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Family firms play a crucial role in the DACH region (Germany, Austria, Switzerland). They are characterized by a long tradition, a strong connection to the region, and a well-established network. However, family firms also face challenges, especially in finding a suitable successor. Wealthy entrepreneurial families are increasingly opting to establish Single Family Offices (SFOs) as a solution to this challenge. An SFO takes on the management and protection of family wealth. Its goal is to secure and grow the wealth over generations. In Germany alone, there are an estimated 350 to 450 SFOs, with 70% of them being established after the year 2000. However, research on SFOs is still in its early stages, particularly regarding the role of SFOs as firm owners. This dissertation delves into an exploration of SFOs through four quantitative empirical studies. The first study provides a descriptive overview of 216 SFOs from the DACH-region. Findings reveal that SFOs exhibit a preference for investing in established companies and real estate. Notably, only about a third of SFOs engage in investments in start-ups. Moreover, SFOs as a group are heterogeneous. Categorizing them into three groups based on their relationship with the entrepreneurial family and the original family firm reveals significant differences in their asset allocation strategies. Subsequent studies in this dissertation leverage a hand-collected sample of 173 SFO-owned firms from the DACH region, meticulously matched with 684 family-owned firms from the same region. The second study focusing on financial performance indicates that SFO-owned firms tend to exhibit comparatively poorer financial performance than family-owned firms. However, when members of the SFO-owning family hold positions on the supervisory or executive board of the firm, there's a notable improvement. The third study, concerning cash holdings, reveals that SFO-owned firms maintain a higher cash holding ratio compared to family-owned firms. Notably, this effect is magnified when the SFO has divested its initial family firms. Lastly, the fourth study regarding capital structure highlights that SFO-owned firms tend to display a higher long-term debt ratio than family-owned firms. This suggests that SFO-owned firms operate within a trade-off theory framework, like private equity-owned firms. Furthermore, this effect is stronger for SFOs that sold their original family firm. The outcomes of this research are poised to provide entrepreneurial families with a practical guide for effectively managing and leveraging SFOs as a strategic long-term instrument for succession and investment planning.
Official business surveys form the basis for national and regional business statistics and are thus of great importance for analysing the state and performance of the economy. However, both the heterogeneity of business data and their high dynamics pose a particular challenge to the feasibility of sampling and the quality of the resulting estimates. A widely used sampling frame for creating the design of an official business survey is an extract from an official business register. However, if this frame does not accurately represent the target population, frame errors arise. Amplified by the heterogeneity and dynamics of business populations, these errors can significantly affect the estimation quality and lead to inefficiencies and biases. This dissertation therefore deals with design-based methods for optimising business surveys with respect to different types of frame errors.
First, methods for adjusting the sampling design of business surveys are addressed. These approaches integrate auxiliary information about the expected structures of frame errors into the sampling design. The aim is to increase the number of sampled businesses that are subject to frame errors. The element-specific frame error probability is estimated based on auxiliary information about frame errors observed in previous samples. The approaches discussed consider different types of frame errors and can be incorporated into predefined designs with fixed strata.
As the second main pillar of this work, methods for adjusting weights to correct for frame errors during estimation are developed and investigated. As a result of frame errors, the assumptions under which the original design weights were determined based on the sampling design no longer hold. The developed methods correct the design weights taking into account the errors identified for sampled elements. Case-number-based reweighting approaches, on the one hand, attempt to reconstruct the unknown size of the individual strata in the target population. In the context of weight smoothing methods, on the other hand, design weights are modelled and smoothed as a function of target or auxiliary variables. This serves to avoid inefficiencies in the estimation due to highly scattering weights or weak correlations between weights and target variables. In addition, possibilities of correcting frame errors by calibration weighting are elaborated. Especially when the sampling frame shows over- and/or undercoverage, the inclusion of external auxiliary information can provide a significant improvement of the estimation quality. For those methods whose quality cannot be measured using standard procedures, a procedure for estimating the variance based on a rescaling bootstrap is proposed. This enables an assessment of the estimation quality when using the methods in practice.
In the context of two extensive simulation studies, the methods presented in this dissertation are evaluated and compared with each other. First, in the environment of an experimental simulation, it is assessed which approaches are particularly suitable with regard to different data situations. In a second simulation study, which is based on the structural survey in the services sector, the applicability of the methods in practice is evaluated under realistic conditions.
Wasserbezogene regulierende und versorgende Ökosystemdienstleistungen (ÖSDL) wurden im Hinblick auf das Abflussregime und die Grundwasserneubildung im Biosphärenreservat Pfälzerwald im Südwesten Deutschlands anhand hydrologischer Modellierung unter Verwendung des Soil and Water Assessment Tool (SWAT+) untersucht. Dabei wurde ein holistischer Ansatz verfolgt, wonach den ÖSDL Indikatoren für funktionale und strukturelle ökologische Prozesse zugeordnet werden. Potenzielle Risikofaktoren für die Verschlechterung von wasserbedingten ÖSDL des Waldes, wie Bodenverdichtung durch Befahren mit schweren Maschinen im Zuge von Holzerntearbeiten, Schadflächen mit Verjüngung, entweder durch waldbauliche Bewirtschaftungspraktiken oder durch Windwurf, Schädlinge und Kalamitäten im Zuge des Klimawandels, sowie der Kli-mawandel selbst als wesentlicher Stressor für Waldökosysteme wurden hinsichtlich ihrer Auswirkungen auf hydrologische Prozesse analysiert. Für jeden dieser Einflussfaktoren wurden separate SWAT+-Modellszenarien erstellt und mit dem kalibrierten Basismodell verglichen, das die aktuellen Wassereinzugsgebietsbedingungen basierend auf Felddaten repräsentierte. Die Simulationen bestätigten günstige Bedingungen für die Grundwasserneubildung im Pfälzerwald. Im Zusammenhang mit der hohen Versickerungskapazität der Bodensubstrate der Buntsandsteinverwitterung, sowie dem verzögernden und puffernden Einfluss der Baumkronen auf das Niederschlagswasser, wurde eine signifikante Minderungswirkung auf die Oberflächenabflussbildung und ein ausgeprägtes räumliches und zeitliches Rückhaltepotential im Einzugsgebiet simuliert. Dabei wurde festgestellt, dass erhöhte Niederschlagsmengen, die die Versickerungskapazität der sandigen Böden übersteigen, zu einer kurz geschlossenen Abflussreaktion mit ausgeprägten Oberflächenabflussspitzen führen. Die Simulationen zeigten Wechselwirkungen zwischen Wald und Wasserkreislauf sowie die hydrologische Wirksamkeit des Klimawandels, verschlechterter Bodenfunktionen und altersbezogener Bestandesstrukturen im Zusammenhang mit Unterschieden in der Baumkronenausprägung. Zukunfts-Klimaprojektionen, die mit BIAS-bereinigten REKLIES- und EURO-CORDEX-Regionalklimamodellen (RCM) simuliert wurden, prognostizierten einen höheren Verdunstungsbedarf und eine Verlängerung der Vegetationsperiode bei gleichzeitig häufiger auftretenden Dürreperioden innerhalb der Vegetationszeit, was eine Verkürzung der Periode für die Grundwasserneubildung induzierte, und folglich zu einem prognostizierten Rückgang der Grundwasserneubildungsrate bis zur Mitte des Jahrhunderts führte. Aufgrund der starken Korrelation mit Niederschlagsintensitäten und der Dauer von Niederschlagsereignissen, bei allen Unsicherheiten in ihrer Vorhersage, wurde für die Oberflächenabflussgenese eine Steigerung bis zum Ende des Jahrhunderts prognostiziert.
Für die Simulation der Bodenverdichtung wurden die Trockenrohdichte des Bodens und die SCS Curve Number in SWAT+ gemäß Daten aus Befahrungsversuchen im Gebiet angepasst. Die günstigen Infiltrationsbedingungen und die relativ geringe Anfälligkeit für Bodenverdichtung der grobkörnigen Buntsandsteinverwitterung dominierten die hydrologischen Auswirkungen auf Wassereinzugsgebietsebene, sodass lediglich moderate Verschlechterungen wasserbezogener ÖSDL angezeigt wurden. Die Simulationen zeigten weiterhin einen deutlichen Einfluss der Bodenart auf die hydrologische Reaktion nach Bodenverdichtung auf Rückegassen und stützen damit die Annahme, dass die Anfälligkeit von Böden gegenüber Verdichtung mit dem Anteil an Schluff- und Tonbodenpartikeln zunimmt. Eine erhöhte Oberflächenabflussgenese ergab sich durch das Wegenetz im Gesamtgebiet.
Schadflächen mit Bestandesverjüngung wurden anhand eines artifiziellen Modells innerhalb eines Teileinzugsgebiets unter der Annahme von 3-jährigen Baumsetzlingen in einem Entwicklungszeitraum von 10 Jahren simuliert und hinsichtlich spezifischer Was-serhaushaltskomponenten mit Altbeständen (30 bis 80 Jahre) verglichen. Die Simulation ließ darauf schließen, dass bei fehlender Kronenüberschirmung die hydrologisch verzögernde Wirkung der Bestände beeinträchtigt wird, was die Entstehung von Oberflächenabfluss begünstigt und eine quantitativ geringfügig höhere Tiefensickerung fördert. Hydrologische Unterschiede zwischen dem geschlossenem Kronendach der Altbestände und Jungbeständen mit annähernden Freilandniederschlagsbedingungen wurden durch die dominierenden Faktoren atmosphärischer Verdunstungsanstoß, Niederschlagsmengen und Kronenüberschirmungsgrad bestimmt. Je weniger entwickelt das Kronendach von verjüngten Waldbeständen im Vergleich zu Altbeständen, je höher der atmosphärische Verdunstungsanstoß und je geringer die eingetragenen Niederschlagsmengen, desto größer war der hydrologische Unterschied zwischen den Bestandestypen.
Verbesserungsmaßnahmen für den dezentralen Hochwasserschutz sollten folglich kritische Bereiche für die Abflussbildung im Wald (CSA) berücksichtigen. Die hohe Sensibilität und Anfälligkeit der Wälder gegenüber Verschlechterungen der Ökosystembedingungen legen nahe, dass die Erhaltung des komplexen Gefüges und von intakten Wechselbeziehungen, insbesondere unter der gegebenen Herausforderung des Klimawandels, sorgfältig angepasste Schutzmaßnahmen, Anstrengungen bei der Identifizierung von CSA sowie die Erhaltung und Wiederherstellung der hydrologischen Kontinuität in Waldbeständen erfordern.
Traditional workflow management systems support process participants in fulfilling business tasks through guidance along a predefined workflow model.
Flexibility has gained a lot of attention in recent decades through a shift from mass production to customization. Various approaches to workflow flexibility exist that either require extensive knowledge acquisition and modelling effort or an active intervention during execution and re-modelling of deviating behaviour. The pursuit of flexibility by deviation is to compensate both of these disadvantages through allowing alternative unforeseen execution paths at run time without demanding the process participant to adapt the workflow model. However, the implementation of this approach has been little researched so far.
