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New method for quantifying power during wheelchair sports propulsion in the field
No full textThe importance of accelerating from a standstill is crucial in dynamic wheelchair sports, as it is closely tied to the ability to generate and apply significant power and net horizontal propulsion force. Assessing and quantifying para-athletes’ physical capabilities could enhance training to performance transition. This study aimed to propose a field method for quantifying total wheelchair propulsion forces and output power, while exploring the usability of the 1080 Motion Sprint. Five para-athletes from the national French wheelchair racing team and seven wheelchair tennis players from the national French team participated. Unloaded and resisted sprints of 50 m and 20 m were performed. Mono-exponential velocity function was deduced using photocells, IMUs, and the 1080 Motion Sprint velocity-time raw data. Net horizontal propulsion force was estimated from Newton’s second law and considered the loads applied by the 1080 Motion Sprint, rolling resistance, and aerodynamic drag. While no significant difference was observed between conditions for theoretical maximal force and maximum power developed, variations were evident in estimated power output and mechanical variables from force-velocity relationships, contingent on the athlete’s classification and sport specialty. The developed protocol can be used by trainers to assess physical capacities during training sessions, guiding subsequent training
Convergence rates for the moment-SoS hierarchy
No full textInternational audienceWe introduce a comprehensive framework for analyzing convergence rates for infinite dimensional linear programming problems (LPs) within the context of the moment-sum-of-squares hierarchy. Our primary focus is on extending the existing convergence rate analysis, initially developed for static polynomial optimization, to the more general and challenging domain of the generalized moment problem. We establish an easy-to-follow procedure for obtaining convergence rates. Our methodology is based on, firstly, a state-of-the-art degree bound for Putinar's Positivstellensatz, secondly, quantitative polynomial approximation bounds, and, thirdly, a geometric Slater condition on the infinite dimensional LP. We address a broad problem formulation that encompasses various applications, such as optimal control, volume computation, and exit location of stochastic processes. We illustrate the procedure at these three problems and, using a recent improvement on effective versions of Putinar's Positivstellensatz, we improve existing convergence rates
Integer and Constraint Programming for the Offline Nanosatellite Partition Scheduling Problem
No full textInternational audienceEffective scheduling of tasks for nanosatellites is essential, given their limited onboard resources and capabilities. In a typical nanosatellite mission, the onboard computer has to run payload tasks linked to the mission objective, such as observations and measurements for science campaigns, but also communication and avionic tasks needed for navigation and safety. We consider a partitioned real-time system where a partition is a job that embeds a set of elementary tasks implementing one of the above-described functions. The offline nanosatellite partition scheduling problem consists in scheduling a set of partitions on a single core processor within a fixed time frame that will be repeated cyclically, while maximizing the number and duration of scheduled payload partitions. The paper first establishes similarities and differences with related scheduling problems. We then prove that the problem is strongly NP-hard. A Mixed Integer Linear Programming (MILP) matheuristic and a Constraint Programming (CP) model are proposed. We compare the MILP and CP approaches in a multi-objective context and demonstrate the relevance of the latter to solve the problem efficiently both on randomly generated instances and on a real nanosatellite case study of the University Space Center of Toulouse: the NIMPH project. The CP model is embedded in NanoSatScheduler, an open source software with a user interface designed for the offline nanosatellite partition scheduling problem. For the NIMPH real-case study, the proposed solution outperforms the semi-manual approach used so far. As a result, the NIMPH team has adopted NanoSatScheduler as an operational tool for their mission
Toward decorrelation of surface oxygen groups from metal dispersion effects in Pd/C hydrogenation catalysts
