University of Virginia

About: University of Virginia is a education organization based out in Charlottesville, Virginia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 52543 authors who have published 113268 publications receiving 5220506 citations. The organization is also known as: U of V & UVa.

The Colors of Investors' Money: The Role of Institutional Investors Around the World

Abstract: We study the role of institutional investors around the world using a comprehensive data set of equity holdings from 27 countries. Domestic, U.S.-, and non-U.S.-based foreign institutions hold comparable shares of non-U.S. corporations. We find that all institutional investors have a strong preference for the stock of large firms and firms with strong governance indicators, while foreign institutions tend to overweight firms that are cross-listed in the U.S. and members of the Morgan Stanley Capital International World Index. We find that firms with higher ownership by foreign and independent institutions (unlike other institutions) have higher firm valuations, better operating performance, and lower capital expenditures. Our results indicate that foreign and independent institutions, with potentially fewer business ties to firms and freer from management influence, are involved in monitoring corporations worldwide.

LSST: From Science Drivers to Reference Design and Anticipated Data Products

Abstract: We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the solar system, exploring the transient optical sky, and mapping the Milky Way. LSST will be a large, wide-field ground-based system designed to obtain repeated images covering the sky visible from Cerro Pachon in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg2 field of view, a 3.2-gigapixel camera, and six filters (ugrizy) covering the wavelength range 320–1050 nm. The project is in the construction phase and will begin regular survey operations by 2022. About 90% of the observing time will be devoted to a deep-wide-fast survey mode that will uniformly observe a 18,000 deg2 region about 800 times (summed over all six bands) during the anticipated 10 yr of operations and will yield a co-added map to r ~ 27.5. These data will result in databases including about 32 trillion observations of 20 billion galaxies and a similar number of stars, and they will serve the majority of the primary science programs. The remaining 10% of the observing time will be allocated to special projects such as Very Deep and Very Fast time domain surveys, whose details are currently under discussion. We illustrate how the LSST science drivers led to these choices of system parameters, and we describe the expected data products and their characteristics.

Pervasiveness and correlates of implicit attitudes and stereotypes

Abstract: Correspondence should be addressed to Brian A. Nosek, Department of Psychology, University of Virginia, 102 Gilmer Hall, Box 400400, Charlottesville, VA 22904, USA. E-mail: nosek@virginia.edu This research was supported by the National Institute of Mental Health (MH-41328, MH-01533, MH-57672, and MH-68447) and the National Science Foundation (SBR-9422241, SBR-9709924, and REC-0634041). The authors are grateful for technical support from N. Sriram, Ethan Sutin, and Lili Wu. Related information is available at http://briannosek.com/ and http://projectimplicit.net/ EUROPEAN REVIEW OF SOCIAL PSYCHOLOGY 2007, 1 – 53, iFirst article

Overview of spatial channel models for antenna array communication systems

Abstract: Spatial antenna diversity has been important in improving the radio link between wireless users. Historically, microscopic antenna diversity has been used to reduce the fading seen by a radio receiver, whereas macroscopic diversity provides multiple listening posts to ensure that mobile communication links remain intact over a wide geographic area. In later years, the concepts of spatial diversity have been expanded to build foundations for emerging technologies, such as smart (adaptive) antennas and position location systems. Smart antennas hold great promise for increasing the capacity of wireless communications because they radiate and receive energy only in the intended directions, thereby greatly reducing interference. To properly design, analyze, and implement smart antennas and to exploit spatial processing in emerging wireless systems, accurate radio channel models that incorporate spatial characteristics are necessary. In this tutorial, we review the key concepts in spatial channel modeling and present emerging approaches. We also review the research issues in developing and using spatial channel models for adaptive antennas.