@article{21819, abstract = {{Many dimensionality and model reduction techniques rely on estimating dominant eigenfunctions of associated dynamical operators from data. Important examples include the Koopman operator and its generator, but also the Schrödinger operator. We propose a kernel-based method for the approximation of differential operators in reproducing kernel Hilbert spaces and show how eigenfunctions can be estimated by solving auxiliary matrix eigenvalue problems. The resulting algorithms are applied to molecular dynamics and quantum chemistry examples. Furthermore, we exploit that, under certain conditions, the Schrödinger operator can be transformed into a Kolmogorov backward operator corresponding to a drift-diffusion process and vice versa. This allows us to apply methods developed for the analysis of high-dimensional stochastic differential equations to quantum mechanical systems.}}, author = {{Klus, Stefan and Nüske, Feliks and Hamzi, Boumediene}}, issn = {{1099-4300}}, journal = {{Entropy}}, title = {{{Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator}}}, doi = {{10.3390/e22070722}}, year = {{2020}}, } @inproceedings{16487, author = {{Bobolz, Jan and Eidens, Fabian and Krenn, Stephan and Slamanig, Daniel and Striecks, Christoph}}, booktitle = {{Proceedings of the 15th ACM Asia Conference on Computer and Communications Security (ASIA CCS ’20),}}, location = {{Taiwan}}, publisher = {{ACM}}, title = {{{Privacy-Preserving Incentive Systems with Highly Efficient Point-Collection}}}, doi = {{10.1145/3320269.3384769}}, year = {{2020}}, } @article{16839, author = {{Sain, Basudeb and Zentgraf, Thomas}}, issn = {{2047-7538}}, journal = {{Light: Science & Applications}}, pages = {{67}}, title = {{{Metasurfaces help lasers to mode-lock}}}, doi = {{10.1038/s41377-020-0312-1}}, volume = {{9}}, year = {{2020}}, } @article{16931, author = {{Zhou, Hongqiang and Sain, Basudeb and Wang, Yongtian and Schlickriede, Christian and Zhao, Ruizhe and Zhang, Xue and Wei, Qunshuo and Li, Xiaowei and Huang, Lingling and Zentgraf, Thomas}}, issn = {{1936-0851}}, journal = {{ACS Nano}}, number = {{5}}, pages = {{5553–5559}}, title = {{{Polarization-Encrypted Orbital Angular Momentum Multiplexed Metasurface Holography}}}, doi = {{10.1021/acsnano.9b09814}}, volume = {{14}}, year = {{2020}}, } @inproceedings{16933, abstract = {{The continuous innovation of its business models is an important task for a company to stay competitive. During this process, the company has to validate various hypotheses about its business models by adapting to uncertain and changing customer needs effectively and efficiently. This adaptation, in turn, can be supported by the concept of Software Product Lines (SPLs). SPLs reduce the time to market by deriving products for customers with changing requirements using a common set of features, structured as a feature model. Analogously, we support the process of business model adaptation by applying the engineering process of SPLs to the structure of the Business Model Canvas (BMC). We call this concept a Business Model Decision Line (BMDL). The BMDL matches business domain knowledge in the form of a feature model with customer needs to derive hypotheses about the business model together with experiments for validation. Our approach is effective by providing a comprehensive overview of possible business model adaptations and efficient by reusing experiments for different hypotheses. We implement our approach in a tool and illustrate the usefulness with an example of developing business models for a mobile application.}}, author = {{Gottschalk, Sebastian and Rittmeier, Florian and Engels, Gregor}}, booktitle = {{Proceedings of the 22nd IEEE International Conference on Business Informatics}}, keywords = {{Business Model Decision Line, Business Model Adaptation, Hypothesis-driven Adaptation, Software Product Line, Feature Model}}, location = {{Antwerp}}, publisher = {{IEEE}}, title = {{{Hypothesis-driven Adaptation of Business Models based on Product Line Engineering}}}, doi = {{10.1109/CBI49978.2020.00022}}, year = {{2020}}, } @inproceedings{16934, abstract = {{To build successful products, the developers have to adapt their product features and business models to uncertain customer needs. This adaptation is part of the research discipline of Hypotheses Engineering (HE) where customer needs can be seen as hypotheses that need to be tested iteratively by conducting experiments together with the customer. So far, modeling support and associated traceability of this iterative process are missing. Both, in turn, are important to document the adaptation to the customer needs and identify experiments that provide most evidence to the customer needs. To target this issue, we introduce a model-based HE approach with a twofold contribution: First, we develop a modeling language that models hypotheses and experiments as interrelated hierarchies together with a mapping between them. While the hypotheses are labeled with a score level of their current evidence, the experiments are labeled with a score level of maximum evidence that can be achieved during conduction. Second, we provide an iterative process to determine experiments that offer the most evidence improvement to the modeled hypotheses. We illustrate the usefulness of the approach with an example of testing the business model of a mobile application.