Publications

2026

• 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
• 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
• Verifying Reasoning Chains Generated by Large Language Models: A Survey
  
  • Jaulmes Bérénice
  • Arouete Jean-Christophe
  • Barry Mariam
  • Alam Mehwish
  ACM Computing Surveys, Association for Computing Machinery, 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.
• PRIMA 2025: Principles and Practice of Multi-Agent Systems
  
  • Dima Catalin
  • Ferrando Angelo
  • Malvone Vadim
  , 2026, 16366. (10.1007/978-3-032-13562-9)
  DOI : 10.1007/978-3-032-13562-9
• Toward Secure RISC-V Microarchitecture: Vulnerability Classes and Defenses
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  IEEE Access, IEEE, 2026, 14, pp.51504-51519. <div xmlns="http://www.tei-c.org/ns/1.0"><head>RISC-</head><p>V is an open instruction set architecture that enables a wide range of processor implementations across academia and industry. While its architectural simplicity and extensibility support rapid innovation, RISC-V processors exhibit microarchitectural behaviors that give rise to side-channel and transient-execution vulnerabilities similar to those observed in other high-performance CPU architectures. This paper presents a structured taxonomy of microarchitectural vulnerabilities in RISC-V processors, organized into five classes based on the affected hardware components and dominant sources of leakage: cache-based channels, speculative and transient execution, branch prediction structures, memory and address translation subsystems, and observation through performance monitoring mechanisms.</p><p>For each class, we examine representative vulnerabilities and documented attacks on existing RISC-V implementations and research prototypes, and analyze the underlying microarchitectural mechanisms that enable information leakage. We further study countermeasures applicable to each vulnerability class, covering both RISC-V-specific proposals and adaptations of mechanisms previously developed for x86 and ARM platforms. The surveyed defenses are systematically analyzed in terms of security coverage, performance overhead, and implementation complexity, highlighting both their strengths and practical limitations. The resulting taxonomy and comparative analysis aim to support RISC-V architects, system designers, and software developers in reasoning about microarchitectural risks and mitigation trade-offs during system design.</p></div> <div xmlns="http://www.tei-c.org/ns/1.0"><head>INDEX TERMS</head><p>RISC-V, microarchitectural vulnerabilities, side-channel attacks, transient execution attacks, cache attacks, microarchitectural data sampling, hardware security, mitigation These include cache timing attacks such as Flush+Reload and Prime+Probe, Spectre v1, Spectre v2, Spectre-v5, Meltdownstyle, TLB-and page-table-based channels TLBleed-style, and side-channels via performance monitoring units (e.g. Cy-cleDrift) [5], [7]-[9], [33]. Notable targets for these demonstrations include SiFive U-class cores, T-Head C906/C910, and BOOM-based designs. These results indicate that high-performance microarchitectural optimizations combined with shared resources can create exploitable leakage even in RISC-V architectures [7].</p></div> <div xmlns="http://www.tei-c.org/ns/1.0"><head>A. MOTIVATION</head><p>RISC-V's open and modular design offers a unique opportunity to integrate security mechanisms early in the processor design lifecycle [10]. However, the wide variation in cores, cache organizations, branch predictors, and speculative execution depths makes it difficult to perform systematic security evaluations. To address this, a structured, componentoriented framework is necessary. Such a framework identifies high-risk microarchitectural structures that leak information, maps known attacks to the underlying microarchitectural structures that enable them. It also evaluates mitigation strategies spanning hardware, firmware, operating systems, and software layers. This motivates a RISC-V-specific taxonomy of microarchitectural vulnerabilities, allowing designers to reason about both vulnerabilities (attacks) and countermeasures (defenses) in a consistent, component-focused manner.</p></div> <div xmlns="http://www.tei-c.org/ns/1.0"><head>B. CONTRIBUTIONS</head><p>This paper makes the following contributions:</p><p>1) We introduce a RISC-V-specific, component-oriented taxonomy of microarchitectural vulnerabilities, categorized by the primary hardware structures through which information leaks. 2) We survey and analyze representative attacks in each category, emphasizing demonstrated RISC-V exploits. Examples include Cache+Time cache attacks, CycleDrift performance-counter side-channels, transient execution attacks on BOOM-class cores, and TLB/page-table leakage channels. 3) We provide a structured set of countermeasures, discussing several defenses per category. These include RISC-V-specific mitigations such as enclave-oriented cores and in-process isolation, as well as adaptations from x86 and ARM such as cache partitioning, fencing, and buffer clearing. 4) We comparatively evaluate the discussed defenses in terms of security coverage, performance overhead, and implementation complexity, summarizing the results in a consolidated reference Table <ref type="table">3</ref> to support informed design decisions. 5) We derive practical recommendations for RISC-V CPU and SoC designers, OS and hypervisor developers, and compiler/toolchain maintainers, emphasizing early integration of isolation mechanisms and defense-in-depth aligned with ongoing RISC-V security extension efforts.</p></div> (10.1109/ACCESS.2026.3679984)
  DOI : 10.1109/ACCESS.2026.3679984
• High-speed photonic-wireless link in the Ka-band using unipolar quantum devices and heterodyne mixing
  
