Wiebeler, C., &; Schumacher, S. (2014). Quantum Yields and Reaction Times of Photochromic Diarylethenes: Nonadiabatic Ab Initio Molecular Dynamics for Normal- and Inverse-Type. The Journal of Physical Chemistry A, 7816–7823.https://doi.org/10.1021/jp506316w 
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Lewandowski, P., Ardizzone, V., Tse, Y. C., Kwong, N. H., Luk, M. H., Lücke, A.,https://doi.org/10.1117/12.2037174__Schumacher, S. (2014). Formation and control of transverse patterns in a quantum fluid of microcavity polaritons. In M. Betz, A. Y. Elezzabi, J.-J. Song, &; K.-T. Tsen (Eds.), Ultrafast Phenomena and Nanophotonics XVIII.https://doi.org/10.1117/12.2037174 
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Meyer, C., Somorovsky, J., Weiss, E., Schwenk, J., Schinzel, S., &; Tews, E. (2014). Revisiting SSL/TLS Implementations: New Bleichenbacher Side Channels and Attacks. In 23rd USENIX Security Symposium (USENIX Security 14) (pp. 733–748). San Diego, CA: USENIX Association.
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Chen, M.-H., Chen, W.-F., &;  Ku, L.-W. (2014). RESOLVE: An Emotion Word Suggestion System Facilitates Language Learners’ Emotional Expressions. In Proceedings of the AsiaCALL 2014.
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Maßbaum, A., &; Sureth-Sloane, C. (2014). Thin Capitalization Rules and Entrepreneurial Capital Structure Decisions. Business Research, 2(2), 147–169.https://doi.org/10.1007/bf03342708 
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Böttcher, S., Hartel, R., &; Thiele, S. (2014). Difference Computation for Grammar-Compressed XML Data. In Proceedings of the Second Australasian Web Conference (pp. 3–12). Australian Computer Society, Inc.
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Böttcher, S., Brandenburg, M., &; Hartel, R. (2014). Keyword Search on DAG-Compressed XML Data. In Proceedings of the Second Australasian Web Conference (pp. 13–20). Australian Computer Society, Inc.
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Böttcher, S., Hartel, R., &; Rabe, J. (2014). Efficient XML Keyword Search Based on DAG-Compression. In Database and Expert Systems Applications - 25th International Conference, DEXA 2014 (pp. 122–137). Cham: Springer.https://doi.org/10.1007/978-3-319-10073-9_11 
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Niendorf, T., Leuders, S., Riemer, A., Brenne, F., Tröster, T., Richard, H. A., &; Schwarze, D. (2014). Functionally Graded Alloys Obtained by Additive Manufacturing. Advanced Engineering Materials, 857–861.https://doi.org/10.1002/adem.201300579 
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Riemer, A., Leuders, S., Thöne, M., Richard, H. A., Tröster, T., &; Niendorf, T. (2014). On the fatigue crack growth behavior in 316L stainless steel manufactured by selective laser melting. Engineering Fracture Mechanics, 15–25.https://doi.org/10.1016/j.engfracmech.2014.03.008 
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Agarwal, M., Fallah Tehrani, A., &; Hüllermeier, E. (2014). Preference-based learning of ideal solutions in TOPSIS-like decision models. Journal of Multi-Criteria Decision Analysis, 22(3–4).
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Krotzky, T., Fober, T., Hüllermeier, E., &; Klebe, G. (2014). Extended graph-based models for enhanced similarity search in Cabase. IEEE/ACM Transactions of Computational Biology and Bioinformatics, 11(5), 878–890.
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Hüllermeier, E. (2014). Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. International Journal of Approximate Reasoning, 55(7), 1519–1534.
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Henzgen, S., Strickert, M., &; Hüllermeier, E. (2014). Visualization of evolving fuzzy-rule-based systems. Evolving Systems, 5, 175–191.
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Busa-Fekete, R., Szörenyi, B., Weng, P., Cheng, W., &; Hüllermeier, E. (2014). Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Machine Learning, 97(3), 327–351.
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Krempl, G., Zliobaite, I., Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V.,  Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explorations, 16(1), 1–10.
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Strickert, M., Bunte, K., Schleif, F. M., &; Hüllermeier, E. (2014). Correlation-based embedding of pairwise score data. Neurocomputing, 141, 97–109.
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Shaker, A., &; Hüllermeier, E. (2014). Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. International Journal of Applied Mathematics and Computer Science, 24(1), 199–212.
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Senge, R., Bösner, S., Dembczynski, K., Haasenritter, J., Hirsch, O., Donner-Banzhoff, N., &; Hüllermeier, E. (2014). Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences, 255, 16–29.
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Donner-Banzhoff, N., Haasenritter, J., Hüllermeier, E., Viniol, A., Bösner, S., &; Becker, A. (2014). The comprehensive diagnostic study is suggested as a design to model the diagnostic process. Journal of Clinical Epidemiology, 2(67), 124–132.
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