Filtern
Erscheinungsjahr
- 2022 (27) (entfernen)
Dokumenttyp
- Dissertation (27) (entfernen)
Schlagworte
- Satellitenfernerkundung (3)
- Algorithmus (2)
- Deutschland (2)
- Englisch (2)
- Meta-Analysis (2)
- Optimierung (2)
- Stress (2)
- Action vs. State Orientation (1)
- Aktienrendite (1)
- Alpen (1)
Institut
- Fachbereich 4 (8)
- Fachbereich 6 (5)
- Fachbereich 1 (4)
- Fachbereich 5 (1)
- Informatik (1)
- Sinologie (1)
Even though in most cases time is a good metric to measure costs of algorithms, there are cases where theoretical worst-case time and experimental running time do not match. Since modern CPUs feature an innate memory hierarchy, the location of data is another factor to consider. When most operations of an algorithm are executed on data which is already in the CPU cache, the running time is significantly faster than algorithms where most operations have to load the data from the memory. The topic of this thesis is a new metric to measure costs of algorithms called memory distance—which can be seen as an abstraction of the just mentioned aspect. We will show that there are simple algorithms which show a discrepancy between measured running time and theoretical time but not between measured time and memory distance. Moreover we will show that in some cases it is sufficient to optimize the input of an algorithm with regard to memory distance (while treating the algorithm as a black box) to improve running times. Further we show the relation between worst-case time, memory distance and space and sketch how to define "the usual" memory distance complexity classes.
The ability to acquire knowledge helps humans to cope with the demands of the environment. Supporting knowledge acquisition processes is among the main goals of education. Empirical research in educational psychology has identified several processes mediated through that prior knowledge affects learning. However, the majority of studies investigated cognitive mechanisms mediating between prior knowledge and learning and neglected that motivational processes might also mediate the influence. In addition, the impact of successful knowledge acquisition on patients’ health has not been comprehensively studied. This dissertation aims at closing knowledge gaps on these topics with the use of three studies. The first study is a meta-analysis that examined motivation as a mediator of individual differences in knowledge before and after learning. The second study investigated in greater detail the extent to which motivation mediated the influence of prior knowledge on knowledge gains in a sample of university students. The third study is a second-order meta-analysis synthesizing the results of previous meta-analyses on the effects of patient education on several health outcomes. The findings of this dissertation show that (a) motivation mediates individual differences in knowledge before and after learning; (b) interest and academic self-concept stabilize individual differences in knowledge more than academic self-efficacy, intrinsic motivation, and extrinsic motivation; (c) test-oriented instruction closes knowledge gaps between students; (d) students’ motivation can be independent of prior knowledge in high aptitude students; (e) knowledge acquisition affects motivational and health-related outcomes; and (f) evidence on prior knowledge and motivation can help develop effective interventions in patient education. The results of the dissertation provide insights into prerequisites, processes, and outcomes of knowledge acquisition. Future research should address covariates of learning and environmental impacts for a better understanding of knowledge acquisition processes.
Issues in Price Measurement
(2022)
This thesis focuses on the issues in price measurement and consists of three chapters. Due to outdated weighting information, a Laspeyres-based consumer price index (CPI) is prone to accumulating upward bias. Therefore, chapter 1 introduces and examines simple and transparent revision approaches that retrospectively address the source of the bias. They provide a consistent long-run time series of the CPI and require no additional information. Furthermore, a coherent decomposition of the bias into the contributions of individual product groups is developed. In a case study, the approaches are applied to a Laspeyres-based CPI. The empirical results confirm the theoretical predictions. The proposed revision approaches are adoptable not only to most national CPIs but also to other price-level measures such as the producer price index or the import and export price indices.
Chapter 2 is dedicated to the measurement of import and export price indices. Such indices are complicated by the impact of exchange rates. These indices are usually also compiled by some Laspeyres type index. Therefore, substitution bias is an issue. The terms of trade (ratio of export and import price index) are therefore also likely to be distorted. The underlying substitution bias accumulates over time. The present article applies a simple and transparent retroactive correction approach that addresses the source of the substitution bias and produces meaningful long-run time series of import and export price levels and, therefore, of the terms of trade. Furthermore, an empirical case study is conducted that demonstrates the efficacy and versatility of the correction approach.
Chapter 3 leaves the field of index revision and studies another issue in price measurement, namely, the economic evaluation of digital products in monetary terms that have zero market prices. This chapter explores different methods of economic valuation and pricing of free digital products and proposes an alternative way to calculate the economic value and a shadow price of free digital products: the Usage Cost Model (UCM). The goal of the chapter is, first of all, to formulate a theoretical framework and incorporate an alternative measure of the value of free digital products. However, an empirical application is also made to show the work of the theoretical model. Some conclusions on applicability are drawn at the end of the chapter.
Der vorliegende Text ist als Mantelpapier im Rahmen einer kumulativen Dissertation an der Universität Trier angenommen worden. Er dient der Zusammenfassung, Reflexion und erweiterten theoretischen Betrachtung der empirischen Einzelbeiträge, die alle einen Einzelaspekt des Gesamtgeschehens „Innovationslabor zur Unterstützung unternehmerischen Lernens und der Entwicklung sozialer Dienstleistungsinnovationen“ behandeln. Dabei wird das Innovationslabor grundsätzlich als Personalentwicklungsmaßnahme aufgefasst. In einem gedanklichen Experiment werden die Ergebnisse auf Organisationen der Erwachsenen- und Weiterbildung übertragen.
Das Besondere dieses Rahmenpapiers ist die Verbindung eines relationalen Raumverständnisses mit der lerntheoretischen Untermauerung des Gegenstandes „Innovationslabor“ aus der Perspektive der Organisationspädagogik und Erwachsenenbildung. Die Ergebnisse zeigen den Lernraum Labor als abseits des Arbeitslebens, als semi-autonom angebundenen Raum, wo Lernprozesse auf unterschiedlichen Ebenen stattfinden und angestoßen werden. Das Labor wird als heterotoper (Lern-)Raum diskutiert. Neu ist auch der Einbezug einer kritischen Perspektive, die bislang im Diskurs um Innovationslabore fehlte: Das Labor wird als prekärer Lernraum charakterisiert. Somit liegt mit dieser Arbeit nun eine grundlegende Ausarbeitung des Labors als Lernraum vor, die zahlreiche weitere Anschlussmöglichkeiten für Forschung ermöglicht.
Hybrid Modelling in general, describes the combination of at least two different methods to solve one specific task. As far as this work is concerned, Hybrid Models describe an approach to combine sophisticated, well-studied mathematical methods with Deep Neural Networks to solve parameter estimation tasks. To combine these two methods, the data structure of artifi- cially generated acceleration data of an approximate vehicle model, the Quarter-Car-Model, is exploited. Acceleration of individual components within a coupled dynamical system, can be described as a second order ordinary differential equation, including velocity and dis- placement of coupled states, scaled by spring - and damping-coefficient of the system. An appropriate numerical integration scheme can then be used to simulate discrete acceleration profiles of the Quarter-Car-Model with a random variation of the parameters of the system. Given explicit knowledge about the data structure, one can then investigate under which con- ditions it is possible to estimate the parameters of the dynamical system for a set of randomly generated data samples. We test, if Neural Networks are capable to solve parameter estima- tion problems in general, or if they can be used to solve several sub-tasks, which support a state-of-the-art parameter estimation method. Hybrid Models are presented for parameter estimation under uncertainties, including for instance measurement noise or incompleteness of measurements, which combine knowledge about the data structure and several Neural Networks for robust parameter estimation within a dynamical system.
For decades, academics and practitioners aim to understand whether and how (economic) events affect firm value. Optimally, these events occur exogenously, i.e. suddenly and unexpectedly, so that an accurate evaluation of the effects on firm value can be conducted. However, recent studies show that even the evaluation of exogenous events is often prone to many challenges that can lead to diverse interpretations, resulting in heated debates. Recently, there have been intense debates in particular on the impact of takeover defenses and of Covid-19 on firm value. The announcements of takeover defenses and the propagation of Covid-19 are exogenous events that occur worldwide and are economically important, but have been insufficiently examined. By answering open research questions, this dissertation aims to provide a greater understanding about the heterogeneous effects that exogenous events such as the announcements of takeover defenses and the propagation of Covid-19 have on firm value. In addition, this dissertation analyzes the influence of certain firm characteristics on the effects of these two exogenous events and identifies influencing factors that explain contradictory results in the existing literature and thus can reconcile different views.
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.
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.
Broadcast media such as television have spread rapidly worldwide in the last century. They provide viewers with access to new information and also represent a source of entertainment that unconsciously exposes them to different social norms and moral values. Although the potential impact of exposure to television content have been studied intensively in economic research in recent years, studies examining the long-term causal effects of media exposure are still rare. Therefore, Chapters 2 to 4 of this thesis contribute to the better understanding of long-term effects of television exposure.
