A convex optimization framework for global tractography

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

We study the glass transition by exploring a broad class of kinetic rules that can significantly modify the normal dynamics of supercooled liquids while maintaining thermal equilibrium. Beyond the usual dynamics of liquids, this class includes dynamics in ...

We study the statistical mechanics and the equilibrium dynamics of a system of classical Heisenberg spins with frustrated interactions on a d -dimensional simple hypercubic lattice, in the limit of infinite dimensionality d -> infinity . In the analysis we ...

We present a combination technique based on mixed differences of both spatial approximations and quadrature formulae for the stochastic variables to solve efficiently a class of optimal control problems (OCPs) constrained by random partial differential equ ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

The optimal pricing of goods, especially when they are new and the innovating firm is a monopolist, must proceed without precise knowledge of the demand curve. This paper provides a pricing method with a relative robustness guarantee by maximizing a perfor ...

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

The present invention relates to a compound of the general formula (I), (II) and (III), more specifically of formula (Ia), (Ib), (Ic)wherein R11 and R12 or R21 and R22 or R31 and R32 are both hydrogen or form together with CHR50 a cyclic moiety or one of R ...

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

We demonstrate the use of both pixelated differential phase contrast (DPC) scanning transmission electron microscopy (STEM) and off-axis electron holography (EH) for the measurement of electric fields and assess the advantages and limitations of each techn ...

In this paper, we set the mathematical foundations of the Dynamical Low-Rank Approximation (DLRA) method for stochastic differential equations (SDEs). DLRA aims at approximating the solution as a linear combination of a small number of basis vectors with r ...

A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

Reduced-order models are indispensable for multi-query or real-time problems. However, there are still many challenges to constructing efficient ROMs for time-dependent parametrized problems. Using a linear reduced space is inefficient for time-dependent n ...

We generalize the class vectors found in neural networks to linear subspaces (i.e., points in the Grassmann manifold) and show that the Grassmann Class Representation (GCR) enables simultaneous improvement in accuracy and feature transferability. In GCR, e ...

Given a hyperelliptic hyperbolic surface S of genus g >= 2, we find bounds on the lengths of homologically independent loops on S. As a consequence, we show that for any lambda is an element of (0, 1) there exists a constant N(lambda) such that every such ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

This paper considers the problem of distributed lossy compression where the goal is to recover one or more linear combinations of the sources at the decoder, subject to distortion constraints. For certain configurations, it is known that codes with algebra ...

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients ...

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

We report on scanningtunneling microscopy (STM) topographs ofindividual metal phthalocyanines (MPc) on a thin salt (NaCl) filmadsorbed on a gold substrate, at tunneling energies within the molecule'selectronic transport gap. Theoretical models of increasin ...

A convex optimization framework for global tractography

Chattez avec Graph Search

Testing Theories of the Glass Transition with the Same Liquid but Many Kinetic Rules

Frustrated magnets in the limit of infinite dimensions: Dynamics and disorder-free glass transition

A Combination Technique for Optimal Control Problems Constrained by Random PDEs

Towards Trustworthy Deep Learning for Image Reconstruction

Monopoly Pricing with Unknown Demand

Scalable constrained optimization

Efficient and insightful descriptors for representing molecular and material space

Biobased surfactant

A ride time-oriented scheduling algorithm for dial-a-ride problems

Measuring electrical properties in semiconductor devices by pixelated STEM and off-axis electron holography (or convergent beams vs. plane waves)

Dynamical Low-Rank Approximation for Stochastic Differential Equations

Density estimation in RKHS with application to Korobov spaces in high dimensions

Non-intrusive data-driven reduced-order modeling for time-dependent parametrized problems

Get the Best of Both Worlds: Improving Accuracy and Transferability by Grassmann Class Representation

Short homology bases for hyperelliptic hyperbolic surfaces

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Distributed Lossy Computation with Structured Codes: From Discrete to Continuous Sources

Robust optimization of control parameters for WEC arrays using stochastic methods

Stochastic adaptive resampling for the estimation of discrete choice models

Character of Electronic States in the Transport Gap of Molecules on Surfaces