Genus 2 curve data - 50625.a.253125.1

Formats: - HTML - YAML - JSON - 2024-04-18T03:03:43.770042

• 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': '1553674325109728329', 'abs_disc': 253125, '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]],[5,[1]]]', 'bad_primes': [3, 5], 'class': '50625.a', 'cond': 50625, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[-1,0,0,0,0,1],[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','324']", 'igusa_inv': "['0','0','0','0','253125']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '50625.a.253125.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '1.96199322672080862605992473433946946214820104636342', 'prec': 173}, 'locally_solvable': True, 'modell_images': ['2.36.1', '3.6480.5'], 'mw_rank': 1, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 3, 'num_rat_wpts': 1, 'real_geom_end_alg': 'C x C', 'real_period': {'__RealLiteral__': 0, 'data': '5.1751222199913060677920247404', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.189560086054', 'prec': 47}, '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': 1, 'torsion_subgroup': '[]', 'two_selmer_rank': 1, 'two_torsion_field': ['5.1.162000.1', [13, -1, 4, 4, -1, 1], [5, 3], 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': [['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': '50625.a.253125.1', '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': '50625.a.253125.1', 'mw_gens': [[[[1, 1], [0, 1], [1, 1]], [[0, 1], [1, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.18956008605378451053614960347031', 'prec': 113}], 'mw_invs': [0], 'num_rat_pts': 3, 'rat_pts': [[1, -1, 1], [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: 13246
    {'conductor': 50625, 'lmfdb_label': '50625.a.253125.1', 'modell_image': '2.36.1', 'prime': 2}
  • id: 13247
    {'conductor': 50625, 'lmfdb_label': '50625.a.253125.1', 'modell_image': '3.6480.5', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 107954
    {'cluster_label': 'c5_1~5', 'label': '50625.a.253125.1', 'p': 3, 'tamagawa_number': 1}
  • id: 107955
