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1,090 results on '"Shi, Yumeng"'

4. Thiol‐Functionalized Conjugated Metal–Organic Frameworks for Stable and Efficient Perovskite Photovoltaics

Author

Liang, Xiao, Singh, Mriganka, Wang, Fei, Fong, Patrick WK, Ren, Zhiwei, Zhou, Xianfang, Wan, Xuejuan, Sutter‐Fella, Carolin M, Shi, Yumeng, Lin, Haoran, Zhu, Quanyao, Li, Gang, and Hu, Hanlin

Subjects
Macromolecular and Materials Chemistry, Chemical Sciences, Physical Chemistry, Engineering, Materials Engineering, MOF/perovskite structure, in situ GIWAXS, perovskite solar cells, thiol-functionalized MOF
Abstract

Metal-organic frameworks (MOFs) have been investigated recently in perovskite photovoltaics owing to their potential to boost optoelectronic performance and device stability. However, the impact of variations in the MOF side chain on perovskite characteristics and the mechanism of MOF/perovskite film formation remains unclear. In this study, three nanoscale thiol-functionalized UiO-66-type Zr-based MOFs (UiO-66-(SH)2 , UiO-66-MSA, and UiO-66-DMSA) are systematically employed and examined in perovskite solar cells (PSCs). Among these MOFs, UiO-66-(SH)2 , with its rigid organic ligands, exhibited a strong interaction with perovskite materials with more efficient suppression of perovskite vacancy defects. More importantly, A detailed and in-depth discussion is provided on the formation mechanism of UiO-66-(SH)2 -assisted perovskite film upon in situ GIWAXS performed during the annealing process. The incorporation of UiO-66-(SH)2 additives substantially facilitates the conversion of PbI2 into the perovskite phase, prolongs the duration of stage I, and induces a delayed phase transformation pathway. Consequently, the UiO-66-(SH)2 -assisted device demonstrates reduced defect density and superior optoelectronic properties with optimized power conversion efficiency of 24.09% and enhanced long-term stability under ambient environment and continuous light illumination conditions. This study acts as a helpful design guide for desired MOF/perovskite structures, enabling further advancements in MOF/perovskite optoelectronic devices.

Published
2024

5. Lossy and Lossless (L$^2$) Post-training Model Size Compression

Author

Shi, Yumeng, Bai, Shihao, Wei, Xiuying, Gong, Ruihao, and Yang, Jianlei

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Deep neural networks have delivered remarkable performance and have been widely used in various visual tasks. However, their huge size causes significant inconvenience for transmission and storage. Many previous studies have explored model size compression. However, these studies often approach various lossy and lossless compression methods in isolation, leading to challenges in achieving high compression ratios efficiently. This work proposes a post-training model size compression method that combines lossy and lossless compression in a unified way. We first propose a unified parametric weight transformation, which ensures different lossy compression methods can be performed jointly in a post-training manner. Then, a dedicated differentiable counter is introduced to guide the optimization of lossy compression to arrive at a more suitable point for later lossless compression. Additionally, our method can easily control a desired global compression ratio and allocate adaptive ratios for different layers. Finally, our method can achieve a stable $10\times$ compression ratio without sacrificing accuracy and a $20\times$ compression ratio with minor accuracy loss in a short time. Our code is available at https://github.com/ModelTC/L2_Compression .

Published
2023

7. Hardware-Aware Graph Neural Network Automated Design for Edge Computing Platforms

Author

Zhou, Ao, Yang, Jianlei, Qi, Yingjie, Shi, Yumeng, Qiao, Tong, Zhao, Weisheng, and Hu, Chunming

Subjects
Computer Science - Machine Learning
Abstract

Graph neural networks (GNNs) have emerged as a popular strategy for handling non-Euclidean data due to their state-of-the-art performance. However, most of the current GNN model designs mainly focus on task accuracy, lacking in considering hardware resources limitation and real-time requirements of edge application scenarios. Comprehensive profiling of typical GNN models indicates that their execution characteristics are significantly affected across different computing platforms, which demands hardware awareness for efficient GNN designs. In this work, HGNAS is proposed as the first Hardware-aware Graph Neural Architecture Search framework targeting resource constraint edge devices. By decoupling the GNN paradigm, HGNAS constructs a fine-grained design space and leverages an efficient multi-stage search strategy to explore optimal architectures within a few GPU hours. Moreover, HGNAS achieves hardware awareness during the GNN architecture design by leveraging a hardware performance predictor, which could balance the GNN model accuracy and efficiency corresponding to the characteristics of targeted devices. Experimental results show that HGNAS can achieve about $10.6\times$ speedup and $88.2\%$ peak memory reduction with a negligible accuracy loss compared to DGCNN on various edge devices, including Nvidia RTX3080, Jetson TX2, Intel i7-8700K and Raspberry Pi 3B+., Comment: Accepted by DAC'23

