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445 results on '"Wu, Jinjian"'

109. Image Quality Assessment Based on Improved Structural SIMilarity

Author

Wu, Jinjian, Qi, Fei, Shi, Guangming, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Lin, Weisi, editor, Xu, Dong, editor, Ho, Anthony, editor, Wu, Jianxin, editor, He, Ying, editor, Cai, Jianfei, editor, Kankanhalli, Mohan, editor, and Sun, Ming-Ting, editor

Published
2012
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112. Two Epochs of Mineralization of Orogenic Gold Deposit in the East Kunlun Orogenic Belt: Constraints from Monazite U–Pb Age, In Situ Sulfide Trace Elements and Sulfur Isotopes in Wulonggou Gold Field

Author

Zhang, Zheming, primary, Zeng, Qingdong, additional, Pan, Tong, additional, Xie, Hailin, additional, Wei, Zhanhao, additional, Fan, Hongrui, additional, Wu, Jinjian, additional, Yang, Kuifeng, additional, Li, Xinghui, additional, and Liang, Gaizhong, additional

Published
2022
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113. Convolutional Neural Networks for Classification of T2DM Cognitive Impairment Based on Whole Brain Structural Features

Author

Tan, Xin, primary, Wu, Jinjian, additional, Ma, Xiaomeng, additional, Kang, Shangyu, additional, Yue, Xiaomei, additional, Rao, Yawen, additional, Li, Yifan, additional, Huang, Haoming, additional, Chen, Yuna, additional, Lyu, Wenjiao, additional, Qin, Chunhong, additional, Li, Mingrui, additional, Feng, Yue, additional, Liang, Yi, additional, and Qiu, Shijun, additional

Published
2022
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117. Altered Functional Network Connectivity of Precuneus and Executive Control Networks in Type 2 Diabetes Mellitus Without Cognitive Impairment

Author

Wu, Jinjian, primary, Kang, Shangyu, additional, Su, Jianpo, additional, Liu, Kai, additional, Fan, Liangwei, additional, Ma, Xiaomeng, additional, Tan, Xin, additional, Huang, Haoming, additional, Feng, Yue, additional, Chen, Yuna, additional, Lyu, Wenjiao, additional, Zeng, Lingli, additional, Qiu, Shijun, additional, and Hu, Dewen, additional

Published
2022
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137. Contribution of YPRO15COverexpression to the Resistance of Saccharomyces cerevisiaeBY4742 Strain to Furfural Inhibitor

Author

Abrha, Getachew Tafere, Li, Qian, Kuang, Xiaolin, Xiao, Difan, Ayepa, Ellen, Wu, Jinjian, Chen, Huan, Zhang, Zhengyue, Liu, Yina, Yu, Xiumei, Xiang, Quanju, and Ma, Menggen

Abstract

Lignocellulosic biomass is still considered a feasible source of bioethanol production. Saccharomyces cerevisiaecan adapt to detoxify lignocellulose-derived inhibitors, including furfural. Tolerance of strain performance has been measured by the extent of the lag phase for cell proliferation following the furfural inhibitor challenge. The purpose of this work was to obtain a tolerant yeast strain against furfural through overexpression of YPR015Cusing the in vivohomologous recombination method. The physiological observation of the overexpressing yeast strain showed that it was more resistant to furfural than its parental strain. Fluorescence microscopy revealed improved enzyme reductase activity and accumulation of oxygen reactive species due to the harmful effects of furfural inhibitor in contrast to its parental strain. Comparative transcriptomic analysis revealed 79 genes potentially involved in amino acid biosynthesis, oxidative stress, cell wall response, heat shock protein, and mitochondrial-associated protein for the YPR015Coverexpressing strain associated with stress responses to furfural at the late stage of lag phase growth. Both up- and down-regulated genes involved in diversified functional categories were accountable for tolerance in yeast to survive and adapt to the furfural stress in a time course study during the lag phase growth. This study enlarges our perceptions comprehensively about the physiological and molecular mechanisms implicated in the YPR015Coverexpressing strain’s tolerance under furfural stress.

