Spontaneous Collective Action: Peripheral Mobilization During the Arab Spring

2017 ◽  
Vol 111 (2) ◽  
pp. 379-403 ◽  
Author(s):  
ZACHARY C. STEINERT-THRELKELD
Keyword(s):  
Social Network ◽  
Collective Action ◽  
Information Diffusion ◽  
Large Scale ◽  
Authoritarian Regimes ◽  
Individual Level ◽  
The Core ◽  
Wide Range ◽  
Models Of Disease ◽  
The Arab Spring

Who is responsible for protest mobilization? Models of disease and information diffusion suggest that those central to a social network (the core) should have a greater ability to mobilize others than those who are less well-connected. To the contrary, this article argues that those not central to a network (the periphery) can generate collective action, especially in the context of large-scale protests in authoritarian regimes. To show that those in the core of a social network have no effect on levels of protest, this article develops a dataset of daily protests across 16 countries in the Middle East and North Africa over 14 months from 2010 through 2011. It combines that dataset with geocoded, individual-level communication from the same period and measures the number of connections of each person. Those on the periphery are shown to be responsible for changing levels of protest, with some evidence suggesting that the core’s mobilization efforts lead to fewer protests. These results have implications for a wide range of social choices that rely on interdependent decision making.

scholarly journals Systems-level neurophysiological state characteristics for drug evaluation in an animal model of levodopa-induced dyskinesia

2016 ◽  
Vol 115 (3) ◽  
pp. 1713-1729 ◽  
Author(s):  
Martin Tamtè ◽  
Ivani Brys ◽  
Ulrike Richter ◽  
Nedjeljka Ivica ◽  
Pär Halje ◽  
...  
Keyword(s):  
Large Scale ◽  
Drug Evaluation ◽  
Brain Structures ◽  
Significant Information ◽  
Animal Models Of Disease ◽  
Physiological Processes ◽  
Wide Range ◽  
The Central Nervous System ◽  
Neurophysiological Mechanisms ◽  
Models Of Disease

Disorders affecting the central nervous system have proven particularly hard to treat, and disappointingly few novel therapies have reached the clinics in recent decades. A better understanding of the physiological processes in the brain underlying various symptoms could therefore greatly improve the rate of progress in this field. We here show how systems-level descriptions of different brain states reliably can be obtained through a newly developed method based on large-scale recordings in distributed neural networks encompassing several different brain structures. Using this technology, we characterize the neurophysiological states associated with parkinsonism and levodopa-induced dyskinesia in a rodent model of Parkinson's disease together with pharmacological interventions aimed at reducing dyskinetic symptoms. Our results show that the obtained electrophysiological data add significant information to conventional behavioral evaluations and hereby elucidate the underlying effects of treatments in greater detail. Taken together, these results potentially open up for studies of neurophysiological mechanisms underlying symptoms in a wide range of neurological and psychiatric conditions that until now have been very hard to investigate in animal models of disease.

The Next ‘Spring’ is Certain to Come – and Certain to be Missed

2014 ◽  
Vol 6 (4) ◽  
pp. 382-406 ◽  
Author(s):  
Witold Mucha
Keyword(s):  
Large Scale ◽  
Task Force ◽  
Authoritarian Regimes ◽  
The Political ◽  
International Crisis ◽  
Starting Point ◽  
To Come ◽  
International Crisis Group ◽  
The Arab Spring ◽  
The Impact

The Arab Spring took policymakers and academics by surprise. The starting point, the scope, nor the impact had been seen coming. This was primarily because of academics’ irrevocable belief in the stabilising power of authoritarian regimes. In light of this failing, the article will critically discuss the production of crisis knowledge on the basis of four major early warning tools. These are World Bank’s greed/grievance model, the predictive model by the Political Instability Task Force, the risk and capacity approach applied by the Failed States Index, and the International Crisis Group. The article will add to the debate in two ways. First, the analysis will show that prevention research can be biased in ways that crucially influence policymakers’ assessment of states at risk. Second, the article will argue in favour of a complementary perspective that includes the analysis of conflicts that do not erupt into large-scale violence against all odds (so-called ‘negative cases’).

scholarly journals A modular computational framework for medical digital twins

2021 ◽  
Vol 118 (20) ◽  
pp. e2024287118
Author(s):  
J. Masison ◽  
J. Beezley ◽  
Y. Mei ◽  
HAL Ribeiro ◽  
A. C. Knapp ◽  
...  
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Model Building ◽  
Heterogeneous Data ◽  
Distributed Model ◽  
Modular Software ◽  
Digital Twins ◽  
Building Process ◽  
Wide Range ◽  
Models Of Disease

