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An ensemble of nodes is synchronized in order to execute a collision-free communication schedule following a time-triggered paradigm [2].
We have showed empirical heuristics to dynamically shape ensemble of nodes to reach a dynamic power-performance tradeoff, specified either in terms of target activity recognition accuracy, or network lifetime [46].
The lower bound for in an ensemble of nodes in a fully connected network then depends on the maximum drift rate, the message staggering delay, and the communication delay.
More rigorously, each tree δ j is represented by an ensemble of nodes κ n, so (delta _{j}~=~{kappa _{0}, kappa _{1}, ldots, kappa _{N^{j}_{kappa }}}), with (boldsymbol {N^{j}_{kappa }}) the total number of nodes in tree δ j in a given moment and κ 0 the root node.
A molecular network is imprinted as a graph and could be considered as an ensemble of nodes (representing biomolecules) and part of them are connected with links, edges (representing interactions and relations of the biomolecules).
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Networks are ensembles of nodes connected into a single computational device.
It is partially for this reason that we have not emphasised the non-random sampling schemes more: a single instance of a network statistic represents only an instance of a sample drawn from an ensemble; for networks sampling of nodes leads to very broad distributions of sample statistics as would be expected for such highly correlated and structured data sets [ 1].
To this end, let us assume that through site-specific analysis, the relative frequencies of type link gains over the node ensemble of size are known a priori, and are given by,.
Methods for network inference such as Bayesian approaches[13] or mutual information[14] require an knowledge of a full distribution of activities across an ensemble of measurements at each node in the network.
Let V k be the set of nodes in the tree ensemble that use gk for the splitting rule.
For each number of unknown nodes, an ensemble of topologies were generated and several values of α and ρ were tested.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com