This paper uses co-integration method and error-correction model to re-examine the relationship between real exchange rate and expected interest rate differentials, including cumulated current account balance, over floating exchange rate periods. As indicated by the dynamic model, I find that there is a long run relationship among the variables using Johansen co-integration method. Final conclusion is that the empirical evidence is provided to show that our error-correction model leads to a good real exchange rate forecast.

The relationship between increased productivity and improved standard of living is not a questioned statement on the global level, but does productivity growth necessarily lead to higher standard of living on the national level? Supported by empirical results it is suggested that a high relative productivity growth should not always be worth striving for, since it translates into decreased welfare, in terms of deteriorated terms of trade. This study attempts to examine the impact of relative productivity on the terms of trade in the OECD-countries and in Sweden, with an error-correction model. Further is an extension of the purpose made in order to estimate the impact of increased relative productivity growth on the welfare. The results suggest that the method for measuring productivity has a great impact on the findings, but concludes that a 1% higher relative labour productivity growth is associated with a 0.23% decline in the terms of trade.

The aim of this investigation was to find out how students react to and make use of oral and written feedback given to them in class. Another aim was to find out if they preferred one form over the other and whether they make more use of that form. The investigation was conducted at a Swedish upper secondary school and consisted of a questionnaire survey and interviews with groups of students and with their teachers. The interviews with the students focused on clarifying some of the results from the questionnaire. The teacher interviews gave the teachers a chance to give their version of what they thought worked better and why they chose to work that way.   The results showed that students welcome feedback, especially positive feedback used for encouragement. However, they also thought that there was a higher limit to the amount of feedback they could benefit from. Too much of one sort could be ignored or perceived as discouraging. The feedback mostly used in class was oral feedback. This was also what the students thought they benefited from the most since it invited to discussion about their work. Most of the students thought the feedback should be delivered in private because it could be embarrassing to receive feedback in front of their peers. Nevertheless, feedback on pronunciation and smaller errors that could be of use for their peers as well was acceptable in front of the class.

Forecasting the stock market is a complex task, partly because of the random walk behavior of the stock price series. The task is further complicated by the noise, outliers and missing values that are common in financial time series. Despite of this, the subject receives a fair amount of attention, which probably can be attributed to the potential rewards that follows from being able to forecast the stock market. Since artificial neural networks are capable of exploiting non-linear relations in the data, they are suitable to use when forecasting the stock market. In addition to this, they are able to outperform the classic autoregressive linear models. The objective of this thesis is to investigate if the stock market can be forecasted, using the so called error correction neural network. This is accomplished through the development of a method aimed at finding the optimum forecast model. The results of this thesis indicates that the developed method can be applied successfully when forecasting the stock market. Of the five stocks that were forecasted in this thesis using forecast models based on the developed method, all generated positive returns. This suggests that the stock market can be forecasted using neural networks.

I propose a computationally efficient method to approximate the inverse of the normal operator arising in the multi-parameter linearized inverse problem for reflection seismology in two and three spatial dimensions. Solving the inverse problem using direct matrix methods like Gaussian elimination is computationally infeasible. In fact, the application of the normal operator requires solving large scale PDE problems. However, under certain conditions, the normal operator is a matrix of pseudodifferential operators. This manuscript shows how to generalize Cramer's rule for matrices to approximate the inverse of a matrix of pseudodifferential operators. Approximating the solution to the normal equations proceeds in two steps: (1) First, a series of applications of the normal operator to specific permutations of the right hand side. This step yields a phase-space scaling of the solution. Phase space scalings are scalings in both physical space and Fourier space. Second, a correction for the phase space scaling. This step requires applying the normal operator once more. The cost of approximating the inverse is a few applications of the normal operator (one for one parameter, two for two parameters, six for three parameters). The approximate inverse is an adequately accurate solution to the linearized inverse problem when it is capable of fitting the data to a prescribed precision. Otherwise, the approximate inverse of the normal operator might be used to precondition Krylov subspace methods in order to refine the data fit. I validate the method on a linearized version of the Marmousi model for constant density acoustics for the one-parameter problem. For the two parameter problem, the inversion of a variable density acoustics layered model corroborates the success of the proposed method. Furthermore, this example details the various steps of the method. I also apply the method to a 1D section of the Marmousi model to test the behavior of the method on complex two-parameter layered models.

