Publications

2026

• Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation
  
  • Lim Gionnieve
  • Tan Bryan Cheng Zhengyu
  • Sim Kellie Yu Hui
  • Shi Weiyan
  • Chew Ming Hui
  • Hee Ming Shan
  • Lee Roy Ka-Wei
  • Perrault Simon
  • Choo Kenny Tsu Wei
  , 2026. The emergence of Large Language Models (LLMs) presents a dual challenge in the fight against disinformation. These powerful tools, capable of generating human-like text at scale, can be weaponised to produce sophisticated disinformation, yet they also hold promise for enhancing mitigation strategies. This paper investigates the complex dynamics between LLMs and disinformation in small, localised settings through a communication game based on online forums, inspired by Werewolf, with 25 participants. We analyse how Disinformers, Moderators, and Users leverage LLMs to advance their goals, revealing both the potential for misuse and combating disinformation. Our findings highlight the varying uses of LLMs depending on the participants' roles and strategies, underscoring the importance of understanding their effectiveness in this context. We conclude by discussing implications for future LLM development and online platform design, advocating for a balanced approach that empowers users and fosters trust while mitigating the risks of LLM-assisted disinformation. (10.1145/3817008)
  DOI : 10.1145/3817008
• Feature Toggle Dynamics in Large-Scale Systems: Prevalence, Growth, Lifespan, and Benchmarking
  
  • Tërnava Xhevahire
  , 2026. <div><p>Feature toggles enable gradual rollouts and experimentation in software systems, yet often persist beyond their intended lifecycle, accumulating as technical debt. Prior research has examined feature toggle interactions and complexity, but no longitudinal study has quantified how toggles evolve over time across different organizational contexts. We analyse over 4,000 toggle events in Kubernetes (10 MLoC, 8.5 years) and GitLab (5 MLoC, 5 years). We find that feature toggle removals lags behind additions in both systems (by roughly 35% and 13%, respectively), leading to growing toggle inventories. Their lifespan patterns also differ notably, with Kubernetes toggles lasting a median of 734 days versus 185 in GitLab. Then, some feature toggles (1.33% and 0.73%, respectively) exceed all previously observed removal durations, becoming de facto permanent. Building on these findings, we propose a benchmarking framework with five key metrics and their empirically derived threshold zones, enabling practitioners to assess and compare toggle management practices across projects. All scripts and data are publicly available.</p></div> (10.5281/zenodo.18773811)
  DOI : 10.5281/zenodo.18773811
• Where Did the Variability Go? From Vibe Coding to Product Lines by Regeneration
  
  • Tërnava Xhevahire
  , 2026. In vibe coding, an emerging AI-driven paradigm, an LLM generates an entire program from a natural language prompt, but what happens to the variability that traditional software engineering carefully builds into code? To answer this question, we conducted an exploratory analysis on 10 vibe coded C/C++ projects, which suggests that there is near-zero in-artifact variability, i.e., at compile and runtime. All variability decisions are resolved at a single new binding time, generation time, the moment the LLM produces the source code. Rather than treating this as a defect to fix, we propose Variability by Regeneration (VbR), to our knowledge the first product-line approach in which the LLM acts as the derivation engine, generating a purpose-built, free of dead code binary for each variant from a declarative specification, while a variant dispatcher transparently routes user requests to the matching binary. We formalise VbR, contrast it with classical SPL derivation, and demonstrate its full pipeline on a wc product family. For SPL engineering, variability in AIgenerated software belongs in the specification, not in the code. (10.5281/zenodo.20730698)
  DOI : 10.5281/zenodo.20730698
• Temporal 2G–5G RF-EMF exposure assessment in ten European countries during one year
  
