Analytical study of level crossings in the Stark-Zeeman spectrum of ground state OH

The ground electronic, vibrational and rotational state of the OH molecule is currently of interest as it can be manipulated by electric and magnetic fields for experimental studies in ultracold chemistry and quantum degeneracy. Based on our recent exact solution of the corresponding effective Stark-Zeeman Hamiltonian, we present an analytical study of the crossings and avoided crossings in the spectrum. These features are relevant to non-adiabatic transitions, conical intersections and Berry phases. Specifically, for an avoided crossing employed in the evaporative cooling of OH, we compare our exact results to those derived earlier from perturbation theory.Comment: 5 figures, to be published in Eur. Phys. J.

IEHCA Summer University on Food and Drink 2018 Report

The Institut Européen d’Histoire et des Cultures de l’Alimentation (IEHCA, European Institute for the History and Cultures of Food) was established in 2001 by the French Ministry of National Education, Higher Education and Research in partnership with the Centre-Val de Loire region and the University of Tours. As a scientific and cultural development agency, it seeks to encourage university research and teaching in connection with “food cultures and heritages” in the humanities and social sciences. The university serves as a key platform for the discussion of new research in Food & Drink Studies. In 2018, 20 researchers from a wide field of disciplines, and exploring varied topics within food and drink studies, gathered to share, discuss and gain a further understanding of current research questions and issues concerned with food, drink and society

An Example of the Use of Interdigital PVDF Transducers to Generate and Receive a High Order Lamb Wave Mode in a Pipe

From a non-destructive evaluation point of view, Lamb waves are a highly attractive means of inspecting a large area of a structure from a single point. Interdigital PVDF transducers have been used previously in signal processing applications [1] to generate acoustic waves in piezoelectric substrates. This paper in conjunction with that of Monkhouse et al [2] aims to provide an overview of the work accomplished so far at Imperial College in the use of interdigital PVDF transducers to transmit and receive Lamb waves in certain structures for non-destructive evaluation purposes. Interdigital PVDF transducers may be permanently bonded to either flat of curved surfaces and this attribute together with their low cost means that they are potentially suitable for“smart structure” applications

Rodent Control on Poultry Farms.

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Locating very high energy gamma ray sources with arc minute accuracy

The angular accuracy of gamma-ray detectors is intrinsically limited by the physical processes involved in photon detection. Although a number of point-like sources were detected by the COS-B satellite, only two were unambiguously identified by time signature with counterparts at longer wavelengths. By taking advantage of the extended longitudinal structure of Very High Energy gamma-ray showers, measurements in the TeV energy range can pinpoint source coordinates to arc minute accuracy. This was demonstrated using Cerenkov air shower imaging techniques. With two telescopes in coincidence, the individual event circular probable error will be 0.13 deg. The half-cone angle of the field of view is effectively 1 deg

Over-Fitting in Model Selection with Gaussian Process Regression

Model selection in Gaussian Process Regression (GPR) seeks to determine the optimal values of the hyper-parameters governing the covariance function, which allows flexible customization of the GP to the problem at hand. An oft-overlooked issue that is often encountered in the model process is over-fitting the model selection criterion, typically the marginal likelihood. The over-fitting in machine learning refers to the fitting of random noise present in the model selection criterion in addition to features improving the generalisation performance of the statistical model. In this paper, we construct several Gaussian process regression models for a range of high-dimensional datasets from the UCI machine learning repository. Afterwards, we compare both MSE on the test dataset and the negative log marginal likelihood (nlZ), used as the model selection criteria, to find whether the problem of overfitting in model selection also affects GPR. We found that the squared exponential covariance function with Automatic Relevance Determination (SEard) is better than other kernels including squared exponential covariance function with isotropic distance measure (SEiso) according to the nLZ, but it is clearly not the best according to MSE on the test data, and this is an indication of over-fitting problem in model selection