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Institute
Analysis of machine learning prediction quality for automated subgroups within the MIMIC III dataset
(2023)
The motivation for this master’s thesis is to explore the potential of predictive data analytics in the field of medicine. For this, the MIMIC-III dataset offers an extensive foundation for the construction of prediction models, including Random Forest, XGBOOST, and deep learning networks. These models were implemented to forecast the mortality of 2,655 stroke patients.
The first part of the thesis involved conducting a comprehensive data analysis of the filtered MIMIC-III dataset.
Subsequently, the effectiveness and fairness of the predictive models were evaluated. Although the performance levels of the developed models did not match those reported in related research, their potential became evident. The results obtained demonstrated promising capabilities and highlighted the effectiveness of the applied methodologies. Moreover, the feature relevance within the XGBOOST model was examined to increase model explainability.
Finally, relevant subgroups were identified to perform a comparative analysis of the prediction performance across these subgroups. While this approach can be regarded as a valuable methodology, it was not possible to investigate underlying reasons for potential unfairness across clusters. Inside the test data, not enough instances remained per subgroup for further fairness or feature relevance analysis.
In conclusion, the implementation of an alternative use case with a higher patient count is recommended.
The code for this analysis is made available via a GitHub repository and includes a frontend to visualize the results.
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.
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.
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.
Electron identification with a likelihood method and measurements of di-electrons for the CBM-TRD
(2017)
In this work a likelihood method has been implemented and investigated as particle identification algorithm for the CBM-TRD.
The creation of the probability distributions for the likelihood method via V0-topologies seems to be feasible and the purity of the obtained samples is sufficient for the usage in the likelihood method.
The comparison between the ANN and the likelihood method shows no differences in the identification performance. The pion suppression factor reaches the same values for the same electron identification efficiencies and the yields of the resulting di-lepton signals are comparable. The signal-to-background ratios for both methods have the same values and show a value of about 10−2 in the invariant mass range of minv = 1.5 - 2.5 GeV/c2, which is expected to be sufficient to provide access to the thermal in-medium and QGP radiation.
The investigation of a detector system without a TRD shows no pion suppression for a momentum above p = 6 GeV/c. Therefore, the background contributions increase drastically and the signal-to-background ratio decreases at all invariant masses, but especially in the invariant mass range of minv = 1.5 - 2.5 GeV/c2.
The background contributions in the invariant mass range of minv = 1.5 - 2.5 GeV/c 2 are also influenced by the selected electron identification efficiency of the TRD, which significantly shifts the fraction of the eπ contributions relative to the total number of pairs.
Anisotropic collective flow of protons resulting from non-central heavy ion collisions is a unique hadronic observable providing information about the early stage of the nuclear collision. The analysis of collective flow in the energy regime between 1-2 AGeV enables the study of the phase diagram of hadronic matter at a high baryochemical potential µb, as well as the analysis of the equation of state at densities up to the threefold of the ground state density ρ0.
The algorithms of the standard event plane method and the scalar product method are used to analyse directed and elliptic flow of protons in a centrality range of 0-40 % most central events.
Prior to the analysis of experimental data, the respective influence of the reconstruction procedure on the algorithms is examined using Monte Carlo simulations based on the Ultra relativistic Quantum Molecular Dynamics (UrQMD) model.
Subsequently, experimental data measured in April 2012 with the High Acceptance DiElectron Spectrometer (HADES) is analysed using both methods. About 7.3 · 109 Au+Au events at a kinetic beam energy of 1.23 AGeV, equivalent to a centre of mass energy of √sNN = 2.42 GeV were recorded. A multi-differential analysis is feasible as the HADES detector provides a good transverse momentum and rapidity coverage.
Both algorithms result in identical values for directed and elliptic flow across all centrality classes within the observable phase space of protons. The calculated integrated value of v2 at mid rapidity is in good agreement with world data.
In April and May 2012 data on Au+Au collisions at beam energies of Ekin = 1.23A GeV were collected with the High Acceptance Di-Electron Spectrometer (HADES) at the GSI Helmholtzzentrum für Schwerionenforschung facility in Darmstadt, Germany. In this thesis, the production of deuterons in this collision system is investigated.
A total number of 2.1 × 109 Au+Au events is selected, containing the most central 0-40% of events. After particle identification, based on a mass determination via time-of-flight and momentum and on a measurement of the energy loss, the transverse mass spectra of the deuteron candidates are extracted for various rapidities and subsequently corrected for acceptance and efficiency.
The inverse slope parameter of a Boltzmann fit applied to the transverse mass spectra at midrapidity, which is referred to as the effective temperature, is extracted. For a static thermal source, this parameter corresponds to the kinetic freeze-out temperature Tkin and is therefore expected to be smaller or equal to the chemical freeze-out temperature Tchem. The extracted effective temperature of Tef f = (190 ± 10) MeV however exceeds the chemical freeze-out temperature that was obtained by a statistical model fit to different particle yields. The effective temperatures of various particle species, obtained in previous analyses, suggest a systematic rise with increasing particle mass, which is confirmed by the deuteron results.
An explanation can be the influence of a collective expansion with a radial expansion velocity βr. By fitting a Siemens-Rasmussen function to the transverse mass spectra, the global temperature of T = (100 ± 8) MeV and radial expansion velocity βr = 0.37 ± 0.01 are obtained. This temperature is still very high and only takes into account the production of deuteron nuclei.
The simultaneous fit of a blast-wave function to the transverse mass spectra of deuterons and other particles, as obtained by previous analyses, considers a velocity profile for the radial expansion velocity and takes into account the production of various particle species. The resulting global temperature Tkin = (68 ± 1) MeV and average transverse expansion velocity hβri = 0.341 ± 0.003 are within the expected range for the collision energy.
The Siemens-Rasmussen fits are also used to extrapolate the transverse mass spectra into unmeasured regions, to integrate them and obtain a rapidity-dependent count rate. This count rate exhibits a thermal shape for central events and shows increasing spectator contributions for more peripheral events.
The invariant yield spectra of the deuterons are compared to those of protons, as obtained by a previous analysis, in the context of a nucleon coalescence model. The hereby extracted nucleon coalescence factor B2 = (4.6 ± 0.1) × 10−3 agrees with the expected result for the beam energy that was studied.
When performing transfer learning in Computer Vision, normally a pretrained model (source model) that is trained on a specific task and a large dataset like ImageNet is used. The learned representation of that source model is then used to perform a transfer to a target task. Performing transfer learning in this way had a great impact on Computer Vision, because it worked seamlessly, especially on tasks that are related to each other. Current research topics have investigated the relationship between different tasks and their impact on transfer learning by developing similarity methods. These similarity methods have in common, to do transfer learning without actually doing transfer learning in the first place but rather by predicting transfer learning rankings so that the best possible source model can be selected from a range of different source models. However, these methods have focused only on singlesource transfers and have not paid attention to multi-source transfers. Multi-source transfers promise even better results than single-source transfers as they combine information from multiple source tasks, all of which are useful to the target task. We fill this gap and propose a many-to-one task similarity method called MOTS that predicts both, single-source transfers and multi-source transfers to a specific target task. We do that by using linear regression and the source representations of the source models to predict the target representation. We show that we achieve at least results on par with related state-of-the-art methods when only focusing on singlesource transfers using the Pascal VOC and Taskonomy benchmark. We show that we even outperform all of them when using single and multi-source transfers together (0.9 vs. 0.8) on the Taskonomy benchmark. We additionally investigate the performance of MOTS in conjunction with a multi-task learning architecture. The task-decoder heads of a multi-task learning architecture are used in different variations to do multi-source transfers since it promises efficiency over multiple singletask architectures and incurs less computational cost. Results show that our proposed method accurately predicts transfer learning rankings on the NYUD dataset and even shows the best transfer learning results always being achieved when using more than one source task. Additionally, it is further examined that even just using one task-decoder head from the multi-task learning architecture promises better transfer learning results, than using a single-task architecture for the same task, which is due to the shared information from different tasks in the multi-task learning architecture in previous layers. Since the MOTS rankings for selecting the MTI-Net task-decoder head with the highest transfer learning performance were very accurate for the NYUD but not satisfying for the Pascal VOC dataset, further experiments need to varify the generalizability of MOTS rankings for the selection of the optimal task-decoder head from a multi-task architecture.
Autonomous steering of an electric bicycle based on sensor fusion using model predictive control
(2019)
In this thesis a control and steering module for an autonomous bicycle was developed. Based on sensor fusion and model predictive control, the module is able to trace routes autonomously.
The system is developed to run on a Raspberry Pi. An ultrasonic sensor and a 2D Lidar sensor are used for distance measurements. The vehicle’s position is determined by using GPS signals. Additionally, a camera is used to capture pictures for the roadside detection. In order to recognize the road and the position of the vehicle on it, computer vision techniques are used. The captured images are denoised, Canny edge detection is performed and a perspective transformation is applied. Thereafter a sliding window algorithm selects the edges belonging to the roadside and a second order polynomial is fitted to the selected data. Based on this, the road curvature and the lateral position of the vehicle on the road are calculated. The implemented software is thus able to detect straight and curved roads as well as the vehicle’s lateral offset.
A route planning module was implemented to navigate the vehicle from the start to the destination coordinates. This is done by creating an abstract graph of the roads and using Dijkstra’s algorithm to determine the shortest path.
