{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:VSNQP3IJ3VNIHOMV2V65Z6WD4S","short_pith_number":"pith:VSNQP3IJ","canonical_record":{"source":{"id":"2402.12729","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-20T05:39:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"88a2e6523c4ae0c4fc96d06ff83ed0c49a7eead8d25c05bf2030678a240da0c6","abstract_canon_sha256":"8d09044cc12f695441471c9e1eaca1145c78ad08b8425c347ff1e7dea30a2bbd"},"schema_version":"1.0"},"canonical_sha256":"ac9b07ed09dd5a83b995d57ddcfac3e4bb4d0b5aec531790bc2b93ba89c61b93","source":{"kind":"arxiv","id":"2402.12729","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.12729","created_at":"2026-07-05T07:47:15Z"},{"alias_kind":"arxiv_version","alias_value":"2402.12729v1","created_at":"2026-07-05T07:47:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.12729","created_at":"2026-07-05T07:47:15Z"},{"alias_kind":"pith_short_12","alias_value":"VSNQP3IJ3VNI","created_at":"2026-07-05T07:47:15Z"},{"alias_kind":"pith_short_16","alias_value":"VSNQP3IJ3VNIHOMV","created_at":"2026-07-05T07:47:15Z"},{"alias_kind":"pith_short_8","alias_value":"VSNQP3IJ","created_at":"2026-07-05T07:47:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:VSNQP3IJ3VNIHOMV2V65Z6WD4S","target":"record","payload":{"canonical_record":{"source":{"id":"2402.12729","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-20T05:39:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"88a2e6523c4ae0c4fc96d06ff83ed0c49a7eead8d25c05bf2030678a240da0c6","abstract_canon_sha256":"8d09044cc12f695441471c9e1eaca1145c78ad08b8425c347ff1e7dea30a2bbd"},"schema_version":"1.0"},"canonical_sha256":"ac9b07ed09dd5a83b995d57ddcfac3e4bb4d0b5aec531790bc2b93ba89c61b93","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:15.573857Z","signature_b64":"0PXYuHV55BNbJlR3iDdQxdlp9UMXAfRy+98gtFzhPLD2x4Vbr2HAWWz+/CjlcyjlhQacnLTs1wPeERoWbV+0Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac9b07ed09dd5a83b995d57ddcfac3e4bb4d0b5aec531790bc2b93ba89c61b93","last_reissued_at":"2026-07-05T07:47:15.573330Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:15.573330Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.12729","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":"2026-07-05T07:47:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0YoNhK//smqT2bCktcEqC4UXRAjVves728TAgi9cmeysoz4ZpGpxEHLr3yBdf8trg2KlRuq659Nfcik8O6bdBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T02:14:23.350610Z"},"content_sha256":"5a40cde36970d1d8e779c06e3600ab0579d902a06a6058f51c720a88e3bd570a","schema_version":"1.0","event_id":"sha256:5a40cde36970d1d8e779c06e3600ab0579d902a06a6058f51c720a88e3bd570a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:VSNQP3IJ3VNIHOMV2V65Z6WD4S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scalable and reliable deep transfer learning for intelligent fault detection via multi-scale neural processes embedded with knowledge","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jiacheng Zhu, Jianliang Ai, Jingqi Tu, Yiqun Dong, Zhongzhi Li","submitted_at":"2024-02-20T05:39:32Z","abstract_excerpt":"Deep transfer learning (DTL) is a fundamental method in the field of Intelligent Fault Detection (IFD). It aims to mitigate the degradation of method performance that arises from the discrepancies in data distribution between training set (source domain) and testing set (target domain). Considering the fact that fault data collection is challenging and certain faults are scarce, DTL-based methods face the limitation of available observable data, which reduces the detection performance of the methods in the target domain. Furthermore, DTL-based methods lack comprehensive uncertainty analysis th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.12729","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/2402.12729/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":"2026-07-05T07:47:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yNqYrzzB6GaVYhO0jDlIOQOF3nrVGTtXzh2WSa3dnPOY27KJt8noGMzOxAYsVwN0/MCerVZkT/1ib9tZPOGwCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T02:14:23.351107Z"},"content_sha256":"8d38a2f5c2b7801eddd154d3d0adde9e2466697f973e6b6563f3804657f51b6f","schema_version":"1.0","event_id":"sha256:8d38a2f5c2b7801eddd154d3d0adde9e2466697f973e6b6563f3804657f51b6f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VSNQP3IJ3VNIHOMV2V65Z6WD4S/bundle.json","state_url":"https://pith