Genus 2 curve data - 61929.a.433503.1

Formats: - HTML - YAML - JSON - 2025-09-24T15:40:35.046657

• 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': '2122667665185905930', 'abs_disc': 433503, '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': '[[3,[1,0,1]],[7,[1,3,9,7]],[983,[1,47,935,-983]]]', 'bad_primes': [3, 7, 983], 'class': '61929.a', 'cond': 61929, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[-1,-1,0,0,0,1],[1,0,0,1]]', 'g2_inv': "['371293/433503','885391/433503','-264823/433503']", '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': "['52','-9503','10241','55488384']", 'igusa_inv': "['13','403','-1567','-45695','433503']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '61929.a.433503.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '1.9192166450290620995349172454127707957115998080427', 'prec': 170}, 'locally_solvable': True, 'modell_images': ['2.6.1'], 'mw_rank': 1, 'mw_rank_proved': True, 'non_maximal_primes': [2], 'non_solvable_places': [], 'num_rat_pts': 3, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '5.0503461073039665935224174729', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.190008427566', 'prec': 47}, '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': 2, 'torsion_order': 1, 'torsion_subgroup': '[]', 'two_selmer_rank': 1, 'two_torsion_field': ['5.1.141552.1', [-5, 3, -5, 3, -1, 1], [5, 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]], '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': '61929.a.433503.1', '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': '61929.a.433503.1', 'mw_gens': [[[[-1, 1], [1, 1], [0, 1]], [[-1, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.19000842756632378963796276235503', 'prec': 113}], 'mw_invs': [0], 'num_rat_pts': 3, 'rat_pts': [[1, -1, 0], [1, -1, 1], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 61929, 'lmfdb_label': '61929.a.433503.1', 'modell_image': '2.6.1', 'prime': 2}
• g2c_tamagawa •

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

  
  
  • id: 124042
    {'cluster_label': 'c2c2_1~2c2_1~2_0', 'label': '61929.a.433503.1', 'local_root_number': 1, 'p': 3, 'tamagawa_number': 1}
