{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AOTVJG2PJUZU6ZSX72CEUM47IC","short_pith_number":"pith:AOTVJG2P","schema_version":"1.0","canonical_sha256":"03a7549b4f4d334f6657fe844a339f40b9600099d2de7b7ef8cec9f37fbb306e","source":{"kind":"arxiv","id":"2312.00236","version":4},"attestation_state":"computed","paper":{"title":"Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Khoa Luu, Pawan Sinha, Samee U. Khan, Xin Li, Xuan-Bac Nguyen","submitted_at":"2023-11-30T22:39:23Z","abstract_excerpt":"Human perception plays a vital role in forming beliefs and understanding reality. A deeper understanding of brain functionality will lead to the development of novel deep neural networks. In this work, we introduce a novel framework named Brainformer, a straightforward yet effective Transformer-based framework, to analyze Functional Magnetic Resonance Imaging (fMRI) patterns in the human perception system from a machine-learning perspective. Specifically, we present the Multi-scale fMRI Transformer to explore brain activity patterns through fMRI signals. This architecture includes a simple yet"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2312.00236","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-30T22:39:23Z","cross_cats_sorted":[],"title_canon_sha256":"739554d950ea5afa4d7f96ced1b8f597b8a27af433fb481feab6f1c783bbea97","abstract_canon_sha256":"adeb60885529d56dfc8b9a4884136d2f7b090bca06ee2d293b68fdb6e2eb5790"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:37.373043Z","signature_b64":"Xq3MKbummsYpdL8OIQyV/KUuHh3VLTLxD/CW+GRWrJ9AE3T9mvwEzSbf4ccHK0QuvkZXLGyMkpyuQYYNTkSUAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"03a7549b4f4d334f6657fe844a339f40b9600099d2de7b7ef8cec9f37fbb306e","last_reissued_at":"2026-07-05T09:40:37.372508Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:37.372508Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Khoa Luu, Pawan Sinha, Samee U. Khan, Xin Li, Xuan-Bac Nguyen","submitted_at":"2023-11-30T22:39:23Z","abstract_excerpt":"Human perception plays a vital role in forming beliefs and understanding reality. A deeper understanding of brain functionality will lead to the development of novel deep neural networks. In this work, we introduce a novel framework named Brainformer, a straightforward yet effective Transformer-based framework, to analyze Functional Magnetic Resonance Imaging (fMRI) patterns in the human perception system from a machine-learning perspective. Specifically, we present the Multi-scale fMRI Transformer to explore brain activity patterns through fMRI signals. This architecture includes a simple yet"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.00236","kind":"arxiv","version":4},"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/2312.00236/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2312.00236","created_at":"2026-07-05T09:40:37.372588+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.00236v4","created_at":"2026-07-05T09:40:37.372588+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.00236","created_at":"2026-07-05T09:40:37.372588+00:00"},{"alias_kind":"pith_short_12","alias_value":"AOTVJG2PJUZU","created_at":"2026-07-05T09:40:37.372588+00:00"},{"alias_kind":"pith_short_16","alias_value":"AOTVJG2PJUZU6ZSX","created_at":"2026-07-05T09:40:37.372588+00:00"},{"alias_kind":"pith_short_8","alias_value":"AOTVJG2P","created_at":"2026-07-05T09:40:37.372588+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.18187","citing_title":"BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding","ref_index":43,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AOTVJG2PJUZU6ZSX72CEUM47IC","json":"https://pith.science/pith/AOTVJG2PJUZU6ZSX72CEUM47IC.json","graph_json":"https://pith.science/api/pith-number/AOTVJG2PJUZU6ZSX72CEUM47IC/graph.json","events_json":"https://pith.science/api/pith-number/AOTVJG2PJUZU6ZSX72CEUM47IC/events.json","paper":"https://pith.science/paper/AOTVJG2P"},"agent_actions":{"view_html":"https://pith.science/pith/AOTVJG2PJUZU6ZSX72CEUM47IC","download_json":"https://pith.science/pith/AOTVJG2PJUZU6ZSX72CEUM47IC.json","view_paper":"https://pith.science/paper/AOTVJG2P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.00236&json=true","fetch_graph":"https://pith.science/api/pith-number/AOTVJG2PJUZU6ZSX72CEUM47IC/graph.json","fetch_events":"https://pith.science/api/pith-number/AOTVJG2PJUZU6ZSX72CEUM47IC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AOTVJG2PJUZU6ZSX72CEUM47IC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AOTVJG2PJUZU6ZSX72CEUM47IC/action/storage_attestation","attest_author":"https://pith.science/pith/AOTVJG2PJUZU6ZSX72CEUM47IC/action/author_attestation","sign_citation":"https://pith.science/pith/AOTVJG2PJUZU6ZSX72CEUM47IC/action/citation_signature","submit_replication":"https://pith.science/pith/AOTVJG2PJUZU6ZSX72CEUM47IC/action/replication_record"}},"created_at":"2026-07-05T09:40:37.372588+00:00","updated_at":"2026-07-05T09:40:37.372588+00:00"}