复杂文摘翻译第六期-(摘自Complex Digest 2016.1月文章)

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复杂文摘翻译第六期-(摘自Complex Digest 2016.1月文章)

Fisher transfer entropy: quantifying the gain in transient sensitivity

Proceedings of the Royal Society A Mathematical Physical & Engineering Sciences, 2015, 471(2184)

by Mikhail Prokopenko, Lionel Barnett, Michael Harré, Joseph T. Lizier, Oliver Obst, X. Rosalind Wang

(Translated by -)

We introduce a novel measure, Fisher transfer entropy (FTE), which quantifies a gain in sensitivity to a control parameter of a state transition, in the context of another observable source. The new measure captures both transient and contextual qualities of transfer entropy and the sensitivity characteristics of Fisher information. FTE is exemplified for a ferromagnetic two-dimensional lattice Ising model with Glauber dynamics and is shown to diverge at the critical point.



Information Flows? A Critique of Transfer Entropies

Phys. Rev. Lett. 116, 238701 (2016)

by Ryan G. James, Nix Barnett, James P. Crutchfield

(Translated by -)

A central task in analyzing complex dynamics is to determine the loci of information storage and the communication topology of information flows within a system. Over the last decade and a half, diagnostics for the latter have come to be dominated by the transfer entropy. Via straightforward examples, we show that it and a derivative quantity, the causation entropy, do not, in fact, quantify the flow of information. At one and the same time they can overestimate flow or underestimate influence. We isolate why this is the case and propose several avenues to alternate measures for information flow. We also address an auxiliary consequence: The proliferation of networks as a now-common theoretical model for large-scale systems, in concert with the use of transfer-like entropies, has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems. This interpretation thus fails to include the effects of polyadic dependencies. The net result is that much of the sophisticated organization of complex systems may go undetected.



Interacting Behavior and Emerging Complexity

Computer Science, 2015

by Alyssa Adams, Hector Zenil, Eduardo Hermo Reyes, Joost Joosten

(Translated by -)

Can we quantify the change of complexity throughout evolutionary processes? We attempt to address this question through an empirical approach. In very general terms, we simulate two simple organisms on a computer that compete over limited available resources. We implement Global Rules that determine the interaction between two Elementary Cellular Automata on the same grid. Global Rules change the complexity of the state evolution output which suggests that some complexity is intrinsic to the interaction rules themselves. The largest increases in complexity occurred when the interacting elementary rules had very little complexity, suggesting that they are able to accept complexity through interaction only. We also found that some Class 3 or 4 CA rules are more fragile than others to Global Rules, while others are more robust, hence suggesting some intrinsic properties of the rules independent of the Global Rule choice. We provide statistical mappings of Elementary Cellular Automata exposed to Global Rules and different initial conditions onto different complexity classes.



On the origin of burstiness in human behavior: The wikipedia edits case

Submitted on 5 Jan 2016 (v1), last revised 8 Jan 2016 (this version, v2)

by Yerali Gandica, Joao Carvalho, Fernando Sampaio Dos Aidos, Renaud Lambiotte, Timoteo Carletti

(Translated by -)

A number of human activities exhibit a bursty pattern, namely periods of very high activity that are followed by rest periods. Records of this process generate time series of events whose inter-event times follow a probability distribution that displays a fat tail. The grounds for such phenomenon are not yet clearly understood. In the present work we use the freely available Wikipedia's editing records to tackle this question by measuring the level of burstiness, as well as the memory effect of the editing tasks performed by different editors in different pages. Our main finding is that, even though the editing activity is conditioned by the circadian 24 hour cycle, the conditional probability of an activity of a given duration at a given time of the day is independent from the latter. This suggests that the human activity seems to be related to the high "cost" of starting an action as opposed to the much lower "cost" of continuing that action.



Understanding the group dynamics and success of teams

Royal Society Open Science, 2016, 3, 160007

by Michael Klug, James P. Bagrow

(Translated by -)

Complex problems often require 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, workload is highly focused across the team, with only a few members performing most work. We find that highly successful teams are significantly more focused than average teams of the same size, that their members have worked on more diverse sets of projects, and the members of highly successful teams are more likely to be core members or 'leads' of other teams. The relations between team success and size, focus and especially team experience cannot be explained by confounding factors such as team age, external contributions from non-team members, nor by group mechanisms such as social loafing. Taken together, these features point to organizational principles that may maximize the success of collaborative endeavors.



The angular nature of road networks

Physics, 2015

by Carlos Molinero, Roberto Murcio, Elsa Arcaute

(Translated by -)

Road networks are characterised by several structural and geometric properties. Their topological structure determines partially its hierarchical arrangement, but since these are networks that are spatially situated and, therefore, spatially constrained, to fully understand the role that each road plays in the system it is fundamental to characterize the influence that geometrical properties have over the network's behaviour. In this work, we percolate the UK's road network using the relative angle between street segments as the occupation probability. We argue that road networks undergo a non-equilibrium first-order phase transition at the moment the main roads start to interconnect forming the spanning percolation cluster. The percolation process uncovers the hierarchical structure of the roads in the network, and as such, its classification. Furthermore, this technique serves to extract the set of most important roads of the network and to create a hierarchical index for each road in the system.



Tracking Urban Activity Growth Globally with Big Location Data

Physics, 2015, 3(4)

by Matthew Daggitt, Anastasios Noulas, Blake Shaw, Cecilia Mascolo

(Translated by -)

In recent decades the world has experienced rates of urban growth unparalleled in any other period of history and this growth is shaping the environment in which an increasing proportion of us live. In this paper we use a longitudinal dataset from Foursquare, a location-based social network, to analyse urban growth across 100 major cities worldwide.

Initially we explore how urban growth differs in cities across the world. We show that there exists a strong spatial correlation, with nearby pairs of cities more likely to share similar growth profiles than remote pairs of cities. Subsequently we investigate how growth varies inside cities and demonstrate that, given the existing local density of places, higher-than-expected growth is highly localised while lower-than-expected growth is more diffuse. Finally we attempt to use the dataset to characterise competition between new and existing venues. By defining a measure based on the change in throughput of a venue before and after the opening of a new nearby venue, we demonstrate which venue types have a positive effect on venues of the same type and which have a negative effect. For example, our analysis confirms the hypothesis that there is large degree of competition between bookstores, in the sense that existing bookstores normally experience a notable drop in footfall after a new bookstore opens nearby. Other place categories however, such as Airport Gates or Museums, have a cooperative effect and their presence fosters higher traffic volumes to nearby places of the same type.



Faster Adaptation in Smaller Populations: Counterintuitive Evolution of HIV during Childhood Infection

PLoS Comput Biol 12(1): e1004694.

by Jayna Raghwani, Samir Bhatt, Oliver G. Pybus

(Translated by -)

Analysis of HIV-1 gene sequences sampled longitudinally from infected individuals can reveal the evolutionary dynamics that underlie associations between disease outcome and viral genetic diversity and divergence. Here we extend a statistical framework to estimate rates of viral molecular adaptation by considering sampling error when computing nucleotide site-frequencies. This is particularly beneficial when analyzing viral sequences from within-host viral infections if the number of sequences per time point is limited. To demonstrate the utility of this approach, we apply our method to a cohort of 24 patients infected with HIV-1 at birth. Our approach finds that viral adaptation arising from recurrent positive natural selection is associated with the rate of HIV-1 disease progression, in contrast to previous analyses of these data that found no significant association. Most surprisingly, we discover a strong negative correlation between viral population size and the rate of viral adaptation, the opposite of that predicted by standard molecular evolutionary theory. We argue that this observation is most likely due to the existence of a confounding third variable, namely variation in selective pressure among hosts. A conceptual non-linear model of virus adaptation that incorporates the two opposing effects of host immunity on the virus population can explain this counterintuitive result.



