Genus 2 curve data - 784.c.614656.1

Formats: - HTML - YAML - JSON - 2024-07-27T14:30:07.603326

• 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': '567926432027860049', 'abs_disc': 614656, 'analytic_rank': 0, 'analytic_rank_proved': True, 'analytic_sha': 1, 'aut_grp_id': '[6,2]', 'aut_grp_label': '6.2', 'aut_grp_tex': 'C_6', 'bad_lfactors': '[[2,[1]],[7,[1,4,7]]]', 'bad_primes': [2, 7], 'class': '784.c', 'cond': 784, 'disc_sign': 1, 'end_alg': 'CM', 'eqn': '[[0,-1,-9,-13,-4,1],[]]', 'g2_inv': "['1248318403996/2401','9291226221/4802','-23245787/9604']", 'geom_aut_grp_id': '[12,4]', 'geom_aut_grp_label': '12.4', 'geom_aut_grp_tex': 'D_6', 'geom_end_alg': 'M_2(Q)', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['398','9016','912086','2401']", 'igusa_inv': "['796','2358','-2348','-1857293','614656']", 'is_gl2_type': True, 'is_simple_base': True, 'is_simple_geom': False, 'label': '784.c.614656.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.3582178305688400308992259460321820582993327584490888593', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.240.1', '3.5760.7'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 3, 'num_rat_wpts': 3, 'real_geom_end_alg': 'M_2(R)', 'real_period': {'__RealLiteral__': 0, 'data': '5.7314852891014404943876151365', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, 'root_number': 1, 'st_group': 'E_3', 'st_label': '1.4.E.3.1a', 'st_label_components': [1, 4, 4, 3, 1, 0], 'tamagawa_product': 9, 'torsion_order': 4, 'torsion_subgroup': '[2,2]', 'two_selmer_rank': 2, 'two_torsion_field': ['3.3.49.1', [1, -2, -1, 1], [3, 1], True]}
• 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': [['2.0.3.1', [1, -1, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], 1]], 'factorsRR_base': ['CC'], 'factorsRR_geom': ['M_2(RR)'], 'fod_coeffs': [1, -2, -1, 1], 'fod_label': '3.3.49.1', 'is_simple_base': True, 'is_simple_geom': False, 'label': '784.c.614656.1', 'lattice': [[['1.1.1.1', [0, 1], [0, 0, 0]], [['2.0.3.1', [1, -1, 1], -1]], ['CC'], [1, -1], 'E_3'], [['3.3.49.1', [1, -2, -1, 1], [-1, -1, 1]], [['1.1.1.1', [0, 1], 