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Positive words carry less information than negative words

We show that the frequency of word use is not only determined by the word length [1] and the average information content [2], but also by its emotional content. We have analyzed three established lexica of affective word usage in English, German, and Spanish, to verify that these lexica have a neutral, unbiased, emotional content. Taking into account the frequency of word usage, we find that words with a positive emotional content are more frequently used.

 

Positive words carry less information than negative words
Garcia D, Garas A and Schweitzer F
EPJ Data Science 2012, 1:3 (18 May 2012)

http://dx.doi.org/10.1140/epjds3

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Rescooped by Complexity Digest from Statistical Physics of Ecological Systems
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Warnings and Caveats in Brain Controllability

In this work we challenge the main conclusions of Gu et al work (Controllability of structural brain networks. Nature communications 6, 8414, doi:10.1038/ncomms9414, 2015) on brain controllability. Using the same methods and analyses on four datasets we find that the minimum set of nodes to control brain networks is always larger than one. We also find that the relationships between the average/modal controllability and weighted degrees also hold for randomized data and the there are not specific roles played by Resting State Networks in controlling the brain. In conclusion, we show that there is no evidence that topology plays specific and unique roles in the controllability of brain networks. Accordingly, Gu et al. interpretation of their results, in particular in terms of translational applications (e.g. using single node controllability properties to define target region(s) for neurostimulation) should be revisited. Though theoretically intriguing, our understanding of the relationship between controllability and structural brain network remains elusive.

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On the records

World record setting has long attracted public interest and scientific investigation. Extremal records summarize the limits of the space explored by a process, and the historical progression of a record sheds light on the underlying dynamics of the process. Existing analyses of prediction, statistical properties, and ultimate limits of record progressions have focused on particular domains. However, a broad perspective on how record progressions vary across different spheres of activity needs further development. Here we employ cross-cutting metrics to compare records across a variety of domains, including sports, games, biological evolution, and technological development. We find that these domains exhibit characteristic statistical signatures in terms of rates of improvement, "burstiness" of record-breaking time series, and the acceleration of the record breaking process. Specifically, sports and games exhibit the slowest rate of improvement and a wide range of rates of "burstiness." Technology improves at a much faster rate and, unlike other domains, tends to show acceleration in records. Many biological and technological processes are characterized by constant rates of improvement, showing less burstiness than sports and games. It is important to understand how these statistical properties of record progression emerge from the underlying dynamics. Towards this end, we conduct a detailed analysis of a particular record-setting event: elite marathon running. In this domain, we find that studying record-setting data alone can obscure many of the structural properties of the underlying process. The marathon study also illustrates how some of the standard statistical assumptions underlying record progression models may be inappropriate or commonly violated in real-world datasets.

 

On the records

Andrew Berdahl, Uttam Bhat, Vanessa Ferdinand, Joshua Garland, Keyan Ghazi-Zahedi, Justin Grana, Joshua A. Grochow, Elizabeth Hobson, Yoav Kallus, Christopher P. Kempes, Artemy Kolchinsky, Daniel B. Larremore, Eric Libby, Eleanor A. Power, Brendan D. Tracey (Santa Fe Institute Postdocs)

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People on the move

Science helps us think more clearly about migration, in part by showing its deep roots. Researchers wielding powerful new methods have discovered ancient, hidden migrations that shaped today's populations. Go back far enough and almost all of us are immigrants, despite cherished stories of ethnic and national origins. Science can also aid the 21 million migrants today who are refugees from violence or famine, according to the United Nations. They need food, medicine, and shelter now, but in the long run it is their mental health that will be key to building new lives, as shown by a case study of the long-persecuted Yezidis. The success of these and other immigrants depends in part on whether new countries spurn or welcome them, and research is starting to show how to manage our long-standing biases against outsiders.

