$, is\n\\begin{equation}\\label{fixed:ordinary-grading}\n \\ell_{0,F}+\\frac{b_F}{z},\\qquad\n b_F=\\lambda\\bigl(nw-j\\log y-nw\\log(w\\lambda)\\bigr),\n\\end{equation}\nup to a scalar multiple of $\\lambda/z$ from a different logarithm\nchoice.  In particular, the remaining operator differentiates none of\n$\\lambda$, $y$, or the Novikov variables.\n\nFor a scalar $b$ independent of $z$, write\n\\[\n \\ell_{-1,F}^{(b)}=z^{-1},\\qquad\n \\ell_{k,F}^{(b)}=z^{-1}(z\\ell_{0,F}+b)^{k+1}\\quad(k\\geq0).\n\\]\nThe binomial identity\n\\begin{equation}\\label{fixed:binomial}\n \\ell_{k,F}^{(b)}=\n   \\sum_{m=-1}^{k}\\binom{k+1}{m+1}b^{k-m}\\ell_{m,F}\n\\end{equation}\nis triangular with diagonal entries one.  Hence these operators are\ninfinitesimal symplectic, and their simultaneous projective equations\nare equivalent to those of the ordinary modes.  We have proved this\nidentification at the classical level.  To obtain the corresponding\nstatement for the potentials, we next implement it by quantum\nRiemann--Roch.\n\n\\subsection{Quantizing the comparison}\n\nThe logarithm in \\eqref{fixed:Delta} contains\n$-\\log(w\\lambda)c_N/z$.  Although this term defines a perfectly good\nclassical multiplication operator, directly exponentiating its\nquantization would require interpreting a translation that is not small\nin $\\lambda^{-1}$.  We avoid that interpretation by first using the\nnormalized characteristic class\n\\begin{equation}\\label{fixed:normalized-class}\n c_0(N_F)=\\prod_x(1+x/a)^{-1}.\n\\end{equation}\nLet $Z_{F,\\mathrm{ntw}}$ and $\\eta_F^{\\mathrm{ntw}}$ denote its descendant\npotential and pairing.  The subscript distinguishes this theory from\nthe inverse Euler twist used in localization.\n\nDefine $\\Delta_{F,0}$ by the formula \\eqref{fixed:Delta}, replacing\n$-\\log(a+x)$ by $-\\log(1+x/a)$ and replacing the half logarithm by\n$\\tfrac12\\log(1+x/a)$.  It maps the ordinary paired space to\n$(\\mathcal H_F,\\eta_F^{\\mathrm{ntw}})$ and is the identity modulo\n$\\lambda^{-1}$.\n\n\\begin{lemma}[Quantum Riemann--Roch in the normalized twist]\n\\label{fixed:qrr-normalized}\nWith the quantization and dilaton translations of\nSection~\\ref{conv:section},\n\\begin{equation}\\label{fixed:qrr}\n Z_{F,\\mathrm{ntw}}=(\\text{invertible scalar})\\,\n                     U_{\\Delta_{F,0}}Z_F.\n\\end{equation}\nThis is an invertible identity formal in $\\lambda^{-1}$ and coefficientwise\nin the full Novikov variables and $\\hbar$.\n\\end{lemma}\n\n\\begin{proof}\nWe use the quantum Riemann--Roch theorem of Coates and\nGivental~\\cite[Theorem~1]{QRR}.  For a multiplicative characteristic class\nwhose logarithm on a line with first Chern class $x$ is $s(x)$, its\nmultiplier in the fixed ordinary pairing has exponent\n\\[\n \\sum_x\\left\\{\\frac1z\\int_0^x s(t)\\,dt+\n       \\sum_{m\\geq1}\\frac{B_{2m}}{(2m)!}z^{2m-1}\n                                         s^{(2m-1)}(x)\\right\\}.\n\\]\nThe theorem identifies the twisted pairing with the ordinary pairing by\nmultiplication by $\\sqrt{c_0(N_F)}$.  Expressing its conclusion as a map\n\\emph{to} the actual twisted space therefore adds $-s(x)/2$ to the\nexponent.  Taking $s(x)=-\\log(1+x/a)$ gives exactly\n$\\Delta_{F,0}$.\n\nThis change of pairing also specifies the affine coordinates of the\nquantized action.  In the ordinary-pairing Fock space of the theorem,\nthe twisted potential is expressed using\n\\[\n \\widetilde q=\\sqrt{c_0(N_F)}(t-z1_F).\n\\]\nMultiplication by $c_0(N_F)^{-1/2}$ returns the twisted Darboux\ncoordinate\n\\[\n q_{\\mathrm{ntw}}=t-z1_F.\n\\]\nThus the paired-space map\n$U_{\\Delta_{F,0}}$ has exactly the dilaton translations stipulated in\n\\eqref{fixed:qrr}.\n\nThe theorem applies to the universal-curve complex\n$R\\pi_*\\ev^*N_F$ on the ordinary stable-map spaces of the smooth\nprojective variety $F$.  Its characteristic class is invertible and\nformal in $\\lambda^{-1}$.  The quantized multiplier and its inverse are\ndefined in that filtration, with the dilaton translation in each paired\nspace.  The scalar factors in quantum Riemann--Roch are immaterial for\nprojective equations.\n\nThe super-space formulation in \\cite[Sections~2--3]{QRR} uses the full\ncohomology of the target.  In particular, its marked-point terms are\neven characteristic-class multiplications and its nodal terms contract\nthe categorical inverse of the pairing.  It therefore has exactly the\nKoszul conventions used here, for arbitrary Hodge types and parity.\n\\end{proof}\n\nThe relation between the two classical multipliers is particularly\nsimple:\n\\begin{equation}\\label{fixed:Delta-normalization}\n \\Delta_F=a^{n/2}\n       \\exp\\left(-\\log(a)c_N/z\\right)\\Delta_{F,0}.\n\\end{equation}\nIf $C$ is an ordinary divisor class, then\n\\[\n [\\ell_{0,F},C/z]=[z\\partial_z,C/z]+[\\mu_F,C/z]=0,\n \\qquad [z,C/z]=0.\n\\]\nHere $R_F$ commutes with $C$, and the two displayed contributions to\nthe first commutator are $-C/z$ and $C/z$.  Consequently $C/z$ commutes\nwith all the ordinary modes and with their scalar translates\n\\eqref{fixed:binomial}.  After the coefficient derivatives have been\nremoved in \\eqref{fixed:ordinary-grading}, the scalar $a^{n/2}$ also\ncancels in conjugation.  Thus $\\Delta_F$ and $\\Delta_{F,0}$ produce the\nsame conjugated mode matrices.  By Lemma~\\ref{fixed:qrr-normalized} and\nthe covariance of Lemma~\\ref{conv:covariance}, the equations for those\nmatrices on $Z_{F,\\mathrm{ntw}}$ are equivalent to the ordinary projective\nequations on $Z_F$.\n\nIt remains to restore the rank factor omitted in\n\\eqref{fixed:normalized-class}.  This changes the paired space as well\nas the invariants, and both changes must be made together.\n\n\\begin{lemma}\\label{fixed:rank-scaling}\nPut $m_F=a^{-n}$.  The actual inverse Euler twist is obtained from the\nnormalized twist by the substitutions\n\\begin{equation}\\label{fixed:scaling}\n \\hbar\\longmapsto\\hbar/m_F,\n \\qquad Q^\\beta y^l\\longmapsto\n     Q^\\beta y^l a^{-\\int_d c_N},\n\\end{equation}\nwhere $d$ is the corresponding actual class on $F$.  Its pairing is\n$\\eta_F^t=m_F\\eta_F^{\\mathrm{ntw}}$.  Under these substitutions the\nquadratic quantizations of\n$\\Delta_{F,0}\\ell_{k,F}\\Delta_{F,0}^{-1}$, for every $k\\geq-1$, in the\ntwo paired spaces agree.\n\\end{lemma}\n\n\\begin{proof}\nOn the moduli space of connected genus-$g$ maps of class $d$,\nRiemann--Roch gives\n\\[\n \\rank(R\\pi_*\\ev^*N_F)=n(1-g)+\\int_d c_N.\n\\]\nThe ratio of the inverse Euler class to the normalized class of this\nvirtual bundle is therefore\n\\[\n a^{-n(1-g)-\\int_d c_N}\n       =m_F^{1-g}a^{-\\int_d c_N}.\n\\]\nThis is precisely the change in the coefficient of $\\hbar^{g-1}Q^\\beta\ny^l$ under \\eqref{fixed:scaling}; exponentiation gives the claimed\nidentity of total potentials.  The pairing follows by the same\ncalculation for degree-zero genus-zero three-point invariants.\n\nThe $qq$ part of quantization uses the pairing divided by $\\hbar$;\nthe $pp$ part uses its inverse multiplied by $\\hbar$.  In the actual\ntwist these are\n\\[\n \\frac{m_F\\eta_F^{\\mathrm{ntw}}}{\\hbar},\\qquad\n \\frac{\\hbar}{m_F}(\\eta_F^{\\mathrm{ntw}})^{-1},\n\\]\nwhich are their normalized counterparts evaluated at $\\hbar/m_F$.\nThe mixed part is unchanged.  The matrices\n$\\Delta_{F,0}\\ell_{k,F}\\Delta_{F,0}^{-1}$ have no Novikov derivatives or\nNovikov dependence, so the curve substitution commutes with their\naction as well.  We use these unshifted matrices here and restore the\nscalar $b_F$ afterwards by \\eqref{fixed:binomial}.  Every actual curve monomial is multiplied\nby a nonzero scalar; hence the substitution is injective and reversible\ncoefficientwise, without identifying any curve classes.\n\\end{proof}\n\nThe preceding argument used quantum Riemann--Roch only as a formal\nidentity in $\\lambda^{-1}$.  We also need its consequence as an identity\nof germs at nonzero $\\lambda$.  To make that passage, first remove the\nscalar $b_F$ by the invertible binomial change\n\\eqref{fixed:binomial}.  Each coefficient of the conjugated matrices\n$\\Delta_{F,0}\\ell_{k,F}\\Delta_{F,0}^{-1}$ is then rational in $\\lambda$.\nThese matrices have finite lower bounds on their loop powers.  The\ncoefficients of $Z_{F,\\mathrm{tw}}$ are likewise rational in $\\lambda$:\nthe torus acts trivially on $F$, and the inverse Euler class on each\nfixed stable-map space is expanded only to its finite cohomological\ndimension.  For fixed genus, class, and number of marks, the descendant\nindices which can occur are bounded for the same reason.\n\nIt follows from the finite-support quantization convention that each\nnonconstant coefficient of\n\\[\n Z_{F,\\mathrm{tw}}^{-1}\n   \\op_{\\eta_F^t}\n     (\\Delta_{F,0}\\ell_{k,F}\\Delta_{F,0}^{-1})\n       Z_{F,\\mathrm{tw}}\n\\]\nis a finite sum of rational functions of $\\lambda$.  Vanishing of its\nformal Laurent expansion at infinity is equivalent to vanishing as a\nrational function, and hence as a germ.  At a fixed genus and curve\ncoefficient, \\eqref{fixed:scaling} only shifts finitely many powers of\n$\\lambda$, so the same argument applies in both directions.  We may now\nrestore $b_F$ and its logarithms using \\eqref{fixed:binomial}.  In\nparticular, no analytic value of the quantized infinite multiplier is\nbeing taken in this passage.\n\n\\begin{proof}[Proof of Proposition~\\ref{fixed:equivalence}]\nLet $F'$ be the other fixed component.  Up to invertible scalar factors,\nthe comparison of potentials follows the chain\n\\[\n\\begin{gathered}\n Z_F\\xrightarrow{\\ U_{\\Delta_{F,0}}\\ }Z_{F,\\mathrm{ntw}}\n       \\xrightarrow{\\ \\eqref{fixed:scaling}\\ }Z_{F,\\mathrm{tw}},\n \\\\\n Z_{F,\\mathrm{tw}}\\xrightarrow{\\ U_{S_f}\\ }\n       \\mathcal A_{F,\\mathrm{tw}}(u_F),\n \\\\\n \\mathcal A_{F,\\mathrm{tw}}(u_F)\n \\mathcal A_{F',\\mathrm{tw}}(u_{F'})\n       \\xrightarrow{\\ U_R\\ }\\mathcal A_W.\n\\end{gathered}\n\\]\nThe arrow labeled \\eqref{fixed:scaling} changes parameters and rescales\nthe pairing as in Lemma~\\ref{fixed:rank-scaling}.  The final row first\nadjoins the other fixed potential and then applies the upper symplectic\ntransformation.\n\nLemma~\\ref{fixed:classical}, the binomial identity, quantum\nRiemann--Roch, and Lemma~\\ref{fixed:rank-scaling} identify projective\nsatisfaction of all ordinary modes on $Z_F$ with projective\nsatisfaction of the corresponding descendant modes on\n$Z_{F,\\mathrm{tw}}$.  The preceding rationality argument gives this\nequivalence over the same germs in $\\lambda$ used by the spectral\nconstruction.\n\nPass next from descendants on $F$ to ancestors at $u_F$.  The\nancestor--descendant identity of Section~\\ref{geo:ancestors} and\nLemma~\\ref{conv:covariance} conjugate the mode matrices by\n\\[\n S_f=S_{F,\\mathrm{tw}}(u_F,z)\n\\]\nand transport their projective equations to\n$\\mathcal A_{F,\\mathrm{tw}}(u_F)$.  On the product of the two fixed\npotentials, an operator supported on $F$ acts only on that factor; its\nprojective equation is consequently equivalent to the equation on\nthe single factor.\n\nFinally apply the upper transformation $R(z)$ of\nProposition~\\ref{loc:factorization}.  Its two identities\n\\[\n S_W=R(z)\\bigoplus_F S_f,\n \\qquad\n \\mathcal A_W=(\\text{scalar})\\,\n      U_R\\prod_F\\mathcal A_{F,\\mathrm{tw}}(u_F)\n\\]\nshow that the resulting classical modes are exactly $A_{k,F}$, by\nProposition~\\ref{spec:zero}, and that their quantum equations are the\nprojective equations on $\\mathcal A_W$.  Each change is invertible, so\nthe implication holds in both directions.  The classical changes of\npaired space are symplectic, proving also the symplectic assertion in\nthe proposition.\n\nAll the quantized changes in this last paragraph are defined in the\nNovikov completion: $u_F$, $S_f-I$, and $R-I$ have positive Novikov\nfiltration.  The fixed-theory series $S_f$ has a finite negative tail\nat each curve coefficient, and $R$ is upper.  Conjugation and\nquantization therefore preserve the lower loop bounds and the finite\nsupport required in Section~\\ref{conv:section}.  In particular, the\ncomparison uses the upper quantization supplied by localization and\nthe fixed-theory ancestor changes; it does not require quantizing a\nnew expansion of $S_W$ itself.\n\\end{proof}\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/08-continuation.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/build/sections/08-continuation.tex", "bytes": 15134, "sha256": "2e67cb1ddef37619e20d4571e6dd5ea63a621cb3595f15f52d6685ac7bf2681f", "content": "\\section{Continuation and the ordinary constraints}\n\\label{cont:section}\n\nThe fixed-component comparison has converted the hypothesis on $B$\ninto equations on one spectral branch of the ancestor theory of $W$.\nWe now continue those equations to every branch. The essential\nfiniteness comes from ancestors: their cotangent powers are bounded\nby the dimension of a moduli space of curves, independently of the\nfiber degree. We then add the branches belonging to $X$ and use the\nsame fixed-component comparison in reverse.\n\n\\subsection{Why the equations are identities of germs}\n\nWrite the connected ancestor potential at background zero as\n\\[\n \\log\\mathcal A_W=\\sum_{g\\geq0}\\hbar^{g-1}\\mathcal F_g.\n\\]\nAll coefficients below are taken in the polynomial equivariant\nbasis of Section~\\ref{geo:section}. In particular we have not\nlocalized the invariants themselves in order to define them.\n\n\\begin{lemma}[Polynomial coefficients and ancestor bounds]\n\\label{cont:polynomiality}\nFix a genus $g$, an actual base class $\\beta$, and a list of\ninsertions $b_i\\bar\\psi_i^{a_i}$, $1\\leq i\\leq n$.\nTheir connected invariant in $W$, summed over fiber degree with\nweight $y^l$, is polynomial in $y$ and $\\lambda$.\nIt vanishes unless the distinguished curve is stable and\n\\begin{equation}\n                   \\sum_i a_i\\leq 3g-3+n.\n \\label{cont:ancestor-bound}\n\\end{equation}\nThe latter bound is independent of $\\beta,l,\\lambda$.\n\\end{lemma}\n\n\\begin{proof}\nBy definition, curve-unstable ancestor terms are omitted. For stable\ndata, the product of cotangent classes is pulled back from\n$\\overline{\\mathcal M}_{g,n}$, whose complex dimension is $3g-3+n$.\nThis proves \\eqref{cont:ancestor-bound}, before integration and\nwithout using equivariant degree.\n\nFor a class $(\\beta,l)$, the virtual dimension of the stable-map\nspace is\n\\[\n (1-g)(\\dim_\\C W-3)+n+\\int_\\beta\\kappa+(r+1)l,\n \\qquad \\kappa=c_1(TB)+c_1(E).\n\\]\nThe insertion degrees are fixed. Proper equivariant pushforward\ntakes values in $\\C[\\lambda]$; it has no negative cohomological\ndegree. Consequently the integral vanishes when this virtual\ndimension exceeds the total insertion degree. Since $l\\geq0$,\nonly finitely many $l$ can occur. Each of the remaining integrals\nis polynomial in $\\lambda$, which proves the claim.\nThis dimension argument uses total cohomological degree; the\nfirst Hodge grading used to define the modes is a separate\ngrading.\n\\end{proof}\n\nThe modes on the $B$ branch are infinitesimal symplectic\nformally at $y=0$ by Proposition~\\ref{fixed:equivalence}.\nBy Proposition~\\ref{spec:zero}, their entries are finite\npolynomials in $\\log y$ with coefficients holomorphic at\nzero. Taylor expansion is injective on this ring, so the\nsymplectic identities hold as germs on a punctured sector.\nThis property\ncontinues entrywise along any path on which the modes continue:\nit is a linear identity between their matrix entries and their\nadjoints. For a branch and logarithm obtained by such\ncontinuation, put\n\\begin{equation}\n \\mathcal E_{k,i}\n   =\\mathcal A_W^{-1}\\op(A_{k,i})\\mathcal A_W .\n \\label{cont:equation}\n\\end{equation}\nThis notation means that the differential operator acts on the\npotential, followed by multiplication by its inverse.\nFor the entire $X$ cluster, the notation $\\mathcal E_{k,X}$\nmeans the same expression with $A_{k,X}=\\sum_{i\\in X}A_{k,i}$,\nusing a single logarithm throughout the cluster. Projective\nsatisfaction means that every coefficient of positive insertion\ndegree in \\eqref{cont:equation} vanishes.\n\n\\begin{lemma}[Finite coefficient tests]\n\\label{cont:finite-tests}\nFix $k\\geq-1$, a power $\\hbar^{G-1}$, a base class $\\beta$, and an\ninsertion monomial of degree $N$ in \\eqref{cont:equation}.\nIts coefficient is a finite sum of holomorphic germs on the\nspectral covering of the generic $(y,\\lambda)$ locus, with the\nchosen logarithms. It continues along every path in that locus.\nNear $y=0$, the coefficient for $\\mathcal E_{k,B}$ is a finite\npolynomial in $\\log y$ with coefficients holomorphic through\nzero; the coefficient for the cluster expression\n$\\mathcal E_{k,X}$ is holomorphic through zero. In these two cases its\nTaylor expansion is the corresponding formal Novikov coefficient\ntest.\n\\end{lemma}\n\n\\begin{proof}\nLet $F=\\log\\mathcal A_W$ and use positive loop coordinates $q$.\nA normally ordered quadratic operator has the schematic form\n\\[\n \\frac{1}{2\\hbar}C(q,q)\n       +\\sum_{\\alpha,\\gamma}B_{\\alpha\\gamma}q^\\gamma\n                    \\partial_\\alpha\n       +\\frac{\\hbar}{2}\n          \\sum_{\\alpha,\\gamma}D_{\\alpha\\gamma}\n                    \\partial_\\alpha\\partial_\\gamma .\n\\]\nThe tensors have the graded symmetry appropriate to their indices.\nDividing its action on $e^F$ by $e^F$ replaces its derivatives by\n$\\partial_\\alpha F$ and by the graded expression\n\\[\n \\partial_\\alpha\\partial_\\gamma F+\n                  (\\partial_\\alpha F)(\\partial_\\gamma F).\n\\]\nThe dilaton translation $q=t-z1$ adds only a fixed constant to\none coordinate and does not affect finiteness.\n\nAt $\\hbar^{G-1}$, the first-derivative term involves\n$\\mathcal F_G$. The second-derivative term involves\n$\\mathcal F_{G-1}$, and the product of first derivatives involves\nthe finitely many pairs $\\mathcal F_g,\\mathcal F_h$ with\n$g+h=G$. Terms with negative genus are absent. The multiplication\nterm occurs only at $\\hbar^{-1}$. For a fixed output monomial,\nthe connected potentials which can occur have at most $N+2$\ndistinguished marks. Lemma~\\ref{cont:polynomiality} therefore\nbounds every differentiated descendant index uniformly,\nindependently of the fiber degree.\n\nThere is also a finite bound on the relevant matrix entries of\n$A_{k,i}$. By Proposition~\\ref{spec:modes} it has loop powers\nat least $-1$ and finite order in $z\\partial_z$.\nIn the second-derivative part, both negative-mode indices have\njust been bounded by the ancestor inequality. In the mixed\npart, the differentiated index is bounded and the multiplied\nindex is either an index of the prescribed output monomial or\nthe dilaton index. In the multiplication part, passage from\na nonnegative mode to a negative one, with lower loop bound\n$-1$, permits only finitely many indices. Thus each of the\nthree parts uses only finitely many loop coefficients of\nthe operator. The $z$ derivatives multiply these entries by\npolynomials in their mode indices and do not change this\nconclusion.\n\nAt fixed $\\beta$, Novikov finiteness leaves only finitely many\ndecompositions into the base classes carried by the operator\nand the one or two connected potentials. The latter\ncoefficients are polynomials in $y$ by\nLemma~\\ref{cont:polynomiality}. The relevant operator entries\nare holomorphic germs that continue with the spectral branches,\nby Proposition~\\ref{spec:modes}. The pairing and its inverse\nare fixed rational matrices in $\\lambda$; we work at a nonzero\n$\\lambda$ germ. This proves the finite-sum assertion and\ncontinuation.\n\nFinally, Proposition~\\ref{spec:zero} gives holomorphic\ncoefficients at zero for the $X$ cluster and a polynomial\ndependence on $\\log y$ for each $B$-branch mode. All operations\nin the coefficient test have just been shown to be finite.\nTaylor expansion therefore commutes with that test and yields\nexactly the formal identity used in\nProposition~\\ref{fixed:equivalence}.\n\\end{proof}\n\nThe last statement is what allows formal identities to start\nanalytic continuation. A finite sum\n$\\sum_{j=0}^J f_j(y)(\\log y)^j$, with $f_j$ holomorphic at zero,\nwhose formal series in $y$ and $\\log y$ vanishes is zero on a\npunctured sector: every Taylor coefficient of every $f_j$\nvanishes. Thus we use convergent germs of individual coefficient\ntests, without requiring convergence of a total potential.\n\n\\subsection{Transport from one branch to the other fixed component}\n\nFix $\\lambda\\neq0$. The branch equation is\n\\begin{equation}\n               y=h^r(h+\\lambda).\n \\label{cont:cover}\n\\end{equation}\nIts critical values are $0$ and\n$(-r)^r\\lambda^{r+1}/(r+1)^{r+1}$. Removing these values from\nthe $y$-plane gives an unramified covering of degree $r+1$.\n\n\\begin{lemma}\n\\label{cont:monodromy}\nThe monodromy of \\eqref{cont:cover} acts transitively on its\n$r+1$ branches.\n\\end{lemma}\n\n\\begin{proof}\nThe inverse image of the complement of the critical values is\nthe complex $h$-plane with finitely many points removed. It is\nconnected and path connected. Given any two points over a\nchosen regular value, join them by a path in this inverse\nimage. Its projection is a loop at that value, whose lift\ntakes the first point to the second. This is transitivity.\n\\end{proof}\n\n\\begin{proposition}\n\\label{cont:projective-transfer}\nIf the ordinary descendant potential of $B$ satisfies its\nordinary modes projectively, then the same is true for $X$.\n\\end{proposition}\n\n\\begin{proof}\nBy Proposition~\\ref{fixed:equivalence}, the hypothesis gives all\nthe projective equations for $A_{k,B}$ on $\\mathcal A_W$ near\n$y=0$. Lemma~\\ref{cont:finite-tests} turns their formal\ncoefficient identities into identities of germs on a\npunctured sector. Their infinitesimal symplectic identities\nhold there as well, by the same fixed-component comparison.\nSymplecticity is an entrywise linear identity, so it\ncontinues together with the operators.\n\nChoose a regular value in this sector. Every coefficient of\neach projective equation continues along any loop based\nthere. The ancestor coefficients themselves return unchanged,\nbecause at fixed base class they are polynomials in $y$.\nThe only change is in the continued spectral branch and\nlogarithm of its mode operator. By\nLemma~\\ref{cont:monodromy}, the continued $B$ branch can be\nany of the $r+1$ branches. The projective equations and\ninfinitesimal symplecticity therefore hold on each branch.\n\nContinuing a logarithm may add $2\\pi\\mathrm i$ times an\ninteger. Proposition~\\ref{spec:modes} gives an invertible\ntriangular binomial change among all modes when this happens.\nConsequently the simultaneous equations hold for any chosen\nlocal logarithm on each branch.\n\nReturn to a punctured neighborhood of $y=0$ and choose the\nsame logarithm on the whole $X$ cluster. Its projectors are\nthe sum of the individual projectors, and its logarithm is\nthe sum of their supported logarithms. In particular\n$D_X=\\sum_{i\\in X}D_i$. These operators contain $z$\nderivatives, so it is useful to record why taking their\npowers still respects this sum. Two-sided support and\ncommutation of each projector with $z$ give\n\\[\n (zD_i)(zD_j)=zD_iP_i zP_jD_j=0\\qquad(i\\neq j).\n\\]\nIt follows that $A_{k,X}=\\sum_{i\\in X}A_{k,i}$ for every\n$k\\geq-1$. Quantization is linear, and a sum of quantities\nindependent of the insertion variables is again independent\nof them. Hence $\\mathcal A_W$ satisfies all cluster modes\nprojectively.\n\nProposition~\\ref{spec:zero} extends the cluster operators\nholomorphically through $y=0$. Taking Taylor coefficients in\nthe equations is legitimate by\nLemma~\\ref{cont:finite-tests}. We may therefore apply\nProposition~\\ref{fixed:equivalence} in the reverse direction,\nwith $F=X$. Its conclusion concerns precisely the ordinary\ndescendant potential $Z_X$, with the full curve labels.\n\\end{proof}\n\n\\subsection{The prescribed scalar normalization}\n\nThe use of projective equations has absorbed the scalar ambiguity\nof quantization. For the ordinary Virasoro operators that\nambiguity is removed by two commutators. We give the scalar\ncalculation because the constant in $L_0$ is part of the\nassertion.\n\n\\begin{lemma}\n\\label{cont:commutators}\nFor every smooth projective complex variety $Y$, with the\noperators of Section~\\ref{conv:section}, one has\n\\begin{equation}\n [L_{-1}^Y,L_1^Y]=-2L_0^Y,\\qquad\n [L_0^Y,L_k^Y]=-kL_k^Y\\quad(k\\geq-1,\\ k\\neq0).\n \\label{cont:exact-commutators}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nThe identities $[\\mu_Y,R_Y]=R_Y$ and\n$[\\ell_{0,Y},z]=z$ give the classical commutators\n\\[\n [\\ell_{-1,Y},\\ell_{1,Y}]=2\\ell_{0,Y},\n \\qquad [\\ell_{0,Y},\\ell_{k,Y}]=k\\ell_{k,Y}.\n\\]\nNormal ordering in our Hamiltonian sign convention reverses\nthe commutator sign, with a scalar from contractions between\nthe two opposite off-diagonal blocks of the polarization;\nsee \\cite[Section~2]{GiventalQuant}. We compute the two\npossible scalars in the full super space.\n\nFor the first commutator, multiplication by $z^{-1}$\ncrosses from the nonnegative to the negative polarization\nonly at mode zero. The nonnegative output of\n$\\ell_{1,Y}$ on the mode $(-z)^{-1}a$ is\n\\[\n                    (1/4-\\mu_Y^2)a\n\\]\nin mode zero. Indeed the terms involving $R_Y$ still have\nnegative powers at this output. Contracting the quadratic\nmode-zero terms in the two orders gives the normal-order\nconstant\n\\[\n -\\frac12\\str(1/4-\\mu_Y^2)\n  =-\\frac{\\chi(Y)}8+\\frac12\\str(\\mu_Y^2)\n  =-2C_Y .\n\\]\nThe parity sign can also be checked on Darboux summands.\nFor an even coordinate,\n\\[\n [q^2/(2\\hbar),\\hbar m\\partial_q^2/2]\n                =-mq\\partial_q-m/2.\n\\]\nFor an odd paired plane with coordinates $\\theta,\\psi$,\n\\[\n [\\theta\\psi/\\hbar,-\\hbar m\\partial_\\psi\\partial_\\theta]\n          =m(1-\\theta\\partial_\\theta-\\psi\\partial_\\psi).\n\\]\nThe scalar is $-m/2$ on an even line and $m$\non an odd paired plane, precisely $-\\str(M)/2$ for\n$M=1/4-\\mu_Y^2$.\nTogether with the classical commutator this is\n$[L_{-1}^Y,L_1^Y]=-2(\\op(\\ell_{0,Y})+C_Y)$.\n\nFor the second commutator with $k>0$, the only crossing of\n$\\ell_{0,Y}$ is its $R_Y/z$ part at mode zero. The\nopposite crossing of $\\ell_{k,Y}$ uses a total of $k-1$\nfactors of $R_Y$: expanding\n$z^{-1}(z\\ell_{0,Y})^{k+1}$, a term that raises loop\npower by one must have exactly this many zero-power\n$R_Y$ factors. Its contraction with the first crossing\ntherefore raises first Hodge degree by $k$. Such an\nendomorphism has zero trace and zero supertrace.\nFor $k=-1$ both crossings have the same direction, so\nthere is no contraction. Thus the second commutator has\nno scalar correction. The constant $C_Y$ commutes with\nevery operator, and translating $q=t-z1_Y$ preserves\nall commutators. This proves\n\\eqref{cont:exact-commutators}.\n\\end{proof}\n\n\\begin{corollary}\n\\label{cont:exact}\nProjective satisfaction of all the ordinary modes by $Z_Y$\nimplies $\\V(Y)$ with exactly the constant specified in\n$L_0^Y$.\n\\end{corollary}\n\n\\begin{proof}\nProjective satisfaction says $L_k^Y Z_Y=c_k Z_Y$, where\n$c_k$ is independent of all insertion variables.\nThe operators differentiate neither the Novikov parameters\nnor $\\hbar$, so they commute with every $c_j$.\nTheir commutators consequently annihilate $Z_Y$.\nThe first identity in \\eqref{cont:exact-commutators}\ngives $L_0^Y Z_Y=0$. The second gives every remaining\nequation in the required range.\n\\end{proof}\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\nThe hypothesis $\\V(B)$ gives projective satisfaction on $B$.\nBy Proposition~\\ref{cont:projective-transfer}, it passes to $X$.\nCorollary~\\ref{cont:exact} gives the prescribed ordinary\nVirasoro equations.\n\nAll constructions used the full cohomology and the\ncategorical inverse pairings. Curve splittings, the shift\ncorrespondence, and the final Taylor expansion preserved\nthe actual integral homology labels. Thus the conclusion\nholds for every genus and curve class, with arbitrary\nordinary descendant insertions, as asserted.\n\\end{proof}\n"}, {"path": "preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/virasoro-constraints-under-projectivization.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Virasoro-Constraints-under-Projectivization-October-5-2026/virasoro-constraints-under-projectivization.pdf", "bytes": 539629, "sha256": "573917aea43c708a8cbef22a209158e5d6ea0e9d71f7786967608483be4709e6", "base64": 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"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/README.md", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/README.md", "bytes": 610, "sha256": "affbb022768fd6664ca99a509a42f24f653cd9919ae144fea162315c84e0c863", "content": "# [Weak and strong normalization in pure type systems](paper.pdf)\n\n**Author:** OpenAI\n\n**Date:** September 25, 2026\n\n## Citation\n\n```bibtex\n@misc{OAI:Weak-and-strong-normalization-in-pure-type-systems-September-25-2026,\n  author = {{OpenAI}},\n  title = {{Weak and strong normalization in pure type systems}},\n  howpublished = {OpenAI Math Release preprint\n                  \\href{https://github.com/openai/math/blob/main/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/paper.pdf}{OAI:Weak-and-strong-normalization-in-pure-type-systems-September-25-2026}},\n  year = {2026}\n}\n```\n\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/paper.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/paper.tex", "bytes": 1637, "sha256": "cfeaa3dce0659ee89cba45f76359ccfd3f3d8b6229c8e091964970a4066d2570", "content": "\\documentclass[11pt]{article}\n\\usepackage[T1]{fontenc}\n\\usepackage{lmodern}\n\\usepackage[margin=1in]{geometry}\n\\usepackage{amsmath,amssymb,amsthm,mathtools}\n\\usepackage{microtype}\n\\usepackage{enumitem}\n\\usepackage{tikz}\n\\usetikzlibrary{arrows.meta,positioning}\n\\usepackage{placeins}\n\\usepackage{needspace}\n\\usepackage[hidelinks]{hyperref}\n\\hypersetup{pdftitle={Weak and strong normalization in pure type systems},\n            pdfauthor={OpenAI}}\n\\newtheorem{theorem}{Theorem}\n\\newtheorem{lemma}[theorem]{Lemma}\n\\newtheorem{proposition}[theorem]{Proposition}\n\\newtheorem{corollary}[theorem]{Corollary}\n\\newtheorem{claim}{Claim}\n\\theoremstyle{definition}\n\\newtheorem{definition}{Definition}[section]\n\\newtheorem{remark}{Remark}[section]\n\\newtheorem{example}{Example}[section]\n\\setlist{itemsep=2pt,topsep=4pt}\n\\title{Weak and strong normalization in pure type systems}\n\\author{OpenAI}\n\\date{September 25, 2026}\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nWe prove that every weakly $\\beta$-normalizing pure type system is strongly\n$\\beta$-normalizing. Both properties quantify over all legal expressions\nin all valid contexts, and reduction acts inside type annotations.\nNo functionality hypothesis is required. This resolves the\n$\\beta$-Barendregt--Geuvers--Klop conjecture.\n\\end{abstract}\n\\setcounter{tocdepth}{1}\n\\tableofcontents\n\\clearpage\n\\input{sections/introduction}\n\\input{sections/metatheory}\n\\input{sections/profiles}\n\\input{sections/observations}\n\\input{sections/wrappers}\n\\input{sections/channels}\n\\input{sections/relational}\n\\input{sections/erasure}\n\\bibliographystyle{plain}\n{\\small\n\\bibliography{references}\n}\n\\end{document}\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/references.bib", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/references.bib", "bytes": 4404, "sha256": "0675e25e4fb05c8c754ac9196db7597db12d435d73cad7344c69c718a300bc57", "content": "@phdthesis{Geuvers1993,\n  author = {Geuvers, Jan Herman},\n  title = {Logics and Type Systems},\n  school = {Katholieke Universiteit Nijmegen},\n  year = {1993},\n  url = {https://www.cs.ru.nl/~herman/PUBS/Proefschrift.pdf}\n}\n\n@article{BartheHatcliffSorensen2001,\n  author = {Barthe, Gilles and Hatcliff, John and S{\\o}rensen, Morten Heine},\n  title = {Weak normalization implies strong normalization in a class of non-dependent pure type systems},\n  journal = {Theoretical Computer Science},\n  volume = {269},\n  number = {1--2},\n  pages = {317--361},\n  year = {2001},\n  doi = {10.1016/S0304-3975(01)00012-3}\n}\n\n@phdthesis{Sorensen1997,\n  author = {S{\\o}rensen, M. H. B.},\n  title = {Normalization in {$\\lambda$}-Calculus and Type Theory},\n  school = {University of Copenhagen},\n  year = {1997},\n  note = {Consulted revised edition, April 1998, DIKU Report 97/27},\n  url = {https://di.ku.dk/forskning/Publikationer/tekniske_rapporter/tekniske-rapporter-1997/97-27.pdf}\n}\n\n@inproceedings{RouxVanDoorn2014,\n  author = {Roux, Cody and van Doorn, Floris},\n  title = {The Structural Theory of Pure Type Systems},\n  editor = {Dowek, Gilles},\n  booktitle = {Rewriting and Typed Lambda Calculi},\n  series = {Lecture Notes in Computer Science},\n  volume = {8560},\n  pages = {364--378},\n  publisher = {Springer},\n  year = {2014},\n  doi = {10.1007/978-3-319-08918-8_25},\n  url = {https://florisvandoorn.com/papers/struct_pts.pdf}\n}\n\n@inproceedings{Mull2023,\n  author = {Mull, Nathan},\n  title = {An Irrelevancy-Eliminating Translation of Pure Type Systems},\n  booktitle = {28th International Conference on Types for Proofs and Programs (TYPES 2022)},\n  series = {Leibniz International Proceedings in Informatics},\n  volume = {269},\n  pages = {7:1--7:21},\n  publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\\\"u}r Informatik},\n  year = {2023},\n  doi = {10.4230/LIPIcs.TYPES.2022.7}\n}\n\n@phdthesis{MullThesis2023,\n  author = {Mull, Nathan},\n  title = {Weak and Strong Normalization of Tiered Pure Type Systems via Type-Preserving Translation},\n  school = {University of Chicago},\n  year = {2023},\n  note = {Consulted draft with title-page date June 16, 2023},\n  url = {https://mailman.cs.uchicago.edu/pipermail/colloquium/attachments/20230502/929cafeb/attachment-0001.pdf}\n}\n\n@book{Girard1989,\n  author = {Girard, Jean-Yves},\n  title = {Proofs and Types},\n  note = {Translated and with appendices by Paul Taylor and Yves Lafont},\n  publisher = {Cambridge University Press},\n  year = {1989},\n  isbn = {0-521-37181-3},\n  url = {https://www.paultaylor.eu/stable/prot.pdf}\n}\n\n@article{Tarski1955,\n  author = {Tarski, Alfred},\n  title = {A lattice-theoretical fixpoint theorem and its applications},\n  journal = {Pacific Journal of Mathematics},\n  volume = {5},\n  number = {2},\n  pages = {285--309},\n  year = {1955},\n  doi = {10.2140/pjm.1955.5.285}\n}\n\n@inproceedings{Hurkens1995,\n  author = {Hurkens, Antonius J. C.},\n  title = {A simplification of {Girard}'s paradox},\n  editor = {Dezani-Ciancaglini, Mariangiola and Plotkin, Gordon},\n  booktitle = {Typed Lambda Calculi and Applications},\n  series = {Lecture Notes in Computer Science},\n  volume = {902},\n  pages = {266--278},\n  publisher = {Springer},\n  year = {1995},\n  doi = {10.1007/BFb0014058}\n}\n\n@misc{Geuvers2007,\n  author = {Geuvers, Herman},\n  title = {Inconsistency of classical logic in type theory},\n  year = {2007},\n  month = nov,\n  note = {Public note},\n  url = {https://www.cs.ru.nl/~herman/PUBS/newnote.pdf}\n}\n\n@article{BartheCoquand2006,\n  author = {Barthe, Gilles and Coquand, Thierry},\n  title = {Remarks on the equational theory of non-normalizing pure type systems},\n  journal = {Journal of Functional Programming},\n  volume = {16},\n  number = {2},\n  pages = {137--155},\n  year = {2006},\n  doi = {10.1017/S0956796803004726}\n}\n\n@misc{Roux2025,\n  author = {Roux, Cody},\n  title = {Internal Proofs of Strong Normalization},\n  howpublished = {TYPES 2025, extended abstract and presentation},\n  year = {2025},\n  month = jun,\n  url = {https://msp.cis.strath.ac.uk/types2025/abstracts/TYPES2025_paper54.pdf},\n  note = {Slides: \\url{https://msp.cis.strath.ac.uk/types2025/slides/TYPES2025-slides54.pdf}}\n}\n\n@phdthesis{Poll1994,\n  author = {Poll, Erik},\n  title = {A Programming Logic Based on Type Theory},\n  school = {Technische Universiteit Eindhoven},\n  year = {1994},\n  doi = {10.6100/IR423044},\n  url = {https://pure.tue.nl/ws/files/1718573/423044.pdf}\n}\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/channels.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/channels.tex", "bytes": 29159, "sha256": "5a3f18e525ee089c2f279a950cc12d62bf4433b7e5278fbde384a5b280f5e4d1", "content": "\\section{Typed data channels}\n\\label{chan-section}\n\nWe retain the labelled proof and data fragment, its fixed ambient context,\nthe proof-formula constructors, and the data-path wrappers constructed\nabove.  In particular, a formula is a stable data type sorted at the logic\nvertex $b$; its proofs have mode $\\mathsf p$.  All data introduced in this\nsection have mode $\\mathsf d$.  Saying that a type is \\emph{sorted at a\nvertex} means that it is typed at every sort belonging to that vertex.  This\nasserts the indicated typings, and does not assert that the vertex is its\nexact profile.\n\nThe aim is to construct a common type of probes that works with different\nchoices of a data type and a data parameter. We first build observations\nand predicates for one type, then query objects and a bundle that retains\nthe chosen parameter. These give a probe type independent of those\nchoices and a second observation channel on that type. The permitted\nlogical quantifier domains are recorded at the end of the section.\n\nFor reference, an even data path $\\rho$ from a vertex at which $T$ is sorted\nhas the operations\n\\[\n \\operatorname{pack}_{\\rho}(t):W_{\\rho}(T),\\qquad\n \\operatorname{use}_{\\rho}(w;x.\\Phi),\n\\]\nwhere $t:T$, $w:W_{\\rho}(T)$, and $\\Phi$ is a formula under $x:T$.\nAn odd data path has the operations\n\\[\n \\operatorname{mk}_{\\rho}(x.\\Phi):W_{\\rho}(T),\\qquad\n \\langle w,t\\rangle_{\\rho}.\n\\]\nThe last expressions in both displays are formulas.  We use the proof\nbuilders of Lemma~\\ref{lem:enc-data-cancellation} in the following form:\n\\begin{equation}\n\\begin{aligned}\n \\operatorname{use}_{\\rho}(\\operatorname{pack}_{\\rho}(t);x.\\Phi)\n       &\\leftrightarrow \\Phi[x:=t] &&(\\rho\\text{ even}),\\\\\n \\langle\\operatorname{mk}_{\\rho}(x.\\Phi),t\\rangle_{\\rho}\n       &\\leftrightarrow \\Phi[x:=t] &&(\\rho\\text{ odd}).\n\\end{aligned}\n\\label{chan-cancellation}\n\\end{equation}\nThese are logical biconditionals with typed proof terms, rather than\nequations permitting replacement inside data.  Subscripts may name a\nparticular resulting type instead of its fixed path.  Even when paths are\nreused, their source type and their role are fixed by that subscript.\n\n\\subsection{The required paths and the first channel}\n\nAssume the configuration to be excluded: $C$ is a plain active strongly\nconnected component of the secondary graph, it has an internal negative\nedge, and\n\\begin{equation}\n\\begin{gathered}\n (I,J,k)\\text{ is a profile triple},\\qquad J,k,d=\\{s\\}\\in C,\\\\\n \\operatorname{Ax}(s)\\ne\\varnothing,\\qquad\n \\operatorname{Ax}(s)\\longrightarrow I\n \\text{ by a primary positive path}.\n\\end{gathered}\n\\label{chan-configuration}\n\\end{equation}\nThe logic component $S$ has its odd closed walk and all-$S$ triple, as in\nthe preceding construction.  We do not assume that $C$ itself has an odd\nclosed walk.\n\n\\begin{lemma}[Choice of paths]\n\\label{chan-paths}\nThere is a retained negative edge associated to a triple $(p,n,g)$, with\n$p,g\\in C$, and there are fixed secondary paths\n\\[\n \\alpha:k\\longrightarrow p\\quad\\text{odd},\\qquad\n \\beta:p\\longrightarrow k\\quad\\text{odd},\\qquad\n \\kappa:g\\longrightarrow J\\quad\\text{even},\n\\]\nall inside $C$.  