The Beholder system is a software client / server system that detects intrusion by monitoring the real-world execution time of critical kernel-level operations. Beholder was designed for use with critical infrastructure systems, especially in the power grid.
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Hyperion is a software system for static analysis of compiled software, enabling the detection of undesirable behavior or the demonstration of correct behavior.
An effective tool for managing and sharing documents and data is needed to effectively support spent fuel activities.
ORNL is developing quantum information tools to help secure the electric grid. Researchers are working to extend the range and reduce the cost of quantum key distribution.
This project will improve our understanding of the dynamic interactions between social and physical networks that make up cities. It uses household level energy consumption and infrastructure records, neighborhood level communication records, digital trace data, and a range of remotely sensed data to quantify relationships between urban infrastructure systems and social structures within that urban environment. This empirical research is necessary to support the next generation of models exploring climate change impacts and resource supply security of cities now and in the future.
PISCEES is a SciDAC Earth System Modeling project with the following goals: (1) To develop and apply robust, accurate, and scalable dynamical cores for ice sheet modeling on structured and unstructured meshes with adaptive refinement, (2) To evaluate ice sheet models using new tools and data sets for verification and validation (V&V) and uncertainty quantification (UQ), (3) to integrate these models and tools into DOE's Accelerated Climate Model for Energy (ACME). Using improved estimates of ice sheet initial conditions, we will simulate decade-to-century-scale evolution of the Greenland and Antarctic ice sheets, running PISCEES ice sheet models both in standalone mode and coupled to ACME. We aim to provide useful, credible predictions, including uncertainty ranges, of future ice-sheet mass loss and resulting changes in climate and sea level.
PISCEES is jointly funded by the Office of Biological and Environmental Research (BER) and the Office of Advanced Scientific Computing Research (ASCR) of the DOE Office of Science.
Principle Investigator: Steve Price - LANL and Esmond Ng – LBNL, Kate Evans - ORNL site PI
PISCEES is jointly funded by the Office of Biological and Environmental Research (BER) and the Office of Advanced Scientific Computing Research (ASCR) of the DOE Office of Science.
Principle Investigator: Steve Price - LANL and Esmond Ng – LBNL, Kate Evans - ORNL site PI
The Accelerated Climate Modeling for Energy (ACME) project is a newly launched project sponsored by the Earth System Modeling (ESM) program within U.S. Department of Energy's (DOE’s) Office of Biological and Environmental Research. ACME is an unprecedented collaboration among eight national laboratories and six partner institutions to develop and apply the most complete, leading-edge climate and Earth system models to challenging and demanding climate-change research imperatives. It is the only major national modeling project designed to address DOE mission needs to efficiently utilize DOE leadership computing resources now and in the future. While the project capabilities will address the critical science questions, its modeling system and related capabilities also can be flexibly applied by the DOE research community to address mission-specific climate change applications from U.S. Energy Sector Vulnerabilities to Climate Change and Extreme Weather.
Quantum computing promises a platform for efficiently solving certain types problems thought to be intractable for traditional computers. The number of qubits needed to be competitive with classical computers varies dramatically depending on the problem. This project seeks to determine the maximum quantum operation rate for a given cooling capacity.
Developing a ground-based, quantum-secured, authenticated time distribution system for the energy grid.
The Oak Ridge National Laboratory's Computational Data Analytics Group's has worked over 12 years in creating text analytics systems to quickly discover meaningful information from raw data. These capabilities focus on six key areas, emphasizing high performance over very large sets of raw documents.
Collecting and Extracting: Collecting millions of documents from databases, Internet, Social Media, and hard drives; extracting text from hundreds of file formats; and translating this information into multiple languages.
Storing and Indexing: Storing and indexing millions of documents in search servers, distributed file systems (MapReduce), relational databases, and file systems.
Recommending: Filtering the full content of millions of documents to recommend the most valuable and relevant information based on a user’s own information, or user selections, or a user’s interactions with information.
Categorize: Grouping items based on the full content of documents using supervised and semi-supervised machine learning methods and targeted search lists.
Clustering: Creating a hierarchical group of documents based on similarity using unsupervised learning methods on the full content of each document.
Visualizing: Showing hierarchies, groups, and relationships among documents that helps the user quickly understand their value, and to see new connections.
This work has resulted in eight issued ( 7,072,883 7,315,858 7,693,903 7,805,446 7,937,389 8,473,314 8,825,710 9,256,649) and one pending patents , several commercial licenses (including Pro2Serve and TextOre), a spin off company (Global Security Information Analysts LLC (GSIA)), an R&D 100 Awards, and scores of peer reviewed research publications.
Collecting and Extracting: Collecting millions of documents from databases, Internet, Social Media, and hard drives; extracting text from hundreds of file formats; and translating this information into multiple languages.
Storing and Indexing: Storing and indexing millions of documents in search servers, distributed file systems (MapReduce), relational databases, and file systems.
Recommending: Filtering the full content of millions of documents to recommend the most valuable and relevant information based on a user’s own information, or user selections, or a user’s interactions with information.
Categorize: Grouping items based on the full content of documents using supervised and semi-supervised machine learning methods and targeted search lists.
Clustering: Creating a hierarchical group of documents based on similarity using unsupervised learning methods on the full content of each document.
Visualizing: Showing hierarchies, groups, and relationships among documents that helps the user quickly understand their value, and to see new connections.
This work has resulted in eight issued ( 7,072,883 7,315,858 7,693,903 7,805,446 7,937,389 8,473,314 8,825,710 9,256,649) and one pending patents , several commercial licenses (including Pro2Serve and TextOre), a spin off company (Global Security Information Analysts LLC (GSIA)), an R&D 100 Awards, and scores of peer reviewed research publications.