Publications

2026

• On the explainability of max-plus neural networks
  
  • Enaieh Ikhlas
  • Fercoq Olivier
  • Ángel García
  , 2026. We investigate the explanability properties of the recently proposed linear-min-max neural networks. At initialization, they can be interpreted as k-medoids with the infinity norm as a distance. Then, they are trained using subgradient descent to better fit the data. The model has been shown to be a universal approximator. Yet, we can trace the decision process because a single most activated neuron is responsible for the value of the output. Using this property, we designed a pixel fragility measure that determines whether changes to a single pixel may be responsible to a change in the classification output. Experiments on the PneumoniaMnist dataset show that this explanation for the output of the neural network compares favorably to SHAP and Integrated Gradient.
• FINER: MLLMs Hallucinate under Fine-grained Negative Queries
  
  • Xiao Rui
  • Kim Sanghwan
  • Xian Yongqin
  • Akata Zeynep
  • Alaniz Stephan
  , 2026. Multimodal large language models (MLLMs) struggle with hallucinations, particularly with fine-grained queries, a challenge underrepresented by existing benchmarks that focus on coarse image-related questions. We introduce FIne-grained NEgative queRies (FINER), alongside two benchmarks: FINER-CompreCap and FINER-DOCCI. Using FINER, we analyze hallucinations across four settings: multi-object, multi-attribute, multi-relation, and "what" questions. Our benchmarks reveal that MLLMs hallucinate when fine-grained mismatches co-occur with genuinely present elements in the image. To address this, we propose FINER-Tuning, leveraging Direct Preference Optimization (DPO) on FINER-inspired data. Finetuning four frontier MLLMs with FINER-Tuning yields up to 24.2% gains (InternVL3.5-14B) on hallucinations from our benchmarks, while simultaneously improving performance on eight existing hallucination suites, and enhancing general multimodal capabilities across six benchmarks. Code, benchmark, and models are available at https://explainableml.github.io/finer-project/.
• What can we do in a symmetry-constrained perspective? The importance of the total charge's status in quantum reference frame frameworks
  
  • Doat Guilhem
  • Vanrietvelde Augustin
  , 2025. The study of quantum reference frames has received renewed interest over the last years, leading to the parallel development of non-equivalent frameworks by different com- munities. We clarify the differences between these frameworks. At the mathematical level, they mainly differ in the kind of symmetry (either weak or strong) employed to constrain the system. We show that this mathematical difference corresponds to a fundamental physical question: whether the global charge associated to the symmetry group is acces- sible to symmetry-constrained observers. In this context, we formulate a definition of a perspective in terms of operational capacities, or lack thereof. Turning to consequences of adopting either approach, we discuss how adopting the weak approach induces an ambi- guity in the momenta included in each perspective and bars from defining reversible QRF transformations. We then review and analyze the existing arguments motivating each approach, and show how they bear upon the problem of charge accessibility. Finally, we introduce a simple operational scenario in which upholding two reasonable physical pos- tulates leads to the conclusion that internal observers could measure the global charge by 1/ performing a relativized interference measurement and 2/ classically communicating.
• Using Locally Learnt Word Representations for better Textual Anomaly Detection
  
  • Breidenstein Alicia
  • Labeau Matthieu
  , 2024, pp.82-91. <div><p>The literature on general purpose textual Anomaly Detection is quite sparse, as most textual anomaly detection methods are implemented as out of domain detection in the context of pre-established classification tasks. Notably, in a field where pre-trained representations and models are of common use, the impact of the pre-training data on a task that lacks supervision has not been studied. In this paper, we use the simple setting of k-classes out anomaly detection and search for the best pairing of representation and classifier. We show that well-chosen embeddings allow a simple anomaly detection baseline such as OC-SVM to achieve similar results and even outperform deep state-of-the-art models.</p></div> (10.18653/v1/2024.insights-1.11)
  DOI : 10.18653/v1/2024.insights-1.11
• Répliquer sans Attendre mais Équitablement
  
