Refine
Year of publication
 1999 (425)
 1998 (116)
 2000 (96)
 2007 (92)
 2018 (89)
 1996 (88)
 2015 (82)
 1995 (81)
 2016 (80)
 2009 (78)
 2014 (77)
 1997 (76)
 2019 (73)
 1994 (70)
 2005 (68)
 2006 (67)
 2008 (66)
 2001 (64)
 2003 (63)
 2013 (62)
 2012 (61)
 2004 (57)
 2010 (56)
 2002 (54)
 2011 (51)
 2017 (51)
 1993 (42)
 1992 (40)
 1991 (33)
 1990 (11)
 2020 (6)
 1989 (5)
 1987 (4)
 1988 (4)
 1979 (3)
 1984 (3)
 1985 (3)
 1980 (1)
 1981 (1)
Document Type
 Preprint (1034)
 Doctoral Thesis (631)
 Report (399)
 Article (213)
 Conference Proceeding (26)
 Diploma Thesis (22)
 Periodical Part (21)
 Working Paper (12)
 Master's Thesis (11)
 Lecture (7)
Language
 English (2399) (remove)
Keywords
 AGRESY (47)
 PARO (25)
 SKALP (15)
 Visualisierung (13)
 Wavelet (13)
 CaseBased Reasoning (11)
 Inverses Problem (11)
 RODEO (11)
 Mehrskalenanalyse (10)
 finite element method (10)
Faculty / Organisational entity
 Fachbereich Mathematik (943)
 Fachbereich Informatik (656)
 Fachbereich Physik (250)
 Fraunhofer (ITWM) (203)
 Fachbereich Maschinenbau und Verfahrenstechnik (112)
 Fachbereich Elektrotechnik und Informationstechnik (82)
 Fachbereich Chemie (59)
 Fachbereich Biologie (43)
 Fachbereich Sozialwissenschaften (26)
 Fachbereich Wirtschaftswissenschaften (12)
Nowadays a large part of communication is taking place on social media platforms such as Twitter, Facebook, Instagram, or YouTube, where messages often include multimedia contents (e.g., images, GIFs or videos). Since such messages are in digital form, computers can in principle process them in order to make our lives more convenient and help us overcome arising issues. However, these goals require the ability to capture what these messages mean to us, that is, how we interpret them from our own subjective points of view. Thus, the main goal of this dissertation is to advance a machine's ability to interpret social media contents in a more natural, subjective way.
To this end, three research questions are addressed. The first question aims at answering "How to model human interpretation for machine learning?" We describe a way of modeling interpretation which allows for analyzing single or multiple ways of interpretation of both humans and computer models within the same theoretic framework. In a comprehensive survey we collect various possibilities for such a computational analysis. Particularly interesting are machine learning approaches where a single neural network learns multiple ways of interpretation. For example, a neural network can be trained to predict userspecific movie ratings from movie features and user ID, and can then be analyzed to understand how users rate movies. This is a promising direction, as neural networks are capable of learning complex patterns. However, how analysis results depend on network architecture is a largely unexplored topic. For the example of movie ratings, we show that the way of combining information for prediction can affect both prediction performance and what the network learns about the various ways of interpretation (corresponding to users).
Since some applicationspecific details for dealing with human interpretation only become visible when going deeper into particular usecases, the other two research questions of this dissertation are concerned with two selected application domains: Subjective visual interpretation and gang violence prevention. The first application study deals with subjectivity that comes from personal attitudes and aims at answering "How can we predict subjective image interpretation one would expect from the general public on photosharing platforms such as Flickr?" The predictions in this case take the form of subjective concepts or phrases. Our study on gang violence prevention is more communitycentered and considers the question "How can we automatically detect tweets of gang members which could potentially lead to violence?" There, the psychosocial codes aggression, loss and substance use serve as proxy to estimate the subjective implications of online messages.
In these two distinct application domains, we develop novel machine learning models for predicting subjective interpretations of images or tweets with images, respectively. In the process of building these detection tools, we also create three different datasets which we share with the research community. Furthermore, we see that some domains such as Chicago gangs require special care due to high vulnerability of involved users. This motivated us to establish and describe an indepth collaboration between social work researchers and computer scientists. As machine learning is incorporating more and more subjective components and gaining societal impact, we have good reason to believe that similar collaborations between the humanities and computer science will become increasingly necessary to advance the field in an ethical way.
Biological clocks exist across all life forms and serve to coordinate organismal physiology with periodic environmental changes. The underlying mechanism of these clocks is predominantly based on cellular transcriptiontranslation feedback loops in which clock proteins mediate the periodic expression of numerous genes. However, recent studies point to the existence of a conserved timekeeping mechanism independent of cellular transcription and translation, but based on cellular metabolism. These metabolic clocks were concluded based upon the observation of circadian and ultradian oscillations in the level of hyperoxidized peroxiredoxin proteins. Peroxiredoxins are enzymes found almost ubiquitously throughout life. Originally identified as H2O2 scavengers, recent studies show that peroxiredoxins can transfer oxidation to, and thereby regulate, a wide range of cellular proteins. Thus, it is conceivable that peroxiredoxins, using H2O2 as the primary signaling molecule, have the potential to integrate and coordinate much of cellular physiology and behavior with metabolic changes. Nonetheless, it remained unclear if peroxiredoxins are passive reporters of metabolic clock activity or active determinants of cellular timekeeping. Budding yeast possess an ultradian metabolic clock termed the Yeast Metabolic Cycle (YMC). The most obvious feature of the YMC is a high amplitude oscillation in oxygen consumption. Like circadian clocks, the YMC temporally compartmentalizes cellular processes (e.g. metabolism) and coordinates cellular programs such as gene expression and cell division. The YMC also exhibits oscillations in the level of hyperoxidized peroxiredoxin proteins.
In this study, I used the YMC clock model to investigate the role of peroxiredoxins in cellular timekeeping, as well as the coordination of cell division with the metabolic clock. I observed that cytosolic 2Cys peroxiredoxins are essential for robust metabolic clock function. I provide direct evidence for oscillations in cytosolic H2O2 levels, as well as cyclical changes in oxidation state of a peroxiredoxin and a model peroxiredoxin target protein during the YMC. I noted two distinct metabolic states during the YMC: low oxygen consumption (LOC) and high oxygen consumption (HOC). I demonstrate that thioldisulfide oxidation and reduction are necessary for switching between LOC and HOC. Specifically, a thiol reductant promotes switching to HOC, whilst a thiol oxidant prevents switching to HOC, forcing cells to remain in LOC. Transient peroxiredoxin inactivation triggered rapid and premature switching from LOC to HOC. Furthermore, I show that cell division is normally synchronized with the YMC and that deletion of typical 2Cys peroxiredoxins leads to complete uncoupling of cell division from metabolic cycling. Moreover, metabolic oscillations are crucial for regulating cell cycle entry and exit. Intriguingly, switching to HOC is crucial for initiating cell cycle entry whilst switching to LOC is crucial for cell cycle completion and exit. Consequently, forcing cells to remain in HOC by application of a thiol reductant leads to multiple rounds of cell cycle entry despite failure to complete the preceding cell cycle. On the other hand, forcing cells to remain in LOC by treating with a thiol oxidant prevents initiation of cell cycle entry.
In conclusion, I propose that peroxiredoxins – by controlling metabolic cycles, which are in turn crucial for regulating the progression through cell cycle – play a central role in the coordination of cellular metabolism with cell division. This proposition, thus, positions peroxiredoxins as active players in the cellular timekeeping mechanism.
In this thesis we study a variant of the quadrature problem for stochastic differential equations (SDEs), namely the approximation of expectations \(\mathrm{E}(f(X))\), where \(X = (X(t))_{t \in [0,1]}\) is the solution of an SDE and \(f \colon C([0,1],\mathbb{R}^r) \to \mathbb{R}\) is a functional, mapping each realization of \(X\) into the real numbers. The distinctive feature in this work is that we consider randomized (Monte Carlo) algorithms with random bits as their only source of randomness, whereas the algorithms commonly studied in the literature are allowed to sample from the uniform distribution on the unit interval, i.e., they do have access to random numbers from \([0,1]\).
By assumption, all further operations like, e.g., arithmetic operations, evaluations of elementary functions, and oracle calls to evaluate \(f\) are considered within the real number model of computation, i.e., they are carried out exactly.
In the following, we provide a detailed description of the quadrature problem, namely we are interested in the approximation of
\begin{align*}
S(f) = \mathrm{E}(f(X))
\end{align*}
for \(X\) being the \(r\)dimensional solution of an autonomous SDE of the form
\begin{align*}
\mathrm{d}X(t) = a(X(t)) \, \mathrm{d}t + b(X(t)) \, \mathrm{d}W(t), \quad t \in [0,1],
\end{align*}
with deterministic initial value
\begin{align*}
X(0) = x_0 \in \mathbb{R}^r,
\end{align*}
and driven by a \(d\)dimensional standard Brownian motion \(W\). Furthermore, the drift coefficient \(a \colon \mathbb{R}^r \to \mathbb{R}^r\) and the diffusion coefficient \(b \colon \mathbb{R}^r \to \mathbb{R}^{r \times d}\) are assumed to be globally Lipschitz continuous.
For the function classes
\begin{align*}
F_{\infty} = \bigl\{f \colon C([0,1],\mathbb{R}^r) \to \mathbb{R} \colon f(x)  f(y) \leq \xy\_{\sup}\bigr\}
\end{align*}
and
\begin{align*}
F_p = \bigl\{f \colon C([0,1],\mathbb{R}^r) \to \mathbb{R} \colon f(x)  f(y) \leq \xy\_{L_p}\bigr\}, \quad 1 \leq p < \infty.
\end{align*}
we have established the following.
\[\]
\(\textit{Theorem 1.}\)
There exists a random bit multilevel Monte Carlo (MLMC) algorithm \(M\) using
\[
L = L(\varepsilon,F) = \begin{cases}\lceil{\log_2(\varepsilon^{2}}\rceil, &\text{if} \ F = F_p,\\
\lceil{\log_2(\varepsilon^{2} + \log_2(\log_2(\varepsilon^{1}))}\rceil, &\text{if} \ F = F_\infty
\end{cases}
\]
and replication numbers
\[
N_\ell = N_\ell(\varepsilon,F) = \begin{cases}
\lceil{(L+1) \cdot 2^{\ell} \cdot \varepsilon^{2}}\rceil, & \text{if} \ F = F_p,\\
\lceil{(L+1) \cdot 2^{\ell} \cdot \max(\ell,1) \cdot \varepsilon^{2}}\rceil, & \text{if} \ F=f_\infty
\end{cases}
\]
for \(\ell = 0,\ldots,L\), for which exists a positive constant \(c\) such that
\begin{align*}
\mathrm{error}(M,F) = \sup_{f \in F} \bigl(\mathrm{E}(S(f)  M(f))^2\bigr)^{1/2} \leq c \cdot \varepsilon
\end{align*}
and
\begin{align*}
\mathrm{cost}(M,F) = \sup_{f \in F} \mathrm{E}(\mathrm{cost}(M,f)) \leq c \cdot \varepsilon^{2} \cdot \begin{cases}
(\ln(\varepsilon^{1}))^2, &\text{if} \ F=F_p,\\
(\ln(\varepsilon^{1}))^3, &\text{if} \ F=F_\infty
\end{cases}
\end{align*}
for every \(\varepsilon \in {]0,1/2[}\).
\[\]
Hence, in terms of the \(\varepsilon\)complexity
\begin{align*}
\mathrm{comp}(\varepsilon,F) = \inf\bigl\{\mathrm{cost}(M,F) \colon M \ \text{is a random bit MC algorithm}, \mathrm{error}(M,F) \leq \varepsilon\bigr\}
\end{align*}
we have established the upper bound
\begin{align*}
\mathrm{comp}(\varepsilon,F) \leq c \cdot \varepsilon^{2} \cdot \begin{cases}
(\ln(\varepsilon^{1}))^2, &\text{if} \ F=F_p,\\
(\ln(\varepsilon^{1}))^3, &\text{if} \ F=F_\infty
\end{cases}
\end{align*}
for some positive constant \(c\). That is, we have shown the same weak asymptotic upper bound as in the case of random numbers from \([0,1]\). Hence, in this sense, random bits are almost as powerful as random numbers for our computational problem.
