5,019 research outputs found
An Online Decoding Schedule Generating Algorithm for Successive Cancellation Decoder of Polar Codes
Successive cancellation (SC) is the first and widely known decoder of polar
codes, which has received a lot of attentions recently. However, its decoding
schedule generating algorithms are still primitive, which are not only complex
but also offline. This paper proposes a simple and online algorithm to generate
the decoding schedule of SC decoder. Firstly, the dependencies among likelihood
ratios (LR) are explored, which lead to the discovery of a sharing factor.
Secondly, based on the online calculation of the sharing factor, the proposed
algorithm is presented, which is neither based on the depth-first traversal of
the scheduling tree nor based on the recursive construction. As shown by the
comparisons among the proposed algorithm and existed algorithms, the proposed
algorithm has advantages of the online feature and the far less memory taken by
the decoding schedule.Comment: 16 pages, 2 figure
Estimating HIV Epidemics for Sub-National Areas
As the global HIV pandemic enters its fourth decade, increasing numbers of
surveillance sites have been established which allows countries to look into
the epidemics at a finer scale, e.g. at sub-national levels. Currently, the
epidemic models have been applied independently to the sub-national areas
within countries. However, the availability and quality of the data vary
widely, which leads to biased and unreliable estimates for areas with very few
data. We propose to overcome this issue by introducing the dependence of the
parameters across areas in a mixture model. The joint distribution of the
parameters in multiple areas can be approximated directly from the results of
independent fits without needing to refit the data or unpack the software. As a
result, the mixture model has better predictive ability than the independent
model as shown in examples of multiple countries in Sub-Saharan Africa.Comment: arXiv admin note: substantial text overlap with arXiv:1411.421
A Hierarchical Model for Estimating HIV Epidemics
As the global HIV pandemic enters its fourth decade, increasing numbers of
surveillance sites have been established which allows countries to look into
the epidemics at a finer scale, e.g. at sub-national level. However, the
epidemic models have been applied independently to the sub-national areas
within countries. An important technical barrier is that the availability and
quality of the data vary widely from area to area, and many areas lack data for
deriving stable and reliable results. To improve the accuracy of the results in
areas with little data, we propose a hierarchical model that utilizes
information efficiently by assuming similar characteristics of the epidemics
across areas within one country. The joint distribution of the parameters in
the hierarchical model can be approximated directly from the results of
independent fits without needing to the refit the data. As a result, the
hierarchical model has better predictive ability than the independent model as
shown in examples of multiple countries in Sub-Saharan Africa
Efficient Bit Sifting Scheme of Post-processing in Quantum Key Distribution
Bit sifting is an important step in the post-processing of Quantum Key
Distribution (QKD) whose function is to sift out the undetected original keys.
The communication traffic of bit sifting has essential impact on the net secure
key rate of a practical QKD system, and it is facing unprecedented challenges
with the fast increase of the repetition frequency of quantum channel. In this
paper, we present an efficient bit sifting scheme whose core is a lossless
source coding algorithm. Both theoretical analysis and experimental results
demonstrate that the performance of our scheme is approaching the Shannon
limit. Our scheme can greatly decrease the communication traffic of the
post-processing of a QKD system, which means it can decrease the secure key
consumption for classical channel authentication and increase the net secure
key rate of the QKD system. Meanwhile, it can relieve the storage pressure of
the system greatly, especially the device at Alice side. Some recommendations
on the application of our scheme to some representative practical QKD systems
are also provided
Improved DC Programming Approaches for Solving the Quadratic Eigenvalue Complementarity Problem
In this paper, we discuss the solution of a Quadratic Eigenvalue
Complementarity Problem (QEiCP) by using Difference of Convex (DC) programming
approaches. We first show that QEiCP can be represented as dc programming
problem. Then we investigate different dc programming formulations of QEiCP and
discuss their dc algorithms based on a well-known method -- DCA. A new local dc
decomposition is proposed which aims at constructing a better dc decomposition
regarding to the specific feature of the target problem in some neighborhoods
of the iterates. This new procedure yields faster convergence and better
precision of the computed solution. Numerical results illustrate the efficiency
of the new dc algorithms in practice.Comment: 23 page
Attention-based Temporal Weighted Convolutional Neural Network for Action Recognition
Research in human action recognition has accelerated significantly since the
introduction of powerful machine learning tools such as Convolutional Neural
Networks (CNNs). However, effective and efficient methods for incorporation of
temporal information into CNNs are still being actively explored in the recent
literature. Motivated by the popular recurrent attention models in the research
area of natural language processing, we propose the Attention-based Temporal
Weighted CNN (ATW), which embeds a visual attention model into a temporal
weighted multi-stream CNN. This attention model is simply implemented as
temporal weighting yet it effectively boosts the recognition performance of
video representations. Besides, each stream in the proposed ATW framework is
capable of end-to-end training, with both network parameters and temporal
weights optimized by stochastic gradient descent (SGD) with backpropagation.
