{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:BA3PVAGDO72ZIDMVYMQMVFNX6A","short_pith_number":"pith:BA3PVAGD","canonical_record":{"source":{"id":"2303.09381","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-16T15:11:17Z","cross_cats_sorted":[],"title_canon_sha256":"45a20c4d77c823f413c2fd3b2dd74a0b2fc03528126d7d86e25187c93974a666","abstract_canon_sha256":"ee0842063c6e873fe68d34aac049c83eefdd28584e2651aced4b82d2977cf097"},"schema_version":"1.0"},"canonical_sha256":"0836fa80c377f5940d95c320ca95b7f01acaa84404c1c75633f0ea83a23e7e27","source":{"kind":"arxiv","id":"2303.09381","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.09381","created_at":"2026-07-05T05:51:52Z"},{"alias_kind":"arxiv_version","alias_value":"2303.09381v1","created_at":"2026-07-05T05:51:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.09381","created_at":"2026-07-05T05:51:52Z"},{"alias_kind":"pith_short_12","alias_value":"BA3PVAGDO72Z","created_at":"2026-07-05T05:51:52Z"},{"alias_kind":"pith_short_16","alias_value":"BA3PVAGDO72ZIDMV","created_at":"2026-07-05T05:51:52Z"},{"alias_kind":"pith_short_8","alias_value":"BA3PVAGD","created_at":"2026-07-05T05:51:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:BA3PVAGDO72ZIDMVYMQMVFNX6A","target":"record","payload":{"canonical_record":{"source":{"id":"2303.09381","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-16T15:11:17Z","cross_cats_sorted":[],"title_canon_sha256":"45a20c4d77c823f413c2fd3b2dd74a0b2fc03528126d7d86e25187c93974a666","abstract_canon_sha256":"ee0842063c6e873fe68d34aac049c83eefdd28584e2651aced4b82d2977cf097"},"schema_version":"1.0"},"canonical_sha256":"0836fa80c377f5940d95c320ca95b7f01acaa84404c1c75633f0ea83a23e7e27","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:51:52.498015Z","signature_b64":"SUKXVLI4WVagIYTuz1dvOtsQlFEedGmust8A0Lqr1UQZrdc7V9JFLgvX1c1RJBMm9YFAg8cfrwD3VvIk5m/ZDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0836fa80c377f5940d95c320ca95b7f01acaa84404c1c75633f0ea83a23e7e27","last_reissued_at":"2026-07-05T05:51:52.497593Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:51:52.497593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.09381","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":"2026-07-05T05:51:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TIOLdiUa/K8K+qubxNUFt0e8cDqxYTDnMtoUW8MdW2V6z2XPUz84+6/KafRe94MhMhFt6CHlDsgWcqyvnhUXAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T18:17:12.954511Z"},"content_sha256":"e53c7877893eb1053d2eafd2e42e1fe73ffcd094feb1da43814c2c123b90760c","schema_version":"1.0","event_id":"sha256:e53c7877893eb1053d2eafd2e42e1fe73ffcd094feb1da43814c2c123b90760c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:BA3PVAGDO72ZIDMVYMQMVFNX6A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-modal Differentiable Unsupervised Feature Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ariel Jaffe, Junchen Yang, Ofir Lindenbaum, Yuval Kluger","submitted_at":"2023-03-16T15:11:17Z","abstract_excerpt":"Multi-modal high throughput biological data presents a great scientific opportunity and a significant computational challenge. In multi-modal measurements, every sample is observed simultaneously by two or more sets of sensors. In such settings, many observed variables in both modalities are often nuisance and do not carry information about the phenomenon of interest. Here, we propose a multi-modal unsupervised feature selection framework: identifying informative variables based on coupled high-dimensional measurements. Our method is designed to identify features associated with two types of l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.09381","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.09381/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":"2026-07-05T05:51:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n2OjAEPdbK3SPV/kyi4N+o1l0AfLND9Y0SyDXQRz3uwgWMmWQXYLXPVImXFCvKwz/mukBI7hbln3TXIwXtPIDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T18:17:12.954998Z"},"content_sha256":"9ed04ef9596a2542b8aa8e4bccea95cc609f45f6528cb62cb5d0c80ad9d175b0","schema_version":"1.0","event_id":"sha256:9ed04ef9596a2542b8aa8e4bccea95cc609f45f6528cb62cb5d0c80ad9d175b0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BA3PVAGDO72ZIDMVYMQMVFNX6A/bundle.json","state_url":"https://pith.