{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:NPAXZIPKHPX3U23SCI4MCOBXX7","short_pith_number":"pith:NPAXZIPK","canonical_record":{"source":{"id":"2405.13970","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2024-05-22T20:11:31Z","cross_cats_sorted":[],"title_canon_sha256":"300c18ee590c9da85844b5e696ccb8189bc11ac3ad725dca25ce9259f9472620","abstract_canon_sha256":"42cefb0d1489bac70dc86f535d58c9a83ca5f23e723e0c3e5f9344471b379fd3"},"schema_version":"1.0"},"canonical_sha256":"6bc17ca1ea3befba6b721238c13837bfe4ddac772693ae136ae00c514da75a41","source":{"kind":"arxiv","id":"2405.13970","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.13970","created_at":"2026-07-05T08:22:08Z"},{"alias_kind":"arxiv_version","alias_value":"2405.13970v1","created_at":"2026-07-05T08:22:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.13970","created_at":"2026-07-05T08:22:08Z"},{"alias_kind":"pith_short_12","alias_value":"NPAXZIPKHPX3","created_at":"2026-07-05T08:22:08Z"},{"alias_kind":"pith_short_16","alias_value":"NPAXZIPKHPX3U23S","created_at":"2026-07-05T08:22:08Z"},{"alias_kind":"pith_short_8","alias_value":"NPAXZIPK","created_at":"2026-07-05T08:22:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:NPAXZIPKHPX3U23SCI4MCOBXX7","target":"record","payload":{"canonical_record":{"source":{"id":"2405.13970","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2024-05-22T20:11:31Z","cross_cats_sorted":[],"title_canon_sha256":"300c18ee590c9da85844b5e696ccb8189bc11ac3ad725dca25ce9259f9472620","abstract_canon_sha256":"42cefb0d1489bac70dc86f535d58c9a83ca5f23e723e0c3e5f9344471b379fd3"},"schema_version":"1.0"},"canonical_sha256":"6bc17ca1ea3befba6b721238c13837bfe4ddac772693ae136ae00c514da75a41","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:22:08.949177Z","signature_b64":"LvfunGMoOx+tBoS7p/bkmpM3WdP6U0eg9iNoGBZ6K5bm/IgrhyI62ISzp+OiR5gXH+EwlCWQhqOvqngVfeoiBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6bc17ca1ea3befba6b721238c13837bfe4ddac772693ae136ae00c514da75a41","last_reissued_at":"2026-07-05T08:22:08.948748Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:22:08.948748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.13970","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":"2026-07-05T08:22:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q7Q8eR4tmBF2Rt94xMS3DHelqdX7TMju+peuzcW9AproMols18RWNCyz+yIlNWF95ZiNG8k6Z/lg2rf7OIxrAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T11:19:23.279556Z"},"content_sha256":"b94c8980a07f5b06bc40200959a0c2b8aed5efc5fbe553be6630ea354e2661b0","schema_version":"1.0","event_id":"sha256:b94c8980a07f5b06bc40200959a0c2b8aed5efc5fbe553be6630ea354e2661b0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:NPAXZIPKHPX3U23SCI4MCOBXX7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jukka-Pekka Onnela, Marcos Matabuena, Oscar Hernan Madrid Padilla, Pavlo Mozharovskyi, Rahul Ghosal","submitted_at":"2024-05-22T20:11:31Z","abstract_excerpt":"Depth measures have gained popularity in the statistical literature for defining level sets in complex data structures like multivariate data, functional data, and graphs. Despite their versatility, integrating depth measures into regression modeling for establishing prediction regions remains underexplored. To address this gap, we propose a novel method utilizing a model-free uncertainty quantification algorithm based on conditional depth measures and conditional kernel mean embeddings. This enables the creation of tailored prediction and tolerance regions in regression models handling comple"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.13970","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/2405.13970/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":"2026-07-05T08:22:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pb1MuiPD4jyPw6/RiteHm2sRjU5ObYDr27xAg0BOXh9if0o5XmzFpweO5Di4dQjfQiRDEwEbZXVGM4fHaS95AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T11:19:23.280483Z"},"content_sha256":"537bcc8f14eadfc7cfae0ef73d0464bcf74856f280cbcb1013242110c9dc317e","schema_version":"1.0","event_id":"sha256:537bcc8f14eadfc7cfae0ef73d0464bcf74856f280cbcb1013242110c9dc317e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NPAXZIPKHPX3U23SCI4MCOBXX7/bundle.json","state_url":"https://pith.science/pith/NPAXZIPKHPX3