La communication est essentielle dans une société de services, notamment pour l’organisation des entreprises et leur ouverture internationale, où le plurilinguisme est central. Cet ouvrage réfléchit à la place et à la gestion de la diversité linguistique et culturelle au travail et dans les institutions. Il propose des solutions en formation, management interculturel et coopération. Par son approche pluridisciplinaire et ses études de cas variées, il intéressera chercheurs, étudiants et professionnels soucieux du rôle des langues dans l’organisation du travail et l’échange de biens et services. Communication is essential in a service society, particularly when it comes to organising companies and opening them up internationally, where multilingualism is central. This book looks at the place and management of linguistic and cultural diversity at work and in institutions. It proposes solutions for training, intercultural management and cooperation. With its multidisciplinary approach and varied case studies, it will be of interest to researchers, students, and professionals concerned about the role of languages in the organisation of work and the exchange of goods and services.

The design of real-time systems is based on assumptions about environmental conditions in which they will operate. We call this their safe operational envelope. Violation of these assumptions, i.e., out-of-envelope environments, can jeopardize timeliness and safety of real-time systems, e.g., by overwhelming them with interrupt storms. A long-lasting debate has been going on over which design paradigm, the time- or event-triggered, is more robust against such behavior. In this work, we investigate the claim that time-triggered systems are immune against out-of-envelope behavior and how event-triggered systems can be constructed to defend against being overwhelmed by interrupt showers. We introduce importance (independently of priority and criticality) as a means to express which tasks should still be scheduled in case environmental design assumptions cease to hold, draw parallels to mixed-criticality scheduling, and demonstrate how event-triggered systems can defend against out-of-envelope behavior.

editorial reviewed

Advancing research in fields such as Simultaneous Localization and Mapping (SLAM) and autonomous navigation critically depends on the availability of reliable and reproducible multimodal datasets. While several influential datasets have driven progress in these domains, they often suffer from limitations in sensing modalities, environmental diversity, and the reproducibility of the underlying hardware setups. To address these challenges, this paper introduces SMapper, a novel open-hardware, multi-sensor platform designed explicitly for, though not limited to, SLAM research. The device integrates synchronized LiDAR, multi-camera, and inertial sensing, supported by a robust calibration and synchronization pipeline that ensures precise spatio-temporal alignment across modalities. Its open and replicable design allows researchers to extend its capabilities and reproduce experiments across both handheld and robot-mounted scenarios. To demonstrate its practicality, we additionally release SMapper-light, a publicly available SLAM dataset containing representative indoor and outdoor sequences. The dataset includes tightly synchronized multimodal data and ground truth trajectories derived from offline LiDAR-based SLAM with sub-centimeter accuracy, alongside dense 3D reconstructions. Furthermore, the paper contains benchmarking results on state-of-the-art LiDAR and visual SLAM frameworks using the SMapper-light dataset. By combining open-hardware design, reproducible data collection, and comprehensive benchmarking, SMapper establishes a robust foundation for advancing SLAM algorithm development, evaluation, and reproducibility. The project's documentation, including source code, CAD models, and dataset links, is publicly available at https://snt-arg.github.io/smapper_docs.

Near-field multiple-input multiple-output (MIMO) radar imaging suffers from high computational load inherently due to irregular spatial sampling with distributed antennas. Existing acceleration methods for near-field MIMO imaging typically rely on interpolation or compensation of measurements and are primarily developed for direct reconstruction. This hinders their ease of adoption for different MIMO geometries and requires further modification for regularized inversion. In this study, we address these challenges by developing a fast regularized reconstruction approach for three-dimensional near-field MIMO imaging based on the Stochastic Proximal Gradient Method. We demonstrate the performance of the developed approach through experimental measurements. The results show a significant improvement in runtime without any notable compromise in reconstruction quality.

The internal representations learned by deep networks are often sensitive to architecture-specific choices, raising questions about the stability, alignment, and transferability of learned structure across models. In this paper, we investigate how structural constraints--such as linear shaping operators and corrective paths--affect the compatibility of internal representations across different architectures. Building on the insights from prior studies on structured transformations and convergence, we develop a framework for measuring and analyzing representational alignment across networks with distinct but related architectural priors. Through a combination of theoretical insights, empirical probes, and controlled transfer experiments, we demonstrate that structural regularities induce representational geometry that is more stable under architectural variation. This suggests that certain forms of inductive bias not only support generalization within a model, but also improve the interoperability of learned features across models. We conclude with a discussion on the implications of representational transferability for model distillation, modular learning, and the principled design of robust learning systems.

