In this thesis I address the fundamental question that how the information gain is possible in the realm of quantum mechanics where a single measurement alters the state of the system. I study an ensemble of particles in some unknown (but product) state in detail and suggest an optimal way of gaining the maximum information and also quantify the corresponding information exactly. We find a rather novel result which is quite different from other well-known definitions of the information gain in quantum theory.

In this thesis I address the fundamental question that how the information gain is possible in the realm of quantum mechanics where a single measurement alters the state of the system. I study an ensemble of particles in some unknown (but product) state in detail and suggest an optimal way of gaining the maximum information and also quantify the corresponding information exactly. We find a rather novel result which is quite different from other well-known definitions of the information gain in quantum theory.

In this thesis I address the fundamental question that how the information gain is possible in the realm of quantum mechanics where a single measurement alters the state of the system. I study an ensemble of particles in some unknown (but product) state in detail and suggest an optimal way of gaining the maximum information and also quantify the corresponding information exactly. We find a rather novel result which is quite different from other well-known definitions of the information gain in quantum theory. / Science, Faculty of / Physics and Astronomy, Department of / Graduate

The  aim  of  the  current  study  was  to investigate  if  Openness  –  to  –  Experience  and  Neuroticism  personality  traits  are associated with curiosity. This will help us to estimate whether knowledge expansion is dependent on a person’s personality and which trait is more willing to invest time on learning.  The  experiment  consisted  of  two  different  sessions.  To estimate  curiosity, 40 subjects first performed a word-synonymy task, where Shannon’s (1948) entropy was estimated  and the result of which lead to the measurement  of uncertainty.  Then in a second session, participants had the option to request for feedback between a few alternative  options  at  a  cost  (time),  and  they  were  also  required  to  estimate  their satisfaction  about  the  answer  on  a  valence  rating  scale.  Finally, participants  were screened  for  personality  traits.  Neurotic  individuals  appeared  to  be  more  willing  in investing time on feedback request, in contrast to open individuals.

Experimental design can be vital when experiments are resource-exhaustive and time-consuming. In this work, we carry out experimental design in the Bayesian framework. To measure the amount of information that can be extracted from the data in an experiment, we use the expected information gain as the utility function, which specifically is the expected logarithmic ratio between the posterior and prior distributions. Optimizing this utility function enables us to design experiments that yield the most informative data about the model parameters. One of the major difficulties in evaluating the expected information gain is that it naturally involves nested integration over a possibly high dimensional domain. We use the Multilevel Monte Carlo (MLMC) method to accelerate the computation of the nested high dimensional integral. The advantages are twofold. First, MLMC can significantly reduce the cost of the nested integral for a given tolerance, by using an optimal sample distribution among different sample averages of the inner integrals. Second, the MLMC method imposes fewer assumptions, such as the asymptotic concentration of posterior measures, required for instance by the Laplace approximation (LA). We test the MLMC method using two numerical examples. The first example is the design of sensor deployment for a Darcy flow problem governed by a one-dimensional Poisson equation. We place the sensors in the locations where the pressure is measured, and we model the conductivity field as a piecewise constant random vector with two parameters. The second one is chemical Enhanced Oil Recovery (EOR) core flooding experiment assuming homogeneous permeability. We measure the cumulative oil recovery, from a horizontal core flooded by water, surfactant and polymer, for different injection rates. The model parameters consist of the endpoint relative permeabilities, the residual saturations and the relative permeability exponents for the three phases: water, oil and microemulsions. We also compare the performance of the MLMC to the LA and the direct Double Loop Monte Carlo (DLMC). In fact, we show that, in the case of the aforementioned examples, MLMC combined with LA turns to be the best method in terms of computational cost.

In epidemiological studies, subjects with disease (prevalent cases) differ from newly diseased (incident cases). They tend to survive longer due to sampling bias, and related covariates will also be biased. Methods for regression analyses have recently been proposed to measure the potential effects of covariates on survival. The goal is to extend the dependence measure of Kent (1983), based on the information gain, in the context of length-biased sampling. In this regard, to estimate information gain and dependence measure for length-biased data, we propose two different methods namely kernel density estimation with a regression procedure and parametric copulas. We will assess the consistency for all proposed estimators. Algorithms detailing how to generate length-biased data, using kernel density estimation with regression procedure and parametric copulas approaches, are given. Finally, the performances of the estimated information gain and dependence measure, under length-biased sampling, are demonstrated through simulation studies.

