Esta tese tem o objetivo de investigar o estatuto dos princípios fundamentais do direito, apresentados por Kant na Doutrina do direito, parte constitutiva da obra Metafísica dos costumes, a fim de mostrar como Kant fundamenta a sua teoria do direito racional. A questão fundamental no que concerne à teoria do direito de Kant, e que há muitos anos suscita o debate entre os intérpretes, diz respeito à possibilidade ou não do direito encontrar os fundamentos de seus conceitos e princípios fundamentais na teoria moral elaborada por ele na Fundamentação da metafísica dos costumes e na Crítica da razão prática. Recentemente pode-se observar um crescente número de estudiosos que afirmam a independência da Doutrina do direito da teoria moral kantiana e, consequentemente, a independência dos princípios a priori do direito do imperativo moral. Contrariamente a essa posição, esta tese procura mostrar que Kant fundamenta o direito em sua teoria moral - o direito pressupõe a teoria moral e seu princípio fundamental, o imperativo moral, - e que essa é a única maneira de interpretarmos a Doutrina do direito se não quisermos fazer afirmações que entrem em contradição com o pensamento do próprio autor. A tese que afirma a fundamentação moral do direito se baseia em duas afirmações, que encontram respaldo nos textos de Kant. São elas: a) as leis jurídicas são uma espécie de leis morais, cujo princípio último é o imperativo categórico – os princípios a priori do direito derivam do princípio supremo da moral; b) o uso da coerção externa para obrigar outrem a cumprir um dever jurídico é moralmente justificável, o que significa que esse tipo de constrangimento imposto ao arbítrio é autorizado por uma lei moral. A tese tem, enfim, o objetivo de reconstruir os argumentos de Kant para mostrar que buscar na sua filosofia moral os fundamentos da teoria do direito está totalmente de acordo com seus textos e com o seu pensamento sistemático. / This thesis aims to investigate the nature of fundamental principles of Right, presented by Kant in the Doctrine of Right, a constituent part of the work Metaphysic of Morals, in order to show how Kant grounded his theory of rational Right. The fundamental question regarding the Kant‟s theory of Right, and that for many years raises debate among interpreters, concerns the possibility or not of the Right to find the foundations of its concepts and principles in moral theory elaborated by him in the Groundwork the Metaphysics of Morals and Critique of Practical Reason. Recently one can observe a growing number of scholars who assert the independence of the Doctrine of Right of Kantian moral theory and accordingly the independence of a priori principles of Right of moral imperative. Contrary to this position, this thesis aims to show that Kant bases the Right on his moral theory - the Right presuppose the moral theory and its fundamental principle, the moral imperative - and that is the only way to interpret the doctrine of Right if not want to make statements that come into conflict with the author's own thought. The thesis argues that the moral foundation of Right rests on two assertions that are supported in the writings of Kant. They are: a) juridical laws are a kind of moral law whose ultimate principle is the Categorical imperative - the a priori principles of Right derived from the supreme principle of morality; b) the use of external coercion to compel another to fulfill a duty legal is morally justifiable, meaning that this type of constraint imposed on the choice is authorized by a moral law. In short, the thesis has the objective of reconstructing Kant's arguments to show that find in his moral philosophy the foundations of the theory of Right is fully consistent with his writings and his systematic thinking.

Behavioral perceptions and cortical auditory evoked potentials (CAEPs) from native English (N=10) and Spanish speakers (N=10) were recorded for speech stimuli that constitute phonetically relevant categories of voicing. The stimuli were synthesized bilabial stop consonant-vowel syllables in a continuum ranging from/ba/to/pa/that varied in voice onset time (VOT) from 0 to 60ms. Different behavioral perceptions were evidenced by significantly different categorical phonetic boundaries between the two groups (p.05). Peak P1, N1, and P2 response latencies and P1–N1 and N1–P2 amplitudes increased significantly with increasing VOTs (p

Background: A link between musical expertise and auditory temporal processing abilities was examined. Material and methods: Trained musicians (n=13) and non-musicians (n=12) were tested on speech tasks (phonetic identification, speech recognition in noise) and non-speech tasks (temporal gap detection). Results: Results indicated musicians had shorter between-channel gap detection thresholds and sharper phonetic identification functions, suggesting that perceptual reorganization following musical training assists basic temporal auditory processes. Conclusions: In general, our results provide a conceptual advance in understanding how musical training influences speech processing, an ability which, when impaired, can affect speech and reading competency.