There are also fixed secondary paths\n\\[\n \\lambda:k\\longrightarrow j\\quad\\text{odd},\\qquad\n \\theta:k\\longrightarrow h\\quad\\text{even},\n\\]\nwhere $j$ and $h$ are direct vertices.  In particular,\n$\\eta=\\beta\\theta:p\\longrightarrow h$ is odd.\n\\end{lemma}\n\n\\begin{proof}\nIf $C$ has an odd closed walk, between any two of its vertices there are\npaths of both parities: choose paths to and from the base of that walk,\nand insert the walk once to change parity.  Choose any internal retained\nnegative $(p,n,g)$ and then choose the three required parities for\n$\\alpha$, $\\beta$, and $\\kappa$.\n\nSuppose instead that every closed walk in $C$ is even.  Color its vertices\nby the parity of a path from a fixed vertex.  The color is well defined:\nappending a fixed return path to two such paths shows that their parities\nare equal.  A positive edge preserves color and a negative edge changes\nit.  The positive edge $J\\longrightarrow k$ of the triple in\n\\eqref{chan-configuration} is retained, since $J,k\\in C\\subseteq H$;\nhence $J$ and $k$ have the same color.  There is a negative edge entering\nthis color.  Indeed, take any internal negative.  If it enters the other\ncolor, follow a directed return path to its source; that path must contain\na negative edge entering the color of $k$.  Choose such an edge\n$p\\longrightarrow g$, with its retained triple $(p,n,g)$.  Thus $g$ has\nthe color of $J,k$, and $p$ has the opposite color.  Connectivity now gives\n$\\kappa$ even and $\\alpha,\\beta$ odd.\n\nFinally, $k$ is plain.  By the definition of plainness it has an odd path\nto a direct vertex $j$ and an even path to a direct vertex $h$.  They need\nnot have the same endpoint and need not stay inside $C$.  Choose them as\n$\\lambda$ and $\\theta$.  Concatenating $\\beta$ with $\\theta$ gives the\nclaimed odd path $\\eta$.\n\\end{proof}\n\nFix also a path $\\delta:d\\longrightarrow k$ inside $C$, with no parity\ncondition.  We use $\\delta$ only to form a type.  Work for the moment under\nthe data declaration $A:s$.  This is a valid declaration because\n$\\operatorname{Ax}(s)$ is nonempty.  Define\n\\begin{equation}\n\\begin{aligned}\n B(A)&=W_{\\delta}(A),&\n L_c(A)&=W_{\\alpha}(B(A)),\\\\\n L(A)&=W_{\\lambda}(B(A)),&\n \\mathsf P_L(A)&=W_{\\eta}(L_c(A)).\n\\end{aligned}\n\\label{chan-basic-types}\n\\end{equation}\nWhen $A$ is fixed, omit it from the notation.  The sort ledger is\n\\[\n\\begin{array}{c|c|l}\n\\text{data type}&\\text{sorted at}&\\text{support}\\\\ \\hline\nA&d=\\{s\\}&A:s\\\\\nB(A)&k&\\delta:d\\longrightarrow k\\\\\nL_c(A)&p&\\alpha:k\\longrightarrow p\\text{ odd}\\\\\nL(A)&j&\\lambda:k\\longrightarrow j\\text{ odd}\\\\\n\\mathsf P_L(A)&h&\\eta:p\\longrightarrow h\\text{ odd}\n\\end{array}\n\\]\nEach line follows from the corresponding data-path formation lemma.\nFor instance, $\\operatorname{mk}_{L}(t.\\Phi)$ binds $t:B(A)$, whereas\n$\\operatorname{mk}_{\\mathsf P_L}(c.\\Phi)$ binds $c:L_c(A)$.  These\ndifferent sources will be important below.\n\n\\begin{definition}[Logical and raw channel values]\n\\label{chan-raw-log}\nFor $y:L$, $c:L_c$, and $P:\\mathsf P_L$, put\n\\[\n\\begin{aligned}\n \\operatorname{Raw}(y)\n   &=\\operatorname{mk}_{L_c}(t.\\langle y,t\\rangle_L):L_c,\\\\\n \\operatorname{Log}(c)\n   &=\\operatorname{mk}_{L}(t.\\langle c,t\\rangle_{L_c}):L,\\\\\n \\operatorname{Round}(y)&=\\operatorname{Log}(\\operatorname{Raw}(y)):L,\\\\\n P@y&=\\langle P,\\operatorname{Raw}(y)\\rangle_{\\mathsf P_L}.\n\\end{aligned}\n\\]\nFor a formula $\\Phi$ under $x:L$, define\n\\[\n \\operatorname{pred}(x.\\Phi)\n   =\\operatorname{mk}_{\\mathsf P_L}\n       (c.\\Phi[x:=\\operatorname{Log}(c)]):\\mathsf P_L.\n\\]\nHere $t:B(A)$ and $c:L_c(A)$ in their respective callbacks.\n\\end{definition}\n\n\\begin{lemma}[Rounding and predicate evaluation]\n\\label{chan-rounding}\nThere are proof builders, for $y:L$ and $t:B(A)$, of\n\\begin{equation}\n \\langle\\operatorname{Round}(y),t\\rangle_L\n       \\leftrightarrow\\langle y,t\\rangle_L.\n\\label{chan-primitive-round}\n\\end{equation}\nFor every formula $\\Phi$ under $x:L$ and every $w:L$, there is a proof of\n\\begin{equation}\n \\operatorname{pred}(x.\\Phi)@w\n       \\leftrightarrow\\Phi[x:=\\operatorname{Round}(w)].\n\\label{chan-predicate-evaluation}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nFor \\eqref{chan-primitive-round}, cancellation for $\\operatorname{Log}$\ngives the comparison with\n$\\langle\\operatorname{Raw}(y),t\\rangle_{L_c}$, and cancellation for\n$\\operatorname{Raw}$ compares this with $\\langle y,t\\rangle_L$.\nApply $\\mathsf{pl}$ to these two biconditionals.  For\n\\eqref{chan-predicate-evaluation}, expand $@$ and cancel\n$\\operatorname{mk}_{\\mathsf P_L}$ at the exact argument\n$c=\\operatorname{Raw}(w)$.  Its callback becomes\n$\\Phi[x:=\\operatorname{Log}(\\operatorname{Raw}(w))]$, which is the\ndisplayed formula by definition.\n\\end{proof}\n\n\\subsection{Bits stored by pairs of odd wrappers}\n\nTwo odd wrappers store one bit while retaining formula observations of\nthe original input. Iterating this construction will distinguish the\nfinite query markers.\n\n\\begin{definition}[A double wrapper]\n\\label{chan-double}\nLet $T_1=W_{\\rho}(T)$ and $T_2=W_{\\sigma}(T_1)$ for two odd data paths.\nUse subscripts $1$ and $2$ for their respective constructors and\nevaluations.  For $t:T$ define two data embeddings\n\\[\n \\operatorname{emb}_1(t)\n   =\\operatorname{mk}_2(f.\\langle f,t\\rangle_1),\\qquad\n \\operatorname{emb}_0(t)\n   =\\operatorname{mk}_2(f.\\neg\\langle f,t\\rangle_1),\n \\qquad f:T_1.\n\\]\nBoth have type $T_2$.  Set\n\\[\n \\operatorname{if}(G;H,H')=(G\\land H)\\lor(\\neg G\\land H'),\n\\]\nand, for $v:T_2$ and a formula $\\Phi$ under $u:T$, set\n\\[\n\\begin{aligned}\n \\operatorname{bit}(v)\n   &=\\langle v,\\operatorname{mk}_1(u.\\top)\\rangle_2,\\\\\n \\operatorname{rec}(v;u.\\Phi)\n   &=\\operatorname{if}\\bigl(\\operatorname{bit}(v);\n          \\langle v,\\operatorname{mk}_1(u.\\Phi)\\rangle_2,\n          \\neg\\langle v,\\operatorname{mk}_1(u.\\Phi)\\rangle_2\\bigr).\n\\end{aligned}\n\\]\n\\end{definition}\n\n\\begin{lemma}[Canonical bit and recovery proofs]\n\\label{chan-double-laws}\nFor $\\epsilon\\in\\{0,1\\}$ there are proofs of the signed bit\n\\[\n \\begin{cases}\n  \\operatorname{bit}(\\operatorname{emb}_1(t)),&\\epsilon=1,\\\\\n  \\neg\\operatorname{bit}(\\operatorname{emb}_0(t)),&\\epsilon=0,\n \\end{cases}\n\\]\nand a proof of\n\\[\n \\operatorname{rec}(\\operatorname{emb}_{\\epsilon}(t);u.\\Phi)\n       \\leftrightarrow\\Phi[u:=t].\n\\]\n\\end{lemma}\n\n\\begin{proof}\nCancel the second wrapper, then the first.  Evaluation of\n$\\operatorname{emb}_1(t)$ against $\\operatorname{mk}_1(u.\\Phi)$ is\nbiconditional to $\\Phi[u:=t]$; evaluation of\n$\\operatorname{emb}_0(t)$ against the same value is biconditional to\n$\\neg\\Phi[u:=t]$.  Each comparison follows by $\\mathsf{pl}$ from the two\ncancellation proofs.  With $\\Phi=\\top$, the proof of $\\top$ and these\ncomparisons give the signed bits.  If $\\epsilon=1$, the definition of\n$\\operatorname{rec}$ then selects the positive comparison.  If\n$\\epsilon=0$, it selects the negation of the negative comparison.  The\npropositional double-negation equivalence is available from\n$\\mathsf{pl}$ by Lemma~\\ref{lem:enc-propositional}, since the logical\nconstructors and classical proof builders have already been derived in\nthe labelled fragment.  Thus\n$\\mathsf{pl}$ gives the recovery biconditional in both cases.\n\\end{proof}\n\nTo record the iteration explicitly, begin with $T_0=L_c$ and define, for\n$1\\le r\\le4$,\n\\[\n T_{2r-1}=W_{\\beta}(T_{2r-2}),\\qquad\n T_{2r}=W_{\\alpha}(T_{2r-1}),\\qquad\n \\operatorname{Big}=T_8.\n\\]\nThe even-indexed types are sorted at $p$ and the odd-indexed types at $k$.\nLet $\\operatorname{emb}^{(r)}$, $\\operatorname{bit}_r$, and\n$\\operatorname{rec}_r$ refer to the $r$th pair.  For a string\n$\\epsilon=(\\epsilon_1,\\ldots,\\epsilon_r)$ put\n\\[\n E_0(c)=c,\\qquad\n E_r(c;\\epsilon)=\\operatorname{emb}^{(r)}_{\\epsilon_r}\n                (E_{r-1}(c;\\epsilon_1,\\ldots,\\epsilon_{r-1})).\n\\]\nThe bit observations at level $r$ are the formulas\n\\[\n\\begin{aligned}\n H_{r,r}(v)&=\\operatorname{bit}_r(v),\\\\\n H_{r,a}(v)&=\\operatorname{rec}_r(v;u.H_{r-1,a}(u))\n                      &&(1\\le a<r).\n\\end{aligned}\n\\]\nSimilarly, for a formula $\\Phi$ under $c:L_c$, define iterated recovery by\n\\[\n R_0(v;c.\\Phi)=\\Phi[c:=v],\\qquad\n R_r(v;c.\\Phi)=\\operatorname{rec}_r(v;u.R_{r-1}(u;c.\\Phi)).\n\\]\nInduction using Lemma~\\ref{chan-double-laws} and $\\mathsf{pl}$ gives the\nappropriate signed proof of every\n$H_{r,a}(E_r(c;\\epsilon))$ and gives\n\\begin{equation}\n R_r(E_r(c;\\epsilon);u.\\Phi)\n       \\leftrightarrow\\Phi[u:=c].\n\\label{chan-iterated-recovery}\n\\end{equation}\nThe induction uses logical comparisons of the displayed formulas; it\ndoes not replace an inner encoded datum by a logically equivalent datum.\n\n\\subsection{Marked queries and point values}\n\nLet\n\\[\n \\mathcal T=\\{G,C_0,C_\\ell,C_r,E_\\ell,E_r,R,\\operatorname{pt}\\},\n \\qquad\n \\mathcal T_{\\mathrm{str}}=\\mathcal T\\setminus\\{\\operatorname{pt}\\}.\n\\]\nThus there are eight marker indices and seven structural indices.\nAssign distinct four-bit strings to the data tag and these markers; for\nexample, in the displayed order, take\n\\[\n\\begin{array}{c|ccccccccc}\n\\text{tag}&\\mathrm{data}&G&C_0&C_\\ell&C_r&E_\\ell&E_r&R&\\operatorname{pt}\\\\ \\hline\n\\text{string}&0000&0001&0010&0011&0100&0101&0110&0111&1000.\n\\end{array}\n\\]\nWrite $\\epsilon(q)$ for the string of a tag $q$, and put\n$E_q(c)=E_4(c;\\epsilon(q)):\\operatorname{Big}$.  For a string\n$\\epsilon\\in\\{0,1\\}^4$, let $\\operatorname{bits}_{\\epsilon}(v)$ be the\nfixed-bracketing conjunction whose $a$th conjunct is $H_{4,a}(v)$ if\n$\\epsilon_a=1$ and $\\neg H_{4,a}(v)$ if $\\epsilon_a=0$.\nAbbreviate this formula by $\\operatorname{bits}_{q}(v)$ for the string of\na tag $q$.  The canonical signed-bit proofs give\n$\\operatorname{bits}_{q}(E_q(c))$ and\n$\\neg\\operatorname{bits}_{q'}(E_q(c))$ whenever $q'\\ne q$: in the\nlatter case use a position where the two strings differ and apply\n$\\mathsf{pl}$.\n\nDefine\n\\[\n \\operatorname{BP}=W_{\\beta}(\\operatorname{Big}),\\qquad\n M(A)=W_{\\alpha}(\\operatorname{BP}).\n\\]\nFor $P:\\mathsf P_L$ and $t\\in\\mathcal T$, put\n\\begin{equation}\n \\operatorname{Enc}_t(P)=\\operatorname{mk}_{\\operatorname{BP}}\\left(\n v.\\bigl(\\operatorname{bits}_{\\mathrm{data}}(v)\n        \\land R_4(v;c.\\langle P,c\\rangle_{\\mathsf P_L})\\bigr)\n        \\lor\\operatorname{bits}_{t}(v)\\right).\n\\label{chan-enc}\n\\end{equation}\nHere $v:\\operatorname{Big}$ and $c:L_c$, so\n$\\operatorname{Enc}_t(P):\\operatorname{BP}$.  Choose the fixed value\n\\[\n c_\\top=\\operatorname{mk}_{L_c}(u.\\top):L_c,\n \\qquad u:B(A),\n\\]\nand define, for $bp:\\operatorname{BP}$,\n\\[\n\\begin{aligned}\n \\operatorname{Mark}_t(bp)\n    &=\\langle bp,E_t(c_\\top)\\rangle_{\\operatorname{BP}},\\\\\n \\operatorname{Dec}(bp)\n    &=\\operatorname{mk}_{\\mathsf P_L}\n                  (c.\\langle bp,E_{\\mathrm{data}}(c)\\rangle_{\\operatorname{BP}})\n                    :\\mathsf P_L,\\\\\n \\operatorname{qry}_t(o,P)\n    &=\\langle o,\\operatorname{Enc}_t(P)\\rangle_M,\n                      \\qquad o:M(A).\n\\end{aligned}\n\\]\nThe sort and value ledger for these constructions is\n\\[\n\\begin{array}{c|c|l}\n\\text{data type}&\\text{sorted at}&\\text{values just constructed}\\\\ \\hline\nT_{2r}&p&E_r(c;\\epsilon)\\quad(0\\le r\\le4)\\\\\nT_{2r-1}&k&\\operatorname{mk}\\text{ values in pair }r\\quad(1\\le r\\le4)\\\\\n\\operatorname{Big}=T_8&p&E_q(c)\\\\\n\\operatorname{BP}&k&\\operatorname{Enc}_t(P)\\\\\n\\mathsf P_L&h&\\operatorname{Dec}(bp)\\\\\nM(A)&p&\\operatorname{mk}_M(bp.\\Phi)\n\\end{array}\n\\]\nAll observations, marks, queries, and recovery expressions are formulas\nsorted at $b$; none is a data value of type $\\operatorname{Big}$ or\n$\\operatorname{BP}$.\n\n\\begin{lemma}[Marker and decoder laws]\n\\label{chan-marker-laws}\nFor $P:\\mathsf P_L$ and $t,u\\in\\mathcal T$ there are proofs of\n\\[\n \\operatorname{Mark}_t(\\operatorname{Enc}_t(P)),\\qquad\n \\neg\\operatorname{Mark}_u(\\operatorname{Enc}_t(P))\\quad(u\\ne t).\n\\]\nFor every $c:L_c$ there is a proof of\n\\[\n \\langle\\operatorname{Dec}(\\operatorname{Enc}_t(P)),c\\rangle_{\\mathsf P_L}\n       \\leftrightarrow\\langle P,c\\rangle_{\\mathsf P_L},\n\\]\nand consequently, for every $y:L$, a proof of\n\\begin{equation}\n \\operatorname{Dec}(\\operatorname{Enc}_t(P))@y\\leftrightarrow P@y.\n\\label{chan-decode}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nFor a marker, cancel \\eqref{chan-enc} at $E_u(c_\\top)$.  The data-string\ntest is false by the signed-bit proofs.  The remaining marker-string\ntest holds exactly when $u=t$, again by those proofs.  Apply\n$\\mathsf{pl}$ to obtain the asserted signed mark.\n\nFor the decoder, first cancel its $\\mathsf P_L$ constructor at the exact\ninput $c$, then cancel \\eqref{chan-enc} at\n$E_{\\mathrm{data}}(c)$.  Here the data-string test holds, the\nmarker-string test fails, and \\eqref{chan-iterated-recovery} compares the\nremaining recovery formula with $\\langle P,c\\rangle_{\\mathsf P_L}$.\nCombine these proofs with $\\mathsf{pl}$.  To obtain \\eqref{chan-decode},\nuse this result at $c=\\operatorname{Raw}(y)$ and expand the definition of\n$@$.  Thus this calculation inserts neither\n$\\operatorname{Round}(y)$ nor an additional rounding of its raw value.\n\\end{proof}\n\nDefine the data values and the formula\n\\begin{equation}\n\\begin{aligned}\n bp_\\bot&=\\operatorname{mk}_{\\operatorname{BP}}(v.\\bot)\n                       :\\operatorname{BP},\\\\\n \\operatorname{dummy}&=\\operatorname{mk}_M(bp.\\top):M(A),\\\\\n D(o)&=\\langle o,bp_\\bot\\rangle_M,\\\\\n \\operatorname{point}(y)\n   &=\\operatorname{mk}_M\n       (bp.\\operatorname{Mark}_{\\operatorname{pt}}(bp)\n                   \\land(\\operatorname{Dec}(bp)@y)):M(A).\n\\end{aligned}\n\\label{chan-point}\n\\end{equation}\n\n\\begin{lemma}[Dummy and point laws]\n\\label{chan-point-laws}\nThere are proofs of\n\\[\n D(\\operatorname{dummy}),\\qquad\n \\neg\\operatorname{Mark}_t(bp_\\bot)\\quad(t\\in\\mathcal T),\\qquad\n \\neg D(\\operatorname{point}(y)),\n\\]\nand there is a proof, for $P:\\mathsf P_L$, of\n\\begin{equation}\n \\operatorname{qry}_{\\operatorname{pt}}(\\operatorname{point}(y),P)\n                  \\leftrightarrow P@y.\n\\label{chan-point-query}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nCancellation compares $D(\\operatorname{dummy})$ with $\\top$, which has\nits derived proof.  It compares every marker of $bp_\\bot$ with $\\bot$,\ngiving the asserted negations by $\\mathsf{pl}$.  Cancellation at\n$bp_\\bot$ compares $D(\\operatorname{point}(y))$ with a conjunction\nhaving $\\operatorname{Mark}_{\\operatorname{pt}}(bp_\\bot)$ as its first\nconjunct, and hence gives its negation.  Finally, cancellation compares\nthe left side of \\eqref{chan-point-query} with\n\\[\n \\operatorname{Mark}_{\\operatorname{pt}}\n       (\\operatorname{Enc}_{\\operatorname{pt}}(P))\n \\land\n \\bigl(\\operatorname{Dec}(\\operatorname{Enc}_{\\operatorname{pt}}(P))@y\\bigr).\n\\]\nThe marker proof and \\eqref{chan-decode} prove the required\nbiconditional by $\\mathsf{pl}$.\n\\end{proof}\n\n\\subsection{A bundle carrying an exact parameter}\n\nDefine two more odd wrappers\n\\begin{equation}\n F(A)=W_{\\beta}(M(A)),\\qquad\n \\operatorname{Single}(A)=W_{\\alpha}(F(A)).\n\\label{chan-single}\n\\end{equation}\nThus $F(A)$ is sorted at $k$ and $\\operatorname{Single}(A)$ is sorted at\n$p$.  We use $\\langle f,o\\rangle_F$ for $f:F(A)$ and $o:M(A)$, and\n$\\langle w,f\\rangle_{\\operatorname{Single}}$ for\n$w:\\operatorname{Single}(A)$ and $f:F(A)$.\n\n\\begin{definition}[Bundle and access]\n\\label{chan-bundle-definition}\nFor $m:M(A)$ and $y:L(A)$, put\n\\[\n \\operatorname{Bundle}(m,y)=\\operatorname{mk}_{\\operatorname{Single}}\n \\left(f.\\operatorname{if}\\bigl(\n    \\langle f,\\operatorname{dummy}\\rangle_F;\n    \\langle f,m\\rangle_F,\n    \\langle f,\\operatorname{point}(y)\\rangle_F\\bigr)\\right)\n       :\\operatorname{Single}(A).\n\\]\nLet $\\Psi(o,x)$ be a formula under $o:M(A),x:L(A)$, independent of the\nnew variable $w:\\operatorname{Single}(A)$.  Its other free data parameters\nare allowed and remain fixed.  Define\n\\begin{equation}\n\\begin{aligned}\n f_i(o)&=\\operatorname{mk}_{F}\\left(o'.\n    \\operatorname{if}\\bigl(D(o');\\bot,\n       \\operatorname{qry}_{\\operatorname{pt}}\n           (o',\\operatorname{pred}(x.\\Psi(o,x)))\\bigr)\\right),\\\\\n f_e(w)&=\\operatorname{mk}_{F}\\left(o.\n    \\operatorname{if}\\bigl(D(o);\\top,\n             \\langle w,f_i(o)\\rangle_{\\operatorname{Single}}\\bigr)\\right),\\\\\n \\operatorname{Access}(w;o,x.\\Psi)\n     &=\\langle w,f_e(w)\\rangle_{\\operatorname{Single}}.\n\\end{aligned}\n\\label{chan-access-definition}\n\\end{equation}\nHere $o,o':M(A)$, both $f_i(o)$ and $f_e(w)$ have type $F(A)$, and\n$\\operatorname{Access}$ is a formula.  The dependence of $f_i$ and $f_e$\non $\\Psi$ is suppressed only in their names.\n\\end{definition}\n\n\\begin{lemma}[Access]\n\\label{chan-access}\nUnder $A:s$, $m:M(A)$, $y:L(A)$ and the proof input $d_m:\\neg D(m)$,\nthere is a proof of\n\\begin{equation}\n \\operatorname{Access}(\\operatorname{Bundle}(m,y);o,x.\\Psi)\n        \\leftrightarrow\\Psi(m,\\operatorname{Round}(y)).\n\\label{chan-access-law}\n\\end{equation}\nThe definitions of $\\operatorname{Bundle}$ and $\\operatorname{Access}$\nthemselves do not use $d_m$.\n\\end{lemma}\n\n\\begin{proof}\nThroughout this proof $w$ abbreviates the exact data expression\n$\\operatorname{Bundle}(m,y)$; it is not a fresh datum with an assumed\nproperty.  For any $f:F(A)$, cancellation of the bundle gives a proof of\n\\begin{equation}\n \\langle w,f\\rangle_{\\operatorname{Single}}\n \\leftrightarrow\n \\operatorname{if}\\bigl(\n    \\langle f,\\operatorname{dummy}\\rangle_F;\n    \\langle f,m\\rangle_F,\n    \\langle f,\\operatorname{point}(y)\\rangle_F\\bigr).\n\\label{chan-bundle-evaluation}\n\\end{equation}\n\nFirst apply cancellation to $f_e(w)$ at\n$\\operatorname{dummy}:M(A)$.  It gives\n\\[\n \\langle f_e(w),\\operatorname{dummy}\\rangle_F\n \\leftrightarrow\n \\operatorname{if}\\bigl(D(\\operatorname{dummy});\\top,\n      \\langle w,f_i(\\operatorname{dummy})\\rangle_{\\operatorname{Single}}\\bigr).\n\\]\nTogether with $D(\\operatorname{dummy})$ from\nLemma~\\ref{chan-point-laws} and the proof of $\\top$, this gives, by\n$\\mathsf{pl}$, a proof of\n$\\langle f_e(w),\\operatorname{dummy}\\rangle_F$.\nCancellation at the exact input $m$ gives\n\\[\n \\langle f_e(w),m\\rangle_F\n \\leftrightarrow\n \\operatorname{if}\\bigl(D(m);\\top,\n                  \\langle w,f_i(m)\\rangle_{\\operatorname{Single}}\\bigr).\n\\]\nUse $d_m$ and $\\mathsf{pl}$ to obtain\n\\[\n \\langle f_e(w),m\\rangle_F\n       \\leftrightarrow\\langle w,f_i(m)\\rangle_{\\operatorname{Single}}.\n\\]\nNow use \\eqref{chan-bundle-evaluation} with $f=f_e(w)$.  Its condition has\nthe positive proof just constructed, so $\\mathsf{pl}$ selects its first\nbranch and gives\n\\begin{equation}\n \\operatorname{Access}(w;o,x.\\Psi)\n       \\leftrightarrow\\langle w,f_i(m)\\rangle_{\\operatorname{Single}}.\n\\label{chan-access-first-selection}\n\\end{equation}\nThis is where the first occurrence of the callback parameter is fixed:\nit is the original $m$ by the exact substitution in cancellation.\n\nFor the second selection, cancellation of $f_i(m)$ at\n$\\operatorname{dummy}$ gives\n\\[\n \\langle f_i(m),\\operatorname{dummy}\\rangle_F\n \\leftrightarrow\n \\operatorname{if}\\bigl(D(\\operatorname{dummy});\\bot,\n       \\operatorname{qry}_{\\operatorname{pt}}\n          (\\operatorname{dummy},\\operatorname{pred}(x.\\Psi(m,x)))\\bigr).\n\\]\nThe proof of $D(\\operatorname{dummy})$ therefore gives a proof of\n$\\neg\\langle f_i(m),\\operatorname{dummy}\\rangle_F$ by $\\mathsf{pl}$.\nCancellation of $f_i(m)$ at $\\operatorname{point}(y)$, followed by\n$\\neg D(\\operatorname{point}(y))$, gives\n\\[\n \\langle f_i(m),\\operatorname{point}(y)\\rangle_F\n \\leftrightarrow\n \\operatorname{qry}_{\\operatorname{pt}}\n       (\\operatorname{point}(y),\\operatorname{pred}(x.\\Psi(m,x))).\n\\]\nThe point-query proof \\eqref{chan-point-query}, at the exact predicate\n$\\operatorname{pred}(x.\\Psi(m,x))$, compares this right side with\n$\\operatorname{pred}(x.\\Psi(m,x))@y$.  In turn,\n\\eqref{chan-predicate-evaluation} compares that formula with\n$\\Psi(m,\\operatorname{Round}(y))$.  Combining these proofs gives\n\\[\n \\langle f_i(m),\\operatorname{point}(y)\\rangle_F\n        \\leftrightarrow\\Psi(m,\\operatorname{Round}(y)).\n\\]\nUse \\eqref{chan-bundle-evaluation} once more, now with $f=f_i(m)$.  Its\ncondition has the negative proof above, so $\\mathsf{pl}$ selects its\nsecond branch and yields\n\\[\n \\langle w,f_i(m)\\rangle_{\\operatorname{Single}}\n        \\leftrightarrow\\Psi(m,\\operatorname{Round}(y)).\n\\]\nFinally combine this with \\eqref{chan-access-first-selection} by\n$\\mathsf{pl}$.  Only predicate evaluation rounded an argument, and that\nargument was $y$.  Every occurrence of $m$ in this derivation comes from\nexact data substitution, without a logical replacement of data.\n\\end{proof}\n\n\\subsection{The common type of probes}\n\nRecall the retained triple $(p,n,g)$ selected in\nLemma~\\ref{chan-paths}.  Its tail type $U_n$ is the previously fixed\npositive data wrapper of a literal sort.  It is sorted at $n$, with the\noperations $\\operatorname{send}_n$ and $\\operatorname{read}_n$ and the\nread--send biconditional \\eqref{eq:enc-read-send}.  Since\n$\\operatorname{Single}(A)$ is sorted at $p$, form\n\\begin{equation}\n \\operatorname{Fun}(A)\n    =\\Pi^{\\mathsf d,\\mathsf d}w:\\operatorname{Single}(A).U_n,\n \\qquad\\text{sorted at }g.\n\\label{chan-fun}\n\\end{equation}\nThis uses precisely the formation rules represented by $(p,n,g)$.\n\nLet $\\zeta$ be the fixed primary positive path\n$\\operatorname{Ax}(s)\\longrightarrow I$ of\n\\eqref{chan-configuration}, and positively wrap the literal $s$:\n\\begin{equation}\n Z=W_{\\zeta}^{+}(s),\\qquad\n A^\\uparrow:Z\\quad(A:s),\\qquad\n z^\\downarrow:s\\quad(z:Z),\\qquad\n (A^\\uparrow)^\\downarrow={}_\\beta A.\n\\label{chan-z}\n\\end{equation}\nThe initial type $s$ is sorted at all of $\\operatorname{Ax}(s)$, so $Z$\nis sorted at $I$.  The superscripts in \\eqref{chan-z} denote the fixed\npositive lift and projection, not additional primitive operations.\n\nNow define the data type\n\\begin{equation}\n V=\\Pi^{\\mathsf d,\\mathsf d}z:Z.\n             W_{\\kappa}(\\operatorname{Fun}(z^\\downarrow)).\n\\label{chan-v}\n\\end{equation}\nUnder $z:Z$ the projection $z^\\downarrow:s$ can be substituted for the\ndata parameter $A$ in every preceding channel type.  The resulting\n$\\operatorname{Fun}(z^\\downarrow)$ is sorted at $g$, and its wrapper\nalong $\\kappa:g\\longrightarrow J$ is sorted at $J$.  The triple\n$(I,J,k)$ therefore sorts $V$ at $k$.  This construction binds the former\nparameter $A$ through $z^\\downarrow$: $V$ has no free $A$ or $m$.\n\nFor $v:V$, $A:s$, $m:M(A)$ and $y:L(A)$, define the formula\n\\begin{equation}\n v\\diamond_{A,m}y\n   =\\operatorname{use}_{\\kappa}\\left(\n        v\\,A^\\uparrow;\n        f.\\operatorname{read}_n(f\\,\\operatorname{Bundle}(m,y))\\right).\n\\label{chan-diamond}\n\\end{equation}\nThe applications displayed here have label $(\\mathsf d,\\mathsf d)$.\nFor clarity, their typing chain is\n\\[\n\\begin{aligned}\n v\\,A^\\uparrow\n   &:W_{\\kappa}(\\operatorname{Fun}((A^\\uparrow)^\\downarrow))\n       =_\\beta W_{\\kappa}(\\operatorname{Fun}(A)),\\\\\n f&:\\operatorname{Fun}(A),\\qquad\n \\operatorname{Bundle}(m,y):\\operatorname{Single}(A),\\\\\n f\\,\\operatorname{Bundle}(m,y)&:U_n.\n\\end{aligned}\n\\]\nThe conversion in the first line is allowed because all paths, tails,\nand role-dependent syntax choices were fixed before substitution.  Since\n$\\kappa$ is even, its $\\operatorname{use}$ operation accepts exactly the\nformula callback in \\eqref{chan-diamond}.  Thus $\\diamond$ is well typed\nwithout a proof assumption about $m$.\n\nThe final data-sort ledger is\n\\[\n\\begin{array}{c|c|l}\n\\text{data type}&\\text{sorted at}&\\text{formation support}\\\\ \\hline\nM(A)&p&W_{\\alpha}(\\operatorname{BP}(A))\\\\\nF(A)&k&W_{\\beta}(M(A))\\\\\n\\operatorname{Single}(A)&p&W_{\\alpha}(F(A))\\\\\nU_n&n&\\text{the retained negative's fixed tail}\\\\\n\\operatorname{Fun}(A)&g&(p,n,g)\\\\\nZ&I&\\zeta:\\operatorname{Ax}(s)\\longrightarrow I\\text{ positive}\\\\\nW_{\\kappa}(\\operatorname{Fun}(z^\\downarrow))&J&\\kappa:g\\longrightarrow J\\text{ even}\\\\\nV&k&(I,J,k)\n\\end{array}\n\\]\nIn particular, neither the use of $M(A)$ as an open parameter nor that of\n$V$ as an input type requires $p$ or $k$ to be direct.\n\n\\subsection{The second channel and permitted quantifiers}\n\nApply the first-channel construction to $V$ itself, which is sorted at\n$k$, using the same three fixed odd paths:\n\\begin{equation}\n X_c=W_{\\alpha}(V),\\qquad\n X=W_{\\lambda}(V),\\qquad\n \\mathsf P_X=W_{\\eta}(X_c).\n\\label{chan-x-types}\n\\end{equation}\nThese types are sorted at $p,j,h$, respectively.  For $x:X$ and $c:X_c$\ndefine\n\\[\n\\begin{aligned}\n \\operatorname{Raw}_X(x)&=\\operatorname{mk}_{X_c}\n                          (v.\\langle x,v\\rangle_X),\\\\\n \\operatorname{Log}_X(c)&=\\operatorname{mk}_{X}\n                          (v.\\langle c,v\\rangle_{X_c}),\\\\\n \\operatorname{Round}_X(x)&=\\operatorname{Log}_X(\\operatorname{Raw}_X(x)),\\\\\n i@x&=\\langle i,\\operatorname{Raw}_X(x)\\rangle_{\\mathsf P_X}\n                         \\qquad(i:\\mathsf P_X),\\\\\n \\operatorname{pred}_X(x.\\Phi)\n   &=\\operatorname{mk}_{\\mathsf P_X}\n                          (c.\\Phi[x:=\\operatorname{Log}_X(c)]).\n\\end{aligned}\n\\]\nThe callbacks defining $\\operatorname{Raw}_X$ and\n$\\operatorname{Log}_X$ bind $v:V$.  Write\n\\[\n [xv]=\\langle x,v\\rangle_X\\qquad(x:X,\\ v:V).\n\\]\nThe proofs of Lemma~\\ref{chan-rounding}, with these types substituted,\ngive\n\\begin{equation}\n\\begin{aligned}\n {}[\\operatorname{Round}_X(x)v]&\\leftrightarrow[xv],\\\\\n \\operatorname{pred}_X(x.\\Phi)@w\n       &\\leftrightarrow\\Phi[x:=\\operatorname{Round}_X(w)].\n\\end{aligned}\n\\label{chan-x-evaluation}\n\\end{equation}\n\nAll logical quantifiers in the subsequent relational construction have\none of the following fixed domain annotations:\n\\[\n\\begin{array}{c|c|c}\n\\text{domain}&\\text{chosen sorting vertex}&\\text{quantifier constructor}\\\\ \\hline\nL(A)&j&\\text{the fixed direct-domain constructor at }j\\\\\nX&j&\\text{the fixed direct-domain constructor at }j\\\\\n\\mathsf P_L(A)&h&\\text{the fixed direct-domain constructor at }h\\\\\n\\mathsf P_X&h&\\text{the fixed direct-domain constructor at }h.\n\\end{array}\n\\]\nHere $j$ and $h$ are direct by Lemma~\\ref{chan-paths}, so each constructor\nhas its required profile triple and fixed proof paths.  Existentials use\nthe same domain annotation through their derived definition.  Other\ngeneric data, including $A:s$, $m:M(A)$, and $v:V$, occur as open\nparameters or as raw data binders whose supporting products have been\ndisplayed above.  Schematic assertions about these parameters denote\nproof builders in valid contexts, not further logical quantifiers.\nEvery data definition in this section is independent of proof variables;\nthe proof input $d_m$ is used only in the proof of\nLemma~\\ref{chan-access}.\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/erasure.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/erasure.tex", "bytes": 6316, "sha256": "301e9d6ced2d562d120dd73db223087e360c9bdd5971004a63d0b024ab9f9a15", "content": "\\section{Exclusion and completion of the proof}\\label{sec:conclusion}\n\n\\subsection{The obstruction to a normal proof}\n\nHere is the precise property of the ambient context used by the encoding.\nLet $\\mathcal F$ be its finite family of terminals.  Each $F\\in\\mathcal F$\nis a beta-normal, sorted data expression containing only\n$(\\mathsf d,\\mathsf d)$ labels.  A \\emph{constant positive telescope} is\nan expression\n\\[\n \\Pi^{\\mathsf d,\\mathsf p}x_1:D_1.\\,\n \\cdots\\Pi^{\\mathsf d,\\mathsf p}x_r:D_r.\\,F,\n \\qquad F\\in\\mathcal F,\n\\]\nwhere $r\\geq0$, the domains are normal data expressions, and each displayed\nbinder is absent from the subsequent suffix.  All the telescopes and suffixes\nused below have specified sort typings.  Let $\\mathcal T$ consist of the\nconverter domains, their suffixes, and all the terminals.\n\nThe ambient context is $\\Delta=\\Delta_{\\mathsf d},\\Delta_{\\mathsf p}$.\nEvery variable in $\\Delta_{\\mathsf d}$ has data mode.  Every declaration\nin $\\Delta_{\\mathsf p}$ is a proof variable of the form\n\\[\n c:\\Pi^{\\mathsf p,\\mathsf p}u:T_c.F_c,\n \\qquad T_c\\in\\mathcal T,\\quad F_c\\in\\mathcal F.\n\\]\nThe domains $T_c$ are the constant positive telescopes just described.\nAll these types are normal.  Their typings, and the suffix typings, persist\nin legal extensions of the context.\n\n\\begin{lemma}[No normal erasure at a terminal]\n\\label{lem:enc-no-normal-proof}\nIn the ambient context above, or in any legal extension by data declarations,\nthere is no proof-mode term $P$ with beta-normal erasure and a judgment\n$P:T$ for any $T\\in\\mathcal T$.\n\\end{lemma}\n\n\\begin{proof}\nChoose a counterexample of least syntax size, allowing all such data\nextensions and all targets in $\\mathcal T$.  A proof-mode term is neither\na sort nor a product.\n\nSuppose first that $P$ is a lambda.  Its body and result have proof mode.\nIts generated type is therefore a product with result label $\\mathsf p$.\nThis type cannot convert to a terminal: a normal terminal is either not a\nproduct, or has outer product labels $(\\mathsf d,\\mathsf d)$.\nConsequently $T$ has a nonempty positive prefix.  Product compatibility\nforces the lambda's labels to be $(\\mathsf d,\\mathsf p)$, with annotation\nconvertible to the first telescope domain.  Its binder is a data variable.\nGeneration gives a body typing whose expected type converts to the\nremaining suffix.  The suffix's recorded sort typing, transported by\ncontext conversion to the lambda annotation and weakened as necessary,\npermits conversion of the body to that suffix.  The body has normal\nerasure and is strictly smaller than $P$, in an allowed extension by one\nmore data declaration.  This contradicts minimality.\n\nOtherwise $P$ is a variable or an application spine.  Every function position\nof a proof-mode spine has proof mode.  Its head cannot be a sort or product,\nand a lambda head with a nonempty spine would create a redex in its erasure.\nThus its head is a proof variable, hence one of the converters $c$.\n\nIf the spine is empty, variable generation says that $T$ converts to\n$\\Pi^{\\mathsf p,\\mathsf p}u:T_c.F_c$.  Both are normal.  The latter\nhas outer labels $(\\mathsf p,\\mathsf p)$, whereas $T$ either has a\n$(\\mathsf d,\\mathsf p)$ prefix or is an all-data terminal.  This is\nimpossible by confluence and product compatibility.\n\nIf the spine is nonempty, let $Q$ be its first argument.  Generation at the\nfirst application and at $c$, followed by product compatibility, forces\nthat application to have labels $(\\mathsf p,\\mathsf p)$ and its domain\nto convert to $T_c$.  Thus $Q$ is a proof-mode term.  The recorded sort\ntyping of $T_c$, weakened to the current context, converts its generated\nargument typing to $Q:T_c$.  Its erasure is normal because it is a subterm\nof the normal erasure of $P$, and its syntax size is strictly smaller.\nThis is another counterexample to minimality.\n\\end{proof}\n\nThe lemma concerns proof mode, rather than every inhabitant of a terminal.\nFor example, the ambient data declaration $e_b:F_b$ is harmless: a data\nvariable cannot be the head of a proof-mode spine.  The reduction-lifting\nlemma ensures that a constructed proof cannot change into such a data term\non the way to an erased normal form.\n\n\\begin{proposition}[Exclusion by system-wide weak normalization]\n\\label{prop:enc-exclusion}\nAssume that every legal expression of the given pure type system is weakly\nbeta-normalizing.  For every primary component containing an odd closed walk and an\nall-component profile triple, condition~\\eqref{obs:forbidden} holds.\n\\end{proposition}\n\n\\begin{proof}\nIf the configuration prohibited in~\\eqref{obs:forbidden} occurred,\nSections~\\ref{sec:enc-wrappers}--\\ref{rel-diagonal}, culminating in\nProposition~\\ref{rel-contradiction}, would give a\nfinite legal labelled context $\\Delta$ of the form above and a proof-mode\nterm\n\\[\n                 \\Delta\\vdash P:F_b.\n\\]\nEvery other temporary assumption in the construction has been discharged\nor instantiated.  Erasure gives an ordinary typing derivation\n$|\\Delta|\\vdash |P|:|F_b|$ using only the original specification.\nThe system-wide hypothesis therefore applies to this particular open\nlegal term $|P|$.  Choose a finite reduction to a full beta-normal form.\nLemma~\\ref{lem:enc-labelled-metatheory} lifts that reduction to a labelled\nreduct $P'$ with the same exact type $F_b$, the same proof mode, and normal\nerasure.  Lemma~\\ref{lem:enc-no-normal-proof} rules out $P'$.\n\\end{proof}\n\n\n\\begin{proof}[Proof of Theorem~\\ref{thm:main}]\nBy Lemma~\\ref{pts:finite-restriction}, it suffices to treat a finite\nspecification. Process the finitely many primary components in dependency\norder. For a component without an odd closed walk use\nProposition~\\ref{prop:signed-component}. For one with no profile triple\nentirely inside it use Proposition~\\ref{prop:one-child-component}.\nEvery remaining component satisfies the hypotheses of\nProposition~\\ref{prop:enc-exclusion}; hence\nProposition~\\ref{obs:interface-theorem} supplies its candidate interface.\nProposition~\\ref{prop:advance-component} therefore advances the\nprocessed invariant at every stage. Once all components are processed,\nProposition~\\ref{prop:interfaces-imply-sn} gives strong normalization\nof every legal expression. This includes expressions in arbitrary valid\nopen contexts and every reduction position in their annotations.\n\\end{proof}\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/introduction.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/introduction.tex", "bytes": 13298, "sha256": "702b7d25ed32919dc78425670e6321d4fd2c322f82ea31a4c8078bc5c87fcfaa", "content": "\\section{Introduction}\\label{sec:introduction}\n\nA pure type system specifies which sorts classify other sorts and which\nsorts may form dependent function types.  A term is \\emph{weakly\n$\\beta$-normalizing} if some sequence of $\\beta$-reductions reaches a normal\nform; it is \\emph{strongly $\\beta$-normalizing} if every such sequence is\nfinite.  The distinction is between existence of a terminating computation\nand termination under every choice of reductions.\n\nWe use the full annotated syntax: reduction is allowed in the domain of\nan abstraction and in both components of a dependent product. Contexts may\nbe open. A legal expression is either side of a derivable typing judgment;\ndeclaration types are included by context validity. The formal rules and\nconventions are given below before the main theorem.\n\n\\subsection{Pure type systems and normalization}\\label{pts:setup}\n\nWe first fix the syntax and the scope of the normalization properties.\nAll contexts in the typing rules are finite, and all reductions include\nreductions inside annotations.\n\n\\begin{definition}[Specification and expressions]\\label{pts:specification}\nA \\emph{pure type specification} is a triple\n$\\mathcal P=(\\mathcal S,\\mathcal A,\\mathcal R)$, where $\\mathcal S$ is a\nset of \\emph{sorts}, $\\mathcal A\\subseteq\\mathcal S^2$ is a relation of\n\\emph{axioms}, and $\\mathcal R\\subseteq\\mathcal S^3$ is a relation of\n\\emph{product rules}. Neither relation is assumed functional.\nUsing a countably infinite set of variables disjoint from $\\mathcal S$, form the\nraw expressions\n\\[\n M,N,A,B ::= x\\mid s\\mid MN\\mid\\lambda x:A.M\\mid\\Pi x:A.B,\n \\qquad s\\in\\mathcal S.\n\\]\nExpressions are identified up to renaming bound variables. In each binder\nthe variable binds in the body or codomain, but not in its annotation or\ndomain. Application associates to the left. Substitution is capture\navoiding; $M[x:=N]$ denotes substitution for one free variable.\n\nA raw context is a finite sequence\n$\\Gamma=(x_1:A_1,\\ldots,x_n:A_n)$ with distinct declared variables.\nIts domain is $\\operatorname{dom}(\\Gamma)=\\{x_1,\\ldots,x_n\\}$.\nWhenever a rule appends $x:A$, its variable $x$ is fresh for the preceding\ncontext and for $A$.\n\\end{definition}\n\n\\begin{definition}[Full beta reduction]\\label{pts:beta}\nThe relation $\\to_\\beta$ is the compatible closure of\n\\[\n             (\\lambda x:A.M)N\\to_\\beta M[x:=N].\n\\]\nCompatibility permits a step in either child of an application, product,\nor lambda. In particular, both\n$\\lambda x:A.M\\to_\\beta\\lambda x:A'.M$ when $A\\to_\\beta A'$ and\n$\\Pi x:A.B\\to_\\beta\\Pi x:A'.B$ are permitted.\nWrite $\\to_\\beta^*$ for its reflexive-transitive closure and\n$=_\\beta$ for the equivalence relation it generates. A normal expression\nhas no beta redex at any position. An expression is \\emph{weakly\nnormalizing} if it reduces to a normal expression, and \\emph{strongly\nnormalizing} if it starts no infinite reduction sequence.\n\\end{definition}\n\nTyping is the least relation closed under these seven rules, with\n$s,s_1,s_2,s_3$ literal sorts.\n\\begin{gather*}\n \\frac{(s_1,s_2)\\in\\mathcal A}{\\varnothing\\vdash s_1:s_2}\n \\;\\textsc{Axiom}\n \\qquad\n \\frac{\\Gamma\\vdash A:s}{\\Gamma,x:A\\vdash x:A}\n \\;\\textsc{Variable}\n \\\\\n \\frac{\\Gamma\\vdash M:B\\qquad\\Gamma\\vdash A:s}\n      {\\Gamma,x:A\\vdash M:B}\n \\;\\textsc{Weakening}\n \\\\\n \\frac{\\Gamma\\vdash A:s_1\\qquad\n       \\Gamma,x:A\\vdash B:s_2\\qquad(s_1,s_2,s_3)\\in\\mathcal R}\n      {\\Gamma\\vdash\\Pi x:A.B:s_3}\n \\;\\textsc{Product}\n \\\\\n \\frac{\\Gamma,x:A\\vdash M:B\\qquad\\Gamma\\vdash\\Pi x:A.B:s}\n      {\\Gamma\\vdash\\lambda x:A.M:\\Pi x:A.B}\n \\;\\textsc{Abstraction}\n \\\\\n \\frac{\\Gamma\\vdash M:\\Pi x:A.B\\qquad\\Gamma\\vdash N:A}\n      {\\Gamma\\vdash MN:B[x:=N]}\n \\;\\textsc{Application}\n \\\\\n \\frac{\\Gamma\\vdash M:A\\qquad\\Gamma\\vdash B:s\\qquad A=_\\beta B}\n      {\\Gamma\\vdash M:B}\n \\;\\textsc{Conversion}.\n\\end{gather*}\nThe relation is understood relative to the fixed specification.\nWhen two specifications are compared we indicate the system on the\njudgment. A derivation is always finite, including a finite conversion\nchain witnessing each use of $=_\\beta$.\n\n\\begin{definition}[Contexts, legality, and system normalization]\n\\label{pts:normalization}\nA context $(x_1:A_1,\\ldots,x_n:A_n)$ is \\emph{valid} if each $A_i$ is\ntyped at some literal sort in the preceding prefix. An expression is\n\\emph{sorted} in $\\Gamma$ if $\\Gamma\\vdash A:s$ for some $s\\in\\mathcal S$.\nIt is \\emph{legal} in $\\Gamma$ if it is the subject or the expected type\nof a derivable judgment in $\\Gamma$.\n\nThe system is weakly normalizing, respectively strongly normalizing, if\nevery legal expression in every context is weakly normalizing,\nrespectively strongly normalizing. These are system-wide properties;\nneither statement replaces its universal quantifier by a hypothesis\nabout a single expression. The reduction relation acts on the expression\nitself, including its annotations; it does not unfold declarations in\nthe external context.