Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation

Monte Carlo tree search (MCTS) is extremely popular in computer Go which determines each action by enormous simulations in a broad and deep search tree. However, human experts select most actions by pattern analysis and careful evaluation rather than brute search of millions of future nteractions. In this paper, we propose a computer Go system that follows experts way of thinking and playing. Our system consists of two parts. The first part is a novel deep alternative neural network (DANN) used to generate candidates of next move. Compared with existing deep convolutional neural network (DCNN), DANN inserts recurrent layer after each convolutional layer and stacks them in an alternative manner. We show such setting can preserve more contexts of local features and its evolutions which are beneficial for move prediction. The second part is a long-term evaluation (LTE) module used to provide a reliable evaluation of candidates rather than a single probability from move predictor. This is consistent with human experts nature of playing since they can foresee tens of steps to give an accurate estimation of candidates. In our system, for each candidate, LTE calculates a cumulative reward after several future interactions when local variations are settled. Combining criteria from the two parts, our system determines the optimal choice of next move. For more comprehensive experiments, we introduce a new professional Go dataset (PGD), consisting of 253233 professional records. Experiments on GoGoD and PGD datasets show the DANN can substantially improve performance of move prediction over pure DCNN. When combining LTE, our system outperforms most relevant approaches and open engines based on MCTS.Comment: AAAI 201

Analysis of the Family of Generalized Cumulative Sum Type Control Charts

As an aid to the practitioners, various Phase II quality control charts have been developed to monitor for a change in the parameters of the distribution of a quality measurement. In this project, the family of generalized cumulative sum type charts was studied. An equivalent chart version that requires fewer parameters was given. Some useful integral equations were derived for determining the run length distribution of the lower and upper one-sided charts. The Markov chain methods were also given. The parameters unknown version was presented and the performance analysis was studied for the chart for monitoring for a change in the mean of a normal distribution. The design and analysis of a chart when the quality measurement follows a gamma distribution was given, which includes the design and analysis of a chart for monitoring for a change in the standard deviation of a normal distribution

Virtual Cellular Multi-period Formation under the Dynamic Environment

AbstractVirtual cellular manufacturing is an innovative way of production organization which both in the production of flexibility and efficient to meet today's rapid development of science and technology and replacement of products. The key process of the design of virtual cellular manufacturing system—cell formation is the focus of research. In order to meet the characteristics of small batch and dynamically changing market demand, this paper studies the problems of virtual cellular multi-period dynamic reconfiguration. A reconfigurable system programming model is developed. The model incorporates parameters of the problems of product dynamic demand, machine capacity, operation sequence, balanced workload, alternative routings and batch setting. The objective of mixed integer programming model is to minimize the total costs of operation, moving raw materials, inventory holding and process routes setup. Though a case study, demonstrates the feasibility and validity of the model in reality

New treatment methods for myocardial infarction

For a long time, cardiovascular clinicians have focused their research on coronary atherosclerotic cardiovascular disease and acute myocardial infarction due to their high morbidity, high mortality, high disability rate, and limited treatment options. Despite the continuous optimization of the therapeutic methods and pharmacological therapies for myocardial ischemia–reperfusion, the incidence rate of heart failure continues to increase year by year. This situation is speculated to be caused by the current therapies, such as reperfusion therapy after ischemic injury, drugs, rehabilitation, and other traditional treatments, that do not directly target the infarcted myocardium. Consequently, these therapies cannot fundamentally solve the problems of myocardial pathological remodeling and the reduction of cardiac function after myocardial infarction, allowing for the progression of heart failure after myocardial infarction. Coupled with the decline in mortality caused by acute myocardial infarction in recent years, this combination leads to an increase in the incidence of heart failure. As a new promising therapy rising at the beginning of the twenty-first century, cardiac regenerative medicine provides a new choice and hope for the recovery of cardiac function and the prevention and treatment of heart failure after myocardial infarction. In the past two decades, regeneration engineering researchers have explored and summarized the elements, such as cells, scaffolds, and cytokines, required for myocardial regeneration from all aspects and various levels day and night, paving the way for our later scholars to carry out relevant research and also putting forward the current problems and directions for us. Here, we describe the advantages and challenges of cardiac tissue engineering, a contemporary innovative therapy after myocardial infarction, to provide a reference for clinical treatment

Sparse Matrix for ECG Identification with Two-Lead Features

Electrocardiograph (ECG) human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods