{"as_of":"2026-08-23T02:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2d69e7ca871a7029dc042d11e10f89a33a3ae0fe39d91f6cef3e6dfe535740aa","coverage":[{"denominator":111,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T21:57:28.582998Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.03961/citation-record","integrity":"/paper/2412.03961/integrity","json":"/paper/2412.03961/citation-record.json","paper":"/paper/2412.03961"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.313671Z","title":"Health information systems, electronic medical records, and big data in global healthcare: Progress and challenges in oecd countries,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.313671Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:6ee6ca7bb71a2e57a49efeb49e453a5ee9c952e77d6326f2ba7daab2c2e0d1d8","observation_id":"cb337876-39f5-4772-a857-45a900f673a6","resolution":{"observed_at":"2026-08-11T21:57:28.313671Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.317159Z","title":"Making sense of big data in health research: towards an EU action plan,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.317159Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:715e54462df3f27435c815a7608266665677a42d158cf9f86e77362edc0142fb","observation_id":"77f7a465-cbc7-4116-8a6a-4c42396fbcf2","resolution":{"observed_at":"2026-08-11T21:57:28.317159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.320130Z","title":"Creating value in health care through big data: opportunities and policy implications,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.320130Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:9bf5cf2ee9ed64114adca6a8fea000af42ae07468ec954b76f5871483c196003","observation_id":"23302fcd-bfbc-4c0b-8425-fbf0d72447f0","resolution":{"observed_at":"2026-08-11T21:57:28.320130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.322639Z","title":"Developing public policy to advance the use of big data in health care,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.322639Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:5318ba8fb1ccdb684018c44fd986b67183ad4003a3047be3749ead888ff8a2a1","observation_id":"6d8fbddd-ce56-4f76-b519-ae506642dbe2","resolution":{"observed_at":"2026-08-11T21:57:28.322639Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.325118Z","title":"Big data for health,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.325118Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:c1b660b3bd61dcca223b076578ec58ea6c54c473a519c9535a379669438022c5","observation_id":"d717bcf6-1e68-4e33-9e9f-f85cbddfe667","resolution":{"observed_at":"2026-08-11T21:57:28.325118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.327528Z","title":"Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.327528Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:44058d9c2af8d844d72adb128e92a1cea98bb2630382eb58d538728900091fde","observation_id":"19e2fede-a292-49b6-a2e2-31a748877f3d","resolution":{"observed_at":"2026-08-11T21:57:28.327528Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.330082Z","title":"Personalized care planning for diabetes: policy lessons from systematic reviews of consultation and self-management interventions,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.330082Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:e6d4dd01beef4849c33993dae59a9cc4bc8ba320ee7bc46e530b978667112089","observation_id":"70f364b4-6852-44f9-a2c4-65d03ca6ec0a","resolution":{"observed_at":"2026-08-11T21:57:28.330082Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.332326Z","title":"Data driven analytics for personalized healthcare,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.332326Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:e099ff5312e02959c79d9a670674cd4da6502d4777d43f6bfd14d4066f77976e","observation_id":"5b172f80-2b1a-454f-8726-483b22f48597","resolution":{"observed_at":"2026-08-11T21:57:28.332326Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.334640Z","title":"Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.334640Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:ad6a90a976f2f9dd152ba3d48f8b03c3e6f6aa248eed200e51a83d11eb4f2f48","observation_id":"c2002827-f45e-4cb8-918b-be95834b938a","resolution":{"observed_at":"2026-08-11T21:57:28.334640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.337130Z","title":"Enhancing healthcare quality in hospitals through electronic health records: A systematic review,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.337130Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:21bcb7710c977f95fd0dbe0fad4ed41be72b49f39ef85d44972d4f50708c56c7","observation_id":"ee9ae9a0-9b75-4066-bfab-9177af8fe590","resolution":{"observed_at":"2026-08-11T21:57:28.337130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.339411Z","title":"Challenges and opportunities of big data in health care: a systematic review,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.339411Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:937299424f2d924df9d7507bc5871b1807b2403767bb49cde3f1a8097e20b38a","observation_id":"be935b52-5367-4e25-ab86-09ae533045d4","resolution":{"observed_at":"2026-08-11T21:57:28.339411Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.341541Z","title":"A structured analysis to study the role of 12 machine learning and deep learning in the healthcare sector with big data analytics,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.341541Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:b722415dd3b9119cc9a8b3e131ef5bd668c336ca0cac4bba5307a1da73dcbf4f","observation_id":"c1e7adb4-1f4f-49f6-a9ac-942c5475921e","resolution":{"observed_at":"2026-08-11T21:57:28.341541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.10492","last_updated":"2024-05-21T01:18:22Z","snapshot_observed_at":"2026-08-16T13:52:00.281440Z","submitted_at":"2024-05-17T01:58:23Z","title":"Automatic News Generation and Fact-Checking System Based on Language Processing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.10492","snapshot_observed_at":"2026-08-11T21:57:28.344149Z","title":"Automatic News Generation and Fact-Checking System Based on Language Processing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.344149Z"},"links":{"cited_paper":"/paper/2405.10492","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:d06606f2c76b315b9e08c97d72f2b71e29daafaa6c49b920788553fe6160f10d","observation_id":"d6371e96-17b7-409f-9027-b16878b6fc60","resolution":{"observed_at":"2026-08-11T21:57:28.344149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.347507Z","title":"AI and big data in healthcare: towards a more comprehensive research framework for multimorbidity,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.347507Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:75f972f65787469297c33caea7e7825f00c75b1e5c305462e2e73daa4f047fa7","observation_id":"d46bbf44-79e0-4732-9c23-a5c8e4fd375d","resolution":{"observed_at":"2026-08-11T21:57:28.347507Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.349866Z","title":"Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.349866Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:5a3a09a0e21809e6fda7342fcde356d18116ecd78e34e9cf9ca70babdac4cafb","observation_id":"e192e956-32a5-402e-a7a4-e0f3cddf5559","resolution":{"observed_at":"2026-08-11T21:57:28.349866Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.352344Z","title":"Data processing and text mining technologies on electronic medical records: a review,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.352344Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:e89b32f3e80957400e9452ab78888f4434cc0e1358c0ee1976b62b929041f887","observation_id":"fabfc477-8bf5-44c6-9619-bba52632fbd2","resolution":{"observed_at":"2026-08-11T21:57:28.352344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.354756Z","title":"Artificial intelligence approaches using natural language processing to advance EHR-based clinical research,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.354756Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:dd634ae390271b0f6bac3409fa6194ed5ddd4878215ad9ead00e429f0b893d14","observation_id":"1bc8948a-d6f3-4319-9cbc-091b6639c9a0","resolution":{"observed_at":"2026-08-11T21:57:28.354756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.357233Z","title":"Simplified Fixed Frequency Phase Shift Modulation for A Novel Single-Stage Single Phase Series-Resonant AC-DC Converter,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.357233Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:d103e91e66a7476ec71d3d033e9cfb014653c785d8fbcfb229773b8a10579cbf","observation_id":"65049b19-c6f9-422c-bfb3-f4457aafa4eb","resolution":{"observed_at":"2026-08-11T21:57:28.357233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.360122Z","title":"Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.360122Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:e0782fb7b585d0d7d895d0fbab9f117b0ad3503cb11c2c5945569eec73dafd60","observation_id":"a9291aff-eab1-404c-a9f3-322a73f106d7","resolution":{"observed_at":"2026-08-11T21:57:28.360122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.362921Z","title":"Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.362921Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:3980121c1b2a68978bfca0d0480f47fa82f1d02d23cd8ae1a010aadb2cfb5535","observation_id":"7e283dba-69d8-493a-b3a3-349ad00e4318","resolution":{"observed_at":"2026-08-11T21:57:28.362921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.365433Z","title":"Utilizing home health care electronic health records for telehomecare patients with heart failure: a decision tree approach to detect associations with rehospitalizations,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.365433Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:4f3e720c4352e7527c414eb17247d74d3d25e621b898fe050ef0ebe207917e62","observation_id":"5c1e1f6f-aea6-4ed1-b20c-9908f00e0bc2","resolution":{"observed_at":"2026-08-11T21:57:28.365433Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.367939Z","title":"Combining structured and unstructured data for predictive models: a deep learning approach,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.367939Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:2130cc53c2a17c6d71bd65270d0ac90e9cd48511b607fb1a1018b66b0e88eb63","observation_id":"90e385bb-c7c5-409e-aeef-24c67acf63b4","resolution":{"observed_at":"2026-08-11T21:57:28.367939Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.371394Z","title":"Predicting cardiovascular health trajectories in time- series electronic health records with LSTM models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.371394Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:a51d7ab93cccf1e13b0105e7d777b5add1a03b15f4255bb4b2a72607f05ce8b3","observation_id":"5dbf340b-69cc-4027-97f3-e47e41aabeca","resolution":{"observed_at":"2026-08-11T21:57:28.371394Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.373940Z","title":"Occluded person re-identification with deep learning: A survey and perspectives,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.373940Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:566b08d5cb837185f4917526f5486d5071ca8d05e02a4c7c92c9f4b4371020eb","observation_id":"0bec181a-782a-43fa-9199-0501f54d8cd1","resolution":{"observed_at":"2026-08-11T21:57:28.373940Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.376557Z","title":"Implementation and use of disease diagnosis systems for electronic medical records based on machine learning: A complete review,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.376557Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:c899f2797c75700813f84717ae05f517dce1c69e36403a51630d794ada52fd8f","observation_id":"14f4e3b6-438e-46f6-bfdd-70d237bb868f","resolution":{"observed_at":"2026-08-11T21:57:28.376557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.379116Z","title":"Named entity recognition method of Chinese EMR based on BERT-BiLSTM-CRF,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.379116Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:baf1d1a0443779502fe331f8c9fa6e80590085710c05dea0d84080b9d2477066","observation_id":"3ade94ff-e3ba-4c1e-ae9c-47a0f0168217","resolution":{"observed_at":"2026-08-11T21:57:28.379116Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.381880Z","title":"Research of clinical named entity recognition based on Bi-LSTM- CRF,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.381880Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:d87f3167eae6496c9549398958acdad31eed6f705cb1698e2d5f5bd24907c6eb","observation_id":"4da3e5a8-9630-47be-8141-bd63dae39412","resolution":{"observed_at":"2026-08-11T21:57:28.381880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.384422Z","title":"Deformation depth decoupling network for point cloud domain adaptation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.384422Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:0c0c74a80fa2bf421507443f0dfba4182c9919e19498d486fa8a82a7c0143e24","observation_id":"a9928c8f-6059-4887-885a-eccf49631c9f","resolution":{"observed_at":"2026-08-11T21:57:28.384422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.386965Z","title":"Systematic evaluation of research progress on natural language processing in medicine over the past 20 years: bibliometric study on PubMed,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.386965Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:5d39e6870f5c43413437b1c3e31d0a6b2df10cae99888970c0feaf6e711a6500","observation_id":"dd0d5364-8ec2-4a86-a6c2-cfb28d10570b","resolution":{"observed_at":"2026-08-11T21:57:28.386965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.389122Z","title":"Research