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Information theoretic aspects of the two-dimensional Ising model

We present numerical results for various information theoretic properties of the square lattice Ising model. First, using a bond propagation algorithm, we find the difference 2HL(w)−H2L(w) between entropies on cylinders of finite lengths L and 2L with open end cap boundaries, in the limit L→∞. This essentially quantifies how the finite length correction for the entropy scales with the cylinder circumference w. Secondly, using the transfer matrix, we obtain precise estimates for the information needed to specify the spin state on a ring encircling an infinitely long cylinder. Combining both results, we obtain the mutual information between the two halves of a cylinder (the “excess entropy” for the cylinder), where we confirm with higher precision but for smaller systems the results recently obtained by Wilms et al., and we show that the mutual information between the two halves of the ring diverges at the critical point logarithmically with w. Finally, we use the second result together with Monte Carlo simulations to show that also the excess entropy of a straight line of n spins in an infinite lattice diverges at criticality logarithmically with n. We conjecture that such logarithmic divergence happens generically for any one-dimensional subset of sites at any two-dimensional second-order phase transition. Comparing straight lines on square and triangular lattices with square loops and with lines of thickness 2, we discuss questions of universality.

 

Information theoretic aspects of the two-dimensional Ising mode

Hon Wai Lau and Peter Grassberger

Phys. Rev. E 87, 022128 (2013)

http://link.aps.org/doi/10.1103/PhysRevE.87.022128

 

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The Matthew effect in empirical data

The Matthew effect describes the phenomenon that in societies the rich tend to get richer and the potent even more powerful. It is closely related to the concept of preferential attachment in network science, where the more connected nodes are destined to acquire many more links in the future than the auxiliary nodes. Cumulative advantage and success-breads-success also both describe the fact that advantage tends to beget further advantage. The concept is behind the many power laws and scaling behaviour in empirical data, and it is at the heart of self-organization across social and natural sciences. Here we review the methodology for measuring preferential attachment in empirical data, as well as the observations of the Matthew effect in patterns of scientific collaboration, socio-technical and biological networks, the propagation of citations, the emergence of scientific progress and impact, career longevity, the evolution of common English words and phrases, as well as in education and brain development. We also discuss whether the Matthew effect is due to chance or optimisation, for example related to homophily in social systems or efficacy in technological systems, and we outline possible directions for future research.


The Matthew effect in empirical data
Matjaz Perc

http://arxiv.org/abs/1408.5124

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Why the ALS Ice Bucket Challenge Went Viral

Why the ALS Ice Bucket Challenge Went Viral | Papers | Scoop.it

If your social media feeds haven’t been clogged with videos of the ALS Ice Bucket Challenge this week, then you’re in the minority.
The campaign involves people dumping buckets of ice water on themselves (or being doused by others), sharing a video of the experience and nominating others to give it a try as a way to build awareness of amyotrophic lateral sclerosis (ALS.) Otherwise known as “Lou Gehrig’s Disease,” ALS is a progressive disease that causes nerve cells to deteriorate and eventually leads to total paralysis and death.

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Cyclic dominance in evolutionary games: A review

Rock is wrapped by paper, paper is cut by scissors, and scissors are crushed by rock. This simple game is popular among children and adults to decide on trivial disputes that have no obvious winner, but cyclic dominance is also at the heart of predator-prey interactions, the mating strategy of side-blotched lizards, the overgrowth of marine sessile organisms, and the competition in microbial populations. Cyclical interactions also emerge spontaneously in evolutionary games entailing volunteering, reward, punishment, and in fact are common when the competing strategies are three or more regardless of the particularities of the game. Here we review recent advances on the rock-paper-scissors and related evolutionary games, focusing in particular on pattern formation, the impact of mobility, and the spontaneous emergence of cyclic dominance. We also review mean-field and zero-dimensional rock-paper-scissors models and the application of the complex Ginzburg-Landau equation, and we highlight the importance and usefulness of statistical physics for the successful study of large-scale ecological systems. Directions for future research, related for example to dynamical effects of coevolutionary rules and invasion reversals due to multi-point interactions, are outlined as well.


Cyclic dominance in evolutionary games: A review
Attila Szolnoki, Mauro Mobilia, Luo-Luo Jiang, Bartosz Szczesny, Alastair M. Rucklidge, Matjaz Perc

http://arxiv.org/abs/1408.6828

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Controlling extreme events on complex networks

Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network “mobile” can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed.


