Résultats de recherche

High-Sensitivity Birefringence Mapping Using Near-Circularly Polarized Light I will describe several techniques for mapping a two-dimensional birefringence distribution, which can be classified according to the optical schemes and principles of work:Illumination geometry (transmitted light/reflected light)Image acquisition (sequential acquisition/simultaneous acquisition)Polarization control (electrically controlled variable retardance/mechanical rotation).This classification facilitates a comparative analysis of the capabilities and limitations in these methods for birefringence characterization. Polychromatic polarizing microscopy (PPM) provides unique capabilities to alternative methods. It leverages vector interference to generate vivid, full-spectrum colors at extremely low retardances, down to < 10 nm. PPM is a significant departure from conventional polarizing microscopes that rely on Newton interference, which requires retardances above 400 nm for color formation. Furthermore, PPM's color output directly reflects the orientation of the birefringent material, a feature absent in conventional microscopy where color is solely determined by retardance.Joint seminar of ILEE & NISM!Le séminaire est accessible à des personnes externes également, pas besoin de s'inscrire. Plus d'infos

Abstract Detecting semantic code clones in logic programs is a longstanding challenge, due to the lack of a unified definition of semantic similarity and the diversity of syntactic expressions that can represent similar behaviours. This thesis introduces a formal and flexible framework for semantic clone detection based on Constrained Horn Clauses (CHC). The approach considers two predicates as semantic clones if they can be independently transformed, via semantics-preserving program transformations, into a common third predicate. At the core of the method lies anti-unification, a process that computes the most specific generalisation of two predicates by identifying their shared structural patterns. The framework is parametric in regard with the allowed program transformations, the notion of generality, and the so-called quality estimators that steer the anti-unification process. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Katrien Beuls - University of Namur, BelgiumProf. Jean-Marie Jacquet - University of Namur, BelgiumProf. Temur Kutsia - Johannes Kepler University, AustriaProf. Frédéric Mesnard - University of the Reunion, Reunion IslandProf. Paul Van Eecke - Free University of Brussels, Belgium

Deadlines Opening of abstract submissions and registrations: 15 September 2025Deadline to submit indicative title and summary: 30 November 2025Deadline for final abstract submissions: 01 May 2026Early bird registration deadline: 01 March 2026 More information on the website

Abstract In recent decades, the volume of data generated worldwide has grown exponentially, significantly accelerating advancements in machine learning. This explosion of data has led to an increased need for effective data exploration techniques, giving rise to a specialized field known as dimensionality reduction. Dimensionality reduction methods are used to transform high-dimensional data into a low-dimensional space (typically 2D or 3D), so that it can be easily visualized and understood by humans. Algorithms such as Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE) have become essential tools for visualizing complex datasets. These techniques play a critical role in exploratory data analysis and in interpreting complex models like Convolutional Neural Networks (CNNs). Despite their widespread adoption, dimensionality reduction techniques, particularly non-linear ones, often lack interpretability. This opacity makes it difficult for users to understand the meaning of the visualizations or the rationale behind specific low-dimensional representations. In contrast, the field of supervised machine learning has seen significant progress in explainable AI (XAI), which aims to clarify model decisions, especially in high-stakes scenarios. While many post-hoc explanation tools have been developed to interpret the outputs of supervised models, there is still a notable gap in methods for explaining the results of dimensionality reduction techniques. This research investigates how post-hoc explanation techniques can be integrated into dimensionality reduction algorithms to improve user understanding of the resulting visualizations. Specifically, it explores how interpretability methods originally developed for supervised learning can be adapted to explain the behavior of non-linear dimensionality reduction algorithms. Additionally, this work examines whether the integration of post-hoc explanations can enhance the overall effectiveness of data exploration. As these tools are intended for end-users, we also design and evaluate an interactive system that incorporates explanatory mechanisms. We argue that combining interpretability with interactivity significantly improves users' understanding of embeddings produced by non-linear dimensionality reduction techniques. In this research, we propose enhancements to an existing post-hoc explanation method that adapts LIME for t-SNE. We introduce a globally-local framework for fast and scalable explanations of t-SNE embeddings. Furthermore, we present a completely new approach that adapts saliency map-based explanations to locally interpret non-linear dimensionality reduction results. Lastly, we introduce our interactive tool, Insight-SNE, which integrates our gradient-based explanation method and enables users to explore low-dimensional embeddings through direct interaction with the explanations. Jury Prof. Wim Vanhoof - University of Namur, BelgiumProf. Benoit Frénay - University of Namur, BelgiumProf. Bruno Dumas - University of Namur, BelgiumProf. John Lee - University of Louvain, BelgiumProf. Luis Galarraga - University of Rennes, France

Le 11 décembre 2024, l'Université de Reims-Champagne-Ardenne a accueilli l'évènement de lancement du projet ORION dont l’Université de Namur est partenaire. Ce projet, financé pour 4 ans par les fonds FEDER et INTERREG, vise à améliorer la gestion de l’eau dans le val de Meuse tout en préservant les écosystèmes du val de Meuse, un fleuve traversant la France et la Belgique. 

Max Collinet, professeur de géologie à la Faculté des sciences et chercheur au sein de l’Institute of Life, Earth and Environment (ILEE), vient d’obtenir un financement équipement (EQP) du F.R.S – FNRS à la suite des appels dont les résultats ont été publiés en décembre 2024.  

Comment concilier télétravail et travail en présentiel ? Comment encadrer ces pratiques professionnelles pour renforcer les dimensions innovantes et durables du travail hybride ? C’est à toutes ces questions que Michel Ajzen, spécialiste en management des organisations, s’intéresse dans le cadre de ses missions d’enseignement au sein du département des sciences de gestion de l’UNamur. Ses recherches se concentrent sur le travail hybride et l'innovation organisationnelle, avec une approche transdisciplinaire visant à réinventer les pratiques managériales pour relever les défis contemporains.