Publications

2025

• 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 < 0.001), Alpha/V˙CO2 (p < 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 < 0.001). The time-to-exhaustion was correlated with Theta/V˙CO2 (p < 0.001) and Alpha/V˙CO2 (p < 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.
• Visionerves: Automatic and Reproducible Hybrid AI for Peripheral Nervous System Recognition Applied to Endometriosis Cases
  
  • La Barbera Giammarco
  • Bonnot Enzo
  • Isla Thomas
  • Pablo de la Plata Juan
  • Dunoyer de Segonzac Joy-Rose
  • Attali Jennifer
  • Lozach Cécile
  • Bellucci Alexandre
  • Marcellin Louis
  • Fournier Laure
  • Gori Pietro
  • Sarnacki Sabine
  • Bloch Isabelle
  , 2025, pp.113-124. Endometriosis often leads to chronic pelvic pain and possible nerve involvement, yet imaging the peripheral nerves remains a challenge. We introduce Visionerves, a novel hybrid AI framework for peripheral nervous system recognition from multi-gradient DWI and morphological MRI data. Unlike conventional tractography, Visionerves encodes anatomical knowledge through fuzzy spatial relationships, removing the need for selection of manual ROIs. The pipeline comprises two phases: (A) automatic segmentation of anatomical structures using a deep learning model, and (B) tractography and nerve recognition by symbolic spatial reasoning. Applied to the lumbosacral plexus in 10 women with (confirmed or suspected) endometriosis, Visionerves demonstrated substantial improvements over standard tractography, with Dice score improvements of up to 25% and spatial errors reduced to less than 5 mm. This automatic and reproducible approach enables detailed nerve analysis and paves the way for non-invasive diagnosis of endometriosis-related neuropathy, as well as other conditions with nerve involvement. (10.1007/978-3-032-05825-6_11)
  DOI : 10.1007/978-3-032-05825-6_11
• Self-Supervised Multiview Xray Matching
  
  • Dabboussi Mohamad
  • Huard Malo
  • Gousseau Yann
  • Gori Pietro
  , 2025. Accurate interpretation of multi-view radiographs is crucial for diagnosing fractures, muscular injuries, and other anomalies. While significant advances have been made in AI-based analysis of single images, current methods often struggle to establish robust correspondences between different X-ray views, an essential capability for precise clinical evaluations. In this work, we present a novel self-supervised pipeline that eliminates the need for manual annotation by automatically generating a many-to-many correspondence matrix between synthetic X-ray views. This is achieved using digitally reconstructed radiographs (DRR), which are automatically derived from unannotated CT volumes. Our approach incorporates a transformer-based training phase to accurately predict correspondences across two or more X-ray views. Furthermore, we demonstrate that learning correspondences among synthetic X-ray views can be leveraged as a pretraining strategy to enhance automatic multiview fracture detection on real data. Extensive evaluations on both synthetic and real X-ray datasets show that incorporating correspondences improves performance in multi-view fracture classification.
• ding-01 :ARG0: An AMR Corpus for Spontaneous French Dialogue
  
  • Kang Jeongwoo
  • Boritchev Maria
  • Coavoux Maximin
  , 2025, Proceedings of the 16th International Conference on Computational Semantics, pp.40-50. We present our work to build a French semantic corpus by annotating French dialogue in Abstract Meaning Representation (AMR). Specifically, we annotate the DinG corpus, consisting of transcripts of spontaneous French dialogues recorded during the board game Catan. As AMR has insufficient coverage of the dynamics of spontaneous speech, we extend the framework to better represent spontaneous speech and sentence structures specific to French. Additionally, to support consistent annotation, we provide an annotation guideline detailing these extensions. We publish our corpus under a free license (CC-SA-BY). We also train and evaluate an AMR parser on our data. This model can be used as an assistance annotation tool to provide initial annotations that can be refined by human annotators. Our work contributes to the development of semantic resources for French dialogue.
• How dataset diversity affects generalization in ML-based NIDS
  