\n\\end{definition}\n\n\n\n\\begin{theorem}[The $\\beta$-Barendregt--Geuvers--Klop conjecture]\n\\label{thm:main}\nLet $\\mathcal P=(\\mathcal S,\\mathcal A,\\mathcal R)$ be any pure type system.\nIf every legal expression of $\\mathcal P$, in every valid context, is weakly\n$\\beta$-normalizing, then every such expression is strongly\n$\\beta$-normalizing. No functionality assumption is imposed on\n$\\mathcal A$ or $\\mathcal R$.\n\\end{theorem}\n\nBoth quantifiers in Theorem~\\ref{thm:main} range over the whole system.\nThe theorem does not assert that an individual term with a normal form is\nstrongly normalizing. In particular, the proof may use weak normalization\nof expressions other than the one whose strong normalization is being\nestablished. The result concerns $\\beta$-reduction; it makes no assertion\nabout adding $\\eta$-reduction.\n\n\\paragraph{History and significance.}\nGeuvers formulates the system-wide conjecture, for both $\\beta$ and\n$\\beta\\eta$ reduction, in his thesis \\cite[Conjecture~8.1.2]{Geuvers1993}.\nTheorem~\\ref{thm:main} establishes its $\\beta$ instance for arbitrary\nspecifications. It allows a proof that every legal expression has a normal\nform to serve also as a proof of termination under every choice of beta\nreductions. This includes computations inside types and annotations,\nwhich may continue after the computational body has reached normal form.\nThe hypothesis remains a property of the whole system: the argument can\nuse normal forms of open expressions other than the expression under study.\n\nOne line of earlier work reduces strong normalization to weak\nnormalization of translated terms. The translation must preserve typing,\nso the products available in the source specification are decisive.\nS{\\o}rensen's continuation-passing construction proves the implication for\ngeneralized nondependent, clean, negatable systems\n\\cite[Theorem~3.5.20]{Sorensen1997}; the uniform nondependent result of\nBarthe, Hatcliff and S{\\o}rensen develops this approach\n\\cite{BartheHatcliffSorensen2001}. Mull's thesis weakens the cleanliness\nand negatability restrictions within the nondependent tiered setting\n\\cite[Theorem~2]{MullThesis2023}. Here the sorts form a finite axiom chain\nand each product inherits its codomain's sort. In particular, the theorem\nallows a weakly clean alternative at each non-top tier that does not require\nnegatability, alongside an alternative retaining negatability and a\nfurther cleanliness condition. These results explain both the force of\ntype-preserving translations and the importance of the product rules they\nrequire.\n\nStructural transformations offer a complementary way to reduce the\nproblem. Roux and van Doorn prove weak-normalization preservation for\ndisjoint sums and a specified family of added product rules, using\nlabelled syntax and dependency erasure\n\\cite[Sections~3--4]{RouxVanDoorn2014}. Mull proves that the\nBarendregt--Geuvers--Klop implication transfers from the irrelevance\nreduction of a tiered system to that system\n\\cite[Theorem~47]{Mull2023}. This is a conditional reduction of the\nconjecture within the tiered class. More recently, Roux proposes an\ninternal proof translation with reported constructions for the simply\ntyped lambda calculus, System~F, and a trivial recursive type\n\\cite[slides~16--17]{Roux2025}. The general specification considered here\nneed not have a hierarchy of sorts, functional axiom or product relations,\nor the products required by any of these translations.\n\nAnnotations impose a separate constraint on comparisons between systems.\nBarthe and Coquand exhibit nonnormalizing pure type systems in which\nerasing lambda-domain annotations identifies terms of the same type and\ncontext that are not beta-convertible\n\\cite[Theorem~17]{BartheCoquand2006}. The auxiliary erasure used below\nretains all annotations. It removes only proof/data mode labels, and its\nreduction-lifting property is proved for each ordinary typed beta step.\n\n\\paragraph{Proof strategy.}\nA finite derivation of a counterexample uses only a finite part of the\nspecification, so the proof first reduces to finite sort and rule sets.\nUnder weak normalization and confluence, every legal expression then has\na unique normal form. The \\emph{sort profile} of a normal type records\nall sorts at which it can be typed. Retaining this set accommodates\nnonfunctional specifications; tracking its growth under substitution\nreplaces an appeal to uniqueness of types.\n\nThe candidate argument follows the Tait--Girard reducibility tradition\n\\cite[Chapters~6 and~14]{Girard1989}. Its candidates are sets of strongly\nnormalizing terms closed under the head expansions needed here; their\nlattice, product tests and substitution laws are proved explicitly,\nwithout requiring reduction closure. Profiles form a finite signed graph:\nin a dependent product, the domain reverses inclusion between candidates\nand the codomain preserves it. Processing its strongly connected\ncomponents in dependency order establishes that a strongly normalizing\nfunction applied to any finite, correctly typed stack of strongly\nnormalizing arguments remains strongly normalizing. This excludes a\nsmallest nonnormalizing application and yields the theorem.\n\nComponents whose signs are consistent admit an ordered fixed point by\nTarski's theorem \\cite{Tarski1955}. A second construction handles products\nwith at most one child in the current component. The remaining components\nrequire observation tables, indexed by stacks of normal arguments and\nauxiliary values. Negative domain occurrences make the equations for\nthese spaces circular. The construction resolves that circularity using\nwell-founded recursion for some profiles and alternating finite stages\nfor the others. Its key bound is uniform over all observations of a fixed\ntyped expression, although the possible arguments and stack lengths are\nunbounded. Semantic substitution and reduction transport then give the\ncandidate equality required at a dependent application.\n\nThis last construction is conditional on a graph exclusion. The forbidden\nconfiguration records the profile pattern forced if substitution exposes\nnew product nodes counted by the recursion's measure on normal types.\nWeak normalization rules it out through a second, typed argument. The\nproduct rules in such a configuration support a small\nformula calculus and a relational realization of Hurkens's\nwell-foundedness paradox \\cite{Hurkens1995}, using the presentation of\nGeuvers \\cite[Section~2]{Geuvers2007}.\n\nRealizing that argument requires more than reproducing its final diagonal\nsteps. The derived formula calculus can quantify only over data types\nsupported by particular product rules in the profile graph. Moreover, the\ngeneral cancellation law for wrappers compares formula observations by\nlogical equivalence; this gives\nno permission to replace a data expression inside a function.\nTwo data channels provide the predicates and observations needed by the\ndiagonal. Their access construction retains an open parameter exactly\nwhile rounding a second data argument. Explicit validity and\nextensionality conditions then state which observations and predicates\nrespect the resulting comparisons. The guarded diagonal uses precisely\nthese conditions, and every logical quantifier is checked against the\navailable product rules.\n\nThe resulting labelled term proves a designated terminal formula in a\ncontext where a direct syntactic descent excludes every proof-mode term\nwith normal erasure. Data inhabitants of the same terminal do not affect\nthis statement. Erasure leaves all type annotations intact, and every\nordinary reduction of the erased typed proof lifts to a labelled\nreduction. Weak normalization would therefore give the excluded normal\nerasure. This contradiction supplies the graph exclusion and completes\nthe candidate argument.\n\n\\paragraph{Organization.}\nSection~\\ref{pts:metatheory} establishes the required PTS metatheory.\nSections~\\ref{sec:profiles} and~\\ref{sec:simple-components} develop the\ncandidate interface and its two simpler realizations.\nSection~\\ref{obs:section} constructs the remaining interpretation,\nconditional on the graph exclusion. Sections~\\ref{sec:enc-wrappers},\n\\ref{chan-section} and~\\ref{rel-section} build the logical and relational\nrealization. Section~\\ref{rel-diagonal} gives the guarded diagonal\nargument. Section~\\ref{sec:conclusion} proves the exclusion and completes\nthe theorem.\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/metatheory.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/metatheory.tex", "bytes": 24651, "sha256": "0874263e6b3d437fb79fcce6a41f73c34b59315cc6fa8cd9fa4510ffce2a485e", "content": "\n\\section{Structural properties}\\label{pts:metatheory}\n\nThe following facts let us use finite open contexts and chosen typing\nderivations without assuming uniqueness of sorts or product rules.\nThese are standard properties of pure type systems; compare\n\\cite[Chapter~2]{Poll1994}. We give the proofs needed here, especially the\nsteps concerning annotations and nonfunctional specifications.\n\n\\begin{lemma}[Validity and scope]\\label{pts:validity}\nIf $\\Gamma\\vdash M:A$, then $\\Gamma$ is valid, and\n$\\mathrm{FV}(M)\\cup\\mathrm{FV}(A)\\subseteq\\operatorname{dom}(\\Gamma)$.\nFor each declaration of $\\Gamma$, its sorting judgment in the preceding\nprefix occurs as a subderivation of the given derivation.\n\\end{lemma}\n\n\\begin{proof}\nInduct on the derivation. An axiom has empty context. The variable and\nweakening rules supply the sorting judgment for their last declaration;\nthe induction hypothesis supplies the earlier ones. Every other rule\nhas a premise with unchanged context, so its induction hypothesis\nsupplies validity and the required subderivations. Scope follows in the\nsame induction. For application, use\n\\[\n \\mathrm{FV}(B[x:=N])\\subseteq\n       (\\mathrm{FV}(B)\\setminus\\{x\\})\\cup\\mathrm{FV}(N).\n\\]\nThe binder cases remove their fresh bound variable from the free\nvariables of the body.\n\\end{proof}\n\n\\begin{lemma}[Thinning by insertion]\\label{pts:thinning}\nSuppose $\\Gamma\\vdash M:A$, and suppose that $\\Delta$ is a valid context\ncontaining the declarations of $\\Gamma$ unchanged and in their original\norder. Then $\\Delta\\vdash M:A$.\n\\end{lemma}\n\n\\begin{proof}\nInduct on the size of the given derivation. For an axiom, weaken through\nthe declarations of $\\Delta$. For a variable or weakening rule, split\n$\\Delta$ just after the last declaration of the source context.\nTransport the rule's premises to the part before that declaration by\ninduction, reapply the rule, and then weaken through the remaining\nsuffix. The necessary sorting judgments for that suffix are supplied\nby validity of $\\Delta$.\n\nAt a binder, first rename its bound variable fresh for $\\Delta$.\nIts domain sorting is a premise of product formation or a smaller\nsubderivation supplied by Lemma~\\ref{pts:validity}. Induction transports\nthis sorting to $\\Delta$, making the extended target context valid.\nInduction therefore also transports the body or codomain premise to\nthat extension. Reapply the original rule. Application and conversion\ntransport their unchanged-context premises and reapply their rule.\n\\end{proof}\n\n\\begin{lemma}[Typed substitution]\\label{pts:substitution}\nLet $\\Gamma=(x_1:A_1,\\ldots,x_n:A_n)$ and let $\\Delta$ be valid.\nSuppose a simultaneous substitution $\\sigma$ satisfies\n\\[\n \\Delta\\vdash\\sigma(x_i):A_i[\\sigma|_{\\{x_1,\\ldots,x_{i-1}\\}}]\n \\qquad(1\\leq i\\leq n).\n\\]\nThen $\\Gamma\\vdash M:A$ implies $\\Delta\\vdash M\\sigma:A\\sigma$.\nIn particular, if\n\\[\n \\Gamma,x:D,\\Theta\\vdash M:A,\n \\qquad \\Gamma\\vdash N:D,\n\\]\nthen\n\\[\n \\Gamma,\\Theta[x:=N]\\vdash M[x:=N]:A[x:=N].\n\\]\n\\end{lemma}\n\n\\begin{proof}\nRaw substitution preserves beta conversion. Indeed, a substituted root\ncontraction is a contraction after renaming bound variables fresh;\nthe required equality is the composition law for capture-avoiding\nsubstitutions. Compatibility extends this to every reduction position,\nand hence to conversion chains.\n\nFor the simultaneous assertion, induct on the source derivation.\nThin axioms to $\\Delta$. At a variable use its assigned judgment, and\nat weakening discard the assignment for the last declaration. Under a\nbinder $x:D$, choose $y$ fresh for $\\Delta$ and the images of $\\sigma$.\nTransport the domain sorting by induction, extend the target context\nto $\\Delta,y:D\\sigma$, and extend the substitution by $x\\mapsto y$.\nThe earlier assignments thin to this context; the new assignment is\nthe variable rule. Apply induction to the binder premise and reapply\nproduct formation or abstraction with the original axiom or rule\nchoices. Application uses substitution composition. Conversion uses\nthe transported sorting of its target and preservation of raw\nconversion.\n\nFor single substitution with a dependent suffix, construct the target\ncontext one declaration at a time. Start with the identity assignments\non $\\Gamma$ and the image $x\\mapsto N$. Transport the first suffix\ndeclaration's sort judgment by the simultaneous assertion, append that\ndeclaration, thin the old assignments, and assign its new variable to\nitself. Repeat through $\\Theta$, then transport the desired judgment.\nThis also proves validity of the substituted target context.\n\\end{proof}\n\n\\begin{lemma}[Conversion of a declaration]\\label{pts:context-conversion}\nSuppose $\\Gamma\\vdash D:s$, $\\Gamma\\vdash D':s'$, and $D=_\\beta D'$.\nReplacing $x:D$ by $x:D'$ in any valid context\n$\\Gamma,x:D,\\Theta$ preserves validity and every derivable judgment,\nwith the subject, expected type, and later declarations unchanged.\nThe converse replacement has the same property.\n\\end{lemma}\n\n\\begin{proof}\nIn $\\Gamma,x:D'$ the variable rule gives $x:D'$. Thin the old sorting\nof $D$ to this context and convert to obtain $x:D$. Thus the identity\non the old variables is a typed substitution from $\\Gamma,x:D$ to\n$\\Gamma,x:D'$. As in the suffix construction in\nLemma~\\ref{pts:substitution}, transport each later sorting judgment,\nappend the same declaration, and extend the identity substitution.\nFinally transport the desired judgment. Interchanging $D,D'$ proves\nthe reverse assertion. Nothing requires $s=s'$.\n\\end{proof}\n\n\\Needspace{4\\baselineskip}\n\\begin{lemma}[Generation]\\label{pts:generation}\nFor derivable judgments in an arbitrary pure type system:\n\\begin{enumerate}\n\\item If $\\Gamma\\vdash x:T$, then $x:D$ occurs in $\\Gamma$ and\n      $T=_\\beta D$.\n\\item If $\\Gamma\\vdash s:T$, then $(s,u)\\in\\mathcal A$ and\n      $T=_\\beta u$ for some sort $u$.\n\\item If $\\Gamma\\vdash\\Pi x:D.E:T$, then for some\n      $(a,b,c)\\in\\mathcal R$,\n      \\[\n       \\Gamma\\vdash D:a,\\qquad\n       \\Gamma,x:D\\vdash E:b,\\qquad T=_\\beta c.\n      \\]\n\\item If $\\Gamma\\vdash\\lambda x:D.M:T$, then for some $E,c$,\n      \\[\n       \\Gamma,x:D\\vdash M:E,\\qquad\n       \\Gamma\\vdash\\Pi x:D.E:c,\\qquad\n       T=_\\beta\\Pi x:D.E.\n      \\]\n\\item If $\\Gamma\\vdash MN:T$, then for some $D,E,x$,\n      \\[\n       \\Gamma\\vdash M:\\Pi x:D.E,\\qquad\n       \\Gamma\\vdash N:D,\\qquad T=_\\beta E[x:=N].\n      \\]\n\\end{enumerate}\nEvery immediate syntactic child of a typable expression is typable\nin the corresponding context, with a binder declaration added for a\nbody or codomain.\n\\end{lemma}\n\n\\begin{proof}\nInduct on the derivation, stripping any final conversion and weakening\nsteps. Conversion composes the asserted conversion of expected types.\nFor weakening, thin all extracted premises back to the original\ncontext, including under a fresh binder. When neither rule is last,\nthe outer syntax identifies its introducing rule and supplies the\ndisplayed premises. The child assertion follows from these premises;\nfor a lambda's annotation use validity of the body context.\n\\end{proof}\n\n\\begin{lemma}[Correctness of types]\\label{pts:correctness}\nIf $\\Gamma\\vdash M:A$, then either $A$ is a literal sort or\n$\\Gamma\\vdash A:s$ for some sort $s$. Moreover, every type assigned\nto an application is sorted, including an assigned type that is itself\na literal sort. Consequently every nonliteral legal expression is\ntypable.\n\\end{lemma}\n\n\\begin{proof}\nInduct on the derivation. The axiom and product conclusions have literal\nsorts as expected types. Variable, weakening, abstraction, and conversion\nuse their sorting premises, thinning where necessary. In the application\ncase, induction on the function premise sorts its product type, since\nthat type is not a literal sort. Product generation then gives a sorting\nof the codomain under the binder. Substitution sorts the instantiated\nresult type. This also proves the stronger application assertion at\na direct application rule; final weakening preserves it by thinning,\nand final conversion explicitly sorts the new expected type. Every\nlegal expression that occurs as an expected type is therefore either\ntypable or a literal sort.\n\\end{proof}\n\n\\subsection{Conversion and preservation of typing}\n\\label{pts:reduction-metatheory}\n\nThe next lemma concerns raw expressions. Thus it applies before any\nnormalization assumption, regardless of which products a specification\ncan form.\n\n\\begin{lemma}[Confluence and product compatibility]\\label{pts:confluence}\nFull beta reduction on raw expressions is confluent. Therefore distinct\nsorts are not convertible, no product is convertible to a sort, and\n\\[\n \\Pi x:D.E=_\\beta\\Pi x:D'.E'\n \\quad\\Longrightarrow\\quad\n D=_\\beta D'\\ \\hbox{ and }\\ E=_\\beta E',\n\\]\nafter consistently renaming the bound variables.\n\\end{lemma}\n\n\\begin{proof}\nDefine parallel contraction $\\Rightarrow$ by reflexive variable and\nsort clauses, homomorphic clauses for all expression constructors, and\nthe additional clause\n\\[\n \\frac{D\\Rightarrow D'\\qquad M\\Rightarrow M'\\qquad N\\Rightarrow N'}\n      {(\\lambda x:D.M)N\\Rightarrow M'[x:=N']}.\n\\]\nIn particular the homomorphic binder clauses reduce both children.\nInduction on a parallel derivation proves parallel substitution:\nif $M\\Rightarrow M'$ and $N\\Rightarrow N'$, then\n$M[x:=N]\\Rightarrow M'[x:=N']$. For its contracting case, rename\nthe contracted binder fresh and use composition of substitutions;\nthe other cases reapply the corresponding homomorphic clause.\n\nDefine $M^*$, its complete development, recursively by developing every\nchild, with\n\\[\n ((\\lambda x:D.P)Q)^*=P^*[x:=Q^*]\n\\]\nat an application whose original function is a lambda. At any other\napplication use $(PQ)^*=P^*Q^*$. We claim that\n$M\\Rightarrow N$ implies $N\\Rightarrow M^*$. Prove this by induction\non the parallel derivation. At an original root redex, either the\nfirst parallel step was homomorphic, in which case contract the root\nin the second step, or it contracted the root, in which case parallel\nsubstitution applies. At an original nonredex application, use the\nhomomorphic clause in the second step, even if its function has become\na lambda. The remaining cases follow by the corresponding\nhomomorphic clauses and induction. Thus any two parallel reducts of\n$M$ parallel-reduce to $M^*$, proving the diamond property.\n\nEvery ordinary step is parallel. Conversely, every parallel step is\na finite sequence of ordinary steps: first reduce the children and\nthen perform its root contraction, if any. Hence the reflexive-transitive\nclosures of the two relations coincide, and beta reduction is confluent.\nConvertible expressions consequently have a common reduct. Reducing\na product never removes its outer constructor, whereas a sort is\nirreducible. Comparing the common reducts gives all the asserted\ncompatibility and separation properties.\n\\end{proof}\n\n\\begin{lemma}[Subject reduction]\\label{pts:subject-reduction}\nIf $\\Gamma\\vdash M:A$ and $M\\to_\\beta^*M'$, then\n$\\Gamma\\vdash M':A$, with the exact original expected type.\n\\end{lemma}\n\n\\begin{proof}\nIt suffices to consider one step. Induct on the typing derivation.\nThere are no steps from sorts or variables. At a final weakening or\nconversion, apply induction to its subject premise and reapply the\nsame rule.\n\nAt product formation, a step in the domain preserves its original sort\nby induction. Lemma~\\ref{pts:context-conversion} transports the codomain\nsorting to the changed binder context, and the same product rule\nre-forms the result. A codomain step is handled by induction on its\ntyping premise.\n\nAt abstraction, a step in the body is immediate by induction. For an\nannotation step $D\\to_\\beta D'$, the supporting judgment\n$\\Gamma\\vdash\\Pi x:D.E:c$ is a strictly smaller premise derivation.\nApply induction to its corresponding domain step to get\n$\\Gamma\\vdash\\Pi x:D'.E:c$. Product generation sorts $D'$;\ncontext conversion then transports the body judgment to\n$\\Gamma,x:D'$. Abstraction gives the changed lambda its new product\ntype. Convert back to the old product type, which is sorted by the\noriginal supporting premise.\n\nAt application, a step in the function preserves its product type by\ninduction. A step $N\\to_\\beta N'$ in the argument gives the new\nresult type $E[x:=N']$; convert this to $E[x:=N]$, whose sorting was\nproved in Lemma~\\ref{pts:correctness}.\n\nIt remains to treat a root contraction\n$(\\lambda x:D_0.P)N\\to_\\beta P[x:=N]$. Suppose the application\npremises assign the function type $\\Pi x:D.E$ and the argument\ntype $D$. Generation of the function judgment gives some $E_0,c$\nsuch that\n\\[\n \\Gamma,x:D_0\\vdash P:E_0,\n \\qquad\\Gamma\\vdash\\Pi x:D_0.E_0:c,\n \\qquad\\Pi x:D_0.E_0=_\\beta\\Pi x:D.E.\n\\]\nProduct compatibility gives $D_0=_\\beta D$ and $E_0=_\\beta E$.\nGeneration of the supporting product sorts $D_0$, so convert\n$N:D$ to $N:D_0$. Substitution yields\n$\\Gamma\\vdash P[x:=N]:E_0[x:=N]$.\nConvert to the old application type $E[x:=N]$, which is sorted by\nLemma~\\ref{pts:correctness}. This completes every reduction position,\nincluding positions inside annotations.\n\\end{proof}\n\n\\subsection{Neutral expressions and normal forms}\n\\label{pts:neutrals}\n\nAn expression $xN_1\\cdots N_k$, with $k\\geq0$, is called\n\\emph{neutral}. Its arguments need not be normal. This restricted\nclass has unique types even when the specification does not.\n\n\\begin{lemma}[Uniqueness for neutral expressions]\\label{pts:neutral-uniqueness}\nIf $H$ is neutral and $\\Gamma\\vdash H:A$ and $\\Gamma\\vdash H:B$,\nthen $A=_\\beta B$. If $\\Gamma\\vdash H:s$ for a literal sort $s$,\nthen $s$ itself is sorted in $\\Gamma$. Every normal typable application\nis neutral.\n\\end{lemma}\n\n\\begin{proof}\nInduct on the length of the neutral spine. At a variable, generation\nidentifies both types with its declaration type up to conversion.\nFor $HN$, generation of its two typings gives function product types\n$\\Pi x:D.E$ and $\\Pi x:D'.E'$. Induction makes these convertible.\nProduct compatibility gives $E=_\\beta E'$, so substitution makes\ntheir instantiated codomains convertible. These codomains are\nconvertible to the original two result types by generation.\n\nIf a variable has type $s$, its declaration type $D$ is sorted by\nvalidity and thinning, and $D=_\\beta s$ by generation. Confluence\nand irreducibility of $s$ give $D\\to_\\beta^*s$. Subject reduction\ntherefore sorts $s$. For a nonempty neutral spine, the stronger\napplication assertion in Lemma~\\ref{pts:correctness} sorts its\nexpected type directly.\n\nFinally, the head of a normal application cannot be a lambda, since\nthat would give a redex. It cannot be a sort or product: generation\nmakes any type of either head convertible to a sort, whereas the\nfirst application requires a product type. Product/sort separation\nexcludes this. The only remaining head is a variable.\n\\end{proof}\n\n\\begin{remark}\\label{pts:nonfunctionality}\nGeneral uniqueness of types is false under our hypotheses. For example,\nif $(r,a)$ and $(r,b)$ are axioms with distinct $a,b$, then $r$ has\nthe two nonconvertible types $a,b$. Lemma~\\ref{pts:neutral-uniqueness}\ndoes not apply to the sort $r$. No later use of uniqueness concerns\nan arbitrary expression.\n\\end{remark}\n\n\\begin{lemma}[Normalizing judgments]\\label{pts:normalizing-judgments}\nAssume that every legal expression is weakly normalizing. Write\n$M^\\#$ for its normal form, uniquely determined up to alpha equivalence.\nIf $\\Gamma\\vdash M:A$, then\n\\[\n                \\Gamma\\vdash M^\\#:A\n                \\qquad\\text{and}\\qquad\n                \\Gamma\\vdash M^\\#:A^\\#.\n\\]\nThe second judgment is also available with $M$ in place of $M^\\#$.\nIf $\\Gamma\\vdash A:s$, then $\\Gamma\\vdash A^\\#:s$.\nA sorted normal expression is either a sort, a neutral expression,\nor a product.\n\\end{lemma}\n\n\\begin{proof}\nWeak normalization supplies normal forms; confluence gives uniqueness.\nSubject reduction proves the first judgment and the assertion about\nsorting. Correctness of types says that $A$ is sorted or is a literal\nsort. In the first case subject reduction sorts $A^\\#$, allowing\nconversion of the expected type. In the second case $A^\\#=A$, so\nthere is nothing to convert if that sort is unsorted. Finally a\nnormal application is neutral by Lemma~\\ref{pts:neutral-uniqueness},\nand a lambda cannot have a sort type by generation and product/sort\nseparation.\n\\end{proof}\n\n\\subsection{Finite specifications and a common context}\n\\label{pts:finite-and-context}\n\nFor the main implication it is enough to work with a finite\nspecification. We will then place all finite contexts inside one\ncountable context, while each judgment continues to use only a finite\nprefix.\n\n\\begin{lemma}[Finite restriction]\\label{pts:finite-restriction}\nLet $\\mathcal P=(\\mathcal S,\\mathcal A,\\mathcal R)$ be a pure type\nsystem.  An expression is called \\emph{legal} if it occurs as the subject\nor the expected type of a derivable judgment.  If every legal expression\nof $\\mathcal P$ is weakly normalizing for full $\\beta$-reduction, but some\nlegal expression is not strongly normalizing, then there are finite sets\n\\[\n  \\mathcal S_0\\subseteq\\mathcal S,\\qquad\n  \\mathcal A_0\\subseteq\\mathcal A\\cap\\mathcal S_0^2,\\qquad\n  \\mathcal R_0\\subseteq\\mathcal R\\cap\\mathcal S_0^3\n\\]\nsuch that the restricted system\n$\\mathcal P_0=(\\mathcal S_0,\\mathcal A_0,\\mathcal R_0)$ has the same two\nproperties.\n\\end{lemma}\n\n\\begin{proof}\nFirst recall how legality relates to typability.  If $E$ is a subject,\nit is typable by definition.  If $E$ is an expected type, correctness of\ntypes says that either $E$ is a literal sort or $\\Gamma\\vdash E:s$ for\nsome context $\\Gamma$ and sort $s$.  Thus every nonliteral legal\nexpression is typable.  Also, context validity says that each declaration\ntype is sorted in its preceding context, so declaration types introduce\nno further kind of untypable expression.  Literal sorts have no\n$\\beta$-redexes.\n\nChoose a legal expression $E$ that is not strongly normalizing.  It is\nnot a literal sort, so fix a finite derivation of a judgment\n$\\Gamma\\vdash E:B$.  For every use of conversion in this derivation,\nchoose a finite raw $\\beta$-conversion chain witnessing its side\ncondition.  Let $\\mathcal S_0$ contain all sorts occurring in the\nderivation and these chains.  Let $\\mathcal A_0$ and $\\mathcal R_0$\ncontain the axiom pairs and formation triples used in the derivation,\nenlarging $\\mathcal S_0$ to contain their entries if necessary.  These\nsets are finite, and the same derivation and conversion chains establish\n$\\Gamma\\vdash E:B$ in $\\mathcal P_0$.\n\nA full $\\beta$-step introduces no new sort symbol: substitution can only\ncopy symbols already present in the redex, and contextual reduction has\nthe same property, including reduction in annotations.  Consequently\nthe infinite reduction from $E$ witnessing failure of strong\nnormalization is also a reduction in the raw syntax over\n$\\mathcal S_0$.\n\nConversely, every judgment derivable in $\\mathcal P_0$ is derivable in\n$\\mathcal P$.  If $F$ is legal in $\\mathcal P_0$, the weak-normalization\nhypothesis therefore supplies a finite reduction $F\\to_\\beta^*N$ in\n$\\mathcal P$ with $N$ normal.  No term in this reduction contains a sort\noutside $\\mathcal S_0$.  The reduction is thus available in\n$\\mathcal P_0$, and normality is unchanged, since it is the absence of a\nsyntactic $\\beta$-redex.  This proves weak normalization of every legal\nexpression of $\\mathcal P_0$, while $E$ remains a legal expression that\nis not strongly normalizing.\n\\end{proof}\n\n\\begin{lemma}[A saturated context]\\label{pts:saturated-context}\nSuppose that the sort set $\\mathcal S$ is at most countable, and that\nthe variable set is countably infinite.  There is a possibly empty\ncountable sequence of declarations $\\Omega$ with the following\nproperties.  A judgment over $\\Omega$ means a judgment derivable over\nsome finite initial segment of $\\Omega$.\n\\begin{enumerate}\n\\item Every finite initial segment of $\\Omega$ is valid.  An infinite\nset of variable names is disjoint from all its declared names.\n\\item If $P$ is a finite initial segment of $\\Omega$ and\n$P\\vdash D:s$, then $\\Omega$ contains infinitely many declarations\n$y:D$ after $P$, with distinct declared names.\n\\item Every finite valid context embeds, by an injective renaming of\nits declared variables, as a subsequence of a finite initial segment\nof $\\Omega$.  If $\\Gamma\\vdash M:A$, such an embedding $\\rho$ gives\n$P\\vdash M\\rho:A\\rho$ for a finite initial segment $P$ containing the\nembedded context.\n\\item If $P\\vdash D:s$ for a finite initial segment $P$, and $F$ is any\nfinite set of variable names, there is a larger finite initial segment\n$Q$ ending in a declaration $y:D$, where\n$y\\notin F\\cup\\operatorname{dom}(P)$, and $Q\\vdash y:D$.\n\\end{enumerate}\nMoreover, finitely many judgments over $\\Omega$ hold over a common\nfinite initial segment.  Injective renaming in the third clause\npreserves and reflects weak and strong normalization.\n\\end{lemma}\n\n\\begin{proof}\nPartition the variable names into disjoint infinite sets $V_0$ and\n$V_1$.  Only names in $V_0$ will be declared in $\\Omega$; names in\n$V_1$ remain available for bound variables.  Because $\\mathcal S$ and\nthe variable set are countable, the set of finite raw expressions is\ncountable.  We work up to $\\alpha$-equivalence, or equivalently choose\nrepresentatives when enumerating expressions.\n\nChoose a sequence of pairs $(p,D)$, with $p$ a nonnegative integer and\n$D$ a raw expression, in which every pair occurs infinitely often.\nConstruct finite contexts $\\Delta_n$ starting with the empty context.\nAt stage $n$, consider the scheduled pair $(p,D)$.  If $\\Delta_n$ has\nat least $p$ declarations and its initial segment of length $p$ derives\n$D:s$ for some sort $s$, append a declaration $y:D$, choosing\n$y\\in V_0\\setminus\\operatorname{dom}(\\Delta_n)$.  Otherwise leave\n$\\Delta_n$ unchanged.  This is a set-theoretic construction and does\nnot require a decision procedure for typing.  In the append case,\nscope gives $\\mathrm{FV}(D)\\subseteq\n\\operatorname{dom}(\\Delta_n)$, and thinning gives\n$\\Delta_n\\vdash D:s$.  Thus the new declaration is fresh and the\nextended context is valid.  Induction proves validity at every stage.\n\nLet $\\Omega$ be the sequence obtained by retaining all declarations\nappended in this construction.  Every finite initial segment appears\nat some stage and is valid.  No name from $V_1$ is declared.  If\n$P\\vdash D:s$ and $P$ has length $p$, then, once $P$ has been\nconstructed, every later occurrence of the pair $(p,D)$ appends a new\ndeclaration of type $D$.  There are infinitely many such occurrences.\nThis proves the first two clauses, including the case where no\ndeclaration is ever possible and $\\Omega$ is empty.\n\nFor the embedding clause, write a finite valid context as\n\\[\n  \\Gamma=(x_1:D_1,\\ldots,x_m:D_m).\n\\]\nInduct on its prefixes.  Suppose an injective renaming $\\rho_i$ embeds\nthe first $i$ declarations, in their original order, into a finite\ninitial segment $P_i$ of $\\Omega$.  Validity supplies\n\\[\n  x_1:D_1,\\ldots,x_i:D_i\\vdash D_{i+1}:s\n\\]\nfor some sort $s$.  Renaming and then thinning yield\n$P_i\\vdash D_{i+1}\\rho_i:s$.  By the second clause, a later\ndeclaration has the form $y:D_{i+1}\\rho_i$, with $y$ fresh for the\nalready chosen names.  Set $\\rho_{i+1}(x_{i+1})=y$ and take an initial\nsegment through this declaration.  This retains all previously\nembedded declarations unchanged and in order.  The induction begins\nwith the empty context and proves the desired embedding.\n\nRenaming a derivation gives\n$\\Gamma\\rho\\vdash M\\rho:A\\rho$.  The chosen initial segment is a\nvalid extension by insertion of $\\Gamma\\rho$, so thinning gives the\nasserted judgment over that initial segment.  All substitutions and\nrenamings here are capture-avoiding; the reserved names permit bound\nvariables to be renamed away from the declarations whenever needed.\n\nFor the fourth clause, only finitely many of the infinitely many\ndeclarations of type $D$ after $P$ can have their declared names in\n$F$.  Choose any other one and let $Q$ be the initial segment ending\nthere.  Thinning the judgment for $D$ to the prefix immediately before\nthat declaration, followed by the variable rule, gives $Q\\vdash y:D$.\nIn particular, when finitely many substitution images have their free\nvariables in $P$, this $y$ is fresh for all those images.\n\nFinally, take the longest among finitely many initial segments\nwitnessing judgments over $\\Omega$ and thin all the judgments to it.\nAn injective renaming of the finitely many variables of a context can\nbe extended to a bijective renaming of the whole variable set.  Such\na renaming commutes with full $\\beta$-reduction and its inverse does\ntoo.  It therefore preserves and reflects normal forms, finite\nnormalizing reductions, and infinite reductions.  This proves the\nlast assertions.\n\\end{proof}\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/observation-bounds.inc.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/observation-bounds.inc.tex", "bytes": 14423, "sha256": "fbb1ac9a7300a97fcd09331dcccf5e4aa83e620680286f3e708c469039f49cfd", "content": "\\subsection{Uniform finite bounds for typed evaluation}\n\\label{obs:bound-section}\n\nWe verify that the preceding clauses define actual values in the stated\nspaces, including at free minus layers.  In this subsection a typing tree\nis fixed, but its typed substitution and its parameters may vary.  Bounds\nwill depend only on the finite tree and an integer, and not on any of\nthese images or parameters.  We use the images of $C_k$ in $C_*$ without\nfurther notation.\n\nTwo environments for the same tree are called \\emph{image-compatible} if their\nterm images are pointwise convertible.  Their actual normalized\ndeclaration types consequently agree.  For $r\\geq 0$, say that they\n\\emph{agree to level $r$} if, in addition, corresponding plain and minus\nparameters are equal and corresponding plus parameters are\n$\\equiv_r$-equivalent.  Parameters at inactive declarations are absent.\nAn environment is \\emph{minus-bounded by $k$} if all its minus parameters\nbelong to $C_k$.  In particular, being minus-bounded by zero means having\nno minus parameters.  Every finite environment is minus-bounded by some\n$k$, since $C_*$ is the increasing union of its stages.\n\n\\paragraph{The three binder facts.}\n\\label{obs:bound-binder-facts}\nThe following consequences of the graph definitions will be used\nrepeatedly.  Suppose a normalized product has profile $K$, domain profile\n$I$, open codomain profile $J$, and instantiated codomain profile $L$.\nIf $K$ and $L$ are active, the positive ascent $L\\longrightarrow\nJ\\longrightarrow K$ makes $J$ active.  If also $I\\in H$, the retained\nnegative edge $I\\longrightarrow K$ makes $I$ active.  Therefore:\n\\begin{enumerate}\n\\item If $K$ is plain, every active frame payload and every active\ncontinuation is plain: an active edge into a plain vertex starts at a\nplain vertex, and the same holds for a positive path into a plain vertex.\n\\item If $K$ and $L$ are free, they have the same sign; a free domain\npayload has the opposite sign to $K$.\n\\item If $K,L\\in S$, then $J\\in S$, since $J$ lies on a path between two\nvertices of the primary strongly connected component $S$.  Hence an\nactive $I$ is direct.  A free direct vertex is plus, so such a domain is\nnever free minus.\n\\end{enumerate}\nThe last assertion is the reason that universal quantification over\npayloads in a product candidate preserves a fixed minus bound.\n\n\\begin{lemma}[Uniform finite bounds]\\label{obs:bound-statement}\nFor every fixed finite tree $t$ there are bounds for each of its\n$\\operatorname{Val}$ and $\\operatorname{Read}$ evaluations, denoted below\nby $F_t(k)$ when the evaluation is understood, with\n$F_t(k)\\geq k+1$, satisfying the following assertions.\n\\begin{enumerate}\n\\item Every evaluation is defined for every typed environment.\n\\item In any environment minus-bounded by $k$, a free minus\n$\\operatorname{Val}$ output belongs to $C_{F_t(k)}$.\n\\item For two environments minus-bounded by $k$ and agreeing to level\n$F_t(k)$, plain and minus $\\operatorname{Val}$ outputs are equal,\n$\\operatorname{Read}$ outputs are equal, and plus\n$\\operatorname{Val}$ outputs are $\\equiv_k$-equivalent.\n\\end{enumerate}\nThe output spaces and their indices in the two environments agree:\npointwise convertibility of the images gives the same normalized actual\ntypes, expression profiles, normalized argument keys, and plain type\ntags.  Increasing a chosen bound preserves all three assertions.\n\\end{lemma}\n\\begin{proof}\nWe prove the statement together with totality by induction on syntax\nsize, proving $\\operatorname{Val}$ before $\\operatorname{Read}$ at a\ngiven size.  All calls to $\\operatorname{Read}$ from a product\n$\\operatorname{Val}$ concern proper children; the only same-size call\nis from the remaining $\\operatorname{Read}$ clause to\n$\\operatorname{Val}$.  Thus this is a well-founded order.  A bound for a\nnode is the maximum of the finitely many bounds described below for its\npossible clauses.  Conditional variation of the actual profiles does\nnot introduce infinitely many cases: the estimates distinguish only\ninactive, plain, plus, and minus layers and the finitely many syntactic\nevaluation clauses.\n\n\\paragraph{Lookup and defaults.}\n\\label{obs:bound-leaves}\nA minus lookup already lies in $C_k$, and a plus lookup agrees under\n$\\equiv_k$ whenever the environments agree to any level at least $k$.\nPlain lookups agree exactly.  Constant-by-ending defaults lie in $C_1$\non the minus side.  Sort defaults and skipped-application defaults\ntherefore satisfy the assertions with bound $k+1$.\n\n\\paragraph{Lambdas at plain and plus layers.}\n\\label{obs:bound-positive-lambdas}\nWrite $l$ for the bound of the body evaluation at $k$.  A lambda at a\nplain layer is defined using the plain prefix bijection.  Each of its\nframes introduces an identical plain parameter, or no parameter, when\ntwo matching frames are compared.  Its active continuation is also\nplain, by the first binder fact.  The extended environments remain\nminus-bounded by $k$, so body comparison gives equal frame outputs.\nTogether with the equal default base, the prefix bijection gives equal\nlambda values.  This holds simultaneously for all frames and all\ncontinuations; the same $l$ works for every one.