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Restricted solid on solid surface growth models can be mapped onto binary lattice gases.
This transformation mirrors what might occur in a simple, biologically plausible neural network in which inputs are mapped onto binary responses via weights updated with a supervised, winner-takes-all rule (Ratcliff et al. 1999).
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For categorical predictor variables, one level is mapped onto the intercept and binary dummy variables are created for the other levels.
Each bit sequence is encoded with code rate R = N/K ′ (ENC), interleaved by a layer-specific interleaver (π m ) and mapped onto the complex plane via binary phase shift keying (BPSK), which leads to the layer-wise symbols x m ′.
To keep using two opposed terms, decoy and sensor, is problematic, especially since the NLR field already has 'sensor' and 'helper' NLRs and that this binary is specifically mapped onto NLR pairs, such as those described here.
We also observe that the levels of Out are more distinct (in terms of their mapping onto binary values) than the levels of I2.
At the source, a sequence of information bits3 c are mapped onto symbols x s=μ[c]∈{−1,+1} from a binary phase-shift keying (BPSK) constellation and broadcasted to the relay.
Each user's binary data from the source is mapped onto complex-valued symbols d l and spread in the frequency domain using a user-specific Walsh-Hadamard spreading code.
The sTOL is a fully resolved binary tree, with each internal node either being mapped onto a known ancestral species or left unlabelled as a hypothetical unknown ancestor.
Each stroke is mapped onto a single key.
The Tree is mapped onto a circle.
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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
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