This thesis is devoted to the study of operators on discrete structures. The operators are supposed to be self-adjoint and obey a certain translation invariance property. The discrete structures are given as Cayley graphs via finitely generated groups. Here, sofic groups and amenable groups are in the center of our considerations. Note that every finitely generated amenable group is sofic. We investigate the spectrum of a discrete self-adjoint operator by studying a sequence of finite dimensional analogues of these operators. In the setting of amenable groups we obtain these approximating operators by restricting the operator in question to finite subsets Qn , n ∈ N. These finite dimensional operators are self-adjoint and therefore admit a well-defined normalized eigenvalue counting function. The limit of the normalized eigenvalue counting functions when |Qn | → ∞ (if it exists) is called the integrated density of states (IDS). It is a distribution function of a probability measure encoding the distribution of the spectrum of the operator in question on the real axis. In this thesis, we prove the existence of the IDS in various geometric settings and for different types of operators. The models we consider include deterministic as well as random situations. Depending on the specific setting, we prove existence of the IDS as a weak limit of distribution functions or even as a uniform limit. Moreover, in certain situations we are able to express the IDS via a semi-explicit formula using the trace of the spectral projection of the original operator. This is sometimes referred to as the validity of the Pastur-Shubin trace formula. In the most general geometric setting we study, the operators are defined on Cayley graphs of sofic groups. Here we prove weak convergence of the eigenvalue counting functions and verify the validity of the Pastur-Shubin trace formula for random and non-random operators . These results apply to operators which not necessarily bounded or of finite hopping range. The methods are based on resolvent techniques. This theory is established without having an ergodic theorem for sofic groups at hand. Note that ergodic theory is the usual tool used in the proof of convergence results of this type. Specifying to operators on amenable groups we are able to prove stronger results. In the discrete case, we show that the IDS exists uniformly for a certain class of finite hopping range operators. This is obtained by using a Banach space-valued ergodic theorem. We show that this applies to eigenvalue counting functions, which implies their convergence with respect to the Banach space norm, in this case the supremum norm. Thus, the heart of this theory is the verification of the Banach space-valued ergodic theorem. Proceeding in two steps we first prove this result for so-called ST-amenable groups. Then, using results from the theory of ε-quasi tilings, we prove a version of the Banach space-valued ergodic theorem which is valid for all amenable groups. Focusing on random operators on amenable groups, we prove uniform existence of the IDS without the assumption that the operator needs to be of finite hopping range or bounded. Moreover, we verify the Pastur-Shubin trace formula. Here we present different techniques. First we show uniform convergence of the normalized eigenvalue counting functions adapting the technique of the Banach space-valued ergodic theorem from the deterministic setting. In a second approach we use weak convergence of the eigenvalue counting functions and additionally obtain control over the convergence at the jumps of the IDS. These ingredients are applied to verify uniform existence of the IDS. In both situations we employ results from the theory of large deviations, in order to deal with long-range interactions.

Thema der Dissertation ist die Untersuchung von asymptotischen Eigenschaften im Wechselspiel von Diffusion und Wellenausbreitung. Es geht um diskrete, zufällige Schrödingeroperatoren, die in die diskrete Wärmeleitungsgleichung eingefügt werden. Das Ensemble der Lösungen kann mit der vom diskreten Laplace erzeugten Irrfahrt in kontinuierlicher Zeit und der Feynman-Kac-Formel stochastisch interpretiert werden. So werden Methoden aus der Theorie der großen Abweichungen anwendbar. Neben dem stochastischen Zugang können die Schrödingeroperatoren auch spektraltheoretisch untersucht werden. In der Dissertation wird das Wechselspiel dieser beiden Herangehensweisen im Hinblick auf die asymptotischen Eigenschaften der Momente, der integrierten Zustandsdichte und der Korrelationsfunktion betrachtet.

