Genus 2 curve data - 482790.a.482790.1

Formats: - HTML - YAML - JSON - 2024-06-06T19:19:19.782562

• 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': '96042222202525445', 'abs_disc': 482790, 'analytic_rank': 1, 'analytic_rank_proved': True, 'analytic_sha': 8, 'aut_grp_id': '[4,2]', 'aut_grp_label': '4.2', 'aut_grp_tex': 'C_2^2', 'bad_lfactors': '[[2,[1,2,3,2]],[3,[1,-1,1,3]],[5,[1,-1,3,5]],[7,[1,5,11,7]],[11,[1,-2,1]],[19,[1,-5,23,-19]]]', 'bad_primes': [2, 3, 5, 7, 11, 19], 'class': '482790.a', 'cond': 482790, 'disc_sign': -1, 'end_alg': 'Q x Q', 'eqn': '[[-14,33,-61,69,-61,33,-14],[1,0,0,1]]', 'g2_inv': "['1137181886990894545487699/482790','2007695068464697012064/21945','548820173942387686/121']", 'geom_aut_grp_id': '[4,2]', 'geom_aut_grp_label': '4.2', 'geom_aut_grp_tex': 'C_2^2', 'geom_end_alg': 'Q x Q', 'globally_solvable': 0, 'has_square_sha': False, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['258956','284225353','22936728525979','61797120']", 'igusa_inv': "['64739','162788032','522481586340','1831248013908059','482790']", 'is_gl2_type': True, 'is_simple_base': False, 'is_simple_geom': False, 'label': '482790.a.482790.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '1.660517151848147439980779728685910394038288487515', 'prec': 167}, 'locally_solvable': False, 'modell_images': ['2.45.1', '3.90.1'], 'mw_rank': 1, 'mw_rank_proved': True, 'non_solvable_places': [0], 'num_rat_pts': 0, 'num_rat_wpts': 0, 'real_geom_end_alg': 'R x R', 'real_period': {'__RealLiteral__': 0, 'data': '1.6169090294032607692265192494', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.51348502657', 'prec': 44}, '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': 2, 'torsion_subgroup': '[2]', 'two_selmer_rank': 5, 'two_torsion_field': ['8.0.34369956362649600.1', [7945, -1540, -3134, 132, 583, -44, 42, 0, 1], [8, 9], 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], ['1.1.1.1', [0, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], 'factorsRR_base': ['RR', 'RR'], 'factorsRR_geom': ['RR', 'RR'], 'fod_coeffs': [0, 1], 'fod_label': '1.1.1.1', 