Engineers at the University of Florida have built a photonic chip that performs convolutions, the most compute-heavy operation in modern AI, using light instead of electricity and delivering roughly ...

With the growing complexity of I/O software stacks and the rise of data-intensive workloads, optimizing I/O performance is essential for enhancing overall system performance on HPC clusters. While ...

Linear regression is the most fundamental machine learning technique to create a model that predicts a single numeric value. One of the three most common techniques to train a linear regression model ...

Researchers at China University of Petroleum (East China) present a comprehensive AI-driven strategy that enhances the efficiency and reliability of materials research across semiconductors, ...

Abstract: For any linear and time-invariant system, its output is the linear convolution between the variable input sequence and the constant system impulse response. When the input is long and the ...

Already registered? Click here to login now. Linear electromagnetic devices — such as linear motors, generators, actuators, and magnetic gears — play a vital role in precision motion control, energy ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The choice of the exchange-correlation functional and dispersion correction ...

Visual Attention Networks (VANs) leveraging Large Kernel Attention (LKA) have demonstrated remarkable performance in diverse computer vision tasks, often outperforming Vision Transformers (ViTs) in ...

🖼️ Parallel Image Convolution, applying a blur filter to images. Written in C, optimized in three different ways: MPI, MPI & OpenMP and CUDA.

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with two-way interactions between predictor variables. Standard linear regression predicts a single numeric value ...

ABSTRACT: Rainfall-induced landslides threaten mountainous regions globally, yet existing models face challenges in real-time, large-scale prediction due to dependency on post-event data. This study ...