We introduce DeepABM, a computational framework for agent-based modeling that leverages geometric message passing for simulating action and interactions over large agent populations. Using DeepABM allows scaling simul...
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We introduce DeepABM, a framework for agent-based modeling that leverages geometric message passing of graph neural networks for simulating action and interactions over large agent populations. Using DeepABM allows sc...
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This paper addresses how to construct a RBAC-compatible attribute-based encryption (ABE) for secure cloud storage, which provides a user-friendly and easy-to-manage security mechanism without user intervention. Simila...
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In this work, K-partitioning of signed or weighted bipartite graph problem has been introduced, which appears as a real life problem where the partitions of bipartite graph represent two different entities and the edg...
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ISBN:
(纸本)9781479915194
In this work, K-partitioning of signed or weighted bipartite graph problem has been introduced, which appears as a real life problem where the partitions of bipartite graph represent two different entities and the edges between the nodes of the partitions represent the relationships among them. A typical example is the set of people and their opinions, whose strength is represented as signed numerical values. Using the weights on the edges, these bipartite graphs can be partitioned into two or more clusters. In political domain, a cluster represents strong relationship among a group of people and a group of issues. In the paper, we formally define the problem and compare different heuristics, and show through both real and simulated data the effectiveness of our approaches.
Business-to-business (B2B) Electronic Business (EC) is speedily getting popular and companies are increasingly using this notion and technology for business transaction and data exchange among each other. Traditionall...
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Statistical estimation of large-scale dynamic systems governed by stochastic partial differential equations is important in a wide range of scientific applications. However, the realization of computationally efficien...
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ISBN:
(纸本)0818688211
Statistical estimation of large-scale dynamic systems governed by stochastic partial differential equations is important in a wide range of scientific applications. However, the realization of computationally efficient algorithms for statistical estimation of such dynamic systems is very difficult. Conventional linear least squares methods are impractical for both computational and storage reasons. A previously-developed multiscale estimation methodology has been successfully applied to a number of large-scale static estimation problems. In this paper we apply the multiscale approach to the more challenging dynamic estimation problems, introducing a recursive procedure that efficiently propagates multiscale models for the estimation errors in a manner analogous to, but more efficient than, the Kalman filter's propagation of the error covariances. We illustrate our research in the context of 1-D and 2-D diffusive processes.
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