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The Solar Orbiter merged magnetic field
No full textInternational audienceContext. In situ studies of the solar wind require precise magnetic field measurements at all frequencies. The Solar Orbiter mission carries two magnetometers to measure the solar wind magnetic field: the fluxgate magnetometer (MAG), which is best suited for frequencies from DC to a few Hertz, and the search coil magnetometer (SCM), which is best suited for frequencies above a few Hertz.Aims: The aim of this paper is to produce a merged magnetic field data product that takes the best of both instruments and provides the community with high quality, easy to use magnetic field data over a wide range of frequencies.Methods: We first compared the two instruments in their overlapping frequency range, then we performed the merging in Fourier space using a weighted function determined by the sensitivity of the two sensors.Results: The two instruments are found to give consistent results in their overlapping frequency range. SCM has a lower gain than MAG by 14% around 1 Hz and MAG is delayed by about 20 ms with respect to SCM, and the merged magnetic field takes care of these discrepancies. It is basically identical to MAG data below 2 Hz and to SCM data above about 15 Hz (with amplitude increased by 14%). We show that the merged magnetic field is suitable to analyse waves and turbulence over a broad frequency range, in particular by confirming that ion cyclotron waves can lower the level of energy at the sub ionic scales. The merged magnetic field is distributed as daily files containing the magnetic field at either 256 or 4096 Hz, and either in the radial-tangential-normal co-ordinates or in the spacecraft reference frame co-ordinates
The dependence of the amino acid backbone conformation on the translated synonymous codon is not statistically significant
No full textInternational audienceThe correlation between synonymous codon usage and secondary structure in translated proteins has been widely demonstrated. This usage plays a capital role in tuning translational rates and protein folding kinetics, indirectly influencing multiple biological processes. A recent report [A. A. Rosenberg, A. Marx, A. M. Bronstein, Nat. Commun. 13 , 2815 (2022).] suggests that the translated synonymous codon influences the ( ϕ , ψ ) dihedral angles within secondary structure elements. If true, this conclusion would have strong consequences in several scientific fields, including structural biology and protein design, where results would depend on DNA sequence rather than protein sequence. Here, we show that the original statistical methodology used in the referred study was formally incorrect. Furthermore, when using a correct approach, we demonstrate that the influence of the codon on the distribution of the dihedral angles is not statistically significant for any type of secondary structure
Multi-parameter Module Approximation: an efficient and interpretable invariant for multi-parameter persistence modules with guarantees
No full textInternational audienceTopological data analysis (TDA) is a rapidly growing area of data science, whose most common descriptor is persistent homology, which tracks the topological changes in growing families of subsets of the data set itself, called filtrations, and encodes them in an algebraic object, called a persistence module. The algorithmic and theoretical properties of persistence modules are now well understood in the single-parameter case, that is, when there is only one filtration (e.g., feature scale) to study. In contrast, much less is known in the multi-parameter case, where several filtrations (e.g., scale and density) are used simultaneously. Since multi-parameter persistence modules usually encode information that is invisible to their single-parameter counterparts, it is critical to build tractable proxies for them, ideally with some theoretical robustness guarantees. In this article, we introduce a new parameterized family of topological descriptors, taking the form of candidate decompositions, for multi-parameter persistence modules, and we a identify a subfamily of these descriptors, that we call approximate decompositions, that are controllable approximations, in the sense that they preserve diagonal barcodes. Then, we introduce MMA (Multipersistence Module Approximation): an algorithm based on matching functions for computing instances of candidate decompositions with some precision parameter delta . By design, MMA can handle an arbitrary number of filtrations, and has bounded complexity and running time. Moreover, we prove the robustess of MMA: when computed with so-called compatible matching functions, we show that MMA produces approximate decompositions (and we prove that such matching functions exist for n = 2 filtrations). Next, we restrict the focus on modules that can be decomposed into interval summands. In that case, compatible matching functions always exist, and we show that, for small enough delta, the approximate decompositions obtained with such compatible matching functions by MMA have an approximation error (in terms of the standard interleaving and bottleneck distances) that is bounded by delta, and that reaches zero for an even smaller, positive precision delta_exact. Finally, we present empirical evidence validating that MMA has state-of-the-art performance and running time on several data sets
