
Jiahui Xu
Articles
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Jun 30, 2024 |
mdpi.com | Buhan Wang |Renfu Jia |Jiahui Xu |Yi Wei
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Apr 1, 2024 |
scirp.org | Wei Chen |Mingzhen Wan |Jiahui Xu |Jing Zhong
1. IntroductionIn many applications, such as the studies of financial and biomedical data, the response variable usually is positive. For modelling the relationship between the positive response and a set of explanatory variables, a natural idea is that first take an appropriate transformation for the response, e.g., the logarithmic transformation, then some common regression models, such as the linear regression or quantile regression, can be employed based on the transformed data.
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Jun 6, 2023 |
mdpi.com | Jiahui Xu |Anqi Xu |Renfu Jia |Buhan Wang
1. IntroductionExcessive greenhouse gas emissions contribute to global warming. In order to alleviate climate extremes and achieve the goal of carbon neutrality, many countries have issued relevant CER policy documents and regulations. For instance, the UK government released a ten-point plan for a green industrial revolution in November 2020, which places “clean growth” at the heart of a new industrial strategy to promote a win–win situation of carbon neutrality and economic recovery.
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Jun 2, 2023 |
onlinelibrary.wiley.com | Jiahui Xu |Ao Zhang |Jianjun Gao
A direct parameter extraction method of EEHEMT nonlinear empirical model is proposed in this paper. An improved DC model is also presented by considering the channel length modulation parameter ( ) as a function of gate-to-source voltage. Model verification is carried out by comparison of measured and simulated current–voltage characteristics and S-parameters.
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May 23, 2023 |
arxiv.org | Chen Zhang |Jiahui Xu |Bin Wang |Yiming Chen
Training or finetuning large-scale language models (LLMs) such as GPT-3 requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One practical area of research is to treat these models as black boxes and interact with them through their inference APIs.
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