Four MPC controllers were implemented to control the movements of the vehicle. They are based on state space equations derived from the linear single-track vehicle model. This relatively straightforward model makes it possible to predict the vehicle behavior and is efficient to compute. Each controller was built with different parameters for different vehicle speeds to account for the non-linearity of the system. The controllers simulate the future states of the system at each timeslot and select appropriate control signals for steering, throttle and brakes.
In this thesis, all the components of the steering and control module were individually validated. It was established that the each individual component works as expected and certain constraints and accuracy limits were identified. Finally, the closed loop capabilities of the system were assessed using a test vehicle. Despite some limitations imposed by this setup, it was shown that the control module is indeed capable of autonomously navigating a vehicle and avoiding collisions.
Computational workflow optimization for magnetic fluctuation measurements of 3D nano-tetrapods
(2021)
The detailed understanding of micro–and nanoscale structures, in particular their magnetization dynamics, dominates contemporary solid–state physics studies. Most investigations already identified an abundance of phenomena in one–and two–dimensional nanostructures. The following thesis focuses on the magnetic fingerprint of three–dimensional CoFe nano–magnets, specifically the temporal development of their hysteresis loop. These nano–magnets were grown in a tetrahedral pattern on top of a highly susceptible home–build GaAs/AlGaAs micro–Hall sensor using focused electron beam induced deposition (FEBID).
During the measurements, utmost efforts were employed to exemplify current best research practices. The data life cycle of the present thesis is based upon open–source data science tools and packages. Data acquisition and analysis required self–written automated algorithms to handle the extensive quantity of data. Existing instrumental-controlling software was improved, and new Python packages were devised to analyze and visualize the gathered data. The open–source Python data analysis framework (ana) was developed to facilitate computational reproducibility. This framework transparently analyses and visualizes the gathered data automatically using Continuous Analysis tools based on GitLab and Continuous Integration. This automatization uses bespoke scripts combined with virtualization tools like Docker to facilitate reproducible and device–independent results.
The hysteresis loops reveal distinct differences in subsequently measured loops with identical initial experimental parameters, originating from the nano–magnet’s magnetic noise. This noise amplifies in regions where switching processes occur. In such noise–prone regions, the time–dependent scrutinization reveals presumably thermally induced metastable magnetization states. The frequency–dependent power spectral density uncovers a characteristic 1/f² behavior at noise–prone regions with metastable magnetization states.
The internet has often been considered a 'technology of freedom' – a nearly revolutionary tool believed to flatten social hierarchies and democratize access to media by 'giving voice' to everybody equally. Contradictory to this point of view, research has shown the existence of a 'digital divide,' the phenomenon that access to and use of the internet, as well as the outcomes derived from this use, correlate with pre-existing inequalities.
Based on ethnographic fieldwork among activists in Dakar, Senegal, this thesis analyzes how inequalities shape and are shaped by the relationships between activists and smartphones. Do smartphones indeed flatten social hierarchies, or are inequalities rather reproduced – or even reinforced – through them?
Frankfurt as a global international city is home to transcultural people with diverse linguistic biographies and migration backgrounds. As teachers exert significant influence on the language practice of their students and their awareness of self and others, it is crucial to examine the language ideologies and attitudes on multilingualism of teachers who work in different schools in Frankfurt. The online questionnaire was selected as the data collection
method for the combination of qualitative and quantitative analysis where teachers were asked to select their opinion on statements that were designed to represent concurring viewpoints of separate bilingualism and flexible bilingualism. The study builds on existing evidence that multiple factors dynamically shape teachers' attitudes towards multilingualism.
School-level support and cooperation between educational institutions seems to be necessary to establish horizontal continuity and help students benefit from language-sensitive didactic methods, such as translanguaging.
During RUN3 (2021-2023) of the Large Hadron Collider, the Time Projection Chamber (TPC) of ALICE will be operated with quadruple stacks of Gas Electron Multipliers (GEMs). This technology will allow to overcome the rate limitation due to the gated operation of the Multi-Wire Proportional Chambers (MWPCs) used in RUN1 (2009-2013) and RUN2 (2015-2018).
As part of the Upgrade project, long-term irradiation tests, so called "ageing tests", have been carried out. A test setup with a detector using a quadruple stack of 10x10cm2 GEMs was built and operated in Ar-CO2 and Ne-CO2-N2 gas mixtures. The detector performance such as gas gain and energy resolution were monitored continuously. In addition, outgassing tests of materials used for the assembly process of the upgraded TPC were performed. To reach the expected dose of the GEM-based TPC, the detector was operated at much higher gains than the TPC. It was found, that the GEMs could keep their performance within the projected lifetime of the TPC. Most of the tested materials showed no negative impact on the detector. For the tested epoxy adhesive no certain conclusion could be drawn.
