Vorticity and divergence in the solar photosphere

We have studied an outstanding sequence of continuum images of the solar granulation from Pic du Midi Observatory. We have calculated the horizontal vector flow field using a correlation tracking algorithm, and from this determined three scalar field: the vertical component of the curl; the horizontal divergence; and the horizontal flow speed. The divergence field has substantially longer coherence time and more power than does the curl field. Statistically, curl is better correlated with regions of negative divergence - that is, the vertical vorticity is higher in downflow regions, suggesting excess vorticity in intergranular lanes. The average value of the divergence is largest (i.e., outflow is largest) where the horizontal speed is large; we associate these regions with exploding granules. A numerical simulation of general convection also shows similar statistical differences between curl and divergence. Some individual small bright points in the granulation pattern show large local vorticities

Turbulent small-scale dynamo action in solar surface simulations

We demonstrate that a magneto-convection simulation incorporating essential physical processes governing solar surface convection exhibits turbulent small-scale dynamo action. By presenting a derivation of the energy balance equation and transfer functions for compressible magnetohydrodynamics (MHD), we quantify the source of magnetic energy on a scale-by-scale basis. We rule out the two alternative mechanisms for the generation of small-scale magnetic field in the simulations: the tangling of magnetic field lines associated with the turbulent cascade and Alfvenization of small-scale velocity fluctuations ("turbulent induction"). Instead, we find the dominant source of small-scale magnetic energy is stretching by inertial-range fluid motions of small-scale magnetic field lines against the magnetic tension force to produce (against Ohmic dissipation) more small-scale magnetic field. The scales involved become smaller with increasing Reynolds number, which identifies the dynamo as a small-scale turbulent dynamo.Comment: accepted by Ap

Loop Evolution Observed with AIA and Hi-C

In the past decade, the evolution of EUV loops has been used to infer the loop substructure. With the recent launch of High Resolution Coronal Imager (Hi-C), this inference can be validated. In this presentation we discuss the first results of loop analysis comparing AIA and Hi-C data. In the past decade, the evolution of EUV loops has been used to infer the loop substructure. With the recent launch of High Resolution Coronal Imager (Hi-C), this inference can be validated. In this presentation we discuss the first results of loop analysis comparing AIA and Hi-C data

Sounding Rocket Instrument Development at UAHuntsville/NASA MSFC

We present an overview of solar sounding rocket instruments developed jointly by NASA Marshall Space Flight Center and the University of Alabama in Huntsville. The High Resolution Coronal Imager (Hi-C) is an EUV (19.3 nm) imaging telescope which was flown successfully in July 2012. The Chromospheric Lyman-Alpha SpectroPolarimeter (CLASP) is a Lyman Alpha (121.6 nm) spectropolarimeter developed jointly with the National Astronomical Observatory of Japan and scheduled for launch in 2015. The Marshall Grazing Incidence X-ray Spectrograph is a soft X-ray (0.5-1.2 keV) stigmatic spectrograph designed to achieve 5 arcsecond spatial resolution along the slit

Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination

Organizations cannot address demographic disparities that they cannot see. Recent research on machine learning and fairness has emphasized that awareness of sensitive attributes, such as race and sex, is critical to the development of interventions. However, on the ground, the existence of these data cannot be taken for granted. This paper uses the domains of employment, credit, and healthcare in the United States to surface conditions that have shaped the availability of sensitive attribute data. For each domain, we describe how and when private companies collect or infer sensitive attribute data for antidiscrimination purposes. An inconsistent story emerges: Some companies are required by law to collect sensitive attribute data, while others are prohibited from doing so. Still others, in the absence of legal mandates, have determined that collection and imputation of these data are appropriate to address disparities. This story has important implications for fairness research and its future applications. If companies that mediate access to life opportunities are unable or hesitant to collect or infer sensitive attribute data, then proposed techniques to detect and mitigate bias in machine learning models might never be implemented outside the lab. We conclude that today's legal requirements and corporate practices, while highly inconsistent across domains, offer lessons for how to approach the collection and inference of sensitive data in appropriate circumstances. We urge stakeholders, including machine learning practitioners, to actively help chart a path forward that takes both policy goals and technical needs into account

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types