Search Results for author: Samir Suweis

Found 6 papers, 0 papers with code

Soft trade-offs and the stochastic emergence of diversification in E. coli evolution experiments

no code implementations20 Jul 2023 Roberto Corral López, Samir Suweis, Sandro Azaele, Miguel A. Muñoz

This introduces a natural new source of stochasticity which allows one to account for the empirically observed variability as well as to make predictions for the likelihood of evolutionary branching to be observed, thus helping to bridge the gap between theory and experiments.

Generalized Lotka-Volterra Systems with Time Correlated Stochastic Interactions

no code implementations6 Jul 2023 Samir Suweis, Francesco Ferraro, Sandro Azaele, Amos Maritan

The dynamics of species communities are typically modelled considering fixed parameters for species interactions.

Effect of delay on the emergent stability patterns in Generalized Lotka-Volterra ecological dynamics

no code implementations22 Oct 2021 Meghdad Saeedian, Emanuele Pigani, Amos Maritan, Samir Suweis, Sandro Azaele

Finally, we introduce a measure of stability that holds for out of equilibrium dynamics and we show that in the oscillatory regime induced by the delay stability increases for increasing ecosystem diversity.

Relation

The emergence of cooperation from shared goals in the Systemic Sustainability Game of common pool resources

no code implementations1 Oct 2021 Chengyi Tu, Paolo DOdorico, Zhe Li, Samir Suweis

The sustainable use of common-pool resources (CPRs) is a major environmental governance challenge because of their possible over-exploitation.

Effective Resource-Competition Model for Species Coexistence

no code implementations2 Apr 2021 Deepak Gupta, Stefano Garlaschi, Samir Suweis, Sandro Azaele, Amos Maritan

Finally, we analytically compute the distribution of the population sizes of coexisting species.

Deep learning systems as complex networks

no code implementations28 Sep 2018 Alberto Testolin, Michele Piccolini, Samir Suweis

Thanks to the availability of large scale digital datasets and massive amounts of computational power, deep learning algorithms can learn representations of data by exploiting multiple levels of abstraction.

Natural Language Understanding Object Recognition +1

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