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This thesis investigates the comprehension of the passive voice in three distinct populations. First, the comprehension of passives by adult German speakers was studied, followed by an examination of how German-speaking children comprehend the structure. Finally, bilingual Mandarin-English speakers were tested on their comprehension of the passive voice in English, which is their L2. An integral part of testing the comprehension in all three populations is the use of structural priming. In each of the three distinct parts of the research, structural priming was used for a specific reason. In the study involving adult German speakers, productive and receptive structural priming was directly compared. The goal was to see the effect the two priming modalities have on language comprehension. In the study on German-acquiring children, structural priming was an important tool in answering the question regarding the delayed acquisition of the passive voice. Finally, in the study on the bilingual population, cross-linguistic priming was used to investigate the importance of word order in the priming effect, since Mandarin and English have different word orders in passive voice sentences.
In this thesis, deficits in theory of mind (ToM) and executive function (EF) were examined in tandem and separately as risk factors for conduct problems, including different forms and functions of aggressive behavior. All three reported studies and the additional analyses were based on a large community sample of N = 1,657 children, including three waves of a longitudinal study covering middle childhood and the transition to early adolescence (range 6 to 13 years) over a total of about three years. All data were analyzed with structural equation modeling.
Altogether, the results of all the conducted studies in this thesis extend previous research and confirm the propositions of the SIP model (Crick & Dodge, 1994) and of the amygdala theory of violent behavior (e.g., Blair et al., 2014) besides other accounts. Considering the three main research questions, the results of the thesis suggest first that deficits in ToM are a risk factor for relational and physical aggression from a mean age of 8 to 11 years under the control of stable between-person differences in aggression. In addition, earlier relationally aggressive behavior predicts later deficits in ToM in this age range, which confirms transactional relations between deficits in ToM and aggressive behavior in children (Crick & Dodge, 1994). Further, deficits in ToM seem to be a risk factor for parent-rated conduct problems cross-sectionally in an age range from 9 to 13 years. Second, deficits in cool EF are a risk factor for later physical, relational, and reactive aggression but not for proactive aggression over a course of three years from middle childhood to early adolescence. Habitual anger seems to mediate the relation between cool EF and physical, and as a trend also relational, aggression. Deficits in emotional and inhibitory control and planning have a direct effect on the individual level of conduct problems under the control of interindividual differences in conduct problems at a mean age of 8 years, but not on the trajectory of conduct problems over the course from age 8 to 11. Third, when deficits in cool EF and ToM are studied in tandem cross-sectionally at the transition from middle childhood to early adolescence, deficits in cool EF seem to play only an indirect role through deficits in ToM as a risk factor for conduct problems. Finally, all results hold equal for females and males in the conducted studies.
The results of this thesis emphasize the need to intervene in the transactional processes between deficits in ToM and in EF and conduct problems, including different forms and functions of aggression, particularly in the socially sensible period from middle and late childhood to early adolescence.
Spatio-temporal data denotes a category of data that contains spatial as well as temporal components. For example, time-series of geo-data, thematic maps that change over time, or tracking data of moving entities can be interpreted as spatio-temporal data.
In today's automated world, an increasing number of data sources exist, which constantly generate spatio-temporal data. This includes for example traffic surveillance systems, which gather movement data about human or vehicle movements, remote-sensing systems, which frequently scan our surroundings and produce digital representations of cities and landscapes, as well as sensor networks in different domains, such as logistics, animal behavior study, or climate research.
For the analysis of spatio-temporal data, in addition to automatic statistical and data mining methods, exploratory analysis methods are employed, which are based on interactive visualization. These analysis methods let users explore a data set by interactively manipulating a visualization, thereby employing the human cognitive system and knowledge of the users to find patterns and gain insight into the data.
This thesis describes a software framework for the visualization of spatio-temporal data, which consists of GPU-based techniques to enable the interactive visualization and exploration of large spatio-temporal data sets. The developed techniques include data management, processing, and rendering, facilitating real-time processing and visualization of large geo-temporal data sets. It includes three main contributions:
- Concept and Implementation of a GPU-Based Visualization Pipeline.
