Latest AI and machine learning research in hiv/aids for healthcare professionals.
Foundation models for single-cell transcriptomics promise to learn generalizable representations of cellular states. However, recent evidence suggests they often fail to outperform simple machine learning baselines. Furthermore, their ability to generalize across unseen experimental conditions remains poorly understood, particularly in plants, where rigorous evaluation beyond cell type annotation ...
Epigenetic clocks based on DNA methylation patterns are among the most accurate molecular correlates of chronological age, yet widely used clocks are predominantly empirical models with limited explicit characterization of the underlying methylation variability, lacking a direct connection to the physical mechanisms of aging. In this work, we bridge this gap by introducing an information-theoretic...
Bio-based alternatives for conventional rigid foams have proven to be good substituents owing to their enhanced sustainability and competitive perform...
Background: HIV testing is the entry point into the diagnosis, treatment and viral suppression cascade, yet in many low and middle income countries th...
Glaucoma is a group of eye diseases that damage the optic nerve, often caused by elevated intraocular pressure. It is a leading cause of irreversible ...
Visual localization becomes extremely challenging in planetary-like terrains characterized by low texture, perceptual aliasing, harsh illumination, an...
AI researchers describe state-of-the-art models as one thing repeated at scale: the Transformer, wired identically for text, pixels, or speech. Neuros...
Controllable image generation remains challenging for creative professionals, who often require precise regional control over materials, object identi...
In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and image m...
Where should the question go in a vision-language model (VLM) prompt: before the image or after it? Intuition says before: knowing what is asked shoul...
Discovering functional peptides across vast sequence space remains a formidable challenge, particularly when experimental training data is scarce. We ...
Low-power event-based Analog Front-Ends (AFEs) are essential for building efficient, end-to-end neuromorphic signal processing systems. In this paper,...
Tuberculosis (TB) is a major global health challenge, with many cases remaining undiagnosed due to limited access to screening and diagnostic services...
Vaccination is one of the most effective public health interventions. However, vaccine efficacy varies widely among individuals, as immunity arises fr...
Blood-based biomarkers discovered by machine learning often lack disease specificity and cross-population robustness for clinical applications. We des...
Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LL...
We study timestep allocation for score-based diffusion sampling, where a learned reverse-time dynamics is discretized on a finite grid. Uniform and ha...
Retention in antiretroviral therapy care remains a major challenge in high-burden settings such as Malawi, where substantial loss to follow up undermi...
Predicting how cells respond to genetic and chemical perturbations is a central challenge in drug discovery and functional genomics. A growing ecosyst...
Low-resolution face recognition (LR-FR) remains a challenging task due to poor feature extraction and aggregation, as probe images often contain limit...