Genus 2 curve data - 1624.a.831488.1

Formats: - HTML - YAML - JSON - 2024-05-21T15:37:17.009082

• 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': '1626099242564292047', 'abs_disc': 831488, '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,-1]],[7,[1,1,7,7]],[29,[1,3,31,29]]]', 'bad_primes': [2, 7, 29], 'class': '1624.a', 'cond': 1624, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[1,1,2,2,0,1],[0,1,0,1]]', 'g2_inv': "['-2401/116','9065/232','-49/464']", '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': "['28','2269','13167','-103936']", 'igusa_inv': "['28','-1480','112','-546816','-831488']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '1624.a.831488.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.6270122538052269949350069802915137114748315566319268292', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.60.1'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2], 'non_solvable_places': [], 'num_rat_pts': 5, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '10.032196060883631918960111685', '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': 16, 'torsion_order': 16, 'torsion_subgroup': '[16]', 'two_selmer_rank': 1, 'two_torsion_field': ['6.0.4615408.3', [16, -26, 8, 1, 5, -1, 1], [6, 3], 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': '1624.a.831488.1', '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': '1624.a.831488.1', 'mw_gens': [[[[0, 1], [1, 1], [0, 1]], [[-1, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [16], 'num_rat_pts': 5, 'rat_pts': [[-1, 1, 1], [0, -1, 1], [0, 1, 1], [1, -1, 0], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 1624, 'lmfdb_label': '1624.a.831488.1', 'modell_image': '2.60.1', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 24496
    {'label': '1624.a.831488.1', 'p': 2, 'tamagawa_number': 16}
  • id: 24497
    {'cluster_label': 'c4c2_1~2_0', 'label': '1624.a.831488.1', 'p': 7, 'tamagawa_number': 1}
  • id: 24498
    {'cluster_label': 'c4c2_1~2_0', 'label': '1624.a.831488.1', 'p': 29, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '1624.a.831488.1', 'plot': 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xl4VOXB/vF7yMaWDIYlIRIQFYExqIiRiv5ECwLKUhRXFKEqoiIatxd5q1X7VhBUFGVQUGsri2iVICpqoSKviAsvSAVEEQuyxiDCJAHMen5/PJ0MAYIBknlm5nw/13WuczJLcmeM9e5zznkej%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%2BtVIBBQSkqK7TiAVYwAAgAAuAwFEAAAwGUogAAAAC5DAQQAAHAZCiAAAIDLUAABAABchgIIAADgMhRAAAAAl6EAAgAAuAwFEABQp/73f/9X/fv3V0ZGhjwej%2BbOnVvlecdx9PDDDysjI0MNGjTQBRdcoDVr1lR5za5duzRkyBB5vV55vV4NGTJEu3fvDuevAcQUCiAAoE7t2bNHp59%2BuiZPnnzI5ydMmKCJEydq8uTJWrZsmdLT03XRRRepsLCw8jWDBw/WypUr9f777%2Bv999/XypUrNWTIkHD9CkDMYS1gAEDYeDwe5ebmauDAgZLM6F9GRoZycnI0evRoSVJxcbHS0tI0fvx4jRgxQmvXrpXP59Nnn32mrl27SpI%2B%2B%2BwznXPOOfrmm2/Uvn37Gv1s1gIGQhgBBABYs2HDBuXl5alXr16VjyUlJal79%2B5aunSpJOnTTz%2BV1%2ButLH%2BS9Jvf/EZer7fyNYdSXFysgoKCKhsAgwIIALAmLy9PkpSWllbl8bS0tMrn8vLy1KJFi4Pe26JFi8rXHMq4ceMqrxn0er3KzMysxeRAdKMAAgCs83g8Vb52HKfKYwc%2Bf6jXHGjMmDEKBAKV2%2BbNm2svMBDl4m0HAAC4V3p6uiQzyteyZcvKx/Pz8ytHBdPT0/Xjjz8e9N4dO3YcNHK4v6SkJCUlJdVyYiA2MAIIALCmbdu2Sk9P14IFCyofKykp0eLFi9WtWzdJ0jnnnKNAIKAvvvii8jWff/65AoFA5WsAHBlGAAEAdaqoqEjr16%2Bv/HrDhg1auXKlUlNT1bp1a%2BXk5Gjs2LFq166d2rVrp7Fjx6phw4YaPHiwJKljx47q06ePhg8frqlTp0qSbr75ZvXr16/GdwADqIppYAAAdeqjjz7ShRdeeNDjQ4cO1V//%2Blc5jqNHHnlEU6dO1a5du9S1a1f5/X5lZWVVvvbnn3/WHXfcoXnz5kmSBgwYoMmTJ6tJkyY1zsE0MEAIBRAA4AoUQCCEawABAABchgIIAADgMhRAAAAAl6EAhsHatdLChbZTAAAAGEwDU8fmz5f69pXatpW%2B/146zKT1AAAAYcEIYB274AKpUSNpwwZpvzlMAQAArKEA1rGGDaUBA8zx7Nl2swAAAEgUwLC4%2Bmqzf/11qbzcbhYAAAAKYBj07i01aSJt2yZ9/LHtNADgLn6/Xz6fT9nZ2bajABGDAhgGSUnSZZeZY04DA0B4jRw5Ul9//bWWLVtmOwoQMSiAYRI8DfzGG1Jpqd0sAADA3SiAYXLhhVKLFtLOncwJCAAA7KIAhkl8vHTlleZ41iy7WQAAgLtRAMPommvMfu5cae9eu1kAAIB7UQDD6JxzpDZtpKIi6d13bacBAABuRQEMI48nNArIaWAAAGALBTDMBg82%2B/nzpd277WYBAADuRAEMs06dpKwsqaREevNN22kAAIAbUQAt4DQwAACwiQJoQfA08KJFZnk4AACAcKIAWnDCCVK3bpLjsDQcAAAIPwqgJddea/YzZ9rNAQAA3IcCaMkVV5jVQVaskL75xnYaAADgJhRAS5o3l3r1MseMAgIAgHCiAFp03XVmP2uWuR4QAFD7/H6/fD6fsrOzbUcBIobHcagetuzZI6Wlmf3SpWapOABA3SgoKJDX61UgEFBKSortOIBVjABa1KiRdOml5njGDLtZAACAe1AALQueBn7tNam01G4WAADgDhRAy3r0MKeBd%2B6U3n/fdhoAAOAGFEDL4uNDS8NxGhgAAIQDBTACDBli9vPmSYGA3SwAACD2UQAjQOfOUseO0i%2B/SG%2B%2BaTsNAACIdRTACODxhG4GmT7dbhYAABD7KIARIlgAP/pI2rTJahQAABDjKIARonVrqXt3c8zScAAAoC5RACPI9deb/fTpLA0HAADqDgUwglx%2BuVS/vrR2rbR8ue00AAAgVlEAI0hKijRwoDl%2B5RW7WQAAQOyiAEaY4GngV19laTgAAFA3KIAR5qKLzNJwP/0kvfee7TQAEP38fr98Pp%2Bys7NtRwEihsdxuN0g0txzjzRxojRokPTGG7bTAEBsKCgokNfrVSAQUEpKiu04gFWMAEagoUPN/u23pZ9/tpsFAADEHgpgBDrtNOn006WSEmn2bNtpAABArKEARqjgKODf/mY3BwAAiD0UwAg1eLAUFyd98YX0zTe20wAAgFhCAYxQaWnSxRebY0YBAQBAbaIARrBhw8x%2B%2BnSpvNxqFAAAEEMogBGsXz8pNVXaulVauNB2GgAAECsogBEsKUm65hpz/Ne/Wo0CAABiCAUwwgVPA8%2BdK%2B3ebTUKAACIERTACNeli5SVJf3yi/Taa7bTAEDtKysr0wMPPKC2bduqQYMGOvHEE/WnP/1JFRUVla9xHEcPP/ywMjIy1KBBA11wwQVas2aNxdRAdKMARjiPJzQK%2BPLLVqMAQJ0YP368nn/%2BeU2ePFlr167VhAkT9Pjjj%2BvZZ5%2BtfM2ECRM0ceJETZ48WcuWLVN6erouuugiFRYWWkwORC/WAo4CP/4oHX%2B8uRP466%2Bljh1tJwKA2tOvXz%2BlpaXppZdeqnxs0KBBatiwoaZPny7HcZSRkaGcnByNHj1aklRcXKy0tDSNHz9eI0aMqNHPYS1gIIQRwCiQlib17WuOGQUEEGvOO%2B88/fOf/9S6deskSf/617%2B0ZMkSXXLJJZKkDRs2KC8vT7169ap8T1JSkrp3766lS5dayQxEu3jbAVAzw4ZJ8%2BZJr7wijR0rxfNPDkCMGD16tAKBgDp06KC4uDiVl5fr0Ucf1TX/mQYhLy9PkpSWllblfWlpafrhhx%2Bq/b7FxcUqLi6u/LqgoKAO0gPRiRHAKNG3r9S8uTkd/N57ttMAQO157bXXNGPGDM2aNUsrVqzQ3/72Nz3xxBP62wHLIHk8nipfO45z0GP7GzdunLxeb%2BWWmZlZJ/mBaEQBjBKJidJ115ljTgMDiCX33Xef7r//fl199dXq1KmThgwZorvuukvjxo2TJKWnp0sKjQQG5efnHzQquL8xY8YoEAhUbps3b667XwKIMhTAKHLDDWb/9ttSfr7dLABQW/bu3at69ar%2B5yguLq5yGpi2bdsqPT1dCxYsqHy%2BpKREixcvVrdu3ar9vklJSUpJSamyATAogFEkK0vKzpbKyqQZM2ynAYDa0b9/fz366KN69913tXHjRuXm5mrixIm69NJLJZlTvzk5ORo7dqxyc3O1evVqDRs2TA0bNtTgwYMtpweiE9PARJnnn5duvVXy%2BaTVq808gQAQzQoLC/Xggw8qNzdX%2Bfn5ysjI0DXXXKM//vGPSkxMlGSu93vkkUc0depU7dq1S127dpXf71dWVlaNfw7TwAAhFMAoEwhILVtK%2B/ZJn30mde1qOxEARAcKIBDCKeAo4/VKgwaZ4/3mTAUAAKgxCmAUuvFGs589W9qzx24WAAAQfSiAUah7d%2Bmkk6TCQunvf7edBgAARBsKYBTyeEKjgC%2B%2BaDcLAACIPhTAKDV0qBQXJ33yifTNN7bTAACAaEIBjFIZGdJ/1knnZhAAAHBEKIBR7KabzP5vf5NKSuxmAQAA0YMCGMUuucTMCbhjhzRvnu00AAAgWlAAo1h8vDRsmDl%2B4QWrUQAAQBShAEa54N3ACxZIP/xgNwsARCK/3y%2Bfz6fs7GzbUYCIwVJwMaBnT%2Bmf/5QefFD6059spwGAyMRScEAII4AxYPhws//LX6SyMrtZAABA5KMAxoCBA6VmzaStW6X33rOdBgAARDoKYAxISjITQ0vcDAIAAH4dBTBGBE8Dv/uutGWL3SwAACCyUQBjRPv2UvfuUkUFK4MAAIDDowDGkJtvNvuXXpLKy%2B1mAQAAkYsCGEMuu0xq2lTavJmbQQAAQPUogDGkfv3QzSBTp9rNAgAAIhcFMMYETwPPn29GAgEAAA5EAYwx7dtLF1xgbgZ58UXbaQAAQCSiAMagW24x%2BxdfZGUQAABwMApgDBo4UGreXNq2TXr7bdtpAABApKEAxqCkJOmGG8wxN4MAAIADUQBj1M03Sx6P9MEH0vff204DAAAiCQUwRp14otS7tzlmFBCAm/n9fvl8PmVnZ9uOAkQMj%2BM4ju0QqBtvvWWuB2zWzKwPnJRkOxEA2FNQUCCv16tAIKCUlBTbcQCrGAGMYX37Sq1aST/9JL3xhu00AAAgUlAAY1h8fGhi6Oees5sFAABEDgpgjLvpJlMEP/lE%2Buor22kAAEAkoADGuJYtzXWAkjRlit0sAAAgMlAAXWDkSLOfMUMqKLCbBQAA2EcBdIHu3aWOHaU9e6RXXrGdBgAA2EYBdAGPR7rtNnM8ZYrExD8AALgbBdAlhgyRGjWS1q6VPvrIdhoAAGATBdAlvF5TAiXJ77ebBQAA2EUBdJHgzSBz55qVQQAAgDtRAF0kK0s6/3ypvJz1gQEAcDMKoMvcfrvZT5smlZTYzQIAAOygALrMwIFSRoaUn8/6wAAAuBUF0GUSEqRbbjHHzz5rNwsAhIPf75fP51N2drbtKEDE8DgOs8K5TV6e1Lq1VFoq/d//SV262E4EAHWvoKBAXq9XgUBAKSkptuMAVjEC6ELp6dIVV5hjRgEBAHAfCqBLjRpl9rNnSzt22M0CAADCiwLoUl27SmedJRUXSy%2B8YDsNAAAIJwqgS3k8oVHA556Tysrs5gEAAOFDAXSxq66SWrQwq4Lk5tpOAwAAwoUC6GJJSdKIEeb4mWfsZgEAAOFDAXS5W26R4uOlJUukL7%2B0nQYAAIQDBdDlMjJCU8JMmmQ3CwAACA8KIHTHHWb/6qtmiTgAABDbKIBQ167S2WdLJSXS1Km20wBwo61bt%2Bq6665T06ZN1bBhQ51xxhlavnx55fOO4%2Bjhhx9WRkaGGjRooAsuuEBr1qyxmBiIbhRAyOOR7rzTHE%2BZYoogAITLrl27dO655yohIUHvvfeevv76az355JNq0qRJ5WsmTJigiRMnavLkyVq2bJnS09N10UUXqbCw0GJyIHqxFjAkmdJ3wgnS9u3S9OnSddfZTgTALe6//3598skn%2Bvjjjw/5vOM4ysjIUE5OjkaPHi1JKi4uVlpamsaPH68RwekMfgVrAQMhjABCkpSYKN12mzl%2B%2BmmJ/1sAIFzmzZuns846S1dccYVatGihzp0764X9lijasGGD8vLy1KtXr8rHkpKS1L17dy1dutRGZCDqUQBR6ZZbzNyAy5dL/G8qgHD597//reeee07t2rXTBx98oFtuuUV33HGHXnnlFUlSXl6eJCktLa3K%2B9LS0iqfO5Ti4mIVFBRU2QAYFEBUatYsdOr3qafsZgHgHhUVFTrzzDM1duxYde7cWSNGjNDw4cP13HPPVXmdx%2BOp8rXjOAc9tr9x48bJ6/VWbpmZmXWSH4hGFEBUkZNj9rm50saNVqMAcImWLVvK5/NVeaxjx47atGmTJCk9PV2SDhrty8/PP2hUcH9jxoxRIBCo3DZv3lzLyYHoRQFEFVlZUs%2BeUkWF9OyzttMAcINzzz1X3377bZXH1q1bpzZt2kiS2rZtq/T0dC1YsKDy%2BZKSEi1evFjdunWr9vsmJSUpJSWlygbAoADiIHfdZfYvvigxwwKAunbXXXfps88%2B09ixY7V%2B/XrNmjVL06ZN08iRIyWZU785OTkaO3ascnNztXr1ag0bNkwNGzbU4MGDLacHohPTwOAgFRWSzyd9%2B625Izg4RyAA1JV33nlHY8aM0Xfffae2bdvq7rvv1vDhwyufdxxHjzzyiKZOnapdu3apa9eu8vv9ysrKqvHPYBoYIIQCiEN6/nnp1lultm2l776T4uJsJwKAY0MBBEI4BYxDuv56KTVV2rBBeust22kAAEBtogDikBo2NPMCStLEiXazAACA2kUBRLVGjpQSEqRPPpG%2B%2BMJ2GgAAUFsogKhWRoZ0zTXmmFFAAABiBwUQh3X33Wb/xhvSDz/YzQIAAGoHBRCHdfrpUo8eUnm5NGmS7TQAAKA2UADxq%2B65x%2BxffFEKBOxmAQAAx44CiF/Vp4%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%2B6dVKHDpLjSKtWSVlZthMBQFUFBQXyer0KBAJKSUmxHQewihFA1IpTTpEuu8wcP/643SwAAODwKICoNaNHm/2sWdKmTXazAACA6lEAUWuys6ULL5TKyqSJE22nAQAA1aEAolbdf7/Zv/CCtHOn3SwAAODQKICoVRddJJ1xhrR3rzR5su00AADgUCiAqFUeT%2BhawGeflfbssZsHAAAcjAKIWnf55dKJJ5pTwC%2B9ZDsNAAA4EAUQtS4%2BXrrvPnP85JNSaandPAAAoCoKIOrE0KFSWpqZDubVV22nAQAA%2B6MAok40aGDWCJakCROkigq7eQAAQAgFEHXm1lul5GRpzRrp3XdtpwEAAEEUQNSZJk1MCZSkxx6zmwUAAIRQAFGncnKkxERp6VLp449tpwEAABIFEHWsZUtp2DBzzCggABv8fr98Pp%2Bys7NtRwEihsdxHMd2CMS29eul9u3NjSBffSV16mQ7EQA3KigokNfrVSAQUEpKiu04gFWMAKLOnXyyNGiQOR4/3m4WAABAAUSYBJeHmz1b2rjRahQAAFyPAoiw6NJF6tlTKi83q4MAAAB7KIAIm%2BAo4EsvST/9ZDcLAABuRgFE2PToYUYC9%2B2Tnn3WdhoAANyLAoiw8Xik//ovczx5srRnj908AAC4FQUQYTVokHTSSdLPP5tTwQAAIPwogAiruDjp3nvN8cSJUlmZ3TwAALgRBRBhN3So1Ly59MMP0uuv204DAID7UAARdg0aSKNGmePHH5dYiwbA/saNGyePx6OcnJzKx4qLizVq1Cg1a9ZMjRo10oABA7RlyxaLKYHoRgGEFbfdJjVsKK1cKf3zn7bTAIgUy5Yt07Rp03TaaadVeTwnJ0e5ubmaPXu2lixZoqKiIvXr10/l5eWWkgLRjQIIK5o2lW64wRw/8YTdLAAiQ1FRka699lq98MILOu644yofDwQCeumll/Tkk0%2BqZ8%2Be6ty5s2bMmKFVq1Zp4cKFFhMD0YsCCGvuukuqV0/64ANp1SrbaQDYNnLkSPXt21c9e/as8vjy5ctVWlqqXr16VT6WkZGhrKwsLV26NNwxgZhAAYQ1J55opoWRzB3BANxr9uzZWrFihcaNG3fQc3l5eUpMTKwyKihJaWlpysvLq/Z7FhcXq6CgoMoGwKAAwqp77jH7mTOl7dvtZgFgx%2BbNm3XnnXdqxowZql%2B/fo3f5ziOPB5Ptc%2BPGzdOXq%2B3csvMzKyNuEBMoADCqq5dpW7dpNJSacoU22kA2LB8%2BXLl5%2BerS5cuio%2BPV3x8vBYvXqxnnnlG8fHxSktLU0lJiXbt2lXlffn5%2BUpLS6v2%2B44ZM0aBQKBy27x5c13/KkDUoADCurvvNvvnnjPrBANwlx49emjVqlVauXJl5XbWWWfp2muvrTxOSEjQggULKt%2Bzfft2rV69Wt26dav2%2ByYlJSklJaXKBsCItx0A%2BN3vpBNOkDZulGbMkIYPt50IQDglJycrKyurymONGjVS06ZNKx%2B/8cYbdc8996hp06ZKTU3Vvffeq06dOh10wwiAmmEEENbFx4cmhn7mGSaGBnCwp556SgMHDtSVV16pc889Vw0bNtTbb7%2BtuLg429GAqORxHP5zC/t275ZatZL27DETQ//2t7YTAYg1BQUF8nq9CgQCnA6G6zECiIjQpIl0/fXmePJku1kAAIh1FEBEjNtvN/u33pK4WQ8AgLpDAUTE8PmkCy%2BUKiqkqVNtpwEAIHZRABFRbr3V7F96ycwNCAAAah8FEBFl4EApLU3Ky5PmzbOdBgCA2EQBRERJSJB%2B/3tz/OKLdrMAABCrKICIODfeaPYffCBt2WI3CwAAsYgCGAarV0v33CNt2GA7SXQ4%2BWTp/PPNhNDTp9tOAwBA7KEAhsHTT0sTJ0onnWSucfvwQ1a7%2BDVDh5r99Ol8VgAA1DYKYBhccYXUq5cpMm%2B9JfXoIXXqJD3/vFRUZDtdZBo0SKpfX1q7VvrXv2ynARDN/H6/fD6fsrOzbUcBIgZLwYXR2rXSs89Kr7xiljyTJK9XGjZMuu026ZRTrMaLOJdfLr35pnT//dK4cbbTAIh2LAUHhDACGEYdO0pTpkhbt0pPPWWudQsEpEmTpPbtpYsuknJzpbIy20kjwxVXmP0bb3AaGACA2sQIoEUVFdI//iH5/dK774ZKTqtW0vDh0k03SRkZdjPaVFgoNWsmlZRIa9aYlUIA4GgxAgiEMAJoUb16Up8%2B0ttvS99/L40ebQrPli3SQw9JrVtLl11mpkOpqLCdNvySk83ScJI0f77dLAAAxBIKYIRo21Z67DFT/mbOlM47TyovN6eE%2B/QxdxA/%2Bqi0bZvtpOHVp4/ZL1hgNwcAALGEU8ARbM0aaepUMxXK7t3msbg46ZJLzOnhiy82K2fEslWrpNNOkxo2NJ9BrP%2B%2BAOoOp4CBEApgFNi3T/r736UXXpCWLAk9np4uXX%2B9WTqtQwd7%2BepSRYWUmmpullm%2BXDrzTNuJAEQrCiAQwingKNCggSl6H39sppK5916peXMpL0%2BaMMHcXdytmzRtWmikMFbUqyd16WKOV660mwUAgFhBAYwyHTpIjz9urhWcM0fq18%2BcFv70U2nECDMqePXV5qaJ0lLbaWtHVpbZr11rNwcAALGCAhilEhOlSy81dxBv2WJK4amnSsXF0muvSX37mulk7rxTWrYsuufRa9vW7H/4wW4OAABiBQUwBqSnm9PCq1ZJ//d/0h13mFPE%2BfnSM89IZ59tVhl56KHoHEVr3tzsd%2B60mwMAgFhBAYwhHo%2B5Xm7SJLPayDvvmNPBDRpI69dLf/qTmUz59NOlsWPNY9EgMdHsY%2BWUNgAAtlEAY1RCgjkN/OqrZiRwxgzzdXy89NVX0h/%2BILVrJ3XubOYXjOSRwaIis2/UyG4OAABiBQXQBRo3lq691owI/vij9OKLZt3huDhzZ%2B0DD5iRwY4dpTFjpM8/j6yVR777zuxbt7abAwCAWME8gC62c6c0d6705pvSwoVVT7GmpZkRw379pJ49zbJstmRnm2sbX3jBTIANAEeDeQCBEAogJJmJlufPN4XwvfekwsLQc/HxZmm63r2lXr2kM84w8/OFw%2BLF0gUXmFPaW7ZILVqE5%2BcCiB1%2Bv19%2Bv1/l5eVat24dBRAQBRCHUFJiitc775hSeODNIqmpppRdcIF0/vlmnr64uNrPsWWL9P/%2Bn7Rxo5nj8Pnna/9nAHAPRgCBEAogftX69dL770v/%2BIf00UdVRwclKSVF%2Bs1vpK5dzenaM8%2BUMjLMXclHo6JCys2VRo2Stm%2BXTj7ZzGXYpMkx/yoAXIwCCIRQAHFESkvN9XiLFplRwqVLQ3fp7q9ZMzMy2KGDudu4bVspM9NcW5iaKjVsaAqi45hCuX279M030iefmBVOvv/efB%2Bfz4xCtmkT3t8TQOyhAAIhFEAck7IyMwH1p59KX3xhyuHatTW7izguTiovP/RzKSlmFZP77zdlEQCOFQUQCKEAotbt2yd9/bW0Zo0Z1fv%2Be3Md39atZk