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2,648 results on '"Xu, Yingying"'

56. The Impact of Online and Offline Learning Motivation on Learning Performance: The Mediating Role of Positive Academic Emotion

Author

Zhu, Yonghai, Xu, Shengmei, Wang, Wenguang, Zhang, Li, Liu, Di, Liu, Ziling, and Xu, Yingying

Abstract

Learning performance is an important indicator of online learning, is related to the quality of online education and the performance of students. Previous studies have found that learners' learning performance is related to learning motivation and academic emotion, but the role of non-intellectual factors such as academic emotion has received less attention in empirical research. In order to make up for this gap, this study explored the mediating effect of positive academic emotions on college students' online learning motivation and online learning performance, as well as the differences in mediating effects under different learning methods (i.e., online learning and offline learning). The data comes from 1088 college students who participate in online courses and offline courses in China. This study adopts correlation analysis and multiple mediation analysis to analyze the intermediary effect of online and offline students and multi-group comparisons. The results showed that the (1) online learning motivation is positively affecting online learning performance and positive academic emotions, and positive academic emotions are related to online learning performance. There is also a positive correlation between the dimensions of the three; (2) positive academic emotions part of the intermediary role between college students' online learning motivation and online learning performance; (3) the intermediary effect of positive academic emotions in offline learning is significantly greater than that of online learning. This study is not only conducive to understanding academic emotions, learning motives and learning performance, but also provides important enlightenment online learning performance management in promoting open curriculum construction.

Published
2022
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57. Biomarkers of aging

Author

Bao, Hainan, Cao, Jiani, Chen, Mengting, Chen, Min, Chen, Wei, Chen, Xiao, Chen, Yanhao, Chen, Yu, Chen, Yutian, Chen, Zhiyang, Chhetri, Jagadish K., Ding, Yingjie, Feng, Junlin, Guo, Jun, Guo, Mengmeng, He, Chuting, Jia, Yujuan, Jiang, Haiping, Jing, Ying, Li, Dingfeng, Li, Jiaming, Li, Jingyi, Liang, Qinhao, Liang, Rui, Liu, Feng, Liu, Xiaoqian, Liu, Zuojun, Luo, Oscar Junhong, Lv, Jianwei, Ma, Jingyi, Mao, Kehang, Nie, Jiawei, Qiao, Xinhua, Sun, Xinpei, Tang, Xiaoqiang, Wang, Jianfang, Wang, Qiaoran, Wang, Siyuan, Wang, Xuan, Wang, Yaning, Wang, Yuhan, Wu, Rimo, Xia, Kai, Xiao, Fu-Hui, Xu, Lingyan, Xu, Yingying, Yan, Haoteng, Yang, Liang, Yang, Ruici, Yang, Yuanxin, Ying, Yilin, Zhang, Le, Zhang, Weiwei, Zhang, Wenwan, Zhang, Xing, Zhang, Zhuo, Zhou, Min, Zhou, Rui, Zhu, Qingchen, Zhu, Zhengmao, Cao, Feng, Cao, Zhongwei, Chan, Piu, Chen, Chang, Chen, Guobing, Chen, Hou-Zao, Chen, Jun, Ci, Weimin, Ding, Bi-Sen, Ding, Qiurong, Gao, Feng, Han, Jing-Dong J., Huang, Kai, Ju, Zhenyu, Kong, Qing-Peng, Li, Ji, Li, Jian, Li, Xin, Liu, Baohua, Liu, Feng, Liu, Lin, Liu, Qiang, Liu, Qiang, Liu, Xingguo, Liu, Yong, Luo, Xianghang, Ma, Shuai, Ma, Xinran, Mao, Zhiyong, Nie, Jing, Peng, Yaojin, Qu, Jing, Ren, Jie, Ren, Ruibao, Song, Moshi, Songyang, Zhou, Sun, Yi Eve, Sun, Yu, Tian, Mei, Wang, Shusen, Wang, Si, Wang, Xia, Wang, Xiaoning, Wang, Yan-Jiang, Wang, Yunfang, Wong, Catherine C. L., Xiang, Andy Peng, Xiao, Yichuan, Xie, Zhengwei, Xu, Daichao, Ye, Jing, Yue, Rui, Zhang, Cuntai, Zhang, Hongbo, Zhang, Liang, Zhang, Weiqi, Zhang, Yong, Zhang, Yun-Wu, Zhang, Zhuohua, Zhao, Tongbiao, Zhao, Yuzheng, Zhu, Dahai, Zou, Weiguo, Pei, Gang, and Liu, Guang-Hui

Published
2023
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71. PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images

Author

Xu, Yingying, Cai, Ming, Lin, Lanfen, Zhang, Yue, Hu, Hongjie, Peng, Zhiyi, Zhang, Qiaowei, Chen, Qingqing, Mao, Xiongwei, Iwamoto, Yutaro, Han, Xian-Hua, Chen, Yen-Wei, and Tong, Ruofeng