This work proposes a novel approach to flexibility by deviation. The approach aims at supporting process participants during the execution of a workflow through suggesting work items based on predefined strategies or experiential knowledge even in case of deviations. The developed concepts combine two renowned methods from the field of artificial intelligence - constraint satisfaction problem solving with process-oriented case-based reasoning. This mainly consists of a constraint-based workflow engine in combination with a case-based deviation management. The declarative representation of workflows through constraints allows for implicit flexibility and a simple possibility to restore consistency in case of deviations. Furthermore, the combined model, integrating procedural with declarative structures through a transformation function, increases the capabilities for flexibility. For an adequate handling of deviations the methodology of case-based reasoning fits perfectly, through its approach that similar problems have similar solutions. Thus, previous made experiences are transferred to currently regarded problems, under the assumption that a similar deviation has been handled successfully in the past.
Necessary foundations from the field of workflow management with a focus on flexibility are presented first.
As formal foundation, a constraint-based workflow model was developed that allows for a declarative specification of foremost sequential dependencies of tasks. Procedural and declarative models can be combined in the approach, as a transformation function was specified that converts procedural workflow models to declarative constraints.
One main component of the approach is the constraint-based workflow engine that utilizes this declarative model as input for a constraint solving algorithm. This algorithm computes the worklist, which is proposed to the process participant during workflow execution. With predefined deviation handling strategies that determine how the constraint model is modified in order to restore consistency, the support is continuous even in case of deviations.
The second major component of the proposed approach constitutes the case-based deviation management, which aims at improving the support of process participants on the basis of experiential knowledge. For the retrieve phase, a sophisticated similarity measure was developed that integrates specific characteristics of deviating workflows and combines several sequence similarity measures. Two alternative methods for the reuse phase were developed, a null adaptation and a generative adaptation. The null adaptation simply proposes tasks from the most similar workflow as work items, whereas the generative adaptation modifies the constraint-based workflow model based on the most similar workflow in order to re-enable the constraint-based workflow engine to suggest work items.
The experimental evaluation of the approach consisted of a simulation of several types of process participants in the exemplary domain of deficiency management in construction. The results showed high utility values and a promising potential for an investigation of the transfer on other domains and the applicability in practice, which is part of future work.
Concluding, the contributions are summarized and research perspectives are pointed out.
Energy transport networks are one of the most important infrastructures for the planned energy transition. They form the interface between energy producers and consumers and their features make them good candidates for the tools that mathematical optimization can offer. Nevertheless, the operation of energy networks comes with two major challenges. First, the nonconvexity of the equations that model the physics in the network render the resulting problems extremely hard to solve for large-scale networks. Second, the uncertainty associated to the behavior of the different agents involved, the production of energy, and the consumption of energy make the resulting problems hard to solve if a representative description of uncertainty is to be considered.
In this cumulative dissertation we study adaptive refinement algorithms designed to cope with the nonconvexity and stochasticity of equations arising in energy networks. Adaptive refinement algorithms approximate the original problem by sequentially refining the model of a simpler optimization problem. More specifically, in this thesis, the focus of the adaptive algorithm is on adapting the discretization and description of a set of constraints.
In the first part of this thesis, we propose a generalization of the different adaptive refinement ideas that we study. We sequentially describe model catalogs, error measures, marking strategies, and switching strategies that are used to set up the adaptive refinement algorithm. Afterward, the effect of the adaptive refinement algorithm on two energy network applications is studied. The first application treats the stationary operation of district heating networks. Here, the strength of adaptive refinement algorithms for approximating the ordinary differential equation that describes the transport of energy is highlighted. We introduce the resulting nonlinear problem, consider network expansion, and obtain realistic controls by applying the adaptive refinement algorithm. The second application concerns quantile-constrained optimization problems and highlights the ability of the adaptive refinement algorithm to cope with large scenario sets via clustering. We introduce the resulting mixed-integer linear problem, discuss generic solution techniques, make the link with the generalized framework, and measure the impact of the proposed solution techniques.
The second part of this thesis assembles the papers that inspired the contents of the first part of this thesis. Hence, they describe in detail the topics that are covered and will be referenced throughout the first part.
THE NONLOCAL NEUMANN PROBLEM
(2023)
Instead of presuming only local interaction, we assume nonlocal interactions. By doing so, mass
at a point in space does not only interact with an arbitrarily small neighborhood surrounding it,
but it can also interact with mass somewhere far, far away. Thus, mass jumping from one point to
another is also a possibility we can consider in our models. So, if we consider a region in space, this
region interacts in a local model at most with its closure. While in a nonlocal model this region may
interact with the whole space. Therefore, in the formulation of nonlocal boundary value problems
the enforcement of boundary conditions on the topological boundary may not suffice. Furthermore,
choosing the complement as nonlocal boundary may work for Dirichlet boundary conditions, but
in the case of Neumann boundary conditions this may lead to an overfitted model.
In this thesis, we introduce a nonlocal boundary and study the well-posedness of a nonlocal Neu-
mann problem. We present sufficient assumptions which guarantee the existence of a weak solution.
As in a local model our weak formulation is derived from an integration by parts formula. However,
we also study a different weak formulation where the nonlocal boundary conditions are incorporated
into the nonlocal diffusion-convection operator.
After studying the well-posedness of our nonlocal Neumann problem, we consider some applications
of this problem. For example, we take a look at a system of coupled Neumann problems and analyze
the difference between a local coupled Neumann problems and a nonlocal one. Furthermore, we let
our Neumann problem be the state equation of an optimal control problem which we then study. We
also add a time component to our Neumann problem and analyze this nonlocal parabolic evolution
equation.
As mentioned before, in a local model mass at a point in space only interacts with an arbitrarily
small neighborhood surrounding it. We analyze what happens if we consider a family of nonlocal
models where the interaction shrinks so that, in limit, mass at a point in space only interacts with
an arbitrarily small neighborhood surrounding it.
Survey data can be viewed as incomplete or partially missing from a variety of perspectives and there are different ways of dealing with this kind of data in the prediction and the estimation of economic quantities. In this thesis, we present two selected research contexts in which the prediction or estimation of economic quantities is examined under incomplete survey data.
These contexts are first the investigation of composite estimators in the German Microcensus (Chapters 3 and 4) and second extensions of multivariate Fay-Herriot (MFH) models (Chapters 5 and 6), which are applied to small area problems.
Composite estimators are estimation methods that take into account the sample overlap in rotating panel surveys such as the German Microcensus in order to stabilise the estimation of the statistics of interest (e.g. employment statistics). Due to the partial sample overlaps, information from previous samples is only available for some of the respondents, so the data are partially missing.
MFH models are model-based estimation methods that work with aggregated survey data in order to obtain more precise estimation results for small area problems compared to classical estimation methods. In these models, several variables of interest are modelled simultaneously. The survey estimates of these variables, which are used as input in the MFH models, are often partially missing. If the domains of interest are not explicitly accounted for in a sampling design, the sizes of the samples allocated to them can, by chance, be small. As a result, it can happen that either no estimates can be calculated at all or that the estimated values are not published by statistical offices because their variances are too large.
Coastal erosion describes the displacement of land caused by destructive sea waves,
currents or tides. Due to the global climate change and associated phenomena such as
melting polar ice caps and changing current patterns of the oceans, which result in rising
sea levels or increased current velocities, the need for countermeasures is continuously
increasing. Today, major efforts have been made to mitigate these effects using groins,
breakwaters and various other structures.
This thesis will find a novel approach to address this problem by applying shape optimization
on the obstacles. Due to this reason, results of this thesis always contain the
following three distinct aspects:
The selected wave propagation model, i.e. the modeling of wave propagation towards
the coastline, using various wave formulations, ranging from steady to unsteady descriptions,
described from the Lagrangian or Eulerian viewpoint with all its specialties. More
precisely, in the Eulerian setting is first a steady Helmholtz equation in the form of a
scattering problem investigated and followed subsequently by shallow water equations,
in classical form, equipped with porosity, sediment portability and further subtleties.
Secondly, in a Lagrangian framework the Lagrangian shallow water equations form the
center of interest.
The chosen discretization, i.e. dependent on the nature and peculiarity of the constraining
partial differential equation, we choose between finite elements in conjunction
with a continuous Galerkin and discontinuous Galerkin method for investigations in the
Eulerian description. In addition, the Lagrangian viewpoint offers itself for mesh-free,
particle-based discretizations, where smoothed particle hydrodynamics are used.
The method for shape optimization w.r.t. the obstacle’s shape over an appropriate
cost function, constrained by the solution of the selected wave-propagation model. In
this sense, we rely on a differentiate-then-discretize approach for free-form shape optimization
in the Eulerian set-up, and reverse the order in Lagrangian computations.
Behavioural traces from interactions with digital technologies are diverse and abundant. Yet, their capacity for theory-driven research is still to be constituted. In the present cumulative dissertation project, I deliberate the caveats and potentials of digital behavioural trace data in behavioural and social science research. One use case is online radicalisation research. The three studies included, set out to discern the state-of-the-art of methods and constructs employed in radicalization research, at the intersection of traditional methods and digital behavioural trace data. Firstly, I display, based on a systematic literature review of empirical work, the prevalence of digital behavioural trace data across different research strands and discern determinants and outcomes of radicalisation constructs. Secondly, I extract, based on this literature review, hypotheses and constructs and integrate them to a framework from network theory. This graph of hypotheses, in turn, makes the relative importance of theoretical considerations explicit. One implication of visualising the assumptions in the field is to systematise bottlenecks for the analysis of digital behavioural trace data and to provide the grounds for the genesis of new hypotheses. Thirdly, I provide a proof-of-concept for incorporating a theoretical framework from conspiracy theory research (as a specific form of radicalisation) and digital behavioural traces. I argue for marrying theoretical assumptions derived from temporal signals of posting behaviour and semantic meaning from textual content that rests on a framework from evolutionary psychology. In the light of these findings, I conclude by discussing important potential biases at different stages in the research cycle and practical implications.
No Longer Printing the Legend: The Aporia of Heteronormativity in the American Western (1903-1969)
(2023)
This study critically investigates the U.S.-American Western and its construction of sexuality and gender, revealing that the heteronormative matrix that is upheld and defended in the genre is consistently preceded by the exploration of alternative sexualities and ways to think gender beyond the binary. The endeavor to naturalize heterosexuality seems to be baked in the formula of the U.S.-Western. However, as I show in this study, this endeavor relies on an aporia, because the U.S.-Western can only ever attempt to naturalize gender by constructing it first, hence inevitably and simultaneously construct evidence that supports the opposite: the unnaturalness and contingency of gender and sexuality.
My study relies on the works of Raewyn Connell, Pierre Bourdieu, and Judith Butler, and amalgamates in its methodology established approaches from film and literary studies (i.e., close readings) with a Foucaultian understanding of discourse and discourse analysis, which allows me to relate individual texts to cultural, socio-political and economical contexts that invariably informed the production and reception of any filmic text. In an analysis of 14 U.S.-Westerns (excluding three excursions) that appeared between 1903 and 1969 I give ample and minute narrative and film-aesthetical evidence to reveal the complex and contradictory construction of gender and sexuality in the U.S.-Western, aiming to reveal both the normative power of those categories and its structural instability and inconsistency.
