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195 results on '"Li, Xueqian"'

2. Probing dark matter particles from evaporating primordial black holes via electron scattering in the CDEX-10 experiment

Author

Zhang, Zhenhua, Yang, Litao, Yue, Qian, Kang, Kejun, Li, Yuanjing, An, Haipeng, Greeshma, C., Chang, Jianping, Chen, Yunhua, Cheng, Jianping, Dai, Wenhan, Deng, Zhi, Fang, Changhao, Geng, Xinping, Gong, Hui, Guo, Qiuju, Guo, Tao, Guo, Xuyuan, He, Li, He, Shengming, Hu, Jinwei, Huang, Hanxiong, Huang, Tuchen, Jiang, Lin, Karmakar, S., Li, Haubin, Li, Hanyu, Li, Jianmin, Li, Jin, Li, Qianyun, Li, Renmingjie, Li, Xueqian, Li, Yulan, Liang, Yifan, Liao, Bin, Lin, FongKay, Lin, ShinTed, Liu, Jiaxuan, Liu, Shukui, Liu, Yandong, Liu, Yu, Liu, Yuanyuan, Ma, Hao, Mao, Yucai, Nie, Qiyuan, Ning, Jinhua, Pan, Hui, Qi, Ningchun, Ren, Jie, Ruan, Xichao, Singh, Monoj Kumar, Sun, Tianxi, Tang, Changjian, Tian, Yang, Wang, Guangfu, Wang, Junzheng, Wang, Li, Wang, Qing, Wang, Yufeng, Wang, Yunxiang, Wong, Henry Tsz-King, Wu, Shiyong, Wu, Yucheng, Xing, Haoyang, Xu, Rui, Xu, Yin, Xue, Tao, Yan, Yulu, Yi, Nan, Yu, Chunxu, Yu, Haijun, Yue, Jianfeng, Zeng, Ming, Zeng, Zhi, Zhang, Bingtao, Zhang, Fengshou, Zhang, Lei, Zhang, Zhenyu, Zhao, Jizhong, Zhao, Kangkang, Zhao, Minggang, Zhou, Jifang, Zhou, Zuying, and Zhu, Jingjun

Published
2024
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29. Electron-ion collider in China

Author

Anderle, Daniele P., Bertone, Valerio, Cao, Xu, Chang, Lei, Chang, Ningbo, Chen, Gu, Chen, Xurong, Chen, Zhuojun, Cui, Zhufang, Dai, Lingyun, Deng, Weitian, Ding, Minghui, Feng, Xu, Gong, Chang, Gui, Longcheng, Guo, Feng-Kun, Han, Chengdong, He, Jun, Hou, Tie-Jiun, Huang, Hongxia, Huang, Yin, KumeričKi, KrešImir, Kaptari, L. P., Li, Demin, Li, Hengne, Li, Minxiang, Li, Xueqian, Liang, Yutie, Liang, Zuotang, Liu, Chen, Liu, Chuan, Liu, Guoming, Liu, Jie, Liu, Liuming, Liu, Xiang, Liu, Tianbo, Luo, Xiaofeng, Lyu, Zhun, Ma, Boqiang, Ma, Fu, Ma, Jianping, Ma, Yugang, Mao, Lijun, Mezrag, Cédric, Moutarde, Hervé, Ping, Jialun, Qin, Sixue, Ren, Hang, Roberts, Craig D., Rojo, Juan, Shen, Guodong, Shi, Chao, Song, Qintao, Sun, Hao, Sznajder, Paweł, Wang, Enke, Wang, Fan, Wang, Qian, Wang, Rong, Wang, Ruiru, Wang, Taofeng, Wang, Wei, Wang, Xiaoyu, Wang, Xiaoyun, Wu, Jiajun, Wu, Xinggang, Xia, Lei, Xiao, Bowen, Xiao, Guoqing, Xie, Ju-Jun, Xie, Yaping, Xing, Hongxi, Xu, Hushan, Xu, Nu, Xu, Shusheng, Yan, Mengshi, Yan, Wenbiao, Yan, Wencheng, Yan, Xinhu, Yang, Jiancheng, Yang, Yi-Bo, Yang, Zhi, Yao, Deliang, Ye, Zhihong, Yin, Peilin, Yuan, C.-P., Zhan, Wenlong, Zhang, Jianhui, Zhang, Jinlong, Zhang, Pengming, Zhang, Yifei, Chang, Chao-Hsi, Zhang, Zhenyu, Zhao, Hongwei, Chao, Kuang-Ta, Zhao, Qiang, Zhao, Yuxiang, Zhao, Zhengguo, Zheng, Liang, Zhou, Jian, Zhou, Xiang, Zhou, Xiaorong, Zou, Bingsong, and Zou, Liping

Published
2021
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36. Seismic Resilience Evaluation of Reinforced Concrete Frame Considering the Effect of Mainshock-Aftershock Sequences.