    {'cluster_label': 'c5_1~4', 'label': '50625.a.253125.1', 'p': 5, 'tamagawa_number': 2}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '50625.a.253125.1', 'plot': 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CwEQAC4hvPnzdq94mIT%2BD744NLjWQYPdnfr3n03x7MAiGwEQAC4iONIu3a5U7olJVJZmfc1PXu6HT6OZwEQbQiAACDp2DH3eJaiImn/fu94mzZSVpYb%2Bnr18qdOAGgIBEAAVvryS2nVKrfLt2mTd7x5c2nkSHfjxpAhUny8P7UCQEMjAAKwQm2ttHmzG/hWrjRn9NV1551uh2/0aKlVK39qBYDGRgAEELP27nU3bixZIp044R3v2tXt8GVlSbfc4kuZANDkrA6AwWBQwWBQoVDI71IANIDTp806vnDo27XLO56SIo0b53b5%2BvbleBYAdopzHMfxuwi/lZeXKxAIqKysTKmpqX6XA%2BA6VVebI1nC07rr1nmvWYuPl%2B66y711Y9gws7YPAGxndQcQQHS5nmvW%2BvZ1p3XHjZP4Ox0AXIoACCCiHTrkTule7Zq18CM93Z86ASCaEAABRJTwNWvh0Ld9u3c8Odns0M3JMYGPa9YAoP4IgAB8FQqZa9bC07qXu2ZtyBB3WnfkSK5ZA4CbRQAE0KQcR9q92w18S5ea3bt19ejhbtzgmjUAaHgEQACN7uRJcw5fOPTt3esdb9PGBL1w6OOaNQBoXARAAA2uqso9nqWwUNqwwXT%2Bwpo3l0aMcAPfV7/KNWsA0JQIgABumuNIO3a4Hb5lyy49niUjww18Y8dKrVv7UioAQARAADfoiy/cwFdUZI5rqatTJ/fGjexsc%2B0aACAyEAABXJdz56SVK91p3S1bvONJSdKYMW6Xj%2BNZACByEQABXJbjSNu2mbBXVCStWGFCYF0DB5rz%2BCZOlEaNMiEQABD5CIAALjhyxDute/GtG126mLAXPoQ5Lc2fOgEAN8fqABgMBhUMBhUKhfwuBfDFuXPSqlWmy3e5ad2WLc2GjXCXLyODaV0AiAVxjlP3cAY7lZeXKxAIqKysTKncHI8YFt6tW1goLV5srly7eFp30CAT%2BHJyuHUDAGKV1R1AwAbHj5t7dcNdvs8/946Hp3Vzc820bseO/tQJAGg6BEAgxtTUSGvWuF2%2B9eu9hzCHd%2Bvm5pouX//%2BTOsCgG0IgEAM2LPHhL3Fi6WSEqm83DuemWkCX26uNHo0u3UBwHYEQCAKVVaa2zbCoa%2B01Dvevr07rZuTY6Z5AQAIIwACUcBxpO3bpUWLzGPlSqm62h2Pjzd36%2BbmSpMmSYMHS82a%2BVcvACCyEQCBCHX6tNm8EQ59F2/e6N7dhL3cXGnCBCkQ8KdOAED0IQACEaK2Vtq0yYS9ggKzkaPuEZVJSdK4cW7o69OHzRsAgBtDAAR8dOKE2a1bUGDW8n3xhXe8b18T%2BCZNMjt3k5P9qRMAEFsIgEATqq2VNm40ga%2BgQFq71jwX1rq1lJVlAt/kyWaaFwCAhkYABBrZqVOmy5efb6Z3L%2B7yZWaasDd5srl5o0ULf%2BoEANiDAAg0MMeRtm2TFi40oe%2BDD7xr%2BVJSzI0bkyebTl96un%2B1AgDsRAAEGkBlpdmxm59vHgcPesczMkzgmzJFGjWKLh8AwF9WB8BgMKhgMKhQ3fYMcJ327JEWLDCdvmXLpKoqdyw52RzNkpdnQh9r%2BQAAkSTOcereEmqn8vJyBQIBlZWVKTU11e9yEKHOn5dWrzahb8ECaedO73iPHtLUqSbwjRvHjl0AQOSyugMIXMvp02bjxnvvmR9PnnTH4uPNpo2pU82jb1/O5QMARAcCIHCR3btN4Js/31y5dv68O9aunVnLN3Wq2cDRpo1/dQIAcKMIgLBeKCR9%2BKEJfPPnSzt2eMf79ZOmTzfr%2BUaMkBL4UwMAiHL8rwxW%2BvJLackSad480%2B2rezZfQoK5dWPaNPPo1cu/OgEAaAwEQFjj5EmzeePdd816vrNn3bHUVLN5Y/p0M8XL1C4AIJYRABHTDh40Xb533pGWL/ceyJyeLt1zj3mMGcPZfAAAexAAEXNKS6W33zaPdeu8Y3fcIc2YYULf4MHs2gUA2IkAiKgXvnrtrbfMY/t2dywuTrr7bulrXzPBj/V8AABIzfwuoCHNmTNHPXv2VFJSkoYMGaKVK1f6XRJuxObN0re%2BJXXsaB4PPyxt3ep5ieNIGzdKP/2p1KePNGCA9PzzJvwlJEg5OdJLL0mHDkmrVkk//jHhDwCAsJi5CeRvf/ubHn74Yc2ZM0cjR47U7373O/3hD3/Qjh071K1bt6t%2BLzeBRJDFi838bN171SQpKUnO/Pe0qX22/vu/pTfflD77zB1OTJRyc6V77zU7d9u2bdqyAQCIJjETAO%2B66y4NHjxYv/3tby88169fP82YMUOzZ8%2B%2B6vcSACNEKCT17CkdOHDZ4QMJPdT9/G45/2hcJyebHbv332/O6EtJacpiAQCIXjGxBrC6ulobNmzQk08%2B6Xk%2BJydHq1evvuT1VVVVqqrTYSovL7%2Bhn9dxHFVUVNzQ9%2BIyiouvGP4kKXB%2Br7Kbv6fkyeP1ta%2BZjl%2BrVmbMcaQb/BgBAJZKSUlRnKW7AWMiAB4/flyhUEidOnXyPN%2BpUycdOXLkktfPnj1bzz333E3/vBUVFQoEAjf9PqiHmhnSP27sAADgZtg88xcTATDs4hTvOM5lk/1TTz2lH/3oRxe%2BLi8vV3p6er1/vpSUFJWVlV1xfOjQoVp38TkkNyHS3%2B9G3vP0aXMw89/%2BJjnvv68CTbn6NyxeLA0f3mT1%2BfGekf5%2B4T8vBw4caLD/cEb6rznSf99Ew2fSGO8Z6e8XDZ%2BLbZ/zxZ9JisVrh2IiAHbo0EHx8fGXdPu%2B%2BOKLS7qCkpSYmKjExMSb/nnj4uKu%2Boc6Pj6%2BQf9mEenvd73vWVMjFRRIr71mrmGrrg6PTNbJ5H7q8eXOy39jZqbZ3tvI9fn9npH%2BfmGpqakN9r6R/muOht83UmR/Jo3xnpH%2BfmGR/LnY%2BDlLDfuZRKuYOAamRYsWGjJkiIqKijzPFxUV6e677/apKmnmzJlWvd/V3tNxpE2bpH/9V6lLF7PR9623TPjr31%2BaPVvat0/q8f4bUvv2l75Bhw7S6683Wn2R9J6R/n6NIdJ/zdHw%2B6ahRcOvOdLfrzFEw685GmpEDO0CDh8D89JLL2nEiBF6%2BeWX9fvf/17bt29X9%2B7dr/q97AJuPMeOSW%2B8Ib3yivcov06dpH/6J3PE34ABF93IceiQ9NJLCi1cqPUbN2rgT36ixH/9V6lz5yavH5fiz0vk4TOJTHwukYfPxBUTU8CS9OCDD%2BrEiRN6/vnndfjwYWVmZio/P/%2Ba4Q8NLxQyG3r/8Aezvq%2BmxjyfmGg6f488YmZyE670u69LF%2Bn553X%2B6adVMHu2Bj71lPlmRITExEQ9%2B%2ByzDbKMAg2DzyQy8blEHj4TV8x0AG8GfyNoGIcOSX/8o3ns2%2Bc%2BP2SI9Oij0je/yQHNAABEgpjpAMIftbXSkiXSb39rjmYJhczzbdqY6d3vfc9M8QIAgMhBAMQNKSuTXn1VmjNHKi11nx81Svrnf5buu8/c1AEAACIPARD1Uloq/frXJvxVVprnUlLMur5/%2BRezoxcAAEQ2AiCuyXGk5culF1%2BUFixwn8/IkJ54QvrWt6TWrf2rDwAA1E9MnAOIxhEKSW%2B%2BKQ0bJo0fb8JfXJw0dapUVCR99JHp%2BjV0%2BFuxYoWmTZumLl26KC4uTvPmzWvYnwBXVN9/94cPH9ZDDz2kPn36qFmzZvrhD3/YRJXapb6fy9tvv62JEyeqY8eOSk1N1YgRI7R48eImqtYO9f1MVq1apZEjR6p9%2B/ZKTk5W37599ctf/rKJqrXHzfz/4/3331dCQoIGDhzYiBVGDgIgLlFTY87t69dPeuABaf16KSnJhL2dO83tHdnZF53d14AqKys1YMAA/eY3v2mcnwBXVN9/