1]], ['M_2(RR)'], [3, 1], 'E_1']], 'ring_base': [1, -1], 'ring_geom': [3, 1], 'spl_facs_coeffs': [[[6405, 427, -2135], [494116, '123529/2', '-370587/2']]], 'spl_facs_condnorms': [64], 'spl_facs_labels': ['3.3.49.1-64.1-a3'], 'spl_fod_coeffs': [1, -2, -1, 1], 'spl_fod_gen': [-1, -1, 1], 'spl_fod_label': '3.3.49.1', 'st_group_base': 'E_3', 'st_group_geom': 'E_1'}
• 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': '784.c.614656.1', 'mw_gens': [[[[0, 1], [1, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]], [[[0, 1], [1, 1], [1, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [2, 2], 'num_rat_pts': 3, 'rat_pts': [[-1, 0, 1], [0, 0, 1], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 152
    {'conductor': 784, 'lmfdb_label': '784.c.614656.1', 'modell_image': '2.240.1', 'prime': 2}
  • id: 153
    {'conductor': 784, 'lmfdb_label': '784.c.614656.1', 'modell_image': '3.5760.7', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 146002
    {'label': '784.c.614656.1', 'p': 2, 'tamagawa_number': 3}
  • id: 146003
    {'cluster_label': 'c2c3_2~3_0', 'label': '784.c.614656.1', 'p': 7, 'tamagawa_number': 3}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '784.c.614656.1', 'plot': 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OlzgAAOaNHSvNmsWiDxweBdASLVq00Msvv6x//OMfmjJlinJzc9WyZUtt375dubm5kqTU1NQSH5Oamlr82JFkZWXJ5/MVHxkZGRX2NQAASmfpUmnIEHc8fjyLPnAoj%2BM4jukQqHy7du3Sqaeeqvvuu08XXHCBMjMztWXLFtWvX7/4Of369VNOTo4WLVp0xM9TWFiowsLC4j%2BHw2FlZGQoFArJ6/VW6NcAADjUN99IzZpJ27dLvXtLU6dy3h8OxQygpWrUqKHGjRtr48aNxauBfz3bl5eXd8is4K8lJSXJ6/WWOAAAZuzeLXXs6Ja/pk2lZ5%2Bl/OHwKICWKiws1Oeff6769eurQYMGSktL05IlS4of37Nnj5YvX66WLVsaTAkAKC3Hkfr3lzZskOrU4Uof%2BG2JpgOgctxzzz1q166dTjzxROXl5WnkyJEKh8Pq1auXPB6PBg0apFGjRqlhw4Zq2LChRo0apeTkZHXr1s10dABAKUyYIE2bJiUkSK%2B%2BKp14oulEiGYUQEts3rxZXbt21ffff6%2B6devqggsu0Jo1a3TSSSdJku677z79%2BOOPuuOOO5Sfn68WLVpo8eLFSklJMZwcAHA0K1ZId9/tjseMkS67zGweRD8WgSCiwuGwfD4fi0AAoJJs2SKdf7703XfuFT9mzuS8Pxwd5wACABCj9uyRbrzRLX%2BNG0vPP0/5Q%2BlQAAEAiFF33y2tWiX5fO6ijxo1TCdCrKAAAgAQg6ZNk4JBdzx9unTaaWbzILZQAAEAiDEffijdeqs7fughqW1bs3kQeyiAAADEkB9%2BkG64QfrpJ%2Bnqq6WHHzadCLGIAggAQIzYv1/q1Uv66ivppJOkGTOkKvyfHMeA/2wAAIgRo0dL8%2BdLSUnSnDlS7dqmEyFWUQAREcFgUH6/X4FAwHQUAIhL774rPfCAO37mGfdav8CxYiNoRBQbQQNA5G3ZIjVpIuXlSb17S1Onst8fyocZQAAAoti%2BfVLXrm75a9zY3fqF8ofyogACABDFHnxQeu89KSVFev11KTnZdCLEAwogAABR6u23pb/%2B1R0//7x0%2Bulm8yB%2BUAABAIhCmzdLN93kjgcMkDp1MpsH8YUCCABAlDlw3t/27dL550tjx5pOhHhDAQQAIMoMHy6tXOme9/fKK%2B6%2Bf0AkUQABAIgi77wjjRrljidPlk47zWwexCcKIAAAUWLbNqlHD8lxpFtukbp0MZ0I8YoCCABAFHAcqW9faetW6ayzpKeeMp0I8YwCCABAFHj2WWnBAqlaNWnWLPb7Q8WiAAIAYNjnn0uDB7vjMWOkc881mwfxjwIIAIBBe/ZI3btLP/0ktWolDRxoOhFsQAFERASDQfn9fgUCAdNRACCmjBghbdggnXCC9OKLUhX%2Bz4xK4HEcxzEdAvEjHA7L5/MpFArJ6/WajgMAUW3NGikzU9q/X3rtNemPfzSdCLbg3xkAABjw449Sr15u%2BevenfKHykUBBADAgAcekL78UkpPl555xnQa2IYCCABAJVu9Who/3h1PmSLVqmU2D%2BxDAQQAoBIVFrobPjuOdNNN0rXXmk4EG1EAAQCoRKNGufv%2BpaYenAUEKhsFEACASvLFF1JWljt%2B%2Bmmpdm2zeWAvCiAAAJXAcaTbb5f27pXatJFuvNF0ItiMAggAQCWYOVNatkyqXt1d9evxmE4Em1EAAQCoYAUF0r33uuMHHpAaNDCbB6AAAgBQwR57TNq6VTr1VGnwYNNpAAogAAAV6uuvD672HT9eSkoymweQKICIkGAwKL/fr0AgYDoKAESVYcOkPXukK66Q2rY1nQZweRzHcUyHQPwIh8Py%2BXwKhULyer2m4wCAUevXS82auQs%2BPvhAOu8804kAFzOAAABUkPvvd2%2B7d6f8IbpQAAEAqAD/%2Bpe0eLGUmCiNGGE6DVASBRAAgApwoPT16SOdcorZLMCvUQABAIiwdeukJUukhISDbwMD0YQCCABAhI0Z49527y6dfLLRKMBhUQABAIigb76R5s51x/fcYzQKcEQUQAAAImjiRGn/funKK6XGjU2nAQ6PAggAQIQUFkpTp7rjgQPNZgF%2BCwUQAIAIefNNaft26Xe/k6691nQa4MgogAAARMhLL7m3vXu7%2B/8B0YoCCABABGzf7m78LEk9e5rNAhwNBRAAgAh44w1p3z73km9nnGE6DfDbKICIiGAwKL/fr0AgYDoKABjx5pvubceOZnMApeFxHMcxHQLxIxwOy%2BfzKRQKyev1mo4DAJWisFCqXVvavVvasMGdBQSiGTOAAACU0%2BrVbvlLTZXOPdd0GuDoKIAAAJTTe%2B%2B5t5ddJnk8ZrMApUEBBACgnFavdm8zM83mAEqLAggAQDk4jvTBB%2B64eXOzWYDSogACAFAOeXnu4fFIjRqZTgOUDgUQAIBy%2BPJL9/bkk6XkZKNRgFKjAAIAUA6bNrm3p55qNgdQFhRAk9ascXcMrVFDqllTuvFG6f33Tacqu/37pWeflc45R6pTx71v5Ejphx/M5gKASvC//7m3v/%2B92RxAWVAATXnzTenii6V589zNo3btkl5/3V1CtmiR6XSl5zhS9%2B7SHXdIH38s7d3r3v/449If/kAJBBD3vv/eva1Xz2wOoCwogCbs3Sv17%2B9eNPLX9uxxH9u/v/JzHYu335Zmzz78Yx9/7BZBAIhjoZB7e/zxZnMAZZFoOkAscxxHBQUFR3mO9Oij0t/%2BdnAyrLUW6lXlHvmD/vtf6e9/ly69NHJhI2THDmnoUOmVV9w/v6gpuv4Xj4d/daupU6UhQyovIABUsvDPf%2BF5PAfHiA0pKSnyWLpzN9cCLocD170FAACxx%2Bbr1lMAy6E0M4CSe7rfjBnSRx%2B5/0JsVvgvTdt%2B7W9/0DvvSE2bRiipKxAIaN26deX6HHv2uLOZc%2Ba4Jz4/kj9AN/44vfjxsKQMSTmSvJLUsGGFLGyJxNcSDa9RGa8TDoeVkZGhnJycCv2LLl6%2BX5XxGvxMou81yvMzueUW6bXXpFGjpAEDjv78ePh%2BVdZrVPTvis0zgLwFXA4ej6dU/0H27OkeB10jne2XPvvs8B/QtKl7QckIS0hIiMgv0JAhv3hXd92dUvPphzzH%2B/OhP/1JqoBf2kh9LaZfozJfx%2Bv1VujrxNP3i59J9L1ONP9MDrwRVFRUur/u4uX7VVk/E6nif1dsxCIQU2bNkurWPfT%2B%2BvWl6YcWqkgYUJp/mpZVICD99a%2BHf%2ByGG0r3z%2BFjUCFfi4HXqMzXqWjx9P3iZxJ9rxPNP5MDiz9Ku%2BlBvHy/ovlngqPjLWCTtm2Tpkxxt32pUkW65hr3vYQTTjCdrOzWrpWee047PvpIJ6xfr63PP6%2B0Pn3crwtGHThX1eZzXaINP5PoU56fyeOPS/fd5%2B6IVUH/frcWvysVh7eATapbV7r/fveIdc2bS82bq2jbNqlePSW0b0/5ixJJSUl6%2BOGHlZSUZDoKfsbPJPqU52eSkeHefvtthEOB35UKxAwgIop/rQGwzdq1UosW7hk8W7aYTgOUDlM0AACUwxlnuLdbt0r5%2BWazAKVFAQQAoBx8Pumkk9xxdrbZLEBpUQABACinZs3c27VrzeYASosCCABAObVs6d6uWGE2B1BaFEAgTmVlZSkQCCglJUX16tVThw4d9O9//9t0LPwsKytLHo9HgwYNMh3Fev/73//Uo0cPnXDCCUpOTtZ5552n9evXl%2BlzXHKJe/vee9LevRUQ0iL79u3TAw88oAYNGqh69eo65ZRT9Mgjj2j//v2mo8UVCiAQp5YvX64BAwZozZo1WrJkifbt26dWrVpp165dpqNZb926dZo8ebLOOecc01Gsl5%2Bfr8zMTFWtWlULFy7UZ599prFjx%2Br4A7s7l1KTJlKdOlJBgbRqVQWFtcTo0aP13HPPacKECfr88881ZswYPf7443rmmWdMR4sr7AMIxKlFixaV%2BPMLL7ygevXqaf369frDH/5gKBV27typ7t27a8qUKRo5cqTpONYbPXq0MjIy9MILLxTfd/LJJ5f58xzYy3/aNGn%2B/IMzgii71atX67rrrlObNm0kuT%2BPWbNm6f0KuK68zZgBREQEg0H5/X4FAgHTUXAEoVBIklS7dm3DSew2YMAAtWnTRldeeaXpKJA0f/58NWvWTDfeeKPq1aunJk2aaMqUKcf0uTp0cG/nzJHYYffYXXTRRVq6dKm%2B/PJLSdKHH36olStX6tprrzWcLL4wA4iIGDBggAYMGFC8ETSii%2BM4uvvuu3XRRRepUaNGpuNYa/bs2frggw%2B0bt0601Hws02bNunZZ5/V3Xffrfvvv19r167VnXfeqaSkJN10001l%2BlxXXy3VqCH997/SmjXShRdWUOg4N2TIEIVCIZ155plKSEhQUVGRHnvsMXXt2tV0tLhCAQQs8Kc//UkfffSRVq5caTqKtXJycvTnP/9Zixcv1nHHHWc6Dn62f/9%2BNWvWTKNGjZIkNWnSRJ9%2B%2BqmeffbZMhfA5GSpY0f3beCXXqIAHqtXXnlF06dP18yZM3X22WcrOztbgwYNUnp6unr16mU6XtzgLWAgzg0cOFDz58/Xu%2B%2B%2Bq9///vem41hr/fr1ysvLU9OmTZWYmKjExEQtX75cTz/9tBITE1VUVGQ6opXq168vv99f4r6zzjpL3x7jhX1793ZvZ82SWG91bO69914NHTpUXbp0UePGjdWzZ0/dddddysrKMh0trjADCMQpx3E0cOBAzZs3T8uWLVODBg1MR7LaFVdcoY8//rjEfX369NGZZ56pIUOGKCEhwVAyu2VmZh6yPdKXX36pkw5c2qOMLr1UOvVU6auv3BJ4yy0RCGmZ3bt3q0qVkvNTCQkJbAMTYRRAIE4NGDBAM2fO1JtvvqmUlBTl5uZKknw%2Bn6pXr244nX1SUlIOOf%2ByRo0aOuGEEzgv06C77rpLLVu21KhRo9SpUyetXbtWkydP1uTJk4/p81WpIt12m3TffdIzz0h9%2B0oeT4RDx7l27drpscce04knnqizzz5bGzZs0Lhx43TzzTebjhZXPI7DWiVEzoFFIKFQSF6v13Qcq3mO8H%2BdF154Qb0PvE8Foy699FKdd955evLJJ01Hsdrf//53DRs2TBs3blSDBg109913q1%2B/fsf8%2BXbskDIypN27pX/%2BU7riigiGtUBBQYEefPBBzZs3T3l5eUpPT1fXrl310EMPqVq1aqbjxQ0KICKKAggA0p/%2BJAWDUqtW0j/%2BYToNcCgWgQAAEGGDB0sJCdLixRK7/iAaUQABAIiwBg2k7t3d8YgRZrMAh0MBBACgAvzlL%2B4s4FtvuRtDA9GEAggAQAU4/XTpwF7Sw4ZxeThEFwogAAAVZPhwqVo1adkyaeFC02mAgyiAAABUkBNPlO680x3fe6%2B0b5/ZPMABFEBERDAYlN/vVyAQMB0FAKLK/fdLJ5wgffaZNGmS6TSAi30AEVHsAwgAh5o4URowQKpVS9q40S2EgEnMAAIAUMFuvVU65xwpP9%2BdEQRMowACAFDBEhOlCRPc8ZQpbAsD8yiAAABUgosvlnr1creD6d9f2rvXdCLYjAIIAEAlefxxqXZt6cMPpXHjTKeBzSiAAABUkrp1pbFj3fHw4e6CEMAECiAAAJWoVy/pyiuln36SbrlF2r/fdCLYiAIIAEAl8nikyZOlGjWk995zt4gBKhsFEACAStaggTR6tDseMkT6z3/M5oF9KIAAABhw%2B%2B3SZZdJu3dLvXtLRUWmE8EmFEAAAAyoUkWaOlVKSZH%2B9S/piSdMJ4JNKIAAABhy8snSk0%2B64wcflLKzjcaBRSiAiIhgMCi/369AIGA6CgDElD59pA4d3I2hu3eXfvzRdCLYwOM4jmM6BOJHOByWz%2BdTKBSS1%2Bs1HQcAYsK2be61gnNzpQEDDl42DqgozAACAGBY3brSiy%2B642BQWrDAaBxYgAIIAEAUaN1auusud9ynj7Rli9k8iG8UQAAAokRWltSkibR9u9SjB1vDoOJQAAEAiBJJSdKsWe5VQt59Vxo1ynQixCsKIAAAUeSMMw5eHm74cGn5cqNxEKcogAAARJmbbnKP/fulbt3cVcJAJFEAAQCIQhMnSmee6S4G6dHDLYNApFAAAQCIQjVqSK%2B9JlWvLi1ezPmAiCwKIAAAUapRo4PnAz78sPTOO2bzIH5QAAEAiGK9e7v7Au7fL3Xtyv6AiAwKIAAAUW7CBKlxYykvT%2Brc2b1uMFAeFEAAAKJccrI0Z46UkiKtXCkNG2Y6EWIdBRAREQwG5ff7FQgETEcBgLjUsOHB6wWPHesWQuBYeRzHcUyHQPwIh8MWdgrnAAAQGUlEQVTy%2BXwKhULyer2m4wBA3Ln3XumJJ9zZwHXr3I2jgbJiBhAAgBiSlSVdcolUUCB17Cjt3Gk6EWIRBRAAgBiSmCjNni3Vry999pnUt6/Ee3koKwogAAAxJi3N3SQ6MVF69VVp/HjTiRBrKIAAAMSgzMyDxe%2B%2B%2B6Rly4zGQYyhAAIAEKMGDHCvE1xUJHXqJG3ebDoRYgUFEACAGOXxSJMmSeeeK23bJt1wg1RYaDoVYgEFEACAGJacLM2dK9WqJa1dKw0caDoRYgEF0BK9e/eWx%2BMpcVxwwQUlnlNYWKiBAweqTp06qlGjhtq3b6/NvJ8AAFHvlFOkWbPcGcEpU6TJk00nQrSjAFrk6quv1tatW4uPt99%2Bu8TjgwYN0rx58zR79mytXLlSO3fuVNu2bVVUVGQoMQCgtFq3lh57zB0PHCitWWM2D6JboukAqDxJSUlKS0s77GOhUEh/%2B9vfNG3aNF155ZWSpOnTpysjI0P//Oc/1bp168qMCgA4BkOHSu%2B/774lfMMN0vr17pYxwK8xA2iRZcuWqV69ejr99NPVr18/5eXlFT%2B2fv167d27V61atSq%2BLz09XY0aNdKqVatMxAUAlJHH414v%2