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase. The PA block consists of an intra-phase attention (Intra-PA) module and an inter-phase attention (Inter-PA) module to capture channel-wise self-dependencies and cross-phase interdependencies, respectively. Thus it enables the network to learn more representative multi-phase features by refining the PV features according to the channel dependencies and recalibrating the ART features based on the learned interdependencies between phases. We propose a PA-based multi-scale fusion (MSF) architecture to embed the PA blocks in the network at multiple levels along the encoding path to fuse multi-scale features from multi-phase images. Moreover, a 3D boundary-enhanced loss (BE-loss) is proposed for training to make the network more sensitive to boundaries. To evaluate the performance of our proposed PA-ResSeg, we conducted experiments on a multi-phase CT dataset of focal liver lesions (MPCT-FLLs). Experimental results show the effectiveness of the proposed method by achieving a dice per case (DPC) of 0.77.87, a dice global (DG) of 0.8682, a volumetric overlap error (VOE) of 0.3328 and a relative volume difference (RVD) of 0.0443 on the MPCT-FLLs. Furthermore, to validate the effectiveness and robustness of PA-ResSeg, we conducted extra experiments on another multi-phase liver tumor dataset and obtained a DPC of 0.8290, a DG of 0.9132, a VOE of 0.2637 and a RVD of 0.0163. The proposed method shows its robustness and generalization capability in different datasets and different backbones., Comment: A self-archive version to be published in Medical Physics, awaiting minor revision

Published
2021
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75. High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood

Author

Suotsalo, Kimmo, Xu, Yingying, Corander, Jukka, and Pensar, Johan

Subjects
Statistics - Computation
Abstract

Learning vector autoregressive models from multivariate time series is conventionally approached through least squares or maximum likelihood estimation. These methods typically assume a fully connected model which provides no direct insight to the model structure and may lead to highly noisy estimates of the parameters. Because of these limitations, there has been an increasing interest towards methods that produce sparse estimates through penalized regression. However, such methods are computationally intensive and may become prohibitively time-consuming when the number of variables in the model increases. In this paper we adopt an approximate Bayesian approach to the learning problem by combining fractional marginal likelihood and pseudo-likelihood. We propose a novel method, PLVAR, that is both faster and produces more accurate estimates than the state-of-the-art methods based on penalized regression. We prove the consistency of the PLVAR estimator and demonstrate the attractive performance of the method on both simulated and real-world data.

Published
2020
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76. Prediction of thermoelectric-figure-of-merit based on autoencoder and light gradient boosting machine.

Author

Xu, Yingying, Liu, Xinyi, and Wang, Jifen

Subjects
*THERMOELECTRIC generators, *MACHINE learning, *DEEP learning, *BOOSTING algorithms, *THERMOELECTRIC materials, *MATERIALS handling, *FORECASTING
Abstract

The evaluation of thermoelectric materials relies significantly on the thermoelectric figure of merit, ZT, which serves as a crucial parameter in assessing their properties. The accurate prediction of ZT values can be accomplished by utilizing machine learning models to learn material characteristics. However, factors such as the size of the dataset, model hyperparameters, and data quality can all impact the accuracy of machine learning. In contrast to previous research where high-dimensional features were simply discarded to transform them into low-dimensional ones, deep learning models such as autoencoder can extract more effective information. Therefore, in this article, the combination of autoencoders and the Light Gradient Boosting Machine (LightGBM) is employed to learn the chemical characteristics and ZT values of various materials. The reliability of the model was confirmed by achieving an R2 score of 0.94 during tenfold cross-validation. 130 000 materials were predicted and screened, the temperature dependence of the screened materials was studied in depth, and 13 materials with high ZT values were identified. Four of the 13 most promising candidates identified are existing thermoelectric materials, while nine are ideal candidates for future experimental studies and validation. This work utilizes autoencoders for extensive prediction and screening of promising materials, providing an effective approach for handling high-dimensional material data. [ABSTRACT FROM AUTHOR]

Published
2024
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78. Research on the Design of Systematic Model of Hybrid Learning and the Research on Effective Model

Author

Zhu, Yonghai, Xu, Yingying, Peng, Yingjie, Xiao, Hongwei, Cai, Yanghe, Jiang, Jiechang, Zhou, Chujun, Huang, Zhijian, Zhang, Xiang, Huang, Ronghuai, Series Editor, Kinshuk, Series Editor, Jemni, Mohamed, Series Editor, Chen, Nian-Shing, Series Editor, Spector, J. Michael, Series Editor, Zeng, Haijun, editor, Li, Zhisheng, editor, Guo, Jiong, editor, and Zhang, Zhuo, editor

Published
2023
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79. Learning performance in inverse Ising problems with sparse teacher couplings

Author

Abbara, Alia, Kabashima, Yoshiyuki, Obuchi, Tomoyuki, and Xu, Yingying

Subjects
Condensed Matter - Disordered Systems and Neural Networks, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We investigate the learning performance of the pseudolikelihood maximization method for inverse Ising problems. In the teacher-student scenario under the assumption that the teacher's couplings are sparse and the student does not know the graphical structure, the learning curve and order parameters are assessed in the typical case using the replica and cavity methods from statistical mechanics. Our formulation is also applicable to a certain class of cost functions having locality; the standard likelihood does not belong to that class. The derived analytical formulas indicate that the perfect inference of the presence/absence of the teacher's couplings is possible in the thermodynamic limit taking the number of spins $N$ as infinity while keeping the dataset size $M$ proportional to $N$, as long as $\alpha=M/N > 2$. Meanwhile, the formulas also show that the estimated coupling values corresponding to the truly existing ones in the teacher tend to be overestimated in the absolute value, manifesting the presence of estimation bias. These results are considered to be exact in the thermodynamic limit on locally tree-like networks, such as the regular random or Erd\H{o}s--R\'enyi graphs. Numerical simulation results fully support the theoretical predictions. Additional biases in the estimators on loopy graphs are also discussed., Comment: 29 pages, 8 figures

Published
2019
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