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Forecasting synchronizability of complex networks from data

Given a complex networked system whose topology and dynamical equations are unknown, is it possible to foresee that a certain type of collective dynamics can potentially emerge in the system, provided that only time-series measurements are available? We address this question by focusing on a commonly studied type of collective dynamics, namely, synchronization in coupled dynamical networks. We demonstrate that, using the compressive-sensing paradigm, even when the coupling strength is not uniform so that the network is effectively weighted, the full topology, the coupling weights, and the nodal dynamical equations can all be uncovered accurately. The reconstruction accuracy and data requirement are systematically analyzed, in a process that includes a validation of the reconstructed eigenvalue spectrum of the underlying coupling matrix. A master stability function (MSF), the fundamental quantity determining the network synchronizability, can then be calculated based on the reconstructed dynamical system, the accuracy of which can be assessed as well. With the coupling matrix and MSF fully uncovered, the emergence of synchronous dynamics in the network can be anticipated and controlled. To forecast the collective dynamics on complex networks is an extremely challenging problem with significant applications in many disciplines, and our work represents an initial step in this important area.

 

Forecasting synchronizability of complex networks from data

Authors: Ri-Qi Su, Xuan Ni, Wen-Xu Wang, and Ying-Cheng Lai

Phys. Rev. E 85, 056220 (2012)

http://pre.aps.org/abstract/PRE/v85/i5/e056220

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Regularity underlies erratic population abundances in marine ecosystems

The abundance of a species' population in an ecosystem is rarely stationary, often exhibiting large fluctuations over time. Using historical data on marine species, we show that the year-to-year fluctuations of population growth rate obey a well-defined double-exponential (Laplace) distribution. This striking regularity allows us to devise a stochastic model despite seemingly irregular variations in population abundances. The model identifies the effect of reduced growth at low population density as a key factor missed in current approaches of population variability analysis and without which extinction risks are severely underestimated. The model also allows us to separate the effect of demographic stochasticity and show that single-species growth rates are dominantly determined by stochasticity common to all species. This dominance—and the implications it has for interspecies correlations, including co-extinctions—emphasizes the need for ecosystem-level management approaches to reduce the extinction risk of the individual species themselves.


Regularity underlies erratic population abundances in marine ecosystems
Jie Sun, Sean P. Cornelius, John Janssen, Kimberly A. Gray, Adilson E. Motter
J. R. Soc. Interface 2015 12 20150235; http://dx.doi.org/10.1098/rsif.2015.0235


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On the tail risk of violent conflict and its underestimation

We examine all possible statistical pictures of violent conflicts over common era history with a focus on dealing with incompleteness and unreliability of data. We apply methods from extreme value theory on log-transformed data to remove compact support, then, owing to the boundedness of maximum casualties, retransform the data and derive expected means. We find the estimated mean likely to be at least three times larger than the sample mean, meaning severe underestimation of the severity of conflicts from naive observation. We check for robustness by sampling between high and low estimates and jackknifing the data. We study inter-arrival times between tail events and find (first-order) memorylessless of events. The statistical pictures obtained are at variance with the claims about "long peace".


On the tail risk of violent conflict and its underestimation
Pasquale Cirillo, Nassim Nicholas Taleb

http://arxiv.org/abs/1505.04722

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Understanding Brains: Details, Intuition, and Big Data

Understanding Brains: Details, Intuition, and Big Data | Papers | Scoop.it

Understanding how the brain works requires a delicate balance between the appreciation of the importance of a multitude of biological details and the ability to see beyond those details to general principles. As technological innovations vastly increase the amount of data we collect, the importance of intuition into how to analyze and treat these data may, paradoxically, become more important.


