{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:26ZBGU6KYDVKG57DKTHDG3DZGN","short_pith_number":"pith:26ZBGU6K","canonical_record":{"source":{"id":"1610.08013","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-23T20:34:10Z","cross_cats_sorted":[],"title_canon_sha256":"33ccb94285ac3e67e03b87cd81c70bad33c48e7b45ac3ab995f4d941a761b64e","abstract_canon_sha256":"440e10befbf4c1647d3bfde21fd21d151c63b031cee1010f4aa758a4ea2db881"},"schema_version":"1.0"},"canonical_sha256":"d7b21353cac0eaa377e354ce336c7933711c5b10e3676b95d934994831ff83b5","source":{"kind":"arxiv","id":"1610.08013","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.08013","created_at":"2026-05-18T01:01:16Z"},{"alias_kind":"arxiv_version","alias_value":"1610.08013v1","created_at":"2026-05-18T01:01:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.08013","created_at":"2026-05-18T01:01:16Z"},{"alias_kind":"pith_short_12","alias_value":"26ZBGU6KYDVK","created_at":"2026-05-18T12:29:52Z"},{"alias_kind":"pith_short_16","alias_value":"26ZBGU6KYDVKG57D","created_at":"2026-05-18T12:29:52Z"},{"alias_kind":"pith_short_8","alias_value":"26ZBGU6K","created_at":"2026-05-18T12:29:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:26ZBGU6KYDVKG57DKTHDG3DZGN","target":"record","payload":{"canonical_record":{"source":{"id":"1610.08013","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-23T20:34:10Z","cross_cats_sorted":[],"title_canon_sha256":"33ccb94285ac3e67e03b87cd81c70bad33c48e7b45ac3ab995f4d941a761b64e","abstract_canon_sha256":"440e10befbf4c1647d3bfde21fd21d151c63b031cee1010f4aa758a4ea2db881"},"schema_version":"1.0"},"canonical_sha256":"d7b21353cac0eaa377e354ce336c7933711c5b10e3676b95d934994831ff83b5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:01:16.939564Z","signature_b64":"Li2dT9+NYdZ618IMVnG55x4DY9jXmuvxU630MV1u+ydIf+ws+3HiYSGS7mYkMtx03o4gznH1slW9V6pgJG8vBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7b21353cac0eaa377e354ce336c7933711c5b10e3676b95d934994831ff83b5","last_reissued_at":"2026-05-18T01:01:16.939074Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:01:16.939074Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1610.08013","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":"2026-05-18T01:01:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cdBf6rj7tFFtAHAhK/2eDcrU7xxoIy4d5Q/GIGFcxGh+bBBW+zuLJ04qMRpJtidfCkRowkKvWFujIFUm1rX+CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T11:51:59.384231Z"},"content_sha256":"ac066071b0cdc01a2edeac90393da00b0ecbb37f677ebd55430e913c16d0d158","schema_version":"1.0","event_id":"sha256:ac066071b0cdc01a2edeac90393da00b0ecbb37f677ebd55430e913c16d0d158"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:26ZBGU6KYDVKG57DKTHDG3DZGN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jiangwen Sun, Jinbo Bi, Tingyang Xu","submitted_at":"2016-10-23T20:34:10Z","abstract_excerpt":"Longitudinal analysis is important in many disciplines, such as the study of behavioral transitions in social science. Only very recently, feature selection has drawn adequate attention in the context of longitudinal modeling. Standard techniques, such as generalized estimating equations, have been modified to select features by imposing sparsity-inducing regularizers. However, they do not explicitly model how a dependent variable relies on features measured at proximal time points. Recent graphical Granger modeling can select features in lagged time points but ignores the temporal correlation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.08013","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":""},"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":"2026-05-18T01:01:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hp7D/c8R1C18Y2PJB9VGFkWoQEOPHp4Ty4bXc4PSy1gGssFD7ZFg7bU2WCCW/njthbQpx4nYX3+v4t1q18OeBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T11:51:59.384617Z"},"content_sha256":"163412b0bcbd652c7ff2b9e4562616c7b582f73972ce286eac82df190e02a850","schema_version":"1.0","event_id":"sha256:163412b0bcbd652c7ff2b9e4562616c7b582f73972ce286eac82df190e02a850"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/26ZBGU6KYDVKG57DKTHDG3DZGN/bundle.json","state_url":"https://pith.