1,030 research outputs found
A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation
Kidney transplantation is the preferred treatment for people suffering from
end-stage renal disease. Successful kidney transplants still fail over time,
known as graft failure; however, the time to graft failure, or graft survival
time, can vary significantly between different recipients. A significant
biological factor affecting graft survival times is the compatibility between
the human leukocyte antigens (HLAs) of the donor and recipient. We propose to
model HLA compatibility using a network, where the nodes denote different HLAs
of the donor and recipient, and edge weights denote compatibilities of the
HLAs, which can be positive or negative. The network is indirectly observed, as
the edge weights are estimated from transplant outcomes rather than directly
observed. We propose a latent space model for such indirectly-observed weighted
and signed networks. We demonstrate that our latent space model can not only
result in more accurate estimates of HLA compatibilities, but can also be
incorporated into survival analysis models to improve accuracy for the
downstream task of predicting graft survival times.Comment: This work has been accepted to BIBM 202
T-Crowd: Effective Crowdsourcing for Tabular Data
Crowdsourcing employs human workers to solve computer-hard problems, such as
data cleaning, entity resolution, and sentiment analysis. When crowdsourcing
tabular data, e.g., the attribute values of an entity set, a worker's answers
on the different attributes (e.g., the nationality and age of a celebrity star)
are often treated independently. This assumption is not always true and can
lead to suboptimal crowdsourcing performance. In this paper, we present the
T-Crowd system, which takes into consideration the intricate relationships
among tasks, in order to converge faster to their true values. Particularly,
T-Crowd integrates each worker's answers on different attributes to effectively
learn his/her trustworthiness and the true data values. The attribute
relationship information is also used to guide task allocation to workers.
Finally, T-Crowd seamlessly supports categorical and continuous attributes,
which are the two main datatypes found in typical databases. Our extensive
experiments on real and synthetic datasets show that T-Crowd outperforms
state-of-the-art methods in terms of truth inference and reducing the cost of
crowdsourcing
Development and characterization of a laser-induced acoustic desorption source
A laser-induced acoustic desorption source, developed for use at central
facilities, such as free-electron lasers, is presented. It features prolonged
measurement times and a fixed interaction point. A novel sample deposition
method using aerosol spraying provides a uniform sample coverage and hence
stable signal intensity. Utilizing strong-field ionization as a universal
detection scheme, the produced molecular plume is characterized in terms of
number density, spatial extend, fragmentation, temporal distribution,
translational velocity, and translational temperature. The effect of desorption
laser intensity on these plume properties is evaluated. While translational
velocity is invariant for different desorption laser intensities, pointing to a
non-thermal desorption mechanism, the translational temperature increases
significantly and higher fragmentation is observed with increased desorption
laser fluence.Comment: 8 pages, 7 figure
Generalized convergence of the deep BSDE method: a step towards fully-coupled FBSDEs and applications in stochastic control
We are concerned with high-dimensional coupled FBSDE systems approximated by
the deep BSDE method of Han et al. (2018). It was shown by Han and Long (2020)
that the errors induced by the deep BSDE method admit a posteriori estimate
depending on the loss function, whenever the backward equation only couples
into the forward diffusion through the Y process. We generalize this result to
fully-coupled drift coefficients, and give sufficient conditions for
convergence under standard assumptions. The resulting conditions are directly
verifiable for any equation. Consequently, unlike in earlier theory, our
convergence analysis enables the treatment of FBSDEs stemming from stochastic
optimal control problems. In particular, we provide a theoretical justification
for the non-convergence of the deep BSDE method observed in recent literature,
and present direct guidelines for when convergence can be guaranteed in
practice. Our theoretical findings are supported by several numerical
experiments in high-dimensional settings.Comment: 25 pages, 3 figures, 1 tabl
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