This useful study presents a biologically realistic, large-scale cortical model of the rat's non-barrel somatosensory cortex, investigating synaptic plasticity of excitatory connections under varying ...

Research team proposed new data placement algorithms for scratch-pad memory (SPM) in embedded systems. Their fine-grained and ...

Despite an increasing consensus regarding the significance of properly identifying the most suitable clustering method for a given problem, a surprising amount of educational research, including both ...

Functional clustering is a computational technique that groups samples, for instance proteins, into clusters with similar functions. Clustering is the first step in forming a network of functions ...

Rough clustering has attracted increasing attention due to well dealing with the fuzziness and uncertainty of data. It is well known that it needs to manually set the threshold to determine the upper ...

The University at Buffalo is an Internet2 member. Through New York State's advanced research network (NYSERNet), CCR and UB have access to all major high speed communication networks commonly referred ...

Unsupervised learning techniques play a pivotal role in unraveling protein folding landscapes, constructing Markov State Models, expediting replica exchange simulations, and discerning drug binding ...

clustering sampling silhouette partitioning k-means unsupervised-learning data-clustering clustering-algorithm structure-learning pattern-discovery weighted-clustering-algorithm k-means-clustering ...

This repository focuses on methods for compiling, summarizing, and analyzing unstructured and semi-structured data, including text, images, and audio. The course covers algorithms and techniques for ...