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Assembly bias and local Primordial non-Gaussianity from DESI DR1 Quasars
No full textInternational audienceThe analysis of the large-scale clustering of quasars (QSO) observed by the Dark Energy Spectroscopic Instrument (DESI) represents a promising avenue for constraining local Primordial non-Gaussianity (PNG), parameterized by . The signal to be constrained is the scale-dependent bias induced in the 2-point clustering of the considered tracer sample. The resulting constraints on , however, are fully degenerate with the local PNG bias parameter , dependent on the assembly bias parameter . Using IllustrisTNG hydrodynamical simulations, we select a QSO sample reflecting the selection criteria and properties of DESI QSOs, and provide a robust prior for , and thus for , building on the findings of Fondi et al. 2024. We find a distribution with mean with weak redshift dependence, stable to selection noise and consistent with the expected recent merger history typical of quasar-hosting halos. By comparing with the CAMELS simulations we demonstrate that this prior is robust to astrophysical assumptions and cosmic variance. Finally, applying this prior to the DESI DR1 dataset, we derive updated constraints on local PNG, obtaining
An Effective Version of the p-Curvature Conjecture for Order One Differential Equations
No full textWe develop an effective version of Kronecker's Theorem on the splitting of polynomials, based on asymptotic arguments proposed by the Chudnovsky brothers, coming from Hermite-Padé approximation. In conjunction with Honda's proof of the p-curvature conjecture for order one equations with polynomial coefficients we use this to deduce an effective version of the Grothendieck p-curvature conjecture for order one equations. More precisely, we bound the number of primes for which the p-curvature of a given differential equation has to vanish in terms of the height and the degree of the coefficients, in order to conclude it has a non-zero algebraic solution. Using this approach, we describe an algorithm that decides algebraicity of solutions of differential equation of order one using p-curvatures, and report on an implementation in SageMath
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
Continuous microstructure variations with graded properties in directed energy deposition
No full textInternational audienceDirected energy deposition additive manufacturing is a versatile technique for fabricating complex geometries, where precise control of process parameters is crucial for tailoring microstructure and part properties. Microstructure control strategies usually involve variation of material composition (i.e., functionally graded materials) or interlayer time delay. However, the obtained microstructures are usually uniform in the print direction and exhibit sharp transitions from one layer to the next in the build direction. This paper targets continuous microstructural variation by exploiting active cooling strategies to control cooling conditions. To do so, the scanning speed is continuously varied, necessitating accommodating the bead size variations with non-standard trajectory generation based on a phenomenological law. The proposed strategy is demonstrated on thin-wall structures made of IN718 using a powder-based laser directed energy deposition. The results reveal a continuous microstructural transition along the print direction, characterized by two distinct microstructural regimes with markedly different morphological features and crystallographic textures. This demonstrates the capability of scanning speed modulation to engineer heterogeneous microstructures within a single component, offering insights into tailoring material properties for specific engineering applications.</div
Association between long-term exposure to air pollution on the risk of infection by SARS-CoV-2 virus and COVID-19 disease in the French CONSTANCES cohort
No full textInternational audienceLong-term air pollution exposure has been associated with increased risk of SARS-CoV-2 infection. However, few studies have used individual-level data, and even fewer serology data.We aimed to investigate the association between long-term exposure to air pollution and 1) SARS-CoV-2 infection and 2) COVID-19 disease, among adults from the French CONSTANCES population-based cohort.SARS-CoV-2 infection was assessed in May–November 2020 using ELISA test serology; COVID-19 disease was self-reported as medical diagnosis of SARS-CoV-2 infection and associated symptoms. Annual 2019 exposures to particulate matter with an aerodynamic diameter of ≤2.5 (PM2.5), black carbon (BC) and nitrogen dioxide (NO2) were estimated using hybrid land-use regression models and assigned at pre-pandemic residential address.We estimated log-binomial risk ratios (RRs for an interquartile range increase), adjusting for individual and area-level covariates.The population included 33,974 participants, among which 1695 (4.99 %) had a SARS-CoV-2 infection, and 802 (2.8 %) reported COVID-19 disease. Exposure to PM2.5 and NO2 were associated with a higher probability of SARS-CoV-2 infection (Adjusted RRs (aRR[95 %CI]): 1.28[1.10: 1.50] for PM2.5, 1.21[1.07: 1.37] for NO2) and COVID-19 disease (1.41[1.11: 1.79] for PM2.5, 1.40[1.18: 1.66] for NO2). Exposure to BC was associated with a higher probability of COVID-19 disease (1.20[1.03: 1.39]) but not SARS-CoV-2 infection (1.07[0.97–1.19]). Stratified analyses showed higher risks for men, age >60, or having pre-existent chronic disease.Using individual data, long-term exposure to air pollution was associated with increased risks of SARS-CoV-2 infection and COVID-19 disease. These results could help to better understand or prevent respiratory infections
“Write your model almost as you would on paper and Michel will take care of the rest!” Michel Juillard’s contribution to macroeconomics in historical perspective
