Refine
Year of publication
Document Type
- Master's Thesis (121) (remove)
Has Fulltext
- yes (121)
Keywords
- Beschleunigerphysik (2)
- E-Learning (2)
- Erwachsenenbildung (2)
- Experiment (2)
- Ionenstrahl (2)
- Konstruktion (2)
- Lehren mit neuen Medien (2)
- Lehrerbildung (2)
- Lernen mit neuen Medien (2)
- Machine Learning (2)
Institute
- Physik (61)
- Informatik und Mathematik (10)
- Gesellschaftswissenschaften (8)
- Mathematik (7)
- Informatik (5)
- Geowissenschaften (4)
- Erziehungswissenschaften (3)
- Sportwissenschaften (3)
- Psychologie (2)
- Sprachwissenschaften (2)
Ausgehend von Scheffers Beschreibung Existentieller Probleme untersucht die Masterarbeit die Problembehandlung solcher. Dabei versucht sich der Autor an einer vorläufigen Begriffsdefinition Existentieller Apparate und untersucht im folgenden, wie sich diese von der Arbeitsweise, ihrer Struktur und anderer Merkmale von herkömmlichen Apparaten unterscheiden. Die Diskussion erfolgt anhand der Problembearbeitungskapazitäten von der Flüchtlingshilfe auf Lesbos, Impfzentren und der Fridays-for-Future-Bewegung, die jeweils für Sich genommen speziell nur im Kontext jeweiliger Existentieller Probleme existieren.
The thesis delves into two research questions, drawing from the 2022 Final Report of the Colombian Truth Commission. It critically examines historical power differentials originating from the colonial period, probing their role in legitimizing and perpetuating violence against ethnic groups, women, and individuals diverging from cisgender or heteronormative norms within Colombian society. Additionally, the research explores the pervasive influence exerted by the colonial legacy on the foundational structures of Colombian societal organization. Employing discourse analysis as its methodological approach, the thesis undertakes the task of deconstructing and reconstructing the Report, thereby elucidating emergent and contingent discursive meanings that situate coloniality within the realms of cognition, language, and affect. Emphasizing the presence of counter-hegemonic knowledge within the Report, the thesis integrates its findings into a robust theoretical and conceptual framework, facilitating a nuanced and systematic comprehension of the underlying causes of violence perpetrated against marginalized groups and the environment. These causes are intricately linked to the intertwined and hybridized power structures that have endured since the colonial era.
Power structures of Eurocentric origin, alongside mental constructs imposed by European invaders over centuries — such as anthropocentrism, racism, internal colonialism, heteropatriarchy, cisnormativity, and classism — were gradually naturalized and institutionalized within Colombian society. This process has been perpetuated through the state's reproduction of these patterns since the inception of Colombia as a nation-state. Consequently, hierarchical discursive constructions, posited as universal and self-evident, have marginalized certain groups and justified environmental degradation. While the internal armed conflict exacerbated these issues, it did not create them; rather, it intensified pre-existing violences, targeting individuals, communities, and their territories.
The thesis also underscores the portrayal of otherness within the modern-colonial world-system, rooted in principles of domination and subalternity, perpetuating colonial patterns of thought and action, reinforcing the hegemonic cosmovision. Notions of superiority and inferiority that predate the establishment of the Colombian nation-state have influenced social categories, subject positions, and identities, resulting in disproportionate, differential, and cumulative harm inflicted upon subalternized population groups. This contributes to a culture of 'justified' violence.
The research underscores the profound entrenchment of coloniality in the structures of Colombian society and the various inherent logics of violence within its conflicts. Coloniality, a comprehensive framework encompassing colonial patterns of thought and action, originating from the invasion and conquest of Abya Yala, continuously shapes the contemporary realities of societies in diverse (re-)configurations, leaving indelible imprints. The effects of these dynamics are manifold, ranging from the imperative of monogamous sexuality according to Judeo-Christian principles as a self-evident norm to the view of nature as a resource rather than a unit comprising both people and environment. In other words, colonial patterns are deeply embedded in all structures of society. A key recommendation emerging from this thesis is to underscore the imperative to recognize and question the persistence of colonial patterns in social and individual lives.
The research urges recognition and interrogation of these persistent colonial patterns in societal and individual structures, advocating for transformative paradigms that challenge conventional thought patterns and foster self-reflection among Colombians. The report, functioning as a political instrument, holds the potential to significantly contribute to the formation of subjectivities that break away from the epistemic schemes of modernity/coloniality. The research and its findings create a political space for questioning the universalist notion of the Eurocentric civilizing project, the scientific rationality of the universal subject, and the presumed neutrality of its forms of knowledge. This opens avenues for questioning, disputing, and transforming entrenched paradigms.
