Publications

2025

• Cyberattacks Detection in Cooperative Intelligent Transportation System (C-ITS)
  
  • Wehby Ayoub
  , 2025. Modern vehicles are becoming smarter, communicating with each other to enhance traffic flow and improve safety. However, this connectivity also exposes them to cyberattacks, particularly Distributed Denial of Service (DDoS) attacks where malicious actors overwhelm vehicular networks with fake messages, disrupting real-time communication between cars. This can lead to issues ranging from navigation failures to serious accidents. This research focuses on a specific type of DDoS attack that exploits Cooperative Awareness Messages (CAMs), which cars use to share their location and speed. We propose an approach for a distributed Intrusion Detection System (IDS) designed to mitigate DDoS attacks in a connected car environment. Initially, we assessed the impact of CAM-DDoS attacks on connected cars through various simulation scenarios. These scenarios revealed a decrease in the CAM broadcast rate from legitimate cars and an accumulation of invalid data. Subsequently, we proposed a distributed architecture for an IDS and assessed the effectiveness of three supervised ensemble learning algorithms against these attacks. Given the challenge of generalizability in machine learning models, we also evaluated the robustness of our DDoS detection approach against other types of DDoS attacks, such as JamDDoS. The results show that all DDoS messages were processed uniformly, which explains the models' robust performance against studied DDoS attacks. Additionally, we demonstrated how a DDoS attack can be transformed into a Sybil DDoS attack that avoids DDoS detectors in a safety-critical connected car environment. To address this, we developed and evaluated an optimized data-handling approach that serves as the foundation for our proposed ensemble hard voting detection model. The results obtained demonstrate an effective attack detection with reduced false alarms against Sybil DDoS attacks.
• Arithmétisation de la partie entière et applications à la cryptographie
  
  • Berthet Pierre-Augustin
  • Tavernier Cédric
  , 2025. Dans ce travail nous proposons une méthodologie pour calculer la partie entière à l'aide uniquement d'additions et de multiplications, la rendant ainsi compatible avec le chiffrement homomorphe.
• Cartographier l'intelligence artificielle : collecte et analyse d'un corpus de chartes et manifestes sur l'éthique de l'IA
  
  • Viard Tiphaine
  • Delarue Simon
  • Gornet Mélanie
  • Boritchev Maria
  , 2025. Nous présentons ici un corpus de 436 chartes et manifestes au sujet de l'éthique de l'intelligence artificielle, ainsi qu'une analyse discursive de celui-ci, permettant de comprendre les enjeux autour desquels le monde social de l'éthique de l'IA se structure. Nous mettons à disposition le corpus, sa documentation, et le code permettant son analyse.
• Rapid Thermalization of Dissipative Many-Body Dynamics of Commuting Hamiltonians
  
  • Kochanowski Jan
  • Alhambra Álvaro
  • Capel Ángela
  • Rouzé Cambyse
  Communications in Mathematical Physics, Springer Verlag, 2025, 406 (8), pp.176. Abstract Quantum systems typically reach thermal equilibrium rather quickly when coupled to a thermal environment. The usual way of bounding the speed of this process is by estimating the spectral gap of the dissipative generator. However the gap, by itself, does not always yield a reasonable estimate for the thermalization time in many-body systems: without further structure, a uniform lower bound on it only constrains the thermalization time to grow polynomially with system size. Here, instead, we show that for a large class of geometrically-2-local models of Davies generators with commuting Hamiltonians, the thermalization time is much shorter than one would naïvely estimate from the gap: at most logarithmic in the system size. This yields the so-called rapid mixing of dissipative dynamics. The result is particularly relevant for 1D systems, for which we prove rapid thermalization with a system size independent decay rate only from a positive gap in the generator. We also prove that systems in hypercubic lattices of any dimension, and exponential graphs, such as trees, have rapid mixing at high enough temperatures. We do this by introducing a novel notion of clustering which we call “strong local indistinguishability” based on a max-relative entropy, and then proving that it implies a lower bound on the modified logarithmic Sobolev inequality (MLSI) for nearest neighbour commuting models. This has consequences for the rate of thermalization towards Gibbs states, and also for their relevant Wasserstein distances and transportation cost inequalities. Along the way, we show that several measures of decay of correlations on Gibbs states of commuting Hamiltonians are equivalent, a result of independent interest. At the technical level, we also show a direct relation between properties of Davies and Schmidt dynamics, that allows to transfer results of thermalization between both. (10.1007/s00220-025-05353-y)
  DOI : 10.1007/s00220-025-05353-y
• Clustering cell nuclei on microgrooves for disease diagnosis using deep learning
  
