Publications

2026

• Self-Supervised Quantification of Despeckling Uncertainties Applied to Unsupervised Radiometric Change Detection in SAR imaging
  
  • Bultingaire Thomas
  • Kervazo Christophe
  • Denis Loïc
  • Tupin Florence
  , 2026. The Synthetic Aperture Radar (SAR) imaging modality is well-suited for change detection tasks. In particular, its robustness to day-night cycles and cloud coverage is critical for certain applications, such as disaster assessment and monitoring the deforestation in old-growth forests that are frequently cloudcovered. However, its use is often impeded by the speckle phenomenon, causing strong fluctuations in the radar images, which are difficult to distinguish from actual temporal changes. To suppress these fluctuations, we propose to resort to a stateof-art despeckling method. In addition, to better take into account the residual fluctuations in the areas that are the most challenging to restore, we quantify despeckling uncertainties, which enables reaching a constant rate of false positive detections. We also show that incorporating spatial correlations into the despeckling uncertainties model can further improve change detection, especially in highly textured regions such as building areas. The proposed SAR Despeckling Uncertainty Change detection frameworK (SAR-DUCK) is applied to TerraSAR-X and Sentinel-1 images, demonstrating improved detection rates compared to methods from the state-of-the-art, as well as a constant and controlled false alarm rate. Supplementary results and an implementation of the proposed method are available on GitLab at https://gitlab.telecom-paris.fr/ring/sar-duck.
• Cross-Core Covert Channel for RISC-V: Implementation, Countermeasures and Cross-Platform Analysis
  
  • Khan Mahreen
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2026. <div><p>Cache-based covert channels exploit microarchitectural timing differences to enable unauthorized communication between processes. While extensively studied on x86 architectures, such channels remain underexplored in the emerging RISC-V ecosystem. This paper presents the design and implementation of a novel prefetcher and cache timing covert channel for RISC-V platforms that exploits the timing difference between cached and uncached memory accesses. Our implementation supports both standardized RISC-V cache management extensions (Zicbom and Zicbop) and vendor-specific instructions (T-Head C910 custom instructions), demonstrating cross-platform portability across heterogeneous RISC-V implementations. The sender encodes bits by selectively prefetching or flushing a shared cache line, while the receiver decodes information by measuring memory access latency. Through careful synchronization using POSIX shared memory and atomic operations, we achieve reliable bit transmission on both the RISC-V gem5 full-system simulator (Sifive U54 core) and physical RISC-V Beagle-V Ahead (T-Head C910 core). Our paper contributes to understanding the security implications of cache and prefetcher management instructions in RISC-V systems and provides a foundation for developing detection and mitigation strategies for this emerging architecture.</p></div>
• Taylor-SWFT: fast discrete Statistical Wave Field Theory using Taylor expansion for late reverberation Work under review
  
  • Rodrigues Marius
  • Lalay Louis
  • Badeau Roland
  • Richard Gaël
  • Fontaine Mathieu
  , 2026. Dynamic room acoustic simulation aims to render the acoustic effects of an environment in real time while accounting for potentially moving sources and receivers. In this context, the efficient synthesis of the long-term room response, also known as late reverberation, remains challenging because of the intricate relationship between room geometry and acoustic behavior. This paper introduces Taylor-SWFT, an efficient implementation of key results from Statistical Wave Field Theory (SWFT) for the geometry-aware dynamic synthesis of late reverberation. The method is evaluated on the Benchmark for Room Acoustical Simulation (BRAS) and achieves competitive performance compared with classical approaches, while substantially reducing computational cost.
• Virtual PUF: Built-in Model for Highly Reliable and Secure PUF
  
  • Nasir Neelam
  • Cheng Wei
  • Kühne Ulrich
  • Graba Tarik
  • Danger Jean-Luc
  , 2026. Strong Physical Unclonable Functions (PUFs) with challengeresponse protocols provide a cost-effective authentication solution for resource-limited devices. However, they are susceptible to modeling attacks. An effective countermeasure for multi-bin PUFs -such as the Ring Oscillator PUF (RO-PUF) and Loop PUF -is the use of Non-Monotonic Quantization (NMQ) of the response. However, NMQ requires quite high quantization levels in order to effectively improve the security. This makes the PUF unreliable, rendering it impractical for authentication purposes. In this paper, we present a solution called the Virtual PUF : It generates a lightweight PUF model at device start-up, which is then used instead of the physical PUF during authentication. This allows the elimination of the noise impact and renders the PUF fully reliable and secure against ML attacks at the same time. As a proof of concept, we present an FPGA implementation based on a Loop PUF -a PUF relying on a single ring oscillator -and we show that beside its perfect reliability and high security, the Virtual PUF can be designed in a lightweight manner. However, the model built by the Virtual PUF may differ from the enrolled model and cause mismatch errors between the two models, which could deteriorate the authentication protocol. We show that the mismatch error measured in different configurations and environments can be optimized towards an acceptable range and/or managed by the authentication protocol.
• SCALMU: Synthetically-trained Coupling of Adaptive Learned Multiplicative Updates for Hyperspectral-Multispectral Fusion
  
  • Xu Xinxin
  • Gousseau Yann
  • Kervazo Christophe
  • Ladjal Saïd
  , 2026. HyperSpectral-MultiSpectral Image (HSI-MSI) fusion enables high-resolution hyperspectral imaging by combining the rich spectral information of low-spatial-resolution hyperspectral images with the detailed spatial structure of multispectral images. 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 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 data scarcity for training, we additionally generate a synthetic HSI-MSI dataset via the dead leaves model, enabling synthetic supervision. SCALMU is then trained end-to-end on this dataset. Experiments demonstrate SCALMU's superiority over state-of-the-art methods on several datasets. The code is available at \url{https://github.com/xinxinxu99/SCALMU.git}
• New approaches to CLT for stable random variables
  
  • Coutin Laure
  • Decreusefond Laurent
  • Huang Lorick
  , 2026. In this paper, we present novel approaches to the Central Limit Theorem (CLT) for stable random variables, particularly focusing on cases where the distribution has heavy tails. The study develops three independent methods, each addressing different aspects of convergence in the context of α-stable distributions. We explore both non-integrable and integrable cases, offering new derivations and highlighting the distinct normalization required when dealing with heavy-tailed distributions. Special attention is given to the Stein method, adapted to handle stable laws, leveraging Fourier techniques and Poisson process representations. We provide convergence rates and quantify the Wasserstein distance between sums of independent and identically distributed random variables and their limiting stable distributions. Our findings extend existing results by offering intrinsic methods for stable CLTs, with applications to distributions such as Pareto, where classical CLT normalization fail.
• How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphs
  
  • Perez Mathilde
  • Romero Raphaël
  • Lijffijt Jefrey
  • Laclau Charlotte
  , 2026. Link prediction models are increasingly used to recommend interactions in evolving networks, yet their impact on network structure is typically assessed from static snapshots. In particular, observed homophily conflates intrinsic interaction tendencies with amplification effects induced by network dynamics and algorithmic feedback. We propose a temporal framework based on multivariate Hawkes processes that disentangles these two sources and introduce an instantaneous bias measure derived from interaction intensities, capturing current reinforcement dynamics beyond cumulative metrics. We provide a theoretical characterization of the stability and convergence of the induced dynamics, and experiments show that the proposed measure reliably reflects algorithmic feedback effects across different link prediction strategies.
• It’s All About the Confidence: An Unsupervised Approach for Multilingual Historical Entity Linking using Large Language Models
  
  • Santini Cristian
  • van Erp Marieke
  • Alam Mehwish
  , 2026. Despite the recent advancements in NLP with the advent of Large Language Models (LLMs), Entity Linking (EL) for historical texts remains challenging due to linguistic variation, noisy inputs, and evolving semantic conventions. Existing solutions either require substantial training data or rely on domain-specific rules that limit scalability. In this paper, we present MHEL-LLaMo (Multilingual Historical Entity Linking with Large Language MOdels), an unsupervised ensemble approach combining a Small Language Model (SLM) and an LLM. MHEL-LLaMo leverages a multilingual bi-encoder (BELA) for candidate retrieval and an instruction-tuned LLM for NIL prediction and candidate selection via prompt chaining. Our system uses SLM's confidence scores to discriminate between easy and hard samples, applying an LLM only for hard cases. This strategy reduces computational costs while preventing hallucinations on straightforward cases. We evaluate MHEL-LLaMo on four established benchmarks in six European languages (English, Finnish, French, German, Italian and Swedish) from the 19th and 20th centuries. Results demonstrate that MHEL-LLaMo outperforms state-of-the-art models without requiring fine-tuning, offering a scalable solution for low-resource historical EL. Our error analysis reveals that 41\% of false predictions exhibit semantic proximity to ground truth entities, highlighting the LLM's accurate disambiguation of historical references.
• On Gossip Algorithms for Machine Learning with Pairwise Objectives
  
  • Colin Igor
  • Bellet Aurélien
  • Clémençon Stephan
  • Salmon Joseph
  , 2026. In the IoT era, information is more and more frequently picked up by connected smart sensors with increasing, though limited, storage, communication and computation abilities. Whether due to privacy constraints or to the structure of the distributed system, the development of statistical learning methods dedicated to data that are shared over a network is now a major issue. Gossip-based algorithms have been developed for the purpose of solving a wide variety of statistical learning tasks, ranging from data aggregation over sensor networks to decentralized multi-agent optimization. Whereas the vast majority of contributions consider situations where the function to be estimated or optimized is a basic average of individual observations, it is the goal of this article to investigate the case where the latter is of pairwise nature, taking the form of a U -statistic of degree two. Motivated by various problems such as similarity learning, ranking or clustering for instance, we revisit gossip algorithms specifically designed for pairwise objective functions and provide a comprehensive theoretical framework for their convergence. This analysis fills a gap in the literature by establishing conditions under which these methods succeed, and by identifying the graph properties that critically affect their efficiency. In particular, a refined analysis of the convergence upper and lower bounds is performed.
• A POP ⋆ is Born: Formal Predictable Out-of-Order Processor Model
  
  • Rouizi Lilia
  • Binder Benjamin
  • Asavoae Mihail
  • Ermis Engin
  • Rieg Lionel
  • Brandner Florian
  , 2026. Modern processors, even at the mid-range level, include multi-level caches, pipelines with branch predictors, or Out-of-Order (OoO) execution. While these are essential for average-case performance, they also increase the complexity of worst-case execution time analysis. OoO execution, for instance, is prone to timing anomalies and, due to the lack of efficient abstractions, quickly leads to state-space explosion. Consequently, it remains highly challenging in the context of critical real-time systems. This work proposes the first generic approach to predictable OoO execution, which is formally modeled and proven in the F* language and experimentally evaluated through simulations in gem5. Performance is evaluated on different processor models for MiBench and Embench programs. The average slowdown for an ARM A710-like processor model amounts to about 18.3% due to an implementation particularity of gem5. Eliminating bias from this issue reduces the slowdown to only 8.8%-10.4%.
• Bandwidth-Scalable Neural Behavioral Modeling of Wideband RF Power Amplifiers : NARX Neural Networks and a Unified Figure of Merit
  
  • Pham Trong Thuy
  , 2026. Wideband RF power amplifiers (PAs) face a fundamental efficiency-linearitytrade-off in which nonlinearities and memory effects degrade in-band waveformfidelity and generate out-of-band spectral regrowth. To resolve this problem,accurate behavioral models serve as essential digital surrogates for thedevelopment of Digital Predistortion (DPD). By characterizing complex memoryeffects and spectral regrowth, these models enable safe and systematic designevaluation without the risks of extensive hardware iterations. This thesisdevelops bandwidth-scalable neural behavioral modeling methods for wideband PAs,emphasizing accuracy, robustness under bandwidth variation, andimplementation-relevant complexity. A structured recurrent formulation based onthe nonlinear autoregressive neural network with exogenous inputs (NARXNN) isestablished as a favorable accuracy-complexity compromise relative to polynomialbaselines and representative neural network alternatives. To improve modelingfidelity in strongly nonlinear regimes, a piecewise NARXNN (PW-NARXNN)architecture is proposed by segmenting the operating space and trainingspecialized NARXNN submodels; on the reference dataset, PW-NARXNN improvesNMSE to -39.2 dB with a moderate increase in parameters (666 compare to377 of global NARXNN), and yields improved spectral fidelity. Bandwidthgeneralization is then investigated on a measured multi-band 5G-NR datasetspanning 20-100 MHz acquired on an LDMOS PA, using standard,interpolation, and extrapolation validation schemes to quantify robustness underbandwidth changes. Finally, a bandwidth-aware Figure of Merit (FoM) isintroduced to unify comparison by combining mean accuracy, prediction stability,bandwidth sensitivity, and a logarithmic complexity penalty, providing a compactranking and consistently identifying NARXNN as the most favorable trade-offamong evaluated baselines.
• Where Experts Disagree, Models Fail: Detecting Implicit Legal Citations in French Court Decisions
  
  • Floro Avrile
  • Dhorasoo Tamara
  • Pellez Soline
  • Holzenberger Nils
  , 2026. Computational methods applied to legal scholarship hold the promise of analyzing law at scale. We start from a simple question: how often do courts implicitly apply statutory rules? This requires distinguishing legal reasoning from semantic similarity. We focus on implicit citation of the French Civil Code in first-instance court decisions and introduce a benchmark of 1,015 passage-article pairs annotated by three legal experts. We show that expert disagreement predicts model failures. Inter-annotator agreement is moderate (κ = 0.33) with 43% of disagreements involving the boundary between factual description and legal reasoning. Our supervised ensemble achieves F1 = 0.70 (77% accuracy), but this figure conceals an asymmetry: 68% of false positives fall on the 33% of cases where the annotators disagreed. Despite these limits, reframing the task as top-k ranking and leveraging multi-model consensus yields 76% precision at k = 200 in an unsupervised setting. Moreover, the remaining false positives tend to surface legally ambiguous applications rather than obvious errors.
• From Weights to Layers : Deep Neural Network Compression for Efficient Inference
  
  • Quétu Victor
  , 2026. Deep learning models continue to grow in depth and computational cost, yet modern inference pipelines remain constrained by latency, memory, and energy budgets. This thesis investigates where redundant computation hides in over-parameterized architectures, and how to remove it safely. We first analyze the Sparse Double Descent phenomenon and show how aggressive sparsification can paradoxically enhance generalization. We characterize this behavior and propose regularization and distillation-based approaches supported by an entropy-based metric. Building on this metric, we introduce three familiesof depth-reduction strategies: entropy-based pruning (EGP, EASIER), BatchNorm-guided layer collapse(TLC), and Optimal Transport–based inductive regularization (LaCoOT). Together, these methods reduce up to 70% of network depth across CNNs, Transformers, and diffusion models, often with minimal performance degradation, and sometimes even gains in accuracy. Finally, we extend the notion of redundancy to the operand by proposing FOLDER, a training-free token-pruning module that accelerates multimodal LLMs by up to 2.4 times with preserved or improved performance. Collectively, these contributions advance the understanding of redundancy in deep networks and propose general strategies for improving inference efficiency, paving the way toward more sustainable and adaptive deep learning models.
• Towards a systemic framework for assessing the environmental rebound effects of Artificial Intelligence
  
  • Delarue Simon
  , 2025.
• Simulation of Fog-Induced Attenuation and Inter-Symbol Interference in Mid-and Long-Infrared Free-Space Optical Communications
  
  • Breton Vicente Alberto
  • Sorrente Béatrice
  • Roux Aurélien
  • Fade Julien
  • Da Silva Anabela
  • Grillot Frédéric
  , 2026. <div><p>Free-space optical (FSO) communications offer large bandwidths but are strongly affected by atmospheric fog. Recent advances in quantum cascade lasers enable high-speed transmission in the mid-and long-infrared ranges (3-6 and 8-12 µm), which are expected to provide increased robustness compared to the conventional visible or 1.55 µm bands. In this work, light propagation through fog is modeled using a time-domain approximate radiative transfer equation solver, accounting for wavelength-dependent scattering, absorption, and droplet size distributions of a realistic fog scenario, demonstrating its advantages against visibility-based models for satisfactory evaluation of FSO links performance in the mid-and long-infrared. The model is applied to an example optical communication link to assess attenuation and inter-symbol interference effects. Quantitative results indicate that long-infrared wavelengths exhibit reduced temporal spreading and improved tolerance to fog-induced impairments.</p></div>
• Continuous-variable quantum communication
  
  • Usenko Vladyslav
  • Acín Antonio
  • Alléaume Romain
  • Andersen Ulrik
  • Diamanti Eleni
  • Gehring Tobias
  • Hajomer Adnan A. E.
  • Kanitschar Florian
  • Pacher Christoph
  • Pirandola Stefano
  • Pruneri Valerio
  Reviews of Modern Physics, American Physical Society, 2026, 98 (1), pp.015003. Tremendous progress in experimental quantum optics during the past decades enabled the advent of quantum technologies, one of which is quantum communication. Aimed at novel methods for more secure or efficient information transfer, quantum communication has developed into an active field of research and proceeds toward full-scale implementations and industrialization. Continuous-variable methods of multi-photon quantum state preparation, manipulation, and coherent detection, as well as the respective theoretical tools of phase-space quantum optics, offer the possibility to make quantum communication efficient, applicable and accessible, thus boosting the development of the field. We review the methodology, techniques and protocols of continuous-variable quantum communication, from the first theoretical ideas, through milestone implementations, to the recent developments, covering quantum key distribution as well as other quantum communication schemes, suggested on the basis of continuous-variable states and measurements. (10.1103/mgj7-t6d3)
  DOI : 10.1103/mgj7-t6d3
• An order-reversing embedding of Turing degrees into Arthur-Nimue-Merlin degrees
  
  • Abou-Samra Jean
  • Madore David Alexander
  , 2026. <div><p>The Arthur-Nimue-Merlin degrees are a generalization of the Turing degrees introduced by Kihara as a tangible description of the partially ordered set of Lawvere-Tierney topologies on the effective topos (equivalently, subtoposes of the effective topos). They are defined in terms of a three-player game that introduces both angelic and demonic nondeterminism into oracle queries. We construct an order embedding of the Turing degrees with their order reversed into the Arthur-Nimue-Merlin degrees, whose image we call the "co-Turing degrees"; we then study the order relationship of these co-Turing degrees with the (naturally embedded) Turing degrees within the Arthur-Nimue-Merlin degrees.</p></div>
• The Hi-Audio Online Platform for Recording and Distributing Multi-Track Music Datasets
  
  • Gil Panal José M
  • David Aurélien
  • Richard Gaël
  EURASIP Journal on Audio, Speech, and Music Processing, SpringerOpen, 2026 (Special issue on "Signal Processing for the Internet of Sounds"). This paper introduces the Hi-Audio online platform, an open-source tool designed to support musicians and researchers in the field of Music Information Retrieval (MIR). The platform enables the recording, uploading, and sharing of multitrack musical compositions, aiming to build an open-access audio database to advance research in music technology. Uploaded audio files are automatically analyzed upon synchronization with the server, leveraging signal processing techniques and machine learning models to generate rich metadata. The platform facilitates remote and asynchronous collaboration via a web-based interface accessible at hiaudio.fr. Furthermore, a novel built-in method for accurate and robust round-trip latency estimation in the browser is proposed and integrated into the platform, demonstrating its applicability in real-world distributed recording scenarios. Finally, an initial user evaluation with musicians was conducted to assess usability and practical relevance under realistic usage conditions. The evaluation combined task-based performance analysis with standardized usability and workload measures. The results indicate high task completion rates for core recording functions and show that the platform can be used effectively by musicians with minimal prior training. (10.1186/s13636-026-00459-0)
  DOI : 10.1186/s13636-026-00459-0
• Compression in 3D Gaussian Splatting: A Survey of Methods, Trends, and Future Directions
  
  • Ali Muhammad Salman
  • Zhang Chaoning
  • Cagnazzo Marco
  • Valenzise Giuseppe
  • Tartaglione Enzo
  • Bae Sung-Ho
  IEEE Transactions on Circuits and Systems for Video Technology, Institute of Electrical and Electronics Engineers, 2026, pp.1-1. <div><p>Gaussian Splatting (3DGS) has recently emerged as a pioneering approach in explicit scene rendering and computer graphics. Unlike traditional neural radiance field (NeRF) methods, which typically rely on implicit, coordinatebased models to map spatial coordinates to pixel values, 3DGS utilizes millions of learnable 3D Gaussians. Its differentiable rendering technique and inherent capability for explicit scene representation and manipulation positions 3DGS as a potential game-changer for the next generation of 3D reconstruction and representation technologies. This enables 3DGS to deliver realtime rendering speeds while offering unparalleled editability levels. However, despite its advantages, 3DGS suffers from substantial memory and storage requirements, posing challenges for deployment on resource-constrained devices. In this survey, we provide a comprehensive overview focusing on the scalability and compression of 3DGS. We begin with a detailed background overview of 3DGS, followed by a structured taxonomy of existing compression methods. Additionally, we analyze and compare current methods from the topological perspective, evaluating their strengths and limitations in terms of fidelity, compression ratios, and computational efficiency. Furthermore, we explore how advancements in efficient NeRF representations can inspire future developments in 3DGS optimization. Finally, we conclude with current research challenges and highlight key directions for future exploration.</p></div> (10.1109/TCSVT.2026.3676048)
  DOI : 10.1109/TCSVT.2026.3676048
• Group Conversational Agents
  
  • Yeo Shunyi
  • Zhang Tianyi
  • Batteman Scott
  • Hsieh Gary
  • Kim Young-Ho
  • Perrault Simon
  • Li Jiannan
  • Tang Anthony
  , 2026. Conversational agents that participate in or mediate group interaction introduce challenges that extend beyond supporting individual users, raising new questions about how agents participate in and influence groups. To characterise this emerging design space, we present a systematic review of 53 peer-reviewed studies on group conversational agents (GCAs). We analyse how GCAs intervene in group-level processes, including participation regulation, conflict mediation, task alignment, and execution support. Using concepts from group research as an analytic lens, we organise prior GCA work around recurring group interactional challenges (orientation, conflict, alignment, and execution), and examine the roles agents are designed to play in addressing these challenges. We find that GCAs are predominantly designed as short-term, role-bounded interventions targeting isolated challenges in bounded interactional contexts. We further identify recurring structural tensions in GCA design, including tradeoffs between visibility and discretion, proactivity and group autonomy, and agent authority and group ownership. Together, these findings clarify how current GCAs are positioned within group interaction, surface the implicit assumptions embedded in their designs, and outline open questions for future research on conversational agents as group-level interventions.
• Microarchitectural Espionage: FPGA-Based Security Analysis of Branch Prediction in RISC-V Out-of-Order Cores
  
  • Khan Mahreen
  • Bin Mohd Shahfie Muhammad Emir
  • Mushtaq Maria
  • Pacalet Renaud
  • Apvrille Ludovic
  , 2026, pp.1-7. <div><p>Modern processor microarchitectural optimizations, while enhancing performance, inadvertently introduce side channels that can leak sensitive information through timing variations. This paper presents an FPGA-based security testbed for studying branch predictor side-channel vulnerabilities in open-source RISC-V out-of-order cores. We demonstrate a configurable platform built on the Berkeley Out-of-Order Machine (BOOM) core, adapted for resource-constrained FPGA deployment with customizable branch predictor configurations. Through baremetal execution and cycle-accurate timing measurements, we implement and evaluate three classes of timing attacks: Conditional Branch Prediction Attacks (CBPA), Indirect Branch Prediction Attacks (IBPA), and a practical smart-lock application attack. Our results show that simplified one-level predictors exhibit deterministic timing separations of 9 to 17 cycles, enabling perfect secret recovery with 100% accuracy for 16-bit secrets within 500 measurement rounds. We further demonstrate practical attack scenarios, including the extraction of a randomly-generated 4-digit smart-lock code, and evaluate the impact of branch predictor complexity on attack feasibility. This work provides an open-source framework for reproducible microarchitectural security research on RISC-V platforms, enabling evaluation of both attacks and countermeasures.</p></div> (10.1109/ISDFS69419.2026.11459082)
  DOI : 10.1109/ISDFS69419.2026.11459082
• 3D Imaging Contribution in Pediatric Surgical Oncology: A Multistakeholder Assessment Study
  
  • Pio Luca
  • Kassir Rani
  • La Barbera Giammarco
  • Lozach Cecile
  • Bonnot Enzo
  • Isla Thomas
  • Pablo de la Plata Alcalde Juan
  • Gori Pietro
  • Bloch Isabelle
  • Sarnacki Sabine
  Scientific Reports, Nature Publishing Group, 2026 (16), pp.14264-1:14264-10. Introduction: Medical imaging is crucial for surgical planning, yet surgeons struggle with mental transformation of 2D images into 3D representations, particularly in complex pediatric pelvic anatomy. This study evaluated perceived benefits of 3D imaging with tractography compared to conventional 2D MRI in pediatric pelvic tumor surgery.<p>Methods: A nationwide study assessed three groups: non-medical personnel (n=30), medical trainees (residents and fellows; primary analysis n=61, excluding 3 medical students), and senior pediatric surgeons (n=12). Using 3-Tesla MRI with specialized protocols including highresolution CoroT2cube and diffusion tensor imaging, participants evaluated five clinical cases in both 2D and 3D formats using 7-point Likert scales. Statistical analysis employed Wilcoxon paired tests with Bonferroni correction.</p><p>Results: All groups showed significant improvements in perceived understanding with 3D imaging. Non-medical personnel scores increased from 4.24 (±0.69) to 6.27 (±0.28) (p&lt;0.001), particularly in understanding disease and surgical objectives. Medical trainees improved from 5.08 (±0.61) to 6.42 (±0.49) (p&lt;0.001), with enhanced understanding of surgical objectives and anatomical relationships. Senior surgeons' scores increased from 5.02 (±0.69) to 6.33 (±0.52) (p&lt;0.001), showing significant improvements in preoperative planning and family communication. Effect sizes were substantial across groups (Cohen's d: 2.80, 1.90, and 1.52 respectively), though the within-subject design likely contributes to effect size inflation.</p><p>Discussion: This study provides preliminary evidence for perceived 3D imaging value in pediatric pelvic tumor surgery. Improved anatomical comprehension among non-medical personnel may benefit informed consent, while enhanced visualization aids surgical education and planning. High surgeon acceptance (92%) suggests strong acceptability, though these exploratory findings require validation before implementation recommendations can be made.</p><p>Prospective studies evaluating objective clinical outcomes, workflow integration and costeffectiveness require further study.</p> (10.1038/s41598-026-44543-z)
  DOI : 10.1038/s41598-026-44543-z
• Analytical solution of radiative transfer equation of light radiance in turbid slab with inner-medium source under P3-1D approximation
  
  • Fade Julien
  • Roux Aurélien
  • Breton Vicente Alberto
  • Sorrente Béatrice
  • Grillot Frederic
  • Silva Anabela Da
  , 2026. A generalized model of the 1-dimensional radiative transfer equation of the light radiance in a turbid slad is detailed, under the P-3 approximation, including the possibility to model a continuous plane-wave source located at any depth within the scattering slab. This analytical model, which requires significant evolution of the P3-1D model is extensively described and validated by comparison with Monte-Carlo numerical experiments. A series of numerical simulations illustrates some of the modelling possibilities offered by this extended model, which makes it possible to continuously model the transition between a classical slab geometry and a semi-infinite geometry.
• Of All StrIPEs: Investigating Structure-informed Positional Encoding for Efficient Music Generation
  
  • Agarwal Manvi
  • Wang Changhong
  • Richard Gael
  , 2026. 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.
• Artifact: PSMark: a distributed IoT benchmark for publish/subscribe under domain-based workloads
  
  • Badolato Christian
  • Samson Nathan
  • Hajj Hassan Houssam
  • Huang Chih-Kai
  • Bouloukakis Georgios
  • Pappachan Primal
  • Yus Roberto
  , 2026. This artifact paper presents a guide for PSMark, a distributed benchmarking framework to evaluate Publish/Subscribe (pub/sub) systems against real-world representative IoT workloads. PSMark addresses limitations in existing pub/sub benchmarks by supporting: (i) heterogeneous device behaviors (e.g, varying payload sizes, publication rates, and connection stability); (ii) distributed multi-node deployments; and (iii) cross-protocol evaluation across MQTT and DDS.