  • Didier Pierre
  • Balzo Livia Del
  • Spitz Olivier
  • Gacemi Djamal
  • Vasanelli Angela
  • Sirtori Carlo
  Journal of Lightwave Technology, Institute of Electrical and Electronics Engineers (IEEE)/Optical Society of America(OSA), 2026, pp.1-9. (10.1109/JLT.2026.3744676)
  DOI : 10.1109/JLT.2026.3744676
• 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.
• Is Phase Really Needed for Weakly-Supervised Dereverberation ?
  
  • Rodrigues Marius
  • Bahrman Louis
  • Badeau Roland
  • Richard Gaël
  , 2026. In unsupervised or weakly-supervised approaches for speech dereverberation, the target clean (dry) signals are considered to be unknown during training. In that context, evaluating to what extent information can be retrieved from the sole knowledge of reverberant (wet) speech becomes critical. This work investigates the role of the reverberant (wet) phase in the time-frequency domain. Based on Statistical Wave Field Theory, we show that late reverberation perturbs phase components with white, uniformly distributed noise, except at low frequencies. Consequently, the wet phase carries limited useful information and is not essential for weakly supervised dereverberation. To validate this finding, we train dereverberation models under a recent weak supervision framework and demonstrate that performance can be significantly improved by excluding the reverberant phase from the loss function.
• Stein's functional method for weakly dependent random variables
  
  • Coutin Laure
  • Darou Kebe Sérigné
  • Decreusefond Laurent
  , 2026. <div><p>This article extends Stein's functional method to establish explicit rates of convergence in the Wasserstein-1 distance for Donsker's invariance principle under weak dependence. Our approach first reduces the problem of bounding distances between distributions on a function space to estimating distances between finite-dimensional marginals. We then employ the block technique, which is standard in Stein's methodology, to handle dependence. While our analysis focuses on φ-mixing sequences, the method extends to other forms of weak dependence admitting suitable covariance inequalities.</p></div>
• 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>
• Representation learning of human cortical folding to reveal long lasting neurodevelopmental signatures
  
  • Laval Julien
  • Guiavarch Robin
  • Dufournet Antoine Joachim
  • Menasria Racim
  • Drabczuk Barthélémy
  • Mendoza Cristobal
  • Tounsi Saeb
  • Baroud Chikh Abdelghani
  • Bourenane Merieme
  • Troiani Vanessa
  • Snyder William
  • Patti Marisa
  • Mylene Moyal
  • Plaze Marion
  • Cachia Arnaud
  • Santacroce Federica
  • Committeri Giorgia
  • Cury Claire
  • de Matos Kevin
  • Colliot Olivier
  • Sun Zhong Yi
  • Fischer Clara
  • Frouin Vincent
  • Gori Pietro
  • Rivière Denis
  • Chavas Joël
  • Mangin Jean-Francois
  , 2026. The human brain folds in utero, primarily during late gestation. Shortly after birth, cortical folding patterns are established and remain stable thereafter, making them promising early neurodevelopmental markers. Yet it is unclear whether the representations given by current neuroimaging foundation models capture cortical folding variability. Here, we introduce Champollion, a self-supervised learning framework that learns interpretable local representations of cortical folding from structural MRI. Optimized on representative folding-related tasks, Champollion accurately captures known folding patterns across cortical regions and external datasets. In a comprehensive benchmark, it consistently outperforms neuroimaging and general-purpose foundation models. Furthermore, Champollion reveals richer genetic associations than conventional morphometric descriptors and identifies localized folding signatures associated with incomplete hippocampal inversion, prematurity, and maternal smoking. These results establish cortical folding as a rich and largely untapped source of neurodevelopmental information, and Champollion provides a unified framework for discovering, localizing and interpreting long lasting cortical folding signatures. (10.5281/zenodo.22229006)
  DOI : 10.5281/zenodo.22229006
• 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>
• 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
• Convergence rate for the coupon collector's problem with Stein's method
  
