{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:HL2535PXXU6ITQU6T6LFCM5HZL","short_pith_number":"pith:HL2535PX","canonical_record":{"source":{"id":"2607.20819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-23T01:12:28Z","cross_cats_sorted":[],"title_canon_sha256":"68fbedbf16a49912fb6a624f662beb9eb61cbbcf5fe17fd6690cd92c3fac932e","abstract_canon_sha256":"23f1ebb73f1adeef44aea7271dbc402d94b6f4263050ce9166298bda20a7c087"},"schema_version":"1.0"},"canonical_sha256":"3af5ddf5f7bd3c89c29e9f965133a7caf2ca799b21d92cc5aa298aaf1f668f66","source":{"kind":"arxiv","id":"2607.20819","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.20819","created_at":"2026-07-24T00:23:36Z"},{"alias_kind":"arxiv_version","alias_value":"2607.20819v1","created_at":"2026-07-24T00:23:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.20819","created_at":"2026-07-24T00:23:36Z"},{"alias_kind":"pith_short_12","alias_value":"HL2535PXXU6I","created_at":"2026-07-24T00:23:36Z"},{"alias_kind":"pith_short_16","alias_value":"HL2535PXXU6ITQU6","created_at":"2026-07-24T00:23:36Z"},{"alias_kind":"pith_short_8","alias_value":"HL2535PX","created_at":"2026-07-24T00:23:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:HL2535PXXU6ITQU6T6LFCM5HZL","target":"record","payload":{"canonical_record":{"source":{"id":"2607.20819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-23T01:12:28Z","cross_cats_sorted":[],"title_canon_sha256":"68fbedbf16a49912fb6a624f662beb9eb61cbbcf5fe17fd6690cd92c3fac932e","abstract_canon_sha256":"23f1ebb73f1adeef44aea7271dbc402d94b6f4263050ce9166298bda20a7c087"},"schema_version":"1.0"},"canonical_sha256":"3af5ddf5f7bd3c89c29e9f965133a7caf2ca799b21d92cc5aa298aaf1f668f66","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-24T00:23:36.307323Z","signature_b64":"kHa9vVLXOEki7pd1CWIuVqgwlt8hLj4E4Z89VERhguPbCEs3pT9EvBDHEXtFvBwrfgTeevxnzw6kxNaWHqQ9Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3af5ddf5f7bd3c89c29e9f965133a7caf2ca799b21d92cc5aa298aaf1f668f66","last_reissued_at":"2026-07-24T00:23:36.306427Z","signature_status":"signed_v1","first_computed_at":"2026-07-24T00:23:36.306427Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.20819","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":"2026-07-24T00:23:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ERnNTicWu262gF3ZFFJGEvjHzyBZustFOiFCRVbm6ayZlqdc33fogUdbgodjuEs0KLB9f+9Dv1VbaBDbatBdBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T13:34:21.917259Z"},"content_sha256":"28309693b5335edf62e64d50cf47def3fa6428224da8f2f1511666db5c50f1c6","schema_version":"1.0","event_id":"sha256:28309693b5335edf62e64d50cf47def3fa6428224da8f2f1511666db5c50f1c6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:HL2535PXXU6ITQU6T6LFCM5HZL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Explainable graph attention network for stress recognition (StressGAT) via differential action units","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Giorgos Giannakakis, Nikolaos Smyrnis, Stefanos Gkikas, Thomas Kassiotis","submitted_at":"2026-07-23T01:12:28Z","abstract_excerpt":"Stress is a dynamic process characterized by significant individual variability in facial expression. Traditional architectures, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), often overlook person-specific baselines or lack the representational capacity to model the non-linear temporal progression of distress due to sequential bottlenecks and rigid grid-based constraints. Furthermore, many deep learning models lack the interpretability required for clinical deployment. This study introduces StressGAT, a Graph Attention Network that leverages the relational "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.20819","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/2607.20819/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":"2026-07-24T00:23:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"olXHlsQePM+Cx6b3XbP7VfoIW/GxFOOjPtJ6MiYn6Mgnb4N5I7hBG5IMKR8OLn719VhZPgEfhNWjwP8z2NE8Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T13:34:21.917575Z"},"content_sha256":"7391570757813d1e3ca08fb292c063d53cdb8c70f700d638abacf2d043046c90","schema_version":"1.0","event_id":"sha256:7391570757813d1e3ca08fb292c063d53cdb8c70f700d638abacf2d043046c90"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HL2535PXXU6ITQU6T6LFCM5HZL/bundle.json","state_url":"https://pith.science