  • Kuznetsov Petr
  • Perion Maxence
  • Tucci Piergiovanni Sara
  , 2026. La réplication assure la disponibilité des systèmes distribués sujets aux pannes et ceux dont la convergence est garantie uniquement à terme (eventual consistency) comme les CRDTs (Conflict-free Replicated Data Types), peuvent répondre aux requêtes sans attendre. Cependant, l'asynchronisme et la concurrence forcent les opérations à être réordonnées, altérant les effets originaux et bloquant la stabilisation des résultats. De plus, un utilisateur du système peut être en famine si toutes ses opérations sont réordonnées au moins une fois. Nous formalisons le problème résolu par les types de données répliqués sans attente en tant que réplication à terme de machine à état. Nous l'augmentons ensuite avec les propriétés de stabilité et d'équité assurant, respectivement, que les répliques partagent un préfixe stable grandissant d'opérations, et qu'aucun utilisateur n'est en famine. Nous présentons finalement une construction générique où les répliques échangent leurs vues locales sous forme de graphe et les unifient avec une fonction de réconciliation. Nous proposons une fonction de réconciliation assurant stabilité et équité.
• Trust-based attack detection model for connected cars using a Subjective Logic based framework
  
  • Ismail Ahmad
  • Fadlallah Ahmad
  • Bassi Francesca
  • Khatoun Rida
  , 2026. Traditional Voting Classifiers (Hard and Soft voting) assign fixed weights to individual models. In many cases, a machine learning model performs differently depending on the predicted class and the context. Hard and soft voting are static in nature which often prevents the full potential of ensemble methods from being realized. In contrast, Subjective Logic offers a probabilistic framework that effectively accounts for information uncertainty and the trustworthiness of information sources. This paper introduces a novel subjective logic based binary ensemble classifier that takes conflict between models and individual model performance into account to modify model weights in real time and improve the ensemble predictions.
• DELICATE: Diachronic Entity LInking using Classes And Temporal Evidence
  
  • Santini Cristian
  • Barzaghi Sebastian
  • Sernani Paolo
  • Frontoni Emanuele
  • Alam Mehwish
  Journal on Computing and Cultural Heritage, Association for Computing Machinery, 2026. In spite of the remarkable advancements in the field of Natural Language Processing, the task of Entity Linking (EL) remains challenging in the field of humanities due to complex document typologies, lack of domain-specific datasets and models, and long-tail entities, i.e., entities under-represented in Knowledge Bases (KBs). The goal of this paper is to address these issues with two main contributions. The first contribution is DELICATE, a novel neuro-symbolic method for EL on historical Italian which combines a BERT-based encoder with contextual information from Wikidata to select appropriate KB entities using temporal plausibility and entity type consistency. The second contribution is ENEIDE, a multi-domain EL corpus in historical Italian semi-automatically extracted from two annotated editions spanning from the 19th to the 20th century and including literary and political texts. Results show how DELICATE outperforms other EL models in historical Italian even if compared with larger architectures with billions of parameters. Moreover, further analyses reveal how DELICATE confidence scores and features sensitivity provide results which are more explainable and interpretable than purely neural methods.
• VOX2Surf: Faithful surface extraction from coarse binary voxels
  
  • Jetti Hari Hara Gowtham
  • Qin Leiheng
  • Huynh Chi
  • Khawand Joe
  • Sureshkumar Anandhu
  • Vining Nicholas
  • Cani Marie-Paule
  • Parakkat Amal Dev
  • Sheffer Alla
  Computers and Graphics, Elsevier, 2026, 138, pp.104649. Coarse binary voxel grids (under 100³ ) provide a simple interface enabling non-expert users to create a coarse approximation of diverse geometric content. Converting voxelized content into piecewise-smooth geometric models that reflect user intent can greatly increase the attractiveness of such interfaces. While multiple methods exist for surfacing binary voxel grids, they by and large target much higher grid resolutions. Applying these to coarse inputs often produces unintuitive results. We introduce VOX2Surf, a novel method for reconstructing user-intended surfaces from coarse binary voxel grids. We observe that a key challenge in achieving this goal is to correctly identify viewer-expected sharp features in these inputs. While human observers easily mentally separate sharp grid edges that are an artefact of the voxel representation from those depicting intended sharp features, existing techniques struggle to distinguish between them. We employ a learning-based approach, targeted at coarse data, to accurately recover the intended sharp features and utilize them for piecewise-smooth surface fitting. After identifying voxels containing sharp features, we employ a novel geometric reconstruction method to extract a curve network from these voxels. We use the loops of this network as the boundaries of our surface patches and use physically based simulation to smooth both the network curves and the surface patches. Extensive comparisons demonstrate that VOX2Surf achieves better approximation of the input voxelized surfaces compared to alternatives. More importantly, our user study confirms that our results are visually significantly better aligned with viewer expectations when presented with the input surfaces than those produced by alternative approaches. (10.1016/j.cag.2026.104649)
  DOI : 10.1016/j.cag.2026.104649
• Leveraging LiDAR datasets to improve SAR tomography: a diffusion model approach
  
