Publications

2026

• Revealing the Power Dynamics of Collaborative Sense-Making Supported by Participatory Data Physicalization
  
  • Cazacu Silvia
  • Panagiotidou Georgia
  • Moere Andrew Vande
  , 2026, pp.1186, Pages 1 - 21. While it is proven that the individual construction of a data physicalization aids personal sense-making, little is known about how sense-making is negotiated when it is shared by multiple, co-located participants. Since participatory data physicalization can inadvertently prioritize dominant views, we interpreted data feminism principles to design a collaborative physicalization construction process that empowers stakeholders and participants to co-determine how meanings are represented. This process revealed how the interplay of physical and non-physical actions during construction negotiations supported collaborative sense-making among 14 groups of 55 participants during 4 workshops, enabling us to articulate how explicit power is embodied by the physicalization artifact and negotiated between authoring and collaborating participants, and facilitators; whereas tacit power operates through artifact meanings, participant identity and design decisions. By providing one operationalization of data-feminist critique into the form of design requirements, our contributions support the design of more equitable physicalization and visualization construction methods. (10.1145/3772318.3791052)
  DOI : 10.1145/3772318.3791052
• Challenges in Synchronous & Remote Collaboration Around Visualization
  
  • Brehmer Matthew
  • Cordeil Maxime
  • Hurter Christophe
  • Itoh Takayuki
  • Büschel Wolfgang
  • Jasim Mahmood
  • Prouzeau Arnaud
  • Saffo David
  • Bartram Lyn
  • Carpendale Sheelagh
  • Zhu-Tian Chen
  • Cunningham Andrew
  • Dwyer Tim
  • Huron Samuel
  • Itoh Masahiko
  • Joshi Alark
  • Kiyokawa Kiyoshi
  • Kuzuoka Hideaki
  • Lee Bongshin
  • Molina León Gabriela
  • Reiterer Harald
  • Ryskeldiev Bektur
  • Schwabish Jonathan
  • Smith Brian A
  • Sumi Yasuyuki
  • Suzuki Ryo
  • Tang Anthony
  • Yang Yalong
  • Zhao Jian
  , 2026, pp.Article No.: 1007, 1-17. We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior research efforts of an international group of 29 experts from across human computer interaction and visualization sub-communities. The challenges are anchored around five collaborative activities that exhibit a centrality of visualization and multimodal communication. These activities include exploratory data analysis, creative ideation, visualization-rich presentations, joint decision making grounded in data, and real-time data monitoring. The challenges also reflect the changing dynamics of these activities in the face of recent advances in extended reality (XR) and artificial intelligence (AI). As an organizing scheme for future research at the intersection of visualization and computer-supported cooperative work, we align the challenges with a sequence of four sets of research and development activities: technological choices, social factors, AI assistance, and evaluation. (10.1145/3772318.3791117)
  DOI : 10.1145/3772318.3791117
• Spatio-Temporal Delta-Sigma Modulation for Massive MIMO with Low Resolution DACs
  
  • Schlegel Nicolas
  • Jabbour Chadi
  • Ciblat Philippe
  , 2026, pp.1-6. Going toward extreme multiple input multiple output (MIMO) antenna systems, each individual radio frequency (RF) chain will require cost and energy efficient implementations. Among the power hungry components is the digital-to-analog converter (DAC). While lowering the resolution can reduce power consumption, the introduced quantization error will lead to degraded performance. In this paper, we propose to use spatio-temporal delta sigma modulation (DSM) upstream of the low-resolution converter in a downlink massive MIMO orthogonal frequency division multiplexing (OFDM) scenario. Its role is to shape quantization noise toward unused frequency resources and directions of space. At the heart lies an optimization based design of the modulator's noise transfer function (NTF) using available channel state information (CSI). A constraint to shape quantization noise away from adjacent frequency bands is studied as well. Numerical results show that the recently thriving spatial DSM is outperformed by its spatio-temporal counterpart. Additional filtering is however still required to reach spectral masks set by current communication standards. (10.1109/WCNC65185.2026.11555620)
  DOI : 10.1109/WCNC65185.2026.11555620
• AI Agents and the Future of Deliberation: Designing Human-AI Collaboration for Democratic Dialogue
  
  • Zhang Weiyu
  • Yeo Shunyi
  • Perrault Simon
  • Pei Jiaxin
  • Liddo Anna De
  • Veri Francesco
  • Flechtner Maurice
  • Saggion Horacio
  , 2026, pp.Article No.: 986, Pages 1 - 6. As societies grapple with increasing polarization and information complexity, the need for constructive, inclusive, and well-informed deliberation has reached an unparalleled level. At the same time, AI agents, ranging from LLMs and multi-agent simulations and systems to conversational assistants and reflective companions, are rapidly reshaping how people communicate, reason together, and form collective judgments. These technologies hold the potential to scale democratic participation, foster inclusivity by bridging linguistic and cultural barriers, and introduce new forms of collaborative reasoning. Yet they also pose epistemic challenges to established notions of authenticity, legitimacy, and human autonomy in civic dialogue. This panel brings together leading researchers from Asia, Europe and North America to examine how AI technologies are transforming deliberation as both a social process and a design problem. It interrogates AI's role in shaping deliberative norms, influencing group dynamics, and redefining what it means to "reason together" in hybrid human-AI spaces. Through interactive polling, structured debates, and audience co-deliberation, the session invites CHI participants to collectively explore how we can design responsible, inclusive, and trustworthy deliberation interfaces that preserve the democratic values of deliberation while embracing the creative potential of AI. (10.1145/3772363.3790075)
  DOI : 10.1145/3772363.3790075
• Optimizing coexistence of quantum continuous variable and classical signals in the C-band
  
  • Sharma Tanya
  • Purkayastha Ambashri
  • Schiavon Matteo
  • Alvarez Velasquez Juan Rafael
  • Lourdiane Mounia
  • Ware Cédric
  • Fabre Nicolas
  , 2026 (PC14078-65).
• Synthetic Abundance Maps for Unsupervised Super-Resolution of Hyperspectral Remote Sensing Images
  
  • Xu Xinxin
  • Gousseau Yann
  • Kervazo Christophe
  • Ladjal Saïd
  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2026. Hyperspectral single image super-resolution (HS-SISR) aims to enhance the spatial resolution of hyperspectral images to fully exploit their spectral information. While considerable progress has been made in this field, most existing methods are supervised and require ground truth data for training—data that is often unavailable in practice. To overcome this limitation, we propose a novel unsupervised training framework for HS-SISR, based on synthetic abundance data, where no high-resolution ground-truth reference is required for training. The approach begins by unmixing the hyperspectral image into endmembers and abundances. A neural network is then trained to perform abundance super-resolution using synthetic abundances only. These synthetic abundance maps are generated from a dead leaves model whose characteristics are inherited from the low-resolution image to be super-resolved and from the known point spread function (PSF) of the hyperspectral sensor. This trained network is subsequently used to enhance the spatial resolution of the original image's abundances, and the final super-resolution hyperspectral image is reconstructed by combining them with the endmembers. Experimental results demonstrate both the training value of the synthetic data and the effectiveness of the proposed method across 3 datasets, 3 scaling factors, and several evaluation metrics. The code is available at https://github.com/xinxinxu99/SISR-DL.git
• Direction-aware Ambisonic Speech Separation Using Edge-Conditioned Graph Convolution
  
  • Kelley Liam
  • Fontaine Mathieu
  • Peeters Geoffroy
  , 2026. The conventional Direction-Aware Ambisonic Speech Separation (DAASS) paradigm involves spatial filtering followed by post-filtering refinement. A number of approaches have been proposed to jointly perform spatial filtering and refinement using end-to-end neural methods, using as input full ambisonic signals and spatially filtered signals without explicitly modeling their spatial relationships. In this paper, we propose to unify these different input types directly in the model architecture with a spatial relationship graph. In particular, we design a novel edge-conditioned graph convolution block which aims to replace standard 1D convolutional blocks and be better suited to ambisonic signals. For our experiments, we develop a new synthetic DAASS dataset called Spatial LibriMix which will be available online upon acceptance. Our method matches or outperforms baseline performance with a significantly reduced parameter count (66M to 7.5M).
• Adaptive gradient domain normalization for one-sided unsupervised medical image synthesis
  
  • Paniagua Kévin
  • Conze Pierre-Henri
  • Jaouen Vincent
  • Angelini Elsa
  , 2026.
• Leveraging Whole Slide Difficulty in Multiple Instance Learning to Improve Prostate Cancer Grading
  
  • Arrivat Marie
  • Peyret Rémy
  • Angelini Elsa
  • Gori Pietro
  , 2026, pp.1-4. Multiple Instance Learning (MIL) has been widely applied in histopathology to classify Whole Slide Images (WSIs) with slide-level diagnoses. While the ground truth is established by expert pathologists, the slides can be difficult to diagnose for non-experts and lead to disagreements between the annotators. In this paper, we introduce the notion of Whole Slide Difficulty (WSD), based on the disagreement between an expert and a non-expert pathologist. We propose two different methods to leverage WSD, a multi-task approach and a weighted classification loss approach, and we apply them to Gleason grading of prostate cancer slides. Results show that integrating WSD during training consistently improves the classification performance across different feature encoders and MIL methods, particularly for higher Gleason grades (i.e., worse diagnosis). (10.1109/ISBI61048.2026.11515564)
  DOI : 10.1109/ISBI61048.2026.11515564
• Structure preserving adversarial diffusion for unpaired medical image synthesis
  
  • Paniagua Kévin
  • Conze Pierre-Henri
  • Jaouen Vincent
  • Angelini Elsa
  , 2026, pp.1-5. Recent works have established diffusion models as viable candidates for unpaired medical image translation, by incorporating supervision through non-diffusive models in an end-to-end fashion.. Existing models typically rely on cycleconsistent, two-sided, non-diffusive modules of relatively high computational cost where structure preservation is not explicit. In this work, we propose SPAD, a novel one-sided approach that explicitly preserves the source domain firstorder (gradient) orientations, while enabling flexible contrast adaptation between source and target domains. Our approach provides direct edge guidance for the diffusive module, leading to better structural preservation. Experiments performed on image synthesis tasks using BraTS, CERMEP, and Gold Atlas datasets demonstrate that our model improved translation fidelity and reduced training time by over 50% compared to SynDiff while keeping the same diffusion architecture. The reduction in computational cost allowed us to train 3D variants, which achieved consistently higher quantitative scores. Code will be publicly shared on GitHub upon acceptance. (10.1109/ISBI61048.2026.11515588)
  DOI : 10.1109/ISBI61048.2026.11515588
• Fast tomographic SAR inversion by unrolling a greedy algorithm for sparse reconstruction
  
  • Mendes Cristiano Ulondu
  • Denis Loïc
  • Kervazo Christophe
  • Tupin Florence
  , 2026. <div><p>As a range-based imaging technique, synthetic aperture radar (SAR) is agnostic to targets' elevation. In urban areas, tomographic synthetic aperture radar (TomoSAR) retrieves the position of sparsely distributed targets along the elevation direction thanks to the phase information from multiple acquisitions of the same area. Compressive sensing (CS)-based methods have received a lot of attention during the last decade, producing satisfactory reconstructions from a limited number of acquisitions. To improve their performance and scalability for pixel-wise TomoSAR inversion, data-driven methods based on the deep unrolling of basis pursuit algorithms have recently been proposed. However, they depend on a discretization of the elevation direction, often leading to artifacts which have to be corrected in a post-processing step. In this paper, we present an approach inspired by the iterations of a greedy algorithm for sparse reconstruction that estimates continuous elevation positions of targets together with well-calibrated uncertainty predictions. Our approach achieves performances comparable to reference CS-based methods while offering significantly faster processing. The trained models and the code used in the different experiments are available at place holder.</p></div>
• Evaluating DV/CV-QKD Architectures for SAFE Long-Term Secure Storage: A Risk Model and ILP-Based Cost Optimization Approach
  
  • Tasdighi Alireza
  • Alléaume Romain
  , 2026, pp.1-9. This paper presents a unified cost model and optimization framework for evaluating hybrid discrete variable / continuous variable quantum key distribution (DV/CV-QKD) architectures within the SAFE (Secure and Efficient) longterm secure storage (LTSS) protocol. The proposed methodology jointly models the information-theoretic and computational aspects of storage security over extended time horizons and integrates a stochastic integer linear programming (ILP) formulation with a quantitative risk model. By combining Sample-Average Approximation (SAA) with an analytically tractable compromise probability, the framework identifies feasible and cost-optimal SAFE topologies under heterogeneous transmission budgets, keyrate constraints, and security tolerances. Simulation results over randomly generated metropolitan-scale deployments reveal how DV, CV, and multiplexed (MUX) QKD modalities are adaptively selected to minimize infrastructure cost while maintaining a target global compromise probability ϵ. The findings show that mixed DV/CV configurations—particularly when combined with multiplexing—achieve significant cost reductions compared to single-modality networks, paving the way towards the deployment of cost-efficient network infrastructures able to leverage existing QKD technology to provide practical cryptographic advantage for long-term secure storage. (10.1109/QCNC69040.2026.00029)
  DOI : 10.1109/QCNC69040.2026.00029
• Automated Derivation of Formal Properties from Requirements
  
  • Sultan Bastien
  • Apvrille Ludovic
  • Saqui-Sannes Pierre De
  , 2026, pp.1-8. Early detection of design errors in the life cycle of systems is one of the many benefits one may expect from using an model-based systems engineering approach. The formal verification of models is thus an issue. Among verification techniques, the paper focuses on model checking. Taking as input a set of properties and a model, model checking outputs a “yes/no” answer for each property saying whether the property is verified or not. The relevance of this proof relies on the relevance of the defined properties. The paper proposes solutions for automated generation of properties from a SysML model and, more specifically, from the requirements expressed in the SysML model. We introduce an AI- and syntactic rule-based process that assists systems engineers in the generation of syntactically correct CTL properties, along with complementary contributions for computing and propagating requirement coverage from formal verification results, backtracing them directly in the SysML requirement diagram. The approach is mathematically defined, with explanatory comments accompanying the formalism to support comprehension. The proposed approach is further implemented by the free software TTool-AI. The latter is an open source SysML toolkit extended with an LLM-based assistant. A satellite system and an embedded maritime system serve as case studies. (10.1109/SysCon66367.2026.11503633)
  DOI : 10.1109/SysCon66367.2026.11503633
• Breaking exponential complexity in games of ordered preference: A tractable reformulation
  
  • Lee Dong Ho
  • Li Jingqi
  • Peters Lasse
  • Bakirtzis Georgios
  • Fridovich-Keil David
  , 2026. Games of ordered preference (GOOPs) model multi-player equilibrium problems in which each player maintains a distinct hierarchy of strictly prioritized objectives. Existing approaches solve GOOPs by deriving and enforcing the necessary optimality conditions that characterize lexicographically constrained Nash equilibria through a single-level reformulation. However, the number of primal and dual variables in the resulting KKT system grows exponentially with the number of preference levels, leading to severe scalability challenges. We derive a compact reformulation of these necessary conditions that preserves the essential primal stationarity structure across hierarchy levels, yielding a "reduced" KKT system whose size grows polynomially with both the number of players and the number of preference levels. The reduced system constitutes a relaxation of the complete KKT system, yet it remains a valid necessary condition for local GOOP equilibria. For GOOPs with quadratic objectives and linear constraints, we prove that the primal solution sets of the reduced and complete KKT systems coincide. More generally, for GOOPs with arbitrary (but smooth) nonlinear objectives and constraints, the reduced KKT conditions recover all local GOOP equilibria but may admit spurious non-equilibrium solutions. We introduce a second-order sufficient condition to certify when a candidate point corresponds to a local GOOP equilibrium. We also develop a primal-dual interior-point method for computing a local GOOP equilibrium with local quadratic convergence. The resulting framework enables scalable and efficient computation of GOOP equilibria beyond the tractable range of existing exponentially complex formulations.
• Importance Sampling Optimization with Laplace Principle
  
  • Dragomir Radu-Alexandru
  • Portier François
  • Priser Victor
  , 2026. Grid search and random search are widely used techniques for hyperparameter tuning in machine learning, especially when gradient information is unavailable. In these methods, a finite set of candidate configurations is evaluated, and the best-performing one is selected. We propose a simple and computationally inexpensive refinement of this paradigm: instead of selecting a single best point, we form a weighted average of the evaluated configurations, where the weights are chosen using an importance sampling scheme inspired by the Laplace principle. This scheme can be implemented as a post-processing step on top of a random search, with no additional function evaluations. We also propose an iterative variant, where the sampling distributions are chosen adaptively to generate new candidate points around the previous estimate, in the spirit of Evolution Strategy (ES) methods. In a general non-convex setting, we show that, after n evaluations, the error of the proposed methods is of smaller order than n -2/(d+2) . This compares favorably to random search or grid search rates of n -1/d as soon as d &gt; 2. We illustrate the practical benefits of this averaging strategy on several examples.
• 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.
• The Role of Averages in CV-QKD over Fast Fading Channels
  
