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selected.
OOps, I forgot to do this while on the road

   Barry Wellman

    A vision is just a vision if it's only in your head
    Step by step, link by link, putting it together
                  Streisand/Sondheim
    The earth to be spannd, connected by network -- Walt Whitman
        It's Always Something -- Roseanne Roseannadanna
  _______________________________________________________________________
   NetLab Network                 FRSC                      INSNA Founder
   Distinguished Visiting Scholar   Social Media Lab   Ryerson University
   Distinguished Senior Advisor     	     University Learning Academy
   NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman
   https://urldefense.proofpoint.com/v2/url?u=http-3A__www.chass.utoronto.ca_-7Ewellman&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=HlN_6OpToY3hIvC-WUnfY8OB-3oliB2lsbTSa_wWj-Q&e=            https://urldefense.proofpoint.com/v2/url?u=http-3A__amzn.to_zXZg39&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=uoSFGHoulDD3rPffW_x9bra2JXLxGRT8ts9yHQ1Lo88&e=
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---------- Forwarded message ----------
Date: Mon, 5 Feb 2018 12:05:40 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
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To: "[utf-8] Barry" <[log in to unmask]>
Subject: [utf-8] Latest Complexity Digest Posts

Learn about the latest and greatest related to complex systems research. More at https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D556da8928b-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=eb1aYHMJwE5GHYVFCWrrGxnUizczmXwBvF9S5jBi4-Q&e=



Dynamic patterns of information flow in complex networks

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D59bc17bf8a-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=TCRkjSZXlczbhYxoeUqA8A5veY2YZl1ibNdyq78dnjY&e=

Although networks are extensively used to visualize information flow in biological, social and technological systems, translating topology into dynamic flow continues to challenge us, as similar networks exhibit fundamentally different flow patterns, driven by different interaction mechanisms. To uncover a network˙˙s actual flow patterns, here we use a perturbative formalism, analytically tracking the contribution of all nodes/paths to the flow of information, exposing the rules that link structure and dynamic information flow for a broad range of nonlinear systems. We find that the diversity of flow patterns can be mapped into a single universal function, characterizing the interplay between the system˙˙s topology and its dynamics, ultimately allowing us to identify the network˙˙s main arteries of information flow. Counter-intuitively, our formalism predicts a family of frequently encountered dynamics where the flow of information avoids the hubs, favoring the network˙˙s
peripheral pathways, a striking disparity between structure and dynamics.


Dynamic patterns of information flow in complex networks
Uzi Harush & Baruch Barzel
Nature Communicationsvolume 8, Article number: 2181 (2017)
doi:10.1038/s41467-017-01916-3

Source: www.nature.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D0e16da3fc1-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=NsU8Xlia4Ygc36kQm963zbP3mpDge3xtADQ36nvUSBw&e=)



Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks

    Interaction patterns in human communication networks are characterized by intermittency and unpredictable timing (burstiness). Simulated spreading dynamics through such networks are slower than expected. A technology for automated recording of social interactions of individual honeybees, developed by the authors, enables one to study these two phenomena in a nonhuman society. Specifically, by analyzing more than 1.2 million bee social interactions, we demonstrate that burstiness is not a human-specific interaction pattern. We furthermore show that spreading dynamics on bee social networks are faster than expected, confirming earlier theoretical predictions that burstiness and fast spreading can co-occur. We expect that these findings will inform future models of large-scale social organization, spread of disease, and information transmission.


Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks
Tim Gernat, Vikyath D. Rao, Martin Middendorf, Harry Dankowicz, Nigel Goldenfeld and Gene E. Robinson
PNAS 2018; published ahead of print January 29, 2018, https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D01fdf60487-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=A2w1naTpSMfdC8QdshqmELvIhxc2CiqDe8KxN9TnKgQ&e=

Source: www.pnas.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D851aa6aae1-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=EuWHZW1bPcZKQlSfvsD-DL_mIYoCEKkxQx0HyV_fdAE&e=)



Why hiring the ˙˙best˙˙ people produces the least creative results ˙˙ Scott E Page

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D6fdab5db18-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=pmuoTuUKMRxEiDIHFqZTpEQtHuXtq34UhK0C1-abMIg&e=

Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and even preschools test, score and hire the ˙˙best˙˙. This all but guarantees not creating the best team. Ranking people by common criteria produces homogeneity. And when biases creep in, it results in people who look like those making the decisions. That˙˙s not likely to lead to breakthroughs. As Astro Teller, CEO of X, the ˙˙moonshoot factory˙˙ at Alphabet, Google˙˙s parent company, has said: ˙˙Having people who have different mental perspectives is what˙˙s important. If you want to explore things you haven˙˙t explored, having people who look just like you and think just like you is not the best way.˙˙ We must see the forest.

