{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:BUZOICVNDTJIWZIO6UU5Q4EZ3C","short_pith_number":"pith:BUZOICVN","canonical_record":{"source":{"id":"2102.09150","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-18T04:10:12Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"b1b19a9b6ef619436961bf17d395f9f8acc9aae17c58c0398acc2a654a2b3cd9","abstract_canon_sha256":"d01c1a30602f774707801b55407aad8f8f9045ebe9e6b5162d200414b7b220a8"},"schema_version":"1.0"},"canonical_sha256":"0d32e40aad1cd28b650ef529d87099d88cf49c7ed24ffd52b04f89b00151b491","source":{"kind":"arxiv","id":"2102.09150","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.09150","created_at":"2026-07-05T02:16:20Z"},{"alias_kind":"arxiv_version","alias_value":"2102.09150v1","created_at":"2026-07-05T02:16:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.09150","created_at":"2026-07-05T02:16:20Z"},{"alias_kind":"pith_short_12","alias_value":"BUZOICVNDTJI","created_at":"2026-07-05T02:16:20Z"},{"alias_kind":"pith_short_16","alias_value":"BUZOICVNDTJIWZIO","created_at":"2026-07-05T02:16:20Z"},{"alias_kind":"pith_short_8","alias_value":"BUZOICVN","created_at":"2026-07-05T02:16:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:BUZOICVNDTJIWZIO6UU5Q4EZ3C","target":"record","payload":{"canonical_record":{"source":{"id":"2102.09150","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-18T04:10:12Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"b1b19a9b6ef619436961bf17d395f9f8acc9aae17c58c0398acc2a654a2b3cd9","abstract_canon_sha256":"d01c1a30602f774707801b55407aad8f8f9045ebe9e6b5162d200414b7b220a8"},"schema_version":"1.0"},"canonical_sha256":"0d32e40aad1cd28b650ef529d87099d88cf49c7ed24ffd52b04f89b00151b491","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:16:20.098115Z","signature_b64":"k+gfR6kyuOlN2EBpi2O8D4fS1JD0JJQshLefks0p2QG2Slv7gWelNeI57usW+qDavYDYP51dNh4TMPxPz1a0Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d32e40aad1cd28b650ef529d87099d88cf49c7ed24ffd52b04f89b00151b491","last_reissued_at":"2026-07-05T02:16:20.097558Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:16:20.097558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2102.09150","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":"2026-07-05T02:16:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sx6RasGBJf6KhhYZEHC1KLilc6HNTJrmS0ejaKagEBqkb/GLcccAw0uA5rUE/bG385HjSWKWEGTkZA1X8KqGDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T13:27:01.256189Z"},"content_sha256":"a1ded4aea077709d7650fa1b399a4c41c3b1d9050302031d20ef2cb56f8140a6","schema_version":"1.0","event_id":"sha256:a1ded4aea077709d7650fa1b399a4c41c3b1d9050302031d20ef2cb56f8140a6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:BUZOICVNDTJIWZIO6UU5Q4EZ3C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Enhanced Adversarial Network with Combined Latent Features for Spatio-Temporal Facial Affect Estimation in the Wild","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CV","authors_text":"Bj\\\"orn Schuller, Decky Aspandi, Federico Sukno, Xavier Binefa","submitted_at":"2021-02-18T04:10:12Z","abstract_excerpt":"Affective Computing has recently attracted the attention of the research community, due to its numerous applications in diverse areas. In this context, the emergence of video-based data allows to enrich the widely used spatial features with the inclusion of temporal information. However, such spatio-temporal modelling often results in very high-dimensional feature spaces and large volumes of data, making training difficult and time consuming. This paper addresses these shortcomings by proposing a novel model that efficiently extracts both spatial and temporal features of the data by means of i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.09150","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/2102.09150/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":"2026-07-05T02:16:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YmeXTgn59T54QyftpzmtsHwooZQ0kjlH2MhMqDG/joLe6Io18n3uJ8CBII/MOXCFC2NWNba2g2tNDQE43+0NAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T13:27:01.256696Z"},"content_sha256":"4d1e16e15bede2e24d45e77a123f08e09bb50216c4125bc18d4a94f9bb7629bd","schema_version":"1.0","event_id":"sha256:4d1e16e15bede2e24d45e77a123f08e09bb50216c4125bc18d4a94f9bb7629bd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BUZOICVNDTJIWZIO6UU5Q4EZ3C/bundle.json","state_url":"https://pith.science/pith/BUZOICVNDTJIWZIO6UU5Q4EZ3C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BUZOICVNDTJIWZIO6UU5Q4EZ3C/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":"2026-08-18T13:27:01Z","links":{"resolver":"https://pith.science/pith/BUZOICVNDTJIWZIO6UU5Q4EZ3C","bundle":"https://pith.science/pith/BUZOICVNDTJIWZIO6UU5Q4EZ3C/bundle.json","state":"https://pith.science/pith/BUZOICVNDTJIWZIO6UU5Q4EZ3C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BUZOICVNDTJIWZIO6UU5Q4EZ3C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:BUZOICVNDTJIWZIO6UU5Q4EZ3C","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":"d01c1a30602f774707801b55407aad8f8f9045ebe9e6b5162d200414b7b220a8","cross_cats_sorted":["cs.HC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-18T04:10:12Z","title_canon_sha256":"b1b19a9b6ef619436961bf17d395f9f8acc9aae17c58c0398acc2a654a2b3cd9"},"schema_version":"1.0","source":{"id":"2102.09150","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.09150","created_at":"2026-07-05T02:16:20Z"},{"alias_kind":"arxiv_version","alias_value":"2102.09150v1","created_at":"2026-07-05T02:16:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.09150","created_at":"2026-07-05T02:16:20Z"},{"alias_kind":"pith_short_12","alias_value":"BUZOICVNDTJI","created_at":"2026-07-05T02:16:20Z"},{"alias_kind":"pith_short_16","alias_value":"BUZOICVNDTJIWZIO","created_at":"2026-07-05T02:16:20Z"},{"alias_kind":"pith_short_8","alias_value":"BUZOICVN","created_at":"2026-07-05T02:16:20Z"}],"graph_snapshots":[{"event_id":"sha256:4d1e16e15bede2e24d45e77a123f08e09bb50216c4125bc18d4a94f9bb7629bd","target":"graph","created_at":"2026-07-05T02:16:20Z","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/2102.09150/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Affective Computing has recently attracted the attention of the research community, due to its numerous applications in diverse areas. In this context, the emergence of video-based data allows to enrich the widely used spatial features with the inclusion of temporal information. However, such spatio-temporal modelling often results in very high-dimensional feature spaces and large volumes of data, making training difficult and time consuming. This paper addresses these shortcomings by proposing a novel model that efficiently extracts both spatial and temporal features of the data by means of i","authors_text":"Bj\\\"orn Schuller, Decky Aspandi, Federico Sukno, Xavier Binefa","cross_cats":["cs.HC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-18T04:10:12Z","title":"An Enhanced Adversarial Network with Combined Latent Features for Spatio-Temporal Facial Affect Estimation in the Wild"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.09150","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:a1ded4aea077709d7650fa1b399a4c41c3b1d9050302031d20ef2cb56f8140a6","target":"record","created_at":"2026-07-05T02:16:20Z","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":"d01c1a30602f774707801b55407aad8f8f9045ebe9e6b5162d200414b7b220a8","cross_cats_sorted":["cs.HC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-02-18T04:10:12Z","title_canon_sha256":"b1b19a9b6ef619436961bf17d395f9f8acc9aae17c58c0398acc2a654a2b3cd9"},"schema_version":"1.0","source":{"id":"2102.09150","kind":"arxiv","version":1}},"canonical_sha256":"0d32e40aad1cd28b650ef529d87099d88cf49c7ed24ffd52b04f89b00151b491","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0d32e40aad1cd28b650ef529d87099d88cf49c7ed24ffd52b04f89b00151b491","first_computed_at":"2026-07-05T02:16:20.097558Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:16:20.097558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"k+gfR6kyuOlN2EBpi2O8D4fS1JD0JJQshLefks0p2QG2Slv7gWelNeI57usW+qDavYDYP51dNh4TMPxPz1a0Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:16:20.098115Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.09150","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a1ded4aea077709d7650fa1b399a4c41c3b1d9050302031d20ef2cb56f8140a6","sha256:4d1e16e15bede2e24d45e77a123f08e09bb50216c4125bc18d4a94f9bb7629bd"],"state_sha256":"18479b0f1e27e8a2f21a637e8eb821a9de9a7aba5ef2542a619b12a37ed4e953"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"45w1zEYrdCq0mMIKh0r5R4jFR/xhgwgO4OCh57nz8olC7qZiSfWiezntiq11GHngixPibEIAuNbea9vIVSvcAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T13:27:01.260724Z","bundle_sha256":"ab0748bc52258312e0924564607ac98abad37edadd1da268b29ada08e719dfb2"}}