Results 71 to 80 of about 29,271 (183)
This study presents improvements to the non‐hydrostatic version of the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), enabling stable global simulations at 1.4‐km resolution. A systematic comparison with the hydrostatic version at resolutions from 9 to 1.4 km shows that non‐hydrostatic effects emerge in ...
Jozef Vivoda +3 more
wiley +1 more source
ABSTRACT In privacy protection of control systems, a trade‐off between control performance and privacy level is often pointed out. Our goal in this paper is to improve this trade‐off by shaping the frequency of noise added for privacy protection when the control objective is to track a reference signal, which is taken as a piece of information whose ...
Rintaro Watanabe +3 more
wiley +1 more source
Initial State Privacy of Nonlinear Systems on Riemannian Manifolds
ABSTRACT In this paper, we investigate initial state privacy protection for discrete‐time nonlinear closed systems. By capturing Riemannian geometric structures inherent in such privacy challenges, we refine the concept of differential privacy through the introduction of an initial state adjacency set based on Riemannian distances.
Le Liu, Yu Kawano, Antai Xie, Ming Cao
wiley +1 more source
Meander‐Bend Erosion Dynamics Along a Gravel‐Bed River: Insights From Short‐Term UAV Monitoring
ABSTRACT Riverbank erosion is a natural process in meandering rivers that contributes to sediment supply and geomorphic diversity, yet it can threaten infrastructure and human activities within the floodplain. Recently, many studies have used high‐resolution remote sensing technologies to measure bank erosion, but they often focus on technical aspects ...
Katarina Pavlek +2 more
wiley +1 more source
Infinitely many weak solutions of the p-Laplacian equation with nonlinear boundary conditions. [PDF]
Lu FY, Deng GQ.
europepmc +1 more source
Infinitely many homoclinic solutions for second order nonlinear difference equations with p-Laplacian. [PDF]
Sun G, Mai A.
europepmc +1 more source
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma +4 more
wiley +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
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
Hölder regularity for parabolic fractional p-Laplacian. [PDF]
Liao N.
europepmc +1 more source

