Latest AI and machine learning research in cultural competence for healthcare professionals.
Background Beta diversity quantifies pairwise differences between two or more communities through matrix transformations, which are either naive to phylogeny or phylogenetically aware. Methods have recently been introduced that also consider compositionality and sparsity and that display an increased magnitude of pseudo-F scores as produced by PERMANOVA to measure effect size. In this study, we as...
Spatial understanding remains a key challenge in vision-language models. Yet it is still unclear whether such understanding is truly acquired, and if so, through what mechanisms. We present a controllable 1D image-text testbed to probe how left-right relational understanding emerges in Transformer-based vision and text encoders trained with a CLIP-style contrastive objective. We train lightweight ...
Large language models (LLMs) increasingly operate in high-stakes settings including healthcare and medicine, where demographic attributes such as race...
Prior research demonstrates that performance of language models on reasoning tasks can be influenced by suggestions, hints and endorsements. However, ...
Reinforcement learning approaches for therapeutic peptide generation suffer from mode collapse, converging to narrow regions of sequence space even wh...
Vision-language pre-training (VLP) models are vulnerable to adversarial examples, particularly in black-box scenarios. Existing multimodal attacks oft...
Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to al...
Pre-trained models operating directly on raw bytes have achieved promising performance in encrypted network traffic classification (NTC), but often su...
A model that avoids stereotypes in a lab benchmark may not avoid them in deployment. We show that measured bias shifts dramatically when prompts menti...
Three-dimensional electron microscopy (3D EM) enables the quantitative analysis of cellular ultrastructure. However, large-scale segmentation of whole...
ObjectiveOur goal was to create open-source software for closed-loop EEG-TMS that allows researchers to rapidly prototype and develop novel stimulatio...
Medical imaging datasets often suffer from class imbalance and limited availability of pathology-rich cases, which constrains the performance of machi...
When training machine learning (ML) models for potential deployment in a healthcare setting, it is essential to ensure that they do not replicate or e...
Recent works have implemented machine learning based solutions for many complex classification tasks including pulse shape discrimination in radiation...
Domain generalized semantic segmentation is an essential computer vision task, for which models only leverage source data to learn semantic segmentati...
The global impact of COVID-19 has caused a significant rise in the demand for psychological counseling services, creating pressure on existing mental ...
A range of generative machine learning models for the design of novel molecules and materials have been proposed in recent years. Models that can gene...
Subject-consistent generation (SCG)-aiming to maintain a consistent subject identity across diverse scenes-remains a challenge for text-to-image (T2...
Recent works have revisited the infamous task ``Name That Dataset'', demonstrating that non-medical datasets contain underlying biases and that the ...
Recent work has revisited the infamous task Name that dataset and established that in non-medical datasets, there is an underlying bias and achieved...