Genus 2 curve data - 7200.c.43200.1

Formats: - HTML - YAML - JSON - 2026-05-28T21:07:23.205369

• 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': '1467905062211340893', 'abs_disc': 43200, 'analytic_rank': 1, 'analytic_rank_proved': True, 'analytic_sha': 1, 'aut_grp_id': '[4,2]', 'aut_grp_label': '4.2', 'aut_grp_tex': 'C_2^2', 'bad_lfactors': '[[2,[1,1]],[3,[1,0,-1]],[5,[1,2,1]]]', 'bad_primes': [2, 3, 5], 'class': '7200.c', 'cond': 7200, 'disc_sign': 1, 'end_alg': 'Q x Q', 'eqn': '[[-12,0,9,0,-3],[0,1,0,1]]', 'g2_inv': "['30772479098000/27','425947988390/9','7838798656/3']", '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': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['4360','4024','5725876','172800']", 'igusa_inv': "['2180','197346','23751936','3208444191','43200']", 'is_gl2_type': True, 'is_simple_base': False, 'is_simple_geom': False, 'label': '7200.c.43200.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.69652329917089518956999042716339730527731018299564', 'prec': 173}, 'locally_solvable': True, 'modell_images': ['2.90.5', '3.720.4'], 'mw_rank': 1, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 6, 'num_rat_wpts': 0, 'real_geom_end_alg': 'R x R', 'real_period': {'__RealLiteral__': 0, 'data': '9.3742501358747564497812063193', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.445810569854', 'prec': 47}, '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': 6, 'torsion_order': 6, 'torsion_subgroup': '[6]', 'two_selmer_rank': 2, 'two_torsion_field': ['8.0.12960000.1', [1, 0, -3, 0, 8, 0, -3, 0, 1], [8, 3], 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], ['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': '7200.c.43200.1', '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': [[[-11], [-17]], [[-71], [-1837]]], 'spl_facs_condnorms': [240, 30], 'spl_facs_labels': ['240.a4', '30.a8'], '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': '7200.c.43200.1', 'mw_gens': [[[[2, 1], [1, 1], [0, 1]], [[4, 1], [0, 1], [0, 1], [0, 1]]], [[[1, 1], [0, 1], [0, 1]], [[0, 1], [0, 1], [0, 1], [-1, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.44581056985369160581501709736115', 'prec': 113}], 'mw_invs': [0, 6], 'num_rat_pts': 6, 'rat_pts': [[-2, 4, 1], [-2, 6, 1], [1, -1, 0], [1, 0, 0], [2, -6, 1], [2, -4, 1]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 2757
    {'conductor': 7200, 'lmfdb_label': '7200.c.43200.1', 'modell_image': '2.90.5', 'prime': 2}
  • id: 2758
    {'conductor': 7200, 'lmfdb_label': '7200.c.43200.1', 'modell_image': '3.720.4', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  local_root_number	numeric
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 137607
    {'label': '7200.c.43200.1', 'local_root_number': 1, 'p': 2, 'tamagawa_number': 2}
  • id: 137608
    {'cluster_label': 'cc2_1~2c2_1~2c2_1~2_0', 'label': '7200.c.43200.1', 'local_root_number': -1, 'p': 3, 'tamagawa_number': 3}
  • id: 137609
    {'cluster_label': 'c2c2_1~2c2_1~2_0', 'label': '7200.c.43200.1', 'local_root_number': 1, 'p': 5, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '7200.c.43200.1', 'plot': 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7GZzo4MGD%2Bumnn/K/3rx5s9LS0lSlShXVrl1bgwYN0siRI1W/fn3Vr19fI0eOVIUKFdSzZ0%2BLqQEgdLAMDM7K1q3mlPC%2BfWbHkOnTTTkEStLy5ct17bXXnnR/7969NXnyZPl8Pj311FOaMGGCDhw4oBYtWig1NVWNGjUq8nuwDAyASEYBxFlbvlxq395sF/f889Kjj9pOBAQeBRBAJOMaQJy1tm2ll14yx489Ji1caDUOAAA4SxRAnJN%2B/aT77jPrAvbsKf3nP7YTAQCAoqIA4py4XGYUsF07KSvL7Biyc6ftVAAAoCgogDhnZctKb70lNWggbd8udekiHTxoOxUAADgTCiCKpXJl6d13papVpS%2B/lJKTpdxc26kAAMDpUABRbPXqSYsWSeXLmzLYvz97BgMAEMoogAiIli2lmTPNtYETJ0ojRthOBAAAToUCiIC5%2BWZp3Dhz/OST0oQJdvMAAAD/KIAIqPvvl/72N3Pcr5%2BZJAIAAEILBbAEZGebm1M8/bR0773mOsDbbpM%2B%2BMB2IqDoUlNT5fF4lJSUZDsKAAQNW8GVgBkzzMSIrl2lHj3MNmply9pOFVx5eWaB6DfflKKjpSVLpDZtbKcCio6t4ABEMkYAS8CHH0perzRlinTjjVJcnHTXXdLSpZG7ZErp0tK0aebnPXxY%2BtOfpM8%2Bs50KAABIjACWiGPHpNWrzWjY229Lu3cXPFatmnTrrWZk8OqrpVIRVsmPl79lyyS3W/r4Y%2Bnyy22nAs6MEUAAkYwCWMLy8qSVK6U5c6R33pH27St4LCFB6t7dLKbcvLlZUiUSHDwo3XCDKcGVK0sffST94Q%2B2UwGnRwEEEMkogBbl5poRsTlzpLlzzWni4%2BrWNUXwL3%2BRGjUK/zKYmSl17Ch9%2BqkpgUuXMhKI0EYBBBDJKIAhIjvbzJadPVtauFDKyip4rGFDUwSTk82uG%2BEqI0O6/npTAt1u8/O2aGE7FeAfBRBAJKMAhqCsLGnxYlMG33tPOnq04LEWLczs2u7dpfh4exnPVWammRiyerUUE2O2jrv6atupgJNRAAFEMgpgiPv9d2nePLPN2iefmAklkpks0q6dKYN//rMUTv8%2BHTwodeliJoZER5ufr2NH26mAwiiAACIZBTCMpKebmcQzZxZeUqV8ealzZ7Po8g03SFFR9jIW1eHDZvbze%2B%2BZNRFnzJC6dbOdCihAAQQQySiAYernn6VZs0xx%2BuGHgvurVDFF6vbbpSuvDO3JI0ePSr16mVLrckkvv2x2EFFenvSf/5hZMk2aSOXK2Y4KB6IAAohkFMAw5/NJX31liuCsWdKuXQWPXXCBKYK9ekkXX2wt4mnl5ZldUiZMMF8v7PyqOqWNkGv7dnNH1arS4MFSSkpot1lEHAoggEhGAYwgeXnmurrp080agwcPFjzWvLkpgsnJplOFEp9P%2BvvfpQPPjNM4DfT/pCFDpNGjSzYYHI0CCCCSUQAj1KFD0oIFpgx%2B8IEph5JUpozZmeOOO8yfIXN2NTtbh6vWUvTBff4fj4qStm0z%2B%2BgBJYACCCCSRdjGYziuQgWzduC770q//iqNHWt238jNNcXwllvMziMDBkjr1plROKtWrDh1%2BZPMBYMLF5ZcHgAAIhgF0AHi4qQHH5S%2B/FLasEF69FFT/vbvl1JTzenhRo3MGdadOy2FPHIkMM8Biik1NVUej0dJSUm2owBA0HAK2KHy8syevFOmmHX4jnerUqXMbh133mnW6iuxU8Tp6VLt2lJOzqmfs3691KxZCQWC03EKGEAkYwTQoUqXNosvz5xputfEiVLr1mah6ffeMzuNJCRIDzxgZhkHXXy8WcjwFD7RtXrug2b2T1UDABABGAFEIT/%2BKE2eLE2daq4dPK5ZM6lPH7PzSJUqQXrzQ4fMNOVFiwrd/UtcK7XYvUD7VE09e0qvvWZ2EAGCiRFAAJGMAgi/8vKkpUulN94wk0aO70dcrpzZeq5vX%2Bnaa80p44Bbv96UwNxc6brrpDZt9PLLZjQyN1e6/HJz2joxMQjvDfwXBRBAJKMA4ox%2B%2B80sNP3669LXXxfcX7euKYJ33inVrBn8HMuXm%2B3jfvtNql5devtt6eqrg/%2B%2BcCYKIIBIRgFEkfl8Zibx66%2BbawczMsz9pUqZNQXvuku68Uaz1mCwbNkide1qdoorU8Ysb3P//WwSgsCjAAKIZBRAnJNDh8wI3KuvSqtXF9yfkGCuFezb12xFFwxZWaZszp5tvr7zTrOPcPnywXk/OBMFEEAkowCi2H74wUzMmDJF2vfftZxdLqlDB%2Bmee6TOnaWyZQP7nj6f9M9/SkOHmpnLV1whzZ3LdYEIHAoggEhGAUTAHD0qzZ9vlpT5%2BOOC%2B%2BPjpb/%2BVbr7bnPdYCB99JGZOPzbb1K1atJbb0nXXBPY94AzUQABRDIKIILi55/NqOCkSdLu3eY%2Bl8tM6r3nHrPIdKBGBbdskW6%2BWUpL47pABA4FEEAkowAiqHJyzDIyEyeaZWWOi4sz1wredZd04YUnvGDfPmnCBGnxYrPmS7t2ps3Vrn3a9zl0yIwwzpxpvr7rLrPNXVRU4H8mOAMFEEAkowCixPzyixkVfOONglFBSWrf3owK3nTJRkV1vFbatavwC2NjpfffN1uVnMb/Xhd4zTXSO%2B9I558fhB8GEY8CCCCSUQBR4nJypIULC0YFj/8GflbmSjXPXeP/RbVrmwZZuvQZv//770s9ekiZmVL9%2BubrevUC%2BAPAESiAACIZBRBWbd5s1hX8dOI3%2BnjvZad/8uLFZsHBItiwwTx12zZzuvmDD6QmTQIQGI5BAQQQyYKxkRdQZHXrSs88I3348s9nfO6xTT8V%2Bfs2aiStXWtK3%2B7dZtu6r74qTlIAACIHBRAhoXRiwhmf0//ZBKWkSN98U7TvWaOG2T6uZUvpwAGpY0cz4gicTmpqqjwej5KSkmxHAYCg4RQwQkejRtK33/p96DfX%2BUrw/aqjKidJathQ6t7d7A186aWnX/LF6y0YAfzDH6TPPgvudnWIDJwCBhDJGAFE6Jg4UapU6eT7y5RRzMyJmv5mOd10k1na5dtvpSefNEXwkkukRx4xi09nZ5/8crfbTDqpXFlav16aPj34PwoAAKGMEUCElu%2B/l/7xD2nRIrMOYPv2pt01b57/lN9/NzuOvPWW2Qnk6NGCl5cvL7VqZW7NmkkNGpidSFwuacgQafJkqVcvaerUkv/REF4YAQQQySiACGsZGWaG77vvmj/T08/8mlGjpMceC342hDcKIIBIRgFExPD5pB9%2BkFatkj7/XPr6a7Ml3f795vH4eOn226Vnn2WHEJwZBRBAJKMAIuLl5ZlyyMQPnA0KIIBIxj%2BJiHhF2DwEAABHYRYwAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEgBOkpqbK4/EoKSnJdhQACBp2AgEAP9gJBEAkYwQQAADAYSiAAAAADkMBBAAAcBgKIAAAgMNQAAEAAByGAggAAOAwFEAAAACHoQACAAA4DAUQAADAYSiAAAAADkMBBAAAcBgKIAAAgMOUsR0gnPl8PmVmZtqOASAAsrOzlZ2dnf/18b/bGRkZtiIBCLKYmBi5XC7bMaxw%2BXw%2Bn%2B0Q4SojI0Nut9t2DAAAcA68Xq9iY2Ntx7CCAlgMpxsBzMjIUGJiorZv337Ov1xJSUlat27dOecL59fz%2BRX/9XyGZ/f6/x0B3LVrl5o3b67vvvtONWvWDPr7R9rrA/H7V9wM4f56/g4H//Nz8gggp4CLweVynfEvZWxs7Dn/xS1dunSx/sMZ7q%2BX%2BPwC8X%2BmfIbF%2BwxjYmL4/IqhOL9/gcgQ7q%2BX%2BDts%2B3cwUjEJJIT179/f0a8vLtv5bb8%2BEGz/DLZfX1y289t%2BfSDY/hlsv764bOe3/XqcGqeAg%2BT49YFOvr6gOPj8io/PsHh27NiRf/qoVq1atuOEHX7/io/PsHj4/E6v9PDhw4fbDhGpSpcurbZt26pMGc60nws%2Bv%2BLjMzx32dnZGjNmjFJSUlSxYkXbccISv3/Fx2dYPHx%2Bp8YIIAD4wegBgEjGNYAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCGATDhw/XJZdcoooVK6py5cpq3769PvvsM9uxwkZOTo6GDh2qxo0bq2LFikpISNAdd9yhnTt32o4WNubOnauOHTuqatWqcrlcSktLsx0JDrJy5Up17txZCQkJcrlcmj9/vu1IYWXUqFFKSkpSTEyMqlevrq5du2rjxo22Y4WNl19%2BWZdddln%2BAtCtWrXS%2B%2B%2B/bztWyKEABsHFF1%2BscePG6ZtvvtHq1at1wQUXqEOHDtq7d6/taGHh0KFDWr9%2BvZ544gmtX79ec%2BfO1Y8//qguXbrYjhY2srKydOWVV%2Bq5556zHQUOlJWVpSZNmmjcuHG2o4SlFStWqH///lq7dq2WLl2q3NxcdejQQVlZWbajhYVatWrpueee0xdffKEvvvhCf/zjH3XTTTfp22%2B/tR0tpLAMTAk4vpzERx99pHbt2tmOE5bWrVun5s2ba%2BvWrapdu7btOGFjy5Ytqlu3rr766is1bdrUdpywwjIwgeFyuTRv3jx17drVdpSwtXfvXlWvXl0rVqxQmzZtbMcJS1WqVNGYMWPUt29f21FCBisjBtnRo0c1ceJEud1uNWnSxHacsOX1euVyuXTeeefZjgIAJcrr9UoyJQZnJy8vT2%2B99ZaysrLUqlUr23FCCgUwSBYvXqzk5GQdOnRINWrU0NKlS1W1alXbscLSkSNH9Nhjj6lnz56MxABwFJ/Pp4ceekhXXXWVGjVqZDtO2Pjmm2/UqlUrHTlyRJUqVdK8efPk8XhsxwopXANYTDNmzFClSpXyb6tWrZIkXXvttUpLS9OaNWt0/fXXq3v37tqzZ4/ltKHpVJ%2BhZCaEJCcn69ixYxo/frzFlKHrdJ8fzl5qaqo8Ho%2BSkpJsRwE0YMAAff3115o1a5btKGGlQYMGSktL09q1a9WvXz/17t1b3333ne1YIYVrAIspMzNTu3fvzv%2B6Zs2aio6OPul59evXV58%2BfZSSklKS8cLCqT7DnJwcde/eXb/88os%2B%2BeQTnX/%2B%2BRZThq7T/Q5yDeC54xrAwOAawHM3cOBAzZ8/XytXrlTdunVtxwlr7du3V7169TRhwgTbUUIGp4CLKSYmRjExMWd8ns/nU3Z2dgkkCj/%2BPsPj5W/Tpk1atmwZ5e80ivo7CCA8%2BHw%2BDRw4UPPmzdPy5cspfwHAv8EnowAGWFZWlp599ll16dJFNWrU0G%2B//abx48drx44d6tatm%2B14YSE3N1e33nqr1q9fr8WLFysvL0/p6emSzEXQUVFRlhOGvv3792vbtm35ayceX0MsPj5e8fHxNqPBAQ4ePKiffvop/%2BvNmzcrLS1NVapUYRZ/EfTv318zZ87UggULFBMTk//fP7fb7fcMEwobNmyYbrjhBiUmJiozM1OzZ8/W8uXLtWTJEtvRQosPAXX48GHfzTff7EtISPBFRUX5atSo4evSpYvv888/tx0tbGzevNknye9t2bJltuOFhUmTJvn9/J588knb0cKG1%2Bv1SfJ5vV7bUcLOsmXL/P7%2B9e7d23a0sHCq//5NmjTJdrSw0KdPH1%2BdOnV8UVFRvmrVqvnatWvn%2B/DDD23HCjlcAwgAfnANIIBIxixgAAAAh6EAAgAAOAyTQAAACICdO6Xt26VSpaSqVaXERKkM/8oiRPGrCQBAMezbJ3XvLi1bVvj%2BqCjJ45GaN5euukr64x%2BlmjXtZAT%2BF5NAAMAPJoGgqB55RPrnP6XSpaVataRjx6Q9eyR/y841bCh16iR16SK1bGlGC/3y%2BaQtW8wQYmJiMOPDobgGEACAYvj9d/Pn3XebzrZtm3TokPTLL9I775iCeMUVksslffut9Pzz0pVXmrI4cKC0erUpjflefVWqX1%2B68EKpdm2paVNp3jwbPxoiGCOAAOAHI4Aoqvffl268USpb1pS55s39P%2B%2B336QPP5QWLZLefVfKyCh4LDFR6tlTGnx4pOJeevzkF7tc0tSp0u23B%2BeHgONQAAHADwogisrnk7p1M6N9cXHSqlVmAO90jh6VPvpIevNNM7iXkSFV1n79qpqK1hH/L0pIkLZuZWYJAoJTwAAAFIPLJb3xhtSkibR7t3TttdIPP5z%2BNVFRZtRw8mTzmrfekoY3XXDq8ieZacYrVwY0O5yLAggAJ0hNTZXH41FSUpLtKAgjsbHSBx%2BYWb%2B//ipnu36nAAAVvElEQVRdfbX0%2BedFe2358tKtt0oP/DXzjM/d8/OZnwMUBaeAAcAPTgHjXOzda0b2vvhCio6Wpk%2BX/vznIr54zRozO%2BQUclVaF2qzGl6fqLvvljp3NtcdAueCEUAAAAKkWjWzHuANN0iHD0u33CKNHGmuEzyj1q2l04w8/7vazdquRC1ZYr5vYqI0bJiZbQycLQogAAABVKmStHChNGCA%2Bfrxx80M30OHivDit9%2BWLrnk5PtbtdI1Gydq0yZp6FAz2WT3bmnUKKlePalDBzMJJScnoD8KIhingAHAD04BIxBeecWs9ZebW7Cc3wUXnOFFubnS/PlmmnCZMuZcb4cOZrbJf%2BXkmJI5caJZWua4uDjpr381axJeeGFQfiRECAogAPhBAUSgrFxpJnns3Sudf740Z47Url3gvv/mzWbt6EmTpPT0gvuvu84UwZtuMrOOgRNRAAHADwogAmnbNjMZ5MsvzfZvo0dLDz1UaFCv2HJyzCLTEyZIS5cWXHdYvbrUu7cpg2danxDOQQEEAD8ogAi0w4elfv2kKVPM18nJ0muvSRUrBv69Nm8233vSJGnXroL727Y1RfDPfzbLz8C5KIAA4AcFEMHg80mpqdLgweZSvyZNzHWBdesG5/1ycsy2c6%2B%2BKi1ZUrDncOXKZle5u%2B6SLrssOO%2BN0EYBBAA/KIAIplWrzHWBe/ZIVaqYLeECeV2gP