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 $\bar{w}$-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