Merge branch 'dev' into main

.github/workflows/testing_ci.yml

@@ -20,7 +20,7 @@ jobs:
             fail-fast: false
             matrix:
                 os: [ubuntu-latest, windows-latest, macOS-latest]
-                python-version: ["3.7", "3.10"]
+                python-version: ["3.7", "3.11"]
 
         steps:
             - name: Check out the repo code

.github/workflows/testing_daily.yml

@@ -15,7 +15,7 @@ jobs:
             fail-fast: false
             matrix:
                 os: [ubuntu-latest, windows-latest, macOS-latest]
-                python-version: ["3.7", "3.10"]
+                python-version: ["3.7", "3.11"]
                 torch-version: ["1.13.1"]
         steps:
             - name: Check out the repo code

PyPOTS_Classification.ipynb

@@ -116,7 +116,7 @@
     "    n_classes=physionet2012_dataset[\"n_classes\"],\n",
     "    n_layers=2,\n",
     "    d_model=physionet2012_dataset[\"n_features\"] * 4,\n",
-    "    d_inner=256,\n",
+    "    d_ffn=256,\n",
     "    n_heads=2,\n",
     "    dropout=0.3,\n",
     "    batch_size=32,\n",

PyPOTS_Imputation.ipynb

@@ -1192,7 +1192,6 @@
     "from pypots.utils.metrics import calc_mae\n",
     "\n",
     "# initialize the model\n",
-    "# initialize the model\n",
     "mrnn = MRNN(\n",
     "    n_steps=physionet2012_dataset['n_steps'],\n",
     "    n_features=physionet2012_dataset['n_features'],\n",

README.md

@@ -1,4 +1,4 @@
-<a href="https://github.com/WenjieDu/PyPOTS">
+<a href="https://github.com/WenjieDu/BrewPOTS">
     <img alt="BrewPOTS logo" src="https://pypots.com/figs/pypots_logos/BrewPOTS/logo_FFBG.svg" width="180" align="right">
 </a>
 
@@ -18,14 +18,24 @@ The tutorials here are for PyPOTS users to quick start their practice, not for a
 So we didn't fine tune the hyper-parameters of each models in the tutorials.
 You can tune the hyper-parameters by yourself to get better performance on the tutorial dataset PhysioNet-2012 or on your own datasets.
 
+Besides BrewPOTS, you can also find a simple and quick-start tutorial notebook on Google Colab
+<a href="https://colab.research.google.com/drive/1HEFjylEy05-r47jRy0H9jiS_WhD0UWmQ">
+<img src="https://img.shields.io/badge/GoogleColab-PyPOTS_Tutorials-F9AB00?logo=googlecolab&logoColor=white" alt="Colab tutorials" align="center"/>
+</a>.
+
 Enjoy it! ☕️ And have fun!
 
 
 ## ❖ Citing BrewPOTS/PyPOTS
-The paper introducing PyPOTS project is available on arXiv at [this URL](https://arxiv.org/abs/2305.18811),
-and we are pursuing to publish it in prestigious academic venues, e.g. JMLR (track for
-[Machine Learning Open Source Software](https://www.jmlr.org/mloss/)). If the tutorials in BrewPOTS are helpful to your work,
-please cite PyPOTS project as below and 🌟star this repository to make others notice it. 🤗 Thank you!
+The paper introducing PyPOTS is available [on arXiv](https://arxiv.org/abs/2305.18811),
+A short version of it is accepted by the 9th SIGKDD international workshop on Mining and Learning from Time Series ([MiLeTS'23](https://kdd-milets.github.io/milets2023/))).
+**Additionally**, PyPOTS has been included as a [PyTorch Ecosystem](https://pytorch.org/ecosystem/) project.
+We are pursuing to publish it in prestigious academic venues, e.g. JMLR (track for
+[Machine Learning Open Source Software](https://www.jmlr.org/mloss/)). If you use PyPOTS in your work,
+please cite it as below and 🌟star this repository to make others notice this library. 🤗
+
+There are scientific research projects using PyPOTS and referencing in their papers.
+Here is [an incomplete list of them](https://scholar.google.com/scholar?as_ylo=2022&q=%E2%80%9CPyPOTS%E2%80%9D&hl=en).
 
 <p align="center">
 <a href="https://github.com/WenjieDu/PyPOTS">
@@ -34,23 +44,17 @@ please cite PyPOTS project as below and 🌟star this repository to make others
 </p>
 
 ``` bibtex
-@article{du2023PyPOTS,
+@article{du2023pypots,
 title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},
 author={Wenjie Du},
+journal={arXiv preprint arXiv:2305.18811},
 year={2023},
-eprint={2305.18811},
-archivePrefix={arXiv},
-primaryClass={cs.LG},
-url={https://arxiv.org/abs/2305.18811},
-doi={10.48550/arXiv.2305.18811},
 }
 ```
-
 or
-
 > Wenjie Du. (2023).
 > PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series.
-> arXiv, abs/2305.18811.https://arxiv.org/abs/2305.18811
+> arXiv, abs/2305.18811. https://arxiv.org/abs/2305.18811
 
 
 <details>