At much higher doses than expected for the upgraded TPC, a new phenomenon was observed, which changed the hole geometry of the GEMs and led to a degradation of the energy resolution. Even though its occurrence is not expected during the lifetime of the GEM-based TPC, simulations were carried out to study this effect more systematically. The simulations confirmed, that a change of the hole geometries of the GEMs, lead to an increase of the local gain variation, which results in a decrease of the energy resolution.
Furthermore the effect of methane as quench gas on GEMs was studied, even though this gas is not foreseen to be used in the TPC. From ageing tests with single-wire proportional counters it is well known that hydrocarbons are produced in the plasma of the avalanches, which cover the electrodes and lead to a degradation of the detector performance. Even though GEMs have a quite different geometry, the ageing tests showed, that also this technology tends to methane-induced ageing. A loss of gas gain as well as a degradation of the energy resolution due to deposits on the electrodes was monitored. A qualitative and quantitative comparison between ageing in GEMs and proportional counters was performed.
Software updates are a critical success factor in mobile app ecosystems. Through publishing regular updates, platform providers enhance their operating systems for the benefit of both end users and third-party developers. It is also a way of attracting new customers. However, this platform evolution poses the risk of inadvertently introducing software problems, which can severely disturb the ecosystem’s balance by compromising its foundational technologies. So far, little to no research has addressed this issue from a user-centered perspective. The thesis at hand draws on IS post-adoption literature to investigate the potential negative influences of operating system updates on mobile app users. The release of Apple’s iOS 13 update serves as research object. Based on over half a million user reviews from the AppStore, data mining techniques are applied to study the impact of the new platform version. The results show that iOS 13 caused complications with a large number of popular apps, leading to a significant decline in user ratings and an uptrend in negative sentiment. Feature requests, functional complaints, and device compatibility are identified as the three major issue categories. These issue types are compared in terms of their quantifiable negative effect on users’ continuance intention. In essence, the findings contribute to IS research on post-adoption behavior and provide guidance to ecosystem participants in dealing with update-induced platform issues.
In thesis I investigate the possibility that at the smallest length scale (Planck scale) the very notion of "dimension" needs to be revisited. Due to "quantum effects" spacetime might become very turbulent at these scales and properties like those of "fractals" emerge, including a "scale dependent dimension". It seems that this "spontaneous dimensional reduction" and the appearance of a minimal physical length are very general effects that most approaches to quantum gravity share. Main emphasis is given to the"spectral dimension" and its calculation for strings and p-branes.
Virtual machines are for the most part not used inside of high-energy physics (HEP) environments. Even though they provide a high degree of isolation, the performance overhead they introduce is too great for them to be used. With the rising number of container technologies and their increasing separation capabilities, HEP-environments are evaluating if they could utilize the technology. The container images are small and self-contained which allows them to be easily distributed throughout the global environment. They also offer a near native performance while at the same time aproviding an often acceptable level of isolation. Only the needed services and libraries are packed into an image and executed directly by the host kernel. This work compared the performance impact of the three container technologies Docker, rkt and Singularity. The host kernel was additionally hardened with grsecurity and PaX to strengthen its security and make an exploitation from inside a container harder. The execution time of a physics simulation was used as a benchmark. The results show that the different container technologies have a different impact on the performance. The performance loss on a stock kernel is small; in some cases they were even faster than no container. Docker showed overall the best performance on a stock kernel. The difference on a hardened kernel was bigger than on a stock kernel, but in favor of the container technologies. rkt showed performed in almost all cases better than all the others.
In this thesis, Planck size black holes are discussed. Specifically, new families of black holes are presented. Such black holes exhibit an improved short scale behaviour and can be used to implement gravity self-complete paradigm. Such geometries are also studied within the ADD large extra dimensional scenario. This allows black hole remnant masses to reach the TeV scale. It is shown that the evaporation endpoint for this class of black holes is a cold stable remnant. One family of black holes considered in this thesis features a regular de Sitter core that counters gravitational collapse with a quantum outward pressure. The other family of black holes turns out to nicely fit into the holographic information bound on black holes, and lead to black hole area quantization and applications in the gravitational entropic force. As a result, gravity can be derived as emergent phenomenon from thermodynamics.
The thesis contains an overview about recent quantum gravity black hole approaches and concludes with the derivation of nonlocal operators that modify the Einstein equations to ultraviolet complete field equations.
Recently, Aumüller and Dietzfelbinger proposed a version of a dual-pivot Quicksort, called "Count", which is optimal among dual-pivot versions with respect to the average number of key comparisons required. In this master's thesis we provide further probabilistic analysis of "Count". We derive an exact formula for the average number of swaps needed by "Count" as well as an asymptotic formula for the variance of the number of swaps and a limit law. Also for the number of key comparisons the asymptotic variance and a limit law are identified. We also consider both complexity measures jointly and find their asymptotic correlation.
