Symmetry has been a very useful tool to researchers in various scientific fields. At its most basic, symmetry refers to the invariance of an object to some transformation, or set of transformations. Usually one searches for, and uses information concerning an existing symmetry within given data, structure or concept to somehow improve algorithm performance or compress the search space. This thesis examines the effects of imposing or inducing symmetry on a search space. That is, the question being asked is whether only existing symmetries can be useful, or whether changing reference to an intuition-based definition of symmetry over the evaluation function can also be of use. Within the context of optimization, symmetry induction as defined in this thesis will have the effect of equating the evaluation of a set of given objects. Group theory is employed to explore possible symmetrical structures inherent in a search space. Additionally, conditions when the search space can have a symmetry induced on it are examined. The idea of a neighborhood structure then leads to the idea of opposition-based computing which aims to induce a symmetry of the evaluation function. In this context, the search space can be seen as having a symmetry imposed on it. To be useful, it is shown that an opposite map must be defined such that it equates elements of the search space which have a relatively large difference in their respective evaluations. Using this idea a general framework for employing opposition-based ideas is proposed. To show the efficacy of these ideas, the framework is applied to popular computational intelligence algorithms within the areas of Monte Carlo optimization, estimation of distribution and neural network learning. The first example application focuses on simulated annealing, a popular Monte Carlo optimization algorithm. At a given iteration, symmetry is induced on the system by considering opposite neighbors. Using this technique, a temporary symmetry over the neighborhood region is induced. This simple algorithm is benchmarked using common real optimization problems and compared against traditional simulated annealing as well as a randomized version. The results highlight improvements in accuracy, reliability and convergence rate. An application to image thresholding further confirms the results. Another example application, population-based incremental learning, is rooted in estimation of distribution algorithms. A major problem with these techniques is a rapid loss of diversity within the samples after a relatively low number of iterations. The opposite sample is introduced as a remedy to this problem. After proving an increased diversity, a new probability update procedure is designed. This opposition-based version of the algorithm is benchmarked using common binary optimization problems which have characteristics of deceptivity and attractive basins characteristic of difficult real world problems. Experiments reveal improvements in diversity, accuracy, reliability and convergence rate over the traditional approach. Ten instances of the traveling salesman problem and six image thresholding problems are used to further highlight the improvements. Finally, gradient-based learning for feedforward neural networks is improved using opposition-based ideas. The opposite transfer function is presented as a simple adaptive neuron which easily allows for efficiently jumping in weight space. It is shown that each possible opposite network represents a unique input-output mapping, each having an associated effect on the numerical conditioning of the network. Experiments confirm the potential of opposite networks during pre- and early training stages. A heuristic for efficiently selecting one opposite network per epoch is presented. Benchmarking focuses on common classification problems and reveals improvements in accuracy, reliability, convergence rate and generalization ability over common backpropagation variants. To further show the potential, the heuristic is applied to resilient propagation where similar improvements are also found.

Symmetry has been a very useful tool to researchers in various scientific fields. At its most basic, symmetry refers to the invariance of an object to some transformation, or set of transformations. Usually one searches for, and uses information concerning an existing symmetry within given data, structure or concept to somehow improve algorithm performance or compress the search space. This thesis examines the effects of imposing or inducing symmetry on a search space. That is, the question being asked is whether only existing symmetries can be useful, or whether changing reference to an intuition-based definition of symmetry over the evaluation function can also be of use. Within the context of optimization, symmetry induction as defined in this thesis will have the effect of equating the evaluation of a set of given objects. Group theory is employed to explore possible symmetrical structures inherent in a search space. Additionally, conditions when the search space can have a symmetry induced on it are examined. The idea of a neighborhood structure then leads to the idea of opposition-based computing which aims to induce a symmetry of the evaluation function. In this context, the search space can be seen as having a symmetry imposed on it. To be useful, it is shown that an opposite map must be defined such that it equates elements of the search space which have a relatively large difference in their respective evaluations. Using this idea a general framework for employing opposition-based ideas is proposed. To show the efficacy of these ideas, the framework is applied to popular computational intelligence algorithms within the areas of Monte Carlo optimization, estimation of distribution and neural network learning. The first example application focuses on simulated annealing, a popular Monte Carlo optimization algorithm. At a given iteration, symmetry is induced on the system by considering opposite neighbors. Using this technique, a temporary symmetry over the neighborhood region is induced. This simple algorithm is benchmarked using common real optimization problems and compared against traditional simulated annealing as well as a randomized version. The results highlight improvements in accuracy, reliability and convergence rate. An application to image thresholding further confirms the results. Another example application, population-based incremental learning, is rooted in estimation of distribution algorithms. A major problem with these techniques is a rapid loss of diversity within the samples after a relatively low number of iterations. The opposite sample is introduced as a remedy to this problem. After proving an increased diversity, a new probability update procedure is designed. This opposition-based version of the algorithm is benchmarked using common binary optimization problems which have characteristics of deceptivity and attractive basins characteristic of difficult real world problems. Experiments reveal improvements in diversity, accuracy, reliability and convergence rate over the traditional approach. Ten instances of the traveling salesman problem and six image thresholding problems are used to further highlight the improvements. Finally, gradient-based learning for feedforward neural networks is improved using opposition-based ideas. The opposite transfer function is presented as a simple adaptive neuron which easily allows for efficiently jumping in weight space. It is shown that each possible opposite network represents a unique input-output mapping, each having an associated effect on the numerical conditioning of the network. Experiments confirm the potential of opposite networks during pre- and early training stages. A heuristic for efficiently selecting one opposite network per epoch is presented. Benchmarking focuses on common classification problems and reveals improvements in accuracy, reliability, convergence rate and generalization ability over common backpropagation variants. To further show the potential, the heuristic is applied to resilient propagation where similar improvements are also found.

