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When the full stock of a new product is quickly sold in a few days or weeks, one has the impression that new technologies develop and conquer the market in a very easy way. This may be true for some new technologies, for example the cell phone, but not for others, like the blue-ray. Novelty, usefulness, advertising, price, and fashion are the driving forces behind the adoption of a new product. But, what are the key factors that lead to adopt a new technology? In this paper we propose and investigate a simple model for the adoption of an innovation which depends mainly on three elements: the appeal of the novelty, the inertia or resistance to adopt it, and the interaction with other agents. Why, when, and how fast innovations are adopted S. Gonçalves, M.F. Laguna and J.R. Iglesias Eur. Phys. J. B (2012) 85: 192
http://dx.doi.org/10.1140/epjb/e2012-30082-6
Over the past decade or so, my colleagues and I have taken a close look at why people cheat, using a variety of experiments and looking at a panoply of unique data sets—from insurance claims to employment histories to the treatment records of doctors and dentists. What we have found, in a nutshell: Everybody has the capacity to be dishonest, and almost everybody cheats—just by a little. Except for a few outliers at the top and bottom, the behavior of almost everyone is driven by two opposing motivations. On the one hand, we want to benefit from cheating and get as much money and glory as possible; on the other hand, we want to view ourselves as honest, honorable people. Sadly, it is this kind of small-scale mass cheating, not the high-profile cases, that is most corrosive to society.
Synthetic biology has advanced the design of standardized control devices that program cellular functions and metabolic activities in living organisms1. Rational interconnection of these synthetic switches resulted in increasingly complex designer networks that execute input-triggered genetic instructions with precision, robustness and computational logic reminiscent of electronic circuits2, 3. Using trigger-controlled transcription factors, which independently control gene expression4, 5, and RNA-binding proteins that inhibit the translation of transcripts harbouring specific RNA target motifs6, 7, we have designed a set of synthetic transcription–translation control devices that could be rewired in a plug-and-play manner. Programmable single-cell mammalian biocomputers Simon Ausländer, David Ausländer, Marius Müller, Markus Wieland & Martin Fussenegger Nature (2012) http://dx.doi.org/10.1038/nature11149
The metabolic network of a living cell involves several hundreds or thousands of interconnected biochemical reactions. Previous research has shown that under realistic conditions only a fraction of these reactions is concurrently active in any given cell. This is partially determined by nutrient availability, but is also strongly dependent on the metabolic function and network structure. Here, we establish rigorous bounds showing that the fraction of active reactions is smaller (rather than larger) in metabolic networks evolved or engineered to optimize a specific metabolic task, and we show that this is largely determined by the presence of thermodynamically irreversible reactions in the network. Why Optimal States Recruit Fewer Reactions in Metabolic Networks Joo Sang Lee, Takashi Nishikawa, Adilson E. Motter http://arxiv.org/abs/1206.0766
Some contemporary views of the universe assume information and computation to be key in understanding and explaining the basic structure underpinning physical reality. We introduce the Computable Universe exploring some of the basic arguments giving foundation to these visions. We will focus on the algorithmic and quantum aspects, and how these may fit and support the computable universe hypothesis. Introducing the Computable Universe Hector Zenil http://arxiv.org/abs/1206.0376
Intel to turn London into smart cities playgroundBusiness GreenThe institute will also install a so-called "ambient intelligence platform" later this year, featuring sensors capable of collecting environmental data such as energy consumption and...
Via Richard Kastelein & Adriana Hamacher
A crucial step in recent theories of human origins is the emergence of strong pair-bonding between males and females accompanied by a dramatic reduction in the male-to-male conflict over mating and an increased investment in offspring. How such a transition from promiscuity to pair-bonding could be achieved is puzzling. Many species would, indeed, be much better off evolutionarily if the effort spent on male competition over mating was redirected to increasing female fertility or survivorship of offspring. Males, however, are locked in a “social dilemma,” where shifting one’s effort from “appropriation” to “production” would give an advantage to free-riding competitors and therefore, should not happen. Human origins and the transition from promiscuity to pair-bonding
Sergey Gavrilets PNAS, Published online before print May 29, 2012, http://dx.doi.org/10.1073/pnas.1200717109
From the frontiers of research on ice dynamics in its broadest sense, this review surveys the structures of ice, the patterns or morphologies it may assume, and the physical and chemical processes in which it is involved. Open questions in the various fields of ice research in nature are highlighted, ranging from terrestrial and oceanic ice on Earth, to ice in the atmosphere, to ice on other Solar System bodies and in interstellar space. Ice structures, patterns, and processes: A view across the icefields Thorsten Bartels-Rausch et al. Rev. Mod. Phys. 84, 885–944 (2012) http://link.aps.org/doi/10.1103/RevModPhys.84.885
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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Suggested by
Keith Warren
