Genus 2 curve data - 1083.a.1083.1

Formats: - HTML - YAML - JSON - 2024-07-27T04:31:46.850207

• 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': '2293160133751546483', 'abs_disc': 1083, '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': '[[3,[1,3,5,3]],[19,[1,0,-1]]]', 'bad_primes': [3, 19], 'class': '1083.a', 'cond': 1083, 'disc_sign': -1, 'end_alg': 'Q x Q', 'eqn': '[[0,0,0,-1],[1,1,1,1]]', 'g2_inv': "['17210368/1083','-175616/1083','68992/361']", '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': "['56','244','928','4332']", 'igusa_inv': "['28','-8','264','1832','1083']", 'is_gl2_type': True, 'is_simple_base': False, 'is_simple_geom': False, 'label': '1083.a.1083.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.18922944023328556136238842343039493886382600930026', 'prec': 173}, 'locally_solvable': True, 'modell_images': ['2.15.2', '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': '22.662454042123746562396605354', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.0751491854736', 'prec': 50}, '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': 3, 'torsion_subgroup': '[3]', 'two_selmer_rank': 1, 'two_torsion_field': ['6.0.69312.1', [1, 0, 1, -2, 1, 0, 1], [6, 11], 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': '1083.a.1083.1', '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': [[[112], [-1304]], [[-32], [8]]], 'spl_facs_condnorms': [57, 19], 'spl_facs_labels': ['57.a1', '19.a3'], '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': '1083.a.1083.1', 'mw_gens': [[[[1, 1], [0, 1], [0, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]], [[[0, 1], [1, 1], [0, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.075149185473647505649610222433415', 'prec': 117}], 'mw_invs': [0, 3], 'num_rat_pts': 6, 'rat_pts': [[-1, -1, 1], [-1, 1, 1], [0, -1, 1], [0, 0, 1], [1, -1, 0], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 279
    {'conductor': 1083, 'lmfdb_label': '1083.a.1083.1', 'modell_image': '2.15.2', 'prime': 2}
  • id: 280
    {'conductor': 1083, 'lmfdb_label': '1083.a.1083.1', 'modell_image': '3.720.4', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 3536
