1. 2019
  2. Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., & Hammer, B. (2019). Feature Relevance Bounds for Ordinal Regression. ArXiv e-prints.
  3. Marlevi, D., Ruijsink, B., Balmus, M., Dillon-Murphy, D., Fovargue, D., Pushparajah, K., ... Nordsletten, D. A. (2019). Estimation of Cardiovascular Relative Pressure Using Virtual Work-Energy. Scientific Reports, 9, [1375]. https://doi.org/10.1038/s41598-018-37714-0
  4. Vermeeren, M., Bravetti, A., & Seri, M. (2019). Contact variational integrators. Manuscript submitted for publication.
  5. van Vugt, M. K., Moye, A., Pollock, J., Johnson, B., Bonn-Miller, M. O., Gyatso, K., ... Fresco, D. M. (2019). Tibetan Buddhist monastic debate: Psychological and neuroscientific analysis of a reasoning-based analytical meditation practice. In Imagining the Brain: Episodes in the History of Brain Research (Progress in brain research). Elsevier. https://doi.org/10.1016/bs.pbr.2018.10.018
  6. Sillitti, A., Schomaker, L., Anakabe, J. F., Basurko, J., Dam, P., Ferreira, H., ... Zurutuza, U. (2019). Providing Proactiveness: Data Analysis Techniques Portfolios. In M. Albano, E. Jantunen, G. Papa, & U. Zurutuza (Eds.), The MANTIS Book : Cyber Physical System Based Proactive Collaborative Maintenance (pp. 145-238). Gistrup (DK): River Publishers.
  7. Babai, M., Wilkinson, M. H. F., & Chowdhury, A. S. (2019). A Graph Formalism for Time and Memory Efficient Morphological Attribute-Space Connected Filters. In Mathematical Morphology and Its Applications to Signal and Image Processing: Book Series: Lecture Notes in Computer Science (pp. 281-294). (Mathematical Morphology and Its Applications to Signal and Image Processing). Springer International Publishing. https://doi.org/10.1007/978-3-030-20867-7_22
  8. Bosnic, A., & Spenader, J. (2019). Acquisition Path of Distributive Markers in Serbian and Dutch: Evidence from an Act-Out Task. In M. M. Brown, & B. Dailey (Eds.), Proceedings of the 43rd Boston University Conference on Language Development (pp. 94-108). Somerville, MA: Cascadilla Press.
  9. Shafiee Kamalabad, M. (2019). Advanced non-homogeneous dynamic Bayesian network models for statistical analyses of time series data. [Groningen]: University of Groningen.
  10. Dijkstra, K., van de Loosdrecht, J., Schomaker, L. R. B., & Wiering, M. A. (2019). CentroidNet: A Deep Neural Network for Joint Object Localization and Counting. In U. Brefeld, E. Curry, E. Daly, B. MacNamee, A. Marascu, F. Pinelli, M. Berlingerio, ... N. Hurly (Eds.), ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases (pp. 585-601). ( Lecture Notes in Computer Science; Vol. 11053). Cham: Springer. https://doi.org/10.1007/978-3-030-10997-4_36
  11. Kind, A., & Azzopardi, G. (Accepted/In press). Computer-Aided Detection System for Diabetic Retinopathy using Retinal Fundus Images. In Computer Analysis of Images and Patterns
  12. Okafor, E. (2019). Deep learning for animal recognition. [Groningen]: University of Groningen.
  13. Keshavarzi Zafarghandi, A., Verheij, B., & Verbrugge, L. (Accepted/In press). Embedding Probabilities, Utilities and Decisions in a Generalization of Abstract Dialectical Frameworks. In International Symposium on Imprecise Probability: Theories and Applications (ISIPTA)
  14. Szabó, R. (2019). Inhomogeneous contact process and percolation. [Groningen]: Rijksuniversiteit Groningen.
  15. Charalampidou, S. (2019). Managing technical debt through software metrics, refactoring and traceability. [Groningen]: University of Groningen.
  16. Song, W. (2019). Matrix-based techniques for (flow-)transition studies. [Groningen]: University of Groningen.
  17. Vugt, M. V. (2019). Mindfulness as a Potential Tool for Productivity. In C. Sadowski, & T. Zimmermann (Eds.), Rethinking Productivity in Software Engineering (pp. 293-302). (Rethinking Productivity in Software Engineering). Apress. https://doi.org/10.1007/978-1-4842-4221-6_25
  18. van der Heiden, H. J. L. (2019). Modelling viscous effects in offshore flow problems: A numerical study. [Groningen]: Rijksuniversiteit Groningen.
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