The developed visualization methods are based on the concept of a GPU-based visualization pipeline, in which all steps -- processing, mapping, and rendering -- are implemented on the GPU. With this concept, spatio-temporal data is represented directly in GPU memory, using shader programs to process and filter the data, apply mappings to visual properties, and finally generate the geometric representations for a visualization during the rendering process. Data processing, filtering, and mapping are thereby executed in real-time, enabling dynamic control over the mapping and a visualization process which can be controlled interactively by a user.
- Attributed 3D Trajectory Visualization.
A visualization method has been developed for the interactive exploration of large numbers of 3D movement trajectories. The trajectories are visualized in a virtual geographic environment, supporting basic geometries such as lines, ribbons, spheres, or tubes. Interactive mapping can be applied to visualize the values of per-node or per-trajectory attributes, supporting shape, height, size, color, texturing, and animation as visual properties. Using the dynamic mapping system, several kind of visualization methods have been implemented, such as focus+context visualization of trajectories using interactive density maps, and space-time cube visualization to focus on the temporal aspects of individual movements.
- Geographic Network Visualization.
A method for the interactive exploration of geo-referenced networks has been developed, which enables the visualization of large numbers of nodes and edges in a geographic context. Several geographic environments are supported, such as a 3D globe, as well as 2D maps using different map projections, to enable the analysis of networks in different contexts and scales. Interactive filtering, mapping, and selection can be applied to analyze these geographic networks, and visualization methods for specific types of networks, such as coupled 3D networks or temporal networks have been implemented.
As a demonstration of the developed visualization concepts, interactive visualization tools for two distinct use cases have been developed. The first contains the visualization of attributed 3D movement trajectories of airplanes around an airport. It allows users to explore and analyze the trajectories of approaching and departing aircrafts, which have been recorded over the period of a month. By applying the interactive visualization methods for trajectory visualization and interactive density maps, analysts can derive insight from the data, such as common flight paths, regular and irregular patterns, or uncommon incidents such as missed approaches on the airport.
The second use case involves the visualization of climate networks, which are geographic networks in the climate research domain. They represent the dynamics of the climate system using a network structure that expresses statistical interrelationships between different regions. The interactive tool allows climate analysts to explore these large networks, analyzing the network's structure and relating it to the geographic background. Interactive filtering and selection enables them to find patterns in the climate data and identify e.g. clusters in the networks or flow patterns.
The last years have shown an increasing sophistication of attacks against enterprises. Traditional security solutions like firewalls, anti-virus systems and generally Intrusion Detection Systems (IDSs) are no longer sufficient to protect an enterprise against these advanced attacks. One popular approach to tackle this issue is to collect and analyze events generated across the IT landscape of an enterprise. This task is achieved by the utilization of Security Information and Event Management (SIEM) systems. However, the majority of the currently existing SIEM solutions is not capable of handling the massive volume of data and the diversity of event representations. Even if these solutions can collect the data at a central place, they are neither able to extract all relevant information from the events nor correlate events across various sources. Hence, only rather simple attacks are detected, whereas complex attacks, consisting of multiple stages, remain undetected. Undoubtedly, security operators of large enterprises are faced with a typical Big Data problem.
In this thesis, we propose and implement a prototypical SIEM system named Real-Time Event Analysis and Monitoring System (REAMS) that addresses the Big Data challenges of event data with common paradigms, such as data normalization, multi-threading, in-memory storage, and distributed processing. In particular, a mostly stream-based event processing workflow is proposed that collects, normalizes, persists and analyzes events in near real-time. In this regard, we have made various contributions in the SIEM context. First, we propose a high-performance normalization algorithm that is highly parallelized across threads and distributed across nodes. Second, we are persisting into an in-memory database for fast querying and correlation in the context of attack detection. Third, we propose various analysis layers, such as anomaly- and signature-based detection, that run on top of the normalized and correlated events. As a result, we demonstrate our capabilities to detect previously known as well as unknown attack patterns. Lastly, we have investigated the integration of cyber threat intelligence (CTI) into the analytical process, for instance, for correlating monitored user accounts with previously collected public identity leaks to identify possible compromised user accounts.
In summary, we show that a SIEM system can indeed monitor a large enterprise environment with a massive load of incoming events. As a result, complex attacks spanning across the whole network can be uncovered and mitigated, which is an advancement in comparison to existing SIEM systems on the market.
