Model-based reinforcement learning: A survey

Reinforcement learning is an important branch of machine learning and artificial intelligence. Compared with traditional reinforcement learning, model-based reinforcement learning obtains the action of the next state by the model that has been learned, and then optimizes the policy, which greatly improves data efficiency. Based on the present status of research on model-based reinforcement learning at home and abroad, this paper comprehensively reviews the key techniques of model-based reinforcement learning, summarizes the characteristics, advantages and defects of each technology, and analyzes the application of model-based reinforcement learning in games, robotics and brain science

Topological Photonic Phase in Chiral Hyperbolic Metamaterials

Recently the possibility of achieving one-way backscatter immune transportation of light by mimicking the topological order present within certain solid state systems, such as topological insulators, has received much attention. Thus far however, demonstrations of non-trivial topology in photonics have relied on photonic crystals with precisely engineered lattice structures, periodic on the scale of the operational wavelength and composed of finely tuned, complex materials. Here we propose a novel effective medium approach towards achieving topologically protected photonic surface states robust against disorder on all length scales and for a wide range of material parameters. Remarkably, the non-trivial topology of our metamaterial design results from the Berry curvature arising from the transversality of electromagnetic waves in a homogeneous medium. Our investigation therefore acts to bridge the gap between the advancing field of topological band theory and classical optical phenomena such as the Spin Hall effect of light. The effective medium route to topological phases will pave the way for highly compact one-way transportation of electromagnetic waves in integrated photonic circuits.Comment: 11 pages, 3 figures. To appear in PR

Users’ Perception and Utility of Health Information Based on WeChat

Objective: To explore the ability of users to identify the health information spread on WeChat platform, and then to discuss the utility of such information on them. Methods: Questionnaire survey and descriptive statistical methods were used to collect and analyze the data. The process of “get health related information, judge true or false, production related behavior” was used to design the survey problem, finally, the paper and network surveys were investigated in users. Results: The proportion of accurate recognition, ambiguous recognition, none recognition and none classification were 21.55%, 63.26%, 4.41%, 10.78% respectively. The average frequency of identification, transmission, guidance and none behavior of originally true health information were 52.00, 78.40, 45.20, 31.80 respectively, and originally false health information were 21.30, 27.70, 14.10, 5.50 respectively.Conclusions: Most WeChat users surveyed lack the ability to accurately identify the authenticity of health information, and improvement of citizens’ health literacy has long way to go. WeChat is an effective platform for the dissemination of health information, but also provides a fertile soil for the spread of false health information. Additionally, users are not aware of the problem of their ability to identify information, even if produced a false judgment of false health information, most of the active users will also lead this kind of information still widely spread

Fault diagnosis for rotating machinery based on multi-differential empirical mode decomposition

The fault diagnosis of rotating machinery has crucial significance for the safety of modern industry, and the fault feature extraction is the key link of the diagnosis process. As an effective time-frequency method, Empirical Mode Decomposition (EMD) has been widely used in signal processing and feature extraction. However, the mode mixing phenomenon may lead to confusion in the identification of multi frequency signals and restricts the applications of EMD. In this paper, a novel method based on Multi-Differential Empirical Mode Decomposition (MDEMD) was proposed to extract the energy distribution characteristics of fault signals. Firstly, multi-order differential signals were deduced and decomposed by EMD. Then, their energy distribution characteristics were extracted and utilized to construct the feature matrix. Finally, taking the feature matrix as input, the classifiers were applied to diagnosis the existence and severity of rotating machinery faults. Simulative and practical experiments were implemented respectively, and the results demonstrated that the proposed method, i.e. MDEMD, is able to eliminate the mode mixing effectively, and the feature matrix extracted by MDEMD has high separability and universality, furthermore, the fault diagnosis based on MDEMD can be accomplished more effectively and efficiently with satisfactory accuracy

A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method

A Multistep Extending Truncation Method towards Model Construction of Infinite-State Markov Chains

The model checking of Infinite-State Continuous Time Markov Chains will inevitably encounter the state explosion problem when constructing the CTMCs model; our method is to get a truncated model of the infinite one; to get a sufficient truncated model to meet the model checking of Continuous Stochastic Logic based system properties, we propose a multistep extending advanced truncation method towards model construction of CTMCs and implement it in the INFAMY model checker; the experiment results show that our method is effective

MTR4 drives liver tumorigenesis by promoting cancer metabolic switch through alternative splicing.