  • Castillo-Celeita Miguel
  • Schiavon Matteo
  Entropy, MDPI, 2026, 28 (4), pp.388. This work presents a study of continuous-variable quantum key distribution (CV-QKD) protocols over fast-fading channels, typically found in free-space communication links. Two eavesdropping models are considered to evaluate their security under collective attacks: Holevo bound average (HBA) and covariance matrix average (CMA). In the HBA approach, the Holevo bound is averaged over the channel transmittance. In contrast, the CMA method calculates the Holevo bound from the average covariance matrix. Analytical expressions are developed for both strategies. The two methods also differ in how they calculate the mutual information between the legitimate parties. The results demonstrate that the SKR is significantly influenced by how you treat channel fluctuations, highlighting the importance of choosing the model that best describes the actual implementation of the protocol. (10.3390/e28040388)
  DOI : 10.3390/e28040388
• 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
  
  • 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.
• LLM-as-a-qualitative-judge: automating error analysis in natural language generation
  
  • Chirkova Nadezhda
  • Ajayi Tunde Oluwaseyi
  • Aycock Seth
  • Mujahid Zain Muhammad
  • Perlić Vladana
  • Borisova Ekaterina
  • Vartampetian Markarit
  , 2026, pp.99-132. Prompting large language models (LLMs) to evaluate generated text, known as LLM-as-ajudge, has become a standard evaluation approach in natural language generation (NLG), but is primarily used as a quantitative tool, i.e. with numerical scores as main outputs. In this work, we propose LLM-as-a-qualitative-judge, an LLM-based evaluation approach with the main output being a structured report of common issue types in the NLG system outputs. Our approach is targeted at providing developers with meaningful insights on what improvements can be done to a given NLG system and consists of two main steps, namely open-ended per-instance issue analysis and clustering of the discovered issues using an intuitive cumulative algorithm. We also introduce a strategy for evaluating the proposed approach, coupled with ∼300 annotations of issues in instances from 12 NLG datasets. Our results show that instancespecific issues output by LLM-as-a-qualitativejudge match those annotated by humans in 2/3 cases, and that LLM-as-a-qualitative-judge is capable of producing error type reports resembling the reports composed by human annotators. We also demonstrate in a case study how the use of LLM-as-a-qualitative-judge can substantially improve NLG systems performance. Our code and data are publicly available. (10.18653/v1/2026.mme-main.7)
  DOI : 10.18653/v1/2026.mme-main.7
• 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.
• Data Metrics with Statistical Depths and Kernel Methods
  
  • Castellanos Salinas Arturo
  , 2026. Data is ubiquitous nowadays, with more and more virtualization of the real world. That raises the question: how well can we understand and picture data? “Mathematics and the Picturing of Data” was the title of the seminal work by John Tukey in 1975 that started the field of statistical data depths, and it is suggesting, of course, to rely on mathematical techniques, while keeping the idea of simplicity of a picture. The challenge is to keep the balance between fidelity to the complexity of the data and simplicity to be understandable by the human mind. In computational statistics, this challenge is often translated as using computational power to analyze a finite number of samples, that should approximate well an underlying distribution, yet to compute relevant characteristics of such distribution. Because the computational complexity is bound to increase with the number of samples, a trade-off appears where the statistical approximation error is hoped to decrease fast enough with the number of samples.In this work, we design tools to understand better data under the constraints that their sample complexity should not grow too high. First, we focus on designing new data depth functions with two kind of extensions of the Tukey depth. One extension is done by changing its geometrical nature, inspired by kernel methods to change from the classical Euclidean inner product. The other is by seeing the Tukey depth as classification risk and changing the space of classifiers as well as the loss function. In both cases, we manage to guarantee parametric rate for this non-parametric methods.In a another part, we also consider understanding distribution by looking at distances to discriminate them. The Wasserstein distance from optimal transport is a well-known distance to quantify the difference of two distributions, however its sample complexity suffers from the curse of dimensionality. On the other hand, we show that the Maximum Mean Discrepancy (MMD), a kernel-based distance enjoying good sample complexity, is sometimes too lenient for discriminating distributions, and we suggest to use a distance in-between those two that has higher discriminating power than MMD at the cost of a slight increase of the sample complexity.All of these methods can be used to prevent contamination of data to ruin our statistical analyses, whether it is by detecting outliers using data depth scores or by enhancing the discrimination of distributions via distances.
• 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.