{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ANKCUHTIRPLURKNPLSJ2N6SFRW","short_pith_number":"pith:ANKCUHTI","schema_version":"1.0","canonical_sha256":"03542a1e688bd748a9af5c93a6fa458da47b4730692a511bb5a70b36e2352996","source":{"kind":"arxiv","id":"2501.10926","version":1},"attestation_state":"computed","paper":{"title":"A Semantic Approach to Successive Interference Cancellation for Multiple Access Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Kaiming Shen, Mingxiao Li, Shuguang Cui","submitted_at":"2025-01-19T03:03:27Z","abstract_excerpt":"Differing from the conventional communication system paradigm that models information source as a sequence of (i.i.d. or stationary) random variables, the semantic approach aims at extracting and sending the high-level features of the content deeply contained in the source, thereby breaking the performance limits from the statistical information theory. As a pioneering work in this area, the deep learning-enabled semantic communication (DeepSC) constitutes a novel algorithmic framework based on the transformer--which is a deep learning tool widely used to process text numerically. The main goa"},"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":"2501.10926","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2025-01-19T03:03:27Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"cffd458b79a3cf1edb3643b40f0b1f3f2c525a473093f888c4872ed1d69be197","abstract_canon_sha256":"24b1f9b43f498a42aca490ff3e6e19e9dd3741bf9e5c8ba02361606e0702c1c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:03:00.844025Z","signature_b64":"lOP6orhs9zOFxypOwfnPx/VNAB83zlU0TsKHcjFCk+lEB75/9tfjG75rJ3OhyIH5c2rChp6qjFnPAh9hBLYLAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03542a1e688bd748a9af5c93a6fa458da47b4730692a511bb5a70b36e2352996","last_reissued_at":"2026-07-05T10:03:00.843445Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:03:00.843445Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Semantic Approach to Successive Interference Cancellation for Multiple Access Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Kaiming Shen, Mingxiao Li, Shuguang Cui","submitted_at":"2025-01-19T03:03:27Z","abstract_excerpt":"Differing from the conventional communication system paradigm that models information source as a sequence of (i.i.d. or stationary) random variables, the semantic approach aims at extracting and sending the high-level features of the content deeply contained in the source, thereby breaking the performance limits from the statistical information theory. As a pioneering work in this area, the deep learning-enabled semantic communication (DeepSC) constitutes a novel algorithmic framework based on the transformer--which is a deep learning tool widely used to process text numerically. The main goa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10926","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/2501.10926/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":"2501.10926","created_at":"2026-07-05T10:03:00.843506+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.10926v1","created_at":"2026-07-05T10:03:00.843506+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10926","created_at":"2026-07-05T10:03:00.843506+00:00"},{"alias_kind":"pith_short_12","alias_value":"ANKCUHTIRPLU","created_at":"2026-07-05T10:03:00.843506+00:00"},{"alias_kind":"pith_short_16","alias_value":"ANKCUHTIRPLURKNP","created_at":"2026-07-05T10:03:00.843506+00:00"},{"alias_kind":"pith_short_8","alias_value":"ANKCUHTI","created_at":"2026-07-05T10:03:00.843506+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/ANKCUHTIRPLURKNPLSJ2N6SFRW","json":"https://pith.science/pith/ANKCUHTIRPLURKNPLSJ2N6SFRW.json","graph_json":"https://pith.science/api/pith-number/ANKCUHTIRPLURKNPLSJ2N6SFRW/graph.json","events_json":"https://pith.science/api/pith-number/ANKCUHTIRPLURKNPLSJ2N6SFRW/events.json","paper":"https://pith.science/paper/ANKCUHTI"},"agent_actions":{"view_html":"https://pith.science/pith/ANKCUHTIRPLURKNPLSJ2N6SFRW","download_json":"https://pith.science/pith/ANKCUHTIRPLURKNPLSJ2N6SFRW.json","view_paper":"https://pith.science/paper/ANKCUHTI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.10926&json=true","fetch_graph":"https://pith.science/api/pith-number/ANKCUHTIRPLURKNPLSJ2N6SFRW/graph.json","fetch_events":"https://pith.science/api/pith-number/ANKCUHTIRPLURKNPLSJ2N6SFRW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ANKCUHTIRPLURKNPLSJ2N6SFRW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ANKCUHTIRPLURKNPLSJ2N6SFRW/action/storage_attestation","attest_author":"https://pith.science/pith/ANKCUHTIRPLURKNPLSJ2N6SFRW/action/author_attestation","sign_citation":"https://pith.science/pith/ANKCUHTIRPLURKNPLSJ2N6SFRW/action/citation_signature","submit_replication":"https://pith.science/pith/ANKCUHTIRPLURKNPLSJ2N6SFRW/action/replication_record"}},"created_at":"2026-07-05T10:03:00.843506+00:00","updated_at":"2026-07-05T10:03:00.843506+00:00"}