In dieser interdisziplinär geprägten Arbeit wird zunächst eine Übersicht über kugelbasierte Modelle und die algorithmischen Ansätze zur Generierung zufälliger Kugelpackungen gegeben. Ein Algorithmus aus der Gruppe der Kollektiven-Umordnungs-Algorithmen -- der Force-Biased-Algorithmus -- wird ausführlich erläutert und untersucht. Dabei werden die für den Force-Biased-Algorithmus als essenziell geltenden Verschiebungsfunktionen bezüglich ihres Einflusses auf den erreichbaren Volumenanteil der Packungen untersucht. Nicht nur aus der Literatur bekannte, sondern auch neu entwickelte Verschiebungsfunktionen werden hierbei betrachtet. Daran anschließend werden Empfehlungen zur Auswahl geeigneter Verschiebungsfunktionen gegeben. Einige mit dem Force-Biased-Algorithmus generierte Kugelpackungen, zum Beispiel hochdichte monodisperse Packungen, lassen den Schluss zu, dass insbesondere strukturelle Umbildungsvorgänge an solchen Packungen sehr gut zu untersuchen sind. Aus diesem Grund besitzt das Modell der mit dem Force-Biased-Algorithmus dicht gepackten harten Kugeln große Bedeutung in der Materialwissenschaft, insbesondere in der Strukturforschung. In einem weiteren Kapitel werden wichtige Kenngrößen kugelbasierter Modelle erläutert, wie z. B. spezifische Oberfläche, Volumenanteil und die Kontaktverteilungsfunktionen. Für einige besonders anwendungsrelevante Kenngrößen (z. B. die spezifische Oberfläche) werden Näherungsformeln entwickelt, an Modellsystemen untersucht und mit bekannten Näherungen aus der Literatur verglichen. Zur Generierung und Analyse der Kugelpackungen wurde im Rahmen dieser Arbeit die Simulationssoftware „SpherePack“ entwickelt, deren Aufbau unter dem Aspekt des Softwareengineerings betrachtet wird. Die Anforderungen an dieses Simulationssystem sowie dessen Architektur werden hier beschrieben, einschließlich der Erläuterung einzelner Berechnungsmodule. An ausgewählten praxisnahen Beispielen aus der Materialwissenschaft kann die Vielfalt der Einsatzmöglichkeiten eines Simulationssystems zur Generierung und Analyse von zufälligen dicht gepackten Kugelsystemen gezeigt werden. Vor allem die hohe Aussagekraft der Untersuchungen in Bezug auf Materialeigenschaften unterstreicht die Bedeutung des Modells zufällig dicht gepackter harter Kugeln in der Materialforschung und verwandten Forschungsgebieten.

Nachrichtentechnische Systeme werden stets durch unvermeidbare zufällige Störungen beeinflußt. Neben anderen Komponenten sind davon besonders Oszillatoren betroffen. Die durch die Störungen verursachten zufälligen Schwankungen in der Oszillatorausgabe können als Amplituden- und Phasenabweichungen modelliert werden. Dabei zeigt sich, daß vor allem zufällige Phasenfluktuationen von Bedeutung sind. Zufällige Phasenfluktuationen können unter Verwendung stochastischer Prozesse zweiter Ordnung mit kurzem oder langem Gedächtnis modelliert werden. Inhalt der Dissertation ist die Herleitung eines Verfahrens zur Simulation zufälliger Phasenfluktuationen von Oszillatoren mit kurzem Gedächtnis unter Berücksichtigung von Datenblattangaben.