This dissertation has three consecutive topics. First, we propose a novel class of independence measures for testing independence between two random vectors based on the discrepancy between the conditional and the marginal characteristic functions. If one of the variables is categorical, our asymmetric index extends the typical ANOVA to a kernel ANOVA that can test a more general hypothesis of equal distributions among groups. The index is also applicable when both variables are continuous. Second, we develop a sufficient variable selection procedure based on the new measure in a large p small n setting. Our approach incorporates marginal information between each predictor and the response as well as joint information among predictors. As a result, our method is more capable of selecting all truly active variables than marginal selection methods. Furthermore, our procedure can handle both continuous and discrete responses with mixed-type predictors. We establish the sure screening property of the proposed approach under mild conditions. Third, we focus on a model-free sufficient dimension reduction approach using the new measure. Our method does not require strong assumptions on predictors and responses. An algorithm is developed to find dimension reduction directions using sequential quadratic programming. We illustrate the advantages of our new measure and its two applications in high dimensional data analysis by numerical studies across a variety of settings.

Spatially explicit ecological models rely on spatially exhaustive data layers that have scales appropriate to the ecological processes of interest. Such data layers are often categorical raster maps derived from high-resolution, remotely sensed data that must be scaled to a lower spatial resolution to make them compatible with the scale of ecological analysis. Statistical functions commonly used to aggregate categorical data are majority-, nearest-neighbor- and random-rule. For heterogeneous landscapes and large scaling factors, however, use of these functions results in two critical issues: (1) ignoring large portions of information present in the high-resolution grid cells leads to high and uncontrolled loss of information in the scaled dataset; and (2) maintaining classes from the high-resolution dataset at the lower spatial resolution assumes validity of the classification scheme at the low-resolution scale, failing to represent recurring mixes of heterogeneous classes present in the low-resolution grid cells. The proposed new scaling algorithm resolves these issues, aggregating categorical data while simultaneously controlling for information loss by generating a non-hierarchical, representative, classification system valid at the aggregated scale. Implementing scaling parameters, that control class-label precision effectively reduced information loss of scaled landscapes as class-label precision increased. In a neutral-landscape simulation study, the algorithm consistently preserved information at a significantly higher level than the other commonly used algorithms. When applied to maps of real landscapes, the same increase in information retention was observed, and the scaled classes were detectable from lower-resolution, remotely sensed, multi-spectral reflectance data with high accuracy. The framework developed in this research facilitates scaling-parameter selection to address trade-offs among information retention, label fidelity, and spectral detectability of scaled classes. When generating high spatial resolution land-cover maps, quantifying effects of sampling intensity, feature-space dimensionality and classifier method on overall accuracy, confidence estimates, and classifier efficiency allowed optimization of the mapping method. Increase in sampling intensity boosted accuracies in a reasonably predictable fashion. However, adding a second image acquired when ground conditions and vegetation phenology differed from those of the first image had a much greater impact, increasing classification accuracy even at low sampling intensities, to levels not reached with a single season image.

