Genus 2 curve data - 94533.a.283599.1

Formats: - HTML - YAML - JSON - 2024-04-30T07:58:31.338188

• 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': '2121780235283698787', 'abs_disc': 283599, 'analytic_rank': 3, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[3,[1,3,5,3]],[31511,[1,308,31202,-31511]]]', 'bad_primes': [3, 31511], 'class': '94533.a', 'cond': 94533, 'disc_sign': -1, 'end_alg': 'Q', 'eqn': '[[0,2,3,0,-3,-1,1],[1]]', 'g2_inv': "['-32768/283599','-1123328/283599','-271360/31511']", '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': "['16','-13148','-387256','-1134396']", 'igusa_inv': "['8','2194','38160','-1127089','-283599']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '94533.a.283599.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.94568398654693614015502382792219378201438275', 'prec': 153}, 'locally_solvable': True, 'modell_images': [], 'mw_rank': 3, 'mw_rank_proved': True, 'non_maximal_primes': [], 'non_solvable_places': [], 'num_rat_pts': 18, 'num_rat_wpts': 0, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '12.557814143844038068893013743', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.0376532084212', 'prec': 50}, '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': 1, 'torsion_subgroup': '[]', 'two_selmer_rank': 3, 'two_torsion_field': ['6.4.2016704.1', [-1, 2, -1, 6, -3, -2, 1], [6, 16], 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': '94533.a.283599.1', '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': '94533.a.283599.1', 'mw_gens': [[[[-1, 1], [1, 1], [0, 1]], [[-3, 1], [0, 1], [0, 1], [1, 1]]], [[[-1, 1], [0, 1], [1, 1]], [[-1, 1], [-1, 1], [0, 1], [0, 1]]], [[[0, 1], [-1, 1], [1, 1]], [[-1, 1], [-1, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.57528354934329460204535298161305', 'prec': 113}, {'__RealLiteral__': 0, 'data': '0.24650195263753333103527883054048', 'prec': 113}, {'__RealLiteral__': 0, 'data': '0.30692533474405946605983089351669', 'prec': 113}], 'mw_invs': [0, 0, 0], 'num_rat_pts': 18, 'rat_pts': [[-7, -259, 8], [-7, -253, 8], [-2, -8, 1], [-2, 7, 1], [-1, -225, 7], [-1, -118, 7], [-1, -1, 1], [-1, 0, 1], [0, -1, 1], [0, 0, 1], [1, -2, 1], [1, -1, 0], [1, 1, 0], [1, 1, 1], [2, -1, 1], [2, 0, 1], [4, -1288, 9], [4, 559, 9]], 'rat_pts_v': False}
• lmfdb_label 94533.a.283599.1 does not appear in g2c_galrep
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 166205