and application progress of Chinese medical knowledge graph,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.389122Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:8a4379d6b0d558511c49079141582dd17dbbfc74fc7de8409159e1a12a8c6335","observation_id":"f8203e09-fbeb-4263-be35-755d366cf896","resolution":{"observed_at":"2026-08-11T21:57:28.389122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.391250Z","title":"A data-driven fault detection approach for Modular Reconfigurable Flying Array based on the Improved Deep Forest,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.391250Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:30b40507c0024f1e0dfde83a013fb4e8a1481fb01d15c2e8eb3be9d926afa7d8","observation_id":"9b3919f6-f843-4166-851a-50c7f253f9d8","resolution":{"observed_at":"2026-08-11T21:57:28.391250Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.393249Z","title":"PFVAE: a planar flow-based 13 variational auto-encoder prediction model for time series data,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.393249Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:e743915b61b3f9ef38d87bff83579be45447efa365ca75453e270ff5a728acc9","observation_id":"ebfa5b1c-42ed-476d-a533-a08ffffa6d36","resolution":{"observed_at":"2026-08-11T21:57:28.393249Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.467614Z","title":null,"venue":null,"work_id":"6a96d6b7-826b-4329-a8f5-8b67f1892906","year":2000},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.395297Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:85dfb020991287624a1ee98d74795a602fb574506b20f177f3d8e9348f893b70","observation_id":"9ae90e40-4f09-4052-bbc0-174cbf757757","resolution":{"observed_at":"2026-08-11T21:57:29.470182Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.460180Z","title":"Inducing decision trees with an ant colony optimization algorithm,","venue":null,"work_id":"d7ea40d5-4783-4a97-819f-56ab18d2b712","year":2012},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.397489Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:38c16a779d4ae6ddcdb4b010ad41192853b1b5331b85a0aa626a243f51a60262","observation_id":"0ed5c959-8682-4f62-a341-45fcb6016af9","resolution":{"observed_at":"2026-08-11T21:57:29.462948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.453145Z","title":"Decision tree (DT): a valuable tool for water resources engineering,","venue":null,"work_id":"227ab6c8-3943-4da7-9011-ae1ad8ec0dee","year":2022},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.400058Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:1206bf290942fc5d465609ea368d8b7b2f31e7bcee525a7b33ec301ec04ccaad","observation_id":"88e73d2c-881a-4fb0-9e76-78a9e2609c26","resolution":{"observed_at":"2026-08-11T21:57:29.455553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.446721Z","title":"Optimization of automated garbage recognition model based on resnet- 50 and weakly supervised cnn for sustainable urban development,","venue":null,"work_id":"5b84d202-6785-4b1c-aa9e-c782d51c1a3d","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.402116Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:9fa27df19741b28d847c06448bd6a9c0b7a96576347b4afbf9784bc7ef6bb30b","observation_id":"b1e5caaa-760c-4e50-b81d-69addfc8db63","resolution":{"observed_at":"2026-08-11T21:57:29.449154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.440456Z","title":"Unsupervised K- means clustering algorithm,","venue":null,"work_id":"30c20a37-4aeb-4362-ae50-209818c23900","year":2020},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.404294Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:b89ec1e9c24a19f38ceec623244c6174d2c1ebf741a7f4293f99ddccf62a1fa6","observation_id":"7e00c9c7-6d95-4c97-a7fc-02235a7fce75","resolution":{"observed_at":"2026-08-11T21:57:29.442880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.406374Z","title":"HCFNN: High-order coverage function neural network for image classification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.406374Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:455411f056a85ea292116b5b7459acf7ea17306dbd3dde51b1238008fba4d9f7","observation_id":"b932b050-541c-4752-84b6-a939c8b7967f","resolution":{"observed_at":"2026-08-11T21:57:28.406374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.433885Z","title":"Bayesianism—Its scope and limits,","venue":null,"work_id":"f7301a89-9100-4e5a-88a1-e79c8050f263","year":2002},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.408793Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:6d07dfa62dc28dc87de580d608997ea88b1e34f9a7447b9a6db9d4c69090520e","observation_id":"5f5493d9-48f3-4190-a481-eb645988d8b2","resolution":{"observed_at":"2026-08-11T21:57:29.436591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.426633Z","title":"Support vector machines based non- contact fault diagnosis system for bearings,","venue":null,"work_id":"511d0ff3-51b2-445a-b51b-2a3b3419a1e6","year":2020},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.411364Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:d586b82deb7f9820ac809562c9bc3589c5d6b339cae7bb54ef4364acf1291442","observation_id":"03112b87-9f62-4f9a-bfb6-222c629c0fea","resolution":{"observed_at":"2026-08-11T21:57:29.429574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.419118Z","title":"Lightgbm: A highly efficient gradient boosting decision tree,","venue":null,"work_id":"28feeae1-4ae4-4d75-972e-ba9697bb48e0","year":2017},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.413815Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:5fee70276cd1bc88ea9c19323d9b7870e5a68d7d293e18836342b70182f6fe10","observation_id":"9690f882-bf12-432b-8c17-d38d3960ff35","resolution":{"observed_at":"2026-08-11T21:57:29.422104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.411287Z","title":"CLU-CNNs: Object detection for medical images,","venue":null,"work_id":"af6dc398-e6d5-49ef-9484-ec07df1c35a1","year":2019},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.416407Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:f430ce3569b9092b31193268e337f9c61d09e716be353a60e3f22db438242ea4","observation_id":"be6bd7cd-7546-41a2-b8c8-19e140de33ec","resolution":{"observed_at":"2026-08-11T21:57:29.414176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.404004Z","title":"3D deep learning on medical images: a review,","venue":null,"work_id":"89c554c1-3523-415a-b26a-a6c6e9943e0a","year":2020},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.419014Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:fd5e7b98207addfdf3abcfced242af84692fe77bbbb2b8cd372f6ec85551228f","observation_id":"3b3fb116-402f-49c1-9228-255960e048e1","resolution":{"observed_at":"2026-08-11T21:57:29.406785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.396554Z","title":"Predicting