{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:NIQJKH4O23223KLG5SP4LFDAAT","short_pith_number":"pith:NIQJKH4O","schema_version":"1.0","canonical_sha256":"6a20951f8ed6f5ada966ec9fc5946004e6ced4d37c0d5cafd23c617953020ea5","source":{"kind":"arxiv","id":"2110.08021","version":2},"attestation_state":"computed","paper":{"title":"StreaMulT: Streaming Multimodal Transformer for Heterogeneous and Arbitrary Long Sequential Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.MM"],"primary_cat":"cs.LG","authors_text":"2), (2) Universit\\'e Paris-Saclay, C\\'eline Hudelot (2) ((1) Institut de Recherche Technologique SystemX, CentraleSup\\'elec, Michel Batteux (1), MICS), Myriam Tami (2), Victor Pellegrain (1","submitted_at":"2021-10-15T11:32:17Z","abstract_excerpt":"The increasing complexity of Industry 4.0 systems brings new challenges regarding predictive maintenance tasks such as fault detection and diagnosis. A corresponding and realistic setting includes multi-source data streams from different modalities, such as sensors measurements time series, machine images, textual maintenance reports, etc. These heterogeneous multimodal streams also differ in their acquisition frequency, may embed temporally unaligned information and can be arbitrarily long, depending on the considered system and task. Whereas multimodal fusion has been largely studied in a st"},"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":"2110.08021","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-15T11:32:17Z","cross_cats_sorted":["cs.CL","cs.MM"],"title_canon_sha256":"a6a97d340b10cc868e123beaec3c27fc19b310a18cd1a846fe465411bd2c54d7","abstract_canon_sha256":"9bc810f95c6d2829ff62b92beda78a311cad8e6d4b5a03daa5b1384e9bf9207b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:52.600957Z","signature_b64":"Ad6Dn8iOsuPQRRJc+Ye3cLAslkrrKsF4Y61p6vjrXMfBl5fMsU7ugV/ZGbFww5inyPQuf2IPdrucXKUsErfDCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a20951f8ed6f5ada966ec9fc5946004e6ced4d37c0d5cafd23c617953020ea5","last_reissued_at":"2026-07-05T07:47:52.600439Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:52.600439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"StreaMulT: Streaming Multimodal Transformer for Heterogeneous and Arbitrary Long Sequential Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.MM"],"primary_cat":"cs.LG","authors_text":"2), (2) Universit\\'e Paris-Saclay, C\\'eline Hudelot (2) ((1) Institut de Recherche Technologique SystemX, CentraleSup\\'elec, Michel Batteux (1), MICS), Myriam Tami (2), Victor Pellegrain (1","submitted_at":"2021-10-15T11:32:17Z","abstract_excerpt":"The increasing complexity of Industry 4.0 systems brings new challenges regarding predictive maintenance tasks such as fault detection and diagnosis. A corresponding and realistic setting includes multi-source data streams from different modalities, such as sensors measurements time series, machine images, textual maintenance reports, etc. These heterogeneous multimodal streams also differ in their acquisition frequency, may embed temporally unaligned information and can be arbitrarily long, depending on the considered system and task. Whereas multimodal fusion has been largely studied in a st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.08021","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/2110.08021/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":"2110.08021","created_at":"2026-07-05T07:47:52.600507+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.08021v2","created_at":"2026-07-05T07:47:52.600507+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.08021","created_at":"2026-07-05T07:47:52.600507+00:00"},{"alias_kind":"pith_short_12","alias_value":"NIQJKH4O2322","created_at":"2026-07-05T07:47:52.600507+00:00"},{"alias_kind":"pith_short_16","alias_value":"NIQJKH4O23223KLG","created_at":"2026-07-05T07:47:52.600507+00:00"},{"alias_kind":"pith_short_8","alias_value":"NIQJKH4O","created_at":"2026-07-05T07:47:52.600507+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/NIQJKH4O23223KLG5SP4LFDAAT","json":"https://pith.science/pith/NIQJKH4O23223KLG5SP4LFDAAT.json","graph_json":"https://pith.science/api/pith-number/NIQJKH4O23223KLG5SP4LFDAAT/graph.json","events_json":"https://pith.science/api/pith-number/NIQJKH4O23223KLG5SP4LFDAAT/events.json","paper":"https://pith.science/paper/NIQJKH4O"},"agent_actions":{"view_html":"https://pith.science/pith/NIQJKH4O23223KLG5SP4LFDAAT","download_json":"https://pith.science/pith/NIQJKH4O23223KLG5SP4LFDAAT.json","view_paper":"https://pith.science/paper/NIQJKH4O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.08021&json=true","fetch_graph":"https://pith.science/api/pith-number/NIQJKH4O23223KLG5SP4LFDAAT/graph.json","fetch_events":"https://pith.science/api/pith-number/NIQJKH4O23223KLG5SP4LFDAAT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NIQJKH4O23223KLG5SP4LFDAAT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NIQJKH4O23223KLG5SP4LFDAAT/action/storage_attestation","attest_author":"https://pith.science/pith/NIQJKH4O23223KLG5SP4LFDAAT/action/author_attestation","sign_citation":"https://pith.science/pith/NIQJKH4O23223KLG5SP4LFDAAT/action/citation_signature","submit_replication":"https://pith.science/pith/NIQJKH4O23223KLG5SP4LFDAAT/action/replication_record"}},"created_at":"2026-07-05T07:47:52.600507+00:00","updated_at":"2026-07-05T07:47:52.600507+00:00"}