In this thesis, abstract bounds for the normal approximation of Poisson functionals are computed by the Malliavin-Stein method and used to derive central limit theorems for problems from stochastic geometry. As a Poisson functional we denote a random variable depending on a Poisson point process. It is known from stochastic analysis that every square integrable Poisson functional has a representation as a (possibly infinite) sum of multiple Wiener-Ito integrals. This decomposition is called Wiener-Itô chaos expansion, and the integrands are denoted as kernels of the Wiener-Itô chaos expansion. An explicit formula for these kernels is known due to Last and Penrose. Via their Wiener-Itô chaos expansions the so-called Malliavin operators are defined. By combining Malliavin calculus and Stein's method, a well-known technique to derive limit theorems in probability theory, bounds for the normal approximation of Poisson functionals in the Wasserstein distance and vectors of Poisson functionals in a similar distance were obtained by Peccati, Sole, Taqqu, and Utzet and Peccati and Zheng, respectively. An analogous bound for the univariate normal approximation in Kolmogorov distance is derived. In order to evaluate these bounds, one has to compute the expectation of products of multiple Wiener-Itô integrals, which are complicated sums of deterministic integrals. Therefore, the bounds for the normal approximation of Poisson functionals reduce to sums of integrals depending on the kernels of the Wiener-Itô chaos expansion. The strategy to derive central limit theorems for Poisson functionals is to compute the kernels of their Wiener-Itô chaos expansions, to put the kernels in the bounds for the normal approximation, and to show that the bounds vanish asymptotically. By this approach, central limit theorems for some problems from stochastic geometry are derived. Univariate and multivariate central limit theorems for some functionals of the intersection process of Poisson k-flats and the number of vertices and the total edge length of a Gilbert graph are shown. These Poisson functionals are so-called Poisson U-statistics which have an easier structure since their Wiener-Itô chaos expansions are finite, i.e. their Wiener-Itô chaos expansions consist of finitely many multiple Wiener-Itô integrals. As examples for Poisson functionals with infinite Wiener-Itô chaos expansions, central limit theorems for the volume of the Poisson-Voronoi approximation of a convex set and the intrinsic volumes of Boolean models are proven.

In 2016, over 47,000 youths in the state of Florida were served by the Department of Juvenile Justice (DJJ) probation services. While on probation, these youths were exposed to 2 different, and potentially conflicting disciplinary management systems. Youth are under the authority of juvenile probation officers (JPOs), who are bound to a consequence-based management approach. This approach is guided by negative reinforcement. The youths are simultaneously engaged with staff from diversion programs, many of which are strengths-based and guided by positive reinforcement. According to the ecosystemic complexity theory of conflict, exposure to incongruent systems can have negative effects such as confusion and ineffectiveness. By applying a hermeneutic phenomenological approach, I explored the responses to this convergence point from the perspective of 9 strengths-based school counseling staff members who supervise the youth that navigate between these 2 different behavior modification systems. This sample of 9 staff members also work directly with JPOs. Data were collected using iterative versions of semistructured interviews and analyzed using content analysis. Findings revealed that conflict did exist at the convergence point, and that cohesion, on varying levels, also existed, and that solutions to the philosophical incompatibility have emerged. This research contributes to social change by illuminating the possible conflict inherent in implementing incongruent approaches to behavior management, which may inform policymakers regarding program management for juvenile justice.

The dissertation deals with prediction of crude oil price and is tailor-made for such companies which are heavily crude oil related. The main dissertation target is to make sure that such companies can get ready for price changes and safeguard themselves against negative consequences. Crude oil prices are the main factor which affects prices of such final products as petrol. It is a well known fact that quantitative predictions are not reliable and all those who are forced to real on such vague data set for their decision-making are reluctant to use them. That’s how we would like to have at least the correct trend information. The dissertation introduces some concepts originally developed within artificial intelligence theory for the crude oil predictions. Specifically common sense algorithms and qualitative interpretation of some aspects of theory of chaos are the main contribution towards expanding of available prediction tools described by the dissertation. A systematic analysis of a sequence of qualitative solutions is the key part of the dissertation.

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In this thesis, the practice of Chaos magick and the practice and mythology of Discordianism are compared to different subversive techniques used in internet culture and specifically in internet trolling. Chaos magick is described from the sigil-making of Austin Osman Spare through the playbacks of William S Burroughs to contemporary practitioners. The Chaos magick practices unveiled in this investigation are compared to practices in internet culture and specifically internet trolling through avariety of different themes, from memes to doxing to the chaos of apophenia.

