{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WCFS2VECTRGMUA5PL6XNM2CBHY","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":"0c3fe2ff0b5836e24bf2a64353e1c8e314a4aec1ca4483387af5b8697978e262","cross_cats_sorted":["eess.SP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-11T12:20:57Z","title_canon_sha256":"83bb2d680b2ceb21c4105405a0572e1c96051a94807f0ec0c12df62444070a9b"},"schema_version":"1.0","source":{"id":"2411.06917","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.06917","created_at":"2026-06-23T02:12:30Z"},{"alias_kind":"arxiv_version","alias_value":"2411.06917v2","created_at":"2026-06-23T02:12:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.06917","created_at":"2026-06-23T02:12:30Z"},{"alias_kind":"pith_short_12","alias_value":"WCFS2VECTRGM","created_at":"2026-06-23T02:12:30Z"},{"alias_kind":"pith_short_16","alias_value":"WCFS2VECTRGMUA5P","created_at":"2026-06-23T02:12:30Z"},{"alias_kind":"pith_short_8","alias_value":"WCFS2VEC","created_at":"2026-06-23T02:12:30Z"}],"graph_snapshots":[{"event_id":"sha256:dcca7db2153026406afec8ad4b8bfba84f69f845c0e2c41418854289b7a3d3e1","target":"graph","created_at":"2026-06-23T02:12:30Z","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/2411.06917/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The growing deployment of low-cost, distributed sensor networks in environmental and biomedical domains has enabled continuous, large-scale health monitoring. However, these systems often face challenges related to degraded data quality caused by sensor drift, noise, and insufficient calibration -- factors that limit their reliability in real-world applications. Traditional machine learning methods for sensor fusion and calibration rely on extensive feature engineering and struggle to capture spatial-temporal dependencies or adapt to distribution shifts across varying deployment conditions. To","authors_text":"Ismail Nejjar, Keivan Faghih Niresi, Olga Fink","cross_cats":["eess.SP"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-11T12:20:57Z","title":"Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.06917","kind":"arxiv","version":2},"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:c70684c63fd7c2d6cdbcab366ccff9a21e0d369cb546bc2586c34c67a9f1ff98","target":"record","created_at":"2026-06-23T02:12:30Z","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":"0c3fe2ff0b5836e24bf2a64353e1c8e314a4aec1ca4483387af5b8697978e262","cross_cats_sorted":["eess.SP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-11T12:20:57Z","title_canon_sha256":"83bb2d680b2ceb21c4105405a0572e1c96051a94807f0ec0c12df62444070a9b"},"schema_version":"1.0","source":{"id":"2411.06917","kind":"arxiv","version":2}},"canonical_sha256":"b08b2d54829c4cca03af5faed668413e114ef6eb10fbcb729dec4965ae238c27","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b08b2d54829c4cca03af5faed668413e114ef6eb10fbcb729dec4965ae238c27","first_computed_at":"2026-06-23T02:12:30.956762Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-23T02:12:30.956762Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8MyEkCnfAH/Tti6Ar43hH3pXgarI0Rt5tohg923dHbLnWiSV/tQQhdDzEVVbaVnNXVVWEA8UtUV24HtNA7XMCA==","signature_status":"signed_v1","signed_at":"2026-06-23T02:12:30.957232Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.06917","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c70684c63fd7c2d6cdbcab366ccff9a21e0d369cb546bc2586c34c67a9f1ff98","sha256:dcca7db2153026406afec8ad4b8bfba84f69f845c0e2c41418854289b7a3d3e1"],"state_sha256":"0ddaf40056b3e182e1e7c71ac47e9c7a9e9f78860f3f51c12c4b0f77573a6a48"}