  • Roellinger Bettina
  • Thenier Francois
  • Leclech Claire
  • Coirault Catherine
  • Angelini Elsa
  • Barakat Abdul I
  Scientific Reports, Nature Publishing Group, 2025, 15 (1), pp.22476 (1-13). Various diseases including laminopathies and certain types of cancer are associated with abnormal nuclear mechanical properties that influence cellular and nuclear deformations in complex environments. Recently, microgroove substrates designed to mimic the anisotropic topography of the basement membrane have been shown to induce 3D nuclear deformations in various adherent cell types. Importantly, these deformations are different in myoblasts derived from laminopathy patients from those in cells derived from normal individuals. Here we assess the ability of a Variational Autoencoder (VAE) and a Gaussian Mixture Model (GMM) to cluster patches of nuclei of both wildtype myoblasts and myoblasts with laminopathy-associated mutations cultured on microgroove substrates, and we explore the impact of image processing parameters on clustering performance. We show that a standard VAE with GMM is able to cluster nuclei based on their morphologies and degrees of deformations and that these clusters correspond to either wildtype myoblasts or myoblasts with LMNA mutations. The current results suggest that combining deep learning techniques with microgroove substrates enables automatic classification of nuclear deformations and thus provides a promising approach for easy and rapid diagnosis of pathologies that involve abnormalities in nuclear deformation. (10.1038/s41598-025-05788-2)
  DOI : 10.1038/s41598-025-05788-2
• Versatile Rectifiers 2.0: Theory, Design, and Emerging Applications
  
  • Niotaki Kyriaki
  • Song Chaoyun
  • Yang Furong
  • Georgiadis Apostolos
  IEEE Microwave Magazine, Institute of Electrical and Electronics Engineers, 2025, 26 (7), pp.93-102. Conventional rectifiers are used primarily for the rectification of ac, RF, or microwave power into dc. Their major applications are concentrated in energy harvesting (EH) and wireless power transfer (WPT) [1], [2], [3], [4], [5]. Rectifiers can be used as stand-alone components or in combination with complementary energy sources, such as solar or wind. They can also be used beyond simple power conversion. Backscattering and simultaneous wireless information and power transmission (SWIPT) systems are two particularly interesting application examples [6]. (10.1109/MMM.2024.3470708)
  DOI : 10.1109/MMM.2024.3470708
• Neurosymbolic Methods for Rule Mining
  
  • Ławrynowicz Agnieszka
  • Galárraga Luis
  • Alam Mehwish
  • Jaulmes Bérénice
  • Zeman Václav
  • Kliegr Tomáš
  , 2025, pp.1-22. In this chapter, we address the problem of rule mining, beginning with essential background information, including measures of rule quality. We then explore various rule mining methodologies, categorized into three groups: inductive logic programming, path sampling and generalization, and linear programming. Following this, we delve into neurosymbolic methods, covering topics such as the integration of deep learning with rules, the use of embeddings for rule learning, and the application of large language models in rule learning. (10.3233/FAIA250224)
  DOI : 10.3233/FAIA250224
• Neurosymbolic Methods for Dynamic Knowledge Graphs
  