patients' satisfaction with doctors in online medical communities: An approach based on XGBoost algorithm,","venue":null,"work_id":"b98eebb2-1b6f-45e9-b8b0-c7e047004ce9","year":2022},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.421731Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:820f090baaac0e1ae19b06f2023d51d1550416289d653bb4124dd44345e61e72","observation_id":"8f631473-1601-47d6-963d-9e7722b97e26","resolution":{"observed_at":"2026-08-11T21:57:29.399474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.388431Z","title":"An optimization-based diabetes prediction model using CNN and Bi- directional LSTM in real-time environment,","venue":null,"work_id":"6c2593c0-d439-4e1a-8eae-bab8eb19a126","year":2022},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.424622Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:096f45c23be0c183820dbd2bd8c7b46a518d1c3b76fc5d684fb679ef31be9ce1","observation_id":"a6a17e34-8efb-472d-b7ca-f4c6b6af290d","resolution":{"observed_at":"2026-08-11T21:57:29.391720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.380449Z","title":"3D-CNN-SPP: A patient risk prediction system from electronic health records via 3D CNN and spatial pyramid pooling,","venue":null,"work_id":"48f19c68-f684-4f75-b943-3ae2301bd01b","year":2020},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.427193Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:46a3f5bdf5b8c75b05617aee6a1d1c9d5f95d2958b4ed2a02cc62c9212662b04","observation_id":"14f1b455-fcdb-4320-bd1a-301a6034c901","resolution":{"observed_at":"2026-08-11T21:57:29.383442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.372503Z","title":"Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction,","venue":null,"work_id":"6314d0a5-56b4-41fc-b7b4-ccc96158f2d3","year":2021},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.429878Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:9416fffebcbf880bc0fe33750133077c54538f3ea4cfc9b16619a829d8473010","observation_id":"b8882f7c-c914-46ab-b8c1-6cef31a687e9","resolution":{"observed_at":"2026-08-11T21:57:29.375599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.364696Z","title":"Fleet rebalancing for expanding shared e-Mobility systems: A multi- agent deep reinforcement learning approach","venue":null,"work_id":"9c48301d-af9f-4dbf-8547-08709e2b71ed","year":2023},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.432660Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:d3a1418df0da5129945555319a2a0ba4ee66652d3a9cfca5194606916882ab98","observation_id":"750c9527-5de7-4a06-98a1-db4fc3d225db","resolution":{"observed_at":"2026-08-11T21:57:29.367840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.357257Z","title":"Application of Deep Learning Models Based on EfficientDet and OpenPose in User-Oriented Motion Rehabilitation Robot Control","venue":null,"work_id":"aa3076ad-08f9-46f1-9083-0f9a694fa50d","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.435342Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:9bb722260f0ad85df564eed181fa6526912368b237aa373c3a951bd54c9909ec","observation_id":"6f6143d2-cc3f-4bf6-84ba-2ce1d699193b","resolution":{"observed_at":"2026-08-11T21:57:29.359854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.349977Z","title":"Classification of DNA Sequences: Performance Evaluation of Multiple Machine Learning Methods","venue":null,"work_id":"1e0daa50-f276-487f-9b87-e61079498735","year":2022},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.437983Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:e7e6ddad08d14c8d43a44a2fe51f0b9c436526f8e85a21f8678ce3df904e6bd1","observation_id":"9da0587b-9f19-4365-a403-95012e7cff18","resolution":{"observed_at":"2026-08-11T21:57:29.352598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.440693Z","title":"Real-time monitoring of lower limb movement resistance based on deep learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.440693Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:a3f06edae6afbc25eeb47ca9751f1e5452e987937e0382d41133167f22a3c99d","observation_id":"d9157a7a-c588-4850-86c1-d8502d0d55be","resolution":{"observed_at":"2026-08-11T21:57:28.440693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05633","last_updated":"2024-07-08T05:58:49Z","snapshot_observed_at":"2026-08-16T13:36:18.498156Z","submitted_at":"2024-07-08T05:58:49Z","title":"AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing","version":1},"cited_work":{"arxiv_id":"2407.05633","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.05633","snapshot_observed_at":"2026-08-11T21:57:28.993661Z","title":"AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing","venue":"cs.LG","work_id":"e7d6d69a-7cec-4878-8c07-0dd5cf96638c","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.443287Z"},"links":{"cited_paper":"/paper/2407.05633","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:06a684be66b42c40114039df313c836e5c0aad57374f03af3ac11b193e5f86e7","observation_id":"46123842-5f85-462f-9a51-0c580c992098","resolution":{"observed_at":"2026-08-11T21:57:28.997034Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.339409Z","title":"Big data and machine learning in defence","venue":null,"work_id":"3290e622-25d1-4569-ba67-743f1208c524","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.446347Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:658c764d7a29c60af67fbff975c0cd85b5b1b68eda20d0fa2cf46a20c2c70252","observation_id":"f379b201-6ae0-437a-8289-c1da1cdf65d6","resolution":{"observed_at":"2026-08-11T21:57:29.341858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.332151Z","title":"Construction and optimization of health behavior prediction 14 model for the elderly in smart elderly care","venue":null,"work_id":"cbebe865-baf8-43cf-a22a-a407dc6bbee2","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.448997Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:adf6d3064cf0da2002676c08034efd101ec3e5e929699bf580e48243ac3e3dd3","observation_id":"7c848c80-cd08-4fb3-8b11-125cab6b08cf","resolution":{"observed_at":"2026-08-11T21:57:29.335034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.324753Z","title":"Computational study of the role of counterions and solvent dielectric in determining the conductance of B-DNA","venue":null,"work_id":"63d99027-cc39-4b4c-a63d-3e0e21f29d14","year":2023},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.452113Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:6cea84acc566991cca9feb97e84855542ea4d4c6e0268741cb9317df88ff9607","observation_id":"dc00feb5-2388-4b17-868c-522c2ec783bf","resolution":{"observed_at":"2026-08-11T21:57:29.327633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.316806Z","title":"Regional