{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:FLZY34ZWMQ5EX7ZOBHY5TOYA5S","short_pith_number":"pith:FLZY34ZW","canonical_record":{"source":{"id":"1903.09798","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-23T11:07:37Z","cross_cats_sorted":[],"title_canon_sha256":"28512f464087f199c57b3d4f3b405d12a891a083b2e9510b063389ed7917015b","abstract_canon_sha256":"663e627a34adf9a1ec674be18ca33d23e526ab7ffc806e7ee792b5960972d7d7"},"schema_version":"1.0"},"canonical_sha256":"2af38df336643a4bff2e09f1d9bb00ecb88ddf936a2a232e721e1e5027e0e1a2","source":{"kind":"arxiv","id":"1903.09798","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.09798","created_at":"2026-05-17T23:49:59Z"},{"alias_kind":"arxiv_version","alias_value":"1903.09798v2","created_at":"2026-05-17T23:49:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.09798","created_at":"2026-05-17T23:49:59Z"},{"alias_kind":"pith_short_12","alias_value":"FLZY34ZWMQ5E","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"FLZY34ZWMQ5EX7ZO","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"FLZY34ZW","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:FLZY34ZWMQ5EX7ZOBHY5TOYA5S","target":"record","payload":{"canonical_record":{"source":{"id":"1903.09798","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-23T11:07:37Z","cross_cats_sorted":[],"title_canon_sha256":"28512f464087f199c57b3d4f3b405d12a891a083b2e9510b063389ed7917015b","abstract_canon_sha256":"663e627a34adf9a1ec674be18ca33d23e526ab7ffc806e7ee792b5960972d7d7"},"schema_version":"1.0"},"canonical_sha256":"2af38df336643a4bff2e09f1d9bb00ecb88ddf936a2a232e721e1e5027e0e1a2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:59.121815Z","signature_b64":"Rr5x1bVz3PITISb5B3ktu14vO3/y3rCqXIgDYckgPz2nQf8XrPGS0+Xu6WVZtedC6wppJyrEcW0N0RjNJG9xBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2af38df336643a4bff2e09f1d9bb00ecb88ddf936a2a232e721e1e5027e0e1a2","last_reissued_at":"2026-05-17T23:49:59.121195Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:59.121195Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.09798","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t0DE8sUm7NFr4f2ugsMpvqdMvsP7m1kXeGi37cyxxRKDitND2pohjlaRAS8mbtWLG2PKIATe4sX7aKS1u0JxAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T20:56:31.515118Z"},"content_sha256":"03c28e033d2df0d0fdd5d13d4e711f8a09851e9cb74244ab3cc5f8bb1337832b","schema_version":"1.0","event_id":"sha256:03c28e033d2df0d0fdd5d13d4e711f8a09851e9cb74244ab3cc5f8bb1337832b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:FLZY34ZWMQ5EX7ZOBHY5TOYA5S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spatially-weighted Anomaly Detection with Regression Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Asim Munawar, Daiki Kimura, Minori Narita, Ryuki Tachibana","submitted_at":"2019-03-23T11:07:37Z","abstract_excerpt":"Visual anomaly detection is common in several applications including medical screening and production quality check. Although a definition of the anomaly is an unknown trend in data, in many cases some hints or samples of the anomaly class can be given in advance. Conventional methods cannot use the available anomaly data, and also do not have a robustness of noise. In this paper, we propose a novel spatially-weighted reconstruction-loss-based anomaly detection with a likelihood value from a regression model trained by all known data. The spatial weights are calculated by a region of interest "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.09798","kind":"arxiv","version":2},"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":"2026-05-17T23:49:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MzGFePqU26C6iyabic8xEA+bJRxLIyIIgbUlUJofiAruoHhq/zMhGCbqSncsCdUKglD/ct3p6/QpIQrW2jJ/Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T20:56:31.515486Z"},"content_sha256":"161b6f1e2c3c35bf9cc8b4884ba59167d4de32fd7d198aad11f17c97f2375261","schema_version":"1.0","event_id":"sha256:161b6f1e2c3c35bf9cc8b4884ba59167d4de32fd7d198aad11f17c97f2375261"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FLZY34ZWMQ5EX7ZOBHY5TOYA5S/bundle.json","state_url":"https://pith.science/pith/FLZY34ZWMQ5EX7ZOBHY5TOYA5S