Results 91 to 100 of about 39,060 (206)

Diffusion model‐regularized implicit neural representation for computed tomography metal artifact reduction

Quantitative Biology, Volume 14, Issue 2, June 2026.
Abstract Computed tomography (CT) images are often severely corrupted by artifacts in the presence of metals. Existing supervised metal artifact reduction (MAR) approaches suffer from performance instability on known data due to their reliance on limited paired metal‐clean data, which limits their clinical applicability. Moreover, existing unsupervised
Jie Wen, Chenhe Du, Xiao Wang, Yuyao Zhang   +3 more
wiley   +1 more source

Saddle Points of Partial Augmented Lagrangian Functions

Mathematical and Computational Applications
In this paper, we study a class of optimization problems with separable constraint structures, characterized by a combination of convex and nonconvex constraints.
Longfei Huang, Jingyong Tang, Yutian Wang, Jinchuan Zhou   +3 more
doaj   +1 more source

On the Foundational Arguments of Sufficient Dimension Reduction

WIREs Computational Statistics, Volume 18, Issue 2, June 2026.
Contemporary Sufficient Dimension Reduction, a versatile method for extracting material information from data, can serve as a preprocessor for classical modeling and inference, or as a standalone theory that leads directly to statistical inference. ABSTRACT Sufficient dimension reduction (SDR) refers to supervised methods of dimension reduction that ...
R. Dennis Cook
wiley   +1 more source

Smith chart-based particle swarm optimization algorithm for multi-objective engineering problems [PDF]

Iranian Journal of Numerical Analysis and Optimization
Particle swarm optimization (PSO) is a widely recognized bio-inspired algorithm for systematically exploring solution spaces and iteratively iden-tifying optimal points.
A. Falloun, Y. Dursun, A. Ait Madi
doaj   +1 more source

High-dimensional Cost-constrained Regression via Nonconvex Optimization. [PDF]

Technometrics, 2022
Yu G, Fu H, Liu Y.
europepmc   +1 more source

Trust‐region filter algorithms utilizing Hessian information for gray‐box optimization

AIChE Journal, Volume 72, Issue 5, May 2026.
Abstract Optimizing industrial processes often involves gray‐box models that couple algebraic glass‐box equations with black‐box components lacking analytic derivatives. Such systems challenge derivative‐based solvers. The classical trust‐region filter (TRF) algorithm provides a robust framework but requires extensive parameter tuning and numerous ...
Gul Hameed, Tao Chen, Antonio del Rio Chanona, Lorenz T. Biegler, Michael Short   +4 more
wiley   +1 more source

T2$$ {\boldsymbol{T}}_{\mathbf{2}} $$‐Weighted Imaging of Water, Fat and Silicone

Magnetic Resonance in Medicine, Volume 95, Issue 5, Page 2765-2775, May 2026.
ABSTRACT Purpose Magnetic resonance imaging (MRI) is a sensitive method for assessing silicone implant integrity, with T2$$ {T}_2 $$‐weighted imaging being essential for detecting abnormalities in surrounding tissue. Silicone breast imaging protocols often require multiple tailored sequences for species suppression and diagnostic contrast. We propose a
Aizada Nurdinova, Xuetong Zhou, Julio A. Oscanoa, Preya Shah, Kawin Setsompop, Bruce L. Daniel, Brian A. Hargreaves   +6 more
wiley   +1 more source

Spatial Image Gradient Estimation From the Diffusion MRI Profile

Magnetic Resonance in Medicine, Volume 95, Issue 5, Page 2980-2991, May 2026.
ABSTRACT Purpose In the course of diffusion, water molecules encounter varying values for the relaxation‐time properties of the underlying tissue. This factor, which has rarely been accounted for in diffusion MRI (dMRI), is modeled in this work, allowing for the estimation of the gradient of relaxation‐time properties from the dMRI signal. Methods With
Iman Aganj, Thorsten Feiweier, John E. Kirsch, Bruce R. Fischl, Andre J. van der Kouwe   +4 more
wiley   +1 more source

Stabilizing Extreme Few‐Shot ECG Classification via Self‐Supervised Contrastive Pretraining

Annals of Noninvasive Electrocardiology, Volume 31, Issue 3, May 2026.
Under extreme few‐shot ECG classification, supervised training frequently collapses to degenerate solutions. Self‐supervised pretraining stabilizes optimization, eliminates early collapse, and enables reproducible multi‐class rhythm classification with limited labeled data. ABSTRACT In clinical electrocardiogram (ECG) analysis, high‐quality annotations
LiuPing Zeng, JingMei Pan, YangJie Lu, Xian Pan   +3 more
wiley   +1 more source

Multi-Objective Optimization Technique Based on QUBO and an Ising Machine

IEEE Access
With an increase in the complexity of society, solving multi-objective optimization problems (MOPs) has become crucial. In this study, we introduced a novel method called “quadratic unconstrained binary optimization based on the weighted normal ...
Hiroshi Ikeda, Takashi Yamazaki
doaj   +1 more source