BDS satellite clock estimation with triple-frequency ambiguity resolution based on undifferenced observation","venue":null,"work_id":"4a48e871-8478-4e14-a502-a5e2f0affa54","year":2019},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.455303Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:5c21c01a82c679ab2bed891129148d26c2507cabe7dd4ae77d779325670709db","observation_id":"73774849-a0f6-4cec-a3b9-0fcdda0d2e49","resolution":{"observed_at":"2026-08-11T21:57:29.320057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.308814Z","title":"Optimization of automated garbage recognition model based on ResNet-50 and weakly supervised CNN for sustainable urban development","venue":null,"work_id":"3df3b39b-7df7-4d9d-b320-3e8efb9ea407","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.458199Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:63737c97919885964f8b22db2cc9796b7646e21ca0e0ab8eb1ff6d013fba2b48","observation_id":"b0a57e48-b693-4688-a64d-040f096ec002","resolution":{"observed_at":"2026-08-11T21:57:29.311834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.15460","last_updated":"2024-05-24T11:34:45Z","snapshot_observed_at":"2026-08-21T07:58:09.580989Z","submitted_at":"2024-05-24T11:34:45Z","title":"TD3 Based Collision Free Motion Planning for Robot Navigation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.15460","snapshot_observed_at":"2026-08-11T21:57:28.461136Z","title":"TD3 Based Collision Free Motion Planning for Robot Navigation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.461136Z"},"links":{"cited_paper":"/paper/2405.15460","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:184b4c1313f584c89c2b482cb5945f7be38af0d01c565a950c9afea672465cb1","observation_id":"0065b545-d77a-42f4-b1f4-41132cdbbf38","resolution":{"observed_at":"2026-08-11T21:57:28.461136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.300991Z","title":"Reinforcement Learning with Communication Latency with Application to Stop-and-Go Wave Dissipation","venue":null,"work_id":"a2002594-3828-4e5c-a238-60bf8c6a8e92","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.464665Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:109e3b118c87489dea7d40a66d8b0004b6daf5b1003e94fe8fe3f04fc9af2195","observation_id":"772ed9b5-e08e-43d9-9963-e5b3bcbd6f7e","resolution":{"observed_at":"2026-08-11T21:57:29.303915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.467593Z","title":"Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.467593Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:c72a5b7c06f69181ec1b62679c239bc478f6f06656a36c02052dc2dca267ffea","observation_id":"598d3f0a-7fd8-452e-87ec-4c3341d5ac84","resolution":{"observed_at":"2026-08-11T21:57:28.467593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12115","last_updated":"2024-08-22T03:59:52Z","snapshot_observed_at":"2026-08-16T13:24:43.515102Z","submitted_at":"2024-08-22T03:59:52Z","title":"Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.12115","snapshot_observed_at":"2026-08-11T21:57:28.470424Z","title":"Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.470424Z"},"links":{"cited_paper":"/paper/2408.12115","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:8daeb47a0b9e7309366ea25df2e1c400550b96664b529103e442e27f1174cd57","observation_id":"ac4f2b3c-76e5-4684-a6f3-62ca208b84ce","resolution":{"observed_at":"2026-08-11T21:57:28.470424Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.474023Z","title":"Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.474023Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:a0a43221dd754dc8c3a2d1a24db79bd5184e5629d9bb87c2fe7cf02e05a9ef64","observation_id":"8417e002-9f06-4a81-9ca5-9663e1d7520a","resolution":{"observed_at":"2026-08-11T21:57:28.474023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.293221Z","title":"Modeling and Simulation of DNA Origami based Electronic Read-only Memory","venue":null,"work_id":"23111650-d43e-4380-aebc-a7b940326f80","year":2022},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.477598Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:ed25fe88b9fbd40d75b824c2089f9f12b5dbee71540df342f63186d02a0f94d8","observation_id":"5368afe9-9a91-4e24-b8f2-824be19f3067","resolution":{"observed_at":"2026-08-11T21:57:29.296119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.06720","last_updated":"2024-11-11T05:12:15Z","snapshot_observed_at":"2026-08-19T10:59:17.834374Z","submitted_at":"2024-11-11T05:12:15Z","title":"Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm","version":1},"cited_work":{"arxiv_id":"2411.06720","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.06720","snapshot_observed_at":"2026-08-11T21:57:28.757693Z","title":"Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm","venue":"cs.LG","work_id":"f36df1e4-5bf0-4df5-a2a5-69552448deb6","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.480491Z"},"links":{"cited_paper":"/paper/2411.06720","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:018b80af8552ae809b3634a52573ebe6293e6514713ebb90a0fd73f8e3128662","observation_id":"fdebe6f3-aabc-40ad-8aa7-40fb824dcf0f","resolution":{"observed_at":"2026-08-11T21:57:28.760980Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.285185Z","title":"Performance analysis of DNA crossbar arrays for high- density memory storage applications","venue":null,"work_id":"d9614c64-d818-428b-aabe-813eb0eb0d67","year":2023},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.483452Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:4758a9e94cd9b651edd36f459bba70c1b228f402a8211b4691ace70b63ecc83c","observation_id":"4b91e9f3-f039-45a7-ac5f-c87b456dca9f","resolution":{"observed_at":"2026-08-11T21:57:29.288318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.15286","last_updated":"2024-10-20T04:53:50Z","snapshot_observed_at":"2026-08-16T20:12:25.210724Z","submitted_at":"2024-10-20T04:53:50Z","title":"LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.15286","snapshot_observed_at":"2026-08-11T21:57:28.486219Z","title":"LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.486219Z"},"links":{"cited_paper":"/paper/2410.15286","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:db92349df4692af672cc330441fa190525644c9bb5335dbafc973525e158e8c4","observation_id":"72bfb01a-ce52-4baa-84b5-aafc185a6911","resolution":{"observed_at":"2026-08-11T21:57:28.486219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.277403Z","title":"Leveraging artificial intelligence to enhance data security and combat cyber