MoDern-Cloud is a reliable, widely-available, understandable, and ultra-fast reconstruction technique for highly accelerated NMR. This work develops the first cloud-based artificial intelligence platform for multi-dimensional NMR data processing, and is the proof-of-concept demonstration of the effectiveness of merging optimization, deep learning, and cloud computing.
The preprint paper can be seen in https://arxiv.org/abs/2012.14830.
Email: Xiaobo Qu (quxiaobo@xmu.edu.cn) CC: Zi Wang (wangzi1023@stu.xmu.edu.cn)
Homepage: http://csrc.xmu.edu.cn
MoDern-Cloud is an easy-to-use cloud computing platform for processing of non-uniformly sampled (NUS) multi-dimensional NMR spectra. Up to now, MoDern-Cloud uses model-inspired deep learning (MoDern) to fast recover high-quality multi-dimensional spectra from NUS data. The platform also provides a customized retrospectively undersampling technique (NUS simulator) to produce NUS data and the corresponding NUS mask from the fully sampled NMR data.
Now, MoDern-Cloud is available at: http://36.134.147.88:2345/, and we will continue to improve the using feeling.
You can access it using following test accounts:
Account-1: CSG-001 Password-1: CSG@MYTEST_001
Account-2: CSG-002 Password-2: MYTEST@CSG_002
Account-3: CSG-003 Password-3: Modern-Cloud__003
Details on the instructions of MoDern-Cloud are described in its Manual, you can download "Manual_MoDern-Cloud.pdf" here or on our cloud platform.
We also have provided some demo data and scripts on the cloud for the quick try, you can download "Demo_data_scripts_MoDern-Cloud.zip" here or on our cloud platform.
Hope you enjoy the reliable, efficient, and high-performance experience. If you find any questions in using MoDern-Cloud, please email me at wangzi1023@stu.xmu.edu.cn.
The synthetic training datasets used in MoDern are shared at: https://drive.google.com/file/d/1bZAP-ittB94wm0hB3SfVsvqXGwk05tkg/view?usp=sharing.
After requesting the access, please email me at wangzi1023@stu.xmu.edu.cn.
If you want to use the platform and training datasets, please cite the following paper:
Zi Wang et al., Accelerated NMR spectroscopy: Merge Optimization with Deep Learning, arXiv preprint, arXiv:2012.14830, 2020.