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4. Multi-Semantic Interactive Learning for Object Detection

Author

Wang, Shuxin, Zheng, Zhichao, Gu, Yanhui, Zhou, Junsheng, and Chen, Yi

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Single-branch object detection methods use shared features for localization and classification, yet the shared features are not fit for the two different tasks simultaneously. Multi-branch object detection methods usually use different features for localization and classification separately, ignoring the relevance between different tasks. Therefore, we propose multi-semantic interactive learning (MSIL) to mine the semantic relevance between different branches and extract multi-semantic enhanced features of objects. MSIL first performs semantic alignment of regression and classification branches, then merges the features of different branches by semantic fusion, finally extracts relevant information by semantic separation and passes it back to the regression and classification branches respectively. More importantly, MSIL can be integrated into existing object detection nets as a plug-and-play component. Experiments on the MS COCO, and Pascal VOC datasets show that the integration of MSIL with existing algorithms can utilize the relevant information between semantics of different tasks and achieve better performance.

Published
2023

7. Synergistic passivation and stepped-dimensional perovskite analogs enable high-efficiency near-infrared light-emitting diodes

Author

Liu, Yongjie, Tao, Chen, Cao, Yu, Chen, Liangyan, Wang, Shuxin, Li, Pei, Wang, Cheng, Liu, Chenwei, Ye, Feihong, Hu, Shengyong, Xiao, Meng, Gao, Zheng, Gui, Pengbing, Yao, Fang, Dong, Kailian, Li, Jiashuai, Hu, Xuzhi, Cong, Hengjiang, Jia, Shuangfeng, Wang, Ti, Wang, Jianbo, Li, Gang, Huang, Wei, Ke, Weijun, Wang, Jianpu, and Fang, Guojia

Subjects
Affordable and Clean Energy
Abstract

Formamidinium lead iodide (FAPbI3) perovskites are promising emitters for near-infrared light-emitting diodes. However, their performance is still limited by defect-assisted nonradiative recombination and band offset-induced carrier aggregation at the interface. Herein, we introduce a couple of cadmium salts with acetate or halide anion into the FAPbI3 perovskite precursors to synergistically passivate the material defects and optimize the device band structure. Particularly, the perovskite analogs, containing zero-dimensional formamidinium cadmium iodide, one-dimensional δ-FAPbI3, two-dimensional FA2FAn-1PbnI3n+1, and three-dimensional α-FAPbI3, can be obtained in one pot and play a pivotal and positive role in energy transfer in the formamidinium iodide-rich lead-based perovskite films. As a result, the near-infrared FAPbI3-based devices deliver a maximum external quantum efficiency of 24.1% together with substantially improved operational stability. Combining our findings on defect passivation and energy transfer, we also achieve near-infrared light communication with device twins of light emitting and unprecedented self-driven detection.

Published
2022

10. Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network

Author

Wang, Shuxin, Cao, Shilei, Chai, Zhizhong, Wei, Dong, Ma, Kai, Wang, Liansheng, and Zheng, Yefeng

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Fully convolutional neural networks have made promising progress in joint liver and liver tumor segmentation. Instead of following the debates over 2D versus 3D networks (for example, pursuing the balance between large-scale 2D pretraining and 3D context), in this paper, we novelly identify the wide variation in the ratio between intra- and inter-slice resolutions as a crucial obstacle to the performance. To tackle the mismatch between the intra- and inter-slice information, we propose a slice-aware 2.5D network that emphasizes extracting discriminative features utilizing not only in-plane semantics but also out-of-plane coherence for each separate slice. Specifically, we present a slice-wise multi-input multi-output architecture to instantiate such a design paradigm, which contains a Multi-Branch Decoder (MD) with a Slice-centric Attention Block (SAB) for learning slice-specific features and a Densely Connected Dice (DCD) loss to regularize the inter-slice predictions to be coherent and continuous. Based on the aforementioned innovations, we achieve state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS) dataset. Besides, we also test our model on the ISBI 2019 Segmentation of THoracic Organs at Risk (SegTHOR) dataset, and the result proves the robustness and generalizability of the proposed method in other segmentation tasks., Comment: Published by IEEE TMI

Published
2022
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14. Observations of Forbush Decreases of cosmic ray electrons and positrons with the Dark Matter Particle Explorer

