{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:FGWPYURIC44RCRTXZ4NFI4NU4I","short_pith_number":"pith:FGWPYURI","schema_version":"1.0","canonical_sha256":"29acfc52281739114677cf1a5471b4e23c7e8068694aa2f9e9c3840c8937f604","source":{"kind":"arxiv","id":"1708.07451","version":2},"attestation_state":"computed","paper":{"title":"Recovering Structured Data From Superimposed Non-Linear Measurements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","stat.AP"],"primary_cat":"cs.IT","authors_text":"Martin Genzel, Peter Jung","submitted_at":"2017-08-24T15:16:00Z","abstract_excerpt":"This work deals with the problem of distributed data acquisition under non-linear communication constraints. More specifically, we consider a model setup where $M$ distributed nodes take individual measurements of an unknown structured source vector $x_0 \\in \\mathbb{R}^n$, communicating their readings simultaneously to a central receiver. Since this procedure involves collisions and is usually imperfect, the receiver measures a superposition of non-linearly distorted signals. In a first step, we will show that an $s$-sparse vector $x_0$ can be successfully recovered from $O(s \\cdot\\log(2n/s))$"},"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":"1708.07451","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-08-24T15:16:00Z","cross_cats_sorted":["math.IT","stat.AP"],"title_canon_sha256":"1ab9d7f943bbc91e2a1ea83ed3a918f80d9c4284ab6f11011ebae51f6425df92","abstract_canon_sha256":"c8c7fa6392e6078c1520dd84377b30d38021024546c7370b6860532fb4a3de52"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:32:09.515989Z","signature_b64":"DS7jKELX/s1lJZ2FuWvTmyS8UWkBWLKx6GoqTSl2csglh3agS/EyyDlQC4xOF3ZMoJxACN4N/5UTEqdIeoIdDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"29acfc52281739114677cf1a5471b4e23c7e8068694aa2f9e9c3840c8937f604","last_reissued_at":"2026-07-05T00:32:09.515494Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:32:09.515494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recovering Structured Data From Superimposed Non-Linear Measurements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","stat.AP"],"primary_cat":"cs.IT","authors_text":"Martin Genzel, Peter Jung","submitted_at":"2017-08-24T15:16:00Z","abstract_excerpt":"This work deals with the problem of distributed data acquisition under non-linear communication constraints. More specifically, we consider a model setup where $M$ distributed nodes take individual measurements of an unknown structured source vector $x_0 \\in \\mathbb{R}^n$, communicating their readings simultaneously to a central receiver. Since this procedure involves collisions and is usually imperfect, the receiver measures a superposition of non-linearly distorted signals. In a first step, we will show that an $s$-sparse vector $x_0$ can be successfully recovered from $O(s \\cdot\\log(2n/s))$"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.07451","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/1708.07451/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":"1708.07451","created_at":"2026-07-05T00:32:09.515553+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.07451v2","created_at":"2026-07-05T00:32:09.515553+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.07451","created_at":"2026-07-05T00:32:09.515553+00:00"},{"alias_kind":"pith_short_12","alias_value":"FGWPYURIC44R","created_at":"2026-07-05T00:32:09.515553+00:00"},{"alias_kind":"pith_short_16","alias_value":"FGWPYURIC44RCRTX","created_at":"2026-07-05T00:32:09.515553+00:00"},{"alias_kind":"pith_short_8","alias_value":"FGWPYURI","created_at":"2026-07-05T00:32:09.515553+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.04433","citing_title":"Sharp Guarantees for Solving Random Equations with One-Bit Information","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FGWPYURIC44RCRTXZ4NFI4NU4I","json":"https://pith.science/pith/FGWPYURIC44RCRTXZ4NFI4NU4I.json","graph_json":"https://pith.science/api/pith-number/FGWPYURIC44RCRTXZ4NFI4NU4I/graph.json","events_json":"https://pith.science/api/pith-number/FGWPYURIC44RCRTXZ4NFI4NU4I/events.json","paper":"https://pith.science/paper/FGWPYURI"},"agent_actions":{"view_html":"https://pith.science/pith/FGWPYURIC44RCRTXZ4NFI4NU4I","download_json":"https://pith.science/pith/FGWPYURIC44RCRTXZ4NFI4NU4I.json","view_paper":"https://pith.science/paper/FGWPYURI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.07451&json=true","fetch_graph":"https://pith.science/api/pith-number/FGWPYURIC44RCRTXZ4NFI4NU4I/graph.json","fetch_events":"https://pith.science/api/pith-number/FGWPYURIC44RCRTXZ4NFI4NU4I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FGWPYURIC44RCRTXZ4NFI4NU4I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FGWPYURIC44RCRTXZ4NFI4NU4I/action/storage_attestation","attest_author":"https://pith.science/pith/FGWPYURIC44RCRTXZ4NFI4NU4I/action/author_attestation","sign_citation":"https://pith.science/pith/FGWPYURIC44RCRTXZ4NFI4NU4I/action/citation_signature","submit_replication":"https://pith.science/pith/FGWPYURIC44RCRTXZ4NFI4NU4I/action/replication_record"}},"created_at":"2026-07-05T00:32:09.515553+00:00","updated_at":"2026-07-05T00:32:09.515553+00:00"}