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Search Results: 1 - 10 of 33485 matches for " Liang Ye "
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Solving Polynomial Equations with Equation Constraints: the Zero-dimensional Case
Ye Liang
Computer Science , 2014,
Abstract: A zero-dimensional polynomial ideal may have a lot of complex zeros. But sometimes, only some of them are needed. In this paper, for a zero-dimensional ideal $I$, we study its complex zeros that locate in another variety $\textbf{V}(J)$ where $J$ is an arbitrary ideal. The main problem is that for a point in $\textbf{V}(I) \cap \textbf{V}(J)=\textbf{V}(I+J)$, its multiplicities w.r.t. $I$ and $I+J$ may be different. Therefore, we cannot get the multiplicity of this point w.r.t. $I$ by studying $I + J$. A straightforward way is that first compute the points of $\textbf{V}(I + J)$, then study their multiplicities w.r.t. $I$. But the former step is difficult to realize exactly. In this paper, we propose a natural geometric explanation of the localization of a polynomial ring corresponding to a semigroup order. Then, based on this view, using the standard basis method and the border basis method, we introduce a way to compute the complex zeros of $I$ in $\textbf{V}(J)$ with their multiplicities w.r.t. $I$. As an application, we compute the sum of Milnor numbers of the singular points on a polynomial hypersurface and work out all the singular points on the hypersurface with their Milnor numbers.
Real root refinements for univariate polynomial equations
Ye Liang
Computer Science , 2012,
Abstract: Real root finding of polynomial equations is a basic problem in computer algebra. This task is usually divided into two parts: isolation and refinement. In this paper, we propose two algorithms LZ1 and LZ2 to refine real roots of univariate polynomial equations. Our algorithms combine Newton's method and the secant method to bound the unique solution in an interval of a monotonic convex isolation (MCI) of a polynomial, and have quadratic and cubic convergence rates, respectively. To avoid the swell of coefficients and speed up the computation, we implement the two algorithms by using the floating-point interval method in Maple15 with the package intpakX. Experiments show that our methods are effective and much faster than the function RefineBox in the software Maple15 on benchmark polynomials.
Extractions: Computable and Visible Analogues of Localizations for Polynomial Ideals
Ye Liang
Computer Science , 2015,
Abstract: When studying local properties of a polynomial ideal, one usually needs a theoretic technique called localization. For most cases, in spite of its importance, the computation in a localized ring cannot be algorithmically preformed. On the other hand, the standard basis method is very effective for the computation in a special kind of localized rings, but for a general semigroup order the geometry of the localization of a positive-dimensional ideal is difficult to interpret. In this paper, we introduce a new ideal operation called extraction. For an ideal $I$ in a polynomial ring $K[x_1,\ldots,x_n]$ over a field $K$, we use another ideal $J$ to control the primary components of $I$ and the result $\beta(I,J)$ is called the extraction of $I$ by $J$. It is still a polynomial ideal and has a concrete geometric meaning in $\bar{K}^n$, i.e., we keep the branches of $\textbf{V}(I) \subset \bar{K}^n$ that intersect with $\textbf{V}(J) \subset \bar{K}^n$ and delete others, where $\bar{K}$ is the algebraic closure of $K$. This is what we mean by visible. On the other hand, we can use the standard basis method to compute a localized ideal corresponding to $\beta(I,J)$ without a complete primary decomposition, and can do further computation in the localized ring such as determining the membership problem of $\beta(I,J)$. Moreover, we prove that extractions are as powerful as localizations in the sense that for any multiplicatively closed subset $S$ of $K[x_1,\ldots,x_n]$ and any polynomial ideal $I$, there always exists a polynomial ideal $J$ such that $\beta(I,J)=(S^{-1}I)^c$.
A Graph-Based Multivariate Conditional Autoregressive Model
Ye Liang
Statistics , 2014,
Abstract: The conditional autoregressive model is one of the routines for applications with univariate areal data. However, many authors have shown that it is not trivial to extend the univariate specification to multivariate situations. The difficulties lie in many aspects, including validity, interpretability, flexibility and computational feasibility. We approach this problem from an element-based perspective which utilizes given graphical information and builds joint adjacency structures. Our framework is general but three special specifications are discussed in details with Bayesian computations, all of which are related to existing models in literature. We provide an example with public health data, not only to illustrate the implementation, but also to compare these specifications.
An Empirical Study about the Effect Which “Bao Bao” Internet Monetary Funds Make on Deposits in Chinese Commercial Banks  [PDF]
Jinjin Gong, Xiaofen Ye, Zhilong Liang
American Journal of Industrial and Business Management (AJIBM) , 2016, DOI: 10.4236/ajibm.2016.69095
Abstract: This paper chooses 14 listed banks in China June 2013 to March 2016 quarter data as sample, exploding the effect of “Bao bao” Internet monetary funds on deposits in commercial banks based on the econometric model. The empirical results show that “Bao bao” Internet monetary funds have not been diverted the deposits balance of commercial banks, but it increases the cost of deposits of commercial banks, and brings a certain amount of pressure on interest payments of Chinese commercial banks.
