{"approach":"Blind competitor-ladder audit. S0 quotes around the visible midpoint, using one-to-two typical retail tickets of size rather than large exposure that an arbitrageur can consume in full. S1 changes only the probability estimator: upward ask moves reset it to ask-0.5 ticks; downward bid moves to bid+0.5; otherwise it relaxes toward the midpoint and stays inside the observed interval. Opposite-side replenishments are not independently treated as informed recentering. This is an approximate, retail-contaminated crossing estimator, not observation of the true price. Quotes widen near expiry; sizing and execution are identical in S0/S1.","tried":[{"variant":"s0a","mechanism":false,"margin":0.5,"notional":6,"relaxation":0.02,"seeds":[115000,115499],"n":500,"mean":-101.6446028464856,"retail_mean":32.6111461627344,"arb_mean":-134.2557490092234,"paired_vs_frozen_s0":-12.433044675981401,"paired_se":4.722064415204152,"selected":false},{"variant":"s0b","mechanism":false,"margin":1.0,"notional":8,"relaxation":0.02,"seeds":[115000,115499],"n":500,"mean":-128.9238756802482,"retail_mean":16.3488704508226,"arb_mean":-145.2727461310668,"paired_vs_frozen_s0":-39.712317509744,"paired_se":4.07910349816227,"selected":false},{"variant":"s0c","mechanism":false,"margin":1.5,"notional":8,"relaxation":0.02,"seeds":[115000,115499],"n":500,"mean":-147.3930309573692,"retail_mean":5.3649143307988,"arb_mean":-152.7579452881692,"paired_vs_frozen_s0":-58.181472786865,"paired_se":4.256894929664136,"selected":false},{"variant":"s0d","mechanism":false,"margin":1.0,"notional":4,"relaxation":0.02,"seeds":[115000,115499],"n":500,"mean":-89.2115581705042,"retail_mean":11.6147983102244,"arb_mean":-100.8263564807272,"paired_vs_frozen_s0":0.0,"paired_se":0.0,"selected":true},{"variant":"s10","mechanism":true,"margin":1.0,"notional":4,"relaxation":0.0,"seeds":[115000,115499],"n":500,"mean":-55.5595468754612,"retail_mean":19.4341429577214,"arb_mean":-74.9936898331788,"paired_vs_frozen_s0":33.652011295042996,"paired_se":5.277012355676594,"selected":true},{"variant":"s11","mechanism":true,"margin":1.0,"notional":4,"relaxation":0.02,"seeds":[115000,115499],"n":500,"mean":-56.8215992901776,"retail_mean":18.0438031545012,"arb_mean":-74.865402444681,"paired_vs_frozen_s0":32.3899588803266,"paired_se":4.605840979869989,"selected":false},{"variant":"s12","mechanism":true,"margin":1.0,"notional":4,"relaxation":0.1,"seeds":[115000,115499],"n":500,"mean":-80.2530415760442,"retail_mean":17.5369289921816,"arb_mean":-97.7899705682266,"paired_vs_frozen_s0":8.958516594459999,"paired_se":2.7955851961587173,"selected":false},{"variant":"s13","mechanism":true,"margin":1.0,"notional":4,"relaxation":1.0,"seeds":[115000,115499],"n":500,"mean":-87.8047895368274,"retail_mean":13.058562538874,"arb_mean":-100.8633520757022,"paired_vs_frozen_s0":1.4067686336768,"paired_se":0.4773064386028541,"selected":false}],"next":"No promotion is claimed; reserved finalist seeds were never used. Graft the estimator into a stronger capital-management baseline and measure paired incremental value separately. Retail depletion and absent boundary quotes can produce false or censored crossing signals; test a probabilistic crossing filter in a subsequent independently allocated item.","code":"import math\nfrom orderbook_pm_challenge.types import CancelAll, PlaceOrder, Side\n\nclass Strategy:\n    # S0 uses the visible midpoint; S1 changes only estimate().\n    mechanism = True\n    margin = 1.0\n    notional = 4\n    relaxation = 0.0\n\n    def __init__(self):\n        self.previous = None\n        self.price = None\n\n    def estimate(self, bid, ask):\n        midpoint = (bid + ask) / 2\n        if not self.mechanism:\n            return midpoint\n        if self.previous is None:\n            self.price = midpoint\n        else:\n            old_bid, old_ask = self.previous\n            # Arbitrage removes all asks below p and all bids above p.\n            # Inward changes can instead be next-step opposite-side refills.\n            up = ask > old_ask\n            down = bid < old_bid\n            if up and not down:\n                self.price = ask - 0.5\n            elif down and not up:\n                self.price = bid + 0.5\n            else:\n                self.price += self.relaxation * (midpoint - self.price)\n            self.price = min(ask - 0.5, max(bid + 0.5, self.price))\n        self.previous = (bid, ask)\n        return self.price\n\n    def on_step(self, state):\n        bid = state.competitor_best_bid_ticks\n        ask = state.competitor_best_ask_ticks\n        bid = 0 if bid is None else bid\n        ask = 100 if ask is None else ask\n        p = self.estimate(bid, ask)\n        # Widen near expiry as diffusion per remaining step rises.\n        width = self.margin + 0.35 * math.sqrt(2000 / max(20, state.steps_remaining))\n        buy = min(99, max(1, math.floor(p - width)))\n        sell = min(99, max(1, math.ceil(p + width)))\n        actions = [CancelAll()]\n        cash = max(0, state.cash - 0.02)\n        # Cap exposure at a few typical retail tickets; bound collateral.\n        for side, tick, denom in ((Side.BUY, buy, max(0.05, p / 100)),\n                                  (Side.SELL, sell, max(0.05, p / 100))):\n            q = self.notional / denom\n            unit = tick / 100 if side == Side.BUY else 1 - tick / 100\n            covered = state.yes_inventory if side == Side.SELL else 0\n            q = min(q, covered + cash * 0.48 / unit)\n            q = math.floor(q * 100) / 100\n            if q >= 0.01:\n                actions.append(PlaceOrder(side, tick, q))\n                cash -= max(0, q - covered) * unit\n        return actions\n","code_sha256":"eac4e22a59a291df1413275c327d61ab25bfb2792596f95f24d890875cc5b08b","code_s0":"import math\nfrom orderbook_pm_challenge.types import CancelAll, PlaceOrder, Side\n\nclass Strategy:\n    # S0 uses the visible midpoint; S1 changes only estimate().