{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:IDRLUZ4O2FWWRM25FFR6RFZUES","short_pith_number":"pith:IDRLUZ4O","canonical_record":{"source":{"id":"1607.01327","kind":"arxiv","version":8},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-05T16:50:42Z","cross_cats_sorted":[],"title_canon_sha256":"3c9b9b454183c6558e7b3f4404a43f6e348f14826f2cc0a6c930735d7fc0bbf1","abstract_canon_sha256":"1141d8ea607f15170ae313be238eebbd63de00b6da977f44ec3f838b6320f000"},"schema_version":"1.0"},"canonical_sha256":"40e2ba678ed16d68b35d2963e89734248eb1bf6c0a03e4e95c4195b31347232b","source":{"kind":"arxiv","id":"1607.01327","version":8},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.01327","created_at":"2026-07-05T07:54:45Z"},{"alias_kind":"arxiv_version","alias_value":"1607.01327v8","created_at":"2026-07-05T07:54:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.01327","created_at":"2026-07-05T07:54:45Z"},{"alias_kind":"pith_short_12","alias_value":"IDRLUZ4O2FWW","created_at":"2026-07-05T07:54:45Z"},{"alias_kind":"pith_short_16","alias_value":"IDRLUZ4O2FWWRM25","created_at":"2026-07-05T07:54:45Z"},{"alias_kind":"pith_short_8","alias_value":"IDRLUZ4O","created_at":"2026-07-05T07:54:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:IDRLUZ4O2FWWRM25FFR6RFZUES","target":"record","payload":{"canonical_record":{"source":{"id":"1607.01327","kind":"arxiv","version":8},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-05T16:50:42Z","cross_cats_sorted":[],"title_canon_sha256":"3c9b9b454183c6558e7b3f4404a43f6e348f14826f2cc0a6c930735d7fc0bbf1","abstract_canon_sha256":"1141d8ea607f15170ae313be238eebbd63de00b6da977f44ec3f838b6320f000"},"schema_version":"1.0"},"canonical_sha256":"40e2ba678ed16d68b35d2963e89734248eb1bf6c0a03e4e95c4195b31347232b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:54:45.474138Z","signature_b64":"MhOJcmmmXCc2ApdfVYB1vfPu7KTgq+uCq2P/1JDdobrPZAvqY+Be81RxlV32EpXPBNRDV0SLtr+2Q9z0AbU4Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40e2ba678ed16d68b35d2963e89734248eb1bf6c0a03e4e95c4195b31347232b","last_reissued_at":"2026-07-05T07:54:45.473656Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:54:45.473656Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1607.01327","source_version":8,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:54:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zScW6+BjVlXSNPvmQqudwtYYrVyzA1FZ1Tm9qacFAsHKMHa9BUgeidnr4b6tP4ZmlPQCMZAm5XAksPLzddShCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T17:42:52.199901Z"},"content_sha256":"69ab38057eab072d9d3bbac75b2f8f0ea74ea8b8ac78a8f9ebddc56443429175","schema_version":"1.0","event_id":"sha256:69ab38057eab072d9d3bbac75b2f8f0ea74ea8b8ac78a8f9ebddc56443429175"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:IDRLUZ4O2FWWRM25FFR6RFZUES","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature Selection Library (MATLAB Toolbox)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Giorgio Roffo","submitted_at":"2016-07-05T16:50:42Z","abstract_excerpt":"The Feature Selection Library (FSLib) introduces a comprehensive suite of feature selection (FS) algorithms for MATLAB, aimed at improving machine learning and data mining tasks. FSLib encompasses filter, embedded, and wrapper methods to cater to diverse FS requirements. Filter methods focus on the inherent characteristics of features, embedded methods incorporate FS within model training, and wrapper methods assess features through model performance metrics. By enabling effective feature selection, FSLib addresses the curse of dimensionality, reduces computational load, and enhances model gen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.01327","kind":"arxiv","version":8},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1607.01327/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T07:54:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k2m/c5T2YCe7Im/X+3Z3Ei3roHdadyHjPNFIHqeaXbrWIM5dj+x9edKKin3e33w7Bs3KBsCOIvPQl+0ye9Y1BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T17:42:52.200382Z"},"content_sha256":"25bb793b8c55ef0c31bcbcbd86c0fa4c2912733cab005d2ba522eb34bd98a6ff","schema_version":"1.0","event_id":"sha256:25bb793b8c55ef0c31bcbcbd86c0fa4c2912733cab005d2ba522eb34bd98a6ff"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IDRLUZ4O2FWWRM25FFR6RFZUES/bundle.json","state_url":"https://pith.science/pith