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A 3D morpho-space of sepal geometry reveals the importance of organ curvature
No full textInternational audienceHow robust three-dimension (3D) organ shape emerges during morphogenesis is a fundamental question in biology. Addressing this question requires a comprehensive quantification of organ geometry in 3D. To tackle these issues, we considered the sepal of Arabidopsis as a model. Using a unique pipeline allowing to recover 3D sepal morphology, we analysed fifteen mutants affected in different pathways. The results of a Principal Component Analysis reveal sepal curvature as an important parameter accounting for variations in sepal morphology within genotypes. Unexpectedly, despite genetic homogeneity of the wild-type plants and reproducible culture conditions, we found a significant level of variability in sepal morphology. Our data also show that sepal shape from wild-type plants is more robust (less variable) than sepal size, hinting to a possible selective pressure on shape parameters
Finite strain micro-poro-mechanics: Formulation and compared analysis with macro-poro-mechanics
No full textInternational audiencePorous materials are ubiquitous in nature -notably living tissues, which often undergo large deformations and engineering applications. Poromechanics is an established theory to model the response of such materials; however, it is limited in its description of microscale phenomena, and structure-properties relationships. In this paper, we propose a microscopic poromechanical model based on a novel formulation of the micro-poro-mechanics problem, which allows to compute the response of any porous periodic microstructure to any loading involving fluid pressure, macroscopic strain, and/or macroscopic stress. We systematically compare the global response of our micro-model to macro-poromechanics, in both the infinitesimal and finite strain settings, and investigate in particular three mechanisms, namely solid compressibility, strain-pressure coupling and deviatoric-volumetric strain coupling. We notably illustrate how the micro-model can be used to derive macroscopic parameters, and how these parameters depend on microscopic features like pore shape, porosity, material properties, etc. This modeling framework will be the basis for powerful micro-poro-mechanical models of various materials and tissues, where pore-scale phenomena can be incorporated explicitly
TRMT112, a master activator of several methyltransferases modifying factors involved in RNA maturation and translation
No full textInternational audienceMost RNAs and many protein factors involved in mRNA maturation and translation are decorated by numerous and diverse chemical modifications, which contribute to the efficiency, fidelity and regulation of these complex and essential cellular processes. Among those modifications, methylation catalyzed mainly by S-adenosyl-L-methionine (SAM) dependent methyltransferases (MTases) is the most common one. TRMT112 is a small protein acting as an allosteric regulator of several MTases. Initial studies focusing on TRMT112 and its associated MTases were performed in Saccharomyces cerevisiae whereas only few were expanded to human cells, leading to the identification and characterization of four TRMT112 partners in yeast (Trm11, Bud23, Mtq2 and Trm9) and five in human cells (TRMT11, BUD23, MTQ2/HemK2 and two Trm9 orthologues ALKBH8 and TRMT9B). Recent studies have identified several novel MTase partners of human TRMT112, namely METTL5, THUMPD2 and THUMPD3. Interestingly, all these TRMT112-MTase complexes modify factors (RNAs and proteins) involved in mRNA maturation and translation processes and growing evidence supports the importance of these MTases in cancer and correct brain development. In this review, we summarize the current knowledge on TRMT112 protein and its various MTase partners in eukaryotes and archaea
Idealized modeling of stratospheric aerosol injection deployment scenarios with two uncooperative actors
No full textInternational audienc
Méthodes d'assimilation de données pour des simulations lagrangiennes
No full textThis thesis focuses on the development of data assimilation methods for Lagrangian particle-based simulations, with applications to fluid mechanics simulations. We study situations where an ensemble of simulations and observations at discrete times are used to update the state estimate of the system. In this context, the procedure for updating the particle discretization based on available observations is a key issue.First, we adapt the ensemble Kalman filter to correct the fields by modifying only the intensities of the discretization particles. The positions of the particles are then left unchanged or regenerated on a regular grid, resulting in two different methods.We then present an ensemble variational approach to particle position correction. We show that this approach can be combined with the first filters to sequentially correct positions and intensities. We evaluate these different methods on applications in incompressible fluid dynamics discretized by vortex methods, and analyze the effectiveness of the filters on advection problems where position errors can be significant.Cette thèse porte sur le développement de méthodes d'assimilation de données pour les simulations lagrangiennes basées sur une discrétisation particulaire, avec des applications pour la simulation en mécanique des fluides. Nous étudions des situations où un ensemble de simulations et des observations à des temps discrets sont utilisés sont pour corriger l'estimation de l'état du système. Dans ce contexte, la procédure de mise à jour de la discrétisation particulaire à partir des observations disponibles constitue une problématique centrale.Dans un premier temps, nous adaptons le filtre de Kalman d'ensemble pour corriger les champs en modifiant uniquement les intensités des particules de la discrétisation. Les positions des particules restent alors inchangées ou sont régénérées sur une grille régulière, conduisant à deux méthodes distinctes.Ensuite, nous présentons une approche variationnelle d'ensemble pour corriger les positions des particules. Nous montrons que cette approche peut être combinée avec les premiers filtres pour corriger séquentiellement les positions et les intensités. Nous évaluons ces différentes méthodes sur des applications en dynamique des fluides incompressibles discrétisées par des méthodes de vortex, et nous analysons l'efficacité des filtres sur des problèmes d'advection où l'erreur de position peut être importante