9u3mx1L3njDnJI%2BLilJ6tvXjEi63cHNgNBBAQQAPyiACLYdO8yp2HXrpNKlpRdeMDOGA3ldoD95eeYawddekxYsMCORklm4uls3qU8fqU2b4OeAXRRAAPCDAoiScOSIdO%2B90tSp5uu%2BfaXUZw6o3Iw3pI8/NsvAdOok3XZbUC4W3LNHmjZNev116fvvC%2B6vV88sJ9O7t1SrVsDfFiGAAggAflAAUVJ8PunFF6UhQ6SGx77W8rLXqUrOnsJPuugis8VIkNqYzyetXWtOD8%2BZI2X%2Bd8thl8ssJ/PXv5rlZKKjg/L2sIACCAB%2BUABR0pa871O9Tpeo/rEf/T%2BhfXtz7jbIsrLMhiSTJkkrVhTc73ab6wTvvFNq0YJTxOGOAggAflAAUeKWLjU7fpzOxo3SxReXTB5JP/9slq2ZMqXwxJEGDaQ77pB69TJ7EiP8sBcwAACh4LvvzvycEy/UKwH16klPP23WFfzoI1P4KlQwPfTxx6U6dczA5NSp0sGDJRoNxUQBBIATpKamyuPxKCkpyXYUOE21amd%2BTtWqwc/hR6lSZpmaqVPNdnNvvCFdc425dvDjj81kkbg4UxA//NDMNEZo4xQwAPjBKWCUuKwsKSFBysjw%2B/Du2It0/r4fVaZs6Fx8t2WLmUU8bZq0aVPB/TVqSD17msWmmzThesFQRAEEAD8ogLDijTfM9hz/809ztqJ0kxYoqvP1mjMn9Gbj%2BnzSZ5%2BZEcI5c6T9%2Bwsea9jQjAz%2B5S9S7dr2MqIwCiAA%2BEEBhDVLlkjPP2%2Bm4JYqJd14o1ZeNUwdn2ypI0ekq66SFi2SzjvPdlD/jh6V3ntPmj7d5Dx6tOCxa64xSxreeqvZjg72UAABwA8KIKzLzTUFsJS5XH/VKqlzZ8nrlZo2lT74QKpe3XLGM/j9d7OkzPTphZeUKVtWuvFGUwY7dQq9EU0noAACgB8UQISi//xH6thR2r3bLMXy0Ufhs1PH9u3SzJnSjBnSN98U3B8TI918s7lmsF07s/kJgo8CCAB%2BUAARqjZtMkVp%2B3apbl3pk0%2BkCy6wnersfPONKYOzZklbtxbcX62a2Y%2B4Z0%2BpVav8wU8EAQUQAPygACKUbdsm/fGPZqHmxESzS1y9erZTnb1jx6RPPzVl8M03pX37Ch6rXVvq0cPsPtKsGTOJA40CCAB%2BUAAR6nbuNCVw40ZzGnjZMrNlcLjKyTFrCs6aJc2bV7AfsWQ2P0lONrdLL7WXMZJQAAHADwogwkF6uimB339vSuCKFdKFF9pOVXyHD5uZxLNnS4sXS0eOFDzWuLEpgj16hOeoZ6igAAKAHxRAhIvdu6VrrzUlsHZtaeVKs0VbpMjMlBYsMGXwww/NSOFxl19uymC3bpH1M5cECiAA%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%2BbN0%2BzZs7V69WodPHhQnTp1Uh4bogL5XC5p8mSpYUNT/rp3Z1KIk7AXMICwtGXLFtWtW1dfffWVmjZtmn%2B/1%2BtVtWrVNG3aNPXo0UOStHPnTiUmJuq9995Tx44di/T92QsYTrFpk3TFFVJGhjRokPTii7YToSQwAgggonz55ZfKyclRhw4d8u9LSEhQo0aNtOY0FzplZ2crIyOj0A1wgvr1palTzfHYsdJbb9nNg5JBAQQQUdLT0xUVFaXKlSsXuj8uLk7p6emnfN2oUaPkdrvzb4mJicGOCoSMm26Shg41x337slOIE1AAAYSsGTNmqFKlSvm3VatWnfP38vl8crlcp3w8JSVFXq83/7Z9%2B/Zzfi8gHD3zjNSmjZSZaRaJPnzYdiIEUxnbAQDgVLp06aIWLVrkf12zZs0zviY%2BPl5Hjx7VgQMHCo0C7tmzR61btz7l68qVK6dy5coVLzAQxsqUkWbNkpo2lb7%2B2iwS/cortlMhWBgBBBCyYmJidNFFF%2BXfoqOjz/iayy%2B/XGXLltXSpUvz79u1a5c2bNhw2gIIQEpIkKZPN8cTJnA9YCSjAAIIK