Pouvoir identifier la personne en face de soi et être capable de retrouver son nom apparaissent des activités indispensables aux interactions sociales quotidiennes. Or, le nom propre s‟avère être une information spécifique comparée aux autres informations disponibles sur une personne. La récupération de cette information est souvent décrite comme difficile. Pour autant, si les auteurs s‟accordent sur la spécificité du nom propre, l‟examen de l‟organisation en mémoire du nom propre et des informations sémantiques liées aux personnes sera l‟objet de ce travail. De plus, l‟accès aux informations sémantiques s‟avère déficitaire dans la maladie d‟Alzheimer.Une première expérience a permis d‟élaborer un matériel normé sur 210 visages/noms, permettant la sélection précise de stimuli. Afin d'étudier la question de l‟organisation en mémoire des informations sémantiques et plus particulièrement l‟information nom propre, deux études comportementales et trois études électrophysiologiques ont été menées. Dans les deux dernières études, nous avons travaillé sur l‟apprentissage et le maintien en mémoire de l‟information spécifique nom propre auprès d‟une population Alzheimer. La discussion des différents résultats s‟appuie sur les connaissances théoriques actuelles sur le sujet. / Being able to identify the person in front of us and being able to remember his or her name appear to be essential activities to daily social interactions. However, compared to other available information related to a person, the proper name seems to be specific information. Proper name retrieval has been very often described as particularly difficult. Although this work aims to study the organization of semantic information in memory, and more specifically the proper name information. Moreover, access to semantic information is known be deficient for Alzheimer disease.A first study has allowed to develope out a standardized material of 210 faces/names, permitting an accurate selection of stimuli. Two behavioural and three electrophysiological studies have been carried out to study the organization of semantic information in memory, and more specifically the proper name. For the last two experiments, we have worked on learning and how to keep specific information « proper name » into memory for Alzheimer‟s disease patients. Discussion of the different results is based on current theorical knowledge on the subject.

許多社會科學及醫學上的長期追蹤研究上，常會根據研究之需要，而針對某一群人在一段時間內重覆地收集其有關變項（包括類別型反應變項及解釋變項）的資料。這種重覆觀察的資料在統計的文獻上稱為長期追蹤研究資料。在這些長期追蹤研究上，研究者常利用迴歸模型建構的技巧來探討反應變項及解釋變項之間的關係。 一般常用的模型，著重於評估解釋變項對反應變項的當時及短期效應，當解釋變項比反應變項更頻繁地被觀測時，這些模型則不適用。當反應變項可在不同類別間變動時，我們通常有興趣去探討解釋變項如何去影響反應變項的演變或未來走向的趨勢，這種研究可稱之為類別型長期追蹤研究資料的未來趨勢分析。本論文提出了以馬可夫離散時間過程來建立類別型長期追蹤研究資料的模型。此模型不但可以捕捉到解釋變項對反應變項的未來趨勢效應；而且當解釋變項較反應變項更頻繁地被觀測時，本模型也可以利用解釋變項的完整訊息來做出更正確的統計推論。 / Many longitudinal studies in social science and medical science take repeated observations of an categorical outcome, along with several covariates, from follow-up subjects over a certain period of time. Such repeated observations are called longitudinal or panel data in the statistical literature. It is often of interest in these studies to investigate the relationship between the outcome and the covariates through regression modeling techniques. Commonly used models often focus on assessing the contemporary or short term effect of the covariate on the outcome, and can't incorporate time-varying covariates that are observed more or less frequently than the rate we observe the outcome. When the outcome fluctuates among different categories, it is often of interest to assess how covariates effect the evolution or trend of the underlying outcome process. Such assessment can be termed trend analysis of categorical panel data. In this thesis, we propose a Markov chain based regression model for analyzing nominal categorical panel data that are generated by a discrete time outcome process. The proposed model focuses on assessing the trend effect of the covariate on the categorical outcome, and is able to utilize the complete information of the covariates that are observed more or less frequently than the outcome.

