Modeling biomass flows at the farm level: a discussion support tool for farmers.

Many simulation models that are used to assess the impact of mixed farming systems have a high level of complexity that is not suitable for teaching farmers about the impacts of their practices.DOI: 10.1051/agro/2009047

Size effect on magnetism of Fe thin films in Fe/Ir superlattices

In ferromagnetic thin films, the Curie temperature variation with the thickness is always considered as continuous when the thickness is varied from $n$ to $n+1$ atomic planes. We show that it is not the case for Fe in Fe/Ir superlattices. For an integer number of atomic planes, a unique magnetic transition is observed by susceptibility measurements, whereas two magnetic transitions are observed for fractional numbers of planes. This behavior is attributed to successive transitions of areas with $n$ and $n+1$ atomic planes, for which the $T_c$'s are not the same. Indeed, the magnetic correlation length is presumably shorter than the average size of the terraces. Monte carlo simulations are performed to support this explanation.Comment: LaTeX file with Revtex, 5 pages, 5 eps figures, to appear in Phys. Rev. Let

Finite-size scaling in thin Fe/Ir(100) layers

The critical temperature of thin Fe layers on Ir(100) is measured through M\"o{\ss}bauer spectroscopy as a function of the layer thickness. From a phenomenological finite-size scaling analysis, we find an effective shift exponent lambda = 3.15 +/- 0.15, which is twice as large as the value expected from the conventional finite-size scaling prediction lambda=1/nu, where nu is the correlation length critical exponent. Taking corrections to finite-size scaling into account, we derive the effective shift exponent lambda=(1+2\Delta_1)/nu, where Delta_1 describes the leading corrections to scaling. For the 3D Heisenberg universality class, this leads to lambda = 3.0 +/- 0.1, in agreement with the experimental data. Earlier data by Ambrose and Chien on the effective shift exponent in CoO films are also explained.Comment: Latex, 4 pages, with 2 figures, to appear in Phys. Rev. Lett

MCMC based Generative Adversarial Networks for Handwritten Numeral Augmentation

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.In this paper, we propose a novel data augmentation framework for handwritten numerals by incorporating the probabilistic learning and the generative adversarial learning. First, we simply transform numeral images from spatial space into vector space. The Gaussian based Markov probabilistic model is then developed for simulating synthetic numeral vectors given limited handwritten samples. Next, the simulated data are used to pre-train the generative adversarial networks (GANs), which initializes their parameters to fit the general distribution of numeral features. Finally, we adopt the real handwritten numerals to fine-tune the GANs, which increases the authenticity of generated numeral samples. In this case, the outputs of the GANs can be employed to augment original numeral datasets for training the follow-up inference models. Considering that all simulation and augmentation are operated in 1-D vector space, the proposed augmentation framework is more computationally efficient than those based on 2-D images. Extensive experimental results demonstrate that our proposed augmentation framework achieves improved recognition accuracy.This work was supported by grants from the Chinese Scholarship Council (CSC) program

Increased risk of breast cancer among female relatives of patients with ataxia-telangiectasia: a causal relationship?

International audienc

Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes

SMC (Sequential Monte Carlo) is a class of Monte Carlo algorithms for filtering and related sequential problems. Gerber and Chopin (2015) introduced SQMC (Sequential quasi-Monte Carlo), a QMC version of SMC. This paper has two objectives: (a) to introduce Sequential Monte Carlo to the QMC community, whose members are usually less familiar with state-space models and particle filtering; (b) to extend SQMC to the filtering of continuous-time state-space models, where the latent process is a diffusion. A recurring point in the paper will be the notion of dimension reduction, that is how to implement SQMC in such a way that it provides good performance despite the high dimension of the problem.Comment: To be published in the proceedings of MCMQMC 201

Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels

Monte Carlo algorithms often aim to draw from a distribution $\pi$ by simulating a Markov chain with transition kernel $P$ such that $\pi$ is invariant under $P$. However, there are many situations for which it is impractical or impossible to draw from the transition kernel $P$. For instance, this is the case with massive datasets, where is it prohibitively expensive to calculate the likelihood and is also the case for intractable likelihood models arising from, for example, Gibbs random fields, such as those found in spatial statistics and network analysis. A natural approach in these cases is to replace $P$ by an approximation $\hat{P}$. Using theory from the stability of Markov chains we explore a variety of situations where it is possible to quantify how 'close' the chain given by the transition kernel $\hat{P}$ is to the chain given by $P$. We apply these results to several examples from spatial statistics and network analysis.Comment: This version: results extended to non-uniformly ergodic Markov chain

Time series prediction via aggregation : an oracle bound including numerical cost

We address the problem of forecasting a time series meeting the Causal Bernoulli Shift model, using a parametric set of predictors. The aggregation technique provides a predictor with well established and quite satisfying theoretical properties expressed by an oracle inequality for the prediction risk. The numerical computation of the aggregated predictor usually relies on a Markov chain Monte Carlo method whose convergence should be evaluated. In particular, it is crucial to bound the number of simulations needed to achieve a numerical precision of the same order as the prediction risk. In this direction we present a fairly general result which can be seen as an oracle inequality including the numerical cost of the predictor computation. The numerical cost appears by letting the oracle inequality depend on the number of simulations required in the Monte Carlo approximation. Some numerical experiments are then carried out to support our findings

Climate-smart solutions for Mali: Findings from implementing the Climate-Smart Agriculture Prioritization Framework

This info note summarizes findings of a pilot project aiming to develop a participatory framework to prioritize CSA practices and interventions to guide CSA investments in Mali. It was undertaken by researchers from the Malian Association of Awareness to Sustainable Development (AMEDD) and the International Center Tropical Agriculture (CIAT) as part of the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). Implementation was led by the Agency of Environment and Sustainable Development (AEDD) on behalf of the National Science-Policy dialogue platforms on Climate Change, Agriculture and Food Security (CCASA). Supported by the West Africa Regional Program, this research is part of a multi-region Prioritization project funded by CCAFS Flagship 1 on Climate-Smart Agricultural Practices