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@@ -15,7 +15,7 @@ Further Contributors: Mehdi Cherti (MC) (Helmholtz AI HLST, JSC)
     - Grant title: *"Large-Scale Advanced Deep Transfer Learning for Fast, Robust and Affordable COVID-19 X-Ray Diagnostics"*
       - Abstract: "X-Ray imaging based diagnostics offers an affordable, widely available and easily deployable alternative for screening of COVID-19 disease caused by the new coronavirus, the SARS-CoV-2. However, large-scale screening of substantial numbers of images under time pressure may cause human based errors and especially in remote locations without enough skilled personal and specialized physicians, both reliability of diagnostics and its rapid execution can be severely affected, corroborating the results. In this project, we aim on establishing advanced deep transfer learning approaches to enable robust and fast X-Ray based diagnostic tools independent of location and local resources. **Large-scale generic models pre-trained in HPC**, **quickly adaptable to local demands via transfer learning**,  can be deployed as **compact, robust, fast and low cost diagnostic tools**, assisting available or even replacing missing medical personal, thus allowing screening in yet unprecedented speed and quality."
     - grant period 01.11.2020 - 31.10.2021
-    - In the granting period we aim to lay grounds for different forms of advanced distributed deep transfer learning that is both high performant and efficient, offering low cost transfer to different domains and tasks, with focus of transfer on small size COVID-19 X-Ray lung images datasets after pre-training on publically available large scale natural image or medical image datasets. We will use supervised, unsupervised and NAS based transfer learning techniques to evaluate transfer quality.
+    - In the granting period we aim to lay grounds for different forms of advanced distributed deep transfer learning that is both high performant and efficient, offering low cost transfer to different domains and tasks, with **focus of transfer on small size COVID-19 X-Ray lung images datasets after pre-training on publically available large scale natural image or medical image datasets**. We will use supervised, unsupervised and NAS based transfer learning techniques to evaluate transfer quality.
   - on JSC's JUSUF machine (up to 61 nodes with 1x V100 GPU)
     - granted on 01.05.2020, until 31.01.2021 for initial project phase
     - [JUSUF Hardware Specs in detail](https://www.fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/JUSUF/Configuration/Configuration_node.html)