Friends and colleagues who knew Ilya Prigogine well called him “A poet of thermodynamics.” It is an apt description. When Prigogine talked about thermodynamics and irreversible processes, one had the sense he understood or knew more than what his words conveyed. Natural processes all around us are irreversible; it is a fact. Their consequence is not merely to increase the entropy of the universe and destroy order. They can also do the opposite: create highly ordered complex structures with extraordinary properties and create life itself. Prigogine saw this as a profound aspect of nature that thermodynamics has revealed. When he came across the famed South Indian sculpture of Nataraja, the dancing Shiva, that depicts as a cosmic dance the perfect balance between creation and destruction that originate from the same source, he made sure he had a bronze statue of Nataraja of highest artistic quality in his art collection. A picture of it became the cover art for the book Thermodynamic Theory of Structure Stability and Fluctuations, that he coauthored with Paul Glansdorff. It was poetry of thermodynamics, creation and destruction emerging from a common source, a perfectly balanced cosmic dance. One could surmise all this from Prigogine’s discourses on thermodynamics.

 

Dissipative structures and irreversibility in nature: Celebrating 100th birth anniversary of Ilya Prigogine (1917–2003)
Chaos 27, 104501 (2017); https://doi.org/10.1063/1.5008858
Dilip Kondepudi, Tomio Petrosky, and John A. Pojman

Source: aip.scitation.org

As the annual flu season approaches, medical professionals are again encouraging people to get flu shots. Perhaps you are among those who rationalize skipping the shot on the grounds that “I never get the flu” or “if I get sick, I get sick” or “I’m healthy, so I’ll get over it.” What you might not realize is that these vaccination campaigns for flu and other diseases are about much more than your health. They’re about achieving a collective resistance to disease that goes beyond individual well-being — and that is governed by mathematical principles unforgiving of unwise individual choices.

Source: www.quantamagazine.org

The structure of complex networks has been of interest in many scientific and engineering disciplines over the decades. A number of studies in the field have been focused on finding the common properties among different kinds of networks such as heavy-tail degree distribution, small-worldness and modular structure and they have tried to establish a theory of structural universality in complex networks. However, there is no comprehensive study of network structure across a diverse set of domains in order to explain the structural diversity we observe in the real-world networks. In this paper, we study 986 real-world networks of diverse domains ranging from ecological food webs to online social networks along with 575 networks generated from four popular network models. Our study utilizes a number of machine learning techniques such as random forest and confusion matrix in order to show the relationships among network domains in terms of network structure. Our results indicate that there are some partitions of network categories in which networks are hard to distinguish based purely on network structure. We have found that these partitions of network categories tend to have similar underlying functions, constraints and/or generative mechanisms of networks even though networks in the same partition have different origins, e.g., biological processes, results of engineering by human being, etc. This suggests that the origin of a network, whether it’s biological, technological or social, may not necessarily be a decisive factor of the formation of similar network structure. Our findings shed light on the possible direction along which we could uncover the hidden principles for the structural diversity of complex networks.

 

Characterizing the structural diversity of complex networks across domains
Kansuke Ikehara, Aaron Clauset

Source: arxiv.org

Animal aggregations are visually striking, and as such are popular examples of collective behavior in the natural world. Quantitatively demonstrating the collective nature of such groups, however, remains surprisingly difficult. Inspired by thermodynamics, we applied topological data analysis to laboratory insect swarms and found evidence for emergent, material-like states. We show that the swarms consist of a core “condensed” phase surrounded by a dilute “vapor” phase. These two phases coexist in equilibrium, and maintain their distinct macroscopic properties even though individual insects pass freely between them. We further define a pressure and chemical potential to describe these phases, extending theories of active matter to aggregations of macroscopic animals and laying the groundwork for a thermodynamic description of collective animal groups.

 

Phase Coexistence in Insect Swarms
Michael Sinhuber and Nicholas T. Ouellette
Phys. Rev. Lett. 119, 178003 – Published 24 October 2017

Source: journals.aps.org

A question of interest in both theory and practice is if and how familiarity between members of a team, expressed in terms of social network structure, relates to the success of the team in a given task. In this paper we revisit this important question in a novel manner by employing game outcome statistics from Dota 2, a popular team-based multiplayer online game, combined with network data from Steam Community, a social networking service for gamers. We conduct a large-scale analysis of 4168 teams to study how network density, and the minimum and maximum degree of the within-team social network are associated with team performance, and determine how this association is moderated by team skill. We observe that minimum degree is strongly associated with good performance, especially in teams with lower skill. Together with previous results on network density that we corroborate in this paper, our findings suggest that a successful team is not only moderately connected overall, but its members should also individually have not too few nor too many within team connections.

 

Large-scale study of social network structure and team performance in a multiplayer online game
Antti Ukkonen, Juho Hamari

Source: arxiv.org