{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:YUV6N7ZWDOQ5T6MQ5MZJR3ERCY","short_pith_number":"pith:YUV6N7ZW","canonical_record":{"source":{"id":"2607.15018","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-07-16T14:05:37Z","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"title_canon_sha256":"cc90c1e8b5feaf287b4f0f0e1fc340e8ff175d7d7971ab49e0087087332a3bb7","abstract_canon_sha256":"659daf86d2595de38011fc69aeeedb50578734e9de2f1eaf81cd2954811d243b"},"schema_version":"1.0"},"canonical_sha256":"c52be6ff361ba1d9f990eb3298ec91162c758ce1e97664123ce023275da3e11d","source":{"kind":"arxiv","id":"2607.15018","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.15018","created_at":"2026-07-17T01:22:04Z"},{"alias_kind":"arxiv_version","alias_value":"2607.15018v1","created_at":"2026-07-17T01:22:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.15018","created_at":"2026-07-17T01:22:04Z"},{"alias_kind":"pith_short_12","alias_value":"YUV6N7ZWDOQ5","created_at":"2026-07-17T01:22:04Z"},{"alias_kind":"pith_short_16","alias_value":"YUV6N7ZWDOQ5T6MQ","created_at":"2026-07-17T01:22:04Z"},{"alias_kind":"pith_short_8","alias_value":"YUV6N7ZW","created_at":"2026-07-17T01:22:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:YUV6N7ZWDOQ5T6MQ5MZJR3ERCY","target":"record","payload":{"canonical_record":{"source":{"id":"2607.15018","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-07-16T14:05:37Z","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"title_canon_sha256":"cc90c1e8b5feaf287b4f0f0e1fc340e8ff175d7d7971ab49e0087087332a3bb7","abstract_canon_sha256":"659daf86d2595de38011fc69aeeedb50578734e9de2f1eaf81cd2954811d243b"},"schema_version":"1.0"},"canonical_sha256":"c52be6ff361ba1d9f990eb3298ec91162c758ce1e97664123ce023275da3e11d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-17T01:22:04.282533Z","signature_b64":"LdyGo0289o+5X8oF6kK0X7G+ZWYVNQzC1qmy0JMWeeHUhWfHC0tfZw80rK+cObR5A0vggJiULPGbqs/oSHCRDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c52be6ff361ba1d9f990eb3298ec91162c758ce1e97664123ce023275da3e11d","last_reissued_at":"2026-07-17T01:22:04.281339Z","signature_status":"signed_v1","first_computed_at":"2026-07-17T01:22:04.281339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.15018","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":"2026-07-17T01:22:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K/9QVpAIGAbu0I4cj1d2RiSvFN2O5yq4LoxknDL5xtEWeJDi0oh8FxVGpo5DUEeu1lTNTmmLj8TbqEYaA8kKBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T19:08:32.696048Z"},"content_sha256":"97b4497e7a80aab39a8fae110c5a74251c2d2bf8cac6476597faee8b80cc8bfb","schema_version":"1.0","event_id":"sha256:97b4497e7a80aab39a8fae110c5a74251c2d2bf8cac6476597faee8b80cc8bfb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:YUV6N7ZWDOQ5T6MQ5MZJR3ERCY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.CO","stat.ME"],"primary_cat":"stat.ML","authors_text":"Chiun-How Kao, Chun-houh Chen, Han-Ming Wu, Shang-Ying Shiu, ShengLi Tzeng, Shun-Chuan Chang, Yi-Ju Lee, Yin-Jing Tien","submitted_at":"2026-07-16T14:05:37Z","abstract_excerpt":"High-dimensional categorical data arise in genetics, biomedicine, and the social sciences, yet visualization tools for such data remain far less developed than those for continuous variables. Existing methods either scale poorly, rely heavily on low-dimensional displays detached from the original data matrix, or prioritize predictive accuracy over interpretability. To address this gap, we introduce categorical Generalized Association Plots (cGAP), a visualization framework for nominal, ordinal, and binary data that preserves the original data matrix while augmenting it with interpretable geome"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.15018","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/2607.15018/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":"2026-07-17T01:22:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LkM7IR6jo44sxdRJO6dR2bxyzQ3mwU9vToFrLLss4kfusl+luBmhKBSav8EYhD6J+MEXJKglRNc/3B0ps2goDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T19:08:32.696605Z"},"content_sha256":"9cfdd55834aeeb189acb237380be78af8ec0c33c0fa91f3c8bc35b7e5e501df1","schema_version":"1.0","event_id":"sha256:9cfdd55834aeeb189acb237380be78af8ec0c33c0fa91f3c8bc35b7e5e501df1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YUV6N7ZWDOQ5T6MQ5MZJR3ERCY/bundle.json","state_url":