Genus 2 curve data - 1280.a.12800.1

Formats: - HTML - YAML - JSON - 2024-05-02T14:32:09.874320

• 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': '683689193125439946', 'abs_disc': 12800, '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,[1,0,2]],[5,[1,-1,5,-5]]]', 'bad_primes': [2, 5], 'class': '1280.a', 'cond': 1280, 'disc_sign': -1, 'end_alg': 'Q', 'eqn': '[[0,0,-1,-1,1,2],[1]]', 'g2_inv': "['-322102/25','-355377/100','-232441/200']", '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': "['22','-170','-1832','-50']", 'igusa_inv': "['44','534','7684','13235','-12800']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '1280.a.12800.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.4741885652919348352611326030214770929718762393942284909', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.180.3', '3.80.1'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2, 3], 'non_solvable_places': [], 'num_rat_pts': 4, 'num_rat_wpts': 2, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '17.070788350509654069400773709', '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': 4, 'torsion_order': 12, 'torsion_subgroup': '[2,6]', 'two_selmer_rank': 2, 'two_torsion_field': ['4.0.256.1', [1, 0, 0, 0, 1], [4, 2], 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': [['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': '1280.a.12800.1', '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': '1280.a.12800.1', 'mw_gens': [[[[1, 2], [1, 1], [1, 1]], [[-1, 2], [0, 1], [0, 1], [0, 1]]], [[[0, 1], [1, 1]], [[-1, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [2, 6], 'num_rat_pts': 4, 'rat_pts': [[0, -1, 1], [0, 0, 1], [1, -4, 2], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 375
    {'conductor': 1280, 'lmfdb_label': '1280.a.12800.1', 'modell_image': '2.180.3', 'prime': 2}
  • id: 376
    {'conductor': 1280, 'lmfdb_label': '1280.a.12800.1', 'modell_image': '3.80.1', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 12097
    {'label': '1280.a.12800.1', 'p': 2, 'tamagawa_number': 2}
  • id: 12098
    {'cluster_label': 'c3c2_1_0', 'label': '1280.a.12800.1', 'p': 5, 'tamagawa_number': 2}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '1280.a.12800.1', 'plot': 