Publications

2025

• Why Older Adults Integrate Digital Health Technology Into Their Everyday Lives: The Role of Basic Psychological Needs
  
  • Yee Andrew
  • Kwok Bernice
  • Ng Janelle
  • Loy Guan Peng
  • Ng Li Yin
  • Sng Jeremy
  • Perrault Simon
  • Lim Kwan Hui
  • Subburaj Karupppasamy
  Human Behavior and Emerging Technologies, Wiley, 2025, 2025 (1). There is growing consensus on the potential of digital health technology to improve individual health and well‐being outcomes. Existing research examining digital health technology adoption among underserved populations has largely relied upon theoretical lenses with an emphasis on perceived benefits and costs at the preadoption or initial use stage. This study demonstrates an alternative approach, relying on basic psychological needs as an explanatory framework to explore how and why older adults integrate digital health technologies into their everyday lives in a sustained manner. A whole‐of‐community approach was taken to examine this question, with 17 focus groups held with 107 older adults, volunteers, and the people who work with them. Overall, we found that the integration of digital health technology into the day‐to‐day life of older adults is contingent on the different ways in which it satisfies or frustrates the basic needs of competence, autonomy, and relatedness. The social environment, user interface design choices, reward, and feedback systems were found to both satisfy and frustrate needs. Researchers and designers intending to study or implement digital health technology for older adults ought to consider how different implementation decisions impact psychological needs. (10.1155/hbe2/9711661)
  DOI : 10.1155/hbe2/9711661
• Are We Unbiased Yet? A Survey on Model Debiasing for Image Classification
  
  • Ciranni Massimiliano
  • Nahon Rémi
  • Marinelli Davide
  • Murino Vittorio
  • Tartaglione Enzo
  • Pastore Vito Paolo
  , 2025. <div><p>The rapid advancement of machine learning and artificial intelligence (AI) has led to transformative changes across various domains that involve Computer Vision, including healthcare, finance, and autonomous systems. Despite their potential, these technologies raise significant concerns related to privacy, fairness, transparency, reliability, and ethics, especially when dealing with biased data. In recent years, the computer vision community has shown an increasing interest in model debiasing, with strategies designed to identify and mitigate the dependency of deep neural networks on shortcuts corresponding to bias. This survey aims to bring order to this field by providing a comprehensive review of model debiasing methods, with a particular focus on their application in Computer Vision. After defining model bias and discussing its implications, we describe the typical evaluation benchmarks, serving as a foundation for understanding the complexities involved in this critical area of research. We further delve into debiasing methods, providing a categorization while discussing their effectiveness and limitations. Besides, contemporary research trends are explored, among which the growing interest in explainable AI techniques and the ethical implications of biased models are predominant. Throughout this survey, we highlight milestones and open challenges in model debiasing, in an attempt to provide a reference for both experts and researchers interested in this context.</p></div>
• The NeWMe Corpus: A gold standard corpus for the study of Word Meaning Negotiation
  
  • Soler Aina Garí
  • Myrendal Jenny
  • Clavel Chloé
  • Larsson Staffan
  , 2025. Abstract Word Meaning Negotiation (WMN) sequences occur when participants focus on clarifying or negotiating the meaning of a word or phrase, often prompted by questions or challenges. These interactions temporarily shift the conversation to explore nuances of meaning - sometimes resulting in quick clarification when due to insufficient understanding of word meaning, and other times leading to extended debates, such as disagreements on what a word can or should mean. This paper presents the largest and freely available manually annotated corpus of WMNs to date, encompassing spoken dyadic and multiparty conversations as well as online discussions. Our methodology combines searching for WMNs using regular expressions with a detailed annotation scheme that categorizes WMNs into types triggered by non-understanding (NONs: Non-understanding WMN) or disagreement (DINs: Disagreement WMN), and distinguishes between negotiations of situated and potential meanings. We also annotate incomplete negotiations and related phenomena, and analyze inter-annotator agreement to evaluate the reliability of the annotation schema. Preliminary investigations of WMNs in the corpus reveal distinct patterns in WMNs across contexts, with NONs prevalent in spoken interactions and DINs dominating online debates. This resource lays a foundation for studying semantic alignment, developing automated WMN detection, and creating adaptive dialogue systems. Our findings highlight the complexity of WMNs and provide practical insights for their identification and analysis. (10.21203/rs.3.rs-5975927/v1)
  DOI : 10.21203/rs.3.rs-5975927/v1
• Multi-view 3D reconstruction in SAR imaging using inverse rendering
  
