Publications

2025

• Iffy-Or-Not: Critically Evaluating Potential Misinformation With Fallacy Detection and Socratic Questioning Using LLMs
  
  • Lim Gionnieve
  • Kim Juho
  • Perrault Simon
  ACM Transactions on Computer-Human Interaction, Association for Computing Machinery, 2025. Social platforms have expanded opportunities for deliberation with the comments being used to inform one's opinion. However, using such information to form opinions is challenged by unsubstantiated or false content. To enhance the quality of opinion formation and potentially confer resistance to misinformation, we developed Iffy-Or-Not ( ION ), a browser extension that seeks to invoke critical thinking when reading texts. With three features guided by argumentation theory, ION highlights fallacious content, suggests diverse queries to probe them with, and offers deeper questions to consider and chat with others about. From a user study ( \(N=18\) ), we found that ION encourages users to be more attentive to the content, suggests queries that align with or are preferable to their own, and poses thought-provoking questions that expands their perspectives. However, some participants expressed aversion to ION due to misalignments with their information goals and thinking predispositions. Potential backfiring effects with ION are discussed. (10.1145/3771935)
  DOI : 10.1145/3771935
• Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation
  
  • Benigmim Yasser
  • Roy Subhankar
  • Oublal Khalid
  • Marouf Imad Eddine
  • Essid Slim
  • Kalogeiton Vicky
  • Lathuilière Stéphane
  , 2025. The rise of Artificial Intelligence as a Service (AIaaS) democratizes access to pre-trained models via Application Programming Interfaces (APIs), but also raises a fundamental question: how can local models be effectively trained using black-box models that do not expose their weights, training data, or logits, a constraint in which current domain adaptation paradigms are impractical ? To address this challenge, we introduce the Black-Box Distillation (B2D) setting, which enables local model adaptation under realistic constraints: (1) the API model is open-vocabulary and trained on large-scale general-purpose data, and (2) access is limited to one-hot predictions only. We identify that open-vocabulary models exhibit significant sensitivity to input resolution, with different object classes being segmented optimally at different scales, a limitation termed the "curse of resolution". Our method, ATtention-Guided sCaler (ATGC), addresses this challenge by leveraging DINOv2 attention maps to dynamically select optimal scales for black-box model inference. ATGC scores the attention maps with entropy to identify informative scales for pseudo-labelling, enabling effective distillation. Experiments demonstrate substantial improvements under black-box supervision across multiple datasets while requiring only one-hot API predictions. Our code is available at https://github.com/yasserben/ATGC. (10.48550/arXiv.2509.00509)
  DOI : 10.48550/arXiv.2509.00509
• Supervised learning methods for offline reinforcement learning
  
  • Ghanem Abdelghani
  , 2025. Offline Reinforcement Learning (RL) enables policy learning from static trajectory data without environment interaction, presenting unique challenges for effective representation learning and optimization. This thesis investigates supervised learning methods for offline RL, with a focus on sequence modeling approaches using transformer architectures. We present several key contributions that advance both theoretical understanding and empirical performance in this domain.First, we propose Multi-Objective Decision Transformers (MO-DT), which jointly optimize action, state, and return prediction to encourage richer attention patterns compared to single-task approaches. To address the non-smoothness of action distributions, we introduce Trust Region Decision Transformers (TRDT), which augment trajectories with action-space regions to smooth representations and improve cross-modal attention. Second, we develop the Reward-Guided Decision Translator (RGDT), an encoder-decoder architecture that recasts offline RL as sequence-to-sequence modeling, predicting next states rather than actions while directly conditioning on sequences of future returns.Our theoretical contributions include a comprehensive framework based on modified gradient flow analysis that reveals how multi-task training fundamentally shapes optimization dynamics. We prove that gradient descent implicitly encourages task disagreement by minimizing inner products between task gradients, with multi-objective training introducing first-order regularization and sequential training adding potentially harmful second-order corrections. Furthermore, we establish sample complexity bounds for offline RL sequence modeling, identifying critical transitions between small-data and large-data regimes and revealing trade-offs between context coverage breadth and sampling depth.Empirically, our methods significantly outperform vanilla Decision Transformers and match or exceed state-of-the-art baselines on D4RL locomotion benchmarks. Our theoretical predictions accurately forecast optimization trajectories and provide actionable principles for designing effective multi-task training strategies in offline RL. Together, these contributions demonstrate how principled supervised learning approaches can effectively address the challenges of learning from static trajectory data.
• Secure Group Key Dissemination Protocol in Cooperative Vehicular Platooning
  
