Genus 2 curve data - 160000.c.800000.1

Formats: - HTML - YAML - JSON - 2025-12-07T09:23:32.177164

• 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': '951545799334278838', 'abs_disc': 800000, '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]],[5,[1]]]', 'bad_primes': [2, 5], 'class': '160000.c', 'cond': 160000, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[-1,0,0,0,0,1],[]]', 'g2_inv': "['0','0','0']", 'geom_aut_grp_id': '[10,2]', 'geom_aut_grp_label': '10.2', 'geom_aut_grp_tex': 'C_{10}', 'geom_end_alg': 'CM', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': False, 'igusa_clebsch_inv': "['0','0','0','1']", 'igusa_inv': "['0','0','0','0','800000']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '160000.c.800000.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '2.7853392656705846659642101973114990566633214199647', 'prec': 170}, 'locally_solvable': True, 'modell_images': ['2.180.2', '3.1296.1'], 'mw_rank': 1, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 2, 'num_rat_wpts': 2, 'real_geom_end_alg': 'C x C', 'real_period': {'__RealLiteral__': 0, 'data': '4.6125927948154910723464123765', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.20771088608', 'prec': 44}, 'root_number': -1, 'st_group': 'F_{ac}', 'st_label': '1.4.D.4.1a', 'st_label_components': [1, 4, 3, 4, 1, 0], 'tamagawa_product': 2, 'torsion_order': 2, 'torsion_subgroup': '[2]', 'two_selmer_rank': 2, 'two_torsion_field': ['4.0.125.1', [1, -1, 1, -1, 1], [4, 1], True]}
• 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': [['4.0.125.1', [1, -1, 1, -1, 1], -1]], 'factorsRR_base': ['RR'], 'factorsRR_geom': ['CC', 'CC'], 'fod_coeffs': [1, -1, 1, -1, 1], 'fod_label': '4.0.125.1', 'is_simple_base': True, 'is_simple_geom': True, 'label': '160000.c.800000.1', 'lattice': [[['1.1.1.1', [0, 1], [0, 0, 0, 0]], [['1.1.1.1', [0, 1], -1]], ['RR'], [1, -1], 'F_{ac}'], [['2.2.5.1', [-1, -1, 1], [0, 0, -1, 1]], [['2.2.5.1', [-1, -1, 1], -1]], ['RR', 'RR'], [1, -1], 'F_{ab}'], [['4.0.125.1', [1, -1, 1, -1, 1], [0, 1, 0, 0]], [['4.0.125.1', [1, -1, 1, -1, 1], -1]], ['CC', 'CC'], [1, -1], 'F']], 'ring_base': [1, -1], 'ring_geom': [1, -1], 'spl_fod_coeffs': [1, -1, 1, -1, 1], 'spl_fod_gen': [0, 1, 0, 0], 'spl_fod_label': '4.0.125.1', 'st_group_base': 'F_{ac}', 'st_group_geom': 'F'}
