A great day for Nedo Award

It is a big pleasure for Nedo to announce a great activity on Nedo award: during the Complexity 72h, held in IMT two great events happens. First, Tiziano, as part of the organisation,  presented himself with this slide which is a sort of meta-Nedo: a shot of himself presenting a slide with the Nedo can (this one).

Tiziano_72h

You probably cannot see it: Tiziano presenting a slide with Tiziano presenting a slide with Nedo.

Then, the disciple overcame the teacher: few minutes later Federica Parisi (the actual owner of the award) presented the following slide:

Federica_72h

It’s small, but it’s there!

So, congratulations to the new holder of the Nedo Award!

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Kids’ time: baptism by fire of the newcomers, AGAIN!

Dear Nedoers,

Nedo strikes back!

After several reorganizations, we will finally have the baptism by fire of the newcomers: Matteo Bruno, Mirko Hu, Emiliano Marchese, Matteo Serafino will have a Nedo on 5th December at 17.30 (we had to postpone it!), a quarter of hour each. The seminars will take place in the Classroom 1.

For the speakers: check the rules of Nedo seminars and remember, NO SLIDES!

Stay tuNED(o)!

Diego Garlaschelli@IMT

Dear Nedoers,

Diego Garlaschelli (Lorentz Institute for Theoretical Physics, Leiden University) is going to held a seminar on February the 28th, at 09.30, in classroom 2 (San Francesco building).

Maximum entropy for economic and brain networks: network reconstruction, early-warning signals, and module detection

In many cases of practical relevance, one needs to construct ensembles of random networks, or random time series, that obey specified constraints. In these cases, the maximum entropy construction is a natural recipe to generate randomness, however the presence of several heterogeneous constraints leads to important differences with respect to the traditional construction. For instance, in order to reliably estimate the risk of collapse of a financial system, one needs to infer the network of linkages between banks and/or firms, but this network is empirically unaccessible due to confidentiality. One therefore has to reconstruct the network from partial, publicly available information about individual financial institutions. I will discuss various maximum-entropy network reconstruction methods, highlighting the importance of capturing the heterogeneity of the constraints correctly. I will also discuss how ensembles of reconstructed networks can be used as benchmarks to detect early-warning signals of upcoming crises in empirical interbank networks. Then, I will describe maximum-entropy ensembles of constrained time series, and use their properties to empirically identify communities of correlated stocks in financial markets and functional modules of correlated neurons in the brain. I will conclude showing that, in all the cases considered, the presence of an extensive number of constraints leads to a surprising breaking of the equivalence between canonical and microcanonical ensembles, with important consequences for the statistical physics of systems with many constraints.

Stay tuNED(O)!