{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:DQ3PQPTJATL3MARI3R3AXWS32R","short_pith_number":"pith:DQ3PQPTJ","canonical_record":{"source":{"id":"1808.01642","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-08-05T16:19:56Z","cross_cats_sorted":["cs.CV","cs.LG","math.OC","q-bio.NC"],"title_canon_sha256":"47be1789bf78f72c8564c8347c0ed3d5ab158712831d8a16f60d77f074fc497a","abstract_canon_sha256":"a9fcceb320869c7f1e0639931e33930cfafee8204ac421f889ff7df87cce2ab8"},"schema_version":"1.0"},"canonical_sha256":"1c36f83e6904d7b60228dc760bda5bd464f0cc03516fd42698f7eaa830bb2a65","source":{"kind":"arxiv","id":"1808.01642","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.01642","created_at":"2026-05-18T00:08:52Z"},{"alias_kind":"arxiv_version","alias_value":"1808.01642v1","created_at":"2026-05-18T00:08:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.01642","created_at":"2026-05-18T00:08:52Z"},{"alias_kind":"pith_short_12","alias_value":"DQ3PQPTJATL3","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"DQ3PQPTJATL3MARI","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"DQ3PQPTJ","created_at":"2026-05-18T12:32:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:DQ3PQPTJATL3MARI3R3AXWS32R","target":"record","payload":{"canonical_record":{"source":{"id":"1808.01642","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-08-05T16:19:56Z","cross_cats_sorted":["cs.CV","cs.LG","math.OC","q-bio.NC"],"title_canon_sha256":"47be1789bf78f72c8564c8347c0ed3d5ab158712831d8a16f60d77f074fc497a","abstract_canon_sha256":"a9fcceb320869c7f1e0639931e33930cfafee8204ac421f889ff7df87cce2ab8"},"schema_version":"1.0"},"canonical_sha256":"1c36f83e6904d7b60228dc760bda5bd464f0cc03516fd42698f7eaa830bb2a65","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:52.309433Z","signature_b64":"u2MydyHsZYUZOENLb64TD23e4MSrLFj7uWWPXb6V1imCo7S3OOEqlXeZlciS21GFtNujlwRMc5xU5VfJDhEgBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c36f83e6904d7b60228dc760bda5bd464f0cc03516fd42698f7eaa830bb2a65","last_reissued_at":"2026-05-18T00:08:52.308679Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:52.308679Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.01642","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":"2026-05-18T00:08:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lbLOSGBHT1wz436/i1gacxMLd3csP8Y5L8PxDKCpP4iSq1ocLSl1b8x61WR7XUB610IKOhvF+p+PA8DDqIFrAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T10:09:24.853973Z"},"content_sha256":"747653d18ee06f9b30f38fbc359fe23ae3aaaa08aad6f41ee93ef058798b2457","schema_version":"1.0","event_id":"sha256:747653d18ee06f9b30f38fbc359fe23ae3aaaa08aad6f41ee93ef058798b2457"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:DQ3PQPTJATL3MARI3R3AXWS32R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","math.OC","q-bio.NC"],"primary_cat":"stat.ML","authors_text":"Daoqiang Zhang, Muhammad Yousefnezhad","submitted_at":"2018-08-05T16:19:56Z","abstract_excerpt":"In order to decode the human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns. However, the MVP models may not provide stable performance on a new fMRI dataset because the standard pipeline uses disjoint steps for generating these models. Indeed, each step in the pipeline includes an objective function with "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.01642","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":"2026-05-18T00:08:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rVAlJilJRVXWjRzaBccYApM6ATufgC5ASC/oWSIuV0pjMnUjAN5Ny/j2wNSFnflHEouNx7e5TZQRccFCmVOADQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T10:09:24.854347Z"},"content_sha256":"3efec4794165f092d708439966ded5da3441b30aad812e656d873c145299c9f5","schema_version":"1.0","event_id":"sha256:3efec4794165f092d708439966ded5da3441b30aad812e656d873c145299c9f5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DQ3PQPTJATL3MARI3R3AXWS32R/bundle.json","state_url":"https://pith.science/pith/DQ3PQPTJATL3MARI3R3AXWS32R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