Publications

2026

• Random purification channel for passive Gaussian bosons
  
  • Mele Francesco Anna
  • Girardi Filippo
  • Chen Senrui
  • Fanizza Marco
  • Lami Ludovico
  , 2025. (10.48550/arXiv.2512.16878)
  DOI : 10.48550/arXiv.2512.16878
• Efficient learning of bosonic Gaussian unitaries
  
  • Fanizza Marco
  • Iyer Vishnu
  • Lee Junseo
  • Mele Antonio A.
  • Mele Francesco A.
  , 2025. Bosonic Gaussian unitaries are fundamental building blocks of central continuous-variable quantum technologies such as quantum-optic interferometry and bosonic error-correction schemes. In this work, we present the first time-efficient algorithm for learning bosonic Gaussian unitaries with a rigorous analysis. Our algorithm produces an estimate of the unknown unitary that is accurate to small worst-case error, measured by the physically motivated energy-constrained diamond distance. Its runtime and query complexity scale polynomially with the number of modes, the inverse target accuracy, and natural energy parameters quantifying the allowed input energy and the unitary's output-energy growth.<p>The protocol uses only experimentally friendly photonic resources-coherent and squeezed probes, passive linear optics, and heterodyne/homodyne detection. We then employ an efficient classical post-processing routine that leverages a symplectic regularization step to project matrix estimates onto the symplectic group. In the limit of unbounded input energy, our procedure attains arbitrarily high precision using only 2m + 2 queries, where m is the number of modes. To our knowledge, this is the first provably efficient learning algorithm for a multiparameter family of continuous-variable unitaries.</p> (10.48550/arXiv.2510.05531)
  DOI : 10.48550/arXiv.2510.05531
• Non-iid hypothesis testing: from classical to quantum
  
  • de Palma Giacomo
  • Fanizza Marco
  • Mowry Connor
  • O'Donnell Ryan
  , 2025. We study hypothesis testing (aka state certification) in the non-identically distributed setting. A recent work (Garg et al. 2023) considered the classical case, in which one is given (independent) samples from $T$ unknown probability distributions $p_1, \dots, p_T$ on $[d] = \{1, 2, \dots, d\}$, and one wishes to accept/reject the hypothesis that their average $p_{\mathrm{avg}}$ equals a known hypothesis distribution $q$. Garg et al. showed that if one has just $c = 2$ samples from each $p_i$, and provided $T \gg \frac{\sqrt{d}}{ε^2} + \frac{1}{ε^4}$, one can (whp) distinguish $p_{\mathrm{avg}} = q$ from $d_{\mathrm{TV}}(p_{\mathrm{avg}},q) &gt; ε$. This nearly matches the optimal result for the classical iid setting (namely, $T \gg \frac{\sqrt{d}}{ε^2}$). Besides optimally improving this result (and generalizing to tolerant testing with more stringent distance measures), we study the analogous problem of hypothesis testing for non-identical quantum states. Here we uncover an unexpected phenomenon: for any $d$-dimensional hypothesis state $σ$, and given just a single copy ($c = 1$) of each state $ρ_1, \dots, ρ_T$, one can distinguish $ρ_{\mathrm{avg}} = σ$ from $D_{\mathrm{tr}}(ρ_{\mathrm{avg}},σ) &gt; ε$ provided $T \gg d/ε^2$. (Again, we generalize to tolerant testing with more stringent distance measures.) This matches the optimal result for the iid case, which is surprising because doing this with $c = 1$ is provably impossible in the classical case. We also show that the analogous phenomenon happens for the non-iid extension of identity testing between unknown states. A technical tool we introduce may be of independent interest: an Efron-Stein inequality, and more generally an Efron-Stein decomposition, in the quantum setting. (10.48550/arXiv.2510.06147)
  DOI : 10.48550/arXiv.2510.06147
• Receiver Noise Calibration in CV-QKD accounting for Noise Dynamics
  