  • Braiteh Farah-Emma
  • Bassi Francesca
  • Khatoun Rida
  , 2025. Cooperative vehicular platoons improve road safety and reduce congestion through synchronized maneuvers enabled by Vehicle-to-Vehicle (V2V) communication. Vehicles are authenticated using certificates from the Cooperative Intelligent Transport Systems (C-ITS) Public Key Infrastructure (PKI). Short-term certificates, serving as vehicle identifiers, change over time and distance, which may lead to legitimate members being misidentified as attackers in the platoon, resulting in false positives. Additionally, sensitive data, such as platoon IDs, must be protected from impersonation attacks by external vehicles. To address these challenges, we propose a secure group key-based authentication framework that uses post-quantum cryptography and Shamir’s Secret Sharing for key exchange. This ensures accurate member authentication and protection of platoon data. Security analysis using the Scyther tool along with simulations using PLEXE simulator demonstrate the protocol’s effectiveness in securing platoon operations against cyber threats.
• MMA-RAG: A Survey on Multimodal Agentic Retrieval-Augmented Generation
  
  • Perlić Vladana
  • Lebailly Stéphane
  • Malvone Vadim
  • Nguyen Van-Tam
  • Urard Pascal
  , 2025. Multimodal Agentic Retrieval-Augmented Generation (MMA-RAG) marks a significant advancement in AI, empowering large language models to integrate and reason over diverse data types, including text, images, audio, and structured data. This survey provides the first comprehensive overview of the MMA-RAG paradigm, tracing its evolution from traditional text-based RAG to sophisticated multimodal and agentic frameworks. We systematically review foundational literature, analyze key architectures and dominant design patterns, and survey applications across domains such as scientific question answering, document understanding, and healthcare. We conduct a comparative analysis of system components, evaluation benchmarks, and agentic capabilities like planning and tool use, offering a holistic view of the current landscape. Our key insights highlight how multimodal integration and autonomous agents mitigate hallucinations and enhance contextual reasoning, while also surfacing persistent challenges in cross-modal alignment, evaluation, and scalability. We conclude by outlining open research directions and practical implications for next-generation AI systems.
• Ask and Remember: A Questions-Only Replay Strategy for Continual Visual Question Answering
  
  • Marouf Imad Eddine
  • Tartaglione Enzo
  • Lathuilière Stéphane
  • van de Weijer Joost
  , 2025. Continual Learning in Visual Question Answering (VQACL) requires models to learn new visual-linguistic tasks (plasticity) while retaining knowledge from previous tasks (stability). The multimodal nature of VQACL presents unique challenges, requiring models to balance stability across visual and textual domains while maintaining plasticity to adapt to novel objects and reasoning tasks. Existing methods, predominantly designed for unimodal tasks, often struggle to balance these demands effectively. In this work, we introduce QUestion-only replay with Attention Distillation (QUAD), a novel approach for VQACL that leverages only past task questions for regularisation, eliminating the need to store visual data and addressing both memory and privacy concerns. QUAD achieves stability by introducing a question-only replay mechanism that selectively uses questions from previous tasks to prevent overfitting to the current task's answer space, thereby mitigating the out-of-answer-set problem. Complementing this, we propose attention consistency distillation, which uniquely enforces both intra-modal and inter-modal attention consistency across tasks, preserving essential visual-linguistic associations. Extensive experiments on VQAv2 and NExT-QA demonstrate that QUAD significantly outperforms state-of-the-art methods, achieving robust performance in continual VQA.
• Remini: Leveraging Technology-mediated Mutual Reminiscence for Promoting Positive Affect and Feeling of Connectedness among Loved Ones
  
