Genus 2 curve data - 164326.a.328652.1

Formats: - HTML - YAML - JSON - 2024-06-10T09:43:31.174923

• 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': '2174835214048247724', 'abs_disc': 328652, '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': '[[2,[1,2,3,2]],[82163,[1,-43,82205,-82163]]]', 'bad_primes': [2, 82163], 'class': '164326.a', 'cond': 164326, 'disc_sign': -1, 'end_alg': 'Q', 'eqn': '[[0,-1,-2,1,2],[1,0,0,1]]', 'g2_inv': "['-30517578125/328652','220703125/164326','-53562500/82163']", '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': "['500','21049','1530861','-42067456']", 'igusa_inv': "['125','-226','13712','415731','-328652']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '164326.a.328652.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '1.1110933747051825652289933389383545551957834', 'prec': 150}, 'locally_solvable': True, 'modell_images': ['2.6.1'], 'mw_rank': 3, 'mw_rank_proved': True, 'non_maximal_primes': [2], 'non_solvable_places': [], 'num_rat_pts': 15, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '16.734080822936382203519967193', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '0.0331985182354', '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': ['5.3.1314608.2', [1, -5, -3, 9, -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': '164326.a.328652.1', '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': '164326.a.328652.1', 'mw_gens': [[[[-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]]], [[[1, 1], [1, 1], [0, 1]], [[-1, 1], [0, 1], [0, 1], [-1, 1]]]], 'mw_gens_v': True, 'mw_heights': [{'__RealLiteral__': 0, 'data': '0.60327808485006251952996850714310', 'prec': 113}, {'__RealLiteral__': 0, 'data': '0.25220644984089131172838951142189', 'prec': 113}, {'__RealLiteral__': 0, 'data': '0.23194523207533454100695397859701', 'prec': 113}], 'mw_invs': [0, 0, 0], 'num_rat_pts': 15, 'rat_pts': [[-3, -4, 1], [-3, 30, 1], [-2, -10, 3], [-2, -9, 3], [-2, -2, 1], [-2, 9, 1], [-1, -7, 2], [-1, 0, 1], [-1, 0, 2], [0, -1, 1], [0, 0, 1], [1, -2, 1], [1, -1, 0], [1, 0, 0], [1, 0, 1]], 'rat_pts_v': False}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 164326, 'lmfdb_label': '164326.a.328652.1', 'modell_image': '2.6.1', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 25165
    {'label': '164326.a.328652.1', 'p': 2, 'tamagawa_number': 2}
  • id: 25166
    {'cluster_label': 'c4c2_1~2_0', 'label': '164326.a.328652.1', 'p': 82163, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '164326.a.328652.1', 'plot': 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3Jy8lEXAHADAiDypVMnqU0b6eBBqW1bKTXV6YqA4Bk2bJhiYmKyLnFxcU6XBAC5QgBEvvh80qRJUoUK0urV0uOPO10REDx9%2B/ZVUlJS1mXLli1OlwQAuUIARL5VqCBNmWLHY8dKn3/ubD1AsERERKhUqVJHXQDADQiACIjrrrOWMJLUvr20e7ej5QAAPCgjw%2BkK3IMAiIAZOVKKj7ddQu67j11C4D779%2B/XihUrtGLFCknSxo0btWLFCm2m2SUQ8r77zjpTfPqp05W4A3sBI6B%2B/FG66CJbFPLCC1K3bk5XBOTeN998o6ZNmx53/b333qupU6ee8v7sBQwEX1KS9aadMMEGHhISpMWLbY46To4AiIAbPVp6%2BGEpMlJautRGBQEvIAACweP3S9OnSw8%2BKG3fbte1by%2BNGiWVLetoaa7AKWAE3EMPSS1bWkuYO%2B%2BkNQwAILC2bJFuvFH6178s/NWsKX31lS1IJPzlDgEQAVeokDR1qlS%2BvDWH7tPH6YqAgpWYmKj4%2BHglJCQ4XQoQ1jIypHHj7MzSxx9LRYtmb0RwgtkbyAGngFFgZs%2BWWrXKPr7uOmfrAQoap4CBgrNypdS5s/T99/Z548bWh7ZePWfrcitGAFFgrr/e5mZINi9jxw5HywEAuNCBA1K/flKjRhb%2BSpWyRYYLFxL%2B8oMAiAI1cqR07rnWF7B9eykz0%2BmKAABu8dVX9jdk2DDp0CHp5pultWutw0QhEky%2B8PShQEVGSm%2B/bR8//9xWCAMAkJO//pI6dJCaNZN%2B/VWqUkX68EPpgw%2BkypWdri48EABR4OLjpeeft%2BM%2BfaRly5ytBwAQmvx%2B6a23pLp1bTGhzyd17y6tWWOrfhE4BEAExf332y9verrUtq2UkuJ0RQCAULJpky0WbNvWpg3Vqyf997/S%2BPE27w%2BBRQBEUPh80ssv2zD%2B%2BvXSv//tdEUAgFCQkWHTg%2BrVkz77TCpWTBo82M4WNW7sdHXhiwCIoClbVnr9dQuDkydL77/vdEUAACetXCldeqntHpWSIjVpYluKPvmkBUEUHAIggqppU%2Bnxx%2B24Sxfr5g64HY2ggbxJTZWeeOLo1i4TJkjffCPVqeN0dd5AI2gEXXq6dNll0pIl0pVXSnPnSoULO10VkH80ggZObf58a%2Bj888/2%2BU032Tw/VvcGFyOACLqiRaVp06SSJe3d3qhRTlcEAChoSUnWv%2B%2BKKyz8xcZaW5fp0wl/TiAAwhE1a0pjx9rxk0/SGgYAwtnMmbbIY8IE%2B7xTJ2vofPPNztblZQRAOKZDBxv6T0%2BX7r7btvsBAISPXbukO%2B%2BU2rSRtm2TzjnHdveYNEkqXdrp6ryNAAjH%2BHzSSy/ZaYC1a61JNADA/fx%2Bm%2BoTH2%2B7QRUqJPXubat%2BmzZ1ujpIBEA4rFw5acoUOx471t4ZAgDca8sWqVUrO7Pz119Sw4a20nfkSKl4caerw2EEQDjummtspxDJTgsnJztbDwAg7zIzpYkTba7fJ59YH78hQ6zjQ6NGTleHYxEAERJGjZKqV5c2b5Z69XK6GiBv6AMIr/vtN6l5c6lrV2nfPtvBY8UKqX9/6/yA0EMfQISMefOsL6BkvQGbNXO0HCDP6AMIr8nMlMaNk/r1k/75RypRQnr6aenBB%2BnvGuoYAUTIuOIK6YEH7LhLF3sxAQCEpp9/li6/XOrZ016vr7zSFnn07En4cwMCIELK8OFS1ap2OmHwYKerAQAcKyNDevZZW9zx3/9KUVHSiy9KX34p1ajhdHXILQIgQkp0tJSYaMfPPmvtYQAAoeHwqF%2BvXraf79VXSz/9ZHP/CpEoXIX/LoScNm3scuiQ9NBD1k8KAOCcjAzp%2Bedt1O/bb%2B3N%2BksvSZ9/Lp15ptPV4XQQABGSnn/eWgjMnSvNnu10NQDgXb/%2BavP7Hnnk6FG/zp2toT/ciQCIkHT22TaRWLIdQjIynK0HALzG77e5fQ0aSAsX2ly/CRNs1K9aNaerQ34RABGy%2BvSxvSJXr5bee8/paoCTow8gws3WrVLLltaZ4fAK31WrrGk/o37hgT6ACGmDB0sDB9o70BUreOFBaKMPINzO75fefFPq3l1KSpIiI6URI6QePVjkEW7470RI69HDGouuXGmNogEABWPPHumOO2wP36Qk6aKL7I33Qw8R/sJREacLAHJSpoy9GL30kjR5cvZOIXCXf/6Rdu%2B2jeH37JH27rVLUpJtG7V/v10OHLBLaqqUlialp9tq8IwM23HgsEKFrNFs0aJ2KVbMRipKlLBLVJStUoyJsWkEZ5whlS1rlwoV7DpGk4Fsc%2BZI7dtLf/xhv1sDBtjuHkVICWGL/1qEvE6td%2BqMl6aowdtrdahceRXpeK907rlOl%2BVuS5dKb7xhaaxBA3vlL1s2zw%2BTnm5zhTZtkrZsscsff9hl%2B3Zpxw5p167Q29WlaFGpYkWpcmVrPF61qk1qP%2Bss25P67LMtJOZaero0fbo0a5Z9/vHH0p13sh0CQsvy5dLrr9s7sfr1pQ4dlBpVTv36WecFSapd214aLrzQ2VJR8JgDiND2wQfy3323fKmpR1/fs2f2KxZyLzPTeje88srR10dFSe%2B/b7O%2Bj5GWZm0g1q%2B3JrAbNthOLb/%2BauHvyJG5nBQrZhmzTBkbkStd2kbooqPt20dF2ehd8eI2mlesmF2KFLEcVaiQjdr5/fY9MzIsd6WnW42pqTZ6mJJio4nJyXbZu9dy7p490p9/2nW5Ua6cVKuWVKeOVLeuVK%2Beve%2BoUuWY0cOtW6UWLaS1a5UsKUZSkqRSDRpIX3xhSRNwkt8vdesmTZx41NWZxUvo3xXf0fjfW0mym4waZb%2BHCH8EQISuX3%2BV4uOlgwdP/PWpU6V77w1qSa43erT08MMn/JK/RAn9Nuc3LdtWUatWWZ%2Bv1avtvyGnNjwRETZ6FhdnlypVbGStUiUpNtbyT/nyFvRC4bRraqqNSu7YYSOVW7fayOXmzdLvv0sbN0o7d578/mecIZ1/vnTBBTZKcsMz/6fIpf%2BVpKMDoGTB8PPPC/zfBOTohRdsVccJHFCkLi7zi4a%2BWkWtWgW5LjiKAIjQ1bu3vR09mfPPl5YtC149buf3S%2BecY8N3J9FfQzRU/Y%2B7PjraTg3VqiXVrGkPc/bZdrq0YsXwmyC%2Bb5/0yy824rlunQXh1attFPTIMHyBlmqpss%2BVHRcAJWnNGhtCBJxSp4798J7E/kcGKOrZp4JYEEIBcwDzwe/3a9%2B%2BfU6XEb4WL87568uX2yqCUBhWCnFpadKP8/7WRTmEP0mq51usC85PVr16Nvhat6797YiNPfnTvH9/ARQcAmrUsMu112Zfl5Zm%2B1OvXGmrIyt%2BsVDJW7K/nnzMR0na9dG3qlClSjBKBo6XkpJj%2BJMkrV6s5NzOjQgz0dHR8nn0bwgjgPlwuOcXAABwHy/37CQA5kNOI4DJycmKi4vTli1b8vXDlZCQoCVLlpz2/V39GNOnSx06nPzrHTrYnLaCrqMAHqMgakhOtr2TP/3UWjr8/ffRty9fXppWtL0u/uPDkz/ohx9KV12VrzpOh1sfI23PHhVr0EC%2B/70OJEuKk7RFdgr47yJlVTdzrQ5kRmTdp0IF6YYbpH/9S7r44hOPrLr1%2BSiIxwin11InakhKklY27qwm2949%2BY3effeEC8ACWUeoPoaXRwA5BZwPPp/vlC9IpUqVyteLVuHChfP97sS1j3H33dKrr0rffHP81ypWtC1CTqOmUHg%2BAlWDz1dKM2ZI77xjoe/I9TKlSlmWu/pq%2B1i7tuRbP1Rq/LUtjT1Wq1aWTPL4YhgKz6djj1GqlDRsmHUsP/Lqw5eJI7X9lvL64gvpo4%2BsS8yuXdKkSXapXl265x57L3PmmQ7/W0L4MaTweC0Ndg0rV0q33CJp2xAt0lyV1Z7jb9Sypd0ojxN5Q%2BH5DNRjeFXhQYMGDXK6iHCUlpam4cOHq2/fvoqIiDj1HXJw0UUX5bseVz5GoULSbbdpw%2Bo0%2BdavU3Gl6qCkzJtuUuG337a/nsGoo4Ae43Tv7/dbJp4/v7kee6yc3nvPWrNkZNgijY4dLZOMGye1bSslJFhLE59PdtC6tbRtm/y//CKf36/McuXke%2BQRKTHxtLu%2BhsLz6dhjXHSRTZT8%2BWel7dql4ZIeq1dPxSdMkNq2VWSktZC5%2BWbpkUekxo2trc1vv1kYnDdPGjPGpryWLm0LbHw%2BFz8fAX6McHstDVYNb71l7%2Bd27pSizyyrNi/foAoZ2%2BX/%2BWf7vS9bVr6ePaUJE6wxZgHV4ZbH8CJOARcQ9gQNjJQUqWFDacuvaXqy82aNmFRL2zz6nB44YAOiY8bYytTDatWynsO33mpBI7eSt2xRvWrVtPrPP1XqNJpA43jbli5V1Qsv1JYtW1S1atUcb/vPP3bG/ZVXpK%2B%2Byr6%2BZk3p0Uetw1FkZAEX7AK8luZNRobUp092A4UWLWxv38O/4lm/97t3q1S5cs4VCseFWfOG0BEREaGBAwfm%2Bx2rl/n90v33Wx%2B68lUidP/T1fSoB5/T1FTpuedsl4pu3Sz8RUVJXbrYqNG6ddKgQXkLf5IUUaGC7hs4UBFRUQVRticVq1ZNknL1M1qihHTXXdKXX9oI7iOPWGPsDRukrl2tzc6YMfb/72W8luZeUpIN8B8Of336SJ98cvQmP1m/99HRzhSJkMEIIEKS3y/16mXBp3BhW9zgxX2AZ8ywTU82bbLPzzzTgkL79qc1/REFLL%2BjVfv3257Xzz5rzakl26ZuyBCpXbvw67eIwNm4Ubr%2BemtTVLy4NGWKdPvtTleFUEYARMhJT7em9ZMm2eeTJ9u8Ni9JTrbRz7ffts%2BrVrVRvnvuOe3pOgiCQJ2uPHjQNrr5z39spxLJ5nK%2B8AJ7tOJ4ixfbyN/u3bYLz8yZtlMNkBPeTyKkbN9uq1YnTbLRjokTvRf%2BNm2y9iBvv22jn337Wh/X%2B%2B4j/IWqxMRExcfHKyEhISCPV6yYneLfsEEaMcJ2YlmyxH4uevfmtDCyzZwpNW1q4e/886Xvvyf8IXcYAUTI%2BOgjqXNneyGLjpamTbN3tWFv/37p9deljz9WWsohjV3VVCP3dlZk1fJ67z3pkkucLhC5VVALFnbssFP/b71ln9evb63b2GHO26ZMsdfMjAzbsebdd21%2BMJAbjADmw/Tp09WyZUuVK1dOPp9PK1asOOV9pk6dKp/Pd9wl1cNv6XfutHYlN95o4a9hQxvtODL8nc5z7Qpbt9rb9gcekD77TBEL5qr33v76uVBdLZ34Q77Dn9/v16BBg1S5cmUVL15cV155pVavXp3jfQYNGnTcz2dsbGz%2BCkG%2BxMbaSs6PP7ZG0j/9ZKeEP8yhp7ebvPDCC6pevboiIyPVqFEjLViw4KS35TXUjBljZ0cyMmxO8Ecf5Rz%2B5s%2Bfr9atW6ty5cry%2BXyaMWNG0GpFaCIA5kNKSoouu%2BwyDR8%2BPE/3K1WqlLZv337UJdKD/R7S06WxY61B8Vtv2Snfxx6z%2BSy1ax9929N9rkPeffdJv/xy3NVnZP6lCt1vlTIz8/XwI0eO1HPPPafx48dryZIlio2N1dVXX33KPazr1at31M/nqlWr8lUHAqN1a2vu27SptUi65RZp/Hinq8qfd955Rz179lT//v21fPlyNWnSRNdee602b9580vt4/TX0mWdscZhkLYNeeeXU00NSUlLUsGFDjXf7DwwCx49827hxo1%2BSf/ny5ae87ZQpU/wxMTFBqCp0ZWb6/R9/7PfXqeP323pfv/%2BCC/z%2BJUtOfd%2B8PNchb8MGv9/ny34STnSZNeu0Hz4zM9MfGxvrHz58eNZ1qamp/piYGP%2BECRNOer%2BBAwf6GzZseNrf18uSkpL8kvxJSUkF%2Bn3S0/3%2BBx7I/jEZPbpAv12Buuiii/xdu3Y96ro6der4%2B/Tpc8Lbe/01dNSo7P/3AQPs9TSvJPk//PDDwBcHV2EE0AH79%2B/XmWeeqapVq6pVq1Zavny50yUFzYIF0uWXS23aWP%2B6cuWkF1%2B0icueW924erW9judk8eLTfviNGzdqx44datGiRdZ1ERERuuKKK/Ttt9/meN8NGzaocuXKql69uu644w799ttvp10HAq9IERv5e%2BIJ%2B7xnT%2Bn9952t6XQcPHhQS5cuPepnVJJatGiR48%2BoV19DJ0yw9liS9NRTdvHoNrYIAAJgkNWpU0dTp07Vxx9/rLfeekuRkZG67LLLtGHDBqdLK1Dffmsd6S%2B/XFq40HY4ePxxO/vZtautdvWcP/449W0ONwA8DTtecpFpAAAgAElEQVR27JAkVaxY8ajrK1asmPW1E7n44ov12muv6fPPP9ekSZO0Y8cOXXrppfrrr79OuxYEns8nDR4sPfSQfd6%2Bva0adpM///xTGRkZefoZ9epr6Pvv21RhSerXTxowwNl64H4EwFyaNm2aoqKisi45TVLOySWXXKK7775bDRs2VJMmTfTuu%2B%2BqVq1aGjduXIArdt7hPWubN5cuu0yaM8dGLu6/34Lf8OG288GxAvVch7wzzzz1berUyfXDHfu8paenS5J8xwwR%2BP3%2B46470rXXXqtbbrlF5557rpo3b67Zs2dLkl599dVc14Lg8PmsWfqVV9qcwPvvP/WgcijKy8%2Bol15DD/vuO%2Bnuu%2B3/tmtXawwO5Nfp7fzuQW3atNHFF1%2Bc9XmVKlUC8riFChVSQkJCWL17zcyUZs2ygPfdd3ZdkSI2QtGvn1S9es73L6jnOuRceKE9MYcOnfw2edj%2B5NjnLS0tTZKNBFaqVCnr%2Bl27dh034pKTkiVL6txzzw2rn9FAS0xMVGJiojIyMoL%2BvQsXtnYgdetKX38tffGF1LJl0Ms4LeXKlVPhwoWPG%2B3Ly89oOL6GHmnrVummm6S0NJs6M348p30RGIwA5lJ0dLTOOeecrEvx4sUD8rh%2Bv18rVqw46g%2B0W6Wl2Wq0%2BvWlG26w8BcRYactfvnFmjufKvxJBfdch5wKFWx/r5Np3NguuXTs8xYfH6/Y2FjNmTMn6zYHDx7UvHnzdOmll%2Bb6cdPS0rR27dqw%2BBktKN27d9eaNWu0ZMkSR77/WWfZ6J8kJSY6UsJpKVasmBo1anTUz6gkzZkzJ9c/o%2BH0GnqsgwelW2%2B1VlkNGlhvVE9Ol0GBYAQwH/bs2aPNmzfrj//N5Vq/fr0kKTY2Nqtv2j333KMqVapo2LBhkqSnnnpKl1xyiWrWrKnk5GSNHTtWK1asUKKbXrWPsWePTU4eN84a1kq2T223bjY5PRAt5HLzXLvS%2BPH26v7JJ0ddvans%2BTozn7P6fT6fevbsqaFDh6pmzZqqWbOmhg4dqhIlSqht27ZZt2vWrJluuukm9ejRQ5LUq1cvtW7dWtWqVdOuXbs0ZMgQJScn6957781XPShYnTpZb7gvvrCdQtzSFeWRRx5Ru3btdOGFF6px48Z66aWXtHnzZnXt2lWSN15DT2bAAGnRIql0aev5mJ8mz/v379cvR7Sc2rhxo1asWKEyZcqoWrVqAagWruPsImR3mzJlil/ScZeBAwdm3eaKK67w33vvvVmf