4,679 research outputs found
Resolving candidate genes of mouse skeletal muscle QTL via RNA-Seq and expression network analyses
Peer reviewedPublisher PD
Optimal Defaults and Active Decisions
Defaults can have a dramatic influence on consumer decisions. We identify an overlooked but practical alternative to defaults: requiring individuals to make an explicit choice for themselves. We study such "active decisions" in the context of 401(k) saving. We find that compelling new hires to make active decisions about 401(k) enrollment raises the initial fraction that enroll by 28 percentage points relative to a standard opt-in enrollment procedure, producing a savings distribution three months after hire that would take three years to achieve under standard enrollment. We also present a model of 401(k) enrollment and characterize the optimal enrollment regime. Active decisions are optimal when consumers have a strong propensity to procrastinate and savings preferences that are highly heterogeneous. Naive beliefs about future time-inconsistency strengthen the normative appeal of the active-decision enrollment regime. However, financial illiteracy favors default enrollment over active decision enrollment.
Spatially Adaptive Bayesian P-Splines with Heteroscedastic Errors
An increasingly popular tool for nonparametric smoothing are penalized splines (P-splines) which use low-rank spline bases to make computations tractable while maintaining accuracy as good as smoothing splines. This paper extends penalized spline methodology by both modeling the variance function nonparametrically and using a spatially adaptive smoothing parameter. These extensions have been studied before, but never together and never in the multivariate case. This combination is needed for satisfactory inference and can be implemented effectively by Bayesian \mbox{MCMC}. The variance process controlling the spatially-adaptive shrinkage of the mean and the variance of the heteroscedastic error process are modeled as log-penalized splines. We discuss the choice of priors and extensions of the methodology,in particular, to multivariate smoothing using low-rank thin plate splines. A fully Bayesian approach provides the joint posterior distribution of all parameters, in particular, of the error standard deviation and penalty functions. In the multivariate case we produce maps of the standard deviation and penalty functions. Our methodology can be implemented using the Bayesian software WinBUGS
Boundary fields and renormalization group flow in the two-matrix model
We analyze the Ising model on a random surface with a boundary magnetic field
using matrix model techniques. We are able to exactly calculate the disk
amplitude, boundary magnetization and bulk magnetization in the presence of a
boundary field. The results of these calculations can be interpreted in terms
of renormalization group flow induced by the boundary operator. In the
continuum limit this RG flow corresponds to the flow from non-conformal to
conformal boundary conditions which has recently been studied in flat space
theories.Comment: 31 pages, Late
The application of a long period grating sensors to human respiratory plethysmography
A series of nine in-line curvature sensors on a garment are used to monitor the thoracic and abdominal movements of a human during respiration for application to Human Respiratory Plethysmography. These results are used to obtain volumetric tidal changes of the human torso which show agreement with data from a spirometer used simultaneously to recorded the inspired and expired volume at the mouth during both rhythmic and transient breathing patterns. The curvature sensors are based upon long period gratings which are written in a progressive three layered fibre to render them insensitive to refractive index changes. The sensor consists of the long period grating laid upon a carbon fibre ribbon, with this then encapsulated in a low temperature curing silicone rubber. The sensing array is multiplexed and interrogated using a derivative spectroscopy based technique to monitor the response of the LPGs' attenuation bands to curvature. The versatility of this scheme is demonstrated by applying the same garment and sensors to various human body types and sizes. It was also found from statistical analysis of the sensing array data, in conjunction with the measurements taken with a spirometer, that 11 to 12 sensors should be required to obtain an absolute volumetric error of 5%
Minimal Dynamical Triangulations of Random Surfaces
We introduce and investigate numerically a minimal class of dynamical
triangulations of two-dimensional gravity on the sphere in which only vertices
of order five, six or seven are permitted. We show firstly that this
restriction of the local coordination number, or equivalently intrinsic scalar
curvature, leaves intact the fractal structure characteristic of generic
dynamically triangulated random surfaces. Furthermore the Ising model coupled
to minimal two-dimensional gravity still possesses a continuous phase
transition. The critical exponents of this transition correspond to the usual
KPZ exponents associated with coupling a central charge c=1/2 model to
two-dimensional gravity.Comment: Latex, 9 pages, 3 figures, Published versio
Redox-Based Probes for Protein Tyrosine Phosphatases
No AbstractPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83769/1/4423_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/83769/2/anie_201007871_sm_miscellaneous_information.pd
Power Transformations When Fitting Theoretical Models to Data
We investigate power transformations in non-linear regression problems when there is a physical model for the response but little understanding of the underlying error structure. In such circumstances and unlike the ordinary power transformation model, both the response and the model must be transformed simultaneously and in the same way. We show by an asymptotic theory and a small Monte-Carlo study that for estimating the model parameters there is little cost for not knowing the correct transform a priori; this is in dramatic contrast to the results for the usual case that only the response is transformed
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