  • Mendes Cristiano Ulondu
  • Denis Loïc
  • Kervazo Christophe
  • Tupin Florence
  , 2026. <div><p>Synthetic Aperture Radar (SAR) tomography is a 3D imaging technique based on the combination of multiple images acquired from slightly dierent angles. By analyzing the phase shift measured across the dierent images, it is possible to separate scatterers located at dierent heights. This requires solving an inverse problem and is typically performed independently for each pixel, producing a point cloud with large localization uncertainties in the elevation direction. Performing a singlestep tomographic reconstruction with improved spatial regularity is dicult to achieve using a supervised approach, as simulating realistic SAR tomography data corresponding to a given 3D urban scene is a highly complex task. In this paper, we suggest a two-step approach: a rst step using simple pixel-based inversion, and a second step restoring the reconstructed 3D cloud thanks to a diusion model. We train our diusion model on a large dataset of freely available LiDAR point clouds. A wellchosen geometrical projection is applied to represent the 3D points of the cloud visible from the radar as a 2D image. The degradation modeled by the diusion model corresponds both to point omission (non-detections) and to mislocalizations in the elevation direction. Our diusion model, based on the Residual Shifting method, requires as few as 20 diusion steps to produce restored reconstructions. The paper introduces a exible approach to leverage digital surface models from LiDAR datasets and improve 3D tomographic SAR reconstructions.</p></div>
• Chain rules for conditional entropies in quantum cryptography: limitations and improvements
  
  • Wooltorton Lewis
  • Brown Peter
  • Fawzi Omar
  , 2026. Security proofs in quantum cryptography rely on conditional entropies. In a many-round protocol, their estimation is a challenging task; one must account for the most general attacks by an eavesdropper, including those that are not independently and identically distributed (i.i.d.) across all rounds. Chain rules address this problem by relating the conditional entropy of a structured, but non-i.i.d. process to a sum of entropy contributions from each round. They are a key ingredient in entropy accumulation theorems (EATs), which provide a versatile security proof framework for many protocols in quantum cryptography. Recently, chain rules in the setting of trusted devices have lead to tight i.i.d. reductions at a finite number of rounds, and whether analogous results can be recovered in the device-independent (DI) setting has not been addressed. Surprisingly, we show that a natural tightening of the chain rule of Dupuis et al. [Commun. Math. Phys. 379, 867-913, (2020)] that would answer this question affirmatively cannot hold, highlighting a limitation of the current DI security proof approach. Nonetheless, we show that an intermediate improvement is possible by proving a new chain rule in this setting. Following the framework of Arqand et al. [Phys. Rev. X 15, 041013 (2025)], we use our chain rule to provide a slightly tighter version of the Rényi EAT in certain contexts. In addition, we provide a self-contained framework that unifies existing chain rules and compares their applications, framing our results in a broader context.
• Visualizing definitional divergence in high-dimensional data by manifold alignment: Application to 3D right ventricular strain computations
  