La correction du facteur de puissance a été récemment introduite dans les convertisseurs pour remplacer le filtrage passif en amont des convertisseurs et pour répondre aux nouvelles normes en vigueur. La correction de facteur de puissance dite mono-étage a succédé à celle en deux étages qui est caractérisée par un coût et un volume élevés. Cette étude vise deux types d'applications. Les applications à faible puissance (300 W) sous une tension d'alimentation de 230 Veff et une tension de 54 V en sortie. Pour des raisons de coût et de volume, les structures asymétriques Flyback et Sepic sont des solutions répondant au cahier des charges et qui restent incontournables. U ressort également de cette étude, que si les contraintes du convertisseur à résonance série fonctionnant en conduction discontinue sont acceptables, alors ce dernier peut aussi répondre au cahier de charge. Le second type d'applications visées par cette étude concerne la moyenne puissance (3 kW) avec une tension de 150 V en sortie. Dans ce cadre, seules les structures symétriques à résonance à commutateur de courant ou à onduleur de tension sont susceptibles de répondre au cahier des charges. Pour la seconde catégorie, et en vue d'une généralisation, une discussion sur la nature du circuit résonant est faite pour étudier le prélèvement sinusoïdal. En évaluant les avantages et les inconvénients de chaque structure, deux convertisseurs ont été retenus et les prototypes réalisés ont montré un bon facteur de puissance et le respect de la norme en vigueur (EN 61000-3-2).

Cette thèse concerne la production et le contrôle d'hologrammes de haute qualité par un modulateur de phase à cristaux liquides (SLM) et l'utilisation de ces hologrammes pour la manipulation d'atomes froids. Ces hologrammes utilisés pour mettre en forme des faisceaux lasers et créer des potentiels dipolaires doivent être de bonne qualité. Or le SLM a des défauts qui peuvent les dégrader. Pour mesurer ces défauts nous avons développé une méthode reposant sur la mesure par polarimétrie de la biréfringence du SLM. La cartographie de biréfringence permet de mesurer l'hologramme en temps réel sans perturber l'expérience en cours. C'est donc une mesure in-situ. Une fois mesurés les défauts sont corrigés par une rétroaction sur la consigne donnée au SLM. Pour le démontrer nous avons créé un défaut artificiel avec un pointeur laser et nous l'avons réduit à λ/7 soit une amélioration de 42 %. Ensuite avec l'objectif d'appliquer des potentiels produit par un SLM aux atomes froids, j'ai dessiné, conçu puis réalisé en partie une expérience basée sur un jet d'atomes froids de rubidium issu d'un MOT-2D. Dans le chapitre correspondant je présente une étude de la faisabilité d'une expérience de guidage du jet d'atomes froids par un mode de Laguerre Gauss désaccordé vers le bleu de la transition. Nous avons montré que dans un tel guide le chauffage par émission spontanée est plus faible que dans un guide gaussien usuel désaccordé vers le rouge de la transition. De plus en réduisant la divergence naturelle du jet issu du MOT-2D nous devrions augmenter le flux d'atomes et améliorer l'efficacité de chargement d'un MOT-3D par un MOT-2D.

