{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:VLHAZUHI76SPDVPY35REMYNK42","short_pith_number":"pith:VLHAZUHI","canonical_record":{"source":{"id":"2401.12733","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2024-01-23T13:11:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dda906231ecaaa9af819bd5cb4472e7e141cca069dddf39f72e28b9581baeb83","abstract_canon_sha256":"b88dca3645448d67221f4239b6cc49b93a47ca161298df0d1ae3180b0d4caed0"},"schema_version":"1.0"},"canonical_sha256":"aace0cd0e8ffa4f1d5f8df624661aae6942a3cba542dccc9f7d0276806ad2810","source":{"kind":"arxiv","id":"2401.12733","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.12733","created_at":"2026-07-05T07:36:35Z"},{"alias_kind":"arxiv_version","alias_value":"2401.12733v1","created_at":"2026-07-05T07:36:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.12733","created_at":"2026-07-05T07:36:35Z"},{"alias_kind":"pith_short_12","alias_value":"VLHAZUHI76SP","created_at":"2026-07-05T07:36:35Z"},{"alias_kind":"pith_short_16","alias_value":"VLHAZUHI76SPDVPY","created_at":"2026-07-05T07:36:35Z"},{"alias_kind":"pith_short_8","alias_value":"VLHAZUHI","created_at":"2026-07-05T07:36:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:VLHAZUHI76SPDVPY35REMYNK42","target":"record","payload":{"canonical_record":{"source":{"id":"2401.12733","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2024-01-23T13:11:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dda906231ecaaa9af819bd5cb4472e7e141cca069dddf39f72e28b9581baeb83","abstract_canon_sha256":"b88dca3645448d67221f4239b6cc49b93a47ca161298df0d1ae3180b0d4caed0"},"schema_version":"1.0"},"canonical_sha256":"aace0cd0e8ffa4f1d5f8df624661aae6942a3cba542dccc9f7d0276806ad2810","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:36:35.438234Z","signature_b64":"y3UIKcaF1u5fgyrBK6FlRPnV380H+wFM7vCgb4hGcUQr0WPtxYwKWSpvHCiaXtSfpyacEG75FahrCtrIJkBWDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aace0cd0e8ffa4f1d5f8df624661aae6942a3cba542dccc9f7d0276806ad2810","last_reissued_at":"2026-07-05T07:36:35.437821Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:36:35.437821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.12733","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":"2026-07-05T07:36:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ONVMu5onVj5cut+8bekJauoSw3R6TukBiCZD3t4wZ6++nMHNXmGyzd4rwCEdNBa13J6RZFHCjIO9mxBX+v1zCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T08:44:47.650461Z"},"content_sha256":"84b0e77b6cfc3ed7e6240ae48087410b144403588aef545b2b8cae0128bf1723","schema_version":"1.0","event_id":"sha256:84b0e77b6cfc3ed7e6240ae48087410b144403588aef545b2b8cae0128bf1723"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:VLHAZUHI76SPDVPY35REMYNK42","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TNANet: A Temporal-Noise-Aware Neural Network for Suicidal Ideation Prediction with Noisy Physiological Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CY","authors_text":"Fang Liu, Guozhen Zhao, Niqi Liu, Wenqi Ji, Wenting Mu, Xinxin Du, Xu Liu, Yong-jin Liu","submitted_at":"2024-01-23T13:11:05Z","abstract_excerpt":"The robust generalization of deep learning models in the presence of inherent noise remains a significant challenge, especially when labels are subjective and noise is indiscernible in natural settings. This problem is particularly pronounced in many practical applications. In this paper, we address a special and important scenario of monitoring suicidal ideation, where time-series data, such as photoplethysmography (PPG), is susceptible to such noise. Current methods predominantly focus on image and text data or address artificially introduced noise, neglecting the complexities of natural noi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.12733","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/2401.12733/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":"2026-07-05T07:36:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bfPhiFnpE2iqDw4dGbuYShZbbFXm1TI+9wy3CtRFK7uo6JGXrDrvEu7VsdS68ggmS9jVtd5/e7YFZ/X/rOy7Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T08:44:47.651189Z"},"content_sha256":"44ad5e826d9b74d59e86b2be6c842124e9ec3a2711a02d3190ef8ce3e96ee6c5","schema_version":"1.0","event_id":"sha256:44ad5e826d9b74d59e86b2be6c842124e9ec3a2711a02d3190ef8ce3e96ee6c5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VLHAZUHI76SPDVPY35REMYNK42/bundle.json","state_url":"https://