@inproceedings{32796, author = {{Böcker, Joachim}}, booktitle = {{2022 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)}}, location = {{Sorrento, Italy}}, publisher = {{IEEE}}, title = {{{Concept Study of an LLC Converter with Magnetically Resonant Inductor}}}, doi = {{10.1109/speedam53979.2022.9842047}}, year = {{2022}}, } @misc{33033, author = {{Fehring, Lukas}}, title = {{{Combined Ranking and Regression Trees for Algorithm Selection}}}, year = {{2022}}, } @misc{33041, author = {{Luan, Yunhao}}, title = {{{Untersuchung der physikalische Absorptionsmethoden zur Bestimmung von gasseitigen Stoffübergangskoeffizienten für strukturierte Packungen basierend auf hydrodynamischen Analogien}}}, year = {{2022}}, } @misc{32756, author = {{Markewitz, Friedrich}}, booktitle = {{Zeitschrift für Angewandte Linguistik}}, pages = {{130--138}}, title = {{{Hexenverhörprotokolle als sprachhistorisches Korpus. Fallstudien zur Erschließung der frühneuzeitlichen Schriftsprache}}}, volume = {{76}}, year = {{2022}}, } @inproceedings{29934, abstract = {{Tire and road wear are a major source of emissions of nonexhaust particulate matter (PM) and make up the largest share of microplastics in the environment. To reduce tire wear through numerical optimization of a vehicle's suspension system, fast simulations of the representative usage of a vehicle are needed. Therefore, this contribution evaluates if instead of a full simulation of a representative test drive, only specific driving maneuvers resulting from a clustering of the driving data can be used to predict tire wear. As a measure for tire wear, the friction work between tire and road is calculated. It is shown that enough clusters result in negligible deviations between the total friction work of the full simulation and the cluster simulations as well as between the distributions of the friction work over the tire width. The calculation time can be reduced to about 1% of the full simulation.}}, author = {{Muth, Lars and Noll, Christian and Sextro, Walter}}, booktitle = {{Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021}}, editor = {{Orlova, Anna and Cole, David}}, isbn = {{978-3-031-07304-5}}, keywords = {{Tire Wear, Vehicle Dynamics, Clustering, Virtual Test}}, location = {{Saint Petersburg, Russia}}, publisher = {{Springer}}, title = {{{Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data}}}, doi = {{10.1007/978-3-031-07305-2_92}}, year = {{2022}}, } @unpublished{30867, abstract = {{In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. As the latter is known to exhibit a heavy-tail distribution, an algorithm is normally stopped when exceeding a predefined upper time limit. As a consequence, machine learning methods used to optimize an algorithm selection strategy in a data-driven manner need to deal with right-censored samples, a problem that has received little attention in the literature so far. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. Moreover, we adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon. In an extensive experimental evaluation on an adapted version of the ASlib benchmark, we demonstrate that theoretically well-founded methods based on Thompson sampling perform specifically strong and improve in comparison to existing methods.}}, author = {{Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the 36th AAAI Conference on Artificial Intelligence}}, publisher = {{AAAI}}, title = {{{Machine Learning for Online Algorithm Selection under Censored Feedback}}}, year = {{2022}}, } @unpublished{30865, abstract = {{The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection. Over the past decade, the problem has received considerable attention, resulting in a number of different methods for algorithm selection. Although most of these methods are based on machine learning, surprisingly little work has been done on meta learning, that is, on taking advantage of the complementarity of existing algorithm selection methods in order to combine them into a single superior algorithm selector. In this paper, we introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework, essentially combining ideas of meta learning and ensemble learning. In an extensive experimental evaluation, we demonstrate that ensembles of algorithm selectors can significantly outperform single algorithm selectors and have the potential to form the new state of the art in algorithm selection.