Author

Li, Peiran, Li, Xueqian, Wang, Xin, and Wang, Ding

Subjects
EARTHQUAKE aftershocks, REINFORCED concrete, MONTE Carlo method, STRUCTURAL frames, SEISMIC response
Abstract

The effect of aftershocks on the function recovery process of damaged structures after large earthquakes cannot be ignored in the resilience evaluation of buildings and communities. Based on the Monte Carlo simulation method, this paper proposes an approach to evaluate the seismic resilience of reinforced concrete frame considering the effect of mainshock-aftershock sequences. The findings of this study can provide a reference for the seismic performance evaluation of reinforced concrete frame structures under aftershocks. [ABSTRACT FROM AUTHOR]

Published
2023
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38. Large Annular Dipoles Bounded between Single‐Atom Co and Co Cluster for Clarifying Electromagnetic Wave Absorbing Mechanism.

Author

Liu, Hu, Li, Xueqian, Zhao, Xiaoying, Zhang, Min, Liu, Xinhua, Yang, Shichun, Wu, Hongjing, and Ma, Zhenhui

Subjects
*ELECTROMAGNETIC wave absorption, *ELECTROMAGNETIC waves, *MAGNETIC flux leakage, *DIPOLE moments, *DIELECTRIC loss, *ATOMIC clusters
Abstract

It is very challenging to demonstrate the intrinsic feature and absorption mechanism for electromagnetic (EM) wave absorber since dipole polarization loss is always discussed together with magnetic loss, conductive loss, defects/interfacial polarization, and so on. To address this issue, here, a kind of atomic composites is reported, including single‐atom Co and Co cluster with controllable atom dipole to tune the polarization and establish the link between dipole polarization and the EM wave absorption. Using a chemical synthesis route, the atomic composites are fabricated, including Co single‐atom (SA) sites and cluster (Cs) on nitrogen‐doped graphitic carbon (Co1+Cs/NGC). Due to the special design, the effect of magnetic loss, conductive loss, and interfacial polarization on EM wave dissipation can be ignored so that it can only highlight dielectric loss caused by dipole polarization. And, by controlling the Co atoms concentration, it can tune the valence state of Co atoms between 0 to +2 to control dipole polarization and relaxation. As a result, the Co1+Cs/NGC‐2 with Co concentration of 6.0 wt% exhibits optimized dipole moments and thus excellent absorption performance (the reflection loss exceeds −54.3 dB, and the effective absorption bandwidth with RL ≤−10 dB reaches 7.0 GHz at 2.0 mm) due to the effective dipole polarization caused by the large annular dipole bounded between Co SA sites and Co Cs. This study proposes a simplified model to clarify EM wave absorption mechanism from atom view. [ABSTRACT FROM AUTHOR]

Published
2023
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39. Fast Neural Scene Flow

Author

Li, Xueqian, Zheng, Jianqiao, Ferroni, Francesco, Pontes, Jhony Kaesemodel, and Lucey, Simon

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with dense lidar points. The approach utilizes a coordinate neural network to estimate scene flow at runtime, without any training. However, it is up to 100 times slower than current state-of-the-art learning methods. In other applications such as image, video, and radiance function reconstruction innovations in speeding up the runtime performance of coordinate networks have centered upon architectural changes. In this paper, we demonstrate that scene flow is different -- with the dominant computational bottleneck stemming from the loss function itself (i.e., Chamfer distance). Further, we rediscover the distance transform (DT) as an efficient, correspondence-free loss function that dramatically speeds up the runtime optimization. Our fast neural scene flow (FNSF) approach reports for the first time real-time performance comparable to learning methods, without any training or OOD bias on two of the largest open autonomous driving (AV) lidar datasets Waymo Open and Argoverse., 17 pages, 10 figures, 6 tables

