{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:XVPZTPAEHZWNY52LHPURGXFTYB","short_pith_number":"pith:XVPZTPAE","canonical_record":{"source":{"id":"1708.04890","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-16T13:33:41Z","cross_cats_sorted":[],"title_canon_sha256":"7d10e6ad590d800d61fd4d7e8aabbcf348d8cf4b1fc8c2f1492cfc313aeb4532","abstract_canon_sha256":"192c87e69c9a699434217fddc8602af78c115eb6747318e11ef99f88a6bb5adc"},"schema_version":"1.0"},"canonical_sha256":"bd5f99bc043e6cdc774b3be9135cb3c05a1674ad0eefce1e3039dfd6e06c9d54","source":{"kind":"arxiv","id":"1708.04890","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.04890","created_at":"2026-05-18T00:37:55Z"},{"alias_kind":"arxiv_version","alias_value":"1708.04890v1","created_at":"2026-05-18T00:37:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.04890","created_at":"2026-05-18T00:37:55Z"},{"alias_kind":"pith_short_12","alias_value":"XVPZTPAEHZWN","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"XVPZTPAEHZWNY52L","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"XVPZTPAE","created_at":"2026-05-18T12:31:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:XVPZTPAEHZWNY52LHPURGXFTYB","target":"record","payload":{"canonical_record":{"source":{"id":"1708.04890","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-16T13:33:41Z","cross_cats_sorted":[],"title_canon_sha256":"7d10e6ad590d800d61fd4d7e8aabbcf348d8cf4b1fc8c2f1492cfc313aeb4532","abstract_canon_sha256":"192c87e69c9a699434217fddc8602af78c115eb6747318e11ef99f88a6bb5adc"},"schema_version":"1.0"},"canonical_sha256":"bd5f99bc043e6cdc774b3be9135cb3c05a1674ad0eefce1e3039dfd6e06c9d54","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:37:55.242499Z","signature_b64":"HZHsk4wpp1Dbt607DDTgDek0fMmEA7jhyj1dOnWdiuZ9NlMmwv3d06epQ4evZ37qs1f/cmVm9jmMdNBzu4C+AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd5f99bc043e6cdc774b3be9135cb3c05a1674ad0eefce1e3039dfd6e06c9d54","last_reissued_at":"2026-05-18T00:37:55.242055Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:37:55.242055Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.04890","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":"2026-05-18T00:37:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8WCn7Sb2BnYLKogDB0i7rLuTjXd9cux7xvDzxGvLtUMK3r9JUn37rWlxT3Yw5s7vZX+mC3F3NsFLkFYyrH7EBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T17:53:11.553709Z"},"content_sha256":"803bebbe478980a368688bebc7c22f0fa89d08738849eca9fe85c0a35be61dee","schema_version":"1.0","event_id":"sha256:803bebbe478980a368688bebc7c22f0fa89d08738849eca9fe85c0a35be61dee"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:XVPZTPAEHZWNY52LHPURGXFTYB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A deep architecture for unified aesthetic prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Albert Gordo, Naila Murray","submitted_at":"2017-08-16T13:33:41Z","abstract_excerpt":"Image aesthetics has become an important criterion for visual content curation on social media sites and media content repositories. Previous work on aesthetic prediction models in the computer vision community has focused on aesthetic score prediction or binary image labeling. However, raw aesthetic annotations are in the form of score histograms and provide richer and more precise information than binary labels or mean scores. Consequently, in this work we focus on the rarely-studied problem of predicting aesthetic score distributions and propose a novel architecture and training procedure f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.04890","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":"2026-05-18T00:37:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WmSLr+GnkoBV55lxBeAJ2zNPZo0E1l6C9ZksIrwk5ys1uHmFEwFopRli3suWhdmu51QaB6UP4xHqCRaSVJ+1BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T17:53:11.554054Z"},"content_sha256":"b379432d846d4129940704948423a3f68e6fe9b904d3e3cb2f8634200f03daea","schema_version":"1.0","event_id":"sha256:b379432d846d4129940704948423a3f68e6fe9b904d3e3cb2f8634200f03daea"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XVPZTPAEHZWNY52LHPURGXFTYB/bundle.json","state_url":"https://pith.science/pith/XVPZTPAEHZWNY52LHPURGXFTYB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XVPZTPAEHZWNY52LHPURGXFTYB/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":"2026-06-28T17:53:11Z","links":{"resolver":"https://pith.science/pith/XVPZTPAEHZWNY52LHPURGXFTYB","bundle":"https://pith.science/pith/XVPZTPAEHZWNY52LHPURGXFTYB/bundle.json","state":"https://pith.science/pith/XVPZTPAEHZWNY52LHPURGXFTYB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XVPZTPAEHZWNY52LHPURGXFTYB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:XVPZTPAEHZWNY52LHPURGXFTYB","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":"192c87e69c9a699434217fddc8602af78c115eb6747318e11ef99f88a6bb5adc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-16T13:33:41Z","title_canon_sha256":"7d10e6ad590d800d61fd4d7e8aabbcf348d8cf4b1fc8c2f1492cfc313aeb4532"},"schema_version":"1.0","source":{"id":"1708.04890","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.04890","created_at":"2026-05-18T00:37:55Z"},{"alias_kind":"arxiv_version","alias_value":"1708.04890v1","created_at":"2026-05-18T00:37:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.04890","created_at":"2026-05-18T00:37:55Z"},{"alias_kind":"pith_short_12","alias_value":"XVPZTPAEHZWN","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_16","alias_value":"XVPZTPAEHZWNY52L","created_at":"2026-05-18T12:31:56Z"},{"alias_kind":"pith_short_8","alias_value":"XVPZTPAE","created_at":"2026-05-18T12:31:56Z"}],"graph_snapshots":[{"event_id":"sha256:b379432d846d4129940704948423a3f68e6fe9b904d3e3cb2f8634200f03daea","target":"graph","created_at":"2026-05-18T00:37:55Z","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":"Image aesthetics has become an important criterion for visual content curation on social media sites and media content repositories. Previous work on aesthetic prediction models in the computer vision community has focused on aesthetic score prediction or binary image labeling. However, raw aesthetic annotations are in the form of score histograms and provide richer and more precise information than binary labels or mean scores. Consequently, in this work we focus on the rarely-studied problem of predicting aesthetic score distributions and propose a novel architecture and training procedure f","authors_text":"Albert Gordo, Naila Murray","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-16T13:33:41Z","title":"A deep architecture for unified aesthetic prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.04890","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":"sha256:803bebbe478980a368688bebc7c22f0fa89d08738849eca9fe85c0a35be61dee","target":"record","created_at":"2026-05-18T00:37:55Z","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":"192c87e69c9a699434217fddc8602af78c115eb6747318e11ef99f88a6bb5adc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-16T13:33:41Z","title_canon_sha256":"7d10e6ad590d800d61fd4d7e8aabbcf348d8cf4b1fc8c2f1492cfc313aeb4532"},"schema_version":"1.0","source":{"id":"1708.04890","kind":"arxiv","version":1}},"canonical_sha256":"bd5f99bc043e6cdc774b3be9135cb3c05a1674ad0eefce1e3039dfd6e06c9d54","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bd5f99bc043e6cdc774b3be9135cb3c05a1674ad0eefce1e3039dfd6e06c9d54","first_computed_at":"2026-05-18T00:37:55.242055Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:37:55.242055Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HZHsk4wpp1Dbt607DDTgDek0fMmEA7jhyj1dOnWdiuZ9NlMmwv3d06epQ4evZ37qs1f/cmVm9jmMdNBzu4C+AQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:37:55.242499Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.04890","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:803bebbe478980a368688bebc7c22f0fa89d08738849eca9fe85c0a35be61dee","sha256:b379432d846d4129940704948423a3f68e6fe9b904d3e3cb2f8634200f03daea"],"state_sha256":"9b93b3209341c737489dc5ac99b755e596a84eaa81f96db8a8eeb4ab68d8399e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yVLVHL11Ps1o7nH+eMpbc/8Yr9w8WVHJ3b9zstofXNEEKAv6srF8bZmBDunVbzajWVeMuPijRFF0bP6ypWiCAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T17:53:11.556201Z","bundle_sha256":"2e36f6f9a21a86a68b9de3aad7a75b26b2d05211f622edcda0edca1837c8dd5c"}}