De nos jours, avec le développement des nouvelles technologies multimédia, la recherche d’images basée sur le contenu visuel est un sujet de recherche en plein essor avec de nombreux domaines d'application: indexation et recherche d’images, la graphologie, la détection et le suivi d’objets... Un des modèles les plus utilisés dans ce domaine est le sac de mots visuels qui tire son inspiration de la recherche d’information dans des documents textuels. Dans ce modèle, les images sont représentées par des histogrammes de mots visuels à partir d'un dictionnaire visuel de référence. La signature d’une image joue un rôle important car elle détermine la précision des résultats retournés par le système de recherche.Dans cette thèse, nous étudions les différentes approches concernant la représentation des images. Notre première contribution est de proposer une nouvelle méthodologie pour la construction du vocabulaire visuel en utilisant le gain d'information extrait des mots visuels. Ce gain d’information est la combinaison d’un modèle de recherche d’information avec un modèle d'attention visuelle.Ensuite, nous utilisons un modèle d'attention visuelle pour améliorer la performance de notre modèle de sacs de mots visuels. Cette étude de la saillance des descripteurs locaux souligne l’importance d’utiliser un modèle d’attention visuelle pour la description d’une image.La dernière contribution de cette thèse au domaine de la recherche d’information multimédia démontre comment notre méthodologie améliore le modèle des sacs de phrases visuelles. Finalement, une technique d’expansion de requêtes est utilisée pour augmenter la performance de la recherche par les deux modèles étudiés. / Nowadays, along with the development of multimedia technology, content based image retrieval (CBIR) has become an interesting and active research topic with an increasing number of application domains: image indexing and retrieval, face recognition, event detection, hand writing scanning, objects detection and tracking, image classification, landmark detection... One of the most popular models in CBIR is Bag of Visual Words (BoVW) which is inspired by Bag of Words model from Information Retrieval field. In BoVW model, images are represented by histograms of visual words from a visual vocabulary. By comparing the images signatures, we can tell the difference between images. Image representation plays an important role in a CBIR system as it determines the precision of the retrieval results.In this thesis, image representation problem is addressed. Our first contribution is to propose a new framework for visual vocabulary construction using information gain (IG) values. The IG values are computed by a weighting scheme combined with a visual attention model. Secondly, we propose to use visual attention model to improve the performance of the proposed BoVW model. This contribution addresses the importance of saliency key-points in the images by a study on the saliency of local feature detectors. Inspired from the results from this study, we use saliency as a weighting or an additional histogram for image representation.The last contribution of this thesis to CBIR shows how our framework enhances the BoVP model. Finally, a query expansion technique is employed to increase the retrieval scores on both BoVW and BoVP models.

A presença de atributos não importantes, i.e., atributos irrelevantes ou redundantes nos dados, pode prejudicar o desempenho de classificadores gerados a partir desses dados por algoritmos de aprendizado de máquina. O objetivo de algoritmos de seleção de atributos consiste em identificar esses atributos não importantes para removê-los dos dados antes da construção de classificadores. A seleção de atributos em dados monorrótulo, nos quais cada exemplo do conjunto de treinamento é associado com somente um rótulo, tem sido amplamente estudada na literatura. Entretanto, esse não é o caso para dados multirrótulo, nos quais cada exemplo é associado com um conjunto de rótulos (multirrótulos). Além disso, como esse tipo de dados usualmente apresenta relações entre os rótulos do multirrótulo, algoritmos de aprendizado de máquina deveriam considerar essas relações. De modo similar, a dependência de rótulos deveria também ser explorada por algoritmos de seleção de atributos multirrótulos. A abordagem filtro é uma das mais utilizadas por algoritmos de seleção de atributos, pois ela apresenta um custo computacional potencialmente menor que outras abordagens e utiliza características gerais dos dados para calcular as medidas de importância de atributos. tais como correlação de atributo-classe, entre outras. A hipótese deste trabalho é trabalho é que algoritmos de seleção de atributos em dados multirrótulo que consideram a dependência de rótulos terão um melhor desempenho que aqueles que ignoram essa informação. Para tanto, é proposto como objetivo deste trabalho o projeto e a implementação de algoritmos filtro de seleção de atributos multirrótulo que consideram relações entre rótulos. Em particular, foram propostos dois métodos que levam em conta essas relações por meio da construção de rótulos e da adaptação inovadora do algoritmo de seleção de atributos monorrótulo ReliefF. Esses métodos foram avaliados experimentalmente e apresentam bom desempenho em termos de redução no número de atributos e qualidade dos classificadores construídos usando os atributos selecionados. / Irrelevant and/or redundant features in data can deteriorate the performance of the classifiers built from this data by machine learning algorithms. The aim of feature selection algorithms consists in identifying these features and removing them from data before constructing classifiers. Feature selection in single-label data, in which each instance in the training set is associated with only one label, has been widely studied in the literature. However, this is not the case for multi-label data, in which each instance is associated with a set of labels. Moreover, as multi-label data usually exhibit relationships among the labels in the set of labels, machine learning algorithms should take thiis relatinship into account. Therefore, label dependence should also be explored by multi-label feature selection algorithms. The filter approach is one of the most usual approaches considered by feature selection algorithms, as it has potentially lower computational cost than approaches and uses general properties from data to calculate feature importance measures, such as the feature-class correlation. The hypothesis of this work is that feature selection algorithms which consider label dependence will perform better than the ones that disregard label dependence. To this end, ths work proposes and develops filter approach multi-label feature selection algorithms which take into account relations among labels. In particular, we proposed two methods that take into account these relations by performing label construction and adapting the single-label feature selection algorith RelieF. These methods were experimentally evaluated showing good performance in terms of feature reduction and predictability of the classifiers built using the selected features.

