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Flocking algorithm for autonomous flying robots

Animal swarms displaying a variety of typical flocking patterns would not exist without underlying safe, optimal and stable dynamics of the individuals. These patterns can be efficiently reconstructed with simple flocking models, based on three simple rules: cohesion of the flock, repulsion of neighbouring individuals and alignment of velocity between neighbours. When designing robot swarms, the controlling dynamics of the robots can be based on these models. In this paper we present such a flocking algorithm endowing flying robots with the capability of self-organized collective manoeuvring. The main new feature of our approach is that we include a term in the velocity alignment part of the equations which is an analogue of the usual frictional force between point-wise bodies. We also introduce a generalized mathematical model of an autonomous flying robot, based on flight field tests. With simulations, we test the flocking algorithm from the aspects of the most general deficiencies of robotic systems, such as time delay, locality of the communication and inaccuracy of the sensors. Some of these deficiencies often cause instabilities and oscillations in the system. We show that the instabilities can be efficiently reduced in all states of the system by the inclusion of the friction-like velocity alignment, resulting in stable flocking flight of the robots.

 

Flocking algorithm for autonomous flying robots
Csaba Virágh, Gábor Vásárhelyi, Norbert Tarcai, Tamás Szörényi, Gergő Somorjai, Tamás Nepusz, Tamás Vicsek

http://arxiv.org/abs/1310.3601

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A framework for optimal high-level descriptions in science and engineering---preliminary report

Both science and engineering rely on the use of high-level descriptions. These go under various names, including "macrostates," "coarse-grainings," and "effective theories". The ideal gas is a high-level description of a large collection of point particles, just as a a set of interacting firms is a high-level description of individuals participating in an economy and just as a cell a high-level description of a set of biochemical interactions. Typically, these descriptions are constructed in an ad hoc manner, without an explicit understanding of their purpose. Here, we formalize and quantify that purpose as a combination of the need to accurately predict observables of interest, and to do so efficiently and with bounded computational resources. This State Space Compression framework makes it possible to solve for the optimal high-level description of a given dynamical system, rather than relying on human intuition alone. In this preliminary report, we present our framework, show its application to a diverse set of examples in Computer Science, Biology, Physics and Networks, and develop some of technical machinery for evaluating accuracy and computation costs in a variety of systems.

 

A framework for optimal high-level descriptions in science and engineering---preliminary report
David H. Wolpert, Joshua A. Grochow, Eric Libby, Simon DeDeo
arXiv:1409.7403, 2014.
http://arxiv.org/abs/1409.7403

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Island biogeography of the Anthropocene

For centuries, biogeographers have examined the factors that produce patterns of biodiversity across regions. The study of islands has proved particularly fruitful and has led to the theory that geographic area and isolation influence species colonization, extinction and speciation such that larger islands have more species and isolated islands have fewer species (that is, positive species–area and negative species–isolation relationships)1, 2, 3, 4. However, experimental tests of this theory have been limited, owing to the difficulty in experimental manipulation of islands at the scales at which speciation and long-distance colonization are relevant5. Here we have used the human-aided transport of exotic anole lizards among Caribbean islands as such a test at an appropriate scale. In accord with theory, as anole colonizations have increased, islands impoverished in native species have gained the most exotic species, the past influence of speciation on island biogeography has been obscured, and the species–area relationship has strengthened while the species–isolation relationship has weakened. Moreover, anole biogeography increasingly reflects anthropogenic rather than geographic processes. Unlike the island biogeography of the past that was determined by geographic area and isolation, in the Anthropocene—an epoch proposed for the present time interval—island biogeography is dominated by the economic isolation of human populations.


Island biogeography of the Anthropocene
Matthew R. Helmus, D. Luke Mahler & Jonathan B. Losos

Nature 513, 543–546 (25 September 2014) http://dx.doi.org/10.1038/nature13739

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An Information-Theoretic Formalism for Multiscale Structure in Complex Systems

We develop a general formalism for representing and understanding structure in complex systems. In our view, structure is the totality of relationships among a system's components, and these relationships can be quantified using information theory. In the interest of flexibility we allow information to be quantified using any function, including Shannon entropy and Kolmogorov complexity, that satisfies certain fundamental axioms. Using these axioms, we formalize the notion of a dependency among components, and show how a system's structure is revealed in the amount of information assigned to each dependency. We explore quantitative indices that summarize system structure, providing a new formal basis for the complexity profile and introducing a new index, the "marginal utility of information". Using simple examples, we show how these indices capture intuitive ideas about structure in a quantitative way. Our formalism also sheds light on a longstanding mystery: that the mutual information of three or more variables can be negative. We discuss applications to complex networks, gene regulation, the kinetic theory of fluids and multiscale cybernetic thermodynamics.


