Cosmic Hydrogen and Ice Loss Lines

We explain the overall equilibrium-temperature-dependent trend in the exoplanet mass-radius diagram, using the escape mechanisms of hydrogen and relevant volatiles, and the chemical equilibrium calculation of molecular hydrogen (H$_2$) break-up into atomic hydrogen (H). We identify two Cosmic Hydrogen and Ice Loss Lines (CHILLs) in the mass-radius diagram. Gas disks are well known to disperse in ten million years. However, gas-rich planets may lose some or almost all gas on a much longer timescale. We thus hypothesize that most planets that are born out of a hydrogen-gas-dominated nebular disk begin by possessing a primordial H$_2$-envelope. This envelope is gradually lost due to escape processes caused by host-stellar radiation.Comment: Updated version with new sections/figures explaining the escape mechanism and updated mass-radius plots in the Appendix. Comments and suggestions welcome

H-means image segmentation to identify solar thermal features

Properly segmenting multiband images of the Sun by their thermal properties will help determine the thermal structure of the solar corona. However, off-the-shelf segmentation algorithms are typically inappropriate because temperature information is captured by the relative intensities in different passbands, while the absolute levels are not relevant. Input features are therefore pixel-wise proportions of photons observed in each band. To segment solar images based on these proportions, we use a modification of k-means clustering that we call the H-means algorithm because it uses the Hellinger distance to compare probability vectors. H-means has a closed-form expression for cluster centroids, so computation is as fast as k-means. Tempering the input probability vectors reveals a broader class of H-means algorithms which include spherical k-means clustering. More generally, H-means can be used anytime the input feature is a probabilistic distribution, and hence is useful beyond image segmentation applications.Statistic

A New Concept to Measure the Ambipolar Electric Field Driving Ionospheric Outflow

Over the last few decades, the role of ionospheric outflow for the loss of atmospheric constituents, as a plasma supplier to the magnetosphere and hence for the evolution of the Earth’s atmosphere has been recognized. A substantial amount of the outflow is thought to be caused by the presence of an ambipolar electric field aligned with the open magnetic field lines of the polar region. To better understand how the changes in outflow are influenced by the solar and geomagnetic activity, it is critical to get a better understanding of the impact of this electric field, and to be able to measure it under various conditions. However, such measurements are not possible with present techniques. In this paper, we propose a new technique to measure the tiny electric field. This technique builds on existing instrument technology but extends the capability to measure the very small electric fields. We present the underlying design concept and demonstrate that this concept is viable and able to measure the very small ambipolar electric fields thought to play a key role in the polar wind.publishedVersio

Targeted Assembly of Short Sequence Reads

As next-generation sequence (NGS) production continues to increase, analysis is becoming a significant bottleneck. However, in situations where information is required only for specific sequence variants, it is not necessary to assemble or align whole genome data sets in their entirety. Rather, NGS data sets can be mined for the presence of sequence variants of interest by localized assembly, which is a faster, easier, and more accurate approach. We present TASR, a streamlined assembler that interrogates very large NGS data sets for the presence of specific variants, by only considering reads within the sequence space of input target sequences provided by the user. The NGS data set is searched for reads with an exact match to all possible short words within the target sequence, and these reads are then assembled strin-gently to generate a consensus of the target and flanking sequence. Typically, variants of a particular locus are provided as different target sequences, and the presence of the variant in the data set being interrogated is revealed by a successful assembly outcome. However, TASR can also be used to find unknown sequences that flank a given target. We demonstrate that TASR has utility in finding or confirming ge-nomic mutations, polymorphism, fusion and integration events. Targeted assembly is a powerful method for interrogating large data sets for the presence of sequence variants of interest. TASR is a fast, flexible and easy to use tool for targeted assembly

Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images

This paper presents a new multi-task neural network, called BsiNet, to delineate agricultural fields from high-resolution satellite images. BsiNet is modified from a Psi-Net by structuring three parallel decoders into a single encoder to improve computational efficiency. BsiNet learns three tasks: a core task for agricultural field identification and two auxiliary tasks for field boundary prediction and distance estimation, corresponding to mask, boundary, and distance tasks, respectively. A spatial group-wise enhancement module is incorporated to improve the identification of small fields. We conducted experiments on a GaoFen1 and three GaoFen2 satellite images collected in Xinjiang, Fujian, Shandong, and Sichuan provinces in China, and compared BsiNet with 13 different neural networks. Our results show that the agricultural fields extracted by BsiNet have the lowest global over-classification (GOC) of 0.062, global under-classification (GUC) of 0.042, and global total errors (GTC) of 0.062 for the Xinjiang dataset. For the Fujian dataset with irregular and complex fields, BsiNet outperformed the second-best method from the Xinjiang dataset analysis, yielding the lowest GTC of 0.291. It also produced satisfactory results on the Shandong and Sichuan datasets. Moreover, BsiNet has fewer parameters and faster computation than existing multi-task models (i.e., Psi-Net and ResUNet-a D7). We conclude that BsiNet can be used successfully in extracting agricultural fields from high-resolution satellite images and can be applied to different field settings.</p