Results 151 to 160 of about 131,054 (221)

“We have nothing to do with it”: How statements of denial by armed actors shape public perceptions and emotions

open access: yesPolitical Psychology, EarlyView.
Abstract Armed groups operating in conflicts around the world publish statements of denial to dissociate themselves from acts of violence. Existing research argues that armed groups publish denial statements to avoid public backlash, favorably frame the conduct of their campaigns, and distance themselves from unsanctioned actions conducted by rank‐and ...
Ilayda B. Onder, Mark Berlin
wiley   +1 more source

Sensitivity analysis for generalized estimating equation with non‐ignorable missing data

open access: yesScandinavian Journal of Statistics, EarlyView.
Abstract Many incomplete‐data statistical inference procedures are developed under the missing at random (MAR) assumption. However, the MAR assumption has been criticized as being overly strong for real‐data problems, and is unverifiable by using observed data. To handle data that are missing not at random (MNAR), sensitivity analysis has been proposed
Hui Gong, Kin Wai Chan
wiley   +1 more source

Dimension reduction for optimal design problems with Kronecker product structure

open access: yesScandinavian Journal of Statistics, EarlyView.
Abstract This paper is motivated by the problem of optimal allocation of trials in multi‐environment crop variety testing with a large number of varieties. Optimizing the allocation of trials results in the minimization of a design criterion with a Kronecker product structure in the information matrix.
Taras Bodnar, Maryna Prus
wiley   +1 more source

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

open access: yesApplied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang   +3 more
wiley   +1 more source

Diffusional magnetic resonance imaging anonymizing with variational autoencoder

open access: yesQuantitative Biology, Volume 14, Issue 2, June 2026.
Abstract Anonymization is a crucial de‐identification technique that protects data privacy while ensuring its utility for model building. Current generative models such as generative adversarial networks and variational auto‐encoders (VAEs) have been applied to medical image anonymization but mainly focus on general image features, lacking specificity ...
Yunheng Shen   +4 more
wiley   +1 more source

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