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Table 1 shows demographic data of the remaining 6409 patients.
Some spectral bands have been removed due to noise corruption and atmosphere absorption, and only the data of the remaining 188 bands have been used.
To evaluate the predictive power of multi-task learning with respect to novel targets, we performed a leave-one-sequence-out validation, which puts aside the data of a certain target for external testing while using the data of the remaining targets for training.
Sparse mobile crowdsensing, which aims to reduce the overall sensing cost without compromise of data quality, functions by allocating sensing tasks for only a small portion of the target area to be covered while inferring the data of the remaining unsensed area based on the spatial and temporal correlation among the data captured from different sub-areas [92].
The EEG/fMRI data of the remaining 22 patients were pre-processed and analysed using SPM8b.
The segregation data of the remaining markers were studied with the program MultiPoint ([95] [97]; http://www.multiqtl.com).
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The lack of data for the remaining 16 ZIP codes means that we were not able to fully investigate them.
In our analyses, training data consisted of randomly selected sets of 3687 subjects (90% of cohort), while testing data consisted of the remaining 410 subjects (10% of cohort).
We report here the temporal and spatial patterns of the two primary LVs, accounting for 42% of the covariance in the data (each of the remaining LVs accounted for <5% of the covariance).
Although this led to a reduction in the overall number of the patients, the data quality of the remaining patient population was thereby increased.
The data set of the remaining intensity condition was used at a later stage to test whether the population coding model is level invariant.
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