3,110 research outputs found
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score
Mendelian randomization (MR) is a method of exploiting genetic variation to
unbiasedly estimate a causal effect in presence of unmeasured confounding. MR
is being widely used in epidemiology and other related areas of population
science. In this paper, we study statistical inference in the increasingly
popular two-sample summary-data MR design. We show a linear model for the
observed associations approximately holds in a wide variety of settings when
all the genetic variants satisfy the exclusion restriction assumption, or in
genetic terms, when there is no pleiotropy. In this scenario, we derive a
maximum profile likelihood estimator with provable consistency and asymptotic
normality. However, through analyzing real datasets, we find strong evidence of
both systematic and idiosyncratic pleiotropy in MR, echoing the omnigenic model
of complex traits that is recently proposed in genetics. We model the
systematic pleiotropy by a random effects model, where no genetic variant
satisfies the exclusion restriction condition exactly. In this case we propose
a consistent and asymptotically normal estimator by adjusting the profile
score. We then tackle the idiosyncratic pleiotropy by robustifying the adjusted
profile score. We demonstrate the robustness and efficiency of the proposed
methods using several simulated and real datasets.Comment: 59 pages, 5 figures, 6 table
Towards Secure and Safe Appified Automated Vehicles
The advancement in Autonomous Vehicles (AVs) has created an enormous market
for the development of self-driving functionalities,raising the question of how
it will transform the traditional vehicle development process. One adventurous
proposal is to open the AV platform to third-party developers, so that AV
functionalities can be developed in a crowd-sourcing way, which could provide
tangible benefits to both automakers and end users. Some pioneering companies
in the automotive industry have made the move to open the platform so that
developers are allowed to test their code on the road. Such openness, however,
brings serious security and safety issues by allowing untrusted code to run on
the vehicle. In this paper, we introduce the concept of an Appified AV platform
that opens the development framework to third-party developers. To further
address the safety challenges, we propose an enhanced appified AV design schema
called AVGuard, which focuses primarily on mitigating the threats brought about
by untrusted code, leveraging theory in the vehicle evaluation field, and
conducting program analysis techniques in the cybersecurity area. Our study
provides guidelines and suggested practice for the future design of open AV
platforms
RANS Turbulence Model Development using CFD-Driven Machine Learning
This paper presents a novel CFD-driven machine learning framework to develop
Reynolds-averaged Navier-Stokes (RANS) models. The CFD-driven training is an
extension of the gene expression programming method (Weatheritt and Sandberg,
2016), but crucially the fitness of candidate models is now evaluated by
running RANS calculations in an integrated way, rather than using an algebraic
function. Unlike other data-driven methods that fit the Reynolds stresses of
trained models to high-fidelity data, the cost function for the CFD-driven
training can be defined based on any flow feature from the CFD results. This
extends the applicability of the method especially when the training data is
limited. Furthermore, the resulting model, which is the one providing the most
accurate CFD results at the end of the training, inherently shows good
performance in RANS calculations. To demonstrate the potential of this new
method, the CFD-driven machine learning approach is applied to model
development for wake mixing in turbomachines. A new model is trained based on a
high-pressure turbine case and then tested for three additional cases, all
representative of modern turbine nozzles. Despite the geometric configurations
and operating conditions being different among the cases, the predicted wake
mixing profiles are significantly improved in all of these a posteriori tests.
Moreover, the model equation is explicitly given and available for analysis,
thus it could be deduced that the enhanced wake prediction is predominantly due
to the extra diffusion introduced by the CFD-driven model.Comment: Accepted by Journal of Computational Physic
The Regulation of Migration in a Transition Economy: China’s Hukou System
Unlike most countries, China regulates internal migration. Public benefits, access to good quality housing, schools, health care, and attractive employment opportunities are available only to those who have local registration (Hukou). Coincident with the deepening of economic reforms, Hukou has gradually been relaxed since the 1980s, helping to explain an extraordinary surge of migration within China. In this study of interprovincial Chinese migration, we address two questions. First, what is a sensible way of incorporating Hukou into theoretical and empirical models of internal migration? Second, to what extent has Hukou influenced the scale and structure of migration? We incorporate two alternative measures of Hukou into a modified gravity model – the unregistered migrant's: (i) perceived probability of securing Hukou; and (ii) perceived probability of securing employment opportunities available only to those with Hukou. In contrast to previous studies, our model includes a much wider variety of control especially important for the Chinese case. Analyzing the relationship between Hukou and migration using census data for 1985-90, 1995-2000 and 2000-05, we find that migration is very sensitive to Hukou, with the greatest sensitivity occurring during the middle period.internal migration, Hukou, migrant networks, reforms
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