{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PRSX2FQQIPEBTZS4PBW23VDBFY","short_pith_number":"pith:PRSX2FQQ","schema_version":"1.0","canonical_sha256":"7c657d161043c819e65c786dadd4612e022c86aa034cefbfcbc37bdfe8760776","source":{"kind":"arxiv","id":"2403.16021","version":1},"attestation_state":"computed","paper":{"title":"Digital Twin Assisted Intelligent Network Management for Vehicular Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Kaige Qu, Weihua Zhuang","submitted_at":"2024-03-24T05:50:55Z","abstract_excerpt":"The emerging data-driven methods based on artificial intelligence (AI) have paved the way for intelligent, flexible, and adaptive network management in vehicular applications. To enhance network management towards network automation, this article presents a digital twin (DT) assisted two-tier learning framework, which facilitates the automated life-cycle management of machine learning based intelligent network management functions (INMFs). Specifically, at a high tier, meta learning is employed to capture different levels of general features for the INMFs under nonstationary network conditions"},"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":"2403.16021","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-03-24T05:50:55Z","cross_cats_sorted":[],"title_canon_sha256":"ad19520d38b36414d47640b7b389d9496e66543b98a0a0caf53e1939ae15416c","abstract_canon_sha256":"2b483cf40939e697711c61a4b8b586862ee3c11c88c5871f2c0b4facb2bb4068"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:00:09.697269Z","signature_b64":"FymdwIwEW99v7/DB6mcKZGXTMm5hJecgaAG1VIi28DEw6PhycXa34bASI8v5vYLO9EFAtY8BFHxT5XoeppOEAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c657d161043c819e65c786dadd4612e022c86aa034cefbfcbc37bdfe8760776","last_reissued_at":"2026-07-05T08:00:09.696919Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:00:09.696919Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Digital Twin Assisted Intelligent Network Management for Vehicular Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Kaige Qu, Weihua Zhuang","submitted_at":"2024-03-24T05:50:55Z","abstract_excerpt":"The emerging data-driven methods based on artificial intelligence (AI) have paved the way for intelligent, flexible, and adaptive network management in vehicular applications. To enhance network management towards network automation, this article presents a digital twin (DT) assisted two-tier learning framework, which facilitates the automated life-cycle management of machine learning based intelligent network management functions (INMFs). Specifically, at a high tier, meta learning is employed to capture different levels of general features for the INMFs under nonstationary network conditions"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.16021","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/2403.16021/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":"2403.16021","created_at":"2026-07-05T08:00:09.696976+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.16021v1","created_at":"2026-07-05T08:00:09.696976+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.16021","created_at":"2026-07-05T08:00:09.696976+00:00"},{"alias_kind":"pith_short_12","alias_value":"PRSX2FQQIPEB","created_at":"2026-07-05T08:00:09.696976+00:00"},{"alias_kind":"pith_short_16","alias_value":"PRSX2FQQIPEBTZS4","created_at":"2026-07-05T08:00:09.696976+00:00"},{"alias_kind":"pith_short_8","alias_value":"PRSX2FQQ","created_at":"2026-07-05T08:00:09.696976+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/PRSX2FQQIPEBTZS4PBW23VDBFY","json":"https://pith.science/pith/PRSX2FQQIPEBTZS4PBW23VDBFY.json","graph_json":"https://pith.science/api/pith-number/PRSX2FQQIPEBTZS4PBW23VDBFY/graph.json","events_json":"https://pith.science/api/pith-number/PRSX2FQQIPEBTZS4PBW23VDBFY/events.json","paper":"https://pith.science/paper/PRSX2FQQ"},"agent_actions":{"view_html":"https://pith.science/pith/PRSX2FQQIPEBTZS4PBW23VDBFY","download_json":"https://pith.science/pith/PRSX2FQQIPEBTZS4PBW23VDBFY.json","view_paper":"https://pith.science/paper/PRSX2FQQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.16021&json=true","fetch_graph":"https://pith.science/api/pith-number/PRSX2FQQIPEBTZS4PBW23VDBFY/graph.json","fetch_events":"https://pith.science/api/pith-number/PRSX2FQQIPEBTZS4PBW23VDBFY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PRSX2FQQIPEBTZS4PBW23VDBFY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PRSX2FQQIPEBTZS4PBW23VDBFY/action/storage_attestation","attest_author":"https://pith.science/pith/PRSX2FQQIPEBTZS4PBW23VDBFY/action/author_attestation","sign_citation":"https://pith.science/pith/PRSX2FQQIPEBTZS4PBW23VDBFY/action/citation_signature","submit_replication":"https://pith.science/pith/PRSX2FQQIPEBTZS4PBW23VDBFY/action/replication_record"}},"created_at":"2026-07-05T08:00:09.696976+00:00","updated_at":"2026-07-05T08:00:09.696976+00:00"}