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Language statistics at different spatial, temporal, and grammatical scales

Mon, 07/11/2022 - 14:45

Language statistics at different spatial, temporal, and grammatical scales
Fernanda Sánchez-Puig, Rogelio Lozano-Aranda, Dante Pérez-Méndez, Ewan Colman, Alfredo J. Morales-Guzmán, Carlos Pineda, Carlos Gershenson
Statistical linguistics has advanced considerably in recent decades as data has become available. This has allowed researchers to study how statistical properties of languages change over time. In this work, we use data from Twitter to explore English and Spanish considering the rank diversity at different scales: temporal (from 3 to 96 hour intervals), spatial (from 3km to 3000+km radii), and grammatical (from monograms to pentagrams). We find that all three scales are relevant. However, the greatest changes come from variations in the grammatical scale. At the lowest grammatical scale (monograms), the rank diversity curves are most similar, independently on the values of other scales, languages, and countries. As the grammatical scale grows, the rank diversity curves vary more depending on the temporal and spatial scales, as well as on the language and country. We also study the statistics of Twitter-specific tokens: emojis, hashtags, and user mentions. These particular type of tokens show a sigmoid kind of behaviour as a rank diversity function. Our results are helpful to quantify aspects of language statistics that seem universal and what may lead to variations.

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Understanding the coevolution of mask wearing and epidemics: A network perspective

Sun, 07/03/2022 - 08:26

Zirou Qiu, et al.

PNAS 119 (26) e2123355119

Nonpharmaceutical interventions such as mask wearing play a critical role in reducing disease prevalence. Under the dueling dynamics of mask wearing and disease, we observe a robust nonmonotonic relationship between the attack rate (i.e., the fraction of the ever-infected population) and the transmission probability of the disease. Specifically, the attack rate exhibits an abrupt reduction as the transmission probability increases to a critical threshold. Furthermore, we characterize regimes of the transmission probability where multiple waves of infection and mask adoption are expected. Our results highlight the necessity of continued public mask-wearing mandates to suppress the epidemic and effectively prevent its revival.

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Belief propagation for permutations, rankings, and partial orders

Fri, 07/01/2022 - 08:36

George T. Cantwell and Cristopher Moore

Phys. Rev. E 105, L052303

Many datasets give partial information about an ordering or ranking by indicating which team won a game, which item a user prefers, or who infected whom. We define a continuous spin system whose Gibbs distribution is the posterior distribution on permutations, given a probabilistic model of these interactions. Using the cavity method, we derive a belief propagation algorithm that computes the marginal distribution of each node’s position. In addition, the Bethe free energy lets us approximate the number of linear extensions of a partial order and perform model selection between competing probabilistic models, such as the Bradley-Terry-Luce model of noisy comparisons and its cousins.

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Mapping Philanthropic Support of Science

Tue, 06/28/2022 - 17:48

Louis M. Shekhtman, Alexander J. Gates, Albert-László Barabási
While philanthropic support plays an increasing role in supporting research, there is limited quantitative knowledge about the patterns that characterize the distribution of philanthropic support. Here, we map philanthropic funding to universities and research institutions based on IRS tax forms from 685,397 non-profit organizations. We identify nearly one million grants supporting institutions involved in science, finding that in volume and scope, philanthropic funding is comparable to federal research funding. However, whereas federal funding relies on a few large organizations to distribute grants, the philanthropic ecosystem’s support is fragmented among a large number of funders with diverse focus that support research institutions at varying levels. Furthermore, we find that distinct from government support, philanthropic funders tend to focus locally, indicating that other criteria, beyond research excellence, play a role in their funding decisions. We also show evidence of persistence, i.e., once a grant-giving relationship begins, it tends to continue in time. Finally, we discuss the policy implications of our findings for philanthropic funders, individual researchers, the science of science, and for quantitative studies of philanthropy in general.

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Critical drift in a neuro-inspired adaptive network

Mon, 06/27/2022 - 07:59

Silja Sormunen, Thilo Gross, Jari Saramäki
It has been postulated that the brain operates in a self-organized critical state that brings multiple benefits, such as optimal sensitivity to input. Thus far, self-organized criticality has typically been depicted as a one-dimensional process, where one parameter is tuned to a critical value. However, the number of adjustable parameters in the brain is vast, and hence critical states can be expected to occupy a high-dimensional manifold inside a high-dimensional parameter space. Here, we show that adaptation rules inspired by homeostatic plasticity drive a neuro-inspired network to drift on a critical manifold, where the system is poised between inactivity and persistent activity. During the drift, global network parameters continue to change while the system remains at criticality.

