Decentralised networks are naturally robust against certain types of attack. Now one mathematician says advanced geometry shows how to make them even more robust.
One of the common myths about the internet is that it was originally designed during the Cold War to survive nuclear attack. Historians of the internet are quick to point out that this was not at all one of the design goals of the early network, although the decentralised nature of the system turns out to make it much more robust than any kind of centralised network.
Nevertheless, the internet is still vulnerable. For example, the magnitude 9 earthquake and resulting tsunami that struck Japan on 11 March 2011, caused huge damage to the Japanese telecommunications infrastructure.
The Japanese telecom company NTT says it lost 18 exchange buildings and 65,000 telegraph poles in the disaster which also damaged 1.5 million fixed line circuits and 6300 kilometres of cabling.
That raises an interesting question: could the spatial layout of the internet be made any more robust against this kind of damage?
When Romanian singer Maria Tanase died in 1963, almost a million people flooded onto the streets of Bucharest for her funeral. A brief, grainy archival snippet on the internet reveals a sea of mourners parting around Tanase's open coffin and overflowing from every balcony.
''Maria Tanase was greatly loved and greatly respected in Romania,'' says violinist Alexander Balanescu. ''And her funeral was like a state funeral. People from that generation who were there … still remember that day.''
Balanescu was only nine at the time, and left Romania permanently a few years later, but he remembers hearing Tanase's music as a child, and listening to his parents' rapturous accounts of her live performances. It took many decades though, for those memories to resurface as the inspiration for one of his most personal projects.
A Hungarian team has created the first drones that can fly as a coordinated flock. The researchers watched as the ten autonomous robots took to the air in a field outside Budapest, zipping through the open sky, flying in formation or even following a leader, all without any central control.
The documentary revisits the unresolved question of “Who were the Dacians?” It focuses on the Roman Emperor Trajan’s six-year long two military campaigns against Dacia and its King Decebal between 101 and 106 AD. The documentary is not a literal history but an attempt to link past (who were the Dacians) to the present (what is the legacy) visible in the core regions of the Dacian Kingdom surrounding Sarmizgetusa, its center of power and sanctuary. Dacian Carpathian Mountain fortresses are a UNESCO Heritage Site. The film uses Trajan’s column in Rome, also a UNESCO Heritage Site, and its extensive bas-relief depictions combined with illustrations by artist Radu Oltean and contemporary on-location videography to create an artistic interpretation of the events and to cover on-going archaeological research.
The idea that social media sites such as Twitter can predict the future has a controversial history. In the last few years, various groups have claimed to be able to predict everything from the outcome of elections to the box office takings for new movies.
It’s fair to say that these claims have generated their fair share of criticism. So it’s interesting to see a new claim come to light.
Today, Nathan Kallus at the Massachusetts Institute of Technology in Cambridge says he has developed a way to predict crowd behaviour using statements made on Twitter. In particular, he has analysed the tweets associated with the 2013 coup d’état in Egypt and says that the civil unrest associated with this event was clearly predictable days in advance.
The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only some macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze Bitcoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling law between the degree and wealth associated to individual nodes.
Recent advances on human dynamics have focused on the normal patterns of human activities, with the quantitative understanding of human behavior under extreme events remaining a crucial missing chapter. This has a wide array of potential applications, ranging from emergency response and detection to traffic control and management. Previous studies have shown that human communications are both temporally and spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness. We study real anomalous events using country-wide mobile phone data, finding that information flow during emergencies is dominated by repeated communications. We further demonstrate that the observed communication patterns cannot be explained by inherent reciprocity in social networks, and are universal across different demographics.
Competition for attention among users can bring social networks close to the critical point of a phase transition.
A “meme” is an idea, style, or behavior that spreads within society; examples include songs, catch phrases, Internet videos, and fashions. The name was coined by British evolutionary biologist Richard Dawkins to suggest the analogy with a gene: a meme can replicate, mutate, and evolve, competing for success. But what mechanisms determine the popularity of a meme? Reporting in Physical Review Letters, James Gleeson at the University of Limerick, Ireland, and co-workers present a model that describes how memes spread and compete in a social network.
