{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:TOMAWUY5VOHWCKXHSIFUOFWXTT","short_pith_number":"pith:TOMAWUY5","canonical_record":{"source":{"id":"1711.00629","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-02T06:28:45Z","cross_cats_sorted":["cs.LG","q-bio.NC"],"title_canon_sha256":"4f7b403c5ff6ba0c2670ff75f764bff8470e987447727817267e2123f7098d9a","abstract_canon_sha256":"eeee92943bdd7d831177c3a894d2656e5d0b56d2edff111efb4370111538b073"},"schema_version":"1.0"},"canonical_sha256":"9b980b531dab8f612ae7920b4716d79ccbc42c29e9dd7e1d47d7a650315279a9","source":{"kind":"arxiv","id":"1711.00629","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.00629","created_at":"2026-05-18T00:31:29Z"},{"alias_kind":"arxiv_version","alias_value":"1711.00629v1","created_at":"2026-05-18T00:31:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00629","created_at":"2026-05-18T00:31:29Z"},{"alias_kind":"pith_short_12","alias_value":"TOMAWUY5VOHW","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TOMAWUY5VOHWCKXH","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TOMAWUY5","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:TOMAWUY5VOHWCKXHSIFUOFWXTT","target":"record","payload":{"canonical_record":{"source":{"id":"1711.00629","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-02T06:28:45Z","cross_cats_sorted":["cs.LG","q-bio.NC"],"title_canon_sha256":"4f7b403c5ff6ba0c2670ff75f764bff8470e987447727817267e2123f7098d9a","abstract_canon_sha256":"eeee92943bdd7d831177c3a894d2656e5d0b56d2edff111efb4370111538b073"},"schema_version":"1.0"},"canonical_sha256":"9b980b531dab8f612ae7920b4716d79ccbc42c29e9dd7e1d47d7a650315279a9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:29.971324Z","signature_b64":"CvJnz0/rIAukeZzhlF9EBg7j+xP3w6dmUxKJwxJr2zWJHgUdsalSlAvKNPl41AAwExUxDZ46rHGY3bs8i3ZpBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b980b531dab8f612ae7920b4716d79ccbc42c29e9dd7e1d47d7a650315279a9","last_reissued_at":"2026-05-18T00:31:29.970799Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:29.970799Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.00629","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":"2026-05-18T00:31:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cyl64Q6Qh5YTzg+AKE/5Y123uFAcKoPWFnsWC+9PbSHcXDDmc6LOQVeGCWGW5GI6yE2/MYM53tUT2g109JH3BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T06:27:10.638032Z"},"content_sha256":"ab8fe37d7ffa0fb6519657d69f71ae2bdd80478dc3fc7c98ed7ec942a6db0f95","schema_version":"1.0","event_id":"sha256:ab8fe37d7ffa0fb6519657d69f71ae2bdd80478dc3fc7c98ed7ec942a6db0f95"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:TOMAWUY5VOHWCKXHSIFUOFWXTT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.NC"],"primary_cat":"stat.ML","authors_text":"Eric I-Chao Chang, He Gao, Weixuan Kou, Xin Zhang, Yan Xu, Yubo Fan","submitted_at":"2017-11-02T06:28:45Z","abstract_excerpt":"This paper proposes a practical approach for automatic sleep stage classification based on a multi-level feature learning framework and Recurrent Neural Network (RNN) classifier using heart rate and wrist actigraphy derived from a wearable device. The feature learning framework is designed to extract low- and mid-level features. Low-level features capture temporal and frequency domain properties and mid-level features learn compositions and structural information of signals. Since sleep staging is a sequential problem with long-term dependencies, we take advantage of RNNs with Bidirectional Lo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00629","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":"2026-05-18T00:31:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HpeMg4Ay0jgOwC+5OPbaFTPqH9AD36ydehBjYTzfaD2AOlNvCY+60DO4XN0dWXXw552TPjYogZU9BvqLF8j4Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T06:27:10.638392Z"},"content_sha256":"b28d7cce2536015ef11bbf95d7d2743b667940e89c79d1921a9357b58c8a617d","schema_version":"1.0","event_id":"sha256:b28d7cce2536015ef11bbf95d7d2743b667940e89c79d1921a9357b58c8a617d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TOMAWUY5VOHWCKXHSIFUOFWXTT/bundle.json","state_url":"https://pith.science/pith/TOMAWUY5VOHWCKXHSIFUOFWXTT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TOMAWUY5VOHWCKXHSIFUOFWXTT/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":"2026-05-30T06:27:10Z","links":{"resolver":"https://pith.science/pith/TOMAWUY5VOHWCKXHSIFUOFWXTT","bundle":"https://pith.science/pith/TOMAWUY5VOHWCKXHSIFUOFWXTT/bundle.json","state":"https://pith.science/pith/TOMAWUY5VOHWCKXHSIFUOFWXTT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TOMAWUY5VOHWCKXHSIFUOFWXTT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:TOMAWUY5VOHWCKXHSIFUOFWXTT","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":"eeee92943bdd7d831177c3a894d2656e5d0b56d2edff111efb4370111538b073","cross_cats_sorted":["cs.LG","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-02T06:28:45Z","title_canon_sha256":"4f7b403c5ff6ba0c2670ff75f764bff8470e987447727817267e2123f7098d9a"},"schema_version":"1.0","source":{"id":"1711.00629","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.00629","created_at":"2026-05-18T00:31:29Z"},{"alias_kind":"arxiv_version","alias_value":"1711.00629v1","created_at":"2026-05-18T00:31:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00629","created_at":"2026-05-18T00:31:29Z"},{"alias_kind":"pith_short_12","alias_value":"TOMAWUY5VOHW","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TOMAWUY5VOHWCKXH","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TOMAWUY5","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:b28d7cce2536015ef11bbf95d7d2743b667940e89c79d1921a9357b58c8a617d","target":"graph","created_at":"2026-05-18T00:31:29Z","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":"This paper proposes a practical approach for automatic sleep stage classification based on a multi-level feature learning framework and Recurrent Neural Network (RNN) classifier using heart rate and wrist actigraphy derived from a wearable device. The feature learning framework is designed to extract low- and mid-level features. Low-level features capture temporal and frequency domain properties and mid-level features learn compositions and structural information of signals. Since sleep staging is a sequential problem with long-term dependencies, we take advantage of RNNs with Bidirectional Lo","authors_text":"Eric I-Chao Chang, He Gao, Weixuan Kou, Xin Zhang, Yan Xu, Yubo Fan","cross_cats":["cs.LG","q-bio.NC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-02T06:28:45Z","title":"Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00629","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:ab8fe37d7ffa0fb6519657d69f71ae2bdd80478dc3fc7c98ed7ec942a6db0f95","target":"record","created_at":"2026-05-18T00:31:29Z","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":"eeee92943bdd7d831177c3a894d2656e5d0b56d2edff111efb4370111538b073","cross_cats_sorted":["cs.LG","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-02T06:28:45Z","title_canon_sha256":"4f7b403c5ff6ba0c2670ff75f764bff8470e987447727817267e2123f7098d9a"},"schema_version":"1.0","source":{"id":"1711.00629","kind":"arxiv","version":1}},"canonical_sha256":"9b980b531dab8f612ae7920b4716d79ccbc42c29e9dd7e1d47d7a650315279a9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b980b531dab8f612ae7920b4716d79ccbc42c29e9dd7e1d47d7a650315279a9","first_computed_at":"2026-05-18T00:31:29.970799Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:29.970799Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CvJnz0/rIAukeZzhlF9EBg7j+xP3w6dmUxKJwxJr2zWJHgUdsalSlAvKNPl41AAwExUxDZ46rHGY3bs8i3ZpBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:29.971324Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.00629","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ab8fe37d7ffa0fb6519657d69f71ae2bdd80478dc3fc7c98ed7ec942a6db0f95","sha256:b28d7cce2536015ef11bbf95d7d2743b667940e89c79d1921a9357b58c8a617d"],"state_sha256":"a7ceb343ab8d99f1b29fb0cfe507e7e56ed845d526f18a7c2e00f6a471e8abdc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l5xzaIh8Sr9of73Fx7/IynXbYSYux0WJ/VtFUvievVm6otn1BuVnClicua4K01aaN28Jj0DRj89eywbPvbhXCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T06:27:10.640375Z","bundle_sha256":"7fee88cb36bc1f3f960d99672e735228918c94d5081d31db61f2125a7104cbdb"}}