This paper introduces a novel measure to assess similarity between event hydrographs. It is based on Cross Recurrence Plots and Recurrence Quantification Analysis which have recently gained attention in a range of disciplines when dealing with complex systems. The method attempts to quantify the event runoff dynamics and is based on the time delay embedded phase space representation of discharge hydrographs. A phase space trajectory is reconstructed from the event hydrograph, and pairs of hydrographs are compared to each other based on the distance of their phase space trajectories. Time delay embedding allows considering the multi-dimensional relationships between different points in time within the event. Hence, the temporal succession of discharge values is taken into account, such as the impact of the initial conditions on the runoff event. We provide an introduction to Cross Recurrence Plots and discuss their parameterization. An application example based on flood time series demonstrates how the method can be used to measure the similarity or dissimilarity of events, and how it can be used to detect events with rare runoff dynamics. It is argued that this methods provides a more comprehensive approach to quantify hydrograph similarity compared to conventional hydrological signatures.
Previous studies on native language (L1) anaphor resolution have found that monolingual native speakers are sensitive to syntactic, pragmatic, and semantic constraints on pronouns and reflexive resolution. However, most studies have focused on English and other Germanic languages, and little is currently known about the online (i.e., real-time) processing of anaphors in languages with syntactically less restricted anaphors, such as Turkish. We also know relatively little about how 'non-standard' populations such as non-native (L2) speakers and heritage speakers (HSs) resolve anaphors.
This thesis investigates the interpretation and real-time processing of anaphors in German and in a typologically different and as yet understudied language, Turkish. It compares hypotheses about differences between native speakers' (L1ers) and L2 speakers' (L2ers) sentence processing, looking into differences in processing mechanisms as well as the possibility of cross-linguistic influence. To help fill the current research gap regarding HS sentence comprehension, it compares findings for this group with those for L2ers.
To investigate the representation and processing of anaphors in these three populations, I carried out a series of offline questionnaires and Visual-World eye-tracking experiments on the resolution of reflexives and pronouns in both German and Turkish. In the German experiments, native German speakers as well as L2ers of German were tested, while in the Turkish experiments, non-bilingual native Turkish speakers as well as HSs of Turkish with L2 German were tested. This allowed me to observe both cross-linguistic differences as well as population differences between monolinguals' and different types of bilinguals' resolution of anaphors.
Regarding the comprehension of Turkish anaphors by L1ers, contrary to what has been previously assumed, I found that Turkish has no reflexive that follows Condition A of Binding theory (Chomsky, 1981). Furthermore, I propose more general cross-linguistic differences between Turkish and German, in the form of a stronger reliance on pragmatic information in anaphor resolution overall in Turkish compared to German.
As for the processing differences between L1ers and L2ers of a language, I found evidence in support of hypotheses which propose that L2ers of German rely more strongly on non-syntactic information compared to L1ers (Clahsen & Felser, 2006, 2017; Cunnings, 2016, 2017) independent of a potential influence of their L1. HSs, on the other hand, showed a tendency to overemphasize interpretational contrasts between different Turkish anaphors compared to monolingual native speakers. However, lower-proficiency HSs were likely to merge different forms for simplified representation and processing. Overall, L2ers and HSs showed differences from monolingual native speakers both in their final interpretation of anaphors and during online processing. However, these differences were not parallel between the two types of bilingual and thus do not support a unified model of L2 and HS processing (cf. Montrul, 2012).
The findings of this thesis contribute to the field of anaphor resolution by providing data from a previously unexplored language, Turkish, as well as contributing to research on native and non-native processing differences. My results also illustrate the importance of considering individual differences in the acquisition process when studying bilingual language comprehension. Factors such as age of acquisition, language proficiency and the type of input a language learner receives may influence the processing mechanisms they develop and employ, both between and within different bilingual populations.
This dissertation consists of four self-contained papers that deal with the implications of financial market imperfections and heterogeneity. The analysis mainly relates to the class of incomplete-markets models but covers different research topics.
The first paper deals with the distributional effects of financial integration for developing countries. Based on a simple heterogeneous-agent approach, it is shown that capital owners experience large welfare losses while only workers moderately gain due to higher wages. The large welfare losses for capital owners contrast with the small average welfare gains from representative-agent economies and indicate that a strong opposition against capital market opening has to be expected.
