143 research outputs found
Discounted continuous-time constrained Markov decision processes in Polish spaces
This paper is devoted to studying constrained continuous-time Markov decision
processes (MDPs) in the class of randomized policies depending on state
histories. The transition rates may be unbounded, the reward and costs are
admitted to be unbounded from above and from below, and the state and action
spaces are Polish spaces. The optimality criterion to be maximized is the
expected discounted rewards, and the constraints can be imposed on the expected
discounted costs. First, we give conditions for the nonexplosion of underlying
processes and the finiteness of the expected discounted rewards/costs. Second,
using a technique of occupation measures, we prove that the constrained
optimality of continuous-time MDPs can be transformed to an equivalent
(optimality) problem over a class of probability measures. Based on the
equivalent problem and a so-called -weak convergence of probability
measures developed in this paper, we show the existence of a constrained
optimal policy. Third, by providing a linear programming formulation of the
equivalent problem, we show the solvability of constrained optimal policies.
Finally, we use two computable examples to illustrate our main results.Comment: Published in at http://dx.doi.org/10.1214/10-AAP749 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A design of Convolutional Neural Network model for the Diagnosis of the COVID-19
With the spread of COVID-19 around the globe over the past year, the usage of
artificial intelligence (AI) algorithms and image processing methods to analyze
the X-ray images of patients' chest with COVID-19 has become essential. The
COVID-19 virus recognition in the lung area of a patient is one of the basic
and essential needs of clicical centers and hospitals. Most research in this
field has been devoted to papers on the basis of deep learning methods
utilizing CNNs (Convolutional Neural Network), which mainly deal with the
screening of sick and healthy people.In this study, a new structure of a
19-layer CNN has been recommended for accurately recognition of the COVID-19
from the X-ray pictures of chest. The offered CNN is developed to serve as a
precise diagnosis system for a three class (viral pneumonia, Normal, COVID) and
a four classclassification (Lung opacity, Normal, COVID-19, and pneumonia). A
comparison is conducted among the outcomes of the offered procedure and some
popular pretrained networks, including Inception, Alexnet, ResNet50,
Squeezenet, and VGG19 and based on Specificity, Accuracy, Precision,
Sensitivity, Confusion Matrix, and F1-score. The experimental results of the
offered CNN method specify its dominance over the existing published
procedures. This method can be a useful tool for clinicians in deciding
properly about COVID-19
Predicting Stock Price of Construction Companies using Weighted Ensemble Learning
Modeling the behavior of stock price data has always been one of the
challengeous applications of Artificial Intelligence (AI) and Machine Learning
(ML) due to its high complexity and dependence on various conditions. Recent
studies show that this will be difficult to do with just one learning model.
The problem can be more complex for companies of construction section, due to
the dependency of their behavior on more conditions. This study aims to provide
a hybrid model for improving the accuracy of prediction for stock price index
of companies in construction section. The contribution of this paper can be
considered as follows: First, a combination of several prediction models is
used to predict stock price, so that learning models can cover each other's
error. In this research, an ensemble model based on Artificial Neural Network
(ANN), Gaussian Process Regression (GPR) and Classification and Regression Tree
(CART) is presented for predicting stock price index. Second, the optimization
technique is used to determine the effect of each learning model on the
prediction result. For this purpose, first all three mentioned algorithms
process the data simultaneously and perform the prediction operation. Then,
using the Cuckoo Search (CS) algorithm, the output weight of each algorithm is
determined as a coefficient. Finally, using the ensemble technique, these
results are combined and the final output is generated through weighted
averaging on optimal coefficients. The results showed that using CS
optimization in the proposed ensemble system is highly effective in reducing
prediction error. Comparing the evaluation results of the proposed system with
similar algorithms, indicates that our model is more accurate and can be useful
for predicting stock price index in real-world scenarios
Structured risk model
Income and risk coexist, yet investors are often so focused on chasing high
returns that they overlook the potential risks that can lead to high losses.
Therefore, risk forecasting and risk control is the cornerstone of investment.
To address the challenge, we construct a multi-factor risk model on the basis
of the classical multi-factor modeling framework. For the common factors,
inspired by Barra Model's factor classification. we adjust the outliers and
missing values of factor exposure data, normalize and finally orthogonalize
them, before computing factor returns and making further analysis. Factor
return covariance matrix and idiosyncratic return variance matrix are essential
tools to express stock returns in the multi-factor risk model. Firstly, we
calculate the factor return covariance matrix with EWMA. To tackle the
time-series autocorrelation of factor returns, we apply Newey-West adjustment.