Uplift modeling has achieved significant success in various fields, particularly in online marketing. It is a method that primarily utilizes machine learning and deep learning to estimate individual treatment effects. This paper we apply uplift modeling to analyze the effect of company adjustment on their financial status, and we treat these adjustment as treatments or interventions in this study. Although there have been extensive studies and application regarding binary treatments, multiple treatments, and continuous treatments, company adjustment are often more complex than these scenarios, as they constitute a series of multiple time-dependent actions. The effect estimation of company adjustment needs to take into account not only individual treatment traits but also the temporal order of this series of treatments. This study collects a real-world data set about company financial statements and reported behavior in Luxembourg for the experiments. First, we use two meta-learners and three other well-known uplift models to analyze different company adjustment by simplifying the adjustment as binary treatments. Furthermore, we propose a new uplift modeling framework (MTDnet) to address the time-dependent nature of these adjustment, and the experimental result shows the necessity of considering the timing of these adjustment.

Quantum computers allow a near-exponential speed-up for specific applications when compared to classical computers. Despite recent advances in the hardware of quantum computers, their practical usage is still severely limited due to a restricted number of available physical qubits and quantum gates, short coherence time, and high error rates. This paper lays the foundation towards a metric independent approach to quantum circuit optimization based on exhaustive search algorithms. This work uses depth-first search and iterative deepening depth-first search. We rely on ZX calculus to represent and optimize quantum circuits through the minimization of a given metric (e.g. the T-gate and edge count). ZX calculus formally guarantees that the semantics of the original circuit is preserved. As ZX calculus is a non-terminating rewriting system, we utilise a novel set of pruning rules to ensure termination while still obtaining high-quality solutions. We provide the first formalization of quantum circuit optimization using ZX calculus and exhaustive search. We extensively benchmark our approach on 100 standard quantum circuits. Finally, our implementation is integrated in the well-known libraries PyZX and Qiskit as a compiler pass to ensure applicability of our results.

This paper introduces the first fully unsupervised domain adaptation (UDA) framework for unsupervised anomaly detection (UAD). The performance of UAD techniques degrades significantly in the presence of a domain shift, difficult to avoid in a real-world setting. While UDA has contributed to solving this issue in binary and multi-class classification, such a strategy is ill-posed in UAD. This might be explained by the unsupervised nature of the two tasks, namely, domain adaptation and anomaly detection. Herein, we first formulate this problem that we call the two-fold unsupervised curse. Then, we propose a pioneering solution to this curse, considered intractable so far, by assuming that anomalies are rare. Specifically, we leverage clustering techniques to identify a dominant cluster in the target feature space. Posed as the normal cluster, the latter is aligned with the source normal features. Concretely, given a one-class source set and an unlabeled target set composed mostly of normal data and some anomalies, we fit the source features within a hypersphere while jointly aligning them with the features of the dominant cluster from the target set. The paper provides extensive experiments and analysis on common adaptation benchmarks for anomaly detection, demonstrating the relevance of both the newly introduced paradigm and the proposed approach. The code will be made publicly available.

The Federated Learning paradigm facilitates effective distributed machine learning in settings where training data is decentralized across multiple clients. As the popularity of the strategy grows, increasingly complex real-world problems emerge, many of which require balancing conflicting demands such as fairness, utility, and resource consumption. Recent works have begun to recognise the use of a multi-objective perspective in answer to this challenge. However, this novel approach of combining federated methods with multi-objective optimisation has never been discussed in the broader context of both fields. In this work, we offer a first clear and systematic overview of the different ways the two fields can be integrated. We propose a first taxonomy on the use of multi-objective methods in connection with Federated Learning, providing a targeted survey of the state-of-the-art and proposing unambiguous labels to categorise contributions. Given the developing nature of this field, our taxonomy is designed to provide a solid basis for further research, capturing existing works while anticipating future additions. Finally, we outline open challenges and possible directions for further research.