{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:RKLT72MCGC73IUI5YDJT32JI4A","short_pith_number":"pith:RKLT72MC","canonical_record":{"source":{"id":"1605.00355","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-05-02T05:42:10Z","cross_cats_sorted":[],"title_canon_sha256":"82ff999ce20ff2f76b92fcbae86e9f24566cca105212ad15f7dd4c58c0779545","abstract_canon_sha256":"52e97a18d5303ea364d03c2625def7f82462f3fbd5dbabef7defccf4b8dc89b1"},"schema_version":"1.0"},"canonical_sha256":"8a973fe98230bfb4511dc0d33de928e01c7cba2852d884c4fde5f05ecc792e8c","source":{"kind":"arxiv","id":"1605.00355","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1605.00355","created_at":"2026-05-18T01:15:56Z"},{"alias_kind":"arxiv_version","alias_value":"1605.00355v1","created_at":"2026-05-18T01:15:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.00355","created_at":"2026-05-18T01:15:56Z"},{"alias_kind":"pith_short_12","alias_value":"RKLT72MCGC73","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"RKLT72MCGC73IUI5","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"RKLT72MC","created_at":"2026-05-18T12:30:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:RKLT72MCGC73IUI5YDJT32JI4A","target":"record","payload":{"canonical_record":{"source":{"id":"1605.00355","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-05-02T05:42:10Z","cross_cats_sorted":[],"title_canon_sha256":"82ff999ce20ff2f76b92fcbae86e9f24566cca105212ad15f7dd4c58c0779545","abstract_canon_sha256":"52e97a18d5303ea364d03c2625def7f82462f3fbd5dbabef7defccf4b8dc89b1"},"schema_version":"1.0"},"canonical_sha256":"8a973fe98230bfb4511dc0d33de928e01c7cba2852d884c4fde5f05ecc792e8c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:15:56.061438Z","signature_b64":"s3gUXeZfPimITRD71cRinJrkqa8/jHpO47lqeTqhQWj8lxVI44zOu3wY/RYNSDO6rTtZQThtOY6EJ03591WQDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a973fe98230bfb4511dc0d33de928e01c7cba2852d884c4fde5f05ecc792e8c","last_reissued_at":"2026-05-18T01:15:56.060657Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:15:56.060657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1605.00355","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":"2026-05-18T01:15:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nLfkyZUNvuCEugJB27EYkJ6iuM9q28RoHdO5qCcc/ICfQkYbx+W80h2z05xrMfCakHp67cy/+PNgOYybi1D3Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T18:09:44.124646Z"},"content_sha256":"a3d2b5b4f82a1c79de0e06235efaedc19e3c64014acfcd7739813cea9e4fe852","schema_version":"1.0","event_id":"sha256:a3d2b5b4f82a1c79de0e06235efaedc19e3c64014acfcd7739813cea9e4fe852"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:RKLT72MCGC73IUI5YDJT32JI4A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Contrastive Structured Anomaly Detection for Gaussian Graphical Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Abhinav Maurya, Mark Cheung","submitted_at":"2016-05-02T05:42:10Z","abstract_excerpt":"Gaussian graphical models (GGMs) are probabilistic tools of choice for analyzing conditional dependencies between variables in complex systems. Finding changepoints in the structural evolution of a GGM is therefore essential to detecting anomalies in the underlying system modeled by the GGM. In order to detect structural anomalies in a GGM, we consider the problem of estimating changes in the precision matrix of the corresponding Gaussian distribution. We take a two-step approach to solving this problem:- (i) estimating a background precision matrix using system observations from the past with"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.00355","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":"2026-05-18T01:15:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cE5P/emkV+epulpZCRD+yw4eSMOKvqBP0p07VcVrIL0zPCXN/3qo5r+rRGGMzy4ciWUGZCBER7X7bPv8pZZ5Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T18:09:44.125393Z"},"content_sha256":"4e4e0f7956d70e9bab5286673a71525ac49dce68bb111a5a340a13c5c9668b95","schema_version":"1.0","event_id":"sha256:4e4e0f7956d70e9bab5286673a71525ac49dce68bb111a5a340a13c5c9668b95"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A/bundle.json","state_url":