Institution
University of Macau
Education•Macao, Macau, China•
About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Population & Control theory. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors analyzed the work performance of salespeople with a competitive disposition and found a positive link between dispositional competitiveness and discretionary performance, and suggest that salespeople's affective commitment to their organization mediates this link.
255 citations
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TL;DR: In this article, a joint multi-task learning algorithm is proposed to better predict attributes in images using deep convolutional neural networks (CNN), where each CNN will predict one binary attribute.
Abstract: This paper proposes a joint multi-task learning algorithm to better predict attributes in images using deep convolutional neural networks (CNN). We consider learning binary semantic attributes through a multi-task CNN model, where each CNN will predict one binary attribute. The multi-task learning allows CNN models to simultaneously share visual knowledge among different attribute categories. Each CNN will generate attribute-specific feature representations, and then we apply multi-task learning on the features to predict their attributes. In our multi-task framework, we propose a method to decompose the overall model’s parameters into a latent task matrix and combination matrix. Furthermore, under-sampled classifiers can leverage shared statistics from other classifiers to improve their performance. Natural grouping of attributes is applied such that attributes in the same group are encouraged to share more knowledge. Meanwhile, attributes in different groups will generally compete with each other, and consequently share less knowledge. We show the effectiveness of our method on two popular attribute datasets.
255 citations
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TL;DR: A novel neuro-fuzzy model named fuzzy broad learning system (BLS) is proposed by merging the Takagi–Sugeno (TS) fuzzy system into BLS, and the results indicate that fuzzy BLS outperforms other models involved.
Abstract: A novel neuro-fuzzy model named fuzzy broad learning system (BLS) is proposed by merging the Takagi–Sugeno (TS) fuzzy system into BLS. The fuzzy BLS replaces the feature nodes of BLS with a group of TS fuzzy subsystems, and the input data are processed by each of them. Instead of aggregating the outputs of fuzzy rules produced by every fuzzy subsystem into one value immediately, all of them are sent to the enhancement layer for further nonlinear transformation to preserve the characteristic of inputs. The defuzzification outputs of all fuzzy subsystem and the outputs of enhancement layer are combined together to obtain the model output. The ${k}$ -means method is employed to determine the centers of Gaussian membership functions in antecedent part and the number of fuzzy rules. The parameters that need to be calculated in a fuzzy BLS are the weights connecting the outputs of enhancement layer to model output and the randomly initialized coefficients of polynomials in consequent part in fuzzy subsystems, which can be calculated analytically. Therefore, fuzzy BLS retains the fast computational nature of BLS. The proposed fuzzy BLS is evaluated by some popular benchmarks for regression and classification, and compared with some state-of-the-art nonfuzzy and neuro-fuzzy approaches. The results indicate that fuzzy BLS outperforms other models involved. Moreover, fuzzy BLS shows advantages over neuro-fuzzy models regarding to the number of fuzzy rules and training time, which can ease the problem of rule explosion to some extent.
254 citations
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TL;DR: This review focuses on recent progress in the design, fabrication and physicochemical aspects of chitosan-based self-assembled nanomaterials and their applications in drug delivery of different therapeutic agents.
254 citations
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Baqiyatallah University of Medical Sciences1, Medical University of Białystok2, University of Basilicata3, University of Greenwich4, Gazi University5, University of Salerno6, University of Pavia7, University of Macau8, University of Vigo9, University of Santiago de Compostela10, Kermanshah University of Medical Sciences11, Tabriz University of Medical Sciences12, University of Reims Champagne-Ardenne13, University of Rochester14
TL;DR: In this review, flavonoids as classical examples of secondary metabolites are employed to highlight recent advances in understanding how valuable compounds can be regulated at various levels.
253 citations
Authors
Showing all 6766 results
Name | H-index | Papers | Citations |
---|---|---|---|
Henry T. Lynch | 133 | 925 | 86270 |
Chu-Xia Deng | 125 | 444 | 57000 |
H. Vincent Poor | 109 | 2116 | 67723 |
Peng Chen | 103 | 918 | 43415 |
George F. Gao | 102 | 793 | 82219 |
MengChu Zhou | 96 | 1124 | 36969 |
Gang Li | 93 | 486 | 68181 |
Rob Law | 81 | 714 | 31002 |
Zongjin Li | 80 | 630 | 22103 |
Han-Ming Shen | 80 | 237 | 27410 |
Heng Li | 79 | 745 | 23385 |
Lionel M. Ni | 75 | 466 | 28770 |
C. L. Philip Chen | 74 | 482 | 20223 |
Chun-Su Yuan | 72 | 397 | 21089 |
Joao P. Hespanha | 72 | 418 | 39004 |