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587 results on '"Ma, Mingliang"'

6. Theoretical analysis of deep neural networks for temporally dependent observations

Author

Ma, Mingliang and Safikhani, Abolfazl

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Deep neural networks are powerful tools to model observations over time with non-linear patterns. Despite the widespread use of neural networks in such settings, most theoretical developments of deep neural networks are under the assumption of independent observations, and theoretical results for temporally dependent observations are scarce. To bridge this gap, we study theoretical properties of deep neural networks on modeling non-linear time series data. Specifically, non-asymptotic bounds for prediction error of (sparse) feed-forward neural network with ReLU activation function is established under mixing-type assumptions. These assumptions are mild such that they include a wide range of time series models including auto-regressive models. Compared to independent observations, established convergence rates have additional logarithmic factors to compensate for additional complexity due to dependence among data points. The theoretical results are supported via various numerical simulation settings as well as an application to a macroeconomic data set., Comment: 23 pages, 18 pages, accepted for publication in Neural Information Processing Systems

Published
2022

15. Vulpedia: Detecting Vulnerable Ethereum Smart Contracts via Abstracted Vulnerability Signatures

Author

Ye, Jiaming, Ma, Mingliang, Lin, Yun, Ma, Lei, Xue, Yinxing, and Zhao, Jianjun

Subjects
Computer Science - Software Engineering, Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Recent years have seen smart contracts are getting increasingly popular in building trustworthy decentralized applications. Previous research has proposed static and dynamic techniques to detect vulnerabilities in smart contracts. These tools check vulnerable contracts against several predefined rules. However, the emerging new vulnerable types and programming skills to prevent possible vulnerabilities emerging lead to a large number of false positive and false negative reports of tools. To address this, we propose Vulpedia, which mines expressive vulnerability signatures from contracts. Vulpedia is based on the relaxed assumption that the owner of contract is not malicious. Specifically, we extract structural program features from vulnerable and benign contracts as vulnerability signatures, and construct a systematic detection method based on detection rules composed of vulnerability signatures. Compared with the rules defined by state-of-the-arts, our approach can extract more expressive rules to achieve better completeness (i.e., detection recall) and soundness (i.e., precision). We further evaluate Vulpedia with four baselines (i.e., Slither, Securify, SmartCheck and Oyente) on the testing dataset consisting of 17,770 contracts. The experiment results show that Vulpedia achieves best performance of precision on 4 types of vulnerabilities and leading recall on 3 types of vulnerabilities meanwhile exhibiting the great efficiency performance.

Published
2019
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37. LGHAP v2: a global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big Earth data analytics

Author

Bai, Kaixu, primary, Li, Ke, additional, Shao, Liuqing, additional, Li, Xinran, additional, Liu, Chaoshun, additional, Li, Zhengqiang, additional, Ma, Mingliang, additional, Han, Di, additional, Sun, Yibing, additional, Zheng, Zhe, additional, Li, Ruijie, additional, Chang, Ni-Bin, additional, and Guo, Jianping, additional

Published
2024
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46. SARS-CoV-2-specific antibody response characteristics in COVID-19 patients of different ages

Author

Lu Linfang, Yu Siqi, Liu Min, Li Yang, Lei Qing, Lin Mingxi, Lai Danyun, Guo Shujuan, Jiang Hewei, Hou Hongyan, Zheng Yunxiao, Wang Xuening, Ma Mingliang, Zhang Bo, Chen Hong, Xue Junbiao, Zhang Hainan, Qi Huan, Sun Ziyong, Wang Feng, Fan Xionglin, and Xu Zhaowei

Subjects
SARS-CoV-2, protein microarray, age, humoral immunity, IgG and IgM, non-structural/accessory protein, Biochemistry, QD415-436, Genetics, QH426-470
Abstract

Age has been found to be one of the main risk factors for the severity and outcome of COVID-19. However, differences in SARS-CoV-2 specific antibody responses among COVID-19 patients of different age groups remain largely unknown. In this study, we analyzed the IgG/IgM responses to 21 SARS-CoV-2 proteins and 197 peptides that fully cover the spike protein against 731 sera collected from 731 COVID-19 patients aged from 1 to 92 years. We show that there is no overall difference in SARS-CoV-2 antibody responses in COVID-19 patients in the 4 age groups. By antibody response landscape maps, we find that the IgG response profiles of SARS-CoV-2 proteins are positively correlated with age. The S protein linear epitope map shows that the immunogenicity of the S-protein peptides is related to peptide sequence, disease severity and age of the COVID-19 patients. Furthermore, the enrichment analysis indicates that low S1 IgG responses are enriched in patients aged 60. In addition, high responses of non-structural/accessory proteins are enriched in severe COVID-19 patients aged >70. These results suggest the distinct immune response of IgG/IgM to each SARS-CoV-2 protein in patients of different age, which may facilitate a deeper understanding of the immune responses in COVID-19 patients.

Published
2022
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