Infrared Astronomical Satellite (IRAS) Scientific Data Analysis System

The Jet Propulsion Laboratory's Scientific Data Analysis System will process Infrared Astronomical Satellite data and produce a catalog of perhaps a million infrared sources in the sky, as well as other vital information for astronomical research

Analysis of the conditional mutual information in ballistic and diffusive non-equilibrium steady-states

The conditional mutual information (CMI) $\mathcal{I}(A\! : \! C|B)$ quantifies the amount of correlations shared between $A$ and $C$ \emph{given} $B$. It therefore functions as a more general quantifier of bipartite correlations in multipartite scenarios, playing an important role in the theory of quantum Markov chains. In this paper we carry out a detailed study on the behavior of the CMI in non-equilibrium steady-states (NESS) of a quantum chain placed between two baths at different temperatures. These results are used to shed light on the mechanisms behind ballistic and diffusive transport regimes and how they affect correlations between different parts of a chain. We carry our study for the specific case of a 1D bosonic chain subject to local Lindblad dissipators at the boundaries. In addition, the chain is also subject to self-consistent reservoirs at each site, which are used to tune the transport between ballistic and diffusive. As a result, we find that the CMI is independent of the chain size $L$ in the ballistic regime, but decays algebraically with $L$ in the diffusive case. Finally, we also show how this scaling can be used to discuss the notion of local thermalization in non-equilibrium steady-states

The ultra-compact binary candidate KUV 23182+1007 is a bright quasar

KUV 23182+1007 was identified as a blue object in the Kiso UV Survey in the 1980s. Classification-dispersion spectroscopy showed a featureless continuum except for a strong emission line in the region of He II 4686 A. This is a hallmark of the rare AM CVn class of cataclysmic variable star, so we have obtained a high-S/N blue spectrum of this object to check its classification. Instead, the spectrum shows a strong quasar-like emission line centred on 4662 A. Comparison with the SDSS quasar template spectra confirms that KUV 23182+1007 is a quasar with a redshift of z = 0.665.Comment: 4 pages, 2 figures, to appear in IBVS. Data are available from http://www.astro.keele.ac.uk/~jkt

Determining Pesticide and Nitrate Levels in Spring Water in Northwest Arkansas

Occurrences of pesticides in our nations ground water are on the rise. As states become aware of this problem and begin monitoring programs, incidence of contamination will probably increase. Since the problem of pesticides in groundwater is relatively new, little research has centered on the fate of pesticides after they reach the groundwater environment. In Northwest Arkansas efforts to monitor groundwater for pesticides have been small. Twenty-five springs in Northwest Arkansas were sampled in the fall of 1988, and spring of 1989. Analysis for atrazine, alachlor, metolachlor, diuron, and simazine in spring water was preformed using gas liquid chromatography and high performance liquid chromatography. No detectable residues of any of the selected pesticides were found. Northwest Arkansas is a leader in poultry production. Much of the manure from poultry houses is spread on the sourounding pastures. As this litter decomposes nitrates and phosphates are released. Nitrate and phosphate concentrations were also determined on water from the spring samples. No spring exceeded the EPA\u27s limit of 45 mg/L for nitrate in drinking water. The highest concentration for phosphate in any spring was 1.05 mg/L

ASSESSMENT OF RISK WHEN CONTRACT CROPS ARE INCLUDED AMONG OTHER CROP ALTERNATIVES

Crop Production/Industries, Risk and Uncertainty,

Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks

Multitask Learning is a learning paradigm that deals with multiple different tasks in parallel and transfers knowledge among them. XOF, a Learning Classifier System using tree-based programs to encode building blocks (meta-features), constructs and collects features with rich discriminative information for classification tasks in an observed list. This paper seeks to facilitate the automation of feature transferring in between tasks by utilising the observed list. We hypothesise that the best discriminative features of a classification task carry its characteristics. Therefore, the relatedness between any two tasks can be estimated by comparing their most appropriate patterns. We propose a multiple-XOF system, called mXOF, that can dynamically adapt feature transfer among XOFs. This system utilises the observed list to estimate the task relatedness. This method enables the automation of transferring features. In terms of knowledge discovery, the resemblance estimation provides insightful relations among multiple data. We experimented mXOF on various scenarios, e.g. representative Hierarchical Boolean problems, classification of distinct classes in the UCI Zoo dataset, and unrelated tasks, to validate its abilities of automatic knowledge-transfer and estimating task relatedness. Results show that mXOF can estimate the relatedness reasonably between multiple tasks to aid the learning performance with the dynamic feature transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference (GECCO 2020