  • Jiang Zhuoqun
  • Yeo Shunyi
  • Seow Wei Xuan, Donovan
  • Perrault Simon Tangi
  Proceedings of the ACM on Human-Computer Interaction, Association for Computing Machinery (ACM), 2025, 9 (7), pp.1-43. <div>Introduction<p>Reminiscence is an act of recollecting or talking about memories of one's self in the past [10]. Reminiscence serves in our daily lives, e.g. for coping current problems with past experiences, seeking coherence, worth, and meaning for identity building as self functions, or sharing memories to transmit of life lessons, and connecting with others for engagement as prosocial functions [70, 106]. Although reminiscence could be both volitional or non-volitional [28], certain elicitor is necessary as an initial trigger that starts the recall process [107]. People suffering from memory loss or cognitive impairment may find it difficult in conducting reminiscence [57]. Thus, most studies of reminiscence adopts intervention tools that prompt reminiscing in therapeutic context among older adults, for its healthcare significance in mitigating dementing illness and promoting healthy aging [110, 119].</p><p>However, reminiscence scientists argue that we should examine reminiscence from a contextual, lifespan perspective [109], and regard it as a psychologically beneficial activity that can prompt subjective well-being of all ages [14, 38, 94].</p><p>To this end, there is a proliferation of researches in HCI community in technology-mediated reminiscence, especially in</p></div> (10.1145/3757650)
  DOI : 10.1145/3757650
• Mixed Criticality Mission Planning for Autonomous Robot Fleets
  
  • Cordeiro Franco Petrone
  , 2025. This thesis explores the problem of managing uncertainty in multi-robot critical systems planning. The first contribution consists of adapting Mixed-Criticality concepts from safety-critical systems to the robot planning domain. Drawing from previous work in real-time scheduling problems, this thesis reconceptualizes how robots prioritize critical tasks when resources become constrained. The approach classifies robot actions according to their objective's importance and implements multiple cost modes to handle varying environmental conditions. The second contribution is the development of a single-robot framework based on Monte-Carlo Tree-Search that demonstrates increased objective achievement in normal environments while guaranteeing critical objective execution during exceptional conditions. The third contribution is extending this solution to multi-robot systems through an approach that includes robot partitioning strategies and a robust synchronization process for online replanning, enabling robots to adapt to changing conditions in real-time. This multi-robot implementation called RESCUE tackles the additional challenge of preventing objective duplication across robots while maintaining system flexibility. This approach is also generalized to multiple levels of criticality. Finally, the contributions are evaluated through simulation by comparing them to existing Monte-Carlo Tree-Search solutions. The experimental results validate that the framework successfully balances competing priorities: maximizing objective completion during normal operation while ensuring critical task execution during environmental challenges. These contributions advance the field of adaptive planning for uncertain robotic environments with objective criticality by providing a more robust and resilient approach to resource allocation in the face of unpredictable conditions.
• SMACC: Sketching Motion for Articulated Characters with Comics-based annotations
  
  • Legrand Amandine
  • Parakkat Amal Dev
  • Rohmer Damien
  , 2025, pp.1-13. <div><p>We introduce SMACC, a sketch-based system for animating short sequences of 3D articulated characters inspired by 2D comic motion line annotations. SMACC relies on classical rules of motion depiction used in comic books, allowing the depiction of dynamism in static images while being universally understood. Building on this, SMACC introduces an algorithmic interpretation of these principles in the context of a 3D character animation, guided by three fundamental types of motion lines: trajectory, circumfixing and impact. The adaptation to rigged 3D characters relies on the automatic computation of how these motion cues spatially influence the character's skeleton, achieved through a global analysis of sketch annotations relative to the character's pose. The resulting animation is generated by encoding the kinematic clues and constraints into joint angular velocities. Finally, the proof-of-concept demonstrated by SMACC is validated through a user study, which evaluates the effectiveness and accuracy of this sketch-based approach applied to 3D character animation.</p></div> (10.2312/pg.20251255)
  DOI : 10.2312/pg.20251255
• Asynchronous Gossip Algorithms for Rank-Based Statistical Methods
  
