2,091 research outputs found
Residual closeness for helm and sunflower graphs
Vulnerability is an important concept in network analysis related with the ability of the network to avoid intentional attacks or disruption when a failure is produced in some of its components. Often enough, the network is modeled as an undirected and unweighted graph in which vertices represent the processing elements and edges represent the communication channel between them. Different measures for graph vulnerability have been introduced so far to study different aspects of the graph behavior after removal of vertices or links such as connectivity, toughness, scattering number, binding number and integrity. In this paper, we consider residual closeness which is a new characteristic for graph vulnerability. Residual closeness is a more sensitive vulnerability measure than the other measures of vulnerability. We obtain exact values for closeness, vertex residual closeness (VRC) and normalized vertex residual closeness (NVRC) for some wheel related graphs namely helm and sunflower.Publisher's Versio
Closeness and Residual Closeness of Harary Graphs
Analysis of a network in terms of vulnerability is one of the most
significant problems. Graph theory serves as a valuable tool for solving
complex network problems, and there exist numerous graph-theoretic parameters
to analyze the system's stability. Among these parameters, the closeness
parameter stands out as one of the most commonly used vulnerability metric. Its
definition has evolved over time to enhance ease of formulation and
applicability to disconnected structures. Furthermore, based on the closeness
parameter, residual closeness, which is a newer and more sensitive parameter
compared to other existing parameters, has been introduced as a new graph
vulnerability index by Dangalchev. In this study, the outcomes of the closeness
and residual closeness parameters in Harary Graphs have been examined. Harary
Graphs are well-known constructs that are distinguished by having vertices
that are -connected with the least possible number of edges.Comment: 21 pages preprin
'But I thought we were friends?' Life cycles and research relationships
This chapter is concerned with a relatively under-explored aspect of ‘engaged research’ – the nature of friendship relations between researchers and practitioners, and the ethical dilemmas that arise in such relationships. Attention has been paid to the relational aspects of research in the methodology literature, but this chapter focuses more closely on friendship in particular. The chapter is framed around two guiding concerns: how do friendships, formed in and around research, change over time; and in view of friendship conceived in this dynamic fashion, what ethical questions and dilemmas arise for the ‘friends’
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval
While there have been many proposals on making AI algorithms explainable, few
have attempted to evaluate the impact of AI-generated explanations on human
performance in conducting human-AI collaborative tasks. To bridge the gap, we
propose a Twenty-Questions style collaborative image retrieval game,
Explanation-assisted Guess Which (ExAG), as a method of evaluating the efficacy
of explanations (visual evidence or textual justification) in the context of
Visual Question Answering (VQA). In our proposed ExAG, a human user needs to
guess a secret image picked by the VQA agent by asking natural language
questions to it. We show that overall, when AI explains its answers, users
succeed more often in guessing the secret image correctly. Notably, a few
correct explanations can readily improve human performance when VQA answers are
mostly incorrect as compared to no-explanation games. Furthermore, we also show
that while explanations rated as "helpful" significantly improve human
performance, "incorrect" and "unhelpful" explanations can degrade performance
as compared to no-explanation games. Our experiments, therefore, demonstrate
that ExAG is an effective means to evaluate the efficacy of AI-generated
explanations on a human-AI collaborative task.Comment: 2019 AAAI Conference on Human Computation and Crowdsourcin
Closeness centrality in some splitting networks
A central issue in the analysis of complex networks is the assessment of their robustness and vulnerability. A variety of measures have been proposed in the literature to quantify the robustness of networks, and a number of graph-theoretic parameters have been used to derive formulas for calculating network reliability. \textit{Centrality} parameters play an important role in the field of network analysis. Numerous studies have proposed and analyzed several \textit{centrality} measures. We consider \textit{closeness centrality} which is defined as the total graph-theoretic distance to all other vertices in the graph. In this paper, closeness centrality of some splitting graphs is calculated, and exact values are obtained
Studies of highway skew slab-bridges with curbs. A report of an investigation conducted by the Engineering Experiment Station, University of Illinois,
On t.p. of v. 2: A report of an investigation conducted by the Engineering Experiment Station, University of Illinois, in cooperation with the Bureau of Public Roads, U. S. Dept. of Commerce and the Division of Highways, State of Illinois.Bibliographical footnotes.pt. 1. Results of analyses, by V. P. Jensen and J. W. Allen.--pt. 2. Laboratory research, by M. L. Gossard [and others
Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments
In this work we focus on improving the efficiency and generalisation of
learned navigation strategies when transferred from its training environment to
previously unseen ones. We present an extension of the residual reinforcement
learning framework from the robotic manipulation literature and adapt it to the
vast and unstructured environments that mobile robots can operate in. The
concept is based on learning a residual control effect to add to a typical
sub-optimal classical controller in order to close the performance gap, whilst
guiding the exploration process during training for improved data efficiency.
