Summer 2020 Student Survey Highlights [Slides]
Abstract not provided.
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Physical Sciences Vistas (Issue 3 2020)
In this issue, highlights of our outstanding R&D supporting the Laboratory’s nuclear security mission include the following. 1. The essential nuclear materials science contributions of MST’s Materials Properties Team to a range of Lab missions, operations, and initiatives. 2. Work in the Sigma Complex supporting NNSA’s Advanced Manufacturing Development milestones by members of Sigma Division and colleagues across the Laboratory and other NNSA sites. 3. Greg Dale’s role in the Lab’s molybdenum program and today as a technical lead on the Scorpius project. 4. Measurements that reveal the role thermal interfaces play in dynamic compression experiments. 5. The first-ever synthesis of an actinide framework, which offers opportunities for understanding these structures as potential radioactive waste forms and provides new models of actinide species transport in the environment. This issue also showcases, as part of our commitment to simultaneous excellence in mission operations and community relations, the successful high-hazard repair of the LANSCE accelerator, which resulted in a large team Laboratory Distinguished Performance Award, and a virtual Summer Physics Camp for Young Women that brought together participants from around the world, even as COVID kept them socially distant in their homes.
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"Mister Los Alamos": The Life, The Times and The Laboratory of Norris E. Bradbury [Slides]
A presentation discussing Norris Bradbury and his time as the director of the Laboratory. The talk is a historical presentation by Alan Carr.
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Operational and Mission Highlights A Monthly Summary of Top Achievements (Nov 2020)
This publication highlights achievements in nuclear security, science, technology and engineering, mission operations, and community relations at Los Alamos National Laboratory.
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DOE Office of Scientific and Technical Information (OSTI) Artificial Intelligence and Machine Learning
The Department of Energy (DOE) Office of Scientific and Technical Information (OSTI) established its artificial intelligence (AI) team in the summer of 2019. The AI Team's work and research in this space are new endeavors for OSTI; identifying the appropriate areas of research and investigation are priorities for the team and will ensure results and products that support OSTI and the collection, preservation, and dissemination of R&D results. To support OSTI’s strategic plan, the AI Team has started an assessment of the current R&D results corpus (e.g., metadata and full text) collected through ingest products such as E-Link and DOE CODE and disseminated through OSTI.GOV and other discovery applications. This presentation will present applied AI and Machine Learning (ML) approaches to assess and address data challenges and discuss how these data challenges are being evaluated to establish a comprehensive corpus of R&D results, support the reuse of R&D results and its data, and extend these findings to the broader DOE community. This presentation can be presented live or via a recorded presentation.
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Titan on the Red (advanced AI / ML system) [Slides]
Born out of a need to make LANL Weapons Program archival material available to scientists and engineers. Material is the end result of decades of consolidation of mini-libraries and mini archives at LANL. Latest consolidation brought together LANL’s digital archives and physical archives. This houses over 75 years of nuclear weapons research, designs, procedures, videos, photos, and other reports. For the Titan on the Red machine learning project, the system must be able to automatically extract metadata from digitized documents, perform natural-language search, enforce security classification and NTK protocols, be expandable to ingest various data stores (Online Vault, PDMLink, shared drives, SharePoint, etc.), and utilize commercial, public domain, and LANL ontologies.
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Design & Brand Guidelines (V.1.03)
The goal is to revitalize the Lab’s identity so that it reflects a focused and forward-looking approach to solving national security challenges. This goal is achieved through design that is dynamic, accessible, and optimized for 21st century communications.
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Wood-to-Fuel for California's Transportation Sector using Autothermal Pyrolysis: Kickoff Questionnaire for Benefits Analysis of CEC Projects
This is the first of three questionnaires that you will be asked to complete over the course of the agreement. Please type your responses directly into this document in the spaces below each question. Use as much space as you need; the form automatically creates more space within each response field. In addition, you may submit additional documents, spreadsheets, or other files. Acceptable filetypes are MS Word, MS Excel, and PDF. Additional files do not need to follow any particular format, unless specified by the particular questionnaire designed for your project.
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Tribal Colleges and Universities/Advanced Manufacturing Network Initiative (Phase II) (Q1 FY2021 Progress Report)
The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) to 1) align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.