Moreover, we present numerical results for a nonanalyzed adaptive random bit MLMC Euler algorithm, in the particular cases of the Brownian motion, the geometric Brownian motion, the OrnsteinUhlenbeck SDE and the CoxIngersollRoss SDE. We also provide a numerical comparison to the corresponding adaptive random number MLMC Euler method.
A key challenge in the analysis of the algorithm in Theorem 1 is the approximation of probability distributions by means of random bits. A problem very closely related to the quantization problem, i.e., the optimal approximation of a given probability measure (on a separable Hilbert space) by means of a probability measure with finite support size.
Though we have shown that the random bit approximation of the standard normal distribution is 'harder' than the corresponding quantization problem (lower weak rate of convergence), we have been able to establish the same weak rate of convergence as for the corresponding quantization problem in the case of the distribution of a Brownian bridge on \(L_2([0,1])\), the distribution of the solution of a scalar SDE on \(L_2([0,1])\), and the distribution of a centered Gaussian random element in a separable Hilbert space.
Activity recognition has continued to be a large field in computer science over the last two decades. Research questions from 15 years ago have led to solutions that today support our daily lives. Specifically, the success of smartphones or more recent developments of other smart devices (e.g., smartwatches) is rooted in applications that leverage on activity analysis and location tracking (fitness applications and maps). Today we can track our physical health and fitness and support our physical needs by merely owning (and using) a smartphone. Still, the quality of our lives does not solely rely on fitness and physical health but also more increasingly on our mental wellbeing. Since we have learned how practical and easy it is to have a lot of functions, including health support on just one device, it would be specifically helpful if we could also use the smartphone to support our mental and cognitive health if need be.
The ultimate goal of this work is to use sensorassisted location and motion analysis to support various aspects of medically valid cognitive assessments.
In this regard, this thesis builds on Hypothesis 3: Sensors in our ubiquitous environment can collect information about our cognitive state, and it is possible to extract that information. In addition, these data can be used to derive complex cognitive states and to predict possible pathological changes in humans. After all, not only is it possible to determine the cognitive state through sensors but also to assist people in difficult situations through these sensors.
Thus, in the first part, this thesis focuses on the detection of mental state and state changes.
The primary purpose is to evaluate possible starting points for sensor systems in order to enable a clinically accurate assessment of mental states. These assessments must work on the condition that a developed system must be able to function within the given limits of a real clinical environment.
Despite the limitations and challenges of reallife deployments, it was possible to develop methods for determining the cognitive state and wellbeing of the residents. The analysis of the location data provides a correct classification of cognitive state with an average accuracy of 70% to 90%.
Methods to determine the state of bipolar patients provide an accuracy of 7080\% for the detection of different cognitive states (total seven classes) using single sensors and 76% for merging data from different sensors. Methods for detecting the occurrence of state changes, a highlight of this work, even achieved a precision and recall of 95%.
The comparison of these results with currently used standard methods in psychiatric care even shows a clear advantage of the sensorbased method. The accuracy of the sensorbased analysis is 60% higher than the accuracy of the currently used methods.
The second part of this thesis introduces methods to support people’s actions in stressful situations on the one hand and analyzes the interaction between people during highpressure activities on the other.
A simple, acceleration based, smartwatch instant feedback application was used to help laypeople to learn to perform CPR (cardiopulmonary resuscitation) in an emergency on the fly.
The evaluation of this application in a study with 43 laypersons showed an instant improvement in the CPR performance of 50%. An investigation of whether training with such an instant feedback device can support improved learning and lead to more permanent effects for gaining skills was able to confirm this theory.
Last but not least, with the main interest shifting from the individual to a group of people at the end of this work, the question: how can we determine the interaction between individuals within a group of people? was answered by developing a methodology to detect unvoiced collaboration in random adhoc groups. An evaluation with data retrieved from video footage provides an accuracy of up to more than 95%, and even with artificially introduced errors rates of 20%, still an accuracy of 70% precision, and 90% recall can be achieved.
All scenarios in this thesis address different practical issues of today’s health care. The methods developed are based on reallife datasets and realworld studies.
Spincrossover and valence tautomeric complexes are of tremendous interest in the field of molecular electronics, electronic storage devices and information processing. Herein, synthesis and characterization of the spincrossover and valence tautomeric cobalt dioxolene complexes are reported. All the synthesized complexes contain N,N'ditertbutyl2,11diaza[3.3](2,6)pyridinophane (LN4tBu2) as ancillary ligands. Only various types of coligands which are different dioxolene ligands, have been used. The mononuclear cobalt dioxolene complexes have been synthesized by using dideprotonated form of the dioxolene ligand 4,5dichlorocatechol (H2DCCat) as coligands, and the cobalt bis(dioxolene) complexes have been synthesized by using dideprotonated form of the 3,3'dihydroxydiphenoquinone(4,4') (H2(SQSQ)) as coligands.
Analytically pure samples of the complexes [Co(LN4tBu2)(DCCat)] (1), [Co(LN4tBu2)(DCCat)](BPh4) (2b), [Co2(LN4tBu2)2(SQSQ)](BPh4)2.4 DMF (3b), [Co2(LN4tBu2)2(CatSQ)](BF4)2.Et2O (3d), have been synthesized and characterized by Xray crystallography, magnetic and electrochemical measurements. The complexes have been investigated by UV/Vis/NIR, IR, and NMR spectroscopic measurements.
The complex [Co(LN4tBu2)(DCCat)] (1) shows temperature invariant highspin cobalt(II) catecholate state. Oneelectron oxidation of 1 has yielded the complex [Co(LN4tBu2)(DCCat)](BPh4) (2b). The solid state properties of 2b are best described by the lowspin cobalt(III) catecholate state, but the solution state properties of the complex 2b are best described by the valence tautomeric transition from the lowspin cobalt(III) catecholate to the lowspin cobalt(II) semiquinonate state.
For the cobalt bis(dioxolene) complexes, it is found that spincrossover for the two cobalt(II) centers is accompanied by the electronic state changes of the coordinated bis(dioxolene) unit from singlet openshell biradicaloid to singlet closedshell quinonoid form in complex 3b. Approaching similar synthetic method to 3b, but performing the metathesis reaction with sodium tetrafluoroborate rather than sodium tetraphenylborate has resulted in the formation of the complex [Co2(LN4tBu2)2(CatSQ)](BF4)2.Et2O (3d). The solid state properties of the complex are best described by the temperature induced valence tautomeric transition for the lowspin cobalt(III) center which is accompanied by the spincrossover process for the cobalt(II) center. Thus, the electronic state of the complex 3d changes from LSCoIIICatSQCoIILS to HSCoII(SQSQ)CSCoIIHS state upon change in temperature.
Temperatureinduced electronic configuration changes of the (SQSQ)CS2 ligands from openshell biradicaloid to closedshell quinonoid configurations are not observed for the nickel, copper and zinc bis(dioxolene) complexes 4a, 5a and 6b, respectively. For these complexes, the metal ions are bridged by (SQSQ)CS2 ligand and the paramagnetic metal ions are very weakly antiferromagnetically coupled.
More than ten years ago, ERANT1 was shown to act as an ATP/ADP antiporter and to exist in the endoplasmic reticulum (ER) of higher plants. Because structurally different transporters generally mediate energy provision to the ER, the physiological function of ERANT1 was not directly evident.
Interestingly, mutant plants lacking ERANT1 exhibit a photorespiratory phenotype. Although many research efforts were undertaken, the possible connection between the transporter and photorespiration also remained elusive. Here, a forward genetic approach was used to decipher the role of ERANT1 in the plant context and its association to photorespiration.
This strategy identified that additional absence of a putative HADtype phosphatase partially restored the photorespiratory phenotype. Localisation studies revealed that the corresponding protein is targeted to the chloroplast. Moreover, biochemical analyses demonstrate that the HADtype phosphatase is specific for pyridoxal phosphate. These observations, together with transcriptional and metabolic data of corresponding single (ERANT1) and double (ERANT1, phosphatase) lossoffunction mutant plants revealed an unexpected connection of ERANT1 to vitamin B6 metabolism.
Finally, a scenario is proposed, which explains how ERANT1 may influence B6 vitamer phosphorylation, by this affects photorespiration and causes several other physiological alterations observed in the corresponding lossoffunction mutant plants.
Destructive diseases of the lung like lung cancer or fibrosis are still often lethal. Also in case of fibrosis in the liver, the only possible cure is transplantation.
In this thesis, we investigate 3D micro computed synchrotron radiation (SR\( \mu \)CT) images of capillary blood vessels in mouse lungs and livers. The specimen show socalled compensatory lung growth as well as different states of pulmonary and hepatic fibrosis.
During compensatory lung growth, after resecting part of the lung, the remaining part compensates for this loss by extending into the empty space. This process is accompanied by an active vessel growing.
In general, the human lung can not compensate for such a loss. Thus, understanding this process in mice is important to improve treatment options in case of diseases like lung cancer.
In case of fibrosis, the formation of scars within the organ's tissue forces the capillary vessels to grow to ensure blood supply.
Thus, the process of fibrosis as well as compensatory lung growth can be accessed by considering the capillary architecture.
As preparation of 2D microscopic images is faster, easier, and cheaper compared to SR\( \mu \)CT images, they currently form the basis of medical investigation. Yet, characteristics like direction and shape of objects can only properly be analyzed using 3D imaging techniques. Hence, analyzing SR\( \mu \)CT data provides valuable additional information.
For the fibrotic specimen, we apply image analysis methods wellknown from material science. We measure the vessel diameter using the granulometry distribution function and describe the intervessel distance by the spherical contact distribution. Moreover, we estimate the directional distribution of the capillary structure. All features turn out to be useful to characterize fibrosis based on the deformation of capillary vessels.
It is already known that the most efficient mechanism of vessel growing forms small torusshaped holes within the capillary structure, socalled intussusceptive pillars. Analyzing their location and number strongly contributes to the characterization of vessel growing. Hence, for all three applications, this is of great interest. This thesis provides the first algorithm to detect intussusceptive pillars in SR\( \mu \)CT images. After segmentation of raw image data, our algorithm works automatically and allows for a quantitative evaluation of a large amount of data.
The analysis of SR\( \mu \)CT data using our pillar algorithm as well as the granulometry, spherical contact distribution, and directional analysis extends the current stateoftheart in medical studies. Although it is not possible to replace certain 3D features by 2D features without losing information, our results could be used to examine 2D features approximating the 3D findings reasonably well.
Function of two redox sensing kinases from the methanogenic archaeon Methanosarcina acetivorans
(2019)
MsmS is a hemebased redox sensor kinase in Methanosarcina acetivorans consisting of alternating PAS and GAF domains connected to a Cterminal kinase domain. In addition to MsmS, M. acetivorans possesses a second kinase, MA0863 with high sequence similarity. Interestingly, MA0863 possesses an amber codon in its second GAF domain, encoding for the amino acid pyrrolysine. Thus far, no function of this residue has been resolved. In order to examine the heme iron coordination in both proteins, an improved method for the production of heme proteins was established using the Escherichia coli strain Nissle 1917. This method enables the complete reconstitution of a recombinant hemoprotein during protein production, thereby resulting in a native heme coordination. Analysis of the fulllength MsmS and MA0863 confirmed a covalently bound heme cofactor, which is connected to one conserved cysteine residue in each protein. In order to identify the coordinating amino acid residues of the heme iron, UV/vis spectra of different variants were measured. These studies revealed His702 in MsmS and the corresponding His666 in MA0863 as the proximal heme ligands. MsmS has previously been described as a hemebased redox sensor. In order to examine whether the same is true for MA0863, redox dependent kinase assays were performed. MA0863 indeed displays redox dependent autophosphorylation activity, which is independent of heme ligands and only observed under oxidizing conditions. Interestingly, autophosphorylation was shown to be independent of the heme cofactor but rather relies on thiol oxidation. Therefore, MA0863 was renamed in RdmS (redox dependent methyltransferaseassociated sensor). In order to identify the phosphorylation site of RdmS, thin layer chromatography was performed identifying a tyrosine as the putative phosphorylation site. This observation is in agreement with the lack of a socalled Hbox in typical histidine kinases. Due to their genomic localization, MsmS and RdmS were postulated to form twocomponent systems (TCS) with vicinal encoded regulator proteins MsrG and MsrF. Therefore, proteinprotein interaction studies using the bacterial adenylate two hybrid system were performed suggesting an interaction of RdmS and MsmS with the three regulators MsrG/F/C. Due to these multiple interactions these signal transduction pathways should rather be considered multicomponent system instead of two component systems.
