Genus 2 curve data - 784.a.1568.1

Formats: - HTML - YAML - JSON - 2025-10-28T05:25:07.020670

• 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': '312369878683860827', 'abs_disc': 1568, 'analytic_rank': 0, '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]],[7,[1,0,-1]]]', 'bad_primes': [2, 7], 'class': '784.a', 'cond': 784, 'disc_sign': 1, 'end_alg': 'Q x Q', 'eqn': '[[-2,0,3,0,-2],[0,1,0,1]]', 'g2_inv': "['304316815968/49','12641055372/49','14427072']", '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': "['792','120','15228','6272']", 'igusa_inv': "['396','6514','144256','3673295','1568']", 'is_gl2_type': True, 'is_simple_base': False, 'is_simple_geom': False, 'label': '784.a.1568.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.2887965445848189243502666365046684126736890460442209383', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.90.3', '3.2160.21'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 4, 'num_rat_wpts': 2, 'real_geom_end_alg': 'R x R', 'real_period': {'__RealLiteral__': 0, 'data': '20.793351210106962553219197828', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, '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': 2, 'torsion_order': 12, 'torsion_subgroup': '[12]', 'two_selmer_rank': 1, 'two_torsion_field': ['4.0.392.1', [1, 1, 0, -1, 1], [4, 3], 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': '784.a.1568.1', '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': [[[25], [-253]], [[-3], [-27]]], 'spl_facs_condnorms': [14, 56], 'spl_facs_labels': ['14.a5', '56.a4'], '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': '784.a.1568.1', 'mw_gens': [[[[-1, 1], [1, 1], [0, 1]], [[-1, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [12], 'num_rat_pts': 4, 'rat_pts': [[-1, 1, 1], [1, -1, 0], [1, -1, 1], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 142
    {'conductor': 784, 'lmfdb_label': '784.a.1568.1', 'modell_image': '2.90.3', 'prime': 2}
  • id: 143
    {'conductor': 784, 'lmfdb_label': '784.a.1568.1', 'modell_image': '3.2160.21', 'prime': 3}
• g2c_tamagawa •

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

  
  
  • id: 145992
    {'label': '784.a.1568.1', 'local_root_number': -1, 'p': 2, 'tamagawa_number': 2}