Marder E (2015) Understanding Brains: Details, Intuition, and Big Data. PLoS Biol 13(5): e1002147. http://dx.doi.org/10.1371/journal.pbio.1002147 

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The Intrafirm Complexity of Systemically Important Financial Institutions

In November, 2011, the Financial Stability Board, in collaboration with the International Monetary Fund, published a list of 29 "systemically important financial institutions" (SIFIs). This designation reflects a concern that the failure of any one of them could have dramatic negative consequences for the global economy and is based on "their size, complexity, and systemic interconnectedness". While the characteristics of "size" and "systemic interconnectedness" have been the subject of a good deal of quantitative analysis, less attention has been paid to measures of a firm's "complexity." In this paper we take on the challenges of measuring the complexity of a financial institution and to that end explore the use of the structure of an individual firm's control hierarchy as a proxy for institutional complexity. The control hierarchy is a network representation of the institution and its subsidiaries. We show that this mathematical representation (and various associated metrics) provides a consistent way to compare the complexity of firms with often very disparate business models and as such may provide the foundation for determining a SIFI designation. By quantifying the level of complexity of a firm, our approach also may prove useful should firms need to reduce their level of complexity either in response to business or regulatory needs. Using a data set containing the control hierarchies of many of the designated SIFIs, we find that in the past two years, these firms have decreased their level of complexity, perhaps in response to regulatory requirements.


The Intrafirm Complexity of Systemically Important Financial Institutions
Robin L. Lumsdaine, Daniel N. Rockmore, Nicholas Foti, Gregory Leibon, J. Doyne Farmer

http://arxiv.org/abs/1505.02305

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Ecological collapse and the emergence of traveling waves at the onset of shear turbulence

The transition to turbulence exhibits remarkable spatio-temporal behavior that continues to defy detailed understanding. Near the onset to turbulence in pipes, transient turbulent regions decay either directly or, at higher Reynolds numbers through splitting, with characteristic time-scales that exhibit a super-exponential dependence on Reynolds number. Here we report numerical simulations of transitional pipe flow, showing that a zonal flow emerges at large scales, activated by anisotropic turbulent fluctuations; in turn, the zonal flow suppresses the small-scale turbulence leading to stochastic predator-prey dynamics. We show that this "ecological" model of transitional turbulence reproduces the super-exponential lifetime statistics and phenomenology of pipe flow experiments. Our work demonstrates that a fluid on the edge of turbulence is mathematically analogous to an ecosystem on the edge of extinction, and provides an unbroken link between the equations of fluid dynamics and the directed percolation universality class.


Ecological collapse and the emergence of traveling waves at the onset of shear turbulence
Hong-Yan Shih, Tsung-Lin Hsieh, Nigel Goldenfeld

http://arxiv.org/abs/1505.02807

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Multifractal to monofractal evolution of the London's street network

We perform a multifractal analysis of the evolution of London's street network from 1786 to 2010. First, we show that a single fractal dimension, commonly associated with the morphological description of cities, does not su ce to capture the dynamics of the system. Instead, for a proper characterization of such a dynamics, the multifractal spectrum needs to be considered. Our analysis reveals that London evolves from an inhomogeneous fractal structure, that can be described in terms of a multifractal, to a homogeneous one, that converges to monofractality. We argue that London's multifractal to monofracal evolution might be a special outcome of the constraint imposed on its growth by a green belt. Through a series of simulations, we show that multifractal objects, constructed through di usion limited aggregation, evolve towards monofractality if their growth is constrained by a non-permeable boundary.


Multifractal to monofractal evolution of the London's street network
Roberto Murcio, A. Paolo Masucci, Elsa Arcaute, Michael Batty

http://arxiv.org/abs/1505.02760

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Pigment cell movement is not required for generation of Turing patterns in zebrafish skin

Pigment cell movement is not required for generation of Turing patterns in zebrafish skin | Papers | Scoop.it

The zebrafish is a model organism for pattern formation in vertebrates. Understanding what drives the formation of its coloured skin motifs could reveal pivotal to comprehend the mechanisms behind morphogenesis. The motifs look and behave like reaction–diffusion Turing patterns, but the nature of the underlying physico-chemical processes is very different, and the origin of the patterns is still unclear. Here we propose a minimal model for such pattern formation based on a regulatory mechanism deduced from experimental observations. This model is able to produce patterns with intrinsic wavelength, closely resembling the experimental ones. We mathematically prove that their origin is a Turing bifurcation occurring despite the absence of cell motion, through an effect that we call differential growth. This mechanism is qualitatively different from the reaction–diffusion originally proposed by Turing, although they both generate the short-range activation and the long-range inhibition required to form Turing patterns.