7CQ03o3LixOdV75plmipr%2B/Zn3D0DtogACIRRAhJXjSHv2mK283IwCJiczygeg7lEAgZB42wHgLh6PGe1r3Nh2EgAA3IuVQAAAAFyGAggAAOAyFEAAAACXoQACAAC4DAUQAADAZShq4ThaAAAVvklEQVSAAAAALkMBBAAAcBkKIAAgpvn9fvl8PmVnZ9uOAkQMVgIBALgCK4EAIYwAAgAAuAwFEAAAwGUogAAAAC5DAQQAAHAZCiAAAIDLUAABAABchgIIAADgMhRAAAAAl6EAAgAAuAwFEABg1bhx45Sdna3k5GS1aNFCAwcO1LffflvlNcXFxRo1apSaNWumRo0aacCAAdqyZYulxED0owACAKxavHixRo4cqc8%2B%2B0wLFixQWVmZevXqpT179lS%2BJicnR7m5uZo9e7aWLFmioqIi9evXT%2BXl5RaTA9GLtYABABFlx44datGihRYvXqzzzz9fgUBAzZs31/Tp03XVVVdJkrZt26bMzEzNnz9fvXv3rtH3ZS1gIIQRQABARAkEApKk1NRUSdLy5ctVWlqqXr16Vb4mIyNDWVlZWrp0qZWMQLSLtx0AAIAgx3F0991367zzzlNWVpYkKS8vT4mJiTruuOOqvDYtLU15eXnVfq/i4mIVFxdXfl1QUFA3oYEoxAggACBi3H777frqq6/06quv/uprHceRx%2BOp9vlx48bJ6/VWbpmZmbUZFYhqFEAAQEQYNWqU5s2bp0WLFqlVq1aVj6enp6ukpES7du2q8vr8/HylpaVV%2B/3GjBmjQCBQuW3evLnOsgPRhgIIALDKcRzdfvvtmjNnjj788EO1bdu2yvNdunRRQkKCFixYUPnY9u3btXr1anXr1q3a75uUlKSUlJQqGwCDawABAFaNHDlSs2bN0ltvvaXk5OTK6/q8Xq8aNGggr9erG2%2B8Uffcc4%2BaNm2q1NRU3XvvverUqZN69uxpOT0QnZgGBgBgVXXX8b388ssaNmyYJOmXX37Rfffdp1mzZmnfvn3q0aOHpkyZckTX9TENDBBCAQQAuAIFEAjhGkAAAACXoQACAAC4DAUQAADAZSiAAAAALkMBBAAAcBkKIAAAgMtQAAEAAFyGAggAAOAyFEAAAACXoQACAGKa3%2B%2BXz%2BdTdna27ShAxGApOACAK7AUHBDCCCAAAIDLUAABAABchgIIAADgMhRAAAAAl6EAAgAAuAwFEAAAwGUogAAAAC5DAQQAAHAZCiAAAIDLUAABAABchgIIAADgMhRAAAAAl6EAAgAAuAwFEAAQ0/x%2Bv3w%2Bn7Kzs21HASKGx3Ecx3YIAADqWkFBgbxerwKBgFJSUmzHAaxiBBAAAMBlKIAAAAAuQwEEAABwGQogAACAy1AAAQAAXIYCCAAA4DIUQAAAAJehAAIAALgMBRAAAMBlKIAAAAAuQwEEAABwGQogAACAy1AAAQAAXIYCCAAA4DIUQABATPP7/fL5fMrOzrYdBYgYHsdxHNshAACoawUFBfJ6vQoEAkpJSbEdB7CKEUAAAACXoQACAICYwXnNmom3HQAAAKAmSkul7dulLVukzZurbps2me3Pf5Zuvtl20shHAQQAAFY5jrRrl7RtW9Vt69bQtmWL9OOPvz7Ct2lTeDJHOwogAACoE3v2SPn5prjl5YX2%2B2/bt5t9cXHNvmdCgnT88VKrVlJmZmhr3dpsJ55Yt79TrKAAAkAs2blTqldPOu4420kiS1mZtGOH7RRRzXGkwkLpp5%2Bqbjt2hLb8/NA%2BP98UwCORmiplZIS24483%2B1atQqWveXPzJ45jQwEEgFjw%2BuvSuHHSypXm627dpD/%2BUerd224u24qLpf/5H2natFABHD5cmjBBatPGbjZLysqkQEDavducdg1uP/8c2ge3nTvNFjwuLT3yn1e/vpSWFtrS00Nby5ahfcuWUlJS7f%2B%2BODTmAQSAaDd5sjRq1MGP16snzZ4tXXFFlYcrKqTy8qrXUsXFmZd7PHWcNZzKy6VLLpH%2B8Q9JUoEkr6SApJSMDOmzz8y5wyhRXi4VFZmtsNBsBQWh/f5bIBDaCgpM2du923xdWHhsORo0kJo1C23Nm1fdWrQIbWlpUuPGMfZ3FSMogAAQzQoLzTmyoqJDPv1jQiud12qjdhfGae9e6ZdfTAGsTlycFB9vrrNKSDAjMomJZqtf33ydlGSOq9saNDj460O9Z//vlZgY%2Bjr48xISjrE4zJkjDRpU%2BWWVAihJt94qTZly1N/eccxoWkmJGWjcf/vll9B%2B377QPrjt3Rva79lj9sHj4FZUFNoXFZnX16ZGjaQmTczVAqmpZh88Tk2VmjYN7fffGjas3Rywg1PAABDN3nqr2vInSWmlW5S5YbHW67c1%2Bnbl5War6QX5dS0hIVQG4%