{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:OPHOYG7QKJHEZOLSQHH2N2SV6W","short_pith_number":"pith:OPHOYG7Q","schema_version":"1.0","canonical_sha256":"73ceec1bf0524e4cb97281cfa6ea55f5a7f68c1b182d930f269b4ff03ef0af7b","source":{"kind":"arxiv","id":"1111.4555","version":2},"attestation_state":"computed","paper":{"title":"Large Deviations Performance of Consensus+Innovations Distributed Detection with Non-Gaussian Observations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Bruno Sinopoli, Dragana Bajovic, Dusan Jakovetic, Joao Xavier, Jose M. F. Moura","submitted_at":"2011-11-19T13:29:54Z","abstract_excerpt":"We establish the large deviations asymptotic performance (error exponent) of consensus+innovations distributed detection over random networks with generic (non-Gaussian) sensor observations. At each time instant, sensors 1) combine theirs with the decision variables of their neighbors (consensus) and 2) assimilate their new observations (innovations). This paper shows for general non-Gaussian distributions that consensus+innovations distributed detection exhibits a phase transition behavior with respect to the network degree of connectivity. Above a threshold, distributed is as good as central"},"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":"1111.4555","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2011-11-19T13:29:54Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"cb31e46fd351e27804f8707b77a6323038cf42044ce7a91237cf95aff648ae72","abstract_canon_sha256":"3b3e62fe3decc4ea75415688df01b040aafc124d3b943da67a376828a0902633"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:59:28.941803Z","signature_b64":"ntm1KD7F7hUDq3qBdDD7DloRwNMI914q6Vbj7ZjJlNKfeNBG1ebfUT+6iokIo12GTO7zCQtEQ6AsNKeP0ZWuDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73ceec1bf0524e4cb97281cfa6ea55f5a7f68c1b182d930f269b4ff03ef0af7b","last_reissued_at":"2026-05-18T01:59:28.941234Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:59:28.941234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large Deviations Performance of Consensus+Innovations Distributed Detection with Non-Gaussian Observations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Bruno Sinopoli, Dragana Bajovic, Dusan Jakovetic, Joao Xavier, Jose M. F. Moura","submitted_at":"2011-11-19T13:29:54Z","abstract_excerpt":"We establish the large deviations asymptotic performance (error exponent) of consensus+innovations distributed detection over random networks with generic (non-Gaussian) sensor observations. At each time instant, sensors 1) combine theirs with the decision variables of their neighbors (consensus) and 2) assimilate their new observations (innovations). This paper shows for general non-Gaussian distributions that consensus+innovations distributed detection exhibits a phase transition behavior with respect to the network degree of connectivity. Above a threshold, distributed is as good as central"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1111.4555","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":""},"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":"1111.4555","created_at":"2026-05-18T01:59:28.941322+00:00"},{"alias_kind":"arxiv_version","alias_value":"1111.4555v2","created_at":"2026-05-18T01:59:28.941322+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1111.4555","created_at":"2026-05-18T01:59:28.941322+00:00"},{"alias_kind":"pith_short_12","alias_value":"OPHOYG7QKJHE","created_at":"2026-05-18T12:26:37.096874+00:00"},{"alias_kind":"pith_short_16","alias_value":"OPHOYG7QKJHEZOLS","created_at":"2026-05-18T12:26:37.096874+00:00"},{"alias_kind":"pith_short_8","alias_value":"OPHOYG7Q","created_at":"2026-05-18T12:26:37.096874+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/OPHOYG7QKJHEZOLSQHH2N2SV6W","json":"https://pith.science/pith/OPHOYG7QKJHEZOLSQHH2N2SV6W.json","graph_json":"https://pith.science/api/pith-number/OPHOYG7QKJHEZOLSQHH2N2SV6W/graph.json","events_json":"https://pith.science/api/pith-number/OPHOYG7QKJHEZOLSQHH2N2SV6W/events.json","paper":"https://pith.science/paper/OPHOYG7Q"},"agent_actions":{"view_html":"https://pith.science/pith/OPHOYG7QKJHEZOLSQHH2N2SV6W","download_json":"https://pith.science/pith/OPHOYG7QKJHEZOLSQHH2N2SV6W.json","view_paper":"https://pith.science/paper/OPHOYG7Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1111.4555&json=true","fetch_graph":"https://pith.science/api/pith-number/OPHOYG7QKJHEZOLSQHH2N2SV6W/graph.json","fetch_events":"https://pith.science/api/pith-number/OPHOYG7QKJHEZOLSQHH2N2SV6W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OPHOYG7QKJHEZOLSQHH2N2SV6W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OPHOYG7QKJHEZOLSQHH2N2SV6W/action/storage_attestation","attest_author":"https://pith.science/pith/OPHOYG7QKJHEZOLSQHH2N2SV6W/action/author_attestation","sign_citation":"https://pith.science/pith/OPHOYG7QKJHEZOLSQHH2N2SV6W/action/citation_signature","submit_replication":"https://pith.science/pith/OPHOYG7QKJHEZOLSQHH2N2SV6W/action/replication_record"}},"created_at":"2026-05-18T01:59:28.941322+00:00","updated_at":"2026-05-18T01:59:28.941322+00:00"}