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10,733 results on '"Tang, Jun"'

106. Clinical significance of serum estradiol monitoring in women receiving adjuvant aromatase inhibitor for hormone receptor-positive early breast cancer

Author

Dai, Shuqin, Wu, Xingping, Huang, Xuefang, Li, Jibin, Wang, Xi, Wang, Shusen, Tang, Jun, Shi, Yanxia, Xie, Xiaoming, Xu, Fei, Liu, Peng, Huang, Jiajia, Xie, Xinhua, An, Xin, Chen, Meiting, Hong, Rouxi, Xia, Wen, Zheng, Qiufan, Jiang, Kuikui, Zhong, Yongyi, Yuan, Zhongyu, Huang, Yuanyuan, Bi, Xiwen, and Xue, Cong

Published
2024
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126. Vision-Language Pre-Training for Boosting Scene Text Detectors

Author

Song, Sibo, Wan, Jianqiang, Yang, Zhibo, Tang, Jun, Cheng, Wenqing, Bai, Xiang, and Yao, Cong

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Recently, vision-language joint representation learning has proven to be highly effective in various scenarios. In this paper, we specifically adapt vision-language joint learning for scene text detection, a task that intrinsically involves cross-modal interaction between the two modalities: vision and language, since text is the written form of language. Concretely, we propose to learn contextualized, joint representations through vision-language pre-training, for the sake of enhancing the performance of scene text detectors. Towards this end, we devise a pre-training architecture with an image encoder, a text encoder and a cross-modal encoder, as well as three pretext tasks: image-text contrastive learning (ITC), masked language modeling (MLM) and word-in-image prediction (WIP). The pre-trained model is able to produce more informative representations with richer semantics, which could readily benefit existing scene text detectors (such as EAST and PSENet) in the down-stream text detection task. Extensive experiments on standard benchmarks demonstrate that the proposed paradigm can significantly improve the performance of various representative text detectors, outperforming previous pre-training approaches. The code and pre-trained models will be publicly released., Comment: Accepted by CVPR 2022

Published
2022

127. Universal device for two-qubit entangled measurements via photonic quantum walks

Author

Yan, Wen-Zhe, Hou, Zhibo, Tang, Jun-Feng, Xiang, Guo-Yong, Li, Chuan-Feng, Guo, Guang-Can, and Renou, Marc-Olivier

Subjects
Quantum Physics
Abstract

Sophisticated quantum measurements are fundamental to obtain a quantum advantage in many informational problems. Here, we consider the task of guessing a direction encoded in a two-qubit pure state. We experimentally demonstrate that abstention can be used to recover optimal direction guessing (measured in terms of the fidelity and maximum likelihood scores) even from non ideal states. Our protocol uses nine-step photonic quantum walks to implement the optimal five-output two-qubit collective measurements with fidelities above 0.9850. Thanks to abstention, we obtain more than a 10-fold improvement of the direction guessing scores (in terms of deviation to the optimal guessing scores). Our work demonstrates the versatility of photonic quantum walks for implementing many-qubit sophisticated measurements., Comment: Comments welcome! 5 pages + 12 pages appendix

Published
2022

132. The evolutionary dynamics of extrachromosomal DNA in human cancers.

Author

Lange, Joshua, Rose, John, Chen, Celine, Pichugin, Yuriy, Xie, Liangqi, Tang, Jun, Hung, King, Yost, Kathryn, Shi, Quanming, Erb, Marcella, Rajkumar, Utkrisht, Wu, Sihan, Taschner-Mandl, Sabine, Bernkopf, Marie, Swanton, Charles, Liu, Zhe, Huang, Weini, Chang, Howard, Bafna, Vineet, Henssen, Anton, Werner, Benjamin, and Mischel, Paul

Subjects
Biological Evolution, DNA, Extrachromosomal Inheritance, Humans, Neoplasms, Oncogenes
Abstract

Oncogene amplification on extrachromosomal DNA (ecDNA) is a common event, driving aggressive tumor growth, drug resistance and shorter survival. Currently, the impact of nonchromosomal oncogene inheritance-random identity by descent-is poorly understood. Also unclear is the impact of ecDNA on somatic variation and selection. Here integrating theoretical models of random segregation, unbiased image analysis, CRISPR-based ecDNA tagging with live-cell imaging and CRISPR-C, we demonstrate that random ecDNA inheritance results in extensive intratumoral ecDNA copy number heterogeneity and rapid adaptation to metabolic stress and targeted treatment. Observed ecDNAs benefit host cell survival or growth and can change within a single cell cycle. ecDNA inheritance can predict, a priori, some of the aggressive features of ecDNA-containing cancers. These properties are facilitated by the ability of ecDNA to rapidly adapt genomes in a way that is not possible through chromosomal oncogene amplification. These results show how the nonchromosomal random inheritance pattern of ecDNA contributes to poor outcomes for patients with cancer.

Published
2022

133. Traditional Chinese medicine FYTF-919 (Zhongfeng Xingnao oral prescription) for the treatment of acute intracerebral haemorrhage: a multicentre, randomised, placebo-controlled, double-blind, clinical trial

Author

Anderson, Craig, Song, Lili, Guo, Jianwen, Cai, Yefeng, Zhao, Yang, Chen, Xiaoying, You, Chao, Pan, Suyue, Tang, Guanghai, Lu, Yun, Hankey, Graeme J, Al-Shahi Salman, Rustam, Wang, Jiguang, Campbell, Bruce, Chen, Chris, Hu, Rong, Li, Qiang, Li, Xian, Wan, Yingfeng, You, Shoujiang, Zhang, Congcong, Gui, Zhong, Chen, Man, Ouyang, Shuman, Zhao, Xin, Huo, Chengju, Ban, Ru, Wang, Jingru, Chen, Yajie, Zhang, Lilan, Yu, Wenjing, Wang, Borui, Dong, Le, Ning, Yi, Li, Penggang, Wu, Manli, Wang, Dou, Zheng, Yanwen, Huang, Fengyan, Ren, Zhixuan, Zhang, Jingbei, Zheng, Zhihua, Li, Kunhong, Wang, Xinwei, Li, Yongqi, Cao, Jiamin, Zhu, Huaying, Tan, Xinning, Qiu, Xinhong, Yang, Zhongkang, Lou, Zhenzhen, Xie, Tingting, Zhao, Haining, Tang, Yu, Hu, Dafeng, Cui, Wanzhen, Xie, Mingjiang, Wang, Daxiu, Wu, Xiaoshu, Zheng, Yingyi, Mao, Rui, Chen, Hao, Luo, Min, Xue, Suqin, Li, Chunji, Chen, Jiekai, Jiang, Hongtao, Jing, Hongyan, Zhang, Xu, Xu, Yingnan, Yan, Haiguang, Yang, Guang, Wang, Feilong, Lu, Xiaodi, Yan, Li, Che, Fengyuan, Liu, Zhenchuan, Xie, Zhongxiang, He, Feng, Liu, Wanqi, Zhuang, Ruixue, Su, Qi, Zhang, Xuli, Xia, Zhangyong, Xu, Guangjun, Chen, Tuanzhi, Liang, Zai, Jiang, Qingyan, Su, Guanli, Xiao, Liufu, Zhang, Xiaoying, Shi, Liu, Zheng, Wei, Zhang, Yongjian, Chen, Jiao, Yin, Xiang, Jiang, Qiuhua, Zhang, Zhenyu, Huang, Jinqing, Huang, Qianliang, Guo, Honghua, Meng, Wei, Zhang, Bin, Zhang, Yulin, Dong, Chun, Shi, Jie, Tian, Song, Deng, Xiong, Liang, Zhijun, Zhong, Haixiang, Liu, Chubao, Shao, Xiaoguang, Liu, Enzhi, Fu, Weiqi, Sheng, Wei, Chen, Minghui, Xiang, Yong, Chen, Yanping, Long, Teng, Zhang, Jie, Chang, Tinghao, Tang, Xiaoping, Zhao, Long, Wu, Junde, Hu, Yangjingtian, Li, Zheng, Yu, Rutong, Hua, Lei, Wang, Yong, Ren, Yanhong, Zhang, Haibo, Tang, Jun, Shu, Jianzhong, Luo, Chao, Wang, Gaung, Guan, Yueyue, Wan, Yue, Ke, Shangsheng, He, Dandan, Wang, Zhenyi, Lu, Chenghang, Li, Xie, Zhong, Jianbin, Zhong, Simin, Lai, Yanfang, Zhang, Shijun, Wang, Lixin, Li, Guifu, Huang, Lijin, You, Jinsong, Huang, Tao, Zhen, Zheng, Xue, Daojin, Ke, Guo, He, Qiurong, Lei, Li, Guo, Tie, Li, Hui, Pu, Yunxue, Zhu, Hailin, Yang, Gang, Zheng, Maohua, Hou, Wei, Yu, Yue, Yang, Haijun, He, Gang, Ran, Yuan, Fan, Xiaoli, Xu, Lai, Zou, Shihui, Bai, Xiaoxin, Cai, Jun, Chen, Ruicong, Li, Yinbin, Tu, Huai, Ren, Guohua, Zhao, Jiye, Wang, Yunna, Shi, Jianhui, Bai, Yafeng, Lin, Lin, Li, Dazhi, Cai, Ning, Zhang, Yonghui, Yu, Xiaochen, Jiang, Zonghua, Wang, Cong, He, Dehai, Lai, Jiahe, Yuan, Xujuan, Zhang, Zeshun, Zhao, Minqing, Qin, Defang, Huo, Yonglin, Wang, Bangxiang, Chen, Shaohong, Jiao, Xu, Li, Mingfei, Liu, Xiaoqiu, Ren, Xinwen, Ouyang, Menglu, Billot, Laurent, Wang, Xia, Wen, Zehuai, and Anderson, Craig S

Published
2024
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