"https://pith.science/pith/YUV6N7ZWDOQ5T6MQ5MZJR3ERCY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YUV6N7ZWDOQ5T6MQ5MZJR3ERCY/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":"2026-08-21T19:08:32Z","links":{"resolver":"https://pith.science/pith/YUV6N7ZWDOQ5T6MQ5MZJR3ERCY","bundle":"https://pith.science/pith/YUV6N7ZWDOQ5T6MQ5MZJR3ERCY/bundle.json","state":"https://pith.science/pith/YUV6N7ZWDOQ5T6MQ5MZJR3ERCY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YUV6N7ZWDOQ5T6MQ5MZJR3ERCY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:YUV6N7ZWDOQ5T6MQ5MZJR3ERCY","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":"659daf86d2595de38011fc69aeeedb50578734e9de2f1eaf81cd2954811d243b","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-07-16T14:05:37Z","title_canon_sha256":"cc90c1e8b5feaf287b4f0f0e1fc340e8ff175d7d7971ab49e0087087332a3bb7"},"schema_version":"1.0","source":{"id":"2607.15018","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.15018","created_at":"2026-07-17T01:22:04Z"},{"alias_kind":"arxiv_version","alias_value":"2607.15018v1","created_at":"2026-07-17T01:22:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.15018","created_at":"2026-07-17T01:22:04Z"},{"alias_kind":"pith_short_12","alias_value":"YUV6N7ZWDOQ5","created_at":"2026-07-17T01:22:04Z"},{"alias_kind":"pith_short_16","alias_value":"YUV6N7ZWDOQ5T6MQ","created_at":"2026-07-17T01:22:04Z"},{"alias_kind":"pith_short_8","alias_value":"YUV6N7ZW","created_at":"2026-07-17T01:22:04Z"}],"graph_snapshots":[{"event_id":"sha256:9cfdd55834aeeb189acb237380be78af8ec0c33c0fa91f3c8bc35b7e5e501df1","target":"graph","created_at":"2026-07-17T01:22:04Z","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/2607.15018/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"High-dimensional categorical data arise in genetics, biomedicine, and the social sciences, yet visualization tools for such data remain far less developed than those for continuous variables. Existing methods either scale poorly, rely heavily on low-dimensional displays detached from the original data matrix, or prioritize predictive accuracy over interpretability. To address this gap, we introduce categorical Generalized Association Plots (cGAP), a visualization framework for nominal, ordinal, and binary data that preserves the original data matrix while augmenting it with interpretable geome","authors_text":"Chiun-How Kao, Chun-houh Chen, Han-Ming Wu, Shang-Ying Shiu, ShengLi Tzeng, Shun-Chuan Chang, Yi-Ju Lee, Yin-Jing Tien","cross_cats":["cs.LG","stat.CO","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-07-16T14:05:37Z","title":"cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.15018","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":"sha256:97b4497e7a80aab39a8fae110c5a74251c2d2bf8cac6476597faee8b80cc8bfb","target":"record","created_at":"2026-07-17T01:22:04Z","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":"659daf86d2595de38011fc69aeeedb50578734e9de2f1eaf81cd2954811d243b","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-07-16T14:05:37Z","title_canon_sha256":"cc90c1e8b5feaf287b4f0f0e1fc340e8ff175d7d7971ab49e0087087332a3bb7"},"schema_version":"1.0","source":{"id":"2607.15018","kind":"arxiv","version":1}},"canonical_sha256":"c52be6ff361ba1d9f990eb3298ec91162c758ce1e97664123ce023275da3e11d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c52be6ff361ba1d9f990eb3298ec91162c758ce1e97664123ce023275da3e11d","first_computed_at":"2026-07-17T01:22:04.281339Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-17T01:22:04.281339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LdyGo0289o+5X8oF6kK0X7G+ZWYVNQzC1qmy0JMWeeHUhWfHC0tfZw80rK+cObR5A0vggJiULPGbqs/oSHCRDg==","signature_status":"signed_v1","signed_at":"2026-07-17T01:22:04.282533Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.15018","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:97b4497e7a80aab39a8fae110c5a74251c2d2bf8cac6476597faee8b80cc8bfb","sha256:9cfdd55834aeeb189acb237380be78af8ec0c33c0fa91f3c8bc35b7e5e501df1"],"state_sha256":"064ebdf125b399e24f8e50ed199c1fe9e21ce36908069dba7e4f66a18177966b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5CuNYNGlcZ6mb1V5wB0sX0CW5pxO+0wQkydWEV+0GYiTvLnF/RjmMZgTaY7oYLExYmAi/Xg5hdldYFiaQDQtCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T19:08:32.701072Z","bundle_sha256":"0d634200fb1cfdfc6bf7b063cef15e78a76fb5c0801bd0e7e24486b2c2546d4c"}}