Published
2023

40. First results on 76Ge neutrinoless double beta decay from CDEX-1 experiment

Author

Wang, Li, Yue, Qian, Kang, KeJun, Cheng, JianPing, Li, YuanJing, Wong, TszKing Henry, Lin, ShinTed, Chang, JianPing, Chen, JingHan, Chen, QingHao, Chen, YunHua, Deng, Zhi, Du, Qiang, Gong, Hui, He, Li, He, QingJu, Hu, JinWei, Huang, HanXiong, Huang, TengRui, Jia, LiPing, Jiang, Hao, Li, HauBin, Li, Hong, Li, JianMin, Li, Jin, Li, Jun, Li, Xia, Li, XueQian, Li, YuLan, Lin, FongKay, Liu, ShuKui, Ma, Hao, Ma, JingLu, Pan, XingYu, Ren, Jie, Ruan, XiChao, Shen, ManBin, Sharma, Vivek, Singh, Lakhwinder, Singh, Manoj Kumar, Singh, Manoj Kumar, Soma, Arun Kumar, Tang, ChangJian, Tang, WeiYou, Tseng, ChaoHsiung, Wang, JiMin, Wang, Qing, Wu, ShiYong, Wu, YuCheng, Xing, HaoYang, Xu, Yin, Xue, Tao, Yang, LiTao, Yang, SongWei, Yi, Nan, Yu, ChunXu, Yu, HaiJun, Zeng, WeiHe, Zeng, XiongHui, Zeng, Zhi, Zhang, Lan, Zhang, YunHua, Zhao, MingGang, Zhao, Wei, Zhou, JiFang, Zhou, ZuYing, Zhu, JingJun, Zhu, WeiBin, Zhu, ZhongHua, and CDEX Collaboration

Published
2017
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42. Performances of a prototype point-contact germanium detector immersed in liquid nitrogen for light dark matter search

Author

Jiang, Hao, Yang, LiTao, Yue, Qian, Kang, KeJun, Cheng, JianPing, Li, YuanJing, Wong, Henry Tsz-King, Ağartioğlu, M., An, HaiPeng, Chang, JianPing, Chen, JingHan, Chen, YunHua, Deng, Zhi, Du, Qiang, Gong, Hui, He, Li, Hu, JinWei, Hu, QingDong, Huang, HanXiong, Jia, LiPing, Li, HauBin, Li, Hong, Li, JianMin, Li, Jin, Li, Xia, Li, XueQian, Li, YuLan, Liao, Bin, Lin, FongKay, Lin, ShinTed, Liu, ShuKui, Liu, YanDong, Liu, YuanYuan, Liu, ZhongZhi, Ma, Hao, Ma, JingLu, Pan, Hui, Ren, Jie, Ruan, XiChao, Sevda, B., Sharma, Vivek, Shen, ManBin, Singh, Lakhwinder, Singh, Monoj Kumar, Sun, TianXi, Tang, ChangJian, Tang, WeiYou, Tian, Yang, Wang, GuangFu, Wang, JiMin, Wang, Li, Wang, Qing, Wang, Yi, Wu, ShiYong, Wu, YuCheng, Xing, HaoYang, Xu, Yin, Xue, Tao, Yang, SongWei, Yi, Nan, Yu, ChunXu, Yu, HaiJun, Yue, JianFeng, Zeng, XiongHui, Zeng, Ming, Zeng, Zhi, Zhang, FengShou, Zhang, YunHua, Zhao, MingGang, Zhou, JiFang, Zhou, ZuYing, Zhu, JingJun, Zhu, ZhongHua, and CDEX Collaboration

Published
2018
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43. Trading Positional Complexity vs. Deepness in Coordinate Networks

Author

Zheng, Jianqiao, Ramasinghe, Sameera, Li, Xueqian, and Lucey, Simon

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

It is well noted that coordinate-based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier features. Hitherto, the rationale for the effectiveness of these positional encodings has been mainly studied through a Fourier lens. In this paper, we strive to broaden this understanding by showing that alternative non-Fourier embedding functions can indeed be used for positional encoding. Moreover, we show that their performance is entirely determined by a trade-off between the stable rank of the embedded matrix and the distance preservation between embedded coordinates. We further establish that the now ubiquitous Fourier feature mapping of position is a special case that fulfills these conditions. Consequently, we present a more general theory to analyze positional encoding in terms of shifted basis functions. In addition, we argue that employing a more complex positional encoding -- that scales exponentially with the number of modes -- requires only a linear (rather than deep) coordinate function to achieve comparable performance. Counter-intuitively, we demonstrate that trading positional embedding complexity for network deepness is orders of magnitude faster than current state-of-the-art; despite the additional embedding complexity. To this end, we develop the necessary theoretical formulae and empirically verify that our theoretical claims hold in practice., arXiv admin note: substantial text overlap with arXiv:2107.02561

