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The software provides a framework to build a fast look up layer that reflects the holdings of an archive or database and operates between search/discovery system and disk storage. The software utilizes Bloom filter (BF) data structure. The discovery service powered by the Bloom filter layer comes with a very high level of accuracy yet space-saving, since BF compresses data to fit into RAM.
Three novel binary barium arsenides, Ba3As4, Ba5As4, and Ba16As11, were synthesized and their crystal and electronic structures were investigated. Structural data collected via the single-crystal X-ray diffraction method indicate that the anionic substructures of all three novel compounds are composed of structural motifs based on the homoatomic As–As contacts, with [As2]4− dimers found in Ba5As4 and Ba16As11, and an [As4]6− tetramer found in Ba3As4. Ba3As4 and Ba5As4 crystallize in the orthorhombic crystal system—with the non-centrosymmetric space group Fdd2 (a = 15.3680(20) Å, b = 18.7550(30) Å, c = 6.2816(10) Å) for the former, and the centrosymmetric space group Cmce (a = 16.8820(30) Å, b = 8.5391(16) Å, and c = 8.6127(16) Å) for the latter—adopting Eu3As4 and Eu5As4 structure types, respectively. The heavily disordered Ba16As11 structure was solved in the tetragonal crystal system with the space group P4¯21m (a = 12.8944(12) Å and c = 11.8141(17) Å). The Zintl concept can be applied to each of these materials as follows: Ba3As4 = (Ba2+)3[As4]6−, Ba5As4 = (Ba2+)5(As3−)2[As2]4−, and 2 × Ba16As11 = (Ba2+)32(As3−) ≈ 20[As2]4− ≈ 1, pointing to the charge-balanced nature of these compounds. Electronic structure calculations indicate narrow bandgap semiconducting behavior, with calculated bandgaps of 0.47 eV for Ba3As4, 0.34 eV for Ba5As4, and 0.33 eV for Ba16As11.
The Sensory Ego-Sphere (SES) is a short-term memory for a robot in the form of an egocentric, tessellated, spherical, sensory-motor map of the robot s locale. Visual attention enables fast alignment of overlapping images without warping or position optimization, since an attentional point (AP) on the composite typically corresponds to one on each of the collocated regions in the images. Such alignment speeds analysis of the multiple images of the area. Compositing and attention were performed two ways and compared: (1) APs were computed directly on the composite and not on the full-resolution images until the time of retrieval; and (2) the attentional operator was applied to all incoming imagery. It was found that although the second method was slower, it produced consistent and, thereby, more useful APs. The SES is an integral part of a control system that will enable a robot to learn new behaviors based on its previous experiences, and that will enable it to recombine its known behaviors in such a way as to solve related, but novel, task problems with apparent creativity. The approach is to combine sensory-motor data association and dimensionality reduction to learn navigation and manipulation tasks as sequences of basic behaviors that can be implemented with a small set of closed-loop controllers. Over time, the aggregate of behaviors and their transition probabilities form a stochastic network. Then given a task, the robot finds a path in the network that leads from its current state to the goal. The SES provides a short-term memory for the cognitive functions of the robot, association of sensory and motor data via spatio-temporal coincidence, direction of the attention of the robot, navigation through spatial localization with respect to known or discovered landmarks, and structured data sharing between the robot and human team members, the individuals in multi-robot teams, or with a C3 center.
Since the satellite era began, NASA has collected a large volume of Earth science observations for research and applications around the world. The collected and archived satellite data at 12 NASA data centers can also be used for STEM education and activities such as disaster events, climate change, etc. However, accessing satellite data can be a daunting task for non-professional users such as teachers and students because of unfamiliarity of terminology, disciplines, data formats, data structures, computing resources, processing software, programming languages, etc. Over the years, many efforts including tools, training classes, and tutorials have been developed to improve satellite data access for users, but barriers still exist for non-professionals. In this presentation, we will present our latest activity that uses a very popular online video sharing Web site, YouTube (https://www.youtube.com/), for accessing visualizations of our global precipitation datasets at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). With YouTube, users can access and visualize a large volume of satellite data without the necessity to learn new software or download data. The dataset in this activity is a one-month animation for the GPM (Global Precipitation Measurement) Integrated Multi-satellite Retrievals for GPM (IMERG). IMERG provides precipitation on a near-global (60 deg. N-S) coverage at half-hourly time interval, providing more details on precipitation processes and development compared to the 3-hourly TRMM (Tropical Rainfall Measuring Mission) Multisatellite Precipitation Analysis (TMPA, 3B42) product. When the retro-processing of IMERG during the TRMM era is finished in 2018, the entire video will contain more than 330,000 files and will last ~3.6 hours. Future plans include development of flyover videos for orbital data for an entire satellite mission or project. All videos, including the one-month animation, will be uploaded and available at the GES DISC site on YouTube (https://www.youtube.com/user/NASAGESDISC).
