Genus 2 curve data - 472.a.944.1

Formats: - HTML - YAML - JSON - 2024-07-27T08:30:26.440239

• 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': '1886010138829170898', 'abs_disc': 944, 'analytic_rank': 0, '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,[1,1,2]],[59,[1,-11,47,59]]]', 'bad_primes': [2, 59], 'class': '472.a', 'cond': 472, 'disc_sign': -1, 'end_alg': 'Q', 'eqn': '[[0,1,0,-2,-1,1],[1,0,1]]', 'g2_inv': "['-3361400000/59','-118335000/59','-5566400/59']", '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': "['280','760','60604','-3776']", 'igusa_inv': "['140','690','4544','40015','-944']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '472.a.944.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.2274474476920005292300657692297720450965366729119791884', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.120.3'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2], 'non_solvable_places': [], 'num_rat_pts': 5, 'num_rat_wpts': 3, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '29.113273304576067741448418461', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, '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': 16, 'torsion_subgroup': '[2,8]', 'two_selmer_rank': 2, 'two_torsion_field': ['3.1.59.1', [-1, 2, 0, 1], [3, 2], 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': '472.a.944.1', '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': '472.a.944.1', 'mw_gens': [[[[1, 1], [1, 1]], [[-1, 1], [0, 1], [0, 1], [0, 1]]], [[[0, 1], [-1, 1], [1, 1]], [[-1, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [2, 8], 'num_rat_pts': 5, 'rat_pts': [[-1, -1, 1], [0, -1, 1], [0, 0, 1], [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': 472, 'lmfdb_label': '472.a.944.1', 'modell_image': '2.120.3', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 101569
    {'label': '472.a.944.1', 'p': 2, 'tamagawa_number': 2}
  • id: 101570
    {'cluster_label': 'c3c2_1~2_0', 'label': '472.a.944.1', 'p': 59, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '472.a.944.1', 'plot': 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tt6WdO%2B1rNWpIt99urS4uX96x8hzFskR7jnJz7XX32mt2fdCgwiB4rBLtOYp3yf379%2B/vuggcWnJystq3b68UpmgeUjw/R3l5tvDySy/Z%2BJr77pM%2B%2B8zWm8vLs0a8a6%2BVHntMeuEF6bLLrAGvbNkiPsAJJ2jHSSfpySef1N333KOyRb6jv8TCa6hGDalLF2vFLV9eWrDAlnT84gtpyBDb5q5pU3dLycTCcxTrEuU52rlTuuIKm%2BGblGSt1LfcEpmfnSjPUSKgBRCIsp07bQLHBx9YS9/eEwKSk21drQsusKNBg%2BN/PD51x6ddu6w1cNAg26lFsrXWbr1V6tPH9k8GIi0Uki68UJowwXb%2BGTFC6trVdVUoDgRAIAo2bpTGjrXQ9/nntoBqvrQ0Wx%2BuSxe7jPQfdgJgfMvLs/Gdjz5auNB0%2BfLWHXfbbfG9Bhtiy9q19h40a5a9L33wgW1ziMREAASKgedJCxdaC9%2BHH9q2YXtvGVa7tq2k36WL1L69fdIuLgTAxOB50ujRNmN47lz7Ws2aNnv46qvZcg7HZ9EiG2O8YoVNOvvkE9sGEomLAAhESE6OzcgdO9a2RlqyZN/vN21qga9zZ9tjN1pLTxEAE0turnXL3X%2B//bGWbP/h55%2BXzjrLaWmIU9Om2ZaFGzfasJNPP5Xq13ddFYobARA4DkuXWpfup5/auL69u3ZTU611r3NnG8/nagIuATAx5eRIL75oLYChkH3tyiulp56yCSVAUXz0kU0u27HD9gAfO7aIKwwg7rEOYAwbOXKkOnbsqMqVKysQCOiH/Xea9wnP89S/f3/VrFlTpUuXVvv27TVv3rzD3qd///4KBAL7HNWrVz/uWtassYH5N9xgn5AbNJB69LClW7Zts66Ta6%2BV3n/f1ub7/HOpVy934Q/mpZdeUr169VSqVCm1aNFCkydPPuRthw0bdsBrJxAIaGf%2B2iwxomRJmwyyeLHUvbu1KL/9ts0cHzJk3yEHx2rSpEm64IILVLNmTQUCAY0ePfr4f2gcOtrnYcKECQd9Df2YP5snRrzyivVK7NhhS0uNHx/58Ddw4EBlZWUpLS1NVatW1YUXXqiFCxdG9kFwTJiHHcO2bdum1q1b69JLL9UNN9zgupzD2rXLAtCOHbag7e7d1lWVLynJxiilptp4t1Kl7ChR4shdoU888YQGDx6sYcOGqWHDhnr00Ud17rnnauHChUpLSzvk/U499VR98cUXBdeTj3KQ1I4d0uzZ0vTp1kUyZYotz7K3lBTbtaFDBxs83ayZ/a6IHe%2B%2B%2B65uv/12vfTSS2rdurVefvllderUSfPnz1edOnUOep/09PQD/kiVKlUqGuUetSpVpJdfthB400225VyPHraEx%2BuvS40aHfvP3rZtm5o2baprr71WF198ceSKjjPH%2BjwsXLhwn1b3KlWqFEd5R83zbAjBgAF2/dpr7TV0tFu7FcXEiRPVs2dPZWVlac%2BePbr33nvVoUMHzZ8/n2WpHCMAxrBu3bpJklbkD/SJsj17pJUrrZvzp5/sfO1aW7bkl1%2BkX3%2B1PWjDYQt8xyI52WYxlitnR3p64VG%2BvJSe7umf/0zWH/4wSuHwOVqyRPrb397U2LHn6Pnnx6hHj78oLc2C2P5SUlIO2%2BrneRZa166VVq%2B28VRLltjkjXnzbFD0/q0oSUkW8tq3t63X2ra12XKIXYMHD9Z1112n66%2B/XpL07LPP6rPPPtOQIUM0cODAg94nUi3G0dSihfTNN7am5D332AeWZs1sDcnbbju2DyadOnVSp06dIl9snDnW56Fq1aoqX758MVR07HJypOuvl/71L7v%2B4IN2FNeY5E8//XSf60OHDlXVqlX1/fffq23btsXzoCgSAiAkWZj77jvp%2B%2B9tCYC5cy0AHW2wCwSsayo11f7gJCVZ0MrLsxbBXbv2/Zm5udKWLXYc4idKukMffWRjVUwJSZN13322cLJki%2BPmh8jSpaXNm7tr/fpOKlHiWyUlBZSWlqbatU%2BQVErbt1to3bzZ3gwPp0oVGxdz%2BunSGWdIrVpJweDRPSdwZ9euXfr%2B%2B%2B/Vr1%2B/fb7eoUMHTZ069ZD327p1q%2BrWravc3Fw1a9ZMjzzyiE6LgymRycm2YG%2BXLjZM4fPPpd69bSb6m2/a7HNEz2mnnaadO3cqMzNT9913n/7geE2VzZuliy%2B2rt6UFGv1%2B9vfoltD6H8DVitWrBjdB8YBCIA%2BtWmTTVr48kvbcmzBgoPfrmRJ6cQTpRNOkDIypFq1bIB5lSpS5crWShcMWitYmTIW/I70SdLzLAju2GG7G2zbJm3dWhgEw2E7fvtNmjdvtd5660N16XK1cnLKaPNm%2B/rKlSHl5JRSXl5JSfazduyQNmzIf5SakmoWtODl5NgMt4MpW9Z%2Br7p1bVxfw4ZSZqbUpIn9rtGarYvI27hxo3Jzc1WtWrV9vl6tWjWt23sF7r2cfPLJGjZsmJo0aaJwOKznnntOrVu31qxZs3TSSSdFo%2BzjVqeOTUx65RULgOPH2yz0oUNtUhKKV40aNfTKK6%2BoRYsWysnJ0VtvvaVzzjlHEyZMcNbqtWKFbSO5YIG9X7//vg1diSbP89S7d2%2B1adNGjRs3ju6D4wAEwBgxfPhw3XjjjQXXP/nkE50V4TUdli%2B3//SjR1tX0f7dmw0aWGvXaadZ%2BDnlFPtDEukxbfmthCVLWoDc2/7