{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7DKAEKKMJYA5JIWZMLEUMIG7GV","short_pith_number":"pith:7DKAEKKM","schema_version":"1.0","canonical_sha256":"f8d402294c4e01d4a2d962c94620df35644179a0530ef6d066787b6966e62ab4","source":{"kind":"arxiv","id":"2503.06619","version":1},"attestation_state":"computed","paper":{"title":"Synthetic Data Generation for Minimum-Exposure Navigation in a Time-Varying Environment using Generative AI Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Nachiket U. Bapat, Raghvendra V. Cowlagi, Randy C. Paffenroth","submitted_at":"2025-03-09T13:45:15Z","abstract_excerpt":"We study the problem of synthetic generation of samples of environmental features for autonomous vehicle navigation. These features are described by a spatiotemporally varying scalar field that we refer to as a threat field. The threat field is known to have some underlying dynamics subject to process noise. Some \"real-world\" data of observations of various threat fields are also available. The assumption is that the volume of ``real-world'' data is relatively small. The objective is to synthesize samples that are statistically similar to the data. The proposed solution is a generative artific"},"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":"2503.06619","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-09T13:45:15Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"b1f59f3f0c534fa2d092e2507eea6a1d557e5873e2a6a79a0a510c724be139a7","abstract_canon_sha256":"37da2be29b98955367b390a07412b2d266c3bd330aa11f78b11f532ff1d8d5bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:27:38.319831Z","signature_b64":"6XLVu1I+3kAQ37utbDy5kWYJumb1MsVaJ5LZeOMk1OPwgKr3NSCltmHlICt604cSYD/UHchXAIUIAbc5JPKdCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f8d402294c4e01d4a2d962c94620df35644179a0530ef6d066787b6966e62ab4","last_reissued_at":"2026-07-05T10:27:38.319288Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:27:38.319288Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Synthetic Data Generation for Minimum-Exposure Navigation in a Time-Varying Environment using Generative AI Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Nachiket U. Bapat, Raghvendra V. Cowlagi, Randy C. Paffenroth","submitted_at":"2025-03-09T13:45:15Z","abstract_excerpt":"We study the problem of synthetic generation of samples of environmental features for autonomous vehicle navigation. These features are described by a spatiotemporally varying scalar field that we refer to as a threat field. The threat field is known to have some underlying dynamics subject to process noise. Some \"real-world\" data of observations of various threat fields are also available. The assumption is that the volume of ``real-world'' data is relatively small. The objective is to synthesize samples that are statistically similar to the data. The proposed solution is a generative artific"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.06619","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/2503.06619/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":"2503.06619","created_at":"2026-07-05T10:27:38.319363+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.06619v1","created_at":"2026-07-05T10:27:38.319363+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.06619","created_at":"2026-07-05T10:27:38.319363+00:00"},{"alias_kind":"pith_short_12","alias_value":"7DKAEKKMJYA5","created_at":"2026-07-05T10:27:38.319363+00:00"},{"alias_kind":"pith_short_16","alias_value":"7DKAEKKMJYA5JIWZ","created_at":"2026-07-05T10:27:38.319363+00:00"},{"alias_kind":"pith_short_8","alias_value":"7DKAEKKM","created_at":"2026-07-05T10:27:38.319363+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.02438","citing_title":"Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7DKAEKKMJYA5JIWZMLEUMIG7GV","json":"https://pith.science/pith/7DKAEKKMJYA5JIWZMLEUMIG7GV.json","graph_json":"https://pith.science/api/pith-number/7DKAEKKMJYA5JIWZMLEUMIG7GV/graph.json","events_json":"https://pith.science/api/pith-number/7DKAEKKMJYA5JIWZMLEUMIG7GV/events.json","paper":"https://pith.science/paper/7DKAEKKM"},"agent_actions":{"view_html":"https://pith.science/pith/7DKAEKKMJYA5JIWZMLEUMIG7GV","download_json":"https://pith.science/pith/7DKAEKKMJYA5JIWZMLEUMIG7GV.json","view_paper":"https://pith.science/paper/7DKAEKKM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.06619&json=true","fetch_graph":"https://pith.science/api/pith-number/7DKAEKKMJYA5JIWZMLEUMIG7GV/graph.json","fetch_events":"https://pith.science/api/pith-number/7DKAEKKMJYA5JIWZMLEUMIG7GV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7DKAEKKMJYA5JIWZMLEUMIG7GV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7DKAEKKMJYA5JIWZMLEUMIG7GV/action/storage_attestation","attest_author":"https://pith.science/pith/7DKAEKKMJYA5JIWZMLEUMIG7GV/action/author_attestation","sign_citation":"https://pith.science/pith/7DKAEKKMJYA5JIWZMLEUMIG7GV/action/citation_signature","submit_replication":"https://pith.science/pith/7DKAEKKMJYA5JIWZMLEUMIG7GV/action/replication_record"}},"created_at":"2026-07-05T10:27:38.319363+00:00","updated_at":"2026-07-05T10:27:38.319363+00:00"}