no code implementations • 7 Dec 2023 • Weijian Zheng, Jun-Sang Park, Peter Kenesei, Ahsan Ali, Zhengchun Liu, Ian T. Foster, Nicholas Schwarz, Rajkumar Kettimuthu, Antonino Miceli, Hemant Sharma
These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots over time of the evolving microstructure and attributes.
1 code implementation • Mathematics 2022 • S. M. A. Sharif, Rizwan Ali Naqvi, Zahid Mehmood, Jamil Hussain, Ahsan Ali, Seung-Won Lee
The performance of the proposed methods has been densely evaluated and compared with the existing deblurring methods.
no code implementations • 4 May 2022 • Ahsan Ali, Syed Zawad, Paarijaat Aditya, Istemi Ekin Akkus, Ruichuan Chen, Feng Yan
In addition, by providing an end-to-end design, SMLT solves the intrinsic problems in serverless platforms such as the communication overhead, limited function execution duration, need for repeated initialization, and also provides explicit fault tolerance for ML training.
1 code implementation • 20 Apr 2022 • Ahsan Ali, Hemant Sharma, Rajkumar Kettimuthu, Peter Kenesei, Dennis Trujillo, Antonino Miceli, Ian Foster, Ryan Coffee, Jana Thayer, Zhengchun Liu
Extracting actionable information rapidly from data produced by instruments such as the Linac Coherent Light Source (LCLS-II) and Advanced Photon Source Upgrade (APS-U) is becoming ever more challenging due to high (up to TB/s) data rates.
2 code implementations • 28 May 2021 • Zhengchun Liu, Ahsan Ali, Peter Kenesei, Antonino Miceli, Hemant Sharma, Nicholas Schwarz, Dennis Trujillo, Hyunseung Yoo, Ryan Coffee, Naoufal Layad, Jana Thayer, Ryan Herbst, ChunHong Yoon, Ian Foster
Extremely high data rates at modern synchrotron and X-ray free-electron laser light source beamlines motivate the use of machine learning methods for data reduction, feature detection, and other purposes.
no code implementations • 1 Feb 2021 • Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, Feng Yan
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks.
no code implementations • 25 Jan 2020 • Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, Yue Cheng
To this end, we propose TiFL, a Tier-based Federated Learning System, which divides clients into tiers based on their training performance and selects clients from the same tier in each training round to mitigate the straggler problem caused by heterogeneity in resource and data quantity.