1,953 research outputs found
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
to 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 and atomic planes,
for which the '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 by
simulating a Markov chain with transition kernel such that is
invariant under . However, there are many situations for which it is
impractical or impossible to draw from the transition kernel . 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 by an approximation . 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 is to
the chain given by . 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
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