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Genome Sizes and the Benford Distribution

Genome Sizes and the Benford Distribution | Papers | Scoop.it

Simply by assuming that the (protein) coding and non-coding fractions of the genome must have different dynamics and that the non-coding fraction must be particularly versatile and therefore be controlled by a variety of (unspecified) probability distribution functions (pdf’s), we are able to predict that the number of ORFs for Eukaryotes follows a Benford distribution and must therefore have a specific logarithmic form. Using the data for the 1000+ genomes available to us in early 2010, we find that the Benford distribution provides excellent fits to the data over several orders of magnitude.

 

Friar JL, Goldman T, Pérez–Mercader J (2012) Genome Sizes and the Benford Distribution. PLoS ONE 7(5): e36624. http://dx.doi.org/doi:10.1371/journal.pone.0036624

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Methods for quantifying effects of social unrest using credit card transaction data

Societal unrest and similar events are important for societies, but it is often difficult to quantify their effects on individuals, hindering a timely and effective policy-making in emergencies and in particular localized social shocks such as protests. Traditionally, effects are assessed through economic indicators or surveys with relatively low temporal and spatial resolutions. In this work, we compute two behavioral indexes, based on the use of credit card transaction data, for measuring the economic effects of a series of protests on consumer actions and personal consumption. Using data from a metropolitan area in an OECD country, we show that protests affect consumers’ shopping frequency and spending, but in noticeably different ways. The effects show strong temporal and spatial patterns, vary between neighborhoods and customers of different socio-demographical characteristics as well as between merchants of different categories, and suggest interesting subtleties in purchase behavior such as displaced or delayed shopping activities. Our method can generally serve for the real-time monitoring of the effects of major social shocks or events on urban economy and consumer sentiment, providing high-resolution and cost-effective measurement tools to complement traditional economic indicators.

 

Methods for quantifying effects of social unrest using credit card transaction data

Xiaowen Dong, Joachim Meyer, Erez Shmueli, Burçin Bozkaya and Alex Pentland
EPJ Data Science20187:8
https://doi.org/10.1140/epjds/s13688-018-0136-x

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Population Structure Promotes the Evolution of Intuitive Cooperation and Inhibits Deliberation

Population Structure Promotes the Evolution of Intuitive Cooperation and Inhibits Deliberation | Papers | Scoop.it

Spatial structure is one of the most studied mechanisms in evolutionary game theory. Here, we explore the consequences of spatial structure for a question which has received considerable empirical and theoretical attention in recent years, but has not yet been studied from a network perspective: whether cooperation relies on intuitive predispositions or deliberative self-control. We examine this question using a model which integrates the “dual-process” framework from cognitive science with evolutionary game theory, and considers the evolution of agents who are embedded within a social network and only interact with their neighbors. In line with past work in well-mixed populations, we find that selection favors either the intuitive defector strategy which never deliberates, or the dual-process cooperator strategy which intuitively cooperates but uses deliberation to switch to defection when doing so is payoff-maximizing. We find that sparser networks (i.e., smaller average degree) facilitate the success of dual-process cooperators over intuitive defectors, while also reducing the level of deliberation that dual-process cooperators engage in; and that these results generalize across different kinds of networks. These observations demonstrate the important role that spatial structure can have not just on the evolution of cooperation, but on the co-evolution of cooperation and cognition.

 

Population Structure Promotes the Evolution of Intuitive Cooperation and Inhibits Deliberation

Mohsen Mosleh & David G. Rand

Scientific Reports

volume 8, Article number: 6293 (2018)
doi:10.1038/s41598-018-24473-1

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Resisting Influence: How the Strength of Predispositions to Resist Control Can Change Strategies for Optimal Opinion Control in the Voter Model

Resisting Influence: How the Strength of Predispositions to Resist Control Can Change Strategies for Optimal Opinion Control in the Voter Model | Papers | Scoop.it

In this paper, we investigate influence maximization, or optimal opinion control, in a modified version of the two-state voter dynamics in which a native state and a controlled or influenced state are accounted for. We include agent predispositions to resist influence in the form of a probability q with which agents spontaneously switch back to the native state when in the controlled state. We argue that in contrast to the original voter model, optimal control in this setting depends on q: For low strength of predispositions q, optimal control should focus on hub nodes, but for large q, optimal control can be achieved by focusing on the lowest degree nodes. We investigate this transition between hub and low-degree node control for heterogeneous undirected networks and give analytical and numerical arguments for the existence of two control regimes.