Efficient immobilization of the antibody to the substrate is of crucial importance in the development of anti-CD34-based endothelial progenitor cells capturing cardiovascular devices. This should go along with precise control of the antibody orientation by appropriate immobilization technology for retaining antibody activity, like in immunosensors. Recently, great attention was paid to immobilization of anti-CD34 antibody onto substrates by covalent binding, but at random orientation. Here, to investigate the biological effect of antibody orientation, we have prepared two kinds of anti-CD34 antibody coated surfaces, with random immobilization and oriented immobilization. The immunological binding activity (IBA) of the antibody at oriented immobilization was 3.48 times higher than at random immobilization, indicating that the two different surfaces were successfully prepared. The endothelial progenitor cell-capturing capability of oriented antibody-immobilized surface was 1.35 and 1.64 times higher than for the random immobilized surface after seeding for 2 and 12 h under flow condition, respectively. The endothelial progenitor cell-capturing efficiency per antibody by oriented immobilization was 5.16 and 6.26 times higher than for the random after seeding for 2 and 12 h under flow condition, respectively. In addition, the oriented antibody-immobilized surface possessed better blood-compatibility. These results clearly revealed the significance of antibody orientation which could retain its biological effect and may revolutionize the antibody-immobilization protocols used in cardiovascular and other bloodcontacting biomedical devices.

This book is devoted to the study of operators on discrete structures. The operators are supposed to be self-adjoint and obey a certain translation invariance property. The discrete structures are given as Cayley graphs via finitely generated groups. Here, sofic groups and amenable groups are in the center of our considerations. Note that every finitely generated amenable group is sofic. We investigate the spectrum of a discrete self-adjoint operator by studying a sequence of finite dimensional analogues of these operators. In the setting of amenable groups we obtain these approximating operators by restricting the operator in question to finite subsets Qn , n ∈ N. These finite dimensional operators are self-adjoint and therefore admit a well-defined normalized eigenvalue counting function. The limit of the normalized eigenvalue counting functions when |Qn | → ∞ (if it exists) is called the integrated density of states (IDS). It is a distribution function of a probability measure encoding the distribution of the spectrum of the operator in question on the real axis. We prove the existence of the IDS in various geometric settings and for different types of operators. The models we consider include deterministic as well as random situations. Depending on the specific setting, we prove existence of the IDS as a weak limit of distribution functions or even as a uniform limit. Moreover, in certain situations we are able to express the IDS via a semi-explicit formula using the trace of the spectral projection of the original operator. This is sometimes referred to as the validity of the Pastur-Shubin trace formula. In the most general geometric setting we study, the operators are defined on Cayley graphs of sofic groups. Here we prove weak convergence of the eigenvalue counting functions and verify the validity of the Pastur-Shubin trace formula for random and non-random operators . These results apply to operators which not necessarily bounded or of finite hopping range. The methods are based on resolvent techniques. This theory is established without having an ergodic theorem for sofic groups at hand. Note that ergodic theory is the usual tool used in the proof of convergence results of this type. Specifying to operators on amenable groups we are able to prove stronger results. In the discrete case, we show that the IDS exists uniformly for a certain class of finite hopping range operators. This is obtained by using a Banach space-valued ergodic theorem. We show that this applies to eigenvalue counting functions, which implies their convergence with respect to the Banach space norm, in this case the supremum norm. Thus, the heart of this theory is the verification of the Banach space-valued ergodic theorem. Proceeding in two steps we first prove this result for so-called ST-amenable groups. Then, using results from the theory of ε-quasi tilings, we prove a version of the Banach space-valued ergodic theorem which is valid for all amenable groups. Focusing on random operators on amenable groups, we prove uniform existence of the IDS without the assumption that the operator needs to be of finite hopping range or bounded. Moreover, we verify the Pastur-Shubin trace formula. Here we present different techniques. First we show uniform convergence of the normalized eigenvalue counting functions adapting the technique of the Banach space-valued ergodic theorem from the deterministic setting. In a second approach we use weak convergence of the eigenvalue counting functions and additionally obtain control over the convergence at the jumps of the IDS. These ingredients are applied to verify uniform existence of the IDS. In both situations we employ results from the theory of large deviations, in order to deal with long-range interactions.