We study the time evolution of driven quantum systems using analytic, algebraic, and numerical methods. First, we obtain analytic solutions for driven free and oscillator systems by shifting the coordinate and phase of the undriven wave function. We also factorize the quantum evolution operator using the generators of the Lie algebra comprising the Hamiltonian. We obtain coupled ODE's for the time evolution of the Lie algebra parameters. These parameters allow us to find physical properties of oscillator dynamics. In particular we find phase-space trajectories and transition probabilities. We then search for chaotic behavior in the Lie algebra parameters as a signature for dynamical chaos in the quantum system. We plot the trajectories, transition probabilities, and Lyapunov exponents for a wide range of the following physical parameters: strength and duration of the driving force, frequency difference, and anharmonicity of the oscillator. We identify conditions for the appearance of chaos in the system.

Wind energy is a field with a large number of uncertainties. The random nature of the weather conditions, including wind speed, wind direction, and turbulence intensity, influences the energy output and the structural safety of a wind farm, making its performance fluctuate and difficult to predict. The uncertainties presented in the energy output and structure lifetime lead to increased investment risk. There are possibilities to reduce the risk associated with these uncertainties by optimizing the design of the farm or the wind turbine, with respect to the stochastic parameters. The goal of this project is to improve the wind farm optimization problem by providing accurate and computationally efficient annual energy production (AEP) estimates, which is a uncertainty quantification that is required in every optimization step. Uncertainty quantification has been recognized as a challenge in the wind energy industry, as the chaotic nature of the weather condition complicates the prediction of energy production. High-fidelity wind farm models usually employ advanced models like Large Eddy Simulation or Reynolds averaged Navier-Stokes equation for better accuracy. However, the prolonged computation time of these high-fidelity models make the traditional uncertainty quantification approach like the Monte-Carlo simulation or other integration techniques infeasible for larger wind farms.  To overcome this limitation, the report proposes the use of generalized polynomial chaos expansion (PCE) to characterize the AEP as a function of wind speed and wind direction. PCE is a technique that approximates a random variable using a series of orthogonal polynomials, the polynomials are chosen based on the target distribution. This report explains how a surrogate model of the AEP can be constructed using PCE, which can be used in optimization or model analysis. The objective of the thesis work is to minimize the number of model evaluations required for obtaining an accurate energy response surface. Different ideas of non-intrusive PCE are implemented and explored in this project. The report demonstrates that, the multi-element polynomial chaos fitted by point collocation, with a dependent polynomial basis, is not only able to make accurate and stable (with respect to the placement of the measurements) energy predictions, but also produces realistic energy response surface. / Vindkraft är en bransch med många osäkerheter, där väderförhållandena påverkar energiproduktionen och strukturens livslängd. Denna osäkerhet ökar investeringsrisken, men kan minskas genom optimering av vindkraftverkets design med hänsyn till de stokastiska parametrarna. Syftet med denna rapport är att förbättra optimeringsproblemet för vindkraftverk genom att ge noggranna och effektiva årliga energiproduktionsberäkningar (AEP), vilket krävs vid varje optimeringssteg. I rapporten används polynomial chaos expansion (PCE) för att approximera AEP och minska antalet nödvändiga modellutvärderingar. Resultaten visar att PCE är en effektiv metod för att göra energiprognoser.