  • Barbier--Renard Emile
  , 2025. Synthetic Aperture Radar (SAR) imaging is a powerful tool for Earth observation, offering the ability to capture data regardless of daylight or weather conditions. However, traditional methods for 3D reconstruction from SAR images often rely on interferometric techniques, which require strict constraints to guarantee the coherence of the acquisitions.In this thesis, we explore the potential of adapting the Differentiable Inverse Rendering (DIR) approach to SAR. These physics-informed methods have seen recent advancements in the optical domain following the publication of Neural Radiance Fields (NeRF) but have not yet been applied to active imaging systems. By developing a DIR technique for SAR intensity images, we aim to achieve a flexible solution for 3D reconstruction from a few views.First, we introduce a novel differentiable SAR rendering model, a central requirement for developing a DIR method. Our model uses a rasterisation strategy to efficiently synthesise SAR images from a Digital Surface Model (DSM) and a map of backscattering coefficients. It leverages the distinct geometry of SAR to ensure the differentiability of the rendering process, in particular for the computation of shadows. We validate our model using EMPRISE®, a state-of-the-art RADAR simulator developed by ONERA.We subsequently propose two different reconstruction strategies. The first one is directly inspired by NeRF and optimises the parameters of a neural network modelling the scene. The second strategy simplifies the tuning of hyperparameters and improves explainability by modelling the scene with stacks of grids of increasing resolutions. Both strategies use the unique properties of our rendering model to enable coarse-to-fine reconstructions, ensuring faster optimisation and more accurate reconstruction of structures of different scales.We demonstrate our method on images simulated using EMPRISE® under different conditions, before showing applications on Sentinel-1 ascending/descending image pairs.
• RibbonSculpt: Voronoi Ball based 3D Sculpting from Sparse VR Ribbons
  
  • Sureshkumar Anandhu
  • Parakkat Amal Dev
  • Bonneau Georges-Pierre
  • Hahmann Stefanie
  • Cani Marie-Paule
  , 2025, pp.1-11. We introduce RibbonSculpt, the first method for interactive freeform shape design in VR through progressive sketching of sparse, oriented ribbons. Instead of reconstructing a surface from a fully drawn VR sketch, our method allows the real-time creation and progressive refinement of a closed surface of any topological genus, thanks to the continuous update of a volumetric proxy. The latter corresponds to a filtered subset of the Voronoi balls defined by the user-sketched ribbons. At each visualization step, a mesh extracted from the proxy is beautified through Laplacian-based energy minimization, yielding a smooth surface that interpolates the ribbons. Guided by this surface, users can easily refine their design by adding or removing ribbons, which sculpts, in return, the set of Voronoi balls forming the proxy. Our results, supported by user studies, show that RibbonSculpt allows VR users to easily and quickly draft the 3D shapes they have in mind. (10.1145/3757377.3763994)
  DOI : 10.1145/3757377.3763994
• Side channel resistance of Europe's choices for lattice-based post-quantum cryptography : FALCON and FrodoKEM
  
  • Berthet Pierre-Augustin
  , 2025. There is a high chance that a quantum computer will be able to break current asymmetric cryptography primitives in the coming decade. To address this threat, new cryptography primitives, i.e., Post-Quantum Cryptography (PQC), are studied. In this thesis, we look at lattice-based PQC selected by European agencies, specifically the signature FALCON and the Key Encapsulation Mechanism (KEM) FrodoKEM. We study their resistance to Side Channel Analysis (SCA).The first contribution of this thesis is the study of SCA countermeasures for the FALCON signature. This signature uses floating-point arithmetic, and a masking countermeasure for the floor function is proposed. Similarly, a masking design based on the Newton-Raphson method is used to protect the inversion. Both operands are used in FALCON's Gaussian sampler. Formal security proofs using the MIMO-SNI model are provided to demonstrate the resistance of the design.In another chapter, ciphertext malleability in lattice-based KEMs is used to recover the secret message by targeting the decoding function in FrodoKEM's decapsulation. By combining chosen ciphertexts and side channel analysis, new strategies are proposed to improve the efficiency of the attack: more optimal choices of ciphertext alterations, adaptative attack, partial recovery with brute-force of remaining bits.A final contribution of this manuscript proposes to re-use the same ciphertext malleability but as a countermeasure to protect the secret message. By performing the attack in a random and controlled manner as the defender, it is possible to disrupt the attacker. The countermeasure requires several adjustments, and it is recommended to deploy it alongside a shuffling countermeasure to protect the secret key as well.
• Deep learning for SAR image enhancement : multi-modal fusion and interferometric denoising
  
