Few-Shot Learning Based on Convolutional Denoising Auto-encoder Relational Network

2022 ◽  
pp. 103-112
Author(s):  
Xinyu Xiang ◽  
Ping Zhang ◽  
Qiang Yuan ◽  
Renping Li ◽  
Runqiao Hu ◽  
...  
Keyword(s):  
Relational Network

Zastosowanie analizy sieci społecznych w badaniach elit parlamentarnych (na przykładzie eurodeputowanych z Polski)

2014 ◽  
pp. 8-24 ◽  
Author(s):  
Piotr Swacha
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
European Parliament ◽  
Political Groups ◽  
Graphical Presentation ◽  
Relational Network

The purpose of this article is to present the possibilities of using social network analysis (SNA) in the study of the European Parliament elite. This study focuses on organisational connections between Polish members of the European Parliament (seventh term). Official organisational relationships of Polish MEPs include common membership in: political groups, authorities of parliamentary committees and delegations, Parliament’s Bureau, Conference of Presidents, Conference of Committee (and Delegation) Chairs. UCInet and Netdraw programmes were used to calculate SNA’s basic measures and to prepare graphical presentation of relational network created by the Polish MEPs. On this basis main characteristics of the network were presented and MEPs who had the best network locations were distinguished.

Learning Aligned Image-Text Representations Using Graph Attentive Relational Network

2021 ◽  
pp. 1-1
Author(s):  
Ya Jing ◽  
Wei Wang ◽  
Liang Wang ◽  
Tieniu Tan
Keyword(s):  
Relational Network

scholarly journals Roman Catholicism and Eastern Orthodoxy: Can linguistic and semiotic analysis clarify their contrasts?

2017 ◽  
Vol 3 ◽  
pp. 187-194
Author(s):  
William J. Sullivan ◽  
Sarah Tsiang
Keyword(s):  
Semiotic Analysis ◽  
Roman Catholicism ◽  
Eastern Orthodoxy ◽  
Life After Death ◽  
First Approximation ◽  
Unleavened Bread ◽  
Great Schism ◽  
Relational Network ◽  
Surface Comparison

The western and eastern branches of Christianity, broadly speaking Roman Catholicism (RC) and Eastern Orthodoxy (EO), have been formally separate for almost a millennium. Yet they share the fundamental dogmas laid down by the first ecumenical councils. History and politics are entwined in the disputes since the Great Schism of 1054, but even earlier there was controversy over basic dogmatic questions and other doctrinal matters. Some, like using leavened or unleavened bread for Consecration, are now considered “matters of custom,” not requiring argument. Other matters are said to block reunification. One of these is Purgatory, for which EO does not even have a term, making a direct comparison difficult. We begin our analysis with the RC teachings on Purgatory, its locus, characteristics, and functions, and provide a simple relational network that shows Purgatory in relation to the afterlife, in particular to Heaven and Hell. With EO we begin with the teachings about life after death and provide a first approximation of Heaven and Hell and their relation to Paradise and Hades, both in characteristics and functions. Again, a simple relational network is enlightening. A surface comparison between the two networks distinguishes between those beliefs about the afterlife that are shared between RC and EO and those parts which house differences. It is these differences that must be subject to careful semiotic analysis to discover whether they are etic and possibly serious but not grounds for mutual excommunication or emic and a true barrier to reunification. We leave the possibly lengthy semiotic analysis for a subsequent study.

Topological Semiotic Knowledge Representation

2018 ◽  
pp. 45-78
Keyword(s):  
Knowledge Representation ◽  
Cognitive Models ◽  
Topological Model ◽  
Network Structures ◽  
Conceptual Blending ◽  
Cortical Level ◽  
Structural Relations ◽  
Relational Network

This chapter describes cognitive models that organize implicit symbols into meaningful relational network structures. With an understanding of implicit symbols, there is evidence that informational processes on the cortical level can create and maintain multileveled hierarchically nested graphs and diagram – like structures. This topological model reflects hierarchically ordered knowledge of world structure and processes. Suggested models reflect systems, and they have structural relations embedded in the model. Ability to generate on fly new meaningful graphs and diagrams allows for modeling phenomena of intelligence like analogies, conceptual blending, and many others.

Dynamics and Evolutional Patterns of Social Networks

2011 ◽  
pp. 156-170
Author(s):  
Yingzi Jin ◽  
Yutaka Matsuo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Real World ◽  
Dynamic Network ◽  
Network Evolution ◽  
Belief Formation ◽  
Network Mining ◽  
Longitudinal Network Analysis ◽  
Relational Network

Previous chapters focused on the models of static networks, which consider a relational network at a given point in time. However, real-world social networks are dynamic in nature; for example, friends of friends become friends. Social network research has, in recent years, paid increasing attention to dynamic and longitudinal network analysis in order to understand network evolution, belief formation, friendship formation, and so on. This chapter focuses mainly on the dynamics and evolutional patterns of social networks. The chapter introduces real-world applications and reviews major theories and models of dynamic network mining.

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