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+<div align="center">
+<h1> DisMouse: Disentangling Information from Mouse Movement Data </h1>
+    
+**[Guanhua Zhang][4], &nbsp; [Zhiming Hu][5], &nbsp; [Andreas Bulling][6]** <br>
+**ACM UIST 2024**, Pittsburgh, USA <br>
+**[[Project][2]]** **[[Paper][7]]** </div>
+----------------
+
+# Virtual environment setup
+We recommend to setup a virtual environment using Anaconda. <br>
+1. Create a conda environment and install dependencies
+   ```shell
+   conda env create --name dismouse --file=environment.yaml
+   conda activate dismouse
+    ```
+2. Clone our repository to download our code
+   ```shell
+   git clone this_repo.git
+   ```
+
+# Run the code to train DisMouse
+Our code supports training using GPUs. You can also assign a particular card via CUDA_VISIBLE_DEVICES (e.g., the following commands use GPU card no.3). Simply execute
+```shell
+CUDA_VISIBLE_DEVICES=3 python main.py
+```
+
+## Use your own configurations
+Change parameters in ```templates.py -> mouse_autoenc``` and ```config.py -> TrainConfig```
+
+## Train on your mouse dataset
+Plug in your data loader function in ```dataset.py -> loadDataset``` (line 6)
+
+
+# Citation 
+If you find our code useful or use it in your own projects, please cite our paper:
+```
+@inproceedings{zhang24_uist,
+  title = {DisMouse: Disentangling Information from Mouse Movement Data},
+  author = {Zhang, Guanhua and Hu, Zhiming and Bulling, Andreas},
+  year = {2024},
+  pages = {1--13},
+  booktitle = {Proc. ACM Symposium on User Interface Software and Technology (UIST)},
+  doi = {10.1145/3654777.3676411}
+}
+```
+
+# Acknowledgements
+Our work is built on the codebase of [Diffusion Autoencoders][1]. Thanks to the authors for sharing their code.
+
+[1]: https://diff-ae.github.io/
+[2]: https://perceptualui.org/publications/zhang24_uist/
+[4]: https://scholar.google.com/citations?user=NqkK0GwAAAAJ&hl=en
+[5]: https://scholar.google.com/citations?hl=en&user=OLB_xBEAAAAJ
+[6]: https://www.perceptualui.org/people/bulling/
+[7]: https://perceptualui.org/publications/zhang24_uist.pdf
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