Controlling extreme events on complex networks
• Yu-Zhong Chen, Zi-Gang Huang & Ying-Cheng Lai

Scientific Reports 4, Article number: 6121 http://dx.doi.org/10.1038/srep06121


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Evolving Modular Genetic Regulatory Networks with a Recursive, Top-Down Approach

Being able to design genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a 'top-down' approach to evolving small GRNs and then use these to recursively boot-strap the identification of larger, more complex, modular GRNs. We start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. Successful solutions found in canonical DE where we truncated small interactions to zero, with or without an interaction penalty term, invariably contained many excess interactions. In contrast, by incorporating aggressive pruning and the penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.

 

"Evolving Modular Genetic Regulatory Networks with a Recursive, Top-Down Approach"
Javier Garcia-Bernardo, Margaret J. Eppstein
arXiv:1408.5380, 2014
http://arxiv.org/abs/1408.5380

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Revealing networks from dynamics: an introduction

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.


Revealing networks from dynamics: an introduction
Marc Timme, Jose Casadiego

http://arxiv.org/abs/1408.2963

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Wheels when you need them

Wheels when you need them | Papers | Scoop.it

Some 600 cities in 52 countries have introduced bike-sharing systems, according to the Earth Policy Institute in Washington, D.C.; Vienna, which was among the early adopters in 2003, now has 1500 bikes. (The largest system, in Wuhan, China, has 90,000.) Nearly all of them share the same problem: Riders tend to take some routes—downhill, for example—and not others. As a result, the bikes tend to collect in a few places.


Wheels when you need them
Chelsea Wald

Science 22 August 2014:
Vol. 345 no. 6199 pp. 862-863
http://dx.doi.org/10.1126/science.345.6199.862

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Evolution of regulatory networks towards adaptability and stability in a changing environment

Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find sparse and heterogeneous connectivity patterns to emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structures different depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments.

 

"Evolution of regulatory networks towards adaptability and stability in a changing environment"

Deok-Sun Lee, arXiv:1408.4221, 2014
http://arxiv.org/abs/1408.4221

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Nowcasting economic and social data: when and why search engine data fails, an illustration using Google Flu Trends

Obtaining an accurate picture of the current state of the economy is particularly important to central banks and finance ministries, and of epidemics to health ministries. There is increasing interest in the use of search engine data to provide such 'nowcasts' of social and economic indicators. However, people may search for a phrase because they independently want the information, or they may search simply because many others are searching for it. We consider the effect of the motivation for searching on the accuracy of forecasts made using search engine data of contemporaneous social and economic indicators. We illustrate the implications for forecasting accuracy using four episodes in which Google Flu Trends data gave accurate predictions of actual flu cases, and four in which the search data over-predicted considerably. Using a standard statistical methodology, the Bass diffusion model, we show that the independent search for information motive was much stronger in the cases of accurate prediction than in the inaccurate ones. Social influence, the fact that people may search for a phrase simply because many others are, was much stronger in the inaccurate compared to the accurate cases. Search engine data may therefore be an unreliable predictor of contemporaneous indicators when social influence on the decision to search is strong.


Nowcasting economic and social data: when and why search engine data fails, an illustration using Google Flu Trends

http://arxiv.org/abs/1408.0699

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Inside Money, Procyclical Leverage, and Banking Catastrophes

We explore a model of the interaction between banks and outside investors in which the ability of banks to issue inside money (short-term liabilities believed to be convertible into currency at par) can generate a collapse in asset prices and widespread bank insolvency. The banks and investors share a common belief about the future value of certain long-term assets, but they have different objective functions; changes to this common belief result in portfolio adjustments and trade. Positive belief shocks induce banks to buy risky assets from investors, and the banks finance those purchases by issuing new short-term liabilities. Negative belief shocks induce banks to sell assets in order to reduce their chance of insolvency to a tolerably low level, and they supply more assets at lower prices, which can result in multiple market-clearing prices. A sufficiently severe negative shock causes the set of equilibrium prices to contract (in a manner given by a cusp catastrophe), causing prices to plummet discontinuously and banks to become insolvent. Successive positive and negative shocks of equal magnitude do not cancel; rather, a banking catastrophe can occur even if beliefs simply return to their initial state. Capital requirements can prevent crises by curtailing the expansion of balance sheets when beliefs become more optimistic, but they can also force larger price declines. Emergency asset price supports can be understood as attempts by a central bank to coordinate expectations on an equilibrium with solvency.

