{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:P5THBJ7SSPCT5YXGWVV5RPFSQW","short_pith_number":"pith:P5THBJ7S","canonical_record":{"source":{"id":"2507.11922","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-07-16T05:37:42Z","cross_cats_sorted":["stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"cff749d7327a65ec3d306c9e4af51fb02102e9d674ca93f9f28d860c04ffa42c","abstract_canon_sha256":"936e798f287e5d76abcccd82b279b12d3d055074e476c7832780d26a1f52d017"},"schema_version":"1.0"},"canonical_sha256":"7f6670a7f293c53ee2e6b56bd8bcb285b44f524cdbda46c0987893e9f0f3acde","source":{"kind":"arxiv","id":"2507.11922","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.11922","created_at":"2026-05-17T23:39:01Z"},{"alias_kind":"arxiv_version","alias_value":"2507.11922v2","created_at":"2026-05-17T23:39:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.11922","created_at":"2026-05-17T23:39:01Z"},{"alias_kind":"pith_short_12","alias_value":"P5THBJ7SSPCT","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"P5THBJ7SSPCT5YXG","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"P5THBJ7S","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:P5THBJ7SSPCT5YXGWVV5RPFSQW","target":"record","payload":{"canonical_record":{"source":{"id":"2507.11922","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-07-16T05:37:42Z","cross_cats_sorted":["stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"cff749d7327a65ec3d306c9e4af51fb02102e9d674ca93f9f28d860c04ffa42c","abstract_canon_sha256":"936e798f287e5d76abcccd82b279b12d3d055074e476c7832780d26a1f52d017"},"schema_version":"1.0"},"canonical_sha256":"7f6670a7f293c53ee2e6b56bd8bcb285b44f524cdbda46c0987893e9f0f3acde","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:01.205650Z","signature_b64":"QDScjjIROzcZh7zCFeVNKP+YOECZexLkFUxSGNweZbfwQMcYEMCsTxQ6ecJjUtykvmpK8xLvqsgDXda51zfZCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f6670a7f293c53ee2e6b56bd8bcb285b44f524cdbda46c0987893e9f0f3acde","last_reissued_at":"2026-05-17T23:39:01.204908Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:01.204908Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.11922","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SkFhFcseuJZDUiFYt07UWJ99xGPj8v8nMnLczMcPQVrXe6DFyYdFMVY2RRaSUJHXSyT05+PsiXH/RjWXtt22AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T10:26:44.432132Z"},"content_sha256":"b0ebde5bb27e906b4f9e38b9dfc46b8257b4a68815b7c2c01cc90301545372d4","schema_version":"1.0","event_id":"sha256:b0ebde5bb27e906b4f9e38b9dfc46b8257b4a68815b7c2c01cc90301545372d4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:P5THBJ7SSPCT5YXGWVV5RPFSQW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Signal Proportion Estimation Through Leveraging Arbitrary Covariance Structures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Jingtian Bai, Xinge Jessie Jeng","submitted_at":"2025-07-16T05:37:42Z","abstract_excerpt":"Accurately estimating the proportion of true signals among a large number of variables is crucial for enhancing the precision and reliability of scientific research. Traditional signal proportion estimators often assume independence among variables and specific signal sparsity conditions, limiting their applicability in real-world scenarios where such assumptions may not hold. This paper introduces a novel signal proportion estimator that leverages arbitrary covariance dependence information among variables, thereby improving performance across a wide range of sparsity levels and dependence st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.11922","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EhnuWe7qbbnBfvXAdTfnrrJOa/+7TwXu9qlo1hUd9rziUMUVew0S98+76YVXA3uQtOyJ2Zi9LVWatU9170XlAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T10:26:44.432795Z"},"content_sha256":"89019368123613bea458abb0608aceaed0bfa2e5dcf40abbfafb97e9ff77cbbf","schema_version":"1.0","event_id":"sha256:89019368123613bea458abb0608aceaed0bfa2e5dcf40abbfafb97e9ff77cbbf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P5THBJ7SSPCT5YXGWVV5RPFSQW/bundle.json","state_url":"https://pith.science/pith/P5THBJ7SSPCT5YXGWVV5RPFSQW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