\n\nA lambda at a plus layer is a full table.  It is defined on every raw\nstack using the gate in the evaluation definition.  For each individual\nvalid first frame, its minus payload, if present, belongs to some finite\nstage of $C_*$.  Together with the finitely many original environment\nparameters this gives a finite bound for the extended environment.\nTotality of the smaller body tree therefore defines the requested body\nvalue.  This use of totality does not require a single bound for all\npossible payloads in the full table.\n\nFor comparison under $\\equiv_k$, however, only stacks whose minus\npayloads all lie in $C_k$ are inspected.  Compare the same such stack in\nthe two tables.  The gate agrees in the two environments.  If it is\nvalid, its first frame introduces either the same minus parameter in\n$C_k$, the same plain parameter, or none.  Body comparison at $k$ thus\napplies.  For a continue frame the body has plus sign and its\n$\\equiv_k$-agreement gives the same observation on the restricted tail.\nFor an exit frame the body is plain and agrees exactly.  Invalid stacks\nand the empty stack have matching defaults.  This proves\n$\\equiv_k$-agreement of the plus lambda using the bound $l$.\n\n\\paragraph{Lambdas at minus layers.}\n\\label{obs:bound-minus-lambdas}\nAgain let $l$ be the body's bound at $k$, so $l\\geq k+1\\geq 1$.\nFirst define the lambda's behavior on raw stacks by the stated gate and\nbody clauses.  Every valid first frame introduces a plus parameter, a\nplain parameter, or none; no new minus parameter is introduced.  The\nbody is therefore total in an environment still minus-bounded by $k$.\n\nConsider two original environments agreeing to level $l$, and two\nstacks with identical keys, flags, none/plain payloads, and pairwise\n$\\equiv_l$-equivalent plus payloads.  Their gates agree.  At a valid\nfirst frame the extended environments still agree to level $l$ and\nremain minus-bounded by $k$.  If the frame continues, the two body\noutputs are the same minus value $c\\in C_l$.  Its behavior factors\nthrough $\\equiv_{l-1}$, and hence also through $\\equiv_l$, on the plus\npayloads of the tails.  It therefore gives equal observations on the\ntwo tails.  If the frame exits, body comparison gives equal plain\nvalues.  Defaults and empty observations also agree.\n\nApply this argument first with the same original environment on both\nsides.  It proves uniform invariance of the lambda behavior under\n$\\equiv_l$ in all plus payloads.  The exact factorization result for\nfree spaces then represents it by an element of $C_{l+1}$.  Apply the\nargument next to different original environments and the same stack on\nboth sides.  Their represented behaviors agree on every stack; the\nbehavior representation is injective, so their minus values are equal.\nThus $l+1$ is a valid output and comparison bound for the minus lambda.\n\n\\paragraph{Applications.}\n\\label{obs:bound-applications}\nLet $f$ and $n$ be bounds for the function and argument evaluations,\nrespectively.  Actual normalized argument keys agree in image-compatible\nenvironments.  A function at an inactive layer returns the default.  A\nfunction at a plain layer has only plain active dependencies, by the\nfirst binder fact.  The function and any evaluated argument payload\ntherefore agree exactly by their induction hypotheses at $k$, and the\nplain prefix operation gives equal outputs.\n\nSuppose the function is plus.  Argument comparison at $k$ makes a minus\npayload exactly equal in the two environments and puts it in\n$C_{n(k)}$.  Set\n\\[\n                 a=\\max\\{k,n(k)\\}.\n\\]\nThe original environments are also minus-bounded by $a$.  Function\ncomparison at $a$ gives $\\equiv_a$-agreement, provided the environments\nagree to level $f(a)$.  Every prefixed stack needed to compare a\ncontinuing output under $\\equiv_k$ has its new minus payload in\n$C_{n(k)}\\subseteq C_a$ and all tail minus payloads in\n$C_k\\subseteq C_a$.  The function observations therefore agree on\nthese stacks.  An exit is a single such frame and gives equal tagged\nplain values, hence equal results after projection.  If the payload is\nnone or plain, the same estimate applies with no use of $C_{n(k)}$.\n\nSuppose instead the function is minus.  Function comparison at $k$\ngives the same value in $C_j$, where\n\\[\n                         j=f(k).\n\\]\nApply argument comparison at $b=\\max\\{k,j\\}$.  If the argument payload\nis plus, the two payloads agree under $\\equiv_b$, hence under\n$\\equiv_j$.  Since the function behavior already factors through\n$\\equiv_{j-1}$, inserting these payloads gives the same observations\non identical arbitrary tails.  A continue slice remains in $C_j$ by\nthe slicing result, and the two slices are equal by injectivity of the\nbehavior representation.  An exit gives equal plain values.  Equal\nplain payloads and absent payloads are immediate special cases.\n\nAll these calls, together with the required output stages, are dominated\nby the finite expression\n\\begin{equation}\n  1+\\max\\bigl\\{k,f(k),n(k),\n       f(\\max\\{k,n(k)\\}),n(\\max\\{k,f(k)\\})\\bigr\\}.\n  \\label{obs:bound-application-formula}\n\\end{equation}\nNo monotonicity of $f$ or $n$ is needed: the displayed arguments are\nthe precise ones used above.  A plus active function can only have a\nfree plus active continuation, and a minus one only a free minus\nactive continuation, so the cases prove the required output assertion\nas well as comparison.\n\n\\paragraph{Product candidates and product overrides.}\n\\label{obs:bound-products}\nConsider a product candidate whose actual expression profile is in $S$.\nUse the domain child's $\\operatorname{Read}$ comparison at $k$ whenever\nthat Read is requested.  It gives exactly the same domain candidate in\nthe two environments, and hence exactly the same set of testers.  If\nthe domain Read is not requested, the typed strongly normalizing tester\nset is already identical because the actual normalized domains agree.\n\nFor each common tester $n$, the instantiated actual codomain types\nagree.  When their profile lies in $S$, the third binder fact says\nthat an active domain payload is plain or plus, never minus.  Pair\neach payload choice with the identical choice in the other environment.\nThis preserves the minus bound $k$ and agreement at every level\nalready required for the original environments.  The codomain child's\n$\\operatorname{Read}$ comparison at $k$ therefore gives equal codomain\ncandidates for every tester and every payload choice.  Its bound is\nuniform in the raw term $n$ and the payload: both are variable images\nor parameters for the same fixed smaller tree.  If the instantiated\nprofile is outside $S$, the required typed strong-normalization\ncondition is identical on the two sides.  Thus all the universally\nquantified membership conditions defining the product candidate agree,\nso the candidate itself is equal.  The maximum of the two child bounds\nand $k+1$ suffices.\n\nThe product $\\operatorname{Val}$ override places this candidate in one\ncomponent of the empty-stack $\\mathcal B$ observation, leaving the\nother components and all nonempty observations at their defaults.\nThe actual component index is the same in image-compatible environments.\nFor a minus output such a table is represented already in $C_1$,\nbecause its nonempty observations are constant by ending and its empty\nobservation contains no payload.  For a plus output its observations\nagree exactly, in particular under $\\equiv_k$.  At a plain layer the\ndefault continuations and equal base give equality by the prefix\nbijection.  This proves the override case with the same child bounds.\n\n\\paragraph{The remaining Read clauses.}\n\\label{obs:bound-read}\nThe default Read clauses are immediate.  At a nonproduct whose Read\nuses its $\\operatorname{Val}$ base, use the already proved\nsame-size $\\operatorname{Val}$ bound.  Plain and minus values agree\nexactly; plus values agree under $\\equiv_k$.  The latter also gives\nequal empty observations, even when $k=0$, since the empty stack\nbelongs to every restriction.  Reading the same actual-expression\ncomponent therefore gives the same candidate.  This completes the\nsimultaneous induction.\n\\end{proof}\n\n\\paragraph{Exact invariance for identical parameters.}\n\\label{obs:bound-exact-invariance}\nAs a consequence, image-compatible environments with identical parameters\ngive identical outputs of every kind.  For plain, minus, and Read\noutputs choose a common finite minus bound and use the comparison\nstatement.  For plus outputs, fix any raw observation stack.  Its\nfinitely many minus payloads and the finitely many minus parameters of\nthe original environments lie in some common $C_k$.  The environments\nagree to every level because their parameters are identical, so the\ncomparison statement gives $\\equiv_k$-agreement of the plus outputs\nand hence equality on this stack.  The stack was arbitrary, proving\nequality of the full tables.\n\nThe finite-bound proof applies to arbitrary typed trees; it assumes\nneither normal syntax nor goodness.  In particular its uniformity is\nunaffected by the lengths of reductions needed to normalize varying\nraw images.  Nonfunctional axioms and product rules also cause no new\ncase: the fixed tree stores its selected judgments, while compatibility\naligns the corresponding actual normalized indices.\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/observation-free-spaces.inc.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/observation-free-spaces.inc.tex", "bytes": 13885, "sha256": "33a49c98d9e20faf0dfd2579048c1060b4e03675ac56343c4dd59450ce7fa128", "content": "\\subsection{Free observation spaces}\n\\label{obs:free-spaces}\n\nThe construction in this subsection is purely set-theoretic.  The typing\nconditions on frames will be imposed by evaluation; they do not restrict\nthe tables constructed here.  Fix the set $\\mathcal Q$ of normal syntax\nover $\\Omega$, with bound names identified up to $\\alpha$-conversion.\nLet $\\mathcal T_{\\mathrm{pl}}$ be the set of actual plain active types,\nand suppose that the nonempty spaces $\\mathcal K_D$ have already been\nconstructed for $D\\in\\mathcal T_{\\mathrm{pl}}$.  The fixed set of tagged\nplain values is\n\\begin{equation}\n  \\mathcal V_{\\mathrm{pl}}\n   =\\coprod_{D\\in\\mathcal T_{\\mathrm{pl}}}\n       \\bigl(\\{D\\}\\times\\mathcal K_D\\bigr).\n  \\label{obs:free-plain-values}\n\\end{equation}\nThus a member of $\\mathcal V_{\\mathrm{pl}}$ remembers both its type $D$ and its value\n$v\\in\\mathcal K_D$.  Let $\\mathcal B$ be the set of base observations:\na member chooses a candidate at every actual type.  Write\n$\\mathbf t\\in\\mathcal B$ for the choice of the top candidate everywhere.\nFor the present construction only the set $\\mathcal B$ and its\ndistinguished member $\\mathbf t$ are needed.  Put\n\\[\n  \\mathcal O=\\{\\star\\}\\sqcup\\mathcal V_{\\mathrm{pl}},\n\\]\nwhere the union is disjoint.  In particular, both output sets\n$\\mathcal B$ and $\\mathcal O$ have specified default elements, even if\nthere are no plain active types.\n\n\\paragraph{Frames, stacks, and tables.}\nFor any set $Y$ define the payload set\n\\[\n  V(Y)\n   =\\{\\mathrm{none}\\}\n      \\sqcup\\bigl(\\{\\mathrm{opp}\\}\\times Y\\bigr)\n      \\sqcup\\bigl(\\{\\mathrm{plain}\\}\\times\\mathcal V_{\\mathrm{pl}}\\bigr).\n\\]\nThe three tags make these alternatives disjoint.  Define continue and\nexit frames by\n\\[\n  F_c(Y)=\\mathcal Q\\times V(Y)\\times\\{c\\},\n  \\qquad\n  F_e(Y)=\\mathcal Q\\times V(Y)\\times\\{e\\}.\n\\]\nHere $Y$ is the space of opposite-sign payloads.  The set of non-exiting\nstacks and the set of exiting stacks are, respectively,\n\\begin{equation}\n  S_c(Y)=\\coprod_{n\\geq 0} F_c(Y)^n,\n  \\qquad\n  S_e(Y)=\\coprod_{n\\geq 0}\n                 \\bigl(F_c(Y)^n\\times F_e(Y)\\bigr).\n  \\label{obs:free-stacks}\n\\end{equation}\nThus $S_c(Y)$ contains the empty stack $\\epsilon$, and an exit frame\noccurs exactly once, at the end of a stack in $S_e(Y)$.  Set\n\\begin{equation}\n  \\operatorname{Tab}(Y)\n      =\\mathcal B^{S_c(Y)}\\times\\mathcal O^{S_e(Y)}.\n  \\label{obs:free-tab}\n\\end{equation}\nA table $t=(t_c,t_e)$ gives a base observation on each non-exiting\nstack and an exit observation on each exiting stack.  Its default\n$d_Y$ is specified by\n\\[\n  (d_Y)_c(\\sigma)=\\mathbf t,\n  \\qquad\n  (d_Y)_e(\\tau)=\\star.\n\\]\nConsequently $\\operatorname{Tab}(Y)$ is nonempty for every $Y$,\nincluding $Y=\\emptyset$.\n\nWe seek two spaces whose tables take values of the opposite sign as\npayloads.  Their construction must solve two different problems.  A plus\nvalue should accept every finite stack of minus values.  A minus value\nmust be representable at a finite stage, even though it is tested on\narbitrarily many plus values and arbitrarily long stacks.  Choosing a\nstage separately for each stack would not give that representation.\n\nThe construction below supplies a space $C_*$ of minus values and the\nfull space $P_*=\\operatorname{Tab}(C_*)$ of plus tables.  Increasing\nstages $C_r$ give restrictions\n$\\rho_r:P_*\\to P_r=\\operatorname{Tab}(C_r)$ of plus tables.\nA table on plus payloads represents a minus value precisely when\none index $r$ works for every observation: replacing each plus payload\n$p$ by $p'$ with $\\rho_r(p)=\\rho_r(p')$ leaves the table output\nunchanged.  Finally, fixing any continue prefix must again give a value\nof the same sign; on the minus side it must retain the same representing\nstage.  These are the representation and slicing properties that typed\nevaluation will use.  We first construct the stages and then prove this\ncharacterization.\n\n\\paragraph{Relabeling and contravariance.}\nA map $f:Y\\to Z$ induces stack maps\n$S_a(f):S_a(Y)\\to S_a(Z)$ for $a\\in\\{c,e\\}$.\nThese replace $(\\mathrm{opp},y)$ by $(\\mathrm{opp},f(y))$ in each\npayload position.  Keys, flags, and the other payload alternatives\nare unchanged.  Define\n\\begin{equation}\n  \\operatorname{Tab}(f):\\operatorname{Tab}(Z)\n                  \\longrightarrow\\operatorname{Tab}(Y),\n  \\qquad\n  \\bigl(\\operatorname{Tab}(f)(t)\\bigr)_a\n                   =t_a\\circ S_a(f).\n  \\label{obs:free-contravariance}\n\\end{equation}\nRelabeling preserves identities and composition.  Hence\n\\[\n  \\operatorname{Tab}(\\operatorname{id}_Y)\n     =\\operatorname{id}_{\\operatorname{Tab}(Y)},\n  \\qquad\n  \\operatorname{Tab}(g\\circ f)\n     =\\operatorname{Tab}(f)\\circ\\operatorname{Tab}(g).\n\\]\nIt also preserves the default tables:\n$\\operatorname{Tab}(f)(d_Z)=d_Y$.\n\nWe will use the following two elementary facts.\n\\begin{enumerate}\n\\item If $f$ is injective, then $S_c(f)$ and $S_e(f)$ are injective,\nand $\\operatorname{Tab}(f)$ is surjective.  Indeed, given a table on\n$Y$, define a table on $Z$ by transporting its values to the images\nof $S_c(f)$ and $S_e(f)$, and assigning $\\mathbf t$ or $\\star$ on the\nrespective complements.  Injectivity makes the transported values\nunambiguous, and restriction recovers the given table.\n\\item If $f$ is surjective, then $S_c(f)$ and $S_e(f)$ are surjective,\nand $\\operatorname{Tab}(f)$ is injective.  To lift a stack, lift its\nfinitely many opposite payloads through $f$.  If two tables become\nequal after precomposition with these surjective stack maps, they\nagree on every stack and therefore are equal.\n\\end{enumerate}\nIn particular these statements apply when an opposite payload set is\nempty; the stacks without opposite payloads remain available.\n\n\\paragraph{The alternating chain.}\nDefine, for $r=0,1,2,\\ldots$,\n\\begin{equation}\n  C_0=\\emptyset,\n  \\qquad P_r=\\operatorname{Tab}(C_r),\n  \\qquad C_{r+1}=\\operatorname{Tab}(P_r).\n  \\label{obs:free-stages}\n\\end{equation}\nLet $i_0:C_0\\to C_1$ be the empty map.  Inductively, once\n$i_r:C_r\\to C_{r+1}$ is defined, put\n\\begin{equation}\n  j_r=\\operatorname{Tab}(i_r):P_{r+1}\\longrightarrow P_r,\n  \\qquad\n  i_{r+1}=\\operatorname{Tab}(j_r):C_{r+1}\\longrightarrow C_{r+2}.\n  \\label{obs:free-bonding}\n\\end{equation}\nThe preceding facts prove by induction that every $i_r$ is injective\nand every $j_r$ is surjective.  Each $j_r$ is restriction to stacks\nwhose opposite payloads lie in the embedded copy of $C_r$; its\nsurjectivity has the explicit default-extension proof above.\n\nFor $r\\leq s$, write $i_{r,s}:C_r\\to C_s$ for the composite of the\ninjections, with $i_{r,r}$ the identity.  Let $C_*$ be the set colimit\nof this chain.  Explicitly, take the disjoint union of the $C_r$ and\nidentify $(r,x)$ with $(s,y)$ precisely when, at some common stage\n$m\\geq r,s$,\n\\[\n  i_{r,m}(x)=i_{s,m}(y).\n\\]\nThe canonical maps $e_r:C_r\\to C_*$ are injective and satisfy\n$e_{r+1}\\circ i_r=e_r$.  Their images form an increasing union\nequal to $C_*$.  We may therefore regard the stages as nested\nsubsets after these identifications.  Every finite family of elements\nof $C_*$ lies in a common stage: choose a representing stage for\neach element and then take their maximum.  Every element in fact\nhas a representative in some $C_{r+1}$, since $C_0$ is empty.\n\n\\paragraph{Final plus tables and finite-stage agreement.}\nPut\n\\begin{equation}\n  P_*=\\operatorname{Tab}(C_*),\n  \\qquad\n  \\rho_r=\\operatorname{Tab}(e_r):P_*\\longrightarrow P_r.\n  \\label{obs:free-final-plus}\n\\end{equation}\nEach $\\rho_r$ is surjective, by default extension along the injection\n$e_r$.  Contravariance and the identities for $e_r$ give\n\\begin{equation}\n  \\rho_r=j_r\\circ\\rho_{r+1}.\n  \\label{obs:free-restriction-compatibility}\n\\end{equation}\nFor $p,p'\\in P_*$ write\n\\begin{equation}\n  p\\equiv_r p' \\quad\\Longleftrightarrow\\quad\n           \\rho_r(p)=\\rho_r(p').\n  \\label{obs:free-equivalence}\n\\end{equation}\nAgreement at a larger stage implies agreement at every smaller\nstage, by repeated use of\n\\eqref{obs:free-restriction-compatibility}.\n\nEvery stack in $S_a(C_*)$, for $a\\in\\{c,e\\}$, belongs to the image\nof $S_a(e_r)$ for some $r$: it has only finitely many opposite\npayloads, which lie in a common stage.  The same holds for any\nfinite family of stacks, with one common $r$.  It follows that\n\\begin{equation}\n  \\bigl(p\\equiv_r p'\\text{ for every }r\\bigr)\n       \\quad\\Longleftrightarrow\\quad p=p'.\n  \\label{obs:free-separation}\n\\end{equation}\nFor the forward implication, lift any specified final stack from a\ncommon stage and evaluate the equality $\\rho_r(p)=\\rho_r(p')$ on\nthat lift.  The reverse implication is immediate.  No assertion\nthat a plus table itself belongs to a finite stage is needed.\n\n\\paragraph{Final minus behaviors.}\nFor each $r\\geq0$, define\n\\begin{equation}\n  \\beta_r=\\operatorname{Tab}(\\rho_r):\n        C_{r+1}=\\operatorname{Tab}(P_r)\n            \\longrightarrow\\operatorname{Tab}(P_*).\n  \\label{obs:free-behavior-stage}\n\\end{equation}\nIn coordinates, for $u\\in C_{r+1}$ and $\\sigma\\in S_a(P_*)$,\n\\[\n  \\bigl(\\beta_r(u)\\bigr)_a(\\sigma)\n       =u_a\\bigl(S_a(\\rho_r)(\\sigma)\\bigr).\n\\]\nThus each plus payload of a final stack is first restricted to\n$P_r$.  Since $\\rho_r$ is surjective, $\\beta_r$ is injective.\nMoreover,\n\\begin{align*}\n  \\beta_{r+1}\\circ i_{r+1}\n    &=\\operatorname{Tab}(\\rho_{r+1})\n                          \\circ\\operatorname{Tab}(j_r)\\\\\n    &=\\operatorname{Tab}(j_r\\circ\\rho_{r+1})\n      =\\operatorname{Tab}(\\rho_r)=\\beta_r.\n\\end{align*}\nThese maps consequently induce a well-defined map\n\\begin{equation}\n  \\beta:C_*\\longrightarrow\\operatorname{Tab}(P_*),\n  \\qquad\n  \\beta(e_{r+1}(u))=\\beta_r(u).\n  \\label{obs:free-behavior}\n\\end{equation}\nIt is injective: represent any two proposed equal-behavior elements\nat a common stage $C_{r+1}$ and use the injectivity of $\\beta_r$.\nWe henceforth evaluate a minus value through this behavior map.\n\nThe defaults are compatible with all bonding maps.  In particular,\nthe default elements $d_{P_r}\\in C_{r+1}$ represent one element of\n$C_*$, whose behavior is $d_{P_*}$.  The plus default is\n$d_{C_*}\\in P_*$.  Both free spaces are therefore nonempty.\n\n\\paragraph{Exact characterization by a uniform stage bound.}\nFix $r$.  For $a\\in\\{c,e\\}$ and $\\sigma,\\tau\\in S_a(P_*)$ write\n$\\sigma\\sim_r\\tau$ when they have identical lengths, keys, flags,\nand payload alternatives, their plain payloads are identical, and\neach corresponding pair of opposite payloads $p,p'$ satisfies\n$p\\equiv_r p'$.  Equivalently,\n\\begin{equation}\n  \\sigma\\sim_r\\tau\n    \\quad\\Longleftrightarrow\\quad\n  S_a(\\rho_r)(\\sigma)=S_a(\\rho_r)(\\tau).\n  \\label{obs:free-stack-equivalence}\n\\end{equation}\nThis equivalence follows directly from the disjoint payload tags\nand the definition of stack relabeling.\n\nFor a final table $t\\in\\operatorname{Tab}(P_*)$, the following\nconditions are equivalent:\n\\begin{enumerate}\n\\item $t$ belongs to the image of $\\beta_r$.\n\\item For both $a=c$ and $a=e$, the equality\n      $\\sigma\\sim_r\\tau$ implies $t_a(\\sigma)=t_a(\\tau)$.\n\\end{enumerate}\nThe first condition implies the second by\n\\eqref{obs:free-behavior-stage}.  Conversely, each\n$S_a(\\rho_r)$ is surjective, since a finite stack of elements of\n$P_r$ can be lifted through the surjection $\\rho_r$.  For\n$\\eta\\in S_a(P_r)$ define $u_a(\\eta)$ to be $t_a(\\sigma)$ for\nany lift $\\sigma$ of $\\eta$.  Such a lift exists, and any two lifts\nare $\\sim_r$-equivalent by\n\\eqref{obs:free-stack-equivalence}.  The second condition therefore\nmakes the definition unambiguous.  It defines a unique\n$u\\in\\operatorname{Tab}(P_r)=C_{r+1}$ with $\\beta_r(u)=t$.\nThis proves the equivalence and uniqueness of the factor.\n\nConsequently\n\\begin{equation}\n  \\beta(C_*)\n    =\\bigcup_{r\\geq0}\\beta_r(C_{r+1})\n    =\\left\\{\n       t\\in\\operatorname{Tab}(P_*):\n       \\begin{array}{l}\n       \\text{for some }r,\\text{ each }t_a\\text{ is constant}\\\\\n       \\text{on every }\\sim_r\\text{-class},\\quad a\\in\\{c,e\\}\n       \\end{array}\n       \\right\\}.\n  \\label{obs:free-uniform-factorization}\n\\end{equation}\nThe one stage $r$ here is uniform over all stack lengths, keys,\npayloads, and both kinds of ending.  The preceding argument proves\nfactorization for precisely this uniform condition.\n\n\\paragraph{Continue-prefix slicing.}\nIf $\\sigma\\in S_c(Y)$ and $t\\in\\operatorname{Tab}(Y)$, define\nthe slice $t|\\sigma\\in\\operatorname{Tab}(Y)$ by\n\\begin{equation}\n  (t|\\sigma)_a(\\tau)=t_a(\\sigma\\tau),\n       \\qquad \\tau\\in S_a(Y),\\quad a\\in\\{c,e\\},\n  \\label{obs:free-slice}\n\\end{equation}\nwhere juxtaposition denotes concatenation.  The prefix has only\ncontinue frames, so concatenation preserves the kind of ending.\nThis immediately gives slices of plus tables, by taking $Y=C_*$.\n\nFor minus tables, slicing preserves the representing stage.\nFor $u\\in C_{r+1}$ and $\\sigma\\in S_c(P_*)$,\nstack relabeling commutes with concatenation and gives\n\\begin{equation}\n  \\beta_r(u)|\\sigma\n     =\\beta_r\\bigl(u|S_c(\\rho_r)(\\sigma)\\bigr).\n  \\label{obs:free-slice-bound}\n\\end{equation}\nTo check this equality, evaluate either side on\n$\\tau\\in S_a(P_*)$; both values are\n\\[\n  u_a\\bigl(S_c(\\rho_r)(\\sigma)\\,\n                 S_a(\\rho_r)(\\tau)\\bigr).\n\\]\nThe sliced table inside $\\beta_r$ belongs to\n$\\operatorname{Tab}(P_r)=C_{r+1}$.  Thus a minus value represented\nat $C_{r+1}$ has every continue-prefix slice represented at that\nsame stage.  Because $\\beta$ is injective, this defines an intrinsic\nslice on $C_*$, independent of the chosen representing stage.\nEquivalently, fixing a prefix preserves invariance under\n$\\sim_r$ on all remaining payloads.  Default tables slice to\ndefault tables.  Define\n\\[\n \\mathcal K_T=\\begin{cases}P_*,&T\\text{ is free plus},\\\\\n C_*,&T\\text{ is free minus}.\\end{cases}\n\\]\nEvaluate a minus value through the injective behavior map $\\beta$.\nThus a plus value is an arbitrary table, whereas a proposed minus\nbehavior defines a value only after one uniform stage bound has been\nproved.  The evaluation of a lambda at a free minus type will be the\nplace where this distinction matters.\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/observations.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/observations.tex", "bytes": 39000, "sha256": "bf764b021f29fde45b42186717ba75b875b5f4aa2a34f5673dd4545ce3817981", "content": "\\section{Observation spaces and substitution-stable candidates}\n\\label{obs:section}\n\nWe now construct the candidate interpretation for the remaining primary\ncomponent.  Throughout this section, $S$ is the current component of the\nprimary graph and $U$ is its predecessor-closed processed part.  We retain\nthe hypothesis of weak normalization for every legal expression.  We use\nthe preceding profile and candidate lemmas, including\n$G_T=\\operatorname{Ty}(T)\\cap\\mathrm{SN}_\\beta$ when $\\pi(T)\\in U$.\nThe new input will be the exclusion of a particular finite graph\nconfiguration, stated below and proved in the next part of the argument.\nNo strong-normalization hypothesis is imposed on substitutions in this\nsection.\n\nThe interpretation of a neutral type must account for substitutions\nthat replace its head by a product.  We therefore give variables parameters\nthat record candidates, and interpret the syntax of a substituted term\nto determine which candidate to read.  At a function type\n$T=\\Pi x:D.E$, such a parameter records a base observation.  The\nconstruction supplies spaces only at selected types; whenever the\ninstantiated result $(E[q/x])^\\#$ has a space, a normal argument\n$q:D$, together with a parameter for $q$ when its type requires one,\nselects a next value in that result space.  The function's space can\ntherefore depend on the spaces for both its arguments and its results.  These dependencies\ncan cycle, so ordinary recursion on the displayed type does not suffice.\n\nThe graph below identifies which spaces are needed and separates two\nways to construct them.  Some admit a description by finite observation\npaths or a decreasing count of type nodes.  For the others we build two\nspaces together: unrestricted tables on one side and tables with a\nuniform finite bound on their payload dependence on the other.  After\nconstructing the spaces, we prove that every finite typed syntax tree\ncan be evaluated in them.  Substitution and reduction require an\nadditional condition, called goodness, which holds for the normal\nsyntax used to define our candidates.  That final transport result will\nsupply the specialization property of\nthe candidate interface.\n\n\\subsection{Which values require observations?}\n\\label{obs:graphs}\n\nThere are two different profiles to keep apart.  A neutral type sorted\nat $s$ has expression profile $\\{s\\}$; a value interpreting that type\nis indexed by its expected type $s$, whose profile is\n$\\operatorname{Ax}(s)$.  These axiom profiles are the starting points\nfor deciding which parameter spaces are needed.  The remaining graph\ndefinitions follow the dependencies of those spaces toward\ndomains that are tested by products within $S$.\n\nLet $H$ be the closure under primary positive paths of\n\\[\n \\{\\operatorname{Ax}(s):\\{s\\}\\in S,\n                         \\ \\operatorname{Ax}(s)\\ne\\varnothing\\}.\n\\]\nThe \\emph{secondary graph} has vertex set $H$.  It retains all primary\npositive edges between these vertices.  It retains the negative edge\n$I\\longrightarrow K$ belonging to a profile triple $(I,J,K)$ precisely\nwhen $I,J,K\\in H$.  A vertex $I\\in H$ is \\emph{direct} if some profile\ntriple $(I,J,K)$ has $J,K\\in S$; here $J,K$ need not belong to $H$.\nA vertex is \\emph{active} if it has a secondary path to a direct vertex.\nOnly the induced graph on active vertices will be used below.\n\nAn active vertex is \\emph{plain} if paths from it to direct vertices\noccur with both parities.  Otherwise it is \\emph{free}; its sign is $+$\nif those paths are even and $-$ if they are odd.  A path of length zero\nis even, so a free direct vertex has sign $+$.\n\nThis last observation will control the later uniform bounds.  A product\ntest whose product and instantiated codomain profiles remain in $S$\nhas its open codomain profile in $S$ as well, by their positive ascent.\nA domain profile that is active is therefore direct.  Such a test may\nquantify over parameters at plain or free plus profiles, but never at\nfree minus profiles.\nThis fact will let one finite dependence bound survive all the parameter\nchoices in a product test.\n\n\\begin{lemma}[Signs and dependencies]\\label{obs:graph-lemma}\nAn active predecessor of a plain vertex is plain.  Along an edge between\nfree vertices, the sign is preserved on a positive edge and reversed on\na negative edge.\n\nLet $T=\\Pi x:D.E$ be a normalized actual type, put\n$I=\\pi(D)$, $J=\\pi(E)$, and $K=\\pi(T)$, and let\n$q\\in\\operatorname{Ty}(D)$.  Put $T_q=(E[q/x])^\\#$ and $L=\\pi(T_q)$.\nIf $K,L$ are active, then $J$ is active, and $I$ is active whenever\n$I\\in H$.  If instead $K,L\\in S$, then $J\\in S$ and every active $I$\nis direct.\n\\end{lemma}\n\\begin{proof}\nPrepending the edge in question to paths to a direct vertex proves the\nsign statements.  Profile growth and the product edge give the positive\npath\n\\[\n                         L\\longrightarrow J\\longrightarrow K,\n\\]\nwhere the first edge is omitted when $L=J$.  Since $L\\in H$ and $H$ is\npositive-path closed, $J\\in H$.  Its path to the active vertex $K$ makes\n$J$ active.  If $I\\in H$, the negative edge of $(I,J,K)$ is retained,\nso $I$ is active too.  When $K,L\\in S$, the displayed path cannot leave\ntheir primary strongly connected component.  Thus $J\\in S$, and the\ndefinition of direct applies.\n\\end{proof}\n\nThe \\emph{layer} of a type $T$ is $\\pi(T)$.  A type is active, plain, or\nfree of a given sign when its layer is so; an empty layer is inactive.\nThese terms concern the type of a value, not the profile of the value\nitself.\n\nHere is the promised graph hypothesis.\n\\begin{equation}\\label{obs:forbidden}\n\\begin{gathered}\n\\text{There is no plain active secondary strongly connected component $C$}\\\\[-2pt]\n\\text{with an internal negative edge, a profile triple $(I,J,K)$ with $J,K\\in C$,}\\\\[-2pt]\n\\text{and a sort $s$ such that $\\{s\\}\\in C$, $\\operatorname{Ax}(s)\\ne\\varnothing$, and}\\\\[-2pt]\n                  \\operatorname{Ax}(s)\\longrightarrow I\n                  \\text{ by a primary positive path.}\n\\end{gathered}\n\\end{equation}\nAll results in this section are conditional on\n\\eqref{obs:forbidden}.  The later typed contradiction will derive this\ncondition from the same system-wide weak-normalization hypothesis.\n\n\\subsection{Actual frames and plain observation spaces}\n\\label{obs:plain-spaces}\n\nLet $\\mathcal B$ be the set of assignments choosing one candidate at\nevery actual type.  Its distinguished member $\\mathbf t$ chooses every\ntop candidate.  All types and expressions here are syntax over finite\nprefixes of $\\Omega$, modulo $\\alpha$-equivalence, so their collections\nand $\\mathcal B$ are sets.\n\nWe construct a nonempty set $\\mathcal K_T$ of parameters for every\nactive actual type $T$.  For a plain type we require the following\nprecise prefix description.  If $T=\\Pi x:D.E$, a \\emph{legal frame at\n$T$} consists of a normal term $q\\in\\operatorname{Ty}(D)$ for which\n$T_q=(E[q/x])^\\#$ is active, together with a parameter\n$v\\in\\mathcal K_D$ if $D$ is active.  If $D$ is inactive there is one\nabsent-payload symbol in place of $v$.  Its target is $T_q$.  A\nnonproduct type has no legal frames.  Write $\\mathcal F_T$ for this\nset of frames.  The desired description is the bijection\n\\begin{equation}\\label{obs:plain-prefix}\n \\mathcal K_T\\ \\simeq\\\n \\mathcal B\\times\n       \\prod_{a\\in\\mathcal F_T}\\mathcal K_{\\operatorname{target}(a)}.\n\\end{equation}\nWe call its first coordinate the base and its $a$-coordinate the next\nvalue.  By Lemma~\\ref{obs:graph-lemma}, all active spaces occurring on\nthe right are plain when $T$ is plain, and their secondary components\nprecede or equal that of $T$.\n\n\\begin{lemma}[Construction of plain spaces]\\label{obs:plain-construction}\nUnder \\eqref{obs:forbidden}, the plain spaces admit\n\\eqref{obs:plain-prefix}.  They also admit distinguished values whose\nbases are $\\mathbf t$ and whose next values are distinguished values.\n\\end{lemma}\n\\begin{proof}\nProcess the finitely many plain secondary components in source order.\nSuppose first that a component $C$ has no internal negative edge.\nEvery active domain used by a legal frame at a type of layer in $C$\nthen lies in an earlier component: otherwise the frame's triple, whose\nopen codomain is active, would contribute an internal negative edge.\nThus all frame alphabets can be specified without using a space whose\nlayer belongs to $C$.\n\nFor $\\pi(T)\\in C$, consider finite paths of legal frames beginning at\n$T$.  A continuing path has all its target layers in $C$, and includes\nthe empty path.  An exiting path has all but its final target layer in\n$C$ and its final target in an earlier component.  Define $\\mathcal K_T$\nto be the set of tables assigning a member of $\\mathcal B$ to each\ncontinuing path and a member of the final target space to each exiting\npath.  Split a table according to the empty path and the first frame.\nFor a frame staying in $C$, its suffix table is exactly a table of the\nsame kind starting at its target.  For an exiting frame its output is\nalready a value of the target space.  This gives the bijection\n\\eqref{obs:plain-prefix} in both directions.  The constant top outputs\nand previously chosen exit defaults define the distinguished table.\n\nNow let $C$ have an internal negative edge.  For every normalized type\n$T$ of layer in $C$, define a positive integer $p_C(T)$ as follows.\nCount its root.  At a product with child layers $I,J$, continue into\nthe codomain when $J\\in C$, and into the domain when $I\\in C$ and\n$J$ is active.  Apply the same rule recursively at counted children,\nusing the binder context in the codomain.  This counts a subset of a\nfinite syntax tree.  Profile invariance makes the count independent of\nthe prefix and unchanged by valid thinning or context conversion.\n\nFor a legal frame at $T=\\Pi x:D.E$, write\n$I=\\pi(D)$, $J=\\pi(E)$, $K=\\pi(T)$, and $L=\\pi(T_q)$.\nA same-$C$ domain has smaller count.  We claim\nthat its same-$C$ target also satisfies\n\\begin{equation}\\label{obs:potential-drop}\n               p_C(T_q)\\le p_C(E)<p_C(T).\n\\end{equation}\nHere $J\\in C$ follows from the positive ascent in\nLemma~\\ref{obs:graph-lemma}; hence $p_C(E)$ is defined.\n\nTo prove the first inequality, thin $T$ and $q$ to a common prefix,\nchoosing the displayed binder fresh.  Associate to every counted node\nof $(E[q/x])^\\#$ its position string.  We show that the same position\nis a counted node of $E$.  The induction along a position maintains\nthat the corresponding pre- and post-substitution parent profiles both\nlie in $C$; at the root these are $J$ and $L$.  At a pre-substitution\nproduct the post normal form is a product with componentwise substituted\nnormal forms.  Write $I_0,J_0,K_0$ for its pre domain, codomain, and\nparent profiles, and $I_1,J_1,K_1$ for the post profiles.  Profile growth\ngives $I_1\\supseteq I_0$ and $J_1\\supseteq J_0$, also under binders\nafter context conversion.\n\nIf the post codomain is counted, then $J_1\\in C$, and the positive\npath $J_1\\longrightarrow J_0\\longrightarrow K_0$ has both endpoints\nin $C$.  Hence $J_0\\in C$, so the pre codomain is counted too.  If\nthe post domain is counted, then $I_1\\in C$ and $J_1$ is active.\nThe inclusion paths put $I_0,J_0$ in $H$.  Since $K_0\\in C\\subseteq H$,\nthe pre triple has a retained negative edge $I_0\\longrightarrow K_0$.\nThe path\n\\[\n                  I_1\\longrightarrow I_0\\longrightarrow K_0\n\\]\nagain has endpoints in $C$, so $I_0\\in C$.  Also the positive edge\n$J_0\\longrightarrow K_0$ makes $J_0$ active.  Thus the pre domain is\ncounted.  In either case the pre- and post-substitution profiles of the\nselected child lie in $C$, which preserves the induction invariant for\nthe next position.  Inclusion edges in these paths are omitted when\nthe profiles are equal.\n\nThe association could fail only if a pre neutral leaf became a product.\nSorts are unchanged, and a neutral with any other head remains neutral\nafter substitution and normalization.  Its head must therefore be the\nsubstituted variable $x$.  Let its singleton profile be $\\{s\\}$;\nthis lies in $C$.  The sort $s$ is sorted, so\n$\\operatorname{Ax}(s)\\ne\\varnothing$.  Trace the original $x$-headed\nspine backward through its normalized successive typing products.\nAt each application the result layer has a primary positive path to\nthe function layer.  Starting with the layer\n$\\operatorname{Ax}(s)$ of its type $s$, concatenate these paths to\nobtain a positive path to $\\pi(D)=I$.  The outer product has\n$J,K\\in C$.  This is forbidden by \\eqref{obs:forbidden}.  No leaf\ntherefore expands.  Position strings give the required injection of\ncounted nodes and prove \\eqref{obs:potential-drop}.\n\nDefine \\eqref{obs:plain-prefix} by induction on $p_C(T)$, simultaneously\nfor all types with the same count.  Every same-component space on its\nright is already constructed by the strict inequalities just proved;\nall other spaces come from earlier components.  The product of the\nspecified nonempty sets is nonempty, with the explicit element whose\nbase is $\\mathbf t$ and whose next values are the already specified\ndefaults.  This completes the construction.\n\\end{proof}\n\n\\input{sections/observation-free-spaces.inc.tex}\n\n\\subsection{Finite typing trees and environments}\n\\label{obs:trees}\n\nEvaluation must remember the particular product used to type an\napplication.  We therefore equip the finite syntax tree of a judgment\n$\\Gamma\\vdash M:T$ with generation data.  At each node we store its\nexpected-type judgment and a core typing converting to that expectation:\nthe declaration at a variable; the axiom target at a sort; the two child\nsorts and the formation triple at a product; the annotation sort, body\ntype and supporting product typing at a lambda; and the function's\nexact product expectation and the argument's domain expectation at an\napplication.  We include the annotation's typing at a lambda.  These\ndata exist by generation; their witnesses are finite.  They do not\nrequire uniqueness of sorts or formation rules.\n\nThe following operations on these trees will be used.  Valid thinning\nand context conversion transport all judgments and generation witnesses.\nAt a variable the implicit core declaration is changed accordingly.\nRetargeting a root changes its expected type to a convertible type at\nwhich the same expression is typed; it retains the core data and all\nchildren.  Capture-avoiding substitution transports the stored judgments\nand grafts copies of the argument tree at occurrences of the substituted\nvariable, thinning and retargeting those copies as needed.  These\noperations use only the previously established metatheory.\n\nAn environment for a finite generic context $\\Gamma$ is a typed\nsubstitution $\\xi:\\Gamma\\to\\Omega$, with arbitrary raw term images,\ntogether with a parameter $\\theta_x\\in\\mathcal K_{(A\\xi)^\\#}$ for each\ndeclaration $x:A$ whose actual type $(A\\xi)^\\#$ is active.  Declaration\ntypes are evaluated at their prefixes.  The notation $A\\xi$ may also\nuse the whole substitution, since no later variable is free in $A$.