{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:V5EHBMKDTIOFUIINDKNEUG26OG","short_pith_number":"pith:V5EHBMKD","canonical_record":{"source":{"id":"2412.04184","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2024-12-05T14:23:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"712b8d4feab2bbef5a28d19c1b3475f48e485159ea484bfe974342cbc30767ef","abstract_canon_sha256":"eaa6b0451f59c192eb06cbe9860366e5f4438962b5d2072bbabba2a42ac24fe3"},"schema_version":"1.0"},"canonical_sha256":"af4870b1439a1c5a210d1a9a4a1b5e71935990347dcc4d81176fecfd59d3553b","source":{"kind":"arxiv","id":"2412.04184","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.04184","created_at":"2026-07-05T11:15:40Z"},{"alias_kind":"arxiv_version","alias_value":"2412.04184v1","created_at":"2026-07-05T11:15:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04184","created_at":"2026-07-05T11:15:40Z"},{"alias_kind":"pith_short_12","alias_value":"V5EHBMKDTIOF","created_at":"2026-07-05T11:15:40Z"},{"alias_kind":"pith_short_16","alias_value":"V5EHBMKDTIOFUIIN","created_at":"2026-07-05T11:15:40Z"},{"alias_kind":"pith_short_8","alias_value":"V5EHBMKD","created_at":"2026-07-05T11:15:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:V5EHBMKDTIOFUIINDKNEUG26OG","target":"record","payload":{"canonical_record":{"source":{"id":"2412.04184","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2024-12-05T14:23:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"712b8d4feab2bbef5a28d19c1b3475f48e485159ea484bfe974342cbc30767ef","abstract_canon_sha256":"eaa6b0451f59c192eb06cbe9860366e5f4438962b5d2072bbabba2a42ac24fe3"},"schema_version":"1.0"},"canonical_sha256":"af4870b1439a1c5a210d1a9a4a1b5e71935990347dcc4d81176fecfd59d3553b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:40.958016Z","signature_b64":"IYeXnapHuEqGzJSEZIAryFMJpXitFHC0k+CWpRZOzklOJnxu0sqox6yaq78diiYd4CVyyo2B4bD5HkhSSQviBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af4870b1439a1c5a210d1a9a4a1b5e71935990347dcc4d81176fecfd59d3553b","last_reissued_at":"2026-07-05T11:15:40.956998Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:40.956998Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.04184","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":"2026-07-05T11:15:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bYlbmHll4qMGjvyOBA8M3INAytehK73TbNOnXJ0GMw1VgVu1fkzjNOH6fAO3bFlXeNYoXT1h1It66IOjSBxvCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T10:20:24.865432Z"},"content_sha256":"1164736516a26d3c0b4d0d7a3998782ec925226fb698ec3a7479c1c806dd0d42","schema_version":"1.0","event_id":"sha256:1164736516a26d3c0b4d0d7a3998782ec925226fb698ec3a7479c1c806dd0d42"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:V5EHBMKDTIOFUIINDKNEUG26OG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modeling Eye Gaze Velocity Trajectories using GANs with Spectral Loss for Enhanced Fidelity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Alexander Szorkovszky, Anis Yazidi, Pedro Lencastre, Pedro Lind, Rujeena Mathema, Shailendra Bhandari","submitted_at":"2024-12-05T14:23:40Z","abstract_excerpt":"Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models like Markov models often fail to capture the complex temporal dependencies and distributional nuance inherent in eye gaze trajectories data. This study introduces a GAN framework employing LSTM and CNN generators and discriminators to generate high-fidelity synthetic eye gaze velocity trajectories. We conducted a comprehensive evaluation of four GAN architectures: CNN-CNN, LSTM-CNN, CNN-LSTM, and LSTM-LSTM trained und"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04184","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/2412.04184/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":"2026-07-05T11:15:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uCqBkZGQ5xUVJxeKNsxWX3YjGmanrtvhbhnRfFG8WB5mw5N2OhML2TCnyBf63Mwjh9QPXzWx/TA39VDLPC7ACA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T10:20:24.866483Z"},"content_sha256":"97cadd49035cf0fd2e8ba3184e59d0aad249484dc678b67196902b02d9f54eaa","schema_version":"1.0","event_id":"sha256:97cadd49035cf0fd2e8ba3184e59d0aad249484dc678b67196902b02d9f54eaa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V5EHBMKDTIOFUIINDKNEUG26OG/bundle.json","state