@article{47947, author = {{Rüsing, Michael and Weigel, Peter O. and Zhao, Jie and Mookherjea, Shayan}}, issn = {{1932-4510}}, journal = {{IEEE Nanotechnology Magazine}}, keywords = {{Electrical and Electronic Engineering, Mechanical Engineering}}, number = {{4}}, pages = {{18--33}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Toward 3D Integrated Photonics Including Lithium Niobate Thin Films: A Bridge Between Electronics, Radio Frequency, and Optical Technology}}}, doi = {{10.1109/mnano.2019.2916115}}, volume = {{13}}, year = {{2019}}, } @article{47946, author = {{Zhao, Jie and Rüsing, Michael and Mookherjea, Shayan}}, issn = {{1094-4087}}, journal = {{Optics Express}}, keywords = {{Atomic and Molecular Physics, and Optics}}, number = {{9}}, publisher = {{The Optical Society}}, title = {{{Optical diagnostic methods for monitoring the poling of thin-film lithium niobate waveguides}}}, doi = {{10.1364/oe.27.012025}}, volume = {{27}}, year = {{2019}}, } @misc{48019, author = {{Meusel, Sarah and Abendroth, Sonja and Hoeft, Maike and Albers, Timm}}, title = {{{Leitfaden zur Gestaltung von Zugängen}}}, year = {{2019}}, } @inproceedings{46343, abstract = {{This paper addresses multimodality of multi-objective (MO) optimization landscapes. Contrary to common perception of local optima, according to which they are hindering the progress of optimization algorithms, it will be shown that local efficient sets in a multi-objective setting can assist optimizers in finding global efficient sets. We use sophisticated visualization techniques, which rely on gradient field heatmaps, to highlight those insights into landscape characteristics. Finally, the MO local optimizer MOGSA is introduced, which exploits those observations by sliding down the multi-objective gradient hill and moving along the local efficient sets.}}, author = {{Grimme, Christian and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)}}, editor = {{Deb, Kalyanmoy and Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}}, pages = {{126–138}}, publisher = {{Springer}}, title = {{{Multimodality in Multi-Objective Optimization — More Boon than Bane?}}}, doi = {{10.1007/978-3-030-12598-1_11}}, volume = {{11411}}, year = {{2019}}, } @article{46345, abstract = {{It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.}}, author = {{Kerschke, Pascal and Hoos, Holger H and Neumann, Frank and Trautmann, Heike}}, journal = {{Evolutionary Computation (ECJ)}}, number = {{1}}, pages = {{3–45}}, title = {{{Automated Algorithm Selection: Survey and Perspectives}}}, doi = {{10.1162/evco_a_00242}}, volume = {{27}}, year = {{2019}}, } @article{46344, abstract = {{Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time.}}, author = {{Carnein, Matthias and Trautmann, Heike}}, journal = {{Business and Information Systems Engineering (BISE)}}, number = {{3}}, pages = {{277–297}}, title = {{{Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms}}}, volume = {{61}}, year = {{2019}}, } @inproceedings{46340, abstract = {{Recommender systems aim to provide personalized suggestions to customers which products to buy or services to consume. They can help to increase sales by helping customers discover new and relevant products. Traditionally, recommender systems use the purchase history of a customer, e.g., the purchased quantity or properties of the items. While this allows to build personalized recommendations, it is a very limited view of the problem. Nowadays, extensive information about customers and their personal preferences is available which goes far beyond their purchase behaviour. For example, customers reveal their preferences in social media, by their browsing habits and online search behaviour or their interest in specific newsletters. In this paper, we investigate how information from different sources and channels can be collected and incorporated into the recommendation process. We demonstrate this, based on a real-life case study of a retailer with several million transactions. We discuss how to employ a recommender system in this scenario, evaluate various recommendation strategies and describe how to incorporate information from different sources and channels, both internal and external. Our results show that the recommendations can be better tailored to the personal preferences of customers.