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Degree correlations in signed social networks

We investigate degree correlations in two online social networks where users are connected through different types of links. We find that, while subnetworks in which links have a positive connotation, such as endorsement and trust, are characterized by assortative mixing by degree, networks in which links have a negative connotation, such as disapproval and distrust, are characterized by disassortative patterns. We introduce a class of simple theoretical models to analyze the interplay between network topology and the superimposed structure based on the sign of links. Results uncover the conditions that underpin the emergence of the patterns observed in the data, namely the assortativity of positive subnetworks and the disassortativity of negative ones. We discuss the implications of our study for the analysis of signed complex networks.


Degree correlations in signed social networks
Valerio Ciotti, Ginestra Bianconi, Andrea Capocci, Francesca Colaiori, Pietro Panzarasa

http://arxiv.org/abs/1412.1024

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Structural Properties of Ego Networks

The structure of real-world social networks in large part determines the evolution of social phenomena, including opinion formation, diffusion of information and influence, and the spread of disease. Globally, network structure is characterized by features such as degree distribution, degree assortativity, and clustering coefficient. However, information about global structure is usually not available to each vertex. Instead, each vertex's knowledge is generally limited to the locally observable portion of the network consisting of the subgraph over its immediate neighbors. Such subgraphs, known as ego networks, have properties that can differ substantially from those of the global network. In this paper, we study the structural properties of ego networks and show how they relate to the global properties of networks from which they are derived. Through empirical comparisons and mathematical derivations, we show that structural features, similar to static attributes, suffer from paradoxes. We quantify the differences between global information about network structure and local estimates. This knowledge allows us to better identify and correct the biases arising from incomplete local information.


Structural Properties of Ego Networks
Sidharth Gupta, Xiaoran Yan, Kristina Lerman

http://arxiv.org/abs/1411.6061

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tom cockburn's curator insight, December 4, 2014 9:17 AM

An interesting study of the granular aspects as well as the interfaces in networks

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A Unifying Framework for Measuring Weighted Rich Clubs

Network analysis can help uncover meaningful regularities in the organization of complex systems. Among these, rich clubs are a functionally important property of a variety of social, technological and biological networks. Rich clubs emerge when nodes that are somehow prominent or ‘rich’ (e.g., highly connected) interact preferentially with one another. The identification of rich clubs is non-trivial, especially in weighted networks, and to this end multiple distinct metrics have been proposed. Here we describe a unifying framework for detecting rich clubs which intuitively generalizes various metrics into a single integrated method. This generalization rests upon the explicit incorporation of randomized control networks into the measurement process. We apply this framework to real-life examples, and show that, depending on the selection of randomized controls, different kinds of rich-club structures can be detected, such as topological and weighted rich clubs.


A Unifying Framework for Measuring Weighted Rich Clubs
• Jeff Alstott, Pietro Panzarasa, Mikail Rubinov, Edward T. Bullmore & Petra E. Vértes

Scientific Reports 4, Article number: 7258 http://dx.doi.org/10.1038/srep07258

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tom cockburn's curator insight, December 4, 2014 9:18 AM

More details on nodes

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Systemic risk analysis in reconstructed economic and financial networks

The assessment of fundamental properties for economic and financial systems, such as systemic risk, is systematically hindered by privacy issues − that put severe limitations on the available information. Here we introduce a novel method to reconstruct partially-accessible networked systems of this kind. The method is based on the knowledge of the fitnesses, i.e., intrinsic node-specific properties, and of the number of connections of only a limited subset of nodes. Such information is used to calibrate a directed configuration model which can generate ensembles of networks intended to represent the real system, so that the real network properties can be estimated within the generated ensemble in terms of mean values of the observables. Here we focus on estimating those properties that are commonly used to measure the network resilience to shock and crashes. Tests on both artificial and empirical networks shows that the method is remarkably robust with respect to the limitedness of the information available, thus representing a valuable tool for gaining insights on privacy-protected economic and financial systems.


Systemic risk analysis in reconstructed economic and financial networks
Giulio Cimini, Tiziano Squartini, Andrea Gabrielli, Diego Garlaschelli

http://arxiv.org/abs/1411.7613

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The Scaling of Human Contacts in Reaction-Diffusion Processes on Heterogeneous Metapopulation Networks

We present new empirical evidence, based on millions of interactions on Twitter, confirming that human contacts scale with population sizes. We integrate such observations into a reaction-diffusion metapopulation framework providing an analytical expression for the global invasion threshold of a contagion process. Remarkably, the scaling of human contacts is found to facilitate the spreading dynamics. Our results show that the scaling properties of human interactions can significantly affect dynamical processes mediated by human contacts such as the spread of diseases, and ideas.


The Scaling of Human Contacts in Reaction-Diffusion Processes on Heterogeneous Metapopulation Networks
Michele Tizzoni, Kaiyuan Sun, Diego Benusiglio, Márton Karsai, Nicola Perra

http://arxiv.org/abs/1411.7310

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Uncertainty in climate science and climate policy

This essay, written by a statistician and a climate scientist, describes our view of the gap that exists between current practice in mainstream climate science, and the practical needs of policymakers charged with exploring possible interventions in the context of climate change. By `mainstream' we mean the type of climate science that dominates in universities and research centres, which we will term `academic' climate science, in contrast to `policy' climate science; aspects of this distinction will become clearer in what follows.
In a nutshell, we do not think that academic climate science equips climate scientists to be as helpful as they might be, when involved in climate policy assessment. Partly, we attribute this to an over-investment in high resolution climate simulators, and partly to a culture that is uncomfortable with the inherently subjective nature of climate uncertainty.


Uncertainty in climate science and climate policy
Jonathan Rougier, Michel Crucifix

http://arxiv.org/abs/1411.6878

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Social media for large studies of behavior

CONCLUSIONS. The biases and issues highlighted above will not affect all research in the same way. Well-reasoned judgment on the part of authors, reviewers, and editors is warranted here. Many of the issues discussed have well-known solutions contributed by other fields such as epidemiology, statistics, and machine learning. In some cases, the solutions are difficult to fit with practical realities (e.g., as in the case of proper significance testing) whereas in other cases the community simply has not broadly adopted best practices (e.g., independent data sets for testing machine learning techniques) or the existing solutions may be subject to biases of their own. Regardless, a crucial step is to resolve the disconnect that exists between this research community and other (often related) fields with methods and practices for managing analytical bias.


Social media for large studies of behavior
Derek Ruths, Jürgen Pfeffer

Science 28 November 2014:
Vol. 346 no. 6213 pp. 1063-1064
http://dx.doi.org/10.1126/science.346.6213.1063


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tom cockburn's curator insight, November 30, 2014 5:26 AM

Seems a sensible conclusion regarding big data

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Predicting scientific success based on coauthorship networks

We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100,000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between authors who have highly cited papers and those who do not. We further show that a Machine Learning classifier, based only on coauthorship network centrality metrics measured at the time of publication, is able to predict with high precision whether an article will be highly cited five years after publication. By this we provide quantitative insight into the social dimension of scientific publishing – challenging the perception of citations as an objective, socially unbiased measure of scientific success.


Predicting scientific success based on coauthorship networks
Emre Sarigöl, René Pfitzner, Ingo Scholtes, Antonios Garas and Frank Schweitzer

EPJ Data Science 2014, 3:9  http://dx.doi.org/10.1140/epjds/s13688-014-0009-x

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Modeling social dynamics in a collaborative environment

Wikipedia is a prime example of today’s value production in a collaborative environment. Using this example, we model the emergence, persistence and resolution of severe conflicts during collaboration by coupling opinion formation with article editing in a bounded confidence dynamics. The complex social behavior involved in editing articles is implemented as a minimal model with two basic elements; (i) individuals interact directly to share information and convince each other, and (ii) they edit a common medium to establish their own opinions. Opinions of the editors and that represented by the article are characterised by a scalar variable. When the pool of editors is fixed, three regimes can be distinguished: (a) a stable mainstream article opinion is continuously contested by editors with extremist views and there is slow convergence towards consensus, (b) the article oscillates between editors with extremist views, reaching consensus relatively fast at one of the extremes, and (c) the extremist editors are converted very fast to the mainstream opinion and the article has an erratic evolution. When editors are renewed with a certain rate, a dynamical transition occurs between different kinds of edit wars, which qualitatively reflect the dynamics of conflicts as observed in real Wikipedia data.