Source: aeon.co (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3De5cdd9eb3e-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=k3eu-zq5wEXS4DjesJGpNP6liLupcl9YN-FdHCjdsJs&e=)



Temporal patterns behind the strength of persistent ties

    Social networks are made out of strong and weak ties having very different structural and dynamical properties. But what features of human interaction build a strong tie? Here we approach this question from a practical way by finding what are the properties of social interactions that make ties more persistent and thus stronger to maintain social interactions in the future. Using a large longitudinal mobile phone database we build a predictive model of tie persistence based on intensity, intimacy, structural and temporal patterns of social interaction. While our results confirm that structural (embeddedness) and intensity (number of calls) features are correlated with tie persistence, temporal features of communication events are better and more efficient predictors for tie persistence. Specifically, although communication within ties is always bursty we find that ties that are more bursty than the average are more likely to decay, signaling that tie strength is not only
reflected in the intensity or topology of the network, but also on how individuals distribute time or attention across their relationships. We also found that stable relationships have and require a constant rhythm and if communication is halted for more than 8 times the previous communication frequency, most likely the tie will decay. Our results not only are important to understand the strength of social relationships but also to unveil the entanglement between the different temporal scales in networks, from microscopic tie burstiness and rhythm to macroscopic network evolution.


Temporal patterns behind the strength of persistent ties
Henry Navarro, Giovanna Miritello, Arturo Canales and Esteban Moro
EPJ Data Science 2017 6:31
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D65130e055d-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=f_9tvAJiAv7yXgInQKnzcJkTkPUFBfSs7-2mqEYk_Ic&e=

Source: epjdatascience.springeropen.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D4b332cb822-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=h9U7wEhWyFBFg3DO_Y-jDyoa9y9aI4QACxn2K7ct7S8&e=)



How often does the best team win? A unified approach to understanding randomness in North American sport

    Statistical applications in sports have long centered on how to best separate signal (e.g. team talent) from random noise. However, most of this work has concentrated on a single sport, and the development of meaningful cross-sport comparisons has been impeded by the difficulty of translating luck from one sport to another. In this manuscript, we develop Bayesian state-space models using betting market data that can be uniformly applied across sporting organizations to better understand the role of randomness in game outcomes. These models can be used to extract estimates of team strength, the between-season, within-season, and game-to-game variability of team strengths, as well each team's home advantage. We implement our approach across a decade of play in each of the National Football League (NFL), National Hockey League (NHL), National Basketball Association (NBA), and Major League Baseball (MLB), finding that the NBA demonstrates both the largest dispersion in talent and
the largest home advantage, while the NHL and MLB stand out for their relative randomness in game outcomes. We conclude by proposing new metrics for judging competitiveness across sports leagues, both within the regular season and using traditional postseason tournament formats. Although we focus on sports, we discuss a number of other situations in which our generalizable models might be usefully applied.


How often does the best team win? A unified approach to understanding randomness in North American sport
Michael J. Lopez, Gregory J. Matthews, Benjamin S. Baumer

Source: arxiv.org (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Da95e5f60a7-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=21rRODjiOJoDn6DXP45UknOfSQhYwF2Ub3a0Aztx86I&e=)



Intermediate Levels of Network Heterogeneity Provide the Best Evolutionary Outcomes

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D659d821a8a-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=wTdHlnIQtpkCxm4y-ufGBV9AzkWYjMLfx3p6KOnbDJc&e=

Complex networks impact the diffusion of ideas and innovations, the formation of opinions, and the evolution of cooperative behavior. In this context, heterogeneous structures have been shown to generate a coordination-like dynamics that drives a population towards a monomorphic state. In contrast, homogeneous networks tend to result in a stable co-existence of multiple traits in the population. These conclusions have been reached through the analysis of networks with either very high or very low levels of degree heterogeneity. In this paper, we use methods from Evolutionary Game Theory to explore how different levels of degree heterogeneity impact the fate of cooperation in structured populations whose individuals face the Prisoner˙˙s Dilemma. Our results suggest that in large networks a minimum level of heterogeneity is necessary for a society to become evolutionary viable. Moreover, there is an optimal range of heterogeneity levels that maximize the resilience of the
society facing an increasing number of social dilemmas. Finally, as the level of degree heterogeneity increases, the evolutionary dominance of either cooperators or defectors in a society increasingly depends on the initial state of a few influential individuals. Our findings imply that neither very unequal nor very equal societies offer the best evolutionary outcome.