Diese Arbeit beginnt mit einer Analyse von stochastischen Rückwärtsdifferentialgleichungen (BSDEs) mit Sprüngen, getragen von zufälligen Maßen mit ggf. unendlicher Aktivität und zeitlich inhomogenem Kompensator. Unter konkreten, in Anwendungen leicht verifizierbaren Bedingungen liefern wir Existenz-, Eindeutigkeits- und Vergleichsergebnisse beschränkter Lösungen für eine Klasse von Generatorfunktionen, die nicht global Lipschitz-stetig im Sprungintegranden sein brauchen. Der übrige Teil der Arbeit behandelt robuste Bewertung und Hedging in unvollständigen Märkten. Wir verfolgen den No-Good-Deal-Ansatz, der Good-Deal-Grenzen liefert, indem nur eine Teilmenge der risikoneutralen Maße mit ökonomischer Bedeutung betrachtet wird (z.B. Grenzen für instantanen Sharpe-Ratio, optimale Wachstumsrate oder erwarteten Nutzen). Durchweg untersuchen wir ein Konzept des Good-Deal-Hedgings für welches Hedgingstrategien als Minimierer geeigneter dynamischer Risikomaße auftreten, was optimale Risikoteilung mit der Markt erlaubt. Wir zeigen, dass Hedging mindestens im-Mittel-selbstfinanzierend ist, also, dass Hedgefehler unter geeigneten A-priori-Bewertungsmaßen eine Supermartingaleigenschaft haben. Wir leiten konstruktive Ergebnisse zu Good-Deal-Bewertung und -Hedging im Rahmen von Prozessen mit Sprüngen durch BSDEs mit Sprüngen, sowie im Brown''schen Fall mit Driftunsicherheit durch klassische BSDEs und mit Volatilitätsunsicherheit durch BSDEs zweiter Ordnung her. Wir liefern neue Beispiele, die insbesondere für versicherungs- und finanzmathematische Anwendungen von Bedeutung sind. Bei Ungewissheit des Real-World-Maßes führt ein Worst-Case-Ansatz bei Annahme mehrerer Referenzmaße zu Good-Deal-Hedging, welches robust bzgl. Unsicherheit, im Sinne von gleichmäßig über alle Referenzmaße mindestens im-Mittel-selbstfinanzierend, ist. Daher ist bei hinreichend großer Driftunsicherheit Good-Deal-Hedging zur Risikominimierung äquivalent. / This thesis starts by an analysis of backward stochastic differential equations (BSDEs) with jumps driven by random measures possibly of infinite activity with time-inhomogeneous compensators. Under concrete conditions that are easy to verify in applications, we prove existence, uniqueness and comparison results for bounded solutions for a class of generators that are not required to be globally Lipschitz in the jump integrand. The rest of the thesis deals with robust valuation and hedging in incomplete markets. The focus is on the no-good-deal approach, which computes good-deal valuation bounds by using only a subset of the risk-neutral measures with economic meaning (e.g. bounds on instantaneous Sharpe ratios, optimal growth rates, or expected utilities). Throughout we study a notion of good-deal hedging consisting in minimizing some dynamic risk measures that allow for optimal risk sharing with the market. Hedging is shown to be at least mean-self-financing in that hedging errors satisfy a supermartingale property under suitable valuation measures. We derive constructive results on good-deal valuation and hedging in a jump framework using BSDEs with jumps, as well as in a Brownian setting with drift uncertainty using classical BSDEs and with volatility uncertainty using second-order BSDEs. We provide new examples which are particularly relevant for actuarial and financial applications. Under ambiguity about the real-world measure, a worst-case approach under multiple reference priors leads to good-deal hedging that is robust w.r.t. uncertainty in that it is at least mean-self-financing uniformly over all priors. This yields that good-deal hedging is equivalent to risk-minimization if drift uncertainty is sufficiently large.

