Results for “distributed inference” — Page 32

NASA NTRS1997

Gaseous abundances and methane supersaturation in Titan's troposphere

Various properties of Titan's troposphere are inferred from an analysis of Voyager I infrared spectrometer (IRIS) data between 200 and 600/ cm. Two homogeneous spectral averages acquired at widely separated emission angles are chosen for the analysis. Both data sets are associated with northern low latitudes very close to that of the radio science ingress occultation point. Solutions require simultaneous nonlinear least-squares fits to the two IRIS data sets, coupled with iteration of the radio occultation refractivity data. Values and associated 1-sigma uncertainties of several parameters are inferred from our analysis. These include mole fractions for molecular hydrogen (approx. 0.0011), argon (small), and methane near the surface ( approx. 0.057). Solutions are also obtained for the hydrogen parafraction (close to equilibrium, with considerable uncertainty), air temperature near the surface ( approx. 93 K), surface surface temperature discontinuity (approx. 1 K), and maximum degree of methane supersaturation in the upper troposphere (approx. 1.5). Actual values for the above-mentioned parameters depend on the amount of ethane cloud near the tropopause. There is no evidence for methane clouds in the upper troposphere, nor is their presence compatible with large degrees of supersaturation. A wave number dependence for the stratospheric haze opacity is inferred similar to that found for a polymeric residue created in laboratory discharge experiments. This haze appears to be uniformly distributed with latitude between altitudes of 40 and 160 km, provided those nighttime data at southern high latitudes that are discounted.

Samuelson, Robert E.↗

NASA NTRS2011

CALIPSO Detection of an Asian Tropopause Aerosol Layer

The first four years of the CALIPSO lidar measurements have revealed the existence of an aerosol layer at the tropopause level associated with the Asian monsoon season in June, July and August. This Asian Tropopause Aerosol Layer (ATAL) extends geographically from Eastern Mediterranean (down to North Africa) to Western China (down to Thailand), and vertically from 13 to 18 km. The Scattering Ratio inferred from CALIPSO shows values between 1.10. 1.15 on average with associated depolarization ratio of less than 5%. The Gaussian distribution of the points indicates that the mean value is statistically driven by an enhancement of the background aerosol level and not by episodic events such as a volcanic eruption or cloud contamination. Further satellite observations of aerosols and gases as well as field campaigns are urgently needed to characterize this layer, which is likely to be a significant source of non-volcanic aerosols for the global upper troposphere with a potential impact on its radiative and chemical balance

Vemier, J.-P.↗

NASA NTRS2021

Loss of Control Detection for Commercial Transports Using Conditional Variational Autoencoders

This work describes a detector for the loss of control of a commercial transport in flight. The detector has a belief state defined by the latent variable stochastic modeling of a conditional variational autoencoder (CVAE) constructed with bidirectional recurrent layers. In 2000, the Boeing Company and the NASA Langley Research Center jointly developed a quantitative set of metrics for defining loss-of-control (LOC) for a commercial transport. We use the thresholds for these quantitative metrics to define a condition vector for training the CVAE. We demonstrate through experimentation that reconstruction probability is an accurate indicator that the vehicle has shifted to an LOC state. Second, we introduce a technique for inferring that the vehicle is experiencing a flight state change is approaching by measuring a shift in the sampling Gaussian distributions of the latent space. We provide an analysis of its applicability to flight data from a NASA generic commercial transport-type aircraft.

Newton H Campbell↗

NASA NTRS2021

Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model

The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

Robert S. Schrom↗

NASA NTRSDate not supplied

Particle Size Distribution Parameters from SAGE III/ISS Extinction Spectra

Stratospheric aerosols play key roles in the chemistry and radiation balance of the atmosphere and are a key input parameter for global chemistry and climate models. The degree to which aerosols impact chemistry and radiation balance depends primarily on the relative abundance of different sized particles within the sample volume, often referred to as the particle size distribution (PSD). If the PSD is accurately known then other key modeling parameters (e.g., surface area density and effective radius) can be derived. Historically, occultation observations from orbital instruments such as SAGE III/ISS have been used to infer these PSD parameters by inverting the extinction coefficient spectra. However, past efforts routinely failed to account for measurement uncertainty and lacked a rigorous estimate of the inferred PSD uncertainty. We carried out a series of simulations to evaluate the accuracy of these inferences and, for every valid SAGE III/ISS extinction spectrum, determined the range of PSD parameters that fell withing the bounds of the extinction error bars. Special application of this method was applied to estimate the impact of the 2022 Hunga Tonga eruption had on particle size distributions.

