{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4BP72JB7UAOVBLYG74Q5KA4CFB","short_pith_number":"pith:4BP72JB7","schema_version":"1.0","canonical_sha256":"e05ffd243fa01d50af06ff21d503822847b170881e88269d9e87190748ebed13","source":{"kind":"arxiv","id":"2302.02951","version":1},"attestation_state":"computed","paper":{"title":"Noise-cleaning the precision matrix of fMRI time series","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cond-mat.stat-mech","authors_text":"Andrea Gabrielli, Carlo Lucibello, Francesca Santucci, Miguel Ib\\'a\\~nez-Berganza, Tommaso Gili","submitted_at":"2023-02-06T17:32:17Z","abstract_excerpt":"We present a comparison between various algorithms of inference of covariance and precision matrices in small datasets of real vectors, of the typical length and dimension of human brain activity time series retrieved by functional Magnetic Resonance Imaging (fMRI). Assuming a Gaussian model underlying the neural activity, the problem consists in denoising the empirically observed matrices in order to obtain a better estimator of the true precision and covariance matrices. We consider several standard noise-cleaning algorithms and compare them on two types of datasets. The first type are time "},"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":"2302.02951","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2023-02-06T17:32:17Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5bf7c01fb97a01af0f1c2ae0318c4f1dce800c7a5b6a8346064de57eb473c23c","abstract_canon_sha256":"9740d91c2ccea1bb691b8eb6f3089f49adf83325da060660c90ff06a30804a59"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:39:08.383757Z","signature_b64":"HQCOqhc2FzRj/hneFO6i0AsMW+YlP+HRLP4kBYW0iKWtU8KQfExC/9i+QEMr5U6NFKLdInnddQpvKz5LN5LzAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e05ffd243fa01d50af06ff21d503822847b170881e88269d9e87190748ebed13","last_reissued_at":"2026-07-05T05:39:08.383286Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:39:08.383286Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Noise-cleaning the precision matrix of fMRI time series","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cond-mat.stat-mech","authors_text":"Andrea Gabrielli, Carlo Lucibello, Francesca Santucci, Miguel Ib\\'a\\~nez-Berganza, Tommaso Gili","submitted_at":"2023-02-06T17:32:17Z","abstract_excerpt":"We present a comparison between various algorithms of inference of covariance and precision matrices in small datasets of real vectors, of the typical length and dimension of human brain activity time series retrieved by functional Magnetic Resonance Imaging (fMRI). Assuming a Gaussian model underlying the neural activity, the problem consists in denoising the empirically observed matrices in order to obtain a better estimator of the true precision and covariance matrices. We consider several standard noise-cleaning algorithms and compare them on two types of datasets. The first type are time "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.02951","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/2302.02951/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":"2302.02951","created_at":"2026-07-05T05:39:08.383362+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.02951v1","created_at":"2026-07-05T05:39:08.383362+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.02951","created_at":"2026-07-05T05:39:08.383362+00:00"},{"alias_kind":"pith_short_12","alias_value":"4BP72JB7UAOV","created_at":"2026-07-05T05:39:08.383362+00:00"},{"alias_kind":"pith_short_16","alias_value":"4BP72JB7UAOVBLYG","created_at":"2026-07-05T05:39:08.383362+00:00"},{"alias_kind":"pith_short_8","alias_value":"4BP72JB7","created_at":"2026-07-05T05:39:08.383362+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4BP72JB7UAOVBLYG74Q5KA4CFB","json":"https://pith.science/pith/4BP72JB7UAOVBLYG74Q5KA4CFB.json","graph_json":"https://pith.science/api/pith-number/4BP72JB7UAOVBLYG74Q5KA4CFB/graph.json","events_json":"https://pith.science/api/pith-number/4BP72JB7UAOVBLYG74Q5KA4CFB/events.json","paper":"https://pith.science/paper/4BP72JB7"},"agent_actions":{"view_html":"https://pith.science/pith/4BP72JB7UAOVBLYG74Q5KA4CFB","download_json":"https://pith.science/pith/4BP72JB7UAOVBLYG74Q5KA4CFB.json","view_paper":"https://pith.science/paper/4BP72JB7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.02951&json=true","fetch_graph":"https://pith.science/api/pith-number/4BP72JB7UAOVBLYG74Q5KA4CFB/graph.json","fetch_events":"https://pith.science/api/pith-number/4BP72JB7UAOVBLYG74Q5KA4CFB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4BP72JB7UAOVBLYG74Q5KA4CFB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4BP72JB7UAOVBLYG74Q5KA4CFB/action/storage_attestation","attest_author":"https://pith.science/pith/4BP72JB7UAOVBLYG74Q5KA4CFB/action/author_attestation","sign_citation":"https://pith.science/pith/4BP72JB7UAOVBLYG74Q5KA4CFB/action/citation_signature","submit_replication":"https://pith.science/pith/4BP72JB7UAOVBLYG74Q5KA4CFB/action/replication_record"}},"created_at":"2026-07-05T05:39:08.383362+00:00","updated_at":"2026-07-05T05:39:08.383362+00:00"}