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Nonlocal Methods for Arbitrary Data Sources

Nonlocal Methods for Arbitrary Data Sources

Objective

In NoMADS we focus on data processing and analysis techniques which can feature potentially very complex, nonlocal, relationships within the data. In this context, methodologies such as spectral clustering, graph partitioning, and convolutional
neural networks have gained increasing attention in computer science and engineering within the last years, mainly from a combinatorial point of view. However, the use of nonlocal methods is often still restricted to academic pet projects. There is a large gap between the academic theories for nonlocal methods and their practical application to real-world problems. The reason these methods work so well in practice is far from fully understood.
Our aim is to bring together a strong international group of researchers from mathematics (applied and computational analysis, statistics, and optimisation), computer vision, biomedical imaging, and remote sensing, to fill the current gaps between theory and applications of nonlocal methods. We will study discrete and continuous limits of nonlocal models by means of mathematical analysis and optimisation techniques, resulting in investigations on scale-independent
properties of such methods, such as imposed smoothness of these models and their stability to noisy input data, as well as the development of resolution-independent, efficient and reliable computational techniques which scale well
with the size of the input data. As an overarching applied theme we focus in particular on image data arising in biology and medicine, which offers a rich playground for structured data processing and has direct impact on society, as well as discrete point clouds, which represent an ambitious target for unstructured data processing. Our long-term vision is to discover fundamental mathematical principles for the characterisation of nonlocal operators, the development of new robust and efficient algorithms, and the implementation of those in high quality software products for real-world application.
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Coordinator

FRIEDRICH-ALEXANDER-UNIVERSITAET ERLANGEN NUERNBERG

Address

Schlossplatz 4
91054 Erlangen

Germany

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 139 500

Participants (21)

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Universitat Pompeu Fabra

Spain

EU Contribution

€ 22 500

UNIVERSITE DE BORDEAUX

France

EU Contribution

€ 76 500

UNIVERSITE DE CAEN NORMANDIE

France

EU Contribution

€ 36 000

ECOLE NATIONALE SUPERIEURE D'INGENIEURS DE CAEN

France

EU Contribution

€ 54 000

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE

United Kingdom

EU Contribution

€ 238 500

UNIVERSITA DEGLI STUDI DI GENOVA

Italy

EU Contribution

€ 40 500

TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY

Israel

EU Contribution

€ 36 000

INSTITUTO SUPERIOR TECNICO

Portugal

EU Contribution

€ 54 000

POLITECNICO DI MILANO

Italy

EU Contribution

€ 4 500

THE UNIVERSITY OF NOTTINGHAM

United Kingdom

EU Contribution

€ 81 000

ECOLE POLYTECHNIQUE

France

EU Contribution

€ 40 500

UNIVERSITEIT TWENTE

Netherlands

EU Contribution

€ 112 500

UNIVERSITE LYON 1 CLAUDE BERNARD

France

EU Contribution

€ 31 500

ASTRAZENECA UK LIMITED

United Kingdom

MEDIGUIDE LTD

Israel

EU Contribution

€ 27 000

CAMELOT BIOMEDICAL SYSTEMS SRL

Italy

EU Contribution

€ 27 000

THE MATHWORKS LIMITED

United Kingdom

CLK GMBH

Germany

DATEXIM

France

CLINICAL SCIENCE SYSTEMS

Netherlands

WESTFAELISCHE WILHELMS-UNIVERSITAET MUENSTER

Germany

EU Contribution

€ 90 000

Partners (2)

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THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

CARNEGIE MELLON UNIVERSITY

Project information

Grant agreement ID: 777826

Status

Ongoing project

  • Start date

    1 March 2018

  • End date

    28 February 2022

Funded under:

H2020-EU.1.3.3.

  • Overall budget:

    € 1 111 500

  • EU contribution

    € 1 111 500

Coordinated by:

FRIEDRICH-ALEXANDER-UNIVERSITAET ERLANGEN NUERNBERG

Germany