\nAn extension by $x:=n$ is typed whenever $n$ has the actual normalized\ndomain type; conversion gives its required raw type.  At an active\ndomain it may be accompanied by any parameter in the corresponding\nspace.  At an inactive domain no parameter is supplied.\n\nTwo environments are \\emph{compatible} if their raw images are\npointwise convertible and their corresponding parameters are equal.\nTheir actual normalized types are equal, so this equality of parameters\nis well-typed.  All prefix changes are understood by thinning.\n\n\\subsection{Evaluation and reading candidates}\n\\label{obs:evaluation}\n\nFor a typing tree $t$ of $M:T$ and an environment $(\\xi,\\theta)$, define\n\\[\n \\operatorname{Val}(t;\\xi,\\theta)\\in\\mathcal K_{(T\\xi)^\\#}\n \\quad\\text{when $(T\\xi)^\\#$ is active.}\n\\]\nFor a tree of $M:s$ with literal expected sort, define\n$\\operatorname{Read}(t;\\xi,\\theta)$ to be a candidate at the actual\nexpression $(M\\xi)^\\#$.  The two constructions are distinct: the\nspace index of the first is the actual \\emph{type}; the candidate index\nof the second is the actual \\emph{expression}.\n\nEvery active space has a base in $\\mathcal B$: its prefix base when\nplain, and its empty-stack output when free.  Write\n$\\operatorname{base}(v)[A]$ for the candidate at actual type $A$ selected\nby that base.\n\nFirst specify the product candidate used in both constructions.\nFor a product tree with children $D:a,E:b$, put\n\\[\n D_0=(D\\xi)^\\#,\n \\qquad A=((\\Pi x:D.E)\\xi)^\\#,\n \\qquad A_n=(E\\xi[x:=n])^\\#.\n\\]\nThe binder is chosen fresh for the images.\nWhen $\\pi(A)\\in S$, define $\\mathcal P(t;\\xi,\\theta)$ to consist of\nthe strongly normalizing $h\\in\\operatorname{Ty}(A)$ satisfying the\nfollowing test.  The tester $n$ ranges over\n\\[\n \\begin{cases}\n \\operatorname{Read}(D:a;\\xi,\\theta),&\\pi(D_0)\\in S,\\\\\n \\operatorname{Ty}(D_0)\\cap\\mathrm{SN}_\\beta,&\\pi(D_0)\\notin S.\n \\end{cases}\n\\]\nIf $\\pi(A_n)\\in S$, require\n\\begin{equation}\\label{obs:product-test}\n h\\,n\\in\\operatorname{Read}(E:b;\\xi[x:=n],\\theta[x:=v])\n\\end{equation}\nfor every $v\\in\\mathcal K_{D_0}$ when $D_0$ is active, and for the\nunique parameter-free extension otherwise.  If $\\pi(A_n)\\notin S$,\nrequire just that $h\\,n$ be strongly normalizing at its exact type\n$A_n$.  Normalization and conversion identify $A_n$ with the\ninstantiation of the normalized product $A$.  The candidate test lemma\ntherefore shows that $\\mathcal P$ is a candidate whenever its child\nReads are defined.\n\nThe clauses for $\\operatorname{Val}$ are as follows.\n\\begin{enumerate}\n\\item A variable looks up its parameter.  A sort has the distinguished\n      value of its actual type.\n\\item At a product with literal core sort $s$, start with the\n      distinguished value.  If $\\{s\\}\\in S$ and the profile of the\n      actual product expression is in $S$, replace its base coordinate\n      at that expression by $\\mathcal P$.  All other coordinates and\n      later observations stay distinguished.\n\\item At a lambda $\\lambda x:D.P$, the base is $\\mathbf t$.\n      If its actual type is plain, prescribe its next value at every\n      legal frame $(q,v)$ to be\n      \\begin{equation}\\label{obs:lambda-equation}\n       \\operatorname{Val}(P;\\xi[x:=q],\\theta[x:=v]).\n      \\end{equation}\n      The absent-payload convention applies when the domain is\n      inactive.  Product compatibility gives the asserted target type.\n\n      At a free actual type, give the following behavior on raw stacks.\n      For a nonempty stack, check that its first key $q$ is a normal\n      term at the actual normalized domain and that its next actual\n      type is active.  Check its payload: absent at an inactive domain,\n      an opposite-sign free value at a free domain, or a plain value\n      with exactly the domain's type tag at a plain domain.  Finally\n      require a continue flag exactly when the next type is free, and\n      an exit flag exactly when it is plain.  Lemma~\\ref{obs:graph-lemma}\n      gives the same sign at a free next type and the opposite sign at\n      a free domain.  On a valid continue frame, observe\n      \\eqref{obs:lambda-equation} on the remaining stack.  On a valid\n      exit frame, return that value tagged with its plain target type.\n      On an invalid stack, return the distinguished output for its\n      ending.  The finite-bound lemma below proves that this behavior\n      represents a member of the minus space when the lambda is minus.\n\\item At an application $F\\,N$ with active actual result type, return\n      its distinguished value if the function's actual type is\n      inactive.  Otherwise use the frame with key $(N\\xi)^\\#$ and\n      payload $\\operatorname{Val}(N)$ when its actual domain is active.\n      At a plain function take its next value.  At a free function take\n      a continue slice when the result is free.  When the result is\n      plain, take the exit output and project its correctly tagged\n      value, using the distinguished target value for a missing or\n      wrong tag.  The graph lemma and subject reduction make this a\n      legal frame with the asserted target.\n\\end{enumerate}\n\nThe clauses for $\\operatorname{Read}(M:s)$ are these.  If the actual\nexpression profile is outside $S$, or if $M$ is a literal sort, use the\ntop candidate.  At product syntax with actual profile in $S$, use\n$\\mathcal P$.  At all other syntax with actual profile in $S$, use\n\\begin{equation}\\label{obs:read-base}\n \\operatorname{base}(\\operatorname{Val}(M:s))[\\,(M\\xi)^\\#\\,]\n\\end{equation}\nif $\\{s\\}\\in S$ and $\\operatorname{Ax}(s)$ is active; use top otherwise.\nThe layer required for the Val in \\eqref{obs:read-base} is exactly\n$\\operatorname{Ax}(s)$.\n\nVal calls Read only on strict syntax children.  Read may call Val at\nthe same root.  Thus the intended recursion order is syntax size,\nwith Val preceding Read at a fixed size.  The next lemma establishes\ntotality as well as the only representability issue, at a minus lambda.\nIt also establishes compatible-image invariance without assuming that\nthe raw images are strongly normalizing.\n\n\\input{sections/observation-bounds.inc.tex}\n\n\\begin{lemma}[Structural invariance]\\label{obs:structural-invariance}\nCompatible environments give identical Val and Read outputs.  Valid\nthinning and context conversion, with transported trees and compatible\nold environments, preserve both outputs.  Retargeting a root to a\nconvertible expected type preserves Val.  All these operations commute with capture-avoiding renaming of generic\ndeclared and bound variables, reindexing the substitution and parameters\nwhile leaving their actual images and values unchanged.\n\\end{lemma}\n\\begin{proof}\nFor compatible environments, apply the finite-bound lemma.  Plain,\nminus, and Read equality follows at once.  For plus equality, fix any\nfinite raw stack.  Its finitely many minus payloads lie in some common\n$C_k$; equality through level $k$ gives equality on that stack.  Thus\nthe tables are equal.\n\nFor the structural operations, induct in the Val/Read syntax order.\nActual normalized types and frame keys agree.  At a binder, the raw\ndomain types are equal or convertible sorted types, so the same actual\nterm gives a typed extension on both sides and the induction hypothesis\napplies for every matching payload.  This proves equality of all\nprefix coordinates or raw observations.  At a product candidate it\nproves equality of domain tests and, for every raw tester and payload,\nof the codomain candidates.  The application clause then extracts the\nsame coordinate or slice.  The other clauses are immediate from lookup,\ndefaults, and base projection.  Retargeting changes no core clause or\nchild and leaves the actual index unchanged.  Bound variables may be chosen fresh throughout. Reindexing a generic\ndeclaration leaves its actual image, parameter, and every subsequent actual\ntype unchanged; the same induction therefore also proves equivariance\nunder renaming generic declarations.\n\\end{proof}\n\n\\subsection{Good evaluations and substitution}\n\\label{obs:goodness}\n\nArbitrary stored trees need not respect substitution: the default used\nat an inactive function can hide syntax that later becomes relevant.\nWe identify the trees for which this cannot occur.  Goodness will\ndepend on the raw images $\\xi$, but never on their parameters $\\theta$.\n\nFor an active Val evaluation, define goodness by the following clauses.\nA lambda is good if its body is good under every typed raw extension\n$x:=n$ for which the actual next type is active.  An application with\ninactive actual function layer is good if its syntactic head is a\nvariable.  An application with active function layer is good if the\nfunction is good and the argument is good whenever its actual layer is\nactive.  A product whose base is overridden is good if its product\ncandidate is good.  Lookups and all other default clauses are good\nwithout further conditions.\n\nThe goodness of a used product candidate means that its domain Read is\ngood when used, and its codomain Read is good under \\emph{every} typed\nraw extension having actual codomain profile in $S$.  There is no\ncandidate-membership restriction on these extensions.  For Read,\ndefaults outside actual profile $S$, and sorts, are good.  A product\nRead in $S$ is good when its product candidate is good.  A remaining\nRead in $S$, with expected sort $s$, is good if $\\{s\\}\\in S$ and,\nwhen $\\operatorname{Ax}(s)$ is inactive, its syntactic head is a\nvariable; when that layer is active, its Val must be good.\nThese are again definitions by the Val/Read syntax order, and their\nbinder clauses quantify over raw terms, not over reduction sequences.\n\n\\begin{lemma}[Basic goodness facts]\\label{obs:goodness-basic}\nGoodness has the structural invariances of\nLemma~\\ref{obs:structural-invariance}.  If the actual expression profile\nand $\\{s\\}$ are in $S$ and $\\operatorname{Ax}(s)$ is active, then\nRead$(M:s)$ is always the base projection\n\\eqref{obs:read-base}, at every syntactic shape, and its goodness is\nequivalent to Val goodness.\n\nEvery normal syntax tree is good for active Val, under every typed raw\nsubstitution.  A normal tree $M:s$ is good for Read whenever its generic\nexpression profile belongs to $U\\cup S$.\n\\end{lemma}\n\\begin{proof}\nStructural invariance follows by the same recursion as for evaluation,\nmatching all typed raw binder extensions by conversion.  For the base\nprojection assertion, the sort case gives top on both sides, the\nproduct case is exactly the override, and all other cases use\n\\eqref{obs:read-base}.  In the product case its literal core sort equals\n$s$ because it is convertible to $s$.  The goodness clauses agree for\nthe same reason.\n\nProve the normal-syntax claims simultaneously in the Val/Read syntax\norder and universally over typed raw substitutions.  Normal applications\nare variable-headed, so skipped ones are good.  Lambda bodies are\nnormal and the induction hypothesis applies to every typed extension.\nAt an overridden normal product, its generic profile contains the\ncore sort $s$, with $\\{s\\}\\in S$.  The positive inclusion edge places\nthat generic profile, and hence both child profiles, in $U\\cup S$.\nThe child Read induction hypotheses prove all required goodness\nconditions.  The same predecessor argument applies at product Read.\nFor a nonsort, nonproduct normal Read whose actual profile is in $S$,\nthe generic profile cannot be in $U$, since growth from $U$ stays in\n$U$.  It is therefore in $S$.  The normal expression is neutral and\nhas singleton generic profile $\\{s\\}$, giving exactly the required\nRead condition; its Val, when needed, is good by induction.\n\\end{proof}\n\n\\begin{lemma}[Substitution of a good value]\\label{obs:substitution}\nLet a tree over $\\Gamma,x:D,\\Delta$ be given, and let $u$ be a tree of\n$\\Gamma\\vdash N:D$.  Substitute $u$ capture-avoidably at occurrences\nof $x$, transporting the context, expectations, and generation data.\nGive the resulting context a typed environment, and give the original\ncontext a compatible environment whose image of $x$ is convertible to\nthe actual expression $N\\xi$.  Require the remaining corresponding\nparameters to agree.\n\nLet $I=\\pi((D\\xi)^\\#)$.  Assume that $I$ is active whenever $I\\in H$.\nIf $I$ is active, assume Val$(u;\\xi)$ is good and use its value as the\noriginal parameter of $x$.  Then a good Val or Read evaluation before\nsubstitution is good after substitution, and the two outputs are equal.\n\\end{lemma}\n\\begin{proof}\nComposition of capture-avoiding substitutions makes corresponding\nactual images convertible and corresponding actual types equal.\nInduct on the original tree in the Val/Read order.  An active lookup\nof $x$ is exactly the chosen value of $u$; thinning and retargeting\npreserve it by structural invariance.  Other lookups agree.\n\nAt a binder, rename its variable fresh for $N$.  The same raw binder\nimage and payload extend both environments, the hypotheses on $I$\nremain unchanged, and the induction hypothesis applies.  This gives\nequality at every legal frame or every raw observation and proves all\nuniversally quantified goodness requirements.  At a product the root\npersists.  Domain Reads agree by induction; for every raw tester and\npayload the codomain Reads agree by the binder argument.  Thus the\nentire product candidates agree and remain good.  At an unskipped\napplication, the evaluated children agree by induction and its normal\nargument keys agree by conversion, so it extracts the same output.\n\nIt remains to justify the skipped clauses, where an arbitrary change of\nhead would invalidate the argument.  Suppose a skipped, variable-headed\napplication had head $x$.  Trace its successive actual typing products\nbackward along the spine.  There is a primary positive path from its\nactive result layer to $I$, passing through its supposedly inactive\nfunction layer.  The path stays in $H$ by positive closure.  By\nhypothesis $I$ is active; consequently every vertex on the path reaches\na direct vertex and is active.  This is a contradiction.  The head is\ntherefore a different variable and persists under substitution, so the\nskipped application stays good and keeps its default.\n\nFor a skipped nonproduct Read, goodness supplies $\\{s\\}\\in S$ and a\nvariable head.  If that head were $x$, the same spine argument would\nstart at $\\operatorname{Ax}(s)$.  This is nonempty because a neutral\ntyped at $s$ has sorted $s$, and it is a seed in $H$.  It would reach\nthe active vertex $I$, forcing $\\operatorname{Ax}(s)$ active, contrary\nto this being a skipped Read.  Thus that head also persists.  In the\nactive case use the all-shape base projection of\nLemma~\\ref{obs:goodness-basic} and the Val induction already proved,\neven if substitution changes the root shape.  Read outside actual\nprofile $S$ stays the same default.  This exhausts the clauses.\n\\end{proof}\n\n\\subsection{Reduction and normal-tree independence}\n\\label{obs:reduction}\n\n\\begin{lemma}[Reduction transport]\\label{obs:reduction-transport}\nA one-step full beta reduction of a typed expression transports its\ntree to a tree of the reduct at the same expected type.  For every\nenvironment, good Val and Read evaluations remain good and their\noutputs are preserved.  This includes reductions in annotations.\n\\end{lemma}\n\\begin{proof}\nConstruct the transported tree independently of its environment.  At\ncontextual steps transport the changed child.  A change in a product\ndomain context-converts its open codomain tree.  A change in a lambda\nannotation also transports the supporting product typing by subject\nreduction and context-converts the open body.  A change in an\napplication argument changes its core result type to a convertible\none; subject reduction retains the root expectation.\n\nInduct on the reduction position, simultaneously for Val and Read.\nAt evaluated children use the induction hypothesis.  At binders apply\nit under every typed raw extension required by goodness; structural\ninvariance handles the context-converted unchanged children.  Actual\ntypes, gates, and argument keys are unchanged.  At a skipped\nvariable-headed spine, a reduction can only be inside its arguments;\nthe head and skipped condition persist.  Ignored children need no\nsemantic property, only the transported typing.  For a remaining Read\nwith active base projection, apply the Val statement already proved\nat the same root.  A Read outside actual profile $S$ remains default.\n\nAt a root contraction $(\\lambda x:D.P)N\\longrightarrow P[N/x]$,\ngeneration and product compatibility align the lambda annotation and\nbody type with the application's stored product.  Retarget the\nargument to the annotation type, substitute its tree in the body, and\nretarget the result to the old expectation.\n\nConsider a good active Val.  The application cannot have been skipped,\nsince its syntactic head is a lambda.  Let $K,L$ be the actual function\nand result layers, respectively.  They are active.  By\nLemma~\\ref{obs:graph-lemma}, the actual domain layer $I$ is active\nwhenever it belongs to $H$.  The argument is good if $I$ is active.\nEvaluation uses the valid normal key $(N\\xi)^\\#$ and, when required,\nthe payload Val$(N)$.  The lambda equation gives its body value with\nthis key, including at an exit where the lambda supplied the correct\ntag.  The raw image $N\\xi$ is convertible to its normal form.\nStructural invariance therefore identifies this body value with the\none at the raw image, with the same payload.  Lambda goodness supplies\nbody goodness for both typed extensions.  Lemma~\\ref{obs:substitution}\nnow identifies that value with the beta contractum and proves its\ngoodness.  No strong normalization of $N\\xi$ was used.\n\nFor a Read with actual profile in $S$, a skipped clause at the root is\nimpossible because the head is a lambda.  Its goodness therefore\nprovides the active all-shape base projection, and the preceding Val\nargument applies.  This proves the root case and the lemma.\n\\end{proof}\n\n\\begin{lemma}[Independence on normal syntax]\\label{obs:normal-independence}\nOn a fixed normal expression in a fixed generic context, Val is\nindependent of its stored tree whenever the root expectations are\nconvertible.  Read is independent of the stored tree and of its chosen\nliteral expected sort.\n\\end{lemma}\n\\begin{proof}\nInduct in the Val/Read syntax order, comparing the same environments.\nVariables use the same declaration parameter and sorts use defaults.\nFor product Val, convertible expected types force its two literal core\nsorts to agree.  The override condition is therefore identical.  For\nproduct Read, the product candidate compares the child Reads\nindependently of their sort choices, by induction.  Its raw extensions\nand actual indices agree, so the candidates are equal.\n\nFor a lambda, product compatibility aligns the body expectations up to\nconversion.  The annotation is fixed syntax.  The body Val induction\ntherefore compares every frame and observation.  In a normal\napplication the function is variable-headed, so its generic type is\nunique up to conversion.  Product compatibility aligns the domains and\ncodomains in the two application trees.  The function and argument\nVal induction hypotheses give the same keys, payloads, and selected\noutputs.  Finally, a normal nonsort, nonproduct expression is neutral\nand has a unique literal sort.  Thus any nondefault Read uses the same\nVal base.  These are all normal syntactic shapes.  Only neutral type\nuniqueness has been used; functionality of the specification is not\nassumed.\n\\end{proof}\n\n\\subsection{The candidate interface}\n\\label{obs:interface}\n\nWe can now return from raw observations to the candidate interpretation.\nFor a generic normalized type $T$ with $\\pi(T)\\in S$, choose any\nliteral sort typing of $T$ and put\n\\begin{equation}\\label{obs:R-definition}\n        R_T(\\xi,\\theta)=\\operatorname{Read}(T;\\xi,\\theta).\n\\end{equation}\nIt is independent of the choice by\nLemma~\\ref{obs:normal-independence}.  At generic profiles outside $S$\nuse the typed-SN top, as in the candidate interface.  At actual\nprofiles in $U$, all candidates are top by the already established\nequality with $G$.\n\n\\begin{proposition}[Observation interpretation]\\label{obs:interface-theorem}\nAssume system-wide weak normalization and \\eqref{obs:forbidden}.\nThen \\eqref{obs:R-definition} gives candidates with the required\nweakening and context-conversion invariance and nonempty parameter\nextensions.  It has the product test clause and the specialization\nproperty: if $T=\\Pi x:D.E$ is generic normalized of profile in $S$,\n$\\Gamma\\vdash N:D$, and $N\\xi$ passes its domain test, then the test\nof a candidate member $h$ entails\n\\[\n                   h\\,(N\\xi)\\in R_{(E[N/x])^\\#}(\\xi,\\theta).\n\\]\nThe ordinary typed-SN clause applies when the instantiated actual\nprofile lies in $U$.\n\\end{proposition}\n\\begin{proof}\nCandidate membership and the product clause are the Read construction\nand the candidate test lemma.  The interpretation's invariances follow\nfrom structural invariance and normal-tree independence.  Every active\nspace has its distinguished member, so arbitrary context extensions\ncan always be supplied with parameters.\n\nFor specialization, if the actual product profile is in $U$, its\ncandidate is the top and hence equals $G$.  The domain tester is\nstrongly normalizing.  Propagation of $G$ along this typed argument\ngives the required typed strong normalization at the instantiated\nprofile, which also lies in $U$.  We may therefore suppose the actual\nproduct profile $K$ is in $S$.  First suppose that the actual\ninstantiated codomain profile $L$ is in $S$, and let $I$ be the actual\ndomain profile.  Lemma~\\ref{obs:graph-lemma} places the open codomain profile in $S$.\nThus if $I\\in H$, the definition makes $I$ direct and hence active;\nin particular, every active $I$ is direct.  Normalize the generic argument\n$N$ to $N^\\#$, retaining its type $D$ by subject reduction.  If $I$ is\nactive, choose as binder payload\n\\[\n                     v=\\operatorname{Val}(N^\\#:D;\\xi,\\theta).\n\\]\nThis exists and is good by normal-syntax goodness.  The product test\nquantifies over every payload and therefore includes this choice.\n\nIts open codomain $E$ is normal, with generic profile in $U\\cup S$,\nso its Read is good.  Apply Lemma~\\ref{obs:substitution} to substitute\n$N^\\#$ there, using the raw image $N\\xi$.  This image is convertible\nto $N^\\#\\xi$, as required.  The lemma identifies the tested open\ncodomain Read with the good Read of the generic expression\n$E[N^\\#/x]$.  This expression is typed by substitution and hence\nweakly normalizing by the system hypothesis.  Transport its tree along\na normalizing sequence using Lemma~\\ref{obs:reduction-transport}.\nIts normal form is precisely $(E[N/x])^\\#$, because $N$ and $N^\\#$\nare convertible.  Normal-tree independence identifies the final Read\nwith \\eqref{obs:R-definition} at that type.\n\nFor completeness, its generic profile belongs to $U\\cup S$: it has a\npositive ascent to the original product profile.  It cannot belong\nto $U$ when its actual profile is $L\\in S$, because profile growth\nfrom $U$ stays in $U$.  Thus \\eqref{obs:R-definition} is indeed the\nappropriate generic $S$ entry.  If the actual instantiated profile is\nin $U$, the test demands typed strong normalization and the processed\ncomponent identity identifies this with the required top candidate.\nThese are the only actual profiles reached from the current component.\n\\end{proof}\n\nNothing in this construction requires $H$ to be nonempty.  If it is\nempty there are no active spaces or parameters.  A normal neutral Read\nof generic profile in $S$ would have profile $\\{s\\}$ with sorted $s$,\nwhich would supply the nonempty seed $\\operatorname{Ax}(s)$ in $H$.\nThus that case cannot occur, and the sort/product clauses complete the\nsame construction without an extra assumption.\n\nThe uses of weak normalization in this section are now explicit.  It\nprovides the normal forms of actual types, argument keys, and raw typed\nsubstitutions, and it normalizes the generic expressions used in the\nspecialization proof.  Strong normalization enters only through the\ncandidate tests already specified in the normalization interface.  The\nremaining task is to derive \\eqref{obs:forbidden} from weak normalization.\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/profiles.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/profiles.tex", "bytes": 42115, "sha256": "61bc613ca8b48a02d16a4a1fd91cc269273133e21f5f8a0b0ce3c87dc54debcc", "content": "\n\\section{Sort profiles and normalization candidates}\n\\label{sec:profiles}\n\nThroughout this section the pure type specification is finite and every\nlegal expression is weakly normalizing for full beta reduction. We use\nthe substitution, generation, context-conversion, subject-reduction and\nconfluence properties established in the preliminaries. Write $M^\\#$\nfor the normal form of a legal expression, up to alpha conversion.\nIn particular, the expected type of a judgment has a normal form: it is\neither sorted or already a literal sort. No uniqueness of sorts or\nproduct rules is assumed.\n\nWe first associate a finite set of sorts to each type. These sets will\norder the normalization argument. Within one part of that order we\nshall construct sets of terms that pass certain application tests. The\nmain result of the section is an interface theorem: any construction\nwith the specified substitution and product properties advances the\nnormalization argument by one component.\n\n\\subsection{Profiles and the dependency graph}\n\nFor a sort $s$ and sets of sorts $I,J$, put\n\\[\n \\operatorname{Ax}(s)=\\{t:(s,t)\\in\\mathcal A\\},\\qquad\n \\operatorname{Out}(I,J)\n =\\{c:\\text{some }a\\in I,\\ b\\in J\\text{ satisfy }(a,b,c)\\in\\mathcal R\\}.\n\\]\nFor an expected type $T$ in a valid context $\\Gamma$, define its\n\\emph{profile} by\n\\[\n             \\pi_\\Gamma(T)=\\{s:\\Gamma\\vdash T^\\#:s\\}.\n\\]\nThe profile can be empty when $T$ is an unsorted literal sort. When the\ncontext is clear we omit its subscript. A \\emph{neutral expression} is\na variable followed by zero or more arguments.\n\n\\begin{lemma}[Normal profiles]\n\\label{lem:normal-profiles}\nA sorted normal expression is a sort, a neutral expression, or a\nproduct. Its profile is determined as follows:\n\\begin{enumerate}\n\\item a literal sort $s$ has profile $\\operatorname{Ax}(s)$;\n\\item a neutral expression has a singleton profile $\\{s\\}$, and\n      $\\operatorname{Ax}(s)$ is nonempty;\n\\item a normal product $\\Pi x:D.E$ has profile\n\\[\n \\operatorname{Out}\\bigl(\\pi_\\Gamma(D),\n                         \\pi_{\\Gamma,x:D}(E)\\bigr).\n\\]\n\\end{enumerate}\nProfiles are unchanged by valid thinning and context conversion.\nIf $\\xi:\\Gamma\\to\\Delta$ is a typed substitution, then\n\\begin{equation}\n                  \\pi_\\Gamma(T)\n                    \\subseteq\\pi_\\Delta(T\\xi).\n\\label{eq:profile-growth}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nA normal application is variable-headed: a lambda head would be a\nredex, and a sort or product cannot have a product type by generation\nand product compatibility. A lambda cannot have a literal sort type\nfor the same reason. Generation for a sort gives exactly its axiom\ntargets. Generation and product formation give the two inclusions in\nthe formula for a product, separately for every applicable rule.\n\nFor a neutral expression, its type is unique up to conversion. Indeed,\nthe assertion for its head variable follows from its declaration.\nInductively, generation and compatibility of convertible products\nidentify the domain and instantiated codomain at each further\napplication. Thus two literal sort types of a neutral are identical.\nIts literal sort type $s$ is itself sorted. For a lone variable its\ndeclaration type is sorted and convertible to $s$; confluence makes\nthat declaration type reduce to $s$, and subject reduction sorts $s$.\nFor a nonempty application spine, correctness of the expected type\nalready gives a sorting of $s$. Generation for $s$ now implies\n$\\operatorname{Ax}(s)\\ne\\varnothing$.\n\nThinning preserves the profile by induction on normal syntax. At a\nneutral, an existing sort is retained and uniqueness excludes any new\none. At a product apply the induction also in its extended binder\ncontext. Literal sorts do not depend on the context. Context conversion\npreserves all relevant judgments in both directions, hence preserves\nprofiles as well.\n\nFinally, if $s\\in\\pi_\\Gamma(T)$, substitution gives\n$\\Delta\\vdash T^\\#\\xi:s$. Reduction of $T$ to $T^\\#$ commutes with\nsubstitution, so $T^\\#\\xi$ and $T\\xi$ have the same normal form.\nSubject reduction therefore gives\n$\\Delta\\vdash(T\\xi)^\\#:s$. This proves the inclusion. For a\nsubstituted product the outer product persists, and its components\nnormalize separately; context conversion transports the open\ncodomain to the normalized domain. The same inclusion consequently\nholds at corresponding product children.\n\\end{proof}\n\nA nonempty profile is \\emph{feasible} if it occurs in some valid\ncontext. Since the specification is finite, there are only finitely\nmany feasible profiles.\n\n\\begin{lemma}[Feasible profiles]\n\\label{lem:feasible-profiles}\nThe feasible profiles form the smallest family containing\n\\[\n \\operatorname{Ax}(s)\\quad\\hbox{and}\\quad\\{s\\}\n       \\qquad\\bigl(\\operatorname{Ax}(s)\\ne\\varnothing\\bigr)\n\\]\nand closed under nonempty values of $\\operatorname{Out}$.\n\\end{lemma}\n\n\\begin{proof}\nNecessity follows by induction on a sorted normal expression, using\nLemma~\\ref{lem:normal-profiles}. Conversely, $s$ witnesses\n$\\operatorname{Ax}(s)$, and a variable declared at type $s$ witnesses\n$\\{s\\}$. Given normal witnesses for $I$ and $J$, rename their finite\ncontexts apart and combine those contexts by thinning. Form a product\nwith the first witness as domain and the second as a codomain\nindependent of its fresh binder. Its profile is exactly\n$\\operatorname{Out}(I,J)$. These witnesses are normal.\n\\end{proof}\n\nCall $(I,J,K)$ a \\emph{profile triple} when $I,J,K$ are feasible and\n$K=\\operatorname{Out}(I,J)$. Define the \\emph{primary graph}, a finite signed directed graph on\nthe feasible profiles, by adding:\n\\begin{itemize}\n\\item a positive edge $L\\to J$ whenever $L\\supsetneq J$;\n\\item for each profile triple $(I,J,K)$, a negative edge $I\\to K$\n      and a positive edge $J\\to K$.\n\\end{itemize}\nParallel edges retain their signs and their associated triples. Here and\nbelow a path means a finite directed walk: vertices and edges may repeat.\nIts parity is the parity of its number of negative edges; a\nlength-zero path is even.\n\nIf $T=\\Pi x:D.E$ has profile $K$, write $I=\\pi(D)$ and\n$J=\\pi(E)$. For every typed $n:D$, profile growth gives\n\\begin{equation}\n L=\\pi\\bigl((E[x:=n])^\\#\\bigr)\\supseteq J,\n \\qquad L\\longrightarrow J\\longrightarrow K\n \\quad\\hbox{by positive edges},\n\\label{eq:codomain-ascent}\n\\end{equation}\nwhere the first edge is omitted when $L=J$.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tikzpicture}[>=stealth,baseline=(current bounding box.center)]\n\\node (I) at (0,1.1) {$I=\\pi(D)$};\n\\node (L) at (-1.4,-.5) {$L\\supseteq J$};\n\\node (J) at (1,-.5) {$J=\\pi(E)$};\n\\node (K) at (4,.45) {$K=\\pi(\\Pi x:D.E)$};\n\\draw[->] (I) -- node[above] {$-$} (K);\n\\draw[->] (J) -- node[below right] {$+$} (K);\n\\draw[->] (L) -- node[above] {$+$} (J);\n\\end{tikzpicture}\n\\caption{The domain and open codomain point to their product. An\ninstantiated codomain has profile $L\\supseteq J$ and also has a\npositive path to the product. The edge $L\\to J$ is omitted if $L=J$.}\n\\label{fig:profile-dependencies}\n\\end{figure}\n\nOrder the strongly connected components so that every predecessor\ncomponent is processed first. At a given stage let $S$ be the current\ncomponent and $U$ the union of the already processed components.\nThus $U$ is predecessor-closed, and every predecessor of $S$ lies in\n$U\\cup S$. The graph immediately gives the following facts:\n\\begin{enumerate}\n\\item the domain and open codomain of an $S$ product have profiles\n      in $U\\cup S$;\n\\item every instantiated codomain of that product has profile in\n      $U\\cup S$;\n\\item any feasible enlargement of a profile in $S$ lies in $U\\cup S$;\n\\item any feasible enlargement of a profile in $U$ lies in $U$.\n\\end{enumerate}\nAll statements also hold after valid thinning or context conversion.\n\n\\subsection{Application tests and candidates}\n\nUse the saturated context $\\Omega$ from the preliminaries. A judgment\nover $\\Omega$ means a judgment over some finite prefix; finitely many\njudgments may always be thinned to a common prefix. Profile invariance\nmakes their profiles independent of this choice. An \\emph{actual\ntype} is a normal sorted expression over $\\Omega$, or a literal sort.\nFor such a type put\n\\[\n \\operatorname{Ty}(T)=\\{h:\\Omega\\vdash h:T\\},\\qquad\n \\top_T=\\operatorname{Ty}(T)\\cap\\operatorname{SN}_\\beta.\n\\]\nHere and below strong normalization includes reduction in annotations.\n\nA \\emph{tested stack at $T$} is a finite list defined inductively.\nThe empty list is always a tested stack. If $T=\\Pi x:D.E$,\n$n\\in\\top_D$, and $\\vec m$ is a tested stack at\n\\[\n                     T_n=(E[x:=n])^\\#,\n\\]\nthen $(n,\\vec m)$ is a tested stack at $T$. This definition follows\nthese exact successive types; it does not range over alternative\ntypings of a term. Define\n\\[\n G_T=\\{h\\in\\operatorname{Ty}(T):\n            h\\,\\vec n\\in\\operatorname{SN}_\\beta\n            \\text{ for every tested stack }\\vec n\\text{ at }T\\}.\n\\]\nWe shall establish, component by component, the invariant\n\\begin{equation}\n                    G_T=\\top_T\\qquad(\\pi(T)\\in U).\n\\label{eq:processed-invariant}\n\\end{equation}\n\n\\begin{lemma}[Elementary properties of the tests]\n\\label{lem:test-properties}\nFor every actual type $T$, $G_T\\subseteq\\top_T$. If $T$ is a literal\nsort, equality holds. Every variable typed at $T$ belongs to $G_T$.\nIf $T=\\Pi x:D.E$, $h\\in G_T$, and $n\\in\\top_D$, then\n$h\\,n\\in G_{T_n}$.\n\\end{lemma}\n\n\\begin{proof}\nThe empty stack gives the inclusion; at a literal sort it is the only\nstack. A variable followed by finitely many strongly normalizing\narguments is strongly normalizing: its head never contracts, and a\nreduction decreases the sum of the arguments' reduction heights.\nThis proves the assertion for variables. For propagation, append any\ntested stack at $T_n$ to $n$ and apply the definition of $G_T$.\nApplication and conversion give the required exact typing at $T_n$.\n\\end{proof}\n\n\\begin{lemma}[Head expansion for strong normalization]\n\\label{lem:sn-head-expansion}\nSuppose that $D,P,n,m_1,\\ldots,m_k$ are strongly normalizing and that\n\\[\n                    P[x:=n]m_1\\cdots m_k\n\\]\nis strongly normalizing. Then\n$(\\lambda x:D.P)n m_1\\cdots m_k$ is strongly normalizing.\n\\end{lemma}\n\n\\begin{proof}\nEvery strongly normalizing expression has a finite maximal reduction\nlength. Indeed, reduction is finitely branching; if lengths were\nunbounded, at least one immediate reduct would again have unbounded\nlengths, and iterating this choice would give an infinite reduction.\nWrite $\\ell(Q)$ for this maximal length and induct on\n\\[\n        \\ell(D)+\\ell(P)+\\ell(n)+\\sum_{i=1}^k\\ell(m_i).\n\\]\nThe head reduct is strongly normalizing by hypothesis. Every other\nimmediate reduct changes one displayed component and strictly lowers\nthis sum. Its head contractum is either unchanged or is a reduct of\nthe old head contractum. For a step in $n$, contract the corresponding\nstep in each of its finitely many copies in $P[x:=n]$; there may be\nzero copies. For a step in the annotation, the contractum is unchanged.\nThus the new contractum is strongly normalizing, and the induction\napplies. Every immediate reduct is strongly normalizing, which proves\nthe claim.\n\\end{proof}\n\n\\begin{definition}[Candidate at an actual type]\n\\label{def:candidate}\nA candidate at $T$ is a set $C$ satisfying\n$G_T\\subseteq C\\subseteq\\top_T$ and the following head-expansion\nproperty: if\n\\[\n r=(\\lambda x:D.P)n\\,\\vec m\\in\\operatorname{Ty}(T),\n\\]\nall displayed components $D,P,n,\\vec m$ are strongly normalizing, and\n$P[x:=n]\\vec m\\in C$, then $r\\in C$. Denote the candidates at $T$\nby $\\mathcal C(T)$.\n\\end{definition}\n\nReduction closure is not part of this definition. All subsequent\ncandidate arguments use only the properties just stated.\nThis is a local variant of the reducibility-candidate method\n\\cite[Chapters~6 and~14]{Girard1989}; the next lemma proves its required\nlattice and product closure directly.\n\n\\begin{lemma}[Candidate lattice and product tests]\n\\label{lem:candidate-lattice}\nThe inclusion-ordered set $\\mathcal C(T)$ is a complete lattice with\nlargest element $\\top_T$. Write $\\bot_T$ for its least element.\nFor an actual product $T=\\Pi x:D.E$, choose a set\n$A\\subseteq\\top_D$. For each $n\\in A$ choose any family of candidates\n$C_{n,a}\\in\\mathcal C(T_n)$ indexed by a set $Q_n$. Then\n\\begin{equation}\n \\{h\\in\\top_T:\\text{for every }n\\in A\\text{ and }a\\in Q_n,\n                                  \\ h n\\in C_{n,a}\\}\n\\label{eq:product-test-candidate}\n\\end{equation}\nis a candidate at $T$.\n\\end{lemma}\n\n\\begin{proof}\nLemma~\\ref{lem:sn-head-expansion} shows that $\\top_T$ is a candidate.\nIntersections of candidates preserve all three requirements of\nDefinition~\\ref{def:candidate}; take the empty intersection to be\n$\\top_T$. Therefore every family has a meet, and its join is the\nintersection of its common upper bounds. This family of upper bounds\nis nonempty because it contains $\\top_T$. In particular, the least\ncandidate is the intersection of all candidates.\n\nFor \\eqref{eq:product-test-candidate}, a term in $G_T$ passes every\ntest by Lemma~\\ref{lem:test-properties} and the containment\n$G_{T_n}\\subseteq C_{n,a}$. Suppose a head contractum belongs to\n\\eqref{eq:product-test-candidate} and its expansion has strongly\nnormalizing components. Lemma~\\ref{lem:sn-head-expansion} gives the\nexpansion's membership in $\\top_T$. Append a prescribed $n\\in A$ to\nits spine. The enlarged head contractum lies in $C_{n,a}$ for every\n$a$, and all enlarged components are strongly normalizing. Head\nexpansion in $C_{n,a}$ gives the required membership of the enlarged\nspine. This proves every test, without using reduction closure of a\ncandidate.\n\\end{proof}\n\nAt profiles in $U$, the invariant\n\\eqref{eq:processed-invariant} forces every candidate to equal\n$\\top_T$. This is why previously processed types need no further\nchoices.\n\n\\subsection{The interface for one component}\n\\label{subsec:candidate-interface}\n\nWe now specify exactly what a component construction must supply.\nThe context $\\Gamma$ and its syntax will be called \\emph{generic};\na typed substitution $\\xi:\\Gamma\\to\\Omega$ supplies raw actual\nimages. An environment also contains auxiliary parameters $\\theta$\nfor the declarations. Each construction below specifies their sets.\nRequire an empty parameter environment and compatible parameter\nextensions along every typed context extension. Restrictions to\npreceding declarations are understood when interpreting declaration\ntypes.