Familiarity and Preference for Pitch Probability Profiles

We investigated familiarity and preference judgments of participants toward a novel musical system. We exposed participants to tone sequences generated from a novel pitch probability profile. Afterward, we either asked participants to identify more familiar or we asked participants to identify preferred tone sequences in a two-alternative forced-choice task. The task paired a tone sequence generated from the pitch probability profile they had been exposed to and a tone sequence generated from another pitch probability profile at three levels of distinctiveness. We found that participants identified tone sequences as more familiar if they were generated from the same pitch probability profile which they had been exposed to. However, participants did not prefer these tone sequences. We interpret this relationship between familiarity and preference to be consistent with an inverted U-shaped relationship between knowledge and affect. The fact that participants identified tone sequences as even more familiar if they were generated from the more distinctive (caricatured) version of the pitch probability profile which they had been exposed to suggests that the statistical learning of the pitch probability profile is involved in gaining of musical knowledge.

Note on 'Growth, Cycles, and Stabilization Policy'

This note presents an analysis which generalizes the results reached by Blackburn and Pelloni (2005) on the relationship between short-term stabilization policy and long-term growth by considering both deliberate (internal) and serendipitous (external) learning mechanisms for productivity growth.

Behaviours in video

With a significant increment of the number of digital cameras used for various purposes, there is a demanding call for advanced video analysis techniques that can be used to systematically interpret and understand the semantics of video contents, which have been recorded in security surveillance, intelligent transportation, health care, video retrieving and summarization. Understanding and interpreting human behaviours based on video analysis have observed competitive challenges due to non-rigid human motion, self and mutual occlusions, and changes of lighting conditions. To solve these problems, advanced image and signal processing technologies such as neural network, fuzzy logic, probabilistic estimation theory and statistical learning have been overwhelmingly investigated.

A Single-Frame Super-Resolution Innovative Approach

Super-resolution refers to the process of obtaining a high resolution image from one or more low resolution images. In this work, we present a novel method for the super-resolution problem for the limited case, where only one image of low resolution is given as an input. The proposed method is based on statistical learning for inferring the high frequencies regions which helps to distinguish a high resolution image from a low resolution one. These inferences are obtained from the correlation between regions of low and high resolution that come exclusively from the image to be super-resolved, in term of small neighborhoods. The Markov random fields are used as a model to capture the local statistics of high and low resolution data when they are analyzed at different scales and resolutions. Experimental results show the viability of the method.

Segmentation and Clustering of Textual Sequences: a Typological Approach

The long term goal of this research is to develop a program able to produce an automatic segmentation and categorization of textual sequences into discourse types. In this preliminary contribution, we present the construction of an algorithm which takes a segmented text as input and attempts to produce a categorization of sequences, such as narrative, argumentative, descriptive and so on. Also, this work aims at investigating a possible convergence between the typological approach developed in particular in the field of text and discourse analysis in French by Adam (2008) and Bronckart (1997) and unsupervised statistical learning.

Modulation de l'apprentissage visuel par stimulation électrique transcrânienne à courant direct du cortex préfrontal

Le traitement visuel répété d’un visage inconnu entraîne une suppression de l’activité neuronale dans les régions préférentielles aux visages du cortex occipito-temporal. Cette «suppression neuronale» (SN) est un mécanisme primitif hautement impliqué dans l’apprentissage de visages, pouvant être détecté par une réduction de l’amplitude de la composante N170, un potentiel relié à l’événement (PRE), au-dessus du cortex occipito-temporal. Le cortex préfrontal dorsolatéral (CPDL) influence le traitement et l’encodage visuel, mais sa contribution à la SN de la N170 demeure inconnue. Nous avons utilisé la stimulation électrique transcrânienne à courant direct (SETCD) pour moduler l’excitabilité corticale du CPDL de 14 adultes sains lors de l’apprentissage de visages inconnus. Trois conditions de stimulation étaient utilisées: inhibition à droite, excitation à droite et placebo. Pendant l’apprentissage, l’EEG était enregistré afin d’évaluer la SN de la P100, la N170 et la P300. Trois jours suivant l’apprentissage, une tâche de reconnaissance était administrée où les performances en pourcentage de bonnes réponses et temps de réaction (TR) étaient enregistrées. Les résultats indiquent que la condition d’excitation à droite a facilité la SN de la N170 et a augmentée l’amplitude de la P300, entraînant une reconnaissance des visages plus rapide à long-terme. À l’inverse, la condition d’inhibition à droite a causé une augmentation de l’amplitude de la N170 et des TR plus lents, sans affecter la P300. Ces résultats sont les premiers à démontrer que la modulation d’excitabilité du CPDL puisse influencer l’encodage visuel de visages inconnus, soulignant l’importance du CPDL dans les mécanismes d’apprentissage de base.

