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The other microbiome--exploring the human virome

The other microbiome--exploring the human virome | Papers | Scoop.it

In June 2012, scientists around the world simultaneously published a series of papers spearheaded by the US National Institutes of Health’s Human Microbiome Project (HMP) that characterized the fundamentals of the microbiome in healthy individuals (3). By definition, the microbiome includes all microbes in the human body: bacteria, viruses, and fungi. Most initial HMP research, however, focused on bacteria because there is a standardized and thorough protocol for isolating and characterizing bacterial genes from the slurry of DNA in human feces or saliva swabs. Viruses, in contrast, have so far been the forgotten siblings of the microbiome family. But a growing cadre of researchers argues that the human virome is probably at least as important to human health as our bacterial inhabitants.

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The Evolutionary Origins of Hierarchy

Hierarchy is a ubiquitous organizing principle in biology, and a key reason evolution produces complex, evolvable organisms, yet its origins are poorly understood. Here we demonstrate for the first time that hierarchy evolves as a result of the costs of network connections. We confirm a previous finding that connection costs drive the evolution of modularity, and show that they also cause the evolution of hierarchy. We further confirm that hierarchy promotes evolvability in addition to evolvability caused by modularity. Because many biological and human-made phenomena can be represented as networks, and because hierarchy is a critical network property, this finding is immediately relevant to a wide array of fields, from biology, sociology, and medical research to harnessing evolution for engineering.

 

Mengistu H, Huizinga J, Mouret J-B, Clune J (2016) The Evolutionary Origins of Hierarchy. PLoS Comput Biol 12(6): e1004829. doi:10.1371/journal.pcbi.1004829

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Bottles, bags, ropes and toothbrushes: the struggle to track ocean plastics

Bottles, bags, ropes and toothbrushes: the struggle to track ocean plastics | Papers | Scoop.it
Scientists know that there is a colossal amount of plastic in the oceans. But they don’t know where it all is, what it looks like or what damage it does.
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Caging and Uncaging Genetics

Caging and Uncaging Genetics | Papers | Scoop.it

It is important for biology to understand if observations made in highly reductionist laboratory settings generalise to harsh and noisy natural environments in which genetic variation is sorted to produce adaptation. But what do we learn by studying, in the laboratory, a genetically diverse population that mirrors the wild? What is the best design for studying genetic variation? When should we consider it at all? The right experimental approach depends on what you want to know.

 

Little TJ, Colegrave N (2016) Caging and Uncaging Genetics. PLoS Biol 14(7): e1002525. doi:10.1371/journal.pbio.1002525

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Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation

Effective study of ocean processes requires sampling over the duration of long (weeks to months) oscillation patterns. Such sampling requires persistent, autonomous underwater vehicles that have a similarly, long deployment duration. The spatiotemporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. In this paper, we consider the combination of two methods for reducing navigation and localization error: a predictive approach based on ocean model predictions and a prior information approach derived from terrain-based navigation. The motivation for this work is not only for real-time state estimation but also for accurately reconstructing the actual path that the vehicle traversed to contextualize the gathered data, with respect to the science question at hand. We present an application for the practical use of priors and predictions for large-scale ocean sampling. This combined approach builds upon previous works by the authors and accurately localizes the traversed path of an underwater glider over long-duration, ocean deployments. The proposed method takes advantage of the reliable, short-term predictions of an ocean model, and the utility of priors used in terrain-based navigation over areas of significant bathymetric relief to bound uncertainty error in dead-reckoning navigation. This method improves upon our previously published works by (1) demonstrating the utility of our terrain-based navigation method with multiple field trials and (2) presenting a hybrid algorithm that combines both approaches to bound navigational error and uncertainty for long-term deployments of underwater vehicles. We demonstrate the approach by examining data from actual field trials with autonomous underwater gliders and demonstrate an ability to estimate geographical location of an underwater glider to <100 m over paths of length >2 km. Utilizing the combined algorithm, we are able to prescribe an uncertainty bound for navigation and instruct the glider to surface if that bound is exceeded during a given mission.

