The point of departure of this thesis is derived from a modern tendency to create a dichotomy between selfless love and human flourishing. Modern attempts to liberate the human being from heteronomous oppression and the moral norms promoting this have sometimes led to the conclusion that selfless love is harmful to human flourishing. Such a conclusion has gained momentum also through modernist re-conceptualisations of the self as an autonomous but empty consciousness which must guard itself against determination by the other. In effect, significant thinkers have replaced the notion of selfless love with a call for self-assertion over against the other, as key to the individual person’s well-being. This has been matched by Christian dismissals of the individual’s pursuit of human flourishing. In the face of modern insights into the ‘desirous’ nature of the human being, modern Christian theology has equally struggled to sustain the tension between the traditional Christian notion of selfless or self-giving love and human beings’ desire to affirm themselves and to find personal fulfilment in this world. Strands of Christian theology have, for instance, affirmed a self-surrendering love at the cost of dismissing the individual’s worldly desires entirely. In this thesis, I outline this situation in modern thought and its problematic consequences. With a view to discerning whether selfless love and human flourishing can be re-connected, I then undertake close studies of the theologian Paul Tillich’s and the moral philosopher and novelist Iris Murdoch’s conceptualisations of the self and of love. As I will argue, Tillich’s and Murdoch’s engagement with modern thought leads them to develop accounts of the self, which correspond with understandings of love as both selfless and conducive to human flourishing. On the basis of their thought I thus argue that selfless love and human flourishing can be understood as interdependent even today.

The aim of this diploma thesis is to analyze, design and create the architecture of the WSageNt system web interface. The main focus of the system will be traffic monitoring and topology control of the network. The work describes basic technologies, design principles and implementation methods.

This thesis explores how jazz musicians in Sweden experience jazz culture in terms of hierarchies and structures of power through a feminist phenomenological perspective. Through qualitative interviews with four jazz musicians with experience of attending different music colleges in Sweden, I explore their experiences of jazz culture in general, as well as specific contexts which are common within the culture. Using my theoretical approach, mainly consosting of the theories of Sara Ahmed and Iris Young, I review my informants´ possibilities to orientate themselves toward and within Swedish jazz culture. The informants all have different views of who holds the authority within the jazz culture. The study shows that male representation, masculine patterns of movement and structures of class differences has an impact on the informants possibilities to orientate themselves within the culture.

A presente dissertação aborda o processo de identificação por biometria das estruturas oculares, analisando suas bases biológicas e tecnológicas e sua aplicabilidade e confiabilidade para utilização jurídica. Está dividida em três partes. Na primeira, são analisados aspectos do conceito de identidade em suas diversas acepções e do processo de identificação médico legal por meio da utilização de diferentes métodos. Na segunda, passa-se a estudar a biometria da íris e da retina, abordando suas bases biológicas e tecnológicas. Na terceira e última parte, estuda-se a aplicabilidade e confiabilidade da biometria ocular para finalidades jurídicas, como método de perícia da identidade. / The purpose of this work is to study the identification process by ocular biometrics, from its biological and technological basis to its forensic and legal system applications. It is divided into three sections. The first section discusses the concept of identity and the identification methods used by forensic professionals. The second section discusses the ocular biometrics methods using iris and retinal traits, emphasizing their biological as well as their technological basis. The third and last section discusses the reliability and applicability of these biometrics modalities as identification methods in legal and forensic issues.

