{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5NN55CNCENO56KOQADKASQ6TOW","short_pith_number":"pith:5NN55CNC","schema_version":"1.0","canonical_sha256":"eb5bde89a2235ddf29d000d40943d375850ced09e2caedcd19dff50fde67b22b","source":{"kind":"arxiv","id":"2602.07252","version":2},"attestation_state":"computed","paper":{"title":"Beyond Euclidean Summaries: Online Change Point Detection for Distribution-Valued Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Xiaoyu Chen, Yingyan Zeng, Yujing Huang","submitted_at":"2026-02-06T23:04:37Z","abstract_excerpt":"Existing online change-point detection (CPD) methods rely on fixed-dimensional Euclidean summaries, implicitly assuming that distributional changes are well captured by moment-based or feature-based representations. They can obscure important changes in distributional shape or geometry. We propose an intrinsic distribution-valued CPD framework that treats streaming batch data as a stochastic process on the 2-Wasserstein space. Our method detects changes in the law of this process by mapping each empirical distribution to a tangent space relative to a pre-change Fr\\'echet barycenter, yielding a"},"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":"2602.07252","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2026-02-06T23:04:37Z","cross_cats_sorted":[],"title_canon_sha256":"4da37b0478f2e39e31c4cd29bcb38d9891f4c84c54b5f6d396fe93eba4c6c1eb","abstract_canon_sha256":"86bb231a92c21622a08517d06ce533d2e04252a64d4faa8922046cc7833cf38c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:03:17.982616Z","signature_b64":"VOHVB6B3y1JMADG9DuG8ak+WAxw1KT3/EYHyJ+5UCLVhrtt9E+CUcyxaXu/9o1HjZjP8DTvJcdTKs9olPrH6BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb5bde89a2235ddf29d000d40943d375850ced09e2caedcd19dff50fde67b22b","last_reissued_at":"2026-05-22T01:03:17.981869Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:03:17.981869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Euclidean Summaries: Online Change Point Detection for Distribution-Valued Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Xiaoyu Chen, Yingyan Zeng, Yujing Huang","submitted_at":"2026-02-06T23:04:37Z","abstract_excerpt":"Existing online change-point detection (CPD) methods rely on fixed-dimensional Euclidean summaries, implicitly assuming that distributional changes are well captured by moment-based or feature-based representations. They can obscure important changes in distributional shape or geometry. We propose an intrinsic distribution-valued CPD framework that treats streaming batch data as a stochastic process on the 2-Wasserstein space. Our method detects changes in the law of this process by mapping each empirical distribution to a tangent space relative to a pre-change Fr\\'echet barycenter, yielding a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.07252","kind":"arxiv","version":2},"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/2602.07252/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":"2602.07252","created_at":"2026-05-22T01:03:17.981981+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.07252v2","created_at":"2026-05-22T01:03:17.981981+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.07252","created_at":"2026-05-22T01:03:17.981981+00:00"},{"alias_kind":"pith_short_12","alias_value":"5NN55CNCENO5","created_at":"2026-05-22T01:03:17.981981+00:00"},{"alias_kind":"pith_short_16","alias_value":"5NN55CNCENO56KOQ","created_at":"2026-05-22T01:03:17.981981+00:00"},{"alias_kind":"pith_short_8","alias_value":"5NN55CNC","created_at":"2026-05-22T01:03:17.981981+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5NN55CNCENO56KOQADKASQ6TOW","json":"https://pith.science/pith/5NN55CNCENO56KOQADKASQ6TOW.json","graph_json":"https://pith.science/api/pith-number/5NN55CNCENO56KOQADKASQ6TOW/graph.json","events_json":"https://pith.science/api/pith-number/5NN55CNCENO56KOQADKASQ6TOW/events.json","paper":"https://pith.science/paper/5NN55CNC"},"agent_actions":{"view_html":"https://pith.science/pith/5NN55CNCENO56KOQADKASQ6TOW","download_json":"https://pith.science/pith/5NN55CNCENO56KOQADKASQ6TOW.json","view_paper":"https://pith.science/paper/5NN55CNC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.07252&json=true","fetch_graph":"https://pith.science/api/pith-number/5NN55CNCENO56KOQADKASQ6TOW/graph.json","fetch_events":"https://pith.science/api/pith-number/5NN55CNCENO56KOQADKASQ6TOW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5NN55CNCENO56KOQADKASQ6TOW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5NN55CNCENO56KOQADKASQ6TOW/action/storage_attestation","attest_author":"https://pith.science/pith/5NN55CNCENO56KOQADKASQ6TOW/action/author_attestation","sign_citation":"https://pith.science/pith/5NN55CNCENO56KOQADKASQ6TOW/action/citation_signature","submit_replication":"https://pith.science/pith/5NN55CNCENO56KOQADKASQ6TOW/action/replication_record"}},"created_at":"2026-05-22T01:03:17.981981+00:00","updated_at":"2026-05-22T01:03:17.981981+00:00"}