Pw9ixY/XWWz00ZMiFqlGjcMXbG264Q6tWrdKYMZ8WBMatWy1Qbt9ugW/XLmthzMuTcnJ2qmfPG3XZZV301792VVqaLbhcubK1GiKxBfZL8Z7nHfC1fK1atVKrVq0Krrdu3VrNmzfXCy%2B8oOeff75Y64ykQEC68UZbyPfKK611v0sX6Y47pIEDDz5sApHRqFEjNdpr8OUZZ5yhVatW6amnnnISAL/91lYi%2BOUX%2B7A7dqx9IIi2Xr16afbs2frqq6%2Bi/%2BA4AG8BMaJz585q2bJlwfVatWpF5Odu2WJrhg0bJn399b7fa9xY%2BuMfbTxb69YWhlzb/3nI%2BV8f7bp161Rjr7UtfvnlF1WrVk0lSljdR669lIYN%2B1nJyePUti37GvlF5cqVlZycfEBrX/7rpyiSkpKUlZWlxYsXF0eJxa5hQ2nqVKlfP%2BmZZ2yZmO%2B%2Bk/79b2vJR3S0atVK/8ofeBdFI0dKf/2r9ZA0a2ZDaSL05%2BWo3HLLLRozZowmTZqk2oxFiAkEwBiRlpZ22BmtR2vJEunZZ6U33rCWMcla8tq3tzEgF1xgXbqxZv/nwfM8Va9eXePGjSsYg7Vr1y5NnDhRgwYNKvLPzcnJ0YIFCyLeqhrLsrOzlZ2drdy9p2P7TIkSJdSiRQuNGzdOF110UcHXx40bpy5duhTpZ3iepx9%2B%2BEFNmjQprjKLXYkS0uDBUps2tmvIhAlSVpaNDYzjXyuuzJw5c58PscXN86Snn7YtAz3Pun/feSf6k9Y8z9Mtt9yiUaNGacKECapXr150C8CheYhZmzZt8mbOnOmNHTvWk%2BS988473syZM721a9ce8j4LF3relVd6XlKS59l/e89r1MjznnzS837%2BOYrFR9Djjz/uBYNBb%2BTIkd6cOXO8K664wqtRo4YXDocLbnP22Wd7L7zwQsH1Pn36eBMmTPCWLVvmffPNN97//d//eWlpad6KFStc/ApOhUIhT5IXCoVcl%2BLEO%2B%2B846Wmpnqvv/66N3/%2BfO/222/3ypYtW/Ba6Natm9evX7%2BC2/fv39/79NNPvaVLl3ozZ870rr32Wi8lJcWbNm2aq18houbN87wGDey9IS3N8z7//NC33bJlizdz5kxv5syZniRv8ODB3syZM72ffvopegXHgCM9D/369fO6detWcPtnnnnGGzVqlLdo0SJv7ty5Xr9%2B/TxJ3n/%2B85%2Bo1Ltrl%2Bd17174N6BHD8/bvTsqD32Am2%2B%2B2QsGg96ECRO8tWvXFhzbt293UxAKEABj2NChQz1JBxwPPvjgAbfdsMHzbr7Z85KTC//Td%2BrkeePGeV5eXvRrj6S8vDzvwQcf9KpXr%2B6VLFnSa9u2rTdnzpx9blO3bt19npc///nPXo0aNbzU1FSvZs2aXteuXb158%2BZFufLY4PcA6Hmel52d7dWtW9crUaKE17x5c2/ixIkF32vXrp139dVXF1y//fbbvTp16nglSpTwqlSp4nXo0MGbOnWqg6qLz6ZNnteunb1PpKR43ttvH/x248ePP%2Bh70N7Plx8c6Xm4%2BuqrvXbt2hXcftCgQV79%2BvW9UqVKeRUqVPDatGnjjR07Niq1bt7seX/8o/3bBgKe98wzbv8GHOx5k%2BQNHTrUXVHwPM/z2Aouznmeje/r08em%2BEu2p%2BPDD7ORNwxbweFgcnJsAeARI2zCyMsv29IxiF/Lltn7/4IFtrrBiBE23Ac4GMYAxrFffrE38I8/tuu/%2B51tCN%2Bundu6AMS%2BkiVtMeAKFWzx6O7dbcb8XpPwEUe%2B%2Bkq66CJb7qp2bRvf2ayZ66oQy9i0Kk59/bX95/74Y3sjHzTIlnkg/AEoqqQk6cUXba1AybaSGz7cbU04em%2B9JZ1zjoW/Fi1s60rCH46EABiH3nnHZvOuXWtr9X33nc30Yl0vAEcrELClYXr1suvXXCONG%2Be0JBRRXp7thnTVVbbuadeu0sSJUs2aritDPCAAxplXX5WuuML%2Bs190kS3wyYLqkbP3LiU7d9pC0kCiCwSk556zBaP37JEuuUT68UfXVeFwtm%2BXLr9cGjDArvfrJ733no39A4qCSSBx5F//krp1s/NevewNO9K7dCQyz5N%2B%2BkmaN09auNAGTK9cKf38s42nDIVs4ez9/0ekptpuIeXL24LT1arZFnG1a9sWefXq2S4q1avH5rZxTAJBUeXk2OLwX31lvQvffstOObFo7Vrb1WX6dHt/euUVa7kFjgYBME5MnmxjPHbvlm691RZ5jsWwEUs2bZKmTLFdEL79Vpoxw0JecUlLk04%2B2fYRbtzYJuX87ncWDF0iAOJorF8vNW9uH4yuu0567TXXFWFvs2bZTN/Vq6VKlWynD0fbCyPOEQDjwIYNFiTWrZMuu8ym9tPyd6AdO2z8y%2BefS//9rzR79oG3SU2VGjWy1o369a0Fr1Yt2xKrYkVr7ShVysZTep4F7h07rGVw82YbZL1%2BvbRmjbRqlW2wnt%2BSuP/eyvmqVbM/qC1a2F7LWVnRHaNDAMTRmjjR9hD2POmTT6TzznNdESTbxu3yy6Vt2%2Bx97KOPrPcBOBYEwDhwxRU28SMz01qyGONRaNUqW%2B7go4%2Bk8eNt3N7eTj7Ztr9q1crCV2amhcBIy8mRli619bfmzZPmzrUAumjRgV3KknUft2xpxxlnWDgsXTrydUkEQBybv//dehrq17fXdMmSrivyL8%2BzJb5697YPmuecY%2BP9KlRwXRniGQEwxk2ebM37SUkW/lq0cF2RewsWWLfHqFG29M3eMjKkDh2kc8%2B1FoyqVd3UmG/bNguCM2ZYrdOnS/PnH9hamJpqrYStW9vRpk3kaicA4lhs2SI1bGg9Dy%2B8UDhLGNG1Z490%2B%2B1SdrZdv/56W7exOD7Iwl8IgDHunHOkL7%2B0xVn/8Q/X1bgzf77073/bp9758wu/HghIZ55pq93/6U/SqafG/tjIrVstDH7zjR1ff23dyvtr2FA66yw72ra17upj%2Bd0IgDhWQ4ZIPXpIdepYCzdLTUXX1q3W5Tt2rP3fHzRIuuOO2H%2BPQ3wgAMai3bulkSMV%2BtcYffbRbk0M/EH95nVTxin%2Bmo63ZIl1fb/7rnWp5ktNtZmKXbtKnTu7b%2BU7Xp4nLV9uk1WmTLEZmHv/vvlq17aFvvOPk046wh%2BCxYulV19V%2BMcfFfzwQ4UmTVL6WWcV2%2B%2BBxLMzlKO/135PbbeOVbuz8lTzqnNtrZgyZVyXlvB%2B/tkme8ycaeOShw%2B39zwgUgiAsWbjRuu//OGHfb9es6atzpqZ6aauKFm92gLfiBH7du%2BmplrX7mWXWegrX95djdGwebOFwcmTpUmTbLHvPXv2vU316rYgePv2FggbNdorEL74ok0X9zyFJQUlhSSl9%2B1rzQjAkfz8s33SWrBg36%2BfcIL0xRc2OBDFYt48qVMnG%2BNcpYqNc27Z0nVVSDQEwFhz0UXS6NEH/17DhrY6a4K1/2/aJL3/vvT22xZ48l%2BRycnWBX755dKFF/p7wPO2bdZdPHGiHd98YwtW7y0/EF5a91td9EQrBf73RO4TACV7si%2B%2BOKr1Iw6de64FvYNp3vzAAbiIiEmTbI2/336zt/xPPpFOPNF1VUhEBMBYsnKlrSp8qPVEJOnTT6WOHaNXUzHZulWlpnAHAAAgAElEQVT64ANr6fvss31bt9q0sZnPl15qn35xoJ07CwPhhAk2jjAnx743TFfrar1ZcNsDAmC7dnYn4FB%2B/NHWSjqcKVNsAC4iZtQoe%2B/LybGndswYW%2BsPKA4M6Y0l8%2BYdPvxJNqU0TgPgzp2WX0eMsC6NHTsKv9esmQ0t%2BvOfbcA5Dq9UqcLu3wcfLAyEEyZIrZ%2BaJW079H1zps/WmmX2WSPBGpMRKQdbRPNgtyEARsyrr0o33WR/Arp0sffJ4loaCpAIgLGlKH2ccdYPumuXLcr8zjvWsx0OF36vQQP7tHvFFUdubMDh7R0INamiNP7Qt129vYIa1Lclc/Lv0749gRB7qVjxyLeJs/eiWPbkk1LfvnZ%2B/fU2%2B5oZ1yhudAHHEs%2BzQR9Llhz8%2B6VL2xYUMf7Gmx/63n/fujQ2by78Xu3aNpHjiitsTUMCRzF4/XX7K/I/%2B3cB/zPjAd207iHt3r3v3fIDYbt2dnniifz7%2BNaePTbZY82ag38/GLRJIswGPi6eJ/XvLz38sF3v10967DH%2B3yE6CICxZuxYm/Gw/5RPSXr6aVsKPgbt2GFbsI0caeNWfvut8HvVqkmXXGLdu61bs41dscvJsdkzU6ZI2i8ANmwoff21tpWsqKlTrct4wgRboHr/QFirloXBtm0PMssYie%2B992wG1sGGpbz8stS9e/RrSiCeJ91zj/T443Z94EALgEC0EABj0aRJ0iOPyPvvfxXwPH2v5io/oK/q3/Nn15XtY8MG6eOPbTLHZ59J27cXfq9aNVuz6tJLLUAkJ7ur05e2bZMeeUR6/XWFN260AHjddUofOPCgM2u2bbOJJPmB8NtvDwyEVaoULkrdtq3tT82/a2Lb/N44zbviUbXJnWRfaNlSuvtuG6SGY%2BZ50n33WWufJD3zjO32AUQTATCWbdumG67do9feC%2BrEE61Bp3p1d%2BXk5dmipJ98YsHvm2/23ee2Th1bxaZrV2vpIxzEgNxchVetUrBevaPaCWT79sJZxpMm2fn%2B%2Byynp9s%2BxmedZTO3s7LoEUwkeXm2WtDo0VLrpls1aUKeksqzk0wkDBhgAVCyPX5vucVtPfAnAmCM%2B/VX6fe/t50iGjSwLtYmTaLz2J4nrVghjR9vy4F98YW1%2Bu2tWTNbmLlLF%2Bm00%2BgijEWR2AouJ8e6ifMXpp46dd8JPZINWm/e3MJg69Y2QdTlBxYcO8%2BzLccGD5ZKlLDW4ebNXVeVGPK315Okp56S%2BvRxWw/8iwAYB5YssTVZV6ywHTFuu83eNCL9x3X3btuC7Ouv7Q/8pEm2Ev3eypWz4WWdOtneu7VrR7YGRF5x7AWcm2urgHz1lYXCr76S1q498Hb16kmtWllL4RlnWLdxiRIRKQHFZOdOCyhDh9r1YcOkq692WlLC%2BOAD6yXxPOmBB6SHHnJdEfyMABgnNmyQrrvO1s%2BTLAief761vLVte3QzNnNzbXLfokW2y9Pcubbz3OzZB3bzpaRIp58unX227Qp1xhn8AY83xREA95ffWjxlSuExd%2B6%2BQwQkW66meXMbStaypb22TjiBluNYMWuWhb1Zs2yy1j/%2BId1wg%2BuqEsOsWdYqvn27Pacvv8zrHm4RAOOI59n4u0cftVa6vaWlSSedZDM3K1a0FWOSky3sbd8uhUIWIteutf129x/gny8YtD/KZ55pXXlnnCGVLVv8vxuKTzQC4MEfV5o2zV6rX39t53svCZSvcmUb5pCVZUsD/f73tvU1fxyj55dfbM7QkCH2nlG5sjR8uO2/jeP366/22l6xwnpzPv6Ydf7gHgEwTs2ZY%2BvsjRtnW3Luvy/skaSkWKvhySdLp54qNW1qLTP167NMS6JxFQD353nW6jxtms0ynjbNWkUO9mGkalV7PTZvbuNMTzvNXq%2B8NiNr%2BXKbhPDKK4Wz%2BC%2B5RHrhBcZvRorn2cpeY8bYa/i772J%2BKVf4BAEwAezaJS1eLC1bZi18mzfbuny5uRb0Spe2lr3Kle1NPSPDWlj4BOoPsRIADyYnx4YeTJ9ufxi/%2B06aP99eu/srV87GEOYfTZpIjRtL5ctHv%2B54tn27DSV54w3bmjH/L0BWlq1Jd/bZbutLNK%2B8It14ow2d%2BeYb%2BzADxAICIJDgYjkAHsyOHdYyOGOGLTv0ww/W4p2Tc/Db16plrdiZmXaccoq1bFeuHN26Y9n69bZQ%2B5gxNoxk2157RXfoYOvLd%2BhAt3ukrVljr8ctW2J6HX/4FAEQSHDxFgAPZs8eaeFCC4azZ9sxZ46NZz2UihVtZ8WGDW18bIMGNsShfn3rgkvUsON50sqV1to0ZYot7D1nzr63OeEE6corpWuvtecFxePKK6URI2wm/FdfsTYqYgsBEEhwiRAADyUUkubNs2P%2BfJvVvmCBBaDDSU%2B3JWpOOEGqW9cWMc/IsGWNate2oRKxPtvd86SNG234x48/2nMwe7a1mm7adODtTzutcOWA3/8%2BcQNwrPjuO%2BtWDwRsnDZdv4g1BEAgwSVyADyU7dstGC1aZMfixbaeZv442aKoXFmqUcO2Naxa1Y7KlaVKleyoUMHGHwaDNgs/Lc12QjmeYOV5Vns4bPtp//qrhbxffpHWrZN%2B/tlaPVeutBml%2By/GnS811cZJnnmm7dTSrp3Vj%2Bi58EJb9%2B%2Bvf5Xeest1NcCBCIBAgvNjADyc7dstPC1fLv30kx0rV9qi56tXW0A82ln1eytTxiZelSxprYipqXYEAoWzmPPybKLL7t32WDt32tjHbdsOXDvxSGrXtjGPp5xiE2OaNbPLUqWO/XfA8Vm2zLrWPc9apE8%2B2XVFwIGYBwrAV8qUKZwwcjD5Xatr11qr27p11gK3YYN9fdMmO377zY5QSNq6tTC4bd9euKTKsUpKspbFChWs1bFKFeuWrlHDAl9GRmEXdunSx/dYiLw33rDXQ4cOhD/ELgIgAOwlELDAVaWKdaMWRV6ehb5t2%2Bxyxw5r1du1y1r59uyx2%2BSHxKQkmxCQ3zpYqpQFubJlbbmbcuUYoxfP3n/fLq%2B6ym0dwOEQAIEElZ2drezsbOUebFE9RFRSUmFwg7%2BtXm0TkpKSbNINEKsYAwgkOMYAAtHz/vvSpZfarN8ZM1xXAxwaGysBABAh8%2BbZJcu%2BINYRAAEAiJCffrLLE090WwdwJARAAAAiJH8RbrYiRKwjAAIAECH5a0iyDiNiHQEQAIAISfnf2hq7d7utAzgSAiAAABESDNrlb7%2B5rQM4EgIgAAARUqOGXa5e7bYO4EgIgAAAREj9%2Bna5eLHbOoAjIQACABAhjRvb5axZbusAjoQACABAhDRrZtvArVlDNzBiGwEQAIAIKVeucBeQCROclgIcFgEQAIAI%2BuMf7fKzz9zWARwOARCIUbt379Zdd92lJk2aqGzZsqpZs6auuuoq/fzzz65LA3AY559vl2PHsh4gYhcBEIhR27dv14wZM3T//fdrxowZGjlypBYtWqTOnTu7Lg3AYbRuLVWtKm3eLH35petqgIMLeJ7nuS4CQNFMnz5dp59%2Bun766SfVqVOnSPcJh8MKBoMKhUJKT08v5goBSFKPHtKQIVK3btKbb7quBjgQLYBAHAmFQgoEAipfvvwhb5OTk6NwOLzPASC6unWzy//8R%2BK/IGIRARCIEzt37lS/fv105ZVXHrYlb%2BDAgQoGgwVHRkZGFKsEIEmtWkknnyxt3y6NGOG6GuBABEAgRgwfPlzlypUrOCZPnlzwvd27d%2Bvyyy9XXl6eXnrppcP%2BnLvvvluhUKjgWLVqVXGXDmA/gYDUvbudv/SSxGArxBrGAAIxYsuWLVq/fn3B9Vq1aql06dLavXu3LrvsMi1btkxffvmlKlWqdFQ/lzGAgBubN0u1akk7dkjjx0vt27uuCChECyAQI9LS0tSgQYOCY%2B/wt3jxYn3xxRdHHf4AuFOhgnT11Xb%2BzDNuawH2RwsgEKP27Nmjiy%2B%2BWDNmzNBHH32katWqFXyvYsWKKlGiRJF%2BDi2AgDsLF0qnnGJdwPPn2zkQC2gBBGLU6tWrNWbMGK1evVrNmjVTjRo1Co6pU6e6Lg9AETRqJHXpYueDBrmtBdgbLYBAgqMFEHDr22%2Blli2l5GRp8WKpXj3XFQG0AAIAUKxOP13q0EHKzZUee8x1NYAhAAIAUMweeMAuhw2Tli93WgogiQAIAECxa91aOvdcac8e6eGHXVcDEAABAIiKRx%2B1yzfflH780W0tAAEQAIAoOP10qXNnKS9Puu8%2B19XA7wiAAABEyYABtk3cf/5js4MBVwiAAABESePGhbuD9O3LHsFwhwAIAEAUPfSQVLKkNHGiNHas62rgVwRAAACiqE4d6bbb7LxvX5sZDEQbARAAgCi7%2B26pUiVpwQLptddcVwM/IgACABBl5ctL/fvb%2BQMPSKGQ03LgQwRAAAAcuPFGqVEjacMGtohD9BEAgQSVnZ2tzMxMZWVluS4FwEGkpkpPPWXnzz4rLVvmth74S8DzmIQOJLJwOKxgMKhQKKT09HTX5QDYi%2BdJHTtK48ZJXbva%2BoBANNACCACAI4GANHiwlJQkjRwpjR/vuiL4BQEQAACHGjeWbrrJzm%2B/XcrNdVsP/IEACACAYw8/LFWoIM2eLb36qutq4AcEQAAAHKtUyUKgJN13n/Trr27rQeIjAAIAEANuukk69VRp06bCNQKB4kIABAAgBqSkSM89Z%2BcvvSTNneu2HiQ2AiAAADHinHNsOZjcXOnWW22ZGKA4EAABAIghTz8tlSplS8KwLiCKCwEQAIAYcsIJUt%2B%2Bdt6nj7R9u9NykKAIgAAAxJi77pLq1JFWrpQGDXJdDRIRARAAgBhTpox1BUsWAJcvd1sPEg8BEACAGHTxxTYpJCdH6t3bdTVINARAAABiUCBgy8IkJ0ujR0uffea6IiQSAiAAADHq1FOlW26x89tuk3btclsPEgcBEACAGNa/v1S1qrRwofT8866rQaIgAAIAEMOCQenxx%2B38oYektWvd1oPEQAAEElR2drYyMzOVlZXluhQAx%2Bnqq6WWLaWtWwvXCASOR8Dz2GgGSGThcFjBYFChUEjp6emuywFwjKZPtxDoedKUKdKZZ7quCPGMFkAAAOJAVpb0t7/Zea9etl8wcKwIgAAAxInHHrMxgTNnSq%2B95roaxDMCIAAAcaJqVenhh%2B383nulX391Ww/iFwEQAIA40qOHrQ%2B4aZN0//2uq0G8IgACABBHUlKkF16w83/8Q5o1y209iE8EQAAA4swf/iBdeqmUl2c7hLCeB44WARAAgDj01FNS6dLSxInSv//tuhrEGwIgAABxqE4dqV8/O7/jDmnbNrf1IL4QAAEAiFN33inVrSutXi0NGuS6GsQTAiAAAHGqdGnp6aft/IknpOXL3daD%2BEEABAAgjnXtKp19tpSTY13BQFEQAAEAiGOBgPTcc1JysjRypPTll64rQjwgAAIAEOcaN5ZuvtnOb7tN2rPHbT2IfQRAAAASwEMPSRUrSnPnSi%2B/7LoaxDoCIAAACaBiRemRR%2Bz8gQfYJxiHRwAEACBBdO8uNWli4e/BB11Xg1hGAAQSVHZ2tjIzM5WVleW6FABRkpIiPfusnQ8ZIs2b57YexK6A57GDIJDIwuGwgsGgQqGQ0tPTXZcDIAq6dpVGjZLOPVf67DObKQzsjRZAAAASzFNPSSVKSOPGSR995LoaxCICIAAACebEE6W//93O%2B/SRdu1yWw9iDwEQAIAEdM89UrVq0uLF0osvuq4GsYYACABAAkpPlwYMsPOHH5Y2bHBbD2ILARAAgAR1zTVSs2ZSKMSyMNgXARAAgASVnFy4LMzLL7MsDAoRAAEASGDt2tmyMHl5Uu/erqtBrCAAAgCQ4J54QkpNlT7/XPr4Y9fVIBYQAAEASHD160u33Wbnd9wh7dnjth64RwAEAMAH7r1XqlxZWrDAxgPC3wiAAAD4QPny0kMP2Xn//tJvvzktB44RAAEA8Inu3aVTTpE2bpQee8x1NXCJAAjEiRtvvFGBQEDP5q/pAABHKSVFevJJO3/uOWn5crf1wB0CIBAHRo8erWnTpqlmzZquSwEQ584/XzrnHNsf%2BO67XVcDVwiAQIxbs2aNevXqpeHDhys1NdV1OQDiXCAgPf20Xb77rvTNN64rggsEQCCG5eXlqVu3brrzzjt16qmnui4HQIJo2tS2iZNsWRjPc1oOHCAAAjFs0KBBSklJ0a233lrk%2B%2BTk5CgcDu9zAMD%2BHnlEKl1amjJFGjnSdTWINgIgECOGDx%2BucuXKFRwTJ07Uc889p2HDhikQCBT55wwcOFDBYLDgyMjIKMaqAcSrWrWs9U%2BS%2BvWzMYHwj4Dn0fALxIItW7Zo/fr1Bdffe%2B893XvvvUpKKvyclpubq6SkJGVkZGjFihUH/Tk5OTnKyckpuB4Oh5WRkaFQKKT09PRiqx9A/NmyRTrpJGn9epsVfBSdDYhzBEAgRm3atElr167d52sdO3ZUt27ddO2116pRo0ZF%2BjnhcFjBYJAACOCgXn5ZuukmqVIlaelSKRh0XRGiIcV1AQAOrlKlSqpUqdI%2BX0tNTVX16tWLHP4A4Eiuu0569lnpxx%2Blxx%2BXBg50XRGigTGAAAD4WEqKNGiQnT/7rLRqldt6EB10AQMJji5gAEfieVK7dtLkybY8zNChritCcaMFEAAAnwsECreIe%2BMNac4ct/Wg%2BBEAAQCAWraULr3UWgPvust1NShuBEAAACBJeuwxGxP4ySfS%2BPGuq0FxIgACAABJUoMG0o032vldd7FFXCIjAAIAgAL33y%2BVKydNny69/77ralBcCIAAAKBAtWpSnz52fs890u7dbutB8SAAAgCAffTpI1WpIi1ZIr32mutqUBwIgAAAYB9padIDD9j5ww9L27a5rQeRRwAEAAAH6N5dqldPWrdOeu4519Ug0giAAADgACVKSI88YueDBkmbNrmtB5FFAAQAAAd1xRVS06ZSOCw9/rjrahBJBEAAAHBQSUm2OLQkvfiitHq123oQOQRAIEFlZ2crMzNTWVlZrksBEMc6dZLOOkvaudMmhCAxBDyPdb6BRBYOhxUMBhUKhZSenu66HABxaMoUqU0bKTlZmj9fatjQdUU4XrQAAgCAw2rdWvrTn6Tc3MLlYRDfCIAAAOCIBgywy3fflX74wW0tOH4EQAAAcERNm9qsYEm69163teD4EQABAECRPPSQjQP8%2BGMbF4j4RQAEAABFctJJ0t/%2BZuf33isxjTR%2BEQABAECR3X%2B/7RIycaL0xReuq8GxIgACAIAiy8iQbr7Zzu%2B7j1bAeEUABAAAR%2BXuu6UyZaRvv5XGjHFdDY4FARAAAByVatWkW2%2B18wcekPLy3NaDo0cABAAAR%2B3OO6X0dGn2bOm991xXg6NFAAQAAEetYkWpd28779/fdglB/CAAAgCAY/L3v1sQ/PFH6e23XVeDo0EABAAAxyQ93bqCJVskevdut/Wg6AiAAADgmPXqJVWpIi1dKr31lutqUFQEQAAAcMzKlZPuusvOH3lE2rXLbT0oGgIgAAA4LjffLFWvLq1YIQ0b5roaFAUBEEhQ2dnZyszMVFZWlutSACS4MmWkfv3s/NFHpZwct/XgyAKexyYuQCILh8MKBoMKhUJKT093XQ6ABLVzp1S/vvTzz9JLLxVuF4fYRAsgAAA4bqVK2RZxkvTYY7QCxjoCIAAAiIjrr5dq1ZJWr5Zee811NTgcAiAAAIiIUqWke%2B6x84EDrVsYsYkACAAAIua666TataU1a6TXX3ddDQ6FAAgAACKmZMnCsYC0AsYuAiAAAIiovVsB//lP19XgYAiAAAAgokqWLFwXcOBAZgTHIgIgAACIuOuuk2rWtBnBtALGHgIgAACIuFKlClsBH3%2BcPYJjDQEQAAAUi%2Buvl2rUkFaulN54w3U12BsBEAAAFIvSpaW%2Bfe38scek3bvd1oNCBEAAAFBsuneXqlaVVqyQ/vUv19UgHwEQAAAUmzJlpDvusPPHHpP27HFbDwwBEAAAFKubb5YqVZKWLJHefdd1NZAIgAAAoJiVKyf17m3nAwZIeXlu6wEBEAAAREHPnlL58tKCBdLIka6rAQEQAAAUu2BQuvVWO3/0Ucnz3NbjdwRAIEFlZ2crMzNTWVlZrksBAEnSbbdZd/CsWdLYsa6r8beA55HBgUQWDocVDAYVCoWUnp7uuhwAPnfXXdITT0gtW0pffy0FAq4r8idaAAEAQNT07m3bxE2bJv33v66r8S8CIAAAiJpq1aQbbrDzAQPc