{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:LK32BFGKGK62DC3XLHD5OIYRYL","short_pith_number":"pith:LK32BFGK","schema_version":"1.0","canonical_sha256":"5ab7a094ca32bda18b7759c7d72311c2f035c7685f5ce41377c7cca0fa6b57b5","source":{"kind":"arxiv","id":"1710.05247","version":1},"attestation_state":"computed","paper":{"title":"On Hashing-Based Approaches to Approximate DNF-Counting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LO","authors_text":"(2) Rice University), Aditya A. Shrotri (2), Kuldeep S. Meel (1), Moshe Y. Vardi (2) ((1) National University of Singapore","submitted_at":"2017-10-14T23:22:09Z","abstract_excerpt":"Propositional model counting is a fundamental problem in artificial intelligence with a wide variety of applications, such as probabilistic inference, decision making under uncertainty, and probabilistic databases. Consequently, the problem is of theoretical as well as practical interest. When the constraints are expressed as DNF formulas, Monte Carlo-based techniques have been shown to provide a fully polynomial randomized approximation scheme (FPRAS). For CNF constraints, hashing-based approximation techniques have been demonstrated to be highly successful. Furthermore, it was shown that has"},"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":"1710.05247","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LO","submitted_at":"2017-10-14T23:22:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"372c5d764b3ef9ba88f313d08905c4b465d9eaee220db7b4cab1cc4a6221e840","abstract_canon_sha256":"ecab55cacab03e6d8b15e3b1c73393c8e80e1661023ee1bcfbc6926d80397a32"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:53.878111Z","signature_b64":"5ZN4OAvcm3kqhUyJbvC0uUOLp3QHCB7UJ7pkOSjpNf3KDsQXhV8K0EkOZv+j8IYyK+0CVLv3cQ7tZdZg7OF1DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ab7a094ca32bda18b7759c7d72311c2f035c7685f5ce41377c7cca0fa6b57b5","last_reissued_at":"2026-05-18T00:32:53.877443Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:53.877443Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Hashing-Based Approaches to Approximate DNF-Counting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LO","authors_text":"(2) Rice University), Aditya A. Shrotri (2), Kuldeep S. Meel (1), Moshe Y. Vardi (2) ((1) National University of Singapore","submitted_at":"2017-10-14T23:22:09Z","abstract_excerpt":"Propositional model counting is a fundamental problem in artificial intelligence with a wide variety of applications, such as probabilistic inference, decision making under uncertainty, and probabilistic databases. Consequently, the problem is of theoretical as well as practical interest. When the constraints are expressed as DNF formulas, Monte Carlo-based techniques have been shown to provide a fully polynomial randomized approximation scheme (FPRAS). For CNF constraints, hashing-based approximation techniques have been demonstrated to be highly successful. Furthermore, it was shown that has"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.05247","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":"1710.05247","created_at":"2026-05-18T00:32:53.877549+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.05247v1","created_at":"2026-05-18T00:32:53.877549+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.05247","created_at":"2026-05-18T00:32:53.877549+00:00"},{"alias_kind":"pith_short_12","alias_value":"LK32BFGKGK62","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"LK32BFGKGK62DC3X","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"LK32BFGK","created_at":"2026-05-18T12:31:28.150371+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/LK32BFGKGK62DC3XLHD5OIYRYL","json":"https://pith.science/pith/LK32BFGKGK62DC3XLHD5OIYRYL.json","graph_json":"https://pith.science/api/pith-number/LK32BFGKGK62DC3XLHD5OIYRYL/graph.json","events_json":"https://pith.science/api/pith-number/LK32BFGKGK62DC3XLHD5OIYRYL/events.json","paper":"https://pith.science/paper/LK32BFGK"},"agent_actions":{"view_html":"https://pith.science/pith/LK32BFGKGK62DC3XLHD5OIYRYL","download_json":"https://pith.science/pith/LK32BFGKGK62DC3XLHD5OIYRYL.json","view_paper":"https://pith.science/paper/LK32BFGK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.05247&json=true","fetch_graph":"https://pith.science/api/pith-number/LK32BFGKGK62DC3XLHD5OIYRYL/graph.json","fetch_events":"https://pith.science/api/pith-number/LK32BFGKGK62DC3XLHD5OIYRYL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LK32BFGKGK62DC3XLHD5OIYRYL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LK32BFGKGK62DC3XLHD5OIYRYL/action/storage_attestation","attest_author":"https://pith.science/pith/LK32BFGKGK62DC3XLHD5OIYRYL/action/author_attestation","sign_citation":"https://pith.science/pith/LK32BFGKGK62DC3XLHD5OIYRYL/action/citation_signature","submit_replication":"https://pith.science/pith/LK32BFGKGK62DC3XLHD5OIYRYL/action/replication_record"}},"created_at":"2026-05-18T00:32:53.877549+00:00","updated_at":"2026-05-18T00:32:53.877549+00:00"}