  • Alam Mehwish
  • Gesese Genet Asefa
  • Paris Pierre-Henri
  , 2025. Knowledge graphs (KGs) have recently been used for many tools and applications, making them rich resources in structured format. However, in the real world, KGs grow due to the additions of new knowledge in the form of entities and relations, making these KGs dynamic. This chapter formally defines several types of dynamic KGs and summarizes how these KGs can be represented. Additionally, many neurosymbolic methods have been proposed for learning representations over static KGs for several tasks such as KG completion and entity alignment. This chapter further focuses on neurosymbolic methods for dynamic KGs with or without temporal information. More specifically, it provides an insight into neurosymbolic methods for dynamic (temporal or non-temporal) KG completion and entity alignment tasks. It further discusses the challenges of current approaches and provides some future directions. (10.3233/FAIA250223)
  DOI : 10.3233/FAIA250223
• Associations of Prepandemic Lung Function and Structure with COVID-19 Outcomes: The C4R Study
  
  • Balte Pallavi
  • Kim John
  • Sun Yifei
  • Allen Nori
  • Angelini Elsa
  • Arynchyn Alexander
  • Barr R. Graham
  • Blaha Michael
  • Bowler Russell
  • Carr Jeff
  • Cole Shelley
  • Couper David
  • Demmer Ryan
  • Doyle Margaret
  • Elkind Mitchell
  • San José Estépar Raúl
  • Garcia-Bedoya Olga
  • Garudadri Suresh
  • Hansel Nadia
  • Hermann Emilia
  • Hoffman Eric
  • Humphries Stephen
  • Hunninghake Gary
  • Kaplan Robert
  • Krishnan Jerry
  • Laine Andrew
  • Lee Joyce
  • Lynch David
  • Make Barry
  • Matsushita Kunihiro
  • Mckleroy Will
  • Min Yuan-I
  • Naik Sneha
  • O’connor George
  • O’driscoll Olivia
  • Oren Eyal
  • Podolanczuk Anna
  • Post Wendy
  • Pottinger Tess
  • Regan Elizabeth
  • Rusk Annie
  • Salvatore Mary
  • Schwartz David
  • Smith Benjamin
  • Sotres-Alvarez Daniela
  • Umans Jason
  • Vasan Ramachandran
  • Washko George
  • Wenzel Sally
  • Woodruff Prescott
  • Xanthakis Vanessa
  • Ortega Victor
  • Oelsner Elizabeth
  American Journal of Respiratory and Critical Care Medicine, American Thoracic Society, 2025, 211 (7), pp.1196-1210. Abstract Rationale Increased risk of coronavirus disease (COVID-19) hospitalization and death has been reported among patients with clinical lung disease. Objectives To test the association of objective measures of prepandemic lung function and structure with COVID-19 outcomes in U.S. adults. Methods Prepandemic obstruction (FEV1/FVC < 0.70) and restriction (FEV1/FVC ⩾ 0.7, FVC < 80%) were defined based on the most recent spirometry exam conducted in 11 prospective U.S. general population–based cohorts. Severe obstruction was classified by FEV1 < 50%. Percentage emphysema, percentage high-attenuation areas, and interstitial lung abnormalities were defined on computed tomography in a subset. Incident COVID-19 was ascertained via questionnaires, serosurvey, and medical records from 2020 to 2023 and classified as severe (hospitalized or fatal) or nonsevere. Cause-specific hazard models were adjusted for sociodemographics, anthropometry, smoking, comorbidities, and COVID-19 vaccination status. Measurements and Main Results Among 29,323 participants (mean age, 67 yr), there were 748 severe incident COVID-19 cases over median follow-up of 17.3 months from March 1, 2020. Greater hazards of severe COVID-19 were associated with severe obstruction (vs. normal; adjusted hazard ratio [aHR], 2.11; 95% confidence interval [CI], 1.02–1.27), restriction (vs. normal; aHR, 1.40; 95% CI, 1.12–1.76), and percentage emphysema (highest vs. lowest quartile; aHR, 1.64; 95% CI, 1.03–2.61), but not greater high-attenuation areas or interstitial lung abnormalities. COVID-19 vaccination provided greater absolute risk reduction in these groups. Results were similar in participants without smoking, obesity, or clinical cardiopulmonary disease. Conclusions Prepandemic severe spirometric obstruction, spirometric restriction, and greater percentage emphysema lung on computed tomography were associated with risk of severe COVID-19. These findings support enhanced COVID-19 risk mitigation for individuals with impaired lung health and warrant further mechanistic studies on interactions of lung function, structure, and vulnerability to acute respiratory illnesses. (10.1164/rccm.202408-1656OC)
  DOI : 10.1164/rccm.202408-1656OC
• Prototyping Custom Hardware Performance Counters in gem5 Simulator: A Framework for RISC-V Side-Channel Attack Assessment
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025. <div><p>Microarchitectural side-channel attacks exploit performancecentric optimizations such as caches and branch predictors to leak sensitive data. While RISC-V processors are increasingly adopted, tools to evaluate their resilience to these attacks remain limited. This paper presents the first framework for designing custom hardware performance counters (HPCs) in the gem5 simulator for RISC-V, targeting the assessment of side-channel attacks. Although gem5 itself can provide valuable statistics to assess attacks, real-world systems do not have access to such detailed insights. To address this gap, we extend gem5 by creating a new component to prototype HPCs for RISC-V. We modify gem5 to create three HPCs to evaluate branch mispredictions, and L1 instruction and data cache misses. These parameters are critical for identifying microarchitectural side-channel attack patterns. To validate our framework, we test it under various workload conditions on Flush+Fault [7], a recent RISC-V attack. Our simulations detect a 491.4% increase in branch mispredictions and a 231.4% rise in instruction cache misses during Flush+Fault execution compared to baseline benign OS processes. These deviations directly align with its exploitation mechanisms of branch mistraining and cache interference. The framework correlates these HPC deviations with attack attempts, enabling vulnerability analysis of components such as cache and branch predictors. Furthermore, it lays the foundation for a prototype evaluation platform that integrates custom HPCs into gem5 for testing realistic detection mechanisms and countermeasures. To our knowledge, no prior work implements custom HPCs in gem5 for RISC-V security analysis. Our goal is to establish a RISC-V security evaluation platform for analyzing attack patterns, testing detection mechanisms, and guiding secure hardware design.</p></div>
• Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates
  