Cleaning an ion beam from unwanted fractions is crucial for intense ion beams. This thesis will explore separation methods using a collimation channel, electric and magnetic dipoles and a velocity selector for low intensity beams on an experimental basis. In addition, statistical data of degassing events during the commissioning of a pentode extraction system for beam energies from 20 - 120keV will be presented.
The aim of this thesis is finding a geometric configuration that allows electron insertion into a Gabor plasma lens in order to increase the density of the confined electrons and provide ignition conditions at parameters where ignition is not possible. First, simulations using CST and bender were conducted to investigate several geometric configurations in terms of their performance of inserting electrons manually. One particular design has been chosen as a basis for an experiment. In order to prepare the experiment, further simulations using the code bender have been conducted to investigate the density distribution that is formed inside the Gabor lens when inserting electrons transversally in compliance with the chosen design. Additionally, bender was used to investigate the impact of the initial electron energy on the distribution inside the lens. Simulations with and without space charge effects have shown a significant impact of the space charge effects on the resulting density dstribution. Therefore, space charge effects have proven to be the major electron redistribution process. A given electron source was characterised in order to find the performance under the conditions inside a Gabor lens. In particular, a transversal magnetic field that will be present in the experiment has to be compensated by shielding the inner regions of the source by a μ-metal layer. Using a μ-metal shield, transversal magnetic fields are sufficiently tolerable to perform measurements in a Gabor lens. Additionally, operating close to 100 eV electron energy yields a maximum in the emitted current. Adding a Wehnelt cylinder to the electron source furthermore improves the extracted current to roughly 1 mA. A test stand consisting of a newly designed anode for the Gabor lens, as well as a terminal for the electron source, was constructed. The electron source was thoroughly characterised in the environment of the Gabor lens and the ignition properties of the new system were evaluated. In further experiments, electron beam assisted ignition by increasing the residual gas pressure was observed and the impact of the position of the electron source on the ignition properties was investigated. In addition, ignition of a sub-critical state, that is a state consisting of potential, magnetic field and pressure that did not yet perform ignition by itself, was performed by increasing the extracted current from the electron source. Finally, the electron source was used to influence a pre-ignited plasma. The density was measured, which was increased by the use of the electron source in most cases. This project is part of the EDEN collaboration (Electron DENsity boosting) of the NNP Group at IAP Frankfurt with INFN institutes in Bologna and Catania.
Computing the diameter of a graph is a fundamental part of network analysis. Even if the data fits into main memory the best known algorithm needs O(n2) [3] with high probability to compute the exact diameter. In practice this is usually too costly. Therefore, heuristics have been developed to approximate the diameter much faster. The heuristic “double sweep lower bound” (dslb) has reasonably good results and needs only two Breadth-First Searches (BFS). Hence, dslb has a complexity of O(n+m). If the data does not fit into main memory, an external-memory algorithm is needed. In this thesis the I/O model by Vitter and Shriver [4] is used. It is widely accepted and has produced suitable results in the past. The best known external-memory BFS implementation has an I/O-complexity of W(pn B + sort(n)) for sparse graphs [5]. But this is still very expensive compared to the I/O complexity of sorting with O(N/B * logM/B (N/B)). While there is no improvement for the external-memory computation of BFS yet, Meyer published a different approach called “Parallel clustering growing approach” (PAR_APPROX) that is a trade-off between the I/O complexity and the approximation guarantee [6].
In this thesis different existing approaches will be evaluated. Also, PAR_APPROX will be implemented and analyzed if it is viable in practice. One main result will be that it is difficult to choose the parameter in a way that PAR_APPROX is reasonably fast for every graph class without using the semi external-memory Single Source Shortest Path (SSSP) implementation by [1]. However, the gain is small compared to external-memory BFS using this approach. Therefore, the approach PAR_APPROX_R will be developed. Furthermore, a lower bound for the expected error of PAR_APPROX_R will be proved on a carefully chosen difficult input class. With PAR_APPROX_R the desired gain will be reached.
The measurement of dielectrons (electron-positron pairs) allows to investigate the properties of strongly interacting matter, in particular the Quark-Gluon Plasma (QGP), which is created in relativistic heavy-ion collisions at the LHC. The evolution of the collision can be probed via dielectrons since electrons do not interact strongly and are created during all stages of the collision. One of the interests in dielectron measurements is motivated by possible modifications of the electromagnetic emission spectrum in the QGP, where pp collisions are used as a medium-free reference. The dielectron spectrum consists of contributions from various processes. In order to estimate contributions of known dielectron sources, simulations of the so-called dielectron cocktail are performed. In this thesis, dielectron cocktails in minimum bias pp collisions at p s = 7 TeV, p–Pb collisions at p sNN = 5.02 TeV and in central (0-10%) and semi-central (20-50%) Pb–Pb collisions at p sNN = 2.76 TeV at the LHC are presented.
