Genus 2 curve data - 5329.b.5329.1

Formats: - HTML - YAML - JSON - 2024-07-27T03:10:27.082367

• 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': '391968563378657010', 'abs_disc': 5329, 'analytic_rank': 2, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[73,[1,2,1]]]', 'bad_primes': [73], 'class': '5329.b', 'cond': 5329, 'disc_sign': 1, 'end_alg': 'RM', 'eqn': '[[0,-1,0,1],[1,0,1,1]]', 'g2_inv': "['-229345007/5329','6540849/5329','7698365/5329']", 'geom_aut_grp_id': '[2,1]', 'geom_aut_grp_label': '2.1', 'geom_aut_grp_tex': 'C_2', 'geom_end_alg': 'RM', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['188','3721','413963','-682112']", 'igusa_inv': "['47','-63','-3485','-41941','-5329']", 'is_gl2_type': True, 'is_simple_base': True, 'is_simple_geom': True, 'label': '5329.b.5329.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.25812129774410191195202641991721242525285898210', 'prec': 163}, 'locally_solvable': True, 'modell_images': ['2.12.2', '3.2160.18'], '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': '24.093313919044251169327764753', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.0107133995187', 'prec': 50}, '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': 1, 'torsion_subgroup': '[]', 'two_selmer_rank': 2, 'two_torsion_field': ['5.1.341056.1', [4, -2, -4, 2, 0, 1], [5, 4], 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': [['2.2.5.1', [-1, -1, 1], -1]], 'factorsQQ_geom': [['2.2.5.1', [-1, -1, 1], -1]], 'factorsRR_base': ['RR', 'RR'], 'factorsRR_geom': ['RR', 'RR'], 'fod_coeffs': [0, 1], 'fod_label': '1.1.1.1', 'is_simple_base': True, 'is_simple_geom': True, 'label': '5329.b.5329.1', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['2.2.5.1', [-1, -1, 1], -1]], ['RR', 'RR'], [1, -1], 'G_{3,3}']], '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': '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': '5329.b.5329.1', 'mw_gens': [[[[0, 1], [1, 1], [0, 1]], [[0, 1], [0, 1], [0, 1], [-1, 1]]], [[[0, 1], [1, 1], [0, 1]], [[-1, 1], [0, 1], [0, 1], [-1, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.13658958321348895344899573686271', 'prec': 113}, {'__RealLiteral__': 0, 'data': '0.092601280436678151972966262336165', 'prec': 117}], 'mw_invs': [0, 0], 'num_rat_pts': 10, 'rat_pts': [[-1, -1, 1], [-1, 0, 1], [0, -1, 1], [0, 0, 1], [1, -8, 2], [1, -3, 1], [1, -3, 2], [1, -1, 0], [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: 2028
