Publications

2025

• 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
• 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. (10.70675/2a5c3019zb14cz41bdzbe8bz42175951619c)
  DOI : 10.70675/2a5c3019zb14cz41bdzbe8bz42175951619c
• 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. (10.70675/7f1c15a9z3c9bz4a61z8bf8z8bc5790ee4ed)
  DOI : 10.70675/7f1c15a9z3c9bz4a61z8bf8z8bc5790ee4ed
• 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. (10.70675/1aa6d967z4564z46b6z94c3ze7d1c1e04e1a)
  DOI : 10.70675/1aa6d967z4564z46b6z94c3ze7d1c1e04e1a
• 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.123030-1:123030-11. 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.
• 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>
• SuperviZ - Supervision et orchestration de la sécurité - Rapport d’avancement à mi-projet
  
  • Debar Hervé
  • Mé Ludovic
  • Leneutre Jean
  • Nicomette Vincent
  • François Jérôme
  • Gouy-Pailler Cédric
  • Blanc Gregory
  • Mocanu Stéphane
  , 2025, pp.1-58. Ce document constitue le rapport à mi-parcours du projet SuperviZ. Il regroupe l’ensemble des livrables à mi-parcours des 6 lots du projet (un chapitre par lot), identifiés sous les codes L02 à L07. Le projet SuperviZ s’intéresse à la détection, à la réponse et à la remédiation des attaques informatiques, sujets regroupés sous l’appellation de “supervision de sécurité”. Fondamentale dans le contexte des SI d’entreprise, la supervision l’est encore plus dans le cas des systèmes cyber-physiques. En effet, avec des “objets” (dispositifs de nature et de capacité très hétérogène) qui devraient à terme être tous, ou presque, connectés, la surface d’attaque augmente significativement. Le projet SuperviZ adresse des défis de la supervision dans ce double contexte IT et OT. Il a permis de lancer à ce jour 13 thèses (pour 11 initialement prévues) et 5 postdoc (2 sont terminés et 2 restent à pourvoir).
• Efficient Quantum Measurements: Computational Max-and Measured Rényi Divergences and Applications
  
  • Yángüez Álvaro
  • Hahn Thomas A
  • Kochanowski Jan
  , 2025. Quantum information processing is limited, in practice, to efficiently implementable operations. This motivates the study of quantum divergences that preserve their operational meaning while faithfully capturing these computational constraints. Using geometric, computational, and information theoretic tools, we define two new types of computational divergences, which we term computational max-divergence and computational measured Rényi divergences. Both are constrained by a family of efficient binary measurements, and thus useful for state discrimination tasks in the computational setting. We prove that, in the infinite-order limit, the computational measured Rényi divergence coincides with the computational max-divergence, mirroring the corresponding relation in the unconstrained information-theoretic setting. For the many-copy regime, we introduce regularized versions and establish a one-sided computational Stein bound on achievable hypothesis-testing exponents under efficient measurements, giving the regularized computational measured relative entropy an operational meaning. We further define resource measures induced by our computational divergences and prove an asymptotic continuity bound for the computational measured relative entropy of resource. Focusing on entanglement, we relate our results to previously proposed computational entanglement measures and provide explicit separations from the information-theoretic setting. Together, these results provide a principled, cohesive approach towards state discrimination tasks and resource quantification under computational constraints.
• Image Pre-Segmentation from Shadow Masks
  
  • Heep Moritz
  • Parakkat Amal Dev
  • Zell Eduard
  , 2025, pp.1-7. Image segmentation has gained a lot of attention in the past. When working with photometric stereo data, we discovered that shadow cues provide valuable spatial information, especially when combining multiple images of the same scene under different lighting conditions. In the following, we present a robust method to pre-segment images, relying heavily on shadow masks as the main input. We first detect object contours from light to shadow transitions. In the second step, we run an image segmentation algorithm based on Delaunay triangulation that is capable of closing the gaps between contours. Our method requires spatial input data but is free from training data. Initial results look promising, generating pre-segmentations close to recent data-driven image segmentation algorithms. (10.2312/vmv.20251239)
  DOI : 10.2312/vmv.20251239
• Digital twin for estimating QoT statistics in presence of PDL and transceiver imperfections
  
  • Purkayastha Ambashri
  • Delezoide Camille
  • Bajaj Vinod
  • Lourdiane Mounia
  • Ware Cédric
  • Layec Patricia
  , 2025, pp.1-4. We propose a physics-based digital twin to predict the statistical QoT distribution of a realistic optical lightpath. We demonstrate up to 0.73 dB accuracy improvement in worst-case SNR prediction for short distance transmissions in linear regime. ©2025 The authors. (10.1109/ECOC66593.2025.11263322)
  DOI : 10.1109/ECOC66593.2025.11263322
• EEG–Metabolic Coupling and Time Limit at VO2max During Constant-Load Exercise
  
