Extremism propagation in social networks with hubs

One aspect of opinion change that has been of academic interest is the impact of people with extreme opinions (extremists) on opinion dynamics. An agent-based model has been used to study the role of small-world social network topologies on general opinion change in the presence of extremists. It has been found that opinion convergence to a single extreme occurs only when the average number of network connections for each individual is extremely high. Here, we extend the model to examine the effect of positively skewed degree distributions, in addition to small-world structures, on the types of opinion convergence that occur in the presence of extremists. We also examine what happens when extremist opinions are located on the well-connected nodes (hubs) created by the positively skewed distribution. We find that a positively skewed network topology encourages opinion convergence on a single extreme under a wider range of conditions than topologies whose degree distributions were not skewed. The importance of social position for social influence is highlighted by the result that, when positive extremists are placed on hubs, all population convergence is to the positive extreme even when there are twice as many negative extremists. Thus, our results have shown the importance of considering a positively skewed degree distribution, and in particular network hubs and social position, when examining extremist transmission

From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation

A network-based approach is presented to investigate the cerebrovascular flow patterns during atrial fibrillation (AF) with respect to normal sinus rhythm (NSR). AF, the most common cardiac arrhythmia with faster and irregular beating, has been recently and independently associated with the increased risk of dementia. However, the underlying hemodynamic mechanisms relating the two pathologies remain mainly undetermined so far; thus the contribution of modeling and refined statistical tools is valuable. Pressure and flow rate temporal series in NSR and AF are here evaluated along representative cerebral sites (from carotid arteries to capillary brain circulation), exploiting reliable artificially built signals recently obtained from an in silico approach. The complex network analysis evidences, in a synthetic and original way, a dramatic signal variation towards the distal/capillary cerebral regions during AF, which has no counterpart in NSR conditions. At the large artery level, networks obtained from both AF and NSR hemodynamic signals exhibit elongated and chained features, which are typical of pseudo-periodic series. These aspects are almost completely lost towards the microcirculation during AF, where the networks are topologically more circular and present random-like characteristics. As a consequence, all the physiological phenomena at microcerebral level ruled by periodicity - such as regular perfusion, mean pressure per beat, and average nutrient supply at cellular level - can be strongly compromised, since the AF hemodynamic signals assume irregular behaviour and random-like features. Through a powerful approach which is complementary to the classical statistical tools, the present findings further strengthen the potential link between AF hemodynamic and cognitive decline.Comment: 12 pages, 10 figure

Caracterização e classificação dos solos do Campo Experimental do Cerrado da Embrapa Amapá, Estado do Amapá.

bitstream/item/63154/1/Oriental-Doc43.pd

A Dynamic Model of Cascades on Random Networks with a Threshold Rule

Cascades on random networks are typically analyzed by assuming they map onto percolation processes and then are solved using generating function formulations. This approach assumes that the network is infinite and weakly connected, yet furthermore approximates a dynamic cascading process as a static percolation event. In this paper we propose a dynamic Markov model formulation that assumes a finite network with arbitrary average nodal degree. We apply it to the case where cascades follow a threshold rule, that is, that a node will change state ("flip") only if a fraction, exceeding a given threshold, of its neighbors has changed state previously. The corresponding state transition matrix, recalculated after each step, records the probability that a node of degree k has i flipped neighbors after j steps in the cascade's evolution. This theoretical model reproduces a number of behaviors observed in simulations but not yet reported in the literature. These include the ability to predict cascades in a domain previously predicted to forbid cascades without assuming that the network is locally tree-like, and, due to the dynamic nature of the model, a "near death" behavior in which cascades initially appear about to die but later explode. Cascades in the "no cascades" region require a sufficiently large seed of initially flipped nodes whose size scales with the size of the network or else the cascade will die out. Our theory also predicts the well known properties of cascades, for instance that a single node seed can start a global cascade in the appropriate regime regardless of the (finite) size of the network. The theory and simulations developed here are compared with a foundational paper by Watts which used generating function theory.Comment: Rev 1: Added citation to prior work by Gleeson and Cahalane. Revised abstract to sui

Modularity produces small-world networks with dynamical time-scale separation

The functional consequences of local and global dynamics can be very different in natural systems. Many such systems have a network description that exhibits strong local clustering as well as high communication efficiency, often termed as small-world networks (SWN). We show that modular organization in otherwise random networks generically give rise to SWN, with a characteristic time-scale separation between fast intra-modular and slow inter-modular processes. The universality of this dynamical signature, that distinguishes modular networks from earlier models of SWN, is demonstrated by processes as different as spin-ordering, synchronization and diffusion.Comment: 6 pages, 7 figures. Published version, Results and figures of additional dynamics have been include

