Genus 2 curve data - 295.a.295.1

Formats: - HTML - YAML - JSON - 2024-07-27T06:52:52.178539

• 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': '1445678309922023239', 'abs_disc': 295, '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': '[[5,[1,2,6,5]],[59,[1,-3,55,59]]]', 'bad_primes': [5, 59], 'class': '295.a', 'cond': 295, 'disc_sign': -1, 'end_alg': 'Q', 'eqn': '[[0,0,-1],[1,0,0,1]]', 'g2_inv': "['14348907/295','629856/295','-186624/295']", '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': "['108','-39','20835','37760']", 'igusa_inv': "['27','32','-256','-1984','295']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '295.a.295.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.1492683688832182090588207138996158657800381952357760759', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.60.1'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2, 7], 'non_solvable_places': [], 'num_rat_pts': 5, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '29.256600301110768975528859924', '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': 1, 'torsion_order': 14, 'torsion_subgroup': '[14]', 'two_selmer_rank': 1, 'two_torsion_field': ['6.2.435125.1', [-1, -2, 4, -4, 6, 0, 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': '295.a.295.1', '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': '295.a.295.1', 'mw_gens': [[[[0, 1], [1, 1], [0, 1]], [[-1, 1], [0, 1], [0, 1], [-1, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [14], 'num_rat_pts': 5, 'rat_pts': [[0, -1, 1], [0, 0, 1], [1, -1, 0], [1, -1, 1], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 295, 'lmfdb_label': '295.a.295.1', 'modell_image': '2.60.1', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 63398
    {'cluster_label': 'c4c2_1~2_0', 'label': '295.a.295.1', 'p': 5, 'tamagawa_number': 1}
  • id: 63399
