{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:7DKAEKKMJYA5JIWZMLEUMIG7GV","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":"37da2be29b98955367b390a07412b2d266c3bd330aa11f78b11f532ff1d8d5bc","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-09T13:45:15Z","title_canon_sha256":"b1f59f3f0c534fa2d092e2507eea6a1d557e5873e2a6a79a0a510c724be139a7"},"schema_version":"1.0","source":{"id":"2503.06619","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.06619","created_at":"2026-07-05T10:27:38Z"},{"alias_kind":"arxiv_version","alias_value":"2503.06619v1","created_at":"2026-07-05T10:27:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.06619","created_at":"2026-07-05T10:27:38Z"},{"alias_kind":"pith_short_12","alias_value":"7DKAEKKMJYA5","created_at":"2026-07-05T10:27:38Z"},{"alias_kind":"pith_short_16","alias_value":"7DKAEKKMJYA5JIWZ","created_at":"2026-07-05T10:27:38Z"},{"alias_kind":"pith_short_8","alias_value":"7DKAEKKM","created_at":"2026-07-05T10:27:38Z"}],"graph_snapshots":[{"event_id":"sha256:d1bc6d67ce45232e1a9a9b2bb72624b2b03892bda2034320ce9f33077c62e276","target":"graph","created_at":"2026-07-05T10:27:38Z","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/2503.06619/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Nachiket U. Bapat, Raghvendra V. Cowlagi, Randy C. Paffenroth","cross_cats":["cs.SY","eess.SY"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-09T13:45:15Z","title":"Synthetic Data Generation for Minimum-Exposure Navigation in a Time-Varying Environment using Generative AI Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.06619","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:da604e1bdef1db01f5d8e2451775475f42cfad8bb3606acf0c3d6ca90d03e9bf","target":"record","created_at":"2026-07-05T10:27:38Z","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":"37da2be29b98955367b390a07412b2d266c3bd330aa11f78b11f532ff1d8d5bc","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-09T13:45:15Z","title_canon_sha256":"b1f59f3f0c534fa2d092e2507eea6a1d557e5873e2a6a79a0a510c724be139a7"},"schema_version":"1.0","source":{"id":"2503.06619","kind":"arxiv","version":1}},"canonical_sha256":"f8d402294c4e01d4a2d962c94620df35644179a0530ef6d066787b6966e62ab4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f8d402294c4e01d4a2d962c94620df35644179a0530ef6d066787b6966e62ab4","first_computed_at":"2026-07-05T10:27:38.319288Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:27:38.319288Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6XLVu1I+3kAQ37utbDy5kWYJumb1MsVaJ5LZeOMk1OPwgKr3NSCltmHlICt604cSYD/UHchXAIUIAbc5JPKdCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:27:38.319831Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.06619","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:da604e1bdef1db01f5d8e2451775475f42cfad8bb3606acf0c3d6ca90d03e9bf","sha256:d1bc6d67ce45232e1a9a9b2bb72624b2b03892bda2034320ce9f33077c62e276"],"state_sha256":"f300cea46a5659e423b358b04a5e5e3493996039faedd7103c115f4845c4cbe3"}