{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:X6DLHXIAZQQAE2RWHZQJIZ77GS","short_pith_number":"pith:X6DLHXIA","schema_version":"1.0","canonical_sha256":"bf86b3dd00cc20026a363e609467ff34b03dde420c8f4463afb333551bc00314","source":{"kind":"arxiv","id":"1502.04169","version":2},"attestation_state":"computed","paper":{"title":"Computationally Tractable Algorithms for Finding a Subset of Non-defective Items from a Large Population","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Abhay Sharma, Chandra R. Murthy","submitted_at":"2015-02-14T05:49:55Z","abstract_excerpt":"In the classical non-adaptive group testing setup, pools of items are tested together, and the main goal of a recovery algorithm is to identify the \"complete defective set\" given the outcomes of different group tests. In contrast, the main goal of a \"non-defective subset recovery\" algorithm is to identify a \"subset\" of non-defective items given the test outcomes. In this paper, we present a suite of computationally efficient and analytically tractable non-defective subset recovery algorithms. By analyzing the probability of error of the algorithms, we obtain bounds on the number of tests requi"},"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":"1502.04169","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2015-02-14T05:49:55Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"9733d8a5878c7072faec61f540df4f740c69e56e73bc1b7a1702c959bd0e28d8","abstract_canon_sha256":"2a58955d6bbde3c56375739d48e0d349e773e3af38c4b3d230d116453b46d0da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:55.459735Z","signature_b64":"NqvL7ErkjknKxXuvuGgS/hdMySHw2El3+XOnGocWfmDJMqhVTHra3iRTUI14EawhqP+Rl9K3crxlWs6HF3wKBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf86b3dd00cc20026a363e609467ff34b03dde420c8f4463afb333551bc00314","last_reissued_at":"2026-05-18T01:19:55.459205Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:55.459205Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Computationally Tractable Algorithms for Finding a Subset of Non-defective Items from a Large Population","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Abhay Sharma, Chandra R. Murthy","submitted_at":"2015-02-14T05:49:55Z","abstract_excerpt":"In the classical non-adaptive group testing setup, pools of items are tested together, and the main goal of a recovery algorithm is to identify the \"complete defective set\" given the outcomes of different group tests. In contrast, the main goal of a \"non-defective subset recovery\" algorithm is to identify a \"subset\" of non-defective items given the test outcomes. In this paper, we present a suite of computationally efficient and analytically tractable non-defective subset recovery algorithms. By analyzing the probability of error of the algorithms, we obtain bounds on the number of tests requi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.04169","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":"1502.04169","created_at":"2026-05-18T01:19:55.459301+00:00"},{"alias_kind":"arxiv_version","alias_value":"1502.04169v2","created_at":"2026-05-18T01:19:55.459301+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.04169","created_at":"2026-05-18T01:19:55.459301+00:00"},{"alias_kind":"pith_short_12","alias_value":"X6DLHXIAZQQA","created_at":"2026-05-18T12:29:47.479230+00:00"},{"alias_kind":"pith_short_16","alias_value":"X6DLHXIAZQQAE2RW","created_at":"2026-05-18T12:29:47.479230+00:00"},{"alias_kind":"pith_short_8","alias_value":"X6DLHXIA","created_at":"2026-05-18T12:29:47.479230+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/X6DLHXIAZQQAE2RWHZQJIZ77GS","json":"https://pith.science/pith/X6DLHXIAZQQAE2RWHZQJIZ77GS.json","graph_json":"https://pith.science/api/pith-number/X6DLHXIAZQQAE2RWHZQJIZ77GS/graph.json","events_json":"https://pith.science/api/pith-number/X6DLHXIAZQQAE2RWHZQJIZ77GS/events.json","paper":"https://pith.science/paper/X6DLHXIA"},"agent_actions":{"view_html":"https://pith.science/pith/X6DLHXIAZQQAE2RWHZQJIZ77GS","download_json":"https://pith.science/pith/X6DLHXIAZQQAE2RWHZQJIZ77GS.json","view_paper":"https://pith.science/paper/X6DLHXIA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1502.04169&json=true","fetch_graph":"https://pith.science/api/pith-number/X6DLHXIAZQQAE2RWHZQJIZ77GS/graph.json","fetch_events":"https://pith.science/api/pith-number/X6DLHXIAZQQAE2RWHZQJIZ77GS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X6DLHXIAZQQAE2RWHZQJIZ77GS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X6DLHXIAZQQAE2RWHZQJIZ77GS/action/storage_attestation","attest_author":"https://pith.science/pith/X6DLHXIAZQQAE2RWHZQJIZ77GS/action/author_attestation","sign_citation":"https://pith.science/pith/X6DLHXIAZQQAE2RWHZQJIZ77GS/action/citation_signature","submit_replication":"https://pith.science/pith/X6DLHXIAZQQAE2RWHZQJIZ77GS/action/replication_record"}},"created_at":"2026-05-18T01:19:55.459301+00:00","updated_at":"2026-05-18T01:19:55.459301+00:00"}