Introduction to Focus Issue: Oscillations and Dynamic Instabilities in Chemical Systems: Dedicated to Irving R. Epstein on occasion of his 70th birthday

Chaos 25, 064201 (2015)

by István Z. Kiss and John A. Pojman

(Translated by -)

Oscillations and Dynamic Instabilities in Chemical Systems” is the title of the Gordon Research Conference inaugurated in 1982 by Irving R. Epstein, whom we honor with this Focus Issue. Oscillations and dynamic instabilities in chemical systems comprise the study of dynamical phenomena in chemically reacting systems far from equilibrium. Systematic exploration of this area began with investigations of the temporal behavior of the Belousov-Zhabotinsky oscillating reaction, discovered accidentally in the former Soviet Union in the 1950s. The field soon advanced into chemical waves in excitable media and propagating fronts. With the systematic design of oscillating reactions in the 1980s and the discovery of Turing patterns in the 1990s, the scope of these studies expanded dramatically. The articles in this Focus Issue provide an overview of the development and current state of the field with special emphasis on the contributions of Irving Epstein.



How Can Evolution Learn?

Trends in Ecology & Evolution, 2015, 31(2):147-157

by Richard A. Watson, Eörs Szathmáry

(Translated by -)

The theory of evolution links random variation and selection to incremental adaptation. In a different intellectual domain, theory links incremental adaptation (e.g., from positive and/or negative reinforcement) to intelligent . Specifically, theory explains how incremental adaptation can acquire knowledge from past experience and use it to direct future toward favourable outcomes. Until recently such cognitive seemed irrelevant to the 'uninformed' process of evolution. In our opinion, however, new results formally linking evolutionary processes to the principles of might provide solutions to several evolutionary puzzles - the evolution of evolvability, the evolution of ecological organisation, and evolutionary transitions in individuality. If so, the ability for evolution to learn might explain how it produces such apparently intelligent designs.



Pantheon 1.0, a manually verified dataset of globally famous biographies

Scientific Data 3, Article number: 150075 (2016)

by Amy Zhao Yu, Shahar Ronen, Kevin Hu, Tiffany Lu & César A. Hidalgo

(Translated by -)

We present the Pantheon 1.0 dataset: a manually verified dataset of individuals that have transcended linguistic, temporal, and geographic boundaries. The Pantheon 1.0 dataset includes the 11,341 biographies present in more than 25 languages in Wikipedia and is enriched with: (i) manually verified demographic information (place and date of birth, gender) (ii) a taxonomy of occupations classifying each biography at three levels of aggregation and (iii) two measures of global popularity including the number of languages in which a biography is present in Wikipedia (L), and the Historical Popularity Index (HPI) a metric that combines information on L, time since birth, and page-views (2008–2013). We compare the Pantheon 1.0 dataset to data from the 2003 book, Human Accomplishments, and also to external measures of accomplishment in individual games and sports: Tennis, Swimming, Car Racing, and Chess. In all of these cases we find that measures of popularity (L and HPI) correlate highly with individual accomplishment, suggesting that measures of global popularity proxy the historical impact of individuals.



The physics of life

Nature, 2016, 529(7584):16-18

by Gabriel Popkin

(Translated by -)

First, Zvonimir Dogic and his students took microtubules — threadlike proteins that make up part of the cell's internal 'cytoskeleton' — and mixed them with kinesins, motor proteins that travel along these threads like trains on a track. Then the researchers suspended droplets of this cocktail in oil and supplied it with the molecular fuel known as adenosine triphosphate (ATP).

To the team's surprise and delight, the molecules organized themselves into large-scale patterns that swirled on each droplet's surface. Bundles of microtubules linked by the proteins moved together “like a person crowd-surfing at a concert”, says Dogic, a physicist at Brandeis University in Waltham, Massachusetts.

With these experiments, published1 in 2012, Dogic's team created a new kind of liquid crystal. Unlike the molecules in standard liquid-crystal displays, which passively form patterns in response to electric fields, Dogic's components were active. They propelled themselves, taking energy from their environment — in this case, from ATP. And they formed patterns spontaneously, thanks to the collective behaviour of thousands of units moving independently.

These are the hallmarks of systems that physicists call active matter, which have become a major subject of research in the past few years. Examples abound in the natural world — among them the leaderless but coherent flocking of birds and the flowing, structure-forming cytoskeletons of cells. They are increasingly being made in the laboratory: investigators have synthesized active matter using both biological building blocks such as microtubules, and synthetic components including micrometre-scale, light-sensitive plastic 'swimmers' that form structures when someone turns on a lamp. Production of peer-reviewed papers with 'active matter' in the title or abstract has increased from less than 10 per year a decade ago to almost 70 last year, and several international workshops have been held on the topic in the past year.



Understanding Noise in Twentieth-Century Physics and Engineering

Perspectives on Science, January-February 2016 Volume: 24, Number: 1 January-February 2016: 1-6.

by Chen-Pang Yeang and Joan Lisa Bromberg

(Translated by -)

Noise is a common experience in the contemporary world. Din from traffic, construction sites, factories, and neighbors bother urban residents. Radio listeners, television watchers, and mobile phone users have to endure statics and fading from time to time. Music lovers have debated whether jazz, atonal composition, rock and roll, rap, and abstract expressionism are art or nuisance. Scientists try to retrieve genuine signals from fluctuating data. Engineers design devices, software, or systems to filter out disturbance to the normal functioning of technology. Mathematicians and physicists examine randomness. Traders and economists attempt to predict markets’ future trends beneath highly irregular commodity prices. Decision makers cope with all kinds of uncertainty. No matter whether we understand the term as annoying sound or random fluctuations, we simply cannot live a life without encountering noise.

Despite its ubiquity in modern times, noise has rarely been a focus of historical studies of recent science and technology. There may be an obvious reason for this lack of attention: noise largely reveals what science and technology are not, instead of what they are. Noise is an environmental plight of industrialization, an obstacle to the advancement of scientific knowledge, a subversive force to technology, a barrier to prediction, estimation, and control, and a symptom of disorder. As a result, noise often exposes the limitations of science and technology. While such limitations have played an important part in the development of science or technology, they are generally conceived as a background to this development, and thus scarcely become the subject of close investigations.

A number of recent historiographical turns, however, have begun to change this situation. Inspired by the cultural histories of senses that flourished.



No tradeoff between versatility and robustness in gene circuit motifs

Physica A: Statistical Mechanics and its Applications, 2016, 449:192-199

by Joshua L. Payne

(Translated by -)

Circuit motifs are small directed subgraphs that appear in real-world networks significantly more often than in randomized networks. In the Boolean model of gene circuits, most motifs are realized by multiple circuit genotypes. Each of a motif’s constituent circuit genotypes may have one or more functions, which are embodied in the expression patterns the circuit forms in response to specific initial conditions. Recent enumeration of a space of nearly 17 million three-gene circuit genotypes revealed that all circuit motifs have more than one function, with the number of functions per motif ranging from 12 to nearly 30,000. This indicates that some motifs are more functionally versatile than others. However, the individual circuit genotypes that constitute each motif are less robust to mutation if they have many functions, hinting that functionally versatile motifs may be less robust to mutation than motifs with few functions. Here, I explore the relationship between versatility and robustness in circuit motifs, demonstrating that functionally versatile motifs are robust to mutation despite the inherent tradeoff between versatility and robustness at the level of an individual circuit genotype.