  • Ricard Guillaume
  • Jaouën Yves
  • Alléaume Romain
  , 2025, pp.043287. Continuous-Variable Quantum Key Distribution (CV-QKD) relies on accurate noise calibration at the receiver to ensure the security of quantum communication. Traditional calibration methods often oversimplify noise characteristics, neglecting the impact of local oscillator (LO) noise and the critical role of noise spectral properties, which can lead to imprecise Shot Noise Calibration (SNC). Our contributions are threefold: 1) we propose an operational framework for calibration, relying on the notion of stationarity 2) in this framework, we give a method allowing us to derive the optimal calibration duration for a given experiment 3) leveraging our knowledge of noise spectral properties, we introduce a novel SNC method. This work also formalizes the calibration procedures, addressing implicit assumptions and providing a better foundation for the certification of CV-QKD protocols, of which calibration is a fundamental part. We demonstrate that our improved calibration technique offers higher performance and higher tolerance to receiver imperfections, which can enhance the performance and cost-effectiveness of CV-QKD systems. (10.48550/arXiv.2509.07549)
  DOI : 10.48550/arXiv.2509.07549
• Phy2-ExposNet: A Physics-Informed Neural Network for Urban EMF Exposure Mapping
  
  • Li Shuangning
  • Zhang Yarui
  • Wang Shanshan
  • Wiart Joe
  IEEE Open Journal of Antennas and Propagation, IEEE, 2026, pp.1-13. Accurate electromagnetic field (EMF) exposure mapping is critical for wireless network planning, environmental monitoring, and the deployment of next generation communication systems. The mapping results can be converted into the form of a radio map, a key technology in digital twin communication systems, which describes the wireless signal propagation characteristics at every location in a specific area. Existing deep learning approaches treat propagation estimation as a pure regression problem and do not enforce physical consistency in the predicted fields. In this paper, we propose Phy2-ExposNet, a novel neural network framework that decouples exposure mapping into a physics-informed estimation stage and a transformer-based residual refinement stage. It first estimates the fields under two physical constraints and then refines the resulting exposure map by capturing long-range interactions and complex spatial propagation patterns. Experiments demonstrate that the proposed method achieves lower estimation error while significantly reducing model complexity compared to existing approaches. It achieves around 15% relative error reduction over baselines, while using over 80% fewer parameters than conventional physics-informed models. Ablation results further reveal that the physics-informed design is crucial for capturing complex propagation effects, particularly in boundary and shadow regions. (10.1109/OJAP.2026.3707005)
  DOI : 10.1109/OJAP.2026.3707005
• Computational aspects of the trace norm contraction coefficient
  
  • Delsol Idris
  • Fawzi Omar
  • Kochanowski Jan
  • Ramachandran Akshay
  , 2026. We show that approximating the trace norm contraction coefficient of a quantum channel within a constant factor is NP-hard. Equivalently, this shows that determining the optimal success probability for encoding a bit in a quantum system undergoing noise is NP-hard. This contrasts with the classical analogue of this problem that can clearly be solved efficiently. We also establish the NP-hardness of deciding if the contraction coefficient is equal to 1, i.e., the channel can perfectly preserve a bit. As a consequence, deciding if a non-commutative graph has an independence number of at least 2 is NPhard. In addition, we establish a converging hierarchy of semidefinite programming upper bounds on the contraction coefficient.
• I-INR: Iterative Implicit Neural Representations
  
  • Haider Ali
  • Ali Muhammad Salman
  • Qamar Maryam
  • Khalil Tahir
  • Kim Soo Ye
  • Oh Jihyong
  • Tartaglione Enzo
  • Bae Sung-Ho
  , 2026, 40 (6), pp.503, 4520-4528. Implicit Neural Representations (INRs) have revolutionized signal processing and computer vision by modeling signals as continuous, differentiable functions parameterized by neural networks. However, INRs are prone to the spectral bias problem, limiting their ability to retain high-frequency information, and often struggle with noise robustness. Motivated by recent trends in iterative refinement processes, we propose Iterative Implicit Neural Representations (I-INRs). This novel plug-and-play framework iteratively refines signal reconstructions to restore high-frequency details, improve noise robustness, and enhance generalization, ultimately delivering superior reconstruction quality. I-INRs integrate seamlessly into existing INR architectures with only a 0.5–2% increase in parameters. During reconstruction, the iterative refinement adds just 0.8–1.6% additional FLOPs over the baseline while delivering a substantial performance boost of up to +2.0 PSNR. Extensive experiments demonstrate that I-INRs consistently outperform WIRE, SIREN, and Gauss across various computer vision tasks, including image fitting, image denoising, and object occupancy prediction. (10.1609/aaai.v40i6.4245)
  DOI : 10.1609/aaai.v40i6.4245
• Convergence of the Cumulant Expansion and Polynomial-Time Algorithm for Weakly Interacting Fermions
  
