Publications related to Adaptive Approximation of Shapes

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

EPFL2022

Chemiscope: interactive structure-property explorer for materials and molecules

Michele Ceriotti, Guillaume André Jean Fraux

The number of materials or molecules that can be created by combining different chemical elements in various proportions and spatial arrangements is enormous. Computational chemistry can be used to generate databases containing billions of potential struct ...

2020

Group Approximation in Cayley Topology and Coarse Geometry, Part II: Fibred Coarse Embeddings

Masato Mimura

The objective of this series is to study metric geometric properties of disjoint unions of Cayley graphs of amenable groups by group properties of the Cayley accumulation points in the space of marked groups. In this Part II, we prove that a disjoint union ...

2019

Two-step phase demodulation algorithm based on quadratic phase parameter estimation using state space analysis

Pramod Rastogi, Rishikesh Dilip Kulkarni

We propose a noise robust two-step phase shifting algorithm for the evaluation of random and unknown phase step. In this algorithm, the phase within a small size two-dimensional window around a user selected pixel in an interferogram is approximated as a q ...

2018

Fourier dimensionality reduction of radio-interferometric data

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Vijay Kartik

Next-generation radio-interferometers face a computing challenge with respect to the imaging techniques that can be applied in the big data setting in which they are designed. Dimensionality reduction can thus provide essential savings of computing resourc ...

2017

A Fourier dimensionality reduction model for big data interferometric imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Vijay Kartik

Data dimensionality reduction in radio interferometry can provide savings of computational resources for image reconstruction through reduced memory footprints and lighter computations per iteration, which is important for the scalability of imaging method ...

Oxford Univ Press2017

Graph-based Methods for Visualization and Clustering

Johann Paratte

The amount of data that we produce and consume is larger than it has been at any point in the history of mankind, and it keeps growing exponentially. All this information, gathered in overwhelming volumes, often comes with two problematic characteristics: ...

EPFL2017

On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems

Daniel Kressner, André Uschmajew

Low-rank tensor approximation techniques attempt to mitigate the overwhelming complexity of linear algebra tasks arising from high-dimensional applications. In this work, we study the low-rank approximability of solutions to linear systems and eigenvalue p ...

Elsevier Science Inc2016

Renormalization and blow up for wave maps from $S^2 \times \mathbb{R}$ to $S^2$

Sohrab Mirshams Shahshahani

We construct a one parameter family of nite time blow ups to the co-rotational wave maps problem from

S^2\times R

to

S^2

parameterized by

\nu \epsilon (\frac{1}{2},1]

. The longitudinal function

u(t,\alpha)

which is the main object of study will be ...

2016

RENORMALIZATION AND BLOW-UP FOR WAVE MAPS FROM S-2 x R TO S-2

Sohrab Mirshams Shahshahani

We construct a one parameter family of finite time blow-ups to the co-rotational wave maps problem from S-2 x R to S-2, parameterized by nu is an element of (1/2, 1]. The longitudinal function u(t, alpha) which is the main object of study will be obtained ...

American Mathematical Society2016

Adaptive Approximation of Shapes

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Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Chemiscope: interactive structure-property explorer for materials and molecules

Group Approximation in Cayley Topology and Coarse Geometry, Part II: Fibred Coarse Embeddings

Two-step phase demodulation algorithm based on quadratic phase parameter estimation using state space analysis

Fourier dimensionality reduction of radio-interferometric data

A Fourier dimensionality reduction model for big data interferometric imaging

Graph-based Methods for Visualization and Clustering

On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems

Renormalization and blow up for wave maps from $S^2 \times \mathbb{R}$ to $S^2$

RENORMALIZATION AND BLOW-UP FOR WAVE MAPS FROM S-2 x R TO S-2

Fourier dimensionality reduction of radio-interferometric data

Two-step phase demodulation algorithm based on quadratic phase parameter estimation using state space analysis

Graph-based Methods for Visualization and Clustering

A Fourier dimensionality reduction model for big data interferometric imaging

Renormalization and blow up for wave maps from $S^2 \times \mathbb{R}$ to $S^2$

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Chemiscope: interactive structure-property explorer for materials and molecules

Group Approximation in Cayley Topology and Coarse Geometry, Part II: Fibred Coarse Embeddings

RENORMALIZATION AND BLOW-UP FOR WAVE MAPS FROM S-2 x R TO S-2

On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems