{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:X2MWWBZNFEYLLNRP66BS4SNTAX","short_pith_number":"pith:X2MWWBZN","schema_version":"1.0","canonical_sha256":"be996b072d2930b5b62ff7832e49b305ef119e3fba447148876d3960f3a1b726","source":{"kind":"arxiv","id":"1312.5859","version":1},"attestation_state":"computed","paper":{"title":"Multiscale adaptive smoothing models for the hemodynamic response function in fMRI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Hongtu Zhu, Jianqing Fan, Jiaping Wang, Kelly Giovanello, Weili Lin","submitted_at":"2013-12-20T09:12:03Z","abstract_excerpt":"In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are developed in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial information. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and frequenc"},"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":"1312.5859","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2013-12-20T09:12:03Z","cross_cats_sorted":[],"title_canon_sha256":"51c7883758c6a3f2e6ebab75fc6714fde5d95ae701f9f1812cc8e3bdbfe1fc87","abstract_canon_sha256":"9c73afa7ab58cb220f97dd90614c4aeae56fe6bab395954320bd093f58c36e4d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:04:07.666146Z","signature_b64":"hbIeE/VVSm8wZVRyI7rc6KG+OdOi9BqSjkvQTgBEH4in/fVPagxUlk6386U3QtItgzE/xzYIEym84hKHylw0Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be996b072d2930b5b62ff7832e49b305ef119e3fba447148876d3960f3a1b726","last_reissued_at":"2026-05-18T03:04:07.665506Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:04:07.665506Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multiscale adaptive smoothing models for the hemodynamic response function in fMRI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Hongtu Zhu, Jianqing Fan, Jiaping Wang, Kelly Giovanello, Weili Lin","submitted_at":"2013-12-20T09:12:03Z","abstract_excerpt":"In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are developed in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial information. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and frequenc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1312.5859","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1312.5859","created_at":"2026-05-18T03:04:07.665589+00:00"},{"alias_kind":"arxiv_version","alias_value":"1312.5859v1","created_at":"2026-05-18T03:04:07.665589+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.5859","created_at":"2026-05-18T03:04:07.665589+00:00"},{"alias_kind":"pith_short_12","alias_value":"X2MWWBZNFEYL","created_at":"2026-05-18T12:28:06.772260+00:00"},{"alias_kind":"pith_short_16","alias_value":"X2MWWBZNFEYLLNRP","created_at":"2026-05-18T12:28:06.772260+00:00"},{"alias_kind":"pith_short_8","alias_value":"X2MWWBZN","created_at":"2026-05-18T12:28:06.772260+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/X2MWWBZNFEYLLNRP66BS4SNTAX","json":"https://pith.science/pith/X2MWWBZNFEYLLNRP66BS4SNTAX.json","graph_json":"https://pith.science/api/pith-number/X2MWWBZNFEYLLNRP66BS4SNTAX/graph.json","events_json":"https://pith.science/api/pith-number/X2MWWBZNFEYLLNRP66BS4SNTAX/events.json","paper":"https://pith.science/paper/X2MWWBZN"},"agent_actions":{"view_html":"https://pith.science/pith/X2MWWBZNFEYLLNRP66BS4SNTAX","download_json":"https://pith.science/pith/X2MWWBZNFEYLLNRP66BS4SNTAX.json","view_paper":"https://pith.science/paper/X2MWWBZN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1312.5859&json=true","fetch_graph":"https://pith.science/api/pith-number/X2MWWBZNFEYLLNRP66BS4SNTAX/graph.json","fetch_events":"https://pith.science/api/pith-number/X2MWWBZNFEYLLNRP66BS4SNTAX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X2MWWBZNFEYLLNRP66BS4SNTAX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X2MWWBZNFEYLLNRP66BS4SNTAX/action/storage_attestation","attest_author":"https://pith.science/pith/X2MWWBZNFEYLLNRP66BS4SNTAX/action/author_attestation","sign_citation":"https://pith.science/pith/X2MWWBZNFEYLLNRP66BS4SNTAX/action/citation_signature","submit_replication":"https://pith.science/pith/X2MWWBZNFEYLLNRP66BS4SNTAX/action/replication_record"}},"created_at":"2026-05-18T03:04:07.665589+00:00","updated_at":"2026-05-18T03:04:07.665589+00:00"}