Electronic structure calculations constitute a valuable tool to predict the properties of materials. In this study we propose an efficient scheme to study correlated electron systems with essentially only one free parameter, the screening length of the Coulomb potential. A general reformulation of the exchange energy of the correlated electron shell is combined with this method in order to analyze the calculations. The results are interpreted in terms of different polarization channels, due to different multipoles. The method is applied to various actinide compounds, in order to increase the understanding of the complicate behaviour of 5f electrons in these systems. We studied the non-magnetic phase of δ-Pu, where the spin polarization is taken over by a spin-orbit-like term that does not break the time reversal symmetry. We also find that a non-trivial high multipole of the magnetization density, the triakontadipole, constitutes the ordering parameter in the mysterious hidden order phase of the heavy-fermion superconductor URu2Si2. This type of multipolar ordering is also found to play an essential role in the hexagonal-based superconductors UPd2Al3,  UNi2Al3 and UPt3 and in the dioxide insulators UO2, NpO2 and PuO2. The triakontadipole moments are also present in all magnetic actinides we considered, except for Cm. These results led us to formulate a new set of rules for the ground state of a system, that are valid in presence of strong spin-orbit coupling interaction instead of those of Hund; the Katt's rules. Finally, we applied our method to a new class of high-Tc superconductors, the Fe-pnictides, where the Fe 3d electrons are moderately correlated. In these materials we obtain the stabilization of a low spin moment solution, in agreement with experiment, over a large moment solution, due to the gain in exchange energy in the formation of large multipoles of the spin magnetization density. / Felaktigt tryckt som Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 705

Signalų technologijų magistro darbo tema yra aktuali, nes nebepakanka įprastų informacijos įvedimo priemonių. Todėl ieškoti ir apdoroti informaciją, valdyti sudėtingus įrenginius ir programas daug patogiau būtų jei kompiuteriai ir įvairūs įrenginiai suprastų žmogaus kalbą. Pasaulyje panašios sistemos kuriamos jau daugelį metų. Tačiau šiuo metu lietuvių kalbos atpažinimo sistemos yra dar tik kūrimo stadijoje. Darbe nagrinėjamas Lietuvių kalbos žodžių atpažinimas skirstant juos į kategorijas ir naudojant paslėptuosius markovo modelius. Šio tyrimo tikslas – ištirti lietuvių kalbos žodžių skirstymo į kategorijas įtaką atpažinimo tikslumui.Taip pat tiriamas žodžių grupių bei pavienių žodžių atpažinimas. Akustinis modelis sukurtas su HTK paketu, kuris naudojasi paslėptųjų Markovo modelių metodika. Žodžių skirstymas į kategorijas aprašytas Backus-Naur formatu. Eksperimentai bus atliekami ir rezultatai gaunami naudojant, Julius programinės įrangos įrankius bei šio paketo, žodžių kategorijų pagrindu veikiančią, Julian kalbos atpažinimo sistemą. Geriausi rezultatai gauti bandant atpažinti pavienius žodžius suskirstytus į kategorijas. Atpažinimo tikslumas siekia 91 %. Bandant atpažinti žodžių sekas, nesuskirstytas į kategorijas, gautas atpažinimo tikslumas tesiekia 51 %. Microsoft Office Word 2003 meniu valdymo atpažinimo tikslumas siekia 82 %. / The theme of Master project of signal technology is actual, because not enough usual information introduction ways. Therefore information search and processing, complicated devices and programs control would be more handily if computers and devices understood human speech. Similar systems are designing for many years in the world. However Lithuanian speech recognition systems are still developing in nowadays. The thesis treats of isolated Lithuanian words recognition dividing them into category and using Hidden Markov Models. The idea of research is to explore categorization of Lithuanian words influence on the accuracy of recognition. The recognition of single words and word groups is under research too. Acoustic model is constructed by using HTK toolkit which is based on Hidden Markov Models. Categorization of words is described with Backus-Naur form. Experiments are made with Julius software speech recognition system Julian witch performs words category based recognition. Best results are got trying to recognize single words set into categories. The accuracy rate of recognition reaches 91 %. While trying to recognize uncategorized word sequences – the accuracy rate of recognition reaches only 51 %. The accuracy rate of Microsoft Office Word 2003 control menu recognition reaches 82 %.