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Engels, Gregor}}, booktitle = {{Business Modeling and Software Design}}, editor = {{Shishkov, Boris}}, keywords = {{Hypothesis Engineering, Model-based, Customer Need Adaptation, Business Model, Product Features}}, location = {{Potsdam}}, pages = {{276--286}}, publisher = {{Springer International Publishing}}, title = {{{Model-based Hypothesis Engineering for Supporting Adaptation to Uncertain Customer Needs}}}, doi = {{10.1007/978-3-030-52306-0_18}}, volume = {{391}}, year = {{2020}}, } @inproceedings{16939, author = {{Triebus, Marcel and Tröster, Thomas}}, booktitle = {{Proceedings 4th International Conference Hybrid Materials & Structures}}, location = {{Web-Conference}}, title = {{{A Holistic Approach to Optimization-Based Design of Hybrid Materials}}}, year = {{2020}}, } @techreport{17019, abstract = {{The scientific impact of research papers is multi-dimensional and can be determined quantitatively by means of citation analysis and qualitatively by means of content analysis. Accounting for the widely acknowledged limitations of pure citation analysis, we adopt a knowledge-based perspective on scientific impact to develop a methodology for content-based citation analysis which allows determining how papers have enabled knowledge development in subsequent research (knowledge impact). As knowledge development differs between research genres, we develop a new knowledgebased citation analysis methodology for the genre of standalone literature reviews (LRs). We apply the suggested methodology to the IS business value domain by manually coding 22 LRs and 1,228 citing papers (CPs) and show that the results challenge the assumption that citations indicate knowledge impact. We derive implications for distinguishing knowledge impact from citation impact in the LR genre. Finally, we develop recommendations for authors of LRs, scientific evaluation committees and editorial boards of journals how to apply and benefit from the suggested methodology, and we discuss its efficiency and automatization.}}, author = {{Schryen, Guido and Wagner, Gerit and Benlian, Alexander}}, keywords = {{Scientific impact, knowledge impact, content-based citation analysis, methodology}}, title = {{{Distinguishing Knowledge Impact from Citation Impact: A Methodology for Analysing Knowledge Impact for the Literature Review Genre}}}, year = {{2020}}, } @inproceedings{17055, abstract = {{Understanding a new literature corpus can be a grueling experience for junior scholars. Nevertheless, corresponding guidelines have not been updated for decades. We contend that the traditional strategy of skimming all papers and reading selected papers afterwards needs to be revised. Therefore, we design a new strategy that guides the overall exploratory process by prioritizing influential papers for initial reading, followed by skimming the remaining papers. Consistent with schemata theory, starting with in-depth reading allows readers to acquire more substantial prior content schemata, which are representa-tive for the literature corpus and useful in the following skimming process. To this end, we develop a prototype that identifies the influential papers from a set of PDFs, which is illustrated in a case study in the IT business value domain. With the new strategy, we envision a more efficient process of exploring unknown literature corpora.}}, author = {{Wagner, Gerit and Empl, Philipp and Schryen, Guido}}, booktitle = {{28th European Conference on Information Systems (ECIS 2020)}}, keywords = {{Reading and skimming, Exploring literature, Review methodology, Design science research, Schemata theory}}, location = {{Marrakesh, Morocco}}, title = {{{Designing a Novel Strategy for Exploring Literature Corpora}}}, year = {{2020}}, } @inproceedings{17089, author = {{Dreiling, Dmitrij and Itner, Dominik Thor and Feldmann, Nadine and Gravenkamp, Hauke and Henning, Bernd}}, location = {{Nürnberg}}, publisher = {{AMA Service GmbH}}, title = {{{Increasing the sensitivity in the determination of material parameters by using arbitrary loads in ultrasonic transmission measurements}}}, doi = {{10.5162/SMSI2020/D1.3}}, year = {{2020}}, } @article{15414, author = {{Schryen, Guido}}, journal = {{Communications of the ACM}}, number = {{9}}, pages = {{35 -- 37}}, title = {{{Integrating Management Science into the HPC Research Ecosystem}}}, volume = {{63}}, year = {{2020}}, } @inproceedings{15490, author = {{Claes, Leander and Baumhögger, Elmar and Rüther, Torben and Gierse, Jan and Tröster, Thomas and Henning, Bernd}}, booktitle = {{Fortschritte der Akustik - DAGA 2020}}, pages = {{1077--1080}}, title = {{{Reduction of systematic measurement deviation in acoustic absorption measurement systems}}}, year = {{2020}}, } @article{15513, abstract = {{This interview is part of the special issue (01/2020) on “High Performance Business Computing” to be published in the journal Business & Information Systems Engineering. The interviewee Utz-Uwe Haus is Senior Research Engineer @ CRAY European Research Lab (CERL)). A bio of him is included at the end of the interview.