  • van Elst Anna
  • Colin Igor
  • Clémençon Stephan
  , 2025, pp.448-455. <div><p>As decentralized AI and edge intelligence become increasingly prevalent, ensuring robustness and trustworthiness in such distributed settings has become a critical issue-especially in the presence of corrupted or adversarial data. Traditional decentralized algorithms are vulnerable to data contamination as they typically rely on simple statistics (e.g., means or sum), motivating the need for more robust statistics. In line with recent work on decentralized estimation of trimmed means and ranks, we develop gossip algorithms for computing a broad class of rank-based statistics, including L-statistics and rank statisticsboth known for their robustness to outliers. We apply our method to perform robust distributed two-sample hypothesis testing, introducing the first gossip algorithm for Wilcoxon rank-sum tests. We provide rigorous convergence guarantees, including the first convergence rate bound for asynchronous gossip-based rank estimation. We empirically validate our theoretical results through experiments on diverse network topologies.</p></div> (10.1109/FLTA67013.2025.11336445)
  DOI : 10.1109/FLTA67013.2025.11336445
• Secure implementation for post-quantum cryptography
  
  • Spyropoulos Maxime
  , 2025. The goal of the thesis is to improve the software security of components implementing post-quantum cryptography. More specifically, the aim is to identify and correct vulnerabilities to auxiliary channel attacks.
• Physically Informed Spatial Regularization for Sound Event Localization and Detection
  
  • Liu Haocheng
  • Di Carlo Diego
  • Nugraha Aditya Arie
  • Yoshii Kazuyoshi
  • Richard Gaël
  • Fontaine Mathieu
  , 2025. Building Sound Event Localization and Detection (SELD) models that are robust to diverse acoustic environments remains one of the major challenges in multichannel signal processing, as reflections and reverberation can significantly confuse both the source direction and event detection. Introducing priors such as microphone geometry or room impulse response (RIR) into the model has proven effective in addressing this issue. Existing methods typically incorporate such priors in a deterministic way, often through data augmentation to enlarge data diversity. However, the uncertainty arising from the complex nature of audio acoustics remains largely underexplored in the SELD literature and naturally call for incorporating a stochastic modeling of acoustic prior. In this paper, we propose regularizing deep learning based SELD models with a physically constructed spatial covariance matrix (SCM) based on the estimated direction of arrival (DOA) and sound event detection (SED).
• IS³ : Generic Impulsive--Stationary Sound Separation in Acoustic Scenes using Deep Filtering
  
  • Berger Clémentine
  • Stamatiadis Paraskevas
  • Badeau Roland
  • Essid Slim
  , 2025. We are interested in audio systems capable of performing a differentiated processing of stationary backgrounds and isolated acoustic events within an acoustic scene, whether for applying specific processing methods to each part or for focusing solely on one while ignoring the other. Such systems have applications in real-world scenarios, including robust adaptive audio rendering systems (e.g., EQ or compression), plosive attenuation in voice mixing, noise suppression or reduction, robust acoustic event classification or even bioacoustics. To this end, we introduce IS³, a neural network designed for Impulsive--Stationary Sound Separation, that isolates impulsive acoustic events from the stationary background using a deep filtering approach, that can act as a pre-processing stage for the above-mentioned tasks. To ensure optimal training, we propose a sophisticated data generation pipeline that curates and adapts existing datasets for this task. We demonstrate that a learning-based approach, build on a relatively lightweight neural architecture and trained with well-designed and varied data, is successful in this previously unaddressed task, outperforming the Harmonic--Percussive Sound Separation masking method, adapted from music signal processing research, and wavelet filtering on objective separation metrics.
• Meaning Representation Frameworks and Reasoning in the Era of Large Language Models
  