 

People on the move
Elizabeth Culotta

Science 19 May 2017:
Vol. 356, Issue 6339, pp. 676-677
DOI: 10.1126/science.356.6339.676

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Soft computing-based traffic density estimation using automated traffic sensor data under Indian conditions

Traffic density is an indicator of congestion and the present study explores the use of data-driven techniques for real time estimation and prediction of traffic density. Data-driven techniques require large database, which can be achieved only with the help of automated sensors. However, the available automated sensors developed for western traffic may not work for heterogeneous and lane-less traffic. Hence, the performance of available automated sensors was evaluated first to identify the best inputs to be used for the chosen application.
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Optimal incentives for collective intelligence

Diversity of information and expertise among group members has been identified as a crucial ingredient of collective intelligence. However, many factors tend to reduce the diversity of groups, such as herding, groupthink, and conformity. We show why the individual incentives in financial and prediction markets and the scientific community reduce diversity of information and how these incentives can be changed to improve the accuracy of collective forecasting. Our results, therefore, suggest ways to improve the poor performance of collective forecasting seen in recent political events and how to change career rewards to make scientific research more successful.

 

Optimal incentives for collective intelligence
Richard P. Mann and Dirk Helbing

PNAS

10.1073/pnas.1618722114

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Graph Theoretic Properties of the Darkweb

We collect and analyze the darkweb (a.k.a. the "onionweb") hyperlink graph. We find properties highly dissimilar to the well-studied world wide web hyperlink graph; for example, our analysis finds that >87% of darkweb sites never link to another site. We compare our results to prior work on world-wide-web and speculate about reasons for their differences. We conclude that in the term "darkweb", the word "web" is a connectivity misnomer. Instead, it is more accurate to view the darkweb as a set of largely isolated dark silos.

 

Graph Theoretic Properties of the Darkweb
Virgil Griffith, Yang Xu, Carlo Ratti

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The Role of Network Analysis in Industrial and Applied Mathematics

Many problems in industry --- and in the social, natural, information, and medical sciences --- involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. Because the study of networks is concerned explicitly with connectivity between different entities, it has become very prominent in industrial settings, and this importance has been accentuated further amidst the modern data deluge. In this article, we discuss the role of network analysis in industrial and applied mathematics, and we give several examples of network science in industry.

 

The Role of Network Analysis in Industrial and Applied Mathematics
Mason A. Porter, Sam D. Howison

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A thermodynamic analysis of the spider silk and the importance of complexity

The spider silk is one of the most interesting bio-materials investigated in the last years. One of the main reasons that brought scientists to study this organized system is its high level of resistance if compared to other artificial materials characterized by higher density. Subsequently, researchers discovered that the spider silk is a complex system formed by different kinds of proteins, organized (or disorganized) to guarantee the required resistance, which is function of the final application and of the environmental conditions. Some spider species are able to make different silks, up to twelve, having a composition that seems to be function of the final use (i.e. dragline web, capture web, etc). The aim of this paper is to analyze the properties of the spider silk by means of a thermodynamic approach, taking advantage of the well-known theories applied to polymers, and to try to underline and develop some intriguing considerations. Moreover, this study can be taken as an example to introduce and discuss the importance of the concept of optionality and of the anti-fragile systems proposed by N. N. Thaleb in his book "Antifragile: Things that gain from disorder".

 

A thermodynamic analysis of the spider silk and the importance of complexity
S.Ripandelli, D.Pugliese, U.Lucia

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The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System

Natural evolution has produced a tremendous diversity of functional organisms. Many believe an essential component of this process was the evolution of evolvability, whereby evolution speeds up its ability to innovate by generating a more adaptive pool of offspring. One hypothesized mechanism for evolvability is developmental canalization, wherein certain dimensions of variation become more likely to be traversed and others are prevented from being explored (e.g. offspring tend to have similarly sized legs, and mutations affect the length of both legs, not each leg individually). While ubiquitous in nature, canalization almost never evolves in computational simulations of evolution. Not only does that deprive us of in silico models in which to study the evolution of evolvability, but it also raises the question of which conditions give rise to this form of evolvability. Answering this question would shed light on why such evolvability emerged naturally and could accelerate engineering efforts to harness evolution to solve important engineering challenges. In this paper we reveal a unique system in which canalization did emerge in computational evolution. We document that genomes entrench certain dimensions of variation that were frequently explored during their evolutionary history. The genetic representation of these organisms also evolved to be highly modular and hierarchical, and we show that these organizational properties correlate with increased fitness. Interestingly, the type of computational evolutionary experiment that produced this evolvability was very different from traditional digital evolution in that there was no objective, suggesting that open-ended, divergent evolutionary processes may be necessary for the evolution of evolvability.

 

The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System
Joost Huizinga, Kenneth O. Stanley, Jeff Clune

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Evolving as a holobiont

Evolving as a holobiont | Papers | Scoop.it

Some of the most exciting recent advances in biology have been in our understanding of how the microbiome—the community of bacteria, fungi, and other single-celled microorganisms—influences host functions and behaviors. From the way we eat, to the way we think, to our susceptibility to diseases (just to name a few), the microbiome has a huge impact on human physiology. But microbiomes aren’t just for humans, or even just for mammals. The composition and function of microbiomes are critical for most animals and plants, so much so that many scientists believe that hosts and their microbiomes should be considered as single ecological unit—the holobiont. Given their ubiquity and importance, researchers are now investigating how this symbiotic relationship between hosts and microbes has evolved over time.

 

Richardson LA (2017) Evolving as a holobiont. PLoS Biol 15(2): e2002168. https://doi.org/10.1371/journal.pbio.2002168

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Reversing the irreversible: from limit cycles to emergent time symmetry

In 1979 Penrose hypothesized that the arrows of time are explained by the hypothesis that the fundamental laws are time irreversible. That is, our reversible laws, such as the standard model and general relativity are effective, and emerge from an underlying fundamental theory which is time irreversible. In Cort\^{e}s and Smolin (2014a, 2014b, 2016) we put forward a research program aiming at realizing just this. The aim is to find a fundamental description of physics above the planck scale, based on irreversible laws, from which will emerge the apparently reversible dynamics we observe on intermediate scales. Here we continue that program and note that a class of discrete dynamical systems are known to exhibit this very property: they have an underlying discrete irreversible evolution, but in the long term exhibit the properties of a time reversible system, in the form of limit cycles. We connect this to our original model proposal in Cort\^{e}s and Smolin (2014a), and show that the behaviours obtained there can be explained in terms of the same phenomenon: the attraction of the system to a basin of limit cycles, where the dynamics appears to be time reversible. Further than that, we show that our original models exhibit the very same feature: the emergence of quasi-particle excitations obtained in the earlier work in the space-time description is an expression of the system's convergence to limit cycles when seen in the causal set description.

 

Reversing the irreversible: from limit cycles to emergent time symmetry
Marina Cortês, Lee Smolin

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Where There is Life There is Mind: In Support of a Strong Life-Mind Continuity Thesis

Where There is Life There is Mind: In Support of a Strong Life-Mind Continuity Thesis | Papers | Scoop.it

This paper considers questions about continuity and discontinuity between life and mind. It begins by examining such questions from the perspective of the free energy principle (FEP). The FEP is becoming increasingly influential in neuroscience and cognitive science. It says that organisms act to maintain themselves in their expected biological and cognitive states, and that they can do so only by minimizing their free energy given that the long-term average of free energy is entropy. The paper then argues that there is no singular interpretation of the FEP for thinking about the relation between life and mind. Some FEP formulations express what we call an independence view of life and mind. One independence view is a cognitivist view of the FEP. It turns on information processing with semantic content, thus restricting the range of systems capable of exhibiting mentality. Other independence views exemplify what we call an overly generous non-cognitivist view of the FEP, and these appear to go in the opposite direction. That is, they imply that mentality is nearly everywhere. The paper proceeds to argue that non-cognitivist FEP, and its implications for thinking about the relation between life and mind, can be usefully constrained by key ideas in recent enactive approaches to cognitive science. We conclude that the most compelling account of the relationship between life and mind treats them as strongly continuous, and that this continuity is based on particular concepts of life (autopoiesis and adaptivity) and mind (basic and non-semantic).

