Martin Delecluse, Stéphane Sanchez, Sylvain Cussat-Blanc, Nicolas Schneider, J. Welcomme
We propose a new collaborative guidance platform for a team of robots that should protect a fixed ground target from one or several threats. The team of robots performs high-level behaviors. These are hand-coded since they consist in driving the robots to some given position. However, deciding when and how to use these behaviors is much more challenging. Scripting high-level interception strategies is a complex problem and applicable to few specific application contexts. We propose to use a gene regulatory network to regulate high-level behaviors and to enable the emergence of efficient and robust interception strategies.
{"title":"High-level behavior regulation for multi-robot systems","authors":"Martin Delecluse, Stéphane Sanchez, Sylvain Cussat-Blanc, Nicolas Schneider, J. Welcomme","doi":"10.1145/2598394.2598454","DOIUrl":"https://doi.org/10.1145/2598394.2598454","url":null,"abstract":"We propose a new collaborative guidance platform for a team of robots that should protect a fixed ground target from one or several threats. The team of robots performs high-level behaviors. These are hand-coded since they consist in driving the robots to some given position. However, deciding when and how to use these behaviors is much more challenging. Scripting high-level interception strategies is a complex problem and applicable to few specific application contexts. We propose to use a gene regulatory network to regulate high-level behaviors and to enable the emergence of efficient and robust interception strategies.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115116379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, inspired by speed-up and speed-down (SUSD) mechanism observed by the fish swarm avoiding light, an SUSD strategy is proposed to develop new swarm intelligence based optimization algorithms to enhance the accuracy and efficiency of swarm optimization algorithms. By comparing with the global best solution, each particle adaptively speeds up and speeds down towards the best solution. Specifically, a new directed speed term is added to the original particle swarm optimization (PSO) algorithm or other PSO variations. Due to the SUSD mechanism, the algorithm shows a great improvement of the accuracy and convergence rate compared with the original PSO and other PSO variations. The numerical evaluation is provided by solving recent benchmark functions in IEEE CEC 2013.
{"title":"A speed-up and speed-down strategy for swarm optimization","authors":"Haopeng Zhang, Fumin Zhang, Qing Hui","doi":"10.1145/2598394.2602285","DOIUrl":"https://doi.org/10.1145/2598394.2602285","url":null,"abstract":"In this paper, inspired by speed-up and speed-down (SUSD) mechanism observed by the fish swarm avoiding light, an SUSD strategy is proposed to develop new swarm intelligence based optimization algorithms to enhance the accuracy and efficiency of swarm optimization algorithms. By comparing with the global best solution, each particle adaptively speeds up and speeds down towards the best solution. Specifically, a new directed speed term is added to the original particle swarm optimization (PSO) algorithm or other PSO variations. Due to the SUSD mechanism, the algorithm shows a great improvement of the accuracy and convergence rate compared with the original PSO and other PSO variations. The numerical evaluation is provided by solving recent benchmark functions in IEEE CEC 2013.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115271156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guang-Wei Zhang, Zhi-hui Zhan, Ke-Jing Du, Wei-neng Chen
This paper proposes a novel normalization group strategy (NGS) to extend brain storm optimization (BSO) for power electronic circuit (PEC) design and optimization. As different variables in different dimensions of the PEC represent different circuit components such as resistor, capacitor, or inductor, they have different physical significances and various search space that are even not in comparable range. Therefore, the traditional group method used in BSO, which is based on the solution position information, is not suitable when solving PEC. In order to overcome this issue, the NGS proposed in this paper normalizes different dimensions of the solution to the same comparable range. This way, the grouping operator of BSO can work when using BSO to solve PEC. The NGS based BSO (NGBSO) approach has been implemented to optimize the design of a buck regulator in PEC. The results are compared with those obtained by using genetic algorithm (GA) and particle swarm optimization (PSO). Results show that the NGBSO algorithm outperforms GA and PSO in our PEC design and optimization study. Moreover, the NGS can be regarded as an efficient method to extend BSO to real-world application problems whose dimensions are with different physical significances and search ranges.
