Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
To make clinically grounded decisions, medical AI agents are expected to go beyond simple recognition and be capable of tool retrieval, evidence acquisition, and integration. Existing benchmarks largely evaluate isolated perception or single-turn question answering, and therefore provide limited visibility into failures of planning, tool recruitment, and rollout reliability. We introduce MedCTA, a...
Flow matching and diffusion models enable conditional generation across domains ranging from images to proteins, with recent extensions to out-of-distribution contexts. Yet generative models of neural time series have largely remained restricted to categorical conditioning, precluding compositional and zero-shot generalization. In this work, we propose a per-timestep conditioned diffusion transfor...
High-content imaging assays quantify cellular responses to chemical and genetic perturbations, yet continuous trajectories of individual cells are uno...
In this report, we present our champion solution for the DataMFM Challenge Track 2: Chart Understanding. This track requires models to recover structu...
Occlusion-based attribution methods provide an intuitive way to estimate feature importance by perturbing input features and measuring the resulting c...
Most existing multi-exposure HDR methods follow a fixed feed-forward reconstruction paradigm, making them prone to ghosting artifacts in complex dynam...
Online task-free continual learning (TFCL) requires intelligent agents to sequentially accumulate knowledge from an unbounded, non-stationary data str...
Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owne...
Multimodal large language models (MLLMs) have raised new privacy challenges. On the data side, user-provided inputs often include unpredictable sensit...
Background: Selective attention enables the prioritization of behaviorally relevant information in complex sensory environments. Despite substantial e...
Pathological gait datasets remain scarce due to privacy, recruitment, cost, and movement variability. Our work presents a multimodal LLM-guided framew...
Modern diffusion models generate high-quality images and videos, but their iterative denoising process makes inference expensive. Feature caching acce...
Vision-Language Models (VLMs), such as CLIP, have shown strong zero-shot generalization but remain highly vulnerable to adversarial perturbations, pos...
Background: We previously examined the burden and predictors of sustained mental health care engagement in Ugandan first episode psychosis patients by...
Objective: Despite the complex and non-linear progression of diabetes, its shared pathways with atherosclerotic cardiovascular disease (ASCVD) are con...
Adaptive behavior requires flexibly shifting between exploiting familiar rewards and exploring novel opportunities. These explore-exploit decisions ar...
Introduction: Regulated cell death (RCD) pathways profoundly influence tumor progression and immune modulation. In prior work, we constructed a compre...
Non-canonical HLA-presented peptides are promising therapeutic targets, but their low abundance makes them difficult to reproducibly identify and quan...
The Northwest Forest Plan (NWFP) Passive Acoustic Monitoring (PAM) program is a large-scale interagency biodiversity monitoring framework designed to ...
Understanding and modeling how a single human genome concurrently encodes gene regulatory programs for thousands of cell types remains a central chall...