The key result of their analysis is that the competition between memes turns the social network into a so-called critical system, i.e., a system close to the critical point of a phase transition. In such a state, minor disturbances lead to avalanches of events that drive the system to a new phase, e.g., one in which certain memes go viral. As expected for a critical state, the authors show that many statistical properties exhibit certain regularities. In particular, they are able to predict distributions of popularity following power laws whose exponents are close to empirical values.
Using open source data, we observe the fascinating dynamics of nighttime light. Following a global economic regime shift, the planetary center of light can be seen moving eastwards at a pace of about 60 km per year. Introducing spatial light Gini coefficients, we find a universal pattern of human settlements across different countries and see a global centralization of light. Observing 160 different countries we document the expansion of developing countries, the growth of new agglomerations, the regression in countries suffering from demographic decline and the success of light pollution abatement programs in western countries.
Idea debunked that young trees have the edge on their older siblings in carbon accumulation.
Many foresters have long assumed that trees gradually lose their vigour as they mature, but a new analysis suggests that the larger a tree gets, the more kilos of carbon it puts on each year.
“The trees that are adding the most mass are the biggest ones, and that holds pretty much everywhere on Earth that we looked,” says Nathan Stephenson, an ecologist at the US Geological Survey in Three Rivers, California, and the first author of the study, which appears today in Nature. “Trees have the equivalent of an adolescent growth spurt, but it just keeps going.”
Effective point-of-use devices for providing safe drinking water are urgently needed to reduce the global burden of waterborne disease. Here we show that plant xylem from the sapwood of coniferous trees – a readily available, inexpensive, biodegradable, and disposable material – can remove bacteria from water by simple pressure-driven filtration. Approximately 3 cm3 of sapwood can filter water at the rate of several liters per day, sufficient to meet the clean drinking water needs of one person. The results demonstrate the potential of plant xylem to address the need for pathogen-free drinking water in developing countries and resource-limited settings.
Has natural selection led to adaptations of Lévy flight foraging, as stated on the respective Wikipedia page? Random walks with scale-free jump length distributions were indeed shown to optimize the search for sparse targets as supported by extensive movement data of many animal species and humans. Here we demonstrate that small variations of the search conditions strongly modify these claims: In the presence of a bias, underwater currents for sea predators or winds for airborne searchers, a Lévy searcher easily overshoots the target, and Brownian strategies become advantageous. Even in the absence of a bias, there exist conditions for which a Brownian strategy may effect faster target localization. Our results show clear limitations for the universality of Lévy flight foraging.
If advancing civilization relies on social networks, the world is in trouble. According to newly published research by University of Oregon psychologist Azim Shariff, individuals relying on their social groups can find solutions but also pre-empt the motivation for independent analytical thinking.
Social networks encompass many scenarios, from divisions within organizations, to fraternities and sororities, to connections on Facebook and Twitter. The four-member research team is not proclaiming a doomsday scenario; it is studying the impacts of social learning in networks from a broad cultural perspective.
While social learning "is a key cultural mechanism that improves the performance of individuals and groups," writes Shariff and international colleagues in the introduction of their paper placed online by the Journal of the Royal Society Interface, watching and copying others while seeking solutions has some limitations on analytical development that drives innovation.
Why does a mouse's heart beat about the same number of times in its lifetime as an elephant's, although the mouse lives about a year, while an elephant sees 70 winters come and go? Why do small plants and animals mature faster than large ones? Why has nature chosen such radically different forms as the loose-limbed beauty of a flowering tree and the fearful symmetry of a tiger?
These questions have puzzled life scientists since ancient times. Now an interdisciplinary team of researchers from the University of Maryland and the University of Padua in Italy propose a thought-provoking answer based on a famous mathematical formula that has been accepted as true for generations, but never fully understood. In a paper published the week of Feb. 17, 2014 in the Proceedings of the National Academy of Sciences, the team offers a re-thinking of the formula known as Kleiber's Law. Seeing this formula as a mathematical expression of an evolutionary fact, the team suggests that plants' and animals' widely different forms evolved in parallel, as ideal ways to solve the problem of how to use energy efficiently.