Pigment cell movement is not required for generation of Turing patterns in zebrafish skin
• D. Bullara & Y. De Decker

Nature Communications 6, Article number: 6971 http://dx.doi.org/10.1038/ncomms7971

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A multilevel multimodal circuit enhances action selection in Drosophila

Natural events present multiple types of sensory cues, each detected by a specialized sensory modality. Combining information from several modalities is essential for the selection of appropriate actions. Key to understanding multimodal computations is determining the structural patterns of multimodal convergence and how these patterns contribute to behaviour. Modalities could converge early, late or at multiple levels in the sensory processing hierarchy. Here we show that combining mechanosensory and nociceptive cues synergistically enhances the selection of the fastest mode of escape locomotion in Drosophila larvae. In an electron microscopy volume that spans the entire insect nervous system, we reconstructed the multisensory circuit supporting the synergy, spanning multiple levels of the sensory processing hierarchy. The wiring diagram revealed a complex multilevel multimodal convergence architecture. Using behavioural and physiological studies, we identified functionally connected circuit nodes that trigger the fastest locomotor mode, and others that facilitate it, and we provide evidence that multiple levels of multimodal integration contribute to escape mode selection. We propose that the multilevel multimodal convergence architecture may be a general feature of multisensory circuits enabling complex input–output functions and selective tuning to ecologically relevant combinations of cues.


A multilevel multimodal circuit enhances action selection in Drosophila
• Tomoko Ohyama, Casey M. Schneider-Mizell, Richard D. Fetter, Javier Valdes Aleman, Romain Franconville, Marta Rivera-Alba, Brett D. Mensh, Kristin M. Branson, Julie H. Simpson, James W. Truman, Albert Cardona & Marta Zlatic

Nature 520, 633–639 (30 April 2015) http://dx.doi.org/10.1038/nature14297

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Ranking in interconnected multilayer networks reveals versatile nodes

Ranking in interconnected multilayer networks reveals versatile nodes | Papers | Scoop.it

The determination of the most central agents in complex networks is important because they are responsible for a faster propagation of information, epidemics, failures and congestion, among others. A challenging problem is to identify them in networked systems characterized by different types of interactions, forming interconnected multilayer networks. Here we describe a mathematical framework that allows us to calculate centrality in such networks and rank nodes accordingly, finding the ones that play the most central roles in the cohesion of the whole structure, bridging together different types of relations. These nodes are the most versatile in the multilayer network. We investigate empirical interconnected multilayer networks and show that the approaches based on aggregating—or neglecting—the multilayer structure lead to a wrong identification of the most versatile nodes, overestimating the importance of more marginal agents and demonstrating the power of versatility in predicting their role in diffusive and congestion processes.


Ranking in interconnected multilayer networks reveals versatile nodes
Manlio De Domenico, Albert Solé-Ribalta, Elisa Omodei, Sergio Gómez & Alex Arenas

Nature Communications 6, Article number: 6868 http://dx.doi.org/10.1038/ncomms7868 ;

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Dmitry Alexeev's curator insight, May 6, 10:03 AM

 insight on the networks

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Ebola—challenge and revival of theoretical epidemiology: Why Extrapolations from early phases of epidemics are problematic

At the beginning of the second half of the 20th century, there was a widespread belief that science and in particular medicine had progressed so far that Nature could be brought under complete control. It seemed that healthcare and pharmacology were in the position to prevent or to cure almost all diseases. In the 1980s, for example, the pharmaceutical industry stopped the search for new antibiotic drugs that would be badly needed nowadays in the light of the universal capabilities of bacteria to develop resistance factors. At about the same time previously unknown or unnoticed virus transmitted infectious human diseases appeared: acquired immunodeficiency syndrome caused by human immunodeficiency virus (HIV), Ebola caused by Ebola virus (EBOV) and four related other strains of filoviridae, as well as severe acquired respiratory syndrome (SARS) brought about by SARS coronavirus. Caused by prions and not by a virus is been bovine spongiform encephalopathy (BSE). Nevertheless, it gave rise to an equally serious new epidemic. These and other cases as well as the consequences of the “antivaccination movement”, for example, the recent reoccurrence of pertussis and measles, revived a need of reliable models in epidemiology. In particular, the recent Ebola epidemic starting in December 2013 in West Africa initiated a new boom in theoretical work on infectious disease dynamics


