Publications

2026

• Quantum Gibbs Sampling in Infinite Dimensions: Generation, Mixing Times and Circuit Implementation
  
  • Becker Simon
  • Rouzé Cambyse
  • Salzmann Robert
  , 2026. We develop a rigorous and implementable framework for Gibbs sampling of infinite-dimensional quantum systems governed by unbounded Hamiltonians. Extending dissipative Gibbs samplers beyond finite dimensions raises fundamental obstacles, including ill-defined generators, the absence of spectral gaps on natural Banach spaces, and tensions between implementability and convergence guarantees. We overcome these issues by constructing KMS-symmetric quantum Markov semigroups on separable Hilbert spaces that are both well-posed and efficiently implementable on qubit hardware. Our generation theory is based on the abstract framework of Dirichlet forms, adapted here to the case of algebras of bounded operators over separable Hilbert spaces. Leveraging the spectral properties of our self-adjoint generators, we establish quantitative convergence results in trace distance, including regimes of fast thermalization. In contrast, we also identify Hamiltonians for which a naive choice of generators guaranteeing implementability generally comes at the cost of losing convergence of the associated evolutions, thereby establishing a strong trade-off between implementability and convergence. Our framework applies to a wide class of models, including Schrödinger operators, Gaussian systems, and Bose-Hubbard Hamiltonians, and provides a unified approach linking rigorous infinite-dimensional analysis with algorithmic Gibbs state preparation. (10.48550/arXiv.2604.01192)
  DOI : 10.48550/arXiv.2604.01192
• Simulating Thermal Properties of Bose-Hubbard Models on a Quantum Computer
  
  • Becker Simon
  • Rouzé Cambyse
  • Salzmann Robert
  , 2026. While recent advances have established efficient quantum algorithms for preparing Gibbs states of finite-dimensional systems, comparable complexity results for bosonic and other infinite-dimensional models remain unexplored. We introduce the first general rigorous Gibbs sampling framework for bosonic many-body systems, showing that physically relevant bosonic models admit gapped dissipative generators, enabling efficient preparation of thermal states. Although our results hold for broad classes of models, we illustrate them using Bose-Hubbard Hamiltonians, both within and beyond the mean-field regime. In both cases, we show that the associated dissipative generators maintain a positive spectral gap, thereby implying exponential convergence to the thermal state. Our argument in the multi-mode case is based on a finite-rank reduction of the dissipative dynamics, which allows us to control the generator via compact perturbations and deduce the discreteness of the spectrum and the stability of the gap. We apply our results to provide efficient preparation of the corresponding Gibbs state on qubit hardware, and by that a quantum algorithm to compute thermal properties of the associated model. This provides the first mathematically controlled route to Gibbs sampling in infinite-dimensional systems, with implications for quantum simulation, thermalization, and many-body complexity, where quantum advantages may arise. (10.48550/arXiv.2604.06077)
  DOI : 10.48550/arXiv.2604.06077
• Distortion-aware STAP adaptive beamforming for robust GNSS anti-jamming in high-dynamics environments
  
  • Lagarde Elise
  • Leborgne Fabien
  • Abedrrabba Sarra
  • Dubroca Norbert
  • Leonardon Mathieu
  • Roblin Christophe
  • Cousin Jean-Christophe
  • Gomes Joan
  • Oriol Stephane
  , 2026. he combination of Spatio-Temporal Adaptive Processing (STAP) and Controlled Reception Pattern Antennas (CRPAs) enhances the robustness of Global Navigation Satellite System (GNSS) anti-jamming receivers, particularly for autonomous launcher navigation where continuous Position, Velocity, and Time (PVT) availability is critical. In this architecture, STAP acts as a spatio-temporal filtering stage on array signals, while adaptive beamforming (ABF) algorithms compute and steer the filter weights to shape the antenna pattern and place spatial nulls under high-dynamics conditions involving strong carrier accelerations. Common techniques used to drive the STAP weights include Minimum Variance Distortionless Response (MVDR), Minimum Mean Square Error (MMSE), and Linearly Constrained Minimum Variance (LCMV). This paper demonstrates that, despite their theoretical optimality, such ABF-driven STAP approaches may struggle in complex real-world conditions, particularly in the presence of significant launcher dynamics, including rapid attitude variations and high acceleration profiles. To illustrate these limitations, two complementary sets of results are presented. The first is based on simulated data generated using open-source tools such as gnss-sdr-sim, allowing controlled emulation of dynamic interference and motion scenarios. The second relies on testbed experiments conducted in a controlled, conducted environment at the European Commission’s Joint Research Centre (JRC). The results show that variations in interference power, waveform type, and direction of arrival, combined with changes in GNSS signal geometry and carrier dynamics, introduce biases of varying magnitude. Significant nonlinear effects and distortions of the cross-correlation functions are observed, which degrade acquisition and tracking performance and may ultimately lead to PVT degradation or loss. These impairments are primarily induced by the conducted Radio Frequency (RF) chain, including front-end nonlinearities and gain increases introduced by the Low Noise Amplifier (LNA) and Automatic Gain Control (AGC), initially designed to improve GNSS signal detectability. Under such conditions, while adaptive beamforming algorithms may still compute STAP filter weights, the resulting interference cancellation may be insufficient to guarantee robust PVT tracking across all scenario configurations. More generally, classical adaptive beamforming approaches are sensitive to steering vector mismatch and covariance estimation errors induced by RF nonlinearities, high jammer-to-signal ratios (JSR), AGC saturation, and quantization effects. Compensating for these effects often requires increasing the number or complexity of constraints or regularization mechanisms, which in turn raises computational complexity, particularly in high-dynamics scenarios requiring frequent weight updates and highlights the need for accurate distortion modeling to properly dimension ABF constraints. This work highlights the need to revisit traditional ABF algorithms used to drive STAP filter weights to explicitly account for nonlinear and distortion effects, particularly in high-dynamics scenarios where robust PVT tracking is required. It also opens the way toward hybrid approaches combining classical digital signal processing with lightweight machine learning–based techniques, in which computationally frugal data-driven models assist in robust covariance estimation, distortion-aware constraint adaptation, and tracking of dynamic interference conditions, thereby improving robustness and maintaining reliable GNSS-based autonomy in RF contested environments.
• Computing the free energy of quantum Coulomb gases and molecules via quantum Gibbs sampling
  
  • Becker Simon
  • Rouzé Cambyse
  • Salzmann Robert
  , 2026. We develop a quantum algorithm for estimating the free energy as well as the total Gibbs state of interacting quantum Coulomb gases and molecular systems in dimensions $d \in \{2,3\}$ at finite temperature. These systems lie beyond the reach of existing methods due to their singular interactions and infinite-dimensional Hilbert space structure. First, we show that the free energy of the full many-body Hamiltonian can be approximated by that of the same Hamiltonian with a finite-rank low-energy truncation of the interaction, with an explicit error bound polynomial in the particle number. This reduces the problem to a controlled finite-rank perturbation problem. Second, we introduce a quantum Gibbs sampling scheme tailored to this truncated system, based on a class of quantum Markov semigroups. Our main analytical result establishes that the associated generator has a strictly positive spectral gap for every truncation, implying exponential convergence to the target Gibbs state. This provides, to our knowledge, the first rigorous mixing-time guarantee for Gibbs sampling in a Coulomb interacting continuous-variable quantum system. Finally, we give an explicit quantum circuit implementation of the dynamics and derive an end-to-end complexity bound for approximating the free energy and the Gibbs state itself. Our results provide a mathematically rigorous route to quantum algorithms for free energy estimation in interacting quantum systems, without relying on classical approximations such as the Born-Oppenheimer reduction. (10.48550/arXiv.2604.15263)
  DOI : 10.48550/arXiv.2604.15263
• A Low-Cost Multiplatform VLC System Prototype for Indoor Attocell Downlink Communication
  
  • Nascimento Alaf
  • Costa Wesley
  • Camporez Higor
  • Zwaag Klaas
  • Santos Francisco
  • Silva Jair
  , 2026. Devices operating in the Radio Frequency (RF) spectrum face capacity limitations due to licensed bandwidth and rapidly growing connectivity demand. Visible Light Communication (VLC) emerges as a complementary alternative, leveraging existing lighting infrastructure for energy-efficient indoor communication. However, for widespread adoption, VLC devices must achieve a cost comparable to established technologies. This work proposes a Plug-and-Play (PnP) USB dongle for VLC data reception based on low-cost hardware, along with a multiplatform interface for reactive data visualization. Our prototype achieves error-free downlink communication in attocell scenarios over distances up to 280 cm under line-of-sight (LOS) conditions.
• Balanced Latent Semantics and Signal Fidelity for EEG Representation Learning
  
  • Nguyen Van-Chien
  • Tran Trung-Hieu
  • Doan Tuan-Kiet
  • Pham Quang Hung
  • Vu Ngoc-Son
  • Le Duc Han
  • Phan Huy
  • Le Nguyen Phi
  • Simidjievski Nikola
  • Tardieu Samuel
  • Nguyen Van-Tam
  , 2026. <div><p>Electroencephalography (EEG) is critical for neurological diagnosis but suffers from low SNR and subject variability. Current foundation models relying on raw signal reconstruction often overfit to local noise. We propose STELAR, a foundation model with a dual-space objective combining patch-level masked latent prediction for semantic stability with masked reconstruction for raw signal fidelity. To balance these objectives, we introduce MTPE-GB, a validation-driven gradient balancer that adaptively weights tasks without manual tuning or computational overhead. STELAR achieves state-of-the-art linear probing performance across diverse EEG benchmarks, demonstrating robust generalization.</p></div>
• The ALERT Dataset: Benchmarking Anomaly Detection of Non-Stationary Vibrational Signals
  
  • Emelchenkov Anton
  • Fontaine Mathieu
  • Mahé Hervé
  • Roueff François
  , 2026. In recent years, automatic audio anomaly detection has gained considerable attention. However, most existing methods and benchmarks assume stationary or periodic signals, limiting their applicability to industrial environments characterized by non-stationary operating regimes such as speed ramps and transient load variations. We introduce the ALERT Dataset, a large-scale collection of non-stationary vibration recordings from electric powertrains acquired on an industrial end-of-line test bench. Each recording captures ramp-up and ramp-down phases with continuously varying rotational speed and includes synchronized speed measurements to enable explicit conditioning on operational dynamics. The dataset comprises 224 healthy training recordings and 80 healthy test recordings, along with an additional 80-sample hold-out set reserved for anomaly generation. From this hold-out set, multiple anomalous test suites (80 samples each) are constructed via expert-designed amplitude-based degradations and structured noise perturbations at varying signal-to-noise ratios, simulating realistic fault scenarios. Models are evaluated by discriminating these anomalies from the 80 healthy test recordings under a one-class learning paradigm. The benchmark further supports diverse protocols, including zero-shot cross-phase testing. To our knowledge, the ALERT Dataset is the first large-scale collection of non-stationary industrial vibration signals with synchronized speed references, addressing a critical gap in existing benchmarks. The dataset is publicly available on Zenodo. (10.5281/zenodo.18759681)
  DOI : 10.5281/zenodo.18759681
• Person-Centric Annotations of LAION-400M: Auditing Bias and Its Transfer to Models
  
  • Girrbach Leander
  • Alaniz Stephan
  • Smith Genevieve
  • Darrell Trevor
  • Akata Zeynep
  , 2026. Vision-language models trained on large-scale multimodal datasets show strong demographic biases, but the role of training data in producing these biases remains unclear. A major barrier has been the lack of demographic annotations in web-scale datasets such as LAION-400M. We address this gap by creating person-centric annotations for the full dataset, including over 276 million bounding boxes, perceived gender and race/ethnicity labels, and automatically generated captions. These annotations are produced through validated automatic labeling pipelines combining object detection, multimodal captioning, and finetuned classifiers. Using them, we uncover demographic imbalances and harmful associations, such as the disproportionate linking of men and individuals perceived as Black or Middle Eastern with crime-related and negative content. We also show that a linear fit predicts 60-70% of gender bias in CLIP and Stable Diffusion from direct co-occurrences in the data. Our resources establish the first large-scale empirical link between dataset composition and downstream model bias. Code is available at https://github.com/ExplainableML/LAION-400M-Person-Centric-Annotations.
• NeuroSnitch: Exploiting Inter-Spike Interval Statistics for Timing Side-Channel Attacks on Noisy Neuromorphic Systems
  
  • Khan Mahreen
  • Mushtaq Maria
  • Apvrille Ludovic
  , 2026. <div><p>Neuromorphic computing promises energy-efficient solutions for embedded and edge systems, but introduces unique security challenges and a new attack surface. This paper presents NeuroSnitch, a first-ever timing side-channel attack to leverage subtle statistical variations in Inter-Spike Intervals (ISIs) on Spiking Neural Networks (SNNs) to extract secret information. We show that secret data, when modulating a neuron's input current, can be profiled through higher-order ISI statistics-mean, variance, skewness, and kurtosis-even under realistic noise sources, including observation noise, current fluctuation, and voltage jitter. Using the Leaky Integrateand-Fire (LIF) neuron model, we demonstrate that a Random Forest classifier can achieve 98.41% character-level classification accuracy on noisy ISI traces, enabling complete recovery of a 33-character secret string. This work exposes a previously underexplored and robust timing leakage vector in SNNs, underscoring the urgent need for tailored security measures in this emerging computing paradigm, particularly for sensitive embedded and IoT applications.</p></div> (10.1145/YYYYYYY.YYYYYYY)
  DOI : 10.1145/YYYYYYY.YYYYYYY
• Study of Training Dynamics for Memory-Constrained Fine-Tuning
  
  • Quélennec Aël
  • Mozharovskyi Pavlo
  • Nguyen Van-Tam
  • Tartaglione Enzo
  , 2026. Memory-efficient training of deep neural networks has become increasingly important as models grow larger while deployment environments impose strict resource constraints. We propose TraDy, a novel transfer learning scheme leveraging two key insights: layer importance for updates is architecture-dependent and determinable a priori, while dynamic stochastic channel selection provides superior gradient approximation compared to static approaches. We introduce a dynamic channel selection approach that stochastically resamples channels between epochs within preselected layers. Extensive experiments demonstrate TraDy achieves state-of-the-art performance across various downstream tasks and architectures while maintaining strict memory constraints, achieving up to 99% activation sparsity, 95% weight derivative sparsity, and 97% reduction in FLOPs for weight derivative computation.
• Pushbroom Satellite Source Identification Using PRNU: A Sentinel-2 Application
  
  • Serfaty Matthieu
  • Gardella Marina
  • Bammey Quentin
  • Grompone von Gioi Rafael
  • de Franchis Carlo
  , 2026. In this work, we take a step toward robust source identification for pushbroom satellite detectors by addressing source detector attribution in satellite imagery using Photo-Response Non-Uniformity (PRNU). As natural camera-based methods do not generalize well to satellite images, we adapt PRNU analysis to pushbroom sensors by extracting 1D reference signals per detector and comparing them with test segments. Experiments on Sentinel-2 data demonstrate that reliable fingerprints can be constructed from just a few images, making it possible to verify the authenticity of a L1B Sentinel-2 source.
• Adaptive Multi-UAV Relay Deployment Framework in Satellite Aerial Ground Integrated Systems
  
  • Bhola Bhola
  • Chen Yu-Jia
  • Balakrishnan Ashutosh
  • De Swades
  • Wang Li-Chun
  , 2026, pp.1-16. <div><p>The sixth-generation (6G) communication networks are expected to provide high data rates, ultra-reliable communication, and massive connectivity, especially in challenging environments such as dense urban areas and disaster-affected regions. However, traditional terrestrial-only networks face significant challenges in these scenarios, including signal blockages from high-rise buildings, traffic congestion, and dynamic user distributions. To address these limitations, we propose the adaptive multi-UAV deployment (AMUD) framework within satellite airground integrated networks (SAGINs). The AMUD framework dynamically deploys amplify-and-forward multiple unmanned aerial vehicle relay (UAVr) in with low Earth orbit (LEO) satellites to improve coverage, alleviate congestion, and ensure reliable communication in non-line-of-sight and high-demand conditions. We formulate an optimization problem that aims to jointly maximize the energy efficiency of the total network and the total capacity while ensuring the fairness of the total capacity and satisfying the user's requirements. The simulation results demonstrate that AMUD improves the total capacity of the network, improves the total energy efficiency, and increases the fairness of the capacity compared to traditional LEO satellite and ground base station (LEO-GBS) only systems.</p></div> (10.48550/arXiv.2604.20466)
  DOI : 10.48550/arXiv.2604.20466
• INSTANT: COMPRESSING GRADIENTS AND ACTIVATIONS FOR RESOURCE-EFFICIENT TRAINING
  
  • Doan Tuan-Kiet
  • Tran Trung-Hieu
  • Tartaglione Enzo
  • Simidjievski Nikola
  • Nguyen Van-Tam
  , 2026. <div><p>Deep learning has advanced at an unprecedented pace. This progress has led to a significant increase in its complexity. However, despite extensive research on accelerating inference, training deep models directly within a resource-constrained budget remains a considerable challenge due to its high computational and memory requirements. In this paper, we introduce INSTANT (compressIng gradieNtS and acTivAtions for resource-efficieNt Training), a method designed to address both the computational and the memory bottlenecks when training. INSTANT reduces resource demands during backpropagation by projecting gradients and activations into a low-rank subspace and performing computation within that compressed representation. Experimental results demonstrate that INSTANT achieves a 15× reduction in computational cost and 32× reduction in activation memory with negligible impact on model performance. The code is available at INSTANT. * Equal contribution.</p><p>• We introduce a low-cost calibration technique to generate calibrated orthonormal bases for tensor projection, enabling significant reductions in memory and computations (Sec. 3.2). • We project activation tensors and gradients onto these orthonormal bases. To our knowledge, this is the first work to exploit the low-rank structure of activation gradients for all types of data distribution. We provide an error analysis of our gradient compression, illustrating that a high compression ratio is achievable with limited performance degradation (Sec. 3.3). • We evaluate INSTANT across multiple datasets and model architectures, consistently demonstrating good performance, achieving up to 32× memory savings and 15× computational cost reduction with only a 1% trade-off in accuracy compared to vanilla fine-tuning (Sec. 4).</p></div> <div>RELATED WORK<p>Activation compression. Activation compression is a recently emerging research direction that addresses the memory challenges during training. This approach offers several key advantages based on the following observations: (i) model weights remain uncompressed during training, thereby preserving their expressive capacity; (ii) activations are often large and exhibit significant redundancy, making them suitable for compression (Sakr &amp; Khailany, 2024; Miles et al., 2024). (Nguyen et al., 2024) applies SVD to compress activations to reduce huge memory usage for activations. However, this approach raises substantial computational overhead due to the high cost of performing SVD in each training iteration. (Sakr &amp; Khailany, 2024) (ESPACE) tackles SVD computational expense by using calibrated subspaces, which are periodically updated, to compress activations. They enable activation compression in the forward pass, reducing computational overhead in both the forward and backward phases. However, ESPACE is prone to error accumulation, as it relies on the universal fixed subspace across varying activations.</p><p>Optimizer state compression. Weight gradients are inherently low-rank (Yang et al., 2023a). Previous studies (Bernstein et al., 2018; Vogels et al., 2019) have leveraged this characteristic to address communication bottlenecks in distributed learning by reducing inter-device data transmission. GaLore (Zhao et al., 2024) and its variances (Muhamed et al., 2024; Shamshoum et al., 2025) leverage the low-rank property of weight gradients for compressing them to reduce memory usage in the optimizer state significantly. CompAct Shamshoum et al. ( 2025) further reduces the memory overhead</p></div>
• Polynomial-time thermalization and Gibbs sampling from system-bath couplings
  
  • Slezak Samuel
  • Scandi Matteo
  • Alhambra Álvaro M.
  • França Daniel Stilck
  • Rouzé Cambyse
  , 2026. Many physical phenomena, including thermalization in open quantum systems and quantum Gibbs sampling, are modeled by Lindbladians approximating a system weakly coupled to a bath. Understanding the convergence speed of these Lindbladians to their steady states is crucial for bounding algorithmic runtimes and thermalization timescales. We study two such families of processes: one characterizing a repeated-interaction Gibbs sampling algorithm, and another modeling open many-body quantum thermalization. We prove that both converge in polynomial time for several non-commuting systems, including high-temperature local lattices, weakly interacting fermions, and 1D spin chains. These results demonstrate that simple dissipative quantum algorithms can prepare complex Gibbs states and that Lindblad dynamics accurately capture thermal relaxation. Our proofs rely on a novel technical result that extrapolates spectral gap lower bounds from quasi-local Lindbladians to the non-local generators governing these dynamics.
• Drop the mask! GAMM—A Taxonomy for Graph Attributes Missing Mechanisms
  