.science/pith/VSNQP3IJ3VNIHOMV2V65Z6WD4S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VSNQP3IJ3VNIHOMV2V65Z6WD4S/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":"2026-08-06T02:14:23Z","links":{"resolver":"https://pith.science/pith/VSNQP3IJ3VNIHOMV2V65Z6WD4S","bundle":"https://pith.science/pith/VSNQP3IJ3VNIHOMV2V65Z6WD4S/bundle.json","state":"https://pith.science/pith/VSNQP3IJ3VNIHOMV2V65Z6WD4S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VSNQP3IJ3VNIHOMV2V65Z6WD4S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:VSNQP3IJ3VNIHOMV2V65Z6WD4S","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":"8d09044cc12f695441471c9e1eaca1145c78ad08b8425c347ff1e7dea30a2bbd","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-20T05:39:32Z","title_canon_sha256":"88a2e6523c4ae0c4fc96d06ff83ed0c49a7eead8d25c05bf2030678a240da0c6"},"schema_version":"1.0","source":{"id":"2402.12729","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.12729","created_at":"2026-07-05T07:47:15Z"},{"alias_kind":"arxiv_version","alias_value":"2402.12729v1","created_at":"2026-07-05T07:47:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.12729","created_at":"2026-07-05T07:47:15Z"},{"alias_kind":"pith_short_12","alias_value":"VSNQP3IJ3VNI","created_at":"2026-07-05T07:47:15Z"},{"alias_kind":"pith_short_16","alias_value":"VSNQP3IJ3VNIHOMV","created_at":"2026-07-05T07:47:15Z"},{"alias_kind":"pith_short_8","alias_value":"VSNQP3IJ","created_at":"2026-07-05T07:47:15Z"}],"graph_snapshots":[{"event_id":"sha256:8d38a2f5c2b7801eddd154d3d0adde9e2466697f973e6b6563f3804657f51b6f","target":"graph","created_at":"2026-07-05T07:47:15Z","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/2402.12729/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep transfer learning (DTL) is a fundamental method in the field of Intelligent Fault Detection (IFD). It aims to mitigate the degradation of method performance that arises from the discrepancies in data distribution between training set (source domain) and testing set (target domain). Considering the fact that fault data collection is challenging and certain faults are scarce, DTL-based methods face the limitation of available observable data, which reduces the detection performance of the methods in the target domain. Furthermore, DTL-based methods lack comprehensive uncertainty analysis th","authors_text":"Jiacheng Zhu, Jianliang Ai, Jingqi Tu, Yiqun Dong, Zhongzhi Li","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-20T05:39:32Z","title":"Scalable and reliable deep transfer learning for intelligent fault detection via multi-scale neural processes embedded with knowledge"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.12729","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":"sha256:5a40cde36970d1d8e779c06e3600ab0579d902a06a6058f51c720a88e3bd570a","target":"record","created_at":"2026-07-05T07:47:15Z","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":"8d09044cc12f695441471c9e1eaca1145c78ad08b8425c347ff1e7dea30a2bbd","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-20T05:39:32Z","title_canon_sha256":"88a2e6523c4ae0c4fc96d06ff83ed0c49a7eead8d25c05bf2030678a240da0c6"},"schema_version":"1.0","source":{"id":"2402.12729","kind":"arxiv","version":1}},"canonical_sha256":"ac9b07ed09dd5a83b995d57ddcfac3e4bb4d0b5aec531790bc2b93ba89c61b93","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ac9b07ed09dd5a83b995d57ddcfac3e4bb4d0b5aec531790bc2b93ba89c61b93","first_computed_at":"2026-07-05T07:47:15.573330Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:47:15.573330Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0PXYuHV55BNbJlR3iDdQxdlp9UMXAfRy+98gtFzhPLD2x4Vbr2HAWWz+/CjlcyjlhQacnLTs1wPeERoWbV+0Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:47:15.573857Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.12729","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5a40cde36970d1d8e779c06e3600ab0579d902a06a6058f51c720a88e3bd570a","sha256:8d38a2f5c2b7801eddd154d3d0adde9e2466697f973e6b6563f3804657f51b6f"],"state_sha256":"325e23cc3f73b3f2828d5daa72437709ab747fa0fdd750cc64a1c2760c5e69ec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N+D/Weudo8jk4X+5/RjNdSYiclUmJLyYU8XhKFVlR8GJVNuQGCcG9U690RibgDteNhMor1sc6evQ6MQYq+bYBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T02:14:23.357870Z","bundle_sha256":"09692f083124d2d9debdccb772569a8134f9f0f9051106598b82722da01d8732"}}