  • Gaya Victor
  , 2025. This thesis focuses on the denoising of synthetic aperture radar (SAR) images, targeting speckle in amplitude imaging and phase noise in SAR interferometry (InSAR). These issues are central to many remote sensing applications, from land use mapping to terrestrial displacement estimation.While traditional methods based on statistical models and non-local filters have made it possible to better preserve structures, they are reaching their limits in terms of current requirements for accuracy and generalization. Deep learning offers new perspectives by learning directly from representations adapted to the specificities of the radar signal.We propose two major methodological contributions. The first concerns SAR denoising through multimodal SAR-optical fusion. By exploiting the complementarity between modalities, we develop a self-supervised approach that guides filtering using optical data, preserving geometric and textural details. This method does not require ground truth by exploiting the independence between the real and imaginary parts of the radar data.The second contribution concerns the joint denoising of phase and coherence in InSAR, a critical step for estimating displacement or topography. Based on projected multi-channel data, our method reduces the multichannel problem to single-channel processing while integrating appropriate spatial regularization, stabilizing the estimation despite strong radiometric or temporal variations.The experimental results demonstrate significant quantitative and qualitative improvements for downstream applications (deformation detection, environmental monitoring, mapping). This thesis bridges the gap between classical radar signal processing and modern machine learning, highlighting the complementarity between physical modeling, multimodal data, and self-supervision.
• Adaptive Compression : From Visual Data to Efficient and Transferable Models
  
  • Spadaro Gabriele
  , 2025. The exponential growth of visual content has made compression a fundamental challenge in modern communication systems. While traditional codecs achieved remarkable success, their rigid design limits their performance. Learned Image Compression emerged as a data-driven alternative, in which models directly minimize a rate-distortion loss function. Despite their results, these methods suffer from limited flexibility, since models are trained to attain a fixed rate-distortion trade-off, as well as poor generalization across novel visual domains and a lack of perceptual control. This thesis aims to investigate deep learning-based compression methods and to address the key limitations that currently hinder their deployment. Moreover, we go beyond the traditional definition of compression, proposing strategies that enhance efficiency, adaptability, and generalization capabilities by compressing the models and their internal representations.In this context, we show how the integration of learning-based modules can significantly enhance compression performance. This improvement occurs not only by replacing specific components of standardized codecs, but also by defining end-to-end methods in which the entire compression pipeline consists of learnable modules. Interestingly, in this latter scenario, we demonstrate how the use of alternative graph-based paradigms can be effectively applied for compression tasks, while also showing their potential as general-purpose backbones for visual feature extraction.Beyond improving compression, this thesis also proposes a unified adapter-based strategy to overcome the structural limitations of learned codecs. Considering a model-adaptation perspective, we demonstrate how adapters enable continuous control over the rate-distortion and distortion-perception trade-offs. Furthermore, they enhance the generalization capability of a pre-trained model to novel visual domains.These advances make learned codecs more versatile for heterogeneous real-world applications.Finally, we demonstrate how model and representation pruning methods allow not only to reduce the complexity of a model, but also to improve generalization and transferability capabilities of a pre-trained model.Here, the notion of compression is extended to models and their representations. This perspective highlights its role not only as a tool for efficiency but also as a principle for designing adaptive and robust neural models.
• Approximating Queries on Probabilistic Graphs
  
  • Amarilli Antoine
  • van Bremen Timothy
  • Gaspard Octave
  • Meel Kuldeep S.
  Logical Methods in Computer Science, Logical Methods in Computer Science Association, 2025, 21 (4). Query evaluation over probabilistic databases is notoriously intractable -- not only in combined complexity, but often in data complexity as well. This motivates the study of approximation algorithms, and particularly of combined FPRASes, with runtime polynomial in both the query and instance size. In this paper, we focus on tuple-independent probabilistic databases over binary signatures, i.e., probabilistic graphs, and study when we can devise combined FPRASes for probabilistic query evaluation. We settle the complexity of this problem for a variety of query and instance classes, by proving both approximability results and (conditional) inapproximability results doubled with (unconditional) DNNF provenance circuit size lower bounds. This allows us to deduce many corollaries of possible independent interest. For example, we show how the results of Arenas et al. on counting fixed-length strings accepted by an NFA imply the existence of an FPRAS for the two-terminal network reliability problem on directed acyclic graphs: this was an open problem until now. We also show that one cannot extend a recent result of van Bremen and Meel that gives a combined FPRAS for self-join-free conjunctive queries of bounded hypertree width on probabilistic databases: neither the bounded-hypertree-width condition nor the self-join-freeness hypothesis can be relaxed. We last show how our methods can give insights on the evaluation and approximability of regular path queries (RPQs) on probabilistic graphs in the data complexity perspective, showing in particular that some of them are (conditionally) inapproximable. (10.46298/lmcs-21(4:30)2025)
  DOI : 10.46298/lmcs-21(4:30)2025
• Contextual knowledge representation for neurosymbolic Artifical Intelligence reasoning
  