9%2BzZ01%2BtWjV/sWLF/OXLl/e3aNHC/%2B233wa/%2BABYt87v79bN7y9ePLstQZUqfv/IkX7/3r2B/V65ea5dbfFiv79/f//mOx/zt9RnfinT/9pr%2BX/YzMxM/8CBA/2xsbH%2BiIgI/%2BWXX%2B5ftWrVUbc588wzj3oeb7/9dn%2BlSpX8RYsW9VeuXNl/8803%2B1evXp3/YjwgWG1gTiQz0%2B8vW9Z%2BD5ctC/q3z5fExET/mWee6S9WrJj/ggsu8M%2BbNy/ra%2BH8GpqThQuzu0R98EH%2BH%2B/rr78%2B4Wvokc8tvIWt4JAnfr8t5hgz5uhBq/POs62qbr/d9jHF6RswQPrPf%2Bx5/PRT6aqrnK4IuVVQW8HlVoMG0qpVNgp49dVB//YIkIMH7TV17VqbOz1litMVIRwxBxC5kpJip3nr17cJ5p98YhORW7eWvvpKWrbMprMR/vJv0CDb4eHgQXt%2Bv/7a6YrgFv/8Yx/dcvoXJzZ%2BvIW/ChVslTdQEBgBRI5%2B%2B0164QVp8mRp7167LipK6tDB%2Bo%2Bdc46z9YWr1FTbG/nzz20hzZtvSjff7HRVOBUnRwCTkqQyZWwV/vbtgZl7i%2BBLSrLFcn//ba%2B7HTs6XRHCFSOAOE5mpp3mbdPGAt6zz1r4q1FDev55a0swdizhryBFRkozZthq6rQ06V//koYNc2ePNwTH9On2u1u3LuHPzRITLfzVrSux7goFiQCILMnJtpI3Pt527Zg50wJHy5bW1%2B/nn21V74maNyPwIiOt%2B3/37vb/0K%2BfnRo%2BPBILHJaeLo0caceEBvdKT7fTv5L9vtPyBQWJAAitWWMho0oVO627fr0UHS09%2BKDt1/vZZ9L110uF%2BGkJusN7vk6YIBUtaq0gzjvPttND6EhMTFR8fLwSEhIc%2Bf4jRmTvrf2/7ilwoU8/tdP3FSpIt93mdDUId8wB9KhDh6SPPrLTDUcuMqhb18LgPfdYCEToWLJEuuMOm5fp80mPPmp7wYZrn2w3cmIO4Ny50jXXSBkZ0muv5dxbHKHtrrtsvu/DD7P4AwWPAOgxO3ZIL79sI0rbttl1hQrZXLPu3a3lCNsMha7kZBulPbwt7znn2P9ls2bO1gUT7AC4aJG1e9m/3079TpnC769bZWZK5ctbY/0FC6T/%2Bz%2BnK0K4IwB6gN8vffutjfa9/77NM5HsxaZzZ9tCikbw7jJzpp3q%2B9/GKLr9dmnUKKlqVWfr8rpgBsDPP7c5oSkp9sbtk09sxTjc6aefpHPPlUqWtEUgRYs6XRHCHbO6wlhKiu2/e/759m7yrbcs/F1yifT669KWLdLTTxP%2B3Kh1a5u72aOHjeC%2B845Uq5Y0cKCNBiF8%2Bf0W9q%2B7zn7HmzeXPv6Y8Od2y5fbx0aNCH8IDgJgGNqwweaQVKkideki/fijrSjt2FFaulT67jvp7rv5g%2BF2MTG2avuHHyzgHzhgcwLPOccWjqSlOV0hAm37dluQ1bu3nTJs316aPdtGjeBuP/9sH%2BvWdbYOeAcBMExkZNgoQMuWNhI0erQ1FD37bBst2LbNmopecIHTlSLQzj9fmj9feu8969W4c6et4K5VS5o4MYcguHWr7RvGkGHIy8y00fz4eFspGhFhDdpfeYXdd8LF9u32kWkcCBYCoMv9%2Bac0fLj94b/hBtsD1OezUYJPPrHRwEcftR0CEL58PmsWvWaNBYNKlaTNm22eYI0a9oYgK%2BctWSJdeaUUF2ebx1aqZInx8D5iCCkLF0oXX2yj%2BXv32inCpUulbt1Y8BFO9u2zj/RZRbAQAF3q%2B%2B9t1V/VqlLfvtKmTRbyeveWfvnFGjdfey29%2B7ymWDELBr/%2BaqGvcmUb/X34Yct7Yzr%2BqMwrm0rz5mXfaf9%2BO2fcqpUNNSHPCqIP4LJlNtezSRM7zR8dba1BFi2S6tUL2LdBiMjIsI80f0awEA9cJDXV2n9cdJGNCLz2mp3ea9TI2j9s3Wq7AZx9ttOVwmnFi0v//rf1DJw4UapZ00aPqk0ZpEL/pJz4Tl9/becXkWfdu3fXmjVrtGTJknw9jt9v2fz66%2B33etYsCwRdumTP7S1SJEBFI6RERtpHBuIRLLyUuMCmTdbr7eWX7ZSvZCM9t99uvfsuvtjZ%2BhC6IiIsPHTqJM16P1XX3z4z5zu8%2B66lDwTVgQO2knvcOBv5k2z0/s47pQEDbD4nwlu5cvbx8Gs8UNAIgCHK75e%2B/NLOzM2cmX1mrlo1m9d13322XRCQG4UKSW1apErKyPF2W9alqEwKq0qDwe%2BXVqywhRxvvJG9x3NkpE3v6NXLVnTDG%2BLi7OPvvztaBjyEU8AhJjnZQl98vHX4/%2BgjC3/NmknTp9vcrr59CX84DaVLS3Xq5HiTsd9frIoVbeRp%2BnRvnY6aPn26WrZsqXLlysnn82nFihW5v/PixdaVuVIl%2B7x9e1upcQK//ioNHWpNfy%2B4wH7f9%2B6VzjpLGjbM%2BnNOmED485rDo7xr1zpbB7yDnUBCxLp1tlPHq69mrwaLirKRgO7d6Q2FAJk40YaQT%2BBARIwuq/irlm8um3VdiRLWWqhNG1tUVLFisAoNvtdff10bN25U5cqV1blzZy1fvlznnXfeqe/40UfSrbdK6elKlhQjKUlSqYgI6aOP5G/RUitXWpum6dNt1O%2BwiAhbvd%2Bxo73hY9GWd23ZYmd4Che2Fl6MwqOgEQAdlJFhrVrGjZPmzMm%2Bvk4dC3333CMFaT95eMmjj0rPP2/nIA8rW1aaMUP%2By/5P339vUwE/%2BMDmnx7pwgula66xsHLJJeHZg%2B73339X9erVcxcA09Ptr/aOHZJ0dACU9GfUWbog5ldt2Zad7AoXlpo2le64wwYNS5cuqH8J3MTvt9PA27ZJc%2BeyvzcKHgHQAXv22LyfxMTs%2BR6FClkXjh49bGsn%2BnuhQG3YIE2bZpuONmhg53xLlDjqJn6/bU/10Ue2GvXw4oTDSpSQLrtMuvxya1WSkHDcQ7hSXgJg%2BvTpKnrLLVmfHxsAJamZ5uq74s3UvLl04402mnp4wj9wpHvusW06H3/c%2BrsCBYkAGESrVtlo3xtv2Ko/STrjDFuh%2BcADNgcICFXbt0uff27Nxr/8Utq16%2BivFykinXeetSm68EJrY1K3rvv2NT1VAExOttO4y5ZJSaMGauC2wdlf0/EBcEXvaar9VFsVLx6M6uFmb70ltW1rZ4GYC4iCRgAMglmzpGeflb75Jvu6Bg1s84W2bcNj1ATe4vdLq1fbz/T8%2BbZbxeGtrI5UrJgtaDr3XGteXLeuVLu2VL26s6ePp02bpvvvvz/r808//VRNmjSRlB0A589fqYiIc7V%2Bvf0xXr3a3sRt3Jj9OJdrnubpyqzPTxQA9f33NjwKnEJSki3wO3jQftbq13e6IoQzAmAQdO1qc%2B8LF5ZuusmCX5MmnOZF%2BPD7beu5RYss7yxdaqePk5NPfPtChWzqXPXqNvJdrZrtalO5shQba38Ey5e3RRIFUeuWLfu0fv0e/flnYe3eXURpaWW0Y0cxbd4s/fxzmlau3C%2Bp7EkfIy7OVvA2aiQ9/HK8ojbbcM1xAfCCC066Ghg4kRtvtGkXjz0mjRjhdDUIZwTAIFizxk77duuW3esJCHd%2Bv42WrVwp/fST/R6sWyf9/LOUcpLNSI5VooRtcRgTYwuiSpa06yIjLRwWLWpvrHw%2B%2B36ZmdKhQ7Y2IzXVplqkpNjK%2BuRka7eyd2/2tlunUqmSteeoU8dGMOvXt9H7skdmwx9/tFUxu3cfHQArVZK%2B%2BuqUrXeAI334oXTzzbbifvPm8FxohdBAAAQQVH6/LZr99VcLiJs2WQuMrVvtNPKOHdLu3RbkClJ0tP2RjY21kcdy5Q6oZMm/FBGxU0OGdNTUqQPUsGENxcbGKjY2NucH27VLmjRJybNmKWbRIiUNGqRSPXockxSBUztyYfnbb9uOT0BBIAACCDl%2Bv43U7dljC5WTkmwUb/9%2BG9U7cMDmSaWn22je4VexwoVtMUrRojZCWLy4jRpGRdkIYunSNqJYpkz23quHTZ06VR06dDiuloEDB2rQoEG5qjs5OVkxMTFKSkpSKXo44TQNGiQ99ZRt8/ndd0wXQsEgAAJAgBAAEQg7d9oo4MGDtsjqf%2BuTgICi7zwAACGkYkXbTVCybQOBgkAABAAgxDz%2BuE1p%2BOwz22oaCDQCIADkU2JiouLj45VAvz8EyNlnS%2B3a2fGTTzpbC8ITcwABIECYA4hA2rjRGqenp9vuO1dd5XRFCCeMAAIAEIKqV5cOb1jz2GPW5xIIFAIgAAAh6sknrWfl0qXSm286XQ3CCQEQAIAQVaGC1LevHffpk/tddIBTIQACABDCHn7Y9szetk0aPtzpahAuCIAAAISwyEjp2Wft%2BJlnbBtFIL8IgAAAhLibbpKaN5fS0qR//zt7%2B0PgdBEAASCf6AOIgubzSePH2z7Xs2dLM2Y4XRHcjj6AABAg9AFEQevf37aHq1pVWrPGVggDp4MRQAAAXKJ/f%2BsPuHWrNGCA09XAzQiAAAC4RIkS0osv2vHYsdIPPzhbD9yLAAgAgIu0bCm1bWs7g3TqZFvFAXlFAAQAwGWef14qU0b68UdrDQPkFQEQAACXqVBBGj3ajgcPltatc7YeuA8BEAAAF7r7bumaa6w34H33SRkZTlcENyEAAkA%2B0QcQTvD5pIkTpago6dtvrU8gkFv0AQSAAKEPIJwwYYLUrZutEF65UqpRw%2BmK4AaMAAIA4GJdukhNm0r//CN17Girg4FTIQACAOBihQpJkydLJUtK8%2BdLL7zgdEVwAwIgAAAuV726NGKEHT/%2BuPTrr87Wg9BHAAQAIAx065Z9KrhDB04FI2cEQAAAwsCRp4IXLLCt4oCTIQACABAmqleXRo2y4759pZ9/drYehC4CIAAAYeT%2B%2B6Wrr5ZSU6V776VBNE6MAAgA%2BUQjaIQSn096%2BWWpVClp0aLsEUHgSDSCBoAAoRE0QsmUKdYXsFgxaelSqX59pytCKGEEEACAMNS%2BvXT99