Artificial intelligence (AI) can offer individualized dietary guidance based on multimodal data collected from various sources, including wearable sensors, high-dimensional multiomics and biomarker analyses, behavioral tracking, and self-reported dietary intake, enabling the emergence of precision nutrition. However, the predictive power and fairness of these models rely on the quality of the data inputs, and measurement […]
Digital phenotyping of affect and stress in emerging adults
BackgroundDepression is highly heterogeneous and difficult to monitor or predict in daily life. One strategy for monitoring depressive symptoms is digital phenotyping, the real-time tracking of behaviors via personal devices. Digital phenotyping may be especially useful for predicting mood in emerging adults, a developmental period characterized by heightened rates of depression and smartphone use. However, […]
Digital marketing of e-cigarettes in Southeast Asia: a neglected digital health and platform governance challenge for youth protection
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Editorial: Advances in generative artificial intelligence for mental health
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A bridge, not a destination: YouTube viewer perspectives on AI mental health support and human therapy
BackgroundArtificial intelligence (AI) tools are increasingly used for mental health support, yet little is known about how they are understood outside clinical trials and survey-based research.MethodsThis study examined public perceptions of AI mental health support through a convergent mixed-methods analysis of 7,949 YouTube comments posted across ten videos discussing AI and mental health. Quantitative analyses […]
Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation
arXiv:2606.09923v1 Announce Type: cross Abstract: Neural operators such as the Fourier Neural Operator (FNO) have emerged as powerful surrogates for solving partial differential equations (PDEs), achieving speedups of several orders of magnitude over traditional numerical solvers. However, deploying these models in safety-critical engineering applications — such as thermal management of electronic components and battery systems […]
An information-geometric framework for mapping maximum potential biodiversity
arXiv:2606.09906v1 Announce Type: cross Abstract: Biodiversity measures are often used descriptively: one computes a diversity index from an observed or estimated community composition and maps the resulting values across space. Conservation planning, however, also requires a site-specific benchmark against which the observed community can be compared. This chapter develops an information-geometric framework for such emphpotential […]
A Source Domain is All You Need: Source-Only Cross-OS Transfer Learning for APT Anomaly Detection via Semantic Alignment and Optimal Transport
arXiv:2606.10216v1 Announce Type: cross Abstract: Advanced Persistent Threats (APTs) are stealthy, multi-stage cyberattacks whose detection is difficult due to scarce labeled traces, severe class imbalance, and the challenge of generating realistic malicious behavior. These challenges are amplified in cross-operating-system (cross-OS) settings, where a detector trained on one source platform must be deployed on an unlabeled […]
Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models
arXiv:2606.10338v1 Announce Type: cross Abstract: Machine unlearning is increasingly important for large language models, yet unlearning in Mixture-of-Experts (MoE) architectures remains underexplored. Unlike dense models, MoE architectures employ a router at each layer to assign each token to a sparse subset of experts. In this work, we observe that forget data often activates a small […]
Generalized-CVO: Fast and Correspondence-Free Local Point Cloud Registration with Second Order Riemannian Optimization
arXiv:2606.10019v1 Announce Type: cross Abstract: We propose a fast and correspondence-free local point cloud registration method that leverages geometric surface structure and reproducing kernel Hilbert space (RKHS) embeddings. The method represents point clouds as continuous functions with point-wise anisotropic kernels that encode local geometry. This formulation improves alignment along surface normals while relaxing alignment along […]
FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching
arXiv:2606.10124v1 Announce Type: cross Abstract: Federated learning (FL) is often subject to aggregation variance if clients do not consistently participate in training rounds. While reusing stale model updates from inactive clients is a common technique to reduce this variance, we find that with skewed client participation, the resulting update staleness can become severe enough to […]
When is Enough Enough? A Proposed Termination Point for the Number of Replicates in Computational Simulations
arXiv:2606.10109v1 Announce Type: new Abstract: Computational simulation provides a powerful toolkit for in silico experimentation. However, while the field has developed best practices for the design and implementation of such models, there remains ambiguity in discussions about how to understand and/or interpret their results due to their inherent ability to overwhelm traditional frequentist statistics by […]