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3,233 results on '"Zhang, Yuwei"'

120. Low-Dose CT Denoising Using a Structure-Preserving Kernel Prediction Network

Author

Xu, Lu, Zhang, Yuwei, Liu, Ying, Wang, Daoye, Zhou, Mu, Ren, Jimmy, Wei, Jingwei, and Ye, Zhaoxiang

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

Low-dose CT has been a key diagnostic imaging modality to reduce the potential risk of radiation overdose to patient health. Despite recent advances, CNN-based approaches typically apply filters in a spatially invariant way and adopt similar pixel-level losses, which treat all regions of the CT image equally and can be inefficient when fine-grained structures coexist with non-uniformly distributed noises. To address this issue, we propose a Structure-preserving Kernel Prediction Network (StructKPN) that combines the kernel prediction network with a structure-aware loss function that utilizes the pixel gradient statistics and guides the model towards spatially-variant filters that enhance noise removal, prevent over-smoothing and preserve detailed structures for different regions in CT imaging. Extensive experiments demonstrated that our approach achieved superior performance on both synthetic and non-synthetic datasets, and better preserves structures that are highly desired in clinical screening and low-dose protocol optimization., Comment: ICIP2021

Published
2021

121. Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report

Author

Ignatov, Andrey, Malivenko, Grigory, Timofte, Radu, Chen, Sheng, Xia, Xin, Liu, Zhaoyan, Zhang, Yuwei, Zhu, Feng, Li, Jiashi, Xiao, Xuefeng, Tian, Yuan, Wu, Xinglong, Kyrkou, Christos, Chen, Yixin, Zhang, Zexin, Peng, Yunbo, Lin, Yue, Dutta, Saikat, Das, Sourya Dipta, Shah, Nisarg A., Kumar, Himanshu, Ge, Chao, Wu, Pei-Lin, Du, Jin-Hua, Batutin, Andrew, Federico, Juan Pablo, Lyda, Konrad, Khojoyan, Levon, Thanki, Abhishek, Paul, Sayak, and Siddiqui, Shahid

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

Camera scene detection is among the most popular computer vision problem on smartphones. While many custom solutions were developed for this task by phone vendors, none of the designed models were available publicly up until now. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms. For this, the participants were provided with a large-scale CamSDD dataset consisting of more than 11K images belonging to the 30 most important scene categories. The runtime of all models was evaluated on the popular Apple Bionic A11 platform that can be found in many iOS devices. The proposed solutions are fully compatible with all major mobile AI accelerators and can demonstrate more than 100-200 FPS on the majority of recent smartphone platforms while achieving a top-3 accuracy of more than 98%. A detailed description of all models developed in the challenge is provided in this paper., Comment: Mobile AI 2021 Workshop and Challenges: https://ai-benchmark.com/workshops/mai/2021/. arXiv admin note: substantial text overlap with arXiv:2105.08630; text overlap with arXiv:2105.07825, arXiv:2105.07809, arXiv:2105.08629

Published
2021

122. KuraNet: Systems of Coupled Oscillators that Learn to Synchronize

Author

Ricci, Matthew, Jung, Minju, Zhang, Yuwei, Chalvidal, Mathieu, Soni, Aneri, and Serre, Thomas

Subjects
Nonlinear Sciences - Adaptation and Self-Organizing Systems, Computer Science - Machine Learning
Abstract

Networks of coupled oscillators are some of the most studied objects in the theory of dynamical systems. Two important areas of current interest are the study of synchrony in highly disordered systems and the modeling of systems with adaptive network structures. Here, we present a single approach to both of these problems in the form of "KuraNet", a deep-learning-based system of coupled oscillators that can learn to synchronize across a distribution of disordered network conditions. The key feature of the model is the replacement of the traditionally static couplings with a coupling function which can learn optimal interactions within heterogeneous oscillator populations. We apply our approach to the eponymous Kuramoto model and demonstrate how KuraNet can learn data-dependent coupling structures that promote either global or cluster synchrony. For example, we show how KuraNet can be used to empirically explore the conditions of global synchrony in analytically impenetrable models with disordered natural frequencies, external field strengths, and interaction delays. In a sequence of cluster synchrony experiments, we further show how KuraNet can function as a data classifier by synchronizing into coherent assemblies. In all cases, we show how KuraNet can generalize to both new data and new network scales, making it easy to work with small systems and form hypotheses about the thermodynamic limit. Our proposed learning-based approach is broadly applicable to arbitrary dynamical systems with wide-ranging relevance to modeling in physics and systems biology., Comment: 9 pages, 4 figures, 5 videos, supplementary information and figures

Published
2021

123. Why don't you tell me? The mediating role of self-concealment in the relationship between Machiavellianism and break-up distress

Author

Geng, Yaoguo, Zhan, Tingting, Zhang, Yuwei, Shi, Liping, Yu, Jiejing, and Jin, Wenjing

Subjects
Self-perception -- Health aspects, Romantic love -- Health aspects, Couples -- Psychological aspects -- Health aspects, Machiavellianism (Psychology) -- Health aspects, Stress (Psychology) -- Risk factors, Psychology and mental health
Abstract

It has been well documented that Machiavellianism has a positive effect on break-up distress. However, there are few research explored the internal mechanism. In this study, we investigated the mediating role of self-concealment and the moderating role of gender. Machiavellianism Personality Scale, Self-Concealment Scale and Break-up Distress Scale was distributed through an online questionnaire platform. A sample of 869 undergraduate students was received, and their age ranged from 16 to 25 years old (M = 19.48, SD = 1.15). As we predicted, the relationship between Machiavellianism and break-up distress was partially mediated by self-concealment. The direct effect of Machiavellianism on break-up distress and the mediating effect of self-concealment were moderated by gender. Specifically, compared with boys, the effect of Machiavellianism on self-concealment was stronger for girls, while the effect of Machiavellianism on break-up distress was stronger for boys. These findings confirm how Machiavellianism affects break-up distress and provide new intervention ideas for solving the psychological crisis of college students after the dissolution of romantic relationships., Author(s): Yaoguo Geng [sup.1] [sup.2] , Tingting Zhan [sup.1] , Yuwei Zhang [sup.2] , Liping Shi [sup.2] , Jiejing Yu [sup.2] , Wenjing Jin [sup.2] Author Affiliations: (1) grid.207374.5, 0000 [...]

Published
2023
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140. Switching LPV Tracking Controller Design for Longitudinal Motion of Aircraft with Uncertain Scheduling Parameters

Author

Zhang, Yuwei, Wang, Shaoping, Wang, Xingjian, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yan, Liang, editor, and Deng, Yimin, editor

Published
2023
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