S. Peitz, J. Stenner, V. Chidananda, O. Wallscheid, S. L. Brunton, and K. Taira, “Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning,”10.1016/j.physd.2024.134096__Physica D: Nonlinear Phenomena, vol. 461, p. 134096, 2024, doi:10.1016/j.physd.2024.134096 .
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S. Dorociak, Implementierung eines Algorithmus zur motivbasierten Schnitt-Sparsifizierung. 2024.
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M. Khedkar and E. Bodden, “Toward an Android Static Analysis Approach for Data Protection,” presented at the 9th International Conference on Mobile Software Engineering and Systems 2024, Lisbon, Portugal, 2024.
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arXiv: 2402.07889

S. E. Otto, S. Peitz, and C. W. Rowley, “Learning Bilinear Models of Actuated Koopman Generators from  Partially-Observed Trajectories,”10.1137/22M1523601__SIAM Journal on Applied Dynamical Systems, vol. 23, no. 1, pp. 885–923, 2024, doi:10.1137/22M1523601 .
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arXiv: 2209.09977

A.-K. Wickert, M. Schlichtig, M. Vogel, L. Winter, M. Mezini, and E. Bodden, Supporting Error Chains in Static Analysis for Precise Evaluation Results and Enhanced Usability. 2024.
LibreCat: /record/52663

Q. A. Ahmed, T. Wiersema, and M. Platzner, “Post-configuration Activation of Hardware Trojans in FPGAs,”10.1007/s41635-024-00147-5__Journal of Hardware and Systems Security, 2024, doi:10.1007/s41635-024-00147-5 .
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DOI: 10.1007/s41635-024-00147-5

S. Hüsing, C. Schulte, S. Sparmann, and M. Bolte, “Using Worked Examples for Engaging in Epistemic Programming Projects,”10.1145/3626252.3630961__2024, doi:10.1145/3626252.3630961 .
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L. Hu, I. Habernal, L. Shen, and D. Wang, “Differentially Private Natural Language Models: Recent Advances and Future Directions,” in Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024, 2024, pp. 478–499.
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T. Igamberdiev, D. N. L. Vu, F. Kuennecke, Z. Yu, J. Holmer, and I. Habernal, “DP-NMT: Scalable Differentially Private Machine Translation,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, 2024, pp. 94–105.
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K. Razavi, S. Ghafouri, M. Mühlhäuser, P. Jamshidi, and L. Wang, “Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling,” presented at the The 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024, Athens, Greece, 2024.
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C. De Groote, A Dispersion Algorithm for Robot Swarms Inside Polygonal Boundary Shapes. 2024.
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R. Doddegowda, Optimal Drone Strategies For Packet Delivery. 2024.
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H. Thakur, Evaluating the Implications. 2024.
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O. L. Preuß, J. Rook, and H. Trautmann, “On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems,” in Applications of Evolutionary Computation, Cham: Springer Nature Switzerland, 2024.
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DOI: 10.1007/978-3-031-56852-7_20

M. Vinzent Seiler, J. Rook, J. Heins, O. L. Preuß, J. Bossek, and H. Trautmann, “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP,”10.1109/ssci52147.2023.10372008__2024, doi:10.1109/ssci52147.2023.10372008 .
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F. Philipp, M. Schaller, K. Worthmann, S. Peitz, and F. Nüske, “Error bounds for kernel-based approximations of the Koopman operator,”10.1016/j.acha.2024.101657__Applied and Computational Harmonic Analysis , vol. 71, Art. no. 101657, 2024, doi:10.1016/j.acha.2024.101657 .
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DOI: 10.1016/j.acha.2024.101657
arXiv: 2301.08637

M. Lass, T. Kenter, C. Plessl, and M. Brehm, “Characterizing Microheterogeneity in Liquid Mixtures via Local Density Fluctuations,”10.3390/e26040322__Entropy, vol. 26, no. 4, Art. no. 322, 2024, doi:10.3390/e26040322 .
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K. Olgu, T. Kenter, J. Nunez-Yanez, and S. Mcintosh-Smith, “Optimisation and Evaluation of Breadth First Search with oneAPI/SYCL on Intel FPGAs: from Describing Algorithms to Describing Architectures,”10.1145/3648115.3648134__2024, doi:10.1145/3648115.3648134 .
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DOI: 10.1145/3648115.3648134

S. Ghafouri et al., “IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency,” Journal of Systems Research (JSys).
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L. Höper and C. Schulte, “Empowering Students for the Data-Driven World: A Qualitative Study of the Relevance of Learning about Data-Driven Technologies,”10.15388/infedu.2024.19__Informatics in Education, 2024, doi:10.15388/infedu.2024.19 .
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DOI: 10.15388/infedu.2024.19