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A new surrogate microstructure generator for porous materials with applications to the buffer layer of TRISO nuclear fuel particles
No full textInternational audienc
Comparative physicochemical study of dielectric barrier discharge and post-discharge plasmas to treat non-small cell lung carcinoma in murine models
No full textInternational audienceWhile cold atmospheric plasmas (CAPs) are increasingly explored for cancer therapy, it remains unclear how distinct device configurations translate into differences in tissue coupling, safety, and therapeutic efficacy. To address this gap, a comparative evaluation of the two following CAP sources has been conducted: the ORJET (atmospheric pressure plasma jet in outer ring electrode configuration) and the PoDBD (post-discharge delivered by a dielectric barrier device with a grounded-mesh electrode). Electrical behavior is quantified on an equivalent electrical human body model, while optical emission spectroscopy and surface-oxidation assays are achieved on transdermal membranes and polyethylene substrates to characterize the nature and diffusion of plasma-generated reactive species. Thermal safety is examined in mice through real-time temperature monitoring and histological analysis while antitumor efficacy is determined in a syngeneic model of non-small cell lung cancer (NSCLC) treated five times. The two devices display fundamentally different modes of tissue coupling: ORJET delivers localized interfacial electric field while PoDBD exposes tissue solely to reactive oxygen and nitrogen species-rich post-discharge. Despite these differences, both generate similar reactive-species signatures, preserve tissue integrity when operated within safe thermal limits, and significantly slow tumor progression compared with controls, with no difference between devices. These findings indicate that therapeutic activity arises predominantly from reactive-species chemistry rather than electrical coupling, supporting the applicability of diverse CAP technologies for oncological treatment
Characterization and forecast of global influenza subtype dynamics
No full textInternational audienceAbstract The subtype composition of seasonal influenza waves varies in space and time. Influenza subtypes A/H1N1, A/H3N2 and B tend to have different impacts on population groups; therefore, understanding the drivers of their cocirculation and anticipating their composition is important for epidemic preparedness. FluNet provides data on influenza specimens by subtype for more than 150 countries. However, owing to surveillance variations across countries, global analyses usually focus on subtype compositions, a kind of data difficult to treat with advanced statistical methods. We used compositional data analysis to circumvent the problem and study trajectories of annual subtype compositions of countries. Here we first examine global trends from 2000 to 2023. We identify a few seasons which stood out for the strong within-country subtype dominance due to either a new virus/clade taking over (2003/2004 season, A/H1N1pdm pandemic) or subtypes’ spatial segregation (coronavirus disease 2019 pandemic). Second, we show that geographical factors, most notably international mobility, concurred in shaping countries’ composition trajectories between 2010 and 2019. Trajectories clustered in two macroregions characterized by subtype alternation versus persistent mixing. Finally, we define five algorithms for forecasting the next year’s composition and found that incorporating the global history of subtype composition in a Bayesian hierarchical vector autoregressive model improved predictions compared with naive methods. The joint analysis of spatiotemporal dynamics of influenza subtypes worldwide reveals a hidden structure in subtype circulation that can be used to improve predictions of the subtype composition of next year’s epidemic according to place
Simple generators of rational function fields
No full textConsider a subfield of the field of rational functions in several indeterminates. We present an algorithm that, given a set of generators of such a subfield, finds a simple generating set. We provide an implementation of the algorithm and show that it improves upon the state of the art both in efficiency and the quality of the results. Furthermore, we demonstrate the utility of simplified generators through several case studies from different application domains, such as structural parameter identifiability. The main algorithmic novelties include performing only partial Gröbner basis computation via sparse interpolation and efficient search for polynomials of a fixed degree in a subfield of the rational function field
Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes
No full textSampling configurations at thermodynamic equilibrium is a central challenge in statistical physics. Boltzmann Generators (BGs) tackle it by combining a generative model with a Monte Carlo (MC) correction step to obtain asymptotically unbiased samples from an unnormalized target. Most current BGs use classic MC mechanisms such as importance sampling, which both require tractable likelihoods from the backbone model and scale poorly in high-dimensional, multi-modal targets. We study BGs built on annealed Monte Carlo (aMC), which is designed to overcome these limitations by bridging a simple reference to the target through a sequence of intermediate densities. Diffusion models (DMs) are powerful generative models and have already been incorporated into aMC-based recalibration schemes via the diffusion-induced density path, making them appealing backbones for aMC-BGs. We provide an empirical meta-analysis of DM-based aMC-BGs on controlled multi-modal Gaussian mixtures (varying mode separation, number of modes, and dimension), explicitly disentangling inference effects from learning effects by comparing (i) a perfectly learned DM and (ii) a DM trained from data. Even with a perfect DM, standard integrations using only first-order stochastic denoising kernels fail systematically, whereas second-order denoising kernels can substantially improve performance when covariance information is available. We further propose a deterministic aMC integration based on first-order transport maps derived from DMs, which outperforms the stochastic first-order variant at higher computational cost. Finally, in the learned-DM setting, all DM-aMC variants struggle to produce accurate BGs; we trace the main bottleneck to inaccurate DM log-density estimation
