Genus 2 curve data - 8281.a.8281.1

Formats: - HTML - YAML - JSON - 2026-06-21T03:42:35.963427

• 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': '464560706679296141', 'abs_disc': 8281, 'analytic_rank': 2, 'analytic_rank_proved': True, 'analytic_sha': 1, 'aut_grp_id': '[4,2]', 'aut_grp_label': '4.2', 'aut_grp_tex': 'C_2^2', 'bad_lfactors': '[[7,[1,0,-1]],[13,[1,0,-1]]]', 'bad_primes': [7, 13], 'class': '8281.a', 'cond': 8281, 'disc_sign': 1, 'end_alg': 'Q x Q', 'eqn': '[[0,0,-1,-3,-1],[1,1,1,1]]', 'g2_inv': "['60466176/8281','-7091712/8281','10368/169']", 'geom_aut_grp_id': '[4,2]', 'geom_aut_grp_label': '4.2', 'geom_aut_grp_tex': 'C_2^2', 'geom_end_alg': 'Q x Q', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['72','1236','15984','33124']", 'igusa_inv': "['36','-152','392','-2248','8281']", 'is_gl2_type': True, 'is_simple_base': False, 'is_simple_geom': False, 'label': '8281.a.8281.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.394457174677436682449358573573010166371211208091', 'prec': 167}, 'locally_solvable': True, 'modell_images': ['2.30.2', '3.2160.21'], 'mw_rank': 2, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 10, 'num_rat_wpts': 0, 'real_geom_end_alg': 'R x R', 'real_period': {'__RealLiteral__': 0, 'data': '23.537474444794969055220841094', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.150828185939', 'prec': 47}, 'root_number': 1, 'st_group': 'SU(2)xSU(2)', 'st_label': '1.4.B.1.1a', 'st_label_components': [1, 4, 1, 1, 1, 0], 'tamagawa_product': 1, 'torsion_order': 3, 'torsion_subgroup': '[3]', 'two_selmer_rank': 2, 'two_torsion_field': ['4.2.1456.1', [1, -2, -2, 0, 1], [4, 5], 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], ['1.1.1.1', [0, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], 'factorsRR_base': ['RR', 'RR'], 'factorsRR_geom': ['RR', 'RR'], 'fod_coeffs': [0, 1], 'fod_label': '1.1.1.1', 'is_simple_base': False, 'is_simple_geom': False, 'label': '8281.a.8281.1', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], ['RR', 'RR'], [2, -1], 'G_{3,3}']], 'ring_base': [2, -1], 'ring_geom': [2, -1], 'spl_facs_coeffs': [[[352], [-6616]], [[-48], [-216]]], 'spl_facs_condnorms': [91, 91], 'spl_facs_labels': ['91.b2', '91.a1'], 'spl_fod_coeffs': [0, 1], 'spl_fod_gen': [0], 'spl_fod_label': '1.1.1.1', 'st_group_base': 'G_{3,3}', 'st_group_geom': 'G_{3,3}'}