Author

Alemanno, Francesca, An, Qi, Azzarello, Philipp, Barbato, Felicia Carla Tiziana, Bernardini, Paolo, Bi, XiaoJun, Cai, MingSheng, Casilli, Elisabetta, Catanzani, Enrico, Chang, Jin, Chen, DengYi, Chen, JunLing, Chen, ZhanFang, Cui, MingYang, Cui, TianShu, Cui, YuXing, Dai, HaoTing, De Benedittis, Antonio, De Mitri, Ivan, de Palma, Francesco, Deliyergiyev, Maksym, Di Santo, Margherita, Ding, Qi, Dong, TieKuang, Dong, ZhenXing, Donvito, Giacinto, Droz, David, Duan, JingLai, Duan, KaiKai, D'Urso, Domenico, Fan, RuiRui, Fan, YiZhong, Fang, Fang, Fang, Kun, Feng, ChangQing, Feng, Lei, Fusco, Piergiorgio, Gao, Min, Gargano, Fabio, Gong, Ke, Gong, YiZhong, Guo, DongYa, Guo, JianHua, Han, ShuangXue, Hu, YiMing, Huang, GuangShun, Huang, XiaoYuan, Huang, YongYi, Ionica, Maria, Jiang, Wei, Kong, Jie, Kotenko, Andrii, Kyratzis, Dimitrios, Li, ., Lei, ShiJun, Li, WenHao, Li, WeiLiang, Li, Xiang, Li, XianQiang, Liang, YaoMing, Liu, ChengMing, Liu, Hao, Liu, Jie, Liu, ShuBin, Liu, Yang, Loparco, Francesco, Luo, ChuanNing, Ma, Miao, Ma, PengXiong, Ma, Tao, Ma, XiaoYong, Marsella, Giovanni, Mazziotta, Mario Nicola, Mo, Dan, Niu, XiaoYang, Pan, Xu, Parenti, Andrea, Peng, WenXi, Peng, XiaoYan, Perrina, Chiara, Qiao, Rui, Rao, JiaNing, Ruina, Arshia, Salinas, MariaMunoz, Shangguan, Zhi, Shen, WeiHua, Shen, ZhaoQiang, Shen, ZhongTao, Silveri, Leandro, Song, JingXing, Stolpovskiy, Mikhail, Su, Hong, Su, Meng, Sun, HaoRan, Sun, ZhiYu, Surdo, Antonio, Teng, XueJian, Tykhonov, Andrii, Wang, JinZhou, Wang, LianGuo, Wang, Shen, Wang, ShuXin, Wang, XiaoLian, Wang, Ying, Wang, YanFang, Wang, YuanZhu, Wei, DaMing, Wei, JiaJu, Wei, YiFeng, Wu, Di, Wu, Jian, Wu, LiBo, Wu, Sha Sha, Wu, Xin, Xia, ZiQing, Xu, EnHeng, Xu, HaiTao, Xu, ZhiHui, Xu, ZunLei, Xue, GuoFeng, Xu, ZiZong, Yang, HaiBo, Yang, Peng, Yang, YaQing, Yao, Hui Jun, Yu, YuHong, Yuan, GuanWen, Yuan, Qiang, Yue, Chuan, Zang, JingJing, Zhang, ShengXia, Zhang, WenZhang, Zhang, Yan, Zhang, Yi, Zhang, YongJie, Zhang, YunLong, Zhang, YaPeng, Zhang, YongQiang, Zhang, ZhiYong, Zhang, Zhe, Zhao, Cong, Zhao, HongYun, Zhao, XunFeng, Zhou, ChangYi, Zhu, Yan, Chen, Wei, Feng, Li, Luo, Xi, and Zhu, ChengRui

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

The Forbush Decrease (FD) represents the rapid decrease of the intensities of charged particles accompanied with the coronal mass ejections (CMEs) or high-speed streams from coronal holes. It has been mainly explored with ground-based neutron monitors network which indirectly measure the integrated intensities of all species of cosmic rays by counting secondary neutrons produced from interaction between atmosphere atoms and cosmic rays. The space-based experiments can resolve the species of particles but the energy ranges are limited by the relative small acceptances except for the most abundant particles like protons and helium. Therefore, the FD of cosmic ray electrons and positrons have just been investigated by the PAMELA experiment in the low energy range ($<5$ GeV) with limited statistics. In this paper, we study the FD event occurred in September, 2017, with the electron and positron data recorded by the Dark Matter Particle Explorer. The evolution of the FDs from 2 GeV to 20 GeV with a time resolution of 6 hours are given. We observe two solar energetic particle events in the time profile of the intensity of cosmic rays, the earlier and weak one has not been shown in the neutron monitor data. Furthermore, both the amplitude and recovery time of fluxes of electrons and positrons show clear energy-dependence, which is important in probing the disturbances of the interplanetary environment by the coronal mass ejections., Comment: This article is dedicated to the 72nd anniversary of People's Republic of China

