Latest AI and machine learning research in cultural competence for healthcare professionals.
We present EDDY (Exact-marginal Diversification via Divergence-free dYnamics), a guidance mechanism for diffusion and flow matching models that promotes diversity among samples generated while maintaining quality. EDDY exploits symmetries of the Fokker-Planck equation, using drift perturbations that change particle trajectories while preserving the evolving marginal distribution. We instantiate th...
GUI grounding is a critical capability for enabling GUI agents to execute tasks such as clicking and dragging. However, in complex scenarios like the ScreenSpot-Pro benchmark, existing models often suffer from suboptimal performance. Utilizing the proposed \textbf{Masked Prediction Distribution (MPD)} attribution method, we identify that the primary sources of errors are twofold: high image resolu...
Staphylococcus aureus produces a broad range of enterotoxins that act as superantigens, disrupting host immune responses and resulting in a myriad of ...
High-quality datasets that span broad sequence diversity are essential for understanding protein sequence-function relationships beyond local mutation...
Sound Event Detection (SED) plays a vital role in audio understanding, with applications in surveillance, smart cities, healthcare, and multimedia ind...
Clinical decision-making is a critical competency for nurses particularly in resource-constrained healthcare systems where frontline practitioners mus...
The rapid rise of large language models (LLMs) and foundation models has accelerated efforts to build artificial intelligence (AI) agents for mental h...
Fairness in machine learning remains challenging due to its ethical complexity, the absence of a universal definition, and the need for context-specif...
The scarcity of high-quality annotated medical data, particularly in mental health, poses a significant bottleneck for training robust machine learnin...
Background: Emerging evidence suggests that the oral microbiome may contribute to aberrant gut immune responses in Inflammatory Bowel Disease (IBD). M...
Class-level evaluation can conceal substantial performance disparities across subconcepts within the same class, causing models that perform well on a...
Background: The rapid expansion of medical literature has led to substantial variability and frequent contradictions in study findings, making it incr...
Knowledge distillation (KD) represents a vital mechanism to transfer expertise from complex teacher networks to efficient student models. However, in ...
Knowledge distillation (KD) is a well-known technique to effectively compress a large network (teacher) to a smaller network (student) with little sac...
Training reliable respiratory sound classification models remains challenging due to the limited size and subject diversity of datasets. Ensemble meth...
Computer-Aided Design (CAD) models are defined by their construction history: a parametric recipe that encodes design intent. However, existing large-...
In recent years, the integration of multimodal machine learning in wellbeing assessment has offered transformative potential for monitoring mental hea...
With the rapid growth of video data, Composed Video Retrieval (CVR) has emerged as a novel paradigm in video retrieval and is receiving increasing att...
Large-scale dataset distillation requires storing auxiliary soft labels that can be 30-40x larger on ImageNet-1K and 200x larger on ImageNet-21K than ...
Multimodal Large Language Models (MLLMs) have been increasingly used as automatic evaluators-a paradigm known as MLLM-as-a-Judge. However, their relia...