 

Resisting Influence: How the Strength of Predispositions to Resist Control Can Change Strategies for Optimal Opinion Control in the Voter Model

Markus Brede, Valerio Restocchi and Sebastian Stein

 

Front. Robot. AI, 17 April 2018 | https://doi.org/10.3389/frobt.2018.00034

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Financial networks and stress testing: Challenges and new research avenues for systemic risk analysis and financial stability implications

Network models, stress testing methods and early warning systems are attracting growing interest both among scholars and practitioners. In this short paper, we illustrate some of the insights they have to offer both in terms of new fundamental scientific understanding of the emergence systemic risk and in terms of concrete applications to the policy areas of financial stability and macro-prudential policy. Finally, we discuss new research pathways to address the challenging questions still open, including multiplex networks, big financial data, and climate-finance.

 

Financial networks and stress testing: Challenges and new research avenues for systemic risk analysis and financial stability implications
Stefano Battiston, Serafin Martinez-Jaramillo

Journal of Financial Stability
Volume 35, April 2018, Pages 6-16

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See Also: Special Issue: Network models, stress testing and other tools for financial stability monitoring and acroprudential policy design and implementation
Edited by Dr Serafín Martínez Jaramillo, Dr.Stefano Battiston
Volume 35, Pages 1-242 (April 2018)

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Reservoir Computing Using Nonuniform Binary Cellular Automata

The reservoir computing (RC) paradigm utilizes a dynamical system (a reservoir) and a linear classifier (a readout layer) to process data from sequential classification tasks. In this paper, the usage of cellular automata (CAs) as a reservoir is investigated. The use of CAs in RC has been showing promising results. In this paper, it is shown that some cellular automaton (CA) rules perform better than others and the reservoir performance is improved by increasing the size of the CA reservoir itself. In addition, the usage of parallel loosely coupled (nonuniform) CA reservoirs, where each reservoir has a different CA rule, is investigated. The experiments performed on nonuniform CA reservoirs provide valuable insights into CA reservoir design. The results herein show that some rules do not work well together, while other combinations work remarkably well. This suggests that nonuniform CAs could represent a powerful tool for novel CA reservoir implementations.

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Bursty Human Dynamics

Bursty dynamics is a common temporal property of various complex systems in Nature but it also characterises the dynamics of human actions and interactions. At the phenomenological level it is a feature of all systems that evolve heterogeneously over time by alternating between periods of low and high event frequencies. In such systems, bursts are identified as periods in which the events occur with a rapid pace within a short time-interval while these periods are separated by long periods of time with low frequency of events. As such dynamical patterns occur in a wide range of natural phenomena, their observation, characterisation, and modelling have been a long standing challenge in several fields of research. However, due to some recent developments in communication and data collection techniques it has become possible to follow digital traces of actions and interactions of humans from the individual up to the societal level. This led to several new observations of bursty phenomena in the new but largely unexplored area of human dynamics, which called for the renaissance to study these systems using research concepts and methodologies, including data analytics and modelling. As a result, a large amount of new insight and knowledge as well as innovations have been accumulated in the field, which provided us a timely opportunity to write this brief monograph to make an up-to-date review and summary of the observations, appropriate measures, modelling, and applications of heterogeneous bursty patterns occurring in the dynamics of human behaviour.

