Genus 2 curve data - 1147.a.35557.2

Formats: - HTML - YAML - JSON - 2025-10-19T21:36:06.895132

• 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': '631810108885965391', 'abs_disc': 35557, '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': '[[31,[1,1,31,31]],[37,[1,-5,31,37]]]', 'bad_primes': [31, 37], 'class': '1147.a', 'cond': 1147, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[0,1,-32,0,6,1],[0,1]]', 'g2_inv': "['8985379753611493376/35557','283731159059005440/35557','10642156427543552/35557']", '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': "['12352','2309104','8338761079','142228']", 'igusa_inv': "['6176','1204440','279006977','68117844088','35557']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '1147.a.35557.2', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.3580802637401832836989249440508629973616432114442666574', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.120.3'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2], 'non_solvable_places': [], 'num_rat_pts': 3, 'num_rat_wpts': 3, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '2.8646421099214662695913995524', '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': 2, 'torsion_order': 4, 'torsion_subgroup': '[2,2]', 'two_selmer_rank': 2, 'two_torsion_field': ['3.3.148.1', [1, -3, -1, 1], [3, 2], 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': [['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': '1147.a.35557.2', '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': '1147.a.35557.2', 'mw_gens': [[[[0, 1], [1, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]], [[[0, 1], [4, 1], [1, 1]], [[0, 1], [-1, 2], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [2, 2], 'num_rat_pts': 3, 'rat_pts': [[-4, 2, 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

  
  {'conductor': 1147, 'lmfdb_label': '1147.a.35557.2', 'modell_image': '2.120.3', 'prime': 2}
• g2c_tamagawa •

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

  
  
  • id: 6439
    {'cluster_label': 'c3c2_1_0', 'label': '1147.a.35557.2', 'local_root_number': 1, 'p': 31, 'tamagawa_number': 2}
