Jordan C. Rozum, Jorge Gómez Tejeda Zañudo, Xiao Gan, Dávid Deritei and Réka Albert
Science Advances  16 Jul 2021:
Vol. 7, no. 29, eabf8124
DOI: 10.1126/sciadv.abf8124

We present new applications of parity inversion and time reversal to the emergence of complex behavior from simple dynamical rules in stochastic discrete models. Our parity-based encoding of causal relationships and time-reversal construction efficiently reveal discrete analogs of stable and unstable manifolds. We demonstrate their predictive power by studying decision-making in systems biology and statistical physics models. These applications underpin a novel attractor identification algorithm implemented for Boolean networks under stochastic dynamics. Its speed enables resolving a long-standing open question of how attractor count in critical random Boolean networks scales with network size and whether the scaling matches biological observations. Via 80-fold improvement in probed network size (N = 16,384), we find the unexpectedly low scaling exponent of 0.12 ± 0.05, approximately one-tenth the analytical upper bound. We demonstrate a general principle: A system’s relationship to its time reversal and state-space inversion constrains its repertoire of emergent behaviors.

Read the full article at: advances.sciencemag.org

Sheryl L. Chang, Oliver M. Cliff, Mikhail Prokopenko
As of July 2021, there is a continuing outbreak of the B.1.617.2 (Delta) variant of SARS-CoV-2 in Sydney, Australia. The outbreak is of major concern as the Delta variant is estimated to have twice the reproductive number to previous variants that circulated in Australia in 2020, which is worsened by low levels of acquired immunity in the population. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, in terms of both mitigation (case isolation, home quarantine) and suppression (school closures, social distancing). Our nowcasting modelling indicated that the level of social distancing currently attained in Sydney is inadequate for the outbreak control. A counter-factual analysis suggested that if 80% of agents comply with social distancing, then at least a month is needed for the new daily cases to reduce from their peak to below ten. A small reduction in social distancing compliance to 70% lengthens this period to over two months.

Read the full article at: arxiv.org

Thayer Alshaabi, Jane L. Adams, Michael V. Arnold, Joshua R. Minot, David R. Dewhurst, Andrew J. Reagan, Christopher M. Danforth, and Peter Sheridan Dodds

Science Advances  16 Jul 2021:

Vol. 7, no. 29, eabe6534
DOI: 10.1126/sciadv.abe6534

In real time, Twitter strongly imprints world events, popular culture, and the day-to-day, recording an ever-growing compendium of language change. Vitally, and absent from many standard corpora such as books and news archives, Twitter also encodes popularity and spreading through retweets. Here, we describe Storywrangler, an ongoing curation of over 100 billion tweets containing 1 trillion 1-grams from 2008 to 2021. For each day, we break tweets into 1-, 2-, and 3-grams across 100+ languages, generating frequencies for words, hashtags, handles, numerals, symbols, and emojis. We make the dataset available through an interactive time series viewer and as downloadable time series and daily distributions. Although Storywrangler leverages Twitter data, our method of tracking dynamic changes in n-grams can be extended to any temporally evolving corpus. Illustrating the instrument’s potential, we present example use cases including social amplification, the sociotechnical dynamics of famous individuals, box office success, and social unrest.

Read the full article at: advances.sciencemag.org

Dion R. J. O’Neale, Shaun C. Hendy, and Demival Vasques Filho

Front. Big Data, 14 July 2021

Agglomeration and spillovers are key phenomena of technological innovation, driving regional economic growth. Here, we investigate these phenomena through technological outputs of over 4,000 regions spanning 42 countries, by analyzing more than 30 years of patent data (approximately 2.7 million patents) from the European Patent Office. We construct a bipartite network—based on revealed comparative advantage—linking geographic regions with areas of technology and compare its properties to those of artificial networks using a series of randomization strategies, to uncover the patterns of regional diversity and technological ubiquity. Our results show that the technological outputs of regions create nested patterns similar to those of ecological networks. These patterns suggest that regions need to dominate various technologies first (those allegedly less sophisticated), creating a diverse knowledge base, before subsequently developing less ubiquitous (and perhaps more sophisticated) technologies as a consequence of complementary knowledge that facilitates innovation. Finally, we create a map—the Patent Space Network—showing the interactions between technologies according to their regional presence. This network reveals how technology across industries co-appear to form several explicit clusters, which may aid future works on predicting technological innovation due to agglomeration and spillovers.

Read the full article at: www.frontiersin.org