 

Bursty Human Dynamics
Márton Karsai, Hang-Hyun Jo, Kimmo Kaski

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It’s Time to Study AI on Its Own Terms

It’s Time to Study AI on Its Own Terms | Papers | Scoop.it

What if physiologists were the only people who study human behavior at all scales: from how the human body functions, to how social norms emerge, to how the stock market functions, to how we create, share, and consume culture?

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Seeking Open-Ended Evolution in Swarm Chemistry II: Analyzing Long-Term Dynamics via Automated Object Harvesting

We studied the long-term dynamics of evolutionary Swarm Chemistry by extending the simulation length ten-fold compared to earlier work and by developing and using a new automated object harvesting method. Both macroscopic dynamics and microscopic object features were characterized and tracked using several measures. Results showed that the evolutionary dynamics tended to settle down into a stable state after the initial transient period, and that the extent of environmental perturbations also affected the evolutionary trends substantially. In the meantime, the automated harvesting method successfully produced a huge collection of spontaneously evolved objects, revealing the system's autonomous creativity at an unprecedented scale.

 

Seeking Open-Ended Evolution in Swarm Chemistry II: Analyzing Long-Term Dynamics via Automated Object Harvesting
Hiroki Sayama

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The Dynamics of Interacting Swarms

Swarms are self-organized dynamical coupled agents which evolve from simple rules of communication. They are ubiquitous in nature, and be- coming more prominent in defense applications. Here we report on a preliminary study of swarm collisions for a swarm model in which each agent is self-propelling but globally communicates with other agents. We generalize previous models by investigating the interacting dynamics when delay is introduced to the communicating agents. One of our major find- ings is that interacting swarms are far less likely to flock cohesively if they are coupled with delay. In addition, parameter ranges based on coupling strength, incidence angle of collision, and delay change dramatically for other swarm interactions which result in flocking, milling, and scattering.

 

The Dynamics of Interacting Swarms
Carl Kolon, Ira B. Schwartz

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The Emergence of Life: Some Notes on the Origin of Biological Information

The emergence of life is best understood in terms of the dynamics and evolution of systems of chemical replicating entities endowed with genetic polymers. To understand the current emphasis of origin‐of‐life research in the abiotic appearance of replicative polymers and genetic information, this chapter considers Ernst Haeckel's assumption of cell nuclei together with his characterization of the Monera as the oldest biological group. Haeckel's ideas exerted an extraordinary influence in many 19th Century naturalists and philosophers, who conceived cells as chemical machines and developed research programs on experimental abiogenesis that can be considered as a direct intellectual predecessor of current efforts on synthetic biology. Some of their results have their contemporary equivalents in the proposals of the advocates of complexity theories that attempt to explain the origin and nature of life on the basis of complexity theory and self‐assembly phenomena.

 

The Emergence of Life: Some Notes on the Origin of Biological Information

Antonio Lazcano
Life Sciences, Information Sciences, 1

Book Editor(s): Thierry Gaudin Dominique Lacroix Marie‐Christine Maurel Jean‐Charles Pomerol

https://doi.org/10.1002/9781119452713.ch1 

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New paper showing how a fundamental law of universal computation also applies to weaker forms of computation and how this can evaluate the effectivity of measures of complexity