Ranking lists are an essential methodology to succinctly summarize outstanding items, computed over database tables or crowdsourced in dedicated websites. In this thesis, we propose the usage of automatically generated, entitycentric rankings to discover insights in data. We present PALEO, a framework for data exploration through reverse engineering topk database queries, that is, given a database and a sample topk input list, our approach, aims at determining an SQL query that returns results similar to the provided input when executed over the database. The core problem consist of finding selection predicates that return the given items, determining the correct ranking criteria, and evaluating the most promising candidate queries first. PALEO operates on subset of the base data, uses data samples, histograms, descriptive statistics, and further proposes models that assess the suitability of candidate queries which facilitate limitation of false positives. Furthermore, this thesis presents COMPETE, a novel approach that models and computes dominance over userprovided input entities, given a database of topk rankings. The resulting entities are found superior or inferior with tunable degree of dominance over the input seta very intuitive, yet insightful way to explore pros and cons of entities of interest. Several notions of dominance are defined which differ in computational complexity and strictness of the dominance conceptyet, interdependent through containment relations. COMPETE is able to pick the most promising approach to satisfy a user request at minimal runtime latency, using a probabilistic model that is estimating the result sizes. The individual flavors of dominance are cast into a stack of algorithms over inverted indices and auxiliary structures, enabling pruning techniques to avoid significant data access over large datasets of rankings.
Wine and alcoholic fermentations are complex and fascinating ecosystems. Wine aroma is shaped by the wine’s chemical compositions, in which both microbes and grape constituents play crucial roles. Activities of the microbial community impact the sensory properties of the final product, therefore, the characterisation of microbial diversity is essential in understanding and predicting sensory properties of wine. Characterisation has been challenging with traditional approaches, where microbes are isolated and therefore analyzed outside from their natural environment. This causes a bias in the observed microbial composition structure. In addition, true community interactions cannot be studied using isolates. Furthermore, the multiplex ties between wine chemical and sensory compositions remain evasive due to their multivariate and nonlinear nature. Therefore, the sensorial outcome arising from different microbial communities has remained inconclusive.
In this thesis, microbial diversity during Riesling wine fermentations is investigated with the aim to understand the roles of microbial communities during fermentations and their links to sensory properties. With the advancement of highthroughput tools based ‘omic methods, such as nextgeneration sequencing (NGS) technologies, it is now possible to study microbial communities and their functions without isolation by culturing. This developing field and its potential to wine community is reviewed in Chapter 1. The standardisation of methods remains challenging in the field. DNA extraction is a key step in capturing the microbial diversity in samples for generating NGS data, therefore, DNA extraction methods are evaluated in Chapter 2. In Chapter 3, machine learning is utilized in guiding raw data mining generated by the untargeted GCMS analysis. This step is crucial in order to take full advantages of the large scope of data generated by ‘omic methods. These lay a solid foundation for Chapters 4 and 5 where microbial community structures and their outputs  chemical and sensory compositions are studied by using approaches and tools based on multiple ‘omics methods.
The results of this thesis show first that by using novel statistical approaches, it is possible to extract meaningful information from heterogeneous biological, chemical and sensorial data. Secondly, results suggest that the variation in wine aroma, might be related
to microbial interactions taking place not only inside a single community, but also the
IV
interactions between communities, such as vineyard and winery communities. Therefore, the true sensory expression of terroir might be masked by the interaction between two microbial communities, although more work is needed to uncover this potential relationship. Such potential interaction mechanisms were uncovered between non Saccharomyces yeast and bacteria in this work and unexpected novel bacterial growth was observed during alcohol fermentation. This suggests new layers in understanding of wine fermentations. In the future, multiomic approaches could be applied to identify biological pathways leading to specific wine aroma as well as investigate the effects upon specific winemaking conditions. These results are relevant not just for the wine industry, but also to other industries where complex microbial networks are important. As such, the approaches presented in this thesis might find widely use in the food industry.
Carotenoids are organic lipophilic tetraterpenes ubiquitously present in Nature and found across the three domains of life (Archaea, Bacteria and Eukaryotes). Their structure is characterized by an extensive conjugated doublebond system, which serves as a lightabsorbing chromophore, hence determining its colour, and enables carotenoids to absorb energy from other molecules and to act as antioxidant agents. Humans obtain carotenoids mainly via the consumption of fruits and vegetables, and to a smaller extent from other food sources such as fish and eggs. The concentration of carotenoids in the human plasma and tissues has been positively associated with a lower incidence of several chronic diseases including, cancer, diabetes, macular degeneration and cardiovascular conditions, likely due to their antioxidant properties. However, an important aspect of carotenoids, namely β and αcarotene and βcryptoxanthin, in human health and development, is their potential to be converted by the body into Vitamin A.
Yet, bioavailability of carotenoids is relatively low (< 30%) and dependent, among others, on dietary factors, such as amount and type of dietary lipids and the presence of dietary fibres. One dietary factor that has been found to negatively impact carotenoid bioaccessibility and cellular uptake in vitro is high concentrations of divalent cations during simulated gastrointestinal digestion. Nevertheless, the mechanism of action of divalent cations remains unclear. The goal of this thesis was to better understand how divalent cations act during digestion and modulate carotenoid bioavailability. In vitro trials of simulated gastrointestinal digestion and cellular uptake were run to investigate how varying concentrations of calcium, magnesium and zinc affected the bioaccessibility of both pure carotenoids and carotenoids from food matrices. In order to validate or refute results obtained in vitro, a randomized and double blinded placebo controlled crossover postprandial trial (24 male participants) was carried out, testing the effect of 3 supplementary calcium doses (0 mg, 500 mg and 1000 mg) on the bioavailability of carotenoids from a spinach based meal. In vitro trials showed that addition of the divalent cations significantly decreased the bioaccessibility of both pure carotenoids (P < 0.001) and those from food matrices (P < 0.01). This effect was dependent on the type of mineral and its concentration. Strongest effects were seen for increasing concentrations of calcium followed by magnesium and zinc. The addition of divalent cations also altered the physicochemical properties, i.e. viscosity and surface tension, of the digestas. However, the extent of this effect varied according to the type of matrix. The effects on bioaccessibility and physicochemical properties were accompanied by variations of the zetapotential of the particles in solution. Taken together, results from the in vitro trials strongly suggested that divalent cations were able to bind bile salts and other surfactant agents, affecting their solubility. The observed i) decrease in macroviscosity, ii) increase in surface tension, and the iii) reduction of the zetapotential of the digesta, confirmed the removal of surfactant agents from the system, most likely due to precipitation as a result of the lower solubility of the mineralsurfactant complexes. As such, micellarization of carotenoids was hindered, explaining their reduced bioaccessibility. As for the human trial, results showed that there was no significant influence of supplementation with either 500 or 1000 mg of supplemental calcium (in form of carbonate) on the bioavailability of a spinach based meal, as measured by the areaunder curve of carotenoid concentrations in the plasmatriacylglycerol rich fraction, suggesting that the in vitro results are not supported in such an in vivo scenario, which may be explained by the initial low bioaccessibility of spinach carotenoids and the dissolution kinetics of the calcium pills. Further investigations are necessary to understand how divalent cations act during in vivo digestion and potentially interact with lipophilic nutrients and food constituents.
Indentation into a metastable austenite may induce the phase transformation to the bcc phase. We study this process using
atomistic simulation. At temperatures low compared to the equilibrium transformation temperature, the indentation triggers the
transformation of the entire crystallite: after starting the transformation, it rapidly proceeds throughout the simulation crystallite.
The microstructure of the transformed sample is characterized by twinned grains. At higher temperatures, around the equilibrium
transformation temperature, the crystal transforms only locally, in the vicinity of the indent pit. In addition, the indenter
produces dislocation plasticity in the remaining austenite. At intermediate temperatures, the crystal continuously transforms
throughout the indentation process.
Cell migration is essential for embryogenesis, wound healing, immune surveillance, and
progression of diseases, such as cancer metastasis. For the migration to occur, cellular
structures such as actomyosin cables and cellsubstrate adhesion clusters must interact.
As cell trajectories exhibit a random character, so must such interactions. Furthermore,
migration often occurs in a crowded environment, where the collision outcome is deter
mined by altered regulation of the aforementioned structures. In this work, guided by a
few fundamental attributes of cell motility, we construct a minimal stochastic cell migration
model from groundup. The resulting model couples a deterministic actomyosin contrac
tility mechanism with stochastic cellsubstrate adhesion kinetics, and yields a welldefined
piecewise deterministic process. The signaling pathways regulating the contractility and
adhesion are considered as well. The model is extended to include cell collectives. Numer
ical simulations of single cell migration reproduce several experimentally observed results,
including anomalous diffusion, tactic migration, and contact guidance. The simulations
of colliding cells explain the observed outcomes in terms of contact induced modification
of contractility and adhesion dynamics. These explained outcomes include modulation
of collision response and group behavior in the presence of an external signal, as well as
invasive and dispersive migration. Moreover, from the single cell model we deduce a pop
ulation scale formulation for the migration of noninteracting cells. In this formulation,
the relationships concerning actomyosin contractility and adhesion clusters are maintained.
Thus, we construct a multiscale description of cell migration, whereby single, collective,
and population scale formulations are deduced from the relationships on the subcellular
level in a mathematically consistent way.
Spatial regression models provide the opportunity to analyse spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarises the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In contrast to previous simulations, this study evaluates the bias of the impacts rather than the regression coefficients and additionally provides results for situations with a nonspatial omitted variable bias. Results reveal that the most commonly used spatial autoregressive (SAR) and spatial error (SEM) specifications yield severe drawbacks. In contrast, spatial Durbin specifications (SDM and SDEM) as well as the simple SLX provide accurate estimates of direct impacts even in the case of misspecification. Regarding the indirect `spillover' effects, several  quite realistic  situations exist in which the SLX outperforms the more complex SDM and SDEM specifications.
On the Effect of Nanofillers on the Environmental Stress Cracking Resistance of Glassy Polymers
(2019)
It is well known that reinforcing polymers with small amounts of nanosized fillers is one of the most effective methods for simultaneously improving their mechanical and thermal properties. However, only a small number of studies have focused on environmental stress cracking (ESC), which is a major issue for premature failures of plastic products in service. Therefore, the contribution of this work focused on the influence of nanoSiO2 particles on the morphological, optical, mechanical, thermal, as well as environmental stress cracking properties of amorphousbased nanocomposites.
Polycarbonate (PC), polystyrene (PS) and poly(methyl methacrylate) (PMMA) nanocomposites containing different amounts and sizes of nanoSiO2 particles were prepared using a twinscrew extruder followed by injection molding. Adding a small amount of nanoSiO2 caused a reduction in optical properties but improved the tensile, toughness, and thermal properties of the polymer nanocomposites. The significant enhancement in mechanical and thermal properties was attributed to the adequate level of dispersion and interfacial interaction of the SiO2 nanoparticles in the polymer matrix. This situation possibly increased the efficiency of stress transfer across the nanocomposite components. Moreover, the data revealed a clear dependency on the filler size. The polymer nanocomposites filled with smaller nanofillers exhibited an outstanding enhancement in both mechanical properties and transparency compared with nanocomposites filled with larger particles. The best compromise of strength, toughness, and thermal properties was achieved in PCbased nanocomposites. Therefore, special attention to the influence of nanofiller on the ESC resistance was given to PC.
