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Telemetry data processing problems noting error sources, probability distribution of counts in counting processes, sensor relay during motion in varying field, etc
A nuclear physics example of statistical bootstrap is used on the MARATHON nucleon structure function ratio data in the quark momentum fraction regions x B → 0 and x B → 1. The extrapolated F 2 ratio as quark momentum fraction x B → 1 is $\frac{F^n_2}{F^p_2}$ → 0.4 ± 0.05 and this value is compared to theoretical predictions. The extrapolated ratio when x B → 0 favors the simple model of isospin symmetry with the complete dominance of sea quarks at low momentum fraction. At high- x B , the proton quark distribution function ratio d/u is derived from the F 2 ratio and found to be d/u → 1/6. Our extrapolated values for both the $\frac{F^n_2}{F^p_2}$ ratio and the d/u parton distribution function ratio are within uncertainties of perturbative QCD values from quark counting, helicity conservation arguments, and a Dyson-Schwinger equation with a contact interaction model. In addition, it is possible to match the statistical bootstrap value to theoretical predictions by allowing two compatible models to act simultaneously in the nucleon wave function. Finally, one such example is nucleon wave functions composed of a linear combination of a quark-diquark state and a three-valence quark correlated state with coefficients that combine to give the extrapolated F 2 ratio at x B = 1.
The Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) [1] provides comprehensive guidance for the development and implementation of radiological surveys conducted to demonstrate compliance with a dose- or risk-based cleanup goal during final status survey. The MARSSIM methodology follows an integrated strategy combining direct measurements and sampling to assess uniform residual contamination, and scanning surveys to identify localized areas of elevated activity. Building surveys assessing residual surface contamination present unique challenges from reliance on field measurements that may be subject to less than ideal environmental conditions. Additionally, limited characterization and excessive conservatism incorporated into the conceptual site model may result in derived concentration guideline levels (DCGL) and, as a consequence of relatively inefficient field instrumentation, corresponding attributable count rates generally comparable to background. The presence of elevated ambient radon concentrations and associated progeny deposition both exacerbate these intrinsic survey challenges and introduce a series of technical, logistic, and administrative obstacles that can wreak havoc on the statistical legitimacy of each component of the MARSSIM process. Short-lived radon progeny including polonium-218, lead-214, bismuth-214, and polonium-214 produce excessive alpha and beta radiation that may decrease instrument sensitivity through an increase in background count rates, and, at worst, may result in incorrect survey unit failure through the simulation of residual surface activity. Aside from the obvious solution of exhaustive radon mitigation, the primary objectives are to distinguish between radon progeny deposition and actual surface activity, and collect sufficient defensible data. The following is a technical discussion of the consequences of performing building MARSSIM surveys in the presence of elevated ambient radon atmospheres, conceptual examples, and an overview of practical strategies to minimize the effects of radon interference while collecting sufficient defensible data to complete the survey. (authors)
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.
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.
The observable universe contains density perturbations on scales larger than any finite volume survey. Perturbations on scales larger than a survey can measure degrade its power to constrain cosmological parameters. The dependence of survey observables such as the weak lensing power spectrum on these long-wavelength modes results in super-sample covariance. Accurately forecasting parameter constraints for future surveys requires accurately accounting for the super-sample effects. If super-sample covariance is in fact a major component of the survey error budget, it may be necessary to investigate mitigation strategies that constrain the specific realization of the long-wavelength modes. We present a Fisher matrix based formalism for approximating the magnitude of super-sample covariance and the effectiveness of mitigation strategies for realistic survey geometries. We implement our formalism in the public code SuperSCRAM: Super-Sample Covariance Reduction and Mitigation. We illustrate SuperSCRAM with an example application, where the modes contributing to supersample covariance in the WFIRST weak lensing survey are constrained by the low-redshift galaxy number counts in the wider LSST footprint. We find that super-sample covariance increases the volume of the error ellipsoid in 7D cosmological parameter space by a factor of 4.5 relative to Gaussian statistical errors only, but our simple mitigation strategy more than halves the contamination, to a factor of 2.0.
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.
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.
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.
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.
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.
An in situ counted ion implantation experiment improving the error on the number of ions required to form a single optically active silicon vacancy (SiV) defect in diamond 7-fold compared to timed implantation is presented. Here, traditional timed implantation relies on a beam current measurement followed by implantation with a preset pulse duration. It is dominated by Poisson statistics, resulting in large errors for low ion numbers. Instead, our in situ detection, measuring the ion number arriving at the substrate, results in a 2-fold improvement of the error on the ion number required to generate a single SiV compared to timed implantation. Through postimplantation analysis, the error is improved 7-fold compared to timed implantation. SiVs are detected by photoluminescence spectroscopy, and the yield of 2.98% is calculated through the photoluminescence count rate. Hanbury–Brown–Twiss interferometry is performed on locations potentially hosting single-photon emitters, confirming that 82% of the locations exhibit single photon emission statistics.
Abstract Background Prophylactic platelet transfusions prevent bleeding in hemato‐oncology patients, but it is unclear how any benefit varies between patients. Our aim was to assess if patients with different baseline risks for bleeding benefit differently from a prophylactic platelet transfusion strategy. Study design and methods Using the data from the randomized controlled TOPPS trial (Trial of Platelet Prophylaxis), we developed a prediction model for World Health Organization grades 2, 3, and 4 bleeding risk (defined as at least one bleeding episode in a 30 days period) and grouped patients in four risk‐quartiles based on this predicted baseline risk. Predictors in the model were baseline platelet count, age, diagnosis, disease modifying treatment, disease status, previous stem cell transplantation, and the randomization arm. Results The model had a c‐statistic of 0.58 (95% confidence interval [CI] 0.54–0.64). There was little variation in predicted risks (quartiles 46%, 47%, and 51%), but prophylactic platelet transfusions gave a risk reduction in all risk quartiles. The absolute risk difference (ARD) was 3.4% (CI −12.2 to 18.9) in the lowest risk quartile (quartile 1), 7.4% (95% CI −8.4 to 23.3) in quartile 2, 6.8% (95% CI −9.1 to 22.9) in quartile 3, and 12.8% (CI −3.1 to 28.7) in the highest risk quartile (quartile 4). Conclusion In our study, generally accepted bleeding risk predictors had limited predictive power (expressed by the low c‐statistic), and, given the wide confidence intervals of predicted ARD, could not aid in identifying subgroups of patients who might benefit more (or less) from prophylactic platelet transfusion.
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.
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.
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.
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.
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.