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van der Zwet, K., Barros, A. I., van Engers, T. M., & Sloot, P. M. A. (2023). Promises and pitfalls of computational modelling for insurgency conflicts. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 20(3), 333-350. https://doi.org/10.1177/15485129211073612 -
Sileno, G., Spanakis, J., & van Dijck, G. (2023). Preface. In G. Sileno, J. Spanakis, & G. van Dijck (Eds.), Legal Knowledge and Information Systems: JURIX 2023: The Thirty-sixth Annual Conference, Maastricht, the Netherlands, 18–20 December 2023 (pp. vii-ix). (Frontiers in Artificial Intelligence and Applications; Vol. 379). IOS Press. https://ebooks.iospress.nl/volumearticle/65560 -
Bernasco, W., Hoeben, E. M., Koelma, D., Liebst, L. S., Thomas, J., Appelman, J., Snoek, C. G. M., & Lindegaard, M. R. (2023). Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing. Sociological Methods and Research, 52(3), 1239–1287. https://doi.org/10.1177/00491241221099554 -
Papadopoulos, K., Papadimitriou, P., & Papagianni, C. (2023). Deterministic and Probabilistic P4-Enabled Lightweight In-Band Network Telemetry. IEEE Transactions on Network and Service Management, 20(4), 4909 - 4922. https://doi.org/10.1109/TNSM.2023.3301839 -
The ATLAS Collaboration (2023). Search for heavy Higgs bosons with flavour-violating couplings in multi-lepton plus b-jets final states in pp collisions at 13 TeV with the ATLAS detector. Journal of High Energy Physics, 2023(12), Article 081. https://doi.org/10.1007/JHEP12(2023)081 -
Gugushvili, S., van der Meulen, F., Schauer, M., & Spreij, P. (2023). Nonparametric Bayesian volatility learning under microstructure noise. Japanese Journal of Statistics and Data Science, 6(1), 551-571. https://doi.org/10.1007/s42081-022-00185-9 -
Jones, D. L., Fuentes, B., Arenas-Díaz, F., Remonsellez, F., van Hall, R., Atkinson, B. S., Mooney, S. J., & Bol, R. (2023). Life at the extreme: Plant-driven hotspots of soil nutrient cycling in the hyper-arid core of the Atacama Desert. Soil Biology and Biochemistry, 184, Article 109128. https://doi.org/10.1016/j.soilbio.2023.109128 -
Lippert, F., Kranstauber, B., van Loon, E. E., & Forré, P. (2023). Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems (NeurIPS 2023): 10-16 December 2023, New Orleans, Louisana, USA (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2306.08445 -
Acernese, F., Antier, S., Bhardwaj, U., Bulten, H. J., Caudill, S., Kalaghatgi, C., Leonova, E., Linde, F., London, L., Miller, B., Mukherjee, S., Nissanke, S., Phukon, K. S., Raaijmakers, G., van Bakel, N., van Beuzekom, M., van den Brand, J. F. J., Van Den Broeck, C., Vardaro, M., & Virgo DetChar group (2023). Virgo detector characterization and data quality: tools. Classical and Quantum Gravity, 40(18), Article 185005. https://doi.org/10.48550/arXiv.2210.15634, https://doi.org/10.1088/1361-6382/acdf36
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