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Data for figures 6 and 8 describing shine-through power computed in NUBEAM and effective diffusivities in the USER model
A number of detailed gravimetric geoids of portions of the world's oceans from marine gravity measurements were constructed. The geoids were constructed by computing 1 x 1 deg or 10 x 10 deg averages of free-air anomaly data and subtracting these values from currently used satellite derived Earth models. The resulting difference gravity anomalies are then integrated over a sphere using a simplified form of Stoke's equation to obtain a difference geoid. This difference geoid is added to the satellite derived model to obtain a 1 x 1 deg or 10 x 10 deg total gravimetric geoid. The geoid undulations are studied by comparison of the altimeter measurements with the morphology of the ocean floor. Utilizing a combination of altimetry data, gravity and seismic reflection data, geophysical models of the earth can be constructed.
The effects of raindrop shape on the attenuation and propagation phase shift at vertical and horizontal linear polarizations is examined, focusing on the implications of these effects on rain measurements by radars. Analytic expressions for the imaginary and real components of the forward scattering amplitude function are dervied for a wavelength of 2.2 cm. It is shown that the summation attenuation rate and the rate of summation propagation phase shift are both insensitive to the shapes of raindrops.
The Tharsis region of Mars is critical to any study of Martian tectonics. This region (65 N and 65 S latitude; 45 W to 157.5 W longitude) is characterized by an asymmetrical dome-shaped topographic high approximately 8000 km across. Affecting over 25 percent of the surface area, this region has been the center of most of the major tectonic and volcanic activity that has taken place on the Martian surface. Lineament studies are the primary tool available for studying tectonic processes on terrestrial planets such as Mars. At least three major lineament systems can be delineated in the Tharsis region; north-south and east-west trending lineament systems are superimposed on an older northwest trending lineament system. Four centers of uplift have been identified based on the occurrence of radial fracture patterns. Preliminary results indicate that the formation of the Tharsis Dome may not have resulted from a single uplift event, but may instead have resulted from as many as four uplift events. The northwest trending fracture may represent a pre-existing zone of weakness which contributed to the early formation of the Tharsis Dome. The 1:2,000,000 scale photo mosaic maps of Mars were examined and lineaments were identified. The end points of each lineament were measured and recorded in an X-Y reference coordinate frame.
Contents include list of publications resulting from grant: Magneto-Hydrodynamic Model of the Solar Corona and Interplanetary Medium. Coronal magnetic field topology and source of fast solar wind. Geophysical Importance of Global Magnetic Field Geometry and Density Distribution in Solar Wind Modeling. Empirical Model of the Corona-Solar Wind with Multiple Current Sheets. Model of the Solar Wind and its Comparison with ULYSSES. Physical properties of a coronal hole from a coronal diagnostic spectrometer, Mauna Loa Coronagraph, and LASCO observations during the Whole Sun Month. Semi-Empirical 2-D MHD Model of the Solar corona and Solar Wind: Energy Flow in the Corona. Source Region of High and Low Speed Wind during the Spartan. Three-dimensional coronal density structure. Cone Model for Halo CME's: Application to Space Weather Forecasting.
Lecture about abstract interpretation. This lecture starts with a brief introduction to validation and verification using formal methods. It then demonstrates IKOS (Inference Kernel for Open Static Analyzers), a static analyzer for C/C++ based on Abstract Interpretation. Then, it describes in details the theory of Abstract Interpretation, a mathematical framework to over-approximate the reachable states of a program.
The Majorana Demonstrator is a leading experiment searching for neutrinoless double-beta decay with high purity germanium detectors (HPGe). Machine learning provides a new way to maximize the amount of information provided by these detectors, but the data-driven nature makes it less interpretable compared to traditional analysis. An interpretability study reveals the machine's decision-making logic, allowing us to learn from the machine to feedback to the traditional analysis. In this work, we have presented the first machine learning analysis of the data from the Majorana Demonstrator; this is also the first interpretable machine learning analysis of any germanium detector experiment. Two gradient boosted decision tree models are trained to learn from the data, and a game-theory-based model interpretability study is conducted to understand the origin of the classification power. By learning from data, this analysis recognizes the correlations among reconstruction parameters to further enhance the background rejection performance. By learning from the machine, this analysis reveals the importance of new background categories to reciprocally benefit the standard Majorana analysis. This model is highly compatible with next-generation germanium detector experiments like LEGEND since it can be simultaneously trained on a large number of detectors.
Three different data analysis techniques - shape factor, parameter estimation, and deconvolution - have been applied to the same set of satellite radiation measurements to determine their effect on the estimated radiation field. The measurements are from a wide-angle, horizon-to-horizon, nadir-pointing sensor. The shape factor technique reduces each measurement to a radiant exitance at the top of the atmosphere by simple division by a constant. The parameter estimation technique processes all measurements together as a batch and defines the radiant exitance as a least-squares fit to the data. The deconvolution technique takes advantage of the fact that spherical harmonics are the eigenfunctions of the measurement operator. All three techniques are derived, and their assumptions, advantages and disadvantages are discussed. Their results are compared globally, zonally, regionally and on a spatial spectrum basis. All three techniques give comparable results for global parameters; however, results on a regional scale were quite different. The standard deviations of the regional differences in radiant exitance varied from 7.4 to 13.5 W/sq m. Of the three techniques, the parameter estimation technique produced the best regional results and is the choice of the author.
The RTCA standard (DO-178C) for developing avionic software and getting certification credits includes an extension (DO-333) that describes how developers can use static analysis in certification. In this paper, we give an overview of the IKOS static analysis framework that helps developing static analyses that are both precise and scalable. IKOS harnesses the power of Abstract Interpretation and makes it accessible to a larger class of static analysis developers by separating concerns such as code parsing, model development, abstract domain management, results management, and analysis strategy. The benefits of the approach is demonstrated by a buffer overflow analysis applied to flight control systems.
Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, in this study, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.
Cluster analysis is a data mining technique that is meant ot simplify the process of classifying data points. The basic clustering process requires an input of data points and the number of clusters wanted. The clustering algorithm will then pick starting C points for the clusters, which can be either random spatial points or random data points. It then assigns each data point to the nearest C point where "nearest usually means Euclidean distance, but some algorithms use another criterion. The next step is determining whether the clustering arrangement this found is within a certain tolerance. If it falls within this tolerance, the process ends. Otherwise the C points are adjusted based on how many data points are in each cluster, and the steps repeat until the algorithm converges,
This viewgraph presentation provides a cost benefit analysis of three next-generation forward-looking turbulence sensing systems: X band turbulence radar system for convective turbulence, LIDAR based turbulence systems to sense clear air turbulence and a combined hybrid system. Parameters for the cost benefit analysis were established using a business model which considered injury rates, cost of injuries, indirect costs, market penetration rate estimates and product success characteristics. Topics covered include: study approach, business case equations, data acquisition, benchmark analysis. Data interpretation from the cost benefit analysis is presented. The researchers conclude that the market potential for these products is based primarily on injury cost reduction and that X band radar systems have the greatest chance for commercial success.
This work seeks to advance the state of the art in HPC I/O performance analysis and interpretation. In particular, we demonstrate effective techniques to: (1) model output performance in the presence of I/O interference from production loads; (2) build features from write patterns and key parameters of the system architecture and configurations; (3) employ suitable machine learning algorithms to improve model accuracy. We train models with five popular regression algorithms and conduct experiments on two distinct production HPC platforms. We find that the lasso and random forest models predict output performance with high accuracy on both of the target systems. We also explore use of the models to guide adaptation in I/O middleware systems, and show potential for improvements of at least 15% from model-guided adaptation on 70% of samples, and improvements up to 10× on some samples for both of the target systems.
Analysis and interpretation of time series data is easiest when the data values occur at uniform intervals in time, but actual data may have differing data sampling frequencies, such as monthly and daily readings. Applying data analysis techniques, such as smoothing, to such a data set may not give a representative result between time segments. The ability to automatically distinguish time segments of differing data frequency would provide a means for applying data analysis independently to each segment, though a suitable blending at segment boundaries would be required. A method for detecting frequency changes was developed and applied to Gaussian and median smoothing of hydraulic head data from groundwater wells at the U.S. Department of Energy Hanford Site in southeastern Washington state. The process identifies time segments of high-frequency (daily) or low-frequency (greater than daily) data using adjusted-bandwidth Gaussian kernel density estimation and a threshold value, which are further refined to address small blocks of low-frequency data within larger blocks of high-frequency data. User-selectable levels of smoothing are then applied independently to the time segments prior to combining the segment results for a single smoothed data set. This time segment identification approach provides effective low- and high-frequency data separation, which provides a method to apply data analysis independently to each time segment.
A means for side-looking airborne radar (SLAR) imagery to become a more widely used data source in geoscience and agriculture is suggested by providing interpretation keys as an easily implemented interpretation model. Interpretation problems faced by the researcher wishing to employ SLAR are specifically described, and the use of various types of image interpretation keys to overcome these problems is suggested. With examples drawn from agriculture and vegetation mapping, direct and associate dichotomous image interpretation keys are discussed and methods of constructing keys are outlined. Initial testing of the keys, key-based automated decision rules, and the role of the keys in an information system for agriculture are developed.
These programs involve: (1) analysis and interpretation of EUVE spectrometer observations of the active giant beta Cet in comparison to the Hyades giant theta(1) Tau, and (2) analysis and interpretation of EUVE spectrometer observations of the BY Dra systems FK Aqr, DH Leo, and BH Lyn. EUVE carried out observations of beta Cet, but has yet to perform an observation of theta(1) Tau. In beta Cet, a number of Fe lines from high ionization species were observed, up to Fe XXIV. The spectrum overall resembles that seen in the active binary Capella (alpha Aur). All three BY Dra systems were observed by EUVE, and show evidence of high temperature (approximately 10(exp 7) K) plasma; FK Aqr and DH Leo show significant variability in their Deep Survey lightcurves. In FK Aqr, spectral differences between its 'quiescent' and 'active' states suggest possible differences in the plasma density. In DH Leo, the Deep Survey lightcurve, taken over nearly 8 days, shows a distinct period of approximately 1.05 days, similar to the photometric period. The emission measure distributions of all three systems are rather similar in shape, and can be well-represented by a power law with slope approximately 1.5 from 6.2-7.0 in log T.
The detection of a phenological event (the brown wave-vegetation senescence) for specific forest and crop types using ERTS-1 imagery is described. Data handling techniques included computer analysis and photo interpretation procedures. Computer analysis of ERTS-1 multispectral scanner digital tapes in all bands was used to give the relative changes of spectral reflectance with time of forests and specified crops. These data were obtained for a number of the study's twenty-four sites located within four north-south corridors across the United States. Analysis of ground observation photography and ERTS-1 imagery for sites in the Appalachian Corridor and Mississippi Valley Corridor indicates that the recession of vegetation development can be detected very well. Tentative conclusions are that specific phenological events such as crop maturity or leaf fall can be mapped for specific sites and possibly for entire regions.
Progress in research into the global morphology of gravity wave activity using UARS data is described for the period March-June, 1998. Highlights this quarter include further progress in the analysis and interpretation of CRISTA temperature variances; model-generated climatologies of mesospheric gravity wave activity using the HWM-93 wind and temperature model; and modeling of gravity wave detection from space-based platforms. Preliminary interpretations and recommended avenues for further analysis are also described.