<p>α₁- and α₂-Adrenoceptors are involved in various physiological events in the eye: blood flow regulation, aqueous humor dynamics and pupil regulation. The α₁- and α₂-adrenoceptors can be further subdivided into six subtypes (α_1A, α_1B, α_1D, α_2A , α_2B, and α_2C ). Currently available α1- and α₂-adrenergic drugs are not selective for the different subtypes and some ocular adrenergics have undesirable side-effects, both local and systemic. A better understanding of the subtype distribution in the eye would be useful when designing new drugs with greater efficacy and fewer adverse effects; this applies especially to the treatment of glaucoma. The purpose of the thesis was therefore to identify and localize the different subtypes of α₁- and α₂-adrenoceptors in the eye. </p><p>The identities of the α₁-adrenoceptor subtypes were studied in various parts of pig and albino rabbit eyes by radioligand binding. In the pig retina and in the albino rabbit iris, ciliary body and retina, mixed populations of α_1A- and α_1B-adrenoceptors were localized. In the rabbit choroid only the α_1A-adrenoceptor subtype was detected. </p><p>The α₂-adrenoceptor subtypes were also characterized by radioligand binding, in different parts of the pig eye. In the iris, ciliary body and choroid, only α_2A-adrenoceptors were localized, while in the retina, mostly α_2A-adrenoceptors and a minor population of α_2C-adrenoceptors were identified. High densities of α_2A-adrenoceptors were found in the ciliary body and choroid.</p><p>The effect of α₂-adrenoceptor agonists on the porcine ciliary artery was studied on a small-vessel myograph. α₂-Adrenoceptor agonists proved to be potent vasoconstrictors in the porcine ciliary artery and it was found that the vasoconstriction induced by brimonidine was mediated by the α_A-adrenoceptor.</p>

α1- and α2-Adrenoceptors are involved in various physiological events in the eye: blood flow regulation, aqueous humor dynamics and pupil regulation. The α1- and α2-adrenoceptors can be further subdivided into six subtypes (α1A, α1B, α1D, α2A , α2B, and α2C ). Currently available α1- and α2-adrenergic drugs are not selective for the different subtypes and some ocular adrenergics have undesirable side-effects, both local and systemic. A better understanding of the subtype distribution in the eye would be useful when designing new drugs with greater efficacy and fewer adverse effects; this applies especially to the treatment of glaucoma. The purpose of the thesis was therefore to identify and localize the different subtypes of α1- and α2-adrenoceptors in the eye. The identities of the α1-adrenoceptor subtypes were studied in various parts of pig and albino rabbit eyes by radioligand binding. In the pig retina and in the albino rabbit iris, ciliary body and retina, mixed populations of α1A- and α1B-adrenoceptors were localized. In the rabbit choroid only the α1A-adrenoceptor subtype was detected. The α2-adrenoceptor subtypes were also characterized by radioligand binding, in different parts of the pig eye. In the iris, ciliary body and choroid, only α2A-adrenoceptors were localized, while in the retina, mostly α2A-adrenoceptors and a minor population of α2C-adrenoceptors were identified. High densities of α2A-adrenoceptors were found in the ciliary body and choroid. The effect of α2-adrenoceptor agonists on the porcine ciliary artery was studied on a small-vessel myograph. α2-Adrenoceptor agonists proved to be potent vasoconstrictors in the porcine ciliary artery and it was found that the vasoconstriction induced by brimonidine was mediated by the αA-adrenoceptor.

The objective of this thesis is to determine and analyze the dose rate to personnel throughout the proposed IRIS nuclear power plant. To accomplish this objective, complex models of the IRIS plant have been devised, advanced transport theory methods employed, and computationally intense simulations performed. IRIS is an advanced integral, light water reactor with a 335 MWe expected power output (1000 MWth). Due to its integral design, the IRIS pressure vessel has a large downcomer region. The large downcomer and the neutron reflector provide a great deal of additional shielding. This increase in shielding ensures that the IRIS design easily accomplishes the regulatory dose limits for radiation workers. However, The IRIS project set enhanced objectives of further reducing the dose rate to significantly lower levels, comparable or below the limit allowed for general public. The IRIS nuclear power plant design is very compact and has a rather complex geometric structure. Programs that use conventional methods would take too much time or would be unable to provide an answer for such a challenging deep penetration problem. Therefore, the modeling of the power plant was done using a hybrid methodology for automated variance reduction implemented into the MAVRIC sequence of the SCALE6 program package. The methodology is based on the CADIS and FW-CADIS methods. The CADIS method was developed by J.C. Wagner and A. Haghighat. The FW-CADIS method was developed by J.C. Wagner and D. Peplow. Using these methodologies in the MAVRIC code sequence, this thesis shows the dose rate throughout most of the inhabitable regions of the IRIS nuclear power plant. This thesis will also show the regions that are below the dose rate reduction objective set by the IRIS shielding team.