The ESC resistance of the materials was investigated under static loading with and without the presence of stresscracking agents. Interestingly, the incorporation of nanoSiO2 greatly enhanced the ESC resistance of PC in all investigated fluids. This result was particularly evident with the smaller quantities and sizes of nanoSiO2. The enhancement in ESC resistance was more effective in mild agents and air, where the quality of the deformation process was vastly altered with the presence of nanoSiO2. This finding confirmed that the new structural arrangements on the molecular scale induced by nanoparticles dominate over the ESC agent absorption effect and result in greatly improving the ESC resistance of the materials. This effect was more pronounced with increasing molecular weight of PC due to an increase in craze stability and fibril density. The most important and new finding is that the ESC behavior of polymerbased nanocomposites/ stresscracking agent combinations can be scaled using the Hansen solubility parameter. Thus allowed us to predict the risk of ESC as a function of the filler content for different stresscracking agents without performing extensive tests. For a comparison of different amorphous polymerbased nanocomposites at a given nanoSiO2 particle content, the ESC resistance of materials improved in the following order: PMMA/SiO2 < PS/SiO2 < low molecular weight PC/SiO2 < high molecular weight PC/SiO2. In most cases, nanocomposites with 1 vol.% of nanoSiO2 particles exhibited the largest improvement in ESC resistance.
However, the remarkable improvement in the ESC resistance—particularly in PCbased nanocomposites—created some challenges related to material characterization because testing times (failure time) significantly increased. Accordingly, the superposition approach has been applied to construct a master curve of crack propagation model from the available shortterm tests at different temperatures. Good agreement of the master curves with the experimental data revealed that the superposition approach is a suitable comparative method for predicting slow crack growth behavior, particularly for longduration cracking tests as in mild agents. This methodology made it possible to minimize testing time.
Additionally, modeling and simulations using the finite element method revealed that multifield modeling could provide reasonable predictions for diffusion processes and their impact on fracture behavior in different stress cracking agents. This finding suggests that the implemented model may be a useful tool for quick screening and mitigating the risk of ESC failures in plastic products.
Most modern multiprocessors offer weak memory behavior to improve their performance in terms of throughput. They allow the order of memory operations to be observed differently by each processor. This is opposite to the concept of sequential consistency (SC) which enforces a unique sequential view on all operations for all processors. Because most software has been and still is developed with SC in mind, we face a gap between the expected behavior and the actual behavior on modern architectures. The issues described only affect multithreaded software and therefore most programmers might never face them. However, multithreaded bare metal software like operating systems, embedded software, and realtime software have to consider memory consistency and ensure that the order of memory operations does not yield unexpected results. This software is more critical as general consumer software in terms of consequences, and therefore new methods are needed to ensure their correct behavior.
In general, a memory system is considered weak if it allows behavior that is not possible in a sequential system. For example, in the SPARC processor with total store ordering (TSO) consistency, all writes might be delayed by store buffers before they eventually are processed by the main memory. This allows the issuing process to work with its own written values before other processes observed them (i.e., reading its own value before it leaves the store buffer). Because this behavior is not possible with sequential consistency, TSO is considered to be weaker than SC. Programming in the context of weak memory architectures requires a proper comprehension of how the model deviates from expected sequential behavior. For verification of these programs formal representations are required that cover the weak behavior in order to utilize formal verification tools.
This thesis explores different verification approaches and respectively fitting representations of a multitude of memory models. In a joint effort, we started with the concept of testing memory operation traces in regard of their consistency with different memory consistency models. A memory operation trace is directly derived from a program trace and consists of a sequence of read and write operations for each process. Analyzing the testing problem, we are able to prove that the problem is NPcomplete for most memory models. In that process, a satisfiability (SAT) encoding for given problem instances was developed, that can be used in reachability and robustness analysis.
In order to cover all program executions instead of just a single program trace, additional representations are introduced and explored throughout this thesis. One of the representations introduced is a novel approach to specify a weak memory system using temporal logics. A set of linear temporal logic (LTL) formulas is developed that describes all properties required to restrict possible traces to those consistent to the given memory model. The resulting LTL specifications can directly be used in model checking, e.g., to check safety conditions. Unfortunately, the derived LTL specifications suffer from the state explosion problem: Even small examples, like the Peterson mutual exclusion algorithm, tend to generate huge formulas and require vast amounts of memory for verification. For this reason, it is concluded that using the proposed verification approach these specifications are not well suited for verification of real world software. Nonetheless, they provide comprehensive and formally correct descriptions that might be used elsewhere, e.g., programming or teaching.
Another approach to represent these models are operational semantics. In this thesis, operational semantics of weak memory models are provided in the form of reference machines that are both correct and complete regarding the memory model specification. Operational semantics allow to simulate systems with weak memory models step by step. This provides an elegant way to study the effects that lead to weak consistent behavior, while still providing a basis for formal verification. The operational models are then incorporated in verification tools for multithreaded software. These state space exploration tools proved suitable for verification of multithreaded software in a weak consistent memory environment. However, because not only the memory system but also the processor are expressed as operational semantics, some verification approach will not be feasible due to the large size of the state space.
Finally, to tackle the beforementioned issue, a state transition system for parallel programs is proposed. The transition system is defined by a set of structural operational semantics (SOS) rules and a suitable memory structure that can cover multiple memory models. This allows to influence the state space by use of smart representations and approximation approaches in future work.
Economics of Downside Risk
(2019)
Ever since establishment of portfolio selection theory by Markowitz (1952), the use of Standard deviation as a measure of risk has heavily been criticized. The aim of this thesis is to refine classical portfolio selection and asset pricing theory by using a downside deviation risk measure. It is defined as belowtarget semideviation and referred to as downside risk.
Downside efficient portfolios maximize expected payoff given a prescribed upper bound for downside risk and, thus, are analogs to meanvariance efficient portfolios in the sense of Markowitz. The present thesis provides an alternative proof of existence of downside efficient portfolios and identifies a sufficient criterion for their uniqueness. A specific representation of their form brings structural similarity to meanvariance efficient portfolios to light. Eventually, a separation theorem for the existence and uniqueness of portfolios that maximize the tradeoff between downside risk and return is established.
The notion of a downside risk asset market equilibrium (DRAME) in an asset market with finitely many investors is introduced. This thesis addresses the existence and uniqueness Problem of such equilibria and specifies a DRAME pricing formula. In contrast to prices obtained from the meanvariance CAPM pricing formula, DRAME prices are arbitragefree and strictly positive.
The final part of this thesis addresses practical issues. An algorithm that allows for an effective computation of downside efficient portfolios from simulated or historical financial data is outlined. In a simulation study, it is revealed in which scenarios downside efficient portfolios
outperform meanvariance efficient portfolios.
The simulation of physical phenomena involving the dynamic behavior of fluids and gases
has numerous applications in various fields of science and engineering. Of particular interest
is the material transport behavior, the tendency of a flow field to displace parts of the
medium. Therefore, many visualization techniques rely on particle trajectories.
Lagrangian Flow Field Representation. In typical Eulerian settings, trajectories are
computed from the simulation output using numerical integration schemes. Accuracy concerns
arise because, due to limitations of storage space and bandwidth, often only a fraction
of the computed simulation time steps are available. Prior work has shown empirically that
a Lagrangian, trajectorybased representation can improve accuracy [Agr+14]. Determining
the parameters of such a representation in advance is difficult; a relationship between the
temporal and spatial resolution and the accuracy of resulting trajectories needs to be established.
We provide an error measure for upper bounds of the error of individual trajectories.
We show how areas at risk for high errors can be identified, thereby making it possible to
prioritize areas in time and space to allocate scarce storage resources.
Comparative Visual Analysis of Flow Field Ensembles. Independent of the representation,
errors of the simulation itself are often caused by inaccurate initial conditions,
limitations of the chosen simulation model, and numerical errors. To gain a better understanding
of the possible outcomes, multiple simulation runs can be calculated, resulting in
sets of simulation output referred to as ensembles. Of particular interest when studying the
material transport behavior of ensembles is the identification of areas where the simulation
runs agree or disagree. We introduce and evaluate an interactive method that enables application
scientists to reliably identify and examine regions of agreement and disagreement,
while taking into account the local transport behavior within individual simulation runs.
ParticleBased Representation and Visualization of Uncertain Flow Data Sets. Unlike
simulation ensembles, where uncertainty of the solution appears in the form of different
simulation runs, momentbased Eulerian multiphase fluid simulations are probabilistic in
nature. These simulations, used in process engineering to simulate the behavior of bubbles in
liquid media, are aimed toward reducing the need for realworld experiments. The locations
of individual bubbles are not modeled explicitly, but stochastically through the properties of
locally defined bubble populations. Comparisons between simulation results and physical
experiments are difficult. We describe and analyze an approach that generates representative
sets of bubbles for momentbased simulation data. Using our approach, application scientists
can directly, visually compare simulation results and physical experiments.
The usage of sensors in modern technical systems and consumer products is in a rapid increase. This advancement can be characterized by two major factors, namely, the mass introduction of consumer oriented sensing devices to the market and the sheer amount of sensor data being generated. These characteristics raise subsequent challenges regarding both the consumer sensing devices' reliability and the management and utilization of the generated sensor data. This thesis addresses these challenges through two main contributions. It presents a novel framework that leverages sentiment analysis techniques in order to assess the quality of consumer sensing devices. It also couples semantic technologies with big data technologies to present a new optimized approach for realization and management of semantic sensor data, hence providing a robust means of integration, analysis, and reuse of the generated data. The thesis also presents several applications that show the potential of the contributions in reallife scenarios.
Due to the broad range, growing feature set and fast release pace of new sensorbased products, evaluating these products is very challenging as standard product testing is not practical. As an alternative, an endtoend aspectbased sentiment summarizer pipeline for evaluation of consumer sensing devices is presented. The pipeline uses product reviews to extract the sentiment at the aspect level and includes several components namely, product name extractor, aspects extractor and a lexiconbased sentiment extractor which handles multiple sentiment analysis challenges such as sentiment shifters, negations, and comparative sentences among others. The proposed summarizer's components generally outperform the stateoftheart approaches. As a use case, features of the market leading fitness trackers are evaluated and a dynamic visual summarizer is presented to display the evaluation results and to provide personalized product recommendations for potential customers.
The increased usage of sensing devices in the consumer market is accompanied with increased deployment of sensors in various other fields such as industry, agriculture, and energy production systems. This necessitates using efficient and scalable methods for storing and processing of sensor data. Coupling big data technologies with semantic techniques not only helps to achieve the desired storage and processing goals, but also facilitates data integration, data analysis, and the utilization of data in unforeseen future applications through preserving the data generation context. This thesis proposes an efficient and scalable solution for semantification, storage and processing of raw sensor data through ontological modelling of sensor data and a novel encoding scheme that harnesses the split between the statements of the conceptual model of an ontology (TBox) and the individual facts (ABox) along with inmemory processing capabilities of modern big data systems. A sample use case is further introduced where a smartphone is deployed in a transportation bus to collect various sensor data which is then utilized in detecting street anomalies.
In addition to the aforementioned contributions, and to highlight the potential use cases of sensor data publicly available, a recommender system is developed using running route data, used for proximitybased retrieval, to provide personalized suggestions for new routes considering the runner's performance, visual and nature of route preferences.
This thesis aims at enhancing the integration of sensing devices in daily life applications through facilitating the public acquisition of consumer sensing devices. It also aims at achieving better integration and processing of sensor data in order to enable new potential usage scenarios of the raw generated data.