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FLZY34ZWMQ5EX7ZOBHY5TOYA5S/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":"2026-06-22T20:56:31Z","links":{"resolver":"https://pith.science/pith/FLZY34ZWMQ5EX7ZOBHY5TOYA5S","bundle":"https://pith.science/pith/FLZY34ZWMQ5EX7ZOBHY5TOYA5S/bundle.json","state":"https://pith.science/pith/FLZY34ZWMQ5EX7ZOBHY5TOYA5S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FLZY34ZWMQ5EX7ZOBHY5TOYA5S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:FLZY34ZWMQ5EX7ZOBHY5TOYA5S","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":"663e627a34adf9a1ec674be18ca33d23e526ab7ffc806e7ee792b5960972d7d7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-23T11:07:37Z","title_canon_sha256":"28512f464087f199c57b3d4f3b405d12a891a083b2e9510b063389ed7917015b"},"schema_version":"1.0","source":{"id":"1903.09798","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.09798","created_at":"2026-05-17T23:49:59Z"},{"alias_kind":"arxiv_version","alias_value":"1903.09798v2","created_at":"2026-05-17T23:49:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.09798","created_at":"2026-05-17T23:49:59Z"},{"alias_kind":"pith_short_12","alias_value":"FLZY34ZWMQ5E","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"FLZY34ZWMQ5EX7ZO","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"FLZY34ZW","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:161b6f1e2c3c35bf9cc8b4884ba59167d4de32fd7d198aad11f17c97f2375261","target":"graph","created_at":"2026-05-17T23:49:59Z","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":"Visual anomaly detection is common in several applications including medical screening and production quality check. Although a definition of the anomaly is an unknown trend in data, in many cases some hints or samples of the anomaly class can be given in advance. Conventional methods cannot use the available anomaly data, and also do not have a robustness of noise. In this paper, we propose a novel spatially-weighted reconstruction-loss-based anomaly detection with a likelihood value from a regression model trained by all known data. The spatial weights are calculated by a region of interest ","authors_text":"Asim Munawar, Daiki Kimura, Minori Narita, Ryuki Tachibana","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-23T11:07:37Z","title":"Spatially-weighted Anomaly Detection with Regression Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.09798","kind":"arxiv","version":2},"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":"sha256:03c28e033d2df0d0fdd5d13d4e711f8a09851e9cb74244ab3cc5f8bb1337832b","target":"record","created_at":"2026-05-17T23:49:59Z","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":"663e627a34adf9a1ec674be18ca33d23e526ab7ffc806e7ee792b5960972d7d7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-23T11:07:37Z","title_canon_sha256":"28512f464087f199c57b3d4f3b405d12a891a083b2e9510b063389ed7917015b"},"schema_version":"1.0","source":{"id":"1903.09798","kind":"arxiv","version":2}},"canonical_sha256":"2af38df336643a4bff2e09f1d9bb00ecb88ddf936a2a232e721e1e5027e0e1a2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2af38df336643a4bff2e09f1d9bb00ecb88ddf936a2a232e721e1e5027e0e1a2","first_computed_at":"2026-05-17T23:49:59.121195Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:59.121195Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Rr5x1bVz3PITISb5B3ktu14vO3/y3rCqXIgDYckgPz2nQf8XrPGS0+Xu6WVZtedC6wppJyrEcW0N0RjNJG9xBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:59.121815Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.09798","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:03c28e033d2df0d0fdd5d13d4e711f8a09851e9cb74244ab3cc5f8bb1337832b","sha256:161b6f1e2c3c35bf9cc8b4884ba59167d4de32fd7d198aad11f17c97f2375261"],"state_sha256":"00a8bc8e6b585a0c3e6fd6693afddced2710696f19dddf353d571d752fee5873"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sYprN53eDZcW42VeCVDn23hDfAJJZa81L3tFhh5aDjOl+PYQtozVJvifDIY7uLy8DAPTwxMKx9cxxbwp7s3NAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-22T20:56:31.517542Z","bundle_sha256":"39be3488fe665e37f6417269d11dafa2a97c1f96f6cb6dae9bc77ff3f36902ce"}}