Genus 2 curve data - 50000.b.800000.1

Formats: - HTML - YAML - JSON - 2024-06-06T21:00:33.972423

• 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': '1135833421995703169', 'abs_disc': 800000, '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': '[[2,[1]],[5,[1]]]', 'bad_primes': [2, 5], 'class': '50000.b', 'cond': 50000, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[0,-1,0,5,0,-4,1],[1]]', 'g2_inv': "['39062500','0','25000']", 'geom_aut_grp_id': '[2,1]', 'geom_aut_grp_label': '2.1', 'geom_aut_grp_tex': 'C_2', 'geom_end_alg': 'RM', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': False, 'igusa_clebsch_inv': "['100','625','14905','32']", 'igusa_inv': "['500','0','80000','10000000','800000']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '50000.b.800000.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '1.94023588064284930415403888461159521224558277884689', 'prec': 173}, 'locally_solvable': True, 'modell_images': ['2.6.2', '3.216.1'], 'mw_rank': 1, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 4, 'num_rat_wpts': 0, 'real_geom_end_alg': 'R x R', 'real_period': {'__RealLiteral__': 0, 'data': '17.117580699192261106509521990', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.566737763571', 'prec': 47}, 'root_number': -1, 'st_group': 'N(SU(2)xSU(2))', 'st_label': '1.4.B.2.1a', 'st_label_components': [1, 4, 1, 2, 1, 0], 'tamagawa_product': 5, 'torsion_order': 5, 'torsion_subgroup': '[5]', 'two_selmer_rank': 1, 'two_torsion_field': ['5.1.800000.1', [-8, -10, -20, 0, 0, 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': [['2.2.5.1', [-1, -1, 1], -1]], 'factorsRR_base': ['RR'], 'factorsRR_geom': ['RR', 'RR'], 'fod_coeffs': [-1, -1, 1], 'fod_label': '2.2.5.1', 'is_simple_base': True, 'is_simple_geom': True, 'label': '50000.b.800000.1', 'lattice': [[['1.1.1.1', [0, 1], [0, 0]], [['1.1.1.1', [0, 1], -1]], ['RR'], [1, -1], 'N(G_{3,3})'], [['2.2.5.1', [-1, -1, 1], [0, 1]], [['2.2.5.1', [-1, -1, 1], -1]], ['RR', 'RR'], [1, -1], 'G_{3,3}']], 'ring_base': [1, -1], 'ring_geom': [1, -1], 'spl_fod_coeffs': [-1, -1, 1], 'spl_fod_gen': [0, 1], 'spl_fod_label': '2.2.5.1', 'st_group_base': 'N(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': '50000.b.800000.1', 'mw_gens': [[[[1, 1], [0, 1], [0, 1]], [[0, 1], [0, 1], [-2, 1], [1, 1]]], [[[0, 1], [1, 1], [0, 1]], [[0, 1], [0, 1], [0, 1], [1, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.56673776357146209655764469998242', 'prec': 113}], 'mw_invs': [0, 5], 'num_rat_pts': 4, 'rat_pts': [[0, -1, 1], [0, 0, 1], [1, -1, 0], [1, 1, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 13146