This thesis, which is organized in two independent parts, presents work on distributional semantics and on variable selection. In the first part, we introduce a new method for learning good word representations using large quantities of unlabeled sentences. The method is based on a probabilistic model of sentence, using a hidden Markov model and a syntactic dependency tree. The latent variables, which correspond to the nodes of the dependency tree, aim at capturing the meanings of the words. We develop an efficient algorithm to perform inference and learning in those models, based on online EM and approximate message passing. We then evaluate our models on intrinsic tasks such as predicting human similarity judgements or word categorization, and on two extrinsic tasks: named entity recognition and supersense tagging. In the second part, we introduce, in the context of linear models, a new penalty function to perform variable selection in the case of highly correlated predictors. This penalty, called the trace Lasso, uses the trace norm of the selected predictors, which is a convex surrogate of their rank, as the criterion of model complexity. The trace Lasso interpolates between the $\ell_1$-norm and $\ell_2$-norm. In particular, it is equal to the $\ell_1$-norm if all predictors are orthogonal and to the $\ell_2$-norm if all predictors are equal. We propose two algorithms to compute the solution of least-squares regression regularized by the trace Lasso, and perform experiments on synthetic datasets to illustrate the behavior of the trace Lasso.

「隱形冠軍」是全球各行業冠軍企業，其有許多成功關鍵因子，惟本研究欲針對「深耕顧客關係管理」著手，透過蒐集、鑽研、整理及分析其成功關鍵因素，以作為歸納分析整理之依循。 而經選拔出來的卓越「中堅企業」則是台灣優秀的中小企業，一樣有其優秀的成功特質，算是國內的隱形冠軍企業，相對於輔導「中堅企業」來說，其為政府重點輔導使其更具國際競爭力的施政經濟方針，經由分析專注本業且深耕顧客關係管理的「中堅企業」，也能產生出個案公司建議之方策。 但是「深耕顧客關係管理」是否果真適合一般企業借鏡效法？或是部分借鏡？或是全部效法？這部分須經過個案公司本身的體質檢查，須經過效益評估，才是企業能委身經營努力的目標，這就是尋求「企業營運價值（效益）最大化」，策略上的合理經營，採取最優的營運政策，使企業總價值達到最大化。 投資營運管理目標應與企業多個利益集團有關，從長期發展來看，不能只強調某一利益，而置其他利益於不顧，經過效益評估後才是企業經營管理的最優目標，因此本研究透過歸納分析，縝密的效益評估，終而產出最有利個案公司的具體建議。

Reducing interference noise in a noisy speech recording has been a challenging task for many years yet has a variety of applications, for example, in handsfree mobile communications, in speech recognition, and in hearing aids. Traditional single-channel noise reduction schemes, such as Wiener filtering, do not work satisfactorily in the presence of non-stationary background noise. Alternatively, supervised approaches, where the noise type is known in advance, lead to higher-quality enhanced speech signals. This dissertation proposes supervised and unsupervised single-channel noise reduction algorithms. We consider two classes of methods for this purpose: approaches based on nonnegative matrix factorization (NMF) and methods based on hidden Markov models (HMM).  The contributions of this dissertation can be divided into three main (overlapping) parts. First, we propose NMF-based enhancement approaches that use temporal dependencies of the speech signals. In a standard NMF, the important temporal correlations between consecutive short-time frames are ignored. We propose both continuous and discrete state-space nonnegative dynamical models. These approaches are used to describe the dynamics of the NMF coefficients or activations. We derive optimal minimum mean squared error (MMSE) or linear MMSE estimates of the speech signal using the probabilistic formulations of NMF. Our experiments show that using temporal dynamics in the NMF-based denoising systems improves the performance greatly. Additionally, this dissertation proposes an approach to learn the noise basis matrix online from the noisy observations. This relaxes the assumption of an a-priori specified noise type and enables us to use the NMF-based denoising method in an unsupervised manner. Our experiments show that the proposed approach with online noise basis learning considerably outperforms state-of-the-art methods in different noise conditions.  Second, this thesis proposes two methods for NMF-based separation of sources with similar dictionaries. We suggest a nonnegative HMM (NHMM) for babble noise that is derived from a speech HMM. In this approach, speech and babble signals share the same basis vectors, whereas the activation of the basis vectors are different for the two signals over time. We derive an MMSE estimator for the clean speech signal using the proposed NHMM. The objective evaluations and performed subjective listening test show that the proposed babble model and the final noise reduction algorithm outperform the conventional methods noticeably. Moreover, the dissertation proposes another solution to separate a desired source from a mixture with arbitrarily low artifacts.  Third, an HMM-based algorithm to enhance the speech spectra using super-Gaussian priors is proposed. Our experiments show that speech discrete Fourier transform (DFT) coefficients have super-Gaussian rather than Gaussian distributions even if we limit the speech data to come from a specific phoneme. We derive a new MMSE estimator for the speech spectra that uses super-Gaussian priors. The results of our evaluations using the developed noise reduction algorithm support the super-Gaussianity hypothesis. / <p>QC 20130916</p>

