{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:ADAALLEQLXK5WLGEP56Y6CMFFS","short_pith_number":"pith:ADAALLEQ","canonical_record":{"source":{"id":"1903.09245","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-03-21T21:42:19Z","cross_cats_sorted":["cs.AI","cs.CC","stat.ML"],"title_canon_sha256":"23b6213abad6d73705df29209fc24995d4a8ce284ecbd884b3a8936a16135593","abstract_canon_sha256":"17e41f5c84eb3d64e3ab05e7479cc9f54b53c4176f5273b29369366822aae81f"},"schema_version":"1.0"},"canonical_sha256":"00c005ac905dd5db2cc47f7d8f09852cbea348dcecb0f96925e19e492335e188","source":{"kind":"arxiv","id":"1903.09245","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.09245","created_at":"2026-05-17T23:50:40Z"},{"alias_kind":"arxiv_version","alias_value":"1903.09245v1","created_at":"2026-05-17T23:50:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.09245","created_at":"2026-05-17T23:50:40Z"},{"alias_kind":"pith_short_12","alias_value":"ADAALLEQLXK5","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"ADAALLEQLXK5WLGE","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"ADAALLEQ","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:ADAALLEQLXK5WLGEP56Y6CMFFS","target":"record","payload":{"canonical_record":{"source":{"id":"1903.09245","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-03-21T21:42:19Z","cross_cats_sorted":["cs.AI","cs.CC","stat.ML"],"title_canon_sha256":"23b6213abad6d73705df29209fc24995d4a8ce284ecbd884b3a8936a16135593","abstract_canon_sha256":"17e41f5c84eb3d64e3ab05e7479cc9f54b53c4176f5273b29369366822aae81f"},"schema_version":"1.0"},"canonical_sha256":"00c005ac905dd5db2cc47f7d8f09852cbea348dcecb0f96925e19e492335e188","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:40.218280Z","signature_b64":"dN2Uxod5fiPZV39M27YGT6sAGLyjEIGbmFWiRtDjuUsG/E65NIrZGHeT7G7Mfbp9GbofuzDZVNu22hXjWJ+xBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00c005ac905dd5db2cc47f7d8f09852cbea348dcecb0f96925e19e492335e188","last_reissued_at":"2026-05-17T23:50:40.217632Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:40.217632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.09245","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:50:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FKEaSRPEKUwukDAWx3YZAYPPTz6SHDrlHpiebTfCspskt8q3y9LRgbXI732lbK1fUeYF0F4qC6Kbzfras2D3Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T14:59:27.515296Z"},"content_sha256":"f884592482efc588ce423ae4b396d203e93281b92ac23a2598ba592094de149d","schema_version":"1.0","event_id":"sha256:f884592482efc588ce423ae4b396d203e93281b92ac23a2598ba592094de149d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:ADAALLEQLXK5WLGEP56Y6CMFFS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Emily Mower Provost, Melvin G McInnis, Soheil Khorram","submitted_at":"2019-03-21T21:42:19Z","abstract_excerpt":"DTW calculates the similarity or alignment between two signals, subject to temporal warping. However, its computational complexity grows exponentially with the number of time-series. Although there have been algorithms developed that are linear in the number of time-series, they are generally quadratic in time-series length. The exception is generalized time warping (GTW), which has linear computational cost. Yet, it can only identify simple time warping functions. There is a need for a new fast, high-quality multisequence alignment algorithm. We introduce trainable time warping (TTW), whose c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.09245","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:50:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WDSK/VIcPYk9xx4VUqXH1Jin9pY75sWc/cv+1VWa92WsulEcp48qaJ/XJa+IWvJJ8OtbTvMhgqJj/YGSGEgKDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T14:59:27.516000Z"},"content_sha256":"b250b0f4c4fba40e90082a371af24a01af9ca8669759e8edf7caaea52454b64e","schema_version":"1.0","event_id":"sha256:b250b0f4c4fba40e90082a371af24a01af9ca8669759e8edf7caaea52454b64e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ADAALLEQLXK5WLGEP56Y6CMFFS/bundle.json","state_url":"https://pith.science/pith/ADAALLEQLXK5WLGEP56Y6CMFFS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ADAALLEQLXK5WLGEP56Y6CMFFS