Genus 2 curve data - 461.a.461.1

Formats: - HTML - YAML - JSON - 2024-07-27T14:09:08.562085

• 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': '2294054356344303861', 'abs_disc': 461, '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': '[[461,[1,1,461,461]]]', 'bad_primes': [461], 'class': '461.a', 'cond': 461, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[-2,3,0,-3,0,1],[0,0,0,1]]', 'g2_inv': "['70288881159168/461','2923824242304/461','161508086208/461']", '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': "['1176','144','66456','1844']", 'igusa_inv': "['588','14382','467132','16957923','461']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '461.a.461.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.2458864264726567276756390789225886309416527367578934646', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.6.1'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2, 7], 'non_solvable_places': [], 'num_rat_pts': 3, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '12.048434897160179656106314867', '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': 1, 'torsion_order': 7, 'torsion_subgroup': '[7]', 'two_selmer_rank': 0, 'two_torsion_field': ['5.1.7376.1', [2, -2, 0, 2, -2, 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': '461.a.461.1', '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': '461.a.461.1', 'mw_gens': [[[[2, 1], [1, 1], [0, 1]], [[4, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [7], 'num_rat_pts': 3, 'rat_pts': [[-2, 4, 1], [1, -1, 0], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 461, 'lmfdb_label': '461.a.461.1', 'modell_image': '2.6.1', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  {'cluster_label': 'c4c2_1~2_0', 'label': '461.a.461.1', 'p': 461, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '461.a.461.1', 'plot': 