{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:P2PO5DKNNEUTRZ63AXU26QJ5JM","short_pith_number":"pith:P2PO5DKN","schema_version":"1.0","canonical_sha256":"7e9eee8d4d692938e7db05e9af413d4b128f706b4eef9c64b43c1cb21f068fc0","source":{"kind":"arxiv","id":"1904.07828","version":1},"attestation_state":"computed","paper":{"title":"An Efficient Formula Synthesis Method with Past Signal Temporal Logic","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LO","authors_text":"Ebru Aydin Gol, Mert Ergurtuna","submitted_at":"2019-04-16T17:12:24Z","abstract_excerpt":"In this work, we propose a novel method to find temporal properties that lead to the unexpected behaviors from labeled dataset. We express these properties in past time Signal Temporal Logic (ptSTL). First, we present a novel approach for finding parameters of a template ptSTL formula, which extends the results on monotonicity based parameter synthesis. The proposed method optimizes a given monotone criteria while bounding an error. Then, we employ the parameter synthesis method in an iterative unguided formula synthesis framework. In particular, we combine optimized formulas iteratively to de"},"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":"1904.07828","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LO","submitted_at":"2019-04-16T17:12:24Z","cross_cats_sorted":[],"title_canon_sha256":"6b78797073e85af50453321cb98c09f9b4c92fe5a04ed533a2e0e8ce6c070b0e","abstract_canon_sha256":"35cf724141a1865f66eaad6576f99f8658f40c67bb44ce0fa8bec1c51749c7bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:24.123027Z","signature_b64":"z9feZClhbNFLdgJTKBGMO6evIUdnoERfCYk94+h9t378AbJV1LRijD/tFN7IVWJgE+059NmLkzMBO1m6aCK4Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e9eee8d4d692938e7db05e9af413d4b128f706b4eef9c64b43c1cb21f068fc0","last_reissued_at":"2026-05-17T23:48:24.122535Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:24.122535Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Efficient Formula Synthesis Method with Past Signal Temporal Logic","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LO","authors_text":"Ebru Aydin Gol, Mert Ergurtuna","submitted_at":"2019-04-16T17:12:24Z","abstract_excerpt":"In this work, we propose a novel method to find temporal properties that lead to the unexpected behaviors from labeled dataset. We express these properties in past time Signal Temporal Logic (ptSTL). First, we present a novel approach for finding parameters of a template ptSTL formula, which extends the results on monotonicity based parameter synthesis. The proposed method optimizes a given monotone criteria while bounding an error. Then, we employ the parameter synthesis method in an iterative unguided formula synthesis framework. In particular, we combine optimized formulas iteratively to de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.07828","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":"1904.07828","created_at":"2026-05-17T23:48:24.122613+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.07828v1","created_at":"2026-05-17T23:48:24.122613+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.07828","created_at":"2026-05-17T23:48:24.122613+00:00"},{"alias_kind":"pith_short_12","alias_value":"P2PO5DKNNEUT","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"P2PO5DKNNEUTRZ63","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"P2PO5DKN","created_at":"2026-05-18T12:33:24.271573+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/P2PO5DKNNEUTRZ63AXU26QJ5JM","json":"https://pith.science/pith/P2PO5DKNNEUTRZ63AXU26QJ5JM.json","graph_json":"https://pith.science/api/pith-number/P2PO5DKNNEUTRZ63AXU26QJ5JM/graph.json","events_json":"https://pith.science/api/pith-number/P2PO5DKNNEUTRZ63AXU26QJ5JM/events.json","paper":"https://pith.science/paper/P2PO5DKN"},"agent_actions":{"view_html":"https://pith.science/pith/P2PO5DKNNEUTRZ63AXU26QJ5JM","download_json":"https://pith.science/pith/P2PO5DKNNEUTRZ63AXU26QJ5JM.json","view_paper":"https://pith.science/paper/P2PO5DKN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.07828&json=true","fetch_graph":"https://pith.science/api/pith-number/P2PO5DKNNEUTRZ63AXU26QJ5JM/graph.json","fetch_events":"https://pith.science/api/pith-number/P2PO5DKNNEUTRZ63AXU26QJ5JM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P2PO5DKNNEUTRZ63AXU26QJ5JM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P2PO5DKNNEUTRZ63AXU26QJ5JM/action/storage_attestation","attest_author":"https://pith.science/pith/P2PO5DKNNEUTRZ63AXU26QJ5JM/action/author_attestation","sign_citation":"https://pith.science/pith/P2PO5DKNNEUTRZ63AXU26QJ5JM/action/citation_signature","submit_replication":"https://pith.science/pith/P2PO5DKNNEUTRZ63AXU26QJ5JM/action/replication_record"}},"created_at":"2026-05-17T23:48:24.122613+00:00","updated_at":"2026-05-17T23:48:24.122613+00:00"}