• 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': '8281.a.8281.1', 'mw_gens': [[[[1, 1], [1, 1], [0, 1]], [[-1, 1], [0, 1], [0, 1], [0, 1]]], [[[1, 1], [0, 1], [0, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]], [[[0, 1], [1, 1], [0, 1]], [[-1, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.60081861853895960233897239336771', 'prec': 113}, {'__RealLiteral__': 0, 'data': '0.28478430133753731836958218994740', 'prec': 113}], 'mw_invs': [0, 0, 3], 'num_rat_pts': 10, 'rat_pts': [[-1, -1, 1], [-1, 1, 1], [0, -1, 1], [0, 0, 1], [1, -11, 2], [1, -4, 2], [1, -1, 0], [1, 0, 0], [2, -11, 1], [2, -4, 1]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 3188
    {'conductor': 8281, 'lmfdb_label': '8281.a.8281.1', 'modell_image': '2.30.2', 'prime': 2}
  • id: 3189
    {'conductor': 8281, 'lmfdb_label': '8281.a.8281.1', 'modell_image': '3.2160.21', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  local_root_number	numeric
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 151917
    {'cluster_label': 'c2c2_1~2c2_1~2_0', 'label': '8281.a.8281.1', 'local_root_number': -1, 'p': 7, 'tamagawa_number': 1}
  • id: 151918
    {'cluster_label': 'c2c2_1~2c2_1~2_0', 'label': '8281.a.8281.1', 'local_root_number': -1, 'p': 13, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '8281.a.8281.1', 'plot': 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cOFEul6tci4uLs7osAKgxBED43FVXSatXSy1bmusCu3WTUlLM2oGAVVq0aKFdu3aVtHXr1lldEgDUGAIgLHHOOdLXX0tDh5rg9%2BijJhju22d1ZXCqkJAQxcXFlbQGDRpYXRIA1BgCICwTESG99Zb0979LtWpJixZJrVoxJAxrbN68WQkJCWratKkGDx6sX375pdLX5uXlKSsrq1wDADshAMJSLpd0222mN/Dcc82kkG7dpPHjpfx8q6uDU7Rv314zZ87Uf/7zH/39739XRkaGOnXqpP3791f4%2BpSUFLnd7pKWmJjo44oBoHpYCBp%2B4/Bhs3/w9Onmfvv20uzZ0tlnW1sXnOfw4cM6%2B%2Byz9fDDD2vUqFEnPJ%2BXl6e8vLyS%2B1lZWUpMTGQhaAC2QQ8g/EadOmaR6LffNrOEv/7aDAm/8YbEnynwpTp16uj888/X5s2bK3w%2BPDxc0dHR5RoA2AkBEH5n0CDpu%2B%2Bkyy4zvYLDh0t9%2B0q7dlldGZwiLy9PGzZsUHx8vNWlAECNIADCLzVuLH32mfT881JYmPThh1KLFtI//0lvILxv9OjRWrp0qbZs2aKvv/5a1157rbKysjRs2DCrSwOAGkEAhN8KDpZGj5a%2B%2BcYsGH3woHT99VL//uwgAu/asWOH/vSnP6l58%2Ba65pprFBYWppUrV6pJkyZWlwYANYJJILCF/Hxp0iTpz382x2639MILZl9hl8vq6uB0WVlZcrvdTAIBYBv0AMIWQkPN0jDffCO1bStlZpprA7t2lTZutLo6AADshQAIWzn/fGnFCunFF81C0kuXShdcIE2YIOXmWl0dAAD2QACE7YSESKNGST/8IPXqJR07Jj35pNlbeOFCq6sDAMD/EQBhW2edJX30kfTee1JCgvTzz9KVV0pXXy39zi5egNekpqYqKSlJycnJVpcCAKeFSSAICNnZ0hNPSH/5i1RQYJaOefBBaexYKSrK6uoQ6JgEAsBu6AFEQIiKMrOCv/tO6t7dDAunpEjNmklvvikVFlpdIQAA/oMAiIBy3nnSJ59I779v9hDOyJBuu82sI/if/1hdHQAA/oEAiIDjcpnrANevN7OF69Y1PYM9e0qXX26WkgEAwMkIgAhYYWFmtvBPP0kPPGDWEvz0U7OO4HXXST/%2BaHWFAABYgwCIgFe/vvTSS9KmTdINN5gewn/9y%2BwtPGyYmT0MAICTEADhGGedJc2aJX37rRkiLiqSZs6UmjeXbrmFIAgAcA4CIBzn/PPNJJFVq8xC0oWF0vTpJgjeeCNDwwCAwEcAhGMlJ5udQ1auLA2Cs2ZJSUnStddKa9ZYXSH8HQtBA7ArFoIGfrNmjfTUU9L8%2BaWPdesmPfSQ1KOHuXYQqAgLQQOwG3oAgd%2B0bWuGhr//Xho61Ow5/PnnpnfwggukadOk3FyrqwQAoProAQQqsX279PLL0t//LuXkmMcaNJDuvNO0hARr64P/oAcQgN0QAIGTOHTIhMC//lVKTzePhYRIAwZII0ZIF1/M8LDTEQAB2A0BEDhFBQXSvHnSK69IX35Z%2BnjLltJdd0nXXy%2B53dbVB%2BsQAAHYDQEQqIL//U%2BaOlWaPVs6etQ8FhEhDRpk9h7u2JFeQSchAAKwGwIgUA2HDpnFpF9/3ew97HHeedLNN5vJJHFx1tUH3yAAArAbAiDgBcXF0ldfmWsF3323tFcwONgsIXPjjVLfvlLt2tbWiZpBAARgNwRAwMuysqR33jG7i6xYUfp4dLSZOHL99VKXLiYcwt5SU1OVmpqqwsJCbdq0iQAIwDYIgEAN2rjR7C4ya5ZZVsYjLk4aONBcM9ihgxTEipy2Rg8gALshAAI%2BUFRkZg7Pni3961/SgQOlz515pnTddWb7OcKgPREAAdgNARDwsWPHpE8%2BMcPE8%2BdL2dmlz8XHS/37m3bZZVJoqHV14tQRAAHYDQEQsFBurvSf/0jvvSd98IG5ftCjbl2pd2/p6qvNRBLWGPRfBEAAdkMABPxEXp702Wdmsen586W9e0ufCwmRLr1UuuoqEwqbNfuddQa3bJH%2B9jdpwwazd92NN0qXXOKT9%2BBUBEAAdkMABPxQYaGZQbxggWkbN5Z//v/%2BT%2BrZ07SuXaXIyN%2BemDlTuvVWs21JWTffLL35JqtT1xACIAC7IQACNvDTT2aIeOFCadkycx2hR2io1KmTNOjCjbpzSgu5CgsrPsnUqWbPOngdARCA3RAAAZvJyZE%2B/1z6%2BGPTtmwxj7%2BkB/SAXq78A5OSpB9%2B8E2RDkMABGA3BEDAxoqLpZ9/NrOKO0/srlZ7P/vd13/7TYHOvzCYpWa8jAAIwG5CrC4AQNW5XNIf/mCaltST3qv8tYfk1oUXBatuXalzZ%2Bnii81t27ZsUVdVZXcCAQA7oQcQCBQLFpg1YyoxP3GErj8wRYcPl388NFRq3Vrq2NG09u2lJk2YL3I66AEEYDcEQCBQFBWZAPjhhyc%2Bl5gorVypgoYJSkuTli%2BX/vtf0zIyTnx5bKyUnCy1a2d6CNu2NSvKoGIEQAB2QwAEAkl%2BvvTss9Jrr0m//ipFREhDhkgTJpg9545TXCxt3WqWnFmxQlq5UkpLO3EVGclkyIsuktq0MT2GrVtLCQn0FEoEQAD2QwAEAlFxsdljLiLCrCJ9Go4elf73P2n1amnVKmnNGmnTpopfW7%2B%2BdMEFUqtW0vnnSy1bmsnGJesSOkTWkiVyd%2B2qzK%2B%2BUnTHjlaXAwAnRQAEcFJZWdLataUtLU368UezYHVFzjrLBMHzzpPOPVdq3ty0Bg0CrMfw66%2Bl229X1nffyS0pU1J0hw7StGnmzQOAnyIA%2BkBOjrl1Wq8IAtvRo9L69dK330rffSetW2eWGdy9u/KPiY4unbV89tlS06YmLDZpYoaYbTUbedMmc3FkdraypNIAKElxcSYlx8ZaWiIAVIYA6ANTp0ojRkj16plfdp5feMff1q1rbZ2AN%2BzbZ4Lh%2BvVmO%2BIffzRb2W3fbkamf09MjNSokbm2MCHB5KeGDU3PYUyM%2BR464wzJ7TZhMizMN%2B%2BpQnfcYfZclk4MgJI0caK59hIA/BABsBqKi4uVnZ190tc99ZT0/PMnP190tOkFady4tJW9X69egA2fwVFyc82uJb/8Ytq2beb%2B9u3Sjh3SkSOnf86QENOzXru2aeHhJhSGhJgWHCwFBZnvG5fLBFBPKyoyQ9iFheWPCwpMK3vsue95XVFRsdYeOVfxMlOosyQlSkpXaQBMc12o3hFLS2oJDS2tLzxcqlXLXKJZu7Z5D5GR5mdAdLQJuGecYb7n69c3AbhBA/NxALwnKipKLof%2BYiUAVoNn5h8AALAfJ8/cJwBWQ9kewOTkZK1evbrK5zp8WEpPN70hd989SUOGPKL0dNNLkp4u7dlz8nOEhpqVPhITTVu06HU99dQdJY81alS1HoTqvjePrKwsJSYmKj09vdrfcN6qyZvn8rfz%2BOPn21vnKSiQ2rXrpgULPtfhw6b3MDdXysszK