Publications

2026

• Antennes et métamatériaux
  
  • Begaud Xavier
  , 2026. Les antennes sont des éléments clés des systèmes de télécommunications, des Radar ou pour l’internet des objets mais leurs performances en termes de bande de fréquences de fonctionnement, de gain et d’encombrement sont contraintes notamment par les limites physiques des matériaux conventionnels. Les métamatériaux sont apparus au début des années 2000 et ont suscité depuis de nombreux travaux. Il s’agit de structures artificielles ayant des propriétés électromagnétiques extraordinaires, telles qu’un indice de réfraction négatif ou une permittivité et une perméabilité contrôlées. Ces propriétés qui n’existent pas à l’état naturel offrent de nouveaux degrés de liberté pour la conception d’antennes et permettent de concevoir de nouveau systèmes antennaires capables d’améliorer le rayonnement, de le contrôler et de réduire son encombrement. L’étude des interactions entre l’antenne et le métamatériau représente un domaine de recherche dynamique associant la physique, l’électromagnétisme et l’ingénierie des systèmes de télécommunications. Les travaux sur les métamatériaux sont très nombreux et il est important de définir précisément les termes utilisés pour s’y retrouver et mieux se rendre compte de l’étendue des applications potentielles. Une fois la terminologie connue, les premiers travaux portant sur la permittivité et la perméabilité négatives rappelés vont démontrer comment il est possible de synthétiser ces matériaux sous les hypothèses importantes de modélisations et d’homogénéisation. D’autres métamatériaux comme les ligne composites main droite main gauche, les surfaces hautes impédances, les conducteurs magnétiques artificiels vont aussi être présentés et leurs caractéristiques essentielles permettant de les dimensionner seront détaillées. Cette première phase est essentielle pour utiliser correctement les métamatériaux avec les antennes. Dans de nombreuses applications, les antennes sont en très grande proximité des métamatériaux et elles interagissent nécessairement avec ces derniers. Les problématiques sont variées et adressent ainsi la réduction des dimensions en volume ou en épaisseur, l’augmentation de la directivité voire du gain, la formation ou le balayage de faisceau. L’idée est donc de donner au lecteur un certain nombre de méthodes pour dimensionner l’antenne avec le métamatériaux quand cela est possible, de préciser certaines limites ou plus simplement de définir l’intérêt de la solution à métamatériaux proposée. Le lecteur trouvera en fin d’article un glossaire des termes et des expressions importantes de l’article, ainsi qu’un tableau des notations et des symboles utilisés. (10.51257/a-v1-e3315)
  DOI : 10.51257/a-v1-e3315
• Geometrical acoustics meets the statistical wave field theory
  
  • Badeau Roland
  , 2026. The statistical wave field theory mathematically establishes the statistical laws of the solutions to the wave equation in a bounded domain, which hold at high frequency. It provides the closed-form expressions of the power distribution and the correlations of the wave field jointly over time, frequency and space, in terms of the geometry and the specific admittance of the boundary surface. In our previous article, the mathematical foundations of the theory have been reworked at the fundamental level of the eigenfunctions of the Laplace operator. We thus introduced a random wave model of the complex Robin Laplacian, which accounts for the absortion of energy at the boundary surface. In this article, based on this model, we establish the first and second order statistics of the room impulse response. The previous predictions of the theory are thus retrieved, and their accuracy is improved. In addition, this approach allows us to achieve the unification of geometrical acoustics, which is generally used to predict the first echoes that appear in early reverberation, and of probabilistic models, which are generally used to predict the statistical properties of late reverberation.
• Statistical wave field theory: Random wave model of the complex Robin Laplacian
  
