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 -