{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YIWN5DJPCWSPN3YENS72HTCVTY","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":"b8b84af478b873e1faf9be7f9292df8391569f95079391ac0cca3e66dbfd4304","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-20T17:41:11Z","title_canon_sha256":"b4bb97adcb74f0dd318d510d64013cdfef8523c53ee43737858c2cab5f1d7d04"},"schema_version":"1.0","source":{"id":"2412.16098","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.16098","created_at":"2026-07-05T09:53:47Z"},{"alias_kind":"arxiv_version","alias_value":"2412.16098v2","created_at":"2026-07-05T09:53:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.16098","created_at":"2026-07-05T09:53:47Z"},{"alias_kind":"pith_short_12","alias_value":"YIWN5DJPCWSP","created_at":"2026-07-05T09:53:47Z"},{"alias_kind":"pith_short_16","alias_value":"YIWN5DJPCWSPN3YE","created_at":"2026-07-05T09:53:47Z"},{"alias_kind":"pith_short_8","alias_value":"YIWN5DJP","created_at":"2026-07-05T09:53:47Z"}],"graph_snapshots":[{"event_id":"sha256:f2639c121600335ca3887d443e5ade3cab5674a487d7994b6a6bb0315c17ef7e","target":"graph","created_at":"2026-07-05T09:53:47Z","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/2412.16098/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI mod","authors_text":"Aaron Wilson, Ali Boyaci, Haowen Xu, Jianming Lian","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-20T17:41:11Z","title":"Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.16098","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":"sha256:044ce3708e5a3a277c43c42af9ec7bda2255dd651927dd329f608c05bc144b4c","target":"record","created_at":"2026-07-05T09:53:47Z","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":"b8b84af478b873e1faf9be7f9292df8391569f95079391ac0cca3e66dbfd4304","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-20T17:41:11Z","title_canon_sha256":"b4bb97adcb74f0dd318d510d64013cdfef8523c53ee43737858c2cab5f1d7d04"},"schema_version":"1.0","source":{"id":"2412.16098","kind":"arxiv","version":2}},"canonical_sha256":"c22cde8d2f15a4f6ef046cbfa3cc559e2651a7c4524a1581db7c78e48f620da0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c22cde8d2f15a4f6ef046cbfa3cc559e2651a7c4524a1581db7c78e48f620da0","first_computed_at":"2026-07-05T09:53:47.388111Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:53:47.388111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kxfY6EcuBLjgHsBRdlGqKFnMuO2txzkFCCfUTUXmC55/c9qCcXhYrxCRwgwuFbkIuxjRGimcZLKrdW3iiCFNAg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:53:47.388586Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.16098","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:044ce3708e5a3a277c43c42af9ec7bda2255dd651927dd329f608c05bc144b4c","sha256:f2639c121600335ca3887d443e5ade3cab5674a487d7994b6a6bb0315c17ef7e"],"state_sha256":"793bc0cdbfaa92bfd790a6a9c0ab887835e01a8e9aacb795af77190fca4373bf"}