attacks","venue":null,"work_id":"18d87b77-5809-45fa-a579-058157a193a4","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.489359Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:7130de5db095e8677a890d37a00ea963587a873a2098f53800b1e26b9a49f142","observation_id":"67347bdd-a216-4613-af13-f714d86224d2","resolution":{"observed_at":"2026-08-11T21:57:29.280295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.08395","last_updated":"2024-09-16T04:42:10Z","snapshot_observed_at":"2026-08-16T13:19:00.144223Z","submitted_at":"2024-09-12T20:48:28Z","title":"Graphical Structural Learning of rs-fMRI data in Heavy Smokers","version":2},"cited_work":{"arxiv_id":"2409.08395","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.08395","snapshot_observed_at":"2026-08-11T21:57:28.740172Z","title":"Graphical Structural Learning of rs-fMRI data in Heavy Smokers","venue":"q-bio.QM","work_id":"e517dd91-738e-4bb0-8a53-03f8bf841e73","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.492109Z"},"links":{"cited_paper":"/paper/2409.08395","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:4cbd6b1550d1b24d75db03005093a3fc1373f4211a7308d4bcebf4e78d838b1c","observation_id":"a9325888-a8dc-448b-83ba-8031366039b6","resolution":{"observed_at":"2026-08-11T21:57:28.743514Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.494521Z","title":"EITNet: An IoT- enhanced framework for real-time basketball action recognition","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.494521Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:8ef043c9483cb4887ad8eb8aaed30a859d057c21aca4b7d5565d3a0d2cdd1070","observation_id":"265c7f9c-6f2e-4839-b151-6a21926623c5","resolution":{"observed_at":"2026-08-11T21:57:28.494521Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.265750Z","title":"Using Automated Vehicle Data as a Fitness Tracker for Sustainability","venue":null,"work_id":"fdc2c2d0-a327-430f-94c1-68b584161327","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.496686Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:78c163e1ec46c8cb5e8b37d9fa4f0530af281281da786164d6a216d4fdf5edaa","observation_id":"957214eb-4306-4c88-a4be-435603b9a6ae","resolution":{"observed_at":"2026-08-11T21:57:29.268617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.13059","last_updated":"2024-09-19T19:22:52Z","snapshot_observed_at":"2026-08-16T13:17:04.127772Z","submitted_at":"2024-09-19T19:22:52Z","title":"Comprehensive Overview of Artificial Intelligence Applications in Modern Industries","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.13059","snapshot_observed_at":"2026-08-11T21:57:28.499210Z","title":"Comprehensive Overview of Artificial Intelligence Applications in Modern Industries","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.499210Z"},"links":{"cited_paper":"/paper/2409.13059","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:ff8b5e8db6a9c039a9ace2a82bb96aa63fca4cfd4ec3b1a6eaa567bb27e62a83","observation_id":"b9559535-8955-472f-9e24-fc495f53bbdd","resolution":{"observed_at":"2026-08-11T21:57:28.499210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.502364Z","title":"Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.502364Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:465dbed5ca9611489d6e98bd2f12dac005825d1c9cb1a6f9d7d901db87749e64","observation_id":"fab50495-2fe7-4c38-97e9-3a36cb78ff3c","resolution":{"observed_at":"2026-08-11T21:57:28.502364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.505062Z","title":"DSEM-NeRF: Multimodal feature fusion and global-local attention for enhanced 3D scene reconstruction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.505062Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:4867f2a6eb30eb613fadb1f8960a4c24afa5a8c35ba5e74e1c1255f96ede52c9","observation_id":"1f8f929c-c7ad-475e-b002-33a54181979e","resolution":{"observed_at":"2026-08-11T21:57:28.505062Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.507901Z","title":"Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.507901Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:c3f4cb422df2f68bbfa509ebc3551b7e29a3a255e4a2c2736ce7887eedcd4e21","observation_id":"49777985-6be6-406d-a324-28ec1406e238","resolution":{"observed_at":"2026-08-11T21:57:28.507901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16068","last_updated":"2024-08-30T03:38:06Z","snapshot_observed_at":"2026-08-16T13:23:06.531175Z","submitted_at":"2024-08-28T18:08:11Z","title":"Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16068","snapshot_observed_at":"2026-08-11T21:57:28.510586Z","title":"Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.510586Z"},"links":{"cited_paper":"/paper/2408.16068","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:73d32c5ae27d9298af7961a4098c2922327353939d5a9fea3666f8a4dd07d6eb","observation_id":"1f846376-69b8-4145-8b67-a66e4965832d","resolution":{"observed_at":"2026-08-11T21:57:28.510586Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.245745Z","title":"Runtime monitoring of accidents in driving recordings with multi-type logic in empirical models","venue":null,"work_id":"b90e5f84-e894-4181-bcc2-dd4da899baf2","year":2023},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.513525Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:c6a7ca572cf949ef90d3459886927d01a6658dc350be70347bfe329ff9a4f750","observation_id":"d70c37d8-1a7f-4f44-818d-6a6663bf15a5","resolution":{"observed_at":"2026-08-11T21:57:29.248841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.14972","last_updated":"2024-09-23T12:42:35Z","snapshot_observed_at":"2026-08-16T13:16:16.835534Z","submitted_at":"2024-09-23T12:42:35Z","title":"Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments","version":1},"cited_work":{"arxiv_id":"2409.14972","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.14972","snapshot_observed_at":"2026-08-11T21:57:28.717315Z","title":"Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments","venue":"cs.RO","work_id":"0af55eeb-ada8-4ef0-ae15-5d2c91907dd9","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.516199Z"},"links":{"cited_paper":"/paper/2409.14972","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:823f328bfd7bbfd0344346e757949a53ebbec26a2670b84b5e3f9ff07a404f48","observation_id":"5a37cf09-b262-43c5-862d-7b38f65898c2","resolution":{"observed_at":"2026-08-11T21:57:28.720236Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.519068Z","title":"Recording brain activity while listening to music using wearable EEG devices combined with Bidirectional Long Short-Term Memory Networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.519068Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:891d402a5158f25fe7b13f64deddef85a60d63f66010c3ccc4444df116c0862f","observation_id":"b92a4c0e-c965-42cb-a0a3-29b1810ce432","resolution":{"observed_at":"2026-08-11T21:57:28.519068Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.234395Z","title":"Traffic smoothing via connected & automated vehicles: A modular, hierarchical control design deployed in a 