  • id: 145993
    {'cluster_label': 'c2c2_1~2c2_1~2_0', 'label': '784.a.1568.1', 'local_root_number': -1, 'p': 7, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '784.a.1568.1', 'plot': 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zCO2a5dUsuW0tq10lln2eXgmjVdpwKODXsBA/AyRgBxzI4/Xpo7V6pTR1q9WurUScrKcp0KAAAcDAUQpaJePSuBxx1nawR26yZlZ7tOBQAASkIBRKlp2FCaM0eqVk16/30pKUnKy3OdCgAA/BYFEKWqeXPprbekqChp1izp%2Buul/HzXqQAAwIEogCh1l10mTZ8uVaggvfaarRNICQQAIHxQAFEmOnWSpk2zLeOmTJFuvJESiNAZPny4AoFAsUd8fLzrWAAQNiiAKDPdutkIYPny0uTJUu/e3BOI0DnrrLO0ffv2wsfKlStdRwKAsFHBdQB429/%2BJgUC0tVXS6%2B8YjODp06VKlZ0nQxeV6FCBUb9AOAgGAFEmevRQ3rzTSt9b75pI4O//uo6Fbxu/fr1qlOnjurXr6%2BkpCRt2LDhkO/Pzs5WRkZGsQcAeBUFECHRtavNDq5cWXrnHal9e4n/vqKsNGvWTJMnT9bcuXP17LPPKj09XS1atNCuXbsO%2BjVjxoxRbGxs4SMxMTGEiQEgtNgKDiG1cKFNEMnMlJo0kd57T6pd23UqeF1WVpb%2B9Kc/adCgQUpJSSnxPdnZ2co%2BYPXyjIwMJSYmshUcAE9iBBAhddFF0vz5Uq1a0ooVUosW0vr1rlPB66pVq6ZGjRpp/SH%2BskVFRal69erFHgDgVRRAhNy550pLlkinnCJt2CBdeKE9B8pKdna2vv76ayUkJLiOAgBhgQIYAps3231vWVmuk4SPU0%2B10nf%2B%2BdKuXdKll0qvv%2B46FbxiwIABWrBggTZu3KhPP/1UPXr0UEZGhnr16uU6GgCEBQpgCEybJnXuLNWsKbVuLY0fL61ZI/n97svate1ycJcutjzM1VdLw4dLBQWukyHSfffdd7r66qt1%2Bumnq1u3bqpUqZI%2B%2BeQT1atXz3U0AAgLTAIJgSeflB5/XNq0qfj5k06y2bDt29v2adHRTuI5l58vDR5sxViSuneXXnrJv78PhIeMjAzFxsYyCQSAJ1EAQyQYlNats1mv770nLVhgo177VaxoEyTat5c6dJDOOMMWUPaTF16QbrlFys2VGjaUZs60S8WACxRAAF5GAXTkl1/s8ud770lz5thkiAPVr29FsEMHqVUrqUoVJzFDbskSGwFMT5diY20LuS5dXKeCH1EAAXgZBTAMBIO2FMqcOfZYsEDKySl6vUoVmyTRsaM9TjrJXdZQ2LbNtpDbPzN44EBp1Ci2j0NoUQABeBkFMAxlZUkffVRUCLduLf56o0ZWBDt1kpo3l8qXd5OzLOXkSIMGSf/8pz2/8ELp1Velk092Ggs%2BQgEE4GUUwDAXDEorV1oRfOcdaenS4rNkjz/eLhN37ixdfrnktf9O/fvfUp8%2B0p49dkl44kQpKcl1KvgBBRCAl1EAI8yuXdJ//iO9%2B67dP/jzz0WvVawoXXyx3TPXubN3Rss2bpSuuUb65BN7fu210oQJUo0abnPBm1JTU5Wamqr8/HytW7eOAgjAkyiAESwvT1q8WHr7bXusW1f89UaNpCuusMd550X2rOK8PGnkSLsXsKBAOvFE6fnnpbZtXSeDVzECCMDLKIAesm6dFcHZs6X//rf4peK6dYvK4CWXSJUqOYt5TJYula6/XvrmG3vep4/06KOMBqL0UQABeBkF0KN27bL7Bt96yy4ZH7gNXWys3Td45ZW27mCkLbiclSUNGSI99ZQ9T0iwS8JXXhnZo5wILxRAAF5GAfSBffukjz%2BWZs2y0cEdO4pei4qyy6jduxdtVxcpFi2Sbrqp6NJ3585WCtntC6WBAgjAyyiAPlNQYJMpZs60Qrj/Uqpky8m0aiX16CF17Wp79Ya7ffukhx6Sxo2zHUSqVJHuv1/q39/KLXC0KIAAvIwC6GPBoLRqlTRjhj2%2B%2BqrotXLlbGu6v/1N6tZNio93l/NwrFkj3XqrtHChPT/1VOmxx2ytRC4L42hQAAF4GQUQhdavtyI4fbq0fHnR%2BUDAlpe56iq7VHzCCe4yHkowKE2dagtIb99u59q0kcaPtxnRwJGgAALwMgogSrRpky3C/Oab0qefFp0vV862pUtKspHB445zFvGgMjNtuZjHH7cdRcqVk3r3tmVk6tZ1nQ6RggIIwMsogPhDmzdbEZw2rfjIYMWKUrt2tkhzly5S1aruMpZkwwZp8GAb0ZSkypWlu%2B6yc%2BFYXBFeKIAAvIwCiCPy7bdWBF9/3bao269aNVuG5e9/ly67TKpQwV3G31q6VBo40BbNlmwZnIEDpTvvlGJi3GZD%2BKIAAvAyCiCO2urV0muvSa%2B%2Batu17Ve7to0KXned1LhxeEzCCAZtL%2BWhQ23iiyTFxVkRvO22yFsLEWWPAgjAyyiAOGbBoC0tM3WqjQzu2lX0WqNGUq9etn9vOMwkLiiwjMOH26QXyYrggAFWBBkRBHsBA/ADCiBKVW6u7TwyZYotOp2dbefLl7f7BW%2B4wRZsdr0VXV6eFdZ//MMua0u2CPZdd0n9%2BnGPIBgBBOBtFECUmZ9%2Bkt54Q3rpJRsh3C8uzu4V7NNHatjQWTxJVgRffdVmDe/fUSQ62tYUvOce22YO/kQBBOBlFECExNq10osvSpMnF63RJ0nNmknJyVLPnm7vw8vPt5nOo0cXTW6pVMkuXw8YIDVo4C4b3KAAAvAyCiBCKi9PmjtXev556e237blk5e%2Baa6S%2BfaVzz3WXLxiU3n1XGjNGWrLEzgUCNsN54ECpeXN32RBaFEAAXkYBhDM7dkgvvyw991zRhAxJOv98K4JXX23Ly7iyaJHtMfzOO0XnWra0Itipk93XCO%2BiAALwMgognAsGpQULpH/9y3Yfycmx89WrS9dfL91yi3TWWe7yrV4tPfqoTRrJzbVzp51m9wj26hV%2BC2CjdFAAAXgZBRBh5YcfbNLIpElFs3Ml6aKLbJmWK690N4N42zbpqaekiROln3%2B2c8cfbxNGbr89PJa5QemhAALwMgogwlJBgfThh1a2Zs%2B2SRqSlazkZOnmm6UTT3STbe9e6YUXpCeeKFoAu1Ilm9mckuJ2tBKlhwIIwMsogAh7330nPfusXSJOT7dz5ctLV1xhI2%2BtWrnZbSQ/X5o5Uxo/vvgyN%2B3a2czhSy8Nj11QcHQogAC8jAKIiJGba4UrNVVauLDo/Bln2OXhXr3svkEXli61Ijhzpo1eSrYN3sCB0t/%2BJlWs6CYXjh4FEICXUQARkVatkp5%2B2nYc2bvXzlWrZvsP33qrdPbZbnJ9%2B61dGn7hBemXX%2BzcSSdJ/fvbwtcuZzXj8LAVHAA/oAAiomVk2OLSTz8tff110fm//MWKYPfuUlRU6HPt2iU984z05JM2sUWyHVDuvNO2mqtRI/SZcGQYAQTgZRRAeEIwKM2fb0Vw5syiSSNxcdKNN9qkkT/9KfS5fv3V1jp85BFpwwY7FxNjJTAlxWYRIzxRAAF4GQUQnrNtm00aefZZ6fvvi863aWMziK%2B4IvRLyeTlFW01t2qVnYuOlu66yyaMMCIYfiiAALyMAgjPysuzXTwmTpTef99GCSWpVi1bYLpPH%2BnPfw5tpoIC6a23pJEjpbQ0O3fccdLQodIdd0iVK4c2Dw6OAgjAyyiA8IWNG23LuRdflLZvLzrfvLnUu7fUs2doR%2BGCQbtUPWyYtGaNnatfX3rsMRuhZPkY9yiAALyMAghfycuT5syRnn9eevfdonsFo6KkLl1sMed27UJ3iTg/3yax3H%2B/XbqWbJ/hZ55xt9A1DAUQgJdRAOFb6enSK6/YJI399%2BVJdkm2Rw8bFbz4YqlChbLPkpVl9wc%2B8oitd1ijhvTa6I1q98MUC9qggV23rlmz7MNAEgUQgLdRAOF7waDdjzdlivT668UvEdeqJXXtKnXrZjuOlPWSMmvW2CXpNstGaaQeUHkVFL1Ytao11iuvLNsQkEQBBOBtFEDgAPn5tpzMtGnSjBm2nt9%2BMTHS5ZfbJdp27aTatcsmQ94bM1ShZ/eSX6xUyYYrTzutbL45ClEAAXgZBRA4iNxcK4MzZtjM3QNHBiXb6q1NG%2Bmyy6SWLW1Zl1Jx0UXSokUHf/2ee