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T14:59:27Z","links":{"resolver":"https://pith.science/pith/ADAALLEQLXK5WLGEP56Y6CMFFS","bundle":"https://pith.science/pith/ADAALLEQLXK5WLGEP56Y6CMFFS/bundle.json","state":"https://pith.science/pith/ADAALLEQLXK5WLGEP56Y6CMFFS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ADAALLEQLXK5WLGEP56Y6CMFFS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:ADAALLEQLXK5WLGEP56Y6CMFFS","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":"17e41f5c84eb3d64e3ab05e7479cc9f54b53c4176f5273b29369366822aae81f","cross_cats_sorted":["cs.AI","cs.CC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-03-21T21:42:19Z","title_canon_sha256":"23b6213abad6d73705df29209fc24995d4a8ce284ecbd884b3a8936a16135593"},"schema_version":"1.0","source":{"id":"1903.09245","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.09245","created_at":"2026-05-17T23:50:40Z"},{"alias_kind":"arxiv_version","alias_value":"1903.09245v1","created_at":"2026-05-17T23:50:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.09245","created_at":"2026-05-17T23:50:40Z"},{"alias_kind":"pith_short_12","alias_value":"ADAALLEQLXK5","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"ADAALLEQLXK5WLGE","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"ADAALLEQ","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:b250b0f4c4fba40e90082a371af24a01af9ca8669759e8edf7caaea52454b64e","target":"graph","created_at":"2026-05-17T23:50:40Z","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"},"paper":{"abstract_excerpt":"DTW calculates the similarity or alignment between two signals, subject to temporal warping. However, its computational complexity grows exponentially with the number of time-series. Although there have been algorithms developed that are linear in the number of time-series, they are generally quadratic in time-series length. The exception is generalized time warping (GTW), which has linear computational cost. Yet, it can only identify simple time warping functions. There is a need for a new fast, high-quality multisequence alignment algorithm. We introduce trainable time warping (TTW), whose c","authors_text":"Emily Mower Provost, Melvin G McInnis, Soheil Khorram","cross_cats":["cs.AI","cs.CC","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-03-21T21:42:19Z","title":"Trainable Time Warping: Aligning Time-Series in the Continuous-Time Domain"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.09245","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:f884592482efc588ce423ae4b396d203e93281b92ac23a2598ba592094de149d","target":"record","created_at":"2026-05-17T23:50:40Z","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":"17e41f5c84eb3d64e3ab05e7479cc9f54b53c4176f5273b29369366822aae81f","cross_cats_sorted":["cs.AI","cs.CC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-03-21T21:42:19Z","title_canon_sha256":"23b6213abad6d73705df29209fc24995d4a8ce284ecbd884b3a8936a16135593"},"schema_version":"1.0","source":{"id":"1903.09245","kind":"arxiv","version":1}},"canonical_sha256":"00c005ac905dd5db2cc47f7d8f09852cbea348dcecb0f96925e19e492335e188","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"00c005ac905dd5db2cc47f7d8f09852cbea348dcecb0f96925e19e492335e188","first_computed_at":"2026-05-17T23:50:40.217632Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:50:40.217632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dN2Uxod5fiPZV39M27YGT6sAGLyjEIGbmFWiRtDjuUsG/E65NIrZGHeT7G7Mfbp9GbofuzDZVNu22hXjWJ+xBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:50:40.218280Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.09245","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f884592482efc588ce423ae4b396d203e93281b92ac23a2598ba592094de149d","sha256:b250b0f4c4fba40e90082a371af24a01af9ca8669759e8edf7caaea52454b64e"],"state_sha256":"43f2f69586ded5d7c28722ec6011b3a3c022a49a5a1c368bb99bad9a8e82143d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HTWlgaedmUpqcI8oFbghtE5bs0D0mSJ0ho/qi8JwcffiDCySBj6ZeVo4OUj7qUGHxqkCOYsm/iDjgTvoCJ8vAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T14:59:27.519355Z","bundle_sha256":"43a7310d017058e7ad94000411d51edc6f9655e54d547bf872f5072168802839"}}