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Exact Designs for OofA Experiments Under a Transition-Effect Model

In the chemical, pharmaceutical, and food industries, sometimes the order of adding a set of components has an impact on the final product. These are instances of the order-of-addition (OofA) problem, which aims to find the optimal sequence of the co

Externí odkaz: http://arxiv.org/abs/2411.03504

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Akademický článek

AKAP12 Expression in HER2-positive Breast Cancer and Its Clinical Significance

Autor: CHENG Qian, LIANG Ruipeng, ZHENG Jiayi, FENG Lijin, LIU Weiwei, LIN Qing

Publikováno v: Zhongliu Fangzhi Yanjiu, Vol 47, Iss 6, Pp 441-445 (2020)

Objective To investigate the expression of AKAP12 in HER2-positive breast cancer and its relation with clinicopathological factors and clinical significance. Methods UALCAN database was used to explore AKAP12 mRNA expression in breast cancer and para

Externí odkaz: https://doaj.org/article/2e02d75c184d4e60a06c6c8b83d76f3b

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Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization

Autor: Yang, Ling, Zheng, Jiayi, Wang, Heyuan, Liu, Zhongyi, Huang, Zhilin, Hong, Shenda, Zhang, Wentao, Cui, Bin

Out-of-distribution (OOD) graph generalization are critical for many real-world applications. Existing methods neglect to discard spurious or noisy features of inputs, which are irrelevant to the label. Besides, they mainly conduct instance-level cla

Externí odkaz: http://arxiv.org/abs/2306.15902

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KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP

Autor: Wang, Yufei, Zheng, Jiayi, Xu, Can, Geng, Xiubo, Shen, Tao, Tao, Chongyang, Jiang, Daxin

This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited. The existing solutions either leverage task-independent heuristic rules (e.g., Synonym Replacement) or fine-tune general-purpose pre-trained lan

Externí odkaz: http://arxiv.org/abs/2206.10265

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Spatial Autoregressive Coding for Graph Neural Recommendation

Autor: Zheng, Jiayi, Yang, Ling, Wang, Heyuan, Yang, Cheng, Li, Yinghong, Hu, Xiaowei, Hong, Shenda

Graph embedding methods including traditional shallow models and deep Graph Neural Networks (GNNs) have led to promising applications in recommendation. Nevertheless, shallow models especially random-walk-based algorithms fail to adequately exploit n

Externí odkaz: http://arxiv.org/abs/2205.09489

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