}}, author = {{Schryen, Guido and Kliewer, Natalia and Fink, Andreas}}, journal = {{Business & Information Systems Engineering}}, number = {{01/2020}}, pages = {{21 -- 23}}, title = {{{Interview with Utz-Uwe Haus on “High Performance Computing in Economic Environments: Opportunities and Challenges"}}}, volume = {{62}}, year = {{2020}}, } @article{15022, author = {{Schryen, Guido}}, journal = {{European Journal of Operational Research}}, number = {{1}}, pages = {{1 -- 18}}, publisher = {{Elsevier}}, title = {{{Parallel computational optimization in operations research: A new integrative framework, literature review and research directions}}}, volume = {{287}}, year = {{2020}}, } @article{16197, abstract = {{Nonlinear Pancharatnam–Berry phase metasurfaces facilitate the nontrivial phase modulation for frequency conversion processes by leveraging photon‐spin dependent nonlinear geometric‐phases. However, plasmonic metasurfaces show some severe limitation for nonlinear frequency conversion due to the intrinsic high ohmic loss and low damage threshold of plasmonic nanostructures. Here, the nonlinear geometric‐phases associated with the third‐harmonic generation process occurring in all‐dielectric metasurfaces is studied systematically, which are composed of silicon nanofins with different in‐plane rotational symmetries. It is found that the wave coupling among different field components of the resonant fundamental field gives rise to the appearance of different nonlinear geometric‐phases of the generated third‐harmonic signals. The experimental observations of the nonlinear beam steering and nonlinear holography realized in this work by all‐dielectric geometric‐phase metasurfaces are well explained with the developed theory. This work offers a new physical picture to understand the nonlinear optical process occurring at nanoscale dielectric resonators and will help in the design of nonlinear metasurfaces with tailored phase properties.}}, author = {{Liu, Bingyi and Sain, Basudeb and Reineke, Bernhard and Zhao, Ruizhe and Meier, Cedrik and Huang, Lingling and Jiang, Yongyuan and Zentgraf, Thomas}}, issn = {{2195-1071}}, journal = {{Advanced Optical Materials}}, number = {{9}}, publisher = {{Wiley}}, title = {{{Nonlinear Wavefront Control by Geometric-Phase Dielectric Metasurfaces: Influence of Mode Field and Rotational Symmetry}}}, doi = {{10.1002/adom.201902050}}, volume = {{8}}, year = {{2020}}, } @inproceedings{16219, abstract = {{Network function virtualization (NFV) proposes to replace physical middleboxes with more flexible virtual network functions (VNFs). To dynamically adjust to everchanging traffic demands, VNFs have to be instantiated and their allocated resources have to be adjusted on demand. Deciding the amount of allocated resources is non-trivial. Existing optimization approaches often assume fixed resource requirements for each VNF instance. However, this can easily lead to either waste of resources or bad service quality if too many or too few resources are allocated. To solve this problem, we train machine learning models on real VNF data, containing measurements of performance and resource requirements. For each VNF, the trained models can then accurately predict the required resources to handle a certain traffic load. We integrate these machine learning models into an algorithm for joint VNF scaling and placement and evaluate their impact on resulting VNF placements. Our evaluation based on real-world data shows that using suitable machine learning models effectively avoids over- and underallocation of resources, leading to up to 12 times lower resource consumption and better service quality with up to 4.5 times lower total delay than using standard fixed resource allocation.}}, author = {{Schneider, Stefan Balthasar and Satheeschandran, Narayanan Puthenpurayil and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft)}}, location = {{Ghent, Belgium}}, publisher = {{IEEE}}, title = {{{Machine Learning for Dynamic Resource Allocation in Network Function Virtualization}}}, year = {{2020}}, } @article{16249, abstract = {{Timing plays a crucial role in the context of information security investments. We regard timing in two dimensions, namely the time of announcement in relation to the time of investment and the time of announcement in relation to the time of a fundamental security incident. The financial value of information security investments is assessed by examining the relationship between the investment announcements and their stock market reaction focusing on the two time dimensions. Using an event study methodology, we found that both dimensions influence the stock market return of the investing organization. Our results indicate that (1) after fundamental security incidents in a given industry, the stock price will react more positively to a firm’s announcement of actual information security investments than to announcements of the intention to invest; (2) the stock price will react more positively to a firm’s announcements of the intention to invest after the fundamental security incident compared to before; and (3) the stock price will react more positively to a firm’s announcements of actual information security investments after the fundamental security incident compared to before. Overall, the lowest abnormal return can be expected when the intention to invest is announced before a fundamental information security incident and the highest return when actual investing after a fundamental information security incident in the respective industry.