AI deciphers long-range DNA signals behind RNA splicing

Accurate RNA splicing is essential for gene expression and human health, yet predicting how DNA sequence variations affect ...
The Scientist

Postdoc Portrait: Denis Turcu Explores How Neural Circuit Constraints Shape Brain Function

Allen Institute and University of Washington postdoctoral researcher Denis Turcu uses computational models to study how the ...
GitHub

Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural Networks

This paper is dedicated to the memory of my grandma. This is probably the only paper in my life that was written in a hospital the entire time. This is also at the same time the very first work in my ...
IEEE

Intelligent Channel Coding Using Graph Neural Networks: A Machine Learning Perspective

Abstract: Wireless communication is rapidly evolving, and this evolution has led to increasing demands for better error correction, lower latency, and higher ...
IEEE

Enhancing Fault Detection and Classification in Wind Farm Power Generation Using Convolutional Neural Networks (CNN) by Leveraging LVRT Embedded in Numerical Relays

Abstract: Real-time fault detection and classification are important for power system stability and resilience of the power grid to minimize downtime and prevent cascading failures. Numerical relays ...
electronicsforu

Top 20 MATLAB Project Ideas

MATLAB (short for Matrix Laboratory) is a powerful software tool used for technical computing and visualization. It is widely used in a variety of fields, including engineering, science, finance, and ...
InfoQ

Building Neural Networks with TensorFlow.NET

TensorFlow is an open-source framework developed by Google scientists and engineers for numerical computing. TensorFlow.NET is a library that provides a .NET Standard binding for TensorFlow, allowing ...
PNAS

A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level

We demonstrate that a neural network automatically solves, explains, and generates university-level problems from the largest Massachusetts Institute of Technology (MIT) mathematics courses at a human ...
PNAS

Learning probabilistic neural representations with randomly connected circuits

We present a theory of neural circuits’ design and function, inspired by the random connectivity of real neural circuits and the mathematical power of random projections. Specifically, we introduce a ...