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Publications associées (35)

Outlier-free spline spaces for isogeometric discretizations of biharmonic and polyharmonic eigenvalue problems

We present outlier-free isogeometric Galerkin discretizations of eigenvalue problems related to the biharmonic and the polyharmonic operator in the univariate setting. These are Galerkin discretizations in certain spline subspaces that provide accurate app ...

Lausanne2023

Application of optimal spline subspaces for the removal of spurious outliers in isogeometric discretizations

Espen Sande

We show that isogeometric Galerkin discretizations of eigenvalue problems related to the Laplace operator subject to any standard type of homogeneous boundary conditions have no outliers in certain optimal spline subspaces. Roughly speaking, these optimal ...

ELSEVIER SCIENCE SA2022

Leverage Point Identification Method for LAV-Based State Estimation

Mario Paolone, Guglielmo Frigo, Ali Abur, Mathias Dorier

In this paper we enunciate and rigorously demonstrate a new lemma that, based on a previously proposed theorem, proves the identifiability of leverage points in state estimation with specific reference to the Least Absolute Value (LAV) estimator. In this c ...

2021

Single-projection reconstruction technique for positioning monodisperse spheres in 3D with a divergent x-ray beam

The measurement of the position of single-sized spheres in 3D from a single, divergent, radiographic projection is addressed in the present study with the development of a novel method. Generally speaking, the location of the shadow cast by a single sphere ...

2021

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Pascal Fua, Mathieu Salzmann, Zheng Dang, Kwang Moo Yi, Fei Wang, Yinlin Hu

Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square problem, which can be done by finding the eigenvector corresponding to the smal ...

IEEE COMPUTER SOC2021

Cortico–reticulo–spinal circuit reorganization enables functional recovery after severe spinal cord contusion

Grégoire Courtine, Bernard Schneider, Quentin Barraud, Léonie Asboth, Mark Andrew Anderson, Lucia Florinda Friedli Wittler, Laetitia Danielle Philippine Baud, Polina Shkorbatova, Janine Beauparlant, Galyna Pidpruzhnykova, Selin Anil, Julie Kreider, Cristina Martinez Gonzalez, Elodie Rey

Severe spinal cord contusions interrupt nearly all brain projections to lumbar circuits producing leg movement. Failure of these projections to reorganize leads to permanent paralysis. Here we modeled these injuries in rodents. A severe contusion abolished ...

2018

Complex heavy-quark potential and Debye mass in a gluonic medium from lattice QCD

Yannis Burnier

We improve and extend our study of the complex in-medium heavy-quark potential and its Debye mass m(D) in a gluonic medium with a finer scan around the deconfinement transition and newly generated ensembles closer to the thermodynamic limit. On the lattice ...

Amer Physical Soc2017

A matrix DEIM technique for model reduction of nonlinear parametrized problems in cardiac mechanics

Alfio Quarteroni, Andrea Manzoni

When relying on Newton iterations to solve nonlinear problems in the context of Reduced Basis (RB) methods, the assembling of the RB arrays during the online stage depends on the dimension of the underlying high-fidelity approximation. This is more of an i ...

Elsevier Science Sa2017

The regularized tau estimator: A robust and efficient solution to ill-posed linear inverse problems

Martin Vetterli, Benjamin Bejar Haro, Marta Martinez-Camara

Linear inverse problems are ubiquitous. Often the measurements do not follow a Gaussian distribution. Additionally, a model matrix with a large condition number can complicate the problem further by making it ill-posed. In this case, the performance of pop ...

2017

A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation

Jean-Yves Le Boudec, Mario Paolone, Lorenzo Zanni

In this paper, we present a new method for the estimation of the prediction-error covariances of a Kalman filter (KF), which is suitable for step-varying processes. The method uses a series of past innovations (i.e., the difference between the upcoming mea ...

Institute of Electrical and Electronics Engineers2017

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