  • Folco Maxime Di
  • Bernardino Gabriel
  • Clarysse Patrick
  • Duchateau Nicolas
  IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2026, pp.1-12. <div><p>Medical imaging studies often rely on a single sample per subject, assuming it is representative of their physiological traits. However, variations in how input descriptors are defined or computed (e.g. due to a lack of consensus in the scientific field) may have a crucial impact on the analysis, and are hardly considered in practice. In this paper, we propose an original strategy based on representation learning to estimate a parametric map reflecting the impact of such definitional differences on a given physiological descriptor, previously extracted from medical images. We consider the different definitions or computations of such physiological descriptors as different high-dimensional data, potentially of heterogeneous types. We specifically focus on myocardial deformation (strain), for which there is limited agreement on its definition. We first use manifold alignment to match the latent representations associated with the different definitions of this descriptor. Then, we formulate plausible distributions in the latent space to represent definitional divergence across descriptors, from which we reconstruct a high-dimensional parametric map to visualize such definitional divergence.</p><p>Due to the lack of proper ground truth for this specific clinical application, we first demonstrate this methodology on toy experiments and then expand the evaluation on right ventricular strain data from subjects obtained from 3D echocardiographic image sequences, for which different types of strain are available at each point of the right ventricle endocardial surface mesh. Beyond this illustrative application, our methodology has the potential to be generalised to many other population analyses considering heterogeneous high-dimensional descriptors.</p></div> (10.1109/TMI.2026.3698240)
  DOI : 10.1109/TMI.2026.3698240
• Reinforcement learning for quantum processes with memory
  
  • Lumbreras Josep
  • Huang Ruo Cheng
  • Hu Yanglin
  • Fanizza Marco
  • Gu Mile
  , 2026. In reinforcement learning, an agent interacts sequentially with an environment to maximize a reward, receiving only partial, probabilistic feedback. This creates a fundamental exploration-exploitation trade-off: the agent must explore to learn the hidden dynamics while exploiting this knowledge to maximize its target objective. While extensively studied classically, applying this framework to quantum systems requires dealing with hidden quantum states that evolve via unknown dynamics. We formalize this problem via a framework where the environment maintains a hidden quantum memory evolving via unknown quantum channels, and the agent intervenes sequentially using quantum instruments. For this setting, we adapt an optimistic maximum-likelihood estimation algorithm. We extend the analysis to continuous action spaces, allowing us to model general positive operator-valued measures (POVMs). By controlling the propagation of estimation errors through quantum channels and instruments, we prove that the cumulative regret of our strategy scales as $\tilde{O}(\sqrt{K})$ over $K$ episodes. Furthermore, via a reduction to the multi-armed quantum bandit problem, we establish information-theoretic lower bounds demonstrating that this sublinear scaling is strictly optimal up to polylogarithmic factors. As a physical application, we consider state-agnostic work extraction. When extracting free energy from a sequence of non-i.i.d. quantum states correlated by a hidden memory, any lack of knowledge about the source leads to thermodynamic dissipation. In our setting, the mathematical regret exactly quantifies this cumulative dissipation. Using our adaptive algorithm, the agent uses past energy outcomes to improve its extraction protocol on the fly, achieving sublinear cumulative dissipation, and, consequently, an asymptotically zero dissipation rate.
• MMA-RAG: A Survey on Multimodal Agentic Retrieval-Augmented Generation
  
  • Perlić Vladana
  • Lebailly Stéphane
  • Malvone Vadim
  • Nguyen Van-Tam
  • Urard Pascal
  , 2026. Multimodal Agentic Retrieval-Augmented Generation (MMA-RAG) represents a paradigm shift in artificial intelligence, evolving beyond static text-based retrieval to empower systems with autonomous planning and reasoning capabilities over heterogeneous data types, including text, images, audio, and structured data. This survey provides a comprehensive analysis of the MMA-RAG landscape, introducing a unified taxonomy organized along the complementary axes of modality (text-only vs. multimodal) and agency (static vs. agentic). We systematically review foundational literature and state-of-the-art frameworks to identify dominant architectural patterns—specifically Hierarchical Orchestration, Iterative Refinement, and Domain-Specific Workflow Emulation—that characterize current systems. The survey evaluates the application of these patterns across diverse domains, such as healthcare, document understanding, and embodied AI, while surfacing persistent challenges in cross-modal alignment, system scalability, and the fragmentation of evaluation benchmarks. Moving beyond a retrospective review, we propose a consolidated research agenda to transition MMA-RAG from descriptive models to auditable, high-stakes decision-support agents. This roadmap identifies critical architectural opportunities, conceptualizing frameworks such as Reasoning Trace Tables for authentic in-context learning, Calibration-Aware Compositional Embedding Hubs for precise grounding, and Dual-Coded Episodic Memory to overcome text-bottlenecking, accompanied by a call for novel metrics to rigorously validate agentic reasoning and safety.
• Convex combinations of bosonic pure-loss channels
  
  • Catalano Giuseppe
  • Fanizza Marco
  • Mele Francesco Anna
  • de Palma Giacomo
  • Giovannetti Vittorio
  , 2026. The pure-loss channel is a fundamental model for describing noise in bosonic quantum platforms. It is characterised by a single parameter, the transmissivity, which quantifies the fraction of the input energy that reaches the output of the channel. In realistic scenarios, however, such as free-space quantum communication, the transmissivity is not fixed but fluctuates from one channel use to another. In this setting, the overall channel is effectively described as a convex combination of pure-loss channels, known as a fading channel. Despite its practical relevance, the quantum Shannon theory of the fading channel has remained largely unexplored. Here, we address this gap, specifically investigating degradability, anti-degradability, entanglement breakingness, and capacities of the fading channel. Of particular relevance to practical quantum-internet applications, we prove that entanglement distribution and quantum key distribution can always be achieved at a strictly positive rate over any fading channel, no matter how noisy it is or how strongly the transmissivity fluctuates, provided the channel is not completely noisy. Moreover, we prove that thermal states, which are optimal for a broad class of static bosonic Gaussian channels, fail to achieve the entanglement-assisted classical capacity of fading channels: non-Gaussian Fock-diagonal states strictly outperform all Gaussian encodings. Most strikingly, we identify regimes where the coherent information of thermal inputs vanishes, while optimized non-Gaussian states achieve strictly positive values, thereby activating the channel for quantum communication. For a paradigmatic binary fading model we establish this result analytically, deriving the exact capacity-achieving state in closed form. For general fading distributions, we design an iterative variational algorithm to optimize the coherent and mutual information.
• KARLA: Knowledge-base Augmented Retrieval for Language Models
  
  • Crespin Francois
  • Suchanek Fabian M.
  • Holzenberger Nils
  , 2026. We propose a new method that allows an LLM to automatically pull in factual knowledge from a knowledge base during token generation. This means that (1)~factual knowledge in the LLM output can be updated without retraining the LLM, (2)~facts in the LLM output can be traced to the knowledge base for transparency and explainability, and (3)~smaller models can achieve the same factual accuracy as larger models. Our core idea is to train the model to produce special tokens that trigger a query to the knowledge base. Our experiments show that our method improves factual grounding in both short and long-form generation, and allows factual revisions to take effect through KB edits rather than parameter updates.
• Identifying Security Vulnerabilities in Source Code with Safety Verification
  
  • Kissi Salim Yahia
  • Ameur-Boulifa Rabéa
  • Seladji Yassamine
  International Journal of Critical Computer-Based Systems (IJCCBS), 2026, 12 (01), pp.45 - 70. Ensuring the security of modern software systems is critical due to their increasing complexity and interconnectedness. While automated testing and bug-finding tools have made progress, detecting security flaws--especially those linked to runtime behaviour-- remains a significant challenge. Existing research in software security has largely focused on source code analysis, often overlooking the role of the execution environment and underlying platform. This narrow focus can miss vulnerabilities that arise from machine-dependent behaviours, leading to undetected and potentially critical flaws. To bridge this gap, we introduce a novel approach that analyses source code with a deep awareness of its execution environment, recognising that many vulnerabilities arise from subtle mismatches between the two. Our focus is on detecting arithmetic errors in C/C++ programs by accounting for how the execution context shapes program behaviour. The core of our contribution is a knowledge base that captures the influence of the execution environment on program behaviour using precise logical formulas. This knowledge base enables our algorithm to reformulate vulnerability detection as a satisfiability problem, making it amenable to automated reasoning with formal methods. This article presents the construction of our knowledge base, and details how it integrates with our algorithm to support accurate and environment-aware vulnerability detection. (10.1504/IJCCBS.2026.153768)
  DOI : 10.1504/IJCCBS.2026.153768
• Towards sample-optimal learning of bosonic Gaussian quantum states
  