2y2CMoUBRCAl1EAgcNQUCAtXy69/bZNIvnii%2BKvly8vnXuuFcHmzaWmTW1W71HN5q1cWcrOPvjrLVpIixcfxQfjSFAAAXgZBRA4Cjt2SB9%2BaI9586TNm3//nho1bE/ihg2lM86wq7YnnywlJh56T%2BBgXJwCB157/q22baW5c4/5Z0DJ2AsYgB9QAIFSsGWLXbVdulT67DPpyy%2BlnJyDvz862rapi4mxAb9y5aR9%2B6Sff5bu3XKbbgk%2Bc/Avfv55298OZYoRQABeRgEEykBOjvT119JXX0mrV0tr10rffGMjhZmZh/7ak7RZywIX6ITgzt%2B/eO65dvmXLUPKHAUQgJdRAIEQy8iwpf1277YymJ1t9xhGRUmxsVLdulLdrHUqN2iArVZdUCBVqSJde60tFMjGwSFBAQTgZRRAIJz98IP044%2B2LUhMjOs0vkIBBOBlIdjjAMBRq1XLHgAAlKJyrgMAAAAgtCiAAAAAPkMBBAAA8BkKIAAAgM8wCeQYBINBZf7Rom4AIkJ2drayD9iCb/8/2xkZGa4iAShjMTExChzVnp2Rj2VgjsH%2BZSIAAEDk8fMyTxTAY3DgCGDTpk21bNmyUvnccPysjIwMJSYmauvWraXyD0s4/oyl%2BVn83iPvs347Arh9%2B3ZdcMEFWrNmjerWresslx8/K1z/3ofj76o0Pytcf%2B9l%2BVl%2BHgHkEvAxCAQChf%2BQlC9fvtT%2BX0S4fpYkVa9evVQ%2BL1x/xtL6LH7v3vgsyf4Dwe8%2B9J8lhd/f%2B3D9XXn99x7OnxXJmARSSm6//XbPf1ZpCtefsbQ%2Bi9%2B7Nz6rNIXrzxiun1WawvGf63D9rNIUrj9juP6%2BQo1LwDgsbIvlBr93d7777rvCy2Ennnii6zi%2Bwt97N/i9%2BwsjgDgsUVFRevDBBxUVFeU6iq/we3dn/%2B%2Bc333o8ffeDX7v/sIIIACUgNEQAF7GJBAAQPj49FNp3Tqpdm3pssuk8uVdJwI8iQIIAHBvzRrp2multLTCU8HERH0/bJKWxbXXtm3Snj1SdrYUCEiVK0sxMVLNmlJ8vHTiiVJiop0H8Me4BAwAJeAScAj9%2BKPUqJGUnv67l7JVSS21WJ/r/MP6qMRE6fTTpTPPlM4%2BW2rSRGrYUKpUqbRDA5GNEUAAgFvPPVdi%2BZOkKOVozHHjNLHVG6pRw0b4CgqkffukzExp1y5p%2B3Zp61bpl1/sz61bpQ8/POAzoqwIXnih9Je/SBddJMXFhehnA8IUI4D4Q7m5ubr//vs1Z84cbdiwQbGxsWrdurXGjh2rOnXquI7naTNmzNCkSZP0%2Beefa9euXVqxYoUaN27sOpYvMAIYQhdfLC1cePDXo6Ot7R1CMGgDievXS//7n7RqlfTll9KKFdJPP/3%2B/Y0aSa1bS23a2LevWvUYf4YINmbMGM2YMUP/%2B9//VKVKFbVo0UIPP/ywTj/9dNfRUIYogPhDe/bsUY8ePZScnKxzzjlHP/30k%2B6%2B%2B27l5eVp%2BfLlruN52pQpU7Rx40bVqVNHycnJFMAQSE1NVWpqqvLz87Vu3ToKYCj8UQGsVk3au/eoPjoYlL79VvrkE2nJEvs2q1cXf0/lylKrVlKnTlKXLnY/oZ%2B0a9dOSUlJatq0qfLy8nTfffdp5cqVWrNmjapVq%2BY6HsoIBRBHZdmyZbrgggu0efNmnXTSSa7jeN6mTZtUv359CmAIMQIYQqNHS/fdd/DXu3eXpk8vtW/3ww/SvHnSBx9Ic%2BfaJeMDNW0qdesm9eghnXpqqX3biPHDDz/ohBNO0IIFC3TRRRe5joMywkLQOCp79uxRIBBQjRo1XEcBEOmSk6UTTijxpdxARe3qM6hUv12tWtJVV0nPPitt3iytXCmNHSu1aGEzjJctk4YMkU47TTr3XGncOGnLllKNENb27NkjSapZs6bjJChLFEAcsX379unee%2B/VNddcw8gIgGNXq5bN2mjYsNjpbaqjbsF/68%2B9LtAbb9jl3NIWCNi3HTxYWrzYJpRMmmT3BpYvb/cQDh4s1atnV6qffdaWo/GqYDColJQU/eUvf1HD3/zvAW%2BhAOJ3pk6dqujo6MLHokWLCl/Lzc1VUlKSCgoK9PTTTztM6T2H%2Br0DnteokQ3FLVokPf