Mécanismes hybrides de partage des risques : un pont entre l'assurance traditionnelle et les solutions peer-to-peer
No full textThis thesis investigates hybrid risk-sharing mechanisms that combine traditional insurance instruments with peer-to-peer (P2P) pooling schemes. It responds to structural limitations in conventional insurance—such as solvency constraints, coverage caps, and index mismatches in parametric insurance—by proposing a layered architecture that integrates ex-ante financial protection with ex-post solidarity, to improve collective efficiency and fairness.Chapter 1 lays the theoretical foundations of P2P risk-sharing by modeling how heterogeneous agents can allocate losses collectively. It introduces a fixed point approach to construct risk-sharing rules that are both Pareto-optimal and actuarially fair. Through detailed numerical analyses, the chapter examines how individual risk aversion, heavy-tailed distributions, and inter-agent dependence affect the optimal allocation of aggregate losses.The second chapter presents a hybrid model for managing natural catastrophe risks. It combines capped traditional insurance with a government-coordinated redistributive taxation scheme that covers residual losses in the event of insurer default. The proposed P2P mechanism is grounded in optimal taxation theory and is empirically assessed on simulated data for 221 European regions. Results demonstrate a substantial reduction in regional disutility and improved fairness across territories.The third chapter addresses basis risk in parametric insurance, particularly for solar energy producers. It constructs an optimized weather index to homogenize the gap between realized losses and payouts, and develops a P2P mechanism to redistribute residual basis risk among producers. Using high-frequency data from 50 solar farms in southern Germany, the model achieves a 55% reduction in basis risk variance, thus enhancing the robustness and acceptability of parametric contracts.Cette thèse explore la conception et l’évaluation de mécanismes hybrides de gestion du risque, articulant des outils d’assurance traditionnels et des dispositifs de mutualisation pair-à-pair (P2P). Face aux limites des approches assurantielles classiques – liées à la solvabilité, aux plafonds de garantie, ou encore à l'inadéquation des indices en assurance paramétrique – elle propose une architecture combinant couverture ex-ante et solidarité ex-post, dans une perspective d'efficacité collective et d'équité redistributive.Le Chapitre 1 pose les fondements théoriques du partage de risque dans un cadre P2P, en développant un modèle général de répartition des pertes entre agents hétérogènes. À l’aide d’une approche par point fixe, il caractérise les règles de partage qui sont à la fois Pareto-optimales et actuariellement équitables. Une analyse numérique approfondie permet d’examiner l’impact de l’aversion au risque, des distributions de pertes à queues lourdes et de la dépendance entre les agents sur la structure des allocations optimales.Le Chapitre 2 introduit un mécanisme hybride pour la couverture des risques de catastrophes naturelles. Il combine une assurance classique à garantie limitée avec une taxation redistributive entre régions, activée ex-post en cas de défaillance de l’assureur. Le gouvernement y joue un rôle de coordinateur central, organisant le partage P2P des pertes résiduelles selon des règles optimales de taxation. Le mécanisme est évalué à partir de données simulées sur 221 régions européennes, révélant des gains substantiels en termes de réduction des désutilités régionales et d’équité interterritoriale.Le Chapitre 3 traite du risque de base en assurance paramétrique, notamment pour les producteurs d’énergie solaire. Il propose la construction d’un indice climatique optimisé afin d’homogénéiser les écarts entre pertes réelles et indemnités, et introduit un mécanisme de compensation P2P pour redistribuer le risque de base entre fermes solaires. À partir de données empiriques collectées sur 50 installations dans le sud de l’Allemagne, le modèle réduit la variance du risque de base de 55%, renforçant ainsi la robustesse des contrats paramétriques et leur acceptabilité
Compressed verification for post-quantum signatures with long-term public keys