Published
2021
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22. Thoughts on 'Taking the Enemy First' of Air Defense Forces

Author

Jiang, Hao, Liu, Weixin, Wang, Shuxin, Yu, Kang, 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, Long, Shengzhao, editor, and Dhillon, Balbir S., editor

Published
2023
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23. A Brief Discussion of Winning Mechanism of Intelligentized Warfare

Author

Wang, Shuxin, Wang, Yujin, Ou, Hongyan, Liu, Weixin, Li, Zhong, 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, Long, Shengzhao, editor, and Dhillon, Balbir S., editor

Published
2023
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24. An Analysis of the U.S. Red Flag Military Exercises

Author

Liu, Weixin, Jiang, Hao, Ou, Hongyan, Wang, Shuxin, Yu, Kang, 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, Long, Shengzhao, editor, and Dhillon, Balbir S., editor

Published
2023
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25. Research on Operational Effectiveness Evaluation of a Certain Type of Equipment

Author

Ou, Hongyan, Liu, Qian, Wu, Di, Wang, Junfei, Wang, Shuxin, Liu, Weixin, 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, Long, Shengzhao, editor, and Dhillon, Balbir S., editor

Published
2023
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35. LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation

Author

Wang, Shuxin, Cao, Shilei, Wei, Dong, Wang, Renzhen, Ma, Kai, Wang, Liansheng, Meng, Deyu, and Zheng, Yefeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images. The main idea is to treat one-shot segmentation as a classical atlas-based segmentation problem, where voxel-wise correspondence from the atlas to the unlabelled data is learned. Subsequently, segmentation label of the atlas can be transferred to the unlabelled data with the learned correspondence. However, since ground truth correspondence between images is usually unavailable, the learning system must be well-supervised to avoid mode collapse and convergence failure. To overcome this difficulty, we resort to the forward-backward consistency, which is widely used in correspondence problems, and additionally learn the backward correspondences from the warped atlases back to the original atlas. This cycle-correspondence learning design enables a variety of extra, cycle-consistency-based supervision signals to make the training process stable, while also boost the performance. We demonstrate the superiority of our method over both deep learning-based one-shot segmentation methods and a classical multi-atlas segmentation method via thorough experiments., Comment: Accepted to Proc. IEEE Conf. Computer Vision and Pattern Recognition 2020

Published
2020

40. ASPSCR1-TFE3 reprograms transcription by organizing enhancer loops around hexameric VCP/p97

Author

Pozner, Amir, Li, Li, Verma, Shiv Prakash, Wang, Shuxin, Barrott, Jared J., Nelson, Mary L., Yu, Jamie S. E., Negri, Gian Luca, Colborne, Shane, Hughes, Christopher S., Zhu, Ju-Fen, Lambert, Sydney L., Carroll, Lara S., Smith-Fry, Kyllie, Stewart, Michael G., Kannan, Sarmishta, Jensen, Bodrie, John, Cini M., Sikdar, Saif, Liu, Hongrui, Dang, Ngoc Ha, Bourdage, Jennifer, Li, Jinxiu, Vahrenkamp, Jeffery M., Mortenson, Katelyn L., Groundland, John S., Wustrack, Rosanna, Senger, Donna L., Zemp, Franz J., Mahoney, Douglas J., Gertz, Jason, Zhang, Xiaoyang, Lazar, Alexander J., Hirst, Martin, Morin, Gregg B., Nielsen, Torsten O., Shen, Peter S., and Jones, Kevin B.

Published
2024
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41. A multicenter clinical AI system study for detection and diagnosis of focal liver lesions

Author

Ying, Hanning, Liu, Xiaoqing, Zhang, Min, Ren, Yiyue, Zhen, Shihui, Wang, Xiaojie, Liu, Bo, Hu, Peng, Duan, Lian, Cai, Mingzhi, Jiang, Ming, Cheng, Xiangdong, Gong, Xiangyang, Jiang, Haitao, Jiang, Jianshuai, Zheng, Jianjun, Zhu, Kelei, Zhou, Wei, Lu, Baochun, Zhou, Hongkun, Shen, Yiyu, Du, Jinlin, Ying, Mingliang, Hong, Qiang, Mo, Jingang, Li, Jianfeng, Ye, Guanxiong, Zhang, Shizheng, Hu, Hongjie, Sun, Jihong, Liu, Hui, Li, Yiming, Xu, Xingxin, Bai, Huiping, Wang, Shuxin, Cheng, Xin, Xu, Xiaoyin, Jiao, Long, Yu, Risheng, Lau, Wan Yee, Yu, Yizhou, and Cai, Xiujun

Published
2024
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