    {'cluster_label': 'c4c2_1~2_0', 'label': '1083.a.1083.1', 'p': 3, 'tamagawa_number': 1}
  • id: 3537
    {'cluster_label': 'c2c2_1~2c2_1~2_0', 'label': '1083.a.1083.1', 'p': 19, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '1083.a.1083.1', 'plot': 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eefZj/hl15iSBju4ziORo4cqXfeeUeffvqpWrVqVa7XMwkEQBBDwIgIvXubIeHTTzchsG9facQIdg%2BBuwwfPlyvvfaaXn/9daWmpmrLli3asmWL9u3bZ7s0ABGGHkBElP37pTFjzExhyawdOH261KaN3bqA6uDxeEo9//LLL2vo0KFHfD09gACCCICISB98IF11lZklnJQkPfaYdMMNZncRAKUjAAIIYggYEemii6TvvjO3OTnSjTdKvXqZnUQAAMDhEQARsdLSpPnzpccflxITpXnzzJDwnDm2KwMAILwRABHRYmKkUaNCewn/8YeZMHLttWb9QAAAcDACIKJC27bSV19Jt99urgN88UWpXTuzowgAACiJAIiokZQkTZhgQl/z5tK6dVLnztLIkdKePZaLAwAgjBAAEXXOPVf6/nvp%2BuvN/UmTpJNPpjcQAIAgAiCiUmqq9Oyz0ocfSunp0tq1pjfwxhu5NhAAAAIgolq3btIPP4R6A595RjrpJOm99%2BzWBQCATQRARD2v1/QG/utfUsuW0oYNUo8e0l/%2BwrqBAAB3IgDCNc4/31wbeNttZvmYGTOkE0%2BUXnhBKiy0XR0AANWHAAhXSUkx%2Bwh/9ZV0yinSrl3SdddJ551nhoqBaJSVlaWMjAxlZmbaLgVAmGAvYLhWfr40caJ0331mmZi4OOmWW8z9mjVtVwdUPvYCBhBEDyBcKy5OuvVW6ccfpT59TCCcMMEMC7/5psSfRgCAaEUAhOs1bSq98440d67UooX0%2B%2B/SFVdIXbuacAgAQLQhAAL/dckl0sqV0tixUmKimTXcrp3Za/jPP21XBwBA5SEAAsUkJ0v33296/nr3NsPCTz4ptWolTZ5s7gMAEOkIgEApWraUZs82O4mceKK0fbv0t7%2BZmcMffWS7OgAAKoYACBxGt27Sd99JTz0l1aljloq58EKpe3eWjQEARC4CIHAE8fHSiBHS6tXmesD4eOmDD8z1gddeK23caLtCAADKhwAIlFGdOtLjj5vrA/v1M7uHvPiidNxx0v/%2Br1lUGgCASEAABMrpuOOkt96SliyRzj5bysmRHn5YOvZYc7t37yFeyH5zAIAwQQAEjlKHDtKiRdKcOVKbNqYH8H//1wTBSZOk3FyZ1aSfe05q29asPF27thlP3rTJdvkAABdjKzigEhQUSNOmmTUE160z59LTpY%2BaXafW/37h4Bekp5suxGOOqdY64W5sBQcgiB5AoBLExkpDhkirVklPPy01aiQ12fB56eFPkjZsMGkRAAAL6AEEqsC%2BfdLqC27QyZ8/e%2BgnJSebLUYSEqqvMLgaPYAAgugBBKpAcrJ0cqM/Dv%2Bkffuk3burpyC4WlZWljIyMpSZmWm7FABhggAIVJUTTzzsw1tiGukfWbXYZxhVbvjw4frxxx%2B1dOlS26UACBMEQKCqXHedmfl7CM8UDtM998WoaVPpjjtYUBoAUH0IgEBVadZMeumlUkNg4UXd1XrKXTrpJCk7W3r0Ual5c%2Bmqq6Tvv6/%2BUgEA7sIkEKCqrVolTZ5skl3t2tKVV0o9e0qxsSoslN5/X5owQVq4MPSSrl2lW24x%2Bw7H8GcaKgmTQAAEEQCBMPHVV6Yn8O23Q5uGnHCCdNNNZomZmjXt1ofIRwAEEEQABMLMunXSxInSCy%2BY4WFJ8vmkv/5VuvFGsxUdcDQIgACCCIBAmAoEpClTpKeektasCZ2/6CJp%2BHCpe3ezADVQVgRAAEEEQCDMFRZKH3xg9hd%2B//3Q%2BaZNzUTjv/5VatzYXn2IHARAAEEEQCCC/Oc/Zj7JSy9JO3eac7GxZk7Jtdea3kF6BXEoBEAAQQRAIALl5Ehvvik9%2B6z02Weh802aSEOHSldfLR17rLXyEKYIgACCCIBAhFu50kwYmTo11CsoSeeea9YVvPxyKTXVXn0IHwRAAEEEQCBK5OZK775rhoc/%2BkgK/stOTpYuvVQaPFjq0uWwm5MgyhEAAQQRAIEo9Pvv0quvSq%2B8YtahDmrYUOrfXxo4UDr9dMnjsVcjqh8BEEAQARCIYo4jLV1qhodnzpS2bw891rKlCYP9%2B0snn0wYdAMCIIAgAiDgEnl50ocfStOnS7NnS3v3hh47/nhzreBll0nt2hEGoxUBEEAQARBwoT17pHnzTK/ge%2B%2BZ6weDjj1W6tvXtNNPZy/iaJCVlaWsrCwVFBTol19%2BIQACIAACbpedLc2da5aV%2BeADs8RMUFqa1KuX1Lu3dP75UlKSvTpRcfQAAggiAAIosnu32W1k1ixp/vzQXsSSVKOG1LWr1KOHaew%2BEnkIgACCCIAASpWbK336qVlaZu5cM7O4uP/5H7PzyEUXSWedJcXHWykT5UAABBBEAARwRI4jLV9urhucP1/66qvQOoOSVLOmGSLu2tWsNXjCCUwkCUcEQABBBEAA5fbHH2ZG8fvvm0Wniy8vI0nHHGMCYefOpjVrZqdOlEQABBBEAARQIYWF0rJl0oIFpn32WclZxZIJgOedZ9rZZ0utWtFDaAMBEEAQARBApdq3z4TAjz%2BWPvnELERdUFDyOQ0amOsGg%2B3UU5lhXB0IgACCCIAAqtTu3SYQLlwoLV5sAuGBPYTx8WYB6tNPN%2B2006TWraXYWDs1RysCIIAgAiCAapWbK339tbRkiWmffy5t3Xrw82rUMDON27eXTjnFHGdk0FNYEQRAAEEEQABWOY60bp3pGfzyS3P77bdmt5IDxcaaGcZt20onnSS1aWNay5ZSXFy1lx5xCIAAggiAAMJOQYH0yy/SN9%2BYCSbLlknffSft3Fn68%2BPjzcSSE04wrVUr0449VmrUiAknkqTCQgX%2B9S/5unWTf/VqeY87znZFACwiAAKICI4jbdworVgh/fCDuV25UvrpJzPx5FCSk00PYYsWZjZys2ZS06ampaeb7e6ivvfwjTek0aMVWLdOPkn%2BuDh5Bw6UJk2SUlNtVwfAAgIggIhWWCht2GCC4KpVpudw9WppzRrpt9/M44cTE2NmJTdubMJgo0ZSw4amNWgg1atnWt26Uu3aUkpKhPUozpkj9ekjOY4CkgmAkrySWaTx44%2BtlgfADgIggKi1f78JgWvXmusMf/vNtPXrTWjcuFHKzy/fe8bHS3XqSLVqST6faV6vaTVrmpaSYlqNGqYHMtiSkqTExFBLSDAtPj7U4uLMtY6xsaFjj6cCobN9ezOGLh0cACUTADt3Pso3BxCpCIAV4DiOsrOzbZcB4CgVFppdTTZvNjORt2wx7Y8/TNu%2B3bQdO6Rdu8ofFiuTx2N6K4vfFm/B5xQPio0L12vp3rZF9wOS0iVtUCgAvhA/TPcnPVLi6xx4fODXKF5H8RYbW/I4GGKDQTZ4HGzx8SUDcPFAHAzISUmmFT8OtuLhukaNkoE7Jqay/wsgGqWmpsoTUV36lYcAWAHBGXUAACDyuHlGPAGwAsrTA5iZmamlS5dW6OtV9D0q8vpAIKD09HRt2LDhqP%2Bx2Ky/Ml5f0ffgM7T/%2BVW0hqp%2BfUGB6WUsKAgdFxaa48JC03r06KE5c%2BbLcVSiSQff5uXuU4s%2Bpylx6%2B%2BSSu8B3Prwy9rdtW%2BJOkp7v2C74or%2Bmj59pgoLzf1gXcXrDN4e%2BL0E25gx9%2Bnee/%2Bf9u%2BX8vLMuf37VXQ/N9e0/ftDxzk5odulS1fouOPaat8%2BMwEoJ0fau/fgBcYrS0JCaLg/NTVfy5Z9op49O6levXjVqqWiVru2acWPS7tmNJx/BqvjPcLpd6GbewCjfe5blfJ4PGX%2B4Y2Nja3wXxkVfY/KqMHr9R71e9iuPxz%2BG0ju/gxtf36VUYPt1ycm7lCbNmV9vVcac6d0000HnjUBsFUreW8ZZMZcy6hGjd/UoUPF/hs%2B%2BuhHuvHGJ4769RkZ1%2BmHH3486HxhoQmEe/aE2u7dB7exYx/VsGG3Kztbys6WAoGDm99vbiUTRIOXBRj9NHdu2WoNXjMabHXrSlu3jtff/%2B4tmlxUt65KHNete/iZ6bZ/BivrPSL5d2E0IABWk%2BHDh1t/j8qowebXt/36ynoPm18/0l9fGWx/D9X%2B%2BpEjzYWNEyaY7rWgk0%2BWZs8uV/g7qq9fBe9xqNfHxIQm4BxOQUGyylJCQYEJiH6/9Oefpm3atFcDB/5NDz30rHJykrRrl4razp2htmtXqEdz69YDd7vpq0cfPfzXrlXLhML69UMz0YOtfftJmjs39Hj9%2BmYSUlk7ssLhv6Htr2%2B7/nDAEDDKhB0EKo7PsGL4/CpoyxZtffFFpd1zjzbNmKFG/fvbrigilfXn0HHMsPTOnWYSUTAY7thxcAtONNq%2B3QTHo5GQEAqIwVB4uFanjr2JMvxbDg/0AKJMEhMTNXbsWCUmJtouJWLxGVYMn18FpaUp5vrrpXvuUdz559uuJmKV9efQ4wn1Rqanl/398/NNCAzOQA%2B24rPSg8fBYem9e01v46ZNppVFTEzpYbFBg9Bx8cePNCxdHvxbDg/0AAKAS9DzEp327Su5dFHwuLS2bZsZ0i4vj8f0GhYPi4fqbQyeJ9%2BFNwIgALgEARCS6S08UlAs3nbuDM0IL4%2BaNUsGwkNd0xhstWub9SJRPQiAAOASBEAcjfx8c43igcGweIjctq3kkPXRLJoe7GUMzog%2B1G3xVqdOuecx4b8IgADgEgRAVAfHMTOmD7xesbTrGYPtaIalg7zekqFw6FBpwIBK%2B3aiFpNAAABApfF4Qgtht2pVttfk5YVmQhffgrH4LOkDj4MzpoNrN/76q7nfpUvVfF/Rht0SUWTWrFm68MILVa9ePXk8Hi1fvvyIr5kyZYo8Hs9BLScnpxoqjhxH89m6leM4uv/%2B%2B9W4cWMlJyerU6dOWrly5WFfc//99x/0M5iWllZNFSPaPP3002rRooWSkpJ06qmnavHixYd8Lr8Dy27RokXq2bOnGjduLI/Ho9mzZxc9Fh8vpaVJJ50kdeokXXaZNGyYdPfd0mOPSa%2B8Is2bJ33xhbR6tbkuMT/f9Cb%2B/LP02WfSnDnSlCnSRRdZ%2BxYjCgEQRfbs2aOOHTvqoYceKtfrvF6vNm/eXKIlJSVVUZWR6Wg/Wzd65JFH9Nhjj2nSpElaunSp0tLS1LVr1yNuu9imTZsSP4MrVqyopooRTWbOnKlRo0ZpzJgxWrZsmc455xx1795d69evP%2BRr%2BB1YNnv27FG7du00adKkSnm/2FhzXeAJJ0hnnSX17ClddZUJkTgyhoBRZPDgwZKkdevWlet19LYc2dF%2Btm7jOI6eeOIJjRkzRn37mv1pX3nlFTVs2FCvv/66hg0bdsjXxsXF8XOICnvsscd0zTXX6Nprr5UkPfHEE/rwww/1zDPPaPz48aW%2Bht%2BBZdO9e3d1797ddhn4L3oAUWG7d%2B9Ws2bNdMwxx%2BiSSy7RsmXLbJeECPXrr79qy5Yt6tatW9G5xMREnXfeeVqyZMlhX7t69Wo1btxYLVq00IABA7R27dqqLhdRZv/%2B/frmm29K/PxJUrdu3Q7788fvQEQiAiAqpHXr1poyZYrmzJmj6dOnKykpSR07dtTq1attl4YItGXLFklSw4YNS5xv2LBh0WOlOeOMMzR16lR9%2BOGHev7557VlyxadddZZ2rFjR5XWi%2Biyfft2FRQUlOvnj9%2BBiFQEQJeaNm2aatasWdQOd5Hz4Zx55pm68sor1a5dO51zzjl64403dPzxx%2Bupp56q5IojR2V9tm5w4GeVl5cnyQypFec4zkHniuvevbv69euntm3bqkuXLpo/f74kM3wMKSsrSxkZGcrMzLRdSkQoz88fvwMRqbgG0