    {'conductor': 50000, 'lmfdb_label': '50000.b.800000.1', 'modell_image': '2.6.2', 'prime': 2}
  • id: 13147
    {'conductor': 50000, 'lmfdb_label': '50000.b.800000.1', 'modell_image': '3.216.1', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 107024
    {'label': '50000.b.800000.1', 'p': 2, 'tamagawa_number': 5}
  • id: 107025
    {'cluster_label': 'c1c5_1~4_0', 'label': '50000.b.800000.1', 'p': 5, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '50000.b.800000.1', 'plot': 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JSuu47yFwkogAAA4JimTjWrfiQmSk89ZTsNAoECCAAAKrR5s1nvV5IeeURKTbWbB4FBAQQAAOXy%2B81Zv/v2SR07Sv362U6EQKEAAgCAcr3xhvTOO1JMjDRtGnP%2BRRL%2BKgEAwFF27ZIGDTLb2dnSmWfazYPAogACAICjDBsmbd8utWxZegwgIgcFEAAAlPHRR9JLL0kulzR9uhQXZzsRAo0CCFRSTk6OMjIylJCQoAYNGqhnz55at25dma/Jz8/XwIEDVb9%2BfcXHx6tHjx76%2BeefLSUGgOO3f7%2BZ80%2BS7r5buuACu3kQHBRAoJIWLlyoAQMGaOnSpZo3b54KCgqUmZmp/fv3l3xNVlaW3nzzTc2cOVOffvqp9u3bp%2B7du6uwsNBicgCovDFjpB9%2BMMu95eTYToNgYSk4oIp27typBg0aaOHChbrooovk9Xp18skn65VXXtGNN94oSdq6davS0tL03nvvqWvXrpV6XpaCA2DLsmVmupeiIum996Ru3WwnQrAwAghUkdfrlSTVq1dPkrRy5UodOXJEmZmZJV%2BTmpqqVq1aafHixVYyAkBl5edLffua8nfzzZS/SBdtOwAQjvx%2Bv4YMGaJOnTqpVatWkqS8vDzFxsaqbt26Zb42OTlZeXl5FT5Xfn6%2B8vPzS/7s8/mCExoAjuHhh6Wvv5YaNJCmTLGdBsHGCCBQBffcc49Wr16t119//Q%2B/1u/3y%2BVyVXh/Tk6O3G53ySUtLS2QUQHgD335Zenxfs88IyUl2c2D4KMAAsdp4MCBevvttzV//nydeuqpJbenpKTo8OHD2r17d5mv37Fjh5KTkyt8vuzsbHm93pLL5s2bg5YdAH6voEC6/XZzfe210vXX206E6kABBCrJ7/frnnvu0axZs/Txxx%2BradOmZe5v166dYmJiNG/evJLbtm3bpq%2B%2B%2BkodO3as8Hnj4uKUmJhY5gIA1WXSJCk3V6pbV/J4zNx/iHwcAwhU0oABA/Taa6/prbfeUkJCQslxfW63W7Vq1ZLb7Vbfvn01dOhQJSUlqV69eho2bJhat26tLl26WE4PAEdbu1YaP95sT5kipaTYzYPqwzQwQCVVdBzfSy%2B9pL/%2B9a%2BSpEOHDmn48OF67bXXdPDgQXXu3FlTp049ruP6mAYGQHUoLDSTPC9bJl1xhTRnDqN/TkIBBEIMBRBAdXjsMWn4cCkx0Zz9%2B5tDmuEAHAMIAIDDrFsnjR5tth9/nPLnRBRAAAAcpLDQnPV76JCUmWm24TwUQAAAHOSpp6TFi6WEBGn6dI77cyoKIAAADrF%2BvTRqlNl%2B7DGpUSO7eWAPBRAAAAcoKird9duli3THHbYTwSYKIAAADvD009Knn0q1a0svvMCuX6ejAAIAEOE2bJCys832o49KjRvbzQP7KIAAAESw4l2/Bw9Kl10m9etnOxFCAQUQAIAI9vTT0qJFUny89OKL7PqFQQEEQoTH41F6eroyMjJsRwEQIb7/vuyu3yZNrMZBCGEpOCDEsBQcgEAoKpIuvVT65BNz/eGHUg2GffB/%2BFYAACACTZ1qyl/xrl/KH36LbwcAACLMxo3SyJFm%2B%2B9/l5o2tZsHoYcCCABABCkqkvr2lfbvly6%2BWOrf33YihCIKIAAAEWT6dGn%2BfKlWLXb9omJ8WwAAECE2b5aGDzfbEydKp51mNw9CFwUQAIAI4PebSZ737pU6dpQGDrSdCKGMAggAQAR49VXp/feluDiz6zcqynYihDIKYDXIy5OefNIcmAsAQKDt2CFlZZntsWOlFi3s5kHoowAGWWGh1Lu3%2BYfZo4f0yy%2B2EwEAIs2gQdKuXVLbttKwYbbTIBxQAIOsRg3pz382Q/Lvvmv%2BcX7yie1UAIBIMWeO9L//a/6/eeEFKSbGdiKEAwpgkLlc0h13SMuWSc2aSVu2mCV5xo6VCgpspwMAhLO9e6W77zbbQ4ZI7drZzYPwQQGsJmedJa1cKd16qzkWcMIEM0HnDz/YTgYACFejR5upX5o2lcaPt50G4YQCWI1q15Zefll67TUpMVFavNgUw5deMqfvAwBQWStXSk8/bbafe0466SS7eRBeKIAW/PnP0pdfShdeKO3bJ91%2Bu3TNNdL27baTwSaPx6P09HRlZGTYjgIgxBUWmjn/iorM/ymZmbYTIdy4/H7GnmwpLJQee8wM4R85IiUlSR6P1KuXOXYQzuTz%2BeR2u%2BX1epWYmGg7DoAQ5PFI99wjud3St99KKSm2EyHcMAJoUVSUNGKE9PnnZlfwr79KN90kXX%2B9mTsQAIDf27FDuv9%2Bs/3ww5Q/VA0FMAS0aSMtXy6NGSNFR0uzZknp6RwbCAA42siRktcrnXOOdNddttMgXLELOMR8%2BaU5JjA31/z50kvNwb3NmtnNherDLmAAFVm%2BXDr/fLO9ZInUvr3dPAhfjACGmLPOMnMGTpok1aolzZ8vtW5tTu8/dMh2OgCALX6/NHiw2b71VsofTgwFMARFR0vDh0tffSV17SodPiyNGye1aiW9957tdAAAG15/XVq6VIqPl3JybKdBuKMAhrD/%2BR/p/ffNEj%2BpqdL330tXXmnWFN6wwXY6AEB1OXRIys4229nZ0imn2M2D8EcBDHEul5kW5ttvzQLf0dHSO%2B9IZ54p3XefORAYABDZPB5p0yapYUPp3nttp0EkoACGiYQE6dFHpTVrSncLP/qodPrp0tSpZh5BAEDk8XqliRPN9oQJrPiBwKAAhpkWLcxu4TlzpObNpV9%2BkQYMMMcH/uc/TBsDAJHmiSekXbvMz/9bb7WdBpGCAhiGXC5zLOCaNdIzz0gnnyx9952ZQLp9e%2Bmjj2wnBAAEwp490pQpZnvCBLOAABAIFMAwFhNjRv82bDDLycXHmzmiunQx8wcuWmQ7IQDgRDz9tNkFfOaZ0nXX2U6DSEIBjACJieY3w%2B%2B/lwYNkmJjpQULpIsukjp3lj75xHZCAMDxOnBAevJJs33//VIN/sdGAPHtFEGSk80Piw0bpH79zAjhxx9LF19syuDcuRwjCADh4h//MGvEN20q3XCD7TSINBTACJSWZpaPW7/erBMZG2t2B3frZtaOfP11qaDAdkr8nsfjUXp6ujIyMmxHAWCZ3y899ZTZzsoyU4ABgcRawA6wZYs0ebI0bZq0f7%2B5rVEjs7u4b1%2BpTh27%2BVAWawEDmD9fuuwyqXZt8zOcHwUINEYAHaBhQ%2Bnxx6WffjLHCjZoYCYUHTZMOvVU6Z57pG%2B%2BsZ0SAFDshRfM9V/%2BQvlDcDAC6ECHDkmvvmqmFvj669LbL7vM7DK%2B%2Bmqz2xh2MAIIOJvPZ47pPnRIWrFCOvdc24kQiRgBdKCaNaW//c3MI/jhh6bw1ahhThjp1cscQzhihLRune2kAOA8b71lyl%2BLFlK7drbTIFJRAB3M5TLTxMyeLW3caKYZSEmRduyQJk0yP3wuuECaPt1MRgoACL7//Mdc9%2Bplfk4DwcAuYJRx5IhZZu7FF82Sc0VF5va4OKl7d3M8SrduZhQRwcEuYMC5Dh2SkpLMHIBffCG1bWs7ESIVBRAV2rrVHCv4z3%2BWPVYwIcHsNr7hBikzkzIYaBRAwLk%2B/tjsmTnlFHP2LyOACBZ2AaNCqanSffeZYwW/%2BKL0rOG9e00xvPpqsw5xr17Sa69Ju3fbTgwA4W3BAnN92WWUPwQXBRB/yOUyuyEefdRMJfPpp9LgwaYM7tsnvfGG2TV88snSJZeYr/vqK1YdAYDjtWyZub7gArs5EPnYBYwq8/vNFAWzZ5uz1tauLXt/w4bSn/4kdelifps95RQ7OcNGfr40e7Z8a9fKPWGCvFu3KpEPDXCUBg2knTul5cslFgVCMFEAETA//CC9%2B6703ntmN8ahQ2Xvb968dF3iTp3MaiTs4vg/H35ohlF37JBPkluSNyFBidOmSTfdZDsdgGrw669S/fpme/9%2B6aST7OZBZKMAIigOHjS7iufNkz76yBxD%2BPvvtIYNpQ4dpPPPN5ezzzbLHjnOhg3SWWeZ0/6k0gIoKTEqyizk3KGDzYSVVlAgeb3meNA9e8y112u2fT6zvXdv6WXfPvMf3f795nvm4EHzi0N%2BvnT4sDkrvaBAKiws%2B/0TFWXWRo2NNSch1aplTk5KTJTq1jX/iTZoYL7HGjWSTj9dOuMMTlhCaFu%2B3PwsTE01J4AAwUQBRLXYvdv0mIULzfUXX5j/2H/L5TJzD7Zta8pgmzZSq1bmh2EwRgrz800x8XpNOSkuJPv2mSJy4EBpITl82FwKCkovhYWl0%2BQUq1Gj9FJcUmJizCU21kynExdnikjx5dwZWTrtnSdLnqNMAZTM6db/%2BlfgP4BjyM%2BXdu0yf2%2B7dpVefv217J%2BLbyv%2BOp%2BvWmMel6goqWVL06UzM6UrrmCEBaFl9mzpmmtMCVy61HYaRDoK4Anw%2B/3au3ev7Rhh6cABKTfX/Mb7%2Bedme9u28r82MVFq1sxcTjtNatLEjOqkpEj16pn/xAsKTHHbvdscP7Nzp5nQeudO6ZdfzPWvv5YtK/n51fqWK/SJOuksrSn5s09SmqTNMgVwp5KUUfcH1a4txceXvZx0khn9OukkUybj4kzRjI42hcflMiNnRUXmMzp82BTagwdLR9727SstwcWjdSf62cTHS2536SUx0VwnJJhL7drmuvg9xMeb91GrVmlJLn4fv30vLpd5L0VFZnTwyBGT9eBB8z58PpP/11%2Bl7dvNVEabN5tB1t%2BXU7dbGjRIuvde8/yAbf/8pzRwoDl2%2Bt//tp3GGRISEuRy6LFIFMATUDxfGwAACD9Onm%2BVAngC/mgE0OfzKS0tTZs3bz7ub7CMjAytWLGiSrmq%2BtjqfFxlPpu9e6XVq83KJO%2B8Y0ZyTkSNGr%2BoY8f6Ou200pHEhg3NMWPFI1M1ax69uznYn0th586K%2Bvzzkj//fgRwT92meuLWVcrLKx3Z3L7dXAdCQoKUn79BF198uho1Mp9L48ZmTeikJKlOHTOCV94vyaH2PVPZ19u2TbruOjPB%2BXnnmWNVA5XzRB9r%2B7OJpMedyOdS1dc8kcedfvpk7dw5VNdfb1ZjCubrOeF7pjKPdfIIYLTtAOHM5XJV6h9OYmLicf8Di4qKqvJvJVV9bHU/Tir9bLZvL90V/MUX0pdfmrOKj6V%2B/dLC5veby969FR%2BHVlSUqE8/NSenVPxezC7J4kvNmtKmTbPUtWuiYmPNsXzFuySjosyxfsU/O4ozFBWZ4wO3bPl/6tkzUQUFZldl8XGExSc5FB9f%2BNm%2BQ2pT3mfzf5cju72aMiV4v6Ga32HOqbAESea91q1rdrknJZnrevWknTvv15NPJqpu3dL7i7fr1jW7WePiynu%2BE/%2BeqSxzvGYTzZ%2BfqJUrzRnqn31Wevxmv36m4JYnnP4dStX7syZcHidV7XM5kdes6uNq1IjR/x35W%2BH3ZCBfz7xO5H7PnOhjIx0FMEQNGDCg2h9bXY/Lz5eWL4%2BSNES33FJLubkVj%2B6lpEjp6ebSvLk5k3PJkleVnX1zucVCMqVq504zSlY8UrZzp/TBB18oLe3skpMXis9U9XrN8WOSKW7FZ6iWOrOKB2RfoPnz//irDqnWMe8viq2prpeqzDGAtWuX/rn4GMBataSPP35PPXteUVJSa9QwpbSwsOwxgAcOlJ7wsnev9Omnq9W4cZuSM3eLL7t2ma8vLDTHUv7yy%2B/T3aIxY479/mrVKnscYGKiFBMzR7fcUvY9FR8DWKuWOf7v94X70KFoSVdo7txoxcWV/358PpP5l1/M3/3PP5vvrYKC/6pnz7K5OnSQxo6VunatOHs4/TusqlD/eXGijzsR1Z31T39qr1df/f3Pn%2BC9XlWF09%2B9je%2BbcMEu4CBiTVfj4EFpyRIz8vLJJ%2Bbstt%2BfZFB8BnC7duYM4LZtzVnAxXNiBVtRUWkh2r/fFIoDB0y5KB6xKz7poPgM4IKC0lG/4vfgcpWeBVx8AkPxWcAxMaVnAP/2TOCTpz8s96QHSrIcdRbw3XdLHk/1fBDlOHTo6LN/yzsbuLgw7tkTemcEx8WVnmHesaM5C7hJE9upAoefNeULt8/l3Xel7t2lc86RVq4M7muF22eDwGMEMIji4uI0duxYxVU0VBWhioqkVaukDz4wx1Z99tnRha9%2Bfb%2BSktbp5ptPV6dO0WrXzhyLZkuNGmZkysrPweH9pNeeM8NVv1enjjRkSPVn%2Bo2aNc2xkg0bHt/jCgtLz8r1ekvPNC6ecue38wAWF%2B5jzQNYVFSkbdu26ZRTTlF0dI0yU%2BucdJIZTfztPIDJyWYKocaNTfYv4BsfAAAc2UlEQVQaEbzwpVN/1vyRcPtcTj3VXG/aFPzXCrfPBoHHCCACwus1hW/OHGnu3KNPUEhNlS69tHQlkGbNWAWkjB9%2BkO64Q/r449IRwHbtlDh9uhkSBRDx9u4t/SV01y7zywwQLBRAVNm2bWbi0tmzpfnzzWhNsdq1zfq/xWsBN29O4auUDRvkW7NG7muvZdcM4ECNG5sRwAULzC/MQLCwCxjHZetWM0Hpv/4lLV5cdnmuZs2kq64yKyx06mR2z%2BE4nX66WcMMgCOdc44pgCtWUAARXBRA/KE9e0zpe%2B0181vpb0vf%2BedL114rXX21GeUDAFRdx45mr8qiRdKwYbbTIJJRAFGuwkJzTN/LL0tvvVX2JI4OHaQbbzQT6RYftAwAOHGXXGKuFywwJ0BF8780giSCz4uz68iRIxoxYoRat26t%2BPh4paam6pZbbtHWrVttRzumjRulBx4wx6FccYXZ1ZufL515ppSTI/34o9n1O3jwiZW/WbNmqWvXrqpfv75cLpdWrVoVsPeAyPLJJ5/oqquuUmpqqlwul2bPnm07UkjIyclRRkaGEhIS1KBBA/Xs2VPr1q2zHSskPPvss2rTpk3JJMcdOnTQ%2B%2B%2B/bztWpZxzjjmL3ec79qT1gZCTkyOXy6WsrKzgvhBCEgUwSA4cOKDc3FyNHj1aubm5mjVrlr777jv16NHDdrSjFBZKb78tdetmlkh7%2BGFpyxaz4sOgQWY%2BqjVrpJEjTTEMhP379%2BuCCy7QI488EpgnRMTav3%2B/zjrrLD3zzDO2o4SUhQsXasCAAVq6dKnmzZungoICZWZmav/%2B/bajWXfqqafqkUce0eeff67PP/9cl112ma6%2B%2Bmp9/fXXtqP9oago6corzfasWcF7nRUrVmjatGlq06a8dYjgBJwFXI1WrFih8847Tz/99JMaNWpkO4527ZJeeMHMMfzbeaf%2B9CczI0mPHuUv4xVIP/74o5o2baovvvhCbdu2De6LhQkmaK2Yy%2BXSm2%2B%2BqZ6/X9YD2rlzpxo0aKCFCxfqoosush0n5NSrV0%2BPPvqo%2BvbtazvKH5ozx5xQl5JipgeNigrs8%2B/bt0/nnHOOpk6dqoceekht27bVlClTAvsiCHmMAFYjr9crl8ulOnXqWM2xbp3Uv7/ZhTtihCl/SUnS8OHShg3m2L8bbgh%2B%2BQMQOF6vV5IpOihVWFiomTNnav/%2B/erQoYPtOJWSmWl%2BJufl6ZhrdFfVgAEDdOWVV6pLly6Bf3KEDQ4vrSaHDh3SyJEj1bt3b2ujOosXS5Mmmd29xeO%2Bbdua3bw33WTWYIU9Ho9HHo9HhYWFtqMgzPj9fg0ZMkSdOnVSq1atbMcJCWvWrFGHDh106NAh1a5dW2%2B%2B%2BabS09Ntx6qU2Fipd2/p6afNXprLLw/cc8%2BcOVO5ublasWJF4J4UYYkRwACZMWOGateuXXJZtGhRyX1HjhzRTTfdpKKiIk2dOrVac/n90n//a%2BaTuuACc0av3292L8yfL%2BXmSrfdFtzyd6zPBqUGDBigtWvX8oMZx%2B2ee%2B7R6tWr9frrr9uOEjKaN2%2BuVatWaenSperfv79uvfVWrV271nasSrvjDnM9e7Y5JjsQNm/erMGDB%2BvVV19VzZo1A/OkCFscAxgge/fu1fbt20v%2B3LBhQ9WqVUtHjhxRr1699MMPP%2Bjjjz9WUlJSteTx%2B83C4hMmmAlFJSkmRurTx%2BzqbdGiWmJIqvizkTgGsDwcA1gxjgE82sCBAzV79mx98sknatq0qe04IatLly467bTT9Pzzz9uOUmkXXWTmAxw1ypycd6Jmz56ta665RlG/OaiwsLBQLpdLNWrUUH5%2Bfpn7ENnYBRwgCQkJSkhIKHNbcflbv3695s%2BfXy3lz%2B83a/GOGSN9/rm5rVYtqV8/M6low4ZBj3CU8j4bACfG7/dr4MCBevPNN7VgwQLK3x/w%2B/3K/%2B2EpmEgK8sUwGeflbKzzRKbJ6Jz585as2ZNmdtuu%2B02tWjRQiNGjKD8OQwFMEgKCgp0/fXXKzc3V3PmzFFhYaHy8vIkmYO0Y4OwTtrCheY3xcWLzZ/j46UBA6ShQ0NvdbFdu3Zp06ZNJfMiFs9flpKSopSUFJvREGL27dunDRs2lPx548aNWrVqlerVqxcSZ9PbMmDAAL322mt66623lJCQUPLzxe12l4ywO9WoUaPUrVs3paWlae/evZo5c6YWLFiguXPn2o52XK6%2B2qwOuWGDNG2aNGTIiT1fQkLCUceIxsfHKykpiWNHnciPoNi4caNfUrmX%2BfPnB/S1vvzS7%2B/Wze83439%2Bf82afv/QoX7/9u0BfZmAeumll8r9bMaOHWs7mnVer9cvye/1em1HCQnz588v93vl1ltvtR3Nqop%2Bvrz00ku2o1l3%2B%2B23%2Bxs3buyPjY31n3zyyf7OnTv7P/jgA9uxqmT6dPNz/ZRT/P4DBwL//BdffLF/8ODBgX9ihDyOAQxjW7ZIo0eb5dr8frNk0N/%2BZm5LTbWdDlXFMYAAih0%2BLJ1xhpmu64knzG5hIBA4CzgMHThgTu5o1kx66SVT/m64QfrmG3OsCOUPACJDbKx0//1mOydH2rfPbh5EDgpgGPH7zdq8LVpIY8eaItixo7Rkibn99NNtJwQABNptt5llOnfsMKOAQCBQAMPE2rVS587SjTdKmzdLjRpJM2eaxcLbt7edDgAQLDEx0kMPme1Jk6TfzKoFVBkFMMTt32%2BWazvrLDNxc82a0rhx0rffmjLoctlOCAAItl69pHPPNbuAx461nQaRgJNAQti770p3320O/pXMlABTpkhNmliNhSDjJBAA5Vm0yEwOXaOGtGqV1Lq17UQIZ4wAhqDt283oXvfupvw1biy9845ZEojyBwDOdOGF0vXXS0VF0uDBpWu6A1VBAQwhfr/0yitSy5bmpI6oKLN6x9dfmzIIAHC2Rx81hwLNny/9%2B9%2B20yCcUQBDxNat0lVXSbfcIu3eLbVtKy1fbv6xx8fbTgcACAVNmpjjwiWzMgjTwqCqKICW%2Bf3SjBnSmWeaY/5iY82i38uXS%2BecYzsdqpPH41F6eroyMjJsRwEQwkaMkJo2lX7%2B2cwJC1QFJ4FY9Ouv0l13lQ7jn3uuWdXjzDOtxoJlnAQC4I/MmWP2GkVHS198IbGUL44XI4CWzJtnzuD697/NP%2BDx46XFiyl/AIA/1r271LOnVFAg9etnTgwBjgcFsJrl50tDh0qZmdK2bWZVj6VLpTFjzGSfAABUxlNPmWPEFy%2BWXnjBdhqEGwpgNVq/3izd9vjj5s/9%2B0srV0rt2tnNBQAIP2lppSuE3HefGVQAKosCWE1ef92c1JGbKyUlSW%2B9JU2dKp10ku1kAIBwNXCgOX7c65UGDbKdBuGEAhhkhYXm%2BIzevc3p%2BhdfLH35pdSjh%2B1kAIBwFxUlTZ9urv/9bzO4AFQGBTDIoqLMVC8ulzR6tPTRR1LDhrZTAQAiRdu20vDhZvvuu6U9e%2BzmQXhgGphqcPCgtGKFWcMR%2BCNMAwPgeB08KJ11ljnW/G9/M6OCwLEwAlgNatWi/AEAgqdWLenFF832Cy%2BYvU3AsVAAAQCIABdeaHYBS9Idd7BMHI6NAggAQIR45BGpUSNp40Zp1CjbaRDKKIAAAESIhITS4/%2BeeUZatMhuHoQuCiAAABEkM1O6/XYzA0XfvtKBA7YTIRRRAAEAiDCTJ5spx9avN1OQAb9HAQRChMfjUXp6ujIyMmxHARDm6tSRpk0z2088IS1ZYjcPQg/zAAIhhnkAAQTKX/8q/eMfUrNm0qpVZroYQGIEEACAiPXEE9Ipp0jffSeNGWM7DUIJBRAAgAhVt670/PNm%2B/HHpaVL7eZB6KAAAgAQwa66SurTRyoqMruEDx60nQihgAIIAECEmzJFSkmR1q2Txo61nQahgAIIAECEq1ev9KzgyZOlZcvs5oF9FEAAABzgqqukm28u3RV86JDtRLCJAggAgEM8%2BaTZFfztt9K4cbbTwCYKIAAADlGvnvTcc2b70Uel5cvt5oE9FECgEo4cOaIRI0aodevWio%2BPV2pqqm655RZt3bq1zNft3r1bffr0kdvtltvtVp8%2BfbRnzx5LqQHgaFdfLfXubXYF33ablJ9vOxFsoAAClXDgwAHl5uZq9OjRys3N1axZs/Tdd9%2BpR48eZb6ud%2B/eWrVqlebOnau5c%2Bdq1apV6tOnj6XUAFC%2Bp56SGjSQ1q6VJkywnQY2sBQcUEUrVqzQeeedp59%2B%2BkmNGjXSN998o/T0dC1dulTnn3%2B%2BJGnp0qXq0KGDvv32WzVv3rxSz8tScACqw6xZ0nXXSVFRZlfwOefYToTqxAggUEVer1cul0t16tSRJC1ZskRut7uk/ElS%2B/bt5Xa7tXjxYlsxAaBc114r9eolFRaas4IPH7adCNWJAghUwaFDhzRy5Ej17t27ZJQuLy9PDRo0OOprGzRooLy8vAqfKz8/Xz6fr8wFAKrDM89I9etLa9ZIOTm206A6UQCBcsyYMUO1a9cuuSxatKjkviNHjuimm25