{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:AWPNCYWU7FQ5TPBIPYZCSZFJCS","short_pith_number":"pith:AWPNCYWU","schema_version":"1.0","canonical_sha256":"059ed162d4f961d9bc287e322964a9149b096fb0f99b8459a3b8e5db7763928f","source":{"kind":"arxiv","id":"1911.03878","version":2},"attestation_state":"computed","paper":{"title":"An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Dingzhu Wen, Jinke Ren, Kaibin Huang, Qunsong Zeng, Xiaoyang Li","submitted_at":"2019-11-10T08:59:21Z","abstract_excerpt":"The 5G network connecting billions of Internet-of-Things (IoT) devices will make it possible to harvest an enormous amount of real-time mobile data. Furthermore, the 5G virtualization architecture will enable cloud computing at the (network) edge. The availability of both rich data and computation power at the edge has motivated Internet companies to deploy artificial intelligence (AI) there, creating the hot area of edge-AI. Edge learning, the theme of this project, concerns training edge-AI models, which endow on IoT devices intelligence for responding to real-time events. However, the trans"},"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":"1911.03878","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-11-10T08:59:21Z","cross_cats_sorted":["cs.LG","math.IT"],"title_canon_sha256":"943c47d58b95dba9fd6c7cf045abf6eadfe2edb2f3a6668e24ed5e14c1e07324","abstract_canon_sha256":"26b7aae899971ab9767cc154123c423b86fc79894afc5b73c9c8a3b037d2762e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:24:42.388807Z","signature_b64":"rKJQu04NGSdM8+bZ6++qh90ScHJ3EyfOh1oFEMwU6UDmJZiXZiOp7aKKH3927ueGGZZD5nuZJU89N7RwiMPTDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"059ed162d4f961d9bc287e322964a9149b096fb0f99b8459a3b8e5db7763928f","last_reissued_at":"2026-07-05T00:24:42.388233Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:24:42.388233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Dingzhu Wen, Jinke Ren, Kaibin Huang, Qunsong Zeng, Xiaoyang Li","submitted_at":"2019-11-10T08:59:21Z","abstract_excerpt":"The 5G network connecting billions of Internet-of-Things (IoT) devices will make it possible to harvest an enormous amount of real-time mobile data. Furthermore, the 5G virtualization architecture will enable cloud computing at the (network) edge. The availability of both rich data and computation power at the edge has motivated Internet companies to deploy artificial intelligence (AI) there, creating the hot area of edge-AI. Edge learning, the theme of this project, concerns training edge-AI models, which endow on IoT devices intelligence for responding to real-time events. However, the trans"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.03878","kind":"arxiv","version":2},"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/1911.03878/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":"1911.03878","created_at":"2026-07-05T00:24:42.388297+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.03878v2","created_at":"2026-07-05T00:24:42.388297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.03878","created_at":"2026-07-05T00:24:42.388297+00:00"},{"alias_kind":"pith_short_12","alias_value":"AWPNCYWU7FQ5","created_at":"2026-07-05T00:24:42.388297+00:00"},{"alias_kind":"pith_short_16","alias_value":"AWPNCYWU7FQ5TPBI","created_at":"2026-07-05T00:24:42.388297+00:00"},{"alias_kind":"pith_short_8","alias_value":"AWPNCYWU","created_at":"2026-07-05T00:24:42.388297+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/AWPNCYWU7FQ5TPBIPYZCSZFJCS","json":"https://pith.science/pith/AWPNCYWU7FQ5TPBIPYZCSZFJCS.json","graph_json":"https://pith.science/api/pith-number/AWPNCYWU7FQ5TPBIPYZCSZFJCS/graph.json","events_json":"https://pith.science/api/pith-number/AWPNCYWU7FQ5TPBIPYZCSZFJCS/events.json","paper":"https://pith.science/paper/AWPNCYWU"},"agent_actions":{"view_html":"https://pith.science/pith/AWPNCYWU7FQ5TPBIPYZCSZFJCS","download_json":"https://pith.science/pith/AWPNCYWU7FQ5TPBIPYZCSZFJCS.json","view_paper":"https://pith.science/paper/AWPNCYWU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.03878&json=true","fetch_graph":"https://pith.science/api/pith-number/AWPNCYWU7FQ5TPBIPYZCSZFJCS/graph.json","fetch_events":"https://pith.science/api/pith-number/AWPNCYWU7FQ5TPBIPYZCSZFJCS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AWPNCYWU7FQ5TPBIPYZCSZFJCS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AWPNCYWU7FQ5TPBIPYZCSZFJCS/action/storage_attestation","attest_author":"https://pith.science/pith/AWPNCYWU7FQ5TPBIPYZCSZFJCS/action/author_attestation","sign_citation":"https://pith.science/pith/AWPNCYWU7FQ5TPBIPYZCSZFJCS/action/citation_signature","submit_replication":"https://pith.science/pith/AWPNCYWU7FQ5TPBIPYZCSZFJCS/action/replication_record"}},"created_at":"2026-07-05T00:24:42.388297+00:00","updated_at":"2026-07-05T00:24:42.388297+00:00"}