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Sponsored AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI

Author

Hwamin Lee

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Open access Oct 2026

Evaluating Large Language Model–Derived Outputs as Psychological Measures: A Five-Domain Psychometric Framework With Application to Daily Stress

Abstract Background Large language models (LLMs) are increasingly used to quantify psychological constructs from natural language, yet their outputs are typically evaluated only as predictions of an external label. When that label is itself imperfect, optimizing prediction conflates label agreement with construct measu...

Inyong Jeong, Soohyun Park, M. Jhon et al. · 0 citations
Open access Sep 2026

Machine learning-based early risk stratification for pulmonary thromboembolism in critically ill patients undergoing chest CT: a retrospective cohort study using CT-confirmed outcomes

Pulmonary thromboembolism is a preventable cause of death in critically ill patients, and thromboprophylaxis decisions in the intensive care unit are made under diagnostic uncertainty. Risk scores derived from general inpatients discriminate poorly in intensive care. We developed and internally validated a machin...

Jeonghwan Kim, Inyong Jeong, Se-Jin Ahn et al. · 0 citations
Conference Jul 2026

AnchorDL: Dual-Locality-based Scheduling for Serverless Inference Workflows

Serverless computing has emerged as an attractive deployment model for deep learning model inference workflows, enabling elastic scaling and fine-grained resource billing across function instances. However, scheduling in this setting introduces a competing-objective challenge: placement decisions simultaneously govern...

Min Chang Kim, Hyungjun Kim, Hokun Park et al. · 0 citations
Conference Jul 2026

BYSTANDER: State-Aware End-to-End Latency Prediction for Heterogeneous LLM Inference Scheduling

Large Language Model (LLM) inference services increasingly rely on heterogeneous GPU clusters to balance cost and performance. However, request routing in such environments is challenging because schedulers must account for hardware heterogeneity, dynamic workload characteristics, and bursty arrivals. Existing approach...

Minjae Jung, Hyungjun Kim, Hokun Park et al. · 0 citations

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