8,775 research outputs found
Solitary waves and their stability in colloidal media: semi-analytical solutions
Spatial solitary waves in colloidal suspensions of spherical dielectric
nanoparticles are considered. The interaction of the nanoparticles is modelled
as a hard-sphere gas, with the Carnahan-Starling formula used for the gas
compressibility. Semi-analytical solutions, for both one and two spatial
dimensions, are derived using an averaged Lagrangian and suitable trial
functions for the solitary waves. Power versus propagation constant curves and
neutral stability curves are obtained for both cases, which illustrate that
multiple solution branches occur for both the one and two dimensional
geometries. For the one-dimensional case it is found that three solution
branches (with a bistable regime) occur, while for the two-dimensional case two
solution branches (with a single stable branch) occur in the limit of low
background packing fractions. For high background packing fractions the power
versus propagation constant curves are monotonic and the solitary waves stable
for all parameter values. Comparisons are made between the semi-analytical and
numerical solutions, with excellent comparison obtained.Comment: Paper to appear in Dynamics of Continuous, Discrete and Impulsive
Systems, Series
Data management study, volume 5. Appendix E - Contractor data package quality assurance /QA/ Final report
Manufacturing verification tests for quality assurance and control data management on Voyager spacecraf
Hierarchical Models for Relational Event Sequences
Interaction within small groups can often be represented as a sequence of
events, where each event involves a sender and a recipient. Recent methods for
modeling network data in continuous time model the rate at which individuals
interact conditioned on the previous history of events as well as actor
covariates. We present a hierarchical extension for modeling multiple such
sequences, facilitating inferences about event-level dynamics and their
variation across sequences. The hierarchical approach allows one to share
information across sequences in a principled manner---we illustrate the
efficacy of such sharing through a set of prediction experiments. After
discussing methods for adequacy checking and model selection for this class of
models, the method is illustrated with an analysis of high school classroom
dynamics
Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation
Nonparametric Bayesian approaches to clustering, information retrieval,
language modeling and object recognition have recently shown great promise as a
new paradigm for unsupervised data analysis. Most contributions have focused on
the Dirichlet process mixture models or extensions thereof for which efficient
Gibbs samplers exist. In this paper we explore Gibbs samplers for infinite
complexity mixture models in the stick breaking representation. The advantage
of this representation is improved modeling flexibility. For instance, one can
design the prior distribution over cluster sizes or couple multiple infinite
mixture models (e.g. over time) at the level of their parameters (i.e. the
dependent Dirichlet process model). However, Gibbs samplers for infinite
mixture models (as recently introduced in the statistics literature) seem to
mix poorly over cluster labels. Among others issues, this can have the adverse
effect that labels for the same cluster in coupled mixture models are mixed up.
We introduce additional moves in these samplers to improve mixing over cluster
labels and to bring clusters into correspondence. An application to modeling of
storm trajectories is used to illustrate these ideas.Comment: Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006
Data management study, volume 5. Appendix C - Contractor data package manufacturing /MG/ Final report
Manufacturing contractor data project for Voyager spacecraft system
Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models
We express the mean and variance terms in a double exponential regression
model as additive functions of the predictors and use Bayesian variable
selection to determine which predictors enter the model, and whether they enter
linearly or flexibly. When the variance term is null we obtain a generalized
additive model, which becomes a generalized linear model if the predictors
enter the mean linearly. The model is estimated using Markov chain Monte Carlo
simulation and the methodology is illustrated using real and simulated data
sets.Comment: 8 graphs 35 page
Phosphorus fertilizer placement and profitability.
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Micronutrient fertilization on a typic acrorthox at Manaus, Brazil.
bitstream/item/210629/1/Micronutrient-Fertilization-on....pd
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