diff --git a/README.md b/README.md index af785f75..e7ca46cc 100644 --- a/README.md +++ b/README.md @@ -21,13 +21,13 @@ Let's dive in and set up your own literature survey adventure! ### Getting Started Follow these simple steps to set up your literature survey website: -1. Click on the ```Use this template``` button to create your own repository based on this template. +1. Click on the ```Use this template``` button to create your own repository based on this template. If you would like to access your repository via URL, please make created repository public. -2. Open up the ```app/data/test.tsv``` file in your favorite code editor or Excel. +2. Open up the ```app/data/query.tsv``` file in your favorite code editor or Excel. -3. Under the `Title` column, give titles to your topics. Under the `Use` column, write 1 if you want to use the article for recommendations or 0 if you just want to display the article. Under the URL column, specify the correspondng URLs to Semantic Scholar article. +3. Under the `Title` column, give titles to your topics. Under the `Use` column, write 1 if you want to use the article for recommendations or 0 if you just want to display the article. **Under the URL column, specify the corresponding URLs to Semantic Scholar (https://www.semanticscholar.org/) article.** Submit your edited file by clicking on `Commit changes...`. In the `Commit message` add a prefix by using one of these keywords **feat:, fix: or chore:**, e.g., "feat: added one paper". After committing changes to the `main` branch directly, the workflows should start automatically. -4. Create a venv: +4. (Optional) Create a venv: ``` > python -m venv env # On Windows @@ -37,32 +37,34 @@ Follow these simple steps to set up your literature survey website: > source env/bin/activate ``` -5. Install all the necessary requirements: +5. (Optional) Install all the necessary requirements: ``` > pip3 install -r requirements.txt ``` -6. It's time to fetch some literature! Run the ```literature_fetch_recommendation_api.py``` script to grab the recommended articles from Semantic Scholar: +6. (Optional) It's time to fetch some literature! Run the ```literature_fetch_recommendation_api.py``` script to grab the recommended articles from Semantic Scholar: ``` > cd app/code > python3 literature_fetch_recommendation_api.py ``` -7. Now, fire up MkDocs locally to view the recommended articles: +7. (Optional) Now, fire up MkDocs locally to view the recommended articles: ``` > mkdocs serve ``` Head over to the localhost link that pops up in your terminal. -8. This repository includes a `mkdocs-deploy.yml` [workflow](https://github.com/VirtualPatientEngine/literatureSurvey/blob/main/.github/workflows/mkdocs-deploy.yml) that uses GitHub Actions to automatically execute the specified script and deploy the literature survey system as a [GitHub Pages website](https://virtualpatientengine.github.io/literatureSurvey/). Feel free to edit to based on your project needs or use it as it is. +8. This repository includes a `mkdocs-deploy.yml` [workflow](https://github.com/VirtualPatientEngine/literatureSurvey/blob/main/.github/workflows/mkdocs-deploy.yml) that uses GitHub Actions to automatically execute the specified script **once a week** and deploy the literature survey system as a [GitHub Pages website](https://virtualpatientengine.github.io/literatureSurvey/). Feel free to edit to based on your project needs or use it as it is. -> To host your