 

Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation
Andrew Stuntz, Jonathan Scott Kelly and Ryan N. Smith

Front. Robot. AI, 29 April 2016 | http://dx.doi.org/10.3389/frobt.2016.00023

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Instagram photos reveal predictive markers of depression

Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners' average diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Photos posted by depressed individuals were more likely to be bluer, grayer, and darker. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These findings suggest new avenues for early screening and detection of mental illness.

 

Instagram photos reveal predictive markers of depression
Andrew G. Reece, Christopher M. Danforth

http://arxiv.org/abs/1608.03282

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Influence of selfish and polite behaviours on a pedestrian evacuation through a narrow exit: A quantitative characterisation

We study the influence of selfish vs. polite behaviours on the dynamics of a pedestrian evacuation through a narrow exit. To this end, experiments involving about 80 participants with distinct prescribed behaviours are performed; reinjection of participants into the setup allowed us to improve the statistics. Notwithstanding the fluctuations in the instantaneous flow rate, we find that a stationary regime is almost immediately reached. The average flow rate increases monotonically with the fraction c\_s of vying (selfish) pedestrians, which corresponds to a "faster-is-faster" effect in our experimental conditions; it is also positively correlated with the average density of pedestrians in front of the door, up to nearly close-packing. At large c\_s , the flow displays marked intermittency, with bursts of quasi-simultaneous escapes. In addition to these findings, we wonder whether the effect of cooperation is specific to systems of intelligent beings, or whether it can be reproduced by a purely mechanical surrogate. To this purpose, we consider a bidimensional granular flow through an orifice in which some grains are made "cooperative" by repulsive magnetic interactions which impede their mutual collisions.

 

Influence of selfish and polite behaviours on a pedestrian evacuation through a narrow exit: A quantitative characterisation
Alexandre Nicolas, Sebastián Bouzat, Marcelo Kuperman

http://arxiv.org/abs/1608.04863

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Haploid-Diploid Algorithms

This short paper uses the recently presented idea that the fundamental haploid-diploid lifecycle of all eukaryote organisms exploits a rudimentary form of the Baldwin effect. The general approach presented here differs from all previous known work using diploid representations within evolutionary computation. The role of recombination is also changed from that previously considered in both natural and artificial evolution under the new theory. Using the NK model of fitness landscapes and the RBNK model of gene regulatory networks it is here shown that varying landscape ruggedness varies the benefit of a haploid-diploid approach in comparison to the traditional haploid representation in both cases.

 

Haploid-Diploid Algorithms
Larry Bull

http://arxiv.org/abs/1608.05578

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A First Look at User Activity on Tinder

Mobile dating apps have become a popular means to meet potential partners. Although several exist, one recent addition stands out amongst all others. Tinder presents its users with pictures of people geographically nearby, whom they can either like or dislike based on first impressions. If two users like each other, they are allowed to initiate a conversation via the chat feature. In this paper we use a set of curated profiles to explore the behaviour of men and women in Tinder. We reveal differences between the way men and women interact with the app, highlighting the strategies employed. Women attain large numbers of matches rapidly, whilst men only slowly accumulate matches. To expand on our findings, we collect survey data to understand user intentions on Tinder. Most notably, our results indicate that a little effort in grooming profiles, especially for male users, goes a long way in attracting attention.

 

A First Look at User Activity on Tinder
Gareth Tyson, Vasile C. Perta, Hamed Haddadi, Michael C. Seto

http://arxiv.org/abs/1607.01952

Complexity Digest's insight:

Seem very similar to other primate mating strategies.

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Introduction to Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems

In the last years, network scientists have directed their interest to the multi-layer character of real-world systems, and explicitly considered the structural and dynamical organization of graphs made of diverse layers between its constituents. Most complex systems include multiple subsystems and layers of connectivity and, in many cases, the interdependent components of systems interact through many different channels. Such a new perspective is indeed found to be the adequate representation for a wealth of features exhibited by networked systems in the real world. The contributions presented in this Focus Issue cover, from different points of view, the many achievements and still open questions in the field of multi-layer networks, such as: new frameworks and structures to represent and analyze heterogeneous complex systems, different aspects related to synchronization and centrality of complex networks, interplay between layers, and applications to logistic, biological, social, and technological fields.

