{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EPK25B4ZCQ323XGIC6KBFSTCXK","short_pith_number":"pith:EPK25B4Z","schema_version":"1.0","canonical_sha256":"23d5ae87991437addcc8179412ca62baba42f52ea4809a0780c1dbb785d8ab3f","source":{"kind":"arxiv","id":"2404.14497","version":1},"attestation_state":"computed","paper":{"title":"Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"cs.NI","authors_text":"Mingzhe Chen, Yuchen Liu, Zhaohui Yang, Zifan Zhang","submitted_at":"2024-04-22T18:02:17Z","abstract_excerpt":"In recent years, the complexity of 5G and beyond wireless networks has escalated, prompting a need for innovative frameworks to facilitate flexible management and efficient deployment. The concept of digital twins (DTs) has emerged as a solution to enable real-time monitoring, predictive configurations, and decision-making processes. While existing works primarily focus on leveraging DTs to optimize wireless networks, a detailed mapping methodology for creating virtual representations of network infrastructure and properties is still lacking. In this context, we introduce VH-Twin, a novel time"},"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":"2404.14497","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-04-22T18:02:17Z","cross_cats_sorted":["cs.LG","eess.SP"],"title_canon_sha256":"7a80c78ad62b1a49afa83ef0f41f96118f6abe7b3548e9ed88492dacdc25faad","abstract_canon_sha256":"8406dbc8fb7e747e9d73156090219e4bbd032a58bfccfc8da98520f681aecfa8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:06.001879Z","signature_b64":"PW5pCS+ZnqVkzAXyJHVQFsdysSIoDFoz8lMSrH2owzkMoMa0OU9YAZRpMQdIw+ooXct6E5Vq2vhGAMrLTWcCAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23d5ae87991437addcc8179412ca62baba42f52ea4809a0780c1dbb785d8ab3f","last_reissued_at":"2026-07-05T08:11:06.001413Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:06.001413Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"cs.NI","authors_text":"Mingzhe Chen, Yuchen Liu, Zhaohui Yang, Zifan Zhang","submitted_at":"2024-04-22T18:02:17Z","abstract_excerpt":"In recent years, the complexity of 5G and beyond wireless networks has escalated, prompting a need for innovative frameworks to facilitate flexible management and efficient deployment. The concept of digital twins (DTs) has emerged as a solution to enable real-time monitoring, predictive configurations, and decision-making processes. While existing works primarily focus on leveraging DTs to optimize wireless networks, a detailed mapping methodology for creating virtual representations of network infrastructure and properties is still lacking. In this context, we introduce VH-Twin, a novel time"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.14497","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/2404.14497/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":"2404.14497","created_at":"2026-07-05T08:11:06.001471+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.14497v1","created_at":"2026-07-05T08:11:06.001471+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.14497","created_at":"2026-07-05T08:11:06.001471+00:00"},{"alias_kind":"pith_short_12","alias_value":"EPK25B4ZCQ32","created_at":"2026-07-05T08:11:06.001471+00:00"},{"alias_kind":"pith_short_16","alias_value":"EPK25B4ZCQ323XGI","created_at":"2026-07-05T08:11:06.001471+00:00"},{"alias_kind":"pith_short_8","alias_value":"EPK25B4Z","created_at":"2026-07-05T08:11:06.001471+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.05116","citing_title":"Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EPK25B4ZCQ323XGIC6KBFSTCXK","json":"https://pith.science/pith/EPK25B4ZCQ323XGIC6KBFSTCXK.json","graph_json":"https://pith.science/api/pith-number/EPK25B4ZCQ323XGIC6KBFSTCXK/graph.json","events_json":"https://pith.science/api/pith-number/EPK25B4ZCQ323XGIC6KBFSTCXK/events.json","paper":"https://pith.science/paper/EPK25B4Z"},"agent_actions":{"view_html":"https://pith.science/pith/EPK25B4ZCQ323XGIC6KBFSTCXK","download_json":"https://pith.science/pith/EPK25B4ZCQ323XGIC6KBFSTCXK.json","view_paper":"https://pith.science/paper/EPK25B4Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.14497&json=true","fetch_graph":"https://pith.science/api/pith-number/EPK25B4ZCQ323XGIC6KBFSTCXK/graph.json","fetch_events":"https://pith.science/api/pith-number/EPK25B4ZCQ323XGIC6KBFSTCXK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EPK25B4ZCQ323XGIC6KBFSTCXK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EPK25B4ZCQ323XGIC6KBFSTCXK/action/storage_attestation","attest_author":"https://pith.science/pith/EPK25B4ZCQ323XGIC6KBFSTCXK/action/author_attestation","sign_citation":"https://pith.science/pith/EPK25B4ZCQ323XGIC6KBFSTCXK/action/citation_signature","submit_replication":"https://pith.science/pith/EPK25B4ZCQ323XGIC6KBFSTCXK/action/replication_record"}},"created_at":"2026-07-05T08:11:06.001471+00:00","updated_at":"2026-07-05T08:11:06.001471+00:00"}