Sampling frame

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Publications associées (28)

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Mohammadhossein Gorji

The efficiency of stochastic particle schemes for large scale simulations relies on the ability to preserve a uniform distribution of particles in the whole physical domain. While simple particle split and merge algorithms have been considered previously, ...

2019

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

François Maréchal, Nils Christian Schüler, Sébastien Cajot

Interactive optimization methods are particularly suited for letting human decision makers learn about a problem, while a computer learns about their preferences to generate relevant solutions. For interactive optimization methods to be adopted in practice ...

2019

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Pascal Frossard, Arun Venkitaraman

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse ...

IEEE2019

Approximation of Non-decaying Signals from Shift-Invariant Subspaces

Michaël Unser, Quy Ha Nguyen

In our recent work, the sampling and reconstruction of non-decaying signals, modeled as members of weighted-L-p spaces, were shown to be stable with an appropriate choice of the generating kernel for the shift-invariant reconstruction space. In this paper, ...

2019

Rapid identification and interpretation of gene‐environment associations using the new R.SamBada landscape genomics pipeline

Stéphane Joost, Oliver Michele Selmoni, Estelle Rochat, Sylvie Stucki, Kevin Leempoel, Elia Vajana, Solange Catherine Gaillard

Samβada is a genome‐environment association (GEA) software, designed to search for signatures of local adaptation. However, pre‐ and post‐processing of data can be labour‐intensive, preventing wider uptake of the method. We have now developed R.SamBada, an ...

2019

Sampling at unknown locations: Uniqueness and reconstruction under constraints

Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Golnooshsadat Elhami, Michalina Wanda Pacholska

Traditional sampling results assume that the sample locations are known. Motivated by simultaneous localization and mapping (SLAM) and structure from motion (SfM), we investigate sampling at unknown locations. Without further constraints, the problem is of ...

2018

Driven-dissipative quantum Monte Carlo method for open quantum systems

Vincenzo Savona, Alexandra Nagy

We develop a real-time full configuration-interaction quantum Monte Carlo approach to model driven-dissipative open quantum systems with Markovian system-bath coupling. The method enables stochastic sampling of the Liouville–von Neumann time evolution of t ...

2018

Tidal Interactions and Mergers in Intermediate-redshift EDisCS Clusters

Pascale Jablonka

We study the fraction of tidal interactions and mergers (TIMs) with well-identified observability timescales (f(TIM)) in group, cluster, and accompanying field galaxies and its dependence on redshift (z), cluster velocity dispersion (sigma), and environmen ...

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