    {'cluster_label': 'c4c2_1~2_0', 'label': '295.a.295.1', 'p': 59, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '295.a.295.1', 'plot': 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6ZNs50GiGynnnqqNm3aVDKWLFliOxIAl6IA4rBq1ChdF3DECGYBgeMRFxen1NTUkpGcnGw7EgCXogDiiIYMMbuD/PCD9NZbttMAkWvFihVKT09XvXr1dN111%2BnXX3897Ovz8vIUDAbLDAAoDxRAHNEJJ5gSKJlZwMJCq3GAiNSiRQtNmDBBH374oV5%2B%2BWVlZ2frvPPO09atWw/5OY8%2B%2Bqh8Pl/JqFOnTggTA4hmHsdxHNshEP6CQenkk6Xt26U33pCuv952IiCy7dq1Sw0aNNBdd92lIcX/wtpPXl6e8vLySj4OBoOqU6eOAoGAvF5vqKICiELMAOKoeL3SnXea45Ejpb177eYBIl21atXUtGlTrVix4pCvSUhIkNfrLTMAoDxQAHHUBg6UkpOllSulCRNspwEiW15enn788UelpaXZjgLAhSiAOGqJidLdd5vjhx6S9jkzBeAIhg4dqjlz5mjVqlX65ptv1K1bNwWDQfXp08d2NAAuRAHEMfnrX6W0NGntWumVV2ynASLH%2BvXr1aNHDzVu3Fhdu3ZVpUqV9PXXXysjI8N2NAAuxE0gOGYvvCANGGCK4MqVUtWqthMB7hAMBuXz%2BbgJBMBxYwYQx6xfPykjQ9q0SfL7bacBAADHigKIY1apkjR8uDl%2B7DGzRAwAAIgcFED8Ib16SY0bS9u2SU8/bTsNAAA4FhRA/CFxceZOYMkUwC1b7OYBAABHjwKIP6xbN%2BmMM6SdO82pYAAAEBkogPjDYmKkUaPM8fPPS%2BvX280DAACODgUQx%2BWSS6TWrc2i0MWnhAEAQHijAOK4eDzS6NHm%2BNVXpcNsawoAAMIEBRDH7fzzpcsukwoLpQcesJ0GAAAcCQUQ5WLUKDMbOGWKtHCh7TQAAOBwKIAoF82aST16mON777WbBQAAHB4FEOXmoYfM%2BoAffijNnm07DQAAOBQKIMpNgwZS//7meNgwyXHs5gEAAAdHAUS5uv9%2BqWpVaf58ado022kAAMDBUABRrlJTpSFDzPF990kFBXbzANHA7/crMzNTWVlZtqMAiBIex%2BFEHcpXIGBOB2/dKr38stSvn%2B1EQHQIBoPy%2BXwKBALyer224wCIYMwAotz5fGb2T5JGjJB%2B/91qHAAAsB8KICrErbdKdetKGzZIY8faTgMAAPZFAUSFSEgo3Rv4scekbdvs5gEAAKUogKgw118vNW0q7dhhSiAAAAgPFEBUmNhY6dFHzfFzz0nr19vNAwAADAogKtSll0pt2kh79kjDh9tOAwAAJAogKpjHI40ZY47Hj5eWLbMaBwAAiAKIEGjZUrrqKqmoSLr3XttpAAAABRAhMXq0uSZw%2BnTpq69spwEAwN0ogAiJJk2kG24wx3ffLbH/DAAA9lAAETIjRkiVK0tffim9957tNAAAuBcFECFTu7Y0aJA5vuceqbDQbh4AANyKAoiQGjZMOuEEaelSaeJE22kAAHAnCiBCqnp1M/snSQ8%2BKOXl2c0DAIAbUQARcrfdJqWnS2vXSn//u%2B00AAC4DwUQIVe1qrkhRJJGjZJ27rQaBwAA16EAwoq%2BfaWGDaXffpP%2B7/9spwEAwF0ogLAiPl56%2BGFz/NRT0tatdvMAAOAmFEBY07271Ly5FAyW7hcM4EB%2Bv1%2BZmZnKysqyHQVAlPA4DnsywJ6ZM6XLL5eqVJF%2B%2BUVKS7OdCAhfwWBQPp9PgUBAXq/XdhwAEYwZQFh16aXSuedKv/9u9gsGAAAVjwIIqzwe6ZFHzPFLL0nr1tnNAwCAG1AAYV27dtIFF0j5%2BcwCAgAQChRAWOfxSCNHmuN//ENas8ZuHgAAoh0FEGGhTRvpwgulvXulxx6znQYAgOhGAUTYGD7cPL76qrR%2Bvd0sAABEMwogwkabNmbk50tPPmk7DQAA0YsCiLBy333m8aWXpC1b7GYBACBaUQARVjp0kM46y6wL%2BNxzttMAABCdKIAIKx6PNGyYOfb7pd277eYBACAaUQARdrp2lerXl7ZulSZMsJ0GAIDoQwFE2ImNlW6/3Rw/%2B6zEbtUAAJQvCiDC0g03SImJ0k8/SZ9%2BajsNAADRhQKIsOT1Sr17m%2BMXX7SbBQCAaEMBRNjq3988Tp8u5eTYzQIAQDShACJsnX66lJUlFRRIkyfbTgMAQPSgACKsFZ8GpgACAFB%2BKIAIa927SzEx0oIF0urVttMAABAdKIAIaykpUuvW5nj6dLtZAFv8fr8yMzOVlZVlOwqAKEEBRNi7/HLz%2BMEHdnMAtgwYMEDLli3TggULbEcBECUogAh7HTqYxy%2B%2BMDeEAACA40MBRNhr2lTy%2BaRdu6QffrCdBgCAyEcBRNiLiZHOOMMcL15sNwsAANGAAoiI0KSJeVyxwm4OAACiAQUQEaFOHfO4caPdHAAARAMKICKC12sec3Pt5gAAIBpQABERiorMo8djNwcAANGAAoiIsHWreaxe3W4OAACiAQUQEeHHH81j/fp2cwAAEA0ogAh7hYXSnDnm%2BJxz7GYBACAaUAAR9v7zHyknx5z%2BPe8822kAAIh8FECEtaIi6eGHzXGfPlKlSnbzAAAQDSiACGt%2BvzRvnlStmnTnnbbTAAAQHSiACFsffigNGWKOH3tMSk%2B3mwcAgGhBAURYmjpV6tJFKiiQevSQBgywnQgAgOhBAURY2bPHnOrt1k3KyzMlcPx4FoAGAKA8UQARFhxHev99qVkz6cknzXODB0tvv82NHwAAlLc42wHgbo5jrvUbNUr68kvzXGqqNG6cdMUVdrMBABCtKICwIidHmjRJeukl6aefzHMJCdLAgdL990s%2Bn918QDjx%2B/3y%2B/0qLCy0HQVAlPA4juPYDgF32LxZmjHDnNb9%2BGOzw4ckJSVJ/fpJf/ubVLu23YxAOAsGg/L5fAoEAvJ6vbbjAIhgzACiwvz%2Bu1nD79NPzWne774zp3yLZWVJN9wg/fnPEn%2BXAQAQOhRAlAvHkdaulRYskL7%2B2hS/BQukvXvLvu6ss6SrrpK6d5caNbKTFQAAt6MA4pjt2iX9%2BKO0dKn0/ffS4sXSokXS1q0HvjY9XWrXTrroIunii6W0tNDnBQAAZVEAcVB5edLq1dIvv0grV0orVkg//ywtXy6tWXPwz4mPl5o2lc45R2rZUjr/fKl%2BfdbwAwAg3FAAXchxzGzd%2BvVmrFtnxpo1ZqxeLW3cWPZ6vf0lJ0unnmoK3%2BmnS82bm%2BOEhJC9DQAA8AdRAKOE45hTszk5pWPzZjOys6VNm0rHxo1Sfv6Rv2a1amYG75RTpIYNzTV7jRtLTZpINWtW/HsCAAAVgwIYhvbulXbskLZtk7ZvL33cutUcb91qxpYtpeO338w2asciOVmqU0c66SSpbl0zMjKkk082IzmZ07cAAEQjCmAILFtmlkAJBqVAoPSxeOzYUfq4Y4eZyfujKleWatWSUlJKR2qqGWlpZqSnm0dO1wIA4E4UwBB4913pvvuO/fO8XqlGDal6dfNYPE480YyaNUtHcrIZiYnlnx8AAEQXCmAINGokdehgCp3Xa7Y583qlE04wxz6fOd53%2BHxSHL87AACgArAVHABECLaCA1BeYmwHAAAAQGhRAAEAAFyGAggAAOAyFEAACHf5%2BdKkSdLAgebjjz46/FY9AHAE3AQCAOHs11/NMgK//qqgJJ%2BkgCRvq1bSzJlmyQAAOEbMAAJAOLvySlMC9/fVV9Ktt4Y%2BD4CowAwgAISY45izurt3S7//brZxzMszz%2BXnm%2B0g9%2B6VkhZ8qjPvbF/yeWVmACUVxcZp7j/XqiglTfHxKhkJCWZXoMqVpSpVzKhUydKbBRCWWGr4ODiOo507d9qOAcACx5Fyc6WcHLMX92%2B/le7XvW1b6daOwaAZO3easWuXKX5FRUf%2BNQbqK52yz8fB/R5VWKAR18zVJ%2BpwxK8VGytVq2ZGYqJZjD4pqXRh%2BurVzSL0%2B%2B82lJxsno/hfBGiUFJSkjwu3fSeGcDjULwoKwAAiDxuXlSdAngcjmUGMCsrSwsWLDjqr12Rrw8Gg6pTp47WrVt31H/wwyn/sb7eTe/XTe9VKr/3m50tLV4sLVkiLVsmLV8urVhhTsseSdWqUq1apTNm8%2BbNUK9enVW9eulWj8WzbYmJ5rFqVXNatmpV6bzzsvTttwd/v3nr16tS8%2Bby7N1r3q%2BkOpLWyZwCVt26JvghpuccRzr77Fb6%2BOOvtHu3mXnMzTVj504zMxkImLFjh7R9uzR9%2BhxlZrbV1q3Sli3mNUcjNlaqU0c6%2BWSpfn0zTjlFatjQPFe8tWUk/%2BxGwp/lSHl9uLxfN88Acgr4OHg8nqP%2BgxsbG3tM/8qo6NdLktfrjdj8vN/Dc9N7lY7t/cbExGr9eq8%2B%2BUT64gtp7lxpw4aDvzYuToqJWa127U7WySdLGRmmc9WubUZamil1%2B8rMHKXnn//zUWePizvM%2B83MlO67TxoxoszTXknemBjpqafM%2BdnDiI8vVEbG0X8/MzOHaf78ZSUf5%2BWZ09zZ2dLGjWZs2CCtW2fGl1%2BulVRXe/dKq1ebMXt22a9ZqZLZE/3UU6Xt22/V7NleNWtmvpdH83dvuPx5Drc/y%2BH2sxjp79dtKIAhMmDAgLB6/bEKt/y83/ITbtkr4r06jvTNN9I//ylt3vyNTj217H%2BPiZGaNJGaN5dOP930rj/9ycxcjRs385gylfv7HT5cSk2VnnxSWrlSklTYrJk0apR02WUVnichwczs1alz8Nf7/TP0178O0MaN5mblX34xY8UKM5P688/mRpcffjBDGqQuXcznVq8unXmmdPbZUlaW1KKFdNJJxxT3iPnL8/Xh8Gf5eL5%2BuL3%2BWIVbnkjHKWAXctuG8m56v256r9KR329BgTRxoulOS5eWPl%2B5snT%2B%2BdIFF5jHs846cCYv7DiONsyfr5NattS6det00vE2pRApKpLWrjWn1otL4OLF5uOCggNff9JJUqtWUuvW0tln56plyyRX/HnmZxehxgygCyUkJGj48OFKSEiwHSUk3PR%2B3fRepcO/3//%2BV%2BrTx1zXJ5nr7bp2lbp3ly66yHwcUTweVapfX5Ii6vc3JsbMpJ58snTppaXP5%2BWZUv7dd9K330rz50vffy%2BtXy9NmWKGlKhq1XaqX78quvRS6ZJLzGRoNOJnF6HGDCCAqDNnjtSpkzn1eOKJ0rBh0s03R/6mGdE%2Ba5KbKy1YIH35pfT552at699/L/uarCyzNvbVV0uNG9vJCUQDCiCAqJKXJzVoYG5UuPhicwq4Zk3bqcpHtBfA/eXnS19/bbY%2B/uADM1O4rzPOMLO8119vij6Ao0cBBBBVPv9cattWSkmRVq0yy61EC7cVwP1lZ0szZkjvvCN9/HHpNYQJCaYEDhtmlp0BcGQUQABR5auvzI0dNWuaJUmqVbOdqPy4vQDua8sWc53gK69IixaZ52JjzfbIo0dHwE09OC6OYy4PKF7XMjfX7LKTm2vWn6xXz3bC8MfmPi6zd%2B9eDRs2TE2bNlW1atWUnp6u3r17a%2BPGjbajHZd33nlHF198sWrWrCmPx6NFxX8jHMb48ePl8XgOGHv27AlB4orxR74P4c5xHI0YMULp6emqUqWKLrjgAi3d95be/ZxzjnTCCdu1ZYuUmDhDHk9VeTwepUbr3QMR7oUXXlC9evVUuXJlnXXWWfriiy8O%2