Broadly speaking, the goals of the project are to develop techniques to use manifold learning to develop reduced‐order and surrogate models for "hyper‐reduction" of very high‐dimensional complex multi‐scale systems. This is being achieved by employing a newly proposed form of manifold projection and learning that leverages recent advancements in computational geometry and data‐driven modeling. In particular, we are applying a manifold projection technique to project the solutions of very high‐dimensional systems onto the so‐called Grassmannmanifold, a Reimannian manifold comprised of orthonormal matrices. We then apply data‐driven machine learning techniques to classify the solutions on the manifold (e.g. clustering techniques) according to their proximity on the manifold and leverage a further nonlinear dimension reduction to organize the structured data on the manifold. Finally, we are developing novel techniques that enable us to directly interpolate the hyper‐reduced data such that we can predict the solution of the complex, high‐ dimensional system without need to call the full expensive computational model. Given their adherence to the underlying structure of the solution of the physical system, it is expected that these approximate solutions will be sufficiently constrained so as to (approximately) adhere to physical principles.
The processing requirements for graphite/PMR-15 polyimide composites developed to demonstrate the structural integrity of polyimide composite structural elements at temperatures up to 589K (600 F) are described. Major tasks included: quality assurance development; materials and process development; specification verification; flat panel fabrication; stiffened panel fabrication; honeycomb panel fabrication; chopped fiber moldings; and demonstration component fabrication. Materials, processing, and quality assurance documents were prepared from experimentally derived data. Structural elements consisting of flat panels, corrugated stiffeners, I-beams, hat stiffeners, honeycomb panels, and chopped fiber moldings were made and tested. Property data from 219K (-65 F) to 589K (600 F) were obtained. All elements were made in a production environment. The size of each element was sufficient to insure production capability and structural component applicability. Problems associated with adhesive bonding, laminate and structural element analysis, material variability, and test methods were addressed.
Written language and conventional data structures for representing scientific procedures suffer from low process detail, often fail to accurately represent protocols, and lack universality. New strategies for the handling of experimental data are needed to provide viable process information for both humans and machines. In this work, we present the universal workflow language (UWL) and interface (UWLi). UWL is a findable, accessible, interoperable, and reusable (FAIR)-compatible, graph-based data architecture that can capture arbitrary scientific procedures through workflow representation, and UWLi is an accompanying software package for building, manipulating, and interpreting UWL entries. The UWL format was found to be highly effective in identifying deficiencies in the reported process details of high-impact, peer-reviewed scientific journals, and in simulated scenarios, the graph format was shown to be more effective than conventional methods in predictively modeling the outcome of diverse scientific protocols. Implementation of UWL could enable more accurate scientific communication and more impactful process datasets.
Because of its far-reaching applications in geophysics and materials science, quartz has been one of the most extensively examined materials under dynamic compression. Despite 50 years of active research, questions remain concerning the structure and transformation of SiO 2 under shock compression. Continuum gas-gun studies have established that under shock loading quartz transforms through an assumed mixed-phase region to a dense high-pressure phase. While it has often been assumed that this high-pressure phase corresponds to the stishovite structure observed in static experiments, there have been no crystal structure data confirming this. In this study, we use gas-gun shock compression coupled with in situ synchrotron x-ray diffraction to interrogate the crystal structure of shock-compressed α-quartz up to 65 GPa. Our results reveal that α-quartz undergoes a phase transformation to a disordered metastable phase as opposed to crystalline stishovite or an amorphous structure, challenging long-standing assumptions about the dynamic response of this fundamental material.
The research has 2 thrusts: 1. new data architectures and tech-transfer-ready prototype tools that use write optimized data structures (WODS) to track real-world events, provide value to analysts, and support our cyber missions; and 2. algorithm research to address infinite streams of data, including expiration and sustainability.