Previously referred to as ‘miraculous’ in the scientific literature because of its powerful properties and its wide application as optimal solution to the problem of induction/inference, (approximations to) Algorithmic Probability (AP) and the associated Universal Distribution are (or should be) of the greatest importance in science. Here we investigate the emergence, the rates of emergence and convergence, and the Coding-theorem like behaviour of AP in Turing-subuniversal models of computation. We investigate empirical distributions of computing models in the Chomsky hierarchy. We introduce measures of algorithmic probability and algorithmic complexity based upon resource-bounded computation, in contrast to previously thoroughly investigated distributions produced from the output distribution of Turing machines. This approach allows for numerical approximations to algorithmic (Kolmogorov-Chaitin) complexity-based estimations at each of the levels of a computational hierarchy. We demonstrate that all these estimations are correlated in rank and that they converge both in rank and values as a function of computational power, despite fundamental differences between computational models. In the context of natural processes that operate below the Turing universal level because of finite resources and physical degradation, the investigation of natural biases stemming from algorithmic rules may shed light on the distribution of outcomes. We show that up to 60% of the simplicity/complexity bias in distributions produced even by the weakest of the computational models can be accounted for by Algorithmic Probability in its approximation to the Universal Distribution. Coding theorem-like behaviour and emergence of the Universal Distribution. Correlation in rank (distributions were sorted in terms of each other) of empirical output distributions as compared to the output distribution of TM(5, 2). A progression towards greater correlation is noticed as a function of increasing computational power. Bold black labels are placed at their Chomsky level and gray labels are placed within the highest correlated level. Shannon entropy and lossless compression (Compress) distribute values below or at about the first 2 Chomsky types, as expected. It is not surprising to see the LBA with runtime 107 further deviate in ranking, because LBA after 27 steps produced the highest frequency strings, which are expected to converge faster. Eventually LBA 107 (which is none other than TM(4,2)) will converge to TM(5,2). An empirical bound of non-halting models seems to be low LBA even when increasing the number of states (or symbols for CA).

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CITIZEN DATA SCIENCE FOR SOCIAL GOOD IN COMPLEX SYSTEMS

The confluence of massive amounts of openly available data, sophisticated machine learning algorithms and an enlightened citizenry willing to engage in data science presents novel opportunities for crowd sourced data science for social good. In this submission, I present vignettes of data science projects that I have been involved in and which have impact in various spheres of life and on social good. Complex systems are all around us: from social networks to transportation systems, cities, economies and financial markets. Understanding these complex systems may lead to solutions for problems ranging from famines, global crises, poverty, climate change and sustainable living despite over-population. Big data and citizen data science allows unprecedented computational power and collective intelligence to be brought to bear on fundamental challenges facing humanity like poverty, diseases, famines and developmental challenges.

 

CITIZEN DATA SCIENCE FOR
SOCIAL GOOD IN COMPLEX SYSTEMS

Soumya Banerjee
INDECS 16(1), 88-91, 2018
DOI 10.7906/indecs.16.1.6

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Optimal diversification strategies in the networks of related products and of related research areas

Optimal diversification strategies in the networks of related products and of related research areas | Papers | Scoop.it

Countries and cities are likely to enter economic activities that are related to those that are already present in them. Yet, while these path dependencies are universally acknowledged, we lack an understanding of the diversification strategies that can optimally balance the development of related and unrelated activities. Here, we develop algorithms to identify the activities that are optimal to target at each time step. We find that the strategies that minimize the total time needed to diversify an economy target highly connected activities during a narrow and specific time window. We compare the strategies suggested by our model with the strategies followed by countries in the diversification of their exports and research activities, finding that countries follow strategies that are close to the ones suggested by the model. These findings add to our understanding of economic diversification and also to our general understanding of diffusion in networks.

 

Optimal diversification strategies in the networks of related products and of related research areas
Aamena Alshamsi, Flávio L. Pinheiro & Cesar A. Hidalgo

Nature Communications volume 9, Article number: 1328 (2018)
doi:10.1038/s41467-018-03740-9

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The Silurian Hypothesis: Would it be possible to detect an industrial civilization in the geological record?

If an industrial civilization had existed on Earth many millions of years prior to our own era, what traces would it have left and would they be detectable today? We summarize the likely geological fingerprint of the Anthropocene, and demonstrate that while clear, it will not differ greatly in many respects from other known events in the geological record. We then propose tests that could plausibly distinguish an industrial cause from an otherwise naturally occurring climate event.