  • Coumes Simon
  , 2025. The field of Knowledge Representation and Reasoning is concerned with the representation of information about reality in a form that is both human-readable and machine-processable.It has been a part of artificial intelligence since its inception, and has produced many important formalisms and systems.One key aspect of knowledge is the context in which it is expressed. This has been identified early on in the field and matches with our common experience: understanding a statement or judging its validty often require to know in what context it was meant.Historically, there has been some work aiming at producing logics implementing a general notion of context. None of them saw a lot of adoption, in part because they lack either sufficient expressive power or because they were not sufficiently usable.This dissertation presents Qiana, a logic of context powerful enough for almost all types of context representation. It is also compatible with various automated reasoning tools, as is demonstrated by the code provided which allows automated reasoning with Qiana. This makes use of the pre-existing theorem prover Vampire -- though any other compatible prover can freely be used instead.By providing a powerful logic for context representation with the possibility of concrete computations without (much) overhead, Qiana paves the way for larger prevalence of logics of context, making it possible to build other reasoning on top of such logics like has been done --for example-- for epistemic logics or description logics.
• Bypass Synchronization Primitives: GhostRace Attack and Mitigation on RISC-V
  
  • Khan Mahreen
  • Priscilla Maria
  • Victor Matheus
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  • Naviner Lirida
  , 2025. <div><p>In communication and network security systems, synchronization primitives (such as mutexes, semaphores, and spinlocks) are essential for ensuring safe access to shared data. They prevent data races and enforce memory consistency across threads, which is critical for communication stacks and cryptographic libraries. However, the recent GhostRace vulnerability exploits speculative execution to microarchitecturally bypass synchronization primitives, creating speculative race conditions that leak data across threads. This fundamentally undermines the security guarantees of critical communication infrastructure. While GhostRace is tested on x86 architecture, its implications for RISC-V remain unexplored. RISC-V is increasingly adopted in networking and communication hardware, making its security analysis essential. In this paper, we evaluate GhostRace on RISC-V using both the BeagleV-Fire RISC-V board and the gem5 full-system simulator. We demonstrate successful speculative synchronization bypasses and validate the instruction serialization mitigation. We also analyze microarchitectural behavior, including cache misses and branch mispredictions, using gem5 full system simulations. This paper provides the first comprehensive characterization of GhostRace on RISC-V, highlighting the need for secure hardware-software co-design.</p></div>
• Opcode Analysis of Real Encryption-Based Microarchitectural Attacks Using gem5
  
  • Awais Muhammad
  • Khan Mahreen
  • Mushtaq Maria
  • Naviner Lirida
  • Yahya Jawad Haj
  • Bruguier Florent
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025, pp.377-384. Can we trust architectural simulators to predict real-world security vulnerabilities? This work utilizes gem5 cycle-accurate simulation to dissect the side-channel leakage of specific AES-NI hardware instructions. By analyzing opcodes like AESENC and AESKEYGENASSIST, we achieved an 89% key recovery rate in simulation, closely mirroring results on physical Intel hardware (96%). Our findings provide a roadmap for identifying microarchitectural footprints and hardening software against Flush+Reload attacks before the code ever touches physical silicon. (10.1109/ComComAp68359.2025.11353163)
  DOI : 10.1109/ComComAp68359.2025.11353163
• Opcode Analysis of Real Encryption-Based Microarchitectural Attacks Using gem5
  
