{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:QTODG3N7JBOSK63QPMMBZ2ZTEC","short_pith_number":"pith:QTODG3N7","canonical_record":{"source":{"id":"1511.06653","kind":"arxiv","version":8},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-20T15:47:55Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ffb0e2cf7458cb2cfde10f5213d4f83f617c8f07e5eaf010483967f14c3d8d03","abstract_canon_sha256":"53a6fdc3380fd4d6aa40e43905ab8ebe45face0521317c5e166b3ec19b900d0c"},"schema_version":"1.0"},"canonical_sha256":"84dc336dbf485d257b707b181ceb3320a5282d1a4dda453458ed75005f5d1f05","source":{"kind":"arxiv","id":"1511.06653","version":8},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.06653","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"arxiv_version","alias_value":"1511.06653v8","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.06653","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"pith_short_12","alias_value":"QTODG3N7JBOS","created_at":"2026-05-18T12:29:37Z"},{"alias_kind":"pith_short_16","alias_value":"QTODG3N7JBOSK63Q","created_at":"2026-05-18T12:29:37Z"},{"alias_kind":"pith_short_8","alias_value":"QTODG3N7","created_at":"2026-05-18T12:29:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:QTODG3N7JBOSK63QPMMBZ2ZTEC","target":"record","payload":{"canonical_record":{"source":{"id":"1511.06653","kind":"arxiv","version":8},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-20T15:47:55Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ffb0e2cf7458cb2cfde10f5213d4f83f617c8f07e5eaf010483967f14c3d8d03","abstract_canon_sha256":"53a6fdc3380fd4d6aa40e43905ab8ebe45face0521317c5e166b3ec19b900d0c"},"schema_version":"1.0"},"canonical_sha256":"84dc336dbf485d257b707b181ceb3320a5282d1a4dda453458ed75005f5d1f05","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:00.807670Z","signature_b64":"Wrz0Ob9VRZtJ+OtvVDGCNLsZosla5Wqkk4taHQV1760KAZzzh5iVZ+74MHpR1SStgJEQeEnC31STjivwJD0FDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84dc336dbf485d257b707b181ceb3320a5282d1a4dda453458ed75005f5d1f05","last_reissued_at":"2026-05-18T00:11:00.806879Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:00.806879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.06653","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":"2026-05-18T00:11:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h702XRJQdjD48dETDaz0Ul3haAusIxGKBukmyfRm5xF/K6Zp+Zii6TGx+QmOoef1o1KOmxUSg/2Ut73O8cUFDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T15:01:03.371701Z"},"content_sha256":"000eb758567bd0ad3a5b00b3dcd204d7d64b4f00b831b4a581729fe2cb4f0650","schema_version":"1.0","event_id":"sha256:000eb758567bd0ad3a5b00b3dcd204d7d64b4f00b831b4a581729fe2cb4f0650"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:QTODG3N7JBOSK63QPMMBZ2ZTEC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recurrent Semi-supervised Classification and Constrained Adversarial Generation with Motion Capture Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Christopher Pal, David Kanaa, F\\'elix G. Harvey, Julien Roy","submitted_at":"2015-11-20T15:47:55Z","abstract_excerpt":"We explore recurrent encoder multi-decoder neural network architectures for semi-supervised sequence classification and reconstruction. We find that the use of multiple reconstruction modules helps models generalize in a classification task when only a small amount of labeled data is available, which is often the case in practice. Such models provide useful high-level representations of motions allowing clustering, searching and faster labeling of new sequences. We also propose a new, realistic partitioning of a well-known, high quality motion-capture dataset for better evaluations. We further"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.06653","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":""},"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":"2026-05-18T00:11:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yt77AKYeGK5HPzftD8QuOQWQIqHgxvgCvb7wcCoP1jyRLYA8Gk3VMrF264AUYHvgBxHV7LX7JDBnK2Ty0e04Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T15:01:03.372422Z"},"content_sha256":"49e50d65c12d8d049e8a069b4dff5bc9facce703dd5430724ad86bceb70b63ad","schema_version":"1.0","event_id":"sha256:49e50d65c12d8d049e8a069b4dff5bc9facce703dd5430724ad86bceb70b63ad"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QTODG3N7JBOSK63QPMMBZ2ZTEC/bundle.json