In this thesis, we describe linear codes over prime fields obtained from incidence designs of iterated line graphs of complete graphs Li(Kn) where i = 1, 2. In the binary case, results are extended to codes from neighbourhood designs of the line graphs Li+1(Kn) using certain elementary relations. Codes from incidence designs of complete graphs, Kn, and neighbourhood designs of their line graphs, L1(Kn) (the so-called triangular graphs), have been considered elsewhere by others. We consider codes from incidence designs of L1(Kn) and L2(Kn), and neighbourhood designs of L2(Kn) and L3(Kn). In each case, basic parameters of the codes are determined. Further, we introduce a family of vertex-transitive graphs 􀀀n that are embeddable into the strong product L1(Kn) ⊠ K2, of triangular graphs and K2, a class which at first sight may seem unnatural but, on closer look, is a repository of graphs rich with combinatorial structures. For instance, unlike most regular graphs considered here and elsewhere that only come with incidence and neighbourhood designs, 􀀀n also has what we have termed as 6-cycle designs. These are designs in which the point set contains vertices of the graph and every block contains vertices of a 6-cycle in the graph. Also, binary codes from incidence matrices of these graphs have other minimum words in addition to incidence vectors of the blocks. In addition, these graphs have induced subgraphs isomorphic to the family Hn of complete porcupines (see Definition 4.11). We describe codes from incidence matrices of 􀀀n and Hn and determine their parameters.

With the wide deployment of data intensive Internet applications and continued advances in sensing technology and biotechnology, large multidimensional datasets, possibly containing privacy-conscious information have been emerging. Mining such datasets has become increasingly common in business integration, large-scale scientific data analysis, and national security. The proposed research aims at exploring the geometric properties of the multidimensional datasets utilized in statistical learning and data mining, and providing novel techniques and frameworks for mining very large datasets while protecting the desired data privacy. The first main contribution of this research is the development of iVIBRATE interactive visualization-based approach for clustering very large datasets. The iVIBRATE framework uniquely addresses the challenges in handling irregularly shaped clusters, domain-specific cluster definition, and cluster-labeling of the data on disk. It consists of the VISTA visual cluster rendering subsystem, and the Adaptive ClusterMap Labeling subsystem. The second main contribution is the development of ``Best K Plot'(BKPlot) method for determining the critical clustering structures in multidimensional categorical data. The BKPlot method uniquely addresses two challenges in clustering categorical data: How to determine the number of clusters (the best K) and how to identify the existence of significant clustering structures. The method consists of the basic theory, the sample BKPlot theory for large datasets, and the testing method for identifying no-cluster datasets. The third main contribution of this research is the development of the theory of geometric data perturbation and its application in privacy-preserving data classification involving single party or multiparty collaboration. The key of geometric data perturbation is to find a good randomly generated rotation matrix and an appropriate noise component that provides satisfactory balance between privacy guarantee and data quality, considering possible inference attacks. When geometric perturbation is applied to collaborative multiparty data classification, it is challenging to unify the different geometric perturbations used by different parties. We study three protocols under the data-mining-service oriented framework for unifying the perturbations: 1) the threshold-satisfied voting protocol, 2) the space adaptation protocol, and 3) the space adaptation protocol with a trusted party. The tradeoffs between the privacy guarantee, the model accuracy and the cost are studied for the protocols.

The aim of this study was to investigate the development of release from proactive interference (RPI) and its relations with executive working memory functions. 101 primary school children (aged 6-12 years) and 20 young adults (aged 22-30 years) participated in the study. The main task, the Categorical Free Recall Test, comprised 12 items from 3 different categories (animals, fruits, clothes). The purpose of the main task was to examine both the development of the RPI pattern and the categorization ability during childhood. As our results showed, the categorization ability and the RPI pattern were already present in the 1st graders. Although overall memory span increased with age, there was no significant development for the categorization and the RPI effect. For the additional tasks, the Word Span Test (WST, to measure the phonological WM capacity), the Wisconsin Card Sorting Test (WCST, to measure both the categorization ability and executive WM functions), and the Listening Span Test (LST, to examine executive and complex WM functions), the results indicated that children also improved with age. Overall memory capacity in the main task was best predicted by the WST / however, memory of serial position was best predicted by the LST. These findings are in accordance with the view that the WST measures the phonological working memory span, whereas the LST measures complex working memory and executive functions. The comparisons between the adult and the child sample revealed that except for the RPI pattern adults were better on all tasks than the children. The lack of a consistent RPI pattern for the adults may be due to the relatively short stimulus list.