We exploit this tight coupling and propose a novel deployment strategy,
switching Residual Reactive Navigation (sRRN), which yields efficient
trajectories whilst probabilistically switching to a classical controller in
cases of high policy uncertainty. Our approach achieves improved performance
over end-to-end alternatives and can be incorporated as part of a complete
navigation stack for cluttered indoor navigation tasks in the real world. The
code and training environment for this project is made publicly available at
https://sites.google.com/view/srrn/home.Comment: Accepted as a conference paper at ICRA2020. Project site available at
https://sites.google.com/view/srrn/hom
The emergence of self-organisation in social systems: the case of the geographic industrial clusters
The objective of this work is to use complexity theory to propose a new interpretation of industrial clusters. Industrial clusters constitute a specific type of econosphere, whose driving principles are self-organisation, economies of diversity and a configuration that optimises the exploration of diversity starting from the configuration of connectivity of the system. This work shows the centrality of diversity by linking complexity theory (intended as "a method for understanding diversity"') to different concepts such as power law distributions, self-organisation, autocatalytic cycles and connectivity.I propose a method to distinguish self-organising from non self-organising agglomerations, based on the correlation between self-organising dynamics and power law network theories. Self-organised criticality, rank-size rule and scale-free networks theories become three aspects indicating a common underlying pattern, i.e. the edge of chaos dynamic. I propose a general model of development of industrial clusters, based on the mutual interaction between social and economic autocatalytic cycle. Starting from Kauffman's idea(^2) on the autocatalytic properties of diversity, I illustrate how the loops of the economies of diversity are based on the expansion of systemic diversity (product of diversity and connectivity). My thesis provides a way to measure systemic diversity. In particular I introduce the distinction between modular innovation at the agent level and architectural innovation at the network level and show that the cluster constitutes an appropriate organisational form to manage the tension and dynamics of simultaneous modular and architectural innovation. The thesis is structured around two propositions: 1. Self-organising systems are closer to a power law than hierarchical systems or aggregates (collection of parts). For industrial agglomerations (SLLs), the closeness to a power law is related to the degree of self-organisation present in the agglomeration, and emerges in the agglomeration’s structural and/or behavioural properties subject to self-organising dynamic.2. Self-organising systems maximise the product of diversity times connectivity at a rate higher than hierarchical systems
Measuring regional creative capacity: A literature review for rural-specific approaches
Recent theories on regional creative capacity often focus on urban regions without taking into account rural regions. In addition, the application of such analyses to rural regions may lead to misrepresentation or misunderstanding of rural creative capacity. Against this background, the aim of the present study is to integrate the existing literature on different components of creative capacity, namely, knowledge, innovation, entrepreneurship and networks, in order to build a more comprehensive framework for rural creative capacity and its evaluation. In the light of the perspective from the empirical literature review on the evaluation of creative capacity in rural regions, various empirical measurements seem to misrepresent or underestimate the creative capacity of rural regions. Therefore, there is a clear need to use the locality in relation to its dynamics, i.e. tacit knowledge, cultural heritage and social and physical environment as the main and basic measurement unit for creative capacity analysis. 2010 Taylor & Francis
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