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Search Results: 1 - 10 of 127073 matches for " Shuangfei LI "
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The Transmission of Pricing Information of Dually-Listed between Hong Kong and New York Stock Exchange  [PDF]
Shuangfei LI, Shou CHEN
Journal of Service Science and Management (JSSM) , 2009, DOI: 10.4236/jssm.2009.24041
Abstract: The study investigates the transmission of pricing information between Hong Kong Stock Exchange and New York Stock Exchange. Using the opening and closing stock prices of these two markets from Jan. 2003 to Apr. 2007 with the method of Seemingly Unrelated Regression, we draw the conclusions that: 1) intraday returns of Chinese dually-listed stocks is influenced more obviously by Hang Seng Index than Dow-Jones Average; 2) transmission of pricing information is only from New York to Hong Kong; 3) intraday returns of stocks from New York Stock Exchange has a remarkable influence on that of the next day in Hongkong market, but the stocks price of Hong Kong Stock Exchange has no relation with which of New York Stock Exchange.
Access Control Attacks on PLC Vulnerabilities  [PDF]
Yong Wang, Jinyong Liu, Can Yang, Lin Zhou, Shuangfei Li, Zhaoyan Xu
Journal of Computer and Communications (JCC) , 2018, DOI: 10.4236/jcc.2018.611028
Abstract: In Industrial Control Systems (ICS), security issues are getting more and more attention. The number of hacking attacks per year is endless, and the attacks on industrial control systems are numerous. Programmable Logic Controller (PLC) is one of the main controllers of industrial processes. Since the industrial control system network is isolated from the external network, many people think that PLC is a safety device. However, virus attacks in recent years, such as Stuxnet, have confirmed the erroneousness of this idea. In this paper, we use the vulnerability of Siemens PLC to carry out a series of attacks, such as S7-200, S7-300, S7-400, S7-1200 and so on. We read the data from the PLC output and then rewrite the data and write it to the PLC. We tamper with the writing of data to achieve communication chaos. When we attack the primary station, all slave devices connected to the primary station will be in a state of communication confusion. The attack methods of us can cause delay or even loss of data in the communications from the Phasor Data Concentrator (PMU) to the data concentrator. The most important thing is that our attack method generates small traffic and short attack time, which is difficult to be identified by traditional detection methods.
Cloning and expression analysis in mature individuals of two chicken type-II GnRH (cGnRH-II) genes in common carp (Cyprinus carpio)
Li Shuangfei,Hu Wei,Wang Yaping,Zhu Zuoyan
Science China Life Sciences , 2004, DOI: 10.1360/03yc0117
Abstract: Gonadotropin-releasing hormone (GnRH) is a conservative neurodecapeptide family, which plays a crucial role in regulating the gonad development and in controlling the final sexual maturation in vertebrate. Two differing cGnRH-II cDNAs of common carp, namely cGnRH-II cDNA1 and cDNA2, were firstly cloned from the brain by rapid amplification of cDNA end (RACE) and reverse transcriptionpolymerase chain reaction (RT-PCR). The length of cGnRH-II cDNA1 and cDNA2 was 622 and 578 base pairs (bp), respectively. The cGnRH-II precursors encoded by two cDNAs consisted of 86 amino acids, including a signal peptide, cGnRH-II decapeptide and a GnRH-associated peptide (GAP) linked by a Gly-Lys-Arg proteolytic site. The results of intron trapping and Southern blot showed that two differing cGnRH-II genes in common carp genome were further identified, and that two genes might exist as a single copy. The multi-gene coding of common carp cGnRH-II gene offered novel evidence for gene duplication hypothesis. Using semi-quantitative RT-PCR, expression and relative expression levels of cGnRH-II genes were detected in five dissected brain regions, pituitary and gonad of common carp. With the exception of no mRNA2 in ovary, two cGnRH-II genes could be expressed in all the detected tissues. However, expression levels showed an apparent difference in different brain regions, pituitary and gonad. According to the expression characterization of cGnRH-II genes in brain areas, it was presumed that cGnRH-II might mainly work as the neurotransmitter and neuromodulator and also operate in the regulation for the GnRH releasing. Then, the expression of cGnRH-II genes in pituitary and gonad suggested that cGnRH-II might act as the autocrine or paracrine regulator.
Cloning and expression analysis in mature individuals of two chicken type-II GnRH (cGnRH-II) genes in common carp (Cyprinus carpio)

LI Shuangfei,HU Wei,WANG Yaping,ZHU Zuoyan,

中国科学C辑(英文版) , 2004,
Influence of Glutamic Acid on the Properties of Poly(xylitol glutamate sebacate) Bioelastomer
Weifu Dong,Ting Li,Shuangfei Xiang,Piming Ma,Mingqing Chen
Polymers , 2013, DOI: 10.3390/polym5041339
Abstract: In order to further improve the biocompatibility of xylitol based poly(xylitol sebacate) (PXS) bioelastomer, a novel kind of amino acid based poly(xylitol glutamate sebacate) (PXGS) has been successfully prepared in this work by melt polycondensation of xylitol, N-Boc glutamic acid and sebacic acid. Differential scanning calorimetry (DSC) results indicated the glass-transition temperatures could be decreased by feeding N-Boc glutamic acid. In comparison to PXS, PXGS exhibited comparable tensile strength and much higher elongation at break at the same ratio of acid/xylitol. The introduction of glutamic acid increased the hydrophilicity and in vitro degradation rate of the bioelastomer. It was found that PXGS exhibited excellent properties, such as tensile properties, biodegradability and hydrophilicity, which could be easily tuned by altering the feeding monomer ratios. The amino groups in the PXGS polyester side chains are readily functionalized, thus the biomelastomers can be considered as potential biomaterials for biomedical application.