The Time Projection Chamber (TPC), a large gaseous detector, is the main particle identification device of the ALICE experiment at the CERN LHC. The desired performance of the TPC defines the requirements for the gas mixture used in the detector. The active volume was filled with either Ne-CO2 (90-10) or Ne-CO2-N2 (90-10-5) during the first LHC running period. For LHC Run 2 the gas mixture is changed to Ar-CO2. Calculations of relevant gas properties are performed for Ar-based gas mixtures and compared to Ne-based gas mixtures to identify the most suitable Ar mixture. The drift velocity of ions in Ar is lower than in Ne. The closing time of the gating grid has to be adjusted accordingly to avoid drift field distortions due to back-drifting ions. The drift times of ions in the TPC readout chambers are calculated for the respective gas mixtures to determine the time to collect all ions from the amplification region. For LHC Run 3 the TPC readout chambers will be upgraded. The Multiwire Proportional Chambers (MWPCs) will be replaced by readout chambers based on Gas Electron Multipliers (GEMs) which are operated in continuous mode. As a consequence an ion backflow of the order of 1% causes significant space-charge distortions in the TPC drift volume. Similar distortions are expected in data taken specifically for the study of space-charge effects at the end of Run 1. The gating grid of the MWPCs is operated in the open state allowing the ions from the amplification region to enter the drift volume. The magnitude of the distortions in this data is measured and compared to the expectations for the TPC upgrade and results from current simulations.
This work proposes to employ the (bursty) GLO model from Bingmer et. al (2011) to model the occurrence of tropical cyclones. We develop a Bayesian framework to estimate the parameters of the model and, particularly, employ a Markov chain Monte Carlo algorithm. This also allows us to develop a forecasting framework for future events.
Moreover, we assess the default probability of an insurance company that is exposed to claims that occur according to a GLO process and show that the model is able to substantially improve actuarial risk management if events occur in oscillatory bursts.
In the second half of the last century, a brave idea of providing aid to developing countries not only because of humanitarian reasons, but also because of a global economic interdependency was born. International organizations shared this view with a great enthusiasm and significantly increased their engagement in low-income countries believing that these nations need to and can be helped. This movement stirred an interest of the academia and, as a result, research on the outcomes of international donors’ engagement in recipient countries started.
The main aim of this thesis is to analyze a broad spectrum of literature on aid effectiveness in developing countries and to summarize the main findings concerning an impact of state fragility and conflict on the efficiency of donor engagement. However, the main focus of this research lies in an own empirical analysis of some well-established hypotheses, as well as in a statistical testing of the outcomes obtained by other authors in a relevant field of study.
The effectiveness of foreign aid...
This work derived the value of α-induced production cross sections of 77Kr and 77Br at α-energies of 12 MeV and 14 MeV, the thick target yields of 77Kr and 77Br at α-energies of 11.19 MeV, 13 MeV and 15.1 MeV and the thick target yield of 80Br as well as 80mBr at an α-energy of 15.1 MeV using the activation technique...
In situ rainwater harvesting has a long history in arid and semi-arid regions of the world buffering water shortages for human consumption and agriculture. In the context of an Integrated Water Resource Management (IWRM) in the Cuvelai Basin in northern Namibia, roof top rainwater harvesting is being introduced to a rural community for the irrigation of household scale gardens for the cultivation of horticulture products. This study elaborates how harvested rainwater can be used for garden irrigation in a sustainable manner evaluating ecologic, economic and social implications. Considering local conditions eight cropping scenarios were designed, including different criteria as well as one and two annual planting seasons. These schemes were tested under present climate conditions and under three future climate change scenarios for 2050 with the help of a tank model designed to model monthly tank inflows and outflows. Special attention was laid on risk and uncertainty aspects of varying inter-annual and interseasonal precipitation and future climate change. A framework for the assessment of sustainability was adapted to the purposes of this study and indicators have been developed in order to assess the cropping and irrigation schemes for sustainability.
The study found that with the given tank size of 30 m³, depending on crop scenario, under optimized conditions a garden area of 60 to 90 m³ can be irrigated. The choice of crops highly impacts water use efficiency and economic profitability, compared to the considerably lower impact of amount of annual planting seasons and future climate change. In the case of worsening future climate conditions, adaptation measures need to be taken as especially the economic as well as the environmental situation are expected to exacerbate due to expected decreases in yields and revenues. Already under present conditions however, the economic dimension represents the most limiting factor to sustainability, particularly due to the excessive investment costs of the rainwater harvesting and gardening facility. Nonetheless, rainwater harvesting in combination with gardening can be regarded as successful in securing household nutrition, providing sufficient horticulture products for household consumption or market sale. At the same time with the optimal choice of crops the investment costs can be recovered within the end of the lifespan of the facility.