91VVVerYsaOefvppDWC3T6Op7%2BeyYsUKTZw4Ufn5%2BdqwYYPGjx%2BvadOmadOmTY1cqT3q%2B5m0atVKjz/%2BuFasWKGdO3fqmWee0TPPPKOXX365kSu1y43%2B/6OsrEzf/va3lZWV1UiVRR6OgRHHwIRVV0t/%2BpP085%2B7x7i0bSs9/riZ6u3YselriouL0zvvvKMZM2Y0/U9uufr%2Bux83bpwGDhyo//qv/2rkyux2o38m%2BvfvrwcffFA/%2B9nPGqkye93oZ/L1r39drVq10p///OdGqsxu9flcvvGNb%2Bj2229XfHy85s2bp82bNzdBhf6yugMYDAaVkZGhoUOH%2Bl2Kr0Ihs6mjTx%2Bzg3ffPnNTxwsvSPv3S88/70/4A9AwamtrVVFRoXbt2vldCv5h06ZNWr16tcaOHet3KdZ75ZVXtHv3bj377LN%2Bl9KkrN4EMnPmTM2cOfNCB9A2jmPW9T35pLRjh3muUyfz9Q9%2BwDEuQKx48cUXVVlZqQceeMDvUqx366236tixYzp//rxmzZql73//%2B36XZLVPP/1UTz75pFauXKmEK15PFZvs%2BtXigs2bpf/1v6Rly8zX7dqZ4DdzptSypa%2BlAWhAc%2BfO1axZs/Tuu%2B8qLS3N73Kst3LlSp05c0Zr1qzRk08%2Bqdtuu03f/OY3/S7LSqFQSA899JCee%2B459e7d2%2B9ymhwB0DInT0pPPy397nemA5iUJP3wh9JPfmJu7wAQO/72t7/pe9/7nt58801lZ2f7XQ4k9ezZU5J0xx136OjRo5o1axYB0CcVFRVav369Nm3apMcff1ySWS7hOI4SEhJUWFioCRMm%2BFxl4yEAWsJxpNdfl370I%2Bn4cfPcgw9K//EfUrdu/tYGoOHNnTtXjz76qObOnau8vDy/y8FlOI6jqqoqv8uwVmpqqrZt2%2BZ5bs6cOSopKdHf//73C2E9VhEALXDwoPTYY9KiRebr/v2lYFCK1LXHZ86c0a5duy58vWfPHm3evFnt2rVTN9Jqo7rWv/unnnpKn3/%2BuV577bULrwnvljtz5oyOHTumzZs3q0WLFsrIyGjy%2BmNVfT%2BXuXPn6tvf/rZ%2B9atfafjw4Tpy5IgkKTk52cr1zo2hvp9JMBhUt27d1LdvX0nmXMBf/OIXeuKJJ3ypP1bV53Np1qyZMjMzPd%2BflpampKSkS56PSQ6csrIyR5JTVlbmdykNbu5cx2nTxnEkx0lMdJyf/9xxqqv9rurqli5d6ki65PHII4/4XVrMu9a/%2B0ceecQZO3as53su9/ru3bs3ee2xrL6fy9ixY/kz1Mjq%2B5n8%2Bte/dvr37%2B%2B0bNnSSU1NdQYNGuTMmTPHCYVC/vwCYtSN/DesrmeffdYZMGBA0xTrM84BVGyeA/jll%2Bbsvj/%2B0Xw9bJg54%2B8ff/kEAAAWs/ocwFh14IA0apQJf3Fx0v/5P9KqVYQ/AABgsAYwxmzaJOXlSYcPSx06SHPnmmvbAAAAwgiAMWTVKmnKFKmiQsrMNIc8d%2B/ud1UAACDSMAUcI9askSZNMuFv7FgTBgl/AADgcugAxoBdu6SpU6XKSikrS5o/n9s8AADAldEBjHJnz0pf%2B5p04oT01a9K775L%2BAMAAFdndQAMBoPKyMjQ0KFD/S7lhv30p9JHH0mdOpnw16qV3xUBAIBIxzmAit5zALdskQYNMte8FRSYNYAAAADXYnUHMNo9%2B6wJfw88QPgDAADXjw6gorMDePCg1K2bCYA7dkj9%2BvldEQAAiBZ0AKPUvHkm/I0aRfgDAAD1QwCMUqtXmx%2BZ%2BgUAAPVFAIxSu3ebHzMy/K0DAABEHwJglPryS/Nj69b%2B1gEAAKIPATBKhfeqnDzpbx0AACD6EACjVN%2B%2B5sfNm/2tAwAARB8CYJQaM8b8%2BN57ZjcwAADA9SIARqnp06WkJGn7dmnFCr%2BrAQAA0YQAGKXatJG%2B8x3zzz/9qVRb62s5AAAgihAAo9gzz0itWpkzAV96ye9qAABAtLA6AAaDQWVkZGjo0KF%2Bl3JDunaV/t//M//84x9LGzb4Ww8AAIgO3AWs6LwLOKy21qwHXLhQ6tLFdAO7d/e7KgAAEMms7gDGgmbNpDfekPr3lw4dkrKzpQMH/K4KAABEMgJgDAgEpEWLpJ49pV27pNGjpU8%2B8bsqAAAQqQiAMeLWW6Vly6Tbb5f27ZNGjJBKSvyuCgAARCICYAzp1k1atUoaPlw6dUrKyZFeeIGDogEAgBcBMMakpZnO38MPS6GQ9G//Jk2dKh096ndlAAAgUhAAY1BysvSnP0m//a2UmCjl50t33CG99ZbflQEAgEhAAIxRcXHSv/yLtG6dCX/Hjkn33Sfde6/0%2Bed%2BVwcAAPxEAIxxd9xhQuDTT0sJCdLbb0v9%2Bkn/%2BZ9STY3f1QEAAD8QAC2QmCj93/8rrV8v3XWXVFFhbg65806poIBNIgA8o3UuAAAYP0lEQVQA2IYAaJEBA8xNIb//vdShg/Txx9KUKWa38MaNflcHAACaCgHQMs2aSd//vvTpp6YL2KKFVFwsDRkiPfigCYUAACC2EQAt1aaN9ItfmMD30EPmuf/%2Bb3Ol3MMPc5MIAACxzOoAGAwGlZGRoaFDh/pdim969jR3CW/ZIt1zj1RbK73%2Butko8o1vSFu3%2Bl0hAABoaHGOwxaA8vJyBQIBlZWVKTU11e9yfLVhg/T889L8%2Be5zU6aYA6XHjDHHywAAgOhmdQcQlxoyRHr3XWnzZumBB8yawfx8adw4s4P4r3/l%2BBgAAKIdHUDRAbyaXbukF1%2BUXn1VOnfOPHfrrdLjj5vNJO3b%2B1oeAAC4AQRAEQCvx7Fj0pw5UjBo/lmSkpLMBpLHH5cGDfK3PgAAcP0IgCIA1se5c2Ya%2BFe/MtPEYcOHS//jf0j332/uIgYAAJGLACgC4I1wHOn9901H8K233HWBbdtK3/629Nhj5kgZAAAQeQiAIgDerKNHpT/%2BUXr5ZWnfPvf5u%2B%2BWvvc9s5mkdWv/6gMAAF4EQBEAG0ooJBUWmiD43nvma8mEv/vvlx59VBo5kqNkAADwGwFQBMDGcPiw9NprpjP46afu8716SY88Ym4b6dHjGm8S/q1JYgQAoEFF/TmAb7/9tnJzc9WhQwfFxcVpc92dCfBN587ST35irpRbuVL67ndNJ3D3bulnPzM3kIwdK/3hD9Lp0xd984oV5vTp5s3NIy9PWrXKl18HAACxKOo7gH/%2B85%2B1Z88edenSRY899pg2bdqkgQMH1us96AA2jcpKs2HktdekkhK3wZeYKE2bJv3TP0l5595S82896M4fhyUkSG%2B%2BKc2Y0fSFAwAQY6I%2BAIbt3btXPXv2JABGiQMHpL/8Rfrzn6Xt281zCarRwbh0dXKOXv6bunQxu0wSEpquUAAAYlDUTwHfiKqqKpWXl3seaFrp6WaKeNs2aeNG6cc/lh5qt/jK4U%2BSDh2SioubrkgAAGKUlQFw9uzZCgQCFx7p6el%2Bl2StuDhzi8gvfiG98h/Hrvn6z9YeU2z0rAEA8E9UBcA33nhDrVu3vvBYuXLlDb3PU089pbKysguPAwcONHCluBHN%2Bve75msemNVPvXpJ//Zv0vr1IgwCAHADomoNYEVFhY4edacIu3btquR/3DvGGsAYMXiwtGnTZYd2t/2q7qxap7Nn3ed69JDuvVe67z5p2DCpWVT9lQYAAH9E1f8uU1JSdNttt114JHPpbOyZO1fq2vXS59PT1WvtX/TFF2Yz8P33Sy1bSnv3Si%2B%2BKI0YIXXvLv3P/yktX37pJmIAAOCKqg7g5Zw8eVL79%2B/XoUOHlJeXp7/%2B9a/q06ePbrnlFt1yyy3X9R50ACPM6dPSq69KixaZRYKTJknf%2BY4UCHhedvasVFAg/f3v0sKFUkWFO9axo3TPPdLXvy5NmGCOmgEAAEbUB8BXX31V3/3udy95/tlnn9WsWbOu6z0IgNHv3DlzDd1bb5lr6E6dcsdSUqSpU80RgpMnm68BALBZ1AfAhkAAjC01NWYa%2BK23pHffNdfShbVoIWVlmTA4fbp0nU1iAABiCgFQBMBYVlsrrV0rzZsnvfOO917iuDjprrvMVPE990h9%2B3LtMADADgRAEQBt4TjSzp0mDM6bJ61b5x2/7TbTFZw%2BXRo5kgtHAACxiwAoAqCtDh2S5s8308QlJVJ1tTvWtq00ZYq5o3jSpEv2nwAAENUIgCIAwuwgXrzYBMKFC6WTJ92xhARpzBgTBqdONZ1CAACiGQFQBEB4nT8vffCBCYMLFkgff%2Bwd79vXBMG8PDNV3Ly5P3UCAHCjCIAiAOLqdu1yO4MrVpiAGBYISLm5JgxOnmzOHwQAINIRAEUAxPUrKzNTxQsWmEOojx93x%2BLipOHDzdrBvDxp4EB2FQMAIpPVATAYDCoYDCoUCqm0tJQAiHoJhaQPPzRhcOFCacsW73jnzqYrOGWKlJ3NRhIAQOSwOgCG0QFEQzh40HQFFy6Uioulykp3LCFBGjXKhMHJk6X%2B/ekOAgD8QwAUARANr6rKrBdcuNCEwtJS73h6utsdnDCB6%2BkAAE2LACgCIBrf7t1Sfr55LFtm7i4Oa97cdAcnT6Y7CABoGgRAEQDRtM6eNXcV5%2Beb7uDu3d7xW281h09PmsTaQQBA4yAAigAIf336qQmCBQWXdgfj46URI0xncNIks7O4WTPfSgUAxAgCoAiAiBxffmm6g4sWXX7tYFqaOXcwJ8c80tL8qRMAEN0IgCIAInLt2WPC4KJF5r7iM2fcsbg4afBgEwhzc02nkFtJAADXgwAoAiCiQ3W1tHq1GwgvPncwJUXKynIDYc%2Be/tQJAIh8BEARABGdDh%2BWCgvNzSRFRd5bSSTp9tvNNHFurjR%2BvNS6tT91AgAiDwFQBEBEv9paaeNG0xksLDSdwlDIHW/eXLr7bnf94KBBbCYBAJsRAEUAROwpK5OWLjXdwcWLzVrCujp0kCZOdDeTdOniT50AAH8QAEUARGxzHGnXLtMZLCy8dDOJJGVmumFw9GipZUt/agUANA0CoAiAsEtNjfTBB24gXL/ehMSwxEQTAnNyTJfwzjuZLgaAWGN1AAwGgwoGgwqFQiotLSUAwkonTkjFxW4gPHjQO96pk7mRJBwIO3f2p04AQMOxOgCG0QEEDMeRPvnErBssLDQ3k5w9631NeLp44kRpzBimiwEgGhEARQAEriR89mC4O7hxo3e6uEULadQod0MJV9UBQHQgAIoACFyv48fNJpLCQnP24P793vEOHcxh1BMnmke3bv7UCQC4OgKgCIDAjQhPFxcVmcfSpZfuLu7Tx3QGs7OlceMk/ngBQGQgAIoACDSEmhppzRo3EH74oTmgOiwhQRo%2B3ITBiROlYcPMcwCApkcAFAEQaAynT5vp4nAg3L3bO56aKk2YYMJgdra5ui4uzp9aAcA2BEARAIGmsGePGwaXLJFOnfKOd%2Bvmrh3MyjLrCQEAjYMAKAIg0NRCIbOjOBwIV682O47rGjTI3V08cqSUlORPrQAQiwiAIgACfquslFasMAdSFxVJ27Z5x5OSzO0k4QOpuZ0EAG4OAVAEQCDSHDlipokLC00oPHTIO96xo3vcTHY2x80AQH0RAEUABCKZ40g7d7rTxcuWmY5hXb17u2Fw/HgpEPClVACIGgRAEQCBaFJdLa1d6wbCdevMmsKw%2BHhzxEz4uJm77jI3lgAAXARAEQCBaFZWZg6hLioy08Wlpd7x1q2lsWPdHcb9%2BnHcDABYHQCDwaCCwaBCoZBKS0sJgEAM2L/fe9zM8ePe8S5dTHcw/Ojc2Z86AcBPVgfAMDqAQGyqrZW2bHF3F69cKZ07531NZqY7XTxmjOkYAkCsIwCKAAjY4tw56f333Q7hpk1mk0lY8%2BbSiBHudPGQIVxXByA2EQBFAARsdfy4ua4u3CHcu9c7Hgh4r6u77TbWDwKIDQRAEQABmE7g7t1uGCwpMfcZ19W9uztdzHV1AKIZAVAEQACXCoWkDRvc3cXvvy/V1HhfM2iQuZkkO1saNYrr6gBEDwKgCIAArq3udXWFhdJHH3nHw9fVhdcPcl0dgEhGABQBEED9HTniThdf7bq68JQx19UBiCQEQBEAAdyci6%2BrW75cOnPG%2B5rwdXUTJ5rr