{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:FHV3RH4VKMMUITR6DWGSO2VMFC","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":"1c3f6ddd41524cd437047bf84084a30f7b4d8338568430a8019e05ddd63532fb","cross_cats_sorted":["cs.CV","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-21T06:51:57Z","title_canon_sha256":"757815114375a09a99627e6e2c289b2449a65e2481bfdbb0a071b6b7ee2c061d"},"schema_version":"1.0","source":{"id":"1606.06439","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1606.06439","created_at":"2026-05-18T01:12:09Z"},{"alias_kind":"arxiv_version","alias_value":"1606.06439v1","created_at":"2026-05-18T01:12:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.06439","created_at":"2026-05-18T01:12:09Z"},{"alias_kind":"pith_short_12","alias_value":"FHV3RH4VKMMU","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"FHV3RH4VKMMUITR6","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"FHV3RH4V","created_at":"2026-05-18T12:30:15Z"}],"graph_snapshots":[{"event_id":"sha256:66e2b1e3f78a169226548ec79fcbc97c025e1cdc9011a7a80a32aacff5bc2c14","target":"graph","created_at":"2026-05-18T01:12:09Z","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":"Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions. However, the state of the art, based on total variation or graph-net, is computationally costly. Here we introduce sparsity in the local neighborhood of each voxel with social-sparsity, a structured shrinkage operator. We find that, on brain imaging classification problems, social-sparsity performs almost as well as total-variation models and better than graph-net, for a fraction of the computational cost. It also ver","authors_text":"Bertrand Thirion (NEUROSPIN, Ga\\\"el Varoquaux (PARIETAL, L2S), Matthieu Kowalski (PARIETAL, NEUROSPIN), PARIETAL)","cross_cats":["cs.CV","q-bio.NC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-21T06:51:57Z","title":"Social-sparsity brain decoders: faster spatial sparsity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.06439","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:c98a852f3c8dbacedf711e95423dc3af1b859e28c4a23d56d0a6e04fc61cbade","target":"record","created_at":"2026-05-18T01:12:09Z","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":"1c3f6ddd41524cd437047bf84084a30f7b4d8338568430a8019e05ddd63532fb","cross_cats_sorted":["cs.CV","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-21T06:51:57Z","title_canon_sha256":"757815114375a09a99627e6e2c289b2449a65e2481bfdbb0a071b6b7ee2c061d"},"schema_version":"1.0","source":{"id":"1606.06439","kind":"arxiv","version":1}},"canonical_sha256":"29ebb89f955319444e3e1d8d276aac28a77136a94bc3e23acef71cce0942c1ff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"29ebb89f955319444e3e1d8d276aac28a77136a94bc3e23acef71cce0942c1ff","first_computed_at":"2026-05-18T01:12:09.559800Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:12:09.559800Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"N4yrnaT0xoDim7/VQIuTd0Q5BMG4Xen/jiJSqy7qujNU802/ks255T8Zj4+EnPqgo+tmf2DspwP3vpzLzMM8Ag==","signature_status":"signed_v1","signed_at":"2026-05-18T01:12:09.560450Z","signed_message":"canonical_sha256_bytes"},"source_id":"1606.06439","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c98a852f3c8dbacedf711e95423dc3af1b859e28c4a23d56d0a6e04fc61cbade","sha256:66e2b1e3f78a169226548ec79fcbc97c025e1cdc9011a7a80a32aacff5bc2c14"],"state_sha256":"1036bc3a4b8ed04fe0a745235925267c8e2da07756ff269116a806ddc01c8a50"}