Search Results for author: Yixuan Chen

Found 5 papers, 1 papers with code

Balancing reaction-diffusion network for cell polarization pattern with stability and asymmetry

no code implementations14 Jan 2024 Yixuan Chen, Guoye Guan, Lei-Han Tang, Chao Tang

Once again, a generic set of kinetic parameters moves the interface towards either the anterior or posterior end, yet a polarized pattern can be stabilized through spatial tuning of one or more parameters coupled to intracellular or extracellular cues.

Adaptive Distributionally Robust Planning for Renewable-Powered Fast Charging Stations Under Decision-Dependent EV Diffusion Uncertainty

no code implementations11 Oct 2023 Yujia Li, Feng Qiu, Yixuan Chen, Yunhe Hou

When deploying fast charging stations (FCSs) to support long-distance trips of electric vehicles (EVs), there exist indirect network effects: while the gradual diffusion of EVs directly influences the timing and capacities of FCS allocation, the decisions for FCS allocations, in turn, impact the drivers' willingness to adopt EVs.

ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation

1 code implementation3 Aug 2023 Xueying Du, Mingwei Liu, Kaixin Wang, Hanlin Wang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, Yiling Lou

Third, we find that generating the entire class all at once (i. e. holistic generation strategy) is the best generation strategy only for GPT-4 and GPT-3. 5, while method-by-method generation (i. e. incremental and compositional) is better strategies for the other models with limited ability of understanding long instructions and utilizing the middle information.

Class-level Code Generation

A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data

no code implementations3 Jan 2021 Shen Jia, Yulin Zhang, Yiming Mao, Jiawei Gao, Yixuan Chen, YuXuan Jiang, Haochen Luo, Kebo Lv, Jionglong Su

The biological functions in cancer development of 50 selected genes is also explored through enrichment analysis and the results show that 12 out of 16 of our top features have already been identified to be specific with cancer and we also propose some more genes to be tested for future studies.

Dimensionality Reduction feature selection

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