Travis N Knepp↗

NASA NTRS1992

Petrogenesis of the Zagami meteorite - Inferences from synchrotron X-ray (SXRF) microprobe and electron microprobe analyses of pyroxenes

Major and minor element abundances in pyroxene, principally pigeonite, from Zagami meteorite samples were determined by electron microprobe; abundances of trace elements Ni, Cu, Zn, and Ga were obtained by synchrotron X-ray (SXRF) microprobe. Abundances of Ni and Cr in the magnesian zones and cores are partially decoupled from abundances of other compatible (Mg) and incompatible (Fe,Ti) elements. The textures, chemical zoning, and element decouplings in the pigeonites are consistent with igneous crystal growth at moderate cooling rates, not isothermally or near equilibrium. From element partitioning and mass balance, Zagami is estimated to contain 20 +/- 5 percent or less cumulus pyroxene (half augite and half pigeonite), half the proportion suggested by equilibrium distribution of Fe/Mg between pigeonite and melt. This inconsistency may be resolved if all pigeonite/melt element partitioning was affected by the rapid growth of the pigeonites. The inference that the pyroxene zoning patterns reflect only magmatic events is consistent with an igneous age of 180 m.y. for Zagami.

Treiman, A. H.↗

NASA NTRS1974

Photoballistics of volcanic jet activity at Stromboli, Italy

Two night eruptions of the volcano Stromboli were studied through 70-mm photography. Single-camera techniques were used. Particle sphericity, constant velocity in the frame, and radial symmetry were assumed. Properties of the particulate phase found through analysis include: particle size, velocity, total number of particles ejected, angular dispersion and distribution in the jet, time variation of particle size and apparent velocity distribution, averaged volume flux, and kinetic energy carried by the condensed phase. The frequency distributions of particle size and apparent velocities are found to be approximately log normal. The properties of the gas phase were inferred from the fact that it was the transporting medium for the condensed phase. Gas velocity and time variation, volume flux of gas, dynamic pressure, mass erupted, and density were estimated. A CO2-H2O mixture is possible for the observed eruptions. The flow was subsonic. Velocity variations may be explained by an organ pipe resonance. Particle collimation may be produced by a Magnus effect.

Chouet, B.↗

NASA NTRS1977

Analytic model approach to the inversion of scattering data

An analytic model approach is applied to several simple atmospheric inversion problems. This method gives a sharp determination of aerosol size distribution parameters. It is shown that this analytic approach, together with ground level point sampling data measurements, can be used to infer information on the tropospheric ozone profile.

Green, A. E. S.↗

NASA NTRS1979

High-latitude field-aligned current sources and induced electric fields

Using a simple, planar model of the high latitude ionosphere with an enhanced conductivity auroral ring, the electric potential is computed for a pair of field-aligned current sheets inferred from observations. The relationship of various characteristics of the electric potential pattern to features of the field-aligned current distribution are elucidated in the context of a generalized field-aligned current Fourier analysis. On the basis of the analysis and observations to date, it is concluded that boundary layer dynamo action is the prevalent mechanism in the solar wind/magnetosphere/ionosphere interaction.

Barbosa, D. D.↗

NASA NTRS1989

Upper-tropospheric influences on Western Pacific tropical cyclone intensity change as inferred from satellite ozone observations

The use of Nimbus-7 TOMS data to diagnose changes in tropical cyclone intensity is demonstrated. The relationship between upper-tropospheric environmental total ozone distribution and intensity for a tropical cyclone case study is examined. The climatological total ozone patterns are used to identify the upper-tropospheric features to differentiate between intensifying and nonintensifying Western Pacific tropical cyclones.

Rodgers, Edward B.↗

NASA NTRS1992

The early Martian atmosphere

Atmospheric noble gases on Mars, Earth, and Venus are severely and variably depleted with respect to solar abundances, and are characterized by isotopic signatures that differ from planet to planet and from meteoritic and inferred solar compositions. Earlier theories based on simple accretion of meteoritic veneers as planetary volatile sources are unable to replicate these distributions, and so attention is now focusing on evolutionary processes that can fractionate both elements and isotopes, operating either on the early planets themselves or in large preplanetary planetesimals.