'is_simple_base': False, 'is_simple_geom': False, 'label': '482790.a.482790.1', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], ['RR', 'RR'], [2, -1], 'G_{3,3}']], 'ring_base': [2, -1], 'ring_geom': [2, -1], 'spl_facs_coeffs': [[[25249], [-4011857]], [[11257], [1194355]]], 'spl_facs_condnorms': [3135, 154], 'spl_facs_labels': ['3135.b1', '154.b1'], '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': '482790.a.482790.1', 'mw_gens': [[[[1, 1], [-3, 8], [1, 1]], [[-1, 4], [11, 32], [0, 1], [0, 1]]], [[[1, 1], [-2, 5], [1, 1]], [[-3, 10], [21, 50], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.51348502656979433144692701649465', 'prec': 113}], 'mw_invs': [0, 2], 'num_rat_pts': 0, 'rat_pts': [], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 41254
    {'conductor': 482790, 'lmfdb_label': '482790.a.482790.1', 'modell_image': '2.45.1', 'prime': 2}
  • id: 41255
    {'conductor': 482790, 'lmfdb_label': '482790.a.482790.1', 'modell_image': '3.90.1', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 103660
    {'label': '482790.a.482790.1', 'p': 2, 'tamagawa_number': 1}
  • id: 103661
    {'cluster_label': 'c4c2_1~2_0', 'label': '482790.a.482790.1', 'p': 3, 'tamagawa_number': 1}
  • id: 103662
    {'cluster_label': 'c1c3c2_1~2_1_-1', 'label': '482790.a.482790.1', 'p': 5, 'tamagawa_number': 1}
  • id: 103663
    {'cluster_label': 'c4c2_1~2_0', 'label': '482790.a.482790.1', 'p': 7, 'tamagawa_number': 1}
  • id: 103664
    {'cluster_label': 'c2c2c2_1~2_1~2_-1~2', 'label': '482790.a.482790.1', 'p': 11, 'tamagawa_number': 1}
  • id: 103665
    {'cluster_label': 'c4c2_1~2_0', 'label': '482790.a.482790.1', 'p': 19, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '482790.a.482790.1', 'plot': 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wGwpCSx2/ejj8x7DRQbAiDgge7dpS98wf3Ys8%2B6dw%2BnY/lys1TF4cPSpZdKTz8tdeyYdZmuolHphz%2BM377zTjOD2Au1tWY5FUk6//ymxw8eNJNeJDO%2BMdeu81iYPP986cUXTfetJH35y9KuXZmd66STzKQByQTJadNyqw25ee21%2BFp%2BkhnPd%2BGF6T33ww9NK7FkhiNccEHihJF588wkLKCYEAABj9x6q/v9hw6ZD5RMrVkjXXKJmXV87rlmvbKSkpxKdDVrVrzLq2tX6Zpr8v8aMStWSJ98Yq67BcDXXjMhMdXxTGzaFA/e559vZkfHWoz27pV%2B%2BtPMz9n4Pf7LX6Rly3KrEdlbtCjx9sUXp//78ec/N13%2BpfEi0NGoWQgdKCYEQMAj55xj1s1z89vfxoNPOt56y3T3xhYjXrw4/y1/kln2JTbrV5Kuvda71j8pPv6vXTvTwpcs1mIn5R4AG59r7Fjz7xVXSMOGmevz5plwnonBg814wJh77smtRmRv8eLE25n8vDQe/xcLgMkTk556Kru6gNaKAAh4aMIE9/t37GjaYpHKv/5l1vTbtUv6zGek554zW1l54aGHEge833STN68TEwtlp5/uviRLS%2BMDs3mt5HNdeaX5d88e08qaqcatgM89J/3f/2VfI7Lz1ltN15F0m9Hrpr7etN5K5uci9kfbkCGJj3vhBdP6DhQLAiDgoRtukI480v1YOpNBdu6UPvc56b33zOLOS5eaSQ1e%2BdWv4teHDjXbYXllx454WPrc55oej0Ti47Jybf07dCi%2Bc0us9S/mggvi12PjETNx5ZWJ73HjFlQUxsqVibe7dDF7/qbj5ZdN%2BJcSu3379EmcyLV/vxk3ChQLAiDgoWBQuvpq92PLlkkbN6Z%2B7p49Jpxs3mxax/7yFzNz1SubNpnlZGLcFprOp5a6d5cti3fJ5hoA16yJf8gnn6vx93TnzszP3bFjYqh8/HFaigot9odCTHLrXXPcun8lMyyhV6/Ex%2BZr%2B0egNSAAeigcDqu8vFwVsbUnYKVUk0GkxBa3xvbvNx9Gf/ub2b/3z3%2BWTjnFm/pi/vjHxNuVld6%2BXiwAduxo1jJMdTwQcB8fmM1rBQLSmDGJx4JBs0%2BsJO3end35GweHaJTxYoWWHABPOin958YC4JFHmslVjXXvnnh7w4aMSwNaLQKghyZOnKgNGzbojeT/nWCV00%2BP7xqR7MEHTdhr7JNPpC9%2BUXrlFRNMFi2Kr1/npcazHLt182bP38Zi3WmjRrlPaImFtsGDs9uyze1cp53m3oUemwGa7cSaxl2Hktl9AoWxf7/0j38k3tezZ3rP3bo1vt7l%2BefH/xCISd7dhQCIYkIABAogVSvgnj2JLW%2BHD5uZt889Z5aweOSRpmPWvPD3v8c/CCXzYZiPvYRTeestM64x9lrJtm83XdKpjmfiwIH4vr1u59q/Pz4jO9ugefzx0qBB8dsvvGDWMIT3Nm5sOns73QCYqvs3JjkA/utfTf9gA9oqAiBQAFdeaXaycNN4MsjXv27GkEmme/iyy7yvTUrcjk1q2k2aby2N/2tcT64BcOVKqa7OXHcL0%2B%2B%2BG7%2Be6j1KR%2BPlYGpr4wtYw1v/%2BlfT%2B2ILfLckFgADgaatuJIZftGY4yQuNg20ZQRAoABKS81uE27WrDFjmKZNiy8Q/ZOfSDffXLj6Xnkl8Xbj1iwvxAJgly7uA/Zjx0tL3ccHZvNaRxwhjR7d9PjatfHruXR7J38dsaVF4C23XXWSg5ub/fvjkzoGD07c/7e582Q7ThRobQiAQIFMmJC6W/Wqq%2BKLCN9%2Bu7kU0quvxq%2B3aycNHOjda9XXxz94zzvPvF6yxuMDO3XK7fViAXD4cPcleZYsMf926mT2DM5WcgBs/D2Fd6qrm96XTgB88cX4bG237l/J/WePAIhiQQAECuTEExPXnGss1q10882m9a%2BQ3n3XrMkXc9JJ0qc%2B5d3rvf66mSkruXfv/v3v8Xpy7f7dtcvMpE51rkjE7KcsmdbBXHY9Oe20xHXj3nwz851FkLlsWwBbGv8nuf88ZLpnNNBaEQCBAvr611MfO%2B%2B81MvCeCk2QSLGy8WfpZbH/%2BVz%2B7cXXzTjtiT38X/33BNvBZo0KbfXKi2V%2BveP396/n11BCiG2vmNjybN53cRafrt3T93y63Yet9cD2iICIFBAF12UejHn995z7w71Wmy2bUyuW661JBbw%2BvZ1X68tdvzoo9Pfzqul1zrqqPievzHvvCPNmmWun3KK%2BySATCUvHEwA9N6BA03vKylp/jnr18dbDi%2B8MPXQDLffx9iEIqCtIwACBdSunXTLLe7H3n7