{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:NUSPC5MIAYPEW4EOCXX5HKJ575","short_pith_number":"pith:NUSPC5MI","canonical_record":{"source":{"id":"1710.04030","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-10-11T12:13:42Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"9b0fdca40cc25f653d74cc2dd602ab2bfd5396ea371d4b9b276644f9bee8fa01","abstract_canon_sha256":"26dcfe7d027cac061cb3a11327a3b5cc1c4a40dc01f8d8fca74cf3d74a50f011"},"schema_version":"1.0"},"canonical_sha256":"6d24f17588061e4b708e15efd3a93dff41868824b97f9ab61ebc6cbb2a9733d2","source":{"kind":"arxiv","id":"1710.04030","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.04030","created_at":"2026-05-18T00:33:05Z"},{"alias_kind":"arxiv_version","alias_value":"1710.04030v1","created_at":"2026-05-18T00:33:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04030","created_at":"2026-05-18T00:33:05Z"},{"alias_kind":"pith_short_12","alias_value":"NUSPC5MIAYPE","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"NUSPC5MIAYPEW4EO","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"NUSPC5MI","created_at":"2026-05-18T12:31:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:NUSPC5MIAYPEW4EOCXX5HKJ575","target":"record","payload":{"canonical_record":{"source":{"id":"1710.04030","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-10-11T12:13:42Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"9b0fdca40cc25f653d74cc2dd602ab2bfd5396ea371d4b9b276644f9bee8fa01","abstract_canon_sha256":"26dcfe7d027cac061cb3a11327a3b5cc1c4a40dc01f8d8fca74cf3d74a50f011"},"schema_version":"1.0"},"canonical_sha256":"6d24f17588061e4b708e15efd3a93dff41868824b97f9ab61ebc6cbb2a9733d2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:33:05.269994Z","signature_b64":"GozTPiPoGod+FbfFMz/XDDwUWxLnmVoYSvj0VWloN/rBUSlCs8IwDG9FrQZlRqQLP7tgudGLzmttx4asCVgwDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d24f17588061e4b708e15efd3a93dff41868824b97f9ab61ebc6cbb2a9733d2","last_reissued_at":"2026-05-18T00:33:05.269458Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:33:05.269458Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.04030","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":"2026-05-18T00:33:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EJwus+1rFiAA9X0LF1ZI2eroaoeFVGkLfHJ2EI/+ehc0bxyeOWc++K6IUyqR9dD9P0uvbtlkZJOGZBkN6TjZAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T20:45:44.137773Z"},"content_sha256":"3cef08a7edcf2b221a467b8878d3b509da10bf8df6fea0a059cdec3b42509107","schema_version":"1.0","event_id":"sha256:3cef08a7edcf2b221a467b8878d3b509da10bf8df6fea0a059cdec3b42509107"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:NUSPC5MIAYPEW4EOCXX5HKJ575","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sparsity estimation in compressive sensing with application to MR images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Anders Garpebring, Jianfeng Wang, Jun Yu, Zhiyong Zhou","submitted_at":"2017-10-11T12:13:42Z","abstract_excerpt":"The theory of compressive sensing (CS) asserts that an unknown signal $\\mathbf{x} \\in \\mathbb{C}^N$ can be accurately recovered from $m$ measurements with $m\\ll N$ provided that $\\mathbf{x}$ is sparse. Most of the recovery algorithms need the sparsity $s=\\lVert\\mathbf{x}\\rVert_0$ as an input. However, generally $s$ is unknown, and directly estimating the sparsity has been an open problem. In this study, an estimator of sparsity is proposed by using Bayesian hierarchical model. Its statistical properties such as unbiasedness and asymptotic normality are proved. In the simulation study and real "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04030","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":"2026-05-18T00:33:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IZV7EwoAakGM8AWFsBxVn6FAt1HlWUT4n5S67AJrw68aHyFa56eOwc8nRCna6aO3WP7DBYQMNJW7NlIhxjiiCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T20:45:44.138133Z"},"content_sha256":"847f1c9ed715ee909a4f395765d92d57a3594c66bdbbf5a27309428c16bde0a2","schema_version":"1.0","event_id":"sha256:847f1c9ed715ee909a4f395765d92d57a3594c66bdbbf5a27309428c16bde0a2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NUSPC5MIAYPEW4EOCXX5HKJ575/bundle.json","state_url":"https://pith.science/pith/NUSPC5MIAYPEW4EOCXX5HKJ575/