@inproceedings{66968, author = {{Razmi, Nasrin and Mukherjee, Sourav and Matthiesen, Bho and Dekorsy, Armin and Popovski, Petar}}, booktitle = {{2025 14th International ITG Conference on Systems, Communications and Coding (SCC)}}, publisher = {{IEEE}}, title = {{{Sparse Incremental Aggregation in Satellite Federated Learning}}}, doi = {{10.1109/ieeeconf62907.2025.10949091}}, year = {{2025}}, } @inproceedings{66969, author = {{Razmi, Nasrin and Matthiesen, Bho and Dekorsy, Armin and Popovski, Petar}}, booktitle = {{2024 IEEE Globecom Workshops (GC Wkshps)}}, publisher = {{IEEE}}, title = {{{Energy-Aware Federated Learning in Satellite Constellations}}}, doi = {{10.1109/gcwkshp64532.2024.11100966}}, year = {{2025}}, } @inbook{60208, author = {{Kruse, Iris and Kanning, Julian}}, booktitle = {{Praktiken der Erinnerung}}, editor = {{Ballis, Anja and Sosna, Anette and Wrobel, Dieter}}, publisher = {{Wissenschaftlicher Verlag Trier}}, title = {{{Zwischen Ablehnung und Befürwortung . Professionelle Beliefs zu Holocaust-Bilderbüchern im Unterricht von Klasse 4}}}, year = {{2025}}, } @article{63053, author = {{Hernández, Carlos and Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Cuate, Oliver and Trautmann, Heike and Schütze, Oliver}}, journal = {{IEEE Transactions on Evolutionary Computation}}, keywords = {{Optimization, Evolutionary computation, Hands, Proposals, Convergence, Computational efficiency, Artificial intelligence, Accuracy, Approximation algorithms, Aerospace electronics, Multi-objective optimization, evolutionary algorithms, nearly optimal solutions, multimodal optimization, archiving, continuation}}, pages = {{1747--1760}}, title = {{{An Evolutionary Approach for the Computation of ∈-Locally Optimal Solutions for Multi-Objective Multimodal Optimization}}}, doi = {{10.1109/TEVC.2025.3637276}}, year = {{2025}}, } @misc{67026, author = {{Leinfellner, Stefanie and Lammer, Christina}}, title = {{{Soziale Nachhaltigkeit – Perspektiven für Mentoring-Programme}}}, year = {{2025}}, } @article{67188, author = {{Eckel, Julia and Reinerth, Maike Sarah and Schamal, Vera}}, journal = {{animationstudies 2.0}}, title = {{{Editorial: Animation and Agency}}}, year = {{2025}}, } @misc{67187, booktitle = {{animationstudies 2.0}}, editor = {{Eckel, Julia and Reinerth, Maike Sarah and Schamal, Vera}}, title = {{{animationstudies 2.0. Theme: Animation and Agency}}}, year = {{2025}}, } @article{67189, author = {{Eckel, Julia}}, journal = {{animationstudies 2.0}}, title = {{{AI Anymation and Agency}}}, year = {{2025}}, } @inproceedings{63193, abstract = {{The integration of data-driven models and specifically machine learning for conditon monitoring and predictive maintenance into companies, especially small and medium-sized enterprises, offers significant opportunities in reducing costs, operating more sustainably, and maintaining long-term competitiveness. However, many small and medium-sized enterprises lack the necessary resources and expertise to derive knowledge from data and integrate their own machine learning based solutions. To address this challenge, a framework is presented that enables the automated generation of data-driven models with a particular focus on condition monitoring and predictive maintenance, but applicable to other use cases as well. Using a dataset from the 2022 data challenge of the prognostics and health management society, it is demonstrated that the framework can generate high-performing models, achieving F1-scores up to 0.998, exemplarily for a classification task.