Latest AI and machine learning research in covid-19 for healthcare professionals.
Large pretrained diffusion models have significantly enhanced the quality of generated videos, and yet their use in real-time streaming remains limited. Autoregressive models offer a natural framework for sequential frame synthesis but require heavy computation to achieve high fidelity. Diffusion distillation can compress these models into efficient few-step variants, but existing video distillati...
Predicting hospital outcomes for patients with severe acute respiratory infections is critical for risk stratification and resource planning, yet heterogeneous electronic health record (EHR) data, class imbalance, and evolving clinical practice present persistent methodological challenges for machine learning (ML) approaches. We conducted a retrospective cohort study using EHR data harmonized to t...
Magnetoencephalography (MEG) and electroencephalography (EEG) source imaging requires solving an ill-posed inverse problem for which numerous algorith...
Understanding dynamic 3D environments in a spatially continuous and temporally consistent manner is fundamental for robotics and autonomous driving. W...
Unified diffusion editors often rely on a fixed, shared backbone for diverse tasks, suffering from task interference and poor adaptation to heterogene...
Understanding spatial affordances -- comprising the contact regions of object interaction and the corresponding contact poses -- is essential for robo...
While recent image generation models demonstrate a remarkable ability to handle a wide variety of image generation tasks, this flexibility makes them ...
Vision-language models encode continuous geometry that their text pathway fails to express: a 6,000-parameter linear probe extracts hand joint angles ...
Acquiring per-frame video annotations remains a primary bottleneck for deploying computer vision in specialized domains such as medical imaging, where...
While recent multimodal large language models (MLLMs) have made impressive strides, they predominantly employ a conventional autoregressive architectu...
Accurate classification of BRCA1 and BRCA2 variants is essential for cancer risk assessment and therapy selection, yet over one-third remain variants ...
Most genetic variants contributing to complex diseases reside in the noncoding genome. While common variants uncovered by genome-wide association stud...
The prediction of protein-protein interactions is central to structural biology, yet leading models are often computationally expensive, creating an a...
Many real-world computer vision tasks, such as depth completion, must handle inputs with arbitrarily shaped regions of missing or invalid data. For Co...
Tumour typing from whole-genome sequencing is increasingly accurate, yet molecular subtyping from somatic variants remains challenging because of tumo...
Open-set semantic mapping enables language-driven robotic perception, but current instance-centric approaches are bottlenecked by context-depriving an...
Backgrounds: Most rare coding variants in monogenic disease genes remain classified as Variants of Uncertain Significance (VUS), limiting their use in...
While protein language models (PLMs) have shown great promise for protein design, their performance is fundamentally constrained by the diversity and ...
DNA length analysis is essential for genomic workflows including next-generation sequencing and fragmentomics based diagnostics. Conventional approach...
Personalized AI assistants must recall and reason over long-term user memory, which naturally spans multiple modalities and sources such as images, vi...