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2,300 results on '"Liu, Xiaoxiao"'

1. LTRDetector: Exploring Long-Term Relationship for Advanced Persistent Threats Detection

Author

Liu, Xiaoxiao, Xu, Fan, Wang, Nan, Zhao, Qinxin, Zhang, Dalin, Zhao, Xibin, and Liu, Jiqiang

Subjects
Computer Science - Cryptography and Security
Abstract

Advanced Persistent Threat (APT) is challenging to detect due to prolonged duration, infrequent occurrence, and adept concealment techniques. Existing approaches primarily concentrate on the observable traits of attack behaviors, neglecting the intricate relationships formed throughout the persistent attack lifecycle. Thus, we present an innovative APT detection framework named LTRDetector, implementing an end-to-end holistic operation. LTRDetector employs an innovative graph embedding technique to retain comprehensive contextual information, then derives long-term features from these embedded provenance graphs. During the process, we compress the data of the system provenance graph for effective feature learning. Furthermore, in order to detect attacks conducted by using zero-day exploits, we captured the system's regular behavior and detects abnormal activities without relying on predefined attack signatures. We also conducted extensive evaluations using five prominent datasets, the efficacy evaluation of which underscores the superiority of LTRDetector compared to existing state-of-the-art techniques.

Published
2024

3. Stromal Cell-SLIT3/Cardiomyocyte-ROBO1 Axis Regulates Pressure Overload-Induced Cardiac Hypertrophy

Author

Liu, Xiaoxiao, Li, Baolei, Wang, Shuyun, Zhang, Erge, Schultz, Megan, Touma, Marlin, Da Rocha, Andre Monteiro, Evans, Sylvia M, Eichmann, Anne, Herron, Todd, Chen, Ruizhen, Xiong, Dingding, Jaworski, Alexander, Weiss, Stephen, and Si, Ming-Sing

Subjects
Biomedical and Clinical Sciences, Medical Physiology, Cardiovascular Medicine and Haematology, Genetics, Pediatric, Cardiovascular, Heart Disease, Heart Disease - Coronary Heart Disease, Aetiology, 2.1 Biological and endogenous factors, Animals, Humans, Mice, Cardiomegaly, Cells, Cultured, Disease Models, Animal, Fibrosis, Hypertrophy, Left Ventricular, Membrane Proteins, Mice, Inbred C57BL, Mice, Knockout, Myocytes, Cardiac, Nerve Tissue Proteins, Receptors, Immunologic, Ventricular Remodeling, ROBO1, axon guidance, fibroblasts, fibrosis, myocytes, cardiac, stromal cells, ventricular pressure, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundRecently shown to regulate cardiac development, the secreted axon guidance molecule SLIT3 maintains its expression in the postnatal heart. Despite its known expression in the cardiovascular system after birth, SLIT3's relevance to cardiovascular function in the postnatal state remains unknown. As such, the objectives of this study were to determine the postnatal myocardial sources of SLIT3 and to evaluate its functional role in regulating the cardiac response to pressure overload stress.MethodsWe performed in vitro studies on cardiomyocytes and myocardial tissue samples from patients and performed in vivo investigation with SLIT3 and ROBO1 (roundabout homolog 1) mutant mice undergoing transverse aortic constriction to establish the role of SLIT3-ROBO1 in adverse cardiac remodeling.ResultsWe first found that SLIT3 transcription was increased in myocardial tissue obtained from patients with congenital heart defects that caused ventricular pressure overload. Immunostaining of hearts from WT (wild-type) and reporter mice revealed that SLIT3 is secreted by cardiac stromal cells, namely fibroblasts and vascular mural cells, within the heart. Conditioned media from cardiac fibroblasts and vascular mural cells both stimulated cardiomyocyte hypertrophy in vitro, an effect that was partially inhibited by an anti-SLIT3 antibody. Also, the N-terminal, but not the C-terminal, fragment of SLIT3 and the forced overexpression of SLIT3 stimulated cardiomyocyte hypertrophy and the transcription of hypertrophy-related genes. We next determined that ROBO1 was the most highly expressed roundabout receptor in cardiomyocytes and that ROBO1 mediated SLIT3's hypertrophic effects in vitro. In vivo, Tcf21+ fibroblast and Tbx18+ vascular mural cell-specific knockout of SLIT3 in mice resulted in decreased left ventricular hypertrophy and cardiac fibrosis after transverse aortic constriction. Furthermore, α-MHC+ cardiomyocyte-specific deletion of ROBO1 also preserved left ventricular function and abrogated hypertrophy, but not fibrosis, after transverse aortic constriction.ConclusionsCollectively, these results indicate a novel role for the SLIT3-ROBO1-signaling axis in regulating postnatal cardiomyocyte hypertrophy induced by pressure overload.