 
Brummitt CD, Sethi R, Watts DJ (2014) Inside Money, Procyclical Leverage, and Banking Catastrophes. PLoS ONE 9(8): e104219. http://dx.doi.org/10.1371/journal.pone.0104219
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Systems Thinking and the Future of Cities

Systems Thinking and the Future of Cities | Papers | Scoop.it

The idea that nothing exists in isolation−but only as part of a system−has long been embedded in folklore, religious scriptures, and common sense. Yet, systems dynamics as a science has yet to transform the way we conduct the public business. This article first briefly explores the question of why advances in systems theory have failed to transform public policy. The second part describes the ways in which our understanding of systems is growing−not so much from theorizing, but from practical applications in agriculture, building design, and medical science. The third part focuses on whether and how that knowledge and systems science can be deployed to improve urban governance in the face of rapid climate destabilization so that sustainability becomes the norm, not the occasional success story.


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Erika Harrison's curator insight, August 15, 7:49 PM

In Brief

The idea that nothing exists in isolation−but only as part of a system−has long been embedded in folklore, religious scriptures, and common sense. Yet, systems dynamics as a science has yet to transform the way we conduct the public business. This article first briefly explores the question of why advances in systems theory have failed to transform public policy. The second part describes the ways in which our understanding of systems is growing−not so much from theorizing, but from practical applications in agriculture, building design, and medical science. The third part focuses on whether and how that knowledge and systems science can be deployed to improve urban governance in the face of rapid climate destabilization so that sustainability becomes the norm, not the occasional success story.


Key Concepts

Reducing wholes to parts lies at the core of the scientific worldview we inherited from Galileo, Bacon, Descartes, and their modern acolytes in the sciences of economics, efficiency, and management.The decades between 1950 and 1980 were the grand era for systems theory. However despite a great deal of talk about systems, we continue to administer, organize, analyze, manage, and govern complex ecological systems as if they were a collection of isolated parts and not an indissoluble union of energy, water, soils, land, forests, biota, and air.Much of what we have learned about managing real systems began in agriculture. One of the most important lessons being that land is an evolving organism of interrelated parts soils, hydrology, biota, wildlife, plants, animals, and people.The challenge is to transition organized urban complexity built on an industrial model and designed for automobiles, sprawl, and economic growth into coherent, civil, and durable places.A systems perspective to urban governance is a lens by which we might see more clearly through the fog of change, and potentially better manage the complex cause and effect relationships between social and ecological phenomena. The application of systems offers at least six possibilities to improve urban governance.

 

A system is an interconnected set of elements that is coherently organized in a way that achieves something . . . . [it] must consist of three kinds of things: elements, interconnections, and a function or purpose.
—Donella Meadows, Thinking in Systems1

 

A system [is] (a) a set of units or elements interconnected so that changes in some elements or their relations produce changes in other parts of the system, and (b) the entire system exhibits properties and behaviors that are different from those of the parts.
—Robert Jervis, Systems Effects 2

 

One of the most important ideas in modern science is the idea of a system; and it is almost impossible to define.
—Garrett Hardin, The Cybernetics of Competition3

Tobias Beckwith's curator insight, August 16, 1:45 PM

One of the things that gives real wizards their "powers," is the ability to see the world as systems within systems within systems... and then finding the leverage points, where a small action in one part of the system might cause a very large response elsewhere...

 

This post and article discuss that whole idea in a bit more depth. I found it to be a good read.

Gary Bamford's curator insight, August 20, 2:08 AM

Non-linear futures.

