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Results: 715
Number of items: 715
  • King, R., Palavalli, B., Roy, D., Menon, N., Sloot, P. M. A., Pfeffer, K., & Lees, M. (2017). Socio-economic data from slums in Bangalore, India [Data set]. UK Data Archive. https://doi.org/10.5255/ukda-sn-852705
  • Roy, D., Lees, M. H., Pfeffer, K., & Sloot, P. M. A. (2017). Modelling the impact of household life cycle on slums in Bangalore. Computers, Environment and Urban Systems, 64, 275-287. https://doi.org/10.1016/j.compenvurbsys.2017.03.008
  • Kovalchuk, S. V., Abuhay, T. M., Altintas, I., Norman, M. L., Lees, M. H., Krzhizhanovskaya, V. V., Dongarra, J., & Sloot, P. M. A. (2017). Data through the Computational Lens. Journal of Computational Science, 20, 81-84. https://doi.org/10.1016/j.jocs.2017.05.003
  • Koumoutsakos, P., Lees, M., Krzhizhanovskaya, V., Dongarra, J., & Sloot, P. (Eds.) (2017). International Conference on Computational Science, ICCS 2017: 12-14 June 2017, Zurich, Switzerland. (Procedia Computer Science; Vol. 108). Elsevier. http://www.sciencedirect.com/science/journal/18770509/108/
  • Traag, V. A., Quax, R., & Sloot, P. M. A. (2017). Modelling the distance impedance of protest attendance. Physica A: Statistical Mechanics and its Applications, 468, 171-182. https://doi.org/10.1016/j.physa.2016.10.054
  • Sloot, P. M. A. (2017). More likely we would be ritually slaughtered. In S. Thurner (Ed.), 43 Visions for Complexity (pp. 65-66). (Exploring complexity; Vol. 3). World Scientific. https://doi.org/10.1142/9789813206854_0033
  • Open Access
    Melnikov, V., Krzhizhanovskaya, V. V., & Sloot, P. M. A. (2017). Models of Pedestrian Adaptive Behaviour in Hot Outdoor Public Spaces. Procedia Computer Science, 108, 185-194. https://doi.org/10.1016/j.procs.2017.05.006
  • Open Access
    Quax, R., Har-Shemesh, O., & Sloot, P. M. A. (2017). Quantifying Synergistic Information Using Intermediate Stochastic Variables. Entropy, 19(2), Article 85. https://doi.org/10.3390/e19020085
  • Open Access
    de Graaf, C. S. L., Kandhai, D., & Sloot, P. M. A. (2017). Efficient Estimation of Sensitivities for Counterparty Credit Risk with the Finite Difference Monte Carlo Method. Journal of Computational Finance, 21(1), 83-113. https://doi.org/10.2139/ssrn.2521431, https://doi.org/10.21314/JCF.2016.325
  • Open Access
    Presbitero, A., Quax, R., Krzhizhanovskaya, V., & Sloot, P. (2017). Anomaly Detection in Clinical Data of Patients Undergoing Heart Surgery. Procedia Computer Science, 108, 99-108. https://doi.org/10.1016/j.procs.2017.05.002
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