This study proofs that the Western up until 1969 did not find a stable pattern to represent the gender binary. The U.S.-Western is not necessarily always looking to confirm or stabilize governing constructs of (gendered) power. However, it without fail explores and negotiates its legitimacy. Heterosexuality and male hegemony are never natural, self-evident, incontestable, or preordained. Quite conversely: the U.S.-Western repeatedly – and in a surprisingly diverse and versatile way – reveals the illogical constructedness of the heteronormative matrix.
My study therefore offers a fresh perspective on the genre and shows that the critical exploration and negotiation of the legitimacy of heteronormativity as a way to organize society is constitutive for the U.S.-Western. It is the inquiry – not necessarily the affirmation – of the legitimacy of this model that gives the U.S.-Western its ideological currency and significance as an artifact of U.S.-American popular culture.
Non-probability sampling is a topic of growing relevance, especially due to its occurrence in the context of new emerging data sources like web surveys and Big Data.
This thesis addresses statistical challenges arising from non-probability samples, where unknown or uncontrolled sampling mechanisms raise concerns in terms of data quality and representativity.
Various methods to quantify and reduce the potential selectivity and biases of non-probability samples in estimation and inference are discussed. The thesis introduces new forms of prediction and weighting methods, namely
a) semi-parametric artificial neural networks (ANNs) that integrate B-spline layers with optimal knot positioning in the general structure and fitting procedure of artificial neural networks, and
b) calibrated semi-parametric ANNs that determine weights for non-probability samples by integrating an ANN as response model with calibration constraints for totals, covariances and correlations.
Custom-made computational implementations are developed for fitting (calibrated) semi-parametric ANNs by means of stochastic gradient descent, BFGS and sequential quadratic programming algorithms.
The performance of all the discussed methods is evaluated and compared for a bandwidth of non-probability sampling scenarios in a Monte Carlo simulation study as well as an application to a real non-probability sample, the WageIndicator web survey.
Potentials and limitations of the different methods for dealing with the challenges of non-probability sampling under various circumstances are highlighted. It is shown that the best strategy for using non-probability samples heavily depends on the particular selection mechanism, research interest and available auxiliary information.
Nevertheless, the findings show that existing as well as newly proposed methods can be used to ease or even fully counterbalance the issues of non-probability samples and highlight the conditions under which this is possible.
Modern decision making in the digital age is highly driven by the massive amount of
data collected from different technologies and thus affects both individuals as well as
economic businesses. The benefit of using these data and turning them into knowledge
requires appropriate statistical models that describe the underlying observations well.
Imposing a certain parametric statistical model goes along with the need of finding
optimal parameters such that the model describes the data best. This often results in
challenging mathematical optimization problems with respect to the model’s parameters
which potentially involve covariance matrices. Positive definiteness of covariance matrices
is required for many advanced statistical models and these constraints must be imposed
for standard Euclidean nonlinear optimization methods which often results in a high
computational effort. As Riemannian optimization techniques proved efficient to handle
difficult matrix-valued geometric constraints, we consider optimization over the manifold
of positive definite matrices to estimate parameters of statistical models. The statistical
models treated in this thesis assume that the underlying data sets used for parameter
fitting have a clustering structure which results in complex optimization problems. This
motivates to use the intrinsic geometric structure of the parameter space. In this thesis,
we analyze the appropriateness of Riemannian optimization over the manifold of positive
definite matrices on two advanced statistical models. We establish important problem-
specific Riemannian characteristics of the two problems and demonstrate the importance
of exploiting the Riemannian geometry of covariance matrices based on numerical studies.
Even though proper research on Cauchy transforms has been done, there are still a lot of open questions. For example, in the case of representation theorems, i.e. the question when a function can be represented as a Cauchy transform, there is 'still no completely satisfactory answer' ([9], p. 84). There are characterizations for measures on the circle as presented in the monograph [7] and for general compactly supported measures on the complex plane as presented in [27]. However, there seems to exist no systematic treatise of the Cauchy transform as an operator on $L_p$ spaces and weighted $L_p$ spaces on the real axis.
This is the point where this thesis draws on and we are interested in developing several characterizations for the representability of a function by Cauchy transforms of $L_p$ functions. Moreover, we will attack the issue of integrability of Cauchy transforms of functions and measures, a topic which is only partly explored (see [43]). We will develop different approaches involving Fourier transforms and potential theory and investigate into sufficient conditions and characterizations.
For our purposes, we shall need some notation and the concept of Hardy spaces which will be part of the preliminary Chapter 1. Moreover, we introduce Fourier transforms and their complex analogue, namely Fourier-Laplace transforms. This will be of extraordinary usage due to the close connection of Cauchy and Fourier(-Laplace) transforms.
In the second chapter we shall begin our research with a discussion of the Cauchy transformation on the classical (unweighted) $L_p$ spaces. Therefore, we start with the boundary behavior of Cauchy transforms including an adapted version of the Sokhotski-Plemelj formula. This result will turn out helpful for the determination of the image of the Cauchy transformation under $L_p(\R)$ for $p\in(1,\infty).$ The cases $p=1$ and $p=\infty$ are playing special roles here which justifies a treatise in separate sections. For $p=1$ we will involve the real Hardy space $H_{1}(\R)$ whereas the case $p=\infty$ shall be attacked by an approach incorporating intersections of Hardy spaces and certain subspaces of $L_{\infty}(\R).$
The third chapter prepares ourselves for the study of the Cauchy transformation on subspaces of $L_{p}(\R).$ We shall give a short overview of the basic facts about Cauchy transforms of measures and then proceed to Cauchy transforms of functions with support in a closed set $X\subset\R.$ Our goal is to build up the main theory on which we can fall back in the subsequent chapters.
The fourth chapter deals with Cauchy transforms of functions and measures supported by an unbounded interval which is not the entire real axis. For convenience we restrict ourselves to the interval $[0,\infty).$ Bringing once again the Fourier-Laplace transform into play, we deduce complex characterizations for the Cauchy transforms of functions in $L_{2}(0,\infty).$ Moreover, we analyze the behavior of Cauchy transform on several half-planes and shall use these results for a fairly general geometric characterization. In the second section of this chapter, we focus on Cauchy transforms of measures with support in $[0,\infty).$ In this context, we shall derive a reconstruction formula for these Cauchy transforms holding under pretty general conditions as well as results on the behaviur on the left half-plane. We close this chapter by rather technical real-type conditions and characterizations for Cauchy transforms of functions in $L_p(0,\infty)$ basing on an approach in [82].
The most common case of Cauchy transforms, those of compactly supported functions or measures, is the subject of Chapter 5. After complex and geometric characterizations originating from similar ideas as in the fourth chapter, we adapt a functional-analytic approach in [27] to special measures, namely those with densities to a given complex measure $\mu.$ The chapter is closed with a study of the Cauchy transformation on weighted $L_p$ spaces. Here, we choose an ansatz through the finite Hilbert transform on $(-1,1).$
The sixth chapter is devoted to the issue of integrability of Cauchy transforms. Since this topic has no comprehensive treatise in literature yet, we start with an introduction of weighted Bergman spaces and general results on the interaction of the Cauchy transformation in these spaces. Afterwards, we combine the theory of Zen spaces with Cauchy transforms by using once again their connection with Fourier transforms. Here, we shall encounter general Paley-Wiener theorems of the recent past. Lastly, we attack the issue of integrability of Cauchy transforms by means of potential theory. Therefore, we derive a Fourier integral formula for the logarithmic energy in one and multiple dimensions and give applications to Fourier and hence Cauchy transforms.
Two appendices are annexed to this thesis. The first one covers important definitions and results from measure theory with a special focus on complex measures. The second appendix contains Cauchy transforms of frequently used measures and functions with detailed calculations.
The COVID-19 pandemic has affected schooling worldwide. In many places, schools closed for weeks or months, only part of the student body could be educated at any one time, or students were taught online. Previous research discloses the relevance of schooling for the development of cognitive abilities. We therefore compared the intelligence test performance of 424 German secondary school students in Grades 7 to 9 (42% female) tested after the first six months of the COVID-19 pandemic (i.e., 2020 sample) to the results of two highly comparable student samples tested in 2002 (n = 1506) and 2012 (n = 197). The results revealed substantially and significantly lower intelligence test scores in the 2020 sample than in both the 2002 and 2012 samples. We retested the 2020 sample after another full school year of COVID-19-affected schooling in 2021. We found mean-level changes of typical magnitude, with no signs of catching up to previous cohorts or further declines in cognitive performance. Perceived stress during the pandemic did not affect changes in intelligence test results between the two measurements.
COVID-19 was a harsh reminder that diseases are an aspect of human existence and mortality. It was also a live experiment in the formation and alteration of disease-related attitudes. Not only are these attitudes relevant to an individual’s self-protective behavior, but they also seem to be associated with social and political attitudes more broadly. One of these attitudes is Social Darwinism, which holds that a pandemic benefits society by enabling nature “to weed out the weak”. In two countries (N = 300, N = 533), we introduce and provide evidence for the reliability, validity, and usefulness of the Disease-Related Social Darwinism (DRSD) Short Scale measuring this concept. Results indicate that DRSD is meaningful related to other central political attitudes like Social Dominance Orientation, Authoritarianism and neoliberalism. Importantly, the scale significantly predicted people’s protective behavior during the Pandemic over and above general social Darwinism. Moreover, it significantly predicted conservative attitudes, even after controlling for Social Dominance Orientation.
Die Dissertation beschäftigt sich mit einer neuartigen Art von Branch-and-Bound Algorithmen, deren Unterschied zu klassischen Branch-and-Bound Algorithmen darin besteht, dass
das Branching durch die Addition von nicht-negativen Straftermen zur Zielfunktion erfolgt
anstatt durch das Hinzufügen weiterer Nebenbedingungen. Die Arbeit zeigt die theoretische Korrektheit des Algorithmusprinzips für verschiedene allgemeine Klassen von Problemen und evaluiert die Methode für verschiedene konkrete Problemklassen. Für diese Problemklassen, genauer Monotone und Nicht-Monotone Gemischtganzzahlige Lineare Komplementaritätsprobleme und Gemischtganzzahlige Lineare Probleme, präsentiert die Arbeit
verschiedene problemspezifische Verbesserungsmöglichkeiten und evaluiert diese numerisch.
Weiterhin vergleicht die Arbeit die neue Methode mit verschiedenen Benchmark-Methoden
mit größtenteils guten Ergebnissen und gibt einen Ausblick auf weitere Anwendungsgebiete
und zu beantwortende Forschungsfragen.
The forensic application of phonetics relies on individuality in speech. In the forensic domain, individual patterns of verbal and paraverbal behavior are of interest which are readily available, measurable, consistent, and robust to disguise and to telephone transmission. This contribution is written from the perspective of the forensic phonetic practitioner and seeks to establish a more comprehensive concept of disfluency than previous studies have. A taxonomy of possible variables forming part of what can be termed disfluency behavior is outlined. It includes the “classical” fillers, but extends well beyond these, covering, among others, additional types of fillers as well as prolongations, but also the way in which fillers are combined with pauses. In the empirical section, the materials collected for an earlier study are re-examined and subjected to two different statistical procedures in an attempt to approach the issue of individuality. Recordings consist of several minutes of spontaneous speech by eight speakers on three different occasions. Beyond the established set of hesitation markers, additional aspects of disfluency behavior which fulfill the criteria outlined above are included in the analysis. The proportion of various types of disfluency markers is determined. Both statistical approaches suggest that these speakers can be distinguished at a level far above chance using the disfluency data. At the same time, the results show that it is difficult to pin down a single measure which characterizes the disfluency behavior of an individual speaker. The forensic implications of these findings are discussed.
Redox-driven biogeochemical cycling of iron plays an integral role in the complex process network of ecosystems, such as carbon cycling, the fate of nutrients and greenhouse gas emissions. We investigate Fe-(hydr)oxide (trans)formation pathways from rhyolitic tephra in acidic topsoils of South Patagonian Andosols to evaluate the ecological relevance of terrestrial iron cycling for this sensitive fjord ecosystem. Using bulk geochemical analyses combined with micrometer-scale-measurements on individual soil aggregates and tephra pumice, we document biotic and abiotic pathways of Fe released from the glassy tephra matrix and titanomagnetite phenocrysts. During successive redox cycles that are controlled by frequent hydrological perturbations under hyper-humid climate, (trans)formations of ferrihydrite-organic matter coprecipitates, maghemite and hematite are closely linked to tephra weathering and organic matter turnover. These Fe-(hydr)oxides nucleate after glass dissolution and complexation with organic ligands, through maghemitization or dissolution-(re)crystallization processes from metastable precursors. Ultimately, hematite represents the most thermodynamically stable Fe-(hydr)oxide formed under these conditions and physically accumulates at redox interfaces, whereas the ferrihydrite coprecipitates represent a so far underappreciated terrestrial source of bio-available iron for fjord bioproductivity. The insights into Fe-(hydr)oxide (trans)formation in Andosols have implications for a better understanding of biogeochemical cycling of iron in this unique Patagonian fjord ecosystem.
We use a novel sea-ice lead climatology for the winters of 2002/03 to 2020/21 based on satellite observations with 1 km2 spatial resolution to identify predominant patterns in Arctic wintertime sea-ice leads. The causes for the observed spatial and temporal variabilities are investigated using ocean surface current velocities and eddy kinetic energies from an ocean model (Finite Element Sea Ice–Ice-Shelf–Ocean Model, FESOM) and winds from a regional climate model (CCLM) and ERA5 reanalysis, respectively. The presented investigation provides evidence for an influence of ocean bathymetry and associated currents on the mechanic weakening of sea ice and the accompanying occurrence of sea-ice leads with their characteristic spatial patterns. While the driving mechanisms for this observation are not yet understood in detail, the presented results can contribute to opening new hypotheses on ocean–sea-ice interactions. The individual contribution of ocean and atmosphere to regional lead dynamics is complex, and a deeper insight requires detailed mechanistic investigations in combination with considerations of coastal geometries. While the ocean influence on lead dynamics seems to act on a rather long-term scale (seasonal to interannual), the influence of wind appears to trigger sea-ice lead dynamics on shorter timescales of weeks to months and is largely controlled by individual events causing increased divergence. No significant pan-Arctic trends in wintertime leads can be observed.
Regional climate models are a valuable tool for the study of the climate processes and climate change in polar regions, but the performance of the models has to be evaluated using experimental data. The regional climate model CCLM was used for simulations for the MOSAiC period with a horizontal resolution of 14 km (whole Arctic). CCLM was used in a forecast mode (nested in ERA5) and used a thermodynamic sea ice model. Sea ice concentration was taken from AMSR2 data (C15 run) and from a high-resolution data set (1 km) derived from MODIS data (C15MOD0 run). The model was evaluated using radiosonde data and data of different profiling systems with a focus on the winter period (November–April). The comparison with radiosonde data showed very good agreement for temperature, humidity, and wind. A cold bias was present in the ABL for November and December, which was smaller for the C15MOD0 run. In contrast, there was a warm bias for lower levels in March and April, which was smaller for the C15 run. The effects of different sea ice parameterizations were limited to heights below 300 m. High-resolution lidar and radar wind profiles as well as temperature and integrated water vapor (IWV) data from microwave radiometers were used for the comparison with CCLM for case studies, which included low-level jets. LIDAR wind profiles have many gaps, but represent a valuable data set for model evaluation. Comparisons with IWV and temperature data of microwave radiometers show very good agreement.
The benefits of prosocial power motivation in leadership: Action orientation fosters a win-win
(2023)
Power motivation is considered a key component of successful leadership. Based on its dualistic nature, the need for power (nPower) can be expressed in a dominant or a prosocial manner. Whereas dominant motivation is associated with antisocial behaviors, prosocial motivation is characterized by more benevolent actions (e.g., helping, guiding). Prosocial enactment of the power motive has been linked to a wide range of beneficial outcomes, yet less has been investigated what determines a prosocial enactment of the power motive. According to Personality Systems Interactions (PSI) theory, action orientation (i.e., the ability to self-regulate affect) promotes prosocial enactment of the implicit power motive and initial findings within student samples verify this assumption. In the present study, we verified the role of action orientation as an antecedent for prosocial power enactment in a leadership sample (N = 383). Additionally, we found that leaders personally benefited from a prosocial enactment strategy. Results show that action orientation through prosocial power motivation leads to reduced power-related anxiety and, in turn, to greater leader well-being. The integration of motivation and self-regulation research reveals why leaders enact their power motive in a certain way and helps to understand how to establish a win-win situation for both followers and leaders.
Repeatedly encountering a stimulus biases the observer’s affective response and evaluation of the stimuli. Here we provide evidence for a causal link between mere exposure to fictitious news reports and subsequent voting behavior. In four pre-registered online experiments, participants browsed through newspaper webpages and were tacitly exposed to names of fictitious politicians. Exposure predicted voting behavior in a subsequent mock election, with a consistent preference for frequent over infrequent names, except when news items were decidedly negative. Follow-up analyses indicated that mere media presence fuels implicit personality theories regarding a candidate’s vigor in political contexts. News outlets should therefore be mindful to cover political candidates as evenly as possible.
Addition of Phosphogypsum to Fire-Resistant Plaster Panels:
A Physic–Mechanical Investigation
(2023)
Gypsum (GPS) has great potential for structural fire protection and is increasingly used in construction due to its high-water retention and purity. However, many researchers aim to improve its physical and mechanical properties by adding other organic or inorganic materials such as fibers, recycled GPS, and waste residues. This study used a novel method to add non-natural GPS from factory waste (phosphogypsum (PG)) as a secondary material for GPS. This paper proposes to mix these two materials to properly study the effect of PG on the physico-mechanical properties and fire performance of two Tunisian GPSs (GPS1 and GPS2). PG initially replaced GPS at 10, 20, 30, 40, and 50% weight percentage (mixing plan A). The PGs were then washed with distilled water several times. Two more mixing plans were run when the pH of the PG was equal to 2.4 (mixing plan B), and the pH was equal to 5 (mixing plan C). Finally, a comparative study was conducted on the compressive strength, flexural strength, density, water retention, and mass loss levels after 90 days of drying, before/after incineration of samples at 15, 30, 45, and 60 min. The results show that the mixture of GPS1 and 30% PG (mixing plan B) obtained the highest compressive strength (41.31%) and flexural strength (35.03%) compared to the reference sample. The addition of 10% PG to GPS1 (mixing plan A) improved fire resistance (33.33%) and the mass loss (17.10%) of the samples exposed to flame for 60 min compared to GPS2. Therefore, PG can be considered an excellent insulating material, which can increase physico-mechanical properties and fire resistance time of plaster under certain conditions.
People are increasingly concerned about how meat affects the environment, human health, and animal welfare, yet eating and enjoying meat remains a norm. Unsurprisingly, many people are ambivalent about meat—evaluating it as both positive and negative. Here, we propose that meat-related conflict is multidimensional and depends on people’s dietary group: Omnivores’ felt ambivalence relates to multiple negative associations that oppose a predominantly positive attitude towards meat, and veg*ans’ ambivalence relates to various positive associations that oppose a predominantly negative attitude. A qualitative study (N = 235; German) revealed that omnivores and veg*ans experience meat-related ambivalence due to associations with animals, sociability, sustainability, health, and sensory experiences. To quantify felt ambivalence in these domains, we developed the Meat Ambivalence Questionnaire (MAQ). We validated the MAQ in four pre-registered studies using self-report and behavioral data (N = 3,485; German, UK, representative US). Both omnivores and veg*ans reported meat-related ambivalence, but with differences across domains and their consequences for meat consumption. Specifically, ambivalence was associated with less meat consumption in omnivores (especially sensory-/animal-based ambivalence) and more meat consumption in veg*ans (especially sensory-/socially-based ambivalence). Network analyses shed further light on the nomological net of the MAQ while controlling for a comprehensive set of determinants of meat consumption. By introducing the MAQ, we hope to provide researchers with a tool to better understand how ambivalence accompanies behavior change and maintenance.
Properties Evaluation of Composite Materials Based on Gypsum Plaster and Posidonia Oceanica Fibers
(2023)
Estimating the amount of material without significant losses at the end of hybrid casting is a problem addressed in this study. To minimize manufacturing costs and improve the accuracy of results, a correction factor (CF) was used in the formula to estimate the volume percent of the material in order to reduce material losses during the sample manufacturing stage, allowing for greater confidence between the approved blending plan and the results obtained. In this context, three material mixing schemes of different sizes and shapes (gypsum plaster, sand (0/2), gravel (2/4), and Posidonia oceanica fibers (PO)) were created to verify the efficiency of CF and more precisely study the physico-mechanical effects on the samples. The results show that the use of a CF can reduce mixing loss to almost 0%. The optimal compressive strength of the sample (S1B) with the lowest mixing loss was 7.50 MPa. Under optimal conditions, the addition of PO improves mix volume percent correction (negligible), flexural strength (5.45%), density (18%), and porosity (3.70%) compared with S1B. On the other hand, the addition of PO thermo-chemical treatment by NaOH increases the compressive strength (3.97%) compared with PO due to the removal of impurities on the fiber surface, as shown by scanning electron microscopy. We then determined the optimal mixture ratio (PO divided by a mixture of plaster, sand, and gravel), which equals 0.0321 because Tunisian gypsum contains small amounts of bassanite and calcite, as shown by the X-ray diffraction results.
The publication of statistical databases is subject to legal regulations, e.g. national statistical offices are only allowed to publish data if the data cannot be attributed to individuals. Achieving this privacy standard requires anonymizing the data prior to publication. However, data anonymization inevitably leads to a loss of information, which should be kept minimal. In this thesis, we analyze the anonymization method SAFE used in the German census in 2011 and we propose a novel integer programming-based anonymization method for nominal data.
