{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:GKZBVFDAAD5VLO6OHRP32QNEIR","short_pith_number":"pith:GKZBVFDA","canonical_record":{"source":{"id":"2002.03848","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2020-02-10T15:11:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"23cd59d2d28a6d15180247d98ed3797432da78698ff67fc66ccbc93fd39e25c1","abstract_canon_sha256":"bfd4633982333b2bde3b90aeeb9af03f41e2023ffdabf57958eb6e863939a0df"},"schema_version":"1.0"},"canonical_sha256":"32b21a946000fb55bbce3c5fbd41a44440a67394acdfa0ad650de038f07f29fe","source":{"kind":"arxiv","id":"2002.03848","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.03848","created_at":"2026-07-05T05:12:06Z"},{"alias_kind":"arxiv_version","alias_value":"2002.03848v2","created_at":"2026-07-05T05:12:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.03848","created_at":"2026-07-05T05:12:06Z"},{"alias_kind":"pith_short_12","alias_value":"GKZBVFDAAD5V","created_at":"2026-07-05T05:12:06Z"},{"alias_kind":"pith_short_16","alias_value":"GKZBVFDAAD5VLO6O","created_at":"2026-07-05T05:12:06Z"},{"alias_kind":"pith_short_8","alias_value":"GKZBVFDA","created_at":"2026-07-05T05:12:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:GKZBVFDAAD5VLO6OHRP32QNEIR","target":"record","payload":{"canonical_record":{"source":{"id":"2002.03848","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2020-02-10T15:11:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"23cd59d2d28a6d15180247d98ed3797432da78698ff67fc66ccbc93fd39e25c1","abstract_canon_sha256":"bfd4633982333b2bde3b90aeeb9af03f41e2023ffdabf57958eb6e863939a0df"},"schema_version":"1.0"},"canonical_sha256":"32b21a946000fb55bbce3c5fbd41a44440a67394acdfa0ad650de038f07f29fe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:12:06.883726Z","signature_b64":"hDJf14jeOuZRPwfqfaHfHrqIHpvXwVkUamsUVoKsSUVyrJVHw5D6yAfTHLzNICgjmPSq6wmGIgvrwhC6bxRlBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"32b21a946000fb55bbce3c5fbd41a44440a67394acdfa0ad650de038f07f29fe","last_reissued_at":"2026-07-05T05:12:06.883310Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:12:06.883310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.03848","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:12:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nbCCmfCEKLzbkBxKySiQZ75gvrDrQNfl5ZLNGpMF+6eeulzfy8FBkxRE0olLcBU0g3VLyPSP2Dz+FnTibqdABg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:19:40.153635Z"},"content_sha256":"dc33cc8312a648025d835cdd54f50458123ab62703fe9154df29148efd02e392","schema_version":"1.0","event_id":"sha256:dc33cc8312a648025d835cdd54f50458123ab62703fe9154df29148efd02e392"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:GKZBVFDAAD5VLO6OHRP32QNEIR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Time Series Alignment with Global Invariances","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Laetitia Chapel, Nicolas Courty, R\\'emi Flamary, Romain Tavenard, Titouan Vayer, Yann Soullard","submitted_at":"2020-02-10T15:11:50Z","abstract_excerpt":"Multivariate time series are ubiquitous objects in signal processing. Measuring a distance or similarity between two such objects is of prime interest in a variety of applications, including machine learning, but can be very difficult as soon as the temporal dynamics and the representation of the time series, {\\em i.e.} the nature of the observed quantities, differ from one another. In this work, we propose a novel distance accounting both feature space and temporal variabilities by learning a latent global transformation of the feature space together with a temporal alignment, cast as a joint"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.03848","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/2002.03848/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:12:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NAD2u3Kh/LYINysCF3VolAWMCaZxt5kOoLphDf85JCM1q7ScJAVtETOgVIZn2h+ETfy3BMQYoyaZG9L6XeFBDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:19:40.154210Z"},"content_sha256":"89fc588fad298f440e48d10d7831b74d355cfd70e7534cc31242b6cddaf0e71a","schema_version":"1.0","event_id":"sha256:89fc588fad298f440e48d10d7831b74d355cfd70e7534cc31242b6cddaf0e71a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GKZBVFDAAD5VLO6OHRP32QNEIR/bundle.json","state_url":"https://pith.science/pith/GKZBVFDAAD5VLO6OHRP32QNEIR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GKZBVFDAAD5VLO6OHRP32QNEIR/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":"2026-08-09T00:19:40Z","links":{"resolver":"https://pith.science/pith/GKZBVFDAAD5VLO6OHRP32QNEIR","bundle":"https://pith.science/pith/GKZBVFDAAD5VLO6OHRP32QNEIR/bundle.json","state":"https://pith.science/pith/GKZBVFDAAD5VLO6OHRP32QNEIR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GKZBVFDAAD5VLO6OHRP32QNEIR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:GKZBVFDAAD5VLO6OHRP32QNEIR","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":"bfd4633982333b2bde3b90aeeb9af03f41e2023ffdabf57958eb6e863939a0df","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2020-02-10T15:11:50Z","title_canon_sha256":"23cd59d2d28a6d15180247d98ed3797432da78698ff67fc66ccbc93fd39e25c1"},"schema_version":"1.0","source":{"id":"2002.03848","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.03848","created_at":"2026-07-05T05:12:06Z"},{"alias_kind":"arxiv_version","alias_value":"2002.03848v2","created_at":"2026-07-05T05:12:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.03848","created_at":"2026-07-05T05:12:06Z"},{"alias_kind":"pith_short_12","alias_value":"GKZBVFDAAD5V","created_at":"2026-07-05T05:12:06Z"},{"alias_kind":"pith_short_16","alias_value":"GKZBVFDAAD5VLO6O","created_at":"2026-07-05T05:12:06Z"},{"alias_kind":"pith_short_8","alias_value":"GKZBVFDA","created_at":"2026-07-05T05:12:06Z"}],"graph_snapshots":[{"event_id":"sha256:89fc588fad298f440e48d10d7831b74d355cfd70e7534cc31242b6cddaf0e71a","target":"graph","created_at":"2026-07-05T05:12:06Z","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/2002.03848/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multivariate time series are ubiquitous objects in signal processing. Measuring a distance or similarity between two such objects is of prime interest in a variety of applications, including machine learning, but can be very difficult as soon as the temporal dynamics and the representation of the time series, {\\em i.e.} the nature of the observed quantities, differ from one another. In this work, we propose a novel distance accounting both feature space and temporal variabilities by learning a latent global transformation of the feature space together with a temporal alignment, cast as a joint","authors_text":"Laetitia Chapel, Nicolas Courty, R\\'emi Flamary, Romain Tavenard, Titouan Vayer, Yann Soullard","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2020-02-10T15:11:50Z","title":"Time Series Alignment with Global Invariances"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.03848","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:dc33cc8312a648025d835cdd54f50458123ab62703fe9154df29148efd02e392","target":"record","created_at":"2026-07-05T05:12:06Z","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":"bfd4633982333b2bde3b90aeeb9af03f41e2023ffdabf57958eb6e863939a0df","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2020-02-10T15:11:50Z","title_canon_sha256":"23cd59d2d28a6d15180247d98ed3797432da78698ff67fc66ccbc93fd39e25c1"},"schema_version":"1.0","source":{"id":"2002.03848","kind":"arxiv","version":2}},"canonical_sha256":"32b21a946000fb55bbce3c5fbd41a44440a67394acdfa0ad650de038f07f29fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"32b21a946000fb55bbce3c5fbd41a44440a67394acdfa0ad650de038f07f29fe","first_computed_at":"2026-07-05T05:12:06.883310Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:12:06.883310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hDJf14jeOuZRPwfqfaHfHrqIHpvXwVkUamsUVoKsSUVyrJVHw5D6yAfTHLzNICgjmPSq6wmGIgvrwhC6bxRlBg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:12:06.883726Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.03848","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dc33cc8312a648025d835cdd54f50458123ab62703fe9154df29148efd02e392","sha256:89fc588fad298f440e48d10d7831b74d355cfd70e7534cc31242b6cddaf0e71a"],"state_sha256":"e12f97e48696f2b8c83d2623618c9a3edbd597042f21f27b5ce57441f350fba4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g+FEXPy56b2eeERlbj9PSEyRzK0oJ0t6fyTNSNwS4E2XvS45HkWi4u52wMI6V1kljhBCNY35UjyGAphxGMGKDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T00:19:40.159105Z","bundle_sha256":"621b450b72b477d12d539496cd5430a5ce371ace464fc4afd72901a3d9d3798c"}}