The Experts below are selected from a list of 383109 Experts worldwide ranked by ideXlab platform
Xiang Wang - One of the best experts on this subject based on the ideXlab platform.
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A Polarity Optimization Algorithm Taking Into Account Polarity Conversion Sequence
IEEE Access, 2019Co-Authors: Zhenxue He, Limin Xiao, Xiang WangAbstract:The Polarity conversion sequence directly determines Polarity conversion efficiency and then affects Polarity optimization efficiency. However, few studies have focused on the Polarity conversion sequence problem of Reed-Muller (RM) circuits. In this paper, we propose a continuous Hopfield neural network (CHNN)-based Polarity conversion algorithm (CHNNPCA) for Mixed Polarity RM (MPRM) circuits, which uses the CHNN to solve the best Polarity conversion sequence of Polarity set waiting for evaluation before converting the Polarity set. Moreover, based on the CHNNPCA, a Polarity optimization algorithm (POA) is proposed to improve the Polarity optimization efficiency of MPRM circuits. The experimental results on MCNC benchmark circuits show that for the large-scale Polarity set, the CHNNPCA is superior to the mixed Polarity conversion algorithm based on the tabular technique in terms of Polarity conversion efficiency. Furthermore, compared to the traditional Polarity optimization algorithm neglecting Polarity conversion sequence, the POA has a considerable advantage in improving Polarity optimization efficiency, especially for large-scale circuits. The POA can be extended to improve the Polarity optimization efficiency of fixed Polarity RM circuits.
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An efficient and fast Polarity optimization approach for mixed Polarity Reed-Muller logic circuits
Frontiers of Computer Science in China, 2017Co-Authors: Zhenxue He, Limin Xiao, Fei Gu, Shubin Su, Rong Zhang, Longbing Zhang, Li Ruan, Xiang WangAbstract:Although the genetic algorithm has been widely used in the Polarity optimization of mixed Polarity Reed-Muller (MPRM) logic circuits, few studies have taken into account the Polarity conversion sequence. In order to improve the efficiency of Polarity optimization of MPRM logic circuits, we propose an efficient and fast Polarity optimization approach (FPOA) considering the Polarity conversion sequence. The main idea behind the FPOA is that, firstly, the best Polarity conversion sequence of the Polarity set waiting for evaluation is obtained by using the proposed hybrid genetic algorithm (HGA); secondly, each of Polarity in the Polarity set is converted according to the best Polarity conversion sequence obtained by HGA. Our proposed FPOA is implemented in C and a comparative analysis has been presented for MCNC benchmark circuits. The experimental results show that for the circuits with more variables, the FPOA is highly effective in improving the efficiency of Polarity optimization of MPRM logic circuits compared with the traditional Polarity optimization approach which neglects the Polarity conversion sequence and the improved Polarity optimization approach with heuristic technique.
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Optimization of best Polarity searching for mixed Polarity reed-muller logic circuit
2015 28th IEEE International System-on-Chip Conference (SOCC), 2015Co-Authors: Limin Xiao, Zhenxue He, Rong Zhang, Li Ruan, Xiang WangAbstract:At present, although genetic algorithm (GA) is widely used in best Polarity searching of MPRM logic circuit, there are few literatures pay attention to the Polarity conversion sequence of the Polarity set waiting for evaluation. An improved best Polarity searching approach (IBPSA) based on GA is presented to optimize the Polarity conversion sequence of Polarity set and speed up the best Polarity searching of MPRM logic circuits. In addition, we present an improved nearest neighbor (INN) to obtain the best Polarity conversion sequence of the Polarity set waiting for evaluation in each generation of GA and apply elitism strategy to IBPSA to guarantee its global convergence. Our proposed IBPSA is implemented in C and a comparative analysis has been presented for MCNC benchmark circuits. The experimental results show that the IBPSA can greatly reduce the time of best Polarity searching of MPRM logic circuits compared to the approaches neglecting Polarity conversion sequence.
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Polarity Searching for MPRM Logic Circuit Based on Improved Adaptive Genetic Algorithm
2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th I, 2015Co-Authors: Xiang Wang, Zhenxue He, Rong Zhang, Li Ruan, Weike Wang, Lin Li, Quanneng Shen, Limin XiaoAbstract:In combinational logic circuits, expressing logic functions in terms of Mixed Polarity Reed-Muller (MPRM) expansions draws more and more attention for its advantages over Boolean logic and Fixed Polarity Reed-Muller (FPRM) expansions. For n-variable MPRM logic function, the Polarity directly determines the expression form of the circuits, and then influences the power dissipation of the circuit. However, many literatures tend to research the optimization of the MPRM ignoring the Polarity traversal sequence for large-scale circuits. This paper presents an Improved Adaptive Genetic Algorithm (IAGA) to optimize the best Polarity traversal sequence of MPRM logic circuits to speed up the Polarity optimization. The proposed algorithm has been carried out in C language, and a comparative analysis has been presented for MCNC benchmark circuits. The results show that this algorithm gives best Polarity and does well in reducing the time of Polarity searching.