In the first part of this thesis, we prove that a fundamental variant of the underlying SAFE optimization problem is NP-hard. This justifies the use of heuristic approaches for large data sets. In the second part, we propose a new anonymization method belonging to microaggregation methods, specifically designed for nominal data. This microaggregation method replaces rows in a microdata set with representative values to achieve k-anonymity, ensuring each data row is identical to at least k − 1 other rows. In addition to the overall dissimilarities of the data rows, the method accounts for errors in resulting frequency tables, which are of high interest for nominal data in practice. The method employs a typical two-step structure: initially partitioning the data set into clusters and subsequently replacing all cluster elements with representative values to achieve k-anonymity. For the partitioning step, we propose a column generation scheme followed by a heuristic to obtain an integer solution, which is based on the dual information. For the aggregation step, we present a mixed-integer problem formulation to find cluster representatives. To this end, we take errors in a subset of frequency tables into account. Furthermore, we show a reformulation of the problem to a minimum edge-weighted maximal clique problem in a multipartite graph, which allows for a different perspective on the problem. Moreover, we formulate a mixed-integer program, which combines the partitioning and the aggregation step and aims to minimize the sum of chi-squared errors in frequency tables.
Finally, an experimental study comparing the methods covered or developed in this work shows particularly strong results for the proposed method with respect to relative criteria, while SAFE shows its strength with respect to the maximum absolute error in frequency tables. We conclude that the inclusion of integer programming in the context of data anonymization is a promising direction to reduce the inevitable information loss inherent in anonymization, particularly for nominal data.
The microbial enzyme alkaline phosphatase contributes to the removal of organic phosphorus compounds from wastewaters. To cope with regulatory threshold values for permitted maximum phosphor concentrations in treated wastewaters, a high activity of this enzyme in the biological treatment stage, e.g., the activated sludge process, is required. To investigate the reaction dynamics of this enzyme, to analyze substrate selectivities, and to identify potential inhibitors, the determination of enzyme kinetics is necessary. A method based on the application of the synthetic fluorogenic substrate 4-methylumbelliferyl phosphate is proven for soils, but not for activated sludges. Here, we adapt this procedure to the latter. The adapted method offers the additional benefit to determine inhibition kinetics. In contrast to conventional photometric assays, no particle removal, e.g., of sludge pellets, is required enabling the analysis of the whole sludge suspension as well as of specific sludge fractions. The high sensitivity of fluorescence detection allows the selection of a wide substrate concentration range for sound modeling of kinetic functions.
- Fluorescence array technique for fast and sensitive analysis of high sample numbers
- No need for particle separation – analysis of the whole (diluted) sludge suspension
- Simultaneous determination of standard and inhibition kinetics
Startups are essential agents for the evolution of economies and the creative destruction of established market conditions for the benefit of a more effective and efficient economy. Their significance is manifested in their drive for innovation and technological advancements, their creation of new jobs, their contribution to economic growth, and their impact on increased competition and increased market efficiency. By reason of their attributes of newness and smallness, startups often experience a limitation in accessing external financial resources. Extant research on entrepreneurial finance examines the capital structure of startups, various funding tools, financing environments in certain regions, and investor selection criteria among other topics. My dissertation contributes to this research area by examining the becoming increasingly important funding instrument of venture debt. Prior research on venture debt only investigated the business model of venture debt, the concept of venture debt, the selection criteria of venture debt providers, and the role of patents in the venture debt provider’s selection process. Based on qualitative and quantitative methods, the dissertation outlines the emergence of venture debt in Europe as well as the impact of venture debt on startups to open up a better understanding of venture debt.
The results of the qualitative studies indicate that venture debt was formed based on a ‘Kirznerian’ entrepreneurial opportunity and venture debt impacts startups positive and negative in their development via different impact mechanisms.
Based on these results, the dissertation analyzes the empirical impact of venture debt on a startup’s ability to acquire additional financial resources as well as the role of the reputation of venture debt providers. The results suggest that venture debt increases the likelihood of acquiring additional financial resources via subsequent funding rounds and trade sales. In addition, a higher venture debt provider reputation increases the likelihood of acquiring additional financial resources via IPOs.
Diese Dissertation beschäftigt sich mit der Fragestellung, ob und wie Intersektionalität als analytische Perspektive für literarische Texte eine nützliche Ergänzung für ethnisch geordnete Literaturfelder darstellt. Diese Fragestellung wird anhand der Analyse dreier zeitgenössischer chinesisch-kanadischer Romane untersucht.
In der Einleitung wird die Relevanz der Themenbereiche Intersektionalität und asiatisch-kanadische Literatur erörtert. Das darauffolgende Kapitel bietet einen historischen Überblick über die chinesisch-kanadische Einwanderung und geht detailliert auf die literarischen Produktionen ein. Es wird aufgezeigt, dass, obwohl kulturelle Güter auch zur Artikulation von Ungleichheitsverhältnissen aufgrund von zugeschriebener ethnischer Zugehörigkeit entstehen, ein Diversifizierungsbestreben innerhalb der literarischen Gemeinschaft von chinesisch-kanadischen Autor:innen identifiziert werden kann. Das dritte Kapitel widmet sich dem Begriff „Intersektionalität“ und stellt, nach einer historischen Einordnung des Konzeptes mit seinen Ursprüngen im Black Feminism, Intersektionalität als bindendes Element zwischen Postkolonialismus, Diversität und Empowerment dar – Konzepte, die für die Analyse (kanadischer) Literatur in dieser Dissertation von besonderer Relevanz sind. Anschließend wird die Rolle von Intersektionalität in der Literaturwissenschaft aufgegriffen. Die darauffolgenden exemplarischen Analysen von Kim Fus For Today I Am a Boy, Wayson Choys The Jade Peony und Yan Lis Lily in the Snow veranschaulichen die vorangegangen methodischen Überlegungen. Allen drei Romanen vorangestellt ist die Kontextualisierung des jeweiligen Werkes als chinesisch-kanadisch, aber auch bisher vorgenommene Überlegungen, die diese Einordnung infrage stellen. Nach einer Zusammenfassung des Inhalts folgt eine intersektionale Analyse auf der inhaltlichen Ebene, die in den familiären und weiteren sozialen Bereich unterteilt ist, da sich die Hierarchiemechanismen innerhalb dieser Bereiche unterscheiden oder gegenseitig verstärken, wie aus den Analysen hervorgeht. Anschließend wird die formale Analyse mit einem intersektionalen Schwerpunkt in einem separaten Unterkapitel näher beleuchtet. Ein drittes Unterkapitel widmet sich einem dem jeweiligen Roman spezifischen Aspekt, der im Zusammenhang mit einer intersektionalen Analyse von besonderer Relevanz ist. Die Arbeit schließt mit einem übergreifenden Fazit, welches die wichtigsten Ergebnisse aus der Analyse zusammenfasst und mit weiteren Überlegungen zu den Implikationen dieser Dissertation, vor allem im Hinblick auf sogenannte kanadische „master narratives“, die eine weitreichende, kontextuelle Relevanz für das Arbeiten mit literarischen Texten aufweisen und durch einen intersektionalen literarischen Ansatz in Zukunft gegebenenfalls gewinnbringend ergänzt werden können.
While humans find it easy to process visual information from the real world, machines struggle with this task due to the unstructured and complex nature of the information. Computer vision (CV) is the approach of artificial intelligence that attempts to automatically analyze, interpret, and extract such information. Recent CV approaches mainly use deep learning (DL) due to its very high accuracy. DL extracts useful features from unstructured images in a training dataset to use them for specific real-world tasks. However, DL requires a large number of parameters, computational power, and meaningful training data, which can be noisy, sparse, and incomplete for specific domains. Furthermore, DL tends to learn correlations from the training data that do not occur in reality, making DNNs poorly generalizable and error-prone.
Therefore, the field of visual transfer learning is seeking methods that are less dependent on training data and are thus more applicable in the constantly changing world. One idea is to enrich DL with prior knowledge. Knowledge graphs (KG) serve as a powerful tool for this purpose because they can formalize and organize prior knowledge based on an underlying ontological schema. They contain symbolic operations such as logic, rules, and reasoning, and can be created, adapted, and interpreted by domain experts. Due to the abstraction potential of symbols, KGs provide good prerequisites for generalizing their knowledge. To take advantage of the generalization properties of KG and the ability of DL to learn from large-scale unstructured data, attempts have long been made to combine explicit graph and implicit vector representations. However, with the recent development of knowledge graph embedding methods, where a graph is transferred into a vector space, new perspectives for a combination in vector space are opening up.
In this work, we attempt to combine prior knowledge from a KG with DL to improve visual transfer learning using the following steps: First, we explore the potential benefits of using prior knowledge encoded in a KG for DL-based visual transfer learning. Second, we investigate approaches that already combine KG and DL and create a categorization based on their general idea of knowledge integration. Third, we propose a novel method for the specific category of using the knowledge graph as a trainer, where a DNN is trained to adapt to a representation given by prior knowledge of a KG. Fourth, we extend the proposed method by extracting relevant context in the form of a subgraph of the KG to investigate the relationship between prior knowledge and performance on a specific CV task. In summary, this work provides deep insights into the combination of KG and DL, with the goal of making DL approaches more generalizable, more efficient, and more interpretable through prior knowledge.
Do Personality Traits, Trust and Fairness Shape the Stock-Investing Decisions of an Individual?
(2023)
This thesis is comprised of three projects, all of which are fundamentally connected to the choices that individuals make about stock investments. Differences in stock market participation (SMP) across countries are large and difficult to explain. The second chapter focuses on differences between Germany (low SMP) and East Asian countries (mostly high SMP). The study hypothesis is that cultural differences regarding social preferences and attitudes towards inequality lead to different attitudes towards stock markets and subsequently to different SMPs. Using a large-scale survey, it is found that these factors can, indeed, explain a substantial amount of the country differences that other known factors (financial literacy, risk preferences, etc.) could not. This suggests that social preferences should be given a more central role in programs that aim to enhance SMP in countries like Germany. The third chapter documented the importance of trust as well as herding for stock ownership decisions. The findings show that trust as a general concept has no significant contribution to stock investment intention. A thorough examination of general trust elements reveals that in group and out-group trust have an impact on individual stock market investment. Higher out group trust directly influences a person's decision to invest in stocks, whereas higher in-group trust increases herding attitudes in stock investment decisions and thus can potentially increase the likelihood of stock investments as well. The last chapter investigates the significance of personality traits in stock investing and home bias in portfolio selection. Findings show that personality traits do indeed have a significant impact on stock investment and portfolio allocation decisions. Despite the fact that the magnitude and significance of characteristics differ between two groups of investors, inexperienced and experienced, conscientiousness and neuroticism play an important role in stock investments and preferences. Moreover, high conscientiousness scores increase stock investment desire and portfolio allocation to risky assets like stocks, discouraging home bias in asset allocation. Regarding neuroticism, a higher-level increases home bias in portfolio selection and decreases willingness to stock investment and portfolio share. Finally, when an investor has no prior experience with portfolio selection, patriotism generates home bias. For experienced investors, having a low neuroticism score and a high conscientiousness and openness score seemed to be a constant factor in deciding to invest in a well-diversified international portfolio
This thesis deals with REITs, their capital structure and the effects on leverage that regulatory requirements might have. The data used results from a combination of Thomson Reuters data with hand-collected data regarding the REIT status, regulatory information and law variables. Overall, leverage is analysed across 20 countries in the years 2007 to 2018. Country specific data, manually extracted from yearly EPRA reportings, is merged with company data in order to analyse the influence of different REIT restrictions on a firm's leverage.