\n    mechanism = False\n    margin = 1.0\n    notional = 4\n    relaxation = 0.0\n\n    def __init__(self):\n        self.previous = None\n        self.price = None\n\n    def estimate(self, bid, ask):\n        midpoint = (bid + ask) / 2\n        if not self.mechanism:\n            return midpoint\n        if self.previous is None:\n            self.price = midpoint\n        else:\n            old_bid, old_ask = self.previous\n            # Arbitrage removes all asks below p and all bids above p.\n            # Inward changes can instead be next-step opposite-side refills.\n            up = ask > old_ask\n            down = bid < old_bid\n            if up and not down:\n                self.price = ask - 0.5\n            elif down and not up:\n                self.price = bid + 0.5\n            else:\n                self.price += self.relaxation * (midpoint - self.price)\n            self.price = min(ask - 0.5, max(bid + 0.5, self.price))\n        self.previous = (bid, ask)\n        return self.price\n\n    def on_step(self, state):\n        bid = state.competitor_best_bid_ticks\n        ask = state.competitor_best_ask_ticks\n        bid = 0 if bid is None else bid\n        ask = 100 if ask is None else ask\n        p = self.estimate(bid, ask)\n        # Widen near expiry as diffusion per remaining step rises.\n        width = self.margin + 0.35 * math.sqrt(2000 / max(20, state.steps_remaining))\n        buy = min(99, max(1, math.floor(p - width)))\n        sell = min(99, max(1, math.ceil(p + width)))\n        actions = [CancelAll()]\n        cash = max(0, state.cash - 0.02)\n        # Cap exposure at a few typical retail tickets; bound collateral.\n        for side, tick, denom in ((Side.BUY, buy, max(0.05, p / 100)),\n                                  (Side.SELL, sell, max(0.05, p / 100))):\n            q = self.notional / denom\n            unit = tick / 100 if side == Side.BUY else 1 - tick / 100\n            covered = state.yes_inventory if side == Side.SELL else 0\n            q = min(q, covered + cash * 0.48 / unit)\n            q = math.floor(q * 100) / 100\n            if q >= 0.01:\n                actions.append(PlaceOrder(side, tick, q))\n                cash -= max(0, q - covered) * unit\n        return actions\n","tuning_score":{"seeds":[115000,115499],"n":500,"s1_mean":-55.5595468754612,"s0_mean":-89.2115581705042,"mechanism_diff":33.652011295042996,"mechanism_se":5.277012355676594},"paired":{"seeds":[200000,200999],"n":1000,"complete":true,"mechanism_diff":35.6444743113832,"mechanism_se":3.7645511946271233,"mechanism_retail_diff":8.6550883031914,"mechanism_arb_diff":26.9893860081846,"reference_diff":-152.5603923628106,"reference_se":6.571232549532504,"s1_mean":-52.8645142104283,"s0_mean":-88.5089885218115,"b_mean":99.6958781523823,"failures":[]},"graft":"Track previous competitor bid/ask and a fair-price estimate. On ask rising alone set p=ask-0.5 ticks; on bid falling alone set p=bid+0.5. Otherwise relax toward midpoint, clipping to [bid+0.5,ask-0.5]. Inward quote changes may be opposite-side replenishments. Feed p into existing sizing/quoting; keep everything else unchanged.","seconds_per_simulation":0.15078736361046322,"evidence":{"simulator_commit":"b244530ee2a6247b5f930830eeca9dd950da0e7e","config":"Unmodified ChallengeConfig() and ParameterVariance(), 2000 steps; run_batch(factory,base_config=ChallengeConfig(),variance=ParameterVariance(),n_simulations=1,seed_start=seed,workers=1); no sandbox.","standard_error":"sample standard deviation of paired per-seed differences divided by sqrt(n); ddof=1","initial_timing":{"seeds":[115000,115019],"n":20,"seconds":3.1639775899238884,"seconds_per_simulation":0.15819887949619443},"freeze":{"s0":{"name":"s0d","params":[false,1.0,4,0.02],"mean":-89.2115581705042},"s1":{"name":"s10","relaxation":0.0,"mean":-55.5595468754612},"code_s0_sha256":"d785eb0274df15894bad6e06dd13131eebabd7c61f23f84a2aac339f088e172e","code_sha256":"eac4e22a59a291df1413275c327d61ab25bfb2792596f95f24d890875cc5b08b"},"reference_scores_sha256":"bde43171c0191751c603d429d3c46ce55bae11086c2826bbb386c9e7ad58c470","reference_usage":"Only fixture.H numeric scores and decoding metadata used; no reference strategy inspected.","source_replay":"Frozen source files loaded through the simulator loader reproduced total, retail, and arbitrage edge on each of 20 tuning seeds (115000..115019), for both S0 and S1.","collateral_checks":"384 deterministic action checks across cash/inventory levels and boundary/missing quotes passed; no extra seed simulations.","audit_checks":"Direct deterministic engine checks: all spreads 1..4, initial probability .505, upward arbitrage to .565 and downward to .435; confirms ask=57 and bid=43 respectively after replenishment. Not additional seed simulations.","holdout_per_seed":{"start":200000,"n":1000,"columns":["s0_total","s1_total","s1_minus_s0_retail","s1_minus_s0_arb"],"encoding":"base64(zlib(little-endian float64 row-major)); 