scholarly journals Stability of Local Information-Based Centrality Measurements Under Degree Preserving Randomizations

2018 ◽  
pp. 395-403
Author(s):  
Chandni Saxena ◽  
M. N. Doja ◽  
Tanvir Ahmad
Keyword(s):  
Local Information

Do You Want to See the Tree? Ignore the Forest

2014 ◽  
Vol 61 (3) ◽  
pp. 205-214 ◽  
Author(s):  
Nicolas Poirel ◽  
Claire Sara Krakowski ◽  
Sabrina Sayah ◽  
Arlette Pineau ◽  
Olivier Houdé ◽  
...  
Keyword(s):  
Visual Recognition ◽  
Local Level ◽  
Local Information ◽  
Visual Environment ◽  
Global Information ◽  
Focus Attention ◽  
Global Level ◽  
Hierarchical Stimuli ◽  
Priming Paradigm ◽  
The One

The visual environment consists of global structures (e.g., a forest) made up of local parts (e.g., trees). When compound stimuli are presented (e.g., large global letters composed of arrangements of small local letters), the global unattended information slows responses to local targets. Using a negative priming paradigm, we investigated whether inhibition is required to process hierarchical stimuli when information at the local level is in conflict with the one at the global level. The results show that when local and global information is in conflict, global information must be inhibited to process local information, but that the reverse is not true. This finding has potential direct implications for brain models of visual recognition, by suggesting that when local information is conflicting with global information, inhibitory control reduces feedback activity from global information (e.g., inhibits the forest) which allows the visual system to process local information (e.g., to focus attention on a particular tree).

A Distributed Mechanism for Probing Overlay Path Bandwidth Using Local Information Exchange

2014 ◽  
Vol E97.B (5) ◽  
pp. 981-995
Author(s):  
Tien Hoang DINH ◽  
Go HASEGAWA ◽  
Masayuki MURATA
Keyword(s):  
Information Exchange ◽  
Local Information

Geolocation technology and local information in mobile telephony

Netcom ◽  
2006 ◽  
Vol 20 (1) ◽  
pp. 9-25 ◽  
Author(s):  
Yoshio Arai
Keyword(s):  
Local Information ◽  
Mobile Telephony

Informal Risk Sharing with Local Information

2018 ◽  
Author(s):  
Attila Ambrus ◽  
Wayne Gao ◽  
Pau Milan
Keyword(s):  
Risk Sharing ◽  
Local Information ◽  
Informal Risk Sharing

Generalised fuzzy local information C-means clustering algorithm

2012 ◽  
Vol 48 (23) ◽  
pp. 1468 ◽  
Author(s):  
S. Krinidis ◽  
M. Krinidis
Keyword(s):  
Clustering Algorithm ◽  
Local Information

scholarly journals Local Information as an Essential Factor for Quantum Entanglement

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 728
Author(s):  
Zhaofeng Su
Keyword(s):  
Quantum Entanglement ◽  
Quantum Information Processing ◽  
Local Information ◽  
Essential Factor ◽  
Qubit System ◽  
Local Vectors ◽  
Correlation Parameters ◽  
The One ◽  
Local Parameters

Quantum entanglement is not only a fundamental concept in quantum mechanics but also a special resource for many important quantum information processing tasks. An intuitive way to understand quantum entanglement is to analyze its geometric parameters which include local parameters and correlation parameters. The correlation parameters have been extensively studied while the role of local parameters have not been drawn attention. In this paper, we investigate the question how local parameters of a two-qubit system affect quantum entanglement in both quantitative and qualitative perspective. Firstly, we find that the concurrence, a measure of quantum entanglement, of a general two-qubit state is bounded by the norms of local vectors and correlations matrix. Then, we derive a sufficient condition for a two-qubit being separable in perspective of local parameters. Finally, we find that different local parameters could make a state with fixed correlation matrix separable, entangled or even more qualitatively entangled than the one with vanished local parameters.

scholarly journals A Study of OWA Operators Learned in Convolutional Neural Networks

2021 ◽  
Vol 11 (16) ◽  
pp. 7195
Author(s):  
Iris Dominguez-Catena ◽  
Daniel Paternain ◽  
Mikel Galar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Practical Method ◽  
Local Information ◽  
Weighted Averaging ◽  
Global Information ◽  
Owa Operator ◽  
Owa Operators ◽  
Ordered Weighted Averaging

Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the exponential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.

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