Genus 2 curve data - 353.a.353.1

Formats: - HTML - YAML - JSON - 2024-04-19T01:24:42.096777

• g2c_curves •

  Column	Type
  Lhash	text
  abs_disc	bigint
  analytic_rank	smallint
  analytic_rank_proved	boolean
  analytic_sha	smallint
  aut_grp_id	text
  aut_grp_label	text
  aut_grp_tex	text
  bad_lfactors	text
  bad_primes	integer[]
  class	text
  cond	bigint
  disc_sign	smallint
  end_alg	text
  eqn	text
  g2_inv	text
  geom_aut_grp_id	text
  geom_aut_grp_label	text
  geom_aut_grp_tex	text
  geom_end_alg	text
  globally_solvable	smallint
  has_square_sha	boolean
  hasse_weil_proved	boolean
  igusa_clebsch_inv	text
  igusa_inv	text
  is_gl2_type	boolean
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  leading_coeff	numeric
  locally_solvable	boolean
  modell_images	text[]
  mw_rank	smallint
  mw_rank_proved	boolean
  non_maximal_primes	integer[]
  non_solvable_places	jsonb
  num_rat_pts	smallint
  num_rat_wpts	smallint
  real_geom_end_alg	text
  real_period	numeric
  regulator	numeric
  root_number	smallint
  st_group	text
  st_label	text
  st_label_components	integer[]
  tamagawa_product	smallint
  torsion_order	smallint
  torsion_subgroup	text
  two_selmer_rank	smallint
  two_torsion_field	jsonb

  
  {'Lhash': '1325289956928886660', 'abs_disc': 353, 'analytic_rank': 0, 'analytic_rank_proved': True, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[353,[1,10,362,353]]]', 'bad_primes': [353], 'class': '353.a', 'cond': 353, 'disc_sign': -1, 'end_alg': 'Q', 'eqn': '[[0,0,1],[1,1,0,1]]', 'g2_inv': "['229345007/353','6021734/353','565504/353']", 'geom_aut_grp_id': '[2,1]', 'geom_aut_grp_label': '2.1', 'geom_aut_grp_tex': 'C_2', 'geom_end_alg': 'Q', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['188','817','30871','45184']", 'igusa_inv': "['47','58','256','2167','353']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '353.a.353.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.1859131786554872680710289056816858155417289571348692034', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.10.1'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2, 11], 'non_solvable_places': [], 'num_rat_pts': 4, 'num_rat_wpts': 0, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '22.495494617313959436594497587', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, 'root_number': 1, 'st_group': 'USp(4)', 'st_label': '1.4.A.1.1a', 'st_label_components': [1, 4, 0, 1, 1, 0], 'tamagawa_product': 1, 'torsion_order': 11, 'torsion_subgroup': '[11]', 'two_selmer_rank': 0, 'two_torsion_field': ['6.0.22592.1', [1, 0, -1, 0, 2, -2, 1], [6, 13], False]}