literature survey system online, you must place the YML file in the `.github/workflows/` folder. Once you have pushed you code to GitHub, under the [Actions](https://github.com/VirtualPatientEngine/literatureSurvey/actions) tab, you'll find the ongoing `mkdocs-deploy.yml` workflow. Once this workflow finishes, head over to the [Settings/Pages](https://github.com/VirtualPatientEngine/literatureSurvey/settings/pages) tab. From there, choose `Deploy from a branch` in the Source section. Under the Branch subsection, select `gh-pages` and root from the dropdown menus, then click `Save`. +> To host your literature survey system online, you must place the YML file in the `.github/workflows/` folder. Once you have pushed you code to GitHub, under the [Actions](https://github.com/VirtualPatientEngine/literatureSurvey/actions) tab, you'll find the ongoing `mkdocs-deploy.yml` workflow (this might take even 1h or more depending on the current workload of compute servers and length of the publication list). Once this workflow finishes, head over to the [Settings/Pages](https://github.com/VirtualPatientEngine/literatureSurvey/settings/pages) tab. From there, choose `Deploy from a branch` in the Source section. Under the Branch subsection, select `gh-pages` and root from the dropdown menus, then click `Save`. -9. Change site_url, theme:/logo:, repo_url, and repo_name in ```base.yml``` to the values related to your project. +9. Under `About` section of your repository, head to the gear symbol and check the box `Use your GitHub Pages website` and `Save changes`. You will see an URL to your literature survey repository under `About` section of the `Code` tab. -10. If you'd like to edit the home page of the website, head over to `docs/index.md` to make the changes. +10. Change site_url, theme:/logo:, repo_url, and repo_name in ```base.yml``` to the values related to your project. -11. (Optional) Edit custom.css if you'd like to change the styling of web pages. +11. If you'd like to edit the home page of the website, head over to `docs/index.md` to make the changes. + +12. (Optional) Edit custom.css if you'd like to change the styling of web pages. ### Bugs? Feature Requests? If you encounter any bugs or have brilliant ideas for new features, please head over to the [Issues](https://github.com/VirtualPatientEngine/literatureSurvey/issues) and let us know. diff --git a/app/data/query.tsv b/app/data/query.tsv index a390c86a..e36cfc41 100644 --- a/app/data/query.tsv +++ b/app/data/query.tsv @@ -1,77 +1,13 @@ Topic Use URL Time-series forecasting 1 https://www.semanticscholar.org/paper/A-Survey-on-Graph-Neural-Networks-for-Time-Series%3A-Jin-Koh/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9?utm_source=direct_link Time-series forecasting 1 https://www.semanticscholar.org/paper/Graph-Guided-Network-for-Irregularly-Sampled-Time-Zhang-Zeman/455bfc515eb279cc09023faa1f78c6efb61224ba?utm_source=direct_link -Time-series forecasting 1 https://www.semanticscholar.org/paper/Taming-Local-Effects-in-Graph-based-Spatiotemporal-Cini-Marisca/e2a83369383aff37224170c1ae3d3870d5d9e419?utm_source=direct_link -Time-series forecasting 1 https://www.semanticscholar.org/paper/Sparse-Graph-Learning-from-Spatiotemporal-Time-Cini-Zambon/0d01d21137a5af9f04e4b16a55a0f732cb8a540b?utm_source=direct_link -Time-series forecasting 1 https://www.semanticscholar.org/paper/Graph-Deep-Learning-for-Time-Series-Forecasting-Cini-Marisca/ccea298edb788edf821aef58f0952c3e8debc25a?utm_source=direct_link -Time-series forecasting 1 https://www.semanticscholar.org/paper/Large-Language-Models-Are-Zero-Shot-Time-Series-Gruver-Finzi/123acfbccca0460171b6b06a4012dbb991cde55b?utm_source=direct_link -Time-series forecasting 1 https://www.semanticscholar.org/paper/Graph-Mamba%3A-Towards-Long-Range-Graph-Sequence-with-Wang-Tsepa/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f?utm_source=direct_link -Time-series forecasting 1 https://www.semanticscholar.org/paper/A-decoder-only-foundation-model-for-time-series-Das-Kong/f45f85fa1beaa795c24c4ff86f1f2deece72252f?utm_source=direct_link -Time-series forecasting 1 https://www.semanticscholar.org/paper/UniTS%3A-Building-a-Unified-Time-Series-Model-Gao-Koker/bcbcc2e1af8bcf6b07edf866be95116a8ed0bf91?utm_source=direct_link -Time-series forecasting 1 https://www.semanticscholar.org/paper/Unified-Training-of-Universal-Time-Series-Woo-Liu/4a111f7a3b56d0468f13104999844885157ef17d?utm_source=direct_link -Time-series forecasting 1 https://www.semanticscholar.org/paper/Time-LLM%3A-Time-Series-Forecasting-by-Reprogramming-Jin-Wang/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277?utm_source=direct_link -Time-series forecasting 1 https://www.semanticscholar.org/paper/Tiny-Time-Mixers-(TTMs)%3A-Fast-Pre-trained-Models-of-Ekambaram-Jati/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6?utm_source=direct_link -Time-series forecasting 0 https://www.semanticscholar.org/paper/Self-Supervised-Contrastive-Pre-Training-For-Time-Zhang-Zhao/648d90b713997a771e2c49f02cd771e8b7b10b37?utm_source=direct_link -Time-series forecasting 0 https://www.semanticscholar.org/paper/Domain-Adaptation-for-Time-Series-Under-Feature-and-He-Queen/5bd2c0acaf58c25f71617db2396188c74d29bf14?utm_source=direct_link -Time-series forecasting 0 https://www.semanticscholar.org/paper/AZ-whiteness-test%3A-a-test-for-signal-uncorrelation-Zambon-Alippi/c3c94ccc094dcf546e8e31c9a42506302e837524?utm_source=direct_link -Time-series forecasting 0 https://www.semanticscholar.org/paper/Graph-state-space-models-Zambon-Cini/279cd637b7e38bba1dd8915b5ce68cbcacecbe68?utm_source=direct_link -Symbolic regression 1 https://www.semanticscholar.org/paper/Discovering-governing-equations-from-data-by-sparse-Brunton-Proctor/5d150cec2775f9bc863760448f14104cc8f42368?utm_source=direct_link -Symbolic regression 1 https://www.semanticscholar.org/paper/Robust-learning-from-noisy%2C-incomplete%2C-data-via-Reinbold-Kageorge/60d0d998fa038182b3b69a57adb9b2f82d40589c?utm_source=direct_link -Symbolic regression 1 https://www.semanticscholar.org/paper/Data-driven-discovery-of-coordinates-and-governing-Champion-Lusch/3c9961153493370500020c81527b3548c96f81e0?utm_source=direct_link -Symbolic regression 1 https://www.semanticscholar.org/paper/Chaos-as-an-intermittently-forced-linear-system-Brunton-Brunton/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2?utm_source=direct_link Symbolic regression 1 https://www.semanticscholar.org/paper/Sparse-identification-of-nonlinear-dynamics-for-in-Kaiser-Kutz/b2eb064f432557c59ce99834d7dc7817e4687271?utm_source=direct_link Symbolic regression 1 https://www.semanticscholar.org/paper/Inferring-Biological-Networks-by-Sparse-of-Dynamics-Mangan-Brunton/06a0ba437d41a7c82c08a9636a4438c1b5031378?utm_source=direct_link Symbolic regression 1 https://www.semanticscholar.org/paper/SINDy-PI%3A-a-robust-algorithm-for-parallel-implicit-Kaheman-Kutz/4971f9abd024e40fbbdff2e9492745b68a6bca01?utm_source=direct_link