Concept

Linear differential equation

Related publications (416)

MeCP2-E1 isoform is a dynamically expressed, weakly DNA-bound protein with different protein and DNA interactions compared to MeCP2-E2

Background: MeCP2-a chromatin-binding protein associated with Rett syndrome-has two main isoforms, MeCP2-E1 and MeCP2-E2, differing in a few N-terminal amino acid residues. Previous studies have shown brain region-specific expression of these isoforms whic ...

2019

A high-order discontinuous Galerkin approximation to ordinary differential equations with applications to elastodynamics

Alfio Quarteroni, Niccolò Dal Santo

The aim of this work is to propose and analyse a new high-order discontinuous Galerkin finite element method for the time integration of a Cauchy problem for second-order ordinary differential equations. These equations typically arise after space semidisc ...

OXFORD UNIV PRESS2018

Sur quelques équations aux dérivées partielles en lien avec le lemme de Poincaré

David Valentin Strütt

In this thesis, we study two distinct problems. The first problem consists of studying the linear system of partial differential equations which consists of taking a k-form, and applying the exterior derivative 'd' to it and add the wedge product with a 1- ...

EPFL2018

ON A GENERALIZATION OF THE POINCARE LEMMA TO EQUATIONS OF THE TYPE dw plus a Lambda w = f

David Valentin Strütt

We study the system of linear partial differential equations given by dw + a Lambda w = f, on open subsets of R-n, together with the algebraic equation da Lambda u = beta, where a is a given 1-form, f is a given (k + 1)-form, beta is a given k + 2-form, w ...

2018

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit [12, 9], thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Quantification Of Resting-State Fmri Networks Driven By Hemodynamically Informed Spatiotemporal Regularization

Dimitri Nestor Alice Van De Ville, Fikret Isik Karahanoglu, Younes Farouj

The brain's spontaneous fluctuations measured by functional magnetic resonance imaging during rest cluster into recurrent activity patterns known as resting-state networks (RSNs). The spatial organization of RSNs in health and disease has been immensely in ...

IEEE2018

Transforming cumulative hazard estimates

Mats Julius Stensrud, Pal Christie Ryalen

Time-to-event outcomes are often evaluated on the hazard scale, but interpreting hazards may be difficult. Recently in the causal inference literature concerns have been raised that hazards actually have a built-in selection bias that prevents simple causa ...

2018

On the theory of drainage area for regular and non-regular points

Sara Bonetti

The drainage area is an important, non-local property of a landscape, which controls surface and subsurface hydrological fluxes. Its role in numerous ecohydrological and geomorphological applications has given rise to several numerical methods for its comp ...

2018

MATHICSE Technical Report : Random time step probabilistic methods for uncertainty quantification in chaotic and geometric numerical integration

Assyr Abdulle, Giacomo Garegnani

A novel probabilistic numerical method for quantifying the uncertainty induced by the time integration of ordinary differential equations (ODEs) is introduced. Departing from the classical strategy to randomize ODE solvers by adding a random forcing term, ...

MATHICSE2018

A theoretical study of COmpRessed SolvING for advection-diffusion-reaction problems

Fabio Nobile, Simone Brugiapaglia

We present a theoretical analysis of the CORSING (COmpRessed SolvING) method for the numerical approximation of partial differential equations based on compressed sensing. In particular, we show that the best s-term approximation of the weak solution of a ...

2018

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