This paper presents a modular software design for the construction of computational modeling technology that will help implement precision medicine. In analogy to a common industrial strategy used for preventive maintenance of engineered products, medical digital twins are computational models of disease processes calibrated to individual patients using multiple heterogeneous data streams. They have the potential to help improve diagnosis, prognosis, and personalized treatment for a wide range of medical conditions. Their large-scale development relies on both mechanistic and data-driven techniques and requires the integration and ongoing update of multiple component models developed across many different laboratories. Distributed model building and integration requires an open-source modular software platform for the integration and simulation of models that is scalable and supports a decentralized, community-based model building process. This paper presents such a platform, including a case study in an animal model of a respiratory fungal infection.

scholarly journals Quantifying segregation in an integrated urban physical-social space

2019 ◽  
Vol 16 (160) ◽  
pp. 20190536 ◽  
Author(s):  
Yang Xu ◽  
Alexander Belyi ◽  
Paolo Santi ◽  
Carlo Ratti
Keyword(s):  
Social Network ◽  
Large Scale ◽  
Social Space ◽  
Human Mobility ◽  
Economic Status ◽  
Physical Space ◽  
Two Dimensions ◽  
Time Resolved ◽  
Individual Level ◽  
The Individual

Our knowledge of how cities bring together different social classes is still limited. Much effort has been devoted to investigating residential segregation, mostly over well-defined social groups (e.g. race). Little is known of how mobility and human communications affect urban social integration. The dynamics of spatial and social-network segregation and individual variations along these two dimensions are largely untapped. In this article, we put forward a computational framework based on coupling large-scale information on human mobility, social-network connections and people’s socio-economic status (SES), to provide a breakthrough in our understanding of the dynamics of spatio-temporal and social-network segregation in cities. Building on top of a social similarity measure, the framework can be used to depict segregation dynamics down to the individual level, and also provide aggregate measurements at the scale of places and cities, and their evolution over time. By applying the methodology in Singapore using large-scale mobile phone and socio-economic datasets, we find a relatively higher level of segregation among relatively wealthier classes, a finding that holds for both social and physical space. We also highlight the interplay between the effect of distance decay and homophily as forces that determine communication intensity, defining a notion of characteristic ‘homophily distance’ that can be used to measure social segregation across cities. The time-resolved analysis reveals the changing landscape of urban segregation and the time-varying roles of places. Segregations in physical and social space are weakly correlated at the individual level but highly correlated when grouped across at least hundreds of individuals. The methodology and analysis presented in this paper enable a deeper understanding of the dynamics of human segregation in social and physical space, which can assist social scientists, planners and city authorities in the design of more integrated cities.

scholarly journals Mathematical Profile Test: A Preliminary Evaluation of an Online Assessment for Mathematics Skills of Children in Grades 1–6

2020 ◽  
Vol 10 (8) ◽  
pp. 126
Author(s):  
Giannis Karagiannakis ◽  
Marie-Pascale Noël
Keyword(s):  
Large Scale ◽  
Numerical Cognition ◽  
Assessment Tool ◽  
Standardized Test ◽  
Primary School Children ◽  
Preliminary Evaluation ◽  
The Core ◽  
School Grades ◽  
Visual Spatial ◽  
Wide Range

The domain of numerical cognition still lacks an assessment tool that is theoretically driven and that covers a wide range of key numerical processes with the aim of identifying the learning profiles of children with difficulties in mathematics (MD) or dyscalculia. This paper is the first presentation of an online collectively administered tool developed to meet these goals. The Mathematical Profile Test (MathPro Test) includes 18 subtests that assess numerical skills related to the core number domain or to the visual-spatial, memory or reasoning domains. The specific aim of this paper is to present the preliminary evaluation both of the sensitivity and the psychometric characteristics of the individual measures of the MathPro Test, which was administered to 622 primary school children (grades 1–6) in Belgium. Performance on the subtests increased across all grades and varied along the level of difficulty of the items, supporting the sensitivity of the test. The MathPro Test also showed satisfactory internal consistency and significant and stable correlation with a standardized test in mathematics across all grades. In particular, the achievement in mathematics was strongly associated with the performance on the subtests assessing the reasoning and the visuospatial domains throughout all school grades, whereas associations with the core number and memory tasks were found mainly in the younger children. MD children performed significantly lower than their peers; these differences in performance on the MathPro subtests also varied according to the school grades, informing us about the developmental changes of the weaknesses of children with MD. These results suggest that the MathPro Test is a very promising tool for conducting large scale research and for clinicians to sketch out the mathematical profile of children with MD or dyscalculia.

scholarly journals A Large-Scale Comparative Study of Informal Social Networks in Firms

