{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:MSV7N4MJGFP3FVE2T4L3DW2P4M","short_pith_number":"pith:MSV7N4MJ","canonical_record":{"source":{"id":"2504.11187","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-04-15T13:42:41Z","cross_cats_sorted":[],"title_canon_sha256":"ec20b47325dfd856561907944b0e391c27dbc0088c1fcff1b7728f2fe6bcd03f","abstract_canon_sha256":"291f606b035f6317ad54f513b45249cee0a2c7e8ca1153709ce26035c684ea34"},"schema_version":"1.0"},"canonical_sha256":"64abf6f189315fb2d49a9f17b1db4fe30d5f0c9fd209b36e44ad9d85e4bbe8b1","source":{"kind":"arxiv","id":"2504.11187","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.11187","created_at":"2026-07-05T10:49:30Z"},{"alias_kind":"arxiv_version","alias_value":"2504.11187v1","created_at":"2026-07-05T10:49:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.11187","created_at":"2026-07-05T10:49:30Z"},{"alias_kind":"pith_short_12","alias_value":"MSV7N4MJGFP3","created_at":"2026-07-05T10:49:30Z"},{"alias_kind":"pith_short_16","alias_value":"MSV7N4MJGFP3FVE2","created_at":"2026-07-05T10:49:30Z"},{"alias_kind":"pith_short_8","alias_value":"MSV7N4MJ","created_at":"2026-07-05T10:49:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:MSV7N4MJGFP3FVE2T4L3DW2P4M","target":"record","payload":{"canonical_record":{"source":{"id":"2504.11187","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-04-15T13:42:41Z","cross_cats_sorted":[],"title_canon_sha256":"ec20b47325dfd856561907944b0e391c27dbc0088c1fcff1b7728f2fe6bcd03f","abstract_canon_sha256":"291f606b035f6317ad54f513b45249cee0a2c7e8ca1153709ce26035c684ea34"},"schema_version":"1.0"},"canonical_sha256":"64abf6f189315fb2d49a9f17b1db4fe30d5f0c9fd209b36e44ad9d85e4bbe8b1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:49:30.471248Z","signature_b64":"Acu+tErsxolviDSxo+Hqf3qrn6RIc9IcQvBPxxh1GURR5pM1sU+ncjvrTFrFPSv47MgDxfUTn8j69vbB7Ji+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64abf6f189315fb2d49a9f17b1db4fe30d5f0c9fd209b36e44ad9d85e4bbe8b1","last_reissued_at":"2026-07-05T10:49:30.470673Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:49:30.470673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.11187","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":"2026-07-05T10:49:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ip2O8HxsbQkJjLRi82luxKUE91ajdD7A8o7s9DiT0qKHbU1ILFtfgnbPDupZRWG0ip+dFfU7JyV3sqfMsNgjDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T20:20:03.781401Z"},"content_sha256":"cf3fecfe2bd7666e4ae14b8e37643a07b0e353ba3b9e84e8222ba27f10a2b328","schema_version":"1.0","event_id":"sha256:cf3fecfe2bd7666e4ae14b8e37643a07b0e353ba3b9e84e8222ba27f10a2b328"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:MSV7N4MJGFP3FVE2T4L3DW2P4M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Spatial-Sign based Direct Approach for High Dimensional Sparse Quadratic Discriminant Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Anqing Shen, Long Feng","submitted_at":"2025-04-15T13:42:41Z","abstract_excerpt":"In this paper, we study the problem of high-dimensional sparse quadratic discriminant analysis (QDA). We propose a novel classification method, termed SSQDA, which is constructed via constrained convex optimization based on the sample spatial median and spatial sign covariance matrix under the assumption of an elliptically symmetric distribution. The proposed classifier is shown to achieve the optimal convergence rate over a broad class of parameter spaces, up to a logarithmic factor. Extensive simulation studies and real data applications demonstrate that SSQDA is both robust and efficient, p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.11187","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/2504.11187/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":"2026-07-05T10:49:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gXuNleBLC2/1+OKKCt96hLNFAP+FN1ZvfxH+5Nk8woNKndrw91RvhGoux744WaeCPX+/pGcnZanfooNLWa9ACw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T20:20:03.782338Z"},"content_sha256":"c8722b99e6e430a25a836ed1fbb722d8686518f76feb869f6fe53ecade455c8a","schema_version":"1.0","event_id":"sha256:c8722b99e6e430a25a836ed1fbb722d8686518f76feb869f6fe53ecade455c8a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MSV7N4MJGFP3FVE2T4L3DW2P4M/bundle.json","state_url":"https