\n\nFor each normal generic expected type $T$ with\n$\\pi_\\Gamma(T)\\in S$, the construction supplies a candidate\n\\[\n       R_{\\Gamma,T}(\\xi,\\theta)\n                     \\in\\mathcal C\\bigl((T\\xi)^\\#\\bigr).\n\\]\nFor any other normal generic expected type use the convention\n\\[\n              R_{\\Gamma,T}(\\xi,\\theta)=\\top_{(T\\xi)^\\#}.\n\\]\nWhen no ambiguity arises write simply $R_T$. Actual profiles obtained\nfrom generic profiles in $S$ lie in $U\\cup S$, and an actual profile\nin $U$ makes the candidate equal to top.\n\nThe required properties are the following.\n\\begin{enumerate}\n\\item\\label{item:interface-invariance}\n\\emph{Invariance.} Valid thinning, context conversion, and pointwise\nconvertible actual images preserve the candidates on old syntax when\nthe old parameters are unchanged. Capture-avoiding renaming of generic declared and bound variables preserves\nthe interpretation, with the substitution and parameter environment reindexed\non declared variables and all actual images and parameter values unchanged.\n\n\\item\\label{item:interface-product}\n\\emph{Product equality.} Let $T=\\Pi x:D.E$ have generic profile in\n$S$ and actual profile in $S$. For each $n\\in R_D$, specify a\nnonempty collection $Q_{T,n}$ of compatible parameter extensions to\n$\\Gamma,x:D$ with substitution $\\xi[x\\mapsto n]$. Then $R_T$\nequals the following product-test candidate. Its members are exactly\nthe $h\\in\\top_{(T\\xi)^\\#}$ such that, for every $n\\in R_D$:\n\\begin{itemize}\n\\item if the actual type $(E(\\xi[x\\mapsto n]))^\\#$ has profile\n      in $S$, then $h n\\in R_E(\\xi[x\\mapsto n],\\theta')$ for\n      every $\\theta'\\in Q_{T,n}$;\n\\item if that actual profile lies in $U$, then $h n$ is strongly\n      normalizing at that exact normal type.\n\\end{itemize}\nApplication is well typed because the actual domain of the normal\nproduct $(T\\xi)^\\#$ is $(D\\xi)^\\#$; substitution and normalization\nidentify its instantiated codomain with the displayed actual type.\nThe generic domain profile belongs to $U\\cup S$, so $R_D$ is the\ncandidate supplied by the construction or the prescribed top in $U$.\n\n\\item\\label{item:interface-specialization}\n\\emph{Specialization.} In the product situation, if\n$\\Gamma\\vdash N:D$, $N\\xi\\in R_D$, and $h\\in R_T$, then\n\\begin{equation}\n          h(N\\xi)\\in\n               R_{\\Gamma,(E[x:=N])^\\#}(\\xi,\\theta).\n\\label{eq:interface-specialization}\n\\end{equation}\nIt is enough to find one of the tested parameter extensions for which\nthe codomain candidate is contained in the candidate on the right.\nIf the actual codomain profile is in $U$, this follows directly from\nthe strong-normalization test.\n\\end{enumerate}\nBoth directions of the product-equality requirement are required: the lambda case uses its\nintroduction direction and application uses its elimination direction.\n\nA typed environment $(\\xi,\\theta)$ is \\emph{admissible} if, for each\ndeclaration $x:B$ of $\\Gamma$, its image satisfies the following\ncondition, evaluated over the preceding declarations:\n\\[\n \\begin{cases}\n \\xi(x)\\in R_{B^\\#},&\\pi(B)\\in S,\\\\\n \\xi(x)\\in\\operatorname{SN}_\\beta,&\\pi(B)\\in U,\\\\\n \\xi(x)\\in G_{(B\\xi)^\\#},&\\pi(B)\\notin U\\cup S.\n \\end{cases}\n\\]\nThe exact typings in these conditions follow from typed substitution\nand conversion. Admissibility imposes no separate restriction on the\nauxiliary parameters.\n\n\\begin{lemma}[Transport of strongly normalizing terms]\n\\label{lem:sn-fundamental}\nAssume \\eqref{eq:processed-invariant} and an implementation of\nthe three interface requirements in Section~\\ref{subsec:candidate-interface}\nfor $S$. If $\\Gamma\\vdash M:A$, the expression $M$ is strongly\nnormalizing, and $(\\xi,\\theta)$ is admissible, then\n\\begin{equation}\n                         M\\xi\\in R_{\\Gamma,A^\\#}(\\xi,\\theta).\n\\label{eq:sn-fundamental}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nInduct lexicographically on $(\\ell(M),|M|)$, where $|M|$ is syntax\nsize including annotations. The induction is simultaneous over all\ntypings, contexts, and admissible environments. A proper subexpression\nhas no larger reduction height and smaller size; a beta reduct has\nstrictly smaller height. Typed substitution gives all required actual\ntypings throughout.\n\nWe shall repeatedly use a fresh-variable extension. If a binder has\nraw generic annotation $B$, then $B\\xi$ is sorted over a finite\nprefix of $\\Omega$. Saturation supplies a fresh variable $z$ declared\nat that raw type, beyond a common prefix for the finite expressions\nin question. Choose $z$ outside the free names of all other images.\nIt belongs to $G_{(B\\xi)^\\#}$ by\nLemma~\\ref{lem:test-properties}, and hence satisfies whichever of\nthe three admissibility conditions applies. Any allowed parameter\nextension can be used. Invariance retains the assumptions on old\nvariables. If the induction proves the body with $x$ sent to $z$\nstrongly normalizing, renaming $z$ back to a bound $x$ proves strong\nnormalization of the open substituted body.\n\nFor a sort, $M\\xi=M$ is normal, and its expected normal type is a\nliteral sort. At a literal sort $G$ equals top, so this suffices for\nmembership in any candidate. For a product, apply the induction to\nits domain and to its open codomain under a fresh-variable extension.\nThe two substituted components are strongly normalizing, so the\nproduct is strongly normalizing. Its expected normal type is again\na literal sort, giving the conclusion.\n\nLet $M=\\lambda x:B.P$. The induction on $B$, and on $P$ under a\nfresh-variable extension, proves strong normalization of the actual\nannotation and open body. Hence $M\\xi$ is strongly normalizing.\nThis already proves the conclusion unless both the generic and the\nactual normalized expected types have profiles in $S$. In the latter\ncase write $A^\\#=\\Pi x:D.E$. Generation and product compatibility\nidentify $D$ with the normal form of $B$ and identify $E$ with the\nnormal body type, after context conversion under the binder.\n\nContext-convert the generic body judgment to\n$\\Gamma,x:D\\vdash P:E$. Take an arbitrary $n\\in R_D$ and arbitrary\nprescribed parameter extension in $Q_{A^\\#,n}$. Convert $n$ to the\nactualized type $D\\xi$ and extend this context's substitution by\n$x\\mapsto n$. This is admissible:\nthe generic domain profile is in $U\\cup S$, and the test condition is\nexactly the required candidate or SN condition. Apply the induction\nto the context-converted judgment for $P$ in this very extension.\nIt gives the required codomain candidate for the head contractum of\n$(M\\xi)n$. The actual annotation, open body and $n$ are strongly\nnormalizing. Head expansion in that codomain candidate proves the\ntest. If the actual codomain profile is in $U$, use any compatible\nparameter extension, the body's strong normalization, and\nLemma~\\ref{lem:sn-head-expansion}. We have verified every product\ntest; the introduction direction of\nthe product-equality requirement proves the conclusion.\n\nFor an application, inspect its entire left-associated spine. If its\nhead is a lambda, write\n\\[\n                M=(\\lambda x:B.P)N\\,\\vec L.\n\\]\nContract its first redex generically to $M'$. Subject reduction gives\n$\\Gamma\\vdash M':A$ and $\\ell(M')<\\ell(M)$, so the induction gives\n$M'\\xi\\in R_{A^\\#}$. The components $B,P,N,\\vec L$ are proper\nsubexpressions. Their inductions, using a fresh-variable extension for\nthe open $P$, give strong normalization of every component after\nsubstitution. Substitution commutes with the displayed contraction up\nto alpha conversion. Head expansion in $R_{A^\\#}$ therefore proves\nthe result. This step does not invoke reduction closure of a candidate.\n\nThe remaining case is a neutral spine $xN_1\\cdots N_k$, including\n$k=0$. Starting at the normal form of the declaration type of $x$,\ngeneration and neutral uniqueness determine its successive normal\nexpected types. Whenever a next argument occurs, the preceding type\nis a product; convert that argument to its normalized domain and take\nthe normalized instantiated codomain as the next type. The induction\napplied to each $N_i$ at this exact domain gives its candidate\nmembership and, in particular, strong normalization after substitution.\nTyped substitution and confluence identify the actual successive\ntypes with the normal forms of the corresponding generic types.\n\nTrack membership along the spine. A $G$ prefix propagates by\nLemma~\\ref{lem:test-properties}. If the head declaration has profile\nin $U$, admissibility and profile growth put it in actual top in $U$,\nhence in $G$ by \\eqref{eq:processed-invariant}. If a prefix has generic\nprofile in $S$, an actual profile in $U$ likewise gives $G$. Otherwise\nits candidate product test applies to the next argument, and\n\\eqref{eq:interface-specialization} gives the next candidate. That\nnext generic profile lies in $U\\cup S$ by the positive codomain path;\non reaching $U$ its membership is again in $G$. A head declaration\noutside $U\\cup S$ starts in $G$ by admissibility and requires no\nfurther restriction on profiles. Thus the final prefix belongs either\nto the desired candidate or to its subset $G$. No other spine head\ncan type as an application. This completes the induction.\n\\end{proof}\n\n\\begin{proposition}[Advancing one component]\n\\label{prop:advance-component}\nUnder the hypotheses of Lemma~\\ref{lem:sn-fundamental},\n\\eqref{eq:processed-invariant} also holds for every actual type with\nprofile in $S$.\n\\end{proposition}\n\n\\begin{proof}\nFix $h\\in\\top_T$ with $\\pi(T)\\in S$ and any tested stack\n$(n_1,\\ldots,n_k)$ at $T$. Choose a common finite prefix $\\Gamma$\nof $\\Omega$ supporting the relevant exact typings and use the\nidentity substitution. Choose parameters successively; all variable\nimages lie in $G$ and therefore the environment is admissible.\nApply Lemma~\\ref{lem:sn-fundamental} separately to $h$ and to each\n$n_i$ at its exact successive domain. Each is strongly normalizing\nby hypothesis or by the definition of a tested stack.\n\nStart with the resulting candidate membership of $h$ and iterate the\nproduct tests and specialization along the stack. Each encountered\ntype has profile in $U\\cup S$. In $S$ specialization supplies the\nnext candidate membership; in $U$ the invariant supplies $G$, which\npropagates across every remaining frame. Thus $h n_1\\cdots n_k$ is\nstrongly normalizing. The argument never assumes strong normalization\nof an intermediate generic application and never applies the\nfundamental lemma to such an application. Since the stack was\narbitrary, $h\\in G_T$. The reverse inclusion is\nLemma~\\ref{lem:test-properties}.\n\\end{proof}\n\n\\begin{proposition}[Completion of the interface argument]\n\\label{prop:interfaces-imply-sn}\nIf the interface can be implemented at every strongly connected\ncomponent, then every legal expression of the PTS is strongly\nnormalizing.\n\\end{proposition}\n\n\\begin{proof}\nProcess the finitely many components and apply\nProposition~\\ref{prop:advance-component}. At the end, $G_T=\\top_T$\nfor every actual type with nonempty profile. Empty-profile actual\ntypes are literal sorts, for which this equality already holds by\nLemma~\\ref{lem:test-properties}.\n\nSuppose a typable expression is not strongly normalizing and choose\none of least syntax size, among all valid contexts and typings. Its\nproper syntactic children are typable in the corresponding contexts,\nincluding under binders, and so are strongly normalizing. A variable\nor sort is normal; a product or lambda with strongly normalizing\nchildren is strongly normalizing. The expression must therefore be\nan application $f n$, with $f,n$ strongly normalizing. Generation\ntypes $f$ at a product and $n$ at its domain. Normalize that product,\nconvert these typings, and embed the finite context into $\\Omega$.\nThe resulting normal product is an actual type with nonempty profile:\nit is sorted by correctness of types and cannot be a literal sort.\nThe equality $G_T=\\top_T$ and its one-frame test now give strong\nnormalization of $f n$, a contradiction. This also handles an empty\nfeasible-profile graph, since the hypothetical application would\nitself produce a feasible product profile.\n\nFinally, every legal expression is typable by validity and correctness\nof types, or is a literal sort. Literal sorts are normal. Hence the\nconclusion holds with the full legal-expression and open-context scope.\n\\end{proof}\n\nThe remaining task is to construct the interface. We now do so in two\ncases using only the profile graph. The other components require the\nadditional semantic construction developed later.\n\nFigure~\\ref{fig:component-contracts} separates the construction of an\ninterface from its use in the normalization induction. For the third\ncase, the observation construction has an additional graph hypothesis.\nThe typed obstruction proves that hypothesis from system-wide weak\nnormalization, independently of the candidate interpretation. Thus the\nthird branch, like the first two, supplies the full interface before\nthe processed invariant is advanced.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tikzpicture}[>=Stealth,\n  contract/.style={draw,rounded corners,align=center,font=\\small,\n                   inner sep=6pt,text width=4.2cm}]\n\\node[contract,text width=11.8cm] (input) at (0,0)\n  {Finite specification and system-wide weak normalization;\\\\\n   current component $S$, processed predecessors $U$, and $G_T=\\top_T$ on $U$};\n\\node[contract] (simple) at (-3.2,-2.25)\n  {No odd closed walk, or no all-$S$ profile triple\\\\\n   Propositions~\\ref{prop:signed-component} and~\\ref{prop:one-child-component}};\n\\node[contract] (remaining) at (3.2,-2.25)\n  {Remaining components: observation construction and typed exclusion\\\\\n   Propositions~\\ref{obs:interface-theorem} and~\\ref{prop:enc-exclusion}};\n\\node[contract,text width=10.7cm] (interface) at (0,-4.25)\n  {Candidate interface for $S$ (Section~\\ref{subsec:candidate-interface}):\\\\\n   invariance, product equality in both directions, and specialization};\n\\node[contract,text width=10.7cm] (advance) at (0,-5.85)\n  {$G_T=\\top_T$ also on $S$ (Proposition~\\ref{prop:advance-component});\\\\\n   after all components, strong normalization (Proposition~\\ref{prop:interfaces-imply-sn})};\n\\draw (input.south) -- (0,-.95);\n\\draw[->] (0,-.95) -| (simple.north);\n\\draw[->] (0,-.95) -| (remaining.north);\n\\draw (simple.south) -- (-3.2,-3.45) -- (0,-3.45);\n\\draw (remaining.south) -- (3.2,-3.45) -- (0,-3.45);\n\\draw[->] (0,-3.45) -- (interface.north);\n\\draw[->] (interface) -- (advance);\n\\end{tikzpicture}\n\\caption{The contracts of one component step. The right branch combines\nthe conditional observation construction with the independent proof of\nits graph hypothesis. The arrows to the interface assert the complete\nthree-part contract, before any use of component advancement.}\n\\label{fig:component-contracts}\n\\end{figure}\n\\FloatBarrier\n\n\\section{Two component constructions}\n\\label{sec:simple-components}\n\nKeep a current component $S$ and the invariant on its processed\npredecessors $U$. The first construction uses signs to compensate for\nthe opposite variances of domain and codomain. The second uses a\nsingle chain in each normal type when its two product children cannot\nboth remain in $S$.\n\n\\subsection{Components without an odd closed walk}\n\n\\begin{proposition}[The signed fixed-point construction]\n\\label{prop:signed-component}\nIf $S$ has no odd closed walk, it admits the candidate interface.\n\\end{proposition}\n\n\\begin{proof}\nChoose a vertex of $S$. Assign to each vertex the parity of any path\nfrom the chosen vertex to it. This is well defined: append a return\npath to two proposed paths; different parities would produce an odd\nclosed walk. Give even vertices sign $+$ and odd vertices sign $-$.\nEvery internal positive edge preserves sign, and every internal\nnegative edge reverses sign.\n\nLet $\\mathcal T_S$ be the set of actual types with profile in $S$.\nIt is a set, since these are finite syntax over the countable context\n$\\Omega$. On\n\\[\n                      \\mathcal L=\n                  \\prod_{T\\in\\mathcal T_S}\\mathcal C(T)\n\\]\nuse inclusion in coordinates with sign $+$ and reverse inclusion in\ncoordinates with sign $-$. This is a complete lattice by\nLemma~\\ref{lem:candidate-lattice}. Extend a vector $v$ to actual\ntypes in $U$ by $v_T=\\top_T$.\n\nDefine an operator $F:\\mathcal L\\to\\mathcal L$. At a nonproduct\ntype set $F(v)_T=\\top_T$. At $T=\\Pi x:D.E$, let\n\\[\n F(v)_T=\\{h\\in\\top_T:\\text{for every }n\\in v_D,\n                                      \\ h n\\in v_{(E[x:=n])^\\#}\\}.\n\\]\nAll indices belong to $U\\cup S$ by the dependency graph, and this\nis a candidate by Lemma~\\ref{lem:candidate-lattice}.\n\nThe operator is monotone in the mixed order. In the usual inclusion\norder its product clause is antitone in the domain coordinate and\nmonotone in every instantiated-codomain coordinate. An internal domain\nhas the opposite sign to $T$ by its negative edge. An instantiated\ncodomain in $S$ has the same sign as $T$ by its positive path through\nthe open codomain: the intermediate vertex must also lie in $S$,\nsince a vertex in $U$ has all its predecessors in $U$. Coordinates\nin $U$ are constant. These observations\ngive exactly the required coordinatewise monotonicity, in either sign\nof the output.\n\nTarski's fixed-point theorem \\cite[Theorem~1]{Tarski1955} gives a fixed\npoint. We recall its short existence proof, which needs no continuity\nassumption. Let $a$ be the supremum of all\n$v$ satisfying $v\\le F(v)$. For every such $v$, monotonicity gives\n$v\\le F(v)\\le F(a)$, so $a\\le F(a)$. Then\n$F(a)\\le F(F(a))$, making $F(a)$ one of the elements whose supremum\nis $a$; hence $F(a)\\le a$. Fix such a vector $a$.\n\nNo auxiliary parameters are needed. For a generic normal type $T$\nwith profile in $S$, define $R_T=a_{(T\\xi)^\\#}$ if its actual\nprofile is in $S$, and top otherwise. At a generic product with\nactual profile in $S$, its actual normalized domain and all actual\ninstantiated codomains are exactly the indices in the operator clause.\nThus the fixed-point equality gives the required product equality,\nwith the unique parameter extension.\n\nThe actual normalized type determines the candidate, so thinning,\ncontext conversion and pointwise convertible images preserve it.\nFor specialization the two actual type expressions\n\\[\n E(\\xi[x\\mapsto N\\xi])\n       \\quad\\hbox{and}\\quad ((E[x:=N])^\\#)\\xi\n\\]\nare convertible by substitution composition and normalization.\nTheir normal forms are identical. The two candidates are consequently\nequal when that profile is in $S$, and both are top in $U$. A generic\nchild in $U$ remains there under substitution, so no additional case\narises. This proves all interface requirements.\n\\end{proof}\n\n\\subsection{Components with at most one internal product child}\n\nSuppose now that there is no profile triple $(I,J,K)$ with\n$I,J,K\\in S$. For a normal type $T$ of profile in $S$, start at its\nroot. At a product continue to its child of profile in $S$, if it has\none; there is at most one. Stop at a nonproduct or at a product with\nno such child. This is a finite path in the syntax tree.\n\nIf the path ends at a neutral expression whose head $y$ is free in\nthe starting context of $T$, define the \\emph{support} of $T$ to be\n$y$. Define its \\emph{polarity} to be $+$ or $-$ according to whether\nthe path takes an even or odd number of domain steps. In every other\ncase the support is empty. Also declare support empty at a generic\nprofile outside $S$. In particular a head bound strictly inside\n$T$ is not its support. The support of a codomain, considered in its\nown extended context, may of course be its immediately preceding\nbinder.\n\nAssign each declaration of a generic context a bit $0$ or $1$;\nthese are the parameters $\\theta$. At a neutral we use the candidate\nextremum selected by its head's bit, with $0$ selecting bottom and $1$\nselecting top. Define candidates recursively on normal generic types\nas follows. At an actual profile in $U$ use top. At a generic profile\noutside $S$ also use top, as in the interface. Otherwise:\n\\begin{itemize}\n\\item at a sort use top;\n\\item at a neutral with head $y$ use\n      $\\bot_{(T\\xi)^\\#}$ or $\\top_{(T\\xi)^\\#}$ according to\n      $\\theta(y)$;\n\\item at a product $T=\\Pi x:D.E$ use the product-test candidate\n      in Section~\\ref{subsec:candidate-interface}. There is one prescribed\n      bit for $x$: use $0$ if the support of $E$ is $x$ with positive\n      polarity, $1$ if it is $x$ with negative polarity, and $1$ if\n      $x$ is not supported. If $E$ has profile in $U$, its bit is\n      irrelevant and we again choose $1$.\n\\end{itemize}\nEvery recursive child is smaller syntax; candidate existence follows\nfrom Lemma~\\ref{lem:candidate-lattice}. Context variables can always\nbe assigned bits, and the prescribed extension is nonempty.\n\n\\begin{lemma}[Dependence on support]\n\\label{lem:support-dependence}\nFor fixed typed actual images, the candidate of an $S$ type depends\non the bits of its starting context only through its support, if any.\nValid thinning and context conversion preserve the support, its\npolarity, and the candidates. Pointwise convertible actual images\npreserve the candidates when old bits agree.\n\\end{lemma}\n\n\\begin{proof}\nInduct on the normal type. Sorts and actual-$U$ cases do not use\nbits. A neutral uses only its head bit. At a product, any child in\n$U$ is interpreted as top; only the unique $S$ child can depend on\nold bits. Its support is propagated to the parent unless it is the\nproduct's own binder, whose bit is fixed by the displayed rule.\nThis proves the dependence assertion. Profile invariance preserves\nthe same product path and its domain parity under thinning and\ncontext conversion. In those operations and under convertible images,\nthe actual normal types agree. The recursive product tests then have\nidentical domains, codomains and prescribed bits, proving the stated\ninvariances.\n\\end{proof}\n\nUse the notation $A\\preceq_+B$ for $A\\subseteq B$ and\n$A\\preceq_-B$ for $A\\supseteq B$. Regard signs as $+1$ and $-1$\nwhen multiplying them.\n\n\\begin{lemma}[Comparison after generic substitution]\n\\label{lem:support-substitution}\nLet $\\Gamma,y:B,\\Delta$ be valid, let $\\Gamma\\vdash N:B$, and\nlet $T$ be a normal type of profile in $S$ in that context. Put\n\\[\n \\Gamma'=\\Gamma,\\Delta[y:=N],\\qquad\n                         T'=(T[y:=N])^\\#.\n\\]\nTake typed substitutions of the two contexts into $\\Omega$ whose\nimages are compatible with composition: the pre-substitution image\nof $y$ is convertible to the actual image of $N$, and the other\nimages agree up to conversion. Equip the substitutions with bits\n$\\theta,\\theta'$. Fix a comparison sign $\\delta\\in\\{+,-\\}$ and\nassume:\n\\begin{enumerate}\n\\item the bits agree on any variable supported by both $T$ and $T'$;\n\\item if $y$ supports $T$ with polarity $\\varepsilon$, then its\n      pre-substitution bit is $0$ when $\\delta\\varepsilon=+$ and\n      $1$ when $\\delta\\varepsilon=-$.\n\\end{enumerate}\nThen, at their common actual normal type,\n\\[\n             R_T(\\xi,\\theta)\n                  \\preceq_\\delta R_{T'}(\\xi',\\theta').\n\\]\n\\end{lemma}\n\n\\begin{proof}\nInduct on the pre-substitution syntax of $T$. The actual normal\ntypes agree by substitution composition and confluence. If their\nprofile lies in $U$, both candidates are top. Otherwise that profile\nlies in $S$, and so does the generic profile of $T'$: profile growth\nputs it in $U\\cup S$, and a generic profile in $U$ could only have\nactual profile in $U$.\n\nA sort is unchanged. A neutral whose head is not $y$ remains a\nneutral with the same head after substitution and normalization; if\nits actual profile is in $S$, the supported head is common and its\nbit agrees. At a neutral headed by $y$, its polarity is positive and\nthe prescribed bit chooses the least candidate for forward inclusion,\nor the greatest candidate for reverse inclusion. This compares it\nwith every possible candidate of $T'$. In particular we do not\nrecurse into the possibly larger normal expression obtained by\nsubstituting for $y$.\n\nLet $T=\\Pi x:D.E$, choosing $x$ fresh for $N$ and for the\nsubstitution data. The post-substitution type is again a product,\nwith its two substituted components normalized. Context-convert the\nopen codomain to the normalized post domain. To compare the product\ntest candidates in direction $\\delta$, compare their domain\ncandidates in direction $-\\delta$. On each common test compare their\ncodomain candidates in direction $\\delta$, extending the actual\nimages by that same raw test and using the respective prescribed\nbits for $x$. For example, for forward inclusion every post domain\ntest must be a pre domain test, and its pre codomain candidate must\nbe contained in its post codomain candidate.\n\nIf a pre child is in $U$, its generic substitute remains in $U$ and\nboth actual child candidates are top. If its generic substitute\ngrows into $U$, the common actual child profile is again in $U$,\nwith the same conclusion. In all other required child comparisons,\nboth generic child profiles are in $S$. Such a post child must be\nthe same unique $S$ child as before substitution: a pre off-path\nchild in $U$ cannot grow into $S$. Every common supported variable\nother than the new binder $x$ therefore inherits the agreement\ncondition from the parent. If $y$ supports the child, the prescribed\nextremum condition is also inherited: a domain step reverses both\nthe comparison direction and the support polarity, and a codomain\nstep reverses neither.\n\nIt remains to check the bits of $x$ when it is supported in both\nopen codomains. The pre support path ends at a neutral with head\n$x$, which substitution for $y$ cannot change. Every product on\nthat path persists. The post support path must follow the same\npositions: a pre off-path child remains in $U$, while truncation\nof the path into $U$ would prevent the post codomain from being\nsupported by $x$. Thus both paths have the same number of domain\nsteps, their polarities agree, and the prescribed bits of $x$\nagree. If substitution exposes $x$ through a neutral headed by\n$y$, that portion of the induction instead stops at the extremum\ncase already proved; it imposes no further shared-support condition.\n\nAll hypotheses of the child inductions now hold. Their inclusions\ngive the desired product inclusion by the universal test definition,\nin either direction. This completes the induction.\n\\end{proof}\n\n\\begin{proposition}[The one-child construction]\n\\label{prop:one-child-component}\nIf no profile triple has all three vertices in $S$, then the\nbit-parametrized candidates above implement the candidate interface.\n\\end{proposition}\n\n\\begin{proof}\nCandidate existence and product equality follow from the recursion.\nLemma~\\ref{lem:support-dependence} gives the required invariances.\nTo verify specialization, let\n$T=\\Pi x:D.E$, take $\\Gamma\\vdash N:D$ with $N\\xi\\in R_D$,\nand let $h\\in R_T$. Its product test gives\n\\[\n h(N\\xi)\\in R_E(\\xi[x\\mapsto N\\xi],\\theta_x)\n\\]\nwhen the actual codomain profile lies in $S$; here $\\theta_x$ is\nthe prescribed binder extension. Apply\nLemma~\\ref{lem:support-substitution} to substitution for $x$ in\n$E$, with comparison direction $+$. All old bits agree. The\nprescribed bit of $x$ is exactly $0$ for positive support and $1$\nfor negative support, as that lemma requires. It follows that the\nlast candidate is contained in\n$R_{(E[x:=N])^\\#}(\\xi,\\theta)$, proving specialization. At an\nactual codomain profile in $U$, the strong-normalization test gives\nspecialization directly. Arbitrary bits are allowed on the starting\ncontext, and every prescribed or arbitrary binder extension exists.\nAll interface requirements are satisfied.\n\\end{proof}\n\nConsequently, only components having both an odd closed walk and a\nprofile triple entirely inside the component remain. The next\nconstruction will provide their interface under a stated graph\nexclusion. Once that exclusion is proved from system-wide weak\nnormalization, Proposition~\\ref{prop:interfaces-imply-sn} completes\nthe normalization argument.\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/relational.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/relational.tex", "bytes": 55402, "sha256": "c021e9bc1273ec699815429d0e2ff543ecd6bbcc53b4689f8caf85de20168d4f", "content": "\\section{Relational coding of the diagonal argument}\n\\label{rel-section}\n\nWe continue with the fixed labelled wrappers, their data cancellation law\n(Lemma~\\ref{lem:enc-data-cancellation}), and their predicate evaluation laws\n\\eqref{chan-predicate-evaluation} and \\eqref{chan-x-evaluation}.\nIn particular, for either of the\npredicate spaces under consideration,\n\\[\n \\operatorname{pred}(y.\\Phi)@w\n       \\ \\leftrightarrow\\ \\Phi[\\operatorname{Round}w/y].\n\\]\nUntil a specialization is explicitly made, \\(A:s\\) and \\(m:M(A)\\) are\nopen data parameters.  Write \\(L=L(A)\\).  The variables \\(y,z,w,a\\)\nrange over \\(L\\), \\(P,P'\\) over \\(\\mathsf P_L\\), \\(x,x'\\) over \\(X\\),\nand \\(i,i'\\) over \\(\\mathsf P_X\\).  The spaces \\(V,X,\\mathsf P_X\\)\nare independent of \\(A,m\\).  All displayed quantifiers use the fixed\ndirect-vertex annotations: \\(j\\) for \\(L,X\\) and \\(h\\) for\n\\(\\mathsf P_L,\\mathsf P_X\\).  The other parameters are parameters of\nterm builders, not additional logical quantifiers.\n\nThe operations \\(\\mathsf{pl}\\), \\(\\mathsf{All}\\), \\(\\mathsf{Ex}\\),\n\\(\\mathsf{Gd}\\), \\(\\operatorname{to}\\), and\n\\(\\operatorname{back}\\) have the typed expansions already constructed.\nFor clarity, when a proof below says that specified proofs yield a\nformula by \\(\\mathsf{pl}\\), that formula is its target.  Quantifiers\nare treated as propositional atoms until explicitly introduced or\ninstantiated.  An existential is eliminated only into the displayed\ntarget, which is independent of its locally opened witnesses.\n\nOur goal is a proof of the terminal formula $F_b$ in the ambient labelled\ncontext. First we construct relations and probes for open parameters\n$A,m$. Next we choose $A_0,m_0$ and prove admissibility. At that\nspecialization, three tagged copies turn the query formulas into class\nand pairing laws. Section~\\ref{rel-diagonal} then uses these laws to\nconstruct the terminal proof.\n\n\\subsection{Generic relations and probes}\n\n\\begin{definition}[Admissible queries and observational equivalence]\n\\label{rel-admissible}\nDefine\n\\[\n\\begin{aligned}\n P\\approx P'&=\\forall y.\\,(P@y\\leftrightarrow P'@y),\\\\\n i\\approx i'&=\\forall x.\\,(i@x\\leftrightarrow i'@x),\\\\\n \\operatorname{Adm}\n   &=\\neg D(m)\\land\n       \\bigwedge_{t\\ {\\rm structural}}\\forall P,P'.\\,\n       \\bigl(P\\approx P'\\Rightarrow\n          (\\operatorname{qry}_t(m,P)\\leftrightarrow\n           \\operatorname{qry}_t(m,P'))\\bigr),\\\\\n \\operatorname{Good}(P)\n   &=\\bigl(\\forall y.\\,(P@y\\leftrightarrow\n                     P@\\operatorname{Round}y)\\bigr)\n           \\land\\operatorname{qry}_G(m,P),\\\\\n y\\sim z&=\\forall P.\\,\n       \\bigl(\\operatorname{Good}(P)\\Rightarrow\n                      (P@y\\leftrightarrow P@z)\\bigr).\n\\end{aligned}\n\\]\nThe finite Boolean operations have fixed bracketing.  From\n\\(ad:\\operatorname{Adm}\\) obtain \\(d_m:\\neg D(m)\\) by projection.\nFor each structural \\(t\\), projection followed by two instantiations\nand application defines\n\\[\n \\operatorname{qr}_t(e):\n \\operatorname{qry}_t(m,P)\\leftrightarrow\n \\operatorname{qry}_t(m,P')\n \\qquad(e:P\\approx P').\n\\]\n\\end{definition}\n\n\\begin{lemma}[Equivalence and rounding]\n\\label{rel-equivalence}\nThe relation \\(\\sim\\) has reflexivity, symmetry, and transitivity\nbuilders.  There is a builder\n\\(\\operatorname{RoundRel}(y):y\\sim\\operatorname{Round}y\\).\nFor \\(e:a\\sim a'\\) and \\(f:c\\sim c'\\) there is a builder\n\\[\n \\operatorname{cong}(e,f):\n       (a\\sim c)\\leftrightarrow(a'\\sim c').\n\\]\nThese builders require no admissibility assumption.\n\\end{lemma}\n\\begin{proof}\nReflexivity is\n\\(\\Lambda P.\\lambda^*h.\\mathsf{pl}_{P@y\\leftrightarrow P@y}()\\).\nFor symmetry, under \\(e:y\\sim z\\), introduce \\(P,h\\), instantiate\n\\(e[P]\\cdot h\\), and apply \\(\\mathsf{pl}\\) to reverse its\nbiconditional.  For transitivity, under\n\\(e:y\\sim z\\) and \\(f:z\\sim w\\), introduce \\(P,h\\) and apply\n\\(\\mathsf{pl}_{P@y\\leftrightarrow P@w}\\) to\n\\(e[P]\\cdot h,f[P]\\cdot h\\).\nFor rounding introduce \\(P,h\\), project\n\\(\\forall w.(P@w\\leftrightarrow P@\\operatorname{Round}w)\\)\nfrom \\(h:\\operatorname{Good}(P)\\), and instantiate it at \\(y\\).\nWrite these builders as \\(\\operatorname{refl}\\),\n\\(\\operatorname{sym}\\), and \\(\\operatorname{trans}\\).\nThe two components of the last biconditional are\n\\[\n\\begin{aligned}\n &\\lambda^*u.\\operatorname{trans}\n       (\\operatorname{sym}(e),\\operatorname{trans}(u,f)),\\\\\n &\\lambda^*u.\\operatorname{trans}\n       (e,\\operatorname{trans}(u,\\operatorname{sym}(f))).\n\\end{aligned}\n\\]\nIn the first line \\(u:a\\sim c\\), and in the second\n\\(u:a'\\sim c'\\), so the endpoints are respectively\n\\(a'\\sim c'\\) and \\(a\\sim c\\).\n\\end{proof}\n\n\\begin{definition}[Class, union, and pairing predicates]\n\\label{rel-pair-definition}\nPut\n\\[\n\\begin{aligned}\n c_y&=\\operatorname{pred}(w.w\\sim y),\\\\\n u_{yz}&=\\operatorname{pred}(w.(w\\sim y)\\lor(w\\sim z)),\\\\\n \\operatorname{Is}_t(y)&=\\operatorname{qry}_{C_t}(m,c_y)\n                         &&(t=0,\\ell,r),\\\\\n T_t(y,z)&=\\operatorname{Is}_0(y)\\land\n             \\operatorname{Is}_t(z)\\land\n             \\operatorname{qry}_{E_t}(m,u_{yz})\n                         &&(t=\\ell,r),\\\\\n \\operatorname{form}(P,a,z)\n       &=\\bigl(\\exists y.\\,(T_\\ell(y,z)\\land P@y)\\bigr)\n                         \\lor T_r(a,z),\\\\\n \\operatorname{Pair}(P,a)&=\n             \\operatorname{pred}(z.\\operatorname{form}(P,a,z)).\n\\end{aligned}\n\\]\nThese are data definitions, independent of any proof of\n\\(\\operatorname{Adm}\\).\n\\end{definition}\n\n\\begin{lemma}[Congruence of the relational predicates]\n\\label{rel-pair-congruence}\nThere are unrounded evaluation proofs\n\\[\n c_y@w\\leftrightarrow(w\\sim y),\\qquad\n u_{yz}@w\\leftrightarrow((w\\sim y)\\lor(w\\sim z)).\n\\]\nEquivalent parameters give \\(c_y\\approx c_{y'}\\) and\n\\(u_{yz}\\approx u_{y'z'}\\).  Under \\(ad:\\operatorname{Adm}\\),\nthe predicates \\(\\operatorname{Is}_t,T_t\\) respect \\(\\sim\\).\nMoreover, still under \\(ad\\), and under \\(P\\approx P'\\),\n\\(a\\sim a'\\), and \\(z\\sim z'\\)\nthere are proofs of\n\\[\n \\operatorname{form}(P,a,z)\\leftrightarrow\n       \\operatorname{form}(P',a',z'),\n\\]\nand, under the first two of these assumptions, of\n\\(\\operatorname{Pair}(P,a)\\approx\\operatorname{Pair}(P',a')\\).\nIn particular, under \\(ad\\) there is\n\\[\n \\operatorname{pv}(P,a,z):\n  \\operatorname{Pair}(P,a)@z\n       \\leftrightarrow\\operatorname{form}(P,a,z).\n\\]\n\\end{lemma}\n\\begin{proof}\nPredicate evaluation first gives\n\\[\n c_y@w\\leftrightarrow(\\operatorname{Round}w\\sim y).\n\\]\nApply relation congruence with\n\\(\\operatorname{RoundRel}(w)\\) and \\(\\operatorname{refl}(y)\\);\npropositional chaining gives the first displayed evaluation.\nFor the union do this separately for \\(y\\) and \\(z\\), then chain\ntheir disjunctions.  For \\(e:y\\sim y'\\), congruence with\n\\(\\operatorname{refl}(w),e\\), together with the two class\nevaluations, gives \\(c_y@w\\leftrightarrow c_{y'}@w\\).\nIntroduce \\(w\\) to obtain the class comparison.  For the union\nuse the two parameter congruences inside the disjunction before\nintroducing \\(w\\).\n\nApply \\(\\operatorname{qr}_{C_t}\\) to the class comparison to\nobtain the \\(\\operatorname{Is}_t\\) comparison.  For \\(T_t\\),\ncombine the two resulting \\(\\operatorname{Is}\\) comparisons and\n\\(\\operatorname{qr}_{E_t}\\) applied to the union comparison.\nFor \\(\\operatorname{form}\\), fix \\(y\\).  The \\(T_\\ell\\)\ncomparison with its first argument unchanged, together with\n\\((P\\approx P')[y]\\), gives\n\\[\n (T_\\ell(y,z)\\land P@y)\\leftrightarrow\n           (T_\\ell(y,z')\\land P'@y).\n\\]\nApply \\(\\mathsf{Ex}_y\\), then combine its result with the\n\\(T_r(a,z)\\leftrightarrow T_r(a',z')\\) comparison by\n\\(\\mathsf{pl}\\).  Predicate evaluation for \\(\\operatorname{Pair}\\)\nhas right side\n\\(\\operatorname{form}(P,a,\\operatorname{Round}z)\\).\nThe just-proved form congruence, with \\(P,a\\) unchanged and\n\\(\\operatorname{RoundRel}(z)\\), gives \\(\\operatorname{pv}\\).\nFinally fix \\(z\\), combine both \\(\\operatorname{pv}\\) proofs\nwith form congruence at unchanged \\(z\\), and introduce \\(z\\).\n\\end{proof}\n\n\\begin{definition}[Lifts and uniform probes]\n\\label{rel-lifts}\nDefine data, still with open \\(A,m\\), by\n\\[\n\\begin{aligned}\n \\operatorname{Part}(v)&=\\operatorname{pred}(y.v\\diamond_m y),\\\\\n \\operatorname{sb}(v,m,a)&=\\operatorname{qry}_R\n                    (m,\\operatorname{Pair}(\\operatorname{Part}(v),a)),\\\\\n \\operatorname{lift}_m(a)&=\\operatorname{mk}_X(v.\\operatorname{sb}(v,m,a)).\n\\end{aligned}\n\\]\nFor \\(i:\\mathsf P_X\\), define \\(z_i:V\\) by the following data\nlambda.  At \\(z:Z\\), pack along the even path \\(g\\to J\\) the\ndata function\n\\[\n \\lambda^{\\mathsf d,\\mathsf d}w:\n             \\operatorname{Single}(z^\\downarrow).\n \\operatorname{send}_n\n    \\bigl(\\operatorname{Access}\n          (w;o,y.i@\\operatorname{lift}_o(y))\\bigr),\n\\]\nwhere all channel types and the lift in its body use\n\\(A=z^\\downarrow\\).  This defines the body of\n\\(\\lambda^{\\mathsf d,\\mathsf d}z:Z.\\,\\cdots\\).\nThe variable \\(o\\) is the data parameter supplied by\n\\(\\operatorname{Access}\\).  Thus \\(z_i\\) has no free \\(A,m\\).\nPut\n\\[\n\\begin{aligned}\n \\operatorname{le}(i,x)&=[x z_i],\\\\\n x\\simeq x'&=\\forall i.\\,(\\operatorname{le}(i,x)\\leftrightarrow \\operatorname{le}(i,x')),\\\\\n \\operatorname{Valid}(x)&=\\forall i,i'.\\,\n       \\bigl(i\\approx i'\\Rightarrow\n                    (\\operatorname{le}(i,x)\\leftrightarrow \\operatorname{le}(i',x))\\bigr),\\\\\n \\operatorname{Ext}(i)&=\\forall x,x'.\\,\n       \\bigl(x\\simeq x'\\Rightarrow(i@x\\leftrightarrow i@x')\\bigr),\\\\\n \\operatorname{Resp}(v)&=\\forall y,z.\\,\n       \\bigl(y\\sim z\\Rightarrow\n                    (v\\diamond_m y\\leftrightarrow v\\diamond_m z)\\bigr).\n\\end{aligned}\n\\]\n\\end{definition}\n\n\\begin{lemma}[Evaluation, validity, and response]\n\\label{rel-lift-properties}\nThe relation \\(\\simeq\\) is an equivalence relation, with the\ncongruence builder of Lemma~\\ref{rel-equivalence}, and\n\\[\n \\operatorname{RoundRel}_X(x):x\\simeq\\operatorname{Round}_Xx.\n\\]\nData cancellation supplies\n\\[\n \\operatorname{LP}(i,a):\n       \\operatorname{le}(i,\\operatorname{lift}_m a)\\leftrightarrow \\operatorname{sb}(z_i,m,a).