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NASA NTRS2015

Relationships Between Excessive Heat and Daily Mortality over the Coterminous U.S

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. Using National Land Data Assimilation System (NLDAS) meteorological reanalysis data, we have developed several measures of extreme heat to enable assessments of the impacts of heat on public health over the coterminous U.S. These measures include daily maximum and minimum air temperatures, daily maximum heat indices and a new heat stress variable called Net Daily Heat Stress (NDHS) that gives an integrated measure of heat stress (and relief) over the course of a day. All output has been created on the NLDAS 1/8 degree (approximately 12 km) grid and aggregated to the county level, which is the preferred geographic scale of analysis for public health researchers. County-level statistics have been made available through the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. We have examined the relationship between excessive heat events, as defined in eight different ways from the various daily heat metrics, and heat-related and all-cause mortality defined in CDC's National Center for Health Statistics 'Multiple Causes of Death 1999-2010' dataset. To do this, we linked daily, county-level heat mortality counts with EHE occurrence based on each of the eight EHE definitions by region and nationally for the period 1999-2010. The objectives of this analysis are to determine (1) whether heat-related deaths can be clearly tied to excessive heat events, (2) what time lags are critical for predicting heat-related deaths, and (3) which of the heat metrics correlates best with mortality in each US region. Results show large regional differences in the correlations between heat and mortality. Also, the heat metric that provides the best indicator of mortality varied by region. Results from this research will potentially lead to improvements in our ability to anticipate and mitigate any significant impacts of extreme heat events on health.

heat↗

NASA NTRS1966

Processing of telemetry data generated by sensors moving in a varying field.

Telemetry data processing problems noting error sources, probability distribution of counts in counting processes, sensor relay during motion in varying field, etc

STATISTICAL COMMUNICATION THEORY↗

NASA NTRS1984

The HEAO 1 A-4 catalog of high-energy X-ray sources

Results are reported from an all-sky survey carried out at high X-ray energies (13-180 keV) from August 1977 until January 1979 using data obtained with the UCSD/MIT Hard X-Ray and Low-Energy Gamma-Ray Instrument on the HEAO 1 satellite. Visual displays are presented which indicate qualitatively the location, intensities, and time variability of the detected high-energy X-ray sources. A model-dependent procedure for the quantitative analysis of the sky survey data is described. The results of this procedure are presented in tabular form and include fitted count rates in four broad energy bands for about 70 sources. All sources which were detected at a level of statistical significance of not less than about 6 sigma were clearly evident in the visual displays of sky survey data. The survey is therefore complete, except in regions of source confusion, down to an intensity level of about 1/75 of the Crab Nebula in the 13-80 keV band. Forty-four sources were detected in the 40-80 keV energy band, and 14 were detected in the 80-180 keV band. Although most of the detected sources are galactic, seven are extragalactic.

Levine, A. M.↗

NASA NTRS1990

Blood biochemical and cellular changes during decompression and simulated extravehicular activity

Blood biochemical and cellular parameters were measured in human subjects before and after exposure to a decompression schedule involving 6 h of oxygen prebreathing. The exposure was designed to simulate extravehicular activity for 6 h (subjects performed exercise while exposed to 29.6 kPa). There were no significant differences between blood samples from subjects who were susceptible (n = 11) versus those who were resistant (n = 27) to formation of venous gas emboli. Although several statistically significant (P less than 0.05) changes in blood parameters were observed following the exposure (increases in white blood cell count, prothrombin time, and total bilirubin, and decreases in triglycerides, very-low-density lipoprotein cholesterol, and blood urea nitrogen), the changes were small in magnitude and blood factor levels remained within normal clinical ranges. Thus, the decompression schedule used in this study is not likely to result in blood changes that would pose a threat to astronauts during extravehicular activity.

Decompression Sickness/blood/etiology/prevention &↗

NASA NTRSDate not supplied

QuantifyML: How Good is My Machine Learning Model?

