Abstract: Space-time adaptive processing (STAP) based on sparse Bayesian learning (SBL) can significantly improve clutter suppression performance utilizing clutter sparsity. However, the existing ...
The Experimental Learning Symposium was held in November as an opportunity for over 50 undergraduates in health science ...
Abstract: Sparse Bayesian Learning (SBL) is recognized for its efficacy in sparse signal recovery, the computational demand escalates significantly with increasing data dimensionality due to the ...
This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...
Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...
I have to admit it: I’ve always been a nerd. I loved school. I loved university. And yes, I’m seriously contemplating a PhD, not for career advancement, but simply for the joy of diving deep into ...
Researchers at The University of Texas at Arlington have developed a new computational tool that helps scientists pinpoint proteins known as transcriptional regulators that control how genes turn on ...
Modern technology offers new possibilities for transforming teaching. By Anant Agarwal Anant Agarwal is an education innovator. This personal reflection is part of a series called Turning Points, in ...
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