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  • Boulogne, G. F. (2023). FED 2023/71. 71. Case note on: HR, 3/03/23, ECLI:NL:HR:2023:330 Fiscaal Tijdschrift FED, 2023(13/14), 10-18.
  • Kassoti, E., & Idriz, N. (2023). The CJEU and the Rule of Law in the EU’s External Action. In L. M. Hinojosa-Martínez, & C. Pérez-Bernárdez (Eds.), Enhancing the Rule of Law in the European Union’s External Action (pp. 63-84). Edward Elgar Publishing. https://doi.org/10.4337/9781035312320.00013
  • Open Access
    Chakraborty, S., & Hampton, S. D. (2023). A 4d non-BPS NS-NS microstate. Journal of High Energy Physics, 2023(9), Article 99. https://doi.org/10.1007/JHEP09(2023)099
  • Open Access
    Lostaglio, M., Belenchia, A., Levy, A., Hernández-Gómez, S., Fabbri, N., & Gherardini, S. (2023). Kirkwood-Dirac quasiprobability approach to the statistics of incompatible observables. Quantum, 7, Article 1128. https://doi.org/10.22331/q-2023-10-09-1128
  • Open Access
    Voskamp, A., Fritz, S. A., Köcke, V., Biber, M. F., Nogueira Brockmeyer, T., Bertzky, B., Forrest, M., Goldstein, A., Henderson, S., Hickler, T., Hof, C., Kastner, T., Lang, S., Manning, P., Mascia, M. B., McFadden, I. R., Niamir, A., Noon, M., O'Donnell, B., ... Böhning-Gaese, K. (2023). Utilizing multi-objective decision support tools for protected area selection. One Earth, 6(9), 1143-1156. https://doi.org/10.1016/j.oneear.2023.08.009
  • Open Access
    Salvatore, C. (2023). Inference with non-probability samples and survey data integration: a science mapping study. Metron, 81(1), 83-107. https://doi.org/10.1007/s40300-023-00243-6
  • Dignum, E., Boterman, W., Flache, A., & Lees, M. (2023). Modeling Mechanisms of School Segregation and Policy Interventions: A Complexity Perspective. In J. Mikyška, C. de Mulatier, M. Paszynski, V. V. Krzhizhanovskaya, J. J. Dongarra, & P. M. A. Sloot (Eds.), Computational Science – ICCS 2023: 23rd International Conference, Prague, Czech Republic, July 3–5, 2023 : proceedings (Vol. III, pp. 74-89). (Lecture Notes in Computer Science; Vol. 14075). Springer. https://doi.org/10.1007/978-3-031-36024-4_6
  • Hooftman, D., Mohammadi Ziabari, S. S., & Snijder, J. (2023). Exploring CycleGAN for Bias Reduction in Gender Classification: Generative Modelling for Diversifying Data Augmentation. In H. Lu, M. Blumenstein, S.-B. Cho, C.-L. Liu, Y. Yagi, & T. Kamiya (Eds.), Pattern Recognition: 7th Asian Conference, ACPR 2023, Kitakyushu, Japan, November 5–8, 2023 : proceedings (Vol. III, pp. 26-40). (Lecture Notes in Computer Science; Vol. 14408). Springer. https://doi.org/10.1007/978-3-031-47665-5_3
  • Deshamudre, R., Mohammadi Ziabari, S. S., & van Houten, M. (2023). Enhancing AI Adoption in Healthcare: A Data Strategy for Improved Heart Disease Prediction Accuracy Through Deep Learning Techniques. In P. Delir Haghighi, E. Pardede, G. Dobbie, V. Yogarajan, N. A. S. ER, G. Kotsis, & I. Khalil (Eds.), Information Integration and Web Intelligence: 25th International Conference, iiWAS 2023, Denpasar, Bali, Indonesia, December 4–6, 2023 : proceedings (pp. 13-19). (Lecture Notes in Computer Science; Vol. 14416). Springer. https://doi.org/10.1007/978-3-031-48316-5_2
  • Open Access
    Jeub, L. G. S., Colavizza, G., Dong, X., Bazzi, M., & Cucuringu, M. (2023). Local2Global: a distributed approach for scaling representation learning on graphs. Machine Learning, 112(5), 1663-1692. https://doi.org/10.1007/s10994-022-06285-7
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