{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SPOHAH5XNKCHOWURVI2UARAP4P","short_pith_number":"pith:SPOHAH5X","schema_version":"1.0","canonical_sha256":"93dc701fb76a84775a91aa3540440fe3c0c6f98dbf2656e067882e054e074add","source":{"kind":"arxiv","id":"2411.05870","version":3},"attestation_state":"computed","paper":{"title":"An Adaptive Online Smoother with Closed-Form Solutions and Information-Theoretic Lag Selection for Conditional Gaussian Nonlinear Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","math.DS","math.PR","physics.data-an","stat.ME"],"primary_cat":"eess.SY","authors_text":"Marios Andreou, Nan Chen, Yingda Li","submitted_at":"2024-11-07T21:08:18Z","abstract_excerpt":"Data assimilation (DA) combines partial observations with dynamical models to improve state estimation. Filter-based DA uses only past and present data and is the prerequisite for real-time forecasts. Smoother-based DA exploits both past and future observations. It aims to fill in missing data, provide more accurate estimations, and develop high-quality datasets. However, the standard smoothing procedure requires using all historical state estimations, which is storage-demanding, especially for high-dimensional systems. This paper develops an adaptive-lag online smoother for a large class of c"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.05870","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2024-11-07T21:08:18Z","cross_cats_sorted":["cs.SY","math.DS","math.PR","physics.data-an","stat.ME"],"title_canon_sha256":"e8952ed143b9a9d116e7da5fc0a2e6da3c171aaa3204eee36ea5ce50bbfbc3ab","abstract_canon_sha256":"69e2cf348c612f3e651bccdc5130819d5b038704438486c5115089d2e446f526"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T01:11:53.349660Z","signature_b64":"t/Y8/W6mW5rn9fQqF6Lkm+kmPeKQFXQfLnzrY+s4WgMVXIHvttosTwPd1zits1QSLklfGlaGJksPz692r38hDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93dc701fb76a84775a91aa3540440fe3c0c6f98dbf2656e067882e054e074add","last_reissued_at":"2026-06-23T01:11:53.349101Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T01:11:53.349101Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Adaptive Online Smoother with Closed-Form Solutions and Information-Theoretic Lag Selection for Conditional Gaussian Nonlinear Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","math.DS","math.PR","physics.data-an","stat.ME"],"primary_cat":"eess.SY","authors_text":"Marios Andreou, Nan Chen, Yingda Li","submitted_at":"2024-11-07T21:08:18Z","abstract_excerpt":"Data assimilation (DA) combines partial observations with dynamical models to improve state estimation. Filter-based DA uses only past and present data and is the prerequisite for real-time forecasts. Smoother-based DA exploits both past and future observations. It aims to fill in missing data, provide more accurate estimations, and develop high-quality datasets. However, the standard smoothing procedure requires using all historical state estimations, which is storage-demanding, especially for high-dimensional systems. This paper develops an adaptive-lag online smoother for a large class of c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.05870","kind":"arxiv","version":3},"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/2411.05870/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.05870","created_at":"2026-06-23T01:11:53.349183+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.05870v3","created_at":"2026-06-23T01:11:53.349183+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.05870","created_at":"2026-06-23T01:11:53.349183+00:00"},{"alias_kind":"pith_short_12","alias_value":"SPOHAH5XNKCH","created_at":"2026-06-23T01:11:53.349183+00:00"},{"alias_kind":"pith_short_16","alias_value":"SPOHAH5XNKCHOWUR","created_at":"2026-06-23T01:11:53.349183+00:00"},{"alias_kind":"pith_short_8","alias_value":"SPOHAH5X","created_at":"2026-06-23T01:11:53.349183+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2604.25157","citing_title":"A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SPOHAH5XNKCHOWURVI2UARAP4P","json":"https://pith.science/pith/SPOHAH5XNKCHOWURVI2UARAP4P.json","graph_json":"https://pith.science/api/pith-number/SPOHAH5XNKCHOWURVI2UARAP4P/graph.json","events_json":"https://pith.science/api/pith-number/SPOHAH5XNKCHOWURVI2UARAP4P/events.json","paper":"https://pith.science/paper/SPOHAH5X"},"agent_actions":{"view_html":"https://pith.science/pith/SPOHAH5XNKCHOWURVI2UARAP4P","download_json":"https://pith.science/pith/SPOHAH5XNKCHOWURVI2UARAP4P.json","view_paper":"https://pith.science/paper/SPOHAH5X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.05870&json=true","fetch_graph":"https://pith.science/api/pith-number/SPOHAH5XNKCHOWURVI2UARAP4P/graph.json","fetch_events":"https://pith.science/api/pith-number/SPOHAH5XNKCHOWURVI2UARAP4P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SPOHAH5XNKCHOWURVI2UARAP4P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SPOHAH5XNKCHOWURVI2UARAP4P/action/storage_attestation","attest_author":"https://pith.science/pith/SPOHAH5XNKCHOWURVI2UARAP4P/action/author_attestation","sign_citation":"https://pith.science/pith/SPOHAH5XNKCHOWURVI2UARAP4P/action/citation_signature","submit_replication":"https://pith.science/pith/SPOHAH5XNKCHOWURVI2UARAP4P/action/replication_record"}},"created_at":"2026-06-23T01:11:53.349183+00:00","updated_at":"2026-06-23T01:11:53.349183+00:00"}