Our previous studies have demonstrated that RNA can serve as a template for double-strand break (DSB) repair in the yeast Saccharomyces cerevisiae using synthetic RNA-containing oligonucleotides (oligos). Following this initial work, we show that the RNA tract of RNA-containing oligos can be copied into DNA to transfer a genetic change at the chromosomal level also in the bacterium Escherichia coli and in human cells. Exploiting the use of oligos containing ribonucleoside monophosphates (rNMPs), we developed a molecular approach to generate RNA/DNA hybrids of chosen sequence and structure at the chromosomal level in both yeast and E. coli cells. Such technique allows us to study how rNMPs present in the DNA genome of cells are tolerated by cells, what factors recognize and target rNMPs in DNA and to what extent the embedded rNMPs may alter genome integrity. Here we proved that mispaired rNMPs embedded into genomic DNA, if not removed, serve as templates for DNA synthesis during chromosomal replication and produce a genetic change. We discovered that mispaired rNMPs that are embedded in genomic DNA are not only targeted by ribonucleases H (RNases H) but also by the mismatch repair (MMR) system both in yeast and in E. coli. Our data reveal novel substrates for the MMR system, and also uncover an unpredicted competition between RNase H and MMR for the RNA/DNA mispairs.

This thesis consists of four papers dealing with estimation and inference for quantile regression of longitudinal data, with an emphasis on nonlinear models. The first paper extends the idea of quantile regression estimation from the case of cross-sectional data with independent errors to the case of linear or nonlinear longitudinal data with dependent errors, using a weighted estimator. The performance of different weights is evaluated, and a comparison is also made with the corresponding mean regression estimator using the same weights. The second paper examines the use of bootstrapping for bias correction and calculations of confidence intervals for parameters of the quantile regression estimator when longitudinal data are used. Different weights, bootstrap methods, and confidence interval methods are used. The third paper is devoted to evaluating bootstrap methods for constructing hypothesis tests for parameters of the quantile regression estimator using longitudinal data. The focus is on testing the equality between two groups of one or all of the parameters in a regression model for some quantile using single or joint restrictions. The tests are evaluated regarding both their significance level and their power. The fourth paper analyzes seven longitudinal data sets from different parts of the biostatistics area by quantile regression methods in order to demonstrate how new insights can emerge on the properties of longitudinal data from using quantile regression methods. The quantile regression estimates are also compared and contrasted with the least squares mean regression estimates for the same data set. In addition to looking at the estimates, confidence intervals and hypothesis testing procedures are examined.

In practice, most approaches for text error detection and correction are based on a conventional domain-dependent background dictionary that represents a fixed and static collection of correct words of a given language and, as a result, satisfactory correction can only be achieved if the dictionary covers most tokens of the underlying correct text. Again, most approaches for text correction are for only one or at best a very few types of errors. The purpose of this thesis is to propose an unsupervised approach to detecting and correcting text errors, that can compete with supervised approaches and answer the following questions: Can an unsupervised approach efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature? What is the magnitude of error coverage, in terms of the number of errors that can be corrected? We conclude that (1) it is possible that an unsupervised approach can efficiently detect and correct a text containing multiple errors of both syntactic and semantic nature. Error types include: real-word spelling errors, typographical errors, lexical choice errors, unwanted words, missing words, prepositional errors, article errors, punctuation errors, and many of the grammatical errors (e.g., errors in agreement and verb formation). (2) The magnitude of error coverage, in terms of the number of errors that can be corrected, is almost double of the number of correct words of the text. Although this is not the upper limit, this is what is practically feasible. We use engineering approaches to answer the first question and theoretical approaches to answer and support the second question. We show that finding inherent properties of a correct text using a corpus in the form of an n-gram data set is more appropriate and practical than using other approaches to detecting and correcting errors. Instead of using rule-based approaches and dictionaries, we argue that a corpus can effectively be used to infer the properties of these types of errors, and to detect and correct these errors. We test the robustness of the proposed approach separately for some individual error types, and then for all types of errors. The approach is language-independent, it can be applied to other languages, as long as n-grams are available. The results of this thesis thus suggest that unsupervised approaches, which are often dismissed in favor of supervised ones in the context of many Natural Language Processing (NLP) related tasks, may present an interesting array of NLP-related problem solving strengths.