My thesis addresses a systematic approach to stochastic models in population genetics; in particular, the Wright-Fisher models affected only by the random genetic drift. I used various mathematical methods such as Probability, PDE, and Geometry to answer an important question: \"How do genetic change factors (random genetic drift, selection, mutation, migration, random environment, etc.) affect the behavior of gene frequencies or genotype frequencies in generations?”. In a Hardy-Weinberg model, the Mendelian population model of a very large number of individuals without genetic change factors, the answer is simple by the Hardy-Weinberg principle: gene frequencies remain unchanged from generation to generation, and genotype frequencies from the second generation onward remain also unchanged from generation to generation. With directional genetic change factors (selection, mutation, migration), we will have a deterministic dynamics of gene frequencies, which has been studied rather in detail. With non-directional genetic change factors (random genetic drift, random environment), we will have a stochastic dynamics of gene frequencies, which has been studied with much more interests. A combination of these factors has also been considered. We consider a monoecious diploid population of fixed size N with n + 1 possible alleles at a given locus A, and assume that the evolution of population was only affected by the random genetic drift. The question is that what the behavior of the distribution of relative frequencies of alleles in time and its stochastic quantities are. When N is large enough, we can approximate this discrete Markov chain to a continuous Markov with the same characteristics. In 1931, Kolmogorov first introduced a nice relation between a continuous Markov process and diffusion equations. These equations called the (backward/forward) Kolmogorov equations which have been first applied in population genetics in 1945 by Wright. Note that these equations are singular parabolic equations (diffusion coefficients vanish on boundary). To solve them, we use generalized hypergeometric functions. To know more about what will happen after the first exit time, or more general, the behavior of whole process, in joint work with J. Hofrichter, we define the global solution by moment conditions; calculate the component solutions by boundary flux method and combinatorics method. One interesting property is that some statistical quantities of interest are solutions of a singular elliptic second order linear equation with discontinuous (or incomplete) boundary values. A lot of papers, textbooks have used this property to find those quantities. However, the uniqueness of these problems has not been proved. Littler, in his PhD thesis in 1975, took up the uniqueness problem but his proof, in my view, is not rigorous. In joint work with J. Hofrichter, we showed two different ways to prove the uniqueness rigorously. The first way is the approximation method. The second way is the blow-up method which is conducted by J. Hofrichter. By applying the Information Geometry, which was first introduced by Amari in 1985, we see that the local state space is an Einstein space, and also a dually flat manifold with the Fisher metric; the differential operator of the Kolmogorov equation is the affine Laplacian which can be represented in various coordinates and on various spaces. Dynamics on the whole state space explains some biological phenomena.

In geotechnical engineering, the uncertainties such as the variability and uncertainty inherent in the geotechnical properties have caught more and more attentions from researchers and engineers. They have found that a single “Factor of Safety” calculated by traditional deterministic analyses methods can not represent the slope stability exactly. Recently in order to provide a more rational mathematical framework to incorporate different types of uncertainties in the slope stability estimation, reliability analyses and non-deterministic methods, which include probabilistic and non probabilistic (imprecise methods) methods, have been applied widely. In short, the slope non-deterministic analysis is to combine the probabilistic analysis or non probabilistic analysis with the deterministic slope stability analysis. It cannot be regarded as a completely new slope stability analysis method, but just an extension of the slope deterministic analysis. The slope failure probability calculated by slope non-deterministic analysis is a kind of complement of safety factor. Therefore, the accuracy of non deterministic analysis is not only depended on a suitable probabilistic or non probabilistic analysis method selected, but also on a more rigorous deterministic analysis method or geological model adopted. In this thesis, reliability concepts have been reviewed first, and some typical non-deterministic methods, including Monte Carlo Simulation (MCS), First Order Reliability Method (FORM), Point Estimate Method (PEM) and Random Set Theory (RSM), have been described and successfully applied to the slope stability analysis based on a numerical simulation method-Strength Reduction Method (SRM). All of the processes have been performed in a commercial finite difference code FLAC and a distinct element code UDEC. First of all, as the fundamental of slope reliability analysis, the deterministic numerical simulation method