{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:SAAZRLYV6T4IUEDTMLZPZ7GZ5U","short_pith_number":"pith:SAAZRLYV","canonical_record":{"source":{"id":"1703.00641","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-03-02T07:15:41Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"b3a3231bc54db0adba970bbd5df450de049e6259a9cd4767f58555cb36caf60d","abstract_canon_sha256":"12d50aee90476fa5bde5be31d3f19d5096b552bcfc18b2e50cc88d4dca89d8c4"},"schema_version":"1.0"},"canonical_sha256":"900198af15f4f88a107362f2fcfcd9ed3a6e76f74c7415c10c3c65cf20641381","source":{"kind":"arxiv","id":"1703.00641","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.00641","created_at":"2026-05-18T00:09:05Z"},{"alias_kind":"arxiv_version","alias_value":"1703.00641v2","created_at":"2026-05-18T00:09:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.00641","created_at":"2026-05-18T00:09:05Z"},{"alias_kind":"pith_short_12","alias_value":"SAAZRLYV6T4I","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SAAZRLYV6T4IUEDT","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SAAZRLYV","created_at":"2026-05-18T12:31:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:SAAZRLYV6T4IUEDTMLZPZ7GZ5U","target":"record","payload":{"canonical_record":{"source":{"id":"1703.00641","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-03-02T07:15:41Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"b3a3231bc54db0adba970bbd5df450de049e6259a9cd4767f58555cb36caf60d","abstract_canon_sha256":"12d50aee90476fa5bde5be31d3f19d5096b552bcfc18b2e50cc88d4dca89d8c4"},"schema_version":"1.0"},"canonical_sha256":"900198af15f4f88a107362f2fcfcd9ed3a6e76f74c7415c10c3c65cf20641381","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:05.869796Z","signature_b64":"h9zvvsDeBEPOStDyFsVIiq4nCcNvRMhpj9X51ccTIET8OaWgytKiKwuKtonguKyYWlpVVFl+sPdIC26JA7a7Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"900198af15f4f88a107362f2fcfcd9ed3a6e76f74c7415c10c3c65cf20641381","last_reissued_at":"2026-05-18T00:09:05.869277Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:05.869277Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.00641","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":"2026-05-18T00:09:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iL3e6M4oUgfh3FedSdA5eDeIx36iAzsnmqZcviD8WFZ2SFLdLnpijhlLrC43dKXrg9PKeLg16iNW+jUpidCbBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T14:12:49.046780Z"},"content_sha256":"182461e0d44cd87b2fb92cf2041a9f6e1e7ad1ad5cecd3f1f159a213ea318977","schema_version":"1.0","event_id":"sha256:182461e0d44cd87b2fb92cf2041a9f6e1e7ad1ad5cecd3f1f159a213ea318977"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:SAAZRLYV6T4IUEDTMLZPZ7GZ5U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Dong Yin, Kannan Ramchandran, Ramtin Pedarsani, Yudong Chen","submitted_at":"2017-03-02T07:15:41Z","abstract_excerpt":"In this paper, we consider the mixture of sparse linear regressions model. Let ${\\beta}^{(1)},\\ldots,{\\beta}^{(L)}\\in\\mathbb{C}^n$ be $ L $ unknown sparse parameter vectors with a total of $ K $ non-zero coefficients. Noisy linear measurements are obtained in the form $y_i={x}_i^H {\\beta}^{(\\ell_i)} + w_i$, each of which is generated randomly from one of the sparse vectors with the label $ \\ell_i $ unknown. The goal is to estimate the parameter vectors efficiently with low sample and computational costs. This problem presents significant challenges as one needs to simultaneously solve the demi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.00641","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":"2026-05-18T00:09:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uVALXG+OJy+UGl8/AHA78bZAGK564AZCCAXxB4hQcY/cxwxXkR3I9phkGJi0PUSN71ZGJqORJxvODmnXEXvzAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T14:12:49.047133Z"},"content_sha256":"bc905d1e91f87d4469c988a2e9e09e18d0e3e8e18c14f6240d114ed3c0d694fe","schema_version":"1.0","event_id":"sha256:bc905d1e91f87d4469c988a2e9e09e18d0e3e8e18c14f6240d114ed3c0d694fe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SAAZRLYV6T4IUEDTMLZPZ7GZ5U/bundle.json","state_url":"https://pith.science/pith/SAAZRLYV6T4IUEDTMLZPZ7GZ5U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SAAZRLYV6T4IUEDTMLZPZ7GZ5U/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":"2026-05-21T14:12:49Z","links":{"resolver":"https://pith.science/pith/SAAZRLYV6T4IUEDTMLZPZ7GZ5U","bundle":"https://pith.science/pith/SAAZRLYV6T4IUEDTMLZPZ7GZ5U/bundle.json","state":"https://pith.science/pith/SAAZRLYV6T4IUEDTMLZPZ7GZ5U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SAAZRLYV6T4IUEDTMLZPZ7GZ5U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:SAAZRLYV6T4IUEDTMLZPZ7GZ5U","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":"12d50aee90476fa5bde5be31d3f19d5096b552bcfc18b2e50cc88d4dca89d8c4","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-03-02T07:15:41Z","title_canon_sha256":"b3a3231bc54db0adba970bbd5df450de049e6259a9cd4767f58555cb36caf60d"},"schema_version":"1.0","source":{"id":"1703.00641","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.00641","created_at":"2026-05-18T00:09:05Z"},{"alias_kind":"arxiv_version","alias_value":"1703.00641v2","created_at":"2026-05-18T00:09:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.00641","created_at":"2026-05-18T00:09:05Z"},{"alias_kind":"pith_short_12","alias_value":"SAAZRLYV6T4I","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SAAZRLYV6T4IUEDT","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SAAZRLYV","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:bc905d1e91f87d4469c988a2e9e09e18d0e3e8e18c14f6240d114ed3c0d694fe","target":"graph","created_at":"2026-05-18T00:09:05Z","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":"In this paper, we consider the mixture of sparse linear regressions model. Let ${\\beta}^{(1)},\\ldots,{\\beta}^{(L)}\\in\\mathbb{C}^n$ be $ L $ unknown sparse parameter vectors with a total of $ K $ non-zero coefficients. Noisy linear measurements are obtained in the form $y_i={x}_i^H {\\beta}^{(\\ell_i)} + w_i$, each of which is generated randomly from one of the sparse vectors with the label $ \\ell_i $ unknown. The goal is to estimate the parameter vectors efficiently with low sample and computational costs. This problem presents significant challenges as one needs to simultaneously solve the demi","authors_text":"Dong Yin, Kannan Ramchandran, Ramtin Pedarsani, Yudong Chen","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-03-02T07:15:41Z","title":"Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.00641","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":"sha256:182461e0d44cd87b2fb92cf2041a9f6e1e7ad1ad5cecd3f1f159a213ea318977","target":"record","created_at":"2026-05-18T00:09:05Z","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":"12d50aee90476fa5bde5be31d3f19d5096b552bcfc18b2e50cc88d4dca89d8c4","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-03-02T07:15:41Z","title_canon_sha256":"b3a3231bc54db0adba970bbd5df450de049e6259a9cd4767f58555cb36caf60d"},"schema_version":"1.0","source":{"id":"1703.00641","kind":"arxiv","version":2}},"canonical_sha256":"900198af15f4f88a107362f2fcfcd9ed3a6e76f74c7415c10c3c65cf20641381","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"900198af15f4f88a107362f2fcfcd9ed3a6e76f74c7415c10c3c65cf20641381","first_computed_at":"2026-05-18T00:09:05.869277Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:05.869277Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"h9zvvsDeBEPOStDyFsVIiq4nCcNvRMhpj9X51ccTIET8OaWgytKiKwuKtonguKyYWlpVVFl+sPdIC26JA7a7Bg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:05.869796Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.00641","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:182461e0d44cd87b2fb92cf2041a9f6e1e7ad1ad5cecd3f1f159a213ea318977","sha256:bc905d1e91f87d4469c988a2e9e09e18d0e3e8e18c14f6240d114ed3c0d694fe"],"state_sha256":"61e91ced4783b96cd40a3a2ed4e707d0274a50c8b46f96a1f2836723f47f659c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9PqTswykt7m+4r+lTV0nkVN59sPtgdKA+wUIiA2ah25xDrGYB4LQd0LWnH+hO8jIygyqiKQgHReLn56wwgAEDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T14:12:49.049142Z","bundle_sha256":"4e630ca413b71e0e6ca64f033df5afacf99cd196cff28a122aae01498c9ff017"}}