Publications

2025

• Polar coordinate-based 2D pose prior with neural distance field
  
  • Gan Qi
  • Nguyen Sao Mai
  • Fenaux Eric
  • Clémençon Stéphan
  • El Yacoubi Mounim
  , 2025, pp.6144-6152. Human pose capture is essential for sports analysis, enabling precise evaluation of athletes' movements. While deep learning-based human pose estimation (HPE) models from RGB videos have achieved impressive performance on public datasets, their effectiveness in real-world sports scenarios is often hindered by motion blur, occlusions, and domain shifts across different pose representations. Fine-tuning these models can partially alleviate such challenges but typically requires large-scale annotated data and still struggles to generalize across diverse sports environments. To address these limitations, we propose a 2D pose prior-guided refinement approach based on Neural Distance Fields (NDF). Unlike existing approaches that rely solely on angular representations of human poses, we introduce a polar coordinate-based representation that explicitly incorporates joint connection lengths, enabling a more accurate correction of erroneous pose estimations. Additionally, we define a novel non-geodesic distance metric that separates angular and radial discrepancies, which we demonstrate is better suited for polar representations than traditional geodesic distances. To mitigate data scarcity, we develop a gradient-based batch-projection augmentation strategy, which synthesizes realistic pose samples through iterative refinement. Our method is evaluated on a long jump dataset, demonstrating its ability to improve 2D pose estimation across multiple pose representations, making it robust across different domains. Experimental results show that our approach enhances pose plausibility while requiring only limited training data. Code is available at: https://github.com/QGAN2019/polar-NDF. (10.1109/CVPRW67362.2025.00612)
  DOI : 10.1109/CVPRW67362.2025.00612
• From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
  
  • Bouniot Quentin
  • Redko Ievgen
  • Mallasto Anton
  • Laclau Charlotte
  • Struckmeier Oliver
  • Arndt Karol
  • Heinonen Markus
  • Kyrki Ville
  • Kaski Samuel
  , 2025. In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explaining the upward trend of their performance, however, remains difficult as different DNN architectures of comparable depth and width -common factors associated with their expressive power -may exhibit a drastically different performance even when trained on the same dataset. In this paper, we introduce the concept of the non-linearity signature of DNN, the first theoretically sound solution for approximately measuring the non-linearity of deep neural networks. Built upon a score derived from closed-form optimal transport mappings, this signature provides a better understanding of the inner workings of a wide range of DNN architectures and learning paradigms, with a particular emphasis on the computer vision task. We provide extensive experimental results that highlight the practical usefulness of the proposed non-linearity signature and its potential for long-reaching implications. The code for our work is available at https: //github.com/qbouniot/AffScoreDeep.
• Auto-Reg: A Dynamic AutoML Framework for Streaming Regression
  
  • Verma Nilesh
  • Bifet Albert
  • Pfahringer Bernhard
  • Bahri Maroua
  , 2025, 15873, pp.245-256. <div><p>Automated Machine Learning (AutoML) has transformed the development of machine learning pipelines. However, its application to data streams presents unique challenges, such as adapting to evolving data distributions and ensuring real-time performance. While substantial progress has been made in streaming classification, advancements in streaming regression remain limited. To address this gap, we propose Auto-Reg, an AutoML framework tailored specifically for data stream regression. Auto-Reg introduces two key components: a dynamic budget adjustment mechanism for efficient resource allocation and a Probability-Weighted Hyperparameter Search (PWHS) strategy that balances exploration and exploitation. Comprehensive experiments on both real-world and synthetic datasets, supported by theoretical and empirical evaluations, demonstrate that Auto-Reg consistently outperforms state-of-theart data stream regression models in terms of predictive accuracy.</p></div> (10.1007/978-981-96-8183-9_20)
  DOI : 10.1007/978-981-96-8183-9_20
• Multi-bit Quantizer Design for Distributed Parameter Estimation
  