 

The Silurian Hypothesis: Would it be possible to detect an industrial civilization in the geological record?
Gavin A. Schmidt, Adam Frank

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Synergy from reproductive division of labor and genetic complexity drive the evolution of sex

Computer experiments that mirror the evolutionary dynamics of sexual and asexual organisms as they occur in nature were used to test features proposed to explain the evolution of sexual recombination. Results show that this evolution is better described as a network of interactions between possible sexual forms, including diploidy, thelytoky, facultative sex, assortation, bisexuality, and division of labor between the sexes, rather than a simple transition from parthenogenesis to sexual recombination. Diploidy was shown to be fundamental for the evolution of sex; bisexual reproduction emerged only among anisogamic diploids with a synergistic division of reproductive labor; and facultative sex was more likely to evolve among haploids practicing assortative mating. Looking at the evolution of sex as a complex system through individual-based simulations explains better the diversity of sexual strategies known to exist in nature, compared to classical analytical models.

 

Synergy from reproductive division of labor and genetic complexity drive the evolution of sex

Klaus Jaffe

Journal of Biological Physics

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How Criticality of Gene Regulatory Networks Affects the Resulting Morphogenesis under Genetic Perturbations

Whereas the relationship between criticality of gene regulatory networks (GRNs) and dynamics of GRNs at a single-cell level has been vigorously studied, the relationship between the criticality of GRNs and system properties at a higher level has not been fully explored. Here we aim at revealing a potential role of criticality of GRNs in morphogenesis, which is hard to uncover through the single-cell-level studies, especially from an evolutionary viewpoint. Our model simulated the growth of a cell population from a single seed cell. All the cells were assumed to have identical intracellular GRNs. We induced genetic perturbations to the GRN of the seed cell by adding, deleting, or switching a regulatory link between a pair of genes. From numerical simulations, we found that the criticality of GRNs facilitated the formation of nontrivial morphologies when the GRNs were critical in the presence of the evolutionary perturbations. Moreover, the criticality of GRNs produced topologically homogeneous cell clusters by adjusting the spatial arrangements of cells, which led to the formation of nontrivial morphogenetic patterns. Our findings correspond to an epigenetic viewpoint that heterogeneous and complex features emerge from homogeneous and less complex components through the interactions among them. Thus, our results imply that highly structured tissues or organs in morphogenesis of multicellular organisms might stem from the criticality of GRNs.

 

How Criticality of Gene Regulatory Networks Affects the Resulting Morphogenesis under Genetic Perturbations
Hyobin Kim and Hiroki Sayama
Artificial Life
https://doi.org/10.1162/artl_a_00262

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Behavior Classification for Turing Machines

A classification for Turing machines is described. Quantitative descriptors for Turing machine behavior are used for measuring repetitiveness, periodicity, complexity and entropy. These descriptors allowed identifying several kinds of behavior for Turing machines, using an approach based on machine learning. The classification was tested and generality was probed over different configurations of Turing machines.

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Multilayer Networks in a Nutshell

Complex systems are characterized by many interacting units that give rise to emergent behavior. A particularly advantageous way to study these systems is through the analysis of the networks that encode the interactions among the system's constituents. During the last two decades, network science has provided many insights in natural, social, biological and technological systems. However, real systems are more often than not interconnected, with many interdependencies that are not properly captured by single layer networks. To account for this source of complexity, a more general framework, in which different networks evolve or interact with each other, is needed. These are known as multilayer networks. Here we provide an overview of the basic methodology used to describe multilayer systems as well as of some representative dynamical processes that take place on top of them. We round off the review with a summary of several applications in diverse fields of science.

 

Multilayer Networks in a Nutshell
Alberto Aleta, Yamir Moreno

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Sixteenth-Century Pharmacology and the Controversy between Reductionism and Emergentism

Although in the sixteenth century some pharmacological powers were widely ascribed to celestial influences, alternative views of the nature of such powers began to be developed: Reductionism, according to which all pharmacological powers could be understood as combinations of the powers of elementary qualities, and emergentism, according to which some pharmacological powers are irreducible to combinations of the powers of elementary but arise out of their combination and interaction. The former view can be traced in the work of Francisco Valles (1524–1592) and Thomas Erastus (1524–1583), the latter view in the work of Girolamo Mercuriale (1530–1606) and Jacob Schegk (1511–1587).

