Genus 2 curve data - 15104.c.966656.1

Formats: - HTML - YAML - JSON - 2024-07-27T07:13:31.206240

• 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': '1610760359549582575', 'abs_disc': 966656, 'analytic_rank': 0, '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]],[59,[1,11,47,-59]]]', 'bad_primes': [2, 59], 'class': '15104.c', 'cond': 15104, 'disc_sign': -1, 'end_alg': 'Q', 'eqn': '[[0,1,-5,0,3,1],[]]', 'g2_inv': "['-3361400000/59','-118335000/59','-5566400/59']", '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': "['280','760','60604','-3776']", 'igusa_inv': "['560','11040','290816','10243840','-966656']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '15104.c.966656.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '1.2773155963132457989554010534101548033336356895183402844', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.120.3'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2], 'non_solvable_places': [], 'num_rat_pts': 3, 'num_rat_wpts': 3, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '5.1092623852529831958216042136', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, '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': 4, 'torsion_order': 4, 'torsion_subgroup': '[2,2]', 'two_selmer_rank': 2, 'two_torsion_field': ['3.1.59.1', [-1, 2, 0, 1], [3, 2], 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': '15104.c.966656.1', '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': '15104.c.966656.1', 'mw_gens': [[[[0, 1], [-1, 1], [1, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]], [[[0, 1], [1, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [2, 2], 'num_rat_pts': 3, 'rat_pts': [[0, 0, 1], [1, 0, 0], [1, 0, 1]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 15104, 'lmfdb_label': '15104.c.966656.1', 'modell_image': '2.120.3', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 20647
    {'label': '15104.c.966656.1', 'p': 2, 'tamagawa_number': 4}
  • id: 20648
    {'cluster_label': 'c3c2_1~2_0', 'label': '15104.c.966656.1', 'p': 59, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '15104.c.966656.1', 'plot': 