{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:Y7HBBXYMW6KPYPSLGCU27DCB2X","short_pith_number":"pith:Y7HBBXYM","canonical_record":{"source":{"id":"1402.1035","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2014-02-05T13:30:25Z","cross_cats_sorted":[],"title_canon_sha256":"25850932c2d6e35792af600f0691a880472e8c91c647fbade4c5078a44f35368","abstract_canon_sha256":"3d9e2528f571922aff026e22fdc76473b36ce73d493022c9a21f45ed617df5be"},"schema_version":"1.0"},"canonical_sha256":"c7ce10df0cb794fc3e4b30a9af8c41d5f43e51ea14518919664c07563518c3ef","source":{"kind":"arxiv","id":"1402.1035","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1402.1035","created_at":"2026-05-18T03:00:00Z"},{"alias_kind":"arxiv_version","alias_value":"1402.1035v2","created_at":"2026-05-18T03:00:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.1035","created_at":"2026-05-18T03:00:00Z"},{"alias_kind":"pith_short_12","alias_value":"Y7HBBXYMW6KP","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"Y7HBBXYMW6KPYPSL","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"Y7HBBXYM","created_at":"2026-05-18T12:28:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:Y7HBBXYMW6KPYPSLGCU27DCB2X","target":"record","payload":{"canonical_record":{"source":{"id":"1402.1035","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2014-02-05T13:30:25Z","cross_cats_sorted":[],"title_canon_sha256":"25850932c2d6e35792af600f0691a880472e8c91c647fbade4c5078a44f35368","abstract_canon_sha256":"3d9e2528f571922aff026e22fdc76473b36ce73d493022c9a21f45ed617df5be"},"schema_version":"1.0"},"canonical_sha256":"c7ce10df0cb794fc3e4b30a9af8c41d5f43e51ea14518919664c07563518c3ef","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:00:00.027368Z","signature_b64":"LJfLrAH5h0jr+hP0hc/5IbHdyzvgZYx0NT0BzlUMTi7HXOQ27sUEg9gWDu+U9fCwQ0epym8wGqvWwMdqVWVECA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7ce10df0cb794fc3e4b30a9af8c41d5f43e51ea14518919664c07563518c3ef","last_reissued_at":"2026-05-18T03:00:00.026522Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:00:00.026522Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1402.1035","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":"2026-05-18T03:00:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qQt6SnTJ9vS5yPlPCRFSm8xX7J0dncWUyqLbelkxy/+I/uBF79vJkr9ryPGC+P2KCSS0oYtY2xlpTqUXs1hgBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T09:41:17.973785Z"},"content_sha256":"cb94d6af1596ac7569f72e7f45fbe134baeaccba9eb98c3a739baf1951708c30","schema_version":"1.0","event_id":"sha256:cb94d6af1596ac7569f72e7f45fbe134baeaccba9eb98c3a739baf1951708c30"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:Y7HBBXYMW6KPYPSLGCU27DCB2X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Model-based Sketching and Recovery with Expanders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Bubacarr Bah, Luca Baldassarre, Volkan Cevher","submitted_at":"2014-02-05T13:30:25Z","abstract_excerpt":"Linear sketching and recovery of sparse vectors with randomly constructed sparse matrices has numerous applications in several areas, including compressive sensing, data stream computing, graph sketching, and combinatorial group testing. This paper considers the same problem with the added twist that the sparse coefficients of the unknown vector exhibit further correlations as determined by a known sparsity model. We prove that exploiting model-based sparsity in recovery provably reduces the sketch size without sacrificing recovery quality. In this context, we present the model-expander iterat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.1035","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":"2026-05-18T03:00:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BEkSGt3LZn6rpj66Oqqlf5lWaf0zbn7JtakGEaOlacBN12x7NMgNiW7NjxxEIh6o+JkGAsKMxdhMCgrD8TuUBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T09:41:17.974445Z"},"content_sha256":"20e56fc99a043b3f4c5e19a46144a2ab5ccb453d1eaddbe45e15a979643ee3c1","schema_version":"1.0","event_id":"sha256:20e56fc99a043b3f4c5e19a46144a2ab5ccb453d1eaddbe45e15a979643ee3c1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y7HBBXYMW6KPYPSLGCU27DCB2X/bundle.json","state_url":"https://pith.science