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Search Results: 1 - 10 of 34919 matches for " Xiaowei Zhou "
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Experiments with Two New Boosting Algorithms  [PDF]
Xiaowei Sun, Hongbo Zhou
Intelligent Information Management (IIM) , 2010, DOI: 10.4236/iim.2010.26047
Abstract: Boosting is an effective classifier combination method, which can improve classification performance of an unstable learning algorithm. But it dose not make much more improvement of a stable learning algorithm. In this paper, multiple TAN classifiers are combined by a combination method called Boosting-MultiTAN that is compared with the Boosting-BAN classifier which is boosting based on BAN combination. We describe experiments that carried out to assess how well the two algorithms perform on real learning problems. Fi- nally, experimental results show that the Boosting-BAN has higher classification accuracy on most data sets, but Boosting-MultiTAN has good effect on others. These results argue that boosting algorithm deserve more attention in machine learning and data mining communities.
A new pricing model of China’s parallel rail lines under the diversified property rights
Shaoni Zhou,Qiusheng Zhang,Xiaowei Wu
Journal of Industrial Engineering and Management , 2013, DOI: 10.3926/jiem.591
Abstract: Purpose: The purpose of this paper is to study on the pricing of China railway company under the background of diversified property rights, especially the pricing of the parallel line system that belong to different owners. Design/methodology/approach: Through theoretical analysis of the main influential factors of railway pricing, this paper designs a basic quotation system for the parallel railway lines. Findings: The transaction price of parallel line consists of two parts, which are fixed railway network price and variable network using price. Practical implications: Through the reasonable designing of fixed network price and variable network using price, it can not only lead to high profitability and low government subsidy, but also can ensure remaining more railway network resources and fulfill the social responsibilities. Originality/value: The conclusions of this study will lay the foundation for the harmonious development of Chinese railway network under the diversified property rights.
Left Derivations and Strong Commutativity Preserving Maps on Semiprime $Γ$-Rings
Xiaowei Xu,Jing Ma,Yuan Zhou
Mathematics , 2012,
Abstract: In this paper, firstly as a short note, we prove that a left derivation of a semiprime $\Gamma$-ring $M$ must map $M$ into its center, which improves a result by Paul and Halder and some results by Asci and Ceran. Also we prove that a semiprime $\Gamma$-ring with a strong commutativity preserving derivation on itself must be commutative and that a strong commutativity preserving endomorphism on a semiprime $\Gamma$-ring $M$ must have the form $\sigma(x)=x+\zeta(x)$ where $\zeta$ is a map from $M$ into its center, which extends some results by Bell and Daif to semiprime $\Gamma$-rings.
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
Xiaowei Zhou,Can Yang,Weichuan Yu
Computer Science , 2011,
Abstract: Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios.
Pose and Shape Estimation with Discriminatively Learned Parts
Menglong Zhu,Xiaowei Zhou,Kostas Daniilidis
Computer Science , 2015,
Abstract: We introduce a new approach for estimating the 3D pose and the 3D shape of an object from a single image. Given a training set of view exemplars, we learn and select appearance-based discriminative parts which are mapped onto the 3D model from the training set through a facil- ity location optimization. The training set of 3D models is summarized into a sparse set of shapes from which we can generalize by linear combination. Given a test picture, we detect hypotheses for each part. The main challenge is to select from these hypotheses and compute the 3D pose and shape coefficients at the same time. To achieve this, we optimize a function that minimizes simultaneously the geometric reprojection error as well as the appearance matching of the parts. We apply the alternating direction method of multipliers (ADMM) to minimize the resulting convex function. We evaluate our approach on the Fine Grained 3D Car dataset with superior performance in shape and pose errors. Our main and novel contribution is the simultaneous solution for part localization, 3D pose and shape by maximizing both geometric and appearance compatibility.
Multi-Image Matching via Fast Alternating Minimization
Xiaowei Zhou,Menglong Zhu,Kostas Daniilidis
Computer Science , 2015,
Abstract: In this paper we propose a global optimization-based approach to jointly matching a set of images. The estimated correspondences simultaneously maximize pairwise feature affinities and cycle consistency across multiple images. Unlike previous convex methods relying on semidefinite programming, we formulate the problem as a low-rank matrix recovery problem and show that the desired semidefiniteness of a solution can be spontaneously fulfilled. The low-rank formulation enables us to derive a fast alternating minimization algorithm in order to handle practical problems with thousands of features. Both simulation and real experiments demonstrate that the proposed algorithm can achieve a competitive performance with an order of magnitude speedup compared to the state-of-the-art algorithm. In the end, we demonstrate the applicability of the proposed method to match the images of different object instances and as a result the potential to reconstruct category-specific object models from those images.
