{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:36WZOSBMOQYGMXO6SLQZH3LAVO","short_pith_number":"pith:36WZOSBM","schema_version":"1.0","canonical_sha256":"dfad97482c7430665dde92e193ed60ab8ea3c58d3a57b2983d0ede48696b9faa","source":{"kind":"arxiv","id":"1703.03352","version":1},"attestation_state":"computed","paper":{"title":"A log-linear time algorithm for constrained changepoint detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.GN","stat.ML"],"primary_cat":"stat.CO","authors_text":"Guillaume Bourque, Guillem Rigaill, Paul Fearnhead, Toby Dylan Hocking","submitted_at":"2017-03-09T17:17:39Z","abstract_excerpt":"Changepoint detection is a central problem in time series and genomic data. For some applications, it is natural to impose constraints on the directions of changes. One example is ChIP-seq data, for which adding an up-down constraint improves peak detection accuracy, but makes the optimization problem more complicated. We show how a recently proposed functional pruning technique can be adapted to solve such constrained changepoint detection problems. This leads to a new algorithm which can solve problems with arbitrary affine constraints on adjacent segment means, and which has empirical time "},"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":"1703.03352","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2017-03-09T17:17:39Z","cross_cats_sorted":["q-bio.GN","stat.ML"],"title_canon_sha256":"12dbb3cf7f524dfa96e9ed6de42b971503e87b387df79434034b5108b8cb4c1e","abstract_canon_sha256":"a01579f04816d934cfe1119e762d11c073cfc54af92caf33468713245519139c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:00.620515Z","signature_b64":"huC66QP8k6BID9R+zvatmRUg9qpv/P3Z3nwxYqACoiDKjrTu24fnOQKyidDVvpg80vxmLpIker1DyI+MjXilCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dfad97482c7430665dde92e193ed60ab8ea3c58d3a57b2983d0ede48696b9faa","last_reissued_at":"2026-05-18T00:49:00.619780Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:00.619780Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A log-linear time algorithm for constrained changepoint detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.GN","stat.ML"],"primary_cat":"stat.CO","authors_text":"Guillaume Bourque, Guillem Rigaill, Paul Fearnhead, Toby Dylan Hocking","submitted_at":"2017-03-09T17:17:39Z","abstract_excerpt":"Changepoint detection is a central problem in time series and genomic data. For some applications, it is natural to impose constraints on the directions of changes. One example is ChIP-seq data, for which adding an up-down constraint improves peak detection accuracy, but makes the optimization problem more complicated. We show how a recently proposed functional pruning technique can be adapted to solve such constrained changepoint detection problems. This leads to a new algorithm which can solve problems with arbitrary affine constraints on adjacent segment means, and which has empirical time "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.03352","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":""},"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":"1703.03352","created_at":"2026-05-18T00:49:00.619903+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.03352v1","created_at":"2026-05-18T00:49:00.619903+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.03352","created_at":"2026-05-18T00:49:00.619903+00:00"},{"alias_kind":"pith_short_12","alias_value":"36WZOSBMOQYG","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"36WZOSBMOQYGMXO6","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"36WZOSBM","created_at":"2026-05-18T12:30:58.224056+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/36WZOSBMOQYGMXO6SLQZH3LAVO","json":"https://pith.science/pith/36WZOSBMOQYGMXO6SLQZH3LAVO.json","graph_json":"https://pith.science/api/pith-number/36WZOSBMOQYGMXO6SLQZH3LAVO/graph.json","events_json":"https://pith.science/api/pith-number/36WZOSBMOQYGMXO6SLQZH3LAVO/events.json","paper":"https://pith.science/paper/36WZOSBM"},"agent_actions":{"view_html":"https://pith.science/pith/36WZOSBMOQYGMXO6SLQZH3LAVO","download_json":"https://pith.science/pith/36WZOSBMOQYGMXO6SLQZH3LAVO.json","view_paper":"https://pith.science/paper/36WZOSBM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.03352&json=true","fetch_graph":"https://pith.science/api/pith-number/36WZOSBMOQYGMXO6SLQZH3LAVO/graph.json","fetch_events":"https://pith.science/api/pith-number/36WZOSBMOQYGMXO6SLQZH3LAVO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/36WZOSBMOQYGMXO6SLQZH3LAVO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/36WZOSBMOQYGMXO6SLQZH3LAVO/action/storage_attestation","attest_author":"https://pith.science/pith/36WZOSBMOQYGMXO6SLQZH3LAVO/action/author_attestation","sign_citation":"https://pith.science/pith/36WZOSBMOQYGMXO6SLQZH3LAVO/action/citation_signature","submit_replication":"https://pith.science/pith/36WZOSBMOQYGMXO6SLQZH3LAVO/action/replication_record"}},"created_at":"2026-05-18T00:49:00.619903+00:00","updated_at":"2026-05-18T00:49:00.619903+00:00"}