Defining Global Gene Expression Changes of the Hypothalamic-Pituitary-Gonadal Axis in Female sGnRH-Antisense Transgenic Common Carp (Cyprinus carpio)
Jing Xu,Wei Huang,Chengrong Zhong,Daji Luo,Shuangfei Li,Zuoyan Zhu,Wei Hu
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0021057
Abstract: The hypothalamic-pituitary-gonadal (HPG) axis is critical in the development and regulation of reproduction in fish. The inhibition of neuropeptide gonadotropin-releasing hormone (GnRH) expression may diminish or severely hamper gonadal development due to it being the key regulator of the axis, and then provide a model for the comprehensive study of the expression patterns of genes with respect to the fish reproductive system.
Dropout Training of Matrix Factorization and Autoencoder for Link Prediction in Sparse Graphs
Shuangfei Zhai,Zhongfei Zhang
Computer Science , 2015,
Abstract: Matrix factorization (MF) and Autoencoder (AE) are among the most successful approaches of unsupervised learning. While MF based models have been extensively exploited in the graph modeling and link prediction literature, the AE family has not gained much attention. In this paper we investigate both MF and AE's application to the link prediction problem in sparse graphs. We show the connection between AE and MF from the perspective of multiview learning, and further propose MF+AE: a model training MF and AE jointly with shared parameters. We apply dropout to training both the MF and AE parts, and show that it can significantly prevent overfitting by acting as an adaptive regularization. We conduct experiments on six real world sparse graph datasets, and show that MF+AE consistently outperforms the competing methods, especially on datasets that demonstrate strong non-cohesive structures.
Semisupervised Autoencoder for Sentiment Analysis
Shuangfei Zhai,Zhongfei Zhang
Computer Science , 2015,
Abstract: In this paper, we investigate the usage of autoencoders in modeling textual data. Traditional autoencoders suffer from at least two aspects: scalability with the high dimensionality of vocabulary size and dealing with task-irrelevant words. We address this problem by introducing supervision via the loss function of autoencoders. In particular, we first train a linear classifier on the labeled data, then define a loss for the autoencoder with the weights learned from the linear classifier. To reduce the bias brought by one single classifier, we define a posterior probability distribution on the weights of the classifier, and derive the marginalized loss of the autoencoder with Laplace approximation. We show that our choice of loss function can be rationalized from the perspective of Bregman Divergence, which justifies the soundness of our model. We evaluate the effectiveness of our model on six sentiment analysis datasets, and show that our model significantly outperforms all the competing methods with respect to classification accuracy. We also show that our model is able to take advantage of unlabeled dataset and get improved performance. We further show that our model successfully learns highly discriminative feature maps, which explains its superior performance.
Manifold Regularized Discriminative Neural Networks
Shuangfei Zhai,Zhongfei Zhang
Computer Science , 2015,
Abstract: Unregularized deep neural networks (DNNs) can be easily overfit with a limited sample size. We argue that this is mostly due to the disriminative nature of DNNs which directly model the conditional probability (or score) of labels given the input. The ignorance of input distribution makes DNNs difficult to generalize to unseen data. Recent advances in regularization techniques, such as pretraining and dropout, indicate that modeling input data distribution (either explicitly or implicitly) greatly improves the generalization ability of a DNN. In this work, we explore the manifold hypothesis which assumes that instances within the same class lie in a smooth manifold. We accordingly propose two simple regularizers to a standard discriminative DNN. The first one, named Label-Aware Manifold Regularization, assumes the availability of labels and penalizes large norms of the loss function w.r.t. data points. The second one, named Label-Independent Manifold Regularization, does not use label information and instead penalizes the Frobenius norm of the Jacobian matrix of prediction scores w.r.t. data points, which makes semi-supervised learning possible. We perform extensive control experiments on fully supervised and semi-supervised tasks using the MNIST dataset and set the state-of-the-art results on it.
Peroxidase Conjugate of Cellulose Nanocrystals for the Removal of Chlorinated Phenolic Compounds in Aqueous Solution
Ruming Yang,He Tan,Fanglin Wei,Shuangfei Wang
Biotechnology , 2008,
Abstract: The study was conducted to immobilize peroxidase (E.C. on to the rodlike cellulose nanocrystals after activation with cyanogen bromide treatment. The resulted bioactive conjugates were used to remove chlorinated phenolic compounds in aqueous solution. Gas phase Fourier transfer infrared spectroscopy was used for the detection and quantification of ammonia released from the immobilization reactions in situ. Results revealed that cyanogen bromide treatment of cellulose nanocrystals generated cyclic imidocarbonate group and cellulose carbamate. Covalent bonding between the activated nanoparticles and peroxidase generated ammonia as one of byproducts and the ammonia generation at an elevated temperature was more significant. Immobilization of enzyme at room temperature resulted in the bioactive conjugates with enzyme activity of 594 unit g-1. Increase of immobilization temperature to 50°C led to thermal deactivation of enzyme although immobilization probably proceeded fast. Comparing to its soluble counterpart, the immobilized peroxidase demonstrated high removal of chlorinated phenolic compounds. This capability might be attributed to the stabilization effect of immobilization toward enzyme deactivation and the precipitate formation of oxidized phenol products inducing by the amino group from carbamate on the bioactive conjugates.
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