Repository: Helmholtz-AI-Energy/propulate (language: python)

Issue:
Update publication list in README



Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index b9cd846..091cc42 100644
--- a/README.md
+++ b/README.md
@@ -40,17 +40,27 @@ next generation using the fitness values of all candidates it evaluated and rece
 It was already successfully applied in several accepted scientific publications. Applications include grid load
 forecasting, remote sensing, and structural molecular biology:
 
->
+> J. Debus, C. Debus, G. Dissertori, et al. **PETNet–Coincident Particle Event Detection using Spiking Neural Networks**.
+> 2024 Neuro Inspired Computational Elements Conference (NICE), La Jolla, CA, USA, pp. 1-9 ( 2024).
+> https://doi.org/10.1109/NICE61972.2024.10549584
+
+> D. Coquelin, K. Flügel, M. Weiel, et al. **AB-Training: A Communication-Efficient Approach for Distributed Low-Rank
+> Learning**. arXiv preprint (2024). https://doi.org/10.48550/arXiv.2405.01067
+
+> D. Coquelin, K. Flügel, M. Weiel, et al. **Harnessing Orthogonality to Train Low-Rank Neural Networks**. arXiv
+> preprint (2024). https://doi.org/10.48550/arXiv.2401.08505
+
 > A. Weyrauch, T. Steens, O. Taubert, et al. **ReCycle: Fast and Efficient Long Time Series Forecasting with Residual
-> Cyclic Transformers**. arXiv preprint (2024). https://doi.org/10.48550/arXiv.2405.03429
+> Cyclic Transformers**. 2024 IEEE Conference on Artificial Intelligence (CAI), Singapore, pp. 1187-1194 (2024).
+> https://doi.org/10.1109/CAI59869.2024.00212
 
-> O. Taubert, F. von der Lehr, A. Bazarova, et al. **RNA contact prediction by data efficient deep learning**. Commun
-> Biol 6, 913 (2023). https://doi.org/10.1038/s42003-023-05244-9
+> O. Taubert, F. von der Lehr, A. Bazarova, et al. **RNA contact prediction by data efficient deep learning**.
+> Communications Biology 6(1), 913 (2023). https://doi.org/10.1038/s42003-023-05244-9
 
 > D. Coquelin, K. Flügel, M. Weiel, et al. **Harnessing Orthogonality to Train Low-Rank Neural Networks**. arXiv
 > preprint (2023). https://doi.org/10.48550/arXiv.2401.08505
 
-> Y. Funk, M. Götz, & H. Anzt. **Prediction of optimal solvers for sparse linear systems using deep learning**.
+> Y. Funk, M. Götz, and H. Anzt. **Prediction of optimal solvers for sparse linear systems using deep learning**.
 > Proceedings of the 2022 SIAM Conference on Parallel Processing for Scientific Computing (pp. 14-24). Society for
 > Industrial and Applied Mathematics (2022). https://doi.org/10.1137/1.9781611977141.2
 
diff --git a/coverage.svg b/coverage.svg
deleted file mode 100644
index 0d660de..0000000
--- a/coverage.svg
+++ /dev/null
@@ -1,1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="114" height="20" role="img" aria-label="coverage: 85.67%"><title>coverage: 85.67%</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="114" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="61" height="20" fill="#555"/><rect x="61" width="53" height="20" fill="#97ca00"/><rect width="114" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="315" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="510">coverage</text><text x="315" y="140" transform="scale(.1)" fill="#fff" textLength="510">coverage</text><text aria-hidden="true" x="865" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="430">85.67%</text><text x="865" y="140" transform="scale(.1)" fill="#fff" textLength="430">85.67%</text></g></svg>
\ No newline at end of file


Test patch:
diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml
index 4baee8b..b7df2d3 100644
--- a/.github/workflows/python-test.yml
+++ b/.github/workflows/python-test.yml
@@ -45,10 +45,15 @@ jobs:
               coverage run --rcfile=./pyproject.toml -m pytest
               mpirun -n 8 coverage run --rcfile=./pyproject.toml -m mpi4py -m pytest --with-mpi
               coverage combine
-              coverage report -m
+              coverage report -m --format markdown > cov_report.txt
               coverage xml
 
           - name: Upload coverage reports to Codecov
             uses: codecov/codecov-action@v4.0.1
             with:
               token: ${{ secrets.CODECOV_TOKEN }}
+
+          - name: Post coverage report to PR
+            uses: marocchino/sticky-pull-request-comment@v2
+            with:
+              path: cov_report.txt