  • Badeau Roland
  , 2026. The statistical wave field theory mathematically establishes the statistical laws of the solutions to the wave equation in a bounded domain, which hold at high frequency. It provides the closed-form expressions of the power distribution and the correlations of the wave field jointly over time, frequency and space, in terms of the geometry and the specific admittance of the boundary surface. In this paper, the mathematical foundations of the theory are reworked at the fundamental level of the eigenfunctions of the Laplace operator. We thus introduce a random wave model of the complex Robin Laplacian, which encompasses and generalizes previous works dedicated to the real Robin Laplacian, and which accounts for the absorption of energy at the boundary surface. This model will allow us to establish the first and second order statistics of the room impulse response, and to finally achieve the unification of geometrical acoustics, which is generally used to predict the first echoes that appear in early reverberation, and of probabilistic models, which are generally used to predict the statistical properties of late reverberation.
• Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent
  
  • Larue Guillaume
  • Dufrène Louis-Adrien
  • Lampin Quentin
  • Ghauch Hadi
  • Rekaya Ben Othman Ghaya
  , 2026. Parity functions are fundamental Boolean operations with critical applications across machine learning, cryptography, and error correction. Yet, learning high-dimensional parity functions poses significant challenges: in a general setting, standard neural network architectures typically require exponential sample complexity, making gradient-based optimization intractable for large number of inputs N. We demonstrate that compact product-based neural architectures combined with stochastic data sparsity (Bernoulli inputs with pe≤1/N) and appropriate hyperparameter choice enable efficient parity learning, with theoretical guarantees of convergence. Experiments validate our theory across dimensions up to N=100,000, with empirical evidence showing optimal hyperparameter choices for pe and learning rate α, as well as polynomial complexity scaling laws. This work establishes fundamental connections between architectural inductive bias and data sparsity, opening new possibilities for neural arithmetic, structured reasoning, binary neural networks, and machine learning applied to automated protocol discovery.
• Feature-aware (Hyper)graph Generation via Next-Scale Prediction
  
  • Gailhard Dorian
  • Tartaglione Enzo
  • Naviner Lirida
  • Giraldo Jhony H.
  , 2026. Graph generative models perform well on small structured data but struggle to scale to large, complex structures. Hierarchical approaches improve scalability but often ignore node and edge features, which are critical in real-world applications, particularly for hypergraphs that model higher-order relationships. In this paper, we propose FAHNES (feature-aware (hyper)graph generation via next-scale prediction), a hierarchical framework that jointly generates topology and features for graphs and hypergraphs. FAHNES builds multi-scale representations through node coarsening and localized expansion, guided by a novel hierarchical scale encoding that controls granularity and ensures cross-scale consistency. Experiments on synthetic, 3D mesh, and graph point cloud datasets demonstrate competitive or state-of-the-art performance while uniquely scaling to featured large-scale graphs and hypergraphs. Our code is open source.
• Spatiotemporal Imputation with Graph-Informed Flow Matching
  
  • Zhang Zepeng
  • Einizade Aref
  • Giraldo Jhony H
  • Fink Olga
  , 2026. Missing data is a common challenge in spatiotemporal systems, arising in applications such as air quality monitoring and urban traffic management. Traditional machine learning approaches, like recurrent and graph neural networks, rely on iterative propagation, which tends to accumulate errors over time and space. Recent diffusionbased methods mitigate error propagation but require iterative sampling and often depend on problem-agnostic Gaussian priors, limiting both efficiency and effectiveness. To address these limitations, we propose GiFlow, a Graph-Informed Flow Matching framework for spatiotemporal imputation. GiFlow replaces the typical Gaussian prior with a graph-informed prior constructed via spatiotemporal filtering of observable signals, which better aligns the source distribution to the target and thereby simplifies the generation trajectory. The flow field is parameterized by a hybrid vector field model that integrates spatial attention, temporal attention, and spatiotemporal propagation, enabling joint modeling of spatial and temporal dependencies. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed GiFlow outperforms the state-of-the-art approaches in spatiotemporal imputation.
• On the consistency of state machines, use cases and block diagrams using dependency graphs and Large Language Models
  
