Affine Processes On Positive Semidefinite Matrices

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IMPROVED VARIANTS OF THE HUTCH plus plus ALGORITHM FOR TRACE ESTIMATION

Daniel Kressner, Alice Cortinovis, Ulf David Persson

This paper is concerned with two improved variants of the Hutch++ algorithm for estimating the trace of a square matrix, implicitly given through matrix-vector products. Hutch++ combines randomized low-rank approximation in a first phase with stochastic tr ...

SIAM PUBLICATIONS2022

Enhanced confinement in diverted negative-triangularity L-mode plasmas in TCV

Olivier Sauter, Stefano Coda, Alessandro Pau, Benoît Labit, Laurie Porte, Antoine Pierre Emmanuel Alexis Merle, Umar Sheikh, Cedric Kar-Wai Tsui, Alessandro Marinoni, Matteo Vallar, Filippo Bagnato

The favorable confinement properties of negative-triangularity (NT) tokamak configurations were discovered in the TCV tokamak in the late 1990s and were documented over the two following decades, through investigations of predominantly electron-heated plas ...

2022

Study of impurity ion transport using charge exchange spectroscopy on TCV

Filippo Bagnato

The study of impurity transport in fusion plasmas has gained interest over the years due to its implication with tokamak performances. In TCV (Tokamak à Configuration Variable), as in many other facilities, impurity ions can be studied through the CXRS (Ch ...

EPFL2022

On Randomized Trace Estimates for Indefinite Matrices with an Application to Determinants

Daniel Kressner, Alice Cortinovis

Randomized trace estimation is a popular and well-studied technique that approximates the trace of a large-scale matrix B by computing the average of x(T) Bx for many samples of a random vector X. Often, B is symmetric positive definite (SPD) but a number ...

2022

Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints

Enrico Amico

Background: Functional connectomes (FCs) have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential ...

MARY ANN LIEBERT, INC2021

Robust Differentiable SVD

Pascal Fua, Mathieu Salzmann, Wei Wang, Yinlin Hu, Zheng Dang

Eigendecomposition of symmetric matrices is at the heart of many computer vision algorithms. However, the derivatives of the eigenvectors tend to be numerically unstable, whether using the SVD to compute them analytically or using the Power Iteration (PI) ...

2021

Diverted negative triangularity plasmas on DIII-D: the benefit of high confinement without the liability of an edge pedestal

Stefano Coda, Alessandro Marinoni

Diverted discharges at negative triangularity on the DIII-D tokamak sustain normalized confinement and pressure levels typical of standard H-mode scenarios (H (98y2) similar or equal to 1, beta (N) similar or equal to 3) without developing an edge pressure ...

IOP Publishing Ltd2021

Sublinear Algorithms for Spectral Graph Clustering

Aidasadat Mousavifar

This thesis focuses on designing spectral tools for graph clustering in sublinear time. With the emergence of big data, many traditional polynomial time, and even linear time algorithms have become prohibitively expensive. Processing modern datasets requir ...

EPFL2021

Nonlocal effects in negative triangularity TCV plasmas

Olivier Sauter, Stefano Coda, Laurent Villard, Stephan Brunner, Zhouji Huang, Gabriele Merlo, Claudio Marini

Global gradient driven gyrokinetic simulations performed with the Gyrokinetic Electromagnetic Numerical Experiment (GENE) code are used to investigate Tokamak a configuration variable (TCV) plasmas with negative triangularity. Considering limited L-mode pl ...

2021

Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses

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

Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be solved by ﬁnding the eigenvector corresponding to the smallest, or zero, eigenvalue of a matrix representing a linear sy ...

2018