Institution
Dalian University of Technology
Education•Dalian, China•
About: Dalian University of Technology is a education organization based out in Dalian, China. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 60890 authors who have published 71921 publications receiving 1188356 citations. The organization is also known as: Dàlián Lǐgōng Dàxué.
Papers published on a yearly basis
Papers
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21 Jul 2017
TL;DR: In this article, a weighted maximum mean discrepancy (MMD) model is proposed to exploit the class prior probability on source and target domains, whose challenge lies in the fact that the class label in target domain is unavailable.
Abstract: In domain adaptation, maximum mean discrepancy (MMD) has been widely adopted as a discrepancy metric between the distributions of source and target domains. However, existing MMD-based domain adaptation methods generally ignore the changes of class prior distributions, i.e., class weight bias across domains. This remains an open problem but ubiquitous for domain adaptation, which can be caused by changes in sample selection criteria and application scenarios. We show that MMD cannot account for class weight bias and results in degraded domain adaptation performance. To address this issue, a weighted MMD model is proposed in this paper. Specifically, we introduce class-specific auxiliary weights into the original MMD for exploiting the class prior probability on source and target domains, whose challenge lies in the fact that the class label in target domain is unavailable. To account for it, our proposed weighted MMD model is defined by introducing an auxiliary weight for each class in the source domain, and a classification EM algorithm is suggested by alternating between assigning the pseudo-labels, estimating auxiliary weights and updating model parameters. Extensive experiments demonstrate the superiority of our weighted MMD over conventional MMD for domain adaptation.
408 citations
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TL;DR: In this article, a color level-set model is proposed for structural shape and topology optimization in a multi-material domain, which is an alternative approach to the popular homogenization-based methods of rule of mixtures for multiphase modeling.
403 citations
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TL;DR: A B ODIPY-based HClO probe (BClO) with ultrasensitivity, fast response, and high selectivity, in which the pyrrole group at the meso position has an "enhanced PET" effect on the BODIPY fluorophore.
Abstract: Reactive oxygen species (ROS) and cellular oxidant stress have long been associated with cancer. Unfortunately, the role of HClO in tumor biology is much less clear than for other ROS. Herein, we report a BODIPY-based HClO probe (BClO) with ultrasensitivity, fast response (within 1 s), and high selectivity, in which the pyrrole group at the meso position has an "enhanced PET" effect on the BODIPY fluorophore. The detection limit is as low as 0.56 nM, which is the highest sensitivity achieved to date. BClO can be facilely synthesized by a Michael addition reaction of acryloyl chloride with 2,4-dimethylpyrrole and applied to image the basal HClO in cancer cells for the first time and the time-dependent HClO generation in MCF-7 cells stimulated by elesclomol, an effective experimental ROS-generating anticancer agent.
402 citations
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TL;DR: N-doped nanodiamond/Si rod array (NDD/Si RA) was proposed as an efficient nonmetallic electrocatalyst for CO2 reduction and preferentially and rapidly converted CO2 to acetate over formate with an onset potential of -0.36 V, overcoming the usual limitation of low selectivity for C2 products.
Abstract: Electrochemical reduction of CO2 is an attractive technique for reducing CO2 emission and converting it into useful chemicals, but it suffers from high overpotential, low efficiency or poor product selectivity. Here, N-doped nanodiamond/Si rod array (NDD/Si RA) was proposed as an efficient nonmetallic electrocatalyst for CO2 reduction. It preferentially and rapidly converted CO2 to acetate over formate with an onset potential of −0.36 V (vs RHE), overcoming the usual limitation of low selectivity for C2 products. Moreover, faradic efficiency of 91.2–91.8% has been achieved for CO2 reduction at −0.8 to −1.0 V. Its superior performance for CO2 reduction can be attributed to its high overpotential for hydrogen evolution and N doping, where N-sp3C species was highly active for CO2 reduction. Electrokinetic data and in situ infrared spectrum revealed the main pathway for CO2 reduction might be CO2 → CO2•– → (COO)2• → CH3COO–.
402 citations
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23 Jun 2014TL;DR: An efficient optimization method is developed to generate reliable intermediate results for kernel estimation based on a simple yet effective L0-regularized prior based on intensity and gradient for text image deblurring.
Abstract: We propose a simple yet effective 0-regularized prior based on intensity and gradient for text image deblurring. The proposed image prior is motivated by observing distinct properties of text images. Based on this prior, we develop an efficient optimization method to generate reliable intermediate results for kernel estimation. The proposed method does not require any complex filtering strategies to select salient edges which are critical to the state-of-the-art deblurring algorithms. We discuss the relationship with other deblurring algorithms based on edge selection and provide insight on how to select salient edges in a more principled way. In the final latent image restoration step, we develop a simple method to remove artifacts and render better deblurred images. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art text image deblurring methods. In addition, we show that the proposed method can be effectively applied to deblur low-illumination images.
400 citations
Authors
Showing all 61205 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yang Yang | 171 | 2644 | 153049 |
Yury Gogotsi | 171 | 956 | 144520 |
Hui Li | 135 | 2982 | 105903 |
Michael I. Posner | 134 | 414 | 104201 |
Anders Hagfeldt | 129 | 600 | 79912 |
Jian Zhou | 128 | 3007 | 91402 |
Chao Zhang | 127 | 3119 | 84711 |
Bin Wang | 126 | 2226 | 74364 |
Chi Lin | 125 | 1313 | 102710 |
Tao Zhang | 123 | 2772 | 83866 |
Bo Wang | 119 | 2905 | 84863 |
Zhenyu Zhang | 118 | 1167 | 64887 |
Liang Cheng | 116 | 1779 | 65520 |
Anthony G. Fane | 112 | 565 | 40904 |
Xuelong Li | 110 | 1044 | 46648 |