{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:EBVG5O3AKSVDX5MPM7CA6GYIEG","short_pith_number":"pith:EBVG5O3A","canonical_record":{"source":{"id":"1204.0746","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-04-03T17:45:49Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"73a53100b7ba3c2dd8e8370d313c5b8c5188189115438050751748742b6518d1","abstract_canon_sha256":"c43e96ee06bccfc873a567afafd02627afe8df6fbcabdc0c58426ac921230895"},"schema_version":"1.0"},"canonical_sha256":"206a6ebb6054aa3bf58f67c40f1b0821baa771d3e3686aa6b02789d574282224","source":{"kind":"arxiv","id":"1204.0746","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1204.0746","created_at":"2026-05-18T03:58:38Z"},{"alias_kind":"arxiv_version","alias_value":"1204.0746v1","created_at":"2026-05-18T03:58:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1204.0746","created_at":"2026-05-18T03:58:38Z"},{"alias_kind":"pith_short_12","alias_value":"EBVG5O3AKSVD","created_at":"2026-05-18T12:27:04Z"},{"alias_kind":"pith_short_16","alias_value":"EBVG5O3AKSVDX5MP","created_at":"2026-05-18T12:27:04Z"},{"alias_kind":"pith_short_8","alias_value":"EBVG5O3A","created_at":"2026-05-18T12:27:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:EBVG5O3AKSVDX5MPM7CA6GYIEG","target":"record","payload":{"canonical_record":{"source":{"id":"1204.0746","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-04-03T17:45:49Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"73a53100b7ba3c2dd8e8370d313c5b8c5188189115438050751748742b6518d1","abstract_canon_sha256":"c43e96ee06bccfc873a567afafd02627afe8df6fbcabdc0c58426ac921230895"},"schema_version":"1.0"},"canonical_sha256":"206a6ebb6054aa3bf58f67c40f1b0821baa771d3e3686aa6b02789d574282224","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:58:38.605344Z","signature_b64":"ZLbkWG+2gagB8hfPsCT7slUq1B8IIHweWfwqGUInZxI0mfB1GYqvo3cPnMm90+NZgvappz6fCkYknQQjNGoLBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"206a6ebb6054aa3bf58f67c40f1b0821baa771d3e3686aa6b02789d574282224","last_reissued_at":"2026-05-18T03:58:38.604886Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:58:38.604886Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1204.0746","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":"2026-05-18T03:58:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m6oXzKmDQ8ABO8kYK0VCs8Yriu2arOxFEt2Yr1BdxPtbahJw2kh1bxyj1EgQywPZFIstDyXwwgk4TgGtJI/RDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T14:31:14.194532Z"},"content_sha256":"82da0ef398337fc996f2cf725fa8ff85a4927961135491817e81d8a37013ef7a","schema_version":"1.0","event_id":"sha256:82da0ef398337fc996f2cf725fa8ff85a4927961135491817e81d8a37013ef7a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:EBVG5O3AKSVDX5MPM7CA6GYIEG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Gradually Atom Pruning for Sparse Reconstruction and Extension to Correlated Sparsity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Mahrokh G. Shayesteh, Seyed Hossein Hosseini","submitted_at":"2012-04-03T17:45:49Z","abstract_excerpt":"We propose a new algorithm for recovery of sparse signals from their compressively sensed samples. The proposed algorithm benefits from the strategy of gradual movement to estimate the positions of non-zero samples of sparse signal. We decompose each sample of signal into two variables, namely \"value\" and \"detector\", by a weighted exponential function. We update these new variables using gradient descent method. Like the traditional compressed sensing algorithms, the first variable is used to solve the Least Absolute Shrinkage and Selection Operator (Lasso) problem. As a new strategy, the seco"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1204.0746","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":"2026-05-18T03:58:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QHaExz6JzpcDLwiXTwocHQlept2TnaZB31eXk80tkSKcMTI20xey+ug9t5drSGsOGZjWW4Ze/sXJTmCzgkFbCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T14:31:14.194893Z"},"content_sha256":"e85b10131744b0210662ca0499babaf1b01b77486acaf6849ddb974a919f0518","schema_version":"1.0","event_id":"sha256:e85b10131744b0210662ca0499babaf1b01b77486acaf6849ddb974a919f0518"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EBVG5O3AKSVDX5MPM7CA6GYIEG/bundle.json","state_url":"https://pith.science