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Zu, Chen,Jie, Biao,Liu, Mingxia,Chen, Songcan,Shen, Dinggang,Zhang, Daoqiang SPRINGER SCIENCE AND BUSINESS MEDIA 2016 BRAIN IMAGING AND BEHAVIOR Vol.10 No.4
<P>Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI.</P>
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Chen Haisheng,Zhou Wei,Liang Mingxia,Lan Hailin 인하대학교 정석물류통상연구원 2009 인하대학교 정석물류통상연구원 학술대회 Vol.2009 No.10
Whether a company"s ownership structure is good or not is often justified by corporate performance and technical efficiency. Statistics in this article finds that among listed companies after technology mergers and acquisitions (M&A), above half of them have short-term benefits decline significantly. Considering the Strong reliance of knowledge transfer on the environment, while taking the restraining level of benefit decline after technology M&A as a short-term evaluation standard for ownership structure choice, this paper researches the critical role of ownership structures in curbing the decrease of benefits after technology M&A. And the empirical data of listed companies indicates that in listed companies with high ownership concentration, the "supporting effect" of majority shareholders" behavior can exert more influence to curb the benefit decline after technology M&A than the "tunneling effect". Moreover, the ownership balance in large degree will confine the majority stockholders" will and behaviro, which therefore can weaken their "support effect"and aggravate the benefit edcline after technology M&A.
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Hierarchical Saliency: A New Salient Target Detection Framework
Bin Chen,Xuezhuan Zhao,Lishen Pei,Tao Li,Mingxia Li 제어·로봇·시스템학회 2016 International Journal of Control, Automation, and Vol.14 No.1
Simulating the shift character of visual attention, we propose a novel concept of hierarchical saliencyand develop a detection framework. First, a given image is over-segmented into coarse and fine layers whichrespond to two scale superpixels. Then, we estimate the saliency maps from coarse to fine. In the coarse layer, wepresent a new self-adaptive algorithm to construct the superpixels graph, employing the manifold ranking approachto optimize it. In the fine layer, sparse reconstruction is used to obtain the saliency regions. At last, we proposea Restricted Voting Strategy (RVS) to fuse two layer saliency maps into one hierarchical saliency map. Differentfrom the prior methods, the targets of the final map are labeled layer-wise. The final result can be directly applied tomore high-level computer vision tasks in various situations. For the requirement of hierarchical saliency evaluation,we construct the CAS-HAS dataset. We exhaustively evaluate the framework on the proposed data set and threebenchmark data sets. The experiment performance is comparable with the sate-of-the-art approaches.
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ZHI JIANG,HAIRONG ZHANG,ZHONGPENG WANG,MINGXIA CHEN,WENFENG SHANGGUAN 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2008 NANO Vol.3 No.4
The simultaneous catalytic removal of NOx and soot over the rare earth element (REE) oxide-based mixture oxides loaded with potassium and transition nanosized metal oxide (designated as M/K/REE oxide) was investigated by using temperature-programmed reaction (TPR). The influence of the type of REE oxides together with the type and amount of transitional metal oxides on the catalytic removal activity was discussed. K/Nd2O3 was found to be the most active oxide among the REE oxides to simultaneous remove the NOx and soot under lean conditions. Chromium oxide was more active than the other transition metal oxides on enhancing the activity of soot oxidation of Nd2O3 loaded with potassium. The optimum loading level of chromium was about 10 wt%, with ignition temperature at about 237°C and the conversion ratio NO → N2 about 24.1%. The Mn-loading on K/Nd2O3 resulted in the biggest conversion efficiency of NO to N2 at about 30.2%. The increasing catalytic reaction of NOx–soot activities is attributed to the formation of complex crystalline phase in the catalyst together with the improving contacting between catalysts and soot.
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