  • Serrano Richard
  • Jeudy Baptiste
  • Laclau Charlotte
  • Largeron Christine
  , 2026, 16513, pp.298–311. Exploring missing data in attributed graphs introduces unique challenges beyond those found in tabular datasets. In this work, we extend the taxonomy for missing data mechanisms to attributed graphs by proposing GAMM (Graph Attributes Missing Mechanisms), a framework that systematically links missingness probability to both node attributes and the underlying graph structure. Our taxonomy enriches the conventional definitions of masking mechanisms by introducing graph-specific dependencies. We empirically demonstrate that state-of-the-art imputation methods, while effective on traditional masks, significantly struggle when confronted with these more realistic graph-aware missingness scenarios. (10.1007/978-3-032-23833-7_22)
  DOI : 10.1007/978-3-032-23833-7_22
• Tighter Bounds for Query Answering with Guarded TGDs
  
  • Amarilli Antoine
  • Benedikt Michael
  Logical Methods in Computer Science, Logical Methods in Computer Science Association, 2026. We consider the complexity of the open-world query answering problem, where we wish to determine certain answers to conjunctive queries over incomplete datasets specified by an initial set of facts and a set of guarded TGDs. This problem has been well-studied in the literature and is decidable but with a high complexity, namely, it is 2EXPTIME complete. Further, the complexity shrinks by one exponential when the arity is fixed. We show in this paper how we can obtain better complexity bounds when considering separately the arity of the guard atom and that of the additional atoms, called the side signature. Our results make use of the technique of linearizing guarded TGDs, introduced in Gottlob, Manna, and Pieris. Specifically, we present a variant of the linearization process, making use of a restricted version of the chase that we recently introduced. Our results imply that open-world query answering with guarded TGDs can be solved in EXPTIME with arbitrary-arity guard relations if we simply bound the arity of the side signature; and that the complexity drops to NP if we fix the side signature and bound the width of the dependencies.
• The Knowledge in Neurosymbolic Artificial Intelligence
  
  • Alam Mehwish
  , 2026. Recent advances in Artificial Intelligence have been largely driven by neural, data-driven approaches, yet these methods often lack interpretability, robustness, and reasoning. Symbolic AI, while offering explicit knowledge representation and reasoning guarantees, has historically struggled with scalability and noise. Neurosymbolic AI seeks to bridge this gap by integrating neural learning with symbolic representations, enabling systems that combine data efficiency, generalization, and interpretability. This manuscript contributes to this paradigm by focusing on Knowledge Graphs (KGs) as a central symbolic foundation within neurosymbolic systems. It advances embedding-based methods for KG completion, extending them from instance-level factual knowledge to entity types, taxonomies, and schema-level structures. The work further proposes approaches for multilingual KG construction in the social sciences, combining symbolic alignment with neural representations to support cross-lingual and interdisciplinary analysis. Additionally, it demonstrates how KG embeddings can enhance downstream language understanding tasks, including coreference resolution and short text classification, particularly in knowledge-sparse and multilingual settings. Situated within established taxonomy of Neurosymbolic AI systems, these contributions illustrate how structured symbolic knowledge and neural models can be effectively integrated to support semantic understanding, reasoning, and real-world applications.
• Doppler-Shannon Association in Vehicular Networks: A Stochastic Geometry Analysis
  
