{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:4HKIHTH24LPSJJA2Q2ITUZCA4A","short_pith_number":"pith:4HKIHTH2","canonical_record":{"source":{"id":"2401.09793","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-18T08:26:33Z","cross_cats_sorted":[],"title_canon_sha256":"8fe5c4adddf6963ea83ed3a91c3eba18c9a9dc782d87dc8bd6e2c345922c09c8","abstract_canon_sha256":"75a1b3331700ac76addd2956b66a7b41c778bcab2086655fd7ad11a560c1d235"},"schema_version":"1.0"},"canonical_sha256":"e1d483ccfae2df24a41a86913a6440e01bee50fd2e5b56f5b2494956acf6bf3c","source":{"kind":"arxiv","id":"2401.09793","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.09793","created_at":"2026-07-05T11:47:10Z"},{"alias_kind":"arxiv_version","alias_value":"2401.09793v6","created_at":"2026-07-05T11:47:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.09793","created_at":"2026-07-05T11:47:10Z"},{"alias_kind":"pith_short_12","alias_value":"4HKIHTH24LPS","created_at":"2026-07-05T11:47:10Z"},{"alias_kind":"pith_short_16","alias_value":"4HKIHTH24LPSJJA2","created_at":"2026-07-05T11:47:10Z"},{"alias_kind":"pith_short_8","alias_value":"4HKIHTH2","created_at":"2026-07-05T11:47:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:4HKIHTH24LPSJJA2Q2ITUZCA4A","target":"record","payload":{"canonical_record":{"source":{"id":"2401.09793","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-18T08:26:33Z","cross_cats_sorted":[],"title_canon_sha256":"8fe5c4adddf6963ea83ed3a91c3eba18c9a9dc782d87dc8bd6e2c345922c09c8","abstract_canon_sha256":"75a1b3331700ac76addd2956b66a7b41c778bcab2086655fd7ad11a560c1d235"},"schema_version":"1.0"},"canonical_sha256":"e1d483ccfae2df24a41a86913a6440e01bee50fd2e5b56f5b2494956acf6bf3c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:47:10.817944Z","signature_b64":"rSKBxTLQrfoKwN8Of2jWqXXk2BTX+Gt9BrK1psbxTMSdW9JDXnQhz1gIcsQ6adyGpTjC6uVbKDI1F/wfF9TaCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1d483ccfae2df24a41a86913a6440e01bee50fd2e5b56f5b2494956acf6bf3c","last_reissued_at":"2026-07-05T11:47:10.817193Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:47:10.817193Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.09793","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:47:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IxXUyKm/XeF/1/vWcWvk35UjoR2YE1b6T+oPjyR0mYiyrLN9Xy5lJGUtxgLrdHiHxpgUnbCKwncPMMyawfgDCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T22:05:18.940289Z"},"content_sha256":"7075534f813e1490a68a7978b3cdbbccca0f916d9a4ce9fdc11a2846f51be130","schema_version":"1.0","event_id":"sha256:7075534f813e1490a68a7978b3cdbbccca0f916d9a4ce9fdc11a2846f51be130"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:4HKIHTH24LPSJJA2Q2ITUZCA4A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Kaixiang Yang, Weizheng Wang, Yiyuan Yang, Zhijie Zhong, Zhiwen Yu","submitted_at":"2024-01-18T08:26:33Z","abstract_excerpt":"Time series anomaly detection is a pivotal task in data analysis, yet it poses the challenge of discerning normal and abnormal patterns in label-deficient scenarios. While prior studies have largely employed reconstruction-based approaches, which limit the models' representational capacities. Moreover, existing deep learning-based methods are not sufficiently lightweight. Addressing these issues, we present PatchAD, our novel, highly efficient multiscale patch-based MLP-Mixer architecture that utilizes contrastive learning for representation extraction and anomaly detection. With its four dist"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.09793","kind":"arxiv","version":6},"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/2401.09793/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":"2026-07-05T11:47:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kbxgVsqz/LVCWSZ7Uimx4PqA0sdiyN6qPM8M8R7U0eupfrdbXcJqELQ4z1xkp/blEj+h0Z7R6/h4kLD2DrmqDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T22:05:18.941052Z"},"content_sha256":"16951f54cc9520a2b0ed6d8436cc0ca9becfd78099333ff9c5314ea321319e69","schema_version":"1.0","event_id":"sha256:16951f54cc9520a2b0ed6d8436cc0ca9becfd78099333ff9c5314ea321319e69"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4HKIHTH24LPSJJA2Q2ITUZCA4A/bundle.json","state_url":"https://pith.science/pith/4HKIHTH24LPSJJA2Q2ITUZCA4A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4HKIHTH24LPSJJA2Q2ITUZCA4A/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":"2026-08-13T22:05:18Z","links":{"resolver":"https://pith.science/pith/4HKIHTH24LPSJJA2Q2ITUZCA4A","bundle":"https://pith.science/pith/4HKIHTH24LPSJJA2Q2ITUZCA4A/bundle.json","state":"https://pith.science/pith/4HKIHTH24LPSJJA2Q2ITUZCA4A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4HKIHTH24LPSJJA2Q2ITUZCA4A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4HKIHTH24LPSJJA2Q2ITUZCA4A","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":"75a1b3331700ac76addd2956b66a7b41c778bcab2086655fd7ad11a560c1d235","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-18T08:26:33Z","title_canon_sha256":"8fe5c4adddf6963ea83ed3a91c3eba18c9a9dc782d87dc8bd6e2c345922c09c8"},"schema_version":"1.0","source":{"id":"2401.09793","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.09793","created_at":"2026-07-05T11:47:10Z"},{"alias_kind":"arxiv_version","alias_value":"2401.09793v6","created_at":"2026-07-05T11:47:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.09793","created_at":"2026-07-05T11:47:10Z"},{"alias_kind":"pith_short_12","alias_value":"4HKIHTH24LPS","created_at":"2026-07-05T11:47:10Z"},{"alias_kind":"pith_short_16","alias_value":"4HKIHTH24LPSJJA2","created_at":"2026-07-05T11:47:10Z"},{"alias_kind":"pith_short_8","alias_value":"4HKIHTH2","created_at":"2026-07-05T11:47:10Z"}],"graph_snapshots":[{"event_id":"sha256:16951f54cc9520a2b0ed6d8436cc0ca9becfd78099333ff9c5314ea321319e69","target":"graph","created_at":"2026-07-05T11:47:10Z","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/2401.09793/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Time series anomaly detection is a pivotal task in data analysis, yet it poses the challenge of discerning normal and abnormal patterns in label-deficient scenarios. While prior studies have largely employed reconstruction-based approaches, which limit the models' representational capacities. Moreover, existing deep learning-based methods are not sufficiently lightweight. Addressing these issues, we present PatchAD, our novel, highly efficient multiscale patch-based MLP-Mixer architecture that utilizes contrastive learning for representation extraction and anomaly detection. With its four dist","authors_text":"Kaixiang Yang, Weizheng Wang, Yiyuan Yang, Zhijie Zhong, Zhiwen Yu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-18T08:26:33Z","title":"PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.09793","kind":"arxiv","version":6},"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":"sha256:7075534f813e1490a68a7978b3cdbbccca0f916d9a4ce9fdc11a2846f51be130","target":"record","created_at":"2026-07-05T11:47:10Z","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":"75a1b3331700ac76addd2956b66a7b41c778bcab2086655fd7ad11a560c1d235","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-18T08:26:33Z","title_canon_sha256":"8fe5c4adddf6963ea83ed3a91c3eba18c9a9dc782d87dc8bd6e2c345922c09c8"},"schema_version":"1.0","source":{"id":"2401.09793","kind":"arxiv","version":6}},"canonical_sha256":"e1d483ccfae2df24a41a86913a6440e01bee50fd2e5b56f5b2494956acf6bf3c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e1d483ccfae2df24a41a86913a6440e01bee50fd2e5b56f5b2494956acf6bf3c","first_computed_at":"2026-07-05T11:47:10.817193Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:47:10.817193Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rSKBxTLQrfoKwN8Of2jWqXXk2BTX+Gt9BrK1psbxTMSdW9JDXnQhz1gIcsQ6adyGpTjC6uVbKDI1F/wfF9TaCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:47:10.817944Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.09793","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7075534f813e1490a68a7978b3cdbbccca0f916d9a4ce9fdc11a2846f51be130","sha256:16951f54cc9520a2b0ed6d8436cc0ca9becfd78099333ff9c5314ea321319e69"],"state_sha256":"56ecaa71ee02c6345c64c26e7205dbf10aeea35c4d280696da04d3da84819e11"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tx7fKFdH9cU7vfXRAsGfUB7jQjmYDfARl21UQMPSOsQX10tlEOAqQPwXUjdhGbGB9YC2pnWgfB9x4aNT1xkHBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T22:05:18.967121Z","bundle_sha256":"02a62e117ffe742b2d113b25db1a5fabffec802c2887b3d569b51f00f69b7da6"}}