  • id: 124043
    {'cluster_label': 'c4c2_1_0', 'label': '61929.a.433503.1', 'local_root_number': 1, 'p': 7, 'tamagawa_number': 2}
  • id: 124044
    {'cluster_label': 'c4c2_1~2_0', 'label': '61929.a.433503.1', 'local_root_number': -1, 'p': 983, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '61929.a.433503.1', 'plot': 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AAQnCiAYajkNHBmprR9u90sAAAg%2BFAAw1Dr1lKbNtKBA9KECbbTAACAYEMBDFP332/248ZJzPQIAACORAEMU336SLVrS6tWSfPn204DAACCCQUwTEVFSSkp5njsWLtZAABAcKEAhrEHHjD7KVO4GQRA8GMtYCBwWAs4zCUmSt98I40cKT35pO00APDbWAsYcB4jgGGuZBTwzTdZGQQAABgUwDDXp49ZGeSXX6S5c22nAQAAwYACGOZq15b69jXH3AwCAAAkCqArPPig2U%2BdKm3ZYjcLAACwjwLoAq1aSR07SgcPSv/4h%2B00AADANgqgS5SMAo4bZ4ogAJyKgwcP6o9//KPi4%2BNVo0YNnX322frLX/6i4iPuMvP7/Ro%2BfLji4uJUo0YNderUST/%2B%2BKPF1ACORgF0iZ49pfr1pZwcacYM22kAhKoRI0bo73//u9LT07VixQqNHDlSL730kv72t7%2BVvmbkyJEaPXq00tPTtWTJEsXGxuq6667T7t27LSYHcCQKoEuccYZ0zz3m%2BI037GYBELoWLVqkm266SV27dlXz5s3Vs2dPJSUl6ZtvvpFkRv9eeeUV/eEPf9Att9yili1basKECdq7d68mT55sOT2AEhRAFymZE3DWLOnXX%2B1mARCarrjiCv33v//VqlWrJEnfffedFixYoBtvvFGStHbtWuXm5iopKan0PZGRkbr66qu1cOFCK5kBHCvCdgAEzjnnSF26SDNnSn//u/TSS7YTAQg1Q4cOlc/nU4sWLVS1alUdOnRIzz//vG6//XZJUm5uriQpJiamzPtiYmK0fv36cj%2BzsLBQhYWFpT/n5%2Bc7lB5ACUYAXeahh8z%2B7belffvsZgEQej744ANNnDhRkydP1rJlyzRhwgS9/PLLmjBhQpnXeTyeMj/7/f5jHiuRlpYmr9dbujVp0sSx/AAMCqDLdO0qNW0q7dwp/etfttMACDVPPvmknn76afXp00etWrVSamqqHnvsMaWlpUmSYmNjJR0eCSyxbdu2Y0YFSwwbNkw%2Bn690y8nJcfZLAKAAuk3VqoenhMnIsJsFQOjZu3evqlQp%2B38dVatWLZ0GJj4%2BXrGxsZo9e3bp8wcOHND8%2BfPVoUOHcj8zMjJSderUKbMBcBYF0IXuvVeqXl1assRsAFBR3bt31/PPP69PP/1U69atU2ZmpkaPHq2bb75Zkjn1O3jwYL3wwgvKzMzU8uXL1a9fP9WsWVMpKSmW0wMo4fH7/X7bIRB4qanSxInSXXdJ77xjOw2AULF79249%2B%2ByzyszM1LZt2xQXF6fbb79df/rTn1S9enVJ5nq/5557TmPHjlVeXp7atWunjIwMtWzZskK/Iz8/X16vVz6fj9FAwCEUQJfKypLat5ciI6WNG6UGDWwnAgCDAgg4j1PALtWunXTppVJhofTWW7bTAACAQKIAupTHIw0caI7feEM6dMhuHgAAEDgUQBfr00eqV09av16aPt12GgAAECgUQBerUUO67z5zfMQ67gAAIMxRAF3u4YelKlWk//5X%2Bukn22kAAEAgUABdrlkzKTnZHKen280CwN0yMjKUkJCgxMRE21GAsMc0MNDcudK110q1akmbNkler%2B1EANyMaWAA5zECCF1zjXThhVJBgTR%2BvO00AADAaRRAyOORBg0yx3/7G1PCAAAQ7iiAkCT17SvVrSutWSPNmGE7DQAAcBIFEJLM9X8lU8K89prdLAAAwFkUQJQaMMBMCTNnjvTjj7bTAAAAp1AAUap5c6lHD3PMKCAAAOGLAogyHn3U7N99V9qxw24WAADgDAogyrjySql1a2nfPmncONtpAACAEyiAKMPjOTwKmJ4uFRXZzQMAACofBRDH6NNHiokxq4J89JHtNAAAoLJRAHGMyEjpoYfM8ZgxdrMAcA/WAgYCh7WAUa5t26SmTaXCQumrr6QOHWwnAuAWrAUMOI8RQJQrOlpKSTHHr7xiNwsAAKhcFEAc12OPmf1HH0nr19vNAgAAKg8FEMfVqpXUubNUXMzE0AAAhBMKIE6oZBRw3DgpP99uFgAAUDkogDihLl2kFi2k3bulf/zDdhoAAFAZKIA4oSpVpCFDzPFrr0kHD9rNAwAATh8FEL%2Bpb1%2BpQQNzI8iUKbbTAACA00UBxG%2BqUUMaMMAcjxolMXMkAAChjQKICnn4YbNCyNdfSwsW2E4DAABOBwUQFRIdLd15pzkeNcpuFgAAcHoogKiwkptBpk2TVq2ymwVA%2BGEtYCBwWAsYJ6V7d2n6dOmBB6S//912GgDhiLWAAecxAoiT8sQTZj9hgrRtm90sAADg1FAAcVKuukpKTJT275cyMmynAQAAp4ICiJPi8RweBczIkPbutZsHAACcPAogTtott0jx8dKOHdL48bbTAACAk0UBxEmLiJAef9wcjx4tHTpkNw8AADg5FECckrvvlurXl9askT76yHYaAIG0adMm9e3bV/Xr11fNmjV1ySWXaOnSpaXP%2B/1%2BDR8%2BXHFxcapRo4Y6deqkH3/80WJiAEejAOKU1Kx5eHm4l15ieTjALfLy8tSxY0dVq1ZNn332mX766SeNGjVKdevWLX3NyJEjNXr0aKWnp2vJkiWKjY3Vddddp927d1tMDuBIzAOIU7Z9u9S0qbkjeO5c6ZprbCcC4LSnn35aX331lb788styn/f7/YqLi9PgwYM1dOhQSVJhYaFiYmI0YsQIPfDAA7/5O5gHEHAeI4A4ZWedJd1zjzkeMcJuFgCBMW3aNLVp00a9evVSdHS0WrdurXHjxpU%2Bv3btWuXm5iopKan0scjISF199dVauHChjcgAykEBxGl5/HGpShVp1izpu%2B9spwHgtDVr1uiNN97Queeeq1mzZunBBx/UI488on/%2B85%2BSpNzcXElSTExMmffFxMSUPne0wsJC5efnl9kAOIsCiNNy9tlSr17meORIu1kAOK%2B4uFiXXnqpXnjhBbVu3VoPPPCA%2BvfvrzfeeKPM6zweT5mf/X7/MY%2BVSEtLk9frLd2aNGniWH4ABgUQp%2B2pp8z%2Bgw%2BkdeusRgHgsIYNGyohIaHMYxdccIE2bNggSYqNjZWkY0b7tm3bdsyoYIlhw4bJ5/OVbjk5OQ4kB3AkCiBO26WXStddZ%2BYDHDXKdhoATurYsaN%2B/vnnMo%2BtWrVKzZo1kyTFx8crNjZWs2fPLn3%2BwIEDmj9/vjp06FDuZ0ZGRqpOnTplNgDOogCiUvz/zX566y1zdzCA8PTYY48pKytLL7zwgn755RdNnjxZb775pgb8/7xQHo9HgwcP1gsvvKDMzEwtX75c/fr1U82aNZWSkmI5PYASTAODSuH3S23bSt98I/3xj9Jf/2o7EQCnTJ8%2BXcOGDdPq1asVHx%2BvIUOGqH///qXP%2B/1%2BPffccxo7dqzy8vLUrl07ZWRkqGXLlhX6fKaBAZxHAUSl%2BegjqWdP6cwzpQ0bpNq1bScCEIoogIDzOAWMStOjh3TuuVJenjR2rO00AADgeCiAqDRVqx6%2BFnD0aKmw0G4eAABQPgogKlXfvlJcnLR5szRxou00AACgPBRAVKrISGnIEHM8cqSZGgYAAAQXCiAq3f33mxtBVq2SpkyxnQYAAByNAohKFxUlDRxojl980UwRAwAAggcFEI545BGpZk1p2TLpiAUBAOC4MjIylJCQoMTERNtRgLDHPIBwzODB0quvSp06SfPm2