pith.science/pith/VLHAZUHI76SPDVPY35REMYNK42/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VLHAZUHI76SPDVPY35REMYNK42/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":"2026-08-07T08:44:47Z","links":{"resolver":"https://pith.science/pith/VLHAZUHI76SPDVPY35REMYNK42","bundle":"https://pith.science/pith/VLHAZUHI76SPDVPY35REMYNK42/bundle.json","state":"https://pith.science/pith/VLHAZUHI76SPDVPY35REMYNK42/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VLHAZUHI76SPDVPY35REMYNK42/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:VLHAZUHI76SPDVPY35REMYNK42","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":"b88dca3645448d67221f4239b6cc49b93a47ca161298df0d1ae3180b0d4caed0","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2024-01-23T13:11:05Z","title_canon_sha256":"dda906231ecaaa9af819bd5cb4472e7e141cca069dddf39f72e28b9581baeb83"},"schema_version":"1.0","source":{"id":"2401.12733","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.12733","created_at":"2026-07-05T07:36:35Z"},{"alias_kind":"arxiv_version","alias_value":"2401.12733v1","created_at":"2026-07-05T07:36:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.12733","created_at":"2026-07-05T07:36:35Z"},{"alias_kind":"pith_short_12","alias_value":"VLHAZUHI76SP","created_at":"2026-07-05T07:36:35Z"},{"alias_kind":"pith_short_16","alias_value":"VLHAZUHI76SPDVPY","created_at":"2026-07-05T07:36:35Z"},{"alias_kind":"pith_short_8","alias_value":"VLHAZUHI","created_at":"2026-07-05T07:36:35Z"}],"graph_snapshots":[{"event_id":"sha256:44ad5e826d9b74d59e86b2be6c842124e9ec3a2711a02d3190ef8ce3e96ee6c5","target":"graph","created_at":"2026-07-05T07:36:35Z","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/2401.12733/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The robust generalization of deep learning models in the presence of inherent noise remains a significant challenge, especially when labels are subjective and noise is indiscernible in natural settings. This problem is particularly pronounced in many practical applications. In this paper, we address a special and important scenario of monitoring suicidal ideation, where time-series data, such as photoplethysmography (PPG), is susceptible to such noise. Current methods predominantly focus on image and text data or address artificially introduced noise, neglecting the complexities of natural noi","authors_text":"Fang Liu, Guozhen Zhao, Niqi Liu, Wenqi Ji, Wenting Mu, Xinxin Du, Xu Liu, Yong-jin Liu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2024-01-23T13:11:05Z","title":"TNANet: A Temporal-Noise-Aware Neural Network for Suicidal Ideation Prediction with Noisy Physiological Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.12733","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":"sha256:84b0e77b6cfc3ed7e6240ae48087410b144403588aef545b2b8cae0128bf1723","target":"record","created_at":"2026-07-05T07:36:35Z","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":"b88dca3645448d67221f4239b6cc49b93a47ca161298df0d1ae3180b0d4caed0","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2024-01-23T13:11:05Z","title_canon_sha256":"dda906231ecaaa9af819bd5cb4472e7e141cca069dddf39f72e28b9581baeb83"},"schema_version":"1.0","source":{"id":"2401.12733","kind":"arxiv","version":1}},"canonical_sha256":"aace0cd0e8ffa4f1d5f8df624661aae6942a3cba542dccc9f7d0276806ad2810","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aace0cd0e8ffa4f1d5f8df624661aae6942a3cba542dccc9f7d0276806ad2810","first_computed_at":"2026-07-05T07:36:35.437821Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:36:35.437821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"y3UIKcaF1u5fgyrBK6FlRPnV380H+wFM7vCgb4hGcUQr0WPtxYwKWSpvHCiaXtSfpyacEG75FahrCtrIJkBWDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:36:35.438234Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.12733","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:84b0e77b6cfc3ed7e6240ae48087410b144403588aef545b2b8cae0128bf1723","sha256:44ad5e826d9b74d59e86b2be6c842124e9ec3a2711a02d3190ef8ce3e96ee6c5"],"state_sha256":"800f6d4a79b048d9f9d1ec99e0c48b66f079f7f4adb5d11a53898fe6b73e232d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PmlhY0LkzfXHnR2Zd7XptR1HSpZtTbfJ0JFZX2zSiRPkr+xglbl7b/e31z+pYOLm+esweUNdx92MBDvbnDFmBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T08:44:47.656863Z","bundle_sha256":"53c48223d7615feb1dfb7929bfb0732a09a4e4a67ffa48203c05d6060e3b7ff8"}}