TlBr raw material purification, crystal growth, annealing, detector fabrication and characterisation for gamma-ray detector applications

The research reported in this thesis dealt with single crystals of thallium bromide grown for gamma-ray detector applications. The crystals were used to fabricate room temperature gamma-ray detectors. Routinely produced TlBr detectors often are poor quality. Therefore, this study concentrated on developing the manufacturing processes for TlBr detectors and methods of characterisation that can be used for optimisation of TlBr purity and crystal quality. The processes under concern were TlBr raw material purification, crystal growth, annealing and detector fabrication. The study focused on single crystals of TlBr grown from material purified by a hydrothermal recrystallisation method. In addition, hydrothermal conditions for synthesis, recrystallisation, crystal growth and annealing of TlBr crystals were examined. The final manufacturing process presented in this thesis deals with TlBr material purified by the Bridgman method. Then, material is hydrothermally recrystallised in pure water. A travelling molten zone (TMZ) method is used for additional purification of the recrystallised product and then for the final crystal growth. Subsequent processing is similar to that described in the literature. In this thesis, literature on improving quality of TlBr material/crystal and detector performance is reviewed. Aging aspects as well as the influence of different factors (temperature, time, electrode material and so on) on detector stability are considered and examined. The results of the process development are summarised and discussed. This thesis shows the considerable improvement in the charge carrier properties of a detector due to additional purification by hydrothermal recrystallisation. As an example, a thick (4 mm) TlBr detector produced by the process was fabricated and found to operate successfully in gamma-ray detection, confirming the validity of the proposed purification and technological steps. However, for the complete improvement of detector performance, further developments in crystal growth are required. The detector manufacturing process was optimized by characterisation of material and crystals using methods such as X-ray diffraction (XRD), polarisation microscopy, high-resolution inductively coupled plasma mass (HR-ICPM), Fourier transform infrared (FTIR), ultraviolet and visual (UV-Vis) spectroscopy, field emission scanning electron microscope (FESEM) and energy-dispersive X-ray spectroscopy (EDS), current-voltage (I-V) and capacity voltage (CV) characterisation, and photoconductivity, as well direct detector examination.

Computing the Stochastic Complexity of Simple Probabilistic Graphical Models

Minimum Description Length (MDL) is an information-theoretic principle that can be used for model selection and other statistical inference tasks. There are various ways to use the principle in practice. One theoretically valid way is to use the normalized maximum likelihood (NML) criterion. Due to computational difficulties, this approach has not been used very often. This thesis presents efficient floating-point algorithms that make it possible to compute the NML for multinomial, Naive Bayes and Bayesian forest models. None of the presented algorithms rely on asymptotic analysis and with the first two model classes we also discuss how to compute exact rational number solutions.

Matrix Decomposition Methods for Data Mining : Computational Complexity and Algorithms

Matrix decompositions, where a given matrix is represented as a product of two other matrices, are regularly used in data mining. Most matrix decompositions have their roots in linear algebra, but the needs of data mining are not always those of linear algebra. In data mining one needs to have results that are interpretable -- and what is considered interpretable in data mining can be very different to what is considered interpretable in linear algebra. --- The purpose of this thesis is to study matrix decompositions that directly address the issue of interpretability. An example is a decomposition of binary matrices where the factor matrices are assumed to be binary and the matrix multiplication is Boolean. The restriction to binary factor matrices increases interpretability -- factor matrices are of the same type as the original matrix -- and allows the use of Boolean matrix multiplication, which is often more intuitive than normal matrix multiplication with binary matrices. Also several other decomposition methods are described, and the computational complexity of computing them is studied together with the hardness of approximating the related optimization problems. Based on these studies, algorithms for constructing the decompositions are proposed. Constructing the decompositions turns out to be computationally hard, and the proposed algorithms are mostly based on various heuristics. Nevertheless, the algorithms are shown to be capable of finding good results in empirical experiments conducted with both synthetic and real-world data.

Linguistic grammars with very low complexity

We have presented an overview of the FSIG approach and related FSIG gram- mars to issues of very low complexity and parsing strategy. We ended up with serious optimism according to which most FSIG grammars could be decom- posed in a reasonable way and then processed efficiently.

Language universals and linguistic complexity : Three case studies in core argument marking

In this dissertation I study language complexity from a typological perspective. Since the structuralist era, it has been assumed that local complexity differences in languages are balanced out in cross-linguistic comparisons and that complexity is not affected by the geopolitical or sociocultural aspects of the speech community. However, these assumptions have seldom been studied systematically from a typological point of view. My objective is to define complexity so that it is possible to compare it across languages and to approach its variation with the methods of quantitative typology. My main empirical research questions are: i) does language complexity vary in any systematic way in local domains, and ii) can language complexity be affected by the geographical or social environment? These questions are studied in three articles, whose findings are summarized in the introduction to the dissertation. In order to enable cross-language comparison, I measure complexity as the description length of the regularities in an entity; I separate it from difficulty, focus on local instead of global complexity, and break it up into different types. This approach helps avoid the problems that plagued earlier metrics of language complexity. My approach to grammar is functional-typological in nature, and the theoretical framework is basic linguistic theory. I delimit the empirical research functionally to the marking of core arguments (the basic participants in the sentence). I assess the distributions of complexity in this domain with multifactorial statistical methods and use different sampling strategies, implementing, for instance, the Greenbergian view of universals as diachronic laws of type preference. My data come from large and balanced samples (up to approximately 850 languages), drawn mainly from reference grammars. The results suggest that various significant trends occur in the marking of core arguments in regard to complexity and that complexity in this domain correlates with population size. These results provide evidence that linguistic patterns interact among themselves in terms of complexity, that language structure adapts to the social environment, and that there may be cognitive mechanisms that limit complexity locally. My approach to complexity and language universals can therefore be successfully applied to empirical data and may serve as a model for further research in these areas.