Ebola—challenge and revival of theoretical epidemiology: Why Extrapolations from early phases of epidemics are problematic
Peter Schuster

Complexity
Early View

10.1002/cplx.21694

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A set of measures to quantify the dynamicity of longitudinal social networks

This study proposes a new set of measures for longitudinal social networks (LSNs). A LSN evolves over time through the creation and/or deletion of links among a set of actors (e.g., individuals or organizations). The current literature does feature some methods, such as multiagent simulation models, for studying the dynamics of LSNs. These methods have mainly been utilized to explore evolutionary changes in LSNs from one state to another and to explain the underlying mechanisms for these changes. However, they cannot quantify different aspects of a LSN. For example, these methods are unable to quantify the level of dynamicity shown by an actor in a LSN and its contribution to the overall dynamicity shown by that LSN. This article develops a set of measures for LSNs to overcome this limitation. We illustrate the benefits of these measures by applying them to an exploration of the Enron crisis. These measures successfully identify a significant but previously unobserved change in network structures (both at individual and group levels) during Enron's crisis period.


A set of measures to quantify the dynamicity of longitudinal social networks
Shahadat Uddin, Arif Khan andMahendra Piraveenan

Complexity
Early View

http://dx.doi.org/10.1002/cplx.21690

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Neural Computations Mediating One-Shot Learning in the Human Brain

There are at least two distinct learning strategies for identifying the relationship between a cause and its consequence: (1) incremental learning, in which we gradually acquire knowledge through trial and error, and (2) one-shot learning, in which we rapidly learn from only a single pairing of a potential cause and a consequence. Little is known about how the brain switches between these two forms of learning. In this study, we provide evidence that the amount of uncertainty about the relationship between cause and consequence mediates the transition between incremental and one-shot learning. Specifically, the more uncertainty there is about the causal relationship, the higher the learning rate that is assigned to that stimulus. By imaging the brain while participants were performing the learning task, we also found that uncertainty about the causal association is encoded in the ventrolateral prefrontal cortex and that the degree of coupling between this region and the hippocampus increases during one-shot learning. We speculate that this prefrontal region may act as a “switch,” turning on and off one-shot learning as required.


Lee SW, O’Doherty JP, Shimojo S (2015) Neural Computations Mediating One-Shot Learning in the Human Brain. PLoS Biol 13(4): e1002137. http://dx.doi.org/10.1371/journal.pbio.1002137 ;

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Logistic Growth and Ergodic Properties of Urban Forms

Urban morphology has presented significant intellectual challenges to mathematicians and physicists ever since the eighteenth century, when Euler first explored the famous Konigsberg bridges problem. Many important regularities and allometries have been observed in urban studies, including Zipf's law and Gibrat's law, rendering cities attractive systems for analysis within statistical physics. Nevertheless, a broad consensus on how cities and their boundaries are defined is still lacking. Applying percolation theory to the street intersection space, we show that growth curves for the maximum cluster size of the largest cities in the UK and in California collapse to a single curve, namely the logistic. Subsequently, by introducing the concept of the condensation threshold, we show that natural boundaries of cities can be well defined in a universal way. This allows us to study and discuss systematically some of the allometries that are present in cities, thus casting light on the concept of ergodicity as related to urban street networks.


Logistic Growth and Ergodic Properties of Urban Forms
A. Paolo Masucci, Elsa Arcaute, Jiaqiu Wang, Erez Hatna, Kiril Stanilov, Michael Batty

http://arxiv.org/abs/1504.07380

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Why Our Genome and Technology Are Both Riddled With “Crawling Horrors”

Why Our Genome and Technology Are Both Riddled With “Crawling Horrors” | Papers | Scoop.it

When we build complex technologies, despite our best efforts and our desire for clean logic, they often end up being far messier than we intend. They often end up kluges: inelegant solutions that work just well enough. And a reason they end up being messy—despite being designed and engineered—is because fundamentally the way they grow and evolve is often more similar to biological systems than we realize.


http://nautil.us/blog/why-our-genome-and-technology-are-both-riddled-with-crawling-horrors

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Jeff Hawkins on Firing Up the Silicon Brain