  • van Bladel Han
  • Veludo Adriana Fernandes
  • Loizeau Nicolas
  • Röösli Martin
  • Maule Milena
  • Vecsei Zsuzsanna
  • Molnár Orsolya
  • Vrijkotte Tanja
  • Polanska Kinga
  • Politański Piotr
  • Mamrot Paweł
  • Wang Shanshan
  • Wiart Joe
  • Grellier James
  • Kovalenko Anastasiia
  • Hulls Paige
  • de Vocht Frank
  • Vaupotiĉ Nina
  • Guxens Mònica
  • Joseph Wout
  Science of the Total Environment, Elsevier, 2026, 1047, pp.182037. Due to the rapid evolution and densification of mobile communication networks, there is an increased interest in the long-term assessment of environmental exposure to radiofrequency electromagnetic fields (RF-EMF). While numerous studies have investigated RF-EMF exposure using short-term or spatial measurement campaigns, the number of comprehensive analyses capturing temporal variability across multiple frequency bands and countries remain limited. This pilot study aims to establish a multi-country sensor network to collect data on long-term RF-EMF exposure, including 5G, and to investigate whether temporal trends occur. Twenty frequency-selective sensors were deployed at fixed indoor and outdoor locations in ten European countries, continuously measuring E-field (electric field) strengths in four mobile communication frequency bands (806 MHz, 942 MHz, 1842 MHz, and 3625 MHz) over a period of 17 months with a temporal resolution of 1 s. The raw measurement data were calibrated and analyzed to describe RF-EMF exposure levels, temporal patterns, and variability across environments and spatial characteristics. A strong and recurring diurnal pattern was observed across the four frequency bands. The highest day-night contrasts occurred in the 806 MHz and 1842 MHz bands, where nighttime values decreased by 35.1% and 48.4%, respectively compared to daytime values. Milder contrasts were observed between weekdays and weekends with the most pronounced decrease of 16.6% for the 1842 MHz band. Temporal variability, quantified using the R-factor (i.e. the ratio of the median field strength to the maximum field strength measured during the period of interest), varied substantially across frequency bands and locations, with lower R-factors observed for higher-frequency bands (with a median R-factor of 0.45 for 3625 GHz), indicating a greater variability. Given the scope and duration of this study, it aims to serve as a pilot study for long-term exposure monitoring at fixed sites in multiple countries. (10.1016/j.scitotenv.2026.182037)
  DOI : 10.1016/j.scitotenv.2026.182037
• Look But Don't Touch with Sparse Autoencoders for Unlearning in Diffusion Models
  
  • Cassano Enrico
  • Renzulli Riccardo
  • Ahmed Rayyan
  • Grangetto Marco
  • Alaniz Stephan
  , 2026. Sparse autoencoders (SAEs) have recently been proposed as interpretable tools for concept-level manipulation, under the assumption that isolated features can serve as controllable intervention points. In this work, we systematically evaluate this assumption in the context of object erasure and steering in diffusion models. We show that while SAEs reliably detect and localize semantic concepts within diffusion model activations, direct intervention in their latent space frequently induces out-of-distribution activations, resulting in severe visual artifacts. To disentangle detection from intervention, we use SAE activations purely as semantic detectors to identify image regions containing the target object, and replace those patch embeddings with the ones that do not contain it. This detection-based replacement preserves the diffusion model's activation statistics and produces significantly cleaner erasure results than latent steering. Our findings reveal a fundamental gap between concept detection and concept intervention in diffusion models: monosemantic or sparse features are not inherently suitable as control knobs for steering. These results position SAEs as powerful interpretability tools for analyzing generative models, but highlight important limitations when used for direct manipulation, such as unlearning.
• Training-free Uncertainty Guidance for Complex Visual Tasks with MLLMs
  
  • Kim Sanghwan
  • Xiao Rui
  • Alaniz Stephan
  • Xian Yongqin
  • Akata Zeynep
  , 2026. Multimodal Large Language Models (MLLMs) often struggle with fine-grained perception, such as identifying small objects in high-resolution images or detecting key moments in long videos. Existing methods typically rely on complex, task-specific fine-tuning, which reduces generalizability and increases system complexity. In this work, we propose an effective, training-free framework that uses an MLLM's intrinsic uncertainty as proactive guidance. Our core insight is that a model's uncertainty decreases when provided with relevant visual information. We introduce a unified mechanism that scores candidate visual inputs by response uncertainty, enabling the model to autonomously focus on the most informative data. We apply this simple principle to three challenging visual tasks: Visual Search, Long Video Understanding, and Temporal Grounding, allowing off-the-shelf MLLMs to achieve performance competitive with specialized, fine-tuned systems. Our results demonstrate that leveraging intrinsic uncertainty is a powerful strategy for improving fine-grained multimodal performance.
• HELIOS: Lightweight real-time scheduling with energy readiness guarantees
  
