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B. Renner et al., “Perspective: A Conceptual Framework for Adaptive Personalized Nutrition Advice Systems,”10.1016/j.advnut.2023.06.009__Advances in Nutrition, 2023, doi:10.1016/j.advnut.2023.06.009 .
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C. Demir and A.-C. Ngonga Ngomo, “Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras,” ECML-PKDD, 2023.
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C. Demir and A.-C. Ngonga Ngomo, “Neuro-Symbolic Class Expression Learning,” International Joint Conference on Artificial Intelligence, 2023.
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A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. 2023.
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I. Gräßler and D. Preuß, “Automatisierte Abhängigkeitsanalyse von Anforderungen zur Wirkkettenmodellierung,” in Stuttgarter Symposium für Produktentwicklung SSP 2023, Stuttgart, 2023.
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C. Offen and S. Ober-Blöbaum, “Learning discrete Lagrangians for variational PDEs from data and detection of travelling waves,”10.1007/978-3-031-38271-0_57__in Geometric Science of Information, Saint-Malo, Palais du Grand Large, France, 2023, vol. 14071, pp. 569–579, doi:10.1007/978-3-031-38271-0_57 .
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S. Ober-Blöbaum and C. Offen, “Variational Learning of Euler–Lagrange Dynamics from Data,”10.1016/j.cam.2022.114780__Journal of Computational and Applied Mathematics, vol. 421, p. 114780, 2023, doi:10.1016/j.cam.2022.114780 .
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E. Dierkes, C. Offen, S. Ober-Blöbaum, and K. Flaßkamp, “Hamiltonian Neural Networks with Automatic Symmetry Detection,”10.1063/5.0142969__Chaos, vol. 33, no. 6, Art. no. 063115, 2023, doi:10.1063/5.0142969 .
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H. M. A. Zahera, F. Vitiugin, M. Sherif, C. Castillo, and A.-C. Ngonga Ngomo, “Using Pre-trained Language Models for Abstractive DBpedia Summarization: A Comparative Study,” presented at the SEMANTiCS 2023, Leipzig, Germany, 2023.
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C. Greiff et al., “Calibration of Large Coherent MIMO Radar Arrays: Channel Imbalances and 3D Antenna Positions,”10.23919/IRS57608.2023.10172475__presented at the 2023 24th International Radar Symposium (IRS), Berlin, Germany, 2023, doi:10.23919/IRS57608.2023.10172475 .
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N. Vöing, M. Arnold, and S. Reisas, Lehrentwicklung durch Scholarship of Teaching and Learning – mit SoTL Hochschullehre beforschen, weiterentwickeln und teilen. e-teaching.org, 2023.
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M. Arnold, N. Vöing, and S. Reisas, “Scholarship of Teaching and Learning (SoTL) als Motor für eine inter- und transdisziplinäre Hochschuldidaktik,” in Hochschulbildung: Lehre und Forschung, Bielefeld, Germany: transcript Verlag, 2023.
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S. S. Hotegni, S. Peitz, and M. B. Berkemeier, “Multi-Objective Optimization for Sparse Deep Neural Network Training,” arXiv:2308.12243. 2023.
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M. Dellnitz et al., “Efficient time stepping for numerical integration using reinforcement  learning,”10.1137/21M1412682__SIAM Journal on Scientific Computing, vol. 45, no. 2, pp. 9–5, 2023, doi:10.1137/21M1412682 .
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M. Arnold, N. Vöing, and S. Reisas, “Scholarship of Teaching and Learning (SoTL) als Motor für eine inter- und transdisziplinäre Hochschuldidaktik,” in Hochschulbildung: Lehre und Forschung, Bielefeld, Germany: transcript Verlag, 2023.
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M. Rossel and G. Meschut, “Modeling and Parameterization for a 3D Simulation of Clinching with an Extensible Die,” in Lecture Notes in Mechanical Engineering, Cham: Springer Nature Switzerland, 2023.
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R. Reeschke, F. K. Haase, L. Dautzenberg, W. Krutsch, and C. Reinsberger, “Training matters: Heading incidence and characteristics in children’s and youth football (soccer) players,”10.1111/sms.14408__Scandinavian Journal of Medicine &; Science in Sports, vol. 33, no. 9, pp. 1821–1830, 2023, doi:10.1111/sms.14408 .
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E. Yigitbas, M. Schmidt, A. Bucchiarone, S. Gottschalk, and G. Engels, “GaMoVR: Gamification-Based UML Learning Environment in Virtual Reality,” Science of Computer Programming, 2023.
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C. Soyka, M. Ullrich, M. Striewe, and N. Schaper, “Comparison of Required Competences and Task Material in Modeling Education,”10.18417/EMISA.18.7__Enterprise Modelling and Information Systems Architectures, vol. 18, Art. no. 7, 2023, doi:10.18417/EMISA.18.7 .
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