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3XmcjvX%2Bx/HX2CXGEq1TdBJGkmVCVMpJOpWjfde%2BWtKe076c0HJKNYSKsvWrn44W51QqUXFKlvbQdkgKpRmSwcz9%2B%2BP7i%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%2BGABLYMWKFXTs2JHKlSvzz3/%2Bk08%2B%2BYT77ruP2rVrR12aJEmxlUhAbi507Ai9e4dr/b5KURdQngwaNIisrCxGjhy54V7Dhg2jK0iSpJhbuzaEvhEjwvUvv0BhIVQy4fwuVwBL4Pnnn6dt27acdNJJNGjQgFatWjHi179xm1FQUEB%2Bfv4mL0mSVDq%2B/x66dAnhLyMD7r4bRo82/BWHAbAEvvzyS4YOHUrjxo15%2BeWXueSSS%2Bjbty9PPvnkZj9%2BwIABZGZmbnhlZWUluWJJktLTrFnQti289RZkZsKkSXDNNSEIauucBVwCVapUoW3btkyfPn3Dvb59%2BzJz5kxmzJjxm48vKCig4D%2BeRs3PzycrK8tZwJIkbYexY%2BGCC2DNGmjSBJ57LvxTxecKYAnsuuuuZGdnb3KvWbNmLFy4cLMfX7VqVWrVqrXJS5IkbZv16%2BHqq%2BHMM0P4O/rocPLX8FdyBsAS6NixI/Pmzdvk3vz589lrr70iqkiSpHj44Qc46ii4775w/Ze/hJW/zMxo6yqvfEyyBK644goOOugg7rrrLk4%2B%2BWTeffddhg8fzvDhw6MuTZKktPX%2B%2B3DccfDVV1CjBowcCSedFHVV5ZvPAJbQiy%2B%2BSP/%2B/VmwYAGNGjXiyiuv5MILLyzW5%2Bbn55OZmekzgJIkFdNTT8F554X2LnvvDRMnQosWUVdV/hkAk8gAKElS8axfD/37w733huuuXWH8eKhbN9q60oXPAEqSpJSyfDl067Yx/F13HfzjH4a/0uQzgJIkKWXMmgXHHw8LF/q8X1lyBVCSJKWEkSPDPN%2BFC6Fx49DixfBXNgyAkiQpUgUFcOml4bBHQQEceyy8%2By40bx51ZenLAChJkiKzaBEceig88kgY43b77eGkb%2B3aUVeW3nwGMAlyc3PJzc2lsLAw6lIkSUoZr78Op54Ky5ZBnTphxNtRR0VdVTzYBiaJbAMjSRIkEnDPPaHNS1ERHHAATJgQ%2BvwpOVwBlCRJSZOXB%2BecE7Z5Ac4%2BG4YOherVIy0rdgyAkiQpKT74AE44AT7/HKpUgQcfhIsuCs/%2BKbkMgJIkqcw98QRccgmsWQN77gn/%2B7%2BQkxN1VfHlKWBJklRm1qwJq3znnBPe7tYNZs82/EXNAChJksrEl1%2BGxs4jRoRt3ltvhUmToF69qCuTW8CSJKnUPfdcOOCRlwc77RRavHTtGnVV%2BpUrgJIkqdSsWwfXXAM9eoTw16EDzJlj%2BEs1rgBKkqRS8c03cMopMH16uL7iChg4MJz4VWoxAEqSpO320ktw1lmwfDnUqgUjR8Lxx0ddlbbELWBJkrTN1q%2BHG24II9yWL4dWrcIpX8NfanMFUJIkbZPFi%2BH002HatHB96aXwt79BtWrR1qWtcwUwCXJzc8nOzibHpkeSpDTx0kthhu%2B0aVCzJjz1FAwZYvgrLzISiUQi6iLiIj8/n8zMTPLy8qhVq1bU5UiSVGLr1sHNN4fDHRBC4NNPQ%2BPG0dalknELWJIkFcvChWHL9%2B23w7VbvuWXAVCSJG3V88%2BHcW4rVoRTvo8%2BCiedFHVV2lY%2BAyhJkraooAAuvxz%2B/OcQ/tq2DY2dDX/lmwFQkiRt1oIFcNBB8OCD4fqKK8L27957R1uXtp9bwJIk6TdGjw7P%2BP38M9SrB088AUcfHXVVKi0GQEmStMHKldCrVwiAAIceCmPHwu67R1uXSpdbwJIkCYD33oPWrUP4q1ABbrsNXnvN8JeOXAGUJCnmiopCO5e//CX0%2BcvKgnHjoFOnqCtTWTEASpIUY0uWwNlnw%2BTJ4fqEE2DECKhTJ9q6VLbcApYkKaZefBFatgzhr3p1GDYMnnnG8BcHBsAkcBawJCmV/PIL9OkDxx4Ly5aFEDhrFlx0EWRkRF2dksFZwEnkLGBJUtQ%2B%2BCCMc/v443Ddr1%2BY61u1arR1KblcAZQkKQaKimDwYMjJCeFv553hn/%2BE%2B%2B83/MWRh0AkSUpzS5aEOb6vvBKujzkGHnsMGjSItCxFyBVASZLS2MSJ0KJFCH/VqsGQIfD884a/uHMFUJKkNLRqVXi%2B77HHwnWrVmGiR7Nm0dal1OAKoCRJaWbGjHCy97HHwqnea6%2BFf/3L8KeNDIDbaMCAAWRkZNCvX7%2BoS5EkCQhTPG6%2BOUzw%2BPLLMNFjyhQYNAiqVIm6OqUSt4C3wcyZMxk%2BfDj7779/1KVIkgTAZ5/BmWeGfn4AZ5wBDz8MtWtHW5dSkyuAJbRq1SrOOOMMRowYQR1bpUuSIlZUBA89FJ7xmzUrTPF46ikYM8bwpy0zAJZQr169OProo/njH/8YdSmSpJj75hs48kjo2xfWrIGuXeHDD%2BGUU6KuTKnOLeASeOqpp5g9ezYzZ84s1scXFBRQUFCw4To/P7%2BsSpMkxUgiEU709u4NeXlhju8998BllznKTcXjCmAxLVq0iMsvv5wxY8ZQrVq1Yn3OgAEDyMzM3PDKysoq4yolSelu2TI46SQ466wQ/g48EObMgV69DH8qPmcBF9PEiRM57rjjqFix4oZ7hYWFZGRkUKFCBQoKCjZ5H2x%2BBTArK8tZwJKkbfLcc3DRRbB0KVSqBLfcAtdfH96WSsK/MsXUpUsXPvzww03unXvuuTRt2pTrrrvuN%2BEPoGrVqlR1wKIkaTv99BNcfjk8%2BWS4bt4cRo8OBz%2BkbWEALKaaNWuy3377bXKvRo0a1KtX7zf3JUkqLa%2B8AuedB4sXQ4UKcPXVcPvt4PqCtocBUJKkFLRyZQh7w4eH6332gSeegIMOirYupQefAUyi/Px8MjMzfQZQkvS7Xn89rPr9%2B9/huk8fGDgQdtgh2rqUPlwBlCQpRaxaBdddB0OGhOuGDWHkSOjcOcqqlI5sAyNJUgqYMgVatNgY/i65BD74wPCnsmEAlCQpQitXhgbOhx8OX38Ne%2B0Fr74KQ4dCzZpRV6d0ZQCUJCkikyeHVb%2BhQ8P1JZeEUW5dukRbl9KfzwBKkpRkeXnhhO%2Bjj4brhg3hscfCKqCUDK4ASpKURJMmhUbOv4a/3r3Dqp/hT8lkAEyC3NxcsrOzycnJiboUSVJEfvgBzjwTjjkmNHXeZx%2BYOhUeegh23DHq6hQ39gFMIvsASlL8JBLwzDOhl9/SpWGaxxVXhGke9vVTVHwGUJKkMvLtt%2BGE73PPhevmzeHxx%2BHAA6OtS3ILWJKkUpZIwIgRkJ0dwl%2BlSnDzzTBrluFPqcEVQEmSStGCBXDRRfDGG%2BE6Jyec8G3RItKypE24AihJUilYty7M623RIoS/HXaAv/0NZsww/Cn1uAIoSdJ2eu89uOACeP/9cH3EETBsGDRqFG1d0pa4AihJ0jZatQquvBLatQvhr25dGDUKXn7Z8KfU5gqgJEnbYNKkcMJ34cJwffrpcP/90KBBtHVJxWEAlCSpBJYsgX794Omnw3XDhmGWb7dukZYllYhbwJIkFUNRUXiur1mzEP4qVgzzfD/6yPCn8scVQEmStuKjj0JrlxkzwnWbNqHPX6tW0dYlbStXAJPAWcCSVD6tXg3XXx%2BC3owZYWbv4MHwzjuGP5VvzgJOImcBS1L5MWkS9O4NX38dro87Dh58EPbYI9KypFLhCqAkSf/hm2/gxBPhmGNC%2BMvKCuPcnn3W8Kf0YQCUJAlYvz60cWnWDCZMCIc8rroKPvkEunePujqpdHkIRJIUezNmwKWXbpzk0aEDPPII7L9/tHVJZcUVQElSbP3wA1x4IRx0UAh/derA8OHw1luGP6U3VwAlSbFTVAQjR8J114UQCHDOOXD33VC/fqSlSUlhAJQkxcqcOdCr18aefvvtFyZ5dOoUbV1SMrkFLEmKhZ9%2Bgj59oG3bjT397r0XZs82/Cl%2BXAGUJKW1oiIYPRquvRaWLg33TjkF7rsPdt892tqkqBgAJUlpa%2B7c0Mz57bfDddOm8PDD0KVLtHVJUXMLWJKUdlasCMGvTZsQ/mrUgIEDw0lfw5/kCmBS5ObmkpubS2FhYdSlSFJa%2B/V07/XXw/Ll4d7JJ4ftXqd4SBs5CziJnAUsSWXn3XfDqt/MmeG6WbOw3Xv44dHWJaUit4AlSeXa0qVw/vnQrl0IfzVrhhW/9983/Elb4hawJKlcWrcOcnPh1lshLy/c69kTBg2CXXaJtDQp5RkAJUnlzquvQt%2B%2B8Omn4bp167Dd26FDtHVJ5YVbwJKkcuOrr%2BD44%2BGII0L422knGDYsPP9n%2BJOKzxVASVLK%2B/lnGDAgTO4oKICKFcM4t1tvhTp1oq5OKn8MgJKklJVIwLhxYYrHt9%2BGe126wODB0Lx5tLVJ5ZlbwCUwYMAAcnJyqFmzJg0aNKBHjx7Mmzcv6rIkKS3NnAkdO8KZZ4bw16gRPPssTJ5s%2BJO2lwGwBKZOnUqvXr3417/%2BxeTJk1m/fj1du3bl559/jro0SUobS5bAuefCgQfCjBlhisdf/wqffALHHQcZGVFXKJV/NoLeDsuWLaNBgwZMnTqVQw45ZKsfbyNoSdqyNWvg/vvhrrtg1apw76yzwrN/u%2B8ebW1SuvEZwO2Q9/%2BNp%2BrWrbvZ9xcUFFBQULDhOj8/Pyl1SVJ5kkiErd2rr4avvw732rULz/m1axdpaVLacgt4GyUSCa688ko6derEfvvtt9mPGTBgAJmZmRteWVlZSa5SklLbnDlw2GFw4okh/O22G4weDdOnG/6ksuQW8Dbq1asXkyZN4q233mKPLUwY39wKYFZWllvAkmLvu%2B/gxhvh8cfDCmC1anDNNXDddeGZP0