/pith/HL2535PXXU6ITQU6T6LFCM5HZL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HL2535PXXU6ITQU6T6LFCM5HZL/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":"2026-08-14T13:34:21Z","links":{"resolver":"https://pith.science/pith/HL2535PXXU6ITQU6T6LFCM5HZL","bundle":"https://pith.science/pith/HL2535PXXU6ITQU6T6LFCM5HZL/bundle.json","state":"https://pith.science/pith/HL2535PXXU6ITQU6T6LFCM5HZL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HL2535PXXU6ITQU6T6LFCM5HZL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:HL2535PXXU6ITQU6T6LFCM5HZL","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":"23f1ebb73f1adeef44aea7271dbc402d94b6f4263050ce9166298bda20a7c087","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-23T01:12:28Z","title_canon_sha256":"68fbedbf16a49912fb6a624f662beb9eb61cbbcf5fe17fd6690cd92c3fac932e"},"schema_version":"1.0","source":{"id":"2607.20819","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.20819","created_at":"2026-07-24T00:23:36Z"},{"alias_kind":"arxiv_version","alias_value":"2607.20819v1","created_at":"2026-07-24T00:23:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.20819","created_at":"2026-07-24T00:23:36Z"},{"alias_kind":"pith_short_12","alias_value":"HL2535PXXU6I","created_at":"2026-07-24T00:23:36Z"},{"alias_kind":"pith_short_16","alias_value":"HL2535PXXU6ITQU6","created_at":"2026-07-24T00:23:36Z"},{"alias_kind":"pith_short_8","alias_value":"HL2535PX","created_at":"2026-07-24T00:23:36Z"}],"graph_snapshots":[{"event_id":"sha256:7391570757813d1e3ca08fb292c063d53cdb8c70f700d638abacf2d043046c90","target":"graph","created_at":"2026-07-24T00:23:36Z","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/2607.20819/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Stress is a dynamic process characterized by significant individual variability in facial expression. Traditional architectures, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), often overlook person-specific baselines or lack the representational capacity to model the non-linear temporal progression of distress due to sequential bottlenecks and rigid grid-based constraints. Furthermore, many deep learning models lack the interpretability required for clinical deployment. This study introduces StressGAT, a Graph Attention Network that leverages the relational ","authors_text":"Giorgos Giannakakis, Nikolaos Smyrnis, Stefanos Gkikas, Thomas Kassiotis","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-23T01:12:28Z","title":"Explainable graph attention network for stress recognition (StressGAT) via differential action units"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.20819","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":"sha256:28309693b5335edf62e64d50cf47def3fa6428224da8f2f1511666db5c50f1c6","target":"record","created_at":"2026-07-24T00:23:36Z","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":"23f1ebb73f1adeef44aea7271dbc402d94b6f4263050ce9166298bda20a7c087","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-23T01:12:28Z","title_canon_sha256":"68fbedbf16a49912fb6a624f662beb9eb61cbbcf5fe17fd6690cd92c3fac932e"},"schema_version":"1.0","source":{"id":"2607.20819","kind":"arxiv","version":1}},"canonical_sha256":"3af5ddf5f7bd3c89c29e9f965133a7caf2ca799b21d92cc5aa298aaf1f668f66","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3af5ddf5f7bd3c89c29e9f965133a7caf2ca799b21d92cc5aa298aaf1f668f66","first_computed_at":"2026-07-24T00:23:36.306427Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-24T00:23:36.306427Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kHa9vVLXOEki7pd1CWIuVqgwlt8hLj4E4Z89VERhguPbCEs3pT9EvBDHEXtFvBwrfgTeevxnzw6kxNaWHqQ9Ag==","signature_status":"signed_v1","signed_at":"2026-07-24T00:23:36.307323Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.20819","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:28309693b5335edf62e64d50cf47def3fa6428224da8f2f1511666db5c50f1c6","sha256:7391570757813d1e3ca08fb292c063d53cdb8c70f700d638abacf2d043046c90"],"state_sha256":"45e9e62523e6cd422e57b96e64f87d20b82c9629aa2ac4ee50a747217c597362"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FdmGVto7LAwszbUa43yd/mhZdcMYOx6/ZjTj9C1vCpZtGp7qJJH4LKEpEj6LN6JvXpVmdUFhpHOA+gEkkx+SDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T13:34:21.920910Z","bundle_sha256":"54d3560ca0c86c935149b87778cfa5b8d90725b001a30a834b08b2f57c20b8d1"}}