science/pith/26ZBGU6KYDVKG57DKTHDG3DZGN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/26ZBGU6KYDVKG57DKTHDG3DZGN/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":"2026-05-30T11:51:59Z","links":{"resolver":"https://pith.science/pith/26ZBGU6KYDVKG57DKTHDG3DZGN","bundle":"https://pith.science/pith/26ZBGU6KYDVKG57DKTHDG3DZGN/bundle.json","state":"https://pith.science/pith/26ZBGU6KYDVKG57DKTHDG3DZGN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/26ZBGU6KYDVKG57DKTHDG3DZGN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:26ZBGU6KYDVKG57DKTHDG3DZGN","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":"440e10befbf4c1647d3bfde21fd21d151c63b031cee1010f4aa758a4ea2db881","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-23T20:34:10Z","title_canon_sha256":"33ccb94285ac3e67e03b87cd81c70bad33c48e7b45ac3ab995f4d941a761b64e"},"schema_version":"1.0","source":{"id":"1610.08013","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.08013","created_at":"2026-05-18T01:01:16Z"},{"alias_kind":"arxiv_version","alias_value":"1610.08013v1","created_at":"2026-05-18T01:01:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.08013","created_at":"2026-05-18T01:01:16Z"},{"alias_kind":"pith_short_12","alias_value":"26ZBGU6KYDVK","created_at":"2026-05-18T12:29:52Z"},{"alias_kind":"pith_short_16","alias_value":"26ZBGU6KYDVKG57D","created_at":"2026-05-18T12:29:52Z"},{"alias_kind":"pith_short_8","alias_value":"26ZBGU6K","created_at":"2026-05-18T12:29:52Z"}],"graph_snapshots":[{"event_id":"sha256:163412b0bcbd652c7ff2b9e4562616c7b582f73972ce286eac82df190e02a850","target":"graph","created_at":"2026-05-18T01:01:16Z","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":"Longitudinal analysis is important in many disciplines, such as the study of behavioral transitions in social science. Only very recently, feature selection has drawn adequate attention in the context of longitudinal modeling. Standard techniques, such as generalized estimating equations, have been modified to select features by imposing sparsity-inducing regularizers. However, they do not explicitly model how a dependent variable relies on features measured at proximal time points. Recent graphical Granger modeling can select features in lagged time points but ignores the temporal correlation","authors_text":"Jiangwen Sun, Jinbo Bi, Tingyang Xu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-23T20:34:10Z","title":"Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.08013","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:ac066071b0cdc01a2edeac90393da00b0ecbb37f677ebd55430e913c16d0d158","target":"record","created_at":"2026-05-18T01:01:16Z","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":"440e10befbf4c1647d3bfde21fd21d151c63b031cee1010f4aa758a4ea2db881","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-10-23T20:34:10Z","title_canon_sha256":"33ccb94285ac3e67e03b87cd81c70bad33c48e7b45ac3ab995f4d941a761b64e"},"schema_version":"1.0","source":{"id":"1610.08013","kind":"arxiv","version":1}},"canonical_sha256":"d7b21353cac0eaa377e354ce336c7933711c5b10e3676b95d934994831ff83b5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d7b21353cac0eaa377e354ce336c7933711c5b10e3676b95d934994831ff83b5","first_computed_at":"2026-05-18T01:01:16.939074Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:01:16.939074Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Li2dT9+NYdZ618IMVnG55x4DY9jXmuvxU630MV1u+ydIf+ws+3HiYSGS7mYkMtx03o4gznH1slW9V6pgJG8vBw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:01:16.939564Z","signed_message":"canonical_sha256_bytes"},"source_id":"1610.08013","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ac066071b0cdc01a2edeac90393da00b0ecbb37f677ebd55430e913c16d0d158","sha256:163412b0bcbd652c7ff2b9e4562616c7b582f73972ce286eac82df190e02a850"],"state_sha256":"d96949710aaf065c74226f985cf05c6012a7c594ade6a8c06a50ec723357efb2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2x0sYOv/UyMHwTVpjrIzJJ+aN+euAr9CrIpiTmKeN3bKK7I+MvEW+V9YDfU20thrxyp6fsB3xnZ9QJBKK2b5DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T11:51:59.386742Z","bundle_sha256":"afc3258874aee7e17cce9bc720716a8ac7e739244fc0b65ec66e797132f13981"}}