Application of Bioreactor in Stem Cell Culture  [PDF]
Yongxin Zhang, Xianghan Wang, Mao Pong, Liang Chen, Zhijia Ye
Journal of Biomedical Science and Engineering (JBiSE) , 2017, DOI: 10.4236/jbise.2017.1011037
Abstract: Stem cells (SCs), the undifferentiated biological cells, have the infinite capacity to self-renew and the pluripotent ability to differentiate. SCs and their derived products offer great promise for biomedical applications such as cell therapy, tissue engineering, regenerative medicine and drug screening. However, the clinical applications of SCs require a large amount of SCs with high quality and the number of SCs from their tissue resources is very limited. Large-scale expansion is needed to generate homogeneous SCs with good biological characteristics for clinical application. This necessitates a bioreactor system to provide controllable and stable conditions for stem cell (SC) culture. Traditional methods of bioreactor for maintenance and expansion of cells rely on two-dimensional (2-D) culture techniques, leading to loss self-renewal ability and differentiation capacity upon long-term culture. New approaches for SC expansion with bioreactor employ three-dimensional (3-D) cell growth to mimic their environment in vivo. In this review, we summarize the application of bioreactors in SC culture.
Adaptive diffusion kernel learning from biological networks for protein function prediction
Liang Sun, Shuiwang Ji, Jieping Ye
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-162
Abstract: In this paper, we address the issue of learning an optimal diffusion kernel, in the form of a convex combination of a set of pre-specified kernels constructed from biological networks, for protein function prediction. Most prior work on this kernel learning task focus on variants of the loss function based on Support Vector Machines (SVM). Their extensions to other loss functions such as the one based on Kullback-Leibler (KL) divergence, which is more suitable for mining biological networks, lead to expensive optimization problems. By exploiting the special structure of the diffusion kernel, we show that this KL divergence based kernel learning problem can be formulated as a simple optimization problem, which can then be solved efficiently. It is further extended to the multi-task case where we predict multiple functions of a protein simultaneously. We evaluate the efficiency and effectiveness of the proposed algorithms using two benchmark data sets.Results show that the performance of linearly combined diffusion kernel is better than every single candidate diffusion kernel. When the number of tasks is large, the algorithms based on multiple tasks are favored due to their competitive recognition performance and small computational costs.Many types of genomic data can be represented as a graph (network), where the nodes represent genes or proteins, and edges may represent similarities between protein sequences, edges in a metabolic pathway, and physical interactions between proteins [1]. Machine learning tools have been commonly used to analyze biological networks for knowledge discovery and pattern analysis [2]. In this paper, we focus on learning from biological networks for protein function prediction. This problem has been studied extensively by using computational approaches recently [1]. Neighborhood-based methods [3,4] assign functions to proteins based on the most frequent functions within a neighborhood of the protein and they differ mainly in how the "neigh
Construction of plant seed-specific expression vectors pSCB and pSCAB and the obtainment of transgenicBrassica napus H165 expressing poly-3-hydroxybutyrate synthetic genes
Liang Ye,Cong Li,Yanru Song
Chinese Science Bulletin , 2000, DOI: 10.1007/BF02886081
Abstract: The seed-specific promoter and transit peptide were amplified and fused to the three genesphbA, phbB andphbC encoding PHB synthetic enzymes, respectively. Seed-specific expression vectors pSCB containingphbC andphbB, and pSCAB containingphbC, phbB andphbA, were constructed by introducing the genes with promoter and peptide into the binary vector pBI101. TransgenicBrassica napus H165 were obtained byAgrobacterium-mediated transformation with these vectors. They were confirmed by PCR, Southern and RT-PCR analyses.
Progress of PHA production in transgenic plants
Tao Wang,Liang Ye,Yanru Song
Chinese Science Bulletin , 1999, DOI: 10.1007/BF02886149
Abstract: With its completely biodegradable ability, good physical and processing properties, and biocompatability, PHA has become the most attractive alternative to take the place of chemo-plastics and eliminate environmental pollution caused by chemo-plastics. Small-scale commercial production has been realized by bacterial fermentation, but thus produced PHA is too expensive to compete with chemo-plastics. However, it is very prospective to lower biodegradable plastic price by producing PHA in transgenic plants. Presently PHB/PHBV biosynthetic genes have been transferred into plants and expressed, but it is still far away from meeting the demands of commercial production. Enhancing research on the regulation of carbon and fatty acid metabolisms, and their biochemical and molecular mechanisms is the key to achieving this aim.
Cloning and sequencing ofphbA gene of poly-β-hydroxybuty rate synthesis and its expression analysis
Liang Ye,Cong Li,Yanru Song
Chinese Science Bulletin , 1999, DOI: 10.1007/BF02885988
Abstract: The gene of the first key enzyme of poly-β-hydroxybuty rate synthesis, 3-ketothiolase, has been amplified and cloned from chromosomal DNA ofAlcaligenes eutrophus H16 by PCR. DNA sequencing results show thatphbA cloned in pBluescriptSK+ has an identical sequence with that reported previously except for one base pair. The plant constitutive expression vector has been constructed and tobacco has been transformed in order to examine thephbA gene function and the efficiency ofctp gene product. SDS-polyacrylamide gel electrophoresis result shows that thectp gene product could direct foreign protein into plastid efficiently andphbA gene could be translated into corresponding protein with correct size. The enzyme activity analysis of 3-ketothiolase shows that the enzyme could catalyze acetyl-CoA to form acetoacetyl-CoA.
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