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Results: 80,477
Number of items: 80,477
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van Gelder, M. (2018). Graffiti in Venetië: Teksten, tekeningen en posters in een vroegmoderne Italiaanse stad. Tijdschrift voor Geschiedenis, 131(1), 73-94. https://doi.org/10.5117/TVGESCH2018.1.GELD -
The ATLAS Collaboration (2018). Comparison between simulated and observed LHC beam backgrounds in the ATLAS experiment at Ebeam=4 TeV. Journal of Instrumentation, 13(12), Article P12006. https://doi.org/10.1088/1748-0221/13/12/P12006 -
Chopard, B., Falcone, J.-L., Kunzli, P., Veen, L., & Hoekstra, A. G. (2018). Multiscale Modeling: recent progress and open questions. Multiscale and Multidisciplinary Modeling, Experiments and Design, 1(1), 57–68. https://doi.org/10.1007/s41939-017-0006-4 -
Davis, R. I., Altmeyer, S., & Reineke, J. (2018). Response-time analysis for fixed-priority systems with a write-back cache. Real-Time Systems, 54(4), 912–963. https://doi.org/10.1007/s11241-018-9305-z -
Klinkert, W. (2018). [Bespreking van: W.J.E. van Rijn (2015) Overstag en toch op koers : de Koninklijke Marine en haar personeel 1945-2005]. BMGN - Low Countries Historical Review, 133, Article review 18. https://doi.org/10.18352/bmgn-lchr.10509 -
Shi, Z., Zhang, L., Liu, Y., Cao, X., Ye, Y., Cheng, M.-M., & Zheng, G. (2018). Crowd Counting With Deep Negative Correlation Learning. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition: proceedings : 18-22 June 2018, Salt Lake City, Utah (pp. 5382-5390). IEEE Computer Society. https://doi.org/10.1109/CVPR.2018.00564 -
Yang, H., Burghouwt, G., Wang, J., Boonekamp, T., & Dijst, M. (2018). The implications of high-speed railways on air passenger flows in China. Applied Geography, 97, 1-9. https://doi.org/10.1016/j.apgeog.2018.05.006 -
Melsen, L. A., Vos, J., & Boelens, R. (2018). What is the role of the model in socio-hydrology? Discussion of “Prediction in a socio-hydrological world”*. Hydrological Sciences Journal, 63(9), 1435-1443. https://doi.org/10.1080/02626667.2018.1499025 -
Li, C., & de Rijke, M. (2018). Incremental sparse Bayesian ordinal regression. Neural Networks, 106, 294-302. https://doi.org/10.1016/j.neunet.2018.07.015
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