Energy and CO2 fluxes in temperate deciduous broadleaf forests: using local add-covariance data for a global optimization of ORCHIDEE model

Non peer reviewedPublisher PD

Hamiltonian Monte Carlo avec réflexions, et application au calcul du volume de polytopes

This paper studies HMC with reflections on the boundary of a domain, providing an enhanced alternative to Hit-and-run (HAR) to sample a target distribution in a bounded domain. We make three contributions. First, we provide a convergence bound, paving the way to more precise mixing time analysis. Second, we present a robust implementation based on multi-precision arithmetic – a mandatory ingredient to guarantee exact predicates and robust constructions. Third, we use our HMC random walk to perform polytope volume calculations, using it as an alternative to HAR within the volume algorithm by Cousins and Vempala. The tests, conducted up to dimension 50, show that the HMC RW outperforms HAR.Ce papier étudie HMC avec réflexions au bord du domaine, donnant une meilleure alternative a Hit-and-Run (HAR) pour échantillonner une distribution cible dans un domaine borné. Nous apportons trois contributions. Premièrement, nous prouvons une borne de convergence, préparant le terrain pour une analyse plus précise du mixing time. Deuxièmement, nous produisons une implémentation robuste basée sur l’arithmétique multi-precision. Troisièmement, nous utilisons HMC avec réflexions comme une alternative à HAR pour calculer le volume de polytopes pour l’algorithme de Cousins et Vempala. Les tests, conduits jusqu’en dimension 50 montrent que HMC avec réflexions est plus performant que HAR

Amélioration des calculs de volume de polytope basés sur le Monte Carlo Hamiltonien avec des réflexions sur les bords et des arithmétiques édulcorées

International audienceComputing the volume of a high dimensional polytope is a fundamental problem in geometry, also connected to the calculation of densities of states in statistical physics, and a central building block of such algorithms is the method used to sample a target probability distribution. This paper studies Hamiltonian Monte Carlo (HMC) with reflections on the boundary of a domain, providing an enhanced alternative to Hit-and-run (HAR) to sample a target distribution restricted to the polytope. We make three contributions. First, we provide a convergence bound, paving the way to more precise mixing time analysis. Second, we present a robust implementation based on multi-precision arithmetic, a mandatory ingredient to guarantee exact predicates and robust constructions. We however allow controlled failures to happen, introducing the Sweeten Exact Geometric Computing (SEGC) paradigm. Third, we use our HMC random walk to perform H-polytope volume calculations, using it as an alternative to HAR within the volume algorithm by Cousins and Vempala. The systematic tests conducted up to dimension $n$ = 100 on the cube, the isotropic and the standard simplex show that HMC significantly outperforms HAR both in terms of accuracy and running time. Additional tests show that calculations may be handled up to dimension $n$ = 500. These tests also establish that multiprecision is mandatory to avoid exits from the polytope

Constraining a global ecosystem model with multi-site eddy-covariance data

Peer reviewedPublisher PD

User-specific frequency band and time segment selection with high class distinctiveness for Riemannian BCIs

International audienceUser-specific settings are known to enhance Brain-Computer Interface (BCI) performances. In particular, the optimal frequency band and time segment for oscillatory activity classification are highly user-dependent and many selection methods have been developed in the past two decades. However, it has not been studied well whether those conventional methods can provide optimal settings for the Riemannian BCIs, a recent family of BCI systems that utilize different data representation, based on covariance matrices, compared to conventional BCI pipelines. In this work, we proposed a novel frequency band and time segment selection method considering class distinctiveness on the Riemannian manifold. The class distinctiveness of each combination of frequency band and time segment is quantified based on inter-class distance and intra-class variance on the Riemannian manifold. An advantage of this method is the user-specific settings can be adjusted without computationally heavy optimization steps. To the best of our knowledge, this is the first optimization method for selecting both the frequency band and the time segment on the Riemannian manifold. We evaluated the contributions of the 3 different selection models (frequency band, time or frequency band+time), comparing classification accuracy with a baseline (a fixed frequency band of 8-30 Hz and a fixed 2s time window) and a conventional popular method for non-Riemannian BCIs, on the BCI competition IV dataset 2a (2-class motor imagery). Our method showed higher average accuracy than baseline and a conventional method in all three models, and especially the frequency band selection model showed the highest performance. This preliminary result suggests the importance of developing new selection algorithms considering the properties of the manifold, rather than directly applying methods developed prior to the rise of Riemannian BCIs as they are

Emergence of Temporal and Spatial Synchronous Behaviors in a Foraging Swarm

International audienceBiological populations often exhibit complex and efficient behaviors, where temporal and spatial couplings at the macro-scale population level emerge from interactions at the micro-scale individual level, without any centralized control. This paper specifically investigates the emergence of behavioral synchronization and the division of labor in a foraging swarm of robotic agents. A deterministic model is proposed and used by each agent to decide whether it goes foraging, based on local cues about its fellow ants' behavior. This individual model, based on the competition of two spiking neurons, results in a self-organized division of labor at the population level. Depending on the strength and occurrences of interactions among individuals, the population behavior displays either an asynchronous, or a synchronous aperiodic, or a synchronous periodic division of labor. Further, the benefits of synchronized individual behaviors in terms of overall foraging efficiency are highlighted in a 2D spatial simulation

Ensemble learning based on functional connectivity and Riemannian geometry for robust workload estimation

International audienceContext Passive Brain-Computer Interface (pBCI) has recently gained in popularity through its applications, e.g. workload and attention assessment. Nevertheless, one of the main limitations remains the important intra-and inter-subject variability. We propose a robust approach relying on ensemble learning, grounded in functional connectivity and Riemannian geometry to mitigate the high variability of the data with a large and diverse panel of classifiers

A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle

Acknowledgements. This work was mainly funded by the EU FP7 CARBONES project (contracts FP7-SPACE-2009-1-242316), with also a small contribution from GEOCARBON project (ENV.2011.4.1.1-1-283080). This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program; DE-FG02-04ER63917 and DE-FG02-04ER63911), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval and Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California-Berkeley, University of Virginia. Philippe Ciais acknowledges support from the European Research Council through Synergy grant ERC-2013-SyG-610028 “IMBALANCE-P”. The authors wish to thank M. Jung for providing access to the GPP MTE data, which were downloaded from the GEOCARBON data portal (https://www.bgc-jena.mpg.de/geodb/projects/Data.php). The authors are also grateful to computing support and resources provided at LSCE and to the overall ORCHIDEE project that coordinate the development of the code (http://labex.ipsl.fr/orchidee/index.php/about-the-team).Peer reviewedPublisher PD