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The scattering dynamics of electron capture in e+$$ {\mathrm{e}}^{+} $$‐H(1 s) scattering under dense semi‐classical plasma (DSCP) environments has been investigated theoretically. Coupled multi‐channel two‐body Lippmann‐Schwinger equations have been solved by retaining e+$$ {\mathrm{e}}^{+} $$+H(1 s) and p + Ps(1 s) channels to calculate the cross sections (CS) of the electron capture process at intermediate and high incident energies. The effective interaction of the plasma charged particles is modelled by a pseudopotential which is a function of two parameters, namely the plasma screening strength and the de Broglie wavelength. A detailed study is made to explore the changes in the CSs of the above‐mentioned process with respect to the variation in the plasma screening strength and de Broglie wavelength. Significant changes are found to take place, when the screening strength and the de Broglie wavelength are varied. Specifically, the sharp minimum in the differential CS moves toward the forward direction with increasing de Broglie wavelength at a given screening strength. [ABSTRACT FROM AUTHOR]

The effects of the non-ideality (NI) of classical plasmas on the scattering of positron from the ground state of hydrogen atom have been investigated by solving coupled multi-channel two-body Lippmann–Schwinger equations. Elastic e + - H (1 s) amplitude, Ps(1s) formation amplitude and elastic p - P s (1 s) amplitude are calculated by considering the coupling of e + - H (1 s) and p - P s (1 s) channels. A pseudopotential which is a function of the Debye length and NI parameter is used to describe the effective interaction potential in non-ideal classical plasmas (NICP). Precise eigenfunctions of H(1s) and Ps(1s) under NICP, obtained within the framework of Rayleigh-Ritz variational principle, are used to evaluate the scattering amplitudes. A comprehensive study is made on the changes brought about in the differential and the total cross sections for the e + - H (1 s) elastic scattering, p - P s (1 s) elastic scattering and Ps(1s) formation due to the variation of the NI parameter over a wide range. It is found that the differential cross section for Ps(1s) formation possesses some rich structures. [ABSTRACT FROM AUTHOR]

Quantum mechanical calculations have been accomplished to study the dynamics of the reaction: p + H(1s) → H(nlm) + p in dense semi‐classical hydrogen plasma. Interactions among the charged particles in plasma are represented by a pseudopotential which takes care of the collective effects at large distances and quantum effect of diffraction at small distances. Various capture cross sections are computed for the incident proton energy lying within 10 to 500 keV by applying a distorted wave method which uses a variationally determined closed‐form wave function of hydrogen atom. Moreover, an inclusive study is made to explore the effects of screening of plasma and quantum diffraction on various capture cross sections for a wide range of thermal Debye length and de Broglie wave length. It has been found that various cross sections suffer considerable changes due to varying Debye length and de Broglie wave length. [ABSTRACT FROM AUTHOR]

Effects of nonideality of classical plasma on the reaction: p + H(1s) → H(nlm) + p has been investigated by carrying out fully quantum mechanical calculations within the framework of a first‐order distorted wave method. Scattering amplitude is calculated conveniently by employing a simple, variationally determined wave function of hydrogen atom embedded in nonideal classical plasma. A detailed study is made on the changes in electron transfer cross sections due to the nonideality of plasma varying from 0 to 4 and the incident proton energy lying between 10 and 500 keV. It has been found that nonideality of plasma causes substantial change in capture cross section. [ABSTRACT FROM AUTHOR]

Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. Gaussian Process regression (GPR) is a popular technique for modeling the input-output relations of a set of variables under the assumption that the weight vector has a Gaussian prior. However, it is challenging to apply GPR to large data sets since prediction based on the learned model requires inversion of an order n kernel matrix. Approximate solutions for sparse Gaussian Processes have been proposed for sparse problems. However, in almost all cases, these solution techniques are agnostic to the input domain and do not preserve the similarity structure in the data. As a result, although these solutions sometimes provide excellent accuracy, the models do not have interpretability. Such interpretable sparsity patterns are very important for many applications. We propose a new technique for sparse GPR that allows us to compute a parsimonious model while preserving the interpretability of the sparsity structure in the data. We discuss how the inverse kernel matrix used in Gaussian Process prediction gives valuable domain information and then adapt the inverse covariance estimation from Gaussian graphical models to estimate the Gaussian kernel. We solve the optimization problem using the alternating direction method of multipliers that is amenable to parallel computation. We compare the performance of this algorithm to different existing methods for sparse covariance regression in terms of both speed and accuracy. We demonstrate the performance of our method in terms of accuracy, scalability, and interpretability on two different satellite data sets from the climate domain. © 2013 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 6: 205-220, 2013 [ABSTRACT FROM AUTHOR]

The article presents a study which described a novel and efficient algorithm for anomaly detection in distributed databases. The proposed method centralizes a very small sample from the different data subsets at different locations. It was found that the algorithm is highly relevant to both earth sciences and aeronautics by describing applications in these domains. The performance of algorithm is demonstrated on the NASA MoDIS satellite images and simulated aviation data set generated by the Commercial Modular Aero-Propulsion System Simulation (CMAPSS).