  • Sadeddine Zacchary
  , 2025. Large Language Models (LLMs) are now used for a wide range of tasks, many of which require reasoning abilities. However, these abilities remain limited and lack transparency. This thesis explores how to improve the reasoning, transparency and robustness of LLMs by integrating symbolic structures.First, we conduct an analysis of the societal issues arising from the new role of LLMs in our access to knowledge. Fifteen major issues are identified, as well as current and potential mitigation strategies, drawing on both technical solutions and regulatory approaches.The thesis then focuses on Meaning Representation Frameworks (MRFs), which encode the semantics of natural language into graph structures. A comprehensive survey of MRFs is presented, introducing a new classification based on their structural properties, as well as the available resources, empirical use and research directions. This repositions MRFs as computational artifacts capable of complementing neural models in complex tasks.Building upon this, the thesis introduces VANESSA, a neuro-symbolic reasoning system that integrates MRFs with LLMs, as well as new representation and symbolic parsing process. VANESSA uses this representation to decompose reasoning problems into three simpler subtasks: parsing, natural language inference (NLI) and formal solving. Experimental results show that VANESSA achieves performance comparable to LLMs on logical reasoning tasks, while producing outputs that are traceable and explainable, illustrating the added value of hybrid architectures.Finally, the thesis addresses the problem of step-by-step verification of reasoning chains, which are produced by LLMs. A novel benchmark of nearly 5,000 annotated reasoning steps is presented, assessing both logical validity and factual correctness. Though LLMs are able to detect some errors, neuro-symbolic approaches such as VANESSA achieve comparable performance while providing valuable transparency.Overall, the thesis advocates a hybrid vision of language-based artificial intelligence, where LLMs and symbolic structures are not competing paradigms but complementary tools. It opens new perspectives towards AI systems that are not only powerful, but also responsible, trustworthy and interpretable, combining the flexibility of neural models with the rigor of symbolic reasoning.
• Photonic Chaos in Quantum Cascade Lasers : Foundations and Applications in Free-Space Optical Systems
  
  • Zaminga Sara
  , 2025. This doctoral thesis explores the use of chaotic light for next-generation free-space optical (FSO) communication systems, focusing on quantum cascade lasers (QCLs) operating in the long-wave infrared (LWIR) atmospheric window. At the core of the study are distributed-feedback (DFB) QCLs, whose unique dynamics are investigated using the Effective Semiconductor Maxwell-Bloch Equations (ESMBEs).We reveal how physical effects—such as a non-zero linewidth enhancement factor (LEF) and fast spatial hole burning (SHB)—alongside geometrical factors like cavity length and facet coatings, govern both the spectral stability and intrinsic modulation response. These mechanisms are critical to understanding the transition from single-mode to multimode longitudinal emission as the bias current increases.In the presence of external optical feedback, we show that photonic chaos emerges through the interplay between internal longitudinal modes and external cavity modes—not from undamped relaxation oscillations, as in interband lasers. The onset of chaos requires feedback strengths nearly two orders of magnitude higher than in diode lasers, consistent with the quasi-Class A nature of QCLs.Building on this insight, we demonstrate two pioneering applications. First, we realize the first LWIR chaos-based LiDAR system, achieving sub-centimeter precision and meter-range resolution—currently limited by detector bandwidth. Second, we present a chaos-based random number generator (RNG) using DFB QCLs, reaching bit-rates up to 2.5 Gbps—marking a first in this spectral region.We further examine the resilience of chaotic signals against atmospheric turbulence in the C-band, at 1.55 µm. Using a spatial light modulator to emulate turbulence in the laboratory environment and a self-configurable programmable photonic processor at the receiver end, we recover the degraded chaotic dynamics due to propagation through a turbulent medium, validating the feasibility of turbulence-hardened FSO links.This work lays the foundation for a new class of LWIR photonic systems that harness deterministic chaos as a resource. By bridging advanced laser physics, nonlinear dynamics, and real-world applications, it paves the way for high-speed, secure, and turbulence-resilient FSO technologies—unlocking new possibilities in remote sensing, telecommunications, and information security.
• Phase Diagram of Dropout for Two-Layer Neural Networks in the Mean-Field Regime
  
