Chemically specific coarse‐graining of polymers: Methods and prospects
Coarse‐grained (CG) modeling is an invaluable tool for the study of polymers and other soft
matter systems due to the span of spatiotemporal scales that typify their physics and …
matter systems due to the span of spatiotemporal scales that typify their physics and …
A survey of fuzzy clustering algorithms for pattern recognition. I
A Baraldi, P Blonda - IEEE Transactions on Systems, Man, and …, 1999 - ieeexplore.ieee.org
Clustering algorithms aim at modeling fuzzy (ie, ambiguous) unlabeled patterns efficiently.
Our goal is to propose a theoretical framework where the expressive power of clustering …
Our goal is to propose a theoretical framework where the expressive power of clustering …
Enhanced binding of the N501Y‐mutated SARS‐CoV‐2 spike protein to the human ACE2 receptor: insights from molecular dynamics simulations
Recently, severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) variants (B. 1.1.
7 and B. 1351) have emerged harbouring mutations that make them highly contagious. The …
7 and B. 1351) have emerged harbouring mutations that make them highly contagious. The …
Topology-preserving class-incremental learning
A well-known issue for class-incremental learning is the catastrophic forgetting
phenomenon, where the network's recognition performance on old classes degrades …
phenomenon, where the network's recognition performance on old classes degrades …
Testing the manifold hypothesis
The hypothesis that high dimensional data tend to lie in the vicinity of a low dimensional
manifold is the basis of manifold learning. The goal of this paper is to develop an algorithm …
manifold is the basis of manifold learning. The goal of this paper is to develop an algorithm …
In Silico Exploration of the Molecular Mechanism of Clinically Oriented Drugs for Possibly Inhibiting SARS-CoV-2's Main Protease
Currently, the new coronavirus disease 2019 (COVID-19) is a global pandemic without any
well-calibrated treatment. To inactivate the SARS-CoV-2 virus that causes COVID-19, the …
well-calibrated treatment. To inactivate the SARS-CoV-2 virus that causes COVID-19, the …
Nonlinear dimensionality reduction by locally linear embedding
Many areas of science depend on exploratory data analysis and visualization. The need to
analyze large amounts of multivariate data raises the fundamental problem of dimensionality …
analyze large amounts of multivariate data raises the fundamental problem of dimensionality …
A global geometric framework for nonlinear dimensionality reduction
Scientists working with large volumes of high-dimensional data, such as global climate
patterns, stellar spectra, or human gene distributions, regularly confront the problem of …
patterns, stellar spectra, or human gene distributions, regularly confront the problem of …
A growing neural gas network learns topologies
B Fritzke - Advances in neural information processing …, 1994 - proceedings.neurips.cc
An incremental network model is introduced which is able to learn the important topological
relations in a given set of input vectors by means of a simple Hebb-like learning rule. In …
relations in a given set of input vectors by means of a simple Hebb-like learning rule. In …
Principal manifolds and nonlinear dimensionality reduction via tangent space alignment
We present a new algorithm for manifold learning and nonlinear dimensionality reduction.
Based on a set of unorganized data points sampled with noise from a parameterized …
Based on a set of unorganized data points sampled with noise from a parameterized …