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Research on Alcoholics Anonymous and other self-help programs has shown mixed results regarding lifetime participation at the individual level. No one has systematically studied whether lifetime membership contributes to the success of the program as a whole. This project analyzes the relationship of lifetime membership and program growth using a series of agent-based models. Results demonstrate that programs encouraging lifetime attendance produce more groups and recruit more members than programs encouraging graduation. Rapid graduation disrupts the availability of senior members to help newcomers, limiting program growth. Self-help programs may improve program effectiveness by encouraging long-term attendance. An Agent-Based Model of Lifetime Attendance and Self-Help Program Growth Danielle Hiance, Nathan Doogan, Keith Warren, Ian M. Hamilton & Marilyn Lewis Journal of Social Work Practice in the Addictions Volume 12, Issue 2, 2012 http://dx.doi.org/10.1080/1533256X.2012.669702
When addressing real world problems in international migration research, invariably the topic of migration-related policy comes to the fore. Policies may look to address specific issues such as the integration of migrants, but in most cases they can be boiled down to limiting the flows of some types of migrant or encouraging the flows of others. Judging the impact of both migration flows and migration policy can only be done effectively with good information, but too often data on migration are poor and thus the evidence base lacking. Global migration modelling: A review of key policy needs and research centres Adam Dennett and Pablo Mateos http://www.bartlett.ucl.ac.uk/casa/publications/working-paper-184
We show that the frequency of word use is not only determined by the word length [1] and the average information content [2], but also by its emotional content. We have analyzed three established lexica of affective word usage in English, German, and Spanish, to verify that these lexica have a neutral, unbiased, emotional content. Taking into account the frequency of word usage, we find that words with a positive emotional content are more frequently used. Positive words carry less information than negative words
Garcia D, Garas A and Schweitzer F
EPJ Data Science 2012, 1:3 (18 May 2012) http://dx.doi.org/10.1140/epjds3
Biological systems are dynamic. Proteins fold into three-dimensional shapes to serve as catalysts, motors, or regulators. A fertilized egg divides exponentially, and gradients and internal cell states choreograph fetus formation. Neurons become active or are inhibited in shifting spatial and temporal patterns as an animal moves through its environment. Flocks of birds migrate together, and schools of fish form and disperse to avoid predators and forage for food. The only constant in biological systems is change. Learning Biology by Recreating and Extending Mathematical Models
Hillel J. Chiel, Jeffrey P. Gill, Jeffrey M. McManus, and Kendrick M. Shaw Science 25 May 2012:
Vol. 336 no. 6084 pp. 993-994
http://dx.doi.org/10.1126/science.1214192
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Predator-prey interactions are vital to the stability of many ecosystems [1]. Yet, few studies have considered how they are mediated due to substantial challenges in quantifying behavior over appropriate temporal and spatial scales. Here, we employ high-resolution sonar imaging to track the motion and interactions among predatory fish and their schooling prey in a natural environment. In particular, we address the relationship between predator attack behavior and the capacity for prey to respond both directly and through collective propagation of changes in velocity by group members (...) Our results highlight the importance of collective behavior to the strategies employed by both predators and prey under conditions of considerable informational constraints. The Dynamics of Coordinated Group Hunting and Collective Information Transfer among Schooling Prey Nils Olav Handegard, Kevin M. Boswell, Christos C. Ioannou, Simon P. Leblanc, Dag B. Tjøstheim, Iain D. Couzin Current Biology, 07 June 2012
http://dx.doi.org/10.1016/j.cub.2012.04.050
In empirical studies of friendship networks participants are typically asked, in interviews or questionnaires, to identify some or all of their close friends, resulting in a directed network in which friendships can, and often do, run in only one direction between a pair of individuals. Here we analyze a large collection of such networks representing friendships among students at US high and junior-high schools and show that the pattern of unreciprocated friendships is far from random. In every network, without exception, we find that there exists a ranking of participants, from low to high, such that almost all unreciprocated friendships consist of a lower-ranked individual claiming friendship with a higher-ranked one. Friendship networks and social status Brian Ball, M. E. J. Newman http://arxiv.org/abs/1205.6822
We develop a sequence of models describing information transmission and
decision dynamics for a network of individual agents subject to multiple
sources of influence. Our general framework is set in the context of an
impending natural disaster, where individuals, represented by nodes on the
network, must decide whether or not to evacuate. Collective Decision Dynamics in the Presence of External Drivers Danielle S. Bassett, David L. Alderson, Jean M. Carlson http://arxiv.org/abs/1206.1120
Two broad models of peloton dynamics are explored. The first is an energetic model that describes peloton dynamics that oscillate through observable phase states as they emerge from collision avoidance and riders' coupled energy outputs. These phases exhibit behavioural characteristics such as convection patterns and synchronization, among others. Under the second, economic model, we discuss some basic parameters of the peloton as a system of economic exchange, and identify the resources within a peloton for which riders compete and cooperate. These include the energy savings of drafting, a near-front positional resource, and an information resource. The complex dynamics of bicycle pelotons Hugh Trenchard http://arxiv.org/abs/1206.0816
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Suggested by
Segismundo
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Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability.
