Scientific Publications of the University of Toulouse II Le Mirail
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Synthèse et perspectives : approches transformatives, changement d'échelle et implications pour l'Enseignement Supérieur et la Recherche
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The relationships to work of young french generation Z
No full textInternational audienceThis exploratory study examines the relationships to work of young French members of Generation Z and highlights their heterogeneity. A total of 273 young people (students with or without a job, young people in an apprenticeship program, young people in employment and unemployed young people) were interviewed using a questionnaire. Their responses were analyzed using a hierarchical classification analysis based on a mixed factorial analysis. In addition, four young people from each cluster were re-interviewed to explain their questionnaire responses. The results reveal four “clusters” according to the factors that young people consider important in their relationships to work. In particular, they highlight two opposing views, i.e., the low or high importance attached to work and the preference for pleasure-seeking or more “materialistic” aspects of work. Practical recommendations for professionals who guide or manage young people are also provided
Uniform Value and Decidability in Ergodic Blind Stochastic Games
No full textInternational audienceWe study a class of two-player zero-sum stochastic games known as \textit{blind stochastic games}, where players neither observe the state nor receive any information about it during the game. A central concept for analyzing long-duration stochastic games is the \textit{uniform value}. A game has a uniform value if for every \varepsilon>0, Player 1 (resp., Player 2) has a strategy such that, for all sufficiently large , his average payoff over stages is at least (resp., at most ). Prior work has shown that the uniform value may not exist in general blind stochastic games. To address this, we introduce a subclass called \textit{ergodic blind stochastic games}, defined by imposing an ergodicity condition on the state transitions. For this subclass, we prove the existence of the uniform value and provide an algorithm to approximate it, establishing the \textit{decidability} of the approximation problem. Notably, this decidability result is novel even in the single-player setting of Partially Observable Markov Decision Processes (POMDPs). Furthermore, we show that no algorithm can compute the uniform value exactly, emphasizing the tightness of our result. Finally, we establish that the uniform value is independent of the initial belief
PINN-based Identification of Spatially Varying Elastic Moduli from Experimental Full-Field Displacement Data
No full textInternational audienceRecent advances inimaging techniques and digital image correlation (DIC) have made it possible to acquire rich full-field displacement data, opening the door to the proper identification of material properties. Yet, conventional identification methods face severe limitations when tackling high-dimensional parameter spaces: constitutive models with large numbers of parameters or spatially varying properties. To address this challenge, we extend the physics-informed neural network (PINN)-based inverse framework into an effective methodology tailored to realistic experimental mechanics settings. The proposed approach builds on a mixed PINN formulation, where displacement and stress fields are represented by distinct neural networks (NNs), and incorporates several key innovations such as: enforcement of multiple global mechanical equilibria to exploit experimentally accessible reaction forces; Fourier features embeddings into the NNs to capture high-frequency components; finite element meshes for representing property fields; and a dedicated initialization and alternating minimization strategy ensuring convergence in high-dimensional coupled mechanical and NN parameter spaces. The study focuses on the identification of spatially distributed elastic moduli. The methodology is first validated on synthetic data, accurately recovering a complex Young’s modulus distribution. It is then applied to DIC-based experimental displacements from a perforated plate loaded to failure. The approach successfully identifies homogeneous elastic constants in the initial regime, in agreement with the well-known finite element model updating method, and subsequently reconstructs Young’s modulus fields that reveal early damage localization and magnitude well before macroscopic crack initiation. To the best of our knowledge, this is the first time that such a field has been identified in experimental mechanics. The methodology proved not only to be accurate but also computationally efficient, only requiring standard computational resources
SNN-Based Online Learning of Concepts and Action Laws in an Open World
No full textInternational audienceWe present the architecture of a fully autonomous, bio-inspired cognitive agent built around a spiking neural network (SNN) implementing the agent's semantic memory. This agent explores its universe and learns concepts of objects/situations and of its own actions in a one-shot manner. While object/situation concepts are unary, action concepts are triples made up of an initial situation, a motor activity, and an outcome. They embody the agent's knowledge of its universe's action laws. Both kinds of concepts have different degrees of generality. To make decisions the agent queries its semantic memory for the expected outcomes of envisaged actions and chooses the action to take on the basis of these predictions. Our experiments show that the agent handles new situations by appealing to previously learned general concepts and rapidly modifies its concepts to adapt to environment changes
Does increasing human impact across the holocene result in simplification of vegetation composition and diversity across Europe? A pollen-based spatio-temporal approach
No full textInternational audienceLand use and climate change are the primary drivers of current biodiversity loss, but have had different impacts on biodiversity across the Holocene epoch. To enhance our understanding of current changes in diversity and its impact on ecosystem functions, knowledge of long-term interactions between vegetation diversity, land use change, and climatic change is crucial. Grid-cell estimates of quantified regional vegetation cover (RVest) based on pollen data from 1607 sites across Europe, transformed using the REVEALS (Regional Vegetation Estimates from Large Sites) model, have been used to explore spatiotemporal changes in vegetation and regional diversity during the Holocene (25 time windows covering the period from 11.7 ka cal BP to present). Space-time constrained clustering of the RVests identified six dominant vegetation types (VTs): Mediterranean vegetation, open vegetation, Abies-Fagus forest, broadleaved mixed forest, coniferous mixed forest, Betula woodland, whose spatial extent changes over the Holocene. The study explored REVEALS α-diversity (richness of taxa, richness ofabundant taxa, and evenness) within each grid cell as well as spatial REVEALS β-diversity (spatial variations in composition within one time frame) and turnover (temporal variation in composition within one grid cell) within each vegetation type. Changes in location, size, taxa composition, and REVEALS diversity of the vegetation types characterised four phases during the Holocene. The first (pioneer: 11.7–9.2 ka cal BP) and second (summer-green forest: 9.2 ka to 5.2 ka cal BP) phases generally showed higher REVEALS β-diversity and lower REVEALS evenness. The third phase (mixed semi-natural forest: 5.2 ka to 1.7 ka cal BP) is characterised by expansion of open vegetation and reflects increased human impact on the environment caused by increasing use of land for agricultural production. The final phase (from 1.7 ka cal BP) saw rapid transformations: open vegetation not only expanded, but also shifted in composition, with major increases in cereals and other anthropogenic indicators. This signals a clear intensification of land-use impact over the last two millennia. Across central Europe, vegetation became increasingly homogenised, dominated by a few widespread species. As a result, both REVEALS evenness and spatial β-diversity plummeted—marking a profound loss of ecological complexity.In short, human-driven landscape openness did not simply reshape the vegetation—it rewrote the rules of diversity across the continent
A Proximal Algorithm for Joint Blood Flow Computation and Tissue Motion Compensation in Doppler Ultrafast Ultrasound Imaging
No full textInternational audienceAccurate tissue-clutter rejection and blood flow estimation remain challenging in ultrasound imaging. Traditionally, this estimation is performed by assuming static tissues. Only a few preprocessing techniques attempt to deal with the more realistic but challenging scenario where the tissues are moving. This paper tackles this scenario and presents a novel method for computing blood flow from moving tissues in ultrafast ultrasound imaging. The proposed computational ultrasound imaging method solves a global inverse problem that jointly computes blood flow, tissues, and their motions. The resulting cost function incorporates each component specificity using appropriate regularizations and is fully convex. The cost function is minimized using an alternating proximal-forward-backward algorithm with convergence guarantees. Furthermore, the proposed method is integrated into a multi-resolution scheme for large motions. The experiments show that the proposed method accurately compensates tissue motions, improving the precision of blood flow computation compared to previous methods. Experiments on in vivo images demonstrate the effectiveness of the proposed method in realistic scenarios with large motions.</div