We present QuantifyML, which applies model counting to assess the learn ability, safety, and robustness of machine learning models. Typically, the efficacy of machine learning models is determined by computing their accuracy statistically on test datasets. However, this may be misleading, if the test data is not representative of the problem that is being studied. With QuantifyML we aim to precisely quantify the extent to which machine learning models have learned and generalized from the given data. In QuantifyML, a trained model is translated into aC program, which is fed to the CBMC model checking tool to produce a formula in Conjunctive Normal Form (CNF), which in turn is analyzed with state-of-the-art model counters to efficiently obtain precise countsw.r.t different outputs. QuantifyML enables i) evaluating the learnability of models by comparing the counts for the outputs to ground truth, ex-pressed as logical predicates (if available), ii) comparing the performance of different models that may be built with different machine learning algorithms (e.g., decision-trees vs. neural networks), and iii) quantifying the safety and robustness of trained models.

Deep Neural Networks↗

NASA NTRS2020

The Chandra Deep Wide-field Survey: A New Chandra Legacy Survey in the Bootes Field. I. X-ray Point Source Catalog, Number Counts, and Multiwavelength Counterparts

We present a new, ambitious survey performed with the Chandra X-ray Observatory of the 9.3 deg2 Boötes field of the NOAO Deep Wide-Field Survey. The wide field probes a statistically representative volume of the universe at high redshift. The Chandra Deep Wide-field Survey exploits the excellent sensitivity and angular resolution of Chandra over a wide area, combining 281 observations spanning 15 yr, for a total exposure time of 3.4 Ms, and detects 6891 X-ray point sources down to limiting fluxes of 4.7 × 10−16, 1.5 × 10−16, and 9 × 10−16 erg cm−2 s−1 in the 0.5–7, 0.5–2, and 2–7 keV bands, respectively. The robustness and reliability of the detection strategy are validated through extensive, state-of-the-art simulations of the whole field. Accurate number counts, in good agreement with previous X-ray surveys, are derived thanks to the uniquely large number of point sources detected, which resolve 65.0% ± 12.8% of the cosmic X-ray background between 0.5 and 2 keV and 81.0% ± 11.5% between 2 and 7 keV. Exploiting the wealth of multiwavelength data available on the field, we assign redshifts to ∼94% of the X-ray sources, estimate their obscuration, and derive absorption-corrected luminosities. We provide an electronic catalog containing all of the relevant quantities needed for future investigations.

Catalogs↗

NASA NTRS2002

Global Frequency and Distribution of Lightning as Observed from Space by the Optical Transient Detector

The Optical Transient Detector (OTD) is a space-based instrument specifically designed to detect and locate lightning discharges as it orbits the Earth. This instrument is a scientific payload on the MicroLab-1 satellite that was launched into a low-earth, 70 deg. inclination orbit in April 1995. Given the orbital trajectory of the satellite, most regions of the earth are observed by the OTD instrument more than 400 times during a one year period, and the average duration of each observation is 2 minutes. The OTD instrument optically detects lightning flashes that occur within its 1300x1300 sq km field-of-view during both day and night conditions. A statistical examination of OTD lightning data reveals that nearly 1.4 billion flashes occur annually over the entire earth. This annual flash count translates to an average of 44 +/- 5 lightning flashes (intracloud and cloud-to-ground combined) occurring around the globe every second, which is well below the traditional estimate of 100 flashes per second that was derived in 1925 from world thunder-day records. The range of uncertainty for the OTD global totals represents primarily the uncertainty (and variability) in the flash detection efficiency of the instrument. The OTD measurements have been used to construct lightning climatology maps that demonstrate the geographical and seasonal distribution of lightning activity for the globe. An analysis of this annual lightning distribution confirms that lightning occurs mainly over land areas, with an average land:ocean ratio of 10:1. A dominant Northern Hemisphere summer peak occurs in the annual cycle, and evidence is found for a tropically-driven semiannual cycle.

Christian, Hugh J.↗

NASA NTRS2008

Landing Site Dispersion Analysis and Statistical Assessment for the Mars Phoenix Lander

The Mars Phoenix Lander launched on August 4, 2007 and successfully landed on Mars 10 months later on May 25, 2008. Landing ellipse predicts and hazard maps were key in selecting safe surface targets for Phoenix. Hazard maps were based on terrain slopes, geomorphology maps and automated rock counts of MRO's High Resolution Imaging Science Experiment (HiRISE) images. The expected landing dispersion which led to the selection of Phoenix's surface target is discussed as well as the actual landing dispersion predicts determined during operations in the weeks, days, and hours before landing. A statistical assessment of these dispersions is performed, comparing the actual landing-safety probabilities to criteria levied by the project. Also discussed are applications for this statistical analysis which were used by the Phoenix project. These include using the statistical analysis used to verify the effectiveness of a pre-planned maneuver menu and calculating the probability of future maneuvers.

Bonfiglio, Eugene P.↗

NASA NTRS2012

A Statistical Test of Uniformity in Solar Cycle Indices

Several indices are used to characterize the solar activity cycle. Key among these are: the International Sunspot Number, the Group Sunspot Number, Sunspot Area, and 10.7 cm Radio Flux. A valuable aspect of these indices is the length of the record -- many decades and many (different) 11-year cycles. However, this valuable length-of-record attribute has an inherent problem in that it requires many different observers and observing systems. This can lead to non-uniformity in the datasets and subsequent erroneous conclusions about solar cycle behavior. The sunspot numbers are obtained by counting sunspot groups and individual sunspots on a daily basis. This suggests that the day-to-day and month-to-month variations in these numbers should follow Poisson Statistics and be proportional to the square-root of the sunspot numbers themselves. Examining the historical records of these indices indicates that this is indeed the case - even with Sunspot Area and 10.7 cm Radio Flux. The ratios of the RMS variations to the square-root of the indices themselves are relatively constant with little variation over the phase of each solar cycle or from small to large solar cycles. There are, however, important step-like changes in these ratios associated with changes in observer and/or observer system. Here we show how these variations can be used to construct more uniform datasets.

Hathaway David H.↗

NASA NTRS1979

Endogenic craters on basaltic lava flows - Size frequency distributions

Circular crater forms, termed collapse depressions, which occur on many basalt flows on the earth have also been detected on the moon and Mars and possibly on Mercury and Io. The admixture of collapse craters with impact craters would affect age determinations of planetary surface units based on impact crater statistics by making them appear anomalously old. In the work described in the present paper, the techniques conventionally used in planetary crater counting were applied to the determination of the size range and size frequency distribution of collapse craters on lava flows in Idaho, California, and New Mexico. Collapse depressions range in size from 3 to 80 m in diameter; their cumulative size distributions are similar to those of small impact craters on the moon.

Greeley, R.↗

NASA NTRS2019

Discovery of Activities via Statistical Clustering of Fixation Patterns

Human behavior often consists of a series of distinct activities, each characterized by a unique pattern of interaction with the visual environment. This is true even in a restricted domain, such as a pilot flying an airplane; in this case, activities with distinct visual signatures might be things like communicating, navigating, monitoring, etc. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these hypothetical activities. We compare, not individual fixations, but groups of fixations aggregated over a fixed time interval (Tau). We assume that the environment has been divided into a finite set of discrete areas-of-interest (AOIs). For a given time interval, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector, where N is the number of AOIs. These proportions can be converted to integer counts by multiplying by Tau divided by the average fixation duration, a parameter that we fix at 283 milliseconds. We compare different intervals by computing the chi-squared statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. We cluster the intervals, first by merging adjacent intervals that are sufficiently similar, optionally shifting the boundary between non-merged intervals to maximize the difference. Then we compare and cluster non-adjacent intervals. The method is evaluated using synthetic data generated by a hand-crafted set of activities. While the method generally finds more activities than put into the simulation, we have obtained agreement as high as 80 percent between the inferred activity labels and ground truth.