 

Kirchhoff, M.D.; Froese, T. Where There is Life There is Mind: In Support of a Strong Life-Mind Continuity Thesis. Entropy 2017, 19, 169.

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PAFit: An R Package for Modeling and Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks

Many real-world systems are profitably described as complex networks that grow over time. Preferential attachment and node fitness are two ubiquitous growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are standard statistical packages for estimating the structural properties of complex networks, there is no corresponding package when it comes to the estimation of growth mechanisms. This paper introduces the R package PAFit, which implements well-established statistical methods for estimating preferential attachment and node fitness, as well as a number of functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure good performance for large-scale networks. In this paper, we first introduce the main functionalities of PAFit using simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks.

 

PAFit: An R Package for Modeling and Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks
Thong Pham, Paul Sheridan, Hidetoshi Shimodaira

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Evolutionary games on scale-free multiplex networks

Evolutionary games on structured populations have been studied extensively in recent years. In reality, social interactions take place in different domains, which naturally requires a multiplex description. The impact of the multiplex nature of human interactions on the evolution of cooperation has recently attracted a lot of attention, however, the fundamental mechanisms at play are still not well understood. Here, we show that the interplay between the structural organization of the multiplex and the assumptions about the dynamical coupling between the layers leads to very different outcomes. We show that the organization of the multiplex can enable mutual spatial selection, which refers to the formation of overlapping clusters of cooperators in different layers that can survive in social dilemmas. Furthermore, heterogeneity and degree correlations lead to topological enslavement, which means that the hubs dominate the game dynamics inducing payoff irrelevance. Our findings reveal the fundamental mechanisms at play and provide a new perspective for understanding the evolution of cooperation on realistic structured populations.

 

Evolutionary games on scale-free multiplex networks
Kaj-Kolja Kleineberg

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Machine learning: the power and promise of computers that learn by example

What is the potential of machine learning over the next 5-10 years? And how can we develop this technology in a way that benefits everyone? 

The Royal Society’s machine learning project has been investigating these questions, and has today launched a report setting out the action needed to maintain the UK’s role in advancing this technology while ensuring careful stewardship of its development.

Machine learning is a form of artificial intelligence that allows computer systems to learn from examples, data, and experience. Through enabling computers to perform specific tasks intelligently, machine learning systems can carry out complex processes by learning from data, rather than following pre-programmed rules.
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Locally noisy autonomous agents improve global human coordination in network experiments

Coordination in groups faces a sub-optimization problem and theory suggests that some randomness may help to achieve global optima. Here we performed experiments involving a networked colour coordination game in which groups of humans interacted with autonomous software agents (known as bots). Subjects (n = 4,000) were embedded in networks (n = 230) of 20 nodes, to which we sometimes added 3 bots. The bots were programmed with varying levels of behavioural randomness and different geodesic locations. We show that bots acting with small levels of random noise and placed in central locations meaningfully improve the collective performance of human groups, accelerating the median solution time by 55.6%. This is especially the case when the coordination problem is hard. Behavioural randomness worked not only by making the task of humans to whom the bots were connected easier, but also by affecting the gameplay of the humans among themselves and hence creating further cascades of benefit in global coordination in these heterogeneous systems.

