Results 141 to 150 of about 10,590 (247)

Smart molecular design of NIR‐II organic fluorophores through self‐driven iterative evolution, deep learning, and fragment‐based assembly

open access: yesSmart Molecules, EarlyView.
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

DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection

open access: yesIEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
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

Enhancing generalized spectral clustering with embedding Laplacian graph regularization

open access: yesCAAI Transactions on Intelligence Technology, EarlyView.
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

Boosted unsupervised feature selection for tumor gene expression profiles

open access: yesCAAI Transactions on Intelligence Technology, EarlyView.
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]

open access: yesEur Phys J C Part Fields
Chiefa A, Del Debbio L, Kenway R.
europepmc   +1 more source

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