A computer software system has been developed to automatically generate two-dimensional coordinates from algebraic transformations. For topologically complex regions, a smooth assembly of the transformations can be used to automatically produce a composite mesh where a general gridded format is retained. The algebraic mesh generation system consists of a collection of operator subroutines which are applied to an established data structure and which automatically perform the necessary parts of mesh construction from a sequence of multisurface transformations. The system operators are discussed, taking into account the data base, the order of application, direct surface generators, geometric surface operators, surface generators from existing surfaces, transverse operators, mesh operators, assembly operators, and data visualization operators. Attention is given to applications related to airfoils.
The Adaptable I/O System (ADIOS) represents the culmination of substantial investment in Scientific Data Management, and it has demonstrated success for several important extreme-scale science cases. However, looking towards the exascale and beyond, we see the development of yet more stringent data management requirements that require new abstractions. Therefore, there is an opportunity to attempt to connect the traditional realms of HPC I/O optimization with the Database / Data Management community. As such, in this paper we offer some specific examples from our ongoing work in managing data structures, services, and performance at the extreme scale for scientific computing. Using the publish/subscribe model afforded by ADIOS, we demonstrate a set of services that connect data format, metadata, queries, data reduction, and high-performance delivery. The resulting publish/subscribe framework facilitates connection to on-line workflow systems to enable the dynamic capabilities that will be required for exascale science.
Abstract Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.
Many applications from precision agriculture, environmental monitoring and transportation networks rely on data collected across space and time over a large geographic area. Missing data poses a significant challenge for any data-driven inference and control tasks. Data imputation or the estimation of missing data can help fill these gaps by utilizing inherent spatial relationships and temporal patterns. A variety of spatiotemporal imputation models have been developed to address missing data in spatiotemporal datasets. However, these classical methods rely on the assumption that the underlying data follows a smooth trend and fail to provide accurate estimates when there is a large number of missing points in the data. Even though there are machine learning driven tensor completion approaches such as convolutional neural network based tensor completion (CoSTCo) that capture the non-linear relationships in the dataset, the transductive nature makes the algorithm less scalable. Thus, existing approaches for estimating the missing information do not effectively capture all dimensions of the spatiotemporal data structure, resulting in erroneous predictions and poor performance. The main contributions of this paper are: (1) We propose a novel inductive framework (G-LSTM) for missing data imputation that integrates a graph neural network with LSTMs to effectively capture both spatial and temporal dependencies. (2) Experimental results on a traffic dataset demonstrate that the proposed GNN integrated with an LSTM framework achieves improved imputation and maintains steady performance even when there are extreme missing conditions in comparison with the state-of-the-art imputation framework (i.e, CoSTCo). (3) The simulation results on a traffic network show up to 69% reduction in mean absolute error and 61% reduction in root mean square error when compared to CoSTCo.
In certain kinds of geophysical surveys, the fields are continua, but measured at discrete points referenced by their position or time of measurement. Systems of this kind are better modelled by databases built from basic data structures attuned to representing traverses across continua that are not of pre-defined fixed length. The general Array DataBase is built on arrays (ordered sequencies of data) with each array holding data elements of one type. The arrays each occupy their own physical data set, in turn inter-related by a hierarchy to other arrays over the same space/time reference points. The GADB illustrates the principle that a data facility should reflect the fundamental properties of its data, and support retrieval based on the application's view. The GADB is being tested by its use in NASA's project MAGSAT.
Rapid progress in low-cost commercial PC-class multimedia workstation technology will potentially have a dramatic impact on the productivity of distributed work groups of 50-100 software developers. Hypermedia/multimedia involves the seamless integration in a graphical user interface (GUI) of a wide variety of data structures, including high-resolution graphics, maps, images, voice, and full-motion video. Hypermedia will normally require the manipulation of large dynamic files for which relational data base technology and SQL servers are essential. Basic machine architecture, special-purpose video boards, video equipment, optical memory, software needed for animation, network technology, and the anticipated increase in productivity that will result for the introduction of hypermedia technology are covered. It is suggested that the cost of the hardware and software to support an individual multimedia workstation will be on the order of $10,000.