100- cav flow smoothing experiment","venue":null,"work_id":"49f1aa6d-c1b6-46fb-b2ac-c8141c27545d","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.521828Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:f0f91efa319dab6607dce05da6f52a01d3a6f634a180f4ab6dfafcbb10ad0ed4","observation_id":"a56581a2-8264-4381-9deb-6f5a7c7183a1","resolution":{"observed_at":"2026-08-11T21:57:29.237086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.227026Z","title":"A machine learning approach for accurate and real-time DNA sequence identification","venue":null,"work_id":"987ebdb1-f5a3-4533-b091-3af098bb26b5","year":2021},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.524528Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:b9e02e81e404844b290e44e9f07268bc5d52fc36942ea97c3859bf54fb7f4856","observation_id":"a0c66619-d20e-47f9-8a8d-b68fda52a203","resolution":{"observed_at":"2026-08-11T21:57:29.229858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.219741Z","title":"Performance evaluation of QUIC with BBR in satellite internet","venue":null,"work_id":"fd6576c5-9c1b-479f-be1a-691712091f91","year":2018},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.527133Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:8167e78f6f72f268721b53518cae75b8317e9e46914762fa656021f0c743d869","observation_id":"b7dc7a17-8e0c-4430-94a0-bea6bc932f17","resolution":{"observed_at":"2026-08-11T21:57:29.222253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.211611Z","title":"Music genre classification with transformer classifier","venue":null,"work_id":"1d955658-d183-44ae-8a88-8c96f2b7c929","year":2020},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.529757Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:6a58d6fd3864d1e5f53ee52fb09a4235cbcef2b6ee507ddbef53616da355d722","observation_id":"7334af8c-9a2a-4819-8ade-048a3a68a794","resolution":{"observed_at":"2026-08-11T21:57:29.214854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.532436Z","title":"Research on empirical correction models of GPS Block IIF and BDS satellite inter-frequency clock bias","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.532436Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:e098b145cdafb241b346f536797bcd9236af4f1d63cb5164d718f68eee97a057","observation_id":"5adb0fa4-e1f5-4e39-a473-211c90136135","resolution":{"observed_at":"2026-08-11T21:57:28.532436Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.13987","last_updated":"2024-09-14T06:09:54Z","snapshot_observed_at":"2026-08-16T13:41:15.121701Z","submitted_at":"2024-06-20T04:26:45Z","title":"Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.13987","snapshot_observed_at":"2026-08-11T21:57:28.535546Z","title":"Image anomaly detection and prediction scheme based on SSA optimized ResNet50- BiGRU model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.535546Z"},"links":{"cited_paper":"/paper/2406.13987","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:6864a6c3565577a133d4d0467f13d824a264174099945807d21129484522c2e9","observation_id":"7e227fe6-9b0a-4f22-983a-ee61fed202f3","resolution":{"observed_at":"2026-08-11T21:57:28.535546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.538804Z","title":"Enhancing human pose estimation in sports training: Integrating spatiotemporal transformer for improved accuracy and real-time performance","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.538804Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:2f429556c5316c072dd3f70fe1bb6f0a6744290535bae329ae0be60da88d478d","observation_id":"37f6d791-12fa-4586-943d-07e59e580e64","resolution":{"observed_at":"2026-08-11T21:57:28.538804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.13113","last_updated":"2024-06-19T00:01:01Z","snapshot_observed_at":"2026-08-17T03:57:28.979016Z","submitted_at":"2024-06-19T00:01:01Z","title":"CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset","version":1},"cited_work":{"arxiv_id":"2406.13113","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.13113","snapshot_observed_at":"2026-08-11T21:57:28.700525Z","title":"CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset","venue":"cs.CV","work_id":"78f054f6-0e72-422c-abcb-242c7d248c83","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.541834Z"},"links":{"cited_paper":"/paper/2406.13113","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:f86e0bb6867708872097f7cc24a8e3f5d36c9e197517c6f848a9c2a4fad21be6","observation_id":"6dedb7ac-d90e-42ed-a277-adcd2c5628ac","resolution":{"observed_at":"2026-08-11T21:57:28.703419Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.195743Z","title":"Accel-gcn: High-performance gpu accelerator design for graph convolution networks","venue":null,"work_id":"92ddec02-c010-4ae0-8120-50ad41f88e2d","year":2023},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.545533Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:9dc8ba3c54c4a03a0712f97bc393ecac03a627417a038de7633fa27dd91621c0","observation_id":"03e1a50f-ff6e-4e09-867d-6c14ed2b58fa","resolution":{"observed_at":"2026-08-11T21:57:29.198723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.188043Z","title":"MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training","venue":null,"work_id":"0b9e46df-00d0-443e-ab4a-0a35000d4e37","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.548888Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:4580f30416c7bc85fa95c342272eb92fba5c84b5f7374565bdc9c504f223b435","observation_id":"230b14c5-c385-49f2-a13f-17c18f8d46d7","resolution":{"observed_at":"2026-08-11T21:57:29.190962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.180358Z","title":"Robust domain generalization for multi-modal object recognition","venue":null,"work_id":"d3584d99-d720-46a5-bb10-0a58c51fb784","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.551959Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:467621777e11479e4db1dc5c4a6ba5df6d4b7c2c21c6ddd7cd93b0f3b36696b4","observation_id":"8ff78725-807f-482b-b753-64d4026bd0e4","resolution":{"observed_at":"2026-08-11T21:57:29.183192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.555022Z","title":"The Design of Autonomous UAV Prototypes for Inspecting Tunnel Construction Environment","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.555022Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:d7f1a5f40c2a7c2d3bbb0211175a2701612c2bad2cf146741bdd2d165e3aac87","observation_id":"8f98a299-41b8-4dad-9c0b-92e89719f5b7","resolution":{"observed_at":"2026-08-11T21:57:28.555022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.168088Z","title":"Design and Implementation of Intelligent Robot Control System Integrating Computer Vision and Mechanical