MIT has been selected from among 55 institutions that submitted 157 proposals to host a new Intel research center that will concentrate on what’s come to be called “big data” — new techniques for organizing and making sense of the huge amounts of information generated by Web users and new networked sensors.
Via Ashish Umre
Thirty authors of different disciplines, ranging from cognitive science and linguistics to mathematics and physics, address the topic of language origin and evolution. Language dynamics is investigated through an interdisciplinary effort, involving field and synthetic experiments, modelling and comparison of the theoretical predictions with empirical data. The result consists in new insights that significantly contribute to the ongoing debate on the origin and the evolution of language. In this Topical Issue the state of the art of this novel and fertile approach is reported by major experts of the field. Language Dynamics Andrea Baroncelli, Vittorio Loreto & Francesca Tria Advances in Complex Systems Volume: 15, Issues: 3-4(2012) 1203002 http://www.worldscinet.com/acs/15/1503n04/S0219525912030026.html
Existing models of network communities do not capture dense community overlaps. We present the Community-Affiliation Graph Model (AGM), a conceptual model of network community structure, which reliably captures the overall structure of networks as well as the overlapping nature of network communities. Structure and Overlaps of Communities in Networks Jaewon Yang, Jure Leskovec http://arxiv.org/abs/1205.6228
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Suggested by
bgcswall
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Given a complex networked system whose topology and dynamical equations are unknown, is it possible to foresee that a certain type of collective dynamics can potentially emerge in the system, provided that only time-series measurements are available? We address this question by focusing on a commonly studied type of collective dynamics, namely, synchronization in coupled dynamical networks. We demonstrate that, using the compressive-sensing paradigm, even when the coupling strength is not uniform so that the network is effectively weighted, the full topology, the coupling weights, and the nodal dynamical equations can all be uncovered accurately. The reconstruction accuracy and data requirement are systematically analyzed, in a process that includes a validation of the reconstructed eigenvalue spectrum of the underlying coupling matrix. A master stability function (MSF), the fundamental quantity determining the network synchronizability, can then be calculated based on the reconstructed dynamical system, the accuracy of which can be assessed as well. With the coupling matrix and MSF fully uncovered, the emergence of synchronous dynamics in the network can be anticipated and controlled. To forecast the collective dynamics on complex networks is an extremely challenging problem with significant applications in many disciplines, and our work represents an initial step in this important area. Forecasting synchronizability of complex networks from data Authors: Ri-Qi Su, Xuan Ni, Wen-Xu Wang, and Ying-Cheng Lai Phys. Rev. E 85, 056220 (2012) http://pre.aps.org/abstract/PRE/v85/i5/e056220
We pose the central problem of defining a measure of complexity, specifically for spatial systems in general, city systems in particular. The measures we adopt are based on Shannon’s (1948) definition of information. We introduce this measure and argue that increasing information is equivalent to increasing complexity and we show that for spatial distributions, this involves a trade-off between the density of the distribution and the number of events that characterize it; as cities get bigger and are characterized by more events – more places or locations, information increases, all other things being equal. But sometimes the distribution changes at a faster rate than the number of events and thus information can decrease even if a city grows. Entropy, complexity and Spatial Information Michael Batty, Robin Morphet, Paolo Massuci, and Kiril Stanilov http://www.bartlett.ucl.ac.uk/casa/publications/working-paper-185
A main characteristic of social media is that its diverse content, copiously generated by both standard outlets and general users, constantly competes for the scarce attention of large audiences. Out of this flood of information some topics manage to get enough attention to become the most popular ones and thus to be prominently displayed as trends. Equally important, some of these trends persist long enough so as to shape part of the social agenda. How this happens is the focus of this paper. Long trend dynamics in social media
Wang C and Huberman BA
EPJ Data Science 2012, 1:2 (18 May 2012) http://dx.doi.org/10.1140/epjds2
Complex networks are often constructed by aggregating empirical data over time, such that a link represents the existence of interactions between the endpoint nodes and the link weight represents the intensity of such interactions within the aggregation time window. The resulting networks are then often considered static. More often than not, the aggregation time window is dictated by the availability of data, and the effects of its length on the resulting networks are rarely considered. Here, we address this question by studying the structural features of networks emerging from aggregating empirical data over different time intervals, focussing on networks derived from time-stamped, anonymized mobile telephone call records. Our results show that short aggregation intervals yield networks where strong links associated with dense clusters dominate; the seeds of such clusters or communities become already visible for intervals of around one week. Effects of time window size and placement on the structure of an aggregated communication network
Krings G, Karsai M, Bernhardsson S, Blondel VD and Saramäki J
EPJ Data Science 2012, 1:4 (18 May 2012) http://dx.doi.org/10.1140/epjds4
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