    {'conductor': 5329, 'lmfdb_label': '5329.b.5329.1', 'modell_image': '2.12.2', 'prime': 2}
  • id: 2029
    {'conductor': 5329, 'lmfdb_label': '5329.b.5329.1', 'modell_image': '3.2160.18', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  {'cluster_label': 'c2c2_1~2c2_1~2_0', 'label': '5329.b.5329.1', 'p': 73, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '5329.b.5329.1', 'plot': 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GAweEgDUHkEQBdLTja3h5OYDALAns2bN0uS0tLSDnk8LS2t7Lmfe%2Bihh%2BTz%2Bcpa06ZNa7xOIJYQAF3uttvMduZMad06u7UAcDePx3PI147jHPZYqZEjRyoQCJS1dfwHBlQJAdDl2raVunaVSkq4PzAAO9LT0yXpsN6%2BrVu3HtYrWCoxMVFer/eQBqDyCIAo6wV89lmpsNBuLQDcp2XLlkpPT9esgy5GLiws1Mcff6xzzz3XYmVA7KpluwDY17evlJEhbdokvfGGmRwCANVp9%2B7dWrVqVdnX%2Bfn5ysvLU6NGjdSsWTPl5uZqzJgxat26tVq3bq0xY8YoKSlJAwcOtFg1ELvoAYRq15Z%2B/WuzP2GC3VoAxKYvvvhCnTp1UqdOnSRJI0aMUKdOnfTnP/9ZknT33XcrNzdXt99%2Bu84880xt2LBBH3zwgerXr2%2BzbCBmsQ4gJEkbN0rNm0sHDkhffy2deqrtigCg8lgHEKgaegAhScrMlPr1M/t%2Bv91aAABAzSIAoswdd5jtiy9KO3farQUAANQcAiDKXHCB1L69tHcv9wcGACCWEQBRxuMJ9QJOmGDWBgQAALGHAIhDXHut5PNJq1ZJ779vuxoAAFATCIA4RL160pAhZn/8eLu1AACAmkEAxGGGDTPbd981PYEAACC2EABxmJNOki65RHIcloQBACAWEQBRruHDzfb556Xdu%2B3WAgAAqhcBEOXq1cv0BAYC0gsv2K4GAMrn9/uVnZ2tnJwc26UAUYVbwaFCTzwh/eY3Utu20pIlZpkYAIhE3AoOqBp6AFGhG24ws4KXLZNmz7ZdDQAAqC4EQFTI6zUhUDK9gQAAIDYQAHFEpZNB3n5b%2Bu47u7UAAIDqQQDEEbVpE1oShoWhAQCIDQRAHNWdd5rtc89Ju3bZrQUAABw/AiCOqmdP6eSTpWBQmjzZdjUAAOB4EQBxVHFxoV7AceOkkhK79QAAgONDAESlDBok%2BXzSypXmHsEAACB6EQBRKfXqSTfdZPYfe8xuLQAA4PgQAFFpd9xhhoNnz5YWL7ZdDQAAOFYEQFRaixZSv35m//HHrZYCAACOAwEQVZKba7Yvvij9%2BKPdWgAAwLEhAKJKzjtPOuMMaf9%2BaeJE29UAAIBjQQBElXg8oV5Av18qLLRbDwB38/v9ys7OVk5Oju1SgKjicRzHsV0EokthobkecNMm6YUXpOuus10RALcLBoPy%2BXwKBALyer22ywEiHj2AqLKEBGnYMLP/6KPmPsEAACB6EABxTG65RapbV1q4UPrkE9vVAACAqiAA4pikpkrXX2/2x461WwsAAKgaAiCOWelkkDfflFatslsLAACoPAIgjlnbttIll5hrAJ94wnY1AACgsgiAOC4jRpjtc89JO3bYrQUAAFQOARDHpXt3qUMHac8e6ZlnbFcDAAAqgwCI4%2BLxSL/9rdkfN04qKrJbDwAAODoCII7bwIFSWpq0fr302mu2qwEAAEdDAMRxS0wMLQw9diwLQwMAEOkIgKgWt90m1akjffmlNHeu7WoAAMCREABRLVJTpcGDzT4LQwMAENkIgKg2pZNBZs6UVqywWwsAAKgYARDV5uSTpV/9ylwD%2BNhjtqsB4AZ%2Bv1/Z2dnKycmxXQoQVTyOwyX7qD4ffih16ybVrSutWyelpNiuCIAbBINB%2BXw%2BBQIBeb1e2%2BUAEY8eQFSrLl2kTp2kffukp56yXQ0AACgPARDVyuMJ3R5u/HipoMBuPQAA4HAEQFS7q6%2BWsrKkzZull16yXQ0AAPg5AiCqXe3a0p13mn0WhgYAIPIQAFEjfv1rKTlZWrxYmjXLdjUAAOBgBEDUiAYNpBtvNPssDA0AQGQhAKLG/OY3Ulyc9P77picQAABEBgIgakyrVlK/fmafXkAAACIHARA16ne/M9upU82sYAAAYB8BEDXqnHOkzp2lwkJpwgTb1QAAAIkAiDAo7QWcMMHcIQQAANhFAESN69tXatFC2rZNmjLFdjUAAIAAiBpXq5aZESxJjz0mlZTYrQcAALcjACIshg6VvF7p22%2Bl996zXQ0AAO5GAERYeL3STTeZfZaEAVBd/H6/srOzlZOTY7sUIKp4HIc7tSI81q41awOWlEiLFkkdOtiuCECsCAaD8vl8CgQC8nq9tssBIh49gAib5s2lK64w%2B48%2BarcWAADcjACIsMrNNdupU6WtW%2B3WAgCAWxEAEVbnnCOddZZZGPqpp2xXAwCAOxEAEVYeT6gXcMIEqaDAbj0AALgRARBh17%2B/lJkpbdkivfqq7WoAAHAfAiDCrnZtadgws//44xLz0AEACC8CIKy4%2BWYpMVH68ktp/nzb1QAA4C4EQFjRuLE0cKDZHzfObi0AALgNARDWDB9utq%2B/Lm3aZLcWAADchAAIazp1ks47TzpwQJo40XY1AGwaNWqUPB7PIS09Pd12WUDMIgDCqtLJIE8/LRUV2a0FgF3t2rXTpk2byto333xjuyQgZtWyXQDc7YorpLQ0MwQ8Y4Z05ZW2KwJgS61atej1A8KEHkBYlZBgZgRLZmFoAO61cuVKZWZmqmXLlrrmmmu0evXqCo8tKChQMBg8pAGoPI/jsAob7Fq3TmrRQiopkZYtk045xXZFAMLt3Xff1d69e9WmTRtt2bJFo0eP1rfffqslS5YoJSXlsONHjRqlBx544LDHA4GAvF5vOEoGohoBEBGhTx9p5kzpt7%2BVxo61XQ0A2/bs2aMTTzxRd999t0aMGHHY8wUFBSo46F6SwWBQTZs2JQAClcQQMCLCLbeY7f/9n7R/v91aANiXnJysDh06aOXKleU%2Bn5iYKK/Xe0gDUHkEQESEiy%2BWTjhB2r5dmj7ddjUAbCsoKNCyZcuUkZFhuxQgJhEAERHi46WhQ83%2BpEl2awEQfnfddZc%2B/vhj5efn67PPPlP//v0VDAY1ePBg26UBMYkAiIgxZIjZzpkjrV1rtxYA4bV%2B/XoNGDBAJ598si6//HIlJCTo008/VfPmzW2XBsQkJoEgonTvbgLggw9Kf/qT7WoARItgMCifz8ckEKCS6AFERCkd7ZkyReJXEwAAagYBEBHl8sulpCRp5Urp889tVwMAQGwiACKi1Ksn9e1r9l96yW4tAADEKgIgIs6AAWb72mvm7iAAAKB6EQARcXr0kHw%2BadMm6X//s10NAACxhwCIiJOYaG4NJ0lvvGG3FgAAYhEBEBHp8svN9t//ZjYwAADVjQCIiNSjh%2BkJXLNGWrrUdjUAAMQWAiAiUnKy1LWr2X/vPbu1AAAQawiAiFi9epntrFl26wAQufx%2Bv7Kzs5WTk2O7FCCqcCs4RKxFi6SOHc3C0Dt3SrVr264IQKTiVnBA1dADiIjVvr3UqJG0d6%2BUl2e7GgAAYgcBEBErLk7q3Nnsz59vtxYAAGIJARAR7cwzzXbhQrt1AAAQSwiAiGinnWa2ixfbrQMAgFhCAEREO%2BUUs12xggWhAQCoLgRARLQWLcx21y5pxw6rpQAAEDMIgIhodetKDRua/c2b7dYCAECsIAAi4jVqZLb0AAIAUD0IgIh4deqYbUGB3ToAAIgVBEBEvJISs43jXysAANWCH6mIeMGg2darZ7cOAABiBQEQEe3AgdDkj/R0u7UAABArCICIaKtWScXFUlKSlJlpuxoAAGIDARARbd48s%2B3UiWsAARzO7/crOztbOTk5tksBogo/UhHR3nrLbLt1s1sHgMg0bNgwLV26VAsWLLBdChBVCICIWFu3Sm%2B