Nur77 au sein des désordres dopaminergiques inhérents à la schizophrénie, la maladie de Parkinson ou la dépendance aux drogues d'abus

Cette thèse vise à mieux comprendre le rôle du facteur de transcription Nur77 au sein des fonctions physiologiques et pathologiques des voies de neurotransmission dopaminergiques des gaglions de la base. Nous basant sur une implication motrice de Nur77 au sein des dyskinésies induites à la L-DOPA (LIDs), nous avons voulu tester in vivo son implication éventuelle dans les phénomènes moteurs et de sensibilisation associés à l’amphétamine ainsi qu’une implication possible du récepteur nucléaire aux rétinoïdes RXR dont nous avons déjà démontré une implication dans les effets moteurs des antipsychotiques. Un deuxième volet de la recherche à consisté en l’étude de l’implication des extracellular signal-regulated kinase (ERK) et de la protéine kinase C (PKC) dans l’induction de l’ARNm des membres de la famille des Nurs suite à l’activation des cascades de signalisation des récepteurs dopamiergiques D1 et D2 in vivo pour une meilleure compréhension des mécanismes physiopatholoiques liés aux désordres dopaminergiques. Pour ce faire, nous avons, premièrement, soumis des souris sauvages et Nur77-/- à un paradigme de sensibilisation à l’amphétamine ainsi qu’a différents agonistes antagonistes RAR/RXR afin de tester l’implication de Nur77 et d’un éventuel complexe Nur77/RXR dans les effets de l’amphétamine. Deuxièmement, soumis des souris sauvages à une combinaison d’agonistes D1/D2 ou une injection d’antagoniste D2 avec ou sans inhibiteur (ERK1/2 ou PKC) afin de tester l’implication de ces kinases sur l’induction de l’ARNm des membres de la famille des Nurs par hybridation in situ. Nous avons ainsi pu démontrer 1- un rôle moteur de Nur77 dans les effets liés à l’amphétamine notamment avec une absence de stéréotypies et un allongement de la durée de la phase de locomotion chez les souris Nur77-/-; ainsi qu’un rôle éventuel du complexe potentiel Nur77/RXR dans les D1 à mieux définir. 2- un rôle des kinases ERKs et PKCs dans les cascades de signalisation des récepteurs dopaminergiques D1 et D2 menant à l’induction des ARNms de Nur77, Nor-1 et Nurr-1. En perspective, ces résultats nous ouvrent la voie vers une implication éventuelle de Nur77 dans les mécanismes d’apprentissage que sont le Long Terme Potentiation (LTP) et la Long Terme Depotentialisation (LTD) liés aux LIDs et à l’amphétamine.

FEATURE SELECTION AND COMPARISON OF TWO NAÏVE BAYES CLASSIFICATION METHODS IN THE CONTEXT OF SPAM FILTERING

Treating e-mail filtering as a binary text classification problem, researchers have applied several statistical learning algorithms to email corpora with promising results. This paper examines the performance of a Naive Bayes classifier using different approaches to feature selection and tokenization on different email corpora

A Note on the Generalization Performance of Kernel Classifiers with Margin

We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The bounds are derived through computations of the $V_gamma$ dimension of a family of loss functions where the SVM one belongs to. Bounds that use functions of margin distributions (i.e. functions of the slack variables of SVM) are derived.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

Neural Networks

We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view learning algorithms as methods for finding parameter values that look probable in the light of the data. We discuss basic issues in representation and learning, and treat some of the practical issues that arise in fitting networks to data. We also discuss links between neural networks and the general formalism of graphical models.

