Sentence examples for dynamic membership from inspiring English sources
"Those two papers, particularly the one around dynamic membership, will explain why having a third of malicious nodes is actually harder than just having 33% of malicious nodes.
However adding dynamic membership, sharding, a data layer then a currency is a much larger proposition, which is why Parsec has been in stealth mode while it is being developed.
So there's an element of proof of stake involved too, bound up with additional planned characteristics of the Safe Network — related to dynamic membership and sharding (Lambert says MaidSafe has additional whitepapers on both those elements coming soon).
And so the fact that we'll have something coming out in that, after we have the dynamic membership stuff coming out, is going to be quite exciting to see the reaction to that as well".
We supposed the location sensitivity parameter is independent to images, therefore, in this model a static membership function is used for location sensitivity and only texture sensitivity and luminance sensitivity have dynamic membership functions.
Then, in order to find point, the average of the texture sensitivity of all blocks in the image is computed as shown in (5), where is the number of blocks in the image: (5). Figure 2 Dynamic membership function for texture sensitivity.
In prior research, most schemes have not considered practical usages, while [3, 4] worked on the search schemes of dynamic group membership changes without re-encrypting documents.
It achieves high quality ACK trees while keeping the tree maintenance overhead reasonably low in the presence of dynamic group membership and route change.
A detailed view of cluster #54, involved in transcription from TATA box promoters, highlights this pattern of dynamic complex membership (rightmost of Fig. 2D).
Teamwork in healthcare is often characterised by highly dynamic team membership, participation on multiple teams and rapid team formation.
Second, the temporal dynamics of spatial network interactions is modeled by a weighted time-evolving graph, and then a data-driven unsupervised learning algorithm based on the dynamic behavioral mixed-membership model (DBMM) is adopted to identify behavioral patterns of brain networks during the temporal evolution process of spatial overlaps/interactions.