Modeling social dynamics in a collaborative environment
Gerardo Iñiguez, János Török, Taha Yasseri, Kimmo Kaski and János Kertész

EPJ Data Science 2014, 3:7  http://dx.doi.org/10.1140/epjds/s13688-014-0007-z

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Phenotypic Plasticity, the Baldwin Effect, and the Speeding up of Evolution: the Computational Roots of an Illusion

An increasing number of dissident voices claim that the standard neo-Darwinian view of genes as 'leaders' and phenotypes as 'followers' during the process of adaptive evolution should be turned on its head. This idea is older than the rediscovery of Mendel's laws of inheritance and has been given several names before its final 'Baldwin effect' label. A condition for this effect is that environmentally induced variation such as phenotypic plasticity or learning is crucial for the initial establishment of a population. This gives the necessary time for natural selection to act on genetic variation and the adaptive trait can be eventually encoded in the genotype. An influential paper published in the late 1980s showed the Baldwin effect to happen in computer simulations, and claimed that it was crucial to solve a difficult adaptive task. This generated much excitement among scholars in various disciplines that regard neo-Darwinian accounts to explain the evolutionary emergence of high-order phenotypic traits such as consciousness or language almost hopeless. Here, we use analytical and computational approaches to show that a standard population genetics treatment can easily crack what the scientific community has granted as an unsolvable adaptive problem without learning. The Baldwin effect is once again in need of convincing theoretical foundations.


Phenotypic Plasticity, the Baldwin Effect, and the Speeding up of Evolution: the Computational Roots of an Illusion
Mauro Santos, Eörs Szathmáry, José F. Fontanari

http://arxiv.org/abs/1411.6843

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Scouting the Mandelbrot set with memory

An exploratory study is made on the dynamics of the map defining the Mandelbrot set endowed with memory (m) of past iterations.


Scouting the Mandelbrot set with memory
Ramón Alonso-Sanz

Complexity
Early View

http://dx.doi.org/10.1002/cplx.21632

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Spatiotemporal Detection of Unusual Human Population Behavior Using Mobile Phone Data

With the aim to contribute to humanitarian response to disasters and violent events, scientists have proposed the development of analytical tools that could identify emergency events in real-time, using mobile phone data. The assumption is that dramatic and discrete changes in behavior, measured with mobile phone data, will indicate extreme events. In this study, we propose an efficient system for spatiotemporal detection of behavioral anomalies from mobile phone data and compare sites with behavioral anomalies to an extensive database of emergency and non-emergency events in Rwanda. Our methodology successfully captures anomalous behavioral patterns associated with a broad range of events, from religious and official holidays to earthquakes, floods, violence against civilians and protests. Our results suggest that human behavioral responses to extreme events are complex and multi-dimensional, including extreme increases and decreases in both calling and movement behaviors. We also find significant temporal and spatial variance in responses to extreme events. Our behavioral anomaly detection system and extensive discussion of results are a significant contribution to the long-term project of creating an effective real-time event detection system with mobile phone data and we discuss the implications of our findings for future research to this end. 


Spatiotemporal Detection of Unusual Human Population Behavior Using Mobile Phone Data
Adrian Dobra, Nathalie E. Williams, Nathan Eagle

http://arxiv.org/abs/1411.6179

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Twitter "Exhaust" Reveals Patterns of Unemployment

Twitter "Exhaust" Reveals Patterns of Unemployment | Papers | Scoop.it

Twitter data mining reveals surprising detail about socioeconomic indicators but at a fraction of the cost of traditional data-gathering methods, say computational sociologists.

