As social networks continue to develop, accurately identifying key nodes has become crucial. Existing centrality algorithms often rely on local network structures and are susceptible to changes in the degree of indivi...
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When hackers carry out network attacks on Beidou satellites, which affect the visible number of satellites and signal transmission, ships cannot perform normal positioning. Therefore, under the premise of network atta...
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This paper introduces a distributed parallel Alternating Direction Method of Multipliers (ADMM) algorithm for solving the distributed constrained optimization problem over directed graphs. To effectively handle the ef...
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In this paper, we consider stochastic realization theory of Linear Switched Systems (LSS) with i.i.d. switching. We characterize minimality of stochastic LSSs and show existence and uniqueness (up to isomorphism) of m...
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This research seeks to research on English vocabulary classification method based on clustering algorithms. Vocabulary has long been the principal focus of teaching in undergraduate English studies as it is the essent...
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Semantic relationship is the logical connection between the parameters, application, and significance of two or more objects. In this paper, we analyze and select appropriate algorithms for determining semantic relati...
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Lasso is widely known as a sparse estimation method for regression coefficients in linear regression models, and the Alternating Direction Method of Multipliers (ADMM) is one of the representative computational method...
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This article proposes a distributed task allocation algorithm based on local information for unmanned aerial vehicle (UAV) swarm. A Part of the UAVs which are in the information exchange range participate in the local...
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This paper proposes a high-precision and efficient voxelization algorithm for the development of voxelization in the preprocessing stage of industrial simulation. The main idea is to establish an octree data structure...
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Many questions in science center around the fundamental problem of understanding causal relationships. However, most constraint-based causal discovery algorithms, including the well-celebrated PC algorithm, often incu...
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Many questions in science center around the fundamental problem of understanding causal relationships. However, most constraint-based causal discovery algorithms, including the well-celebrated PC algorithm, often incur an exponential number of conditional independence (CI) tests, posing limitations in various applications. Addressing this, our work focuses on characterizing what can be learned about the underlying causal graph with a reduced number of CI tests. We show that it is possible to a learn a coarser representation of the hidden causal graph with a polynomial number of tests. This coarser representation, named Causal Consistent Partition Graph (CCPG), comprises of a partition of the vertices and a directed graph defined over its components. CCPG satisfies consistency of orientations and additional constraints which favor finer partitions. Furthermore, it reduces to the underlying causal graph when the causal graph is identifiable. As a consequence, our results offer the first efficient algorithm for recovering the true causal graph with a polynomial number of tests, in special cases where the causal graph is fully identifiable through observational data and potentially additional interventions. Copyright 2024 by the author(s)
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