A presença de atributos não importantes, i.e., atributos irrelevantes ou redundantes nos dados, pode prejudicar o desempenho de classificadores gerados a partir desses dados por algoritmos de aprendizado de máquina. O objetivo de algoritmos de seleção de atributos consiste em identificar esses atributos não importantes para removê-los dos dados antes da construção de classificadores. A seleção de atributos em dados monorrótulo, nos quais cada exemplo do conjunto de treinamento é associado com somente um rótulo, tem sido amplamente estudada na literatura. Entretanto, esse não é o caso para dados multirrótulo, nos quais cada exemplo é associado com um conjunto de rótulos (multirrótulos). Além disso, como esse tipo de dados usualmente apresenta relações entre os rótulos do multirrótulo, algoritmos de aprendizado de máquina deveriam considerar essas relações. De modo similar, a dependência de rótulos deveria também ser explorada por algoritmos de seleção de atributos multirrótulos. A abordagem filtro é uma das mais utilizadas por algoritmos de seleção de atributos, pois ela apresenta um custo computacional potencialmente menor que outras abordagens e utiliza características gerais dos dados para calcular as medidas de importância de atributos. tais como correlação de atributo-classe, entre outras. A hipótese deste trabalho é trabalho é que algoritmos de seleção de atributos em dados multirrótulo que consideram a dependência de rótulos terão um melhor desempenho que aqueles que ignoram essa informação. Para tanto, é proposto como objetivo deste trabalho o projeto e a implementação de algoritmos filtro de seleção de atributos multirrótulo que consideram relações entre rótulos. Em particular, foram propostos dois métodos que levam em conta essas relações por meio da construção de rótulos e da adaptação inovadora do algoritmo de seleção de atributos monorrótulo ReliefF. Esses métodos foram avaliados experimentalmente e apresentam bom desempenho em termos de redução no número de atributos e qualidade dos classificadores construídos usando os atributos selecionados. / Irrelevant and/or redundant features in data can deteriorate the performance of the classifiers built from this data by machine learning algorithms. The aim of feature selection algorithms consists in identifying these features and removing them from data before constructing classifiers. Feature selection in single-label data, in which each instance in the training set is associated with only one label, has been widely studied in the literature. However, this is not the case for multi-label data, in which each instance is associated with a set of labels. Moreover, as multi-label data usually exhibit relationships among the labels in the set of labels, machine learning algorithms should take thiis relatinship into account. Therefore, label dependence should also be explored by multi-label feature selection algorithms. The filter approach is one of the most usual approaches considered by feature selection algorithms, as it has potentially lower computational cost than approaches and uses general properties from data to calculate feature importance measures, such as the feature-class correlation. The hypothesis of this work is that feature selection algorithms which consider label dependence will perform better than the ones that disregard label dependence. To this end, ths work proposes and develops filter approach multi-label feature selection algorithms which take into account relations among labels. In particular, we proposed two methods that take into account these relations by performing label construction and adapting the single-label feature selection algorith RelieF. These methods were experimentally evaluated showing good performance in terms of feature reduction and predictability of the classifiers built using the selected features.

A B S T R A C T title: Multimodal Image Processing in Art Investigation author: Jan Blažek department: Department of Image Processing, IITA of the CAS supervisor: RNDr. Barbara Zitová PhD., Institute of Information Theory and Automation supervisor's e-mail address: zitova@utia.cas.cz abstract: Art investigation and digital image processing demar- cate an interdisciplinary field of the presented thesis. Over the past 8 years we have published thirteen papers belonging to this field of research. This thesis presents the current state of the art and puts these papers into context. Our research is focused on modalities in the visible and near-infrared parts of the spectrum and affects vari- ous tasks of art investigation. For studying the spectral response of paint materials, we suggest a low-cost mobile multi-band acquisition system and a calibration method extended by a light source with an adjustable wavelength. We created the m3art database of the spectral responses of pigments, available for comparison and public use. The central point of our research is underdrawing detection and visual- ization. For this purpose we have developed: acquisition guidelines based on optical properties of the topmost non-transparent layer, a visualization technique for comparison of modalities, and a signal separation technique...