  • Awais Muhammad
  • Khan Mahreen
  • Mushtaq Maria
  • Naviner Lirida
  • Haj-Yahya Jawad
  • Bruguier Florent
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2025, pp.377-384. Modern processors use performance-boosting techniques like speculative execution and multi-level caching. Unfortunately, these same optimizations can unintentionally create security vulnerabilities known as microarchitectural sidechannels. The core novelty of this work is a precise, simulationdriven method to pinpoint exactly how individual cryptographic instructions leak information. We present a systematic analysis of common AES instructions (such as AESENC, AESDEC, and AESKEYGENASSIST), measuring their vulnerability to Flush+Reload side-channel attacks. By using cycle-accurate gem5 simulations, we were able to isolate and analyze each instruction's unique leakage pattern. The AESENC instruction causes significant timing variations and L1 data cache misses (76%) due to its S-box table lookups. The AESKEYGENASSIST instruction shows the highest latency, stemming from its iterative key expansion process. Nearby branch instructions (like JNE) worsen the leakage by introducing frequent mispredictions. A key finding is that our gem5 simulation environment reliably mirrors real hardware behaviour, achieving 89% key recovery accuracy compared to 96% on a physical Intel CPU. This validation confirms that simulations can effectively capture these subtle vulnerabilities without real-world noise. We also show that smaller caches amplify leakage by concentrating memory accesses, while larger caches only reduce, not eliminate, the risk. Ultimately, our findings provide actionable insights for developers to harden cryptographic code and for designers creating future detection tools. This work demonstrates the critical role of architectural simulation in identifying and understanding security vulnerabilities before chips are even built. (10.1109/ComComAp68359.2025.11353163)
  DOI : 10.1109/ComComAp68359.2025.11353163
• Domain Adaptation in the era of Foundation Models
  
  • Benigmim Mohammed-Yasser
  , 2025. Artificial intelligence models that analyze images need to understand what each pixel represents, such as identifying cars and pedestrians in street scenes. Building such models for semantic segmentation typically requires collecting many manually labeled images, a time-consuming and expensive process. To address this bottleneck, domain adaptation has emerged: models are trained on accessible source data (such as synthetic images) and adapted to realworld target domains. However, this approach faces domain shift, where models trained on one data distribution fail to generalize to another at deployment. This challenge is especially pronounced in urban driving due to variations in weather, lighting, and geography. This thesis explores how Foundation Models, large AI systems pre-trained on millions of images, can overcome domain shift. These models learn powerful general-purpose representations that can be adapted to diverse applications. We investigate four practical scenarios under severe resource constraints. First, in one-shot domain adaptation, we generate synthetic datasets from a single target image by personalizing text-to-image diffusion models to capture target style while introducing diverse scenes. Second, we develop a collaborative framework where multiple foundation models work synergistically to generalize to unseen environments. Third, we distill knowledge from proprietary black-box AI services accessed via APIs without access to model internals. Finally, we improve open-vocabulary semantic segmentation by identifying which text descriptions work best for each object type. This work enables effective computer vision with unlabeled data and limited resources, democratizing access to advanced AI capabilities.
• Kramers-Kronig detection in the quantum regime
  
  • Pousset Thomas
  • Federico Maxime
  • Alléaume Romain
  • Fabre Nicolas
  Physical Review Research, American Physical Society, 2025, 7 (4), pp.043287. We investigate the quantization of the Kramers-Kronig (KK) detection technique, initially developed for classical coherent communications. This detection method involves combining the state of interest with a local oscillator on an unbalanced beamsplitter, followed by direct detection and digital signal processing. Intensity measurements in the case of spectrally engineered fields allow for the "digital measurement" of the phase of classical and quantum fields. We show that, to the first order of the local oscillator's amplitude, KK detection is a Gaussian measurement that allows for the estimation of both quadratures of the electric field, similar to double homodyne detection. We study in detail how KK detection operates in the case of bosonic coherent states, pure single-mode and mixed states, as well as the nature of the phase information it measures. Finally, we propose an alternative spectral tomography technique for single-photon states inspired by KK detection. (10.48550/arXiv.2407.20827)
  DOI : 10.48550/arXiv.2407.20827
• Deep reinforcement learning for joint optimization of scheduling and resource allocation in mobile ad hoc networks
  
  • Nérondat Sylvain
  , 2025. The central theme of the thesis concerns the use of deep reinforcement learning techniques to solve the joint scheduling and resource allocation problem in mobile ad hoc communication systems. The context of this work is clustered ad hoc networks where nodes are grouped in clusters whose composition can vary dynamically over time. Within each cluster, the scheduling and resource allocation task is performed by a dedicated node that receives the communication requests. The problem we propose to solve is to find the optimal joint scheduling and resource allocation under certain quality of service (QoS) constraints. The global optimization problem has a high dimensional state and action space and cannot be solved by analytical methods, hence the use of new reinforcement learning techniques using deep neural networks. The general objective of the thesis is to study new solutions and their performance for the implementation of joint scheduling and resource allocation algorithms at the radio access level in the cross-optimization paradigm applied to clustered mobile ad hoc networks.
• Mission aware cyber‐physical security
  