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Unique Building Identifier (UBID): Public Sector Implementation Guide
Buildings generate data throughout their lifecycle – about ownership & taxation, usage, zoning, code compliance, energy use, and retrofits. State and local governments collect this data after it flows through growing networks of people and systems. But collecting data is only half the battle; what’s really needed is information – the actionable insights that lead to successful policy outcomes.
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The archives of the future
The term “artificial intelligence” (AI) - essentially programming machines to think like humans - conjures up different emotions in different people. Some view it with fear, imagining a malevolent AI similar to Skynet in the Terminator movies. Others see something benevolent, such as the character Data in Star Trek: The Next Generation . Still others see it as a means to advance the frontiers of science, engineering and technology to new levels not possible through traditional means. At the National Security Research Center (NSRC or the Center), which is the classified library at Los Alamos National Laboratory, we see AI as a tool to help us go through the monumental tasks we have in digitizing, cataloging, and searching our collections.
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Lessons Learned-Building 1090 Laboratory Construction Modifications
The purpose of this document is to disseminate lessons learned from the Sandia National Laboratories (SNL) Building 1090 modification project. The following sections summarize lessons learned at various phases of the project.
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Finding of Effect and Mitigation Documentation for the Power and Communications System in Mercury, Area 23, Nevada National Security Site, Nye County, Nevada
The U.S. Department of Energy, National Nuclear Security Administration Nevada Field Office (NNSA/NFO) proposes to make multiple upgrades and additions to the power and communications (P&C) system related to modernization activities in the town of Mercury at the Nevada National Security Site (NNSS) in Nye County, Nevada. The purpose of this letter report is to submit documentation related to the finding of effect and mitigation for the undertaking of modifications to and mitigation of the P&C system in Mercury. This submission is intended to comply with the stipulations in the Programmatic Agreement between the National Nuclear Security Administration Nevada Field Office and the Nevada State Historic Preservation Officer Regarding Modernization and Operational Maintenance of the Nevada National Security Site, at Mercury in Nye County, Nevada, hereafter referred to as the Mercury PA. The town of Mercury and the immediate surrounding area have been designated the Mercury Historic District (MHD; SHPO Resource #D230), which is a property eligible to the National Register of Historic Places under Criteria A and C for its importance in supporting nuclear testing and scientific research from 1951 through 1992. During the initial architectural survey (Reno et al. 2018), the P&C system was identified as contributing to the eligibility of the district.
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A library of AI-assisted FAIR water cycle and related disturbance datasets to enable model training, parameterization and validation
This whitepaper is responsive to focal area Data acquisition and assimilation enabled by machine learning, AI, and advanced methods. Here we describe how FAIR (Findable, Accessible, Reusable, Interoperable) datasets related to water cycle extremes are essential for successful implementation of ML in Earth System and other models. We also describe how AI can be used to acquire and integrate water cycle data related to extreme events to create a library of FAIR datasets for training and evaluating algorithms.
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A new era of observationally-infused E3SM: GANs for unifying imagery archives
This paper presents an idea to develop a “Rosetta Stone” for unifying observations from various satellite or remote sensors into a common format that would vastly advance our ability to exploit existing datasets for improving predictability within Earth System Models (ESMs). While the applications of such a unified archive are broad, we believe it will be a critical step toward ushering in a new generation of ESMs that are richly informed, guided by, and validated by extensive observational data. With the vast quantity of both remotely-sensed and in-situ data streams available and coming online, new approaches are needed that can harmonize and thus fully exploit these expensive datasets. While we present the broader idea, we point to examples of applications that impact the water cycle and its representation in ESMs.
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A Quantum-AI Framework for Extreme Weather Prediction
The frequency and intensity of extreme weather events in North America will likely increase with a changing climate. High resolution simulations are necessary to advance process-based understanding of weather events at local scales accompanying future changes in global atmospheric circulation.
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On AI Prediction of Hydrological Processes Based on Integration of Retrospective and Forecasting ML Techniques
Focus areas: (1) Data assimilation enabled by machine learning, unsupervised learning (including deep learning), and (2) Predictive modeling through the use of AI techniques and AI-derived model components, comprising a hierarchy of models.
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Quarterly Postdoc Program Data [Slides]
Abstract not provided.
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