Published
2024

10. Genomic and transcriptomic landscape of human gastrointestinal stromal tumors

Author

Xie, Feifei, Luo, Shuzhen, Liu, Dongbing, Lu, Xiaojing, Wang, Ming, Liu, Xiaoxiao, Jia, Fujian, Pang, Yuzhi, Shen, Yanying, Zeng, Chunling, Ma, Xinli, Tang, Daoqiang, Tu, Lin, Yang, Linxi, Cheng, Yumei, Luo, Yuxiang, Xie, Fanfan, Hou, Hao, Huang, Tao, Ni, Bo, Zhuang, Chun, Zhao, Wenyi, Li, Ke, Zheng, Xufen, Bi, Wenbo, Jia, Xiaona, He, Yi, Wang, Simin, Cao, Hui, Wu, Kui, and Wang, Yuexiang

Published
2024
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18. BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Author

Manubens-Gil, Linus, Zhou, Zhi, Chen, Hanbo, Ramanathan, Arvind, Liu, Xiaoxiao, Liu, Yufeng, Bria, Alessandro, Gillette, Todd, Ruan, Zongcai, Yang, Jian, Radojević, Miroslav, Zhao, Ting, Cheng, Li, Qu, Lei, Liu, Siqi, Bouchard, Kristofer E, Gu, Lin, Cai, Weidong, Ji, Shuiwang, Roysam, Badrinath, Wang, Ching-Wei, Yu, Hongchuan, Sironi, Amos, Iascone, Daniel Maxim, Zhou, Jie, Bas, Erhan, Conde-Sousa, Eduardo, Aguiar, Paulo, Li, Xiang, Li, Yujie, Nanda, Sumit, Wang, Yuan, Muresan, Leila, Fua, Pascal, Ye, Bing, He, Hai-yan, Staiger, Jochen F, Peter, Manuel, Cox, Daniel N, Simonneau, Michel, Oberlaender, Marcel, Jefferis, Gregory, Ito, Kei, Gonzalez-Bellido, Paloma, Kim, Jinhyun, Rubel, Edwin, Cline, Hollis T, Zeng, Hongkui, Nern, Aljoscha, Chiang, Ann-Shyn, Yao, Jianhua, Roskams, Jane, Livesey, Rick, Stevens, Janine, Liu, Tianming, Dang, Chinh, Guo, Yike, Zhong, Ning, Tourassi, Georgia, Hill, Sean, Hawrylycz, Michael, Koch, Christof, Meijering, Erik, Ascoli, Giorgio A, and Peng, Hanchuan

Subjects
Biological Sciences, Bioengineering, Networking and Information Technology R&D (NITRD), Neurosciences, Benchmarking, Microscopy, Imaging, Three-Dimensional, Neurons, Algorithms, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