The systems in industrial automation management (IAM) are information systems. The management parts of such systems are software components that support the manufacturing processes. The operational parts control highly plugcompatible devices, such as controllers, sensors and motors. Process variability and topology variability are the two main characteristics of software families in this domain. Furthermore, three roles of stakeholders  requirement engineers, hardwareoriented engineers, and software developers  participate in different derivation stages and have different variability concerns. In current practice, the development and reuse of such systems is costly and timeconsuming, due to the complexity of topology and process variability. To overcome these challenges, the goal of this thesis is to develop an approach to improve the software product derivation process for systems in industrial automation management, where different variability types are concerned in different derivation stages. Current stateoftheart approaches commonly use generalpurpose variability modeling languages to represent variability, which is not sufficient for IAM systems. The process and topology variability requires more usercentered modeling and representation. The insufficiency of variability modeling leads to low efficiency during the staged derivation process involving different stakeholders. Up to now, product line approaches for systematic variability modeling and realization have not been well established for such complex domains. The modelbased derivation approach presented in this thesis integrates feature modeling with domainspecific models for expressing processes and topology. The multivariability modeling framework includes the metamodels of the three variability types and their associations. The realization and implementation of the multivariability involves the mapping and the tracing of variants to their corresponding software product line assets. Based on the foundation of multivariability modeling and realization, a derivation infrastructure is developed, which enables a semiautomated software derivation approach. It supports the configuration of different variability types to be integrated into the staged derivation process of the involved stakeholders. The derivation approach is evaluated in an industrygrade case study of a complex software system. The feasibility is demonstrated by applying the approach in the case study. By using the approach, both the size of the reusable core assets and the automation level of derivation are significantly improved. Furthermore, semistructured interviews with engineers in practice have evaluated the usefulness and easeofuse of the proposed approach. The results show a positive attitude towards applying the approach in practice, and high potential to generalize it to other related domains.
Novel image processing techniques have been in development for decades, but most
of these techniques are barely used in real world applications. This results in a gap
between image processing research and realworld applications; this thesis aims to
close this gap. In an initial study, the quantification, propagation, and communication
of uncertainty were determined to be key features in gaining acceptance for
new image processing techniques in applications.
This thesis presents a holistic approach based on a novel image processing pipeline,
capable of quantifying, propagating, and communicating image uncertainty. This
work provides an improved image data transformation paradigm, extending image
data using a flexible, highdimensional uncertainty model. Based on this, a completely
redesigned image processing pipeline is presented. In this pipeline, each
step respects and preserves the underlying image uncertainty, allowing image uncertainty
quantification, image preprocessing, image segmentation, and geometry
extraction. This is communicated by utilizing meaningful visualization methodologies
throughout each computational step.
The presented methods are examined qualitatively by comparing to the Stateof
theArt, in addition to user evaluation in different domains. To show the applicability
of the presented approach to real world scenarios, this thesis demonstrates
domainspecific problems and the successful implementation of the presented techniques
in these domains.
The focus of this work is to provide and evaluate a novel method for multifield topologybased analysis and visualization. Through this concept, called Pareto sets, one is capable to identify critical regions in a multifield with arbitrary many individual fields. It uses ideas found in graph optimization to find common behavior and areas of divergence between multiple optimization objectives. The connections between the latter areas can be reduced into a graph structure allowing for an abstract visualization of the multifield to support data exploration and understanding.
The research question that is answered in this dissertation is about the general capability and expandability of the Pareto set concept in context of visualization and application. Furthermore, the study of its relations, drawbacks and advantages towards other topologicalbased approaches. This questions is answered in several steps, including consideration and comparison with related work, a thorough introduction of the Pareto set itself as well as a framework for efficient implementation and an attached discussion regarding limitations of the concept and their implications for run time, suitable data, and possible improvements.
Furthermore, this work considers possible simplification approaches like integrated singlefield simplification methods but also using common structures identified through the Pareto set concept to smooth all individual fields at once. These considerations are especially important for realworld scenarios to visualize highly complex data by removing small local structures without destroying information about larger, global trends.
To further emphasize possible improvements and expandability of the Pareto set concept, the thesis studies a variety of different real world applications. For each scenario, this work shows how the definition and visualization of the Pareto set is used and improved for data exploration and analysis based on the scenarios.
In summary, this dissertation provides a complete and sound summary of the Pareto set concept as ground work for future application of multifield data analysis. The possible scenarios include those presented in the application section, but are found in a wide range of research and industrial areas relying on uncertainty analysis, timevarying data, and ensembles of data sets in general.
In this thesis, we consider the problem of processing similarity queries over a dataset of topk rankings and class constrained objects. Topk rankings are the most natural and widely used technique to compress a large amount of information into a concise form. Spearman’s Footrule distance is used to compute the similarity between rankings, considering how well rankings agree on the positions (ranks) of ranked items. This setup allows the application of metric distancebased pruning strategies, and, alternatively, enables the use of traditional inverted indices for retrieving rankings that overlap in items. Although both techniques can be individually applied, we hypothesize that blending these two would lead to better performance. First, we formulate theoretical bounds over the rankings, based on Spearman's Footrule distance, which are essential for adapting existing, inverted index based techniques to the setting of topk rankings. Further, we propose a hybrid indexing strategy, designed for efficiently processing similarity range queries, which incorporates inverted indices and metric space indices, such as M or BKtrees, resulting in a structure that resembles both indexing methods with tunable emphasis on one or the other. Moreover, optimizations to the inverted index component are presented, for early termination and minimizing bookkeeping. As vast amounts of data are being generated on a daily bases, we further present a distributed, highly tunable, approach, implemented in Apache Spark, for efficiently processing similarity join queries over topk rankings. To combine distancebased filtering with inverted indices, the algorithm works in several phases. The partial results are joined for the computation of the final result set. As the last contribution of the thesis, we consider processing knearestneighbor (kNN) queries over classconstrained objects, with the additional requirement that the result objects are of a specific type. We introduce the MISP index, which first indexes the objects by their (combination of) class belonging, followed by a similarity search sub index for each subset of objects. The number of such subsets can combinatorially explode, thus, we provide a cost model that analyzes the performance of the MISP index structure under different configurations, with the aim of finding the most efficient one for the dataset being searched.
Previously in this journal we have reported on fundamental transversemode selection (TMS#0) of broad area semiconductor lasers
(BALs) with integrated twiceretracted 4f setup and filmwaveguide lens as the Fouriertransform element. Now we choose and
report on a simpler approach for BALTMS#0, i.e., the use of a stable confocal longitudinal BAL resonator of length L with a
transverse constriction.The absolute value of the radius R of curvature of both mirrorfacets convex in one dimension (1D) is R = L
= 2f with focal length f.The round trip length 2L = 4f againmakes up for a Fourieroptical 4f setup and the constriction resulting
in a resonatorinternal beam waist stands for a Fourieroptical lowpass spatial frequency filter. Good TMS#0 is achieved, as long
as the constriction is tight enough, but filamentation is not completely suppressed.
1. Introduction
Broad area (semiconductor diode) lasers (BALs) are intended
to emit high optical output powers (where “high” is relative
and depending on the material system). As compared to
conventional narrow stripe lasers, the higher power is distributed
over a larger transverse crosssection, thus avoiding
catastrophic optical mirror damage (COMD). Typical BALs
have emitter widths of around 100 ????m.
Thedrawback is the distribution of the high output power
over a large number of transverse modes (in cases without
countermeasures) limiting the portion of the light power in
the fundamental transverse mode (mode #0), which ought to
be maximized for the sake of good light focusability.
Thus techniques have to be used to support, prefer, or
select the fundamental transverse mode (transverse mode
selection TMS#0) by suppression of higher order modes
already upon buildup of the laser oscillation.
In many cases reported in the literature, either a BAL
facet, the
A measurement technique, i.e. reflectance anisotropy/difference spectroscopy (RAS/RDS), which had originally been developed for insitu
epitaxial growth control, is employed here for insitu realtime etchdepth control during reactive ion etching (RIE) of cubic crystalline III/V
semiconductor samples. Temporal optical FabryPerot oscillations of the genuine RAS signal (or of the average reflectivity) during etching due
to the ever shrinking layer thicknesses are used to monitor the current etch depth. This way the achievable insitu etchdepth resolution has
been around 15 nm. To improve etchdepth control even further, i.e. down to below 5 nm, we now use the optical equivalent of a mechanical
vernier scale– by employing FabryPerot oscillations at two different wavelengths or photon energies of the RAS measurement light – 5%
apart, which gives a vernier scale resolution of 5%. For the AlGaAs(Sb) material system a 5 nm resolution is an improvement by a factor of 3
and amounts to a precision in insitu etchdepth control of around 8 lattice constants.
In this thesis we consider the directional analysis of stationary point processes. We focus on three nonparametric methods based on second order analysis which we have defined as Integral method, Ellipsoid method, and Projection method. We present the methods in a general setting and then focus on their application in the 2D and 3D case of a particular type of anisotropy mechanism called geometric anisotropy. We mainly consider regular point patterns motivated by our application to real 3D data coming from glaciology. Note that directional analysis of 3D data is not so prominent in the literature.
We compare the performance of the methods, which depends on the relative parameters, in a simulation study both in 2D and 3D. Based on the results we give recommendations on how to choose the methods´ parameters in practice.
We apply the directional analysis to the 3D data coming from glaciology, which consist in the locations of airbubbles in polar ice cores. The aim of this study is to provide information about the deformation rate in the ice and the corresponding thinning of ice layers at different depths. This information is substantial for the glaciologists in order to build ice dating models and consequently to give a correct interpretation of the climate information which can be found by analyzing ice cores. In this thesis we consider data coming from three different ice cores: the Talos Dome core, the EDML core and the Renland core.
Motivated by the ice application, we study how isotropic and stationary noise influences the directional analysis. In fact, due to the relaxation of the ice after drilling, noise bubbles can form within the ice samples. In this context we take two classification algorithms into consideration, which aim to classify points in a superposition of a regular isotropic and stationary point process with Poisson noise.
We introduce two methods to visualize anisotropy, which are particularly useful in 3D and apply them to the ice data. Finally, we consider the problem of testing anisotropy and the limiting behavior of the geometric anisotropy transform.
This thesis addresses several challenges for sustainable logistics operations and investigates (1) the integration of intermediate stops in the route planning of transportation vehicles, which especially becomes relevant when alternativefuel vehicles with limited driving range or a sparse refueling infrastructure are considered, (2) the combined planning of the battery replacement infrastructure and of the routing for battery electric vehicles, (3) the use of mobile load replenishment or refueling possibilities in environments where the respective infrastructure is not available, and (4) the additional consideration of the flow of goods from the end user in backward direction to the point of origin for the purpose of, e.g., recapturing value or proper disposal. We utilize models and solution methods from the domain of operations research to gain insights into the investigated problems and thus to support managerial decisions with respect to these issues.
Magnetoelastic coupling describes the mutual dependence of the elastic and magnetic fields and can be observed in certain types of materials, among which are the socalled "magnetostrictive materials". They belong to the large class of "smart materials", which change their shape, dimensions or material properties under the influence of an external field. The mechanical strain or deformation a material experiences due to an externally applied magnetic field is referred to as magnetostriction; the reciprocal effect, i.e. the change of the magnetization of a body subjected to mechanical stress is called inverse magnetostriction. The coupling of mechanical and electromagnetic fields is particularly observed in "giant magnetostrictive materials", alloys of ferromagnetic materials that can exhibit several thousand times greater magnitudes of magnetostriction (measured as the ratio of the change in length of the material to its original length) than the common magnetostrictive materials. These materials have wide applications areas: They are used as variablestiffness devices, as sensors and actuators in mechanical systems or as artificial muscles. Possible application fields also include robotics, vibration control, hydraulics and sonar systems.
Although the computational treatment of coupled problems has seen great advances over the last decade, the underlying problem structure is often not fully understood nor taken into account when using black box simulation codes. A thorough analysis of the properties of coupled systems is thus an important task.
The thesis focuses on the mathematical modeling and analysis of the coupling effects in magnetostrictive materials. Under the assumption of linear and reversible material behavior with no magnetic hysteresis effects, a coupled magnetoelastic problem is set up using two different approaches: the magnetic scalar potential and vector potential formulations. On the basis of a minimum energy principle, a system of partial differential equations is derived and analyzed for both approaches. While the scalar potential model involves only stationary elastic and magnetic fields, the model using the magnetic vector potential accounts for different settings such as the eddy current approximation or the full Maxwell system in the frequency domain.
The distinctive feature of this work is the analysis of the obtained coupled magnetoelastic problems with regard to their structure, strong and weak formulations, the corresponding function spaces and the existence and uniqueness of the solutions. We show that the model based on the magnetic scalar potential constitutes a coupled saddle point problem with a penalty term. The main focus in proving the unique solvability of this problem lies on the verification of an infsup condition in the continuous and discrete cases. Furthermore, we discuss the impact of the reformulation of the coupled constitutive equations on the structure of the coupled problem and show that in contrast to the scalar potential approach, the vector potential formulation yields a symmetric system of PDEs. The dependence of the problem structure on the chosen formulation of the constitutive equations arises from the distinction of the energy and coenergy terms in the Lagrangian of the system. While certain combinations of the elastic and magnetic variables lead to a coupled magnetoelastic energy function yielding a symmetric problem, the use of their dual variables results in a coupled coenergy function for which a mixed problem is obtained.
