A Deep Learning Method For Spatio-temporal Detection Of Atypical Activity In NGSS Camera Data.
Abstract not provided.
Smith, Michael↗
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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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Exploring deep learning methods for characterizing near-source characteristics of buried explosions from seismic data
Item purpose: SnT virtual poster showing LYNM work Internal peer review status: This poster has been peer reviewed HQ review status: this poster has been approved by HQ
Harding, Jennifer Logan [Sandia National Laborator↗
Analysis and Test of Deep Flaws in Thin Sheets of Aluminum and Titanium. Volume 1: Program Summary and Data Analysis
Six thicknesses of 2219-T87 aluminum base metal surface flaw and center crack specimens ranging from 9.53 to 0.635 mm (0.375 to 0.025 inch) were tested at temperatures ranging from 295K to 20K. Additionally, 6A1-4V STA titanium base metal specimens were tested in three thicknesses 3.18, 2.03, and 1.02 mm (0.125, 0.080 and 0.040 inch) at room temperature. All tests were conducted on uniaxial specimens. Results were analyzed and compared with previously developed data to establish a criterion for proof testing thin walled pressure vessels. The data analysis and exact flaw size dimensions are presented.
Finger, R. W.↗
Radio reference frame stability from VLBI data
Intercontinental radio interferometric measurements between NASA's Deep Space Network stations yield a catalog of positions of 106 extragalactic radio sources, uniformly distributed between -45 and +85 deg declination. Almost all of the source positions have formal uncertainties between 0.5 and 3 milliarcseconds. Estimates of three independent sets of 32 source coordinates at average epochs 1980.0, 1983.2, and 1984.5 assess the level of stability of the reference frame over a five-year period. Comparisons of the three sets of source coordinates show a number of cases of greater than 2 sigma differences, particularly between the 1984-85 and 1981-83 periods. No sources, however, show 2 sigma shifts among both pairs of epochs. Upper limits of the order of 1 mas/yr can be placed on the time rates of change of the 32 source coordinates.
Sovers, O. J.↗
Decoder Synchronization for Deep Space Missions
The Consultative committee for Space Data STandards (CCSDS) recommends that space communication links employ a concatenated error-correcting channel-coding system in which the inner code is a convolutional (7, 2/2) code and the outer code is a (255,223) Reed-Solomon code.
Decoder↗
Nanoporous Tio2 Water Training Data
Data and input files used to train a Deep Potential (DP) model for the nanoporous TiO2-water interface. The DeepMD-kit code was used to train the DP. Information about data format, and how to use DeepMD-kit can be found at https://docs.deepmodeling.com/projects/deepmd/en/master/
08 HYDROGEN↗
Programming with BIG data in R: Scaling analytics from one to thousands of nodes
Here, we present a tutorial overview showing how one can achieve scalable performance with R. We do so by utilizing several package extensions, including those from the pbdR project. These packages consist of high performance, high-level interfaces to and extensions of MPI, PBLAS, ScaLAPACK, I/O libraries, profiling libraries, and more. While these libraries shine brightest on large distributed platforms, they also work rather well on small clusters and often, surprisingly, even on a laptop with only two cores. Our tutorial begins with recommendations on how to get more performance out of your R code before considering parallel implementations. Because R is a high-level language, a function can have a deep hierarchy of operations. For big data, this can easily lead to inefficiency. Profiling is an important tool to understand the performance of an R code for both serial and parallel improvements.
97 MATHEMATICS AND COMPUTING↗
Determination of Ceres Physical Parameters Using Radiometric and Optical Data
The Dawn spacecraft was launched on September 27th, 2007. Its mission is to rendezvous with and observe the two largest bodies in the main asteroid belt, Vesta and Ceres. It has completed over a year’s worth of direct observations of Vesta from early 2011 through late 2012. In the spring of 2015, the Dawn spacecraft entered orbit around the asteroid Ceres for the start of what is expected to be more than a year of science operations. The science data collected from this encounter consist of infrared (IR) images and spectra, visible images through a number of color filters, gamma ray detections and measurements of the Ceres gravity field. These data will be collected during several science phases: an Approach phase (1500000-4860 km from Ceres), a Survey orbit (4860 km radius), a High Altitude Mapping Orbit (HAMO) (1940 km radius) and a Low Altitude Mapping Orbit (LAMO) (855 km radius). The Approach phase included three Rotational Characterization (RC) opportunities. Designing each science orbit and successfully transferring into that orbit requires a sufficiently accurate estimate of Ceres physical parameters (body fixed frame, GM and harmonics). This paper focuses on work performed to estimate Ceres physical parameters using Deep Space Network (DSN) radiometric tracking data and optical measurements derived from science camera imagery. This paper describes planning for the data acquisition, as well as processing techniques and methodology. The trajectories predicted by the gravity field estimations are also compared with the actual as-flown trajectories. Observations of the gravity at high altitudes are found to be sufficient to design precision orbits at lower altitudes. Follow-up analysis after successfully reaching LAMO is included, as is a discussion of lessons learned.