  • Spadaro Gabriele
  • Presta Alberto
  • Giraldo Zuluaga Jhony Heriberto
  • Grangetto Marco
  • Hu Wei
  • Valenzise Giuseppe
  • Fiandrotti Attilio
  • Tartaglione Enzo
  , 2025. Efficient compression of low-bit-rate point clouds is critical for bandwidth-constrained applications. However, existing techniques mainly focus on high-fidelity reconstruction, requiring many bits for compression. This paper proposes a "Denoising Diffusion Probabilistic Model" (DDPM) architecture for point cloud compression (DDPM-PCC) at low bit-rates. A PointNet encoder produces the condition vector for the generation, which is then quantized via a learnable vector quantizer. This configuration allows to achieve a low bitrates while preserving quality. Experiments on ShapeNet and ModelNet40 show improved rate-distortion at low rates compared to standardized and state-of-the-art approaches. We publicly released the code at https://github.com/EIDOSLAB/DDPM-PCC. (10.1109/icme59968.2025.11209228)
  DOI : 10.1109/icme59968.2025.11209228
• Neural Velocity for hyperparameter tuning
  
  • Dalmasso Gianluca
  • Bragagnolo Andrea
  • Tartaglione Enzo
  • Fiandrotti Attilio
  • Grangetto Marco
  , 2025, pp.1-8. Hyperparameter tuning, such as learning rate decay and defining a stopping criterion, often relies on monitoring the validation loss. This paper presents NeVe, a dynamic training approach that adjusts the learning rate and defines the stop criterion based on the novel notion of "neural velocity". The neural velocity measures the rate of change of each neuron’s transfer function and is an indicator of model convergence: sampling neural velocity can be performed even by forwarding noise in the network, reducing the need for a held-out dataset. Our findings show the potential of neural velocity as a key metric for optimizing neural network training efficiently. (10.1109/IJCNN64981.2025.11229080)
  DOI : 10.1109/IJCNN64981.2025.11229080
• Décoder le pouvoir de persuasion dans les concours d'éloquence : une étude sur la capacité des modèles de langues à évaluer la prise de parole en public
  
