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The above figure graphs sensitivity to different bit-flip rates. The green line represents a full 100 epoch training without bit-flips injected. CSMD ORNL Computer Science and Mathematics

Understanding Soft Error Sensitivity of Deep Learning Models and Frameworks through Checkpoint Alteration

October 1, 2021

The above figure illustrates a timing path change in a PTP network when a switch fails. CSMD ORNL Computer Science and Mathematics

Recent Advances in Precision Clock Synchronization Protocols for Power Grid Control Systems

October 1, 2021

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CSAT Chemical Security Portal Version 5.1.00 Released

October 1, 2021

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CSAT Portal Version 2.1.12 Released September 2021

October 1, 2021

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ORNL CSAT First External System to Fully Connect to DHS VENOM, Enhancing System Security

October 1, 2021

The Cheetah-Savanna workflow suite. Cheetah is the user interface to compose campaigns using Python. Savanna is the execution engine to run a campaign on a target HPC machine. CSMD ORNL Computer Science and Mathematics Oak Ridge

A Co-design Framework for Online Data Analysis and Reduction

October 1, 2021

The radar plot shows relative, qualitative improvements to the iFR-LOOP workflow along the six workflow properties. CSMD ORNL Computer Science and Mathematics

Reusability First: Toward FAIR Workflows

October 1, 2021

Application request flow through protected PCS and vWAF configurations CSMD ORNL Computer Science and Mathematics Oak Ridge National Lab

Protecting Websites from Cross-Site Scripting (XSS) Attacks: A Novel Configuration using Pulse Secure© Pulse Connect Secure© and Virtual Web Application Firewall (vWAF)

October 1, 2021

Comparing default (red) and optimized (blue) methods for in transit visualization with Sensei. The optimized approach was made possible by innovations in distributed metadata handling. Image Credit: B. Loring CSMD ORNL Computer Science and Mathematics Divison

Improving In Transit and In Situ Analysis and Visualization

October 1, 2021

Progressive data retrieval workflow: Given a requested tolerance 𝜏0, the retriever fetches data to form a low-resolution/precision representation; later, as the requested tolerance changes to 𝜏0, the retriever fetches additional data and add it with the existing data to form a high-resolution/precision representation

Error-controlled Progressive Retrieval of Scientific Data

October 1, 2021

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