A new kernel (core program) within an operating system gives researchers a cleaner view of what's happening inside a ...

Are two sets of data genuinely different, or is it because of randomness? This question, known as the two-sample testing problem, becomes notoriously difficult in modern datasets, because they are ...

Abstract: The performance of reproducing kernel Hilbert space-based methods is known to be sensitive to the choice of the reproducing kernel. Choosing an adequate reproducing kernel can be challenging ...

The Linux kernel, foundational for servers, desktops, embedded systems, and cloud infrastructure, has been under heightened scrutiny. Several vulnerabilities have been exploited in real-world attacks, ...

Why it matters: The kernel space is the core component of a computer operating system, where critical hardware management and device driver code reside in memory. If a kernel-level driver malfunctions ...

This project proposes a Stochastic Variational Deep Kernel Learning method for the data-driven discovery of a low-dimensional representation of a dynamical system, from high-dimensional data at ...

With the increasing growth of artificial intelligence—introduction of large language models (LLMs) and generative AI—there has been a growing demand for more efficient graphics processing units (GPUs) ...

AMD is warning about a high-severity CPU vulnerability named SinkClose that impacts multiple generations of its EPYC, Ryzen, and Threadripper processors. The vulnerability allows attackers with Kernel ...

Large Language Models (LLMs) face deployment challenges due to latency issues caused by memory bandwidth constraints. Researchers use weight-only quantization to address this, compressing LLM ...

Large language models (LLMs) by themselves are less than meets the eye; the moniker “stochastic parrots” isn’t wrong. Connect LLMs to specific data for retrieval-augmented generation (RAG) and you get ...