Adapting to Adaptations: Behavioural Strategies that are Robust to Mutations and Other Organisational-Transformations

Scientific Reports 6, Article number: 18963 (2016)

by Matthew D. Egbert & Juan Pérez-Mercader

(Translated by -)

Genetic mutations, infection by parasites or symbionts, and other events can transform the way that an organism’s internal state changes in response to a given environment. We use a minimalistic computational model to support an argument that by behaving “interoceptively,” i.e. responding to internal state rather than to the environment, organisms can be robust to these organisational-transformations. We suggest that the robustness of interoceptive behaviour is due, in part, to the asymmetrical relationship between an organism and its environment, where the latter more substantially influences the former than vice versa. This relationship means that interoceptive behaviour can respond to the environment, the internal state and the interaction between the two, while exteroceptive behaviour can only respond to the environment. We discuss the possibilities that (i) interoceptive behaviour may play an important role of facilitating adaptive evolution (especially in the early evolution of primitive life) and (ii) interoceptive mechanisms could prove useful in efforts to create more robust synthetic life-forms.



The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

Social Science Electronic Publishing, 2015

by Gerard P Cachon, Kaitlin M Daniels, Ruben Lobel

(Translated by -)

Recent platforms, like Uber and Lyft, offer service to consumers via “self-scheduling” providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a “surge pricing” policy, which pays providers a fixed commission of its dynamic price. We find that the optimal contract substantially increases the platform's profit relative to contracts that have a fixed price or fixed wage (or both) and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized in the press and has garnered the attention of regulators due to concerns for the welfare of providers and consumers. However, we find that providers and consumers are generally better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity.



The Anthropocene is functionally and stratigraphically distinct from the Holocene

Science 08 Jan 2016: Vol. 351, Issue 6269, pp.

by CN Waters, J Zalasiewicz, C Summerhayes, AD Barnosky, C Poirier et al.

(Translated by -)

Human activity is leaving a pervasive and persistent signature on Earth. Vigorous debate continues about whether this warrants recognition as a new geologic time unit known as the Anthropocene. We review anthropogenic markers of functional changes in the Earth system through the stratigraphic record. The appearance of manufactured materials in sediments, including aluminum, plastics, and concrete, coincides with global spikes in fallout radionuclides and particulates from fossil fuel combustion. Carbon, nitrogen, and phosphorus cycles have been substantially modified over the past century. Rates of sea-level rise and the extent of human perturbation of the climate system exceed Late Holocene changes. Biotic changes include species invasions worldwide and accelerating rates of extinction. These combined signals render the Anthropocene stratigraphically distinct from the Holocene and earlier epochs.



Predicting links in ego-networks using temporal information

EPJ Data Science 2016, 5:1

by Lionel Tabourier, Anne-Sophie Libert and Renaud Lambiotte

(Translated by -)

Link prediction appears as a central problem of network science, as it calls for unfolding the mechanisms that govern the micro-dynamics of the network. In this work, we are interested in ego-networks, that is the mere information of interactions of a node to its neighbors, in the context of social relationships. As the structural information is very poor, we rely on another source of information to predict links among egos’ neighbors: the timing of interactions. We define several features to capture different kinds of temporal information and apply machine learning methods to combine these various features and improve the quality of the prediction. We demonstrate the efficiency of this temporal approach on a cellphone interaction dataset, pointing out features which prove themselves to perform well in this context, in particular the temporal profile of interactions and elapsed time between contacts.



The power of crowds

Science 01 Jan 2016:Vol. 351, Issue 6268, pp. 32-33

by Pietro Michelucci, Janis L. Dickinson

(Translated by -)

Human computation, a term introduced by Luis von Ahn (1), refers to distributed systems that combine the strengths of humans and computers to accomplish tasks that neither can do alone (2). The seminal example is reCAPTCHA, a Web widget used by 100 million people a day when they transcribe distorted text into a box to prove they are human. This free cognitive labor provides users with access to Web content and keeps websites safe from spam attacks, while feeding into a massive, crowd-powered transcription engine that has digitized 13 million articles from The New York Times archives (3). But perhaps the best known example of human computation is Wikipedia. Despite initial concerns about accuracy (4), it has become the key resource for all kinds of basic information. Information science has begun to build on these early successes, demonstrating the potential to evolve human computation systems that can model and address wicked problems (those that defy traditional problem-solving methods) at the intersection of economic, environmental, and sociopolitical systems.



Operational resilience: concepts, design and analysis

Scientific Reports 6, Article number: 19540 (2016)

by Alexander A. Ganin, Emanuele Massaro, Alexander Gutfraind, Nicolas Steen, Jeffrey M. Keisler, Alexander Kott, Rami Mangoubi, Igor Linkov

(Translated by -)

Building resilience into today's complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This study proposes quantitative measures that implement the definition of engineering resilience advanced by the National Academy of Sciences. The approach is applicable across physical, information, and social domains. It evaluates the critical functionality, defined as a performance function of time set by the stakeholders. Critical functionality is a source of valuable information, such as the integrated system resilience over a time interval, and its robustness. The paper demonstrates the formulation on two classes of models: 1) multi-level directed acyclic graphs, and 2) interdependent coupled networks. For both models synthetic case studies are used to explore trends. For the first class, the approach is also applied to the Linux operating system. Results indicate that desired resilience and robustness levels are achievable by trading off different design parameters, such as redundancy, node recovery time, and backup supply available. The nonlinear relationship between network parameters and resilience levels confirms the utility of the proposed approach, which is of benefit to analysts and designers of complex systems and networks.



Ants determine their next move at rest: motor planning and causality in complex systems

Royal Society Open Science, 2016, 3(1)

by ER Hunt,RJ Baddeley,A Worley,AB Sendovafranks,NR Franks

(Translated by -)

To find useful work to do for their colony, individual eusocial animals have to move, somehow staying attentive to relevant social information. Recent research on individual Temnothorax albipennis ants moving inside their colony’s nest found a power-law relationship between a movement’s duration and its average speed; and a universal speed profile for movements showing that they mostly fluctuate around a constant average speed. From this predictability it was inferred that movement durations are somehow determined before the movement itself. Here, we find similar results in lone T. albipennis ants exploring a large arena outside the nest, both when the arena is clean and when it contains chemical information left by previous nest-mates. This implies that these movement characteristics originate from the same individual neural and/or physiological mechanism(s), operating without immediate regard to social influences. However, the presence of pheromones and/or other cues was found to affect the inter-event speed correlations. Hence we suggest that ants’ motor planning results in intermittent response to the social environment: movement duration is adjusted in response to social information only between movements, not during them. This environmentally flexible, intermittently responsive movement behaviour points towards a spatially allocated division of labour in this species. It also prompts more general questions on collective animal movement and the role of intermittent causation from higher to lower organizational levels in the stability of complex systems.



The DARPA Twitter Bot Challenge

Computer, 2016, 49(6):38-46

by VS Subrahmanian, A Azaria, S Durst, V Kagan

(Translated by -)

A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes. There is thus a growing need to identify and eliminate "influence bots" - realistic, automated identities that illicitly shape discussion on sites like Twitter and Facebook - before they get too influential. Spurred by such events, DARPA held a 4-week competition in February/March 2015 in which multiple teams supported by the DARPA Social Media in Strategic Communications program competed to identify a set of previously identified "influence bots" serving as ground truth on a specific topic within Twitter. Past work regarding influence bots often has difficulty supporting claims about accuracy, since there is limited ground truth (though some exceptions do exist [3,7]). However, with the exception of [3], no past work has looked specifically at identifying influence bots on a specific topic. This paper describes the DARPA Challenge and describes the methods used by the three top-ranked teams.



