{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:R4ZY5UCYZ77EOTXV3QJXOU5E4I","short_pith_number":"pith:R4ZY5UCY","canonical_record":{"source":{"id":"1601.06602","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-01-25T13:56:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"14bd6fd5b5359ba0e39ae4ec7745b1d353c73db3b05d3c480c4f0bba5a4c47b8","abstract_canon_sha256":"5c6d6a331d2e6c980b67cda34c0678240dd7209238aa2542146f458962710645"},"schema_version":"1.0"},"canonical_sha256":"8f338ed058cffe474ef5dc137753a4e214dde29c62fe9afafd4f4b7b558b6a4c","source":{"kind":"arxiv","id":"1601.06602","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1601.06602","created_at":"2026-05-18T01:12:55Z"},{"alias_kind":"arxiv_version","alias_value":"1601.06602v3","created_at":"2026-05-18T01:12:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.06602","created_at":"2026-05-18T01:12:55Z"},{"alias_kind":"pith_short_12","alias_value":"R4ZY5UCYZ77E","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"R4ZY5UCYZ77EOTXV","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"R4ZY5UCY","created_at":"2026-05-18T12:30:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:R4ZY5UCYZ77EOTXV3QJXOU5E4I","target":"record","payload":{"canonical_record":{"source":{"id":"1601.06602","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-01-25T13:56:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"14bd6fd5b5359ba0e39ae4ec7745b1d353c73db3b05d3c480c4f0bba5a4c47b8","abstract_canon_sha256":"5c6d6a331d2e6c980b67cda34c0678240dd7209238aa2542146f458962710645"},"schema_version":"1.0"},"canonical_sha256":"8f338ed058cffe474ef5dc137753a4e214dde29c62fe9afafd4f4b7b558b6a4c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:55.007313Z","signature_b64":"ApBnW/nePcjNyrlzuJdETmO6HojuYF2f7AMJ7a/kJr+wkmjtHsCWpgc+lLQw4oOpYqnPjs8ZIqYBBj2ccXs9Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f338ed058cffe474ef5dc137753a4e214dde29c62fe9afafd4f4b7b558b6a4c","last_reissued_at":"2026-05-18T01:12:55.006980Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:55.006980Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1601.06602","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:12:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wyHxtkvyXXk4KBshxdDmBzF/GHscfc3Xq2cgAARF8NdEzFzvQogXl8pr7p+IJOgl7Mp6fNJgzvtEOI1h/RE7Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T00:29:40.558251Z"},"content_sha256":"33b496c2e75d4972e2e3fa8bbcbf8ac2d56a32470b12ed7df36c7dd77918c4ec","schema_version":"1.0","event_id":"sha256:33b496c2e75d4972e2e3fa8bbcbf8ac2d56a32470b12ed7df36c7dd77918c4ec"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:R4ZY5UCYZ77EOTXV3QJXOU5E4I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Fabio Ramos, Markus Schneider, Wolfgang Ertel","submitted_at":"2016-01-25T13:56:59Z","abstract_excerpt":"We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (EXPoSE), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with EXPoSE can be done in linear time and online (incremental) learning takes constant time per instan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.06602","kind":"arxiv","version":3},"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":"2026-05-18T01:12:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1cQs7WDpzFzHKQnPwK1rv8mSd8tjE//HuOumzz9p2alNVvZSt1DnseoImg2mlUSn28pP4NI0uujS+IDa9sIYDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T00:29:40.558640Z"},"content_sha256":"fcfe758939b336f8b12558b38938c2bded9b56fcc0d6042870f9d8b128b40e47","schema_version":"1.0","event_id":"sha256:fcfe758939b336f8b12558b38938c2bded9b56fcc0d6042870f9d8b128b40e47"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R4ZY5UCYZ77EOTXV3QJXOU5E4I/bundle.json","state_url":"https://pith.science/pith/R4ZY5UCYZ77EOTXV3QJXOU5E4I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R4ZY5UCYZ77EOTXV3QJXOU5E4I/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":"2026-06-09T00:29:40Z","links":{"resolver":"https://pith.science/pith/R4ZY5UCYZ77EOTXV3QJXOU5E4I","bundle":"https://pith.science/pith/R4ZY5UCYZ77EOTXV3QJXOU5E4I/bundle.json","state":"https://pith.science/pith/R4ZY5UCYZ77EOTXV3QJXOU5E4I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R4ZY5UCYZ77EOTXV3QJXOU5E4I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:R4ZY5UCYZ77EOTXV3QJXOU5E4I","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":"5c6d6a331d2e6c980b67cda34c0678240dd7209238aa2542146f458962710645","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-01-25T13:56:59Z","title_canon_sha256":"14bd6fd5b5359ba0e39ae4ec7745b1d353c73db3b05d3c480c4f0bba5a4c47b8"},"schema_version":"1.0","source":{"id":"1601.06602","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1601.06602","created_at":"2026-05-18T01:12:55Z"},{"alias_kind":"arxiv_version","alias_value":"1601.06602v3","created_at":"2026-05-18T01:12:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.06602","created_at":"2026-05-18T01:12:55Z"},{"alias_kind":"pith_short_12","alias_value":"R4ZY5UCYZ77E","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"R4ZY5UCYZ77EOTXV","created_at":"2026-05-18T12:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"R4ZY5UCY","created_at":"2026-05-18T12:30:41Z"}],"graph_snapshots":[{"event_id":"sha256:fcfe758939b336f8b12558b38938c2bded9b56fcc0d6042870f9d8b128b40e47","target":"graph","created_at":"2026-05-18T01:12:55Z","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":"We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (EXPoSE), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about the type or shape of the underlying data distribution. We show that offline (batch) learning with EXPoSE can be done in linear time and online (incremental) learning takes constant time per instan","authors_text":"Fabio Ramos, Markus Schneider, Wolfgang Ertel","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-01-25T13:56:59Z","title":"Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.06602","kind":"arxiv","version":3},"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":"sha256:33b496c2e75d4972e2e3fa8bbcbf8ac2d56a32470b12ed7df36c7dd77918c4ec","target":"record","created_at":"2026-05-18T01:12:55Z","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":"5c6d6a331d2e6c980b67cda34c0678240dd7209238aa2542146f458962710645","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-01-25T13:56:59Z","title_canon_sha256":"14bd6fd5b5359ba0e39ae4ec7745b1d353c73db3b05d3c480c4f0bba5a4c47b8"},"schema_version":"1.0","source":{"id":"1601.06602","kind":"arxiv","version":3}},"canonical_sha256":"8f338ed058cffe474ef5dc137753a4e214dde29c62fe9afafd4f4b7b558b6a4c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8f338ed058cffe474ef5dc137753a4e214dde29c62fe9afafd4f4b7b558b6a4c","first_computed_at":"2026-05-18T01:12:55.006980Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:12:55.006980Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ApBnW/nePcjNyrlzuJdETmO6HojuYF2f7AMJ7a/kJr+wkmjtHsCWpgc+lLQw4oOpYqnPjs8ZIqYBBj2ccXs9Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:12:55.007313Z","signed_message":"canonical_sha256_bytes"},"source_id":"1601.06602","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:33b496c2e75d4972e2e3fa8bbcbf8ac2d56a32470b12ed7df36c7dd77918c4ec","sha256:fcfe758939b336f8b12558b38938c2bded9b56fcc0d6042870f9d8b128b40e47"],"state_sha256":"c4b82d3c48aa69b9be0dd61a97d3155c63ad4af62ad89cc49a6e92fd532f8647"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BrMg51UcTY4J9qCcDFUUPXB8xKUxFOY5+gdw/V6yKYz9nwNo3Gu9vyb7ETmWvw54cF5/swNgz8wYop+QTxJeBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T00:29:40.560687Z","bundle_sha256":"8e4c4182e9476bf8793c09454c8c743c893c9f7c1e1399ab531b872dcac89094"}}