Superfluid Transition in a Chiron Gas

Low temperature measurements of the magnetic susceptibility of LSCO suggest that the superconducting transition is associated with the disappearance of a vortex liquid. In this note we wish to draw attention to the fact that spin-orbit-like interactions in a poorly conducting layered material can lead to a new type of quantum ground state with spin polarized soliton-like charge carriers as the important quantum degree of freedom. In 2-dimensions these solitons are vortex-like, while in 3-dimensional systems they are monopole-like. In either case there is a natural mechanism for the pairing of spin up and spin down solitons, and we find that at low temperatures there is a cross-over transition as a function of carrier density between a state where the solitons are free and a condensate state where the spin up and spin down solitons in neighboring layers are paired.Comment: 10 pages, 1 figur

How to Host a Data Competition: Statistical Advice for Design and Analysis of a Data Competition

Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the underlying problem of interest to the host. This paper outlines some important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition based on our experience. It also describes a post-competition analysis that enables robust and efficient assessment of the strengths and weaknesses of solutions from different competitors, as well as greater understanding of the regions of the input space that are well-solved. The post-competition analysis, which complements the leaderboard, uses exploratory data analysis and generalized linear models (GLMs). The GLMs not only expand the range of results we can explore, they also provide more detailed analysis of individual sub-questions including similarities and differences between algorithms across different types of scenarios, universally easy or hard regions of the input space, and different learning objectives. When coupled with a strategically planned data generation approach, the methods provide richer and more informative summaries to enhance the interpretation of results beyond just the rankings on the leaderboard. The methods are illustrated with a recently completed competition to evaluate algorithms capable of detecting, identifying, and locating radioactive materials in an urban environment.Comment: 36 page

A Deep Relevance Matching Model for Ad-hoc Retrieval

In recent years, deep neural networks have led to exciting breakthroughs in speech recognition, computer vision, and natural language processing (NLP) tasks. However, there have been few positive results of deep models on ad-hoc retrieval tasks. This is partially due to the fact that many important characteristics of the ad-hoc retrieval task have not been well addressed in deep models yet. Typically, the ad-hoc retrieval task is formalized as a matching problem between two pieces of text in existing work using deep models, and treated equivalent to many NLP tasks such as paraphrase identification, question answering and automatic conversation. However, we argue that the ad-hoc retrieval task is mainly about relevance matching while most NLP matching tasks concern semantic matching, and there are some fundamental differences between these two matching tasks. Successful relevance matching requires proper handling of the exact matching signals, query term importance, and diverse matching requirements. In this paper, we propose a novel deep relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model employs a joint deep architecture at the query term level for relevance matching. By using matching histogram mapping, a feed forward matching network, and a term gating network, we can effectively deal with the three relevance matching factors mentioned above. Experimental results on two representative benchmark collections show that our model can significantly outperform some well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape

Adaptive Optics Observations of the Galactic Center Young Stars

Adaptive Optics observations have dramatically improved the quality and versatility of high angular resolution measurements of the center of our Galaxy. In this paper, we quantify the quality of our Adaptive Optics observations and report on the astrometric precision for the young stellar population that appears to reside in a stellar disk structure in the central parsec. We show that with our improved astrometry and a 16 year baseline, including 10 years of speckle and 6 years of laser guide star AO imaging, we reliably detect accelerations in the plane of the sky as small as 70 microarcsec/yr/yr (~2.5 km/s/yr) and out to a projected radius from the supermassive black hole of 1.5" (~0.06 pc). With an increase in sensitivity to accelerations by a factor of ~6 over our previous efforts, we are able to directly probe the kinematic structure of the young stellar disk, which appears to have an inner radius of 0.8". We find that candidate disk members are on eccentric orbits, with a mean eccentricity of = 0.30 +/- 0.07. Such eccentricities cannot be explained by the relaxation of a circular disk with a normal initial mass function, which suggests the existence of a top-heavy IMF or formation in an initially eccentric disk.Comment: 7 pages, 4 figures, SPIE Astronomical Telescopes and Instrumentation 201

The dust morphology of the elliptical Galaxy M86 with SPIRE

We present Herschel-SPIRE observations at 250–500 μm of the giant elliptical galaxy M 86 and examine the distribution of the resolved cold dust emission and its relation with other galactic tracers. The SPIRE images reveal three dust components: emission from the central region; a dust lane extending north-south; and a bright emission feature 10 kpc to the south-east. We estimate that ~10^6 M_☉ of dust is spatially coincident with atomic and ionized hydrogen, originating from stripped material from the nearby spiral NGC 4438 due to recent tidal interactions with M 86. The gas-to-dust ratio of the cold gas component ranges from ~20–80. We discuss the different heating mechanisms for the dust features

Proximity and anomalous field-effect characteristics in double-wall carbon nanotubes

Proximity effect on field-effect characteristic (FEC) in double-wall carbon nanotubes (DWCNTs) is investigated. In a semiconductor-metal (S-M) DWCNT, the penetration of electron wavefunctions in the metallic shell to the semiconducting shell turns the original semiconducting tube into a metal with a non-zero local density of states at the Fermi level. By using a two-band tight-binding model on a ladder of two legs, it is demonstrated that anomalous FEC observed in so-called S-M type DWCNTs can be fully understood by the proximity effect of metallic phases.Comment: 4 pages, 4 figure