The University of Toulouse (French: Université de Toulouse) is a university in France that was established by papal bull in 1229, making it one of the earliest universities to emerge in Europe. Since the closing of the university in 1793 due to the French Revolution, the University of Toulouse no longer exists as a single institution. However, there have been several independent "successor" universities inheriting the name. The current consortium of French universities, grandes écoles and other institutions of higher education and research in Toulouse and the surrounding region is known as Université fédérale de Toulouse Midi-Pyrénées.
Last-mile delivery by Uncrewed Aerial Vehicles (UAVs) has gained increasing attention as a promising solution for urban parcel transport, offering a sustainable and cost-effective alternative to traditional ground-based logistics. Such operations, however, introduce significant risks, requiring effective mitigation strategies and efficient path-planning tools. Risk-aware path planning in complex urban environments remains computationally demanding, as it involves a highly combinatorial sequential decision-making problem while requiring efficient online replanning to ensure reactivity. Efficient planners often rely on graph-based search methods, in which the heuristic used to guide the search plays a crucial role. However, in risk-aware path planning, heuristic estimation is particularly challenging, as risk evaluation forms part of the cost estimation and differs significantly from classical line-of-sight or Euclidean approaches. To address this limitation, this study presents a novel Deep Learning framework for estimating accurate and admissible heuristic functions in risk-aware UAV path planning. To this end, a comprehensive database of cost-to-go maps was generated using realistic 3D urban scenarios and a graph-based optimisation framework. This dataset was used to train a Vision Transformer (ViT) network capable of providing accurate cost-to-go estimates for various objective-function configurations. The proposed architecture incorporates an admissibility correction mechanism that regulates the trade-off between heuristic accuracy and admissibility, promoting either solution optimality or search efficiency. The method was benchmarked against conventional heuristics in terms of computation time and cost-to-go. Results show speed-ups of up to 20 times compared to traditional approaches while maintaining near-optimal solutions, with marginal increases in average path cost of the order of 0.1%.
This study develops an optimal transport (OT) framework for measuring pairwise airline supply dissimilarity across heterogeneous route networks. By representing each airline as a distribution of seat capacity or flight frequency across routes and defining route-level transportation costs, the framework provides a flexible and interpretable approach to comparing carriers beyond traditional measures based on multimarket contact or aggregate concentration. To illustrate its versatility, three OT-based dissimilarity indices are introduced to capture differences in seasonal supply positioning, geographic market proximity, and route-length structure. The framework is applied to European scheduled passenger flights for 32 airlines between 2016 and 2025. The empirical results show that airline relationships are multidimensional, with different patterns of similarity emerging across carrier types and network characteristics. A longitudinal analysis relative to the 2016 baseline reveals heterogeneous adjustment trajectories across airlines and indicates post-pandemic convergence in some dimensions of network organization alongside persistent differentiation in others. Overall, the proposed OT framework provides a general tool for inferring airline similarity from transport distances, assessing resilience and recovery following shocks, and examining structural change over time. The framework can also support the construction of airline peer groups for benchmarking, and the medium- to long-term monitoring of structural network recovery following major market shocks.
In this paper, we propose a new Blind Source Separation (BSS) method for (under)-determined convolutive mixtures of speech signals based on Time-Frequency (TF) masking and recombination of observations, exploiting the sparsity of source signals in the TF domain. Unlike conventional TF-masking-based approaches that rely directly on observation vectors for clustering, the proposed method introduces a more robust feature based on the complex cosine similarity between observation vectors and a set of reference vectors. This leads to more reliable TF clustering and improved separation performance, particularly in challenging underdetermined scenarios. A key contribution of this work is the blind estimation of Relative Transfer Functions (RTFs) of the sources, without requiring any prior acoustic knowledge. Building on this, we propose a novel RTF-based recombination strategy that replaces the classical TF-masking reconstruction step. This significantly reduces the artifacts typically introduced by masking-based BSS methods. Our test results demonstrate that our method outperforms several existing TF-masking-based methods.
Constraint-based clustering incorporates user knowledge into the clustering process through pairwise constraints, which specify whether two elements should be linked (Must-Link) or separated (Cannot-Link). Active constraint-based clustering extends this framework by selecting constraints iteratively to improve clustering quality while minimizing user effort. While effective, its performance strongly depends on the choice of queried constraints. Existing active approaches often ignore the underlying data distribution, which leads to a non-informed exploration of the data space and results in constraints that are uninformative or unrepresentative. We introduce FALCON (Functional Active Learning with Constraints based on criticisms and Neighborhood prototypes), a novel active constraint-based clustering method. FALCON formulates constraint selection as a modelling task that uses a minimal set of representative samples (prototypes) to approximate the data distribution, identifying areas with poor coverage (criticisms). Both prototypes and criticisms are then selected from this model to generate pairwise constraints that direct queries towards the most informative and homogeneous regions of the data space. Extensive experiments on 31 real-world datasets demonstrate that FALCON consistently outperforms state-of-the-art methods in terms of query efficiency, clustering accuracy, and robustness.
The resource-constrained project scheduling problem is one of the most investigated problems in the project scheduling literature, and has a rich history. This article provides a perspective on this challenging scheduling problem, without having the ambition to provide a complete overview. Instead, the article does aim to summarize a number of reasons why this problem has been so intensely investigated from different perspectives. It will be shown that this scheduling problem has many faces, and therefore deserves a lot of research time from a computational and theoretical point of view as well as from a practical point of view. An overview of possible extensions to other problems and a detailed overview of the used (both heuristic and exact) solution methods will be given. In addition, the data used will be discussed and interesting avenues for further research will be mentioned throughout the different sections.