science/pith/BA3PVAGDO72ZIDMVYMQMVFNX6A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BA3PVAGDO72ZIDMVYMQMVFNX6A/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":"2026-08-16T18:17:12Z","links":{"resolver":"https://pith.science/pith/BA3PVAGDO72ZIDMVYMQMVFNX6A","bundle":"https://pith.science/pith/BA3PVAGDO72ZIDMVYMQMVFNX6A/bundle.json","state":"https://pith.science/pith/BA3PVAGDO72ZIDMVYMQMVFNX6A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BA3PVAGDO72ZIDMVYMQMVFNX6A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:BA3PVAGDO72ZIDMVYMQMVFNX6A","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":"ee0842063c6e873fe68d34aac049c83eefdd28584e2651aced4b82d2977cf097","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-16T15:11:17Z","title_canon_sha256":"45a20c4d77c823f413c2fd3b2dd74a0b2fc03528126d7d86e25187c93974a666"},"schema_version":"1.0","source":{"id":"2303.09381","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.09381","created_at":"2026-07-05T05:51:52Z"},{"alias_kind":"arxiv_version","alias_value":"2303.09381v1","created_at":"2026-07-05T05:51:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.09381","created_at":"2026-07-05T05:51:52Z"},{"alias_kind":"pith_short_12","alias_value":"BA3PVAGDO72Z","created_at":"2026-07-05T05:51:52Z"},{"alias_kind":"pith_short_16","alias_value":"BA3PVAGDO72ZIDMV","created_at":"2026-07-05T05:51:52Z"},{"alias_kind":"pith_short_8","alias_value":"BA3PVAGD","created_at":"2026-07-05T05:51:52Z"}],"graph_snapshots":[{"event_id":"sha256:9ed04ef9596a2542b8aa8e4bccea95cc609f45f6528cb62cb5d0c80ad9d175b0","target":"graph","created_at":"2026-07-05T05:51:52Z","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/2303.09381/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-modal high throughput biological data presents a great scientific opportunity and a significant computational challenge. In multi-modal measurements, every sample is observed simultaneously by two or more sets of sensors. In such settings, many observed variables in both modalities are often nuisance and do not carry information about the phenomenon of interest. Here, we propose a multi-modal unsupervised feature selection framework: identifying informative variables based on coupled high-dimensional measurements. Our method is designed to identify features associated with two types of l","authors_text":"Ariel Jaffe, Junchen Yang, Ofir Lindenbaum, Yuval Kluger","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-16T15:11:17Z","title":"Multi-modal Differentiable Unsupervised Feature Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.09381","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:e53c7877893eb1053d2eafd2e42e1fe73ffcd094feb1da43814c2c123b90760c","target":"record","created_at":"2026-07-05T05:51:52Z","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":"ee0842063c6e873fe68d34aac049c83eefdd28584e2651aced4b82d2977cf097","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-16T15:11:17Z","title_canon_sha256":"45a20c4d77c823f413c2fd3b2dd74a0b2fc03528126d7d86e25187c93974a666"},"schema_version":"1.0","source":{"id":"2303.09381","kind":"arxiv","version":1}},"canonical_sha256":"0836fa80c377f5940d95c320ca95b7f01acaa84404c1c75633f0ea83a23e7e27","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0836fa80c377f5940d95c320ca95b7f01acaa84404c1c75633f0ea83a23e7e27","first_computed_at":"2026-07-05T05:51:52.497593Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:51:52.497593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SUKXVLI4WVagIYTuz1dvOtsQlFEedGmust8A0Lqr1UQZrdc7V9JFLgvX1c1RJBMm9YFAg8cfrwD3VvIk5m/ZDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:51:52.498015Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.09381","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e53c7877893eb1053d2eafd2e42e1fe73ffcd094feb1da43814c2c123b90760c","sha256:9ed04ef9596a2542b8aa8e4bccea95cc609f45f6528cb62cb5d0c80ad9d175b0"],"state_sha256":"746282506e469580e6334f70cb3d261fead328673f7cf5a68f2a760a0d20fa4b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3iS3jKg8dQDyYNRHUDLwK8dv3lI6aJi4wGmlBUbOafcAQ6fIMs7SyHFW7az04IkwO4RtFqYwgUbtKLFOGjL+Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T18:17:12.960648Z","bundle_sha256":"fb7f94dda3c5331aa03a5ddc6f7a197c5d9ad8e8585443526d0932163ec7a29a"}}