Published
2022

45. Research on Consumer Trust Mechanism in China's B2C E-Commerce Platform for Second-Hand Cars.

Author

Li, Xueqian, Ma, Jiaqi, Zhou, Xinyu, and Yuan, Ruixia

Abstract

The rapid development of China's e-commerce industry has led to the rise of China's second-hand car e-commerce. With the increasingly rich trust theory and the rapid development of e-commerce platforms, the issue of online consumer trust has attracted more and more scholars' attention. This paper takes China's B2C second-hand car e-commerce platforms as an example, combines the second-hand car research conclusions and consumer trust theory, and conducts a systematic study on the formation of consumer trust in second-hand car e-commerce platforms. Based on the trust of individual consumers, system environment, website/APP and platform companies, this paper explores the influencing factors of consumer trust and constructs the influencing factors model of trust formation. The empirical study was conducted by using the structural equation model and multiple regression to verify the degree of fitting of the theoretical hypothesis and the model. The research results have a certain reference value for the development of second-hand car e-commerce platforms. [ABSTRACT FROM AUTHOR]

Published
2023
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46. Exploration of risk factors of platelet transfusion refractoriness and its impact on the prognosis of hematopoietic stem cell transplantation: a retrospective study of patients with hematological diseases.

Author

Song, Xiaofei, Qi, Jiaqian, Li, Xueqian, Zhou, Meng, He, Jingyi, Chu, Tiantian, and Han, Yue

Subjects
HEMATOPOIETIC stem cell transplantation, BLOOD diseases, CHRONIC leukemia, BLOOD platelet transfusion, LYMPHOBLASTIC leukemia, CHRONIC myeloid leukemia
Abstract

Platelet transfusion refractoriness (PTR) is an intractable issue in hematological patients, which increases bleeding risks and hospitalization costs to a great extent. We reviewed 108 patients with hematological diseases including acute leukemia, myelodysplastic syndrome, aplastic anemia, and others who received allogeneic hematopoietic stem cell transplantation (HSCT) from January 2019 through December 2020. After multivariable logistic regression, we found that splenomegaly (odds ratio [OR] = 26.98, p <.001) and JAK mutation (OR = 17.32, p =.024) were independent risk factors for PTR. During the period of transplantation, patients in the PTR group had a significantly higher platelet transfusion demand, which was reflected in the increased number of platelet transfusions (10.23 ± 6.696 vs. 5.06 ± 1.904, p <.001). After multivariate adjustment, PTR turned out to be independently associated with worse overall survival (hazard ratio = 2.794, 95% confidence interval = 1.083–7.207, p =.034). In conclusion, we found that splenomegaly and JAK gene mutation were independent risk factors for PTR in patients with hematological diseases. A history of PTR prior to allo-HSCT indicates a poor prognosis. What is the context? Platelet transfusion refractoriness is a critical issue, and it greatly increases bleeding risks and hospitalization costs. Patients with hematological diseases tend to develop PTR. PTR results from immune and nonimmune factors and the latter account for 80–90%. At present, there are few studies focused on the inducing factors of PTR, and the specific mechanism is not clear. What is new? In this study, we investigated 108 patients with hematological disorders who received allogeneic HSCT from January 2019 to December 2020. We found that splenomegaly and JAK gene mutation were independent risk factors for PTR in patients with hematological diseases. PTR had a passive effect on the prognosis of patients after HSCT, as indicated by worse OS and a trend toward lower platelets after transplantation. PTR might affect megakaryocyte reconstitution after transplantation. What is the impact? This study provides evidence that hematological patients with splenomegaly should be alert to the occurrence of PTR, which often indicates a worse prognosis of transplantation. Spleen reduction and JAK inhibitors in the treatment of PTR are worth exploring. Abbreviations PLT: platelets; PTR: platelet transfusion refractoriness; HSCT: hematopoietic stem cell transplantation; OR: odds ratio; HR: hazard ratio; CI: confidence interval; IQR: interquartile range; SD: standard deviation; HLA: human leukocyte antigen; HPA: human platelet antigen; OS: overall survival; RFS: relapse free survival; PI: post-transfusion increment; PPR: percentage platelet recovery; CCI: corrected count increment; ICU: intensive care unit; AA: aplastic anemia; MDS: myelodysplastic syndrome; AML: acute myeloid leukemia; ALL: acute lymphocytic leukemia; CML: chronic myeloid leukemia; CMML: chronic myelomonocytic leukemia; MPN: myeloproliferative neoplasm; SI: splenic irradiation; Abs: antibodies; CR: complete remission; DAC: decitabine; GVHD: graft-versus-host disease; BM: bone marrow; PB: peripheral blood [ABSTRACT FROM AUTHOR]

