Latest AI and machine learning research in geriatrics for healthcare professionals.
Recently, significant progress has been made in developing reasoning-capable Large Language Models (LLMs) through long Chain-of-Thought (CoT) techniques. However, this long-CoT reasoning process imposes substantial memory overhead due to the large Key-Value (KV) Cache memory overhead. Post-training KV Cache quantization has emerged as a promising compression technique and has been extensively st...
Advances in markerless motion capture are expanding access to biomechanical movement analysis, making it feasible to obtain high-quality movement data from outpatient clinics, inpatient hospitals, therapy, and even home. Expanding access to movement data in these diverse contexts makes the challenge of performing downstream analytics all the more acute. Creating separate bespoke analysis code fo...
Deep learning (DL) identification of vertebral fractures and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment (VFA) ima...
BACKGROUND: Atrial fibrillation (AF) is one of the primary etiologies for ischemic stroke, and it is of paramount importance to delineate the risk phe...
Brain network analysis plays a crucial role in diagnosing and monitoring neurodegenerative disorders such as Alzheimer's disease (AD). Existing appr...
Current large vision-language models (LVLMs) typically employ a connector module to link visual features with text embeddings of large language mode...
This study introduces Reverse-Speech-Finder (RSF), a groundbreaking neural network backtracking architecture designed to enhance Alzheimer's Disease...
Front-end engineering involves a complex workflow where engineers conceptualize designs, translate them into code, and iteratively refine the implem...
Front-end engineering involves a complex workflow where engineers conceptualize designs, translate them into code, and iteratively refine the implem...
BACKGROUND: The incidence of cardiovascular metabolic diseases (CMD) continues to rise among middle-aged and elderly populations, affecting not only p...
Peptide compounds demonstrate considerable potential as therapeutic agents due to their high target affinity and low toxicity, yet their drug develo...
To encode point clouds containing both geometry and attributes, most learning-based compression schemes treat geometry and attribute coding separate...
We tackle the problem of monocular-to-stereo video conversion and propose a novel architecture for inpainting and refinement of the warped right vie...
As vision-based machine learning models are increasingly integrated into autonomous and cyber-physical systems, concerns about (physical) adversaria...
BACKGROUND: The English National Health Service (NHS) strives for a fair, diverse, and inclusive workplace, but Black and Minority Ethnic (BME) repres...
BACKGROUND: Neurological disorders, ranging from common conditions like Alzheimer's disease that is a progressive neurodegenerative disorder and remai...
Dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) is implicated in several human diseases, including DYRK1A syndrome, cancer, and...
We propose a novel framework for integrating fragmented multi-modal data in Alzheimer's disease (AD) research using large language models (LLMs) and...
We introduce a data-driven approach for enabling word-level timestamp prediction in the Canary model. Accurate timestamp information is crucial for ...
With recent breakthroughs in large-scale modeling, the Segment Anything Model (SAM) has demonstrated significant potential in a variety of visual ap...