  • Balakrishnan Ashutosh
  • Baccelli François
  • Jhawar Sanjoy Kumar
  • Martins Philippe
  , 2026. Historically, in cellular networks, the user equipment (UE) association with a base station (BS) has been largely confined to nearest BS association strategies. Unlike cellular networks wherein the BSs are static, vehicular networks are prone to high mobility, resulting in the UE experiencing a Doppler shift. The Doppler shift further results in the fading channel exhibiting high temporal variability. In this work, we present a novel stochastic geometry framework, modeling the effects of the Doppler shift on the physical layer network performance through the coherence time. First, the statistics of the Doppler shift are studied in a two-and three-dimensional Poisson point process (PPP) setting. The distribution and the geometric factors influencing the Doppler shift are analyzed. Next, we propose Doppler shift aware utilities, modeling the Doppler shift induced Shannon rate achievable in time-selective fading links. Through these utilities, we analytically study the outage and coverage probabilities for a UE to achieve a given quality of service. These probabilities are computed based on the optimal BS association in vehicular networks, which goes beyond the closest Euclidean distance. In the UE-BS line of sight, the novel BS association policies factor the BS direction of motion and the fading gain, in addition to the Euclidean distance. We show that the nearest BS is no longer the optimal BS association, resulting in non-convex coverage tessellations associated with the BSs. Our analytical and simulation results illustrate the need for a Doppler shift correction, complementing the ideal SNR based Shannon performance. The network performance achieved through the proposed Doppler-Shannon based association is proved, analytically as well as through simulations, to be superior to the classical Shannon based association achieving spatially averaged gains up to 90% and 22% in a 2D and 3D setting, respectively.
• Functional requirements decomposition in set-based design
  
  • Sun Minghui
  • Chen Zhaoyang
  • Bakirtzis Georgios
  • Jafarzadeh Hassan
  • Fleming Cody
  Design Science, Cambridge University Press, 2026, 12, pp.e12, 1-35. Designing systems is typically uncertain and ambiguous at the early stages. Set-based design (SBD) supports alternative exploration and gradual uncertainty reduction during the early lifecycle, making it practical for complex system design. In parallel, functional requirements decomposition helps to advance the design incrementally. However, current literature on SBD lacks formal guidance on how to decompose functional requirements. To bridge this gap, we introduce a four-step method to decompose functional requirements for SBD hierarchically. We systematically define, reason and narrow the sets, breaking down the functional requirements into formal sub-requirements. This method allows parallel abstraction, ensuring the resulting system satisfies the top-level functional requirements (10.1017/dsj.2026.10053)
  DOI : 10.1017/dsj.2026.10053
• Proving execution platform contracts
  
  • Bourgeoisat Dorian
  • Brandner Florian
  • Kühne Ulrich
  Microprocessors and Microsystems: Embedded Hardware Design, Elsevier, 2026, 122, pp.105273. Since the discovery of the Spectre vulnerability and its media coverage, micro-architectural side channel attacks have gained considerable interest, both in research and the general public. These side channels threaten many usage scenarios that have become central to everybody’s daily lives. Be it cloud services, where virtual machines share the same physical hardware, be it web applications on different tabs of one’s web browser, or be it applications on one’s smart phone. In all these cases, sensitive data might be processed by software running on a hardware platform along with potentially malicious other pieces of software. Due to the micro-architectural side channels the malicious software may observe how this sensitive data is processed and thus extract information on the data – effectively leaking sensitive information. New vulnerabilities and counter-measures are discovered and publicized at a frantic rate, calling for a sea change in the way micro-architecture designs and software process sensitive data. Hardware/software contracts were recently proposed to establish a formal framework, providing formal guarantees against information leakage and allowing a clear separation of concerns between hardware and software. However, these contracts only focus on the interactions between the micro-architecture and the application software, missing a crucial element that governs and mediates most of these interactions: system software – such as operating systems or hypervisors. The objective of this work is to fill this gap and extend the contracts to include the operating system (OS). For this we formalize thread and memory management policies provided by the OS on top of a hardware model in order to study information leakage on such a platform. We first show through an exploration of concrete examples that the OS plays a crucial role in providing security guarantees to software processing sensitive data. Finally, we apply an inductive proof strategy on these formal platform models in order to establish contracts between the software, the OS, and the hardware. (10.1016/j.micpro.2026.105273)
  DOI : 10.1016/j.micpro.2026.105273
• Verification of statistical wave field theory reverberation time predictions
  