 

Introduction to Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems
Stefano Boccaletti, Regino Criado, Miguel Romance and Joaquín J. Torres

Chaos 26, 065101 (2016); http://dx.doi.org/10.1063/1.4953595

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Understanding Predictability and Exploration in Human Mobility

Predictive models for human mobility have important applications in many fields such as traffic control, ubiquitous computing and contextual advertisement. The predictive performance of models in literature varies quite broadly, from as high as 93% to as low as under 40%. In this work we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users for periods between 3 months and one year. We show that it is easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover we demonstrate how the temporal and spatial resolution of the data can have strong influence on the accuracy of prediction. Finally we uncover that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.

 

Understanding Predictability and Exploration in Human Mobility
Andrea Cuttone, Sune Lehmann, Marta C. González

http://arxiv.org/abs/1608.01939

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Fruitful symbioses between termites and computers

The living-together of distinct organisms in a single termite nest along with the termite builder colony, is emblematic in its ecological and evolutionary significance. On top of preserving biodiversity, these interspecific and intraspecific symbioses provide useful examples of interindividual associations thought to underly transitions in organic evolution. Being interindividual in nature, such processes may involve emergent phenomena and hence call for analytical solutions provided by computing tools and modelling, as opposed to classical biological methods of analysis. Here we provide selected examples of such solutions, showing that termite studies may profit from a symbiotic-like link with computing science to open up wide and new research avenues in ecology and evolution.

 

Fruitful symbioses between termites and computers
Og DeSouza, Elio Tuci, Octavio Miramontes

http://arxiv.org/abs/1608.05367

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The Physics behind Systems Biology


Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (‘in silico’) experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology. Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the ‘Physics core’ of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from its Theoretical Physics foundation.



Via Bernard Ryefield
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Noise and Function

Noise is widely understood to be something that interferes with a signal or process. Thus, it is generally thought to be destructive, obscuring signals and interfering with function. However, early in the 20th century, mechanical engineers found that mechanisms inducing additional vibration in mechanical systems could prevent sticking and hysteresis. This so-called "dither" noise was later introduced in an entirely different context at the advent of digital information transmission and recording in the early 1960s. Ironically, the addition of noise allows one to preserve information that would otherwise be lost when the signal or image is digitized. As we shall see, the benefits of added noise in these contexts are closely related to the phenomenon which has come to be known as stochastic resonance, the original version of which appealed to noise to explain how small periodic fluctuations in the eccentricity of the earth's orbit might be amplified in such a way as to bring about the observed periodic transitions in climate from ice age to temperate age and back. These noise-induced transitions have since been invoked to explain a wide array of biological phenomena, including the foraging and tracking behavior of ants. Many biological phenomena, from foraging to gene expression, are noisy, involving an element of randomness. In this paper, we illustrate the general principles behind dithering and stochastic resonance using examples from image processing, and then show how the constructive use of noise can carry over to systems found in nature.

 

Noise and Function
Steven Weinstein, Theodore P. Pavlic

http://arxiv.org/abs/1608.04824

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Disease Surveillance on Complex Social Networks

Disease Surveillance on Complex Social Networks | Papers | Scoop.it

As public health agencies strive to harness big data to improve outbreak surveillance, they face the challenge of extracting meaningful information that can be directly used to improve public health, without incurring additional costs. In this article, we address the question: Which nodes in a social network should be selectively monitored to detect and monitor outbreaks as early and accurately as possible? We derive best-case performance scenarios, and show that a practical strategy for data collection–recruiting friends of randomly selected individuals–is expected to perform reasonably well, in terms of the timing and reliability of the epidemiological information collected.

