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Projeto Cicit Webinar realiza seminário on-line amanhã (24)

O convidado desta edição é o professor Fernando Santos

O projeto Cicit Webinar, que tem realizado seminários mensais on-line, promove mais um encontro amanhã (24). O evento, de tema “Usando topologia para entender o cérebro e sistemas complexos", acontecerá às 16h, no canal do Cicit no YouTube. O palestrante desta edição será o professor Fernando A. N. Santos.

O Cicit é uma ação extensionista desenvolvida por professores do Núcleo Interdisciplinar de Ciências Exatas e da Natureza (Nicen) e estudantes das ciências exatas do Campus do Agreste da UFPE. A proposta é apresentar, por meio de eventos on-line, diversos tópicos relacionados às ciências exatas e à inovação tecnológica, dentro de um contexto interdisciplinar, com o intuito de estimular o espírito científico, apresentar as linhas de pesquisa desenvolvidas no campus e fortalecer a colaboração entre pesquisadores.

Resumo

Over the past decades, methods and concepts of differential topology were used in classical statistical mechanics to describe phase transitions. In parallel, methods of stochastic topology were used to generalize the so-called giant component transition to simplicial complexes. In this talk, we show that it is possible to put those ideas together, by using methods of Topological Data Analysis (TDA) to characterize topological phase transitions in high-order networks and complex systems. Under certain conditions, topological phase transitions in networks are characterized by i) the zeros of the Euler characteristic (or the singularities of the Euler entropy); ii) the emergence of multidimensional topological holes in a network; and iii) signal changes in the mean node curvature of a network. The geometric nature of the transitions can be interpreted, under specific hypotheses, as an extension of percolation to high-dimensional objects. We illustrate those ideas in functional brain networks, both in healthy and disease groups, in empirical protein interaction networks, as well as other theoretical network models. Due to the universal character of phase transitions and noise robustness of TDA, our findings open perspectives towards establishing reliable topological and geometrical fingerprints for networks. Finally, inspired by the long-term relation between topology and theoretical physics, we point at the possibility of finding high-order network analogs to this relation that have the potential to lead to basic principles in network science.

Mais informações
cicit.webinar@gmail.com

Data da última modificação: 23/09/2020, 17:43