Crop Biotechnology could help achieve a more food-secure world. However, the strong opposition to GM food, particularly in Europe, will undoubtedly affect the diffusion of GM crops worldwide, delaying or preventing the world from realizing the potential benefits of GM crops in terms of food security. This braking effect could deprive the world of a potential tool to increase or stabilize the future worldwide availability of food under a changing or more volatile climate. It is therefore essential to understand how the opposition to GM food has and will affect the diffusion of biotechnological innovations worldwide in order to estimate the effect of this opposition on global food security. <p> The main objective of the thesis is to estimate the loss in global food security if the EU does not relax their opposition to GM food. To meet this objective a market model is combined with a GM diffusion model to create a global food security (GFS) model. The focus of the model is GM wheat, due to the vital importance of conventional wheat to global food security. This approach allows us to evaluate dynamic economic responses to food production shocks, such as climate change. The GFS model is calibrated using production, consumption and price data for wheat. A number of scenarios are analyzed to consider the range of potential effects of the EU opposition on global food security. The results of the analyses will better inform the ongoing GM policy debates, which often ignore food security impacts.

Since William Gibson includes a Rastafarian Enclave and a theological compass in <i>Neuromancer</i>, this can be used to examine the troublesome natures of the two Artificial Intelligences, Wintermute and Neuromancer. The Rastafarian's beliefs and interpretations of Babylon and Zion, the oppressors and the liberated, add political significance to the Tessier-Ashpool's and their enslaved Artificial Intelligences. Since the Artificial Intelligences are both created of Babylon the Tessier-Ashpools and also wanting to be free of them, they are something outside of both. In the Artificial Intelligences, then, Gibson collapses the straightforward dichotomy of Babylon and Zion.

Texte remanié de: Thèse de doctorat--Histoire--Aix-Marseille 1, 1995. Titre de soutenance : La presse d'opposition au franquisme durant la dernière décennie de la dictature et la transition démocratique. / En appendice, choix de documents. Bibliogr. p. 449-482. Index. Résumés en français, espagnol et anglais.

Texte remanié de: Th. doct.--Hist. et civilisation--Paris--École des hautes études en sciences sociales, 1999. / Bibliogr. p. 307-310.

Texte remanié de: Thèse--Science politique--Montpellier 1, 1983. / Bibliogr. p. 253-265.

published_or_final_version / Chinese / Master / Master of Philosophy

This study focuses on support and opposition in an attempted merger between three Norwegian university colleges.  The university colleges that took part in the merger process were Buskerud University College, Vestfold University College and Østfold University College, all three located in the south-east part of Norway. The study takes its point of departure in the fact that the merger process was terminated before a decision to merge was reached. It aims to describe the two discourses of support and opposition, and explain change in discursive practices from support to opposition. The thesis is divided into three parts. In the first part the conceptual framework used in the analysis is presented, as well as the area of mergers in higher education and methodological aspects.  Four conceptual pairs are used to analyse the merger process; support/opposition, idea/operationalisation, outwards/inwards and modern/traditional. The second part is constructed as a play with acts presenting the merger process in a chronological order where the conceptual pairs are used to describe and explain discursive practices. The third and final part focuses on general insights into the construction of support and opposition in the terminated merger process. The study shows that the use of different discursive practices evolved into antagonism between the three university colleges leading to the termination of the merger process. Supporting the idea to merge in order to achieve university status was not enough to support the merger if the operationalisation of how the merger process was to be conducted, and how the merged organisation was to be constructed led to a disadvantageous power position.  In addition, discursive practices of support and opposition were conditioned by discourses of the education market and academic drift. / This study focuses on support and opposition in an attempted merger between three Norwegian university colleges. It aims to describe and explain how discourses of support and opposition conditioned the outcome of the merger process.The thesis is divided into three parts. In the first part the four conceptual pairs – support/opposition, idea/operationalisation, outwards/inwards and modern/traditional – used to analyse the merger process, are introduced, as well as the area of mergers in higher education and methodological aspects. The second part is constructed as a play with acts presenting the merger process in a chronological order. The third and final part focuses on general insights into the construction of support for and opposition to the merger.The study shows how different discursive practices evolved into antagonism between the three university colleges leading to the termination of the merger process. Support for the idea of the merger as a means of achieving university status was not sufficient; the operationalisation of the merger process itself and the proposed construction of the merged organisation resulted in power struggles. In addition, support and opposition were conditioned by discourses of the education market and academic drift.

The Sweden Democrats are very clear in what way they want to be described as; as the only opposition party in the Swedish parliament. Is it possible that Swedish media helps in creating that description? This essay examines in what way Swedish media are describing the Sweden Democrats in condition to the establishment. By doing a discourse analysis, the essay will find out which discourse that is dominating about the Sweden Democrats in the two Swedish newspapers Aftonbladet and Expressen. The results shows that the dominate discourse is describing the party as an outsider in relationship to the establishment, but that in the same time a normalization is going on in times where the elections are far away. It also shows that it's only in the question about immigration that this 7-to-1 picture is described, which also is the party’s main question. In other political questions, media shows a picture of a party that try to widen their politics to a direction towards both of the blocks.