 

Herrera JL, Srinivasan R, Brownstein JS, Galvani AP, Meyers LA (2016) Disease Surveillance on Complex Social Networks. PLoS Comput Biol 12(7): e1004928. doi:10.1371/journal.pcbi.1004928

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Ten Simple Rules for Writing a Postdoctoral Fellowship

Rule 1: Start Early and Gather Critical Information
Rule 2: Create a Game Plan and Write Regularly
Rule 3: Find Your Research Niche
Rule 4: Use Your Specific Aims Document as Your Roadmap
Rule 5: Build a First-Rate Team of Mentors
Rule 6: Develop a Complete Career Development Training Plan
Rule 7: STOP! Get Feedback
Rule 8: Tell a Consistent and Cohesive Story
Rule 9: Follow Specific Requirements and Proofread for Errors and Readability
Rule 10: Recycle and Resubmit

 

Yuan K, Cai L, Ngok SP, Ma L, Botham CM (2016) Ten Simple Rules for Writing a Postdoctoral Fellowship. PLoS Comput Biol 12(7): e1004934. doi:10.1371/journal.pcbi.1004934

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My Text in Your Handwriting

My Text in Your Handwriting | Papers | Scoop.it

There are many scenarios where we wish to imitate a specific author's pen-on-paper handwriting style. Rendering new text in someone's handwriting is difficult because natural handwriting is highly variable, yet follows both intentional and involuntary structure that makes a person's style self-consistent.
We present an algorithm that renders a desired input string in an author's handwriting. An annotated sample of the author's handwriting is required; the system is flexible enough that historical documents can usually be used with only a little extra effort. Experiments show that our glyph-centric approach, with learned parameters for spacing, line thickness, and pressure, produces novel images of handwriting that look hand-made to casual observers, even when printed on paper.

 

My Text in Your Handwriting
Tom S.F. Haines, Oisin Mac Aodha, and Gabriel J. Brostow
University College London
Transactions on Graphics 2016

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From A to B: A new approach to the limits of predictability of human mobility patterns

Next place prediction algorithms are invaluable tools, capable of increasing the efficiency of a wide variety of tasks, ranging from reducing the spreading of diseases to better resource management in areas such as urban planning and communication networks. In this work we estimate upper and lower limits on the predictability of human mobility to help assess the performance of competing algorithms. We do this using GPS traces from 604 individuals participating in a multiyear long experiment, The Copenhagen Networks study. Earlier works, focusing on the prediction of a participants whereabouts in the next time bin, have found very high upper limits (> 90%). We show that these upper limits, at least for some spatiotemporal scales, are mainly driven by the fact that humans tend to stay in the same place for long periods of time. This leads us to propose a new approach, focusing on the prediction of the next Point of Interest. By removing the trivial parts of human mobility behaviour, we show that the predictability of human mobility is significantly lower than implied by earlier works.

 

From A to B: A new approach to the limits of predictability of human mobility patterns
Edin Lind Ikanovic, Anders Mollgaard

http://arxiv.org/abs/1608.06419

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The Complexity of Dynamics in Small Neural Circuits

The mesoscopic level of brain organization, describing the organization and dynamics of small circuits of neurons including from few tens to few thousands, has recently received considerable experimental attention. It is useful for describing small neural systems of invertebrates, and in mammalian neural systems it is often seen as a middle ground that is fundamental to link single neuron activity to complex functions and behavior. However, and somewhat counter-intuitively, the behavior of neural networks of small and intermediate size can be much more difficult to study mathematically than that of large networks, and appropriate mathematical methods to study the dynamics of such networks have not been developed yet. Here we consider a model of a network of firing-rate neurons with arbitrary finite size, and we study its local bifurcations using an analytical approach. This analysis, complemented by numerical studies for both the local and global bifurcations, shows the emergence of strong and previously unexplored finite-size effects that are particularly hard to detect in large networks. This study advances the tools available for the comprehension of finite-size neural circuits, going beyond the insights provided by the mean-field approximation and the current techniques for the quantification of finite-size effects.

 

Fasoli D, Cattani A, Panzeri S (2016) The Complexity of Dynamics in Small Neural Circuits. PLoS Comput Biol 12(8): e1004992. doi:10.1371/journal.pcbi.1004992

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Fecal Transplants: What Is Being Transferred?