/IDRLUZ4O2FWWRM25FFR6RFZUES/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IDRLUZ4O2FWWRM25FFR6RFZUES/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":"2026-08-21T17:42:52Z","links":{"resolver":"https://pith.science/pith/IDRLUZ4O2FWWRM25FFR6RFZUES","bundle":"https://pith.science/pith/IDRLUZ4O2FWWRM25FFR6RFZUES/bundle.json","state":"https://pith.science/pith/IDRLUZ4O2FWWRM25FFR6RFZUES/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IDRLUZ4O2FWWRM25FFR6RFZUES/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:IDRLUZ4O2FWWRM25FFR6RFZUES","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":"1141d8ea607f15170ae313be238eebbd63de00b6da977f44ec3f838b6320f000","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-05T16:50:42Z","title_canon_sha256":"3c9b9b454183c6558e7b3f4404a43f6e348f14826f2cc0a6c930735d7fc0bbf1"},"schema_version":"1.0","source":{"id":"1607.01327","kind":"arxiv","version":8}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.01327","created_at":"2026-07-05T07:54:45Z"},{"alias_kind":"arxiv_version","alias_value":"1607.01327v8","created_at":"2026-07-05T07:54:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.01327","created_at":"2026-07-05T07:54:45Z"},{"alias_kind":"pith_short_12","alias_value":"IDRLUZ4O2FWW","created_at":"2026-07-05T07:54:45Z"},{"alias_kind":"pith_short_16","alias_value":"IDRLUZ4O2FWWRM25","created_at":"2026-07-05T07:54:45Z"},{"alias_kind":"pith_short_8","alias_value":"IDRLUZ4O","created_at":"2026-07-05T07:54:45Z"}],"graph_snapshots":[{"event_id":"sha256:25bb793b8c55ef0c31bcbcbd86c0fa4c2912733cab005d2ba522eb34bd98a6ff","target":"graph","created_at":"2026-07-05T07:54:45Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1607.01327/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The Feature Selection Library (FSLib) introduces a comprehensive suite of feature selection (FS) algorithms for MATLAB, aimed at improving machine learning and data mining tasks. FSLib encompasses filter, embedded, and wrapper methods to cater to diverse FS requirements. Filter methods focus on the inherent characteristics of features, embedded methods incorporate FS within model training, and wrapper methods assess features through model performance metrics. By enabling effective feature selection, FSLib addresses the curse of dimensionality, reduces computational load, and enhances model gen","authors_text":"Giorgio Roffo","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-05T16:50:42Z","title":"Feature Selection Library (MATLAB Toolbox)"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.01327","kind":"arxiv","version":8},"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":"sha256:69ab38057eab072d9d3bbac75b2f8f0ea74ea8b8ac78a8f9ebddc56443429175","target":"record","created_at":"2026-07-05T07:54:45Z","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":"1141d8ea607f15170ae313be238eebbd63de00b6da977f44ec3f838b6320f000","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-05T16:50:42Z","title_canon_sha256":"3c9b9b454183c6558e7b3f4404a43f6e348f14826f2cc0a6c930735d7fc0bbf1"},"schema_version":"1.0","source":{"id":"1607.01327","kind":"arxiv","version":8}},"canonical_sha256":"40e2ba678ed16d68b35d2963e89734248eb1bf6c0a03e4e95c4195b31347232b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"40e2ba678ed16d68b35d2963e89734248eb1bf6c0a03e4e95c4195b31347232b","first_computed_at":"2026-07-05T07:54:45.473656Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:54:45.473656Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MhOJcmmmXCc2ApdfVYB1vfPu7KTgq+uCq2P/1JDdobrPZAvqY+Be81RxlV32EpXPBNRDV0SLtr+2Q9z0AbU4Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:54:45.474138Z","signed_message":"canonical_sha256_bytes"},"source_id":"1607.01327","source_kind":"arxiv","source_version":8}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:69ab38057eab072d9d3bbac75b2f8f0ea74ea8b8ac78a8f9ebddc56443429175","sha256:25bb793b8c55ef0c31bcbcbd86c0fa4c2912733cab005d2ba522eb34bd98a6ff"],"state_sha256":"1eb8e41604dfc5d154c7f644f9c04e29e2fc38e45bf0bb5a9ecd84ade8bb64e4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y87jEQRY6clU/N14tJFeUIEzRES0CzVqRI64l8fq1FiGM1vtuKhv1n3DlBe+N9jW+HKaSdZU7U8r8bNKy02DDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T17:42:52.204150Z","bundle_sha256":"ad56c6cdda202260415c8dc87fd6063491aa7e61de005c9fcbf782c48ed14cc7"}}