}}, author = {{Löwen, Alexander and Quirin, Dennis and Hesse, Marc and Aimiyekagbon, Osarenren Kennedy and Sextro, Walter}}, booktitle = {{2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA)}}, location = {{Porto}}, publisher = {{IEEE}}, title = {{{Facilitating the Automated Generation of Data-Driven Models for the Diagnostics and Prognostics of Technical Systems}}}, doi = {{10.1109/etfa65518.2025.11205799}}, year = {{2025}}, } @book{67219, author = {{Ptashnyk, Stefaniya}}, isbn = {{9783111624020}}, publisher = {{De Gruyter}}, title = {{{Multilingualität in der Stadt}}}, doi = {{10.1515/9783111624495}}, year = {{2025}}, } @inbook{54774, author = {{Varga, Eva}}, booktitle = {{Diskurse und Praktiken der Heterogenität. Pädagogisches Handeln in heterogenen Settings. Mit einem Fokus auf die Elementar- und Primarbildung.}}, editor = {{Günesli, Habib and Albers, Timm and Mombeck, Mona M. and Jesuthasan, Jonitta}}, pages = {{239--248}}, publisher = {{Beltz Juventa}}, title = {{{Das Linguistic Soundscaping im Elementarbereich. Ein methodischer Ansatz für die Erfahrung von sprachlicher und sprachbezogener Heterogenität. }}}, year = {{2025}}, } @inbook{66491, author = {{Taskaya, Ayce and Jenninger, Isabell}}, booktitle = {{Ausbilder-Handbuch: Aufgaben, Konzepte, Praxisbeispiele}}, editor = {{Dietl, Stefan F. and Schmidt, Hermann and Weiß, Reinhold and Wittwer, Wolfgang}}, isbn = {{978-3871561658}}, pages = {{1--16}}, publisher = {{Deutscher Wirtschaftsdienst}}, title = {{{Die Standardberufsbildposition "Umweltschutz und Nachhaltigkeit" im Betrieb umsetzen}}}, year = {{2025}}, } @article{60681, author = {{Díaz Canales, Edith Benjamina and Avila Galarza, Alfredo and Schlüter, Sabine and Lacayo Escobar, Erick and Schlüter, Alexander}}, issn = {{3044-5221}}, journal = {{Journal of Sustainable Development Indicators}}, number = {{2}}, pages = {{1--26}}, publisher = {{SDEWES Centre}}, title = {{{Implementing Strategic Environmental Assessment in the Global South, a Challenge: Nicaragua as a Case Study}}}, doi = {{10.13044/j.sdi.d2.0592}}, volume = {{1}}, year = {{2025}}, } @unpublished{63602, abstract = {{We show that, on a smoothly paracompact convenient manifold $M$ modeled on a convenient space with the bornological approximation property, the dual map of a Poisson bracket factors as a smooth section of the vector bundle $L_{skew}^2(T^*M,\mathbb R)$.}}, author = {{Michor, P. W. and Rahangdale, Praful}}, title = {{{Poisson bivectors on infinite dimensional manifolds}}}, year = {{2025}}, } @inbook{67279, author = {{Schreiber, Christine and Patrzek, Justine}}, booktitle = {{Psychologiedidaktik an allgemeinbildenden und beruflichen Schulen}}, isbn = {{9783662694800}}, keywords = {{Unterrichtsfach Psychologie}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Urteilsbildung als ein didaktisches Modell für den Psychologieunterricht}}}, doi = {{10.1007/978-3-662-69481-7_5}}, year = {{2025}}, } @inbook{67278, author = {{Patrzek, Justine and Schreiber, Christine}}, booktitle = {{Psychologiedidaktik an allgemeinbildenden und beruflichen Schulen}}, isbn = {{9783662694800}}, keywords = {{Unterrichtsfach Psychologie}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Paradigmenorientierte Didaktik des Psychologieunterrichts}}}, doi = {{10.1007/978-3-662-69481-7_2}}, year = {{2025}}, } @inbook{67281, author = {{Bender, Elena and Iobidze, Maia}}, booktitle = {{Psychologiedidaktik an allgemeinbildenden und beruflichen Schulen: Ein Lehrbuch mit Unterrichtsmaterialien }}, editor = {{Scharlau, Ingrid and Bender, Elena and Patrzek, Justine and Schreiber, Christine}}, isbn = {{978-3-662-69480-1}}, keywords = {{Unterrichtsfach Psychologie, Psychologiedidaktik}}, pages = {{55--88}}, publisher = {{Springer Nature}}, title = {{{Lernfelddidaktik im Psychologieunterricht an berufsbildenden Schulen}}}, year = {{2025}}, } @article{51159, abstract = {{Sparsity is a highly desired feature in deep neural networks (DNNs) since it ensures numerical efficiency, improves the interpretability of models (due to the smaller number of relevant features), and robustness. In machine learning approaches based on linear models, it is well known that