  • id: 6440
    {'cluster_label': 'c3c2_1~2_0', 'label': '1147.a.35557.2', 'local_root_number': 1, 'p': 37, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '1147.a.35557.2', 'plot': 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Hu2cwoYoYNlYABYo6Lr%2BF599VXdcccdkqSDBw9q5MiRmjt3rg4cOKAOHTpo8uTJpzSxg2VgEO7%2B%2BU%2BpUyeWfAllFEAA8DMKIMLZzp1Sq1ZSYaE0cKA0darpRDgdXAMIAAAq5cgRqV8/p/wlJ0sTJphOhNNFAQQAAJUyYYL0/vvSWWc5S7/UqmU6EU4XBRAA/CQ7O1vJyclKSUkxHQXwu08/lR580NmfMEFq2dJsHpwZrgEEAD/jGkCEmz17pP/7P2nDBqlnT%2BmNN6STrI2OEMAIIAAAqJDPJ919t1P%2BmjaVpk%2Bn/IUDCiAAAKjQ5MnS3/4m1aghvf66s%2B4fQh8FEAAAnNDq1dIDDzj7zzwjcXlr%2BKAAAgCA43g8Uu/e0qFDUvfu0n33mU4Ef6IAAgCAcnw%2B6a67pO%2B%2Bk849V3r1Va77CzcUQAAAUM4LL0jz5jnX/b3xhlSvnulE8DcKIAAAKJOfL40c6ew/9xzX/YUrCiAA%2BAkLQSPU/fij1KuXdPiw83XwYNOJECgsBA0AfsZC0AhFpaVS167SBx9IzZpJK1dK/PENX4wAAgAA/fGPTvmrWVN66y3KX7ijAAIAYLn335cef9zZnzqV%2B/zagAIIAIDFNm6UbrvNWfrl97%2BXMjJMJ0IwUAABALDUwYPSzTdLu3Y5s30nTjSdCMFCAQQAwEI%2Bn5SZKa1ZI8XFOdf9RUWZToVgoQACAGCh6dOlV16RqlWTcnKkJk1MJ0IwUQABALDMihXSkCHO/pNPSh07ms2D4KMAAoCfsBA0QsH27c51f4cOSTfdJI0aZToRTGAhaADwMxaCRlVVUuKM9n38sXThhc5IIH9E7cQIIAAAlhg%2B3Cl/MTHS/PmUP5tRAAEAsMBf/iL9%2Bc/O/qxZzggg7EUBBAAgzK1cKQ0a5Ow/8ojUvbvZPDCPAggAQBgrLHQme3i9Unq69OijphOhKqAAAgAQpg4dknr2lLZscU75zprlrPsH8McAAIAw5PNJgwdL//qX5HZLb7/NpA8cQwEEAD9hHUBUJS%2B95Nztw%2BWSXntNatbMdCJUJawDCAB%2BxjqAMG3JEqlTJ%2BnwYWn8eBZ7xvEYAQQAIIxs2OBc93f4sNS3r/SHP5hOhKqIAggAQJjYu9eZ6fvTT9Lll0szZjingIFfogACABAGjhyRMjKkL7%2BUEhKcO33UrGk6FaoqCiAAq3z88ce68cYblZiYKJfLpQULFpR73ufzady4cUpMTFTNmjV1zTXX6KuvvjKUFqi8sWOdmb5RUdKCBVLjxqYToSqjAAKwSnFxsVq3bq1Jkyad8PlnnnlGzz//vCZNmqSVK1cqISFBnTp10t69e4OcFKi8uXOlp55y9mfMkNq0MZsHVR%2BzgAFYy%2BVyaf78%2BerRo4ckZ/QvMTFRw4YN06ifp016vV7Fx8fr6aef1qCj99L6FcwCRjCtWCFdfbVzp49Ro5xZv8CvYQQQAH62YcMGFRYWKi0treyxqKgoXX311crLy6vwfV6vV0VFReU2IBg2bZJ69Dh2m7ejo4DAr6EAAsDPCgsLJUnx8fHlHo%2BPjy977kSysrLkdrvLtqSkpIDmBCSpuFjq3t2512/LltLs2dzmDZXHHxUA%2BAXXL9bN8Pl8xz32v0aPHi2Px1O2bdq0KdARYbmjM34/%2B0xq0EBauFCKiTGdCqEkwnQAAKgqEhISJDkjgY0aNSp7fMeOHceNCv6vqKgoRUVFBTwfcNTYsc4yL5GRztdzzzWdCKGGEUAA%2BFnTpk2VkJCgRYsWlT126NAhLV26VO