\n\\]\nUnder \\(ad:\\operatorname{Adm}\\) there are builders\n\\begin{align}\n z_i\\diamond_m y&\\leftrightarrow\n             i@\\operatorname{lift}_m(\\operatorname{Round}y),\n                  \\label{rel-probe-rounded}\\\\\n \\operatorname{LC}(e)&:\n       \\operatorname{lift}_m(a)\\simeq\\operatorname{lift}_m(a')\n                    \\qquad(e:a\\sim a'), \\label{rel-lift-congruence}\\\\\n &\\operatorname{Valid}(\\operatorname{lift}_m a). \\label{rel-lift-valid}\n\\end{align}\nIf also \\(ex:\\operatorname{Ext}(i)\\), then\n\\begin{equation}\n z_i\\diamond_m y\\leftrightarrow i@\\operatorname{lift}_m y\n \\quad\\hbox{and}\\quad \\operatorname{Resp}(z_i).\n \\label{rel-probe-exact}\n\\end{equation}\nIf \\(h:\\operatorname{Resp}(v)\\), then\n\\begin{equation}\n \\operatorname{Part}(v)@y\\leftrightarrow v\\diamond_m y.\n \\label{rel-part-exact}\n\\end{equation}\n\\end{lemma}\n\\begin{proof}\nFor \\(\\simeq\\), introduce \\(i\\) and use propositional\nreflexivity, symmetry, or transitivity of the instantiated\nbiconditionals.  Its congruence builder is the pair displayed\nin Lemma~\\ref{rel-equivalence}, with the relation replaced by\n\\(\\simeq\\).  Primitive \\(X\\)-evaluation is preserved by\n\\(\\operatorname{Round}_X\\).  Apply this preservation at \\(z_i\\)\nand introduce \\(i\\) to obtain \\(\\operatorname{RoundRel}_X\\).\nCancellation of \\(\\operatorname{mk}_X\\) gives \\(\\operatorname{LP}\\)\nat the exact input \\(z_i\\).\n\nFor \\eqref{rel-probe-rounded}, the application \\(z_iA^\\uparrow\\)\nbeta-reduces to the same pack and data lambda at \\(A\\), since\n\\((A^\\uparrow)^\\downarrow=_\\beta A\\) and the wrapper choices\nare fixed.  The pack/use cancellation leaves the read of that\nfunction applied to \\(\\operatorname{Bundle}(m,y)\\).\nAfter its raw beta contraction, read/send cancellation leaves\n\\[\n \\operatorname{Access}\n   (\\operatorname{Bundle}(m,y);o,w.i@\\operatorname{lift}_o(w)).\n\\]\nThe Access builder, applied to \\(d_m\\), compares this with\n\\(i@\\operatorname{lift}_m(\\operatorname{Round}y)\\).\nChain these three biconditionals by \\(\\mathsf{pl}\\).\n\nTo obtain \\(\\operatorname{LC}(e)\\), introduce \\(i\\).\nPair congruence applied to the reflexive comparison of\n\\(\\operatorname{Part}(z_i)\\) and to \\(e\\), followed by\n\\(\\operatorname{qr}_R\\), compares \\(\\operatorname{sb}(z_i,m,a)\\) and\n\\(\\operatorname{sb}(z_i,m,a')\\).  Combine it with the two \\(\\operatorname{LP}\\)\nproofs and introduce \\(i\\).\n\nFor validity introduce \\(i,i'\\) and \\(e:i\\approx i'\\).\nThere is a comparison\n\\(\\operatorname{Part}(z_i)\\approx\\operatorname{Part}(z_{i'})\\):\nat \\(y\\), predicate evaluation gives the two expressions\n\\(z_i\\diamond_m\\operatorname{Round}y\\) and\n\\(z_{i'}\\diamond_m\\operatorname{Round}y\\).\nApply \\eqref{rel-probe-rounded} at precisely\n\\(\\operatorname{Round}y\\), and use\n\\[\n e[\\operatorname{lift}_m\n               (\\operatorname{Round}(\\operatorname{Round}y))].\n\\]\nThese proofs propositionally entail the required evaluation\ncomparison; introduce \\(y\\).  Apply Pair congruence with \\(a\\)\nunchanged, then \\(\\operatorname{qr}_R\\), and finally the two\n\\(\\operatorname{LP}\\) proofs.  Introducing \\(i,i',e\\) proves\n\\eqref{rel-lift-valid}.\n\nFor \\eqref{rel-probe-exact}, apply \\(ex\\) at\n\\(\\operatorname{lift}_m y,\\operatorname{lift}_m(\\operatorname{Round}y)\\)\nto \\(\\operatorname{LC}(\\operatorname{RoundRel}(y))\\).\nCombine its result with \\eqref{rel-probe-rounded}.  Under\n\\(e:y\\sim z\\), apply \\(ex\\) at the two lifts to\n\\(\\operatorname{LC}(e)\\), and combine this with the exact\nprobe evaluations at \\(y,z\\).  Introducing \\(y,z,e\\) proves\n\\(\\operatorname{Resp}(z_i)\\).\nFinally predicate evaluation gives\n\\(\\operatorname{Part}(v)@y\\leftrightarrow\nv\\diamond_m\\operatorname{Round}y\\).\nCombine it with \\(h[y][\\operatorname{Round}y]\\cdot\n\\operatorname{RoundRel}(y)\\) to obtain \\eqref{rel-part-exact}.\n\\end{proof}\n\n\\subsection{The laws needed for the diagonal argument}\n\\label{rel-target-laws}\n\nThe relations and uniform probes are now defined.  Before choosing\n$A$ and $m$, we identify the laws their specialization must provide.\nFor $i:\\mathsf P_X$, define\n\\begin{equation}\n \\operatorname{Ind}(i)=\n \\forall x:X.\\bigl(\\operatorname{Valid}(x)\\Rightarrow\n       (\\operatorname{le}(i,x)\\Rightarrow i@x)\\bigr).\n \\label{rel-ind-definition}\n\\end{equation}\nThis formula says that $i$ contains every valid $x$ for which\n$\\operatorname{le}(i,x)$ holds.  The name records its role in the\nwell-foundedness diagonal; it does not assume a pre-existing order on $X$.\n\nWe shall construct data operations $x\\mapsto\\delta x:X$ and\n$i\\mapsto Si:\\mathsf P_X$, and a data term\n$\\operatorname{WF}:X$.  Their required properties are\n\\begin{align*}\n &\\operatorname{Valid}(\\delta x),\\qquad\n                    \\operatorname{Valid}(\\operatorname{WF}),\\\\\n &\\operatorname{Ext}(i)\\ \\Longrightarrow\\\n     \\operatorname{Ext}(Si)\\ \\text{ and }\\\n                  (Si@x\\leftrightarrow i@\\delta x),\\\\\n &\\operatorname{Ext}(i),\\ \\operatorname{Valid}(x)\n       \\ \\Longrightarrow\\\n       \\bigl(\\operatorname{le}(i,\\delta x)\n                      \\leftrightarrow\\operatorname{le}(Si,x)\\bigr),\\\\\n &\\operatorname{Ext}(i)\\ \\Longrightarrow\\\n       \\bigl(\\operatorname{le}(i,\\operatorname{WF})\n                              \\leftrightarrow\\operatorname{Ind}(Si)\\bigr).\n\\end{align*}\nHere the outer implication signs describe proof builders with the\ndisplayed inputs, as elsewhere in the construction.  We also require\n$x\\simeq x'$ to imply $\\delta x\\simeq\\delta x'$.\nThe logical binders introduced in the final diagonal proof range only\nover $X$ or $\\mathsf P_X$; it also uses the previously proved laws at\ntheir original domains.\n\nThese laws already explain the intended role of $\\operatorname{WF}$.\nFor an extensional $i$, a proof of $\\operatorname{Ind}(i)$ gives a\nproof of $\\operatorname{Ind}(Si)$: apply the proof of\n$\\operatorname{Ind}(i)$ at\nthe valid point $\\delta x$, using the predecessor comparison to turn\n$\\operatorname{le}(Si,x)$ into $\\operatorname{le}(i,\\delta x)$.\nThe shift evaluation then gives the required conclusion $Si@x$.\nThe last displayed law then gives\n$\\operatorname{le}(i,\\operatorname{WF})$, so validity of\n$\\operatorname{WF}$ gives $i@\\operatorname{WF}$.  Section~\\ref{rel-diagonal}\nuses this consequence with a specified negative predicate to derive\n$\\bot$; it will prove the construction with every guard in place.\n\nTo obtain the predecessor comparison, we will encode three distinguishable\ncopies of $X$ in $L$.  The class and edge queries will identify the\ncorresponding elements of those copies.  Observations of the pairing\npredicate on one copy will be equivalent to the evaluations of the\nrepresented predicate; observations on another will identify the point\nof evaluation up to $\\simeq$.  The $R$ query can then express\n$\\operatorname{le}$ at that point.  Its validity guard is essential:\nit permits transport between the corresponding\n$\\operatorname{le}$ formulas for the represented and original predicates.\nThe next two subsections build\nthese copies and queries before the guarded diagonal uses them.\n\n\\subsection{Three tagged copies and one admissible parameter}\n\nThe types $V,X,\\mathsf P_X$ and the probes $z_i$ were constructed\nuniformly, before any choice of $A,m$.  We now use them to choose\n$A_0$ and then $m_0$.  The observations defining $m_0$ may use those\nalready constructed probes; they never use $m_0$ itself.  This order\nwill allow us to prove admissibility after defining the data term.\n\nWe now choose a fixed \\(A_0:s\\).  Choose the secondary path\n\\(k\\to d\\), inside \\(C\\), so that its concatenation with the\npreviously fixed \\(d\\to k\\) has even parity and at least four\nnegative edges, and put \\(A_0=W_{k\\to d}(V)\\).\nFor completeness, in a component with an odd closed walk a\ndetour changes the parity if necessary; in a component with a\nconsistent two-coloring the return paths already have matching\nparities.  A closed detour through an internal negative exists\nby strong connectivity.  Inserting it twice preserves parity\nand increases the number of negatives.  Repeating this insertion\ngives the claimed choice.\n\nSplit the resulting wrapper from \\(V\\) to \\(B(A_0)\\) into\nfour odd segments \\(\\rho_1,\\ldots,\\rho_4\\).  This is possible\nby cutting after the first, second, and third negative;\nthe remaining number of negatives is odd.  Let \\(T_0=V\\) and\n\\(T_a=W_{\\rho_a}(T_{a-1})\\), so \\(T_4=B(A_0)\\).\nThe fixed odd path \\(k\\to j\\), denoted \\(\\rho_5\\) here, gives\n\\(T_5=L(A_0)\\).  The first odd wrapper defines\n\\[\n y_1(x)=\\operatorname{mk}_{\\rho_1}(v.[xv]):T_1.\n\\]\nUse the double-embedding construction of Definition~\\ref{chan-double}\nand Lemma~\\ref{chan-double-laws} on \\((\\rho_2,\\rho_3)\\)\nand on \\((\\rho_4,\\rho_5)\\).  Denote their signed embeddings\nby \\(E^{(1)}_\\epsilon:T_1\\to T_3\\) and\n\\(E^{(2)}_\\epsilon:T_3\\to T_5\\), their bit observations by\n\\(B_1,B_2\\), and their recovery formula builders by\n\\(\\mathcal R_1,\\mathcal R_2\\).  These are notation for the\nprevious data macros, not logical function spaces.\nFix distinct strings, for example\n\\[\n (b_1(0),b_2(0))=(0,0),\\quad\n (b_1(\\ell),b_2(\\ell))=(1,0),\\quad\n (b_1(r),b_2(r))=(0,1),\n\\]\nand define\n\\[\n\\begin{aligned}\n x^t&=E^{(2)}_{b_2(t)}(E^{(1)}_{b_1(t)}(y_1(x))),\\\\\n H_2(y)&=B_2(y),\\\\\n H_1(y)&=\\mathcal R_2(y;c.B_1(c)),\\\\\n O_i(y)&=\\mathcal R_2\n       (y;c.\\mathcal R_1(c;u.\\langle u,z_i\\rangle_{\\rho_1})).\n\\end{aligned}\n\\]\nHere \\(\\langle u,z_i\\rangle_{\\rho_1}\\) uses the first odd\nwrapper, whose input type is \\(V\\).\n\n\\begin{lemma}[Tagged observations]\n\\label{rel-tags}\nFor \\(t=0,\\ell,r\\) there are proofs of the signed bits\n\\(H_a(x^t)\\) if \\(b_a(t)=1\\), and \\(\\neg H_a(x^t)\\) if\n\\(b_a(t)=0\\), and proofs\n\\[\n O_i(x^t)\\leftrightarrow \\operatorname{le}(i,x).\n\\]\nEach \\(H_a\\) and \\(O_i\\) is invariant, by biconditional, under\n\\(y\\mapsto\\operatorname{Round}y\\).\nDefine\n\\[\n \\operatorname{Ae}(y,z)=\n   \\bigwedge_{a=1,2}(H_a(y)\\leftrightarrow H_a(z))\n    \\land\\bigl(\\forall i.\\,(O_i(y)\\leftrightarrow O_i(z))\\bigr).\n\\]\nThen \\(\\operatorname{Ae}\\) is an equivalence relation and\nthere is \\(\\operatorname{AeRound}(y):\n\\operatorname{Ae}(y,\\operatorname{Round}y)\\).\nFor each tag \\(t\\), its restriction compares exactly \\(\\simeq\\):\n\\[\n \\operatorname{Ae}(x^t,x'^{\\,t})\\leftrightarrow x\\simeq x'.\n\\]\nFor distinct tags \\(t,u\\) there is a proof of\n\\(\\neg\\operatorname{Ae}(x^t,x'^{\\,u})\\).\n\\end{lemma}\n\\begin{proof}\nThe two double-embedding builders give their signed bits and\nrecover each callback at its exact input.  Apply them successively\nto \\(B_1\\) and to \\(u\\mapsto\\langle u,z_i\\rangle_{\\rho_1}\\).\nFor the latter, first-wrapper cancellation gives\n\\(\\langle y_1(x),z_i\\rangle_{\\rho_1}\\leftrightarrow[xz_i]\\).\nPropositional chaining gives all the asserted canonical\nevaluations.  For a general \\(y\\), the outer bit and every\nouter recovery are Boolean combinations of primitive\n\\(L\\)-evaluations at arguments independent of \\(y\\).\nPrimitive Round preservation at those arguments, followed\nby \\(\\mathsf{pl}\\), therefore gives each invariance proof.\n\nReflexivity of \\(\\operatorname{Ae}\\) pairs the propositional\nbit reflexivities with\n\\(\\Lambda i.\\mathsf{pl}_{O_i(y)\\leftrightarrow O_i(y)}()\\).\nFor symmetry, project both bit comparisons and the observation\nuniversal, reverse the former by \\(\\mathsf{pl}\\), instantiate\nthe latter at \\(i\\), reverse it, and introduce \\(i\\).\nFor transitivity perform these projections on both premises,\ncompose the bit comparisons, and at each \\(i\\) compose the\ntwo observation comparisons before introducing \\(i\\).\nReassemble the conjunctions by \\(\\mathsf{pl}\\).\nThe invariance proofs supply the two bit components of\n\\(\\operatorname{AeRound}\\) and, after introduction of \\(i\\),\nits observation component.  The congruence builder for\n\\(\\operatorname{Ae}\\) is the explicit two-component\ntransitivity construction of Lemma~\\ref{rel-equivalence}.\n\nGiven \\(e:x\\simeq x'\\), the canonical signed bits imply\nthe two same-tag bit comparisons.  At \\(i\\), combine \\(e[i]\\)\nwith both canonical \\(O_i\\) evaluations, then introduce \\(i\\).\nThis proves \\(\\operatorname{Ae}(x^t,x'^{\\,t})\\).\nConversely project its observation universal, instantiate at\n\\(i\\), and chain the two canonical evaluations to obtain\n\\(\\operatorname{le}(i,x)\\leftrightarrow \\operatorname{le}(i,x')\\); introduce \\(i\\).\nFor \\(t\\ne u\\), choose a coordinate where their fixed strings\ndiffer.  The two signed-bit proofs contradict the comparison\nat that coordinate projected from an assumed\n\\(\\operatorname{Ae}(x^t,x'^{\\,u})\\).\nAbstract that assumption to obtain the negation.\n\\end{proof}\n\n\\begin{definition}[The specialized query object]\n\\label{rel-specialized-object}\nAt \\(A_0\\), for \\(P:\\mathsf P_L\\), define\n\\[\n\\begin{aligned}\n \\mathcal P_G(P)&=\\forall y,z.\\,\n  \\bigl(\\operatorname{Ae}(y,z)\\Rightarrow(P@y\\leftrightarrow P@z)\\bigr),\\\\\n \\mathcal P_{C_t}(P)&=\\exists x.\\forall w.\\,\n                    (P@w\\leftrightarrow\\operatorname{Ae}(w,x^t)),\\\\\n \\mathcal P_{E_t}(P)&=\\exists x.\\forall w.\\,\n       \\bigl(P@w\\leftrightarrow\n        (\\operatorname{Ae}(w,x^0)\\lor\\operatorname{Ae}(w,x^t))\\bigr),\\\\\n i_P&=\\operatorname{pred}_X(x.P@(x^\\ell)),\\\\\n \\mathcal P_R(P)&=\\operatorname{Ext}(i_P)\\Rightarrow\n     \\forall x.\\bigl(\\operatorname{Valid}(x)\\Rightarrow\n                    (P@(x^r)\\Rightarrow \\operatorname{le}(i_P,x))\\bigr).\n\\end{aligned}\n\\]\nHere \\(C_t\\) has \\(t=0,\\ell,r\\) and \\(E_t\\) has \\(t=\\ell,r\\).\nDefine\n\\[\n m_0=\\operatorname{mk}_M\n   \\left(bp.\\bigvee_{t\\ {\\rm structural}}\n       \\bigl(\\operatorname{Mark}_t(bp)\\land\n                       \\mathcal P_t(\\operatorname{Dec}(bp))\\bigr)\\right).\n\\]\nThe formulas \\(\\mathcal P_t\\) use \\(z_i\\), tags, and observations\nalready defined independently of \\(m_0\\); this is a data term\nwith no recursive occurrence of itself.\n\\end{definition}\n\n\\begin{lemma}[Respect and admissibility]\n\\label{rel-query-realization}\nFor \\(d:i\\approx i'\\) there is a builder\n\\[\n \\operatorname{ExtC}(d):\n       \\operatorname{Ext}(i)\\leftrightarrow\\operatorname{Ext}(i').\n\\]\nFor \\(e:P\\approx P'\\), every structural \\(t\\) has a builder\n\\(\\mathcal P_t(P)\\leftrightarrow\\mathcal P_t(P')\\).\nConsequently there are proofs\n\\[\n q_t(P):\\operatorname{qry}_t(m_0,P)\\leftrightarrow\\mathcal P_t(P)\n \\quad(t\\ {\\rm structural}),\\qquad\n ad:\\operatorname{Adm}\\big|_{A=A_0,m=m_0}.\n\\]\n\\end{lemma}\n\\begin{proof}\nUnder \\(x,x'\\), the evaluations \\(d[x],d[x']\\) propositionally\nentail\n\\[\n (x\\simeq x'\\Rightarrow(i@x\\leftrightarrow i@x'))\n \\leftrightarrow\n (x\\simeq x'\\Rightarrow(i'@x\\leftrightarrow i'@x')).\n\\]\nApply \\(\\mathsf{All}_{x,x'}\\) to obtain\n\\(\\operatorname{ExtC}(d)\\).\n\nFor \\(G\\), under \\(y,z\\), apply \\(\\mathsf{pl}\\) to\n\\(e[y],e[z]\\) with target the comparison of the two implications\nwhose common antecedent is \\(\\operatorname{Ae}(y,z)\\);\nthen apply \\(\\mathsf{All}_{y,z}\\).\nFor \\(C_t\\), under \\(x,w\\), the proof \\(e[w]\\) compares\n\\[\n P@w\\leftrightarrow\\operatorname{Ae}(w,x^t)\n \\quad\\hbox{with}\\quad\n P'@w\\leftrightarrow\\operatorname{Ae}(w,x^t).\n\\]\nApply \\(\\mathsf{All}_w\\) and then \\(\\mathsf{Ex}_x\\).\nFor \\(E_t\\), use exactly the same two typed operations, with\ntheir common right side now\n\\(\\operatorname{Ae}(w,x^0)\\lor\\operatorname{Ae}(w,x^t)\\).\n\nFor \\(R\\), predicate evaluation at \\(x\\) for \\(i_P,i_{P'}\\)\nhas right sides\n\\[\n P@((\\operatorname{Round}_Xx)^\\ell)\n \\quad\\hbox{and}\\quad P'@((\\operatorname{Round}_Xx)^\\ell).\n\\]\nCombine these evaluations with\n\\(e[(\\operatorname{Round}_Xx)^\\ell]\\) and introduce \\(x\\),\nobtaining \\(d:i_P\\approx i_{P'}\\).\nThe proof \\(\\operatorname{ExtC}(d)\\) compares the two\nantecedents of \\(\\mathcal P_R\\).\nUnder \\(x,v:\\operatorname{Valid}(x)\\), combine\n\\[\n e[x^r],\\qquad v[i_P][i_{P'}]\\cdot d\n\\]\nwith target\n\\[\n (P@(x^r)\\Rightarrow \\operatorname{le}(i_P,x))\n   \\leftrightarrow(P'@(x^r)\\Rightarrow \\operatorname{le}(i_{P'},x)).\n\\]\nApply \\(\\mathsf{Gd}_{v:\\operatorname{Valid}(x)}\\), then\n\\(\\mathsf{All}_x\\).  This compares the consequents of\n\\(\\mathcal P_R\\).  Combine it propositionally with\n\\(\\operatorname{ExtC}(d)\\).  In particular, the change of\npredicate in \\(le\\) has used its explicit validity guard.\n\nData cancellation at \\(\\operatorname{Enc}_t(P)\\) compares\n\\(\\operatorname{qry}_t(m_0,P)\\) with the defining disjunction\nevaluated there.  The signed marker proofs eliminate every\ndisjunct except \\(t\\), and give a comparison with\n\\(\\mathcal P_t(\\operatorname{Dec}(\\operatorname{Enc}_t(P)))\\).\nIntroduce \\(y\\) in the decoder evaluation comparison to get\n\\[\n \\operatorname{Dec}(\\operatorname{Enc}_t(P))\\approx P.\n\\]\nUse the property-respect builder just proved and chain to\nobtain \\(q_t(P)\\).  Cancellation at \\(bp_\\bot\\) and the\nnegations of all its structural markers give \\(\\neg D(m_0)\\).\nFor each structural \\(t\\), form\n\\[\n \\Lambda P,P'.\\lambda^*e.\\,\n \\mathsf{pl}\\bigl(q_t(P),q_t(P'),\n                      \\text{property-respect}_t(e)\\bigr)\n\\]\nwith target\n\\(\\operatorname{qry}_t(m_0,P)\\leftrightarrow\n\\operatorname{qry}_t(m_0,P')\\).\nTogether with \\(\\neg D(m_0)\\), these are exactly the\nconjuncts of the displayed admissibility formula.\n\\end{proof}\n\nFrom this point onward \\(A=A_0\\), \\(m=m_0\\), and \\(ad\\) is\nthe proof just constructed.\n\n\\subsection{Pairing laws at the fixed specialization}\n\n\\begin{lemma}[The coded equivalence is the observation equivalence]\n\\label{rel-observation-equivalence}\nThere are proofs\n\\[\n \\operatorname{GA}(P):\n       \\operatorname{Good}(P)\\leftrightarrow\\mathcal P_G(P),\n \\qquad\n \\operatorname{SA}(y,z):\n       (y\\sim z)\\leftrightarrow\\operatorname{Ae}(y,z).\n\\]\nFor each \\(t\\), there are builders\n\\[\n \\operatorname{Tag}_t(e):x^t\\sim x'^{\\,t}\n                  \\quad(e:x\\simeq x'),\\qquad\n \\operatorname{TagBack}_t(f):x\\simeq x'\n                  \\quad(f:x^t\\sim x'^{\\,t}).\n\\]\nFor \\(t\\ne u\\) there is\n\\(\\operatorname{diff}_{t,u}:\\neg(x^t\\sim x'^{\\,u})\\).\n\\end{lemma}\n\\begin{proof}\nThe proof \\(q_G(P)\\) identifies the second conjunct of\n\\(\\operatorname{Good}(P)\\).  Its property implies the first:\nthe builder\n\\[\n \\lambda^*g.\\Lambda y.\\,\n      g[y][\\operatorname{Round}y]\\cdot\\operatorname{AeRound}(y)\n\\]\nhas that implication as type.  Combining these by\n\\(\\mathsf{pl}\\) gives \\(\\operatorname{GA}(P)\\).\n\nFor an observation \\(\\Phi\\) equal to \\(H_1,H_2\\), or \\(O_i\\)\nwith \\(i\\) temporarily free, put\n\\(P_\\Phi=\\operatorname{pred}(w.\\Phi(w))\\).\nPredicate evaluation and Round invariance give, at every \\(u\\),\nan evaluation proof \\(P_\\Phi@u\\leftrightarrow\\Phi(u)\\).\nUnder \\(a:\\operatorname{Ae}(u,w)\\), project the relevant bit\ncomparison, or project and instantiate its observation\nuniversal at the free \\(i\\).  Combine it with the two evaluations\nto get \\(P_\\Phi@u\\leftrightarrow P_\\Phi@w\\).\nIntroducing \\(u,w,a\\) proves \\(\\mathcal P_G(P_\\Phi)\\);\napply \\(\\operatorname{back}(\\operatorname{GA}(P_\\Phi),-)\\)\nto get \\(\\operatorname{Good}(P_\\Phi)\\).\n\nUnder \\(e:y\\sim z\\), the proof\n\\(e[P_\\Phi]\\cdot\\operatorname{Good}(P_\\Phi)\\), combined\nwith the two evaluations, gives \\(\\Phi(y)\\leftrightarrow\\Phi(z)\\).\nTake the two bit instances.  For \\(O_i\\), introduce \\(i\\)\nafter obtaining this comparison.  Their conjunction proves\n\\(\\operatorname{Ae}(y,z)\\).  Conversely, under\n\\(a:\\operatorname{Ae}(y,z)\\), the term\n\\[\n \\Lambda P.\\lambda^*g.\\,\n       \\bigl(\\operatorname{to}(\\operatorname{GA}(P),g)\\bigr)\n                       [y][z]\\cdot a\n\\]\nproves \\(y\\sim z\\).  Abstract the two premises and pair the\ndirections to obtain \\(\\operatorname{SA}\\).\n\nGiven \\(e:x\\simeq x'\\), use the same-tag direction of\nLemma~\\ref{rel-tags}, then \\(\\operatorname{back}(\\operatorname{SA},-)\\).\nGiven \\(f:x^t\\sim x'^{\\,t}\\), use\n\\(\\operatorname{to}(\\operatorname{SA},f)\\) and the reverse\nsame-tag direction.  For distinct tags, introduce\n\\(f:x^t\\sim x'^{\\,u}\\), map it forward through\n\\(\\operatorname{SA}\\), and apply the negation from\nLemma~\\ref{rel-tags}.  These define the three asserted builders.\n\\end{proof}\n\n\\begin{lemma}[Class and edge witnesses]\n\\label{rel-witnesses}\nFor \\(t=0,\\ell,r\\) there is a proof\n\\[\n \\operatorname{Is}_t(y)\\leftrightarrow\n                         \\exists x.\\,(y\\sim x^t).\n\\]\nFor \\(t=\\ell,r\\) there is a proof\n\\[\n T_t(y,z)\\leftrightarrow\n             \\exists x.\\,((y\\sim x^0)\\land(z\\sim x^t)).\n\\]\n\\end{lemma}\n\\begin{proof}\nFor the first forward implication, take\n\\(h:\\operatorname{Is}_t(y)\\), apply\n\\(\\operatorname{to}(q_{C_t}(c_y),h)\\), and open the result\nas \\(x,p\\), where\n\\[\n p:\\forall w.\\,(c_y@w\\leftrightarrow\\operatorname{Ae}(w,x^t)).\n\\]\nThe class evaluation and reflexivity prove \\(c_y@y\\).\nApply \\(p[y]\\) forward and \\(\\operatorname{SA}(y,x^t)\\)\nbackward to obtain \\(y\\sim x^t\\).  Witness the opened \\(x\\)\nin the target existential.  The target contains no free\noccurrence of either local witness.\nFor the reverse, open \\(x,e:y\\sim x^t\\).\nAt \\(w\\), the class evaluation, congruence with\n\\(\\operatorname{refl}(w),e\\), and\n\\(\\operatorname{SA}(w,x^t)\\) yield\n\\(c_y@w\\leftrightarrow\\operatorname{Ae}(w,x^t)\\).\nIntroduce \\(w\\), witness \\(x\\) in \\(\\mathcal P_{C_t}(c_y)\\),\nand apply \\(q_{C_t}(c_y)\\) backward.\n\nFor the edge forward implication, take \\(h:T_t(y,z)\\).\nProject both \\(\\operatorname{Is}\\) conjuncts and its query.\nUse the first equivalence and open their witnesses as\n\\[\n a,e_a:y\\sim a^0,\\qquad\n b',e_{b'}:z\\sim b'^{\\,t}.\n\\]\nMap the query forward through \\(q_{E_t}(u_{yz})\\) and\nopen its witness as \\(x,p\\), with\n\\[\n p:\\forall w.\\,\n   \\bigl(u_{yz}@w\\leftrightarrow\n    (\\operatorname{Ae}(w,x^0)\\lor\\operatorname{Ae}(w,x^t))\\bigr).\n\\]\nThe union evaluation and reflexivity prove \\(u_{yz}@y\\)\nand \\(u_{yz}@z\\).  Apply \\(p\\) at each of these points and\nthe comparisons \\(\\operatorname{SA}\\); the resulting proofs\nhave types\n\\[\n (y\\sim x^0)\\lor(y\\sim x^t),\\qquad\n (z\\sim x^0)\\lor(z\\sim x^t).\n\\]\nThe term\n\\[\n \\lambda^*f.\\operatorname{diff}_{0,t}\\cdot\n        \\operatorname{trans}(\\operatorname{sym}(e_a),f)\n\\]\nnegates \\(y\\sim x^t\\).  The corresponding term\n\\[\n \\lambda^*f.\\operatorname{diff}_{t,0}\\cdot\n        \\operatorname{trans}(\\operatorname{sym}(e_{b'}),f)\n\\]\nnegates \\(z\\sim x^0\\).  Propositional elimination gives\n\\(y\\sim x^0\\) and \\(z\\sim x^t\\); pair them and witness \\(x\\).\nAll three openings have the original target existential.\n\nFor the reverse, open \\(x,e\\) and project\n\\(e_0:y\\sim x^0\\), \\(e_t:z\\sim x^t\\).\nWitness \\(x\\) with \\(e_0\\) or \\(e_t\\) in the first\nequivalence backward to obtain the two \\(\\operatorname{Is}\\)\nconjuncts.  At \\(w\\), the union evaluation and congruences\nalong \\(e_0,e_t\\) compare \\(u_{yz}@w\\) with\n\\((w\\sim x^0)\\lor(w\\sim x^t)\\).  Use the two\n\\(\\operatorname{SA}\\) proofs and introduce \\(w\\), obtaining\nthe property required for the witness \\(x\\) in\n\\(\\mathcal P_{E_t}(u_{yz})\\).  Apply \\(q_{E_t}(u_{yz})\\)\nbackward and conjoin the three results.\n\\end{proof}\n\n\\begin{lemma}[The two pairing laws]\n\\label{rel-pair-laws}\nUnder \\(h:\\operatorname{Resp}(v)\\), let\n\\(P_x=\\operatorname{Pair}(\\operatorname{Part}(v),x^0)\\).\nFor every \\(x':X\\) there are proofs\n\\begin{align}\n P_x@(x'^{\\,\\ell})&\\leftrightarrow\n                         v\\diamond_m(x'^{\\,0}), \\label{rel-pair-left}\\\\\n P_x@(x'^{\\,r})&\\leftrightarrow x\\simeq x'. \\label{rel-pair-right}\n\\end{align}\n\\end{lemma}\n\\begin{proof}\nFor \\eqref{rel-pair-left} forward, first negate\n\\(T_r(x^0,x'^{\\,\\ell})\\).  Given such a proof, map it\nforward through Lemma~\\ref{rel-witnesses} and open its\nwitness \\(u\\) into \\(\\bot\\).  Its second conjunct is\n\\(x'^{\\,\\ell}\\sim u^r\\), contradicted by\n\\(\\operatorname{diff}_{\\ell,r}\\).\nGiven \\(p:P_x@(x'^{\\,\\ell})\\), use\n\\(\\operatorname{pv}\\) and this negation to extract\n\\[\n \\exists y.\\,\n       (T_\\ell(y,x'^{\\,\\ell})\\land\\operatorname{Part}(v)@y).\n\\]\nOpen it as \\(y,q\\), and open the edge witness from the\nfirst conjunct as \\(u,q_0,q_\\ell\\), where\n\\[\n q_0:y\\sim u^0,\\qquad q_\\ell:x'^{\\,\\ell}\\sim u^\\ell.\n\\]\nThe proof \\(\\operatorname{TagBack}_\\ell(q_\\ell)\\) is\n\\(x'\\simeq u\\).  Symmetrize it and apply\n\\(\\operatorname{Tag}_0\\), then compose with \\(q_0\\),\nto get \\(e:y\\sim x'^{\\,0}\\).\nFrom the second conjunct of \\(q\\), the unrounded Part\nevaluation gives \\(v\\diamond_m y\\).\nApply \\(h[y][x'^{\\,0}]\\cdot e\\) forward to obtain the\ntarget \\(v\\diamond_m(x'^{\\,0})\\).  Each opening uses this\nfixed target.\n\nFor the left reverse implication, a proof of\n\\(v\\diamond_m(x'^{\\,0})\\) gives\n\\(\\operatorname{Part}(v)@(x'^{\\,0})\\) by\n\\eqref{rel-part-exact} backward.  Witness \\(x'\\) with\nthe two reflexivities in the edge equivalence backward\nto get \\(T_\\ell(x'^{\\,0},x'^{\\,\\ell})\\).\nPair these, witness \\(y=x'^{\\,0}\\), inject the resulting\nexistential into \\(\\operatorname{form}\\), and apply\n\\(\\operatorname{pv}\\) backward.\n\nFor \\eqref{rel-pair-right} forward, negate\n\\(\\exists y.(T_\\ell(y,x'^{\\,r})\\land\\operatorname{Part}(v)@y)\\).\nOpen an assumed existential into \\(\\bot\\), open its\n\\(T_\\ell\\) witness, and contradict its second conjunct\n\\(x'^{\\,r}\\sim u^\\ell\\) with \\(\\operatorname{diff}_{r,\\ell}\\).\nThus \\(\\operatorname{pv}\\) maps a proof of\n\\(P_x@(x'^{\\,r})\\) to \\(T_r(x^0,x'^{\\,r})\\).\nOpen its witness \\(u\\), with\n\\(x^0\\sim u^0\\) and \\(x'^{\\,r}\\sim u^r\\).\nThe two same-tag converses give \\(x\\simeq u\\) and\n\\(x'\\simeq u\\).  Compose the first with the symmetry of\nthe second to prove \\(x\\simeq x'\\).\nFor the reverse, given \\(e:x\\simeq x'\\), witness \\(x\\)\nin the edge equivalence with reflexivity at \\(x^0\\) and\n\\(\\operatorname{Tag}_r(\\operatorname{sym}(e)):\nx'^{\\,r}\\sim x^r\\).  This gives \\(T_r(x^0,x'^{\\,r})\\);\ninject it into \\(\\operatorname{form}\\), then apply\n\\(\\operatorname{pv}\\) backward.\nAbstract each input proof and pair the two directions.\n\\end{proof}\n\n\\section{The guarded diagonal argument}\n\\label{rel-diagonal}\n\nThe pairing laws are now available at the fixed specialization. We use\nthem to compare a probe with a predicate on $X$, and then derive a proof\nof $F_b$.\n\nThe construction below adapts the well-foundedness paradox of Hurkens\n\\cite{Hurkens1995}, as presented by Geuvers~\\cite[Section~2]{Geuvers2007}.\nWe prove each identity with the validity and extensionality hypotheses\nrequired by the present encoding.\n\nWe retain the specialization $A=A_0$, $m=m_0$ and the constructed\nproof $ad:\\operatorname{Adm}$.  Thus every relation and every occurrence\nof $\\diamond_m$ below uses this fixed specialization.  We use the Pair\nlaws of Lemma~\\ref{rel-pair-laws}, the query comparison $q_R$, and the\npreviously constructed lift and tag builders.  All displayed proof\nbuilders are in the labelled fragment.  In particular, $\\Lambda$ and\n$\\lambda^*$ denote the established logical introduction rules, and\n$\\mathsf{pl}_F$ denotes the established propositional proof builder\nwith target formula $F$.  Its use to construct a biconditional supplies\nboth implication directions; it never replaces a data expression by\na logically equivalent expression.  The data parameters $v:V$ used\nbelow are open parameters, not additional logical quantifier domains.\n\n\\begin{lemma}[The predicate represented by a response]\n\\label{rel-response-predicate}\nFor every data parameter $v:V$, define\n\\[\n i_v=\\operatorname{pred}_X(x'.\\,v\\diamond_m(x'^{\\,0}))\n       :\\mathsf P_X.\n\\]\nThis data definition requires no proof hypothesis.  Given\n$h:\\operatorname{Resp}(v)$, there are builders\n\\[\n E_h(x):i_v@x\\leftrightarrow v\\diamond_m(x^0),\n \\qquad ex_v:\\operatorname{Ext}(i_v).\n\\]\nMoreover, for $x:X$ and $u:\\operatorname{Valid}(x)$, there is a builder\n\\begin{equation}\n \\operatorname{SB}(v,h,x,u):\n \\operatorname{sb}(v,m,x^0)\\leftrightarrow\n                         \\operatorname{le}(i_v,x).\n \\label{rel-sb-law}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nThe predicate evaluation law gives\n\\[\n i_v@x\\leftrightarrow\n      v\\diamond_m((\\operatorname{Round}_Xx)^0).\n\\]\nOn the other hand,\n\\[\n h[x^0][(\\operatorname{Round}_Xx)^0]\\cdot\n       \\operatorname{Tag}_0(\\operatorname{RoundRel}_X(x))\n :v\\diamond_m(x^0)\\leftrightarrow\n      v\\diamond_m((\\operatorname{Round}_Xx)^0).\n\\]\nApplying $\\mathsf{pl}$ to these two biconditionals at the stated\ntarget gives $E_h(x)$.  For $e:x\\simeq x'$, put\n\\[\n ex_v=\\Lambda x,x'.\\lambda^*e.\\,\n \\mathsf{pl}_{\\,i_v@x\\leftrightarrow i_v@x'}\n \\bigl(E_h(x),E_h(x'),\n       h[x^0][x'^{\\,0}]\\cdot\\operatorname{Tag}_0(e)\\bigr).\n\\]\nThe final premise has type\n$v\\diamond_m(x^0)\\leftrightarrow v\\diamond_m(x'^{\\,0})$,\nso this is a proof of $\\operatorname{Ext}(i_v)$.\n\nFix $x:X$ and write\n\\[\n P_x=\\operatorname{Pair}(\\operatorname{Part}(v),x^0),\n \\qquad\n i_{P_x}=\\operatorname{pred}_X(x'.\\,P_x@(x'^{\\,\\ell})).\n\\]\nThe Pair laws, under the present hypothesis $h$, supply\n\\begin{align*}\n L_h(x')&:P_x@(x'^{\\,\\ell})\n                  \\leftrightarrow v\\diamond_m(x'^{\\,0}),\\\\\n R_h(x')&:P_x@(x'^{\\,r})\\leftrightarrow x\\simeq x'.\n\\end{align*}\nAt an arbitrary $x':X$, denote the two predicate evaluation proofs by\n\\begin{align*}\n r_{P_x}(x')&:i_{P_x}@x'\n              \\leftrightarrow P_x@((\\operatorname{Round}_Xx')^\\ell),\\\\\n r_v(x')&:i_v@x'\n              \\leftrightarrow v\\diamond_m((\\operatorname{Round}_Xx')^0).\n\\end{align*}\nUsing $L_h(\\operatorname{Round}_Xx')$ between them gives\n\\[\n d=\\Lambda x'.\\,\n      \\mathsf{pl}_{\\,i_{P_x}@x'\\leftrightarrow i_v@x'}\n      (r_{P_x}(x'),r_v(x'),\n       L_h(\\operatorname{Round}_Xx'))\n      :i_{P_x}\\approx i_v.\n\\]\nConsequently\n\\[\n ex_P=\\operatorname{back}(\\operatorname{ExtC}(d),ex_v)\n       :\\operatorname{Ext}(i_{P_x}).\n\\]\n\nWe first construct both directions of\n\\begin{equation}\n \\mathcal P_R(P_x)\\leftrightarrow\\operatorname{le}(i_v,x),\n \\label{rel-r-property-law}\n\\end{equation}\nwhere, by the definition of $\\mathcal P_R$,\n\\[\n \\mathcal P_R(P_x)=\\operatorname{Ext}(i_{P_x})\\Rightarrow\n \\forall x':X.\\bigl(\\operatorname{Valid}(x')\\Rightarrow\n (P_x@(x'^{\\,r})\\Rightarrow\\operatorname{le}(i_{P_x},x'))\\bigr).\n\\]\nFor the forward direction, assume $g:\\mathcal P_R(P_x)$ and put\n\\[\n p_x=\\operatorname{back}(R_h(x),\\operatorname{refl}(x))\n           :P_x@(x^r).\n\\]\nThen\n\\[\n (g\\cdot ex_P)[x]\\cdot u\\cdot p_x\n      :\\operatorname{le}(i_{P_x},x),\n \\qquad\n u[i_{P_x}][i_v]\\cdot d:\n      \\operatorname{le}(i_{P_x},x)\\leftrightarrow\n      \\operatorname{le}(i_v,x).\n\\]\nThus the forward builder is\n\\[\n \\lambda^*g.\\,\n \\operatorname{to}\\bigl(u[i_{P_x}][i_v]\\cdot d,\n                      (g\\cdot ex_P)[x]\\cdot u\\cdot p_x\\bigr).\n\\]\nFor the reverse direction, assume $l:\\operatorname{le}(i_v,x)$.\nIntroduce $ex':\\operatorname{Ext}(i_{P_x})$, $x':X$,\n$u':\\operatorname{Valid}(x')$, and $p:P_x@(x'^{\\,r})$.\nThe proof $ex'$ need not be used.  Set\n\\[\n e=\\operatorname{to}(R_h(x'),p):x\\simeq x'.\n\\]\nHere $e[i_v]:\\operatorname{le}(i_v,x)\\leftrightarrow\n\\operatorname{le}(i_v,x')$, whereas\n\\[\n u'[i_{P_x}][i_v]\\cdot d:\n \\operatorname{le}(i_{P_x},x')\\leftrightarrow\n \\operatorname{le}(i_v,x').\n\\]\nThe required proof of $\\operatorname{le}(i_{P_x},x')$ is therefore\n\\[\n \\operatorname{back}\\bigl(u'[i_{P_x}][i_v]\\cdot d,\n                         \\operatorname{to}(e[i_v],l)\\bigr).\n\\]\nAbstracting, in order, over $p,u',x',ex',l$ gives the reverse\ndirection of \\eqref{rel-r-property-law}; pairing the directions gives\nthe displayed biconditional.\n\nFinally, by definition,\n$\\operatorname{sb}(v,m,x^0)=\\operatorname{qry}_R(m,P_x)$.\nThe query comparison is\n\\[\n q_R(P_x):\\operatorname{qry}_R(m,P_x)\n                         \\leftrightarrow\\mathcal P_R(P_x).\n\\]\nComposing its forward direction with the forward direction of\n\\eqref{rel-r-property-law}, and composing the two reverse directions\nin the reverse order, gives \\eqref{rel-sb-law}.\n\\end{proof}\n\n\\begin{lemma}[The shift and its predecessor comparison]\n\\label{rel-shift}\nDefine the data operations\n\\[\n \\delta x=\\operatorname{lift}_m(x^0):X,\n \\qquad\n Si=\\operatorname{pred}_X(x.\\,i@\\delta x):\\mathsf P_X.\n\\]\nFor every $x:X$ there is a proof\n$V_\\delta(x):\\operatorname{Valid}(\\delta x)$, and for\n$e:x\\simeq x'$ there is a proof\n$\\operatorname{DeltaC}(e):\\delta x\\simeq\\delta x'$.\nFor $ex:\\operatorname{Ext}(i)$ there are builders\n\\begin{align}\n s_{ex}(x)&:Si@x\\leftrightarrow i@\\delta x,\n                                    \\label{rel-shift-evaluation}\\\\\n \\operatorname{ExtS}(i,ex)&:\\operatorname{Ext}(Si),\n                                    \\label{rel-shift-extensionality}\\\\\n d_s&:i_{z_i}\\approx Si.             \\label{rel-shift-comparison}\n\\end{align}\nFor the additional input $u:\\operatorname{Valid}(x)$ there is a builder\n\\begin{equation}\n a_{ex,u}:\\operatorname{le}(i,\\delta x)\n                    \\leftrightarrow\\operatorname{le}(Si,x).\n \\label{rel-shift-predecessor}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nThe established validity proof for a lift, applied to $x^0$, is\n$V_\\delta(x)$.  Congruence is the composite\n\\[\n \\operatorname{DeltaC}(e)\n       =\\operatorname{LC}(\\operatorname{Tag}_0(e)).\n\\]\nNeither construction requires $x$ to be valid or any predicate to\nbe extensional.\n\nThe predicate evaluation law gives\n$Si@x\\leftrightarrow i@\\delta(\\operatorname{Round}_Xx)$.\nThe proof\n\\[\n ex[\\delta x][\\delta(\\operatorname{Round}_Xx)]\\cdot\n      \\operatorname{DeltaC}(\\operatorname{RoundRel}_X(x))\n :i@\\delta x\\leftrightarrow\n      i@\\delta(\\operatorname{Round}_Xx)\n\\]\nand that evaluation give $s_{ex}(x)$ by $\\mathsf{pl}$ at the target\nin \\eqref{rel-shift-evaluation}.  Set\n\\[\n \\operatorname{ExtS}(i,ex)=\n \\Lambda x,x'.\\lambda^*e.\\,\n \\mathsf{pl}_{\\,Si@x\\leftrightarrow Si@x'}\n \\bigl(s_{ex}(x),s_{ex}(x'),\n       ex[\\delta x][\\delta x']\\cdot\\operatorname{DeltaC}(e)\\bigr).\n\\]\nThis has the type in \\eqref{rel-shift-extensionality}.\n\nFrom $ex$, the earlier response builder gives\n$h_i:\\operatorname{Resp}(z_i)$, and the earlier exact evaluation\ngives, for every $y:L$, a proof\n\\[\n Z_{ex}(y):z_i\\diamond_m y\\leftrightarrow\n                i@\\operatorname{lift}_m(y).\n\\]\nIn particular, at $y=x^0$ the right-hand side is $i@\\delta x$.\nTogether with $E_{h_i}(x)$ and $s_{ex}(x)$ this gives\n\\[\n d_s=\\Lambda x.\\,\n \\mathsf{pl}_{\\,i_{z_i}@x\\leftrightarrow Si@x}\n \\bigl(E_{h_i}(x),Z_{ex}(x^0),s_{ex}(x)\\bigr).\n\\]\n\nNow assume $u:\\operatorname{Valid}(x)$.  The three available\nbiconditionals, with their exact endpoints, are\n\\begin{align*}\n \\operatorname{LP}(i,x^0)&:\n \\operatorname{le}(i,\\delta x)\n       \\leftrightarrow\\operatorname{sb}(z_i,m,x^0),\\\\\n \\operatorname{SB}(z_i,h_i,x,u)&:\n \\operatorname{sb}(z_i,m,x^0)\n       \\leftrightarrow\\operatorname{le}(i_{z_i},x),\\\\\n u[i_{z_i}][Si]\\cdot d_s&:\n \\operatorname{le}(i_{z_i},x)\n       \\leftrightarrow\\operatorname{le}(Si,x).\n\\end{align*}\nThe forward direction of $a_{ex,u}$ is\n\\[\n \\lambda^*t.\\,\n \\operatorname{to}\\bigl(u[i_{z_i}][Si]\\cdot d_s,\n  \\operatorname{to}\\bigl(\\operatorname{SB}(z_i,h_i,x,u),\n   \\operatorname{to}(\\operatorname{LP}(i,x^0),t)\\bigr)\\bigr).\n\\]\nIts reverse direction is\n\\[\n \\lambda^*t.\\,\n \\operatorname{back}\\bigl(\\operatorname{LP}(i,x^0),\n  \\operatorname{back}\\bigl(\\operatorname{SB}(z_i,h_i,x,u),\n   \\operatorname{back}(u[i_{z_i}][Si]\\cdot d_s,t)\\bigr)\\bigr).\n\\]\nPairing them proves \\eqref{rel-shift-predecessor}.  The only use of\n$u$ is in the response comparison and in transport at the fixed\nargument $x$; no unconditional replacement of $i_{z_i}$ by $Si$\ninside $\\operatorname{le}$ has been made.\n\\end{proof}\n\n\\begin{lemma}[Induction respects predicate equivalence]\n\\label{rel-ind-congruence}\nFor $d:i\\approx i'$ there is a builder\n\\[\n \\operatorname{IndC}(d):\n       \\operatorname{Ind}(i)\\leftrightarrow\\operatorname{Ind}(i').\n\\]\nThis builder needs no extensionality hypothesis on either predicate.\n\\end{lemma}\n\n\\begin{proof}\nFor $x:X$ and $u:\\operatorname{Valid}(x)$ put\n\\[\n c_u=u[i][i']\\cdot d:\n       \\operatorname{le}(i,x)\\leftrightarrow\\operatorname{le}(i',x).\n\\]\nThe two directions, paired in the displayed order, are\n\\begin{align*}\n &\\lambda^*l.\\Lambda x.\\lambda^*u.\\lambda^*t'.\\,\n    \\operatorname{to}\\bigl(d[x],\n       l[x]\\cdot u\\cdot\\operatorname{back}(c_u,t')\\bigr),\\\\\n &\\lambda^*l'.\\Lambda x.\\lambda^*u.\\lambda^*t.\\,\n    \\operatorname{back}\\bigl(d[x],\n       l'[x]\\cdot u\\cdot\\operatorname{to}(c_u,t)\\bigr).\n\\end{align*}\nIn the first line $l:\\operatorname{Ind}(i)$ and\n$t':\\operatorname{le}(i',x)$; in the second\n$l':\\operatorname{Ind}(i')$ and $t:\\operatorname{le}(i,x)$.\nThus every application has the exact antecedent required by\n\\eqref{rel-ind-definition}.\n\\end{proof}\n\n\\begin{lemma}[A valid induction object]\n\\label{rel-wf}\nDefine, without proof hypotheses,\n\\[\n \\operatorname{WF}=\\operatorname{mk}_X(v.\\,\\operatorname{Ind}(i_v)):X.\n\\]\nThere is a proof $V_{\\operatorname{WF}}:\n\\operatorname{Valid}(\\operatorname{WF})$.  For\n$ex:\\operatorname{Ext}(i)$ there is a builder\n\\begin{equation}\n w_{ex}:\\operatorname{le}(i,\\operatorname{WF})\n                       \\leftrightarrow\\operatorname{Ind}(Si).\n \\label{rel-wf-evaluation}\n\\end{equation}\nFinally, from $i:\\mathsf P_X$, $ex:\\operatorname{Ext}(i)$ and\n$l:\\operatorname{Ind}(i)$ there is a builder\n\\begin{equation}\n b(i,ex,l):i@\\operatorname{WF}.\n \\label{rel-induction-builder}\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nSince $\\operatorname{le}(i,\\operatorname{WF})=[\\operatorname{WF}z_i]$,\nthe data cancellation law gives, for every $i$, a proof\n\\[\n F(i):\\operatorname{le}(i,\\operatorname{WF})\n                   \\leftrightarrow\\operatorname{Ind}(i_{z_i}).\n\\]\nThis evaluation uses neither $\\operatorname{Resp}(z_i)$ nor\n$\\operatorname{Ext}(i)$.\n\nTo prove validity, take arbitrary $i,i':\\mathsf P_X$ and\n$d:i\\approx i'$.  We construct\n\\[\n D(d):i_{z_i}\\approx i_{z_{i'}}\n\\]\nwithout introducing extensionality assumptions.  At $x:X$ put\n$t=(\\operatorname{Round}_Xx)^0:L$.  The predicate evaluation law\nand the earlier rounded evaluation of $z_i$ give the following\nchain of biconditionals:\n\\[\n i_{z_i}@x\n \\ \\leftrightarrow\\ z_i\\diamond_m t\n \\ \\leftrightarrow\\ i@\\operatorname{lift}_m(\\operatorname{Round}t)\n \\ \\leftrightarrow\\ i'@\\operatorname{lift}_m(\\operatorname{Round}t)\n \\ \\leftrightarrow\\ z_{i'}\\diamond_m t\n \\ \\leftrightarrow\\ i_{z_{i'}}@x.\n\\]\nThe middle comparison is exactly\n$d[\\operatorname{lift}_m(\\operatorname{Round}t)]$.\nThe other four comparisons are the two predicate evaluations and\nthe two rounded evaluations.  