%2BEcO1a%2Bd8/T6yeZnkBPr2BwsDRmzGhNnvyCgoPNfU%2BPnacH8fjboCCpoCBPsY8OV91P50uquAcw88Z7tP%2BBp0t6EI8dM7Xl5ZXWeviwuZby8GEz0SY729zOnfup2rTprgMHzJD6vn0VL81zvIgI6ZxzpGbNzOSbN98cpU8%2BeUlnnnnc6MEjj0ivvlr%2Bg2vVkt58U7rqqgrP7U9fA9782vZWTf54Hm%2Bdyx9/lnjrXE7uAWQruGpwuVwl3wzBwcHV%2BsaIjpbi483Cu48//r5efvmZcs/n5ppwuG2bWbdt27by7ddfzS%2BXLVtMMx7S3XeXrddcm96kSflh5rKtomHm6r63E99rdLXP582avHUufzuPhz99vr353sLCjigpqfrneuaZLzVwYBXOM2m81HGh%2Bcb7TfRvTZGRip44SolNq1bf6tWPaenSa0ruFxdLBw%2BaRbt//dUMme/YUfrzwDOkfuSImZTz7beej3xDLVuaYeSLLjI/X67LnqYLjg9/kvkhc8st5sLNpk1PeNofvwa88bUt%2Bd9788efb5J//Szx9rmciADoJSNGjKjRc9WqZf6qb9as4o8pKCj9hbBtmwmLixb9oDp1WpTcz82Vdu0ybeXKis8TEVF63aHntk2bKVq82Nw/80z/mM1c05/vQDiPN/nje7O8posukt57T7rtNtNF53HmmdLs2RWGqKrW5HKZP87q1TPL61Tk2DETBDduNBNwvv9eWrJkn/bsidH%2B/dInn5h2rf5S%2BT%2Bclye9/ro0adJJa6oqvr59dx5vn8sbAvm92Q1DwA5RXGx%2BR5UNiMe3UxlmlsxsZU8YLNsaNSq9dbtP7ElktwTf4vPtI3l5ypg%2BXfF33aVd06YpbujQ0158uybl5powuHq1tGpFoabP%2Bv3ajnW5XGFffOKj6qqGr23f4vMdmPznpxRqlMtVOpOwbduKX5Oba6439FyLWPZ2xw5zm5UlHTpk2rp1lf97ERGly3l4bhs2jNCAAe9q9epaatLEDHnXqVMz79fR/vEPaepURW3YoH316inipZekUaPMdQZ%2B4tgxc71bTo7KXcPnaXl55jVlZ%2BB6eK7dKzuzNjzczKaNiDCt7IzaGs1iubnS1Kmq9dtyMLVeftkUftttpkg/UKtW6X7Od90VLP074nenXH%2B4JErTrpJuvlnq29d8jv1NeHi4JkyYoHCmRfsEn%2B/ARA8gTktWVmkg9ITCHTvMdUmea5MOHjz180VGmiAYF1faYmNNi4sza8B5WkREzb2vgHHnnWYI73gXXigtWWK6Zr3g6FHpwAHzf3188/yBUPY4K0vKzDShLzOz3GVzNa5OHbOkSt26Zgg1Jqa0ef4oatiw9OsuJuYUs1turtSzp7R06YnrAA4YIL37rpkx4m9uvdXsVFKJa/We/q1rJZk/3O67T7rrLikqylcFAvAFAiC87sgRaefO0lC4c%2BeJbdeu01/3rU6d0l/YnoWBY2LMBe6e5rlOyrNgcGSkg9ZO/OILqVu3yp9/5BEpJUWSuSQgJ8eEMU9IKxvayga6AwfKh70DB0wPnTeEh5v/V896eLVqmRYebnqeQkNVMjvX8/9YVGRaQUH5WbVHj5bOqM3OrnqNQUEmEMbHm0CYkGCO4%2BNLF6hOSJDi356skIdHSapkIei335YGDareJ6gmbNkitW8v7d174nNdumjjlMWa8Y8QTZtWellIw4bmssCbbnLQ9xMQ4AiAsExWlpnVuGuXufW03bvLt717q/7LPDjY9Px4mmcHCbfb9GhER5tbz0K8deqUNs9wYtlg4gknvhrdKyw0771syMnNNeHZE3aOHDG37VJvVPOvZ1V6rn0hsUo%2BM0OZmeZzX9k%2BvqcqKMiEbM%2BCxZ7jsp9vz64dns%2B7p0VFmVaTw7P5%2BSbgZmaa0HrokLndv998TXmWV9mzx3ydZWSY50/1J%2BK3ukAXyFwHUVEAzOzYQ0fnfqwGDfxmNLjU5s3Sww9LH3xgvhAiI6Xbb5f%2B/OeSrva8PGnOHOnpp6WffjIfdt110qxZLEgNBAICIPxecbHp0dmzx/zi9vzy3rvX/DLft8/c7t9veqg8t8eO1VxNQUHle6lCQ0vXifO0oKATd6HwvB9PL9bxu094erWOHTOtqOjUa1qs7uquz373NcEqUJFK04gnIJcNbmWHSysKeJ7j6OjA6w0qKDBfV2X/KNm1q7TXumwP9q6CGMVov6SKA%2BA6tdQFWqfg4Ip7Ej3HcXGlvY0%2Bu95u1iwztlv2eo3zzjPD1i1blntpfr40ebL02GPm%2BMEHpRde8FGdAGoMARABqbjY9JCVvQ7NM9zpuRYtK6t08d2cnNIJCZ7m6WXztNMJYzXB5Srd8szTM%2BnppaxTR3pw8x3qsfVvlX780YZN9O38rSU9cm63%2BdhAC3G%2BUFQkFSa3V%2BjaVZIqDoAfh1%2BtK/PfP62vm/r1S69/9VwD69kP2bMnsufyh6ioKv7fffqp1KNHxV/QsbFmDZl69U546plnpHHjzAoA27dX4d8F4FeYBQyfyM/P12OPPaaFCxfql19%2BkdvtVvfu3TVp0iQlJCR4/d9zuUrDUaNG3jln2evNPD10nt0dyvbgea5P8/T0Hf8nlmcnCk8ru/tE2R5Fz56xYWFm6Dkk5CS/8L%2B5XWpbeQCsff8d6tDBO58LpwsKkoJG3CHduqrS1/Scd4fyLjc912V7ED1rce7cWdrDuHu3%2BZrx9GSvX3/yGsLCKr721dOTW/ZSB88lDrVrS83HT1KdylLp7t3alTJ2/7K5AAAczElEQVRdW/o/qL17zSSvjRulr76S1q41L2nTpgqfMC9ZtmyZnn/%2BeX3zzTfatWuX5s2bp379%2BllXUIBLSUnR3Llz9eOPP6p27drq1KmTnn32WTVv3tzq0uAF9ADCJzIzM3Xttddq%2BPDhatWqlQ4ePKj7779fBQUFWrNmjdXlBY6nnzZjdcfJ6tBB0UuWcPGWNxUVSUOGSO%2B8c2IP4L33Sn/5nQWXKzjV/v2l1yKWvRZ2z57S5rkE4ujRqpXsUpHyFapgVd4t%2BbF6qJc%2BPuHxoCBp4EBpyhQTOq2waNEi/fe//1WbNm00YMAAAmAN69mzpwYPHqzk5GQVFBRo3LhxWrdundavX686rOFlewRAWGb16tVq166dtm3bpsaNG1tdTuBYvtzs87phg45GRmr4l19q9KpVujA52erKAk9RkfT%2B%2B8p6/XW5P/lEmX36KHrECDPEWoOOHCm9BtYzscUzU7vsJQ/HX%2BJw9Eixtu0KVYgqnwG0OLS37kz8SDExpvf87LNNr1/XrmZY2l%2B4XC4CoI/t3btXDRs21NKlS3XppZdaXQ6qiSFgWCYzM1Mul0t169a1upTAcsklpknavXWrZjdtqtH%2BuJpvIAgKkq65Rure3Yy3/uMfPllwOyLC7OndpMnpfqRL6tNL%2BvDDSl9x%2BctX6ue7K30aDpaZmSlJqlfBNaKwHz9cpRROkJubq0ceeURDhgxhayHAlx59tPLpxk2bSkOH%2BrYe2EJxcbFGjRqliy%2B%2BWC2PmykOeyIAokbMnj1bkZGRJW358uUlz%2BXn52vw4MEqKirS1KlTLawyMPze5xo4QceO0vvvS2edVf7xSy%2BVPv%2BcLT9QoXvuuUffffed5syZY3Up8BKGgFEj%2Bvbtq/bt25fcb/TbVNz8/HwNHDhQW7Zs0eeff07vnxdU9rkGKtW7t/Tzz9KyZWYhzfPOk1q0sLoq%2BKmRI0dqwYIFWrZsmc4880yry4GXEABRI6KiohR1XE%2BCJ/xt3rxZX3zxhepbNZUwwFT0uQZOKihI6tLF6irgx4qLizVy5EjNmzdPS5YsUdOmTa0uCV5EAIRPFBQU6Nprr9XatWv14YcfqrCwUBkZGZLMBcVhYWEWVxhYDhw4oO3bt2vnzp2SpI0bN0qS4uLiFOdPUzltLjU1VampqSqs7r56OCU5OTn6ybMvnaQtW7YoLS1N9erVYyWBGjBixAj985//1Pz58xUVFVXyM9vtdqt27doWV4fqYhkY%2BMTWrVsr/evxiy%2B%2BUBd6IrxqxowZuvnmm094fMKECZo4caLvCwpwWVlZcrvdyszM5LKGGrRkyRJ17dr1hMeHDRumGTNm%2BL6gAOeqZOX56dOn66abbvJtMfA6AiAAVBMBEIDdMAsYAADAYQiAAAAADkMABAAAcBgCIAAAgMMQAAEAAByGAAgAAOAwBEAAqKLU1FQlJSUpOTnZ6lIA4LSwDiAAVBPrAAKwG3oAAQAAHIYACAAA4DAEQAAAAIchAAIAgBPt3y9NmiR16iS1ayeNGSNt22Z1VfASJoEAQDUxCQQB5%2BefpS5dpB07yj8eFSV99JF0ySWWlAXvCbG6AAAA4B0FBVJenpSfLxUWSp4unqAgKThYCgmRwsLMrcv1Oye69dYTw58kZWdLf/qTtHWrOQlsi/89AABsYNcuae1aacMG6ZdfpPR0afduad8%2B6dAhKSfHBL9T4XJJ4eFS7dqmRURIdepIkZHSufpRb/x3aeUf/Ouv%2BukvHyqofz%2BdcYbkdpuACXshAAIA4KdWrZJmzZIWLjShz1uKi6XcXNMOHiz/nFs/n/TjXxv9k14cbY5dLqluXal%2BfalePXNbtsXElG8NGpjHQ0O9935w%2BgiAAFBFqampSk1NVWFhodWlIMCsXy%2BNGCEtWVL6WFCQdN55UsuW0h/%2BIDVuLMXFmVBVt665PC8iQqpVy4Sr4GATzoqLTSssND2E%2BflmmNgTAI8