{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:PAK6JO6U2FEL3EF5JMQJ7NBR5R","short_pith_number":"pith:PAK6JO6U","canonical_record":{"source":{"id":"1709.03189","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-09-10T22:27:37Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"f982ac8bf29b6debdce893b3ab4eccad8b663ecbfbf4a2f89394556b6a4e67d7","abstract_canon_sha256":"71850d4c8ae222d018a5af13a68577d72559ab2297505e9dde82a12cbe228082"},"schema_version":"1.0"},"canonical_sha256":"7815e4bbd4d148bd90bd4b209fb431ec789bc16fa6a5604f8a9e53adbee39fb4","source":{"kind":"arxiv","id":"1709.03189","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.03189","created_at":"2026-05-18T00:35:39Z"},{"alias_kind":"arxiv_version","alias_value":"1709.03189v1","created_at":"2026-05-18T00:35:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.03189","created_at":"2026-05-18T00:35:39Z"},{"alias_kind":"pith_short_12","alias_value":"PAK6JO6U2FEL","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_16","alias_value":"PAK6JO6U2FEL3EF5","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_8","alias_value":"PAK6JO6U","created_at":"2026-05-18T12:31:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:PAK6JO6U2FEL3EF5JMQJ7NBR5R","target":"record","payload":{"canonical_record":{"source":{"id":"1709.03189","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-09-10T22:27:37Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"f982ac8bf29b6debdce893b3ab4eccad8b663ecbfbf4a2f89394556b6a4e67d7","abstract_canon_sha256":"71850d4c8ae222d018a5af13a68577d72559ab2297505e9dde82a12cbe228082"},"schema_version":"1.0"},"canonical_sha256":"7815e4bbd4d148bd90bd4b209fb431ec789bc16fa6a5604f8a9e53adbee39fb4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:39.097396Z","signature_b64":"wqAQygEFzizW/AFaO7aP/7NSgPArly9Rr0/JyanY/qv2tjOtS0fhDxy/KBCBiLVy/7U5nqcZLPuwVBmCcgkWBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7815e4bbd4d148bd90bd4b209fb431ec789bc16fa6a5604f8a9e53adbee39fb4","last_reissued_at":"2026-05-18T00:35:39.096881Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:39.096881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.03189","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":"2026-05-18T00:35:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lsUZZVCHG/ZJMctaU/4xC7qoq2oWcftC7ZJiOvfdZtUFmsafFGEzp1UMNUCnenC9uJSyYEENIPFkkV09OARGBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T10:54:45.404825Z"},"content_sha256":"35dce9e1ee16c74c2bfef6b4ef5e31c5381f138b738f78c4293dc85cab25d52b","schema_version":"1.0","event_id":"sha256:35dce9e1ee16c74c2bfef6b4ef5e31c5381f138b738f78c4293dc85cab25d52b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:PAK6JO6U2FEL3EF5JMQJ7NBR5R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Data Discovery and Anomaly Detection Using Atypicality: Theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Anders H{\\o}st-Madsen, Chad Walton, Elyas Sabeti","submitted_at":"2017-09-10T22:27:37Z","abstract_excerpt":"A central question in the era of 'big data' is what to do with the enormous amount of information. One possibility is to characterize it through statistics, e.g., averages, or classify it using machine learning, in order to understand the general structure of the overall data. The perspective in this paper is the opposite, namely that most of the value in the information in some applications is in the parts that deviate from the average, that are unusual, atypical. We define what we mean by 'atypical' in an axiomatic way as data that can be encoded with fewer bits in itself rather than using t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.03189","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":""},"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":"2026-05-18T00:35:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oQNh4B1X+UFmtpxKyS0rVaddUu8+VO2j2w4oZ+Vuw6/yp2fj2E/ZKQhr4KVK7pHH+szNL1RKOJAfSW7LqQubCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T10:54:45.405171Z"},"content_sha256":"44473dd0642b9d7cf0ef4a0a50370b6649e55b5d71f96ee548637d2ae4a99953","schema_version":"1.0","event_id":"sha256:44473dd0642b9d7cf0ef4a0a50370b6649e55b5d71f96ee548637d2ae4a99953"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PAK6JO6U2FEL3EF5JMQJ7NBR5R/bundle.json","state_