{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:NX47OMOVQLSTCXCHNFR6PACLCZ","short_pith_number":"pith:NX47OMOV","canonical_record":{"source":{"id":"2201.01819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-01-05T21:03:29Z","cross_cats_sorted":["cs.CL","cs.CV"],"title_canon_sha256":"5b5c237a469e598a49a484ac5396a4eb523b55010ad754d7fc88ffe25258a0fa","abstract_canon_sha256":"ba5003c3170472e726025a31ff8b2ab1a7405cef43c482201fa320ec4c137a5a"},"schema_version":"1.0"},"canonical_sha256":"6df9f731d582e5315c476963e7804b1653d677af4fedc226c710c7513cd42a0a","source":{"kind":"arxiv","id":"2201.01819","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.01819","created_at":"2026-07-05T03:48:10Z"},{"alias_kind":"arxiv_version","alias_value":"2201.01819v1","created_at":"2026-07-05T03:48:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.01819","created_at":"2026-07-05T03:48:10Z"},{"alias_kind":"pith_short_12","alias_value":"NX47OMOVQLST","created_at":"2026-07-05T03:48:10Z"},{"alias_kind":"pith_short_16","alias_value":"NX47OMOVQLSTCXCH","created_at":"2026-07-05T03:48:10Z"},{"alias_kind":"pith_short_8","alias_value":"NX47OMOV","created_at":"2026-07-05T03:48:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:NX47OMOVQLSTCXCHNFR6PACLCZ","target":"record","payload":{"canonical_record":{"source":{"id":"2201.01819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-01-05T21:03:29Z","cross_cats_sorted":["cs.CL","cs.CV"],"title_canon_sha256":"5b5c237a469e598a49a484ac5396a4eb523b55010ad754d7fc88ffe25258a0fa","abstract_canon_sha256":"ba5003c3170472e726025a31ff8b2ab1a7405cef43c482201fa320ec4c137a5a"},"schema_version":"1.0"},"canonical_sha256":"6df9f731d582e5315c476963e7804b1653d677af4fedc226c710c7513cd42a0a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:48:10.625417Z","signature_b64":"A4mdWwIjGJimNbBiDDacGWXNxGaGsyK7bVYcy9oRu+IFbG1KDCENPn6hTBHp3zw1Ia4Qj19j2Grcs3LKd20uDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6df9f731d582e5315c476963e7804b1653d677af4fedc226c710c7513cd42a0a","last_reissued_at":"2026-07-05T03:48:10.624996Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:48:10.624996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2201.01819","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":"2026-07-05T03:48:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OzeAN11HoffIo1ENv3RFLS2I0f7ifzFhYxK8EfcN220BwvXC3FjTSbt2d6cxoZUEVzhdH8cETozK8qJDCypcAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T08:08:36.993409Z"},"content_sha256":"700914a3332d64a9cba172fe1493af0ae0247dc750c4a1da3c5d3afa9c0187d5","schema_version":"1.0","event_id":"sha256:700914a3332d64a9cba172fe1493af0ae0247dc750c4a1da3c5d3afa9c0187d5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:NX47OMOVQLSTCXCHNFR6PACLCZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Formal Analysis of Art: Proxy Learning of Visual Concepts from Style Through Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CV"],"primary_cat":"cs.LG","authors_text":"Ahmed Elgammal, Diana Kim, Marian Mazzone","submitted_at":"2022-01-05T21:03:29Z","abstract_excerpt":"We present a machine learning system that can quantify fine art paintings with a set of visual elements and principles of art. This formal analysis is fundamental for understanding art, but developing such a system is challenging. Paintings have high visual complexities, but it is also difficult to collect enough training data with direct labels. To resolve these practical limitations, we introduce a novel mechanism, called proxy learning, which learns visual concepts in paintings though their general relation to styles. This framework does not require any visual annotation, but only uses styl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.01819","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/2201.01819/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":"2026-07-05T03:48:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IgFmvg+1/mVV+qxgqh6piFLOnF8hrue58+2QjxA3C4QABHlEXHmNjGXG/w3vQ3+gHyxixr7rm795mR6MvYNAAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T08:08:36.993911Z"},"content_sha256":"d994b455afe3f52e4c92994a29a1ff20be4a45998e43e92ab300668d9cd533b7","schema_version":"1.0","event_id":"sha256:d994b455afe3f52e4c92994a29a1ff20be4a45998e43e92ab300668d9cd533b7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NX47OMOVQLSTCXCHNFR6PACLCZ/bundle.json","state_url":"https://pith.science