The second paper considers the puzzling observation of capital flows from poor to rich countries and the accompanying changes in domestic economic development. Motivated by the mixed results from the literature, we employ an incomplete-markets model with different types of idiosyncratic risk and borrowing constraints. Based on different scenarios, we analyze under what conditions the presence of financial market imperfections contributes to explain the empirical findings and how the conditions may change with different model assumptions.
The third paper deals with the interplay of incomplete information and financial market imperfections in an incomplete-markets economy. In particular, it analyzes the impact of incomplete information about idiosyncratic income shocks on aggregate saving. The results show that the effect of incomplete information is not only quantitatively substantial but also qualitatively ambiguous and varies with the influence of the income risk and the borrowing constraint.
Finally, the fourth paper analyzes the influence of different types of fiscal rules on the response of key macroeconomic variables to a government spending shock. We find that a strong temporary increase in public debt contributes to stabilizing consumption and leisure in the first periods following the change in government spending, whereas a non-debt-intensive fiscal rule leads to a faster recovery of consumption, leisure, capital and output in later periods. Regarding optimal debt policy, we find that a debt-intensive fiscal rule leads to the largest aggregate welfare benefit and that the individual welfare gain is particularly high for wealth-poor agents.
Metamaterial devices
(2018)
Digital fabrication machines such as 3D printers excel at producing arbitrary shapes, such as for decorative objects. In recent years, researchers started to engineer not only the outer shape of objects, but also their internal microstructure. Such objects, typically based on 3D cell grids, are known as metamaterials. Metamaterials have been used to create materials that, e.g., change their volume, or have variable compliance.
While metamaterials were initially understood as materials, we propose to think of them as devices.
We argue that thinking of metamaterials as devices enables us to create internal structures that offer functionalities to implement an input-process-output model without electronics, but purely within the material’s internal structure. In this thesis, we investigate three aspects of such metamaterial devices that implement parts of the input-process-output model: (1) materials that process analog inputs by implementing mechanisms based on their microstructure, (2) that process digital signals by embedding mechanical computation into the object’s microstructure, and (3) interactive metamaterial objects that output to the user by changing their outside to interact with their environment. The input to our metamaterial devices is provided directly by the users interacting with the device by means of physically pushing the metamaterial, e.g., turning a handle, pushing a button, etc.
The design of such intricate microstructures, which enable the functionality of metamaterial devices, is not obvious. The complexity of the design arises from the fact that not only a suitable cell geometry is necessary, but that additionally cells need to play together in a well-defined way. To support users in creating such microstructures, we research and implement interactive design tools. These tools allow experts to freely edit their materials, while supporting novice users by auto-generating cells assemblies from high-level input. Our tools implement easy-to-use interactions like brushing, interactively simulate the cell structures’ deformation directly in the editor, and export the geometry as a 3D-printable file. Our goal is to foster more research and innovation on metamaterial devices by allowing the broader public to contribute.
Today, more than half of the world’s population lives in urban areas. With a high density of population and assets, urban areas are not only the economic, cultural and social hubs of every society, they are also highly susceptible to natural disasters. As a consequence of rising sea levels and an expected increase in extreme weather events caused by a changing climate in combination with growing cities, flooding is an increasing threat to many urban agglomerations around the globe.
To mitigate the destructive consequences of flooding, appropriate risk management and adaptation strategies are required. So far, flood risk management in urban areas is almost exclusively focused on managing river and coastal flooding. Often overlooked is the risk from small-scale rainfall-triggered flooding, where the rainfall intensity of rainstorms exceeds the capacity of urban drainage systems, leading to immediate flooding. Referred to as pluvial flooding, this flood type exclusive to urban areas has caused severe losses in cities around the world. Without further intervention, losses from pluvial flooding are expected to increase in many urban areas due to an increase of impervious surfaces compounded with an aging drainage infrastructure and a projected increase in heavy precipitation events. While this requires the integration of pluvial flood risk into risk management plans, so far little is known about the adverse consequences of pluvial flooding due to a lack of both detailed data sets and studies on pluvial flood impacts. As a consequence, methods for reliably estimating pluvial flood losses, needed for pluvial flood risk assessment, are still missing.