url":"https://pith.science/pith/PAK6JO6U2FEL3EF5JMQJ7NBR5R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PAK6JO6U2FEL3EF5JMQJ7NBR5R/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":"2026-06-21T10:54:45Z","links":{"resolver":"https://pith.science/pith/PAK6JO6U2FEL3EF5JMQJ7NBR5R","bundle":"https://pith.science/pith/PAK6JO6U2FEL3EF5JMQJ7NBR5R/bundle.json","state":"https://pith.science/pith/PAK6JO6U2FEL3EF5JMQJ7NBR5R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PAK6JO6U2FEL3EF5JMQJ7NBR5R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:PAK6JO6U2FEL3EF5JMQJ7NBR5R","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":"71850d4c8ae222d018a5af13a68577d72559ab2297505e9dde82a12cbe228082","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-09-10T22:27:37Z","title_canon_sha256":"f982ac8bf29b6debdce893b3ab4eccad8b663ecbfbf4a2f89394556b6a4e67d7"},"schema_version":"1.0","source":{"id":"1709.03189","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.03189","created_at":"2026-05-18T00:35:39Z"},{"alias_kind":"arxiv_version","alias_value":"1709.03189v1","created_at":"2026-05-18T00:35:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.03189","created_at":"2026-05-18T00:35:39Z"},{"alias_kind":"pith_short_12","alias_value":"PAK6JO6U2FEL","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_16","alias_value":"PAK6JO6U2FEL3EF5","created_at":"2026-05-18T12:31:37Z"},{"alias_kind":"pith_short_8","alias_value":"PAK6JO6U","created_at":"2026-05-18T12:31:37Z"}],"graph_snapshots":[{"event_id":"sha256:44473dd0642b9d7cf0ef4a0a50370b6649e55b5d71f96ee548637d2ae4a99953","target":"graph","created_at":"2026-05-18T00:35:39Z","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":"A central question in the era of 'big data' is what to do with the enormous amount of information. One possibility is to characterize it through statistics, e.g., averages, or classify it using machine learning, in order to understand the general structure of the overall data. The perspective in this paper is the opposite, namely that most of the value in the information in some applications is in the parts that deviate from the average, that are unusual, atypical. We define what we mean by 'atypical' in an axiomatic way as data that can be encoded with fewer bits in itself rather than using t","authors_text":"Anders H{\\o}st-Madsen, Chad Walton, Elyas Sabeti","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-09-10T22:27:37Z","title":"Data Discovery and Anomaly Detection Using Atypicality: Theory"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.03189","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":"sha256:35dce9e1ee16c74c2bfef6b4ef5e31c5381f138b738f78c4293dc85cab25d52b","target":"record","created_at":"2026-05-18T00:35:39Z","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":"71850d4c8ae222d018a5af13a68577d72559ab2297505e9dde82a12cbe228082","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-09-10T22:27:37Z","title_canon_sha256":"f982ac8bf29b6debdce893b3ab4eccad8b663ecbfbf4a2f89394556b6a4e67d7"},"schema_version":"1.0","source":{"id":"1709.03189","kind":"arxiv","version":1}},"canonical_sha256":"7815e4bbd4d148bd90bd4b209fb431ec789bc16fa6a5604f8a9e53adbee39fb4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7815e4bbd4d148bd90bd4b209fb431ec789bc16fa6a5604f8a9e53adbee39fb4","first_computed_at":"2026-05-18T00:35:39.096881Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:35:39.096881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wqAQygEFzizW/AFaO7aP/7NSgPArly9Rr0/JyanY/qv2tjOtS0fhDxy/KBCBiLVy/7U5nqcZLPuwVBmCcgkWBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:35:39.097396Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.03189","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:35dce9e1ee16c74c2bfef6b4ef5e31c5381f138b738f78c4293dc85cab25d52b","sha256:44473dd0642b9d7cf0ef4a0a50370b6649e55b5d71f96ee548637d2ae4a99953"],"state_sha256":"0d6b30713e8fbccbb3f9b5346a5219a59f0670418af4a3bb76a473e586ce2857"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3TvGIQVkErPC0s8tESU7miDJ+qftncThg6EGz3vMsZ+0+R7+l592ifHKNEu/UsYQ4Tnu8W7F+0af6ToiSjFNBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-21T10:54:45.407137Z","bundle_sha256":"8d5a220505aa646a4ac3840be072343b2ac281aee0857449c2ce1cfddf294af2"}}