  • Chen Hongrui
  • Rouzé Cambyse
  • Chen Jielun
  • Jiang Jiaqing
  • Scalet Samuel
  • Zhan Yongtao
  • Chan Garnet Kin-Lic
  • Ying Lexing
  • Tong Yu
  , 2025. We propose a randomized algorithm to compute the log-partition function of weakly interacting fermions with polynomial runtime in both the system size and precision. Although weakly interacting fermionic systems are considered tractable for many computational methods such as the diagrammatic quantum Monte Carlo, a mathematically rigorous proof of polynomial runtime has been lacking. In this work we first extend the proof techniques developed in previous works for proving the convergence of the cumulant expansion in periodic systems to the non-periodic case. A key equation used to analyze the sum of connected Feynman diagrams, which we call the tree-determinant expansion, reveals an underlying tree structure in the summation. This enables us to design a new randomized algorithm to compute the log-partition function through importance sampling augmented by belief propagation. This approach differs from the traditional method based on Markov chain Monte Carlo, whose efficiency is hard to guarantee, and enables us to obtain a algorithm with provable polynomial runtime. (10.48550/arXiv.2512.12010)
  DOI : 10.48550/arXiv.2512.12010
• Free space optical communications in fog: comparing wavelengths for intersymbol interference resilience
  
  • Breton Alberto
  • Sorrente Béatrice
  • Fade Julien
  • Silva Anabela Da
  • Grillot Frédéric
  , 2026, 13890, pp.138900V. Free-space optical (FSO) communications at 1.55 μm are gaining increasing recognition due to their high data rates and smaller beam divergence compared to radio-frequency systems. However, FSO links are highly sensitive to adverse atmospheric conditions and turbulence, which limits their practicality in urban environments, particularly in the presence of fog. To mitigate these impairments, the use of longer wavelengths has been proposed. The aim of this work is to investigate the effects of fog on optical communication links operating at 1.55 μm and 10.3 μm. The study focuses on the temporal spreading of transmitted optical pulses caused by multiple scattering interactions between photons and water droplets. This pulse broadening can significantly limit the achievable data rate due to inter-symbol interference (ISI). A radiative transfer model based on the radiative transfer equation (RTE) is employed to compute the impulse response of a foggy atmospheric slab. These impulse responses are then applied to an amplitude-modulated optical signal to assess system performance under fog conditions. The results show that the link operating at 10.2 μm is less affected by temporal spreading than the 1.55 μm link, demonstrating improved robustness against fog-induced ISI degradation. (10.1117/12.3079603)
  DOI : 10.1117/12.3079603
• Scalable Information Theoretic Evaluation of the Rank Statistics in Side-Channel Attacks
  
  • Béguinot Julien
  • Rioul Olivier
  • Masure Loïc
  • Standaert François-Xavier
  • Cheng Wei
  • Guilley Sylvain
  IACR Transactions on Cryptographic Hardware and Embedded Systems, IACR, 2026, 2026 (1), pp.53-81. Evaluating the security of a device against side-channel attacks is a difficult task. One prominent strategy for this purpose is to characterize the distribution of the rank of the correct key among the different key hypotheses produced by a maximum likelihood attack, depending on the number of measured traces. In practice, evaluators can estimate some statistics of the rank that are used as security indicators—e.g., the arithmetic and geometric mean rank, the median rank, the α-marginal guesswork, or the success rate of level L. Yet, a direct estimation becomes time-consuming as security levels increase.In this work, we provide new bounds on these figures of merit in terms of the mutual information between the secret and its side-channel leakages. These bounds provide theoretical insights on the evolution of the figures of merit in terms of noise level, computational complexity (how many keys are evaluated) and data complexity (how many side-channel traces are used for the attack). To the best of our knowledge, these bounds are the first to formally characterize security guarantees that depend on the computational power of the adversary, based on a measure of their informational leakages. It follows that our results enable fast shortcut formulas for the certification laboratories, potentially enabling them to speed up the security evaluation process. We demonstrate the tightness of our bounds on both synthetic traces (in a controlled environment) and real-world traces from two popular datasets (Aisylab/AES_HD and SMAesH). (10.46586/tches.v2026.i1.53-81)
  DOI : 10.46586/tches.v2026.i1.53-81
• Docker does not Guarantee Reproducibility
  
  • Malka Julien
  • Zacchiroli Stefano
  • Zimmermann Théo
  , 2026. <div><p>The reproducibility of software environments is a critical concern in modern software engineering, with ramifications ranging from the effectiveness of collaboration workflows to software supply chain security and scientific reproducibility. Containerization technologies like Docker address this problem by encapsulating software environments into shareable filesystem snapshots known as images. While Docker is frequently cited in the literature as a tool that enables reproducibility in theory, the extent of its guarantees and limitations in practice remains under-explored.</p><p>In this work, we address this gap through two complementary approaches. First, we conduct a systematic literature review to examine how Docker is framed in scientific discourse on reproducibility and to identify documented best practices for writing Dockerfiles enabling reproducible image building. Then, we perform a large-scale empirical study of 5298 Docker builds collected from GitHub workflows. By rebuilding these images and comparing the results with their historical counterparts, we assess the real reproducibility of Docker images and evaluate the effectiveness of the best practices identified in the literature.</p></div>
• Thinking Before Constraining: A Unified Decoding Framework for Large Language Models
  
  • Nguyen Ngoc Trinh Hung
  • Silva Alonso
  • Zumot Laith
  • Tupikina Liubov
  • Aghasaryan Armen
  • Alam Mehwish
  , 2026. Natural generation allows Language Models (LMs) to produce free-form responses with rich reasoning, but the lack of guaranteed structure makes outputs difficult to parse or verify. Structured generation, or constrained decoding, addresses this drawback by producing content in standardized formats such as JSON, ensuring consistency and guaranteed-parsable outputs, but it can inadvertently restrict the model's reasoning capabilities. In this work, we propose a simple approach that combines the advantages of both natural and structured generation. By allowing LLMs to reason freely until specific trigger tokens are generated, and then switching to structured generation, our method preserves the expressive power of natural language reasoning while ensuring the reliability of structured outputs. We further evaluate our approach on several datasets, covering both classification and reasoning tasks, to demonstrate its effectiveness, achieving a substantial gain of up to 27% in accuracy compared to natural generation, while requiring only a small overhead of 10-20 extra tokens.
• Towards Robust Secure Compilation in Presence of Speculative Execution
  
  • Clément Léopold
  • Kühne Ulrich
  • Brandner Florian
  • Pacalet Renaud
  , 2026. Time-based side-channel attacks have been known since 1996. Many such attacks use the differences in execution time between program executions caused e.g. by cache hits/misses or non-constant-time instructions. To counter them, programmers use constant time programming, a set of empirical rules that are supposed to avoid a secret leaking via execution time variations. However, until the discovery of the Spectre attack, the verifications were based on the instruction set architecture (ISA) specification that does not take into account speculative execution. Speculation widely varies between actual ISA implementations. Creating a precise model is mostly impractical for verification. In our current work, we incorporate speculative semantics into the compiler CompCert. An additional pass has been added that inserts speculation barriers in order to counter information leakage from transient executions. We have succeeded to prove the preservation of constant time for a simple and overly aggressive strategy, which inserts such barriers after each branch instruction. In this paper, we briefly describe the challenges that remain to be addressed.
• A Survey on Verifying Reasoning Chains Generated by Large Language Models
  
  • Jaulmes Bérénice
  • Arouete Jean-Christophe
  • Barry Mariam
  • Alam Mehwish
  , 2026. Large Languages Models (LLMs) are currently being extensively employed for many Natural Language Processing tasks such as question answering, natural language inference, document summarization etc. Chain-of-Thought (CoT) prompting guides LLMs with the reasoning steps, compelling them to generate reasoning chains. While some of the reasoning chains may follow a correct thought process, they can also suffer from hallucinations, leading to errors in answer generation. Recently, many articles have targeted the problem of verifying these reasoning chains from various aspects. Despite this recent attention, to the best of our knowledge, no comprehensive survey currently summarizes these studies on CoT verification. This work addresses that gap by presenting a detailed overview of the methods for verifying reasoning chains and categorizing them according to their methodology. This paper introduces a novel taxonomy of classification of the methods introduced so far and mainly divides them into approaches that assess entire chains versus those that examine individual steps. This paper also reviews benchmarks for evaluating CoT reasoning and verification methods and further discusses the challenges and future directions associated with these methods. By compiling and analyzing these approaches, our survey aims to advance the understanding and development of robust reasoning techniques in LLMs.
• LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation
  
  • Haffoudhi Samy
  • Suchanek Fabian M
  • Holzenberger Nils
  , 2026. Entity linking (mapping ambiguous mentions in text to entities in a knowledge base) is a foundational step in tasks such as knowledge graph construction, question-answering, and information extraction. Our method, LELA, is a modular coarse-to-fine approach that leverages the capabilities of large language models (LLMs), and works with different target domains, knowledge bases and LLMs, without any fine-tuning phase. Our experiments across various entity linking settings show that LELA is highly competitive with fine-tuned approaches, and substantially outperforms the non-fine-tuned ones.
• The Inverse Drum Machine: Source Separation Through Joint Transcription and Analysis-by-Synthesis
  
  • Torres Bernardo
  • Peeters Geoffroy
  • Richard Gaël
  IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2026, 34, pp.84-95. We present the Inverse Drum Machine, a novel approach to Drum Source Separation that leverages an analysis-by-synthesis framework combined with deep learning. Unlike recent supervised methods that require isolated stem recordings for training, our approach is trained on drum mixtures with only transcription annotations. IDM integrates Automatic Drum Transcription and One-shot Drum Sample Synthesis, jointly optimizing these tasks in an end-to-end manner. By convolving synthesized one-shot samples with estimated onsets, akin to a drum machine, we reconstruct the individual drum stems and train a Deep Neural Network on the reconstruction of the mixture. Experiments on the StemGMD dataset demonstrate that IDM achieves separation quality comparable to state-of-the-art supervised methods that require isolated stems data. (10.1109/TASLPRO.2025.3629286)
  DOI : 10.1109/TASLPRO.2025.3629286
• U-DREAM: Unsupervised Dereverberation guided by a Reverberation Model
  
  • Bahrman Louis
  • Rodrigues Marius
  • Fontaine Mathieu
  • Richard Gaël
  IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2026, 34, pp.1552-1563. This paper explores the outcome of training state-of-the-art dereverberation models with supervision settings ranging from weakly-supervised to virtually unsupervised, relying solely on reverberant signals and an acoustic model for training. Most of the existing deep learning approaches typically require paired dry and reverberant data, which are difficult to obtain in practice. We develop instead a sequential learning strategy motivated by a maximum-likelihood formulation of the dereverberation problem, wherein acoustic parameters and dry signals are estimated from reverberant inputs using deep neural networks, guided by a reverberation matching loss. Our most data-efficient variant requires only 100 reverberation-parameter-labeled samples to outperform an unsupervised baseline, demonstrating the effectiveness and practicality of the proposed method in low-resource scenarios. (10.1109/TASLPRO.2026.3671615)
  DOI : 10.1109/TASLPRO.2026.3671615
• Multimodal Cultural Heritage Knowledge Graph Extension with Language and Vision Models
  
  • Zhang Yang
  • Mimouni Nada
  • Moissinac Jean-Claude
  • Hamdi Fayçal
  Journal on Computing and Cultural Heritage, Association for Computing Machinery, 2026. The preservation and interpretation of cultural heritage increasingly rely on digital technologies, among which Knowledge Graphs (KGs) stand out for their ability to structure vast amounts of data. However, the construction and expansion of these KGs often face challenges due to the diverse and complex nature of cultural heritage information. In this paper, we propose a novel approach for extending KG resources in the domain of cultural heritage, which we applied to French data. First, we introduce a new knowledge graph in the domain of French cultural heritage, WJoconde, which is distinguished by its multimodality as it integrates both textual and image information of the entities. We further introduce three variants of WJoconde to facilitate downstream research, such as Knowledge Graph Completion (KGC). We also built a comprehensive benchmark for KGC methods on our dataset. Second, we propose a new framework for extending cultural heritage KGs using multi-modal approaches leveraging Large Language Models (LLMs) and Vision-Language Models (VLMs), which includes automated data extraction from unstructured resources combined with a special validation pipeline for grounding the output of both models, to further extend WJoconde. Our results show that by integrating the rich text and image information in cultural heritage data, we can efficiently enhance KGs with high reliability. We open-source all code and benchmark datasets with text and images, as well as the original data with an interactive access point (10.48550/arXiv.2605.17669)
  DOI : 10.48550/arXiv.2605.17669
• A Decade of Software Reproducibility in the Nix Package Ecosystem
  