Therefore, this thesis investigates how pluvial flood losses to private households can be reliably estimated, based on an improved understanding of the drivers of pluvial flood loss. For this purpose, detailed data from pluvial flood-affected households was collected through structured telephone- and web-surveys following pluvial flood events in Germany and the Netherlands.
Pluvial flood losses to households are the result of complex interactions between impact characteristics such as the water depth and a household’s resistance as determined by its risk awareness, preparedness, emergency response, building properties and other influencing factors. Both exploratory analysis and machine-learning approaches were used to analyze differences in resistance and impacts between households and their effects on the resulting losses. The comparison of case studies showed that the awareness around pluvial flooding among private households is quite low. Low awareness not only challenges the effective dissemination of early warnings, but was also found to influence the implementation of private precautionary measures. The latter were predominately implemented by households with previous experience of pluvial flooding. Even cases where previous flood events affected a different part of the same city did not lead to an increase in preparedness of the surveyed households, highlighting the need to account for small-scale variability in both impact and resistance parameters when assessing pluvial flood risk.
While it was concluded that the combination of low awareness, ineffective early warning and the fact that only a minority of buildings were adapted to pluvial flooding impaired the coping capacities of private households, the often low water levels still enabled households to mitigate or even prevent losses through a timely and effective emergency response.
These findings were confirmed by the detection of loss-influencing variables, showing that cases in which households were able to prevent any loss to the building structure are predominately explained by resistance variables such as the household’s risk awareness, while the degree of loss is mainly explained by impact variables.
Based on the important loss-influencing variables detected, different flood loss models were developed. Similar to flood loss models for river floods, the empirical data from the preceding data collection was used to train flood loss models describing the relationship between impact and resistance parameters and the resulting loss to building structures. Different approaches were adapted from river flood loss models using both models with the water depth as only predictor for building structure loss and models incorporating additional variables from the preceding variable detection routine.
The high predictive errors of all compared models showed that point predictions are not suitable for estimating losses on the building level, as they severely impair the reliability of the estimates. For that reason, a new probabilistic framework based on Bayesian inference was introduced that is able to provide predictive distributions instead of single loss estimates. These distributions not only give a range of probable losses, they also provide information on how likely a specific loss value is, representing the uncertainty in the loss estimate.
Using probabilistic loss models, it was found that the certainty and reliability of a loss estimate on the building level is not only determined by the use of additional predictors as shown in previous studies, but also by the choice of response distribution defining the shape of the predictive distribution. Here, a mix between a beta and a Bernoulli distribution to account for households that are able to prevent losses to their building’s structure was found to provide significantly more certain and reliable estimates than previous approaches using Gaussian or non-parametric response distributions.
The successful model transfer and post-event application to estimate building structure loss in Houston, TX, caused by pluvial flooding during Hurricane Harvey confirmed previous findings, and demonstrated the potential of the newly developed multi-variable beta model for future risk assessments. The highly detailed input data set constructed from openly available data sources containing over 304,000 affected buildings in Harris County further showed the potential of data-driven, building-level loss models for pluvial flood risk assessment.
In conclusion, pluvial flood losses to private households are the result of complex interactions between impact and resistance variables, which should be represented in loss models. The local occurrence of pluvial floods requires loss estimates on high spatial resolutions, i.e. on the building level, where losses are variable and uncertainties are high.
Therefore, probabilistic loss estimates describing the uncertainty of the estimate should be used instead of point predictions. While the performance of probabilistic models on the building level are mainly driven by the choice of response distribution, multi-variable models are recommended for two reasons:
First, additional resistance variables improve the detection of cases in which households were able to prevent structural losses.
Second, the added variability of additional predictors provides a better representation of the uncertainties when loss estimates from multiple buildings are aggregated.
This leads to the conclusion that data-driven probabilistic loss models on the building level allow for a reliable loss estimation at an unprecedented level of detail, with a consistent quantification of uncertainties on all aggregation levels. This makes the presented approach suitable for a wide range of applications, from decision support in spatial planning to impact- based early warning systems.
