Genus 2 curve data - 126859.b.126859.1

Formats: - HTML - YAML - JSON - 2024-04-24T14:53:59.048238

• 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': '639796208606119598', 'abs_disc': 126859, 'analytic_rank': 1, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[126859,[1,-552,127410,-126859]]]', 'bad_primes': [126859], 'class': '126859.b', 'cond': 126859, 'disc_sign': -1, 'end_alg': 'Q', 'eqn': '[[-1,-8,-29,-38,-8,1],[1]]', 'g2_inv': "['-68232727503987277824/126859','-2836879788910804992/126859','-156952622199877632/126859']", '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': "['18528','46416','283353360','-507436']", 'igusa_inv': "['9264','3568168','1828822192','1052596477616','-126859']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '126859.b.126859.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '4.9535581930389571041186827315353136878211859962440', 'prec': 170}, 'locally_solvable': True, 'modell_images': ['2.6.1'], 'mw_rank': 1, 'mw_rank_proved': True, 'non_maximal_primes': [2], 'non_solvable_places': [], 'num_rat_pts': 1, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '3.8037139635287906108631408918', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.30229513589', 'prec': 44}, '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': 1, 'torsion_order': 1, 'torsion_subgroup': '[]', 'two_selmer_rank': 1, 'two_torsion_field': ['5.3.2029744.1', [-1, -5, 10, -6, -1, 1], [5, 5], 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': '126859.b.126859.1', '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': '126859.b.126859.1', 'mw_gens': [[[[1, 1], [3, 1], [1, 1]], [[0, 1], [1, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '1.3022951358948742444345400687595', 'prec': 110}], 'mw_invs': [0], 'num_rat_pts': 1, 'rat_pts': [[1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 126859, 'lmfdb_label': '126859.b.126859.1', 'modell_image': '2.6.1', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  {'cluster_label': 'c3c2_1~2_0', 'label': '126859.b.126859.1', 'p': 