{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:OP7NSGPM63PQBI3FUPLO3ELEQ6","short_pith_number":"pith:OP7NSGPM","canonical_record":{"source":{"id":"1212.0451","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2012-12-03T17:06:41Z","cross_cats_sorted":["stat.AP","stat.ML"],"title_canon_sha256":"1ebd855fb93aff840ea1c43fcb85639a0c751f4086e3cde6e49f081b007208b5","abstract_canon_sha256":"02d3284a1491b582c88af84f559058e9fc479992cb9d1aee0ba8c8bd95b2e09a"},"schema_version":"1.0"},"canonical_sha256":"73fed919ecf6df00a365a3d6ed9164878cebd60978f7056f7df2615c0a47d2d8","source":{"kind":"arxiv","id":"1212.0451","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1212.0451","created_at":"2026-05-18T02:28:48Z"},{"alias_kind":"arxiv_version","alias_value":"1212.0451v2","created_at":"2026-05-18T02:28:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1212.0451","created_at":"2026-05-18T02:28:48Z"},{"alias_kind":"pith_short_12","alias_value":"OP7NSGPM63PQ","created_at":"2026-05-18T12:27:16Z"},{"alias_kind":"pith_short_16","alias_value":"OP7NSGPM63PQBI3F","created_at":"2026-05-18T12:27:16Z"},{"alias_kind":"pith_short_8","alias_value":"OP7NSGPM","created_at":"2026-05-18T12:27:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:OP7NSGPM63PQBI3FUPLO3ELEQ6","target":"record","payload":{"canonical_record":{"source":{"id":"1212.0451","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2012-12-03T17:06:41Z","cross_cats_sorted":["stat.AP","stat.ML"],"title_canon_sha256":"1ebd855fb93aff840ea1c43fcb85639a0c751f4086e3cde6e49f081b007208b5","abstract_canon_sha256":"02d3284a1491b582c88af84f559058e9fc479992cb9d1aee0ba8c8bd95b2e09a"},"schema_version":"1.0"},"canonical_sha256":"73fed919ecf6df00a365a3d6ed9164878cebd60978f7056f7df2615c0a47d2d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:28:48.391264Z","signature_b64":"SZ674Vst+JXKYPclo+LBN+EcGRZ6y1AEDSbywbPuC0I9LnvUDLedpa+a6OoHNyp5s6FyM+epxy4ogzxFGXFMDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73fed919ecf6df00a365a3d6ed9164878cebd60978f7056f7df2615c0a47d2d8","last_reissued_at":"2026-05-18T02:28:48.390881Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:28:48.390881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1212.0451","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:28:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zpwO2T+wekD7+e2e9KPfxi7W9hOXOSe80pxms31PY6EtqciFQ5BjTPj9ElsomGdkL/OyiC0qoPV07TTIOEDfAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T01:56:13.356220Z"},"content_sha256":"fed330c5230e459b68afbe0d3ab57a7f475cdf523c66e04b0332cd2af1ab9402","schema_version":"1.0","event_id":"sha256:fed330c5230e459b68afbe0d3ab57a7f475cdf523c66e04b0332cd2af1ab9402"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:OP7NSGPM63PQBI3FUPLO3ELEQ6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-blind Source Separation via Sparse Representations and Online Dictionary Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"cs.SD","authors_text":"Jarvis D. Haupt, Sirisha Rambhatla","submitted_at":"2012-12-03T17:06:41Z","abstract_excerpt":"This work examines a semi-blind single-channel source separation problem. Our specific aim is to separate one source whose local structure is approximately known, from another a priori unspecified background source, given only a single linear combination of the two sources. We propose a separation technique based on local sparse approximations along the lines of recent efforts in sparse representations and dictionary learning. A key feature of our procedure is the online learning of dictionaries (using only the data itself) to sparsely model the background source, which facilitates its separat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.0451","kind":"arxiv","version":2},"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":"2026-05-18T02:28:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lKEmiFIzfynH6Lfcqc9jHaXxSYzlL7WouylZIjMrxhI7KB8lHmP5RDEa1l24drqhzWpb+Ut3olSiEWNI/WHGAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T01:56:13.356918Z"},"content_sha256":"f3a277e22a78bbe64329ede9a7cb5e869a750f52ca24a22b6b7126d3dcb53da5","schema_version":"1.0","event_id":"sha256:f3a277e22a78bbe64329ede9a7cb5e869a750f52ca24a22b6b7126d3dcb53da5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OP7NSGPM63PQBI3FUPLO3ELEQ6/bundle.json","state_url":"https://pith.science/pith/OP7NSGPM63PQBI3FUPLO3ELEQ6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OP7NSGPM63PQBI3FUPLO3ELEQ6/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":"2026-05-26T01:56:13Z","links":{"resolver":"https://pith.science/pith/OP7NSGPM63PQBI3FUPLO3ELEQ6","bundle":"https://pith.science/pith/OP7NSGPM63PQBI3FUPLO3ELEQ6/bundle.json","state":"https://pith.science/pith/OP7NSGPM63PQBI3FUPLO3ELEQ6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OP7NSGPM63PQBI3FUPLO3ELEQ6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:OP7NSGPM63PQBI3FUPLO3ELEQ6","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":"02d3284a1491b582c88af84f559058e9fc479992cb9d1aee0ba8c8bd95b2e09a","cross_cats_sorted":["stat.AP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2012-12-03T17:06:41Z","title_canon_sha256":"1ebd855fb93aff840ea1c43fcb85639a0c751f4086e3cde6e49f081b007208b5"},"schema_version":"1.0","source":{"id":"1212.0451","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1212.0451","created_at":"2026-05-18T02:28:48Z"},{"alias_kind":"arxiv_version","alias_value":"1212.0451v2","created_at":"2026-05-18T02:28:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1212.0451","created_at":"2026-05-18T02:28:48Z"},{"alias_kind":"pith_short_12","alias_value":"OP7NSGPM63PQ","created_at":"2026-05-18T12:27:16Z"},{"alias_kind":"pith_short_16","alias_value":"OP7NSGPM63PQBI3F","created_at":"2026-05-18T12:27:16Z"},{"alias_kind":"pith_short_8","alias_value":"OP7NSGPM","created_at":"2026-05-18T12:27:16Z"}],"graph_snapshots":[{"event_id":"sha256:f3a277e22a78bbe64329ede9a7cb5e869a750f52ca24a22b6b7126d3dcb53da5","target":"graph","created_at":"2026-05-18T02:28:48Z","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":"This work examines a semi-blind single-channel source separation problem. Our specific aim is to separate one source whose local structure is approximately known, from another a priori unspecified background source, given only a single linear combination of the two sources. We propose a separation technique based on local sparse approximations along the lines of recent efforts in sparse representations and dictionary learning. A key feature of our procedure is the online learning of dictionaries (using only the data itself) to sparsely model the background source, which facilitates its separat","authors_text":"Jarvis D. Haupt, Sirisha Rambhatla","cross_cats":["stat.AP","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2012-12-03T17:06:41Z","title":"Semi-blind Source Separation via Sparse Representations and Online Dictionary Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.0451","kind":"arxiv","version":2},"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:fed330c5230e459b68afbe0d3ab57a7f475cdf523c66e04b0332cd2af1ab9402","target":"record","created_at":"2026-05-18T02:28:48Z","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":"02d3284a1491b582c88af84f559058e9fc479992cb9d1aee0ba8c8bd95b2e09a","cross_cats_sorted":["stat.AP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2012-12-03T17:06:41Z","title_canon_sha256":"1ebd855fb93aff840ea1c43fcb85639a0c751f4086e3cde6e49f081b007208b5"},"schema_version":"1.0","source":{"id":"1212.0451","kind":"arxiv","version":2}},"canonical_sha256":"73fed919ecf6df00a365a3d6ed9164878cebd60978f7056f7df2615c0a47d2d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73fed919ecf6df00a365a3d6ed9164878cebd60978f7056f7df2615c0a47d2d8","first_computed_at":"2026-05-18T02:28:48.390881Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:28:48.390881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SZ674Vst+JXKYPclo+LBN+EcGRZ6y1AEDSbywbPuC0I9LnvUDLedpa+a6OoHNyp5s6FyM+epxy4ogzxFGXFMDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:28:48.391264Z","signed_message":"canonical_sha256_bytes"},"source_id":"1212.0451","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fed330c5230e459b68afbe0d3ab57a7f475cdf523c66e04b0332cd2af1ab9402","sha256:f3a277e22a78bbe64329ede9a7cb5e869a750f52ca24a22b6b7126d3dcb53da5"],"state_sha256":"cc1506e4283f185666f432d6b92631e4e60b4e5fc205f8b0726b54dc53308794"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"imF19A/ad08qAnG2O3UglGit2os4YnyTMnBmj4hsqnocxXOxoCMKjD0tGR06ZqTYlFljAY33ui4+UqrZyVqRBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T01:56:13.360423Z","bundle_sha256":"d189068dd4ef3ca7e53965098c61aad7be4b0e67aed82611bbe610498cbe2c38"}}