  • Chen Senrui
  • Mele Francesco Anna
  • Fanizza Marco
  • Li Alfred
  • Mann Zachary
  • Huang Hsin-Yuan
  • Chen Yanbei
  • Preskill John
  , 2026. Continuous-variable systems enable key quantum technologies in computation, communication, and sensing. Bosonic Gaussian states emerge naturally in various such applications, including gravitational-wave and dark-matter detection. A fundamental question is how to characterize an unknown bosonic Gaussian state from as few samples as possible. Despite decades-long exploration, the ultimate efficiency limit remains unclear. In this work, we study the necessary and sufficient number of copies to learn an $n$-mode Gaussian state, with energy less than $E$, to $\varepsilon$ trace distance closeness with high probability. We prove a lower bound of $\Omega(n^3/\varepsilon^2)$ for Gaussian measurements, matching the best known upper bound up to doubly-log energy dependence, and ${\Omega}(n^2/\varepsilon^2)$ for arbitrary measurements. We further show an upper bound of $\widetilde{O}(n^2/\varepsilon^2)$ given that the Gaussian state is promised to be either pure or passive. Interestingly, while Gaussian measurements suffice for nearly optimal learning of pure Gaussian states, non-Gaussian measurements are provably required for optimal learning of passive Gaussian states. Finally, focusing on learning single-mode Gaussian states via non-entangling Gaussian measurements, we provide a nearly tight bound of $\widetilde\Theta(E/\varepsilon^2)$ for any non-adaptive schemes, showing adaptivity is indispensable for nearly energy-independent scaling. As a key technical tool of independent interest, we establish stringent bounds on the trace distance between Gaussian states in terms of the total variation distance between their Wigner functions. In particular, this yields a nearly tight sample complexity of $\widetilde{\Theta}(n^{2}/\varepsilon^{2})$ for learning the Wigner distribution of any Gaussian state to $\varepsilon$ total variation distance, achievable with Gaussian measurements. Our results greatly advance quantum learning theory in the bosonic regimes and have practical impact in quantum sensing and benchmarking applications.
• A Stable SVM Quantile Regression Algorithm for Heavily Censored Data
  
  • Lamalle Florian
  • Clémençon Stéphan
  • Feuillard Vincent
  • Sabourin Anne
  , 2026. This paper introduces a novel framework for quantile regression with censored observation. Our primary focus is on addressing the challenges posed by heavily censored datasets, which are prevalent in many real-world applications yet remain underexplored in the existing literature. The proposed approach, TIQ-SVM (Truncated IPCW Quantile SVM) relies on an adaptive truncation mechanism aimed at stabilizing the Inverse Probability of Censoring Weighting (IPCW) strategy in a quantile SVM framework. While theoretical guarantees within a non-asymptotic and model-agnostic framework are limited, notable exceptions include the work by Kosorok (2017). A significant limitation of existing approaches is their inability to handle heavy censoring effectively, primarily due to a central requirement that the survival function of the censoring should be bounded from below. This limitation often leads to numerical instabilities in heavily censored settings, restricting the applicability of these methods. In response to these challenges, our contribution introduces an adaptive truncation technique designed to stabilize the IPCW cost function, thereby accommodating heavy censoring scenarios. This innovative approach not only enhances the robustness of the regression framework but also broadens its applicability to datasets with substantial censoring. Beyond the theoretical guarantees in the form of generalization bounds we establish, through extensive numerical experiments, we demonstrate the efficacy and stability of our proposed method, showcasing its potential to advance the field of quantile regression for censored data. Our findings suggest that this approach can significantly improve the handling of heavily censored datasets, offering a promising direction for future research and practical applications.
• Hardness of M-LWE with General Distributions and Applications to Leaky Variants
  
  • Boudgoust Katharina
  • Jeudy Corentin
  • Tairi Erkan
  • Wen Weiqiang
  , 2026, 16551, pp.3-37. The Module Learning With Errors (M-LWE) problem has become a fundamental hardness assumption for lattice-based cryptography. It offers an attractive trade-o between strong robustness guarantees, sometimes directly based on worst-case lattice problems, and efficiency of the subsequent cryptographic primitives. Different flavors of M-LWE have then been introduced towards improving performance. Such variants look at different secret-error distributions and might allow for additional hints on the secret-error vector. Existing hardness results however only cover restricted classes of said distributions, or are tailored to specific leakage models. This lack of generality hinders the design of efficient and versatile cryptographic schemes, as each new distribution or leakage model requires a separate and nontrivial hardness evaluation In this work, we address this limitation by establishing the hardness of M-LWE under general distributions. As a first step, we show that M-LWE remains hard when the error vector follows an arbitrary bounded distribution with sufficient entropy, with some restriction on the number of samples. Building on this, we then reduce to the Hermite Normal Form (HNF) where the secret-error vector follows said arbitrary distribution. Overall, our result shows the actual shape of the distribution does not matter, as long as it keeps sufficient entropy. To demonstrate the versatility of our framework, we further analyze a range of leakage scenarios. By examining the residual entropy given the leakage, we show that our results of M-LWE with general distributions encompass various types of leakage. More precisely, we cover exact and approximate linear hints which are widely used in recent cryptographic designs, as well as quadratic, and even non-algebraic forms, some of which were not yet covered by any theoretical hardness guarantees. The generality of our results aims at facilitating future cryptographic designs and security analyses. (10.1007/978-3-032-26731-3_1)
  DOI : 10.1007/978-3-032-26731-3_1
• A Noise-Shaping SAR ADC With Second-Order Input Prediction
  
  • Nguyen-Thien Viet
  • Jabbour Chadi
  • Nguyen Minh-Tien
  • Delorme Nicolas
  , 2026, pp.2982-2986. This paper presents a low-power noise-shaping (NS) SAR ADC with a second-order input prediction algorithm. This algorithm uses polynomial interpolation to predict the bits of the next sample. Since the ADC only needs to convert the small difference between the input signal and the predicted code, the energy per conversion and the number of bit cycles per conversion can be reduced. The prototype ADC is designed in a 130-nm CMOS process. It consumes 270 µW from a 1.2-V supply while operating at 16 MS/s. The simulated SNDR is 86.3 dB over a bandwidth of 500 kHz, corresponding to an SNDR-based Schreier figure of merit (FoM) of 179 dB. (10.1109/ISCAS66217.2026.11562036)
  DOI : 10.1109/ISCAS66217.2026.11562036
• Arena: a kubernetes-based testbed for evaluating application deployment across the computing continuum
  
  • Huang Chih-Kai
  • Krouti Konstantinos
  • Markopoulou Stella
  • Tserpes Konstantinos
  • Bouloukakis Georgios
  , 2026. This paper introduces Arena, a Kubernetes-based testbed for evaluating application deployment across computing continuum environments (IoT/Edge/Cloud). Arena enables the emulation of diverse computing nodes using Docker containers and leverages Kubernetes for testbed management. Arena integrates the Chaos Mesh framework to simulate network characteristics and Prometheus with Grafana tools for monitoring and visualization purposes. Experiments on the Grid'5000 platform with a Google microservice application demonstrate that Arena's container-based emulation achieves similar resource usage patterns to virtual machine-based nodes, and its network chaos injection effectively enforces network constraints. Results highlight Arena's capability to provide a practical and reproducible environment for testing containerized applications across diverse computing continuum nodes.
• Co-investment in mobile edge computing with infrastructure update and dynamic participation
  
  • Sakr Amal
  • Araldo Andrea
  • Chahed Tijani
  • Kofman Daniel
  , 2026. Mobile Edge Computing (MEC) requires Network Operators (NOs) to undertake substantial infrastructure investments, while most revenues are captured by Service Providers (SPs) offering end-user applications. This cost-revenue imbalance discourages NOs from investing in MEC deployment, despite increasing demand for low-latency and bandwidth-intensive services. This paper proposes a co-investment scheme in which players, i.e., one NO and multiple SPs, jointly deploy, maintain, and share MEC infrastructure over multiple decision epochs. We devise a new coalitional game model that captures the planning of resources, their allocation among players, and cost and revenue sharing. To address fluctuating user demand and evolving participation incentives, we design a mechanism that updates resources and allows the dynamic entrance and exit of players over time. We sustain cooperation through a compensation scheme. Numerical results show that combining resource updates with dynamic participation increases the total payoff and strengthens the NO's incentive to invest.
• Generic neural network model for estimating exposure levels in industrial environments
  