Bdp9f2aTkz267TaPFi3y6JNP8nTZZVJhofTcc9K555qCGAk%2B//xzde7cWenp6fJ4PHr33XdtRyoXB3tfBQVmm8TVq80NP19%2BKc2aZYr80KHL5fEMkcczXB7Pk/J4xsnjmax27XJ1wQVmqaDGjc36m16vWaOzWjUz09%2BggdSsmXTeeVLHjmaGGEfGXcAus3v3bi1cuFAPPPCAmjVrpu3bt2vw4MG64oor9O3%2BF9hEkF27dqlVq1bq3r27brrppqP%2BPK/Xq%2BXLl5d5rnLlyuUdL2T%2B6PchnD3%2B%2BON6%2BumnNX78eDVq1EiPPPKIOnTooOXLlyspKemA18fHS1deOU2vv/5nOU5nNWoU0BNPBHXuuUex%2BS5CasqUKRo8eLBeeOEFtWrVSuPGjVOnTp20bNky1a1b96Cfc7Cf2dTUBF14oTkt3Lu3WWrmvvukceNC8S6Oz65du9SsWTPdcMMNuvrqq23HOay8PFPgtm8v3TmmeM/rfUcgIP3yy5%2BUnf2CYmJ8knJ13XW1jrCzTmNJTx/w7P6Lih9M1apmxrd4n%2BsjrI2O/6EAuozP59NHH31U5rnnnntO55xzjtauXXvI/%2BmGu169ekkCsp1IAAAWT0lEQVSSVq9efUyfF20zQ3/0%2BxCuHMfRM888o/vuu09du3aVJL3%2B%2ButKSUnR5MmT1b9//4N%2BXkbGWtWvf4tyc1/Tzz/Hq0uXE9Wli3T33VLLlqF8Bzicp59%2BWn/5y1/Ur18/SdIzzzyjDz/8UC%2B%2B%2BKIeffTRg37O4X5mzzlHatdOmjzZLEIdCTp16qROnTqF9Nd0HLMV4G%2B/lY4tW8zYurV0bNtmxtatpuztf0f24SXvc%2BwtU/4qVzazeMVbJHq9Un7%2BFs2b96H69u2mmjUTlJSkA0ZiYul2isXH1aodcidEHAEFEAoEAvJ4PDrBhf9sys3NVUZGhgoLC9W8eXM9/PDDOuOMM2zHwv%2BsWrVK2dnZ6tixY8lzCQkJatu2rebOnXvIAihJmzb9S0lJC5SYeL927bpG06fHaPp0s%2BjzjTea9QCTkw/56ahg%2Bfn5%2Bu6773T33XeXeb5jx46aO3fuIT9v/5/Z2257TFu3nqb335emTzfXgUlm2R%2B3KSyUNm82aylu2GC27du0ydw8k51t/tvmzWZrv6PZ5/pgYmLMckrVq5uZtuLH4uHzlT4Wj3btztALL4xR9%2B4d5fWarQH3N3v2D2rX7nrNnn2/9uzZo8zMTN1///1q167d8X1TcEgUQJfbs2eP7r77bvXs2dN1F5U3adJE48ePV9OmTRUMBvXss8%2BqVatWWrx4sRpyK2FYyM7OliSlpKSUeT4lJUVr1qw55Oe1aNFCEyZMUKNGjbR582bde29fLVlyqYqKrtV333n03XfS7bebu4U7dzZrBjZqdHT70qJ8bNmyRYWFhQf9vS3%2Bfd9Xbq7k8Zyp/v1nKz%2B/oZYti9Mnn0gzZ5Zd/yUzUxozRrr88gqNb01urpndXL7c7Az466%2BlezBv2HDw3VUOpWpV84%2Bg5GRz01TNmmY5nX1HjRqlo3p1M1t37DNui5SWtvuwyzGlpaXppZde0llnnaW8vDy98cYbat%2B%2BvWbPnq02bdoc6y%2BIo0ABjHKTJk0qM0sya9YstW7dWpK5IeS6665TUVGRXnjhBVsRj9nh3tOxaNmypVrucz6wVatWOvPMM/Xcc89p7Nix5ZK1IpXX9yGc7P%2BeZs6cKcmc9tuX4zgHPLevfU%2BpNW3aVOeee64aNGigW2/dpqSkWzVxorRwofTpp2bccYfZgqxtW3MH8bnnSqeeai40Dxs5OWZBPMm0gCj5B5vH4yk5Jbl%2BvfTzz/UUCFyjBx4wpWbVKrO3sOmEpx/kKxSoVq2NuuGGuurSxZzij7giX1QkffGFrpaUtHZtydO//27WQZw716yBuGiR%2BZ4cTmyslJZmbpZITzfHaWlmB5WUFDNq1TIjnHbDady4sRrvs7L3ueeeq3Xr1unJJ5%2BkAFaQcPrfGyrAFVdcoRYtWpR8XLt2bUmm/F1zzTVatWqVPv3004ia/TvUezpeMTExysrK0ooVK8rl61W0ivo%2B2LT/e8r733mq7OxspaWllTyfk5NzwMzR4VSrVk1NmzbVpk1L9OCDpvD98ov0739LM2dKX3xhysekSWZIZv3A5s3NaNpUOu006U9/MrMiIS0YeXlmunL8eLMysiQ1aSLddZd0//0hDHLs9u4115UVn3bcvLn0dOSGDSmSPlX37qcpEDCd1ughSXrkkQO/Xs2a5k7QzEzze3LGGdIrrwzS5s2/6rHHZoXqbZWvDz6QBgyQfv1Vb0vSoEHKefFt3ZP2uibPq6eDLUpQq5b5PpxyirkDtl49s9VenTqm7IXVP1yOQ8uWLTVx4kTbMaJWlPwxwaEkJSUdcKdkcflbsWKFPvvsM50YYUvoH%2Bw9lQfHcbRo0SI1bdq03L92Raio74NN%2B78nx3GUmpqqjz76qOTazPz8fM2ZM0djxow56q%2Bbl5enH3/8scwMaYMGpgjecYe0e7eZZfn8c/M4f76ZkZo3z4x9Va9uFhsu/os3I8P8xXvSSWbWpXr1ci6IN95o7mrY186d0gMPmL/p97uGriIUFZk7O/e987P4BoHimwS2bi29kWDLFnNjwY4dh/uqsZLaacOG0md8PmnPnp910klF6tChSckewg0amFG9etmv4DiO7rhjfsT8zB7gm2%2BkLl1Ki/3/1PrpC93/04X6l75XenqSWreWWrQwhbdpU/fsevLf//63zD/8UL4ogC5TUFCgbt26aeHChXrvvfdUWFhYcr1NjRo1VOlgV%2BdGgG3btmnt2rUl6xkWLxORmppacsdg7969Vbt27ZK7C0eOHKmWLVuqYcOGCgaDGjt2rBYtWiS/32/nTZSDo/k%2BRBKPx6PBgwdr9OjRatiwoRo2bKjRo0eratWq6tmzZ8nr2rdvr6uuukq33XabJGno0KHq3Lmz6tatq5ycHD3yyCMKBoPq06fPQX%2BdqlWliy4yQzKF5%2BefzWnixYulJUukZcukNWtMAZo/34yDqVSp9DRb8XVVNWqUvUh%2B37sYq1Qxv37lymZUqmSWsomPl%2BJX/az4f/5Th%2BqTzhNPKP%2BWQSqIr6KCAnP9V35%2B6cjLk/bsMeP3383YvduMXbtKF87NzTWdMjdXCgZLRyBgRjD4R38HzfViNWuWnn5MSTGnI9PSpNWr5%2BnFFx/UQw/1V8eOp2nSpHF6%2BeWX9cknS5WRYX5md%2ByorWuvjdKf2UcfPaD8Faun1Vp%2Bz%2BtKG3Vb5J3SlrlZZ%2BXKlSUfr1q1SosWLVKNGjVUt25d3XPPPdqwYYMmTJggydwBfvLJJ%2BvUU09Vfn6%2BJk6cqKlTp2rq1Km23kL0c%2BAqq1atciQddHz22We24/1hr7322kHf0/Dhw0te07ZtW6dPnz4lHw8ePNipW7euU6lSJSc5Odnp2LGjM3fu3NCHL0dH832INEVFRc7w4cOd1NRUJyEhwWnTpo2zZMmSMq/JyMgo8x6vvfZaJy0tzYmPj3fS09Odrl27OkuXLj3uLLt2Oc7ixY4zdarjPP6449x6q%2BNcdpnjnH6649So4ThmgY3yG4P0f2WeCPzv9zOwz3MX6uNy/3UPNSpXdpy0NMfJzHScVq0cp3Nnx%2Bnb13GGDHGcUaMcZ9w4872ZM8dxli1znN9%2Bc5zCwsN/T/1%2Bv5ORkeFUqlTJOfPMM505c%2BaU/Leo/5mNjz/8N/zii20n/MM%2B%2B%2Byzg/6/qPj3s0%2BfPk7btm1LXj9mzBinQYMGTuXKlZ3q1as7559