Goal-Conditioned Reinforcement Learning (GCRL) is a popular framework for training agents to solve multiple tasks in a single environment. It is cru- cial to train an agent on a diverse set of goals to ensure that it can learn to generalize to unseen downstream goals. Therefore, current algorithms try to learn to reach goals while simultaneously exploring the environment for new ones (Aubret et al., 2021; Mendonca et al., 2021). This creates a form of the prominent exploration-exploitation dilemma. To relieve the pres- sure of a single agent having to optimize for two competing objectives at once, this thesis proposes the novel algorithm family Goal-Conditioned Re- inforcement Learning with Prior Intrinsic Exploration (GC-π), which sep- arates exploration and goal learning into distinct phases. In the first ex- ploration phase, an intrinsically motivated agent explores the environment and collects a rich dataset of states and actions. This dataset is then used to learn a representation space, which acts as the distance metric for the goal- conditioned reward signal. In the final phase, a goal-conditioned policy is trained with the help of the representation space, and its training goals are randomly sampled from the dataset collected during the exploration phase. Multiple variations of these three phases have been extensively evaluated in the classic AntMaze MuJoCo environment (Nachum et al., 2018). The fi- nal results show that the proposed algorithms are able to fully explore the environment and solve all downstream goals while using every dimension of the state space for the goal space. This makes the approach more flexible compared to previous GCRL work, which only ever uses a small subset of the dimensions for the goals (S. Li et al., 2021a; Pong et al., 2020).
WaterGAP (Water - Global Assessment and Prognosis) is a tool for modeling global water use and water availability. It participates among other models in the ISIMIP initiative (The Inter-Sectoral Impact Model Intercomparison Project). As part of this initiative, the water temperature should be calculated by participating hydrological models because it plays a vital role in many chemical, physical and biological processes. Therefore, the subject of this master thesis is to implement the physically based surface water temperature computation after VAN BEEK ET AL. (2012) and WANDERS ET AL. (2019) into WaterGAP and compare the results to the statistical regression approach by PUNZET ET AL. (2012). The computation is validated with observed water temperature data obtained from the GEMStat water quality database. The results are good for arctic and temperate latitudes. Surface water temperatures for tropical rivers are overestimated, most likely due to the overestimation of precipitation temperatures, incoming radiation and groundwater temperatures. The comparison with the regression model by PUNZET ET AL. (2012) shows matching results. The regression model even matches with WaterGAP results for most of the simulations of the future under climate change conditions, where the regression model should stop working due to changing environmental parameters. Several assumptions had to be made in order to implement the water temperature calculation in Water-GAP. These include, e.g., discharge temperatures for power plant cooling water, precipitation and surface runoff temperatures. For model improvements, perhaps three different values for the different regions of the world should be used to cool down the precipitation and surface runoff. The model could also be improved by refining the ice formation calculation, especially for the conditions when the ice melts, breaks up and is transported downstream. Furthermore, the feedback to the river channel roughness could be implemented if ice has formed. The WaterGAP model upgraded with the water temperature calculation will help the ISIMIP initiative in the future.
The reanalysis products and derived products, ERA5 (Copernicus Climate Change Service, 2018) and W5E5 (WATCH Forcing Data (WFD) methodology applied to ERA5) (LANGE ET AL., 2021) have been recently published initiating a new phase of scientific research utilizing these datasets. ERA5 and W5E5 offer the possibility to reduce insecurities in model results through their improved quality compared to previous climate reanalyses (CUCCHI ET AL., 2020). The suitability of either climate forcing as input for the hydrological model WaterGAP and the influence of the models specific calibration routine has been evaluated with four model experiments. The model was validated by analysing the models ability to produce reasonable values for global water balance components and to reproduce observed discharge in 1427 basins as well as total water storage anomalies in 143 basins using well established efficiency metrics. Bias correction of W5E5 was found to lead to more global realistic mean precipitation and consequently discharge and AET values. In an uncalibrated model setup ERA5 results in better performances across all efficiency metrics. Model results produced with W5E5 as climate input were strongly improved through calibration ultimately leading to the best performances out of all four model experiments. However, model performances considerably improved through calibration with both climate forcings hence calibration was found to have the strongest effect on model performance. Furthermore, spatial differences in performance of either forcing were identified. Snow-dominated regions show an overall better performance with ERA5, while wetter and warmer regions are better represented with W5E5. Finally, it can be concluded that W5E5 should be preferred as climate input for impact modelling; however, depending on the spatial scale and region ERA5 should at least be considered, in particular for snow-dominated regions.
We give theorems about asymptotic normality of general additive functionals on patricia tries, derived from results on tries. These theorems are applied to show asymptotic normality of the distribution of random fringe trees in patricia tries. Formulas for asymptotic mean and variance are given. The proportion of fringe trees with 𝑘 keys is asymptotically, ignoring oscillations, given by (1−𝜌(𝑘))/(𝐻 +𝐽)𝑘(𝑘−1) with the source entropy 𝐻, an entropy-like constant 𝐽, that is 𝐻 in the binary case, and an exponentially decreasing function 𝜌(𝑘). Another application gives asymptotic normality of the independence number and the number of 𝑘-protected nodes.
