The Experts below are selected from a list of 4782 Experts worldwide ranked by ideXlab platform
N C Santos - One of the best experts on this subject based on the ideXlab platform.
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transit timing measurements with the model independent Barycenter method application to the lhs 6343 system
Astronomy and Astrophysics, 2012Co-Authors: M Oshagh, G Boue, Nader Haghighipour, M Montalto, P Figueira, N C SantosAbstract:We present a model-independent technique for calculating the time of mid-transits. This technique, named “Barycenter method”, uses the light-curve’s symmetry to determine the transit timing by calculating the transit light-curve Barycenter. Unlike the other methods of calculating mid-transit timing, this technique does not depend on the parameters of the system and central star. We demonstrate the capabilities of the Barycenter method by applying this technique to some known transiting systems including several Kepler confirmed planets. Results indicate that for complete and symmetric transit lightcurves, the Barycenter method achieves the same precision as other techniques, but with fewer assumptions and much faster. Among the transiting systems studied with the Barycenter method, we focus in particular on LHS 6343C, a brown dwarf that transits a member of an M+M binary system, LHS 6343AB. We present the results of our analysis, which can be used to set an upper limit on the period and mass of a possible second small perturber.
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transit timing measurements with the model independent Barycenter method application to the lhs 6343 system
arXiv: Earth and Planetary Astrophysics, 2011Co-Authors: M Oshagh, G Boue, Nader Haghighipour, M Montalto, P Figueira, N C SantosAbstract:We present a model-independent technique for calculating the time of mid-transits. This technique, named "Barycenter method", uses the light-curve's symmetry to determine the transit timing by calculating the transit light-curve Barycenter. Unlike the other methods of calculating mid-transit timing, this technique does not depend on the parameters of the system and central star. We demonstrate the capabilities of the Barycenter method by applying this technique to some known transiting systems including several \emph{Kepler} confirmed planets. Results indicate that for complete and symmetric transit lightcurves, the Barycenter method achieves the same precision as other techniques, but with fewer assumptions and much faster. Among the transiting systems studied with the Barycenter method, we focus in particular on LHS 6343C, a brown dwarf that transits a member of an M+M binary system, LHS 6343AB. We present the results of our analysis, which can be used to set an upper limit on the period and mass of a possible second small perturber.
Jean-luc Starck - One of the best experts on this subject based on the ideXlab platform.
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Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning
SIAM Journal on Imaging Sciences, 2018Co-Authors: Morgan Schmitz, Gabriel Peyre, Matthieu Heitz, Nicolas Bonneel, Fred Maurice Ngolè Mboula, David Coeurjolly, Marco Cuturi, Jean-luc StarckAbstract:This article introduces a new non-linear dictionary learning method for histograms in the probability simplex. The method leverages optimal transport theory, in the sense that our aim is to reconstruct histograms using so called displacement interpolations (a.k.a. Wasserstein Barycenters) between dictionary atoms; such atoms are themselves synthetic histograms in the probability simplex. Our method simultaneously estimates such atoms, and, for each datapoint, the vector of weights that can optimally reconstruct it as an optimal transport Barycenter of such atoms. Our method is computationally tractable thanks to the addition of an entropic regularization to the usual optimal transportation problem, leading to an approximation scheme that is efficient, parallel and simple to differentiate. Both atoms and weights are learned using a gradient-based descent method. Gradients are obtained by automatic differentiation of the generalized Sinkhorn iterations that yield Barycenters with entropic smoothing. Because of its formulation relying on Wasserstein Barycenters instead of the usual matrix product between dictionary and codes, our method allows for non-linear relationships between atoms and the reconstruction of input data. We illustrate its application in several different image processing settings.
M Oshagh - One of the best experts on this subject based on the ideXlab platform.
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transit timing measurements with the model independent Barycenter method application to the lhs 6343 system
Astronomy and Astrophysics, 2012Co-Authors: M Oshagh, G Boue, Nader Haghighipour, M Montalto, P Figueira, N C SantosAbstract:We present a model-independent technique for calculating the time of mid-transits. This technique, named “Barycenter method”, uses the light-curve’s symmetry to determine the transit timing by calculating the transit light-curve Barycenter. Unlike the other methods of calculating mid-transit timing, this technique does not depend on the parameters of the system and central star. We demonstrate the capabilities of the Barycenter method by applying this technique to some known transiting systems including several Kepler confirmed planets. Results indicate that for complete and symmetric transit lightcurves, the Barycenter method achieves the same precision as other techniques, but with fewer assumptions and much faster. Among the transiting systems studied with the Barycenter method, we focus in particular on LHS 6343C, a brown dwarf that transits a member of an M+M binary system, LHS 6343AB. We present the results of our analysis, which can be used to set an upper limit on the period and mass of a possible second small perturber.