{"title":"Normalization group brain storm optimization for power electronic circuit optimization","authors":"Guang-Wei Zhang, Zhi-hui Zhan, Ke-Jing Du, Wei-neng Chen","doi":"10.1145/2598394.2598433","DOIUrl":"https://doi.org/10.1145/2598394.2598433","url":null,"abstract":"This paper proposes a novel normalization group strategy (NGS) to extend brain storm optimization (BSO) for power electronic circuit (PEC) design and optimization. As different variables in different dimensions of the PEC represent different circuit components such as resistor, capacitor, or inductor, they have different physical significances and various search space that are even not in comparable range. Therefore, the traditional group method used in BSO, which is based on the solution position information, is not suitable when solving PEC. In order to overcome this issue, the NGS proposed in this paper normalizes different dimensions of the solution to the same comparable range. This way, the grouping operator of BSO can work when using BSO to solve PEC. The NGS based BSO (NGBSO) approach has been implemented to optimize the design of a buck regulator in PEC. The results are compared with those obtained by using genetic algorithm (GA) and particle swarm optimization (PSO). Results show that the NGBSO algorithm outperforms GA and PSO in our PEC design and optimization study. Moreover, the NGS can be regarded as an efficient method to extend BSO to real-world application problems whose dimensions are with different physical significances and search ranges.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116634617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Ren, J. Suzuki, Chonho Lee, A. Vasilakos, Shingo Omura, Katsuya Oba
This paper proposes and evaluates a multiobjective evolutionary game theoretic framework for adaptive and stable application deployment in clouds that support dynamic voltage and frequency scaling (DVFS) for CPUs. The proposed framework, called Cielo, aids cloud operators to adapt the resource allocation to applications and their locations according to the operational conditions in a cloud (e.g., workload and resource availability) with respect to multiple conflicting objectives such as response time performance, recourse utilization and power consumption. Moreover, Cielo theoretically guarantees that each application performs an evolutionarily stable deployment strategy, which is an equilibrium solution under given operational conditions. Simulation results verify this theoretical analysis; applications seek equilibria to perform adaptive and evolutionarily stable deployment strategies. Cielo allows applications to successfully leverage DVFS to balance their response time performance, resource utilization and power consumption.
{"title":"Balancing performance, resource efficiency and energy efficiency for virtual machine deployment in DVFS-enabled clouds: an evolutionary game theoretic approach","authors":"Y. Ren, J. Suzuki, Chonho Lee, A. Vasilakos, Shingo Omura, Katsuya Oba","doi":"10.1145/2598394.2605693","DOIUrl":"https://doi.org/10.1145/2598394.2605693","url":null,"abstract":"This paper proposes and evaluates a multiobjective evolutionary game theoretic framework for adaptive and stable application deployment in clouds that support dynamic voltage and frequency scaling (DVFS) for CPUs. The proposed framework, called Cielo, aids cloud operators to adapt the resource allocation to applications and their locations according to the operational conditions in a cloud (e.g., workload and resource availability) with respect to multiple conflicting objectives such as response time performance, recourse utilization and power consumption. Moreover, Cielo theoretically guarantees that each application performs an evolutionarily stable deployment strategy, which is an equilibrium solution under given operational conditions. Simulation results verify this theoretical analysis; applications seek equilibria to perform adaptive and evolutionarily stable deployment strategies. Cielo allows applications to successfully leverage DVFS to balance their response time performance, resource utilization and power consumption.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127232587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Welcome to the Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS 2014)! Evolutionary computation (EC) and multi-agent systems and simulation (MASS) both involve populations of agents. EC is a learning technique by which populations of individual agents adapt according to the selection pressures exerted by an environment; MASS seeks to understand how to coordinate the actions of a population of (possibly selfish) autonomous agents that share an environment so that some outcome is achieved. Both EC and MASS have top-down and bottom up features. For example, some aspects of multi-agent system engineering (e.g., mechanism design) are concerned with how top-down structure can constrain or influence individual decisions. Similarly, most work in EC is concerned with how to engineer selective pressures to drive the evolution of individual behavior towards some desired goal. Multi-agent simulation (also called agent-based modeling) addresses the bottom-up issue of how collective behavior emerges from individual action. Likewise, the study of evolutionary dynamics within EC (for example in coevolution) often considers how population-level phenomena emerge from individual-level interactions. Thus, at a high level, we may view EC and MASS as examining and utilizing analogous processes. It is therefore natural to consider how knowledge gained within EC may be relevant to MASS, and vice versa; indeed, applications and techniques from one field have often made use of technologies and algorithms from the other field. Studying EC and MASS in combination is warranted and has the potential to contribute to both fields. The ECoMASS Workshop at GECCO has a successful history as a forum for exploring precisely this intersection, and we are looking forward to another year of stimulating discussion, bringing together experts as well as novices in both areas, to engage in dialogue about their work. This year's participants bring a variety of research topics for discussion, including migratory flows, the beating of the heart, embedded systems, pelotons and cyclists, and flocking behavior. This year, we will also be adding a "research slam". We will be inviting presenters who are presenting in the general conference to give us a quick presentation about their work. We hope that this will further encourage additional lively discussion about a wide range of topics. We also encourage presenters to demonstrate their software live during a breakout session, enabling additional opportunities for one-on-one dialogue and discussion of actual running systems. Through these additions to the workshop, we hope to continue to innovate, and develop the conversations around the interesting intersection of evolutionary computation and multi-agent systems and simulations!
{"title":"Session details: Workshop: eighth annual workshop on evolutionary computation and multi-agent systems and simulation","authors":"Forrest Stoendahl, W. Rand","doi":"10.1145/3250286","DOIUrl":"https://doi.org/10.1145/3250286","url":null,"abstract":"Welcome to the Eighth Annual Workshop on Evolutionary Computation and Multi-Agent Systems and Simulation (ECoMASS 2014)! Evolutionary computation (EC) and multi-agent systems and simulation (MASS) both involve populations of agents. EC is a learning technique by which populations of individual agents adapt according to the selection pressures exerted by an environment; MASS seeks to understand how to coordinate the actions of a population of (possibly selfish) autonomous agents that share an environment so that some outcome is achieved. Both EC and MASS have top-down and bottom up features. For example, some aspects of multi-agent system engineering (e.g., mechanism design) are concerned with how top-down structure can constrain or influence individual decisions. Similarly, most work in EC is concerned with how to engineer selective pressures to drive the evolution of individual behavior towards some desired goal. Multi-agent simulation (also called agent-based modeling) addresses the bottom-up issue of how collective behavior emerges from individual action. Likewise, the study of evolutionary dynamics within EC (for example in coevolution) often considers how population-level phenomena emerge from individual-level interactions. Thus, at a high level, we may view EC and MASS as examining and utilizing analogous processes. It is therefore natural to consider how knowledge gained within EC may be relevant to MASS, and vice versa; indeed, applications and techniques from one field have often made use of technologies and algorithms from the other field. Studying EC and MASS in combination is warranted and has the potential to contribute to both fields. The ECoMASS Workshop at GECCO has a successful history as a forum for exploring precisely this intersection, and we are looking forward to another year of stimulating discussion, bringing together experts as well as novices in both areas, to engage in dialogue about their work. This year's participants bring a variety of research topics for discussion, including migratory flows, the beating of the heart, embedded systems, pelotons and cyclists, and flocking behavior. This year, we will also be adding a \"research slam\". We will be inviting presenters who are presenting in the general conference to give us a quick presentation about their work. We hope that this will further encourage additional lively discussion about a wide range of topics. We also encourage presenters to demonstrate their software live during a breakout session, enabling additional opportunities for one-on-one dialogue and discussion of actual running systems. Through these additions to the workshop, we hope to continue to innovate, and develop the conversations around the interesting intersection of evolutionary computation and multi-agent systems and simulations!","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125363286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we study the evolutionary dynamics of the public goods game where the population of mobile individuals is divided into separate groups. We extend the usual discrete strategy game, by introducing "conditional investors" who have a real-value genetic trait that determines their level of risk aversion, or willingness to invest into the common pool. At the end of each round of the game, each individual has an opportunity to (a) update their risk aversion trait using a form of imitation from within their current group, and (b) to switch groups if they are not satisfied with their payoff in their current group. Detailed simulation experiments show that investment levels can be maintained within groups. The mean value of the risk aversion trait is significantly lower in smaller groups and is correlated with the underlying migration mode. In the conditional migration scenarios, levels of investment consistent with risk aversion emerge.