The presented models are supplemented with numerical simulations carried out with MATLAB for different examples including a 1D EulerBernoulli beam under magnetic influence and a 2D magnetostrictive plate in the state of plane stress. The simulations are based on material data of TerfenolD, a giant magnetostrictive materials used in many industrial applications.
A distributional solution framework is developed for systems consisting of linear hyperbolic partial differential equations (PDEs) and switched differential algebraic equations (DAEs) which are coupled via boundary conditions. The unique solvability is then characterize in terms of a switched delay DAE. The theory is illustrated with an example of electric power lines modeled by the telegraph equations which are coupled via a switching transformer where simulations confirm the predicted impulsive solutions.
Graphs and flow networks are important mathematical concepts that enable the modeling and analysis of a large variety of real world problems in different domains such as engineering, medicine or computer science. The number, sizes and complexities of those problems permanently increased during the last decades. This led to an increased demand of techniques that help domain experts in understanding their data and its underlying structure to enable an efficient analysis and decision making process.
To tackle this challenge, this work presents several new techniques that utilize concepts of visual analysis to provide domain scientists with new visualization methodologies and tools. Therefore, this work provides novel concepts and approaches for diverse aspects of the visual analysis such as data transformation, visual mapping, parameter refinement and analysis, model building and visualization as well as user interaction.
The presented techniques form a framework that enriches domain scientists with new visual analysis tools and help them analyze their data and gain insight from the underlying structures. To show the applicability and effectiveness of the presented approaches, this work tackles different applications such as networking, product flow management and vascular systems, while preserving the generality to be applicable to further domains.
In this thesis, we deal with the worstcase portfolio optimization problem occuring in discretetime markets.
First, we consider the discretetime market model in the presence of crash threats. We construct the discrete worstcase optimal portfolio strategy by the indifference principle in the case of the logarithmic utility. After that we extend this problem to general utility functions and derive the discrete worstcase optimal portfolio processes, which are characterized by a dynamic programming equation. Furthermore, the convergence of the discrete worstcase optimal portfolio processes are investigated when we deal with the explicit utility functions.
In order to further study the relation of the worstcase optimal value function in discretetime models to continuoustime models we establish the finitedifference approach. By deriving the discrete HJB equation we verify the worstcase optimal value function in discretetime models, which satisfies a system of dynamic programming inequalities. With increasing degree of fineness of the time discretization, the convergence of the worstcase value function in discretetime models to that in continuoustime models are proved by using a viscosity solution method.
Study 1 (Chapter 2) is an empirical case study that concerns the nature of teaching–learning transactions that facilitate selfdirected learning in vocational education and training of young adults in England. It addresses in part the concern that fostering the skills necessary for selfdirected learning is an important endeavor of vocational education and training in many contexts internationally. However, there is a distinct lack of studies that investigate the extent to which facilitation of selfdirected learning is present within vocational education and training in different contexts. An exploratory thematic qualitative analysis of inspectors’ comments within general Further Education college Ofsted inspection reports was conducted to investigate the balance of control of the learning process between teacher and learner within vocational education and training of young adults in England. A clear difference between outstanding and inadequate provision is reported. Inadequate provision was overwhelmingly teacherdirected. Outstanding provision reflected a collaborative relationship between teacher and learner in directing the learning process, despite the Ofsted framework not explicitly identifying the need for learner involvement in directing the learning process. The chapter offers insight into the understanding of how an effective balance of control of learning between teacher and learner may be realized in vocational education and training settings and highlights the need to consider the modulating role of contextual factors.
Following the further research directions outlined in Chapter 2, study 2 (Chapter 3) is a theoretical chapter that addresses the issue that fostering adult learners’ competence to adapt appropriately to our everchanging world is a primary concern of adult education. The purpose of the chapter is novel and examines whether the consideration of modes of learning (instruction, performance, and inquiry) could assist in the design of adult education that facilitates selfdirected learning and enables learners to think and perform adaptively. The concept of modes of learning originated from the typology of Houle (1980). However, to date, no study has reached beyond this typology, especially concerning the potential of using modes of learning in the design of adult education. Specifically, an apparent oversight in adult learning theory is the foremost importance of the consideration of whether inquiry is included in the learning process: its inclusion potentially differentiates the purpose of instruction, the nature of learners’ performance, and the underlying epistemological positioning. To redress this concern, two models of modes of learning are proposed and contrasted. The reinforcing model of modes of learning (instruction, performance, without inquiry) promotes teacherdirected learning. A key consequence of employing this model in adult education is that learners may become accustomed to habitually reinforcing patterns of perceiving, thinking, judging, feeling, and acting—performance that may be rather inflexible and represented by a distinct lack of a perceived need to adapt to social contextual changes: a lack of motivation for selfdirected learning. Rather, the adapting model of modes of learning (instruction, performance, with inquiry) may facilitate learners to be adaptive in their performance—by encouraging an enhanced learner sensitivity toward changing social contextual conditions: potentially enhancing learners’ motivation for selfdirected learning.
In line with the further research directions highlighted in Chapter 3, concerning the need to consider the nature and treatment of educational experiences that are conductive to learner growth and development, study 3 (Chapter 4) presents a systematic review of the experiential learning theory; a theory that perhaps cannot be uncoupled from selfdirected learning theory, especially in regard to understanding the cognitive aspect of selfdirected learning, which represents an important direction for further research on selfdirected learning. D. A. Kolb’s (1984) experiential learning cycle is perhaps the most scholarly influential and cited model regarding experiential learning theory. However, a key issue in interpreting Kolb’s model concerns a lack of clarity regarding what constitutes a concrete experience, exactly. A systematic literature review was conducted in order to examine: what constitutes a concrete experience and what is the nature of treatment of a concrete experience in experiential learning? The analysis revealed five themes: learners are involved, active, participants; knowledge is situated in place and time; learners are exposed to novel experiences, which involves risk; learning demands inquiry to specific realworld problems; and critical reflection acts as a mediator of meaningful learning. Accordingly, a revision to Kolb’s model is proposed: experiential learning consists of contextually rich concrete experience, critical reflective observation, contextualspecific abstract conceptualization, and pragmatic active experimentation. Further empirical studies are required to test the model proposed. Finally, in Chapter 5 key findings of the studies are summarized, including that the models proposed in Chapters 3 and 4 (Figures 2 and 4, respectively) may be important considerations for further research on selfdirected learning.
Wearable activity recognition aims to identify and assess human activities with the help
of computer systems by evaluating signals of sensors which can be attached to the human
body. This provides us with valuable information in several areas: in health care, e.g. fluid
and food intake monitoring; in sports, e.g. training support and monitoring; in entertainment,
e.g. humancomputer interface using body movements; in industrial scenarios, e.g.
computer support for detected work tasks. Several challenges exist for wearable activity
recognition: a large number of nonrelevant activities (null class), the evaluation of large
numbers of sensor signals (curse of dimensionality), ambiguity of sensor signals compared
to the activities and finally the high variability of human activity in general.
This thesis develops a new activity recognition strategy, called invariants classification,
which addresses these challenges, especially the variability in human activities. The
core idea is that often even highly variable actions include short, more or less invariant
subactions which are due to hard physical constraints. If someone opens a door, the
movement of the hand to the door handle is not fixed. However the door handle has to
be pushed to open the door. The invariants classification algorithm is structured in four
phases: segmentation, invariant identification, classification, and spotting. The segmentation
divides the continuous sensor data stream into meaningful parts, which are related
to subactivities. Our segmentation strategy uses the zero crossings of the central difference
quotient of the sensor signals, as segment borders. The invariant identification finds
the invariant subactivities by means of clustering and a selection strategy dependent on
certain features. The classification identifies the segments of a specific activity class, using
models generated from the invariant subactivities. The models include the invariant
subactivity signal and features calculated on sensor signals related to the subactivity. In
the spotting, the classified segments are used to find the entire activity class instances in
the continuous sensor data stream. For this purpose, we use the position of the invariant
subactivity in the related activity class instance for the estimation of the borders of the
activity instances.
In this thesis, we show that our new activity recognition strategy, built on invariant
subactivities, is beneficial. We tested it on three human activity datasets with wearable
inertial measurement units (IMU). Compared to previous publications on the same
datasets we got improvement in the activity recognition in several classes, some with a
large margin. Our segmentation achieves a sensible method to separate the sensor data in
relation to the underlying activities. Relying on subactivities makes us independent from
imprecise labels on the training data. After the identification of invariant subactivities,
we calculate a value called cluster precision for each sensor signal and each class activity.
This tells us which classes can be easily classified and which sensor channels support
the classification best. Finally, in the training for each activity class, our algorithm selects
suitable signal channels with invariant subactivities on different points in time and
with different length. This makes our strategy a multidimensional asynchronous motif
detection with variable motif length.
Small concentrations of alloying elements can modify the
α
α

γ
γ
phase transition temperature
T
c
Tc
of Fe. We study this effect using an atomistic model based on a set of manybody interaction potentials for iron and several alloying elements. Freeenergy calculations based on perturbation theory allow us to determine the change in
T
c
Tc
introduced by the alloying element. The resulting changes are in semiquantitative agreement with experiment. The effect is traced back to the shape of the pair potential describing the interaction between the Fe and the alloying atom
Adjustment Effects of Maximum Intensity Tolerance During WholeBody Electromyostimulation Training
(2019)
Intensity regulation during wholebody electromyostimulation (WBEMS) training is mostly controlled by subjective scales such as CR10 Borg scale. To determine objective training intensities derived from a maximum as it is used in conventional strength training using the onerepetitionmaximum (1RM), a comparable maximum in WBEMS is necessary. Therefore, the aim of this study was to examine, if there is an individual maximum intensity tolerance plateau after multiple consecutive EMS application sessions. A total of 52 subjects (24.1 ± 3.2 years; 76.8 ± 11.1 kg; 1.77 ± 0.09 m) participated in the longitudinal, observational study (38 males, 14 females). Each participant carried out four consecutive maximal EMS applications (T1–T4) separated by 1 week. All muscle groups were stimulated successively until their individual maximum and combined to a wholebody stimulation index to carry out a possible statement for the development of the maximum intensity tolerance of the whole body. There was a significant main effect between the measurement times for all participants (p < 0.001; ????2 = 0.39) as well as gender specific for males (p = 0.001; ????2 = 0.18) and females (p < 0.001; ????2 = 0.57). There were no interaction effects of gender × measurement time (p = 0.394). The maximum intensity tolerance increased significantly from T1 to T2 (p = 0.001) and T2 to T3 (p < 0.001). There was no significant difference between T3 and T4 (p = 1.0). These results indicate that there is an adjustment of the individual maximum intensity tolerance to a WBEMS training after three consecutive tests. Therefore, there is a need of several habituation units comparable to the identification of the individual 1RM in conventional strength training. Further research should focus on an objective intensityspecific regulation of the WBEMS based on the individual maximum intensity tolerance to characterize different training areas and therefore generate specific adaptations to a WBEMS training compared to conventional strength training methods.
Muscular imbalances of the trunk muscles are held responsible for changes in body posture. At the same time, wholebody electromyostimulation (WBEMS) has been established as a new training method that enables simultaneous stimulation of many muscle groups. This study was aiming to analyze if a 10 weeks WBEMS training changes posturerelevant parameters and/or improves isometric strength of the trunk extensors and flexors, and if there are differences based on stimulation at 20 Hz and 85 Hz. Fifty eight untrained adult test persons were divided into three groups (control, CON; training with 20 Hz stimulation, TR20; training with 85 Hz, TR85). Anthropometric parameters, trunk extension and flexion forces and torques, and posture parameters were determined before (n = 58) and after (n = 53: CON: n = 15, TR20: n = 19, TR85: n = 19) a 10 weeks WBEMS training program (15 applications, 9 exercises). Differences between the groups were calculated for pre and posttests using univariate ANOVA and between the test times using repeated (2 × 3) ANOVA. Comparisons of pairs were calculated post hoc based on Fisher (LSD). No differences between the groups were found for the posture parameters. The post hoc analysis of both trunk flexion and trunk extension forces and torques showed a significant difference between the groups TR85 and CON but no difference between the other group pairs. A 10 weeks wholebody electrostimulation training with a stimulation frequency of 85 Hz in contrast to training with a stimulation frequency of 20 Hz improves the trunk muscle strength of an untrained group but does not significantly change posture parameters.