","state_url":"https://pith.science/pith/QTODG3N7JBOSK63QPMMBZ2ZTEC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QTODG3N7JBOSK63QPMMBZ2ZTEC/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":"2026-05-25T15:01:03Z","links":{"resolver":"https://pith.science/pith/QTODG3N7JBOSK63QPMMBZ2ZTEC","bundle":"https://pith.science/pith/QTODG3N7JBOSK63QPMMBZ2ZTEC/bundle.json","state":"https://pith.science/pith/QTODG3N7JBOSK63QPMMBZ2ZTEC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QTODG3N7JBOSK63QPMMBZ2ZTEC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:QTODG3N7JBOSK63QPMMBZ2ZTEC","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":"53a6fdc3380fd4d6aa40e43905ab8ebe45face0521317c5e166b3ec19b900d0c","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-20T15:47:55Z","title_canon_sha256":"ffb0e2cf7458cb2cfde10f5213d4f83f617c8f07e5eaf010483967f14c3d8d03"},"schema_version":"1.0","source":{"id":"1511.06653","kind":"arxiv","version":8}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.06653","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"arxiv_version","alias_value":"1511.06653v8","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.06653","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"pith_short_12","alias_value":"QTODG3N7JBOS","created_at":"2026-05-18T12:29:37Z"},{"alias_kind":"pith_short_16","alias_value":"QTODG3N7JBOSK63Q","created_at":"2026-05-18T12:29:37Z"},{"alias_kind":"pith_short_8","alias_value":"QTODG3N7","created_at":"2026-05-18T12:29:37Z"}],"graph_snapshots":[{"event_id":"sha256:49e50d65c12d8d049e8a069b4dff5bc9facce703dd5430724ad86bceb70b63ad","target":"graph","created_at":"2026-05-18T00:11:00Z","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":"We explore recurrent encoder multi-decoder neural network architectures for semi-supervised sequence classification and reconstruction. We find that the use of multiple reconstruction modules helps models generalize in a classification task when only a small amount of labeled data is available, which is often the case in practice. Such models provide useful high-level representations of motions allowing clustering, searching and faster labeling of new sequences. We also propose a new, realistic partitioning of a well-known, high quality motion-capture dataset for better evaluations. We further","authors_text":"Christopher Pal, David Kanaa, F\\'elix G. Harvey, Julien Roy","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-20T15:47:55Z","title":"Recurrent Semi-supervised Classification and Constrained Adversarial Generation with Motion Capture Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.06653","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":"sha256:000eb758567bd0ad3a5b00b3dcd204d7d64b4f00b831b4a581729fe2cb4f0650","target":"record","created_at":"2026-05-18T00:11:00Z","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":"53a6fdc3380fd4d6aa40e43905ab8ebe45face0521317c5e166b3ec19b900d0c","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-11-20T15:47:55Z","title_canon_sha256":"ffb0e2cf7458cb2cfde10f5213d4f83f617c8f07e5eaf010483967f14c3d8d03"},"schema_version":"1.0","source":{"id":"1511.06653","kind":"arxiv","version":8}},"canonical_sha256":"84dc336dbf485d257b707b181ceb3320a5282d1a4dda453458ed75005f5d1f05","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"84dc336dbf485d257b707b181ceb3320a5282d1a4dda453458ed75005f5d1f05","first_computed_at":"2026-05-18T00:11:00.806879Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:00.806879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Wrz0Ob9VRZtJ+OtvVDGCNLsZosla5Wqkk4taHQV1760KAZzzh5iVZ+74MHpR1SStgJEQeEnC31STjivwJD0FDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:00.807670Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.06653","source_kind":"arxiv","source_version":8}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:000eb758567bd0ad3a5b00b3dcd204d7d64b4f00b831b4a581729fe2cb4f0650","sha256:49e50d65c12d8d049e8a069b4dff5bc9facce703dd5430724ad86bceb70b63ad"],"state_sha256":"17ed94cbb3b70ee1f233e42f7f0f11dd73629a8822b28d16da47cb37a2ac36fb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/xsX6DuKSab8MUp70akUDBzIVohRy1vmrIuYandGLZzGhwF4xPBwg49Og9pqA4XOu0fLlFsTgiV8L8073UlrBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T15:01:03.376665Z","bundle_sha256":"998511e39652b13e03c8f828ea23b0609b137eb968f064bf50bcc6ba88e29daa"}}