Pulsation of EE Cam

EE Cam is a previously little studied Delta Scuti pulsator with amplitudes between those of the HADS (High-Amplitude Delta Scuti stars) group and the average low-amplitude pulsators. Since the size of stellar rotation determines both which pulsation modes are selected by the star as well as their amplitudes, the star offers a great opportunity to examine the astrophysical connections. Extensive photometric measurements covering several months were carried out. 15 significant pulsation frequencies were extracted. The dominant mode at 4.934 cd$^{-1}$ was identified as a radial mode by examining the phase shifts at different wavelengths. Medium-dispersion spectra yielded a $v\sin i$ value of $40 \pm 3$ km s$^{-1}$. This shows that EE Cam belongs to the important transition region between the HADS and normal Delta Scuti stars.Comment: 13 pages, 3 figures, 3 table

PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. This work is an important first step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.Comment: 14 pages, 5 figures, submitted for review to JML

Automating biomedical data science through tree-based pipeline optimization

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators---such as synthetic feature constructors---that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.Comment: 16 pages, 5 figures, to appear in EvoBIO 2016 proceeding

Microwave (SSM/I) Estimates of the Precipitation Rate to Improve Numerical Atmospheric Model Forecasts

Delay in the spin-up of precipitation early in numerical atmospheric forecasts is a deficiency correctable by diabatic initialization combined with diabatic forcing. For either to be effective requires some knowledge of the magnitude and vertical placement of the latent heating fields. Until recently the best source of cloud and rain water data was the remotely sensed vertical integrated precipitation rate or liquid water content. Vertical placement of the condensation remains unknown. Some information about the vertical distribution of the heating rates and precipitating liquid water and ice can be obtained from retrieval techniques that use a physical model of precipitating clouds to refine and improve the interpretation of the remotely sensed data. A description of this procedure and an examination of its 3-D liquid water products, along with improved modeling methods that enhance or speed-up storm development is discussed

Hall of Mirrors Scattering from an Impurity in a Quantum Wire

This paper develops a scattering theory to examine how point impurities affect transport through quantum wires. While some of our new results apply specifically to hard-walled wires, others--for example, an effective optical theorem for two-dimensional waveguides--are more general. We apply the method of images to the hard-walled guide, explicitly showing how scattering from an impurity affects the wire's conductance. We express the effective cross section of a confined scatterer entirely in terms of the empty waveguide's Green's function, suggesting a way in which to use semiclassical methods to understand transport properties of smooth wires. In addition to predicting some new phenomena, our approach provides a simple physical picture for previously observed effects such as conductance dips and confinement-induced resonances.Comment: 19 pages, 8 figures. Accepted for publication in Physical Review B. Minor additions to text, added reference