  • Malka Julien
  • Zacchiroli Stefano
  • Zimmermann Théo
  Empirical Software Engineering, Springer Verlag, 2026. <div><p>We report a large-scale empirical study of two aspects of software reproducibility-rebuildability and bitwise reproducibility-in the Nix package ecosystem. Using 29 evenly spaced historical snapshots over a decade of history of the nixpkgs repository (2015-2024) we attempted to rebuild and bitwise-compare the build outputs of tens of thousands of packages per snapshot. Our experiment produced a dataset of build metadata and logs for 1 321 000 package builds and preserved 166 523 diffoscopes for unreproducible outputs.</p><p>We find that functional package management enables extremely high rebuildability over time (near-universal ability to reconstitute historical build environments and rebuild software packages), while bitwise reproducibility has steadily improved and reaches a high point in recent years (up to 93% in 2024). Early years show substantially lower bitwise reproducibility, indicating that functional package management alone does not guarantee bitwiseidentical outputs, and that the observed high level of bitwise reproducibility is not solely due to the package management approach. Common causes of unreproducibility, both in the rebuildability and bitwise reproducibility dimensions, include management of dates in build and test processes; we quantify their prevalence and other common causes using manual analysis of logs of rebuild failures and automated analysis of diffoscopes.</p></div> (10.1007/s10664-026-10924-1)
  DOI : 10.1007/s10664-026-10924-1
• Of All StrIPEs: Investigating Structure-informed Positional Encoding for Efficient Music Generation
  
  • Agarwal Manvi
  • Wang Changhong
  • Richard Gaël
  IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2026, 34, pp.1388-1400. While music remains a challenging domain for generative models like Transformers, a two-pronged approach has recently proved successful: inserting musically-relevant structural information into the positional encoding (PE) module and using kernel approximation techniques based on Random Fourier Features (RFF) to lower the computational cost from quadratic to linear. Yet, it is not clear how such RFF-based efficient PEs compare with those based on rotation matrices, such as Rotary Positional Encoding (RoPE). In this paper, we present a unified framework based on kernel methods to analyze both families of efficient PEs. We use this framework to develop a novel PE method called RoPEPool, capable of extracting causal relationships from temporal sequences. Using RFF-based PEs and rotation-based PEs, we demonstrate how seemingly disparate PEs can be jointly studied by considering the interactions they induce between two descriptive levels of the data: the input, capturing quickly-varying components, and the prior, capturing slowly-varying components. For empirical validation, we use a symbolic music generation task, namely, melody harmonization. We show that RoPEPool, combined with highly-informative structural priors, outperforms all methods. (10.1109/TASLPRO.2026.3662483)
  DOI : 10.1109/TASLPRO.2026.3662483
• Generalised contextuality of continuous variable quantum theory can be revealed with a single projective measurement
  
  • Jokinen Pauli
  • Weilenmann Mirjam
  • Plávala Martin
  • Pellonpää Juha-Pekka
  • Kiukas Jukka
  • Uola Roope
  arxiv.org, 2026. Generalized contextuality is a possible indicator of non-classical behaviour in quantum information theory. In finite-dimensional systems, this is justified by the fact that noncontextual theories can be embedded into some simplex, i.e. into a classical theory. We show that a direct application of the standard definition of generalized contextuality to continuous variable systems does not envelope the statistics of some basic measurements, such as the position observable. In other words, we construct families of fully classical, i.e. commuting, measurements that nevertheless can be used to show contextuality of quantum theory. To overcome the apparent disagreement between the two notions of classicality, that is commutativity and noncontextuality, we propose a modified definition of generalised contextuality for continuous-variable systems. The modified definition is based on a physically-motivated approximation procedure, that uses only finite sets of measurement effects. We prove that in the limiting case this definition corresponds exactly to an extension of noncontextual models that benefits from non-constructive response functions. In the process, we discuss the extension of a known connection between contextuality and no-broadcasting to the continuous-variable scenario, and prove structural results regarding fixed points of infinite-dimensional entanglement breaking channels. (10.48550/arXiv.2601.14067)
  DOI : 10.48550/arXiv.2601.14067
• Rate of convergence of the conditioned random walk towards the Brownian bridge
  
