Mining of repeated patterns from HTML documents is the key step towards Web-based data mining and knowledge extraction. Many web crawling applications need efficient repeated patterns mining techniques to generate the...
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Spin-transfer torque random access memory (STT-RAM) is one of the most promising substitutes for universal main memory and cache due to its excellent scalability, high density and low leakage power. Nevertheless, the ...
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Network-on-chip system plays an important role to improve the performance of chip multiprocessor systems. As the complexity of the network increases, congestion problem has become the major performance bottleneck and ...
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In this paper, we consider a wide class of constrained nonconvex regularized minimization problems, where the constraints are linearly constraints. It was reported in the litera- ture that nonconvex regularization usu...
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High-dimensional data arising from diverse scientific research fields and industrial development have led to increased interest in sparse learning due to model parsimony and computational advantage. With the assumptio...
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High-dimensional data arising from diverse scientific research fields and industrial development have led to increased interest in sparse learning due to model parsimony and computational advantage. With the assumption of sparsity, many computational problems can be handled efficiently in practice. Structured sparse learning encodes the structural information of the variables and has been quite successful in numerous research fields. With various types of structures discovered, sorts of structured regularizations have been proposed. These regularizations have greatly improved the efficacy of sparse learning algorithms through the use of specific structural information. In this article, we present a systematic review of structured sparse learning including ideas, formulations, algorithms, and applications. We present these algorithms in the unified framework of minimizing the sum of loss and penalty functions, summarize publicly accessible software implementations, and compare the computational complexity of typical optimization methods to solve structured sparse learning problems. In experiments, we present applications in unsupervised learning, for structured signal recovery and hierarchical image reconstruction, and in supervised learning in the context of a novel graph-guided logistic regression.
Applying graph clustering algorithms in real world networks needs to overcome two main challenges: the lack of prior knowledge and the scalability issue. This paper proposes a novel method based on the topological fea...
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There are a lot of important and sensitive data in databases, which need to be protected from attacks. To secure the data, Cryptography support is an effective mechanism. However, a tradeoff must be made between the p...
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This paper addresses the issue of fault tolerance in parallel computing, and proposes a new method named parallel recomputing. Such method achieves fault recovery automatically by using surviving processes to recomput...
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A radiation hardening algorithm named as state-conservation on 2nd order clock and data recovery (CDR) system is presented in this paper. This proposed algorithm is used to resist the single event transient (SET) of C...
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Environment-Driven adaptation is an important means ensuring software integrity. Confronted with scenarios not anticipated during the developmental stage, the predefined adaptability of the software should be adjusted...
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