University of Guelph

About: University of Guelph is a education organization based out in Guelph, Ontario, Canada. It is known for research contribution in the topics: Population & Gene. The organization has 26542 authors who have published 50553 publications receiving 1715255 citations. The organization is also known as: U of G & Guelph University.

Relativistic theory of tidal Love numbers

Abstract: In Newtonian gravitational theory, a tidal Love number relates the mass multipole moment created by tidal forces on a spherical body to the applied tidal field. The Love number is dimensionless, and it encodes information about the body's internal structure. We present a relativistic theory of Love numbers, which applies to compact bodies with strong internal gravities; the theory extends and completes a recent work by Flanagan and Hinderer, which revealed that the tidal Love number of a neutron star can be measured by Earth-based gravitational-wave detectors. We consider a spherical body deformed by an external tidal field, and provide precise and meaningful definitions for electric-type and magnetic-type Love numbers; and these are computed for polytropic equations of state. The theory applies to black holes as well, and we find that the relativistic Love numbers of a nonrotating black hole are all zero.

The Borrower's Curse: Optimism, Finance and Entrepreneurship

Abstract: This paper argues that most of the facts characterising small-scale businesses, including high failure rates, reliance on bank credit rather than equity finance, relatively low interest rate margins, and credit rationing, can be explained by a tendency for those who are excessively optimistic to dominate new entrants. Drawing on findings in psychology, we model entrants as relatively naive optimisers. Banks on the other hand are viewed as well informed and efficient processors of information. 'The over-weening conceit which the greater part of men have of their own abilities, is an ancient evil remarked by the philosophers and moralists of all ages. Their absurd presumption in their own good fortune, has been less taken notice of. It is, however, if possible still more universal. There is no man living who, when in tolerable health and spirits, has not some share of it. The chance of gain is by every man more or less overvalued, and the chance of loss is by most men under-valued, and scarce by any man, who is in tolerable health and spirits, valued more than it is worth.'

Workgroup Report: Public Health Strategies for Reducing Aflatoxin Exposure in Developing Countries

Abstract: Consecutive outbreaks of acute aflatoxicosis in Kenya in 2004 and 2005 caused > 150 deaths. In response, the Centers for Disease Control and Prevention and the World Health Organization convened a workgroup of international experts and health officials in Geneva, Switzerland, in July 2005. After discussions concerning what is known about aflatoxins, the workgroup identified gaps in current knowledge about acute and chronic human health effects of aflatoxins, surveillance and food monitoring, analytic methods, and the efficacy of intervention strategies. The workgroup also identified public health strategies that could be integrated with current agricultural approaches to resolve gaps in current knowledge and ultimately reduce morbidity and mortality associated with the consumption of aflatoxin-contaminated food in the developing world. Four issues that warrant immediate attention were identified: a) quantify the human health impacts and the burden of disease due to aflatoxin exposure; b) compile an inventory, evaluate the efficacy, and disseminate results of ongoing intervention strategies; c) develop and augment the disease surveillance, food monitoring, laboratory, and public health response capacity of affected regions; and d) develop a response protocol that can be used in the event of an outbreak of acute aflatoxicosis. This report expands on the workgroup's discussions concerning aflatoxin in developing countries and summarizes the findings.

Recurrent Neural Networks

Abstract: This chapter presents an introduction to recurrent neural networks for readers familiar with artificial neural networks in general, and multi-layer perceptrons trained with gradient descent algorithms (back-propagation) in particular. A recurrent neural network (RNN) is an artificial neural network with internal loops. These internal loops induce recursive dynamics in the networks and thus introduce delayed activation dependencies across the processing elements (PEs) in the network.