 

Sixteenth-Century Pharmacology and the Controversy between Reductionism and Emergentism

Andreas Blank
Perspectives on Science
Volume 26 | Issue 2 | March-April 2018
p.157-184

https://doi.org/10.1162/POSC_a_00271

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Logical Gates via Gliders Collisions

An elementary cellular automaton with memory is a chain of finite state machines (cells) updating their state simultaneously and by the same rule. Each cell updates its current state depending on current states of its immediate neighbours and a certain number of its own past states. Some cell-state transition rules support gliders, compact patterns of non-quiescent states translating along the chain. We present designs of logical gates, including reversible Fredkin gate and controlled NOT gate, implemented via collisions between gliders.


Logical Gates via Gliders Collisions
Genaro J. Martinez, Andrew Adamatzky, Kenichi Morita

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Methodological Strategies in Microbiome Research and their Explanatory Implications

Early microbiome research found numerous associations between microbial community patterns and host physiological states. These findings hinted at community-level explanations. “Top-down” experiments, working with whole communities, strengthened these explanatory expectations. Now, “bottom-up” mechanism-seeking approaches are dissecting communities to focus on specific microbes carrying out particular biochemical activities (e.g., choline metabolism pathways, Clostridium difficile suppression). To understand the interplay between methodological and explanatory scales, we examine claims of “dysbiosis,” when host illness is proposed as the consequence of a community state. Our analysis concludes with general observations about how methodologies relate to explanations, and the implications for microbiome research.

 

Methodological Strategies in Microbiome Research and their Explanatory Implications

Maureen A. O’Malley and Derek J. Skillings

Perspectives on Science
Volume 26 | Issue 2 | March-April 2018
p.239-265
https://doi.org/10.1162/POSC_a_00274

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Bridging the Timescales of Single-Cell and Population Dynamics

Bridging the Timescales of Single-Cell and Population Dynamics | Papers | Scoop.it

How are granular details of stochastic growth and division of individual cells reflected in smooth deterministic growth of population numbers? We provide an integrated, multiscale perspective of microbial growth dynamics by formulating a data-validated theoretical framework that accounts for observables at both single-cell and population scales. We derive exact analytical complete time-dependent solutions to cell-age distributions and population growth rates as functionals of the underlying interdivision time distributions, for symmetric and asymmetric cell division. These results provide insights into the surprising implications of stochastic single-cell dynamics for population growth. Using our results for asymmetric division, we deduce the time to transition from the reproductively quiescent (swarmer) to the replication-competent (stalked) stage of the Caulobacter crescentus life cycle. Remarkably, population numbers can spontaneously oscillate with time. We elucidate the physics leading to these population oscillations. For C. crescentus cells, we show that a simple measurement of the population growth rate, for a given growth condition, is sufficient to characterize the condition-specific cellular unit of time and, thus, yields the mean (single-cell) growth and division timescales, fluctuations in cell division times, the cell-age distribution, and the quiescence timescale.

 

Bridging the Timescales of Single-Cell and Population Dynamics
Farshid Jafarpour, Charles S. Wright, Herman Gudjonson, Jedidiah Riebling, Emma Dawson, Klevin Lo, Aretha Fiebig, Sean Crosson, Aaron R. Dinner, and Srividya Iyer-Biswas
Phys. Rev. X 8, 021007 – Published 5 April 2018

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Toward Growing Robots: A Historical Evolution from Cellular to Plant-Inspired Robotics

