815 research outputs found
Patterns of Influenza Vaccination Coverage in the United States from 2009 to 2015
Background: Globally, influenza is a major cause of morbidity,
hospitalization and mortality. Influenza vaccination has shown substantial
protective effectiveness in the United States. We investigated state-level
patterns of coverage rates of seasonal and pandemic influenza vaccination,
among the overall population in the U.S. and specifically among children and
the elderly, from 2009/10 to 2014/15, and associations with ecological factors.
Methods and Findings: We obtained state-level influenza vaccination coverage
rates from national surveys, and state-level socio-demographic and health data
from a variety of sources. We employed a retrospective ecological study design,
and used mixed-model regression to determine the levels of ecological
association of the state-level vaccinations rates with these factors, both with
and without region as a factor for the three populations. We found that
health-care access is positively and significantly associated with mean
influenza vaccination coverage rates across all populations and models. We also
found that prevalence of asthma in adults are negatively and significantly
associated with mean influenza vaccination coverage rates in the elderly
populations. Conclusions: Health-care access has a robust, positive association
with state-level vaccination rates across different populations. This
highlights a potential population-level advantage of expanding health-care
access.Comment: 10 pages, 2 figure
Neural Generalized Ordinary Differential Equations with Layer-varying Parameters
Deep residual networks (ResNets) have shown state-of-the-art performance in
various real-world applications. Recently, the ResNets model was
reparameterized and interpreted as solutions to a continuous ordinary
differential equation or Neural-ODE model. In this study, we propose a neural
generalized ordinary differential equation (Neural-GODE) model with
layer-varying parameters to further extend the Neural-ODE to approximate the
discrete ResNets. Specifically, we use nonparametric B-spline functions to
parameterize the Neural-GODE so that the trade-off between the model complexity
and computational efficiency can be easily balanced. It is demonstrated that
ResNets and Neural-ODE models are special cases of the proposed Neural-GODE
model. Based on two benchmark datasets, MNIST and CIFAR-10, we show that the
layer-varying Neural-GODE is more flexible and general than the standard
Neural-ODE. Furthermore, the Neural-GODE enjoys the computational and memory
benefits while performing comparably to ResNets in prediction accuracy
Contagion Source Detection in Epidemic and Infodemic Outbreaks: Mathematical Analysis and Network Algorithms
This monograph provides an overview of the mathematical theories and
computational algorithm design for contagion source detection in large
networks. By leveraging network centrality as a tool for statistical inference,
we can accurately identify the source of contagions, trace their spread, and
predict future trajectories. This approach provides fundamental insights into
surveillance capability and asymptotic behavior of contagion spreading in
networks. Mathematical theory and computational algorithms are vital to
understanding contagion dynamics, improving surveillance capabilities, and
developing effective strategies to prevent the spread of infectious diseases
and misinformation.Comment: Suggested Citation: Chee Wei Tan and Pei-Duo Yu (2023), "Contagion
Source Detection in Epidemic and Infodemic Outbreaks: Mathematical Analysis
and Network Algorithms", Foundations and Trends in Networking: Vol. 13: No.
2-3, pp 107-251. http://dx.doi.org/10.1561/130000006
A High-Performance Triple Patterning Layout Decomposer with Balanced Density
Triple patterning lithography (TPL) has received more and more attentions
from industry as one of the leading candidate for 14nm/11nm nodes. In this
paper, we propose a high performance layout decomposer for TPL. Density
balancing is seamlessly integrated into all key steps in our TPL layout
decomposition, including density-balanced semi-definite programming (SDP),
density-based mapping, and density-balanced graph simplification. Our new TPL
decomposer can obtain high performance even compared to previous
state-of-the-art layout decomposers which are not balanced-density aware, e.g.,
by Yu et al. (ICCAD'11), Fang et al. (DAC'12), and Kuang et al. (DAC'13).
Furthermore, the balanced-density version of our decomposer can provide more
balanced density which leads to less edge placement error (EPE), while the
conflict and stitch numbers are still very comparable to our
non-balanced-density baseline
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