Engineering","venue":null,"work_id":"077bd6ed-d330-4b50-b281-bdd2b4517a49","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.558184Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:43031a9a6cfc7c42fb2fd40718caae1f02c19095808e9a002f9bf9289759bd95","observation_id":"d308afc8-6e6a-45ab-bfe1-919f55d062c8","resolution":{"observed_at":"2026-08-11T21:57:29.171050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19192","last_updated":"2024-04-30T01:41:03Z","snapshot_observed_at":"2026-08-16T13:56:46.198820Z","submitted_at":"2024-04-30T01:41:03Z","title":"Mix of Experts Language Model for Named Entity Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19192","snapshot_observed_at":"2026-08-11T21:57:28.560907Z","title":"Mix of Experts Language Model for Named Entity Recognition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.560907Z"},"links":{"cited_paper":"/paper/2404.19192","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:082b48aa2984769ef3576374e5ad912cda776090543153158be9854e1ecf0138","observation_id":"35b0948f-a019-43fa-9905-8af5389cf769","resolution":{"observed_at":"2026-08-11T21:57:28.560907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.160321Z","title":"TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA- LSTM Forecasting model","venue":null,"work_id":"0e05c5d3-50a2-4e65-819e-aaa6e62694e6","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.563780Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:f588236bdaaffcca8c54593a7dc1b66d133db32b42ba1d7a29c5a10cdbeeb9a6","observation_id":"4b1597ec-8088-4d27-8e65-3b4d0a49f162","resolution":{"observed_at":"2026-08-11T21:57:29.163387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.152610Z","title":"Automatic News Generation and Fact-Checking System Based on Language Processing","venue":null,"work_id":"661372c3-4232-4042-9976-f52e7b0dad0d","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.566393Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:a9d57d2809161c94ac04a9b1b98d1390b6e5b94a66e5891ce45dbfbb4161e7b0","observation_id":"2f39e5f9-0f70-4ed2-b7d9-29a274de1ac2","resolution":{"observed_at":"2026-08-11T21:57:29.155486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.569003Z","title":"Application of Deep Learning for Automatic Identification of Hazardous Materials and Urban Safety Supervision","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.569003Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:e109c8c9a516ba5fbc4c3ad4476e19d43628ab88f8c792bd6dd1a3849900d3f3","observation_id":"2a764702-a667-44be-a782-3342b7490e06","resolution":{"observed_at":"2026-08-11T21:57:28.569003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:29.140582Z","title":"GTA-Net: An IoT- integrated 3D human pose estimation system for real-time adolescent sports posture correction","venue":null,"work_id":"dae19e33-0cda-45a6-af7d-92ffb9c4cd79","year":2025},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.571540Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:ed22505aeea2c2c9576e7128169dbf314304d452224bd19de62ea06421f35bc0","observation_id":"e082ef64-7cf9-4816-a443-c9058f30b0bf","resolution":{"observed_at":"2026-08-11T21:57:29.143642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.13885","last_updated":"2024-11-21T06:52:48Z","snapshot_observed_at":"2026-08-20T01:48:16.480420Z","submitted_at":"2024-11-21T06:52:48Z","title":"Trajectory Tracking Using Frenet Coordinates with Deep Deterministic Policy Gradient","version":1},"cited_work":{"arxiv_id":"2411.13885","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.13885","snapshot_observed_at":"2026-08-11T21:57:28.681698Z","title":"Trajectory Tracking Using Frenet Coordinates with Deep Deterministic Policy Gradient","venue":"cs.RO","work_id":"5c0323af-7893-491f-8186-76562a04e039","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.574079Z"},"links":{"cited_paper":"/paper/2411.13885","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:6348c4c36bee66d618bea411a3a55aac09caaf2478fd6af38edd685e34c945a0","observation_id":"41716f8e-f148-4b3a-b05a-15cdec735db6","resolution":{"observed_at":"2026-08-11T21:57:28.685402Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04821","last_updated":"2024-06-07T10:47:33Z","snapshot_observed_at":"2026-08-21T07:58:31.036696Z","submitted_at":"2024-06-07T10:47:33Z","title":"Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04821","snapshot_observed_at":"2026-08-11T21:57:28.576793Z","title":"Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.576793Z"},"links":{"cited_paper":"/paper/2406.04821","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:a5f8e6f2933d068e7ee4c67d441548bb16e23f63f3d1cf153f757b357fce301a","observation_id":"b99cee3a-a168-48f5-a077-4b208a7c783c","resolution":{"observed_at":"2026-08-11T21:57:28.576793Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1117/12.3044221","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:57:28.631767Z","title":"Harnessing XGBoost for robust biomarker selection of obsessive-compulsive disorder (OCD) from adolescent brain cognitive development (ABCD) data","venue":null,"work_id":"293dab8f-14ef-4360-8f85-a3d1de7c8534","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.580052Z"},"links":{"citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:d20f2f82fa2fb6850bb1b13c1aa33cd992d621c72d71ad7d2cfb6739689c125f","observation_id":"a39620d1-605d-443e-9a9e-807f4a99eb2e","resolution":{"observed_at":"2026-08-11T21:57:28.637527Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.12676","last_updated":"2024-11-19T17:29:59Z","snapshot_observed_at":"2026-08-16T01:09:51.896158Z","submitted_at":"2024-11-19T17:29:59Z","title":"IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose","version":1},"cited_work":{"arxiv_id":"2411.12676","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.12676","snapshot_observed_at":"2026-08-11T21:57:28.660322Z","title":"IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose","venue":"cs.CV","work_id":"dea0e5a6-a310-46b5-9276-41df30dc687c","year":2024},"citing_paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-11T21:57:28.582998Z"},"links":{"cited_paper":"/paper/2411.12676","citing_paper":"/paper/2412.03961"},"observation_digest":"sha256:fd577a3e690241941cbb4cee5c35b185cea566513f1ce9813a445c7181d1a4d1","observation_id":"e34d8f64-73c9-4c4d-afaa-addc920c7d64","resolution":{"observed_at":"2026-08-11T21:57:28.664185Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.03961","last_updated":"2024-12-05T08:26:07Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-20T01:48:44.162712Z","submitted_at":"2024-12-05T08:26:07Z","title":"Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":54,"verified_exact":8,"verified_fuzzy":38},"total_outbound_references":111},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 100 of 111 outbound references and 0 inbound Pith citation observations for arXiv:2412.03961."}