  • Plets David
  • Apostolidis Christos
  • Wang Shanshan
  • Valič Blaž
  • Martens Luc
  • Samaras Theodoros
  • Gajšek Peter
  Journal of Radiological Protection, IOP Publishing, 2026, 46 (2), pp.021518 (1-14). Abstract This study describes a neural network-based method for estimating exposure levels in industrial environments, without requiring detailed technical inputs, allowing usage of the model by layman people or by workers active in these areas. A pipeline based on Blender environments and MATLAB ray-tracing simulations is created and after defining a set of 11 candidate input parameters for the model, more than 20 000 different wireless configurations are simulated, varying the different environmental and wireless input parameters. A correlation analysis shows that main inputs influencing the exposure levels in the industrial area are the transmit power of the antennas, the density of clutter in the area, the density of transmitters in the area, and the height and location of the transmitters. A multi-layer fully connected neural network regression model is developed to predict median ( E 50 ) and 95th percentile ( E 95 ) exposure levels in industrial areas. Testing the obtained model on an unseen dataset of environments with E 50 values between 0 and 3.25 V m −1 and E 95 values between 0 and 7 V m −1 , demonstrates the good prediction performance of the model: root-mean-square error values below 0.173 V m −1 and R 2 values above 95% are obtained. Subsequently, the model is validated with measurement data collected in three distinct realistic industrial environments. The average absolute deviation of the model predictions with respect to the measurements is limited to 20.4%. This novel and broadly accessible approach demonstrates that it is possible to reliably estimate exposure levels in realistic environments without having to rely on external experts or on dedicated complex software. (10.1088/1361-6498/ae6c32)
  DOI : 10.1088/1361-6498/ae6c32
• Integrating Visual Features in Multiple Hypothesis Tracking Through Self-Supervised Learning
  
  • Reme Raphael
  • Newson Alasdair
  • Angelini Elsa
  • Olivo-Marin Jean-Christophe
  • Lagache Thibault
  , 2026, pp.1-5. Multiple Object Tracking (MOT) is a crucial step in the automatic analysis of biological processes. In this work, we introduce VISUALMHT, a Bayesian Multiple Hypothesis Tracking algorithm integrating self-supervised visual features to leverage robust target identity and consistently track biological objects. This novel tracking algorithm achieves state-of-the-art results on the SINETRA synthetic datasets and demonstrates robust neuron tracking in the freshwater cnidarian Hydra vulgaris, highlighting the benefit of combining probabilistic motion models with learned visual representations. (10.1109/ISBI61048.2026.11515572)
  DOI : 10.1109/ISBI61048.2026.11515572
• AudioCAN: Enhanced Few-Shot Audio Classification via Energy-Guided Temporal Cross Attention
  
  • Zhuang Xuanyu
  • Peeters Geoffroy
  • Richard Gaël
  IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2026, 33, pp.2215-2219. <div><p>Despite the growing research interest and practical potential of few-shot audio classification, its efficacy remains limited by the temporal sparsity of target sound events and the presence of non-target acoustic interference within audio samples. In this work, we leverage the cross attention mechanism to address these challenges. While the Cross Attention Network (CAN) has demonstrated superior performance in few-shot image classification by highlighting semantically relevant regions between support and query features, we propose AudioCAN by extending CAN to audio domain through two-fold, audiospecific modifications. In particular, we first reformulate the original 2D spatial cross attention to 1D temporal version to prioritize the time frames containing discriminative acoustic information, thereby mitigating interference from non-target sounds. Secondly, we introduce an energy-guided masking strategy to synthesize a pseudo-query set from the support set via data augmentation, which then serves as a guidance for optimizing the 1D temporal cross attention during training. Experimental results on several few-shot audio classification benchmarks demonstrate that AudioCAN achieves state-of-the-art performance in 5-way-1shot settings while remaining highly competitive in 5-way-5-shot configurations.</p></div> (10.1109/LSP.2026.3694791)
  DOI : 10.1109/LSP.2026.3694791