  • Poinsard Luc
  • Berthomier Christian
  • Clémençon Michel
  • Brandewinder Marie
  • Essid Slim
  • Damon Cécilia
  • Rigaud François
  • Bénichoux Alexis
  • Maby Emmanuel
  • Fornoni Lesly
  • Bouchet Patrick
  • Beers Pascal Van
  • Massot Bertrand
  • Revol Patrice
  • Creveaux Thomas
  • Collet Christian
  • Mattout Jérémie
  • Pialoux Vincent
  • Billat Véronique
  Journal of Functional Morphology and Kinesiology, MDPI, 2025, 10 (4), pp.369-1:369-25. Background: Exercise duration at maximum oxygen uptake (V˙O2max) appears to be influenced not only by metabolic factors but also by the interplay between brain dynamics and ventilatory regulation. This study examined how cortical activity, assessed via electroencephalography (EEG), relates to performance and acute fatigue regulation during a constant-load cycling test. We hypothesized that oscillatory activity in the theta, alpha, and beta bands would be associated with ventilatory coordination and endurance capacity. Methods: Thirty trained participants performed a cycling test to exhaustion at 90% maximal aerobic power. EEG and gas exchange were continuously recorded; ratings of perceived exertion were assessed immediately after exhaustion. Results: Beta power was negatively correlated with time spent at V˙O2max (r = −0.542, p = 0.002). Theta and Alpha power alone showed no direct associations with endurance, but EEG–metabolic ratios revealed significant correlations. Specifically, the time to reach V˙O2max correlated with Alpha/V˙O2 (p &lt; 0.001), Alpha/V˙CO2 (p &lt; 0.001), and Beta/V˙CO2 (p = 0.002). The time spent at V˙O2max correlated with Theta/V˙O2 (p = 0.002) and Theta/V˙CO2 (p &lt; 0.001). The time-to-exhaustion was correlated with Theta/V˙CO2 (p &lt; 0.001) and Alpha/V˙CO2 (p &lt; 0.001). Conclusions: These findings indicate that cortical oscillations were associated with different aspects of acute fatigue regulation. Beta activity was associated with fatigue-related neural strain, whereas Theta and Alpha bands, when normalized to metabolic load, were consistent with a role in ventilatory coordination and motor control. EEG–metabolic ratios may provide exploratory indicators of brain–metabolism interplay during high-intensity exercise and could help guide future brain-body interactions in endurance performance. (10.3390/jfmk10040369)
  DOI : 10.3390/jfmk10040369
• Superviz25-SQL: High-Quality Dataset to Empower Unsupervised SQL Injection Detection Systems
  
  • Quetel Grégor
  • Alata Eric
  • Gimenez Pierre-François
  • Robert Thomas
  • Pautet Laurent
  , 2025, Computer Security. Esorics 2025 International Workshops: Anubis 2025, Secai 2025, Secassure 2025, Stmus 2025, Toulouse, France, September 22-24, 2025, (Lecture Notes in Computer Science #1623), pp.1-20. The digitalization of public and private services has led to more sophisticated and serious cybersecurity threats. Among them, SQL injection attacks leverage user inputs to remotely execute malicious actions on a database, such as data exfiltration and deletion, or privilege escalation. They are regularly classified as one of the most prominent threats to web services. Intrusion detection systems are widely used to detect such injection attacks and react to them, but it is difficult to assess their actual effectiveness and compare them because of a lack of high-quality datasets. Current SQL injection detection datasets lack diversity, are poorly documented, and the generated samples are not representative of real-world infrastructures. This article presents a new dataset Superviz25-SQ , whose design is structured around four quality dimensions: realism, diversity, benchmarking capabilities and the presence of good documentation. We examine the dataset diversity using lexical, syntactic and semantic metrics, and demonstrate that its size is sufficient to evaluate data-intensive detectors. Finally, we provide nine classical and state-of-the art SQL injection detection pipelines as baselines for future works.
• Practical Advantage of Classical Communication in Entanglement Detection
  
