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1,439 results on '"Li, Yifeng"'

63. Deep Evolutionary Learning for Molecular Design

Author

Li, Yifeng, Ooi, Hsu Kiang, and Tchagang, Alain

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence
Abstract

In this paper, we propose a deep evolutionary learning (DEL) process that integrates fragment-based deep generative model and multi-objective evolutionary computation for molecular design. Our approach enables (1) evolutionary operations in the latent space of the generative model, rather than the structural space, to generate novel promising molecular structures for the next evolutionary generation, and (2) generative model fine-tuning using newly generated high-quality samples. Thus, DEL implements a data-model co-evolution concept which improves both sample population and generative model learning. Experiments on two public datasets indicate that sample population obtained by DEL exhibits improved property distributions, and dominates samples generated by multi-objective Bayesian optimization algorithms.

Published
2020

64. 5G Antenna Distribution in Substations Considering Electromagnetic Interference Factors

Author

Liu, Xingfa, Tang, Bo, Zhang, Can, Qi, Daokun, Yao, Yushan, Li, Yifeng, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Dong, Xuzhu, editor, Yang, Qingxin, editor, and Ma, Weiming, editor

Published
2023
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65. Channel Path Simulation and Channel Loss Calculation of 5G Substation

Author

Li, Yifeng, Liu, Xingfa, Tang, Bo, Cai, Chenlin, Zhang, Longbin, Yao, Yushan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Dong, Xuzhu, editor, Yang, Qingxin, editor, and Ma, Weiming, editor

Published
2023
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68. Analysis of epidemic trends and status of schistosomiasis in Jiangxi Province from 2002 to 2021

Author

LI Yifeng, LI Zongguang, HE Tingting, XIE Jingzi, LYU Shangbiao, YUAN Min, and LIN Dandan

Subjects
schistosomiasis, epidemic trend, joinpoint regression model, jiangxi province, Medicine
Abstract

ObjectiveTo analyze the epidemic trends and current status in the prevalence of schistosomiasis in Jiangxi Province from 2002 to 2021， and to provide evidence for formulating and optimizing schistosomiasis control strategies in the province.MethodsEpidemiological data of schistosomiasis in Jiangxi Province from 2002 to 2021 were collected. The Joinpoint regression model was used to investigate the epidemic trends of schistosomiasis， and the annual average percent change （AAPC） was used to assess the changing trend of schistosomiasis infection in humans and bovines. SPSS 25.0 software was used to analyze the target achievement status， infection rate of schistosomiasis in humans and the areas with snails in 2021 to evaluate the differences among various districts and epidemic regions.ResultsBy 2021， 24 counties （61.50%） in the province had achieved the elimination goal， with hilly and marsh counties reaching 82.60% and 31.30%， respectively. The seropositive rate of schistosomiasis in human was 3.51% across the province. However， the seropositive rate in marshland areas （4.77%） was significantly higher than that in hilly areas （1.23 %） （χ2=3 827.51， P

Published
2023
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69. Pulsar Timing Observations with Haoping Radio Telescope

Author

Luo, Jintao, Gao, Yuping, Yang, Tinggao, Zhao, Chengshi, Tong, Minglei, Rao, Yongnan, Li, Yifeng, Li, Bian, Zhu, Xingzhi, Qiao, Haihua, and Lu, Xiaochun

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We report pulsar timing observations carried out in L-band with NTSC's 40-meter Haoping Radio Telescope (HRT), which was constructed in 2014. The observations were carried out using the pulsar machine we developed. Timing observations toward millisecond pulsar J0437-4715 obtains a timing residual (r.m.s) of 397ns in the time span of 284 days. And our observations successfully detected Crab pulsar's glitch that happened on July 23rd, 2019., Comment: 10 pages, 8 figures, accepted for publication in RAA (Res. Astron. Astrophys.)

Published
2020
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80. Exploring Deep Anomaly Detection Methods Based on Capsule Net

Author

Li, Xiaoyan, Kiringa, Iluju, Yeap, Tet, Zhu, Xiaodan, and Li, Yifeng

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

In this paper, we develop and explore deep anomaly detection techniques based on the capsule network (CapsNet) for image data. Being able to encoding intrinsic spatial relationship between parts and a whole, CapsNet has been applied as both a classifier and deep autoencoder. This inspires us to design a prediction-probability-based and a reconstruction-error-based normality score functions for evaluating the "outlierness" of unseen images. Our results on three datasets demonstrate that the prediction-probability-based method performs consistently well, while the reconstruction-error-based approach is relatively sensitive to the similarity between labeled and unlabeled images. Furthermore, both of the CapsNet-based methods outperform the principled benchmark methods in many cases., Comment: Presented in the "ICML 2019 Workshop on Uncertainty & Robustness in Deep Learning", June 14, Long Beach, California, USA

Published
2019

81. Defending Adversarial Attacks by Correcting logits

Author

Li, Yifeng, Xie, Lingxi, Zhang, Ya, Zhang, Rui, Wang, Yanfeng, and Tian, Qi

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

Generating and eliminating adversarial examples has been an intriguing topic in the field of deep learning. While previous research verified that adversarial attacks are often fragile and can be defended via image-level processing, it remains unclear how high-level features are perturbed by such attacks. We investigate this issue from a new perspective, which purely relies on logits, the class scores before softmax, to detect and defend adversarial attacks. Our defender is a two-layer network trained on a mixed set of clean and perturbed logits, with the goal being recovering the original prediction. Upon a wide range of adversarial attacks, our simple approach shows promising results with relatively high accuracy in defense, and the defender can transfer across attackers with similar properties. More importantly, our defender can work in the scenarios that image data are unavailable, and enjoys high interpretability especially at the semantic level.

Published
2019

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