    {'cluster_label': 'c4c2_1_0', 'label': '94533.a.283599.1', 'p': 3, 'tamagawa_number': 2}
  • id: 166206
    {'cluster_label': 'c4c2_1~2_0', 'label': '94533.a.283599.1', 'p': 31511, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '94533.a.283599.1', 'plot': 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iAAIoUDgcVigUUlpamutSAJSiPXuka6%2BVsrOliy%2B2A/GNRtAADolG0EB8u%2B8%2BaehQG/VbulRKTXVdEUobI4AAAPjYkiV5rV4eeojw5xcEQAAAfCo7W7rmGrsE3LWrdPnlritCWSEAAgDgUxMmSF9/LR11lPT441Ig4LoilBUCIAAAPrRkiTRypJ0/9JBUp47TclDGCIAAAPjMnj3W6Hn3brv0e%2BWVritCWSMAAgDgM2PHSvPnS9Wq2XZvXPr1HwIgAAA%2BsnChdM89dv7II1Lt2m7rgRsEwDKwbJk0cKCUk%2BO6EgCAn2VlSVddZat/u3WTevRwXRFcSXBdQLzbuVPq3Flat0766Sfp5ZelChVcVwUA8KORI6Vvv5Vq1pQee4xLv37GCGApq1RJ%2Buc/pcREacoU6fzzpcxM11UBAPxm9mxp/Hg7f%2BIJ6Zhj3NYDtwiAZaBbN%2Bn996XKlaXp06VzzpG2bXNdFQDALzIz7dLv3r224veii1xXBNcIgGXk3HOlGTNsxdWcOdJpp0lr17quCihcOBxWKBRSWlqa61IAFMNtt0krVkj16kmTJrmuBtEg4Hme57oIP1m6VPrzn20%2BYJ060rRpUvPmrqsCCheJRBQMBpWenq6UlBTX5QA4AtOn2/uOJH34oV2FAhgBLGNNm0qzZkmhkLRhg40EzpjhuioAQDzavl269lo779%2Bf8Ic8BEAH6teXvvhCOuMMKSNDOu886dlnXVcFAIg3/frZYEOTJtb8GchFAHSkWjUblu/Z0/ox9e4tDR5sE3QBACiuV1%2BVXntNKl9eeuklW4gI5CIAOpSUZH0Bhw2zz8eOlS65hDYxAIDi%2Bekn6e9/t/NhwyTWceH3CICOBQK2Jc%2BLL1qD6Lfflk49lRXCAICi2btXuvpq6ddfLfjddZfrihCNCIBR4sorpU8%2BscacixZJJ58sffaZ66oAALHm4Yeljz6yjQheftk2IgB%2BjwAYRTp0kL76SmrVSvr5Z%2Bnss22jbhr1AAAOx3ffSXfcYecPPCA1buy2HkQvAmCUOfZYWyHco4ctDunfX%2BrVS/rtN9eVAQCi2e7d0hVXSFlZ1l3ihhtcV4RoRgCMQpUrS6%2B8Yr%2B95a7eat9e%2BuEH15UBAKLVyJHSggVSjRrSM8/YHHOgIATAKBUISAMHWpPoWrWkxYulNm2kKVNcVwYAiDZffimNG2fnTzwh1a7tth5EPwJglDvzTOmbb6TTT7em0Zdeao09d%2B50XRkAIBpkZEhXXWWrf6%2B6Srr4YtcVIRYQAGNAnTrSxx9Ld95pnz/2mHTKKTbZFyhN4XBYoVBIaTQRA6LWrbdKK1faHPJJk1xXg1gR8DzWmMaS6dPtN7wtW6SKFaWJE22iL3M9UJoikYiCwaDS09OVkpLiuhwA//P%2B%2B1LXrvYe8PHHdtUIOByMAMaYzp2tT2DnztKuXXY5uGtXadMm15UBAMrS1q1Snz52fssthD8cGQJgDEpNlaZOlR580LaT%2B89/pGbNpDfecF0ZAKAseJ4NAGzeLP3xj9J997muCLGGABijypWTBgyQvv5aatFC2rZN%2BtvfrH/g1q2uqwMAlKbXXrOuELmtwipWdF0RYg0BMMY1aybNmycNGWIvBJMnS02b2gsDszsBIP5s2iT9/e92PnSotQgDjhQBMA5UqCCNGiXNnm3hb8sWaxdz0UXS%2BvWuqwMAlBTPs4V/27fbtqFDhriuCLGKABhH0tKk%2BfOlYcOkhATp3XdtbsjDD0s5Oa6rAwAU17/%2BZa/tiYnS88/bR6AoaAMTp5Yska67Tpozxz5v00Z69FGpbVu3dSE20QYmxqxZI735ppSZKbVrJ51zDr2i4sCWLVIoZHO%2B775bGj7cdUWIZQTAOJaTIz35pDR4sJSebq//ffrYarGaNV1Xh1hCAIwRnmf9QB5%2B2LaFyNWsmfTvf0sNG7qrDcXWo4fN827e3BYAMvqH4iAA%2BsDmzdJtt9lKMUk66ijbNLxfP15AcHgIgCXL86SsLNvScdcuafduKTvbjtzcFgjYav%2BEBPt/mpRkKz0rV7bb8jVuXN6WQb/XpIm0dKmtFkPMmTpV6tLF/k3Mm8fCDxQfAdBHvvhC6t9fWrjQPm/cWJowIa%2BLPFCQmA6A2dm2XcIPP0i1a9vqqCpVSuSp9%2B61y3GbNtkvWps3Sz//bK2Ytm61ifq//CL9%2BquNwmdk2PHbb8VbpZ%2BUJCUnS8GgVK2aVKOGVKv6Hj387rFK%2Ba2QrvBvvWV/f8SUzExb4LdmjW37dv/9ritCPCAA%2BkxOjvTss7Zy7Oef7bYzzpDGj2d%2BIAoWswHwyy%2Blyy47cDl8MGgTYnv2POSXRyLSqlXS6tXS2rX2BrxunfTTT/aUGzZIe/YUr8RAwFbyJyba4Fy5cnab51nAzM62EcJD/TkhLdVSNSv0MR83v1lL%2B/xTTZtKJ53EVJBYceedNrh77LG2B3wJ/f4CnyMA%2BlQkIo0ZY7uJZGXZbRdfLN17r60cBiQpHA4rHA4rJydH33//fWwFwHXrbO5bJHLwfeXLSzNnSqedpm3bpOXLbYBwxQo7fvzRju3bD%2B%2BPqlHDdug55hg7ata026pXtxG6atWklBQ7kpPtDbxyZbukm5h4eCPwnmeXi3/7Tdqxw/5a6ek2wrhtm5T93xXqM65Roc8xTrfrTo3b93mdOnYp8eSTba3IKadYPkb0%2BO9/bc7fnj22%2Bvf8811XhHhBAPS5deusbcyLL9obTLly0hVX2OqyE05wXR2iRUyOAN51l/2WU4DPql%2BgboF3tG1b4U9To4Z03HF2HHusVL%2B%2BHfXqWYBKTbURvKhw0knWAqAAj145W9PT22nJEhvZ/P2rfyBgOwt17CidfbZdHahatZRrRoE8T/rLX6QPPrD5f%2B%2B/77oixBMCICTZe8bQofYbpmQDJFddZe%2BhJ57otja4FysB0PNsHt6330pNr2uvOmvmFPjYiJIVlI0O1qtnc2IbNbJ/7yecYEfDhjZiFzPeeUfq1i3/CYa/SxAZGdKiRdY7dN48ayS/atWBX5KYaCHw/POlCy%2B04IuyM22aBcDERLv0y2sxShIBEAf46isb/fvgA/u8XDlrPTB4sE1Chj9FYwDMzrbLYwsX2rFokR25c1s/0%2Bk6XV8U%2BPVZVatr2efb1KhRnM2pmjLF/sP%2B%2BKN9XqmS1KuXNHGinRdiwwbps8%2Bkjz%2BWPvzQ5j7ur107ez3o3t0udaP05OTYaOzSpdKgQbZgDyhJBEDka%2B5cazQ6bVrebV27SnfcIXXowKphv3EdAHftkhYvlr75xo4FC2yUL3f%2B6v4CARvJG5J0v6769raCn/Saa2xFVDzyPBvay8y0CWTVqhXpKb7/3gYN33nH1tPkvlskJNjrQd%2B%2BUqdO9osiStYLL0hXX20/uh9/LNKPECgUARCF%2BuYbafRo6x6R%2By%2BlXTtp4EDrJlFgPzLElbIMgFlZFva%2B/tqO%2BfNtikJ%2B2xkmJ9soScuW9rFFCxuprlxZtjqiZUtbvvt7VavadU9WPB22jRttcPGll%2BznkqtJE%2Bs93auXLWpB8e3ZI/3hD9LKlbb69/bbXVeEeEQAxGFZvtx6T734orWkkGxC/N//LvXubRPlEb9KKwDm5EjLltnUg3nz7OPixfm3PDn6aKl167yjVSvp%2BOMPMfq0apVtf/Pxx3m3tWghPfaY1L59if09/GbJEttl6IUX8hZZ165t7Ur69rU%2BhSi6l16yOdg1a9o/4biaooCoQQDEEdm8WQqH7f1z61a7rWJFmxfUr5%2B1k0D8KakAuH69TS%2BYO9cC39dfW0uT36tRw/4t5bYoadPGFiAUeerBDz/kNYJu1arI9eNAGRl2Ff2BB6yjgGSrpSdMsLZSTBU5cp5nV%2B2XLLGrL4MHu64I8YoAiCLZtUv6179sy9EFC/Jub93aRgC6d7eeZwdZtEiaPt3OzzvP2lYguv3wgyKvv67g0KFKnzFDKWeffVhftnOnXb6dMyfv2L8fc64qVSzkpaXlHccdR3iIJbt3S889Z/OGN2602zp3lp54QmrQwG1tMePnn6UpU7Tym1910zPN9Wnlv2jd%2BnI66ijXhSFeEQBRLJ5n7SMefdTmB%2BVeHq5c2TZguPZa6dRTpUDmDhsm/H0jqwsukF55hWsc0WjPHrt8%2BtJLiniegpLSJaX86U/Sm2/aNdn/8Tybajd7tjRrloW9BQtspe7%2BypWz3szt2tnOM23bSqEQ29PGi8xM21Vo3Diby5mcLD3%2B%2BGFtuuJv48ZJI0YcsKrp55TjVfOLd/glGaWGAIgSs3WrzQl6%2Bmlrz5HrxBOld5MuVWjplPy/sGdPC4GILgMGSA89JEmKSHkBUNLeP52hryfM1JdfWuCbNctaiPxeaqqFvdzj5JPJ%2Bn7w/fe2yHrWLPv8ppusCw1BPx%2BvvGLd9/OTmmrfzJhqRolYQQBEifM8axnx7LPS669Lx2Su1AqdqHIq4J9a%2BfK23O3YY8u2UBTs11%2BlunVt3zEdHAAlqa3m6ivlbSCdkGCLbtu3zzsaNOBSrl9lZ0v33GPbS0rWNWDy5CjaNSVatGxpU2MK8sgjttoOKGEEwGLwPE8ZGRmuy4hqO3ZI3975itq/1K/wBz71lHTppWVTlF/t3XvYDdu2vzld1a/N%2B3lEJNWXtE55AXB0xXu04E837xvda9Xqf%2B1XgP28%2B67NJNi922Z8PP88fQP3%2BfXXQ0%2BSvPBCu7SCUpGcnKyAT39LJQAWQ%2B7KSAAAEHuiaXejskYALIaijgCmpaXpq6%2B%2BKtFaSuM5S%2Bx5t2%2B3rqb5bdsgaYcq6w/6rzIUVN26tlakZ0/bi3Ufz7MZ5pUqFTqRKKq/D2XwnAc8765dNnowe/ZBj/HattWKB/6taTMr6cMP7SH7995L0G4tLxfS0XttX7WDRgADAdt/7bjjSq7eEhZLz1sazxmJRFS/fn2tW7euxN/gilLv5MnWISAQsLaMrVuXzPMejqj%2BmV14ofTJJwXf/8IL9phiiqXvbVk%2Br59HANnHoRgCgUCRXljLly9f4i/IpfGcJfa8KSm2dciYMfnevf2qQbo0sb7eeMPahNx/vx1nnSXddOMedX3jKpV7520LkOXK2dLRl1/%2BXUIswXrzEZM/s1deyTf8SZLmzdPzp7%2BuR9R/300NGkjnnGPf97POklLfHCH94x8HfFnK/w717m3Nykqy3hIWS89bWrVKUkpKSlTUe/310syZ1j7q7rvzzzy%2B/Jnde6/UsWP%2B3c/btLHfhktgy6VY%2Bt7G4vPGovIjR44c6boIP2rbtu2hHxQFz1liz3vWWTZyt3ChjUxJ2lutmgLDh%2Buo%2B4fq/AsCuvlm63iwY4ftfbl61V6NntJI1Zd%2BkbcPmOdJP/1kDcZ69sx3g8yo/j6UwXPue96bbrLvVQFqBbZo9bnX66abbLHvPffYHK2TTrKd0tS2rXTMMdI33yhrxw6NlXRnlSqqePPN0oMPluhErlj6mZXW85b0c2ZlZWns2LEaPHiwkkpha46i1Nu2rfUOXblSuuQS%2B%2BdVEs97eH92lP7Mjj1WattWP0%2Bfryq/WXd9LyFBgUsukV57rUSXzcfS9zYWnzfWcAkYZWvnTmV%2B8onO69JF72/erJT83gEkrVkj/ffKUer8%2BbCCn6ttW9tSAvk74QR7py3A3vrHqtzaNYd%2Bnj17tPG991Tn4ou1btky1fvDH0qwSJSWsty/%2BUicf7703nt2QeDOO11XEz1uuEGa98Q3qqZ/6p3lQ5XcuLHrkhDnWIuFslWpkhLOPltnjRihpEIW0DRoIHVe81Thz1UK80PiSpMmhd5drslhvsEkJirh9NMlSUls%2BhwzkpKSNGLEiFIZ/SuOU0%2B1j0uWuK0j2vz6q7RArZXYuYcqsH0KygBzAFHmkpKSdFgzD3J3mS%2BI5x1RaxPfOfHE4t2/n9wQEW1hAgU77P9nZSx31kZ%2Be0D7We6uOV27nif%2Bm6Es8M6J6JWaWvj9iYmEv8J8803h9%2B%2B/iTNQRnJ3jGEw%2BUC5DbJzt9MEShvvnihbS5ZIDzxgx3ff5fuQ7GxrHjsmYWjhz3XeeaVQYBzZuLHQu7NWb9y3tqZQP/5omz1Lhw6VwCF89JF9zK8NjJ/Vr7xN1%2BsJ/fG9cdK0aXZ1AyhFLAJB2cjMlC6/3JLd/rp1k156SapcWUuWSC%2B%2BaJ9u2mR3v68u6qKpBz9faqotcKhUqfRrj1WdO0v/938F3v2xOqp7zY913nnSn/9sLWBq1tzvAXv2WOO2559XxPPytoLr2FGaMoUhHByxBQss%2BJUrZwu96tVzXVFTIvKCAAAd7klEQVSUuP9%2B7Rk8TInZu/Jua9RIevttqWlTd3UhrhEAUTa6d7eWBvn4rvUVunzvS1q4MO%2B2mjVtM/kbbpAaznxOGj3aRrSqVpWuukoaNYpNRQ/lzTet10YBrqk0Wc/vvOyA21q2tI49HTtKnT64VUnhiZLy2Qu4Y0fr5gscppwc6YwzbJ/wnj2tTSVkjRF79sz/vtq1peXLpeTksq0JvkAAROlbtcoWHBRwSSNb5XWcVmtLYj116WL5rksX8l2JuOEG65n4e717a89jT%2BuLL%2Bxq0/Tp0uLFeXenKF3rVVdVlSkpnwAo2Srsk08u3fpxxD777DNNmDBB8%2BfP18aNG/X222/rwhLYSaK4Bg%2BWxo613%2BGWLrX2d5ANiRY2H/fRR6Ubbyy7euAbzAFE6fvss0LnsyQoR89f85k2brQrHhddRPgrMY8/Lv3nP/ZNbd3aOj2/95709NNKTLSBvPHjpUWL7LL7q69KffpIF9b5al/