Published
2023
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138. A Novel Algorithm for Online Spike Detection

Author

Liu Zuozhi, Sun Zuoming, Shi Guangming, Wu Jinjian, and Xie Xuemei

Subjects
Engineering (General). Civil engineering (General), TA1-2040
Abstract

Recordings of extracellular spikes have been widely used in various fields ranging from basic neuroscientific research to clinical applications. However, in the extracellular recording system, how to accurately detect spikes from the recorded signal in real time is still a major challenging work. Although the existing algorithms for online spike detection have made great progress, there still remains much room for improvement in terms of accuracy. In this paper, we propose a new method for high accuracy and real time spike detection. Concretely, differential operator is firstly employed to accentuate spikes in the signal for its simplicity and strong capacity to detect significant changes. Then, by exploiting the structural features of spikes, the resolution parameter is introduced to improve the performance of differential operator. Finally, a simple and effective measure is utilized to further reduce the influence of background noise, which makes spike detection more accurate. The results of simulated and real data show that the proposed method is able to precisely detect spikes while maintaining low computational complexity.

Published
2018
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139. An Improved Extreme Learning Machine Based on Full Rank Cholesky Factorization

Author

Liu Zuozhi, Wu JinJian, and Wang Jianpeng

Subjects
Engineering (General). Civil engineering (General), TA1-2040
Abstract

Extreme learning machine (ELM) is a new novel learning algorithm for generalized single-hidden layer feedforward networks (SLFNs). Although it shows fast learning speed in many areas, there is still room for improvement in computational cost. To address this issue, this paper proposes an improved ELM (FRCFELM) which employs the full rank Cholesky factorization to compute output weights instead of traditional SVD. In addition, this paper proves in theory that the proposed FRCF-ELM has lower computational complexity. Experimental results over some benchmark applications indicate that the proposed FRCF-ELM learns faster than original ELM algorithm while preserving good generalization performance.

Published
2018
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148. Quality Assessment of UGC Videos Based on Decomposition and Recomposition

Author

Liu, Yongxu, Wu, Jinjian, Li, Leida, Dong, Weisheng, and Shi, Guangming

Abstract

The prevalence of short-video applications imposes more requirements for video quality assessment (VQA). User-generated content (UGC) videos are captured under an unprofessional environment, thus suffering from various dynamic degradations, such as camera shaking. To cover the dynamic degradations, existing recurrent neural network-based UGC-VQA methods can only provide implicit modeling, which is unclear and difficult to analyze. In this work, we consider explicit motion representation for dynamic degradations, and propose a motion-enhanced UGC-VQA method based on decomposition and recomposition. In the decomposition stage, a dual-stream decomposition module is built, and VQA task is decomposed into single frame-based quality assessment problem and cross frames-based motion understanding. The dual streams are well grounded on the two-pathway visual system during perception, and require no extra UGC data due to knowledge transfer. Hierarchical features from shallow to deep layers are gathered to narrow the gaps from tasks and domains. In the recomposition stage, a progressively residual aggregation module is built to recompose features from the dual streams. Representations with different layers and pathways are interacted and aggregated in a progressive and residual manner, which keeps a good trade-off between representation deficiency and redundancy. Extensive experiments on UGC-VQA databases verify that our method achieves the state-of-the-art performance and keeps a good capability of generalization. The source code will be available in https://github.com/Sissuire/DSD-PRO.

Published
2023
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149. Dynamic Expert-Knowledge Ensemble for Generalizable Video Quality Assessment

Author

Chen, Pengfei, Li, Leida, Li, Haoliang, Wu, Jinjian, Dong, Weisheng, and Shi, Guangming

Abstract

Despite the impressive progress of supervised methods in quality assessment for in- the-wild videos, models trained on one domain often fail to generalize well to others due to the domain shifts caused by distortion diversity and content variation. Domain generalizable video quality assessment (VQA) methods that can work across domains remain an open research challenge. Although combining more data following the mixed-domain training strategy can improve the generalization performance to a certain extent, the specific knowledge from each source domain, which could potentially be useful for improving unseen domain generalization, is ignored in this principle. Motivated by this, we propose a domain generalizable VQA method named Dynamic Ensemble of Expert-Knowledge (DEEK), a novel framework that dynamically exploits the expert-knowledge from each source domain to achieve a generalizable ensemble prediction. Specifically, based on the multiple experts each trained to specialize in a particular source domain, we aim to exploit complementary information provided by the expert-knowledge. We effectively train an ensemble model by proposing a quality-sensitive InfoNCE loss to regularize the collaborative training of all experts in the contrastive learning formulation, aiming to exploit complementary information provided by the expert-knowledge when forming the ensemble. By dynamically integrating the experts according to their relevances to the target data, these expert-knowledge could be leveraged for better generalization. Experiments on five VQA datasets verify that our approach outperforms the state-of-the-arts by large margins.

Published
2023
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