{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:RXFVA7AKRWHQJ4Z74THSKNFI3Q","short_pith_number":"pith:RXFVA7AK","canonical_record":{"source":{"id":"1302.6787","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-02-27T14:14:02Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"5b9bd050cd884185f9a09dbb3314044739eb9bbd48742ce192c6ac183b501973","abstract_canon_sha256":"86c98dde70b3d29df384d0968e24d3052577fd9d25752e7774440cbd705b2077"},"schema_version":"1.0"},"canonical_sha256":"8dcb507c0a8d8f04f33fe4cf2534a8dc1fde586ed5979d1812de0a09ecaecde4","source":{"kind":"arxiv","id":"1302.6787","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1302.6787","created_at":"2026-05-18T03:32:18Z"},{"alias_kind":"arxiv_version","alias_value":"1302.6787v1","created_at":"2026-05-18T03:32:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1302.6787","created_at":"2026-05-18T03:32:18Z"},{"alias_kind":"pith_short_12","alias_value":"RXFVA7AKRWHQ","created_at":"2026-05-18T12:27:59Z"},{"alias_kind":"pith_short_16","alias_value":"RXFVA7AKRWHQJ4Z7","created_at":"2026-05-18T12:27:59Z"},{"alias_kind":"pith_short_8","alias_value":"RXFVA7AK","created_at":"2026-05-18T12:27:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:RXFVA7AKRWHQJ4Z74THSKNFI3Q","target":"record","payload":{"canonical_record":{"source":{"id":"1302.6787","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-02-27T14:14:02Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"5b9bd050cd884185f9a09dbb3314044739eb9bbd48742ce192c6ac183b501973","abstract_canon_sha256":"86c98dde70b3d29df384d0968e24d3052577fd9d25752e7774440cbd705b2077"},"schema_version":"1.0"},"canonical_sha256":"8dcb507c0a8d8f04f33fe4cf2534a8dc1fde586ed5979d1812de0a09ecaecde4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:32:18.780392Z","signature_b64":"xfT6wToDGyWSSAu+X1O8Bxbpam0nJmhMnMqP0eKzQy08F6uxYv8rDf4bTWIWG1DGQsUxzPb0M/GJneV34cufAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8dcb507c0a8d8f04f33fe4cf2534a8dc1fde586ed5979d1812de0a09ecaecde4","last_reissued_at":"2026-05-18T03:32:18.779946Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:32:18.779946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1302.6787","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:32:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SeJq+2RjpXbvtNHD4iAAUZh6PPBkniZz4z3PfHFtastCehvL7c0gBI/bz6HtPH3O1VEJIP34pAHj+cLT3aXYCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T18:22:46.278467Z"},"content_sha256":"0b4a935b9e55510c83ee650c77fd04a87777005b3353dad4bc05d491d2f2a55a","schema_version":"1.0","event_id":"sha256:0b4a935b9e55510c83ee650c77fd04a87777005b3353dad4bc05d491d2f2a55a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:RXFVA7AKRWHQJ4Z74THSKNFI3Q","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Approximation Algorithms for the Loop Cutset Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"cs.AI","authors_text":"Ann Becker, Dan Geiger","submitted_at":"2013-02-27T14:14:02Z","abstract_excerpt":"We show how to find a small loop curser in a Bayesian network.  Finding such a loop cutset is the first step in the method of conditioning for inference.  Our algorithm for finding a loop cutset, called MGA, finds a loop cutset which is guaranteed in the worst case to contain less than twice the number of variables contained in a minimum loop cutset.  We test MGA on randomly generated graphs and find that the average ratio between the number of instances associated with the algorithms' output and the number of instances associated with a minimum solution is 1.22."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1302.6787","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:32:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bu8CnyAVsEKKOiLMrKxDe+DY/9/WflBPykt3Wjh+mrXVqRcWmcXC5wHzCIM8/8kPZJEqWuBq9HgbeLDNdRLmCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T18:22:46.279165Z"},"content_sha256":"04848373077a5e094505c2c558d2b63b900298c926c246206d75a7dca7960f22","schema_version":"1.0","event_id":"sha256:04848373077a5e094505c2c558d2b63b900298c926c246206d75a7dca7960f22"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RXFVA7AKRWHQJ4Z74THSKNFI3Q/bundle.json","state_url":"https://pith.science/pith/RXFVA7AKRWHQJ4Z