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Control energy scaling in temporal networks

Fri, 01/19/2018 - 19:45

In practical terms, controlling a network requires manipulating a large number of nodes with a comparatively small number of external inputs, a process that is facilitated by paths that broadcast the influence of the (directly-controlled) driver nodes to the rest of the network. Recent work has shown that surprisingly, temporal networks can enjoy tremendous control advantages over their static counterparts despite the fact that in temporal networks such paths are seldom instantaneously available. To understand the underlying reasons, here we systematically analyze the scaling behavior of a key control cost for temporal networks–the control energy. We show that the energy costs of controlling temporal networks are determined solely by the spectral properties of an “effective” Gramian matrix, analogous to the static network case. Surprisingly, we find that this scaling is largely dictated by the first and the last network snapshot in the temporal sequence, independent of the number of intervening snapshots, the initial and final states, and the number of driver nodes. Our results uncover the intrinsic laws governing why and when temporal networks save considerable control energy over their static counterparts.


Control energy scaling in temporal networks
Aming Li, Sean P. Cornelius, Yang-Yu Liu, Long Wang, Albert-László Barabási


Understanding predictability and exploration in human mobility

Fri, 01/19/2018 - 17:51

Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors – in terms of modeling approaches and spatio-temporal characteristics of the data sources – have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.


Understanding predictability and exploration in human mobility
Andrea Cuttone, Sune Lehmann and Marta C. González
EPJ Data Science20187:2


Community energy storage: A smart choice for the smart grid?

Fri, 01/19/2018 - 15:46

•We compare batteries deployed in 4500 individual households with 200 communities.

•Using real demand, PV data and locations we form community microgrids.

•We find that community batteries are more effective for distributed PV integration.

•Internal rates of return depend on the number of PV households.


Community energy storage: A smart choice for the smart grid?
Edward Barbour, David Parra, Zeyad Awwad, Marta C.González

Applied Energy
Volume 212, 15 February 2018, Pages 489-497


Socioeconomic characterization of regions through the lens of individual financial transactions

Fri, 01/19/2018 - 13:45

People are increasingly leaving digital traces of their daily activities through interacting with their digital environment. Among these traces, financial transactions are of paramount interest since they provide a panoramic view of human life through the lens of purchases, from food and clothes to sport and travel. Although many analyses have been done to study the individual preferences based on credit card transaction, characterizing human behavior at larger scales remains largely unexplored. This is mainly due to the lack of models that can relate individual transactions to macro-socioeconomic indicators. Building these models, not only can we obtain a nearly real-time information about socioeconomic characteristics of regions, usually available yearly or quarterly through official statistics, but also it can reveal hidden social and economic structures that cannot be captured by official indicators. In this paper, we aim to elucidate how macro-socioeconomic patterns could be understood based on individual financial decisions. To this end, we reveal the underlying interconnection of the network of spending leveraging anonymized individual credit/debit card transactions data, craft micro-socioeconomic indices that consists of various social and economic aspects of human life, and propose a machine learning framework to predict macro-socioeconomic indicators.


Hashemian B, Massaro E, Bojic I, Murillo Arias J, Sobolevsky S, Ratti C (2017) Socioeconomic characterization of regions through the lens of individual financial transactions. PLoS ONE 12(11): e0187031.


From Animals to Animats: 15th International Conference on the Simulation of Adaptive Behavior 2018

Fri, 01/19/2018 - 10:46

The objective of this interdisciplinary conference is to bring together researchers in computer science, artificial intelligence, artificial life, control, robotics, neurosciences, ethology, evolutionary biology and related fields in order to further our understanding of the behaviours and underlying mechanisms that allow natural and artificial animals to adapt and survive in uncertain environments. The conference will focus on experiments with well-defined models including robot models, computer simulation models and mathematical models designed to help characterise and compare various organisational principles or architectures underlying adaptive behaviour in real animals and in synthetic agents, the animats.


Scientists just uncovered the cause of a massive epidemic which killed the Aztecs, using 500-year-old teeth

Thu, 01/18/2018 - 09:16

Nearly 500 years ago, in what we know call Mexico, a disease started rippling through the population.


It bore the name cocoliztli, meaning ‘pestilence,’ and it killed between five and 15 million people in just three years. As many plagues were at the time, it proved deadly and mysterious, burning through entire populations. Occurring centuries before John Snow’s work on cholera gave rise to epidemiology, data on the disease’s devastation was sparse. Over the years, researchers and historians attempted to pin the blame for the illness on measles, plague, viral hemorrhagic fevers like Ebola, and typhoid fever—a disease caused by a variation of the bacteria Salmonella enterica.


