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Your search keyword '"Xiao, Jingjing"' showing total 741 results
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741 results on '"Xiao, Jingjing"'

3. A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray

Author

Fan, Weijie, Yang, Yi, Qi, Jing, Zhang, Qichuan, Liao, Cuiwei, Wen, Li, Wang, Shuang, Wang, Guangxian, Xia, Yu, Wu, Qihua, Fan, Xiaotao, Chen, Xingcai, He, Mi, Xiao, JingJing, Yang, Liu, Liu, Yun, Chen, Jia, Wang, Bing, Zhang, Lei, Yang, Liuqing, Gan, Hui, Zhang, Shushu, Liu, Guofang, Ge, Xiaodong, Cai, Yuanqing, Zhao, Gang, Zhang, Xi, Xie, Mingxun, Xu, Huilin, Zhang, Yi, Chen, Jiao, Li, Jun, Han, Shuang, Mu, Ke, Xiao, Shilin, Xiong, Tingwei, Nian, Yongjian, and Zhang, Dong

Published
2024
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7. Cascadable in-memory computing based on symmetric writing and read out

Author

Wang, Lizheng, Xiong, Junlin, Cheng, Bin, Dai, Yudi, Wang, Fuyi, Pan, Chen, Cao, Tianjun, Liu, Xiaowei, Wang, Pengfei, Chen, Moyu, Yan, Shengnan, Liu, Zenglin, Xiao, Jingjing, Xu, Xianghan, Wang, Zhenlin, Shi, Youguo, Cheong, Sang-Wook, Zhang, Haijun, Liang, Shi-Jun, and Miao, Feng

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

The building block of in-memory computing with spintronic devices is mainly based on the magnetic tunnel junction with perpendicular interfacial anisotropy (p-MTJ). The resulting asymmetric write and read-out operations impose challenges in downscaling and direct cascadability of p-MTJ devices. Here, we propose that a new symmetric write and read-out mechanism can be realized in perpendicular-anisotropy spin-orbit (PASO) quantum materials based on Fe3GeTe2 and WTe2. We demonstrate that field-free and deterministic reversal of the perpendicular magnetization can be achieved by employing unconventional charge to z-spin conversion. The resulting magnetic state can be readily probed with its intrinsic inverse process, i.e., z-spin to charge conversion. Using the PASO quantum material as a fundamental building block, we implement the functionally complete set of logic-in-memory operations and a more complex nonvolatile half-adder logic function. Our work highlights the potential of PASO quantum materials for the development of scalable energy-efficient and ultrafast spintronic computing., Comment: Accepted by Science Advances

Published
2022

23. P-ADMMiRNN: Training RNN with Stable Convergence via An Efficient and Paralleled ADMM Approach

Author

Tang, Yu, Kan, Zhigang, Sun, Dequan, Xiao, Jingjing, Lai, Zhiquan, Qiao, Linbo, and Li, Dongsheng

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

It is hard to train Recurrent Neural Network (RNN) with stable convergence and avoid gradient vanishing and exploding problems, as the weights in the recurrent unit are repeated from iteration to iteration. Moreover, RNN is sensitive to the initialization of weights and bias, which brings difficulties in training. The Alternating Direction Method of Multipliers (ADMM) has become a promising algorithm to train neural networks beyond traditional stochastic gradient algorithms with the gradient-free features and immunity to unsatisfactory conditions. However, ADMM could not be applied to train RNN directly since the state in the recurrent unit is repetitively updated over timesteps. Therefore, this work builds a new framework named ADMMiRNN upon the unfolded form of RNN to address the above challenges simultaneously. We also provide novel update rules and theoretical convergence analysis. We explicitly specify essential update rules in the iterations of ADMMiRNN with constructed approximation techniques and solutions to each sub-problem instead of vanilla ADMM. Numerical experiments are conducted on MNIST, IMDb, and text classification tasks. ADMMiRNN achieves convergent results and outperforms the compared baselines. Furthermore, ADMMiRNN trains RNN more stably without gradient vanishing or exploding than stochastic gradient algorithms. We also provide a distributed paralleled algorithm regarding ADMMiRNN, named P-ADMMiRNN, including Synchronous Parallel ADMMiRNN (SP-ADMMiRNN) and Asynchronous Parallel ADMMiRNN (AP-ADMMiRNN), which is the first to train RNN with ADMM in an asynchronous parallel manner. The source code is publicly available., Comment: 13 pages, 12 figures

Published
2020

24. Research on Evaluation Model of Road Congestion of Tourist Attraction Based on Spatial Syntax and Neural Network Method -- A Case of Gulangyu Island,Xiamen,China

Author

Su, QingMu, Xiao, JingJing, Yu, XuanHe, and Chen, iang

Subjects
Physics - Physics and Society
Abstract

In order to more accurately predict the pedestrian flow and understand the interactive relationship between tourist space and pedestrians, this paper uses spatial syntax and neural network methods to construct an evaluation model of tourist road congestion. This model makes full use of the advantages of neural network method and spatial syntax. For example, neural network method can objectively and dynamically assign the weight of attractions, and it can estimate the weight of other attractions through training. Analysis, we can clearly understand the connection relationship between roads; then we use mathematical formulas to effectively combine the road network structure and landscape attractions, which can correspond to the street network structure, the distribution of attractions and pedestrian movement The ability to estimate road congestion in low and inconsistent situations. We experimented with Gulangyu Island in Xiamen. As a result, we found that 1.the attractions of Gulangyu Island are mainly located on the edge of the island, and the attraction of several attractions that sell tickets reaches above 0.9; 2.The topological model of spatial syntax can better predict the walking results of tourists in Gulangyu Island; 3.The road accessibility and the distribution of scenic spots in Gulangyu Island have no great spatial correlation, but the model can predict the degree of road congestion To bring it closer to the truth. The results of our research can be used as a basis for future tourism space management and can enrich the research of tourism space., Comment: in Chinese

Published
2020

25. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs

Author

Liao Jun, Yang Xianwu, Yang Jiejie, Xiao Jingjing, Liu Xuyang, Zhuo Yingquan, Yang Jiafei, and Gu Huajian

Subjects
fractalkine, cx3cr1, hepatopulmonary syndrome, tube formation, macrophages, Biology (General), QH301-705.5
Abstract

This study aimed to investigate effects of pulmonary fractalkine (FKN/CX3CL1) on angiogenesis and tube formation. Tube forming capability of pulmonary vascular endothelial cells (PVECs) was evaluated. CCK-8 assay was used to evaluate proliferation of PVECs. RT-PCR assay was used to determine angiogenesis specific biomarkers. Western blot was applied to identify CX3CR1, Akt, phosphorylated Akt (p-Akt), Erk1/2, phosphorylated Erk1/2 (p-Erk1/2), vascular endothelial growth factor A (VEGFA), and inducible nitric oxide synthase (iNOS) expression. VEGF-A and platelet-derived growth factor (PDGF) levels were examined using ELISA. FKN was safe and triggered tube formation in PVECs. FKN significantly enhanced VEGF-A, PDGF, and iNOS gene transcription compared to the Control group (p < 0.05). CX3CR1 interfering (LV5-CX3CR1 shRNA) remarkably reduced CX3CR1 expression compared to those in LV5 blank group (p < 0.05). Ratios of p-Akt/Akt and p-Erk/Erk were significantly decreased in CX3CR1 shRNA-treated PVECs administered Akt inhibitor (or Erk inhibitor) and 10 ng/mL FKN compared to CX3CR1 shRNA-treated PVECs administered 10 ng/mL FKN (p < 0.05). FKN increased VEGF-A and iNOS expression through activating Akt/Erk pathway. FKN promoted VEGF-A/iNOS expression and triggered p-Akt/Akt and p-Erk/Erk pathway through modulating CX3CR1. FKN-treated macrophages enhanced activation of Akt/Erk pathway. FKN-treated macrophages enhanced PDGF and VEGF-1 expression in PVECs. FKN modulated pulmonary angiogenesis and tube formation through modulating CX3CR1 and growth factors and activating p-Akt/Akt and p-Erk/Erk signaling pathway.

Published
2023
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34. Contrast-enhanced CT radiomics for predicting lymph node metastasis in pancreatic ductal adenocarcinoma: a pilot study.

Author

Li, Ke, Yao, Qiandong, Xiao, Jingjing, Li, Meng, Yang, Jiali, Hou, Wenjing, Du, Mingshan, Chen, Kang, Qu, Yuan, Li, Lian, Li, Jing, Wang, Xianqi, Luo, Haoran, Yang, Jia, Zhang, Zhuoli, and Chen, Wei

Subjects
Humans, Carcinoma, Pancreatic Ductal, Pancreatic Neoplasms, Lymphatic Metastasis, Tomography, X-Ray Computed, Pilot Projects, Decision Support Techniques, Models, Theoretical, Aged, Middle Aged, Female, Male, CT, Lymph node metastasis, Pancreatic ductal adenocarcinoma, Radiomics, Digestive Diseases, Cancer, Biomedical Imaging, Pancreatic Cancer, Rare Diseases, Patient Safety, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Good Health and Well Being, Oncology and Carcinogenesis, Nuclear Medicine & Medical Imaging
Abstract

BACKGROUND:We developed a computational model integrating clinical data and imaging features extracted from contrast-enhanced computed tomography (CECT) images, to predict lymph node (LN) metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS:This retrospective study included 159 patients with PDAC (118 in the primary cohort and 41 in the validation cohort) who underwent preoperative contrast-enhanced computed tomography examination between 2012 and 2015. All patients underwent surgery and lymph node status was determined. A total of 2041 radiomics features were extracted from venous phase images in the primary cohort, and optimal features were extracted to construct a radiomics signature. A combined prediction model was built by incorporating the radiomics signature and clinical characteristics selected by using multivariable logistic regression. Clinical prediction models were generated and used to evaluate both cohorts. RESULTS:Fifteen features were selected for constructing the radiomics signature based on the primary cohort. The combined prediction model for identifying preoperative lymph node metastasis reached a better discrimination power than the clinical prediction model, with an area under the curve of 0.944 vs. 0.666 in the primary cohort, and 0.912 vs. 0.713 in the validation cohort. CONCLUSIONS:This pilot study demonstrated that a noninvasive radiomics signature extracted from contrast-enhanced computed tomography imaging can be conveniently used for preoperative prediction of lymph node metastasis in patients with PDAC.

Published
2020

46. MyoFold: Joint myocardial tissue composition and wall motion quantification via a highly folded sequence

Author

Guo, Rui, primary, Fan, Yingwei, additional, Liu, Bowei, additional, Qian, Xiaofeng, additional, Dai, Jiahuan, additional, Si, Dongyue, additional, Wang, Yuanyuan, additional, Wang, Ancong, additional, Dong, Guozhao, additional, Jin, Yongsheng, additional, Xiao, Jingjing, additional, Ding, Haiyan, additional, and Tang, Xiaoying, additional

Published
2024
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47. Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study

Author

Xie, Puguang, primary, Wang, Hao, additional, Xiao, Jun, additional, Xu, Fan, additional, Liu, Jingyang, additional, Chen, Zihang, additional, Zhao, Weijie, additional, Hou, Siyu, additional, Wu, Dongdong, additional, Ma, Yu, additional, and Xiao, Jingjing, additional

Published
2024
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