This paper provides the very first definition of “growing robots”: a category of robots that imitates biological growth by the incremental addition of material. Although this nomenclature is quite new, the concept of morphological evolution, which is behind growth, has been extensively addressed in engineering and robotics. In fact, the idea of reproducing processes that belong to living systems has always attracted scientists and engineers. The creation of systems that adapt reliably and effectively to the environment with their morphology and control would be beneficial for many different applications, including terrestrial and space exploration or the monitoring of disasters or dangerous environments. Different approaches have been proposed over the years for solving the morphological adaptation of artificial systems, e.g., self-assembly, self-reconfigurability, evolution of virtual creatures, plant inspiration. This work reviews the main milestones in relation to growing robots, starting from the original concept of a self-replicating automaton to the achievements obtained by plant inspiration, which provided an alternative solution to the challenges of creating robots with self-building capabilities. A selection of robots representative of growth functioning is also discussed, grouped by the natural element used as model: molecule, cell, or organism growth-inspired robots. Finally, the historical evolution of growing robots is outlined together with a discussion of the future challenges toward solutions that more faithfully can represent biological growth.

 

Toward Growing Robots: A Historical Evolution from Cellular to Plant-Inspired Robotics

Emanuela Del Dottore, Ali Sadeghi, Alessio Mondini, Virgilio Mattoli and Barbara Mazzolai

Front. Robot. AI, 14 March 2018 | https://doi.org/10.3389/frobt.2018.00016


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Spatial diffusion and churn of social media

Innovative ideas, products or services spread on social networks that, in the digital age, are maintained to large extent via telecommunication tools such as emails or social media. One of the intriguing puzzles in social contagion under such conditions is the role of physical space. It is not understood either how geography influences the disappearance of products at the end of their life-cycle. In this paper, we utilize a unique dataset compiled from a Hungarian on-line social network (OSN) to uncover novel features in the spatial adoption and churn of digital technologies. The studied OSN was established in 2002 and failed in international competition about a decade later. We find that early adopter towns churn early; while individuals tend to follow the churn of nearby friends and are less influenced by the churn of distant contacts. An agent-based Bass Diffusion Model describes the process how the product gets adopted in the overall population. We show the limitations of the model regarding the spatial aspects of diffusion and identify the directions of model corrections. Assortativity of adoption time, urban scaling of adoption over the product life-cycle and a distance decay function of diffusion probability are the main factors that spatial diffusion models need to account for.

 

Spatial diffusion and churn of social media
Balázs Lengyel, Riccardo Di Clemente, János Kertész, Marta C. González

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Success in books: a big data approach to bestsellers

Reading remains the preferred leisure activity for most individuals, continuing to offer a unique path to knowledge and learning. As such, books remain an important cultural product, consumed widely. Yet, while over 3 million books are published each year, very few are read widely and less than 500 make it to the New York Times bestseller lists. And once there, only a handful of authors can command the lists for more than a few weeks. Here we bring a big data approach to book success by investigating the properties and sales trajectories of bestsellers. We find that there are seasonal patterns to book sales with more books being sold during holidays, and even among bestsellers, fiction books sell more copies than nonfiction books. General fiction and biographies make the list more often than any other genre books, and the higher a book’s initial place in the rankings, the longer the book stays on the list as well. Looking at patterns characterizing authors, we find that fiction writers are more productive than nonfiction writers, commonly achieving bestseller status with multiple books. Additionally, there is no gender disparity among bestselling fiction authors but nonfiction, most bestsellers are written by male authors. Finally we find that there is a universal pattern to book sales. Using this universality we introduce a statistical model to explain the time evolution of sales. This model not only reproduces the entire sales trajectory of a book but also predicts the total number of copies it will sell in its lifetime, based on its early sales numbers. The analysis of the bestseller characteristics and the discovery of the universal nature of sales patterns with its driving forces are crucial for our understanding of the book industry, and more generally, of how we as a society interact with cultural products.

 

Success in books: a big data approach to bestsellers
Burcu Yucesoy, Xindi Wang, Junming Huang and Albert-László BarabásiEmail authorView ORCID ID profile
EPJ Data Science20187:7
https://doi.org/10.1140/epjds/s13688-018-0135-y

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See Also: http://bestsellers.barabasilab.com 

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