Eye Movements↗

NASA NTRS2004

The X-Ray Variable Sky

A novel approach to search for rapid time variability of every source, whether persistent or transient, within the entire ROSAT detector field has been developed. Semi-automated software statistically tests the photon arrival time event list for source variability and reports significant (greater than 5 sigma) variable enhancement in the photon count rate on time scales ranging from 5-500 seconds. The computational algorithms have been thoroughly tested, and are able to discriminate between false detections induced by instrumental effects, and real X-ray variable sources (XRVs); when carefully scrutinized by the PI. The analysis has proven to be successful, with both periodic and transient XRVs having been found. Some of these are entirely new discoveries, while others have been detected in previous X-ray missions, yet their nature remained elusive since source identification and analysis had yet to be performed. On average, one new (unidentified) XRV is found per ROSAT PSPC field analyzed. In some cases the XRVs show only a transient signature (a burst type event) with no persistent X-ray or other known (optical) counterpart. The ambitious goal of this project was to process as much of the ROSAT PSPC archive data as feasibly possible to discover XRVs and analyze this data.

Stringfellow, Guy↗

NASA NTRS2019

Discovery of Activities via Statistical Clustering of Fixation Patterns

Human behavior often consists of a series of distinct activities, each characterized by a unique signature of visual behavior. This is true even in a restricted domain, such as piloting an aircraft, where patterns of visual signatures might represent activities like communicating, navigating, and monitoring. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these activities based on their behavioral signatures. The method is in some respects similar to recurrence analysis, but here we compare not individual fixations, but groups of fixations aggregated over a fixed time interval. The duration of this interval is a parameter that we will refer to as τ. We assume that the environment has been divided into a set of N different areas-of-interest (AOIs). For a given interval of time of duration τ, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector. These proportions can be converted to counts by multiplying by τ divided by the average fixation duration (another parameter that we fix at 280 milliseconds). We compare different intervals by computing the chi-square statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. We have investigated the method using a set of 10 synthetic "activities," that sample 4 AOIs. Four of these activities visit 3 of the 4 AOIs, with equal probability; as there are four different ways to leave-one- out, there are four such activities. Similarly, there are six different activities that leave-two-out. Sequences of simulated behavior were generated by running each activity for 40 seconds, in sequence, for a total of 6.7 minutes. The figure to the right shows the matrix of chi-square statistics, using a value of 2.8 seconds for τ, corresponding to 10 fixations. Low values (dark) indicate poor evidence for activity differences, while high values (bright) indicate strong evidence. The dark squares along the main diagonal each correspond to the forty second intervals in which the activity was held constant; the 4x4 block at the lower left corresponds to the four leave-one-out activities, while the 6x6 block in the upper right corresponds to the leave-two-out activities. (The anti-diagonal pattern of white squares indicates those activity pairs that share no AOIs.) The chi-square values can be binarized by choosing a particular significance level; we are interested in grouping bins that represent the same activity, effectively accepting the null hypothesis. Therefore, we may adopt a relatively lax criterion; for example, choosing a p-value of 0.2 means that two behaviors that have only a 1-in-5 chance of being produced by a single activity might nevertheless be clustered together. We have explored several methods to perform clustering on the data and solving for the activity probabilities. Greedy methods begin by selecting the time bin that is similar to the most (or least) other bins, and then forming a cluster from it and all other non-discriminable bins. These methods show mediocre performance, as they do not take into account temporal contiguity. Preliminary results indicate that methods that "grow" clusters in time from seed points perform better.

activity analysis↗

NASA NTRS2018

Discovery of Activities via Statistical Clustering of Fixation Patterns

Human behavior often consists of a series of distinct activities, each characterized by a unique pattern of interaction with the visual environment. This is true even in a restricted domain, such as a piloting an aircraft, where activities with distinct visual signatures might be things like communicating, navigating, and monitoring. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these hypothetical activities. The method is in some respects similar to recurrence analysis, but here we compare not individual fixations, but groups of fixations aggregated over a fixed time interval. The duration of this interval is a parameter that we will refer to as delta. We assume that the environment has been divided into a set of N different areas-of-interest (AOIs). For a given interval of time of duration delta, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector. These proportions can be converted to integer counts by multiplying by delta divided by the average fixation duration (another parameter that we fix at 280 milliseconds). We compare different intervals by computing the chi-square statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. The method has been applied to approximately 100 hours of eye movement data collected from pilots in a high-fidelity B747 flight simulator, and the results have been compared to synthetic data in which the each activity is represented as first-order Markov process with random probabilities assigned to the AOIs. Randomly-generated synthetic activities can require thousands of fixations to be discriminated with statistical significance, while the human data can be clustered using averaging windows of some 10's of seconds, suggesting that the actual activities are much more narrowly focused than random Markov models.