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On the Complexity and Behaviour of Cryptocurrencies Compared to Other Markets

We show that the behaviour of Bitcoin has interesting similarities to stock and precious metal markets, such as gold and silver. We report that whilst Litecoin, the second largest cryptocurrency, closely follows Bitcoin's behaviour, it does not show all the reported properties of Bitcoin. Agreements between apparently disparate complexity measures have been found, and it is shown that statistical, information-theoretic, algorithmic and fractal measures have different but interesting capabilities of clustering families of markets by type. The report is particularly interesting because of the range and novel use of some measures of complexity to characterize price behaviour, because of the IRS designation of Bitcoin as an investment property and not a currency, and the announcement of the Canadian government's own electronic currency MintChip.


On the Complexity and Behaviour of Cryptocurrencies Compared to Other Markets
Daniel Wilson-Nunn, Hector Zenil

http://arxiv.org/abs/1411.1924

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Social and Natural Sciences Differ in Their Research Strategies, Adapted to Work for Different Knowledge Landscapes

Do different fields of knowledge require different research strategies? A numerical model exploring different virtual knowledge landscapes, revealed two diverging optimal search strategies. Trend following is maximized when the popularity of new discoveries determine the number of individuals researching it. This strategy works best when many researchers explore few large areas of knowledge. In contrast, individuals or small groups of researchers are better in discovering small bits of information in dispersed knowledge landscapes. Bibliometric data of scientific publications showed a continuous bipolar distribution of these strategies, ranging from natural sciences, with highly cited publications in journals containing a large number of articles, to the social sciences, with rarely cited publications in many journals containing a small number of articles. The natural sciences seem to adapt their research strategies to landscapes with large concentrated knowledge clusters, whereas social sciences seem to have adapted to search in landscapes with many small isolated knowledge clusters. Similar bipolar distributions were obtained when comparing levels of insularity estimated by indicators of international collaboration and levels of country-self citations: researchers in academic areas with many journals such as social sciences, arts and humanities, were the most isolated, and that was true in different regions of the world. The work shows that quantitative measures estimating differences between academic disciplines improve our understanding of different research strategies, eventually helping interdisciplinary research and may be also help improve science policies worldwide.


Jaffe K (2014) Social and Natural Sciences Differ in Their Research Strategies, Adapted to Work for Different Knowledge Landscapes. PLoS ONE 9(11): e113901. http://dx.doi.org/10.1371/journal.pone.0113901

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tom cockburn's curator insight, December 4, 2014 9:25 AM

Intriguing study worth a second look

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Influence of sociodemographic characteristics on human mobility

Human mobility has been traditionally studied using surveys that deliver snapshots of population displacement patterns. The growing accessibility to ICT information obtained from portable digital media has recently opened the possibility of going beyond such fixed pictures, exploring human behavior at high spatio-temporal resolutions. Mobile phone records, geolocated tweets, check-ins from Foursquare or geotagged photos, have contributed to this purpose at different scales from cities to countries and in different areas of the world. Many of these previous works have lacked, however, details on the attributes of the individuals. In this work, we analyze credit-card transaction records as mobility proxies and assess the influence of sociodemographic characteristics on the way people move and spend their money in cities. In particular, we focus on Barcelona and Madrid, the two most populated cities of Spain, and by examining the geolocated credit-card transactions of individuals living in the two provinces, we find that consumption habits and mobility patterns vary according to gender, age and occupation. Differences in distance traveled and travel purpose are observed between younger and older people, but, curiously, either between males and females of similar age. While mobility displays some generic features, here we show that sociodemographic characteristics play a relevant role and must be taken into account for human mobility modelization.


