{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:5RYG66OED23JPJ2PTZBWAVGPK7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e5165602b7b84cc8cd243df2b21014b45731f797d21125f9a32ca34973df6b08","cross_cats_sorted":["cs.AI","cs.IT","cs.LG","math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NI","submitted_at":"2025-03-23T12:27:20Z","title_canon_sha256":"0116ed121293480b27fffb230234d7004e287740a502600cd09889dc30825936"},"schema_version":"1.0","source":{"id":"2504.03690","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.03690","created_at":"2026-07-05T10:44:46Z"},{"alias_kind":"arxiv_version","alias_value":"2504.03690v1","created_at":"2026-07-05T10:44:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.03690","created_at":"2026-07-05T10:44:46Z"},{"alias_kind":"pith_short_12","alias_value":"5RYG66OED23J","created_at":"2026-07-05T10:44:46Z"},{"alias_kind":"pith_short_16","alias_value":"5RYG66OED23JPJ2P","created_at":"2026-07-05T10:44:46Z"},{"alias_kind":"pith_short_8","alias_value":"5RYG66OE","created_at":"2026-07-05T10:44:46Z"}],"graph_snapshots":[{"event_id":"sha256:6720d92e26635a7b268789fc33df8de208afdae285b0f6b6cde320dbe8dcf706","target":"graph","created_at":"2026-07-05T10:44:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2504.03690/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We consider multiple transmitters aiming to communicate their source signals (e.g., images) over a multiple access channel (MAC). Conventional communication systems minimize interference by orthogonally allocating resources (time and/or bandwidth) among users, which limits their capacity. We introduce a machine learning (ML)-aided wireless image transmission method that merges compression and channel coding using a multi-view autoencoder, which allows the transmitters to use all the available channel resources simultaneously, resulting in a non-orthogonal multiple access (NOMA) scheme. The rec","authors_text":"Can Karamanli, Deniz Gunduz, Selim F. Yilmaz","cross_cats":["cs.AI","cs.IT","cs.LG","math.IT"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NI","submitted_at":"2025-03-23T12:27:20Z","title":"Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.03690","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:02a15c48ea6a8a820b4d77a40df3861f2771972da8b0e1535fec203a5fd6b4f2","target":"record","created_at":"2026-07-05T10:44:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e5165602b7b84cc8cd243df2b21014b45731f797d21125f9a32ca34973df6b08","cross_cats_sorted":["cs.AI","cs.IT","cs.LG","math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NI","submitted_at":"2025-03-23T12:27:20Z","title_canon_sha256":"0116ed121293480b27fffb230234d7004e287740a502600cd09889dc30825936"},"schema_version":"1.0","source":{"id":"2504.03690","kind":"arxiv","version":1}},"canonical_sha256":"ec706f79c41eb697a74f9e436054cf57c1f11ae917e16ea7f18c6a5088f60949","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ec706f79c41eb697a74f9e436054cf57c1f11ae917e16ea7f18c6a5088f60949","first_computed_at":"2026-07-05T10:44:46.380170Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:44:46.380170Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"R3oHIYPEvP56ycYrjL3PT1Ta4xa/10o+KyvVbVCpzrTVdT9e1yRmjfQpAMk77zlAvGDF9LGV0/hhvVI2N0ROCA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:44:46.380683Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.03690","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:02a15c48ea6a8a820b4d77a40df3861f2771972da8b0e1535fec203a5fd6b4f2","sha256:6720d92e26635a7b268789fc33df8de208afdae285b0f6b6cde320dbe8dcf706"],"state_sha256":"d0e4fce44a707f1f8022c9a6dd8f63823927afa20d1874ac58ff291b46bc458b"}