 

Locally noisy autonomous agents improve global human coordination in network experiments

Hirokazu Shirado & Nicholas A. Christakis

Nature 545, 370–374 (18 May 2017) doi:10.1038/nature22332

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The advantage of being slow: the quasi-neutral contact process

According to the competitive exclusion principle, in a finite ecosystem, extinction occurs naturally when two or more species compete for the same resources. An important question that arises is: when coexistence is not possible, which mechanisms confer an advantage to a given species against the other(s)? In general, it is expected that the species with the higher reproductive/death ratio will win the competition, but other mechanisms, such as asymmetry in interspecific competition or unequal diffusion rates, have been found to change this scenario dramatically. In this work, we examine competitive advantage in the context of quasi-neutral population models, including stochastic models with spatial structure as well as macroscopic (mean-field) descriptions. We employ a two-species contact process in which the "biological clock" of one species is a factor of α slower than that of the other species. Our results provide new insights into how stochasticity and competition interact to determine extinction in finite spatial systems. We find that a species with a slower biological clock has an advantage if resources are limited, winning the competition against a species with a faster clock, in relatively small systems. Periodic or stochastic environmental variations also favor the slower species, even in much larger systems.

 

The advantage of being slow: the quasi-neutral contact process
Marcelo Martins de Oliveira, Ronald Dickman

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DeepStack: Expert-level artificial intelligence in heads-up no-limit poker

DeepStack: Expert-level artificial intelligence in heads-up no-limit poker | Papers | Scoop.it
Artificial intelligence masters poker
Computers can beat humans at games as complex as chess or go. In these and similar games, both players have access to the same information, as displayed on the board. Although computers have the ultimate poker face, it has been tricky to teach them to be good at poker, where players cannot see their opponents' cards. Moravčík et al. built a code dubbed DeepStack that managed to beat professional poker players at a two-player poker variant called heads-up no-limit Texas hold'em. Instead of devising its strategy beforehand, DeepStack recalculated it at each step, taking into account the current state of the game. The principles behind DeepStack may enable advances in solving real-world problems that involve information asymmetry.


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Identifying and modeling the structural discontinuities of human interactions

Identifying and modeling the structural discontinuities of human interactions | Papers | Scoop.it

The idea of a hierarchical spatial organization of society lies at the core of seminal theories in human geography that have strongly influenced our understanding of social organization. Along the same line, the recent availability of large-scale human mobility and communication data has offered novel quantitative insights hinting at a strong geographical confinement of human interactions within neighboring regions, extending to local levels within countries. However, models of human interaction largely ignore this effect. Here, we analyze several country-wide networks of telephone calls - both, mobile and landline - and in either case uncover a systematic decrease of communication induced by borders which we identify as the missing variable in state-of-the-art models. Using this empirical evidence, we propose an alternative modeling framework that naturally stylizes the damping effect of borders. We show that this new notion substantially improves the predictive power of widely used interaction models. This increases our ability to understand, model and predict social activities and to plan the development of infrastructures across multiple scales.

 

Grauwin, S. et al. Identifying and modeling the structural discontinuities of human interactions. Sci. Rep. 7, 46677; doi: 10.1038/srep46677 (2017)

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The mind in the machine: Demis Hassabis on artificial intelligence

The mind in the machine: Demis Hassabis on artificial intelligence | Papers | Scoop.it
The scientific method might be the single most powerful idea humans have ever had, and progress since the Enlightenment has been simply astonishing. But we are now at a critical juncture where many of the systems we need to master are fiendishly complex, from climate change to macroeconomic issues to Alzheimer’s disease. Whether we can solve these challenges — and how fast we can get there — will affect the future wellbeing of billions of people and the environment we all live in.

The problem is that these challenges are so complex that even the world’s top scientists, clinicians and engineers can struggle to master all the intricacies necessary to make the breakthroughs required.
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Diffusion Geometry Unravels the Emergence of Functional Clusters in Collective Phenomena

Collective phenomena emerge from the interaction of natural or artificial units with a complex organization. The interplay between structural patterns and dynamics might induce functional clusters that, in general, are different from topological ones. In biological systems, like the human brain, the overall functionality is often favored by the interplay between connectivity and synchronization dynamics, with functional clusters that do not coincide with anatomical modules in most cases. In social, sociotechnical, and engineering systems, the quest for consensus favors the emergence of clusters. Despite the unquestionable evidence for mesoscale organization of many complex systems and the heterogeneity of their interconnectivity, a way to predict and identify the emergence of functional modules in collective phenomena continues to elude us. Here, we propose an approach based on random walk dynamics to define the diffusion distance between any pair of units in a networked system. Such a metric allows us to exploit the underlying diffusion geometry to provide a unifying framework for the intimate relationship between metastable synchronization, consensus, and random search dynamics in complex networks, pinpointing the functional mesoscale organization of synthetic and biological systems.

