In the "NN training" step of the concurrent learning process, four NNPES models were trained at the same time with the same network architectures but different random seeds for their parameters. The batch number was set to a relatively medium value (400,000) to improve the efficiency without too much loss of accuracy. When we got the final dataset, one NNPES model was retrained based on it and the batch number was increased to 4,000,000 to guarantee the accuracy.