John G. Collard - One of the best experts on this subject based on the ideXlab platform.
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Cell Polarity proteins and cancer
Seminars in Cancer Biology, 2012Co-Authors: Saskia I. J. Ellenbroek, Sandra Iden, John G. CollardAbstract:Cell Polarity is essential in many biological processes and required for development as well as maintenance of tissue integrity. Loss of Polarity is considered both a hallmark and precondition for human cancer. Three conserved Polarity protein complexes regulate different modes of Polarity that are conserved throughout numerous cell types and species. These complexes are the Crumbs, Par and Scribble complex. Given the importance of cell Polarity for normal tissue homeostasis, aberrant Polarity signaling is suggested to contribute to the multistep processes of human cancer. Most human cancers are formed from epithelial cells. Evidence confirming the roles for Polarity proteins in different phases of the oncogenic trajectory comes from functional studies using mammalian cells as well as Drosophila and zebrafish models. Furthermore, several reports have revealed aberrant expression and localization of Polarity proteins in different human tumors. In this review we will give an overview on the current data available that couple Polarity signaling to tumorigenesis, particularly in epithelial cells.
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Tiam1 takes PARt in cell Polarity.
Trends in Cell Biology, 2006Co-Authors: A. E E Mertens, D. Michiel Pegtel, John G. CollardAbstract:Cell Polarity is an essential requirement for the proper tissue development of complex organisms. This is underscored by in vivo studies showing that loss of cell Polarity contributes to the formation and progression of tumours. Evolutionary conserved multiprotein complexes, such as the Par3–Par6–aPKC or, in short, the Par Polarity complex, regulate the establishment of cell Polarity. The small Rho GTPases CDC42 and Rac control the activation of the Par Polarity complex. Evidence now implicates the Rac activator Tiam1 as a crucial component of the Par complex in regulating neuronal (axonal) and epithelial (apical–basal) Polarity. Our current knowledge places Tiam1 at the centre of a pivotal biological process, the establishment and maintenance of cell Polarity, and suggests that deregulation of the Tiam1–Par complex contributes to tumourigenicity.
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Tiam1 takes PARt in cell Polarity
Trends in Cell Biology, 2006Co-Authors: A. E E Mertens, D. Michiel Pegtel, John G. CollardAbstract:Cell Polarity is an essential requirement for the proper tissue development of complex organisms. This is underscored by in vivo studies showing that loss of cell Polarity contributes to the formation and progression of tumours. Evolutionary conserved multiprotein complexes, such as the Par3-Par6-aPKC or, in short, the Par Polarity complex, regulate the establishment of cell Polarity. The small Rho GTPases CDC42 and Rac control the activation of the Par Polarity complex. Evidence now implicates the Rac activator Tiam1 as a crucial component of the Par complex in regulating neuronal (axonal) and epithelial (apical-basal) Polarity. Our current knowledge places Tiam1 at the centre of a pivotal biological process, the establishment and maintenance of cell Polarity, and suggests that deregulation of the Tiam1-Par complex contributes to tumourigenicity. © 2006 Elsevier Ltd. All rights reserved.
Zhenxue He - One of the best experts on this subject based on the ideXlab platform.
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A Polarity Optimization Algorithm Taking Into Account Polarity Conversion Sequence
IEEE Access, 2019Co-Authors: Zhenxue He, Limin Xiao, Xiang WangAbstract:The Polarity conversion sequence directly determines Polarity conversion efficiency and then affects Polarity optimization efficiency. However, few studies have focused on the Polarity conversion sequence problem of Reed-Muller (RM) circuits. In this paper, we propose a continuous Hopfield neural network (CHNN)-based Polarity conversion algorithm (CHNNPCA) for Mixed Polarity RM (MPRM) circuits, which uses the CHNN to solve the best Polarity conversion sequence of Polarity set waiting for evaluation before converting the Polarity set. Moreover, based on the CHNNPCA, a Polarity optimization algorithm (POA) is proposed to improve the Polarity optimization efficiency of MPRM circuits. The experimental results on MCNC benchmark circuits show that for the large-scale Polarity set, the CHNNPCA is superior to the mixed Polarity conversion algorithm based on the tabular technique in terms of Polarity conversion efficiency. Furthermore, compared to the traditional Polarity optimization algorithm neglecting Polarity conversion sequence, the POA has a considerable advantage in improving Polarity optimization efficiency, especially for large-scale circuits. The POA can be extended to improve the Polarity optimization efficiency of fixed Polarity RM circuits.