In a paper published this week in Nature Ecology & Evolution, researchers present evidence that the latter was the most likely candidate in this cast of microbial miscreants. The study was pre-printed in biorxiv last year. The researchers detected the genome of a different variety of Salmonella enterica (the specific variety is Paratyphi C) in teeth of individuals buried in a cemetery historically linked to the deadly outbreak.


The researchers used a technique called MALT (MEGAN Alignment Tool) to analyze DNA left behind in the pulp of the teeth. MALT takes a sample of material, in this case from a tooth, and compares it to 6,247 known bacterial genomes. The results identified Salmonella enterica in 10 burials associated with the epidemic.


Complexity, Development, and Evolution in Morphogenetic Collective Systems

Wed, 01/17/2018 - 17:56

Many living and non-living complex systems can be modeled and understood as collective systems made of heterogeneous components that self-organize and generate nontrivial morphological structures and behaviors. This chapter presents a brief overview of our recent effort that investigated various aspects of such morphogenetic collective systems. We first propose a theoretical classification scheme that distinguishes four complexity levels of morphogenetic collective systems based on the nature of their components and interactions. We conducted a series of computational experiments using a self-propelled particle swarm model to investigate the effects of (1) heterogeneity of components, (2) differentiation/re-differentiation of components, and (3) local information sharing among components, on the self-organization of a collective system. Results showed that (a) heterogeneity of components had a strong impact on the system’s structure and behavior, (b) dynamic differentiation/re-differentiation of components and local information sharing helped the system maintain spatially adjacent, coherent organization, (c) dynamic differentiation/re-differentiation contributed to the development of more diverse structures and behaviors, and (d) stochastic re-differentiation of components naturally realized a self-repair capability of self-organizing morphologies. We also explored evolutionary methods to design novel self-organizing patterns, using interactive evolutionary computation and spontaneous evolution within an artificial ecosystem. These self-organizing patterns were found to be remarkably robust against dimensional changes from 2D to 3D, although evolution worked efficiently only in 2D settings.


Complexity, Development, and Evolution in Morphogenetic Collective Systems
Hiroki Sayama


Mobility and Congestion in Dynamical Multilayer Networks with Finite Storage Capacity

Wed, 01/17/2018 - 15:44

Multilayer networks describe well many real interconnected communication and transportation systems, ranging from computer networks to multimodal mobility infrastructures. Here, we introduce a model in which the nodes have a limited capacity of storing and processing the agents moving over a multilayer network, and their congestions trigger temporary faults which, in turn, dynamically affect the routing of agents seeking for uncongested paths. The study of the network performance under different layer velocities and node maximum capacities, reveals the existence of delicate trade-offs between the number of served agents and their time to travel to destination. We provide analytical estimates of the optimal buffer size at which the travel time is minimum and of its dependence on the velocity and number of links at the different layers. Phenomena reminiscent of the Slower Is Faster (SIF) effect and of the Braess’ paradox are observed in our dynamical multilayer set-up.


Mobility and Congestion in Dynamical Multilayer Networks with Finite Storage Capacity
Sabato Manfredi, Edmondo Di Tucci, Vito Latora


Global Systems Science: How to Address Humanity’s Challenges

Tue, 01/16/2018 - 23:43

Presentation by Dirk Helbing


Call for Papers | ALIFE 2018

Tue, 01/16/2018 - 22:26

The 2018 Conference on Artificial Life (ALIFE 2018)

A Hybrid of the European Conference on Artificial Life (ECAL) and the International Conference on the Synthesis and Simulation of Living Systems (ALife)

July 23-27, 2018
Tokyo, Japan

The “ALIFE 2018” conference will be a stimulating home for a rich and diverse research community in Artificial Life and related fields from around the world, with a special emphasis on encouraging communication and building bridges between the different research threads that make Artificial Life such an exciting field. Following in the tradition of recent artificial life conferences, the meeting will also have an overall theme that reflects the global nature of the first joint conference: Beyond AI. We believe that AI is just a side effect of ALIFE and we believe that this conference is going to be a turning point for both ALIFE and AI researchers.

We are inviting especially contributions to solve new challenges in ALife. Since the first ALife conference in 1987, the computational landscape has been completely reshaped in terms of scale, means, capacity, and spheres of application in our society. The use of massive real-world data has now the potential to offer an important new avenue for ALife, to help us understand the nature of living systems by understanding bridges between simple idealized models and complex data-rich phenomena? An epistemology for a modern artificial life that can operate at scale and in partnership with data, but without sacrificing the complexity of the systems that we observe, has yet to be achieved.

Submissions are welcome on all topics.
By widening the focus of artificial life, the field can avoid conventional approaches and be a source of radically new concepts, methods, models, and technologies.

