Analyzing Peptide Torsional Dynamics: An Angular-Displacement PCA Pipeline for Short-Horizon Prediction from Molecular Dynamics. [PDF]
Albrizzi L +5 more
europepmc +1 more source
A smart design strategy for NIR‐II organic fluorophores is proposed by combining self‐driven Iterative evolution, deep Learning, and fragment‐based assembly. This work establishes a broadly applicable approach for molecular design, accelerating the discovery of NIR‐II fluorophores and extending to optoelectronic materials and therapeutic compounds ...
Yu Zhang +6 more
wiley +1 more source
An Approach to Fisher-Rao Metric for Infinite Dimensional Non-Parametric Information Geometry. [PDF]
Cheng B, Tong H.
europepmc +1 more source
DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley +1 more source
A user centric group authentication scheme for secure communication. [PDF]
Gerenli O, Karabulut-Kurt G, Ozdemir E.
europepmc +1 more source
DO-PI-EATCNet: Efficient-Attention- and Dream-Optimization-Based Channel Selection for EEG Motor Imagery Classification. [PDF]
Shen X, Zhong H, Gu Y, Han R.
europepmc +1 more source
Enhancing generalized spectral clustering with embedding Laplacian graph regularization
Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang +5 more
wiley +1 more source
Simple picture of how output from ChatGPT-like AI shifts from good to bad. [PDF]
Johnson NF, Huo FY.
europepmc +1 more source
Boosted unsupervised feature selection for tumor gene expression profiles
Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi +5 more
wiley +1 more source
Quantitative understanding of PDF fits and their uncertainties. [PDF]
Chiefa A, Del Debbio L, Kenway R.
europepmc +1 more source

