Results 161 to 170 of about 31,104 (274)
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
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
Positive periodic solution for p-Laplacian neutral Rayleigh equation with singularity of attractive type. [PDF]
Xin Y, Liu H, Cheng Z.
europepmc +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
Multiplicity and asymptotic behavior of solutions to a class of Kirchhoff-type equations involving the fractional p-Laplacian. [PDF]
Shen L.
europepmc +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
ABSTRACT Accurately predicting line loss rates is crucial for effective management in distribution networks, particularly for short‐term multihorizon forecasts ranging from 1 hour to 1 week. In this study, we propose attention‐GCN–LSTM, a novel method that integrates graph convolutional networks (GCN), long short‐term memory (LSTM) and a three‐level ...
Jie Liu +4 more
wiley +1 more source
Robust Partial Multi‐Label Learning Under Dual Noise via Joint Subspace Learning
ABSTRACT Partial Multi‐label Learning (PML) deals with the ambiguity where each instance is annotated with a set of candidate labels, and only a subset of which is valid. While existing PML methods focus primarily on label disambiguation, they often rely on the assumption of a clean feature space.
Yuanjian Zhang +4 more
wiley +1 more source
Abstract We estimate the price impact of very nearby concurrently listed properties in the Sydney housing market and assess their competition effects. We apply a hedonic model with spatiotemporal effects regularized via a graph Laplacian prior at the month‐by‐SA2 regional level to seven SA4 subregions of metropolitan Sydney. The model structure enables
Willem P. Sijp, Mengheng Li
wiley +1 more source
High-frequency energy fusion (HFEF) for nuclei segmentation with boundary-aware loss. [PDF]
Yin W, He W, Shang B, Zhao B, Wu X.
europepmc +1 more source