  • Bakirtzis Georgios
  • Carter Bryan
  • Fleming Cody
  • Elks Carl
  Systems Engineering, Wiley, 2025, 29 (2), pp.355-367. Perimeter cybersecurity, while essential, has proven insufficient against sophisticated, coordinated, and cyber‐physical attacks. In contrast, mission‐centric cybersecurity emphasizes finding evidence of attack impact on mission success, allowing for targeted resource allocation to mitigate vulnerabilities and protect critical assets. Mission Aware is a systems‐theoretic cybersecurity analysis that identifies components that, if compromised, destabilize the overall mission. It generates evidence by finding potential attack vectors relevant to mission‐linked elements and traces this evidence to mission requirements, prioritizing high‐impact vulnerabilities relative to mission objectives. Mission Aware is an informational tool for system resilience by unifying cybersecurity analysis with core systems engineering goals. (10.1002/sys.70018)
  DOI : 10.1002/sys.70018
• On the Role and Robustness of Self-Supervised and Multiple Instance Learning Approaches for Digital Pathology - Application to Sjögren's Syndrome
  
  • Mammadov Ali
  , 2025. Digital pathology has revolutionized medical diagnosis by enabling the digitization of tissue samples into Whole Slide Images (WSIs). However, WSIs are extremely large and high-resolution, which makes applying deep learning models directly very challenging. A common solution is to divide each slide into smaller patches, treating each slide as a bag of patches with a single global label rather than requiring costly pixel-level annotations. This approach naturally leads to Multiple Instance Learning (MIL), which has become the preferred method for WSI classification. MIL has two main paradigms: instance-based and embedding-based. In instance-based MIL, each patch is classified independently, and the patch-level predictions are aggregated to determine the slide label. In embedding-based MIL, patch embeddings are first combined into a single representation before classifying the slide. While instance-based methods are more interpretable, embedding-based methods have traditionally been favored due to their robustness with weak feature extractors. However, recent advances in Self-Supervised Learning (SSL) have dramatically improved feature quality. Despite this, many studies still prioritize embedding-based MIL. To investigate this, we conducted 710 experiments across four datasets, comparing 12 MIL strategies, six SSL methods with four backbones, four foundation models, and several pathology-specific techniques. We also introduce four novel instance-based MIL methods not previously used in pathology. Our results show that, with high-quality SSL features, simple instance-based MIL methods with few parameters match or outperform state-of-the-art embedding-based MIL methods. These findings suggest that efforts should focus on designing effective SSL feature extractors rather than developing increasingly complex embedding-based MIL models, especially since instance-based methods are naturally more interpretable for clinicians. A second challenge in MIL is high run-to-run performance variability, which can reach 10-15 AUC points due to random weight initialization, batch ordering, and learning rate differences. To address this, we propose a Multi-Fidelity Model Fusion strategy: multiple models are trained for a few epochs, and the most promising ones are selected and ensembled based on validation scores. This approach reduces variability, improves reproducibility, simplifies hyperparameter tuning, and maintains computational efficiency. We validated it extensively with over 2,000 experiments on multiple datasets, initialization strategies, and MIL models, confirming its effectiveness. Finally, we apply these techniques to Sjögren's syndrome (SjS), a chronic autoimmune disease affecting salivary and lacrimal glands. The clinical gold standard involves quantifying lymphocytic foci in labial salivary gland biopsies, but manual scoring is time-consuming and prone to inter-observer variability. While SSL-enhanced MIL can achieve high performance, it frames the problem as a classification task and is sometimes not sufficiently interpretable for clinical use in cases like SjS diagnosis, which requires step-by-step reasoning. To address this, we propose a fully automated, clinically interpretable pipeline that replicates the pathologist's workflow: candidate foci are detected using unsupervised clustering, filtered with a supervised tissue classifier, and aggregated to compute a focus score. Using a curated dataset from Saint-Joseph Hospital, our method achieves strong diagnostic performance while maintaining transparency and clinical relevance.
• Optimal Active Cyber Defence Strategies based on Multi-Agent System Verification
  