An Information-Theoretic Formalism for Multiscale Structure in Complex Systems
Benjamin Allen, Blake C. Stacey, Yaneer Bar-Yam

http://arxiv.org/abs/1409.4708

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Strategic Islands in Economic Games: Isolating Economies From Better Outcomes

Many of the issues we face as a society are made more problematic by the rapidly changing context in which important decisions are made. For example buying a petrol powered car is most advantageous when there are many petrol pumps providing cheap petrol whereas buying an electric car is most advantageous when there are many electrical recharge points or high capacity batteries available. Such collective decision-making is often studied using economic game theory where the focus is on how individuals might reach an agreement regarding the supply and demand for the different energy types. But even if the two parties find a mutually agreeable strategy, as technology and costs change over time, for example through cheaper and more efficient batteries and a more accurate pricing of the total cost of oil consumption, so too do the incentives for the choices buyers and sellers make, the result of which can be the stranding of an industry or even a whole economy on an island of inefficient outcomes. In this article we consider the issue of how changes in the underlying incentives can move us from an optimal economy to a sub-optimal economy while at the same time making it impossible to collectively navigate our way to a better strategy without forcing us to pass through a socially undesirable “tipping point”. We show that different perturbations to underlying incentives results in the creation or destruction of “strategic islands” isolated by disruptive transitions between strategies. The significant result in this work is the illustration that an economy that remains strategically stationary can over time become stranded in a suboptimal outcome from which there is no easy way to put the economy on a path to better outcomes without going through an economic tipping point.


Entropy 2014, 16(9), 5102-5121; http://dx.doi.org/10.3390/e16095102

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BIG DATA SOCIETY: Age of Reputation or Age of Discrimination?

If we want Big Data to create societal progress, more transparency and participatory opportunities are needed to avoid discrimination and ensure that they are used in a scientifically sound, trustable, and socially beneficial way.

Have you ever "enjoyed" an extra screening at the airport because you happened to sit next to someone from a foreign country? Have you been surprised by a phone call offering a special service or product, because you visited a certain webpage? Or do you feel your browser reads your mind? Then, welcome to the world of Big Data, which mines the tons of digital traces of our daily activities such as web searches, credit card transactions, GPS mobility data, phone calls, text messages, facebook profiles, cloud storage, and more. But are you sure you are getting the best possible product, service, insurance or credit contract? I am not.


BIG DATA SOCIETY: Age of Reputation or Age of Discrimination?

By Dirk Helbing

http://futurict.blogspot.ch/2014/09/big-data-society-age-of-reputation-or.html

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Eli Levine's curator insight, September 26, 8:41 AM

Personally, I think it's more likely to increase peoples' abilities to make prejudicial choices about other people in addition to the actual technical advantages that this will bring about.  Thus, the universe doesn't so much as change for the better, but rather muddle on under the same basic principles.  We are only humans, after all.  Our descendants are not going to fall far from the tree and, unless something dramatic happens to our population and to the way that our people think and feel about themselves and our world (an x-event), there will likely only be questionable or low probability change as we settle into a kind of equilibrium, for better and for worse, that we may choose to die in, rather than evolve and make a change.

 

Thus, we're more than likely going to take a significant hit as a species, in spite of our abilities to avoid the problems in the first place.

 

Silly brains....

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Exoplanets

It is hard to imagine now, and the younger people in the field will not remember this, but there was a period when the search for exoplanets had rather a bad reputation, based on a number of high-profile claims that were subsequently disproved. Although there was broad agreement, even by the 1980s, that planet formation ought to be a natural part of the star-formation process, at least for low-mass stars, we were still basing our assumptions on what we might find using the Solar System as a template.


