This repository contains the source code used in our paper titled Yihe Pang, Bin Liu. DisoFLAG: Accurate prediction of protein intrinsic disorder and its functions using graph-based interaction ...

A new technical paper titled “Massively parallel and universal approximation of nonlinear functions using diffractive processors” was published by researchers at UCLA. “Nonlinear computation is ...

Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs large-scale nonlinear computations using linear materials. Reported in ...

The solution of the nonlinear differential equation Y″ = F(x, Y, Y′) with two-point boundary conditions is approximated by a quintic or cubic spline function y(x ...

Activation functions play a critical role in AI inference, helping to ferret out nonlinear behaviors in AI models. This makes them an integral part of any neural network, but nonlinear functions can ...

Recent work has established an alternative to traditional multi-layer perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). The general KAN framework uses learnable activation ...

ABSTRACT: In the realm of nonlinear physics, it is crucial to establish precise traveling wave solutions and solitary wave solutions for a variety of nonlinear models, as this aids our exploration of ...

Advancements in neuroscience research have led to significant progress in understanding the complex dynamics of the brain. However, the nonlinear properties of brain function still present a challenge ...

In-context learning (ICL) enables LLMs to adapt to new tasks by including a few examples directly in the input without updating their parameters. However, selecting appropriate in-context examples ...