  • Costacèque Bruno
  • Decreusefond Laurent
  Stochastic Processes and their Applications, Elsevier, 2026, 193, pp.104835. The functional characterization of a measure, an essential but delicate aspect of Stein's method, is shown to be accessible for stable probability distributions on convex cones. This notion encompasses the usual stable distributions \textit{e.g.} Gaussian, Pareto, \textit{etc.} but also the max-stable distributions: Weibull, Gumbel and Fréchet. We use the definition of max-stability to define a Markov process whose invariant measure is the stable measure of interest. In this paper, we focus on the Gumbel distribution and show how this construction can be applied to estimate the rate of convergence in the classical coupon collector's problem. (10.1016/j.spa.2025.104835)
  DOI : 10.1016/j.spa.2025.104835
• Equitable Alignment of LLMs Across Languages with Regularized Direct Preference Optimization
  
  • Leteno Thibaud
  • Gourru Antoine
  • Laclau Charlotte
  • Gravier Christophe
  , 2026. Aligning large language models with human values is a critical challenge for ensuring public safety. This alignment is commonly achieved through methods such as Direct Preference Optimization and RLAIF/RLHF. Despite extensive research, prior studies show that models often fail to maintain consistent alignment performance across languages, resulting in unequal treatment of users. To address this issue, we introduce a regularization term in the Direct Preference Optimization fine-tuning process that penalizes discrepancies in model behavior between languages using a bilingual corpus. Our results demonstrate that this approach effectively reduces the alignment gap across languages following fine-tuning.
• 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, pp.1-14. 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. (10.1109/TMM.2026.3733013)
  DOI : 10.1109/TMM.2026.3733013
• THE INTERNET ARCHIVE MUSIC DATASET
  
  • Stamatiadis Paraskevas
  • Miranda Bernardo V
  • Berger Clémentine
  • Richard Gaël
  • Fontaine Mathieu
  • Essid Slim
  , 2026. <div><p>We introduce the Internet Archive Music Dataset (IAMD), a large-scale collection of captioned music segments derived from the Internet Archive. To the best of our knowledge, IAMD constitutes the largest publicly available music-caption dataset to date with over 34,000 hours of audio, providing a valuable benchmark for training and evaluating music understanding and generative models. The dataset is built from content declared to be distributed under Creative Commons licenses, and cross-referencing with MusicBrainz is done to improve license information reliability. To annotate IAMD, we present an automatic captioning pipeline that augments base captions produced by an audio-language model (ALM) with textual metadata sourced from the Internet Archive and imputed metadata obtained using audio classification models. Caption quality is assessed objectively and subjectively, and results indicate that the annotation pipeline is reliable and does not degrade caption quality with scaling.</p></div>
• Digital Education: Shaping Sustainable Lifelong Learning for All in the Era of AI
  
  • Hamonic Ella
  • Sharrock Rémi
  , 2026, 15733, pp.XI, 234. (10.1007/978-3-032-00056-9)
  DOI : 10.1007/978-3-032-00056-9
• Computational Aspects of the Trace Norm Contraction Coefficient
  
  • Delsol Idris
  • Fawzi Omar
  • Kochanowski Jan
  • Ramachandran Akshay
  IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 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 analog 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 NP-hard. In addition, we establish a converging hierarchy of semidefinite programming upper bounds on the contraction coefficient. (10.1109/tit.2026.3743741)
  DOI : 10.1109/tit.2026.3743741
• Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos
  
  • Parolari Luca
  • Gori Pietro
  • Ballan Lamberto
  • Biffi Carlo
  • Folgoc Loic Le
  , 2026. Learning robust representations of polyp tracklets is key to enabling multiple AI-assisted colonoscopy applications, from polyp characterization to automated reporting and retrieval. Supervised contrastive learning is an effective approach for learning such representations, but it typically relies on correct positive and negative definitions. Collecting these labels requires linking tracklets that depict the same underlying polyp entity throughout the video, which is costly and demands specialized clinical expertise. In this work, we leverage the sequential workflow of colonoscopy procedures to derive self-supervised associations from temporal structure. Since temporally derived associations are not guaranteed to be correct, we introduce a noise-aware contrastive loss to account for noisy associations. We demonstrate the effectiveness of the learned representations across multiple downstream tasks, including polyp retrieval and re-identification, size estimation, and histology classification. Our method outperforms prior self-supervised and supervised baselines, and matches or exceeds recent foundation models across all tasks, using a lightweight encoder trained on only 27 videos. Code is available at github.com/lparolari/ntssl.
• Agentic Much? Adoption of Coding Agents on GitHub
  