126859, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '126859.b.126859.1', 'plot': 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TpYceMp0IZ4sCCAAATsuypP79pf/8R7r4YmnuXKl6ddOpcLYogAAA4LSeeEJauFCKjbUv%2Bqhf33QinAsKIAAAOKVly6T777fHU6ZILN8e%2BSiAAADgZ%2BXlSb17S2Vl9iHgrCzTiVARKIAAAOCkAgHpllukH36QWrSQpk2zb/2CyEcBhFE5OTnyer1K43gCAISde%2B6R1q%2BX6tSR3nxTiosznQgVxWVZlmU6BOD3%2B%2BXxeOTz%2BZSQkGA6DgA43osvSnfcYc/4vfsu9/uLNswAAgCAEJs2SXfdZY8feYTyF40ogAAAIGjPHvtmz4GA1LWr9Oc/m06EykABBAAAkqTSUum226SdO6VLLpFmz5aq0RSiEj9WAAAgyV7bd%2BlSqWZNacEC6fzzTSdCZaEAAgAALVggZWfb4%2Befl5o3N5sHlYsCCACAw33%2BudSvnz2%2B9177MDCiGwUQAAAH8/ulm2%2BW9u%2BXOnSQHn/cdCJUBQogAAAOVVYm3X67PQN40UXSq69KNWqYToWqQAEEAMChHntMWrhQio21V/pITDSdCFWFAggAgAO9//7Re/xNnSpdeaXZPKhaFEAAABzm66/tCz0sS8rKsjc4CwUQAAAHOXDAvuijqEhq00aaMsV0IphAAQQAwCGOzPh98ol9vt8bb0hut%2BlUMIECCKNycnLk9XqVlpZmOgoARL2nnpJeeUWKiZFee82%2B8hfO5LIsyzIdAvD7/fJ4PPL5fEpISDAdBwCizocfSp062ev9PvOMNGSI6UQwiRlAAACiXG6u1KuXXf4yM6W77zadCKZRAAEAiGKHDkm//730ww9Sy5bSjBmSy2U6FUyjAAIAEKUsSxo0SPrXv6R69eybPcfFmU6FcEABBAAgSj37rPTii1K1atL8%2BVKjRqYTIVxQAAEAiEJr1kj33GOPH3tM6tjRbB6EFwogAABRJj9fuuUWqaRE6tlTGjnSdCKEGwogAABRpLjYLn35%2BVKzZtILL3DRB05EAQQAIIoMHy7985%2BSxyMtWCDVrm06EcIRBdDhsrOzlZaWpvj4eCUmJup3v/udvvjii5B9AoGAhgwZovr166tWrVrq3r27du3aFbJPbm6uunXrplq1aql%2B/foaOnSoiouLq/KtAIDjvfSSlJNjj%2BfMkZo0MZsH4YsC6HArVqzQ4MGD9dFHH2np0qU6fPiw0tPTdeDAgeA%2Bw4YN04IFCzR//nytXr1a%2B/fvV9euXVVaWipJKi0t1U033aQDBw5o9erVmj9/vt544w2NGDHC1NsCAMfZtEkaONAeP/yw1LWr2TwIbywFhxDff/%2B9EhMTtWLFCl1zzTXy%2BXy64IILNHv2bPXu3VuStHv3bjVo0EDvvfeeOnfurEWLFqlr167Ky8tTSkqKJGn%2B/Pnq37%2B/CgsLy7W0G0vBAcDZ27NHatVK2rlTuukm6a237Fu/AD%2BHXw%2BE8Pl8kqS6detKkjZu3KiSkhKlp6cH90lJSVFqaqrWrFkjSVq7dq1SU1OD5U%2BSOnfurEAgoI0bN1ZhegBwntJSKSPDLn%2BXXGIf%2BqX84XRiTAdA%2BLAsS8OHD9fVV1%2Bt1NRUSVJBQYFiY2NVp06dkH2TkpJUUFAQ3CcpKSnk9Tp16ig2Nja4z/ECgYACgUDwY7/fX5FvBQAc46GHpCVLpJo17ZU%2Bzj/fdCJEAv6NgKC7775bn3zyiV555ZXT7mtZllzH3FfAdZJ7DBy/z7Gys7Pl8XiCW4MGDc4%2BOAA41FtvSePH2%2BOZM6XLLzebB5GDAghJ0pAhQ/TWW2/pww8/1C9%2B8Yvg88nJySouLlZRUVHI/oWFhcFZv%2BTk5BNm%2BoqKilRSUnLCzOARY8aMkc/nC255eXkV/I4AILpt3y5lZtrjoUPtw8BAeVEAHc6yLN1999168803tWzZMjVu3Djk9VatWqlGjRpaunRp8Ln8/Hxt3bpV7du3lyS1a9dOW7duVX5%2BfnCfJUuWyO12q1WrVif9vm63WwkJCSEbAKB8DhyQfv97ye%2BXrrpKmjjRdCJEGq4CdrhBgwZp3rx5%2Bvvf/66mTZsGn/d4PIqLi5Mk3XXXXXrnnXc0a9Ys1a1bVyNHjtSePXu0ceNGVa9eXaWlpWrRooWSkpI0YcIE7d27V/3799fvfvc7TZkypVw5uAoYAMrHsqR%2B/eyLPZKS7Nu/HHMNHlAuFECH%2B7lz9F588UX1799fknTo0CGNGjVK8%2BbN08GDB3XDDTdo2rRpIeft5ebmatCgQVq2bJni4uKUkZGhiRMnyu12lysHBRAAymfaNGnwYKl6dWnZMumaa0wnQiSiACIsUAAB4PTWr5euvloqKZEmTJBGjjSdCJGKcwABAIgAe/ZIPXva5e/mmyUWW8K5oAACABDmysrsK35zc6VLL5VefFH6mTN4gHKhAAIAEOays6VFi6TzzpNef13yeEwnQqSjAAIAEMaWL7dX%2B5DsC0CuuMJoHEQJCiAAAGHqu%2B%2Bk226zDwH37y/94Q%2BmEyFaUAABAAhDpaVSnz5SQYHUrJmUk2M6EaIJBRAAgDA0frz0j39INWtKr71mPwIVhQIIAECYWblSeuQRezx9uvTrXxuNgyhEAQQAIIzs2SNlZNjn/fXrZ29ARaMAwqicnBx5vV6lpaWZjgIAxlmWdMcd0rffSpddxnl/qDwsBYewwFJwAHB0nd/YWGndOqlFC9OJEK2YAQQAIAx8%2BunR5d0ef5zyh8pFAQQAwLBAwD7v79Ah6cYbpXvuMZ0I0Y4CCACAYQ88IH3yiXTBBazzi6pBAQQAwKDly6XJk%2B3xX/8qJScbjQOHoAACAGCI328v8WZZ0p/%2BJHXrZjoRnIICCACAIcOHSzt3So0bH50FBKoCBRAAAAMWL7YP%2Bbpc0qxZUny86URwEgogAABVzOeTsrLs8T33SNdcYzYPnIcCCABAFbvvPmnXLumSS6RHHzWdBk5EAQQAoAqtXCnNmGGPn39eqlnTbB44EwUQAIAqEghIAwbY46ws6dprjcaBg1EAAQCoIhMmSJ9/LiUl2cu9AaZQAAEAqALffHP0fL/Jk6U6dczmgbNRAGFUTk6OvF6v0tLSTEcBgEp17732Wr/XXy/ddpvpNHA6l2VZlukQgN/vl8fjkc/nU0JCguk4AFChli6V0tOlmBjp448lr9d0IjgdM4AAAFSiw4ft2T9JGjyY8ofwQAEEAKASvfii9OmnUt260sMPm04D2CiAAABUkp9%2BOlr6HnyQCz8QPiiAAABUkqlTpfx8qWFD6a67TKcBjqIAAgBQCfbvl554wh6PHSu53WbzAMeiAAIAUAmmTZP27JGaNJH69DGdBghFAQQAoIIdOmTf7FmS7r/fvv0LEE4ogAAAVLDZs6XvvpMuvpjZP4QnCiAAABXIsqSnnrLHw4ZJNWqYzQOcDAUQAIAK9OGH0mefSbVrS3fcYToNcHIUQAAAKtCMGfZjZqbk8ZjNAvwcCiAAABVkzx5p4UJ7nJVlNgtwKhRAGJWTkyOv16u0tDTTUQDgnL32mlRcLF1xhdSypek0wM9zWZZlmQ4B%2BP1%2BeTwe%2BXw%2BJSQkmI4DAGfluuuk5cvtG0CPGmU6DfDzmAEEAKACfP%2B9tHKlPe7Z02wW4HQogAAAVIBFi6SyMqlFC6lRI9NpgFOjAAIAUAGWLLEf/%2Bd/zOYAyoMCCADAObIs%2B/5/ktSxo9ksQHlQAAEAOEc7d0q7d9urfrRtazoNcHoUQAAAztGGDfbj5ZdLcXFmswDlQQEEAOAcffyx/ci9/xApKIAAAJyjbdvsx2bNzOYAyosCCADAOfr6a/vx0kvN5gDKiwIIAMA52rXLfrz4YrM5gPKKMR3A6X780b56LD/fvot8UZHk90sHDkgHD9prSh4%2BLJWWHv2catWk6tWlmBj7irPYWMntls47z95q1jy61aol1a59dIuPtze3W3K5zL1vAIgWpaXSnj32OCnJbBagvCiAVWzvXmnOHGnpUmnTJvu2ASbExEgJCaGbx3Pi%2BNjnjt2OFMmaNe1CCgBO9eOP9n0AJen8881mAcqLAngOLMvSjz/%2BWO79t2%2BXOnWyZ/mOdf750oUXSvXrS3Xq2MWqVi17Ni821i5r1arZm2XZSw2VldkzgyUlUiBgPx48aI9/%2Bske//STvR04IO3ff3RWUbI/d%2B9ee6ts114rPf106NJIgUBAgUAg%2BPGR/45%2Bv7/yAwFABSostB9dLvtv8DF/2hDm4uPj5XLo4TCXZR35dwvOlN/vl8fjMR0DAACcBZ/Pp4SEBNMxjKAAnoPjZwDT0tK04cjdQE9izx7pllvsQ78nEx9/dAawZk37ZqLHzgAe%2BUdKWZm0cuUqtW37/1RcrOB26JC9HTx4dAawpKQi37HtyHmF1auXb//evaXhw%2B1Dx0ccPwOYn5%2BvK6%2B8Up999pkuuuiiCk4c6nQ/p0j8XtH2nvx%2Bvxo0aKC8vLwq%2BePMzyn8v09V/k6c6Xv67jvpssvs8b59Z3Z%2BdbT9nKrqe1XU74OTZwA5BHwOXC5XyC9e9erVT/mLmJAg/etf0tq19jmAGzdKX3xhXwQSCNjnkZT/iPJN%2Bsc/yp81JsYulkcuBDlyHt/JzgE89ry/gQNv07vvvhJy7l9lnvMXHx9f6X/cT/dzisTvFY3vSZISEhKi6r9fNP6cqvI9SVXzO3Eu76lGDfsf8FXxvc5ENP7uSVX3NyIaUQAr0ODBg0%2B7j8sltW9vb0dYln1e4PFXAR86ZBfDk10FvGLFMqWnX3/CVcBxcfZ25ArgI5vbfXbvadSoq6Puxqbl%2BTlF2veKxvdUlfg5hf/3qUpn%2Bp6OHBEpLbXPqz6TAhiNP6do/J2IRhwCRljYtWtXcDr/F7/4hek4MOzI%2BbVOPj8HocL9dyIlxb6d14YNUuvWptNEv3D/fYgE3MADYcH93ylK99lOVSKquN1uPfzww/w%2BICjcfycaNLAfc3PN5nCKcP99iATMACIs8K85AJGsTx9p3jwpO1saPdp0GuD0mAEEAOAceb3249atZnMA5UUBBADgHLVoYT/%2B3G2%2BgHBDAQQA4BylpdmP27aduNoTEI4ogAAAnKPExKM3g161ymwWoDwogADCxiOPPCKXyxWyJScnm46FKrJy5Up169ZNKSkpcrlcWrhwYcjrlmXpkUceUUpKiuLi4nTttdfq008/NZT2RNdfbz8uWWI2RzQ53e9E//79T/ib0bZtW0NpIwsFEEBYadasmfLz84Pbli1bTEdCFTlw4ICuuOIKTZ069aSvP/HEE5o8ebKmTp2qDRs2KDk5WZ06dQpZktOkG2%2B0H997z77BP87d6X4nJOnGG28M%2BZvx3nvvVWHCyMVKIADCSkxMDLN%2BDtWlSxd16dLlpK9ZlqWnnnpKDzzwgHr06CFJeumll5SUlKR58%2BZpwIABVRn1pDp2tFdk%2BuYb6eOPj14YgrN3qt%2BJI9xuN38zzgIzgADCyvbt25WSkqLGjRvr1ltv1ddff206EsLAN998o4KCAqWnpwefc7vd6tChg9asWWMw2VG1aklHusqrr5rN4iTLly9XYmKiLrvsMmVlZamwsNB0pIhAAYRROTk58nq9SjtyCR0crU2bNnr55Zf1/vvva%2BbMmSooKFD79u21Z88e09FgWEFBgSQpKSkp5PmkpKTga%2BHg1lvtx7lzpbIys1mcoEuXLpo7d66WLVumSZMmacOGDbr%2B%2BusVCARMRwt7HAKGUYMHD9bgwYODK4HA2Y491NO8eXO1a9dOl1xyiV566SUNHz7cYDKEC5fLFfKxZVknPGdSt26Sx2MvCbdsmX1YGJWnd%2B/ewXFqaqpat26thg0b6t133w2eKoCTYwYQQNiqVauWmjdvru3bt5uOAsOOnON1/GxfYWHhCbOCJsXF2cvCSdKMGWazONGFF16ohg0b8jejHCiAAMJWIBDQtm3bdOGFF5qOAsMaN26s5ORkLV26NPhccXGxVqxYofbt2xtMdqKBA%2B3