Adsorption and Diffusion of Cisplatin Molecules in Nanoporous Materials: A Molecular Dynamics Study
(2019)
Using molecular dynamics simulations, the adsorption and diffusion of cisplatin drug molecules in nanopores is investigated for several inorganic materials. Three different materials are studied with widelyvarying properties: metallic gold, covalent silicon, and silica. We found a strong influence of both the van der Waals and the electrostatic interaction on the adsorption behavior on the pore walls, which in turn influence the diffusion coefficients. While van der Waals forces generally lead to a reduction of the diffusion coefficient, the fluctuations in the electrostatic energy induced by orientation changes of the cisplatin molecule were found to help desorb the molecule from the wall.
The main focus of the research lies in the interpretation and application of results and correlations of soil properties from in situ testing and subsequent use in terramechanical applications. The empirical correlations and current procedures were mainly developed for medium to large depths, and therefore they were reevaluated and adjusted herein to reflect the current state of knowledge for the assessment of nearsurface soil. For testing technologies, a field investigation to a moon analogue site was carried out. Focus was placed in the assessment of the near surface soil properties. Samples were collected for subsequent analysis in laboratory conditions. Further laboratory experiments in extraterrestrial soil simulants and other terrestrial soils were conducted and correlations with relative density and shear strength parameters were attempted. The correlations from the small scale laboratory experiments, and the new reevaluated correlation for relative density were checked against the data from the field investigation. Additionally, single tiresoil tests were carried out, which enable the investigation of the localized soil response in order to advance current wheel designs and subsequently the vehicle’s mobility. Furthermore, numerical simulations were done to aid the investigation of the tiresoil interaction. Summing up, current relationships for estimating relative density of near surface soil were reevaluated, and subsequently correlated to shear strength parameters that are the main input to model soil in numerical analyses. Single tiresoil tests were carried out and were used as a reference to calibrate the interaction of the tire and the soil and subsequently were utilized to model rolling scenarios which enable the assessment of soil trafficability and vehicle’s mobility.
In this dissertation we apply financial mathematical modelling to electricity markets. Electricity is different from any other underlying of financial contracts: it is not storable. This means that electrical energy in one time point cannot be transferred to another. As a consequence, power contracts with disjoint delivery time spans basically have a different underlying. The main idea throughout this thesis is exactly this twodimensionality of time: every electricity contract is not only characterized by its trading time but also by its delivery time.
The basis of this dissertation are four scientific papers corresponding to the Chapters 3 to 6, two of which have already been published in peerreviewed journals. Throughout this thesis two model classes play a significant role: factor models and structural models. All ideas are applied to or supported by these two model classes. All empirical studies in this dissertation are conducted on electricity price data from the German market and Chapter 4 in particular studies an intraday derivative unique to the German market. Therefore, electricity market design is introduced by the example of Germany in Chapter 1. Subsequently, Chapter 2 introduces the general mathematical theory necessary for modelling electricity prices, such as Lévy processes and the Esscher transform. This chapter is the mathematical basis of the Chapters 3 to 6.
Chapter 3 studies factor models applied to the German dayahead spot prices. We introduce a qualitative measure for seasonality functions based on three requirements. Furthermore, we introduce a relation of factor models to ARMA processes, which induces a new method to estimate the mean reversion speed.
Chapter 4 conducts a theoretical and empirical study of a pricing method for a new electricity derivative: the German intraday cap and floor futures. We introduce the general theory of derivative pricing and propose a method based on the HullWhite model of interest rate modelling, which is a onefactor model. We include week futures prices to generate a price forward curve (PFC), which is then used instead of a fixed deterministic seasonality function. The idea that we can combine all market prices, and in particular futures prices, to improve the model quality also plays the major role in Chapter 5 and Chapter 6.
In Chapter 5 we develop a HeathJarrowMorton (HJM) framework that models intraday, dayahead, and futures prices. This approach is based on two stochastic processes motivated by economic interpretations and separates the stochastic dynamics in trading and delivery time. Furthermore, this framework allows for the use of classical dayahead spot price models such as the ones of Schwartz and Smith (2000), Lucia and Schwartz (2002) and includes many model classes such as structural models and factor models.
Chapter 6 unifies the classical theory of storage and the concept of a risk premium through the introduction of an unobservable intrinsic electricity price. Since all tradable electricity contracts are derivatives of this actual intrinsic price, their prices should all be derived as conditional expectation under the riskneutral measure. Through the intrinsic electricity price we develop a framework, which also includes many existing modelling approaches, such as the HJM framework of Chapter 5.
Under the notion of CyberPhysical Systems an increasingly important research area has
evolved with the aim of improving the connectivity and interoperability of previously
separate system functions. Today, the advanced networking and processing capabilities
of embedded systems make it possible to establish strongly distributed, heterogeneous
systems of systems. In such configurations, the system boundary does not necessarily
end with the hardware, but can also take into account the wider context such as people
and environmental factors. In addition to being open and adaptive to other networked
systems at integration time, such systems need to be able to adapt themselves in accordance
with dynamic changes in their application environments. Considering that many
of the potential application domains are inherently safetycritical, it has to be ensured
that the necessary modifications in the individual system behavior are safe. However,
currently available stateofthepractice and stateoftheart approaches for safety assurance
and certification are not applicable to this context.
To provide a feasible solution approach, this thesis introduces a framework that allows
“justintime” safety certification for the dynamic adaptation behavior of networked
systems. Dynamic safety contracts (DSCs) are presented as the core solution concept
for monitoring and synthesis of decentralized safety knowledge. Ultimately, this opens
up a path towards standardized service provision concepts as a set of safetyrelated runtime
evidences. DSCs enable the modular specification of relevant safety features in
networked applications as a series of formalized demandguarantee dependencies. The
specified safety features can be hierarchically integrated and linked to an interpretation
level for accessing the scope of possible safe behavioral adaptations. In this way, the networked
adaptation behavior can be conditionally certified with respect to the fulfilled
DSC safety features during operation. As long as the continuous evaluation process
provides safe adaptation behavior for a networked application context, safety can be
guaranteed for a networked system mode at runtime. Significant safetyrelated changes
in the application context, however, can lead to situations in which no safe adaptation
behavior is available for the current system state. In such cases, the remaining DSC
guarantees can be utilized to determine optimal degradation concepts for the dynamic
applications.
For the operationalization of the DSCs approach, suitable specification elements and
mechanisms have been defined. Based on a dedicated GUIengineering framework it is
shown how DSCs can be systematically developed and transformed into appropriate runtime
representations. Furthermore, a safety engineering backbone is outlined to support
the DSC modeling process in concrete application scenarios. The conducted validation
activities show the feasibility and adequacy of the proposed DSCs approach. In parallel,
limitations and areas of future improvement are pointed out.
We report the design, fabrication and experimental investigation of a spectrally wideband terahertz spatial light modulator (THzSLM) based on an array of 768 actuatable mirrors with each having a length of 220 μm and a width of 100 μm. A mirror length of several hundred micrometers is required to reduce diffraction from individual mirrors at terahertz frequencies and to increase the pixeltopixel modulation contrast of the THzSLM. By means of spatially selective actuation, we used the mirror array as reconfigurable grating to spatially modulate terahertz waves in a frequency range from 0.97 THz to 2.28 THz. Over the entire frequency band, the modulation contrast was higher than 50% with a peak modulation contrast of 87% at 1.38 THz. For spatial light modulation, almost arbitrary spatial pixel sizes can be realized by grouping of mirrors that are collectively switched as a pixel. For fabrication of the actuatable mirrors, we exploited the intrinsic residual stress in chromecopperchrome multilayers that forces the mirrors into an upstanding position at an inclination angle of 35°. By applying a bias voltage of 37 V, the mirrors were pulled down to the substrate. By hysteretic switching, we were able to spatially modulate terahertz radiation at arbitrary pixel modulation patterns.
Topological insulators (TI) are a fascinating new state of matter. Like usual insulators, their band structure possesses a band gap, such that they cannot conduct current in their bulk. However, they are able to conduct current along their edges and surfaces, due to edge states that cross the band gap. What makes TIs so interesting and potentially useful are these robust unidirectional edge currents. They are immune to significant defects and disorder, which means that they provide scatteringfree transport.
In photonics, using topological protection has a huge potential for applications, e.g. for robust optical data transfer [13] – even on the quantum level [4, 5] – or to make devices more stable and robust [6, 7]. Therefore, the field of topological insulators has spread to optics to create the new and active research field of topological photonics [810].
Welldefined and controllable model systems can help to provide deeper insight into the mechanisms of topologically protected transport. These model systems provide a vast control over parameters. For example, arbitrary lattice types without defects can be examined, and single lattice sites can be manipulated. Furthermore, they allow for the observation of effects that usually happen at extremely short timescales in solids. Model systems based on photonic waveguides are ideal candidates for this.
They consist of optical waveguides arranged on a lattice. Due to evanescent coupling, light that is inserted into one waveguide spreads along the lattice. This coupling of light between waveguides can be seen as an analogue to electrons hopping/tunneling between atomic lattice sites in a solid.
The theoretical basis for this analogy is given by the mathematical equivalence between Schrödinger and paraxial Helmholtz equation. This means that in these waveguide systems, the role of time is assigned to a spatial axis. The field evolution along the waveguides' propagation axis z thus models the temporal evolution of an electron's wavefunction in solid states. Electric and magnetic fields acting on electrons in solids need to be incorporated into the photonic platform by introducing artificial fields. These artificial gauge fields need to act on photons in the same way that their electromagnetic counterparts act on electrons. E.g., to create a photonic analogue of a topological insulator the waveguides are bent helically along their propagation axis to model the effect of a magnetic field [3]. This means that the fabrication of these waveguide arrays needs to be done in 3D.
In this thesis, a new method to 3D microprint waveguides is introduced. The inverse structure is fabricated via direct laser writing, and subsequently infiltrated with a material with higher refractive index contrast. We will use these model systems of evanescently coupled waveguides to look at different effects in topological systems, in particular at Floquet topological systems.
We will start with a topologically trivial system, consisting of two waveguide arrays with different artificial gauge fields. There, we observe that an interface between these trivial gauge fields has a profound impact on the wave vector of the light traveling across it. We deduce an analog to Snell's law and verify it experimentally.
Then we will move on to Floquet topological systems, consisting of helical waveguides. At the interface between two Floquet topological insulators with opposite helicity of the waveguides, we find additional trivial interface modes that trap the light. This allows to investigate the interaction between trivial and topological modes in the lattice.
Furthermore, we address the question if topological edge states are robust under the influence of timedependent defects. In a onedimensional topological model (the SuSchriefferHeeger model [11]) we apply periodic temporal modulations to an edge waveguide. We find Floquet copies of the edge state, that couple to the bulk in a certain frequency window and thus depopulate the edge state.
In the twodimensional Floquet topological insulator, we introduce single defects at the edge. When these defects share the temporal periodicity of the helical bulk waveguides, they have no influence on a topological edge mode. Then, the light moves around/through the defect without being scattered into the bulk. Defects with different periodicity, however, can – likewise to the defects in the SSH model – induce scattering of the edge state into the bulk.
In the end we will briefly highlight a newly emerging method for the fabrication of waveguides with low refractive index contrast. Moreover, we will introduce new ways to create artificial gauge fields by the use of orbital angular momentum states in waveguides.
While the design step should be free from computational related constraints and operations due to its artistic aspect, the modeling phase has to prepare the model for the later stages of the pipeline.