  • Bi Yue
  • Ciblat Philippe
  • Wu Yue
  • Hua Cunqing
  , 2025. <div><p>We consider sensors deployed in diverse locations measuring a common parameter through noisy observations. These observations are quantized to be sent to a fusion center doing the estimation of the common parameter. We design these quantizers to minimize the worst-case mean square error for common parameter estimation. Relying on an asymptotic regime in terms of sensors' number and on random multi-bit quantizer per sensor, we provide a relevant continuous distribution for the thresholds of these quantizers via signomial programming. Through numerical simulations, we show that the proposed quantizers outperform the uniformly-distributed one and some deterministic ones even when the number of sensors is limited.</p></div>
• Stochastic Activation based Broadcast Push-Sum for Distributed Estimation
  
  • Bi Yue
  • Ciblat Philippe
  • Wu Yue
  • Hua Cunqing
  , 2025, pp.1-5. <div><p>Distributed estimation systems enable nodes to estimate a target parameter in a collaborative manner. These systems are useful in sensor networks or distributed machine learning. Here, we explore distributed estimation in graph-connected networks without a fusion center, where nodes exchange information with neighbors to estimate this target parameter synchronously. Due to packet collision, there is a tradeoff between the number of exchanges and the quality of these exchanges. To fix this issue, we propose to activate the nodes randomly. The main contribution of the paper is to determine an activation rate offering a good target estimation quality as fast as possible.</p></div> (10.1109/SSP64130.2025.11073203)
  DOI : 10.1109/SSP64130.2025.11073203
• InteractOR: Interacting with Images during Surgery in Mixed Reality through Real-time Instrument Segmentation
  
  • Karoui Nour
  • Bloch Isabelle
  • Avellino Ignacio
  , 2025.
• Massive parallelization of projection-based depths
  
  • Leone Leonardo
  • Mozharovskyi Pavlo
  • Bounie David
  , 2025. This article introduces a novel methodology for the massive parallelization of projection-based depths, addressing the computational challenges of data depth in high-dimensional spaces. We propose an algorithmic framework based on Refined Random Search (RRS) and demonstrate significant speedup (up to 7,000 times faster) on GPUs. Empirical results on synthetic data show improved precision and reduced runtime, making the method suitable for large-scale applications. The RRS algorithm (and other depth functions) are available in the Python-library data-depth (https://data-depth.github.io/) with ready-to-use tools to implement and to build upon this work. (10.48550/arXiv.2506.08262)
  DOI : 10.48550/arXiv.2506.08262
• Co-investment under uncertainty: coalitional game formulation and application to edge computing
  
  • Sakr Amal
  • Araldo Andrea
  • Chahed Tijani
  • Patanè Rosario
  • Kofman Daniel
  , 2025. <div><p>We focus on the deployment of large scale Edge Computing (EC) infrastructure, which requires substantial capital and operational costs. The Infrastructure Providers (InPs) are reluctant to make these investments, as the revenues from edge services, e.g., augmented reality, may be mainly captured by the respective Service Providers (SPs), and not the InPs. This is the main reason for the limited deployment of EC. We tackle this economic aspect by proposing a co-investment strategy, in which all players, i.e., one InP and multiple SPs, form a coalition to deploy an optimal amount of resources, dynamically allocate them over an investment period, and fairly share costs and revenues. The major challenge is to ensure that the coalition is stable, i.e., the co-investment is profitable for all players under uncertainty of revenues, which comes from the uncertainty of future user demand. We address this challenge with a novel stochastic coalitional game formulation, which allows us to analytically compute a lower bound on the probability that the grand coalition is stable. Numerical results show a large value for the lower bound, even with high levels of uncertainty, provided that the number of SPs is relatively small, their loads are comparable, and the investment period is long enough.</p></div>
• Performance Analysis of Network Sensing in the Distributed MIMO Radar System
  