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COmlB48KD39tO00AADA1yiAfnD55dLq1dKNN0qHD0vt2kkTJwbOdYElS0qvvmqOp0yRUlLs5gEAAL5FAfSTSpWkL7%2BU7r1XOnlSGjBAeughc4o4EDRvLnXpYrL16WPuAQCAM1EA/Sg01Ey0%2BMc/zDIsb7whtWplTr0GgnHjpLJlpRUrpHfftZ0GAAD4CgXQzzweadAgae5cqVw5aelSqXFjacsW28mk6tXz9gkeMkT6/Xe7eQAAgG9QAC1p105audLMDN6xQ7ruOmn%2BfNupzGzg%2BHizZM2wYbbTAAAAX6AAWlS3rvTdd1KzZmZySPv25jSszckhoaHSa6%2BZ4zfekFatspcFAAD4BgXQsipVpIULpfvvNxMvBg%2BWevWSjh2zl%2BnGG6WePU0RfeghKTvbXhYAAFD8KIABIDTUjLZNnGj25n33XTMrd98%2Be5leesksCP3999LLL9vLAeD82AkEQGGxE0iAWbTILMdy6JBUrZo0Z47UqJGdLG%2B/Ld13n1SmjLRpk1Srlp0cAAqGnUAAFBQjgAGmZUtzXeAVV0i//irdcIM0bZqdLPfcY04HHzlidgvhfxUAAHAGCmAAql3bTL5o185cC9ijh/TYY1JOjn9zeDzS669LYWHS559L06f79/MBAIBvUAADVESE9OmneUux/OMf0q23mlPD/nTZZXn7A/fvb5aHAQAAwY0CGMBKlJCee0764AOpdGnpiy/MotGbN/s3x2OPSfXqmR1L%2Bvf372cDTrF8%2BXK1b99esbGx8ng8mjNnTr7nvV6vRo4cqdjYWJUuXVo33XSTNvv7X3YArkEBDAJdupjt2WrUkLZvN4tG/89/O3wqNNRMCClRQpoxw%2BxiAqBwMjMzVb9%2BfU2aNOmMz7/44osaP368Jk2apDVr1ig6Olq33HKLDh8%2B7OekANyAWcBB5MABUwaXLjWPhw83W7eV8FONHzpUevFFKSbGjEJWqOCfzwWcxuPxaPbs2UpKSpJkRv9iY2M1YMAADR06VJKUlZWlqlWrauzYsXrwwQcL9L7MAgZQUIwABpEqVaQvv5QefdQ8fvZZKSlJSkvzz%2BePHGmuCdy3TxowwD%2BfCbjBzp07lZqaqlatWuV%2BLSwsTDfeeKNWrFhhMRkAp6IABplSpcyC0e%2B8Y2bnfvaZuS7whx98/9mlS0tTp5rZwe%2B%2Baz4bQNGlpqZKkqpWrZrv61WrVs197kyysrKUnp6e7wYABUEBDFI9e0rffCNVry799JN07bX%2BuS7wr3%2BVBg0yxw88YCaGACgeHo8n32Ov13va1/5szJgxioyMzL3FxcX5OiIAh6AABrFGjaS1a81izYcPSx07miVbTp707eeOGmUWqk5Nlfr08e1nAW4QHR0tSaeN9u3fv/%2B0UcE/e%2BKJJ5SWlpZ727Nnj09zAnAOCmCQi4qSFi7MW55l9GizgLQv1wu86CJzCjgkxCxRM3Om7z4LcINatWopOjpaCxcuzP3a8ePHtWzZMl1//fVn/b6wsDBFRETkuwFAQVAAHaBUKenll6X33jPlbMECMzq4YYPvPrNRI%2Bmpp8zxww9Le/f67rMAJ8jIyFBKSopSUlIkmYkfKSkp2r17tzwejwYMGKDnn39es2fP1qZNm9SrVy%2BVKVNG3bt3t5wcgBOxDIzDpKSYU8G7dplJG//8p%2BSr/36cOCE1aSKtWSPdfLOZoeyvJWmAYLN06VI1b978tK/37NlT77zzjrxer5555hm9/vrrOnTokK699lolJyerbt26Bf4MloEBUFAUQAc6eNCUvi%2B/NI/795deesmMFBa3n36SGjSQjhwx29WdmiACwP8ogAAKivEaB6pUSZo/P28f4YkTpRYtzPp9xa1OHWnCBHM8bJgZgQQAAIGNAuhQISFmH%2BE5c6TwcLNkzDXXmPvi1ru31KGDdPy41K2blJlZ/J8BAACKDwXQ4Tp0kP79b%2BnKK82yLc2bmxHB4jzx7/GYaw1jY6WtW/N2KgEAAIGJAugCdepIq1aZ0bnsbLONW/fuUkZG8X1G5crS%2B%2B%2BbMvj22ywNAwBAIKMAukS5ctL06Wa5mJIlTUG79lrpxx%2BL7zOaN89bGqZ3b2nbtuJ7bwBnl5ycrPj4eCUkJNiOAiBIMAvYhb79Vurc2UwKKVfOjNh17lw8752dbZaEWb5cuvpqaeVKszYhAN9jFjCAgmIE0IWaNJHWrzdbyGVkSF26SAMHmnX9iqpkSTPSWLmymRF8aocSAAAQOCiALlW1qrRokTRkiHk8YYI5hfvrr0V/72rVTAn0eKQ33jDbxgEAgMBBAXSxkiWlsWOl2bOliAhzaviaa6TFi4v%2B3rfcIo0YYY4fekj6/vuivycAACgeFEAoKUlau1a66ipp/35T3p5/Xjp5smjv%2B/TTUps20tGj0m23SYcOFU9eAABQNBRASJJq1zYTNnr1MsXvySelxETp998v/D1LlDBLw9SsKf38s3TnnVJOTnElBgAAF4oCiFxlykhTp5pFncPCpH/9y5wSXrPmwt%2BzUiVp1iwzE3jBAmn48OLLCwAALgwFEKe57z6zcPQll0i//CI1bSolJ1/47iENGphSKZlTyx9%2BWHxZAQBA4VEAcUZXX22uC%2BzY0ezx27ev2T3k8OELe78775QGDTLHvXqZZWgAFA8WggZQWCwEjXPyes0SMUOHmkWeL7tM%2BvhjqW7dwr9XTo7Urp30%2BedS9erSd99JMTHFnxlwKxaCBlBQjADinDwes0j0smVmfb%2BtW6XGjaX/%2B7/Cv1dIiDRjhnT55dLevVKHDtKRI8WfGQAAnBsFEAVy/fXmtG3r1mZZl169zLWChS1w5ctLn30mVaxoJpfcfXfRl5sBAACFQwFEgVWpIs2fLz37rBkZfPtt6brrpJ9%2BKtz71K5tFp8uVUr65BPp8cd9kxcAAJwZBRCFUqKEWeB50SIpKkrauFFq2FCaObNw79OsmSmQkvTSS9JrrxV/VgAAcGYUQFyQFi2klBTpxhuljAzpjjukRx6Rjh0r%2BHv06CGNGmWO%2B/WT5szxTVYAAJAfBRAXLCbGjAQOG2YeT54sNWlidv0oqCeflHr3NtcB3nGH9M03vskKAADyUABRJCVLSs89Z3b5qFRJWrfO7B4ya1bBvt/jMad/ExPN6GH79tKGDb7NDACA21EAUSzatDGnhJs0kdLTpdtuk/r3l7Kyzv%2B9JUua5WGaNJH%2B%2BMPMNC7MKCIAACgcCiCKTfXq0pIl0mOPmcevvCLdcIO0a9f5v7dMGbM8zFVXSampUsuW0q%2B/%2BjQu4BjsBAKgsNgJBD4xb55Z4%2B/QIbP23zvvmIWfzyc11ZTG7dvNgtHLlpnZxgDOj51AABQUI4DwiXbtzMLR115rTusmJZkdRY4fP/f3RUebiSVxcdKPP5qRwIMH/ZMZAAC3oADCZ2rUkJYvN8VPMnsKN2sm/fLL%2Bb/vq69MGdy4UbrlFjOSCAAAigcFED4VGiqNG2fW%2BCtfXlq9WmrQwFzvdy6XXmpKYJUqZiSREggAQPGhAMIvOnQwRS4hwRS5xERp8GDpxImzf098vLR4sVS5srR2rTkd/Pvv/ssMAIBTUQDhNzVrmoWe%2B/c3j8eNM6eEd%2B8%2B%2B/fUrWtmFlepYtYYbNFCOnDAL3EBAHAsCiD8KjRUevlls1B0ZKS0apV09dXnPiV8qgRWrSp9/710003Svn1%2BiwwAgONQAGFFx45nPiV8tlnCV15ploSpVk3asqVgk0kAAMCZUQBhTa1aZz4lfLZid9llZlZxzZpmncCmTc1SMQAAoHAogLDq1Cnh2bPzZglffbX06adnfv3FF5vSeMUV0t69ZtHof//bv5mBQMNOIAAKi51AEDB27ZK6dpW%2B%2B8487t9fGjtWCgs7/bX//a/Utq0pf%2BXKmQLZsqVf4wIBh51AABQUI4AIGDVrSl9/nbdw9MSJUpMm0s8/n/7aypXNEjEtWkgZGdLf/ibNnOnXuAAABC0KIALKqYWj586VKlY06/81aHDmchceLs2fL3XubNYTvOMOs9sIAAA4NwogAlL79lJKipnocfiwKXe9e0tHjuR/XViYNGOG1K%2BfeTxwoLmdPOn/zAAABAsKIAJWXJxZ/%2B/JJyWPR/rnP6VGjaQNG/K/LiTEnC4eO9Y8njDBXEt49Kj/MwMXauTIkfJ4PPlu0dHRtmMBcCgKIAJayZLS6NHSokVSTIz0ww9S48bSpEnSn6cveTzSkCHStGlSqVLSxx%2Bb/YMPHrSXHSisK6%2B8Uvv27cu9bdy40XYkAA5FAURQaNHC7AJy661SVpY55ZuYePq2cN27S19%2BaXYZ%2BfZb6a9/NWsGAsGgZMmSio6Ozr1VqVLFdiQADkUBRNCoUsVsGTdxorn2b948qV496fPP87/uppukFSukGjWkbduk664zj4FAt23bNsXGxqpWrVrq1q2bduzYYTsSAIdiHUAEpQ0bzGjf5s3mcZ8%2B0osvSmXK5L0mNdVMJvn3v01hfPddqUsXO3mB81mwYIGOHDmiOnXq6D//%2BY9Gjx6tH3/8UZs3b1alSpXO%2BD1ZWVnKysrKfZyenq64uDjWAQRwXhRABK2jR6WhQ6VXXzWPL7vMlLzGjfNek5kp3Xln3s4iY8dKjz1mrhkEAllmZqYuueQSDRkyRANPLY75P0aOHKlnnnnmtK9TAAGcD6eAEbRKl5ZeeUX64gspNlbaulW6/noza/jUoEjZstInn0iPPmoeDx0qPfKIlJ1tLzdQEGXLllW9evW0bdu2s77miSeeUFpaWu5tz549fkwIIJhRABH0WrWSNm40awXm5EjPP2%2BWi1mzxjx/apmYl182I39TpkgdO5rRQSBQZWVl6YcfflBMTMxZXxMWFqaIiIh8NwAoCAogHKFiRWn6dLP8S5Uq0qZNZvLH4MF5Ra9/fzMaeNFFZgLJzTebPYWBQDB48GAtW7ZMO3fu1OrVq3X77bcrPT1dPXv2tB0NgANRAOEot90mbdliJoicPGm2lbvySlP4JDPy99VXUoUK0urVUrNm0q%2B/2s0MSNLevXt1xx136LLLLlOnTp0UGhqqVatWqUaNGrajAXAgJoHAsebPlx5%2BWNq92zxu314aP16qXduUxNatpb17pTuil%2BifV72iMj%2BuMwsIdu9uvjEy0u4fACik9PR0RUZGMgkEwHlRAOFomZnSs8%2Ba4pedbXYJ6ddPGjZMysiQpjUcr2EHB53%2BjVdcIS1fLlWu7P/QwAWiAAIoKAogXOHHH6UBA8yMYUmKiJBG3vWzBrx2qTxn%2B1egd2/pjTf8FxIoIgoggILiGkC4wuWXmx1DFiyQ6teX0tOlI8lvn738SWZWydGj/gsJAICfUADhKm3aSOvWSR99JDWo8Mu5X5yZKR086J9gQBEkJycrPj5eCQkJtqMACBKcAoZreZ98Sp7nnzv7C8qWlQ4cMCtOA0GAU8AACooRQLiW5757pRLn%2BFegRw/KHwDAkSiAcK%2BLLzYLBZ7JlVdKz51jdBAAgCDGKWBg%2BXLp1VfNxYGn1gF84AEzVRgIIpwCBlBQFEAAcAgKIICC4hQwAACAy1AAAQAAXIYCCAAA4DIUQAAAAJehAAJAkGMnEACFxSxgAHAIZgEDKChGAAEAAFyGAggAAOAyFEAAAACXoQACAAC4DAUQAADAZSiAAAAALkMBBAAAcBkKIAAAcAxWNy4YCiAABDl2AoHb/ec/0sSJUsOG0vz5ttMEB3YCAQCHYCcQuElmpvTpp9L770tffinl5Jivd%2B0qzZxpN1swKGk7AAAAQEGcOCEtXChNny7NmWNK4CmNG0t33SV162YvXzChAAIAgIB18qT09dfSjBnSxx9LBw/mPXfxxdKdd0o9ekh16tjLGIwogAAAIKCcPCmtWiV98IEpfb/9lvdcVJQ5zdu9u3TttZLHYy9nMKMAAgAA606Vvo8%2BMqVv796858qXlzp2lO64Q2reXCpJeykyfoQAAMCKnBxzeveTT6RZs/KP9IWHSx06SF26SK1aSWFh9nI6EQUQAAD4zbFj0uLFpvB9%2Bqn03//mPRcRIbVvL3XuLLVuLV10kb2cTkcBBAAAPnXokLRggZm5u2CBlJGR91zFilJionTbbdIttzDS5y8UQAAAUOx27JA%2B%2B0yaO1davlzKzs57LjZWSkoy1/XdeKNUqpS9nG5FAQSAIJecnKzk5GTlnFoJF7AgO1tauVL6179M8duyJf/z8fHmmr6kJKlRI6kEe5FZxU4gAOAQ7AQCfztwQPr8c7P92hdfmFO9p4SESE2bmtO7iYlS7dr2cuJ0jAACAIACycmR1qwOTjq0AAAWT0lEQVQxpW/BAnP852GkihWlNm2kdu3MfYUK9rLi3CiAAADgrPbuNXvtfvGF2Ybtz6N8klS/vnTrrdLf/iZdd50Z%2BUPgowACAIBcmZlm0saXX5rb/17LV7681LKl1LatGeWLjbWTE0VDAQQAwMVycqS1a6VFi8wI34oV0vHjec97PFJCglmXr3Vrs/0aO3EEP/4RAgDgIl6vtG2bKXyLFklLlkh//JH/NX/5i9l945ZbzGhfxYp2ssJ3KIAAADhcaqr01Vem8H31lbRnT/7nIyPNHrunCt%2Bll5qRPzgXBRAAAIfJyJCWLTOndBctkjZvzv98aKh0/fWm7LVsKTVsyGldt%2BEfdxF4vV4dPnzYdgwALpWVlaWsrKzcx6f%2BPkpPT7cVCZacPCmtX29G95YskVavNtf2/dlVV0k33WRuf/2rVKZM3nNHjvgzbeAIDw%2BXx6VDnSwEXQSnFl0FAADBx82LplMAi6AwI4AJCQlas2bNBX9Wenq64uLitGfPniL/shY1S3G%2BTyBlKa6fcSD9mYrrfQLp51tceQLp53uh7/O/I4D79u1T48aNtWXLFlWrVs2vWQL9fZzyO7x3r9lbd9486dtvs/XnE3nlypl9dVu0MLeLL/ZtluJ%2BHxs/XzePAHIKuAg8Hk%2BBf0lDQkKK5f8yIiIiivw%2BxZWlON4nkLKcUtSfcaD9mQLpn5MUOL/DgfTzLc73kcx/1Pgd9k0Wyf%2B/w3/8Ic2cKb3/vvTtt/mfq1vXLMLctq25pq9UKd9m8cf7BMrfEU5HAfSTPn362I6Qq7iyFMf7BFKW4hJof6ZA%2BudUXALpzxRo71McAu3PFEj/vItLQfJs2SKNHy9Nny4dPWq%2B5vGY/XU7dZLS0t7ViBF3%2ByWLP9%2BnOARSlkDFKeAgwSbvvsfP2Lf4%2Bfre3r17c0%2BhVa9e3XYcx/HX7/Cvv0pDhkgzZuTts1u3rtSrl9Stm1SEs/sBjb8j/IsRwCARFhamESNGKCwszHYUx%2BJn7Fv8fH3v1M%2BWn7Fv%2BON3eM4cqWdP6dRE7o4dpYEDpSZNnL8uH39H%2BBcjgADgEIygBLdZs6Tbbzejfo0bS5MnS9dcYzsVnIoRQAAALEtLk%2B6/35S/%2B%2B6TpkxhYWb4VgnbAQAAcLt586RDh6TLL6f8wT8ogAAQ5JKTkxUfH6%2BEhATbUXCB9u41940bU/7gHxTAIDFy5EhdfvnlKlu2rCpUqKCWLVtq9erVtmM5xokTJzR06FDVq1dPZcuWVWxsrO6%2B%2B2799ttvtqM5xqxZs9S6dWtVrlxZHo9HKSkptiM5Rp8%2BfbRly5ZiWcwXp1u%2BfLnat2%2Bv2NhYeTwezZkzp9g/4y9/Mffr1%2BfN/HWLMWPGKCEhQeHh4YqKilJSUpK2bt1qO5bjUQCDRJ06dTRp0iRt3LhR33zzjWrWrKlWrVrpwIEDtqM5wpEjR7Ru3To9/fTTWrdunWbNmqWffvpJiYmJtqM5RmZmppo0aaIXXnjBdhSgUDIzM1W/fn1NmjTJZ5/RurUUFiZt3CgtXOizjwlIy5YtU58%2BfbRq1SotXLhQ2dnZatWqlTIzM21HczRmAQepU7P9Fi1apJtvvtl2HEdas2aNGjdurF9%2B%2BUV/OfW/5yiyXbt2qVatWlq/fr2uvvpq23EchVnAvufxeDR79mwlJSUV%2B3v//e/Syy9LtWubkcBy5Yr9I4LCgQMHFBUVpWXLlqlZs2a24zgWI4BB6Pjx43rjjTcUGRmp%2BvXr247jWGlpafJ4PCpfvrztKABcYORIqXp1aft2qXdv950KPiUtLU2SVLFiRctJnI0CGETmzZuncuXK6aKLLtKECRO0cOFCVa5c2XYsRzp27Jgef/xxde/enZEUAH4RGWl2/yhZ0uz9%2B%2ByzthP5n9fr1cCBA9W0aVPVrVvXdhxHowAGoGnTpqlcuXK5t6%2B//lqS1Lx5c6WkpGjFihVq06aNunTpov3791tOG5zO9jOWzISQbt266eTJk3rttdcspgxe5/r5Aji7pk2l5GRzPHKkWRLGTfr27asNGzZoxowZtqM4HtcABqDDhw/rP//5T%2B7jatWqqXTp0qe97tJLL9W9996rJ554wp/xHOFsP%2BMTJ06oS5cu2rFjhxYvXqxKlSpZTBm8zvU7zDWAvsM1gL7ny2sA/%2Bzpp6XRo832b%2B%2B8I919t08/LiD069dPc%2BbM0fLly1WrVi3bcRyP1YYCUHh4uMLDw8/7Oq/Xq6ysLD8kcp4z/YxPlb9t27ZpyZIllL8iKOjvMIAze/ZZ6Y8/pEmTpHvuMUXwrrtsp/INr9erfv36afbs2Vq6dCnlz08ogEEgMzNTzz33nBITExUTE6ODBw/qtdde0969e9W5c2fb8RwhOztbt99%2Bu9atW6d58%2BYpJydHqampksyFyKGhoZYTBr/ff/9du3fvzl1b8dQ6X9HR0YqOjrYZDTinjIwMbd%2B%2BPffxzp07lZKSoooVK/pshQCPR5o4UTpxQnr9dalnTyknR%2BrVyycfZ1WfPn00ffp0ffrppwoPD8/9uzcyMvKMZ79QTLwIeEePHvV27NjRGxsb6w0NDfXGxMR4ExMTvd99953taI6xc%2BdOr6Qz3pYsWWI7niNMnTr1jD/fESNG2I4W9CZNmuS94oorvHXq1PFK8qalpdmO5ChLliw54%2B9uz549ff7ZOTle70MPeb1mTrDXO2WKzz/S7872d%2B/UqVNtR3M0rgEEAIfgGkBn8nqlAQOkV14xjydMMI%2BBomAWMAAAAczjMQtEDxliHv/972aCCMM3KAoKIAAAAc7jkV54QXrmGfP46aelYcMogbhwFEAAAIKAxyMNHy6NG2cev/CC9Oij0smTdnMhOFEAAQAIIgMHmgWiPR6zTMz995sZwkBhUAABAAgyDz4ovfuuVKKENHWqWSPwxAnbqRBMKIAAAAShHj2kDz80ewfPmCF17SodP247FYIFBRAAgCB1223S7NlSaKi579RJOnbMdioEAwogAABBrF076bPPpIsukv71LykpSTp61HYqBDoKIAAEueTkZMXHxyshIcF2FFjSqpU0f75Upoz0xRdShw6UQJwbO4EAgEOwEwiWL5f%2B9jcpM1Nq2VKaO1diO12cCSOAAAA4RLNm0uefS%2BXKSYsWMRKIs6MAAgDgIE2bSgsWSGXLSgsXmokhWVm2UyHQUAABAHCYpk3zrgn8/HOpc2eWiEF%2BFEAAAByoWbO82cGffSZ17y5lZ9tOhUBBAQQAwKFatJDmzDHrBH7yiXTvvewdDIMCCACAg7VubXYMCQmR3ntP6ttXYv0PUAABAHC4Dh1M%2BfN4pMmTpaeesp0ItlEAAQBwgTvuMOVPkp5/Xho3zm4e2EUBBIAgx04gKKgHH5TGjDHHgwdL//d/dvPAHnYCAQCHYCcQFITXKz32mBkBDAmRPv1UuvVW26ngb4wAAgDgIh6P9OKL0l13STk5Upcu0urVtlPB3yiAAAC4TIkS0ltvSW3aSEeOSO3aSdu22U4Ff6IAAgDgQqVKSR99JDVqJP33v1LbttKBA7ZTwV8ogAAQAHr16iWPx5Pvdt1119mOBYcrV06aN0%2BqVUv6%2BWepfXvp6FHbqeAPFEAACBBt2rTRvn37cm/z58%2B3HQkuULWqtGCBVKGCuRbwrrvYLcQNKIAAECDCwsIUHR2de6tYsaLtSHCJyy7Lv2XcsGG2E8HXKIAAECCWLl2qqKgo1alTR71799b%2B/fvP%2BfqsrCylp6fnuwEXqlkzMzFEksaOlaZOtZsHvsU6gAAQAD744AOVK1dONWrU0M6dO/X0008rOztba9euVVhY2Bm/Z%2BTIkXrmmWdO%2BzrrAKIohg%2BXRo0yk0QWLTLFEM5DAQQAP5s2bZoefPDB3McLFizQDTfckO81%2B/btU40aNTRz5kx16tTpjO%2BTlZWlrKys3Mfp6emKi4ujAKJIvF6pWzfpww%2BlSpWkNWvMJBE4S0nbAQDAbRITE3XttdfmPq5Wrdppr4mJiVGNGjW07RyLs4WFhZ11dBC4UB6POf3788/S2rVSYqK0YoUUHm47GYoTBRAA/Cw8PFzh5/mv6cGDB7Vnzx7FxMT4KRWQp0wZs0Vco0bSpk3S3XebySElmDngGPyjBADLMjIyNHjwYK1cuVK7du3S0qVL1b59e1WuXFkdO3a0HQ8uVa2aNHu2mRk8Z440erTtRChOFEAAsCwkJEQbN25Uhw4dVKdOHfXs2VN16tTRypUrzztSCPjSdddJU6aY4xEjpM8%2Bs5sHxYdJIADgEOnp6YqMjGQSCIpd375ScrIUEWEmhdSpYzsRiooRQAAAcE7jx0tNmkjp6VKnTlJGhu1EKCoKIAAAOKfQUOmjj6ToaGnzZumBB8xyMQheFEAAAHBeMTGmBIaESDNmmFPCCF4UQAAIcsnJyYqPj1dCQoLtKHC4pk2ll14yxwMHSt99ZzcPLhyTQADAIZgEAn/weqXOnc26gDVqSOvWSRUr2k6FwmIEEAAAFJjHI731lnTJJdIvv0j33MP1gMGIAggAAAolMtJcDxgaKs2dK738su1EKCwKIAAAKLQGDaQJE8zx0KHSv/9tNw8KhwIIAAAuyMMPS7fdJp04IXXrZtYJRHCgAAIAgAvi8Uhvvmkmg/z8s/TII7YToaAogAAA4IJVqCBNm2bWB5w2TXr/fduJUBAUQAAAUCRNmkgjRpjjRx6RduywmwfnRwEEAABFNmyYWSj68GGpRw8pO9t2IpwLBRAAghw7gSAQhISY078REdLKldLzz9tOhHNhJxAAcAh2AkEgmDbNjACGhJgiyP%2BXBCZGAAEAQLHp3l3q2lXKyZHuuks6csR2IpwJBRAAABQbj0d67TUpNlbaulV6/HHbiXAmFEAAAFCsKlaU3n7bHL/6qrR4sd08OB0FEAAAFLvWraWHHjLH99zDLiGBhgIIAAB84qWXpFq1pN27pcGDbafBn1EAAQCAT5QrJ02dao7ffFP68ku7eZCHAggAAHzmxhulfv3M8f33cyo4UFAAAQCAT40ZI118sbRnjzRkiO00kCiAABD02AkEga5sWemtt8zx669LS5bYzQN2AgEAx2AnEAS6hx%2BWpkyRLrlE2rBBKlPGdiL3YgQQAAD4xdixUvXq0s8/SyNG2E7jbhRAAADgFxER0uTJ5nj8eGndOrt53IwCCAAA/KZdO7NX8MmTZlZwdrbtRO5EAQQAAH41caJUoYK0fr05hv9RAAEAgF9VrWp2CZGk4cOlX36xm8eNKIAAAMDv7rlHatZMOnJE6ttXYk0S/6IAAgAAvytRwiwJU6qUNG%2BeNHu27UTuQgEEgCDHQtAIVldckbczSP/%2BUkaG3TxuwkLQAOAQLASNYHT0qHTlldLOndKgQdI//mE7kTswAggAAKwpXVqaNMkcv/yytGmT3TxuQQEEAABW/e1vUseOUk6O1KcPE0L8gQIIAACsmzDBjAYuXy5Nn247jfNRAAEAgHU1akhPPWWOBw%2BW0tPt5nE6CiAA%2BNisWbPUunVrVa5cWR6PRykpKae9JisrS/369VPlypVVtmxZJSYmau/evRbSAvYMGiTVri2lpkrPPms7jbNRAAHAxzIzM9WkSRO98MILZ33NgAEDNHv2bM2cOVPffPONMjIy1K5dO%2BXk5PgxKWBXWFje1nATJ0o//mg3j5OxDAwA%2BMmuXbtUq1YtrV%2B/XldffXXu19PS0lSlShW999576tq1qyTpt99%2BU1xcnObPn6/WrVsX6P1ZBgZO0b69WRy6dWtpwQLJ47GdyHkYAQQAy9auXasTJ06oVatWuV%2BLjY1V3bp1tWLFirN%2BX1ZWltLT0/PdACeYMEEKDZW%2B%2BMIUQRQ/CiAAWJaamqrQ0FBVqFAh39erVq2q1NTUs37fmDFjFBkZmXuLi4vzdVTAL2rXlv7%2Bd3M8cKCUlWU3jxNRAAGgGE2bNk3lypXLvX399dcX/F5er1eec5z7euKJJ5SWlpZ727NnzwV/FhBonnxSio6Wtm%2BXXn3VdhrnoQACQDFKTExUSkpK7q1Ro0bn/Z7o6GgdP35chw4dyvf1/fv3q2rVqmf9vrCwMEVEROS7AU4RHi6NGWOOR42S9u%2B3m8dpKIAAUIzCw8NVu3bt3Fvp0qXP%2Bz0NGzZUqVKltHDhwtyv7du3T5s2bdL111/vy7hAQLv7bumaa8yagMOH207jLBRAAPCx33//XSkpKdqyZYskaevWrUpJScm9vi8yMlL33XefBg0apK%2B%2B%2Bkrr169Xjx49VK9ePbVs2dJmdMCqEiXM/sCS9Oab7BNcnCiAAOBjc%2BfOVYMGDXTrrbdKkrp166YGDRpoypQpua%2BZMGGCkpKS1KVLFzVp0kRlypTRZ599ppCQEFuxgYBwww3SbbdJJ0%2BahaJRPFgHEAAcgnUA4VQ//yxdcYV04oRZF7BNG9uJgh8jgAAAIKBdconUr585HjxYYoOcoqMAAgCAgPfUU1KFCtLmzdLUqbbTBD8KIAAACHgVKkhPP22Ohw%2BXMjPt5gl2FEAACHLJycmKj49XQkKC7SiATz3yiFSrlrRvnzR%2BvO00wY1JIADgEEwCgRvMmCF1724Wit6%2BXYqKsp0oODECCAAAgkbXrlLDhtLhw9Lo0bbTBC8KIAAACBolSkhjx5rjKVOkHTvs5glWFEAAABBUbr5ZatXKrAvIFnEXhgIIAACCzpgx5n76dOn77%2B1mCUYUQAAAEHSuucZcD%2Bj1SsOG2U4TfCiAAAAgKI0aJYWESPPnS99%2BaztNcKEAAgCAoHTppdK995rjYcPMaCAKhgIIAACC1vDhUliYtHy59OWXttMEDwogAAQ5dgKBm1WvbnYIkcx%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%3D'}