%2B8Okia8fiL2nJ2Z/3wg9Szp9S%2BvU3wKEu1a0s33yy9/75NTJ440YpfIGD3D958s607ePXVdvk4P79s84TaHXdCh7FZAAAaN0lEQVTcoa%2B%2B%2Bkqvvfaa6ygoY9wDiN/JzMzUjh07Cp/XrVtXVapUUW5urq666ipt2LBBH3/8sY4//niHKb3nYL93iXsAXeAeQLcCgYBmzpypDh26avRoacwYKSdHqlBB6ttXGjbMylqobN0qvfqqNHmyrVm9X2KidMMN0o032ihhJOvXr59mzZqlhQsXqn79%2Bq7joIxRAHFY9pe/9evXa968eapVq5brSL5CAQw9CqBb%2Bwtg165dJdnyLv37S2%2B/ba9XqybddZedC%2BWtasGg9MUX0osvWiHcv8RMICC1bWsjhJ07SxUrhi7TsQoGg%2BrXr59mzpyp%2BfPn67TTTnMdCSHAJWD8oby8PPXo0UPLly/X1KlTlZ%2Bfr/T0dKWnpysnJ8d1PE/bvXu30tLStOb/hxzWrl2rtLQ0pR9k0Vwgku3du1dpaWlK%2B//t4DZu3Ki0tDRt2bJFp50mzZ4tffyxzdLNyrLJwyefLN1/v60BGAqBgHTeedKECdK2bdJrr9mWxcGgXRLu3l066SQboYyUiSO33367XnnlFb366quKiYkp/Pf7r7/%2B6joaylIQ%2BAMbN24MSirxMW/ePNfxPO3FF18s8ff%2B4IMPuo7meXv27AlKCu7Zs8d1FN%2BYN29eiX/fe/XqVex9BQXB4MyZweDZZweDVr2CwWrVgsGUlGDw%2B%2B/dZP/mm2Dw3nuDwRNOKMpUrlww2LlzMPjee8Fgfr6bXIfjYP9%2Bf/HFF11HQxniEjAAlIBLwOGvoEB66y3pH/%2Bw2bqS7fl7/fXSgAG2J3Co5eRIs2bZ5JF584rOn3KKdOutdr8gt08jHFAAAaAEFMDIEQxK//mPXRL%2B73/tXCAgXXGFFcH96/uF2tq10jPPSC%2B9VLR0TOXKUlKSdPvt0vnnhz4TsB8FEABKQAGMTEuWSA8/bPcL7te8uRXBrl1tbb9Qy8qyewWffrpopFKSLrhAuu02W%2BKmcuXQ54K/UQAB4ADsBewNX38tjR8vTZlil2Uluwx79912GTY6OvSZgkHbkzg1VXrzzaJcxx8v9ekj3XKLxOorCBUKIACUgBFAb0hPtxm7zzwj7d5t52rUsLJ1xx1S3bpucu3cKT3/vOXavxdxIGCLXd92m9SunZvRSvgHBRAASkAB9JZffrF78R5/XPrmGztXsaLt6JGSIp1zjptc%2BfnSO%2B/Y5eH33y86f/LJtqbgjTeGdsFr%2BAcFEABKQAH0poICW0x6/HjbdW6/Nm3sPsE2bdxMGJFsseuJE22R6f0LTFesKHXrZrufXHKJu2zwHgogAJSAAuh9y5ZJjz4qTZ9uxVCSzj7bimBSkrvdPH79VZo2zcrgp58WnW/QQEpOlnr1ktiMCceKAggAJaAA%2BsemTdITT0jPPWczdiXb4zclRbrpJjcTRvZLS7MiOHWqtHevnatYUbrySst22WVSOfb0wlGgAAJACSiA/vPTTzYp48knpR077FzNmlK/fvZwuYBzZqb0%2BuvSv/4lLV9edL5ePZvV3Lu3HQOHiwIIACWgAPrXvn3S5MnSI48UTRipVs128khJkRIS3OZLS7PRyldeKVpgOhCw0cAbbrD1DqtWdZsR4Y8CCAAloAAiP1/697%2BlMWOsdEm2YHNysjR4sLslZPb79VdpxgzphRekjz8uOl%2B9unTVVXavYMuWTBxBySiAAFACCiD2CwalOXOkUaOkpUvtXFSUFcEhQ6Q6ddzmk6SNG6WXX7albjZvLjp/yinSdddJf/%2B7dOqpzuIhDFEAAaAEFED8VjBoI20jRhQtIVO5si3cfO%2B94TEzt6BAWrjQyuD06UUTRySpWTMrglddJZ1wgruMCA8UQAA4AFvB4Y8Eg9K8edIDD0iLF9u56Gipf397xMS4zbdfVpY0a5Zth/fBB0VL3ZQvL7VuLV1zjd0vyF9vf6IAAkAJGAHEHwkGpblzpfvvlz7/3M7VqmXFsG9fd%2BsIliQ93dYWnDrV1j/cLypK6tjR1j3s2JHJI35CAQSAElAAcbiCQbvcev/90rp1dq5BA5tF3Llz%2BE3CWL9eeu01e/zvf0Xnq