cMe3wYdOLGPZ9vDTx9%2BvLPSNBtXOlo0fNuQ8ePDFInkzduiYMNmxobj3HntaggRQba47r1zfvB97DJBAAqCYmgcCbFi%2BW%2BvUzoSwsTBo4ULruOqlrVxPyfOLCC6Vvv634ubp1pZ07pdq1lZdngt%2BBA6bt339i27evfNu/v/TaxFMVFGSCbsOGJhT%2BXmvYkN7FU0EABIBqIgDCWzIypHPPlTIzpe7dpTffND19Pvf119Lll5tJH2UFB0uzZ0uDBlX51IWFJizu3WsC4Z495njvXnN8fDtw4PQC4/jx0pNPVrk8x2AIGAAAP/Hqqyb8tWljVlsJC7OokPbtzQWIL7wgLVhgxo27dZMefNCsC1gNwcGlQ76noqCgNBzu3m1a2eOMDHM/I8O8Lja2WuU5BgEQgOPddNNNeuutt8o91r59e61cudKiiuBUy5eb2xEjLAx/HueeK73xhsVFmNVm4uNNO5miIhMYcXIEQACQ1LNnT02fPr3kfpjlv33hRIcOmdtTCTs4UVCQHwRnmyAAAoCk8PBwxcXFWV0GHC4mxtz%2B%2Bqu1dSDwsXQjAEhasmSJGjZsqGbNmmn48OHas2eP1SXBgdq0MbfLlllbBwIfs4ABON4777yjyMhINWnSRFu2bNH48eNVUFCgb775RuHh4Se8Pi8vT3l5eSX3s7KylJiYyCxgVNuyZdJll5ndNXbtMrt0ADWBHkAAjjJ79mxFRkaWtOXLl2vQoEG68sor1bJlS/Xp00eLFi3Spk2b9NFHH1V4jpSUFLnd7pKWmJjo43eBQHXxxVKTJmYm8Jw5VleDQEYPIABHyc7O1u7du0vuN2rUSLUr6GY555xzdNttt2nMmDEnPEcPIGrS889LDz9sJuF%2B/z07YKBmMAkEgKNERUUp6iTbKezfv1/p6emKr2QqZnh4eIVDw4A33HGHlJIi/fij6QW84QarK0IgYggYgKPl5ORo9OjRWrFihbZu3aolS5aoT58%2BiomJUf/%2B/a0uDw4UHS099JA5HjfO7LcLeBsBEICjBQcHa926dbr66qvVrFkzDRs2TM2aNdOKFStO2lMI1JT77pPOPFPavl168UWrq0Eg4hpAAKgm9gJGTZgzRxoyxMwEXr9eOussqytCIKEHEAAAPzR4sNSlixkCvuceie4aeBMBEAAAP%2BRySVOnSqGh0kcfSe%2B%2Ba3VFCCQEQAAA/NR555mJIJI0cqS0b5%2B19SBwEAABoIpSU1OVlJSk5ORkq0tBABs7VmrRQtq710wOAbyBSSAAUE1MAkFNW71a6tBBKiqS5s2T%2BvWzuiLYHT2AAAD4ueRkszuIZBaKZigY1UUABADABiZMMEPBe/ZId9/NrGBUDwEQAAAbqFVLmjlTCgmR3nvPrBMIVBUBEAAAm2jTRnrsMXM8YoT066/W1gP7IgACAGAjjz5qrgk8dEi65RaGglE1BEAAAGwkNNQMBdeqJX3yiVksGjhdBEAAAGzm3HOlZ581xw89JG3aZG09sB8CIAAANnTPPVL37mav4KFDpYICqyuCnRAAAaCK2AkEVgoKkqZPl%2BrWlVatkp55xuqKYCfsBAIA1cROILDSP/8pXX%2B9WR5mxQqpbVurK4Id0AMIAICN/elP0sCBZgh46FAzJAycDAEQAAAbc7mkV1%2BV4uOlH3%2BUHnnE6opgBwRAAABsrl49ado0c/zKK9Jnn1lbD/wfARAAgADQs6d0553m%2BOabpcxMa%2BuBfyMAAgAQIJ5/Xjr7bCk9XbrvPqurgT8jAAIAECAiI6W33jLXBb71ljR/vtUVwV8RAAEACCCdO5vdQSTp9tulvXutrQf%2BiQAIAFXEQtDwV088IbVoIe3ZI40YYXU18EcsBA0A1cRC0PBHa9dK7dub9QHfflsaNMjqiuBP6AEEACAAtWkjjRtnju%2B%2BW8rIsLYe%2BBcCIAAAAWrcOOnCC6UDB6S77pIY84MHARAAgAAVGirNmGFu339fmjPH6orgLwiAAAAEsFatpPHjzfHIkQwFwyAAAgAQ4B55RGrd2gwF3303Q8EgAAIAEPBCQ6Xp06WQEGnePOm996yuCFYjAAIA4ACtWkmPPmqO77lH2rfP2npgLQIgAFQRC0HDbsaNk1q2NLuDsFews7EQNABUEwtBw05Wr5Y6dJCKiqQPPpCuusrqimAFegABAHCQ5GRp1ChzfNddUlaWtfXAGgRAAAAc5oknpLPPlnbskMaOtboaWIEACACAw0RESH/7mzmeOlX68ktr64HvEQABBLS5c%2BeqR48eiomJkcvlUlpa2gmvycvL08iRIxUTE6M6deqob9%2B%2B2rFjhwXVAr7TrZt0yy3m%2BPbbpbw8a%2BuBbxEAAQS0w4cPq3Pnzpo0aVKlr7n//vs1b948vf322/ryyy%2BVk5Ojq666SoWFhT6sFPC955%2BXYmOlDRuk3/kWQQBiFjAAR9i6dauaNm2q//3vf7rwwgtLHs/MzFSDBg00a9YsDRo0SJK0c%2BdOJSYmauHCherRo8dJz80sYNjZO%2B9IgwdLYWHSt99K555rdUXwBXoAATjaN998o/z8fF1xxRUljyUkJKhly5b66quvLKwM8I2BA6XevaVjx6Q77mCbOKcgAAJwtIyMDIWFhemMM84o93hsbKwyMjIq/Ji8vDxlZWWVa4BduVxSaqqZGLJsmTRjhtUVwRcIgAACxuzZsxUZGVnSli9fXuVzFRcXy%2BVyVfhcSkqK3G53SUtMTKzyvwP4g7POMkvDSNLo0WwT5wQEQAABo2/fvkpLSytpbdu2PenHxMXF6dixYzp48GC5x/fs2aPY2NgKP2bs2LHKzMwsaenp6V6pH7DSffdJF1wgHTggPfSQ1dWgphEAAQSMqKgo/eEPfyhptWvXPunHXHTRRQoNDdXixYtLHtu1a5e%2B//57derUqcKPCQ8PV3R0dLkG2F1oqPTaa2ZIeMYMMxyMwBVidQEAUJMOHDig7du3a%2BfOnZKkjRs3SjI9f3FxcXK73br11lv14IMPqn79%2BqpXr55Gjx6t888/X927d7eydMDnOnaUhg83i0TfdZeUlmaCIQIPPYAAAtqCBQvUunVrXXnllZKkwYMHq3Xr1nrttddKXjN58mT169dPAwcOVOfOnRUREaEPPvhAwcHBVpUNWCYlRYqJkdavlyZPlvTLL9KmTRLrYgYU1gEEgGpiHUAEmhkzpA9u/reedE1Ui%2BLvzYONG5sZIiNHWlobvIMhYAAAUM6wkNm6UUMVVLaPaPt26d57pT17pD//2bri4BX0AAJANdEDiIBSUGDWhfn114qfDwuT0tOlhg19Wha8i2sAAQBAqa%2B%2Bqjz8SWbLkHnzfFcPagQBEACqKDU1VUlJSUpOTra6FMB7cnK88xr4NYaAAaCaGAJGQNm1y0z4KCio/DXLlkmXXOK7muB19AACAIBS8fHS4MGVP3/RRYS/AEAABAAA5U2dKnXtesLD63WeVj0y14KC4G0MAQNANTEEjIC1ZIm0YIGUn6%2BpP3bTyE/76oILg7VmjcQ66fZGAASAaiIAwgn27pXOOUfKzDRbxQ0fbnVFqA6GgAEAwEk1aCA98YQ5fvRR6dAha%2BtB9RAAAQDAKbn7buncc6V9%2B9gMxO4IgAAA4JSEhkqTJ5vjv/5V2rzZ2npQdQRAAABwynr2lHr1kvLzpdGjra4GVUUABIAqYicQONWLL5pZwAsWSJ99ZnU1qApmAQNANTELGE40cqQ0ZYrUqpX0zTcsC2M39AACAIDTNnGiVLeu9O230owZVleD00UABAAAp61%2BfWn8eHP82GNSTo619eD0EAABAECVjBgh/d//SRkZ0gsvWF0NTgcBEAAAVEl4uPTss%2Bb4%2BeelnTutrQenjgAIAACqbMAAqWNH6cgRacIEq6vBqSIAAgCAKnO5Sod/p02TfvjB2npwagiAAACgWjp1kq65RioqksaMsboanAoCIABUEQtBA6VSUqSQEOmjj6SlS62uBifDQtAAUE0sBA0Yd98tvfqq1K6dtHKlGR6Gf6IHEAAAeMWECVKdOtKqVdK//211Nfg9BEAAAOAVsbHSgw%2Ba43HjpPx8a%2BtB5QiAAADAax58UIqJkTZtkqZPt7oaVIYACAAAvCY62mwNJ0lPPGHWB4T/IQACAACvuvNOqXFjszPIlClWV4OKEAABAIBXhYdLTz5pjidNkjIzra0HJyIAAgAAr7vhBikpSTp4sHSnEPgPAiAAVBELQQOVCw6W/vxnczx5srRnj7X1oDwWggaAamIhaKBixcVmUeg1a6QHHpBeesnqiuBBDyAAAKgRLpf01FPmeOpU6ddfra0HpQiAAACgxlxxhXTJJVJeXmkYhPUIgAAAoMaU7QV8801p61ZLy8FvCIAAAtrcuXPVo0cPxcTEyOVyKS0t7YTXdOnSRS6Xq1wbPHiwBdUCgenSS6Xu3c3WcJ6JIbAWARBAQDt8%2BLA6d%2B6sSZMm/e7rhg8frl27dpW0119/3UcVAs7gCX5vvSX99JO1tUAKsboAAKhJQ4cOlSRtPcm4U0REhOLi4nxQEeBMHTpIvXtLCxeaRaJnzrS6ImejBxAAJM2ePVsxMTFq0aKFRo8erezsbKtLAgLOE0%2BY29mzpU2brK3F6egBBOB4119/vZo2baq4uDh9//33Gjt2rL799lstXry4wtfn5eUpLy%2Bv5H5WVpavSgVsrW1b6aqrpA8/NL2A//iH1RU5Fz2AAALG7NmzFRkZWdKWL19%2BSh83fPhwde/eXS1bttTgwYP1r3/9S59%2B%2BqnWrl1b4etTUlLkdrtLWmJiojffBhDQJk40t3PmSBs3WlqKoxEAAQSMvn37Ki0traS1bdu2Sudp06aNQkNDtXnz5gqfHzt2rDIzM0taenp6dcoGHOWii6S%2BfaWiImYEW4khYAABIyoqSlFRUdU%2Bzw8//KD8/HzFx8dX%2BHx4eLjCw8Or/e8ATjVhgrRggekFfPxxqVkzqytyHnoAAQS0AwcOKC0tTevXr5ckbdy4UWlpacrIyJAk/fzzz3ryySe1Zs0abd26VQsXLtR1112n1q1bq3PnzlaWDgSsNm3MtYBFRewOYhUCIICAtmDBArVu3VpXXnmlJGnw4MFq3bq1XnvtNUlSWFiYPvvsM/Xo0UPNmzfXvffeqyuuuEKffvqpgoODrSwdCGgTJpjbf/6TdQGt4CouLi62uggAsLOsrCy53W5lZmYqOjra6nIA2%2BjdW1q0SLrlFrNNHHyHHkAAAGCJxx83tzNnskewrxEAAQCAJTp0MHsEFxRIzz5rdTXOQgAEAACWGT/e3E6bJv36q7W1OAkBEACqKDU1VUlJSUpOTra6FMC2Lr1UuuQS6dgx6YUXrK7GOZgEAgDVxCQQoHo%2B%2BUTq0UOqXVvatk1q0MDqigIfPYAAAMBSl19u9gk%2BelR6%2BWWrq3EGAiAAALCUyyU9%2Bqg5njJFysy0th4nIAACAADLXX21lJQkZWVJU6daXU3gIwACAADLBQVJY8ea48mTzXAwag4BEAAA%2BIVBg6SzzpL27mVnkJpGAAQAAH4hNFQaPdocv/CClJ9vbT2BjAAIAAD8xi23SA0bmuVg3nnH6moCFwEQAKqIhaAB76tdW7rvPnP83HMSqxXXDBaCBoBqYiFowLsOHZIaN5ays6WPPpJ697a6osBDDyAAAPArdetKt99ujp991tpaAhUBEAAA%2BJ0HHjCTQpYtk77%2B2upqAg8BEAAA%2BJ1GjaTrrzfHzz1nbS2BiAAIAAD8kmdJmHnzpM2bra0l0BAAAQCAX2rRQrrySjMT%2BKWXrK4msBAAAQCA33roIXM7Y4bZIQTeQQAEAAB%2B69JLpbZtpdxcKTXV6moCBwEQAAD4LZer9FrA1FTp6FFr6wkUBEAAqCJ2AgF8Y8AAqUkTad8%2BadYsq6sJDOwEAgDVxE4gQM17%2BWWzNmDz5tL69VIQXVjVwqcPAAD4vVtvldxuaeNGaeFCq6uxPwIgAADwe1FR0vDh5pglYaqPAAgAAGxh5EgpOFj64gspLc3qauyNAAgAAGyhcWPp2mvN8eTJ1tZidwRAAABgG6NGmdu335YyMqytxc4IgAAAwDbatZM6dpSOHZOmTrW6GvsiAAIAAFt54AFz%2B9prZocQnD4CIABUEQtBA9bo319KTDR7A8%2BZY3U19sRC0ABQTSwEDfjec89JY8ZIF1xgZgS7XFZXZC/0AAIAANu57TYpIkL67jtp6VKrq7EfAiAAALCdevWkoUPN8SuvWFuLHREAAQCALd17r7mdP1/autXSUmyHAAggYOXn52vMmDE6//zzVadOHSUkJOjGG2/Uzp07y73u4MGDGjp0qNxut9xut4YOHapDhw5ZVDWAU5WUJHXvLhUVsSTM6SIAAghYR44c0dq1azV%2B/HitXbtWc%2BfO1aZNm9S3b99yrxsyZIjS0tL08ccf6%2BOPP1ZaWpqGesaWAPg1Ty/gG29IR45YW4udMAsYgKOsXr1a7dq107Zt29S4cWNt2LBBSUlJWrlypdq3by9JWrlypTp27Kgff/xRzZs3P%2Bk5mQUMWKewUDrnHGnLFulvf5OGD7e6InugBxCAo2RmZsrlcqlu3bqSpBUrVsjtdpeEP0nq0KGD3G63vvrqK6vKBHCKgoOlESPM8ZQpEt1ap4YACMAxcnNz9cgjj2jIkCElPXUZGRlq2LDhCa9t2LChMirZaDQvL09ZWVnlGgDr3HKLVLu2WRJm%2BXKrq7EHAiCAgDF79mxFRkaWtOVlfhPk5%2Bdr8ODBKioq0tTjrhZ3VbCCbHFxcYWPS1JKSkrJhBG3263ExETvvhEAp%2BWMM6QbbjDHqanW1mIXBEAAAaNv375KS0sraW3btpVkwt/AgQO1ZcsWLV68uNx1enFxcdq9e/cJ59q7d69iY2Mr/HfGjh2rzMzMkpaenl4zbwjAKfMMA8%2BdKx030R8VCLG6AADwlqioKEVFRZV7zBP%2BNm/erC%2B%2B%2BEL169cv93zHjh2VmZmpVatWqV27dpKkr7/%2BWpmZmerUqVOF/054eLjCw8Nr5k0AqJJWraTLLpOioqTsbKur8X/MAgYQsAoKCjRgwACtXbtWH374YbkevXr16iksLEyS1KtXL%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%2BLIlSmWgAAAABJRU5ErkJggg%3D%3D'}