This dissertation is concerned with the design and implementation of a framework for local remeshing and optimization. Based on the experience gathered, a full study about mesh quality criteria is also part of this work.
The contributions can be highlighted as: (1) a local meshing technique based on a completely novel approach constrained to the preservation of the mesh of non interesting areas. With this concept, designers can work on the design details of specific regions of the model without introducing more polygons elsewhere; (2) a tool capable of recovering the shape of a refined area to its decimated version, enabling details on optimized meshes of detailed models; (3) the integration of novel techniques into a single framework for meshing and smoothing which is constrained to surface structure; (4) the development of a mesh quality criteria priority structure, being able to classify and prioritize according to the application of the mesh.
Although efficient meshing techniques have been proposed along the years, most of them lack the possibility to mesh smaller regions of the base mesh, preserving the mesh quality and density of outer areas.
Considering this limitation, this dissertation seeks answers to the following research questions:
1. Given that mesh quality is relative to the application it is intended for, is it possible to design a general mesh evaluation plan?
2. How to prioritize specific mesh criteria over others?
3. Given an optimized mesh and its original design, how to improve the representation of single regions of the first, without degrading the mesh quality elsewhere?
Four main achievements came from the respective answers:
1. The Application Driven Mesh Quality Criteria Structure: Due to high variation in mesh standards because of various computer aided operations performed for different applications, e.g. animation or stress simulation, a structure for better visualization of mesh quality criteria is proposed. The criteria can be used to guide the mesh optimization, making the task consistent and reliable. This dissertation also proposes a methodology to optimize the criteria values, which is adaptable to the needs of a specific application.
2. Curvature Driven Meshing Algorithm: A novel approach, a local meshing technique, which works on a desired area of the mesh while preserving its boundaries as well as the rest of the topology. It causes a slow growth in the overall amount of polygons by making only small regions denser. The method can also be used to recover the details of a reference mesh to its decimated version while refining it. Moreover, it employs a geometric fast and easy to implement approach representing surface features as simple circles, being used to guide the meshing. It also generates quaddominant meshes, with triangle count directly dependent on the size of the boundary.
3. Curvaturebased Method for Anisotropic Mesh Smoothing: A geometricbased method is extended to 3D space to be able to produce anisotropic elements where needed. It is made possible by mapping the original space to another which embeds the surface curvature. This methodology is used to enhance the smoothing algorithm by making the nearly regularized elements follow the surface features, preserving the original design. The mesh optimization method also preserves mesh topology, while resizing elements according to the local mesh resolution, effectively enhancing the design aspects intended.
4. Framework for Local Restructure of Meshed Surfaces: The combination of both methods creates a complete tool for recovering surface details through mesh refinement and curvature aware mesh smoothing.
In computer graphics, realistic rendering of virtual scenes is a computationally complex problem. Stateoftheart rendering technology must become more scalable to
meet the performance requirements for demanding realtime applications.
This dissertation is concerned with core algorithms for rendering, focusing on the
ray tracing method in particular, to support and saturate recent massively parallel computer systems, i.e., to distribute the complex computations very efficiently
among a large number of processing elements. More specifically, the three targeted
main contributions are:
1. Collaboration framework for largescale distributed memory computers
The purpose of the collaboration framework is to enable scalable rendering
in realtime on a distributed memory computer. As an infrastructure layer it
manages the explicit communication within a network of distributed memory
nodes transparently for the rendering application. The research is focused on
designing a communication protocol resilient against delays and negligible in
overhead, relying exclusively on onesided and asynchronous data transfers.
The hypothesis is that a loosely coupled system like this is able to scale linearly
with the number of nodes, which is tested by directly measuring all possible
communicationinduced delays as well as the overall rendering throughput.
2. Ray tracing algorithms designed for vector processing
Vector processors are to be efficiently utilized for improved ray tracing performance. This requires the basic, scalar traversal algorithm to be reformulated
in order to expose a high degree of finegrained data parallelism. Two approaches are investigated: traversing multiple rays simultaneously, and performing
multiple traversal steps at once. Efficiently establishing coherence in a group
of rays as well as avoiding sorting of the nodes in a multitraversal step are the
defining research goals.
3. Multithreaded schedule and memory management for the ray tracing acceleration structure
Construction times of highquality acceleration structures are to be reduced by
improvements to multithreaded scalability and utilization of vector processors. Research is directed at eliminating the following scalability bottlenecks:
dynamic memory growth caused by the primitive splits required for high
quality structures, and toplevel hierarchy construction where simple task par
allelism is not readily available. Additional research addresses how to expose
scatter/gatherfree dataparallelism for efficient vector processing.
Together, these contributions form a scalable, highperformance basis for realtime,
ray tracingbased rendering, and a prototype path tracing application implemented
on top of this basis serves as a demonstration.
The key insight driving this dissertation is that the computational power necessary
for realistic light transport for realtime rendering applications demands massively
parallel computers, which in turn require highly scalable algorithms. Therefore this
dissertation provides important research along the path towards virtual reality.
Many loads acting on a vehicle depend on the condition and quality of roads
traveled as well as on the driving style of the motorist. Thus, during vehicle development,
good knowledge on these further operations conditions is advantageous.
For that purpose, usage models for different kinds of vehicles are considered. Based
on these mathematical descriptions, representative routes for multiple user
types can be simulated in a predefined geographical region. The obtained individual
driving schedules consist of coordinates of starting and target points and can
thus be routed on the true road network. Additionally, different factors, like the
topography, can be evaluated along the track.
Available statistics resulting from travel survey are integrated to guarantee reasonable
trip length. Population figures are used to estimate the number of vehicles in
contained administrative units. The creation of thousands of those georeferenced
trips then allows the determination of realistic measures of the durability loads.
Private as well as commercial use of vehicles is modeled. For the former, commuters
are modeled as the main user group conducting daily drives to work and
additional leisure time a shopping trip during workweek. For the latter, taxis as
example for users of passenger cars are considered. The model of lightduty commercial
vehicles is split into two types of driving patterns, stars and tours, and in
the common traffic classes of longdistance, local and city traffic.
Algorithms to simulate reasonable target points based on geographical and statistical
data are presented in detail. Examples for the evaluation of routes based
on topographical factors and speed profiles comparing the influence of the driving
style are included.
Planar force or pressure is a fundamental physical aspect during any peoplevspeople and peoplevsenvironment activities and interactions. It is as significant as the more established linear and angular acceleration (usually acquired by inertial measurement units). There have been several studies involving planar pressure in the discipline of activity recognition, as reviewed in the first chapter. These studies have shown that planar pressure is a promising sensing modality for activity recognition. However, they still take a niche part in the entire discipline, using ad hoc systems and data analysis methods. Mostly these studies were not followed by further elaborative works. The situation calls for a general framework that can help push planar pressure sensing into the mainstream.
This dissertation systematically investigates using planar pressure distribution sensing technology for ubiquitous and wearable activity recognition purposes. We propose a generic Textile Pressure Mapping (TPM) Framework, which encapsulates (1) design knowledge and guidelines, (2) a multilayered tool including hardware, software and algorithms, and (3) an ensemble of empirical study examples. Through validation with various empirical studies, the unified TPM framework covers the full scope of application recognition, including the ambient, object, and wearable subspaces.
The hardware part constructs a general architecture and implementations in the largescale and mobile directions separately. The software toolkit consists of four heterogeneous tiers: driver, data processing, machine learning, visualization/feedback. The algorithm chapter describes generic data processing techniques and a unified TPM feature set. The TPM framework offers a universal solution for other researchers and developers to evaluate TPM sensing modality in their application scenarios.
The significant findings from the empirical studies have shown that TPM is a versatile sensing modality. Specifically, in the ambient subspace, a sports mat or carpet with TPM sensors embedded underneath can distinguish different sports activities or different people's gait based on the dynamic change of bodyprint; a pressure sensitive tablecloth can detect various dining actions by the force propagated from the cutlery through the plates to the tabletop. In the object subspace, swirl office chairs with TPM sensors under the cover can be used to detect the seater's realtime posture; TPM can be used to detect emotionrelated touch interactions for smart objects, toys or robots. In the wearable subspace, TPM sensors can be used to perform pressurebased mechanomyography to detect muscle and body movement; it can also be tailored to cover the surface of a soccer shoe to distinguish different kicking angles and intensities.
All the empirical evaluations have resulted in accuracies wellabove the chance level of the corresponding number of classes, e.g., the `swirl chair' study has classification accuracy of 79.5% out of 10 posture classes and in the `soccer shoe' study the accuracy is 98.8% among 17 combinations of angle and intensity.
The fifthgeneration mobile telecommunication network is expected to support multiaccess edge computing (MEC), which intends to distribute computation tasks and services from the central cloud to the edge clouds. Toward ultraresponsive, ultrareliable, and ultralowlatency MEC services, the current mobile network security architecture should enable a more decentralized approach for authentication and authorization processes. This paper proposes a novel decentralized authentication architecture that supports flexible and lowcost local authentication with the awareness of context information of network elements such as user equipment and virtual network functions. Based on a Markov model for backhaul link quality as well as a random walk mobility model with mixed mobility classes and traffic scenarios, numerical simulations have demonstrated that the proposed approach is able to achieve a flexible balance between the network operating cost and the MEC reliability.
Cyclic indentation is a technique used to characterize materials by indenting repeatedly on the same location. This technique allows information to be obtained on how the plastic material response changes under repeated loading. We explore the processes underlying this technique using a combined experimental and simulative approach. We focus on the loading–unloading hysteresis and the dependence of the hysteresis width ha,p on the cycle number. In both approaches, we obtain a powerlaw demonstrating ha,p with respect to the hardening exponent e. A detailed analysis of the atomistic simulation results shows that changes in the dislocation network under repeated indentation are responsible for this behavior.
Modern applications in the realms of wireless communication and mobile broadband Internet increase the demand for compact antennas with well defined directivity. Here, we present an approach for the design and implementation of hybrid antennas consisting of a classic feeding antenna that is nearfieldcoupled to a subwavelength resonator. In such a combined structure, the composite antenna always radiates at the resonance frequency of the subwavelength oscillator as well as at the resonance frequency of the feeding antenna. While the classic antenna serves as impedancematched feeding element, the subwavelength resonator induces an additional resonance to the composite antenna. In general, these nearfield coupled structures are known for decades and are lately published as nearfield resonant parasitic antennas. We describe an antenna design consisting of a highfrequency electric dipole antenna at fd=25 GHz that couples to a lowfrequency subwavelength splitring resonator, which emits electromagnetic waves at fSRR=10.41 GHz. The radiating part of the antenna has a size of approximately 3.2mm×8mm×1mm and thus is electrically small at this frequency with a product k⋅a=0.5 . The input return loss of the antenna was moderate at −18 dB and it radiated at a spectral bandwidth of 120 MHz. The measured main lobe of the antenna was observed at 60∘ with a −3 dB angular width of 65∘ in the Eplane and at 130∘ with a −3 dB angular width of 145∘ in the Hplane
The coordination of multiple external representations is important for learning, but yet a difficult task for students, requiring instructional support. The subject in this study covers a typical relation in physics between abstract mathematical equations (definitions of divergence and curl) and a visual representation (vector field plot). To support the connection across both representations, two instructions with written explanations, equations, and visual representations (differing only in the presence of visual cues) were designed and their impact on students’ performance was tested. We captured students’ eye movements while they processed the written instruction and solved subsequent coordination tasks. The results show that students instructed with visual cues (VC students) performed better, responded with higher confidence, experienced less mental effort, and rated the instructional quality better than students instructed without cues. Advanced eyetracking data analysis methods reveal that cognitive integration processes appear in both groups at the same point in time but they are significantly more pronounced for VC students, reflecting a greater attempt to construct a coherent mental representation during the learning process. Furthermore, visual cues increase the fixation count and total fixation duration on relevant information. During problem solving, the saccadic eye movement pattern of VC students is similar to experts in this domain. The outcomes imply that visual cues can be beneficial in coordination tasks, even for students with high domain knowledge. The study strongly confirms an important multimedia design principle in instruction, that is, that highlighting conceptually relevant information shifts attention to relevant information and thus promotes learning and problem solving. Even more, visual cues can positively influence students’ perception of course materials.