Published
2023

19. Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Author

Manubens-Gil, Linus, Zhou, Zhi, Chen, Hanbo, Ramanathan, Arvind, Liu, Xiaoxiao, Liu, Yufeng, Bria, Alessandro, Gillette, Todd, Ruan, Zongcai, Yang, Jian, Radojević, Miroslav, Zhao, Ting, Cheng, Li, Qu, Lei, Liu, Siqi, Bouchard, Kristofer E., Gu, Lin, Cai, Weidong, Ji, Shuiwang, Roysam, Badrinath, Wang, Ching-Wei, Yu, Hongchuan, Sironi, Amos, Iascone, Daniel Maxim, Zhou, Jie, Bas, Erhan, Conde-Sousa, Eduardo, Aguiar, Paulo, Li, Xiang, Li, Yujie, Nanda, Sumit, Wang, Yuan, Muresan, Leila, Fua, Pascal, Ye, Bing, He, Hai-yan, Staiger, Jochen F., Peter, Manuel, Cox, Daniel N., Simonneau, Michel, Oberlaender, Marcel, Jefferis, Gregory, Ito, Kei, Gonzalez-Bellido, Paloma, Kim, Jinhyun, Rubel, Edwin, Cline, Hollis T., Zeng, Hongkui, Nern, Aljoscha, Chiang, Ann-Shyn, Yao, Jianhua, Roskams, Jane, Livesey, Rick, Stevens, Janine, Liu, Tianming, Dang, Chinh, Guo, Yike, Zhong, Ning, Tourassi, Georgia, Hill, Sean, Hawrylycz, Michael, Koch, Christof, Meijering, Erik, Ascoli, Giorgio A., and Peng, Hanchuan

Published
2024
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20. Philosophical Reflections on Artificial Intelligence Reshaping the 'Field' of Physical Education Teaching

Author

Liu, Xiaoxiao, Kamalden, Tengku Fadilah Tengku, Chao, Mengyao, Lovell, Nigel H., Advisory Editor, Oneto, Luca, Advisory Editor, Piotto, Stefano, Advisory Editor, Rossi, Federico, Advisory Editor, Samsonovich, Alexei V., Advisory Editor, Babiloni, Fabio, Advisory Editor, Liwo, Adam, Advisory Editor, Magjarevic, Ratko, Advisory Editor, Mohamed, Zulkifli, editor, Ngali, Mohd Zamani, editor, Sudin, Suhizaz, editor, Ibrahim, Mohamad Fauzi, editor, and Casson, Alexander, editor

Published
2024
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21. Analysis of Knee Injuries in Tai Ji Quan Athletes Based on Selective Functional Movement Assessment Perspective

Author

Liu, Xiaoxiao, Kamalden, Tengku Fadilah Tengku, Chao, Mengyao, Lovell, Nigel H., Advisory Editor, Oneto, Luca, Advisory Editor, Piotto, Stefano, Advisory Editor, Rossi, Federico, Advisory Editor, Samsonovich, Alexei V., Advisory Editor, Babiloni, Fabio, Advisory Editor, Liwo, Adam, Advisory Editor, Magjarevic, Ratko, Advisory Editor, Mohamed, Zulkifli, editor, Ngali, Mohd Zamani, editor, Sudin, Suhizaz, editor, Ibrahim, Mohamad Fauzi, editor, and Casson, Alexander, editor

Published
2024
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23. AMCNet: Adaptive Matching Constraint for Unsupervised Point Cloud Registration

Author

Yu, Feng, Xiao, Zhuohan, Chen, Zhaoxiang, Liu, Li, Jiang, Minghua, Liu, Xiaoxiao, Hu, Xinrong, Peng, Tao, 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, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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24. Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report

Author

Conde, Marcos V., Timofte, Radu, Huang, Yibin, Peng, Jingyang, Chen, Chang, Li, Cheng, Pérez-Pellitero, Eduardo, Song, Fenglong, Bai, Furui, Liu, Shuai, Feng, Chaoyu, Wang, Xiaotao, Lei, Lei, Zhu, Yu, Li, Chenghua, Jiang, Yingying, A, Yong, Wang, Peisong, Leng, Cong, Cheng, Jian, Liu, Xiaoyu, Yin, Zhicun, Zhang, Zhilu, Li, Junyi, Liu, Ming, Zuo, Wangmeng, Jiang, Jun, Kim, Jinha, Zhang, Yue, Zou, Beiji, Zong, Zhikai, Liu, Xiaoxiao, Vega, Juan Marín, Sloth, Michael, Schneider-Kamp, Peter, Röttger, Richard, Kınlı, Furkan, Özcan, Barış, Kıraç, Furkan, Leyi, Li, Uddin, SM Nadim, Ghosh, Dipon Kumar, and Jung, Yong Ju