• 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': '160000.c.800000.1', 'mw_gens': [[[[2, 1], [2, 1], [1, 1]], [[-1, 1], [1, 1], [0, 1], [0, 1]]], [[[-1, 1], [1, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '1.2077108860774697482122174768484', 'prec': 110}], 'mw_invs': [0, 2], 'num_rat_pts': 2, 'rat_pts': [[1, 0, 0], [1, 0, 1]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 25387
    {'conductor': 160000, 'lmfdb_label': '160000.c.800000.1', 'modell_image': '2.180.2', 'prime': 2}
  • id: 25388
    {'conductor': 160000, 'lmfdb_label': '160000.c.800000.1', 'modell_image': '3.1296.1', 'prime': 3}
• g2c_tamagawa •

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

  
  
  • id: 23731
    {'label': '160000.c.800000.1', 'local_root_number': 1, 'p': 2, 'tamagawa_number': 1}
  • id: 23732
    {'cluster_label': 'c5_1~4', 'label': '160000.c.800000.1', 'local_root_number': -1, 'p': 5, 'tamagawa_number': 2}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '160000.c.800000.1', 'plot': 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C35Il0tGj4ePt25vl3PR088cuXdypEwBCrA6A9AIGcCNOnw6/nmX79vDxhATTTzcU%2BgYOZB8fgMhCL2DRCxjA1Z0/b/buLVliAt%2BHH156PcvQoc5p3dGjuZ4FQGSzegYQAC4nGJR273aWdIuKpNLS8Pf06OHM8HE9CwCvIQACgKTjx53rWQoLpQMHwsdbtpRSU53Q17OnO3UCQH0gAAKw0tmz0urVzizfpk3h402aSGPGOAc3hg2TYmLcqRUA6hsBEIAVamqk4mIn8K1aZe7oq23gQGeGb9w4qXlzd2oFgIZGAATgW/v2OQc3li6VSkrCxzt3dmb4UlOlW25xpUwAaHQEQAC%2BceqU2ccXCn27d4ePJyZKEyc6s3x9%2BnA9CwA7EQABeFZVlbmSJbSsu359eJu1mBhpxAin68add5q9fQBgOwIgAM%2B4njZrffo4y7oTJ0pc7QkAlyIAAohohw87S7pXa7MWeqSkuFMnAHgJARBARAm1WQuFvm3bwscTEswJ3YwME/hoswYAdWd1AKQXMOC%2BQMC0WQst616uzdqwYc6y7pgxtFkDgJtFL2DRCxhoTMGgtGePE/iWLTOnd2vr3t05uEGbNQCof1bPAAJoHF9%2Bae7hC4W%2BffvCx1u2NEEvFPposwYADYsACKDeVVY617MUFEgbN5qZv5AmTaRRo5zAd8cdtFkDgMZEAARw04JBaft2Z4Zv%2BfJLr2fp29cJfBMmSC1auFIqAEAEQAA36IsvnMBXWGiua6mtQwen40Zammm7BgCIDARAANfl3Dlp1SpnWXfz5vDx%2BHhp/Hhnlo/rWQAgcvkiAK5cuVLPP/%2B8Nm7cqCNHjuidd97R3Xff7XZZgKcFg9LWrSbsFRZKK1eaEFjb4MHmPr70dGnsWBMCAQCRzxcBsKKiQoMGDdIjjzyie%2B65x%2B1yAM86ejR8WffirhudOpmwF7qEuX17d%2BoEANwcXwTArKwsZWVluV0G4DnnzkmrV5tZvsst6zZrZg5shGb5%2BvZlWRcA/MAXARDA9Qmd1i0okBYvNi3XLl7WHTLEBL6MDLpuAIBfWRkAKysrVVlZeeF5WVmZi9UADevECdNXNzTL9/nn4eOhZd3MTLOs266dO3UCABqPlQFwzpw5mj17tttlAA2iulpau9aZ5duwIfwS5tBp3cxMM8vXrx/LugBgG9/1Ao6