A Unified Framework for Regularization Networks and Support Vector Machines

Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples -- in particular the regression problem of approximating a multivariate function from sparse data. We present both formulations in a unified framework, namely in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics.

A Note on Support Vector Machines Degeneracy

When training Support Vector Machines (SVMs) over non-separable data sets, one sets the threshold $b$ using any dual cost coefficient that is strictly between the bounds of $0$ and $C$. We show that there exist SVM training problems with dual optimal solutions with all coefficients at bounds, but that all such problems are degenerate in the sense that the "optimal separating hyperplane" is given by ${f w} = {f 0}$, and the resulting (degenerate) SVM will classify all future points identically (to the class that supplies more training data). We also derive necessary and sufficient conditions on the input data for this to occur. Finally, we show that an SVM training problem can always be made degenerate by the addition of a single data point belonging to a certain unboundedspolyhedron, which we characterize in terms of its extreme points and rays.

Investigation of support vector machine for the detection of architectural distortion in mammographic images

This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma.

Trajetórias De Acumulação De Capacidades Inovadoras, Mecanismos De Aprendizagem E Fatores Organizacionais Relativos A Atividades Em Gestão De Projetos: Estudo De Caso Comparativo Inter-Empresarial Na Indústria De Bens De Capital E De Celulose E Papel No Brasil

Over the last 40 years there has been a profusion of studies about the ccumulation of technological capacities in firms from developing economies. However, there remain few studies that examine, on a combined basis, the relationship among: the trajectories of technological capacities accumulation; the underlying learning mechanisms; and, the implications of organizational factors for these two variables. Still scarcer are the studies that examine the relationship among these variables along time and based on a comparative case study. This dissertation examines the relationship among the trajectory of accumulation of innovative capacities in complex project management, the learning mechanisms underlying these technological capacities and the intra-organizational factors that influence these learning echanisms. That set of relationships is examined through a comparative and a long-term (1988-2008) case study in a capital goods firm (for the pulp and paper industry) and a pulp mill in Brazil. Based on first-hand quantitative and qualitative empiric evidence, gathered through extensive field research, this dissertation found: 1. Both firms accumulated innovative capacity in project management at the international frontier level (Level 6). However, there was variability between the firms in terms of the nature and speed of accumulation of those capacities. It was also observed that, at this level of innovation, the innovative capacities of both firms are not confined to their organizational boundaries, but they are distributed beyond their boundaries. 2. So that these companies could accumulate those levels of innovative capacities it was necessary to manage several learning mechanisms: leveraging of external knowledge and its internalization in terms of internal apacities of the firm. In other words, as the companies accumulated more innovative levels of capacities for project management, it was necessary to manage different cycles of technological learning. 3. Further, the relationship between the ccumulation of technological capacities and learning was affected positively by intra-organizational factors, such as 'authority disposition', 'mutability of work roles' and 'intensity of internal crises', and negatively by the factor 'singularity of goals'. This dissertation revealed divergent results between firms in two of the four factors studied. These results contribute to advance our understanding of the complexity and variability involved in the process of accumulation of innovative capacities in firms from developing economies. This highlights the growing importance of the organizational and the human resource dimensions of innovation and technological capacity as the company approaches the international frontier. The results suggest to managers that: (i) the good performance in project management in the two firms studied did not occur simply as a result of the pulp and paper Brazilian industry growth, rather as a result of the deliberate construction and accumulation of the capacities through an intensive and coordinated cyclical process of technological learning, (ii) to develop innovative capabilities in project management, besides looking for learning mechanisms they should also look at the organizational factors that influence the learning mechanisms directly, (iii) performance of pulp mill¿s projects is better when projects are implemented together with technology suppliers than when performed only by the mill. This dissertation concludes that capital goods firms have been having a fundamental role for the innovative capabilities accumulation in project management of pulp mills in Brazil (and vice-versa) for a long time. This contradicts some authors' propositions that affirm that: a) equipment suppliers for the pulp and paper industry have been creating little, if any, development of processes or engineering projects in Brazil; b) firms in the pulp and paper industry have little capacity for machinery and equipments projects only taking place in few technological activities, being internal or external to the firm. Finally, some studies are proposed for future research.