  • Qiu Ruizhe
  • Robert Thomas
  • Tardieu Samuel
  • Pautet Laurent
  • Singhoff Frank
  Journal of Systems Architecture, Elsevier, 2026, 178, pp.1038856-1:1038856-10. Energy-harvesting embedded systems, like sensors and medical implants, must satisfy real-time constraints under strict energy limitations. Traditional schedulers, which prioritize timing over energy, often fail to produce valid schedules, particularly for non-preemptive task models. We introduce HELIOS, a lightweight, table-driven scheduler that treats timing and energy as joint first-class constraints while supporting limited-preemptive execution. HELIOS introduces energy readiness, ensuring jobs execute only when both temporal and energy conditions are met. The scheduling problem is formulated as an integer linear programming (ILP) problem, jointly optimizing task ordering, preemption points, and execution under energy constraints. By leveraging non-preemption and energy readiness, HELIOS maintains complexity proportional to job count. A key innovation is the use of worst-case energy footprints to guarantee safe execution via an energy readiness threshold. The table-driven runtime ensures compatibility with resource-constrained kernels. Experiments demonstrate that HELIOS achieves higher schedulability than priority-based baselines. (10.1016/j.sysarc.2026.103885)
  DOI : 10.1016/j.sysarc.2026.103885
• Determinants of radiofrequency electromagnetic fields emitted by smartphones in French cellular telephony networks
  
  • Fontaine Maëlle
  • Guida Florence
  • Moissonnier Monika
  • Beranger Remi
  • Lagroye Isabelle
  • Orlacchio Rosa
  • Laplanche Alexia
  • Dejardin Olivier
  • Conil Emmanuelle
  • Huss Anke
  • Mazloum Taghrid
  • Wiart Joe
  • Danjou Aurélie
  • Schüz Joachim
  • Bories Serge
  • Deltour Isabelle
  Environmental Research, Elsevier, 2026, 305, Part 2, pp.125023-1:125023-11. Smartphones are a major source of radiofrequency electromagnetic field exposure, yet real-life determinants of uplink emissions remain poorly characterised. We conducted the first epidemiological study of smartphone cellular network uplink emissions in a general population sample, recruiting 167 volunteers across three French cities in 2022-2023. Emissions were measured quasi-continuously for a week during everyday activities using the novel DEVIN exposimeter, while the XMobiSensePlus application simultaneously recorded sensor values and smartphone activities in the background. Determinants of UL emission occurrence and level were analysed using logistic and linear regressions. Over 8001 h analysed, voice calls were recorded in 2.4% of time and data uploads in 81.3%. Voice calls were associated to emission occurrence (OR = 7.09, 95% CI: 5.96-8.43). In fully adjusted models, emission levels were associated to calls (+9.16 dBm, CI: 7.15; 11.16), and to Wi-Fi connection with substantially lower cellular emissions outside call periods (-15.37 dBm, CI: -17.28, -13.46). Cellular upload rates differentially increased emissions depending on Wi-Fi connection, while received signal quality differentially reduced emissions depending on call status. Legacy technologies (2G, 3G) were associated with higher emissions than 4G. Results varied across centers. Smartphone brand, operator, and Android version showed no independent association with emitted power after System-on-Chip adjustment. These findings demonstrate that real-life smartphone radiofrequency electromagnetic cellular emissions are shaped by a complex interplay of usage patterns, network conditions, and device characteristics. (10.1016/j.envres.2026.125023)
  DOI : 10.1016/j.envres.2026.125023
• A posteriori closure of turbulence models: Are symmetries preserved?
  
  • Freitas André
  • Um Kiwon
  • Desbrun Mathieu
  • Buzzicotti Michele
  • Biferale Luca
  European Journal of Mechanics - B/Fluids, Elsevier, 2026, 119, pp.204496:1-204496:8. Turbulence modeling remains a longstanding challenge in fluid dynamics. Recent advances in data- driven methods have led to a surge of novel approaches aimed at addressing this problem. This work builds upon our recent work [Phys. Rev. Fluids 10, 044602 (2025)], where we introduced a new closure for a shell model of turbulence using an a posteriori (or solver-in-the-loop) approach. Unlike most deep learning-based models, our method explicitly incorporates physical equations into the neural network framework, ensuring that the closure remains constrained by the underlying physics benefiting from enhanced stability and generalizability. In this paper, we further analyze the learned closure, probing its capabilities and limitations. In particular, we look at joint probability density functions between resolved and unresolved variables, as well as the scale invariance of multipliers (ratios between adjacent shells) within the inertial range. Although our model excels in reproducing high-order statistical moments, it breaks this known symmetry near the cutoff, indicating a fundamental limitation. We discuss the implications of these findings for subgrid-scale modeling in 3D turbulence and outline directions for future research. (10.1016/j.euromechflu.2026.204496)
  DOI : 10.1016/j.euromechflu.2026.204496
• Semi-Supervised Novel Intrusion Detection via Orthogonal Feature Extraction and Score-Space Meta-Classifier Assembly
  
