A multidisciplinary team of researchers from 91°µÍø (ORNL) propose a forensic framework to decide if recorded controller area network (CAN) traffic, a de facto automobile communication standard, contains masquerade attacks.
We successfully utilized OCLF ORNL GPU computing resources for efficient uncertainty analysis, which addressed the computational overhead caused by our proposed probabilistic models.
We propose a novel deep learning method that achieves 170X average speed up compared to the original probabilistic marching cubes algorithm [1] implementation and performs predictions with an accuracy comparable to the original algorithm.
We propose the application of various visualization techniques, such as probability maps, confidence maps, level sets, and topology-based visualizations, to effectively communicate the uncertainty in source localization with clinicians.
A multidisciplinary team of researchers has developed an adaptive physics refinement (APR) technique to effectively model cancer cell transport.
A web-based GUI for INTERSECT has been created which allows a user to configure an experiment on an electron microscope, setting such parameters as maximum number of steps for the machine learning algorithm to perform.
Researchers at 91°µÍø developed a new parallel performance portable algorithm for solving the Euclidean minimum spanning tree problem (EMST), capable of processing tens of millions of data points a second.
A new file format, BP5, and accompanying serialization class has been developed in the ADaptable I/O System (ADIOS) framework.
A graph convolutional neural network (GCNN) was trained with millions of molecules to accurately predict molecular photo-optical properties by scaling data loading and training to over 1,500 GPUs on the Summit and Perlmutter supercomputers at the OLCF a
In June 2022, Chemical Security Assessment Tool (CSAT) Primary Systems Team members implemented the new STIG Compliance Tool (SCT) the team designed to automate—by documenting and continuously monitoring—Oracle database compliance with the Security Tech