_url":"https://pith.science/pith/V5EHBMKDTIOFUIINDKNEUG26OG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V5EHBMKDTIOFUIINDKNEUG26OG/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":"2026-08-12T10:20:24Z","links":{"resolver":"https://pith.science/pith/V5EHBMKDTIOFUIINDKNEUG26OG","bundle":"https://pith.science/pith/V5EHBMKDTIOFUIINDKNEUG26OG/bundle.json","state":"https://pith.science/pith/V5EHBMKDTIOFUIINDKNEUG26OG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V5EHBMKDTIOFUIINDKNEUG26OG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:V5EHBMKDTIOFUIINDKNEUG26OG","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":"eaa6b0451f59c192eb06cbe9860366e5f4438962b5d2072bbabba2a42ac24fe3","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2024-12-05T14:23:40Z","title_canon_sha256":"712b8d4feab2bbef5a28d19c1b3475f48e485159ea484bfe974342cbc30767ef"},"schema_version":"1.0","source":{"id":"2412.04184","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.04184","created_at":"2026-07-05T11:15:40Z"},{"alias_kind":"arxiv_version","alias_value":"2412.04184v1","created_at":"2026-07-05T11:15:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04184","created_at":"2026-07-05T11:15:40Z"},{"alias_kind":"pith_short_12","alias_value":"V5EHBMKDTIOF","created_at":"2026-07-05T11:15:40Z"},{"alias_kind":"pith_short_16","alias_value":"V5EHBMKDTIOFUIIN","created_at":"2026-07-05T11:15:40Z"},{"alias_kind":"pith_short_8","alias_value":"V5EHBMKD","created_at":"2026-07-05T11:15:40Z"}],"graph_snapshots":[{"event_id":"sha256:97cadd49035cf0fd2e8ba3184e59d0aad249484dc678b67196902b02d9f54eaa","target":"graph","created_at":"2026-07-05T11:15:40Z","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/2412.04184/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models like Markov models often fail to capture the complex temporal dependencies and distributional nuance inherent in eye gaze trajectories data. This study introduces a GAN framework employing LSTM and CNN generators and discriminators to generate high-fidelity synthetic eye gaze velocity trajectories. We conducted a comprehensive evaluation of four GAN architectures: CNN-CNN, LSTM-CNN, CNN-LSTM, and LSTM-LSTM trained und","authors_text":"Alexander Szorkovszky, Anis Yazidi, Pedro Lencastre, Pedro Lind, Rujeena Mathema, Shailendra Bhandari","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2024-12-05T14:23:40Z","title":"Modeling Eye Gaze Velocity Trajectories using GANs with Spectral Loss for Enhanced Fidelity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04184","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":"sha256:1164736516a26d3c0b4d0d7a3998782ec925226fb698ec3a7479c1c806dd0d42","target":"record","created_at":"2026-07-05T11:15:40Z","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":"eaa6b0451f59c192eb06cbe9860366e5f4438962b5d2072bbabba2a42ac24fe3","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2024-12-05T14:23:40Z","title_canon_sha256":"712b8d4feab2bbef5a28d19c1b3475f48e485159ea484bfe974342cbc30767ef"},"schema_version":"1.0","source":{"id":"2412.04184","kind":"arxiv","version":1}},"canonical_sha256":"af4870b1439a1c5a210d1a9a4a1b5e71935990347dcc4d81176fecfd59d3553b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"af4870b1439a1c5a210d1a9a4a1b5e71935990347dcc4d81176fecfd59d3553b","first_computed_at":"2026-07-05T11:15:40.956998Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:15:40.956998Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IYeXnapHuEqGzJSEZIAryFMJpXitFHC0k+CWpRZOzklOJnxu0sqox6yaq78diiYd4CVyyo2B4bD5HkhSSQviBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:15:40.958016Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.04184","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1164736516a26d3c0b4d0d7a3998782ec925226fb698ec3a7479c1c806dd0d42","sha256:97cadd49035cf0fd2e8ba3184e59d0aad249484dc678b67196902b02d9f54eaa"],"state_sha256":"2fc835af058133508ad8d51891fb4705548b126bbb904f182712a3e1c0a5ab11"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vw8TNBaWThH19EjRb9xlCWoT/P7CnptcFYmN08oLEMwybDYoUiqzNMe/5Z6s7+4jxxG/bXKEBUEOCv9EXvwXDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T10:20:24.871266Z","bundle_sha256":"3d075970f35b233eaf658366d0cbe10c8ef483de3f8332b5401bc6e33b9ca285"}}