Emergence of Consensus in a Multi-Robot Network: from Abstract Models to Empirical Validation

Robotics and Automation Letters, IEEE , vol.PP, no.99, pp.1 (2016)

by Vito Trianni, Daniele De Simone, Andreagiovanni Reina, Andrea Baronchelli

(Translated by -)

Consensus dynamics in decentralised multiagent systems are subject to intense studies, and several different models have been proposed and analysed. Among these, the naming game stands out for its simplicity and applicability to a wide range of phenomena and applications, from semiotics to engineering. Despite the wide range of studies available, the implementation of theoretical models in real distributed systems is not always straightforward, as the physical platform imposes several constraints that may have a bearing on the consensus dynamics. In this paper, we investigate the effects of an implementation of the naming game for the kilobot robotic platform, in which we consider concurrent execution of games and physical interferences. Consensus dynamics are analysed in the light of the continuously evolving communication network created by the robots, highlighting how the different regimes crucially depend on the robot density and on their ability to spread widely in the experimental arena. We find that physical interferences reduce the benefits resulting from robot mobility in terms of consensus time, but also result in lower cognitive load for individual agents.



How the Cold War Created Astrobiology

Nautilus, Issue 32, Chapter three, January 21, 2016

by Caleb Scharf

(Translated by -)

Astronomy and biology have been circling each other with timid infatuation since the first time a human thought about the possibility of other worlds and other suns. But the melding of the two into the modern field of astrobiology really began on Oct. 4, 1957, when a 23-inch aluminum sphere called Sputnik 1 lofted into low Earth orbit from the desert steppe of the Kazakh Republic. Over the following weeks its gently beeping radio signal heralded a new and very uncertain world. Three months later it came tumbling back through the atmosphere, and humanity’s small evolutionary bump was set on a trajectory never before seen in 4 billion years of terrestrial history.

At the time of the ascent of Sputnik, a 32-year-old American called Joshua Lederberg was working in Australia as a visiting professor at the University of Melbourne. Born in 1925 to immigrant parents in New Jersey, Lederberg was a prodigy. Quick-witted, generous, and with an incredible ability to retain information, he blazed through high school and was enrolled at Columbia University by the time he was 15. Earning a degree in zoology and moving on to medical studies, his research interests diverted him to Yale. There, at age 21, he helped research the nascent field of microbial genetics, with work on bacterial gene transfer that would later earn him a share of the 1958 Nobel Prize.1,2

Like the rest of the planet, Australia was transfixed by the Soviet launch; as much for the show of technological prowess as for the fact that a superpower was now also capable of easily lobbing thermonuclear warheads across continents. But, unlike the people around him, Lederberg’s thoughts were galvanized in a different direction. He immediately knew that another type of invisible wall had been breached, a wall that might be keeping even more deadly things at bay, as well as incredible scientific opportunities.

If humans were about to travel in space, we were also about to spread terrestrial organisms to other planets, and conceivably bring alien pathogens back to Earth. As Lederberg saw it, either we were poised to destroy indigenous life-forms across our solar system, or ourselves. Neither was an acceptable option. When he returned to the United States he quickly threw himself into learning all he could about astronomy and rocketry, and began writing urgent letters to the National Academy of Sciences, alerting his colleagues to the imminent danger.



Transition to Chaos in Random Neuronal Networks

Phys. Rev. X 5, 041030 (2015)

by Jonathan Kadmon and Haim Sompolinsky

(Translated by -)