Nature has created complex biological structures that exhibit intelligent behaviour through an evolutionary process. Thus, intelligence and evolution are intimately connected. This has inspired evolutionary computation (EC) that simulates the evolutionary process to develop powerful techniques such as genetic algorithms (GAs), genetic programming (GP), evolutionary strategies (ES) and evolutionary programming (EP) to solve real-world problems in learning, control, optimization and classification. GP discovers the relationship among data and expresses it as a LISP-S expression i.e., a computer program. Thus the goal of program discovery as a solution for a problem is addressed by GP in the framework of evolutionary computation. In this thesis, we address for the first time the problem of applying GP to mu1ticategory pattern classification. In supervised pattern classification, an input vector of m dimensions is mapped onto one of the n classes. It has a number of application areas such as remote sensing, medical diagnosis etc., A supervised classifier is developed by using a training set that contains representative samples of various classes present in the application. Supervised classification has been done earlier with maximum likelihood classifier: neural networks and fuzzy logic. The major considerations in applying GP to pattern classification are listed below: (i) GP-based techniques are data distribution-free i.e., no a priori knowledge is needed abut the statistical distribution of the data or no assumption such as normal distribution for data needs to be made as in MLC. (ii) GP can directly operate on the data in its original form. (iii) GP can detect the underlying but unknown relationship that mists among data and express it as a mathematical LISP S-expression. The generated LISP S-expressions can be directly used in the application environment. (iv) GP can either discover the most important discriminating features of a class during evolution or it requires minor post-processing of the LISP-S expression to discover the discriminant features. In a neural network, the knowledge learned by the neural network about the data distributions is embedded in the interconnection weights and it requires considerable amount of post-processing of the weights to understand the decision of the neural network. In 2-category pattern classification, a single GP expression is evolved as a discriminant function. The output of the GP expression can be +l for samples of one class and -1 for samples of the other class. When the GP paradigm is applied to an n-class problem, the following questions arise: Ql. As a typical GP expression returns a value (+l or -1) for a 2-class problem, how does one apply GP for the n-class pattern classification problem? Q2. What should be the fitness function during evolution of the GP expressions? Q3. How does the choice of a function set affect the performance of GP-based classification? Q4. How should training sets be created for evaluating fitness during the evolution of GP classifier expressions? Q5. How does one improve learning of the underlying data distributions in a GP framework? Q6. How should conflict resolution be handled before assigning a class to the input feature vector? Q7. How does GP compare with other classifiers for an n-class pattern classification problem? The research described here seeks to answer these questions. We show that GP can be applied to an n-category pattern classification problem by considering it as n 2-class problems. The suitability of this approach is demonstrated by considering a real-world problem based on remotely sensed satellite images and Fisher's Iris data set. In a 2-class problem, simple thresholding is sufficient for a discriminant function to divide the feature space into two regions. This means that one genetic programming classifier expression (GPCE) is sufficient to say whether or not the given input feature vector belongs to that class; i.e., the GP expression returns a value (+1 or -1). As the n-class problem is formulated as n 2-class problems, n GPCEs are evolved. Hence, n GPCE specific training sets are needed to evolve these n GPCEs. For the sake of illustration, consider a 5-class pat tern classification problem. Let n, be the number of samples that belong to class j, and N, be the number of samples that do not belong to class j, (j = 1,..., 5). Thus, N1=n2+n3+n4+n5 N2=n1+n3+n4+n5 N3=n1+n2+n4+n5 N4=n1+n2+n3+n5 N5=n1+n2+n3+n4 Thus, When the five class problem is formulated as five 2-class problems. we need five GPCEs as discriminant functions to resolve between n1 and N1, n2 and N2, n3 and N3, n4 and N4 and lastly n5 and N5. Each of these five 2-class problems is handled as a separate 2-class problem with simple thresholding. Thus, GPCE# l resolves between samples of class# l and the remaining n - 1 classes. A training set is needed to evaluate the fitness of GPCE during its evolution. If we directly create the training set, it leads to skewness (as n1 < N1). To overcome the skewness, an interleaved data format is proposed for the training set of a GPCE. For example, in the training set of GPCE# l, samples of class# l are placed alternately between samples of the remaining n - 1 classes. Thus, the interleaved data format is an artifact to create a balanced training set. Conventionally, all the samples of a training set are fed to evaluate the fitness of every member of the population in each generation. We call this "global" learning 3s GP tries to learn the entire training set at every stage of the evolution. We have introduced incremental learning to simplify the task of learning for the GP paradigm. A subset of the training set is fed and the size of the subset is gradually increased over time to cover the entire training data. The basic motivation for incremental learning is to improve learning during evolution as it is easier to learn a smaller task and then to progress from a smaller task to a bigger