://pith.science/pith/MSV7N4MJGFP3FVE2T4L3DW2P4M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MSV7N4MJGFP3FVE2T4L3DW2P4M/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":"2026-08-18T20:20:03Z","links":{"resolver":"https://pith.science/pith/MSV7N4MJGFP3FVE2T4L3DW2P4M","bundle":"https://pith.science/pith/MSV7N4MJGFP3FVE2T4L3DW2P4M/bundle.json","state":"https://pith.science/pith/MSV7N4MJGFP3FVE2T4L3DW2P4M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MSV7N4MJGFP3FVE2T4L3DW2P4M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MSV7N4MJGFP3FVE2T4L3DW2P4M","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":"291f606b035f6317ad54f513b45249cee0a2c7e8ca1153709ce26035c684ea34","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-04-15T13:42:41Z","title_canon_sha256":"ec20b47325dfd856561907944b0e391c27dbc0088c1fcff1b7728f2fe6bcd03f"},"schema_version":"1.0","source":{"id":"2504.11187","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.11187","created_at":"2026-07-05T10:49:30Z"},{"alias_kind":"arxiv_version","alias_value":"2504.11187v1","created_at":"2026-07-05T10:49:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.11187","created_at":"2026-07-05T10:49:30Z"},{"alias_kind":"pith_short_12","alias_value":"MSV7N4MJGFP3","created_at":"2026-07-05T10:49:30Z"},{"alias_kind":"pith_short_16","alias_value":"MSV7N4MJGFP3FVE2","created_at":"2026-07-05T10:49:30Z"},{"alias_kind":"pith_short_8","alias_value":"MSV7N4MJ","created_at":"2026-07-05T10:49:30Z"}],"graph_snapshots":[{"event_id":"sha256:c8722b99e6e430a25a836ed1fbb722d8686518f76feb869f6fe53ecade455c8a","target":"graph","created_at":"2026-07-05T10:49:30Z","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/2504.11187/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we study the problem of high-dimensional sparse quadratic discriminant analysis (QDA). We propose a novel classification method, termed SSQDA, which is constructed via constrained convex optimization based on the sample spatial median and spatial sign covariance matrix under the assumption of an elliptically symmetric distribution. The proposed classifier is shown to achieve the optimal convergence rate over a broad class of parameter spaces, up to a logarithmic factor. Extensive simulation studies and real data applications demonstrate that SSQDA is both robust and efficient, p","authors_text":"Anqing Shen, Long Feng","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-04-15T13:42:41Z","title":"A Spatial-Sign based Direct Approach for High Dimensional Sparse Quadratic Discriminant Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.11187","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:cf3fecfe2bd7666e4ae14b8e37643a07b0e353ba3b9e84e8222ba27f10a2b328","target":"record","created_at":"2026-07-05T10:49:30Z","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":"291f606b035f6317ad54f513b45249cee0a2c7e8ca1153709ce26035c684ea34","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-04-15T13:42:41Z","title_canon_sha256":"ec20b47325dfd856561907944b0e391c27dbc0088c1fcff1b7728f2fe6bcd03f"},"schema_version":"1.0","source":{"id":"2504.11187","kind":"arxiv","version":1}},"canonical_sha256":"64abf6f189315fb2d49a9f17b1db4fe30d5f0c9fd209b36e44ad9d85e4bbe8b1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"64abf6f189315fb2d49a9f17b1db4fe30d5f0c9fd209b36e44ad9d85e4bbe8b1","first_computed_at":"2026-07-05T10:49:30.470673Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:49:30.470673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Acu+tErsxolviDSxo+Hqf3qrn6RIc9IcQvBPxxh1GURR5pM1sU+ncjvrTFrFPSv47MgDxfUTn8j69vbB7Ji+Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:49:30.471248Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.11187","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cf3fecfe2bd7666e4ae14b8e37643a07b0e353ba3b9e84e8222ba27f10a2b328","sha256:c8722b99e6e430a25a836ed1fbb722d8686518f76feb869f6fe53ecade455c8a"],"state_sha256":"baee5a77ae2f97e040f147e61cdf804d2041a4cd02e1065df04e574f75758e8e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SPTmiGXBzECB/0PjpC6wwO/sQgnDAfULm2heK6VC6knUghR8wZYLatBFBn8/lqpJrVAhZ8g5uEUnxC33rOMsAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T20:20:03.789916Z","bundle_sha256":"f81e7d1d54a99c28e118970b0364a2eec9330890e336b971445f42eab5fe2080"}}