{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:AU2CE6SSCRI2AZYKX4WB4JAYRT","short_pith_number":"pith:AU2CE6SS","canonical_record":{"source":{"id":"1907.01953","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2019-07-02T10:03:54Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4118c29c486295dbe6bfb4e627a3fab313d412fbdc385db84795491c914c3f72","abstract_canon_sha256":"c867f9b8aa17bb4e2f1997ad2d9540a43f15fa2a35b553ff54fdf9a29a2241a6"},"schema_version":"1.0"},"canonical_sha256":"0534227a521451a0670abf2c1e24188cf0d00f506b85bebd0cbf4406ce9bfcc6","source":{"kind":"arxiv","id":"1907.01953","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.01953","created_at":"2026-05-17T23:41:34Z"},{"alias_kind":"arxiv_version","alias_value":"1907.01953v1","created_at":"2026-05-17T23:41:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.01953","created_at":"2026-05-17T23:41:34Z"},{"alias_kind":"pith_short_12","alias_value":"AU2CE6SSCRI2","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"AU2CE6SSCRI2AZYK","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"AU2CE6SS","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:AU2CE6SSCRI2AZYKX4WB4JAYRT","target":"record","payload":{"canonical_record":{"source":{"id":"1907.01953","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2019-07-02T10:03:54Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4118c29c486295dbe6bfb4e627a3fab313d412fbdc385db84795491c914c3f72","abstract_canon_sha256":"c867f9b8aa17bb4e2f1997ad2d9540a43f15fa2a35b553ff54fdf9a29a2241a6"},"schema_version":"1.0"},"canonical_sha256":"0534227a521451a0670abf2c1e24188cf0d00f506b85bebd0cbf4406ce9bfcc6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:34.391968Z","signature_b64":"hzjMz11cpYbq/uATvrJSvoIwlVeNgaYu8W8sgLY5fZuNmuBi8JeaVlJPmQZOH7Q0ADkPr/+Pxt6OQVmHm6GxAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0534227a521451a0670abf2c1e24188cf0d00f506b85bebd0cbf4406ce9bfcc6","last_reissued_at":"2026-05-17T23:41:34.391424Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:34.391424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.01953","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":"2026-05-17T23:41:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l5Oe/D9ojL/+qF9gnUgV7OoT5ThhXwo4o83Gq0MlvgjC8sOzKL/xp+QUABiVFSQIM5Qbdlash0RKXge2bKtuCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:33:48.775402Z"},"content_sha256":"8af0a8538079dee2a9fa12ae3a444f65dca19d7daeddb7ec0d553ca018f4e4e3","schema_version":"1.0","event_id":"sha256:8af0a8538079dee2a9fa12ae3a444f65dca19d7daeddb7ec0d553ca018f4e4e3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:AU2CE6SSCRI2AZYKX4WB4JAYRT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Transfer Learning For Whole-Brain fMRI Analyses","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"eess.IV","authors_text":"Armin W. Thomas, Klaus-Robert M\\\"uller, Wojciech Samek","submitted_at":"2019-07-02T10:03:54Z","abstract_excerpt":"The application of deep learning (DL) models to the decoding of cognitive states from whole-brain functional Magnetic Resonance Imaging (fMRI) data is often hindered by the small sample size and high dimensionality of these datasets. Especially, in clinical settings, where patient data are scarce. In this work, we demonstrate that transfer learning represents a solution to this problem. Particularly, we show that a DL model, which has been previously trained on a large openly available fMRI dataset of the Human Connectome Project, outperforms a model variant with the same architecture, but whi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.01953","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":"2026-05-17T23:41:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G0Nbm1vF4u0q3zwYth0UBalBXahjLryJHb+UE9s8vxuIni6wv4frJ0Kr44W2whotlFSPnv1cQ4rGRHmOGlSKAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T03:33:48.775769Z"},"content_sha256":"d4271118c9bdcb3245fb1d1789b93229274f7a94131bc073f438b0484617f3b3","schema_version":"1.0","event_id":"sha256:d4271118c9bdcb3245fb1d1789b93229274f7a94131bc073f438b0484617f3b3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AU2CE6SSCRI2AZYKX4WB4JAYRT/bundle.json","state_url":"https://pith.science/pith/AU2CE6SSCRI2AZYKX4WB4JAYRT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AU2CE6SSCRI2AZYKX4WB4JAYRT/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":"2026-05-26T03:33:48Z","links":{"resolver":"https://pith.science/pith/AU2CE6SSCRI2AZYKX4WB4JAYRT","bundle":"https://pith.science/pith/AU2CE6SSCRI2AZYKX4WB4JAYRT/bundle.json","state":"https://pith.science/pith/AU2CE6SSCRI2AZYKX4WB4JAYRT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AU2CE6SSCRI2AZYKX4WB4JAYRT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:AU2CE6SSCRI2AZYKX4WB4JAYRT","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":"c867f9b8aa17bb4e2f1997ad2d9540a43f15fa2a35b553ff54fdf9a29a2241a6","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2019-07-02T10:03:54Z","title_canon_sha256":"4118c29c486295dbe6bfb4e627a3fab313d412fbdc385db84795491c914c3f72"},"schema_version":"1.0","source":{"id":"1907.01953","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.01953","created_at":"2026-05-17T23:41:34Z"},{"alias_kind":"arxiv_version","alias_value":"1907.01953v1","created_at":"2026-05-17T23:41:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.01953","created_at":"2026-05-17T23:41:34Z"},{"alias_kind":"pith_short_12","alias_value":"AU2CE6SSCRI2","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"AU2CE6SSCRI2AZYK","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"AU2CE6SS","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:d4271118c9bdcb3245fb1d1789b93229274f7a94131bc073f438b0484617f3b3","target":"graph","created_at":"2026-05-17T23:41:34Z","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":"The application of deep learning (DL) models to the decoding of cognitive states from whole-brain functional Magnetic Resonance Imaging (fMRI) data is often hindered by the small sample size and high dimensionality of these datasets. Especially, in clinical settings, where patient data are scarce. In this work, we demonstrate that transfer learning represents a solution to this problem. Particularly, we show that a DL model, which has been previously trained on a large openly available fMRI dataset of the Human Connectome Project, outperforms a model variant with the same architecture, but whi","authors_text":"Armin W. Thomas, Klaus-Robert M\\\"uller, Wojciech Samek","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2019-07-02T10:03:54Z","title":"Deep Transfer Learning For Whole-Brain fMRI Analyses"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.01953","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":"sha256:8af0a8538079dee2a9fa12ae3a444f65dca19d7daeddb7ec0d553ca018f4e4e3","target":"record","created_at":"2026-05-17T23:41:34Z","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":"c867f9b8aa17bb4e2f1997ad2d9540a43f15fa2a35b553ff54fdf9a29a2241a6","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2019-07-02T10:03:54Z","title_canon_sha256":"4118c29c486295dbe6bfb4e627a3fab313d412fbdc385db84795491c914c3f72"},"schema_version":"1.0","source":{"id":"1907.01953","kind":"arxiv","version":1}},"canonical_sha256":"0534227a521451a0670abf2c1e24188cf0d00f506b85bebd0cbf4406ce9bfcc6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0534227a521451a0670abf2c1e24188cf0d00f506b85bebd0cbf4406ce9bfcc6","first_computed_at":"2026-05-17T23:41:34.391424Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:34.391424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hzjMz11cpYbq/uATvrJSvoIwlVeNgaYu8W8sgLY5fZuNmuBi8JeaVlJPmQZOH7Q0ADkPr/+Pxt6OQVmHm6GxAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:34.391968Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.01953","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8af0a8538079dee2a9fa12ae3a444f65dca19d7daeddb7ec0d553ca018f4e4e3","sha256:d4271118c9bdcb3245fb1d1789b93229274f7a94131bc073f438b0484617f3b3"],"state_sha256":"3fd0ca489251957e2394d8d9b0392069df01d2db24c33140cd1c290660c00769"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jCj1y2DX14A41ZZ9GMifcOl97DZcJk7Lqh3UJ9uFVUF3gVNsQQcywOVd9fnPOF1SoTEgpgzWgUoQ/sJgPyv9Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T03:33:48.778113Z","bundle_sha256":"faa1b7d838d6729b3afb339555388ec480c3340dabf3372c710a8c3bc161f3cc"}}