auto update @ 2023-12-12T03:53:02Z Asia/Shanghai

README.md

@@ -141,7 +141,7 @@ Papers of federated learning in Nature(and its sub-journals), Cell, Science(and
 
 |Title                                                           |    Affiliation    |    Venue                    |    Year    |    Materials|
 | ------------------------------------------------------------ | ----------- | --------------------- | ---- | ------------------------------------------------------------ |
-|Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning | Tsinghua University | Nat. Commun. | 2023 | [PUB](https://www.nature.com/articles/s41467-023-43883-y) |
+| Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning | Tsinghua University | Nat. Commun. | 2023 | [[PUB](https://www.nature.com/articles/s41467-023-43883-y)] |
 | Advocating for neurodata privacy and neurotechnology regulation | Columbia University | Nat. Protoc. (Perspective) | 2023 | [[PUB](https://www.nature.com/articles/s41596-023-00873-0)] |
 | Federated benchmarking of medical artificial intelligence with MedPerf | IHU Strasbourg; University of Strasbourg; Dana-Farber Cancer Institute; Weill Cornell Medicine; Harvard T.H. Chan School of Public Health; MIT; Intel | Nat. Mach. Intell. | 2023 | [[PUB](https://www.nature.com/articles/s42256-023-00652-2)] [[PDF](https://arxiv.org/abs/2110.01406)] [[CODE](https://github.com/mlcommons/MedPerf)] |
 | Algorithmic fairness in artificial intelligence for medicine and healthcare | Harvard Medical School; Broad Institute of Harvard and Massachusetts Institute of Technology; Dana-Farber Cancer Institute | Nat. Biomed. Eng. (Perspective) | 2023 | [[PUB](https://www.nature.com/articles/s41551-023-01056-8)] [[PDF](https://arxiv.org/abs/2110.00603)] |