Assessing the Rental Value of Residential Properties: An Abductive Learning Networks Approach

This paper attempts to estimate rental value of residential properties using Abductive Learning Networks (ALN), and artificial intelligence technique. The results indicate that the ALN model provides an accurate estimation of rents with only seven input variables, while other multivariate statistical techniques do not. The ALN model automatically selects the best network structure, node types and coefficients, and therefore it simplifies the maintenance of the model. Once the final model is synthesized, the ALN model becomes very compact, rapidly executable and cost-effective.

A recurrent neural network for classification of unevenly sampled variable stars

Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus time ("light curves"). Unlike in many other physical domains, however, large (and source-specific) temporal gaps in data arise naturally due to intranight cadence choices as well as diurnal and seasonal constraints. With nightly observations of millions of variable stars and transients from upcoming surveys, efficient and accurate discovery and classification techniques on noisy, irregularly sampled data must be employed with minimal human-in-the-loop involvement. Machine learning for inference tasks on such data traditionally requires the laborious hand-coding of domain-specific numerical summaries of raw data ("features"). Here we present a novel unsupervised autoencoding recurrent neural network (RNN) that makes explicit use of sampling times and known heteroskedastic noise properties. When trained on optical variable star catalogs, this network produces supervised classification models that rival other best-in-class approaches. We find that autoencoded features learned on one time-domain survey perform nearly as well when applied to another survey. These networks can continue to learn from new unlabeled observations and may be used in other unsupervised tasks such as forecasting and anomaly detection.Comment: 23 pages, 14 figures. The published version is at Nature Astronomy (https://www.nature.com/articles/s41550-017-0321-z). Source code for models, experiments, and figures at https://github.com/bnaul/IrregularTimeSeriesAutoencoderPaper (Zenodo Code DOI: 10.5281/zenodo.1045560

A deep learning approach to diabetic blood glucose prediction

We consider the question of 30-minute prediction of blood glucose levels measured by continuous glucose monitoring devices, using clinical data. While most studies of this nature deal with one patient at a time, we take a certain percentage of patients in the data set as training data, and test on the remainder of the patients; i.e., the machine need not re-calibrate on the new patients in the data set. We demonstrate how deep learning can outperform shallow networks in this example. One novelty is to demonstrate how a parsimonious deep representation can be constructed using domain knowledge

Direct NN-body code on low-power embedded ARM GPUs

This work arises on the environment of the ExaNeSt project aiming at design and development of an exascale ready supercomputer with low energy consumption profile but able to support the most demanding scientific and technical applications. The ExaNeSt compute unit consists of densely-packed low-power 64-bit ARM processors, embedded within Xilinx FPGA SoCs. SoC boards are heterogeneous architecture where computing power is supplied both by CPUs and GPUs, and are emerging as a possible low-power and low-cost alternative to clusters based on traditional CPUs. A state-of-the-art direct $N$-body code suitable for astrophysical simulations has been re-engineered in order to exploit SoC heterogeneous platforms based on ARM CPUs and embedded GPUs. Performance tests show that embedded GPUs can be effectively used to accelerate real-life scientific calculations, and that are promising also because of their energy efficiency, which is a crucial design in future exascale platforms.Comment: 16 pages, 7 figures, 1 table, accepted for publication in the Computing Conference 2019 proceeding

Molecular Line Observations of Infrared Dark Clouds: Seeking the Precursors to Intermediate and Massive Star Formation

We have identified 41 infrared dark clouds from the 8 micron maps of the Midcourse Space Experiment (MSX), selected to be found within one square degree areas centered on known ultracompact HII regions. We have mapped these infrared dark clouds in N2H+(1-0), CS(2-1) and C18O(1-0) emission using the Five College Radio Astronomy Observatory. The maps of the different species often show striking differences in morphologies, indicating differences in evolutionary state and/or the presence of undetected, deeply embedded protostars. We derive an average mass for these clouds using N2H+ column densities of ~2500 solar masses, a value comparable to that found in previous studies of high mass star forming cores using other mass tracers. The linewidths of these clouds are typically ~2.0 - 2.9 km/s. Based on the fact that they are dark at 8 micron, compact, massive, and have large velocity dispersions, we suggest that these clouds may be the precursor sites of intermediate and high mass star formation.Comment: Accepted to ApJS, 22 pages, 10 pages of figures. For full-resolution images, see http://www.astro.lsa.umich.edu/~seragan/pubs/fcrao/figures.tar.g