Firing patterns in the central nervous system often exhibit strong temporal irregularity and considerable heterogeneity in time-averaged response properties. Previous studies suggested that these properties are the outcome of the intrinsic chaotic dynamics of the neural circuits. Indeed, simplified rate-based neuronal networks with synaptic connections drawn from Gaussian distribution and sigmoidal nonlinearity are known to exhibit chaotic dynamics when the synaptic gain (i.e., connection variance) is sufficiently large. In the limit of an infinitely large network, there is a sharp transition from a fixed point to chaos, as the synaptic gain reaches a critical value. Near the onset, chaotic fluctuations are slow, analogous to the ubiquitous, slow irregular fluctuations observed in the firing rates of many cortical circuits. However, the existence of a transition from a fixed point to chaos in neuronal circuit models with more realistic architectures and firing dynamics has not been established. In this work, we investigate rate-based dynamics of neuronal circuits composed of several subpopulations with randomly diluted connections. Nonzero connections are either positive for excitatory neurons or negative for inhibitory ones, while single neuron output is strictly positive with output rates rising as a power law above threshold, in line with known constraints in many biological systems. Using dynamic mean field theory, we find the phase diagram depicting the regimes of stable fixed-point, unstable-dynamic, and chaotic-rate fluctuations. We focus on the latter and characterize the properties of systems near this transition. We show that dilute excitatory-inhibitory architectures exhibit the same onset to chaos as the single population with Gaussian connectivity. In these architectures, the large mean excitatory and inhibitory inputs dynamically balance each other, amplifying the effect of the residual fluctuations. Importantly, the existence of a transition to chaos and its critical properties depend on the shape of the single-neuron nonlinear input-output transfer function, near firing threshold. In particular, for nonlinear transfer functions with a sharp rise near threshold, the transition to chaos disappears in the limit of a large network; instead, the system exhibits chaotic fluctuations even for small synaptic gain. Finally, we investigate transition to chaos in network models with spiking dynamics. We show that when synaptic time constants are slow relative to the mean inverse firing rates, the network undergoes a transition from fast spiking fluctuations with constant rates to a state where the firing rates exhibit chaotic fluctuations, similar to the transition predicted by rate-based dynamics. Systems with finite synaptic time constants and firing rates exhibit a smooth transition from a regime dominated by stationary firing rates to a regime of slow rate fluctuations. This smooth crossover obeys scaling properties, similar to crossover phenomena in statistical mechanics. The theoretical results are supported by computer simulations of several neuronal architectures and dynamics. Consequences for cortical circuit dynamics are discussed. These results advance our understanding of the properties of intrinsic dynamics in realistic neuronal networks and their functional consequences.



Monitoring Potential Drug Interactions And Reactions Via Network Analysis Of Instagram User Timelines

Pac Symp Biocomput. 2016;21:492-503.

by Correia RB1, Li L, Rocha LM

(Translated by -)

Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products-including cannabis-which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Most social media analysis focuses on Twitter and Facebook, but Instagram is an increasingly important platform, especially among teens, with unrestricted access of public posts, high availability of posts with geolocation coordinates, and images to supplement textual analysis. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected close to 7000 user timelines spanning from October 2010 to June 2015.We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that Instagram contains much drug- and pathology specific data for public health monitoring of DDI and ADR, and that complex network analysis provides an important toolbox to extract health-related associations and their support from large-scale social media data.



Global multi-layer network of human mobility

arXiv:1601.05532v1 [cs.SI] (Submitted on 21 Jan 2016)

by Alexander Belyi, Iva Bojic, Stanislav Sobolevsky, Izabela Sitko, Bartosz Hawelka, Lada Rudikova, Alexander Kurbatski, Carlo Ratti

(Translated by -)

Recent availability of geo-localized data capturing individual human activity together with the statistical data on international migration opened up unprecedented opportunities for a study on global mobility. In this paper we consider it from the perspective of a multi-layer complex network, built using a combination of three datasets: Twitter, Flickr and official migration data. Those datasets provide different but equally important insights on the global mobility: while the first two highlight short-term visits of people from one country to another, the last one - migration - shows the long-term mobility perspective, when people relocate for good. And the main purpose of the paper is to emphasize importance of this multi-layer approach capturing both aspects of human mobility at the same time. So we start from a comparative study of the network layers, comparing short- and long- term mobility through the statistical properties of the corresponding networks, such as the parameters of their degree centrality distributions or parameters of the corresponding gravity model being fit to the network. We also focus on the differences in country ranking by their short- and long-term attractiveness, discussing the most noticeable outliers. Finally, we apply this multi-layered human mobility network to infer the structure of the global society through a community detection approach and demonstrate that consideration of mobility from a multi-layer perspective can reveal important global spatial patterns in a way more consistent with other available relevant sources of international connections, in comparison to the spatial structure inferred from each network layer taken separately.