Abstract: Recognizing and diagnosing the state of traffic flow is an important research area, which is the basis of improving the level of traffic management and the quality of traffic information services. However, due to the increasing amount of traffic data collected, the traffic management system is facing the problem of "information surplus". After finishing several process, including data preprocessing, attribute reduction and rule acquisition, finally obtained the knowledge rules of the traffic flow’s state. Using the method of knowledge discovery can reveal some hidden, unknown and valuable information from the huge amount of traffic flow information, so as to provide rules and decisionmaking basis for traffic management department.

Abstract: Factor investing is one king of quantitative investing methodologies for portfolio construction based on factors. Factors with different style are extracted from multiple sources such as market data, fundamental information from financial statements, sentimental information from the Internet, etc. Numerous style factors are defined by Barra model proposed by Morgan Stanley Capital International(MSCI) to explain the return a portfolio. Multiple factors are usually integrated linearly when being put to use, which ensure stability of the process of integration and enhance the effectiveness of integrated factors. In this work, we integrate factors by machine learning and deep learning methodologies to explore deeper information among multiple style factors defined by MSCI Barra model. Multi-factors indexes are compiled using Smart Beta Index methodology proposed by MSCI. And the results shows non-linear integration by deep neural network can enhance the profitability and stability of the index compiled according to the integrated factor.

Abstract: As a core task and important part of Information Extraction，Entity Relation Extraction can realize the identification of the semantic relation between entity pairs. And it plays an important role in semantic understanding of sentences and the construction of entity knowledge base. It has the potential of employing distant supervision method, end-to-end model and other deep learning model with the creation of large datasets. In this review, we compare the contributions and defect of the various models that have been used for the task, to help guide the path ahead.

Abstract: Traffic congestion seriously affect citizens’ life quality. Many researchers have paid much attention to the task of short-term traffic congestion forecasting. However, the performance of the traditional traffic congestion forecasting approaches is not satisfactory. Moreover, most neural network models cannot capture the features at different moments effectively. In this paper, we propose an Attention-based long short-term memory (LSTM) recurrent neural network. We evaluate the prediction architecture on a real-time traffic data from Gray-Chicago-Milwaukee (GCM) Transportation Corridor in Chicagoland. The experimental results demonstrate that our method outperforms the baselines for the task of congestion prediction.

Abstract: This paper presents a remarkable model for portfolio selection using inverse-variance weighting and machine learning techniques such as hierarchical clustering algorithms. This method allows building diversified portfolios that have a good balance sector exposure and style exposure, respect to momentum, size, value, short-term reversal, and volatility. Furthermore, we compare performance for seven hierarchical algorithms: Single, Complete, Average, Weighted, Centroid, Median and Ward Linkages. Results show that the Average Linkage algorithm has the best Cophenetic Correlation Coefficient. The proposed method using the best linkage criteria is tested against real data over a two-year dataset of one-minute American stocks returns. The portfolio selection model achieves a good financial return and an outstanding result in the annual volatility of 3.2%. The results suggest good behavior in performance indicators with a Sharpe ratio of 0.89, an Omega ratio of 1.16, a Sortino ratio of 1.29 and a beta to S&P of 0.26.

Abstract: In the present work, the European style fixed strike Asian call option with arithmetic and continuous averaging is numerically evaluated where the volatility, the risk free interest rate and the dividend yield are functions of the time. A finite difference scheme consisting of second order HODIE scheme for spatial discretization and two-step backward differentiation formula for temporal discretization is applied. The scheme is proved to be second order accurate in space and time both. The numerical results are in accordance with analytical results.

Abstract: Portfolio optimization in stock markets has been investigated by many researchers. It looks for a subset of assets able to maintain a good trade-o control between risk and return. Several algorithms have been proposed to portfolio management. These algorithms use known return and correlation data to build subset of recommended assets. Dynamic stock correlation networks, whose vertices represent stocks and edges represent the correlation between them along the time, can also be used as input by these algorithms. This study proposes the denition of the constants of the classic mean-variance analysis using machine learning and weighted link prediction in stock networks (named as MLink). To assess the performance of MLink, experiments were performed using real data from the Brazilian Stock Exchange. In these experiments, MLink was compared with mean-variance analysis (MVA), a popular methods for portfolio optimization. According to the experimental results, the use of weighted link prediction in stock networks as input produced the best performance in the portfolio optimization task, resulting in a capital increase of 41% in 84 days.