Vertical Bloch Line (VBL) memory is a recently conceived, integrated, solid-state, block-access, VLSI memory which offers the potential of 1Gbit/sq cm real storage density, gigabit per second data rates, and sub-millisecond average access times simultaneously at relatively low mass, volume, and power values when compared to alternative technologies. VBL's are micromagnetic structures within magnetic domain walls which can be manipulated using magnetic fields from integrated conductors. The presence or absence of VBL pairs are used to store binary information. At present, efforts are being directed at developing a single-chip memory using 25Mbit/sq cm technology in magnetic garnet material which integrates, at a single operating point, the writing, storage, reading, and amplification functions needed in a memory. This paper describes the current design architecture, functional elements, and supercomputer simulation results which are used to assist the design process. The current design architecture uses three metal layers, two ion implantation steps for modulating the thickness of the magnetic layer, one ion implantation step for assisting propagation in the major line track, one NiFe soft magnetic layer, one CoPt hard magnetic layer, and one reflective Cr layer for facilitating magneto-optic observation of magnetic structure. Data are stored in a series of elongated magnetic domains, called stripes, which serve as storage sites for arrays of VBL pairs. The ends of these stripes are placed near conductors which serve as VBL read/write gates. A major line track is present to provide a source and propagation path for magnetic bubbles. Writing and reading, respectively, are achieved by converting magnetic bubbles to VBL's and vice versa. The output function is effected by stretching a magnetic bubble and detecting it magnetoresistively. Experimental results from the past design cycle created four design goals for the current design cycle. First, the bias field ranges for the stripes and the major line needed to be matched. Second, the magnetic field barrier between the stripe and the read/write gates needed to be reduced. Third, current conductor routing needed to be improved to reduce occurrences of open-circuiting, short-circuiting, and eddy-current shielding. Fourth, a modified Co-alloy was needed with an increased coercivity and controlled magnetization to allow VBL stabilization to occur without affecting stripe stability.
NASA's Atmospheric Tomography Mission (ATom) deployed in each of the four seasons during 2016-2018, the DC-8 aircraft in order to establish global-scale datasets intended to improve the representation of chemically reactive gases in global atmospheric chemistry models (ACMs). The Global Modeling Initiative (GMI) executed simulations for each ATom flight using the GMI Chemistry Transport Model (GMI-CTM) to provide species concentrations of chemical gases along the DC-8 flight transects. To solve the problem of translating the GMI-CTM simulation data to the unique spatial resolutions of each ATom flight, the GMI ICARTT Processing Software (GMI-IPS) was developed.The GMI-IPS is written in Python and provides data processing, flight extraction, and visualization support for aircraft research projects using ICARTT format, which is a standard format for airborne instrument data. Additionally, the GMI-IPS interpolates global gridded model data from Hierarchical Data Format (HDF) to ICARTT compatible flight transects. Software classes for instruments and collections provided by the ATom DC-8 aircraft such as MER10, MMS, etc. are derived from a common base class. Other functionality provided by the GMI-IPS are: deriving missing flight entries along a transect, reading ICARTT entries from file, and providing Python data structures for storing flight and model information, and more.The GMI-IPS is GIT source controlled, has approximately 30,000 lines of code, and supports parallelization across data collections. It delivered GMI-CTM data for more than forty distinct DC-8 aircraft flights that took place under ATom. The output ICARTT files adhere to format standard V1.1, and pass the scan utility provided by NASA LaRC Airborne Science Data for Atmospheric Composition. This presentation will include a software and methods overview, and results from ATom, including assessments using the GMI-CTM showing how well observations from ATom flight transects represent a broader region.
The box-counting approach for fractal dimension calculation is scaled up for big data using a data structure named box locality index (BLI). The BLI is constructed as key-value pair style with the key (an integer array) indexing the location of a “box” (i.e., a gird cell on the multi-dimensional space embedding a given dataset) and the value counting the number of data points inside the box (called “box occupancy”). Compared with the traditionally used E-dim tree, the BLI avoids complex hierarchical structure and encodes only necessary information required by box-counting approach for fractal dimension calculation. Moreover, the key-value pair nature of BLI, together with the fact that the box occupancy is aggregatable, grants box-counting approach the needed scalability for fractal dimension calculation of big data using distributed computing techniques (e.g., MapReduce and Spark). Taking the advantage of the BLI, MapReduce and Spark methods for fractal dimension calculation of big data are developed, which conduct box-counting for each grid level as a cascade of MapReduce/Spark jobs in a bottom-up fashion. In an empirical validation, the MapReduce and Spark methods demonstrated good effectiveness and efficiency in fractal calculation of a big synthetic dataset. In summary, this work provides an efficient solution for the estimation of intrinsic dimension of big data that is essential for many machine learning methods and data analytics.