Published
2023
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47. Neural Scene Flow Prior

Author

Li, Xueqian, Pontes, Jhony Kaesemodel, and Lucey, Simon

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

Before the deep learning revolution, many perception algorithms were based on runtime optimization in conjunction with a strong prior/regularization penalty. A prime example of this in computer vision is optical and scene flow. Supervised learning has largely displaced the need for explicit regularization. Instead, they rely on large amounts of labeled data to capture prior statistics, which are not always readily available for many problems. Although optimization is employed to learn the neural network, the weights of this network are frozen at runtime. As a result, these learning solutions are domain-specific and do not generalize well to other statistically different scenarios. This paper revisits the scene flow problem that relies predominantly on runtime optimization and strong regularization. A central innovation here is the inclusion of a neural scene flow prior, which uses the architecture of neural networks as a new type of implicit regularizer. Unlike learning-based scene flow methods, optimization occurs at runtime, and our approach needs no offline datasets -- making it ideal for deployment in new environments such as autonomous driving. We show that an architecture based exclusively on multilayer perceptrons (MLPs) can be used as a scene flow prior. Our method attains competitive -- if not better -- results on scene flow benchmarks. Also, our neural prior's implicit and continuous scene flow representation allows us to estimate dense long-term correspondences across a sequence of point clouds. The dense motion information is represented by scene flow fields where points can be propagated through time by integrating motion vectors. We demonstrate such a capability by accumulating a sequence of lidar point clouds., accepted by NeurIPS 2021 as "spotlight"

