Genus 2 curve data - 2348.b.37568.1

Formats: - HTML - YAML - JSON - 2024-05-20T11:27:48.395443

• 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': '312483698350368126', 'abs_disc': 37568, 'analytic_rank': 1, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[2,[1,1,1]],[587,[1,-35,621,-587]]]', 'bad_primes': [2, 587], 'class': '2348.b', 'cond': 2348, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[0,0,-1,2,-4,1],[1,1,0,1]]', 'g2_inv': "['1/37568','165/9392','953/2348']", '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': False, 'igusa_clebsch_inv': "['4','-15839','-1111055','4808704']", 'igusa_inv': "['1','660','15248','-105088','37568']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '2348.b.37568.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.35551685849805571265083019138230838029630083280081', 'prec': 173}, 'locally_solvable': True, 'modell_images': [], 'mw_rank': 1, 'mw_rank_proved': True, 'non_maximal_primes': [], 'non_solvable_places': [], 'num_rat_pts': 8, 'num_rat_wpts': 0, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '16.450123479718159880055272205', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.00720393495194', 'prec': 54}, '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': 3, 'torsion_order': 1, 'torsion_subgroup': '[]', 'two_selmer_rank': 1, 'two_torsion_field': ['6.2.37568.1', [-1, 2, -1, 0, 2, -2, 1], [6, 16], 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': '2348.b.37568.1', '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': '2348.b.37568.1', 'mw_gens': [[[[1, 1], [-2, 1], [1, 1]], [[-1, 1], [-1, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.0072039349519449480818045250415315', 'prec': 120}], 'mw_invs': [0], 'num_rat_pts': 8, 'rat_pts': [[0, -1, 1], [0, 0, 1], [1, -2, 1], [1, -1, 0], [1, -1, 1], [1, 0, 0], [3, -29, 2], [3, -18, 2]], 'rat_pts_v': True}