Towards History-aware Sensitivity Analysis For Time Series
No full textExplaining the outcome of dynamic systems is non-trivial due to the temporal nature and correlation of the input variables. In this work, we propose a framework of history-aware sensitivity analysis for stationary time-series to quantify different memory effects and clarify their roles. For this purpose, we decompose the output time series into non-correlated components, namely the instantaneous component and the memory components. The latter are sorted in decreasing order of variance to reflect the importance of the variables. We highlight the compensation phenomena between the resulting components and illustrate them in the case of independent variables in a linear setting. To enable history-aware explanations, variance-based sensitivity indices are derived from the obtained decomposition. We demonstrate the effectiveness of our methodology in providing insights to explain output time-series in both synthetic and real-world cases
Post-COVID Inflation & the Monetary Policy Dilemma: An Agent-Based Scenario Analysis
No full text50 pages, 34 figuresInternational audienceThe economic shocks that followed the COVID-19 pandemic have brought to light the difficulty, both for academics and policy makers, of describing and predicting the dynamics of inflation. This paper offers an alternative modelling approach. We study the 2020-2023 period within the well-studied Mark-0 Agent-Based Model, in which economic agents act and react according to plausible behavioural rules. We include a mechanism through which trust of economic agents in the Central Bank can de-anchor. We investigate the influence of regulatory policies on inflationary dynamics resulting from three exogenous shocks, calibrated on those that followed the COVID-19 pandemic: a production/consumption shock due to COVID-related lockdowns, a supply-chain shock, and an energy price shock exacerbated by the Russian invasion of Ukraine. By exploring the impact of these shocks under different assumptions about monetary policy efficacy and transmission channels, we review various explanations for the resurgence of inflation in the United States, including demand-pull, cost-push, and profit-driven factors. Our main results are four-fold: (i) without appropriate fiscal policy, the shocked economy can take years to recover, or even tip over into a deep recession; (ii) the response to policy is non-monotonic, leading to a narrow window of ''optimal'' policy responses due to the trade-off between inflation and unemployment; (iii) the success of monetary policy in curbing inflation is primarily due to expectation anchoring, rather than to direct impact of interest rate hikes; (iv) the two most sensitive model parameters are those describing wage and price indexation. The results of our study have implications for Central Bank decision-making, and offers an easy-to-use tool that may help anticipate the consequences of different monetary and fiscal policies
Studying Image Diffusion Features for Zero-Shot Video Object Segmentation
No full textAccepted to CVPRW2025International audienceThis paper investigates the use of large-scale diffusion models for Zero-Shot Video Object Segmentation (ZS-VOS) without fine-tuning on video data or training on any image segmentation data. While diffusion models have demonstrated strong visual representations across various tasks, their direct application to ZS-VOS remains underexplored. Our goal is to find the optimal feature extraction process for ZS-VOS by identifying the most suitable time step and layer from which to extract features. We further analyze the affinity of these features and observe a strong correlation with point correspondences. Through extensive experiments on DAVIS-17 and MOSE, we find that diffusion models trained on ImageNet outperform those trained on larger, more diverse datasets for ZS-VOS. Additionally, we highlight the importance of point correspondences in achieving high segmentation accuracy, and we yield state-of-the-art results in ZS-VOS. Finally, our approach performs on par with models trained on expensive image segmentation datasets
Off-the-grid learning of mixtures from a continuous dictionary
No full textWe consider a general non-linear model where the signal is a finite mixture of an unknown, possibly increasing, number of features issued from a continuous dictionary parameterized by a real non-linear parameter. The signal is observed with Gaussian (possibly correlated) noise in either a continuous or a discrete setup. We propose an off-the-grid optimization method, that is, a method which does not use any discretization scheme on the parameter space, to estimate both the non-linear parameters of the features and the linear parameters of the mixture.We use recent results on the geometry of off-the-grid methods to give minimal separation on the true underlying non-linear parameters such that interpolating certificate functions can be constructed. Using also tail bounds for suprema of Gaussian processes we bound the prediction error with high probability. Assuming that the certificate functions can be constructed, our prediction error bound is up to -factors similar to the rates attained by the Lasso predictor in the linear regression model. We also establish convergence rates that quantify with high probability the quality of estimation for both the linear and the non-linear parameters.We develop in full details our main results for two applications: the Gaussian spike deconvolution and the scaled exponential model
Integer multiplication is at least as hard as matrix transposition
No full textWorking in the multitape Turing model, we show how to reduce the problem of matrix transposition to the problem of integer multiplication. If transposing an n × n binary matrix requires Ω(n^2 log n) steps on a Turing machine, then our reduction implies that multiplying n-bit integers requires Ω(n log n) steps. In other words, if matrix transposition is as hard as expected, then integer multiplication is also as hard as expected