Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...

g2o is an open-source C++ framework for optimizing graph-based nonlinear error functions. g2o has been designed to be easily extensible to a wide range of problems ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

Abstract: Subcircuit recognition (SR) is a problem of identifying instances of a small subcircuit in a larger circuit. Despite recent progress toward linear optimization-based SR algorithms, finding a ...

Our library allows automatic bound derivation and computation for general computational graphs, in a similar manner that gradients are obtained in modern deep learning frameworks -- users only define ...

Acquiring and analyzing body information is the first step to sensing and understanding the body. The signals obtained from our body often hold the characteristics of high dimension, non-linearity, ...

Dynamics on networks describe a plethora of physical phenomena, including the viral spread on contact networks, the competition between species on predator–prey networks, and magnetoencephalography ...

Abstract: In recent years, there has been a growing interest in graph signal processing due to its capability to model and analyze irregular data generated by wireless sensor networks (WSNs).

Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit ...

Linear and nonlinear functions are fundamental concepts in mathematics, and it is crucial for students to understand the differences between them. Identifying linear and nonlinear functions can be ...