Chapter 2 empirically investigates whether access to reliable environmental information through television can influence individuals' environmental awareness and pro-environmental behavior. Analyzing exogenous variation in Western television reception in the German Democratic Republic shows that access to objective reporting on environmental pollution can enhance concerns regarding pollution and affect the likelihood of being active in environmental interest groups.
Chapter 3 utilizes the same natural experiment and explores the relationship between exposure to foreign mass media content and xenophobia. In contrast to the state television broadcaster in the German Democratic Republic, West German television regularly confronted its viewers with foreign (non-German) broadcasts. By applying multiple measures for xenophobic attitudes, our findings indicate a persistent mitigating impact of foreign media content on xenophobia.
Chapter 4 deals with another unique feature of West German television. In contrast to East German media, Western television programs regularly exposed their audience to unmarried and childless characters. The results suggest that exposure to different gender stereotypes contained in television programs can affect marriage, divorce, and birth rates. However, our findings indicate that mainly women were affected by the exposure to unmarried and childless characters.
Chapter 5 examines the influence of social media marketing on crowd participation in equity crowdfunding. By analyzing 26,883 investment decisions on three German equity crowdfunding platforms, our results show that startups can influence the success of their equity crowdfunding campaign through social media posts on Facebook and Twitter.
In Chapter 6, we incorporate the concept of habit formation into the theoretical literature on trade unions and contribute to a better understanding of how internal habit preferences influence trade union behavior. The results reveal that such internal reference points lead trade unions to raise wages over time, which in turn reduces employment. Conducting a numerical example illustrates that the wage effects and the decline in employment can be substantial.
Die Effekte diverser Hormone auf das Sozialverhalten von Männern und Frauen sind nicht vollständig geklärt, da eine genaue Messung dieser, sowie eine Ableitung kausaler Zusammenhänge, die Forschung seither vor Herausforderungen stellt. Umso wichtiger sind Studien, welche versuchen für konfundierende Aspekte zu kontrollieren und die hormonellen oder endokrinen Effekte auf das Sozialverhalten und die soziale Kognition zu untersuchen. Während Studien bereits Effekte von akutem Stress auf Sozialverhalten zeigten, sind die zugrundeliegenden neurobiologischen Mechanismen nicht vollständig bekannt, da hierfür ein rein pharmakologischer Ansatz von Nöten wäre. Die wenigen Studien, die einen solchen wählten, zeigen konträre Befunde. Bisherige Untersuchungen mit psychosozialen Stressoren lassen jedoch prosoziale Tendenzen nach Stress sowohl für Männer als auch für Frauen vermuten. Darüber hinaus sind auch Untersuchungen zu weiblichen Geschlechtshormonen und ihrem Einfluss auf Sozialverhalten sowie die soziale Kognition bei Frauen besonders herausfordernd durch die hormonellen Schwankungen während des Menstruationszyklus oder auch Veränderungen durch die Einnahme oraler Kontrazeptiva. Studien die sowohl Zyklusphasen als auch die Effekte von oralen Kontrazeptiva untersuchten, deuten aber bereits auf Unterschiede zwischen den verschiedenen Phasen, sowie Frauen mit natürlichem Zyklus und Einnahme oraler Kontrazeptiva hin.
Der theoretische Teil beschreibt die Grundlagen zur Stressreaktion des Menschen und die hormonellen Veränderungen weiblicher Geschlechtshormone. Folgend, soll ein Kapitel zur aktuellen Forschungslage zu Effekten von akutem Stress auf Sozialverhalten und die soziale Kognition einen Überblick über die bisherige Befundlage schaffen. Die erste empirische Studie, welche die Effekte von Hydrocortison auf das Sozialverhalten und die Emotionserkennung untersucht, soll anschließend in diese aktuelle Befundlage eingeordnet werden und zu der weniger erforschten Sparte der pharmakologischen Studien beitragen. Die zweite empirische Studie befasst sich folgend mit den Effekten weiblicher Geschlechtshormone auf Sozialverhalten und Empathie, genauer wie auch Zyklusphasen und orale Kontrazeptiva (über Hormone vermittelt) einen Einfluss bei Frauen nehmen. Abschließend sollen die Effekte von Stresshormonen bei Männern, und modulierende Eigenschaften weiblicher Geschlechtshormone, Zyklusphasen und oraler Kontrazeptiva bei Frauen, jeweils in Hinblick auf Sozialverhalten und die soziale Kognition diskutiert werden.