  • Song Yi
  • Zhi Kangda
  • Yi Shuangyang
  • Ciblat Philippe
  • Caire Guiseppe
  , 2025. <div><p>This paper investigates the network sensing problem in a distributed multiple-input multiple-output (MIMO) radar system. We first formulate the received signal model in distributed MIMO systems as a function of the target's location. Based on the problem formulation, we derive the Cramér-Rao lower bound (CRLB) of the location estimation error for a single target, whose dependence on the layout of the transmitters (TXs) and receivers (RXs) is revealed. Using the tools from stochastic geometry, we then model the locations of TXs and RXs as homogeneous Poisson Point Process (PPP) and investigate the network-level sensing performance. Particularly, we derive the scaling law for the average estimation error, revealing the impact of various system parameters such as the number of antennas, SNR, TX/RX densities, and path loss exponent. More importantly, we unveil that the estimation error scales with the SNR and the number of antennas to the power of ´1, and with the TX/RX densities to the power of ´γ{2, where γ is the path loss exponent. Our numerical results confirm the accuracy of our theoretical derivations and the correctness of conclusions.</p></div>
• On the Average Random Probing Model
  
  • Béguinot Julien
  • Masure Loïc
  IACR Transactions on Cryptographic Hardware and Embedded Systems, IACR, 2025, 2025 (3), pp.32-55. Masking is one of the main countermeasures against side-channel analysis since it relies on provable security. In this context, “provable” means that a security bound can be exhibited for the masked implementation through a theoretical analysis in a given threat model. The main goal in this line of research is therefore to provide the tightest security bound, in the most realistic model, in the most generic way. Yet, all of these objectives cannot be reached together. That is why the masking literature has introduced a large spectrum of threat models and reductions between them, depending on the desired trade-off with respect to these three goals. In this paper, we focus on three threat models, namely the noisy-leakage model (realistic yet hard to work with), the random probing (unrealistic yet easy to work with), and more particularly a third intermediate model called average random probing. Average random probing has been introduced by Dziembowski et al. at Eurocrypt 2015, in order to exhibit a tight reduction between noisy-leakage and random probing models, recently proven by Brian et al. at Eurocrypt 2024. This milestone has strong practical consequences, since otherwise the reduction from the noisy leakage model to the random probing model introduces a prohibitively high constant factor in the security bound, preventing security evaluators to use it in practice. However, we exhibit a gap between the average random probing definitions of Dziembowski et al. (denoted hereafter by DFS-ARP) and Brian et al. (simply denoted by ARP). Whereas any noisy leakage can be tightly reduced to DFS-ARP, we show in this paper that it cannot be tightly reduced to ARP, unless requiring extra assumptions, e.g., if the noisy leakage is deterministic. Our proof techniques do not involve more tools than the one used so far in such reductions, namely basic probability facts, and known properties of the total variation distance. As a consequence, the reduction from the noisy leakage to the random probing — without high constant factor — remains unproven. This stresses the need to clarify the practical relevance of analyzing the security of masking in the random probing model since most of the current efforts towards improving the constructions and their security proofs in the random probing model might be hindered by potentially unavoidable loss in the reduction from more realistic but currently less investigated leakage models. (10.46586/tches.v2025.i3.32-55)
  DOI : 10.46586/tches.v2025.i3.32-55
• DreamPet: Text Driven Controllable 3D Animal Generation using Gaussian Splatting
  
  • Ramakrishnan Vysakh
  • Nag Sauradip
  • Parakkat Amal Dev
  • Zhu Xiatian
  • Dutta Anjan
  , 2024. Realistic 3D animal generation from text prompts is a significant yet challenging task. Traditional approaches, which use score distillation sampling to optimize 3D formats like meshes or neural fields, often suffer from a lack of detail and designed for fixed shape. To address both limitations, in this work, we introduce DreamPet, a novel framework that explores a retrieval-augmented approach tailored for score distillation and efficiently produces high-quality 3D animal models featuring fine-grained geometry and lifelike textures. Our key insight is that both expressiveness of 2D diffusion models and geometric consistency of 3D animal assets can be fully leveraged by employing the semantically relevant assets directly within the optimization process. Specifically, our method features 1) a Shape-Aware SDS for optimizing appearance and geometry to ensure structural consistency per category, and 2) a Category aware refinement module that addresses the over-saturation issue and further eliminates floating artefacts based on the animal category to produce realistic textures. Extensive experiments demonstrate competitive quality of our method, rendering 3D animals under diverse scenarios.
• Quantized Precoding Under ACLR Constraints with FIR-DACs for Downlink MU-MIMO
  