  • Chizat Lénaïc
  • Marion Pierre
  • Yesbay Yerkin
  , 2025. Dropout is a standard training technique for neural networks that consists of randomly deactivating units at each step of their gradient-based training. It is known to improve performance in many settings, including in the large-scale training of language or vision models. As a first step towards understanding the role of dropout in large neural networks, we study the large-width asymptotics of gradient descent with dropout on two-layer neural networks with the mean-field initialization scale. We obtain a rich asymptotic phase diagram that exhibits five distinct nondegenerate phases depending on the relative magnitudes of the dropout rate, the learning rate, and the width. Notably, we find that the well-studied "penalty" effect of dropout only persists in the limit with impractically small learning rates of order O(1/width). For larger learning rates, this effect disappears and in the limit, dropout is equivalent to a "random geometry" technique, where the gradients are thinned randomly after the forward and backward pass have been computed. In this asymptotic regime, the limit is described by a mean-field jump process where the neurons' update times follow independent Poisson or Bernoulli clocks (depending on whether the learning rate vanishes or not). For some of the phases, we obtain a description of the limit dynamics both in path-space and in distribution-space. The convergence proofs involve a mix of tools from mean-field particle systems and stochastic processes. Together, our results lay the groundwork for a renewed theoretical understanding of dropout in large-scale neural networks.
• TTool-AI: A Large Language Model-Based Assistant for Model Driven Engineering
  
  • Sultan Bastien
  • Apvrille Ludovic
  SN Computer Science, Springer, 2025, 6 (7), pp.886 (1-18). Throughout the history of engineering, successive innovations have been implemented to assist engineers in their tasks, enabling them to focus on high-value activities while minimizing time-consuming and error-prone tasks. Large language models (LLMs) represent one of these innovations, with significant potential for developing new kinds of engineering assistants, as demonstrated by a rich body of recent literature. The paper introduces TTool-AI, a model-driven engineering assistant based on LLMs and integrated within the SysML modeling and formal verification toolkit TTool. TTool-AI enables system architects to generate and incrementally refine various types of SysML diagrams directly from textual specifications with a single click. The core mechanisms of TTool-AI (contextual knowledge injection, automated prompt generation, and iterative feedback) enable it to produce good quality models that can serve as a sound foundation for system architects in MDE processes. Building on our previous work presented at MODELSWARD 2024, this paper provides a comprehensive description of TTool-AI’s MDE assistance features. It introduces new functionalities, including requirement engineering and automated model mutation generation. An evaluation of these features, comparing their performance against Master-level students, demonstrates the tool’s efficacy and suggests a strong potential to significantly enhance engineering productivity by enabling engineers to focus on high-value tasks. (10.1007/s42979-025-04444-w)
  DOI : 10.1007/s42979-025-04444-w
• Numerically Efficient Parametric Inference for Learning Space-Time Hawkes Processes
  
  • Siviero Emilia
  • Staerman Guillaume
  • Clémençon Stéphan
  • Moreau Thomas
  , 2025, pp.1-10. In a wide range of spatio-temporal datasets, from sociology to seismology, self-exciting dynamics are often observed, characterized by event triggering and clustering across both space and time. Space-time Hawkes processes provide a powerful framework to model such phenomena. This paper introduces a flexible parametric inference method to estimate the underlying kernel parameters involved in the intensity function of a space-time Hawkes process based on such data. Our approach combines three core components: 1) kernels with finite support, 2) discretization of the space-time domain, and 3) efficient (possibly approximate) precomputations. The inference method we propose then relies on a gradient-based solver that offers both computational efficiency and strong statistical performance. Alongside a detailed presentation of the algorithmic framework, we present numerical experiments on synthetic and real spatio-temporal data, offering solid empirical evidence of the validity and applicability of the proposed methodology. (10.1109/DSAA65442.2025.11247997)
  DOI : 10.1109/DSAA65442.2025.11247997
• Adaptive Augmented Reality Pathfinding for Parkinson's Disease: Integrating Visual Cueing with User-Directed Navigation
  