KV69eqlM844o%2Bh%2BkyZNKuV9Y2JilJmZ6eq/fqvqs41GB35Wubm5kkxPYKNGjYrOb9u27aBemcNJSUlR27ZtXf1zWNzw4cM1fPjwonUAUbp69eopNjb2oN6%2B8vz88TsQkYIA6FKpqalKTU2t9Pd1HEfLly9X27ZtK/29I0VVfbbR6MDPynEcpaWlacGCBTrllFMkmeuyFi5cqIcffrjM75ubm6uffvpJ55xzTqXXjOiVkJCgU089VQsWLNCll15adH7BggXq3bt3md6D34GIFARAFNm5c6fWr1%2BvTZs2SZJWrVolSUpLSyua4TZkyBA1adKkaDbcAw88oDPPPFOtWrVSIBDQxIkTtXz5cmVlZdn5JsJUWT5bmKG3UaNGady4cWrVqpVatWqlcePGqUaNGho4cGDR8y644AJdeumlGjFihCTp9ttvV8%2BePdW0aVNt27ZNDz74oAKBgK666ipb3woi1K233qq26h8aAAAZOElEQVTBgwfrtNNOU4cOHfTcc89p/fr1uuGGGyTxO7Aidu/erTVr1hTd//XXX7V8%2BXLVqVNHTZs2tViZSznAf7388suOpIPa2LFji55z3nnnOVdddVXR/VGjRjlNmzZ1EhISnPr16zvdunVzlixZUv3Fh7myfLYwCgsLnbFjxzppaWlOYmKic%2B655zorVqwo8ZxmzZqV%2BOz69%2B/vNGrUyImPj3caN27s9O3b11m5cmU1Vx7%2B/H6/I8nx%2B/22SwlrWVlZTrNmzZyEhASnffv2zsKFC4se43fg0fvkk09K/T1Y/PNE9WEvYABwCfYCBhDELGAAAACX4RpAAGHPcaScHGnfPnObkyPt3x9qeXlmX9D8fKmwUCooMK8pLibGtNhYs%2B9oXJy5TUiQEhOlpCQpOVmqUcMcx/DnMYAoRgAEUGUcR9q712zcHmx//int2mVu/X7TAgEpOzt0m50t7d4t7dlj2t691V97SopUs6ZpXq/k80m1aoVanTqm1a1r9iNt0MC0evVMuASAcMavKQBllp8vbd8ubdsm/fGHacWPt2%2BXduwI3e7cKf13ab9KExNjeugSE03vXUJCqEcvNta0YG%2BfJHk8JogGewYLCkK9hcEexJwcU%2Bd/16GWFAqfW7eWrz6Px4TAtDTTGjc27ZhjTEtPN61uXfNcALCBAAi4XEGBCWxbtpRsW7eGboNtx46Dh1bLIj5eql3b9JjVrh3qRfP5Qi011fS0paaaVrOm6YULtuRk0%2BLjK/8zCCooML2Ne/eGeiADgVD7889QD2awRzMYeLdtM8eFhaFAvGLFob9WcrLUrJnUvLlpLVua22OPNY31mgFUJQIgUNVycqSZM6XvvzcJaOBAqUWLKv%2ByeXkmwG3eLG3aZNrmzaEWfGzrVhNaysrjMb1XwSHP%2BvVNq1cvdFu3bqjVqWPCXCT0dsXGhgJoOTYeKVI8TG/dWvKz//13acMG07ZtM9cz/vyzaaWpV09q1Uo67jjp%2BOOlE06QWrc255KSjuKb%2B/lnKbg13ttvS1deWbVpGkBYYxkYoCp9/rnUp4/5P35QTIx0%2B%2B1SOXa2KO7AYBe83bix5P3t28v%2BngcOWxZvDRuGWoMGJtRxjVvF5OaaIPjbb9K6ddKvv5q2dq1pxX9cDuTxmL8fTjxRysiQ2rQxtxkZpqf0IIWF0g03SM8/r4AknyS/JG/TptK8eRI7VgCuRAAEqsrOnab7Zteu0h9/7jnpuuuK7ubnm//xB0Pcxo0Hh7xNm8xzyvqvNj7ehLhGjULXogWDXaNGodagAaEunGRnS2vWSKtXm/bLL6b99JOZNFMaj8cMI7dtK518smnt2kkt3xivmDF3S1LJAChJTZqYL3RUXYoAIhkBEKgqjz0m3XbbIR/eVOtEXd/xx6KAt21b2Ydi4%2BJKTjBo3LhkyAse163LcibRxHHM0PLPP0s//mjaypXmtrRew1jla4OnqRo5myWVEgAlMyw8ZEj1fAMAwgYBEDgKjmMmBRw4cSI4NLt5s3TbF5erW%2BCtw75PLe2SX7WK7sfGmqHW0gJdkyah%2B/XqEexQUnDSyfffh9q%2BFWu0cn%2BroueUFgB/6nSD8ic%2Bo4wM8/MHwB0Y9AH%2By3HM0Ftwxuu2bQfPgg3OjN2yxVzEfziDlHzYxws9MXrsqUQ1aBYKeQ0a8D9hHJ369aXzzzctKP%2B3ZKn54V/33qc1dPvJ5vrB006TTj9dOvNMqUMH8zMJIDrRA4iotnfvodet27atZNu6tfxr1qWmHjxpInhdXdt1c3TqA70P/eKePaU5cyr2DQJH0qGD9MUXkkrvARx%2ByhJNXd1Bu3cf/NKmTc3LO3Y07eSTuVYUiBYEQESMffvMOmvFW/GFh4PHxRclPpodJFJSSs58PXAmbPGwV6PGYd6osFDq3FlatOjgx2rUkBYvltq3L3%2BBQHl88ol04YVSXt7BAbBPH%2Bmdd1RQYK4r/PLLUFux4uBrUlNSTCA85xzTzjzTrGcIIPIQAFGt8vPNLMbgYroHtuJbhu3cac4Fd5Q40pDroSQkhNaqC7bgGnYHtoYNjxDqymv3bumOO6SpU0NptGNH6dFHzf89gerwz39Ko0cr8O23JgB6vfJed500bpz5B1KK7Gxp6VJpyRLps8/MikYHzkBOSJAyM6VOnUw766xK/vcDoMoQAFFmubkl92wt3oJ7ugb3dQ2GvOBtsJU2zFQesbElFxkuvuhwcBHi4gsS169vhmmtL0IcCEj/%2BY/ZBqN5c8vFwK0Cy5bJ1769/Js2yVvOC/wKC6UffjAd1//%2Bt%2BnY3rSp5HPi483fNRdcYK5FPOOMQ%2BZLAJYRAKNQfn5oO6s9e0K3xVtwm6vdu0u27GxzPjv74LZ/f%2BXVmJJistCBLbhVWJ06B7e6dc1WYdbDHBChAoGAfD6f/H6/vF7vkV9wGI5j/qZZtMiMMn/6qdntpLiUFNMz2KWLGYVu3Zp/v0C4IABWA7/fDGPm5oZa8Q3oc3PNcfGWm2uGPHNySt6W1oJhb%2B9ec78yg1ppatQwQezAVnxfV5%2Bv5F6vtWqF9oD1%2BdiBCrChMgPggYKB8F//kj7%2B2ITCP/4o%2BZxjjjFBsHt300tYq1bp7wWg6hEAq8G4cdKYMdX/dT0eE9ZSUkK3NWua22CrWbNkS009%2BDbYvF5zjlmAQGSqygB4oMJCsxbhggXSRx%2BZoePis%2BxjY83lsBdfLF1yidnKjt5BoPoQAKvBE09I99xjroVJTCzZkpJKHh/YkpNDt8WPa9QwxzVqhO4Hj4OBLymJX6gAQqozAB5o3z4zXPzBB9L770urVpV8vHlzEwR79ZLOO49rB4GqRgAEAJewGQAPtHatCYLz55sh4%2BK9g16v6Rns08cMF1suFYhKBEAAcIlwCoDF7dljrh2cM0eaN88syh6UkCB17Sr16yf17m0mhAGoOAIgALhEuAbA4goLzULUs2eb9ssvocdiY83yMv37S5deShgEKoIACAAuEQkBsDjHkX78UZo1S3rrLTOpJCguTurWzYTBPn0YJgbKiwAIAC4RaQHwQKtXS2%2B8Ib35pvTdd6HzSUlma%2B1Bg8w1g0wgAY6MAAgALhHpAbC4n3%2BWZswwrfiM4jp1pAEDpCFDpNNPZyUE4FAIgADgEtEUAIMcR1q2THrtNWn6dGnLltBjrVtLQ4dKgwdLjRtbKxEISwRAAIhyWVlZysrKUkFBgX755ZeoCoDFFRSY2cRTp5rrBvftM%2BdjYszQ8LXXSj16sBMRIBEAAcA1orEH8FACAXOt4MsvS599FjqfliZdfbV03XVSixb26gNsIwACgEu4KQAWt2qV9NJL0pQp0rZt5pzHY/YlvvFGs%2Bh0bKzVEoFqRwAEAJdwawAM2r/fLDb93HNmj%2BKgpk2lv/3NDBHXq2evPqA6EQABwCXcHgCLW7NGevZZ0zO4c6c5l5holpK5%2BWbp5JPt1gdUNQIgALgEAfBg%2B/aZpWQmTZK%2B/TZ0vnNn6bbbzOSRmBh79QFVhQAIAC5BADw0x5GWLJGefNLMIC4oMOdbt5ZuvdUsJZOUZLdGoDIRAAHAJQiAZbN%2BvfTUU%2BZawUDAnGvYUBo1ylwr6PPZrQ%2BoDARAAHAJAmD5BALSCy9ITzwhbdhgznm9ZubwLbdIDRrYrQ%2BoCAIgALgEAfDo5OWZXUYeflj68UdzLjlZGjZMuuMOdhlBZOLSVgAADiM%2B3uwtvGKFNHu2lJlpJo888YTUsqU0cqS0caPtKoHyIQACQATIy8vT6NGj1bZtW6WkpKhx48YaMmSINm3aZLs014iJkXr3lr78UvrwQ6ljRyk318wgPvZYc41g8b2IgXBGAASACLB37159%2B%2B23uvfee/Xtt99q1qxZ%2BuWXX9SrVy/bpbmOxyN16yYtXmz2Hj77bBMEn3zS9AiOHi3t2GG7SuDwuAYQACLU0qVLdfrpp%2Bu3335T06ZNj/h8rgGsGo4j/fOf0n33SV98Yc55vdKdd5pewZQUu/UBpaEHEAAilN/vl8fjUa1atWyX4moej9S1q1lHcM4cs4tIICDdc4903HFmx5H8fNtVAiXRAwgAESgnJ0dnn322Wrdurddee63U5%2BTm5io3N7fofiAQUHp6Oj2AVayw0Owucs890q%2B/mnOtW0sTJkg9epjACNhGDyAAhKFp06apZs2aRW3x4sVFj%2BXl5WnAgAEqLCzU008/fcj3GD9%2BvHw%2BX1FLT0%2BvjtJdLyZGGjhQ%2Bvlnc11g3brmuGdPqUsX6bvvbFcI0AMIAGEpOztbW7duLbrfpEkTJScnKy8vT1dccYXWrl2rjz/%2BWHXr1j3ke9ADGB78fmncOBMGc3NNQLz2Wunvf2cxadhDAASACBEMf6tXr9Ynn3yi%2BvXrl%2Bv1TAKxa906M0P4jTfMfZ9PeuABs7NIfLzV0uBCDAEDQATIz8/XZZddpq%2B//lrTpk1TQUGBtmzZoi1btmj//v22y0MZNG8uzZwpLVoknXKK6RkcNUpq315auNB2dXAbegABIAKsW7dOLVq0KPWxTz75RJ06dTrie9ADGD4KCqQXX5Tuvju0ZuCgQdL//Z/UsKHd2uAOBEAAcAkCYPjZuVMaM8YsFeM4Zlh4/Hizz3AMY3SoQvx4AQBgSZ060jPPmO3lTj3VDAvfeKPZZm7FCtvVIZoRAAEAsCwz04TAiROl1FSzo0j79mYtwZwc29UhGhEAAQAIA7Gx0siR0k8/SX36mN1D/vEPM2Hk889tV4doQwAEACCMNGkivfOO9NZbUlqaWUS6Y0fp1lulvXttV4doQQAEACAM9esn/fijNHSomSDy%2BONSu3Zmz2GgogiAAACEqdq1pZdflt57z/QMrlkjnX22WVC62CYvQLkRAAEACHPdu0s//CANGWJ6Ax95xEwc%2Bf5725UhUhEAAQCIALVqSa%2B8Is2eLdWvb5aJycw0i0cXFtquDpGGAAgAQATp3dv0BvbsKe3fL91%2Bu3ThhdKmTbYrQyQhAAJAlMvKylJGRoYyMzNtl4JK0qCB9O670uTJUnKy9M9/mgki8%2BbZrgyRgq3gAMAl2AouOv38szRwoLRsmbl/883Sww9LiYl260J4owcQAIAI1rq1WSh61Chz/8knzUzhtWvt1oXwRgAEACDCJSaadQLnzjX7C3/9tdlK7p13bFeGcEUABAAgSlxyibR8udShg%2BT3S337SrfdJuXl2a4M4YYACABAFElPlxYuNMFPkh57TLrgAmnzZrt1IbwQAAEAiDLx8dKjj0qzZkmpqdLixWZI%2BN//tl0ZwgUBEACAKHXppeZ6wDZtpC1bpM6dpaefNruJwN0IgAAARLHjj5e%2B%2BEK64gopP18aPly67jr2EnY7AiAAAFGuZk1pxgyzh3BMjPTii6Y3cOtW25XBFgIgAAAu4PFId9whzZ9v9hX%2B/HPptNNCC0jDXQiAAAC4yEUXSV9%2BKZ1wgvT772bRaNYLdB8CIAAALhO8LrBrV2nvXqlfPzM8zOQQ9yAAAgDgQrVqSe%2B9ZyaFOI40erR0/fUsGu0WBEAAAFwqLk6aNMnsHxwTI73wgtlNJBCwXRmqGgEQAACXu%2Bkm6d13pRo1pI8%2BMtcF/v677apQlQiAABDlsrKylJGRoczMTNulIIxdcom0aJGUliatWCGddZa0cqXtqlBVPI7DJZ8A4AaBQEA%2Bn09%2