  • Schlegel Nicolas
  • Jabbour Chadi
  • Valcarce Alvaro
  • Wantiez Eric
  , 2025, pp.548-553. Scaling massive multiuser multiple-input multipleoutput (MIMO) to larger antenna numbers constrains each chain in terms of power consumption and implementation. In this paper, the use of low resolution digital-to-analog converters (DACs) enabling these large scale arrays in downlink multiuser massive MIMO is studied with a focus on reaching adjacent channel leakage ratio (ACLR) targets. A quantization aware precoder with constraints on out-of-band (OOB) emissions is designed based on the multi-block alternate directions method of multipliers (ADMM) algorithm. With conventional DACs, simulation results indicate that these constraints do not provide sufficient ACLR. In response, Finite impulse response (FIR)DACs are introduced as a reconfigurable alternative to analog filters. Numerical results show that ACLR targets can be reached, even at 1 bit, at a lower hardware cost than high resolution DACs. (10.1109/EuCNC/6GSummit63408.2025.11036903)
  DOI : 10.1109/EuCNC/6GSummit63408.2025.11036903
• Tail Index Estimation for Discrete Heavy-Tailed Distributions with Application to Statistical Inference for Regular Markov Chains
  
  • Bertail Patrice
  • Clémençon Stephan
  • Fernández Carlos
  Test, Spanish Society of Statistics and Operations Research/Springer, 2025, 34, pp.691-713. It is the purpose of this paper to investigate the issue of estimating the regularity index $\beta&gt;0$ of a discrete heavy-tailed r.v. $S$, \textit{i.e.} a r.v. $S$ valued in $\mathbb{N}^*$ such that $\mathbb{P}(S&gt;n)=L(n)\cdot n^{-\beta}$ for all $n\geq 1$, where $L:\mathbb{R}^*_+\to \mathbb{R}_+$ is a slowly varying function. Such discrete probability laws, referred to as generalized Zipf's laws sometimes, are commonly used to model rank-size distributions after a preliminary range segmentation in a wide variety of areas such as \textit{e.g.} quantitative linguistics, social sciences or information theory. As a first go, we consider the situation where inference is based on independent copies $S_1,\; \ldots,\; S_n$ of the generic variable $S$. The estimator $\widehat{\beta}$ we propose can be derived by means of a suitable reformulation of the regularly varying condition, replacing $S$'s survivor function by its empirical counterpart. Under mild assumptions, a non-asymptotic bound for the deviation between $\widehat{\beta}$ and $\beta$ is established, as well as limit results (consistency and asymptotic normality). Beyond the i.i.d. case, the inference method proposed is extended to the estimation of the regularity index of a regenerative $\beta$-null recurrent Markov chain. Since the parameter $\beta$ can be then viewed as the tail index of the (regularly varying) distribution of the return time of the chain $X$ to any (pseudo-) regenerative set, in this case, the estimator is constructed from the successive regeneration times. Because the durations between consecutive regeneration times are asymptotically independent, we can prove that the consistency of the estimator promoted is preserved. In addition to the theoretical analysis carried out, simulation results provide empirical evidence of the relevance of the inference technique proposed. (10.1007/s11749-025-00975-9)
  DOI : 10.1007/s11749-025-00975-9
• Estimation de la consommation énergétique de la 5G en France basée sur des données réelles et des modèles analytiques
  
  • Ghali Meriem
  • Busson Anthony
  • Coupechoux Marceau
  , 2025. <div><p>Le changement climatique a incité divers secteurs, y compris le secteur numérique, à évaluer leur impact environnemental, la consommation énergétique étant un facteur clé. Avec le déploiement des réseaux 5G, comprendre leur consommation énergétique est essentiel pour concevoir des infrastructures plus durables. Cette étude propose un modèle pour estimer la consommation énergétique des réseaux 5G, intégrant à la fois des composantes fixes et dépendantes de la charge. Nous appliquons ce modèle au déploiement actuel de la 5G en France. Contrairement aux études précédentes, nous utilisons des données ouvertes et publiques d'ARCEP et de l'INSEE, ce qui rend la paramétrisation de notre modèle plus réaliste et reproductible. Nous mettons en évidence comment la consommation énergétique est influencée par des facteurs tels que la charge de trafic, la densification des réseaux et les différences régionales entre les zones urbaines et rurales. Nos résultats révèlent que : i) Bien que le nombre d'utilisateurs de la 5G augmente en moyenne de 1,5 million par trimestre, la charge du réseau 5G reste faible pour le moment. ii) La consommation énergétique de la 5G est étroitement liée au déploiement de l'infrastructure, les stations de base et les AAU étant actuellement surdimensionnées par rapport à la charge du réseau en France. iii) Les zones rurales présentent une consommation énergétique par utilisateur plus élevée en raison de leur faible densité de population. iv) La consommation énergétique de base d'une station de base 5G est significativement plus élevée que sa consommation énergétique en transmission, soulignant l'importance d'améliorer cette composante.</p></div>
• NickPay, an Auditable, Privacy-Preserving, Nickname-Based Payment System
  
  • Quispe Guillaume
  • Jouvelot Pierre
  • Memmi Gerard
  , 2025. In this paper, we describe the motivation, design, security properties, and a prototype implementation of NickPay, a new privacy-preserving yet auditable payment system built on top of the Ethereum blockchain platform. NickPay offers a strong level of privacy to participants and prevents successive payment transfers from being linked to their actual owners. It is providing the transparency that blockchains ensure and at the same time, preserving the possibility for a trusted authority to access sensitive information, e.g., for audit purposes or compliance with financial regulations. NickPay builds upon the Nicknames for Group Signatures (NGS) scheme, a new signing system based on dynamic ``nicknames'' for signers that extends the schemes of group signatures and signatures with flexible public keys. NGS enables identified group members to expose their flexible public keys, thus allowing direct and natural applications such as auditable private payment systems, NickPay being a blockchain-based prototype of these. (10.1109/ICBC64466.2025.11114708)
  DOI : 10.1109/ICBC64466.2025.11114708
• EU Digital Technologies and Policy Conference (EUDTP 2025) Abstracts and Contributions
  
  • Cordero-Fuertes Juan-Antonio
  • Alam Mehwish
  • Blazy Olivier
  • Alombert Anne
  • Díaz-Rodríguez Natalia
  • Curelariu Teodora
  • Ashok Pratiksha
  • Ciuhu Calina
  • de Luca Stefano
  • Feijóo Claudio
  • Gaubiene Neringa
  • Ghaddar Bissan
  • Giglietto Fabio
  • Gomà Rafael
  • González-Fuster Gloria
  • Grumulaitis Arturas
  • Guintchev Petia
  • Jacob Romain
  • Janciute Laima
  • Kalogeiton Vicky
  • Kariniotakis Georges
  • Knaster Juan
  • Koch Luise
  • Kreer Philipp
  • Krüger Kim
  • Leblanc-Albarel Diane
  • Manner Jukka
  • Mcstay Andrew
  • Ortiz de Zúñiga María
  • Nivaggioli Patrice
  • Popovic Ivanka
  • Ramos Simona
  • Roth Markus
  • Spangenberg Jochen
  • Cripps Christopher
  , 2025.
• Une approche unifiée des activités de conception système et conception d’architecture pour intégrer la cybersécurité au tout début des phases de conception
  
  • Cincilla Pierpaolo
  • Guitton-Ouhamou Patricia
  • Guillot Bertrand
  • Barki Amira
  • Mangé Jean-Baptiste
  • Apvrille Ludovic
  • Chevalier Pascal
  MISC - Multi-System & Internet Security Cookbook, Diamond Connect, 2025, Hors-série Numéro 32 (32), pp.https://connect.ed-diamond.com/misc/mischs-032/vers-une-integration-harmonisee-des-activites-cybersecurite-dans-l-ingenierie-systeme.
• Large Language Models as Search Engines: Societal Challenges
  