  • Vu Minh-Khanh
  • Ene Cristian
  • Mounier Laurent
  • Ramparison Mathias
  , 2026. Signature-based endpoint detection fails against novel payloads whose behavioral patterns do not match any catalogued specimen. A semi-supervised host-based intrusion detection system is introduced that decomposes each system-call window into three structurally independent views, each trained exclusively on benign traces: frequency and rarity (TF-IDF with a one-class Support Vector Machine), local topology (a 1D convolutional autoencoder), and chronological grammar (a GRU next-event predictor). The three anomaly scalars are assembled into a score-space meta-matrix over which an XGBoost layer learns the final decision boundary without accessing raw syscall sequences or malware signatures directly. On the ADFA-LD 32-bit benchmark, the fusion model achieves Area Under the ROC Curve (AUC) 0.9113, Area Under the Precision-Recall Curve (AUPR) 0.8950, and F1 0.8523 at a full-pipeline latency of 0.2217 ms per window. Per-family evaluation across six attack families yields average AUC 0.9073, and cross-path Pearson correlations of 0.43 to 0.46 indicate that the three views occupy productively distinct operating regimes and increase the number of behavioural constraints an attacker must preserve to evade detection.
• LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation
  
  • Haffoudhi Samy
  • Dobričić Nikola
  • Suchanek Fabian
  • Holzenberger Nils
  , 2026. Entity linking is a key component of many downstream NLP systems, yet existing approaches are often tied to the specific target knowledge bases and domains, limiting their real world application. In this paper, we extend LELA, a modular and domain-agnostic LLM-based entity disambiguation method, into a practical Python library that integrates zero-shot Named Entity Recognition (NER) -thereby providing a complete end-toend pipeline for entity-linking in real-world usage. We provide experimental results validating LELA's performance and robustness across diverse entity linking settings. In our demo, users can play with the system on their own input texts.
• Theoretical Comparison of Independent-Based Samplers
  
  • Bertholom François
  • Douc Randal
  • Roueff François
  , 2026. In this work, we analyze the convergence properties of four independent-based Markov Chain Monte-Carlo samplers, the Importance Markov Chain and three algorithms based on multiple tries. We prove that the Multiple-Try Metropolis with Independent Sampling dominates Multiple-Try Metropolis with Independent Balancing and Iterated Sampling Importance Resampling in the Peskun sense, guaranteeing its theoretical superiority over these alternatives. Furthermore, we derive a general formula for obtaining exact uniform convergence rates of these algorithms, assuming that the density ratio between the target and the proposal is bounded. In the unbounded case, while none of the three algorithms based on multiple tries are geometrically ergodic, the Importance Markov Chain only requires a simple exponential moment condition to be geometrically ergodic, or a polynomial moment condition to ensure polynomial ergodicity.
• Understanding Build Reproducibility in the F-Droid Ecosystem
  
  • Nanni Denise
  • Malka Julien
  • Zacchiroli Stefano
  • Zimmermann Théo
  • d'Angelo Gabriele
  , 2026. The security of open source applications benefits considerably from the possibility of rebuilding their source and verifying the output. F-Droid, a prominent distribution for open source Android applications, systematically rebuilds them from source and tests their bitwise reproducibility at app publishing time. However, F-Droid offers no guarantee that app reproducibility will continue to hold in the future. As software ecosystems evolve, reproducibility may degrade, with potential negative consequences for software preservation and security. We present the first empirical study of build reproducibility in the F-Droid app ecosystem. Analyzing historical reproducibility logs, we find that the overall bitwise reproducibility rate has been steadily increasing over time (as new versions of apps are published). We then evaluate how reproducibility holds in time for fixed app versions, by attempting to rebuild 18 904 app versions that F-Droid had previously confirmed bitwise reproducible, published between September 2018 and February 2026, achieving an 83% rebuild success rate, and identify missing dependencies as the dominant cause of failure, accounting for 76% of non-rebuildable cases. Among suc- cessfully rebuilt apps, 94% are also bitwise reproducible—i.e., they still yield bitwise identical artifacts upon rebuild. Together, these results show that while bitwise reproducibility largely holds for apps that can be rebuilt, rebuildability itself is highly sensitive to temporal decay. (10.1145/3820002.3828593)
  DOI : 10.1145/3820002.3828593
• Mutating the "Immutable": A Large-Scale Study of Git Tag Alterations
  