Asymptotic giant branch (AGB) stars are initially low and intermediate mass stars undergoing recurrent hydrogen and helium shell burning. During the advanced stage of stellar evolution AGB stars follow after the helium core burning ceased and are located in the AGB of the Hertzsprung-Russell Diagram. One characteristic is their ability of element synthesis, especially carbon and nitrogen, which they eject in large amounts into the interstellar medium. But AGB stars also feature a slow-neutron capture process called s-process which forms approximately 50 % of all elements between Fe and Bi. The initial mass function emphasizes the importance of the synthesized ejecta of AGB stars since they are much more abundant than massive stars. Therefore, the abundance evolution of many elements in the universe is drastically affected by AGB stars. In order to understand chemical evolution in the universe their behavior must be known since their first appearance. In previous times less heavy elements were produced and available. Hence AGB stars with lower heavy element content, which means lower metallicity, must be investigated. They appear to behave substantially differently than stars of higher metallicity. Another issue is that AGB stars have mass-dependent characteristics from which follows a division into low-mass, massive and super AGB stars. Super AGB stars have the most open issues due to their large masses and initial mass boundaries that separate them from massive stars. Due to large spectroscopic surveys in the last years, many low metallicity stars have been analyzed. These findings make it necessary to complement those studies through stellar modeling. This work makes a step in this direction. The AGB star masses under investigation are 1M⊙, 1.65M⊙, 2M⊙, 3M⊙, 4M⊙, 5M⊙, 6M⊙ and 7M⊙ which include low-mass, massive and super AGB stars. Metallicities of Z = 6 x 10 exp-3 and Z = 1 x 10 exp-4 (for comparison, solar Z ~ 0.02) were chosen. These results are an extension of already available data, covering solar and half-solar metallicity, but without super AGB stars. Therefore physics input includes mainly well-established approaches rather than new theories. New physical approaches are included due to the low metallicity which makes the results a unique set of models. Additionally, extensive s-process network calculations lead to production factors of all included elements and isotopes. The s-process signatures of those stars were analyzed. The stellar evolution simulations presented in this work have been utilized for rate and especially sensitivity studies. One approach done was to analyze s-process branchings at 95Zr and 85Kr for stars at 3M⊙ with Z = 1 x 10 exp-2 and Z = 1 x 10 exp-3 respectively.
In this work we study basic properties of unstable particles and scalar hadronic resonances, respectively, within simple quantum mechanical and quantum field theoretical (effective) models. The term 'particle' is usually assigned to entities, described by physical theories, that are able to propagate over sufficiently large time scales (e.g. from a source to a detector) and hence could be identified in experiments - one especially should be able to measure some of their distinct properties like spin or charge. Nevertheless, it is well known that there exists a huge amount of unstable particles to which it seems difficult to allocate such definite values for their mass and decay width. In fact, for extremely short-lived members of that species, so called resonances, the theoretical description turns out to be highly complicated and requires some very interesting concepts of complex analysis.
In the first chapter, we start with the basic ideas of quantum field theory. In particular, we introduce the Feynman propagator for unstable scalar resonances and motivate the idea that this kind of correlation function should possess complex poles which parameterize the mass and decay width of the considered particle. We also brie
y discuss the problematic scalar sector in particle physics, emphasizing that hadronic loop contributions, given by strongly coupled hadronic intermediate states, dominate its dynamics. After that, the second chapter is dedicated to the method of analytic continuation of complex functions through branch cuts. As will be seen in the upcoming sections, this method is crucial in order to describe physics of scalar resonances because the relevant functions to be investigated (namely, the Feynman propagator of interacting quantm field theories) will also have branch cuts in the complex energy plane due to the already mentioned loop contributions. As is consensus among the physical community, the understanding of the physical behaviour of resonances requires a deeper insight of what is going on beyond the branch cut. This will lead us to the idea of a Riemann surface, a one-dimensional complex manifold on which the Feynman propagator is defined.
We then apply these concepts to a simple non-relativistic Lee model in the third chapter and demonstrate the physical implications, i.e., the motion of the propagator poles and the behaviour of the spectral function. Besides that, we investigate the time evolution of a particle described by such a model. All this will serve as a detailed preparation in order to encounter the rich phenomena occuring on the Riemann surface in quantum field theory. In the last chapter, we finally concentrate on a simple quantm field theoretical model which describes the decay of a scalar state into two (pseudo)scalar ones. It is investigated how the motion of the propagator poles is in
uenced by loop contributions of the two (pseudo)scalar particles. We perform a numerical study for a hadronic system involving a scalar seed state (alias the σ-meson) that couples to pions. The unexpected emergence of a putative stable state below the two-pion threshold is investigated and it is claeifieed under which conditions such a stable state appears.