activity analysis↗

NASA NTRS1976

Support of selected X-ray studies to be performed using data from the Uhuru (SAS-A) satellite

A new measurement of the diffuse X-ray emission sets more stringent upper limits on the fluctuations of the background and on the number counts of X-ray sources with absolute value of b 20 deg than previous measurements. A random sample of background data from the Uhuru satellite gives a relative fluctuation in excess of statistics of 2.0% between 2.4 and 6.9 keV. The hypothesis that the relative fluctuation exceeds 2.9% can be rejected at the 90% confidence level. No discernable energy dependence is evident in the fluctuations in the pulse height data, when separated into three energy channels of nearly equal width from 1.8 to 10.0 keV. The probability distribution of fluctuations was convolved with the photon noise and cosmic ray background deviation (obtained from the earth-viewing data) to yield the differential source count distribution for high latitude sources. Results imply that a maximum of 160 sources could be between 1.7 and 5.1 x 10 to the -11 power ergs/sq cm/sec (1-3 Uhuru counts).

Garmire, G. P.↗

NASA NTRS2021

Unsteady Pressure-Sensitive-Paint Shot Noise Reduction

In the study of unsteady Pressure-Sensitive Paint (uPSP) image data sets, pixel intensity values collected by Phantom cameras from multiple perspectives are used to calculate the surface pressure of an experimental model. This paper focuses on mitigation of shot noise which is introduced into the image data set by the cameras. Shot noise impacts the quality of data collected, increasing the system error in its pressure value calculations. This paper characterises statistical methods to reduce shot noise within experimental data by taking an average or weighted average across an area of pixels of their respective counts. Each combined-pixel has a higher total effective intensity value and therefore exhibits reduced noise throughout its time history. This is demonstrated by calculating the standard deviations of the combined pixel values through time, which decrease as the combined pixel area increases. Finally, these methods are compared across experimental wind tunnel conditions to examine their effectiveness at different Mach numbers. Ultimately, the results are used to recommend a blur filter type and size which will be applied to the raw image data prior to processing, as well as a minimum camera resolution for future experiments. Downstream of the filter application, the software to convert the 2D image data sets into surface pressure readings on a 3D grid use a ratio of decimation that assigns multiple pixels to each 3D grid node. This decimation ratio will also be aligned with the size of the blur filter, resulting in a more manageable data set size and compromised spatial resolution. Combined with the effect of the blur filter, the resulting pixel intensity readings will exhibit significantly reduced shot noise, reducing the magnitude of uncertainty and error in the final calculated pressure. For future tests, the camera spatial resolution must be sufficient to capture small flow and model features even after the recommended blur filter is applied. The image data set is taken from a uPSP experiment of the Space Launch System Block 1 cargo vehicle model in September 2019.

Lucy Tang↗

NASA NTRS2021

Unsteady Pressure-Sensitive Paint (UPSP) Shot Noise Reduction

Lucy Tang↗

NASA NTRS1986

Statistical performance of the intensified charged coupled device

The first analysis of the statistical performance of the intensified charge coupled device (CCD) is presented. The Poisson statistics of the arriving photons, the exponential pulse height distribution of the microchannel plate in the image intensifier, and the effects of discrete digitization of the charge in reading the CCD are included. A range of intensifier gains from the high levels that permit pulse counting to the low levels that can be used with the accumulation of charge on the CCD is considered. As a tool for this analysis, an analytical model of the performance of the detector system that is generally useful for predicting many aspects of system performance under a wide range of conditions is developed. The statistical penalty for operating the intensified CCD in the charge accumulation mode is not severe, and in some applications it may be desirable to accept this penalty to gain the advantages of increased dynamic range through charge accumulation. This investigation has revealed no pathological aspects of the operation of the detector system.

Sandel, B. R.↗