{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:FLRGWNDMYRJ5MIAM2B6TKA33FB","short_pith_number":"pith:FLRGWNDM","schema_version":"1.0","canonical_sha256":"2ae26b346cc453d6200cd07d35037b28609e82c4722ed2bee1f57512dbec0965","source":{"kind":"arxiv","id":"1311.2336","version":1},"attestation_state":"computed","paper":{"title":"Unstructured sequential testing in sensor networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Alexander Tartakovsky, Georgios Fellouris","submitted_at":"2013-11-11T02:38:58Z","abstract_excerpt":"We consider the problem of quickly detecting a signal in a sensor network when the subset of sensors in which signal may be present is completely unknown. We formulate this problem as a sequential hypothesis testing problem with a simple null (signal is absent everywhere) and a composite alternative (signal is present somewhere). We introduce a novel class of scalable sequential tests which, for any subset of affected sensors, minimize the expected sample size for a decision asymptotically, that is as the error probabilities go to 0. Moreover, we propose sequential tests that require minimal t"},"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":"1311.2336","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-11-11T02:38:58Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"92943d68335a60258136fc6f4bd4b83e1aeab014202f6f3b8ae17192556550fc","abstract_canon_sha256":"7dee92cc4b848a3adad62c0a09d6229527b626d0ab7851605e50a1c7696f1620"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:07:32.924420Z","signature_b64":"ZSB2DWe8dTZf/JmSeO7gcqWBiM4VdiuUfs53evWJVyw2pnoYnvrbngz1QbExXkpTD98difLEgmucVsZnMtPVBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ae26b346cc453d6200cd07d35037b28609e82c4722ed2bee1f57512dbec0965","last_reissued_at":"2026-05-18T03:07:32.924006Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:07:32.924006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unstructured sequential testing in sensor networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Alexander Tartakovsky, Georgios Fellouris","submitted_at":"2013-11-11T02:38:58Z","abstract_excerpt":"We consider the problem of quickly detecting a signal in a sensor network when the subset of sensors in which signal may be present is completely unknown. We formulate this problem as a sequential hypothesis testing problem with a simple null (signal is absent everywhere) and a composite alternative (signal is present somewhere). We introduce a novel class of scalable sequential tests which, for any subset of affected sensors, minimize the expected sample size for a decision asymptotically, that is as the error probabilities go to 0. Moreover, we propose sequential tests that require minimal t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.2336","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":""},"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":"1311.2336","created_at":"2026-05-18T03:07:32.924068+00:00"},{"alias_kind":"arxiv_version","alias_value":"1311.2336v1","created_at":"2026-05-18T03:07:32.924068+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.2336","created_at":"2026-05-18T03:07:32.924068+00:00"},{"alias_kind":"pith_short_12","alias_value":"FLRGWNDMYRJ5","created_at":"2026-05-18T12:27:45.050594+00:00"},{"alias_kind":"pith_short_16","alias_value":"FLRGWNDMYRJ5MIAM","created_at":"2026-05-18T12:27:45.050594+00:00"},{"alias_kind":"pith_short_8","alias_value":"FLRGWNDM","created_at":"2026-05-18T12:27:45.050594+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/FLRGWNDMYRJ5MIAM2B6TKA33FB","json":"https://pith.science/pith/FLRGWNDMYRJ5MIAM2B6TKA33FB.json","graph_json":"https://pith.science/api/pith-number/FLRGWNDMYRJ5MIAM2B6TKA33FB/graph.json","events_json":"https://pith.science/api/pith-number/FLRGWNDMYRJ5MIAM2B6TKA33FB/events.json","paper":"https://pith.science/paper/FLRGWNDM"},"agent_actions":{"view_html":"https://pith.science/pith/FLRGWNDMYRJ5MIAM2B6TKA33FB","download_json":"https://pith.science/pith/FLRGWNDMYRJ5MIAM2B6TKA33FB.json","view_paper":"https://pith.science/paper/FLRGWNDM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1311.2336&json=true","fetch_graph":"https://pith.science/api/pith-number/FLRGWNDMYRJ5MIAM2B6TKA33FB/graph.json","fetch_events":"https://pith.science/api/pith-number/FLRGWNDMYRJ5MIAM2B6TKA33FB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FLRGWNDMYRJ5MIAM2B6TKA33FB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FLRGWNDMYRJ5MIAM2B6TKA33FB/action/storage_attestation","attest_author":"https://pith.science/pith/FLRGWNDMYRJ5MIAM2B6TKA33FB/action/author_attestation","sign_citation":"https://pith.science/pith/FLRGWNDMYRJ5MIAM2B6TKA33FB/action/citation_signature","submit_replication":"https://pith.science/pith/FLRGWNDMYRJ5MIAM2B6TKA33FB/action/replication_record"}},"created_at":"2026-05-18T03:07:32.924068+00:00","updated_at":"2026-05-18T03:07:32.924068+00:00"}