{"title":"Risk aversion and mobility in the public goods game","authors":"M. Kirley, Friedrich Burkhard von der Osten","doi":"10.1145/2598394.2599988","DOIUrl":"https://doi.org/10.1145/2598394.2599988","url":null,"abstract":"In this paper, we study the evolutionary dynamics of the public goods game where the population of mobile individuals is divided into separate groups. We extend the usual discrete strategy game, by introducing \"conditional investors\" who have a real-value genetic trait that determines their level of risk aversion, or willingness to invest into the common pool. At the end of each round of the game, each individual has an opportunity to (a) update their risk aversion trait using a form of imitation from within their current group, and (b) to switch groups if they are not satisfied with their payoff in their current group. Detailed simulation experiments show that investment levels can be maintained within groups. The mean value of the risk aversion trait is significantly lower in smaller groups and is correlated with the underlying migration mode. In the conditional migration scenarios, levels of investment consistent with risk aversion emerge.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122680118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Particle Swarm Optimization is fundamentally a stochastic algorithm, where each particle takes into account noisy information from its own history as well as that of its neighborhood. Though basic information-theoretic principles would suggest that less noise indicates greater certainty, the momentum term is simultaneously the least directly-informed and the most deterministically applied. This dichotomy suggests that the typically confident treatment of momentum is misplaced, and that swarm performance can benefit from better-motivated processes that obviate momentum entirely.
{"title":"Under-informed momentum in PSO","authors":"Christopher K. Monson, Kevin Seppi","doi":"10.1145/2598394.2598490","DOIUrl":"https://doi.org/10.1145/2598394.2598490","url":null,"abstract":"Particle Swarm Optimization is fundamentally a stochastic algorithm, where each particle takes into account noisy information from its own history as well as that of its neighborhood. Though basic information-theoretic principles would suggest that less noise indicates greater certainty, the momentum term is simultaneously the least directly-informed and the most deterministically applied. This dichotomy suggests that the typically confident treatment of momentum is misplaced, and that swarm performance can benefit from better-motivated processes that obviate momentum entirely.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122690242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Black-box complexity: from complexity theory to playing mastermind","authors":"Benjamin Doerr, Carola Doerr","doi":"10.1145/2598394.2605352","DOIUrl":"https://doi.org/10.1145/2598394.2605352","url":null,"abstract":"","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114419859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is well-known that Evolutionary Algorithms (EAs) are sensitive to changes in their control parameters, and it is generally agreed that too large a change may turn the EA from being successful to unsuccessful. This work reports on an experimental hybrid visualization scheme for the determination of EA stability according to perturbation of EA parameters. The scheme gives a visual representation of local neighborhoods of the parameter space according to a choice of two perturbation metrics, relating perturbations to EA performance as a variant of Kolmogorov distance. Through visualization and analysis of twelve thousand case study EA runs, we illustrate that we are able to distinguish between EA stability and instability depending upon perturbation and performance metrics. Finally we use what we have learned in the case study to provide a methodology for more general EAs.
{"title":"EA stability visualization: perturbations, metrics and performance","authors":"M. J. Craven, H. C. Jimbo","doi":"10.1145/2598394.2610549","DOIUrl":"https://doi.org/10.1145/2598394.2610549","url":null,"abstract":"It is well-known that Evolutionary Algorithms (EAs) are sensitive to changes in their control parameters, and it is generally agreed that too large a change may turn the EA from being successful to unsuccessful. This work reports on an experimental hybrid visualization scheme for the determination of EA stability according to perturbation of EA parameters. The scheme gives a visual representation of local neighborhoods of the parameter space according to a choice of two perturbation metrics, relating perturbations to EA performance as a variant of Kolmogorov distance. Through visualization and analysis of twelve thousand case study EA runs, we illustrate that we are able to distinguish between EA stability and instability depending upon perturbation and performance metrics. Finally we use what we have learned in the case study to provide a methodology for more general EAs.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121899564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Una-May O’Reilly, Anna I. Esparcia-Alcázar, A. Auger, Carola Doerr, A. Ekárt, G. Ochoa
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Copyright is held by the author/owner(s). GECCO’14, July 12–16, 2014, Vancouver, BC, Canada. ACM 978-1-4503-2881-4/14/07. http://dx.doi.org/10.1145/2598394.2611386. • How can we efficiently disseminate evolutionary computation information to pre-college girls?
{"title":"Women@GECCO 2014","authors":"Una-May O’Reilly, Anna I. Esparcia-Alcázar, A. Auger, Carola Doerr, A. Ekárt, G. Ochoa","doi":"10.1145/2598394.2611386","DOIUrl":"https://doi.org/10.1145/2598394.2611386","url":null,"abstract":"Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Copyright is held by the author/owner(s). GECCO’14, July 12–16, 2014, Vancouver, BC, Canada. ACM 978-1-4503-2881-4/14/07. http://dx.doi.org/10.1145/2598394.2611386. • How can we efficiently disseminate evolutionary computation information to pre-college girls?","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129770573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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