  • Sultan Bastien
  • Apvrille Ludovic
  • Coudert Sophie
  Software and Systems Modeling, Springer Verlag, 2026, pp.1-40. Abstract Model-Driven Engineering aims to support rigorous reasoning about system analysis and design by relying on multiple interconnected modeling views to represent complex systems. However, maintaining consistency across these heterogeneous views remains a major challenge, especially when capturing the subtle semantic and logical dependencies inherent to such systems. Traditional approaches to consistency rely on formal rule-based methods, sometimes complemented by ontologies. Yet, these techniques often fall short when dealing with deeper semantic issues that cannot be explicitly expressed as rules. This paper introduces a combined approach for the automated detection and correction of inconsistencies in multi-view SysML models. The proposed framework articulates three complementary techniques: (1) a Large Language Model (LLM)-based technique for inconsistency detection, (2) a dependency graph-based technique for detecting inconsistencies in a class of logical relationships between model elements and (3) a correction method relying on LLMs constrained by formal rules to enforce consistency constraints. The graph-based technique relies on transforming SysML design models into dependency graphs: The paper first proves that these graphs are in bijective correspondence with the original design models. It then formalizes consistency rules for three diagram types, presents the integrated framework and evaluates its implementation on representative SysML analysis and design diagrams. Overall, the practical illustration shows that the approach is effective for identifying and correcting inconsistencies, although the LLM-based component produces both false positives and false negatives. It also suggests that the dependency graph-based technique is a relevant complement to the LLM-based one for identifying inconsistencies in logical dependencies between model elements. (10.1007/s10270-026-01388-4)
  DOI : 10.1007/s10270-026-01388-4
• Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
  
  • Plaud Roman
  • Perez-Lebel Alexandre
  • Saillenfest Antoine
  • Bonald Thomas
  • Le Morvan Marine
  • Varoquaux Gaël
  • Labeau Matthieu
  , 2026. Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especially critical in Inverse Probability Weighting (IPW) for causal inference, where propensity score errors near 0 and 1 often lead to high bias and variance. We propose a principled framework for deriving task-specific strictly proper scoring rules by matching the local curvature of the downstream error metric. We apply this to the Average Treatment Effect (ATE) estimation, deriving a closed-form loss and its corresponding canonical probability mapping that can be readily integrated with any model like a neural network or a gradient boosting algorithm. Extensive evaluations on causal inference benchmarks demonstrate that our tailored objective consistently outperforms standard likelihood-based and covariate-balancing approaches.
• Medium Identification Based on Correlation Analysis: A DAS Field Trial with Telecom Fibers
  
  • Luo Jiawei
  • Irurozki Ekhine
  • Pruvost Pierre
  • Jaouën Yves
  • Awwad Élie
  , 2026. Distributed acoustic sensing (DAS) enables infrastructure monitoring. However, DAS response strongly depends on cable deployment conditions. Based on field trial data, we propose medium-identification methods using cross-correlation, paving the way toward enhanced DAS data processing.
• Dual representations of classical and quantum entropies: Theory and perspectives
  
  • Rioul Olivier
  , 2026. The MAXENT (maximum entropy) principle gives a natural correspondance between (classical and quantum) statistical mechanics and information theory. Its general formulation leverages on log-concavity (or Klein's inequality) and the celebrated information inequality, which is equivalent to Gibbs' inequality. The resulting convex optimization allows one to relate entropy to the log-canonical partition function by Legendre-Fenchel duality. This can be further generalized to dual variational characterization of relative entropy or Kullback-Leibler divergence, relating Gibbs variationnal principle to Donsker-Vardhan variational representations. From an information theoretical point of view, the Gibbs principle and its associated evidence lower bound serves as the basis of modern machine learning algorithms, such as the expectation-maximization (EM) algorithm and its restriction to parameters output by a neural network. Interestingly, a large class of alternating optimization algorithms including EM and Blahut-Arimoto for computing channel capacity and source rate-distortion functions can be cast in a generic information geometric alternating projections on convex sets of measures. On the other hand, Donsker-Vardhan representations, when restricted to the output of a neural network, allows one to efficiently estimate information measures, and when applied to parametric estimators with quadratic risk, provide HCR-like and CRB-like statistical lower bounds. An alternative derivation on such lower bounds in the Bayesian setting makes use of the Weyl-Heisenberg uncertainty principle, which opens up new perspectives. In this keynote, I will select and review several of the aforementioned topics, and discuss the interactions between classical and quantum interpretations.
• Microarchitectural Analysis of Speculative Execution Patterns in RISC-V using Machine Learning and ISA-Level Masking Wrappers
  
