{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:34WBC6SZLILGEJ3LYFQF5DEUML","short_pith_number":"pith:34WBC6SZ","canonical_record":{"source":{"id":"2403.12284","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-03-18T22:09:48Z","cross_cats_sorted":["q-bio.QM","stat.AP","stat.ME","stat.TH"],"title_canon_sha256":"bdb8739a8d423d881024dceace21ac57af155eef5d7c6f9b255d73ca0d8bf58c","abstract_canon_sha256":"b0b9505299897eecb0044701c303183bd88cf313870dc70dc4fe7183ad4a0762"},"schema_version":"1.0"},"canonical_sha256":"df2c117a595a1662276bc1605e8c9462efe65286135f516949f2303718486c65","source":{"kind":"arxiv","id":"2403.12284","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.12284","created_at":"2026-07-05T07:57:58Z"},{"alias_kind":"arxiv_version","alias_value":"2403.12284v1","created_at":"2026-07-05T07:57:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.12284","created_at":"2026-07-05T07:57:58Z"},{"alias_kind":"pith_short_12","alias_value":"34WBC6SZLILG","created_at":"2026-07-05T07:57:58Z"},{"alias_kind":"pith_short_16","alias_value":"34WBC6SZLILGEJ3L","created_at":"2026-07-05T07:57:58Z"},{"alias_kind":"pith_short_8","alias_value":"34WBC6SZ","created_at":"2026-07-05T07:57:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:34WBC6SZLILGEJ3LYFQF5DEUML","target":"record","payload":{"canonical_record":{"source":{"id":"2403.12284","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-03-18T22:09:48Z","cross_cats_sorted":["q-bio.QM","stat.AP","stat.ME","stat.TH"],"title_canon_sha256":"bdb8739a8d423d881024dceace21ac57af155eef5d7c6f9b255d73ca0d8bf58c","abstract_canon_sha256":"b0b9505299897eecb0044701c303183bd88cf313870dc70dc4fe7183ad4a0762"},"schema_version":"1.0"},"canonical_sha256":"df2c117a595a1662276bc1605e8c9462efe65286135f516949f2303718486c65","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:57:58.321548Z","signature_b64":"j+aUaTQ9BvmTtW5SJC+8fqFi8yKazTyVhwcKCZ7r7Kl3YHEJtdvEytDDZz7UInDSHHhuamfeSRvfFkAzzViFBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df2c117a595a1662276bc1605e8c9462efe65286135f516949f2303718486c65","last_reissued_at":"2026-07-05T07:57:58.320998Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:57:58.320998Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.12284","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:57:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h34wsf3+H6ivvnRnI+Psbich9h27/tDX1HaB3ym3WN6uLjAedyKEgpJvxUIPtltTYs1DglUv/1wFliumMaq1Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:20:58.864481Z"},"content_sha256":"eede75073c098fb0e7dd165e30b89a22f0556ab4bd97531d31504334bbc02a2a","schema_version":"1.0","event_id":"sha256:eede75073c098fb0e7dd165e30b89a22f0556ab4bd97531d31504334bbc02a2a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:34WBC6SZLILGEJ3LYFQF5DEUML","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Wreaths of KHAN: Uniform Graph Feature Selection with False Discovery Rate Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.QM","stat.AP","stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Doudou Zhou, Jiajun Liang, Junwei Lu, Sinian Zhang, Yue Liu","submitted_at":"2024-03-18T22:09:48Z","abstract_excerpt":"Graphical models find numerous applications in biology, chemistry, sociology, neuroscience, etc. While substantial progress has been made in graph estimation, it remains largely unexplored how to select significant graph signals with uncertainty assessment, especially those graph features related to topological structures including cycles (i.e., wreaths), cliques, hubs, etc. These features play a vital role in protein substructure analysis, drug molecular design, and brain network connectivity analysis. To fill the gap, we propose a novel inferential framework for general high dimensional grap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.12284","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.12284/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T07:57:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T3yR7Kzarnk4YhZrXUfmMLSpuniT3yfpHkshZYqRq3wAi/R46kuH+E/+mJYg27LPcYn+iXUzCO7i5Z76jB2+CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T06:20:58.865480Z"},"content_sha256":"71e8fa5ee17db503f0cf30bc25e22724a17131b28ca7356fd55668f3b392eca7","schema_version":"1.0","event_id":"sha256:71e8fa5ee17db503f0cf30bc25e22724a17131b28ca7356fd55668f3b392eca7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/34WBC6SZLILGEJ3LYFQF5DEUML/bundle.json","state_url":"https://pith.science/pith/34WBC6SZLILGEJ3LYFQF5DEUML/