3atTOYDDhm9uxj1/pNny61b282D0ITI4AArLJv3z795z//Kft%2Bw4YNKigoUGxsrJo0aaJhw4bpqaee0gUXXKALLrhATz31lGrVqqW%2BffsaTA048vKk/v2d/dGjpdtvN5sHoYtlYABYZcmSJbr22muPe7xfv36aOXOmfD6fHnvsMU2dOlW7d%2B9WmzZtlJ2drRYtWlT6Z7AMDAJhwwZnfb%2BdO6WbbpLeeotJHzh9FEAA8DMKIPytqEhq10766ivp0kulZcuk6GjTqRDK%2BP8OAABUYaWl0u9%2B55S/Ro2cGb%2BUP5wpCiAAAFXY8OHSe%2B9JNWs65Y97/MIfKIAA4CfZ2dlKTk5WSkqK6SgIE1OnSi%2B84Oz/9a/S5ZebzYPwwTWAAOBnXAMIf/jnP6XOnZ1TwE88IT30kOlECCeMAAIAUMV8843Us6dT/m69VRozxnQihBsKIAAAVciuXdKNN0p79kipqdKMGdJJ7kQInBYKIAAAVURJidS7t/Ttt1KTJs5t3s46y3QqhCMKIAAAVcR99znX/kVHS%2B%2B8I53kFtTAGaEAAgBQBWRnSy%2B95JzunTtXatXKdCKEMwogAACGffSRM/onSVlZUnq62TwIfxRAAAAM%2Bu9/j834ve026Q9/MJ0INqAAAoCfsBA0TtXevVL37tLu3dIVV0jTpzPjF8HBQtAA4GcsBI3KOHJEuvlmacEC5x6/q1ZJiYmmU8EWjAACAGDAE0845S8y0lnuhfKHYKIAAgAQZO%2B%2BKz36qLM/ZYrUpo3ZPLAPBRAAgCD6z3%2BcyR6SdO%2B90p13ms0DO1EAAQAIkv37nev%2BPB6pXTtpwgTTiWArCiAAAEEyeLD0xRdSw4bSG2841/8BJlAAAQAIgr/8RXr1ValaNem116SzzzadCDajAAIAEGDr1zvX%2B0nSY49J111nNg9AAQQAP2EhaJyI1yvdcotz/V%2BHDtLo0aYTASwEDQB%2Bx0LQ%2BF8jRkjPPSfVr%2B9c/9eokelEACOAAAAEzNKl0vPPO/uvvEL5Q9VBAQQAIAD27XPW%2BPP5pLvvlm680XQi4JgI0wHCms8nffihlJPj/EuQkuL8axAXZzoZACDAHn5Y2rBBatLk2CggUFVwDWCgeL3STTdJ779f/vE6daSFC6WrrzaTC0DAcQ0gVq1ybu925IiUmyt17mw6EVAep4AD5ZFHji9/klRUJPXoIe3dG/xMAICAO3LEWfLlyBGpb1/KH6omCmAgHDwoTZ9e8fN79kizZwcvDwAgaGbNklaulGJinNm/QFVEAQyEH36Qdu8%2B%2BWs%2B/zw4WQAAQXPggPTQQ87%2Bww9LCQlm8wAVoQAGQt26ksv1668BEFZYCBpTpkhbtjgTP4YONZ0GqBiTQAKlUydnBnBFvvhCatkyeHkABA2TQOzk9UpNm0rbtknTpkkDBphOBFSMEcBAeeYZqXbtEz83aBDlDwDCzNy5Tvlr3Fjq1890GuDkKICBcuml0vLlUvfuUvXqzmPnnSdNnCi99JLZbAAAv8vOdr4OGSJFRprNAvwaTgEHQ3Gxc2VwXNyvXxsIIDT5fNLbb0szZqjou%2B/kXrdOnjlzVOd3v%2BPvvQW%2B%2BEJq3dopflu2OPf9Baoy7gQSDNHRzgYgPPl80l13STNnln/81lulRYucm8BSAsPa6687X2%2B4gfKH0MApYAA4U2%2B8cXz5O2rmTOd5hLV33nG%2B9uxpNgdQWRRAADhT06ad2fMIaTt2SGvXOvvc9QOhglPAZ8Dn82kvt3QDrOf79lv97wneol981bffOreBRFhassT52ry5cw0gv%2BrQERMTI5ell2cwCeQMHF3rCwAAhB6b1%2BqkAJ6Byo4AFhUVKSkpSZs2bTqlP2gpKSlauXJlwF4fjPcE69hP5z0cO8fur/eUvPKKatx/f9n3RZKSJG2SVEeS/vxn6fbbg57LxM84nd99qB/70KHSX/4iPfDAQT3/fLxVx35UVf29/9p7bB4B5BTwGXC5XKf0H7c6deqc0uurV68e0NcH8z2BPvbTeQ/HzrH77T2DBzuzfd97r9zDdSTVueEG6fe/lyJO/s9tyB57BU7ldx/qx75nj/M1KamGJLuO/Zeq2rGf7ntswCSQKiwzMzOgrw/me4LxM6rq/17B%2BBkcu%2BGfEREhLVjgrAR86aU6Uq%2BeJOnAE084j/9K%2BQtYLgM/43SE%2BrEXFztfT2e1r1A/9jMRTv/NCkWcAg4Cm%2B8LyrFz7LYduyRt3ry57HRY48aNTccJKht/92lpzgDw1KkHNGhQLauO/Sgbf%2B%2BhjhHAIIiKitKjjz6qqKgo01GCjmPn2G109LhtPH4bf/dHb/t%2B8GCEdcd%2BlI2/91DHCCAA%2BBmjIXa5917nFu8PPSQ98YTpNEDlMAIIAMAZOO885%2Bu//202B3AqKIAA4CfZ2dlKTk5WSkqK6SgIolatnK%2BffWY2B3AqOAUMAH7GKWC77NkjxcZKPp%2B0davUqJHpRMCvYwQwyAYNGiSXy6WJEyeajhIU48aN04UXXqjo6GjVq1dPHTt21IoVK0zHCriSkhKNGjVKLVu2VHR0tBITE3X77bdr69atpqMFxbx589S5c2fVr19fLpdLBQUFpiMhwD7%2B%2BGPdeOONSkxMlMvl0oIFC0xHCoqsrCx16pQil2uNJOmGG17UN998YzhVcLz00ktq1apV2dp/qampev/9903HQiVRAINowYIFWrFihRITE01HCZpmzZpp0qRJWrt2rZYvX65zzz1XaWlp2rlzp%2BloAbV//36tWbNGY8eO1Zo1azRv3jz9%2B9//Vnp6uuloQVFcXKz27dtr/PjxpqMgSIqLi9W6dWtNmjTJdJSgWrp0qTIzMzVokLPcz9atbZWWlqbio4sDhrHGjRtr/PjxWrVqlVatWqXrrrtO3bt311dffWU6GiqBU8BBsmXLFrVp00YffPCBunbtqmHDhmnYsGGmYwXd0VNjH374oTp06GA6TlCtXLlSV1xxhb7//ns1adLEdJyg2Lhxo5o2barPPvtMl1xyiek4QWP7KWCXy6X58%2BerR48epqMEzddfSxdfLEVE%2BHT4cLyWLn1LV111lelYQRcbG6tnn31W/fv3Nx0Fv4IRwCA4cuSIMjIyNHLkSF188cWm4xhz6NAhTZs2TW63W61btzYdJ%2Bg8Ho9cLpfq1q1rOgoAP0tOllJSpMOHXZLuUGxsrOlIQVVaWqqcnBwVFxcrNTXVdBxUAvcCDoKnn35aERERGjp0qOkoRrz77ru65ZZbtH//fjVq1EiLFi1S/fr1TccKqoMHD%2BrBBx9U3759rRwRAmwwaJBPK1e6FBX1gC68MMF0nKBYu3atUlNTdfDgQdWuXVvz589XcnKy6VioBEYA/WzOnDmqXbt22bZ06VK98MILmjlzplwul%2Bl4AfXLY1%2B2bJkk6dprr1VBQYHy8vLUpUsX9e7dWzt27DCc1r8qOnbJmRByyy236MiRI5o8ebLBlIFxsmMHbPLpp/erWrUf5fUm6PXXTacJjubNm6ugoED5%2Bfm655571K9fP3399demY6ESuAbQz/bu3avt27eXff/mm2/qoYceUrVqx7p2aWmpqlWrpqSkJG3cuNFAysD45bGfffbZqlmz5nGvu%2BCCC3TXXXdp9OjRwYwXUBUde0lJiXr37q3vvvtOH330keLi4gymDIyT/d65BpBrAG0xZMgQLViwQH36fK7nnotV8%2BbSl19KEZadZ%2BvYsaN%2B85vfaOrUqaaj4FdY9kcz8GJiYhQTE1P2/cCBA3XjjTeWe03nzp2VkZGhO%2B%2B8M9jxAuqXx14Rn88nr9cbhETBc6JjP1r%2Bvv32Wy1evDgsy59U%2Bd%2B7DbKzs5Wdna3S0lLTURAkPp9PQ4YM0fz587VkyRLFx8fq1Velb76RZs2Swuyf%2BV8Vjv%2B%2BhysKYIDFxcUd9x/%2BGjVqKCEhQc2bNzeUKjiKi4v15JNPKj09XY0aNdJPP/2kyZMna/PmzerVq5fpeAF1%2BPBh9ezZU2vWrNG7776r0tJSFRYWSnJmyUVGRhpOGFi7du3SDz/8ULbu4dF10RISEpSQEL7XRmVmZiozM