Month: July 2019

Entropy | Special Issue : Thermodynamics and Information Theory of Living Systems

One of the defining features of living systems is their ability to process, exchange and store large amounts of information at multiple levels of organization, ranging from the biochemical to the ecological. At the same time, living entities are non-equilibrium—possibly at criticality—physical systems that continuously exchange matter and energy with structured environments, all while obeying the laws of thermodynamics. These properties not only lead to the emergence of biological information, but also impose constraints and trade-offs on the costs of such information processing. Some of these costs arise due to the particular properties of the material substrate of living matter in which information processing takes place, while others are universal and apply to all physical systems that process information.

In the past decade, the relationship between thermodynamics and information has received renewed scientific attention, attracting an increasing number of researchers and achieving significant progress. Despite this, the field is full of open problems and challenges at all levels, especially when dealing with biological systems. In spite of these difficulties, continued progress has the potential to fundamentally shape our future understanding of biology.

In this Special Issue we encourage researchers from theoretical biology, statistical physics, neuroscience, information theory, and complex systems to present their research on the connection between thermodynamics and information, with special emphasis on their implications for biological phenomena. We welcome contributions that focus on a particular biological system, as well as contributions that propose general theoretical approaches. We also welcome contributions that use mathematical techniques from statistical physics (variational methods, fluctuation theorems, uncertainty relations, etc.) to investigate biological questions.

Source: www.mdpi.com

Information Pollution by Social Bots

Social media are vulnerable to deceptive social bots, which can impersonate humans to amplify misinformation and manipulate opinions. Little is known about the large-scale consequences of such pollution operations. Here we introduce an agent-based model of information spreading with quality preference and limited individual attention to evaluate the impact of different strategies that bots can exploit to pollute the network and degrade the overall quality of the information ecosystem. We find that penetrating a critical fraction of the network is more important than generating attention-grabbing content and that targeting random users is more damaging than targeting hub nodes. The model is able to reproduce empirical patterns about exposure amplification and virality of low-quality information. We discuss insights provided by our analysis, with a focus on the development of countermeasures to increase the resilience of social media users to manipulation.

 

Information Pollution by Social Bots

Xiaodan Lou, Alessandro Flammini, Filippo Menczer

Source: arxiv.org

Complexity: Science, Engineering or a State of Mind? Towards a Scientific Renaissance

Is complexity a Science? Is it a possibly useful new way of engineering? In this video narrated by Maxi San Miguel it will be argued that Complexity is a new way of thinking necessary for a scientific renaissance that can transform society.

Source: www.youtube.com

Complexity in Medical Informatics

The topics of the accepted articles include but are not limited to the following: machine and deep learning approaches for health data; data mining and knowledge discovery in healthcare; clinical decision support systems; applications of the genetic algorithm in disease screening, diagnosis, and treatment planning; neurofuzzy system based on genetic algorithm for medical diagnosis and therapy support systems; applications of AI in healthcare; applications of artificial neural networks in medical science; electronic medical record and missing data; network and disease modeling (using administrative data); and health analytics and visualization.

 

Complexity
Volume 2019, Article ID 8658124, 2 pages
https://doi.org/10.1155/2019/8658124
Editorial
Complexity in Medical Informatics
Panagiotis Vlamos, Ilias Kotsireas, and Dimitrios Vlachakis

Source: www.hindawi.com

Historical comparison of gender inequality in scientific careers across countries and disciplines

There is extensive, yet fragmented, evidence of gender differences in academia suggesting that women are under-represented in most scientific disciplines, publish fewer articles throughout a career, and their work acquires fewer citations. Here, we offer a comprehensive picture of longitudinal gender discrepancies in performance through a bibliometric analysis of academic careers by reconstructing the complete publication history of over 1.5 million gender-identified authors whose publishing career ended between 1955 and 2010, covering 83 countries and 13 disciplines. We find that, paradoxically, the increase of participation of women in science over the past 60 years was accompanied by an increase of gender differences in both productivity and impact. Most surprisingly though, we uncover two gender invariants, finding that men and women publish at a comparable annual rate and have equivalent career-wise impact for the same size body of work. Finally, we demonstrate that differences in dropout rates and career length explain a large portion of the reported career-wise differences in productivity and impact. This comprehensive picture of gender inequality in academia can help rephrase the conversation around the sustainability of women’s careers in academia, with important consequences for institutions and policy makers.

 

Historical comparison of gender inequality in scientific careers across countries and disciplines

Junming Huang, Alexander J. Gates, Roberta Sinatra, Albert-Laszlo Barabasi

Source: arxiv.org