  • Barkar Alisa
  • Chollet Mathieu
  • Labeau Matthieu
  • Biancardi Beatrice
  • Clavel Chloé
  , 2025, pp.77-90. L’importance des compétences en prise de parole en public (PPP) stimule le développement de systèmes d’évaluation automatisée, mais l’intégration des grandes modèles de langue (LLMs) reste peu explorée. Nous proposons un cadre où les LLMs évaluent des critères issus de la littérature et de retours de formateurs. Nous testons trois approches : des prédictions LLM directes à zéro coup (RMSE 0, 8) par rapport à des prédictions de persuasion basées sur des caractéristiques lexicales fabriquées à la main (RMSE 0, 51) ou basées sur des critères évalués par LLM 0, 6 insérés en entrée dans ElasticNet. L’analyse des liens entre critères et caractéristiques lexicales montre que seul le critère de niveau de langue évalué par LLM est prévisible (score F1 de 0, 56) soulignant les limites actuelles des LLMs pour l’analyse de la PPP. Code source et données disponibles sur GitHub.
• Power-Aware digital twin of coherent optical receiver
  
  • Purkayastha Ambashri
  • Delezoide Camille
  • Boitier Fabien
  • Bajaj Vinod
  • Lourdiane Mounia
  • Ware Cédric
  • Layec Patricia
  , 2025. <div><p>We propose a digital twin of coherent receivers based on an extended physical model for predicting quality of transmission under receiver power variations. We experimentally validate it, demonstrating an accuracy improved by up to 1.5dB.</p></div>
• Deep reinforcement learning for wind farm flow control
  
  • Kadoche Elie
  , 2025. Within wind farms, wake interactions between turbines can significantly reduce overall energy production. Wind farm flow control encompasses methods designed to mitigate these effects through coordinated turbine control. Wake steering, for example, involves redirecting the wake of certain machines to optimize airflow and increase power output. This is achieved via yaw control, by intentionally misaligning certain turbines with the incoming wind. However, designing robust wake steering controllers is challenging: they must adapt to dynamic and uncertain wind conditions, respect turbine actuation constraints, and capture the complexity of physical interactions in large arrays. Reinforcement learning offers a powerful framework for developing more effective controllers. By leveraging principles from artificial intelligence, it enables wind farms to adapt and respond intelligently to diverse wind conditions. A reinforcement learning agent learns from past experience and continuously refines control strategies as the wind changes. This Ph.D. thesis further studies and develops deep reinforcement learning-based wake steering controllers for wind farm flow control. More specifically: 1) it studies the impact of wind dynamics on yaw control and highlights the importance of optimizing strategies over a certain time horizon when variations in wind direction are significant; 2) it investigates how multi-agent reinforcement learning approaches can control large-scale wind farms and capture complex wake interactions in time-varying wind conditions; 3) it leverages self-attention mechanisms within a single-agent setting to build more capable controllers, demonstrating significant improvements in sample efficiency, learning performance and generalization. Overall, this Ph.D. thesis contributes to narrowing the gap between algorithmic advances in deep reinforcement learning and their practical deployment for large-scale wind farm flow control. (10.70675/9e9d9afczffd7z47fdz8a40z3b16520c953a)
  DOI : 10.70675/9e9d9afczffd7z47fdz8a40z3b16520c953a
• Bias in Face Recognition : Assessment and Post-Processing Mitigation
  