has been improved. This method has a higher accuracy than the conventional limit equilibrium methods, because of the reason that the constitutive relationship of soil is considered, and fewer assumptions on boundary conditions of slope model are necessary. However, the construction of slope numerical models, particularly for the large and complicated models has always been very difficult and it has become an obstacle for application of numerical simulation method. In this study, the excellent spatial analysis function of Geographic Information System (GIS) technique has been introduced to help numerical modeling of the slope. In the process of modeling, the topographic map of slope has been gridded using GIS software, and then the GIS data was transformed into FLAC smoothly through the program built-in language FISH. At last, the feasibility and high efficiency of this technique has been illustrated through a case study-Xuecheng slope, and both 2D and 3D models have been investigated. Subsequently, three most widely used probabilistic analyses methods, Monte Carlo Simulation, First Order Reliability Method and Point Estimate Method applied with Strength Reduction Method have been studied. Monte Carlo Simulation which needs to repeat thousands of deterministic analysis is the most accurate probabilistic method. However it is too time consuming for practical applications, especially when it is combined with numerical simulation method. For reducing the computation effort, a simplified Monte Carlo Simulation-Strength Reduction Method (MCS-SRM) has been developed in this study. This method has estimated the probable failure of slope and calculated the mean value of safety factor by means of soil parameters first, and then calculated the variance of safety factor and reliability of slope according to the assumed probability density function of safety factor. Case studies have confirmed that this method can reduce about 4/5 of time compared with traditional MCS-SRM, and maintain almost the same accuracy. First Order Reliability Method is an approximate method which is based on the Taylor\'s series expansion of performance function. The closed form solution of the partial derivatives of the performance function is necessary to calculate the mean and standard deviation of safety factor. However, there is no explicit performance function in numerical simulation method, so the derivative expressions have been replaced with equivalent difference quotients to solve the differential quotients approximately in this study. Point Estimate Method is also an approximate method involved even fewer calculations than FORM. In the present study, it has been integrated with Strength Reduction Method directly. Another important observation referred to the correlation between the soil parameters cohesion and friction angle. Some authors have found a negative correlation between cohesion and friction angle of soil on the basis of experimental data. However, few slope probabilistic studies are found to consider this negative correlation between soil parameters in literatures. In this thesis, the influence of this correlation on slope probability of failure has been investigated based on numerical simulation method. It was found that a negative correlation considered in the cohesion and friction angle of soil can reduce the variability of safety factor and failure probability of slope, thus increasing the reliability of results. Besides inter-correlation of soil parameters, these are always auto-correlated in space, which is described as spatial variability. For the reason that knowledge on this character is rather limited in literature, it is ignored in geotechnical engineering by most researchers and engineers. In this thesis, the random field method has been introduced in slope numerical simulation to simulate the spatial variability structure, and a numerical procedure for a probabilistic slope stability analysis based on Monte Carlo simulation was presented. The soil properties such as cohesion and friction angle were discretized to continuous random fields based on local averaging method. In the case study, both stationary and non-stationary random fields have been investigated, and the influence of spatial variability and averaging domain on the convergence of numerical simulation and probability of failure was studied. In rock medium, the structure faces have very important influence on the slope stability, and the rock material can be modeled as the combination of rigid or deformable blocks with joints in distinct element method. Therefore, much more input parameters like strength of joints are required to input the rock slope model, which increase the uncertainty of the results of numerical model. Furthermore, because of the limitations of the current laboratory and in-site testes, there is always lack of exact values of geotechnical parameters from rock material, even the probability distribution of these variables. Most of time, engineers can only estimate the interval of these variables from the limit testes or the expertise’s experience. In this study, to assess the reliability of the rock slope, a Random Set Distinct Element Method (RS-DEM) has been developed through coupling of Random Set Theory and Distinct Element Method, and applied in a rock slope in Sichuan province China.

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