  • Awais Muhammad
  • Mushtaq Maria
  • Naviner Lirida
  • Haj Jawad
  • Bruguier Florent
  , 2026, pp.1-7. Speculative execution attacks, such as Spectre Variant 1, leak sensitive data through transient microarchitectural effects. This paper presents a machine learning-based framework for analyzing and mitigating speculative execution vulnerability patterns in RISC-V systems. We extract branch, cache, and timing features from gem5 simulations and validate leakage behavior on a SiFive HiFive Premier P550 platform. Supervised models achieve up to 97.1% accuracy in distinguishing benign and speculative-leak execution windows, while unsupervised methods (K-means, HDBSCAN, and Isolation Forest) independently reveal structural and anomaly-based separation. Feature analysis shows that speculative loads and memory latency are the strongest leakage indicators. We further propose lightweight RISC-V assembly wrappers based on index masking and fence instructions. Results demonstrate that these wrappers shift vulnerable execution toward benign microarchitectural behavior. The proposed approach combines simulation, hardware validation, and learning-based analysis for practical speculative vulnerability analysis and mitigation in RISC-V processors. (10.1109/IOLTS69666.2026.11633845)
  DOI : 10.1109/IOLTS69666.2026.11633845
• MEDIATE: multi-faceted implementation of a mixed software/hardware-based zero trust framework for the computing continuum
  
  • Fournaris Apostolos P
  • Haleplidis Evangelos
  • Abdoul-Soukour Shahin
  • Huang Chih-Kai
  • Khoder Niemat
  • Bouloukakis Georgios
  • Brokalakis Andreas
  • Georgopoulos Konstantinos
  • Ioannidis Sotiris
  , 2026. This paper introduces MEDIATE, a framework that brings zero trust principles to the Cloud-Edge-IoT Computing Continuum. MEDIATE integrates several software-based security and policy services operating at the cloud and edge layers with hardware-and software-based monitoring components as IoT devices deployed close to the assets that require security protection. The framework is designed to a three-layer architecture with Orchestrator (Cloud), Overwatch (Edge), and Sentinel (IoT) that collectively enables cross-layer policy enforcement and coordinated threat mitigation. The Orchestrator provides global view and policy consistency, the Overwatch performs local analysis and threat correlation, and the Sentinel acts as lightweight agent for real-time monitoring near critical assets. These components can provide an adaptive security foundation suitable for distributed and large-scale operational domains such as Fourth-Party Logistics (4PL).
• Identifying and Evaluating Misleading Climate Communication with Natural Language Processing
  
  • Calamai Tom
  , 2026. Climate communication is becoming more abundant, but not necessarily more informative. This thesis investigates whether Natural Language Processing (NLP) can help structure climate-related discourse and distinguish substantive content from vague or rhetorical formulations. By examining the literature on greenwashing and major datasets for climate-related NLP tasks, it highlights key limitations, including subjectivity, ambiguity, and noisy data. It then proposes ways to address these issues through annotation schemes and evaluation metrics designed for ambiguity, as well as methods for propagating uncertainty into downstream analyses. Overall, the thesis shows that NLP can make climate-related discourse more explicit and analyzable.
• RV-Sec5: Enhancing Pre-Silicon Security Evaluation of RISC-V Processors through Targeted ISA-Level Instrumentation in the gem5 Simulation Framework
  
