@inproceedings{48632, abstract = {{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.}}, author = {{Koldewey, Christian and Fichtler, Timm and Scholtysik, Michel and Biehler, Jan and Schreiner, Nick and Sommer, Franziska and Schacht, Maximilian and Kaufmann, Jonas and Rabe, Martin and Sedlmeier, Joachim and Dumitrescu, Roman}}, keywords = {{Digital Servitization, Transformation, Capabilities, Maturity, Smart Services}}, location = {{Hawaii}}, title = {{{Exploring Capabilities for the Smart Service Transformation in Manufacturing: Insights from Theory and Practice}}}, year = {{2024}}, } @article{47275, author = {{Herbert, Franziska and Becker, Steffen and Buckmann, Annalina and Kowalewski, Marvin and Hielscher, Jonas and Acar, Yasemin and Dürmuth, Markus and Sasse, M. Angela and Zou, Yixin}}, journal = {{IEEE Symposium on Security and Privacy. IEEE, New York, NY, USA}}, title = {{{Digital Security -- A Question of Perspective. A Large-Scale Telephone Survey with Four At-Risk User Groups}}}, doi = {{10.48550/arXiv.2212.12964}}, year = {{2024}}, } @inproceedings{49354, author = {{Afroze, Lameya and Merkelbach, Silke and von Enzberg, Sebastian and Dumitrescu, Roman}}, booktitle = {{ML4CPS 2023}}, location = {{Hamburg}}, title = {{{Domain Knowledge Injection Guidance for Predictive Maintenance}}}, year = {{2024}}, } @inproceedings{49363, author = {{Scholtysik, Michel and Rohde, Malte and Koldewey, Christian and Dumitrescu, Roman}}, title = {{{Circular Product-Service-System Ideation Canvas – A Framework for the Design of circular Product-Service-System Ideas}}}, year = {{2024}}, } @inproceedings{49364, author = {{Scholtysik, Michel and Rohde, Malte and Koldewey, Christian and Dumitrescu, Roman}}, title = {{{Business strategy taxonomy and solution patterns for the circular economy}}}, year = {{2024}}, } @article{32097, author = {{Weich, Tobias and Guedes Bonthonneau, Yannick and Guillarmou, Colin}}, journal = {{Journal of Differential Geometry (to appear) -- arXiv:2103.12127}}, title = {{{SRB Measures of Anosov Actions}}}, year = {{2024}}, } @inproceedings{50287, author = {{Kruse, Stephan and Schwabe, Tobias and Kneuper, Pascal and Kurz, Heiko G. and Meinecke, March-Michael and Scheytt, Christoph}}, booktitle = {{German Microwave Conference (GeMiC) }}, title = {{{Analysis and Simulation of a Photonic Multiband FMCW Radar Sensor System using Nyquist Pulses}}}, year = {{2024}}, } @article{46469, abstract = {{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. }}, author = {{Offen, Christian and Ober-Blöbaum, Sina}}, issn = {{1054-1500}}, journal = {{Chaos}}, number = {{1}}, publisher = {{AIP Publishing}}, title = {{{Learning of discrete models of variational PDEs from data}}}, doi = {{10.1063/5.0172287}}, volume = {{34}}, year = {{2024}}, } @inproceedings{50476, author = {{Krings, Sarah Claudia and Yigitbas, Enes}}, booktitle = {{Proceedings of the 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2024) (to appear)}}, publisher = {{ACM}}, title = {{{TARPS: A Toolbox for Enhancing Privacy and Security for Collaborative AR}}}, year = {{2024}}, } @inbook{50554, author = {{Prediger, Susanne and Wessel, Lena}}, booktitle = {{Berufs-und Fachsprache Deutsch in Wissenschaft und Praxis}}, editor = {{Efing, Christian and Kalkavan-Aydin, Zeynep}}, isbn = {{978-3-11-074544-3}}, pages = {{363--372}}, publisher = {{DE GRUYTER}}, title = {{{31 Sprachbildung im berufsbezogenen Mathematikunterricht.}}}, volume = {{Band 3}}, year = {{2024}}, } @inproceedings{50066, author = {{Dou, Feng and Wang, Lin and Chen, Shutong and Liu, Fangming}}, booktitle = {{Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)}}, location = {{Vancouver, Canada}}, publisher = {{IEEE}}, title = {{{X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics}}}, year = {{2024}}, } @inproceedings{50065, author = {{Blöcher, Marcel and Nedderhut, Nils and Chuprikov, Pavel and Khalili, Ramin and Eugster, Patrick and Wang, Lin}}, booktitle = {{Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)}}, location = {{Vancouver, Canada}}, publisher = {{IEEE}}, title = {{{Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES}}}, year = {{2024}}, } @inproceedings{50807, author = {{Hu, Haichuan and Liu, Fangming and Pei, Qiangyu and Yuan, Yongjie and Xu, Zichen and Wang, Lin}}, booktitle = {{Proceedings of the ACM Web Conference (WWW)}}, location = {{Singapore}}, publisher = {{ACM}}, title = {{{𝜆Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing}}}, year = {{2024}}, } @unpublished{51160, abstract = {{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.}}, author = {{Philipp, Friedrich M. and Schaller, Manuel and Boshoff, Septimus and Peitz, Sebastian and Nüske, Feliks and Worthmann, Karl}}, booktitle = {{arXiv:2402.02494}}, title = {{{Extended Dynamic Mode Decomposition: Sharp bounds on the sample efficiency}}}, year = {{2024}}, } @article{51208, abstract = {{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.}}, author = {{Gebken, Bennet}}, issn = {{0926-6003}}, journal = {{Computational Optimization and Applications}}, keywords = {{Applied Mathematics, Computational Mathematics, Control and Optimization}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{A note on the convergence of deterministic gradient sampling in nonsmooth optimization}}}, doi = {{10.1007/s10589-024-00552-0}}, year = {{2024}}, } @unpublished{51204, abstract = {{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.}}, author = {{Lutsko, Christopher and Weich, Tobias and Wolf, Lasse Lennart}}, booktitle = {{arXiv:2402.02530}}, title = {{{Polyhedral bounds on the joint spectrum and temperedness of locally symmetric spaces}}}, year = {{2024}}, } @article{51374, author = {{Hasler, David and Hinrichs, Benjamin and Siebert, Oliver}}, issn = {{0022-1236}}, journal = {{Journal of Functional Analysis}}, keywords = {{Analysis}}, number = {{7}}, publisher = {{Elsevier BV}}, title = {{{Non-Fock ground states in the translation-invariant Nelson model revisited non-perturbatively}}}, doi = {{10.1016/j.jfa.2024.110319}}, volume = {{286}}, year = {{2024}}, } @article{32101, author = {{Weich, Tobias and Guedes Bonthonneau, Yannick and Guillarmou, Colin and Hilgert, Joachim}}, journal = {{J. Europ. Math. Soc.}}, pages = {{1--36}}, title = {{{Ruelle-Taylor resonaces of Anosov actions}}}, year = {{2024}}, } @unpublished{51501, author = {{Hilgert, Joachim}}, title = {{{Quantum-Classical Correspondences for Locally Symmetric Spaces}}}, year = {{2024}}, } @article{46019, abstract = {{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.}}, author = {{Sonntag, Konstantin and Peitz, Sebastian}}, journal = {{Journal of Optimization Theory and Applications}}, publisher = {{Springer}}, title = {{{Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems}}}, doi = {{10.1007/s10957-024-02389-3}}, year = {{2024}}, }