KirrmKeDLzQCmpKTQCxietXevCXuLF0tFRdLFk9r9%2B5vAl5kpjRvHaV0AsJ2VM4BxcXGKY2MTPKyiwnTbCIW%2BnTvDx9u0cZZ1MzLMMi8AACG%2BCICnT5/W7t27Lzzfu3eviouL1bp1a3Xt2tXFyoD6EQxK27ZJ%2BfnmsWqVVFXljMfEmN66mZnSlCnS0KFSdLR79QIAIpsvloCXL1%2BuSZMmXfL6ww8/rFdeeeWaP19WVqbk5GSWgBFRTp0yhzdCoe/iwxvdupmwl5kpTZ4sJSe7UycAwHt8EQBvFgEQkaCmRtq0yYS9vDxzkCMQcMbj46WJE53Q17s3hzcAADfGF0vAgFeVlJjTunl5Zi/fF1%2BEj/fpYwLflCnm5G5Cgjt1AgD8hQAINKKaGunjj03gy8uT1q0zr4W0aCGlpprAl5VllnkBAKhvBECggZ08aWb5cnPN8u7Fs3z9%2B5uwl5VlOm80bepOnQAAexAAgXoWDEpbt0qLFpnQ9%2BGH4Xv5EhNNx42sLDPTl5LiXq0AADsRAIF6UFFhTuzm5prHoUPh4337msA3dao0diyzfAAAdxEAgRu0d6%2B0cKGZ6Vu%2BXKrVXEYJCeZqluxsE/rYywcAiCRWB8CcnBzl5OQoUHt9DriC8%2BelNWtM6Fu4UNqxI3y8e3dp2jQT%2BCZO5MQuACBycQ%2BguAcQV3bqlDm48f775o9ffumMxcSYQxvTpplHnz7cywcA8AarZwCBy9mzxwS%2BBQtMy7Xz552x1q3NXr5p08wBjpYt3asTAIAbRQCE9QIB6aOPTOBbsEDavj18/PbbpRkzzH6%2BUaOkWP6tAQB4HP8pg5XOnpWWLpXefdfM9tW%2Bmy821nTdmD7dPHr2dK9OAAAaAgEQ1vjyS3N44733zH6%2BM2ecsaQkc3hjxgyzxMvSLgDAzwiA8LVDh8ws3zvvSCtWhF/InJIi3XWXeYwfz918AAB7EADhOzt3Sm%2B/bR7r14ePDRgg3X23CX1Dh3JqFwBgJwIgPC/Ueu1vfzOPbducsagoafRo6atfNcGP/XwAABAA4VHBoLRpk/TXv5rHrl3OWGys6cLxta%2BZmb5bbnGvTgAAIhEBEJ4RCn1//rP0l79In33mjMXFSZmZ0j33mJO7rVq5VycAAJGOAIiIFgxKW7ZI8%2Beb4LdnjzOWkGBO7N53n7mjLzHRvToBAPASqwMgvYAj16efSvPmSW%2B9Zf48JCHBXNdy//0m9DVv7l6NAAB4Fb2ARS/gSHHokAl8b75plnpD4uJM6HvgARP6WrRwr0YAAPzA6hlAuO/UKXOI4403pJUrzZKvZA5ypKdL//AP5iAHuRwAgPpDAESjq66W8vKk114zbdiqqpyxceOkBx%2BU7r1XatvWvRoBAPAzAiAaRTAoFRdLr7xilnhPnHDG%2BvWTvvlNE/y6dnWtRAAArEEARIM6flyaO1f605/Mad6QDh2kb3xDeughadAgOnIAANCYCICod4GAtGSJ9F//Jb33nlnylcxhjrvukh5%2BWMrIMPv8AABA4%2BM/wag3hw9Lf/yjeezf77w%2BbJj0ne%2BYAx1c0AwAgPsIgLgpNTXS0qXSiy9KCxaY2T9JatnSLO8%2B%2BqhZ4gUAAJGDAIgbUlpqDnS88IK0c6fz%2Btix0v/6X%2BYUb0KCa%2BUBAICrIACiTnbulH77WxP%2BKirMa4mJZl/f975nTvQCAIDIRgDENQWD0ooV0i9/KS1c6Lzet6/05JPmChe6cwAA4B1WB0B6AV9dICC9/bb07/8ubdhgXouKMu3Y/umfpNRUrm8BAMCL6AUsegFfrLratGabM0fatcu8Fh8vffvb0lNPSb17u1oeAAC4SVbPACJcVZX06qvSz3/uXOPSqpX0xBNmqbddO3frAwAA9YMACAUC0uuvS7NnS/v2mdc6dJD%2B%2BZ/NwQ729wEA4C8EQIsFg%2BZQx49/LG3fbl7r0ME8f/xxrnEBAMCvCICWKi6WfvADafly87x1axP8Zs6UmjVztTQAANDACICW%2BfJL6ZlnpD/8wcwAxsebgx3/%2B3%2Bb7h0AAMD/CICWCAbNyd4f/lA6ccK89sAD5oqXrl3drQ0AADQuAqAFDh2SHntMys83z/v1k3JypAkT3K0LAAC4I9rtAtCw3npLGjDAhL%2B4OHPFy6ZNhD8AAGzGDKBPnT1r7u774x/N8zvvNHf89enjbl0AAMB9zAD60MGD0tixJvxFRUn/5/9Iq1cT/