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6,106 results on '"Bai,Jie"'

210. Spread-gram: A spreading-activation schema of network structural learning

Author

Bai, Jie, Li, Linjing, and Zeng, Daniel

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning, J.4, I.2.4
Abstract

Network representation learning has exploded recently. However, existing studies usually reconstruct networks as sequences or matrices, which may cause information bias or sparsity problem during model training. Inspired by a cognitive model of human memory, we propose a network representation learning scheme. In this scheme, we learn node embeddings by adjusting the proximity of nodes traversing the spreading structure of the network. Our proposed method shows a significant improvement in multiple analysis tasks based on various real-world networks, ranging from semantic networks to protein interaction networks, international trade networks, human behavior networks, etc. In particular, our model can effectively discover the hierarchical structures in networks. The well-organized model training speeds up the convergence to only a small number of iterations, and the training time is linear with respect to the edge numbers., Comment: 21 pages, 11 figures

Published
2019

219. Effects of repetitive transcranial magnetic stimulation on pain and emotion of patients with neuropathic pain

Author

ZHANG Kun⁃long, XUE Bai⁃jie, XIAO Wei, SUN Xiao⁃long, WANG Ya⁃yun, and YUAN Hua

Subjects
neuralgia, transcranial magnetic stimulation, mood, rehabilitation, Neurology. Diseases of the nervous system, RC346-429
Abstract

Objective To investigate the effect of high⁃frequency repetitive transcranial magnetic stimulation (rTMS) on pain and emotion in patients with neuropathic pain (NP) combined with anxiety and/or depression. Methods A total of 39 patients with NP combined with anxiety and/or depression from June 2019 to October 2021 were included and randomly divided into conventional treatment combined with sham stimulation group (control group, n=19) and conventional treatment⁃assisted high⁃frequency rTMS (rTMS group, n=20). The Numerical Rating Scale (NRS) was used to evaluate the pain state, Hamilton Anxiety Scale⁃14 (HAMA⁃14) was used to evaluate the anxiety state, and Hamilton Depression Scale⁃17 (HAMD⁃17) was used to evaluate depression state before treatment and at 3 days, 1 week, 2 weeks, 3 weeks and 4 weeks of treatment. Results The NRS score (F=23.573, P=0.000) and HAMD score (F=15.426, P=0.000) before and after treatment were significantly different between the 2 groups. The NRS score and HAMD score in rTMS group were lower than those in control group at 1, 2, 3 and 4 weeks of treatment (P≤0.01, for all). The differences in NRS score (F=317.751, P=0.000), HAMA score (F=81.510, P=0.000) and HAMD score (F=32.773, P=0.000) at different observation times were also statistically significant in the same treatment group. In rTMS group, NRS score and HAMD score at 1, 2, 3 and 4 weeks of treatment were lower than those before treatment and 3 days of treatment (P＜0.01, for all), and HAMA score at 1, 2 and 3 weeks of treatment were lower than those before treatment and 3 days of treatment (P=0.000, for all). HAMA score at 4 weeks of treatment was lower than that before treatment (P=0.007), at 1 week (P=0.014) and 2 weeks (P=0.001) of treatment. In control group, NRS score only at 4 weeks of treatment was lower than that before treatment (P=0.000), HAMA score at 1, 2 and 3 weeks of treatment was lower than those before treatment (P＜0.05, for all), and HAMA score at 1 week of treatment was lower than that at 3 days of treatment (P=0.002). Spearman correlation analysis showed that the degree of pain relief before and after treatment was positively correlated with the ease of anxiety (rs=0.442, P=0.048) and depression (rs=0.705, P=0.001). Conclusions High⁃frequency rTMS is a safe and effective treatment for NP with anxiety and/or depression. It can significantly ease anxiety and depression in patients while relieving pain.

Published
2022
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235. Multi-Channel RPL Protocol Based on Cross-Layer Design in High-Density LLN

Author

Lei, Jianjun, Wang, Tianpeng, Zhao, Xunwei, Zhang, Chunling, Bai, Jie, Wang, Zhigang, Wang, Dan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Lei, editor, Segal, Michael, editor, Chen, Jenhui, editor, and Qiu, Tie, editor

Published
2022
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236. An Algorithm of Millimeter-Wave Radar Target Classification Based on SVM Using Manually Extracted Point Cloud Features

Author

Dong, Lianfei, Zhang, Lu, Bai, Jie, Li, Sen, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, 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, and Zhang, Zhenyuan, editor

Published
2022
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237. CPU-GPU Heterogeneous Parallel Algorithm for Metal Solidification Molecular Dynamics Simulation Based on Artificial Intelligence

Author

Huhemandula, Bai, Jie, Ji, Wenhui, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, 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, Hung, Jason C., editor, Yen, Neil Y., editor, and Chang, Jia-Wei, editor

Published
2022
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238. Learning Interactive Knowledge Graph for Trajectory Prediction

Author

Zhu, Chen, Bai, Jie, Fang, Jianwu, Xue, Jianru, Li, Xu, Yu, Hongkai, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, 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, Hirche, Sandra, 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, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, 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, Wu, Meiping, editor, Niu, Yifeng, editor, Gu, Mancang, editor, and Cheng, Jin, editor

Published
2022
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248. Primary motor hand area corticospinal excitability indicates overall functional recovery after spinal cord injury

Author

Chun-Qiu Dai, Ming Gao, Xiao-Dong Lin, Bai-Jie Xue, Ying Liang, Mu-Lan Xu, Xiang-Bo Wu, Gui-Qing Cheng, Xu Hu, Chen-Guang Zhao, Hua Yuan, and Xiao-Long Sun

Subjects
spinal cord injury, corticospinal excitability, MEP, ADL, motor function, Neurology. Diseases of the nervous system, RC346-429
Abstract

BackgroundAfter spinal cord injury (SCI), the excitability of the primary motor cortex (M1) lower extremity area decreases or disappears. A recent study reported that the M1 hand area of the SCI patient encodes the activity information of both the upper and lower extremities. However, the characteristics of the M1 hand area corticospinal excitability (CSE) changes after SCI and its correlation with extremities motor function are still unknown.MethodsA retrospective study was conducted on the data of 347 SCI patients and 80 healthy controls on motor evoked potentials (MEP, reflection of CSE), extremity motor function, and activities of daily living (ADL) ability. Correlation analysis and multiple linear regression analysis were conducted to analyze the relationship between the degree of MEP hemispheric conversion and extremity motor function/ADL ability.ResultsThe CSE of the dominant hemisphere M1 hand area decreased in SCI patients. In 0–6 m, AIS A grade, or non-cervical injury SCI patients, the degree of M1 hand area MEP hemispheric conversion was positively correlated with total motor score, lower extremity motor score (LEMS), and ADL ability. Multiple linear regression analysis further confirmed the contribution of MEP hemispheric conversion degree in ADL changes as an independent factor.ConclusionThe closer the degree of M1 hand area MEP hemispheric conversion is to that of healthy controls, the better the extremity motor function/ADL ability patients achieve. Based on the law of this phenomenon, targeted intervention to regulate the excitability of bilateral M1 hand areas might be a novel strategy for SCI overall functional recovery.

Published
2023
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