{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:EKNPAFP4JXL53PDPVOGV5TI3Z6","short_pith_number":"pith:EKNPAFP4","canonical_record":{"source":{"id":"1511.05204","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-16T22:19:11Z","cross_cats_sorted":[],"title_canon_sha256":"de7aabe07ef629d952686bf573bfb00d544412a3a1d7c66e838edcf870a9a260","abstract_canon_sha256":"a5319d7c7efbdccd5079e34f6de878daa6aed2eb4bc84c9116338efdbe820374"},"schema_version":"1.0"},"canonical_sha256":"229af015fc4dd7ddbc6fab8d5ecd1bcfbad11055886e54a15ceb5ed83d51f2f0","source":{"kind":"arxiv","id":"1511.05204","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.05204","created_at":"2026-05-18T00:57:18Z"},{"alias_kind":"arxiv_version","alias_value":"1511.05204v1","created_at":"2026-05-18T00:57:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.05204","created_at":"2026-05-18T00:57:18Z"},{"alias_kind":"pith_short_12","alias_value":"EKNPAFP4JXL5","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"EKNPAFP4JXL53PDP","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"EKNPAFP4","created_at":"2026-05-18T12:29:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:EKNPAFP4JXL53PDPVOGV5TI3Z6","target":"record","payload":{"canonical_record":{"source":{"id":"1511.05204","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-16T22:19:11Z","cross_cats_sorted":[],"title_canon_sha256":"de7aabe07ef629d952686bf573bfb00d544412a3a1d7c66e838edcf870a9a260","abstract_canon_sha256":"a5319d7c7efbdccd5079e34f6de878daa6aed2eb4bc84c9116338efdbe820374"},"schema_version":"1.0"},"canonical_sha256":"229af015fc4dd7ddbc6fab8d5ecd1bcfbad11055886e54a15ceb5ed83d51f2f0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:57:18.125520Z","signature_b64":"TzR2QnPkqpPI98FhZdhhgAcsPS5hhKzeQ684Kp8bTv9W+cNKI3NfIizZce2iHB6+9Sdhs+nLWfJVUPa9D4BWBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"229af015fc4dd7ddbc6fab8d5ecd1bcfbad11055886e54a15ceb5ed83d51f2f0","last_reissued_at":"2026-05-18T00:57:18.124904Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:57:18.124904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.05204","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":"2026-05-18T00:57:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dTtHgLixQwZ+qLGHtNyXRC264KrfqU5qxsM6DWLH3dEtpAmXPmJ35ZK4uThDNiffxZZPgPWHB4y9hZQ+CmGMBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:19:06.090442Z"},"content_sha256":"a055fd3487c493360c272775e94490a16f1ce5bdc1ff79f1b4e8ac0f2353907b","schema_version":"1.0","event_id":"sha256:a055fd3487c493360c272775e94490a16f1ce5bdc1ff79f1b4e8ac0f2353907b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:EKNPAFP4JXL53PDPVOGV5TI3Z6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mengyi Liu, Ruiping Wang, Shiguang Shan, Xilin Chen","submitted_at":"2015-11-16T22:19:11Z","abstract_excerpt":"Facial expression is temporally dynamic event which can be decomposed into a set of muscle motions occurring in different facial regions over various time intervals. For dynamic expression recognition, two key issues, temporal alignment and semantics-aware dynamic representation, must be taken into account. In this paper, we attempt to solve both problems via manifold modeling of videos based on a novel mid-level representation, i.e. \\textbf{expressionlet}. Specifically, our method contains three key stages: 1) each expression video clip is characterized as a spatial-temporal manifold (STM) fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.05204","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":"2026-05-18T00:57:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z9YumxIm69FTU75LZvzq9sjq+G1VVvYetWamnZTp3TrG3GI1q1I451n+My48cDWLzvpS56T9TLkSIlRSnwT2CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:19:06.091087Z"},"content_sha256":"2fa4d8fdd249dc3df42f979d993cb14d9b6d8eb348af081ce3ce856cb74774a6","schema_version":"1.0","event_id":"sha256:2fa4d8fdd249dc3df42f979d993cb14d9b6d8eb348af081ce3ce856cb74774a6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EKNPAFP4JXL53PDPVOGV5TI3Z6/bundle.json","state_url":"https://pith.science/pith/EKNPAFP4JXL