gAAgGH1DKAfewFv2mR69R45IrVtK82bJ6WluV0VAACIJPQCln96Aa9eLU2dKpWXS/37m0ueu3VzuyoAABBpWAL2ibVrpSlTTPibMMGEQcIfAAC4HKuXgP1i925p2jSpokJKTZUWLKCbBwAAuDJmAD3uzBnpq1%2BVSkqkO%2B6Q3nuP8AcAAK6OAOhxP/mJ9Pe/Sx06mPDXvLnbFQEAgEhHAPSwzZul3/7W/Pkrr0idOrlaDgAA8AgCoIc995zp8Xv//eYACAAAwPXwVQB84YUX1KNHD8XHx2vYsGFatWqV2yXVvwMHpJkzFWjdVm%2B9F68VGq9fjnnb7aoAAICH%2BCYAzp8/X0899ZSeeeYZbdq0SePGjVNWVpYOHDjgdmn1Z9cuafhw6YUXFHOyRPGq1HitUpd/ukf6l39xuzoAAOARvrkIesSIERo6dKhefPHFC6/dfvvtuvvuuzVnzpyr/qxnLoK%2B6y5zx8vlREeb%2B2B69GjcmgAAgOf44h7Aqqoqbdy4UT/%2B8Y/DXs/IyNCaNWsueX9lZaUqKysvPC8rK7uhv24wGFR5efkN/WydlZSY1h5XUlMjvfSS9PTTjVMPAAAel5iYqKioKLfLcIUvAuCJEycUCATUoUOHsNc7dOigo0ePXvL%2BOXPmaPbs2Tf91y0vL1dycvJNf069%2BcUvzAMAAFxTxK/8NSBfBMCQi1N8MBi8bLJ/%2Bumn9cMf/vDC87KyMqWkpNT5r5eYmKjS0tI6/czw4cO1fv36Ov%2B1dPq0dOut0tmzV37P//2/0syZdf/sm6mrET4vEj8r9M/MwYMH6%2BU3j0j8NTbE5/H9u/dZ9fl5fP/ufh7ff/19VmJiYr18vhf5IgC2bdtWMTExl8z2ffHFF5fMCkpSXFyc4uLibvqvGxUVVed/%2BWJiYm7sX9ikJOmb35Refvny482aSY8/bt53A264rkb4vEj9LElKSkqql8%2BL5F9jJNfm9%2B8/kv9eSnz/bn8e3787n%2BUXvjgF3LRpUw0bNkyFhYVhrxcWFmr06NEuVXV5M29whk6S9O//Lg0bdsnLwaZNpTfekFq3dqeuBv68SP2s%2BhTJv8ZIrq2%2BROqvMZL/XtanSP3O%2BP75LD/zzSng%2BfPn66GHHtLvf/97jRo1Si%2B99JJefvllbdu2Td26dbvqz3rmFLAknTsnzZuns6%2B8oY9XntFHGqkxb/yj7vzGbW5XZhVP/TPjQ3z/7uL7dxffP%2BqDL5aAJemBBx5QSUmJfvazn%2BnIkSPq37%2B/cnNzrxn%2BPCc%2BXnrkEUU/%2BKD%2BacwWbdw4XKNfkFb9g7kJBo0jLi5Ozz33XL1sJUDd8f27i%2B/fXXz/qA%2B%2BmQG8GV79v6nPP5d695YqKqScHOkf/9HtigAAgBcwZ%2BRhnTubg7%2BS9KMfSRs3ulsPAADwBgKgxz3xhJSdbbYGzpgh7d/vdkUAACDSEQA9LjpamjtX6tdPOnxYSkuTDh50uyoAABDJCIA%2BkJws5eebNsC7d0vjxkmffup2VQAAIFIRAD1m5cqVmj59ujp16qSoqCi9%2B%2B67kqQuXaTly6XbbjPLwKNGSUVF7tbqZVf6nq/kyJEjevDBB9W7d29FR0frqaeeaqRK/amu3//bb7%2Bt9PR0tWvXTklJSRo1apQWL17cSNX6S12/%2B9WrV2vMmDFq06aNEhIS1KdPH/3qV79qpGr9p67ff20ffPCBYmNjNXjw4AasEH5BAPSYiooKDRo0SL/73e8uGevaVVq9Who5Ujp5UsrIkJ5/XuKcd91d7Xu%2BnMrKSrVr107PPPOMBg0a1MDV%2BV9dv/%2BVK1cqPT1dubm52rhxoyZNmqTp06dr06ZNDVyp/9T1u2/evLmeeOIJrVy5Ujt27NCzzz6rZ599Vi%2B99FIDV%2BpPdf3%2BQ0pLS/Wtb31LqampDVQZ/Mbqa2BycnKUk5OjQCCgnTt3eu4amKioKL3zzju6%2B%2B67w14/e9Z0hXv9dfN86lTpv/9bukxXPFyHK33PVzJx4kQNHjxYv/71rxu4MjvU9fsP6devnx544AH99Kc/baDK/O9Gv/uvfe1rat68uV4P/SaEG1KX7//rX/%2B6brvtNsXExOjdd99VcXFxI1QIL7N6BnDmzJnavn17vTb7jgQJCdKrr0ovvijFxUm5udKAAdLf/uZ2ZUDjqKmpUXl5uVrfRHtE3JhNmzZpzZo1mjBhgtulWONPf/qT9uzZo%2Beee87tUuAhVgdAP4uKkr73PWn9ehP%2Bjh%2BX7r1Xuucec4E04Ge//OUvVVFRofvvv9/tUqzRpUsXxcXF6Y477tDMmTP13e9%2B1%2B2SrLBr1y79%2BMc/1ty5cxUb65vmXmgEBECfGzDAhMBnnpFiY6W335Zuv136f/9Pqq52uzqg/s2bN0%2BzZs3S/Pnz1b59e7fLscaqVau0YcMG/f73v9evf/1rzZs3z%2B2SfC8QCOjBBx/U7Nmz1atXL7fLgccQAC0QFyf9679KGzZII0ZI5eWmc8jAgVJeHodE4B/z58/Xo48%2Bqj//%2Bc9KS0tzuxyr9OjRQwMGDNBjjz2mH/zgB5o1a5bbJfleeXm5NmzYoCeeeEKxsbGKjY3Vz372M23evFmxsbEq4ioIXAUB0CKDBklr1kgvvyy1bSt98ok5IJKRIX38sdvVATdn3rx5%2Bva3v60333xT2dnZbpdjtWAwqMrKSrfL8L2kpCRt3bpVxcXFFx7f%2B9731Lt3bxUXF2vEiBFul4gIxoYBjzl9%2BrR279594fnevXtVXFys1q1bq2vXrtf8%2Beho6bvfNfsB//Vfpf/8T2nJEmnYMOn%2B%2B6XZs6U%2BfRryV%2BAN1/qen376aX3%2B%2Bed67bXXLrwndOru9OnTOn78uIqLi9W0aVPcdW5nAAAYKUlEQVT17du30ev3urp%2B//PmzdO3vvUt/eY3v9HIkSN19OhRSVJCQoKSk5Nd%2BTV4VV2/%2B5ycHHXt2lV9/uc3jtWrV%2Bs//uM/9OSTT7pSv9fV5fuPjo5W//79w36%2Bffv2io%2BPv%2BR14BJBBEtLS4OSgqWlpW6Xck3Lli0LSrrk8fDDD9/Q5332WTD44IPBoFkIDgajo4PBb34zGPzkk/qt22uu9T0//PDDwQkTJoT9zOXe361bt0av3Q/q%2Bv1PmDChXv%2B9sFldv/vf/va3wX79%2BgWbNWsWTEpKCg4ZMiT4wgsvBAOBgDu/AI%2B7kd97anvuueeCgwYNapxi4WlW3wMYUlZWpuTkZM/dA1iftmyRfvpT6b33zPOoKDMj%2BJOfmL2CAADAP9gDCEkm5L37rjkoMmOGmQ%2BcP9/sG8zOllas4LAIAAB%2BQQBEmGHDzCxgcbGZAYyONhdJT5xoThC/9RbXxwAA4HUsAYsl4KvZvVv65S%2BlV16Rzp0zr3XpIj3xhDlM0qaNq%2BUBAIAbYHUA9Hov4MZ0/Lj0wgtSTo75c0mKj5cefNCEwSFD3K0PAABcP6sDYAgzgNfv3DmzDPyb35hl4pCRI6Xvf1%2B67z7TixgAAEQuAqAIgDciGJQ%2B%2BMDMCP7tb86%2BwFatpG99S3rsMalfP3drBAAAl0cAFAHwZh07Jv3xj9JLL0n79zuvjx4tPfqoOUzSooV79QEAgHAEQBEA60sgIBUUmCD4/vvmuWTC3333Sd/5jjRmjLljEAAAuIcAKAJgQzhyRHrtNTMzuGuX83rPntLDD0sPPSR17%2B5aeQAAWI0AKAJgQwrtFfzv/5b%2B8hfp9GlnbPx4EwTvvVdq2dK9GgEAsA0BUATAxlJRYQ6MvPaaVFTkdBaJi5OmT5e%2B8Q0pK8s8BwAADYcAKAKgGw4elN58U3r9dWnbNuf1li2le%2B4x9wtOmCDFxLhXIwAAfkUAFAHQTcGguU9w7lxzv%2BDnnztjt9xiThB//evmnkEOjwAAUD8IgCIARopAQFq5Upo3T/rrX6WTJ52xrl1NGLz/fumOOwiDAADcDAKgCICRqKrKXCnz1lvSe%2B%2BFHx7p0cMcHLn/fmnYMMIgAAB1ZXUApBewN5w9K%2BXlSfPnSwsXSmfOOGPdu5s9g/feK915pxQd7VqZAAB4htUBMIQZQO%2BoqDBh8M9/lhYtCg%2BDXbpIX/2qCYRjx3KABACAKyEAigDoVWfOmDD417%2BaMFhe7oy1ayfddZf0ta9JkydztQwAALURAEUA9INz58yewb/9zbShq32AJDFRmjZNuvtuc89gYqJ7dQIAEAkIgCIA%2Bk11tbRihQmD771n2tKFNG0qpaaaMDhjhrlqBgAA2xAARQD0s5oaad066d13pXfeCe9LHBUljRhhlorvukvq04cTxQAAOxAARQC0RTAo7dhhwuC770rr14eP33qrmRWcMUMaM0aKjXWnTgAAGhoBUARAWx0%2BLC1YYJaJi4rM3YMhrVpJU6eaHsVTpkjJye7VCQBAfSMAigAIc4J48