Symbolic regression 0 https://www.semanticscholar.org/paper/Multidimensional-Approximation-of-Nonlinear-Systems-Gel%C3%9F-Klus/2b2aa13d4959073f61ad70555bc8c7da7d116196?utm_source=direct_link -Symbolic regression 1 https://www.semanticscholar.org/paper/Learning-Discrepancy-Models-From-Experimental-Data-Kaheman-Kaiser/73dd9c49f205280991826b2ea4b50344203916b4?utm_source=direct_link -Symbolic regression 1 https://www.semanticscholar.org/paper/Discovery-of-Physics-From-Data%3A-Universal-Laws-and-Silva-Higdon/35e2571c17246577e0bc1b9de57a314c3b60e220?utm_source=direct_link -Symbolic regression 1 https://www.semanticscholar.org/paper/Data-driven-discovery-of-partial-differential-Rudy-Brunton/0acd117521ef5aafb09fed02ab415523b330b058?utm_source=direct_link -Symbolic regression 1 https://www.semanticscholar.org/paper/Ensemble-SINDy%3A-Robust-sparse-model-discovery-in-Fasel-Kutz/883547fdbd88552328a6615ec620f96e39c57018?utm_source=direct_link -Symbolic regression 1 https://www.semanticscholar.org/paper/Learning-sparse-nonlinear-dynamics-via-optimization-Bertsimas-Gurnee/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8?utm_source=direct_link -Symbolic regression 1 https://www.semanticscholar.org/paper/A-Unified-Framework-for-Sparse-Relaxed-Regularized-Zheng-Askham/c0fc3882a9976f6a9cdc3a724bce184b786503da?utm_source=direct_link -Neural ODEs 1 https://www.semanticscholar.org/paper/Neural-Ordinary-Differential-Equations-Chen-Rubanova/449310e3538b08b43227d660227dfd2875c3c3c1?utm_source=direct_link -Neural ODEs 1 https://www.semanticscholar.org/paper/Dissecting-Neural-ODEs-Massaroli-Poli/b8db0d2a39ca356abe63a8eabbc5ed9c868f5907?utm_source=direct_link -Neural ODEs 1 https://www.semanticscholar.org/paper/Graph-Neural-Ordinary-Differential-Equations-Poli-Massaroli/8540780e6b9422f7a1264edb70f39d3ff79bb8c1?utm_source=direct_link -Neural ODEs 1 https://www.semanticscholar.org/paper/GRAND%3A-Graph-Neural-Diffusion-Chamberlain-Rowbottom/95eee51c1cb1771e96cd182f47c90a7877461530?utm_source=direct_link -Neural ODEs 1 https://www.semanticscholar.org/paper/Beltrami-Flow-and-Neural-Diffusion-on-Graphs-Chamberlain-Rowbottom/af84c6db6b5c41ca628867ff4a27566e9ca3c69e?utm_source=direct_link -Neural ODEs 1 https://www.semanticscholar.org/paper/Message-Passing-Neural-PDE-Solvers-Brandstetter-Worrall/be8d39424a9010bfc0805385cc91edee383c2e24?utm_source=direct_link -Neural ODEs 0 https://www.semanticscholar.org/paper/Graph-Coupled-Oscillator-Networks-Rusch-Chamberlain/a50a2a191c98dfe045ac2139495ee80ff1338e47?utm_source=direct_link -Neural ODEs 0 https://www.semanticscholar.org/paper/Continuous-PDE-Dynamics-Forecasting-with-Implicit-Yin-Kirchmeyer/d39ad86d4617e069d89b6d62c760c2ba268a2b85?utm_source=direct_link Physics-based GNNs 1 https://www.semanticscholar.org/paper/Learning-rigid-dynamics-with-face-interaction-graph-Allen-Rubanova/d6fdd8fc0c5fc052d040687e72638fb4297661cc?utm_source=direct_link Physics-based GNNs 1 https://www.semanticscholar.org/paper/Graph-network-simulators-can-learn-discontinuous%2C-Allen-Lopez-Guevara/979c112d5ed2f7653990a3591cdfccfad0dc27fd?utm_source=direct_link Physics-based GNNs 1 https://www.semanticscholar.org/paper/Learning-Mesh-Based-Simulation-with-Graph-Networks-Pfaff-Fortunato/9e20f6874feaaf7c9994f9875b1d9cab17a2fd59?utm_source=direct_link -Physics-based GNNs 1 https://www.semanticscholar.org/paper/Physical-Design-using-Differentiable-Learned-Allen-Lopez-Guevara/90cc86274f947b15ec3cc8c1dcfe1fc8db608e03?utm_source=direct_link -Physics-based GNNs 1 https://www.semanticscholar.org/paper/Constraint-based-graph-network-simulator-Rubanova-Sanchez-Gonzalez/e0ee02a573b3d83fec55ed5d7c80f1afa055a7b4?utm_source=direct_link -Physics-based GNNs 1 https://www.semanticscholar.org/paper/Learning-3D-Particle-based-Simulators-from-RGB-D-Whitney-Lopez-Guevara/4fd23f18cfb2105ccadda5a51fed13063d611fff?utm_source=direct_link -Physics-based GNNs 1 https://www.semanticscholar.org/paper/Interaction-Networks-for-Learning-about-Objects%2C-Battaglia-Pascanu/ae42c0cff384495683192b06bd985cdd7a54632a?utm_source=direct_link -Physics-based GNNs 1 https://www.semanticscholar.org/paper/Graph-networks-as-learnable-physics-engines-for-and-Sanchez-Gonzalez-Heess/43879cf527f4918955fd55128baa6745174d8555?utm_source=direct_link -Physics-based GNNs 1 https://www.semanticscholar.org/paper/Relational-inductive-biases%2C-deep-learning%2C-and-Battaglia-Hamrick/3a58efcc4558727cc5c131c44923635da4524f33?utm_source=direct_link -Physics-based GNNs 1 https://www.semanticscholar.org/paper/Learning-to-Simulate-Complex-Physics-with-Graph-Sanchez-Gonzalez-Godwin/c529f5b08675f787cdcc094ee495239592339f82?utm_source=direct_link -Physics-based GNNs 0 https://www.semanticscholar.org/paper/Discovering-Symbolic-Models-from-Deep-Learning-with-Cranmer-Sanchez-Gonzalez/643ac3ef063c77eb02a3d52637c11fe028bfae28?utm_source=direct_link -Physics-based GNNs 0 https://www.semanticscholar.org/paper/Rediscovering-orbital-mechanics-with-machine-Lemos-Jeffrey/2232751169e57a14723bfffb4ab26aa0e0e3839a?utm_source=direct_link -Latent space simulators 1 https://www.semanticscholar.org/paper/Molecular-latent-space-simulators-Sidky-Chen/2d3000d245988a02d3c1060211e9d89c67147b49?utm_source=direct_link -Latent space simulators 1 https://www.semanticscholar.org/paper/Extended-dynamic-mode-decomposition-with-dictionary-Li-Dietrich/80744010d90c8ede052c7ac6ba8c38c9de959c6e?utm_source=direct_link -Latent space simulators 1 https://www.semanticscholar.org/paper/Time-lagged-autoencoders%3A-Deep-learning-of-slow-for-Wehmeyer-No%C3%A9/d8d8e2c04ca47bd628bd2a499e03ad7cd29633da?utm_source=direct_link -Latent space simulators 1 https://www.semanticscholar.org/paper/VAMPnets-for-deep-learning-of-molecular-kinetics-Mardt-Pasquali/58912e2c2aaa77d1448d51e9d9460e06a5b924b9?utm_source=direct_link -Latent space simulators 1 https://www.semanticscholar.org/paper/Nonlinear-Discovery-of-Slow-Molecular-Modes-using-Chen-Sidky/2e7163e31e9b32cec11005678bae9e1dbeb6d573?utm_source=direct_link -Latent space simulators 1 https://www.semanticscholar.org/paper/Variational-Approach-for-Learning-Markov-Processes-Wu-No'e/b921efbb226fe2618ec160563a2bcb5999c7c28f?utm_source=direct_link -Parametrizing using ML 1 https://www.semanticscholar.org/paper/Deep-learning-prediction-of-patient-response-time-Lu-Bender/4e837965494c4edbec4d30832d31ba5639996da8?utm_source=direct_link -Parametrizing using ML 1 https://www.semanticscholar.org/paper/Coupled-Graph-ODE-for-Learning-Interacting-System-Huang-Sun/aaf2145f9998f304513c0c9b530ee9f7750c6f55?utm_source=direct_link -Parametrizing using ML 1 https://www.semanticscholar.org/paper/CellBox%3A-Interpretable-Machine-Learning-for-Biology-Yuan-Shen/6bd28606fbae3449f831248804264c9885e992f9?utm_source=direct_link -Parametrizing using ML 0 https://www.semanticscholar.org/paper/Efficient-Amortised-Bayesian-Inference-for-and-Roeder-Grant/309c5ae93a4cabfd37747abd130866240e265b2d?utm_source=direct_link -PINNs 1 https://www.semanticscholar.org/paper/Solving-real-world-optimization-tasks-using-neural-Seo/23c7b93a379c26c3738921282771e1a545538703?utm_source=direct_link -PINNs 1 https://www.semanticscholar.org/paper/Systems-biology-informed-neural-networks-(SBINN)-Przedborski-Smalley/68d54a4ef82873fd3a0e857ad2c136d65fa17db8?utm_source=direct_link -PINNs 1 https://www.semanticscholar.org/paper/Physics-informed-machine-learning-Karniadakis-Kevrekidis/53c9f3c34d8481adaf24df3b25581ccf1bc53f5c?utm_source=direct_link -PINNs 1 https://www.semanticscholar.org/paper/Physics-Informed-Deep-Learning-(Part-I)%3A-Solutions-Raissi-Perdikaris/fa352e8e4d9ec2f4b66965dd9cea75167950152a?utm_source=direct_link -PINNs 1 https://www.semanticscholar.org/paper/Physics-Informed-Deep-Learning-(Part-II)%3A-Discovery-Raissi-Perdikaris/25903eabbb1830aefa82048212e643eec660de0b?utm_source=direct_link -PINNs 1 https://www.semanticscholar.org/paper/Multistep-Neural-Networks-for-Data-driven-Discovery-Raissi-Perdikaris/a41fe2302296a9d1eabc382415d4049905fddb36?utm_source=direct_link -PINNs 1 https://www.semanticscholar.org/paper/Systems-biology-informed-deep-learning-for-and-Yazdani-Lu/33da5e93b3c9c02256c6a98f8a843ae62e27d436?utm_source=direct_link -PINNs 1 https://www.semanticscholar.org/paper/B-PINNs%3A-Bayesian-Physics-Informed-Neural-Networks-Yang-Meng/acc257947545c8daa968138e317e03edc90e79b0?utm_source=direct_link -Koopman operator 1 https://www.semanticscholar.org/paper/Hamiltonian-Systems-and-Transformation-in-Hilbert-Koopman/bf657b5049c1a5c839369d3948ffb4c0584cd1d2?utm_source=direct_link -Koopman operator 1 https://www.semanticscholar.org/paper/Applied-Koopmanism.-Budi%C5%A1i%C4%87-Mohr/2c9be1e38f978f43427ea5293b3138e0c4fede71?utm_source=direct_link -Koopman operator 1 https://www.semanticscholar.org/paper/Koopman-Invariant-Subspaces-and-Finite-Linear-of-Brunton-Brunton/a3c279828af3621d2c16ac26e5900b970383f60e?utm_source=direct_link -Koopman operator 1 https://www.semanticscholar.org/paper/Deep-learning-for-universal-linear-embeddings-of-Lusch-Kutz/6adeda1af8abc6bc3c17c0b39f635a845476cd9f?utm_source=direct_link -Koopman operator 1 https://www.semanticscholar.org/paper/Deep-learning-models-for-global-coordinate-that-Gin-Lusch/0ce6f9c3d9dccdc5f7567646be7a7d4c6415576b?utm_source=direct_link Koopman operator 1 https://www.semanticscholar.org/paper/From-Fourier-to-Koopman%3A-Spectral-Methods-for-Time-Lange-Brunton/11df7f23f72703ceefccc6367a6a18719850c53e?utm_source=direct_link Koopman operator 1 https://www.semanticscholar.org/paper/Modern-Koopman-Theory-for-Dynamical-Systems-Brunton-Budi%C5%A1i%C4%87/68b6ca45a588d538b36335b23f6969c960cf2e6e?utm_source=direct_link Koopman operator 1 https://www.semanticscholar.org/paper/Parsimony-as-the-ultimate-regularizer-for-machine-Kutz-Brunton/893768d957f8a46f0ba5bab11e5f2e2698ef1409?utm_source=direct_link diff --git a/requirements.txt b/requirements.txt index d8193782..1f1f712c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ astroid==3.0.0 Babel==2.14.0 beautifulsoup4==4.12.3 blinker==1.6.3 -certifi==2024.2.2 +certifi==2024.7.4 charset-normalizer==3.3.2 click==8.1.7 colorama==0.4.6 @@ -17,6 +17,8 @@ fonttools==4.49.0 ghp-import==2.1.0 griffe==0.36.5 idna==3.6 +importlib_metadata==8.1.0 +importlib_resources==6.4.0 iniconfig==2.0.0 isort==5.12.0 itsdangerous==2.1.2 @@ -67,3 +69,4 @@ tzdata==2024.1 urllib3==2.2.1 watchdog==4.0.0 Werkzeug==3.0.0 +zipp==3.19.2