No full textInternational audienceCarbon-supported Pd-based catalysts have found wide applications in hydrogenation of specific functional groups. Surface modification of the support, via the introduction of oxygen functional groups, modulates the metal dispersion and the interaction of reactant(s) with the catalyst surface, consequently tuning its catalytic properties. However, it is difficult to decorrelate the effect of surface oxygen groups from that of the dispersion of the metallic phase. This study aims at decorrelating these effects on the catalytic performance for phenylacetylene hydrogenation by using preformed monodispersed Pd nanoparticles deposited on carbon supports presenting different densities of surface oxygen groups. X-ray photoelectron spectroscopy, temperature-programmed decomposition experiments and transmission electron microscopy were used to analyze the dispersion and oxidation state of Pd and the concentration of surface oxygen groups. The results reveal that such decorrelation is not an easy task, particularly since spillover of the nanoparticles' native capping ligand (oleylamine) occurs during Pd particle deposition. This phenomenon, which depends on the density of oxygen functional groups and the size of Pd particles, impacts the Pd(0)/Pd2+ ratio and the surface Pd/N atomic ratio. These two last parameters, which seem to be interconnected, significantly impact the catalytic performance
A propos des ambiguïtés sémantiques en biotechnologie: des propositions de la part de l'infrastructure européenne IBISBA
No full textInternational audienceDriven by numerous scientific discoveries in biology in the second half of the last century, biotechnology is now set to play an important role as a driver for advanced manufacturing, leveraging the power of living organisms to produce a range of goods and services. Considering this prospect, it is vital that terminology surrounding biotechnology is sufficiently clear to provide a basis for efficient regulation and public buy-in. Despite the apparent clarity of the term biotechnology, its definition is the subject of a longstanding debate and liberal interpretations. Likewise, other more recent terms such as biomanufacturing, synthetic biology and engineering biology also lack consensual definitions despite their use in both scientific and secular circles. Additionally, new terms such as precision fermentation and cellular agriculture, recently introduced in the framework of businessto-business exchanges, appear to call upon imaginaries rather than scientific facts. Herein, we examine the lexical complexity of the biotechnology field and argue that, for the sake of efficient policymaking, it is vital to harmonise the definitions of some core terms, including biotechnology, biomanufacturing, engineering biology and synthetic biology. With this aim in mind, this discussion paper is intended to be useful to policymakers and science communicators, whether in the media or in professional settings.Portée par les nombreuses découvertes scientifiques en biologie réalisées au cours de la seconde moitié du siècle dernier, la biotechnologie est désormais appelée à jouer un rôle important en tant que moteur de la fabrication avancée, exploitant la puissance des organismes vivants pour produire une gamme de biens et de services. Dans ce contexte, il est essentiel que la terminologie relative à la biotechnologie soit suffisamment claire pour constituer la base d'une réglementation efficace et d'une adhésion publique. Malgré l'apparente clarté du terme biotechnologie, sa définition fait l'objet d'un débat de longue date et d'interprétations libérales. De même, d'autres termes plus récents, tels que la bioproduction, la biologie synthétique et la bioingénierie, manquent également de définitions consensuelles, malgré leur utilisation dans les cercles scientifiques et profanes. De plus, de nouveaux termes, tels que fermentation de précision et l'agriculture cellulaire, récemment introduits dans le cadre des échanges inter-entreprises, semblent faire appel à l'imaginaire plutôt qu'à des faits scientifiques. Nous examinons ici la complexité lexicale du domaine des biotechnologies et soutenons que, pour une élaboration efficace des politiques, il est essentiel d'harmoniser les définitions de certains termes clés, notamment biotechnologie, la bioproduction, la bioingénierie et la biologie synthétique. Dans cet objectif, ce document de discussion se veut utile aux décideurs politiques et aux communicateurs scientifiques, que ce soit dans les médias ou dans des contextes professionnels
A Gaussian correlation inequality for plurisubharmonic functions
No full textInternational audienceA positive correlation inequality is established for circular-invariant plurisubharmonic functions, with respect to complex Gaussian measures. The main ingredients of the proofs are the Ornstein-Uhlenbeck semigroup, and another natural semigroup associated to the Gaussian ∂-Laplacian
Mixtures Closest to a Given Measure: A Semidefinite Programming Approach