2021 ◽  
Author(s):  
Abigail Z. Jacobs ◽  
Duncan J. Watts
Keyword(s):  
Network Structure ◽  
Large Scale ◽  
Network Size ◽  
Emergent Properties ◽  
Network Data ◽  
Data Set ◽  
Network Characteristics ◽  
Individual Level ◽  
Industrial Sectors ◽  
Wide Range

Theories of organizations are sympathetic to long-standing ideas from network science that organizational networks should be regarded as multiscale and capable of displaying emergent properties. However, the historical difficulty of collecting individual-level network data for many (N ≫ 1) organizations, each of which comprises many (n ≫ 1) individuals, has hobbled efforts to develop specific, theoretically motivated hypotheses connecting micro- (i.e., individual-level) network structure with macro-organizational properties. In this paper we seek to stimulate such efforts with an exploratory analysis of a unique data set of aggregated, anonymized email data from an enterprise email system that includes 1.8 billion messages sent by 1.4 million users from 65 publicly traded U.S. firms spanning a wide range of sizes and 7 industrial sectors. We uncover wide heterogeneity among firms with respect to all measured network characteristics, and we find robust network and organizational variation as a result of size. Interestingly, we find no clear associations between organizational network structure and firm age, industry, or performance; however, we do find that centralization increases with geographical dispersion—a result that is not explained by network size. Although preliminary, these results raise new questions for organizational theory as well as new issues for collecting, processing, and interpreting digital network data. This paper was accepted by David Simchi-Levi, Special Issue of Management Science: 65th Anniversary.

scholarly journals Bayesian large-scale multiple regression with summary statistics from genome-wide association studies

2016 ◽  
Author(s):  
Xiang Zhu ◽  
Matthew Stephens
Keyword(s):  
Multiple Regression ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Individual Level ◽  
Genome Wide ◽  
Level Data ◽  
Wide Range

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a “Regression with Summary Statistics” (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously-proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously-unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss.

scholarly journals Deep integrative models for large-scale human genomics

2021 ◽  
Author(s):  
Arnor Ingi Sigurdsson ◽  
David Westergaard ◽  
Ole Winther ◽  
Ole Lund ◽  
Søren Brunak ◽  
...  
Keyword(s):  
Large Scale ◽  
Linear Models ◽  
Critical Role ◽  
Measurement Data ◽  
Risk Scores ◽  
Individual Level ◽  
Automatic Integration ◽  
Level Data ◽  
Wide Range ◽  
Clinical Measurements

Polygenic risk scores (PRSs) are expected to play a critical role in achieving precision medicine. PRS predictors are generally based on linear models using summary statistics, and more recently individual- level data. However, these predictors generally only capture additive relationships and are limited when it comes to what type of data they use. Here, we develop a deep learning framework (EIR) for PRS prediction which includes a model, genome-local-net (GLN), we specifically designed for large scale genomics data. The framework supports multi-task (MT) learning, automatic integration of clinical and biochemical data and model explainability. GLN outperforms LASSO for a wide range of diseases, particularly autoimmune disease which have been researched for interaction effects. We showcase the flexibility of the framework by training one MT model to predict 338 diseases simultaneously. Furthermore, we find that incorporating measurement data for PRSs improves performance for virtually all (93%) diseases considered (ROC-AUC improvement up to 0.36) and that including genotype data provides better model calibration compared to measurements alone. We use the framework to analyse what our models learn and find that they learn both relevant disease variants and clinical measurements. EIR is open source and available at https://github.com/arnor-sigurdsson/EIR.

scholarly journals Analyzing, Modeling, and Simulation for Human Dynamics in Social Network

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Yunpeng Xiao ◽  
Bai Wang ◽  
Yanbing Liu ◽  
Zhixian Yan ◽  
Xian Chen ◽  
...  
Keyword(s):  
Social Network ◽  
Human Behavior ◽  
Large Scale ◽  
Empirical Studies ◽  
Real Life ◽  
Power Law Distribution ◽  
Human Dynamics ◽  
Individual Level ◽  
Real Life Data ◽  
Dynamic Phenomena

This paper studies the human behavior in the top-one social network system in China (Sina Microblog system). By analyzing real-life data at a large scale, we find that the message releasing interval (intermessage time) obeys power law distribution both at individual level and at group level. Statistical analysis also reveals that human behavior in social network is mainly driven by four basic elements:social pressure,social identity,social participation, andsocial relationbetween individuals. Empirical results present the four elements' impact on the human behavior and the relation between these elements. To further understand the mechanism of such dynamic phenomena, a hybrid human dynamic model which combines “interest” of individual and “interaction” among people is introduced, incorporating the four elements simultaneously. To provide a solid evaluation, we simulate both two-agent and multiagent interactions with real-life social network topology. We achieve the consistent results between empirical studies and the simulations. The model can provide a good understanding of human dynamics in social network.

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