  • Nougnanke Benoit
  • Blanc Gregory
  • Robert Thomas
  , 2025, pp.269 - 288. Machine Learning-based Network Intrusion Detection Systems (ML-based NIDS) rely heavily on the quality of the datasets used for training and evaluation. However, widely used NIDS benchmarks often suffer from poor data diversity, which limits model generalization and undermines the reliability of evaluation protocols. While prior work has acknowledged this limitation, a systematic framework to quantify dataset diversity and analyze its relationship with performance is still missing. To address this gap, we introduce a structured approach for characterizing dataset diversity in ML-based NIDS, grounded in measurement theory. We distinguish three types of diversity-intra-class, inter-class, and domain-shift-and operationalize their measurement using established metrics such as the Vendi Score and the Jensen-Shannon divergence. Our empirical analysis on the CIC-IDS2018 dataset, spanning sixty diversity-controlled train-test experiments, provides new insights into the relationship between diversity and generalization and demonstrates the value of diversity-aware data sampling for improving evaluation reliability. (10.1007/978-3-032-07884-1_14)
  DOI : 10.1007/978-3-032-07884-1_14
• Translation-Equivariant Self-Supervised Learning for Pitch Estimation with Optimal Transport
  
  • Torres Bernardo
  • Riou Alain
  • Richard Gaël
  • Peeters Geoffroy
  , 2025. In this paper, we propose an Optimal Transport objective for learning one-dimensional translation-equivariant systems and demonstrate its applicability to single pitch estimation. Our method provides a theoretically grounded, more numerically stable, and simpler alternative for training state-of-the-art self-supervised pitch estimators.
• Predictive Learning in Survival Analysis by Empirical Maximization of Harrell's Concordance Index
  
  • Lamalle Florian
  • Clémençon Stéphan
  • Sabourin Anne
  , 2025. The predictive problem analyzed in this paper concerns survival analysis. A $d$-dimensional r.v. $X$ is observed, modelling some information a priori useful to predict a partially observed random duration $T\geq 0$. Motivated by various applications ranging from public health resource management to predictive maintenance in industry, the goal is to build a ranking function $f:\mathbb{R}^d\to \mathbb{R}_+$ for operational prioritization purposes, so that $f(X)$ and $T$ tend to increase or decrease together with (hopefully) largest probability. While Harrell's concordance index ($C$-index) is a natural performance criterion for this problem, the statistical learning framework often encountered in practice stipulates that only right-censored realizations of the duration $T$ are present in the training database. Since discarding censored observations and analyzing only complete ones leads to considerable bias and error, we explain how to calculate an empirical version of the $C$-index in a censored context, which is amenable to optimization. We then establish learning rate bounds for empirical $C$-index maximizers and present numerical results empirically confirming the relevance of this approach.
• Anomaly Detection based on Markov Data: A Statistical Depth Approach
  
  • Clémençon Stéphan
  • Fernández Carlos
  , 2025. The purpose of this article is to extend the notion of statistical depth to the case of sample paths of a Markov chain. Initially introduced to define a center-outward ordering of points in the support of a multivariate distribution, depth functions permit to generalize the notions of quantiles and (signed) ranks for observations in Rd with d > 1, as well as statistical procedures based on such quantities. Here we develop a general theoretical framework for evaluating the depth of a Markov sample path and recovering it statistically from an estimate of its transition probability with (non-) asymptotic guarantees. We also detail some of its applications, focusing particularly on unsupervised anomaly detection. Beyond the theoretical analysis carried out, numerical experiments are displayed, providing empirical evidence of the relevance of the novel concept we introduce here to quantify the degree of abnormality of Markov paths of variable length.
• Adding temporal musical controls on top of pretrained generative models
  
