{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HM4XMUW6IBOEQXVYIOJ2HBV2QM","short_pith_number":"pith:HM4XMUW6","schema_version":"1.0","canonical_sha256":"3b397652de405c485eb84393a386ba8328b80bd9b4310928b5b2f4fb50a0ed3d","source":{"kind":"arxiv","id":"2409.13857","version":1},"attestation_state":"computed","paper":{"title":"Wormhole: Concept-Aware Deep Representation Learning for Co-Evolving Sequences","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Kunpeng Xu, Lifei Chen, Shengrui Wang","submitted_at":"2024-09-20T19:11:39Z","abstract_excerpt":"Identifying and understanding dynamic concepts in co-evolving sequences is crucial for analyzing complex systems such as IoT applications, financial markets, and online activity logs. These concepts provide valuable insights into the underlying structures and behaviors of sequential data, enabling better decision-making and forecasting. This paper introduces Wormhole, a novel deep representation learning framework that is concept-aware and designed for co-evolving time sequences. Our model presents a self-representation layer and a temporal smoothness constraint to ensure robust identification"},"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":"2409.13857","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-20T19:11:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b7500c0a084d0ce896d08ba54595359af89b9622a7cc243bdc6b750509c8f0bd","abstract_canon_sha256":"ef0f1806a6b5012e8274096ab2a6dcecf8bf8378ff27e1972173bec685e0d93b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:10:07.750067Z","signature_b64":"Qh8+Rk/fNk8Ky/9nmgyYCxNtjz8U33YxiaSPP3yRZBnaRQ6s9N6x/EyFTgnQ79X/DbtkUKcNh1z78WLfO8VKCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b397652de405c485eb84393a386ba8328b80bd9b4310928b5b2f4fb50a0ed3d","last_reissued_at":"2026-07-05T09:10:07.749541Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:10:07.749541Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Wormhole: Concept-Aware Deep Representation Learning for Co-Evolving Sequences","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Kunpeng Xu, Lifei Chen, Shengrui Wang","submitted_at":"2024-09-20T19:11:39Z","abstract_excerpt":"Identifying and understanding dynamic concepts in co-evolving sequences is crucial for analyzing complex systems such as IoT applications, financial markets, and online activity logs. These concepts provide valuable insights into the underlying structures and behaviors of sequential data, enabling better decision-making and forecasting. This paper introduces Wormhole, a novel deep representation learning framework that is concept-aware and designed for co-evolving time sequences. Our model presents a self-representation layer and a temporal smoothness constraint to ensure robust identification"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.13857","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2409.13857/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":"2409.13857","created_at":"2026-07-05T09:10:07.749604+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.13857v1","created_at":"2026-07-05T09:10:07.749604+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.13857","created_at":"2026-07-05T09:10:07.749604+00:00"},{"alias_kind":"pith_short_12","alias_value":"HM4XMUW6IBOE","created_at":"2026-07-05T09:10:07.749604+00:00"},{"alias_kind":"pith_short_16","alias_value":"HM4XMUW6IBOEQXVY","created_at":"2026-07-05T09:10:07.749604+00:00"},{"alias_kind":"pith_short_8","alias_value":"HM4XMUW6","created_at":"2026-07-05T09:10:07.749604+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.01480","citing_title":"CORAL: Concept Drift Representation Learning for Co-evolving Time-series","ref_index":56,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HM4XMUW6IBOEQXVYIOJ2HBV2QM","json":"https://pith.science/pith/HM4XMUW6IBOEQXVYIOJ2HBV2QM.json","graph_json":"https://pith.science/api/pith-number/HM4XMUW6IBOEQXVYIOJ2HBV2QM/graph.json","events_json":"https://pith.science/api/pith-number/HM4XMUW6IBOEQXVYIOJ2HBV2QM/events.json","paper":"https://pith.science/paper/HM4XMUW6"},"agent_actions":{"view_html":"https://pith.science/pith/HM4XMUW6IBOEQXVYIOJ2HBV2QM","download_json":"https://pith.science/pith/HM4XMUW6IBOEQXVYIOJ2HBV2QM.json","view_paper":"https://pith.science/paper/HM4XMUW6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.13857&json=true","fetch_graph":"https://pith.science/api/pith-number/HM4XMUW6IBOEQXVYIOJ2HBV2QM/graph.json","fetch_events":"https://pith.science/api/pith-number/HM4XMUW6IBOEQXVYIOJ2HBV2QM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HM4XMUW6IBOEQXVYIOJ2HBV2QM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HM4XMUW6IBOEQXVYIOJ2HBV2QM/action/storage_attestation","attest_author":"https://pith.science/pith/HM4XMUW6IBOEQXVYIOJ2HBV2QM/action/author_attestation","sign_citation":"https://pith.science/pith/HM4XMUW6IBOEQXVYIOJ2HBV2QM/action/citation_signature","submit_replication":"https://pith.science/pith/HM4XMUW6IBOEQXVYIOJ2HBV2QM/action/replication_record"}},"created_at":"2026-07-05T09:10:07.749604+00:00","updated_at":"2026-07-05T09:10:07.749604+00:00"}