Runtime Analysis of the Compact Genetic Algorithm on the LeadingOnes Benchmark
No full textInternational audienceThe compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA)another simple EDA-, the cGA has been subject to extensive mathematical runtime analyses, often showcasing a similar or even superior performance to competing approaches. Surprisingly though, up to date and in contrast to the UMDA and many other heuristics, we lack a rigorous runtime analysis of the cGA on the LEADINGONES benchmark-one of the most studied theory benchmarks in the domain of evolutionary computation.We fill this gap in the literature by conducting a formal runtime analysis of the cGA on LEADINGONES. For the cGA's single parameter-called the hypothetical population size-at least polylogarithmically larger than the problem size, we prove that the cGA samples the optimum of LEADINGONES with high probability within a number of function evaluations quasi-linear in the problem size and linear in the hypothetical population size. For the best hypothetical population size, our result matches, up to polylogarithmic factors, the typical quadratic runtime that many randomized search heuristics exhibit on LEADINGONES. Our analysis exhibits some noteworthy differences in the working principles of the two algorithms which were not visible in previous works
Sparse recovery of Diffusion Dynamics: Handling High-Dimensionality in Repeated Short Trajectories
No full textViewed as systems of interacting particles, high-dimensional stochastic differential equations encode complex interaction structures within their drift component. We propose a novel approach to estimate this drift from independent high-frequency trajectory data observed over a short time horizon. Each trajectory is modelled as the solution of a Brownian-driven stochastic differential equation, while the number of time points within each path tends to infinity. We further assume that the drift function governing the dynamics can be expressed as a linear combination of a growing number of Lipschitz basis functions. To promote accurate recovery of the underlying dynamics under sparsity constraints, we propose a Lasso-regularised likelihood criterion. Under suitable regularity conditions, we establish convergence rates for the resulting estimator and emphasise how they depend on the dimensional parameters of the problem, in particular on the number of observed trajectories. We assess the performance of the estimator on synthetic datasets, both from an estimation and a generative perspective. Finally, we illustrate the practical relevance of the approach on a real-world climate dataset, highlighting its ability to perform variable selection
Leveraging Contrastive Learning for a Similarity-Guided Tampered Document Data Generation Pipeline
No full textDetecting tampered text in document images is a challenging task due to data scarcity. To address this, previous work has attempted to generate tampered documents using rule-based methods. However, the resulting documents often suffer from limited variety and poor visual quality, typically leaving highly visible artifacts that are rarely observed in real-world manipulations. This undermines the model's ability to learn robust, generalizable features and results in poor performance on real-world data. Motivated by this discrepancy, we propose a novel method for generating high-quality tampered document images. We first train an auxiliary network to compare text crops, leveraging contrastive learning with a novel strategy for defining positive pairs and their corresponding negatives. We also train a second auxiliary network to evaluate whether a crop tightly encloses the intended characters, without cutting off parts of characters or including parts of adjacent ones. Using a carefully designed generation pipeline that leverages both networks, we introduce a framework capable of producing diverse, high-quality tampered document images. We assess the effectiveness of our data generation pipeline by training multiple models on datasets derived from the same source images, generated using our method and existing approaches, under identical training protocols. Evaluating these models on various open-source datasets shows that our pipeline yields consistent performance improvements across architectures and datasets
The injective norm of CSS quantum error-correcting codes
No full textIn this paper, we compute the injective norm - a.k.a. geometric entanglement - of standard basis states of CSS quantum error-correcting codes. The injective norm of a quantum state is a measure of genuine multipartite entanglement. Computing this measure is generically NP-hard. However, it has been computed exactly in condensed-matter theory - notably in the context of topological phases - for the Kitaev code and its extensions, in works by Orús and collaborators. We extend these results to all CSS codes and thereby obtain the injective norm for a nontrivial, infinite family of quantum states. In doing so, we uncover an interesting connection to matroid theory and Edmonds' intersection theorem
Topological Signatures of Magnetic Phase Transitions with Majorana Fermions through Local Observables and Quantum Information
No full textInternational audienceThe one-dimensional (1D) quantum spin model can be viewed as a strong-coupling analogue of the Schrieffer-Su-Heeger model with two inequivalent alternating Ising couplings along the wire, associated to the physics of resonating valence bonds. Similar to the quantum Ising model, which differently presents a long-range Neel ordered phase, this model also maps onto a p-wave superconducting wire which shows a topological phase transition with the emergence of low-energy Majorana fermions. We show how signatures of the topological phase transition for the p-wave superconducting wire, i.e. a half Skyrmion, are revealed through local (short-range) spin observables and their derivatives related to the capacitance of the pairing fermion model. Then, we present an edge correspondence through the edge spin susceptibility in the model revealing that the topological phase transition is a metal of Majorana fermions. We justify that the spin magnetization at an edge at very small transverse magnetic field is a good marker of the topological invariant. We identify a correspondence between the quantum information of resonating valence bonds and the charge fluctuations in a p-wave superconductor through our method ``the bipartite fluctuations''. This system may be realized in materials and engineered in quantum circuits, optical lattices