}}, author = {{Carnein, Matthias and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried}}, booktitle = {{Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)}}, pages = {{65–74}}, title = {{{A Recommender System Based on Omni-Channel Customer Data}}}, year = {{2019}}, } @inproceedings{46341, abstract = {{Customer Segmentation aims to identify groups of customers that share similar interest or behaviour. It is an essential tool in marketing and can be used to target customer segments with tailored marketing strategies. Customer segmentation is often based on clustering techniques. This analysis is typically performed as a snapshot analysis where segments are identified at a specific point in time. However, this ignores the fact that customer segments are highly volatile and segments change over time. Once segments change, the entire analysis needs to be repeated and strategies adapted. In this paper we explore stream clustering as a tool to alleviate this problem. We propose a new stream clustering algorithm which allows to identify and track customer segments over time. The biggest challenge is that customer segmentation often relies on the transaction history of a customer. Since this data changes over time, it is necessary to update customers which have already been incorporated into the clustering. We show how to perform this step incrementally, without the need for periodic re-computations. As a result, customer segmentation can be performed continuously, faster and is more scalable. We demonstrate the performance of our algorithm using a large real-life case study.}}, author = {{Carnein, Matthias and Trautmann, Heike}}, booktitle = {{Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)}}, pages = {{280–292}}, title = {{{Customer Segmentation Based on Transactional Data Using Stream Clustering}}}, year = {{2019}}, } @inproceedings{46342, abstract = {{There is a range of phenomena in continuous, global multi-objective optimization, that cannot occur in single-objective optimization. For instance, in some multi-objective optimization problems it is possible to follow continuous paths of gradients of straightforward weighted scalarization functions, starting from locally efficient solutions, in order to reach globally Pareto optimal solutions. This paper seeks to better characterize multimodal multi-objective landscapes and to better understand the transitions from local optima to global optima in simple, path-oriented search procedures.}}, author = {{Grimme, Christian and Kerschke, Pascal and Emmerich, Michael T M and Preuss, Mike and Deutz, André H and Trautmann, Heike}}, booktitle = {{AIP Conference Proceedings}}, pages = {{020052--1--020052--4}}, publisher = {{AIP Publishing}}, title = {{{Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization}}}, doi = {{10.1063/1.5090019}}, year = {{2019}}, } @inbook{48341, author = {{Wille, Manuel}}, booktitle = {{Textgliederungsprinzipien. Ihre Kennzeichnungsformen und Funktionen vom 8. bis 18. Jahrhundert. Akten zum Internationalen Kongress vom 22. bis 24. Juni 2017 an der Universität St. Petersburg}}, editor = {{Simmler, Franz and Baeva, Galina}}, pages = {{507 -- 531}}, publisher = {{Weidler}}, title = {{{Prinzipien und Strategien der Textgliederung in den Zeitungen des 18. Jahrhunderts – Eine computerbasierte Korpusanalyse}}}, volume = {{34}}, year = {{2019}}, } @article{48340, author = {{Wille, Manuel}}, journal = {{Wiener Geschichtsblätter}}, pages = {{163 -- 181}}, publisher = {{Verein für Geschichte der Stadt Wien}}, title = {{{Zeitungen des 18. Jahrhunderts im Kontext des Höflichkeitsdiskurses}}}, volume = {{74}}, year = {{2019}}, } @article{47917, abstract = {{ Companies disclosing negative aspects in sustainability reports often employ legitimation strategies to present mishaps in a favorable light. In incentivized experiments, we find that nonprofessional investors divest from companies with a negative sustainability-related incident, and that symbolic legitimation (which only evasively explains a negative incident) is not a strong enough signal to counter this divestment behavior. Even substantial legitimation (which reports on measures and behavioral change) mitigates the divestment decisions only if the company reports on concrete remediation actions in morally charged situations, such as social or environmental incidents. We elaborate these results in light of signaling and screening theory, and suggest the conceptual extension of “costly signals” to what we call “valuable signals.” We argue that valuable signals need be not only costly for the sender from an economic perspective but also perceived as appropriate by the receiver from a noneconomic perspective. }}, author = {{Hahn, Rüdiger and Reimsbach, Daniel and Kotzian, Peter and Feder, Madeleine and Weißenberger, Barbara E.