Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Modern large reasoning models demonstrate impressive problem-solving capabilities by employing sophisticated reasoning strategies. However, they often struggle to balance efficiency and effectiveness, frequently generating unnecessarily lengthy reasoning chains for simple problems. In this work, we propose AdaCtrl, a novel framework to support both difficulty-aware adaptive reasoning budget allo...
Chiral mobile phase additive (CMPA) technique is an attractive method for chromatographic enantioseparation of chiral analytes. However, establishing chromatographic separation and analysis methods for given chiral analytes often requires extensive trial-and-error experiments, leading to time-consuming processes with high experimental costs. To address this challenge, machine learning (ML) was emp...
Test-time compute has empowered multimodal large language models to generate extended reasoning chains, yielding strong performance on tasks such as...
Large Language Model (LLM) inference is typically memory-intensive, especially when processing large batch sizes and long sequences, due to the larg...
As learned image compression (LIC) methods become increasingly computationally demanding, enhancing their training efficiency is crucial. This paper...
Current large vision-language models (LVLMs) typically employ a connector module to link visual features with text embeddings of large language mode...
Recently, 3D GANs based on 3D Gaussian splatting have been proposed for high quality synthesis of human heads. However, existing methods stabilize t...
Dataset pruning -- selecting a small yet informative subset of training data -- has emerged as a promising strategy for efficient machine learning, ...
Recent studies have shown that vector representations of contextual embeddings learned by pre-trained large language models (LLMs) are effective in ...
Continual post-training adapts a single text-to-image diffusion model to learn new tasks without incurring the cost of separate models, but naive po...
The problems that tobacco workshops encounter include poor curing, inconsistencies in supplies, irregular scheduling, and a lack of oversight, all o...
Adversarial attacks exploiting unrestricted natural perturbations present severe security risks to deep learning systems, yet their transferability ...
BACKGROUND: The English National Health Service (NHS) strives for a fair, diverse, and inclusive workplace, but Black and Minority Ethnic (BME) repres...
The automatic identification of medication states of Parkinson's disease (PD) patients can assist clinicians in monitoring and scheduling personaliz...
Recent advancements in deep models have highlighted the need for intelligent systems that combine continual learning (CL) for knowledge acquisition ...
Colorectal cancer remains a major health concern, with colorectal polyps as key precursors. Endoscopic mucosal resection (EMR) is a common treatment, ...
PURPOSE: This article summarizes a novel methodology of applying machine learning (ML) algorithms trained with external training data to assist with a...
In this study, we introduce a groundbreaking deep learning (DL) model designed for the precise task of classifying common diseases in tea leaves, leve...
Objective: As AI becomes increasingly central to healthcare, there is a pressing need for bioinformatics and biomedical training systems that are pe...
This paper explores the use of contrastive learning and generative adversarial networks for generating realistic underwater images from synthetic im...