Portfolio News and Webcast

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HPC4Energy Innovation initiative launches new call for proposals

The Department of Energy launched the spring 2022 solicitation for the High Performance Computing for Energy Innovation (HPC4EI) initiative, seeking proposals from industry that address key energy and decarbonization-related challenges in domestic manufacturing.

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LLNL Project Targets More Energy-Efficient Steel Production

A Lawrence Livermore National Laboratory-led collaboration targeted at using machine learning to reduce defects and carbon emissions in steelmaking.

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LLNL AI Innovation Incubator to Advance Artificial Intelligence

Lawrence Livermore National Laboratory has established the AI Innovation Incubator to unite artificial intelligence experts from LLNL, industry and academia for the advancement of AI for large-scale scientific and commercial applications.

Machine Learning Methods Technologies

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Natural Language Processing (NLP) is a field of study which aims to program computers to process and analyze large amount of natural language data. In order to accurately and effectively utilize datasets in NLP systems, labeled datasets are a must. In cases like pathology reports, the sub-parts of the report are not programmatically labeled. To solve the unlabeled dataset problem, LLNL…

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Conventional dimension reduction methods aim to maximally preserve the global and/or local geometric structure of a dataset. However, in practice one is often more interested in determining how one or multiple user-selected response function(s) can be explained by the data. To intuitively connect the responses to the data, LLNL scientists developed function preserving projections (FPP), a…

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Clinical images have a wealth of data that are currently untapped by physicians and machine learning (ML) methods alike. Most ML methods require more data than is available to sufficiently train them. In order to obtain all data contained in a clinical image, it is imperative to be able to utilize multimodal, or various types of, data such as tags or identifications, especially where spatial…

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Some COVID-19 diagnoses are utilizing computed tomography (CT)-scans for triage. CT-scans produce immediate results with high sensitivity. The digital images produced by a CT-scan require physicians to identify objects within the image to determine the presence of disease. Object identification can be done using machine learning (ML) techniques such as deep learning (DL) to improve speed and…

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Drug discovery could be significantly sped up by the integration of in silico methods. To this end, LLNL scientists along with other ATOM Consortium members created the ATOM Modeling PipeLine (AMPL). AMPL is an open-source, modular, extensible, end-to-end software pipeline for building and sharing models. It extends the functionality of DeepChem and supports an array of machine learning and…

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When analyzing a dataset, one must not only understand the relationship between the data points, but also the underlying structure of the set. The underlying structure of a dataset is generally estimated from the data on hand, leading to assumptions and less accurate predictions. In order to improve structure learning, LLNL scientists have developed an open source software suite called MTL.…

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MimicGAN represents a new generation of methods that can “self-correct” for unseen corruptions in the data out in the field. This is particularly useful for systems that need to be deployed autonomously without needing constant intervention such as Automated Driver Assistance Systems. MimicGAN achieves this by treating every test sample as “corrupt” by default. The goal is to determine (a) the…

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LLNL has developed a new system, called the Segmentation Ensembles System, that provides a simple and general way to fuse high-level and low-level information and leads to a substantial increase in overall performance of digital image analysis. LLNL researchers have demonstrated the effectiveness of the approach on applications ranging from automatic threat detection for airport security, to…