No full textInternational audienceIn this article, we document Michel Juillard’s contribution to macroeconomics. Best known as the creator of the computer package Dynare, Juillard’s impact extends far beyond software development. We trace his training and career from his first encounter with computers in high school through his ongoing work on Dynare. His contribution to macroeconomics, we argue, is threefold: intellectual (devising algorithms and addressing specific computational problems for a class of models), technical (writing code and developing a computer package), and institutional (establishing and maintaining the governance structures that ensure Dynare’s sustainability as a digital commons). Juillard’s career highlights broader questions about adapting Ostrom’s framework to digital commons development, the principles that govern software development, and the place computational economics should occupy in the history of macroeconomics
Repulsive Monte Carlo on the sphere for the sliced Wasserstein distance
No full textInternational audienceIn this paper, we consider the problem of computing the integral of a function on the unit sphere, in any dimension, using Monte Carlo methods. Although the methods we present are general, our guiding thread is the sliced Wasserstein distance between two measures on R d , which is precisely an integral of the d-dimensional sphere. The sliced Wasserstein distance (SW) has gained momentum in machine learning either as a proxy to the less computationally tractable Wasserstein distance, or as a distance in its own right, due in particular to its built-in alleviation of the curse of dimensionality. There has been recent numerical benchmarks of quadratures for the sliced Wasserstein (Sisouk et al., 2025), and our viewpoint differs in that we concentrate on quadratures where the nodes are repulsive, i.e. negatively dependent. Indeed, negative dependence can bring variance reduction when the quadrature is adapted to the integration task. Our first contribution is to extract and motivate quadratures from the recent literature on determinantal point processes (DPPs) and repelled point processes, as well as repulsive quadratures from the literature specific to the sliced Wasserstein distance. We then numerically benchmark these quadratures. Moreover, we analyze the variance of the UnifOrtho estimator, an orthogonal Monte Carlo estimator introduced by Rowland et al. (2019). Our analysis sheds light on UnifOrtho's success for the estimation of the sliced Wasserstein in large dimensions, as well as counterexamples from the literature. Our final recommendation for the computation of the sliced Wasserstein distance is to use randomized quasi-Monte Carlo in low dimensions and UnifOrtho in large dimensions. DPPbased quadratures only shine when quasi-Monte Carlo also does, while repelled quadratures show moderate variance reduction in general, but more theoretical effort is needed to make them robust.</div
Near‐Inertial Wave Trapping Inside a Fine‐Scale Anticyclonic Eddy During the BioSWOT‐Med 2023 Cruise: Turbulence and Energy Flux
No full textInternational audienceAbstract Near‐inertial waves (NIWs) are an important source of turbulence for the ocean interior. Mesoscale anticyclonic eddies are known to facilitate their propagation at depth while trapping them. However, in situ observations have so far focused on large ( km radius), energetic eddies, whereas most of the ocean is populated by smaller, moderately energetic fine‐scale structures. Are these smaller structures efficient to trap NIWs and enhance turbulence? Here, we present in situ observations from the BioSWOT‐Med 2023 cruise addressing this issue by surveying a fine‐scale frontal area of the North Balearic front in the Mediterranean Sea, assisted by the first high‐resolution Sea Surface Height images of the new Surface Water and Ocean Topography (SWOT) satellite mission during its Calibration/Validation phase. We explore how fine scales modulate the evolution of turbulence below the mixed layer after experiencing two consecutive strong wind events. We show that turbulence remains low in the front and its cyclonic side, while being greatly enhanced in the anticyclonic side. The latter side is dominated by a fine‐scale anticyclone (12.8 km of radius, Rossby number of 0.5) that trapped NIWs, increasing turbulent dissipation level to several 10-8 W/kg . The NIW‐induced vertical kinetic energy flux reach up to 5.1 mW/m2 below the pycnocline and represent ~20 % of the wind power input into inertial motions, higher or similar to previous estimations outside and inside mesoscale anticyclones. Future work is needed to investigate whether these results extend to fine scales elsewhere in the world ocean, especially in regions with larger baroclinic Rossby radius of deformation
Oceanic δ<sup>13</sup> C Fingerprints Caused by Laurentice Ice Sheet Discharges: Model‐Data Comparison During Heinrich Event 4
No full textInternational audienceAbstract This study investigates the sensitivity of the oceanic circulation and of the dissolved inorganic carbon to ice discharge events from the Laurentide ice sheet (LIS), using an isotope‐enabled and coupled climate–ice sheet model, and observations. The ice discharges are triggered by either reduced friction at the ice sheet‐bedrock interface or increased oceanic melt rates in the Hudson Strait ice stream region. The simulated decreases in both scenarios, following freshwater release and the weakening of the Atlantic Meridional Overturning Circulation. The best agreement with the observed anomalies is achieved with large ice volume loss, by reducing ice sheet basal friction. In our model, the freshwater discharges from the LIS need to be amplified to better represent the observed changes. The LIS alone does not seem able to explain the observed oceanic variations, which may indicate that additional processes are required to account for these changes