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Exploring Scientists' Working Timetable

Exploring Scientists' Working Timetable | Complex World | Scoop.it

In our previous study (Wang et al., 2012), we analyzed scientists' working timetable of 3 countries, using realtime downloading data of scientific literatures. In this paper, we make a through analysis about global scientists' working habits. Top 30 countries/territories from Europe, Asia, Australia, North America, Latin America and Africa are selected as representatives and analyzed in detail. Regional differences for scientists' working habits exists in different countries. Besides different working cultures, social factors could affect scientists' research activities and working patterns. Nevertheless, a common conclusion is that scientists today are often working overtime. Although scientists may feel engaged and fulfilled about their hard working, working too much still warns us to reconsider the work - life balance.

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Complex World
Cutting Edge Research about Complex Systems
Curated by Claudia Mihai
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Controlling extreme events on complex networks

Controlling extreme events on complex networks | Complex World | Scoop.it
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 [ldquo]mobile[rdquo] 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.
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Scaling of Chaos versus Periodicity: How Certain is it that an Attractor is Chaotic?

Scaling of Chaos versus Periodicity: How Certain is it that an Attractor is Chaotic? | Complex World | Scoop.it
A small perturbation in a system's parameter can convert its attractor from chaotic to periodic, where the probability of obtaining a chaotic regime scales as a power law with respect to the perturbation size.
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A Brazilian Wunderkind Who Calms Chaos

A Brazilian Wunderkind Who Calms Chaos | Complex World | Scoop.it
Artur Avila’s solutions to ubiquitous problems in chaos theory have “changed the face of the field,” earning him Brazil’s first Fields Medal.
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Computational Linguistics of Twitter Reveals the Existence of Global Superdialects

Computational Linguistics of Twitter Reveals the Existence of Global Superdialects | Complex World | Scoop.it
The first study of dialects on Twitter reveals global patterns that have never been observed before.
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Jean-Michel Livowsky's curator insight, August 9, 1:19 AM

Les bases d'un meta-langage universel ?

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Hidden scaling patterns and universality in written communication

Hidden scaling patterns and universality in written communication | Complex World | Scoop.it
The temporal statistics exhibited by written correspondence appear to be media dependent, with features which have so far proven difficult to characterize. We explain the origin of these difficulties by disentangling the role of spontaneous activity from decision-based prioritizing processes in human dynamics, clocking all waiting times through each agent's ``proper time'' measured by activity. This unveils the same fundamental patterns in written communication across all media (letters, email, sms), with response times displaying truncated power-law behavior and average exponents near $$-${}\frac{3}{2}$. When standard time is used, the response time probabilities are theoretically predicted to exhibit a bimodal character, which is empirically borne out by our newly collected years-long data on email. These perspectives on the temporal dynamics of human correspondence should aid in the analysis of interaction phenomena in general, including resource management, optimal pricing and routing, information sharing, and emergency handling.
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A network framework of cultural history

A network framework of cultural history | Complex World | Scoop.it

The emergent processes driving cultural history are a product of complex interactions among large numbers of individuals, determined by difficult-to-quantify historical conditions. To characterize these processes, we have reconstructed aggregate intellectual mobility over two millennia through the birth and death locations of more than 150,000 notable individuals. The tools of network and complexity theory were then used to identify characteristic statistical patterns and determine the cultural and historical relevance of deviations. The resulting network of locations provides a macroscopic perspective of cultural history, which helps us to retrace cultural narratives of Europe and North America using large-scale visualization and quantitative dynamical tools and to derive historical trends of cultural centers beyond the scope of specific events or narrow time intervals.

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How bird flocks are like liquid helium

How bird flocks are like liquid helium | Complex World | Scoop.it

Mathematical model shows how hundreds of starlings coordinate their movements in flight.

A flock of starlings flies as one, a spectacular display in which each bird flits about as if in a well-choreographed dance. Everyone seems to know exactly when and where to turn. Now, for the first time, researchers have measured how that knowledge moves through the flock—a behavior that mirrors certain quantum phenomena of liquid helium.