llyy3gbdCnTx%2Bef/55pk2btsXwB1C1alWqVq2axMokKbVt7jm/006DgQNhzz2jrU2KEwNgCSQSCfr06cPf//533njjDRo1ahR1SZJULiQS8MwzYYXv1%2Bf8DjwQHnjACR5SFAyAJdCrVy/GjRvHc889R82aNfnuu%2B8AyMzMpHr16hFXJ0mpaeZMuOIKePvtcL377mHF7/TToYJPokuR8BnAEsjYQvOpkSNHcs4552z1820DIylOvvkG%2BveHMWPC9Q47hIke11wT3pYUHVcAS8CsLElb9/PPcPfdcM898Msv4V7PnuG5P/v5SanBAChJKhVFRfDkk/CXv4RpHgCdOoVDH23bRlubpE0ZACVJ223qVLjySpg9O1w3ahRWAU84wdFtUioyAEqSttnnn4dn%2BiZODNe1aoX%2Bfn37gl2wpNRlAJQkldiKFXDnnfDQQ7BuHVSsCBddBLfdBvXrR12dpK0xAEqSim3dOhg2DG69FX74Idw78ki47z5o3jzS0iSVgB2YkiA3N5fs7GxycnKiLkWStkkiAZMmwf77Q58%2BIfxlZ8M//wkvvWT4k8ob%2BwAmkX0AJZVHH30EV10Fr7wSrnfaCW6/HS68ECq5jySVS/6rK0narGXL4OabYfjw0OKlSpVwuOPGGyEzM%2BrqJG0PA6AkaRMFBeFwxx13QH5%2BuHf88aGx8957R1ubpNJhAJQkAeE5v%2BefD9u9X3wR7rVuHRo5H3JItLVJKl0eApEk8eGHcMQR0KNHCH%2B77AKPPw4zZxr%2BpHRkAJSkGFu%2BHC67DA44AF57LTRv7t8f5s%2BHc8%2BFCv5XQkpLbgFLUgytXw9Dh8Itt4SmzhDGtt1zTxjjJim9GQAlKWZefz2c5v3443C9//4weDB07hxpWZKSyMV9SYqJhQvhpJOgS5cQ/urVC6uAs2cb/qS4cQVQktJcQUEY1XbnnfDLL%2BG5vksvDc2c69aNujpJUTAASlIae/nlMLptwYJwffDB8PDDYdtXUny5BZwEzgKWlGyLF8PJJ0O3biH87bILjBkDU6ca/iQ5CzipnAUsqawVFoYVvhtvhFWroGLFsAJ4223gjx1Jv3ILWJLSxNy5cOGF8N574bp9e3jkEWjZMtq6JKUet4AlqZz75ZfQvLlt2xD%2BMjND8Hv7bcOfpM1zBVCSyrG334bzzguTOyC0eRk8GHbdNdq6JKU2VwAlqRxaswauvjqc6p0/PwS%2BiRPh6acNf5K2zhVASSpn5syBM8%2BETz4J12efDfffD3XqRFuXpPLDFUBJKieKikJD53btQvjbeWd44QUYNcrwJ6lkXAGUpHJg%2BXLo2RP%2B%2Bc9w3aMHDB8O9etHW5ek8skAKEkp7t134YQT4JtvoFo1eOABuOgiyMiIujJJ5ZUBUJJS2KhRcPHFsHYt7LsvPPOMkzwkbT%2BfAZSkFFRUFHr7nXtuCH89esDMmYY/SaXDAJgEzgKWVBLr1sE558DAgeH6xhthwgRHuUkqPc4CTiJnAUvamrVr4ZRTQk%2B/ihXhscdCmxdJKk0%2BAyhJKaKwMPT3mzgRqlaF//1fOOaYqKuSlI4MgJKUIq66KhzyqFw5hMBu3aKuSFK68hlASUoBTzwRZvgCjBlj%2BJNUtlwBTBOJRHh2aN26sI3065OdFStCpUpQpUp4W1Lq%2BeILuOyy8PZtt8HJJ0dbj6T0ZwBMUYkELFkCX34J//53aAD77bfw/fdhIsCPP0JeHuTnw88/wy%2B/bP1rVq4MO%2BwAO%2B4ImZlQuzbUqxcmCey8M%2By2G2RlQcOG0KiRJw6lZOnVC1avhs6dw4lfSSprngJOoi2dAi4shI8%2BghkzYNYseP99%2BPRTWLUqwmKBXXaB7Gxo0QJat4b27aFxY6cPSKVpyhQ4/PDwP2iffAL77BN1RZLiwACYDAsXwqBB5I8fT%2BaKFeS1b0/1y6/muUonMGECvPJKWNH7bxUqwJ57hhW5rKywQrfzzmHFrm7dsIJXqxbUqBFW9qpWDf8RqVRpY0grLIT166GgIKwSrl4dgmVeHqxYAT/8AMuWwXffweLFsGgRfP11WGXcnF12gSOOCE1p//SnMJZKUgkVFsLQofDII6z7dAHfFu3Cx23P4U%2BvXeXSu6SkMACWtc8/h06d4PvvyQcygTygFnALt3I7twBhW7ZDB8jJgQMOgP32gz/8ITy7F4W8PJg3Dz7%2BOKxIzpwZVicLCjZ%2BTN260Ls3XH011KwZTZ1SuVNUBCedBM8%2B%2B9v3tWoFU6f6L5SkMmcALGvHHRf6OcBvAmARGdx94eccfPbetGsXVu5S2Zo1YZv6xRfh6afDc4kQnhd8%2BeWwPSxpK559Fk44Ycvvv%2B