• g2c_endomorphisms •

  Column	Type
  factorsQQ_base	jsonb
  factorsQQ_geom	jsonb
  factorsRR_base	jsonb
  factorsRR_geom	jsonb
  fod_coeffs	jsonb
  fod_label	text
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  lattice	jsonb
  ring_base	jsonb
  ring_geom	jsonb
  spl_facs_coeffs	jsonb
  spl_facs_condnorms	jsonb
  spl_facs_labels	jsonb
  spl_fod_coeffs	jsonb
  spl_fod_gen	jsonb
  spl_fod_label	text
  st_group_base	text
  st_group_geom	text

  
  {'factorsQQ_base': [['1.1.1.1', [0, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], -1]], 'factorsRR_base': ['RR'], 'factorsRR_geom': ['RR'], 'fod_coeffs': [0, 1], 'fod_label': '1.1.1.1', 'is_simple_base': True, 'is_simple_geom': True, 'label': '353.a.353.1', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['1.1.1.1', [0, 1], -1]], ['RR'], [1, -1], 'USp(4)']], 'ring_base': [1, -1], 'ring_geom': [1, -1], 'spl_fod_coeffs': [0, 1], 'spl_fod_gen': [0], 'spl_fod_label': '1.1.1.1', 'st_group_base': 'USp(4)', 'st_group_geom': 'USp(4)'}
• g2c_ratpts •

  Column	Type
  label	text
  mw_gens	jsonb
  mw_gens_v	boolean
  mw_heights	numeric[]
  mw_invs	smallint[]
  num_rat_pts	smallint
  rat_pts	jsonb
  rat_pts_v	boolean

  
  {'label': '353.a.353.1', 'mw_gens': [[[[0, 1], [1, 1], [0, 1]], [[-1, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [11], 'num_rat_pts': 4, 'rat_pts': [[0, -1, 1], [0, 0, 1], [1, -1, 0], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 353, 'lmfdb_label': '353.a.353.1', 'modell_image': '2.10.1', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  {'cluster_label': 'c4c2_1~2_0', 'label': '353.a.353.1', 'p': 353, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '353.a.353.1', 'plot': 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wdKF19s1htE5SAAApUrP98sup%2BdbY6B88OHg9c5OcHrw4dLbrm5wWPg/MiR4HVubvC68LG4dscdZp1XHB8BECiD/Hzpyy9NEJw6tehM4qgoqUsXqV8/01q0YGmZikQAhNP5/SZoZWVJBw8eeyyuHToUPBZu2dlFz4trgYX1Q824cdK999quIvQRAIFy4vdLq1aZWcQzZkirVxf9epMmUt%2B%2B0kUXmcWna9SwU2ekIgAi3OTmSj6flJkZPBZuBw4UPS%2BpHTxojllZwXVNK5vLZZ6FLtxiY81kudhY0wL3Aq1KlWOvA/cC51WqSDExx14H7gXOAy062uzudNppdt6HcEIABCrIxo3SJ5%2BYtmCBGdIIiI01vYO9eplJJGedZXoMcfIIgKgsfr8JXV6vtH%2B/OR59Xrj5fKYVPvf5TG9dRYmNNY%2BkVK8uxccHz4tr1aqV3OLiTCt8Hhdnwlzh8ypVGOEINwRAoBIcOGBC4KxZ0mefmXBYWGKidP750gUXmGPz5vyf6YkiAOJE5OebELZvn7R3rzmW1PbvN23fvmDQK8/hz2rVzBqjR7caNY49Ftfi449tMTHlVx8iEwEQqGR%2Bv7R%2BvTR7tjRnjvT55yYgFla/vhkm7t5d6trV7ExCD%2BHxEQCdye83QW73bmnPHtP27g22wHXgWDjwlXW4NCrKbCFZs6ZpHs%2Bxze0OHgPnCQlFzwlrsIEACFiWmyutWCHNm2d2I/nyy2OHhmrWlDp1CrbUVJ4hPBoBMPzl55sett27i2%2BBgHd02Dty5OR/Z7VqUq1aUu3a5lirlvnnLXAdCHeFzwOtRg166hG%2BCIBAiMnOlpYvlxYulBYtMoEwK6vo90RFmV7BtDTTzj3XXFepYqfmUEAADD3Z2dKuXaYFQlzh68L3A6EuL%2B/kflf16uZRisREE95q1y75PBDuatdmRx84FwEQCHG5uWZG8ZIlJgx%2B%2BaW0efOx31e1qtS2rXT22VK7dmZiSevWzlmYmgBYsfx%2BMxs1EOAKB7mdO4sPeUc/2lBaNWpIdeoUbYmJxR9r1zZHghxwYgiAQBjats30Ei5fboaPV640Q2dHi4qSzjhDatPGtFatTDvjjMjrLSQAnpi8PPMc3NE9ckeHu8LXhWeyl1ZMjFS3bjDIBc7r1i16v3Bja0Wg4hEAgQiQny9t2GC2q/v2W9NWrTI9M8WJiTEhsEULM%2BO4WTOpaVNzLykpDJ9rysyU7%2BOP5bn6annXrJG7ZUvbFVUqv9/0tpX07FzhnrnAcc8e83Mnqnr1YHgrHOIKn9erF7x2u8Pw8wQ4AAEQiGDbt0vff2/aDz9IP/4o/fTT8Yfm4uOl0083C1efdlqwNW4sJSebf8GH1IzkJ5%2BUxo2TLzNTHkneqCi5r7xS%2Bs9/zB8TZnJygjNVj57FWnjiQ%2BGJEHv2nFzvnGQmMxQOcH8U7sLwLQVQDAIg4DB%2Bv7R1qwmCP/8srVtn2vr15tnCP1oaIzZWathQatTIHBs0ML2GSUmm5yfQA1SnTiXMknzmGemuuyRJPskEQEluSRowQJo%2BvQJ/%2BbECW3EVXuz36IWCC7fi1pw7ePDkf39cXPFDqiUNvSYmRt6jAABKhwAIoMDhw2aR6g0bpN9%2BM%2BebNpm2ebOUkXFiw4axsUVnXQbWSgusiVZ4cdvCuxUU3nUgsH1U4W2goqOlmLwcVWueLNeuXZKKCYCSspet0pFWbZWXZ555O3Lk2A3nC29iHzgG9kAtvF9qVlawHb0VV%2BHtvMpjgWCXKzhLtfAM1sAs16NbIMzROwegtAiAZeD3%2B5WZmWm7DKDS5OaaYeXt281ElIwM03bsOHa5j4rc5kqS2muZ5qp3wbVPUrKkLQoGwDF6WP/WnRVbSAni44Mh9%2BhFgQsvHlxc83hCbJgdiFAJCQlyOfQhVQJgGQRmHQIAgPDj5FUDCIBlULgHMDU1VStWrCiX1y2v1/L5fEpOTtaWLVvK/AEPxb8vVF%2BrPN/38qyrPF/LVk35%2BWYY98gR6dDe/arZqa2ivfslFd8DmDXrc7natVN0tApaaf9jPxTf91B8LSd83kP1tUL1vQ/F96qk13JyDyA7EJaBy%2BUq%2BIcuOjq63P4rojxfS5LcbneZXy9U/75QfS2pfN53KTT/xpCoKckt3XWn9NBDRW67/6%2BpZ0%2B5%2B3av/Loc%2BlqR/HkP5deSQu%2B9D9X3qrzf93DHUyblZOTIkSH5WuUlVP%2B%2BUH2t8hSKf2PI1PTgg9J99xXZBsIvSZdcIn3wgb26HPha5SVU/75Qfa3yFHH//1CBrxUJGAKOYOyMYAfvuwV79mjHpElKuv12/b5woRp262a7Isfg824P7z3Kgh7ACFa1alU98sgjqsq%2BSpWK992CxERFXXWVJKmKw3YBsY3Puz289ygLegABRAR6QwCg9OgBBAAAcBgCIAAAgMMQAAEAAByGAAgAAOAwBMAINXXqVPXu3Vt16tSRy%2BXSqlWrbJcUUfx%2Bv8aMGaOGDRuqWrVqOu%2B88/Tjjz8e92fGjBkjl8tVpCUlJVVSxcDJe%2Bmll9SkSRPFxcXpnHPO0aJFi0r83gkTJhzzOXe5XMrOzq7EiiPbF198of79%2B6thw4ZyuVz66KOPbJeEMEQAjFBZWVnq3Lmzxo0bZ7uUiPT0009r/PjxevHFF7VixQolJSWpZ8%2BeBVsDlqRVq1bavn17Qfv%2B%2B%2B8rqWLg5Lz33nu6/fbb9eCDD%2Brbb79V165d1bdvX23evLnEn3G73UU%2B59u3b1dcocW6UTZZWVlq27atXnzxRdulIIyxFVyEGjp0qCRp48aNdguJQH6/X88%2B%2B6wefPBBDR48WJI0ceJE1a9fX2%2B//bZuvvnmEn82JiaGXj%2BElfHjx2vEiBG64YYbJEnPPvusZs%2BerZdfflljx44t9mfo3a5Yffv2Vd%2B%2BfW2XgTBHDyBwgn777TdlZGSoV69eBfeqVq2q7t27a%2BnSpcf92fXr16thw4Zq0qSJhgwZol9//bWiywVO2uHDh7Vy5coin3VJ6tWr13E/6wcOHNCpp56qU045Rf369dO3335b0aUCOEEEQOAEZWRkSJLq169f5H79%2BvULvlactLQ0vfnmm5o9e7Zee%2B01ZWRkqFOnTtqzZ0%2BF1gucrN27dysvL%2B%2BEPustWrTQhAkTNGPGDL3zzjuKi4tT586dtX79%2BsooGUApEQAjwOTJk1WjRo2CdrwHtHHijn5/c3NzJZlhrsL8fv8x9wrr27evLr30UrVp00YXXnihZs6cKckMHwOh7EQ%2B6x06dNA111yjtm3bqmvXrnr//ffVrFkzvfDCC5VRKoBS4hnACDBgwAClpaUVXDdq1MhiNZHn6Pc3JydHkukJbNCgQcH9nTt3HtNTcjzx8fFq06YNPSMIWXXq1FF0dPQxvX0n8lmPiopSamoqn3MgxNADGAESEhJ0xhlnFLRq1arZLimiHP3%2BpqSkKCkpSXPnzi34nsOHD2vhwoXq1KlTqV83JydHP/30U5EQCYSS2NhYnXPOOUU%2B65I0d%2B7cUn/W/X6/Vq1axeccCDH0AEaovXv3avPmzdq2bZskae3atZKkpKQkZueVkcvl0u23364nn3xSTZs2VdOmTfXkk0%2BqevXquuqqqwq%2Br0ePHho0aJBuvfVWSdJdd92l/v37q3Hjxtq5c6f%2B8Y9/yOfz6brrrrP1pwB/aPTo0Ro6dKjOPfdcdezYUa%2B%2B%2Bqo2b96sW265RZJ07bXXqlGjRgUzgh999FF16NBBTZs2lc/n0/PPP69Vq1YpPT3d5p8RUQ4cOKBffvml4Pq3337TqlWrVLt2bTVu3NhiZQgnBMAINWPGDA0fPrzgesiQIZKkRx55RGPGjLFUVeS45557dOjQIf31r3/Vvn37lJaWpjlz5ighIaHgezZs2KDdu3cXXG/dulVXXnmldu/erbp166pDhw5atmyZTj31VBt/QsRIT09Xenq68vLybJcSka644grt2bNHjz32mLZv367WrVtr1qxZBZ/bzZs3KyoqOJi0f/9%2B3XTTTcrIyJDH41G7du30xRdfqH379rb%2BhIjz9ddf6/zzzy%2B4Hj16tCTpuuuu04QJEyxVhXDj8vv9fttFRLrff5e2bJHi46UaNaTq1c2xWjUpikF4oFz4fD55PB55vV653W7b5QBASKMHsBK8%2B650113Ffy0%2BPhgMA%2BeFr0/0fqDFxkrHmZAKAAAcjABYCapXl047TcrKMu3gweDXAvd27izf3xkdfWwoLC4sBnojC18X9zOFv16tGuESAIBwxhCwBfn5JgRmZUkHDhQ9BlpJ14XvF3f%2Bf0vUVSiXq/igGLh39LHw10v6nkAQJWDiZDEEDAClRw%2BgBVFRJuzUqCGdwLJxpZKbW3KIPLodOGCCaOD7AqG0pPZ/y9/J7w/eqyjVqpUuNJbmWNy9GD75AAAH41%2BDEaZKFalmTdPKW15e8SHx6HuB64MHgwGz8L2SjtnZwd916JBpFSU2tvgezOM9YxloR18nJJhWo4Z5XQAAQh0BEKUWHR0MOxUhEDALtz8KjcUFzuK%2BFmiBBx4OHzZt377y/RtiY4PvUaC53aYVPvd4THO7g4E9cK9mTYIkAKBiEQARMio6YPr9Zhi7uN7Ko4fDj/cMZuGWmWmOgeHxw4elPXtMK4vq1U0QrFXLtNq1g8fataXExOCxTh3TEhPN0DkAAH%2BEAAjHcLmkuDjTEhPL97Vzc4OBsHDz%2BYLnXq%2B5DjSvN9j27zftwAHzeoHezP/byKXU4uOlunWlevVMC5zXry8lJQVbgwamt5HJNgDgTMwCBkJIXl7RQLhvn2l79x7bAj2Nu3eb45EjJ/a74uKCYbBhQ%2BmUU6RGjYLH5GRzDJfhaGYBA0DpEQCBCOD3m%2BC4a1ew7dwZbDt2mJaRIW3fbr63tJKSpMaNpVNPDbbTTjOtSRPT6xgKCIAAUHoEQMCBDh0KhsFt20z7/XfTtm4NtsCzjcdTr550%2Bumm/elP0hlnSE2bmmOdOpU3zEwABIDSIwACKJbfb4aXN282bdOmYNu4UfrtNzNMfTwej9SsmWnNm0stWphjs2ZmCLo8EQABoPQIgABO2r59Jgj%2B%2Bqu0YYM5rl8v/fKLtGVLyT8XFWV6DFu2NK1VK6l1a3N%2BUjOZ9%2B%2BXb9o0ea6/Xt5Vq%2BRu2/ak/yYAcAICIIAKceiQCYXr1pn288/S2rXmWFLPoctlho5bt5bOPDPYTj/dhMZiPfyw9Mwz8h08KI8kryT3pZdKb7xRcWsKAUCYIwACqFR%2Bv5mQ8tNP0po15vjjj9L335e8fmKNGiYItmsnnXWWdPbZptew6r/HSfffL0nyScEAKEl9%2B0qzZlXOHwUAYYYACCAk%2BP1mxvIPP0jffRdsP/5Y/GSUGjHZ2uw/RbXyTGo8JgBK0sqVJi0CAIpgIWgAIcHlMgtW168v9egRvH/kiBk6XrXKtG%2B/Na3l3q9VS8ffcmX3pM%2BU2O5sFrwGgKPQAwgg7Pj9UsaHi9Xg8q4F94rrAbxfT%2BrV2vcrLU3q0EHq1ElKS%2BPRQAAgAAIITzk5ZquSPSUPAadV%2BUZf5bYr8mNRUVKbNlLnzlKXLubYuHFlFg4A9hEAAYSvp56S7rtPUjEB8KKLdHjaTK1eLS1bJn35pbR0qVnH8GiNG0tdu0rdupnWvDn7JAOIbARAAOFtzBjpX/%2BSLyvLBECXS%2B4//1n63//M9OGjbNtmguCSJdLixeZ5wry8ot9Tv74JguedJ51/vlnAmkAIIJIQAAGEP6/XLAQ9fLi8330nd5s2pf7RAwdMD%2BEXX0iLFpnz7Oyi31O/vgmCPXpIF1xg1iUEgHBGAAQQEcprK7icHOmrr6TPPzdt6dJjA2GTJtKFF5rWo4eUmFim0gGg0hEAAUSEitoLOCfH9ArOny/NmyctX26WpglwuaRzzpF69ZJ695Y6dpSqVCm3Xw8AFYIACCAiVFQAPFpmphkunjdPmjPHLFRdWEKC6Rns3Vu66CIpObnCSgGAk0YABBARKisAHm3bNmnuXGn2bHPcvbvo11u3NkHw4ovNOoQxLL8PIAQQAAFEBFsBsLD8fOmbb6RPPzVt%2BXJzL6BmTbNFcf/%2BUp8%2BUq1aVsoEAAIggMgQCgHwaHv2mJ7BWbNMINy7N/i1mBiz9uCAAdIll5iJJQBQWQiAACJCKAbAwvLyzGSSjz82bc2aol8/80xp0CBp4ECpbVvWHQRQsQiAACJCqAfAo23YYILgRx%2BZ9QcLDxU3aSJdeqk0eLDZuzgqyl6dACITARBARAi3AFjYnj3SJ59I06aZIePC6w42amSC4OWXm0kkhEFoicPvAAAXbElEQVQA5YEACCAihHMALCwrS/rsM2nqVNNDmJkZ/FrDhtJll5kw2LEjYRDAySMAAogIkRIAC8vONkvLfPCBNGOG5PUGv5acLF1xhTRkiHT22TwzCODEEAABRIRIDICF5eSYhafff1%2BaPr1oz2CzZtJVV0lXXmnOAeCPEAABRIRID4CFZWebpWXefdcMExd%2BZrB9e%2Bnqq03PYL169moEENoIgAAigpMCYGGZmWYm8dtvm%2BHivDxzPybGbEd37bVmrcG4OLt1AggtBEAAYS09PV3p6enKy8vTunXrHBcAC9uxw/QKTpokff118H7NmqZHcNgw00PI84IACIAAIoJTewBL8vPP0ltvmbZlS/B%2By5bS8OGmZ7B%2BfXv1AbCLAAggIhAAi5efLy1YIE2YIE2ZIh06ZO5HR5s9iUeMMPsSx8RYLRNAJSMAAogIBMA/5vNJ770nvf662ZYuoFEj0yt4ww3Sqafaqw9A5SEAAogIBMATs2aN9N//miHi3bvNPZfL9Abecot00UX0CgKRjAAIICIQAE9OTo5ZV/DVV6V584L3TzlFuvFG0xo0sFcfgIpBAAQQEQiAZbd%2BvfTaa2aIeM8ecy8mRho0SLr1VqlrV2YQA5GCAAggIhAAy09OjvThh9LLL0tLlgTvn3mmCYJXXy1Vr26vPgBlRwAEEBEIgBVj9WopPd2sLRiYQVyrlhkaHjlSatzYbn0ATg4BEEBEIABWrH37zNDwSy9Jv/5q7kVHS4MHS3fcIXXsaLc%2BACeGAAggIhAAK0denjRzpvTcc9L8%2BcH7aWnSnXea5wWZPQyEvijbBQAAwkd0tNlbeN48Mzw8fLgUGystXy5dfrnUtKn0/PPSgQO2KwVwPPQAAogI9ADas2OHeU7w5ZeDawrWqmWeERw1SqpXz259AI5FAAQQEQiA9h08KE2cKI0fL/3yi7kXFyddf710111SkyZ26wMQxBAwAKBcVK8u/eUv0s8/m2Vk2reXsrPNxJGmTaWhQ80OJADsIwACsCo3N1f33nuv2rRpo/j4eDVs2FDXXnuttm3bZrs0nKToaOnSS81%2Bw/PnS716mckjkyZJrVqZmcMrV9quEnA2AiAAqw4ePKhvvvlGDz30kL755htNnTpV69at04ABA2yXhjJyuaTzz5dmz5a%2B/toEP5dLmjZNOvdc6eKLTUgEUPl4BhBAyFmxYoXat2%2BvTZs2qXEpVxrmGcDwsGaN9OST0jvvSPn55l6vXtKYMawlCFQmegABhByv1yuXy6WaNWvaLgXlLCXFDAWvXWuWkImOlubMkTp1kvr0oUcQqCz0AAIIKdnZ2erSpYtatGihSZMmlfh9OTk5ysnJKbj2%2BXxKTk6mBzDM/Pqr6RGcOFE6csTcu%2Bgi6fHHpbPPtlsbEMnoAQRQqSZPnqwaNWoUtEWLFhV8LTc3V0OGDFF%2Bfr5eeuml477O2LFj5fF4ClpycnJFl44KcPrp0n//W7RHcNYs6ZxzzESSH3%2B0XSEQmegBBFCpMjMztWPHjoLrRo0aqVq1asrNzdXll1%2BuX3/9VfPnz1diYuJxX4cewMi0fr306KPS229Lfr%2BZNHLNNeYe6wgC5YcACMC6QPhbv369FixYoLp1657wazAJJLKsWSM99JA0daq5rlJFuuUW6e9/Z2cRoDwwBAzAqiNHjuiyyy7T119/rcmTJysvL08ZGRnKyMjQ4cOHbZcHS1JSpClTpBUrpAsvlHJzpRdekP70J9MbyF7DQNnQAwjAqo0bN6pJCWN7CxYs0HnnnVeq16EHMLLNmyfdd59ZT1CS6tc3QXDECCkmxm5tQDgiAAKICATAyOf3Sx98ID3wgLRhg7nXsqX09NNmUWmXy259QDhhCBgAEBZcLunyy83zgc8/LyUmSj/9JPXvb4aJV6%2B2XSEQPgiAAICwEhsrjRol/fKLdM89UtWqZs/hdu2kG2%2BUCk0yB1ACAiAAICzVrCk99ZT088%2BmZ9DvN2sKNm0q/fOfUqFVggAchQAIAAhrp50mvfeetHixdO65Umam6Rls3VqaOdN2dUBoIgACACJC587S8uXSG29ISUlmiLhfPzNBZP1629UBoYUACACIGFFR0rBh0rp10t13mwWkZ80yvYF//7t08KDtCoHQQAAEAESchASzPMz330u9e0uHD0tPPGEWmJ4%2B3TwvCDgZARAAELGaN5c%2B/dRsKde4sbRpkzRwoDRggLRxo%2B3qAHsIgACAiOZySYMGmfUD77vPDAt/8onpDXzqKbPNHOA0BEAAgCPEx0tjx5oFo7t3lw4dMoHwnHOkL7%2B0XR1QuQiAAABHadlSWrBAmjBBqlPHPCfYubM0cqTk89muDqgcBEAAgOO4XNJ115mt5IYNM5NCXnrJDAt//LHt6oCKRwAEADhWnTpm3cB586Q//Un6/XczQWTIEGnnTtvVARWHAAggrKWnpyslJUWpqam2S0EYu%2BAC6bvvzNqB0dFmZ5GUFOmdd1gyBpHJ5ffz0QYQ/nw%2Bnzwej7xer9xut%2B1yEMZWrpSuv94EQkm65BLp5ZelBg3s1gWUJ3oAAQAo5JxzpBUrpMceM0vGTJ8utWolTZ5MbyAiBwEQAICjxMZKDz1kegPPPlvat0%2B65hrpsst4NhCRgQAIAEAJ2rSRli0L9gZOnWr2FZ42zXZlQNkQAAEAOI4qVUxv4FdfmUC4a5c0eLBZPoZ1AxGuCIAAAJTCWWeZZwPvvVeKipImTpTOPFP64gvblQEnjgAIAEApVa0qjRtnQl%2BTJtKmTdJ550n33y8dPmy7OqD0CIAAAJygzp3NnsLXX29mBo8bJ3XqJK1da7syoHQIgAAAnISEBOl//5OmTJFq1w7OGH79dZaLQegjAAIAUAaDB5tFoy%2B4QDp4UBoxwmwlt3%2B/7cqAkhEAAQAoo0aNpLlzzVBwTIz0/vtSu3bS8uW2KwOKRwAEAKAcREWZGcKLF5sJIhs3Sl26SP/8p5Sfb7s6oCgCIAAA5SgtTfr2W