Explore the words cloud of the SO-ReCoDi project. It provides you a very rough idea of what is the project "SO-ReCoDi" about.
The following table provides information about the project.
UNIVERSITA COMMERCIALE LUIGI BOCCONI
|Coordinator Country||Italy [IT]|
|Total cost||1˙971˙805 €|
|EC max contribution||1˙971˙805 € (100%)|
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
|Duration (year-month-day)||from 2019-09-01 to 2024-08-31|
Take a look of project's partnership.
|1||UNIVERSITA COMMERCIALE LUIGI BOCCONI||IT (MILANO)||coordinator||1˙971˙805.00|
In a recovery problem, we are interested in recovering structure from data that contains a mix of combinatorial structure and random noise. In a robust recovery problem, the data may contain adversarial perturbations as well. A series of recent results in theoretical computer science has led to algorithms based on the convex optimization technique of Semidefinite Programming for several recovery problems motivated by unsupervised machine learning. Can those algorithms be made robust? Sparsifiers are compressed representations of graphs that speed up certain algorithms. The recent proof of the Kadison-Singer conjecture by Marcus, Spielman and Srivastava (MSS) shows that certain kinds of sparsifiers exist, but the proof does not provide an explicit construction. Dynamics and population protocols are simple models of distributed computing that were introduced to study sensor networks and other lightweight distributed systems, and have also been used to model naturally occurring networks. What can and cannot be computed in such models is largely open. We propose an ambitious unifying approach to go beyond the state of the art in these three domains, and provide: robust recovery algorithms for the problems mentioned above; a new connection between sparsifiers and the Szemeredi Regularity Lemma and explicit constructions of the sparsifiers resulting from the MSS work; and an understanding of the ability of simple distributed algorithms to solve community detection problems and to deal with noise and faults. The unification is provided by a common underpinning of spectral methods, random matrix theory, and convex optimization. Such tools are used in technically similar but conceptually very different ways in the three domains. By pursuing these goals together, we will make it more likely that an idea that is natural and simple in one context will translate to an idea that is deep and unexpected in another, increasing the chances of a breakthrough.
Are you the coordinator (or a participant) of this project? Plaese send me more information about the "SO-RECODI" project.
For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.
Send me an email (firstname.lastname@example.org) and I put them in your project's page as son as possible.
Thanks. And then put a link of this page into your project's website.
The information about "SO-RECODI" are provided by the European Opendata Portal: CORDIS opendata.
Dynamic Growth and Replication in Coacervate ProtocellsRead More
Cliodynamic archaeology: Computational approaches to Final Palaeolithic/earliest Mesolithic archaeology and climate changeRead More
Biomineralizing coatings for maxillofacial implantsRead More