Results 131 to 140 of about 24,633 (240)

Variable selection via thresholding

Scandinavian Journal of Statistics, Volume 53, Issue 1, Page 207-237, March 2026.
Abstract Variable selection comprises an important step in many modern statistical inference procedures. In the regression setting, when estimators cannot shrink irrelevant signals to zero, covariates without relationships to the response often manifest small but nonzero regression coefficients.
Ka Long Keith Ho, Hien Duy Nguyen
wiley   +1 more source

Graph‐guided frequency‐enhanced state space network for 3D spine segmentation from MR images

Journal of Applied Clinical Medical Physics, Volume 27, Issue 2, February 2026.
Abstract Background Accurate spinal MRI segmentation is essential for computer‐aided diagnosis of spinal diseases. Existing methods have limitations in global semantic modeling and boundary delineation due to complex anatomy and imaging artifacts. Purpose Our work aimed to propose a novel Graph‐Guided Frequency‐Enhanced State Space Network (GF‐SSNet ...
Linghui Hong, Zhengchao Zhou, Wanbo Xu, Pingping Wang, Benzheng Wei   +4 more
wiley   +1 more source

Genome‐Wide by Lifetime Environment Interaction Studies of Brain Imaging Phenotypes

Advanced Science, Volume 13, Issue 8, 9 February 2026.
This study explores genome‐wide by lifetime environment interactions on brain imaging phenotypes. Gene‐environment interactions explain more phenotypic variance than main effects, pinpoint regulatory variants, and reveal exposure‐specific biological pathways.
Sijia Wang, Meiyun Wang, Peng Zhang, Jingliang Cheng, Longjiang Zhang, Wenzhen Zhu, Shijun Qiu, Zuojun Geng, Guangbin Cui, Yongqiang Yu, Weihua Liao, Xi‐Nian Zuo, Hui Zhang, Bo Gao, Xiaojun Xu, Tong Han, Zhenwei Yao, Quan Zhang, Feng Liu, Qiang Xu, Jiayuan Xu, Jilian Fu, Nana Liu, Yuan Ji, Jie Tang, Lining Guo, Mengge Liu, Xiaoxiao Xiao, Xiaoxuan Liu, Wei Li, Caihong Wang, Wei Wei, Dapeng Shi, Su Lui, Zhihan Yan, Feng Chen, Jing Zhang, Wen Shen, Yanwei Miao, Dawei Wang, Jia‐Hong Gao, Yunjun Yang, Kai Xu, Junfang Xian, Bing Zhang, Xiaochu Zhang, Zhaoxiang Ye, Le Yu, Wen Qin, Meng Liang, Chunshui Yu, the CHIMGEN Consortium   +51 more
wiley   +1 more source

Explicit Compression Degradation Estimations for Low‐Sampling Single‐Pixel Imaging using Hadamard Basis

Advanced Science, Volume 13, Issue 9, 13 February 2026.
This study proposes a degradation estimation technique to explicitly describe compressive sampling for low‐sampling Hadamard single‐pixel imaging. Blur kernels in explicit degradation models are estimated by the self‐supervised learning method without labeled data and implicit priors.
Haoyu Zhang, Jie Cao, Chang Zhou, Haifeng Yao, Qun Hao   +4 more
wiley   +1 more source

Tunneling‐Controlled Fusion of Short‐ and Long‐Term Memory in SiO2/HfO2‐Based Neuromorphic Device for Time‐Series Prediction

Advanced Science, Volume 13, Issue 7, 3 February 2026.
Based on trap‐assisted tunneling, the devices can fuse STM/LTM, where the low switching energy of 1 pJ and stable low‐power retention (0.2 % loss ratio and 3.05 × 10−11 W) is achieved. Training in a long short‐term memory network it allows to analysis time‐series data and then makes precise long‐term predictions with an error ratio of 4.465 ...
Chengdong Yang, Lihua Xu, Linlin Su, Yue Wu   +3 more
wiley   +1 more source

Inferring Gene Regulatory Networks From Single‐Cell RNA Sequencing Data by Dual‐Role Graph Contrastive Learning

Advanced Science, Volume 13, Issue 9, 13 February 2026.
RegGAIN is a novel and powerful deep learning framework for inferring gene regulatory networks (GRNs) from single‐cell RNA sequencing data. By integrating self‐supervised contrastive learning with dual‐role gene representations, it consistently outperforms existing methods in both accuracy and robustness.
Qiyuan Guan, Jiating Yu, Jieyi Pan, Fan Yuan, Jiadong Ji, Rusong Zhao, Zhi‐Ping Liu, Bingqiang Liu, Ling‐Yun Wu, Duanchen Sun   +9 more
wiley   +1 more source

Signpost Testing to Navigate the Parameter Space of the Gaussian Graphical Model With High‐Dimensional Data

Biometrical Journal, Volume 68, Issue 1, February 2026.
ABSTRACT We evaluate the relevance of external quantitative information on the parameter of a Gaussian graphical model from high‐dimensional data. This information comes in the form of a parameter value available from a related knowledge domain or population.
Kai Ruan, Mark A. van de Wiel, Wessel N. van Wieringen   +2 more
wiley   +1 more source

On the Method of Harmonic Balance for Lumped‐Element Transformer Models

International Journal of Circuit Theory and Applications, Volume 54, Issue 2, Page 625-637, February 2026.
The steady‐state solution of a lumped‐element transformer model with a dry friction‐like hysteresis model depicting the magnetic core is of interest. The harmonic balance method efficiently solves this stiff system. We derive the harmonic balance algorithm, enhance it with performance and convergence improvements, and demonstrate its efficiency by ...
Alexander Sauseng, Alice Reinbacher‐Köstinger, Dennis Albert, Peter Hamberger, Manfred Kaltenbacher, Klaus Roppert   +5 more
wiley   +1 more source

Cartesian MaxGIRF: Model‐based EPI reconstruction incorporating gradient nonlinearity and concomitant field effects

Magnetic Resonance in Medicine, Volume 95, Issue 2, Page 1044-1067, February 2026.
Abstract Purpose Lower field strength scanners with large bore size or complex geometries, and scanners with stronger gradient systems experience increased gradient nonlinearity and concomitant fields, each of which causes distortions in EPI. Current correction approaches based on image‐domain interpolation introduce undesirable spatial blurring.
Nam G. Lee, Sophia X. Cui, Krishna S. Nayak   +2 more
wiley   +1 more source

An Extended Krylov Subspace Method for Decoding Edge‐Based Compressed Images by Homogeneous Diffusion

Numerical Linear Algebra with Applications, Volume 33, Issue 1, February 2026.
ABSTRACT The heat equation is often used to inpaint dropped data in inpainting‐based lossy compression schemes. We propose an alternative way to numerically solve the heat equation by an extended Krylov subspace method. The method is very efficient with respect to the computation of the solution of the heat equation at large times.
Volker Grimm, Kevin Liang
wiley   +1 more source