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z4nXvxC/0CSFK6t9uONRbh8+mSbMrrwOvtY83JFV2wTET9GTtl2NDvM11mCRQg/Mr7zaphLFzio/tzeZ4JVpmLxp4KS0HEDRLT2uVYdIZwUXWUxRBmh4jYDOfMAZZuRlsTTODmB7tWhy+KntyHwxrai3wQSc/ypukeBjyun7PbJqRFQafklQJvk8PmI737JQnCYNZGlr3aqgQOPC/468KZ0aRB7lCynQx8T2h8Jt3pCLKGBfWZFq4g6JwGn6XlUH0hw5REpy48a6RZpMKqQ4d5sn2XIQI9yhzd7HRKJN2AGeTL4oZn8YPOMf3acLci6e85ZT1I9G+iU+9uwlZTUn7XmqKHTgLuIO0YJahWUOftt1eGW3UfiIs5xJEXA2NY9XsJqDjT1FBLJQNS+OHGFN0ykOflpOoZ14yFI+xlrNoa7ntMrWHn+TNwno9/1qf+LORpC0dqh9Mgy+CxuU1NXuAgW8yaLJME0UitH1ExWuC09vJ8n7I4Ug35Jdg8hyD0Lg/VjoQiHLkN1aclGkJlNb+qwAYz2IZuEjtPy0PCef+whJ9qcPOXoVK7oQbQGtRT/7o2hBtNITClWtzG9Vem/IxOVAAqdQpChXxt6C252r6WyY4CJ0OvbNxVhKkHl5rWT3nA3zOplbbNnEBzN095e1oXRQTrJjIPOiCGQ5srxwdBIEISlrnlEAxMvfJ2takECC2d/R6lzQiX7r/KtG2wgnFC+XSMsDV0eibQ+6y+w6J7HZa19vggZ6MnUdIsCuxaqYo0jxFwaOoFtWXyoNs22ZRPdNRBbUw7SatPFyZ89tlaRuVhkHs39u+QNsLLdaQU/yQN8zoL5tGPQgG/otBP+9NpZKBFcOddmhk6WehdQcEBaGWSPzDfVQ2Mv48I961pgr+FtBbPyktcHjLYEPeggFY1W69+Mxn4cuZmIn2zBDTaN7m8foZB86YCYgVFrMBxoY+Y2TocAjYs/hmGyYDD9o05f24FtFv/6/2f1VB0s75kZ5WDBxi6DDjNDPigIM7w19y5N1go1JLnPP6Lq9OR5LVT44GJx0MlmTQiMCo1cb9bkBadTkwxK/LDQJZfw9E3OnlQ9pr269OWh2t2PQRd7KQoy2bvbEbBKRAq45ERuooBbfHA1hARObBM1vtKFM2IiKOmnz6/KQXeggvHn5U0gWrdZQUXpgV2nxR5uohJEJ5AkolbGydMmjalbU6qQm1YysrNGXMwdXj0ND8DH5Ux1XQEp2hDZ/n0esjWJRBl5GTxRGpAIfK+VytMEc24/S7SnYxBqUK33hoPagFLaq+SNU4fRlccshq2/+DmdydDVux4IJL0J8lzHT3QfHgoU2ZhC8Klf54oXiUCyirMjjG9Noi9evhAM0gHShcLpL5dUQZmz07/sgFKVP51CbXFCqClvbymdmsdEOu0MnnGbQYLDxcTsrv40b9zi9/V/mjAW6n77iL7fnB+2WrOMMv/v/2ON5Gb+0OHHtySOgn9ZnmhTaTYnMJZDzwGBNxFLp7sp0N6HZZaFWCSVXjXN0YP9lz07924OP6LT5xuJ5XPx4PcB3xjb7FYOOv38tmMpy7gu2+QbptIw/MCixYFGnZkqF87/fSuKngrWjG9MlOBEkv5H5EjHIiF9vVSex0zBDzFHh5HYGBuupHiiYMgNHHKZETWn0ZdXxL873KyAiltwV7Ar0CoUhO2LN7SBdFF1wcrg4ooUq5HYnnBE1mOReTflGQD9kTjDWEaCdAUiZVnm2BGdOJvnm8tiMFW0351GvF5mOyTl1m27Mf93beq7cbYIK1ArfzJEG+U4BBB/oHHEdZJ5Qt61Z3g26NOj8r871ieDuvXm9mMcI4lcfed/RmgeBz38dWOMow7YQW6jISQh/Zv5Q1pK6TETIetsFCHhdWC1bhuMdD0nVGT0KSH3L8v8sM05FCNTnuSRvJ5kAnVIbwudOKXFZcp+vutUcLcflz0pBvKtizlwBZYQYnyudB9fzOgpqoeCkyUAeWcYJc4Rg20W3w7LdZOCwK+UMTynfCN3rGkiI4HIepZk7oq3yoIjAXk2vzhYdDQ+PXtw4UwyN75gBW7z3FimKTP+mqKA0Gv+prpG03YHR2dVVOzBMUN+nvFhyc++8XKJrGxKPg6TSTuEYfDwwBn4ilaDRDOyn3UTqiBZN0fDSkPhKAovYkmAX0HkHk0ruRd6gyZDAMUu/yCKDRzi7wDi+CTt23RaLQWvP9+TyvFXBs8HZuu/CWgRzSOY48b6lmhxZMvoYQsBl6SUnv0juuAeHflj/nzbuhBH3tzXV0Q+uHcuZxiGQUcCqRGnNlRkGgVulswF4ZNFJJToRGlgZqj508L3ihAwqO7hrUXteBLxFIK8y9h1EXrXTGQrwL6XxQPpPEVgISEK6PwIoJEmfUrvlUcSNboMuE8vRy8+HE/rudABLIWe7w65oyAhMrk28YDAWhquXuXZ0cN4hN/VB1zGgHhKjWuZV0K6u/E98l5M6HT1a3/7J+oI42NgaWUQ1kYXHets/miCI79Hada9iqxqT8GORRuU4PKkw89oU/EQDrotW2vojQMX9wtJZF5ibW1XO3/sU0I1P6LPsbnvOE4zfvtMwpLoFPlE3e2E4bp46Xkm50uKGy3eyi+yAKuPaqIwK+1BFOFRkKOvo9YZPnNkVGGGno4eu7oinXhBsoCqognDUCQO6G6kFX7hOs91g7LBeFxRsRvrwAu2JmUMo0cEPxvPsPUpzSorU5DafuMV0AVBrrXs7vtxnRAK2aV3OkZHXjN/KUtwagCbivbt/26G7BYpadT6HDBJ47gUOn30kiKEh3G/DyDuu76OVxrUoMLllrDS7TWcF3AhG02kxY8Ta8aUbHoAkfmXjj9ZUewnGyOUl/iAKvW4+sYeUn0ybggTm3dGnXQNyaROPHCrhzi97kjDsllG9Map5jQ723CWwmaAhD1ZnLulVoM5OiJ4QnXx0D+Gj3pLmU/Fipz936McUJAgiktzlge/PGiajb1Ff873xZ3ddJa1zFOE7N856m/Lgg9pbPQMNOHcySn0qTKGNE3V4XGkXO80BWIh+0rjYcwa8ujnzfjYZj15s5LFyEEP653RCbzQ0CoL3OurA60CnMY4/FagvIKc8bQAgEwyB2nxPUpQXDU5Mec5RMuFuUUyJtSAR+WxNGWXXzEU9/JNhaBDz1lQcNv8BVBnWIFJ25ywi8GLk+6PpxC0b/z+WdvMwKGiPhMtudrrGnsTR/9CX1weJT7d95HHPyZEy4sVmDhfZ8ClXrBeciTMmEMuScLrTMYIjcNA/T8U7LRfpQvyjlHn0HyQAb4X/Ckvv+mCtwJy4R7PyVQcT5e/JChMvBWpO49eO0CFX+fXoBp1//qIx19sYuZgBbU1ZNcb97wAGXJPnG6GA94Ffh6NtKAB/EwU19ducwF37ZMzD4fnwbIeki8Rm4FSUa7NSxEsvBgIfJVP58BdO8N9G4N+kKhUaegkb4OmP78mMfJIIX8p9IfcpxyR0rN0mXWFgvYTK7fHnKnFGH7RXp/pC0nEFK7pvOkSYJkPWtD3e4JF46eIY/E14aJi1Nz1ARsyLD9vK2APAa44L1dHcYSPHEjnB7hFKD89S2Z2qY81Lm6kHpcsUWd0Wv8K+bscOMtJr+MiAemkcMBSyIO60Y113Plyy7OFD+KOB3jBkKYsJo/Rm7/20+bmUdjSrs4B7pmDnVjFQh93jmkP6wP02bSjawmGAhqdda5YSUE7fU528XU1LD3hCPZioAF8O570NeQ6WCdvig8ldvHA7aXn62ztjXhao2Gg7uYBdySXvlHzy8Hn775tqknCUN52+hlw05JsI66YxG1IAXSaXh3Bl350Zxht8fvZAHIIX3lnjPmC08uWH6emvIFkb1ZG4tDbkQTvZxkYcUDiq6OurkZYWD7t/5osSEM8AzLVr0YL+DwJOZ+W3ERQMaal9lIjzfMqsiRX+H3+d/+ZsiRe/qNFEq/PiIwuqQFxvzOpLT/EHy7tll7cFkeqRNbtqQky0HxhwimmwTSkCL4ge8agyzM94+9UymkRFn0Cdw37jKBzxTehIK1BAzp8d5R9jCG16dZbrmz8wCLhtLFCipN4Mm8/HTUOhZsxc6+ahNRg/T7LPxkiYboxevl73JZF5Hka3t6/gF5CPW8X0sUKP+/+ks51c2kvMdmdEe+fGRoA/4JNBwb4fZwSRBrTpLIg2oTrt8nOumzFp7AsKOPFawQOnX1z1r9L34NL6Pz4xHOnsrx6fkoPahKDXw6n6wHZXNNGo5ZyVg8rieSFLbtuI+zkd9NCQ2Aljn+/e8UEfBjIOOvnaZD9BviqxfS1dGZz3OqmDQRGB61PrvkLQ6/DiJYSzaasDQfFYk/MvzCuQ0Elpnr6UB6VZeWCpk+rBvnrdmf6sBmWbuYR2YygWVeBeMtbVn4OGH6aypPDu4TPU39lseDmuOPzBL0+CDFSiVs9K8M0Pmqp0SeUYJZq4n2ulRKlI79Mx01i4FqauFhmqlguO7ZSvBOjQWo/5CI242boNQymrUibh9kiXvDftHKHGzwe9vmerSBxqo+EtFhoezdufstjCYoZ6q/gGjsxJ8UcFSa5hjB5K+LFzH8ljDPKX87mkMbeVnhlcwyMkH1FSUiKg4mcGy+rPDsdjs2sddE5xZXJ843oyeZV84VhgrvXNJl54WbBDfmKNt5UNFvN0fHaSdUk36oVY1nCHMFhAsWkzaQ+Eh0JBImsLuNlp+3KrFgOErSZfhTBZSqxP2euaj9T39DXDwGK/dxz0RDLZI/akPR2vUfHjE6EFB6NkRtdwSLZ1UWGldwgLPB/xrvcqQHTtYfaF9RcsOlLvr4IJdd7C4LHMxEa6G3n345Kk0j+Pfw9cvb3VpganS5gzyfHr2OPVpf+skGFYEP6HL8rE44/520mgIt5EHv+qTLaSR1OtKtrMccaSUTOpkqisLACPmmy7AKfCBl1e3pYEQ8FpljfjyyQKrCx/RdQh0E8vLs2nJsYJrlXNX9A24Y9ZHdSKHXg7OJW4mRk07QVy3oUSRzBs4NpbeaHrfi8Mxpk5Jv7OG22d+N/Z7Uhi9xZnj7P3QhMq2R7Hn0Q2yW1/Li+C4NsNF/9iRjVwZuPWx2xR0TqJ/pymZiEAfHos6LigQyQOGt9I3fRBzMLINDHP9KgEXUD9ybeiY0YXa2R56eDTC2z+Y6Cq7Cz4s3yMLUf+IcBUqvdh9g0Cj5g3tYo6so5M7tCq8Ge9gKFJB8r+gAeAu2iU1al3GwVKBc/wAPJOl4goi6QkF4wD/m2lQovBa8fq9B6AibmPUg9NzEKaAmaLXz3tSDPw/u2nTN6MJcrCqpSAk/yvre/K/oRH/SozreuY+JgIIVQ6L0jiQwXeagGFunQwWxg54h4pygU6U9P0hkCkSBe8RPZS2BK4S7zHJTHEW4fqkeUVKCNVzwsXC5PJBeSxVo4iaAD0kfNDOZWZAAJ2Xu/WZhRDhj1yUlagV1TKVz01etYfPohpx+GCtSfs39N/CWEFy0z/iQP5cEON7us+rO+zhSXHV4qZoHStNbf0ly4xKiSF/8EqqrCeOvnqR3NFhD7b2EG69yWSA3UzXKT1IH7t45+yH2ojAk05KSP5kQh69/imkfCNdgy6N2FQO/4MPTxubP7yTogRzbgPQfc/93f1tYfr/6engKvi72BBafl4RabfeZ0y8MgT0yUSInQAGNFUsxXnhnAktZBHG79uowsyr0UbRLF5yGPyV+ZxZHvyvb4xxV1OAs9V2N411V6DbOlfzEpAFpQcX4MWcwKIy0dFH2hDM5XtVtPPstByvnxpfa8+XhzK0l+gbiUiySbbRjo13C4e1rZjp1GkMgBV3GJwIz+HVTVjFneBRXnaTw2JybFjCaaLpUUxHmzAKryGdNwTF0mMOOkx1sf3tJcTWrQ3KSbt16pDSMpv3IU8iTBgm/yJCLFXSoh5ObtVSUHr7FHc3Nm9GDxB3+FwoxCsAwUyyedIsA2Ur97s84kIWb3/z+9sRdhrDDgXZ2K0Z4NMthf7bbEwlGBy6M615EciXOK2+Dz8ADp7nxP+2KyDBr5frqmhYq4i/O+yDkgLRyVj82K29jVTDce2Oh+jA/0LD1YsIK8uiHb3a4qKAC2/6k809FgYAhzHBD3xpYNO3uiVBLQ6dj6lfvD3rwaaGyJSqQAuafXZaZ1mMAPDeZb5v8DdhBoQglsP6DU+8T04su/4e7zEHF/s/v9H/96wr7vpUs8Q+n9U3B/H6+KtR30dK3/bKG1UPDW/fOsgHjc40MaSc92BNZyImLkodHtWKtJ30FAov57bh/BMhwQopI7x45cIcN3uwvEYM3kwweRRRysMxosuOsRINCeq42ct/mBfwYsRnGJgV4FVrn3vdCAyQrWpaO11jRa82PhO5EEnDnzaR5hpoC4PV02999qwULL7QZ7nawItk/r8q6xuWBW/EMw21xOWBdKe3VUlOH0aLP71lkfuKAXDkzOoQb7MmzRutO/B5xEE6Y+EgGzs6XD3Tp0CEWXw9RDgUG4FysfSlzoAnSAlfCzd0M4O33JwK77+kR0UWQ8cPJg1AEwciUajScFlBa5E8ygH+FXKKyBJaI74i+itj4AjoBH478OmngSnySXDErDUuRXrJtdv+wbi0fRrFleJAaevW8QwUbzDAe1ihPsQFeR33s/UIZHJ7B7LG3WTVuX+SaoYWjA/yjXLxYZukAfxb/ud/GF0Po7+awXI8o1F19c7X/cjx4MhrHvCJTAXmTrIQOagckvJRjpi4WiVx7QyzkkCQo4dnm6p3kyy++meBMz4L4x55ykBgKwVSUJ0bZ1wBiehktc4mtIfI8h3i9gjQkBq88vMDPC79fZNL57xjBYhvtutRtQxiy58+yq1VHJbbC2MUNFYhN4iA/IxQGrbTvvHs4wyAx8hq9I48QwnN1uFm2KQDXzcNnt28EAhk+/52E1cD/8UWJeJBEGSkUtu1w/8yUBcVrXHq+BXJw0Oh/KfsBHZLOZ6P1eM0E7yv8UeEMP3zV3IkveyUKKIT46++dEuxWTOPst0VSuEvf8G1rUQPa5VOWeuS0geDF9fQcllNI98ynz/ZUfCDN2pnh+YYDJa+Lf9pz54UfUaK2ISQ2wEx3G9f4wQxRqAnraFIIgKnzAxWZdpH/8iO6Nj0zhCMArzzez/82LwBHs+Ra+up5KN78jLEvsUQlOt4/VsO0gDhUfMxlWgGiPxJrB941BO26UC8w5EeH6ulhQe3aMPNg8XPy0InfNJIZyqETA7xyC93wz6y45/oln7iVd3CaiWWVQpqeMJA8On+crgvv97wk00Y10YMivezaE/5eurSf9HVHHhZcv99+yaMEfpgbOZV6VdhQeVq2lSF8KKI75pztSoAAshH/D8KxUGn93T9K3gElGlV7NcrIgVnjXedvg6rgEOvreO+bEYTe4pLkKhFA01Nlz4R/aAGXOUklN7sGeCU6cci/wMJb36o4/30qRBTPc+96FCsoTUjkNP2TBKvJXTwBBylgNKiPb1Ppx+GdwsN9nbyCbWB90T5QIAV92n9+mWvJAB7mVO5lbUPc9P0t93y1A5yGa/HetPMFuG9G0fU24sL/+NntR+Dw/gGOr6D970k54b7KF9EXi1ig82HHejRRo9GswEG+fjLwi9XBX27AQNH7qv7vbgIwDrr4z19QoHG2wIxgNUZ48dchzGffBPov0h5MP9eHZ8FGnurd0nDV4b2Tg6Yaulz6PhHPUhOUnWn+jJZrQd7lLYKKChHUYf9qP3xYBiI5weHwgSu02xifazwWPfGZKrGks2LwIYTzT/kLV9S6fmosrJ0XKAT5tyj/8f4v/8mRvpRKwBnpbiZWs4jCGbLl9oTzCCqDOUTPf2CEHKaCGbbn4lDZx4M3fE4OGgYS9f4G6kFoHwP7tD0PUL6rPUzMlwSHNn3NBpFQWCq3npk354fCIJrShwukqI4k4utaXQjqyn5N0g2XQViq7rX1zGX4GkbKN463jcULtiYyfk0EQtykVi8e2YBJVpg03Vtl8Ge6cU3GUR7tBtD0j79CqOPVVJQekyD45UYbOPvygmEwBXu2PANCEg8i4p+QAYWF0BkxYkVg0ZveH0eW0FpqNVfdLwiGtXR0pgVYIFHKHOXsiITIlPPELdm7WMk3RdY4dllUMB+Q/bPuIupt8WLY7DY48VGJdoTmBpDE/MLvCgktqmeUXfSpIwcf+4QDR2N+OJsyufRsRxBQ1G0iihJilH7/wBXHQwP2OfsX9Y6oIWHwtaU2/8n+kWToPd8qgqudbi2sv7uFs7o0lEoebwgW4Vd23mprQ+6dzKoKSgWkLrS/5yaNgVhMHvzykIVgkY8s3PflAC/StYyH1hwXSs/hVvbgJ45nxZxGJ9AT8rw0yZ1m5ICEaYysbVwa5f3IL7zy2AUF+nkcnTJjgp/l37Wu04pCyV4PGYrnQhH9PK9LRU645LWiU1GnD/xb0bhR3+8Dz3PzBr5s0KP5xEuDvpKM8Ezo5piftyTQkUq91ZoVB+sZMqaP7CyIPM+GNoOCEcjPHUf7+PhB6Zhw+WkrPyC4wU2EORRGoY0tLWXlQsDROUDdUqkFPXYTp2+JGwJHa1S27LQAmqMc0x84UIIvqh7UO+0R0MqmihN05Qb1SxnPynSFUc7yc3vbS+GIpUeUGbfmAIprpzMvfD7xB6c9lykPmUCS+82VZHdrtJ6q/52LXAwScdtFpcGWoP2oapZaSxlsS/9w+W6pwa6s875xuCgU0ViovjRVgpffmpsuedKhci+O6R8OEjAs07M0kecEyPbFxZTbZ07mcWJvcLQLByS+Fnpkp+Ha2+3tIT1XuL2oY23IeA4ij9dK7/7CILyemPLoCGE4w5tek3jTDabl23nv+LvB1tdT9mJJ7KhEfiMu1osZOK+sHOYl6IDmWgOX47we2O/W5Nf20aP6gn8frQP44P1N1bPmznRw9GHf7vEdTnDLaZA3PD2Avd3WHf3gmBQUzp9u2tyJAVMPCfZt+ijQFFH1GM+1RJa60+SJJ354x5E1NGlABSSo7bh0RzTA/0/E6Pd7NEh8vaEn1o4HnHfiEkeyOCBkgzG6dFgN9B71rpOf6HVBnGDv6DUeyKsUSVjkVwTnbanz/whVoH+0/KEvOyP6duTatGbCAzfeCkiUvL0A45XWBCMGGmDl/KPiw7YHmlfkW31/6gwSquPl8CaRhc8Tij/6a2SANL9DUtr7OhYFJ7ZhIqaw3S1yx1vxAhD4u9lG21kI8Oa6EvW9RbH1ipeK2pc3cawGF8drY5XBTZ9ZwpVOCAwbPfSX1FiQ26xODe1NdlTjyUrhKCYE6gz+RhV5ohBjemFSXvcv9qrfgafFewowKfwqmCDpB9enTn0Q08PA36mIcsFiHcQ3LQjush7I6LF4wTK+IeiJc5qBtz643ngRVqokipT94zVP9fOBbjumd+I+glc5M2adaToQ7T4Tpd/Dhl6rDrn2soqAQa6g/s1jE6DRjubW/ScPSU8qMs+dzKfu8tkXX1bFUK7u4aRx11k4RAJQuYuAWUpwqgAjjaqkOxIxl82RbZXZ/ZtlCvD+ZbTi204V+F1xS7KqsgLrGEfWdC6HBrqYcxhmTEWgluh5BkqQBT6rmzrL/E+wIweaH/LzmYG7hFQq0SoKCgXf8ij0CYLRUBRbNaEdUpbwvOx15gJaGasVM6TSAv89k9RZOh14N/im/3MfCYrGn3/NlsUOZdq/PVrcFaFN8EEJib0y4BWxzl6OCMBKj6izaVof42J7Nx5jiaVhrf1duoiI0n/63zFTnFyVyQHGmgTRe7EYoOc/aLRVNYBXIWU90xTCYE/8wEC7QQXm5jBE3kUG0KPWmHFrQgvoLhq3ZjvJgmG7SLnmmgZaufRl5ZWhBxxfS2VUbTMB+6Myqjc3NNDyJzHClXJzVEhJr5JIJQP17ruar+OloI7z5vu/WtRI+mkfOccbInga2578qEcZvnXJ4EeG2QNsfPY9z6QId1Au5bkoPVD1NHqzEu0Hn/jkaK/su0OzIO5GhoQEmvi7mqH0Uhv9Y21a7DWTAdeZ+JbGRlmo8b0nXL3DivLS+8PPXueGpFNvuoleiAAf2e4Xiz1RCP3NsxXsQYJ2/3T4FVic+Ib429lBsapwr9X83Z0ODSARnQnerZZE/W7V0aW5svB1TfKxC6s6yMnIWye/MYBRD7jBeIkLeCjuSRj0CkEOf1Kah78TcFBRvqNl3MflfDY7v2PMh0KUTfefVtmjSOuUKs9mDpj/mX0NL0wcaCp7Zvgfv8HGvgi4uc/BCW9XpeeiEyNP4hXIJlYi/+sPoid7NTjFeqJnBJOPa6TAvPlxlqw/Fr46vFgRvU8Mq0vet0NeiUBy/vupJ/4ykG248Ie5WA9Y8hlW8+/zwPfdRfFn12RguXHyTXucB9AmV0lgygzg1s8g41f2EtBOISCJG3VGZLiR7Uu/HKHk/Tm1y0F0yCpNdPjBK1uER+XrnRepgMhUZ4tlmTWAycChd1taE9pHlluP53jQxOoHggQ6PmDvmRopJaSHIL4F6+QbbCDNIBE9slqG/br0PXm45R/uZyl1v0WRMlDSfq6ou6ECmKppn/sc+GiV29YlJ+UUDHe1Vry/s4s1nS7j9HypDVUxrVmGnRIw2GYYfG5FCSI58KrO8UcAg79Z22LNR2z/U1fH3qQTrnn55I4Moz8q6jFa1q1WBxMq/aERTk1YXFKk6dA4xPo9P5KSGDoFfp+X2SypdKDuT/FqSrMuJOWIB4kvj2O37Jn9tUcp4RnJQOHWhhbMlqY8zTjR8azW1I2YJgbE4/xKV6acB+RLZUpDGKzB8B7D8vSOIvARPJYIfcSLtI43uyeIdFHcV4vMvnJFcNI7fUW7QRGI1s1FLLzNUASbJG9GoBmEudm2S6VTAFfuh0w5VUuYycu4GrRGDyxyIXciNU2Bvl7s49svVkCVR8JIyCIG3t63NE6xasPgtNU9mzQHdDx3WBsgceLrGVjKJzZMQMXXnpDyhwpIvudgVA5TR18IotpsDXWA0bq58UsPgkKFOGb+QnHkFxb61yGDF6RqFIT3RKSAlHlRcP6pFNh6cnR4BnIiAmxUm0oUF1T3WW7dPHIHCBKvv/DbHeIUI9o4fThR4u/V5r0FDnBxeiEmXe0OPzwrP/fbH2DF2D5445XooEfZJLrts06okeZRitFkLPgU9855fTKEd/35FxijLdH2YkBma2E4St05f2YxhQ44J2sEpC4yA96s9dC2aCaWRjqB5trPnzhi59uSuezaYJ3n7tN4XQ/Cgq9F5TAtYFmyAsyaCzlhYf17OXOVN2RH1DfV7Xj/Nx8Q6FHfvUQAlpaS0VPRGkAr45hsRqILmVXttZFvuFDLn3oVXgNZqGV1atHUDYQbvVFx7B7/8294s4+5ej9t4vyyycZDBNVAncB3oDPbCGIefNYx6KWDm0+HfrwwkYMO4760cxgrmPH/6XRqxwbO1BDVPH7PhF5X7vHe+iUN0i9NVf2o9OCRRevAUyljkHZrt/jaLoPUqzJDwvHUofmtnR4oyYBuXrQxAZsFNK0xLYfss4JKEbmJLVYHhN7Hujv8VIZzFnd5H4brw3Ojxpa/liygrE8adBYnBu33UVJNmiEQCY2LZs0Y/Zd/Vg7LjYA5InAMf3in1usiaDzdPRw+8R+HDy614Gc6IavBqlpX4gAkr/LbciBXFW5p0OIzEGr8Fz/KC4l2HoQQHil7xf+FBMiI3l0eYJcEEReTz2XRlKijcCZC4B8VSAFla7mHJZCs25+a+WgBWw3lCpst97FI+RKhTOtfLOHtiunc7WAwwEYsLBtyg27VAn/xuDCaatV5KhAQiFQ/tffM+yrAeUwBvCxUBLqUsH9qnH1YVN6okYK/j+NNu7jareANA8xLSuy90lBSH/e1aV0G2XutEQ1+dkOvLMWd/8TQwq/+0ypFRgzwMPRoIIPrNEqV+vJqZY4WiPs+cPLOqcD8UMvT/E/yQOrzslorlwNZpjnNY3nwYWnYZZWTSARe0VESmJAhYKH5EGARxIIYuovXCx6rQKD/1abSQC3g3cR637qlDYafyue3GEjRfJrkv9lSBsCVZTybbooEXcOF0Xu9f3HDl40K3x2fRVmhE8HqkudRnap28ydld/iVkb6E83MHhemHT2x/8qF5SsOulG1BaClgtE1jxQKHQ5Ez17Q6LC+5zTSfpUKhF+TVCkr2cbfd8bVHFGygq7SSsOG2LcQFaWfaba3gQp8F+mFG6eCAzec97xw33DSXtQ1RMwKthfW+O9eFgciFmZpcXRPogpr4N2MQWJLXZNe+QKDDQG2oZkaBeH75Mcs6UUOArCtNx4mudZ77WrI8pQNb2qZ+54LoUeKT+G9NEkywbmZvX37bCHbXDgljR/53v/CIzcen1mdxTc/pUs1TvcFTkupLP48PJC6/piXOfI4tmbkr53uBHiSW+H3lZxUgmtA80J1fHV67BjrS8//Coa4k1af09HCX7qWxSJ82NGAf5QgVa0Fyb/JZyZaT+vz0I7GQ54JKs0IjMic5CIgIEfQ6rfzf/2kKTcMDJ+mA9UyNIzedGcTtfUnUO+F/0rqF5vPAiEILPiYs44uC37ENM3WaMbxVd3AcemwGo7mx4o07dIjD24jN+K8A9CTccLiv6w4UZNEsj+ik0C0una3N85aI1vwz/sM/doiyx+Xl03/KEBjzhmD+mhr8EW24m0E4inUclZv6qUIC5tnE1546igCxIL19eKc27CdYcNVcZUQYTyWZNilNuGXvcgYvTAtYExt2mJ/r/Je/o2lpV4c0HbxUdgr1MjsLP1dF5qg7nSGleAQSabuwltG0i9ppzLBp28LQ/k0eUrAK1a4OFsAIrgGnhXiAqEg7WeM9An7rR0939zBA5a//oUPRAFieGXzVc8GA16OcgE/d6nDZvS1gidMSQmsvc5u4GMOj7lfmpGm8CJ0R5Z9rJ0TSLYfPpCh0AcNEa0qZrgciwkTyxnr1uPnZXuriNFKQj/W6H9MrC/rhMzunwxSgzrLZJJ15AltizaVQtEIKjnw69qY+fNB2zzGWP/BEj0QmOUr72BDVqyuNwn4C4Ho5olwm0Qyu4t/ZbjzhBBPWazahxuYoJ77RQrpWE6j0LVJUe4KBf4POtMlVBBQN6ayFD62QtflVETUZT7QtwRzyDicN3VWVw27TcuD8/loAsXQ71vLoLffGfTLQyR2Y+CPvDUWfaVaLkk78YbDap3D3T7ikpmfb0+PuaP7ChoBiAAbeBX6lHqmUgKwUzzbC20yoLP9xX/41IRiiSqp0OFSC8GyteNnEM9A7MfkgDU8JGtKnHH4mmQGbJ12TMpkMlNxu7BufUoDVjs8M2esCaJedR733mhT0TkcM6upKgLJkx4uEl6oQ3u2q+0yfEU0L51WShciAs4IVXvdtP/B08A0NypEEN0Mni955XuQmZvf1fJw3IlSQU5Cvk4dj8vgp+jlFGFncsLsfW4099bbm8so8PiiqgWa7lwnkSugXn8Zag6X3frrtKSX0wNpAP/2+LlTEKdc0fFCFhxXCPCzKRqDrQNGc4sQK1TrMjziwJ9x3jb3t6IUpyCq3TPrcUwL1UcXDmGwZ1Ehg/JDNTBrhzTMyR+BHwsykAUU/JwZx+PeU/RJ1QTmfj0wNL4egs32bBaRPFeAhjuZJXbQABNuRap8fYkYdurfeWh3To1uVW15xxfpANhdw+aGMHuzeFjAsu66IvuGzxW8/kgOW4eXXf+uvAJ3U+dGX3Vf+px+HHqmkXQSQ/Enzbqx5KuBu51psKeCjrOIgc/MxbcRZU1VSKJuC5gbt5pxfY4AJ8w0vQR4DDLfcfNe5WFCqakat+AETqL9ux/4QkAB6RoVbDKuScLfN+FZ4yjFWPIsc32KYBB7OP8uIxyaBd+dLBW2XJGgbdiGrFSBGeIacZKIXWcHypny770Mb2B3bI/DaNgLvLQu/wgAlGK8uFGo7ZYTkVKSnCMbk4J7Tk7z4LEWILeH55U9Oh5QD6AnsTnFCNFdMB2s9L0TaqEbp8P3vffuvd0QblcNU4JJvaPZFiBkMj969//uOFZ5Qodpox0bsKcyi/8bjFdxf9Z8PrkxdgipJTCVdpCxExjiwpd7RRGfaNnZWemPQCwcS8+JuY9CYm/K7ImAIFaYadhp4fBC0/netOVoCcRkuMPl9NwAuDWnz8ihjmDhmzHNVpkPf3mmL8GjxQa7UIdMtaVG4U/Gkt51WFnxeGcWfvkGNNtKa6CUmMeC2mhGQnCeHGNXC93kvEqEF+iUJikVbJHF9YitJwAl+4LqO54iVAKV7TRROqsDm8ceKY6HP2NSFgfxlD3LIm83dj2gQAfLU3svf9DWgXk3WLpKdEWxp7eaqzQUheyWC+M0tQaA1+1jaFKoHJwPBtJLCDUruIX9InisBLqniDsNfBeA/qOKkuoWFUwqxUh6fOVAV/hfGsQFpOGiPqJsGa6jP6uxT3+ACzNtq53Q/ZWTk2nmXMFQPDdHkVab8lIOoznGsqMX/3gfgiT02r1ydwvkJmXIVythBnu2bmng7O7iTbfU4W4MX0fw5Q1rpzAdV7Ov19cXSMMoa89B/XO6/eMPtqPT+u2Tw2P96qGCOCoQXqw0ILugA1PrWySqQQU+4Th4+vSDs0XCd8uvSA4U7dZ7fAqxB23zlq9QQJ9I4/tg+NawNV3Ve15yqsodQQWb1HiUHiH+YFeXawYJK/LAjFF95wIzwmXExoSl4UhwMnHY2hlMMzIGPnbnRc1shbyEeOhAJImr9fRQHr/5ecDpduYDFf8818OOqIorUK3519CAWmZC+cePr4ofHpKXdA1qikBxTVBlSwobej/K3zL7lhcVj7q63u/4Qd3TuEfdDBWR4O7w8kM0U4bFbjEQ/9EbBWYLfVeMR2Aj/sv8nog97pI17xwl8aLw3bpiEVQFexzYMrumeh7m/Ul7CT7kBT5Dg8VfLx1icsV5Mu14Ieh69nDXpRwOeFgemh+GSIMTbxPe4kwWdHpdMk7WQgWbavLlqDXmg2SH6l06tABZr2q6CfjRoq3H3Y8tTeui56MKsESYJO/w4rLWUKnhSCr017+ZC2lYzvqRVshAUbDy0gGJhNNDJ5+uKJoykHH0kn/JEZnLDEi9HghCdbTTlArEkDGS7PhO9JAXnVk3uZY0wolFPljVMOwuM62pP8p+OBMXSVeW2D99xW5UOC3IjGkiwM1F1AhOBFmMdKouHZOET/t8Q2gM1GKUbirmaQghwqmOJaJ0NTnuKmN94ogI2abp6Cg1YcMMfKywjPsBtVUfu6ZvRgSb0Wi7mxkDgC7rUTR1OpHX+0kWfYyUkr3ur+G9aNJohhYnCdDngrxhxZjJRg/nM5sPWS2zoelIUlVW7OExg845UvopA4vf4V7MkEgBqBNpCPJVYlKLhxN33G3dsN9SnbO4GxKsDvZaEToC/hRIEyg0RXq3FbP+iLAzfUGsqP1KC3n+6L+aM1MHt2U2xwp/lWMu1S9Zvz9CCslVsSqJhNDxUOBQxTosGmhj78kLPN9hEJrb4a/dpIfFcw8+qBTlgU9B+/KhUB/ZtvJ7/2DvA7lg4uVLOy8MIwf7ZKEVauN+rdP3OoQQw1KybD/whRzm398WMucTBgyHMsjuFGlhizrpMpotD68uB1flxEnSJPby76JwoLHxf57hbwAPGmslaAY1q0MG9SARCZLC5G9oohKRBfvnZ58TUIGhd/jKNJ/kTd/3h62aeaDpkM+sy7TkSiNzqJj6YRinBHNWlinlqVYjzyP/sv/YYG+3e9ldHnhiC+DaKxz6Jg2NppNQ5FikIxk+llomhRbpHDyRNtxhBV31x7GXFCbeRXRARHUGwf019NzSADzkqqO4XOkjBm+cqEiLuEiCYOj764WQ/N6cTGVwObcbypATzig4s4TRjNKLFKnkge7DG30QbA3gNNauvEcIlTjIOzMa9w3XS0v2YdZKHRNHmdKMEY0gdrjNlXheHwRy8qzUSsqDzzyef6X0kXKXmvkSvwgzWwTrUOW8dUX1lhYGZfih608FwL2P8ImSP1sn3UNJB0O49/A9FfGhVS6ElfDMCHd98PpsTZwlWbhuc1b128EuPd94EIwKp6CLGXAoDFvJPN8udFaFNvaww6rQNXNMKkOWt5IP9+XdyDMT6oFeMuXDrFR3INOs+bqlhPJkHH+vNsRvYaORx/Ub1Cu5bnuC3xRJjELj85JNE7hlQvyjze2GGFwgiV08fT+hBQcMe7tqRH4RS5rXeu/+/993zD412NQkYgMs56Z55rS08/mnHdY/FDjq7ZvyP7XqwieyYh1+KjnBDyZklG95+8NHOEhNewwqjtfwheNoILVD+3eBO9UTXbj574qGvBh5Gvpm/ZBFcCx3+839NnHc81t///+29987e87K5uI69ueyZlV1GCGV0yciOJkJIUohkpXQ9U0pCIqMlFSVkZISMr98/n/fv79ftdW7Pc27n+Xw87meZZnCgJ+Y8vpAqDkvp22VW7WaQK8rpQn9NB/rvf7gUr6iCgF+QUK3FiEwnJLNPXT8CR3sGvXbIeKEgaOU3Ly8PqrG/9rSCwAqPdnD1igd+oFV+eu9qkwmwrI7najhoI7bMQTWKEQe0/6L0z+mFM+BKn24bYp8IzTX0Hyv45oiEqmUhowQumOzI0fV31QH60Em/vgpD0Mt9o64GAohpr+SKLhcGxrPKPHBh6TB2en5IWIAGkOFxw1PHRZDj6rOAUwzpaInTp8W0Tx0CSsXoTjZrQMQvpRf4HzdxvvlrFM1GS0TKxaMerGUY+Cv7aq52WQ24Xn0YyGrmR1HfjNIiz4oClveSKusVV8DkpU2/+2YAx/Js0mMRDrGUbWVJ2uqiqcVTM0GinmBHJSql2H7IfYVZzEE/ETJuK1H6pGSHao1T1MZPWIBMEtvvwAZLIBFOffTM7SEOpR65/fQZBXxyejzA9hKB+ZlXt24nGYLBAP93qVEcKN4mV9kPxiJe8Y/nOjxcYO3ne63Xcq4g26vKqEQpgba+y3QP5MvBA5zpLvVtM3hiRqC11NYFmSqerleldDBkZSd5JEQTSd9hznhzmJeB+YmNCy5YuHZ5N/lsDysij4ruF9NVgZec1zW6iiLBXWTx6ypDFJQ23x01JL4kkpjTFGmasYB00pt1m0BhGFQ54SB65dBflbB8so9UJtL8W90L8uGAgwdv5M6Un4WRDcU7DW/O/n/n+yqb9HlpgSn5mNnMoQ+1oRlNsAk0gd8+lvoS/CKowDnYwqIRBw53vHzpkT4Ex0Sou13Rgwitn6ldJORImG3BydDlH5FhC/cIQ5kGTbBLXhxAC9Z5kScMAoPRGgcq/Cd+BlmIZi1jawyAyW5nT+KHHbxLDR7dqGEE2QcJ/qpvtMDpKSOv02E//Uqq7aLKMTBM49HN9WWYWLm/+XDv1yqRq4av+bWZOAwxfwmSFbSEh61ZOQv7KrA7JHk8jVsLGrNidl9rGkDoWfcn9GwygI+Wtxo5y4ZY1Zczy01V0K2yDJzmvBzQ5ztYtXopQ9rdEtKb9pRIfJheSc2MA8YThI/RyyBYEx7PFHqMQDKXqViBZxAnPNYuE0a5Scy++fbPhwZDONY66HpHywieorfR0zTCSDr9KRWflijUtnIw2YfKQGjO6KdhaTmQW1jQjdgCHEEuVo+Bf4WoJCTwIIWIh2Rt+YMBBkuYbHn2HL9PCl4u937Iiqsg6nfl15MP/WlKcsNn87+GkCRrqPl1mwxNuiw4tmGFUMKPFN3rzG6AmU+7z7ri9t/6dnwlDDExAF7OWjr3Ajc4F1JOBZMKA8nVeIH3J5WJW1RtxZQ/SKDbJ7G4ITMKWul42Tn6TQEGi1PPG9igkuh+q2i6AMT6gf6hF2MgrAWqXPhR4wPvdLcaPLXtEUHcXFqMSRfoFH1EmvT9YSjlpEwB0R8Iqaf4NmjDiJWvap/d4GEBkeabL07w2cPp3ps73ZT2MHjrtvopSS4UZXT7VNZHDog0znA9VmwEkvKLLDyrCIS+dHVe6JZGKxfbsL1IAGJ/yzbU8ChCBlP1s7ouNYDFHGbJclKUN/a3e/HQB/d03Q+srLIHx/gmz2AhA1hP0TpaNE6LbGim3aPOI3R0ZDDPMMwUQooZ/52gNAEDDXdd3aMMCDxdkrNwc8RUi+ner00uQMj5XPIb7wrTZaHL0FWDQy8C0wJjKeBsJFwT0JODk0b1DrtuGKDD/t54r0SHgoJYc0dIhCE0r2ao9bU6xFE+UOT/rAGZUQJjpFGc6LGTi/P1Nl6gW8WS4BSs4XW3B3uQjwP8WRtxH6uXBZAyI0kOZYLOutkWi7dYYI8v/VI5bwzkxaz+rtoKyO+sW/fNSl24UWx1LLU6AVhNTX0i8ixh9a90k2aWPXrhV1w/uxSFhFqPtg5VY+CG4dxZvUO/QTH/RK0xYhtH8+vB5kA1E5xSe737vUgGFnZGNUlvGoCll6EnL54Vquei+OhkMJDWqrHBwqcNPEaeFOVBxoBYCh79c9gnLuKLmv5cl4MnNW6mN8a9oaTeVH9QxATacskZ345pIaaquZdZwtaosMb/+JtbKjB/aV106Lo95Ndf8NIf5AGajNrnDFZmUBH60FfyORVMIMmSz485gKTjfmIojSjRQuZ3k3UbGfwyS3Nd8JWGUjI/bJCgMWQdS6qcVWCGua1lnlJxLSi0arGq+qwNbHF/6y4GaAFuX55QHyOAUJ3BRtQFLhDQEX5SRXEKcIH7BbLfY0HrrCRbdIwkAuoFhTxjURj1uJN3ohUDxbx5/TGGFhBblnWXjUoAbI0Gjm4OacFv0z1iDE4E3ol0BAt+EINApyWT1BZahJEO/XOJiQ0OnOo9coJ4IZONIo921gCs4Xmpsp8gkOfd8RDn0YDAN1Wr4mJYmDo3ySuVqA3yR2wkjfhEEJbh1O+Cy9xAs1Lts6+jCYqT9B/kuv47n1tcefNX2y4rXDMMMQ/yS4W9DbLvxsKS6Owpdt1b33Aoto4fM9JFQHWfmHWTWSJh8mxSvoJm5H98VmyvIeO2RiyaKTjvb+cIQ5GxCy9VnWB49vlnOXJh1Ilb3XWRlILIT9RJ2B8mUHyBbqPF2gIUVv7qkWYffi8mF/0oqgh3xk/OxhbogJi57YM1O+3/7pfPaYmyz63hmFBr5VMOLdj9oDYVsvnf/iYJW+DZHrcnuCHtQdJCvgDYyaI7wrZ66G8zGanlc7UAe/yJ74x1IPJ1M1boYVaDHUljjcvWh/Nz0Or7vSUOdILaivrvV05In8NKXiL6APZkRfa1Z74QLW3gJPKN+5A/hNlWFGRBf+7orP5DK5htSdSWfGMLWty1Lz+ekUGLlUx52F8YQA4RXx8b+oI2kmjezJWH4rX5pX5tAaRv80vPW9kT9dsxpOFFRaDEPa4/gF0drIcK/xaHCyDzwHcvqstV4WYqVWoTXgqYemoL3jbpgtbXzpnZGwzAVssrtvNDGuLiHx1bXo2Gv1PaKd5cMdDSLj9+i5wegW5zTkk5F3jHF35tEFYE3SLFqfcxeqD+UfEaaRYLYqpUG03LwMCl52QpGR9DoIrn40QqrxpkfZHUDoqSQn2zsV9svwagnjBqYtOaDtDQ6poV6ukAIVh7snWQAvk+aFCN1t4nvironZ8/7g37++azsnI+QIL8k7jDp3GVVzOVTMgZgC5HN5JKzg+6/Rfk+0/LQ0vL+bSlv6JoZiCkbyzYC+0dOIhlxciDdPjN6ogyJcAzBGHCr97BdZZwBfxk3yFWcbR+MTA7D7XeQzWp06JguttHdWc4DAXURly7YkZAuPrNZcYhZRBNrFi6pXLo/+7tEG3JOFHUiEn52zF9IKNLddYk8YcEkqCbXt1Y4LN9NJQsLYwE/ez7qAc80LyTjAi1szYwMDx72u+lB/q1FxbmSOVRMPLl6qQ59Pf67YwKksrwg5Lit3S8EjiQ294v+iGKWvJmz/myisLTkSWWkwExsCvDMNDGqg8wcKbOP0cH1b2wZvdpi0TnyXfE3ssJQAv9Sp2clAiYrrn57i7dxjFwXy+NstgmXrqb1XKXLR98oxIMJehyYEwXp92XT4s6Jzr0u+NdkRpb46CMIAv0tc4Ivwrg+9/8ljaJrNW/QAH+loF9Cff54LeD1WNSAwmQ8HieXdBNjfKy2Dfsm/gPeUBm+R1+l2hQM2FTm2YBle0/DuKZlWGiMKz2WAmCtFu7ZaH8FLB+qrqWjYUZNM853uPmt8M5HDeIU/+9QRzlDbl+6bkzvIhJNfmqrw66+FpuzjItVM+HpWm4bozsbHXLnt1nAZ2YGDeiPC/UUKqZkF8nQd1SI97JfSzwtmUldExeBXjvcOs7lGLgK7nQ4LgdKRJud47ofsQAnLVqT2cdPCBT/VgPPscDfrnw7SqeZENAScn85Scn5DYajPqnqkJxCfHOo+/WQEcvgKTeCUG/t2YzXZE+MNQav1RdMgdroZupKeVKUKFMv8DmqIvcVFpynOf5kI9wfZ54myok3nqLZG8jmCzmKjXzWiA+ZraNup4kA5+UUcnjEh14sfqkMJhT/3/ji6fefOT6mR7eXrlhFTjHCaoyndGOEnaAsSar1HcRhZR9Q5GOETPYVLuiLZGmA6+xOz/ybCyBEC5I9nWuH9fp7ufywkYHxmmFv1hwZgGb/TUzkQAKNERRvGLph1Ael7d9idh5dGFqR33F+hQ8N50Yvh526r/6pR7yqbR+j1it+vlOHY8tsD1rf2NnagfVe2A8/espbppq1o9lmxwunWhXHrFnhkd0eyVSWkxQGVhDvtmegMPvJDxafQc4TCxj0wZ9Grzv52JwOyqIusna9I2C9BAphbJJiuc5lDt6k/T0bgBYjv+umbOdx/3wIHu/+1AQ5dwjkG9JB6Ac7eDh9dx4mJodor61yIEWvMa9HUI9DuvH8ICDyUl0iUk43/rgJDgVT8qph5mgu1u2a5INXqj2XcECG/lxpDwz52X2zA1mFS6p0U2KgM3VS08+vjVAF8t5I5r1rdCTJ5ezW1+pw5Ubg45+/Qg+7qTVokQxNLRgfvJeljqsYwYlzzUmwCjteaX5PCzspLS0b7DYIxdlaa64ymjEJ/evY+zQ/1PxrAc/vaEJxkfVRVKOduLEs0M6xdVZ4M41cf0HFM5AjCC0GX/UgqR8Q5m3BFF00Ulr4sI/c4Q3/yNi914H+If+FWTF4gBdpV3B6I7gCpslvxcXssNB0nGS2+w46LknGi2xLw1HYpcYT0UgYIotVSF2WiB9L2vOl876MDrraTRqZQ3T2f5JTuMiSN5jaCL9qgGEc+Mw597pAluAXyVHuR3UFfxoONVDC2G1MfINXAg+BOa3xizFQgEf7cqZ3kO/Gs68pkvihj6vFrHyBYShfJ2/bRGvFaB/mXLIh9UAREUftP3Cc6MxuuR7hqzaYOg0xPLS/CT0bf5keMPoChZP4yZkPsojG9X7e2vYQES4P24YsMkHt6KVf4RekQaCbEcL788oIkmm4DN1ZS545zIsVv9FBqiuSgpcsDKF7pzvSSZTAuix9RBZeKQRXFXMPjadYA2LdH41XixW0LpzI4e4iUFZobYMnRLKcOkOR2HJQgrsBwk51fCeBY4PZehzOBkinCVW33jIAXdGwk02q1khPUM+TimRDyjffRRLzK3HUY0x2FYKkUEs9tmifo0D5Gq+yvyWpgJhFY00VSTKaH28nxWbaYyKonHJBZlYeMv4ti+iUQ8Kp6zcn5QU46K+p7QcH/5D/JrkI3j0hBG89b4wXkizQDzTaBPqrsGMLgREGY0xGCDzzs6qAC9f4M0saBDDfcPN6Uz9czzFgpbuMFj9GfNBmI5HhlrVbpBPYnf9gocj0FwMLAlzpEJO0pmW6k4Y1Ftdpz9RSQdlOksTmey20CHb8dHOWxzexHOGz9qbwX3+MtHTCaeAZ3bvx81BBrjna2//sFUTfaJ9aHLxWQyydo+7MSPsCZXFQZZ5GZ6wcpkt7DLfSxxIOVjoMe4STRfLVP5VRgFHkz53/KEfnCw6i9EZtUVB/yQCp0NCkJZ11r7SIC8I+KU/bLggDiStvimoR5Cov3w2gLuGCay3xVMdN5QBaJopc6z1ADMUi/F+wol6QmRZh16owAfO3ohsaQSM35nP/BCxhqwoet+cd7SApbnSL92pBlQvP1VxM2mB1IfrWz6VWCg2F8nYu8mKCvqZoi5sisD+9b96fOROQHb3X+iopDN04b5l0fWSIxaSFgoeIQHQUZ5e/9RwAkavoBNHGNSh8Nmdd08VbFFZg8LFubP+yJ3taSuREwuap/sm8A/1QHm9JrLH8Sux8lrqlvKFVaJD44nHUu1WMNiLJpyzLaH66nSCTLEEyttSnvk6IwxHLNaY6KYkwenFVzKzckXQFM9ef/OcGqXniXz76ssPX6Svr+rKIYh+wMFrT3/IUYzai22ZP4kJJaK4X9SS8PJYh/zSuCWo/7mwRvHEAFQFlY52+CshfE2XYDs5BSIO3ttydVQFw16BmLRqVUiaVXzUJ8eE+vB2ZhcdWCB6fjdpzhQPHp39foU38FD45H475w6RiLxpJl1l14l7lVJ24j2MUNXAm8PxWwSed7mP6NWzgrJ0zEhfxDzx6EZAeGcLFvad/NPZjf/TF5adscj7nNtE/qTJbK4cITjxR41P5vN/+p5gI5B8PPg3jmTTnLTxZAYw9ur8ehGWAdZF3plci6yIBJ+xz/GHAaQr6PHULSJwbZyqvc/VBJ40H9w36ecHmYEM193vh7p2TnuLcUMGoiaKuqyUFCCJvDQ4pOQAF+CkqFffxgCvn/ruRRTpwRqZOyL7YAwQ67xPvSyJIk8kneMt1QR+CuNv96k8oUS/oCttzxMYhMeubVttEYHopaffSgY0xdrFtbyWMCgdZZWwbA5GrOe0f12mQQTF1QesfT+J5UaqbWaJKvAuHPfmlLIxNK+PGKc5MoIXZwtBVVcVKOi/XxaOtoHyKQetDEZ7yBaz46rooUNQVLFu6yQD7XFe3Z9e0aPajWOUF6cMwNUyDvv6vCgI0mtSnPmjB8T3HO2QLQOGhScTsiWtofPdx+cz14RAyca+RWDVAFTuJAgzVrmCpL/d5es/HOA05d40dwEWZd1r9r1pLgZdNt8n/A1NwG/kfftXE12og/ppTgoWtDh2jrQ7URLhj+Fqx0SZodis6ugzUQTWZfVFnCpHgBDDWv3jshq8dBUjnN7FQT2apZ9i0wIC6dkbOdwfiaaSCXpr9cLow1ObxkuWqmBbZfvut6kq0I2VbKn50KC64UplKkMaOKPJRlhNFwaTJ/1fru6J/Pe+34msf845P4lTZDM/Wf0CQXGgrW1dQBNSY7PdtMpM0fpjzrj+BndkEsYru3VLEv4srywGOKiBfZNZ6P5dfqQoEz6Df6cEfuadZdaMf4lE3gqhaQo8WGtrhA/4qsBDuq6q5UpTmH/7VrfDNA7SHGW/3M2MgxUpPXN3o14cwc6/kvw2C3wxCQyR3vMCCJW+9FnOG4Rb97dlNcdxeJkRz15bDijL9ztXbnAE/nRd6t8ZEf5vfcdQ6SqIfidKVazvRmVrAM7lxM3FPzrwh+yFBN5OEonHtEWbiSrBzxtpPubPU2Cp9dXdAkk5MI4lPjg+6o+MPWxYvFNjEYXtzMOgcVWQnWzZfVqpAh0DBqaPsNyocv5vNX6AAZ6oJnI8sdCGW5Gxz4JeawOPw2PzRes+HCpyWcWb/yB+k32nlhnmBCRd1bLfg+0gQ9Cbbr5dGGXZd6527Ykjho1wEQvxU3CU1eHh9rFTAN0wlIbpwsGQEc2KEwvkHkluXsG6A2qeP30rQhjWR/QIPplGiA0Mzk1OWCJWfHjoEX4xeHeJ8VSgmhQUvm5gCyJ7SPS1n/hHq3NArPcOfPqKxhxI8kbNdFIQfNmaZTfSVga1Clpqf1scKu98ayI8g4UFdsVrzu4GoDMim79SyocSxOq5C+algaOZA0fnowRDURTHamS04UZk1uucRmYUaTOuXfVFFrQU3OZuLEXB2uT9M3gzTUQlaKIfSHBH2aIz6C9lCMI5rYwlvtEGt2/Ngk7L1uA14uX1hZoP/vi4F6jla4MY+drdhVIG+OnD8GFGXBjmd1TXrOX5UAPMheA5JMGEqCWubikPkFJ6O3pKGQJXQigfP6vGNZRRlb3hpILGCQqdi/aiYLZKs/w7RBRalzjx+udZ0LyIUNdGGwtcE5Twa8zlAeeilIdCrDyg62zU+5uMC70kVAj6ZHHCNRqhG/Li1hD+6W2jZL7Nf3yvp7dQeYEKfqi8PctmawIt/47qcA+YQpwRu6fjeC+O5G3yVLMeLRAZ5QgcXTrwCtNUd+6RGdh2vTKoL5RByXwM2jd7EFyV39wuGRGH5OET6aE0OtCoISTX1SSEPplps9FbaUKi9UrPdKg43LvPpLPpoAhCD/rS5kRGccXOp+MsKDiB/L55aEK0Dhjyc3zZm7SAPmFWuq7bm0QCpX4r9ZgmWFDrtfmWCsKVa3e/vx9lR0B3VUIgWhCMWyubODtV0WlqR+VT5vZQlrpYcu2pPQh/9muyeH+fSHI85DWl3B7R54NVeLKcEfQueZBKXTQFpTANdzEvZUQl4ZQaJ6MJrq356Z/EJeFdTovwO3oMiLVeG+RN5EazmbdvujXLgOf5Fc7p0xZgtH61lZxoCZNjQnmc/k+Jyl9riniw5BBbgbfDnw0BZQ7Ht011JMj7Ks2f00e1UJat07BReyAas0eVTstKQPWQ60SjlwZcdpxPdL6sgGYeLrxXUFIHv1rX/E5taxCsvB+fc2aHOCj74KJ4vhhqtP3Ncf2cKZolsj6wM9QGcz3/becebSD5G1JlHqRDJOxvkm7tfCFSjySs3WVG8IdknSn4sH4nb87IoG0DKHwZypQui1BQqJ8GxbNg+LzJcXzqE0IEHVBi2RJBzQOyQ10fwhCpZ9V9jeOSoCcdsWEfj4GiugNr/V4h1POqy4fhhwIEXs42T3YzhioV0jCrEFt4LbuQUX2WA0guzxgGHyhAg/dc7naZC2QZjpfe7nU99PHFnqIeRByh0jrBgoYd2L/w2w1IukOHFdDzLlvBWYXfMw3lBqh5WabdklwMOR1JHv51HYGsfM5poz+H/Hg0WMgrkwwpy2k1bS5QgxeBZns1CwP2gUyUw1Mm4N9FN+g7w4UWclyMX/zWBx/eoBPuMepQjCUr951GIO/sOUR2RAy5Jwyo/fqkDsdYPpK/w2jBkRxvT76rlpASHhXZKMKDclp9qH8QDEDj3LfkgAkHmJ843no83AGOaOTYhJSLIMLLO+TG1wXgZcHpW7FWZnBiuMf7Y5QVTB/JjvHLlQTCRp423wQ1wn/9wv8yPxm2OhkMYz+P4P4+7zsYK/ZASV77W+aLcajH0wjfq6cGOUlYvESUKZzYE9gxEGaBkdtNb2NL1cC0qW06f1kdAk5q+TVSqAM7P1Mrfa4Ewt9gb7NsE4OxHNaIIotzsCwdJKE6pAkVJX5HP3U4oYuOsRfN55LRfEFGo3C9AUjleNXE+BnDYmzquX/cH4iVPRf2uVk5YNHx6mq9mSfcXv1KZ2sgAZWD9NuB16Qgys1zOeKiJ3rjP/YRNfPDyXpz7tvPReBBYeD8TjwzCn+89JLuCjegcMdztoPeQDtQjy4+dISE6/S+oWUsMCRm30Spa44KWUZP2uB84dPyceNoRXV4c7QgHrGLogj3EqOrK26I6t2GIlI0AIlznytiQiyA4ttTwzt8jMAyOBMhVi0DZz1yfzCfswfen1PSHqm6QPE49M0pMxwYdSsMX41xQLFc4WcygxGk5HWIHBEzgr3qcn0NXV4Eg/GP93KloPaf5/LPI3LweVXnZsOqPAw9JmstKRGFqy/ESMZeCqO5x56f5LMt4POu4YlsDRFQjpHpP/tQAr02SBV0LsUhCtljg+NmXuBsRn6usdcLzDeEZF45PsP5llyRZF8hARsVxiO1Ypowf/NTUWK1FuzW1BQuFfOidwdTnsaXj4A9Z0lJ9Xgc/HEyVTyokEFhARkXDfKc0aREmGIOikYOwj9q8m/rgQejltogyWE/QvI9GB+ToMvOUY+0X7DCQUCPZJ2EJEzrnXHH6yJwGjqfOfiFDRIZ1/Te1ysDxe1FsWJOYcStfHF4qVEeAh4GUxibukAS56TEaCMetZhULlyiYICDbZUuElIchI+kiotzScD292RZr0NuuXiCPVlyKBHuqmJH29mSgBAnO++u044jifqwup/ECsEiHPXy/vLQ8Gpw9Dm/KZymaSq6rcIGpxVrw2nPaMPFyIsZky8sQTTG4p/eaykYnDsRKMVsBOYlXBrFq44oiOdBgsC0CTCyH7tuTn2YH+oebp200gjLkn9SzVED3nh5nXTmMYKNJxn6+lImsEcmGzfmJoP6e4Odsf3K8KDk+LGEI4pw5GJBQ027ImDcCGPf5u/jKsWI7w18J4jYNsebf+6wQjY/ZZftoX8PcagJbXl4BzdzlPZvxSAZpLBX2W9MscDaT7zZXiL7//St7mZKI25uhshUwH0iMeA8HIiJ/8JMZoBsdWKw3R16NJzwRGuN1gxdoCH/NHVVBaqG5QrlvmrBW15dvoYASUT90+RpdRIGdNZuezEwyyIJ+9/iPYc6WDVtf77WyhkFFIiUaWF9IDzbxuT97gKRu9eMIyL+gEiimx6pmPLtSUVfNmYrYZV4c7mOhVUxE/qWzrh5eaTBCy/bC3RslGiuf+ME54YzGvkqNv6rHAcsFpQ6KNEIPIdfrtqMSqAIqtdX9QwxcFPFVSRwwAiYj+oJn4w3AtpC3mtD/lTINysio4CdFOSPetE/btOC+ma9o54LmpBDYcHGp0+O8ML3DGXGt4k4++e3S/MRYGTU69i1DIDl1Z+QVCZ2lIAST1j484B37vx9hgh9uPMyxf/hlh3ILzzvDq5kBeuzlWLW/HqgoSJgnh2pBMJvIzq4/5pCZ8olVqsGTmAeCqVye60F1RHP12PjD7mniZ5u77Y2aIls2f9K50NFNZIzSfkqwKU3GhFQqw8ZE22vF5lw/90vuF6rxS36gXg962jdmioWPlSQsJ7u0IVyy1cCbO/pUeXuVz+9DWqQr5mLf6egCBKIQUPxpBxY+Bw/dneRDhUn73SIrh8QsX9162xYVEG58+eD7+WH8bHa0iz/YwGngLS95EV1+LCbttH6WAPyL2YVqD5HoDqj/5r1nwTyKJVn1H2lDr3pvz7SmGKBluhGQ2i2A5LnGc8pNzpxcTOmgpslBiD9eTjmzR0c+FqS9lwbNPgv/sf5qSS5zCCedZOySj0GLMvyfNSZZcFa9dLtXz32iGc8/ev7ueMIa/PBc5oGi2qOj9qIBfDAu1Nsuc/5DtsJYZz4ceMBzshJQmOQRxVoWP7fk6KqUJ8zVf78PA3S2q6LGCpkAmVa/NM8/x1i48WB7O67OMjBvnx9gsoGOOjH3xsftptK1vXHyF0bxs3XaBp2/7t/4MQ761aZzwY+Un3Xox8ngbrcALsTW/J/8W+QkHoncIIh3fa/yrVD7gmqILA46oNG6bde/CgTovN5pvMgRhz2eMe4ahzcYbqOtqrnijsQRMWqn2Gv45BJw/UraqtEeqw4Zv03L+yyCJRORwqB/8uYJKNpelTdqSLJFM0GV0ruPH/VpwRrl52HFfRUIOxe2md716c4t63H/Kc7/hLzjkgILmFd4IbTXYVyFUPw8uxlHctVRb0x3WlO5xAq6Gjbx3XIAc1Gut3pSHkwVv+ObTt6h1hpbhUd83GUuGn+yTLtUEdcJ2Y7LrTLw5ZNllh2zi1cQlvI/Q7rbaLR+3QLOaw0vKzIllimkoP5/bPjH+8+wrHIF5QPsGwRM489jS2Q8Qd+0zeleAMsONkcj79/Whcpi64HeAW4oHcVjFwld81AhFz1BUbQEqKoxRrnX10gzhW+y51LZwcCl2ZAXIIIuFNfPPggLwbDNirGntCII4j+CbUT/U1M5nXMrdmRAnudTvJRHx3wE59Yb2Y1h+bVoStR0wg9TDbruL4jCeJ2YmdTvWUA7CzKX5nUE6fR+uqB8R/iH5VnZyJZNMFo3KvJe1gLoreFvZUcZnFzvs5JlE7kECeFDzNhloelHOf8b0oq0Fwk6VAlPEwM+Cl5bV6PGm546eg4JjrCDwKR9oqJGtBuX8pk0dQCOZ9I3pZ4J/TR4itXK/hB1YxyxjVRfwgV5Jn/e60fh1Sz2DxF6SBQ9OLbj9tKkIUJufNoTRtEQfHXnjgfOnFsXoeRoAz/8GYfjjJZA/3aRapkHXtwjhY0MQNRZEq0pvFS0ID0GysZg5cFgMHH3O5zsjTYZiryMQXxoZy4oJLkICmIzI15ZfDDDwgaIaI6//wgl5rey1NSAFWGVp4J6hWCF+OboV08eBRJxV50z5MUhK+57I+HiqBnglZPRoYd4Blb2Nf8Zgnoyso9ZV0vBS8qBS44q9EiB+zb5dsXWOD20zOUJrNGMNZO/1pOzgzsMKROsTJiKD1NtO28sDIEbJH/keGkQ8fMe3or4zDgRbrAfuu7MGiKCK7/KJAFhY60mHgpZXg7Od/6I0MKHDi/Xw7jFkKEd45+rXS0cPVbU2vIgTVc8aM0jnWyhVMVG9Yus5LIK846J11bHtKJypGZfJxwtCSMoESmCeD4vTc9hgt4ZKyC3PWkQYr5zpaSmBZsUtu9fv5OHyof90V3v+VBK3bN3jQDMtD65pQixQs14NSKaA31tgE8VlTz9qoI2J4bq/i3hz3Mh4MicQl9WJKQPqrubwDFYdXHt+o+EDtVc/iyR1jgJU8Hx3aVEMgtHItU1+MHNtq78Yz0HMj4ktuIRS0LfLp1oHmGKg72H53k//TtFPDp5XS/sZRG6POgHMOEGCTNDorF29mC+J2tZz/l8HBTNekVtxsHmquzPhVuIwpGT0r6z2hggbvMbcKZQwcEo3pu1SkJozxXQkr3ODcUae0L3yhThKBVpYdkFzHw+H5iwd0LN3Gd12B54jgdXK2ZUW9owsI7JcsNUikcfDsjckHWVAJ9+7TCOK4mC0PffG2Pe5pCIPMlvZJ9CxhM+rNoIsCP6gYMH3zgkwdDGtfg/Hge6HrkrFiojwEFCRutc4gBVWDZNc/YK0FphOBb7dkjsB7PffKskDi0119rSHjPgagU7nTrqQhCVIAqWSHFBjFh9R7nFVsSeMVPQVMzdYto+ugdq+XoLLFhpvUJ4dURkHkPQzFeYlBoZsUu2foal3WjTE9Y7YDIlmkZ9vyhCtQekTZxw2GA7V12Z6IKBwrQIRF/bcYFZ0PDMnZL4+BhILcG7epXXFMgO7NfjDuafo1eCJhFoRXODV0O5/NwQXRYK1dKAK4vxRApSULR5bjatmefCEg9jsk83lACtGVXEnEikvDMgrHrgRAHGrg/LGL8iRMu15l/cObFQjdr7Do7lxkIhstb0ozJIoJRm6pL8uF4rtF3RfNioMF9V+af53/nu/GKaWeNOMigXzKurbJFBGR/Fsa9PRAFa9v8UskbFGjAbqvi1ygtZDJ0LhhXKYMrD7n8itR//2d5u5F5qDNAVZGSskW/Krw11s7KbD/kMH8/r7+7XTgtDrXEEW0SsPx1rzk/kQQEqJ6+O19nDtLtXdSzqgpArvqyY8wRgdgW29q/DAFo7X0Yp9ksCoRiuoJgL0rkdC34aW0WO5zN8W2Uz9YGwYq9gw/BxlB4pJbe+Ys4Sn8aPqnarg3vM05QfTwTD0P2+fFqdfHwSe/A3K17FUcomW0NONTny8rrFCW8mtB7btbA56MK9Kj/bNffOIJY9tycm7YoIczoxwUpNxPgFFqfC6o3h6jTfOH1Z2aIfRtDHrfoeEDtktU5wooiyKkHx0TWGoPkX5Hr3gPMUHredITtozq4dBZHPfm6QjR5VqW1rb5LlB2qv7ghDbgQ2u71lpZ54mCKd9jMdyV4TLutLSOkBNvk9C9xnZyIwV8/6JU8B9AEDY4WtJmB055w3mK3DXhcma3TuaqAHHJ5eyc/HuaTlOdYAp8O3JgqUxX5pQvXk/P4Tx65g2P59PdE6oM9Yv8jVk0dAT9YTvf3etG9RjzPdPPGvUVJdC+3m/CT3ge9DIhsu3bDD1YKfH82MvrDvYWtKOr7lTiSCKZSfB891HL7TlR/1wBxawk00G0ADrEqT0kjOdBdK03e9gcqwG3GtsrSLQA39rGTt5oPuTfu5xtbUnYU3lX78vmICKQZthpS6+qA9MCSz9XrZtDQZrw8+poRuIjo4K6hCrQv+PR0eShAOvWpABpde6jG4mneGUmgezcx+XJaeOi6V+bliDeE62vkRnm6joAadrptljaITG8THzkc+l28ea8rTTmCF40usn+V8LCOY1Vba2WDnJDX8hcmdeCE85vppk7MIed/ktbG6MHMlxAt/W88COsjPs7Togw/K21XzqXyoRVVyfUpNSmQ1+GVjR3gAPJEI9egQ7+2L9djvLIWCXaaGfw2lgZgE9wR7/zCEeWuURhinvghniX/zZfGTDAV4Xau5qYyFHjWPxXn4EIHPzcfcPhiwNiS0dVC0wDUnKjI+14aQqVXOqsDXT9R2T2hwiSYGgIGVu6d/OILTS+ZVPXdxSFotzJn20kFdRIm1JKnPJBi427jzpYR2NSO0V1VcgCNsxdH+LwO69WZh3xeG1pwNYlUK0kOwaeUMr+QOVMIM3C5wnJEGCGJr+W+2epw/9TzrYAJJbiYHrcs0YggeuXpqeylw/x4cVxwalYXtkc4L3M8MAB+QwHDt2w4eCLZnpkWzYdAcfWTmSUZvBP3TjUecIa/YuTm475u0MCoXz8YJIoCyFVXGjMOffXLhfUryeKgJbCuOM2vBWbcryfPyiojklhzxivU+jBWpRP6xTwctHN2nqeXy0LUWeZEdS4W+GP1rK/zyAlEJhhwuccFDx2K+XnfilyBIuyDQ5cKO4QML1qxCmvBaMPrEw+Pa0KwRZgYHbMefPvSXj3goYy83VzcBffVQdSMlWvFWB+eePezdRjqAWf8b6l6dwHUKTjM9xLPA689pOiXTeTAvXn0y8OjuvDPzci7nIMTqb4fvv/rAAN0lxu6ZZXFoNzB0erXoBw8Fz5V/XCIDGUxZi2+r+YDPbWwDP4MI0jkmcju1kBQnYP3fTUpgYpfUsxU67PCXNuMlsGhfnGXHn9flWcEdIL5R2m6+FBkQZ/CrdP60E8aFGfPHgsvYn4/lv2IRXHatZX1Za7I4QpXbMtaJCJhjur8R7CEzbGjd44J/7f+2qwZcR73jwPOC10hXFAxhffSl2qaA8xAoUZTPsy8iVj4QunaSisZVA/+TomYToSyuVvKfy3xQPNpMfjlNQuke/dFb0dpLMqyuRBk5x0C3I8GHF8UhQAJSlqVkQzHkWQ2PaDt3yZmpmp27j82grN25hSU0azg9tNYHRczj9s+9p5vVdwAHb3N6C05/Iv44SK/YC83DhocbhSpTVFAYtqGa3KjLoySfwn2Fo2F5viMUsPqGGBPspDaxCgiwln5rS1vAfDktxIU5TCFVAqGwGQjN6jzyvQUGJAGt8rFK29xxkAUSjrzIkYN0r9vPMXoHepEhh6XVLwAasVLqk2WcYNt0dUKjfPKcOXm8kXe3f/0RcAi4G3uKCsYbZp/y/KwB9w9/5VNvB2o81Ys/RgyQHNmn9WKx7DQQ/7o+ZCuDEyN6/495WkEeZYKFyw3WECozW7u/mF9xlw2vVJ42Rwwjb1Xe97rAE/XK54NH1GkxvOCqU9fFiXezpLi/XIcLLrYsD4szFDpf9mWtFEfaVG/IGjmB6MVT1/ucDoj2EiaPlOqbAx8RULbJV9kkDznkfN2CYpgy5IR6WBuBL1MGy8XO4z/F3/Uz4PMW9tUoO8qdE0/IxD+PJTPFrZRR0knqaY3WWwQKxiu1Mz7IgUO4gfuMEPIj+AaZOHGA0lYZ3AtPRCpRBgwu8G6MLbkERVefQxqb7K8fLUrCVMayq/CkChS+OoiVcrkhxaWmB02cWIQmrh5vlZCAki+oIo6KQ3i8IFTqL7bElHX6Xum60NhaPuybaMSK/bf/XTPVq2h8Q3iudP0+S99EYTO8MW/ETjkXWOnu5bHBNE0Z9De8yoRmF0wiFpf2CSWtWyFmGOxQLilYPz8Lxe4xc6zFDOrw+5ShRlfgy1whHY5Y7ytIeWXvJpFjAp4nN/g5mXWQJj4W05BypogL/r4Cv97bXjBRDpoIddOdLOz6bK7TAGBhLlq1jIn8KWSeBxQzg8XmP8et8JgIGhSDDMU4Yp8uR49sSmSg2/cmTefmKsA/tgCB22CP7FV9kBIeJceaoKPDOvzc0ADm/JC6WtLOMa72/DRVRUYMF6ncrT0QKE9Xi2yRgdM/e7X19zDwufG7zvVeu9xWUMyvL6nyeHTsD+f/lcDEGGZI7/+7T++J8GvOxYPZRD1qt4oel03hvyfFtx27Sag7bWU8p3Ah7zMfP30Gg7HR1n/21gKJ+RPMTX4NfFBFOb9ROtvGjRt2q0edJsVnOXFSm/ZGwOBjL2+PsEMitVvPTpD2CKuHLsyycrDDula/KWZE34gLEW8+OEGLbKd9hg0ACvEqXJEdOG6CxrNPdonu+oPSRrWPjKxx/7b/3Me+zziwwIfZn0n7Q5w8DZ6xPFDhSHMju1WW53mQoQwqqjIhxLwdNxYUNghDeLLS9zV5bhRlI0cZ3KKP6p288i49zQZNfnkVXyOVoVwmZZvA9rqgNw2r1P/u4bDnxrwnR7aJf7Kw7d5MilDDStTdUXHIb+TReqfKXhN9D1Ol+4j+pe4XEr65LG/NETsN9rE1svD5a3dPw81qJC50QTPQAM7qPnGu2rvu4EOfdTOHWc3CGUdSk2JlTrku0sl5MqikOBs6NVzOL7zpV8CrbEm0Nq1vJjwiRcIWds3pCc5kBTLJzGSbhmYfGe+iRwUQcN1b2M7VAbN8of16RwoALVbSIr622DgFhd7EQ/agBKjonJGhdCCnzAmp/oYckdjov23SSCgo27DSpAZhp5XJOXLBeIS9AJPlGzsEqsC2xRVMkPA+EYV536GE2wMXP28NeePhtle/eC7Zgi0k0apclT24HTOzuDNlA30BaruCLWzQJ/Jv1RFVhm0sE/C5CAkCW+HhBrNOmShuPzxBfWECtxw59WfLyrJwGbeb8lwIAqWeLfFX3xkgS/9NN8utjigH47XdLw7g1Gf8hZdTNwxoC5DKwu3jkG9CzZGrFoSFf549vJ+iQzol1KN5w1JAmvx2MdQPjmYzpvkSEi9jguoC6SODyIF5f7EaFd5MYjW6Ai+T42B74+cXUQy2NAtgiBNmYQslHlH/XIXzQQH9iJRlTEMEk6tkVINsUfsroWMe7npaDy1k2+jnxPOpfXuhM4IArCbzXbWXSAaP1ajNmAngan5UNFWegO4xJG+tOZlDQpfrU90aCkBYVQ96wKPAEifb/R/kKQOAaMErQwdPWCdlt7N2hFAGmXdMnNbCpBC7xHg3KMDwnT83yt5jSGPjGmtfYgdBWTfECoNwMDd1q6KW11y8OhhPNt8pgoo/2v47qzLgJT/4fapnwvAiqzF/abBKNALv4+9TnoSnic2Wo0v1+EIn+wYM9zpwO2GUWo6swbo21GWtFBwAkfX41f4R3Tw+pv2+lMJNfTrL41qM04FuPCszdlWZsBjTVx61MkIA3iDJqtKbfhsOTuju6QGTrsPLTHxGjBEyNQy27yJI1gxX+NR2ibSSIWLL/Jbg62RFHd2gA24Kd6X5MS+wqEJhpRCQSpYPon9tCAUDm/b/zGYXw0HEoYN21qTk0SSJ3e8QocYIbcBn2hyUQHq/xGPzZ9Xhz895EsnRrnRFlH1mUmwDGzxJH4o6aWHhqk/asQWDEiHxB5kpTOj0zMXectlVcH9pAKBZfEomJze6lsMNANT+pPCsebSUH6SeEPa0BnRc3kIu6tYQkQEC+stfktwDfVJ/rjNhaLk2ch1rbnAf2S9PG9HHPQ833lv9KrApawbc+fOcKJtwv6lG7Yy4HUva8okCQPK8itLzZfVgST1W1JClx4xizqhKayHBoJ+CKZEKJ0F1g4S7XS7FJDmIms1TFNDSPdJ1mk2Pvh45/Z2TYk6DHkRrw97mELj39PeXb+EYcVKVVDfVgGk0sTv3U1yBc4kzX9hSy4gZJb9UV2CBJG0nBahke/H3W6JcX7mggUFTEF+HRkebDnbND/zssI9/cmLweE42Murt9J4rAvyheUjVYd6mCPxgemxpyQaoiYKXGqRALeCg+BTacoQl8s+X2eEAW/C6Wlnyz7cCqnd53MMB8RwB3nPQXdl+KylhUJkDCDEl0d1S4UN5f+mwaRmaABNfT71Z3IpWA1f2cp7YQuSbaMPTt+RgoUpbktNfiO4LZQwIzWlDlffC7S70WqA8Ku2WDshJkSiMkk6Hs8MUV5fk7nPCR/G+XlonEECRqzkhupu1hJpODsrI4ZJQd52RnbCSxF0HuQcv3rWCDyoBbi8b7FBJK/g41ApVXgRpXyKrFYG7jWc/VijZQHmf5ZztwKFoeZLlIb1cT14c/XZZc1fJwHDR9njk3ASflRQrSQ9V0UkmfpZB+EyAJ+la1kVcCjsO4s2m744PBpkPj4x7wIVVH2xVUoW6Px0oswXowTA/6ObT+9JAOVY5SfM1/aIJFd01BqMeKDHbtflWHM8fMsU27vIagTbyr8ViU4mKIzrDGvJzWi0TzZkW1FnBemZj/pMG/BgFkYj1oMVA4LyMHaCe5doEttbFK2KhaXqDKa2aX1QJ2FX4ZYjQ491cnKv9LMDN5YysitQG5yK1wnGeJ3/6Vfl+QU9Q/vfRI2fj/6OBKqCjpV6FfaBBpg+1Hvd9FoMWacqC6UFyUKksZdIUU8I/DY3iru9FgJ2GxW+2AYGRKBTHpxLONR9CUbrSA0s/PwYc8ZGxxzeanOG6diyQ+GbOgGdMUWgXTOgGWTFg3tt4043kz0QtmnoXZz6iYQPvYuTW4xQ/kvud/O1w/nv8YF0hmgFsWa5348VciD8UBs1D5UJKD1Ey/kqJyGK6lbmo+O6AOKFUSd2jFFQUZY9bUcowmiaiL5XsgTJ5z9CtMRVoPPKcuvlTjGURC6XquSjjXhG+nTT6WMhMsWK9XE0A9Tivr7xSscgEam6Tbe70Uj63B0cU3ICxMdwLsZpLBITjj5Jv78nic6lMyZQdsSj10Ezvzec9KBPy8XKncwQvoXNTQqa8SLLDlJ9GnJZcDqt1M1xkArpRedKw3ZTAcMv8PXgExUisVJT+4yhAA73tfXf5FxwSqSasf+4KLDdMrhflkiFfB+vpsSy8sHGG/OCya8mEJyRE9970QXubQwyXU7nAgzTn6kjMcbAzB70tNhaFC7Cx6GeIAUoZOLM2omkQlKrZ5qk24UhXCQcd7vcDxiKi/D1WCnoac8Sq4lUQcr1s/TDfz3R0DnPJwvXToPJgP4d63E84GPr/KpdcajYYDV86kQMwqrf/WBDKQ2JapfibwvLAwVTNmJfbsaFmNQxflknAdOpR6/PHOoPdlVIYAfMYKrbiW1Z+lA/nD0DddQxsLgcGlrwxRZUr/gdBIqRANN7BWONYE4Ulr2ETipbocZJZ4qsj9FQGGFSfC7DALTamm5RR2qivDMz4UPBEUjg3KQD17AtOBRwXHmiIQXD/n8YNvM0keWiqu1EvA5yeDtxhuoCHkKSTd7ZyesDSx3DhzB+OhSKXzyqGITQ/wEp5E2j"},"tuning_per_seed":{"start":115000,"n":500,"columns":["s0_total","s1_total"],"encoding":"base64(zlib(little-endian float64 row-major)); 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"},"interpretation":"The estimator improves S0 by 35.64 edge (SE 3.76), mostly through 26.99 lower arbitrage losses, but S1 remains negative and trails B by 152.56 (SE 6.57). This is evidence for testing the estimator as a graft, not for submitting this standalone strategy or claiming a promotion."}}
