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@@ -11,15 +11,15 @@ Further Contributors: Mehdi Cherti (MC) (Helmholtz AI HLST, JSC)
 
 * [Overview - Description of available models, codes and datasets](https://gitlab.version.fz-juelich.de/MLDL_FZJ/juhaicu/jsc_public/sharedspace/playground/covid_xray_deeplearning/wiki/-/blob/master/Description.md)
 * For model training and further experiments, **computing budget is available**,
-  - on JSC's JUSUF machine (up to 61 nodes with 1x V100 GPU)
-  - [JUSUF Hardware Specs in detail](https://www.fz-juelich.de/ias/jsc/EN/Expertise/Supercomputers/JUSUF/Configuration/Configuration_node.html)
-  - Budget limited until 31.10.2020 for initial project phase
-  - Computational time project will be continued after successful initial phase
-  - For collaboration and access to computing resources, please contact Jenia Jitsev (j.jitsev@fz-juelich.de), Mehdi Cherti (m.cherti@fz-juelich.de), or Alex Strube (a.strube@fz-juelich.de)
   - **UPDATE**: 30.10.2020 - COVIDNetX computational time application granted for JUWELS Booster (ca. **3600 GPUs** !)
     - 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.
+  - 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)
+  - For collaboration and access to computing resources, please contact Jenia Jitsev (j.jitsev@fz-juelich.de), Mehdi Cherti (m.cherti@fz-juelich.de), or Alex Strube (a.strube@fz-juelich.de)
   - Collaborating partners will also gain access to common code and dataset repository
 * Initiative's aims are:
   - short-term: provide **strong baseline** for pre-training and **transfer learning** for COVID X-Ray diagnostics using large-scale datasets of images from different domains (using both generic datasets like ImageNet and medical imaging datasets like COVIDx - see [Description of available models, codes and datasets](https://gitlab.version.fz-juelich.de/MLDL_FZJ/juhaicu/jsc_public/sharedspace/playground/covid_xray_deeplearning/wiki/-/blob/master/Description.md))