4K8i2Nz4Rvz5Gn8zMTLVo0UKPPPKI61L2efBBC3%2BS9OSThL990tMPvRirsG3igGKgDQxKjefZa9u3ryaq1yEee855iRJTykrHX/5ixyHUqmX7MPfoIenTROnMwh9/77hEvfaC9XXr0MGOVq1ECwvHzjvvPJ0XJQuk9u6Vhg2zGRySbQHXo4fbmqJKQoJtjlzYb1KJiWVXD3yFAIgSt22bbdX77LN2WbG6OutSVVQl7cr38TkVK6r8ueeWcZUoVPv2tk/Xli353p2jcpqWcL42bbKphm%2B%2BabcnJdn2pe3b29GunVS3bhnWjaixdat09dU2AC1Z%2BBtWyMY%2BvlSlinT22dKMGQU/Jgou3yM%2BEQBRIjxP%2BvRTu7zz5pt5vaySkqROF9bQhr0364Qp4/L92p%2BvuEKphewKAgcqVJCGDrX9hPNR/tqrtfCR4/X11zapf9YsafZse9OfNcuOXPXrWxA85RT72Lo1i7fjmedJ77xj09Y2b7bXgCeftLm9yMfw4fbiuWfPwfe1bm3TN4BSwCIQFEt6urVuefRR6b//zbu9VSupd28pMXGKBg68RvI83bVnj25PSlLC/7YAyElJ0YhIRJd8841atmrl6G%2BAQj38sDRqlCJbttgikMqVldK3r00cTDjw90fPk1assCCYe3z77cHTPxMSpObNLRC2bWtHkyZS%2BfJl99fyi0AgUKaLQJYvl269NW/ULxSyeaUtWpTJHx%2B7pk2TBgyQvv9eki2Mm5PaVacteZp2Syg1BEAUyfLl0qRJ0gsvWIs/ya5mXH65dP31dhlQkjIyMrR58%2BZ9X1e3WjVVWrRICgS0pnZtHffHP2rBggVq2bKlg78FDsvu3YrMmKFgly5KX7tWKfXrH/aX7thhi4XnzpXmzLFjv38O%2ByQn27%2BZtm2ltDRbXNyggU2PQtGVVQBcvVq67z7puees3UtionTbbXbJt2LFUv2j44fnacJll%2BnrD1foy1//rU3l6%2BnHH%2B3/AVAauASMw%2BZ51vpt4kRp6n69mUMhqV8/6corpZSUA78mOTlZyb/vYXXWWfZ8q1eXbsEoGRUqSKedZudHeKm%2BalVbadyxo33uedLatdK8eRYK582T5s%2BXMjKkmTPtyFWjhgXBk0%2B2cNimjV1OPuJQmJVly8t/%2BMH6qv3tb0f890D%2BFiyw14N//Uv7dpXp2lWaMMFGdXF4PM9T//799faXX2rmvJm68cZ6Wv%2BRfW8fesh1dYhXjADikLKzpTfesKt%2BuR0LAgF7ob/pJstzR/KmvH37dq1du1YbNmxQly5dNHnyZDVp0kSpqalKPdT%2Bv3AiEokoGAwqPT1dKb9P%2BcWUnW27An71Vd6xeLHd/ns1ati0qNatbZpBy5bWYrLAy8effGJNyPdfzFKlil3avuaaEv17RIsdO3ZoxYoVkqRWrVpp4sSJ6tixo6pXr65jS6D/SmamvR48%2BeSBcz3POccWenToUOw/wnf69eunV199Ve%2B%2B%2B66aNGmiTz%2BtoO7dq6tSJU8rVwYOuS06UBQEQBQoK8vm940bZ9vBSlLlyva%2BOWCAvfEWxfPPP69r8nnzHTFihEaOHFn0glFqSjMA5mfXLguBX39tI4Tz51vz4PxCYZUq0kknWRhs3tzmmzVrJqVsX20nmfn0MwwEbFPa3KHJODJz5kx1zOfv1atXLz3//PNFes7du22h6uTJNpj6v2m8SkiwzWZuvZWe4MURyPc36FmS2uummxgFROkgAOIgWVnS009b49affrLbatSw0b6//505yX5U1gEwP7t22aKSBQukb76RFi60kLhzZ/6PfzzldvWNTCj4Cbt0kd5/v3SKdW3TJluKm5lpq21yL%2BEfga1bbSvp99%2B3RR2RSN59J5xgvwhee61dVUfJmzFD6tTJ5lMuXy41bOi6IsQbAiD22b3bJnGPGpUX/GrXtsnc119vIy3wp2gIgPnJybGFkwsX2m4mixfbsX69NFvt1E5zC/zaXYlV9cpjGWrSRGrc2PafjotFJ4MHSw88cGBbkZNPtqG7evUK/LLt262lz2ef2VzfBQsO7E%2BcmipdfLFtW9u%2BfZx8r6Jcp04WBC%2B7zEZfgZJEAIRycmwS9/Dhtm2vZM17Bw%2B2Vi6s4kO0BsCCbN8ulTvrDB216LMCH7NVNVRTW/d9HgzatIbc4/jj7WjY0P4/JMTCkrmJE%2B16bH6aNbOUXK6cfv3VgvKCBXZ5fd48G2X6vebNbROZrl2th2M5Ng8tUwsX2nxXz5M%2B/7xIA7lAgQiAPuZ50vTp0h132JuBZNuB3XWXjfgR/JAr1gKgJNuAduDAAu%2BeFeqju%2Bs9peXLbWVyYa%2BE5cvb4FmDBrYSuV49O%2BrUsaN2bfu/4/T/THa2dNxxNvxZgGEt/q3ntnYt8CFNmkinny6deaYt7uLyrnvXXy899ZTNcf3qqxj5RQQxgQDoU4sXS4MGSR9%2BaJ8HgxYEb7qJS704WEwGwPR06x2Tu4Jpfykp9m7auLEkm1%2B4YkXe8eOP0sqVdqxZk/8mDflJSbFLyTVrSkcfLVWvLlWrJh11lP0fS0mx1jhVq9qCqooV7ahQwd7YExJslC13e9icHDt277a5ubt22bS%2BHTusdc6vv9qxbZtUYcV3euCDpoXW96AGaKAelCQde6wtmGnTxnovtm1rNSO6/Pyz/TP99Vfrvdq/v%2BuKEC8IgD7z88%2B2w9fTT9sODRUqSP/4hzRkiL1ZAfsLh8MKh8PKycnR999/H1sBULLJrH372k4LuS91p5wihcN53coPYe9eaeNGC4Jr19rx00820LZ%2Bvd23aVPe9oeuNNL3%2Bl6FN99b/OfbtGPYeIVCFkoRGx5/3LbWS0mRli2zUWeguAiAPpGdbdu1DR9uAyOS9cMdO9bmOQGFickRwP2tWWPDerVrS3/8Y4k/vefZCM2WLXZs3Wqjctu22e2//GKraCMRG73bscNWL%2B/caSN7u3fbKGNOjgVOz7NRwHLlbFQwMTFvtLByZRtBTE62UcVq1eyXt5o1pWsm/FHBDf8tuFAmksWknBzp1FOteXq3brbfOlBcBEAf%2BPJL26kjd55fq1bWV%2Br0093WhdgR8wHQL15/3ZaM5qdTJ%2Bvrgpi0aJENWufkSG%2B9JV10keuKEOtY0xXHtm2T%2BvSxX/gXL7ZRgscft6lPhD8gDl16qW3QXbdu3m2JidJVV1lqQMxq0UK6/XY779fPRpWB4mAEMA55nrV1GTDA5vxJ1s5l7FgmeaNoGAGMMdnZtk9bZqYN%2BbOXWFzYtctWAy9fLl19tfVtBYqKABhn1q2TbrhBmjrVPm/aVHriCZs/AhQVARCIDrNm2VUdz7MdWv7yF9cVIVZxCThOeJ6t7G3WzMJfhQrSvffallmEPwCIDx062NUdSbruOi4Fo%2BgIgHFg/Xr7LfC662yVYbt21kF%2B6FALggCA%2BDFqlPUG3LDBercCRUEAjHGTJ0snnSR98IGUlCTdf7/0xRel0ukCABAFKle2tT7lykkvv2zbPANHigAYo9LTpSuvlHr0sEsAJ59s%2B3reeqttWwUAiF/t2uWtCr7%2BemnzZrf1IPYQAGPQnDm2Euzll%2B03wOHDbWIwo34A4B8jR0rNm1vj8b59C9/PGvg9AmAM2bvXWrmcdpq0erXt%2B/7559Ldd1urLwCAfyQlSS%2B%2BaK//775LWxgcGQJgjPj5Z1voMXiwdYLv3t0WenTo4LoyAIArLVpYxwdJuvlmadUqt/UgdhAAY8CsWdbLdfp0qVIla/fy6qu2DyhQmsLhsEKhkNLS0lyXAqAAgwbZlaEdO6RevWyQADgUGkFHMc%2BTJk2y/9zZ2dIf/iBNmWK9/oCyRCNoILqtWmXzAXfskMaNy1sgAhSEEcAolZkpXXGFNfzMzrb93efNI/wBAA7WsKH00EN2PnSotGiR23oQ/QiAUWj1atu949VXpYQE6Z//tL19k5NdVwYAiFbXXCNdcIG0Z490%2BeW2dzBQEAJglJk503r6LVokHXOM9NFHNrE3EHBdGQAgmgUC0lNP2XvH0qXSkCGuK0I0IwBGkSeflDp1krZtk9q0kb7%2BWvrTn1xXBQCIFTVrSs88Y%2BcTJ0qffOK2HkQvAmAUyMmRbrnFGnlmZ9vuHp9/LtWv77oyAECs%2BetfbW94yVYF//qr23oQnQiAju3YIV14oc3zk6yf0yuvWLsXAACKYuJE6YQTpHXrpP79XVeDaEQAdGjDBrvE%2B/77UsWK0uuv2%2Bot5vsBAIqjalXppZdsu9CXX5beeMN1RYg2BEBHvvtOat9eWrDA5mzMnCn97W%2BuqwIAxIv27W33KMmmGG3c6LYeRBcCoANffGFtXtaulRo3lubMkU45xXVVAIB4M3y47SS1fbvUp49tMABIBMAy9%2B670jnn2KTc9u1tm7fjj3ddFQAgHlWoYJeCk5KkqVOtTQwgEQDL1HPPSd26SVlZUteu0owZUo0arqsCAMSzpk2l0aPtfOBA6ccf3daD6EAALCMPPCBde620d691a3/rLalyZddVAYULh8MKhUJKS0tzXQqAYhgwQDrjDNtm9Oqrrf0Y/C3gecwIKE2eJ40YYe1dJOm222yjblb6IpZEIhEFg0Glp6crJSXFdTkAimDVKql5c2s/NmGCNGiQ64rgEiOApWzXLumDD%2Bx89Ghp/HjCHwCg7DVsKD34oJ0PHWrdKOBfjACWge3bpWnTbHNuIBYxAgjEB8%2BznUKmTrUtR2fPlhITXVcFFxgBLAPVqxP%2BAADuBQK2ErhaNWn%2BfGnsWNcVwRUCIAAAPlKnjvTww3Z%2Bzz3SokVu64EbBEAAAHymZ0/poouk7GypVy9p927XFaGsEQABAPCZQEB67DHrRbtoUV6fQPgHARCIc1dffbUCgcABR7t27VyXBcCxWrWkcNjO77tPWrjQbT0oWwRAwAf%2B/Oc/a%2BPGjfuOqVOnui4JQBS49FLp4ovtUvDVV0t79riuCGWFAAj4QFJSklJTU/cd1atXd10SgCgQCNgoYO6l4DFjXFeEskIABHxg5syZOuaYY9S4cWNdd9112rJli%2BuSAESJWrWkSZPsfNQoackSt/WgbNAIGohzr732mqpWraoGDRpo1apVGjZsmLKzszV//nwlJSXl%2BzVZWVnKysra93kkElH9%2BvVpBA3EKc%2BTLrxQ%2Bve/pbQ0adYsKSHBdVUoTYwAAnHklVdeUdWqVfcdn3/%2BuS677DJ16dJFzZo1U9euXTVt2jR9//33%2Bs9//lPg84wZM0bBYHDfUb9%2B/TL8WwAoa7mrgoNB6auv8raMQ/xiBBCIIxkZGdq8efO%2Bz%2BvWratKlSod9LhGjRqpT58%2BuuOOO/J9HkYAAX969lmpd2%2BpYkVp8WKpUSPXFaG0MMALxJHk5GQlJycX%2Bpht27Zp3bp1ql27doGPSUpKKvDyMID4dc010r/%2BJc2YIV1/vfTRR1I5rhXGJX6sQBzbsWOHBg0apNmzZ2v16tWaOXOmunbtqqOPPloXXXSR6/IARJlAQHrySalyZWnmTOmZZ1xXhNJCAATiWPny5fXtt9/qggsuUOPGjdWrVy81btxYs2fPPuRIIQB/atjQVgNL0m23SRs2uK0HpYM5gAAOKRKJKBgMMgcQ8ImcHKl9e1sQ0q2b9OabritCSWMEEAAAHKB8eenpp60VzFtvSe%2B847oilDQCIAAAOEjz5tKgQXb%2Bj39IkYjbelCyCIAAACBfw4dLJ5wgrV8vDRniuhqUJAIgAADIV6VK0uOP23k4LM2b57YelBwCIAAAKNA550hXXGHbxfXtK2Vnu64IJYEACAAACvXAA1K1atLChdKkSa6rQUkgAAIAgEIdc4w0frydDx8urVvnth4UHwEQQIHC4bBCoZDS0tJclwLAsWuvlU49VcrMlG6%2B2XU1KC4aQQM4JBpBA5Ckb7%2BVWre2eYDvvy916eK6IhQVI4AAAOCwnHSSNGCAnffvL%2B3c6bYeFB0BEAAAHLYRI6R69aRVq6TRo11Xg6IiAAIAgMNWtar0z3/a%2Bfjx0g8/uK0HRUMABAAAR6RbN6lzZ2n3brsUzGqC2EMABAAARyQQkB5%2BWKpQQZo%2BXXrnHdcV4UgRAAEAwBFr1Ei67TY7v%2BUW6bff3NaDI0MABAAARTJ4sFS/vrRmjTR2rOtqcCQIgAAAoEiqVJEmTrTz8eOllSvd1oPDRwAEAABFdvHF0tlnS1lZ0sCBrqvB4SIAAgCAIgsEpEmTpPLlpXfflf7v/1xXhMNBAAQAAMUSClk7GMl2Ctmzx209ODQCIIAChcNhhUIhpaWluS4FQJQbMUKqWVNatkwKh11Xg0MJeB7tGwEULhKJKBgMKj09XSkpKa7LARClnnpKuv56KRi0HUJq1nRdEQrCCCAAACgR114rtWwppadLw4e7rgaFIQACAIASUb583j7BTz4pffut23pQMAIgAAAoMWecIV1yibR3r%2B0QwkSz6EQABAAAJWr8eNsn%2BKOPpPffd10N8kMABAAAJaphw7ym0IMGSbt3u60HByMAAgCAEjd4sHTMMdL330uPP%2B66GvweARAAAJS4lBTpnnvs/O67pV9%2BcVsPDkQABAAApaJ3b6lpU2n7dmnUKNfVYH8EQAAAUCoSEqT777fzhx%2BWVq50Ww/yEAABAECp6dxZ6tTJ9gcePNh1NchFAAQAAKUmEJAmTLCPr78uzZ3ruiJIBEAAhQiHwwqFQkpLS3NdCoAY1qKF1KuXnd92G82ho0HA8/gxAChcJBJRMBhUenq6UlJSXJcDIAb99JPUqJG0a5f07rvS%2Bee7rsjfGAEEAAClrl49acAAO7/zTik72209fkcABAAAZeLOO6UaNaRly6TnnnNdjb8RAAEAQJkIBqUhQ%2Bx85Ejpt9%2BcluNrBEAAAFBm%2BvWTGjSQNmyQJk1yXY1/EQABAECZSUqS7r3XzseOtV1CUPYIgAAAoEz17CmddJKUni6NG%2Be6Gn8iAAIAgDJVvrw0ZoydT5okrV/vth4/IgACAIAy95e/SKedZn0B77nHdTX%2BQwAEAABlLhCQRo%2B282eekX74wW09fkMABAAATpx%2Buo0E5uRII0a4rsZfCIAAAMCZ%2B%2B6zj5MnS4sXu63FTwiAAAoUDocVCoWUlpbmuhQAcaplS%2Blvf5M8Txo%2B3HU1/hHwPM9zXQSA6BaJRBQMBpWenq6UlBTX5QCIM//9r9S0qbR3rzR3rtS2reuK4h8jgAAAwKk//EG68ko7HzbMbS1%2BQQAEAADODR8uJSRI//d/0uefu64m/hEAAQCAc8cfL/XubefDhtmcQJQeAiAAAIgKQ4ZIFSpIn34qffyx62riGwEQAABEhfr1pb597Xz4cEYBSxMBEAAARI0775QqVpRmzZI%2B/NB1NfGLAAgAAKJGnTrSDTfY%2BYgRjAKWFgIgEMPeeustde7cWUcffbQCgYAWLlx40GOysrLUv39/HX300apSpYrOP/98/fTTTw6qBYDDc8cdNgo4Z440fbrrauITARCIYZmZmTr11FM1duzYAh8zYMAAvf3225o8ebK%2B%2BOIL7dixQ3/961%2BVk5NThpUCwOFLTZVuvNHOR45kFLA0sBMIEAdWr16thg0basGCBWrZsuW%2B29PT01WzZk299NJLuuyyyyRJGzZsUP369TV16lR17tz5sJ6fnUAAlLXNm6WGDaWdO6UPPpAO8%2BUKh4kRQCCOzZ8/X3v27NG5556777Y6deqoWbNmmjVrVoFfl5WVpUgkcsABAGWpVq28uYB3380oYEkjAAJxbNOmTapQoYKqVat2wO21atXSpk2bCvy6MWPGKBgM7jvq169f2qUCwEFuv93mAs6eLc2Y4bqa%2BEIABGLEK6%2B8oqpVq%2B47Pi/GXkme5ykQCBR4/%2BDBg5Wenr7vWLduXZH/LAAoqtTUvL6A99zDKGBJSnBdAIDDc/755%2BuUU07Z93ndunUP%2BTWpqanavXu3fvnllwNGAbds2aIOHToU%2BHVJSUlKSkoqXsEAUAJuu016/HHpiy%2BkmTOljh1dVxQfGAEEYkRycrJOPPHEfUelSpUO%2BTVt2rRRYmKiPtyvm%2BrGjRu1ZMmSQgMgAESLunWlPn3s/N573dYSTxgBBGLY9u3btXbtWm3YsEGStHz5ckk28peamqpgMKjevXvr1ltvVY0aNVS9enUNGjRIJ510ks455xyXpQPAYbvjDunJJ6VPPpG%2B/FI69VTXFcU%2BRgCBGPbvf/9brVq1UpcuXSRJ3bt3V6tWrfT444/ve8yDDz6oCy%2B8UJdeeqlOPfVUVa5cWe%2B9957Kly/vqmwAOCL160u9etn5qFFua4kX9AEEcEj0AQTg2o8/Sk2aSDk50tdfS23auK4otjECCAAAot4JJ0g9etj5ffe5rSUeEAABAEBMGDxYCgSkt9%2BWli51XU1sIwACAICYEApJ3brZ%2BZgxbmuJdQRAAAAQM%2B66yz5OniytXOm2llhGAAQAADGjdWupc2dbDDJhgutqYhcBEAAAxJTBg%2B3jc89JGze6rSVWEQABFCgcDisUCiktLc11KQCwz5/%2BJHXoIGVlSf/8p%2BtqYhN9AAEcEn0AAUSb996Tzj9fSk6W1q6VjjrKdUWxhRFAAAAQc7p0kZo1kzIypEcfdV1N7CEAAgCAmFOunO0RLEkPPSTt3Om2nlhDAAQAADHpssukY4%2BVtmyRXnjBdTWxhQAIAABiUmKidOutdn7//dYaBoeHAAgAAGJW795S9erSjz9Kb73luprYQQAEAAAxq0oV6R//sPMJEyR6mxweAiAAAIhp//iHVLGi9NVX0qefuq4mNhAAAQBATKtZU7rmGjtne7jDQwAEAAAxb%2BBAKRCQpk6Vli51XU30S3BdAAAAQHGdeKLUr5/UoIFUr57raqIfW8EBOCS2ggOA%2BMIlYAAFCofDCoVCSktLc10KAKAEMQII4JAYAQSA%2BMIIIAAAgM8QAAEAAHyGAAgAAOAzBEAAAACfIQACAAD4DAEQAADAZwiAAAAAPkMABAAA8BkCIAAAgM8QAAEAAHyGAAgAAOAzBEAABQqHwwqFQkpLS3NdCgCgBAU8z/NcFwEgukUiEQWDQaWnpyslJcV1OQCAYmIEEAAAwGcIgAAAAD5DAAQAAPAZAiAAAIDPEAABAAB8hgAIAADgMwRAAAAAnyEAAgAA%2BAwBEAAAwGcIgAAAAD5DAAQAAPAZAiCAAoXDYYVCIaWlpbkuBQBQggKe53muiwAQ3SKRiILBoNLT05WSkuK6HABAMTECCAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIZG0AAOyfM8ZWRkKDk5WYFAwHU5AIBiIgACAAD4DJeAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DMEQAAAAJ8hAAIAAPgMARAAAMBnCIAAAAA%2BQwAEAADwGQIgAACAzxAAAQAAfIYACAAA4DP/D7v%2BF317xmqgAAAAAElFTkSuQmCC'}