Peitz, S., Stenner, J., Chidananda, V., Wallscheid, O., Brunton, S. L., &; Taira, K. (2024). Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning. Physica D: Nonlinear Phenomena, 461, 134096.https://doi.org/10.1016/j.physd.2024.134096 
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DOI: 10.1016/j.physd.2024.134096

Dorociak, S. (2024). Implementierung eines Algorithmus zur motivbasierten Schnitt-Sparsifizierung.
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Khedkar, M., &; Bodden, E. (2024). Toward an Android Static Analysis Approach for Data Protection. Proceedings of the 9th International Conference on Mobile Software Engineering and Systems. 9th International Conference on Mobile Software Engineering and Systems 2024, Lisbon, Portugal.
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arXiv: 2402.07889

Otto, S. E., Peitz, S., &; Rowley, C. W. (2024). Learning Bilinear Models of Actuated Koopman Generators from  Partially-Observed Trajectories. SIAM Journal on Applied Dynamical Systems, 23(1), 885–923.https://doi.org/10.1137/22M1523601 
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arXiv: 2209.09977

Wickert, A.-K., Schlichtig, M., Vogel, M., Winter, L., Mezini, M., &; Bodden, E. (2024). Supporting Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability.
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Ahmed, Q. A., Wiersema, T., &; Platzner, M. (2024). Post-configuration Activation of Hardware Trojans in FPGAs. Journal of Hardware and Systems Security.https://doi.org/10.1007/s41635-024-00147-5 
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Hüsing, S., Schulte, C., Sparmann, S., &; Bolte, M. (2024). Using Worked Examples for Engaging in Epistemic Programming Projects. Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1.https://doi.org/10.1145/3626252.3630961 
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Hu, L., Habernal, I., Shen, L., &; Wang, D. (2024). Differentially Private Natural Language Models: Recent Advances and Future Directions. In Y. Graham &; M. Purver (Eds.), Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024 (pp. 478–499). Association for Computational Linguistics.
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Igamberdiev, T., Vu, D. N. L., Kuennecke, F., Yu, Z., Holmer, J., &; Habernal, I. (2024). DP-NMT: Scalable Differentially Private Machine Translation. In N. Aletras &; O. De Clercq (Eds.), Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations (pp. 94–105). Association for Computational Linguistics.
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Razavi, K., Ghafouri, S., Mühlhäuser, M., Jamshidi, P., &; Wang, L. (2024). Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling. Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), Colocated with EuroSys 2024. The 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024, Athens, Greece.
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