No full textInternational audienceMixture models, such as Gaussian mixture models (GMMs), are widely used in machine learning to represent complex data distributions. A key challenge, especially in high-dimensional settings, is to determine the mixture order and estimate the mixture parameters. We study the problem of approximating a target measure, available only through finitely many of its moments, by a mixture of distributions from a parametric family (e.g., Gaussian, exponential, Poisson), with approximation quality measured by the 2-Wasserstein or the total variation distance. Unlike many existing approaches, the parameter set is not assumed to be finite; it is modeled as a compact basic semi-algebraic set. We introduce a hierarchy of semidefinite relaxations with asymptotic convergence to the desired optimal value. In addition, when a certain rank condition is satisfied, the convergence is even finite and recovery of an optimal mixing measure is obtained. We also present an application to clustering, where our framework serves either as a stand-alone method or as a preprocessing step that yields both the number of clusters and strong initial parameter estimates, thereby accelerating convergence of standard (local) clustering algorithms
Robust ML Auditing using Prior Knowledge
No full textInternational audienceAmong the many technical challenges to enforcing AI regulations, one crucial yet underexplored problem is the risk of audit manipulation. This manipulation occurs when a platform deliberately alters its answers to a regulator to pass an audit without modifying its answers to other users. In this paper, we introduce a novel approach to manipulation-proof auditing by taking into account the auditor's prior knowledge of the task solved by the platform. We first demonstrate that regulators must not rely on public priors (e.g. a public dataset), as platforms could easily fool the auditor in such cases. We then formally establish the conditions under which an auditor can prevent audit manipulations using prior knowledge about the ground truth. Finally, our experiments with two standard datasets illustrate the maximum level of unfairness a platform can hide before being detected as malicious. Our formalization and generalization of manipulation-proof auditing with a prior opens up new research directions for more robust fairness audits
Quaternion-Based Vision-Transformer for Polycrystalline EBSD Scans Pre-Trained on Large-Scale Synthetic Data
No full textInternational audienceDimensionality reduction is crucial in materials science for extracting patterns from high-dimensional data. This is vital for optimizing material design spaces by cutting computational complexity and for integrating diverse data sources for advanced anomaly detection, identifying structural or functional material deviations. Traditional convolutional autoencoders focus on local features, failing to capture global contextual information essential for predicting material behavior under stress, where overall material properties, grain orientations, morphologies and sizes are key. To address this, a Quaternion-Based Vision Transformer Masked Autoencoder (ViT-MAE) is proposed for polycrystalline materials. This model processes quaternion-valued EBSD orientation maps with a 65% masking ratio, capturing both local and global microstructural features efficiently. Pre-trained on synthetic data using self-supervision, the model achieves robust generalization on real-world EBSD scans by minimizing a misorientation loss function on the quaternion manifold. This work lays the groundwork for advanced multimodal data analysis in materials science, offering an open dataset and pre-trained vision-transformer weights. The goal is to provide a reliable latent space for EBSD orientation maps, advancing future multimodal characterization through latent space merging
Efficient Neuro-Symbolic Learning of Constraints and Objective
No full textIn the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or optimization problems from natural inputs, a task that Large Language Models seem to struggle with. Objectives: We introduce a differentiable neuro-symbolic architecture and a loss function dedicated to learning how to solve NP-hard reasoning problems. Methods: Our new probabilistic loss allows for learning both the constraints and the objective, thus delivering a complete model that can be scrutinized and completed with side constraints. By pushing the combinatorial solver out of the training loop, our architecture also offers scalable training while exact inference gives access to maximum accuracy. Results: We empirically show that it can efficiently learn how to solve NP-hard reasoning problems from natural inputs. On three variants of the Sudoku benchmark -- symbolic, visual, and many-solution --, our approach requires a fraction of training time of other hybrid methods. On a visual Min-Cut/Max-cut task, it optimizes the regret better than a Decision-Focused-Learning regret-dedicated loss. Finally, it efficiently learns the energy optimization formulation of the large real-world problem of designing proteins