Cities are increasingly the fundamental socio-economic units of human societies worldwide, but we still lack a unified characterization of urbanization that captures the social processes realized by cities across time and space. This is especially important for understanding the role of cities in the history of human civilization and for determining whether studies of ancient cities are relevant for contemporary science and policy. As a step in this direction, we develop a theory of settlement scaling in archaeology, deriving the relationship between population and settled area from a consideration of the interplay between social and infrastructural networks. We then test these models on settlement data from the Pre-Hispanic Basin of Mexico to show that this ancient settlement system displays spatial scaling properties analogous to those observed in modern cities. Our data derive from over 1,500 settlements occupied over two millennia and spanning four major cultural periods characterized by different levels of agricultural productivity, political centralization and market development. We show that, in agreement with theory, total settlement area increases with population size, on average, according to a scale invariant relation with an exponent in the range . As a consequence, we are able to infer aggregate socio-economic properties of ancient societies from archaeological measures of settlement organization. Our findings, from an urban settlement system that evolved independently from its old-world counterparts, suggest that principles of settlement organization are very general and may apply to the entire range of human history.
After hearing about Flappy Bird the past couple days, I decided to download its 68,000 iTunes reviews last night. I explain some of the technical details down below, but I honestly don’t think that’s the most interesting story here. In fact, while the internet keeps pushing The Verge’s $50,000-a-day story about the app, I think the onslaught of Flappy Bird downloads that’s happened in the past two weeks is a much more interesting storyline.
Self-organization is thought to play an important role in structuring nervous systems. It frequently arises as a consequence of plasticity mechanisms in neural networks: connectivity determines network dynamics which in turn feed back on network structure through various forms of plasticity. Recently, self-organizing recurrent neural network models (SORNs) have been shown to learn non-trivial structure in their inputs and to reproduce the experimentally observed statistics and fluctuations of synaptic connection strengths in cortex and hippocampus. However, the dynamics in these networks and how they change with network evolution are still poorly understood. Here we investigate the degree of chaos in SORNs by studying how the networks' self-organization changes their response to small perturbations. We study the effect of perturbations to the excitatory-to-excitatory weight matrix on connection strengths and on unit activities. We find that the network dynamics, characterized by an estimate of the maximum Lyapunov exponent, becomes less chaotic during its self-organization, developing into a regime where only few perturbations become amplified. We also find that due to the mixing of discrete and (quasi-)continuous variables in SORNs, small perturbations to the synaptic weights may become amplified only after a substantial delay, a phenomenon we propose to call deferred chaos.
When animals swarm they exhibit a complex collective intelligence that could help us build robots, heal wounds and understand the brain.
We tend to think of swarms as mindless moving masses, not the kind of thoughtful groups that humans form. But humans often behave like a swarm, particularly when it comes to collective decision-making.
During election campaigns, people often believe that sufficiently outspoken minority groups have the power to sway the results. That's unlikely, say Iain Couzin and his team at Princeton University. Their models of voter swarms show that the minority influence, however strong, gets diluted to the point where the group goes with the majority decision – provided the electorate contains enough uninformed and undecided voters who simply copy their neighbours. For better or worse, ignorance plays a significant role in the way democracies operate.
Study the way people make mobile phone calls in metropolitan areas and you can see a city breathe, say computer scientists.
The results reveal some fascinating patterns in city structure. For a start, every city undergoes a kind of respiration in which people converge into the center and then withdraw on a daily basis, almost like breathing. And this happens in all cities. This “suggests the existence of a single ‘urban rhythm’ common to all cities,” say Louail and co.
The question of how an economic system should be structured in order to best promote fairness and equality is one of the most debated subjects of all time. By approaching the complexities of this question from the field of network science, researchers from MIT and other institutions have found that the average degree to which individuals in a society are connected to each other can crucially affect the fairness of income distribution.