Collective Decision Dynamics in the Presence of External Drivers

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. Sources of influence include a one-to-many externally driven global broadcast as well as pairwise interactions, across links in the network, in which agents transmit either continuous opinions or binary actions. We consider both uniform and variable threshold rules on the individual opinion as baseline models for decision-making. Our results indicate that 1) social networks lead to clustering and cohesive action among individuals, 2) binary information introduces high temporal variability and stagnation, and 3) information transmission over the network can either facilitate or hinder action adoption, depending on the influence of the global broadcast relative to the social network. Our framework highlights the essential role of local interactions between agents in predicting collective behavior of the population as a whole.Comment: 14 pages, 7 figure

Competition and Selection Among Conventions

In many domains, a latent competition among different conventions determines which one will come to dominate. One sees such effects in the success of community jargon, of competing frames in political rhetoric, or of terminology in technical contexts. These effects have become widespread in the online domain, where the data offers the potential to study competition among conventions at a fine-grained level. In analyzing the dynamics of conventions over time, however, even with detailed on-line data, one encounters two significant challenges. First, as conventions evolve, the underlying substance of their meaning tends to change as well; and such substantive changes confound investigations of social effects. Second, the selection of a convention takes place through the complex interactions of individuals within a community, and contention between the users of competing conventions plays a key role in the convention's evolution. Any analysis must take place in the presence of these two issues. In this work we study a setting in which we can cleanly track the competition among conventions. Our analysis is based on the spread of low-level authoring conventions in the eprint arXiv over 24 years: by tracking the spread of macros and other author-defined conventions, we are able to study conventions that vary even as the underlying meaning remains constant. We find that the interaction among co-authors over time plays a crucial role in the selection of them; the distinction between more and less experienced members of the community, and the distinction between conventions with visible versus invisible effects, are both central to the underlying processes. Through our analysis we make predictions at the population level about the ultimate success of different synonymous conventions over time--and at the individual level about the outcome of "fights" between people over convention choices.Comment: To appear in Proceedings of WWW 2017, data at https://github.com/CornellNLP/Macro

Proteome and phosphoproteome analysis of brown adipocytes reveals that RICTOR loss dampens global insulin/AKT signaling

Stimulating brown adipose tissue (BAT) activity represents a promising therapy for overcoming metabolic diseases. mTORC2 is important for regulating BAT metabolism, but its downstream targets have not been fully characterized. In this study, we apply proteomics and phosphoproteomics to investigate the downstream effectors of mTORC2 in brown adipocytes. We compare wild-type controls to isogenic cells with an induced knockout of the mTORC2 subunit RICTOR (Rictor-iKO) by stimulating each with insulin for a 30-minute time course. In Rictor-iKO cells, we identify decreases to the abundance of glycolytic and de novo lipogenesis enzymes, and increases to mitochondrial proteins as well as a set of proteins known to increase upon interferon stimulation. We also observe significant differences to basal phosphorylation due to chronic RICTOR loss including decreased phosphorylation of the lipid droplet protein perilipin-1 in Rictor-iKO cells, suggesting that RICTOR could be involved with regulating basal lipolysis or droplet dynamics. Finally, we observe mild dampening of acute insulin signaling response in Rictor-iKO cells, and a subset of AKT substrates exhibiting statistically significant dependence on RICTOR.Fil: Entwisle, Samuel W.. University of Washington; Estados UnidosFil: Martinez Calejman, Camila. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; ArgentinaFil: Valente, Anthony S.. University of Washington; Estados UnidosFil: Lawrence, Robert T.. University of Washington; Estados UnidosFil: Hung, Chien Min. University Of Massachussets. Medical School; Estados UnidosFil: Guertin, David A.. University Of Massachussets. Medical School; Estados UnidosFil: Villen, Judit. University of Washington; Estados Unido

Interpreting syndepositional sediment remobilization and deformation beneath submarine gravity flows; a kinematic boundary layer approach

Turbidite sandstones and related deposits commonly contain deformation structures and remobilized sediment that might have resulted from post-depositional modification such as downslope creep (e.g. slumping) or density-driven loading by overlying deposits. However, we consider that deformation can occur during the passage of turbidity currents that exerted shear stress on their substrates (whether entirely pre-existing strata, sediment deposited by earlier parts of the flow itself or some combination of these). Criteria are outlined here, to avoid confusion with products of other mechanisms (e.g. slumping or later tectonics), which establish the synchronicity between the passage of overriding flows and deformation of their substrates. This underpins a new analytical framework for tracking the relationship between deformation, deposition and the transit of the causal turbidity current, through the concept of kinematic boundary layers. Case study examples are drawn from outcrop (Miocene of New Zealand, and Apennines of Italy) and subsurface examples (Britannia Sandstone, Cretaceous, UK Continental Shelf). Example structures include asymmetric flame structures, convolute lamination, some debritic units and injection complexes, together with slurry and mixed slurry facies. These structures may provide insight into the rheology and dynamics of submarine flows and their substrates, and have implications for the development of subsurface turbidite reservoirs