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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 doctoral dissertation aims at elucidating the development of hot and cool executive functions in middle childhood and at gaining insight about their role in childhood overweight. The dissertation is based on three empirical studies which have been published in peer-reviewed journals. Data from a large 3-year longitudinal study (the “PIER-study”) was used.
The findings presented in the dissertation demonstrated that both hot and cool EF abilities increase during middle childhood. They also supported the notion that hot and cool EF facets are distinguishable from each other in middle childhood, that they have distinct developmental trajectories, and different predictors.
Evidence was found for associations of hot and cool EF with body weight in middle childhood, which is in line with the notion that they might play a role in the self-regulation of eating and the multifactorial etiology of childhood overweight.
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.