Graph Neural Networks for Maximum Constraint Satisfaction

Many combinatorial optimization problems can be phrased in the language of constraint satisfaction problems.We introduce a graph neural network architecture for solving such optimization problems.The architecture is generic; it works for all binary constraint satisfaction problems.Training is unsupervised, and it is sufficient to FAST JOINT CARE +

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Bladder Cancer in South Korea: Analysis of Trends and Risk Factors of Bladder Cancer in South Korea Using a Nationwide Database

Purpose The purpose of this study was to evaluate the incidence rate and trend of bladder cancer in South Korea using a nationwide database.In addition, we aimed to determine the risk factors and their influence on the incidence of bladder cancer.Materials and Methods We extracted data from the health insurance database and estimated the incidence

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The Impending 5G Era and its likely impact on society

This paper looks at the emergence of the fifth generation of wireless networks, commonly referred to by the acronym 5G, from a perspective informed by the literature on digital divides and digital inequality.5G has been designed with the goal of minimizing inequalities in physical Dispenser access, in particular differences in access that arise as

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Fault Diagnosis for Variable Frequency Drive-Fed Induction Motors Using Wavelet Packet Decomposition and Greedy-Gradient Max-Cut Learning

In this paper, a novel fault diagnosis method for Default variable frequency drive (VFD)-fed induction motors is proposed using Wavelet Packet Decomposition (WPD) and greedy-gradient max-cut (GGMC) learning algorithm.The proposed method is developed using experimental stator current data in the lab for two 0.25 HP induction motors fed by a VFD, sub

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