2BPNcXx8aAsW1ri40ChmcHv021m68DDfu2jaTPVaOUUVFabMlZebclxWFvocysrMVloa2peWmtJXUhK2j6GK%2BHgzqpacLKWkhPYHbl5vaGvSJLQPbgkJdvIjMlAAASCa7d79qy/xP7pb5QPMiE9SUqhMBUfXHCd0Wnj/whMsOfuPcAVHtoqLzYjUgaNcwe3Arw/cDvxewe3Ac1LBwnU0ynX4z6ZxeYE%2B%2B7RCTi2tieDxVD9C2qDBobeGDc0/l%2BB%2B/%2BPGjc22/3Fysvn%2BnFLFsaIAAkA0O%2BOMwz5dIY%2Buf/w0tfhE6tJFOu00KStLOumkyBwBKiszRTBYPINFNFgEgyNx%2B2/B0brgCF5wa/OX06W3/1ntzwq07qQ5k%2BrJ4zEjhsERxP1HFfcfbQyeFg9uwVPVwdPW%2B5dqINJxDeAxcBxHhcd6NS0AHKvzzpNWrTrkUws9PTXIefOgx%2BPizE2wJ54onXCCuReiVSuzBe/MjPY7MkvWrlXCeefJU1YmyVwDmClps/5zDeCkSdKwYdbywb7k5GR5XNraKYDHoKCgQF6v13YMAABwFAKBgFJSUmzHsIICeAyOZASwoKBAmZmZ2rx58xH/sWVnZ2vZsmVHnO9o3xfunxlNn02438dnc2g2PpdjeW9Y3rdvn/TmmypdsEBvzJ2r302ZooZXXnnI87yOY5bZ%2Bv57acMG6YcfzFqq8%2BatUFramdq27eivu5PM6dDgdWv7X9cWvOYtuJ8zZ6ZuvPHaKncKH3jHcPB4/8cGDuyjRYveV4MG5mdVN4BT8tFHSvzd7yq/PmgE8L77pAceqNHvFNH/7I/xfW7%2B98nNI4BcA3gMPB7PEf/LkpKScsTviYuLO6r/h3K077P1M6Phs7HxuUh8NtUJ5%2BdyLO8Ny/tSUqTbblPBddfpFq9X11x77WHf6/VKHTtWfcznu01ff/115bqswaW69l/pITgp8M8/h%2BaWW79%2BhxITm1dOUxK8fm/Xrl8LfaueeKJmv15VS6vM4Vy/fqhY7n9zxSMb39ShpsFO%2Bc%2B254WZevvMCUpOrnonbZMmZh%2B/338hI/qffS28T%2BLfJ7ehAEaBkSNHhvV9tn5mOH9etLzvWETL7xgtfzPH8t5o%2B2w8ntBccD7fr7/P739dI0eOVGlpaILi4Nx1%2B89nF5zrLrgtXbpCHTqcWWWevJrdRezIcUKjNsHHDxSvLYfN3Wj3Ng2%2Bpvq7gIPz5DVpIpWULNTvfld1jrwDJ0Nu0cLMo7f/MmVu%2BZsJ53uj5bOJdJwCDpPg9YJuvt6gOnw21eOzOTQ%2Bl%2Bq54bNxHDPCeKhJloMTLO/bJ2U9daNO/Ogvle87cCLo/AatdVXXH6qsqlFQYL7H0YqPN2UwuORZcImz4Lq2wfVsW7QwN%2BJEAjf8zeBgjACGSVJSkh566CElRfttdXWAz6Z6fDaHxudSPTd8NvvPt3fY%2B/BaDJfO%2BUv1T4%2B5SYsePPjx0tLQMmr7r5cbXCs3uD7uTz%2BFtvx88/qyMmn7drMdTny8KYKZmeZu7DZtzN3YbduaO7MzM6uegq5LbvibwcEYAQQAxK4HHpAefVTSASOA3btL771nLhisJSUl5jrJvDzpxx/NPlgGt22Ttm412/bth1%2BOTzLlr00b6eSTpXbtzNa%2Bvdlat656mhk4GhRAAEBs%2B8c/pOeeU8Hq1fKuX6/ApElKGTHC2kSHwVHCTZtC28aNZtuwwewPtxRfgwamCPp80qmnmom9s7LMCCLFEDVFAQQAuEK0XOtWUWFGDNevN9t330nr1plt/frq1yBu3Fjq1Ek6/XSpc2fpzDNNMaxfP7z5ER0ogAAAV4iWAng4ZWVmlPDrr822Zo20erW0du2hi2F8vCmB2dnS2Web7dRTI%2BcGFNhDAQQAuEIsFMDqlJWZEcJ//UtauVL68ktpxQpzs8qBGjc2RbBbN%2Bncc6VzzvmVm2kQk7haIEyGDRsmj8dTZfvNb35jO1bEGTFihDwej55%2B%2BmnbUax7%2BOGH1aFDBzVq1EjHHXecevbsqc8//9x2LOtKS0s1evRoderUSY0aNVJGRoauv/56bdu2zXa0iDBnzhz17t1bzZo1k8fj0cqVK21HQhjEx5trAq%2B5Rho/3lz2uGOHuZ7wjTek0aOlCy80E14XFUkffij9%2Bc/SxRdLTZpUyOP5Uv37f6/cXHO3M2IfBTCM%2BvTpo%2B3bt1du8%2BfPtx0posydO1eff/65MjIybEeJCKeccoomT56sVatWacmSJTrhhBPUq1cv7dixw3Y0q/bu3asVK1bowQcf1IoVKzRnzhytW7dOAwYMsB0tIuzZs0fnnnuuHnvsMdtRYJnHY%2B4kHjRIeuwxU/p27ZK%2B%2BkqaOlXq0WOLmjT5WaYKdNY775ykyy4zE1t36SL9139JCxaocoUXxBZOAYfJsGHDtHv3bs2dO9d2lIi0detWde3aVR988IH69u2rnJwc5eTk2I4VUYKnrxYuXKgePXrYjhNRli1bprPPPls//PCDWrdubTtORNi4caPatm2rL7/8UmeccYbtOBEhlk8BH4tt26Tjj79KffqM08aNJ%2Bqbb6o%2BX7%2B%2B1L271KePdMklZkoaly6fG1MYAQyjjz76SC1atNApp5yi4cOHKz8/33akiFBRUaEhQ4bovvvu06mnnmo7TkQqKSnRtGnT5PV6dfrpp9uOE3ECgYA8Ho%2BaNGliOwoQdcxJl9c1YsRXWrvWFMIZM6Rhw8zKJb/8In3wgXTXXWb6mVNOMceLFplJsxGdKIBhcvHFF2vmzJn68MMP9eSTT2rZsmX67W9/q%2BLDTfbkEuPHj1d8fLzuuOMO21EizjvvvKPGjRurfv36euqpp7RgwQI1a9bMdqyI8ssvv%2Bj%2B%2B%2B/X4MGDGdUBakHLltK110ovvyxt3mzuMn7ySalHDykhwUxF8/TT0m9/a5a8GzpUmjuXU8XRhgJYB2bOnKnGjRtXbh9//LGuuuoq9e3bV1lZWerfv7/ee%2B89rVu3Tu%2B%2B%2B67tuGF14GezePFiTZo0SX/961/lcfE5hUP9zUjShRdeqJUrV2rp0qXq06ePrrzySteNHFf32UjmhpCrr75aFRUVmjJlisWUdhzus0GI3%2B%2BXz%2BdTdna27ShRx%2BMx08bcfbe0cKG5q/jNN83oYLNm5prCV16RLr3UrIF89dXSnDmUwWjANYB1oLCwUD/%2B%2BGPl18cff7waHGK5oXbt2ummm27S6NGjwxnPqgM/m7///e/6wx/%2BoHr7TV9fXl6uevXqKTMzUxs3brSQMvyO5G/mhhtu0JgxY8IZz6rqPpvS0lJdeeWV%2Bve//60PP/xQTZs2tZjSjsP93XAN4MG4BrB6Ho9Hubm5GjhwYI3fU14uffKJlJtrSt%2BmTaHnkpNNKRw82IwchmtdY9Qc/0jqQHJyspKTkw/7mp07d2rz5s1q2bJlmFJFhgM/m5tvvln9%2B/ev8prevXtryJAh%2Bv3vfx/ueNbU5G9GkhzHcd1lA4f6bILl77vvvtOiRYtcWf6kmv/dAHUhLk46/3yzTZwoLVsmvf662TZvNiODr7xiThMPHmxOFXMJc%2BSgAIZBUVGRHn74YQ0aNEgtW7bUxo0b9d///d9q1qyZLr30UtvxrGratOlB//FOSEhQenq62rdvbymVfXv27NGjjz6qAQMGqGXLltq5c6emTJmiLVu26IorrrAdz6qysjJdfvnlWrFihd555x2Vl5crLy9PkpSamqrExETLCe36%2BeeftWnTpsp5Eb/99ltJUnp6utLT021GQ4QpKirS%2BvXrK7/esGGDVq5cqdTU1CO%2Bm97jCa00MmGC9Omn0qxZ0muvST/%2BKD31lNk6d5ZuuMFcY3jccbX9G%2BGIOKhze/fudXr16uU0b97cSUhIcFq3bu0MHTrU2bRpk%2B1oEalNmzbOU089ZTuGVfv27XMuvfRSJyMjw0lMTHRatmzpDBgwwPniiy9sR7Nuw4YNjqRDbosWLbIdz7qXX375kJ/NQw89ZDuadYFAwJHkBAIB21EiwqJFiw75tzJ06NBa%2BxklJY7z1luOM2iQ4yQkOI5ktqQkx7nuOsf5%2BGPHqaiotR%2BHI8A1gAAAV%2BAaQLt27pRmzpReeslMRh2UlSXdeqs0ZIi5dhDhQQEEALgCBTAyOI65XnDqVGn2bGnvXvN4crL0%2B99Lo0ZJJ59sN6MbUAABAK5AAYw8u3ebG0X8fmndOvOYxyP172%2Bmnjn/fFYdqSsUQACAK1AAI1dFhVl3%2BJlnpPnzQ49nZ0v33Sdddpm56xi1h4mgAQCAVfXqSb17S%2B%2B%2BK33zjTRihFmDeNky6corpQ4dpBdflFw2C1adYgQQAOAKjABGlx07pMmTzfbzz%2BaxVq2k0aOlm24yBRFHjwIIAHAFCmB0KiqSpk0z6xH/Z3pLtWwpjRkjDR9OETxanAIGAAARq3Fjc0PI999LU6ZImZnS9u3SHXdI7dqZu4lLS22njD4UQAAAEPHq1zfzBa5fLz33nDkdvGWLdMstUseO0quvmptJUDMUQAAAEDUSE03p%2B%2B47adIkqUULMzo4eLB01lnSwoW2E0YHCiAAIKb5/X75fD5lZ2fbjoJaVL%2B%2BOQ38/ffS//yPmUj6yy/NnIL4ddwEAgBwBW4CiW0//ST9%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%3D%3D'}