• lmfdb_label 2348.b.37568.1 does not appear in g2c_galrep
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 47416
    {'label': '2348.b.37568.1', 'p': 2, 'tamagawa_number': 3}
  • id: 47417
    {'cluster_label': 'c4c2_1~2_0', 'label': '2348.b.37568.1', 'p': 587, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '2348.b.37568.1', 'plot': 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AAIIFBTCIuVwHRgG5DAwAAGqKAhjkrr/eFMHsbOn7722nAQAAwYACGOTatpUuvtgcz5plNwsAAAgOFMAQcOONZv/SSxIL%2BwEAgGOhAIaAK6%2BUGjeWvvtOWr7cdhoA8L/MzEwlJycrNTXVdhQgJLi8XsaMQsH//Z80Y4bZ/%2BtfttMAQN0oLi6W2%2B2Wx%2BNRXFyc7ThA0GIEMESMHGn2r74q7d1rNQoAAAhwFMAQ0bu3dPLJUkmJmRgaAADgaCiAIaJBgwOjgDNmWI0CAAACHAUwhNxwg5kTcOlS6YcfbKcBAACBigIYQtq1OzAn4Esv2c0CAAACFwUwxFRdBp45U6qstJkEAAAEKgpgiLnySikuzlwCzs62nQYAAAQiCmCIadRIGjbMHP/733azAACAwEQBDEH/939mP2%2Be5PHYzQIAAAIPBTAEnXuulJws7dsn/ec/ttMAAIBAQwEMQS6X9Mc/mmOWhQMAAIeiAIaojAwpPFz67DNp/XrbaQCgdjIzM5WcnKzU1FTbUYCQ4PJ6vV7bIVA3rrhCWrBAuuMO6fHHbacBgNorLi6W2%2B2Wx%2BNRXFyc7ThA0GIEMITddJPZv/yyVFZmNwsAAAgcFMAQdtllUqtW0s6d0sKFttMAAIBAQQEMYeHh0o03mmNuBgEAAFUogCGu6jLw%2B%2B9L%2Bfl2swAAgMBAAQxxHTpIF14oeb3SjBm20wAAgEBAAXSAqlHAf/9bqqy0mwUAANhHAXSAq66SmjSRfvxR%2BuAD22kAAIBtFEAHiI6Wrr/eHL/4ot0sAADAPgqgQ1RdBl6wQNqxw24WAABgFwXQIbp0kbp1k8rLpZdesp0GAADYRAF0kFGjzP7FF81dwQAQLFgLGPAv1gJ2kD17zMoge/dK2dnS%2BefbTgQAx4e1gAH/YATQQWJjpeuuM8fPP283CwAAsIcC6DBVl4Fff13atctuFgAAYAcF0GFSU6XOnaXSUmnWLNtpAACADRRAh3G5pFtuMcfPP8/NIAAAOBEF0IFGjJAaNZI2bJA%2B/dR2GgAAUN8ogA7kdkvDhpljbgYBAMB5KIAOVXUZ%2BNVXuRkEAACnoQA61LnnHrgZ5OWXbacBEOieffZZnXXWWYqLi1NcXJzS0tL03nvv%2BZ4vLS3VuHHj1KJFC8XExGjQoEHaunVrtdfYsmWLBg4cqJiYGLVo0ULjx49XWVlZfb8VAKIAOpbLJd16qzmePp2bQQD8vtatW%2Bvhhx/W559/rs8//1wXX3yxBg8erPXr10uSJkyYoPnz5ysrK0vLly9XSUmJ0tPTVVFRIUmqqKjQgAEDtHfvXi1fvlxZWVmaN2%2Be7rzzTptvC3AsVgJxsOJiKSnJrAyybJl0wQW2EwEIJs2aNdOjjz6qq6%2B%2BWi1bttSsWbM0dOhQSdK2bdvUpk0bvfvuu%2BrXr5/ee%2B89paenKz8/X0lJSZKkrKwsjRw5UkVFRTVe1YOVQAD/YATQweLipOHDzfFzz9nNAiB4VFRUKCsrS3v37lVaWppyc3NVXl6uvn37%2Bs5JSkpSSkqKVqxYIUlauXKlUlJSfOVPkvr166fS0lLl5uYe9c8qLS1VcXFxtQ1A7VEAHe5PfzL7efOkoiK7WQAEtnXr1qlx48aKiorSn/70J82fP1/JyckqLCxUZGSkmjZtWu38hIQEFRYWSpIKCwuVkJBQ7fmmTZsqMjLSd86RTJkyRW6327e1adPG/28McCAKoMOdc45ZHaS8XJoxw3YaAIGsY8eOWrt2rVatWqXbbrtNN954ozZs2HDU871er1wul%2B/ng4%2BPds6hJk6cKI/H49vy8/Nr9yYASKIAQgdGAadPlyor7WYBELgiIyPVoUMHdevWTVOmTFHnzp319NNPKzExUWVlZdq9e3e184uKinyjfomJiYeN9O3evVvl5eWHjQweLCoqynfncdUGoPYogNCwYVKTJtL330uLF9tOAyBYeL1elZaWqmvXroqIiNCSJUt8zxUUFCgvL089e/aUJKWlpSkvL08FBQW%2BcxYvXqyoqCh17dq13rMDTkcBhBo1km64wRw/%2B6zdLAAC01//%2Bld98skn%2BuGHH7Ru3Trdd999WrZsmUaMGCG3262bbrpJd955pz788EN98cUXuv7669WpUyddeumlkqS%2BffsqOTlZGRkZ%2BuKLL/Thhx/qrrvu0qhRoxjVAywItx0AgeG226SpU6W335a2bJHatrWdCEAg2b59uzIyMlRQUCC3262zzjpLixYtUp8%2BfSRJTz75pMLDw3Xttddq3759uuSSSzRz5kyFhYVJksLCwvTOO%2B9o9OjR6tWrl6KjozV8%2BHA99thjNt8W4FjMAwifiy%2BWli6V7rtPeugh22kA4HDMAwj4B5eA4XPbbWb/4osSqzMBABC6KIDwGTJEatVK2r5dmj/fdhoAAFBXKIDwiYiQRo0yx888YzcLAACoOxRAVHPLLVJYmPTxx1Jenu00AACgLlAAUc1JJ0mDB5tjRgEBAAhNFEAcZswYs581S2LddQAAQg8FEIe56CLp9NOlkhLp5ZdtpwEAKTMzU8nJyUpNTbUdBQgJzAOII5o2TRo3TjrjDGn9eul31moHgHrDPICAfzACiCO64QapcWNp40YzOTQAAAgdFEAcUVyclJFhjqdNs5sFAAD4FwUQR1V1M8ibb5r1gQEAQGigAOKozjzT3BBSWSk995ztNAAAwF8ogPhdY8ea/QsvSL/9ZjcLAADwDwogftegQVKbNtLOndKrr9pOAwAA/IECiN8VHi7ddps5/uc/7WYBAAD%2BQQHEMd18sxQVJX3%2BubRqle00AACgtiiAOKaWLaVhw8wxo4AAAAQ/CiBqZPx4s3/tNamgwG4WAABQOxRA1Mg550g9e0rl5dL06bbTAACA2qAAosaqRgGfe04qK7ObBYCzZGZmKjk5WampqbajACHB5fV6vbZDIDiUl0t/%2BIO0bZs0e7Y0YoTtRACcpri4WG63Wx6PR3FxcbbjAEGLEUDUWETEgSlhnn7abhYAAHDiKIA4LrfcYqaEycmRVq60nQYAAJwICiCOS3y8dN115njqVLtZAADAiaEA4rhV3Qzy%2BuvSTz/ZzQIAAI4fBRDH7eyzpfPOk/bvl5591nYaAABwvCiAOCF//rPZP/ectG%2Bf3SwAAOD4UABxQgYPltq2lX7%2BWZo713YaAABwPCiAOCHh4dK4ceb46aclZpMEACB4UABxwm66SWrUSFq3Tlq61HYaAABQUxRAnLCmTaWRI83xU09ZjQIAAI4DBRC1UjUlzNtvS99%2BazcLgNDFWsCAf7EWMGrt8sul994z3wlkcmgAdYm1gAH/YAQQtXb77WY/Y4bk8djNAgAAjo0CiFq79FLpzDOlkhLpX/%2BynQYAABwLBRC15nIdmBh66lSzQggAAAhcFED4xfXXS82bSz/%2BKC1YYDsNAAD4PRRA%2BEV0tHTbbeaYKWEAAAhsFED4zejRUkSE9Omn0mef2U4DAACOhgIIv2nVSho2zBwzCggAQOCiAMKvqqaEee01KT/fbhYAAHBkFED41dlnSxdcYO4Ezsy0nQYAABwJBRB%2BVzUKOH26mRsQAAAEFgog/C49XerQQfrlF%2Bmll2ynAQAAh6IAwu/Cwg5MDP3001Jlpd08AIJfZmamkpOTlZqaajsKEBJcXq/XazsEQk9JidSmjRkFfPNNadAg24kAhILi4mK53W55PB7FxcXZjgMELUYAUScaN5ZuucUcP/mk3SwAAKA6CiDqzNix5nLwsmXSF1/YTgMAAKpQAFFn2rSRrrnGHDMKCABA4KAAok7dcYfZZ2VJ27bZzQIAAAwKIOpUaqrUq5dUXs7E0AAABAoKIOpc1Sjg9OnSr7/azQIAACiAqAeDB0vt20s//yzNmmU7DYATMWXKFKWmpio2Nlbx8fEaMmSIvvnmm2rnlJaWaty4cWrRooViYmI0aNAgbd26tdo5W7Zs0cCBAxUTE6MWLVpo/PjxKisrq8%2B3AkAUQNSDsDBp/Hhz/NRTTAwNBKPs7GyNGTNGq1at0pIlS7R//3717dtXe/fu9Z0zYcIEzZ8/X1lZWVq%2BfLlKSkqUnp6uiooKSVJFRYUGDBigvXv3avny5crKytK8efN055132npbgGMxETTqRXGx1Lq1tGeP9O67Uv/%2BthMBqI0dO3YoPj5e2dnZOv/88%2BXxeNSyZUvNmjVLQ4cOlSRt27ZNbdq00bvvvqt%2B/frpvffeU3p6uvLz85WUlCRJysrK0siRI1VUVFSjiZ2ZCBrwD0YAUS/i4qSbbzbHTAkDBD%2BPxyNJatasmSQpNzdX5eXl6tu3r%2B%2BcpKQkpaSkaMWKFZKklStXKiUlxVf%2BJKlfv34qLS1Vbm5uPaYHQAFEvRk/XmrQQFqyRMrLs50GwInyer2644471Lt3b6WkpEiSCgsLFRkZqaZNm1Y7NyEhQYWFhb5zEhISqj3ftGlTRUZG%2Bs45VGlpqYqLi6ttAGqPAoh684c/SFdcYY6fespqFAC1MHbsWH311Vd65ZVXjnmu1%2BuVy%2BXy/Xzw8dHOOdiUKVPkdrt9W5s2bU48OAAfCiDq1YQJZj97trRjh90sAI7fuHHjtHDhQi1dulStW7f2PZ6YmKiysjLt3r272vlFRUW%2BUb/ExMTDRvp2796t8vLyw0YGq0ycOFEej8e35efn%2B/kdAc5EAUS96tVL6tZNKi018wICCA5er1djx47VG2%2B8oY8%2B%2Bkjt27ev9nzXrl0VERGhJUuW%2BB4rKChQXl6eevbsKUlKS0tTXl6eCgoKfOcsXrxYUVFR6tq16xH/3KioKMXFxVXbANQedwGj3s2eLWVkSK1aST/8IEVG2k4E4FhGjx6tuXPn6s0331THjh19j7vdbkVHR0uSbrvtNr399tuaOXOmmjVrprvuuks///yzcnNzFRYWpoqKCnXp0kUJCQl69NFHtWvXLo0cOVJDhgzRP//5zxrl4C5gwD8ogKh3ZWXm%2B4AFBaYMjhhhOxGAYznad/RmzJihkSNHSpJ%2B%2B%2B033X333Zo7d6727dunSy65RM8880y17%2B1t2