This work deals with the determination of the scale parameter ΛM̄S̄ from lattice QCD and perturbation theory results of the static quark-antiquark potential for nf = 2. The investigation is done in momentum space. Lattice methods as well as perturbation theory calculations are introduced. Another part of this work concerns the calculation of the quark-antiquark potential from gauge link configurations for nf = 2 + 1 + 1.
Origin of the German Novel
(1927)
Representations of the reasons and actions of terrorists have appeared in German literature tracing back to the age of Sturm und Drang of the 18th century, most notably in Heinrich von Kleist's Michael Kohlhaas and Friedrich Schiller's Die Räuber, and more recently since the radical actions of the Red Army Faction during the late 1960s and early 1970s, such as in Uli Edel's film, The Baader Meinhof Complex. By referring to Walter Benjamin's system of natural law and positive law, which provides definitions of differing codes of ethics with relation to state laws and personal ethics, one should be able to understand that Michael Kohlhaas, Karl Moor, and the members of the RAF are indeed represented as terrorists. However, their actions and motives are not without an internal ethics, which conflicts with that of their respective state-sanctioned authorities. This thesis reveals the similarities and differences in motives, methods, and use of violence in Schiller, Kleist, and representations of the RAF and explores how the turn to terrorism can arise from a logical realization that ideologies of state law do not align with the personal sense of justice and law of the individual.
The ALICE Time Projection Chamber (TPC) is the main tracking detector of ALICE which was designed to perform well at multiplicities of up to 20000 charged primary and secondary tracks emerging from Pb-Pb collisions. Successful operation of such a large and complex detector requires an elaborate calibration and commissioning. The main goal for the calibration procedures is to provide the information needed for the offline software for the reconstruction of the particle tracks with sufficient precision so that the design performance can be achieved. For a precise reconstruction of particle tracks in the TPC, the calibration of the drift velocity, which in conjunction with the drift time provides the z position of the traversing particles, is essential. In this thesis, an online method for the calibration of the drift velocity is presented. It uses the TPC Laser System which generates 336 straight tracks within the active volume of the TPC. A subset of these tracks, showing sufficiently small distortions, is used in the analysis. The resulting time dependent drift velocity correction parameters are entered into a database and provide start values for the offline reconstruction chain of ALICE. Even though no particle tracking information is used, the online drift velocity calibration is in agreement with the full offline calibration including tracking on the level of about 2 x 10 exp (-4). In chapter 2, a short overview of the ALICE detector, as well as the data taking model of the ALICE, is given. In chapter 3, the TPC detector is described in detail. Lastly in chapter 4, the online drift velocity calibration method is presented, together with a detailed description of the TPC laser system.
As a part of this thesis, a Monte Carlo-based code has been developed capable of simulating the transition of proton beam properties to neutron beam properties as it occurs in the Li-7(p, n)Be-7 reaction. It is able to reproduce not only the angle-integrated energy distributions but it is also capable of predicting the angle-dependent neutron spectra as measured at Forschungszentrum Karlsruhe (Karlsruhe, Germany) and Physikalisch-Technische Bundesanstalt (Braunschweig, Germany). Since the code retains all three spatial dimensions as well as all three velocity dimensions, it provides very detailed information on the neutron beam. The resulting data can aid in many different aspects, for example it can be used in shielding construction, or for lithium target design. In this work, the code is used to predict the neutron beam properties expected at the Frankfurt Neutron Source at Stern-Gerlach-Zentrum (FRANZ) facility. For different proton beam energies, the neutron distribution in x/p_x, y/p_y, and z/p_z is shown as well as a Mollweide projection, which illustrates the kinematic collimation effect that limits the neutron cone opening angle to less than 180 degree.
This study analyzes storyline structure in three Hausa home videos; Mai Kudi (The Rich Man), Sanafahna (with time truth shall dawn) and Albashi (Salary). The study measures storyline structure in these films against a Hollywood film industry model of story writing “the Hero's Journey”. It uses narrative analysis as its analytical tool, and narrative theory as its framework. After analyzing these videos, the study found that the major elements of storyline structure in Vogler's model formed the framework of the storyline structure in Hausa home videos analyzed. However, in spite of the preponderance of these elements within the storyline structure, there are significant variations to Vogler's model. Specifically, Vogler's model has some twelve stages spread on the universal structure of storytelling, i.e. beginning, middle and end. Few of these stages were found to exist in Hausa narrative structure, perhaps due to cultural differences between Western, Indian and Hausa cultures. The study therefore recommends screenwriters and producers to be aware of the existence of standard models of scriptwriting. It also recommends more training for script writers in the Hausa film industry.