Plant-derived Transcription Factors for Orthologous Regulation of Gene Expression in the Yeast Saccharomyces cerevisiae
Control of gene expression by transcription factors (TFs) is central in many synthetic biology projects where tailored expression of one or multiple genes is often needed. As TFs from evolutionary distant organisms are unlikely to affect gene expression in a host of choice, they represent excellent candidates for establishing orthogonal control systems. To establish orthogonal regulators for use in yeast (Saccharomyces cerevisiae), we chose TFs from the plant Arabidopsis thaliana. We established a library of 106 different combinations of chromosomally integrated TFs, activation domains (yeast GAL4 AD, herpes simplex virus VP64, and plant EDLL) and synthetic promoters harbouring cognate cis-regulatory motifs driving a yEGFP reporter. Transcriptional output of the different driver / reporter combinations varied over a wide spectrum, with EDLL being a considerably stronger transcription activation domain in yeast, than the GAL4 activation domain, in particular when fused to Arabidopsis NAC TFs. Notably, the strength of several NAC - EDLL fusions exceeded that of the strong yeast TDH3 promoter by 6- to 10-fold. We furthermore show that plant TFs can be used to build regulatory systems encoded by centromeric or episomal plasmids. Our library of TF – DNA-binding site combinations offers an excellent tool for diverse synthetic biology applications in yeast.
COMPASS: Rapid combinatorial optimization of biochemical pathways based on artificial transcription factors
We established a high-throughput cloning method, called COMPASS for COMbinatorial Pathway ASSembly, for the balanced expression of multiple genes in Saccharomyces cerevisiae. COMPASS employs orthogonal, plant-derived artificial transcription factors (ATFs) for controlling the expression of pathway genes, and homologous recombination-based cloning for the generation of thousands of individual DNA constructs in parallel. The method relies on a positive selection of correctly assembled pathway variants from both, in vivo and in vitro cloning procedures. To decrease the turnaround time in genomic engineering, we equipped COMPASS with multi-locus CRISPR/Cas9-mediated modification capacity. In its current realization, COMPASS allows combinatorial optimization of up to ten pathway genes, each transcriptionally controlled by nine different ATFs spanning a 10-fold difference in expression strength. The application of COMPASS was demonstrated by generating cell libraries producing beta-carotene and co-producing beta-ionone and biosensor-responsive naringenin. COMPASS will have many applications in other synthetic biology projects that require gene expression balancing.
CaPRedit: Genome editing using CRISPR-Cas9 and plant-derived transcriptional regulators for the redirection of flux through the FPP branch-point in yeast. Technologies developed over the past decade have made Saccharomyces cerevisiae a promising platform for production of different natural products. We developed CRISPR/Ca9- and plant derived regulator-mediated genome editing approach (CaPRedit) to greatly accelerate strain modification and to facilitate very low to very high expression of key enzymes using inducible regulators. CaPRedit can be implemented to enhance the production of yeast endogenous or heterologous metabolites in the yeast S. cerevisiae. The CaPRedit system aims to faciltiate modification of multiple targets within a complex metabolic pathway through providing new tools for increased expression of genes encoding rate-limiting enzymes, decreased expression of essential genes, and removed expression of competing pathways. This approach is based on CRISPR/Cas9-mediated one-step double-strand breaks to integrate modules containing IPTG-inducible plant-derived artificial transcription factor and promoter pair(s) in a desired locus or loci. Here, we used CaPRedit to redirect the yeast endogenous metabolic flux toward production of farnesyl diphosphate (FPP), a central precursor of nearly all yeast isoprenoid products, by overexpression of the enzymes lead to produce FPP from glutamate. We found significantly higher beta-carotene accumulation in the CaPRedit-mediated modified strain than in the wild type (WT) strain. More specifically, CaPRedit_FPP 1.0 strain was generated, in which three genes involved in FPP synthesis, tHMG1, ERG20, and GDH2, were inducibly overexpressed under the control of strong plant-derived ATFPs. The beta–carotene accumulated in CaPRedit_FPP 1.0 strain to a level 1.3-fold higher than the previously reported optimized strain that carries the same overexpressed genes (as well as additional genetic modifications to redirect yeast endogenous metabolism toward FPP production). Furthermore, the genetic modifications implemented in CaPRedit_FPP 1.0 strain resulted in only a very small growth defect (growth rate relative to the WT is ~ -0.03).