Jeff Hawkins on Firing Up the Silicon Brain | Papers | Scoop.it

JEFF HAWKINS RECENTLY re-read his 2004 book On Intelligence, where the founder of Palm computing – the company that gave us the first handheld computer and later, first-generation smartphones – explains how the human brain learns. An electrical engineer by training, Hawkins had taken a deep interest in how the brain works and founded the Redwood Neuroscience Institute, a private, nonprofit research organization focused on understanding how the neocortex processes information, at UC Berkeley in 2002.
The big surprise? “There was very little I would change about that book,” Hawkins says. “There’s a lot I would add. There’s a ton of stuff where I know exactly how it works, that I didn’t know when I wrote it.”


http://www.wired.com/2015/05/jeff-hawkins-firing-silicon-brain


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Optimal Census by Quorum Sensing

Bacteria regulate gene expression in response to changes in cell density in a process called quorum sensing. To synchronize their gene-expression programs, these bacteria need to glean as much information as possible about their cell density. Our study is the first to physically model the flow of information in a quorum-sensing microbial community, wherein the internal regulator of the individuals response tracks the external cell density via an endogenously generated shared signal. Combining information theory and Lagrangian formalism, we find that quorum-sensing systems can improve their information capabilities by tuning circuit feedbacks. Our analysis suggests that achieving information benefit via feedback requires dedicated systems to control gene expression noise, such as sRNA-based regulation.


Optimal Census by Quorum Sensing
Thibaud Taillefumier, Ned S. Wingreen

PLoS Comput Biol 11(5): e1004238. http://dx.doi.org/10.1371/journal.pcbi.1004238 ;

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Pablo Vicente Munuera's curator insight, May 17, 4:15 AM

Quorum sensing is an interesting concept!

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On the Optimality and Predictability of Cultural Markets with Social Influence

Social influence is ubiquitous in cultural markets, from book recommendations in Amazon, to song popularities in iTunes and the ranking of newspaper articles in the online edition of the New York Times to mention only a few. Yet social influence is often presented in a bad light, often because it supposedly increases market unpredictability.
Here we study a model of trial-offer markets, in which participants try products and later decide whether to purchase. We consider a simple policy which ranks the products by quality when presenting them to market participants. We show that, in this setting, market efficiency always benefits from social influence. Moreover, we prove that the market converges almost surely to a monopoly for the product of highest quality, making the market both predictable and asymptotically optimal. Computational experiments confirm that the quality ranking policy identifies "blockbusters" in reasonable time, outperforms other policies, and is highly predictable. These results indicate that social influence does not necessarily increase market unpredicatibility. The outcome really depends on how social influence is used.


On the Optimality and Predictability of Cultural Markets with Social Influence
Pascal Van Hentenryck, Andres Abeliuk, Franco Berbeglia, Gerardo Berbeglia

http://arxiv.org/abs/1505.02469

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Duality between Temporal Networks and Signals: Extraction of the Temporal Network Structures

We develop a framework to track the structure of temporal networks with a signal processing approach. The method is based on the duality between networks and signals using a multidimensional scaling technique. This enables a study of the network structure using frequency patterns of the corresponding signals. An extension is proposed for temporal networks, thereby enabling a tracking of the network structure over time. A method to automatically extract the most significant frequency patterns and their activation coefficients over time is then introduced, using nonnegative matrix factorization of the temporal spectra. The framework, inspired by audio decomposition, allows transforming back these frequency patterns into networks, to highlight the evolution of the underlying structure of the network over time. The effectiveness of the method is first evidenced on a toy example, prior being used to study a temporal network of face-to-face contacts. The extraction of sub-networks highlights significant structures decomposed on time intervals.


Duality between Temporal Networks and Signals: Extraction of the Temporal Network Structures
Ronan Hamon, Pierre Borgnat, Patrick Flandrin, Céline Robardet

http://arxiv.org/abs/1505.03044

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Improving measures of topological robustness in networks of networks and suggestion of a novel way to counter both failure propagation and isolation

