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Your search keyword '"Zhang, Haoran"' showing total 119 results
Start Over Author "Zhang, Haoran" Publisher springer science and business media llc
119 results on '"Zhang, Haoran"'

29. GhMYB18 confers Aphis gossypii Glover resistance through regulating the synthesis of salicylic acid and flavonoids in cotton plants

Author

Hu, Zongwei, primary, Zhong, Xiao, additional, Zhang, Haoran, additional, Luo, Xincheng, additional, Wang, Yuxue, additional, Wang, Yi, additional, Liu, Ting, additional, Zhang, Yi, additional, Wang, Xiangping, additional, An, Hongliu, additional, Xu, Dong, additional, Wan, Peng, additional, Yang, Yazhen, additional, and Zhang, Jianmin, additional

Published
2022
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39. Vectorized rooftop area data for 90 cities in China

Author

Zhang, Zhixin, Qian, Zhen, Zhong, Teng, Chen, Min, Zhang, Kai, Yang, Yue, Zhu, Rui, Zhang, Fan, Zhang, Haoran, Zhou, Fangzhuo, Yu, Jianing, Zhang, Bingyue, Lü, Guonian, and Yan, Jinyue

Subjects
Statistics and Probability, Library and Information Sciences, Statistics, Probability and Uncertainty, Computer Science Applications, Education, Information Systems
Abstract

Reliable information on building rooftops is crucial for utilizing limited urban space effectively. In recent decades, the demand for accurate and up-to-date data on the areas of rooftops on a large-scale is increasing. However, obtaining these data is challenging due to the limited capability of conventional computer vision methods and the high cost of 3D modeling involving aerial photogrammetry. In this study, a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery. This framework is used to generate vectorized data for rooftops in 90 cities in China. The data was validated on test samples of 180 km2 across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively. In addition, the generated rooftop area conforms to the urban morphological characteristics and reflects urbanization level. These results demonstrate that the generated dataset can be used for data support and decision-making that can facilitate sustainable urban development effectively.

Published
2022

40. A comparison of approaches to improve worst-case predictive model performance over patient subpopulations

Author

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Institute for Medical Engineering & Science, Pfohl, Stephen R, Zhang, Haoran, Xu, Yizhe, Foryciarz, Agata, Ghassemi, Marzyeh, Shah, Nigam H, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Institute for Medical Engineering & Science, Pfohl, Stephen R, Zhang, Haoran, Xu, Yizhe, Foryciarz, Agata, Ghassemi, Marzyeh, and Shah, Nigam H

Abstract

AbstractPredictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinforcing inequities in care access and quality. Model training approaches that aim to maximize worst-case model performance across subpopulations, such as distributionally robust optimization (DRO), attempt to address this problem without introducing additional harms. We conduct a large-scale empirical study of DRO and several variations of standard learning procedures to identify approaches for model development and selection that consistently improve disaggregated and worst-case performance over subpopulations compared to standard approaches for learning predictive models from electronic health records data. In the course of our evaluation, we introduce an extension to DRO approaches that allows for specification of the metric used to assess worst-case performance. We conduct the analysis for models that predict in-hospital mortality, prolonged length of stay, and 30-day readmission for inpatient admissions, and predict in-hospital mortality using intensive care data. We find that, with relatively few exceptions, no approach performs better, for each patient subpopulation examined, than standard learning procedures using the entire training dataset. These results imply that when it is of interest to improve model performance for patient subpopulations beyond what can be achieved with standard practices, it may be necessary to do so via data collection techniques that increase the effective sample size or reduce the level of noise in the prediction problem.

Published
2022

50. Source-tracking of the Chinese Chikungunya viruses suggests that Indian subcontinent and Southeast Asia act as major hubs for the recent global spread of Chikungunya virus

Author

Qiu, Shaofu, primary, Guo, Jinpeng, additional, Li, Peihan, additional, Li, Peng, additional, Du, Xinying, additional, Hao, Rongzhang, additional, Yang, Chaojie, additional, Wang, Qi, additional, Liu, Hongbo, additional, Zhang, Haoran, additional, Tian, Sai, additional, Shi, Hua, additional, Wen, Liang, additional, An, Daizhi, additional, Yang, Xiaocui, additional, An, Xiaoyuan, additional, Wang, Ligui, additional, Wang, Changjun, additional, and Song, Hongbin, additional

Published
2021
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