P5THBJ7SSPCT5YXGWVV5RPFSQW/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-21T10:26:44Z","links":{"resolver":"https://pith.science/pith/P5THBJ7SSPCT5YXGWVV5RPFSQW","bundle":"https://pith.science/pith/P5THBJ7SSPCT5YXGWVV5RPFSQW/bundle.json","state":"https://pith.science/pith/P5THBJ7SSPCT5YXGWVV5RPFSQW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P5THBJ7SSPCT5YXGWVV5RPFSQW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:P5THBJ7SSPCT5YXGWVV5RPFSQW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"936e798f287e5d76abcccd82b279b12d3d055074e476c7832780d26a1f52d017","cross_cats_sorted":["stat.ME","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-07-16T05:37:42Z","title_canon_sha256":"cff749d7327a65ec3d306c9e4af51fb02102e9d674ca93f9f28d860c04ffa42c"},"schema_version":"1.0","source":{"id":"2507.11922","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.11922","created_at":"2026-05-17T23:39:01Z"},{"alias_kind":"arxiv_version","alias_value":"2507.11922v2","created_at":"2026-05-17T23:39:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.11922","created_at":"2026-05-17T23:39:01Z"},{"alias_kind":"pith_short_12","alias_value":"P5THBJ7SSPCT","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"P5THBJ7SSPCT5YXG","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"P5THBJ7S","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:89019368123613bea458abb0608aceaed0bfa2e5dcf40abbfafb97e9ff77cbbf","target":"graph","created_at":"2026-05-17T23:39:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Accurately estimating the proportion of true signals among a large number of variables is crucial for enhancing the precision and reliability of scientific research. Traditional signal proportion estimators often assume independence among variables and specific signal sparsity conditions, limiting their applicability in real-world scenarios where such assumptions may not hold. This paper introduces a novel signal proportion estimator that leverages arbitrary covariance dependence information among variables, thereby improving performance across a wide range of sparsity levels and dependence st","authors_text":"Jingtian Bai, Xinge Jessie Jeng","cross_cats":["stat.ME","stat.ML","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-07-16T05:37:42Z","title":"Enhancing Signal Proportion Estimation Through Leveraging Arbitrary Covariance Structures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.11922","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b0ebde5bb27e906b4f9e38b9dfc46b8257b4a68815b7c2c01cc90301545372d4","target":"record","created_at":"2026-05-17T23:39:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"936e798f287e5d76abcccd82b279b12d3d055074e476c7832780d26a1f52d017","cross_cats_sorted":["stat.ME","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-07-16T05:37:42Z","title_canon_sha256":"cff749d7327a65ec3d306c9e4af51fb02102e9d674ca93f9f28d860c04ffa42c"},"schema_version":"1.0","source":{"id":"2507.11922","kind":"arxiv","version":2}},"canonical_sha256":"7f6670a7f293c53ee2e6b56bd8bcb285b44f524cdbda46c0987893e9f0f3acde","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f6670a7f293c53ee2e6b56bd8bcb285b44f524cdbda46c0987893e9f0f3acde","first_computed_at":"2026-05-17T23:39:01.204908Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:01.204908Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QDScjjIROzcZh7zCFeVNKP+YOECZexLkFUxSGNweZbfwQMcYEMCsTxQ6ecJjUtykvmpK8xLvqsgDXda51zfZCw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:01.205650Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.11922","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0ebde5bb27e906b4f9e38b9dfc46b8257b4a68815b7c2c01cc90301545372d4","sha256:89019368123613bea458abb0608aceaed0bfa2e5dcf40abbfafb97e9ff77cbbf"],"state_sha256":"8ac82ee2bf2baf5cfcce06049d2b57054a7ca4994a45ec18de40dad7e5f3353c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"thWboeEWpAugIyEWPeKLlOEfDQcoLiApDW0CwLonqVAnP5MA66bVXMrLfkvqkPQ1ZvsPwaNJP7k3xQPkmMeRBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T10:26:44.436161Z","bundle_sha256":"1793c24dba14ed09adcaf7dcefb00a1b5432eb7fa73e9caf9e8976ca4eb714f6"}}