/vjNz5kw74V2CreAAoJzk5ZUut5GTU7quWvHiucWzacFg6dZVu3eXzs7l5ZW9i/MOPa2n9beSj4OSfJICkoqv2u2g/%2BhjdSh5jcdjZhETEkpH5cpmprF4trFqVbN%2BWvHCucVj33XZise%2BS3skJITiu%2BgSjmOmeQsLD/2aDh2k//wndJngKpwCBoBykpBgrgnMyPjjX6OoyNw8UVAgFS26SDr/0K91EpP09o8tFXuCuRwwPt7cBYoI4PFI7dsfvuB16HDo/wYcJ9bPBoAwEhNjimS1alJSq9Olyy475Gs9tw2Q76QkJSaaWT7KX4QZNuyQi%2BoVnFjL3AAEVBAKIACEs8mTzZ2i%2B4qLkwYOPPhaKYgcF14oTZhg7hLax49qonYFH%2BvrFS653RdWcA0gAESCn35ScNYs%2BYYMUWD5cnkbNbKdCOVlzx5pxgwpJ0eB9D%2Bpw%2Bh2WvCtR5UrS6%2B9Jl13ne2AiEYUQACIEMFgUD6fT4FAIKIWb8exyc2VevSQ3nvPfHzXXdKoUdGzwDPCA6eAAQAII4mJ0rvvmuInSY8/Ll18sbmzHCgvFEAAAMJMbKw0Zow0ZYq5IejTT81OIF9%2BaTsZogUFEACAMHXNNWbXmT/9Sdq4UbrgAjMjWFRkOxkiHQUQAIAwlplpSmDPnmbd6GHDzI3h27bZToZIRgEEACDMJSZKEydK48aZdSLfe8%2BcEv76a9vJEKkogAAARACPR7r5ZlP6TjlFWrtWatNGevZZs7MccCwogAAARJDmzaXvvpO6dzfbBg4ebI6DQdvJEEkogAAARBiv19whPHas2QN66lTp7LOl77%2B3nQyRggIIAEAE8njMjoBffCHVqSOtWCG1bGmuFQSOhAIIAEAEa9FCWrjQLBb9%2B%2B9Sr17SbbdJ%2Bfm2kyGcUQABAIhwNWtKM2dKDz5oPvb7pXbtpE2b7OZC%2BKIAAgAQBWJjpZEjpRkzJJ9PmjtXOussad4828kQjiiAAABEkcsvlxYskE491cwAXnCB9OqrtlMh3FAAAQCIMg0bmvUCu3Y11wL%2B5S/SoEFSQYHtZAgXFEAACHN%2Bv1%2BZmZnKysqyHQURJDFReustc1pYMkvGXHqptH273VwIDx7HYf1wAIgEwWBQPp9PgUBAXq/XdhxEkGnTpOuvl3bvlho3NtcJNmxoOxVsYgYQAIAod9VV0ldfmfUCly836wXOmWM7FWyiAAIA4ALNm0vz50vnnCNt2yZ16CC98YbtVLCFAggAgEukpkqzZ0vdupl9hHv3lh56SOJiMPehAAIA4CJVqph9hO%2B6y3w8fLjUr58phHAPCiAAAC4TEyONGSO9%2BKI5fvVV6YorpNxc28kQKhRAAABc6pZbpHffNbOCH3wgXXih9NtvtlMhFCiAAAC4WOfO0mefSSeeaHYQOf98afVq26lQ0SiAAAC4XIsWZpmYunWln3%2BWWrWSli61nQoViQIIAADUuLE0d67ZQ3jjRqlNG7NsDKITBRAAAEiSateWPv/czAhu2ya1b2%2BWjUH0oQACAIASNWpIH39sbgjJzZU6dZJmzbKdCuWNAggAAMpITJRmzjQ3iOzZI3XpYu4WRvSgAAIAgANUrixNnSpdc41ZJLpbN%2Blf/7KdCuWFAggAAA4qPl6aNEnq1UsqLJR69JAmT7adCuWBAggAAA4pLk567TXpxhuloiJTBidNsp0Kx4sCCAAADis2Vnr5ZbNncFGR1Lu39M9/2k6F40EBBAAARxQTI40bV1oCr7%2BeawIjGQUQAAAcleISWHw6uGdPado026nwR1AAAQDAUYuJkV56qfTGkGuvZZ3ASEQBBIAw5/f7lZmZqaysLNtRAEnmmsBXXy1dIqZrV3YMiTQex3Ec2yEAAEcWDAbl8/kUCATk9XptxwG0d6909dXSjBlm8ehPPpHOOcd2KhwNZgABAMAfEh9vbgRp395sG3fJJdIPP9hOhaNBAQQAAH9Y5cpmm7iWLaXt26WOHaVVq2ynwpFQAAEAwHEp3jv4tNOkTZtMCdy82XYqHA4FEAAAHLcaNaQPP5ROPllauVK69FIpGLSdCodCAQQAAOUiPV36z3%2Bk5GRp4UJzg0h%2Bvu1UOBgKIAAAKDcNG0rvvy9VqyZ9/HHpotEILxRAAABQrs4%2BW5o6VYqLkyZNku67z3Yi7I8CCAAAyt3FF0svv2yOH3tM%2Bvvf7eZBWRRAAABQIfr2lUaONMcDBphTwwgPFEAAAFBhHnhAuuEGcx3gtddKixfbTgSJAggAACqQx2NO/154odkt5PLLpY0bbacCBRAAAFSoSpXMTSFNmkjr10tduki7d9tO5W4UQAAAUOFOOMHsFnLiidK335rrA1kexh4KIAAACIn69aVp06T4eOmtt6SHHrKdyL0ogAAAIGRaty5dEmbkSFMEEXoUQAAAEFI33igNGWKO%2B/aVFi2yGseVKIAAACDkxoyROnY0N4NceaX022%2B2E7kLBRAAAIRcXJz05pvSKadIa9aYNQILCmyncg8KIACEOb/fr8zMTGVlZdmOApSr6tWl6dOlxETps8%2Bku%2B6yncg9PI7jOLZDAACOLBgMyufzKRAIyOv12o4DlJt33pGuvtocT54s9ehhN48bMAMIAACs6tpVuvdec9yvn7Rkid08bkABBAAA1j30UOlNIV27SoGA7UTRjQIIAACsi401p3/r1pVWrjTLw3CRWsWhAAIAgLBw4onS22%2BbvYPffVd66inbiaIXBRAAAISNrCzpmWfM8d13S199ZTdPtKIAAgCAsHLLLeZO4MJCsz4gi0SXPwogAAAIKx6P9NJLUuPG0oYNUu/eUlGR7VTRhQIIAADCTmKi9NZbUuXK0gcfSE88YTtRdKEAAgCAsNS0qTR2rDm%2B7z5p3jy7eaIJBRAAAIStfv2k664z1wP26CHt2GE7UXSgAAIAgLDl8Ujjxkn160tr1kg33cT6gOWBAggAAMKa1yu9%2BaYUF2fWCfzHP2wninwUQAAAEPaysqRRo8zxoEHS8uV280Q6CiAAAIgIQ4dK7dub/YJ79pTy820nilwUQAAIgb59%2B8rj8ZQZLVu2tB0LiCgxMdLrr0s1akgLF0oPPmg7UeSiAAJAiFxyySXatGlTyXj//fdtRwIiTu3a0iuvmOPHH5fmzLGbJ1JRAAEgRBISEpSamloyatSoYTsSEJGuukq68UZzN3Dv3lIgYDtR5KEAAkCIzJ49W7Vq1VKjRo100003KScn57Cvz8vLUzAYLDMAGM88Y5aGWbtWGjjQdprI43EcVtMBgIo2ZcoUJSYmKiMjQ6tWrdIDDzyggoICfffdd0pISDjo54wYMUIjR4484PlAICCv11vRkYGwN3eu1Lq12Sd46lSpa1fbiSIHBRAAytmkSZPUv3//ko9nzZql1q1bl3nNpk2blJGRoTfffFNdD/G3Vl5envLy8ko%2BDgaDqlOnDgUQ2Me990qPPirVrCn98IOUkmI7UWSIsx0AAKLNFVdcoRYtWpR8XLt27QNek5aWpoyMDK1YseKQXychIeGQs4MAjBEjpPfflxYvlvr3l6ZNM7uH4PAogABQzpKSkpSUlHTY12zdulXr1q1TWlpaiFIB0alSJWnCBOnss6Xp06WJE6VevWynCn/cBAIAFSw3N1dDhw7VvHnztHr1as2ePVudO3dWzZo1ddVVV9mOB0S800%2BXhg83x7ffLm3caDdPJGAGEAAqWGxsrJYsWaIJEyZox44dSktLU7t27TRlypQjzhQCODrDhkmzZkmXXCIlJ9tOE/64CQQAIkQwGJTP5%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%2B92DYYAAAAASUVORK5CYII%3D'}