7NsBNAm%2B/bt03/%2B85%2By7zds2KCCggLFxsaG9az3zMxMzZ07V2%2B//bZiYmK0f3%2BhBg%2Bupccfr6OHH5Z695aio02nDIwxY8bo%2BuuvV1JSkvbu3aucnBwtWbJEubm5pqOhMnwIunPOOcc3YcIE0zEC7sCBA76bbrrJl5iY6IuMjPQ1atTIl56e7vv0009NRwu4DRs2%2BCSdcFu8eLHpeAH36quvnvDYH330UdPRgsLj8fgk%2BTwej%2BkoQbN48eIT/s779etnOlpAnfjveZSvfv0in%2BTzjR1rOmHg3HXXXb5zzjnHFxkZ6WvQoIGvQ4cOvn/84x%2BmY6GSuAYQAPzM9msAIb31ltSrlxQV5VwLeP75phMB5TELGAAAP7v5ZqlTJ8nrlTIznfsEA1UJBRAAAD9zuaTsbGcE8B//kGbPNp0IKI8CCABAAFxwgfToo87%2BsGHSz/PAgCqBAggAQIAR%2BCSaAAAWZElEQVSMGCFdeqm0a5c0aBCnglF1UAABAAiQGjWkmTOdrwsXOvtAVUABBAA/yc7OVnJyslJSUkxHQRXSqpX0xz86%2B0OHSt99ZzYPIHErOADwO5aBwS%2BVlkrXXistWyalpkoff2zfbeJQtTACCABAgFWv7twazu2WPvlEGjfOdCLYjgIIAEAQnHOONHWqs//UU9JHH5nNA7tRAAEACJI%2BfaT%2B/Z3ZwLfeKm3fbjoRbEUBBAAgiF58Ubr4YmddwNtuc64PBIKNAggAQBDVqiW98Ybz9cMPpSeeMJ0INqIAAgAQZMnJ0pQpzv5jj0mLFpnNA/tQAAEAMCAjQ7r7bud6wL59pc2bTSeCTSiAAOAnLASNU/Xii86t4n78UerVSzp0yHQi2IKFoAHAz1gIGqfiu%2B%2Bkyy6T9uyRMjOlSZNMJ4INGAEEAMCg885zFomWpOxsafZss3lgBwogAACGdesmjR3r7A8cKH3%2Budk8CH8UQADWmDdvnjp37qz69evL5XKpoKDguNd4vV4NGTJE9evXV3R0tNLT07WZq/MRBI8%2BKnXpIh04IP2//yft3m06EcIZBRCANYqLi9W%2BfXuNHz%2B%2BwtcMGzZM8%2BfPV05OjpYvX659%2B/apW7duKmW1XgRY9erSnDlS06bOdYG33soi0QgcJoEAsM7GjRvVtGlTffbZZ7rkkkvKHvd4PGrQoIFmzZqlPn36SJK2bt2qpKQkvffee%2BrcuXOlPp9JIDgTBQVSaqp08KBzWvjxx00nQjhiBBAAfrZ69WqVlJQoLS2t7LHExES1aNFCeXl5BpPBJpdcIk2f7uz/8Y/S22%2BbzYPwRAEEgJ8VFhYqMjJS9erVK/d4fHy8CgsLK3yf1%2BtVUVFRuQ04E7fdJg0d6uxnZEjffGM2D8IPBRBAWJozZ45q165dti1btuy0P8vn88nlclX4fFZWltxud9mWlJR02j8LOOpPf5KuvFLau1fq0UPi/1fAnyiAAMJSenq6CgoKyrbLL7/8V9%2BTkJCgQ4cOafcvpl/u2LFD8fHxFb5v9OjR8ng8ZdumTZvOOD9Qo4b05pvS2WdL69dLd9whHTliOhXCBQUQQFiKiYnR%2BeefX7bVrFnzV99z2WWXqUaNGlq0aFHZY9u2bdOXX36pdu3aVfi%2BqKgo1alTp9wG%2BEN8vPS3v0mRkdL8%2BVJWlulECBcRpgMAQLDs2rVLP/zwg7Zu3SpJ%2BubnC6sSEhKUkJAgt9ut/v37a/jw4YqLi1NsbKxGjBihli1bqmPHjiajw2Jt2ji3hxs40JkV/H//J11/velUCHWMAAKwxsKFC3XppZeqa9eukqRbbrlFl156qaZMmVL2mgkTJqhHjx7q3bu32rdvr1q1aumdd95R9erVTcUGNGCAUwB9PqlvX%2Bm//zWdCKGOdQABwM9YBxCB4PVK11wj5edLrVpJeXlSdLTpVAhVjAACABACoqKkt95yrgv84gvp7rudEUHgdFAAAQAIEWef7cwMjoiQcnKkiRNNJ0KoogACABBCrrxSev55Z3/kSGnJEqNxEKIogADgJ9nZ2UpOTlZKSorpKAhzgwc7dwgpLZV695Y2bzadCKGGSSAA4GdMAkEw7N8vtW8vFRRIV1whffyxc50gUBmMAAIAEIJq1XIWia5XT/r002P3DgYqgwIIAECIOu88ae5cyeWSpk2TXnnFdCKECgogAAAhrEsX6fHHnf1775VWrzabB6GBAggAQIgbM0a68UZnseibb5Z%2B%2Bsl0IlR1FEAAAEJctWrSX/8q/eY30vffS7fd5swQBipCAQQAIAzUrSvNmyfVrCnl5kp//KPpRKjKKIAA4CesAwjTWrWSpk519h9/XHr/fbN5UHWxDiAA%2BBnrAMK0e%2B6RpkyRYmOdSSHnnms6EaoaRgABAAgzEydKKSnSrl1Sr17O5BDgf1EAAQAIM1FR0ptvOiOAq1ZJw4aZToSqhgIIAEAYOuccac4cZ5HoKVOcfeAoCiAAAGGqSxfp4Yed/YEDpa%2B/NpsHVQcFEACAMPboo1KHDtL%2B/VLPntK%2BfaYToSqgAAIAEMaqV3fuF5yYKK1bJ/3%2B9xLrf4ACCABAmGvYUMrJccrgnDnSjBmmE8E0CiAA%2BAkLQaMqu/JK6cknnf0hQ6TPPzebB2axEDQA%2BBkLQaOqOnJESk%2BX/v536YILnEWiY2JMp4IJjAACAGCJatWkv/xFSkqSvv3WmRnMMJCdKIAAAFgkLu7Y9YA5OdL06aYTwQQKIAAAlmnXTnrqKWf/vvukL74wmwfBRwEEAMBCI0ZI118vHTwo9ekjFRebToRgogACAGCho9cDJiZK69dLgwebToRgogACAGCpBg2cRaKrVZNmzpRmzzadCMFCAQQAwGJXXy098oizf889zuxghD8KIAD4CQtBI1Q9/LBTBPftk265RfJ6TSdCoLEQNAD4GQtBIxRt2SK1bi399JP0wAPSc8%2BZToRAYgQQAADo7LOlV15x9p9/Xnr/fbN5EFgUQAAAIMm5TdyQIc5%2Bv35SYaHZPAgcCiAAK5SUlGjUqFFq2bKloqOjlZiYqNtvv11bt24t97rdu3crIyNDbrdbbrdbGRkZ2rNnj6HUQPA984zUsqW0c6d0xx3O/YMRfiiAAKywf/9%2BrVmzRmPHjtWaNWs0b948/fvf/1Z6enq51/Xt21cFBQXKzc1Vbm6uCgoKlJGRYSg1EHxnneXcIu6ss6QPPpBeeMF0IgQCk0AAWGvlypW64oor9P3336tJkyZat26dkpOTlZ%2BfrzZt2kiS8vPzlZqaqvXr16t58%2BaV%2BlwmgSAcTJniLAsTGSmtWCFdconpRPAnRgABWMvj8cjlcqlu3bqSpE8%2B%2BURut7us/ElS27Zt5Xa7lZeXZyomYMSgQVL37tKhQ1LfvtL%2B/aYTwZ8ogACsdPDgQT344IPq27dv2ShdYWGhGjZseNxrGzZsqMKTXA3v9XpVVFRUbgNCncslzZghNWokrVsnjRxpOhH8iQIIICzNmTNHtWvXLtuWLVtW9lxJSYluueUWHTlyRJMnTy73PpfLddxn%2BXy%2BEz5%2BVFZWVtmkEbfbraSkJP8dCGBQ/frO/YIlafJk6d13zeaB/1AAAYSl9PR0FRQUlG2XX365JKf89e7dWxs2bNCiRYvKXaOXkJCg7du3H/dZO3fuVHx8fIU/a/To0fJ4PGXbpk2b/H9AgCGdOkn33%2B/s9%2B8vneCvCEIQk0AAWONo%2Bfv222%2B1ePFiNWjQoNzzRyeBrFixQldccYUkacWKFWrbti2TQGC1gwelK66Q1q6VunWTFi50ThEjdFEAAVjh8OHDuvnmm7VmzRq9%2B%2B675Ub0YmNjFRkZKUm6/vrrtXXrVk2dOlWSNHDgQJ1zzjl65513Kv2zKIAIR2vXSpdf7kwKmTLFmSSC0EUBBGCFjRs3qmnTpid8bvHixbrmmmskSbt27dLQoUO1cOFCSc6p5EmTJpXNFK4MCiDC1fPPS8OHS7VqSQUF0gUXmE6E00UBBAA/owAiXB054lwT%2BNFHUps20vLlUkSE6VQ4HUwCAQAAlVKtmjRzpuR2O4tDZ2WZToTTRQEEAACVlpQkZWc7%2B489Jq1aZTYPTg8FEAAAnJK%2BfaVevaTSUun226UDB0wnwqmiAAKAn2RnZys5OVkpKSmmowAB5XJJL70kJSQ4dwl56CHTiXCqmAQCAH7GJBDY4r33pK5dnUK4eLF09dWmE6GyGAEEAACn5YYbpLvvlnw%2B6Y47pL17TSdCZVEAAQDAaXv%2Beemcc6SNG501AhEaKIAAAOC0xcQ4S8NI0vTpUm6u0TioJAogAAA4I9dcIw0d6uzffbe0Z4/ROKgECiAAADhjWVnS%2BedLW7ZI999vOg1%2BDQUQAACcsVq1nFPBLpfz9e9/N50IJ0MBBAAAftG%2B/bHRv4EDpd27zeZBxSiAAOAnLAQNSE88ITVrJm3dyqngqoyFoAHAz1gIGrbLy5N%2B%2B1tnfcC//91ZLxBVCyOAAADAr9q1k4YNc/YHDpQ8HrN5cDwKIAAA8Lsnnjg2K3jECNNp8EsUQAAA4He1akkvv%2Bzsz5ghffih2TwojwIIAAAC4qqrpMxMZ3/AAGnfPrN5cAwFEAAABExW1rF7BT/0kOk0OIoCCAAAAiYmRpo61dn/85%2BlTz4xmwcOCiAA%2BAnrAAIn1rmz1K%2BfsyxM//6S12s6EVgHEAD8jHUAgePt2iVddJG0Y4c0dqz0%2BOOmE9mNEUAAABBwsbHSpEnO/vjx0pdfms1jOwogAAAIip49pe7dpZIS6e67pdJS04nsRQEEAABB4XJJ2dnOxJAVK6TJk00nshcFEAAABM3ZZ0tPP%2B3sjxkj/fCD2Ty2ogACAICgGjRIat/eWRh68GBndjCCiwIIAACCqlo1ado0qUYN6Z13pL/9zXQi%2B1AAAQBA0CUnSw8%2B6OwPHSrt2WM2j20ogADgJywEDZyaMWOkZs2kbducfQQPC0EDgJ%2BxEDRQeYsXS9dd58wQzsuT2rY1ncgOjAACAABjrr322G3iBg501ghE4FEAAQCAUX/6kxQXJ61dK02caDqNHSiAAADAqPr1nRIoSePGSd9/bzSOFSiAAKwxbtw4XXjhhYqOjla9evXUsWNHrVixotxrdu/erYyMDLndbrndbmVkZGgP0xOBgOvXT7rqKmn/fmnIENNpwh8FEIA1mjVrpkmTJmnt2rVavny5zj33XKWlpWnnzp1lr%2Bnbt68KCgqUm5ur3NxcFRQUKCMjw2BqwA4ul/TSS8fWBlywwHSi8MYsYADWOjpb98MPP1SHDh20bt06JScnKz8/X23atJEk5efnKzU1VevXr1fz5s1P6XOZBQycujFjpKwsKSlJ%2BvprqXZt04nCEyOAAKx06NAhTZs2TW63W61bt5YkffLJJ3K73WXlT5Latm0rt9utvLy8Cj/L6/WqqKio3Abg9Dz8sHTuudKmTdLjj5tOE74ogACs8u6776p27do666yzNGHCBC1atEj169eXJBUWFqphw4bHvadhw4YqLCys8DOzsrLKrhl0u91KSkoKWH4g3NWqJf35z87%2BhAnSV1%2BZzROuKIAAwtKcOXNUu3btsm3ZsmWSpGuvvVYFBQXKy8tTly5d1Lt3b%2B3YsaPsfS6X67jP8vl8J3z8qNGjR8vj8ZRtmzZt8v8BARbp1k3q3l06fFi6915njUD4FwUQQFhKT09XQUFB2Xb55ZdLkqKjo3X%2B%2Beerbdu2evnllxUREaGXX35ZkpSQkKDt27cf91k7d%2B5UfHx8hT8rKipKderUKbcBODMvvCDVrCl9/LE0Z47pNOGHAgggLMXExOj8888v22rWrHnC1/l8Pnm9XklSamqqPB6PPv3007LnV6xYIY/Ho3bt2gUlNwDHOedIY8c6%2ByNGSB6P2TzhhgIIwArFxcUaM2aM8vPz9f3332vNmjW6%2B%2B67tXnzZvXq1UuSdNFFF6lLly4aMGCA8vPzlZ%2BfrwEDBqhbt26VngEMwH%2BGD5eaN5e2b5ceecR0mvBCAQRgherVq2v9%2BvW6%2Beab1axZM3Xr1k07d%2B7UsmXLdPHFF5e9bs6cOWrZsqXS0tKUlpamVq1aadasWQaTA/aKjJQmTXL2J02SPv/cbJ5wwjqAAOBnrAMI%2BFfv3tKbb0rt