The Risk Premium of Treasury Bonds in China  [PDF]
Xiaowei Wu
Journal of Mathematical Finance (JMF) , 2016, DOI: 10.4236/jmf.2016.61015

This paper studied the macroeconomic and the term structure of treasury bonds in the Shanghai Stock Exchange Market. Different from previous studies, we used a group of 122 observed macroeconomic data to construct our model’s macro factor. Therefore the macro factor contained more information than previous studies in predicting the excess return of Treasury bond. Based on the Kalman-Filter estimation, the results show that the macro factor’s risk was compensated through the level factor and slope factor, especially the level factor. Further, based on the decomposition of the yield curve into expected future short rate part and risk premium part, we find that there is some correlation between the variability of the risk premium and monetary policy to some extent.

Effective identification of the three particle modes generated during pulverized coal combustion
DunXi Yu,MingHou Xu,Hong Yao,XiaoWei Liu,Ke Zhou
Chinese Science Bulletin , 2008, DOI: 10.1007/s11434-008-0192-x
Abstract: Based on the mass fraction size distribution of aluminum (Al), an improved method for effectively identifying the modes of particulate matter from pulverized coal combustion is proposed in this study. It is found that the particle size distributions of coal-derived particulate matter actually have three modes, rather than just mere two. The ultrafine mode is mainly generated through the vaporization and condensation processes. The coarse mode is primarily formed by the coalescence of molten minerals, while the newly-found central mode is attributed to the heterogeneous condensation or adsorption of vaporized species on fine residual ash particles. The detailed investigation of the mass fraction size distribution of sulfur (S) further demonstrates the rationality and effectiveness of the mass fraction size distribution of the Al in identifying three particle modes. The results show that not only can the number of particle modes be identified in the mass fraction size distributions of the Al but also can their size boundaries be more accurately defined. This method provides new insights in elucidating particle formation mechanisms and their physico-chemical characteristics.
A MESFET variable-capacitance analytical model
Xiaowei Sun,Jinsheng Luo,Zongming Zhou,Jinrong Cao,Jinting Lin
Chinese Science Bulletin , 1997, DOI: 10.1007/BF02884224
Abstract: A variable-capacitance model suitable for MMIC active voltage-controlled filter has been reported. The analytical expression is also given for the gate capacitance as a function of the gate bias. Since the free carrier move in active region for contributing to the gate capacitance is considered, the results calculated from the new model are in agreement with the experimental results. Hence, the new model is very useful for determining voltage-tuning bandwidth in MMIC active filter or MMIC VCO’s.
Three-dimensional reconstruction and analysis of structure characteristics on senile plaques of Alzheimer’s disease
Ye Wei,Liu Jianwu,Zhou Jiangning,Hu Xiangyou,Tang Xiaowei
Chinese Science Bulletin , 2005, DOI: 10.1007/BF02897380
Abstract: Alzheimer’s disease is a progressive neurodegenerative disorder characterized by the presence of senile plaques primarily composed of amyloid β in brain. Abnormal secretion and aggregation of amyloid β are the key events in pathogenesis of Alzheimer’s disease. Reduction of amyloid β production and inhibition of amyloid β aggregation to form senile plaques are hopeful strategies for the treatment and prevention of Alzheimer’s disease. In the present study, the silver and immunohistochemical staining methods were applied to discover senile plaques in the hippocampus of Alzheimer’s disease patients, and then images were processed and three-dimensionally reconstructed by Matlab and AVS software. The structure characteristics of senile plaques were measured through correlation function calculation and fractal dimension by a computer-aided method. Diffuse plaque had no amyloid center, but classic plaque presented compact central core structure; two types of plaques were both of porous structure, but the sizes of their pores were significantly different. Furthermore, there was difference in fractal dimension value between the diffuse plaque and classic plaque in the two staining methods. The comparison of structure characteristics between two types of plaques indicated that they developed independently. Establishment of the methods for reconstructing the three-dimensional structure of senile plaque and analyzing their structure characteristics is helpful for further study on the aggregation mechanism of senile plaque.
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