  • Rapaport Solal
  • Pautet Laurent
  • Tardieu Samuel
  • Zacchiroli Stefano
  • Zimmermann Théo
  , 2026. Git tags are commonly viewed as immutable references in software development, marking releases and specific repository states that underpin build reproducibility and software supply-chain integrity. Despite their intended immutability, Git allows tags to be altered through deletion or modification via force-pushed updates. The prevalence of such alterations threatens reproducible builds and dependency integrity. We conduct the first large-scale empirical study of tag alterations in public code repositories, analyzing 328.4 M software repositories from Software Heritage and identifying 10.2 M tag alterations affecting 189 k unique repositories. A cross-analysis with Nixpkgs reveals that 32 packages reference tags altered in our dataset, with 7 exhibiting confirmed build errors, providing concrete evidence that tag alterations break reproducible package builds. Our findings challenge the widespread assumption that tags are immutable anchors for released software. We therefore recommend that build systems and package managers pin dependencies to cryptographic commit hashes, that development forges expose tagmutation audit logs, and that the community adopt systematic monitoring of tag alterations as a standard supply-chain security practice. (10.1145/3820002.3828585)
  DOI : 10.1145/3820002.3828585
• NeuralSketch2Surf: Fast Neural Surfacing of Unoriented 3D Sketches
  
  • Ye Hongsheng
  • Sureshkumar Anandhu
  • Zhonghan Wang
  • Cani Marie-Paule
  • Hahmann Stefanie
  • Bonneau Georges-Pierre
  • Parakkat Amal Dev
  , 2026. With recent advances in VR, 3D sketches have emerged as a powerful medium for 3D model creation. However, while they already provide the user with a good perception of the intended shape, they must be surfaced before any reuse in a downstream application. This remains a challenge when sketches are unoriented, i.e., when they are simply sets of unsorted 3D strokes, without any additional normal information. We introduce NeuralSketch2Surf, the first fast and robust neural surfacing solution that processes arbitrarily unoriented sketches at interactive rates. Our approach uses S2V-Net, a new transformer network designed to mesh 3D sketches. Instead of directly inferring complex functions to represent shapes, we focus on predicting an occupancy grid, then refined using a custom smoothing function to create the desired surface. Thanks to a lightweight architecture that enables fast predictions, our method produces results in less than 2 seconds, in contrast to SOTA techniques that can take minutes or even hours. Extensive evaluations demonstrate that our method is not only fast but also generates closed surfaces with high geometric, topological, and perceptual accuracy. (10.1145/3799902.3811227)
  DOI : 10.1145/3799902.3811227
• ExposNet: A Deep Learning Framework for EMF Exposure Prediction in Complex Urban Environments
  
  • Zhang Yarui
  • Wang Shanshan
  • Wiart Joe
  IEEE Transactions on Machine Learning in Communications and Networking, Institute of Electrical and Electronics Engineers, 2026, pp.1-1. The prediction of the electric field (E-field) plays a crucial role in monitoring radiofrequency electromagnetic field (RF-EMF) exposure induced by cellular networks. In this paper, a deep learning framework is proposed to predict E-field levels in complex urban environments. First, the drive test measurement in Paris and Lyon and publicly accessible databases used to construct the training dataset are introduced, with a detailed explanation provided on how these datasets are formulated and integrated to enhance their suitability for Convolutional Neural Networks (CNNs)-based models. Then, the proposed model, ExposNet, which is a lightweight CNN-based framework, is presented. Two variants of the network structure are proposed, enabling per-frequency prediction and total E-field prediction. Extensive experimental analyses are conducted, including the comparison with a standard U-Net baseline and a classical Kriging baseline. Moreover, a detailed ablation study is conducted to quantify the contribution of each input component, and additional generalization experiments are performed on different test subsets. The overall results indicate that, despite being trained and tested on real-world measurements, the model performs well and achieves better accuracy compared to previous studies. (10.1109/TMLCN.2026.3714431)
  DOI : 10.1109/TMLCN.2026.3714431
• TRAPHALT: Indirect Core Halt via Trap Handling to Halt a RISC-V Core from User Mode Through Kernel-Mediated Fault Handling
  