The metabolic switch from oxidative phosphorylation to glycolysis is required for tumorigenesis in order to provide cancer cells with energy and substrates of biosynthesis. Therefore, it is important to elucidate mechanisms controlling the cancer metabolic switch. MTR4 is a RNA helicase associated with a nuclear exosome that plays key roles in RNA processing and surveillance. We demonstrate that MTR4 is frequently overexpressed in hepatocellular carcinoma (HCC) and is an independent diagnostic marker predicting the poor prognosis of HCC patients. MTR4 drives cancer metabolism by ensuring correct alternative splicing of pre-mRNAs of critical glycolytic genes such as GLUT1 and PKM2. c-Myc binds to the promoter of the MTR4 gene and is important for MTR4 expression in HCC cells, indicating that MTR4 is a mediator of the functions of c-Myc in cancer metabolism. These findings reveal important roles of MTR4 in the cancer metabolic switch and present MTR4 as a promising therapeutic target for treating HCC

Shaft orbit identification for rotating machinery based on statistical fuzzy vector chain code and support vector machine

Shaft orbit is a significant diagnosis criterion, and its identification plays an important role in the fault diagnosis of large rotating machinery. The main difficulty of shaft orbit identification is how to extract the shape features automatically and effectively. Therefore, in this paper, a novel method named statistical fuzzy vector chain code (SFVCC) is proposed for the feature extraction of shaft orbit, which has such advantages as invariance, simple calculation and high separability. Furthermore, taking the extracted feature vectors as input, support vector machine (SVM) is utilized to identify various kinds of shaft orbits for rotating machinery. Comparative experiments are implemented, the results reveal that, compared with previous methods, the proposed method can identify the shaft orbit more effectively and efficiently with satisfactory accuracy

Brassinosteroids affect wood development and properties of Fraxinus mandshurica

IntroductionXylem development plays a crucial role in wood formation in woody plants. In recent years, there has been growing attention towards the impact of brassinosteroids (BRs) on this xylem development. In the present study, we evaluated the dynamic variation of xylem development in Fraxinus mandshurica (female parent, M8) and a novel interspecific hybrid F. mandshurica × Fraxinus sogdiana (1601) from May to August 2020.MethodsWe obtained RNA-Seq transcriptomes of three tissue types (xylem, phloem, and leaf) to identify the differences in xylem-differentially expressed genes (X-DEGs) and xylem-specifically expressed genes (X-SEGs) in M8 and 1601 variants. We then further evaluated these genes via weighted gene co-expression network analysis (WGCNA) alongside overexpressing FmCPD, a BR biosynthesis enzyme gene, in transient transgenic F. mandshurica.ResultsOur results indicated that the xylem development cycle of 1601 was extended by 2 weeks compared to that of M8. In addition, during the later wood development stages (secondary wall thickening) of 1601, an increased cellulose content (14%) and a reduced lignin content (11%) was observed. Furthermore, vessel length and width increased by 67% and 37%, respectively, in 1601 compared with those of M8. A total of 4589 X-DEGs were identified, including enzymes related to phenylpropane metabolism, galactose metabolism, BR synthesis, and signal transduction pathways. WGCNA identified hub X-SEGs involved in cellulose synthesis and BR signaling in the 1601 wood formation–related module (CESA8, COR1, C3H14, and C3H15); in contrast, genes involved in phenylpropane metabolism were significantly enriched in the M8 wood formation–related module (CCoAOMT and CCR). Moreover, overexpression of FmCPD in transient transgenic F. mandshurica affected the expression of genes associated with lignin and cellulose biosynthesis signal transduction. Finally, BR content was determined to be approximately 20% lower in the M8 xylem than in the 1601 xylem, and the exogenous application of BRs (24-epi brassinolide) significantly increased the number of xylem cell layers and altered the composition of the secondary cell walls in F. mandshurica.DiscussionOur findings suggest that BR biosynthesis and signaling play a critical role in the differing wood development and properties observed between M8 and 1601 F. mandshurica