Published
2023

9. Oriented lateral growth of two-dimensional materials on c-plane sapphire

Author

Fu, Jui-Han, Min, Jiacheng, Chang, Che-Kang, Tseng, Chien-Chih, Wang, Qingxiao, Sugisaki, Hayato, Li, Chenyang, Chang, Yu-Ming, Alnami, Ibrahim, Syong, Wei-Ren, Lin, Ci, Fang, Feier, Zhao, Lv, Lo, Tzu-Hsuan, Lai, Chao-Sung, Chiu, Wei-Sheng, Jian, Zih-Siang, Chang, Wen-Hao, Lu, Yu-Jung, Shih, Kaimin, Li, Lain-Jong, Wan, Yi, Shi, Yumeng, and Tung, Vincent

Published
2023
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23. A Sparse Matrix Optimization Method for Graph Neural Networks Training

Author

Yao, Tiechui, Wang, Jue, Gu, Junyu, Shi, Yumeng, Liu, Fang, Wang, Xiaoguang, Wang, Yangang, Chi, Xuebin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jin, Zhi, editor, Jiang, Yuncheng, editor, Buchmann, Robert Andrei, editor, Bi, Yaxin, editor, Ghiran, Ana-Maria, editor, and Ma, Wenjun, editor

Published
2023
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24. The Mediating Effect of Mean Thinking Between Stressor and Depression in Senior High School Students

Author

Hu, Xinyu, Shi, Yumeng, Zhang, Xiyue, Zheng, Chenlu, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Ying, Loo Fung, editor, Halili, Ts. Dr. Siti Hajar, editor, and Mishra, Deepanjali, editor

Published
2023
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38. Self-Trapped Exciton Emission in Highly Polar 0D Hybrid Ammonium/Hydronium-Based Perovskites Triggered by Antimony Doping

Author

Zhou, Bo, primary, Fang, Feier, additional, Liu, Zexiang, additional, Zhong, Haizhe, additional, Zhou, Kang, additional, Hu, Hanlin, additional, Min, Jiacheng, additional, Zheng, Fangyuan, additional, Fang, Shaofan, additional, Nie, Jingheng, additional, Huang, Jing-Kai, additional, Li, Lain-Jong, additional, Li, Henan, additional, Wan, Yi, additional, and Shi, Yumeng, additional

Published
2024
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49. Strong Rashba-Edelstein Effect-Induced Spin-Orbit Torques in Monolayer Transition-Metal Dichalcogenide/Ferromagnet Bilayers

Author

Shao, Qiming, Yu, Guoqiang, Lan, Yann-Wen, Shi, Yumeng, Li, Ming-Yang, Zheng, Cheng, Zhu, Xiaodan, Li, Lain-Jong, Amiri, Pedram Khalili, and Wang, Kang L.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract

The electronic and optoelectronic properties of two dimensional materials have been extensively explored in graphene and layered transition metal dichalcogenides (TMDs). Spintronics in these two-dimensional materials could provide novel opportunities for future electronics, for example, efficient generation of spin current, which should enable the efficient manipulation of magnetic elements. So far, the quantitative determination of charge current induced spin current and spin-orbit torques (SOTs) on the magnetic layer adjacent to two-dimensional materials is still lacking. Here, we report a large SOT generated by current-induced spin accumulation through the Rashba-Edelstein effect in the composites of monolayer TMD (MoS$_2$ or WSe$_2$)/CoFeB bilayer. The effective spin conductivity corresponding to the SOT turns out to be almost temperature-independent. Our results suggest that the charge-spin conversion in the chemical vapor deposition-grown large-scale monolayer TMDs could potentially lead to high energy efficiency for magnetization reversal and convenient device integration for future spintronics based on two-dimensional materials., Comment: accepted version

Published
2018
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