bJFo0eP1kcffaTo6GgNHz5cjz32mKKiomqUgwII%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%2B2TJpyxarUQAACEkUQASctm2lCy4wx3Pn2s0CAEAoogAiII0YYfavvGI3B4DAwFrAgH%2BxFjAC0q5dUkKCtH%2B/tHGjdPrpthMBCASsBQz4ByOACEjNmkl9%2Bphj7gYGAMC/KIAIWFddZfbz59vNAQBAqKEAImANGmSmhVmzRsrPt50GAIDQQQFEwGrZUurRwxy/957dLAAAhBIKIAJa//5m//77dnMAABBKKIAIaFU3gixdKlVW2s0CAECooAAioHXtKsXESLt3S3l5ttMAABAaKIAIaBERUvfu5njVKrtZAAAIFRRABLxzzzX73Fy7OQAACBUUQAS8s84yey4BAwDgHxRABLyOHc1%2B82a7OQAACBUUQAS8du3MfscOqbTUbhYAdmRmZio5OVmpqam2owAhweX1er22Q4S6Tz6RXnxRuvxyqW9fqWlT24mCi9crhYebaWB%2B%2BklKSrKdCIAtxcXFcrvd8ng8iouLsx0HCFqMANaDN96QXn5ZGjZMatFC6t1beugh6fPPmduuJlwuKTraHP/2m90sAACEAkYA68Hq1dLrr0vvvitt2FD9uZYtzWTH/fqZfatWdjIGuoYNzeXfH344cEkYgPMwAgj4BwWwnv34o7Rokdk%2B/FDas6f68506mSLYp4903nlmEmSn27swlkYyAAAWDUlEQVRXatzYHHs8Ev/OB5yLAgj4BwXQovJyacUKs87t4sXSmjXm%2B25VIiKknj2lSy4xW2qqecxpvvhCOucc893JXbtspwFgEwUQ8A8KYADZuVP64AOzLVkibdlS/fmYGDMqeNFF0oUXmlIUHm4lar16%2BmlpwgTp0kvN5wLAuSiAgH9QAAOU1yt9%2B625TPzhh9LSpdLPP1c/p3Fjc0PJ%2BeebrVs3KSrKTt664vWapeBycqQnnpBuv912IgA2UQAB/6AABonKSmndOlMEly6VPv5Y%2BuWX6uc0bGiWTevVy2xpaVKzZnby%2Bstbb0mDBpliu2WLFB9vOxEAmyiAgH9QAINURYUphNnZpgx%2B8omZKPlQp58u9ehhtu7dpZSU4LlsnJ9vCm1hofSXv0iPPGI7EQDbKICAf1AAQ4TXK23aJC1fbrYVK8zPh4qONt8d7NZN6trVbB07SmFh9Z/592zYIA0cKH33nSmtq1dLjRrZTgXANgog4B8UwBC2c6e0atWBLSdHKi4%2B/LzoaDP9TOfO0llnmeOUFKl58/rPvGePuelj8mRp3z7p5JPNJe%2B2bes/C4DAQwEE/IMC6CCVlWZUMCfHrEKSmyutXWvm2TuS%2BHjpjDPMCGHHjlKHDtIpp0h/%2BIN/5yf87TczajlvnvTKK2auP8nMhTh7Nt/7A3AABRDwDwqgw1VWSps3myL41Vdmy8szK278nhYtzKjcSSeZ1UsSEsxjzZqZiZobNzY3pUREmKXc9u83I3p79piRyZ9%2BMpd38/LMn1lWduC1TztNeuABs3Sey1WX7x5AsMjMzFRmZqYqKiq0adMmCiBQSxRAHFFJifT119LGjdI335iS%2BO230n//e2CEzp9atZL69zel75JLpAasUg3gCBgBBPyDAojj9ssvZkm7/HwzkldQIBUVmZG93bvN9wxLSsyl3f37zQ0q4eFmRDA21owSJiVJ7dubu5TPOcccM9oH4FgogIB/BMmEIAgkTZqYrXNn20kAAMCJ4EIbAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAIAauTjjz/WwIEDlZSUJJfLpQULFlR73uv16oEHHlBSUpKio6N14YUXav369dXO2b17tzIyMuR2u%2BV2u5WRkaFffvmlPt8GAFEAAQA1tHfvXnXu3FnTpk074vP/%2BMc/9MQTT2jatGnKyclRYmKi%2BvTpoz179vjOGT58uNauXatFixZp0aJFWrt2rTIyMurrLQD4H%2BYBBAAcN5fLpfnz52vIkCGSzOhfUlKSJkyYoHvuuUeSVFpaqoSEBD3yyCO69dZbtXHjRiUnJ2vVqlXq3r27JGnVqlVKS0vT119/rY4dOx7zz2UeQMA/GAEEANTa999/r8LCQvXt29f3WFRUlC644AKtWLFCkrRy5Uq53W5f%2BZOkHj16yO12%2B845VGlpqYqLi6ttAGqPAggAqLXCwkJJUkJCQrXHExISfM8VFhYqPj7%2BsN%2BNj4/3nXOoKVOm%2BL4v6Ha71aZNGz8nB5yJAggA8BvXIWs6er3eao8d%2BvyRzjnYxIkT5fF4fFt%2Bfr5/AwMOxVJwAIBaS0xMlGRG%2BVq1auV7vKioyDcqmJiYqO3btx/2uzt27Dhs5LBKVFSUoqKi6iAx4GyMAAIAaq19%2B/ZKTEzUkiVLfI%2BVlZUpOztbPXv2lCSlpaXJ4/Hos88%2B852zevVqeTwe3zkA6gcjgACAGikpKdG3337r%2B/n777/X2rVr1axZM7Vt21YTJkzQ5MmTdeqpp%2BrUU0/V5MmT1ahRIw0fPlySdMYZZ%2Biyyy7TqFGjNH36dEnSLbfcovT09BrdAQzAf5gGBgBQI8uWLdNFF1102OM33nijZs6cKa/XqwcffFDTp0/X7t271b17d2VmZiolJcV37q5duzR%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%2B/Nvu9e%2B3mAIIcBRAAEPjeeks65RSpe3fz88knS3feKZWX2811sIIC6ZFHpHHjpMcfl3bssJ0IOComggYABLYFC6Qrr5S8XhVLckvySIqTpP79pXfftRpPkjRtmjRhglRRceCx8HDphRekkSNP7DVXr5aefVb65hspPl668UZpyBCpAWM3qD0KIAAgsMXH%2B0bTDiuAkvTZZ5LNJeI%2B/FC69NIjP%2BdySStXHhi5rKmHH5YmTjz88YEDpTfeMOUSqAUKYC14vV7t2bPHdgwACGrbtkk33SStWHH4c6drvVarp%2B/nYkltJOXrQAF8U%2Bm6QXN857hcUkTEgS083OwjIw88FhZmBtIaNDDHB2/h4Qe2qvMjI80WFWX2DRse2EY800vNC/KO%2Bv4qzu2hBovfl8tVww8kN1e6%2BOKjP//gg2a0MYRUVEhlZdJvvx3Yl5aa/cHbvn3Vj3/91Rz/%2BuuBn6%2B8UurTp2Z/bmxsrFw1/h8mtFAAa6FqTUoAABB8nLymNAWwFmoyApiamqqcnBy//HnFxcVq06aN8vPz/fYX1p/5Av316uLzkwL7Pfv79fg7WHt8hocrK5MmT5aysqT9%2B81WUWH2J5du0KcVab5zjzQC%2BIYG64962W95j9fPaqpwVR71%2Bd8UqQTV/IaQb3WyWurnoz7vldREnuOJGIT2ye2O9o2yRkVJ0dHm%2BOB9dLTUqJH5uVEj8/P550vdulV/taP9HXTyCCBfIqgFl8t1zH%2BBh4WF%2Bf3/XcTFxfntNf2dL9BfT/Lv5ycF/nsO9M8w0N9vXXx%2BEp/hoZ54wmyH6yG1SpQKC6s9GqcDBXDkFw9oZJcjv35l5YEyeaStvFzq06e/3nrrPZWXV3/80POO9lzEPScp9pf8o763ve72uurSOO3cKe3cab7OWFR09M8iTi793qfl9X0C1TVrJp1xhtSixYHHqrrNwfv9%2B8u1cOF8XXHFFYqMjPBdCj94q7ocfvBxePjh%2B6rt8ccf1qRJ91a7bH7wZfeqS%2BgHb1FRR94iIqQzz%2ByhDRs2/M6ncHzq6p/jYEYBrGNjxoyxHeF3%2BTtfoL9eXQj09xzon2Ggv99A//ykwH/PtX69f/9bGjBAOtIFqyFDpC5djvqrVYUmIuLoL3/HHelKTq5FvvJ7pLFjj/p0q8xJen1EzV6qokLaf%2BFZ0vKPjnrOL2pyxMd37ZI%2B/VRq2VI680ypUyfprLOkzp2llBQzOiZJxcX75HYP1cyZHsXF/c4HcxxiYmLlz782Afd3MARxCTiIVH3n0MnfWagNPr/a4zOsPT7DE7RkiXT77Spev15uSbsbN1aT22%2BX7r/fDEfZ5PVKV19t7s491MiR0owZx/d6n3xirmMeRcXf/p9%2B/OOD%2Bu9/pc2bpU2bzEwxX38t/fDDkX8nLMyMDnbrJqWk7NNdd52vHTs%2BVIsW/B10KkYAg0hUVJTuv/9%2BRUVF2Y4SlPj8ao/PsPb4DE9Qnz5SXp5Kc3Olbt1UvnGj1Lq17VSGyyW9/rq0cKH0zDPSjz9KHTpIY8aYeQqP13nnSX/5i/SPfxz%2B3AUXKOxvf9XJUWYu7EPvdt271xTBvDxp3Trpyy/NtmOHeSwvT5KiJeWodWuvunaV0tKk3r2lXr3M6CGcgRFAAEDQcNQI6vvvm4mgv/5aSkgwE0Fff735At1x8Hqln36S1qyRPv9cyskxUyfu2nX4ucnJ0oUXShddZPYHf58QoYUCCAAIGo4qgHXI65W%2B/dbMUf3pp9Ly5dKR7rk4%2B2ypb1%2BpXz8zQnic3RMBjAIIAAgaFMC6s3On%2Bfrh0qVmyztkbuvGjc0l5/R0c09OQoKdnPAPCiAAIGhQAOtPYaH0wQfS4sXmavTBU9e4XFKPHtIVV5itQwd7OXFiKIAAgKBBAbSjstJ8h/Cdd6S33jKr1R2sSxfpmmukoUOlU06xkxHHp4HtAKi5kpISjR07Vq1bt1Z0dLTOOOMMPfvss7ZjBZWNGzdq0KBBcrvdio2NVY8ePbRlyxbbsYLSrbfeKpfLpaeeesp2lKBQXl6ue%2B65R506dVJMTIySkpJ0ww03aNu2bbajwQGmTJmi1NRUxcbGKj4%2BXkOGDNE333xT499v0MBMIXP//eZGkq1bpcxM6ZJLzBQza9dK991nRgK7d5eeflravr0O3xBqjQIYRG6//XYtWrRIs2fP1saNG3X77bdr3LhxevPNN21HCwr//e9/1bt3b51%2B%2BulatmyZvvzyS02aNEkNGza0HS3oLFiwQKtXr1ZSUpLtKEHj119/1Zo1azRp0iStWbNGb7zxhjZt2qRBgwbZjgYHyM7O1pgxY7Rq1SotWbJE%2B/fvV9%2B%2BfbV3794Ter2TTpJGjzaXiAsLpRdekC691BTFzz6TJkww56SnmxlySkv9/IZQa1wCDiIpKSkaOnSoJk2a5Husa9euuvzyy/X3v//dYrLgMGzYMEVERGjWrFm2owS1n376Sd27d9f777%2BvAQMGaMKECZowYYLtWEEpJydH5557rn788Ue1bdvWdpyAlpmZqczMTFVUVGjTpk1cAq6lHTt2KD4%2BXtnZ2Tr/dyadPl7bt0uvvirNnm2KYJXmzaWMDOnmm80qJbCPEcAg0rt3by1cuFA//fSTvF6vli5dqk2bNqlfv362owW8yspKvfPOOzrttNPUr18/xcfHq3v37lqwYIHtaEGlsrJSGRkZuvvuu3Um/xavNY/HI5fLpSZNjry0Fw4YM2aMNmzYoJycHNtRQoLH45EkNWvWzK%2Bvm5AgjRsnrV5tpi%2BcONGMBP78s/TUU2ZJuvPOk%2BbOZVTQNgpgEJk6daqSk5PVunVrRUZG6rLLLtMzzzyj3r17244W8IqKilRSUqKHH35Yl112mRYvXqwrrrhCV155pbKzs23HCxqPPPKIwsPDNX78eNtRgt5vv/2me%2B%2B9V8OHD2ckC/XK6/XqjjvuUO/evZWSklJnf07HjtLkyWZhlHfeMcs2h4WZOQdHjJDatpUmTZL4GqwdFMAANWfOHDVu3Ni3ffLJJ5o6dapWrVqlhQsXKjc3V48//rhGjx6tDz74wHbcgHPo51f1ZefBgwfr9ttvV5cuXXTvvfcqPT1dzz33nOW0genQzzA7O1tPP/20Zs6cKZfLZTtewDvSP8NVysvLNWzYMFVWVuqZZ56xmBJONHbsWH311Vd65ZVX6uXPCwuTLr9cmj9f2rJFevBBMypYVCQ99JDUrp1Z4GTNmnqJg//hO4ABas%2BePdp%2B0C1UJ510ktxut%2BbPn68BAwb4Hr/55pu1detWLVq0yEbMgHXo59eyZUu1aNFC999/v/72t7/5Hr/nnnu0fPlyffrppzZiBrRDP8PXXntN9913nxo0OPD/GysqKtSgQQO1adNGPxxtFXqHOtI/w9HR0SovL9e1116r7777Th999JGaN29uMWXwYRqY2hk3bpwWLFigjz/%2BWO3bt7eWo7xcWrBAmjrVjAhWuegiswxyv35mrkHUnXDbAXBksbGxio2N9f1cXFys8vLyav/xlaSwsDBVVlbWd7yAd%2BjnJ0mpqamHTXuwadMmtWvXrj6jBY1DP8NbbrlFAwcOrHZOv379lJGRoT/%2B8Y/1HS/gHenvYFX527x5s5YuXUr5Q73xer0aN26c5s%2Bfr2XLllktf5IUEWHmDbzmGjOn4BNPmJtHqlYh6dLFfH/wqqvMCCL8jwIYJOLi4nTBBRfo7rvvVnR0tNq1a6fs7Gy9/PLLeuKJJ2zHCwp33323hg4dqvPPP18XXXSRFi1apLfeekvLli2zHS0oNG/e/LDCEhERocTERHXs2NFSquCxf/9%2BXX311VqzZo3efvttVVRUqLCwUJL5In4ki6yiDo0ZM0Zz587Vm2%2B%2BqdjYWN/fPbfbrejoaKvZunaV5syRpkwxN4o8/7yZV3DoUOn00838gtddRxH0Oy%2BCRkFBgXfkyJHepKQkb8OGDb0dO3b0Pv74497Kykrb0YLGv/71L2%2BHDh28DRs29Hbu3Nm7YMEC25GCWrt27bxPPvmk7RhB4fvvv/dKOuK2dOlS2/GChsfj8Uryejwe21GCytH%2B7s2YMcN2tMPs3On13n%2B/19ukidcrme2007zeuXO93v37bacLHXwHEAAQNPgOoHMUF0vTpkmPPy7t2mUeS0kxN44MGsR3BGuLu4ABAEDAiYuT/vpX6YcfpL//XWrSRMrLM9PJ9OolHXRjPU4ABRAAAASs2Fjpb3%2BTvvvO3BjSqJG0cqV0/vmmDB7HksY4CAUQAAAEvKZNzcTS334r3XqruSnkzTfNZeE///nAZWLUDAUQAAAEjVatpOeek9atk9LTpf37zXyCp54qPfusVFFhO2FwoAACAAJeZmamkpOTlZqaajsKAsQZZ0hvvSUtWWJGAXftkkaPlvr3t50sOHAXMAAgaHAXMI5k/34zKjhpkvTYY9JNN9lOFPgogACAoEEBxO/5%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%3D'}