1uJnBEAAABBVd94ppaZKEyZYNzCijwAIAACiKiND6tbNzmkFdIMACAAAoq5fPykpyWYDz5rluhr/IQACAICoO%2Bkk6dJL7XzgQLe1%2BBEBEAAAOHH33Xb53nvS4sVua/EbAiAAAHCiaVPp/PNtb%2BAnnnBdjb8QAAEAgDP33GOXb74prVnjthY/IQACAABnWreWzjpL2rVLeuYZ19X4BwEQAAA41a%2BfXf7jH9Kvv7qtxS8IgAAAwKlOnWw84LZtUna262r8gQAIxLgFCxaoc%2BfOCgaDSktLU6tWrbRy5UrXZQFAxAQC0l132fnzz0vbt7utxw8IgEAMW7p0qdq0aaOTTz5ZEyZM0KxZs3T//ferVKlSrksDgIi69FKpXj1p40bp9dddV5P4Ap7nea6LAHBwl19%2BuVJTU/XWW28d888Ih8MKBoMKhUJKT0%2BPYHUAEFlDhkg9ekh169q6gKmpritKXLQAAjEqLy9PY8eOVcOGDdWxY0dVrVpVLVu21OjRow97v5ycHIXD4X0OAIgH11wjVa0q/fST9O9/u64msREAgRj1yy%2B/aOvWrXr88cd13nnn6fPPP9dFF12krl27auLEiYe838CBAxUMBguOjIyMKFYNAMeudGnp1lvtfNAgiT7K4kMXMBAjhg8frhtvvLHg%2BtixY9W%2BfXtdccUVevvttwu%2B3rlzZ5UtW1YjRow46M/JyclRTk5OwfVwOKyMjAy6gAHEhc2bpTp1pK1bpY8/thnCiLwU1wUAMJ07d1bLli0LrlepUkUpKSnKzMzc53annHKKvvrqq0P%2BnJIlS6pkyZLFVicAFKcKFaTu3aXBg60VkABYPAiAQIxIS0tTWlraPl/LysrSwoUL9/naokWLVLdu3WiWBgBR9fe/23IwEydK334rnX6664oSD2MAgRh255136t1339Wrr76qJUuW6MUXX9SHH36oHj16uC4NAIpN7drSX/5i50884baWRMUYQCDG/fOf/9TAgQO1evVqNWrUSA899JC6dOlS5PuzDAyAeDR3rtSkiS0SvWiR1KCB64oSCwEQSHAEQADx6k9/sokgN91kawQicugCBgAAMenOO%2B1y2DBpwwanpSQcAiAAAIhJ7dpJWVnSzp3Siy%2B6riaxEAABAEBMCgSkO%2B6w8%2Bxsaft2t/UkEgIgAACIWV27SvXqSZs2SW%2B84bqaxEEABAAAMSslxdYFlKSnn5Zyc93WkygIgAAAIKb97W%2B2Q8jSpdIHH7iuJjEQAAEAQEwrW1a6%2BWY7f/ppt7UkCgIgAACIeb16SSVKSFOnSl9/7bqa%2BEcABAAAMa9GjcLt4WgFPH4EQAAAEBd697bLUaOkZcvc1hLvCIAAACAuNG4sdewo5eVJzz3nupr4RgAEElR2drYyMzOVlZXluhQAiJg%2Bfezy9delzZvd1hLPAp7nea6LAFB8wuGwgsGgQqGQ0tPTXZcDAMfF86SmTaU5c6RBg6S%2BfV1XFJ9oAQQAAHEjEChcGPqFF6Tdu93WE68IgAAAIK5ceaVUrZq0erX0/vuuq4lPBEAAABBXSpaUeva088GDrVsYR4cACAAA4s5NN1kQ/O47acoU19XEHwIgAACIO1WqSN262fkzz7itJR4RAAEAQFy6/Xa7HD1aWr7cbS3xhgAIAADi0qmnSh062MLQL7zgupr4QgAEAABxK78V8PXXpXDYbS3xhAAIAADiVseO0sknW/gbOtR1NfGDAAgAAOJWUpJ02212/sILUm6u23riBQEQAADEtW7dpAoVpKVLpbFjXVcTHwiAAAAgrpUtK91wg50/%2B6zbWuIFARAAAMS9nj2l5GRp/HhpzhzX1cS%2BFNcFAAAAHK86daRevaSaNaXatV1XE/sCnscOekAiC4fDCgaDCoVCSk9Pd10OACAG0AUMAADgMwRAAAAAnyEAAgkqOztbmZmZysrKcl0KACDGMAYQSHCMAQQA7I8WQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQCBBZWdnKzMzU1lZWa5LAQDEmIDneZ7rIgAUn3A4rGAwqFAopPT0dNflAABiAC2AAAAAPkMABAAA8BkCIAAAgM8QAAEAAHyGAAgAAOAzBEAAAACfIQACAAD4DAEQAADAZwiAAAAAPkMABAAA8BkCIAAAgM8QAAEAAHyGAAgAAOAzBEAAAACfCXie57kuAkDx8TxPW7ZsUVpamgKBgOtyAAAxgAAIAADgM3QBAwAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiAAAP7b6RsAAAAoSURBVIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGf%2BH0uMw7aNpWE8AAAAAElFTkSuQmCC'}