Exoplanets
Leslie Sage
Nature 513, 327 (18 September 2014)

http://dx.doi.org/10.1038/513327a

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toyLIFE: a computational framework to study the multi-level organization of the genotype-phenotype map

The genotype-phenotype map is an essential object in our understanding of organismal complexity and adaptive properties, determining at once genomic plasticity and those constraints that may limit the ability of genomes to attain evolutionary innovations. An exhaustive experimental characterization of the relationship between genotypes and phenotypes is at present out of reach. Therefore, several models mimicking that map have been proposed and investigated, leading to the identification of a number of general features: genotypes differ in their robustness to mutations, phenotypes are represented by a broadly varying number of genotypes, and simple point mutations seem to suffice to navigate the space of genotypes while maintaining a phenotype. However, most current models address only one level of the map (sequences and folded structures in RNA or proteins; networks of genes and their dynamical attractors; sets of chemical reactions and their ability to undergo molecular catalysis), such that many relevant questions cannot be addressed. Here we introduce toyLIFE, a multi-level model for the genotype-phenotype map based on simple genomes and interaction rules from which a complex behavior at upper levels emerges, remarkably plastic gene regulatory networks and metabolism. toyLIFE is a tool that permits the investigation of how different levels are coupled, in particular how and where do mutations affect phenotype or how the presence of certain metabolites determines the dynamics of toyLIFE gene regulatory networks. The possibilities of this model are not exhausted by the results presented in this contribution. It can be easily generalized to incorporate evolution through mutations that change genome length or through recombination, to consider gene duplication or deletion, and therefore to explore further properties of extended genotype-phenotype maps.


toyLIFE: a computational framework to study the multi-level organization of the genotype-phenotype map
Clemente F. Arias, Pablo Catalán, Susanna Manrubia, José A. Cuesta

http://arxiv.org/abs/1409.4904

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Random Walks with Preferential Relocations to Places Visited in the Past and their Application to Biology

Strongly non-Markovian random walks offer a promising modeling framework for understanding animal and human mobility, yet, few analytical results are available for these processes. Here we solve exactly a model with long range memory where a random walker intermittently revisits previously visited sites according to a reinforced rule. The emergence of frequently visited locations generates very slow diffusion, logarithmic in time, whereas the walker probability density tends to a Gaussian. This scaling form does not emerge from the central limit theorem but from an unusual balance between random and long-range memory steps. In single trajectories, occupation patterns are heterogeneous and have a scale-free structure. The model exhibits good agreement with data of free-ranging capuchin monkeys.


Random Walks with Preferential Relocations to Places Visited in the Past and their Application to Biology
Phys. Rev. Lett. 112, 240601 – Published 18 June 2014
Denis Boyer and Citlali Solis-Salas

http://dx.doi.org/10.1103/PhysRevLett.112.240601


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Disease-Induced Resource Constraints Can Trigger Explosive Pandemic

Epidemic spreading is a complex process influenced by the intrinsic properties of a disease, the infrastructure that supports global economic and cultural exchange, and the behavior of individuals. Advances in mathematical epidemiology and network science have led to a better understanding of the risks posed by epidemic spreading and informed containment strategies such as immunization and treatment. However, a challenge that has been typically overlooked is that, as a disease becomes more prevalent, the burden that it places on capital can limit the supply of treatment. Here we study the dynamics of an epidemic when the recovery of sick individuals depends on the availability of healing resources that are generated by the healthy population. We find that epidemics spiral out of control into "explosive'' pandemics if the cost of recovery is above a critical cost. This occurs even when the standard models predict that the infection will not lead to an epidemic. Furthermore, the onset of explosive pandemics is very sudden, and we show through simulations and a mean-field analytical solution that the transition is always discontinuous, independent of the specific structure of the networks of human interaction through which the disease propagates.


Disease-Induced Resource Constraints Can Trigger Explosive Pandemics

Lucas Bottcher,

Olivia Woolley-Meza, Nuno A.M. Araujo, Hans J. Herrmann, Dirk Helbing


http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2496128

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How structurally stable are global socioeconomic systems?

The stability analysis of socioeconomic systems has been centred on answering whether small perturbations when a system is in a given quantitative state will push the system permanently to a different quantitative state. However, typically the quantitative state of socioeconomic systems is subject to constant change. Therefore, a key stability question that has been under-investigated is how strongly the conditions of a system itself can change before the system moves to a qualitatively different behaviour, i.e. how structurally stable the systems is. Here, we introduce a framework to investigate the structural stability of socioeconomic systems formed by a network of interactions among agents competing for resources. We measure the structural stability of the system as the range of conditions in the distribution and availability of resources compatible with the qualitative behaviour in which all the constituent agents can be self-sustained across time. To illustrate our framework, we study an empirical representation of the global socioeconomic system formed by countries sharing and competing for multinational companies used as proxy for resources. We demonstrate that the structural stability of the system is inversely associated with the level of competition and the level of heterogeneity in the distribution of resources. Importantly, we show that the qualitative behaviour of the observed global socioeconomic system is highly sensitive to changes in the distribution of resources. We believe that this work provides a methodological basis to develop sustainable strategies for socioeconomic systems subject to constantly changing conditions.