Caroline Ungher-Sabatier wird heute meist mit dem Altsolo bei der Uraufführung der 9. Symphonie Ludwig van Beethovens in Verbindung gebracht. Ihre größten Erfolge konnte sie jedoch mit der italienischen Oper in den 1830er Jahren feiern. Bisher fand ihre sängerische Laufbahn und ihre Kontakte in Wien – abgesehen von Franz Schubert und Beethoven – kaum Beachtung. Deswegen beschäftigt sich diese Masterarbeit eingehend mit der Künstlerin und Sängerin Caroline Ungher-Sabatier, deren Lebensweg mit der Stadt Wien verknüpft ist. Sie genoss dort ihre musikalische Ausbildung, kehrte, nach ihren großen Erfolgen in Italien, 1839 und 1840 für Gastspiele an das Kärntnertortheater zurück und hielt lebenslang ein Netzwerk zu berühmten und bekannten Persönlichkeiten in Wien aufrecht. Nach ihrem Bühnenabschied war Ungher-Sabatier als Gesangspädagogin tätig und setzte sich auch in Wien für ihre Schülerinnen ein.
Neben ihrer erfolgreichen Karriere in der italienischen Oper pflegte Ungher-Sabatier das deutsche Liedrepertoire und komponierte eigene Lieder.
Ihr Werdegang und ihre Rezeption in Wien werden anhand von zeitgenössischen Artikeln aus Zeitungen und Zeitschriften untersucht. Um einen tieferen Einblick in Caroline Ungher-Sabatiers Wiener Netzwerk nach ihrer Opernkarriere zu ermöglichen, wurden ihr Stammbuch und über hundert Briefe ausgewertet.
AI-based computer vision systems play a crucial role in the environment perception for autonomous driving. Although the development of self-driving systems has been pursued for multiple decades, it is only recently that breakthroughs in Deep Neural Networks (DNNs) have led to their widespread application in perception pipelines, which are getting more and more sophisticated. However, with this rising trend comes the need for a systematic safety analysis to evaluate the DNN's behavior in difficult scenarios as well as to identify the various factors that cause misbehavior in such systems. This work aims to deliver a crucial contribution to the lacking literature on the systematic analysis of Performance Limiting Factors (PLFs) for DNNs by investigating the task of pedestrian detection in urban traffic from a monocular camera mounted on an autonomous vehicle. To investigate the common factors that lead to DNN misbehavior, six commonly used state-of-the-art object detection architectures and three detection tasks are studied using a new large-scale synthetic dataset and a smaller real-world dataset for pedestrian detection. The systematic analysis includes 17 factors from the literature and four novel factors that are introduced as part of this work. Each of the 21 factors is assessed based on its influence on the detection performance and whether it can be considered a Performance Limiting Factor (PLF). In order to support the evaluation of the detection performance, a novel and task-oriented Pedestrian Detection Safety Metric (PDSM) is introduced, which is specifically designed to aid in the identification of individual factors that contribute to DNN failure. This work further introduces a training approach for F1-Score maximization whose purpose is to ensure that the DNNs are assessed at their highest performance. Moreover, a new occlusion estimation model is introduced to replace the missing pedestrian occlusion annotations in the real-world dataset. Based on a qualitative analysis of the correlation graphs that visualize the correlation between the PLFs and the detection performance, this study identified 16 of the initial 21 factors as being PLFs for DNNs out of which the entropy, the occlusion ratio, the boundary edge strength, and the bounding box aspect ratio turned out to be most severely affecting the detection performance. The findings of this study highlight some of the most serious shortcomings of current DNNs and pave the way for future research to address these issues.
Die Arbeit befasst sich mit einer Vereinfachung des von Devroye (1999) geprägten Begriffs der random split trees und verallgemeinert diesen im Sinne von Janson (2019) auf unbeschränkten Verzweigungsgrad. Diese Verallgemeinerung deckt auch preferential attachment trees mit linearen Gewichten ab, wofür ein Beweis von Janson (2019) aufbereitet wird. Zusätzlich bleiben die von Devroye (1999) nachgewiesenen Eigenschaften über die Tiefe der hinzugefügten Knoten erhalten.
Statistical shape models learn to capture the most characteristic geometric variations of anatomical structures given samples from their population. Accordingly, shape models have become an essential tool for many medical applications and are used in, for example, shape generation, reconstruction, and classification tasks. However, established statistical shape models require precomputed dense correspondence between shapes, often lack robustness, and ignore the global surface topology. This thesis presents a novel neural flow-based shape model that does not require any precomputed correspondence. The proposed model relies on continuous flows of a neural ordinary differential equation to model shapes as deformations of a template. To increase the expressivity of the neural flow and disentangle global, low-frequency deformations from the generation of local, high- frequency details, we propose to apply a hierarchy of flows. We evaluate the performance of our model on two anatomical structures, liver, and distal femur. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior, as indicated by its generalization ability and specificity. More so, we demonstrate the effectiveness of our shape model on shape reconstruction tasks and find anatomically plausible solutions. Finally, we assess the quality of the emerging shape representation in an unsupervised setting and discriminate healthy from pathological shapes.