1WTOnWyS8Tt20tVqrjLiLJHAQSAElAAcaRyc6Vnn5WGD5d%2B%2BMHOtW5ti0yfdZbTaCUKBqUvv7T1BadNswWx94uOtjLYo4eVQUYGvYcCCAAloADiaGVk2NIxjz8uZWfbPXf9%2BtnkkXD9qxQM2qXhN96Q3nxT2rKl6LWqVaUOHaTu3e0ycbjc44hjQwEEgBJQAHGsNmywSSGzZtnz%2BHgrhT17ht9l4QMFg9Jnn1kRnD69%2BLIyUVFS27ZSt252edvl7ig4NhRAACgBBRClZe5cGwFcv96et2tnW86dfLLTWIclGLQJLv/%2Btz32/wySjWxedJHtS3zFFdJJJ7nLiSNHAQSAElAAUZqys6WHH7bFpHNy7LLqmDHSHXdI5cq5Tnd4gkFp9WorgjNn2v2DBzr3XFtW5oorpEaNwnuUExRAACgRBRBlYe1a6eabbbFmSfrLX6QXX4zMXTo2bLDL27Nm2XqI%2B9cZlGx0s0sXK4N//Wt4LYkDQwEEgBJQAFFWCgqkSZOkQYNsp46qVaXx423twEgdNdu5U3rnHSuDH3wg7dtX9FpsrM0k7tLFLn8fd5y7nChCAQSAElAAUdY2bZJuvNF2FZFshu3zz0u1azuNdcyysqwEzp5tpXD/kjiS3Tf417/aBJJOnWy9RLhBAQSAElAAEQoFBdKTT9pewtnZVv6mTJHatHGdrHTk50uffmpl8O23pTVrir9%2B2mlWBDt1ssvhlSq5yelHFEAAOAB7AcOFlSttb95Vq%2Bwy8JAhtm5ghQquk5WuDRusCL7zjrRggS2evV9MjC0x07GjXTKOj3eX0w8ogABQAkYAEWq//iqlpEgTJ9rziy%2B2XTq8WoQyMqT335fefVeaM8fuIzzQeedZGezQQWraNHJmS0cKCiAAlIACCFemTZNuuskmiCQk2GLMLVq4TlW2Cgqk5cutDL77rq09eKC4OJtA0qGDjRKyAPWxowACQAkogHBp7Vrbem31altC5amnbJawX6SnS%2B%2B9ZyOD779vo4X7lSsnXXCBXSZu395GChkdPHIUQAAoAQUQru3da7OE33zTnt92m/TEE/5bUy83V1qyxMrgnDl2n%2BSBatWSLr/cRgjbtrXn%2BGMUQAAoAQUQ4SAYtB1D7r/fji%2B7zC4J16jhOpk7W7dK//mPjRB%2B%2BKGUmVn0WiBg%2By8/8oi7fJGCQVMAAMJUICANHWpbr1WrJn30kd0PuGmT62TuJCZKycnSjBnSrl3S/PnS4MHS2WdbST7tNNcJIwMjgAA8p3fv3nr55ZeLnWvWrJk%2B%2BeSTw/4MRgARbtLSbAHl776z9QLffdfuf0ORbdusKMfGuk4S/hgBBOBJ7dq10/bt2wsfc%2BbMcR0JOCaNG0uffCKdc460Y4ctE/P%2B%2B65ThZc6dSh/h4sCCMCToqKiFB8fX/ioWbOm60jAMatbV1q4UGrd2rZc69hReu0116kQiSiAADxp/vz5OuGEE9SgQQMlJydr529Xmf2N7OxsZWRkFHsA4ah6dbv8m5Qk5eVJ115btHg0cLi4BxCA50ybNk3R0dGqV6%2BeNm7cqGHDhikvL0%2Bff/65oqKiSvya4cOHa8SIEb87zz2ACFcFBdKdd0qpqfZ83Dhp4EC3mRA5KIAAItrUqVPV94AVct977z399a9/Lfae7du3q169enr99dfVrVu3Ej8nOztb2dnZhc8zMjKUmJhIAURYCwZtiZjRo%2B35iBHSsGE2exg4FI9tMw3Ab7p06aJmzZoVPq9bt%2B7v3pOQkKB69epp/fr1B/2cqKiog44OAuEqEJBGjbKZr/fdJz34oC2cPHIkJRCHRgEEENFiYmIUExNzyPfs2rVLW7duVUJCQohSAaE1dKgUFSUNGCA99JCdowTiUJgEAsBT9u7dqwEDBmjp0qXatGmT5s%2Bfr86dOysuLk5XXnml63hAmenfX3rsMTt%2B6CErgMDBMAIIwFPKly%2BvlStXavLkyfr555%2BVkJCgVq1aadq0aX84UghEunvusfsC%2B/eXhg%2BXKle2XTKA32ISCACUgJ1AEMnGjpWGDLHjp56S7rjDbR6EHy4BAwDgMffea7OBJalfP2nKFLd5EH4ogAAAeNCIEdJdd9nxDTdIb7/tNg/CCwUQAAAPCgRsUsj110v5%2BdJVV0mLF7tOhXBBAQQAwKPKlZOee07q1Enat8/%2BXLPGdSqEAwogAAAeVrGiNG2adOGF0s8/S%2B3bS9u2uU4F1yiAAHCA1NRUnXnmmWratKnrKECpqVrV7gFs0EDaskXq2FHKzHSdCi6xDAwAlIBlYOBFGzbYSODOnVKHDtJbb0kVWBHYlxgBBADAJ045xUYCK1eW5syRUlJcJ4IrFEAAAHzkggukV16x46eekp5%2B2m0euEEBBADAZ7p3l0aPtuM775Q%2B%2BshtHoQeBRAAAB%2B6917p73%2B3NQL/9jfpm29cJ0IoUQABAPChQEB69lmpWTPpp5%2Bkrl2ZGewnFEAAAHyqcmVpxgwpIUFavVrq3VtibRB/oAACAOBjdepYCaxUyf4cO9Z1IoQCBRAAAJ9r3lyaMMGO77tP%2BuADt3lQ9iiAAABAycnSTTfZJeCrr7YdQ%2BBdFEAAACDJ1gU87zxp1y6bGZyT4zoRygoFEAAOwF7A8LPKlaU335SOO0767DNp4EDXiVBW2AsYAErAXsDws3fekTp3tuPp023haHgLI4AAAKCYTp2KRv/69JE2bnSbB6WPAggAAH5n1CjpwgulPXukpCTuB/QaCiAAAPidihWl116TatSw%2BwGHDXOdCKWJAggAAEpUr570/PN2PG6c9OGHbvOg9FAAAQDAQXXrJvXta8fXXy/9%2BKPbPCgdFEAAAHBIjz0m/fnP0vbtRYtFI7JRAAEAwCFVrSq9%2BqrdF/jWW9Jzz7lOhGNFAQQAAH%2BocWNp9Gg7vuce6Ztv3ObBsaEAAgCAw5KSIl1yiZSVZfcD5ue7ToSjRQEEgAOwFRxwcOXKSS%2B/LFWvLi1dajODEZnYCg4ASsBWcMDBvfyy1Lu33RO4fLl09tmuE%2BFIMQIIAACOyPXXS1dcIeXmSr16sUtIJKIAAgCAIxIISJMmSccfL6WlFU0OQeSgAAIAgCNWu7Y0YYIdjxolffml2zw4MhRAAABwVHr2tJ1C8vLsnsDcXNeJcLgogAAA4KgEAtLTT0s1a9ql4EcecZ0Ih4sCCAAAjlrt2tI//2nHI0ZI//uf2zw4PBRAAABwTK69Vmrf3mYD33STVFDgOhH%2BCAUQAAAck0BAmjhRio6WFi%2B2Y4Q3CiAAADhmJ51UtBzMkCHS99%2B7zYNDowACAIBScdttUrNmUkaGdOedrtPgUCiAAHAA9gIGjl758tKzz0oVKkgzZkhvv%2B06EQ6GvYABoATsBQwcvcGDpXHj7LLw6tV2byDCCyOAAACgVD3wgHTyydKWLdLIka7ToCQUQAAAUKqqVSvaJu6xx6RVq9zmwe9RAAEAQKnr2FG68kopP98mh3DDWXihAAIAgDLxxBNS1arSokXSlCmu0%2BBAFEAAAFAmTjpJGjbMjgcOlPbscZsHRSiAACLKjBkzdPnllysuLk6BQEBpaWm/e092drb69eunuLg4VatWTV26dNF3333nIC2AlBTp9NOlnTulBx90nQb7UQABRJSsrCy1bNlSY8eOPeh77r77bs2cOVOvv/66/vvf/2rv3r3q1KmT8vPzQ5gUgCRVqiQ99ZQdT5jAhJBwwTqAACLSpk2bVL9%2Bfa1YsUKNGzcuPL9nzx7VqlVLU6ZMUc%2BePSVJ27ZtU2JioubMmaPLL7/8sD6fdQCB0tW9uy0O3aqV9NFHtn8w3GEEEICnfP7558rNzVXbtm0Lz9WpU0cNGzbUkiVLHCYD/G38eKlyZWnePOnf/3adBhRAAJ6Snp6uSpUq6bjjjit2vnbt2kpPTz/o12VnZysjI6PYA0DpOflkadAgOx4wQPr1V6dxfI8CCCBsTZ06VdHR0YWPRYsWHfVnBYNBBQ5xzWnMmDGKjY0tfCQmJh719wJQssGDpcREafNm6dFHXafxNwoggLDVpUsXpaWlFT7OP//8P/ya%2BPh45eTk6Keffip2fufOnapdu/ZBv27IkCHas2dP4WPr1q3HnB9AcVWr2h7BkjR2rPT9927z%2BBkFEEDYiomJ0amnnlr4qFKlyh9%2BzXnnnaeKFSvqgw8%2BKDy3fft2rVq1Si1atDjo10VFRal69erFHgBKX8%2BeUsuW0i%2B/SEOGuE7jXxVcBwCAI7F7925t2bJF27ZtkyStXbtWko38xcfHKzY2Vn369FH//v11/PHHq2bNmhowYIAaNWqk1q1bu4wOQDb79/HHpQsusN1B%2BvWTmjZ1ncp/GAEEEFFmz56tJk2aqGPHjpKkpKQkNWnSRBMnTix8z%2BOPP66uXbvqqquuUsuWLVW1alW9/fbbKl%2B%2BvKvYAA7QtKl03XV2nJLCPsEusA4gAJSAdQCBsvXdd1KDBjYbePp0WycQocMIIAAACLkTT7TlYCSbHZyT4zaP31AAAQCAE4MGSfHx0rffSs884zqNv1AAAQCAE9HR0siRdjxypPTzz27z%2BAkFEAAAOHPDDdKZZ0q7d9vagAgNCiAAAHCmQoWi4vfPf0qswR4aFEAAOEBqaqrOPPNMNWVhMiBkOnWSLrpI2rdPevBB12n8gWVgAKAELAMDhNann0rNm0vlyklffSWddZbrRN7GCCAAAHCuWTOpWzepoEAaOtR1Gu%2BjAAIAgLAwapSNAM6eLS1Z4jqNt1EAAQBAWDjjDOnGG%2B14yBC2iCtLFEAAABA2HnxQioqSFi6U5s51nca7KIAAACBsnHiidPvtdnzffXZPIEofBRAAAISVe%2B%2B1XUK%2B%2BEKaMcN1Gm%2BiAAIAgLBSq5aUkmLHDzwg5ee7zeNFFEAAABB2UlKk446Tvv5aevVV12m8hwIIAADCTmysNHCgHY8YIeXmus3jNRRAAAAQlvr1s8vB334rTZ7sOo23UAAB4ADsBQyEj%2BhomxAiSf/4h5ST4zaPl7AXMACUgL2AgfDwyy/Sn/4kpadLkyZJN9/sOpE3MAIIAADCVtWqRaOAo0YxClhaKIAAACCs3XyzlJAgbdkivfii6zTeQAEEAABhrUqVolHA0aMZBSwNFEAAABD2kpOLRgFfesl1mshHAQQAAGGvShVp0CA7HjOGdQGPFQUQAABEhJtvlmrXljZtkqZMcZ0mslEAAQBARKhaVRowwI5Hj5by8tzmiWQUQAAAEDFuvVWKi7PdQaZNc50mclEAAQBAxKhWTbrnHjseNUoqKHCbJ1JRAAHgAGwFB4S/22%2BXYmOlr7%2BWZs50nSYysRUcAJSAreCA8DZsmPTQQ9K550rLl0uBgOtEkYURQAAAEHHuussmhXzxhTR3rus0kYcCCAAAIk5cnNS3rx2PHu02SySiAAIAgIjUv79UsaK0aJG0eLHrNJGFAggAACJS3bpSr152PHas2yyRhgIIAAAi1qBBNgHknXeklStdp4kcFEAAABCxTjtN6t7djseNc5slklAAAQBARLv3XvvztdekzZvdZokUFEAAABDRzjtPuuwyKT9feuwx12kiAwUQAABEvEGD7M/nnpN273abJRJUcB0AAADgWLVpIzVuLNWoIf34o1SzputE4Y0CCAAHSE1NVWpqqvLz811HAXAEAgFpwQKJnRsPD3sBA0AJ2AsYgJdxDyAAAIDPUAABAAB8hgIIAADgMxRAAAAAn6EAAgAA%2BAwFEEBEmTFjhi6//HLFxcUpEAgoLS3td%2B%2B55JJLFAgEij2SkpIcpAWA8EQBBBBRsrKy1LJlS40dO/aQ70tOTtb27dsLH5MmTQpRQgAIfywEDSCiXHfddZKkTZs2HfJ9VatWVXx8fAgSAUDkYQQQgCdNnTpVcXFxOuusszRgwABlZma6jgQAYYMRQACec%2B2116p%2B/fqKj4/XqlWrNGTIEH355Zf64IMPDvo12dnZys7OLnyekZERiqgA4AQjgADC1tSpUxUdHV34WLRo0WF9XXJyslq3bq2GDRsqKSlJ06dP14cffqgvvvjioF8zZswYxcbGFj4SExNL68cAgLDDXsAAwlZmZqZ27NhR%2BLxu3bqqUqWKJLsHsH79%2BlqxYoUaN258yM8JBoOKiorSlClT1LNnzxLfU9IIYGJiInsBA/AkLgEDCFsxMTGKiYk55s9ZvXq1cnNzlZCQcND3REVFKSoq6pi/FwBEAgoggIiye/dubdmyRdu2bZMkrV27VpIUHx%2Bv%2BPh4ffvtt5o6dao6dOiguLg4rVmzRv3791eTJk3UsmVLl9EBIGxwDyCAiDJ79mw1adJEHTt2lCQlJSWpSZMmmjhxoiSpUqVK%2Buijj3T55Zfr9NNP15133qm2bdvqww8/VPny5V1GB4CwwT2AAFCCjIwMxcbGcg8gAE9iBBAAAMBnKIAAAAA%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'}