 

Diffusion Geometry Unravels the Emergence of Functional Clusters in Collective Phenomena
Manlio De Domenico
Phys. Rev. Lett. 118, 168301

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Incoherence-Mediated Remote Synchronization

In previously identified forms of remote synchronization between two nodes, the intermediate portion of the network connecting the two nodes is not synchronized with them but generally exhibits some coherent dynamics. Here we report on a network phenomenon we call incoherence-mediated remote synchronization (IMRS), in which two noncontiguous parts of the network are identically synchronized while the dynamics of the intermediate part is statistically and information-theoretically incoherent. We identify mirror symmetry in the network structure as a mechanism allowing for such behavior, and show that IMRS is robust against dynamical noise as well as against parameter changes. IMRS may underlie neuronal information processing and potentially lead to network solutions for encryption key distribution and secure communication.

 

Incoherence-Mediated Remote Synchronization
Liyue Zhang, Adilson E. Motter, and Takashi Nishikawa
Phys. Rev. Lett. 118, 174102

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Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots

It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using `social bots' deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.

 

Evidence of Complex Contagion of Information in Social Media: An Experiment Using Twitter Bots

Bjarke Mønsted, Piotr Sapieżyński, Emilio Ferrara, Sune Lehmann

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The world of long-range interactions: A bird's eye view

In recent years, studies of long-range interacting (LRI) systems have taken centre stage in the arena of statistical mechanics and dynamical system studies, due to new theoretical developments involving tools from as diverse a field as kinetic theory, non-equilibrium statistical mechanics, and large deviation theory, but also due to new and exciting experimental realizations of LRI systems. In this invited contribution, we discuss the general features of long-range interactions, emphasizing in particular the main physical phenomenon of non-additivity, which leads to a plethora of distinct effects, both thermodynamic and dynamic, that are not observed with short-range interactions: Ensemble inequivalence, slow relaxation, broken ergodicity. We also discuss several physical systems with long-range interactions: mean-field spin systems, self-gravitating systems, Euler equations in two dimensions, Coulomb systems, one-component electron plasma, dipolar systems, free-electron lasers, atoms trapped in optical cavities.

 

The world of long-range interactions: A bird's eye view
Shamik Gupta, Stefano Ruffo

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Robustness and efficiency in interconnected networks with changes in network assortativity

In this study, the effect of assortativity on the robustness and efficiency of interconnected networks was investigated. This involved constructing a network that possessed the desired degree of assortativity. Additionally, an interconnected network was constructed wherein the assortativity between component networks possessed the desired value. With respect to single networks, the results indicated that a decrease in assortativity provided low hop length, high information diffusion efficiency, and distribution of communication load on edges. The study also revealed that excessive assortativity led to poor network performance. In the study, the assortativity between networks was defined and the following results were demonstrated: assortative connections between networks lowered the average hop length and enhanced information diffusion efficiency, whereas disassortative connections between networks distributed the communication loads of internetwork links and enhanced robustness. Furthermore, it is necessary to carefully adjust assortativity based on the node degree distribution of networks. Finally, the application of the results to the design of robust and efficient information networks was discussed.

 

Robustness and efficiency in interconnected networks with changes in network assortativity
Masaya Murakami, Shu Ishikura, Daichi Kominami, Tetsuya Shimokawa and Masayuki Murata
Applied Network Science 2017 2:6
DOI: 10.1007/s41109-017-0025-4

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