53PDPVOGV5TI3Z6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EKNPAFP4JXL53PDPVOGV5TI3Z6/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":"2026-05-25T21:19:06Z","links":{"resolver":"https://pith.science/pith/EKNPAFP4JXL53PDPVOGV5TI3Z6","bundle":"https://pith.science/pith/EKNPAFP4JXL53PDPVOGV5TI3Z6/bundle.json","state":"https://pith.science/pith/EKNPAFP4JXL53PDPVOGV5TI3Z6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EKNPAFP4JXL53PDPVOGV5TI3Z6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:EKNPAFP4JXL53PDPVOGV5TI3Z6","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":"a5319d7c7efbdccd5079e34f6de878daa6aed2eb4bc84c9116338efdbe820374","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-16T22:19:11Z","title_canon_sha256":"de7aabe07ef629d952686bf573bfb00d544412a3a1d7c66e838edcf870a9a260"},"schema_version":"1.0","source":{"id":"1511.05204","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.05204","created_at":"2026-05-18T00:57:18Z"},{"alias_kind":"arxiv_version","alias_value":"1511.05204v1","created_at":"2026-05-18T00:57:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.05204","created_at":"2026-05-18T00:57:18Z"},{"alias_kind":"pith_short_12","alias_value":"EKNPAFP4JXL5","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"EKNPAFP4JXL53PDP","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"EKNPAFP4","created_at":"2026-05-18T12:29:19Z"}],"graph_snapshots":[{"event_id":"sha256:2fa4d8fdd249dc3df42f979d993cb14d9b6d8eb348af081ce3ce856cb74774a6","target":"graph","created_at":"2026-05-18T00:57:18Z","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":"Facial expression is temporally dynamic event which can be decomposed into a set of muscle motions occurring in different facial regions over various time intervals. For dynamic expression recognition, two key issues, temporal alignment and semantics-aware dynamic representation, must be taken into account. In this paper, we attempt to solve both problems via manifold modeling of videos based on a novel mid-level representation, i.e. \\textbf{expressionlet}. Specifically, our method contains three key stages: 1) each expression video clip is characterized as a spatial-temporal manifold (STM) fo","authors_text":"Mengyi Liu, Ruiping Wang, Shiguang Shan, Xilin Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-16T22:19:11Z","title":"Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.05204","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:a055fd3487c493360c272775e94490a16f1ce5bdc1ff79f1b4e8ac0f2353907b","target":"record","created_at":"2026-05-18T00:57:18Z","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":"a5319d7c7efbdccd5079e34f6de878daa6aed2eb4bc84c9116338efdbe820374","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-16T22:19:11Z","title_canon_sha256":"de7aabe07ef629d952686bf573bfb00d544412a3a1d7c66e838edcf870a9a260"},"schema_version":"1.0","source":{"id":"1511.05204","kind":"arxiv","version":1}},"canonical_sha256":"229af015fc4dd7ddbc6fab8d5ecd1bcfbad11055886e54a15ceb5ed83d51f2f0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"229af015fc4dd7ddbc6fab8d5ecd1bcfbad11055886e54a15ceb5ed83d51f2f0","first_computed_at":"2026-05-18T00:57:18.124904Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:57:18.124904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TzR2QnPkqpPI98FhZdhhgAcsPS5hhKzeQ684Kp8bTv9W+cNKI3NfIizZce2iHB6+9Sdhs+nLWfJVUPa9D4BWBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:57:18.125520Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.05204","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a055fd3487c493360c272775e94490a16f1ce5bdc1ff79f1b4e8ac0f2353907b","sha256:2fa4d8fdd249dc3df42f979d993cb14d9b6d8eb348af081ce3ce856cb74774a6"],"state_sha256":"4dcb1c6c533a50dcda5107e4338eae9b57d5ceb6a416788df3fb3c4df8c374dd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hX2lVKragJsgzpdLdUCKJwBb+eubpNH+YuH2rXf0Z6OTGc8JWkJ42yIVhgRcCLdo9W8TJxZ9gAQET/PR62BQAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:19:06.094652Z","bundle_sha256":"2c6018ba2a1d6c9ebc36143287b0fae26e969d42d4e57a56622e9ade63ba7c29"}}