{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:4YUJXSYNGIOXIDEAOGSHRLFVZE","short_pith_number":"pith:4YUJXSYN","canonical_record":{"source":{"id":"1610.02255","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-07T12:42:47Z","cross_cats_sorted":[],"title_canon_sha256":"922b25ff40abd1ad29ea2fb2ec08e16d4b5b17369fd3569a8d77da4ce6cee6d3","abstract_canon_sha256":"128f0d52ac6739867bbc8cbbe19e7f8239b04af4348421dd76c664158ccc2ff1"},"schema_version":"1.0"},"canonical_sha256":"e6289bcb0d321d740c8071a478acb5c914c78ade280fade0bd8af12a24d3bce8","source":{"kind":"arxiv","id":"1610.02255","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.02255","created_at":"2026-05-18T01:02:57Z"},{"alias_kind":"arxiv_version","alias_value":"1610.02255v1","created_at":"2026-05-18T01:02:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.02255","created_at":"2026-05-18T01:02:57Z"},{"alias_kind":"pith_short_12","alias_value":"4YUJXSYNGIOX","created_at":"2026-05-18T12:29:58Z"},{"alias_kind":"pith_short_16","alias_value":"4YUJXSYNGIOXIDEA","created_at":"2026-05-18T12:29:58Z"},{"alias_kind":"pith_short_8","alias_value":"4YUJXSYN","created_at":"2026-05-18T12:29:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:4YUJXSYNGIOXIDEAOGSHRLFVZE","target":"record","payload":{"canonical_record":{"source":{"id":"1610.02255","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-07T12:42:47Z","cross_cats_sorted":[],"title_canon_sha256":"922b25ff40abd1ad29ea2fb2ec08e16d4b5b17369fd3569a8d77da4ce6cee6d3","abstract_canon_sha256":"128f0d52ac6739867bbc8cbbe19e7f8239b04af4348421dd76c664158ccc2ff1"},"schema_version":"1.0"},"canonical_sha256":"e6289bcb0d321d740c8071a478acb5c914c78ade280fade0bd8af12a24d3bce8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:02:57.948907Z","signature_b64":"tMvua82hB7kAg/hJtLbmMiaRU6P6nNON9Wj/ciXTuCDTQdJXFcdtZUP4z5ghDdvxK5mmx854OYLODoCMmJV2Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e6289bcb0d321d740c8071a478acb5c914c78ade280fade0bd8af12a24d3bce8","last_reissued_at":"2026-05-18T01:02:57.948269Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:02:57.948269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1610.02255","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":"2026-05-18T01:02:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7+aDl9Sg7wuMG0+DHrCNPJvfjD6Zn9ASZ1r3DGq4nc+liDBUxuvYN5nb9SYcpya3/CLXJYNFnh5M1+GY4jLrBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T19:26:18.064290Z"},"content_sha256":"94b389c3928d22a15cca0440936399f2cc829c4d80d37389db049b0708ea5c82","schema_version":"1.0","event_id":"sha256:94b389c3928d22a15cca0440936399f2cc829c4d80d37389db049b0708ea5c82"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:4YUJXSYNGIOXIDEAOGSHRLFVZE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Grimaces by Watching TV","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrea Vedaldi, Samuel Albanie","submitted_at":"2016-10-07T12:42:47Z","abstract_excerpt":"Differently from computer vision systems which require explicit supervision, humans can learn facial expressions by observing people in their environment. In this paper, we look at how similar capabilities could be developed in machine vision. As a starting point, we consider the problem of relating facial expressions to objectively measurable events occurring in videos. In particular, we consider a gameshow in which contestants play to win significant sums of money. We extract events affecting the game and corresponding facial expressions objectively and automatically from the videos, obtaini"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.02255","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":"2026-05-18T01:02:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pjX1X8xlHiqqlq/N5b/hgtHHLX6JoNyQmxz89Iu9aoORGlbDM0N+Vc0VEzvsSEHQz6sYaNrEJuIG9Tp+14DjAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T19:26:18.064682Z"},"content_sha256":"0e49649b93d27c737665e969329043e34cfbdb92e119cb3f97c11d04010b14ba","schema_version":"1.0","event_id":"sha256:0e49649b93d27c737665e969329043e34cfbdb92e119cb3f97c11d04010b14ba"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4YUJXSYNGIOXIDEAOGSHRLFVZE/bundle.json","state_url":"https://pith.science/pith/4YUJXSYNGIOXIDEAOGSHRLFVZE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4YUJXSYNGIOXIDEAOGSHRLFVZE/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":"2026-06-02T19:26:18Z","links":{"resolver":"https://pith.science/pith/4YUJXSYNGIOXIDEAOGSHRLFVZE","bundle":"https://pith.science/pith/4YUJXSYNGIOXIDEAOGSHRLFVZE/bundle.json","state":"https://pith.science/pith/4YUJXSYNGIOXIDEAOGSHRLFVZE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4YUJXSYNGIOXIDEAOGSHRLFVZE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:4YUJXSYNGIOXIDEAOGSHRLFVZE","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":"128f0d52ac6739867bbc8cbbe19e7f8239b04af4348421dd76c664158ccc2ff1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-07T12:42:47Z","title_canon_sha256":"922b25ff40abd1ad29ea2fb2ec08e16d4b5b17369fd3569a8d77da4ce6cee6d3"},"schema_version":"1.0","source":{"id":"1610.02255","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.02255","created_at":"2026-05-18T01:02:57Z"},{"alias_kind":"arxiv_version","alias_value":"1610.02255v1","created_at":"2026-05-18T01:02:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.02255","created_at":"2026-05-18T01:02:57Z"},{"alias_kind":"pith_short_12","alias_value":"4YUJXSYNGIOX","created_at":"2026-05-18T12:29:58Z"},{"alias_kind":"pith_short_16","alias_value":"4YUJXSYNGIOXIDEA","created_at":"2026-05-18T12:29:58Z"},{"alias_kind":"pith_short_8","alias_value":"4YUJXSYN","created_at":"2026-05-18T12:29:58Z"}],"graph_snapshots":[{"event_id":"sha256:0e49649b93d27c737665e969329043e34cfbdb92e119cb3f97c11d04010b14ba","target":"graph","created_at":"2026-05-18T01:02:57Z","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":"Differently from computer vision systems which require explicit supervision, humans can learn facial expressions by observing people in their environment. In this paper, we look at how similar capabilities could be developed in machine vision. As a starting point, we consider the problem of relating facial expressions to objectively measurable events occurring in videos. In particular, we consider a gameshow in which contestants play to win significant sums of money. We extract events affecting the game and corresponding facial expressions objectively and automatically from the videos, obtaini","authors_text":"Andrea Vedaldi, Samuel Albanie","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-07T12:42:47Z","title":"Learning Grimaces by Watching TV"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.02255","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":"sha256:94b389c3928d22a15cca0440936399f2cc829c4d80d37389db049b0708ea5c82","target":"record","created_at":"2026-05-18T01:02:57Z","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":"128f0d52ac6739867bbc8cbbe19e7f8239b04af4348421dd76c664158ccc2ff1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-07T12:42:47Z","title_canon_sha256":"922b25ff40abd1ad29ea2fb2ec08e16d4b5b17369fd3569a8d77da4ce6cee6d3"},"schema_version":"1.0","source":{"id":"1610.02255","kind":"arxiv","version":1}},"canonical_sha256":"e6289bcb0d321d740c8071a478acb5c914c78ade280fade0bd8af12a24d3bce8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e6289bcb0d321d740c8071a478acb5c914c78ade280fade0bd8af12a24d3bce8","first_computed_at":"2026-05-18T01:02:57.948269Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:02:57.948269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tMvua82hB7kAg/hJtLbmMiaRU6P6nNON9Wj/ciXTuCDTQdJXFcdtZUP4z5ghDdvxK5mmx854OYLODoCMmJV2Bg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:02:57.948907Z","signed_message":"canonical_sha256_bytes"},"source_id":"1610.02255","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:94b389c3928d22a15cca0440936399f2cc829c4d80d37389db049b0708ea5c82","sha256:0e49649b93d27c737665e969329043e34cfbdb92e119cb3f97c11d04010b14ba"],"state_sha256":"21e6f6f5dd78e48cc94287ad5a2aee1bec3999878aa50eeee537fe9db7f3d699"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bhx5OkjTALNrK5/6DKLdJ0vNgb1n+k3+NBHXKleBrBbjhnmCpDor1VKeexy6xcrWzzWDwhn1VTVDTWIuerHhBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T19:26:18.066592Z","bundle_sha256":"bd22c1108bd27cdf2bdb5a4d1c5c9bdaf93b7a2e5c1bef9cea8a326d0feeedb8"}}