How structurally stable are global socioeconomic systems?
Serguei Saavedra, Rudolf P. Rohr, Luis J. Gilarranz, Jordi Bascompte

http://dx.doi.org/10.1098/ rsif.2014.0693
J. R. Soc. Interface 6 November 2014 vol. 11 no. 100 20140693

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Eli Levine's curator insight, September 14, 2:18 PM

There are most likely a plurality of stable socio-economic systems with different dynamics and levels of short term system stability.  It's likely that, even if there are periods of short term instability, that long term stability will hold, even if instability is a stable feature. 

 

Very interesting points here. 

 

Enjoy! 

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The state of global health in 2014

The global health landscape looks more promising than ever, although progress has been uneven. Here, we describe the current global burden of disease throughout the life cycle, highlighting regional differences in the unfinished agenda of communicable diseases and reproductive, maternal, and child health and the additive burden of emerging noncommunicable diseases and injuries. Understanding this changing landscape is an essential starting point for effective allocation of both domestic and international resources for health.


The state of global health in 2014
Jaime Sepúlveda, Christopher Murray

Science 12 September 2014:
Vol. 345 no. 6202 pp. 1275-1278
http://dx.doi.org/10.1126/science.1257099

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How memory generates heterogeneous dynamics in temporal networks

Empirical temporal networks display strong heterogeneities in their dynamics, which profoundly affect processes taking place on these networks, such as rumor and epidemic spreading. Despite the recent wealth of data on temporal networks, little work has been devoted to the understanding of how such heterogeneities can emerge from microscopic mechanisms at the level of nodes and links. Here we show that long-term memory effects are present in the creation and disappearance of links in empirical networks. We thus consider a simple generative modeling framework for temporal networks able to incorporate these memory mechanisms. This allows us to study separately the role of each of these mechanisms in the emergence of heterogeneous network dynamics. In particular, we show analytically and numerically how heterogeneous distributions of contact durations, of inter-contact durations and of numbers of contacts per link emerge. We also study the individual effect of heterogeneities on dynamical processes, such as the paradigmatic Susceptible-Infected epidemic spreading model. Our results confirm in particular the crucial role of the distributions of inter-contact durations and of the numbers of contacts per link.


How memory generates heterogeneous dynamics in temporal networks
Christian L. Vestergaard, Mathieu Génois, Alain Barrat

http://arxiv.org/abs/1409.1805

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Changing behaviors to prevent noncommunicable diseases

Noncommunicable diseases (NCDs)—largely comprising cardiovascular disease, cancer, chronic pulmonary disease, and diabetes—are the most important global health issues of the 21st century, as measured by both mortality and morbidity. These diseases are as preventable across entire populations as are infectious diseases, but require a different approach—one that involves policy change to promote healthy behaviors.


Changing behaviors to prevent noncommunicable diseases
Dave A. Chokshi, Thomas A. Farley

Science 12 September 2014:
Vol. 345 no. 6202 pp. 1243-1244
http://dx.doi.org/10.1126/science.1259809

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Requisite Variety, Autopoiesis, and Self-organization

Ashby's law of requisite variety states that a controller must have at least as much variety (complexity) as the controlled. Maturana and Varela proposed autopoiesis (self-production) to define living systems. Living systems also require to fulfill the law of requisite variety. A measure of autopoiesis has been proposed as the ratio between the complexity of a system and the complexity of its environment. Self-organization can be used as a concept to guide the design of systems towards higher values of autopoiesis, with the potential of making technology more "living", i.e. adaptive and robust.


Requisite Variety, Autopoiesis, and Self-organization
Carlos Gershenson

http://arxiv.org/abs/1409.7475

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How Information Theory Could Hold the Key to Quantifying Nature

How Information Theory Could Hold the Key to Quantifying Nature | Papers | Scoop.it

The Western Ghats in India rise like a wall between the Arabian Sea and the heart of the subcontinent to the east. The 1,000-mile-long chain of coastal mountains is dense with lush rainforest and grasslands, and each year, clouds bearing monsoon rains blow in from the southwest and break against the mountains’ flanks, unloading water that helps make them hospitable to numerous spectacular and endangered species. The Western Ghats are one of the most biodiverse places on the planet. They were also the first testing ground of an unusual new theory in ecology that applies insights from physics to the study of the environment.