  • Awais Muhammad
  • Mushtaq Maria
  • Naviner Lirida
  • Haj Jawad
  • Bruguier Florent
  , 2026. Modern processors rely on strict privilege separation to prevent user applications from manipulating privileged architectural state directly. In RISC-V systems, user-mode programs cannot access privileged control and status registers (CSRs) or execute privileged instructions. Still, exceptions raised by user programs are handled by the operating system kernel through the trap mechanism, which runs at a higher privilege level. This interaction between user-level faults and kernel trap handling can introduce subtle system behaviours that affect processor control state. In this work, we show that a RISC-V CPU core can be indirectly halted from user mode by deliberately triggering kernel trap handling with carefully crafted illegal instruction sequences. A user-space program forces the kernel to process a fault condition that propagates through OpenSBI's unhandled-fault path to sbi hart hang(), suspending the hart in an infinite wfi() loop. We demonstrate this behaviour on a SiFive HiFive Premier P550 platform running Linux with OpenSBI v1.6. Our results highlight how usertriggered exceptions can cause unexpected system-level effects and stress the need for careful validation of exception-handling paths in RISC-V platforms.
• System-level performance in massive JCAS networks
  
  • Mengoli Emanuele
  • Soprano-Loto Nahuel
  , 2026. <div><p>We propose a dynamic stochastic geometry framework to model and evaluate large-scale joint communication and sensing (JCAS) networks. In this model, base stations are distributed according to a homogeneous Poisson point process, while user equipments and sensing objects undergo localised random motion around their serving base station. Full spectrum reuse with a common waveform generates mutual interference that fundamentally couples the communication and sensing functionalities. Communication performance is characterised through downlink queues with service rates governed by the instantaneous signal-to-interference-plus-noise ratio (SINR) through the Shannon capacity. Sensing performance is captured by a Kalman filter (KF) whose measurement noise covariance depends on the sensing SINR, yielding a non-classical state-dependent filtering problem. We define system-level performance metrics under the Palm distribution of the base station process, distinguishing between primary metrics -namely, time-averaged communication and sensing SINRs -and derived metrics that include queue lengths and KF error covariance. Ergodic theorems permit the consistent estimation of these metrics through empirical spatial and temporal averages. Within this framework, we establish that, for the typical base station, communication and sensing metrics are associated: operating regimes favourable to one functionality tend to benefit the other. Simulation results validate the analytical findings and confirm the tracking accuracy of the proposed filter, further demonstrating that the association measure decreases as the network density increases. This work provides a theoretical foundation for cross-functional resource allocation in JCAS-enabled cellular networks.</p></div>
• Green Energy Driven Integrated Smart Grid and Wireless Networks
  
  • Wang Li-Chun
  • De Swades
  • Balakrishnan Ashutosh
  , 2026, pp.XXIII, 140. Green energy and next-generation wireless systems are no longer independent domains, rather they are rapidly converging to redefine the future of sustainable connectivity. This book presents a forward looking study outlining the design of green energy driven, integrated smart grid and wireless networks. It reimagines the integration of renewable energy with the traditional power grid, by enabling each grid user to not only consume grid energy but to also be a potential energy source, thereby revising the conventional idea of power grids. A grid networked system of such distributed ambient powered nodes is hence envisioned to potentially act as a carbon-free energy producer system to the power grid, in addition to an energy prosumer system. Through its chapters, the book outlines and highlights the importance of sustainability in 6G networks. The authors present insights on the design constraints and challenges in system analysis. The study broadly pertains to analytical modeling of spatio-temporal randomness in cellular networks, presenting novel strategies to mitigate the effects of the dual randomness on network performance. The key ideas presented include joint load and energy balancing, optimum resource provisioning, and aerial offloading. The authors also demonstrate a wider perspective by extending the concept of energy balancing to energy aware residential networks. The book concludes by discussing the scope of integration of AI in wireless networks and motivate the need for green-AI aided future networks. Written for graduate level students in computer science and electrical engineering, as well as industry professionals deploying large-scale green service solutions, this book serves as both a foundational reference and a roadmap toward sustainable, scalable, and intelligent future communication networks. (10.1007/978-3-032-20509-4)
  DOI : 10.1007/978-3-032-20509-4
• 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
• 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.
• 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.
• 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.In press. 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.