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In the first analysis, we tested whether we can use the corpus-derived semantic coordinates of the target objects to decode the BOLD activation patterns elicited using clues.
Therefore, the destination should receive enough linear equation data over k to be able to decode the target packet.
PU can combine some part of linear network coding packets that it initially received with the partially altered linear network coding packets to decode the target packet.
The results demonstrate that the spatial correlation patterns of LFP signals can be used to decode movement target direction.
We tested the hypothesis that spatial correlation patterns of neural data can be used to decode movement target direction.
The results demonstrate that the spatial patterns of LFP signals can be used to decode movement target direction.
We have shown that it is possible to decode movement target direction with high accuracy by exploiting the spatial patterns of LFP recorded from M1 and PMd.
4A and 5A) when the data of all the sessions were combined, which suggests that there was a consistent pattern across channels during these sessions that could be used to decode movement target direction.
We showed that it was possible to decode the implied target object with high accuracy without ever showing the object explicitly, suggesting that the clues triggered a coherent representation of the target object.
For the second experiment, GIMME has been applied to all routes of the first set which OpenLR had not been able to decode in the target map (i.e., a hybrid approach of first using OpenLR and then GIMME has been applied).
Instead of targeting specific markers, whole genome sequencing allows us to decode all 3 billion letters of a person's DNA.
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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