We are honoured to welcome keynote speakers who include:

Rodney Brooks (iRobot, MIT, USA)
Inman Harvey (University of Sussex, UK)
Hiroshi Ishiguro (Osaka University, Japan)
David Oreilly (Artist, USA)
Margaret Boden (University of Sussex, UK)
Kenneth O. Stanley (University of Central Florida, USA).


Serendipity and strategy in rapid innovation

Sat, 01/13/2018 - 16:51

Innovation is to organizations what evolution is to organisms: it is how organizations adapt to environmental change and improve. Yet despite advances in our understanding of evolution, what drives innovation remains elusive. On the one hand, organizations invest heavily in systematic strategies to accelerate innovation. On the other, historical analysis and individual experience suggest that serendipity plays a significant role. To unify these perspectives, we analysed the mathematics of innovation as a search for designs across a universe of component building blocks. We tested our insights using data from language, gastronomy and technology. By measuring the number of makeable designs as we acquire components, we observed that the relative usefulness of different components can cross over time. When these crossovers are unanticipated, they appear to be the result of serendipity. But when we can predict crossovers in advance, they offer opportunities to strategically increase the growth of the product space.


Serendipity and strategy in rapid innovation
T. M. A. Fink, M. Reeves, R. Palma & R. S. Farr
Nature Communications 8, Article number: 2002 (2017)


Quantifying China’s regional economic complexity

Thu, 01/11/2018 - 19:00

China’s regional economic complexity is quantified by modeling 25 years’ public firm data.
High positive correlation between economic complexity and macroeconomic indicators is shown.
Economic complexity has explanatory power for economic development and income inequality.
Multivariate regressions suggest the robustness of these results with controlling socioeconomic factors.


Quantifying China’s regional economic complexity
Jian Gao, Tao Zhou

Physica A: Statistical Mechanics and its Applications
Volume 492, 15 February 2018, Pages 1591-1603


A Mathematician Who Decodes the Patterns Stamped Out by Life

Thu, 01/11/2018 - 16:59

Corina Tarnita deciphers bizarre patterns in the soil created by competing life-forms. She’s found that they can reveal whether an ecosystem is thriving or on the verge of collapse.


From Maps to Multi-dimensional Network Mechanisms of Mental Disorders

Thu, 01/11/2018 - 16:55

The development of advanced neuroimaging techniques and their deployment in large cohorts has enabled an assessment of functional and structural brain network architecture at an unprecedented level of detail. Across many temporal and spatial scales, network neuroscience has emerged as a central focus of intellectual efforts, seeking meaningful descriptions of brain networks and explanatory sets of network features that underlie circuit function in health and dysfunction in disease. However, the tools of network science commonly deployed provide insight into brain function at a fundamentally descriptive level, often failing to identify (patho-)physiological mechanisms that link system-level phenomena to the multiple hierarchies of brain function. Here we describe recently developed techniques stemming from advances in complex systems and network science that have the potential to overcome this limitation, thereby contributing mechanistic insights into neuroanatomy, functional dynamics, and pathology. Finally, we build on the Research Domain Criteria framework, highlighting the notion that mental illnesses can be conceptualized as dysfunctions of neural circuitry present across conventional diagnostic boundaries, to sketch how network-based methods can be combined with pharmacological, intermediate phenotype, genetic, and magnetic stimulation studies to probe mechanisms of psychopathology.


From Maps to Multi-dimensional Network Mechanisms of Mental Disorders
Urs Braun, Axel Schaefer, Richard F. Betzel, Heike Tost, Andreas Meyer-Lindenberg, Danielle S. Bassett

Volume 97, Issue 1, 3 January 2018, Pages 14-31


Modelling indirect interactions during failure spreading in a project activity network

Wed, 01/10/2018 - 14:57

Spreading broadly refers to the notion of an entity propagating throughout a networked system via its interacting components. Evidence of its ubiquity and severity can be seen in a range of phenomena, from disease epidemics to financial systemic risk. In order to understand the dynamics of these critical phenomena, computational models map the probability of propagation as a function of direct exposure, typically in the form of pairwise interactions between components. By doing so, the important role of indirect exposure remains unexplored. In response, we develop a simple model that accounts for the effect of both direct and indirect exposure, which we deploy in the novel context of failure propagation within a real-world engineering project. We show that indirect exposure has a significant effect in key aspects, including the: (a) final spreading event size, (b) propagation rate, and (c) spreading event structure. In addition, we demonstrate the existence of hidden influentials in large-scale spreading events, and evaluate the role of direct and indirect exposure in their emergence. Given the evidence of the importance of indirect exposure, our findings offer new insight on particular aspects that need to be included when modelling network dynamics in general, and spreading processes specifically.


