TY - GEN AU - Stiballe, Alisa AU - Reimer, Jan Dennis AU - Sadeghi-Kohan, Somayeh AU - Hellebrand, Sybille ID - 50284 TI - Modeling Crosstalk-induced Interconnect Delay with Polynomial Regression ER - TY - CONF AU - Hu, Lijie AU - Habernal, Ivan AU - Shen, Lei AU - Wang, Di ED - Graham, Yvette ED - Purver, Matthew ID - 52827 T2 - Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024 TI - Differentially Private Natural Language Models: Recent Advances and Future Directions ER - TY - CONF AB - Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees; however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package. Our goal is to provide a platform for researchers to advance the development of privacy-preserving NMT systems, keeping the specific details of the DP-SGD algorithm transparent and intuitive to implement. We run a set of experiments on datasets from both general and privacy-related domains to demonstrate our framework in use. We make our framework publicly available and welcome feedback from the community. AU - Igamberdiev, Timour AU - Vu, Doan Nam Long AU - Kuennecke, Felix AU - Yu, Zhuo AU - Holmer, Jannik AU - Habernal, Ivan ED - Aletras, Nikolaos ED - De Clercq, Orphee ID - 52842 T2 - Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations TI - DP-NMT: Scalable Differentially Private Machine Translation ER - TY - JOUR AU - Boeddeker, Christoph AU - Subramanian, Aswin Shanmugam AU - Wichern, Gordon AU - Haeb-Umbach, Reinhold AU - Le Roux, Jonathan ID - 52958 JF - IEEE/ACM Transactions on Audio, Speech, and Language Processing KW - Electrical and Electronic Engineering KW - Acoustics and Ultrasonics KW - Computer Science (miscellaneous) KW - Computational Mathematics SN - 2329-9290 TI - TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings VL - 32 ER - TY - CONF AU - Razavi, Kamran AU - Ghafouri, Saeid AU - Mühlhäuser, Max AU - Jamshidi, Pooyan AU - Wang, Lin ID - 53095 T2 - Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024 TI - Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling ER - TY - JOUR AB - In this work, we consider optimal control problems for mechanical systems with fixed initial and free final state and a quadratic Lagrange term. Specifically, the dynamics is described by a second order ODE containing an affine control term. Classically, Pontryagin's maximum principle gives necessary optimality conditions for the optimal control problem. For smooth problems, alternatively, a variational approach based on an augmented objective can be followed. Here, we propose a new Lagrangian approach leading to equivalent necessary optimality conditions in the form of Euler-Lagrange equations. Thus, the differential geometric structure (similar to classical Lagrangian dynamics) can be exploited in the framework of optimal control problems. In particular, the formulation enables the symplectic discretisation of the optimal control problem via variational integrators in a straightforward way. AU - Leyendecker, Sigrid AU - Maslovskaya, Sofya AU - Ober-Blöbaum, Sina AU - Almagro, Rodrigo T. Sato Martín de AU - Szemenyei, Flóra Orsolya ID - 53101 JF - Journal of Computational Dynamics KW - Optimal control problem KW - Lagrangian system KW - Hamiltonian system KW - Variations KW - Pontryagin's maximum principle. SN - 2158-2491 TI - A new Lagrangian approach to control affine systems with a quadratic Lagrange term ER - TY - CONF AU - Dann, Andreas Peter AU - Hermann, Ben AU - Bodden, Eric ID - 35083 TI - UpCy: Safely Updating Outdated Dependencies ER - TY - JOUR AB - As in almost every other branch of science, the major advances in data science and machine learning have also resulted in significant improvements regarding the modeling and simulation of nonlinear dynamical systems. It is nowadays possible to make accurate medium to long-term predictions of highly complex systems such as the weather, the dynamics within a nuclear fusion reactor, of disease models or the stock market in a very efficient manner. In many cases, predictive methods are advertised to ultimately be useful for control, as the control of high-dimensional nonlinear systems is an engineering grand challenge with huge potential in areas such as clean and efficient energy production, or the development of advanced medical devices. However, the question of how to use a predictive model for control is often left unanswered due to the associated challenges, namely a significantly higher system complexity, the requirement of much larger data sets and an increased and often problem-specific modeling effort. To solve these issues, we present a universal framework (which we call QuaSiModO: Quantization-Simulation-Modeling-Optimization) to transform arbitrary predictive models into control systems and use them for feedback control. The advantages of our approach are a linear increase in data requirements with respect to the control dimension, performance guarantees that rely exclusively on the accuracy of the predictive model, and only little prior knowledge requirements in control theory to solve complex control problems. In particular the latter point is of key importance to enable a large number of researchers and practitioners to exploit the ever increasing capabilities of predictive models for control in a straight-forward and systematic fashion. AU - Peitz, Sebastian AU - Bieker, Katharina ID - 21199 JF - Automatica TI - On the Universal Transformation of Data-Driven Models to Control Systems VL - 149 ER - TY - JOUR AB - This paper presents a model of an energy system for a private household extended by a lifetime prognosis. The energy system was designed for fully covering the year-round energy demand of a private household on the basis of electricity generated by a photovoltaic (PV) system, using a hybrid energy storage system consisting of a hydrogen unit and a lithium-ion battery. Hydrogen is produced with a Proton Exchange Membrane (PEM) electrolyser by PV surplus during the summer months and then stored in a hydrogen tank. Mainly during winter, in terms of lack of PV energy, the hydrogen is converted back into electricity and heat by a fuel cell. The model was created in Matlab/Simulink and is based on real input data. Heat demand was also taken into account and is covered by a heat pump. The simulation period is a full year to account for the seasonality of energy production and demand. Due to high initial costs, the longevity of such an energy system is of vital interest. Therefore, this model was extended by a lifetime prediction in order to optimize the dimensioning with the aim of lifetime extension of a hydrogen-based energy system. Lifetime influencing factors were identified on the basis of a literature review and were integrated in the model. An extensive parameter study was performed to evaluate different dimensionings regarding the energy balance and the lifetime of the three components, electrolyser, fuel cell and lithium-ion battery. The results demonstrate the benefits of a holistic modelling approach and enable a design optimization regarding the use of resources, lifetime and self-sufficiency of the system AU - Möller, Marius Claus AU - Krauter, Stefan ID - 35428 IS - 1 JF - Solar SN - 2673-9941 TI - Dimensioning and Lifetime Prediction Model for a Hybrid, Hydrogen-Based Household PV Energy System Using Matlab/Simulink VL - 3 ER - TY - CHAP AU - Ostsieker, Laura AU - Biehler, Rolf ID - 35697 SN - 1869-4918 T2 - Practice-Oriented Research in Tertiary Mathematics Education TI - Supporting Students in Developing Adequate Concept Images and Definitions at University: The Case of the Convergence of Sequences ER - TY - CHAP AU - Kortemeyer, Jörg AU - Biehler, Rolf ID - 35678 SN - 1869-4918 T2 - Practice-Oriented Research in Tertiary Mathematics Education TI - Analyzing the Interface Between Mathematics and Engineering in Basic Engineering Courses ER - TY - CHAP AU - Biehler, Rolf AU - Liebendörfer, Michael AU - Gueudet, Ghislaine AU - Rasmussen, Chris AU - Winsløw, Carl ID - 35669 SN - 1869-4918 T2 - Practice-Oriented Research in Tertiary Mathematics Education TI - Practice-Oriented Research in Tertiary Mathematics Education – An Introduction ER - TY - CHAP AU - Liebendörfer, Michael AU - Büdenbender-Kuklinski, Christiane AU - Lankeit, Elisa AU - Schürmann, Mirko AU - Biehler, Rolf AU - Schaper, Niclas ID - 35681 SN - 1869-4918 T2 - Practice-Oriented Research in Tertiary Mathematics Education TI - Framing Goals of Mathematics Support Measures ER - TY - BOOK ED - Biehler, Rolf ED - Liebendörfer, Michael ED - Gueudet, Ghislaine ED - Rasmussen, Chris ED - Winsløw, Carl ID - 37469 SN - 1869-4918 TI - Practice-Oriented Research in Tertiary Mathematics Education ER - TY - CONF AU - Schrader, Elena AU - Bernijazov, Ruslan AU - Foullois, Marc AU - Hillebrand, Michael AU - Kaiser, Lydia AU - Dumitrescu, Roman ID - 37553 T2 - 2022 IEEE International Symposium on Systems Engineering (ISSE) TI - Examples of AI-based Assistance Systems in context of Model-Based Systems Engineering ER - TY - CONF AU - Richter, Cedric AU - Haltermann, Jan Frederik AU - Jakobs, Marie-Christine AU - Pauck, Felix AU - Schott, Stefan AU - Wehrheim, Heike ID - 35426 T2 - 37th IEEE/ACM International Conference on Automated Software Engineering TI - Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs? ER - TY - CONF AU - Schott, Stefan AU - Pauck, Felix ID - 36848 T2 - 2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM) TI - Benchmark Fuzzing for Android Taint Analyses ER - TY - CONF AU - Pauck, Felix ID - 35427 T2 - 37th IEEE/ACM International Conference on Automated Software Engineering TI - Scaling Arbitrary Android App Analyses ER - TY - JOUR AU - Noé, Reinhold ID - 38280 IS - 1 JF - Journal of Lightwave Technology TI - Consistent Optical and Electrical Noise Figure VL - 41 ER - TY - GEN AB - We consider the data-driven approximation of the Koopman operator for stochastic differential equations on reproducing kernel Hilbert spaces (RKHS). Our focus is on the estimation error if the data are collected from long-term ergodic simulations. We derive both an exact expression for the variance of the kernel cross-covariance operator, measured in the Hilbert-Schmidt norm, and probabilistic bounds for the finite-data estimation error. Moreover, we derive a bound on the prediction error of observables in the RKHS using a finite Mercer series expansion. Further, assuming Koopman-invariance of the RKHS, we provide bounds on the full approximation error. Numerical experiments using the Ornstein-Uhlenbeck process illustrate our results. AU - Philipp, Friedrich AU - Schaller, Manuel AU - Worthmann, Karl AU - Peitz, Sebastian AU - Nüske, Feliks ID - 38031 T2 - arXiv:2301.08637 TI - Error bounds for kernel-based approximations of the Koopman operator ER -