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M/IkdLDD9s%2Bf99/b5s%2Bw9/oAQQAIILNnCn172/Xw4cT/mAIgAAARKjNm23e39690vXXSz17uq4IoYIACABABMrb5HnFCqluXem115j3hwIEQABu7NzpugIgoj3/fMF%2Bfx98ICUkuK4IoYQACCC43npLatFCqlLF7v/5z1J6utuagAgzY4b06KN2/eKLUsuWbutB6GEVMIDgGTIk//T5LEkJkjIlxZctK339tdS2rcPigMiwfr0FvnXr7IzfN99k6BcHIwACCI5Vq6TataWcHEkHBEBJOvNM258CwAnLyZE6dJC%2B%2BUZKTpZ%2B%2BkkqW9Z1VQhF0a4LCGee52nbtm2uywCK1Z490rZtUlaW3W7fbm3HjoK2e7dN6du929qePQVt3z5bgdhx4Tj9%2BX/hT7IAuP%2BtfvhBD1%2BUrk3xdRQTI5UqZXuWBQLWypSRSpe223LlrMXFSfHx0imn2PymU06RonlVg4898YSFvzJlpLQ0C4RZWUd9mm/FxcUpyqfdo/QAnoS8jWwBAED48fNG9ATAk5DXA5iSkqJZs2YV%2B9eLpK%2BTlZWlpKQkrVq1qlh/%2BSLp7%2BxwXyc7W8rIsLZund2uX2%2B3GzbY9R9/WNuvA%2B64lSljPW55vW9ly1rL65krXVqKjbUWCKhQL17JklLdn9/WRe8VbEKWJSlJ0irZEPC%2B6IDee2yhdsacqr17C3oQs7OtV3HXLutl3LnTeh23bSvomczMtF7J4xEVZetQatSwVrOmVKuWHZNVp46UmHj886aC8XMQST/TwXodkCLr7%2B1QX2f5cun886WtW6W775aee67ov0ZxcP0z4OceQAZLTkJUVJTi4%2BNVsmTJoLyDiLSvI0nx8fHF%2BrXC/e9s1y5p9Wpp5UqbQrdly33q3z9eq1fb42vWSBs3Ht/nTEiQKlYsaBUqSKedJpUvb9fly0u9e9%2Bhjz56PX9oNSHBQtxJ2d1N%2BuYxS6X7if9f02236L5Ha57wp9%2B3z/7z27zZwu7GjfalNmyQhg9/Rxdd9GetXWt/Z2vX2sevXWvtxx8P/nzlykkNGkiNGkmNG0tNmlirW/fwfxfB%2BHkL95/pQynu1wEp8v7e9v86u3fbOb9bt9o6qpdesjdeRfk1ilsk/QyECwJgEegZpK3VI%2B3rBEMo/515nr1gL19ubcWKgrZypbU//jjwWQ9pzJiDP1cgIFWtaj1aVatKlSvbdeXK1hITrVWsaB97NBkZZxT9thGxsdKECVKnTvaN769dOzus9CRER1uQPe00C277O%2BWUrYVOQMjNtZ7RlSsL/v6XLrX2%2B%2B/2%2BPbt0i%2B/WDvw22jSRGre3HazadXKrsuVC87PWyj/TIeySPt72//rPPigNHu2vYEbP75owt%2BBXyMSRNr3c7IYAoYTefMnI33%2BxY4d0rJl1pYutaCRd3/58mObnF22rJSUVNCqVy9o1apZ4KtQIYy2edi4URo3Tpu%2B/lqnTZ6sda%2B8osrdu4fU6o3sbPv3WrhQWrBAmjevoO3adfDHlyhhPYVt2lhr105q1iykvqWQ5JfXgeKUlibdcYf9/n/xha0ADif8DLjDyxOcCAQCGjx4sALH0h0VwjzPepKWLCnc8nqT1q8/%2BueoVKlg/lnNmtby5qXVqCGdemoYhbtjcdpp0oAByu3WTapUSSWvvTbkklIgYMO%2BjRsXfjwnx/5d//tf27s6Pd16CNeuLQiIaWn2sWXK2HDcOedI555ru9yUKxf0byWkRcrrgCvp6dK999r1kCHhF/4kfgZcogcQOIrcXPsPfvFiGx7cvy1ZYr18R3LKKbb9XZ06dlu7toW92rUt7JUpE5RvI%2BRE0jv/jAxp1izbc%2B2nn6SZM21hyv6io6UzzpAuvFC6%2BGLp7LNtOBk4EVu22M/T0qXS5ZdLH39sPdHAsSIAArKevA0bpEWLrC1eXNB%2B//3Qw355oqKsp65uXWt16hTc1qljPXg4WCQFwAPl5lpv4HffSd9%2BK02fbvMK9xcbaz2Dl14qXXaZbdobUT29KDa5udKf/mTn/NaqZfP/ypd3XRXCDQEQvrJzp4W6BQss6C1caG3RoiPPx4uOthfa%2BvWlevWs1a1rt7VqHdvCChQWyQHwUJYvl6ZOtU16v/7aepX3V6OGdMUVtkbmoovoHcThPfWU9Je/2OvO99/bQiTgeBEAEXE8z1bPzp9vQS/vdsECW2F7OFFRNiTboIEFvfr17Tov5JUqFbRvwRf8FgD353n2c/nVVzZxf9o028ojT9my1it4zTUWCNlvHnkmT7ZeY8%2BTxo2z7V%2BAE0EARNjyPNv4eN486bffCq/U3Lz58M8rX15q2LCgNWhQEPTodQkePwfAA%2B3cKU2ZYkN6H39sexXmKVXKJvd37mzDfoRB/1q50nr7Nm2S7rxTGjvWdUUIax5QTHJzc73Bgwd7VapU8WJjY73zzz/f%2B/XXX4/4nMGDB3uSCrXExERvyxbPmz7d81JTPe/eez3vnHM879RTPc9i4MEtKsrzatf2vI4dPa93b88bO9aev2FDkL55HNaoUaO8xo0bew0aNPAkeZmZma5LCim5uZ43a5bnDRrkeY0aFf65DgQ875prPO/DDz1v1y7XlR6b1NRUr1atWl4gEPBatWrlTZ8%2B/bAf%2B/rrrx/0%2By/J2xUu32wx2r3b81JS7Oegdevw%2Bfc/nGnTpnmdOnXyqlSp4knyJk6c6Lok3wmtvRcQUYYNG6YRI0YoLS1NDRo00JNPPqlLLrlECxcuVFxc3CGf43lRqlfvMvXv/w/99lspzZsXrXnzSh12IUWJEtZz16SJTaJPTratOxo1sqPJEHp69uypnj17cpb2YURF2erOM86QnnzSerTHj7c2f740caK1U06xXsGuXW3fwVBcQPL%2B%2B%2B%2BrV69eGj16tM4%2B%2B2yNGTNGHTt21Lx581SjRo1DPic%2BPl4LFy4s9FgsXfN66CFbaV6%2BvPThh%2BE/WrFjxw41b95cd9xxh6677jrX5fgSQ8AoFp7nqWrVqurVq5cGDBggScrOzlZiYqKeffZZ9ejRQzk5tvji559tFdsvv0gzZ2Zrz55Dr6hISrLNdZs1k5o2tduGDcP/hdCvGAI%2BPp5n%2Bw/%2B4x/WVq8u%2BLNGjaTu3aUuXWxfyVDRtm1btWrVSi%2B//HL%2BY40bN9bVV1%2BtoUOHHvTxaWlp6tWrl7YeeFKMz73xhgX9qCjps89sfmgkiYqK0sSJE3X11Ve7LsVX6AFEsVi2bJkyMjLUYb%2BdSTduDKh%2B/YF66aXqevddC33btx/4zICk3SpVaoECgQVq0GCX%2Bve/VJdeWlWnnBLM7wAILVFRduRc8%2BbS0KG2ojgtzXqDFiyQ%2BvWTHn1UuvZa6Z57pPPPd9sruGfPHs2ePVsDBw4s9HiHDh30/fffH/Z527dvV82aNZWTk6MWLVroiSeeUMsiP5cwfPznP/bvKdlmz5EW/uAO20aiWKxZkyGphT79tI5uusm2uKheXfr554H67bcrNG2ahb8yZWxD3Icesne5o0fP0Pvvf6bZs0tq4sTTFBf3hh56qJVycja5/paAkFGihG0V8%2Babtgn1q69KKSnS3r3S%2B%2B/bZtNNm0ovv3yoN1nBsXHjRuXk5CgxMbHQ44mJicrIyDjkcxo1aqS0tDRNmjRJ7777rmJjY3X22Wdr8eLFwSg55GzdaoF%2B926pY0fb%2BgUoMo7nICJCvPnmO15s7DleqVKPeiVLfu6VLbv3oIUZJUp4XvnyK73q1T/xxo3zvLlzPW/fviN/3u3bt3uJiYne8OHDg/ONIGgyMzNZBFLEfvnF8%2B6%2B2/PKli34vTvlFM/r18/zVq4Mbi1r1qzxJHnff/99oceffPJJr2HDhsf0OXJycrzmzZt7DzzwQHGUGNJycjzvqqvs37BWLc/btMl1RcVHLAJxgh5AnLDly6UxY6TrrpMefPBm7d49Q3v3PqWcnMu0Y0e0pCyddVaWnnjCNr7NzJTOOed%2BXXTReHXrZj0UJUse%2BWuULVtWzZo1820PAHA8Wra038k1a6SRI22B1Nat0nPP2ak0t90mzZ0bnFpOO%2B00lSxZ8qDevg0bNhzUK3g4JUqUUEpKii9//4cNkyZNss2eP/yQkz5Q9AiAOGb79tmRVv362Wrb2rVtbsqECdLWrVGKj5euvFIaMUL6%2BWdPiYmN9ac/vaK//MWGq2Ji9mjatGk666yzjvlrZmdna/78%2BapSpUoxfmdAZElIsGkVCxdaiLjgAvv9fftt6fTTbXPpH34o3hpiYmLUunVrTZ48udDjkydPPubXAM/zlJ6e7rvf/ylTpEGD7Pqll6TWrd3WgwjlugsSoS072/M%2B/dTzunXzvAoVCg/plixp%2B/E9/rjn/fCD5%2B3dW/i5zzzzjJeQkOBNmDDBmzt3rnfzzTd7VapU8bKysvI/5qKLLvJeeuml/Pt9%2BvTxpk6d6i1dutT78ccfvU6dOnlxcXHe8uXLg/UtI0gYAg6un3/2vM6dbSpG3u/wxRfb/pjF5b333vNKlSrljRs3zps3b57Xq1cvr2zZsvm/z7fddps3cODA/I8fMmSI98UXX3hLlizx5syZ491xxx1edHS0N3PmzOIrMsSsXet5iYn273P77bYvZCTatm2bN2fOHG/OnDmeJG/EiBHenDlzvBUrVrguzTcIgDhITo7nTZnieXfeefBmy%2BXLe96tt3ree%2B953pYtR/48eRtBV65c2QsEAt55553nzZ07t9DH1KxZ0xs8eHD%2B/RtvvNGrUqWKV6pUKa9q1aretdde6/32229F/03COQKgG4sWeV737p4XHV3we92%2Bvef9%2BGPxfL3U1FSvZs2aXkxMjNeqVStv2rRp%2BX92/vnne7fffnv%2B/V69enk1atTwYmJivIoVK3odOnQ4aA5hJNu71/POO8/%2BTZo187wdO1xXVHymTJlyyE2/9/95QPFiH0DkW75cev1121pi5cqCxytXtnl%2B114rnXeeFM3mQTgJqampSk1NVU5OjhYtWsQ%2BgI6sWCE9/bT0f/9nw8OSHTX39NM2xQPB98gj0jPPSHFxtj9qgwauK0IkIwD6XG6u9PnnUmqqHUqf99MQHy/dcIP05z9b6DvaYg3geLERdGhYvlx6/HHbhik317aY6dbNHvPZ1DunPv9cuvxyu37/fTvlBShOBECf2rXLevpeeEHaf4HdxRfbiQJXX81RaiheBMDQMn%2B%2BLTyYONHuly1rPVJ9%2BnDaTnFbvVpq0ULatEnq2VMaNcp1RfADAqDPbN8ujR4tDR8ubdhgjyUkWOi7917bNgIIBgJgaPruOwt9M2fa/dq1bUuZq65yW1ek2rfPdkmYMUNq1Ur6/nvb%2BgUobmwD4xPZ2fYiXqeONGCAhb%2BaNaUXX7R3n8OHE/4A2Mk8P/wgvfOOVK2atGyZzQ288kobLkbRevxxC39xcTb0S/hDsBAAI5zn2T59ycnSww9Lf/wh1a1rw7%2BLF0sPPCCVK%2Be6SgChJCpKuuUWO2N44ECpVCnpk0%2BkJk1sn8%2BcHNcVRoZp06Qnn7TrMWN4E47gYgg4gi1ZIt13n/TVV3a/ShXpscekrl3tBR1wiSHg8DF/vm36Pn263W/TxnYMYLXwidu8WWre3EZg7rjDVmMDwUQPYATKybF36U2bWvgLBOwQ8cWLpbvuIvwBOD6NG0tTp0pjx9qc4Z9%2BsmPnnn/eVg7j%2BHiezblevVqqX9%2Bm4gDBRg9ghFm92s77nDrV7l98sfTKKwwtIPTQAxie1qyR7r5b%2Buwzu3/BBdJbb0nVqzstK6y8/ba9TkdH26KPlBTXFcGP6AGMIJMn21YCU6faFg5jxthjhD8ARaVaNZsPOHasvc5MnWpDmZ984rqy8LB6tXT//Xb9t78R/uAOATACeJ6t8L3sMttHqmVLac4ce5ceFeW6OqCw1NRUJScnK4X/%2BcJWVJR05532OtO6tc1nu/JKWzCSd6oIDuZ5Ng0nM9PmUT7yiOuK4GcMAYe53Fypd2/p73%2B3%2B3fcYfv8sXErQh1DwJFhzx6pf/%2BC16D27W07k/Ll3dYVit54wxbhBQIWnhs3dl0R/IwewDCWm2vvwvNeeJ97Tho3jvAHIHhiYmwE4v33pTJlpH//W2rbVlq0yHVloWXDBtuKS7LdGAh/cI0AGKY8z7ZleP11O6f3rbekvn0Z8gXgRufOtoF0zZrS779LZ55pCxxg%2BvaVtmyxedp9%2BriuBiAAhq0hQ2wSdokStmP/rbe6rgiA351%2Bum0R06aNzQts3176/HPXVbk3Y4a9SY%2BKssV50dGuKwIIgGHp/fft%2BCDJtni58UY/MdAjAAAgAElEQVS39QBAnkqVpClTpMsvl3btsmPkJk1yXZU7ubnSQw/Z9V13WTgGQgEBMMwsXCh1727X/fvbCwoAhJIyZaSPPrJh4b17peuvl774wnVVbrz9ti34iI8vOPYNCAUEwDCyb59tHrpjh3ThhdLTT7uuCAAOrVQpm55yww0WAq%2B9Vpo503VVwbVnj%2B31J0mPPipVrOi2HmB/BMAwMmqUNGuWHcX05pu2%2BAMAQlV0tPWAXXaZDQdfdZW0cqXrqoInLU1asUKqXFl64AHX1QCFEQDDxMaN0uDBdj1sGMcuAQgPMTHSBx/Y6tcNG6wnMDvbdVXFLyfHXqslacAAGxYHQgkBMEw895yUlWUvonfe6boa4MRxEoj/lCtncwLLl5dmz7bh0Ej36afSkiXSqacyVxuhiZNAwkBWlvX4bdsmffyx1KmT64qAk8dJIP7z8cc2DBwVJX33ne0VGKkuv9y2wOnfX3r2WdfVAAejBzAMvP22hb/GjaUrrnBdDQCcmCuvtKPQPE/q2dO2SIlE69dLX35p14zYIFQRAMPA22/b7V13cdIHgPA2bJhtiTJnjjR%2BvOtqisfEiRZuU1Kk%2BvVdVwMcGgEwxG3YYMcrSbanFgCEs4oVC45CGzbMegMjzWef2e0117itAzgSAmCImzrVbps3l6pVc1oKABSJnj2lQMB6AefMcV1N0crNlaZPt%2BtLLnFbC3AkBMAQ99NPdnvOOW7rQOTyPE9DhgxR1apVVbp0aV1wwQX67bffjvicIUOGKCoqqlCrXLlykCpGuKuw4hd9U/FGZSlOTduVla6%2BumCoI8wtWSJlZkqxsbZrAxCqCIAhbt48uz39dLd1IHINGzZMI0aM0KhRozRr1ixVrlxZl1xyibZt23bE5zVp0kTr1q3Lb3Pnzg1SxQhrX30lnXWWzlo9XnHarpi9O6V//Us67zybPBfmFiyw20aNbCNsIFQRAEPc6tV2W6uW0zIQoTzP08iRIzVo0CBde%2B21atq0qd544w3t3LlT//jHP4743OjoaFWuXDm/VeScKxxNbq50zz2H3gl63z7p3nvt/LQwtmqV3fKajVBHAAxxW7bYbfnybutAZFq2bJkyMjLUoUOH/McCgYDOP/98ff/990d87uLFi1W1alXVrl1bN910k5YuXXrEj8/OzlZWVlahBp%2BZNk1atuzwf75%2BvW2eF8a2brXbChXc1gEcDQEwxO3da7cxMW7rQGTKyMiQJCUmJhZ6PDExMf/PDqVt27Z688039eWXX2rs2LHKyMjQWWedpU2bNh32OUOHDlVCQkJ%2BS0pKKppvAuFj/fqjf8wRfu7CQd5rdqlSbusAjoYAGOICAbvdvdttHYgM77zzjsqVK5ff9v7vf6uoAzaY9DzvoMf217FjR1133XVq1qyZ2rdvr08//VSS9MYbbxz2OY888ogyMzPz26q8sTL4R4MGRfMxIYzXbIQLpqiGuAoVpJUrpT/%2BcF0JIsFVV12ltm3b5t/P/t9crIyMDFWpUiX/8Q0bNhzUK3gkZcuWVbNmzbR48eLDfkwgEFAg739H%2BFOrVrY78qxZh/7zRo2kCy4IaklFLW/ol9dshDp6AENc3kTi3393WgYiRFxcnOrVq5ffkpOTVblyZU2ePDn/Y/bs2aNp06bprLPOOubPm52drfnz5xcKkcAhvfWWVLXqwY9XrCi9917YH3dUs6bdLlnitg7gaAiAIa5pU7tNT3dbByJTVFSUevXqpaeffloTJ07Ur7/%2Bqq5du6pMmTK65ZZb8j/u4osv1qhRo/Lv9%2B3bV9OmTdOyZcs0c%2BZMXX/99crKytLtt9/u4ttAOGnYUJo7V2PqDtNUna9Vtc%2BVnnhC%2BvVX2/E%2BzDVpYreLF0s7d7qtBTgShoBDXLt2dpu3szxQ1Pr3769du3bpvvvu05YtW9S2bVt99dVXiouLy/%2BYJUuWaOPGjfn3V69erZtvvlkbN25UxYoV1a5dO/3444%2Bqmdf9ARzB0q3lde/SfvLUT0u/llTbdUVFp2pVa2vXSjNnShde6Loi4NCiPC8ST2KMHNu22ZySvXul336TkpNdVwQUjaysLCUkJCgzM1Px8fGuy0EQ3Xuv9Mor0qWXSl984bqaonfrrdI770gDBkjPPOO6GuDQGAIOcXFxUt4WbUfZlxcAQt6CBdJrr9n1o4%2B6raW4dOpkt//8p0QXC0IVATAMdOlit%2BPGHXoDfQAIB7m51vu3b5%2BFpPPOc11R8ejUSSpd2hbvzZzpuhrg0AiAYeCaa6Rq1Wx/1DffdF0NAJyYv/9dmjpVKlPGriNVuXLS9dfb9ZgxbmsBDocAGAZKlZL69rXrxx5jZRmA8PPDDzYnTpKef16qU8dtPcXtvvvs9t13j%2B0AFCDYCIBh4p57bH%2BpNWukp55yXQ1w4lJTU5WcnKyUlBTXpSBIVqywkYy9e61n7J57XFdU/Nq2tV0csrOlESNcVwMcjFXAYeSjj%2BxFNDpa%2BvFHqXVr1xUBJ45VwP6wcaN07rm2%2BOP006XvvrMhUj/4%2BGPpqquksmWlpUulSpVcVwQUoAcwjFx9tb173rdPuvlmKSvLdUUAcHibN9suBgsWSNWrS59%2B6p/wJ9likJQUaccO2%2BsaCCUEwDDzyitSUpLtMn/bbVJOjuuKAOBgf/whXXyxNGeO9XxNnmwh0E%2Biogr2AXzlFQvCQKggAIaZChWkDz%2BUAgFp0iSpVy/2mQIQWpYvt2Hf9HQLf998IzVq5LoqNy66yHoC9%2B2THn6Y12uEDgJgGGrTRnrjDbseNUoaNIgXFQChYfZs6cwzpYULpRo17BjLvPNx/WrECCkmxk49mTDBdTWAIQCGqRtvtPAnSUOH2jYxubluawLgb%2BPHW89fRobUrJkt%2BGjY0HVV7tWvL/Xvb9cPPihlZrqtB5AIgGGtZ0/pxRftesQIO39y9263NQHwn337bI%2B/G2%2BUdu2SOnaUvv3Wf3P%2BjmTQIKlePWnt2oIwCLhEAAxzDzxgw8HR0bbh6PnnS6tWua4KgF%2BsXSu1by8NG2b3%2B/a17U/Y2aew2NiCM5BffVX697/d1gMQACNAly7Sl19K5ctLP/0ktWwpffKJ66oARLpPPpGaN5emTbPtXcaPl557TipZ0nVloen88wtOCOnenaFguEUAjBAXXST9/LPUqpW0aZN05ZVSjx7sFYjQw0kg4W/7djvN48orbaPnFi1s8ccNN7iuLPQ9%2B6wdg7dypfTQQ66rgZ9xEkiEyc6WHnlEeuEFu1%2B9ui0Wueoq25MKCBWcBBKepk613qulS%2B3%2Bww/bQrRAwGlZYeXbb603MDdX%2BuAD2%2BAfCDZ6ACNMIGALQqZMsXeZq1fbCSJXXCHNn%2B%2B6OgDhKjNTuvde6cILLfzVqCF9/bW93hD%2Bjs8550gDB9r13XczbxtuEAAj1AUXSHPnWm9gqVLS55/btgw9ekhr1riuDkC48Dyb29e4sZ1mIVlomTvXpp7gxAweLJ1xhrRlC6c6wQ0CYAQrU0Z6%2Bmnpt99sCDgnx1af1a1rq4d51wngSBYssLN8b7xRWrfO9rObMkUaM4ZVvicrJsZ2bihXzhbRPPWU64rgNwRAH6hfX/rXv2xH/nPPtXmCo0bZEPHtt0v/%2BY/rCgGEki1bpN69bdTg3/%2B2Id7Bg6X//tdGF1A06tWTXn7Zrh97zIIgECwsAvEZz7N38E88YZO58%2BRtT3D11fbOFChuLAIJPXv22DDv44/bbgKSrfR94QUbOUDx6NrV9nOtWtXOT65Y0XVF8AMCoI/99JNN4P7ww4L5JxUr2r6CXbtKTZs6LQ8RjgAYOnJzbZ7fX/4iLVlijyUn2%2BvDpZe6rc0PduyQUlJsoV6HDjZnuwTjcyhmBEBo9WqbG/jaazbPJ0%2BLFtItt9j8nxo13NWHyEQAdM/zbDPnv/61YCpIYqINR3bvbicMITh%2B/VVq08aO0nviCQvjQHEiACLfvn3SZ59Jr78uffqptHdvwZ%2B1bStdd50NEdev765GRA4CoDt5we/xx20DeckWdfTta/v6lSvntj6/SkuT7rjDev8mT2aVNYoXARCHtGmTbVD67rvSjBn2H0aeRo1sXtDll0tnn23bzADHKjU1VampqcrJydGiRYsIgEGUkyNNmGC7A6Sn22NlytiuAP3723GScKtbN3sTXqmSNGeOzQsEigMBEEe1bp00caK1qVOtpzBPXJy9S73kEunii6WGDTlxBMeGHsDg2bVLevNNafhwafFie6xsWalnT6lPHwsbCA27dknt2tmK63POkb75hjfZKB4EQByXrVulL7%2B04aMvv5T%2B%2BKPwn1etattEXHCBbTlDIMThEACLX0aGreodPbrgd/XUU63H78EHpQoV3NaHQ1u82DaJzsqygP78864rQiQiAOKE5ebaEMVXX9l8le%2B/tz0G91exog0Tn3WWdOaZUuvWUunSbupFaCEAFp%2BffrK9Pt9/37Z2kaSaNaVevaQ772SOXziYMMHmXUu2U0PeNVBUCIAoMrt2ST/8YMPE06dLM2dKu3cX/pjoaKl5c1vt1qaNbX3QqJFUsqSTkuEQAbBobd8uvfee9fjNnl3weLt2Fvyuu45VveGmXz/r/YuLk2bNshEVoKgQAFFssrPtP6LvvrPewR9%2BkNavP/jjypSRWraUWrWy1rKlnTvKhtSRjQB48jzPgsG4cbZga9s2ezwQkDp3tqHelBS3NeLE7d0rtW9vb6ibNJF%2B/JHeWxQdAiCCxvOkFSusZ3DWLBum%2BuUX2wT1QKVK2QteixbS6adbr2GzZuyQH0kIgCduzRrpnXfs9Ih58woer1dPuvtu20rktNPc1Yeik5Fhb4zXrZNuvtn%2B3ZlXjaJAAIRTOTnSwoXWU/jLLzanMD1dysw89McnJloQbNrUWpMmdmIB%2BSH8EACPz5YtthL/H/%2BwlaF5r9yxsTa82727Lb4iHESeGTNst4V9%2B6QXX7SeXeBkEQARcjxPWr7cTibIa//9r7R0aeH9CPeXlGRhsHFjC4SNG1tjX7PQRQA8uq1bpY8/tmPavvqqYEGHZFuEdOliQ70JCe5qRHC88ILUu7fN45w2zRbWASeDAIiwsX27DXfNnWvHJv36q/Tbb4WPrztQpUoWBBs1KmgNG9qKSM7adIsAeGhr11romzjRevr2P5GnaVMbBrz5Zql2bXc1Ivg8T7rpJnszULWqjZgkJrquCuGMAIiwt2WLBcF586zNn2%2B3q1cf/jmBgB1p17Ch1KCB3eZd02tYvDgJpLDcXJsC8dlntr9m3tFseZo0sSHezp3tGv61fbvtnjB/vg33T57Mym6cOAIgIta2bTa/cP58acECawsX2iar%2Bw%2BlHah8eQuC9esXtHr17JahtqLj5x7AtWulr7%2B2Yd1Dbajetq2du3311dZrDeRZsMBWdm/fbtvEDBvmuiKEKwIgfCcnx%2BYYLlwoLVpkt3nB8Ei9hpKtQq5Xr3CrW9dahQpMwD8efgqAf/xhW3lMmWJt/5W7ku3z1r69dMUV1ipXdlMnwsOHH0o33GDX//yndO21butBeCIAAvvZudOC4KJFdrt/27DhyM%2BNj7cgWKdO4dvataUaNTjP80CRGgA9z35efvjB9sD89lvrhd5fVJRt7XHJJdKll9qEfva9xPHo29fOdo6Ls2kDDRq4rgjhhgAIHKOsLOn336UlS%2Bw2ry1ZYvuyHUmJErZSuXZta7VqFVzXrGmTuv12GkqkBMB162wO388/296WM2dKmzcf/HFNmxack33hhcw1xck5cJPomTOlsmVdV4VwQgAEisCuXbZNzdKlFgjzbpcts3bgkXgHio62gFizprUaNew2Kcmuk5Ii78U93ALgnj3Ws/ff/9pK9PR027cyI%2BPgjw0E7Nzrs8%2B2ds45NkUAKErr1llPckaGdMst0ttvMw0Fx44ACBSz3Fx7gc4Lg8uW2RzE5cvtetUq2%2BD1aE491YJg9ep2W61a4Va1qn1MMP4DmDBhgsaMGaPZs2dr06ZNmjNnjlq0aHFsT/7uO2nkSGV9%2B60SMjKU%2BdBDiu/f376BELB5c8Gwf97iofnzbVrAof6dSpSwhRpnnGGT89u0sRNsGNJFMMyYYT3KOTnSqFFSz56uK0K4IAACjuXk2KrQFSssFK5caW3FCguHK1cWnPF6NIGAVKVKQUtMtAUFiYnWKlWyVrGizVk80bD41ltvadmyZapataruuuuuYw%2BAaWl2ZEVurrIkJUjKlBRfpYr9T1a37okVdIw8T9q0yRb75P3d5v295/Xgbtly%2BOfHxdlQbrNmFvLyjiqMtN5ZhJcRI6Q%2BfWye8YwZtoocOBoCIBAGMjMtsOQFl9Wrra1da/MP16w59LyzIylVyoJghQp2bmyFCjYvrXx560k89VTplFNs65u8Fh9vIahsWev5Wr58uWrXrn