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/34WBC6SZLILGEJ3LYFQF5DEUML/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":"2026-08-07T06:20:58Z","links":{"resolver":"https://pith.science/pith/34WBC6SZLILGEJ3LYFQF5DEUML","bundle":"https://pith.science/pith/34WBC6SZLILGEJ3LYFQF5DEUML/bundle.json","state":"https://pith.science/pith/34WBC6SZLILGEJ3LYFQF5DEUML/state.json","well_known_bundle":"https://pith.science/.well-known/pith/34WBC6SZLILGEJ3LYFQF5DEUML/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:34WBC6SZLILGEJ3LYFQF5DEUML","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":"b0b9505299897eecb0044701c303183bd88cf313870dc70dc4fe7183ad4a0762","cross_cats_sorted":["q-bio.QM","stat.AP","stat.ME","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-03-18T22:09:48Z","title_canon_sha256":"bdb8739a8d423d881024dceace21ac57af155eef5d7c6f9b255d73ca0d8bf58c"},"schema_version":"1.0","source":{"id":"2403.12284","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.12284","created_at":"2026-07-05T07:57:58Z"},{"alias_kind":"arxiv_version","alias_value":"2403.12284v1","created_at":"2026-07-05T07:57:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.12284","created_at":"2026-07-05T07:57:58Z"},{"alias_kind":"pith_short_12","alias_value":"34WBC6SZLILG","created_at":"2026-07-05T07:57:58Z"},{"alias_kind":"pith_short_16","alias_value":"34WBC6SZLILGEJ3L","created_at":"2026-07-05T07:57:58Z"},{"alias_kind":"pith_short_8","alias_value":"34WBC6SZ","created_at":"2026-07-05T07:57:58Z"}],"graph_snapshots":[{"event_id":"sha256:71e8fa5ee17db503f0cf30bc25e22724a17131b28ca7356fd55668f3b392eca7","target":"graph","created_at":"2026-07-05T07:57:58Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2403.12284/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graphical models find numerous applications in biology, chemistry, sociology, neuroscience, etc. While substantial progress has been made in graph estimation, it remains largely unexplored how to select significant graph signals with uncertainty assessment, especially those graph features related to topological structures including cycles (i.e., wreaths), cliques, hubs, etc. These features play a vital role in protein substructure analysis, drug molecular design, and brain network connectivity analysis. To fill the gap, we propose a novel inferential framework for general high dimensional grap","authors_text":"Doudou Zhou, Jiajun Liang, Junwei Lu, Sinian Zhang, Yue Liu","cross_cats":["q-bio.QM","stat.AP","stat.ME","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-03-18T22:09:48Z","title":"The Wreaths of KHAN: Uniform Graph Feature Selection with False Discovery Rate Control"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.12284","kind":"arxiv","version":1},"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:eede75073c098fb0e7dd165e30b89a22f0556ab4bd97531d31504334bbc02a2a","target":"record","created_at":"2026-07-05T07:57:58Z","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":"b0b9505299897eecb0044701c303183bd88cf313870dc70dc4fe7183ad4a0762","cross_cats_sorted":["q-bio.QM","stat.AP","stat.ME","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-03-18T22:09:48Z","title_canon_sha256":"bdb8739a8d423d881024dceace21ac57af155eef5d7c6f9b255d73ca0d8bf58c"},"schema_version":"1.0","source":{"id":"2403.12284","kind":"arxiv","version":1}},"canonical_sha256":"df2c117a595a1662276bc1605e8c9462efe65286135f516949f2303718486c65","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df2c117a595a1662276bc1605e8c9462efe65286135f516949f2303718486c65","first_computed_at":"2026-07-05T07:57:58.320998Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:57:58.320998Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j+aUaTQ9BvmTtW5SJC+8fqFi8yKazTyVhwcKCZ7r7Kl3YHEJtdvEytDDZz7UInDSHHhuamfeSRvfFkAzzViFBw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:57:58.321548Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.12284","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eede75073c098fb0e7dd165e30b89a22f0556ab4bd97531d31504334bbc02a2a","sha256:71e8fa5ee17db503f0cf30bc25e22724a17131b28ca7356fd55668f3b392eca7"],"state_sha256":"0c4390930eb0c760a0587aca4f868f961814211078b17fe89155445b5a739127"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lpohopvRK2fmiJJKM3lwpBd+BDDmwN6AEq4Hp7FqicpMle868CSP/QXx8jvSSABh2Mol58gpFfgJGGzEfCxkAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T06:20:58.872171Z","bundle_sha256":"cab5405f9338d8b4af834d779e355ad9b501f80d16c3f102bd4c71725728230a"}}