  • Robbes Romain
  • Matricon Théo
  • Degueule Thomas
  • Hora Andre
  • Zacchiroli Stefano
  ACM Transactions on Software Engineering and Methodology, Association for Computing Machinery, 2026, pp.1-43. In the first half of 2025, coding agents have emerged as a category of development tools that have very quickly transitioned to the practice. Unlike ''traditional'' code completion LLMs such as Copilot, agents like Cursor, Claude Code, or Codex operate with high degrees of autonomy, up to generating complete pull requests starting from a developer-provided task description. This new mode of operation is poised to change the landscape in an even larger way than code completion LLMs did, making the need to study their impact critical. Also, unlike traditional LLMs, coding agents tend to leave more explicit traces in software engineering artifacts, such as co-authoring commits or pull requests. We leverage these traces to present the first large-scale study (128,018 projects) of the adoption of coding agents on GitHub, finding an estimated adoption rate of 22.20%--28.66%, which is very high for a technology only a few months old--and increasing. We carry out an in-depth study of the adopters we identified, finding that adoption is broad: it spans the entire spectrum of project maturity; it includes established organizations; and it concerns diverse programming languages or project topics. At the commit level, we find that commits assisted by coding agents are larger than commits only authored by human developers, and have a large proportion of features and bug fixes. These findings highlight the need for further investigation into the practical use of coding agents. (10.1145/3822180)
  DOI : 10.1145/3822180
• Event Detection and Localization Using a Multiple-Input-Multiple-Output Distributed Fiber Sensor with Birefringence and Phase Estimation
  
  • Prato Diane
  • Sheramin Mehran Mokthari
  • Gabet Renaud
  • Awwad Élie
  , 2026, pp.17. We present a numerical modeling approach for a Multiple-Input-Multiple-Output Distributed Acoustic Sensing (MIMO-DAS) system, incorporating both phase and polarization dynamics. We demonstrate the ability of the proposed architecture to provide a distributed estimation of the fiber effective linear birefringence magnitude and of the optical phase of the backscattered signal to detect and localize dynamic events with a mean spatial resolution of 1.3m. This allows for increased sensitivity to disturbances that act transversely on the fiber, since the estimated fiber effective birefringence magnitude will be responsive to perturbations that break cylindrical symmetry (anisotropic transverse strains), while the phase common to both polarization tributaries will show great sensitivity to pure longitudinal strains. Analyzing these two quantities is therefore of use to discriminate between purely axisymmetric strains and anistropic strains. (10.1117/12.3104760)
  DOI : 10.1117/12.3104760
• SCALMU: Synthetically-trained Coupling of Adaptive Learned Multiplicative Updates for Hyperspectral-Multispectral Fusion
  
  • Xu Xinxin
  • Gousseau Yann
  • Kervazo Christophe
  • Ladjal Saïd
  IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2026. HyperSpectral-MultiSpectral Image (HSI-MSI) fusion aims to recover a high-resolution hyperspectral image from a low-resolution HSI and a high-resolution MSI. Classical methods such as Coupled Nonnegative Matrix Factorization (CNMF) benefit from a strong physical interpretability but suffer from inferior results compared to their deep-learning counterparts. To address this limitation, we propose SCALMU (Synthetically-trained Coupling of Adaptive Learned Multiplicative Updates), a novel blind unrolled neural network architecture that integrates adaptive learnable matrices within the classical framework of CNMF multiplicative updates, improving its results. Due to its architectural proximity with CNMF, the resulting algorithm preserves physical interpretability and nonnegativity constraints. To overcome the scarcity of supervised training data, we generate a synthetic HSI-MSI dataset using the dead leaves model and train SCALMU end-to-end under synthetic supervision. Experiments on several datasets show that SCALMU outperforms state-of-the-art methods and highlights the potential of blind fusion trained with synthetic data. The code is available at \url{https://github.com/xinxinxu99/SCALMU.git} (10.1109/TGRS.2026.3712501)
  DOI : 10.1109/TGRS.2026.3712501