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Competitive Dynamics on Complex Networks

Competitive Dynamics on Complex Networks | Complex World | Scoop.it
We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition.
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A simple generative model of collective online behavior

A simple generative model of collective online behavior | Complex World | Scoop.it

One of the most common strategies in studying complex systems is to investigate and interpret whether any “hidden order” is present by fitting observed statistical regularities via data analysis and then reproducing such regularities with long-time or equilibrium dynamics from some generative model. Unfortunately, many different models can possess indistinguishable long-time dynamics, so the above recipe is often insufficient to discern the relative quality of competing models. In this paper, we use the example of collective online behavior to illustrate that, by contrast, time-dependent modeling can be very effective at disentangling competing generative models of a complex system.

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Mathematicians Explain Why Social Epidemics Spread Faster in Some Countries Than Others

Mathematicians Explain Why Social Epidemics Spread Faster in Some Countries Than Others | Complex World | Scoop.it
Psychologists have always puzzled over why people in Sweden were slower to start smoking and slower to stop. Now a group of mathematicians have worked out why.
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Diseases, symptoms, genes, and proteins linked together in giant network

Diseases, symptoms, genes, and proteins linked together in giant network | Complex World | Scoop.it

The first indication that you're sick is typically one or more symptoms: perhaps a cough, fever, abdominal pain, etc. Symptoms are high-level clinical manifestations of a disease that, at a lower level, is caused by molecular-level components, such as genes and proteins. Understanding the complex ways in which symptoms, diseases, and their underlying molecular mechanisms are related can provide a valuable tool for medical researchers when designing better treatments.

However, this area of research is still very new and not well understood. In a new study published in Nature Communications, researchers XueZhong Zhou, et al., have constructed a human symptoms-disease network (HSDN) that reveals the numerous and sometimes surprising connections between symptoms, diseases, genes, and proteins.

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Biologists discover electric bacteria that eat pure electrons rather than sugar

Biologists discover electric bacteria that eat pure electrons rather than sugar | Complex World | Scoop.it

Some intrepid biologists at the University of Southern California (USC) have discovered bacteria that survives on nothing but electricity -- rather than food, they eat and excrete pure electrons. These bacteria yet again prove the almost miraculous tenacity of life -- but, from a technology standpoint, they might also prove to be useful in enabling the creation of self-powered nanoscale devices that clean up pollution. Some of these bacteria also have the curious ability to form into ‘biocables,’ microbial nanowires that are centimeters long and conduct electricity as well as copper wires — a capability that might one day be tapped to build long, self-assembling subsurface networks for human use.

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Information-based fitness and the emergence of criticality in living systems

Information-based fitness and the emergence of criticality in living systems | Complex World | Scoop.it

Recently, evidence has been mounting that biological systems might operate at the borderline between order and disorder, i.e., near a critical point. A general mathematical framework for understanding this common pattern, explaining the possible origin and role of criticality in living adaptive and evolutionary systems, is still missing. We rationalize this apparently ubiquitous criticality in terms of adaptive and evolutionary functional advantages. We provide an analytical framework, which demonstrates that the optimal response to broadly different changing environments occurs in systems organizing spontaneously—through adaptation or evolution—to the vicinity of a critical point. Furthermore, criticality turns out to be the evolutionary stable outcome of a community of individuals aimed at communicating with each other to create a collective entity.

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Damien Thouvenin's curator insight, August 14, 1:53 AM

Très intéressant. A rapprocher du livre de Shulmann: Living at the Edge of Chaos: Complex Systems in Culture and Psyche 

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The small-world effect is a modern phenomenon

The small-world effect is a modern phenomenon | Complex World | Scoop.it

The "small-world effect" is the observation that one can find a short chain of acquaintances, often of no more than a handful of individuals, connecting almost any two people on the planet. It is often expressed in the language of networks, where it is equivalent to the statement that most pairs of individuals are connected by a short path through the acquaintance network. Although the small-world effect is well-established empirically for contemporary social networks, we argue here that it is a relatively recent phenomenon, arising only in the last few hundred years: for most of mankind's tenure on Earth the social world was large, with most pairs of individuals connected by relatively long chains of acquaintances, if at all. Our conclusions are based on observations about the spread of diseases, which travel over contact networks between individuals and whose dynamics can give us clues to the structure of those networks even when direct network measurements are not available. As an example we consider the spread of the Black Death in 14th-century Europe, which is known to have traveled across the continent in well-defined waves of infection over the course of several years. Using established epidemiological models, we show that such wave-like behavior can occur only if contacts between individuals living far apart are exponentially rare. We further show that if long-distance contacts are exponentially rare, then the shortest chain of contacts between distant individuals is on average a long one. The observation of the wave-like spread of a disease like the Black Death thus implies a network without the small-world effect.