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NUSPC5MIAYPEW4EOCXX5HKJ575/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":"2026-05-26T20:45:44Z","links":{"resolver":"https://pith.science/pith/NUSPC5MIAYPEW4EOCXX5HKJ575","bundle":"https://pith.science/pith/NUSPC5MIAYPEW4EOCXX5HKJ575/bundle.json","state":"https://pith.science/pith/NUSPC5MIAYPEW4EOCXX5HKJ575/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NUSPC5MIAYPEW4EOCXX5HKJ575/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:NUSPC5MIAYPEW4EOCXX5HKJ575","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":"26dcfe7d027cac061cb3a11327a3b5cc1c4a40dc01f8d8fca74cf3d74a50f011","cross_cats_sorted":["stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-10-11T12:13:42Z","title_canon_sha256":"9b0fdca40cc25f653d74cc2dd602ab2bfd5396ea371d4b9b276644f9bee8fa01"},"schema_version":"1.0","source":{"id":"1710.04030","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.04030","created_at":"2026-05-18T00:33:05Z"},{"alias_kind":"arxiv_version","alias_value":"1710.04030v1","created_at":"2026-05-18T00:33:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04030","created_at":"2026-05-18T00:33:05Z"},{"alias_kind":"pith_short_12","alias_value":"NUSPC5MIAYPE","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"NUSPC5MIAYPEW4EO","created_at":"2026-05-18T12:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"NUSPC5MI","created_at":"2026-05-18T12:31:34Z"}],"graph_snapshots":[{"event_id":"sha256:847f1c9ed715ee909a4f395765d92d57a3594c66bdbbf5a27309428c16bde0a2","target":"graph","created_at":"2026-05-18T00:33:05Z","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 theory of compressive sensing (CS) asserts that an unknown signal $\\mathbf{x} \\in \\mathbb{C}^N$ can be accurately recovered from $m$ measurements with $m\\ll N$ provided that $\\mathbf{x}$ is sparse. Most of the recovery algorithms need the sparsity $s=\\lVert\\mathbf{x}\\rVert_0$ as an input. However, generally $s$ is unknown, and directly estimating the sparsity has been an open problem. In this study, an estimator of sparsity is proposed by using Bayesian hierarchical model. Its statistical properties such as unbiasedness and asymptotic normality are proved. In the simulation study and real ","authors_text":"Anders Garpebring, Jianfeng Wang, Jun Yu, Zhiyong Zhou","cross_cats":["stat.AP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-10-11T12:13:42Z","title":"Sparsity estimation in compressive sensing with application to MR images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04030","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":"sha256:3cef08a7edcf2b221a467b8878d3b509da10bf8df6fea0a059cdec3b42509107","target":"record","created_at":"2026-05-18T00:33:05Z","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":"26dcfe7d027cac061cb3a11327a3b5cc1c4a40dc01f8d8fca74cf3d74a50f011","cross_cats_sorted":["stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-10-11T12:13:42Z","title_canon_sha256":"9b0fdca40cc25f653d74cc2dd602ab2bfd5396ea371d4b9b276644f9bee8fa01"},"schema_version":"1.0","source":{"id":"1710.04030","kind":"arxiv","version":1}},"canonical_sha256":"6d24f17588061e4b708e15efd3a93dff41868824b97f9ab61ebc6cbb2a9733d2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6d24f17588061e4b708e15efd3a93dff41868824b97f9ab61ebc6cbb2a9733d2","first_computed_at":"2026-05-18T00:33:05.269458Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:33:05.269458Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GozTPiPoGod+FbfFMz/XDDwUWxLnmVoYSvj0VWloN/rBUSlCs8IwDG9FrQZlRqQLP7tgudGLzmttx4asCVgwDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:33:05.269994Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.04030","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3cef08a7edcf2b221a467b8878d3b509da10bf8df6fea0a059cdec3b42509107","sha256:847f1c9ed715ee909a4f395765d92d57a3594c66bdbbf5a27309428c16bde0a2"],"state_sha256":"ad24ba91ed2113a3644db335b7a3a508ff34e820a181fe53b9a650100c85db8f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cPrMPEUQqN1gv3K9eMxpTNpVEZdiI8N77y3qE8/vA0YGTSHbbJmrfhckPb27mFAoRvvWUiFskH1n3u5oVC+/BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T20:45:44.140339Z","bundle_sha256":"a90264a6277a6fbd834758fa9627930c7c55ad8e9cd071d255ca2837bdb82b62"}}