ABSTRACT: The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and chaotic nature, making its accurate prediction a significant ...

When enterprises fine-tune LLMs for new tasks, they risk breaking everything the models already know. This forces companies to maintain separate models for every skill. Researchers at MIT, the ...

Abstract: This paper proposes a new convolution layer implementation method for the parallel computation of high-dimensional matrices. It can accomplish global convolution or local convolution by ...

Discover how the Eisenhower Matrix can help you prioritize your tasks based upon urgency and importance. Learn how to use the matrix to focus on what truly matters while reducing time spent on ...

CUDA-L2 is a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multiply (HGEMM) CUDA kernels. CUDA-L2 ...

Abstract: The existing frequency domain convolution operation methods mainly focus on studying single frequencies and are difficult to adapt to the construction of small signal models in oscillation ...

High-precision GNSS applications, such as real-time displacement monitoring and vehicle navigation, rely heavily on resolving carrier-phase ambiguities. However, traditional methods like the R-ratio ...

Genomic best linear unbiased prediction (GBLUP) is a key method in genomic prediction, relying on the construction of a genomic relationship matrix (G-matrix). Although various methods for G-matrix ...

Understanding intelligence and creating intelligent machines are grand scientific challenges of our times. The ability to learn from experience is a cornerstone of intelligence for machines and living ...

KU Leuven and Vrije Universiteit Brussel researchers led efforts to improve deep reinforcement learning (RL) for liquid chromatography (LC) method development. Their findings were published in the ...