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Information Entropy-Based Metrics for Measuring Emergences in Artificial Societies

Emergence is a common phenomenon, and it is also a general and important concept in complex dynamic systems like artificial societies. Usually, artificial societies are used for assisting in resolving several complex social issues (e.g., emergency management, intelligent transportation system) with the aid of computer science. The levels of an emergence may have an effect on decisions making, and the occurrence and degree of an emergence are generally perceived by human observers. However, due to the ambiguity and inaccuracy of human observers, to propose a quantitative method to measure emergences in artificial societies is a meaningful and challenging task. This article mainly concentrates upon three kinds of emergences in artificial societies, including emergence of attribution, emergence of behavior, and emergence of structure. Based on information entropy, three metrics have been proposed to measure emergences in a quantitative way. Meanwhile, the correctness of these metrics has been verified through three case studies (the spread of an infectious influenza, a dynamic microblog network, and a flock of birds) with several experimental simulations on the Netlogo platform. These experimental results confirm that these metrics increase with the rising degree of emergences. In addition, this article also has discussed the limitations and extended applications of these metrics.


Information Entropy-Based Metrics for Measuring Emergences in Artificial Societies
Mingsheng Tang  and Xinjun Mao

Entropy 2014, 16, 4583-4602; http://dx.doi.org/10.3390/e16084583

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Programmable self-assembly in a thousand-robot swarm

Self-assembly enables nature to build complex forms, from multicellular organisms to complex animal structures such as flocks of birds, through the interaction of vast numbers of limited and unreliable individuals. Creating this ability in engineered systems poses challenges in the design of both algorithms and physical systems that can operate at such scales. We report a system that demonstrates programmable self-assembly of complex two-dimensional shapes with a thousand-robot swarm. This was enabled by creating autonomous robots designed to operate in large groups and to cooperate through local interactions and by developing a collective algorithm for shape formation that is highly robust to the variability and error characteristic of large-scale decentralized systems. This work advances the aim of creating artificial swarms with the capabilities of natural ones.


Programmable self-assembly in a thousand-robot swarm
Michael Rubenstein, Alejandro Cornejo, Radhika Nagpal

Science 15 August 2014:
Vol. 345 no. 6198 pp. 795-799
http://dx.doi.org/10.1126/science.1254295

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Eli Levine's curator insight, August 16, 1:31 PM

Imagine what we can do as a society with this way of thinking and feeling in our governing bodies. This isn't social engineering, but the submission to discovered, natural law in the society, environment, economy, and universe as a whole.

 

It's all one thing. 

 

Enjoy. 

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How Could Language Have Evolved?

The evolution of the faculty of language largely remains an enigma. In this essay, we ask why. Language's evolutionary analysis is complicated because it has no equivalent in any nonhuman species. There is also no consensus regarding the essential nature of the language “phenotype.” According to the “Strong Minimalist Thesis,” the key distinguishing feature of language (and what evolutionary theory must explain) is hierarchical syntactic structure. The faculty of language is likely to have emerged quite recently in evolutionary terms, some 70,000–100,000 years ago, and does not seem to have undergone modification since then, though individual languages do of course change over time, operating within this basic framework. The recent emergence of language and its stability are both consistent with the Strong Minimalist Thesis, which has at its core a single repeatable operation that takes exactly two syntactic elements a and b and assembles them to form the set {a, b}.


Bolhuis JJ, Tattersall I, Chomsky N, Berwick RC (2014) How Could Language Have Evolved? PLoS Biol 12(8): e1001934. http://dx.doi.org/10.1371/journal.pbio.1001934

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Traffic Ghost Hunting

Traffic Ghost Hunting | Papers | Scoop.it

Few experiences on the road are more perplexing than phantom traffic jams. Most of us have experienced one: The vehicle ahead of you suddenly brakes, forcing you to brake, and making the driver behind you brake. But, soon afterward, you and the cars around you accelerate back to the original speed—and it becomes clear that there were no obstacles on the road, and apparently no cause for the slowdown.
Because traffic quickly resumes its original speed, phantom traffic jams usually don’t cause major delays. But neither are they just minor nuisances. They are hot spots for accidents because they force unexpected braking. And the unsteady driving they cause is not good for your car, causing wear and tear and poor gas mileage.

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Gary Bamford's curator insight, August 30, 2:23 AM

Something to think about when you are in your next traffic jam - the jamiton!

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Longitudinal analysis of microbial interaction between humans and the indoor environment

The bacteria that colonize humans and our built environments have the potential to influence our health. Microbial communities associated with seven families and their homes over 6 weeks were assessed, including three families that moved their home. Microbial communities differed substantially among homes, and the home microbiome was largely sourced from humans. The microbiota in each home were identifiable by family. Network analysis identified humans as the primary bacterial vector, and a Bayesian method significantly matched individuals to their dwellings. Draft genomes of potential human pathogens observed on a kitchen counter could be matched to the hands of occupants. After a house move, the microbial community in the new house rapidly converged on the microbial community of the occupants’ former house, suggesting rapid colonization by the family’s microbiota.