Via Ashish Umre
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Gary Bamford's curator insight, September 27, 12:04 AM

MaxEnt - physics meets ecology.

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First response, revisited

These frameworks have failed miserably in this outbreak, and the WHO has been slow and, so far, ineffective. There has been some progress in disease surveillance, but the world is little better prepared to quickly stamp out a threatening outbreak than it was a decade ago.


http://www.nature.com/news/first-response-revisited-1.15978

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War: Origins and Effects

The International System is a self-organized system and shows emergent behavior. During the timeframe (1495 - 1945), a finite-time singularity and four accompanying accelerating log-periodic cycles shaped the dynamics of the International System. The accelerated growth of the connectivity of the regulatory network of the International System, in combination with its anarchistic structure, produce and shape the war dynamics of the system. Accelerated growth of the connectivity of the International system is fed by population growth and the need for social systems to fulfill basic requirements. The finite-time singularity and accompanying log-periodic oscillations were instrumental in the periodic reorganization of the regulatory network of the International System, and contributed to a long-term process of social expansion and integration in Europa. The singularity dynamic produced a series of organizational innovations. At the critical time of the singularity (1939) the connectivity of the system reached a critical threshold, resulting in a critical transition. This critical transition caused a fundamental reorganization of the International System: Europe transformed from an anarchistic system to cooperative security community. This critical transition also marks the actual globalization of the International System. During the life span of cycles, the war dynamics show chaotic characteristics. Various early-warning signals can be identified, and can probably be used in the current International System. These findings have implications for the social sciences and historical research.


War: Origins and Effects
Ingo Piepers

http://arxiv.org/abs/1409.6163

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Eli Levine's curator insight, September 26, 8:31 AM

Thus we delve closer into the hidden language of our social world.

 

Way cool science!

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Are liars ethical? On the tension between benevolence and honesty

• Deception is sometimes perceived to be ethical.
• Prosocial liars are perceived to be more moral than honest individuals.
• Benevolence may be more important than honesty for judgments of moral character.
• The moral principle of care is sometimes more important than justice.


Are liars ethical? On the tension between benevolence and honesty
Emma E. Levine, Maurice E. Schweitzer

Journal of Experimental Social Psychology
Volume 53, July 2014, Pages 107–117

http://dx.doi.org/10.1016/j.jesp.2014.03.005

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Identification of core-periphery structure in networks

Many networks can be usefully decomposed into a dense core plus an outlying, loosely-connected periphery. Here we propose an algorithm for performing such a decomposition on empirical network data using methods of statistical inference. Our method fits a generative model of core-periphery structure to observed data using a combination of an expectation--maximization algorithm for calculating the parameters of the model and a belief propagation algorithm for calculating the decomposition itself. We find the method to be efficient, scaling easily to networks with a million or more nodes and we test it on a range of networks, including real-world examples as well as computer-generated benchmarks, for which it successfully identifies known core-periphery structure with low error rate. We also demonstrate that the method is immune from the detectability transition observed in the related community detection problem, which prevents the detection of community structure when that structure is too weak. There is no such transition for core-periphery structure, which is detectable, albeit with some statistical error, no matter how weak it is.


Identification of core-periphery structure in networks
Xiao Zhang, Travis Martin, M. E. J. Newman

http://arxiv.org/abs/1409.4813

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Exclusive Queueing Processes and their Application to Traffic Systems

The dynamics of pedestrian crowds has been studied intensively in recent years, both theoretically and empirically. However, in many situations pedestrian crowds are rather static, e.g. due to jamming near bottlenecks or queueing at ticket counters or supermarket checkouts. Classically such queues are often described by the M/M/1 queue that neglects the internal structure (density profile) of the queue by focussing on the system length as the only dynamical variable. This is different in the Exclusive Queueing Process (EQP) in which the queue is considered on a microscopic level. It is equivalent to a Totally Asymmetric Exclusion Process (TASEP) of varying length. The EQP has a surprisingly rich phase diagram with respect to the arrival probability alpha and the service probability beta. The behavior on the phase transition line is much more complex than for the TASEP with a fixed system length. It is nonuniversal and depends strongly on the update procedure used. In this article, we review the main properties of the EQP. We also mention extensions and applications of the EQP and some related models.