Observing statistically significant differences in means across NON-REITs and REITs, causes motivation for further investigations. My results show that variables beyond traditional capital structure determinants impact the leverage of REITs. I find that explicit restrictions on leverage and the distribution of profits have a significant effect on leverage decisions. This supports the notion that the restrictions from EPRA reportings are mandatory. I test for various combinations of regulatory variables that show both in isolation as well as in combination significant effects on leverage.
My main result is the following: Firms that operate under regulation that specifies a maximum leverage ratio, in addition to mandatory high dividend distributions, have on average lower leverage ratios. Further the existence of sanctions has a negative effect on REITs' leverage ratios, indicating that regulation is binding. The analysis clearly shows that traditional capital structure determinants are of second order relevance. This relationship highlights the impact on leverage and financing decisions caused by regulation. These effects are supported by further analysis. Results based on an event study show that REITs have statistically lower leverage ratios compared to NON-REITs. Based on a structural break model, the following effect becomes apparent: REITs increase their leverage ratios in years prior REIT status. As a consequence, the ex ante time frame is characterised by a bunker and adaption process, followed by the transformation in the event. Using an event study and a structural break model, the analysis highlights the dominance of country-specific regulation.
This thesis comprises of four research papers on the economics of education and industrial relations, which contribute to the field of empirical economic research. All of the corresponding papers focus on analysing how much time individuals spend on specific activities. The allocation of available time resources is a decision that individuals make throughout their lifetime. In this thesis, we consider individuals at different stages of their lives - students at school, university students, and dependent employees at the workplace.
Part I includes two research studies on student's behaviour in secondary and tertiary education.
Chapter 2 explores whether students who are relatively younger or older within the school year exhibit differential time allocation. Building on previous findings showing that relatively younger students perform worse in school, the study shows that relatively younger students are aware of their poor performance in school and feel more strain as a result. Nevertheless, there are no clear differences to be found in terms of time spent on homework, while relatively younger students spend more time watching television and less time on sports activities. Thus, the results suggest that the lower learning outcomes are not associated with different time allocations between school-related activities and non-school-related activities.
Chapter 3 analyses how individual ability and labour market prospects affect study behaviour. The theoretical modelling predicts that both determinants increase study effort. The empirical investigation is based on cross-sectional data from the National Educational Panel Study (NEPS) and includes thousands of students in Germany. The analyses show that more gifted students exhibit lower subjective effort levels and invest less time in self-study. In contrast, very good labour market prospects lead to more effort exerted by the student, both qualitatively and quantitatively. The potential endogeneity problem is taken into account by using regional unemployment data as an instrumental variable.
Part II includes two labour economic studies on determinants of overtime. Both studies belong to the field of industrial relations, as they focus on union membership on the one hand and the interplay of works councils and collective bargaining coverage on the other.
Chapter 4 shows that union members work less overtime than non-members do. The econometric approach takes the problem of unobserved heterogeneity into account; but provides no evidence that this issue affects the results. Different channels that could lead to this relationship are analysed by examining relevant subgroups separately. For example, this effect of union membership can also be observed in establishments with works councils and for workers who are very likely to be covered by collective bargaining agreements. The study concludes that the observed effect is due to the fact that union membership can protect workers from corresponding increased working time demands by employers.
Chapter 5 builds on previous studies showing a negative effect of works councils on overtime. In addition to co-determination by works councils at the firm level, collective bargaining coverage is an important factor in the German industrial relations system. Corresponding data was not available in the SOEP for quite some time. Therefore, the study uses recent SOEP data, which also contains information on collective bargaining coverage. A cross-sectional analysis is conducted to examine the effects of works councils in establishments with and without collective bargaining coverage. Similar to studies analysing other outcome variables, the results show that the effect of works councils exists only for employees covered by a collective bargaining agreement.
Striving for sustainable development by combating climate change and creating a more social world is one of the most pressing issues of our time. Growing legal requirements and customer expectations require also Mittelstand firms to address sustainability issues such as climate change. This dissertation contributes to a better understanding of sustainability in the Mittelstand context by examining different Mittelstand actors and the three dimensions of sustainability - social, economic, and environmental sustainability - in four quantitative studies. The first two studies focus on the social relevance and economic performance of hidden champions, a niche market leading subgroup of Mittelstand firms. At the regional level, the impact of 1,645 hidden champions located in Germany on various dimensions of regional development is examined. A higher concentration of hidden champions has a positive effect on regional employment, median income, and patents. At the firm level, analyses of a panel dataset of 4,677 German manufacturing firms, including 617 hidden champions, show that the latter have a higher return on assets than other Mittelstand firms. The following two chapters deal with environmental strategies and thus contribute to the exploration of the environmental dimension of sustainability. First, the consideration of climate aspects in investment decisions is compared using survey data from 468 European venture capital and private equity investors. While private equity firms respond to external stakeholders and portfolio performance and pursue an active ownership strategy, venture capital firms are motivated by product differentiation and make impact investments. Finally, based on survey data from 443 medium-sized manufacturing firms in Germany, 54% of which are family-owned, the impact of stakeholder pressures on their decarbonization strategies is analyzed. A distinction is made between symbolic (compensation of CO₂-emissions) and substantive decarbonization strategies (reduction of CO₂-emissions). Stakeholder pressures lead to a proactive pursuit of decarbonization strategies, with internal and external stakeholders varying in their influence on symbolic and substantial decarbonization strategies, and the relationship influenced by family ownership.
In recent years, the establishment of new makerspaces in Germany has increased significantly. The underlying phenomenon of the Maker Movement is a cultural and technological movement focused on making physical and digital products using open source principles, collaborative production, and individual empowerment. Because of its potential to democratize the innovation and production process, empower individuals and communities, and enable innovators to solve problems at the local level, the Maker Movement has received considerable attention in recent years. Despite numerous indicators, little is known about the phenomenon and its individual members, especially in Germany. Initial research suggests that the Maker Movement holds great potential for innovation and entrepreneurship. However, there is still a gap in understanding how Makers discover, evaluate and exploit entrepreneurial opportunities. Moreover, there is still controversy - both among policy makers and within the maker community itself - about the impact the maker movement has and can have on innovation and entrepreneurship in the future. This dissertation uses a mixed-methods approach to explore these questions. In addition to a quantitative analysis of maker characteristics, the results show that social impact, market size, and property rights have significant effects on the evaluation of entrepreneurial opportunities. The findings within this dissertation expand research in the field of the Maker Movement and offer multiple implications for practice. This dissertation provides the first quantitative data on makers in makerspaces in Germany, their characteristics and motivations. In particular, the relationship between the Maker Movement and entrepreneurship is explored in depth for the first time. This is complemented by the presentation of different identity profiles of the individuals involved. In this way, policy-makers can develop a better understanding of the movement, its personalities and values, and consider them in initiatives and formats.
The German Mittelstand is closely linked to the success of the German economy. Mittelstand firms, thereof numerous Hidden Champions, significantly contribute to Germany’s economic performance, innovation, and export strength. However, the advancing digitalization poses complex challenges for Mittelstand firms. To benefit from the manifold opportunities offered by digital technologies and to defend or even expand existing market positions, Mittelstand firms must transform themselves and their business models. This dissertation uses quantitative methods and contributes to a deeper understanding of the distinct needs and influencing factors of the digital transformation of Mittelstand firms. The results of the empirical analyses of a unique database of 525 mid-sized German manufacturing firms, comprising both firm-related information and survey data, show that organizational capabilities and characteristics significantly influence the digital transformation of Mittelstand firms. The results support the assumption that dynamic capabilities promote the digital transformation of such firms and underline the important role of ownership structure, especially regarding family influence, for the digital transformation of the business model and the pursuit of growth goals with digitalization. In addition to the digital transformation of German Mittelstand firms, this dissertation examines the economic success and regional impact of Hidden Champions and hence, contributes to a better understanding of the Hidden Champion phenomenon. Using quantitative methods, it can be empirically proven that Hidden Champions outperform other mid-sized firms in financial terms and promote regional development. Consequently, the results of this dissertation provide valuable research contributions and offer various practical implications for firm managers and owners as well as policy makers.
Every action we perform, no matter how simple or complex, has a cognitive representation. It is commonly assumed that these are organized hierarchically. Thus, the representation of a complex action consists of multiple simpler actions. The representation of a simple action, in turn, consists of stimulus, response, and effect features. These are integrated into one representation upon the execution of an action and can be retrieved if a feature is repeated. Depending on whether retrieved features match or only partially match the current action episode, this might benefit or impair the execution of a subsequent action. This pattern of costs and benefits results in binding effects that indicate the strength of common representation between features. Binding effects occur also in more complex actions: Multiple simple actions seem to form representations on a higher level through the integration and retrieval of sequentially given responses, resulting in so-called response-response binding effects. This dissertation aimed to investigate what factors determine whether simple actions form more complex representations. The first line of research (Articles 1-3) focused on dissecting the internal structure of simple actions. Specifically, I investigated whether the spatial relation of stimuli, responses, or effects, that are part of two different simple actions, influenced whether these simple actions are represented as one more complex action. The second line of research (Articles 2, 4, and 5) investigated the role of context on the formation and strength of more complex action representations. Results suggest that spatial separation of responses as well as context might affect the strength of more complex action representations. In sum, findings help to specify assumptions on the structure of complex action representations. However, it may be important to distinguish factors that influence the strength and structure of action representations from factors that terminate action representations.
There is no longer any doubt about the general effectiveness of psychotherapy. However, up to 40% of patients do not respond to treatment. Despite efforts to develop new treatments, overall effectiveness has not improved. Consequently, practice-oriented research has emerged to make research results more relevant to practitioners. Within this context, patient-focused research (PFR) focuses on the question of whether a particular treatment works for a specific patient. Finally, PFR gave rise to the precision mental health research movement that is trying to tailor treatments to individual patients by making data-driven and algorithm-based predictions. These predictions are intended to support therapists in their clinical decisions, such as the selection of treatment strategies and adaptation of treatment. The present work summarizes three studies that aim to generate different prediction models for treatment personalization that can be applied to practice. The goal of Study I was to develop a model for dropout prediction using data assessed prior to the first session (N = 2543). The usefulness of various machine learning (ML) algorithms and ensembles was assessed. The best model was an ensemble utilizing random forest and nearest neighbor modeling. It significantly outperformed generalized linear modeling, correctly identifying 63.4% of all cases and uncovering seven key predictors. The findings illustrated the potential of ML to enhance dropout predictions, but also highlighted that not all ML algorithms are equally suitable for this purpose. Study II utilized Study I’s findings to enhance the prediction of dropout rates. Data from the initial two sessions and observer ratings of therapist interventions and skills were employed to develop a model using an elastic net (EN) algorithm. The findings demonstrated that the model was significantly more effective at predicting dropout when using observer ratings with a Cohen’s d of up to .65 and more effective than the model in Study I, despite the smaller sample (N = 259). These results indicated that generating models could be improved by employing various data sources, which provide better foundations for model development. Finally, Study III generated a model to predict therapy outcome after a sudden gain (SG) in order to identify crucial predictors of the upward spiral. EN was used to generate the model using data from 794 cases that experienced a SG. A control group of the same size was also used to quantify and relativize the identified predictors by their general influence on therapy outcomes. The results indicated that there are seven key predictors that have varying effect sizes on therapy outcome, with Cohen's d ranging from 1.08 to 12.48. The findings suggested that a directive approach is more likely to lead to better outcomes after an SG, and that alliance ruptures can be effectively compensated for. However, these effects
were reversed in the control group. The results of the three studies are discussed regarding their usefulness to support clinical decision-making and their implications for the implementation of precision mental health.