WITCBctkr780hmLjZXGjzdhcNo0M1MIAICXEQBFAES48%2BelDz80YXDhQumTT8LH%2B/QxQTA72ywVN2niTp0AANwoAqAIgLi63budmcGVK01ADElOljIzTRjMyjL3DwIAEOkIgCIA4vqVlpql4oULzSXUJ044Y1FR0siRZu9gdrY0eDCnigEAkcnqAEgvYNyMQED66CMTBhctkjZvDh/v2NHMCk6dKqWlcZAEABA5rA6AIcwAoj4cOmRmBRctkpYsMX2LQ2JjTX/iqVNNKOzXj9lBAIB7CIAiAKL%2BVVaa/YKLFplQuHNn%2BHhKijM7OHky7ekAAI2LACgCIBrenj1Sbq55LF9ueheHNGliZgezspgdBAA0DgKgCIBoXGfOmF7FublmdnDPnvDxLl3M5dNTprB3EADQMAiAIgDCXbt2mSCYl3fp7GBMjDRqlJkZnDLFnCyOjnatVACATxAARQBE5Dh71swO5udffu9g%2B/bm3sGMDPNo396dOgEA3ub5APj222/rD3/4gzZu3KiSkhJt2rRJgwcPrtNnEAARqfbuNWEwP9/0Kz592hmLipKGDjWBMDPTzBTSlQQAcD08HwBff/117d27V506ddJjjz1GAIRvVVVJa9Y4gfDiewcTE6XUVCcQ9ujhTp0AgMjn%2BQAYsm/fPvXo0YMACGscOSIVFJjOJIWF4V1JJOm228wycWamNGmS1KKFO3UCACIPAVAEQHhfTY308cdmZrCgwMwUBgLOeJMm0ujRzv7BIUM4TAIANrMyAFZWVqqysvLC87KyMqWkpBAA4RulpdKyZWZ2cPFis5ewtrZtpfR05zBJp07u1AkAcIen5gDmzp2rFi1aXHisWrXqhj5nzpw5Sk5OvvBISUmp50oBdyUnS3ffLb34orlncOdO6Xe/k2bMMEvBJ05I8%2BZJjzwide4sDRgg/ehHJiyeOeN29QCAhuapGcDy8nIdO3bswvPOnTsrISFBEjOAwPWqrpY%2B/NAsFRcUSBs2SLV/F4iLk8aNMzOD6enSwIEsFwOA33gqAF4NewCBG1NSIi1Z4gTCQ4fCxzt0MB1JQoGwY0d36gQA1B/PB8Avv/xSBw4c0OHDh5Wdna233npLvXv31i233KJbbrnluj6DAAgYwaD06admKbigwHQmuXhJuH9/JwyOHy81a%2BZKqQCAm%2BD5APjKK6/okUceueT15557TrNmzbquzyAAApcXunswNDv48cfhy8VNm0pjxzoHSmhVBwDe4PkAWB8IgMD1OXHCdCQpKDB3Dx44ED7etq25jDo93Ty6dnWnTgDA1REARQAEbkRoubiw0DyWLQtvVSdJvXubmcG0NGniRIl/vQAgMhAARQAE6kN1tbR2rRMIP/rIXFAdEhsrjRxpwmB6unTnneY1AEDjIwCKAAg0hFOnzHJxKBDu2RM%2BnpQkTZ5swmBammldFxXlTq0AYBsCoAiAQGPYu9cJg0uXSidPho937ersHUxNNfsJAQANgwAoAiDQ2AIBc6I4FAjXrDEnjmsbMsQ5XTxmjBQf706tAOBHBEARAAG3VVRIK1eaC6kLC6WtW8PH4%2BNNd5LQhdR0JwGAm2N1AMzJyVFOTo4CgYB27txJAAQixNGjZpm4oMCEwsOHw8fbtXOum0lL47oZAKgrqwNgCDOAQOQKBqUdO5zl4uXLzYxhbb16OWFw0iQpOdmVUgHAMwiAIgACXlJVJa1b5wTC9evNnsKQmBhzxUzoupkRI0zHEgCAgwAoAiDgZaWl5hLqwkKzXLxzZ/h4ixbShAnOCePbb%2Be6GQAgAIoACPjJgQPh182cOBE%2B3qmTmR0MPTp2dKdOAHATAVAEQMCvamqkzZud08WrVknnzoW/p39/Z7l4/HgzYwgAfkcAFAEQsMW5c9IHHzgzhJs2mUMmIU2aSKNGOcvFw4bRrg6APxEARQAEbHXihGlXF5oh3LcvfDw5Obxd3a23sn8QgD8QAEUABGBmAvfsccJgUZHpZ1xbt27OcjHt6gB4GQFQBEAAlwoEpI0bndPFH3wgVVeHv2fIENOZJC1NGjuWdnUAvIMAKAIggGur3a6uoED6%2B9/Dx0Pt6kL7B2lXByCSEQBFAARQd0ePOsvFV2tXF1oypl0dgEhidQCkFzCA%2BnBxu7oVK6TTp8PfE2pXl55u2tXxWw0AN1kdAEOYAQRQn2q3q1uyRProo0vb1Y0YYWYHMzJM67omTdyrF4B9CIAiAAJoWKdOmXZ1of2Du3eHjycmmlnB0Axhr15cNwOgYREARQAE0Lj27XP2Dy5dKpWUhI%2BnpDhhMDXV7CcEgPpEABQBEIB7ampMR5LQ/sHVq80Scm1DhjiBkOtmANQHAqAIgAAix5kzpmdxKBBu2RI%2BHh9vehbXvm6G5WIAdUUAFAEQQOS61nUz7ds7h0nS06VOndypE4C3EABFAATgDcGgtH27CYMFBea6mTNnwt/Tt68TBidMkJo3d6dWAJGNACgCIABvqqyUPvzQCYQbN5qQGNKkidkzmJ5uQuGQIXQnAWAQAEUABOAPJSVSUZEJg4WF0v794eNt2phTxaEZQrqTAPYiAIoACMB/gkFz32AoDBYVSeXl4e/p3duEwYwMaeJEqUULV0oF4AICoAiAAPyvutrpTlJQYLqT1NQ4402aSKNGOYFw6FDTsQSAP1kdAOkFDMBWJ0%2Ba7iQFBeaxd2/4eOvWznJxRgbLxYDfWB0AQ5gBBGC7PXucMFhUJJWVhY/36eOEwQkTWC4GvI4AKAIgANR2/rxZLg7tH1y37tLl4jFjnEDI6WLAewiAIgACwNWcOuWcLr7ccnHbts5l1JmZXEYNeAEBUARAALheweCly8UXny7u39%2BZHRw/XkpIcKdWAFdGABQBEABuVHW1tHatCYOLF0sbNoRfRh0XZ0JgaHawf396FwORgAAoAiAA1JeSEmnpUhMGCwqkQ4fCxzt2dMJgWprUrp07dQK2IwCKAAgADSEYlHbscGYHV6yQzp51xqOizH2DmZnmMWqUOWACoOERAEUABIDGcO6ctHq1Ewi3bAkfT0yUJk0yYXDKFOkrX3GnTsAGBEARAAHADUeOOGGwsFA6cSJ8vGdPZ3Zw8mTuHgTqEwFQBEAAcFtNjbRpk7N38IMPzH2EIaG7B0OBcNAg7h4EbgYBUARAAIg05eXmipnFi83js8/Cxzt0cA6TZGRwmASoK6sDIL2AAcAbdu92wmBRkVRRET4%2BbJjZN5iZKY0cyWES4FqsDoAhzAACgHdUVZkl4sWLpfx8afPm8PGkJCk11QmE3bq5UycQyQiAIgACgJcdPerMDhYUmLsIa%2BvTx4TBKVPoTAKEEABFAAQAvwgEpI8/NjOD%2BfmmS0lNjTMeHy9NnOgEwl696EwCOxEARQAEAL86edJ0JsnPNzOEF3cm6d7dLBNnZZmrZhITXSkTaHQEQBEAAcAGwaC0bZuzd3DlSrOfMCQ2Vho71pkdHDiQ2UH4FwFQBEAAsFFFhbRsmbNcvGdP%2BHinTk4YTEuTWrVyp06gIRAARQAEAJirZvLyTBhctiy8b3F0tOlVPGWKWS4eMoSLqOFtng6A1dXVevbZZ5Wbm6vPPvtMycnJSktL0y9%2B8Qt16tTpuj%2BHAAgAqO3cOWnVKhMG8/KkHTvCx9u3d2YHMzKkNm3cqRO4UZ4OgKWlpbr33nv12GOPadCgQTp58qSeeuopnT9/Xhs2bLjuzyEAAgCuZv9%2BJwwuXSqdPu2MRUVJd95pZganTjWXUjM7iEjn6QB4OevXr9edd96p/fv3q2vXrtf1MwRAAMD1Cl1EnZdnHn//e/h427bOyeLMTPMciDS%2BC4BLlixRRkaGTp06dd1hjgAIALhRhw45s4NLlkhlZc4Ys4OIVL4KgOfOndPYsWPVp08fvfHGG1d8X2VlpSorKy88LysrU0pKCgEQAHBTqqulNWuc2cEtW8LH27ULnx1k7yDc4qkAOHfuXD3%2B%2BOMXnufl5WncuHGSzIGQ%2B%2B67TwcOHNDy5cuvGuRmzZql2bNnX/I6ARAAUJ8%2B/9wJg4WFUnm5MxYdLY0Y4cwOcrIYjclTAbC8vFzHjh278Lxz585KSEhQdXW17r//fn322WcqKipSm2v8LxUzgACAxlZdHb53cOvW8PEOHcyp4qlTzcnili3dqRN28FQAvJxQ%2BNu1a5eWLVumdu3a1fkz2AMIAGhshw5JubnO3sHaJ4tjYsy9g9nZJhAOGEBXEtQvTwfA8%2BfP65577tHHH3%2BshQsXqkOHDhfGWrduraZNm17X5xAAAQBuqqqSVq82gTA399J7Bzt3NkvF2dlSaio9i3HzPB0A9%2B3bpx49elx2bNmyZZo4ceJ1fQ4BEAAQSfbtc8JgUVF4V5ImTaTx453ZwV69mB1E3Xk6ANYXAiAAIFKdOyctX%2B4Ewot7Fn/lKyYIZmdLEydK8fFuVAmvIQCKAAgA8I6dO00QXLRIWrnSLB%2BHNGsmTZ7szA5eZz8EWIgAKAIgAMCbTp82rekWLTKh8PPPw8f79zdhMDvbHCqJjXWnTkQeAqAIgAAA7wsGzcXTixaZx9q1Uk2NM96qlbl8OjvbXDdDizq7EQBFAAQA%2BE9JibR4sQmD%2BfnSl186Y1FR0siRJgxOmyYNHMhBEtsQAEUABAD4WyAgrVtnwuDChZe2qOvSxewZnDbN7CFs3tydOtF4CIAiAAIA7HLwoHOQZOlS6cwZZywuTpo0yYTB7Gype3fXykQDsjoA5uTkKCcnR4FAQDt37iQAAgCsE7pmZuFCEwj37Qsf79fPhMFp08yyMQdJ/MHqABjCDCAAAOYgyfbtThhcs8YsH4e0bm0OkEyfbg6UtGrlXq24OQRAEQABALickyfNAZKFC03P4pMnnbGYGGnsWGd2sHdvDpJ4CQFQBEAAAK7l/Hnpww9NGFy40MwU1tazpxMGx4%2BXmjZ1p05cHwKgCIAAANTV3r1OGFy%2BPLwjSWKiWSKePl3KypLatXOtTFwBAVAEQAAAbsbp01JhofT%2B%2B%2BZ08bFjzlhUlOlCMn26mR3s14%2Bl4khAABQBEACA%2BlJTI23YYMLgwoVScXH4ePfuJgxOny5NmMBSsVsIgCIAAgDQUA4edJaKly6VKiudsdpLxVOn0p6uMREARQAEAKAxVFRIS5Y4s4O1l4qjo6XRo80y8YwZUp8%2BLBU3JAKgCIAAADS22kvF778vbd4cPt6zp5kZnDHDXDfTpIk7dfoVAVAEQAAA3HbggJkVXLBAWrYs/FRxy5ZmiXj6dHMRdcuW7tXpFwRAEQABAIgk5eVSQYGZGVy0SDpxwhmLjTX3DM6YYR49erhXp5dZHQDpBQwAQGQLBKS1a00YXLBA2rEjfLx/fycMDh9u9hLi2qwOgCHMAAIA4A27d5sguGCBtHp1eK/iW26R/vmfpR/9yL36vIKcDAAAPOPWW6Uf/tB0H/niC%2Bn116X77jNXyhw9KjGtdX2YARQzgAAAeF1VlQmF/fpJnTu7XU3ki3W7AAAAgJvVtKmUkeF2Fd7BEjAAAIBlCIAAAACWIQACAABYhgAIAABgGQIgAACAZQiAAAAAliEAAgAAWMbqAJiTk6O%2Bfftq%2BPDhbpcCAADQaOgEIjqBAAAAu1g9AwgAAGAjAiAAAIBlCIAAAACWIQACAABYhgAIAABgGQIgAACAZQiAAAAAliEAAgAAWIYACAAAYBkCIAAAgGWsDoD0AgYAADaiF7DoBQwAAOxi9QwgAACAjQiAAAAAliEAAgAAWIYACAAAYBkCIAAAgGUIgAAAAJbxfACcNWuW%2BvTpo%2BbNm6tVq1ZKS0vTunXr3C4LAAAgYnk%2BAPbq1Uu/%2B93vtHXrVq1evVrdu3dXRkaGjh8/7nZpAAAAEcl3F0GHLnVesmSJUlNT6/QzXAQNAABsEOt2AfWpqqpKL730kpKTkzVo0KArvq%2ByslKVlZUXnpeVlTVGeQAAABHB80vAkrRw4UK1aNFC8fHx%2BtWvfqXCwkK1bdv2iu%2BfM2eOkpOTLzxSUlIasVoAAAB3eWoJeO7cuXr88ccvPM/Ly9O4ceNUUVGhI0eO6MSJE3r55ZdVVFSkdevWqX379pf9nMvNAKakpLAEDAAArOCpAFheXq5jx45deN65c2clJCRc8r7bbrtN3/nOd/T0009f1%2BeyBxAAANjEU3sAExMTlZiYeM33BYPBsBk%2BAAAAODwVAC9WUVGhn//855oxY4Y6duyokpISvfDCCzp06JDuu%2B%2B%2B6/6cxMRElZaWXle4BAAA8DpPB8CYmBh98sknevXVV3XixAm1adNGw4cP16pVq9SvX7/r/pyoqCiWfgEAgDU8tQcQAAAAN88X18AAAADg%2BhEAAQAALEMABAAAsAwBEAAAwDIEQAAAAMsQAAEAACxDAAQAALAMARAAAMAyBEAAAADLEAABAAAsQwAEAACwDAEQAADAMgRAAAAAyxAAAQAALEMABAAAsAwBEAAAwDIEQAAAAMsQAAEAACxDAAQAALAMARAAAMAyBEAAAADLEAABAAAsQwAEAACwDAEQAADAMgRAAAAAyxAAAQAALEMABAAAsAwBEAAAwDIEQAAAAMsQAAEAACxDAAQAALAMARAAAMAyBEAAAADLEAABAAAsQwAEAACwDAEQAADAMgRAAAAAyxAAAQAALEMABAAAsAwBEAAAwDIEQAAAAMsQAAEAACxDAAQAALAMARAAAMAyBEAAAADLEAABAAAsQwAEAACwDAEQAADAMgRAAAAAy/x/zYcAqmkhSrkAAAAASUVORK5CYII%3D'}