02uPnm5NUjKZYMgNshkUiwcuXKLb6/4LvvqNKsGRlFRUAIgFnAIkIABOC66%2BAvfynjSktfYSG88ALcdFPY4W7bFl57LeqqpHLglFPgpZe2/P699oIPPkhePVKM1axZk4yYPthuANwOvx7qkCRJ5U%2BcR7MaALfDVlcAf/yRKs2bk7F6NbCFFcA774Q%2BfUq9tpycHGbOnFnqX/dXixfDiBFw//1rgGp07w6jR5fZtwPK/teUzO%2BTn59PVlYWixYtKvMfPun0%2B5YW3%2BfCC8MzFFuy337w9tul/31Jg9%2B7NP4%2B6fgzIZnfa1u/T5xXAFP8qbPUlpGR8fv/otaqBWedBcOGbXr7/1%2Brqc4ZL1/CYTVr8cc/QrNmpddipWLFiqX6Q2T9%2BnAY5PXXYdIkmDbt12bTtejWLUwxKOv/iSrtX1PU3wegVq1aZf690u33rdx/n759fz8A9u5dZv8ylfvfuzT/PpBePxOS%2Bb2S%2BWtKFwbAsjZoEMyeHY7R/oe1GVU4KzGaiW/WY%2BKb4V7duhtPATdvDk2bhpPAdeqUPBj26tVrm8otLAyHO774YtNTwLNnhxYy/%2BnQQ6FJk0k88sjRSekNuK2/plT9PsmSbr9v5f77dOwIt9%2B%2B%2BYMep50WVgjLSLn/vUvz75Msyfz1%2BGeUutwCToY1a2D8eH584gnqTZ3K0vPPp/611/JV5X2ZMCE8D/722%2BHDNqdmzZL1AaxYMfQQTCRCx4l168KYuF/7AK5cGSaI/NoHcOnS0Afw22/Da/36zdeRmRk62nTtCt27h/6E2jZbagquGJkxA4YNY8E/5vHBst34OOdMbn6nh53WY8qfCUo2A2ASLVu2jAYNGrB06VLq16%2B/yfvWrg0rbbNmhQOAn3wC8%2BeHcXDJVrlyCHf77rtxEkibNmFFskKF5NeTjgoKChgwYAD9%2B/enatWqUZejCM2du5ZWrSoDGUybBgcfHHVFioI/E5RsBsAk2pb/w1u9OrRZ%2BXUW8NKlYXLH5mYBFxSEIFlY%2BOvzeWE1sFKlsDpYrVpYKaxZM6wc1qkTVhLr1w8TPnbbDfbYA3bdNXyepOS46KJwqKpx4/C4xY47Rl2RpHRnAEwil/glbc6KFWGlffFiOPVUGDfOnWBJZcsNPUmKWJ06MH58WK1/6im45ZaoK5KU7gyAkpQCDj4YhgwJb99xB9x7b7T1SEpvBkBJShEXXhjCH8A118Bf/7rxeV5JKk0GQKmMDBkyhEaNGlGtWjXatGnDm2%2B%2BucWPHTVqFBkZGb95rdlSbyCVe9OmTePYY49lt912IyMjg4kTJwJw441w661sePvyy8PBLpVvW/rz3pI33nhjsz8TPvvssyRVrHRnAEyC3NxcsrOzycnJiboUJcn//M//0K9fP2644QbmzJnDwQcfzFFHHcXChQu3%2BDm1atViyZIlm7yqVauWxKqVTD///DMtW7bk4Ycf/s37brkF7r8/vP3QQ9CjRzjxr/Lr9/68f8%2B8efM2%2BZnQuHHjMqpQceMp4CTyFHB8tGvXjtatWzN06NAN95o1a0aPHj0YMGDAbz5%2B1KhR9OvXj59%2B%2BimZZSpFZGRk8Pe//50ePXpscv%2BZZ6Bnz9AkvmlT%2BPvfwz9Vvm3pz/s/vfHGGxx22GGsWLGC2rVrJ7E6xYUrgFIpW7t2LbNmzaJr166b3O/atSvTp0/f4uetWrWKvfbaiz322INjjjmGOXPmlHWpSnEnnRTmbu%2B%2BO3z2WRgV%2BdRTUVelZGrVqhW77rorXbp0YcqUKVGXozRiAJRK2fLlyyksLGTnnXfe5P7OO%2B/Md999t9nPadq0KaNGjeL5559n/PjxVKtWjY4dO7JgwYJklKwUlpMTJgR17gyrVm0cF/zzz1FXprK06667Mnz4cCZMmMCzzz5LkyZN6NKlC9OmTYu6NKWJSlEXIKWrjP/q5JtIJH5z71ft27enffv2G647duxI69ateeihh3jwwQfLtE6lvp13hsmTw%2BGQu%2B6CRx8NK4Njx0LbtlFXp7LQpEkTmjRpsuG6Q4cOLFq0iHvvvZdDDjkkwsqULlwBlErZTjvtRMWKFX%2Bz2rd06dLfrApuSYUKFcjJyXEFUBtUqgR33gmvvhq2hOfPhw4d4LbbYN26qKtTMrRv396fCSo1BkCplFWpUoU2bdowefLkTe5PnjyZgw46qFhfI5FIMHfuXHbdddeyKFHl2OGHw/vvw8knw/r1YVWwXbtwT%2Bltzpw5/kxQqXELWCoDV155JWeddRZt27alQ4cODB8%2BnIULF3LJJZcA0LNnT3bfffcNJ4Jvu%2B022rdvT%2BPGjcnPz%2BfBBx9k7ty55ObmRvnLUBlatWoVn3/%2B%2BYbrr776irlz51K3bl323HPP3/3cevXCYZAePaB3b5gzJ2wFX3996B1YtWpZV6%2BS2tqfd//%2B/Vm8eDFPPvkkAA888AANGzakefPmrF27ljFjxjBhwgQmTJgQ1S9B6SahpMnLy0sAiby8vKhLURLk5uYm9tprr0SVKlUSrVu3TkydOnXD%2Bw499NDE2WefveG6X79%2BiT333DNRpUqVRP369RNdu3ZNTJ8%2BPYKqlSxTpkxJAL95/effi%2BJYsiSROP74RCLMDEkkmjZNJKZNK5uate229ud99tlnJw499NANHz9o0KDEH/7wh0S1atUSderUSXTq1CkxadKkaIpXWrIPYBLZB1BSWUgkYMKEsBr4/ffh3gUXwKBBULdutLVJSk0%2BAyhJ5VxGBpx4Inz6aWgRA%2BGkcJMm8MQTzhOW9FsGQElKE3Wd7/KVAAAVAUlEQVTqwPDh8OabkJ0Ny5fDOeeEHoIffRR1dZJSiQFQktJMp07hYMjAgbDDDqFn4AEHwJVXOlNYUmAATILc3Fyys7PJycmJuhRJMVGlClx3XdgWPu44KCyE%2B%2B%2BHffeFJ5%2BEoqKoK5QUJQ%2BBJJGHQCRF5eWXoU8f%2BLWPcIcO8NBD0KZNtHVJioYrgJIUA0ceCR9%2BCAMGQI0aMGNGmDN84YUbTw5Lig8DoCTFRNWqoVn0vHlwxhnhdPCjj4Zt4fvug7Vro65QUrIYACUpZnbfHcaMgbfeClvA%2Bflw9dWw337w4ou2jZHiwAAoSTHVsSO88w489hg0aBCeDzz2WOjWDT7%2BOOrqJJUlA6AkxVjFinDeeSH8XXttOD38yivQsmWYLLJ8edQVSioLBkBJErVqhdFxn3yysW1Mbi7ss09oH%2BPzgVJ6MQBKkjb4wx/g2WdhypSwCpiXFxpI77cfPPeczwdK6cIAKEn6jc6dYdYsGDECdt45bBH36AFdusDcuVFXJ2l7GQAlSZtVsSJccAHMnw/9%2B4c2MlOmQOvWcP75sGRJ1BVK2lYGQEnS76pVC%2B66Cz77DE45JWwDP/44NG4Mf/0r/PJL1BVKKikDYBI4C1hSOmjYEJ56Ct5%2BG9q1g59/hhtvhCZNYNw4nw%2BUyhNnASeRs4AlpYuiohAGr78eFi0K99q1g7/9DQ46KNraJG2dK4CSpBKrUAFOPz2MlbvzzjBf%2BJ13QnPpU06Br7%2BOukJJv8cAKEnaZtWrww03wOefh4MhGRnw9NPQtGk4OJKfH3WFkjbHAChJ2m677AKPPgpz5sDhh0NBAQwcGA6KDB8eGktLSh0GQElSqWnZEl59NTSN3ndfWLoULr4YWrUK9yWlBgOgJKlUZWRA9%2B7w4YdhjFydOuHtI44I9%2BfPj7pCSQbAYlq3bh3XXXcdLVq0oEaNGuy222707NmTb7/9NurSJCklVakC/fqF5wP79oVKleCFF6B5c7jiClixIuoKpfgyABbT6tWrmT17NjfddBOzZ8/m2WefZf78%2BXTv3j3q0iQppdWtC4MHh1XAo4%2BG9evhgQdgn33g4Ydh3bqoK5Tixz6A22HmzJkceOCB/Pvf/2bPPffc6sfbB1CSYPLksAL48cfhulmzsFV85JHR1iXFiSuA2yEvL4%2BMjAxq164ddSmSVG4ccQTMnQtDhsBOO8Gnn0K3bmF18LPPoq5OigdXALfRmjVr6NSpE02bNmXMmDGb/ZiCggIKCgo2XOfn55OVleUKoCT9v59%2BgjvugAcfDFvDlSpBr15wyy3h8IiksuEK4BaMHTuWHXfcccPrzTff3PC%2BdevWceqpp1JUVMSQIUO2%2BDUGDBhAZmbmhldWVlYySpekcqN2bbjvvrAdfOyxIQQOHhz6Bw4dGq4llT5XALdg5cqVfP/99xuud999d6pXr866des4%2BeST%2BfLLL3n99depV6/eFr%2BGK4CSVDKTJ4eTw598Eq5btAgHRg4/PNq6pHRjACyBX8PfggULmDJlCvXr1y/R53sIRJK2bv16eOQRuPnmja1ijj8e7r0XGjWKtjYpXbgFXEzr16/nxBNP5L333mPs2LEUFhby3Xff8d1337F27dqoy5OktFGpEvTuDQsWhH9WrAjPPhtOC994I6xaFXWFUvnnCmAxff311zTawv96Tpkyhc6dO2/1a7gCKEkl9/HHcPnl8Npr4Xr33eHuu%2BG008LUEUklZwBMIgOgJG2bRCLMF77ySvjqq3CvY8dwerh162hrk8ojt4AlSSkvIwN69AiHQ%2B68E3bYAd5%2BG9q2hYsvhuXLo65QKl8MgJKkcqNaNbjhBpg3L2wBJxIwfHhoG/Pww7aNkYrLAChJKnf22APGjYNp06Bly9BQuk%2BfsB08bVrU1UmpzwAoSSq3Dj4YZs0KY%2BXq1IEPP4RDD4UzzoBvv426Oil1GQAlSeVaxYpw6aWhbczFF4fnBceNgyZN4J57wE5d0m8ZACVJaaFevdBA%2Br33oH370C/w2mvDFvGrr0ZdnZRaDIBJkJubS3Z2Njk5OVGXIklpr3XrcEJ45Eho0AA%2B%2BwyOOAJOPhm%2B%2BSbq6qTUYB/AJLIPoCQl108/hZFyublQVAQ1aoTrfv2gSpWoq5Oi4wqgJClt1a4dmkXPnh0aR//8M1x3HRxwALz%2BetTVSdExAEqS0l7LlqE9zKhRUL8%2BfPopdOkCp58OS5ZEXZ2UfAZASVIsVKgAZ58dmkhfdlm4Hj8emjaFwYNtIq14MQBKkmKlTp3wTOC778KBB0J%2BfngmMCcH3nkn6uqk5DAASpJiqU0bmD49tI6pXRvmzoUOHeCSS2DFiqirk8qWAVCSFFsVK4bm0fPmhe3hRAKGDQtNpEePDtdSOjIASpJir0GDcEBk6lTIzoZly6Bnz3BQ5LPPoq5OKn0GQEmS/t8hh8CcOXDXXVC9OkyZEk4Q33ILrFkTdXVS6TEASpL0H6pUgf794eOP4U9/CrOEb78d9t8fXnst6uqk0mEAlCRpMxo1ghdfhGeegV13hQUL4I9/DFvDy5ZFXZ20fQyASeAsYEkqnzIy4MQTQ%2BPoXr3C9ejRoXfgyJEeElH55SzgJHIWsCSVb%2B%2B8AxddBB98EK4POyy0kdl332jrkkrKFUBJkoqpXTt47z0YNGjjIZH994e//jU8KyiVFwZASZJKoHJluPZa%2BOgj6NoVCgrgxhtDY%2Bl//Svq6qTiMQBKkrQN9t4bXnoJxoyBnXYKgfCgg6BvX1i5MurqpN9nAJQkaRtlZMAZZ4Rm0T17hkMhDz0EzZvDP/4RdXXSlhkAJUnaTvXqwRNPwCuvhPYxixbB0UeHcGjLGKUiA6AkSaXkiCPgww/hyiuhQgUYNy6Mlhs3zpYxSi0GQEmSSlGNGnDffeFASIsWsHx5WAk89lj45puoq5MCA6AkSWUgJye0jLnjjjBebtKksBo4bBgUFUVdneLOAChJUhmpUiW0iJkzB9q3D6eDL7kkjJT74ouoq1OcGQAlSSpj2dnw1ltw//2www6hgXSLFvDAA1BYGHV1iiMDYBI4C1iSVLEi9OsXDokcdhj88gtccQUccgjMmxd1dYobZwEnkbOAJUkQngEcPhyuuQZWrYJq1cKzgldcEYKiVNZcAZQkKckqVAjPAn78cRgnt2ZNCIOdOoWm0lJZMwBKkhSRPfcM4%2BQeewxq1QqtYw44AO65x2cDVbYMgJIkRSgjA847L6wGdusGBQVw7bVhNdBnA1VWDICSJKWAPfYI84P/ezXwb39zNVClzwAoSVKK%2BHU18KOPNj4beNVV0LkzfP551NUpnRgAJUlKMVlZ4dnAYcNgxx1DD8GWLSE31ykiKh0GwG108cUXk5GRwQMPPBB1KZKkNJSRARddFPoGdu4Mq1dD795hZXDRoqirU3lnANwGEydO5J133mG33XaLuhRJUppr2BBeew0efBCqVw9v77cfPPEE2MlX28oAWEKLFy%2Bmd%2B/ejB07lsqVK0ddjiQpBipUgD59YO7cMFM4Px/OOQeOPx6WLo26OpVHBsASKCoq4qyzzuKaa66hefPmW/34goIC8vPzN3lJkrSt9t03PA94111QuTJMnBhWAydOjLoylTcGwBIYNGgQlSpVom/fvsX6%2BAEDBpCZmbnhlZWVVcYVSpLSXcWK0L8/zJwJLVrAsmVw3HFw7rlhZVAqDgPgFowdO5Ydd9xxw2vq1KkMHjyYUaNGkZGRUayv0b9/f/Ly8ja8FvnUriSplLRsGULgtdeGAyOjRsH%2B%2B8O0aVFXpvIgI5HwEdLNWblyJd9///2G62eeeYYbbriBChU2ZubCwkIqVKhAVlYWX3/99Va/Zn5%2BPpmZmeTl5VGrVq2yKFuSFENvvQU9e8JXX4UwePXVcMcdULVq1JUpVRkAi%2BmHH35gyZIlm9w78sgjOeusszj33HNp0qTJVr%2BGAVCSVFZWroR%2B/eDxx8P1/vvD2LHhGUHpv1WKuoDyol69etSrV2%2BTe5UrV2aXXXYpVviTJKks1awZxsh17w4XXAAffABt28LAgdC3bzhJLP3Kvw6SJKWRP/85NI/%2B05%2BgoACuuAKOPBIWL466MqUSt4CTyC1gSVKyJBLwyCNhlvAvv0DdujBiROgdKLkCKElSGsrIgEsvhdmzoU0b%2BPFHOOEEOP98WLUq6uoUNQOgJElprGlTmD499A7MyAiHRFq1gnffjboyRckAKElSmqtSJUwPmTIFsrLg88%2BhY8dwr7Aw6uoUBQOgJEkxceih8P77cPLJsH493HADdOkCzimIHwOgJEkxUqcOPPUUjBwJNWrA1KlhqsiECVFXpmQyACZBbm4u2dnZ5OTkRF2KJElkZMA558DcuZCTAytWwIknwsUXw%2BrVUVenZLANTBLZBkaSlGrWrYObboK77w6tY5o1g/Hjw6qg0pcrgJIkxVjlymFayOTJsOuu8Omn0K4dPPxwCIRKTwZASZJEly7hgMjRR4cJIn36QI8e8MMPUVemsmAAlCRJANSvDy%2B8AIMHh9Yxzz8PBxwA06ZFXZlKmwFQkiRtkJEBffvCv/4F%2B%2B4L33wDhx0Gt99uz8B0YgCUJEm/0aoVzJoFPXtCURHccgv88Y/w7bdRV6bSYACUJEmbteOO8MQT8OSToWfgG2%2BELeGXXoq6Mm0vA6AkSfpdZ50VVgNbtoRly%2BCoo%2BD660MLGZVPBkBJkrRVTZqE5wIvuyxcDxoEnTs7Rq68MgBKkqRiqVYNcnPhmWegVi2YPj1sCb/4YtSVqaQMgJIkqUROPBHmzIG2beHHH%2BHYY%2BHaa90SLk8MgEngLGBJUrrZe294663QMgbgnnvcEi5PnAWcRM4CliSlo2efhXPPhfx8qFcPxoyBbt2irkq/xxVASZK0XY4/HmbPhjZtwui4o46CG26A9eujrkxbYgCUJEnb7Q9/gLffhksvDdd33QVdu8J330VblzbPAChJkkpF1aowZAiMHx8aR0%2BZEiaKTJ0adWX6bwZASZJUqk49Fd57D5o3DyuAXbqEvoGeOkgdBkBJklTqmjaFd94JU0QKC8PkkB494Kefoq5MYACUJEllpEaNMEt42LCwPfz88%2BGgyNy5UVcmA6AkSSozGRlw0UXhgEjDhvDll9ChA4wcGXVl8WYAlCRJZa5NG5g1C/70J1izBs47LwTDNWuiriyeDICSJCkp6taFF16A228PK4MjRkCnTvDvf0ddWfwYACVJUtJUqAA33QQvvRSmhsyaFVYHX3kl6srixQCYBM4CliRpU127hvDXtm2YHtKtW2geXVQUdWXx4CzgJHIWsCRJm1qzBvr0gUcfDdd//nM4OZyZGW1d6c4VQEmSFJlq1cKzgCNGQJUq8NxzcOCB8MknUVeW3gyAkiQpchdcAG%2B9BXvsAfPnQ7t2MGFC1FWlLwOgJElKCTk5MHs2HHYYrFoFJ54I/fuHSSIqXQZASZKUMurXDyeCr7oqXA8cGHoH/vhjtHWlGwOgJElKKZUqwb33wvjxUL16CIQ5OfDBB1FXlj4MgJIkKSWdeirMmAGNGm0cIffMM1FXlR4MgCXw6aef0r17dzIzM6lZsybt27dn4cKFUZclSVLaatkSZs6EI46A1avh5JN9LrA0GACL6YsvvqBTp040bdqUN954g/fff5%2BbbrqJatWqRV2aJElprV49%2BMc/4Oqrw/XAgXDssfDTT9HWVZ7ZCLqYTj31VCpXrszo0aO3%2BWvYCFqSpO0zbhycf35oIN24cegb2KxZ1FWVP64AFkNRURGTJk1i33335cgjj6RBgwa0a9eOiRMn/u7nFRQUkJ%2Bfv8lLkiRtu9NPh%2BnTYc89YcGC0C/wxRejrqr8MQAWw9KlS1m1ahUDBw6kW7duvPLKKxx33HEcf/zxTJ06dYufN2DAADIzMze8srKykli1JEnpqVWr8FzgIYfAypXQvXuYI%2ByeZvG5BbwZY8eO5eKLL95wPWnSJDp37sxpp53GuHHjNtzv3r07NWrUYPz48Zv9OgUFBRQUFGy4zs/PJysryy1gSZJKwbp1cPnlMHRouD7lFHj8cdhhh2jrKg8qRV1AKurevTvt2rXbcF2/fn0qVapEdnb2Jh/XrFkz3nrrrS1%2BnapVq1K1atUyq1OSpDirXBmGDAknhXv3hv/5n9AuZvr00EtQW%2BZvz2bUrFmTmjVrbnIvJyeHefPmbXJv/vz57LXXXsksTZIk/ZeLLw4HQU48EU47zfBXHP4WFdM111zDKaecwiGHHMJhhx3GSy%2B9xAsvvMAbb7wRdWmSJMXeIYfAxx/DTjtFXUn54DOAJfD4448zYMAAvvnmG5o0acJtt93Gn//852J/vm1gJElSKjAAJpEBUJIkpQLbwEiSJMWMAVCSJClmDICSJEkxYwCUJEmKGQOgJElSzBgAkyA3N5fs7GxycnKiLkWSJMk2MMlkGxhJkpQKXAGUJEmKGQOgJElSzBgAJUmSYsYAKEmSFDMGQEmSpJgxAEqSJMWMAVCSJClmDICSJEkxYwCUJEmKGQOgJElSzBgAJUmSYsYAmAS5ublkZ2eTk5MTdSmSJElkJBKJRNRFxEV%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%3D%3D'}