Determining protein structure and function experimentally is both costly and time consuming. Transferring function-related protein annotations based on homology-based methods is relatively straightforward for proteins that have sequence identity of more than 40%. However, there are many proteins in the "twilight zone" where sequence similarity with any other protein is very weak, while being structurally similar to several. Such cases require methods that are capable of using and exploiting both sequence and structural similarity. To understand ways of how such methods can and should be designed is the focus of this study. In this thesis, models that use both sequence and structure features are applied on two protein prediction problems that are particularly challenging when relying on sequence alone. Enzyme classification benefits from both kinds of features because on one hand, enzymes can have identical function with limited sequence similarity while on the other hand, proteins with similar fold may have disparate enzyme class annotation. This thesis shows that the full integration of protein sequence and structure-related features (via the use of kernels) automatically places proteins with similar biological properties closer together, leading to superior classification accuracy using Support Vector Machines. Disulfide-bonds link residues in a protein structure, but may appear distant in sequence. Sequence similarity reflecting such structural properties is thus very hard to detect. It is sufficient for the structure to be similar for accurate prediction of disulfide-bonds, but such information is very scarce and predictors that rely on protein structure are not nearly as useful as those operating on sequence alone. This thesis proposes a novel approach based on Kernel Canonical Correlation Analysis that uses structural features during training only. It does so by finding sequence representations that correlate with structural features that are essential for a disulfide bond. The resulting representations enable high prediction accuracy for a range of disulfide-bond problems. The proposed model thus taps the advantage of structural features without requiring protein structure to be available in the prediction process. The merits of this approach should apply to a number of open protein structure prediction problems.

Sociality is widespread across the animal kingdom and explanations for its incidence and persistence are numerous. Whilst various drivers of sociality have been identified and tested, controversies remain and we are still far from a complete understanding of the mechanisms underlying social structure. Here I use a combination of field observations on a free-living population of feral horses Equus caballus and laboratory behavioural experiments on the Pacific beetle roach Diploptera punctata to investigate the drivers of sociality in these species. I explore four key aspects of sociality: the influences of kinship on sociality and social development, the strength and persistence of social bonds, the relationship between inbreeding avoidance and dispersal and the potential influence of individuals on social structure. Whilst kinship is a major driver of social structure in most mammalian species, I present evidence in Chapter Three that horse society is not structured by levels of kinship; however, in Chapter Five, I show that kinship levels to potential mates are significant in female dispersal choices in this species. In Chapter Eight, I provide evidence for significant effects of kinship to companions upon social and physical development in D. punctata, indicating a clear potential benefit of kin-based associations. The stability of social bonds can have substantial effects upon social structure. In Chapter Three, I show that the bonds between female horses show significant stability and are formed independently to kinship levels, a rare result in a non-primate species. I also provide evidence consistent with the hypothesis that these bonds are driven by male harassment. Similarly, in D. punctata, I find in Chapter Eight that female clustering occurs within resting aggregations and that the most likely explanation is the avoidance of male harassment. I therefore propose that this driver of female sociality may be a highly prevalent force structuring animal societies. Inbreeding depression has been demonstrated in a variety of species and contexts. Here I show in Chapter Five that in horses, female dispersal is likely to be influenced by kinship levels with potential mates. In Chapter Four, I then show that more heterozygous males have a higher reproductive success, most likely due to their ability to utilise a larger home range. Finally, local population structure can be highly influenced by individual association choices and behaviour. In Chapter Two, I show that in horses, mothers may allow their sons to postpone dispersal by the maintenance of stronger mother-son bonds, permitting an extended period of social learning. In Chapter Seven, I demonstrate that consistent inter-individual variation in personality traits exists in D. punctata which is stable across life stages, despite age effects on the strength of boldness. This is a source of variation which may be extremely important for decision-making social groups. My main conclusion from this work is that male harassment is often a key driver of sociality which may frequently be overlooked. I also demonstrate that the effects of kinship are far-ranging but not omnipresent. This thesis therefore makes a major contribution to our understanding of the mechanisms underlying animal sociality and presents clear potential avenues for future research.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Computer Science, 2009. / Includes bibliographical references.

This thesis is considering with network traffic analysis and prediction of real networks default services. The first part of this paper is containing the theoretical explanation of the mathematical model’s needs. These models are mainly used as a part of simulation algorithms which are describing the processes of network traffic simulations. The second part is describing the process how to apply the models to mathematically analyze the captured traffic. The capture is including all kind of packet types which can appear on the real network. At the last part of the thesis is described the detailed design of the prediction algorithm’s which are developed in programing language of Matlab® Mathworks®.

Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are widely-used mathematical models for decomposing multivariate data. They capture spatial relationships between variables, but ignore any temporal relationships that might exist between observations. Probabilistic PCA (PPCA) and Probabilistic CCA (ProbCCA) are versions of these two models that explain the statistical properties of the observed variables as linear mixtures of an alternative, hypothetical set of hidden, or latent, variables and explicitly model noise. Both the noise and the latent variables are assumed to be Gaussian distributed. This thesis introduces two new models, named PPCA-AR and ProbCCA-AR, that augment PPCA and ProbCCA respectively with autoregressive processes over the latent variables to additionally capture temporal relationships between the observations. To make PPCA-AR and ProbCCA-AR robust to outliers and able to model leptokurtic data, the Gaussian assumptions are replaced with infinite scale mixtures of Gaussians, using the Student-t distribution. Bayesian inference calculates posterior probability distributions for each of the parameter variables, from which we obtain a measure of confidence in the inference. It avoids the pitfalls associated with the maximum likelihood method: integrating over all possible values of the parameter variables guards against overfitting. For these new models the integrals required for exact Bayesian inference are intractable; instead a method of approximation, the variational Bayesian approach, is used. This enables the use of automatic relevance determination to estimate the model orders. PPCA-AR and ProbCCA-AR can be viewed as linear dynamical systems, so the forward-backward algorithm, also known as the Baum-Welch algorithm, is used as an efficient method for inferring the posterior distributions of the latent variables. The exact algorithm is tractable because Gaussian assumptions are made regarding the distribution of the latent variables. This thesis introduces a variational Bayesian forward-backward algorithm based on Student-t assumptions. The new models are demonstrated on synthetic datasets and on real remote sensing and EEG data.

This thesis gives a comprehensive characterization of lightweight non-oxide ceramic materials for ballistic applications, an overview of production technologies and processing of boron carbide B4C and its ceramic-based composites. A framework for evaluating the ballistic resistance of the material based on mechanical properties is shown there. It can be used in experiments without normalized equipment. The experiments including B4C + Si, B4C + Ti composites, and application of Spark plasma sintering (SPS) were designed according to outputs from the theoretical part. The volume fractions of Si, Ti dopants were optimized based on ongoing chemical reactions during sintering. The obtained samples were subjects of mechanical testing which results were compared to identify the ideal ratio of matrix and reinforcement. As the best suited material for ballistic protection, B4C + 1,0 obj. % reaches these values of parameters; hardness = 3502 ± 122 HV1; fracture toughness KIC = 2,97 ± 0,03 MPam^0,5.

Baigiamajame magistro darbe yra nagrinėjamas Baltijos šalių – Lietuvos, Latvijos ir Estijos BVP, jo kitimo tendencijos bei pagrindiniai makroekonomikos rodikliai – galutinio vartojimo išlaidos, prekių ir paslaugų importas bei eksportas, investicijos, užimtųjų darbo rinkoje skaičius bei BVP defliatorius. Išnagrinėti šių rodiklių apibrėžimai bei ekonominė prasmė, taip pat apžvelgta sukaupta mokslininkų patirtis modeliuojant juos. Siekiant išsiaiškinti ryšius tarp šių rodiklių atlikta koreliacijų bei Grangerio priežastingumo analizė. Modeliuojant, visų pirma, rasti ilgalaikiai sąryšiai tarp kintamųjų, o po to kiekvienai šaliai specifikuotas paklaidų korekcijos modelis bei įvertintas šio modelio tikslumas. Išnagrinėjus praktinius ir teorinius aspektus, pateikiamos baigiamojo darbo išvados ir pasiūlymai. Darbą sudaro 5 dalys: įvadas, analitinė - metodinė dalis, tiriamoji dalis, išvados ir pasiūlymai, literatūros sąrašas. Darbo apimtis – 112 p. teksto be priedų, 60 iliustr., 35 lent., 28 bibliografiniai šaltiniai. Atskirai pridedami darbo priedai. / The gross domestic product of the Baltic States – Lithuania, Latvia and Estonia, has been examined in the master thesis. Trends of the GDP and the main macroeconomic indicators – gross final consumption expenditure, imports and exports of goods and services, investment, labor force and GDP deflator have been analyzed. The definitions of these indicators and an economic sense, as well as researchers' experience in modeling, have been examined. In order to establish the relationship between these variables, analysis of the correlation and Granger causality test has been performed. In particular, modeling started with the finding of the long – term relationship between selected indicators. After that the error correction model has been specified and the accuracy of this model has been estimated. After the examination of the practical and theoretical aspects, the conclusions and recommendations have been presented. Structure: introduction, analytical – methodical part, research part, conclusions and suggestions, references. Thesis consists of: 112 p. text without appendixes, 60 pictures, 35 tables, 28 bibliographical entries. Appendixes included.