Published
2021

50. Design and construction of the BESIII detector

Author

Ablikim, M., An, Z.H., Bai, J.Z., Berger, Niklaus, Bian, J.M., Cai, X., Cao, G.F., Cao, X.X., Chang, J.F., Chen, C., Chen, G., Chen, H.C., Chen, H.X., Chen, J., Chen, J.C., Chen, L.P., Chen, P., Chen, X.H., Chen, Y.B., Chen, M.L., Chu, Y.P., Cui, X.Z., Dai, H.L., Deng, Z.Y., Dong, M.Y., Du, S.X., Du, Z.Z., Fang, J., Fu, C.D., Gao, C.S., Gong, M.Y., Gong, W.X., Gu, S.D., Guan, B.J., Guan, J., Guo, Y.N., Han, J.F., He, K.L., He, M., He, X., Heng, Y.K., Hou, Z.L., Hu, H.M., Hu, T., Huang, B., Huang, J., Huang, S.K., Huang, Y.P., Ji, Q., Ji, X.B., Ji, X.L., Jia, L.K., Jiang, L.L., Jiang, X.S., Jin, D.P., Jin, S., Jin, Y., Lai, Y.F., Lei, G.K., Li, F., Li, G., Li, H.B., Li, H.S., Li, J., Li, J.C., Li, Q.J., Li, L., Li, R.B., Li, R.Y., Li, W.D., Li, W.G., Li, X.N., Li, X.P., Li, X.R., Li, Y.R., Li, W., Lin, D.X., Liu, B.J., Liu, C.X., Liu, F., Liu, G.M., Liu, H., Liu, H.M., Liu, H.W., Liu, J.B., Liu, L.F., Liu, Q., Liu, Q.G., Liu, S.D., Liu, W.J., Liu, X., Liu, X.Z., Liu, Y., Liu, Y.J., Liu, Z.A., Liu, Z.Q., Liu, Z.X., Lu, J.G., Lu, T., Lu, Y.P., Luo, X.L., Ma, H.L., Ma, Q.M., Ma, X., Ma, X.Y., Mao, Z.P., Min, J., Mo, X.H., Nie, J., Nie, Z.D., Ping, R.G., Qian, S., Qiao, Q., Qin, G., Qin, Z.H., Qiu, J.F., Liu, R.G., Ren, Z.Y., Rong, G., Shang, L., Shen, D.L., Shen, X.Y., Sheng, H.Y., Shi, Y.F., Song, L.W., Song, W.Y., Sun, D.H., Sun, G.X., Sun, H.S., Sun, L.J., Sun, S.S., Sun, X.D., Sun, Y.Z., Sun, Z.J., Tan, J.P., Tang, S.Q., Tang, X., Tao, N., Tian, H.L., Tian, Y.R., Wan, X., Wang, D.Y., Wang, J.K., Wang, J.Z., Wang, K., Wang, K.X., Wang, L., Wang, L.J., Wang, L.S., Wang, M., Wang, N., Wang, P., Wang, P.L., Wang, Q., Wang, Y.F., Wang, Z., Wang, Z.G., Wang, Z.Y., Wei, C.L., Wei, S.J., Wen, S.P., Wu, J.J., Wu, L.H., Wu, N., Wu, Y.H., Wu, Y.M., Wu, Z., Xu, M.H., Xia, X.M., Xiang, H.S., Xie, G., Xie, X.X., Xie, Y.G., Xu, G.F., Xu, H., Xu, Q.J., Xue, J.D., Xue, L., Yan, L., Yang, G.A., Yang, H., Yang, H.X., Yang, S.M., Ye, M., Yu, B.X., Yuan, C., Yuan, C.Z., Yuan, Y., Zang, S.L., Zhang, B.X., Zhang, B.Y., Zhang, C.C., Zhang, D.H., Zhang, H.Y., Zhang, J., Zhang, J.W., Zhang, J.Y., Zhang, L.S., Zhang, M., Zhang, Q.X., Zhang, W., Zhang, X.M., Zhang, Y., Zhang, Y.H., Zhang, Y.Y., Zhang, Z.X., Zhang, S.H., Zhao, D.X., Zhao, H.S., Zhao, J.B., Zhao, J.W., Zhao, J.Z., Zhao, L., Zhao, P.P., Zhao, Y.B., Zhao, Y.D., Zheng, B., Zheng, J.P., Zheng, L.S., Zheng, Z.P., Zhou, B.Q., Zhou, G.M., Zhou, J., Zhou, L., Zhou, Z.L., Zhu, H.T., Zhu, K., Zhu, K.J., Zhu, Q.M., Zhu, X.W., Zhu, Y.S., Zhu, Z.A., Zhuang, B.A., Zou, J.H., Zou, X., Zuo, J.X., Wang, L.L., Ye, M.H., Zheng, Y.H., Qiao, C.F., Lu, X.R., Liu, H.B., Hu, J.F., Gu, Y.T., Ruan, X.D., Huang, G.M., Zeng, Y., Yan, Y.H., Chelkov, G., Boyko, I., Dedovich, D., Denysenko, I., Grishin, S., Zhemchugov, A., Xiao, Zhenjun, Ping, Jialun, Guo Chenglin Luo, Libo, Chen, Shenjian, Qi, Ming, Hu, Xiaowei, Zhang, Lei, Li, Xueqian, Yu, Chunxu, Liu, Yubin, Xu, Ye, Zhao, Minggang, Guo, Aiqiang, Guo, Yuping, He, Zhenya, Mao, Y.J., You, Z.Y., Liang, Y.T., Zhang, X.Y., Huang, X.T., Jiao, J.B., Li, X.L., Duan, M.Y., Liu, F.H., Lu, Q.W., Ning, F.P., Wang, X.D., Liang, Yongfei, Tang, Changjian, Zhang, Yiyun, Gao, Y.N., Gong, H., Shao, B.B., Harris, F.A., Kennedy, J.W., Nguyen, X., Olsen, S.L., Rosen, M., Shen, C.P., Varner, G.S., Yu, X., Zhou, Y., Liang, H., Chen, Y., Xue, J., Liu, B., Cheng, Z., Chen, H.F., Li, Cheng, Shao, M., Sun, Y.J., Yan, J., Tang, Z.B., Li, X., Jiang, L., Zhang, Z.P., Wu, J., Xu, Z.Z., Shan, Q., Xue, Z., Wang, X.L., An, Q., Liu, S.B., Guo, J.H., Feng, C.Q., Li, H., Zheng, W., Yan, H., Cao, Z., Liu, X.H., Komamiya, Sachio, Sanuki, Tomoyuki, Yamamura, Taiki, Zhao, Tianchi, and Luo, Mingxing

Published
2010
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