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RKLT72MCGC73IUI5YDJT32JI4A/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":"2026-06-09T18:09:44Z","links":{"resolver":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A","bundle":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A/bundle.json","state":"https://pith.science/pith/RKLT72MCGC73IUI5YDJT32JI4A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RKLT72MCGC73IUI5YDJT32JI4A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:RKLT72MCGC73IUI5YDJT32JI4A","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":"52e97a18d5303ea364d03c2625def7f82462f3fbd5dbabef7defccf4b8dc89b1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-05-02T05:42:10Z","title_canon_sha256":"82ff999ce20ff2f76b92fcbae86e9f24566cca105212ad15f7dd4c58c0779545"},"schema_version":"1.0","source":{"id":"1605.00355","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1605.00355","created_at":"2026-05-18T01:15:56Z"},{"alias_kind":"arxiv_version","alias_value":"1605.00355v1","created_at":"2026-05-18T01:15:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.00355","created_at":"2026-05-18T01:15:56Z"},{"alias_kind":"pith_short_12","alias_value":"RKLT72MCGC73","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"RKLT72MCGC73IUI5","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"RKLT72MC","created_at":"2026-05-18T12:30:41Z"}],"graph_snapshots":[{"event_id":"sha256:4e4e0f7956d70e9bab5286673a71525ac49dce68bb111a5a340a13c5c9668b95","target":"graph","created_at":"2026-05-18T01:15:56Z","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":"Gaussian graphical models (GGMs) are probabilistic tools of choice for analyzing conditional dependencies between variables in complex systems. Finding changepoints in the structural evolution of a GGM is therefore essential to detecting anomalies in the underlying system modeled by the GGM. In order to detect structural anomalies in a GGM, we consider the problem of estimating changes in the precision matrix of the corresponding Gaussian distribution. We take a two-step approach to solving this problem:- (i) estimating a background precision matrix using system observations from the past with","authors_text":"Abhinav Maurya, Mark Cheung","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-05-02T05:42:10Z","title":"Contrastive Structured Anomaly Detection for Gaussian Graphical Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.00355","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:a3d2b5b4f82a1c79de0e06235efaedc19e3c64014acfcd7739813cea9e4fe852","target":"record","created_at":"2026-05-18T01:15:56Z","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":"52e97a18d5303ea364d03c2625def7f82462f3fbd5dbabef7defccf4b8dc89b1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-05-02T05:42:10Z","title_canon_sha256":"82ff999ce20ff2f76b92fcbae86e9f24566cca105212ad15f7dd4c58c0779545"},"schema_version":"1.0","source":{"id":"1605.00355","kind":"arxiv","version":1}},"canonical_sha256":"8a973fe98230bfb4511dc0d33de928e01c7cba2852d884c4fde5f05ecc792e8c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8a973fe98230bfb4511dc0d33de928e01c7cba2852d884c4fde5f05ecc792e8c","first_computed_at":"2026-05-18T01:15:56.060657Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:15:56.060657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s3gUXeZfPimITRD71cRinJrkqa8/jHpO47lqeTqhQWj8lxVI44zOu3wY/RYNSDO6rTtZQThtOY6EJ03591WQDA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:15:56.061438Z","signed_message":"canonical_sha256_bytes"},"source_id":"1605.00355","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a3d2b5b4f82a1c79de0e06235efaedc19e3c64014acfcd7739813cea9e4fe852","sha256:4e4e0f7956d70e9bab5286673a71525ac49dce68bb111a5a340a13c5c9668b95"],"state_sha256":"6619ee791e01b68dca11df9fbd7073d6817e6da14b8c7c7fced63e0c24302cf0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A4R7GKCp3PxVOVR+7IAt19JrxseATfiqGuVNSljvY17sTTfydaJ98G0M7xPr739+hTbcUg/Lyw2guo6VV57gDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T18:09:44.129195Z","bundle_sha256":"9bca78e642bd39334d734e9a6376e0ec091ac753ccaa7e02e462a8ce0e0022d8"}}