there exists a connecting path between the sparsest solution in terms of the $\ell^1$ norm,i.e., zero weights and the non-regularized solution, which is called the regularization path. Very recently, there was a first attempt to extend the concept of regularization paths to DNNs by means of treating the empirical loss and sparsity ($\ell^1$ norm) as two conflicting criteria and solving the resulting multiobjective optimization problem. However, due to the non-smoothness of the $\ell^1$ norm and the high number of parameters, this approach is not very efficient from a computational perspective. To overcome this limitation, we present an algorithm that allows for the approximation of the entire Pareto front for the above-mentioned objectives in a very efficient manner. We present numerical examples using both deterministic and stochastic gradients. We furthermore demonstrate that knowledge of the regularization path allows for a well-generalizing network parametrization.}}, author = {{Amakor, Augustina Chidinma and Sonntag, Konstantin and Peitz, Sebastian}}, journal = {{Machine Learning with Applications}}, publisher = {{Elsevier}}, title = {{{A multiobjective continuation method to compute the regularization path of deep neural networks}}}, doi = {{10.1016/j.mlwa.2025.100625}}, volume = {{19}}, year = {{2025}}, } @article{46579, abstract = {{The Koopman operator has become an essential tool for data-driven analysis, prediction and control of complex systems, the main reason being the enormous potential of identifying linear function space representations of nonlinear dynamics from measurements. Until now, the situation where for large-scale systems, we (i) only have access to partial observations (i.e., measurements, as is very common for experimental data) or (ii) deliberately perform coarse graining (for efficiency reasons) has not been treated to its full extent. In this paper, we address the pitfall associated with this situation, that the classical EDMD algorithm does not automatically provide a Koopman operator approximation for the underlying system if we do not carefully select the number of observables. Moreover, we show that symmetries in the system dynamics can be carried over to the Koopman operator, which allows us to massively increase the model efficiency. We also briefly draw a connection to domain decomposition techniques for partial differential equations and present numerical evidence using the Kuramoto--Sivashinsky equation.}}, author = {{Peitz, Sebastian and Harder, Hans and Nüske, Feliks and Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl}}, journal = {{Journal of Computational Dynamics}}, pages = {{305--324}}, title = {{{Equivariance and partial observations in Koopman operator theory for partial differential equations}}}, doi = {{10.3934/jcd.2024035}}, volume = {{12}}, year = {{2025}}, } @inproceedings{53793, abstract = {{We utilize extreme learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individually using a single model. Despite requiring only few data points (in some cases, our method can learn from a single full-state snapshot), it still achieves high accuracy and can predict the flow of PDEs over long time horizons. Moreover, we show how additional symmetries can be exploited to increase sample efficiency and to enforce equivariance.}}, author = {{Harder, Hans and Peitz, Sebastian}}, booktitle = {{Systems theory in data and optimization}}, editor = {{Berberich, J. and Ianelli, A. and Allgöwer, F.}}, isbn = {{978-3-031-83191-1}}, keywords = {{extreme learning machines, partial differential equations, data-driven prediction, high-dimensional systems}}, pages = {{153--168}}, publisher = {{Springer}}, title = {{{Solving partial differential equations with equivariant extreme learning machines}}}, doi = {{10.1007/978-3-031-83191-1_10}}, year = {{2025}}, }