}}, author = {{Tornede, Alexander and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Machine Learning}}, title = {{{Algorithm Selection on a Meta Level}}}, year = {{2022}}, } @techreport{33070, author = {{Fuhrmann, Anika and Schoch, Rebecca and Rusam, Alexander and Bosse, Michael and Flachmann, Felix and Moritzer, Elmar and Hochrein, Thomas and Bastian, Martin}}, publisher = {{Shaker}}, title = {{{Verbessertes Füllverhalten im Spritzgießprozess durch Schäumen von WPC: Bewertung des Fließverhaltens treibmittelbeladener Polymerschmelzen}}}, year = {{2022}}, } @article{33090, abstract = {{AbstractHeated tool butt welding is a method often used for joining thermoplastics, especially when the components are made out of different materials. The quality of the connection between the components crucially depends on a suitable choice of the parameters of the welding process, such as heating time, temperature, and the precise way how the parts are then welded. Moreover, when different materials are to be joined, the parameter values need to be tailored to the specifics of the respective material. To this end, in this paper, three approaches to tailor the parameter values to optimize the quality of the connection are compared: a heuristic by Potente, statistical experimental design, and Bayesian optimization. With the suitability for practice in mind, a series of experiments are carried out with these approaches, and their capabilities of proposing well-performing parameter values are investigated. As a result, Bayesian optimization is found to yield peak performance, but the costs for optimization are substantial. In contrast, the Potente heuristic does not require any experimentation and recommends parameter values with competitive quality.}}, author = {{Gevers, Karina and Tornede, Alexander and Wever, Marcel Dominik and Schöppner, Volker and Hüllermeier, Eyke}}, issn = {{0043-2288}}, journal = {{Welding in the World}}, keywords = {{Metals and Alloys, Mechanical Engineering, Mechanics of Materials}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials}}}, doi = {{10.1007/s40194-022-01339-9}}, year = {{2022}}, } @unpublished{33150, abstract = {{In this article, we build on previous work to present an optimization algorithm for nonlinearly constrained multi-objective optimization problems. The algorithm combines a surrogate-assisted derivative-free trust-region approach with the filter method known from single-objective optimization. Instead of the true objective and constraint functions, so-called fully linear models are employed and we show how to deal with the gradient inexactness in the composite step setting, adapted from single-objective optimization as well. Under standard assumptions, we prove convergence of a subset of iterates to a quasi-stationary point and if constraint qualifications hold, then the limit point is also a KKT-point of the multi-objective problem.}}, author = {{Berkemeier, Manuel Bastian and Peitz, Sebastian}}, booktitle = {{arXiv:2208.12094}}, title = {{{Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients}}}, year = {{2022}}, } @article{33221, abstract = {{AbstractNon-pharmaceutical interventions are an effective strategy to prevent and control COVID-19 transmission in the community. However, the timing and stringency to which these measures have been implemented varied between countries and regions. The differences in stringency can only to a limited extent be explained by the number of infections and the prevailing vaccination strategies. Our study aims to shed more light on the lockdown strategies and to identify the determinants underlying the differences between countries on regional, economic, institutional, and political level. Based on daily panel data for 173 countries and the period from January 2020 to October 2021 we find significant regional differences in lockdown strategies. Further, more prosperous countries implemented milder restrictions but responded more quickly, while poorer countries introduced more stringent measures but had a longer response time. Finally, democratic regimes and stronger manifested institutions alleviated and slowed down the introduction of lockdown measures.