U23SCI4MCOBXX7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NPAXZIPKHPX3U23SCI4MCOBXX7/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":"2026-08-05T11:19:23Z","links":{"resolver":"https://pith.science/pith/NPAXZIPKHPX3U23SCI4MCOBXX7","bundle":"https://pith.science/pith/NPAXZIPKHPX3U23SCI4MCOBXX7/bundle.json","state":"https://pith.science/pith/NPAXZIPKHPX3U23SCI4MCOBXX7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NPAXZIPKHPX3U23SCI4MCOBXX7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NPAXZIPKHPX3U23SCI4MCOBXX7","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":"42cefb0d1489bac70dc86f535d58c9a83ca5f23e723e0c3e5f9344471b379fd3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2024-05-22T20:11:31Z","title_canon_sha256":"300c18ee590c9da85844b5e696ccb8189bc11ac3ad725dca25ce9259f9472620"},"schema_version":"1.0","source":{"id":"2405.13970","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.13970","created_at":"2026-07-05T08:22:08Z"},{"alias_kind":"arxiv_version","alias_value":"2405.13970v1","created_at":"2026-07-05T08:22:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.13970","created_at":"2026-07-05T08:22:08Z"},{"alias_kind":"pith_short_12","alias_value":"NPAXZIPKHPX3","created_at":"2026-07-05T08:22:08Z"},{"alias_kind":"pith_short_16","alias_value":"NPAXZIPKHPX3U23S","created_at":"2026-07-05T08:22:08Z"},{"alias_kind":"pith_short_8","alias_value":"NPAXZIPK","created_at":"2026-07-05T08:22:08Z"}],"graph_snapshots":[{"event_id":"sha256:537bcc8f14eadfc7cfae0ef73d0464bcf74856f280cbcb1013242110c9dc317e","target":"graph","created_at":"2026-07-05T08:22:08Z","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/2405.13970/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Depth measures have gained popularity in the statistical literature for defining level sets in complex data structures like multivariate data, functional data, and graphs. Despite their versatility, integrating depth measures into regression modeling for establishing prediction regions remains underexplored. To address this gap, we propose a novel method utilizing a model-free uncertainty quantification algorithm based on conditional depth measures and conditional kernel mean embeddings. This enables the creation of tailored prediction and tolerance regions in regression models handling comple","authors_text":"Jukka-Pekka Onnela, Marcos Matabuena, Oscar Hernan Madrid Padilla, Pavlo Mozharovskyi, Rahul Ghosal","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2024-05-22T20:11:31Z","title":"Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.13970","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:b94c8980a07f5b06bc40200959a0c2b8aed5efc5fbe553be6630ea354e2661b0","target":"record","created_at":"2026-07-05T08:22:08Z","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":"42cefb0d1489bac70dc86f535d58c9a83ca5f23e723e0c3e5f9344471b379fd3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2024-05-22T20:11:31Z","title_canon_sha256":"300c18ee590c9da85844b5e696ccb8189bc11ac3ad725dca25ce9259f9472620"},"schema_version":"1.0","source":{"id":"2405.13970","kind":"arxiv","version":1}},"canonical_sha256":"6bc17ca1ea3befba6b721238c13837bfe4ddac772693ae136ae00c514da75a41","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6bc17ca1ea3befba6b721238c13837bfe4ddac772693ae136ae00c514da75a41","first_computed_at":"2026-07-05T08:22:08.948748Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:22:08.948748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LvfunGMoOx+tBoS7p/bkmpM3WdP6U0eg9iNoGBZ6K5bm/IgrhyI62ISzp+OiR5gXH+EwlCWQhqOvqngVfeoiBw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:22:08.949177Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.13970","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b94c8980a07f5b06bc40200959a0c2b8aed5efc5fbe553be6630ea354e2661b0","sha256:537bcc8f14eadfc7cfae0ef73d0464bcf74856f280cbcb1013242110c9dc317e"],"state_sha256":"3851eb0a5ff1380118a507fb122faea0bacd575e15fa80d6e15c817228397edf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"27lprP7MyGpqkNvYlKTNWTfLdjSA9n328OlxFa2FmuFslZgCGe4aInf7H28qOoWLahnSolubNamiYkAMQ5M7AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T11:19:23.285854Z","bundle_sha256":"de8742bf25f64757bc23311bb85a8b41a5052230c91de2c0d9cd7452d8eba2e6"}}