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Only ten midges needed to make a swarm

Only ten midges needed to make a swarm | Complex World | Scoop.it
High-speed cameras reveal when insects become self-organizing.
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Sand Pile Model of the Mind Grows in Popularity

Sand Pile Model of the Mind Grows in Popularity | Complex World | Scoop.it
Support is growing for a decades-old physics idea suggesting that localized episodes of disordered brain activity help keep the overall system in healthy balance
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Physicists eye neural fly data, find formula for Zipf's law

Physicists eye neural fly data, find formula for Zipf's law | Complex World | Scoop.it
Physicists have identified a mechanism that may help explain Zipf's law – a unique pattern of behavior found in disparate systems, including complex biological ones. The journal Physical Review Letters is publishing their mathematical models, which demonstrate how Zipf's law naturally arises when a sufficient number of units react to a hidden variable in a system.
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Jean-Michel Livowsky's curator insight, August 8, 1:57 AM

Maintenant, on sait pourquoi les terroristes du hamaSS volent, et surtout comment.

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The dynamics of correlated novelties

The dynamics of correlated novelties | Complex World | Scoop.it
Novelties are a familiar part of daily life. They are also fundamental to the evolution of biological systems, human society, and technology. By opening new possibilities, one novelty can pave the way for others in a process that Kauffman has called “expanding the adjacent possible”. The dynamics of correlated novelties, however, have yet to be quantified empirically or modeled mathematically. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs. The model, a generalization of Polya's urn, predicts statistical laws for the rate at which novelties happen (Heaps' law) and for the probability distribution on the space explored (Zipf's law), as well as signatures of the process by which one novelty sets the stage for another. We test these predictions on four data sets of human activity: the edit events of Wikipedia pages, the emergence of tags in annotation systems, the sequence of words in texts, and listening to new songs in online music catalogues. By quantifying the dynamics of correlated novelties, our results provide a starting point for a deeper understanding of the adjacent possible and its role in biological, cultural, and technological evolution.
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A mathematical theory proposed by Alan Turing in 1952 can explain the formation of fingers

A mathematical theory proposed by Alan Turing in 1952 can explain the formation of fingers | Complex World | Scoop.it

Alan Turing, the British mathematician (1912-1954), is famous for a number of breakthroughs, which altered the course of the 20th century. In 1936 he published a paper, which laid the foundation of computer science, providing the first formal concept of a computer algorithm. He next played a pivotal role in the Second World War, designing the machines which cracked the German military codes, enabling the Allies to defeat the Nazis in several crucial battles. And in the late 1940's he turned his attention to artificial intelligence and proposed a challenge, now called the Turing test, which is still important to the field today.

His contribution to mathematical biology is less famous, but was no less profound. He published just one paper (1952), but it triggered a whole new field of mathematical enquiry into pattern formation. He discovered that a system with just 2 molecules could, at least in theory, create spotty or stripy patterns if they diffused and chemically interacted in just the right way.

His mathematical equations showed that starting from uniform condition (ie. a homogeneous distribution – no pattern) they could spontaneously self-organise their concentrations into a repetitive spatial pattern. This theory has come to be accepted as an explanation of fairly simple patterns such as zebra stripes and even the ridges on sand dunes, but in embryology it has been resisted for decades as an explanation of how structures such as fingers are formed.

Now a group of researchers from the Multicellular Systems Biology lab at the CRG, led by ICREA Research Professor James Sharpe, has provided the long sought-for data which confirms that the fingers and toes are patterned by a Turing mechanism. "It complements their recent paper (Science 338:1476, 2012), which provided evidence that Hox genes and FGF signaling modulated a hypothetical Turing system. However, at that point the Turing molecules themselves were still not identified, and so this remained as the critical unsolved piece of the puzzle. The new study completes the picture, by revealing which signaling molecules act as the Turing system" says James Sharpe, co-author of the study.

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The Beautiful Phenomena Of Starling Flocks, Explained By Computers

The Beautiful Phenomena Of Starling Flocks, Explained By Computers | Complex World | Scoop.it

On its own, a single starling doesn't elicit much fuss. It's a tennis-ball-sized bird, glossy black in winter, purplish or green in summer, and in autumn, sometimes speckled with white spots. But when starlings congregate in flocks of hundreds of thousands over open fields (something scientists call a "murmuration") they pitch and arc and rush at one another in a bizarre choreography that's puzzled naturalists for hundreds of years. A whole catalog of YouTube videos has documented the black shapes in flight, a movement that looks like the birds are attached to a giant rhythmic gymnast's invisible ribbon.

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The Science of Problem Solving

The Science of Problem Solving | Complex World | Scoop.it
A review of the science behind problem solving, how it functions in the brain and how we can do it better.
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Exploring No Man’s Sky, A Computer Game Forged by Algorithms

Exploring No Man’s Sky, A Computer Game Forged by Algorithms | Complex World | Scoop.it
A new computer game, No Man’s Sky, demonstrates a new way to build computer games filled with diverse flora and fauna.

Sean Murray, one of the creators of the computer game No Man’s Sky, can’t guarantee that the virtual universe he is building is infinite, but he’s certain that, if it isn’t, nobody will ever find out. “If you were to visit one virtual planet every second,” he says, “then our own sun will have died before you’d have seen them all.”

No Man’s Sky is a video game quite unlike any other. Developed for Sony’s PlayStation 4 by an improbably small team (the original four-person crew has grown only to 10 in recent months) at Hello Games, an independent studio in the south of England, it’s a game that presents a traversable universe in which every rock, flower, tree, creature, and planet has been “procedurally generated” to create a vast and diverse play area.

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Prevalence and use of Twitter among scholars

Prevalence and use of Twitter among scholars | Complex World | Scoop.it
Prevalence and use of Twitter among scholars (Infographic on Prevalence and use of Twitter among scholars
http://t.co/skulthbeMe)
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Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence

Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence | Complex World | Scoop.it

Organizational growth processes have consistently been shown to exhibit a fatter-than-Gaussian growth-rate distribution in a variety of settings. Long periods of relatively small changes are interrupted by sudden changes in all size scales. This kind of extreme events can have important consequences for the development of biological and socio-economic systems. Existing models do not derive this aggregated pattern from agent actions at the micro level. We develop an agent-based simulation model on a social network. We take our departure in a model by a Schwarzkopf et al. on a scale-free network. We reproduce the fat-tailed pattern out of internal dynamics alone, and also find that it is robust with respect to network topology. Thus, the social network and the local interactions are a prerequisite for generating the pattern, but not the network topology itself. We further extend the model with a parameter  that weights the relative fraction of an individual's neighbours belonging to a given organization, representing a contextual aspect of social influence. In the lower limit of this parameter, the fraction is irrelevant and choice of organization is random. In the upper limit of the parameter, the largest fraction quickly dominates, leading to a winner-takes-all situation. We recover the real pattern as an intermediate case between these two extremes.

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Understanding the group dynamics and success of teams

Understanding the group dynamics and success of teams | Complex World | Scoop.it

Tackling complex problems often requires coordinated group effort and can consume significant resources, yet our understanding of how teams form and succeed has been limited by a lack of large scale, quantitative data. We analyze activity traces and success levels for ∼150,000 self-organized, online team projects. While larger teams tend to be more successful, the distribution of activity is highly skewed across the team, with only small subsets of members performing most work. This
focused centralization in activity indicates that larger teams succeed not simply by distributing workload, but by acting as a support system for a smaller set of core members. High impact teams are significantly more focused than average teams of the same size, yet are more likely to consist of members with diverse experiences, and these members, even non-core members, are more likely to themselves be core members of other teams. This mixture of size, focus, experience, and diversity points to underlying mechanisms that can be used to maximize the success of collaborative endeavors.

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