The study of interdependent complex networks in the last decade has shown how cascading failure can result in the recursive and complete fragmentation of all connected systems from the destruction of a comparatively small number of nodes. Existing “network of networks” approaches are still in infancy and have shown limits when trying to model the robustness of real-world systems, due to simplifying assumptions regarding network interdependencies and post-attack viability. In order to increase the realism of such models, we challenge such assumptions by validating the following four hypotheses trough experimental results obtained from computer based simulations. Firstly, we suggest that, in the case of network topologies vulnerable to fragmentation, replacing the standard measure of robustness based on the size of the one largest remaining connected component by a new measure allowing secondary components to remain viable when measuring post-attack viability can make a significant improvement to the model. Secondly, we show that it is possible to influence the way failure propagation is balanced between coupled networks while keeping the same overall robustness score by allowing nodes in a given network to have multiple counter parts in another network. Thirdly, we challenge the generalised assumption that partitioning between networks is a good way to increase robustness and find that isolation is a force as equally destructive as the iterative propagation of cascading failure. This result significantly alters where the optimum robustness lies in the balance between isolation and inter-network coupling in such interconnected systems. Finally, we propose a solution to the consequent problem of seemingly ever increasing vulnerability of interdependent networks to both cascading failure and isolation: the use of permutable nodes that would give such systems rewiring capabilities. This last concept could have wide implications when trying to improve the topological resilience of natural or engineered interdependent networks.


Improving measures of topological robustness in networks of networks and suggestion of a novel way to counter both failure propagation and isolation
Mehdi Khoury, Seth Bullock, Gaihua Fu and Richard Dawson

Infrastructure Complexity 2015, 2:1 

http://dx.doi.org/10.1186/s40551-015-0004-9 ;

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Accelerating extinction risk from climate change

Current predictions of extinction risks from climate change vary widely depending on the specific assumptions and geographic and taxonomic focus of each study. I synthesized published studies in order to estimate a global mean extinction rate and determine which factors contribute the greatest uncertainty to climate change–induced extinction risks. Results suggest that extinction risks will accelerate with future global temperatures, threatening up to one in six species under current policies. Extinction risks were highest in South America, Australia, and New Zealand, and risks did not vary by taxonomic group. Realistic assumptions about extinction debt and dispersal capacity substantially increased extinction risks. We urgently need to adopt strategies that limit further climate change if we are to avoid an acceleration of global extinctions.


Accelerating extinction risk from climate change
Mark C. Urban

Science 1 May 2015:
Vol. 348 no. 6234 pp. 571-573
http://dx.doi.org/10.1126/science.aaa4984

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Hierarchical organisation of Britain through percolation theory

Urban systems present hierarchical structures at many different scales. These are observed as administrative regional delimitations, which are the outcome of geographical, political and historical constraints. Using percolation theory on the street intersections and on the road network of Britain, we obtain hierarchies at different scales that are independent of administrative arrangements. Natural boundaries, such as islands and National Parks, consistently emerge at the largest/regional scales. Cities are devised through recursive percolations on each of the emerging clusters, but the system does not undergo a phase transition at the distance threshold at which cities can be defined. This specific distance is obtained by computing the fractal dimension of the clusters extracted at each distance threshold. We observe that the fractal dimension presents a maximum over all the different distance thresholds. The clusters obtained at this maximum are in very good correspondence to the morphological definition of cities given by satellite images, and by other methods previously developed by the authors (Arcaute et al. 2015).


Hierarchical organisation of Britain through percolation theory
Elsa Arcaute, Carlos Molinero, Erez Hatna, Roberto Murcio, Camilo Vargas-Ruiz, Paolo Masucci, Jiaqiu Wang, Michael Batty

http://arxiv.org/abs/1504.08318

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Unveiling patterns of international communities in a global city using mobile phone data

We analyse a large mobile phone activity dataset provided by Telecom Italia for the Telecom Big Data Challenge contest. The dataset reports the international country codes of every call/SMS made and received by mobile phone users in Milan, Italy, between November and December 2013, with a spatial resolution of about 200 meters. We first show that the observed spatial distribution of international codes well matches the distribution of international communities reported by official statistics, confirming the value of mobile phone data for demographic research. Next, we define an entropy function to measure the heterogeneity of the international phone activity in space and time. By comparing the entropy function to empirical data, we show that it can be used to identify the city’s hotspots, defined by the presence of points of interests. Eventually, we use the entropy function to characterize the spatial distribution of international communities in the city. Adopting a topological data analysis approach, we find that international mobile phone users exhibit some robust clustering patterns that correlate with basic socio-economic variables. Our results suggest that mobile phone records can be used in conjunction with topological data analysis tools to study the geography of migrant communities in a global city.


