Discovering Influencers in Opinion Formation over Social Graphs

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observations and exchange the beliefs with their neighbors. In this work, it is shown how the sequence of publicly exchanged beliefs over time allows users to discover rich information about the underlying network topology and about the flow of information over graph. In particular, it is shown that it is possible (i) to identify the influence of each individual agent to the objective of truth learning, (ii) to discover how well informed each agent is, (iii) to quantify the pairwise influences between agents, and (iv) to learn the underlying network topology. The algorithm derived herein is also able to work under non-stationary environments where either the true state of nature or the network topology are allowed to drift over time. We apply the proposed algorithm to different subnetworks of Twitter users, and identify the most influential and central agents merely by using their public tweets (posts)

Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependency

Increased coupling between critical infrastructure networks, such as power and communication systems, will have important implications for the reliability and security of these systems. To understand the effects of power-communication coupling, several have studied interdependent network models and reported that increased coupling can increase system vulnerability. However, these results come from models that have substantially different mechanisms of cascading, relative to those found in actual power and communication networks. This paper reports on two sets of experiments that compare the network vulnerability implications resulting from simple topological models and models that more accurately capture the dynamics of cascading in power systems. First, we compare a simple model of topological contagion to a model of cascading in power systems and find that the power grid shows a much higher level of vulnerability, relative to the contagion model. Second, we compare a model of topological cascades in coupled networks to three different physics-based models of power grids coupled to communication networks. Again, the more accurate models suggest very different conclusions. In all but the most extreme case, the physics-based power grid models indicate that increased power-communication coupling decreases vulnerability. This is opposite from what one would conclude from the coupled topological model, in which zero coupling is optimal. Finally, an extreme case in which communication failures immediately cause grid failures, suggests that if systems are poorly designed, increased coupling can be harmful. Together these results suggest design strategies for reducing the risk of cascades in interdependent infrastructure systems

A new method to determine viscosity of liquids using vibration principles

A new method for determining viscosity of liquids is examined. The method employs the principles of vibration and measures the viscous damping due to the motion of a liquid placed in a cylindrical tube. The apparatus and the test liquid are treated as a dynamic system and the measured mechanical impedances are used to calculate energy dissipation due to the viscous damping. The newly designed apparatus is able to generate shear deformations in the liquid without using moving solid surfaces. A harmonic varying force with a frequency close to the resonance frequency of the system is applied through a piston and the resulting velocities of the oscillations generated in the system are measured. Liquids with higher viscosities result in lower velocities due to the higher damping. Analytical equations are provided to relate the viscous damping of the dynamic system to the viscosity of the liquids. The viscosities obtained from the proposed method are in good agreement with the ones obtained from standard rotational viscometry using a cone and plate geometry

Incumbency advantage is not restricted to established majoritarian systems

To date, most scholarly works have focused on incumbency advantage in the US and consider how it operates in majoritarian contexts. In a recent paper, Mert Moral, H. Ege Ozen and Efe Tokdemir drew on the case of Turkey to explore whether the incumbency operates in multi member district systems. They found that although it is not as marked as in the US context, considerable incumbency advantage persisted in the more proportional system

Dif-MAML: Decentralized Multi-Agent Meta-Learning

The objective of meta-learning is to exploit the knowledge obtained from observed tasks to improve adaptation to unseen tasks. As such, meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of data per task. Given the amount of resources that are needed, it is generally difficult to expect the tasks, their respective data, and the necessary computational capacity to be available at a single central location. It is more natural to encounter situations where these resources are spread across several agents connected by some graph topology. The formalism of meta-learning is actually well-suited to this decentralized setting, where the learner would be able to benefit from information and computational power spread across the agents. Motivated by this observation, in this work, we propose a cooperative fully-decentralized multi-agent meta-learning algorithm, referred to as Diffusion-based MAML or Dif-MAML. Decentralized optimization algorithms are superior to centralized implementations in terms of scalability, avoidance of communication bottlenecks, and privacy guarantees. The work provides a detailed theoretical analysis to show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML objective even in non-convex environments. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting

U.S. Dental School Deans’ Attitudes About Mid–Level Providers

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153725/1/jddj0022033720137711tb05623x.pd

A study of the soil-plant interactions of Pistacia lentiscus L. distributed in the western Anatolian part of Turkey

This study was undertaken with the aim of illuminating the soil-plant interactions of Pistacia lentiscus L., which is a Mediterranean sclerophylleous coastal zone plant in the Western Anatolian part of Turkey. The soil analysis data showed that this plant grows on different kinds of soils such as sandy-clayey-loam, clayey-loam, sandy-loam and loamy texture. Soils are not saline, with pH moderately and slightly alkaline. This species prefers soils with low phosphorus and potassium contents, but with different calcium carbonate and nitrogen contents. Three negative linear correlations were observed between plant calcium and soil pH, plant nitrogen and soil calcium carbonate, plant potassium and soil calcium carbonate

Zero-shot Learning of Individualized Task Contrast Prediction from Resting-state Functional Connectomes

Given sufficient pairs of resting-state and task-evoked fMRI scans from subjects, it is possible to train ML models to predict subject-specific task-evoked activity using resting-state functional MRI (rsfMRI) scans. However, while rsfMRI scans are relatively easy to collect, obtaining sufficient task fMRI scans is much harder as it involves more complex experimental designs and procedures. Thus, the reliance on scarce paired data limits the application of current techniques to only tasks seen during training. We show that this reliance can be reduced by leveraging group-average contrasts, enabling zero-shot predictions for novel tasks. Our approach, named OPIC (short for Omni-Task Prediction of Individual Contrasts), takes as input a subject's rsfMRI-derived connectome and a group-average contrast, to produce a prediction of the subject-specific contrast. Similar to zero-shot learning in large language models using special inputs to obtain answers for novel natural language processing tasks, inputting group-average contrasts guides the OPIC model to generalize to novel tasks unseen in training. Experimental results show that OPIC's predictions for novel tasks are not only better than simple group-averages, but are also competitive with a state-of-the-art model's in-domain predictions that was trained using in-domain tasks' data.Comment: Accepted at DALI@MICCAI 202

Studija o biljkama uz rub cesta (Zapadna Anatolija, Turska)

In this study, roadside plants distributed throughout the link roads of all the cities in West Anatolia in Turkey were investigated. The length of the selected 17 roads is around 2700 km. The total number of samples collected from the study area is 271 taxa belonging to 57 families. Among them, Asteraceae, Fabaceae, Poaceae are the families that have the largest number of taxa, and Bromus L., Rumex L. and Silene L. are the genera that have the largest number of taxa. The most frequently found taxon throughout the selected roads is Valerianella coronata (L.) DC. and therophytes are the most frequently found life form.Istraživane su biljke uz rub svih cesta koje povezuju gradove u Zapadnoj Anatoliji u Turskoj. Duljina 17 izabranih cesta je iznosila oko 2700 km. Prikupljeni uzorci pripadali su 271 svojti, odnosno 57 biljnih porodica. Među njima su najzastupljenije bile porodice Asteraceae, Fabaceae i Poaceae, a među rodovima to su bili Bromus L., Rumex L. i Silene L. Najčešće nađena svojta uz istraživane ceste bila je Valerianella coronata (L.) DC., a najčešći biljni oblik su bili terofiti