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Full-Text Articles in Physical Sciences and Mathematics
Introduction To The Usu Library Of Solutions To The Einstein Field Equations, Ian M. Anderson, Charles G. Torre
Introduction To The Usu Library Of Solutions To The Einstein Field Equations, Ian M. Anderson, Charles G. Torre
Tutorials on... in 1 hour or less
This is a Maple worksheet providing an introduction to the USU Library of Solutions to the Einstein Field Equations. The library is part of the DifferentialGeometry software project and is a collection of symbolic data and metadata describing solutions to the Einstein equations.
Tracking You Through Dns Traffic: Linking User Sessions By Clustering With Dirichlet Mixture Model, Mingxuan Sun, Junjie Zhang, Guangyue Xu, Dae Wook Kim
Tracking You Through Dns Traffic: Linking User Sessions By Clustering With Dirichlet Mixture Model, Mingxuan Sun, Junjie Zhang, Guangyue Xu, Dae Wook Kim
Computer Science and Engineering Faculty Publications
The Domain Name System (DNS), which does not encrypt domain names such as "bank.us" and "dentalcare.com", commonly accurately reflects the specific network services. Therefore, DNS-based behavioral analysis is extremely attractive for many applications such as forensics investigation and online advertisement. Traditionally, a user can be trivially and uniquely identified by the device’s IP address if it is static (i.e., a desktop or a laptop). As more and more wireless and mobile devices are deeply ingrained in our lives and the dynamic IP address such as DHCP has been widely applied, it becomes almost impossible to use one IP address to …
Some Tricks In Parameter Selection For Extreme Learning Machine, Weipeng Cao, Jinzhu Gao, Zhong Ming, Shubin Cai
Some Tricks In Parameter Selection For Extreme Learning Machine, Weipeng Cao, Jinzhu Gao, Zhong Ming, Shubin Cai
All Faculty Presentations - School of Engineering and Computer Science
Extreme learning machine (ELM) is a widely used neural network with random weights (NNRW), which has made great contributions to many fields. However, the relationship between the parameters and the performance of ELM has not been fully investigated yet, i.e. the impact of the number of hidden layer nodes, the randomization range of the weights between the input layer and hidden layer, the randomization range of the threshold of hidden nodes, and the type of activation functions. In this paper, eight benchmark functions are used to study this relationship. We have some interesting findings, such as more hidden layer nodes …