/pith/EBVG5O3AKSVDX5MPM7CA6GYIEG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EBVG5O3AKSVDX5MPM7CA6GYIEG/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":"2026-06-10T14:31:14Z","links":{"resolver":"https://pith.science/pith/EBVG5O3AKSVDX5MPM7CA6GYIEG","bundle":"https://pith.science/pith/EBVG5O3AKSVDX5MPM7CA6GYIEG/bundle.json","state":"https://pith.science/pith/EBVG5O3AKSVDX5MPM7CA6GYIEG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EBVG5O3AKSVDX5MPM7CA6GYIEG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:EBVG5O3AKSVDX5MPM7CA6GYIEG","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":"c43e96ee06bccfc873a567afafd02627afe8df6fbcabdc0c58426ac921230895","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-04-03T17:45:49Z","title_canon_sha256":"73a53100b7ba3c2dd8e8370d313c5b8c5188189115438050751748742b6518d1"},"schema_version":"1.0","source":{"id":"1204.0746","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1204.0746","created_at":"2026-05-18T03:58:38Z"},{"alias_kind":"arxiv_version","alias_value":"1204.0746v1","created_at":"2026-05-18T03:58:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1204.0746","created_at":"2026-05-18T03:58:38Z"},{"alias_kind":"pith_short_12","alias_value":"EBVG5O3AKSVD","created_at":"2026-05-18T12:27:04Z"},{"alias_kind":"pith_short_16","alias_value":"EBVG5O3AKSVDX5MP","created_at":"2026-05-18T12:27:04Z"},{"alias_kind":"pith_short_8","alias_value":"EBVG5O3A","created_at":"2026-05-18T12:27:04Z"}],"graph_snapshots":[{"event_id":"sha256:e85b10131744b0210662ca0499babaf1b01b77486acaf6849ddb974a919f0518","target":"graph","created_at":"2026-05-18T03:58:38Z","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":"We propose a new algorithm for recovery of sparse signals from their compressively sensed samples. The proposed algorithm benefits from the strategy of gradual movement to estimate the positions of non-zero samples of sparse signal. We decompose each sample of signal into two variables, namely \"value\" and \"detector\", by a weighted exponential function. We update these new variables using gradient descent method. Like the traditional compressed sensing algorithms, the first variable is used to solve the Least Absolute Shrinkage and Selection Operator (Lasso) problem. As a new strategy, the seco","authors_text":"Mahrokh G. Shayesteh, Seyed Hossein Hosseini","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-04-03T17:45:49Z","title":"Gradually Atom Pruning for Sparse Reconstruction and Extension to Correlated Sparsity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1204.0746","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":"sha256:82da0ef398337fc996f2cf725fa8ff85a4927961135491817e81d8a37013ef7a","target":"record","created_at":"2026-05-18T03:58:38Z","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":"c43e96ee06bccfc873a567afafd02627afe8df6fbcabdc0c58426ac921230895","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-04-03T17:45:49Z","title_canon_sha256":"73a53100b7ba3c2dd8e8370d313c5b8c5188189115438050751748742b6518d1"},"schema_version":"1.0","source":{"id":"1204.0746","kind":"arxiv","version":1}},"canonical_sha256":"206a6ebb6054aa3bf58f67c40f1b0821baa771d3e3686aa6b02789d574282224","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"206a6ebb6054aa3bf58f67c40f1b0821baa771d3e3686aa6b02789d574282224","first_computed_at":"2026-05-18T03:58:38.604886Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:58:38.604886Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZLbkWG+2gagB8hfPsCT7slUq1B8IIHweWfwqGUInZxI0mfB1GYqvo3cPnMm90+NZgvappz6fCkYknQQjNGoLBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:58:38.605344Z","signed_message":"canonical_sha256_bytes"},"source_id":"1204.0746","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:82da0ef398337fc996f2cf725fa8ff85a4927961135491817e81d8a37013ef7a","sha256:e85b10131744b0210662ca0499babaf1b01b77486acaf6849ddb974a919f0518"],"state_sha256":"ca856845bea277a137766a02c77cfb17e465a632a65deffb75d91fdfe27fba58"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I383c+Rzo55dluv/QlbYfU4kSIVd2ldqhVxwJpD9Ijk7ULJz8KVVqO/vmM7YiTyLciX+57aKlnWO+coZRhgPCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T14:31:14.196988Z","bundle_sha256":"79b46e3b1b2cb47b794ae8e823c45e9c522dfbbe9e551c777d77ecbfbc19cb1f"}}