Search

Pitch Angle Distributions in the radiation belts are well characterized with sinnα. By tracking the exponent ‘n’, termed Pitch Angle Index, we are able to observe persistent and cross energy changes in pitch angle distributions of Van Allen radiation belt electrons using Van Allen Probes particle observations. The pitch angle distributions measurements are well fit down to a single satellite spin, and therefore can track spatially and temporally confined changes to determine connection between particles and waves. We use the Van Allen Probes data in conjunction with Geostationary Operational Environmental Satellites (GOES) spacecraft and several ground magnetometer stations from Canadian Array for Realtime Investigations of Magnetic Activity (CARISMA) and Finnish pulsation magnetometer network of Sodankylä Geophysical Observatory (SGO) to connect particles that become very anisotropic to electromagnetic ion cyclotron (EMIC) waves during a quiet period over two days, June 26 and 27, 2013. The waves and peaks in the particle PADs are both long lasting but spatially separated, suggesting that wave particle interactions in the inner magnetosphere can occur for extend periods of time and have significant impact on the global radiation belts, even during otherwise geomagnetically quiet times and when wave activity is highly localized.

A genomic sketch is a small, probabilistic representation of the set of k-mers in a sequencing dataset. Sketches are building blocks for large-scale analyses that consider similarities between many pairs of sequences or sequence collections. While existing tools can easily compare 10,000s of genomes, relevant datasets can reach millions of sequences and beyond. Popular tools also fail to considerk-mer multiplicities, making them less applicable in quantitative settings. We describe a method called Dashing 2 that builds on the SetSketch data structure. SetSketch is related to HyperLogLog, but discards use of leading zero count in favor of a truncated logarithm of adjustable base. Unlike HLL, SetSketch can perform multiplicity-aware sketching when combined with the ProbMinHash method. Dashing 2 integrates locality-sensitive hashing to scale all-pairs comparisons to millions of sequences. Dashing 2 is free, open source software available athttps://github.com/dnbaker/dashing2

We present a machine-learning-based model of relativistic electron fluxes >1.8 MeV using a neural network approach in the Earth's outer radiation belt. The Outer RadIation belt Electron Neural net model for Relativistic electrons (ORIENT-R) uses only solar wind conditions and geomagnetic indices as input. For the first time, we show that the state of the outer radiation belt can be determined using only solar wind conditions and geomagnetic indices, without any initial and boundary conditions. The most important features for determining outer radiation belt dynamics are found to be AL, solar wind flow speed and density, and SYM-H indices. ORIENT-R reproduces out-of-sample relativistic electron fluxes with a correlation coefficient of 0.95 and an uncertainty factor of ∼2. ORIENT-R reproduces radiation belt dynamics during an out-of-sample geomagnetic storm with good agreement to the observations. In addition, ORIENT-R was run for a completely out-of-sample period between March 2018 and October 2019 when the AL index ended and was replaced with the predicted AL index (lasp.colorado.edu/home/personnel/xinlin.li). It reproduces electron fluxes with a correlation coefficient of 0.92 and an out-of-sample uncertainty factor of ∼3. Furthermore, ORIENT-R captured the trend in the electron fluxes from low-earth-orbit (LEO) SAMPEX, which is a completely out-of-sample data set both temporally and spatially. In sum, the ORIENT-R model can reproduce transport, acceleration, decay, and dropouts of the outer radiation belt anywhere from short timescales (i.e., geomagnetic storms) and very long timescales (i.e., solar cycle) variations.

The Atmospheric Effects of Precipitation through Energetic X-rays (AEPEX) mission is a 6U CubeSat that will monitor energetic electron precipitation from the radiation belts into the upper atmosphere. The primary instrument will image energetic (50–300 keV) X-rays produced in the atmosphere by bremsstrahlung, providing a near-direct signature of electron precipitation. An energetic electron detector will measure the precipitating electron spectrum, while the X-ray observations will be used to determine the absolute flux. X-ray images will be produced with 10-s time resolution and 50–100 km spatial resolution. The 6U spacecraft uses flight heritage spacecraft bus subsystems, including the attitude determination and control, electrical power, and command & data handling systems. AEPEX is designed to be operated from low-Earth orbit at ~ 500 km altitude with a high inclination in order to cover the outer radiation belt. AEPEX will be the first spacecraft mission to measure X-rays in the 50–300 keV energy range emitted by Earth’s atmosphere in response to radiation belt precipitation, and the first to image that precipitation from above.

A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or consistency of the cluster, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span. To address this problem, previous work introduced the ‘scale-agnostic’ G+ discordance metric, however this internal metric is slow to calculate for large data. Furthermore, we show that G+ varies as a function of the proportion of observations in the predicted cluster labels (group balance), which is an undesirable property.To address this problem, we propose a modification of G+, referred to as H+, and demonstrate that H+ does not vary as a function of group balance using a simulation study and with public single-cell RNA-sequencing data. Finally, we provide scalable approaches to estimate H+, which are available in the fasthplus R package.

We present a machine-learning-based model of relativistic electron fluxes >1.8 MeV using a neural network approach in the Earth's outer radiation belt. The Outer RadIation belt Electron Neural net model for Relativistic electrons (ORIENT-R) uses only solar wind conditions and geomagnetic indices as input. For the first time, we show that the state of the outer radiation belt can be determined using only solar wind conditions and geomagnetic indices, without any initial and boundary conditions. The most important features for determining outer radiation belt dynamics are found to be AL, solar wind flow speed and density, and SYM-H indices. ORIENT-R reproduces out-of-sample relativistic electron fluxes with a correlation coefficient of 0.95 and an uncertainty factor of ~2. ORIENT-R reproduces radiation belt dynamics during an out-of-sample geomagnetic storm with good agreement to the observations. In addition, ORIENT-R was run for a completely out-of-sample period between March 2018 and October 2019 when the AL index ended and was replaced with the predicted AL index (lasp.colorado.edu/~lix). It reproduces electron fluxes with a correlation coefficient of 0.92 and an out-of-sample uncertainty factor of ~3. Furthermore, ORIENT-R captured the trend in the electron fluxes from low-earth-orbit (LEO) SAMPEX, which is a completely out-of-sample dataset both temporally and spatially. In sum, the ORIENT-R model can reproduce transport, acceleration, decay, and dropouts of the outer radiation belt anywhere from short timescales (i.e., geomagnetic storms) and very long timescales (i.e., solar cycle) variations., 37 pages, 8 figures

Single-cell RNA-sequencing (scRNA-seq) analyses typically begin by clustering a gene-by-cell expression matrix to empirically define groups of cells with similar expression profiles. We describe new methods and a new open source library, minicore, for efficient k-means++ center finding and k-means clustering of scRNA-seq data. Minicore works with sparse count data, as it emerges from typical scRNA-seq experiments, as well as with dense data from after dimensionality reduction. Minicore’s novel vectorized weighted reservoir sampling algorithm allows it to find initial k-means++ centers for a 4-million cell dataset in 1.5 minutes using 20 threads. Minicore can cluster using Euclidean distance, but also supports a wider class of measures like Jensen-Shannon Divergence, Kullback-Leibler Divergence, and the Bhattachaiyya distance, which can be directly applied to count data and probability distributions. Further, minicore produces lower-cost centerings more efficiently than scikit-learn for scRNA-seq datasets with millions of cells. With careful handling of priors, minicore implements these distance measures with only minor (