TY - CONF AB - Digital Servitization is one of the significant trends affecting the manufacturing industry. Companies try to tackle challenges regarding their differentiation and profitability using digital services. One specific type of digital services are smart services, which are digital services built on data from smart products. Introducing these kinds of offerings into the portfolio of manufacturing companies is not trivial. Moreover, they require conscious action to align all relevant capabilities to realize the respective business goals. However, what capabilities are generally relevant for smart services remains opaque. We conducted a systematic literature review to identify them and extended the results through an interview study. Our analysis results in 78 capabilities clustered among 12 principles and six dimensions. These results provide significant support for the smart service transformation of manufacturing companies and for structuring the research field of smart services. AU - Koldewey, Christian AU - Fichtler, Timm AU - Scholtysik, Michel AU - Biehler, Jan AU - Schreiner, Nick AU - Sommer, Franziska AU - Schacht, Maximilian AU - Kaufmann, Jonas AU - Rabe, Martin AU - Sedlmeier, Joachim AU - Dumitrescu, Roman ID - 48632 KW - Digital Servitization KW - Transformation KW - Capabilities KW - Maturity KW - Smart Services TI - Exploring Capabilities for the Smart Service Transformation in Manufacturing: Insights from Theory and Practice ER - TY - JOUR AU - Herbert, Franziska AU - Becker, Steffen AU - Buckmann, Annalina AU - Kowalewski, Marvin AU - Hielscher, Jonas AU - Acar, Yasemin AU - Dürmuth, Markus AU - Sasse, M. Angela AU - Zou, Yixin ID - 47275 JF - IEEE Symposium on Security and Privacy. IEEE, New York, NY, USA TI - Digital Security -- A Question of Perspective. A Large-Scale Telephone Survey with Four At-Risk User Groups ER - TY - CONF AU - Afroze, Lameya AU - Merkelbach, Silke AU - von Enzberg, Sebastian AU - Dumitrescu, Roman ID - 49354 T2 - ML4CPS 2023 TI - Domain Knowledge Injection Guidance for Predictive Maintenance ER - TY - CONF AU - Scholtysik, Michel AU - Rohde, Malte AU - Koldewey, Christian AU - Dumitrescu, Roman ID - 49363 TI - Circular Product-Service-System Ideation Canvas – A Framework for the Design of circular Product-Service-System Ideas ER - TY - CONF AU - Scholtysik, Michel AU - Rohde, Malte AU - Koldewey, Christian AU - Dumitrescu, Roman ID - 49364 TI - Business strategy taxonomy and solution patterns for the circular economy ER - TY - JOUR AU - Weich, Tobias AU - Guedes Bonthonneau, Yannick AU - Guillarmou, Colin ID - 32097 JF - Journal of Differential Geometry (to appear) -- arXiv:2103.12127 TI - SRB Measures of Anosov Actions ER - TY - CONF AU - Kruse, Stephan AU - Schwabe, Tobias AU - Kneuper, Pascal AU - Kurz, Heiko G. AU - Meinecke, March-Michael AU - Scheytt, Christoph ID - 50287 T2 - German Microwave Conference (GeMiC) TI - Analysis and Simulation of a Photonic Multiband FMCW Radar Sensor System using Nyquist Pulses ER - TY - JOUR AB - We show how to learn discrete field theories from observational data of fields on a space-time lattice. For this, we train a neural network model of a discrete Lagrangian density such that the discrete Euler--Lagrange equations are consistent with the given training data. We, thus, obtain a structure-preserving machine learning architecture. Lagrangian densities are not uniquely defined by the solutions of a field theory. We introduce a technique to derive regularisers for the training process which optimise numerical regularity of the discrete field theory. Minimisation of the regularisers guarantees that close to the training data the discrete field theory behaves robust and efficient when used in numerical simulations. Further, we show how to identify structurally simple solutions of the underlying continuous field theory such as travelling waves. This is possible even when travelling waves are not present in the training data. This is compared to data-driven model order reduction based approaches, which struggle to identify suitable latent spaces containing structurally simple solutions when these are not present in the training data. Ideas are demonstrated on examples based on the wave equation and the Schrödinger equation. AU - Offen, Christian AU - Ober-Blöbaum, Sina ID - 46469 IS - 1 JF - Chaos SN - 1054-1500 TI - Learning of discrete models of variational PDEs from data VL - 34 ER - TY - CONF AU - Krings, Sarah Claudia AU - Yigitbas, Enes ID - 50476 T2 - Proceedings of the 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2024) (to appear) TI - TARPS: A Toolbox for Enhancing Privacy and Security for Collaborative AR ER - TY - CHAP AU - Prediger, Susanne AU - Wessel, Lena ED - Efing, Christian ED - Kalkavan-Aydin, Zeynep ID - 50554 SN - 978-3-11-074544-3 T2 - Berufs-und Fachsprache Deutsch in Wissenschaft und Praxis TI - 31 Sprachbildung im berufsbezogenen Mathematikunterricht. VL - Band 3 ER - TY - CONF AU - Dou, Feng AU - Wang, Lin AU - Chen, Shutong AU - Liu, Fangming ID - 50066 T2 - Proceedings of the IEEE International Conference on Computer Communications (INFOCOM) TI - X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics ER - TY - CONF AU - Blöcher, Marcel AU - Nedderhut, Nils AU - Chuprikov, Pavel AU - Khalili, Ramin AU - Eugster, Patrick AU - Wang, Lin ID - 50065 T2 - Proceedings of the IEEE International Conference on Computer Communications (INFOCOM) TI - Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES ER - TY - CONF AU - Hu, Haichuan AU - Liu, Fangming AU - Pei, Qiangyu AU - Yuan, Yongjie AU - Xu, Zichen AU - Wang, Lin ID - 50807 T2 - Proceedings of the ACM Web Conference (WWW) TI - 𝜆Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing ER - TY - GEN AB - We rigorously derive novel and sharp finite-data error bounds for highly sample-efficient Extended Dynamic Mode Decomposition (EDMD) for both i.i.d. and ergodic sampling. In particular, we show all results in a very general setting removing most of the typically imposed assumptions such that, among others, discrete- and continuous-time stochastic processes as well as nonlinear partial differential equations are contained in the considered system class. Besides showing an exponential rate for i.i.d. sampling, we prove, to the best of our knowledge, the first superlinear convergence rates for ergodic sampling of deterministic systems. We verify sharpness of the derived error bounds by conducting numerical simulations for highly-complex applications from molecular dynamics and chaotic flame propagation. AU - Philipp, Friedrich M. AU - Schaller, Manuel AU - Boshoff, Septimus AU - Peitz, Sebastian AU - Nüske, Feliks AU - Worthmann, Karl ID - 51160 T2 - arXiv:2402.02494 TI - Extended Dynamic Mode Decomposition: Sharp bounds on the sample efficiency ER - TY - JOUR AB - AbstractApproximation of subdifferentials is one of the main tasks when computing descent directions for nonsmooth optimization problems. In this article, we propose a bisection method for weakly lower semismooth functions which is able to compute new subgradients that improve a given approximation in case a direction with insufficient descent was computed. Combined with a recently proposed deterministic gradient sampling approach, this yields a deterministic and provably convergent way to approximate subdifferentials for computing descent directions. AU - Gebken, Bennet ID - 51208 JF - Computational Optimization and Applications KW - Applied Mathematics KW - Computational Mathematics KW - Control and Optimization SN - 0926-6003 TI - A note on the convergence of deterministic gradient sampling in nonsmooth optimization ER - TY - GEN AB - Given a real semisimple connected Lie group $G$ and a discrete torsion-free subgroup $\Gamma < G$ we prove a precise connection between growth rates of the group $\Gamma$, polyhedral bounds on the joint spectrum of the ring of invariant differential operators, and the decay of matrix coefficients. In particular, this allows us to completely characterize temperedness of $L^2(\Gamma\backslash G)$ in this general setting. AU - Lutsko, Christopher AU - Weich, Tobias AU - Wolf, Lasse Lennart ID - 51204 T2 - arXiv:2402.02530 TI - Polyhedral bounds on the joint spectrum and temperedness of locally symmetric spaces ER - TY - JOUR AU - Hasler, David AU - Hinrichs, Benjamin AU - Siebert, Oliver ID - 51374 IS - 7 JF - Journal of Functional Analysis KW - Analysis SN - 0022-1236 TI - Non-Fock ground states in the translation-invariant Nelson model revisited non-perturbatively VL - 286 ER - TY - JOUR AU - Weich, Tobias AU - Guedes Bonthonneau, Yannick AU - Guillarmou, Colin AU - Hilgert, Joachim ID - 32101 JF - J. Europ. Math. Soc. TI - Ruelle-Taylor resonaces of Anosov actions ER - TY - GEN AU - Hilgert, Joachim ID - 51501 TI - Quantum-Classical Correspondences for Locally Symmetric Spaces ER - TY - JOUR AB - We derive efficient algorithms to compute weakly Pareto optimal solutions for smooth, convex and unconstrained multiobjective optimization problems in general Hilbert spaces. To this end, we define a novel inertial gradient-like dynamical system in the multiobjective setting, which trajectories converge weakly to Pareto optimal solutions. Discretization of this system yields an inertial multiobjective algorithm which generates sequences that converge weakly to Pareto optimal solutions. We employ Nesterov acceleration to define an algorithm with an improved convergence rate compared to the plain multiobjective steepest descent method (Algorithm 1). A further improvement in terms of efficiency is achieved by avoiding the solution of a quadratic subproblem to compute a common step direction for all objective functions, which is usually required in first-order methods. Using a different discretization of our inertial gradient-like dynamical system, we obtain an accelerated multiobjective gradient method that does not require the solution of a subproblem in each step (Algorithm 2). While this algorithm does not converge in general, it yields good results on test problems while being faster than standard steepest descent. AU - Sonntag, Konstantin AU - Peitz, Sebastian ID - 46019 JF - Journal of Optimization Theory and Applications TI - Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems ER -