93 research outputs found
Mathematical Models of Ebola Virus Disease and Vaccine Preventable Diseases
This thesis focuses on applying mathematical models to studies on the transmission dynamics and control interventions of infectious diseases such as Ebola virus disease and vaccine preventable diseases.
Many models in studies of Ebola transmission are based on the model by Legrand et al. (2007). However, there are potential issues with the Legrand model. First, the model was originally formulated in a complex form, leading to confusion and hindering its uses in practice. To overcome the difficulty, the Legrand model is reformulated in a much simpler but equivalent form in this thesis. The reformulated model also provides an intuitive understanding of its parameterization. Second, the underlying assumptions of the Legrand model are not mathematically clear for researchers, which might lead to inadvertent misuse of the model. The assumptions are clearly identified through comparison with three models developed with clear assumptions in this thesis, one of which simplifies to the Legrand model. This three models are also built with more realistic sojourns of epidemiological processes. The comparison among these models also demonstrates the importance of the underlying assumptions as they may provide different implications on control strategies.
In addition, a concern about current Ebola models is that many of them consider only infections with typical symptoms, but Ebola presents clinically in a more complicated way. To account crudely for the wide spectrum of clinical symptoms that characterizes Ebola infection, a model is developed including asymptomatic, mild and severe infections. Comparing to the model with only typical symptoms, it shows that modeling the spectrum is important as it could affect estimation of the reproduction number and effectiveness of interventions. Possible effective control strategies are also evaluated. We show that the spectrum of Ebola infection is important in modeling as it has implications for policy-making.
In many parts of the world, people seasonally migrate between rural and urban/peri-urban patches for economic opportunities. Migration meanwhile changes the immunity levels of patches and might increase the chance of recurrent outbreaks of vaccine preventable diseases. A three-patch meta-population model is developed that incorporates spatially explicit migration of individuals. The model is used to evaluate vaccination strategies to mitigate outbreaks. It suggests that rural-urban migration is an important factor in designing public health policies to mitigate vaccine-preventable diseases
Thrust distribution in Higgs decays at the next-to-leading order and beyond
We present predictions for the thrust distribution in hadronic decays of the
Higgs boson at the next-to-leading order and the approximate
next-to-next-to-leading order. The approximate NNLO corrections are derived
from a factorization formula in the soft/collinear phase-space regions. We find
large corrections, especially for the gluon channel. The scale variations at
the lowest orders tend to underestimate the genuine higher order contributions.
The results of this paper is therefore necessary to control the perturbative
uncertainties of the theoretical predictions. We also discuss on possible
improvements to our results, such as a soft-gluon resummation for the 2-jets
limit, and an exact next-to-next-to-leading order calculation for the
multi-jets region
Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification
An efficient and effective person re-identification (ReID) system relieves
the users from painful and boring video watching and accelerates the process of
video analysis. Recently, with the explosive demands of practical applications,
a lot of research efforts have been dedicated to heterogeneous person
re-identification (Hetero-ReID). In this paper, we provide a comprehensive
review of state-of-the-art Hetero-ReID methods that address the challenge of
inter-modality discrepancies. According to the application scenario, we
classify the methods into four categories -- low-resolution, infrared, sketch,
and text. We begin with an introduction of ReID, and make a comparison between
Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and
compare existing datasets for performing evaluations, and survey the models
that have been widely employed in Hetero-ReID. We also summarize and compare
the representative approaches from two perspectives, i.e., the application
scenario and the learning pipeline. We conclude by a discussion of some future
research directions. Follow-up updates are avaible at:
https://github.com/lightChaserX/Awesome-Hetero-reIDComment: Accepted by IJCAI 2020. Project url:
https://github.com/lightChaserX/Awesome-Hetero-reI
An Integrated Enhancement Solution for 24-hour Colorful Imaging
The current industry practice for 24-hour outdoor imaging is to use a silicon
camera supplemented with near-infrared (NIR) illumination. This will result in
color images with poor contrast at daytime and absence of chrominance at
nighttime. For this dilemma, all existing solutions try to capture RGB and NIR
images separately. However, they need additional hardware support and suffer
from various drawbacks, including short service life, high price, specific
usage scenario, etc. In this paper, we propose a novel and integrated
enhancement solution that produces clear color images, whether at abundant
sunlight daytime or extremely low-light nighttime. Our key idea is to separate
the VIS and NIR information from mixed signals, and enhance the VIS signal
adaptively with the NIR signal as assistance. To this end, we build an optical
system to collect a new VIS-NIR-MIX dataset and present a physically meaningful
image processing algorithm based on CNN. Extensive experiments show outstanding
results, which demonstrate the effectiveness of our solution.Comment: AAAI 2020 (Oral
GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction
This paper presents a 3D lidar SLAM system based on improved regionalized
Gaussian process (GP) map reconstruction to provide both low-drift state
estimation and mapping in real-time for robotics applications. We utilize
spatial GP regression to model the environment. This tool enables us to recover
surfaces including those in sparsely scanned areas and obtain uniform samples
with uncertainty. Those properties facilitate robust data association and map
updating in our scan-to-map registration scheme, especially when working with
sparse range data. Compared with previous GP-SLAM, this work overcomes the
prohibitive computational complexity of GP and redesigns the registration
strategy to meet the accuracy requirements in 3D scenarios. For large-scale
tasks, a two-thread framework is employed to suppress the drift further. Aerial
and ground-based experiments demonstrate that our method allows robust odometry
and precise mapping in real-time. It also outperforms the state-of-the-art
lidar SLAM systems in our tests with light-weight sensors.Comment: Accepted by IROS 202
Blur Interpolation Transformer for Real-World Motion from Blur
This paper studies the challenging problem of recovering motion from blur,
also known as joint deblurring and interpolation or blur temporal
super-resolution. The remaining challenges are twofold: 1) the current methods
still leave considerable room for improvement in terms of visual quality even
on the synthetic dataset, and 2) poor generalization to real-world data. To
this end, we propose a blur interpolation transformer (BiT) to effectively
unravel the underlying temporal correlation encoded in blur. Based on
multi-scale residual Swin transformer blocks, we introduce dual-end temporal
supervision and temporally symmetric ensembling strategies to generate
effective features for time-varying motion rendering. In addition, we design a
hybrid camera system to collect the first real-world dataset of one-to-many
blur-sharp video pairs. Experimental results show that BiT has a significant
gain over the state-of-the-art methods on the public dataset Adobe240. Besides,
the proposed real-world dataset effectively helps the model generalize well to
real blurry scenarios
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