Practical Deep Reinforcement Learning Approach for Stock Trading

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns

Remote Antenna Unit Selection Assisted Seamless Handover for High-Speed Railway Communications with Distributed Antennas

To attain seamless handover and reduce the han- dover failure probability for high-speed railway (HSR) com- munication systems, this paper proposes a remote antenna unit (RAU) selection assisted handover scheme where two antennas are installed on high speed train (HST) and distributed antenna system (DAS) cell architecture on ground is adopted. The RAU selection is used to provide high quality received signals for trains moving in DAS cells and the two HST antennas are employed on trains to realize seamless handover. Moreover, to efficiently evaluate the system performance, a new met- ric termed as handover occurrence probability is defined for describing the relation between handover occurrence position and handover failure probability. We then analyze the received signal strength, the handover trigger probability, the handover occurrence probability, the handover failure probability and the communication interruption probability. Numerical results are provided to compare our proposed scheme with the current existing ones. It is shown that our proposed scheme achieves better performances in terms of handover failure probability and communication interruption probability.Comment: 7 figures, accepted by IEEE VTC-Spring, 201

Robust fault detection for networked systems with communication delay and data missing

n this paper, the robust fault detection problem is investigated for a class of discrete-time networked systems with unknown input and multiple state delays. A novel measurement model is utilized to represent both the random measurement delays and the stochastic data missing phenomenon, which typically result from the limited capacity of the communication networks. The network status is assumed to vary in a Markovian fashion and its transition probability matrix is uncertain but resides in a known convex set of a polytopic type. The main purpose of this paper is to design a robust fault detection filter such that, for all unknown inputs, possible parameter uncertainties and incomplete measurements, the error between the residual signal and the fault signal is made as small as possible. By casting the addressed robust fault detection problem into an auxiliary robust H∞ filtering problem of a certain Markovian jumping system, a sufficient condition for the existence of the desired robust fault detection filter is established in terms of linear matrix inequalities. A numerical example is provided to illustrate the effectiveness and applicability of the proposed technique

A Novel Image Segmentation Algorithm Based on Graph Cut Optimization Problem

Image segmentation, a fundamental task in computer vision, has been widely used in recent years in many fields. Dealing with the graph cut optimization problem obtains the image segmentation results. In this study, a novel algorithm with weighted graphs was constructed to solve the image segmentation problem through minimization of an energy function. A binary vector of the segmentation label was defined to describe both the foreground and the background of an image. To demonstrate the effectiveness of our proposed method, four various types of images were used to construct a series of experiments. Experimental results indicate that compared with other methods, the proposed algorithm can effectively promote the quality of image segmentation under three performance evaluation metrics, namely, misclassification error rate, rate of the number of background pixels, and the ratio of the number of wrongly classified foreground pixels

A Multicenter prospective study of poor-grade aneurysmal subarachnoid hemorrhage (AMPAS): observational registry study

BACKGROUND: Poor-grade aneurysmal subarachnoid hemorrhage (aSAH) is associated with very high mortality and morbidity. Our limited knowledge on predictors of long-term outcome in poor-grade patients with aSAH definitively managed comes from retrospective and prospective studies of small case series of patients in single center. The purpose of the AMPAS is to determine the long-term outcomes in poor-grade patients with different managements within different time after aSAH, and identify the independent predictors of the outcome that help guide the decision on definitive management. METHODS/DESIGN: The AMPAS study is a prospective, multicenter, observational registry of consecutive hospitalized patients with poor grade aSAH (WFNS grade IV and V). The aim is to enroll at least 226 poor-grade patients in 11 high-volume medical centers (eg, >150 aSAH cases per year) affiliated to different universities in China. This study will describe poor grade patients and aneurysm characteristics, treatment strategies (modality and time of definitive management), hospitalization complications and outcomes evolve over time. The definitive management is ruptured aneurysm treatment. Outcomes at 3, 6, 12 months after the management were measured using the Glasgow Outcome Scale and the Modified Rankin Scale. DISCUSSION: The AMPAS is the first prospective, multicenter, observational registry of poor grade aSAH with any management. This study will contribute to a better understanding of significant predictors of outcome in poor grade patients and help guide future treatment of the worst patients after aSAH. TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR-TNRC-10001041