In this study we look at the problem of analysing survival data in the presence of longitudinally collected covariates. New methodology for analysing such data has been developed through the use of hidden Markov modeling. Special attention has been given to the case of large information volume, where a preliminary data reduction is necessary. Novel graphical diagnostics have been proposed to assess goodness of fit and significance of covariates. The methodology developed has been applied to the data collected on behaviors of Mexican fruit flies, which were monitored throughout their lives. It has been found that certain patterns in eating behavior may serve as an aging marker. In particular it has been established that the frequency of eating is positively correlated with survival times. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2010-04-26 18:34:01.131

Detractors of sentimental literature argue that such novels are unoriginal and concerned primarily with emotions. Feminist scholars redeem the reputation of sentimental literature to an extent. At present, a multitude of approaches present sentimental authors as active agents, engaging with public issues. Building upon the scholarship of prominent feminist historians and literary critics, this thesis provides direct evidence that three female authors embrace the responsibilities of being a social critic. The Wide, Wide World (1850) by Susan Warner, Uncle Tom’s Cabin (1851) by Harriet Beecher Stowe, and The Hidden Hand (1859) by Emma D.E.N. Southworth provide unique commentaries on the separation of the private and public spheres, market revolution, and religion. Decisive differences between the authors’ opinions reveal a high degree of engagement with the public issues.

This thesis is concerned with the efficient computation of Jacobian matrices of nonlinear vector maps using automatic differentiation (AD). Specifically, we propose the use of two directed edge separator methods, the weighted minimum separator and natural order separator methods, to exploit the structure of the computational graph of the nonlinear system.This allows for the efficient determination of the Jacobian matrix using AD software. We will illustrate the promise of this approach with computational experiments.

The rapid development of corpus-based speech systems such as concatenative synthesis systems for under-resourced languages requires an efficient, consistent and accurate solution with regard to phonetic speech segmentation. Manual development of phonetically annotated corpora is a time consuming and expensive process which suffers from challenges regarding consistency and reproducibility, while automation of this process has only been satisfactorily demonstrated on large corpora of a select few languages by employing techniques requiring extensive and specialised resources. In this work we considered the problem of phonetic segmentation in the context of developing small prototypical speech synthesis corpora for new under-resourced languages. This was done through an empirical evaluation of existing segmentation techniques on typical speech corpora in three South African languages. In this process, the performance of these techniques were characterised under different data conditions and the efficient application of these techniques were investigated in order to improve the accuracy of resulting phonetic alignments. We found that the application of baseline speaker-specific Hidden Markov Models results in relatively robust and accurate alignments even under extremely limited data conditions and demonstrated how such models can be developed and applied efficiently in this context. The result is segmentation of sufficient quality for synthesis applications, with the quality of alignments comparable to manual segmentation efforts in this context. Finally, possibilities for further automated refinement of phonetic alignments were investigated and an efficient corpus development strategy was proposed with suggestions for further work in this direction. / Thesis (M.Ing. (Computer Engineering))--North-West University, Potchefstroom Campus, 2009.

The rapid development of corpus-based speech systems such as concatenative synthesis systems for under-resourced languages requires an efficient, consistent and accurate solution with regard to phonetic speech segmentation. Manual development of phonetically annotated corpora is a time consuming and expensive process which suffers from challenges regarding consistency and reproducibility, while automation of this process has only been satisfactorily demonstrated on large corpora of a select few languages by employing techniques requiring extensive and specialised resources. In this work we considered the problem of phonetic segmentation in the context of developing small prototypical speech synthesis corpora for new under-resourced languages. This was done through an empirical evaluation of existing segmentation techniques on typical speech corpora in three South African languages. In this process, the performance of these techniques were characterised under different data conditions and the efficient application of these techniques were investigated in order to improve the accuracy of resulting phonetic alignments. We found that the application of baseline speaker-specific Hidden Markov Models results in relatively robust and accurate alignments even under extremely limited data conditions and demonstrated how such models can be developed and applied efficiently in this context. The result is segmentation of sufficient quality for synthesis applications, with the quality of alignments comparable to manual segmentation efforts in this context. Finally, possibilities for further automated refinement of phonetic alignments were investigated and an efficient corpus development strategy was proposed with suggestions for further work in this direction. / Thesis (M.Ing. (Computer Engineering))--North-West University, Potchefstroom Campus, 2009.