HBQukXbvMZnGaPXv2KC8vj78Z5UABBBA2Ro4cqRUrVuibb77RunXrdMstt8jv9%2Bv22283HQ1VYP/%2B/dq8ebM2b94syb7wY/PmzcrNzZXL5dKwYcM0fvx4LViwQFu3blX//v1Vs2ZNZWRkGE4eqnlzqUMHqbRUOsXdS1AOp/qd2L9/v0aOHKm1a9dqx44dWr58ubp166b69evr5ptvNpw8AlhAGPD5fJYky%2BfzmY4Cg3r37m1deOGFVo0aNayUlBSrR48e1qeffmo6FqrIhx9%2BaEk6Ybv99tsty7KssrIy6%2BGHH7aSk5Mtt9s60nEKAAAXn0lEQVRtXXPNNdaWLVvMhv4Zf/%2B7ZUmW5fFYFn/Wzt6pfid%2B%2BuknKz093brgggusGjVqWBdffLF1%2B%2B23W7m5uaZjRwSXZXG7Sph35CIQn8%2BnhIQE03EA4JyUlUmpqfbawNnZ0ujRphMBoTgEDABABatWTRozxh5PmiTt3282D3A8CiAAAJXgttukSy%2BVfvhBeuYZ02mAUBRAAAAqQUyM9Mgj9viJJ6S9e43GAUJQAAEAqCS33WZfFezzSePHm04DHEUBBACgklSrJj3%2BuD2eMkViaWuECwogAACV6MYbpU6dpOJiadQo02kAGwUQAIBK5HJJkydL1atLb74pffCB6UQABRAAgEqXmioNGmSPhwyxZwMBkyiAMConJ0der1dpaWmmowBApRo3TkpMlD7/3L43IGASK4EgLLASCAAnmD1b6tdPOu886dNPpV/%2B0nQiOBUzgAAAVJG%2BfaXrr5cOHZIGDJCYgoEpFEAAAKqIyyU9%2B6w9A/jBB9JLL5lOBKeiAAIAUIWaNDm6Qsi990r5%2BUbjwKEogAAAVLERI6RWraR9%2BzgUDDMogAAAVLGYGGnWLKlGDentt6WXXzadCE5DAQQAwIDUVGnsWHt8zz1SXp7ZPHAWCiAAAIaMGiW1bSv5fFL//lJZmelEcAoKIAAAhsTE2Id/a9aUli2Tnn7adCI4BQUQAACDmjQ5ujLImDHSJ5%2BYzQNnoAACAGDYgAFS165SICBlZEgHD5pOhGhHAQQAwDCXS/rrX6WkJHuJuJEjTSdCtKMAAgAQBhITj64MMm2a9Pe/m82D6EYBBAAgTHTubN8kWpL%2B8AduDYPKQwGEUTk5OfJ6vUpLSzMdBQDCwvjxUuvWUlGRfT7g4cOmEyEauSyLBWhgnt/vl8fjkc/nU0JCguk4AGDUV19JLVtKP/4o3X%2B/9OijphMh2jADCABAmLnkEmnmTHucnS0tWWI2D6IPBRAAgDDUu7c0cKBkWVLfvtLu3aYTIZpQAAEACFNPPildcYX0/ffSrbdyPiAqDgUQAIAwdd550muvSfHx0qpV0gMPmE6EaEEBBAAgjDVpIr3wgj1%2B4gnp7bfN5kF0oAACABDmbrlFGjrUHvfrJ339tdk8iHwUQAAAIsCECVLbttK%2BfXYhPHTIdCJEMgogAAARIDZWevVVqX596d//loYMMZ0IkYwC6FAlJSW677771Lx5c9WqVUspKSnq16%2Bfdh93n4GioiJlZmbK4/HI4/EoMzNT%2B/btC9lny5Yt6tChg%2BLi4nTRRRdp3Lhx4v7iAFDxGjSQ5s2TXC7p%2BeePnhsInCkKoEP99NNP2rRpkx588EFt2rRJb775pr788kt17949ZL%2BMjAxt3rxZixcv1uLFi7V582ZlZmYGX/f7/erUqZNSUlK0YcMGTZkyRRMnTtTkyZOr%2Bi0BgCN06iSNG2ePBw2SNm0ymweRiaXgELRhwwZdeeWV2rlzpy6%2B%2BGJt27ZNXq9XH330kdq0aSNJ%2Buijj9SuXTt9/vnnatq0qaZPn64xY8bou%2B%2B%2Bk9vtliQ99thjmjJlinbt2iWXy1Wu781ScABQfmVl0m9/K73zjtSokbRxo1S3rulUiCTMACLI5/PJ5XLp/PPPlyStXbtWHo8nWP4kqW3btvJ4PFqzZk1wnw4dOgTLnyR17txZu3fv1o4dO6o0PwA4RbVq0ssvS7/8