Schooling in Rural East Texas: Contextualizing and Responding to the Needs of African American Students

This critical analysis contextualizes and responds to the current state of education for persons of African descent in rural East Texas, specifically Region VII. The researchers analyzed assessment data, attendance data, demographic data, and discipline data from the Texas Education Agency. Selected data provided a pathway to explore variables that directly impact students’ academic performance and identities. Findings from this study highlight concerns that range from discrepancies in out-of-school suspensions, disproportionate representation of faculty with the student populations, and challenges faced by East Texas schools and school districts to meet state and federal policies and accountability standards. The authors recommend that students, families, teachers, administrators within these communities must work together to create an environment that all parties are valued

First direct observation of a nearly ideal graphene band structure

Angle-resolved photoemission and X-ray diffraction experiments show that multilayer epitaxial graphene grown on the SiC(000-1) surface is a new form of carbon that is composed of effectively isolated graphene sheets. The unique rotational stacking of these films cause adjacent graphene layers to electronically decouple leading to a set of nearly independent linearly dispersing bands (Dirac cones) at the graphene K-point. Each cone corresponds to an individual macro-scale graphene sheet in a multilayer stack where AB-stacked sheets can be considered as low density faults.Comment: 5 pages, 4 figure

On the Identification of High Mass Star Forming Regions using IRAS: Contamination by Low-Mass Protostars

We present the results of a survey of a small sample (14) of low-mass protostars (L_IR < 10^3 Lsun) for 6.7 GHz methanol maser emission performed using the ATNF Parkes radio telescope. No new masers were discovered. We find that the lower luminosity limit for maser emission is near 10^3 Lsun, by comparison of the sources in our sample with previously detected methanol maser sources. We examine the IRAS properties of our sample and compare them with sources previously observed for methanol maser emission, almost all of which satisfy the Wood & Churchwell criterion for selecting candidate UCHII regions. We find that about half of our sample satisfy this criterion, and in addition almost all of this subgroup have integrated fluxes between 25 and 60 microns that are similar to sources with detectable methanol maser emission. By identifying a number of low-mass protostars in this work and from the literature that satisfy the Wood & Churchwell criterion for candidate UCHII regions, we show conclusively for the first time that the fainter flux end of their sample is contaminated by lower-mass non-ionizing sources, confirming the suggestion by van der Walt and Ramesh & Sridharan.Comment: 8 pages with 2 figures. Accepted by Ap

How do methanol masers manage to appear in the youngest star vicinities and isolated molecular clumps?

General characteristics of methanol (CH3OH) maser emission are summarized. It is shown that methanol maser sources are concentrated in the spiral arms. Most of the methanol maser sources from the Perseus arm are associated with embedded stellar clusters and a considerable portion is situated close to compact HII regions. Almost 1/3 of the Perseus Arm sources lie at the edges of optically identified HII regions which means that massive star formation in the Perseus Arm is to a great extent triggered by local phenomena. A multiline analysis of the methanol masers allows us to determine the physical parameters in the regions of maser formation. Maser modelling shows that class II methanol masers can be pumped by the radiation of the warm dust as well as by free-free emission of a hypercompact region hcHII with a turnover frequency exceeding 100 GHz. Methanol masers of both classes can reside in the vicinity of hcHIIs. Modelling shows that periodic changes of maser fluxes can be reproduced by variations of the dust temperature by a few percent which may be caused by variations in the brightness of the central young stellar object reflecting the character of the accretion process. Sensitive observations have shown that the masers with low flux densities can still have considerable amplification factors. The analysis of class I maser surveys allows us to identify four distinct regimes that differ by the series of their brightest lines.Comment: 8 pages, 4 figures, invited presentation at IAU242 "Astrophysical Masers and their environments