  • Sadeddine Zacchary
  • Maxwell Winston
  • Varoquaux Gaël
  • Suchanek Fabian M.
  Sigir Forum, Association for Computing Machinery (ACM), 2025, 59 (1), pp.1-35. Large Language Models (LLMs) may one day replace search engines as the primary portal to information on the Web. In this article, we investigate the societal challenges that such a change could bring. We focus on the roles of LLM Providers, Content Creators, and End Users, and identify 15 types of challenges. With each, we show current mitigation strategies -both from the technical perspective and the legal perspective. We also discuss the impact of each challenge and point out future research opportunities. Large Language Models (LLMs) are increasingly used as portals to information on the Web. Google is rolling out AI overviews above its search results 1 building upon its language models 2 , Microsoft's Bing search engine 3 allows sending the query to Microsoft's Co-pilot, DuckDuckGo 4 and Brave Search 5 offer AI-assisted answers, and browsers such as Opera, Brave, and Edge have built-in AI-plugins for query answering. These developments are changing the way users access information: instead of querying the Web with a search engine, reading one or several result pages, and finding the information, people can now ask their question to the AI assistant, which will synthesize an answer for the user from Web sources. This means that LLMs have the potential to severely disrupt the search engine ecosystem, which has been comparatively stable for the last 25 years, and to completely change the way the Web is used. (10.1145/3769733.376974)
  DOI : 10.1145/3769733.376974
• A Novel Mixture Model for Characterizing Human Aiming Performance Data
  
  • Li Yanxi
  • Young Derek S
  • Rioul Olivier
  • Gori Julien
  Statistical Modelling, SAGE Publications, 2025, 25 (3), pp.236-254. Fitts’ law is often employed as a predictive model for human movement, especially in the field of human-computer interaction. Models with an assumed Gaussian error structure are usually adequate when applied to data collected from controlled studies. However, observational data (often referred to as data gathered ‘in the wild’) typically display noticeable positive skewness relative to a mean trend as users do not routinely try to minimize their task completion time. As such, the exponentially modified Gaussian (EMG) regression model has been applied to aimed movements data. However, it is also of interest to reasonably characterize those regions where a user likely was not trying to minimize their task completion time. In this article, we propose a novel model with a two-component mixture structure—one Gaussian and one exponential—on the errors to identify such a region. An expectation-conditional-maximization (ECM) algorithm is developed for estimation of such a model and some properties of the algorithm are established. The efficacy of the proposed model, as well as its ability to inform model-based clustering, are addressed in this work through extensive simulations and an insightful analysis of a human aiming performance study. (10.1177/1471082X241234139)
  DOI : 10.1177/1471082X241234139
• Character recognition in Byzantine seals with deep neural networks
  
  • Rageau Théophile
  • Likforman-Sulem Laurence
  • Fiandrotti Attilio
  • Eyharabide Victoria
  • Caseau Béatrice
  • Cheynet Jean-Claude
  Digital Applications in Archaeology and Cultural Heritage, Elsevier, 2025, 37, pp.e00403-1:e00403-11. Seals are small coin-shaped artifacts, mostly made of lead, held with strings to seal letters. This work presents the first attempt towards automatic reading of inscribed text on Byzantine seal images. Byzantine seals are generally decorated with iconography on the obverse side and Greek text on the reverse side. Text may include the sender's name, position in the Byzantine aristocracy, and elements of prayers. Both text and iconography are precious literary sources that wait to be exploited electronically, so the development of computerized systems for interpreting seals images is of paramount importance. This work's contribution is hence a deep, two-stages, character reading pipeline for transcribing Byzantine seal images. A first deep convolutional neural network (CNN) detects characters in the seal (character localization). A second convolutional network reads the localized characters (character classification). Finally, a diplomatic transcription of the seal is provided by post-processing the two network outputs. We provide an experimental evaluation of each CNN in isolation and both CNNs in combination. All performances are evaluated by cross-validation. Character localization achieves a mean average precision (mAP) greater than 0.9 at the intersection of union threshold of 0.5. Classification of characters achieves an accuracy greater than 0.92. Such performance compares favorably to similar tasks such as the recognition of inscribed characters on ancient coins. At transcription level, we provide novel performance results in terms of Character Error Rate. This is novel for seal images and differs from results on isolated character recognition. (10.1016/j.daach.2025.e00403)
  DOI : 10.1016/j.daach.2025.e00403
• Exposing Go Hidden Bugs: A Novel Concolic Framework
  
