Artificial Intelligence Approach to the Determination of Physical Properties of Eclipsing Binaries. I. The EBAI Project

Achieving maximum scientific results from the overwhelming volume of astronomical data to be acquired over the next few decades will demand novel, fully automatic methods of data analysis. Artificial intelligence approaches hold great promise in contributing to this goal. Here we apply neural network learning technology to the specific domain of eclipsing binary (EB) stars, of which only some hundreds have been rigorously analyzed, but whose numbers will reach millions in a decade. Well-analyzed EBs are a prime source of astrophysical information whose growth rate is at present limited by the need for human interaction with each EB data-set, principally in determining a starting solution for subsequent rigorous analysis. We describe the artificial neural network (ANN) approach which is able to surmount this human bottleneck and permit EB-based astrophysical information to keep pace with future data rates. The ANN, following training on a sample of 33,235 model light curves, outputs a set of approximate model parameters (T2/T1, (R1+R2)/a, e sin(omega), e cos(omega), and sin i) for each input light curve data-set. The whole sample is processed in just a few seconds on a single 2GHz CPU. The obtained parameters can then be readily passed to sophisticated modeling engines. We also describe a novel method polyfit for pre-processing observational light curves before inputting their data to the ANN and present the results and analysis of testing the approach on synthetic data and on real data including fifty binaries from the Catalog and Atlas of Eclipsing Binaries (CALEB) database and 2580 light curves from OGLE survey data. [abridged]Comment: 52 pages, accepted to Ap

Cholinergic stimulation of arachidonic acid and phosphatidic acid metabolism in C62B glioma cells

Glioma C62B cells were incubated for 18 h with [1-14C]arachidonic acid. Most (80%) of the added [1-14C] arachidonic acid was taken into the intracellular pool; less than 1% of the intracellular [1-14C]arachidonic acid remained unesterified; the rest was present in glycerophospholipids. Acetylcholine stimulation of the prelabeled cells resulted in the rapid accumulation of free [1-14C]arachidonic acid, presumably liberated by hydrolysis from phospholipids. Labeled unesterified [1-14C]arachidonic acid peaked by 90 s and returned to basal levels by 5 min. Paralleling the transient increase of unesterified [1-14C]arachidonic acid were increases in level of radioactivity in an unidentified lipoxygenase metabolite of arachidonic acid and of radioactive phosphatidic acid. The release of arachidonic acid induced by acetylcholine or carbachol was blocked by muscarinic but not nicotinic receptor antagonists; adrenergic or histaminergic receptor agonists were ineffective at stimulating arachidonic acid liberation. In contrast to the transient effects of stimulation with cholinergic agonists, stimulation with the divalent cation ionophore A23187 resulted in a linear increase in the accumulation of liberated arachidonic acid for at least 1 h. Furthermore, the pattern of metabolites synthesized from arachidonic acid in response to ionophore stimulation was more complex than that observed following cholinergic stimulation and included also several metabolites derived from cyclooxygenase activity. We conclude that muscarinic receptor agonists rapidly induce specific changes in arachidonic acid and phosphatidic acid metabolism in a glioma cell line and suggest that similar responses may occur in glial cells and play a physiologically significant role in neural metabolism

Development of a custom on-line ultrasonic vapour analyzer/flowmeter for the ATLAS inner detector, with application to gaseous tracking and Cherenkov detectors

Precision sound velocity measurements can simultaneously determine binary gas composition and flow. We have developed an analyzer with custom electronics, currently in use in the ATLAS inner detector, with numerous potential applications. The instrument has demonstrated ~0.3% mixture precision for C3F8/C2F6 mixtures and < 10-4 resolution for N2/C3F8 mixtures. Moderate and high flow versions of the instrument have demonstrated flow resolutions of +/- 2% F.S. for flows up to 250 l.min-1, and +/- 1.9% F.S. for linear flow velocities up to 15 ms-1; the latter flow approaching that expected in the vapour return of the thermosiphon fluorocarbon coolant recirculator being built for the ATLAS silicon tracker.Comment: Paper submitted to TWEPP2012; Topical Workshop on Electronics for Particle Physics, Oxford, UK, September 17-21, 2012. KEYWORDS: Sonar; Saturated fluorocarbons; Flowmetry; Sound velocity, Gas mixture analysis. 8 pages, 7 figure

Affective Temperaments: Unique Constructs or Dimensions of Normal Personality by Another Name?

Abstract: Background

On limit multiplicites of discrete series representations in spaces of automorphic forms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46616/1/222_2005_Article_BF01388963.pd

The ability of analysts’ recommendations to predict optimistic and pessimistic forecasts

Previous researches show that buy (growth) companies conduct income increasing earnings management in order to meet forecasts and generate positive forecast Errors (FEs). This behavior however, is not inherent in sell (non-growth) companies. Using the aforementioned background, this research hypothesizes that since sell companies are pressured to avoid income increasing earnings management, they are capable, and in fact more inclined, to pursue income decreasing Forecast Management (FM) with the purpose of generating positive FEs. Using a sample of 6553 firm-years of companies that are listed in the NYSE between the years 2005–2010, the study determines that sell companies conduct income decreasing FM to generate positive FEs. However, the frequency of positive FEs of sell companies does not exceed that of buy companies. Using the efficiency perspective, the study suggests that even though buy and sell companies have immense motivation in avoiding negative FEs, they exploit different but efficient strategies, respectively, in order to meet forecasts. Furthermore, the findings illuminated the complexities behind informative and opportunistic forecasts that falls under the efficiency versus opportunistic theories in literature