Kennedy, Brian M.↗
A small core in Vesta inferred from Dawn’s observations
Vesta’s large-scale interior structure had previously been constrained primarily using the gravity and shape data from the Dawn mission. However, these data alone still allow a wide range of possibilities for the differentiation state of the body. The moment of inertia is arguably the most diagnostic parameter related to the radial density distribution of a planetary body, making it crucial for assessing the body’s state of internal differentiation. Determining the moment of inertia requires additional measurements of the amplitudes of small rotational motions, such as precession and nutation. Here we report an updated estimate of the moment of inertia of Vesta inferred from Dawn’s Doppler tracking via the Deep Space Network and onboard imaging data. The recovered value for Vesta’s normalized polar moment of inertia is $\overline{C}$/MR 2 = 0.4208 ± 0.0047 (where M is the mass of Vesta and R is the reference radius), which is only 6.6% lower than the homogeneous value of 0.4505. This value, combined with the gravity field and global shape, suggests that Vesta’s interior has limited density stratification beneath its howardite–eucrite–diogenite-dominated crust. We propose two possible origin scenarios that are consistent with the observed constraints. In the first scenario, Vesta’s interior did not undergo full differentiation due to late accretion. In the second scenario, Vesta originated as an impact remnant of a larger differentiated body re-accreted with non-chondritic bulk composition produced from a catastrophic impact. Vesta did not experience complete differentiation in either scenario, suggesting that its current state reflects a complex interplay between its accretion timing, thermal evolution, redistribution of 26 Al bearing melt and/or impact processes.
CNEOS 2014-01-08 bolide↗
Appalachian Basin Temperature-Depth Maps and Structured Data in support of Feasibility Study of Direct District Heating for the Cornell Campus Utilizing Deep Geothermal Energy
This dataset contains shapefiles and rasters that summarize the results of a stochastic analysis of temperatures at depth in the Appalachian Basin states of New York, Pennsylvania, and West Virginia. This analysis provides an update to the temperature-at-depth maps provided in the Geothermal Play Fairway Analysis of the Appalachian Basin (GPFA-AB) Thermal Quality Analysis (GDR repository 879: https://gdr.openei.org/submissions/879). This dataset improves upon the GPFA-AB dataset by considering several additional uncertainties in the temperature-at-depth calculations, including geologic properties and thermal properties. A Monte Carlo analysis of these uncertain properties and the GPFA-AB estimated surface heat flow was used to predict temperatures at depth using a 1-D heat conduction model. In this data submission, temperatures are provided for depths from 1-5 km in 0.5 km increments. The mean, standard deviation, and selected quantiles of temperatures at these depths are provided as shapefiles with attribute tables that contain the data. Rasters are provided for the mean and standard deviation data. Figures and maps that summarize the data are also provided. For the pixel corresponding to Cornell University, Ithaca, NY, a .csv file containing the 10,000 temperature-depth profiles estimated from the Monte Carlo analysis is provided. These data are summarized in a figure containing violin plots that illustrate the probability of obtaining certain temperatures at depths below Cornell.
15 GEOTHERMAL ENERGY↗
Electronic health record analysis via deep poisson factor models
Electronic Health Record (EHR) phenotyping utilizes patient data captured through normal medical practice, to identify features that may represent computational medical phenotypes. These features may be used to identify at-risk patients and improve prediction of patient morbidity and mortality. We present a novel deep multi-modality architecture for EHR analysis (applicable to joint analysis of multiple forms of EHR data), based on Poisson Factor Analysis (PFA) modules. Each modality, composed of observed counts, is represented as a Poisson distribution, parameterized in terms of hidden binary units. In-formation from different modalities is shared via a deep hierarchy of common hidden units. Activation of these binary units occurs with probability characterized as Bernoulli-Poisson link functions, instead of more traditional logistic link functions. In addition, we demon-strate that PFA modules can be adapted to discriminative modalities. To compute model parameters, we derive efficient Markov Chain Monte Carlo (MCMC) inference that scales efficiently, with significant computational gains when compared to related models based on logistic link functions. To explore the utility of these models, we apply them to a subset of patients from the Duke-Durham patient cohort. We identified a cohort of over 12,000 patients with Type 2 Diabetes Mellitus (T2DM) based on diagnosis codes and laboratory tests out of our patient population of over 240,000. Examining the common hidden units uniting the PFA modules, we identify patient features that represent medical concepts. Experiments indicate that our learned features are better able to predict mortality and morbidity than clinical features identified previously in a large-scale clinical trial.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Learning physics at future $e^–e^+$ colliders with machine