Intermediate Levels of Network Heterogeneity Provide the Best Evolutionary Outcomes
Flávio L. Pinheiro & Dominik Hartmann
Scientific Reports volume 7, Article number: 15242 (2017)
doi:10.1038/s41598-017-15555-7

Source: www.nature.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Dcf158fa27f-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=ChSbfH01DrH_w2doEpw77DrafflE4e1QRWz6PTbj_Ro&e=)



Metric clusters in evolutionary games on scale-free networks

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Ddf6647627a-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=wQ4o-q6QzgGmGYVKuyJ_950dcSWuK6xC8TChBT8GTn8&e=

The evolution of cooperation in social dilemmas in structured populations has been studied extensively in recent years. Whereas many theoretical studies have found that a heterogeneous network of contacts favors cooperation, the impact of spatial effects in scale-free networks is still not well understood. In addition to being heterogeneous, real contact networks exhibit a high mean local clustering coefficient, which implies the existence of an underlying metric space. Here we show that evolutionary dynamics in scale-free networks self-organize into spatial patterns in the underlying metric space. The resulting metric clusters of cooperators are able to survive in social dilemmas as their spatial organization shields them from surrounding defectors, similar to spatial selection in Euclidean space. We show that under certain conditions these metric clusters are more efficient than the most connected nodes at sustaining cooperation and that heterogeneity does not always
favor˙˙but can even hinder˙˙cooperation in social dilemmas.


Metric clusters in evolutionary games on scale-free networks
Kaj-Kolja Kleineberg
Nature Communications

volume 8, Article number: 1888 (2017)
doi:10.1038/s41467-017-02078-y

Source: www.nature.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D90d661faad-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=Jr_OwTSIkLawG4dvsg1A-u0Jcoa-Isug71GnqFgN8qI&e=)



Morphology of travel routes and the organization of cities

    https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D62d4fe116d-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=dPwD1E4xNggWRbiLy9u9ishA7vj4Ab1zHeD3jOX43Hc&e=

The city is a complex system that evolves through its inherent social and 
economic interactions. Mediating the movements of people and resources, 
urban street networks offer a spatial footprint of these activities. Of 
particular interest is the interplay between street structure and its 
functional usage. Here, we study the shape of 472,040 spatiotemporally 
optimized travel routes in the 92 most populated cities in the world, 
finding that their collective morphology exhibits a directional bias 
influenced by the attractive (or repulsive) forces resulting from 
congestion, accessibility, and travel demand. To capture this, we develop 
a simple geometric measure, inness, that maps this force field. In 
particular, cities with common inness patterns cluster together in groups 
that are correlated with their putative stage of urban development as 
measured by a series of socio-economic and infrastructural indicators, 
suggesting a strong connection between urban development, increasing 
physical connectivity, and diversity of road hierarchies.


Morphology of travel routes and the organization of cities
Minjin Lee, Hugo Barbosa, Hyejin Youn, Petter Holme & Gourab Ghoshal
Nature Communications volume 8, Article number: 2229 (2017)
doi:10.1038/s41467-017-02374-7

Source: www.nature.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3Daac822aa56-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=qKnshdbeGSzpJgsY-C9YnEca3TrUlzuR-Ty-8mmNk5U&e=)


Distributed Sequential Consensus in Networks: Analysis of Partially Connected Blockchains with Uncertainty

    This work presents a theoretical and numerical analysis of the conditions under which distributed sequential consensus is possible when the state of a portion of nodes in a network is perturbed. Specifically, it examines the consensus level of partially connected blockchains under failure/attack events. To this end, we developed stochastic models for both verification probability once an error is detected and network breakdown when consensus is not possible. Through a mean field approximation for network degree we derive analytical solutions for the average network consensus in the large graph size thermodynamic limit. The resulting expressions allow us to derive connectivity thresholds above which networks can tolerate an attack.


Distributed Sequential Consensus in Networks: Analysis of Partially Connected Blockchains with Uncertainty
Francisco Prieto-Castrillo, Sergii Kushch, and Juan Manuel Corchado

Complexity
Volume 2017 (2017), Article ID 4832740, 11 pages
https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D03cfcaa42b-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=HamebUTfyqD-R1zy3ZFYy6y4Z_4dO-WzNrr7aYKL-KI&e=

Source: www.hindawi.com (https://urldefense.proofpoint.com/v2/url?u=https-3A__unam.us4.list-2Dmanage.com_track_click-3Fu-3D0eb0ac9b4e8565f2967a8304b-26id-3D1c39498e6a-26e-3D55e25a0e3e&d=DwIFAw&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=vlPQth_lSCowziYidVJWgv0c5cWVjLgcnf8XA7kbYfU&s=4_PHCMq7t-1cntBtrVVfeFR39a47EeGU-8-ObptIajw&e=)

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Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

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