The study of experimental recording of dynamical systems often consists in the analysis of signals produced by that system. Time series analysis consists of a wide range of methodologies ultimately aiming at characterizing the signals and, eventually, gaining insights on the underlying processes that govern the evolution of the system. A standard way to tackle this issue is spectrum analysis, which uses Fourier or Laplace transforms to convert time-domain data into a more useful frequency space. These analytical methods allow to highlight periodic patterns in the signal and to reveal essential characteristics of linear systems. Most experimental signals, however, exhibit strange and apparently unpredictable behavior which require more sophisticated analytical tools in order to gain insights into the nature of the underlying processes generating those signals. This is the case when nonlinearity enters into the dynamics of a system. Nonlinearity gives rise to unexpected and fascinating behavior, among which the emergence of deterministic chaos. In the last decades, chaos theory has become a thriving field of research for its potential to explain complex and seemingly inexplicable natural phenomena. The peculiarity of chaotic systems is that, despite being created by deterministic principles, their evolution shows unpredictable behavior and a lack of regularity. These characteristics make standard techniques, like spectrum analysis, ineffective when trying to study said systems. Furthermore, the irregular behavior gives the appearance of these signals being governed by stochastic processes, even more so when dealing with experimental signals that are inevitably affected by noise. Nonlinear time series analysis comprises a set of methods which aim at overcoming the strange and irregular evolution of these systems, by measuring some characteristic invariant quantities that describe the nature of the underlying dynamics. Among those quantities, the most notable are possibly the Lyapunov ex- ponents, that quantify the unpredictability of the system, and measure of dimension, like correlation dimension, that unravel the peculiar geometry of a chaotic system’s state space. These methods are ultimately analytical techniques, which can often be exactly estimated in the case of simulated systems, where the differential equations governing the system’s evolution are known, but can nonetheless prove difficult or even impossible to compute on experimental recordings. A different approach to signal analysis is provided by information theory. Despite being initially developed in the context of communication theory, by the seminal work of Claude Shannon in 1948, information theory has since become a multidisciplinary field, finding applications in biology and neuroscience, as well as in social sciences and economics. From the physical point of view, the most phenomenal contribution from Shannon’s work was to discover that entropy is a measure of information and that computing the entropy of a sequence, or a signal, can answer to the question of how much information is contained in the sequence. Or, alternatively, considering the source, i.e. the system, that generates the sequence, entropy gives an estimate of how much information the source is able to produce. Information theory comprehends a set of techniques which can be applied to study, among others, dynamical systems, offering a complementary framework to the standard signal analysis techniques. The concept of entropy, however, was not new in physics, since it had actually been defined first in the deeply physical context of heat exchange in thermodynamics in the 19th century. Half a century later, in the context of statistical mechanics, Boltzmann reveals the probabilistic nature of entropy, expressing it in terms of statistical properties of the particles’ motion in a thermodynamic system. A first link between entropy and the dynamical evolution of a system is made. In the coming years, following Shannon’s works, the concept of entropy has been further developed through the works of, to only cite a few, Von Neumann and Kolmogorov, being used as a tool for computer science and complexity theory. It is in particular in Kolmogorov’s work, that information theory and entropy are revisited from an algorithmic perspective: given an input sequence and a universal Turing machine, Kolmogorov found that the length of the shortest set of instructions, i.e. the program, that enables the machine to compute the input sequence was related to the sequence’s entropy. This definition of the complexity of a sequence already gives hint of the differences between random and deterministic signals, in the fact that a truly random sequence would require as many instructions for the machine as the size of the input sequence to compute, as there is no other option than programming the machine to copy the sequence point by point. On the other hand, a sequence generated by a deterministic system would simply require knowing the rules governing its evolution, for example the equations of motion in the case of a dynamical system. It is therefore through the work of Kolmogorov, and also independently by Sinai, that entropy is directly applied to the study of dynamical systems and, in particular, deterministic chaos. The so-called Kolmogorov-Sinai entropy, in fact, is a well-established measure of how complex and unpredictable a dynamical system can be, based on the analysis of trajectories in its state space. In the last decades, the use of information theory on signal analysis has contributed to the elaboration of many entropy-based measures, such as sample entropy, transfer entropy, mutual information and permutation entropy, among others. These quantities allow to characterize not only single dynamical systems, but also highlight the correlations between systems and even more complex interactions like synchronization and chaos transfer. The wide spectrum of applications of these methods, as well as the need for theoretical studies to provide them a sound mathematical background, make information theory still a thriving topic of research. In this thesis, I will approach the use of information theory on dynamical systems starting from fundamental issues, such as estimating the uncertainty of Shannon’s entropy measures on a sequence of data, in the case of an underlying memoryless stochastic process. This result, beside giving insights on sensitive and still-unsolved aspects when using entropy-based measures, provides a relation between the maximum uncertainty on Shannon’s entropy estimations and the size of the available sequences, thus serving as a practical rule for experiment design. Furthermore, I will investigate the relation between entropy and some characteristic quantities in nonlinear time series analysis, namely Lyapunov exponents. Some examples of this analysis on recordings of a nonlinear chaotic system are also provided. Finally, I will discuss other entropy-based measures, among them mutual information, and how they compare to analytical techniques aimed at characterizing nonlinear correlations between experimental recordings. In particular, the complementarity between information-theoretical tools and analytical ones is shown on experimental data from the field of neuroscience, namely magnetoencefalography and electroencephalography recordings, as well as mete- orological data.

This dissertation focuses on the ‘theo-political’ core of US Christian Zionism and Jewish Religious Zionism. The political militancy characterizing two Millenarian/Messianic movements such as Christian Zionism and Jewish Religious Zionism constitutes a still under-researched and under-theorized aspect that, at present, is paramount to address for its immediate and long terms implications in the highly sensitive and volatile Israeli-Palestinian issue, in the US and Israeli domestic domain, and in the wider international community. Although processes of the ‘sacralisation of politics’ and ‘politicisation of religions’ have already manifested themselves in countless forms over past centuries, Christian Zionism and Jewish Religious Zionism are unprecedented phenomena given their unique hybridized nature, political prominence and outreach, mobilizing appeal amongst believers, organizationalcommunicational skills and degree of institutionalization. / Consortium for Peace Studies at Calgary University

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