Longitudinal analysis of microbial interaction between humans and the indoor environment
. Simon Lax, et al.

Science 29 August 2014:
Vol. 345 no. 6200 pp. 1048-1052
http://dx.doi.org/10.1126/science.1254529

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Characterizing Autopoiesis in the Game of Life

Maturana and Varela's concept of autopoiesis defines the essential organization of living systems and serves as a foundation for their biology of cognition and the enactive approach to cognitive science. As an initial step toward a more formal analysis of autopoiesis, this article investigates its application to the compact, recurrent spatiotemporal patterns that arise in Conway's game-of-Life cellular automata. In particular, we demonstrate how such entities can be formulated as self-constructing networks of interdependent processes that maintain their own boundaries. We then characterize the specific organizations of several such entities, suggest a way to simplify the descriptions of these organizations, and briefly consider the transformation of such organizations over time.


Characterizing Autopoiesis in the Game of Life
Randall D. Beer

Artificial Life

http://dx.doi.org/10.1162/ARTL_a_00143

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Epidemic processes in complex networks

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and socio-technical systems. The complex properties of real world networks have a profound impact on the behavior of equilibrium and non-equilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. Here we present a coherent and comprehensive review of the vast research activity concerning epidemic processes, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, epidemiologists, computer and social scientists share a common interest in studying epidemic spreading and rely on very similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while we focus on the main results and the paradigmatic models in infectious disease modeling, we also present the major results concerning generalized social contagion processes. Finally we outline the research activity at the forefront in the study of epidemic spreading in co-evolving and time-varying networks.


Epidemic processes in complex networks
Romualdo Pastor-Satorras, Claudio Castellano, Piet Van Mieghem, Alessandro Vespignani

http://arxiv.org/abs/1408.2701

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Use of differentiated pluripotent stem cells as replacement therapy for treating disease

Use of differentiated pluripotent stem cells as replacement therapy for treating disease | Papers | Scoop.it

Pluripotent stem cells (PSCs) directed to various cell fates holds promise as source material for treating numerous disorders. The availability of precisely differentiated PSC-derived cells will dramatically affect blood component and hematopoietic stem cell therapies and should facilitate treatment of diabetes, some forms of liver disease and neurologic disorders, retinal diseases, and possibly heart disease. Although an unlimited supply of specific cell types is needed, other barriers must be overcome. This review of the state of cell therapies highlights important challenges. Successful cell transplantation will require optimizing the best cell type and site for engraftment, overcoming limitations to cell migration and tissue integration, and occasionally needing to control immunologic reactivity, as well as a number of other challenges. Collaboration among scientists, clinicians, and industry is critical for generating new stem cell–based therapies.


Use of differentiated pluripotent stem cells as replacement therapy for treating disease
Ira J. Fox, George Q. Daley, Steven A. Goldman, Johnny Huard, Timothy J. Kamp, Massimo Trucco

Science 22 August 2014:
Vol. 345 no. 6199
http://dx.doi.org/10.1126/science.1247391

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Laurence Chilcott's curator insight, August 25, 7:54 PM

Today, the simplest and best way to actuate 'stem cell ' therapy is to use the world's only proven 'stem cell' nutrition supplement, which is able to cause the release of some 5 million 'adult stem cells ' from the bone marrow, each and every day . . .because it's a well known fact that these stem cells are able to repair and regenerate , worn ,damaged and diseased cell tissue and thus restore our diseased body's back to health, again!

You may learn more about the healing power of your 'adult stem cells' HERE: http://bodyhealing.stemtech.com

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Lakes under the ice: Antarctica’s secret garden

Lakes under the ice: Antarctica’s secret garden | Papers | Scoop.it
Samples from a lake hidden under 800 metres of ice contain thousands of microbes and hint at vast ecosystems yet to be discovered.


http://www.nature.com/news/lakes-under-the-ice-antarctica-s-secret-garden-1.15729

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Study of a model for the distribution of wealth

An equation for the evolution of the distribution of wealth in a population of economic agents making binary transactions with a constant total amount of "money" has recently been proposed by one of us (RLR). This equation takes the form of an iterated nonlinear map of the distribution of wealth. The equilibrium distribution is known and takes a rather simple form. If this distribution is such that, at some time, the higher momenta of the distribution exist, one can find exactly their law of evolution. A seemingly simple extension of the laws of exchange yields also explicit iteration formulae for the higher momenta, but with a major difference with the original iteration because high order momenta grow indefinitely. This provides a quantitative model where the spreading of wealth, namely the difference between the rich and the poor, tends to increase with time.


Study of a model for the distribution of wealth
Yves Pomeau, Ricardo Lopez-Ruiz

http://arxiv.org/abs/1408.2963

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Ten Simple Rules of Live Tweeting at Scientific Conferences

Ten Simple Rules of Live Tweeting at Scientific Conferences | Papers | Scoop.it

Rule 1: Short Conference Hashtag
Rule 2: Promote the Hashtag
Rule 3: Encourage Tweeting
Rule 4: Conference Twitter Etiquette
Rule 5: Conference Tweet Layout
Rule 6: Keep Conference Discussion Flowing
Rule 7: Differentiate Your Opinions from the Speaker's
Rule 8: Bring Questions up from Outside
Rule 9: Meet Other Live Tweeters Face to Face
Rule 10: Emphasize Impact of Live Tweeting


Ekins S, Perlstein EO (2014) Ten Simple Rules of Live Tweeting at Scientific Conferences. PLoS Comput Biol 10(8): e1003789. http://dx.doi.org/10.1371/journal.pcbi.1003789

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Origin of symbol-using systems: speech, but not sign, without the semantic urge

Natural language—spoken and signed—is a multichannel phenomenon, involving facial and body expression, and voice and visual intonation that is often used in the service of a social urge to communicate meaning. Given that iconicity seems easier and less abstract than making arbitrary connections between sound and meaning, iconicity and gesture have often been invoked in the origin of language alongside the urge to convey meaning. To get a fresh perspective, we critically distinguish the origin of a system capable of evolution from the subsequent evolution that system becomes capable of. Human language arose on a substrate of a system already capable of Darwinian evolution; the genetically supported uniquely human ability to learn a language reflects a key contact point between Darwinian evolution and language. Though implemented in brains generated by DNA symbols coding for protein meaning, the second higher-level symbol-using system of language now operates in a world mostly decoupled from Darwinian evolutionary constraints. Examination of Darwinian evolution of vocal learning in other animals suggests that the initial fixation of a key prerequisite to language into the human genome may actually have required initially side-stepping not only iconicity, but the urge to mean itself. If sign languages came later, they would not have faced this constraint.


Origin of symbol-using systems: speech, but not sign, without the semantic urge
Martin I. Sereno

http://dx.doi.org/10.1098/rstb.2013.0303
Phil. Trans. R. Soc. B 19 September 2014 vol. 369 no. 1651 20130303

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Local rewiring rules for evolving complex networks

The effects of link rewiring are considered for the class of directed networks where each node has the same fixed out-degree. We model a network generated by three mechanisms that are present in various networked systems; growth, global rewiring and local rewiring. During a rewiring phase a node is randomly selected, one of its out-going edges is detached from its destination then re-attached to the network in one of two possible ways; either globally to a randomly selected node, or locally to a descendant of a descendant of the originally selected node. Although the probability of attachment to a node increases with its connectivity, the probability of detachment also increases, the result is an exponential degree distribution with a small number of outlying nodes that have extremely large degree. We explain these outliers by identifying the circumstances for which a set of nodes can grow to very high degree.

 

"Local rewiring rules for evolving complex networks"
Ewan R. Colman, Geoff J. Rodgers, arXiv:1408.3570, 2014
http://arxiv.org/abs/1408.3570

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JIDT: An information-theoretic toolkit for studying the dynamics of complex systems

Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave and Python. We present the principles behind the code design, and provide several examples to guide users

 

"JIDT: An information-theoretic toolkit for studying the dynamics of complex systems"
Joseph T. Lizier, arXiv:1408.3270, 2014
http://arxiv.org/abs/1408.3270

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Eli Levine's curator insight, August 19, 11:11 AM

This could be useful.