LitCovid-sentences  

PMC:7796369 / 10294-11815 JSONTXT 2 Projects

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
T78 0-2 Sentence denotes 3.
T79 3-10 Sentence denotes Results
T80 11-124 Sentence denotes We utilized an 80–20 train-test split to train a 5-layer feed forward neural network, which is shown in Figure 4.
T81 125-258 Sentence denotes We trained the network using the Adam optimizer with a learning rate of 0.001, minimizing the mean absolute error, in batches of 256.
T82 259-355 Sentence denotes Training was stopped after 100 epochs, with the test and validation error displayed in Figure 5.
T83 356-510 Sentence denotes Our final network is 10 Mb in size, which is small enough to be implemented even on the smallest of mobile devices and is able to infer results instantly.
T84 511-778 Sentence denotes More precisely, by entering the parameters (within reasonable ranges) listed in Table 1, our model is able to predict information such as cars and passengers passing through the vaccination center, average wait times throughout the day and overall completion metrics.
T85 779-822 Sentence denotes An example of this can be seen in Figure 6.
T86 823-932 Sentence denotes Our neural network model is capable of prediction orders of magnitudes faster than the full simulation model.
T87 933-1113 Sentence denotes More precisely, predictions were on average computed in 0.027 s, with minimum and maximum times of 0.025 and 0.039 s, respectively, in a simulation of 1000 random input variations.
T88 1114-1166 Sentence denotes Distribution of the results can be seen in Figure 7.
T89 1167-1312 Sentence denotes Notice that the time scale we achieve here is on the order of milliseconds, whereas previously we required minutes to produce these same results.
T90 1313-1439 Sentence denotes We achieve an improvement of over 3000× in terms of speed, largely due to the computation cost of simulating the entire event.
T91 1440-1521 Sentence denotes Furthermore, we do all this locally as opposed to the need for cloud computation.