A Computer Code for the Solution of Large Scale Network Reliability Analysis Problems.

1988 ◽  
Author(s):  
Michael O. Ball
Keyword(s):  
Reliability Analysis ◽  
Large Scale ◽  
Network Reliability ◽  
Computer Code ◽  
Large Scale Network ◽  
Scale Network

Research of the Method for Traffic Generating Based on Genetic Algorithm

2014 ◽  
Vol 701-702 ◽  
pp. 3-7
Author(s):  
Liu Bo
Keyword(s):  
Genetic Algorithm ◽  
Network Traffic ◽  
Large Scale ◽  
Network Reliability ◽  
Actual Usage ◽  
Large Scale Network ◽  
Scale Network ◽  
Self Similar ◽  
Optimal Values

It has great impact on result of the network test or simulation if the test simulated traffic is corresponding to real situation. The network traffic is the superposition of different traffic streams in the actual usage of the network. But because of the complexity and time-consumption to generate different traffic streams, it is difficult to generate the network traffic in the simulation for the large scale network. This paper proposes a kind of method for traffic generating based on genetic algorithm .According to building the self-similar traffic model ,the optimal values of the model’s parameters has been obtained. A case study shows the effectiveness of the method for the network reliability.

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network

Attention Biases Visual Activity in Visual Short-term Memory

2014 ◽  
Vol 26 (7) ◽  
pp. 1377-1389 ◽  
Author(s):  
Bo-Cheng Kuo ◽  
Mark G. Stokes ◽  
Alexandra M. Murray ◽  
Anna Christina Nobre
Keyword(s):  
Large Scale ◽  
Short Term Memory ◽  
Control Mechanisms ◽  
Top Down ◽  
Short Term ◽  
Visual Activity ◽  
Visual Short Term Memory ◽  
Large Scale Network ◽  
Visual Areas ◽  
Scale Network

In the current study, we tested whether representations in visual STM (VSTM) can be biased via top–down attentional modulation of visual activity in retinotopically specific locations. We manipulated attention using retrospective cues presented during the retention interval of a VSTM task. Retrospective cues triggered activity in a large-scale network implicated in attentional control and led to retinotopically specific modulation of activity in early visual areas V1–V4. Importantly, shifts of attention during VSTM maintenance were associated with changes in functional connectivity between pFC and retinotopic regions within V4. Our findings provide new insights into top–down control mechanisms that modulate VSTM representations for flexible and goal-directed maintenance of the most relevant memoranda.

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