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PriorFormer: A Transformer for Real-time Monocular 3D Human Pose Estimation with Versatile Geometric Priors
No full textInternational audienceThis paper proposes a new lightweight Transformer-based lifter that maps short sequences of human 2D joint positions to 3D poses using a single camera. The proposed model takes as input geometric priors including segment lengths and camera intrinsics and is designed to operate in both calibrated and uncalibrated settings. To this end, a masking mechanism enables the model to ignore missing priors during training and inference. This yields a single versatile network that can adapt to different deployment scenarios, from fully calibrated lab environments to in-the-wild monocular videos without calibration.The model was trained using 3D keypoints from AMASS dataset with corresponding 2D synthetic data generated by sampling random camera poses and intrinsics. It was then compared to an expert model trained, only on complete priors, and the validation was done by conducting an ablation study. Results show that both, camera and segment length priors, improve performance and that the versatile model outperforms the expert, even when all priors are available, and maintains high accuracy when priors are missing. Overall the average 3D joint center positions estimation accuracy was as low as 36mm improving state of the art by half a centimeter and at a much lower computational cost. Indeed, the proposed model runs in 380µs on GPU and 1800µs on CPU, making it suitable for deployment on embedded platforms and low-power devices.</p
Planification des activités d'une flotte de robots mobiles autonomes pour la logistique interne de systèmes de production
No full textNational audienc
Decision-Epochs Matter: Unveiling Its Impact on the Stability of Scheduling With Randomly Varying Connectivity
No full textInternational audienceA classical result in queuing theory states that in a parallel-queue single-server model, the maximum stability region is unaffected by scheduling decision epochs, and in particular is the same for preemptive and non-preemptive systems. We examine a scenario where queues are randomly connected to the server and show that, unlike the classical case, the maximum stability region strongly depends on the scheduling decision epochs. We compare three settings: decisions can be made anytime (unconstrained), decisions are made only at departures (non-preemptive), and decisions occur when a γ-rate exponential clock rings. We observe a significant reduction in the stability region in the non-preemptive setting compared to the unconstrained one, showing that a non-preemptive scheduler cannot take opportunistically advantage of the random varying connectivity. Also, in the γ-rate clock setting, one can be arbitrarily close to the maximum stability region in the unconstrained setting if we choose γ large enough. In all the settings, we show that the Longest Connected Queue (LCQ) policy achieves maximum stability. From a methodological viewpoint, we introduce a new theoretical tool called "test for fluid limits" (TFL), which offers a method to determine stability on the basis of a simple formal test
Using a pre-trained machine learning model to estimate the 3d-ground reaction forces during rugby scrummaging with instrumented insoles
No full textInternational audienceINTRODUCTION:Rugby scrummaging represents a crucial phase of the game, characterized by high-intensity physical efforts and a significant impact on match outcomes [1]. The horizontal force generated by the entire pack is a key determinant of scrum success. However, existing measurement systems are unable to provide 3D, individual, and on-field assessments of ground reaction forces (GRF). A previous study developed a Machine Learning (ML) model to predict the 3D-GRF with instrumented insoles during scrummaging but this was conducted on recreationally active subjects without specific scrummaging experience [2]. Thus, this study aimed to investigate to what extent this model can be used to predict the 3D-GRF for elite rugby players.METHODS:Twelve elite rugby players (12 males; age: 20+/-1 ans; height: 191+/-7 cm; weight: 116+/-13 kg) performed three pushing trials of 15 seconds, against a fixed scrum machine. They wore commercial instrumented insoles (Loadsol Pro®, Novel, Germany, 200Hz) inside their shoes with each foot on a force plate (Sensix, 1000Hz) covered with artificial turf. The force plate data served as the reference for 3D-GRF measurements. For each subject, one trial was used to infer data from the pre-trained ML model, while the remaining two trials were utilized to personalize the model for each subject. The model’s performance was assessed by computing the Root Mean Square Error (RMSE) between the prediction and the reference, the correlation coefficient (r), and the percentage of RMSE compared to the mean resultant force.RESULTS:The initial model inference yielded mean RMSE values of 42±9N on the Medio-Lateral (ML) axis, 168±87N on the Antero-Posterior (AP) axis, and 180±49N on the Vertical (V) axis, with correlation coefficients r of 0.708±0.110 (ML), 0.825±0.102 (AP), and 0.571±0.123 (V). RMSE percentages relative to the mean resultant force were 4.2±0.7% (ML), 15.8±5.0% (AP), and 18.0±4.8% (V). After personalization, RMSE were 32±10N (ML), 99±41N (AP), and 135±38N (V), and correlation coefficients r were0.838±0.062 (ML), 0.911±0.044 (AP), and 0.680±0.168 (V). The mean percentages of RMSE compared to the average resultant force were 3.1±0.6% (ML), 9.7±2.8% (AP), and 14.0±5.5% (V).CONCLUSION:The findings of this study indicate that an ML model pre-trained on data from recreationally active individuals without specific rugby scrummaging experience is not optimal for accurately estimating 3D-GRF in elite rugby players. Model personalization for each participant improved performance, suggesting that personalization is a promising approach for enhancing ML model performance when the model is trained on a non-specific dataset. However, further improvement in model performance could be achieved by pre-training the model on a dataset more closely aligned with the data used for personalization, to ensure reliable predictions.References[1] Scott et al., J. Sci. Med. Sport, 2023[2] Pomarat et al., IEEE Xplore, 202
Banc de caractérisation multi-canaux de sources de photons uniques pour les télécommunications fibrées quantiques
No full textNational audienceLa caractérisation de paires de photons, voire de peignes quantiques, est une problématique essentielle du domaine des télécommunications quantiques fibrées. Un banc faible coût a été développé autour de 1550 nm à partir de composants SPADs refroidis, d'un laser pulsé et de différentes solutions de filtrage. Une première application à la caractérisation de la génération de paires de photons par un résonateur fibré non-linéaire est présentée
Young measure relaxation gaps for controllable systems with smooth state constraints
No full textIn this article, we tackle the problem of the existence of a gap corresponding to Young measure relaxations for state-constrained optimal control problems. We provide a counterexample proving that a gap may occur in a very regular setting, namely for a smooth controllable system state constrained to the closed unit ball, provided that the Lagrangian density (i.e., the running cost) is non-convex in the control variables. The example is constructed in the setting of sub-Riemannian geometry with the core ingredient being an unusual admissible curve that exhibits a certain form of resistance to state-constrained approximation. Specifically, this curve cannot be approximated by neighboring admissible curves while obeying the state constraint due to the intricate nature of the dynamics near the boundary of the constraint set. Our example also presents an occupation measure relaxation gap.23 pages, 2 figure
Density, Determinacy, Duality and a Regularized Moment-SOS Hierarchy
No full textThe standard moment-sum-of-squares (SOS) hierarchy is a powerful method for solving global polynomial optimization problems. However, its convergence relies on Putinar's Positivstellensatz, which requires the feasible set to satisfy the algebraic Archimedean property. In this paper, we introduce a regularized moment-SOS hierarchy capable of handling problems on unbounded sets or bounded sets violating the Archimedean property. Adopting a functional analysis viewpoint, we rely on the multivariate Carleman condition for measure determinacy rather than algebraic compactness. We prove that finite degree projections of the quadratic module are dense in the cone of positive polynomials with respect to the square norm induced by the measure. Based on these density results, we prove the convergence of a regularized hierarchy without invoking any Positivstellensatz. Furthermore, we propose a penalized formulation of the hierarchy which, combined with Bernstein-Markov inequalities, provides a monotonically non-decreasing sequence of certified lower bounds on the global minimum. The approach is illustrated on several benchmark problems known to be difficult or ill-posed for the standard hierarchy
Kerr-Brillouin frequency combs in fiber Fabry-Perot resonators
No full textInternational audienceWe report the observation of broadband optical frequency combs in high-Q fiber Fabry-Perot resonators under CW pumping. We develop a new mean-field equation which permits to efficiently simulate the observed spectra and to unveil the physics underlying this phenomenon
Diagnosis test selection for distributed systems under communication and privacy constraints
No full textInternational audienceDistribution is often necessary for large-scale systems because it makes monitoring and diagnosis more manageable from both computational and communication costs perspectives. Decomposing the system into subsystems may also be required to satisfy geographic, functional, or privacy constraints. The selection of diagnosis tests guaranteeing some level of diagnosability must adhere to this decomposition by remaining as local as possible in terms of the required sensor variables. This helps minimize communication costs. In practical terms, this means that the number of interconnections between subsystems should be minimized while keeping diagnosability, i.e., fault isolation capability, at its maximum. This paper differentiates itself from existing literature by leveraging flexibility in forming the subsystems. Through structural analysis and graph partitioning, we address the combined challenges of constrained decomposition of a large-scale system into subsystems and the selection of diagnosis tests that achieve maximal diagnosability with minimal subsystem interconnection. The proposed solution is implemented through an iterative algorithm, which is proven to converge. Its efficiency is demonstrated using a case study in the domain of water networks