Influence of sociodemographic characteristics on human mobility
Maxime Lenormand, Thomas Louail, Oliva G. Cantu-Ros, Miguel Picornell, Ricardo Herranz, Juan Murillo Arias, Marc Barthelemy, Maxi San Miguel, Jose J. Ramasco

http://arxiv.org/abs/1411.7895

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De-anonymizing scale-free social networks by percolation graph matching

We address the problem of social network de-anonymization when relationships between people are described by scale-free graphs. In particular, we propose a rigorous, asymptotic mathematical analysis of the network de-anonymization problem while capturing the impact of power-law node degree distribution, which is a fundamental and quite ubiquitous feature of many complex systems such as social networks. By applying bootstrap percolation and a novel graph slicing technique, we prove that large inhomogeneities in the node degree lead to a dramatic reduction of the initial set of nodes that must be known a priori (the seeds) in order to successfully identify all other users. We characterize the size of this set when seeds are selected using different criteria, and we show that their number can be as small as n^{\epsilon}, for any small {\epsilon>0}. Our results are validated through simulation experiments on a real social network graph.


De-anonymizing scale-free social networks by percolation graph matching
Carla Chiasserini, Michele Garetto, Emilio Leonardi

http://arxiv.org/abs/1411.7296

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Algorithmic complexity for psychology: A user-friendly implementation of the coding theorem method

Kolmogorov-Chaitin complexity has long been believed to be unapproachable when it comes to short sequences (e.g. of length 5-50). However, with the newly developed coding theorem method the complexity of strings of length 2-11 can now be numerically estimated. We present the theoretical basis of algorithmic complexity for short strings (ACSS) and describe an R-package providing functions based on ACSS that will cover psychologists' needs and improve upon previous methods in three ways: (1) ACSS is now available not only for binary strings, but for strings based on up to 9 different symbols, (2) ACSS no longer requires time-consuming computing, and (3) a new approach based on ACSS gives access to an estimation of the complexity of strings of any length. Finally, three illustrative examples show how these tools can be applied to psychology.


Algorithmic complexity for psychology: A user-friendly implementation of the coding theorem method
Nicolas Gauvrit, Henrik Singmann, Fernando Soler-Toscano, Hector Zenil

http://arxiv.org/abs/1409.4080

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tom cockburn's curator insight, December 4, 2014 9:21 AM

Useful

Hector Zenil's curator insight, February 15, 2015 3:21 AM

Forthcoming in Behavior Research Methods (accepted)

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Impact, productivity, and scientific excellence

Citation metrics are becoming pervasive in the quantitative evaluation of scholars, journals and institutions. More then ever before, hiring, promotion, and funding decisions rely on a variety of impact metrics that cannot disentangle quality from productivity, and are biased by factors such as discipline and academic age. Biases affecting the evaluation of single papers are compounded when one aggregates citation-based metrics across an entire publication record. It is not trivial to compare the quality of two scholars that during their careers have published at different rates in different disciplines in different periods of time. We propose a novel solution based on the generation of a statistical baseline specifically tailored on the academic profile of each researcher. By decoupling productivity and impact, our method can determine whether a certain level of impact can be explained by productivity alone, or additional ingredients of scientific excellence are necessary. The method is flexible enough to allow for the evaluation of, and fair comparison among, arbitrary collections of papers --- scholar publication records, journals, and entire institutions; and can be extended to simultaneously suppresses any source of bias. We show that our method can capture the quality of the work of Nobel laureates irrespective of productivity, academic age, and discipline, even when traditional metrics indicate low impact in absolute terms. We further apply our methodology to almost a million scholars and over six thousand journals to quantify the impact required to demonstrate scientific excellence for a given level of productivity.


Impact, productivity, and scientific excellence
Jasleen Kaur, Emilio Ferrara, Filippo Menczer, Alessandro Flammini, Filippo Radicchi

http://arxiv.org/abs/1411.7357

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Inheritance Patterns in Citation Networks Reveal Scientific Memes

Inheritance Patterns in Citation Networks Reveal Scientific Memes | Papers | Scoop.it
Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.