task. Experimental results are presented to show that the interleaved data format and incremental learning improve the performance of the GP classifier. We also show that the GPCEs evolved with an arithmetic function set are able to track variation in the input better than GPCEs evolved with function sets containing logical and nonlinear elements. Hence, we have used arithmetic function set, incremental learning, and interleaved data format to evolve GPCEs in our simulations. AS each GPCE is trained to recognize samples belonging to its own class and reject samples belonging to other classes a strength of association measure is associated with each GPCE to indicate the degree to which it can recognize samples belonging to its own class. The strength of association measures are used for assigning a class to an input feature vector. To reduce misclassification of samples, we also show how heuristic rules can be generated in the GP framework unlike in either MLC or the neural network classifier. We have also studied the scalability and generalizing ability of the GP classifier by varying the number of classes. We also analyse the performance of the GP classifier by considering the well-known Iris data set. We compare the performance of classification rules generated from the GP classifier with those generated from neural network classifier, (24.5 method and fuzzy classifier for the Iris data set. We show that the performance of GP is comparable to other classifiers for the Iris data set. We notice that the classification rules can be generated with very little post-processing and they are very similar to the rules generated from the neural network and C4.5 for the Iris data set. Incremental learning influences the number of generations available for GP to learn the data distribution of classes whose d is -1 in the interleaved data format. This is because the samples belonging to the true class (desired output d is +1) are alternately placed between samples belonging to other classes i.e., they are repeated to balance the training set in the interleaved data format. For example, in the evolution of GPCE for class# l, the fitness function can be fed initially with samples of class#:! and subsequently with the samples of class#3, class#4 and class#. So in the evaluation of the fitness function, the samples of class#kt5 will not be present when the samples of class#2 are present in the initial stages. However, in the later stages of evolution, when samples of class#5 are fed, the fitness function will utilize the samples of both class#2 and class#5. As learning in evolutionary computation is guided by the evaluation of the fitness function, GPCE# l gets lesser number of generations to learn how to reject data of class#5 as compared to the data of class#2. This is because the termination criterion (i.e., the maximum number of generations) is defined a priori. It is clear that there are (n-l)! Ways of ordering the samples of classes whose d is -1 in the interleaved data format. Hence a heuristic is presented to determine a possible order to feed data of different classes for the GPCEs evolved with incremental learning and interleaved data format. The heuristic computes an overlap index for each class based on its spatial spread and distribution of data in the region of overlap with respect to other classes in each feature. The heuristic determines the order in which classes whose desired output d is –1 should be placed in each GPCE-specific training set for the interleaved data format. This ensures that GP gets more number of generations to learn about the data distribution of a class with higher overlap index than a class with lower overlap index. The ability of the GP classifier to learn the data distributions depends upon the number of classes and the spatial spread of data. As the number of classes increases, the GP classifier finds it difficult to resolve between classes. So there is a need to partition the feature space and identify subspaces with reduced number of classes. The basic objective is to divide the feature space into subspaces and hence the data set that contains representative samples of n classes into subdata sets corresponding to the subspaces of the feature space, so that some of the subdata sets/spaces can have data belonging to only p classes (p < n). The GP classifier is then evolved independently for the subdata sets/spaces of the feature space. This results in localized learning as the GP classifier has to learn the data distribution in only a subspace of the feature space rather than in the entire feature space. By integrating the GP classifier with feature space partitioning (FSP), we improve classification accuracy due to localized learning. Although serial computers have increased steadily in their performance, the quest for parallel implementation of a given task has continued to be of interest in any computationally intensive task since parallel implementation leads to a faster execution than a serial implementation As fitness evaluation, selection strategy and population structures are used to evolve a solution in GP, there is scope for a parallel implementation of GP classifier. We have studied distributed GP and massively parallel GP for our approach to GP-based multicategory pattern classification. We present experimental results for distributed GP with Message Passing Interface on IBM SP2 to highlight the speedup that can be achieved over the serial implementation of GP. We also show how data parallelism can be used to further speed up fitness evaluation and hence the execution of the GP paradigm for multicategory pat tern classification. We conclude that GP can be applied to n-category pattern classification and its potential lies in its simplicity and scope for parallel implementation. The GP classifier developed in this thesis can be looked upon as an addition to the earlier statistical, neural and fuzzy approaches to multicategory pattern classification.