/bfavuMJuLQAAxBICICLWo49KhYXSWWdJHTvargYAgNhBAEREWrVKeuwxs//HP9qtBQCAWEMARMQpLJSuu07av1%2B66CKpd2/bFQEAEFsIgIgoJSXSTTdJn30mNWggPfus5PHYrgoAgNhCAETEOHDAhL8XXpDi46WXX5aaN7ddFQAAsaeW7QIASfrhB2ngQGn2bLPe35Qp0sUX264KAIDYRA8grJs5Uzr1VBP%2BkpKk6dNNGAQAADWDAAhrli%2BXLrtM6tPH3O%2B3bVtz7V%2BfPrYrAwAgthEAEXZLl0o33CC1aye9%2BaZUq5Z0993Sl19K7dvbrg4AgNjHNYAIi6Iic1ePp56S3n8/9Hjv3tLDD5vePwAAEB4EQNSY4mJp3jzp1VeladPMRA/JLOty2WXSyJHmLh8AACC8CICoVjt2SP/5j/TOO6bHb%2BvW0HNpadLgwdItt0itWtmrEQAAtyMA4rj88IPp5fvkE%2Bnjj811fI4Tet7nM71911wj9ehhrvcDAAB28eMYlbZ9u/T119LChdIXX0iffy6tXn34cW3bmjX8Lr1UuuACKSEh/LUCcAe/3y%2B/36/i4mLbpQBRxeM4B/fXAGYYd/lyadkyM2N3yRJp8WJp3bryj8/Ols47T/rFL6SuXaWsrPDWCwDBYFA%2Bn0%2BBQEBer9d2OUDEowfQhYqLpY0bpTVrpPx804u3erW0apW0cqX0448Vv7ZFC6lTJ%2BmMM6ScHDOJo0GDcFUOAACqAwEwxuzfbxZV3rgx1DZskNavl77/3vTibdhg7rt7JFlZ0sknm969du3M%2Bnzt2xP2AACIBQTACFdUJG3bZnrltm0zky5K29atpm3ZYrabN0s7d1bufWvVkpo2lVq2NDNyW7WSTjxRat3atHr1ava8AACAPQTAMNi3z0ygCARCbefOUNuxI7Tdvt20HTtM4Nu1q%2Bp/XmKilJEhZWaalpUlnXCCCXxNm0rNmpnn4%2BOr/1wBAEDkIwCGwT/%2BId1//7G/3uORGjWSUlKkxo0PbU2amPX1SltGhhmm9Xiqr34AABBbCIBh4POZ3jafL9QaNDDbhg3NfsOGodaoUailpJjn6a0DAADVhWVgwqC4WIqLo1cOAGoKy8AAVUMPYBjQewcAACJJnO0CAAAAEF4EQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAEQtv9%2Bv7Oxs5eTk2C4FiCqsAwgAiHqsAwhUDT2AAAAALkMABAAAcBkCIAAAgMsQAAEAAFyGAAgAAOAyBEAAAACXIQACAAC4DAEQAADAZWrZLgAA3MBxpL17pR07pEBACgZN27VL2r3btL17Tdu3T9q/37SCAtMKC6WiIunAgVArLpZKSsx7/3xJ/7g40zweKT5eqlUr1GrXNi0hwbTERKlOnVBLSpLq1jXbpCQpOVmqV8%2B0%2BvVDLSnJvD%2BA6EMABOBejiO9/LI0caK0apWUkSENGSLdfLNJRpV4%2Bc6d0rp10vr10saN0qZN0ubN0tat0g8/SD/%2BaNr27SbAxZL4eMnrlXw%2BqUED0xo2NK1RI9NSUqTU1FBr3Ng8Hh9fTUXs3y8984w0aZL5%2BtJLpTvukK6%2Bupr%2BANS47dulceOkV14xvxGdcYb0m99I3brZriymcSs4AO41ZIg0efLhj3frJr3zjukakwlwS5dK334rrVhhsuLq1dKaNebnVVXEx5ug5PWaVr9%2BqHettMctKcn0xJX2zCUmhnrtatc2vXjx8aaV9vLFHXRBT2mPYElJqB3cc1hUZFphYaiHsaDAZKl9%2B0wr7Y3cu1fasyfUS1naY3k8Pzni4kwQbNJESkuT0tNN9s7IkDIzTcvKMts6dY7wRvv3S716SXPnKijJJykgyStJv/61CfaIbJs3SxdcYD5UP/foo1JubpXervTffbX9ghHDCIAA3GnmTKlPnwqffvuisRpf67fKyzM/o44kNdUElqwsE2LS002wadzYtJSUUI9YcnL0D5s6jgmBpcPYgYDpCd2xw2y3bw%2B1bdtCvaA//miOqYrUVOmEE6SmTaXmzaVmzcy2eXMpe%2BbDqv/XeyXp8AAoSe%2B/L/XsWX0njuo3eLA0ZUr5z8XFSatXa2/j5tq61fSqb9lietZLtz/8EOptL%2B1x/%2Bc/pWHDwnsa0Ygh4OPgOI52VfXXfwCR4cknj/h0yuyn9Z5uLPu6WTOpTRvppJOkE0%2BUWrY0IeSEE0yPXWWUlFS9xzCSlV4LmJVV%2BdcUFZlQWPoDvfSH%2BebNoe2mTWY4vaAgFBzz8g5/r4V6Wif%2BtB/82VaSSp58SnGdOx/j2aEm7dsnbV29S01ffqXi2aglJfpH66c0umhkld57/Xrzi0ll1K9fX55o/43sGNEDeByCwaB8Pp/tMgAAwDEIBALyer1HPzAGEQCPw9F6AHNycrRgwYKjvk8wGFTTpk21bt26Sv1DrOz7VvXYmnrvqp5fTdVRk8fyPTz2Gqp6/PEcW1BgLvkbN04as%2B5a9dFbFb84O1uaP98p2lIkAAAZKklEQVR6zdV1fKx8D0sVFBQovnNn1Vq9WpLp%2BWsqaZ1CQ8Cv6QrdpOfKfd/UVGnPni907bVn6pRTzLe7bVszTF9TNR/X8Y6jXatXK/v007XEwv8zBQWhCU4bN0q///1YDRgwQps2qaxt3myuGa2shrV3a1nRiaqr/RUes/3WPyj%2BD/fI6y3/0onjPUc39wAyBHwcPB7PET%2BE8fHxVfrNwuv1Vur4qrxvVWuoyfeu7PnVZB01eX4S38Njfd9wfA/nzDHzAr77zjw3s/7tum7XEQLgrbeaWRoWa66J46P5e3iY22%2BX7rrrkIe8CgXA3i/frlmpXq1YIS1fbibxLFtmZm3/%2BKMkddOzzx76lhkZUocOpp16qtm2bWv5ezhunPTYY/KuXq0dkkoGDVKtMWOkowxvV7aOXbukAwda67PPvFq/XtqwQYdtf/jh568apXHjyn8/rzc0oScjQ3rvvef1xz8OKZvsU7r1%2Bbzy3DRQeq78kK5ateT9423SCeH/HLoBAbAGDauhq1Cr8r5VraEm3zsS6oj186vp966p963p7%2BEjj0i//72ZvJCRIf3pT9KQIZdIv7ut/GsBf/lLEwAt1lxTx0dCHdV27PDhZqLHrFmHP5ebqybXdNNFki666NCndu82YfCJJ2YrI%2BMiLVkiLV4srV0b6s364IPQ8fHxUpMmn2jAABMKO3Y0wfCEE8rvlarWv7vcXOnxx8u%2BjJMU9%2BGHUpcu5rwvuKDCl95%2B%2BzDt2GFC3MFt3bpQuAtdL/fWUefLJCaGZmjv3btSXbq0Lpu1fXBLTj70dX7/3oonZfztb9Knn5pp9gfzeKTx481f8hFEwv930Yoh4AhQei1hrF6LEOvnJ8X%2BOUbz%2BU2ebFZ7kaSbbpLGjjUTF8rMmCFNnKjilSv13%2B%2B%2B01kTJqjuzTebtVZiSDR/D4%2BoqEh64QVtnzhRKZ9/rh%2B7dVNKbq7Uu3eV32rXLmnJEmnRIumbb0xbtKjimcsNGkjt25tWOoR8yikmIFXLqOLKldLJJ1e45k7BaWdr4YRPtXGjGZYtXYvy4N67ffsq90d5vWamdVaWyVyl29L9rCwzm71GRkuDQenpp1U8dapW5eWpVf/%2Bqj1ihHTOOTXwh6FUbP0PF6USExN1//33K/GnNcdiTayfnxT75xit5xcMhpYRu%2B8%2B6S9/Keegvn2lvn11oKBAHz70kDoPHRpz4U%2BK3u/hUdWuLQ0dquLevaUmTVTyyitm7Z1jUL%2B%2BGVU9eGTVcUyY%2BvrrUDBctMgMKe/cKf33v6YdLCnJzBRv0cLMHs/KMsOeKSkmNNarZ9Y3rFXLBKriYpNj9%2B0zay7u2mWW1jnplVd0zhH6aBLzPtO1565Wvlod8bxSUkJhrryWlfWzX4rCzeuV7rpLB4YP18sPPaSRI0eWrcGJmkMPIICY9cYbUv/%2BUuvW5tovFoeNXeHu4SwoMMPIixebVrpQ%2BHffmUBXHf6p3%2Bl3GnvEYy7NWKgdLTqVLZxd2lt3cM9d3brVUw9iS%2Bz9mgsAPyldwLl9e8IfqldiorkWsGPHQx8vKjJ3iFm1ymzXrTPDslu2mPUPAwHTy7d/v7kri%2BOY9Y4TEkyvYHKy6Y3zeqWEXZ2kzyquwalXT28vP0my2XuHqEUABBCzTvxpleBPPzW3PavE7X2B41K7tulxbt26Gt6s4Eqp5d1mVko5PEOGWB67RTSrcAFuAIh2XbuaWb%2BbNkkPPmi7GqCKEhOlN98s/5rGHj2khx8Of02IGVwDCCCmvfKKNGCA2Z8wQbrtNrv1oGbE7CxnycwKeeEFszB5UpJ05ZVS9%2B7Rf1NpWEUPYAQYNWqUTjnlFCUnJ6thw4a66KKL9NlnR7jwIwIVFRXpnnvuUYcOHZScnKzMzEwNGjRIGzduPOLrRo0aJY/Hc0hLT08PU9XV41jPPdL861//Uq9evZSamiqPx6O88m6%2B%2BjOTJ08%2B7Pvn8Xi0f3/FK/uH2zXXSHffbfZvv93MCi4oOOiAAwc0/9579fTJJ%2BuW%2BvVVt5LnHokcx9GoUaOUmZmpunXrqkuXLlqyZMkRXxMLn0Ht3StNm2b23323%2BmZh/KS42PQif/GF6ZB7%2BmnTo3znndLAgdLFF5sVS9q1M/eIzsyUmjQxLTPTXIpw2mlSt27ml5Hf/17y%2B80yfhs3HrrKy4QJE9SyZUvVqVNHZ5xxhj755BMzzHv66aZ16mQuavV4ouLzVxlz585V7969lZmZKY/HoxkzZtguyRW4BjACtGnTRuPHj1erVq20b98%2BPfroo%2BrZs6dWrVqlxse4nEG47d27VwsXLtR9992njh07aseOHcrNzVWfPn30xRdfHPG17dq10%2BzZs8u%2Bjo%2Byq/WP59wjyZ49e3Teeefpyiuv1M0331zp13m9Xi1fvvyQx%2BrUqVPd5R2Xv/3NzIR84AGzpu7s2dLEidJ5JZ9IAwbonA0bVLri2EOSgh98YH5iR5m///3vGjt2rCZPnqw2bdpo9OjR6tGjh5YvX676R7hWLKo/g1OnSsOGmdkVkkn8zZqZQHiUO2WU2r/fLAK9Zo1pa9ea9v33pm3caCZs1JTUVOnMM6Xk5MWaMWOmHn/8fnXrdrYmTpyoG3r10tK2bZW4cGHoBXfeaX6Tads2Kj5/R7Nnzx517NhRQ4YM0RVXXGG7HNdgCDgClQ5lzJ49W927d7ddzjFbsGCBzjrrLK1du1bNmjUr95hRo0ZpxowZUdvjUpHKnHukWrNmjVq2bKmvvvpKpx0lBE2ePFm5ubnauXNnmKo7PjNnSjfeaG5r1UxrtaxWByUdOPx%2B3k58vDyffBJVC9E6jqPMzEzl5ubqnnvukWTul5uWlqaHH35Yt9xyS7mvi%2BrP4Ny55kLPkhIFJfkkBfTTreAaNDCrOmdmynHMjPDvvpNWrzYtPz%2B0X5nO%2Brg4KS0tdHuz9HTTw5eSYprPZ2buJiWF1vhzHBMc9%2B0zo7jbtpnZwN9/b/7c5cvNbOGfd1jWqmX%2B6fXs4ajf6IZqVxgot6YFV12lHu%2B/HzWfv8rweDyaPn26%2Bvbta7uUmEcPYIQpLCzU008/LZ/Pp44/X18gygQCAXk8HjVo0OCIx61cuVKZmZlKTEzU2WefrTFjxqhVqyMvbBrpKnvusWD37t1q3ry5iouLddppp%2BnBBx9Up06dbJdVrt69zXptI0dKrZ%2BdUG74kyRPcbH0z3%2BahQSjRH5%2BvjZv3qyeB93PKzExURdeeKHmzZtXYQCUovgz%2BI9/SCUl5T%2B3c6emdX1KD9b%2Bi1avPvodMZKTzcLNpa15c9OR2KyZuUNGenrNrA%2B%2Bf79ZXHr%2B/APKzZ2mlJR%2B%2BvHHJH3yiZT0yfv6k8oPf5LU7v33tX/Xrqj5/CGyEAAjxFtvvaVrrrlGe/fuVUZGhmbNmqXU1FTbZR2z/fv3695779XAgQOPeEH22WefrSlTpqhNmzbasmWLRo8erXPPPVdLlixRSkpKGCuuPpU991hwyimnaPLkyerQoYOCwaAef/xxnXfeefr666/VulrWwah%2BqanSM89Ie%2Bd/KB3p8rgPPwxbTdVh80%2BLHqalpR3yeFpamtauXVvh66LpMxgMmp6z774z7TfvzdGR7heRtWKOlsjc/iUuzgS5Vq3MNXmtWpnWsqVpqal25lTUqSPl5EhZWVv1m99cpxkz/qf09HM1e7bU5J9zpFUVvzYpENDro0er6a9%2BFTWfP0QQB2H14osvOsnJyWVt7ty5juM4zu7du52VK1c68%2BfPd4YOHeq0aNHC2bJli%2BVqK1bReTiO4xQWFjqXXXaZ06lTJycQCFTpfXfv3u2kpaU5jzzySHWXXG1q6tzD6UjnkJ%2Bf70hyvvrqqyq/b3FxsdOxY0dn%2BPDh1VlutSo99/lxcY5jRunKbdtrN3bGjXOctWttV1y%2Bn38PP/roI0eSs3HjxkOOu%2Bmmm5xevXpV%2Bn1tfgaLihxnzRrHmTPHcSZNcpw//MFxBgxwnLPOcpzU1MO/TTvlLfsiIDn6aVv62PrWXZx333WcFSscp6Ag7KdTJRs2bHAkOfPmzQs9OHLkEf%2BNOpLzt0FLnLw8c3g0fP6ORpIzffp022W4Aj2AYdanTx%2BdffbZZV9nZWVJkpKTk3XSSSfppJNOUufOndW6dWtNmjTJ3BMxAlV0HkVFRbrqqquUn5%2BvOXPmVLkHLDk5WR06dNDKlSurtd7qVFPnHk4VncPxiouLU05OTlR8/xo%2B9ZT0yCMVHvdGUR8NHy4NH25md/bsaVbeOP98c72XbT//Hhb8NLV58%2BbNysjIKHt869ath/UKHklNfgZLZ9MePOFizRpzPV5%2Bvrk27miTLVJTTQ/eiSdK%2Bd/00WnfvFjhsVm39VHWxdV5BjUnNTVV8fHxZT25kqQ%2BfaSHHqrwNSvUWvdOaat7p5jJwUOHxunUU38R0Z8/RA4CYJjVr1//iLPxSjmOU/YfeiQq7zxKA9DKlSv14YcfHtPwUUFBgZYtW6YLLrigukqtdjV17uFU2X%2BHVeU4jvLy8tShQ4dqf%2B/qUnbuI0eaRQI3bDjsmKK6yYq74y6dN88svbZkiWmPPmqGCU891Vykf9ZZ0hlnSG3bmjtAWDmPnziOo/T0dM2aNavsGrDCwkJ9/PHHergKCwYf62fQccxE3PXrze3PSlvpTNq1a81zRUVHfp%2BEBHP9Xenw7M%2BHbA/5vWrxPdLZ/zLLwPxc8%2BbSkCFVOgebEhISdMYZZ2jWrFnq16%2BfebBzZ81NTtYv9uwp9zW7R9yvK9Z6NHOm9NVX5pcVj%2BfvOvHEL/TZZ%2BbfJ0sFoiIEQMv27Nmjv/71r%2BrTp48yMjK0bds2TZgwQevXr9eVV15pu7xKO3DggPr376%2BFCxfqrbfeUnFxcdlvso0aNVLCT/fg6t69u/r166c77rhDknTXXXepd%2B/eatasmbZu3arRo0crGAxq8ODB1s6lqip77pFu%2B/bt%2Bv7778vWLyxdWiI9Pb1sXbhBgwYpKytLD/3UK/HAAw%2BU9VgHg0E98cQTysvLk9/vt3MSVZGSIn38sTR0qJlN%2BpM8ST/%2B4V6d/qv9%2BuWIzUpISNfs2WbNto8%2BMrM2v/7atKeeMq9JSJCys01PYXa2dPLJ5lZgrVpJ9eqF53Q8Ho9yc3M1ZswYtW7dWq1bt9aYMWOUlJSkgQMHlh1X1c9gabDbssXMpN282fTibdxoths2hFoFOeUQ8fHmWryDJ1y0bGm2rVqZGbaVXoWmfXvpgw%2BkW2%2BVFi8OPd61q/Tcc2YmcBQZMWKErr/%2Bep155pk655xz9PTTT%2BtFx1F%2B//6q9%2B9/h9JzRoY0erRmrlulm29%2BXyNHnqxp02rp2Wcd7djRVKtWna/Onc2ygcOHm5VxInllmN27d2vVqtDFjvn5%2BcrLy1OjRo2ibhWFqGJ5CNr19u3b5/Tr18/JzMx0EhISnIyMDKdPnz7O559/bru0Kim9bqy89uGHH5Yd17x5c%2Bf%2B%2B%2B8v%2B/rqq692MjIynNq1azuZmZnO5Zdf7ixZsiT8J3AcKnvuke75558v9xwO/n5deOGFzuDBg8u%2Bzs3NdZo1a%2BYkJCQ4jRs3dnr27HnoNUxR4l9jxjiXSk6nI5x7qY0bHef11x3nrrscp0sXx/F6j3yZVmqq45xxhuP07es4w4Y5zl//6jjPPus4b77pOPPmOc7y5Y6zdWv1XKNWUlLi3H///U56erqTmJjo/OIXv3C%2B%2BeYbx3HM9XU7djhOVtY5zm23%2BZ3//tdx3nrLcc4%2Be5zj9f7RiYt7wElOftY54YS5zrnn7nI6dXKcE05wnISEo16Gdkhr2NBxTj3VcS691HFuvdVxxoxxnBdfdJy5c831lAcOHP95lifw4YfmGsAvvqiZPyBM/H6/07x5cychIcE5/fTTnY8//tg8sXmzc3f79s5DvXo5TmGh4ziHf/569OjpTJy4yBk0yHESE0Pfk8aNHee%2B%2Bxxn0yaLJ3YEH/70vft5O/j/GlQ/1gEEgOPgOOY6tkWLzDDxt9%2BG1nfbvr1q71WnjrnpQ3JyaD25OnXM8HLt2mYZkri40LCe45hVUIqLTedQUZFUWGjudLJvn2l795qeueO9osTrNevgpaebDqjMzFDLygq1pKTj%2B3OOVUzfCu4Y/PijNGmSuePIunXmsYQE6frrpd/9zly2AHcjAAJADQkEQneWWL/eDJNu2mSGU7duNT%2Bkt20ziwSHS0KCCXM%2BnxkhbdjQbBs1Ci1q3LixmWzRuLEJfY0bmzupRDICYPkOHJCmT5fGjpU%2B/TT0eN%2B%2B5jLYs86yVxvsIgACgGUHDpg17oJBEwb37jVt3z7Tc1dYaHr3iotNj1/p/9oej%2BkRjI83vYO1a0uJiabVqWNCW2lvYr16pncxSi5JrTIC4NHNm2fWzv73v0P/hi66SLrvPukXv7BbG8KPAAgAiHoEwMpbtkx6%2BGFzG%2BXSZXe6dDH3yiYIukec7QIAAED4tG0rTZ4srVwp3XKL6Tn%2B6CPpwgtNj%2BDBQ8WIXQRAAABcqEULs5TRd9%2BZlXRq15b%2B8x%2BzxuVllx26sg5iDwEQAAAXa9pUevJJacUKs3Z2XJz05ptmwfMhQ8wEJsQeAiAAAFCLFmb97CVLpCuuMBNFJk82i5r/8Y/hna2OmkcABAAAZU45RXr9dXMt4AUXSPv3S2PGmCA4aZKZjY7oRwAEAACHOftsc7fE6dNN%2BNuyRbrpJrN24P/%2BZ7s6HC8CIAAgavn9fmVnZysnJ8d2KTHJ4zGLRi9eLD3yiFlAfOFC6fzzpUGDzL2hEZ1YBxAAEPVYBzA8tm411wNOmmSuEfR6pdGjpdtvNwuSI3rQAwgAACqlSRPpmWekzz6TzjzT3L3mzjvNcPGXX9quDlVBAAQAAFWSk2MmiTz5pBkW/vJLc23giBHSnj22q0NlEAABAECVxcebBaS//VYaMMDcp/rRR6X27aVZs2xXh6MhAAIAgGOWni699JL0zjtSs2bSmjVSz55mxnAgYLs6VIQACAAAjtsll5hFpO%2B4w8wenjTJ9Aa%2B/77tylAeAiAAAKgW9epJ48aZ9QNPOsncRu7ii81Q8e7dtqvDwQiAAACgWl1wgZSXJw0fbr6eOFE67TQzcQSRgQAIAACqXXKy9MQT0n/%2BIzVtKn33nVlA%2BoEHpAMHbFcHAiAAAKgx3bpJixZJAwea%2BwiPGiVdeKGZLAJ7CIAAAKBGNWggTZ1qmtcrzZtnhoRff/2nA5Yske67T8rNlV54QSoosFqvG3ArOABA1ONWcNEjP9/0Bn76qeRRiea2vVXnL3vm0IMyMqS335Y6dbJTpAsQAAEAUY8AGF2KiqQ//1ly/vY3/U0jyz8oLU1avVpKSgpvcS5BAAQARD0CYBQ6cED705urzraNFR/z7LPSjTeGryYX4RpAAAAQfuvWHTn8SawbU4MIgACAqOX3%2B5Wdna2cnBzbpaCqkpOPfgzDvzWGIWAAQNRjCDhKXXihNHduhU%2BveO6/ajPkvDAW5B70AAIAADv%2B%2BlcpIaHcp2boMp027DxNmxbmmlyCAAgAAOw4/3xp1iypc%2BfQYw0aaP/w32tSz1e1b590zTVmicCSEntlxiKGgAEAUY8h4Biwdq0UDEonniglJam4WLr3Xumf/zRPX365NGVK5S4dxNERAAEAUY8AGLumTJFuvlkqLJROP12aOVPKzLRdVfRjCBgAAESsQYOkOXOk1FRp4ULprLOkr7%2B2XVX0IwACAICIdt550mefSaecIm3YYC4dfP9921VFNwIgAACIeK1aSfPmSV27Srt3S5deKj3/vO2qohcBEAAARIWGDaX33pOuu04qLpaGDpUefFBiNkPVEQABAEDUSEgwE0NGjjRf//nP0rBhJhCi8giAAAAgqng80pgx0vjxZv/JJ6Wrr5YKCmxXFj0IgAAAICoNGya9%2BqrpFXzjDXNd4O7dtquKDgRAAAAQtfr3l955R6pXT/rPf6SLLpL277ddVeQjAAIAgKjWvbtZK7BRI7Nfp47tiiJfLdsFAABwrPx%2Bv/x%2Bv4qZAeB6OTnSokXcJaSyuBUcACDqcSs4oGoYAgYAAHAZAiAAAIDLEAABAABchgAIAADgMgRAAAAAlyEAAgAAuAwBEAAAwGUIgAAAAC5DAAQAAHAZAiAAAIDLEAABAABchgAIAADgMh7HcRzbRQAAcDwcx9GuXbtUv359eTwe2%2BUAEY8ACAAA4DIMAQMAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBl/h%2B68WdUy69hhwAAAABJRU5ErkJggg%3D%3D'}