BKyzpC1bpBtvlPbsMZ0K0YgCaIlrrrlGM2bM0DvvvKOxY8dq3bp1uvzyy1X483Kx3NxcVatWTbVq1SrxcampqcrNzT3i5y0sLFQ4HC5xAADMSUmR3nhD8nqllSulu%2B4ynQjRiAIYh2bMmKGaNWsWHytWrFDnzp3Vpk0bNWrUSO3atdPChQv15Zdf6q233vrNz%2BU4jjwezxEfz8rKks/nKz4yMjIi/eUAAMro9NOlGTPc8cSJ0gsvmM2D6EMBjEPt27dXdnZ28dGsWbNDnlO/fn2ddNJJ2rhxoyQpLS1Ne/bsUX5%2Bfonn5eXlKTU19YivNWzYMIVCoeIjJycnsl8MAOCYtG0rjRjhjvv3d7eIAQ6gAMahlJQUnXbaacVH9erVD3nO9u3blZOTo/r160uSmjZtqqpVq2rJkiXFz9m6das%2B%2BeQTtWzZ8oivlZSUJK/XW%2BIAAESHBx6Q2rd3zwPs2FH67jvTiRAtKIAW2Llzp%2B655x6tXr1a33zzjZYtW6Z27dqpTp06uv766yVJPp9Pffv21eDBg7V06VJt2LBBPXr0UOPGjYtXBQMAYkuVKtK0adKZZ0r/%2B5%2B7KGTvXtOpEA0ogBZISEjQxx9/rOuuu06nn366evXqpdNPP12rV69WSkpK8fPGjx%2BvDh06qFOnTsrMzFRycrIWLFighIQEg%2BkBAOXh9R5cFLJihXT33aYTIRp4HMdxTIdA/AiHw/L5fAqFQrwdDABRZP586brr3PHUqVKfPmbzwCxmAAEAsED79tLw4e749ttZFGI7CiAAAJZ48EG3CBYWsijEdhRAAAAswaIQHEABBADAIl6vNG%2Bee8WQFSu4UoitKIAAAFjmzDMPXikkGORKITaiAAIAYKF27UouClm3zmgcVDIKIAAAlmJRiL0ogIiIYDAov9%2BvQCBgOgoAoJQOLAo54wxp82YWhdiEjaARUWwEDQCx54svpObNpYICaeBA6emnTSdCRWMGEAAAy515pjR9ujt%2B5hnppZfM5kHFowACAAC1by899JA7vu02af16s3lQsSiAAABAkvTww1Lbtu6ikOuvl7ZtM50IFYUCCAAAJB1cFNKwoZSTI3XuLO3bZzoVKgIFEAAAFDv%2BeOmNN6SaNaV335WGDDGdCBWBAggAAErw%2B6UXX3TH48ZJs2YZjYMKQAEEAACHuOEGaehQd9y3r/TRR2bzILIogAAA4LBGjpRatZJ%2B/NFdFJKfbzoRIoUCCAAADishwX379%2BSTpU2bpO7dpaIi06kQCRRAAABwRLVrS/PmSdWrSwsXSiNGmE6ESKAAAgCA33TeedKkSe740Uel%2BfPN5kH5UQABAMBR9ezpXif4wHjjRrN5UD4UQEREMBiU3%2B9XIBAwHQUAUEHGjpUyM6Vw2F0UsnOn6UQ4Vh7HcRzTIRA/wuGwfD6fQqGQvF6v6TgAgAjbulU6/3wpN1fq0kWaOVPyeEynQlkxAwgAAEqtfn3ptdekxERp9mzpqadMJ8KxoAACAIAyuegi9%2B1gSbrnHum998zmQdlRAAEAQJkNHCh16%2BbuC9ipk7Rli%2BlEKAsKIAAAKDOPR5o8WWrUSPruO7cE7t1rOhVKiwIIAACOSY0a0ty5ktcr/etf0r33mk6E0qIAAgCAY9awofTyy%2B74qaekV14xmwelQwEEAADlct110rBh7rhvX%2Bmzz8zmwdFRAAEAQLk98oh0%2BeXSrl3SDTdIBQWmE%2BG3UAABAEC5JSZKs2ZJv/ud9MUX0i23SFxqInpRAAEAQETUq3dwk%2BhXX5Weftp0IhwJBRAAAETMhReW3CR61SqzeXB4FEBERDAYlN/vVyAQMB0FAGDYwIFS587Svn3u/oB5eaYT4dc8jsM79IiccDgsn8%2BnUCgkr9drOg4AwJCCAql5c/d8wCuukP7xDykhwXQqHMAMIAAAiLiUFOn116XkZGnpUmnECNOJ8EsUQAAAUCHOPtu9XJwkjRwpLVpkNg8OogACAIAK07271L%2B/uyVMjx5STo7pRJAogAAAoIKNHy81bSpt3%2B4uCtmzx3QiUAABAECFOu44d19An09as0YaOtR0IlAAAQBAhTvlFOmll9zx%2BPHS3Llm89iOAggAACrFdddJgwe74z59pK%2B%2BMpvHZhRAAABQabKypJYtpXDYPR/wp59MJ7ITBRAAAFSaqlWl2bOlE06QPvjg4IwgKhcFEAAAVKqMDGnaNHc8caK7QASViwIIAAAq3TXXSMOGueNbbpH%2B8x%2BzeWxDAQQAAEY88oh08cXudYM5H7ByUQAREcFgUH6/X4FAwHQUAECMSEyUZs2S6tSRNmzgfMDK5HEcxzEdAvEjHA7L5/MpFArJ6/WajgMAiAGLFrlvCUvSa69Jf/yj2Tw2YAYQAAAYdfXVB68O0revtGmT2Tw2oAACAADjHnnk4P6AnTtzveCKRgEEAADGHdgfsFYt6f33uV5wRaMAAgCAqJCRIb34ojseP15asMBonLhGAQQAAFGjfXvpz392x717S5s3G40TtyiAAAAgqoweLTVtKu3YIXXrJu3bZzpR/KEAAgCAqJKU5J4PmJIirVghPfqo6UTxhwIYB%2BbOnavWrVurTp068ng8ys7OPuQ5hYWFGjhwoOrUqaMaNWqoffv22vyrefVvv/1W7dq1U40aNVSnTh3deeed2sMyLACAAaedJk2a5I4ffVRatsxonLhDAYwDu3btUmZmpv76178e8TmDBg3SvHnzNHv2bK1cuVI7d%2B5U27ZtVVRUJEkqKipSmzZttGvXLq1cuVKzZ8/WnDlzNJht2QEAhnTtKt18s%2BQ4Uvfu0vffm04UP7gSSBz55ptv1KBBA23YsEHnnXde8f2hUEh169bVtGnT1LlzZ0nSli1blJGRobffflutW7fWwoUL1bZtW%2BXk5Cg9PV2SNHv2bPXu3Vt5eXmlvqoHVwIBAETSrl1Ss2bSF19IbdtK8%2BdLHo/pVLGPGUALrF%2B/Xnv37lWrVq2K70tPT1ejRo20atUqSdLq1avVqFGj4vInSa1bt1ZhYaHWr19f6ZkBAJCkGjXc8wGTkqS//1165hnTieIDBdACubm5qlatmmrVqlXi/tTUVOXm5hY/JzU1tcTjtWrVUrVq1YqfcziFhYUKh8MlDgAAIuncc6UnnnDH994rHeZUd5QRBTDGzJgxQzVr1iw%2BVqxYccyfy3EceX4xj%2B45zJz6r5/za1lZWfL5fMVHRkbGMecBAOBIBgyQ2rVzLxHXtav71jCOHQUwxrRv317Z2dnFR7NmzY76MWlpadqzZ4/y8/NL3J%2BXl1c865eWlnbITF9%2Bfr727t17yMzgLw0bNkyhUKj4yMnJOYavCgCA3%2BbxSFOnSunp7vmAgwaZThTbKIAxJiUlRaeddlrxUb169aN%2BTNOmTVW1alUtWbKk%2BL6tW7fqk08%2BUcuWLSVJF154oT755BNt3bq1%2BDmLFy9WUlKSmjZtesTPnZSUJK/XW%2BIAAKAi1KkjTZ/ulsHnn5def910othFAYwDO3bsUHZ2tj777DNJ0r///W9lZ2cXz%2Bj5fD717dtXgwcP1tKlS7Vhwwb16NFDjRs31pVXXilJatWqlfx%2Bv3r27KkNGzZo6dKluueee9SvXz9KHQAgalx2mTR0qDvu10/69luzeWIVBTAOzJ8/X02aNFGbNm0kSV26dFGTJk303HPPFT9n/Pjx6tChgzp16qTMzEwlJydrwYIFSkhIkCQlJCTorbfe0nHHHafMzEx16tRJHTp00BMHzroFACBKjBghNW8u/fCD1KOH9POWtigD9gFERLEPIACgMnz1lXTeedLOne6VQh54wHSi2MIMIAAAiDmnnipNnOiOhw%2BX1qwxGifmUAABAEBM6tHD3RKmqMi9VBxb0ZYeBRAAAMQkj8edBTzpJGnTJmngQNOJYgcFEAAAxKzjj3e3hqlSRXr5ZemVV0wnig0UQAAAENMuuki6/3533L%2B/9IstbXEEiaYDAAAAlNdDD0nLl0vXXSf9xgWs8DMKIAAAiHlVq0rLlrlvBePo%2BDYhIoLBoPx%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%3D'}