{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:KUNLKJ4OC72ESXKTJD6FWQHYLY","short_pith_number":"pith:KUNLKJ4O","canonical_record":{"source":{"id":"2009.09463","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-09-20T16:06:39Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"61bce3b649c32b0ac6db6f858ac88f9762b9a3677a28741f7ddd6e9ed461e606","abstract_canon_sha256":"f5962c61357ccdaef9862f5fe3571654b7b2360256755f719741110fd72aaad7"},"schema_version":"1.0"},"canonical_sha256":"551ab5278e17f4495d5348fc5b40f85e144cbe06c99ee7878e0b1d892bf7a01e","source":{"kind":"arxiv","id":"2009.09463","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.09463","created_at":"2026-07-05T03:44:50Z"},{"alias_kind":"arxiv_version","alias_value":"2009.09463v1","created_at":"2026-07-05T03:44:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.09463","created_at":"2026-07-05T03:44:50Z"},{"alias_kind":"pith_short_12","alias_value":"KUNLKJ4OC72E","created_at":"2026-07-05T03:44:50Z"},{"alias_kind":"pith_short_16","alias_value":"KUNLKJ4OC72ESXKT","created_at":"2026-07-05T03:44:50Z"},{"alias_kind":"pith_short_8","alias_value":"KUNLKJ4O","created_at":"2026-07-05T03:44:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:KUNLKJ4OC72ESXKTJD6FWQHYLY","target":"record","payload":{"canonical_record":{"source":{"id":"2009.09463","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-09-20T16:06:39Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"61bce3b649c32b0ac6db6f858ac88f9762b9a3677a28741f7ddd6e9ed461e606","abstract_canon_sha256":"f5962c61357ccdaef9862f5fe3571654b7b2360256755f719741110fd72aaad7"},"schema_version":"1.0"},"canonical_sha256":"551ab5278e17f4495d5348fc5b40f85e144cbe06c99ee7878e0b1d892bf7a01e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:44:50.282525Z","signature_b64":"+8u224fsG+isMPkhIePdFmj7Yp4Pwq/hPB3NJ/IBAkcM93h4QmZCzeXmjZ60HulY0BEVJH+vY1elEAI2hGTVDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"551ab5278e17f4495d5348fc5b40f85e144cbe06c99ee7878e0b1d892bf7a01e","last_reissued_at":"2026-07-05T03:44:50.282169Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:44:50.282169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2009.09463","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":"2026-07-05T03:44:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8SU66RDdGmoWnQTkObaKI1xx29jNtB/JOvAQeaaSnGp7HFBzTDU3jlJ4fF31+/ro4V0aEcpS1+WKR9oALGkPDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T01:29:19.709275Z"},"content_sha256":"dfa7685ac5a1bc2c975827069457857f287e9cc2931f68e79a5faac6bd8faca2","schema_version":"1.0","event_id":"sha256:dfa7685ac5a1bc2c975827069457857f287e9cc2931f68e79a5faac6bd8faca2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:KUNLKJ4OC72ESXKTJD6FWQHYLY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"COPOD: Copula-Based Outlier Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG"],"primary_cat":"stat.ML","authors_text":"Cezar Ionescu, Nicola Botta, Xiyang Hu, Yue Zhao, Zheng Li","submitted_at":"2020-09-20T16:06:39Z","abstract_excerpt":"Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm called COPOD, which is inspired by copulas for modeling multivariate data distribution. COPOD first constructs an empirical copula, and then uses it to predict tail probabilities of each given data point to determine its level of \"extremeness\". Intuitively, we think of this as calculating an anomalous p-v"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.09463","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/2009.09463/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":"2026-07-05T03:44:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q2nZYEjaiR2P+aFdQo1BmGn0yGyKuERG0VORAd1z/2ZMRmr8csvL1QMdES5SvM9oG+JqUzh6DDJk9nZJA9taBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T01:29:19.712278Z"},"content_sha256":"68343cfaef348d476f1a35518d58899d625476ee0edb44d999e8848c70522bda","schema_version":"1.0","event_id":"sha256:68343cfaef348d476f1a35518d58899d625476ee0edb44d999e8848c70522bda"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KUNLKJ4OC72ESXKTJD6FWQHYLY/bundle.json","state_url":"https://pith.science/pith/KUNLKJ4OC72ESXKTJD6FWQHYLY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KUNLKJ4OC72ESXKTJD6FWQHYLY/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":"2026-08-04T01:29:19Z","links":{"resolver":"https://pith.science/pith/KUNLKJ4OC72ESXKTJD6FWQHYLY","bundle":"https://pith.science/pith/KUNLKJ4OC72ESXKTJD6FWQHYLY/bundle.json","state":"https://pith.science/pith/KUNLKJ4OC72ESXKTJD6FWQHYLY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KUNLKJ4OC72ESXKTJD6FWQHYLY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:KUNLKJ4OC72ESXKTJD6FWQHYLY","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":"f5962c61357ccdaef9862f5fe3571654b7b2360256755f719741110fd72aaad7","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-09-20T16:06:39Z","title_canon_sha256":"61bce3b649c32b0ac6db6f858ac88f9762b9a3677a28741f7ddd6e9ed461e606"},"schema_version":"1.0","source":{"id":"2009.09463","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2009.09463","created_at":"2026-07-05T03:44:50Z"},{"alias_kind":"arxiv_version","alias_value":"2009.09463v1","created_at":"2026-07-05T03:44:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.09463","created_at":"2026-07-05T03:44:50Z"},{"alias_kind":"pith_short_12","alias_value":"KUNLKJ4OC72E","created_at":"2026-07-05T03:44:50Z"},{"alias_kind":"pith_short_16","alias_value":"KUNLKJ4OC72ESXKT","created_at":"2026-07-05T03:44:50Z"},{"alias_kind":"pith_short_8","alias_value":"KUNLKJ4O","created_at":"2026-07-05T03:44:50Z"}],"graph_snapshots":[{"event_id":"sha256:68343cfaef348d476f1a35518d58899d625476ee0edb44d999e8848c70522bda","target":"graph","created_at":"2026-07-05T03:44:50Z","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/2009.09463/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm called COPOD, which is inspired by copulas for modeling multivariate data distribution. COPOD first constructs an empirical copula, and then uses it to predict tail probabilities of each given data point to determine its level of \"extremeness\". Intuitively, we think of this as calculating an anomalous p-v","authors_text":"Cezar Ionescu, Nicola Botta, Xiyang Hu, Yue Zhao, Zheng Li","cross_cats":["cs.IR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-09-20T16:06:39Z","title":"COPOD: Copula-Based Outlier Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.09463","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:dfa7685ac5a1bc2c975827069457857f287e9cc2931f68e79a5faac6bd8faca2","target":"record","created_at":"2026-07-05T03:44:50Z","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":"f5962c61357ccdaef9862f5fe3571654b7b2360256755f719741110fd72aaad7","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-09-20T16:06:39Z","title_canon_sha256":"61bce3b649c32b0ac6db6f858ac88f9762b9a3677a28741f7ddd6e9ed461e606"},"schema_version":"1.0","source":{"id":"2009.09463","kind":"arxiv","version":1}},"canonical_sha256":"551ab5278e17f4495d5348fc5b40f85e144cbe06c99ee7878e0b1d892bf7a01e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"551ab5278e17f4495d5348fc5b40f85e144cbe06c99ee7878e0b1d892bf7a01e","first_computed_at":"2026-07-05T03:44:50.282169Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:44:50.282169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+8u224fsG+isMPkhIePdFmj7Yp4Pwq/hPB3NJ/IBAkcM93h4QmZCzeXmjZ60HulY0BEVJH+vY1elEAI2hGTVDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:44:50.282525Z","signed_message":"canonical_sha256_bytes"},"source_id":"2009.09463","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dfa7685ac5a1bc2c975827069457857f287e9cc2931f68e79a5faac6bd8faca2","sha256:68343cfaef348d476f1a35518d58899d625476ee0edb44d999e8848c70522bda"],"state_sha256":"d1989ae1d74d3ba71bf44afe3db26901fb3598714d6daedc37fe6773631706e7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6WpZYlhH2jyzv9Ju3oEpGyzt3cboV0Mt0HehtbB/AsNE/r1h/uL4BFJ811TtQZsZdPo8TAAvwMaez2uOyGFxDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T01:29:19.728081Z","bundle_sha256":"45e3a347092e5080a6639289a9017623c1b4210cd3cb49e142b476197d1d088a"}}