/v371daWpq%2B%2B%2B47SdLGjRuVlpaWf32f2%2B1W37599fDDD%2Bvjjz/WV199pdtvv12NGzdW%2B/btbUYHwkKHDlJKijm%2B%2B25p82a7eRAcFEAAYWXhwoVq1qyZ/vSnP0mSkpOT1axZM71ywrmqF198UV27dlX37t115ZVXqkKFClq0aJFKly5tKzYQVp56SmrVSvJ6pdtuk3JzbSdCoLEOIAD4wTqAcLotW6QmTcz6gE88IT39tO1ECCRGAAEAwEkuuMBcByhJzz4r/fvfVuMgwCiAAADAr%2BRkqVcv6dgx6fbbzWggIgMFEAAAnNK4cWY0cMsW6cEHbadBoFAAAQDAKcXGmq3iXC5p8mRp/nzbiRAIFEAAAHBaV18tDRliju%2B5R9qzx24eFB8FEAAAnNHTT0uNG0t790r9%2BkmsIRLeKIAAAOCMypUzp4LLlJHmzjXbxiF8UQABAECRNG1q1gSUpIEDpf9uwIMwRAEEAABFlpIiNWsm7d/PqeBwRgEEgBOkpqbK4/EoKSnJdhQgJJUta2YDlyljZgS/%2BabtRDgXbAUHAH6wFRxwesOHmz2Dq1WTvvtOqlrVdiKcDUYAAQDAWRs2TGrUyMwKHjzYdhqcLQogAAA4a1FR0uuvS6VKSdOnS0uW2E6Es0EBBAAA56R5c%2BmBB8zxffdJWVl286DoKIAAAOCcjRgh1akjbd0qPfmk7TQoKgogAAA4Z5UqSePHm%2BOxY6WvvrKbB0VDAQQAAMVy441S9%2B5SXp45FZyXZzsRzoQCCAAAim3sWCk2Vvr8c2nCBNtpcCYUQAAAUGw1akjPPmuOhw1jm7hQRwEEAAAB0a%2BfdPnlktcrDRliOw1OhwIIAAAConRp6eWXJZfLrA24cqXtRDgVCiAAAAiYpCTpnnvMcf/%2BUm6u3TzwjwIIAAACauRI6fzzpQ0bpHHjbKeBPxRAAAAQUFWqSKNGmeMnn5R277abByejAALACVJTU%2BXxeJSUlGQ7ChDW%2BvQxE0IyMqSUFNtp8L9cPp/PZzsEAISajIwMud1ueb1excbG2o4DhKVPP5VatzbHn39urg9EaGAEEAAABEWrVlKvXub4wQclhpxCBwUQAAAEzXPPSRUrmtHAmTNtp8FxFEAAABA0CQlmZxBJeuwx6dAhu3lgUAABAEBQDR4s1akj7dghjRljOw0kCiAAAAiy6Ghp9GhzPHq09OuvdvOAAggAAEpAt25mRvChQ9Lf/mY7DSiAAAAg6Fwu6YUXzPGUKVJamt08TkcBBAAAJaJFCyk52SwH88gjLAtjEwUQAACUmFGjpKgo6eOPpSVLbKdxLgogAAAoMRdcIA0caI4ffVTKy7Max7EogAAAoEQ9/rh03nnShg3S1Km20zgTBRAAAJSoypVNCZSkv/9dOnzYbh4nogACAIASN2CAVLu2WRx63DjbaZyHAggAAEpc%2BfLSU0%2BZ41GjpN9/t5vHaSiAAHCC1NRUeTweJSUl2Y4CRLxevaSGDaUDBwp2CkHJcPl8rMIDAP8rIyNDbrdbXq9XsbGxtuMAEWvBAqlrV6lCBemnn6QaNWwncgZGAAEAgDVdukgtW5ot4p591nYa56AAAgAAa1wuaeRIczxxorR5s908TkEBBAAAVl17rdS%2BvZSTIz39tO00zkABBAAA1h0//Tt1qrRxo90sTkABBAAA1jVvLnXuLB07Jg0fbjtN5KMAAgCAkDBihPlzzhyzTRyChwIIAABCQpMm0q23Sj6f9OSTttNENgogAAAIGcOHm5nBc%2BdKaWm200QuCiAAAAgZDRtKPXqY4%2BNbxSHwKIAAACCk/P3vZhRw/nxGAYOFAggAAELKpZdKycnmmFHA4KAAAgCAkPPEEwWjgP/5j%2B00kYcCCAAAQs6ll0rdu5vj48vDIHAogABwgtTUVHk8HiUlJdmOAjje8VHAd96Rvv3WdprI4vL5fD7bIQAg1GRkZMjtdsvr9So2NtZ2HMCxbr3VFMDkZGnWLNtpIgcjgAAAIGT97W/mzzfflH780W6WSEIBBAAAIatp04I9gkeNsp0mclAAAQBASHv8cfPn9OnS1q12s0QKCiAAAAhpLVpI7dpJubnSmDG200QGCiAAAAh5x0cBX39dSk%2B3myUSUAABAEDIa9tWatlSOnJEGjvWdprwRwEEAAAhz%2BWSUlLM8fjx0u%2B/280T7iiAAAAgLHTqJDVsKGVmSi%2B/bDtNeKMAAgCAsFCqlDR0qDkeO1Y6fNhunnBGAQQQVnJycjR06FA1btxYFStWVEJCgu644w7t3Lmz0PMOHDigXr16ye12y%2B12q1evXvqdc0ZA2EtOlmrXlvbskaZMsZ0mfFEAAYSVQ4cOaf369XriiSe0fv16zZ07Vz/%2B%2BKO6dOlS6Hk9e/ZUWlqalixZoiVLligtLU29evWylBpAoJQtKz38sDn%2Bxz%2BkvDy7ecIVewEDCHvr1q1T8%2BbNtXXrVtWuXVvff/%2B9PB6P1q5dqxYtWkiS1q5dq1atWumHH35QgwYNzvg92QsYCF1ZWWYUcP9%2Bs0Vct262E4UfRgABhD2v1yuXy6XzzjtPkvTpp5/K7Xbnlz9Jatmypdxut9asWWMrJoAAqVhRGjDAHI8ZIzGUdfYogADC2pEjR/TYY4%2BpZ8%2Be%2BSN16enpql69%2BknPrV69utJPsYJsdna2MjIyCt0AhK4BA6Ty5aV166SVK22nCT8UQAAhbcaMGapUqVL%2BbdWqVfmP5eTkKDk5WceOHdP48eMLvc7lcp30vXw%2Bn9/7JWnUqFH5E0bcbrcSExMD%2B4MACKhq1aQ77zTHbA939iiAAEJaly5dlJaWln%2B74oorJJny1717d23evFlLly4tdJ1efHy8du/efdL32rt3r%2BLi4vy%2BT0pKirxeb/5t%2B/btwfmBAATMQw%2BZBaLffVf6/nvbacILBRBASIuJidFFF12Uf4uOjs4vf5s2bdJHH32k888/v9BrWrVqJa/Xq88//zz/vs8%2B%2B0xer1etW7f2%2Bz7lypVTbGxsoRuA0Fa/vnTTTeb4hRfsZgk3zAIGEFZyc3N1yy23aP369Vq8eHGhEb0qVaooKipKknTDDTdo586dmjBhgiTpnnvuUZ06dbRo0aIivQ%2BzgIHw8O9/S1ddJZUrJ23dKp1ikB//gxFAAGFlx44dWrhwoXbs2KGmTZuqRo0a%2BbcTZ/jOmDFDjRs3VocOHdShQwdddtllmjZtmsXkAIKhdWupRQspO9vsEYyiYQQQAPxgBBAIH2%2B%2BKfXoIVWtKm3bJkVH204U%2BhgBBAAAYe3PfzYLQ%2B/bJ82YYTtNeKAAAgCAsFamjPTAA%2BZ47FgWhi4KCiAAAAh7d90lVaokffutxIY/Z1bGdgAAAIDicrul//s/szTMKVZ7wgmYBAIAfjAJBEAk4xQwAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAALACVJTU%2BXxeJSUlGQ7CgAEDbOAAcAPZgEDiGSMAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHMbl8/l8tkMAQKjx%2BXzKzMxUTEyMXC6X7TgAEFAUQAAAAIfhFDAAAIDDUAABAAAchgIIAADgMBRAAAAAh6EAAgAAOAwFEAAAwGEogAAAAA5DAQQAAHAYCiAAAIDDUAABAAAchgIIAADgMBRAAAAAh6EAAgAAOAwFEAAAwGEogAAAAA5DAQQAAHAYCiAAAIDDUAABAAAchgIIAADgMBRAAAAAh6EAAgAAOAwFEAAAwGEogAAAAA5DAQQAAHAYCiAAAIDDUAABAAAchgIIAADgMBRAAAAAh6EAAgAAOAwFEAAAwGEogAAAAA5DAQQAAHAYCiAAAIDDUAABAAAchgIIAADgMBRAAAAAh6EAAgAAOAwFEAAAwGEogAAAAA5DAQQAAHAYCiAAAIDDUAABAAAchgIIAADgMBRAAAAAh6EAAgAAOAwFEAAAwGEogAAAAA5DAQQAAHAYCiAAAIDDUAABAAAc5v8BlyagV/HzHskAAAAASUVORK5CYII%3D'}