Exclusive Queueing Processes and their Application to Traffic Systems
C. Arita, A. Schadschneider

http://arxiv.org/abs/1409.4731

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Fractional dynamics on networks: Emergence of anomalous diffusion and Lévy flights

We introduce a formalism of fractional diffusion on networks based on a fractional Laplacian matrix that can be constructed directly from the eigenvalues and eigenvectors of the Laplacian matrix. This fractional approach allows random walks with long-range dynamics providing a general framework for anomalous diffusion and navigation, and inducing dynamically the small-world property on any network. We obtained exact results for the stationary probability distribution, the average fractional return probability, and a global time, showing that the efficiency to navigate the network is greater if we use a fractional random walk in comparison to a normal random walk. For the case of a ring, we obtain exact analytical results showing that the fractional transition and return probabilities follow a long-range power-law decay, leading to the emergence of Lévy flights on networks. Our general fractional diffusion formalism applies to regular, random, and complex networks and can be implemented from the spectral properties of the Laplacian matrix, providing an important tool to analyze anomalous diffusion on networks.


Fractional dynamics on networks: Emergence of anomalous diffusion and Lévy flights
Phys. Rev. E 90, 032809 – Published 17 September 2014
A. P. Riascos and José L. Mateos

http://dx.doi.org/10.1103/PhysRevE.90.032809

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A Neuroscientist’s Radical Theory of How Networks Become Conscious

A Neuroscientist’s Radical Theory of How Networks Become Conscious | Papers | Scoop.it
It's a question that's perplexed philosophers for centuries and scientists for decades: Where does consciousness come from? Neuroscientist Christof Koch, chief scientific officer at the Allen Institute for Brain Science, thinks he has an answer.

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The Time Scale of Evolutionary Innovation

Evolutionary adaptation can be described as a biased, stochastic walk of a population of sequences in a high dimensional sequence space. The population explores a fitness landscape. The mutation-selection process biases the population towards regions of higher fitness. In this paper we estimate the time scale that is needed for evolutionary innovation. Our key parameter is the length of the genetic sequence that needs to be adapted. We show that a variety of evolutionary processes take exponential time in sequence length. We propose a specific process, which we call ‘regeneration processes’, and show that it allows evolution to work on polynomial time scales. In this view, evolution can solve a problem efficiently if it has solved a similar problem already.


Chatterjee K, Pavlogiannis A, Adlam B, Nowak MA (2014) The Time Scale of Evolutionary Innovation. PLoS Comput Biol 10(9): e1003818. http://dx.doi.org/10.1371/journal.pcbi.1003818

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Symbolic regression of generative network models

Symbolic regression of generative network models | Papers | Scoop.it

Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied “out of the box” to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world networks. We were able to find programs that are simple enough to lead to an actual understanding of the mechanisms proposed, namely for a simple brain and a social network.


Symbolic regression of generative network models
• Telmo Menezes & Camille Roth

Scientific Reports 4, Article number: 6284 http://dx.doi.org/10.1038/srep06284

See Also: https://github.com/telmomenezes/synthetic

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Flora Moon's curator insight, September 14, 11:13 AM

Big data meets systems and can potentially shines a light on system dynamics....

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Dynamic Homeostasis in Packet Switching Networks

In this study, we investigate the adaptation and robustness of a packet switching network (PSN), the fundamental architecture of the Internet. We claim that the adaptation introduced by a transmission control protocol (TCP) congestion control mechanism is interpretable as the self-organization of multiple attractors and stability to switch from one attractor to another. To discuss this argument quantitatively, we study the adaptation of the Internet by simulating a PSN using ns-2. Our hypothesis is that the robustness and fragility of the Internet can be attributed to the inherent dynamics of the PSN feedback mechanism called the congestion window size, or \textit{cwnd}. By varying the data input into the PSN system, we investigate the possible self-organization of attractors in cwnd temporal dynamics and discuss the adaptability and robustness of PSNs. The present study provides an example of Ashby's Law of Requisite Variety in action.


Dynamic Homeostasis in Packet Switching Networks
Mizuki Oka, Hirotake Abe, Takashi Ikegami

http://arxiv.org/abs/1409.1533

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