{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XDJ57CXWBX7OESJI2K4RBTAJVS","short_pith_number":"pith:XDJ57CXW","schema_version":"1.0","canonical_sha256":"b8d3df8af60dfee24928d2b910cc09acb31ef2baf5b7d7dc08ddddf68b81b37c","source":{"kind":"arxiv","id":"2403.12400","version":1},"attestation_state":"computed","paper":{"title":"Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Fanyi Meng, Guangxu Zhu, Hang Li, Tingwei Chen, Xiaoyang Li, Zijian Zhao","submitted_at":"2024-03-19T03:16:52Z","abstract_excerpt":"Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which negatively impacts the performance of the learning models. To overcome this challenge, we propose a deep learning model based on Bidirectional Encoder Representations from Transformers (BERT) for CSI recovery, named CSI-BERT. CSI-BERT can be trained in an self-supervised manner on the target dataset without the need for additional data. Furthermore, unlike traditional interpolation methods that focus on one subcarrier a"},"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":"2403.12400","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-03-19T03:16:52Z","cross_cats_sorted":["cs.AI","eess.SP"],"title_canon_sha256":"846bb476bd30e8d7150ae45af83ce156bc0e52a007b7848f1558e866878bd7e4","abstract_canon_sha256":"6cd566b393764cabcd90c2c05e0ea3a42819e89818d6b7386bb980f4c946eaec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:45:45.547160Z","signature_b64":"O+vOFGBLgHdRtO7AQdo+BwQ7cAErJs5ug/K361fZt0g75dpnn47FsAS+Znj1F3rX+3+PjO4cl3oBCiXnOKcgCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8d3df8af60dfee24928d2b910cc09acb31ef2baf5b7d7dc08ddddf68b81b37c","last_reissued_at":"2026-07-05T09:45:45.546673Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:45:45.546673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Fanyi Meng, Guangxu Zhu, Hang Li, Tingwei Chen, Xiaoyang Li, Zijian Zhao","submitted_at":"2024-03-19T03:16:52Z","abstract_excerpt":"Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which negatively impacts the performance of the learning models. To overcome this challenge, we propose a deep learning model based on Bidirectional Encoder Representations from Transformers (BERT) for CSI recovery, named CSI-BERT. CSI-BERT can be trained in an self-supervised manner on the target dataset without the need for additional data. Furthermore, unlike traditional interpolation methods that focus on one subcarrier a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.12400","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/2403.12400/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":"2403.12400","created_at":"2026-07-05T09:45:45.546733+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.12400v1","created_at":"2026-07-05T09:45:45.546733+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.12400","created_at":"2026-07-05T09:45:45.546733+00:00"},{"alias_kind":"pith_short_12","alias_value":"XDJ57CXWBX7O","created_at":"2026-07-05T09:45:45.546733+00:00"},{"alias_kind":"pith_short_16","alias_value":"XDJ57CXWBX7OESJI","created_at":"2026-07-05T09:45:45.546733+00:00"},{"alias_kind":"pith_short_8","alias_value":"XDJ57CXW","created_at":"2026-07-05T09:45:45.546733+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.01802","citing_title":"BERT4MIMO: A Foundation Model using BERT Architecture for Massive MIMO Channel State Information Prediction","ref_index":30,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XDJ57CXWBX7OESJI2K4RBTAJVS","json":"https://pith.science/pith/XDJ57CXWBX7OESJI2K4RBTAJVS.json","graph_json":"https://pith.science/api/pith-number/XDJ57CXWBX7OESJI2K4RBTAJVS/graph.json","events_json":"https://pith.science/api/pith-number/XDJ57CXWBX7OESJI2K4RBTAJVS/events.json","paper":"https://pith.science/paper/XDJ57CXW"},"agent_actions":{"view_html":"https://pith.science/pith/XDJ57CXWBX7OESJI2K4RBTAJVS","download_json":"https://pith.science/pith/XDJ57CXWBX7OESJI2K4RBTAJVS.json","view_paper":"https://pith.science/paper/XDJ57CXW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.12400&json=true","fetch_graph":"https://pith.science/api/pith-number/XDJ57CXWBX7OESJI2K4RBTAJVS/graph.json","fetch_events":"https://pith.science/api/pith-number/XDJ57CXWBX7OESJI2K4RBTAJVS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XDJ57CXWBX7OESJI2K4RBTAJVS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XDJ57CXWBX7OESJI2K4RBTAJVS/action/storage_attestation","attest_author":"https://pith.science/pith/XDJ57CXWBX7OESJI2K4RBTAJVS/action/author_attestation","sign_citation":"https://pith.science/pith/XDJ57CXWBX7OESJI2K4RBTAJVS/action/citation_signature","submit_replication":"https://pith.science/pith/XDJ57CXWBX7OESJI2K4RBTAJVS/action/replication_record"}},"created_at":"2026-07-05T09:45:45.546733+00:00","updated_at":"2026-07-05T09:45:45.546733+00:00"}