6vhPDQA/EQBFAATQsOpeV1dcLH344aXX1d11l%2BkO5uSYq%2BuaN/evXgD2IQCKAAigcZ0%2Bba6rC68f3LXLO56SYrqC4Q5h794cNwOgcREARQAE0LT27nXXDy5ZIp044R1PT3fDYFaWWU8IAA2JACgCIAD/1NaaG0nC6wdXrTJTyHUNGuQGQo6bAdAQCIAiAAKIHGfPmjuLw4Fw61bveFKSubO47nEzTBcDqC%2BrA2AwGFQwGFQoFFJpaSkBEEDEudZxM2lp7maSiROlLl38qRNAdLE6AIbRAQQQDRxH2rHDhMHCQnPczNmz3tdkZLhhcOxYqVUrf2oFENkIgCIAAohOVVXSBx%2B4gXDDBhMSw5o3N2sGJ040oXDQIG4nAWAQAEUABBAbTpyQSkpMGCwqkvbt8463b292FYc7hNxOAtiLACgCIIDY4zjmvMFwGCwpkSoqvK/p08eEwZwcadw4qXVrX0oF4AMCoAiAAGJfTY17O0lhobmdpLbWHW/eXBoxwg2EgwebG0sAxCYCoAiAAOxz6pS5naSw0Dz27PGOt2vnThfn5DBdDMQaAqAIgACwe7cbBktKpPJy73jfvm4YHDuW6WIg2hEARQAEgLrOnzfTxeH1g2vXXjpdPHKkGwjZXQxEHwKgCIAAcDWnT7u7iy83Xdyhg3sYdW4uh1ED0SCqA2BNTY2eeeYZ5efn67PPPlMgEFB2drb%2B/d//XV3q8V8gAiAAXB/HuXS6%2BOLdxZmZbndwzBgpOdmfWgFcWVQHwLKyMt1333167LHHNGDAAJ06dUo//OEPdf78ea1fv/6634cACAA3pqZGWrPGhMHFi6X1672HUScmmhAY7g5mZnJ3MRAJojoAXs66des0bNgw7du3T92uc9saARAAGsaJE9KSJSYMFhZKBw96xzt3dsNgdrbUsaM/dQK2i7kAWFxcrJycHJ0%2BffqKYa6qqkpVVVUXvi4vL1d6ejoBEAAakONIO3e63cHly6Uvv3TH4%2BLMeYO5ueYxYoTZYAKg8cVUADx37pxGjRqlvn376vXXX7/i62bNmqXnnnvukucJgADQeM6dk1atcgPh1q3e8ZQUafx4EwYnTZK%2B8hV/6gRsEFUB8I033tAPfvCDC18XFBRo9OjRksyGkPvvv1/79%2B/XsmXLrhrk6AACgP8OH3bDYFGRdPy4d7xXL7c7OGECZw8CDSmqAmBFRYWOHj164euuXbsqOTlZNTU1euCBB/TZZ5%2BppKRE7du3r9f7sgYQAPxVWytt2uSuHXz/fXMeYVj47MFwIBwwgLMHgZsRVQHwcsLh79NPP9XSpUvV8QZWFBMAASCyVFSYI2YWLzaPzz7zjnfq5G4myclhMwlQX1EdAM%2BfP697771XGzdu1IIFC9SpU6cLY%2B3atVOLFi2u630IgAAQ2XbtcsNgSYlUWekdHzLErBvMzZWGD2czCXAtUR0A9%2B7dq549e152bOnSpRo3btx1vQ8BEACiR3W1mSJevFhatEjassU7npoqZWW5gbB7d3/qBCJZVAfAhkIABIDodeSI2x0sLDRnEdbVt68Jg5MmcTMJEEYAFAEQAGJFKCRt3Gg6g4sWmVtKamvd8aQkadw4NxD27s3NJLATAVAEQACIVadOmZtJFi0yHcKLbybp0cNME0%2BebI6aSUnxpUygyREARQAEABs4jrR9u7t2cMUKs54wLCFBGjXK7Q7eeSfdQcQuAqAIgABgo8pKaelSd7p4927veJcubhjMzpbatvWnTqAxEABFAAQAmKNmCgpMGFy61HtvcbNm5q7iSZPMdPGgQRxEjehGABQBEADgde6ctHKlCYMFBdLOnd7xtDS3O5iTI9XzAirAdwRAEQABAFe3b58bBpcskc6cccfi4qRhw0xncMoUcyg13UFEOqsDYDAYVDAYVCgUUmlpKQEQAHBN4YOoCwrM46OPvOMdOrg7i3NzzddApLE6AIbRAQQA3KiDB93uYHGxVF7ujtEdRKQiAIoACABoGDU10urVbndw61bveMeO3u4gawfhFwKgCIAAgMbx%2BeduGCwqkioq3LFmzaS77nK7g%2BwsRlMiAIoACABofDU13rWD27Z5xzt1MruKp0wxO4vbtPGnTtiBACgCIACg6R08KOXnu2sH6%2B4sjo835w7m5ZlAeMcd3EqChkUAFAEQAOCv6mpp1SoTCPPzLz13sGtXM1WclydlZXFnMW4eAVAEQABAZNm71w2DJSXeW0maN5fGjHG7g7170x1E/REARQAEAESuc%2BekZcvcQHjxncVf%2BYoJgnl50rhxUlKSH1Ui2hAARQAEAESP0lITBBculFasMNPHYS1bShMmuN3Bbt38qxORjQAoAiAAIDqdOWOuplu40ITCzz/3jmdmmjCYl2c2lSQk%2BFMnIg8BUARAAED0cxxz8PTCheaxZo1UW%2BuOt21rDp/OyzPHzXBFnd0IgCIAAgBiz4kT0uLFJgwuWiSdPOmOxcVJw4ebMDh1qnTnnWwksQ0BUARAAEBsC4WktWtNGFyw4NIr6m691awZnDrVrCFs1cqfOtF0rA6AwWBQwWBQoVBIpaWlBEAAgBUOHHA3kixZIp09644lJkrjx5swmJcn9ejhW5loRFYHwDA6gAAAW4WPmVmwwATCvXu94/37mzA4daqZNmYjSWwgAIoACACAZDaS7NjhhsHVq830cVi7dmYDybRpZkNJ27b%2B1YqbQwAUARAAgMs5dcpsIFmwwNxZfOqUOxYfL40a5XYH%2B/RhI0k0IQCKAAgAwLWcPy998IEJgwsWmE5hXb16uWFwzBipRQt/6sT1IQCKAAgAQH3t2eOGwWXLvDeSpKSYKeJp06TJk6WOHX0rE1dAABQBEACAm3HmjFRUJL33ntldfPSoOxYXZ24hmTbNdAf792eqOBIQAEUABACgodTWSuvXmzC4YIG0ebN3vEcPEwanTZPGjmWq2C8EQBEAAQBoLAcOuFPFS5ZIVVXuWN2p4ilTuJ6uKREARQAEAKApVFZKxcVud7DuVHGzZtLdd5tp4unTpb59mSpuTARAEQABAGhqdaeK33tP2rLFO96rl%2BkMTp9ujptp3tyfOmMVAVAEQAAA/LZ/v%2BkKzp8vLV3q3VXcpo2ZIp42zRxE3aaNf3XGCgKgCIAAAESSigqpsNB0BhculI4fd8cSEsw5g9Onm0fPnv7VGc0IgCIAAgAQqUIhac0aEwbnz5d27vSOZ2a6YXDoULOWENdmdQAMBoMKBoMKhUIqLS0lAAIAEOF27TJBcP58adUq713Ft9wi/e//Lf34x/7VFy2sDoBhdAABAIg%2BJ0%2Bag6fnzzd3FldUSC%2B8YEIgro4AKAIgAADRrrraXEnXv7/Utavf1US%2BBL8LAAAAuFktWkg5OX5XET1YKgkAAGAZAiAAAIBlCIAAAACWIQACAABYhgAIAABgGQIgAACAZQiAAAAAliEAAgAAWIYACAAAYBkCIAAAgGWsDoDBYFAZGRkaOnSo36UAAAA0mTjHcRy/i/BbeXm5AoGAysrKlJqa6nc5AAAAjcrqDiAAAICNCIAAAACWIQACAABYhgAIAABgGQIgAACAZQiAAAAAliEAAgAAWIYACAAAYJmoD4CzZs1S37591apVK7Vt21bZ2dlau3at32UBAABErKgPgL1799ZvfvMbbdu2TatWrVKPHj2Uk5OjY8eO%2BV0aAABARIq5q%2BDC17oVFxcrKyurXt/DVXAAAMAGCX4X0JCqq6v18ssvKxAIaMCAAVd8XVVVlaqqqi58XV5e3hTlAQAARISonwKWpAULFqh169ZKSkrSL3/5SxUVFalDhw5XfP3s2bMVCAQuPNLT05uwWgAAAH9F1RTwG2%2B8oR/84AcXvi4oKNDo0aNVWVmpw4cP6/jx4/r973%2BvkpISrV27VmlpaZd9n8t1ANPT05kCBgAAVoiqAFhRUaGjR49e%2BLpr165KTk6%2B5HW33367Hn30UT311FPX9b6sAQQAADaJqjWAKSkpSklJuebrHMfxdPgAAADgiqoAeLHKykr9/Oc/1/Tp09W5c2edOHFCc%2BbM0cGDB3X//ff7XR4AAEBEiuoAGB8fr48//lh/%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