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

Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level vision tasks operate in the RAW domain (e.g. image denoising, white balance) due to its linear relationship with the scene irradiance, wide-range of information at 12bits, and sensor designs. Despite this, RAW image datasets are scarce and more expensive to collect than the already large and public RGB datasets. This paper introduces the AIM 2022 Challenge on Reversed Image Signal Processing and RAW Reconstruction. We aim to recover raw sensor images from the corresponding RGBs without metadata and, by doing this, "reverse" the ISP transformation. The proposed methods and benchmark establish the state-of-the-art for this low-level vision inverse problem, and generating realistic raw sensor readings can potentially benefit other tasks such as denoising and super-resolution., Comment: ECCV 2022 Advances in Image Manipulation (AIM) workshop

Published
2022

30. NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Author

Li, Yawei, Zhang, Kai, Timofte, Radu, Van Gool, Luc, Kong, Fangyuan, Li, Mingxi, Liu, Songwei, Du, Zongcai, Liu, Ding, Zhou, Chenhui, Chen, Jingyi, Han, Qingrui, Li, Zheyuan, Liu, Yingqi, Chen, Xiangyu, Cai, Haoming, Qiao, Yu, Dong, Chao, Sun, Long, Pan, Jinshan, Zhu, Yi, Zong, Zhikai, Liu, Xiaoxiao, Hui, Zheng, Yang, Tao, Ren, Peiran, Xie, Xuansong, Hua, Xian-Sheng, Wang, Yanbo, Ji, Xiaozhong, Lin, Chuming, Luo, Donghao, Tai, Ying, Wang, Chengjie, Zhang, Zhizhong, Xie, Yuan, Cheng, Shen, Luo, Ziwei, Yu, Lei, Wen, Zhihong, Wu1, Qi, Li, Youwei, Fan, Haoqiang, Sun, Jian, Liu, Shuaicheng, Huang, Yuanfei, Jin, Meiguang, Huang, Hua, Liu, Jing, Zhang, Xinjian, Wang, Yan, Long, Lingshun, Li, Gen, Zhang, Yuanfan, Cao, Zuowei, Sun, Lei, Alexander, Panaetov, Wang, Yucong, Cai, Minjie, Wang, Li, Tian, Lu, Wang, Zheyuan, Ma, Hongbing, Liu, Jie, Chen, Chao, Cai, Yidong, Tang, Jie, Wu, Gangshan, Wang, Weiran, Huang, Shirui, Lu, Honglei, Liu, Huan, Wang, Keyan, Chen, Jun, Chen, Shi, Miao, Yuchun, Huang, Zimo, Zhang, Lefei, Ayazoğlu, Mustafa, Xiong, Wei, Xiong, Chengyi, Wang, Fei, Li, Hao, Wen, Ruimian, Yang, Zhijing, Zou, Wenbin, Zheng, Weixin, Ye, Tian, Zhang, Yuncheng, Kong, Xiangzhen, Arora, Aditya, Zamir, Syed Waqas, Khan, Salman, Hayat, Munawar, Khan, Fahad Shahbaz, Ning, Dandan Gaoand Dengwen Zhouand Qian, Tang, Jingzhu, Huang, Han, Wang, Yufei, Peng, Zhangheng, Li, Haobo, Guan, Wenxue, Gong, Shenghua, Li, Xin, Liu, Jun, Wang, Wanjun, Zhou, Dengwen, Zeng, Kun, Lin, Hanjiang, Chen, Xinyu, and Fang, Jinsheng

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

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of $\times$4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution., Comment: Validation code of the baseline model is available at https://github.com/ofsoundof/IMDN. Validation of all submitted models is available at https://github.com/ofsoundof/NTIRE2022_ESR

Published
2022

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