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108 results on '"Ding, Jieyu"'

2. A Feature Fusion Method Based on DeepONet for Dynamic Equations

Author

Huang, Yin, Ding, Jieyu, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Rui, Xiaoting, editor, and Liu, Caishan, editor

Published
2024
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16. Smoothing piecewise linear activation functions based on mollified square root functions.

Author

Pan, Tony Yuxiang, Yang, Guangyu, Zhao, Junli, and Ding, Jieyu

Subjects
SQUARE root, SMOOTHNESS of functions, SKILLETS, DEEP learning
Abstract

Activation functions(AFs) are crucial components in neural networks for deep learning. Piecewise linear functions(PLFs) have been widely employed to act as AFs, thanks to their computational efficiencies and simplicities. However, PLFs are not completely differentiable, have potential problems of training. The analytical expressions of AFs based on pure PLFs can be smoothed via mollified square root function(MSRF) method, inspired by SquarePlus method of ReLU approximation. In this paper, we propose a proposition defining AFs as maximum or minimum of two PLFs, and transform the results into a smoothed function via MSRF method. Based on MSRF, we modify the well-known AFs composed of two PLFs, three or four PLFs to regularized ones systematically, including ReLU, LReLU, vReLU, Step, Biplolar, BReLU(Bounded ReLU), Htanh(Hard Tanh), Pan(Frying pan function), STF(Soft Thresholding Formulas), HTF(Soft Thresholding Formulas), SReLU(S-shaped ReLU), MReLU(Mexican hat type ReLU), TSF(Trapezoid-shaped function) functions. Additionally, according to the equivalences of SquarePlus and SoftPlus functions, some classic compound AFs, such as ELU, Swish, Mish, SoftSign Logish and DLU functions, can be expressed via MSRF method also. The derivatives of their mollified versions demonstrate their smoothness properties. The proposed method can be extended to AFs composed of multiple PLFs easily, which will be investigated deeply in the future. [ABSTRACT FROM AUTHOR]

Published
2024
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32. 多体系统动力学微分-代数方程L-稳定方法

Author

LI Bowen, null 李博文，丁洁玉，李亚男, DING Jieyu, and LI Yanan

Subjects
Applied Mathematics, Mechanical Engineering
Abstract

针对多体系统动力学微分-代数方程形式，在时间区间上构造L-稳定方法，分别基于等距节点、Chebyshev节点和Legendre节点等非等距节点建立求解格式，依据Ehle定理及猜想，与Pade逼近式对比得到待定矩阵和向量，从而获得L-稳定求解公式，循环求解过程采用Newton迭代法计算.以平面双连杆机械臂系统为例，使用L-稳定方法进行数值仿真，通过改变时间区间节点数和步长对各个指标结果进行比较，并与经典Runge-Kutta法对比.结果表明，该方法具有稳定性好、精度高等优点，适用于长时间情况下的多体系统动力学仿真.

Published
2019

34. Large-scale analysis of 2,152 Ig-seq datasets reveals key features of B cell biology and the antibody repertoire

Author

Yang, Xiujia, primary, Wang, Minhui, additional, Wu, Jiaqi, additional, Shi, Dianchun, additional, Zhang, Yanfang, additional, Zeng, Huikun, additional, Zhu, Yan, additional, Lan, Chunhong, additional, Deng, Yang, additional, Guo, Shixin, additional, Xu, Lijun, additional, Ma, Cuiyu, additional, Zhang, Yanxia, additional, Ou, Jinxia, additional, Liu, Chu-jun, additional, Chen, Yuan, additional, Wang, Qilong, additional, Xie, Wenxi, additional, Guan, Junjie, additional, Ding, Jieyu, additional, Wang, Zhi, additional, Chang, Changqing, additional, Yang, Wei, additional, Zhang, Huijie, additional, Chen, Jun, additional, Qin, Lijie, additional, Zhou, Hongwei, additional, Bei, Jin-Xin, additional, Wei, Lai, additional, Cao, Guangwen, additional, Yu, Xueqing, additional, and Zhang, Zhenhai, additional

Published
2021
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40. The Uproar Over Gene-Edited Babies: A Semantic Network Analysis of CRISPR on Twitter.

Author

Calabrese, Christopher, Ding, Jieyu, Millam, Benjamin, and Barnett, George A.

Subjects
SEMANTIC network analysis, CRISPRS, MUSCULAR dystrophy, GENOME editing, INFANTS, AGRICULTURAL biotechnology, ECOLOGY
Abstract

CRISPR-Cas9, a recent gene editing technology, has potential to greatly impact medicine, agriculture, and the environment; therefore, it is increasingly important to understand the public's perceptions surrounding this technology. This study examined the main themes of CRISPR during a nine-month period on Twitter through semantic network analysis. Three additional networks (before, initial, and after-event stages) were created to understand the effects of the "CRISPR babies" news event. Four themes emerged in the overall network: (1) research/applications of CRISPR, (2) the "CRISPR babies" event, (3) agricultural regulations for biotechnology, and (4) advancements in muscular dystrophy research. The "CRISPR babies" news story dominated the initial and after-event stage network clusters, indicating major events may impact how users discuss socio-scientific issues in online media. The sentiment surrounding CRISPR was mixed, demonstrating concerns over this technology. This study reveals the potential of social media as a tool to understand public discussions around new technologies. [ABSTRACT FROM AUTHOR]

Published
2020
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49. Advantages and challenges in using mobile apps for field experiments: A systematic review and a case study

Author

Boase, Jeffrey, Humphreys, Lee, Zhang, Jingwen, Calabrese, Christopher, Ding, Jieyu, Liu, Mingxuan, and Zhang, Biying

Abstract

As smartphone’s computing power continues to grow and as mobile applications (apps) continue to dominate digital engagement, apps have become a new frontier for advancing field experiment methodology. Using apps may help researchers to scale up the reach, precisely control randomization and experiment materials, collect a variety of objective and self-reported data over time, and more conveniently replicate and adapt an experiment. We performed a systematic review on field experiments involving apps published between 2007 and 2017. Seven databases were scanned using a predefined search strategy. The database search retrieved 4,810 citations; 101 articles met the inclusion criteria. Our review suggests that scholars have only started to employ apps in field experiments in the last 4 years. Most studies only used apps as an experiment treatment instead of an experiment platform; therefore, researchers have yet to fully leverage the advantages. Almost all studies were from the health research domain and 77.2% used randomized controlled trial design. Only 7 studies utilized smartphone sensors for collecting data. Only one study reported cost and ethical concerns regarding using apps for the experiment. Given these findings, we reported a case study that targeted a minority racial group and leveraged the advantages of apps as an experiment platform and as a data collection tool to illustrate practical challenges and lessons learned regarding time, financial cost, and technical support. In conclusion, we suggest apps provide new ways to study causal mechanisms with experiment big data. Limitations of generalizability, retention, and design quality were discussed as well.

Published
2018
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