Fire Rescue Service is territorialized in each region, which means that Fire Rescue Service is present in each region of the Czech Republic as an individual accounting entity. Each accounting entity is obliged to prepare own budget estimate for next year which serves for covering costs related to interventions. Therefore it is beneficial to have an overview about the number of interventions expected in following year. The prediction of the expected interventions should be based utmost on real data from the the last period. Fire Rescue Service units' interventions in traffic accidents are covered from the state budget as well as other performances. The thesis is concerning the statistic survey related to Fire Rescue Service's interventions in traffic accidents. Specified goals of the thesis: G1) The survey of the development in time of the number of traffic accidents associated with South Bohemia Fire Rescue Service's interventions in traffic accidents within one month as the unit of time during the last 5 years. It is considered as the survey of the first of selected parameters concerning Fire Rescue Service's interventions in traffic accidents in the South Bohemia region. G2) The survey of the development in time of costs associated with South Bohemia Fire Rescue Service's interventions in traffic accidents within one month as the unit of time during the last 5 years. It is considered as the survey of the second of selected parameters related to Fire Rescue Service's interventions in traffic accidents in the South Bohemia region. G3) The comparison of the number of interventions and costs, the investigation of the apportionment of the number of interventions and costs in terms of a proper theoretical apportionment. It is considered as the survey concerning the dependence of selected parameters of Fire Rescue Service's interventions in traffic accidents in the South Bohemia region. With regard to a relativelly high number of primary data (entries from about 8 000 traffic accidents in the time period between 7/2010 and 6/2015 in the South Bohemia region, where was always present at least one unit of the South Bohemia Fire Rescue Service), mathematical statistic tools were needed to create a survey of selected parameters of the data file. Determining intervals for individual statistic features is to some extent carried out "by feeling". With regard to it, there was done the sensitivity analysis within which was investigated the influence of minor changes in the number of intervals on apportionment of percent occurrences of monitored statistical features (intervals were determined "by feeling" and based on recommendation from technical literature). For investigation of statistical features were used description statistics and also methods of statistical induction. The information used from technical literature and the interpretation of partial results and conclusions concerning individual sections of the thesis are provided in the section Discussion. The thesis demonstrated that the empirical apportionment of the number of traffic accidents and costs on South Bohemia Fire Rescue Service's interventions in traffic accidents can be considered as standard. Therefore it is correct to use theoretical findings associated with standard apportionment to work with those data. Also the thesis demonstrated that the number of traffic accidents and costs associated with South Bohemia Fire Rescue Service's interventions not only does not decrease but on the contrary increases. It was demonstrated that there is present very high positive correlation between numbers of the traffic accidents and the costs related to South Bohemia Fire Rescue Service's interventions in single months of the monitored time period. This brings us to a conclusion that the increase of costs related to interventions is caused mainly by the increase of the number of traffic accidents where the units of Fire Rescue Service intervene.

The cryptographic algorithms are commonly used as a security item today. In some situations, the special device is used to run the cryptographic algorithm, so the data are protected against the attack from the internet. Naturally, the attack can be loaded on the device as well using the side channel attack. The data are under the great danger, because nowadays plenty of power consumption analyses exist. The side channel attack uses knowledge about the cryptographic algoritm and simple or differential analysis. The diploma thesis focuses on the differential power analysis attack for the data published under the DPA contest. This thesis covers different types of analyss and attacks, and describes the new DPACv4.2 implementation. The correlation analysis is presented for the DPACv4.2 and the possible attack is discussed at the conclusion.