Building Fortress Europe Economic realism, China, and Europe’s investment screening mechanisms
(2023)
This thesis deals with the construction of investment screening mechanisms across the major economic powers in Europe and at the supranational level during the post-2015 period. The core puzzle at the heart of this research is how, in a traditional bastion of economic liberalism such as Europe, could a protectionist tool such as investment screening be erected in such a rapid manner. Within a few years, Europe went from a position of being highly welcoming towards foreign investment to increasingly implementing controls on it, with the focus on China. How are we to understand this shift in Europe? I posit that Europe’s increasingly protectionist shift on inward investment can be fruitfully understood using an economic realist approach, where the introduction of investment screening can be seen as part of a process of ‘balancing’ China’s economic rise and reasserting European competitiveness. China has moved from being the ‘workshop of the world’ to becoming an innovation-driven economy at the global technological frontier. As China has become more competitive, Europe, still a global economic leader, broadly situated at the technological frontier, has begun to sense a threat to its position, especially in the context of the fourth industrial revolution. A ‘balancing’ process has been set in motion, in which Europe seeks to halt and even reverse the narrowing competitiveness gap between it and China. The introduction of investment screening measures is part of this process.
Some of the largest firms in the DACH region (Germany, Austria, Switzerland) are (partially) owned by a foundation and/or a family office, such as Aldi, Bosch, or Rolex. Despite their growing importance, prior research neglected to analyze the impact of these intermediaries on the firms they own. This dissertation closes this research gap by contributing to a deeper understanding of two increasingly used family firm succession vehicles, through four empirical quantitative studies. The first study focuses on the heterogeneity in foundation-owned firms (FOFs) by applying a descriptive analysis to a sample of 169 German FOFs. The results indicate that the family as a central stakeholder in a family foundation fosters governance that promotes performance and growth. The second study examines the firm growth of 204 FOFs compared to matched non-FOFs from the DACH region. The findings suggest that FOFs grow significantly less in terms of sales but not with regard to employees. In addition, it seems that this negative effect is stronger for the upper than for the middle or lower quantiles of the growth distribution. Study three adopts an agency perspective and investigates the acquisition behavior within the group of 164 FOFs. The results reveal that firms with charitable foundations as owners are more likely to undertake acquisitions and acquire targets that are geographically and culturally more distant than firms with a family foundation as owner. At the same time, they favor target companies from the same or related industries. Finally, the fourth study scrutinizes the capital structure of firms owned by single family-offices (SFOs). Drawing on a hand-collected sample of 173 SFO-owned firms in the DACH region, the results show that SFO-owned firms display a higher long-term debt ratio than family-owned firms, indicating that SFO-owned firms follow trade-off theory, similar to private equity-owned firms. Additional analyses show that this effect is stronger for SFOs that sold their original family firm. In conclusion, the outcomes of this dissertation furnish valuable research contributions and offer practical insights for families navigating such intermediaries or succession vehicles in the long term.
We study planned changes in protective routines after the COVID-19 pandemic: in a survey in Germany among >650 respondents, we find that the majority plans to use face masks in certain situations even after the end of the pandemic. We observe that this willingness is strongly related to the perception that there is something to be learned from East Asians’ handling of pandemics, even when controlling for perceived protection by wearing masks. Given strong empirical evidence that face masks help prevent the spread of respiratory diseases and given the considerable estimated health and economic costs of such diseases even pre-Corona, this would be a very positive side effect of the current crisis.
Forest inventories provide significant monitoring information on forest health, biodiversity,
resilience against disturbance, as well as its biomass and timber harvesting potential. For this
purpose, modern inventories increasingly exploit the advantages of airborne laser scanning (ALS)
and terrestrial laser scanning (TLS).
Although tree crown detection and delineation using ALS can be seen as a mature discipline, the
identification of individual stems is a rarely addressed task. In particular, the informative value of
the stem attributes—especially the inclination characteristics—is hardly known. In addition, a lack
of tools for the processing and fusion of forest-related data sources can be identified. The given
thesis addresses these research gaps in four peer-reviewed papers, while a focus is set on the
suitability of ALS data for the detection and analysis of tree stems.
In addition to providing a novel post-processing strategy for geo-referencing forest inventory plots,
the thesis could show that ALS-based stem detections are very reliable and their positions are
accurate. In particular, the stems have shown to be suited to study prevailing trunk inclination
angles and orientations, while a species-specific down-slope inclination of the tree stems and a
leeward orientation of conifers could be observed.
Agricultural monitoring is necessary. Since the beginning of the Holocene, human agricultural
practices have been shaping the face of the earth, and today around one third of the ice-free land
mass consists of cropland and pastures. While agriculture is necessary for our survival, the
intensity has caused many negative externalities, such as enormous freshwater consumption, the
loss of forests and biodiversity, greenhouse gas emissions as well as soil erosion and degradation.
Some of these externalities can potentially be ameliorated by careful allocation of crops and
cropping practices, while at the same time the state of these crops has to be monitored in order
to assess food security. Modern day satellite-based earth observation can be an adequate tool to
quantify abundance of crop types, i.e., produce spatially explicit crop type maps. The resources to
do so, in terms of input data, reference data and classification algorithms have been constantly
improving over the past 60 years, and we live now in a time where fully operational satellites
produce freely available imagery with often less than monthly revisit times at high spatial
resolution. At the same time, classification models have been constantly evolving from
distribution based statistical algorithms, over machine learning to the now ubiquitous deep
learning.
In this environment, we used an explorative approach to advance the state of the art of crop
classification. We conducted regional case studies, focused on the study region of the Eifelkreis
Bitburg-Prüm, aiming to develop validated crop classification toolchains. Because of their unique
role in the regional agricultural system and because of their specific phenologic characteristics
we focused solely on maize fields.
In the first case study, we generated reference data for the years 2009 and 2016 in the study
region by drawing polygons based on high resolution aerial imagery, and used these in
conjunction with RapidEye imagery to produce high resolution maize maps with a random forest
classifier and a gaussian blur filter. We were able to highlight the importance of careful residual
analysis, especially in terms of autocorrelation. As an end result, we were able to prove that, in
spite of the severe limitations introduced by the restricted acquisition windows due to cloud
coverage, high quality maps could be produced for two years, and the regional development of
maize cultivation could be quantified.
In the second case study, we used these spatially explicit datasets to link the expansion of biogas
producing units with the extended maize cultivation in the area. In a next step, we overlayed the
maize maps with soil and slope rasters in order to assess spatially explicit risks of soil compaction
and erosion. Thus, we were able to highlight the potential role of remote sensing-based crop type
classification in environmental protection, by producing maps of potential soil hazards, which can
be used by local stakeholders to reallocate certain crop types to locations with less associated
risk.
In our third case study, we used Sentinel-1 data as input imagery, and official statistical records
as maize reference data, and were able to produce consistent modeling input data for four
consecutive years. Using these datasets, we could train and validate different models in spatially
iv
and temporally independent random subsets, with the goal of assessing model transferability. We
were able to show that state-of-the-art deep learning models such as UNET performed
significantly superior to conventional models like random forests, if the model was validated in a
different year or a different regional subset. We highlighted and discussed the implications on
modeling robustness, and the potential usefulness of deep learning models in building fully
operational global crop classification models.
We were able to conclude that the first major barrier for global classification models is the
reference data. Since most research in this area is still conducted with local field surveys, and only
few countries have access to official agricultural records, more global cooperation is necessary to
build harmonized and regionally stratified datasets. The second major barrier is the classification
algorithm. While a lot of progress has been made in this area, the current trend of many appearing
new types of deep learning models shows great promise, but has not yet consolidated. There is
still a lot of research necessary, to determine which models perform the best and most robust,
and are at the same time transparent and usable by non-experts such that they can be applied
and used effortlessly by local and global stakeholders.
The Second Language Acquisition of English Non-Finite Complement Clauses – A Usage-Based Perspective
(2022)
One of the most essential hypotheses of usage-based theories and many constructionist approaches to language is that language entails the piecemeal learning of constructions on the basis of general cognitive mechanisms and exposure to the target language in use (Ellis 2002; Tomasello 2003). However, there is still a considerable lack of empirical research on the emergence and mental representation of constructions in second language (L2) acquisition. One crucial question that arises, for instance, is whether L2 learners’ knowledge of a construction corresponds to a native-like mapping of form and meaning and, if so, to what extent this representation is shaped by usage. For instance, it is unclear how learners ‘build’ constructional knowledge, i.e. which pieces of frequency-, form- and meaning-related information become relevant for the entrenchment and schematisation of a L2 construction.
To address these issues, the English catenative verb construction was used as a testbed phenomenon. This idiosyncratic complex construction is comprised of a catenative verb and a non-finite complement clause (see Huddleston & Pullum 2002), which is prototypically a gerund-participial (henceforth referred to as ‘target-ing’ construction) or a to-infinitival complement (‘target-to’ construction):
(1) She refused to do her homework.
(2) Laura kept reading love stories.
(3) *He avoids to listen to loud music.
This construction is particularly interesting because learners often show choices of a complement type different from those of native speakers (e.g. Gries & Wulff 2009; Martinez‐Garcia & Wulff 2012) as illustrated in (3) and is commonly claimed to be difficult to be taught by explicit rules (see e.g. Petrovitz 2001).
By triangulating different types of usage data (corpus and elicited production data) and analysing these by multivariate statistical tests, the effects of different usage-related factors (e.g. frequency, proficiency level of the learner, semantic class of verb, etc.) on the representation and development of the catenative verb construction and its subschemas (i.e. target-to and target-ing construction) were examined. In particular, it was assessed whether they can predict a native-like form-meaning pairing of a catenative verb and non-finite complement.
First, all studies were able to show a robust effect of frequency on the complement choice. Frequency does not only lead to the entrenchment of high-frequency exemplars of the construction but is also found to motivate a taxonomic generalisation across related exemplars and the representation of a more abstract schema. Second, the results indicate that the target-to construction, due to its higher type and token frequency, has a high degree of schematicity and productivity than the target-ing construction for the learners, which allows for analogical comparisons and pattern extension with less entrenched exemplars. This schema is likely to be overgeneralised to (less frequent) target-ing verbs because the learners perceive formal and semantic compatibility between the unknown/infrequent verb and this pattern.