}}, author = {{Szubartowicz, Eva and Schryen, Guido}}, journal = {{Journal of Information System Security}}, keywords = {{Event Study, Information Security, Investment Announcements, Stock Price Reaction, Value of Information Security Investments}}, number = {{1}}, pages = {{3 -- 31}}, publisher = {{Information Institute Publishing, Washington DC, USA}}, title = {{{Timing in Information Security: An Event Study on the Impact of Information Security Investment Announcements}}}, volume = {{16}}, year = {{2020}}, } @inproceedings{16285, abstract = {{To decide in which part of town to open stores, high street retailers consult statistical data on customers and cities, but they cannot analyze their customers’ shopping behavior and geospatial features of a city due to missing data. While previous research has proposed recommendation systems and decision aids that address this type of decision problem – including factory location and assortment planning – there currently is no design knowledge available to prescribe the design of city center area recommendation systems (CCARS). We set out to design a software prototype considering local customers’ shopping interests and geospatial data on their shopping trips for retail site selection. With real data on 500 customers and 1,100 shopping trips, we demonstrate and evaluate our IT artifact. Our results illustrate how retailers and public town center managers can use CCARS for spatial location selection, growing retailers’ profits and a city center’s attractiveness for its citizens.}}, author = {{zur Heiden, Philipp and Berendes, Carsten Ingo and Beverungen, Daniel}}, booktitle = {{Proceedings of the 15th International Conference on Wirtschaftsinformatik}}, keywords = {{Town Center Management, High Street Retail, Recommender Systems, Geospatial Recommendations, Design Science Research}}, location = {{Potsdam}}, title = {{{Designing City Center Area Recommendation Systems }}}, doi = {{doi.org/10.30844/wi_2020_e1-heiden}}, year = {{2020}}, } @article{16290, abstract = {{The control of complex systems is of critical importance in many branches of science, engineering, and industry, many of which are governed by nonlinear partial differential equations. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration). However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible. Fortunately, these high- dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required. Advances in machine learning have the potential to revolutionize flow control given its ability to extract principled, low-rank feature spaces characterizing such complex systems.We present a novel deep learning modelpredictive control framework that exploits low-rank features of the flow in order to achieve considerable improvements to control performance. Instead of predicting the entire fluid state, we use a recurrent neural network (RNN) to accurately predict the control relevant quantities of the system, which are then embedded into an MPC framework to construct a feedback loop. In order to lower the data requirements and to improve the prediction accuracy and thus the control performance, incoming sensor data are used to update the RNN online. The results are validated using varying fluid flow examples of increasing complexity.}}, author = {{Bieker, Katharina and Peitz, Sebastian and Brunton, Steven L. and Kutz, J. Nathan and Dellnitz, Michael}}, issn = {{0935-4964}}, journal = {{Theoretical and Computational Fluid Dynamics}}, pages = {{577–591}}, title = {{{Deep model predictive flow control with limited sensor data and online learning}}}, doi = {{10.1007/s00162-020-00520-4}}, volume = {{34}}, year = {{2020}}, } @techreport{23568, abstract = {{We study the structure of power networks in consideration of local protests against certain power lines (’not-in-my-backyard’). An application of a network formation game is used to determine whether or not such protests arise. We examine the existence of stable networks and their characteristics, when no player wants to make an alteration. Stability within this game is only reached if each player is sufficiently connected to a power source but is not linked to more players than necessary. In addition we introduce an algorithm that creates a stable network.}}, author = {{Block, Lukas}}, keywords = {{Network formation, NIMBY, Power networks, Nash stability}}, title = {{{Network formation with NIMBY constraints}}}, year = {{2020}}, }