{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:3LXYW32LKOSC45RESOLM6V3ZTB","short_pith_number":"pith:3LXYW32L","canonical_record":{"source":{"id":"2305.12958","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-22T12:09:14Z","cross_cats_sorted":[],"title_canon_sha256":"0c6ef0f4648e357857026049d91f7ad7d9409c06791e58c1dcd91af3e6623016","abstract_canon_sha256":"9f64a1953847bcdcd6f86ebbf515b08b4f58c192163bf931c76a3aeddc19723a"},"schema_version":"1.0"},"canonical_sha256":"daef8b6f4b53a42e76249396cf57799874fb2dfe708ef6c6b07a0b804d14b63a","source":{"kind":"arxiv","id":"2305.12958","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.12958","created_at":"2026-07-05T06:12:25Z"},{"alias_kind":"arxiv_version","alias_value":"2305.12958v1","created_at":"2026-07-05T06:12:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12958","created_at":"2026-07-05T06:12:25Z"},{"alias_kind":"pith_short_12","alias_value":"3LXYW32LKOSC","created_at":"2026-07-05T06:12:25Z"},{"alias_kind":"pith_short_16","alias_value":"3LXYW32LKOSC45RE","created_at":"2026-07-05T06:12:25Z"},{"alias_kind":"pith_short_8","alias_value":"3LXYW32L","created_at":"2026-07-05T06:12:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:3LXYW32LKOSC45RESOLM6V3ZTB","target":"record","payload":{"canonical_record":{"source":{"id":"2305.12958","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-22T12:09:14Z","cross_cats_sorted":[],"title_canon_sha256":"0c6ef0f4648e357857026049d91f7ad7d9409c06791e58c1dcd91af3e6623016","abstract_canon_sha256":"9f64a1953847bcdcd6f86ebbf515b08b4f58c192163bf931c76a3aeddc19723a"},"schema_version":"1.0"},"canonical_sha256":"daef8b6f4b53a42e76249396cf57799874fb2dfe708ef6c6b07a0b804d14b63a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:12:25.040329Z","signature_b64":"DFoMXNgHailsFx0H3foWCo3+D+1YuLI+ZJwk+nYs0qLFp6v42eHgiKIJCxhEQGd1zjvtN0DZhfM/wH1heNLXCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"daef8b6f4b53a42e76249396cf57799874fb2dfe708ef6c6b07a0b804d14b63a","last_reissued_at":"2026-07-05T06:12:25.039863Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:12:25.039863Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.12958","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":"2026-07-05T06:12:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z8aqQ+bYcyQQWTQLDiN/1006K6kgxDfbEqMpXHZs1IDiLgFiXiDzuSEMHlQcOhhXDxHpCAXQRL54wyONuP9mDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:54:46.589652Z"},"content_sha256":"df5c3301548be703775175b3c42914c8af0849982583c3fa9992522ba5659a52","schema_version":"1.0","event_id":"sha256:df5c3301548be703775175b3c42914c8af0849982583c3fa9992522ba5659a52"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:3LXYW32LKOSC45RESOLM6V3ZTB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Elia Van Wolputte, Hendrik Blockeel, Jonas Soenen, Vincent Vercruyssen, Wannes Meert","submitted_at":"2023-05-22T12:09:14Z","abstract_excerpt":"Most anomaly detection systems try to model normal behavior and assume anomalies deviate from it in diverse manners. However, there may be patterns in the anomalies as well. Ideally, an anomaly detection system can exploit patterns in both normal and anomalous behavior. In this paper, we present AD-MERCS, an unsupervised approach to anomaly detection that explicitly aims at doing both. AD-MERCS identifies multiple subspaces of the instance space within which patterns exist, and identifies conditions (possibly in other subspaces) that characterize instances that deviate from these patterns. Exp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12958","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2305.12958/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T06:12:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yWl6rFkcVk7WHRamcaVMnpnIn6Hw5JoTK7fS/G8gOkCzFSVLk2pGjilYXYKzsNw9pLKrOH9x19ImOqGjFwRoAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:54:46.590276Z"},"content_sha256":"5bf822d2e3bd8b3e912f89c7b6e0cd59b205fe3c2fb00d25be4c0a4f21448e76","schema_version":"1.0","event_id":"sha256:5bf822d2e3bd8b3e912f89c7b6e0cd59b205fe3c2fb00d25be4c0a4f21448e76"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3LXYW32LKOSC45RESOLM6V3ZTB/bundle.json","state_url":"https://pith.science/pith/3LXYW32LKOSC45RESOLM6V3ZTB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3LXYW32LKOSC45RESOLM6V3ZTB/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":"2026-07-09T06:54:46Z","links":{"resolver":"https://pith.science/pith/3LXYW32LKOSC45RESOLM6V3ZTB","bundle":"https://pith.science/pith/3LXYW32LKOSC45RESOLM6V3ZTB/bundle.json","state":"https://pith.science/pith/3LXYW32LKOSC45RESOLM6V3ZTB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3LXYW32LKOSC45RESOLM6V3ZTB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3LXYW32LKOSC45RESOLM6V3ZTB","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":"9f64a1953847bcdcd6f86ebbf515b08b4f58c192163bf931c76a3aeddc19723a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-22T12:09:14Z","title_canon_sha256":"0c6ef0f4648e357857026049d91f7ad7d9409c06791e58c1dcd91af3e6623016"},"schema_version":"1.0","source":{"id":"2305.12958","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.12958","created_at":"2026-07-05T06:12:25Z"},{"alias_kind":"arxiv_version","alias_value":"2305.12958v1","created_at":"2026-07-05T06:12:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12958","created_at":"2026-07-05T06:12:25Z"},{"alias_kind":"pith_short_12","alias_value":"3LXYW32LKOSC","created_at":"2026-07-05T06:12:25Z"},{"alias_kind":"pith_short_16","alias_value":"3LXYW32LKOSC45RE","created_at":"2026-07-05T06:12:25Z"},{"alias_kind":"pith_short_8","alias_value":"3LXYW32L","created_at":"2026-07-05T06:12:25Z"}],"graph_snapshots":[{"event_id":"sha256:5bf822d2e3bd8b3e912f89c7b6e0cd59b205fe3c2fb00d25be4c0a4f21448e76","target":"graph","created_at":"2026-07-05T06:12:25Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2305.12958/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most anomaly detection systems try to model normal behavior and assume anomalies deviate from it in diverse manners. However, there may be patterns in the anomalies as well. Ideally, an anomaly detection system can exploit patterns in both normal and anomalous behavior. In this paper, we present AD-MERCS, an unsupervised approach to anomaly detection that explicitly aims at doing both. AD-MERCS identifies multiple subspaces of the instance space within which patterns exist, and identifies conditions (possibly in other subspaces) that characterize instances that deviate from these patterns. Exp","authors_text":"Elia Van Wolputte, Hendrik Blockeel, Jonas Soenen, Vincent Vercruyssen, Wannes Meert","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-22T12:09:14Z","title":"AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12958","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:df5c3301548be703775175b3c42914c8af0849982583c3fa9992522ba5659a52","target":"record","created_at":"2026-07-05T06:12:25Z","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":"9f64a1953847bcdcd6f86ebbf515b08b4f58c192163bf931c76a3aeddc19723a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-05-22T12:09:14Z","title_canon_sha256":"0c6ef0f4648e357857026049d91f7ad7d9409c06791e58c1dcd91af3e6623016"},"schema_version":"1.0","source":{"id":"2305.12958","kind":"arxiv","version":1}},"canonical_sha256":"daef8b6f4b53a42e76249396cf57799874fb2dfe708ef6c6b07a0b804d14b63a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"daef8b6f4b53a42e76249396cf57799874fb2dfe708ef6c6b07a0b804d14b63a","first_computed_at":"2026-07-05T06:12:25.039863Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:12:25.039863Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DFoMXNgHailsFx0H3foWCo3+D+1YuLI+ZJwk+nYs0qLFp6v42eHgiKIJCxhEQGd1zjvtN0DZhfM/wH1heNLXCg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:12:25.040329Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.12958","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:df5c3301548be703775175b3c42914c8af0849982583c3fa9992522ba5659a52","sha256:5bf822d2e3bd8b3e912f89c7b6e0cd59b205fe3c2fb00d25be4c0a4f21448e76"],"state_sha256":"86cc3e0e22ce4b727d80ebdec7e379ae11d3c2ba9197118c5a449152caac286a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"brQTpD9LMq938+X4MJPeAZRk8zsGRrocFUtBjkXQ06v/qY+qbATvmljo0ddd6Mp9UJEC6ef0eUK4YORLzzANAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T06:54:46.593573Z","bundle_sha256":"043f7de8fe85e03cb17f777c3e41fa6b95a15a98e670bd7ae8079ffecf6ceba2"}}