In industrial recommendation systems, the shift toward Generative Retrieval (GR) is replacing traditional embedding-based nearest neighbor search with Large Language Models (LLMs). These models ...

Abstract: Sparse arrays offer economic advantages by reducing the number of antennas. However, directly utilizing the covariance matrix of sparse array signals for wideband beamforming may lead to the ...

The Nature Index 2025 Research Leaders — previously known as Annual Tables — reveal the leading institutions and countries/territories in the natural and health sciences, according to their output in ...

ABSTRACT: Node renumbering is an important step in the solution of sparse systems of equations. It aims to reduce the bandwidth and profile of the matrix. This allows for the speeding up of the ...

This project focuses on lossless compression techniques optimizing space, time, and energy for multiplications between binary (or ternary) matrix formats and real-valued vectors.

In the field of renewable energy, accurate long-term time series forecasting is crucial for optimizing the operation of power systems and reducing risks. Due to the intermittency of renewable energy ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

A new technical paper titled “Signal processing architecture for a trustworthy 77GHz MIMO Radar” was published by researchers at Fraunhofer FHR, Ruhr University Bochum, and Wavesense Dresden GmbH.

Abstract: Sparse Matrix-Vector Multiplication (SpMV) on GPUs has gained significant attention because of SpMV's importance in modern applications and the increasing computing power of GPUs in the last ...