  • Awais Muhammad
  • Mushtaq Maria
  • Naviner Lirida
  • Yahya Jawad Haj
  • Bruguier Florent
  Pragmatic Cybersecurity, sciltp, 2026, 1 (1), pp.6. The open-source RISC-V Instruction Set Architecture (ISA) is being adopted rapidly in security-sensitive areas such as IoT, edge computing, and aerospace systems, which makes early-stage security validation increasingly important. Yet most existing approaches still depend either on post-silicon testing or on high-level emulation. Neither is well suited to exposing ISA-specific vulnerabilities or microarchitectural side effects during the design phase. As a result, there remains a gap between highlevel security policies and the way hardware actually behaves at runtime, and that gap can leave processors exposed to privilege escalation, memory protection failures, and side-channel leakage that may only become visible late in development. In this paper, we present RV-Sec5, a systematic and policy-driven framework for ISA-level security evaluation built on the gem5 cycle-accurate simulator. RV-Sec5 provides a formal method for translating high-level security invariants, including privilege isolation, Physical Memory Protection (PMP) enforcement, and Control and Status Register (CSR) integrity, into automated cycle-accurate instrumentation points embedded directly in the ISA decoder. By recording precise architectural execution context at instruction commit time, the framework supports specification-driven methodology of privilege escalation attempts and enables systematic correlation between ISA-level events and microarchitectural behavior, including TLB activity and cache state changes, without interfering with functional execution. Our results shows that RV-Sec5 can significantly detects the specification violation on the events that are permitted in User mode, the results shows that after extending the gem5 with the hooks added and ISA modified, it create an overhead on the simulation. The overall overhead of RV-Sec5 for the simulation time is less than 4% and the overhead for the memory usage is less than 2% across the evaluated workloads. RV-Sec5 is a modular, cycle-accurate observation and post-execution detection virtual platform that reduces the gap between architectural security requirements and their enforcement at the Microarchitectural level, using post-silicon testing within the RISC-V processor design flow. (10.53941/pc.2026.100006)
  DOI : 10.53941/pc.2026.100006
• Affinés pour la réussite : Évaluation des représentations dans le domaine de la formation professionnelle
  
  • Breidenstein Alicia
  • Leang Marguerite
  • Labeau Matthieu
  , 2026, pp.65-90. Dans le domaine de la formation professionnelle et du marché du travail, les données sont souvent structurées sous forme de graphes avec attributs textuels, reliant les offres de formation, les CV et les ontologies de certifications et de compétences. Cette configuration présente des défis uniques, car ces données sont organisées selon une hiérarchie de milliers d’étiquettes de classification, et le graphe comporte de nombreuses liaisons manquantes. Avec la difficulté supplémentaire que représente le vocabulaire spécifique au domaine, il est nécessaire d’utiliser une représentation du texte à la fois adaptée et efficace. Dans ce travail, nous évaluons un large éventail de représentations de texte, allant des représentations symboliques aux grands modèles de langue, sur des tâches réelles appliquées à notre jeu de données interne. Nous montrons que les représentations lexicales offrent les meilleures performances sans affinage, mais que les modèles basés sur BERT dominent une fois affinés sur des données spécifiques au domaine. En revanche, les LLM utilisés avec des instructions génératives directes sous-performent, limités par la complexité structurelle des données et le vocabulaire spécifique, et atteignent des performances comparables à celles des modèles basés sur BERT lorsqu'ils sont affinés sur le jeu de données.
• Efficient 3D Deep Learning for Joint DBT Reconstruction and Faithful Uncertainty Estimation: Preliminary Clinical Evaluation
  