  • Prinn Albert G
  • Badeau Roland
  Applied Acoustics, Elsevier, 2026, 250, pp.111337, 1-12. Classical statistical reverberation models, such as Sabine’s and Eyring’s models, provide practitioners with efficient estimates of a room’s reverberation time based on its volume, surface area, and the absorption coefficients of its boundary surfaces. Absorption coefficients quantify sound absorption in terms of energy, which can be of limited use when detailed descriptions of interior acoustic fields are required. In such cases, describing absorption in terms of impedance or admittance is preferable. A statistical theory of wave fields has recently been proposed in the literature. The statistical wave field theory predicts reverberation time at high frequencies based on the locally reacting impedances of the surfaces present in a room. This study verifies the predictions of the statistical wave field theory using various numerical models. Additionally, predictions of the theory are indirectly validated by using the theory to estimate surface impedances from measured data, which are in turn used to predict reverberation times in a shoebox-shaped room and in a reverberation chamber. (10.1016/j.apacoust.2026.111337)
  DOI : 10.1016/j.apacoust.2026.111337
• Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening
  
  • Di Carlo Diego
  • Koyama Shoichi
  • Aditya Arie Nugraha
  • Mathieu Fontaine
  • Yoshiaki Bando
  • Kazuyoshi Yoshii
  , 2026. This paper investigates continuous representations of steering vectors over frequency and microphone/source positions for augmented listening (e.g., spatial filtering and binaural rendering), enabling user-parameterized control of the reproduced sound field. Steering vectors have typically been used for representing the spatial response of a microphone array as a function of the look-up direction. The basic algebraic representation of these quantities assuming an idealized environment cannot deal with the scattering effect of the sound field. One may thus collect a discrete set of real steering vectors measured in dedicated facilities and super-resolve (i.e., upsample) them. Recently, physics-aware deep learning methods have been effectively used for this purpose. Such deterministic super-resolution, however, suffers from the overfitting problem due to the non-uniform uncertainty over the measurement space. To solve this problem, we integrate an expressive representation based on the neural field (NF) into the principled probabilistic framework based on the Gaussian process (GP). Specifically, we propose a physics-aware composite kernel that models the directional incoming waves and the subsequent scattering effect. Our comprehensive comparative experiment showed the effectiveness of the proposed method under data insufficiency conditions. In downstream tasks such as speech enhancement and binaural rendering using the simulated data of the SPEAR challenge, the oracle performances were attained with less than ten times fewer measurements.
• 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
• Promises, Perils, and (Timely) Heuristics for Mining Coding Agent Activity
  
  • Robbes Romain
  • Matricon Théo
  • Degueule Thomas
  • Hora Andre
  • Zacchiroli Stefano
  , 2026. In 2025, coding agents have seen a very rapid adoption. Coding agents leverage Large Language Models (LLMs) in ways that are markedly different from LLM-based code completion, making their study critical. Moreover, unlike LLM-based completion, coding agents leave visible traces in software repositories, enabling the use of MSR techniques to study their impact on SE practices. This paper documents the promises, perils, and heuristics that we have gathered from studying coding agent activity on GitHub. (10.1145/3793302.3793375)
  DOI : 10.1145/3793302.3793375
• Craft-Based Data Physicalization: Opportunities and Challenges
  
  • Bakhtiari Bahare
  • Daneshzand Foroozan
  • Sauvé Kim
  • Bressa Nathalie
  • Huron Samuel
  • Oehlberg Lora
  • Carpendale Sheelagh
  • Somanath Sowmya
  • Perin Charles
  , 2026, pp.Article No.: 931, Pages 1 - 8. This three-hour workshop will gather data visualization and HCI researchers and practitioners to explore the possibilities of data representation using craft techniques. Participants will submit a 2-4 page document including (i) a statement of their craft experience, (ii) representative images of physicalizations they have created using this craft technique, and (iii) a discussion of opportunities and challenges for physicalizing data in their craft domain. During the workshop, participants and organizers will work in groups to brainstorm ways of representing data through their shared craft of interest. Then, every group proposes a synthesis of opportunities and challenges of the craft technique they worked with. Together, the community will chart a research agenda on how craft can ex- pand the design space of data physicalization, inform the creation of more expressive and accessible authoring tools, and raise new questions around aesthetics, accuracy, and the role of slow making in data representation. (10.1145/3772363.3778766)
  DOI : 10.1145/3772363.3778766