Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Aiming at the problems of poor quality of steganographic images and slow network convergence of image steganography models based on deep learning, this paper proposes a Steganography Curriculum Learning training strategy (STCL) for deep learning image steganography models. So that only easy images are selected for training when the model has poor fitting ability at the initial stage, and gradual...
Although large language models (LLMs) have recently achieved remarkable performance on various complex reasoning benchmarks, the academic community still lacks an in-depth understanding of base model training processes and data quality. To address this, we construct a large-scale, difficulty-graded reasoning dataset containing approximately 3.34 million unique queries of varying difficulty level...
Optimization plays a vital role in scientific research and practical applications. However, formulating a concrete optimization problem described in...
Contrastive Language-Image Pre-training (CLIP) has achieved success on multiple downstream tasks by aligning image and text modalities. However, the...
Infrared small target detection (ISTD) is highly sensitive to sensor type, observation conditions, and the intrinsic properties of the target. These...
In recent years, large-scale vision-language models (VLMs) like CLIP have gained attention for their zero-shot inference using instructional text pr...
Our everyday auditory experience is shaped by the acoustics of the indoor environments in which we live. Room acoustics modeling is aimed at establi...
We propose a perceptual chromatic adaptation transform for white balance that makes use of split-quaternions. The novelty of the present work, which...
Endovascular procedures have revolutionized the treatment of vascular diseases thanks to minimally invasive solutions that significantly reduce pati...
While generative artificial intelligence (GenAI) is finding increased adoption in workplaces, current tools are primarily designed for individual us...
Energy consumption in mobile communication networks has become a significant challenge due to its direct impact on Capital Expenditure (CAPEX) and O...
The practical performance of generative diffusion models depends on the appropriate choice of the noise scheduling function, which can also be equiv...
Biological synapses effortlessly balance memory retention and flexibility, yet artificial neural networks still struggle with the extremes of catast...
Large Language Models (LLMs) trained on extensive datasets often learn sensitive information, which raises significant social and legal concerns und...
Reservoir computers (RCs) provide a computationally efficient alternative to deep learning while also offering a framework for incorporating brain-i...
Deep neural networks (DNNs) have recently become the leading method for low-light image enhancement (LLIE). However, despite significant progress, t...
In many applications, especially those involving prediction, models may yield near-optimal performance yet significantly disagree on individual-leve...
Clinical trials are crucial for assessing new treatments; however, recruitment challenges - such as limited awareness, complex eligibility criteria,...
Small language models (SLMs) support efficient deployments on resource-constrained edge devices, but their limited capacity compromises inference pe...
Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research. Structural annotations of MR images are an importan...