  • Quillent Arnaud
  • Bismuth Vincent
  • Bloch Isabelle
  • Kervazo Christophe
  • Ladjal Saïd
  , 2026. Purpose: Digital breast tomosynthesis (DBT) reconstruction is an ill-posed inverse problem due to the limited-angle acquisition and sparse projection sampling, leading to streak artefacts and poor depth resolution in the reconstructed volumes. Although deep learning provides data-driven priors, its application remains hindered by the lack of ground-truth clinical data and concerns regarding model reliability. This study introduces a 3D deep learning framework trained on virtual phantoms to jointly reconstruct DBT volumes and estimate voxel-wise uncertainty. Methods: We generate a synthetic database of phantom-reconstruction pairs derived from breast computed tomography (CT) images on which we train a post-processing 3D deep ensemble to estimate the reconstructed volume. To ensure reliability, we implement faithful regression and decouple the estimation of uncertainty from the main reconstruction task, mitigating training instability. The approach is then evaluated on the synthetic test set along with textured phantoms and clinical images. Results: On synthetic data, our method improves peak signal-to-noise ratio (PSNR) by 30%, normalised root mean squared error (NRMSE) by 50%, and structural similarity index measure (SSIM) by 43% compared to baseline iterative reconstruction. The qualitative analysis demonstrates significantly reduced tissue superposition and improved depth resolution in coronal and sagittal planes. Additionally, the uncertainty maps correlate well with the reconstruction error, but they slightly underestimate its true magnitude. A preliminary clinical evaluation yields plausible glandular localisation, although domain shift introduces some artefacts and texture mismatch. Lastly, we discuss the potential clinical value of uncertainty estimation as a tool to guide radiologists' confidence in reconstructed volumes. Conclusion: We successfully demonstrate a robust 3D deep learning approach for DBT that improves reconstruction quality while providing faithful uncertainty estimates. The model generalises well to synthetic phantoms, with promising translation to clinical images.
• Towards Reliable and Secure RISC-V Systems: Survey of Testability and Security Mechanisms
  
  • Khan Mahreen
  • Mushtaq Maria
  • Apvrille Ludovic
  , 2026. <div><p>RISC-V has emerged as a versatile open-source instruction set architecture, enabling extensible microarchitectures, custom accelerators, and domain-specific processors. Its openness facilitates innovation in testability, safety, and security for safety-critical and security-sensitive applications. This survey provides a comprehensive review of recent research in RISC-V verification and protection mechanisms. We analyze AI-assisted test generation, statistical fault injection frameworks, systemlevel testing, design-for-test architectures, and hardware-software co-verification methods. In the safety domain, we discuss temporal isolation, performance monitoring, debug support, and fault containment strategies. Security mechanisms, including trusted execution environments, memory protection, cryptographic ISA extensions, post-quantum acceleration, and secure debug practices, are evaluated. Open challenges in scalable test coverage, AI-enabled certification, side-channel resilience, and lifecycle management are highlighted. Finally, we outline future research directions that leverage RISC-V's modularity and openness to enable trustworthy computing systems in automotive, aerospace, telecommunications, and edge computing domains.</p></div>
• Joint Visibility Analysis of RIS in Non-Terrestrial Networks through Stochastic Geometry
  
  • Balakrishnan Ashutosh
  • Lee Junse
  • Baccelli François
  , 2026. Non-Terrestrial networks (NTNs) are a key theme in upcoming 6G communications, especially for ubiquitous coverage. Urban environments, comprising of high rise buildings often result in blocking the line of sight (LoS) path between the user equipment (UE) and the NTN base station (NTN-BS). In this paper we investigate the situation where reconfigurable intelligent surfaces (RIS) are deployed on the building roof-tops to ensure multi-hop connectivity between the UE and the NTN-BS. In such a scenario, it becomes crucial to statistically study the LoS visibility of the RIS from the UE as well as from the NTN-BS, hence termed as joint visibility. In this work, accounting for the dual stochasticity arising from the locations of the RIS deployed buildings and the respective random building heights, we statistically study the probability of joint RIS visibility in a two-dimensional (2D) scenario considering a deterministic location of the NTN-BS. Further, we study the joint RIS visibility statistics conditional on the UE-NTN link being LoS or non-LoS. For the RISs deployed as a point point process (PPP) having exponentially distributed heights, the expected RISs jointly visible under the unconditional and conditional geometric settings are derived in closed form. Interestingly, in the 2D setting, the maximum expected RISs jointly visible, unconditionally, is twice the Basel number (π^2 /6). The simulated results are analyzed over building density, average building height, the altitude and position of the NTN-BS. We also illustrate probability heatmaps, demonstrating the strongest chance to have a RIS used conditioned on the system geometry. This study is expected to be useful in planning the deployment of RIS in urban areas, improving the signal and for assessing economic aspects.
• Transitions as the Native Objects of Dispersive Light-Matter Dynamics
  
  • Mohamed Meguebel
  • Federico Maxime
  • Garbe Louis
  • Belabas Nadia
  • Fabre Nicolas
  , 2026. We introduce a framework where light-matter transitions, rather than states, are the primary dynamical objects. Successive compositions of elementary transitions yield multiphoton processes with compact diagrammatic bookkeeping of resonant and off-resonant pathways. This approach enables transparent derivations of effective high-order Hamiltonians in the dispersive regime, foundational to quantum-information applications. Applied to the paradigmatic Jaynes-Cummings model, our framework reveals a photon-number-independent intrinsic Rabi frequency and persistent polaritonic hybridization in the dispersive regime, unifying resonant and dispersive limits.
• Time-frequency Talbot effect as Clifford operations on entangled time-frequency GKP states
  
  • Pousset Thomas
  • Dalidet Romain
  • Labonté Laurent
  • Fabre Nicolas
  , 2026. The Talbot effect-a near-field diffraction phenomenon in which a periodic wavefront self-images at regular distances-can be transposed to the time-frequency domain via the space-time duality between diffraction and dispersive broadening. We exploit this analogy to define the time-frequency (TF) Talbot effect and show that it implements different Clifford operations on TF Gottesman-Kitaev-Preskill (TF-GKP) qubits (Phys. Rev. A 102, 012607), a class of qubit states encoded in the discretised frequency and time-of-arrival degrees of freedom of entangled photon pairs, whose logical basis corresponds to even and odd components of an entangled frequency combs. These states are intrinsically robust against small frequency and temporal displacements, which can be further corrected by linear or nonlinear quantum error-correction schemes. We analyse the role of the comb envelope and peak width relative to the free spectral range, and show that a compromise must be made between the gate fidelity of the Clifford gates induced by TF-Talbot operation and the error-correction capacity of the code. We then demonstrate that the signature of the TF-Talbot effect is directly accessible via the generalised Hong-Ou-Mandel interferometer: all six logical GKP states can be unambiguously distinguished by introducing a frequency shift of half the comb periodicity in one interferometer arm. We conclude with a feasibility analysis based on current experimental technology, identifying the comb finesse as the key figure of merit for both gate performance and correctability. This conclusion extends naturally to quadrature GKP states, where a shear in quadrature phase space is precisely a Talbot effect. (10.1103/s8jp-l4hf)
  DOI : 10.1103/s8jp-l4hf
• Effective Hamiltonians in Cavity and Waveguide QED from Transition-Operator Diagrammatic Perturbation Theory
  
  • Meguebel Mohamed
  • Federico Maxime
  • Garbe Louis
  • Belabas Nadia
  • Fabre Nicolas
  , 2026. We propose an adiabatic-elimination formalism in the dispersive regime based on a transitioncentric perturbation theory. The perturbative expansion is recast into a diagrammatic framework, while adiabatic elimination is implemented through controlled projections onto transition subspaces. Our approach applies systematically at arbitrary perturbation order, and is suited to multilevel systems and multiple qubits in both cavity and waveguide quantum electrodynamics. It ultimately enables the explicit construction of effective higher-order Hamiltonians while bypassing important limitations of existing techniques, thereby providing a practical toolbox for multiphoton processes in the dispersive regime.
• Scalable Models and Simulations for Communicating Embedded Systems
  
  • Rayon Malou
  • Genius Daniela
  • Apvrille Ludovic
  , 2026.
• A Max–Min Neural Network Model for Propositional Fuzzy Logic
  
  • Baaj Ismaïl
  • Bloch Isabelle
  • Rico Agnès
  • Strauss Olivier
  , 2026, 3020, pp.334-348. We propose and study a neural network model for propositional fuzzy logic over $[0,1]$, based on a single max-min neuron. The neuron output is obtained by combining $\max$, $\min$, and a switch parameter through a fuzzy equivalence, and when the switch is Boolean, it reduces to a strict choice between the value obtained by max-min aggregation of the input and weights and its negation. We show that cascades of such neurons can represent the elementary Boolean connectives, and we analyze the neuron equation from two complementary viewpoints: solving for the parameters when the input and output are fixed, and solving for admissible inputs when the parameters and output are fixed. These results are then used to describe a training scheme by forward evaluation and backward propagation of constraints through the layers. (10.1007/978-3-032-28997-1_24)
  DOI : 10.1007/978-3-032-28997-1_24
• Methods for Sizing and Deploying Virtualised Mobile Networks : Application to 5G/6G, Optimisation for Smart Electrical Grids
  
  • Altawil Rosy
  , 2026. Modernising industrial smart grids operated by EDF requires highly reliable communications for Protection, Automation, and Control (PAC) systems. Although 5G network slicing supports mission-critical messages via URLLC, industrial clients lack visibility into the operator's core network, making it hard to guarantee the required 10−5 packet loss probability. Furthermore, traditional allocation models based on Poisson's law fail to capture real-world traffic burstiness, causing resource deficits 30% of the time. Tobridge this visibility gap, a Machine Learning classification model was developed. A LightGBM model was implemented to classify trafficinto URLLC and eMBB slices using experimental data restricted to the lower PHY and MAC layers. Achieving a 97.6% accuracy demonstrates that industrial clients can autonomously monitor traffic without operator-level KPIs. Building on this, a dynamic resource allocation model using Deep Reinforcement Learning (DRL) was introduced. A Deep Q-Network (DQN) agent continuously interacts with the network environment across a continuous state space (URLLC/eMBB loads, cell capacity) to manage the explorationexploitation trade-off. The agent dynamically allocates the optimal guard channels to minimize URLLC packet loss while maintaining fairness toward the eMBB buffer. Combining queuing limits, ML classification, and DQN, this thesis empowers industrial clients to autonomously predict, dimension, and adjust their 5G network slice resources instantaneously.
• CALICE: Continuous bitrate control with Adapted LIC modEl
  
  • Spadaro Gabriele
  • Presta Alberto
  • Giraldo Jhony
  • Fiandrotti Attilio
  • Grangetto Marco
  • Tartaglione Enzo
  ACM Transactions on Multimedia Computing, Communications and Applications, Association for Computing Machinery, 2026, pp.1-21. Learned image compression (LIC) has drawn much attention recently as it outperforms standardized codecs in rate-distortion (RD) efficiency. However, a LIC model is typically trained for a specific RD trade-off, and achieving a different target rate requires retraining the model and storing the weights as a whole, limiting the practical applicability of LIC. In this paper, we introduce CALICE, a framework for achieving continuous bitrate control by plugging into a pre-trained LIC model a set of modular adapters. Unlike similar methods that require a distinct set of adapters for each target rate, our method achieves continuous bitrate control by modulating a single set of adapters via a scalar parameter \(\boldsymbol{\alpha}\) , with a total overhead of less than \(\mathbf{0.35}\boldsymbol{\%}\) of the parameters of the LIC model. This design enables efficient support for multiple distortion objectives by learning lightweight, distortion-aware adapters. We also extend our strategy beyond rate control, demonstrating its ability to provide fine-grained adaptation of perceptual quality along the distortion-perception trade-off. To our knowledge, this is the first method that jointly addresses rate and perceptual control using a unified, low-cost strategy. We publicly released the code at https://github.com/EIDOSLAB/CALICE . (10.1145/3820658)
  DOI : 10.1145/3820658