Publications related to Scaling (geometry)

State-Based Versus Time-Based Estimation of the Gait Phase for Hip Exoskeletons in Steady and Transient Walking

Auke Ijspeert, Mohamed Bouri, Ali Reza Manzoori, Tian Ye

The growing demand for online gait phase (GP) estimation, driven by advancements in exoskeletons and prostheses, has prompted numerous approaches in the literature. Some approaches explicitly use time, while others rely on state variables to estimate the G ...

IEEE 2023

Longitudinal incremental propensity score interventions for limited resource settings

Mats Julius Stensrud, Aaron Leor Sarvet

Many real‐life treatments are of limited supply and cannot be provided to all individuals in the population. For example, patients on the liver transplant waiting list usually cannot be assigned a liver transplant immediately at the time they reach highest ...

2023

Low-Rank Updates Of Matrix Functions Ii: Rational Krylov Methods

Daniel Kressner, Alice Cortinovis, Marcel Schweitzer

This work develops novel rational Krylov methods for updating a large-scale matrix function f(A) when A is subject to low-rank modifications. It extends our previous work in this context on polynomial Krylov methods, for which we present a simplified conve ...

SIAM PUBLICATIONS2021

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 ...

2021

Compress-and-restart block Krylov subspace methods for Sylvester matrix equations

Daniel Kressner, Stefano Massei, Kathryn Dianne Lund

Block Krylov subspace methods (KSMs) comprise building blocks in many state-of-the-art solvers for large-scale matrix equations as they arise, for example, from the discretization of partial differential equations. While extended and rational block Krylov ...

2020

Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity

Ksenia Briling, Alberto Fabrizio, Andrea Grisafi

Machine-learning in quantum chemistry is currently booming, with reported applications spanning all molecular properties from simple atomization energies to complex mathematical objects such as the many-body wavefunction. Due to its central role in density ...

2020

The Role of Reynolds Number Effect and Tip Leakage in Compressor Geometry Scaling at Low Turbulent Reynolds Numbers

Jürg Alexander Schiffmann, Markus Diehl, Christoph Schreiber

In compressor design, a convenient way to save time is to scale an existing geometry to required specifications, rather than developing a new design. The approach works well when scaling compressors of similar size at high Reynolds numbers but becomes more ...

2020

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

Edo Collins

With ever greater computational resources and more accessible software, deep neural networks have become ubiquitous across industry and academia. Their remarkable ability to generalize to new samples defies the conventional view, which holds that complex, ...

EPFL2019

A Dynamic Embedding Model of the Media Landscape

Karl Aberer, Jérémie Rappaz

Information about world events is disseminated through a wide variety of news channels, each with specific considerations in the choice of their reporting. Although the multiplicity of these outlets should ensure a variety of viewpoints, recent reports sug ...

ASSOC COMPUTING MACHINERY2019

Dependence of the L-Mode scrape-off layer power fall-off length on the upper triangularity in TCV

Stefano Coda, Holger Reimerdes, Benoît Labit, Christian Gabriel Theiler, Antoine Pierre Emmanuel Alexis Merle, Roberto Maurizio, Bernhard Sieglin

This paper reports on experimental observations on TCV with a scan in upper triangularity

{\delta }_{\mathrm{up}}

, including negative triangularity, focusing on the power fall-off length

{\lambda }_{{\rm{q}}}

in L-Mode. The upper triangularity is scann ...

2018

Scaling (geometry)

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State-Based Versus Time-Based Estimation of the Gait Phase for Hip Exoskeletons in Steady and Transient Walking

Longitudinal incremental propensity score interventions for limited resource settings

Low-Rank Updates Of Matrix Functions Ii: Rational Krylov Methods

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

Compress-and-restart block Krylov subspace methods for Sylvester matrix equations

Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity

The Role of Reynolds Number Effect and Tip Leakage in Compressor Geometry Scaling at Low Turbulent Reynolds Numbers

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

A Dynamic Embedding Model of the Media Landscape

Dependence of the L-Mode scrape-off layer power fall-off length on the upper triangularity in TCV

Low-Rank Updates Of Matrix Functions Ii: Rational Krylov Methods

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

Dependence of the L-Mode scrape-off layer power fall-off length on the upper triangularity in TCV

State-Based Versus Time-Based Estimation of the Gait Phase for Hip Exoskeletons in Steady and Transient Walking

The Role of Reynolds Number Effect and Tip Leakage in Compressor Geometry Scaling at Low Turbulent Reynolds Numbers

Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity

A Dynamic Embedding Model of the Media Landscape

Longitudinal incremental propensity score interventions for limited resource settings

Compress-and-restart block Krylov subspace methods for Sylvester matrix equations

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization