diff --git a/LICENSE b/LICENSE
index ff4c0fb..8287818 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2021 Echo Ji
+Copyright (c) 2022 Echo Ji
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/README.md b/README.md
index 34b0a34..b3e8e0e 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,9 @@
 # STDEN
 
-This is the implementation of Spatio-temporal Differential Equation Network (STDEN) in paper Towards Physics-guided Neural Networks for Traffic Flow Prediction.
+This is the implementation of Spatio-temporal Differential Equation Network (STDEN) in the following paper:
+Jiahao Ji, Jingyuan Wang, Zhe Jiang, Jiawei Jiang, and Hu Zhang, Towards Physics-guided Neural Networks for Traffic Flow Prediction, AAAI 2022. 
+
+Thanks [chnsh](https://github.com/chnsh/DCRNN_PyTorch) for the model training framework of this project.
 
 ## Requirement
 
@@ -36,4 +39,4 @@ The configuration file of all datasets are as follows:
 |WRS-393|stden_wrs.yaml|
 |ZGC-564|stden_zgc.yaml|
 
-PS: The data is not public and I am not allowed to distribute it.
+Note the data is not public and I am not allowed to distribute it.
\ No newline at end of file
diff --git a/lib/metrics.py b/lib/metrics.py
index 117b0db..e7b2f90 100644
--- a/lib/metrics.py
+++ b/lib/metrics.py
@@ -4,7 +4,7 @@ def masked_mae_loss(y_pred, y_true):
     # print('y_pred: ', y_pred.shape, 'y_true: ', y_true.shape)
     y_true[y_true < 1e-4] = 0
     mask = (y_true != 0).float()
-    mask /= mask.mean() # 将0值的权重分配给非零值
+    mask /= mask.mean() # assign the sample weights of zeros to nonzero-values 
     loss = torch.abs(y_pred - y_true)
     loss = loss * mask
     # trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
@@ -15,7 +15,7 @@ def masked_mape_loss(y_pred, y_true):
     # print('y_pred: ', y_pred.shape, 'y_true: ', y_true.shape)
     y_true[y_true < 1e-4] = 0
     mask = (y_true != 0).float()
-    mask /= mask.mean() # 将0值的权重分配给非零值
+    mask /= mask.mean() 
     loss = torch.abs((y_pred - y_true) / y_true)
     loss = loss * mask
     # trick for nans: https://discuss.pytorch.org/t/how-to-set-nan-in-tensor-to-0/3918/3
diff --git a/lib/utils.py b/lib/utils.py
index dafd6ec..1364123 100644
--- a/lib/utils.py
+++ b/lib/utils.py
@@ -158,7 +158,7 @@ def load_dataset(dataset_dir, batch_size, val_batch_size=None, **kwargs):
             cat_data = np.load(os.path.join(dataset_dir, category + '.npz'))
             data['x_' + category] = cat_data['x']
             data['y_' + category] = cat_data['y']
-    scaler = StandardScaler(mean=data['x_train'].mean(), std=data['x_train'].std()) # 第0维是要预测的量，但是第1维是什么呢？
+    scaler = StandardScaler(mean=data['x_train'].mean(), std=data['x_train'].std()) 
     # Data format
     for category in ['train', 'val', 'test']:
         data['x_' + category] = scaler.transform(data['x_' + category])