In this paper, we develop a distributed algorithm for monitoring the principal components (PCs) for next generation of astronomy petascale data pipelines such as the Large Synoptic Survey Telescopes (LSST). This telescope will take repeated images of the night sky every 20 s, thereby generating 30 terabytes of calibrated imagery every night that will need to be co-analyzed with other astronomical data stored at different locations around the world. Event detection, classification, and isolation in such data sets may provide useful insights to unique astronomical phenomenon displaying astrophysically significant variations: quasars, supernovae, variable stars, and potentially hazardous asteroids. However, performing such data mining tasks is a challenging problem for such high-throughput distributed data streams. In this paper, we propose a highly scalable and distributed asynchronous algorithm for monitoring the PCs of such dynamic data streams and discuss a prototype web-based system PADMINI (Peer-to-Peer Astronomy Data Mining) which implements this algorithm for use by the astronomers. We demonstrate the algorithm on a large set of distributed astronomical data to accomplish well-known astronomy tasks such as measuring variations in the fundamental plane of galaxy parameters. The proposed algorithm is provably correct (i.e., converges to the correct PCs without centralizing any data) and can seamlessly handle changes to the data or the network. Real experiments performed on Sloan Digital Sky Survey (SDSS) catalogue data show the effectiveness of the algorithm. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2011 [ABSTRACT FROM AUTHOR]

The inner product measures how closely two feature vectors are related. It is an important primitive for many popular data mining tasks, for example, clustering, classification, correlation computation, and decision tree construction. If the entire data set is available at a single site, then computing the inner product matrix and identifying the top (in terms of magnitude) entries is trivial. However, in many real-world scenarios, data is distributed across many locations and transmitting the data to a central server would be quite communication intensive and not scalable. This paper presents an approximate local algorithm for identifying top-i inner products among pairs of feature vectors in a large asynchronous distributed environment such as a peer-to-peer (P2P) network. We develop a probabilistic algorithm for this purpose using order statistics and the Hoeffding bound. We present experimental results to show the effectiveness and scalability of the algorithm. Finally, we demonstrate an application of this technique for interest-based community formation in a P2P environment. [ABSTRACT FROM AUTHOR]

High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, such as satellite derived estimates, to monitor AQ. Current satellite-data-based models provide AQ mapping on a kilometer scale at best. In this study, we focus on producing hundred-meter-scale AQ maps for urban environments in developed cities. We examined the feasibility of an image-based object-detection analysis approach using very high-spatial-resolution (2.5 m) commercial satellite imagery. We fed the satellite imagery to a deep neural network (DNN) to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM2.5 and NO2 concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE < 2 µg/m3 and highlight the contribution of specific urban features, such as green areas and roads, to continuous hundred-meter-scale AQ estimations. This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create hundred-meter-scale AQ maps in developing cities, where current and historical ground data are limited. [ABSTRACT FROM AUTHOR]

Cloud detection algorithms are crucial in many remote-sensing applications to allow an optimized processing of the acquired data, without the interference of the cloud fields above the surfaces of interest (e.g., land, coral reefs, etc.). While this is a well-established area of research, replete with a number of cloud detection methodologies, many issues persist for detecting clouds over areas of high albedo surfaces (snow and sand), detecting cloud shadows, and transferring a given algorithm between observational platforms. Particularly for the latter, algorithms are often platform-specific with corresponding rule-based tests and thresholds inherent to instruments and applied corrections. Here, we present a convolutional neural network (CNN) algorithm for the detection of cloud and cloud shadow fields in multi-channel satellite imagery from World-View-2 (WV-2) and Sentinel-2 (S-2), using their Red, Green, Blue, and Near-Infrared (RGB, NIR) channels. This algorithm is developed within the NASA NeMO-Net project, a multi-modal CNN for global coral reef classification which utilizes imagery from multiple remote sensing aircraft and satellites with heterogeneous spatial resolution and spectral coverage. Our cloud detection algorithm is novel in that it attempts to learn deep invariant features for cloud detection utilizing both the spectral and the spatial information inherent in satellite imagery. The first part of our work presents the CNN cloud and cloud shadow algorithm development (trained using WV-2 data) and its application to WV-2 (with a cloud detection accuracy of 89%) and to S-2 imagery (referred to as augmented CNN). The second part presents a new domain adaptation CNN-based approach (domain adversarial NN) that allows for better adaptation between the two satellite platforms during the prediction step, without the need to train for each platform separately. Our augmented CNN algorithm results in better cloud prediction rates as compared to the original S-2 cloud mask (81% versus 48%), but still, clear pixels prediction rate is lower than S-2 (81% versus 91%). Nevertheless, the application of the domain adaptation approach shows promise in better transferring the knowledge gained from one trained domain (WV-2) to another (S-2), increasing the prediction accuracy of both clear and cloudy pixels when compared to a network trained only by WV-2. As such, domain adaptation may offer a novel means of additional augmentation for our CNN-based cloud detection algorithm, increasing robustness towards predictions from multiple remote sensing platforms. The approach presented here may be further developed and optimized for global and multi-modal (multi-channel and multi-platform) satellite cloud detection capability by utilizing a more global dataset. • Convolutional Neural Networks (CNNs) are used for cloud and cloud shadow detection. • CNN-based cloud detector exploits both spatial and spectral data for high accuracy. • Our CNN-based method does not rely on SWIR or thermal IR bands. • Our CNN-based method trained on WV-2 succeeded in predicting Sentinel clouds. • Our DANN-based method improved Sentinel cloud predictions based on WV-2 training. [ABSTRACT FROM AUTHOR]