Our experiments show that the proposed attention mechanism contributes
substantially to the performance gains with the more discriminative snippets by
focusing on more relevant video segments.Comment: 14th International Conference on Artificial Intelligence Applications
and Innovations (AIAI 2018), May 25-27, 2018, Rhodes, Greec
What Can We Learn from the Travelers Data in Detecting Disease Outbreaks -- A Case Study of the COVID-19 Epidemic
Background: Travel is a potent force in the emergence of disease. We
discussed how the traveler case reports could aid in a timely detection of a
disease outbreak. Methods: Using the traveler data, we estimated a few
indicators of the epidemic that affected decision making and policy, including
the exponential growth rate, the doubling time, and the probability of severe
cases exceeding the hospital capacity, in the initial phase of the COVID-19
epidemic in multiple countries. We imputed the arrival dates when they were
missing. We compared the estimates from the traveler data to the ones from
domestic data. We quantitatively evaluated the influence of each case report
and knowing the arrival date on the estimation. Findings: We estimated the
travel origin's daily exponential growth rate and examined the date from which
the growth rate was consistently above 0.1 (equivalent to doubling time < 7
days). We found those dates were very close to the dates that critical
decisions were made such as city lock-downs and national emergency
announcement. Using only the traveler data, if the assumed epidemic start date
was relatively accurate and the traveler sample was representative of the
general population, the growth rate estimated from the traveler data was
consistent with the domestic data. We also discussed situations that the
traveler data could lead to biased estimates. From the data influence study, we
found more recent travel cases had a larger influence on each day's estimate,
and the influence of each case report got smaller as more cases became
available. We provided the minimum number of exported cases needed to determine
whether the local epidemic growth rate was above a certain level, and developed
a user-friendly Shiny App to accommodate various scenarios.Comment: 25 pages, 6 figure
Thirty Years of The Network Scale up Method
Estimating the size of hard-to-reach populations is an important problem for
many fields. The Network Scale-up Method (NSUM) is a relatively new approach to
estimate the size of these hard-to-reach populations by asking respondents the
question, "How many X's do you know," where X is the population of interest
(e.g. "How many female sex workers do you know?"). The answers to these
questions form Aggregated Relational Data (ARD). The NSUM has been used to
estimate the size of a variety of subpopulations, including female sex workers,
drug users, and even children who have been hospitalized for choking. Within
the Network Scale-up methodology, there are a multitude of estimators for the
size of the hidden population, including direct estimators, maximum likelihood
estimators, and Bayesian estimators. In this article, we first provide an
in-depth analysis of ARD properties and the techniques to collect the data.
Then, we comprehensively review different estimation methods in terms of the
assumptions behind each model, the relationships between the estimators, and
the practical considerations of implementing the methods. Finally, we provide a
summary of the dominant methods and an extensive list of the applications, and
discuss the open problems and potential research directions in this area
Evaluating the relative contribution of data sources in a Bayesian analysis with the application of estimating the size of hard to reach populations
When using multiple data sources in an analysis, it is important to
understand the influence of each data source on the analysis and the
consistency of the data sources with each other and the model. We suggest the
use of a retrospective value of information framework in order to address such
concerns. Value of information methods can be computationally difficult. We
illustrate the use of computational methods that allow these methods to be
applied even in relatively complicated settings.
In illustrating the proposed methods, we focus on an application in
estimating the size of hard to reach populations. Specifically, we consider
estimating the number of injection drug users in Ukraine by combining all
available data sources spanning over half a decade and numerous sub-national
areas in the Ukraine. This application is of interest to public health
researchers as this hard to reach population that plays a large role in the
spread of HIV. We apply a Bayesian hierarchical model and evaluate the
contribution of each data source in terms of absolute influence, expected
influence, and level of surprise. Finally we apply value of information methods
to inform suggestions on future data collection.Comment: 24 pages, 7 figures, 2 table
Modeling the Marked Presence-only Data: A Case Study of Estimating the Female Sex Worker Size in Malawi
Certain subpopulations like female sex workers (FSW), men who have sex with
men (MSM), and people who inject drugs (PWID) often have higher prevalence of
HIV/AIDS and are difficult to map directly due to stigma, discrimination, and
criminalization. Fine-scale mapping of those populations contributes to the
progress towards reducing the inequalities and ending the AIDS epidemic. In
2016 and 2017, the PLACE surveys were conducted at 3,290 venues in 20 out of
the total 28 districts in Malawi to estimate the FSW sizes. These venues
represent a presence-only data set where, instead of knowing both where people
live and do not live (presence-absence data), only information about visited
locations is available. In this study, we develop a Bayesian model for
presence-only data and utilize the PLACE data to estimate the FSW size and
uncertainty interval at a -km resolution for all of Malawi. The
estimates can also be aggregated to any desirable level (city/district/region)
for implementing targeted HIV prevention and treatment programs in FSW
communities, which have been successful in lowering the incidence of HIV and
other sexually transmitted infections
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