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P s denotes source node S's transmission power.
Table 1 Summary of commonly used notation Notation Description Notation Description ({mathcal{X}}) Input feature space P (X) Marginal distribution ({mathcal{Y}}) Label space P (Y|X) Conditional distribution ({mathcal{T}}) Predictive learning task P (Y) Label distribution Subscript S Denotes source DS Source domain data Subscript T Denotes target DT Target domain data.
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Let C B SWT1and C B SWT2 denote source and target SWT cubes, respectively.
Let s and d denote source and destination nodes of an arbitrary unicast session, respectively.
S, R, and D denote source node, relay node, and destination node, respectively.
A novel sequential discriminative dictionary learning (SDDL) algorithm is presented to suppress the confusion between the separated signals which we denote source confusion.
To set the notation, let us denote source and target images as S x) and T x), where x ∈ R n stands for the pixel coordinate vector.
Let m be the application message to be transmitted from a sender process, denoted source, to all other processes in the system.
Then we compare our proposed algorithm denoted source PA discussed in the main body of the paper and relay PA mentioned in section 3.6 with the algorithms proposed in [18] and [19] under several performance measures.
The system-level quantities we want to encode are confidence distributions that may be either one- or two-dimensional which we denote source distributions.
x y ∈ {sd, sr, rd} where sd, sr and rd, respectively, denote source-to-destination, source-to-relay, and relay-to-destination links.
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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