SUVGRpk6dWuZxLpfrqOfy%2B/3l3l4sJyen5KQRt9uttLS0wL0RADiGk0%2BWnn7abD/0kLR6td08qD4UQKAcPXr00KpVq0ou5557riRT/nr16qWNGzdq3rx5ZY7RS0lJ0fbt2496rp07dyo5ObnC18rOzpbX6y25bN68OfBvCAAqcOONUs%2BeUkGBdPvt5hqRjwIIlCMhIUGnn356yaVWrVol5W/9%2BvX68MMPlZSUVOYxHTp0kNfr1fLfTKy1bNkyeb1edezYscLXiouLU2JiYpkLAFQXl0uaOlWqU0daudIsFYfIx1nAQCUUFBTouuuuU25urubMmVNmRK9evXqKjY2VJHXr1k1bt27V888/L0m688471bhxY73zzjuVfi3OAgZgw8svm3kB4%2BLMruBmzWwnQjBRAIFK%2BPHHH9W0adNy75s/f74uueQSSdKuXbs0aNAgvf3225LMruRnnnmm5EzhyqAAArDB75cuv1z64AOpUydp4UKpBvsJIxYFEAgxFEAAtvz0k3TmmdL%2B/ZLHI919t%2B1ECBa6PQAAkCQ1biw98ojZHjFC2rTJbh4EDwUQAACUuPtuqWNHad8%2BqX9/s2sYkYcCCAAAStSoIb3wghQbK733nvT667YTIRgogAAAoIyWLaUHHjDbgwdLv/xiNw8CjwIIAACOMmKE1KqVKX9DhthOg0CjAAIAgKPExkrTp5uJol95xUwPg8hBAQQAAOVq314aONBs33WXdOCA3TwIHAogAACo0EMPSWlp0saN0rhxttMgUCiAAACgQgkJZq1gSXr8cWnVKrt5EBgUQCBEeDwepaenKyMjw3YUACije3fphhukwkLpzjvNNcIbS8EBIYal4ACEom3bpBYtJJ9Peuqp0mMDEZ4YAQQAAH/olFNKl4m7/35pyxa7eXBiKIAAAKBS%2BvUzZwbv3SsNGmQ7DU4EBRAAAFRKjRrS889LUVHSrFnSnDm2E6GqKIAAAKDS2rQpXRnknnuYGzBcUQABAMBxGTtWatRI%2BuknacIE22lQFRRAAABwXOLjpaefNtuTJ0tr19rNg%2BNHAQQAAMetRw9zKSiQ%2BveXmFQuvFAAAQBAlTz1lFSrlvTJJ9Irr9hOg%2BNBAQQAAFXSuLE0ZozZHjZM2rPHbh5UHgUQAABU2ZAhZoWQnTulBx6wnQaVRQEEAABVFhsreTxm%2B9lnpdxcu3lQORRAAABwQi67TLrpJqmoSLr7bnON0EYBBAAAJ2zyZKl2bWnZMunll22nwR%2BhAAIhwuPxKD09XRkZGbajAMBxS02Vxo0z2yNGSLt3W42DP%2BDy%2B5m5BwglPp9PbrdbXq9XiYmJtuMAQKUdOSKddZb0zTdmmbjiyaIRehgBBAAAARETIz3zjNmeOlVavdpuHlSMAggAAALmssuk6683J4IMHMgKIaGKAggAAAJq8uTSFUL%2B939tp0F5KIAAACCgGjWSsrPN9rBh0v79dvPgaBRAAAAQcMOGSU2aSFu2SDk5ttPg9yiAAAAg4GrVkh5/3Gw/9pi0caPdPCiLAggAAIKiZ0%2Bpc2cpP9%2BMCCJ0UAABAEBQuFzSlClSVJQ0a5Y0f77tRChGAQQAAEHTqpV0111mOytLKiy0mwcGBRAAAATV%2BPFS3bpmYugXXrCdBhIFEAAABFlSUuk6waNHS16v1TgQBRAAAFSD/v2lFi2knTulhx6ynQYUQAAAEHQxMWaFEEl66inp%2B%2B/t5nE6CiAQIjwej9LT05WRkWE7CgAERbduUmamdPiwdN99ttM4m8vvZ5lmIJT4fD653W55vV4lJibajgMAAfXVV9JZZ0lFRWat4AsvtJ3ImRgBBAAA1aZVK%2BlvfzPbQ4eaIojqRwEEAADVasIEqXZtacUK6fXXbadxJgogAACoVsnJUna22R41Sjp40G4eJ6IAAgCAanfvvdKpp0qbNpmzglG9KIAAAKDa1aolPfyw2Z44UfrlF7t5nIYCCAAArLj5ZunssyWfT3rwQdtpnIUCCAAArKhRQ3r0UbM9daq0YYPdPE5CAQQAANZ07ixdfrlUUGBOCEH1oAACVdCvXz%2B5XC5NmTKlzO27d%2B9Wnz595Ha75Xa71adPH%2B3Zs8dSSgAID5MmSS6X9MYb0vLlttM4AwUQOE6zZ8/WsmXLlJqaetR9vXv31qpVqzR37lzNnTtXq1atUp8%2BfSykBIDw0bq1dMstZvu%2B%2ByTWKAs%2BCiBwHLZs2aJ77rlHM2bMUExMTJn7vvnmG82dO1cvvPCCOnTooA4dOmj69OmaM2eO1q1bZykxAISHBx%2BU4uKkhQul996znSbyUQCBSioqKlKfPn00fPhwnXnmmUfdv2TJErndbp1//vklt7Vv315ut1uLFy%2BuzqgAEHbS0qSBA812drZUWGg3T6SjAAKV9Pe//13R0dEaNGhQuffn5eWpQYMGR93eoEED5eXlVfi8%2Bfn58vl8ZS4A4ETZ2VKdOtKaNdKMGbbTRDYKIFCOGTNmqHbt2iWXhQsX6sknn9TLL78sl8tV4ePKu8/v9x/zMTk5OSUnjbjdbqWlpQXkPQBAuKlXTxo50myPGSPl59vNE8kogEA5evTooVWrVpVcFi9erB07dqhRo0aKjo5WdHS0fvrpJw0dOlRNmjSRJKWkpGj79u1HPdfOnTuVnJxc4WtlZ2fL6/WWXDZv3hystwUAIW/gQCk1VfrpJ%2Bm552yniVwuv59zbYA/8uuvv2rbtm1lbuvatav69Omj2267Tc2bN9c333yj9PR0LVu2TOedd54kadmyZWrfvr2%2B/fZbNW/evFKv5fP55Ha75fV6lZiYGPD3AgChbto0qV8/6eSTpe%2B/lxISbCeKPBRAoIqaNGmirKwsZWVlldzWrVs3bd26Vc8//7wk6c4771Tjxo31zjvvVPp5KYAAnK6gQEpPl9avl8aPN7uDEVjsAgYCaMaMGWrdurUyMzOVmZmpNm3a6JVXXrEdCwDCSnR06drAjz0m/fKL3TyRiBFAIMQwAggAUlGRdO650hdfSEOHmiKIwGEEEAAAhJwaNaSHHzbbHo%2B0ZYvdPJGGAggAAELS5ZdLnTpJhw6V7hJGYFAAAQBASHK5SkcBX3xR%2BuEHu3kiCQUQAACErIsukrp2NWcGjx9vO03koAACAICQVrz799VXpW%2B/tZslUlAAAQBASMvIkK6%2B2pwZPHas7TSRgQIIAABC3oQJ5vpf/5JWr7abJRJQAAEAQMhr00bq1ctsszLIiaMAAgCAsDBunDkz%2BK23pJUrbacJbxRAIER4PB6lp6crIyPDdhQACEktW0q9e5ttjgU8MSwFB4QYloIDgIp9950pgkVF0rJl0nnn2U4UnhgBBAAAYaNZM6lPH7M9bpzVKGGNAggAAMLKAw9IUVHS%2B%2B%2BbUUAcPwogAAAIK6efzijgiaIAAgCAsFM8Cjh3LqOAVUEBBAAAYee000pHAVkj%2BPhRAAEAQFi6//7SYwFXrLCdJrxQAAEAQFg6/XTp5pvNdvFScagcCiAAAAhb998v1aghzZkj5ebaThM%2BKIAAACBsnXGG9Oc/m%2B0HH7SbJZxQAAEAQFi7/36zRvDs2dLq1bbThAcKIAAACGstW0o33GC2H37YbpZwQQEEAABh74EHzPUbb0jffms3SziIth0AAADgRLVuLd1xhzkmsGFD22lCHwUQCBEej0cej0eFhYW2owBAWJo2zXaC8OHy%2B/1%2B2yEAlPL5fHK73fJ6vUpMTLQdBwAQgTgGEAAAwGEogAAAAA5DAQQAAHAYCiAAAIDDUAABAAAchgIIAADgMBRAAAAAh6EAAgAAOAwFEAAAwGEogAAAAA5DAQQAAHAYCiAAAIDDUAABAAAchgIIhAiPx6P09HRlZGTYjgIAiHAuv9/vtx0CQCmfzye32y2v16vExETbcQAAEYgRQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQOA4fPPNN%2BrRo4fcbrcSEhLUvn17bdq0qeT%2B/Px8DRw4UPXr11d8fLx69Oihn3/%2B2WJiAACORgEEKun7779Xp06d1KJFCy1YsEBffvmlRo8erZo1a5Z8TVZWlt58803NnDlTn376qfbt26fu3bursLDQYnIAAMpiKTigkm666SbFxMTolVdeKfd%2Br9erk08%2BWa%2B88opuvPFGSdLWrVuVlpam9957T127dq3U67AUHAAg2BgBBCqhqKhI7777rpo1a6auXbuqQYMGOv/88zV79uySr1m5cqWOHDmizMzMkttSU1PVqlUrLV682EZsAADKRQEEKmHHjh3at2%2BfHnnkEV1%2B%2BeX64IMPdM011%2Bjaa6/VwoULJUl5eXmKjY1V3bp1yzw2OTlZeXl5FT53fn6%2BfD5fmQsAAMFEAQTKMWPGDNWuXbvksm7dOknS1VdfrXvvvVdt27bVyJEj1b17dz333HPHfC6/3y%2BXy1Xh/Tk5OXK73SWXtLS0gL4XAAB%2BjwIIlKNHjx5atWpVyaVt27aKjo5Wenp6ma9r2bJlyVnAKSkpOnz4sHbv3l3ma3bs2KHk5OQKXys7O1ter7fksnnz5sC/IQAAfiPadgAgFCUkJCghIaHMbRkZGSUjgcW%2B%2B%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%3D'}