U4DIFQwDyDgPEYA4ZjHH5ciIqTPP5cWLbKdBgAAlKAAwjFNmkipqeb4xRftZgEAAIdRAOGooUMlj0eaNk1avtx2GgAAIFEA4bDzz5duvdUcp6XZzQIAAAwKIBw3bJjZf/CBtGaN3SwAAIACiAC49FLp%2BuvNqiAvvWQ7DQAAoAAiIJ55xuzHj5e2bLGbBQAAt6MAIiCuvFLq0EEqLJTGjLGdBgAAd6MAIiA8nsPXAr7xhpSXZzcPAABuRgFEwHTtKrVqJe3ZI6Wn204DAIB7UQARMEeOAr76qlRQYDcPgODCWsBA4LAWMALq4EEzN%2BCaNdIrr0iPPmo7EYBgw1rAgPMYAURARURITz1ljkeNkg4csJsHAAA3ogAi4O66S4qNlXJypMmTbacBAMB9KIAIuDPOkB57zByPGCEVF9vNAwCA21AAYcWDD0per7RypfTxx7bTAADgLhRAWFGnjvTww%2BZ4xAiJW5EAAAgcCiCsefRRczp48WLpiy9spwEAwD0ogLAmJkbq188cjxhhNQoAAK5CAYRVTzwhVakiffaZ9P33ttMAAOAOFEBYdc450q23muOXX7abBQAAt6AAwrqSiaHfe8/MDQgAAJxFAYR1bdpInTqZZeJefdV2GgC2sBYwEDisBYyg8OmnUrduZnqYnByzB%2BBOrAUMOI8RQASFG26QLrhAys%2BX/vEP22kAAAhvFEAEhSpVDi8P99pr5nQwAABwBgUQQaNvX6lBA2n9emnqVNtpAAAIXxRABI0aNcwawRI3gwAA4CQKIILKQw9JERHSggXSt9/aTgMAQHiiACKoxMVJPXua4/R0u1kA/La0tDR5PB4NHjy49LHCwkINGjRIDRo0UK1atZScnKyNGzdaTAngaBRABJ2BA83%2BvfekvDy7WQAc35IlS/Tmm2/qoosuKvP44MGDlZmZqffff18LFizQnj171K1bNx06dMhSUgBHowAi6HToIF10kbRvnzRhgu00AMqzZ88e3XHHHRo3bpzOPPPM0sd9Pp/eeustjRo1Sp07d1br1q01ceJE/fDDD5ozZ47FxACORAFE0PF4Dt8M8uabElOVA8FnwIAB6tq1qzp37lzm8aVLl6qoqEhJSUmlj8XFxally5ZauHBhuZ9VWFio/Pz8MhsAZ1EAA2DjRmniRKmw0HaS0HHHHVLNmtKKFdKiRbbTADjS%2B%2B%2B/r2XLliktLe2Y53Jzc1W9evUyo4KSFBMTo9zc3HI/Ly0tTV6vt3Rr0qSJI7kBHEYBDIA33pBSU6UmTaRhw6S1a20nCn516ki9epnjt9%2B2mwXAYTk5OXr00Uc1ceJEnXHGGRV%2Bn9/vl8fjKfe5YcOGyefzlW45OTmVFRfAcVAAA6BhQ6lxY2n7dunFF6VzzpFuvFH65BOJa6KP7%2B67zf7DD831gADsW7p0qbZt26bLLrtMERERioiI0Pz58/Xaa68pIiJCMTExOnDggPKOuoNr27ZtiomJKfczIyMjVadOnTIbAGdRAANg4EAz6jdlinTddeaats8%2Bk5KTpebNpeeeM6eJUdaVV0rNmpn1gadPt50GgCRde%2B21%2BuGHH5SdnV26tWnTRnfccUfpcbVq1TR79uzS92zZskXLly9Xhw4dLCYHcCSP388l9oG2erU0dqz0zjvSjh3msSpVpK5dpfvvl7p0MZMhQ3r6aWnECOnWW6V//9t2GgDl6dSpky655BK98sorkqSHHnpI06dP1zvvvKN69erpiSee0I4dO7R06VJVrVr1Nz8vPz9fXq9XPp%2BP0UDAIYwAWnDuudLLL5tRv0mTpKuukoqLzSnh7t3NqOCzz3KtoCT17m32M2ZIe/fazQKgYsaMGaMePXqod%2B/e6tixo2rWrKlPPvmkQuUPQGAwAhgkVq6Uxo0z896VjApK0rXXSvfcI918s1kr1238fik%2BXlq/Xpo2zRRkAOGNEUDAeYwABokWLaRRo6RNm6QPPjDXCno80n//a6ZEadjQrJP79dfumhfP45G6dTPHn31mNwsAAOGCEcAgtm6duU5w/Hhpw4bDj19wgdSvn9S3r1k7N9xNmybddJN03nnSzz/bTgPAaYwAAs6jAIaA4mJp7lxTBKdMkfbvN49XqSJ17mzmGLz5ZqlWLbs5nZKXJ9WrZ463bpWio%2B3mAeAsCiDgPE4Bh4CSojdpkpSba64V7NjRFMP//McUwJgYs581Szp40HbiynXmmeYUuSQtXWo3CwAA4YACGGK8Xum%2B%2B6QFC6RffpH%2B/GczsXRBgVlurksXqVEjadAgaeHC8Lle8KKLzP6nn%2BzmAAAgHFAAQ9g550jDh5t5BRculB5%2BWGrQQNq2TUpPN6OE8fHSU0%2BZkbNQLoPnnGP269ZZjQHAQRkZGUpISFBiYqLtKEDY4xrAMFNUJM2ZI02eLE2dKu3Zc/i5s8826%2Bv27Clddpm5wzZUjBkjDRki9ekjvfee7TQAnMQ1gIDzGAEMM9WqSTfcIL37rhkJ/Pe/TemrUUNas8asqpGYaEYGhwwxp5JDYT3i6tXNPtyubwQAwAYKYBirUcMsofavf0nbt5v5BXv2lGrWNBMrjxlj1ttt2FC6914z3UqwrrYZeyE8AAAXd0lEQVSxa5fZR0XZzQEAQDigALpErVpmWbUPPzRlcMoUM49g3brm57ffNnPt1a9v1iR%2B443gut5u%2BXKzP/tsuzkAAAgHXAPockVF0vz5ZvRv2jQzMnikFi3MncVJSWbNYhtzDe7da%2B5s3rXLZL3qqsBnABA4XAMIOI8CiFJ%2Bvxlp%2B/RTsy1aVPb6wGrVpPbtpWuukTp1ktq1C8z6xH/8o/T882b0b9UqifXkgfBGAQScRwHEce3aZe4onjVLmj372NHB6tWlNm3MdDPt20tt25qRusri95vrFB9/3Pz84YfmGkYA4Y0CCDiPAogK8fulX381S9LNm2dOxW7ZcuzrYmOlSy%2BVLrlEatlSSkiQzj3X3HhSUQUF0owZ0ujRUlaWeeyJJ6SXXqqc7wIguFEAAedRAHFKSgrhggXmVHFWljl9XFxc/usbNpSaNTMjhNHR5uaTmjWliAhzHeLu3aZQrlwpffedeUwyr3nxRWngwNCatxDAqaMAAs6jAKLSFBSY8vbtt9L335tCuGKFlJd38p/VvLmUkmKKX8OGlR4VQBCjAALOowDCcTt2mEmoN2wwo3zbt5vrC/fuNTeZRESY%2Bf2io6Xf/U5q3dpMVM2IH%2BBOFEDAeRG2AyD81a9vNpb3BHAiGRkZysjI0KFQWJ4ICHGMAAIAggojgIDzWAkEAADAZSiAAAAALkMBBAAAcBkKIAAAgMtQAAEAAFyGAggAAOAyFEAAAACXoQACAAC4DAUQAADAZSiAAAAALkMBBAAEhYyMDCUkJCiRhcMBx7EWMAAgqLAWMOA8RgABAABchgIIAADgMhRAAAAAl6EAAgAAuAwFEAAAwGUogACACktLS1NiYqKioqIUHR2tHj166Oeffy7zmsLCQg0aNEgNGjRQrVq1lJycrI0bN1pKDKA8FEAAQIXNnz9fAwYMUFZWlmbPnq2DBw8qKSlJBQUFpa8ZPHiwMjMz9f7772vBggXas2ePunXrpkOHDllMDuBIzAMIADhl27dvV3R0tObPn6%2BrrrpKPp9PZ511lt59913ddtttkqTNmzerSZMmmjFjhq6//vrf/EzmAQScxwggAOCU%2BXw%2BSVK9evUkSUuXLlVRUZGSkpJKXxMXF6eWLVtq4cKFVjICOFaE7QAAgNDk9/s1ZMgQXXHFFWrZsqUkKTc3V9WrV9eZZ55Z5rUxMTHKzc0t93MKCwtVWFhY%2BnN%2Bfr5zoQFIYgQQAHCKBg4cqO%2B//17vvffeb77W7/fL4/GU%2B1xaWpq8Xm/p1qRJk8qOCuAoFEAAwEkbNGiQpk2bpnnz5qlx48alj8fGxurAgQPKy8sr8/pt27YpJiam3M8aNmyYfD5f6ZaTk%2BNodgAUQADASfD7/Ro4cKCmTJmiuXPnKj4%2Bvszzl112mapVq6bZs2eXPrZlyxYtX75cHTp0KPczIyMjVadOnTIbAGdxDSAAoMIGDBigyZMn6%2BOPP1ZUVFTpdX1er1c1atSQ1%2BvVvffeq8cff1z169dXvXr19MQTT6hVq1bq3Lmz5fQASjANDACgwo53Hd/48ePVr18/SdL%2B/fv15JNPavLkydq3b5%2BuvfZavf766xW%2Bto9pYADnUQABAEGFAgg4j2sAAQAAXIYCCAAA4DIUQAAAAJehAAIAALgMBRAAAMBlKIAAAAAuQwEEAASFjIwMJSQkKDEx0XYUIOwxDyAAIKgwDyDgPEYAAQAAXIYCCAAA4DIUQAAAAJehAAIAALgMBRAAAMBlKIAAAAAuQwEEAABwGQogAACAy1AAAQAAXIYCCAAA4DIUQABAUGAtYCBwWAsYABBUWAsYcB4jgAAAAC5DAQQAAHAZCiAAAIDLUAABAABchgIIAADgMhRAAAAAl6EAAgAAuAwFEAAAwGUogAAAAC5DAQQABAWWggMCh6XgAABBhaXgAOdF2A4AAABwKnw%2B6eefD28rV0r33Sd16WI7WfCjAAIAgKC1b5%2B0Zo20erW0alXZbevWY1/fsiUFsCIogAAAwKo9e0zJ%2B/VX6ZdfDm%2BrV0sbN0onulgtNlY6/3ypRQuzv%2BaawOUOZRRAAADgqOJiafNmU/LWrjX7ksK3Zk35I3lHqlNHOvdc6bzzjt24TPTUUAABAMBpKS6WcnOldeuk9evNfu1asy957MCBE39GvXrSOeeY7Xe/M4WvZN%2BggeTxOP893IQCCAAATmjvXiknx2wbNpht/fqy%2B6KiE39GRITUtKkUHy%2BdffbhraT01a0bmO8CgwIIAICL7d8vbdpkrrXbuPFw0Tty27Hjtz%2BnalWpUSOpefOyW3y82Ro1MiUQwYE/CgAAwtSePabUHVnwjv55%2B/aKfVbt2mYEr0kTs2/W7PC%2BWTMKXqjhjwoAAFuKi6Vp06QJE0wTu%2BAC6cEHpcsu%2B823FhQcPiWbk3N49O7IoufzVSzGGWdIjRubcleyP3qrW5fr8MIJK4GcBr/fr927d9uOAQAhrbCwUIWFhaU/7969WwkJCcrJyQnvlUAOHpTuvFP69NNjn0tLk/%2Bhh7Vpk5kKpeTu2XXrDl%2BDl5dXsV9Tu7YUF2dG6Bo2NPuSn0v2Z57pznIXFRUljxu/uCiAp6VkuSIAABB63LzcIAXwNPj9frVo0UJLliyp8HsSExNP6vUn%2B578/Hw1adLkpP7L%2BWQzOf0dTvb1gfjOp/KeYPvOTmcKxOuD8e83/6ZP//VHjwBu2bJFbdu21U8//aRGjRo5kudU3lPpr2/d2gztHcc/dI%2BeqjpG8fHmLtmSmymaNj18LV7nzsH19zvU/k27eQSQawBPg8fjUdWqVU/q/4BP9vWn%2Bp46depU%2BD3B%2BB2C7TufynuC7TsHIlMgvrMUXH%2B/%2BTft3O%2BIiooK33/TxcUnLH%2BSdH/btRr4ZR1Vrx6gTBZeXyLY/n67QRXbAULdgAEDHH39qb7Hyc8PxHcItu98Ku8Jtu98Kr8j2F5/KoLxOwTb3%2B9g/PdwsoLxO5zw9VWqSGeddcL3r98fo9WrT7wMWrD9/Q6Hf9NuwSngMFNyXaKbrmvgO7vjO0vu/N5u/M4bN24sPS3YuHFj23Gc8%2BST0ssvH/fp6zVT/9H1atZMuvFG6YYbzDq3tWsHMKPD3Pj3O1hUHT58%2BHDbIVC5qlatqk6dOinCRRMy8Z3dw43f223fubCwUC%2B99JKGDRumWrVq2Y7jnDZtpE8%2Bkf73v2OeWt3uDv3noie1IcejHTukb76R3ntPGjVK%2Bvxzads2yes1g4ihfgmb2/5%2BBwtGAAEAQcVVo0I7d5pRwAkTTKu74ALp4Yel%2B%2B%2BXqlRRQYE0b540Y4Y0c6aZCuZIjRub0cEbb5Q6d5bCuS%2BjclEAAQBBxVUF8CT4/WZOwJkzpc8%2BMyOB%2B/cffj4y0pwi7t5dSk425RA4HgogACCoUAArZt8%2BUwJnzDBzSR89OnjZZVKPHtItt0gJCVYiIohRAAEAQYUCePL8fmnFCnNJ4bRp0qJFZe8ebtFC6tXLbC1bhv51gzh9FEAAQFChAJ6%2BbdtMEczMlObMkQ4cOPxcQoLUp490%2B%2B3S735nLyPsYh7AMNOvXz95PJ4