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The Nature of Complex Networks – Sergey N. Dorogovtsev, José F. F. Mendes – Oxford University Press

Sun, 06/26/2022 - 15:43

Sergey N. Dorogovtsev and José F. F. Mendes
Provides a systematic account of the statistical mechanics of complex networks
Covers recent trends, concepts, and theoretical techniques, and emphasises interdisciplinary strands
Broad appeal to researchers in complex systems including theoretical physicists and applied mathematicians as well as epidemiologists
Extensive bibliography and appendices offer excellent reference source for students and researchers

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[3C] Cells, Computers & Clinics. Oeiras, Portugal. 26 – 28 Oct 2022

Fri, 06/24/2022 - 12:13

The global pandemic showed the critical importance of integrating fundamental , computational and clinical research to promote systemic understanding of a global threat to humankind. Global epidemiological assessments informing national and regional policy-making around the world were only made possible due to fundamental mechanistic knowledge of coronavirus biology dating back decades, large-scale data on human mobility patterns enabled by recent technologies, as well as massive onsite and dynamic clinical reporting from health institutions.

It is now clear that complex human diseases can only be tackled by transdisciplinary efforts that integrate fundamental, computational, and clinical research. This is not, however, an easily achievable feat, as fundamental laboratory discoveries are often not directly transferable into clinical settings, with controlled experiments not necessarily reflecting organismic and societal complexity. Only with synergy between fundamental researchers, clinicians, and data scientists can we hope to gain the depth of understanding required to address the physiological mechanisms behind some of the most challenging human diseases at the interface between hosts and pathogens.

The goal of the [3C] Cells, Computers & Clinics Symposium is to do exactly that, to bridge fundamental, computational, and clinical research in the scope of complex diseases, particularly those related to host-pathogen interactions.

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Dynamics of cross-platform attention to retracted papers

Thu, 06/23/2022 - 17:53

Hao Peng, Daniel M. Romero, and Emőke-Ágnes Horvát

PNAS June 14, 2022 119 (25) e2119086119

Scientific retraction has been on the rise recently. Retracted papers are frequently discussed online, enabling the broad dissemination of potentially flawed findings. Our analysis spans a nearly 10-y period and reveals that most papers exhaust their attention by the time they get retracted, meaning that retractions cannot curb the online spread of problematic papers. This is striking as we also find that retracted papers are pervasive across mediums, receiving more attention after publication than nonretracted papers even on curated platforms, such as news outlets and knowledge repositories. Interestingly, discussions on social media express more criticism toward subsequently retracted results and may thus contain early signals related to unreliable work.

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A Ubiquitous Collective Tragedy in Transport

Thu, 06/23/2022 - 15:51

Rafael Prieto Curiel, Humberto González Ramírez, and Steven Bishop

Front. Phys., 16 June 2022

A tragedy of the commons is said to occur when individuals act only in their own interest but, in so doing, create a collective state of a group that is less than optimal due to uncoordinated action. Here, we explore the individual decision-making processes of commuters using various forms of transport within a city, forming a modal share which is then built into a dynamical model using travel time as the key variable. From a randomised start in the distribution of the modal share, assuming that some individuals change their commuting method, favouring lower travel times, we show that a stable modal share is reached corresponding to an equilibrium in the model. Considering the average travel time for all commuters within the city, we show that an optimal result is achieved only if the direct and induced factors and the number of users are equal for all transport modes. For asymmetric factors, the equilibrium reached is always sub-optimal, leading to city travel trajectories being “tragic”, meaning that individuals choose a faster commuting time but create a slower urban mobility as a collective result. Hence, the city evolves, producing longer average commuting times. It is also shown that if a new mode of transport has a small baseline commuting time but has a high induced impact for other users, then introducing it might result in a counter-intuitive result producing more congestion, rather than less.

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Influence maximization in Boolean networks

Mon, 06/20/2022 - 14:40

Thomas Parmer, Luis M. Rocha & Filippo Radicchi 
Nature Communications volume 13, Article number: 3457 (2022)

The optimization problem aiming at the identification of minimal sets of nodes able to drive the dynamics of Boolean networks toward desired long-term behaviors is central for some applications, as for example the detection of key therapeutic targets to control pathways in models of biological signaling and regulatory networks. Here, we develop a method to solve such an optimization problem taking inspiration from the well-studied problem of influence maximization for spreading processes in social networks. We validate the method on small gene regulatory networks whose dynamical landscapes are known by means of brute-force analysis. We then systematically study a large collection of gene regulatory networks. We find that for about 65% of the analyzed networks, the minimal driver sets contain less than 20% of their nodes.

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A Formal Definition of Scale-dependent Complexity and the Multi-scale Law of Requisite Variety

Sun, 06/19/2022 - 16:43

Alexander F. Siegenfeld, Yaneer Bar-Yam
Ashby’s law of requisite variety allows a comparison of systems with their environments, providing a necessary (but not sufficient) condition for system efficacy: a system must possess at least as much complexity as any set of environmental behaviors that require distinct responses from the system. However, the complexity of a system depends on the level of detail, or scale, at which it is described. Thus, the complexity of a system can be better characterized by a complexity profile (complexity as a function of scale) than by a single number. It would therefore be useful to have a multi-scale generalization of Ashby’s law that requires that a system possess at least as much complexity as the relevant set of environmental behaviors *at each scale*. We construct a formalism for a class of complexity profiles that is the first, to our knowledge, to exhibit this multi-scale law of requisite variety. This formalism not only provides a characterization of multi-scale complexity but also generalizes the single constraint on system behaviors provided by Ashby’s law to an entire class of multi-scale constraints. We show that these complexity profiles satisfy a sum rule, which reflects the important tradeoff between smaller- and larger-scale degrees of freedom, and we extend our results to subdivided systems and systems with a continuum of components.

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Lithbea, A New Domain outside the Tree of Life

Sun, 06/19/2022 - 12:44

Gomez-Marquez, J. Lithbea, A New Domain outside the Tree of Life . Preprints 2022, 2022060094 (doi: 10.20944/preprints202206.0094.v1)

As synthetic/artificial life forms become more abundant and sophisticated, an increasing number of bizarre creatures – xenobots, robots, soft A-life entities, genetically engineered organisms, etc. – are invading our society. Therefore, we need to bring order to all this, to establish what is living and what is not. Here, I intend to classify all these non-natural entities and clarify their status with reference to their consideration or not as living beings, leaving the door open to an uncertain future in which perhaps we can see how “the artificial” and “the natural” merge to originate something new. To order all this “new biodiversity” and to also give entry to viruses (which are excluded of the three-domains tree of life), I propose the creation of a new domain, Lithbea (from the name: life-in-the-border entities), in which all these new human-made entities as well as the viruses will be included. Within this domain there would be two kingdoms, Virus and Humade (contraction of human-made), based on their origin, natural or human-made. A brief description of each component of Lithbea is included and the implications for society and biology of this “new biodiversity” is briefly discussed.

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Principles of Biological Design

Wed, 06/15/2022 - 11:00

Online lectures

Created by: Prof. Ricard Solé, Jordi Piñero
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Complex systems for the most vulnerable

Tue, 06/14/2022 - 14:00

Elisa Omodei, Manuel Garcia-Herranz, Daniela Paolotti and Michele Tizzoni

Journal of Physics: Complexity, Volume 3, Number 2

In a rapidly changing world, facing an increasing number of socioeconomic, health and environmental crises, complexity science can help us to assess and quantify vulnerabilities, and to monitor and achieve the UN sustainable development goals. In this perspective, we provide three exemplary use cases where complexity science has shown its potential: poverty and socioeconomic inequalities, collective action for representative democracy, and computational epidemic modeling. We then review the challenges and limitations related to data, methods, capacity building, and, as a result, research operationalization. We finally conclude with some suggestions for future directions, urging the complex systems community to engage in applied and methodological research addressing the needs of the most vulnerable.

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Biology, geometry and information

Sat, 06/11/2022 - 16:29

Jürgen Jost
Theory in Biosciences volume 141, pages 65–71 (2022)

The main thesis developed in this article is that the key feature of biological life is the a biological process can control and regulate other processes, and it maintains that ability over time. This control can happen hierarchically and/or reciprocally, and it takes place in three-dimensional space. This implies that the information that a biological process has to utilize is only about the control, but not about the content of those processes. Those other processes can be vastly more complex that the controlling process itself, and in fact necessarily so. In particular, each biological process draws upon the complexity of its environment.

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Scale, context, and heterogeneity: the complexity of the social space

Fri, 06/10/2022 - 18:32

José Balsa-Barreiro, Mónica Menendez & Alfredo J. Morales 

Scientific Reports volume 12, Article number: 9037 (2022)

The social space refers to physical or virtual places where people interact with one another. It decisively influences the emergence of human behaviors. However, little is known about the nature and complexity of the social space, nor its relationship to context and spatial scale. Recently, the science of complex systems has bridged between fields of knowledge to provide quantitative responses to fundamental sociological questions. In this paper, we analyze the shifting behavior of social space in terms of human interactions and wealth distribution across multiple scales using fine-grained data collected from both official (US Census Bureau) and unofficial data sources (social media). We use these data to unveil how patterns strongly depend upon the observation scale. Therefore, it is crucial for any analysis to be framed within the appropriate context to avoid biased results and/or misleading conclusions. Biased data analysis may lead to the adoption of fragile and poor decisions. Including context and a proper understanding of the spatial scale are essential nowadays, especially with the pervasive role of data-driven tools in decision-making processes.

