Detecting the Presence of Vehicles and Equipment in SAR Imagery Using Image Texture Features

In this work, we present a methodology for monitoring man-made, construction-like activities in low-resolution SAR imagery. Our source of data is the European Space Agency Sentinel-l satellite which provides global coverage at a 12-day revisit rate. Despite limitations in resolution, our methodology enables us to monitor activity levels (i.e. presence of vehicles, equipment) of a pre-defined location by analyzing the texture of detected SAR imagery. Using an exploratory dataset, we trained a support vector machine (SVM), a random binary forest, and a fully-connected neural network for classification. We use Haralick texture features in the VV and VH polarization channels as the input features to our classifiers. Each classifier showed promising results in being able to distinguish between two possible types of construction-site activity levels. This paper documents a case study that is centered around monitoring the construction process for oil and gas fracking wells.Comment: 6 pages, 6 figures, 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR

Dark Matter in Galaxy Clusters: Shape, Projection, and Environment

We explore the intrinsic distribution of dark matter within galaxy clusters, by combining insights from the largest {\em N}-body simulations as well as the largest observational dataset of its kind. Firstly, we study the intrinsic shape and alignment of isodensities of galaxy cluster halos extracted from the MultiDark MDR1 cosmological simulation. We find that the simulated halos are extremely prolate on small scales and increasingly spherical on larger ones. Due to this trend, analytical projection along the line of sight produces an overestimate of the concentration index as a decreasing function of radius, which we quantify by using both the intrinsic distribution of 3D concentrations ($c_{200}$) and isodensity shape on weak and strong lensing scales. We find this difference to be $\sim 18\%$ ($\sim 9\%$) for low (medium) mass cluster halos with intrinsically low concentrations ($c_{200}=1-3$), while we find virtually no difference for halos with intrinsically high concentrations. Isodensities are found to be fairly well-aligned throughout the entirety of the radial scale of each halo population. However, major axes of individual halos have been found to deviate by as much as $\sim 30^{\circ}$. We also present a value-added catalog of our analysis results, which we have made publicly available to download. Following that, we then turn to observational measurements galaxy clusters. Scaling relations of clusters have made them particularly important cosmological probes of structure formation. In this work, we present a comprehensive study of the relation between two profile observables, concentration ($\mathrm{c_{vir}}$) and mass ($\mathrm{M_{vir}}$). We have collected the largest known sample of measurements from the literature which make use of one or more of the following reconstruction techniques: Weak gravitational lensing (WL), strong gravitational lensing (SL), Weak+Strong Lensing (WL+SL), the Caustic Method (CM), Line-of-sight Velocity Dispersion (LOSVD), and X-ray. We find that the concentration-mass (c-M) relation is highly variable depending upon the reconstruction technique used. We also find concentrations derived from dark matter only simulations (at approximately $\mathrm{M_{vir} \sim 10^{14} M_{\odot}}$) to be inconsistent with the WL and WL+SL relations at the $\mathrm{1\sigma}$ level, even after the projection of triaxial halos is taken into account. However, to fully determine consistency between simulations and observations, a volume-limited sample of clusters is required, as selection effects become increasingly more important in answering this. Interestingly, we also find evidence for a steeper WL+SL relation as compared to WL alone, a result which could perhaps be caused by the varying shape of cluster isodensities, though most likely reflects differences in selection effects caused by these two techniques. Lastly, we compare concentration and mass measurements of individual clusters made using more than one technique, highlighting the magnitude of the potential bias which could exist in such observational samples.Finally, we explore the large-scale environment around galaxy clusters using spectroscopically confirmed galaxies from the Sloan Digital Sky Survey (SDSS) Data Release 10. We correlate the angular structure of the distribution of galaxies (out to a distance of $\mathrm{10 h^{-1}\, Mpc}$) around 92 galaxy clusters with their corresponding mass and concentration measurements. We find that the orientation of the cluster environment on this scale has little impact on the value of cluster measurements.Ph.D., Physics -- Drexel University, 201

Globally-scalable Automated Target Recognition (GATR)

