{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:AIP3L7YDUSNMIFHDMGNP7G6FT5","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":"b041aa55dc32445cb0049bf970e7f5eb6a4036e1eaa2e4bcc819892dba48db5d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2022-04-30T21:09:19Z","title_canon_sha256":"835d34a8c89532fb3c4ac73b8fc7b06b5a58f58d6b4746e798e1651f89fa53d5"},"schema_version":"1.0","source":{"id":"2205.00343","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.00343","created_at":"2026-07-05T06:48:30Z"},{"alias_kind":"arxiv_version","alias_value":"2205.00343v2","created_at":"2026-07-05T06:48:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.00343","created_at":"2026-07-05T06:48:30Z"},{"alias_kind":"pith_short_12","alias_value":"AIP3L7YDUSNM","created_at":"2026-07-05T06:48:30Z"},{"alias_kind":"pith_short_16","alias_value":"AIP3L7YDUSNMIFHD","created_at":"2026-07-05T06:48:30Z"},{"alias_kind":"pith_short_8","alias_value":"AIP3L7YD","created_at":"2026-07-05T06:48:30Z"}],"graph_snapshots":[{"event_id":"sha256:1974cc1f61eeb6963498714d1abde4ef429de463d58b9e6362435dafa6672469","target":"graph","created_at":"2026-07-05T06:48:30Z","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/2205.00343/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper addresses the limitations of standard uncertainty models, e.g., robust (norm-bounded) and stochastic (one fixed distribution, e.g., Gaussian), and proposes to model uncertainty via Optimal Transport (OT) ambiguity sets. These constitute a very rich uncertainty model, which enjoys many desirable geometrical, statistical, and computational properties, and which: (1) naturally generalizes both robust and stochastic models, and (2) captures many additional real-world uncertainty phenomena (e.g., black swan events). Our contributions show that OT ambiguity sets are also analytically trac","authors_text":"Florian D\\\"orfler, Hongruyu Chen, Liviu Aolaritei, Nicolas Lanzetti","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2022-04-30T21:09:19Z","title":"Distributional Uncertainty Propagation via Optimal Transport"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.00343","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:cc28fe7b60262a7c7628aa762ad0d55e7b7c99de8c29dd095a932fc1c7170002","target":"record","created_at":"2026-07-05T06:48:30Z","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":"b041aa55dc32445cb0049bf970e7f5eb6a4036e1eaa2e4bcc819892dba48db5d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2022-04-30T21:09:19Z","title_canon_sha256":"835d34a8c89532fb3c4ac73b8fc7b06b5a58f58d6b4746e798e1651f89fa53d5"},"schema_version":"1.0","source":{"id":"2205.00343","kind":"arxiv","version":2}},"canonical_sha256":"021fb5ff03a49ac414e3619aff9bc59f475345bd7b3f7d5f984c2d5ffbb54a86","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"021fb5ff03a49ac414e3619aff9bc59f475345bd7b3f7d5f984c2d5ffbb54a86","first_computed_at":"2026-07-05T06:48:30.870472Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:48:30.870472Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zrJxIQpXvQ5sAPaShSUNp2pK7D9AOi5hw2uAIKKvs8nDiGQuDLNqOmuCQAqz9hp4lGEHCZunQyjxX5p+zUZcDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:48:30.870999Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.00343","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cc28fe7b60262a7c7628aa762ad0d55e7b7c99de8c29dd095a932fc1c7170002","sha256:1974cc1f61eeb6963498714d1abde4ef429de463d58b9e6362435dafa6672469"],"state_sha256":"53d1295fcf5e51046ac5d85a4bb77ec70093c8b34d7993773eaffcf7a45fba76"}