@article{14888, author = {{Chen, Wei-Fan and Ku, Lun-Wei}}, journal = {{圖書館學與資訊科學}}, publisher = {{國立台灣師範大學圖書資訊學研究所}}, title = {{{中文情感語意分析套件 CSentiPackage 發展與應用}}}, year = {{2018}}, } @inproceedings{15081, author = {{Böttcher, Stefan and Hartel, Rita}}, booktitle = {{2018 IEEE International Conference on Big Data (Big Data)}}, isbn = {{9781538650356}}, pages = {{3996--4005}}, title = {{{RECUT: RE-Compressing partially Unordered Trees}}}, doi = {{10.1109/bigdata.2018.8622261}}, year = {{2018}}, } @inproceedings{15082, author = {{Dunst, Alexander and Hartel, Rita}}, booktitle = {{Digital Humanities 2018: Book of Abstracts/Libro de resúmenes}}, pages = {{184--187}}, publisher = {{Red de Humanidades Digitales AC}}, title = {{{Automated Genre and Author Distinction in Comics: Towards a Stylometry for Visual Narrative}}}, year = {{2018}}, } @inbook{15160, author = {{Dunst, Alexander and Hartel, Rita}}, booktitle = {{Empirical Comics Research: Digital, Multimodal, and Cognitive Methods}}, editor = {{Dunst, Alexander and Laubrock, Jochen and Wildfeuer, Janina}}, pages = {{43--61}}, publisher = {{Routledge}}, title = {{{The quantitative analysis of comics: Towards a visual stylometry of graphic narrative}}}, year = {{2018}}, } @inproceedings{15183, author = {{Dunst, Alexander and Hartel, Rita}}, booktitle = {{DHd Konferenz 2018, Kritik der digitalen Vernunft, DHd 2018}}, title = {{{Auf dem Weg zur Visuellen Stilometrie:Automatische Genre- und Autorunterscheidung in graphischen Narrativen}}}, year = {{2018}}, } @article{16038, author = {{Schäfer, D. and Hüllermeier, Eyke}}, journal = {{Machine Learning}}, number = {{5}}, pages = {{903--941}}, title = {{{Dyad ranking using Plackett-Luce models based on joint feature representations}}}, volume = {{107}}, year = {{2018}}, } @unpublished{16292, abstract = {{In a recent article, we presented a framework to control nonlinear partial differential equations (PDEs) by means of Koopman operator based reduced models and concepts from switched systems. The main idea was to transform a control system into a set of autonomous systems for which the optimal switching sequence has to be computed. These individual systems can be approximated very efficiently by reduced order models obtained from data, and one can guarantee equality of the full and the reduced objective function under certain assumptions. In this article, we extend these results to continuous control inputs using convex combinations of multiple Koopman operators corresponding to constant controls, which results in a bilinear control system. Although equality of the objectives can be carried over when the PDE depends linearly on the control, we show that this approach is also valid in other scenarios using several flow control examples of varying complexity.}}, author = {{Peitz, Sebastian}}, booktitle = {{arXiv:1801.06419}}, title = {{{Controlling nonlinear PDEs using low-dimensional bilinear approximations obtained from data}}}, year = {{2018}}, } @unpublished{16293, abstract = {{Kernel transfer operators, which can be regarded as approximations of transfer operators such as the Perron-Frobenius or Koopman operator in reproducing kernel Hilbert spaces, are defined in terms of covariance and cross-covariance operators and have been shown to be closely related to the conditional mean embedding framework developed by the machine learning community. The goal of this paper is to show how the dominant eigenfunctions of these operators in combination with gradient-based optimization techniques can be used to detect long-lived coherent patterns in high-dimensional time-series data. The results will be illustrated using video data and a fluid flow example.