  • Bassal Dimah
  • Medeiros Daniel
  , 2025. <div><p>Parkinson's disease (PD) affects millions worldwide, with gait impairments and freezing of gait (FOG) episodes representing debilitating symptoms that significantly compromise patient mobility, safety, and quality of life. Recent advances in augmented reality (AR) have demonstrated that visual cueing can effectively modify gait parameters in PD patients, with AR cues proving as effective as real-world cues for improving step length, gait speed, and crossing maneuvers. However, existing AR cueing systems, including applications like Holocue, rely primarily on static, pre-positioned cues or continuous cueing paradigms that require patients to adapt to predetermined patterns, poten-tially limiting patient autonomy and confidence in independent navigation. This paper presents a novel adaptive AR pathfinding system that integrates established visual cueing principles with user-directed navigation to enhance both therapeutic effectiveness and patient autonomy.</p></div>
• How to Improve Anomaly Detection for Electric Powertrains in Production?
  
  • Emelchenkov Anton
  • Fontaine Mathieu
  • Mahé Hervé
  • Roueff François
  , 2025. Despite the low noise level of an electric powertrain, its tonality concentrated around a few frequencies can make it painful for the end user. The End Of Line Tester (EOLT) for electric powertrains plays a critical role in ensuring NVH quality standards. Today’s industry-standard solutions predominantly rely on order tracking and amplitude estimation to detect potential defects and compliance versus requirements. These techniques often depend on expert intervention and precise hyperparameters tuning, which undermines their robustness and scalability, especially when faced with rapidly evolving non-stationary signals. To reinforce precision and speed, two key innovations are proposed: (1) a high-resolution method for multi-frequency amplitude estimation in highly oscillatory regimes, equipped with automatic hyperparameter tuning to enhance the accuracy and stability of order tracking; and (2) a neural network-based anomaly detection framework that learns directly from raw signal spectrograms, removing the need for handcrafted signal processing. To support this, we introduce and release the first dataset of non-stationary vibration signals collected from an EOLT, specifically designed for anomaly detection. Our approach sets a new benchmark for automated, data-driven diagnostics in electric powertrain manufacturing.
• Associations between individual and geospatial characteristics and power of 4G signals received by mobile phones
  
  • Laplanche Alexia
  • Guida Florence
  • Moissonnier Monika
  • Launay Ludivine
  • Beranger Remi
  • Lagroye Isabelle
  • Orlacchio Rosa
  • Fontaine Maëlle
  • Bories Serge
  • Mazloum Taghrid
  • Conil Emmanuelle
  • Huss Anke
  • Wiart Joe
  • Danjou Aurélie
  • Schüz Joachim
  • Dejardin Olivier
  • Deltour Isabelle
  Environmental Research, Elsevier, 2025, 286 (3), pp.123030. Background: The Received Signal Strength Indicator (RSSI) measures downlink signal intensity received by smartphones in 4th Generation LTE networks. Objective: This study evaluated how individual, technical, and spatial factors influenced LTE-RSSI during daily activities. Methods: Between November 2022 and October 2023, adults in France used the XMobiSensePlus Android smartphone application to record RSSI and GPS data. Distance to the operator's nearest antenna, obtained from Cartoradio, population and antenna density and urbanicity were analyzed using a geographic information system. Determinants of RSSI were assessed using an autoregressive mixed model incorporating restricted cubic splines for distance. Environmental exposures were estimated at 1800 MHz using conversion factors. Results: From 1,969,913 records of 187 participants, with measurements taken every 30 s over 7.9 days, the average LTE-RSSI was -79.3 dBm. The estimated electric field strength was 0.12 V/m, albeit with large uncertainty. The median distance to the nearest antenna was 536 m. Proximity to antennas increased RSSI. Antenna density positively influenced RSSI (overall β = +0.37 dBm per additional antenna per km2). Lower RSSI was observed in the evening and night, particularly in urban areas. Smartphone's technical parameters (Android version and System-on-a-Chip) influenced RSSI, operators did not. Proximity to antennas had greater impact in rural areas. Conclusion: Urbanicity, distance to the nearest 4G antenna, antenna density, time of day, and smartphone's technical parameters influenced RSSI levels in 4G networks in France, but not operator. (10.1016/j.envres.2025.123030)
  DOI : 10.1016/j.envres.2025.123030
• Fine-Grained Confidentiality and Authenticity Modeling and Verification for Embedded Systems
  
