--- a/README.md
+++ b/README.md
@@ -1,81 +1,81 @@
-# MetaPred
-The repo is code (baseline and the proposed MetaPred) for paper MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records by [Xi Sheryl Zhang](https://www.xi-zhang.net), [Fengyi Tang](https://github.com/af1tang), [Hiroko H. Dodge](https://medicine.umich.edu/dept/neurology/hiroko-dodge-phd), [Jiayu Zhou](https://jiayuzhou.github.io), and [Fei Wang](https://sites.google.com/site/cornellwanglab/home).  
-
-## Overview
-
-MetaPred is a meta-learning framework for Clinical Risk Prediction using limited patient Electronic Health Records (EHRs). We given an example in the following figure:
-
-<p align="center"><img src="figures/task-design.png" alt=" Illustration of the proposed learning procedure" width="500"></p>
-
-Suppose we have multiple domains, our goal is to predict Alzheimer’s disease with few labeled patients, which give rise to a low-resource classification. The idea is to employ labeled patients from high-resource domains and design a learning to transfer framework with sources and a simulated target in meta-learning. There are four steps: (1) constructing episodes by sampling from the source domains and the simulated target domain; (2) learn the parameters of predictors in an episode-by-episode manner; (3) fine-tuning the model parameters on the genuine target domain; (4) predicting the target clinical risk. We respectively implemented Convolutional Neural Network (CNN) and Long-Shot Term Memory (LSTM) Network as base predictors. The model overview (meta-training procedure) is shown as follows:
-
-<p align="center"><img src="figures/MetaPred.png" alt="MetaPred framework overview" width="750"></p>
-
-The entire learning procedure can be viewed as: iteratively transfer the parameter Θ learned from source domains through utilizing it as the initialization of the parameter that needs to be updated in the target domain.
-
-## Results
-
-The learned representations of patients in five disease domains are visualized by t-SNE. In detail, AD, PD, DM, AM, MCI are abbreviations of Alzheimer's Disease, Parkinson's Disease, Dementia, Amnesia and Mild Cognitive Impairment, respectively. As a patient might suffer multiple diseases, there is supposed to be some overlaps among the given domains.
-
-<p align="center"><img src="figures/patient_vis_metapred.png" alt="Visualization of patient representation learned by MetaPred" width="500"></p>
-
-To demonstrate the effectiveness of the proposed MetaPred in the context of domain adaptation, we compare it with the state-of-the-art meta-learning algorithm ``Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks`` (MAML). The results on Alzheimer's Disease domain are presented in terms of AUC and F1-Score.
-
-<p align="center"><img src="figures/vs_maml_ad_cnn.png" alt="Performance comparison of MetaPred and MAML on the top of Alzheimer's Disease" width="500"></p>
-
-
-## Requirements
-This package has the following requirements:
-* `Python 3.x`
-* [TensorFlow 1.5](https://github.com/tensorflow/tensorflow)
-* [Progress Bar](https://progressbar-2.readthedocs.io/en/latest/index.html)
-
-
-## Usage
-### Baseline in Sequential Data Modeling 
-The [baseline implementation](https://github.com/sheryl-ai/MetaPred/tree/master/baselines) includes:
-* Logistic Regression
-* K-Nearest Neighbors 
-* XGBoost 
-* SVM
-* Random Forest
-* MLP
-* LSTM
-* CNN
-
-which can be used in any sort of sequence modeling, especially for EHRs data, directly.
-
-### How to Run
-To run MetaPred on EHR data, you need to revise the learning settings in main.py and the network hyperparameters in model.py. Then run the shell script metapred.sh.
-```bash
-bash metapred.sh
-```
-Our settings of learning procedures are:
-```bash
-python main.py --method='cnn' --metatrain_iterations=10000 --meta_batch_size=32 --update_batch_size=4 --meta_lr=0.001 --update_lr=1e-5 --num_updates=4 --n_total_batches=500000
-```
-or
-```bash
-python main.py --method='rnn' --metatrain_iterations=10000 --meta_batch_size=32 --update_batch_size=4 --meta_lr=0.001 --update_lr=1e-5 --num_updates=4 --n_total_batches=500000
-```
-
-### Additional Material
-There is implementations used in:
-
-Chelsea Finn, Pieter Abbeel, Sergey Levine, [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://arxiv.org/abs/1703.03400), International Conference on Machine Learning (ICML), 2017.
-
-## References 
-If you happen to use our work, please consider citing our paper: 
-```
-@inproceedings{Zhang:2019:MMC:3292500.3330779,
- author = {Zhang, Xi Sheryl and Tang, Fengyi and Dodge, Hiroko H. and Zhou, Jiayu and Wang, Fei},
- title = {MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records},
- booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
- series = {KDD '19},
- year = {2019},
- location = {Anchorage, AK, USA},
- pages = {2487--2495},
-} 
-```
-This paper can be accessed on : [MetaPred] (https://dl.acm.org/citation.cfm?id=3330779)
-
+# MetaPred
+The repo is code (baseline and the proposed MetaPred) for paper MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records by [Xi Sheryl Zhang](https://www.xi-zhang.net), [Fengyi Tang](https://github.com/af1tang), [Hiroko H. Dodge](https://medicine.umich.edu/dept/neurology/hiroko-dodge-phd), [Jiayu Zhou](https://jiayuzhou.github.io), and [Fei Wang](https://sites.google.com/site/cornellwanglab/home).  
+
+## Overview
+
+MetaPred is a meta-learning framework for Clinical Risk Prediction using limited patient Electronic Health Records (EHRs). We given an example in the following figure:
+
+<p align="center"><img src="https://github.com/sheryl-ai/MetaPred/blob/master/figures/task-design.png?raw=true" alt=" Illustration of the proposed learning procedure" width="500"></p>
+
+Suppose we have multiple domains, our goal is to predict Alzheimer’s disease with few labeled patients, which give rise to a low-resource classification. The idea is to employ labeled patients from high-resource domains and design a learning to transfer framework with sources and a simulated target in meta-learning. There are four steps: (1) constructing episodes by sampling from the source domains and the simulated target domain; (2) learn the parameters of predictors in an episode-by-episode manner; (3) fine-tuning the model parameters on the genuine target domain; (4) predicting the target clinical risk. We respectively implemented Convolutional Neural Network (CNN) and Long-Shot Term Memory (LSTM) Network as base predictors. The model overview (meta-training procedure) is shown as follows:
+
+<p align="center"><img src="https://github.com/sheryl-ai/MetaPred/blob/master/figures/MetaPred.png?raw=true" alt="MetaPred framework overview" width="750"></p>
+
+The entire learning procedure can be viewed as: iteratively transfer the parameter Θ learned from source domains through utilizing it as the initialization of the parameter that needs to be updated in the target domain.
+
+## Results
+
+The learned representations of patients in five disease domains are visualized by t-SNE. In detail, AD, PD, DM, AM, MCI are abbreviations of Alzheimer's Disease, Parkinson's Disease, Dementia, Amnesia and Mild Cognitive Impairment, respectively. As a patient might suffer multiple diseases, there is supposed to be some overlaps among the given domains.
+
+<p align="center"><img src="https://github.com/sheryl-ai/MetaPred/blob/master/figures/patient_vis_metapred.png?raw=true" alt="Visualization of patient representation learned by MetaPred" width="500"></p>
+
+To demonstrate the effectiveness of the proposed MetaPred in the context of domain adaptation, we compare it with the state-of-the-art meta-learning algorithm ``Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks`` (MAML). The results on Alzheimer's Disease domain are presented in terms of AUC and F1-Score.
+
+<p align="center"><img src="https://github.com/sheryl-ai/MetaPred/blob/master/figures/vs_maml_ad_cnn.png?raw=true" alt="Performance comparison of MetaPred and MAML on the top of Alzheimer's Disease" width="500"></p>
+
+
+## Requirements
+This package has the following requirements:
+* `Python 3.x`
+* [TensorFlow 1.5](https://github.com/tensorflow/tensorflow)
+* [Progress Bar](https://progressbar-2.readthedocs.io/en/latest/index.html)
+
+
+## Usage
+### Baseline in Sequential Data Modeling 
+The [baseline implementation](https://github.com/sheryl-ai/MetaPred/tree/master/baselines) includes:
+* Logistic Regression
+* K-Nearest Neighbors 
+* XGBoost 
+* SVM
+* Random Forest
+* MLP
+* LSTM
+* CNN
+
+which can be used in any sort of sequence modeling, especially for EHRs data, directly.
+
+### How to Run
+To run MetaPred on EHR data, you need to revise the learning settings in main.py and the network hyperparameters in model.py. Then run the shell script metapred.sh.
+```bash
+bash metapred.sh
+```
+Our settings of learning procedures are:
+```bash
+python main.py --method='cnn' --metatrain_iterations=10000 --meta_batch_size=32 --update_batch_size=4 --meta_lr=0.001 --update_lr=1e-5 --num_updates=4 --n_total_batches=500000
+```
+or
+```bash
+python main.py --method='rnn' --metatrain_iterations=10000 --meta_batch_size=32 --update_batch_size=4 --meta_lr=0.001 --update_lr=1e-5 --num_updates=4 --n_total_batches=500000
+```
+
+### Additional Material
+There is implementations used in:
+
+Chelsea Finn, Pieter Abbeel, Sergey Levine, [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://arxiv.org/abs/1703.03400), International Conference on Machine Learning (ICML), 2017.
+
+## References 
+If you happen to use our work, please consider citing our paper: 
+```
+@inproceedings{Zhang:2019:MMC:3292500.3330779,
+ author = {Zhang, Xi Sheryl and Tang, Fengyi and Dodge, Hiroko H. and Zhou, Jiayu and Wang, Fei},
+ title = {MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records},
+ booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
+ series = {KDD '19},
+ year = {2019},
+ location = {Anchorage, AK, USA},
+ pages = {2487--2495},
+} 
+```
+This paper can be accessed on : [MetaPred] (https://dl.acm.org/citation.cfm?id=3330779)
+