Abstract: The objective of optical super-resolution (SR) imaging is to acquire reliable sub-diffraction information on bioprocesses to facilitate scientific discovery. Structured illumination ...

Abstract: Sparse Bayesian learning (SBL) is an algorithm for high-dimensional data processing based on Bayesian statistical theory. Its goal is to improve the generalization ability and efficiency of ...

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 ...

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 ...

One of the best ways to reduce your vulnerability to data theft or privacy invasions when using large language model artificial intelligence or machine learning, is to run the model locally. Depending ...

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Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities, where they can learn tasks from demonstrations without requiring additional training. A critical ...

In this paper, a fast temporal multiple sparse Bayesian learning (FTMSBL)-based channel estimation method for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) systems is ...