@inbook{6605,
author = {Laubenstein, Désirée and Lindmeier, Christian and Scheer, David and Guthöhrlein, Kirsten},
booktitle = {Inklusion – Chancen und Herausforderungen für Menschen mit geistiger Behinderung},
editor = {Fischer, Erhard and Ratz, Christoph},
isbn = {978-3-7799-3352-6},
pages = {123--140},
publisher = {Beltz},
title = {{Gemeinsamer Unterricht an rheinland-pälzischen Schwerpunktschulen}},
year = {2017},
}
@article{7026,
author = {Zolatanosha, Viktoryia and Reuter, Dirk},
issn = {0167-9317},
journal = {Microelectronic Engineering},
pages = {35--39},
publisher = {Elsevier BV},
title = {{Robust Si 3 N 4 masks for 100 nm selective area epitaxy of GaAs-based nanostructures}},
doi = {10.1016/j.mee.2017.05.053},
volume = {180},
year = {2017},
}
@article{682,
author = {Weber, Nils and Protte, Maximilian and Walter, Felicitas and Georgi, Philip and Zentgraf, Thomas and Meier, Cedrik},
issn = {2469-9950},
journal = {Physical Review B},
number = {20},
publisher = {American Physical Society (APS)},
title = {{Double resonant plasmonic nanoantennas for efficient second harmonic generation in zinc oxide}},
doi = {10.1103/physrevb.95.205307},
volume = {95},
year = {2017},
}
@misc{74,
author = {Knorr, Christoph},
publisher = {Universität Paderborn},
title = {{OpenCL-basierte Videoverarbeitung auf heterogenen Rechenknoten}},
year = {2017},
}
@inproceedings{79,
abstract = {Consider a problem in which $n$ jobs that are classified into $k$ types arrive over time at their release times and are to be scheduled on a single machine so as to minimize the maximum flow time.The machine requires a setup taking $s$ time units whenever it switches from processing jobs of one type to jobs of a different type.We consider the problem as an online problem where each job is only known to the scheduler as soon as it arrives and where the processing time of a job only becomes known upon its completion (non-clairvoyance).We are interested in the potential of simple ``greedy-like'' algorithms.We analyze a modification of the FIFO strategy and show its competitiveness to be $\Theta(\sqrt{n})$, which is optimal for the considered class of algorithms.For $k=2$ types it achieves a constant competitiveness.Our main insight is obtained by an analysis of the smoothed competitiveness.If processing times $p_j$ are independently perturbed to $\hat p_j = (1+X_j)p_j$, we obtain a competitiveness of $O(\sigma^{-2} \log^2 n)$ when $X_j$ is drawn from a uniform or a (truncated) normal distribution with standard deviation $\sigma$.The result proves that bad instances are fragile and ``practically'' one might expect a much better performance than given by the $\Omega(\sqrt{n})$-bound.},
author = {Mäcker, Alexander and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Riechers, Sören},
booktitle = {Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)},
pages = {207--222},
publisher = {Springer},
title = {{Non-Clairvoyant Scheduling to Minimize Max Flow Time on a Machine with Setup Times}},
doi = {10.1007/978-3-319-89441-6},
volume = {10787},
year = {2017},
}
@proceedings{7754,
editor = {Hess, Steffen and Fischer, Holger Gerhard},
publisher = {Gesellschaft für Informatik e.V. und German UPA e.V.},
title = {{Mensch und Computer 2017 - Usability Professionals. Tagungsband}},
year = {2017},
}
@misc{81,
author = {Luo, Linghui},
publisher = {Universität Paderborn},
title = {{MultiSkipList: A Self-stabilizing Overlay Network with Monotonic Searchability maintained}},
year = {2017},
}
@article{8877,
author = {Beutner, Marc and Rüscher, Frederike Anna},
journal = {Proceedings of the 13th International Conference Mobile Learning 2017},
pages = {63 -- 72},
publisher = {IADIS Conference},
title = {{Acceptance of Mobile Learning at SMEs of the Service Sector}},
year = {2017},
}
@misc{86,
author = {Niggemeyer, Laura},
publisher = {Universität Paderborn},
title = {{Kartellabsprachen und vertikale Preisbindungen - Eine wettbewerbspolitische Analyse am Bespiel der Lebensmittelindustrie in Deutschland}},
year = {2017},
}
@inproceedings{8752,
abstract = {In this article we develop a gradient-based algorithm for the solution of multiobjective optimization problems with uncertainties. To this end, an additional condition is derived for the descent direction in order to account for inaccuracies in the gradients and then incorporated into a subdivision algorithm for the computation of global solutions to multiobjective optimization problems. Convergence to a superset of the Pareto set is proved and an upper bound for the maximal distance to the set of substationary points is given. Besides the applicability to problems with uncertainties, the algorithm is developed with the intention to use it in combination with model order reduction techniques in order to efficiently solve PDE-constrained multiobjective optimization problems.},
author = {Peitz, Sebastian and Dellnitz, Michael},
booktitle = {NEO 2016},
isbn = {9783319640624},
issn = {1860-949X},
pages = {159--182},
title = {{Gradient-Based Multiobjective Optimization with Uncertainties}},
doi = {10.1007/978-3-319-64063-1_7},
year = {2017},
}