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transit timing measurements with the model independent Barycenter method application to the lhs 6343 system
arXiv: Earth and Planetary Astrophysics, 2011Co-Authors: M Oshagh, G Boue, Nader Haghighipour, M Montalto, P Figueira, N C SantosAbstract:We present a model-independent technique for calculating the time of mid-transits. This technique, named "Barycenter method", uses the light-curve's symmetry to determine the transit timing by calculating the transit light-curve Barycenter. Unlike the other methods of calculating mid-transit timing, this technique does not depend on the parameters of the system and central star. We demonstrate the capabilities of the Barycenter method by applying this technique to some known transiting systems including several \emph{Kepler} confirmed planets. Results indicate that for complete and symmetric transit lightcurves, the Barycenter method achieves the same precision as other techniques, but with fewer assumptions and much faster. Among the transiting systems studied with the Barycenter method, we focus in particular on LHS 6343C, a brown dwarf that transits a member of an M+M binary system, LHS 6343AB. We present the results of our analysis, which can be used to set an upper limit on the period and mass of a possible second small perturber.
Pascal Frossard - One of the best experts on this subject based on the ideXlab platform.
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node2coords: Graph Representation Learning with Wasserstein Barycenters
IEEE Transactions on Signal and Information Processing over Networks, 2021Co-Authors: Effrosyni Simou, Dorina Thanou, Pascal FrossardAbstract:In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relatively unstable to perturbations of the graph structure. We address these two limitations by proposing node2coords, a representation learning algorithm for graphs, which learns simultaneously a low-dimensional space and coordinates for the nodes in that space. The patterns that span the low dimensional space reveal the graph's most important structural information. The coordinates of the nodes reveal the proximity of their local structure to the graph structural patterns. We measure this proximity with Wasserstein distances that permit to take into account the properties of the underlying graph. Therefore, we introduce an autoencoder that employs a linear layer in the encoder and a novel Wasserstein barycentric layer at the decoder. Node connectivity descriptors, which capture the local structure of the nodes, are passed through the encoder to learn a small set of graph structural patterns. In the decoder, the node connectivity descriptors are reconstructed as Wasserstein Barycenters of the graph structural patterns. The optimal weights for the Barycenter representation of a node's connectivity descriptor correspond to the coordinates of that node in the low-dimensional space. Experimental results demonstrate that the representations learned with node2coords are interpretable, lead to node embeddings that are stable to perturbations of the graph structure and achieve competitive or superior results compared to state-of-the-art unsupervised methods in node classification.
Morgan Schmitz - One of the best experts on this subject based on the ideXlab platform.
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Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning
SIAM Journal on Imaging Sciences, 2018Co-Authors: Morgan Schmitz, Gabriel Peyre, Matthieu Heitz, Nicolas Bonneel, Fred Maurice Ngolè Mboula, David Coeurjolly, Marco Cuturi, Jean-luc StarckAbstract:This article introduces a new non-linear dictionary learning method for histograms in the probability simplex. The method leverages optimal transport theory, in the sense that our aim is to reconstruct histograms using so called displacement interpolations (a.k.a. Wasserstein Barycenters) between dictionary atoms; such atoms are themselves synthetic histograms in the probability simplex. Our method simultaneously estimates such atoms, and, for each datapoint, the vector of weights that can optimally reconstruct it as an optimal transport Barycenter of such atoms. Our method is computationally tractable thanks to the addition of an entropic regularization to the usual optimal transportation problem, leading to an approximation scheme that is efficient, parallel and simple to differentiate. Both atoms and weights are learned using a gradient-based descent method. Gradients are obtained by automatic differentiation of the generalized Sinkhorn iterations that yield Barycenters with entropic smoothing. Because of its formulation relying on Wasserstein Barycenters instead of the usual matrix product between dictionary and codes, our method allows for non-linear relationships between atoms and the reconstruction of input data. We illustrate its application in several different image processing settings.