No full textInternational audienceMany signature applications-such as root certificates, secure software updates, and authentication protocols-involve long-lived public keys that are transferred or installed once and then used for many verifications. This key longevity makes post-quantum signature schemes with conservative assumptions (e.g., structure-free lattices) attractive for long-term security. But many such schemes, especially those with short signatures, suffer from extremely large public keys. Even in scenarios where bandwidth is not a major concern, large keys increase storage costs and slow down verification. We address this with a method to replace large public keys in GPV-style signatures with smaller, private verification keys. This significantly reduces verifier storage and runtime while preserving security. Applied to the conservative, short-signature schemes Wave and Squirrels, our method compresses Squirrels-I keys from 665 kB to 20.7 kB and Wave822 keys from 3.5 MB to 207.97 kB.</div
A sequential variational Bayesian approach to Gaussian process quantile regression for optimization
No full textInternational audienceQuantile regression [1] extends the classical least-squares regression to the estimation of the conditional quantiles of a random variable. In the frequentist approach, one casts the quantile regression into the problem of minimizing a loss function, possibly completed with regularization terms. The Bayesian counterpart, first proposed in [2], formulates the problem as a posterior inference over a function space. Bayesian inference can rely on Markov chain Monte Carlo (MCMC) methods to sample the posterior distribution. Variation Bayesian inference techniques alleviate some of the computational burdens of MCMC by introducing latent variables and providing an analytical approximation of their posterior distribution
Competitive and Revenue-Optimal Pricing with Budgets
No full textInternational audienceIn markets with budget-constrained buyers, competitive equilibria need not be efficient in the utilitarian sense, or maximise the seller's revenue. We consider a setting with multiple divisible goods. Competitive equilibrium outcomes, and only those, are constrained utilitarian efficient, a notion of utilitarian efficiency that respects buyers' demands and budgets. Our main contribution establishes that, when buyers have linear valuations, competitive equilibrium prices are unique and revenue-optimal for a zero-cost seller
Understanding the key challenges in tuberculosis drug discovery: what does the future hold?
No full textInternational audienceIntroduction: Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a major global health concern. It spreads through airborne droplets and has a high mortality rate, particularly without treatment. Drug resistance is rising, with treatments against multidrug-resistant TB (MDR-TB) showing poor treatment success rates. The thick, lipid-rich wall of Mtb and its slow growth reduce antibiotic effectiveness, requiring long treatment courses of 4-6 months. Current therapies often fail against drugresistant strains, highlighting the urgent need for new, short-course treatment, affordable, and combination-friendly drugs.Areas covered: Within this perspective, the authors review and comment on the following topics regarding Mtb resistance emergence and treatment strategies: i) Existing treatment ii) Resistance evolution in Mtb; iii) Key challenges in drug discovery targeting Mtb; iv) emerging strategies and recent advances in Mtb drug discovery, and v) Next-generation approaches. Literature was identified through a search of PubMed, google scholar, and web of science, from January 2010 to March 2025.Expert opinion: AI is accelerating the discovery of bioavailable and safe preclinical drug candidates for TB, though data limitations and biological complexity remain challenging. Future progress requires multi-modal models, open-access datasets, and interdisciplinary collaboration
Chemoselective Cleavage of N -Aryl Phthalimides through a Transamidation Reaction with Solid Sources of Ammonia
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Record-breaking 2023 marine heatwaves
No full textInternational audienceThe year 2023 witnessed an extraordinary surge in marine heatwaves (MHWs) across Earth’s oceans, setting new records in duration, extent, and intensity, with MHW activity totaling 53.6 billion °C days square kilometer—more than three standard deviations above the historical norm since 1982. Notable events include the North Atlantic MHW (276-year return period) and the Southwest Pacific (141 years). Using ECCO2 (Estimating the Circulation and Climate of the Ocean-Phase II) high-resolution daily data, we conducted a mixed-layer heat budget analysis and identified region-specific drivers: enhanced shortwave flux and a shallower mixed layer in the North Atlantic and North Pacific, reduced cloud cover and increased advection in the Southwest Pacific, and oceanic advections in the Tropical Eastern Pacific. The 2023 MHWs highlight the intensifying impacts of a warm climate and the challenges in understanding extreme events