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In one monkey, indeed, the supposed free choice conditions led to lopsided behavior (dataset J2, Table 1): for the T-maze, the animal moved left 92% of the time when that choice was easy, and 0% when it was hard ("diff").
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The proposed method was successfully applied to the pedestrian unsafe behavior (PUB) dataset captured from far-infrared camera at night and its behavior recognition accuracy is confirmed to be higher than that of some algorithms related to CNNs, with a shorter processing time.
On 2 days, we changed the mazes mid-session to achieve better behavior; these datasets were not analyzed.
To observe the relative scalability of the approaches, we introduce the F Hadoop / F HPC metric and investigate its behavior for different dataset sizes.
In the following, we will demonstrate that the standardized C(k) curve is a valuable tool for i) assessing the overall consistency of sample behavior within a dataset, ii) identifying distinct groups of samples, and iii) identifying important subsets of features (e.g. genes).
Following this procedure, we were able to identify one robust pathway that stratified prognosis across all five different sources of datasets; the p38 pathway demonstrated consistent behavior across all datasets.
Using real datasets avoids simulation biases and gives a real picture of mapper behavior, whereas simulated datasets are benchmarks from which all parameters can be controlled.
LLSI of Kim and co-workers [ 57] has a correct behavior for all datasets while BPCA of Oba and co-workers [ 30] is strongly dependant of the dataset.
A great deal of work has been done analyzing human behavior in simpler datasets (KTH [7], Weizmann [8]) where the motions are performed in controlled situations [9 12].
Whilst this may offer insight for the specific publication, it seems unlikely that a single property will satisfactorily explain synergistic behavior for all datasets.
Referring this measure to the performance of human priority maps, the model proves to be the only one able to keep the same behavior through different datasets, showing free of biases.
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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