154 research outputs found
Association of Center Volume with Outcome After Liver and Kidney Transplantation
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73934/1/j.1600-6143.2004.00462.x.pd
LSST Science Book, Version 2.0
A survey that can cover the sky in optical bands over wide fields to faint
magnitudes with a fast cadence will enable many of the exciting science
opportunities of the next decade. The Large Synoptic Survey Telescope (LSST)
will have an effective aperture of 6.7 meters and an imaging camera with field
of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over
20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with
fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a
total point-source depth of r~27.5. The LSST Science Book describes the basic
parameters of the LSST hardware, software, and observing plans. The book
discusses educational and outreach opportunities, then goes on to describe a
broad range of science that LSST will revolutionize: mapping the inner and
outer Solar System, stellar populations in the Milky Way and nearby galaxies,
the structure of the Milky Way disk and halo and other objects in the Local
Volume, transient and variable objects both at low and high redshift, and the
properties of normal and active galaxies at low and high redshift. It then
turns to far-field cosmological topics, exploring properties of supernovae to
z~1, strong and weak lensing, the large-scale distribution of galaxies and
baryon oscillations, and how these different probes may be combined to
constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at
http://www.lsst.org/lsst/sciboo
Staged Models for Interdisciplinary Research
Modellers of complex biological or social systems are often faced with an invidious choice: to use simple models with few mechanisms that can be fully analysed, or to construct complicated models that include all the features which are thought relevant. The former ensures rigour, the latter relevance. We discuss a method that combines these two approaches, beginning with a complex model and then modelling the complicated model with simpler models. The resulting "chain" of models ensures some rigour and relevance. We illustrate this process on a complex model of voting intentions, constructing a reduced model which agrees well with the predictions of the full model. Experiments with variations of the simpler model yield additional insights which are hidden by the complexity of the full model. This approach facilitated collaboration between social scientists and physicists- The complex model was specified based on the social science literature, and the simpler model constrained to agree (in core aspects) with the complicated model
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