DQ3PQPTJATL3MARI3R3AXWS32R/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":"2026-06-12T10:09:24Z","links":{"resolver":"https://pith.science/pith/DQ3PQPTJATL3MARI3R3AXWS32R","bundle":"https://pith.science/pith/DQ3PQPTJATL3MARI3R3AXWS32R/bundle.json","state":"https://pith.science/pith/DQ3PQPTJATL3MARI3R3AXWS32R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DQ3PQPTJATL3MARI3R3AXWS32R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:DQ3PQPTJATL3MARI3R3AXWS32R","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":"a9fcceb320869c7f1e0639931e33930cfafee8204ac421f889ff7df87cce2ab8","cross_cats_sorted":["cs.CV","cs.LG","math.OC","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-08-05T16:19:56Z","title_canon_sha256":"47be1789bf78f72c8564c8347c0ed3d5ab158712831d8a16f60d77f074fc497a"},"schema_version":"1.0","source":{"id":"1808.01642","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.01642","created_at":"2026-05-18T00:08:52Z"},{"alias_kind":"arxiv_version","alias_value":"1808.01642v1","created_at":"2026-05-18T00:08:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.01642","created_at":"2026-05-18T00:08:52Z"},{"alias_kind":"pith_short_12","alias_value":"DQ3PQPTJATL3","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"DQ3PQPTJATL3MARI","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"DQ3PQPTJ","created_at":"2026-05-18T12:32:19Z"}],"graph_snapshots":[{"event_id":"sha256:3efec4794165f092d708439966ded5da3441b30aad812e656d873c145299c9f5","target":"graph","created_at":"2026-05-18T00:08:52Z","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":"In order to decode the human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns. However, the MVP models may not provide stable performance on a new fMRI dataset because the standard pipeline uses disjoint steps for generating these models. Indeed, each step in the pipeline includes an objective function with ","authors_text":"Daoqiang Zhang, Muhammad Yousefnezhad","cross_cats":["cs.CV","cs.LG","math.OC","q-bio.NC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-08-05T16:19:56Z","title":"Multi-Objective Cognitive Model: a supervised approach for multi-subject fMRI analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.01642","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":"sha256:747653d18ee06f9b30f38fbc359fe23ae3aaaa08aad6f41ee93ef058798b2457","target":"record","created_at":"2026-05-18T00:08:52Z","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":"a9fcceb320869c7f1e0639931e33930cfafee8204ac421f889ff7df87cce2ab8","cross_cats_sorted":["cs.CV","cs.LG","math.OC","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-08-05T16:19:56Z","title_canon_sha256":"47be1789bf78f72c8564c8347c0ed3d5ab158712831d8a16f60d77f074fc497a"},"schema_version":"1.0","source":{"id":"1808.01642","kind":"arxiv","version":1}},"canonical_sha256":"1c36f83e6904d7b60228dc760bda5bd464f0cc03516fd42698f7eaa830bb2a65","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c36f83e6904d7b60228dc760bda5bd464f0cc03516fd42698f7eaa830bb2a65","first_computed_at":"2026-05-18T00:08:52.308679Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:08:52.308679Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u2MydyHsZYUZOENLb64TD23e4MSrLFj7uWWPXb6V1imCo7S3OOEqlXeZlciS21GFtNujlwRMc5xU5VfJDhEgBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:08:52.309433Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.01642","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:747653d18ee06f9b30f38fbc359fe23ae3aaaa08aad6f41ee93ef058798b2457","sha256:3efec4794165f092d708439966ded5da3441b30aad812e656d873c145299c9f5"],"state_sha256":"03b64cc1033aea5ac7d4530f314958a1fcf3ecd64d9e149ccc418c84f3620ba9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZbtjrG/cwObXNIBa+oiAQVq8wwgF7nsQQe9thQcvxbGhYztOHA+HjeUDLmO3IQeTVl+ZF/sSgzHZ0ibhNYaPAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T10:09:24.856538Z","bundle_sha256":"f70ce2b5102c8193fa5ff45aab4cdb7b3a20e1418ea6c85e4e3ff6bf54d34497"}}