Information deformation and loss in jet clustering are one of the major limitations for precisely measuring hadronic events at future $e^–e^+$ colliders. Because of their dominance in data, the measurements of such events are crucial for advancing the precision frontier of Higgs and electroweak physics in the next decades. We show that this difficulty can be well-addressed by synergizing the event-level information into the data analysis, with the techniques of deep neutral network. In relation to this, we introduce a CMB-like observable scheme, where the event-level kinematics is encoded as Fox-Wolfram (FW) moments at leading order and multi-spectra of spherical harmonics at higher orders. Then we develop a series of jet-level (w/ and w/o the FW moments) and event-level classifiers, and analyze their sensitivity performance comparatively with two-jet and four-jet events. As an application, we analyze measuring Higgs decay width at $e^–e^+$ colliders with the data of 5ab –1 @240GeV. The precision obtained is significantly better than the baseline ones presented in documents. We expect this strategy to be applied to many other hadronic- event measurements at future $e^–e^+$ colliders, and to open a new angle for evaluating their physics capability.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Chemical models of the deep atmospheres of Jupiter and Saturn
New and updated chemical kinetic data, elemental abundances, and thermodynamic data are used for thermochemical equilibrium and, where relevant, thermochemical kinetic calculations of gas abundances and condensate stability in the hot, deep atmospheres of Jupiter and Saturn. Over 2000 compounds of all naturally occurring elements in the periodic table are considered. The calculations range from 298 to 2000 K and are done for adiabatic models of the two planetary atmospheres. The results predict the abundances of many gases which are potentially observable by the Galileo probe to Jupiter, by the Cassini mission to Saturn, and by Earth-based and Earth-orbital telescopes. In addition, the results also predict many new species which are potentially observable by a new generation of entry probes capable of penetrating deeper into the atmospheres of Jupiter and Saturn.
Fegley, Bruce, Jr.↗
Data-driven Mapping of the Mouse Connectome: The utility of transfer learning to improve the performance of deep learning models performing axon segmentation on light-sheet microscopy images
Light sheet microscopy has made possible the high temporal and spatial 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual process. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through the application of refinements such as transfer learning.
59 BASIC BIOLOGICAL SCIENCES↗
Volumetric Segmentation via Neural Networks Improves Neutron Crystallography Data Analysis
Crystallography is the powerhouse technique for molecular structure determination, with applications in fields ranging from energy storage to drug design. Accurate structure determination, however, relies partly on determining the precise locations and integrated intensities of Bragg peaks in the resulting data. Here, we describe a method for Bragg peak integration that is accomplished using neural networks. The network is based on a U-Net and identifies peaks in three-dimensional reciprocal space through segmentation, allowing prediction of the full 3D peak shape from noisy data that is commonly difficult to process. The procedure for generating appropriate training sets is detailed. Trained networks achieve Dice coefficients of 0.82 and mean IoUs of 0.69. Carrying out integration over entire datasets, it is demonstrated that integrating neural network-predicted peaks results in improved intensity statistics. Furthermore, using a second dataset, the possibility of transfer learning between datasets is shown. Given the ubiquity and growing complexity of crystallography, we anticipate integration by machine learning to play an increasingly important role across the physical sciences. These early results demonstrate the applicability of deep learning techniques for integrating crystallography data and suggest a possible role in the next generation of crystallography experiments.
Sullivan, Brendan T.↗
Practical galaxy morphology tools from deep supervised representation learning
Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. ‘#diffuse’), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100 per cent accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled data sets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code zoobot. Zoobot is accessible to researchers with no prior experience in deep learning.
79 ASTRONOMY AND ASTROPHYSICS↗
Direct Aerosol Radiative Effect Derived From CERES Data Products
The CERES team uses MODIS and VIIRS derived aerosol optical thickness to compute surface and in atmosphere irradiances. In producing the data products, aerosol optical thicknesses derived by Dark Target and Deep Blue algorithms are assimilated in an aerosol transport model. In addition, the aerosol transport model provides aerosol optical thicknesses under cloudy conditions and aerosol optical thicknesses over polar regions. These aerosol optical thicknesses are used for hourly irradiance computations in 11 grids. Global mean top-of-atmosphere direct aerosol radiative effects are -5.2 Wm-2 and -2.2 Wm-2 for, respectively, clear-sky and all-sky conditions. Spatial pattern of the trend of the computed clear-sky direct aerosol radiative effect agrees with that derived from CERES observations. In this presentation, the importance of seamless transition of aerosol optical thicknesses derived from MODIS to those derived from VIIRS is emphasized. Currently, the CERES team is working with MODIS dark target and deep blue teams to achieve the seamless transition in producing CERES Edition 4 data products.
Seiji Kato↗