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An efficient and fast Polarity optimization approach for mixed Polarity Reed-Muller logic circuits
Frontiers of Computer Science in China, 2017Co-Authors: Zhenxue He, Limin Xiao, Fei Gu, Shubin Su, Rong Zhang, Longbing Zhang, Li Ruan, Xiang WangAbstract:Although the genetic algorithm has been widely used in the Polarity optimization of mixed Polarity Reed-Muller (MPRM) logic circuits, few studies have taken into account the Polarity conversion sequence. In order to improve the efficiency of Polarity optimization of MPRM logic circuits, we propose an efficient and fast Polarity optimization approach (FPOA) considering the Polarity conversion sequence. The main idea behind the FPOA is that, firstly, the best Polarity conversion sequence of the Polarity set waiting for evaluation is obtained by using the proposed hybrid genetic algorithm (HGA); secondly, each of Polarity in the Polarity set is converted according to the best Polarity conversion sequence obtained by HGA. Our proposed FPOA is implemented in C and a comparative analysis has been presented for MCNC benchmark circuits. The experimental results show that for the circuits with more variables, the FPOA is highly effective in improving the efficiency of Polarity optimization of MPRM logic circuits compared with the traditional Polarity optimization approach which neglects the Polarity conversion sequence and the improved Polarity optimization approach with heuristic technique.
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Optimization of best Polarity searching for mixed Polarity reed-muller logic circuit
2015 28th IEEE International System-on-Chip Conference (SOCC), 2015Co-Authors: Limin Xiao, Zhenxue He, Rong Zhang, Li Ruan, Xiang WangAbstract:At present, although genetic algorithm (GA) is widely used in best Polarity searching of MPRM logic circuit, there are few literatures pay attention to the Polarity conversion sequence of the Polarity set waiting for evaluation. An improved best Polarity searching approach (IBPSA) based on GA is presented to optimize the Polarity conversion sequence of Polarity set and speed up the best Polarity searching of MPRM logic circuits. In addition, we present an improved nearest neighbor (INN) to obtain the best Polarity conversion sequence of the Polarity set waiting for evaluation in each generation of GA and apply elitism strategy to IBPSA to guarantee its global convergence. Our proposed IBPSA is implemented in C and a comparative analysis has been presented for MCNC benchmark circuits. The experimental results show that the IBPSA can greatly reduce the time of best Polarity searching of MPRM logic circuits compared to the approaches neglecting Polarity conversion sequence.
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Polarity Searching for MPRM Logic Circuit Based on Improved Adaptive Genetic Algorithm
2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th I, 2015Co-Authors: Xiang Wang, Zhenxue He, Rong Zhang, Li Ruan, Weike Wang, Lin Li, Quanneng Shen, Limin XiaoAbstract:In combinational logic circuits, expressing logic functions in terms of Mixed Polarity Reed-Muller (MPRM) expansions draws more and more attention for its advantages over Boolean logic and Fixed Polarity Reed-Muller (FPRM) expansions. For n-variable MPRM logic function, the Polarity directly determines the expression form of the circuits, and then influences the power dissipation of the circuit. However, many literatures tend to research the optimization of the MPRM ignoring the Polarity traversal sequence for large-scale circuits. This paper presents an Improved Adaptive Genetic Algorithm (IAGA) to optimize the best Polarity traversal sequence of MPRM logic circuits to speed up the Polarity optimization. The proposed algorithm has been carried out in C language, and a comparative analysis has been presented for MCNC benchmark circuits. The results show that this algorithm gives best Polarity and does well in reducing the time of Polarity searching.
Marcos Garcia - One of the best experts on this subject based on the ideXlab platform.
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citius a naive bayes strategy for sentiment analysis on english tweets
International Conference on Computational Linguistics, 2014Co-Authors: Pablo Gamallo, Marcos GarciaAbstract:This article describes a strategy based on a naive-bayes classifier for detecting the Polarity of English tweets. The experiments have shown that the best performance is achieved by using a binary classifier between just two sharp Polarity categories: positive and negative. In addition, in order to detect tweets with and without Polarity, the system makes use of a very basic rule that searchs for Polarity words within the analysed tweets/texts. When the classifier is provided with a Polarity lexicon and multiwords it achieves 63% F-score.
Limin Xiao - One of the best experts on this subject based on the ideXlab platform.
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A Polarity Optimization Algorithm Taking Into Account Polarity Conversion Sequence
IEEE Access, 2019Co-Authors: Zhenxue He, Limin Xiao, Xiang WangAbstract:The Polarity conversion sequence directly determines Polarity conversion efficiency and then affects Polarity optimization efficiency. However, few studies have focused on the Polarity conversion sequence problem of Reed-Muller (RM) circuits. In this paper, we propose a continuous Hopfield neural network (CHNN)-based Polarity conversion algorithm (CHNNPCA) for Mixed Polarity RM (MPRM) circuits, which uses the CHNN to solve the best Polarity conversion sequence of Polarity set waiting for evaluation before converting the Polarity set. Moreover, based on the CHNNPCA, a Polarity optimization algorithm (POA) is proposed to improve the Polarity optimization efficiency of MPRM circuits. The experimental results on MCNC benchmark circuits show that for the large-scale Polarity set, the CHNNPCA is superior to the mixed Polarity conversion algorithm based on the tabular technique in terms of Polarity conversion efficiency. Furthermore, compared to the traditional Polarity optimization algorithm neglecting Polarity conversion sequence, the POA has a considerable advantage in improving Polarity optimization efficiency, especially for large-scale circuits. The POA can be extended to improve the Polarity optimization efficiency of fixed Polarity RM circuits.
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An efficient and fast Polarity optimization approach for mixed Polarity Reed-Muller logic circuits
Frontiers of Computer Science in China, 2017Co-Authors: Zhenxue He, Limin Xiao, Fei Gu, Shubin Su, Rong Zhang, Longbing Zhang, Li Ruan, Xiang WangAbstract:Although the genetic algorithm has been widely used in the Polarity optimization of mixed Polarity Reed-Muller (MPRM) logic circuits, few studies have taken into account the Polarity conversion sequence. In order to improve the efficiency of Polarity optimization of MPRM logic circuits, we propose an efficient and fast Polarity optimization approach (FPOA) considering the Polarity conversion sequence. The main idea behind the FPOA is that, firstly, the best Polarity conversion sequence of the Polarity set waiting for evaluation is obtained by using the proposed hybrid genetic algorithm (HGA); secondly, each of Polarity in the Polarity set is converted according to the best Polarity conversion sequence obtained by HGA. Our proposed FPOA is implemented in C and a comparative analysis has been presented for MCNC benchmark circuits. The experimental results show that for the circuits with more variables, the FPOA is highly effective in improving the efficiency of Polarity optimization of MPRM logic circuits compared with the traditional Polarity optimization approach which neglects the Polarity conversion sequence and the improved Polarity optimization approach with heuristic technique.
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Optimization of best Polarity searching for mixed Polarity reed-muller logic circuit
2015 28th IEEE International System-on-Chip Conference (SOCC), 2015Co-Authors: Limin Xiao, Zhenxue He, Rong Zhang, Li Ruan, Xiang WangAbstract:At present, although genetic algorithm (GA) is widely used in best Polarity searching of MPRM logic circuit, there are few literatures pay attention to the Polarity conversion sequence of the Polarity set waiting for evaluation. An improved best Polarity searching approach (IBPSA) based on GA is presented to optimize the Polarity conversion sequence of Polarity set and speed up the best Polarity searching of MPRM logic circuits. In addition, we present an improved nearest neighbor (INN) to obtain the best Polarity conversion sequence of the Polarity set waiting for evaluation in each generation of GA and apply elitism strategy to IBPSA to guarantee its global convergence. Our proposed IBPSA is implemented in C and a comparative analysis has been presented for MCNC benchmark circuits. The experimental results show that the IBPSA can greatly reduce the time of best Polarity searching of MPRM logic circuits compared to the approaches neglecting Polarity conversion sequence.
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Polarity Searching for MPRM Logic Circuit Based on Improved Adaptive Genetic Algorithm
2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th I, 2015Co-Authors: Xiang Wang, Zhenxue He, Rong Zhang, Li Ruan, Weike Wang, Lin Li, Quanneng Shen, Limin XiaoAbstract:In combinational logic circuits, expressing logic functions in terms of Mixed Polarity Reed-Muller (MPRM) expansions draws more and more attention for its advantages over Boolean logic and Fixed Polarity Reed-Muller (FPRM) expansions. For n-variable MPRM logic function, the Polarity directly determines the expression form of the circuits, and then influences the power dissipation of the circuit. However, many literatures tend to research the optimization of the MPRM ignoring the Polarity traversal sequence for large-scale circuits. This paper presents an Improved Adaptive Genetic Algorithm (IAGA) to optimize the best Polarity traversal sequence of MPRM logic circuits to speed up the Polarity optimization. The proposed algorithm has been carried out in C language, and a comparative analysis has been presented for MCNC benchmark circuits. The results show that this algorithm gives best Polarity and does well in reducing the time of Polarity searching.