L'objectif de ce travail est l'analyse et la classification d'expressions faciales.<br />Des expériences en psychologie ont permis de mettre en évidence le fait que l'être humain est capable de reconnaître les émotions sur un visage à partir de la visualisation de l'évolution temporelle de certains points caractéristiques de celui-ci.<br />Nous avons donc tout d'abord proposé un système d'extraction automatique des contours des traits permanents du visage (yeux, sourcils et lèvres).<br /><br /> Dans ce travail nous nous intéressons au problème de la segmentation des yeux et des sourcils.<br />La segmentation des contours des lèvres est basée sur un travail précédent développé au sein du laboratoire.<br />L'algorithme proposé pour l'extraction des contours des yeux et des sourcils est constitué de trois étapes : d'abord la définition de modèles paramétriques pour modéliser au mieux le contour de chaque trait ; ensuite, les modèles choisis sont initialisés sur les images à segmenter grâce à l'extraction d'un ensemble de points caractéristiques ; enfin, les modèles initiaux sont ajustés finement en tenant compte d'information de gradient de luminance.<br />La segmentation des contours des yeux, des sourcils et des lèvres conduit a ce que nous appelons des squelettes d'expressions.<br /><br /> Pour mesurer la déformation des traits caractéristiques, cinq distances caractéristiques sont définies sur ces squelettes.<br />Basé sur l'état de ces distances un ensemble de règles logiques est défini pour chacune des expressions considérées : Sourire, Surprise, Dégout, Colère, Peur, Tristesse, Neutre.<br />Ces règles sont compatibles avec la norme MPEG-4 qui fournit une description des transformations subies par chacun des traits du visage lors de la production des six expressions faciales universelles.<br />Cependant le comportement humain n'étant pas binaire, une expression pure est rarement produite.<br />Pour pouvoir modéliser le doute entre plusieurs expressions et le cas des expressions inconnues, les Modèles de Croyance Transférables sont utilisés comme pro cessus de fusion pour la classification des expressions faciales.<br />Le système de reconnaissance développé tient compte de l'évolution au cours du temps des déformations des traits du visage.<br /><br /> Dans la perspective d'un système audio-visuel de reconnaissance d'expressions émotionelles, une étude préliminaire sur des expressionse vocales a aussi été menée.

Dans cette thèse, nous avons adapté trois versions d'une chaine d'algorithmes de reconnaissance biométrique par l'iris appelés OSIRIS V2, V3, V4 qui correspondent à différentes implémentations de l'approche de J. Daugman pour les besoins d'une implémentation logicielle / matérielle. Les résultats expérimentaux sur la base de données ICE2005 montrent que OSIRIS_V4 est le système le plus fiable alors qu'OSIRIS_V2 est le plus rapide. Nous avons proposé une mesure de qualité de l'image segmentée pour optimiser en terme de compromis coût / performance un système de référence basé sur OSIRIS V2 et V4. Nous nous sommes ensuite intéressés à l'implémentation de ces algorithmes sur des plateformes reconfigurables. Les résultats expérimentaux montrent que l'implémentation matériel / logiciel est plus rapide que l'implémentation purement logicielle. Nous proposons aussi une nouvelle méthode pour le partitionnement matériel / logiciel de l'application. Nous avons utilisé la programmation linéaire pour trouver la partition optimale pour les différentes tâches prenant en compte les trois contraintes : la surface occupée, le temps d'exécution et la consommation d'énergie