}}, author = {{Redlin, Margarete}}, issn = {{0922-680X}}, journal = {{Journal of Regulatory Economics}}, keywords = {{Economics and Econometrics}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Differences in NPI strategies against COVID-19}}}, doi = {{10.1007/s11149-022-09452-9}}, year = {{2022}}, } @article{33220, abstract = {{AbstractWe provide a partial equilibrium model wherein AI provides abilities combined with human skills to provide an aggregate intermediate service good. We use the model to find that the extent of automation through AI will be greater if (a) the economy is relatively abundant in sophisticated programs and machine abilities compared to human skills; (b) the economy hosts a relatively large number of AI-providing firms and experts; and (c) the task-specific productivity of AI services is relatively high compared to the task-specific productivity of general labor and labor skills. We also illustrate that the contribution of AI to aggregate productive labor service depends not only on the amount of AI services available but on the endogenous number of automated tasks, the relative productivity of standard and IT-related labor, and the substitutability of tasks. These determinants also affect the income distribution between the two kinds of labor. We derive several empirical implications and identify possible future extensions.}}, author = {{Gries, Thomas and Naudé, Wim}}, issn = {{2510-5019}}, journal = {{Journal for Labour Market Research}}, keywords = {{General Medicine}}, number = {{1}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Modelling artificial intelligence in economics}}}, doi = {{10.1186/s12651-022-00319-2}}, volume = {{56}}, year = {{2022}}, } @inproceedings{33230, author = {{Daymude, Joshua J. and Richa, Andréa W. and Scheideler, Christian}}, booktitle = {{1st Symposium on Algorithmic Foundations of Dynamic Networks, SAND 2022, March 28-30, 2022, Virtual Conference}}, editor = {{Aspnes, James and Michail, Othon}}, pages = {{12:1–12:19}}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}}, title = {{{Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems}}}, doi = {{10.4230/LIPIcs.SAND.2022.12}}, volume = {{221}}, year = {{2022}}, } @article{33219, author = {{Gries, Thomas and Müller, Veronika and Jost, John T.}}, issn = {{1047-840X}}, journal = {{Psychological Inquiry}}, keywords = {{General Psychology}}, number = {{2}}, pages = {{65--83}}, publisher = {{Informa UK Limited}}, title = {{{The Market for Belief Systems: A Formal Model of Ideological Choice}}}, doi = {{10.1080/1047840x.2022.2065128}}, volume = {{33}}, year = {{2022}}, } @inproceedings{33240, author = {{Götte, Thorsten and Scheideler, Christian}}, booktitle = {{SPAA ’22: 34th ACM Symposium on Parallelism in Algorithms and Architectures, Philadelphia, PA, USA, July 11 - 14, 2022}}, editor = {{Agrawal, Kunal and Lee, I-Ting Angelina}}, pages = {{99–101}}, publisher = {{ACM}}, title = {{{Brief Announcement: The (Limited) Power of Multiple Identities: Asynchronous Byzantine Reliable Broadcast with Improved Resilience through Collusion}}}, doi = {{10.1145/3490148.3538556}}, year = {{2022}}, } @inbook{33248, author = {{Herzig, Bardo}}, booktitle = {{Radikalisierungsnarrative online. Perspektiven und Lehren aus Wissenschaft und Prävention}}, editor = {{Reinke de Buitrago, S.}}, isbn = {{978-3658370428}}, pages = {{273 -- 300}}, publisher = {{Springer VS}}, title = {{{Medienbildung als Radikalisierungsprävention?}}}, year = {{2022}}, } @inbook{33247, author = {{Herzig, Bardo and Martin, Alexander}}, booktitle = {{Handbuch Pädagogikunterricht}}, editor = {{Püttmann, C. and Wortmann, E.}}, isbn = {{978-3825256203}}, pages = {{433 -- 443}}, publisher = {{Waxmann Verlag GmbH}}, title = {{{Lehren und Lernen mit (digitalen) Medien im Pädagogikunterricht}}}, year = {{2022}}, } @inbook{33268, author = {{Goller, Michael and Hilkenmeier, Frederic}}, booktitle = {{Methods for Researching Professional Learning and Development}}, isbn = {{9783031085178}}, issn = {{2210-5549}}, publisher = {{Springer International Publishing}}, title = {{{PLS-Based Structural Equation Modelling: An Alternative Approach to Estimating Complex Relationships Between Unobserved Constructs}}}, doi = {{10.1007/978-3-031-08518-5_12}}, year = {{2022}}, } @inbook{33266, author = {{Goller, Michael and Kyndt, Eva and Paloniemi, Susanna and Damşa, Crina}}, booktitle = {{Methods for Researching Professional Learning and Development}}, isbn = {{9783031085178}}, issn = {{2210-5549}}, publisher = {{Springer International Publishing}}, title = {{{Addressing Methodological Challenges in Research on Professional Learning and Development}}}, doi = {{10.1007/978-3-031-08518-5_1}}, year = {{2022}}, } @inbook{33271, author = {{Leidig, Susann and Köhler, Hanna and Caruso, Carina and Goller, Michael}}, booktitle = {{Methods for Researching Professional Learning and Development}}, isbn = {{9783031085178}}, issn = {{2210-5549}}, publisher = {{Springer International Publishing}}, title = {{{Q Method: Assessing Subjectivity Through Structured Ranking of Items}}}, doi = {{10.1007/978-3-031-08518-5_20}}, year = {{2022}}, }