  • Ballot Gabriel
  , 2025. Many computer and industrial systems are designed at a given point in time using the tools and knowledge available at that time, but evolve little afterwards. This static nature benefits cyberattackers, whose techniques advance frequently and who can observe the system over long periods and plan targeted attacks. Active cyber defence mechanisms—such as moving target defences, which frequently reconfigure the system, and adaptive honeypots, which deceive attackers and study their capabilities—reduce this vulnerability. Finding effective active cyber defence strategies to protect the system and lure attackers poses a major challenge. Current techniques rely on analytical game theory and machine learning, and do not provide a general solution for determining active cyber defence strategies with strong guarantees against multi-step attacks. These limits can be naturally overcome through multi-agent system verification. However, this approach does not account for an essential aspect of cybersecurity: attacker profiles.This thesis proposes tools to model systems where active cyber defences interact with attackers of diverse profiles, formalise security objectives, and extract defence strategies with robust guarantees. A central contribution is the introduction of the capacity concept, which models agent profiles and lays the groundwork for a novel logical formalism: Capacity Alternating-time Temporal Logic (CapATL). It enables the expression and verification of strategic, temporal, and capacity (e.g. profile identification) objectives, thus allowing the characterisation of desired behaviours for active cyber defences. CapATL has been extended to imperfect information (CapATEL) and probabilistic (ATL-SA) settings to account for real-world constraints, requiring the resolution of new theoretical challenges in verification. We show how these formalisms can model a non-trivial adaptive honeypot and derive a strategy suited to the objectives of realism, security, resource management, and attacker profiling.
• Continuous-variable quantum communication
  
  • Usenko Vladyslav
  • Acín Antonio
  • Alléaume Romain
  • Andersen Ulrik
  • Diamanti Eleni
  • Gehring Tobias
  • Hajomer Adnan
  • Kanitschar Florian
  • Pacher Christoph
  • Pirandola Stefano
  • Pruneri Valerio
  , 2025. Tremendous progress in experimental quantum optics during the past decades enabled the advent of quantum technologies, one of which is quantum communication. Aimed at novel methods for more secure or efficient information transfer, quantum communication has developed into an active field of research and proceeds toward full-scale implementations and industrialization. Continuous-variable methods of multi-photon quantum state preparation, manipulation, and coherent detection, as well as the respective theoretical tools of phase-space quantum optics, offer the possibility to make quantum communication efficient, applicable and accessible, thus boosting the development of the field. We review the methodology, techniques and protocols of continuous-variable quantum communication, from the first theoretical ideas, through milestone implementations, to the recent developments, covering quantum key distribution as well as other quantum communication schemes, suggested on the basis of continuous-variable states and measurements. (10.48550/arXiv.2501.12801)
  DOI : 10.48550/arXiv.2501.12801
• Evaluation of Information Leakage in Side Channels
  
  • Béguinot Julien
  , 2025. A symmetric key cryptographic algorithmis deemed robust against cryptanalysis when seen asa function mapping a secret key and a plaintext to a ciphertext. However, the computation of this function may leak some sensitive information about the secret key being manipulated by the underlying hardware circuit. The corresponding attacks are devastating if no proper countermeasure is implemented. Countermeasures have to be implemented and the leakages of a chip have to be evaluated by a certification laboratory before it is deployed. The masking countermeasure essentially amounts to a secret sharing over the wire of a circuit.The goal of my PhD is to leverage information theoretic tools to improve the leakage certification process of a device in the presence of the masking countermeasure.The first aspect is to provide informational bounds on several operational measures of the adver-sary success. This includes the success rate of an attack iin presence of key enumeration and the average enumeration time required to find a correct key (guessing entropy). This is achieved by variations of Fano's inequality and offers Gibb's inequality.The second aspect is to provide information theoretic bounds on the leakages of masked sensitive variable in terms of the leakages of each share. This is achieved by a variation of Mrs Gerber's lemma.The third aspect is to derive a security bound in the presence of computations, especially in the presence of multiplications. I used the complementary Doeblin coefficient to reduce general side channels to the much simpler erasure channels.Finally, connecting the three contributions above we obtain a faster evaluation methodology for laboratories based on information theoretic inequalities.While this thesis is motivated by concrete problems, iit essentially relies on information theoretic derivations. Information leakages are measured by Sibson's α-information. Emphasis is put on desirable mathematical properties such as data processing inequalities, tensorization, Gibbs inequality and Mrs Gerber's lemma.
• Efficient Compact Single-Layer Metasurface RF Energy Harvesters for IoT Applications
  