/pith/NX47OMOVQLSTCXCHNFR6PACLCZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NX47OMOVQLSTCXCHNFR6PACLCZ/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":"2026-08-15T08:08:36Z","links":{"resolver":"https://pith.science/pith/NX47OMOVQLSTCXCHNFR6PACLCZ","bundle":"https://pith.science/pith/NX47OMOVQLSTCXCHNFR6PACLCZ/bundle.json","state":"https://pith.science/pith/NX47OMOVQLSTCXCHNFR6PACLCZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NX47OMOVQLSTCXCHNFR6PACLCZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:NX47OMOVQLSTCXCHNFR6PACLCZ","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":"ba5003c3170472e726025a31ff8b2ab1a7405cef43c482201fa320ec4c137a5a","cross_cats_sorted":["cs.CL","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-01-05T21:03:29Z","title_canon_sha256":"5b5c237a469e598a49a484ac5396a4eb523b55010ad754d7fc88ffe25258a0fa"},"schema_version":"1.0","source":{"id":"2201.01819","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.01819","created_at":"2026-07-05T03:48:10Z"},{"alias_kind":"arxiv_version","alias_value":"2201.01819v1","created_at":"2026-07-05T03:48:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.01819","created_at":"2026-07-05T03:48:10Z"},{"alias_kind":"pith_short_12","alias_value":"NX47OMOVQLST","created_at":"2026-07-05T03:48:10Z"},{"alias_kind":"pith_short_16","alias_value":"NX47OMOVQLSTCXCH","created_at":"2026-07-05T03:48:10Z"},{"alias_kind":"pith_short_8","alias_value":"NX47OMOV","created_at":"2026-07-05T03:48:10Z"}],"graph_snapshots":[{"event_id":"sha256:d994b455afe3f52e4c92994a29a1ff20be4a45998e43e92ab300668d9cd533b7","target":"graph","created_at":"2026-07-05T03:48:10Z","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/2201.01819/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a machine learning system that can quantify fine art paintings with a set of visual elements and principles of art. This formal analysis is fundamental for understanding art, but developing such a system is challenging. Paintings have high visual complexities, but it is also difficult to collect enough training data with direct labels. To resolve these practical limitations, we introduce a novel mechanism, called proxy learning, which learns visual concepts in paintings though their general relation to styles. This framework does not require any visual annotation, but only uses styl","authors_text":"Ahmed Elgammal, Diana Kim, Marian Mazzone","cross_cats":["cs.CL","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-01-05T21:03:29Z","title":"Formal Analysis of Art: Proxy Learning of Visual Concepts from Style Through Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.01819","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":"sha256:700914a3332d64a9cba172fe1493af0ae0247dc750c4a1da3c5d3afa9c0187d5","target":"record","created_at":"2026-07-05T03:48:10Z","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":"ba5003c3170472e726025a31ff8b2ab1a7405cef43c482201fa320ec4c137a5a","cross_cats_sorted":["cs.CL","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-01-05T21:03:29Z","title_canon_sha256":"5b5c237a469e598a49a484ac5396a4eb523b55010ad754d7fc88ffe25258a0fa"},"schema_version":"1.0","source":{"id":"2201.01819","kind":"arxiv","version":1}},"canonical_sha256":"6df9f731d582e5315c476963e7804b1653d677af4fedc226c710c7513cd42a0a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6df9f731d582e5315c476963e7804b1653d677af4fedc226c710c7513cd42a0a","first_computed_at":"2026-07-05T03:48:10.624996Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:48:10.624996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"A4mdWwIjGJimNbBiDDacGWXNxGaGsyK7bVYcy9oRu+IFbG1KDCENPn6hTBHp3zw1Ia4Qj19j2Grcs3LKd20uDA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:48:10.625417Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.01819","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:700914a3332d64a9cba172fe1493af0ae0247dc750c4a1da3c5d3afa9c0187d5","sha256:d994b455afe3f52e4c92994a29a1ff20be4a45998e43e92ab300668d9cd533b7"],"state_sha256":"c249e246b23a917b78a01fc1291fdaa947e5b9994eb6dc62febe3c4231c6fbaf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UWmYtQbFauM7L93kdsWK4Yd35MM6LyUk1a2Skn/hM0ZsVNnb1LHfKGe0iVzcGGN7uEMXbPkpY/JeV1Mma6caCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T08:08:36.997949Z","bundle_sha256":"58041521f1834e2c265a047b4d07aad5f78b00adc6c0056e8831600d7212d1a8"}}