dLBg3YqOD3d6YoQSgiAAACEIZ9PmjRJOuMMadkyaeiQTGnhQumjj6RffnG6PDiMAAjA89LT0/X444/r3HPPVcmSJVW5cmXdc889%2BuOPP5wuDciXSpWkxETpOs3W3YNrSk2aSDfeKNWqJV1zjcTPuGcxBxCA5yUlJelf//qXOnfurIYNG%2Brvv/9Wz549dejQIf3www%2B5fhzmACIU%2BRcsVMYVTVXEf%2Bj4L9apIy1fLkVGBr8wOIoACAAnsGTJEl100UXatGmTqlWrlqv7EAARkq65Rvr885N/fcoUmzAIT%2BEUMACcQFJSknw%2Bn0qXLn3S26SlpSk5OfmoCxBSDh6Uvvgi59vMnBmcWhBSCIAAcIzU1FT16dNHbdu2zXEkb9iwYYqJicm6xMXFBbFKIBcyM6VTneg7dIJTwwh7BEAAnjNt2jRFRUVlXRYsWJD1tfT0dN1xxx3KzMzUCy%2B8kOPj9O3bV0lJSVmXLVu2FHTpQN5ERkqXXprzbZo3D04tCCnMAQTgOfv27dPOnTuzPq9SpYqKFy%2Bu9PR03Xbbbfrtt9/01VdfqWzZsnl6XOYAIiTNnCm1aXPCL6WVraSI39bZtiHwFEYAAXhOdHS0zjnnnKzLkeFvw4YNmjt3bp7DHxCyWreWJkyQoqOPunqt6qh15FztL0T48yJGAAF43qFDh3TLLbdo2bJlmjVrlipWrJj1tTJlyqhYsWK5ehxGABHS9u2TZsyQ9uxRSvX6iu9xlTZv8albN%2BkUsx0QhgiAADzv999/V/Xq1U/4ta%2B//lpXXnllrh6HAAg3%2BfLL7Ol/c%2BYwFdBrCIAAECAEQLhN9%2B42%2BletmrRqFVMBvYQ5gAAAeNSIEdLZZ0ubN0u9ejldDYKJAAgA%2BZSYmKj4%2BHglJCQ4XQqQJ1FR0iuv2PGkSTlvGILwwilgAAgQTgHDrR56SBo3TqpaVfrpJykmxumKUNAYAQQAwOOGDZNq1JC2bpUefdTpahAMBEAAADyuZElpyhQ7njyZU8FeQAAEAABq0sROBUtS585ScrKz9aBgEQABAIAkaehQOxW8ZQurgsMdARAAAEiyU8GTJ9vxpEnS3LnO1oOCQwAEAABZrrjCGkRLUqdO0v79ztaDgkEABAAARxk%2BXDrrLGnTJqlPH6erQUEgAAJAPtEIGuEmKspOAUtSYqI0f76z9SDwaAQNAAFCI2iEm86dpZdfls45R1q5Uipe3OmKECiMAAIAgBMaNUqqUkX65RdpwACnq0EgEQABAMAJxcRIEybY8XPPSUuWOFsPAocACAAATqpVK6ltWykzU7rvPungQacrQiAQAAEAQI5Gj5bKlZNWrZJGjHC6GgQCARAAAOSofHlpzBg7HjJEWrvW2XqQfwRAAABwSnfeKV1/vZ0C7tTJTgnDvQiAAJBP9AGEF/h80osvWo/Ab7%2B1Y7gXfQABIEDoAwgvGD9eevBBC4Jr1khxcU5XhNPBCCAAAMi1Bx6QGje2PYK7d5cYRnInAiAAAMi1QoVsm7iiRaWZM6UPPnC6IpwOAiAAAMiTevWkPn3s%2BMEHpb17na0HeUcABAAAedavn1SrlrRjR3YYhHsQAAEAQJ5FRkovvWTHEydK//2vs/UgbwiAAADgtFxxhdSxox136cI2cW5CAASAfKIPILzsmWdsp5A1a6RRo5yuBrlFH0AACBD6AMKr3nhDatfOTgv/9JNUo4bTFeFUGAEEAAD5ctddUrNmUmqq9QlkaCn0EQABAEC%2BHN4mLiJC%2BuIL6Z13nK4Ip0IABABJgwYNUp06dVSyZEmdccYZat68uRYvXux0WYBr1KxprWEk6eGHpaQkZ%2BtBzgiAACCpVq1aGj9%2BvFatWqWFCxfqrLPOUosWLbR7926nSwNc4/HHs3sDPvGE09UgJywCAYATOLygY%2B7cuWrWrFme7sMiEHjZl19KzZvbaeElS6RGjZyuCCfCCCAAHOPgwYN66aWXFBMTo4YNGzpdDuAqzZpJbdvaQpCuXaWMDKcrwokQAAHgf2bNmqWoqChFRkbq%2Beef15w5c1SuXLmT3j4tLU3JyclHXQBIzz4rlSol/fBD9m4hCC0EQACeM23aNEVFRWVdFixYIElq2rSpVqxYoW%2B//VbXXHONbrvtNu3ateukjzNs2DDFxMRkXeLi4oL1TwBCWmysNGSIHffrJ%2BXwawSHMAcQgOfs27dPO3fuzPq8SpUqKl68%2BHG3q1mzpjp27Ki%2Bffue8HHS0tKUlpaW9XlycrLi4uKYAwjITv0mJEjLl0v33itNnep0RThSEacLAIBgi46OVnR09Clv5/f7jwp4x4qIiFBEREQgSwPCRuHC0gsvSI0bS6%2B%2BKnXuLF12mdNV4TBOAQPwvJSUFPXr10%2BLFi3Spk2btGzZMnXq1Elbt27Vrbfe6nR5gGtdconUqZMdP/CAdOiQs/UgGwEQgOcVLlxY69at0y233KJatWqpVatW2r17txYsWKB69eo5XR7gasOGSWecIa1caSOCCA3MAQSAAKEPIHBiEyZI3bpJMTHS%2BvVSxYpOVwRGAAEAQIHq3NkaQiclSX36OF0NJAIgAAAoYIULS%2BPH2/HUqdJ33zlaDkQABIB8S0xMVHx8vBISEpwuBQhZl1widehgxz16sEOI05gDCAABwhxAIGe7dkk1a0rJydLEiVKXLk5X5F2MAAIAgKCoUEEaPNiO%2B/WT/v7b2Xq8jAAIAACC5oEHpPh46a%2B/pAEDnK7GuwiAAAAgaIoWlcaOteMXX5R%2B%2BsnZeryKAAgAAIKqWTPp5pttIci//y2xGiH4CIAAACDoRo2SIiKkr76SPvzQ6Wq8hwAIAACCrnp1qVcvO%2B7VS0pNdbYeryEAAkA%2B0QcQOD19%2B0pVqkgbN0rPP%2B90Nd5CH0AACBD6AAJ598YbUrt2UsmS0oYNUqVKTlfkDYwAAgAAx7RtK118sZSSYr0BERwEQAAA4JhChaQxY%2Bz41VelpUudrccrCIAAAMBRF18s3XWXtYN5%2BGHawgQDARAAADhu%2BHCpeHFpwQLpgw%2Bcrib8EQABAIDjqlaVeve248cek9LSnK0n3BEAAQBASHjsMalyZWsLc3i7OBQMAiAAAAgJJUtKTz9tx0OGSLt3O1tPOCMAAkA%2B0QgaCJx77pHOP19KTpaeesrpasIXjaABIEBoBA0ExtdfS1ddJRUuLP30k1SnjtMVhR9GAAEAQEhp2lRq3VrKyJAef9zpasITARAAAISckSNtBPDjj6VvvnG6mvBDAAQAACGnTh2pSxc77t1bysx0tp5wQwAEAAAhadAgKTpa%2BuEH6e23na4mvBAAAQBASKpQIXsOYL9%2BNIcOJAIgAAAIWQ8/LFWpIm3aJI0f73Q14YMACAD5RB9AoOCUKCENHmzHTz8t/f23s/WEC/oAAkCA0AcQKBgZGVLDhtLq1bYgZORIpytyP0YAAQBASCtcWBoxwo7HjpW2bHG2nnBAAAQAACHvuuukK66whSADBjhdjfsRAAHgGPfff798Pp9Gjx7tdCkA/sfnyx4FfPVV2yIOp48ACABHmDFjhhYvXqzKlSs7XQqAY1x8sXTLLZLfb21hcPoIgADwP9u2bVOPHj00bdo0FS1a1OlyAJzA00/bnMCZM6WFC52uxr0IgAAgKTMzU%2B3atVPv3r1Vr169XN0nLS1NycnJR10AFKzataX77rPjPn1sNBB5RwAEAEkjRoxQkSJF9NBDD%2BX6PsOGDVNMTEzWJS4urgArBHDYgAFSZKT03/9Ks2c7XY07EQABeM60adMUFRWVdZk3b57GjBmjqVOnyufz5fpx%2Bvbtq6SkpKzLFnpTAEFRpYp0%2BL1av37WJxB5QyNoAJ6zb98%2B7dy5M%2Bvz9957T/3791ehQtnviTMyMlSoUCHFxcXp999/z9Xj0ggaCJ49e6Szz5aSkqTXX5fuvtvpityFAAjA8/766y9t3779qOtatmypdu3aqUOHDqpdu3auHocACATXsGE2Ali9urRunVSsmNMVuUcRpwsAAKeVLVtWZcuWPeq6okWLKjY2NtfhD0DwPfSQNGaMtHGjNHmy1K2b0xW5B3MAAQCAK5UsKT3xhB3/5z/SP/84W4%2BbcAoYAAKEU8BA8KWlWWuYTZukkSOl3r2drsgdGAEEAACuFREhDRpkx8OHS7TjzB0CIAAAcLV27aQ6dWxl8HPPOV2NOxAAASCfEhMTFR8fr4SEBKdLATypcGFp8GA7fu456a%2B/nK3HDZgDCAABwhxAwDmZmVKjRtYSZswYiY15ckYbGAAA4HqFCkkLF9rKYJwap4ABAEBYIPzlHgEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEgn%2BgDCMBt6AMIAAFCH0AAbsEIIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAJBP9AEE4Db0AQSAAKEPIAC3YAQQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACQD7RCBqA29AIGgAChEbQANyCEUAAAACPIQACAAB4DAEQAADAYwiAAAAAHsMiEAAIEL/fr3379ik6Olo%2Bn8/pcgDgpAiAAAAAHsMpYAAAAI8hAAIAAHgMARAAAMBjCIAAAAAeQwAEAADwGAIgAACAxxAAAQAAPIYACAAA4DEEQAAAAI8hAAIAAHgMARAAAMBjCCsZXFkAAAD%2BSURBVIAAAAAeQwAEAADwGAIgAACAxxAAAQAAPIYACAAA4DEEQAAAAI8hAAIAAHgMARAAAMBjCIAAAAAeQwAEAADwGAIgAACAxxAAAQAAPIYACAAA4DEEQAAAAI8hAAIAAHgMARAAAMBjCIAAAAAeQwAEAADwGAIgAACAxxAAAQAAPIYACAAA4DEEQAAAAI8hAAIAAHgMARAAAMBjCIAAAAAeQwAEAADwGAIgAACAxxAAAQAAPIYACAAA4DEEQAAAAI8hAAIAAHgMARAAAMBjCIAAAAAeQwAEAADwGAIgAACAxxAAAQAAPIYACAAA4DEEQAAAAI8hAAIAAHjM/wOlMDvbk5/ZEQAAAABJRU5ErkJggg%3D%3D'}