Inheritance Patterns in Citation Networks Reveal Scientific Memes
Phys. Rev. X 4, 041036 – Published 21 November 2014
Tobias Kuhn, Matjaž Perc, and Dirk Helbing

http://dx.doi.org/10.1103/PhysRevX.4.041036


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The heavy tail of the human brain

The heavy tail of the human brain | Papers | Scoop.it

Fluctuating oscillations are a ubiquitous feature of neurophysiology. Are the amplitude fluctuations of neural oscillations chance excursions drawn randomly from a normal distribution, or do they tell us more? Recent empirical research suggests that the occurrence of ‘anomalous’ (high amplitude) oscillations imbues their probability distributions with a heavier tail than the standard normal distribution. However, not all heavy tails are the same. We provide canonical examples of different heavy-tailed distributions in cortical oscillations and discuss the corresponding mechanisms that each suggest, ranging from criticality to multistability, memory, bifurcations, and multiplicative noise. Their existence suggests that the brain is a strongly correlated complex system that employs many different functional mechanisms, and that likewise, we as scientists should refrain from methodological monism.

The heavy tail of the human brain
James A Roberts, Tjeerd W Boonstra, Michael Breakspear
Current Opinion in Neurobiology 31:164–172, 2014.
http://dx.doi.org/10.1016/j.conb.2014.10.014

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Evolutionary dynamics of time-resolved social interactions

Cooperation among unrelated individuals is frequently observed in social groups when their members combine efforts and resources to obtain a shared benefit that is unachievable by an individual alone. However, understanding why cooperation arises despite the natural tendency of individuals toward selfish behavior is still an open problem and represents one of the most fascinating challenges in evolutionary dynamics. Recently, the structural characterization of the networks in which social interactions take place has shed some light on the mechanisms by which cooperative behavior emerges and eventually overcomes the natural temptation to defect. In particular, it has been found that the heterogeneity in the number of social ties and the presence of tightly knit communities lead to a significant increase in cooperation as compared with the unstructured and homogeneous connection patterns considered in classical evolutionary dynamics. Here, we investigate the role of social-ties dynamics for the emergence of cooperation in a family of social dilemmas. Social interactions are in fact intrinsically dynamic, fluctuating, and intermittent over time, and they can be represented by time-varying networks. By considering two experimental data sets of human interactions with detailed time information, we show that the temporal dynamics of social ties has a dramatic impact on the evolution of cooperation: the dynamics of pairwise interactions favors selfish behavior.


Evolutionary dynamics of time-resolved social interactions
Phys. Rev. E 90, 052825 – Published 25 November 2014
Alessio Cardillo, Giovanni Petri, Vincenzo Nicosia, Roberta Sinatra, Jesús Gómez-Gardeñes, and Vito Latora

http://dx.doi.org/10.1103/PhysRevE.90.052825

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Odor Landscapes in Turbulent Environments

Odor Landscapes in Turbulent Environments | Papers | Scoop.it

A statistical-physics model provides an accurate description of how animals communicate via pheromones in a turbulent atmospheric environment.


Odor Landscapes in Turbulent Environments
Antonio Celani, Emmanuel Villermaux, and Massimo Vergassola
Phys. Rev. X 4, 041015 (2014)

http://dx.doi.org/10.1103/PhysRevX.4.041015

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Google Creates Software That Tells You What It Sees in Images

Google Creates Software That Tells You What It Sees in Images | Papers | Scoop.it
Experimental Google software that can describe a complex scene could lead to better image search or apps to help the visually impaired.
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HOW SOCIETY WORKS: Social order by self-organization

The invention of laws and regulations is celebrated as great success principle of societies and they are, of course, important. However, a major part of social order is based on self-organization, which builds on simple social mechanism. These mechanisms have evolved over historical times and are the basis of the success or failure of civilizations. Currently, many people oppose globalization, because traditional social mechanisms fail to create cooperation and social order under globalized conditions that are increasingly characterized by homogeneous or random interactions. However, I will show that there are other social mechanisms such as reputation systems, which will work in a globalized world, too.


http://futurict.blogspot.nl/2014/11/how-society-workssocial-order-by-self.html

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Eli Levine's curator insight, November 24, 2014 8:08 PM

We could be approaching something fantastically cool, if we don't destroy ourselves in the meantime.

 

Enjoy!

 

https://www.youtube.com/watch?v=akek6cFRZfY