Pepin, Robert O.↗

NASA NTRS1992

Constraints on the thermal evolution of Venus inferred from Magellan data

The impact craters with diameters from 1.5 to 280 km compiled from Magellan observations indicate that the crater population on Venus has a completely spatially random distribution and the size/density distribution of craters with diameters greater than or equal to 35 km is consistent with a 'production' population with an age of 500 plus or minus 250 m.y. The similarity in size distribution from area to area indicates that the crater distribution is independent of crater size. Also, the forms of the modified craters are virtually identical to those of the pristine craters. These observations imply that Venus reset its cratering record by global resurfacing 500 m.y. ago, and resurfacing declined relatively fast. The fact that less than 40 percent of all craters have been modified and that the few volcanically embayed craters are located on localized tectonic regions indicate that only minor and localized volcanism and tectonism have occurred since the latest vigorous resurfacing event approximately 500 m.y. ago and the interior of Venus has been solid and possibly colder than Earth's. This is because the high-temperature lithosphere of Venus would facilitate upward ascending of mantle plumes and result in extensive volcanism if the venusian upper mantle were as hot as or hotter than Earth's. Therefore, the present surface morphology of Venus may provide useful constraints on the pattern of that vigorous convection, and possibly on the thermal state of the venusian mantle. We examine this possibility through numerical calculations of three-dimensional thermal convection models in a spherical shell with temperature- and pressure-dependent Newtonian viscosity, temperature-dependent thermal diffusivity, pressure-dependent thermal expansion coefficient, and time-dependent internal heat production rate solar magnitude.

Arkani-Hamed, Jafar↗

NASA NTRS1992

Differential correction technique for removing common errors in gas filter radiometer measurements

The Measurement of Air Pollution from Satellites (MAPS) gas filter radiometer experiment was designed to measure CO mixing ratios in the earth's atmosphere. MAPS also measures N2O to provide a reference channel for the atmospheric emitting temperature and to detect the presence of clouds. In this paper we formulate equations to correct the radiometric signals based on the spatial and temporal uniformity of the N2O mixing ratio in the atmosphere. Results of an error study demonstrate that these equations reduce the error in inferred CO mixing ratios. Subsequent application of the technique to the MAPS 1984 data set decreases the error in the frequency distribution of mixing ratios and increases the number of usable data points.

Wallio, H. A.↗

NASA NTRS1993

Structure of axisymmetric mantle plumes

The structure of axisymmetric subsolidus thermal plumes in the earth's lower mantle is inferred from calculations of axisymmetric thermal plumes in an infinite Prandtl number fluid with thermally activated viscosity. The velocity and temperature distribution is determined for axisymmetric convection above a heated disk in an incompressible fluid cylinder 2,400 km in height and 1,200 km in diameter. Several calculations of plumes with heat transport in the range 100-400 GW, similar to the advective heat transport at the Hawaiian hotspot, are presented. Hotspot formation by plumes originating at the base of the mantle requires both large viscosity variations and a minimum heat transport.

Olson, Peter↗

NASA NTRS2016

NuSTAR, SWIFT, and GROND Observations of the Flairing MeV Blazar PMN J0641-0320

MeV blazars are a sub-population of the blazar family, exhibiting larger-than-average jet powers, accretion luminosities, and black hole masses. Because of their extremely hard X-ray continua, these objects are best studied in the X-ray domain. Here, we report on the discovery by the Fermi Large Area Telescope and subsequent follow-up observations with NuSTAR, Swift, and GROND of a new member of the MeV blazar family: PMN J0641-0320. Our optical spectroscopy provides confirmation that this is a flat-spectrum radio quasar located at a redshift of z = 1.196. Its very hard NuSTAR spectrum (power-law photon index of approx.1 up to approx. 80 keV) indicates that the emission is produced via inverse Compton scattering off of photons coming from outside the jet. The overall spectral energy distribution of PMN J0641-0320 is typical of powerful blazars and, using a simple one-zone leptonic emission model, we infer that the emission region is located either inside the broad line region or within the dusty torus.

galaxies: active quasars: general↗

NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗

NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗

NASA NTRS2003

Intelligent machines in the twenty-first century: foundations of inference and inquiry

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have learned and what they are designed to understand.

Review↗