Modelling indirect interactions during failure spreading in a project activity network
Christos Ellinas


Learning how to understand complexity and deal with sustainability challenges – A framework for a comprehensive approach and its application in university education

Tue, 01/09/2018 - 16:56

• Sustainability challenges require both specialized and integrative approaches.
• Domination of specialism and reductionism calls for emphasis on comprehensiveness.
• The GHH framework can be used as a tool to add comprehensiveness in education.
• The framework consists of three dimensions: generalism, holism, and holarchism.
• The dialectical approach combines comprehensive and differentiative approaches.


Energy, Information, Feedback, Adaptation, and Self-organization

Tue, 01/09/2018 - 14:50

This unique book offers a comprehensive and integrated introduction to the five fundamental elements of life and society: energy, information, feedback, adaptation, and self-organization. It is divided into two parts. Part I is concerned with energy (definition, history, energy types, energy sources, environmental impact); thermodynamics (laws, entropy definitions, energy, branches of thermodynamics, entropy interpretations, arrow of time); information (communication and transmission, modulation–demodulation, coding–decoding, information theory, information technology, information science, information systems); feedback control (history, classical methodologies, modern methodologies); adaptation (definition, mechanisms, measurement, complex adaptive systems, complexity, emergence); and self-organization (definitions/opinions, self-organized criticality, cybernetics, self-organization in complex adaptive systems, examples in nature).


In turn, Part II studies the roles, impacts, and applications of the five above-mentioned elements in life and society, namely energy (biochemical energy pathways, energy flows through food chains, evolution of energy resources, energy and economy); information (information in biology, biocomputation, information technology in office automation, power generation/distribution, manufacturing, business, transportation), feedback (temperature, water, sugar and hydrogen ion regulation, autocatalysis, biological modeling, control of hard/technological and soft/managerial systems), adaptation and self-organization (ecosystems, climate change, stock market, knowledge management, man-made self-organized controllers, traffic lights control).


Energy, Information, Feedback, Adaptation, and Self-organization
The Fundamental Elements of Life and Society
Spyros G Tzafestas


Using physics, math and models to fight cancer drug resistance

Tue, 01/09/2018 - 11:58

Despite the increasing effectiveness of breast cancer treatments over the last 50 years, tumors often become resistent to the drugs used. While drug combinations could be part of the solution to this problem, their development is very challenging. In this blog post Jorge Zanudo explains how it is possible to combine physical and mathemathical models with clinical and biological data to determine which drug combinations would be most effective in breast cancer therapy.


Workshop “Stochastic models in ecology and evolutionary biology”, 5-7th April 2018, Venice. Registration Open!

Mon, 01/08/2018 - 14:01

Living systems are characterized by the emergence of recurrent dynamical patterns at all scales of magnitude. Self-organized behaviors are observed both in large communities of microscopic components – like neural oscillations and gene network activity – as well as on larger levels – as predator-prey equilibria to name a few. Such regularities are deemed to be universal in the sense they are due to common mechanisms, independent of the details of the system. This belief justifies investigation through quantitative models able to grasp key features while disregarding inessential complications. The attempt of modeling such complex systems leads naturally to consider large families of microscopic identical units. Complexity and self-organization then arise on a macroscopic scale from the dynamics of these minimal components that evolve coupled by interaction terms. Within this scenario, probability theory and statistical mechanics come into play very soon. Aim of the workshop is to bring together scientists with different background – biology, physics and mathematics – interested in stochastic models in ecology and evolutionary biology, to discuss issues and exchange ideas. A partial list of topics includes: stochastic population dynamics, branching processes, interacting particle systems and statistical mechanics models in ecology, robustness and adaptability of ecosystems, resilience and criticality of ecological systems, models and prediction of biodiversity, molecular evolution, and neuroscience.
The style of the workshop will be rather informal. The idea is to have the opportunity to freely share ideas and discuss. Talks will be organised in different thematic sessions, and we will have both colloquia and more technical presentations.


A framework for designing compassionate and ethical artificial intelligence and artificial consciousness

Mon, 01/08/2018 - 12:20

Intelligence and consciousness have fascinated humanity for a long time and we have long sought to replicate this in machines. In this work we show some design principles for a compassionate and conscious artificial intelligence. We present a computational framework for engineering intelligence, empathy and consciousness in machines. We hope that this framework will allow us to better understand consciousness and design machines that are conscious and empathetic. Our hope is that this will also shift the discussion from a fear of artificial intelligence towards designing machines that embed our cherished values in them. Consciousness, intelligence and empathy would be worthy design goals that can be engineered in machines.


Banerjee S. (2018) A framework for designing compassionate and ethical artificial intelligence and artificial consciousness. PeerJ Preprints 6:e3502v2