  • Sharifi Raziyeh
  • Lepage Anne Claire
  • Niotaki Kyriaki
  • Begaud Xavier
  Reviews of Electromagnetics, 2025, 4. In this paper, we extend our previous works on compact and efficient metasurface energy harvesters by introducing additional theoretical analysis and measurement results for evaluating performance in finite arrays. Two metasurface harvesters are studied: a single-band design operating at 2.45 GHz and a dual-band design covering 2.45 GHz and 5.2 GHz (Wi-Fi bands), proposed for IoT applications. This paper introduces the concept of central rows in the finite array, which provides a more realistic and reliable metric for characterizing the capturing efficiency. For both designs, 5×4 finite arrays are analyzed. The simulated capturing efficiency of the central rows reaches 90% at 2.54 GHz for the single-band structure, and 74% at 2.5 GHz and 30% at 5.09 GHz for the dual-band design. Each proposed design has been analyzed independently, fabricated, and measured to verify its performance. (10.53792/RoE/2025/25014)
  DOI : 10.53792/RoE/2025/25014
• Tight PAC-Bayesian Risk Certificates for Contrastive Learning
  
  • van Elst Anna
  • Ghoshdastidar Debarghya
  SIAM Journal on Mathematics of Data Science, Society for Industrial and Applied Mathematics, 2025, 7 (4), pp.1904-1927. (10.1137/24M1715283)
  DOI : 10.1137/24M1715283
• Mining Rules on Tabular Data
  
  • Amat François
  , 2025. Recent years have seen the rise of highly powerful machine-learning models, particularly deep-learning approaches. Their main drawback lies in their “black box” nature: the decision logic remains largely opaque. As a result, deploying them is delicate in critical contexts, for example security, health care, and justice, or more generally wherever users or citizens need to understand an algorithmic prediction. In Europe in particular, the General Data Protection Regulation (GDPR) and other legal frameworks increasingly require algorithmic recommendations to be explainable. The scientific community has therefore turned to Explainable Artificial Intelligence (XAI), which aims either to produce interpretable models or to explain existing ones.In the tabular setting, one has a set of columns (attributes) and many rows (observations), and one typically seeks to explain a model's prediction for each observation. We argue that the need for explanation goes beyond this single objective: any tabular dataset may call for explanations that are understandable by humans. Consider, for example, the operator of a large fleet of aircraft who maintains a table of technical incidents that required intervention. It is useful to know whether incidents occur primarily on a certain aircraft model, whether specific parts fail under certain weather conditions, or whether recently repaired aircraft are more prone to new failures. Such findings, readable by humans, can inform maintenance, logistics, and production.Similarly, imagine an income table including occupation, seniority, gender, ethnicity, company, and location. One may wish to analyze how these factors influence salaries without restricting analysis to a single target column, by exploring, for instance, correlations between company and ethnicity, or cross-row patterns: among people in the same occupation and of the same gender, do those who earn less than the median tend to belong to a particular age group? Such explanations can guide feature engineering, anomaly detection, missing-value imputation, and even the design of recruitment policies, while remaining intelligible to decision-makers.We do not claim to settle the debate between correlation and causation, nor to redefine what qualifies as an “explanation.” Our objective is more modest and more practical: to make sense of tabular data in human-understandable terms by identifying patterns that genuinely help to apprehend them. Recent advances in sub-symbolic learning only underscore the need for symbolic approaches to interpretability. We therefore propose to construct, directly from tabular data, a symbolic model, based on rules and relational constraints, that captures useful and interpretable regularities.
• Expanding bipartite Bell inequalities for maximum multi-partite randomness
  
  • Wooltorton Lewis
  • Brown Peter
  • Colbeck Roger
  Quantum, Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften, 2025, 9, pp.1930. Nonlocal tests on multipartite quantum correlations form the basis of protocols that certify randomness in a device-independent (DI) way. Such correlations admit a rich structure, making the task of choosing an appropriate test difficult. For example, extremal Bell inequalities are tight witnesses of nonlocality, however achieving their maximum violation places constraints on the underlying quantum system, which can reduce the rate of randomness generation. As a result there is often a trade-off between maximum randomness and the amount of violation of a given Bell inequality. Here, we explore this trade-off for more than two parties. More precisely, we study the maximum amount of randomness that can be certified by correlations exhibiting a violation of the Mermin-Ardehali-Belinskii-Klyshko (MABK) inequality. We find that maximum quantum violation and maximum randomness are incompatible for any even number of parties, with incompatibility diminishing as the number of parties grow, and conjecture the precise trade-off. We also show that maximum MABK violation is not necessary for maximum randomness for odd numbers of parties. To obtain our results, we derive new families of Bell inequalities certifying maximum randomness from a technique for randomness certification, which we call "expanding Bell inequalities". Our technique allows one to take a bipartite Bell expression, known as the seed, and transform it into a multipartite Bell inequality tailored for randomness certification, showing how intuition learned in the bipartite case can find use in more complex scenarios. (10.22331/q-2025-12-05-1930)
  DOI : 10.22331/q-2025-12-05-1930