Fecal Transplants: What Is Being Transferred? | Papers | Scoop.it

Fecal transplants are increasingly utilized for treatment of recurrent infections (i.e., Clostridium difficile) in the human gut and as a general research tool for gain-of-function experiments (i.e., gavage of fecal pellets) in animal models. Changes observed in the recipient's biology are routinely attributed to bacterial cells in the donor feces (~10^11 per gram of human wet stool). Here, we examine the literature and summarize findings on the composition of fecal matter in order to raise cautiously the profile of its multipart nature. In addition to viable bacteria, which may make up a small fraction of total fecal matter, other components in unprocessed human feces include colonocytes (~10^7 per gram of wet stool), archaea (~10^8 per gram of wet stool), viruses (~10^8 per gram of wet stool), fungi (~10^6 per gram of wet stool), protists, and metabolites. Thus, while speculative at this point and contingent on the transplant procedure and study system, nonbacterial matter could contribute to changes in the recipient's biology. There is a cautious need for continued reductionism to separate out the effects and interactions of each component.

 

Bojanova DP, Bordenstein SR (2016) Fecal Transplants: What Is Being Transferred? PLoS Biol 14(7): e1002503. doi:10.1371/journal.pbio.1002503

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Ten Simple Rules for Taking Advantage of Git and GitHub

Rule 1: Use GitHub to Track Your Projects
Rule 2: GitHub for Single Users, Teams, and Organizations
Rule 3: Developing and Collaborating on New Features: Branching and Forking
Rule 4: Naming Branches and Commits: Tags and Semantic Versions
Rule 5: Let GitHub Do Some Tasks for You: Integrate
Rule 6: Let GitHub Do More Tasks for You: Automate
Rule 7: Use GitHub to Openly and Collaboratively Discuss, Address, and Close Issues
Rule 8: Make Your Code Easily Citable, and Cite Source Code!
Rule 9: Promote and Discuss Your Projects: Web Page and More
Rule 10: Use GitHub to Be Social: Follow and Watch

 

Perez-Riverol Y, Gatto L, Wang R, Sachsenberg T, Uszkoreit J, Leprevost FdV, et al. (2016) Ten Simple Rules for Taking Advantage of Git and GitHub. PLoS Comput Biol 12(7): e1004947. doi:10.1371/journal.pcbi.1004947

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Simon's fundamental rich-gets-richer model entails a dominant first-mover advantage

Herbert Simon's classic rich-gets-richer model is one of the simplest empirically supported mechanisms capable of generating heavy-tail size distributions for complex systems. Simon argued analytically that a population of flavored elements growing by either adding a novel element or randomly replicating an existing one would afford a distribution of group sizes with a power-law tail. Here, we show that, in fact, Simon's model does not produce a simple power law size distribution as the initial element has a dominant first-mover advantage, and will be overrepresented by a factor proportional to the inverse of the innovation probability. The first group's size discrepancy cannot be explained away as a transient of the model, and may therefore be many orders of magnitude greater than expected. We demonstrate how Simon's analysis was correct but incomplete, and expand our alternate analysis to quantify the variability of long term rankings for all groups. We find that the expected time for a first replication is infinite, and show how an incipient group must break the mechanism to improve their odds of success. Our findings call for a reexamination of preceding work invoking Simon's model and provide a revised understanding going forward.