  • Jerray Jawher
  • Sultan Bastien
  • Apvrille Ludovic
  , 2025, pp.333-344. Handling cybersecurity during system design is mandatory for (critical and) connected embedded systems. Numerous contributions, including standards like ISO 26262, emphasize the need to address cybersecurity as early as possible in the design process. Design space exploration, typically performed early in system design-before software or hardware development-offers an opportunity for early cybersecurity integration. SysML-Sec has demonstrated how cybersecurity concepts can be incorporated into design space exploration. However, its security mechanisms have significant limitations to address some of the modern threats. The paper introduces a new security modeling and verification approach. Our method enables multipattern security channels, allowing multiple security patterns to coexist within a single communication channel. It also supports fine-grained verification of individual write and read operations, ensuring that confidentiality and authenticity are independently validated for each data exchange. Additionally, our approach generates traceable counterexamples for unverified properties, helping engineers identify and address security vulnerabilities. We implemented this technique in TTool/DIPLODOCUS, a UML/SysML-based framework for hardware/software co-design, demonstrating how its enhanced version can now support more advanced security mechanisms, and evaluated it on an automotive case-study. (10.1109/MODELS-C68889.2025.00052)
  DOI : 10.1109/MODELS-C68889.2025.00052
• Tunable Metasurface MIMO Antenna
  
  • Medrar Ghiles
  • Lepage Anne Claire
  • Begaud Xavier
  , 2025.
• Synthetic Abundance Maps for Unsupervised Super-Resolution of Hyperspectral Remote Sensing Images
  
  • Xu Xinxin
  • Gousseau Yann
  • Kervazo Christophe
  • Ladjal Saïd
  , 2025. Hyperspectral single image super-resolution (HS-SISR) aims to enhance the spatial resolution of hyperspectral images to fully exploit their spectral information. While considerable progress has been made in this field, most existing methods are supervised and require ground truth data for training—data that is often unavailable in practice. To overcome this limitation, we propose a novel unsupervised training framework for HS-SISR, based on synthetic abundance data. The approach begins by unmixing the hyperspectral image into endmembers and abundances. A neural network is then trained to perform abundance super-resolution using synthetic abundances only. These synthetic abundance maps are generated from a dead leaves model whose characteristics are inherited from the low-resolution image to be super-resolved. This trained network is subsequently used to enhance the spatial resolution of the original image’s abundances, and the final super-resolution hyperspectral image is reconstructed by combining them with the endmembers. Experimental results demonstrate both the training value of the synthetic data and the effectiveness of the proposed method.
• Quantitative Limit Theorems for Cox-Poisson and Cox-Binomial Point Processes
  
  • Adrat Hamza
  • Decreusefond Laurent
  , 2025. <div><p>This paper establishes quantitative limit theorems for two classes of Cox point processes, quantifying their convergence to a Poisson point process (PPP). We employ Stein's method for PPP approximation, leveraging the generator approach and the Stein-Dirichlet representation formula associated with the Glauber dynamics. First, we investigate a Cox-Poisson process constructed by placing one-dimensional PPPs on the lines of a Poisson line process in $\mathbb{R}^2$. We derive an explicit bound on the convergence rate to a homogeneous PPP as the line intensity grows and the point intensity on each line diminishes. Second, we analyze a Cox-Binomial process on the unit sphere $\mathbb{S}^2$, modeling a system of satellites. This process is generated by placing PPPs on great-circle orbits, whose positions are determined by a Binomial point process. For this model, we establish a convergence rate of order O(1/n) to a uniform PPP on the sphere, where n is the number of orbits. The derived bounds provide precise control over the approximation error in both models, with applications in stochastic geometry and spatial statistics.</p></div>