LLNL’s Distributed Implicit Neural Representation (DINR) is a novel approach to 4D time-space reconstruction of dynamic objects. DINR is the first technology to enable 4D imaging of dynamic objects at sufficiently high spatial and temporal resolutions that are necessary for real world medical and industrial applications.
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CSP-POST provides the capability to inspect all incoming and outgoing emails while providing after-the-fact forensic capabilities. Using commercially available lightweight and serverless technologies, CSP-POST easily collects all email and parses it into easily searchable metadata, enriched and ready for analysis. The web-based application is deployed in a repeatable, testable, and auditable…
LLNL has invented a new system that uses public key cryptography to differentiate between human-generated text and AI-generated text. This invention can be used to validate that text is likely to be human generated for the purposes of sorting or gatekeeping on the internet, can detect cheating on essay assignments, and can be used as an automatic captcha that does away with the hassle of…
The approach is to use peroxides to modify the reaction kinetics in the production of polysiloxanes. A radical initiator in the presence of a hydride-terminated polysiloxane will increase the rate of curing and reduce manufacturing costs. At a minimum a formulation would contain a hydride-terminated polysiloxane, a platinum catalyst, and an initiator that generates radicals. The content of…
The LiDO code combines finite element analysis, design sensitivity analysis and nonlinear programming in a High-Performance Computing (HPC) environment that enables the solution of large-scale structural optimization problems in a computationally efficient manner. Currently, the code uses topology optimization strategies in which a given material is optimally distributed throughout the domain…
LLNL has developed a new method for securely processing protected data on HPC systems with minimal impact on the existing HPC operations and execution environment. It can be used with no alterations to traditional HPC operations and can be managed locally. It is fully compatible with traditional (unencrypted) processing and can run other jobs, unencrypted or not, on the cluster simultaneously…
LLNL has developed a new active memory data reorganization engine. In the simplest case, data can be reorganized within the memory system to present a new view of the data. The new view may be a subset or a rearrangement of the original data. As an example, an array of structures might be more efficiently accessed by a CPU as a structure of arrays. Active memory can assemble an alternative…
LLNL's NeMS system enables network mapping operations by using two LLNL-developed software systems: LLNL's NeMS tool and the Everest visualization system. Each software system can be also used separately for their specific applications. When the two systems are used together as an iterative analysis platform, LLNL's NeMS system provides network security managers and information technology…