}}, issn = {{0007-6503}}, journal = {{Business & Society}}, keywords = {{Social Sciences (miscellaneous), Business, Management and Accounting (miscellaneous)}}, number = {{4}}, pages = {{943--978}}, publisher = {{SAGE Publications}}, title = {{{Legitimation Strategies as Valuable Signals in Nonfinancial Reporting? Effects on Investor Decision-Making}}}, doi = {{10.1177/0007650319872495}}, volume = {{60}}, year = {{2019}}, } @inproceedings{45388, author = {{Dröse, Jennifer}}, booktitle = {{Proceedings of the Eleventh Congress of the European Society for Research in Mathematics Education}}, editor = {{Jankvist, U. T. and van den Heuvel-Panhuizen, M. and Veldhuis, M.}}, publisher = {{Freudenthal Group & ERME}}, title = {{{Comprehending mathematical problem texts – Fostering subject-specific reading strategies for creating mental text representation}}}, year = {{2019}}, } @inproceedings{45389, author = {{Dröse, Jennifer}}, booktitle = {{Proceedings of the Third International Conference on Mathematics Textbook Research and Development }}, editor = {{Rezat, S. and Hattermann, M. and Schumacher, J. and Wuschke, H.}}, pages = {{161--166}}, title = {{{Mathematical and linguistic features of word problems in grade 4 and 5 German textbooks – A compara-tive corpus linguistic approach}}}, year = {{2019}}, } @inproceedings{29867, author = {{Faulwasser, Tim and Flaßkamp, K. and Ober-Blöbaum, Sina and Worthmann, Karl}}, pages = {{490--495}}, title = {{{Towards velocity turnpikes in optimal control of mechanical systems}}}, volume = {{52(16)}}, year = {{2019}}, } @article{48717, author = {{Krause, Daniel}}, journal = {{Sportpädagogik}}, number = {{5}}, pages = {{34--38}}, title = {{{Mit taktischen Grammatiken zum Korberfolg - "Pick and Roll" im Basketball situativ verstehen und anwenden.}}}, volume = {{42}}, year = {{2019}}, } @inbook{48724, author = {{Krause, Daniel and Ramme, Katharina and Weigelt, Matthias}}, booktitle = {{Praktische Ausbildung in der Physiotherapie}}, editor = {{Klemme, Beate and Weyland, Ulrike and Harms, Jan}}, pages = {{59--69}}, publisher = {{Thieme}}, title = {{{Erwerb motorischer und sensorischer Kompetenzen}}}, year = {{2019}}, } @inbook{48617, author = {{Hartung, Olaf}}, booktitle = {{Sprachsensibler Geschichtsunterricht: Von der geschichtsdidaktischen Theorie über die Empirie zur Unterrichtspraxis}}, editor = {{Bertram, Christiane and Kolpatzik, Andrea}}, isbn = {{9783734408564}}, pages = {{64--76}}, publisher = {{Wochenschau Verlag}}, title = {{{‚Gattungskompetenz 3.0‘ – Zur Performativität formbewussten Geschichtslernens}}}, year = {{2019}}, } @inproceedings{15237, abstract = {{This paper presents an approach to voice conversion, whichdoes neither require parallel data nor speaker or phone labels fortraining. It can convert between speakers which are not in thetraining set by employing the previously proposed concept of afactorized hierarchical variational autoencoder. Here, linguisticand speaker induced variations are separated upon the notionthat content induced variations change at a much shorter timescale, i.e., at the segment level, than speaker induced variations,which vary at the longer utterance level. In this contribution wepropose to employ convolutional instead of recurrent networklayers in the encoder and decoder blocks, which is shown toachieve better phone recognition accuracy on the latent segmentvariables at frame-level due to their better temporal resolution.For voice conversion the mean of the utterance variables is re-placed with the respective estimated mean of the target speaker.The resulting log-mel spectra of the decoder output are used aslocal conditions of a WaveNet which is utilized for synthesis ofthe speech waveforms. Experiments show both good disentan-glement properties of the latent space variables, and good voiceconversion performance.}}, author = {{Gburrek, Tobias and Glarner, Thomas and Ebbers, Janek and Haeb-Umbach, Reinhold and Wagner, Petra}}, booktitle = {{Proc. 10th ISCA Speech Synthesis Workshop}}, location = {{Vienna}}, pages = {{81--86}}, title = {{{Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion}}}, doi = {{10.21437/SSW.2019-15}}, year = {{2019}}, } @article{49098, author = {{Krebs, Benjamin and Izsak, L. and Kabst, Rüdiger}}, journal = {{PERSONALquartely}}, title = {{{Corporate Entrepreneurship: HR als Treiber und Begleiter des Wandels}}}, volume = {{4}}, year = {{2019}}, }