Efficient learning by symbol emergence in multilayer network and agent collaboration

2002 ◽  
Author(s):  
S. Tano ◽  
D. Futamura ◽  
Y. Uemura
Keyword(s):  
Multilayer Network ◽  
Agent Collaboration ◽  
Efficient Learning

A performative network theory of attitudes

2020 ◽  
Author(s):  
Michael Quayle
Keyword(s):  
Attitude Change ◽  
Network Structure ◽  
Network Theory ◽  
Social Group ◽  
Group Structure ◽  
Social Identities ◽  
Bipartite Network ◽  
Identity Change ◽  
Multilayer Network ◽  
Dynamic Phenomena

In this paper I propose a network theory of attitudes where attitude agreements and disagreements forge a multilayer network structure that simultaneously binds people into groups (via attitudes) and attitudes into clusters (via people who share them). This theory proposes that people have a range of possible attitudes (like cards in a hand) but these only become meaningful when expressed (like a card played). Attitudes are expressed with sensitivity to their potential audiences and are socially performative: when we express attitudes, or respond to those expressed by others, we tell people who we are, what groups we might belong to and what to think of us. Agreement and disagreement can be modelled as a bipartite network that provides a psychological basis for perceived ingroup similarity and outgroup difference and, more abstractly, group identity. Opinion-based groups and group-related opinions are therefore co-emergent dynamic phenomena. Dynamic fixing occurs when particular attitudes become associated with specific social identities. The theory provides a framework for understanding identity ecosystems in which social group structure and attitudes are co-constituted. The theory describes how attitude change is also identity change. This has broad relevance across disciplines and applications concerned with social influence and attitude change.

Multilayer Network Valuation with Equity Conversions

2018 ◽  
Author(s):  
Matias Puig ◽  
Christoph Siebenbrunner
Keyword(s):  
Multilayer Network

Diffusion

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
Random Walks ◽  
Spectral Properties ◽  
Diffusion Constant ◽  
Network Structures ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Lévy Walk ◽  
Number Of Layers ◽  
Levy Walk ◽  
Speed Up

This chapter addresses diffusion, random walks and congestion in multilayer networks. Here it is revealed that diffusion on a multilayer network can be significantly speed up with respect to diffusion taking place on its single layers taken in isolation, and that sometimes it is possible also to observe super-diffusion. Diffusion is here characterized on multilayer network structures by studying the spectral properties of the supra-Laplacian and the dependence on the diffusion constant among different layers. Random walks and its variations including the Lévy Walk are shown to reflect the improved navigability of multilayer networks with more layers. These results are here compared with the results of traffic on multilayer networks that, on the contrary, point out that increasing the number of layers could be detrimental and could lead to congestion.

Centrality Measures

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
State Of The Art ◽  
Centrality Measures ◽  
Eigenvector Centrality ◽  
Multilayer Networks ◽  
Comprehensive Description ◽  
Multilayer Network ◽  
Suitable Definition ◽  
Katz Centrality ◽  
Pagerank Centrality

Defining the centrality of nodes and layers in multilayer networks is of fundamental importance for a variety of applications from sociology to biology and finance. This chapter presents the state-of-the-art centrality measures able to characterize the centrality of nodes, the influences of layers or the centrality of replica nodes in multilayer and multiplex networks. These centrality measures include modifications of the eigenvector centrality, Katz centrality, PageRank centrality and Communicability to the multilayer network scenario. The chapter provides a comprehensive description of the research of the field and discusses the main advantages and limitations of the different definitions, allowing the readers that wish to apply these techniques to choose the most suitable definition for his or her case study.

scholarly journals The structure of co-publications multilayer network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ghislain Romaric Meleu ◽  
Paulin Yonta Melatagia
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Generation Model ◽  
Small World Network ◽  
Power Law Distribution ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Scientific Papers

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

scholarly journals Task space adaptation via the learning of gait controllers of magnetic soft millirobots

2021 ◽  
pp. 027836492110218
Author(s):  
Sinan O. Demir ◽  
Utku Culha ◽  
Alp C. Karacakol ◽  
Abdon Pena-Francesch ◽  
Sebastian Trimpe ◽  
...  
Keyword(s):  
Human Body ◽  
Degrees Of Freedom ◽  
Bayesian Optimization ◽  
Small Scale ◽  
Soft Robots ◽  
Modeling And Control ◽  
Walking Gait ◽  
Physical Experiments ◽  
Efficient Learning ◽  
Task Environments

Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can directly and non-invasively access confined and hard-to-reach spaces in the human body. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot’s motion and task performance. The applicability of the conventional modeling and control methods is further limited for soft robots at the small-scale owing to their kinematics with virtually infinite degrees of freedom, inherent stochastic variability during fabrication, and changing dynamics during real-world interactions. To address the controller adaptation challenge to dynamically changing task environments, we propose using a probabilistic learning approach for a millimeter-scale magnetic walking soft robot using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme by finding the gait controller parameters while optimizing the stride length of the walking soft millirobot using a small number of physical experiments. To demonstrate the controller adaptation, we test the walking gait of the robot in task environments with different surface adhesion and roughness, and medium viscosity, which aims to represent the possible conditions for future robotic tasks inside the human body. We further utilize the transfer of the learned GP parameters among different task spaces and robots and compare their efficacy on the improvement of data-efficient controller learning.

scholarly journals Technology-mediated teaching and learning process: A conceptual study of educators’ response amidst the Covid-19 pandemic

2021 ◽  
Author(s):  
Kingsley Okoye ◽  
Jorge Alfonso Rodriguez-Tort ◽  
Jose Escamilla ◽  
Samira Hosseini
Keyword(s):  
Teaching And Learning ◽  
Linear Trend ◽  
Univariate Analysis ◽  
Learning Experiences ◽  
Well Being ◽  
Learning Tools ◽  
Main Challenge ◽  
Teachers And Students ◽  
Efficient Learning ◽  
Teaching Learning

AbstractThe COVID-19 pandemic has disrupted many areas of the human and organizational ventures worldwide. This includes new innovative technologies and strategies being developed by educators to foster the rapid learning-recovery and reinstatement of the stakeholders (e.g., teachers and students). Indeed, the main challenge for educators has been on what appropriate steps should be taken to prevent learning loss for the students; ranging from how to provide efficient learning tools/curriculum that ensures continuity of learning, to provision of methods that incorporate coping mechanisms and acceleration of education in general. For several higher educational institutions (HEIs), technology-mediated education has become an integral part of the modern teaching/learning instruction amidst the Covid-19 pandemic, when digital technologies have consequently become an inevitable and indispensable part of learning. To this effect, this study defines a hybrid educational model (HyFlex + Tec) used to enable virtual and in-person education in the HEIs. Practically, the study utilized data usage report from Massive Open Online Courses (MOOCs) and Emotions and Experience Survey questionnaire in a higher education setting for its experiments. To this end, we applied an Exponential Linear trend model and Forecasting method to determine overall progress and statistics for the learners during the Covid-19 pandemic, and subsequently performed a Text Mining and Univariate Analysis of Variance (ANOVA) to determine effects and significant differences that the teaching–learning experiences for the teachers and students have on their energy (learning motivation) levels. From the results, we note that the hybrid learning model supports continuity of education/learning for teachers and students during the Covid-19 pandemic. The study also discusses its innovative importance for future monitoring (tracking) of learning experiences and emotional well-being for the stakeholders in leu (aftermath) of the Covid-19 pandemic.

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