}}, author = {{Klus, Stefan and Peitz, Sebastian and Schuster, Ingmar}}, booktitle = {{arXiv:1805.10118}}, title = {{{Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions}}}, year = {{2018}}, } @inbook{16392, author = {{Feldkord, Björn and Malatyali, Manuel and Meyer auf der Heide, Friedhelm}}, booktitle = {{Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications}}, isbn = {{9783319125671}}, issn = {{0302-9743}}, title = {{{A Dynamic Distributed Data Structure for Top-k and k-Select Queries}}}, doi = {{10.1007/978-3-319-98355-4_18}}, year = {{2018}}, } @article{1430, author = {{Hoffmann, Sandro P. and Albert, Maximilian and Weber, Nils and Sievers, Denis and Förstner, Jens and Zentgraf, Thomas and Meier, Cedrik}}, issn = {{2330-4022}}, journal = {{ACS Photonics}}, keywords = {{tet_topic_phc}}, pages = {{1933--1942}}, publisher = {{American Chemical Society (ACS)}}, title = {{{Tailored UV Emission by Nonlinear IR Excitation from ZnO Photonic Crystal Nanocavities}}}, doi = {{10.1021/acsphotonics.7b01228}}, volume = {{5}}, year = {{2018}}, } @article{13057, author = {{Kampmann, Matthias and Hellebrand, Sybille}}, journal = {{Microelectronics Reliability}}, pages = {{124--133}}, title = {{{Design For Small Delay Test - A Simulation Study}}}, volume = {{80}}, year = {{2018}}, } @misc{13072, author = {{Kampmann, Matthias and Hellebrand, Sybille}}, keywords = {{WORKSHOP}}, title = {{{Optimized Constraints for Scan-Chain Insertion for Faster-than-at-Speed Test}}}, year = {{2018}}, } @article{10129, abstract = {{There are many hard conjectures in graph theory, like Tutte's 5-flow conjecture, and the 5-cycle double cover conjecture, which would be true in general if they would be true for cubic graphs. Since most of them are trivially true for 3-edge-colorable cubic graphs, cubic graphs which are not 3-edge-colorable, often called snarks, play a key role in this context. Here, we survey parameters measuring how far apart a non 3-edge-colorable graph is from being 3-edge-colorable. We study their interrelation and prove some new results. Besides getting new insight into the structure of snarks, we show that such measures give partial results with respect to these important conjectures. The paper closes with a list of open problems and conjectures.}}, author = {{Fiol, M. A. and Mazzuoccolo, Guiseppe and Steffen, Eckhard}}, journal = {{The Electronic Journal of Combinatorics}}, keywords = {{Cubic graph, Tait coloring, Snark, Boole coloring, Berge's conjecture, Tutte's 5-flow conjecture}}, number = {{4}}, title = {{{Measures of Edge-Uncolorability of Cubic Graphs}}}, volume = {{25}}, year = {{2018}}, } @article{10132, author = {{Jin, Ligang and Mazzuoccolo, Giuseppe and Steffen, Eckhard}}, issn = {{1234-3099}}, journal = {{Discussiones Mathematicae Graph Theory}}, pages = {{165--175}}, title = {{{Cores, joins and the Fano-flow conjectures}}}, doi = {{10.7151/dmgt.1999}}, volume = {{38}}, year = {{2018}}, } @article{10142, author = {{Steffen, Eckhard}}, journal = {{Australasian Journal of Combinatorics}}, number = {{1}}, pages = {{153--160}}, title = {{{Approximating Vizing’s independence number conjecture}}}, volume = {{71}}, year = {{2018}}, } @inproceedings{10145, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI)}}, pages = {{2951--2958}}, title = {{{Learning to Rank Based on Analogical Reasoning}}}, year = {{2018}}, } @inproceedings{10148, author = {{El Mesaoudi-Paul, Adil and Hüllermeier, Eyke and Busa-Fekete, Robert}}, booktitle = {{Proc. 35th Int. Conference on Machine Learning (ICML)}}, pages = {{3469--3477}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Ranking Distributions based on Noisy Sorting}}}, year = {{2018}}, } @inproceedings{10149, author = {{Hesse, M. and Timmermann, J. and Hüllermeier, Eyke and Trächtler, Ansgar}}, booktitle = {{Proc. 4th Int. Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, Procedia Manufacturing 24}}, pages = {{15--20}}, title = {{{A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart}}}, year = {{2018}}, } @inbook{10152, author = {{Mencia, E.Loza and Fürnkranz, J. and Hüllermeier, Eyke and Rapp, M.}}, booktitle = {{Explainable and Interpretable Models in Computer Vision and Machine Learning}}, editor = {{Jair Escalante, H. and Escalera, S. and Guyon, I. and Baro, X. and Güclüütürk, Y. and Güclü, U. and van Gerven, M.A.J.}}, pages = {{81--113}}, publisher = {{Springer}}, title = {{{Learning interpretable rules for multi-label classification}}}, year = {{2018}}, } @inproceedings{10181, author = {{Nguyen, Vu-Linh and Destercke, Sebastian and Masson, M.-H. and Hüllermeier, Eyke}}, booktitle = {{Proc. 27th Int.Joint Conference on Artificial Intelligence (IJCAI)}}, pages = {{5089--5095}}, title = {{{Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty}}}, year = {{2018}}, }