  • Xing Wen-Bo
  • Lv Min-Yu
  • Zhang Lingxia
  • Guo Yu
  • Weilenmann Mirjam
  • Wei Zhaohui
  • Li Chuan-Feng
  • Guo Guang-Can
  • Hu Xiao-Min
  • Liu Bi-Heng
  • Navascués Miguel
  • Wang Zizhu
  Physical Review Letters, American Physical Society, 2025, 135 (13), pp.130805. Entanglement is the cornerstone of quantum communication, yet conventional detection relies solely on local measurements. In this Letter, we present an experimental demonstration, based on an improved theoretical framework showing that one-way local operations and classical communication (1-LOCC) can significantly outperform purely local measurements in detecting quantum entanglement. By casting the entanglement detection problem as a semidefinite program, we derive protocols that minimize false negatives at fixed false-positive rates. A variational generative machine-learning algorithm efficiently searches over high-dimensional parameter spaces, identifying states and measurement strategies that exhibit a clear 1-LOCC advantage. Experimentally, we realize a genuine event-ready protocol on a three-dimensional photonic entanglement source, employing fiber delays as short-lived quantum memories. We implement rapid, field-programmable gate array-based sampling of the optimized probabilistic instructions, allowing Bob’s measurement settings to adapt to Alice’s outcomes in real time. Our results validate the predicted 1-LOCC advantage in a realistic noisy setting and reduce the experimental trials needed to certify entanglement. These findings mark a step toward scalable, adaptive entanglement detection methods crucial for quantum networks and computing, paving the way for more efficient generation and verification of high-dimensional entangled states. (10.1103/hlcv-qcnw)
  DOI : 10.1103/hlcv-qcnw
• Nicknames for Group Signatures
  
  • Quispe Guillaume
  • Jouvelot Pierre
  • Memmi Gerard
  , 2025, pp.210-230. Nicknames for Group Signatures (NGS) is a new signature scheme that extends Group Signatures (GS) with Signatures with Flexible Public Keys (SFPK). Via GS, each member of a group can sign messages on behalf of the group without revealing his identity, except to a designated auditor. Via SFPK, anyone can create new identities for a particular user, enabling anonymous transfers with only the intended recipient able to trace these new identities. To prevent the potential abuses that this anonymity brings, NGS integrates flexible public keys into the GS framework to support auditable transfers. In addition to introducing NGS, we describe its security model and provide a mathematical construction proved secure in the Random Oracle Model. As a practical NGS use case, we build NickHat, a blockchain-based token-exchange prototype system on top of Ethereum. (10.1007/978-3-032-06155-3_12)
  DOI : 10.1007/978-3-032-06155-3_12
• Hybrid Quantum Cryptography from Communication Complexity
  
  • Mazzoncini Francesco
  • Bauer Balthazar
  • Brown Peter
  • Alléaume Romain
  Quantum, Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften, 2025, 9, pp.1862. We introduce an explicit construction for a key distribution protocol in the Quantum Computational Timelock (QCT) security model, where one assumes that computationally secure encryption may only be broken after a time much longer than the coherence time of available quantum memories. Taking advantage of the QCT assumptions, we build a key distribution protocol called HM-QCT from the Hidden Matching problem for which there exists an exponential gap in one-way communication complexity between classical and quantum strategies. We establish that the security of HM-QCT against arbitrary i.i.d. attacks can be reduced to the difficulty of solving the underlying Hidden Matching problem with classical information. Legitimate users, on the other hand, can use quantum communication, which gives them the possibility of sending multiple copies of the same quantum state while retaining an information advantage. This leads to an everlasting secure key distribution scheme over n bosonic modes. Such a level of security is unattainable with purely classical techniques. Remarkably, the scheme remains secure with up to O √ n log(n) input photons for each channel use, extending the functionalities and potentially outperforming QKD rates by several orders of magnitudes. (10.22331/q-2025-09-24-1862)
  DOI : 10.22331/q-2025-09-24-1862
• Reducing Variability of Multiple Instance Learning Methods for Digital Pathology
  
  • Mammadov Ali
  • Le Folgoc Loic
  • Hocquet Guillaume
  • Gori Pietro
  , 2025. Digital pathology has revolutionized the field by enabling the digitization of tissue samples into whole slide images (WSIs). However, the high resolution and large size of WSIs present significant challenges when it comes to applying Deep Learning models. As a solution, WSIs are often divided into smaller patches with a global label (i.e., diagnostic) per slide, instead of a (too) costly pixel-wise annotation. By treating each slide as a bag of patches, Multiple Instance Learning (MIL) methods have emerged as a suitable solution for WSI classification. A major drawback of MIL methods is their high variability in performance across different runs, which can reach up to 10-15 AUC points on the test set, making it difficult to compare different MIL methods reliably. This variability mainly comes from three factors: i) weight initialization, ii) batch (shuffling) ordering, iii) and learning rate. To address that, we introduce a Multi-Fidelity, Model Fusion strategy for MIL methods. We first train multiple models for a few epochs and average the most stable and promising ones based on validation scores. This approach can be applied to any existing MIL model to reduce performance variability. It also simplifies hyperparameter tuning and improves reproducibility while maintaining computational efficiency. We extensively validate our approach on WSI classification tasks using 2 different datasets, 3 initialization strategies and 5 MIL methods, for a total of more than 2000 experiments.