%2BmKK6QjR6R77pH695d277ZdGRBEAAQAoJx5PNI770ivvGKWjpk1y0wQWbrUdmWAQQAEAKACuFzSzTeb5wCbNpW2bJG6d5f%2B/W9mCcM%2BAiAAABWobVuzRExgSHj0aOnPf2YbOdhFAAQAoIIlJJgh4RdeMHsLT5kinXuu9MMPtiuDUxEAAQCoBC6XdOut0qJFUnKytH69mTDy7ru2K4MTEQABAKhEaWnSN99IPXuahaOvvFK6804zPAxUFgIggLCWnp6ulJQUpaam2i4FKLU6daRPP5Xuv99cjx8v9eol7dplty44h8vvZy4SgPDn8/nk8Xjk9XrldrttlwOU2tSp0nXXSQcOSI0bSx99ZHYRASoSPYAAAFg0eHBwqZjNm6XOnXkuEBWPAAgAgGUpKdJXX0l9%2BkiHDpnnAh94gC3kUHEIgAAAhICaNaVPPjFbx0nS2LHSoEFSZqbduhCZCIAAAISI6GjpqaekSZPMFnIzZkidOkkbN9quDJGGAAgAQIi5%2Bmrpiy%2BkpCSzWHT79tKSJbarQiQhAAIAEILat5dWrDAzgnftki64QJo82XZViBQEQAAAQtQpp5idQwYNkg4flq65RhozRmIBN5QVARAAgBAWHy99%2BGFwcsijj0pDh0o5OXbrQngjAAIAEOKioszkkNdeMxNFJk82O4fs3Wu7MoQrAiAAAGHihhvMFnJut5kk0rmz9NtvtqtCOCIAAgAQRnr2lBYvNs8H/vyz1LGjtHKl7aoQbgiAAACEmTZtpGXLpLZtpR07pO7dpdmzbVeFcEIABAAgDDVqZIaBL7xQysqS%2BvWTJk60XRXCBQEQAIAw5XZLM2ea5WGOHJGGDTOTRVgmBn%2BEAAgAQBiLjTU9f3ffba7vu08aPVrKz7dbF0IbARAAgDAXFSU9/bQ0fry5fvZZ6dprpdxcu3UhdBEAAQCIEHfcIU2aJMXEmLUCBw6UDh60XRVCEQEQQFhLT09XSkqKUlNTbZcChISrr5amT5eqVZNmzZJ695b277ddFUKNy%2B/nUVEA4c/n88nj8cjr9crtdtsuB7BuyRLp4oslr1c66yyzTEy9erarQqigBxAAgAjUubO0cKEJfatWSd26SVu22K4KoYIACABAhGrbVlq0SGrcWFq7VuraVfrlF9tVIRQQAAEAiGDNmpkQ2LSptGmT6Qn86SfbVcE2AiAAABGucWOza0jr1tL27WbruNWrbVcFmwiAAAA4QFKS9Pnn0tlnS7t2SeefL339te2qYAsBEAAAh0hMlObNkzp0kPbtM/sIL19uuyrYQAAEAMBBataU5swxE0K8XqlnT2npUttVobIRAAEAcJiEBOnTT6XzzpMyM81i0YsX264KlYkACACAA8XHSzNnSj16SAcOSH36mNnCcAYCIAAADlW9uvTxx%2BZZwKwsqW9fegKdggAIAICDVasmzZhRNATyTGDkIwACAOBwgRBYeDh42TLbVaEiEQABAEBBCAxMDOnTh3UCIxkBEEBIufnmm%2BVyufTss8/aLgVwnOrVpU8%2Bkbp0MUvE9O4tffed7apQEQiAAELGRx99pOXLl6thw4a2SwEcKz5emjXLLBa9d695NpC9gyMPARBASPj999916623avLkyapSpYrtcgBHC6wTGNg2rkcPacMG21WhPBEAAViXn5%2BvoUOH6u6771arVq1slwNAwR1DWreWtm83IXDLFttVobwQAAFY99RTTykmJka33XZbqX8mJydHPp%2BvSANQvhITpblzpaZNpU2bzLZxO3fargrlgQAIoFJNnjxZNWrUKGgLFy7Uc889pwkTJsjlcpX6dcaOHSuPx1PQkpOTK7BqwLmSkqT/9/%2Bkxo2ltWvNxJD9%2B21XhbJy%2Bf1%2Bv%2B0iADhHZmamduzYUXD9wQcf6MEHH1RUVPC/R/Py8hQVFaXk5GRt3Lix2NfJyclRTk5OwbXP51NycrK8Xq/cbneF1Q841fr1Znbwzp1S587S7NlmwgjCEwEQgFV79uzR9u3bi9zr3bu3hg4dquHDh6t58%2Baleh2fzyePx0MABCrQ6tVmncD9%2B806gdOnS7GxtqvCyYixXQAAZ0tMTFRiYmKRe1WqVFFSUlKpwx%2BAytG2rTRzpnkW8LPPpGHDpEmTpCgeKAs7/E8GAABKrVMnacoUKSZGeucd6fbbJcYSww9DwAAiAkPAQOV6913pqqtM%2BHvsMemhh2xXhBNBDyAAADhhQ4ZIzz1nzh9%2BWHrtNbv14MQQAAEAwEkZNUp68EFzfsstZlIIwgMBEAAAnLTHH5dGjJDy802v4NKltitCaRAAAQDASXO5pFdekfr1k7Kzpf79pZ9/tl0V/ggBEAAAlElMjJkUkpYm7d0r9e0rZWTYrgrHQwAEAABlFh8vffyxdMYZ0saN0sUXSwcO2K4KJSEAAgCAclG3rlkguk4d6ZtvpCuukI4csV0VikMABAAA5eZPfzI9gdWqSbNmmZnCrDgcegiAAACgXHXoIL39dnCCyDPP2K4IRyMAAgCAcjdwoDR%2BvDm/%2B26zfRxCBwEQAABUiL/9Tbr1VnN%2BzTXSV1/ZrQdBBEAAAFAhXC7p3/%2BWLrrIrBE4YIC0ebPtqiARAAEAQAUKrBF45pnSjh1mwejMTNtVgQAIIKylp6crJSVFqamptksBUIKEBDMzuH596fvvpauukvLybFflbC6/n8nZAMKfz%2BeTx%2BOR1%2BuV2%2B22XQ6AYnz1ldS9uxkOvvNO6V//sl2Rc9EDCAAAKkX79tKECeb8mWekN96wWo6jEQABAEClueIK6eGHzfnNN0tLltitx6kIgAAAoFI98oh02WVSbq40aBAzg20gAAIAgEoVFWWGgs86S9q1S7rkEikry3ZVzkIABAAAlS4%2BXpo%2BXapbV1q1Shoxgj2DKxMBEAAAWNG4sdkiLiZGeu89adw42xU5BwEQAABY07Wr9OKL5vzBB6VZs%2BzW4xQEQAAAYNXNN5vm95tFotevt11R5CMAAgAA655/XurUSfJ6pYED2S6uohEAAQCAdbGx5nnABg2kNWuk4cOZFFKRCIAAACAkJCWZEFilijk%2B/bTtiiIXARAAAISMjh3NcLAkPfCANG%2Be3XoiFQEQAACElJtvNkPA%2BfnSkCHSli22K4o8BEAAABBSXC4pPV1q107avdtsG5eTY7uqyEIABAAAIadaNfMcYK1a0ldfSaNH264oshAAAQBASGrSRJo0yZy/9JL09tt264kkBEAAABCyLrpIeughc37jjWaJGJQdARAAAIS0Rx6RevSQDh6ULr1UOnDAdkXhjwAIIKylp6crJSVFqamptksBUEGio83wb8OG0s8/S7fcwiLRZeXy%2B3kLAYQ/n88nj8cjr9crt9ttuxwAFWDxYum886S8POk//5Fuusl2ReGLHkAAABAWunSRnnjCnN92m7R6td16whkBEAAAhI2775YuvtisC3j55VJmpu2KwhMBEAAAhI2oKGniROmUU6R163ge8GQRAAEAQFhJTJTefTc4OeSNN2xXFH4IgAAAIOx07iz94x/m/NZbWR/wRBEAAQBAWLrnHqlnT%2BnQIemKK8wRpUMABAAAYSkqSnrrLalePemHH9gv%2BEQQAAEAQNiqX9%2BEQEl65RVp6lS79YQLAiAAAAhrvXqZ4WBJuuEGKSPDbj3hIMZ2AQAAAGX1%2BOPSkiXS4MFmSBjHRwAEAABhLzZWWrjQLA2DP8YQMAAAiAiEv9IjAAIAADgMARAAAMBhCIAAAAAOQwAEAABwGAIgAACAwxAAAYS19PR0paSkKDU11XYpABA2XH6/32%2B7CAAoK5/PJ4/HI6/XK7fbbbscAAhp9AACAAA4DAEQAADAYQiAAAAADkMABAAAcBgCIAAAgMMQAAEAAByGAAgAAOAwBEAAAACHIQACAAA4DAEQAADAYQiAAAAADkMABAAAcBgCIAAAgMMQAAGEhJ9%2B%2BkkDBgyQx%2BNRQkKCOnTooM2bN9suCwAiEgEQgHUbNmxQly5d1KJFC33%2B%2BedavXq1HnroIcXFxdkuDQAiksvv9/ttFwHA2YYMGaIqVarorbfeOunX8Pl88ng88nq9crvd5VgdAEQeegABWJWfn6%2BZM2eqWbNm6t27t%2BrVq6e0tDR99NFHtksDgIhFAARg1c6dO3XgwAGNGzdOffr00Zw5czRo0CANHjxYCxcuLPHncnJy5PP5ijQAQOkQAAFUqsmTJ6tGjRoFbe3atZKkSy65RHfccYfOOuss3XffferXr59eeeWVEl9n7Nix8ng8BS05Obmy/gQACHsxtgsA4CwDBgxQWlpawXXdunUVExOjlJSUIt/XsmVLLV68uMTXuf/%2B%2BzV69OiCa5/PRwgEgFIiAAKoVAkJCUpISChyLzU1taAnMGDdunU69dRTS3ydqlWrqmrVqhVSIwBEOgIgAOvuvvtuXXHFFerWrZvOP/98ffbZZ/r444/1%2Beef2y4NACISy8AACAmvv/66xo4dq61bt6p58%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%3D'}