y2%2BWXX247VsA88MAD8ng8euWVV2xHcdTw4cPVokUL1apVS2eeeaY6d%2B6sxYsX247lmKKiIg0dOlStWrVSrVq1FBcXpzvvvFObN2%2B2Hc1xU6ZM0fXXX68GDRrI4/EoOzvbdiRUoi%2B%2B%2BELdu3dXXFycPB6Ppk6dWimfGx0t3XefOTW8bZv07rvmusDq1aWffpL%2B9Cfp3HOl9u2l11%2BXduyolF9bIWlpaUpMTFRUVJSio6PVo0cP/fzzz4ELAEkUwLDUpUsXbdmypXSbMWOG7UgBMXXqVC1evFhxcXG2ozjuvPPOU3p6un744QctWLBAzZs3V1JSkrZv3247miP27t2rZcuW6dlnn9WyZcs0ZcoUrVq1SsnJybajOa6goEAdO3bUiy%2B%2BaDuK4zIyMpSQkKDExETbUQKmoKBAF198sdLT0x37HV6v1Lev9PHHpgyOHy8lJZm5qLOypAEDpIYNpZ49zfWEhw45FkWSNH/%2BfA0YMEBZWVmaPXu2Dh48qKSkJBUUFDj7i1EGp4DDTL9%2B/bRr165K%2B6/IULFp0ya1a9dOs2bNUteuXTV48GANHjzYdqyAKTllNmfOHF177bW24wTEkiVL1LZtW61fv15Nmza1Hcdx69atU3x8vL799ltdcskltuM4yq2ngD0ejzIzM9WjR4%2BA/L7cXDO34D//KR05sNyokXTPPWYEMRD/tLZv367o6GjNnz9fV111lfO/EJIYAQxLn3/%2BuaKjo3Xeeeepf//%2B2rZtm%2B1IjiouLlZqaqqefPJJXXjhhbbjBNyBAwf05ptvyuv16uKLL7YdJ2B8Pp88Ho/q1q1rOwoQkmJjpccek7791hTARx%2BV6teXNm2S/vpXqXlzM6XMZ5%2BZpYud4vP5JEn16tVz7pfgGBTAMHPDDTdo0qRJmjt3rkaNGqUlS5bo97//vQoLC21Hc8yIESMUERGhRx55xHaUgJo%2Bfbpq166tM844Q2PGjNHs2bPVoEED27ECYv/%2B/Xr66aeVkpLiqhEiwCkXXyy98oopf%2B%2B/b%2BYT9Pul6dPNJNPnnWeez8%2Bv3N/r9/s1ZMgQXXHFFWrZsmXlfjhOiAIYwiZNmqTatWuXbl9%2B%2BaVuu%2B02de3aVS1btlT37t312WefadWqVfr0009tx60UR3/n%2BfPn69VXX9U777wjT5jOa1Den7MkXXPNNcrOztbChQvVpUsX9e7dO2xGe4/3nSVzQ0ifPn1UXFys119/3WLKynei7w0EQmSkdNtt0ty50sqV0uDB5hrCX381o4WNG5v9unWV8/sGDhyo77//Xu%2B9917lfCAqjGsAQ9ju3bu1devW0p8bNWqkGjVqHPO6c889V/fdd5%2BGDh0ayHiOOPo7f/jhh/rDH/6gKlUO/7fMoUOHVKVKFTVp0kTrKut/pSw6mT/ne%2B65R8OGDQtkPEcc7zsXFRWpd%2B/eWrNmjebOnav69etbTFn5TvRnzTWA4S/Q1wBWVEGBuYv4tdfMXIOSuYGkVy/pqaekSy89tc8dNGiQpk6dqi%2B%2B%2BELx8fGVFxgVwsrLISwqKkpRUVEnfM2OHTuUk5Ojhg0bBiiVs47%2Bzvfff7%2B6d%2B9e5jXXX3%2B9UlNTdffddwc6niMq8ucsmVMp4XKqv7zvXFL%2BVq9erXnz5oVd%2BZMq/mcNBFKtWtKDD0oPPCDNmiWNHi3Nni198IHZkpKkP/xBquj9G36/X4MGDVJmZqY%2B//xzyp8lFMAwsmfPHg0fPly33nqrGjZsqHXr1umZZ55RgwYNdPPNN9uO54j69esfUwSqVaum2NhYnX/%2B%2BZZSOaugoEDPP/%2B8kpOT1bBhQ%2B3YsUOvv/66Nm7cqF69etmO54iDBw%2BqZ8%2BeWrZsmaZPn65Dhw4pNzdXkrlwvHr16pYTOmfnzp3asGFD6ZyHJfOlxcbGKjY21mY0VII9e/bol19%2BKf157dq1ys7OVr169YLu7naPR%2BrSxWzffSeNHGkK4H/%2BY7YrrzTzC1577YlXGhkwYIAmT56sjz/%2BWFFRUaX/lr1eb7lnN%2BAQP8LG3r17/UlJSf6zzjrLX61aNX/Tpk39d911l3/Dhg22owVUs2bN/GPGjLEdwzH79u3z33zzzf64uDh/9erV/Q0bNvQnJyf7v/76a9vRHLN27Vq/pHK3efPm2Y7nqPHjx5f7vf/85z/bjuYYn8/nl%2BT3%2BXy2ozhu3rx55f753nXXXbajVcivv/r9Dz7o91ev7veb20b8/o4d/f65c4//nuP9Wx4/fnzAcsPv5xpAAEBQces1gKFs0yZpxAjpzTelkitRfv976fnnJRctRhVSuAsYAACclkaNzE0ia9aYlUWqVzd3ErdvL9188%2BGbRxA8KIAAAKBSxMVJ6enSqlXS3Xebu4WnTpVatTI3khxxkzssowACAIKCG9cCDlfNmklvvy0tXy7ddJNZX3jsWOl3v5NeeEHav992QnANIAAgqHANYPj58kvp8celJUvMz82bS6NGmdPDYTqHf9BjBBAAADjqyiulrCxp4kRzveC6ddKtt5o5BFeutJ3OnSiAAADAcVWqSHfcIf38s/THP5pl5%2BbMkS66SBo2TNq713ZCd6EAAgCAgKlVS/rrX6WffpK6dZOKiqQXX5QuvFCaMcN2OvegAAIAgIA7%2B2zpk0/MXcJNm5rTwl27Sn36cLdwIFAAAQCANTfdJP34ozRkiDlN/MEHUkKC9O67Zm0ROIMCCAAArKpd29wV/PXX0iWXSDt3SnfeKXXvLv3/MtioZBRAAAAQFC67zJTA5583q4l8%2Bqm5NnDSJEYDKxsFEAAABI1q1aRnnpGWLZPatJF27ZL69pV69ZL%2B9z/b6cIHBRAAAASdCy%2BUFi2S/vIXKSJC%2Bugjs6Tcf/5jO1l4oAACAICgFBEhPfustHix1KKFlJsrXX%2B9uWGksNB2utBGAQQABAXWAsbxXHqptHSp9PDD5ucxY6T27aVVq%2BzmCmWsBQwACCqsBYwT%2BeQT6e67pR07zN3DY8dKKSm2U4UeRgABAEDI6N5d%2Bu47qVMnac8es7zc/fdL%2B/bZThZaKIAAACCkNGpk1hH%2B058kj0caN07q2FFas8Z2stBBAQQAACGnalXpueekWbOkBg2kb7818wh%2B%2BqntZKGBAggAAELWddeZ8nf55WbOwGeekQ4etJ0q%2BFEAAQBASGvcWJo/30wP8%2B9/m%2BljcGIUQABAhRQVFWno0KFq1aqVatWqpbi4ON15553afNRirXl5eUpNTZXX65XX61Vqaqp27dplKTXconp1s57wuefaThIaKIAAgArZu3evli1bpmeffVbLli3TlClTtGrVKiUnJ5d5XUpKirKzszVz5kzNnDlT2dnZSk1NtZQaQHmYBxAAcMqWLFmitm3bav369WratKlWrFihhIQEZWVlqV27dpKkrKwstW/fXitXrtT555//m5/JPICA8xgBBACcMp/PJ4/Ho7p160qSFi1aJK/XW1r%2BJOnyyy%2BX1%2BvVwoULbcUEcBQukwQAnJL9%2B/fr6aefVkpKSulIXW5urqKjo495bXR0tHJzc8v9nMLCQhUesbBrfn6%2BM4EBlGIEEABQrkmTJql27dql25dffln6XFFRkfr06aPi4mK9/vrrZd7n8XiO%2BSy/31/u45KUlpZWesOI1%2BtVkyZNKveLADgGI4AAgHIlJyeXOZXbqFEjSab89e7dW2vXrtXcuXPLXKcXGxurrVu3HvNZ27dvV0xMTLm/Z9iwYRoyZEjpz/n5%2BZRAwGEUQABAuaKiohQVFVXmsZLyt3r1as2bN0/169cv83z79u3l8/n09ddfq23btpKkxYsXy%2BfzqUOHDuX%2BnsjISEVGRjrzJQCUi7uAAQAVcvDgQd16661atmyZpk%2BfXmZEr169eqpevbok6YYbbtDmzZs1duxYSdL999%2BvZs2a6ZNPPqnQ7%2BEuYMB5FEAAQIWsW7dO8fHx5T43b948derUSZK0c%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%2BT%2Bk%2BPjyvmTfEgAAAABJRU5ErkJggg%3D%3D'}