/pith/Y7HBBXYMW6KPYPSLGCU27DCB2X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y7HBBXYMW6KPYPSLGCU27DCB2X/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":"2026-05-31T09:41:17Z","links":{"resolver":"https://pith.science/pith/Y7HBBXYMW6KPYPSLGCU27DCB2X","bundle":"https://pith.science/pith/Y7HBBXYMW6KPYPSLGCU27DCB2X/bundle.json","state":"https://pith.science/pith/Y7HBBXYMW6KPYPSLGCU27DCB2X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y7HBBXYMW6KPYPSLGCU27DCB2X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:Y7HBBXYMW6KPYPSLGCU27DCB2X","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":"3d9e2528f571922aff026e22fdc76473b36ce73d493022c9a21f45ed617df5be","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2014-02-05T13:30:25Z","title_canon_sha256":"25850932c2d6e35792af600f0691a880472e8c91c647fbade4c5078a44f35368"},"schema_version":"1.0","source":{"id":"1402.1035","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1402.1035","created_at":"2026-05-18T03:00:00Z"},{"alias_kind":"arxiv_version","alias_value":"1402.1035v2","created_at":"2026-05-18T03:00:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.1035","created_at":"2026-05-18T03:00:00Z"},{"alias_kind":"pith_short_12","alias_value":"Y7HBBXYMW6KP","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"Y7HBBXYMW6KPYPSL","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"Y7HBBXYM","created_at":"2026-05-18T12:28:57Z"}],"graph_snapshots":[{"event_id":"sha256:20e56fc99a043b3f4c5e19a46144a2ab5ccb453d1eaddbe45e15a979643ee3c1","target":"graph","created_at":"2026-05-18T03:00:00Z","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":"Linear sketching and recovery of sparse vectors with randomly constructed sparse matrices has numerous applications in several areas, including compressive sensing, data stream computing, graph sketching, and combinatorial group testing. This paper considers the same problem with the added twist that the sparse coefficients of the unknown vector exhibit further correlations as determined by a known sparsity model. We prove that exploiting model-based sparsity in recovery provably reduces the sketch size without sacrificing recovery quality. In this context, we present the model-expander iterat","authors_text":"Bubacarr Bah, Luca Baldassarre, Volkan Cevher","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2014-02-05T13:30:25Z","title":"Model-based Sketching and Recovery with Expanders"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.1035","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:cb94d6af1596ac7569f72e7f45fbe134baeaccba9eb98c3a739baf1951708c30","target":"record","created_at":"2026-05-18T03:00:00Z","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":"3d9e2528f571922aff026e22fdc76473b36ce73d493022c9a21f45ed617df5be","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2014-02-05T13:30:25Z","title_canon_sha256":"25850932c2d6e35792af600f0691a880472e8c91c647fbade4c5078a44f35368"},"schema_version":"1.0","source":{"id":"1402.1035","kind":"arxiv","version":2}},"canonical_sha256":"c7ce10df0cb794fc3e4b30a9af8c41d5f43e51ea14518919664c07563518c3ef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c7ce10df0cb794fc3e4b30a9af8c41d5f43e51ea14518919664c07563518c3ef","first_computed_at":"2026-05-18T03:00:00.026522Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:00:00.026522Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LJfLrAH5h0jr+hP0hc/5IbHdyzvgZYx0NT0BzlUMTi7HXOQ27sUEg9gWDu+U9fCwQ0epym8wGqvWwMdqVWVECA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:00:00.027368Z","signed_message":"canonical_sha256_bytes"},"source_id":"1402.1035","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb94d6af1596ac7569f72e7f45fbe134baeaccba9eb98c3a739baf1951708c30","sha256:20e56fc99a043b3f4c5e19a46144a2ab5ccb453d1eaddbe45e15a979643ee3c1"],"state_sha256":"223bf159397d48d7a3e904cf7633b3c941b8c4a9d59bc82c13149d0fff78f831"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"46BJnZLvWyJNgtTJYtq0NoVrOR8KBpwE312Leq+KNN748g1vUAZb2MNx3CvInVkGE5eD9y40+pdaMoFE2NDwAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T09:41:17.978088Z","bundle_sha256":"5f2f1e39ef5f1fffc80fff699ab96fdbbc719c46528f73c36996820f1f003208"}}