Applying $\\mathsf{pl}$ to those five\nproofs at target $i_{z_i}@x\\leftrightarrow i_{z_{i'}}@x$, then\nabstracting over $x$, constructs $D(d)$.  Therefore\n\\[\n V_{\\operatorname{WF}}=\n \\Lambda i,i'.\\lambda^*d.\\,\n \\mathsf{pl}_{\\,\\operatorname{le}(i,\\operatorname{WF})\n                   \\leftrightarrow\n                   \\operatorname{le}(i',\\operatorname{WF})}\n \\bigl(F(i),F(i'),\\operatorname{IndC}(D(d))\\bigr).\n\\]\n\nNow assume $ex:\\operatorname{Ext}(i)$.  The proof $d_s$ from\n\\eqref{rel-shift-comparison} and Lemma~\\ref{rel-ind-congruence} give\n\\[\n \\operatorname{IndC}(d_s):\n          \\operatorname{Ind}(i_{z_i})\\leftrightarrow\\operatorname{Ind}(Si).\n\\]\nThe forward and reverse directions of $w_{ex}$ are, respectively,\n\\begin{align*}\n &\\lambda^*t.\\,\n   \\operatorname{to}(\\operatorname{IndC}(d_s),\n                     \\operatorname{to}(F(i),t)),\\\\\n &\\lambda^*l_S.\\,\n   \\operatorname{back}(F(i),\n                       \\operatorname{back}(\\operatorname{IndC}(d_s),l_S)).\n\\end{align*}\nTheir pair proves \\eqref{rel-wf-evaluation}.\n\nFor the final assertion, fix $l:\\operatorname{Ind}(i)$.  We first\nconstruct $l_S:\\operatorname{Ind}(Si)$.  Under $x:X$,\n$u:\\operatorname{Valid}(x)$ and $t:\\operatorname{le}(Si,x)$,\nthe proof $\\operatorname{back}(a_{ex,u},t)$ has type\n$\\operatorname{le}(i,\\delta x)$.  Since\n$V_\\delta(x):\\operatorname{Valid}(\\delta x)$, it follows that\n\\[\n l[\\delta x]\\cdot V_\\delta(x)\\cdot\n                   \\operatorname{back}(a_{ex,u},t):i@\\delta x.\n\\]\nApplying the reverse direction of $s_{ex}(x)$ gives $Si@x$.\nThus the required builder is\n\\[\n l_S=\\Lambda x.\\lambda^*u.\\lambda^*t.\\,\n \\operatorname{back}\\bigl(s_{ex}(x),\n       l[\\delta x]\\cdot V_\\delta(x)\\cdot\n                   \\operatorname{back}(a_{ex,u},t)\\bigr).\n\\]\nSince $\\operatorname{back}(w_{ex},l_S)$ has type\n$\\operatorname{le}(i,\\operatorname{WF})$, define\n\\[\n b(i,ex,l)=\n l[\\operatorname{WF}]\\cdot V_{\\operatorname{WF}}\\cdot\n                      \\operatorname{back}(w_{ex},l_S).\n\\]\nIts type is $i@\\operatorname{WF}$, as required.\n\\end{proof}\n\n\\begin{proposition}[The diagonal contradiction]\n\\label{rel-contradiction}\nThe specialized relational construction yields a proof of\n$\\bot=F_b$ in the ambient labelled context.\n\\end{proposition}\n\n\\begin{proof}\nDefine the data formula and data predicate\n\\begin{align*}\n Q(x)&=\\forall i:\\mathsf P_X.\\bigl(\\operatorname{Ext}(i)\n           \\Rightarrow(\\operatorname{le}(i,x)\\Rightarrow i@\\delta x)\\bigr),\\\\\n j_*&=\\operatorname{pred}_X(x.\\,\\neg Q(x)):\\mathsf P_X.\n\\end{align*}\nNeither definition uses proof variables.\n\nFirst we construct\n\\[\n \\operatorname{QC}(e):Q(x)\\leftrightarrow Q(x')\n \\qquad(e:x\\simeq x').\n\\]\nFor any $i:\\mathsf P_X$ and $ex:\\operatorname{Ext}(i)$, put\n\\[\n c_{ex,e}=ex[\\delta x][\\delta x']\\cdot\\operatorname{DeltaC}(e)\n               :i@\\delta x\\leftrightarrow i@\\delta x'.\n\\]\nThe forward direction of $\\operatorname{QC}(e)$ is\n\\[\n \\lambda^*q.\\Lambda i.\\lambda^*ex.\\lambda^*t'.\\,\n \\operatorname{to}\\bigl(c_{ex,e},\n       q[i]\\cdot ex\\cdot\\operatorname{back}(e[i],t')\\bigr),\n\\]\nwhere $q:Q(x)$ and $t':\\operatorname{le}(i,x')$.\nIts reverse direction is\n\\[\n \\lambda^*q'.\\Lambda i.\\lambda^*ex.\\lambda^*t.\\,\n \\operatorname{back}\\bigl(c_{ex,e},\n       q'[i]\\cdot ex\\cdot\\operatorname{to}(e[i],t)\\bigr),\n\\]\nwhere $q':Q(x')$ and $t:\\operatorname{le}(i,x)$.\nPairing these terms gives the asserted congruence.\n\nThe predicate evaluation law supplies\n$j_*@x\\leftrightarrow\\neg Q(\\operatorname{Round}_Xx)$.\nUsing $\\operatorname{QC}(\\operatorname{RoundRel}_X(x))$ and\n$\\mathsf{pl}$ with that evaluation yields\n\\begin{equation}\n n(x):j_*@x\\leftrightarrow\\neg Q(x).\n \\label{rel-negative-evaluation}\n\\end{equation}\nThe extensionality builder is\n\\[\n ex_*=\n \\Lambda x,x'.\\lambda^*e.\\,\n \\mathsf{pl}_{\\,j_*@x\\leftrightarrow j_*@x'}\n       \\bigl(n(x),n(x'),\\operatorname{QC}(e)\\bigr)\n       :\\operatorname{Ext}(j_*).\n\\]\nIn both uses of $\\mathsf{pl}$ the passage from a biconditional\nbetween the $Q$-formulas to the corresponding negated formulas is\npropositional; no predicate is substituted inside data.\n\nWe next construct $l_*:\\operatorname{Ind}(j_*)$.\nIntroduce $x:X$, $u:\\operatorname{Valid}(x)$ and\n$t:\\operatorname{le}(j_*,x)$.  To obtain $j_*@x$, it suffices,\nby the reverse direction of $n(x)$, to obtain $\\neg Q(x)$.\nUnder the further assumption $q:Q(x)$ we have\n\\[\n q[j_*]\\cdot ex_*\\cdot t:j_*@\\delta x,\n\\]\nand hence\n\\begin{equation}\n n_\\delta=\n \\operatorname{to}\\bigl(n(\\delta x),q[j_*]\\cdot ex_*\\cdot t\\bigr)\n                  :\\neg Q(\\delta x).\n \\label{rel-negative-shift}\n\\end{equation}\nTo construct the opposite formula $Q(\\delta x)$, take arbitrary\n$i:\\mathsf P_X$, $ex:\\operatorname{Ext}(i)$ and\n$t':\\operatorname{le}(i,\\delta x)$.  At this point\n$a_{ex,u}$ uses the original proof $u:\\operatorname{Valid}(x)$.\nIts forward direction gives\n\\[\n \\operatorname{to}(a_{ex,u},t'):\\operatorname{le}(Si,x).\n\\]\nBecause $\\operatorname{ExtS}(i,ex):\\operatorname{Ext}(Si)$,\nwe may instantiate $q$ at $Si$ to obtain\n\\[\n q[Si]\\cdot\\operatorname{ExtS}(i,ex)\\cdot\n           \\operatorname{to}(a_{ex,u},t'):Si@\\delta x.\n\\]\nThe forward direction of $s_{ex}(\\delta x)$ turns this into\n$i@\\delta(\\delta x)$.  Therefore\n\\begin{equation}\n \\begin{split}\n p_\\delta={}&\\Lambda i.\\lambda^*ex.\\lambda^*t'.\\,\n \\operatorname{to}\\bigl(s_{ex}(\\delta x),\\\\[-2pt]\n &\\hspace{35mm}\n q[Si]\\cdot\\operatorname{ExtS}(i,ex)\\cdot\n                   \\operatorname{to}(a_{ex,u},t')\\bigr)\n       :Q(\\delta x).\n \\end{split}\n \\label{rel-positive-shift}\n\\end{equation}\nCombining \\eqref{rel-negative-shift} and \\eqref{rel-positive-shift}\ngives $n_\\delta\\cdot p_\\delta:\\bot$.  With these local terms\ninlined in their indicated context, define\n\\[\n l_*=\n \\Lambda x.\\lambda^*u.\\lambda^*t.\\,\n \\operatorname{back}\\bigl(n(x),\n                  \\lambda^*q.\\,n_\\delta\\cdot p_\\delta\\bigr).\n\\]\nIts body has type $j_*@x$, so\n$l_*:\\operatorname{Ind}(j_*)$.\n\nFinally construct $q_0:Q(\\operatorname{WF})$.  Under arbitrary\n$i:\\mathsf P_X$, $ex:\\operatorname{Ext}(i)$ and\n$t:\\operatorname{le}(i,\\operatorname{WF})$, the forward direction\nof $w_{ex}$ gives $\\operatorname{Ind}(Si)$.  Applying the induction\nbuilder to the predicate $Si$ therefore gives\n\\[\n b\\bigl(Si,\\operatorname{ExtS}(i,ex),\n                 \\operatorname{to}(w_{ex},t)\\bigr):Si@\\operatorname{WF}.\n\\]\nHere the builder $b$ is instantiated at $Si$, while the displayed\n$w_{ex}$ and the following $s_{ex}$ are the builders for the\noriginal predicate $i$.  Thus\n\\[\n q_0=\\Lambda i.\\lambda^*ex.\\lambda^*t.\\,\n \\operatorname{to}\\bigl(s_{ex}(\\operatorname{WF}),\n       b(Si,\\operatorname{ExtS}(i,ex),\n                        \\operatorname{to}(w_{ex},t))\\bigr)\n       :Q(\\operatorname{WF}).\n\\]\nOn the other hand,\n$b(j_*,ex_*,l_*):j_*@\\operatorname{WF}$, and hence\n\\[\n \\operatorname{to}\\bigl(n(\\operatorname{WF}),\n                      b(j_*,ex_*,l_*)\\bigr)\n                  :\\neg Q(\\operatorname{WF}).\n\\]\nThe required ambient proof is therefore\n\\[\n \\operatorname{to}\\bigl(n(\\operatorname{WF}),\n                      b(j_*,ex_*,l_*)\\bigr)\\cdot q_0:\\bot.\n\\]\n\\end{proof}\n\n\\begin{remark}[Dependencies and discharged assumptions]\n\\label{rel-diagonal-dependencies}\nAt the fixed specialization, $i_v$ is defined before its\nresponse and extensionality proofs; $\\delta$ and $S$ are data\ndefinitions independent of those proofs; $\\operatorname{WF}$ uses\nthe already defined $i_v$ and $\\operatorname{Ind}$; and $Q,j_*$ use\nonly the previously defined data operations.  In particular,\n$\\operatorname{WF}$ is defined even when $v$ is not responsive.\nIts validity proof uses rounded evaluations and does not assume\nextensionality of the predicates it quantifies over.\n\nThe inputs $h,ex,u$ occur only in proof builders.  Every use of\n\\eqref{rel-sb-law} or \\eqref{rel-shift-predecessor} supplies the\nstated response, extensionality and validity inputs.  Every logical\nquantifier introduced by the displayed tail builders ranges over\n$X$ or $\\mathsf P_X$ with its fixed direct-domain annotation.\nAfter inlining the local builders,\nthe proof of Proposition~\\ref{rel-contradiction} has no free proof\nor data assumptions beyond the ambient context: $A_0,m_0,ad$ are\nthe earlier constructed specialization, and every introduced\nlogical assumption has been abstracted or supplied as an argument.\n\\end{remark}\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/wrappers.tex", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/build/sections/wrappers.tex", "bytes": 29269, "sha256": "7dfbec4b1799ae2e589d49c55b8ef545fb6a925fcba8e4a2751af708cfb12fdc", "content": "\\section{A typed calculus for the forbidden configuration}\n\\label{sec:enc-wrappers}\n\nWe now prove the exclusion used in constructing the payload spaces.  Throughout\nthis section, $S$ is a primary strongly connected component containing an odd\nclosed walk and a profile triple $(a_0,b,k_0)$ with all three vertices in $S$.\nWe construct a small logical calculus inside the given PTS.  Its judgments will\nbe genuine PTS judgments in one finite open context.  The later contradiction\nwill therefore fall under the system-wide weak-normalization hypothesis.\n\nWe say that a type is \\emph{sorted at a profile} $I$ if it has each sort in $I$.\nIts actual profile may be larger than $I$.  All constructions below use only\nthese separate sort judgments, and never uniqueness of sorts or product rules.\nSince $S$ is strongly connected and has an odd closed walk, any two of its\nvertices are joined inside $S$ by paths of both parities: join the first vertex\nto the walk and the walk to the second, and either insert the odd walk or omit\nit.  A path's parity is the number of its negative edges modulo two.\n\n\\subsection{Modes and the ambient context}\n\\label{subsec:enc-modes}\n\nPartition variable names into two infinite classes, of modes $\\mathsf d$\nand $\\mathsf p$.  Sorts have mode $\\mathsf d$.  Products, abstractions and\napplications carry a label\n\\[\n (i,j)\\in\\{(\\mathsf d,\\mathsf d),(\\mathsf d,\\mathsf p),\n                 (\\mathsf p,\\mathsf p)\\}.\n\\]\nIn a binder, $i$ is the bound variable's mode.  A product\n$\\Pi^{i,j}x:A.B$ has mode $\\mathsf d$ and both $A,B$ have mode\n$\\mathsf d$.  An abstraction $\\lambda^{i,j}x:A.M$ has annotation of mode\n$\\mathsf d$ and body and result of mode $j$.  In an application with label\n$(i,j)$, the argument has mode $i$ and the function and result have mode $j$.\nConsequently, an expression of mode $\\mathsf d$ has no free variable of mode\n$\\mathsf p$.  Declaration types and expected types always have mode\n$\\mathsf d$.\n\nThe typing rules are the original PTS rules with the displayed label retained\non the product in abstraction and application rules.  A beta step contracts\nan abstraction and application with matching labels; reduction is closed under\nall syntax positions, including annotations.  Conversion is generated by this\nlabelled reduction.  We suppress application labels only when the displayed\nfunction type determines them.  The labelled metatheory and its relation to\nordinary reduction are proved in Lemma~\\ref{lem:enc-labelled-metatheory}.\nIn particular, the weakening, substitution, conversion and generation\narguments used below apply in these modes.\n\n\\subsection{Reduction after forgetting the modes}\n\\label{sec:normal-erasure}\n\nBefore constructing the witnesses and wrappers, we establish the\nlabelled metatheory and its relation to the given pure type system.  Write $|M|$ for the term obtained by\nforgetting every mode label.  Erasure keeps all applications, binders, and\nannotations.  In particular, a redex in an annotation remains a redex after\nerasure.\n\nLabelling and erasure also occur in the structural transformations of\nRoux and van Doorn \\cite[Sections~3--4]{RouxVanDoorn2014}.\nHere the exact interface is preservation of typing together with lifting\nof every erased beta step. Keeping annotations is part of that interface;\nthe domain-erasure comparison studied by Barthe and Coquand\n\\cite[Theorem~17]{BartheCoquand2006} concerns a different operation.\n\n\\begin{lemma}[Labelled metatheory and reduction lifting]\n\\label{lem:enc-labelled-metatheory}\nThe labelled typing rules have context validity, correctness of types,\nweakening, typed substitution by terms of the declared modes, context\nconversion, generation, and subject reduction at the exact expected type.\nLabelled beta reduction is confluent, and convertible\nlabelled products have the same pair of labels and convertible corresponding\ncomponents.  Erasing a labelled typing derivation gives a typing derivation\nin the original pure type system.\n\nIf $\\Gamma\\vdash M:A$ is a labelled judgment and\n$|M|\\longrightarrow_\\beta N$, there is a labelled $M'$ with\n\\[\n M\\longrightarrow_\\beta M',\\qquad |M'|=N,\n \\qquad \\Gamma\\vdash M':A,\n\\]\nup to alpha conversion.  The mode of $M'$ is the mode of $M$.\nConsequently, every finite reduction of $|M|$ lifts to a typed labelled\nreduction.  If $|M|$ is weakly normalizing, some labelled reduct of $M$ has\nbeta-normal erasure.\n\\end{lemma}\n\n\\begin{proof}\nSubstitution preserves modes by induction on syntax.  A data expression has\nno free proof variable, so substitution for a proof variable cannot introduce\nproof syntax into a data expression.  Substitution for a data variable uses\na data term.  At binders, choose fresh names in the same mode class.\n\nContext validity follows by rule induction, retaining the sort typing of\neach declaration from its introduction premise.\nThe proofs of weakening and typed substitution are inductions on typing\nderivations.  Each binder retains its label, and each substituted term has\nthe mode required by that binder or declaration.  Context conversion follows\nby the identity substitution, typing the changed variable at its old type\nby conversion.  Generation follows by removing final conversions and\nweakenings.  None of these arguments compares distinct possible sort\nassignments.\n\nCorrectness of types is another rule induction: an expected type is a literal\nsort or is itself\nsorted.  At application, the function's product type is sorted by induction;\ngeneration on that product and typed substitution sort the application's\nresult type.  This remains true through conversion and weakening.\n\nFor confluence, use parallel reduction with homomorphic clauses at every\nconstructor, including annotation children, and a contracting clause only\nwhen the application and lambda labels match.  Parallel substitution is\nproved by syntax induction.  Every parallel reduct of a term parallel-reduces\nto its complete development: contract the matching redexes already present\nin the original term and develop all children.  At an unmatched\nlambda--application pair only the homomorphic clause applies.  This proves\nthe triangle property, hence confluence.  The reflexive transitive closures\nof parallel and ordinary reduction agree, since parallel contractions can be\nperformed from the children upwards.  Reduction never changes a product's\nouter constructor or labels.  Common reducts therefore give labelled product\ncompatibility, and a product cannot be convertible to a sort.\n\nSubject reduction now follows by induction on a typing derivation.  At a root\nbeta step, generation and product compatibility identify the lambda's\nannotation with the application's domain up to conversion.  Convert the\nargument to the annotation type, substitute in the body, and convert the\nresult to the original expected type.  A reduction of an application\nargument changes its substituted result type by conversion.  A reduction\nof a product domain is handled by context conversion in the codomain\npremise.  For a lambda annotation step, reduce the corresponding domain in\nits supporting product judgment, context-convert its body premise,\nre-form the lambda, and convert back to the original sorted product type.\nThe other positions, including positions inside\nannotations, follow by induction.  These constructions keep every label.\nErasure of typing follows directly by induction on the labelled derivation.\n\nIt remains to check that an erased redex can be contracted with the labels\npresent.  Every syntactic subterm of a typable term has a typing in the\nappropriate binder context; for an annotation this follows from context\nvalidity.  Consider the subterm at the erased redex.  Its shape is\n\\[\n (\\lambda^{i,j}x:D.P)\\mathbin{@^{i',j}}Q.\n\\]\nThe result modes already force the displayed common $j$.  Lambda generation\ngives a product type with labels $(i,j)$; application generation gives a\nconvertible product type with labels $(i',j)$.  Product compatibility forces\n$i=i'$.  Thus this is a matching labelled redex.  Contract it at the same\nposition.  Capture-free substitution commutes with erasure, so its erasure\nis $N$; subject reduction gives the asserted judgment and substitution\npreserves its mode.  This applies to any position, including one in a\nlambda or product annotation.  Induction on a finite erased reduction proves\nthe remaining assertions.\n\\end{proof}\n\n\\subsection{Witnesses and terminal transformations}\nFor each feasible profile $I$ needed in the finite construction, choose a\nnormal witness $D_I$ sorted at $I$, using only data names and labels\n$(\\mathsf d,\\mathsf d)$.  Such witnesses follow from the profile-generation\nconstruction: literals realize nonempty axiom profiles, variables of sorted\nliteral type realize the singleton generators, and dummy products realize\n$\\operatorname{Out}(I,J)$.  Combine the finitely many witness contexts by\nrenaming their data variables disjointly and weakening.  Extend the result\nby a data variable $e_I:D_I$ for each needed witness.  Denote this finite\nvalid data context by $\\Delta_{\\mathsf d}$.\n\nHere and below every profile triple means\n$K=\\operatorname{Out}(I,J)$.  If $T$ is sorted at $J$, the positive edge\n$J\\longrightarrow K$ associated with this triple sends $T$ to\n\\[\n   \\Pi^{\\mathsf d,j}z:D_I.T,\n   \\qquad j=\\mathsf p\\text{ for proof values},\\quad\n          j=\\mathsf d\\text{ for data values},\n\\]\nwhere $z$ is fresh and absent from $T$.  This type is sorted at every sort\n$c\\in K$: choose $a\\in I,b'\\in J$ with $(a,b',c)\\in\\mathcal R$ and use\nthose two available sort judgments.  The corresponding lift and projection\nare\n\\[\n    \\operatorname{up}(t)=\\lambda^{\\mathsf d,j}z:D_I.t,\n    \\qquad \\operatorname{pr}(w)=w\\,e_I.\n\\]\nThey have the displayed types and\n$\\operatorname{pr}(\\operatorname{up}(t))=_\\beta t$.\nA positive inclusion edge $I'\\longrightarrow I$ with $I\\subseteq I'$\nuses the identity type, lift and projection.\n\nA negative edge $I\\longrightarrow K$ associated with $(I,J,K)$ sends a\ntype $T$ sorted at $I$ to\n$\\Pi^{j,j}z:T.F$, where $F$ is a fixed type sorted at $J$ and $j$ is\n$\\mathsf p$ or $\\mathsf d$ according to the construction.  Formation uses\nexactly the same argument for each $c\\in K$.  Thus a path $\\rho$ defines\na wrapper $W_\\rho(T)$ by successive applications of these operations,\nwith specified tails at negative edges.  Empty paths are permitted, and\n\\[\n     W_{\\rho\\sigma}(T)=W_\\sigma(W_\\rho(T))\n\\]\nby the definition.  We always specify whether the wrapper carries proof\nvalues or data values.\n\nFor every internal negative edge of $S$ with triple $(I,J,K)$, fix the\nproof tail $F_J=D_J$ before choosing any paths. Call these finitely many types the\n\\emph{terminals}; include $F_b=D_b$ by the negative edge of\n$(a_0,b,k_0)$.  Each terminal is normal and has only\n$(\\mathsf d,\\mathsf d)$ labels.\n\n\\begin{lemma}[Terminal transformations]\n\\label{lem:enc-terminal-converters}\nThere is a finite valid extension $\\Delta=\\Delta_{\\mathsf d},\n\\Delta_{\\mathsf p}$ and, for every pair of terminals $F,F'$, a proof\nterm $\\tau_{F,F'}(p):F'$ in $\\Delta,p:F$.  Every declaration of\n$\\Delta_{\\mathsf p}$ has the form\n\\[\n       c:\\Pi^{\\mathsf p,\\mathsf p}u:T.F_J,\n\\]\nwhere $T$ is a normal, constant telescope of\n$(\\mathsf d,\\mathsf p)$ products ending at a terminal.  Its suffixes\nare sorted, and its type contains no free proof variables.\n\\end{lemma}\n\\begin{proof}\nAssociate each terminal with a negative edge producing it.  Lift the input\nterminal along the positive edge of its associated triple, reaching $S$.\nChoose a path in $S$ to the domain vertex of an edge producing the target\nterminal, and append that negative edge.  Carry a proof along this route.\nAt a positive edge use the lift above.  At a negative edge $(I,J,K)$,\nif the currently carried type is $T$, introduce a fresh proof variable\n$c:\\Pi^{\\mathsf p,\\mathsf p}u:T.F_J$ and replace the carried proof $t$\nby $c\\,t:F_J$.  Unless this is the last edge, lift $c\\,t$ along the\npositive edge $J\\longrightarrow K$ of this same triple before continuing.\nThe product type of $c$ is sorted at $K$, so the declaration is valid.\n\nInitially the carried type is a terminal.  Positive edges add constant\n$(\\mathsf d,\\mathsf p)$ prefixes, and negative edges reset the carried\ntype to a terminal before the prescribed lift.  Thus every converter domain\nhas precisely the asserted form.  Its normality and the sorting of each\nsuffix follow from its construction.  The types depend on chosen types and\npaths, not on the input proof.  Choose the finitely many pairwise routes\nfirst and predeclare all their converters in $\\Delta_{\\mathsf p}$;\nweakening makes every resulting transformation available in the same\ncontext.  Types use data only, so the converter declarations do not depend\non one another.\n\\end{proof}\n\n\\subsection{Proof transport and derived logic}\n\\label{subsec:enc-proof-transport}\n\nLet $\\rho$ be a proof path inside $S$ from a profile at which $T$ is sorted, and put\n$W=W_\\rho(T)$.  The following operations are defined in every valid\nextension of $\\Delta$.  All displayed inputs after a semicolon describe\nan open term under the indicated fresh variable.\nFor an even path they have types\n\\[\n  \\operatorname{in}_\\rho(p:T):W,\n  \\qquad \\operatorname{out}_\\rho(w:W;e.k):F\n       \\quad(e:T\\vdash k:F),\n\\]\nand for an odd path they have types\n\\[\n  \\operatorname{in}_\\rho(e.k):W\n       \\quad(e:T\\vdash k:F),\n  \\qquad \\operatorname{out}_\\rho(w:W,p:T):F,\n\\]\nwhere $F$ is any terminal.  Path and terminal subscripts will often be\nomitted, but remain part of each chosen operation.\nFor the empty path, $\\operatorname{in}(p)=p$ and\n$\\operatorname{out}(w;e.k)=k[e:=w]$.\nAppending a positive edge lifts the previous $\\operatorname{in}$ and\nprojects the input to the previous $\\operatorname{out}$.\nFor a negative edge with tail $F'$ appended to an even path $\\rho$, set\n\\begin{align*}\n \\operatorname{in}_{\\rho-}(e.k)\n    &=\\lambda^{\\mathsf p,\\mathsf p}u:W_\\rho(T).\n        \\operatorname{out}_\\rho(u;e.\\tau_{F,F'}(k)),\\\\\n \\operatorname{out}_{\\rho-}(w,p)\n    &=\\tau_{F',F}(w\\,\\operatorname{in}_\\rho(p)).\n\\end{align*}\nFor a negative edge appended to an odd path $\\rho$, set\n\\begin{align*}\n \\operatorname{in}_{\\rho-}(p)\n    &=\\lambda^{\\mathsf p,\\mathsf p}u:W_\\rho(T).\n        \\operatorname{out}_\\rho(u,p),\n       &&\\text{with output terminal }F',\\\\\n \\operatorname{out}_{\\rho-}(w;e.k)\n    &=\\tau_{F',F}(w\\,\\operatorname{in}_\\rho(e.k)).\n\\end{align*}\nThese are recursion equations on path length.  Each abstraction uses the\nalready formed negative-edge product.  Each application uses its domain\nexactly, and the terminal transformations have the displayed input and\noutput types.  This proves the typings by induction; callbacks are thinned\nby insertion before introducing each fresh variable.\n\nCall a type \\emph{stable} if it is a syntactic telescope of proof-result\nproducts, with labels $(\\mathsf d,\\mathsf p)$ or\n$(\\mathsf p,\\mathsf p)$, ending at a terminal.  A telescope of length\nzero is allowed.  Let $Q$ be a sorted stable type independent of a proof\nvariable $e:T$.  The even-path operation $\\operatorname{out}(w;e.k)$\nextends from terminal-valued callbacks to $k:Q$: introduce the successive\nraw arguments $\\vec x$ of $Q$, thin $w$ and the callback into that\ncontext, apply terminal-valued $\\operatorname{out}$ to\n$e.k\\,\\vec x$, and abstract the same arguments $\\vec x$.\nGeneration on the sorted telescope provides the domain sortings and the\nsupporting product judgments for all these abstractions.  In particular,\nif $T$ is stable, the identity callback gives\n\\[\n                    \\operatorname{down}_\\rho(w):T\n                 \\qquad (\\rho\\text{ even}).\n\\]\nA proof wrapper preserves stability of its input.  A proof path containing\na negative edge produces a stable type even if its input was not stable,\nbecause the first such edge ends at a terminal.\n\nA \\emph{formula} will mean a stable data type sorted at every sort of\n$b$. A formula is therefore a data-mode expression used as a type; a proof\nof that formula means a proof-mode inhabitant. The mode of an inhabitant\nis not determined by its expected type.  The next operations give a typed classical logical calculus of\nformulas.  These operations are defined terms, not additional PTS rules.\n\nFix even proof paths $\\alpha:b\\to a_0$, $\\beta:b\\to b$, and\n$\\gamma:k_0\\to b$.  Define\n\\[\n P\\Rightarrow Q=\n W_\\gamma\\bigl(\\Pi^{\\mathsf p,\\mathsf p}p:W_\\alpha(P).\n                                      W_\\beta(Q)\\bigr).\n\\]\nThe product rule is supplied by $(a_0,b,k_0)$, and all wrappers are\nalready typed.  Given $q:Q$ in a context extended by $p:P$, define\n\\begin{align*}\n \\lambda^*p.q\n   &=\\operatorname{in}_\\gamma\\bigl(\n       \\lambda^{\\mathsf p,\\mathsf p}p':W_\\alpha(P).\n       \\operatorname{in}_\\beta(q[p:=\\operatorname{down}_\\alpha(p')])\n                                      \\bigr),\\\\\n f\\cdot p\n   &=\\operatorname{down}_\\beta\\bigl(\n       \\operatorname{down}_\\gamma(f)\\,\n                          \\operatorname{in}_\\alpha(p)\\bigr).\n\\end{align*}\nThey have types $P\\Rightarrow Q$ and $Q$, respectively.  Data formulas\ncannot contain $p$, so the product codomain is independent of this proof\nvariable.\n\nFor a data type $T$ sorted at a direct vertex $I$, fix once and for all\na triple $(I,J,K)$ with $J,K\\in S$, and even proof paths\n$\\alpha_I:b\\to J$ and $\\gamma_I:K\\to b$.  For a formula $Q$ in\ncontext $x:T$, put\n\\[\n  \\forall x:T.Q=\n  W_{\\gamma_I}\\bigl(\\Pi^{\\mathsf d,\\mathsf p}x:T.\n                                       W_{\\alpha_I}(Q)\\bigr).\n\\]\nThe associated introduction and elimination are\n\\[\n \\Lambda x.q=\\operatorname{in}_{\\gamma_I}\n       (\\lambda^{\\mathsf d,\\mathsf p}x:T.\n                                  \\operatorname{in}_{\\alpha_I}(q)),\n \\qquad\n f[t]=\\operatorname{down}_{\\alpha_I}\n                   (\\operatorname{down}_{\\gamma_I}(f)\\,t).\n\\]\nThey are typed using precisely the chosen triple.  The direct vertex and\npaths are part of the domain annotation and remain fixed under\nsubstitution; paths are never reselected from a newly enlarged profile.\nThese constructions show that implication and universal quantification\nagain yield formulas.\n\nSet $\\bot=F_b$, $\\neg P=P\\Rightarrow\\bot$, and\n$\\top=\\bot\\Rightarrow\\bot$.  There are derived operations\n\\[\n        \\operatorname{abs}_P:\\bot\\longrightarrow P,\n        \\qquad \\operatorname{dn}_P:\\neg\\neg P\\longrightarrow P,\n\\]\nwhere the arrows describe input and output judgments, not an assertion that\nthese are primitive function types.  To construct either operation, expose\nthe raw telescope arguments $\\vec x$ of $P$, ending at a terminal $F$.\nFor $f:\\bot$, abstract the terminal body $\\tau_{\\bot,F}(f)$.\nFor $w:\\neg\\neg P$, abstract the terminal body\n\\[\n \\tau_{\\bot,F}\\left(\n       w\\cdot(\\lambda^*p.\\tau_{F,\\bot}(p\\,\\vec x))\\right).\n\\]\nThe applications $p\\,\\vec x$ use the original labels and annotations\nof $P$.  Sorting and abstraction support follow from generation on $P$,\nas in the definition of $\\operatorname{down}$.\n\nFor completeness, the other logical operations used below are the\nfollowing explicit abbreviations and term constructors:\n\\begin{align*}\n P\\land Q&=\\neg(P\\Rightarrow\\neg Q),&\n (p,q)_\\land&=\\lambda^*h.(h\\cdot p)\\cdot q,\\\\\n \\operatorname{fst}(w)\n   &=\\operatorname{dn}_P\n       (\\lambda^*n.w\\cdot(\\lambda^*p.\\lambda^*q.n\\cdot p)),&\n \\operatorname{snd}(w)\n   &=\\operatorname{dn}_Q\n       (\\lambda^*n.w\\cdot(\\lambda^*p.\\lambda^*q.n\\cdot q)),\\\\\n P\\leftrightarrow Q&=(P\\Rightarrow Q)\\land(Q\\Rightarrow P),&\n \\operatorname{to}(e,p)&=\\operatorname{fst}(e)\\cdot p,\\\\\n &&\\operatorname{back}(e,q)&=\\operatorname{snd}(e)\\cdot q,\\\\\n P\\lor Q&=\\neg(\\neg P\\land\\neg Q),&\n \\operatorname{inl}(p)&=\\lambda^*h.\\operatorname{fst}(h)\\cdot p,\\\\\n &&\\operatorname{inr}(q)&=\\lambda^*h.\\operatorname{snd}(h)\\cdot q.\n\\end{align*}\nGiven $w:P\\lor Q$ and branches $p:P\\vdash u:R$ and\n$q:Q\\vdash v:R$, their case term is\n\\[\n \\operatorname{dn}_R\\left(\n   \\lambda^*n.w\\cdot\n       (\\lambda^*p.n\\cdot u,\\lambda^*q.n\\cdot v)_\\land\\right).\n\\]\nExcluded middle has the term\n\\[\n      \\operatorname{em}(P)=\n        \\lambda^*h.\\operatorname{snd}(h)\\cdot\\operatorname{fst}(h)\n          :P\\lor\\neg P.\n\\]\nFor the same permitted quantified domains as above, put\n\\[\n \\exists x:T.Q=\\neg\\forall x:T.\\neg Q.\n\\]\nIf $t:T$ and $p:Q[x:=t]$, a witness term is\n$\\operatorname{wit}(t,p)=\\lambda^*h.h[t]\\cdot p$.\nIf $w:\\exists x:T.Q$ and $u:R$ under $x:T,p:Q$, where $R$ is\nindependent of $x,p$, existential elimination is\n\\[\n \\operatorname{open}_R(w;x,p.u)=\n  \\operatorname{dn}_R\\bigl(\n    \\lambda^*n.w\\cdot(\\Lambda x.\\lambda^*p.n\\cdot u)\\bigr).\n\\]\nEach display can be checked from the introduction and elimination\njudgments already proved.  All new assumptions are introduced with their\nformula or data type as annotation, and terms are weakened when necessary.\n\n\\begin{lemma}[Finite propositional derivations]\n\\label{lem:enc-propositional}\nIf formulas $P_1,\\ldots,P_m$ propositionally entail a formula $R$, there\nis a specified finite term\n$\\mathsf{pl}_R(p_1,\\ldots,p_m):R$ under $p_i:P_i$.\nHere $\\bot$ has truth value false; specified Boolean connectives are\nparsed, and other subformulas, including quantifiers, may be treated as\natoms.\n\\end{lemma}\n\\begin{proof}\nList the finitely many atoms and nest cases on their terms\n$\\operatorname{em}$.  Each resulting branch supplies either the atom or\nits negation.  Recursively build a proof of every true parsed formula and\na proof of the negation of every false parsed formula.  For $\\bot$ the\nrequired negation is the identity term.  For conjunction, a true case\nuses pairing, and a false case contradicts the false conjunct's projection.\nFor disjunction, a true case uses its true injection, and a false case\nuses case analysis to contradict either disjunct.  For implication, a true\ncase either returns a proof of the consequent or derives it by\n$\\operatorname{abs}$ from the false antecedent; a false case contradicts\nthe application of an assumed implication to the already proved antecedent.\nNegation is implication to $\\bot$, and biconditional is its displayed\nconjunction.  If some premise is false, apply its constructed negation to\nthe given proof and use $\\operatorname{abs}_R$.  Otherwise the assumed\ntruth-table entailment makes $R$ true, so use its constructed proof.\nThe recursively typed case terms discharge all branch assumptions.\n\\end{proof}\n\nWe also use three explicit congruence constructors.  For a proof\n$e:P\\leftrightarrow Q$ under data $x:T$, define\n\\[\n \\mathsf{All}_x(e)=\n \\bigl(\\lambda^*h.\\Lambda x.\\operatorname{to}(e,h[x]),\n       \\lambda^*h.\\Lambda x.\\operatorname{back}(e,h[x])\\bigr)_\\land.\n\\]\nIt compares $\\forall x:T.P$ and $\\forall x:T.Q$ with exactly the same\nannotated domain.  Define $\\mathsf{Ex}_x(e)$ by pairing the two functions\nwhich open an existential input, retain its data witness, and use\n$\\operatorname{to}$ or $\\operatorname{back}$ on its proof witness.\nFor a proof $e:P\\leftrightarrow Q$ under $v:D$, where $D$ is a formula,\nput\n\\[\n \\mathsf{Gd}_{v:D}(e)=\n \\bigl(\\lambda^*h.\\lambda^*v.\\operatorname{to}(e,h\\cdot v),\n       \\lambda^*h.\\lambda^*v.\\operatorname{back}(e,h\\cdot v)\\bigr)_\\land.\n\\]\nThis compares $D\\Rightarrow P$ and $D\\Rightarrow Q$.\nMultiple indices mean iteration of the corresponding constructor.\n\nThese conventions make later derivations finite algorithms for labelled\nterms.  A stated propositional consequence invokes\nLemma~\\ref{lem:enc-propositional}; proving the two directions of an\n equivalence means abstracting the two assumptions and pairing; opening an\nexistential means the displayed $\\operatorname{open}$ term at the stated\ntarget.  A schematic data parameter is an open parameter, not an implicit\nuniversal quantifier.  Only displayed $\\Lambda$ operations quantify over\ndata.  Logical equivalences are used to construct proofs and never to\nreplace a data subterm.  All fixed paths, roles and syntax choices are\npreserved under substitution.\n\n\\subsection{Data wrappers and exact cancellation}\n\\label{subsec:enc-data-transport}\n\nFor a sort $u$ with $\\{u\\}\\in S$ and\n$\\operatorname{Ax}(u)\\ne\\varnothing$, fix an even proof path from $b$\nto $\\{u\\}$.  For a formula $P$, its wrapper type along this path is a\ndata expression of type $u$; denote it by $\\operatorname{encode}_u(P)$.\nFix also an even proof path from $\\{u\\}$ to $b$ containing a negative\nedge.  Such a path is obtained from any even one by inserting an odd\nclosed detour twice.  Wrapping a data expression $t:u$ along this second\npath defines a stable formula $\\operatorname{decode}_u(t)$.\nThe two proof-path inclusions and the two stable retractions give a proof\n\\begin{equation}\n \\operatorname{decode}_u(\\operatorname{encode}_u(P))\n                             \\leftrightarrow P.\n \\label{eq:enc-code-decode}\n\\end{equation}\nExplicitly, the forward implication applies the two $\\operatorname{down}$\noperations and the reverse implication applies the two\n$\\operatorname{in}$ operations.  No equality of the two formulas is asserted.\n\nRecall that $H$ is the positive-path closure of the nonempty\n$\\operatorname{Ax}(u)$ with $\\{u\\}\\in S$.  For each tail vertex $J$\nof a retained negative secondary edge, choose such a $u$ and an all-positive\nprimary path $\\eta:\\operatorname{Ax}(u)\\to J$.  Define the data type\n\\[\n                 U_J=W_\\eta(u),\n\\]\nusing data labels.  It is sorted at $J$.  Define\n$\\operatorname{send}_J(P):U_J$ by positively lifting\n$\\operatorname{encode}_u(P):u$, and define\n$\\operatorname{read}_J(t)$ by positively projecting $t:U_J$ to type\n$u$ and applying $\\operatorname{decode}_u$.  Positive\nprojection--lift beta conversion and \\eqref{eq:enc-code-decode} give a\nspecified proof\n\\begin{equation}\n        \\operatorname{read}_J(\\operatorname{send}_J(P))\n                                     \\leftrightarrow P.\n        \\label{eq:enc-read-send}\n\\end{equation}\nThese choices are fixed once for each retained edge.\n\nNow use data wrappers on secondary paths, and on all-positive primary\npaths when indicated.  At a retained negative edge $(I,J,K)$, the tail is\n$U_J$ and the label is $(\\mathsf d,\\mathsf d)$.  For an even data path\n$\\rho$ from the sorting profile of $T$, define\n\\[\n \\operatorname{pack}_\\rho(t:T):W_\\rho(T),\\qquad\n \\operatorname{use}_\\rho(w:W_\\rho(T);x.\\Phi)\\quad\\text{a formula},\n\\]\nwhere $\\Phi$ is a formula under data $x:T$.  For an odd data path define\n\\[\n \\operatorname{mk}_\\rho(x.\\Phi):W_\\rho(T),\\qquad\n \\langle w:W_\\rho(T),t:T\\rangle_\\rho\\quad\\text{a formula}.\n\\]\nAt the empty path use $\\operatorname{pack}(t)=t$ and\n$\\operatorname{use}(w;x.\\Phi)=\\Phi[x:=w]$.\nA positive extension uses the same lift and projection operations as\nbefore.  If a negative edge with tail $U_J$ is appended to an even path,\nput\n\\begin{align*}\n \\operatorname{mk}_{\\rho-}(x.\\Phi)\n   &=\\lambda^{\\mathsf d,\\mathsf d}v:W_\\rho(T).\n           \\operatorname{send}_J(\\operatorname{use}_\\rho(v;x.\\Phi)),\\\\\n \\langle w,t\\rangle_{\\rho-}\n   &=\\operatorname{read}_J(w\\,\\operatorname{pack}_\\rho(t)).\n\\end{align*}\nIf it is appended to an odd path, put\n\\begin{align*}\n \\operatorname{pack}_{\\rho-}(t)\n   &=\\lambda^{\\mathsf d,\\mathsf d}v:W_\\rho(T).\n                         \\operatorname{send}_J(\\langle v,t\\rangle_\\rho),\\\\\n \\operatorname{use}_{\\rho-}(w;x.\\Phi)\n   &=\\operatorname{read}_J(w\\,\\operatorname{mk}_\\rho(x.\\Phi)).\n\\end{align*}\nAll expressions in these definitions have mode $\\mathsf d$.\nPath induction proves their typings: the body of each new lambda has type\n$U_J$, the negative-edge product is sorted by $(I,J,K)$, and each\napplication supplies an argument at exactly its displayed domain.  The\nformula returned by a read is stable by the negative-containing decode\npath.  Fresh variables and weakening keep callbacks in scope.\n\n\\begin{lemma}[Exact data cancellation]\n\\label{lem:enc-data-cancellation}\nFor an even data path there is a proof of\n\\[\n       \\operatorname{use}_\\rho(\\operatorname{pack}_\\rho(t);x.\\Phi)\n                             \\leftrightarrow\\Phi[x:=t],\n\\]\nand for an odd data path there is a proof of\n\\[\n             \\langle\\operatorname{mk}_\\rho(x.\\Phi),t\\rangle_\\rho\n                             \\leftrightarrow\\Phi[x:=t].\n\\]\nThese are uniform term constructors in all the displayed open data\nparameters.  The right-hand sides contain the original input $t$.\n\\end{lemma}\n\\begin{proof}\nInduct on the path.  The empty-path equivalence is propositional\nreflexivity.  A positive extension beta-reduces its projection of a lift,\nso conversion reduces its assertion to the preceding one.  For a negative\nextension, the displayed lambda beta-reduces the left side to\n$\\operatorname{read}_J(\\operatorname{send}_J(\\Psi))$, where $\\Psi$\nis the left side of the previous cancellation assertion.  Apply\n\\eqref{eq:enc-read-send}, the induction hypothesis, and\n$\\mathsf{pl}$ for transitivity of equivalence.  The expansions commute\nwith capture-free substitution because all paths, tails and names are\nfixed hygienically.  Consequently the final callback is exactly\n$\\Phi[x:=t]$ up to labelled conversion, with no extensional substitution\ninside data.\n\\end{proof}\n\nThe preceding constructions use finitely many sorts, profiles, witnesses,\npaths and converters.  Each term in the remainder is built from finitely\nmany of their instances.  Thus all the resulting judgments have the one\nfinite ambient context $\\Delta$, with any explicitly stated local\nparameters.  The next construction uses these operations to encode the\nforbidden profile configuration.\n"}, {"path": "preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/paper.pdf", "url": "https://raw.githubusercontent.com/openai/math/fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb/preprints/Weak-and-strong-normalization-in-pure-type-systems-September-25-2026/paper.pdf", "bytes": 719425, "sha256": "9db295a439634e3169700af595760553afafc9c5db979de9d2ac27a534af63ef", "base64": 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"}], "errors": []}