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Debatten führen nicht immer zu einem Konsens. Selbst die Vorlage von Beweisen bewirkt nicht immer eine Überzeugung der Gegenseite. Dies zeigt sich nicht nur in der Geschichte der Wissenschaften (vgl. Ludwik Fleck, Bruno Latour), sondern auch in der in unterschiedlichen Disziplinen geführten zeitgenössischen Debatte unter dem Label ‚science wars‘ zwischen einem Realismus und Konstruktivismus beziehungsweise Relativismus. Unterschiede in ihren Legitimierungen zeigen systematisch verschiedene Wirklichkeits- und Wahrheitsverständnisse, die sich aus den vom Seinsstandort der Perspektive abhängigen Grundannahmen konstituieren. Über einen wissenssoziologischen Zugriff wird es möglich die (sozio-)strukturlogische Konstitution von Perspektivität zu analysieren, die eine epistemologisch vorstrukturierte Revolvierung untereinander inkommensurabler Beiträge in der Debatte aufdeckt, was als Erklärung für ungelöste Debatten in Wissenschaft, Politik und Alltag überhaupt fungieren kann.
Die vorliegende Arbeit orientiert sich in ihrem Vorgehen an dem von Paul Boghossian veröffentlichten Werk ‚Angst vor der Wahrheit‘ als zeitgenössischen Vertreter eines Neuen Realismus. Hierbei werden zum einen den direkten Bezügen von Boghossian die Aussagen der kritisierten Perspektiven (v.a. Latour und Goodman) gegenübergestellt, als auch zum anderen weitere Spielarten eines Konstruktivismus (kognitionstheoretischer Konstruktivismus nach Maturana und Varela, soziologischer Konstruktivismus nach Berger und Luckmann, Wissenschaftssoziologie am Beispiel von Bloor und Latour, die Systemtheorie von Luhmann sowie postkonstruktivistische Positionen) in den Dimensionen ‚Wissensverständnis‘, ‚Subjektrelevanz‘ und ‚Einstellung zu einer naturalistischen Grundlage‘ vorgestellt. Es wird eine systematische und beidseitige Fehlinterpretation in der Debatte zwischen Realismus und Konstruktivismus sichtbar. Diese wird auf die Seinsgebundenheit von Perspektiven nach dem Verständnis einer mannheimschen Wissenssoziologie zurückgeführt. Anhand einer Rekonstruktion der Erkenntnistheorie des frühen Mannheims (1922: ‚Strukturanalyse der Erkenntnistheorie‘) wird die (sozio-)strukturlogische Konstitution erkenntnistheoretischer Elemente von Grundwissenschaften herausgearbeitet, wodurch denkstilgemäße Objektivierungen (und damit Wahrheitsverständnisse) unterschieden werden können. Diese Unterschiede erklären nicht nur die Inkommensurabilität von heterogenen Perspektiven in Debatten, sondern zeigen auf, dass das Aufeinandertreffen der Debattierenden vorstrukturiert sind. Der Ablauf einer Debatte ist soziostrukturell determiniert. Abschließend wird in der vorliegenden Arbeit diskutiert, inwiefern der verfahrenen Situation einer Debatte entgegengewirkt werden kann und auf welche Weise eine wissenssoziologische Analyse zu einem gegenseitigen Verständnis zwischen debattierenden Parteien beitragen kann.
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.
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.
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.
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.
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.
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.
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.
Anmerkung: Es handelt sich um die 1. Auflage der Dissertation.
2. überarbeitete Auflage siehe:
"https://ubt.opus.hbz-nrw.de/frontdoor/index/index/docId/2166".
Ausgangspunkt der politisch-ikonographischen Untersuchung, in deren Zentrum zwei Staatsporträts König Maximilians II. von Bayern stehen, ist die Beobachtung, dass diese beiden Bildnisse grundsätzlich unterschiedliche Inszenierungsformen wählen. Das erste von Max Hailer gefertigte Werk zeigt Maximilian II. im vollen bayerischen Krönungsornat und greift eine tradierte Darstellungsweise im Staatsporträt auf. Es entstand zwei Jahre nach Maximilians II. Thronbesteigung und damit nach den revolutionären Unruhen der Jahre 1848/49 im Jahr 1850. Das zweite wurde von Joseph Bernhardt 1857 bis 1858 gemalt und im Jahr 1858 zum zehnjährigen Thronjubiläum des Monarchen erstmals präsentiert. Die Inszenierung ändert sich im zweiten Bildnis: Das bayerische Krönungsornat ist der Generalsuniform gewichen, ebenso weitere Details, die sich noch in der ersten Darstellung finden: Draperie und Wappen fehlen, der übliche bayerisch-königliche Thronsessel ist durch einen anderen ersetzt. In den Hintergrund gedrängt ist die Verfassung, immerhin seit 1818 staatliche Rechtsgrundlage des bayerischen Königreichs. Die beiden Staatsporträts Maximilians II. leiten offensichtlich von den Herrscherbildnissen im vollen bayerischen Krönungsornat seines Großvaters Maximilian I. und Vaters Ludwig I. über zu einer solchen in Uniform mit Krönungsmantel wie sie sich bei Napoleon III. und Friedrich Wilhelm IV. finden und wie sie sein Sohn Ludwig II. weiterführte. Es stellt sich somit die Frage, welche Faktoren zu diesem prägnanten Wandel in der Inszenierung Maximilians II. als König von Bayern führten. Die Arbeit geht der These nach, dass beide Darstellungen grundlegend auf eine reaktionäre, gegen die Revolution 1848/49 gerichtete Politik ausgelegt sind, wobei dieser reaktionäre Charakter in Maximilians II. Bildnis von 1858 noch eine Steigerung im Vergleich zu derjenigen von 1850 erfährt. Zudem wandelt sich die innenpolitisch-historische Ausrichtung des ersten Porträts bei der zweiten Darstellung des bayerischen Monarchen in eine außenpolitisch-progressive. Die Legitimation Maximilians II. begründet sich nicht mehr, wie bei ersterem, in der Geschichte und der Herrschaft der Wittelsbacher, sondern in seinen eigenen Errungenschaften und seiner eigenen Herrschaft. Dieser Wechsel der politischen Bildaussage fußt sowohl auf den politischen Veränderungen und Entwicklungen innerhalb und außerhalb Bayerns als auch auf der Entwicklung des Staatsporträts in der Mitte des 19. Jahrhunderts. Nach nur zehn Jahren wird so eine veränderte Botschaft über Maximilians II. Position und Machtanspruch ausgesendet.