Variable Winds and Dust Formation in R Coronae Borealis Stars

We have observed P-Cygni and asymmetric, blue-shifted absorption profiles in the He I 10830 lines of twelve R Coronae Borealis (RCB) stars over short (1 month) and long (3 year) timescales to look for variations linked to their dust-formation episodes. In almost all cases, the strengths and terminal velocities of the line vary significantly and are correlated with dust formation events. Strong absorption features with blue-shifted velocities ~400 km/s appear during declines in visible brightness and persist for about 100 days after recovery to maximum brightness. Small residual winds of somewhat lower velocity are present outside of the decline and recovery periods. The correlations support models in which recently formed dust near the star is propelled outward at high speed by radiation pressure and drags the gas along with it.Comment: AJ in press, 21 pages, 3 figure

Design of magnetic traps for neutral atoms with vortices in type-II superconducting micro-structures

We design magnetic traps for atoms based on the average magnetic field of vortices induced in a type-II superconducting thin film. This magnetic field is the critical ingredient of the demonstrated vortex-based atom traps, which operate without transport current. We use Bean's critical-state method to model the vortex field through mesoscopic supercurrents induced in the thin strip. The resulting inhomogeneous magnetic fields are studied in detail and compared to those generated by multiple normally-conducting wires with transport currents. Various vortex patterns can be obtained by programming different loading-field and transport current sequences. These variable magnetic fields are employed to make versatile trapping potentials.Comment: 11 pages, 14 figure

A fundamental limit to the efficiency of spin-exchange optical pumping of 3He nuclei

We establish the existence of a fundamental limit to the efficiency of spin-exchange optical pumping of 3He nuclei by collisions with spin-polarized alkali-metal atoms. Using accurate ab initio calculations of molecular interactions and scattering properties, we show that the maximum 3He spin polarization that can be achieved in spin-exchange collisions with potassium (39K) and silver (107Ag) atoms is limited by the anisotropic hyperfine interaction. We find that spin exchange in Ag-He collisions occurs much faster than in K-He collisions, suggesting the possibility of using Ag in spin-exchange optical pumping experiments to increase the production rate of hyperpolarized 3He. Our analysis indicates that measurements of trap loss rates of 2S atoms in the presence of cold 3He gas may be used to probe anisotropic spin-exchange interactions in atom-He collisions.Comment: 5 pages, 4 figure

Coexistence of Spin Density Wave and Triplet Superconductivity

We discuss the possibility of coexistence of spin density wave (antiferromagnetism) and triplet superconductivity as a particular example of a broad class of systems where the interplay of magnetism and superconductivity is important. We focus on the case of quasi-one-dimensional metals, where it is known experimentally that antiferromagnetism is in close proximity to triplet superconductivity in the temperature versus pressure phase diagram. Over a narrow range of pressures, we propose an intermediate non-uniform phase consisting of alternating antiferromagnetic and triplet superconducting stripes. Within the non-uniform phase there are also changes between two and three dimensional behavior.Comment: Revtex4, 4 pages, 5 figure

GhostVLAD for set-based face recognition

The objective of this paper is to learn a compact representation of image sets for template-based face recognition. We make the following contributions: first, we propose a network architecture which aggregates and embeds the face descriptors produced by deep convolutional neural networks into a compact fixed-length representation. This compact representation requires minimal memory storage and enables efficient similarity computation. Second, we propose a novel GhostVLAD layer that includes {\em ghost clusters}, that do not contribute to the aggregation. We show that a quality weighting on the input faces emerges automatically such that informative images contribute more than those with low quality, and that the ghost clusters enhance the network's ability to deal with poor quality images. Third, we explore how input feature dimension, number of clusters and different training techniques affect the recognition performance. Given this analysis, we train a network that far exceeds the state-of-the-art on the IJB-B face recognition dataset. This is currently one of the most challenging public benchmarks, and we surpass the state-of-the-art on both the identification and verification protocols.Comment: Accepted by ACCV 201