  • Nabi Sarah
  • Demerlé Nils
  • Peeters Geoffroy
  • Bevilacqua Frédéric
  • Esling Philippe
  , 2025. Recent advances in deep generative modeling have enabled high-quality models for musical audio synthesis. However, these approaches remain difficult to control, confined to simple, static attributes and, most importantly, entail retraining a different computationally-heavy architecture for each new control. This is inefficient and impractical as it requires substantial computational resources. In this paper, we propose a novel approach allowing to add time-varying musical controls on top of any pretrained generative models with an exposed latent space (e.g. neural audio codecs), without retraining or finetuning. Our method supports both discrete and continuous attributes by adapting a rectified flow approach with a latent diffusion transformer. We learn an invertible mapping between pretrained latent variables and a new space disentangling explicit control attributes and style variables that capture the remaining factors of variation. This enables both feature extraction from an input, but also editing those features to generate transformed audio samples. Finally, this also introduces the ability to perform synthesis directly from the audio descriptors. We validate our method with 4 datasets going from different musical instruments up to full music recordings, on which we outperform state-of-the-art taskspecific baselines in terms of both generation quality and accuracy of the control by inferring transferred attributes.
• Compositional shield synthesis for safe reinforcement learning in partial observability
  
  • Carr Steven
  • Bakirtzis Georgios
  • Topcu Ufuk
  IEEE Open Journal of Control Systems, 2025, 4, pp.373 - 384. Agents controlled by the output of reinforcement learning (RL) algorithms often transition to unsafe states, particularly in uncertain and partially observable environments. Partially observable Markov decision processes (POMDPs) provide a natural setting for studying such scenarios with limited sensing. Shields filter undesirable actions to ensure safe RL by preserving safety requirements in the agents' policy. However, synthesizing holistic shields is computationally expensive in complex deployment scenarios. We propose the compositional synthesis of shields by modeling safety requirements by parts, thereby improving scalability. In particular, problem formulations in the form of POMDPs using RL algorithms illustrate that an RL agent equipped with the resulting compositional shielding, beyond being safe, converges to higher values of expected reward. By using subproblem formulations, we preserve and improve the ability of shielded agents to require fewer training episodes than unshielded agents, especially in sparse-reward settings. Concretely, we find that compositional shield synthesis allows an RL agent to remain safe in environments two orders of magnitude larger than other state-of-the-art model-based approaches. (10.1109/ojcsys.2025.3611725)
  DOI : 10.1109/ojcsys.2025.3611725
• On the Moreau envelope properties of weakly convex functions
  
  • Renaud Marien
  • Leclaire Arthur
  • Papadakis Nicolas
  , 2025. In this document, we present the main properties satisfied by the Moreau envelope of weakly convex functions. The Moreau envelope has been introduced in convex optimization to regularize convex functionals while preserving their global minimizers. However, the Moreau envelope is also defined for the more general class of weakly convex function and can be a useful tool for optimization in this context. The main properties of the Moreau envelope have been demonstrated for convex functions and are generalized to weakly convex function in various works. This document summarizes the vast literature on the properties of the Moreau envelope and provides the associated proofs.
• Leveraging gem5 and Machine Learning for End-to-End Detection of Cache-based Side-Channel Attack Patterns
  
  • Awais Muhammad
  • Mushtaq Maria
  • Naviner Lirida
  • Bruguier Florent
  • Benoit Pascal
  • Haj-Yahya Jawad
  , 2025. Side-channel attacks (SCAs) exploit physical leakage vectors, including cache states, timing variations, and power consumption in hardware microarchitectures to compromise computational security. This paper introduces an end-to-end, simulation-driven framework that integrates gem5's cycle-accurate architectural models with unsupervised machine learning to automate SCA detection. We simulate Spectre (V1/V2), Prime+Probe, and Flush+Reload attack pattern workloads to generate fine-grained execution traces encompassing BTB access sequences, memory latency distributions, and pipeline stall events. Temporal feature engineering extracts discriminative signatures through branch predictor entropy calculations and miss sequence autocorrelation, while dimensionality reduction via t-SNE optimizes the feature space. These preprocessed traces train an ensemble of Isolation Forest, Variational Autoencoders, and HDBSCAN models to identify attack patterns without predefined templates. Experimental validation demonstrates 0.92 precision and 0.88 recall for cache-based SCAs (Prime+Probe variants), representing a 92% F1-score improvement over SVM baselines. SHAP-based feature attribution reveals BTB-miss run-lengths and memory controller contention queues as critical attack patterns. This pipeline automatically categorize interpretable attack patterns if they are risky for the system, enabling proactive identification of safe and unsafe instructions for secure hardware through gem5's reconfigurable memory hierarchy and cache partitioning mechanisms.
• Overcoming the Technical Hurdles of IoT Adoption: the FITNESS Project Vision and Insights
  
