The Schwinger Model on a circle: relation between Path Integral and Hamiltonian approaches

We solve the massless Schwinger model exactly in Hamiltonian formalism on a circle. We construct physical states explicitly and discuss the role of the spectral flow and nonperturbative vacua. Different thermodynamical correlation functions are calculated and after performing the analytical continuation are compared with the corresponding expressions obtained for the Schwinger model on the torus in Euclidean Path Integral formalism obtained before.Comment: 40 page

A note on the peeling theorem in higher dimensions

We demonstrate the ``peeling property'' of the Weyl tensor in higher dimensions in the case of even dimensions (and with some additional assumptions), thereby providing a first step towards understanding of the general peeling behaviour of the Weyl tensor, and the asymptotic structure at null infinity, in higher dimensions.Comment: 5 pages, to appear in Class. Quantum Gra

Nonparametric Transient Classification using Adaptive Wavelets

Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classifier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The ranked classifier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, BAGIDIS is nonparametric so it can be used for blind searches for new objects. We demonstrate the effectiveness of our ranked wavelet classifier against the well-tested Supernova Photometric Classification Challenge dataset in which the challenge is to correctly classify light curves as Type Ia or non-Ia supernovae. We train our ranked probability classifier on the spectroscopically-confirmed subsample (which is not representative) and show that it gives good results for all supernova with observed light curve timespans greater than 100 days (roughly 55% of the dataset). For such data, we obtain a Ia efficiency of 80.5% and a purity of 82.4% yielding a highly competitive score of 0.49 whilst implementing a truly "model-blind" approach to supernova classification. Consequently this approach may be particularly suitable for the classification of astronomical transients in the era of large synoptic sky surveys.Comment: 14 pages, 8 figures. Published in MNRA

Economic Consequences of Health Status: A Review of the Evidence

The correlation between health and economic performance is extremely robust across communities and over time. Many factors exogenous to income play an important role in determining health status, including a number of geographical, environmental, and evolutionary factors. This suggests the existence of simultaneous impacts of health on wealth and wealth on health. Potential health impacts on national economic performance are explored, and some important unanswered questions are identified.health, economic growth, human capital

Older Adults and Forgoing Cancer Screening

Although there is a growing recognition that older adults and those with extensive comorbid conditions undergo cancer screening too frequently, there is little information about patients’ perceptions regarding cessation of cancer screening. Information on older adults’ views of screening cessation would be helpful both for clinicians and for those designing interventions to reduce overscreening

Parent Resource Packet - A Guide for New Parents

PDF pages: 8