Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Introduction: Climate change disproportionately affects disadvantaged communities, yet construction workforce education rarely addresses interconnected pathways linking energy efficiency, nature exposure, and public health. Green-blue infrastructure delivers co-optimized benefits: reducing building energy consumption 15-30% while decreasing heat-related mortality by approximately 3.9% per degree C...
Objectives: Explore the perspectives of primary caregivers towards pediatric tissue-based research participation. Design: Cross-sectional. Setting: Two academic pediatric gastroenterology sites in the United States, including one serving a largely rural referral population and one urban clinic population. Participants: Primary caregivers of children who underwent endoscopy between 2017-2018 at UVA...
Decision support systems (DSS) increasingly run retention what-if analysis on synthetic customer populations, because privacy constraints preclude unr...
This paper proposes a Gaussian-Based Shift-Variant filtered backprojection (FBP) neural network, which is designed for the efficient reconstruction of...
Introduce a MinHash-based similarity scheduling framework that constructs a progressive curriculum over neural architecture code for LLM-based neural ...
Underwater object detection is strongly affected by domain shift, where performance can vary significantly across different locations, habitats, and d...
The action space poses a major challenge in robot learning, since it is often high-dimensional, can span long time horizons, and frequently admits mul...
Medical image classifiers are often trained within one source population, yet clinical deployment requires robustness to patients whose appearance, ac...
Geroscience clinical trials need biomarker surrogate endpoints for healthspan. Leading candidates are omics-based composites developed from machine le...
Objective. To evaluate whether open-weight large language models (LLMs) can accurately extract clinical findings from Finnish-language pediatric recor...
Medical image segmentation models can achieve strong benchmark performance while remaining sensitive to scanner, protocol, and institutional variation...
Pose-Free Feed-forward 3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for fast scene reconstruction. However, its performanc...
Faithful visual attribution identifies which image regions support a model prediction. Search-based perturbation methods lead the insertion--deletion ...
Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment a...
Real-world data distributions evolve over time, inducing temporal distribution shift that can substantially degrade the reliability of deployed machin...
Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes, ...
The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting. This objective rests on a pervasive, of...
Model merging offers a practical alternative to conventional continual learning by integrating independently fine-tuned models without retaining previ...
Charts and images appear together throughout scientific publications, yet most computational work does not characterize their coherence. We argue that...
Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and ...