{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:KKICRXQHQD7U7VOIY5LPCQBQBX","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":"9fee776ac8570e425db8b5a90f301aa491c014ee259e98f7fa25fab3c7370dd7","cross_cats_sorted":["cs.CY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-13T20:54:52Z","title_canon_sha256":"a14a7c6bd55f19b041e0f53a2326b54223761a7d667000dbb8986c590736b657"},"schema_version":"1.0","source":{"id":"2311.07744","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.07744","created_at":"2026-07-05T08:11:55Z"},{"alias_kind":"arxiv_version","alias_value":"2311.07744v2","created_at":"2026-07-05T08:11:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.07744","created_at":"2026-07-05T08:11:55Z"},{"alias_kind":"pith_short_12","alias_value":"KKICRXQHQD7U","created_at":"2026-07-05T08:11:55Z"},{"alias_kind":"pith_short_16","alias_value":"KKICRXQHQD7U7VOI","created_at":"2026-07-05T08:11:55Z"},{"alias_kind":"pith_short_8","alias_value":"KKICRXQH","created_at":"2026-07-05T08:11:55Z"}],"graph_snapshots":[{"event_id":"sha256:df4472bd2b59851e7703082714fbe7864c4d28b95b746d97ddf5a0e9eb690c53","target":"graph","created_at":"2026-07-05T08:11:55Z","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/2311.07744/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Irregular multivariate time series data is characterized by varying time intervals between consecutive observations of measured variables/signals (i.e., features) and varying sampling rates (i.e., recordings/measurement) across these features. Modeling time series while taking into account these irregularities is still a challenging task for machine learning methods. Here, we introduce TADA, a Two-stageAggregation process with Dynamic local Attention to harmonize time-wise and feature-wise irregularities in multivariate time series. In the first stage, the irregular time series undergoes tempo","authors_text":"Ahmed Allam, Amina Mollaysa, Manuel Sch\\\"urch, Michael Krauthammer, Xiaochen Zheng, Xingyu Chen","cross_cats":["cs.CY"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-13T20:54:52Z","title":"Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Series"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.07744","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":"sha256:1125412aead0e04a573da90f33cc8bd4ad80ff2ea0e011fa4f619dc5973ff5ea","target":"record","created_at":"2026-07-05T08:11:55Z","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":"9fee776ac8570e425db8b5a90f301aa491c014ee259e98f7fa25fab3c7370dd7","cross_cats_sorted":["cs.CY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-13T20:54:52Z","title_canon_sha256":"a14a7c6bd55f19b041e0f53a2326b54223761a7d667000dbb8986c590736b657"},"schema_version":"1.0","source":{"id":"2311.07744","kind":"arxiv","version":2}},"canonical_sha256":"529028de0780ff4fd5c8c756f140300dc6d651b93fb43cb1e7dc270011ca6df6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"529028de0780ff4fd5c8c756f140300dc6d651b93fb43cb1e7dc270011ca6df6","first_computed_at":"2026-07-05T08:11:55.496050Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:11:55.496050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FhljQIWOKijHBV0gPOs654y8qeY6Z/73jsi70liNtoc4oE85qHrgZCseuZ4zuufgADj54Fjjnn6px07jaFUpAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:11:55.496485Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.07744","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1125412aead0e04a573da90f33cc8bd4ad80ff2ea0e011fa4f619dc5973ff5ea","sha256:df4472bd2b59851e7703082714fbe7864c4d28b95b746d97ddf5a0e9eb690c53"],"state_sha256":"b74eb7879420a201686260a93a2412f605120817d0401e5dafeb47b9d2ce1580"}