This dissertation mainly studies correlation and then portfolio analysis of financial contagion and capital flight, focusing on currency co-movements around the political uncertainty due to the Brexit referendum on 26 June 2016. The correlation, mean, and covariance computations in the analysis are both time-unconditional and time-conditional, and the generalized autoregressive conditional heteroskedasticity (GARCH) and exponentially weighted moving average (EWMA) methods are applied. The correlation analysis in this dissertation (Chapter 1) extends the previous literature on contagion testing based on a single global factor model, bivariate correlation analysis, and heteroskedasticity bias correction. Chapter 1 proposes an alternatively extended framework, assuming that intensification of financial correlations in a state of distress could coincide with rising global-factor-loading variability, provides simple tests to verify the assumptions of the literature and of the extended framework, and considers capital flight other than merely financial contagion. The outcomes show that, compared to the literature, the extended framework can be deemed more verified to the Brexit case. Empirically, with the UK being the shock-originating economy and the sterling value plummeting on the US dollar, there exist contagions to some other major currencies as well as a flight to quality, particularly to the yen, probably suggesting diversification benefits. When the correlation coefficients are time-conditional, or depend more on more recent data, the evidence shows fewer contagions and flights since the political uncertainty in question disappeared gradually over time. After relevant interest rates were partialled out, some previous statistical contagion and flight occurrences became less significant or even insignificant, possibly due to the significant impacts of the interest rates on the corresponding currency correlations. The portfolio analysis in this dissertation (Chapter 2) examines financial contagion and capital flight implied by portfolio reallocations through mean-variance portfolio analysis, and builds on the correlation analysis in Chapter 1. In the correlation analysis, correlations are bivariate, whereas in the portfolio analysis they are multivariate and the risk-return tradeoff is also vitally involved. Portfolio risk minimization and reward-to-risk maximization are the two analytical cases of portfolio optimality taken into consideration. Robust portfolio optimizations, using shrinkage estimations and newly proposed risk-based weight constraints, are also applied. The evidence demonstrates that the portfolio analysis outcomes regarding currency contagions and flights, implying diversification benefits, vary and are noticeably dissimilar from the correlation analysis outcomes of Chapter 1. Subsequently, it could be inferred that the diversification benefits deduced from the portfolio and correlation analyses differ owing to the dominance, during market uncertainty, of the behaviors of the means and (co)variances of all the shock-originating and shock-receiving returns, over the behaviors of just bivariate correlations between the shock-originating and shock-receiving returns. Moreover, corrections of the heteroskedasticity bias inherent in the shock-originating returns, overall, do not have an effect on currency portfolio rebalancing. Additionally, hedging demands could be implied from detected structural portfolio reallocations, probably as a result of variance-covariance shocks rising from Brexit. / This dissertation mainly studies correlation and then portfolio analysis of financial contagion and capital flight, focusing on currency co-movements around the political uncertainty due to the Brexit referendum on 26 June 2016. The correlation, mean, and covariance computations in the analysis are both time-unconditional and time-conditional, and the generalized autoregressive conditional heteroskedasticity (GARCH) and exponentially weighted moving average (EWMA) methods are applied. The correlation analysis in this dissertation (Chapter 1) extends the previous literature on contagion testing based on a single global factor model, bivariate correlation analysis, and heteroskedasticity bias correction. Chapter 1 proposes an alternatively extended framework, assuming that intensification of financial correlations in a state of distress could coincide with rising global-factor-loading variability, provides simple tests to verify the assumptions of the literature and of the extended framework, and considers capital flight other than merely financial contagion. The outcomes show that, compared to the literature, the extended framework can be deemed more verified to the Brexit case. Empirically, with the UK being the shock-originating economy and the sterling value plummeting on the US dollar, there exist contagions to some other major currencies as well as a flight to quality, particularly to the yen, probably suggesting diversification benefits. When the correlation coefficients are time-conditional, or depend more on more recent data, the evidence shows fewer contagions and flights since the political uncertainty in question disappeared gradually over time. After relevant interest rates were partialled out, some previous statistical contagion and flight occurrences became less significant or even insignificant, possibly due to the significant impacts of the interest rates on the corresponding currency correlations. The portfolio analysis in this dissertation (Chapter 2) examines financial contagion and capital flight implied by portfolio reallocations through mean-variance portfolio analysis, and builds on the correlation analysis in Chapter 1. In the correlation analysis, correlations are bivariate, whereas in the portfolio analysis they are multivariate and the risk-return tradeoff is also vitally involved. Portfolio risk minimization and reward-to-risk maximization are the two analytical cases of portfolio optimality taken into consideration. Robust portfolio optimizations, using shrinkage estimations and newly proposed risk-based weight constraints, are also applied. The evidence demonstrates that the portfolio analysis outcomes regarding currency contagions and flights, implying diversification benefits, vary and are noticeably dissimilar from the correlation analysis outcomes of Chapter 1. Subsequently, it could be inferred that the diversification benefits deduced from the portfolio and correlation analyses differ owing to the dominance, during market uncertainty, of the behaviors of the means and (co)variances of all the shock-originating and shock-receiving returns, over the behaviors of just bivariate correlations between the shock-originating and shock-receiving returns. Moreover, corrections of the heteroskedasticity bias inherent in the shock-originating returns, overall, do not have an effect on currency portfolio rebalancing. Additionally, hedging demands could be implied from detected structural portfolio reallocations, probably as a result of variance-covariance shocks rising from Brexit.