  • Conti Jean-Rémy
  , 2025. Face Recognition (FR) systems have reached unprecedented accuracy due to deep learning advances, enabling widespread deployment. Yet, concerns about fairness—especially across demographic subgroups defined by sensitive attributes like gender and ethnicity— persist. This thesis addresses algorithmic bias in FR through two complementary lenses: evaluation and mitigation.The first part introduces a statistically rigorous framework for assessing FR performance and fairness. Focusing on similarity-based ROC analysis, we apply U-statistics theory to derive asymptotically valid confidence intervals for key metrics. We also propose a scalar uncertainty score to quantify the statistical reliability of fairness assessments. This enables practitioners to identify disparities that are not only large but statistically significant.The second part tackles post-processing bias mitigation for frozen, pre-trained FR models. We begin with gender bias, proposing a novel mitigation approach using two operationally relevant fairness metrics: BFAR and BFRR, which capture disparities in false acceptance/rejection rates under fixed-security constraints. We introduce the Ethical Module, a shallow neural network that transforms embeddings using a Fair von Mises-Fisher loss. This loss models group-specific intra-class variance, enabling fairness control without access to sensitive attributes at deployment. The method improves fairness with minimal performance loss and low computational cost.Next, we address racial bias via a post-processing strategy aligned with FR objectives. Using centroid-based pseudo-scores as proxies for similarity, we develop the Centroid Fairness loss, implemented through a lightweight Fairness Module. This module, trained on top of a frozen model, facilitates subgroup alignment and effectively mitigates racial disparities while preserving overall accuracy—challenging the conventional fairness-performance trade-off.Together, these contributions offer a statistically grounded and practically effective toolbox for evaluating and mitigating bias in face recognition. By integrating uncertainty quantification, fairness-aware representation learning, and scalable post-hoc debiasing, this thesis is an additional step towards the development of equitable and trustworthy biometric technologies. (10.70675/00e94bd5zffa2z48bbzb616zc5c21ee3e86b)
  DOI : 10.70675/00e94bd5zffa2z48bbzb616zc5c21ee3e86b
• Home Spaces and Semiflows for the Analysis of Parameterized Petri Nets
  
  • Memmi Gerard
  , 2025, Vol-3998, pp.97-113. After briefly recalling basic notations of Petri Nets, home spaces, and semiflows, we focus on ℱ + , the set of semiflows with non-negative coordinates where the notions of minimality of semiflows and minimality of supports are particularly critical to develop an effective analysis of invariants and behavioral properties of Petri Nets such as boundedness or even liveness. We recall known behavioral properties attached to the notion of semiflows that we associate with extremums. We also recall three known decomposition theorems considering N, Q + , and Q respectively where the decomposition over N is being improved with a necessary and sufficient condition.<p>Then, we regroup a number of properties (old and new) especially around the notions of home spaces and home states which, in combination with semiflows, are used to efficiently support the analysis of behavioral properties. We introduce a new result on the decidability of liveness under the existence of a home state. We introduce new results on the structure and behavioral properties of Petri Nets, illustrating again the importance of considering generating sets of semiflows with non-negative coordinates.</p><p>As examples, we present two related Petri Net modeling arithmetic operations (one of which represents an Euclidean division), illustrating how results on semiflows and home spaces can be methodically used in analyzing the liveness of the parameterized model and underlining the efficiency brought by the combination of these results to the verification engineer.</p>
• Efficient Thermalization and Universal Quantum Computing with Quantum Gibbs Samplers
  
  • Rouzé Cambyse
  • França Daniel Stilck
  • Alhambra Álvaro
  , 2025, pp.1488-1495. The preparation of quantum Gibbs states is a crucial task in quantum computing. In this work, we prove that a recently introduced, efficiently implementable dissipative evolution thermalizes to the Gibbs state in time scaling polynomially or even logarithmically with system size at high enough temperatures for any Hamiltonian that satisfies a Lieb-Robinson bound, such as local Hamiltonians on a lattice. Furthermore, we show the efficient adiabatic preparation of the associated purifications or "thermofield double" states. To the best of our knowledge, these are the first results rigorously establishing the efficient preparation of high-temperature Gibbs states and their purifications. In the low-temperature regime, we show that implementing this family of dissipative evolutions for inverse temperatures polynomial in the system's size is computationally equivalent to standard quantum computations. On a technical level, for high temperatures, our proof makes use of the mapping of the generator of the evolution into a Hamiltonian, and then connecting its convergence to that of the infinite temperature limit. We further present an alternative proof that is based on showing the exponential decay of the so-called oscillator norm, yielding convergence in logarithmic times. For low temperature, we instead perform a perturbation at zero temperature and resort to circuit-to-Hamiltonian mappings akin to the proof of universality of quantum adiabatic computing. Taken together, our results show that a family of quasi-local dissipative evolutions efficiently prepares a large class of quantum many-body states of interest, and has the potential to mirror the success of classical Monte Carlo methods for quantum many-body systems. (10.1145/3717823.3718268)
  DOI : 10.1145/3717823.3718268
• Approximate Hypothesis Testing
  
  • Le Gouic Nicolas
  • Graczyk Robert
  • Moser Stefan M
  , 2025. We establish the sample complexity of Approximate Hypothesis Testing (AHT) where—unlike in classical hypothesis testing—we need only approximate the hypothesis governing the observed samples rather than recover it exactly. We show that the AHT sample complexity scales inversely with the multivariate Bhatthacharyya distance evaluated on a “maximally confusable” subset of hypotheses that is characterized by the chosen distance measure and approximation accuracy. Index terms—hypothesis testing, sample complexity, learning, Bhattacharyya distance, Hellinger distance.
• Optimized Co-Design of Delta Sigma Modulators and Fir-DACs for High Speed Transmitters
  
  • Lima Evelyn
  • Schlegel Nicolas
  • Mathieu Yves
  • Jabbour Chadi
  , 2025, pp.246-250. Delta sigma modulators (DSMs) are a very attractive solution to build flexible high resolution transmitters needed for future massive multiple-input multiple-output (mMIMO) systems. The main challenge in this architecture is its out-of-band (OOB) noise which can be addressed by using FIRDACs. This paper proposes a joint design of the DSM and FIRDACs. The coefficients of the latter are chosen using a multiobjective optimization approach which takes into account the DAC and the DSM noise. The proposed solution is simulated for a 400 MHz 4-Channel signal. Compared to a classical design approach with similar complexity, it achieves 2.86 dB and 16.19 dB improvements for respectively the adjacent and the alternate channel power ratio and error vector magnitude (EVM) of 1.02. (10.1109/NewCAS64648.2025.11107152)
  DOI : 10.1109/NewCAS64648.2025.11107152
• Side-Channel Attack Detection using gem5 and HPCs
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025.
• A 24 GHz Rectifier and its Applications in Energy Harvesting and Wireless Power Transfer Systems
  
  • Wang Yibo
  • Niotaki Kyriaki
  • Lepage Anne Claire
  • Begaud Xavier
  , 2025. This paper presents the design and characterization of a millimeter wave 24 GHz rectifier. A prototype was fabricated on a Rogers 5880 substrate and was characterized over input power and frequency. The rectifier is able to collect power from the FR3 band reserved for Integrated Sensing and Communication. The rectifier exhibits a measured RF-to-dc power conversion efficiency of 21.5% for an input power of about 5 dBm and for a 1.5 kΩ load at 22.6 GHz, while its peak measured efficiency is 34% for an input power of 15 dBm. The potential of the rectifier in energy harvesting and wireless power transfer systems is analysed, showing that the proposed rectifier has the potential to drive low-power devices at a distance of a few meters from a base station or an access point.
• Circulator-Based RF Energy Harvester with Wide Input Power Dynamic Range
  
  • Kibiwott Albert
  • Mohellebi Reda
  • Niotaki Kyriaki
  • Jabbour Chadi
  , 2025, pp.440-444. Radio frequency energy harvesting (RFEH) is a promising solution for powering ultra-low-power (ULP) devices. However, capturing ambient energy is challenging due to low power densities and unpredictable RF power available in the surrounding environment. Moreover, conventional rectifiers exhibit limited power conversion efficiency (PCE) and a narrow input power dynamic range (PDR) due to impedance mismatch and power reflection losses. This paper presents a novel circulatorbased multi-branch RF energy harvester designed to efficiently recycle reflected RF power across multiple rectifier branches, significantly enhancing overall energy conversion efficiency and extending the operational power range. Simulation results at 900 MHz reveal that the proposed architecture achieves a PCE of 81% at an input power of 4 dBm and a PDR of 23 dB (spanning from −15 dBm to 8 dBm). These results demonstrate an 8 dB and 26% improvement in dynamic range and efficiency respectively compared to traditional single-rectifier designs, highlighting the effectiveness of the proposed solution. (10.1109/NewCAS64648.2025.11106982)
  DOI : 10.1109/NewCAS64648.2025.11106982
• Contributions to explainable anomaly detection using data depth
  