  • Decreusefond Laurent
  • Jacquet Antonin
  , 2026. <div><p>We study the rate of convergence of two discrete processes towards the Brownian bridge: the random walk conditioned to be zero at time 2n and the empirical process which appears in the Glivencko-Cantelli theorem. Combining a functional Stein method with a Radon-Nikodym representation of the bridge, we bound the Fortet-Mourier distance between these conditioned processes and the Brownian bridge.</p></div>
• Distilling Learned Image Compression Models: An Analytical Approach for Low-Latency FPGA Deployment
  
  • Mazouz Alaa Eddine
  • Chaudhuri Sumanta
  • Cagnazzo Marco
  • Mitrea Mihai
  • Tartaglione Enzo
  • Zatt Bruno
  • Fiandrotti Attilio
  IEEE Transactions on Multimedia, Institute of Electrical and Electronics Engineers, 2026. <div><p>Learned Image Compression (LIC) models now rival traditional video codecs in rate-distortion (RD) efficiency, spurring interest in hardware-friendly deployments. However, most existing implementations take an LIC model and fit it to a specific hardware platform through time-consuming, lowlevel hardware optimizations. Moreover, these methods are not designed to meet a pre-established target latency, leading to suboptimal complexity-efficiency-latency trade-offs. We propose a paradigm for distilling a student LIC model under a target latency constraint, avoiding the need for low-level hardware redesign. First, we establish an analytical relationship between the number of convolutional channels of a LIC model and its latency. Second, we introduce a framework to distill a large LIC model into a latency-bound student. Finally, we design a pipelined FPGA architecture that employs mixed-precision quantization, parallel processing, and optimized resource allocation for maximum efficiency. RD efficiency is preserved thanks to a hardwarefriendly GDN/iGDN module end-to-end integrated within our LIC pipeline. Experiments on a ZCU102 FPGA show that our approach achieves competitive RD efficiency with explicit latency control, reaching up to 60 fps for HD content while consuming less than 2.14 J/frame.</p></div>
• Statistically Robust Resource Block Allocation for Satellite Communications
  
  • Manapragada Chaitanya
  • Decreusefond Laurent
  • Martins Philippe
  , 2026. It is critical to dimension (accurately estimate capacity of) a satellite system prior to deployment, as it is very expensive to reconfigure launched satellite systems that fail to meet demand or that waste capacity. The fundamental requirement is a dimensioning rule for resource blocks (RBs) given a satellite footprint and a target overload probability (target Quality-of-Service). The rule must be robust to the spatial covariance structure of signal attenuation, which is generally unknown both at the time of pre-deployment dimensioning and afterwards. Existing approaches address parts of this problem, but there does not yet exist a footprint-level RB dimensioning rule for the satellite context. We develop such a rule: starting with a Gaussian attenuation field that induces a covariance structure inspired by classical work on spatial covariance of attenuation, we sample users at random along with their field-based attenuation values, and estimate aggregate RB demand for a target overload probability. We do this in two complementary ways: a Monte Carlo route that gives a simulation-derived RB budget for a given target overload probability, and a concentration route that gives a conservative analytic upper bound on the target overload probability for a given RB budget (such as the one obtained through simulation). Taken together, these complementary approaches give a principled way to dimension RBs for a satellite footprint under spatially correlated attenuation.
• UNSUPERVISED DOMAIN ADAPTATION WITH TARGET-ONLY MARGIN DISPARITY DISCREPANCY
  
  • Miralles Gauthier
  • Le Folgoc Loic
  • Jugnon Vincent
  • Gori Pietro
  , 2026. <div><p>In interventional radiology, Cone-Beam Computed Tomography (CBCT) is a helpful imaging modality that provides guidance to practicians during minimally invasive procedures. CBCT differs from traditional Computed Tomography (CT) due to its limited reconstructed field of view, specific artefacts, and the intra-arterial administration of contrast medium. While CT benefits from abundant publicly available annotated datasets, interventional CBCT data remain scarce and largely unannotated, with existing datasets focused primarily on radiotherapy applications. To address this limitation, we leverage a proprietary collection of unannotated interventional CBCT scans in conjunction with annotated CT data, employing domain adaptation techniques to bridge the modality gap and enhance liver segmentation performance on CBCT. We propose a novel unsupervised domain adaptation (UDA) framework based on the formalism of Margin Disparity Discrepancy (MDD), which improves target domain performance through a reformulation of the original MDD optimization framework. Experimental results on CT and CBCT datasets for liver segmentation demonstrate that our method achieves state-of-the-art performance in UDA, as well as in the few-shot setting.</p></div>