74THSKNFI3Q/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RXFVA7AKRWHQJ4Z74THSKNFI3Q/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-31T18:22:46Z","links":{"resolver":"https://pith.science/pith/RXFVA7AKRWHQJ4Z74THSKNFI3Q","bundle":"https://pith.science/pith/RXFVA7AKRWHQJ4Z74THSKNFI3Q/bundle.json","state":"https://pith.science/pith/RXFVA7AKRWHQJ4Z74THSKNFI3Q/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RXFVA7AKRWHQJ4Z74THSKNFI3Q/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:RXFVA7AKRWHQJ4Z74THSKNFI3Q","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"86c98dde70b3d29df384d0968e24d3052577fd9d25752e7774440cbd705b2077","cross_cats_sorted":["cs.DS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-02-27T14:14:02Z","title_canon_sha256":"5b9bd050cd884185f9a09dbb3314044739eb9bbd48742ce192c6ac183b501973"},"schema_version":"1.0","source":{"id":"1302.6787","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1302.6787","created_at":"2026-05-18T03:32:18Z"},{"alias_kind":"arxiv_version","alias_value":"1302.6787v1","created_at":"2026-05-18T03:32:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1302.6787","created_at":"2026-05-18T03:32:18Z"},{"alias_kind":"pith_short_12","alias_value":"RXFVA7AKRWHQ","created_at":"2026-05-18T12:27:59Z"},{"alias_kind":"pith_short_16","alias_value":"RXFVA7AKRWHQJ4Z7","created_at":"2026-05-18T12:27:59Z"},{"alias_kind":"pith_short_8","alias_value":"RXFVA7AK","created_at":"2026-05-18T12:27:59Z"}],"graph_snapshots":[{"event_id":"sha256:04848373077a5e094505c2c558d2b63b900298c926c246206d75a7dca7960f22","target":"graph","created_at":"2026-05-18T03:32:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We show how to find a small loop curser in a Bayesian network.  Finding such a loop cutset is the first step in the method of conditioning for inference.  Our algorithm for finding a loop cutset, called MGA, finds a loop cutset which is guaranteed in the worst case to contain less than twice the number of variables contained in a minimum loop cutset.  We test MGA on randomly generated graphs and find that the average ratio between the number of instances associated with the algorithms' output and the number of instances associated with a minimum solution is 1.22.","authors_text":"Ann Becker, Dan Geiger","cross_cats":["cs.DS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-02-27T14:14:02Z","title":"Approximation Algorithms for the Loop Cutset Problem"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1302.6787","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0b4a935b9e55510c83ee650c77fd04a87777005b3353dad4bc05d491d2f2a55a","target":"record","created_at":"2026-05-18T03:32:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"86c98dde70b3d29df384d0968e24d3052577fd9d25752e7774440cbd705b2077","cross_cats_sorted":["cs.DS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-02-27T14:14:02Z","title_canon_sha256":"5b9bd050cd884185f9a09dbb3314044739eb9bbd48742ce192c6ac183b501973"},"schema_version":"1.0","source":{"id":"1302.6787","kind":"arxiv","version":1}},"canonical_sha256":"8dcb507c0a8d8f04f33fe4cf2534a8dc1fde586ed5979d1812de0a09ecaecde4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8dcb507c0a8d8f04f33fe4cf2534a8dc1fde586ed5979d1812de0a09ecaecde4","first_computed_at":"2026-05-18T03:32:18.779946Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:32:18.779946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xfT6wToDGyWSSAu+X1O8Bxbpam0nJmhMnMqP0eKzQy08F6uxYv8rDf4bTWIWG1DGQsUxzPb0M/GJneV34cufAw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:32:18.780392Z","signed_message":"canonical_sha256_bytes"},"source_id":"1302.6787","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0b4a935b9e55510c83ee650c77fd04a87777005b3353dad4bc05d491d2f2a55a","sha256:04848373077a5e094505c2c558d2b63b900298c926c246206d75a7dca7960f22"],"state_sha256":"a64c36fa1f6c0cdd6e927ae515b6bcc283c347a51aacaacca17460e77237ea89"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UG0PSnp8WPwuQP+zad2NDIbOCKG6bkwdozaFGKQ9FZ5+zvjkAfSR7vk6vIZKscz0Zu/V+McuAzS20STHY2BCAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T18:22:46.282758Z","bundle_sha256":"ebfb338d18bedc834decafa5c834d94500acea04572a4d13652a272762669ba2"}}