Then we estimate the idiosyncratic return variance matrix in a similar way and
make Newey-West adjustment again to solve the time-series autocorrelation
problem. Since the return of a single share is sensitive to missing values and
outliers, we introduce structural adjustment to improve the matrix.Eventually,
we obtain the return covariance matrix among stocks and compute the risk of
investment portfolio based on it. Furthermore, we search for optimal portfolio
with respect to minimizing risk or maximizing risk-adjusted return with our
model. They provide good Sharpe ratio and information ratio for considering
both absolute risk and active risk. Hence, the multi-factor risk model is
efficient
Driving force induced transition in thermal behavior of grain boundary migration in Ni
Grain boundaries (GBs) that show higher mobility at lower temperatures (i.e.,
anti-thermal or non-Arrhenius behavior) have attracted significant interest in
recent years. In this study, we use atomistic simulations to systematically
investigate the effect of driving force on GB mobility based on a set of
bicrystalline models in Ni. It is found that the thermal behavior of GB
migration strongly depends on temperature and the magnitude of driving forces.
When the driving force is at the zero-driving force limit as induced solely by
thermal fluctuations, the mobility of all GBs investigated in the current study
shows a transition from thermally activated to anti-thermal behavior when the
temperature is increased. As the driving force increases, the transition
temperature at which the mobility peaks would gradually decrease so that for
some GBs only the anti-thermal behavior would be detected. Energy analysis
further reveals that the transition temperature (Ttrans) is linearly related to
both energy barrier per area (E) from NEB simulation and the fitted apparent
activation (Q) energy, and both E and Q are lowered as the driving force
increases. Our work supports the previous theoretical models for GB migration
based on both classical thermal activation and disconnection nucleation.
Furthermore, the current study can be used to improve both models by
considering the influence of driving force with a simple fix to how the energy
barrier for GB migration should be considered. It is expected that this work
advances the current understanding of general GB migration and sheds some light
on a unified theoretical framework in the near future
Hidden Markov latent variable models with multivariate longitudinal data: Hidden Markov Latent Variable Models with Multivariate Longitudinal Data
Cocaine addiction is chronic and persistent, and has become a major social and health problem in many countries. Existing studies have shown that cocaine addicts often undergo episodic periods of addiction to, moderate dependence on, or swearing off cocaine. Given its reversible feature, cocaine use can be formulated as a stochastic process that transits from one state to another, while the impacts of various factors, such as treatment received and individuals’ psychological problems on cocaine use, may vary across states. This paper develops a hidden Markov latent variable model to study multivariate longitudinal data concerning cocaine use from a California Civil Addict Program. The proposed model generalizes conventional latent variable models to allow bidirectional transition between cocaine-addiction states and conventional hidden Markov models to allow latent variables and their dynamic interrelationship. We develop a maximum likelihood approach, along with a Monte Carlo expectation conditional maximization (MCECM) algorithm, to conduct parameter estimation. The asymptotic properties of the parameter estimates and statistics for testing the heterogeneity of model parameters are investigated. The finite sample performance of the proposed methodology is demonstrated by simulation studies. The application to cocaine use study provides insights into the prevention of cocaine use
Direct growth of 2D and 3D graphene nano-structures over large glass substrates by tuning a sacrificial Cu-template layer
We demonstrate direct growth of two-dimensional (2D) and three-dimensional (3D) graphene structures on glass substrates. By starting from catalytic copper nanoparticles of different densities and using chemical vapour deposition (CVD) techniques, different 2D and 3D morphologies can be obtained, including graphene sponge-like, nano-ball and conformal graphene structures. More important, we show that the initial copper template can be completely removed via sublimation during CVD and, if need be, subsequent metal etching. This allows optical transmissions close to the bare substrate, which, combined with electrical conductivity make the proposed technique very attractive for creating graphene with high surface to volume ratio for a wide variety of applications, including antiglare display screens, solar cells, light-emitting diodes, gas and biological plasmonic sensors.Peer ReviewedPostprint (author's final draft
PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving
Pedestrian detection under valet parking scenarios is fundamental for
autonomous driving. However, the presence of pedestrians can be manifested in a
variety of ways and postures under imperfect ambient conditions, which can
adversely affect detection performance. Furthermore, models trained on
publicdatasets that include pedestrians generally provide suboptimal outcomes
for these valet parking scenarios. In this paper, wepresent the Parking
Pedestrian Dataset (PPD), a large-scale fisheye dataset to support research
dealing with real-world pedestrians, especially with occlusions and diverse
postures. PPD consists of several distinctive types of pedestrians captured
with fisheye cameras. Additionally, we present a pedestrian detection baseline
on PPD dataset, and introduce two data augmentation techniques to improve the
baseline by enhancing the diversity ofthe original dataset. Extensive
experiments validate the effectiveness of our novel data augmentation
approaches over baselinesand the dataset's exceptional generalizability.Comment: 9 pages, 6 figure
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