bn62mtmxJvO02GD5f9u2Lb7fm1rr3ySfxbb3GjMk9EMe%2Bn%2Beck9g9W1srfeELppUuEJBmz87PsjfJAfDtt3M/J5rnFgDbt2/%2BOel0/0ruP38s74NiQQAECqy5pV3C4cLVEVPIALhiRXzNPbcAuHq1CYmpjmfi3XfjYysbd//u2mXOHWudu/323F8rJjkAJreu5pPjSOPHm5ASCLSNS0lJ7l3tydzW5Us3AHbo4L4PdQwBEMWMAAgUUG2tdNNNqY8//bT7oHYvJa9r5mUAbLwlm9uYvHyO/3NbS/CZZ8x4r1gr5OWXSz/4QW6v01hyAHRboy5fdu%2BWFi%2BOj3FsCw4flubPz%2B85M913%2Bd//jr//o0dLZWWpHxvbIaax2B8wQFvXwt9JAPIltsXb6tVmyzG3D65Dh6Rf/1r64Q8LU1NdnbRzZ%2BJ9yfuf5lMslJ1%2Buvuiy7HjJ5yQOKEiG43HEm7cKH3jG9KyZfHjX/mKWXcxn%2BMukxcg/uCD/J072THHSJMnS88/7x5UWqN27aQbb8zvOd267pv7fjz3XHx2dnPdv5L7LO50JpgAbQEBMA0rV67UT3/6U61du1Y1NTVauHChxo8f73dZaEMOH5auv958WHfoYFr6pk83i0An%2B%2B1vpe99L7clSdK1d2/T%2B7xaAmbHjvierW6te9GoWRA71fHm1NebsWC7d5sW1PXr4%2BP/PvrI7MQS07Wr9LOfSTfckPnX0JLkdeOSw3W%2B/fzn3p6/LWg8tjOmuQD4zDPx69kEQLe1JIG2iC7gNNTW1mrQoEH65S9/6XcpaKNuu83s%2BduunfTwwybgpFoSZudOacGCwtQVG2/XWMeO3rxWS927y5ebICdJv/tdZmPLjjhCCgbNDObRo804s%2BTlOsrKzP1vveVN%2BJOaBsDaWmaNes3tD6XYz1GyQ4fMH2GSdPLJ7rPQWzoPARDFghbANIwbN07jxo3zuwy0Ud/7njRnjrk%2BZ450xRXm%2Bpe%2BJE2dalqoks2ZY457rba26X1eB8DSUunss1Mfz6ejjjK7iVx6qXTddektEJwLt50jPv6YbkMvuX3PU03UeO01MwZQSm/ZH7cZxgRAFAsCoAfq6upU1%2BjP/mhs2wNY5/7745MM7r5b%2BtrX4sc6dTItUbG16Bp7%2BWXTjen1osx%2BBMCRI90/tGPHe/Uygc3Nww%2Bb3TtKSsyuKfX15sM%2BEDDn3Lo1vgfvffeZlteWZoTmk9v37uOPpVCocDXY5uijm96XKgCmu/xLjNsMYwIgigUB0AMzZ87UjBkz/C4DPvvDH8zEA8kM1v/Od5o%2B5tZbpV/8wn0m55w53u8M4tY96TamKldvvSVt326uuy278f775jGS2Rf53nvdzxMKST/6kenKq6xsuqTOzp2mG/jAAdON/s1v5u9rSIdbAGTZEG%2B5BcBUM4NjATAUks46q%2BVzuwXA5mYNA20JYwA9MH36dEUikYbLtkKv6wHfLVliZjs6jul6vO8%2B98f17%2B%2B%2BHIoUb%2B3ykltLnBdj1loa/9d4yZbm1mX7xjfiNf/4x02Pd%2B8uTZhgrr/ySrw1sFDcwp5XLaow3GaTu3Xdbttm9pmWzJ7c6bQMu3XepNrJB2hrCIAeKC0tVTAYTLjAHq%2B8YtaXq6833UyxCQ2p3Hqr%2B/21tdKDD3pTY4zbmDi3D89cxQJequ3dYgGxQwf38YEx3brF11F84w3ppZeaPub22%2BMh8Xvfy77mbDBmrPB69mx6n1vLXabdv5JUU9P0vhNPTO%2B5QGtHAATy6O9/Nx8uBw6YIPP44y23NFxySdMFhGPmzs1/jY25hZNMF9ZtSX292QFESr29W2zJllGjWl6GZurU%2BPfUrRXwuOPi2%2B399a%2BJH/xeSw6AsbGJ8E6fPk3vc2u5i/0ctGsnpTunLzkAdu1KFzCKBwEwDfv27VNVVZWqqqokSVu3blVVVZWqq6t9rgytyZYtpmtpzx5p0CCz1l86H/4lJan3B9640dv9gQvRAvj66/EPZLfu33/8I/5B21z3b0zfvtJ//qe5vnSp9OabTR8zbVq86/XOOzMuOWvJ37tPfSo/ewwjNbcAmLwE0Mcfx1uLzzzTvdvYTXIApPUPxYQAmIY1a9Zo8ODBGjx4sCRpypQpGjx4sL5X6P4ltFo7dpjwUlNj1hZ7/vnMZn5%2B9aupJ1/ElpDxQjBoAmhjbt1nuXDbkq2xbLZ/%2B/a348HKrRWwRw/zPZWktWvNlmmFkBwAGf3hvVNOaXpf8tJKy5bFf67T7f59//2m62T265d5fUBrxSzgNJx77rly2tKGmyioSMS0/G3ZYoLH0qWZb6d23HHSF75guoyTLV5sZtC6jXXKVUmJ1Lu39M9/xu/buVP67Gfz9xqxgJdqe7fGW7a5jQ90M3Cg%2BSB/%2Bmkz23fz5qbnnjbNbPVWV2daAS%2B5xPvWuA8/TLzt1jqVT1OnmglHbWkruJtuMnXnS%2BfOZgjFe%2B/F73v//cTHZDP%2Bb/36pveVl2deH9BaEQCBHBw4YD5Q1q83Aeb557NvJbj1VvcAGNsf%2BK67cqs1lZNOSgyAjT9Ic7VvnxmHJ7l37zYeH3jeeZntyzt9ugmAhw9L99wj/eY3icd79jRrBYbD0t/%2BZoLi5Zdn93WkK/l752WL0a5dZku7tub7389vAJTMepnpBMDevdNfW9MtAI4alV19QGtEFzCQpfp6s8fsyy%2BbsV7PPJNby9k555iWLTe/%2BY30ySfZn7s5yeOa8hkAV6yI1%2B3Wvbt6dXw/4nTG/zU2YoTZ9k2SHnrIfcbmt78d3yrs%2B9/3vqUsecUnLwNg165mwey2NMYwEDDLI%2BVb7OcgZuPG%2BPUNG%2BJ/4KSz%2B0dMcgAsKZGGD8%2BqPKBVIgACWXAc05X1zDNm7N4TT5gdLnKVan/gHTukJ5/M/fxukvdDzWcAjI3/CwTc1ztsPP4v0wAomYAnmW5et7UWe/WSvvIVc/0f/3BvYc2n5O%2Bdl5MGAgFp0SITah2nbVwOH5a82FK9sjLx9oYNpuVcyq77V5JWrUq8PWaM91sJAoVEAAQydPCgafl76CHzITx/fvrLSrTk%2ButTf8iEw/l5jWTJrY75XLc8FvBOO0069tjUx/v1yy4sjRsnnX66uf7rXzed/SmZruJYK%2BCMGfFg4IXkAHjyyd69FuJOPz1xjGxdnfT/F23QM8%2BYfzt1MsMM0rF5s5S8yMO11%2BZeJ9CaEACBDOzZY1oRnnjC3L7lFunqq/N3/rKy1B80q1bFdzLIp5EjE7sR8/UaNTWm1U1y7/7dt88sESNl1/oXM22a%2BXfvXveQ3Lu39OUvm%2BsbN0qPPJL9azWnvt4Eh5gOHdKf1ILcBAJNW89feMH8vr76qrl93nnpr8n42GOJt4PBptsOAm0dARBI08svS2eckbisyR135P91Uu0MInmzJEyXLolLaWzb1nQ2azZaWt5l%2BfLmxwem64or4t3Ys2e7r2M4fXp8mZ277jJhLd82bEhcRHvIELaBK6QJExID3pNPmklZsfc63e7fw4eb7sAzeTILQKP4EACBFmzYIF1zjRlo/u67icfc9n7NVW1t6jUBH344ccZuviTPbnRbXDlTjbd3Sx6k3/h4u3bpd825KSmRvvUtc/2DD8zWe8n69IlPPnjnHdN9n29r1ybeZsZoYXXtGh8TKpnW5cbjQtOdAPKnP5mfkZijj5amTMlPjUBrQgAEGjlwwIzjWrZMuvtuM9N04EDTbei2FORll5lWhthM1mwcOmRa3F58UZo0yQw2TzXjd98%2BafBg0yLx7LPmgyoSyT2IJu%2B/m48AGNvBZORI9%2B3dYi2pQ4aYD%2B9c3HijWYNRku69172F7zvfiQfrH/wg/7OqkwNgPiYFITPTpyeOaY0NMRg0yAwFaMmBA01b9WfNymxRd6CtYB1AQOY//tNOS/zLPx3r10uf/7y5fvvt0k9%2BktnzN20y3crJOw7iSo0gAAARiElEQVQ0Z88e6Re/MJfGrrgi%2B1muF1xgWtJiEyRia/dla8MGs3i1ZALq5MmJxw8fNo%2BRcuv%2BjSktNa8xbZppIb3mmnggbKxbN1PX1q3SAw%2Bk3oIvG2%2B8kViP26xneOuII6Q//tG0ODfeDSTd1r%2BpUxNb%2BS%2B91EzMAooRARCQ2cUj0/CXrEuXzJ/zxhuZhb/mHHdc9s/t1k0699x4q92LL5qZlKWl2Z2v8fi/devMJZVcJoA09vWvSzNnmoCcThC%2B%2B27TchibIZyL3bsTWwAvuIBWI7%2BceqppHT///Pj2b0uWmGWbkpc8ijl82KwT2XiMbXm5meEPFCsCICDTbVRdbcbfZaN9%2B%2ByWMbnmGrM5fa5Lk5SUSH375naOK6%2BMB8B9%2B8wkjQsuyO5cjQNgczp1yt9YubIyaeJEE%2BzSUV0t/fa3zU%2B6SdeSJYnv4ZVX5n5OZG/ECPMH0ZYt5nZVlQmGX/ua%2BZ07/XTTWrhnj/mZ//nPpVdeiT//zDPNLjOdO/tTP1AIAYdNbj0TDocVDod16NAhvf3224pEIgqyOzxaqd27zYdmbPzcpEnS/ff7W1NbcdVV8VbHjh3NZBRmjfpn06b4GozDhpmg9/bb8eOBgGndbjxrWzKtttOmmUlF7WkeQZEjABZANBpVKBQiAKLVu%2Bii%2BM4JffuasXJoXn29WeQ6tgj15ZebmaTwz6xZ0je/aa4/%2BKBp9VuyxCwN8/rrZqmj/fvN5KRevUyLYGWlee/Y7QO2IAAWAAEQbcXy5WYWcsyLL%2Ba2RIsNFi5MXCT45ZdZAsZvlZXxWebbt0vHH%2B9vPUBrxDIwABqce27ihvdz5/pWSpvReOLAyJGEP7/t3y%2BtXGmun3IK4Q9IhQAIIEFsazVJWrRIev99/2pp7TZvjk%2BckcxSQPDX66%2BbGexS/maYA8WIAAggwaWXxhfTra%2BX5s3zt57WbO7c%2BALhJ58sXXKJv/XArKt55plSz55mVjgAd4wBLADGAKKteeGFeOtJ585mcdxs1jksZjt3Sp/%2BdHwdx0WLTHgGgLaAFkAATZx/vnT11eb6nj1m6zQk%2Bv734%2BFv/HjCH4C2hRbAAqAFEG3Rzp1mEP1HH5ndMt56K7vFrovRxo3SZz9rusiPOspsa5fOXrMA0FrQAgjAVffu0o9/bK4fPChNmeJvPa3J7bfHF8y%2B6y7CH4C2hwAIIKVbbol3bS5eLP3v//pbT2vw4INmmzBJOucc6b/%2By996ACAbdAEXAF3AaMv27DEzK999VzrySGndOql/f7%2Br8seWLWbXiL17zbZ569aZfwGgraEFEECzOnc222h17SrV1prJIQcP%2Bl1V4dXXmy3F9u41W4gtXkz4A9B2EQABtOgznzHdnkceKa1ZI117rXT4sN9VFdakSdLq1VL79tJjj0nDhvldEQBkjwAIIC0jRkjPPy%2BVlUl/%2BpNdi%2Bzeeaf0619LRxxhwt9FF/ldEQDkhgAIIG2jRpmtz7p3l371K2nGDL8r8t6cOWam75FHSgsXSpdd5ndFAJA7JoEUAJNAUGyqq00r2IEDZj/cYrVvnxQKmbF%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%2B64hAsAAEBbZVUAPHjwoNauXavKysqE%2BysrK/Xqq6%2BmfN6%2BffvUp08f9erVSxdddJHWrVvX7OvU1dUpGo0mXAAAAFoLqwLgrl27dOjQIXXv3j3h/u7du2vHjh2uzzn55JM1f/58PfXUU3r00UfVsWNHjRo1Sps3b075OjNnzlQoFGq49O7dO69fBwAAQC6sCoAxgUAg4bbjOE3uixk%2BfLiuvfZaDRo0SGeffbYef/xxfeYzn9H999%2Bf8vzTp09XJBJpuGzbti2v9QMAAOSivd8FFNIxxxyjkpKSJq19H3zwQZNWwVTatWunioqKZlsAS0tLVVpamlOtAAAAXrGqBbBDhw4aOnSoli5dmnD/0qVLNXLkyLTO4TiOqqqq1KNHDy9KBAAA8JxVLYCSNGXKFF133XU644wzNGLECM2bN0/V1dWaMGGCJOn6669Xz549NXPmTEnSjBkzNHz4cPXv31/RaFSzZ89WVVWVwuGwn18GAABA