GATR (Globally-scalable Automated Target Recognition) is a Lockheed Martin software system for real-time object detection and classification in satellite imagery on a worldwide basis. GATR uses GPU-accelerated deep learning software to quickly search large geographic regions. On a single GPU it processes imagery at a rate of over 16 square km/sec (or more than 10 Mpixels/sec), and it requires only two hours to search the entire state of Pennsylvania for gas fracking wells. The search time scales linearly with the geographic area, and the processing rate scales linearly with the number of GPUs. GATR has a modular, cloud-based architecture that uses the Maxar GBDX platform and provides an ATR analytic as a service. Applications include broad area search, watch boxes for monitoring ports and airfields, and site characterization. ATR is performed by deep learning models including RetinaNet and Faster R-CNN. Results are presented for the detection of aircraft and fracking wells and show that the recalls exceed 90% even in geographic regions never seen before. GATR is extensible to new targets, such as cars and ships, and it also handles radar and infrared imagery.Comment: 7 pages, 18 figures, 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR

Conditional -VAE for De Novo Molecular Generation

Deep learning has significantly advanced and accelerated de novo molecular generation. Generative networks, namely Variational Autoencoders (VAEs) can not only randomly generate new molecules but also alter molecular structures to optimize specific chemical properties which are pivotal for drug-discovery. While VAEs have been proposed and researched in the past for pharmaceutical applications, they possess deficiencies that limit their ability to both optimize properties and decode syntactically valid molecules. We present a recurrent, conditional -VAE that disentangles the latent space to enhance post hoc molecule optimization. We create a mutual information driven training protocol and data augmentations to both increase molecular validity and promote longer sequence generation. We demonstrate the efficacy of our framework on the ZINC-250k dataset, achieving SOTA unconstrained optimization results on the penalized LogP (pLogP) and QED scores, while also matching current SOTA results for validity, novelty, and uniqueness scores for random generation. We match the current SOTA on QED for top-3 molecules at 0.948, while setting a new SOTA for pLogP optimization at 104.29, 90.12, 69.68 and demonstrating improved results on the constrained optimization task

astroquery: v0.3.2 release

Functions and classes to access online data resources. Maintainer: @keflavic

astropy/astroquery: v0.4.5

What's Changed Update Spectra URLs by @weaverba137 in https://github.com/astropy/astroquery/pull/2214ALMA integration tests fix by @andamian in https://github.com/astropy/astroquery/pull/2224Fix doc build issues by @bsipocz in https://github.com/astropy/astroquery/pull/2226Allow retrieval from a previous ESO archive request by @gbrammer in https://github.com/astropy/astroquery/pull/1614Turn off default verbosity for TapPlus by @bsipocz in https://github.com/astropy/astroquery/pull/2228Refreshing content of RTD config by @bsipocz in https://github.com/astropy/astroquery/pull/2229NED: String has to remain raw by @bsipocz in https://github.com/astropy/astroquery/pull/2230eJWST prelaunch by @jespinosaar in https://github.com/astropy/astroquery/pull/2140MNT: Cleanup of old unused code and configs by @bsipocz in https://github.com/astropy/astroquery/pull/2231Adding changelog rendering to narrative docs by @bsipocz in https://github.com/astropy/astroquery/pull/2233Mast cloudaccess docs update by @jaymedina in https://github.com/astropy/astroquery/pull/2235eJWST release by @jespinosaar in https://github.com/astropy/astroquery/pull/2238eJWST: remove disclaimer for release by @jespinosaar in https://github.com/astropy/astroquery/pull/2243Fix to allow html downloads with alma package by @andamian in https://github.com/astropy/astroquery/pull/2246Deprecate astroquery/utils/download_file_list.py by @eerovaher in https://github.com/astropy/astroquery/pull/2247Expand contribution guidelines by @keflavich in https://github.com/astropy/astroquery/pull/2248Fix for issue #2237: do not cache results that cannot be parsed. by @mkelley in https://github.com/astropy/astroquery/pull/2253Adding python 3.10 to CI by @bsipocz in https://github.com/astropy/astroquery/pull/2186 New Contributors @gbrammer made their first contribution in https://github.com/astropy/astroquery/pull/1614 Full Changelog: https://github.com/astropy/astroquery/compare/v0.4.4...v0.4.

The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package*

The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates on the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project