Search Results for author: Rajkumar Kettimuthu

Found 9 papers, 9 papers with code

Masked Sinogram Model with Transformer for ill-Posed Computed Tomography Reconstruction: a Preliminary Study

1 code implementation3 Sep 2022 Zhengchun Liu, Rajkumar Kettimuthu, Ian Foster

Inspired by the success of transformer for natural language processing, the core idea of this preliminary study is to consider a projection of tomography as a word token, and the whole scan of the cross-section (A. K. A.

Computed Tomography (CT)

fairDMS: Rapid Model Training by Data and Model Reuse

1 code implementation20 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.

Information Retrieval Retrieval

BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes

3 code implementations22 Jun 2021 Zhengchun Liu, Rajkumar Kettimuthu, Michael E. Papka, Ian Foster

We describe how the task of rescaling suitable DNN training tasks to fit dynamically changing holes can be formulated as a deterministic mixed integer linear programming (MILP)-based resource allocation algorithm, and show that this MILP problem can be solved efficiently at run time.


Fast and accurate learned multiresolution dynamical downscaling for precipitation

1 code implementation18 Jan 2021 Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, Rao Kotamarthi

We compare the four new CNN-derived high-resolution precipitation results with precipitation generated from original high resolution simulations, a bilinear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique.


SeQUeNCe: A Customizable Discrete-Event Simulator of Quantum Networks

1 code implementation25 Sep 2020 Xiaoliang Wu, Alexander Kolar, Joaquin Chung, Dong Jin, Tian Zhong, Rajkumar Kettimuthu, Martin Suchara

We implement a comprehensive suite of network protocols and demonstrate the use of SeQUeNCe by simulating a photonic quantum network with nine routers equipped with quantum memories.

Quantum Physics

BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning

2 code implementations18 Aug 2020 Zhengchun Liu, Hemant Sharma, Jun-Sang Park, Peter Kenesei, Antonino Miceli, Jonathan Almer, Rajkumar Kettimuthu, Ian Foster

When applied to a real experimental dataset, a 3D reconstruction that used peak positions computed by BraggNN yields 15% better results on average as compared to a reconstruction obtained using peak positions determined using conventional 2D pseudo-Voigt fitting.

3D Reconstruction

Scientific Image Restoration Anywhere

2 code implementations12 Nov 2019 Vibhatha Abeykoon, Zhengchun Liu, Rajkumar Kettimuthu, Geoffrey Fox, Ian Foster

We explore this question by evaluating the performance and accuracy of a scientific image restoration model, for which both model input and output are images, on edge computing devices.

Edge-computing Image Denoising +2

Deep Learning Accelerated Light Source Experiments

2 code implementations9 Oct 2019 Zhengchun Liu, Tekin Bicer, Rajkumar Kettimuthu, Ian Foster

Experimental protocols at synchrotron light sources typically process and validate data only after an experiment has completed, which can lead to undetected errors and cannot enable online steering.

TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks

3 code implementations20 Feb 2019 Zhengchun Liu, Tekin Bicer, Rajkumar Kettimuthu, Doga Gursoy, Francesco De Carlo, Ian Foster

We present \TOMOGAN{}, a denoising technique based on generative adversarial networks, for improving the quality of reconstructed images for low-dose imaging conditions.


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