Furthermore, the findings present evidence that less advanced learners (A2-B2) make more coarse-grained generalisations, which are centred around high-frequency and prototypical exemplars/low-scope patterns. In the case of high-proficiency learners (C1-C2), not only does the number of native-like complement choices increase but relational information, such as the semantic subclasses of the verb, form-function contingency and other factors, becomes also relevant for a target-like choice. Thus, the results suggests that with increasing usage experience learners gradually develop a more fine-grained, interconnected representation of the catenative verb construction, which gains more resemblance to the form-meaning mappings of native speakers.
Taken together, these insights highlight the importance for language learning and teaching environments to acknowledge that L2 knowledge is represented in the form of highly interconnected form-meaning pairings, i.e. constructions, that can be found on different levels of abstraction and complexity.
This thesis is concerned with two classes of optimization problems which stem
mainly from statistics: clustering problems and cardinality-constrained optimization problems. We are particularly interested in the development of computational techniques to exactly or heuristically solve instances of these two classes
of optimization problems.
The minimum sum-of-squares clustering (MSSC) problem is widely used
to find clusters within a set of data points. The problem is also known as
the $k$-means problem, since the most prominent heuristic to compute a feasible
point of this optimization problem is the $k$-means method. In many modern
applications, however, the clustering suffers from uncertain input data due to,
e.g., unstructured measurement errors. The reason for this is that the clustering
result then represents a clustering of the erroneous measurements instead of
retrieving the true underlying clustering structure. We address this issue by
applying robust optimization techniques: we derive the strictly and $\Gamma$-robust
counterparts of the MSSC problem, which are as challenging to solve as the
original model. Moreover, we develop alternating direction methods to quickly
compute feasible points of good quality. Our experiments reveal that the more
conservative strictly robust model consistently provides better clustering solutions
than the nominal and the less conservative $\Gamma$-robust models.
In the context of clustering problems, however, using only a heuristic solution
comes with severe disadvantages regarding the interpretation of the clustering.
This motivates us to study globally optimal algorithms for the MSSC problem.
We note that although some algorithms have already been proposed for this
problem, it is still far from being “practically solved”. Therefore, we propose
mixed-integer programming techniques, which are mainly based on geometric
ideas and which can be incorporated in a
branch-and-cut based algorithm tailored
to the MSSC problem. Our numerical experiments show that these techniques
significantly improve the solution process of a
state-of-the-art MINLP solver
when applied to the problem.
We then turn to the study of cardinality-constrained optimization problems.
We consider two famous problem instances of this class: sparse portfolio optimization and sparse regression problems. In many modern applications, it is common
to consider problems with thousands of variables. Therefore, globally optimal
algorithms are not always computationally viable and the study of sophisticated
heuristics is very desirable. Since these problems have a discrete-continuous
structure, decomposition methods are particularly well suited. We then apply a
penalty alternating direction method that explores this structure and provides
very good feasible points in a reasonable amount of time. Our computational
study shows that our methods are competitive to
state-of-the-art solvers and heuristics.
Due to the transition towards climate neutrality, energy markets are rapidly evolving. New technologies are developed that allow electricity from renewable energy sources to be stored or to be converted into other energy commodities. As a consequence, new players enter the markets and existing players gain more importance. Market equilibrium problems are capable of capturing these changes and therefore enable us to answer contemporary research questions with regard to energy market design and climate policy.
This cumulative dissertation is devoted to the study of different market equilibrium problems that address such emerging aspects in liberalized energy markets. In the first part, we review a well-studied competitive equilibrium model for energy commodity markets and extend this model by sector coupling, by temporal coupling, and by a more detailed representation of physical laws and technical requirements. Moreover, we summarize our main contributions of the last years with respect to analyzing the market equilibria of the resulting equilibrium problems.
For the extension regarding sector coupling, we derive sufficient conditions for ensuring uniqueness of the short-run equilibrium a priori and for verifying uniqueness of the long-run equilibrium a posteriori. Furthermore, we present illustrative examples that each of the derived conditions is indeed necessary to guarantee uniqueness in general.
For the extension regarding temporal coupling, we provide sufficient conditions for ensuring uniqueness of demand and production a priori. These conditions also imply uniqueness of the short-run equilibrium in case of a single storage operator. However, in case of multiple storage operators, examples illustrate that charging and discharging decisions are not unique in general. We conclude the equilibrium analysis with an a posteriori criterion for verifying uniqueness of a given short-run equilibrium. Since the computation of equilibria is much more challenging due to the temporal coupling, we shortly review why a tailored parallel and distributed alternating direction method of multipliers enables to efficiently compute market equilibria.
For the extension regarding physical laws and technical requirements, we show that, in nonconvex settings, existence of an equilibrium is not guaranteed and that the fundamental welfare theorems therefore fail to hold. In addition, we argue that the welfare theorems can be re-established in a market design in which the system operator is committed to a welfare objective. For the case of a profit-maximizing system operator, we propose an algorithm that indicates existence of an equilibrium and that computes an equilibrium in the case of existence. Based on well-known instances from the literature on the gas and electricity sector, we demonstrate the broad applicability of our algorithm. Our computational results suggest that an equilibrium often exists for an application involving nonconvex but continuous stationary gas physics. In turn, integralities introduced due to the switchability of DC lines in DC electricity networks lead to many instances without an equilibrium. Finally, we state sufficient conditions under which the gas application has a unique equilibrium and the line switching application has finitely many.
In the second part, all preprints belonging to this cumulative dissertation are provided. These preprints, as well as two journal articles to which the author of this thesis contributed, are referenced within the extended summary in the first part and contain more details.
The forward testing effect is an indirect benefit of retrieval practice. It refers to the finding that retrieval practice of previously studied information enhances learning and retention of subsequently studied other information in episodic memory tasks. Here, two experiments were conducted that investigated whether retrieval practice influences participants’ performance in other tasks, i.e., arithmetic tasks. Participants studied three lists of words in anticipation of a final recall test. In the testing condition, participants were immediately tested on lists 1 and 2 after study of each list, whereas in the restudy condition, they restudied lists 1 and 2 after initial study. Before and after study of list 3, participants did an arithmetic task. Finally, participants were tested on list 3, list 2, and list 1. Different arithmetic tasks were used in the two experiments. Participants did a modular arithmetic task in Experiment 1a and a single-digit multiplication task in Experiment 1b. The results of both experiments showed a forward testing effect with interim testing of lists 1 and 2 enhancing list 3 recall in the list 3 recall test, but no effects of recall testing of lists 1 and 2 for participants’ performance in the arithmetic tasks. The findings are discussed with respect to cognitive load theory and current theories of the forward testing effect.
Advances in eye tracking technology have enabled the development of interactive experimental setups to study social attention. Since these setups differ substantially from the eye tracker manufacturer’s test conditions, validation is essential with regard to the quality of gaze data and other factors potentially threatening the validity of this signal. In this study, we evaluated the impact of accuracy and areas of interest (AOIs) size on the classification of simulated gaze (fixation) data. We defined AOIs of different sizes using the Limited-Radius Voronoi-Tessellation (LRVT) method, and simulated gaze data for facial target points with varying accuracy. As hypothesized, we found that accuracy and AOI size had strong effects on gaze classification. In addition, these effects were not independent and differed in falsely classified gaze inside AOIs (Type I errors; false alarms) and falsely classified gaze outside the predefined AOIs (Type II errors; misses). Our results indicate that smaller AOIs generally minimize false classifications as long as accuracy is good enough. For studies with lower accuracy, Type II errors can still be compensated to some extent by using larger AOIs, but at the cost of more probable Type I errors. Proper estimation of accuracy is therefore essential for making informed decisions regarding the size of AOIs in eye tracking research.
The main focus of this work is to study the computational complexity of generalizations of the synchronization problem for deterministic finite automata (DFA). This problem asks for a given DFA, whether there exists a word w that maps each state of the automaton to one state. We call such a word w a synchronizing word. A synchronizing word brings a system from an unknown configuration into a well defined configuration and thereby resets the system.
We generalize this problem in four different ways.
First, we restrict the set of potential synchronizing words to a fixed regular language associated with the synchronization under regular constraint problem.
The motivation here is to control the structure of a synchronizing word so that, for instance, it first brings the system from an operate mode to a reset mode and then finally again into the operate mode.
The next generalization concerns the order of states in which a synchronizing word transitions the automaton. Here, a DFA A and a partial order R is given as input and the question is whether there exists a word that synchronizes A and for which the induced state order is consistent with R. Thereby, we study different ways for a word to induce an order on the state set.
Then, we change our focus from DFAs to push-down automata and generalize the synchronization problem to push-down automata and in the following work, to visibly push-down automata. Here, a synchronizing word still needs to map each state of the automaton to one state but it further needs to fulfill some constraints on the stack. We study three different types of stack constraints where after reading the synchronizing word, the stacks associated to each run in the automaton must be (1) empty, (2) identical, or (3) can be arbitrary.
We observe that the synchronization problem for general push-down automata is undecidable and study restricted sub-classes of push-down automata where the problem becomes decidable. For visibly push-down automata we even obtain efficient algorithms for some settings.
The second part of this work studies the intersection non-emptiness problem for DFAs. This problem is related to the problem of whether a given DFA A can be synchronized into a state q as we can see the set of words synchronizing A into q as the intersection of languages accepted by automata obtained by copying A with different initial states and q as their final state.
For the intersection non-emptiness problem, we first study the complexity of the, in general PSPACE-complete, problem restricted to subclasses of DFAs associated with the two well known Straubing-Thérien and Cohen-Brzozowski dot-depth hierarchies.
Finally, we study the problem whether a given minimal DFA A can be represented as the intersection of a finite set of smaller DFAs such that the language L(A) accepted by A is equal to the intersection of the languages accepted by the smaller DFAs. There, we focus on the subclass of permutation and commutative permutation DFAs and improve known complexity bounds.
Surveys play a major role in studying social and behavioral phenomena that are difficult to
observe. Survey data provide insights into the determinants and consequences of human
behavior and social interactions. Many domains rely on high quality survey data for decision
making and policy implementation including politics, health, business, and the social
sciences. Given a certain research question in a specific context, finding the most appropriate
survey design to ensure data quality and keep fieldwork costs low at the same time is a
difficult task. The aim of examining survey research methodology is to provide the best
evidence to estimate the costs and errors of different survey design options. The goal of this
thesis is to support and optimize the accumulation and sustainable use of evidence in survey
methodology in four steps:
(1) Identifying the gaps in meta-analytic evidence in survey methodology by a systematic
review of the existing evidence along the dimensions of a central framework in the
field
(2) Filling in these gaps with two meta-analyses in the field of survey methodology, one
on response rates in psychological online surveys, the other on panel conditioning
effects for sensitive items
(3) Assessing the robustness and sufficiency of the results of the two meta-analyses
(4) Proposing a publication format for the accumulation and dissemination of metaanalytic
evidence