@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}}, } @misc{30180, author = {{Ficara, Elena and d'Agostini, Franca }}, booktitle = {{La Stampa}}, title = {{{Perché celebrare Hegel? La sua dialettica è un brand, il suo pensiero una febbre benefica}}}, year = {{2020}}, }