pbRjh9Snj1RaajoVIgkFEJKkQ4cOafTo0crIyAjOwBUUFCgxMfGEfRMTE1VQUBDcJykpKeT1Ix8f2edkAoGA/H5/yAYAKL86daQ335Ti4qTFi6WxY00nQiShADrE3LlzVbt27eC2atWq4GslJSW69dZbVVZWpmnTpoV83skO4VqWFfL88fscOavgVId/s7OzgxeWeDweNWjQ4KzeFwA42RVXSDNm2OP/%2Bz9uEo3yowA6RPfu3bV58%2Bbg1rp1a0l2%2BevVq5e%2B%2BeYbLV26NOT8u%2BTkZH333XcnfK3vv/8%2BOMuXnJx8wkxfYWGhJJ0wM3isMWPGyOfzBbe8vLxzfo8A4ESZmdLddx8db99uNg8iAwXQIeLj43XppZcGt7i4uGD52759uz744APVq1cv5HPatWsnn8%2Bn9evXB59bt26dfD6f2rdvH9xn5cqVKi4uDu6zZMkSpaSkqFGjRj%2Bbx%2B12KyEhIWQDAJydSZOkq66SfD6pRw/pwAHTiRDuuArYoQ4fPqzf//732rRpk955552Q2bq6desqNjZWktSlSxft3r1bM/57jOHOO%2B9Uw4YN9fZ/jzP4fD41bdpU119/ve6//35t375d/fv310MPPaQRI0aUOw9XAQPAucnPl37zG6mgQOrdW3rlFft%2BgcDJUAAdaseOHWrcuPFJX/vwww917bXXSpL27t2roUOH6q233pJkH0qeOnVq8Ephyb4R9ODBg7V%2B/XrVqVNHAwcO1EMPPVTuW8BIFEAAqAirV0vXXScdPixNnCidwb/D4TAUQIQFCiAAVIypU%2B1l4qpXl5YutQshcDzOAQQAIIoMHiz162ffF7BXLyk313QihCMKIAAAUcTlkp59VmrZUvrhB/uikEOHTKdCuKEAAgAQZeLi7JtE16tnLxM3aJDECV84FgUQAIAo1KiRNH%2B%2BvWzciy/as4LAERRAAACiVMeOUna2PR46VPrvMu4ABRAAgGg2apTUs6d9a5jf/17avdt0IoQDCiAAAFHM5ZJeeEFq1sy%2BSXTPntIxizfBoSiAAABEudq1pYULJY/HPgw8bJjpRDCNAgijcnJy5PV6lZaWZjoKAES1Sy%2BV5s61ZwSnT7cvDIFzsRIIwgIrgQBA1fi//5Meekhyu6VVqyT%2B/e1MzAACAOAgDzwgde8uBQL2RSGFhaYTwQQKIAAADlKtmvTyy9Jll0l5eVLv3vYVwnAWCiAAAA7j8UgLFtgXhyxfLt13n%2BlEqGoUQAAAHMjrlV56yR5PnmyvGgLnoAACAOBQPXpIo0fb4z/%2BUdqyxWweVB0KIAAADvaXv0jp6dJPP0k33ywVFZlOhKpAAQQAwMGqV5fmzZMaNZK%2B%2Bkrq21cqKzOdCpWNAggAgMPVqye9%2BaZ03nnSe%2B9J48aZToTKRgEEAABq2VKaMcMejx0rvfOO2TyoXBRAAAAgSerXTxo82B737Sv95z9m86DyUAABAEDQ5MlS%2B/aSz2dfJXzggOlEqAwUQAAAEBQbK732mpSUZN8WJitLsizTqVDRKIAAACBESopdAmNipFdekZ55xnQiVDQKIIzKycmR1%2BtVWlqa6SgAgGP8v/8nTZhgj0eOlFavNpsHFctlWUzswjy/3y%2BPxyOfz6eEhATTcQAAsg/9ZmTYy8QlJ0ubNkkXXmg6FSoCM4AAAOCkXC5p5kypWTOpoEDq3VsqKTGdChWBAggAAH5W7dr2TaITEqRVq6T77jOdCBWBAggAAE7pssukl16yx08%2BKb36qtk8OHcUQAAAcFq/%2B93R2b877pC2bTObB%2BeGAggAAMrlL3%2BRrrvOvjl0jx7S/v2mE%2BFsUQABAEC5xMTYVwSnpEiffy796U/cJDpSUQABAEC5JSYevUn03/4mTZliOhHOBgUQAACckfbtpYkT7fGIEdKaNWbz4MxRAAEAwBkbOlTq1Us6fNh%2BLCw0nQhnggIIAADOmMslPf%2B89KtfSd9%2BK/XpI5WWmk6F8qIAAgCAsxIfL73%2BulSzpvTBB9LYsaYTobwogAAA4Kw1ayY995w9/stfpMWLzeZB%2BVAAAQDAOenTR7rrLvuWMH37Srm5phPhdCiAMConJ0der1dpaWmmowAAzsGTT0qtWkl79ki9e0vFxaYT4VRclsUtHGGe3%2B%2BXx%2BORz%2BdTQkKC6TgAgLPwzTfSb34j7dsnDRtml0KEJ2YAAQBAhWjcWHrpJXv81FPSG2%2BYzYOfRwEEAAAVpnt3adQoe3zHHdJXX5nNg5OjAAIAgAr16KPSVVdJfr/Us6d06JDpRDgeBRAAAFSoGjXsdYLr15f%2B/W/p3ntNJ8LxKIAAAKDCXXSRNGeOPX72WWn%2BfLN5EIoCCAAAKkXnztIDD9jjrCzpyy/N5sFRFEAAAFBpHnlE6tBB2r9f6tWL8wHDBQUQAABUmpgYad486YILpI8/5nzAcEEBhCRpwIABcrlceuqpp0KeLyoqUmZmpjwejzwejzIzM7Vv376QfbZs2aIOHTooLi5OF110kcaNGyfuLw4AOCIlxT4f0OWyzwf8299MJwIFEFq4cKHWrVunlJSUE17LyMjQ5s2btXjxYi1evFibN29WZmZm8HW/369OnTopJSVFGzZs0JQpUzRx4kRNnjy5Kt8CACDMpadLY8bY46ws6T//MZvH6WJMB4BZ3377re6%2B%2B269//77uummm0Je27ZtmxYvXqyPPvpIbdq0kSTNnDlT7dq10xdffKGmTZtq7ty5OnTokGbNmiW3263U1FR9%2BeWXmjx5soYPHy6Xy2XibQEAwtDYsdKqVfbWu7e0Zo3kdptO5UzMADpYWVmZMjMzNWrUKDVr1uyE19euXSuPxxMsf5LUtm1beTwerVmzJrhPhw4d5D7m/%2BDOnTtr9%2B7d2rFjR6W/BwBA5DhyPmC9etKmTUdXDEHVowA62OOPP66YmBgNHTr0pK8XFBQoMTHxhOcTExNVUFAQ3CcpKSnk9SMfH9nnZAKBgPx%2Bf8gGAIh%2Bv/iF9PLL9njKFGnhQrN5nIoC6BBz585V7dq1g9uKFSv09NNPa9asWac8THuy1yzLCnn%2B%2BH2OXAByqq%2BbnZ0dvLDE4/GoQYMGZ/qWAAAR6n/%2BRxoxwh7fcYeUm2s2jxNRAB2ie/fu2rx5c3Bbs2aNCgsLdfHFFysmJkYxMTHauXOnRowYoUaNGkmSkpOT9d13353wtb7//vvgLF9ycvIJM32FhYWSdMLM4LHGjBkjn88X3PLy8ironQIAIsH48dKVV0pFRdJtt0mHD5tO5CwUQIeIj4/XpZdeGtzuvPNOffLJJyGlMCUlRaNGjdL7778vSWrXrp18Pp/Wr18f/Drr1q2Tz%2BdT%2B/btg/usXLlSxcXFwX2WLFmilJSUYJE8GbfbrYSEhJANAOAcsbH28nAJCfbFIA8/bDqRs7gsbtiG/2rUqJGGDRumYcOGBZ/r0qWLdu/erRkzZkiS7rzzTjVs2FBvv/22JMnn86lp06a6/vrrdf/992v79u3q37%2B/HnroIY04Mr9fDn6/Xx6PRz6fjzIIAA7y6qv2FcEul7R0qXTDDaYTOQMzgDiluXPnqnnz5kpPT1d6erouv/xyzZ49O/i6x%2BPR0qVLtWvXLrVu3VqDBg3S8OHDNXz4cIOpAQCRolcv%2B76AliX17Sv99ywiVDJmABEWmAEEAOf66ScpLU367DOpSxfpnXekakxRVSr%2B8wIAAKNq1rSXhzvvPGnRIum4VUlRCSiAAADAuNRU6cgqoqNHSxs3ms0T7SiAAAAgLAwcKN18s1RSYt8aZv9%2B04miFwUQAACEBZdLev55e7WQ7dulIUNMJ4peFEAAABA26taV5s61LwKZNcs%2BNxAVjwIIAADCyjXXSPffb48HDJB27DAaJypRAAEAQNh5%2BGGpXTvJ57PvD8hScRWLAggAAMJOTIx9KDghQfrnP6VHHzWdKLpQAAEAQFhq3FiaPt0ejxtnrxmMikEBhFE5OTnyer1KS0szHQUAEIYyMuxDwGVlUp8%2B9iFhnDuWgkNYYCk4AMDP8fulK66wLwbp21c6Zkl6nCVmAAEAQFhLSJDmzZOqV5fmzJFeecV0oshHAQQAAGGvXTvpz3%2B2x3fdJe3caTZPpKMAAgCAiPDnP0tt29rnAfbrJ5WWmk4UuSiAAAAgIsTE2IeAa9eWVq6UJkwwnShyUQABAEDEuOQS6Zln7PFDD0mbNpnNE6kogAAAIKL07y/16CGVlNi3hvnpJ9OJIg8FEAAARBSXS3ruOenCC6XPP5f%2B939NJ4o8FEAAABBx6tWTZs2yxzk50uLFRuNEHAogAACISOnp0pAh9viOO6Q9e8zmiSQUQAAAELEef1z61a%2Bk/Hxp4ECJ9c3KhwIIAAAiVlycfWuYmBjp9dftMU6PAggAACJaq1bSww/b47vvlnbvNpsnEsSYDgAAAHCuRo%2BW/vEPqVs3KTnZdJrwRwGEUTk5OcrJyVEp6/kAAM5BTIxdAKtxbLNcXJbF6ZIwz%2B/3y%2BPxyOfzKSEhwXQcAACiGj0ZAADAYSiAAAAADkMBBAAAcBgKIAAAgMNQAAEAAByGAggAAOAwFEAAAACHoQACAAA4DAUQAADAYSiAAAAADkMBBAAAcBgKIAAAgMNQAGFUTk6OvF6v0tLSTEcBAMAxXJZlWaZDAH6/Xx6PRz6fTwkJCabjAAAQ1ZgBBAAAcBgKIAAAgMNQAAEAAByGAggAAOAwFEAAAACHoQACAAA4DAUQAADAYSiAAAAADkMBBAAAcBgKoMNt27ZN3bt3l8fjUXx8vNq2bavc3Nzg64FAQEOGDFH9%2BvVVq1Ytde/eXbt27Qr5Grm5uerWrZtq1aql%2BvXra%2BjQoSouLq7qtwIAAMqJAuhgX331la6%2B%2Bmr96le/0vLly/Xxxx/rwQcf1HnnnRfcZ9iwYVqwYIHmz5%2Bv1atXa//%2B/eratatKS0slSaWlpbrpppt04MABrV69WvPnz9cbb7yhESNGmHpbAADgNFgL2MFuvfVW1ahRQ7Nnzz7p6z6fTxdccIFmz56t3r17S5J2796tBg0a6L333lPnzp21aNEide3aVXl5eUpJSZEkzZ8/X/3791dhYWG51/VlLWAAAKoOM4AOVVZWpnfffVeXXXaZOnfurMTERLVp00YLFy4M7rNx40aVlJQoPT09%2BFxKSopSU1O1Zs0aSdLatWuVmpoaLH%2BS1LlzZwUCAW3cuPFnv38gEJDf7w/ZAABA1aAAOlRhYaH279%2Bvxx57TDfeeKOWLFmim2%2B%2BWT169NCKFSskSQUFBYqNjVWdOnVCPjcpKUkFBQXBfZKSkkJer1OnjmJjY4P7nEx2drY8Hk9wa9CgQQW/QwAA8HMogA4xd%2B5c1a5dO7h98cUXkqTf/va3uvfee9WiRQuNHj1aXbt21bPPPnvKr2VZllwuV/DjY8c/t8/xxowZI5/PF9zy8vLO8p0BAIAzFWM6AKpG9%2B7d1aZNm%2BDHF1xwgWJiYuT1ekP2%2B/Wvf63Vq1dLkpKTk1VcXKyioqKQWcDCwkK1b98%2BuM%2B6detCvkZRUZFKSkpOmBk8ltvtltvtPuf3BQAAzhwzgA4RHx%2BvSy%2B9NLh5PB6lpaUFZwKP%2BPLLL9WwYUNJUqtWrVSjRg0tXbo0%2BHp%2Bfr62bt0aLIDt2rXT1q1blZ%2BfH9xnyZIlcrvdatWqVRW8MwAAcKaYAXSwUaNGqXfv3rrmmmt03XXXafHixXr77be1fPlySZLH49Ef//hHjRgxQvXq1VPdunU1cuRINW/eXB07dpQkpaeny%2Bv1KjMzUxMmTNDevXs1cuRIZWVlcTUvAABhitvAONwLL7yg7Oxs7dq1S02bNtXYsWP129/%2BNvj6oUOHNGrUKM2bN08HDx7UDTfcoGnTpoVctJGbm6tBgwZp2bJliouLU0ZGhiZOnHhGh3i5DQwAAFWHAoiwQAEEAKDqcA4gAACAwzADiLBgWZZ%2B/PFHxcfHn/L2MQAA4NxRAAEAAByGQ8AAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwmP8PPD5uMr5UmVIAAAAASUVORK5CYII%3D'}