  • Cassiau Nicolas
  • Achir Nadjib
  • Adjih Cédric
  • Andrieux Guillaume
  • Bechkit Walid
  • Ben Hadj Said Siwar
  • Boissier Olivier
  • Bouferroum Aymen
  • Combes Richard
  • Courrèges Fabien
  • Dakdouk Hiba
  • Dhaouadi Amira
  • Diouris Jean-François
  • Elayoubi Salah Eddine
  • Härri Jérôme
  • Kassi Mihia
  • Lagrange Xavier
  • Liu Yandi
  • Loscri Valeria
  • Mannoni Valerian
  • Maudet Sébastien
  • Mitton Nathalie
  • Mokdad Amina
  • Moulay Emmanuel
  • Nadar Ali
  • Nahon Rémi
  • Négrier Romain
  • Nguyen van Tam
  • Pelov Alexander
  • Perrine Clency
  • Pierre Marc
  • Pillement Sébastien
  • Pottier Antony
  • Poulliat Charly
  • Pousset Yannis
  • Rady Mina
  • Sboui Nourhen
  • Sondi Patrick
  • Toutain Laurent
  , 2025. The Internet of Things (IoT) has emerged as a transformative force, enabling seamless connectivity and data exchange between diverse devices and networks. However, the realization of a truly interoperable, secure, and energy-efficient IoT ecosystem remains a significant challenge. In this paper, we present the vision and the first key findings of the FITNESS project, a comprehensive research initiative funded by the French Research Agency (ANR) as part of the France 2030 program. Our work aims to develop solutions that enable the IoT's full potential, emphasizing scalability, interoperability, security, and sustainability, and enabling seamless connectivity and efficient data transmission between diverse IoT devices and networks. We focus in this paper on three critical areas: IoT architecture and interoperability, the place of Artificial Intelligence in IoT, and energy efficiency. Additionally, we identify and analyze key use cases that demonstrate the practical applications of our research, highlighting the importance of real-world implementation to validate and refine our solutions. Through our dedicated research efforts, we have made significant advances across several key areas, while also laying the groundwork for further development in others. These contributions support the emergence of a more secure, efficient, and interoperable IoT ecosystem, and provide a foundation for adoption by stakeholders seeking to harness its transformative potential. (10.5281/zenodo.17119689)
  DOI : 10.5281/zenodo.17119689
• Additivity and chain rules for quantum entropies via multi-index Schatten norms
  
  • Fawzi Omar
  • Kochanowski Jan
  • Rouzé Cambyse
  • van Himbeeck Thomas
  , 2025. The primary entropic measures for quantum states are additive under the tensor product. In the analysis of quantum information processing tasks, the minimum entropy of a set of states, e.g., the minimum output entropy of a channel, often plays a crucial role. A fundamental question in quantum information and cryptography is whether the minimum output entropy remains additive under the tensor product of channels. Here, we establish a general additivity statement for the optimized sandwiched R´enyi entropy of quantum channels. For that, we generalize the results of [Devetak, Junge, King, Ruskai, CMP 2006] to multi-index Schatten norms. As an application, we strengthen the additivity statement of [Van Himbeeck and Brown, 2025] thus allowing the analysis of time-adaptive quantum cryptographic protocols. In addition, we establish chain rules for R´enyi conditional entropies that are similar to the ones used for the generalized entropy accumulation theorem of [Metger, Fawzi, Sutter, Renner, CMP 2024]
• Subgraph Gaussian Embedding Contrast for Self-supervised Graph Representation Learning
  