  • Valla Romain
  , 2025. Abnormal events are subjects of interest in various fields of application, such as industry, finance and medicine, when they provide information that differs from the general trend. We can see that the arrival of new interconnected sensors, more powerful and in greater numbers, is leading to an increase in the mass of data available for analysis, requiring innovative methods to solve modern challenges. On the one hand, the growing volume of samples requires faster algorithms, while potential data contamination calls for reliable and robust techniques. On the other hand, it is becoming complicated but necessary to interpret certain decisions made by these new tools, as their complexity has increased when coupled with a large quantity of data.The first part of this thesis focuses on the use of data depths, which measure the centrality of data, in the context of anomaly detection. The development of these methods in recent years, particularly their implementation via numerical approximations coupled with optimisation procedures, has made it possible to extend their use to large-scale datasets. We carry out several comparisons with recognised and widely applied methods in this field, while providing experimental (visual) tools to facilitate parameterisation and thus facilitate their use by practitioners.In the second part, we propose a new method for visualising multivariate data based on reducing the dimensionality of the input space with the aim of better visual separation of abnormal observations (=anomalies) in the sense of data depths. Abnormal Component Analysis (ACA) consists in searching for a new orthogonal basis in an unsupervised way to represent the data while providing interpretation opportunities on the input variables responsible for anomalies of interest. The detailed algorithm, together with certain details enabling users to use it, is followed by applications on real data sets to demonstrate the relevance of the technique.Finally, the last part extends the ACA reasoning to functional data in order to take advantage of the continuous nature of many observed phenomena (time series, frequency analysis, etc.). This extension makes use of dictionaries of functions with various properties to extract significant information from different types of anomalies, which are also described using real data sets. This extension proves to be effective for obtaining a "summary" visualisation of the functional data, complemented by an interpretation obtained using the functions employed. (10.70675/5a925aa6z3d5ez4a4ez8d86z2830a101b335)
  DOI : 10.70675/5a925aa6z3d5ez4a4ez8d86z2830a101b335
• Exploiting Heterogeneous Labels in Deep Learning for Medical Image Analysis : Application to the Automated Diagnosis of Liver Diseases
  
  • Sarfati Emma
  , 2025. Liver cancer is one of the deadliest cancers worldwide, with a high mortality-to-incidence ratio and major health and economic consequences. Main risk factors include chronic hepatitis B and C infections, non-alcoholic steatohepatitis (NASH), alcohol-related liver disease, and exposure to hepatotoxic substances. Cirrhosis is the leading predisposing factor for hepatocellular carcinoma (HCC), present in the majority of cases. Cirrhosis and HCC diagnoses are typically based on liver biopsy, an invasive and costly procedure. Biomedical imaging is a less invasive alternative but suffers from high inter-radiologist variability. Radiological annotations, while more accessible, are considered weak labels compared to histological standards. This creates a common imbalance: many weakly annotated images and few strongly labeled ones. In this context, pretraining models using weak supervision can improve performance on rare but critical classification tasks. Contrastive learning, a self-supervised method that structures the representation space by pulling similar samples together, enables the use of unlabeled or partially labeled data. However, the integration of heterogeneous annotations remains underexplored. This thesis proposes strategies combining contrastive learning with supervised training via hybrid loss functions, as well as fully contrastive frameworks incorporating both discrete and continuous clinical metadata. Another approach leverages weak radiological predictors directly to guide the learning process. These methods were applied to cirrhosis classification and small HCC detection from CT scans. Results show improved diagnostic accuracy, reduced inter-expert variability, and potential for earlier and more reliable detection of liver diseases. (10.70675/c8de34e7z1fa6z42cbza183z1e37c1b52362)
  DOI : 10.70675/c8de34e7z1fa6z42cbza183z1e37c1b52362