 

Simon's fundamental rich-gets-richer model entails a dominant first-mover advantage
Peter Sheridan Dodds, David Rushing Dewhurst, Fletcher F. Hazlehurst, Colin M. Van Oort, Lewis Mitchell, Andrew J. Reagan, Jake Ryland Williams, Christopher M. Danforth

http://arxiv.org/abs/1608.06313

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The new challenges of multiplex networks: measures and models

What do societies, the Internet, and the human brain have in common? The immediate answer might be "not that much", but in reality they are all examples of complex relational systems, whose emerging behaviours are largely determined by the non-trivial networks of interactions among their constituents, namely individuals, computers, or neurons. In the last two decades, network scientists have proposed models of increasing complexity to better understand real-world systems. Only recently we have realised that multiplexity, i.e. the coexistence of several types of interactions among the constituents of a complex system, is responsible for substantial qualitative and quantitative differences in the type and variety of behaviours that a complex system can exhibit. As a consequence, multilayer and multiplex networks have become a hot topic in complexity science. Here we provide an overview of some of the measures proposed so far to characterise the structure of multiplex networks, and a selection of models aiming at reproducing those structural properties and at quantifying their statistical significance. Focusing on a subset of relevant topics, this brief review is a quite comprehensive introduction to the most basic tools for the analysis of multiplex networks observed in the real-world. The wide applicability of multiplex networks as a framework to model complex systems in different fields, from biology to social sciences, and the colloquial tone of the paper will make it an interesting read for researchers working on both theoretical and experimental analysis of networked systems.

 

The new challenges of multiplex networks: measures and models
Federico Battiston, Vincenzo Nicosia, Vito Latora

http://arxiv.org/abs/1606.09221

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Robustness and Resilience of cities around the world

The concept of city or urban resilience has emerged as one of the key challenges for the next decades. As a consequence, institutions like the United Nations or Rockefeller Foundation have embraced initiatives that increase or improve it. These efforts translate into funded programs both for action on the ground and to develop quantification of resilience, under the for of an index. Ironically, on the academic side there is no clear consensus regarding how resilience should be quantified, or what it exactly refers to in the urban context. Here we attempt to link both extremes providing an example of how to exploit large, publicly available, worldwide urban datasets, to produce objective insight into one of the possible dimensions of urban resilience. We do so via well-established methods in complexity science, such as percolation theory --which has a long tradition at providing valuable information on the vulnerability in complex systems. Our findings uncover large differences among studied cities, both regarding their infrastructural fragility and the imbalances in the distribution of critical services.

 

Robustness and Resilience of cities around the world
Sofiane Abbar, Tahar Zanouda, Javier Borge-Holthoefer

http://arxiv.org/abs/1608.01709

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The fundamental advantages of temporal networks

Despite the traditional focus of network science on static networks, most networked systems of scientific interest are characterized by temporal links. By disrupting the paths, link temporality has been shown to frustrate many dynamical processes on networks, from information spreading to accessibility. Considering the ubiquity of temporal networks in nature, we must ask: Are there any advantages of the networks' temporality? Here we develop an analytical framework to explore the control properties of temporal networks, arriving at the counterintuitive conclusion that temporal networks, compared to their static (i.e. aggregated) counterparts, reach controllability faster, demand orders of magnitude less control energy, and the control trajectories, through which the system reaches its final states, are significantly more compact than those characterizing their static counterparts. The combination of analytical, numerical and empirical results demonstrates that temporality ensures a degree of flexibility that would be unattainable in static networks, significantly enhancing our ability to control them.

 

The fundamental advantages of temporal networks
Aming Li, Sean P. Cornelius, Yang-Yu Liu, Long Wang, Albert-László Barabási

http://arxiv.org/abs/1607.06168

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State power and elite autonomy: The board interlock network of Chinese non-profits

In response to failures of central planning, the Chinese government has experimented not only with free-market trade zones, but with allowing non-profit foundations to operate in a decentralized fashion. A network study shows how these foundations have connected together by sharing board members, in a structural parallel to what is seen in corporations in the United States. This board interlock leads to the emergence of an elite group with privileged network positions. While the presence of government officials on non-profit boards is widespread, state officials are much less common in a subgroup of foundations that control just over half of all revenue in the network. This subgroup, associated with business elites, not only enjoys higher levels of within-elite links, but even preferentially excludes government officials from the nodes with higher degree. The emergence of this structurally autonomous sphere is associated with major political and social events in the state-society relationship.

 

State power and elite autonomy: The board interlock network of Chinese non-profits
Ji Ma, Simon DeDeo

http://arxiv.org/abs/1606.08103

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