Remedies for Discrimination in Apprenticeship Programs

[Excerpt] Efforts by Negroes to gain admission to apprenticeship programs in the building, machinist, and printing crafts have received widespread attention. Street demonstrations, picketing, entrance blocking, sleepins in union halls, and several violent clashes with police have catapulted the topic into the headlines. Accordingly, the issue has joined the grievances which serve as rallying cries for civil rights spokesmen in every section of the country. In response both to the public furor and to the notable absence of Negro workers in these trades, public authorities have adopted a variety of remedial measures. The main objective of this article is to review the effectiveness of these remedies and to suggest others which are likely to be more successful in solving this important domestic problem

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page

An Unbiased Survey of 500 Nearby Stars for Debris Disks: A JCMT Legacy Program

We present the scientific motivation and observing plan for an upcoming detection survey for debris disks using the James Clerk Maxwell Telescope. The SCUBA-2 Unbiased Nearby Stars (SUNS) Survey will observe 500 nearby main sequence and sub-giant stars (100 of each of the A, F, G, K and M spectral classes) to the 850 micron extragalactic confusion limit to search for evidence of submillimeter excess, an indication of circumstellar material. The survey distance boundaries are 8.6, 16.5, 22, 25 and 45 pc for M, K, G, F and A stars, respectively, and all targets lie between the declinations of -40 deg to 80 deg. In this survey, no star will be rejected based on its inherent properties: binarity, presence of planetary companions, spectral type or age. This will be the first unbiased survey for debris disks since IRAS. We expect to detect ~125 debris disks, including ~50 cold disks not detectable in current shorter wavelength surveys. A substantial amount of complementary data will be required to constrain the temperatures and masses of discovered disks. High resolution studies will likely be required to resolve many of the disks. Therefore, these systems will be the focus of future observational studies using a variety of observatories to characterize their physical properties. For non-detected systems, this survey will set constraints (upper limits) on the amount of circumstellar dust, of typically 200 times the Kuiper Belt mass, but as low as 10 times the Kuiper Belt mass for the nearest stars in the sample (approximately 2 pc).Comment: 11 pages, 7 figures (3 color), accepted by the Publications of the Astronomical Society of the Pacifi

The High-Metallicity Explosion Environment of the Relativistic Supernova 2009bb

We investigate the environment of the nearby (d ~ 40Mpc) broad-lined Type Ic supernova SN 2009bb. This event was observed to produce a relativistic outflow likely powered by a central accreting compact object. While such a phenomenon was previously observed only in long-duration gamma-ray bursts (LGRBs), no LGRB was detected in association with SN 2009bb. Using an optical spectrum of the SN 2009bb explosion site, we determine a variety of ISM properties for the host environment, including metallicity, young stellar population age, and star formation rate. We compare the SN explosion site properties to observations of LGRB and broad-lined SN Ic host environments on optical emission line ratio diagnostic diagrams. Based on these analyses, we find that the SN 2009bb explosion site has a very high metallicity of ~2x solar, in agreement with other broad-lined SN Ic host environments and at odds with the low-redshift LGRB host environments and recently proposed maximum metallicity limits for relativistic explosions. We consider the implications of these findings and the impact that SN 2009bb's unusual explosive properties and environment have on our understanding of the key physical ingredient that enables some SNe to produce a relativistic outflow.Comment: 7 pages, 4 figures, 1 table; accepted for publication in ApJ Letters (replaced to include missing figure