1sAXDzZuuq/N94eKEAKEkdO9pmeMdhzx47LSMz04Lb5s3WNm60lbd//CGtX29t7VobMjvSFkB5qlYt2Aoo71SZ5GTreWWIDaHG82xV8D//aT%2Bjv/zCWdA4OgIgECF277ah5rygk5FR0PJC0IYNFop27Dj5r1emjFS6dI42bVqpOnUqq3z50oqNtbNpAwFrMTEWNEuVki6a93f9%2Bcde%2Bc8/MADmRpXQgJtWamOgmvbutaCWnW1t1y5rO3bYSu1t22wftOzsE6u9UiUbSs%2Bba5m3KKdOHWv06CHcZGXZFIRFi6QOHey9lN8WluH4EAABH9q5s6CXbONG6zXbtMna5s3Wm7Z1q91mZlrLyrLglZNzYl/zGQ3QABXsWntQD6CkNpqpWWpz3J87Lq6g17JCBWsVK1qrVMmGwfcfGg8ETux7AELZ3Lk2/LtrlzRkiDR4sOuKEMoIgACO6J133lGPHj0k2VDTv/71pU4//Wxt3y4tWrRGHTtepzFj3lbVqvW0e7f1ROb13O3ZY9tV7NsnNft2lDp99kD%2B5z24B7CkXuy/WtmnVlapUtZ7mNeTGBtrPY5lyljvXLlyFvri4mxYmp4OwLz1ltSli01V%2BOIL6w0EDoUACOCItm3bpvXr1%2Bffr1atmkqXLi3pOOcAZmbaHMD/jT8f1AP4pz9JH31UHN8C4Cs9ekivvmrzAOfMsekOwIFKuC4AQGiLi4tTvXr18lte%2BDtuCQnS668f%2BkiUWrWkl146qToBmL//XWrZ0qZ3dO5svfDAgQiAAI7b5s2blZ6ernn/O9R24cKFSk9PV8ahdkXe3w03SLNmSd26FZxd9de/2lEaSUnFXDXgD7Gx0gcf2HuuH36QBg50XRFCEUPAAI5bWlqa7rjjjoMeHzx4sIYMGXJMnyPcTgIBws1HH0nXXGPXEydKV1/tth6EFgIgACcIgEDx69PHNopOSLD9AevUcV0RQgVDwAAARKhnnpHOPNPWYHXufOJ7ZyLyEAABAIhQpUpJ771nJ/zMni316%2Be6IoQKAiAAABGsRg3bH1Cyxfb//KfbehAaCIAAAES4yy%2BX%2Bve36%2B7dpaVL3dYD9wiAAIIqNTVVycnJSklJcV0K4CtPPlkwH/Cmm%2BykHvgXq4ABOMEqYCD4Vq60TaI3b5Z69ZJeeMF1RXCFHkAAAHyiRg0pLc2uR46UPv7YaTlwiAAIAICPXHml9PDDdt21q7RqldNy4AgBEAAAn3nmGal1axsK/vOfpX37XFeEYCMAAgDgMzExtj9gXJw0Y4b0%2BOOuK0KwEQABAPChevWkMWPs%2BqmnpKlTnZaDICMAAgDgUzffLN1xh5SbK916q7Rpk%2BuKECwEQAAAfOyll6SGDaU1a6Ru3SQ2h/MHAiAAAD5WtqzNB4yJkSZNkkaPdl0RgoEACCCoOAkECD0tWkjDhtl1nz7S3Llu60Hx4yQQAE5wEggQWjxP6tRJ%2BuwzqUkTadYsqXRp11WhuNADCAAAFBUlvf66lJgo/fab1K%2Bf64pQnAiAAABAklSpkvTGG3admip98onbelB8CIAAACDfpZdKvXrZdbduUkaG23pQPAiAAACgkKFDpdNPl/74g61hIhUBEAAAFBIbK/3jH1IgIH3%2BuQ0HI7IQAAEAwEGaNJGee86u%2B/WT5s1zWw%2BKFgEQAAAc0v33S5ddJu3eLf35z9KePa4rQlEhAAIAgEOKipL%2B7/%2BkChWk9HTpb39zXRGKCgEQQFBxEggQXqpUkcaOtethw6QZM9zWg6LBSSAAnOAkECC8dOtmG0XXqiX95z8Sv7bhjR5AAABwVCNHWvhbvlx66CHX1eBkEQABAMBRxcdLb75p8wLT0qSPPnJdEU4GARAAAByTc88tOCP47rulDRvc1oMTRwAEAADH7PHHpWbN7JSQHj04JSRcEQABAMAxCwSkt96SSpWyYeA333RdEU4EARAAAByX5s2lIUPs%2BsEHpVWrnJaDE0AABAAAx61/f6ltWykrS%2BrenaHgcEMABAAAxy06WnrjDSk2Vpo8WRozxnVFOB4EQABBxUkgQORo2FB65hm77ttXWrrUbT04dpwEAsAJTgIBIkNurnThhdL06dL550vffCOVoHsp5PFPBAAATliJEnZEXNmy0rRpUmqq64pwLAiAAADgpNSpIz37rF0PHCgtWeK2HhwdARAAAJy0e%2B%2B1oeCdO6Vu3WxoGKGLAAgAAE5aiRLSuHE2FDx9OkPBoY4ACPjchAkTdOmll%2Bq0005TVFSU0tPTj/qctLQ0RUVFHdR2794dhIoBhKratQsPBbMqOHQRAAGf27Fjh84%2B%2B2w9k7eXwzGKj4/XunXrCrXY2NhiqhJAuLj3XlsNvHOndOedbBAdqqJdFwDArdtuu02StHz58uN6XlRUlCpXrlwMFQEIZ3lDwaefLk2ZIr36qtSjh%2BuqcCB6AAGckO3bt6tmzZqqXr26OnXqpDlz5rguCUCIqFtXeuopu%2B7XT1q50m09OBgBEMBxa9SokdLS0jRp0iS9%2B%2B67io2N1dlnn63Fixcf9jnZ2dnKysoq1ABErgcekM46S9q2TbrnHoaCQw0BEPCRd955R%2BXKlctvM2bMOKHP065dO916661q3ry5zj33XI0fP14NGjTQSy%2B9dNjnDB06VAkJCfktKSnpRL8NAGGgZEkbCg4EpM8/l95%2B23VF2B9HwQE%2Bsm3bNq1fvz7/frVq1VS6dGlJNgewdu3amjNnjlq0aHHcn/uuu%2B7S6tWr9fnnnx/yz7Ozs5WdnZ1/PysrS0lJSRwFB0S4p5%2BWBg2SypeX5s%2BXKlVyXREkFoEAvhIXF6e4uLgi/7ye5yk9PV3NmjU77McEAgEFAoEi/9oAQlu/ftIHH0jp6TYs/P77riuCxBAw4HubN29Wenq65s2bJ0lauHCh0tPTlZGRkf8xXbp00SOPPJJ//7HHHtOXX36ppUuXKj09Xd27d1d6erruueeeoNcPILSVKmVDwSVLSuPHS5Mmua4IEgEQ8L1JkyapZcuWuuKKKyRJN910k1q2bKlXXnkl/2NWrlypdevW5d/funWr7r77bjVu3FgdOnTQmjVrNH36dLVp0ybo9QMIfa1aSX362PV990msAXOPOYAAnMjKylJCQgJzAAGf2LVLatZMWrLENosePdp1Rf5GDyAAACh2pUtLY8fa9csvS99957YevyMAAgCAoLjwQqlbN7u%2B6y5pv40BEGQEQAAAEDTPPWdbwcyfLx3nEeQoQgRAAAAQNOXLSy%2B%2BaNdPPy0tWOC2Hr8iAAIIqtTUVCUnJyslJcV1KQAc6dxZuvxyac8eqUcPKTfXdUX%2BwypgAE6wChjwtxUrpORkaedO6bXXpO7dXVfkL/QAAgCAoKtZU3riCbvu10/asMFtPX5DAAQAAE48%2BKDUsqW0ZYvUu7fravyFAAgAAJyIjpbGjJGioqR33pH%2B/W/XFfkHARAAADiTkiL17GnX994r7d7tth6/IAACAACnnnxSqlJF%2Bv139gYMFgIgAABwKiFB%2Bvvf7XroUGnRIrf1%2BAEBEAAAOHf99dJll9negPfdJ7FJXfEiAAIAAOeioqRRo6RAQPr6a%2Bm991xXFNkIgACCipNAABxO3brSoEF23bu3lJnptp5IxkkgAJzgJBAAh5KdLZ1%2Bus0DfPDBgrmBKFr0AAIAgJARCEipqXY9apQ0Z47beiIVARAAAISU9u2lzp2l3FxbEJKb67qiyEMABAAAIWfECKlcOenHH6W0NNfVRB4CIAAACDnVqklDhtj1gAHS5s1Oy4k4BEAAABCSHnxQatJE2rixYHUwigYBEAAAhKRSpQoWhIwZI82e7baeSEIABAAAIev886VbbrGTQe6/nwUhRYUACAAAQtpzz7EgpKgRAAEEFSeBADheVatKgwfb9cCB0tatbuuJBJwEAsAJTgIBcDz27JGaN5cWLJAeekgaOdJ1ReGNHkAAABDyYmKkF1%2B061GjpF9/dVtPuCMAAgCAsHDJJdI110g5ObZFDGOYJ44ACAAAwsaIEVJsrDRlivTPf7quJnwRAAEAQNioVUvq39%2Bu%2B/SRdu50Wk7YIgACAICwMmCAlJQkrVwpDRvmuprwRAAEAABhpUwZ6fnn7frZZ6UVK9zWE44IgAAAIOzccIN03nnS7t0FQ8I4dgRAAAAQdqKipL//XSpRQho/Xpoxw3VF4YUACCCoOAkEQFFp0UK66y67fugh2x4Gx4aTQAA4wUkgAIrCH39I9etLmZnS2LHSnXe6rig80AMIAADCVsWKBecEDxokZWW5rSdcEAABAEBY69lTatBA2rBBeuop19WEBwIgAAAIazEx0vDhdj1ypLR0qdt6wkG06wIAAABO1hVXSNdfL7VtK1Wr5rqa0EcPIIDjsnfvXg0YMEDNmjVT2bJlVbVqVXXp0kVr1651XRoAH4uKkj74QOrbVwoEXFcT%2BgiAAI7Lzp079csvv%2Bivf/2rfvnlF02YMEGLFi3SVVdd5bo0AMAxYhsYACdt1qxZatOmjVasWKEaNWoc03PYBgYA3KEHEMBJy8zMVFRUlE455RTXpZvCv7QAAAM3SURBVAAAjgGLQACclN27d2vgwIG65ZZbjtiTl52drezs7Pz7WWzWBQDO0AMI4IjeeecdlStXLr/N2O/Azb179%2Bqmm25Sbm6uRo8efcTPM3ToUCUkJOS3pKSk4i4dAHAYzAEEcETbtm3T%2BvXr8%2B9Xq1ZNpUuX1t69e9W5c2ctXbpU33zzjSpUqHDEz3OoHsCkpCTmAAKAAwwBAziiuLg4xcXFFXosL/wtXrxYU6ZMOWr4k6RAIKAAezMAQEggAAI4Lvv27dP111%2BvX375RZ988olycnKUkZEhSSpfvrxiYmIcVwgAOBqGgAEcl%2BXLl6t27dqH/LMpU6boggsuOKbPwzYwAOAOPYAAjkutWrXE%2B0YACG%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%2BAAAAAElFTkSuQmCC'}