1qwLgFdddZV2796tu%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%2BLKHbRaFShUEiRSETBYNDvcgAAgOVoAQQAALAMARAAAMAyBEAAAADLEAABAAAsQwAEAACwDAEQAADAMgRAAAAAyxAAAQAALEMABAAAsAwBEAAAwDIEQAAAAMsQAAEAACxDAAQAALAMARAAAMAyBEAAAADLEAA9FA6HVV5eroqKCr9LAQAAaBBwHMfxu4hiF41GFQqFFIlEFAwG/S4HAABYjhZAAAAAyxAAAQAALEMABAAAsAwBEAAAwDIEQAAAAMsQAAEAACxDAAQAALAMARAAAMAyBEAAAADLEAABAAAsQwAEAACwDAEQAADAMgRAAAAAyxAAAQAALEMABAAAsEx7vwtoyxzH0d69e5vcX1dXp7q6uobbscdEo9GC1QYAAJpXVlamQCDgdxm%2BCDiO4/hdRFsVjUYVCoX8LgMAAGQhEokoGAz6XYYvCIA5SLcFsKamRsOGDdOGDRvUs2fPvNdRUVGhN954I%2B/n5fwti0aj6t27t7Zt2%2BbZfyJe1t%2BWv/den7%2Btv7dt/fy8t5w/G5m%2Btza3ANIFnINAIJDRfx5lZWWe/GdTUlLi6V8wnL9lwWDQs9fwsv62/r3nvS3e8/Pecv5cePneFgsmgRSBiRMncn4fz%2B81L%2Btv69973tviPT/vLeeHt%2BgCLoD33nuvoUm6V69efpeDPIqNA7V5HEmx4r0tXry3xYv3Nn20ABZAaWlpwr8oHqWlpbrzzjt5b4sQ723x4r0tXry36aMFsAD4iwQAALQmtAACAABYhhbAAogtF2PzdHMAANB6EAABAAAsQxcwAACAZQiAQAvmzJmjfv36qWPHjho6dKhWrVqV8rHz589XIBBocvn4448LWDFysXLlSl188cU6/vjjFQgEtGjRIr9LQoYyfQ%2BXL1/u%2Bnu7cePGAlWMXM2cOVMVFRUqKytTt27dNH78eG3atMnvslo1AiDQjMcee0yTJ0/WHXfcoXXr1unss8/WuHHjVF1dnfI5wWBQNTU1CZeOHTsWsGrkora2VoMGDdIvf/lLv0tBlrJ9Dzdt2pTwe9u/f3%2BPKkS%2BrVixQhMnTtTq1au1dOlS1dfXq7KyUrW1tX6X1moxBhBoxplnnqkhQ4Zo7ty5DfedcsopGj9%2BvGbOnNnk8fPnz9fkyZO1Z8%2BeQpYJjwQCAS1cuFDjx4/3uxRkKZ33cPny5RozZow%2B%2Bugjde7cuYDVwSsffvihunXrphUrVmj06NF%2Bl9Mq0QIIpHDw4EGtXbtWlZWVCfdXVlbq1VdfTfm8ffv2qU%2BfPurVq5cuuugirVu3zutSAeTB4MGD1aNHD40dO1bLli3zuxzkIBKJSJK6dOnicyWtFwEQSGHXrl06dOiQunfvnnB/9%2B7dtWPHDtfnnHzyyZo/f76eeuopPfroo%2BrYsaNGjRqlzZs3F6JkAFno0aOH5s2bpwULFujJJ5/UgAEDNHbsWK1cudLv0pAFx3E0ZcoUnXXWWTr11FP9LqfVau93AUBrl7x2o%2BM4KddzHD58uIYPH95we9SoURoyZIjuv/9%2BzZ4929M6AWRnwIABGjBgQMPtESNGaNu2bbr33nvpPmyDJk2apPXr1%2Bvll1/2u5RWjRZAIIVjjjlGJSUlTVr7Pvjggyatgqm0a9dOFRUVtAACbczw4cP5vW2DbrvtNj311FNatmyZevXq5Xc5rRoBEEihQ4cOGjp0qJYuXZpw/9KlSzVy5Mi0zuE4jqqqqtSjRw8vSgTgkXXr1vF724Y4jqNJkybpySef1EsvvaR%2B/fr5XVKrRxcw0IwpU6bouuuu0xlnnKERI0Zo3rx5qq6u1oQJEyRJ119/vXr27NkwI3jGjBkaPny4%2Bvfvr2g0qtmzZ6uqqkrhcNjPLwMZ2Ldvn955552G21u3blVVVZW6dOmiE044wcfKkK6W3sPp06dr%2B/bt%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'}