20vLljmLRuPMMAIIAACqtOeec5aH%2Bde/pNmzTacJDxRAAABQpSUlHZsQMnIkE0L8gQIIAH6SnZ2t5ORkpaSkmI4ChJ3775cuuMCZEMIdQs4c1wACgJ9xDSAQGLm50vXXS9WrS198ISUnm04UuhgBBAAAIaFLF%2BcOIaWl0tCh3CHkTFAAAQBAyHj%2BeSkqSvrnP6X5802nCV0UQAAAEDLOO8%2BZCCJJDzwgHThgNk%2BoogACAICQ8uCDUuPG0vffS88%2BazpNaKIAAgCAkBIdLf3pT87%2B%2BPHSDz%2BYzROKKIAAACDk9O4tXXmlcwr4D38wnSb0UAABAEDIcbmkF190vr7%2BunOLOFQeBRAA/ISFoIHguuQSacAAZ/%2B%2B%2B5zlYVA5LAQNAH7GQtBA8Ozc6dwhxOORZsyQ%2Bvc3nSg0MAIIAABCVoMG0iOPOPtjxkhFRWbzhAoKIAAACGmDB0vNmkk7dkhPPmk6TWigAAIAgJAWGSk995yzP3Gi9N13ZvOEggjTAQAAAM5U165Sz55SmzbS2WebTlP1UQABAEDIc7mkN980nSJ0cAoYAADAMhRAAAAAy1AAAcBPWAgaQKhgIWgA8DMWggZQ1TECCAAAYBkKIAAAgGUogAAAAJahAAIAAFiGAggAAGAZCiAAAIBlKIAAAACWoQACgJ%2BwEDSAUMFC0ADgZywEDaCqYwQQAADAMhRAAAAAy1AAAVhp0KBBcrlcmjhxYrnHd%2B/erYyMDLndbrndbmVkZGjPnj2GUgJAYFAAAVhnwYIFWrFihRITE497rm/fviooKFBubq5yc3NVUFCgjIwMAykBIHAiTAcAgGDasmWLBg8erA8%2B%2BEBdu3Yt99y6deuUm5ur/Px8tWnTRpI0ffp0paam6ptvvlHz5s1NRAYAv2MEEIA1jhw5ooyMDI0cOVIXX3zxcc9/8skncrvdZeVPktq2bSu32628vLwKP9fr9aqoqKjcBgBVGQUQgDWefvppRUREaOjQoSd8vrCwUA0bNjzu8YYNG6qwsLDCz83Kyiq7ZtDtdispKclvmQEgECiAAMLSnDlzVLt27bJt6dKleuGFFzRz5ky5XK4K33ei53w%2B30nfM3r0aHk8nrJt06ZNfjkGAAgUrgEEEJbS09PLncp98803tWPHDjVp0qTssdLSUg0fPlwTJ07Uxo0blZCQoO3btx/3WTt37lR8fHyFPysqKkpRUVH%2BPQAACCAKIICwFBMTo5iYmLLvBw4cqBtvvLHcazp37qyMjAzdeeedkqTU1FR5PB59%2BumnuuKKKyRJK1askMfjUbt27YIXHgACjAIIwApxcXGKi4sr91iNGjWUkJBQNrv3oosuUpcuXTRgwABNnTpVklMcu3XrxgxgAGGFawAB4H/MmTNHLVu2VFpamtLS0tSqVSvNmjXLdCwA8CuXz%2BfzmQ4BAOGkqKhIbrdbHo9HderUMR0HAI7DCCAAAIBlKIAAAACWoQACgJ9kZ2crOTlZKSkppqMAwElxDSAA%2BBnXAAKo6hgBBAAAsAwFEAAAwDIUQAAAAMtQAAEAACxDAQQAALAMBRAAAMAyLAMDAH7m8/m0d%2B9excTEyOVymY4DAMehAAIAAFiGU8AAAACWoQACAABYhgIIAABgGQogAACAZSiAAAAAlqEAAgAAWIYCCAAAYBkKIAAAgGUogAAAAJahAAIAAFiGAggAAGAZCiAAAIBlKIAAAACWoQACAABYhgIIAABgGQogAACAZSiAAAAAlqEAAgAAWIYCCAAAYBkKIAAAgGUogAAAAJahAAIAAFiGAggAAGAZCiAAAIBlKIAAAACWoQACAABYhgIIAABgGQogAACAZSiAAAAAlqEAAgAAWIYCCAAAYBkKIAAAgGUogAAAAJahAAIAAFiGAggAAGAZCiAAAIBlKIAAAACWoQACAABYhgIIAABgGQogAACAZSiAAAAAlqEAAgAAWIYCCAAAYBkKIAAAgGUogAAAAJahAAIAAFiGAggAAGAZCiAAAIBlKIAAAACWoQACAABY5v8DWDQdDX8lVz8AAAAASUVORK5CYII%3D'}