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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Nonlinear model of infection wavy oscillation of COVID-19 in Japan based on diffusion kinetics
The infectious propagation of SARS-CoV-2 is continuing worldwide, and specifically, Japan is facing severe circumstances. Medical resource maintenance and action limitations remain the central measures. An analysis of long-term follow-up reports in Japan shows that the infection number follows a unique wavy oscillation, increasing and decreasing over time. However, only a few studies explain the infection wavy oscillation. This study introduces a novel nonlinear mathematical model of the new infection wavy oscillation by applying the macromolecule diffusion theory. In this model, the diffusion coefficient that depends on population density gives nonlinearity in infection propagation. As a result, our model accurately simulated infection wavy oscillations, and the infection wavy oscillation frequency and amplitude were closely linked with the recovery rate of infected individuals. In conclusion, our model provides a novel nonlinear contact infection analysis framework.
Understanding HPC Application I/O Behavior Using System Level Statistics
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fASLR: Function-Based ASLR via TrustZone-M and MPU for Resource-Constrained IoT Systems
Not provided.
sputniPIC: an Implicit Particle-in-Cell Code for Multi-GPU Systems
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Mesoscale Gravity Wave Momentum Fluxes from Kilometer-Scale ECMWF Experimental Nature Run
This dataset consists of the ultra high-resolution (km-scale) global gravity wave momentum fluxes, computed from the ECMWF IFS Experimental Nature Run at 1-km (XNR1K) simulation data, using Helmholtz decomposition. The raw model output from the ECMWF XNR1K data is composed of simulations corresponding to Nov 2018 - Feb 2019.
Digital vibration control techniques
Analog vibration control techniques are reviewed and are compared with digital techniques. The advantages of the digital methods over the analog methods are demonstrated. The following topics are covered: (1) methods of computer-controlled random vibration and reverberation acoustic testing; (2) methods of computer-controlled sinewave vibration testing; and (3) methods of computer-controlled shock testing. Basic concepts are stressed rather than specific techniques or equipment. General algorithms are described in the form of block diagrams and flow diagrams. Specific problems and potential problems are discussed. The material is computer sciences oriented but is kept at a level that facilitates an understanding of the basic concepts of computer-controlled induced environmental test systems.
National Aeronautics and Space Administration fundamental research program. Information utilization and evaluation, appendices
Important points presented and recommendations made at an information and decision processes workshop held in Asilomar, California; at a data and information performance workshop held in Houston, Texas; and at a data base use and management workshop held near San Jose, California are summarized. Issues raised at a special session of the Soil Conservation Society of America's remote sensing for resource management conference in Kansas City, Missouri are also highlighted. The goals, status and activities of the NASA program definition study of basic research requirements, the necessity of making the computer science community aware of user needs with respect to information related to renewable resources, performance parameters and criteria for judging federal information systems, and the requirements and characteristics of scientific data bases are among the topics reported.
Characterizations of electrical properties of highT(sub c) superconducting materials
The automated data acquisition system developed in the Space Science Laboratory at Marshall Space Flight Center for measuring electrical properties of high temperature superconductors is described. The acquisition system, consisting of a computer and computer-controlled hardware, allows large numbers of voltage, current, temperature, and magnetic measurements to be performed on bulk and thin film samples. Typical results are shown characterizing transition temperature (T sub c), critical current density (J sub c), and magnetic properties of bulk high T(sub c) materials as a function of temperature.
Laser Research
Eastman Kodak Company, Rochester, New York is a broad-based firm which produces photographic apparatus and supplies, fibers, chemicals and vitamin concentrates. Much of the company's research and development effort is devoted to photographic science and imaging technology, including laser technology. Eastman Kodak is using a COSMIC computer program called LACOMA in the analysis of laser optical systems and camera design studies. The company reports that use of the program has provided development time savings and reduced computer service fees.
Scaling Deep Learning for Cancer Drug Discovery on HPC Systems
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Systemic Theoretic Process Analysis (STPA) Used for Cyber Security and Agile Software Development
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FPGA-accelerated simulation of variable latency memory systems
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NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment
In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.
Accelerating Scientific Applications With SambaNova Reconfigurable Dataflow Architecture
Here, our exploratory work finds that the SambaNova Reconfigurable Dataflow Architecture (RDA) along with the SambaFlow software stack provides for an attractive system and solution to accelerate AI for science workloads. We have observed the efficacy of using the system with a diverse set of science applications and reasoned their suitability for performance gains over traditional hardware. As the Data-Scale system provides for a very large memory capacity, the system can be used to train models that typically do not fit in a GPU. The architecture also provides for deeper integration with upcoming supercomputers at the Argonne Leadership Computing Facility (ALCF), a US Department of Energy Office of Science user facility, to help advance science insights.
NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment
In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.