  • Xie Shifeng
  • Einizade Aref
  • Giraldo Jhony
  , 2026, 16018, pp.430-447. Graph Representation Learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-Supervised Learning (SSL) methods are widely used in GRL because they can avoid expensive human annotation. In this work, we propose a novel Subgraph Gaussian Embedding Contrast (SubGEC) method. Our approach introduces a subgraph Gaussian embedding module, which adaptively maps subgraphs to a structured Gaussian space, ensuring the preservation of input subgraph characteristics while generating subgraphs with a controlled distribution. We then employ optimal transport distances, more precisely the Wasserstein and Gromov-Wasserstein distances, to effectively measure the similarity between subgraphs, enhancing the robustness of the contrastive learning process. Extensive experiments across multiple benchmarks demonstrate that SubGEC outperforms or presents competitive performance against state-of-the-art approaches. Our findings provide insights into the design of SSL methods for GRL, emphasizing the importance of the distribution of the generated contrastive pairs. (10.1007/978-3-032-06106-5_25)
  DOI : 10.1007/978-3-032-06106-5_25
• T-REX: Table -Refute or Entail eXplainer
  
  • Horstmann Tim Luka
  • Geisenberger Baptiste
  • Alam Mehwish
  , 2025. Verifying textual claims against structured tabular data is a critical yet challenging task in Natural Language Processing with broad real-world impact. While recent advances in Large Language Models (LLMs) have enabled significant progress in table fact-checking, current solutions remain inaccessible to non-experts. We introduce T-REX (Table -Refute or Entail eXplainer), the first live, interactive tool for claim verification over multimodal, multilingual tables using state-of-theart instruction-tuned reasoning LLMs. Designed for accuracy and transparency, T-REX empowers non-experts by providing access to advanced fact-checking technology. The system is openly available online.
• Sports Motion Analysis : From Competition Videos to Data-Driven Interpretations
  
  • Gan Qi
  , 2025. Understanding sports motion is essential for performance analysis and training guidance, and has become a growing research area with the advancement of artificial intelligence (AI) in data analysis. Additionally, due to its analyzable physical mechanisms, sports motion serves as a valuable case study for other motion-related domains, such as pedestrian prediction in autonomous driving, abnormal behavior detection in surveillance, disease diagnosis in medicine, and pose tracking in gaming. However, the black-box nature of modern AI models limits our ability to understand their behavior and decisions. This thesis aims to bridge the gap between sports motion analysis and explainable AI (XAI), leveraging AI's representational power while ensuring interpretability.Two main challenges are addressed: (1) the lack of high-quality sports datasets—despite many online videos, competition footage often suffers from low resolution, motion blur, and noisy backgrounds; and (2) the limited research on interpreting sports motions, with few baseline studies in this area.To tackle these, we focus on long jump, which offers two advantages: the availability of high-quality world-class competition videos and biomechanically well-defined motion sequences suitable for interpretation. To support this work, we built three datasets: (1) Olympic triple jump videos with 2D poses and official distances, (2) World Championship long jump videos with 2D poses and biomechanical features, and (3) long jump videos with 2D poses and jump distances from top level competitions.Our study focuses on two levels of data extraction. First, we estimate athlete poses from video, despite challenges like low frame rates and rare poses. We improve pose accuracy with a post-correction method using 2D sports pose priors, modeled as Neural Distance Fields (NDF) in polar coordinates and trained with a gradient-projection-based augmentation method. Second, we estimate biomechanical features from video using a data-free method, reconstructing athlete trajectories from poses via biomechanical modeling.We explored two paths for interpreting sports motion. The first focuses on biomechanical features: we use a classical interpretable machine learning pipeline by training a quantile random forest regressor and applying SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs), and Individual Conditional Expectation (ICE) plots. This reveals insights aligned with existing literature. However, to capture widespread feature interactions, we propose a new method to estimate not only interaction strengths but also where and how interactions occur.The second path focuses on interpreting pose sequences. We analyze black-box time-series models by generating counterfactual explanations using a sparse autoencoder-based model. Experiments show that this approach yields both faithful and robust explanations, contributing to more interpretable and practical human motion analysis from video data.