  • Gorna Karolina
  • Iooss Nicolas
  • Seurin Yannick
  • Khatoun Rida
  , 2025. The widespread adoption of the Go programming language in infrastructure backends and blockchain projects has heightened the need for improved security measures. Established techniques such as unit testing, static analysis, and program fuzzing provide foundational protection mechanisms. Although symbolic execution tools have made significant contributions, opportunities remain to address the complexities of Go's runtime and concurrency model. In this work, we present Zorya, a novel methodology leveraging concrete and symbolic (concolic) execution to evaluate Go programs comprehensively. By systematically exploring execution paths to uncover vulnerabilities beyond conventional testing, symbolic execution offers distinct advantages, and coupling it with concrete execution mitigates the path explosion problem. Our solution employs Ghidra's P-Code as an intermediate representation (IR). This implementation detects runtime panics in the TinyGo compiler and supports both generic and custom invariants. Furthermore, P-Code's generic IR nature enables analysis of programs written in other languages such as C. Future enhancements may include intelligent classification of concolic execution logs to identify vulnerability patterns.
• Deliverable D2.1: Study of Timing Anomalies Documented in the Literature
  
  • Brandner Florian
  • Asăvoae Mihail
  • Bechennec Jean-Luc
  • Carle Thomas
  • Cassé Hugues
  • Faucou Sébastien
  • Rieg Lionel
  , 2025, pp.1-32. In this report, we will study a phenomenon that may have a considerable impact on the computation of the WCET of real-time tasks: Timing Anomalies (timing anomalys (TAs)). These phenomena may make WCET analysis much harder, or even impossible. Even worse they also threaten the validity of schedulability tests, which often manipulate WCET values under the hypothesis that no TAs may occur. In the following we will provide a brief introduction to relevant aspects to understand TAs, notably architecture, WCET analysis, and an intuitive definition of TAs. The remaining sections of the report detail related work on the subject of TAs, with a specific focus on formal definitions of the phenomenon.
• Rate of Convergence in the Functional Central Limit Theorem for Stable Processes
  
  • Coutin Laure
  • Decreusefond Laurent
  • Huang Lorick
  Potential Analysis, Springer Verlag, 2025. In this article, we quantify the functional convergence of the rescaled random walk with heavy tails to a stable process. This generalizes the Generalized Central Limit Theorem for stable random variables in finite dimension. We show that provided we have a control between the random walk or the limiting stable process and their respective affine interpolation, we can lift the rate of convergence obtained for multivariate distributions to a rate of convergence in some functional spaces. (10.1007/s11118-025-10215-2)
  DOI : 10.1007/s11118-025-10215-2
• Efficient 5G Resource Block Scheduling Using Action Branching and Transformer Networks
  
  • Nérondat Sylvain
  • Leturc Xavier
  • Ciblat Philippe
  • Le Martret Christophe
  , 2025, pp.1-6. <div><p>This paper presents a deep reinforcement learningbased scheduling solution tailored for 5G networks. The proposed neural network architecture, utilizing an encoder-only transformer and action branching, is designed to handle large action spaces for resource block allocation in wireless environments. By training on variable number of user equipment scenarios, the solution generalizes well across different configurations. Experimental results in Nokia's wireless suite environment demonstrate superior performance in packet loss, compared to heuristics.</p></div> (10.1109/ICMLCN64995.2025.11140453)
  DOI : 10.1109/ICMLCN64995.2025.11140453
• Railway track monitoring using distributed acoustic sensing (DAS) with standard telecom cable
  
  • Chedid Alex
  • Kabalan Ali
  • Hammi Tarik
  • Garbini Gabriel Papaiz
  • Gabet Renaud
  , 2025, 13639, pp.348. We demonstrate the ability to detect ground vibrations in a railway environment using two Distributed Acoustic Sensing (DAS) configurations. The study employs the standard deviation of the differential phase over time (STDv) as a metric to evaluate the detection capabilities and spatiotemporal localization accuracy of both systems. A demonstration of rail train tracking is presented using a standard optical fiber telecom cable sheathed PEHD, with a detection range extending up to 40 km. (10.1117/12.3062236)
  DOI : 10.1117/12.3062236