{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2011:GT7SSFJW4I6XQ6KZZLFP7QG7JQ","short_pith_number":"pith:GT7SSFJW","canonical_record":{"source":{"id":"1102.5019","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2011-02-24T15:37:09Z","cross_cats_sorted":["math.PR","stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"013f95fd2f2b6a5cad58586e280cd222e19f0de6209c1efa881b87f606787270","abstract_canon_sha256":"bf377be39e441e65dd34137a7d2396cc09121225d79d0d23d04b14d29edb3905"},"schema_version":"1.0"},"canonical_sha256":"34ff291536e23d787959cacaffc0df4c0b08e5e7042fa85a0333a0bb8ef78f69","source":{"kind":"arxiv","id":"1102.5019","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1102.5019","created_at":"2026-05-18T00:10:52Z"},{"alias_kind":"arxiv_version","alias_value":"1102.5019v2","created_at":"2026-05-18T00:10:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1102.5019","created_at":"2026-05-18T00:10:52Z"},{"alias_kind":"pith_short_12","alias_value":"GT7SSFJW4I6X","created_at":"2026-05-18T12:26:30Z"},{"alias_kind":"pith_short_16","alias_value":"GT7SSFJW4I6XQ6KZ","created_at":"2026-05-18T12:26:30Z"},{"alias_kind":"pith_short_8","alias_value":"GT7SSFJW","created_at":"2026-05-18T12:26:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2011:GT7SSFJW4I6XQ6KZZLFP7QG7JQ","target":"record","payload":{"canonical_record":{"source":{"id":"1102.5019","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2011-02-24T15:37:09Z","cross_cats_sorted":["math.PR","stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"013f95fd2f2b6a5cad58586e280cd222e19f0de6209c1efa881b87f606787270","abstract_canon_sha256":"bf377be39e441e65dd34137a7d2396cc09121225d79d0d23d04b14d29edb3905"},"schema_version":"1.0"},"canonical_sha256":"34ff291536e23d787959cacaffc0df4c0b08e5e7042fa85a0333a0bb8ef78f69","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:52.559708Z","signature_b64":"M3+9mbumnqx2QMGop6i1UPbozroNgQImVNwg7xnaH1qYoU5Db3Xp4VIs+B3BpBTaEEE5ZDk28eYOMy5M8O/sCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34ff291536e23d787959cacaffc0df4c0b08e5e7042fa85a0333a0bb8ef78f69","last_reissued_at":"2026-05-18T00:10:52.558983Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:52.558983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1102.5019","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":"2026-05-18T00:10:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sV//b8qn533afsQOJi56QNuYmN2gyx34zhGEikUrahmnK1xnHtqGd1zoPABvypXrL/XSC8IzkEVRRzhYvF1kDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T07:04:06.943268Z"},"content_sha256":"eb5a41e13eade39abb15e2120257ed18e883c645430ba40b1db6769337dfac77","schema_version":"1.0","event_id":"sha256:eb5a41e13eade39abb15e2120257ed18e883c645430ba40b1db6769337dfac77"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2011:GT7SSFJW4I6XQ6KZZLFP7QG7JQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised nonparametric detection of unknown objects in noisy images based on percolation theory","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Mikhail A. Langovoy, Olaf Wittich, Patrick Laurie Davies","submitted_at":"2011-02-24T15:37:09Z","abstract_excerpt":"We develop an unsupervised, nonparametric, and scalable statistical learning method for detection of unknown objects in noisy images. The method uses results from percolation theory and random graph theory. We present an algorithm that allows to detect objects of unknown shapes and sizes in the presence of nonparametric noise of unknown level. The noise density is assumed to be unknown and can be very irregular. The algorithm has linear complexity and exponential accuracy and is appropriate for real-time systems. We prove strong consistency and scalability of our method in this setup with mini"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1102.5019","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":"2026-05-18T00:10:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dzqfOfYUpHaAnYDAfY+W8l8INea50zjlTRwtw99fcgxzxu0v/Z18PBa7+UTM3ewX0JGEvjmnGddbyl7I/1ORDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T07:04:06.944016Z"},"content_sha256":"2905ccd05653f07cc836959c6fb75fee1020adb9d13b224acc6b55821f0119d9","schema_version":"1.0","event_id":"sha256:2905ccd05653f07cc836959c6fb75fee1020adb9d13b224acc6b55821f0119d9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GT7SSFJW4I6XQ6KZZLFP7QG7JQ/bundle.json","state_url":"https://pith.science/pith/GT7SSFJW4I6XQ6KZZLFP7QG7JQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GT7SSFJW4I6XQ6KZZLFP7QG7JQ/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":"2026-06-07T07:04:06Z","links":{"resolver":"https://pith.science/pith/GT7SSFJW4I6XQ6KZZLFP7QG7JQ","bundle":"https://pith.science/pith/GT7SSFJW4I6XQ6KZZLFP7QG7JQ/bundle.json","state":"https://pith.science/pith/GT7SSFJW4I6XQ6KZZLFP7QG7JQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GT7SSFJW4I6XQ6KZZLFP7QG7JQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2011:GT7SSFJW4I6XQ6KZZLFP7QG7JQ","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":"bf377be39e441e65dd34137a7d2396cc09121225d79d0d23d04b14d29edb3905","cross_cats_sorted":["math.PR","stat.ME","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2011-02-24T15:37:09Z","title_canon_sha256":"013f95fd2f2b6a5cad58586e280cd222e19f0de6209c1efa881b87f606787270"},"schema_version":"1.0","source":{"id":"1102.5019","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1102.5019","created_at":"2026-05-18T00:10:52Z"},{"alias_kind":"arxiv_version","alias_value":"1102.5019v2","created_at":"2026-05-18T00:10:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1102.5019","created_at":"2026-05-18T00:10:52Z"},{"alias_kind":"pith_short_12","alias_value":"GT7SSFJW4I6X","created_at":"2026-05-18T12:26:30Z"},{"alias_kind":"pith_short_16","alias_value":"GT7SSFJW4I6XQ6KZ","created_at":"2026-05-18T12:26:30Z"},{"alias_kind":"pith_short_8","alias_value":"GT7SSFJW","created_at":"2026-05-18T12:26:30Z"}],"graph_snapshots":[{"event_id":"sha256:2905ccd05653f07cc836959c6fb75fee1020adb9d13b224acc6b55821f0119d9","target":"graph","created_at":"2026-05-18T00:10:52Z","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 develop an unsupervised, nonparametric, and scalable statistical learning method for detection of unknown objects in noisy images. The method uses results from percolation theory and random graph theory. We present an algorithm that allows to detect objects of unknown shapes and sizes in the presence of nonparametric noise of unknown level. The noise density is assumed to be unknown and can be very irregular. The algorithm has linear complexity and exponential accuracy and is appropriate for real-time systems. We prove strong consistency and scalability of our method in this setup with mini","authors_text":"Mikhail A. Langovoy, Olaf Wittich, Patrick Laurie Davies","cross_cats":["math.PR","stat.ME","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2011-02-24T15:37:09Z","title":"Unsupervised nonparametric detection of unknown objects in noisy images based on percolation theory"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1102.5019","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:eb5a41e13eade39abb15e2120257ed18e883c645430ba40b1db6769337dfac77","target":"record","created_at":"2026-05-18T00:10:52Z","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":"bf377be39e441e65dd34137a7d2396cc09121225d79d0d23d04b14d29edb3905","cross_cats_sorted":["math.PR","stat.ME","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2011-02-24T15:37:09Z","title_canon_sha256":"013f95fd2f2b6a5cad58586e280cd222e19f0de6209c1efa881b87f606787270"},"schema_version":"1.0","source":{"id":"1102.5019","kind":"arxiv","version":2}},"canonical_sha256":"34ff291536e23d787959cacaffc0df4c0b08e5e7042fa85a0333a0bb8ef78f69","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"34ff291536e23d787959cacaffc0df4c0b08e5e7042fa85a0333a0bb8ef78f69","first_computed_at":"2026-05-18T00:10:52.558983Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:52.558983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"M3+9mbumnqx2QMGop6i1UPbozroNgQImVNwg7xnaH1qYoU5Db3Xp4VIs+B3BpBTaEEE5ZDk28eYOMy5M8O/sCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:52.559708Z","signed_message":"canonical_sha256_bytes"},"source_id":"1102.5019","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb5a41e13eade39abb15e2120257ed18e883c645430ba40b1db6769337dfac77","sha256:2905ccd05653f07cc836959c6fb75fee1020adb9d13b224acc6b55821f0119d9"],"state_sha256":"a15d51fde430d5df9d550739ed02b5ab2f96deb70d187d9c76c5ee3db502ead5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ILPZmaFVweF/tH6j2tVi9aIkh5+XBHz8aLWm17nLlJ369o7cVKoaiHnsLjbsqFWFZ6CdmWG0LFts9cvfbTe9DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T07:04:06.948626Z","bundle_sha256":"e49062c0bb3e193b93c4aa78c352ed417c4ef6961a2ca2eb461bca5b9980b9e9"}}