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Flat teams drive scientific innovation

Fri, 06/10/2022 - 16:32

Fengli Xu, Lingfei Wu, and James Evans

PNAS 119 (23) e2200927119

With teams growing in all areas of scientific and scholarly research, we explore the relationship between team structure and the character of knowledge they produce. Drawing on 89,575 self-reports of team member research activity underlying scientific publications, we show how individual activities cohere into broad roles of 1) leadership through the direction and presentation of research and 2) support through data collection, analysis, and discussion. The hidden hierarchy of a scientific team is characterized by its lead (or L) ratio of members playing leadership roles to total team size. The L ratio is validated through correlation with imputed contributions to the specific paper and to science as a whole, which we use to effectively extrapolate the L ratio for 16,397,750 papers where roles are not explicit. We find that, relative to flat, egalitarian teams, tall, hierarchical teams produce less novelty and more often develop existing ideas, increase productivity for those on top and decrease it for those beneath, and increase short-term citations but decrease long-term influence. These effects hold within person—the same person on the same-sized team produces science much more likely to disruptively innovate if they work on a flat, high-L-ratio team. These results suggest the critical role flat teams play for sustainable scientific advance and the training and advancement of scientists.

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The Hidden Benefits of Limited Communication and Slow Sensing in Collective Monitoring of Dynamic Environments

Tue, 06/07/2022 - 16:50

T. Aust, M. S. Talamali, M. Dorigo, H. Hamann, and A. Reina

IRIDIA – Technical Report No. TR/IRIDIA/2022-005

Most of our experiences and also our intuition is usually built
on a linear understanding of systems and processes. Complex systems
in general and more specifically swarm robotics in this context leverage
non-linear effects to self-organise and to ensure that ‘more is different’. In
previous work the non-linear and therefore counter-intuitive effect of ‘less
is more’ was shown for a site-selection swarm scenario. Although it seems
intuitive that being able to communicate over longer distances should be
beneficial, swarms were found to sometimes profit from communication
limitations. Here, we built on this work and show the same effect for the
collective perception scenario in a dynamic environment. We found an
additional effect of ‘slower is faster’. In certain situations, swarms benefit
from sampling their environment less frequently. All our work is based on
simulations using the ARGoS simulator extended with a simulator of the
smart environment for the Kilobot robot called Kilogrid. Our findings are
supported by an intensive empirical approach and a mean-field model.
Both effects seem important for designing swarms

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Nutrient concentrations in food display universal behaviour

Sat, 06/04/2022 - 16:12

Giulia Menichetti & Albert-László Barabási 

Nature Food volume 3, pages375–382 (2022)

Extensive programmes around the world endeavour to measure and catalogue the composition of food. Here we analyse the nutrient content of the full US food supply and show that the concentration of each nutrient follows a universal single-parameter scaling law that accurately captures the eight orders of magnitude in nutrient content variability. We show that the universality is rooted in the biochemical constraints obeyed by the metabolic pathways responsible for nutrient modulation, allowing us to confirm the empirically observed scaling law and to predict its variability in agreement with the data. We propose that the natural nutrient variability in food can be quantitatively formalized. This provides a mathematical rationale for imputing missing values in food composition databases and paves the way towards a quantitative understanding of the impact of food processing on nutrient balance and health effects.

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The emergence of polarization in coevolving networks

Thu, 06/02/2022 - 12:36

Jiazhen Liu, Shengda Huang, Nathan Aden, Neil Johnson, Chaoming Song
Polarization is a ubiquitous phenomenon in social systems. Empirical studies show substantial evidence for opinion polarization across social media. Recent modeling works show qualitatively that polarization emerges in coevolving networks by integrating reinforcing mechanisms and network evolution. However, a quantitative and comprehensive theoretical framework capturing generic mechanisms governing polarization remains unaddressed. In this paper, we discover a universal scaling law for opinion distributions, characterized by a set of scaling exponents. These exponents classify social systems into polarization and depolarization phases. We find two generic mechanisms governing the polarization dynamics, and propose a coevolving framework that counts for opinion dynamics and network evolution simultaneously. We show analytically three different phases including polarization, partial polarization, and depolarization, and the corresponding phase diagram. In the polarized phase, our theory predicts that a bi-polarized community structure emerges naturally from the coevolving dynamics. These theoretical predictions are in line with observations in empirical datasets. Our theory not only accounts for the empirically observed scaling laws but also allows us to quantitatively predict scaling exponents.

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