Bv19er9d2OQhT69ZJ3btLP/9srhOcM0c65xzbVaGy0QMIAACKNG8uffaZ6QH8808zU3j2bNtVobIRAAEAQAl16kgLFki9epkt4/r1k156yXZVqEwEQAAAcJAaNaS335b%2B%2BlepsFC65hppwgTbVaGyEAABAECp4uLM0jB33mnu33mndPfdLBgdDQiAAADgkDwe6eGHTZOk8eOlkSNNryAiFwEQAAAc0Z13SpMnm0CYlWWGhgsKbFeFo0UABAAAZTJsmPTqq1JsrPTKK9KgQWwdF6kIgAAAoMwGDZLeeEOKj5dmzpT695f277ddFcqLAAgAAMqlb1/pnXekxERz26%2BfWS4GkYMACAAAyq1HD7NLSFKSNG8eITDSEAABAMBR6dbNhL%2BkJGn%2BfNMzSAiMDARAAABw1C64wIS/5GTpvfekyy7jmsBIQAAEAAAVcv750ty5oeHg/v2ZHRzuCIAAAKDCLrjAXBOYmCjNnm1mC%2Bfn264Kh0IABIAol5WVpYyMDGVmZtouBVGua1dp1iyzRMybb4b2EUb48TgOO/oBgBsEAgH5fD75/X55vV7b5SCKzZ5trgUsKDCLRz/zjNlBBOGDHkAAAFCp%2BvSRXnvNhL5nnzXbyNHdFF4IgAAAoNINGCA9/7w5fvRRadw4u/WgJAIgAACoEtdcIz32mDm%2B5x7p6aft1oMQAiAAAKgyt9wi3XuvOR4xQpoxw249MAiAAACgSj3wgDR8uLkOcMgQ6cMPbVcEAiAAAKhSHo80caK5LjAvz%2BwbvHSp7arcjQAIAACqXEyM9MorZq3APXukiy%2BWVq%2B2XZV7EQABAEC1SEiQ3n5bOvVUaft26aKLpK1bbVflTgRAAABQbVJTpfnzpZYtpbVrpR49pN27bVflPgRAAABQrRo2lD74QKpXT/rmG3NtIPsGVy8CIABEoGHDhsnj8eiJJ56wXQpwVFq1kubOlZKTTY/gyJHsFlKdCIAAEGFmz56tL7/8Uo0bN7ZdClAhZ54pvf66mSU8ebLZMQTVgwAIABFk48aNGjFihKZNm6b4%2BHjb5QAV1qdPaLeQO%2B80k0RQ9QiAABAhCgsLNXjwYN1xxx1q06bNEZ%2Bfm5urQCBQogHh6OabzS4hknTllawRWB0IgAAQIR5%2B%2BGHFxcXppptuKtPzx48fL5/PV9TS09OruELg6Hg80uOPm7UBc3KkXr2k9ettVxXdCIAAEIamTZummjVrFrWFCxfqySef1JQpU%2BTxeMr0HnfddZf8fn9R27BhQxVXDRy9uDizT3DbttKWLSYEsjxM1fE4DnNuACDcZGdna2uxFXLffPNNjRkzRjExob/bCwoKFBMTo/T0dK1bt%2B6I7xkIBOTz%2BeT3%2B%2BX1equibKDC1q%2BXMjOlbdvM9YFvv212EUHlIgACQATYsWOHNm/eXOLchRdeqMGDB%2Bvqq6/WCSeccMT3IAAiUnz%2BudSpk7R/v3T33dI//mG7ougTZ7sAAMCR1a1bV3Xr1i1xLj4%2BXmlpaWUKf0Ak6dBBeuEFacgQadw4Myw8YIDtqqILnaoAACDsDB5sloWRpKuvlr791m490YYhYABwCYaAEWkKCqSePaX335fS06Wvv5YaNLBdVXSgBxAAAISl2Fhp%2BnTp%2BOOlDRukyy%2BX8vJsVxUdCIAAACBs%2BXzSu%2B9KqanSokXSrbfarig6EAABAEBYa91aeu01czxpkjR1qt16ogEBEAAAhL1evaSxY83xsGFMCqkoAiAAAIgI990n9ehhtovr21fascN2RZGLAAgAACJCTIz06qtSy5bSb7%2BZpWIKC21XFZkIgAAAIGLUri3NmiUlJZnlYR580HZFkYkACAAAIkq7dtLkyeb4/vuljz6yWk5EIgACAICIc9VV0vXXS44jDRok/f677YoiCwEQAKJcVlaWMjIylJmZabsUoFI9%2BaR0yinS9u1S//4sEl0ebAUHAC7BVnCIRv/5j9S%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%2Bh0ndtqDLAAAAAElFTkSuQmCC'}