Unveiling patterns of international communities in a global city using mobile phone data
Paolo Bajardi, Matteo Delfino, André Panisson, Giovanni Petri and Michele Tizzoni

EPJ Data Science 2015, 4:3  http://dx.doi.org/10.1140/epjds/s13688-015-0041-5 ;

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The Fractured Nature of British Politics

The outcome of the British General Election to be held in just over one week's time is widely regarded as the most difficult in living memory to predict. Current polls suggest that the two main parties are neck and neck but that there will be a landslide to the Scottish Nationalist Party with that party taking most of the constituencies in Scotland. The Liberal Democrats are forecast to loose more than half their seats and the fringe parties of whom the UK Independence Party is the biggest are simply unknown quantities. Much of this volatility relates to long-standing and deeply rooted cultural and nationalist attitudes that relate to geographical fault lines that have been present for 500 years or more but occasionally reveal themselves, at times like this. In this paper our purpose is to raise the notion that these fault lines are critical to thinking about regionalism, nationalism and the hierarchy of cities in Great Britain (excluding Northern Ireland). We use a percolation method (Arcaute et al. 2015) to reveal them that treats Britain as a giant cluster of related places each defined from the intersections of the road network at a very fine spatial scale. We break this giant cluster into a detailed hierarchy of sub-clusters by successively reducing a distance threshold which first breaks off some of the Scottish Islands and then reveals the very distinct nations and regions that make up Britain, all the way down to the definition of the largest cities that appear when the threshold reaches 300m. We use these percolation clusters to apportion the 2010 voting pattern to a new hierarchy of constituencies based on these clusters, and this gives us a picture of how Britain might vote on purely geographical lines. We then examine this voting pattern which provides us with some sense of how important the new configuration of political parties might be to the election next week.


The Fractured Nature of British Politics
Carlos Molinero, Elsa Arcaute, Duncan Smith, Michael Batty

http://arxiv.org/abs/1505.00217

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How a well-adapted immune system is organized

The adaptive immune system uses the experience of past infections to prepare its limited repertoire of specialized receptors to protect organisms from future threats. What is the best way of doing this? Building a theoretical framework from first principles, we predict the composition of receptor repertoires that are optimally adapted to minimize the cost of infections from a given pathogenic environment. A naive repertoire can reach these optima through a biologically plausible competitive mechanism. Our findings explain how limited populations of immune receptors can self-organize to provide effective immunity against highly diverse pathogens. Our results also inform the design and interpretation of experiments surveying immune repertoires.


How a well-adapted immune system is organized
Andreas Mayer, Vijay Balasubramanian, Thierry Mora, and Aleksandra M. Walczak

http://dx.doi.org/10.1073/pnas.1421827112 ;

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On the universal structure of human lexical semantics

How universal is human conceptual structure? The way concepts are organized in the human brain may reflect distinct features of cultural, historical, and environmental background in addition to properties universal to human cognition. Semantics, or meaning expressed through language, provides direct access to the underlying conceptual structure, but meaning is notoriously difficult to measure, let alone parameterize. Here we provide an empirical measure of semantic proximity between concepts using cross-linguistic dictionaries. Across languages carefully selected from a phylogenetically and geographically stratified sample of genera, translations of words reveal cases where a particular language uses a single polysemous word to express concepts represented by distinct words in another. We use the frequency of polysemies linking two concepts as a measure of their semantic proximity, and represent the pattern of such linkages by a weighted network. This network is highly uneven and fragmented: certain concepts are far more prone to polysemy than others, and there emerge naturally interpretable clusters loosely connected to each other. Statistical analysis shows such structural properties are consistent across different language groups, largely independent of geography, environment, and literacy. It is therefore possible to conclude the conceptual structure connecting basic vocabulary studied is primarily due to universal features of human cognition and language use.


On the universal structure of human lexical semantics
Hyejin Youn, Logan Sutton, Eric Smith, Cristopher Moore, Jon F. Wilkins, Ian Maddieson, William Croft, Tanmoy Bhattacharya

http://arxiv.org/abs/1504.07843

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