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Mingxing Tan
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2020 – today
- 2024
- [c45]Zhaoqi Leng, Pei Sun, Tong He, Dragomir Anguelov, Mingxing Tan:
PVTransformer: Point-to-Voxel Transformer for Scalable 3D Object Detection. ICRA 2024: 4238-4244 - [c44]Longlong Jing, Ruichi Yu, Xu Chen, Zhengli Zhao, Shiwei Sheng, Colin Graber, Qi Chen, Qinru Li, Shangxuan Wu, Han Deng, Sangjin Lee, Chris Sweeney, Qiurui He, Wei-Chih Hung, Tong He, Xingyi Zhou, Farshid Moussavi, James Guo, Yin Zhou, Mingxing Tan, Weilong Yang, Congcong Li:
STT: Stateful Tracking with Transformers for Autonomous Driving. ICRA 2024: 4442-4449 - [c43]Kan Chen, Runzhou Ge, Hang Qiu, Rami Ai-Rfou, Charles R. Qi, Xuanyu Zhou, Zoey Yang, Scott Ettinger, Pei Sun, Zhaoqi Leng, Mustafa Baniodeh, Ivan Bogun, Weiyue Wang, Mingxing Tan, Dragomir Anguelov:
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting. ICRA 2024: 4766-4773 - [i39]Longlong Jing, Ruichi Yu, Xu Chen, Zhengli Zhao, Shiwei Sheng, Colin Graber, Qi Chen, Qinru Li, Shangxuan Wu, Han Deng, Sangjin Lee, Chris Sweeney, Qiurui He, Wei-Chih Hung, Tong He, Xingyi Zhou, Farshid Moussavi, Zijian Guo, Yin Zhou, Mingxing Tan, Weilong Yang, Congcong Li:
STT: Stateful Tracking with Transformers for Autonomous Driving. CoRR abs/2405.00236 (2024) - [i38]Zhaoqi Leng, Pei Sun, Tong He, Dragomir Anguelov, Mingxing Tan:
PVTransformer: Point-to-Voxel Transformer for Scalable 3D Object Detection. CoRR abs/2405.02811 (2024) - 2023
- [j3]Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen, Minh-Thang Luong, Yonghui Wu, Mingxing Tan, Quoc V. Le:
Combined scaling for zero-shot transfer learning. Neurocomputing 555: 126658 (2023) - [c42]Sheng Li, Garrett Andersen, Tao Chen, Liqun Cheng, Julian Grady, Da Huang, Quoc V. Le, Andrew Li, Xin Li, Yang Li, Chen Liang, Yifeng Lu, Yun Ni, Ruoming Pang, Mingxing Tan, Martin Wicke, Gang Wu, Shengqi Zhu, Parthasarathy Ranganathan, Norman P. Jouppi:
Hyperscale Hardware Optimized Neural Architecture Search. ASPLOS (3) 2023: 343-358 - [c41]Zhaoqi Leng, Guowang Li, Chenxi Liu, Ekin Dogus Cubuk, Pei Sun, Tong He, Dragomir Anguelov, Mingxing Tan:
Lidar Augment: Searching for Scalable 3D LiDAR Data Augmentations. ICRA 2023: 7039-7045 - [c40]Tong He, Pei Sun, Zhaoqi Leng, Chenxi Liu, Dragomir Anguelov, Mingxing Tan:
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection. IROS 2023: 1637-1644 - [i37]Kan Chen, Runzhou Ge, Hang Qiu, Rami Ai-Rfou, Charles R. Qi, Xuanyu Zhou, Zoey Yang, Scott Ettinger, Pei Sun, Zhaoqi Leng, Mustafa Baniodeh, Ivan Bogun, Weiyue Wang, Mingxing Tan, Dragomir Anguelov:
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting. CoRR abs/2304.03834 (2023) - [i36]Tong He, Pei Sun, Zhaoqi Leng, Chenxi Liu, Dragomir Anguelov, Mingxing Tan:
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection. CoRR abs/2309.16870 (2023) - 2022
- [j2]Reza Mahjourian, Jinkyu Kim, Yuning Chai, Mingxing Tan, Ben Sapp, Dragomir Anguelov:
Occupancy Flow Fields for Motion Forecasting in Autonomous Driving. IEEE Robotics Autom. Lett. 7(2): 5639-5646 (2022) - [c39]Yingwei Li, Adams Wei Yu, Tianjian Meng, Benjamin Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Yifeng Lu, Denny Zhou, Quoc V. Le, Alan L. Yuille, Mingxing Tan:
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection. CVPR 2022: 17161-17170 - [c38]Chenxi Liu, Zhaoqi Leng, Pei Sun, Shuyang Cheng, Charles R. Qi, Yin Zhou, Mingxing Tan, Dragomir Anguelov:
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds. ECCV (21) 2022: 158-175 - [c37]Pei Sun, Mingxing Tan, Weiyue Wang, Chenxi Liu, Fei Xia, Zhaoqi Leng, Dragomir Anguelov:
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds. ECCV (10) 2022: 426-442 - [c36]Zhaoqi Leng, Shuyang Cheng, Benjamin Caine, Weiyue Wang, Xiao Zhang, Jonathon Shlens, Mingxing Tan, Dragomir Anguelov:
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds. ECCV (31) 2022: 555-572 - [c35]Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Jay Shi, Shuyang Cheng, Dragomir Anguelov:
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions. ICLR 2022 - [c34]Yanqi Zhou, Xuanyi Dong, Tianjian Meng, Mingxing Tan, Berkin Akin, Daiyi Peng, Amir Yazdanbakhsh, Da Huang, Ravi Narayanaswami, James Laudon:
Towards the Co-design of Neural Networks and Accelerators. MLSys 2022 - [i35]Reza Mahjourian, Jinkyu Kim, Yuning Chai, Mingxing Tan, Benjamin Sapp, Dragomir Anguelov:
Occupancy Flow Fields for Motion Forecasting in Autonomous Driving. CoRR abs/2203.03875 (2022) - [i34]Yingwei Li, Adams Wei Yu, Tianjian Meng, Benjamin Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Bo Wu, Yifeng Lu, Denny Zhou, Quoc V. Le, Alan L. Yuille, Mingxing Tan:
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection. CoRR abs/2203.08195 (2022) - [i33]Tianjian Meng, Golnaz Ghiasi, Reza Mahjourian, Quoc V. Le, Mingxing Tan:
Revisiting Multi-Scale Feature Fusion for Semantic Segmentation. CoRR abs/2203.12683 (2022) - [i32]Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Xiaojie Shi, Shuyang Cheng, Dragomir Anguelov:
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions. CoRR abs/2204.12511 (2022) - [i31]Chenxi Liu, Zhaoqi Leng, Pei Sun, Shuyang Cheng, Charles R. Qi, Yin Zhou, Mingxing Tan, Dragomir Anguelov:
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds. CoRR abs/2210.05018 (2022) - [i30]Pei Sun, Mingxing Tan, Weiyue Wang, Chenxi Liu, Fei Xia, Zhaoqi Leng, Dragomir Anguelov:
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds. CoRR abs/2210.07372 (2022) - [i29]Zhaoqi Leng, Shuyang Cheng, Benjamin Caine, Weiyue Wang, Xiao Zhang, Jonathon Shlens, Mingxing Tan, Dragomir Anguelov:
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds. CoRR abs/2210.13428 (2022) - [i28]Zhaoqi Leng, Guowang Li, Chenxi Liu, Ekin Dogus Cubuk, Pei Sun, Tong He, Dragomir Anguelov, Mingxing Tan:
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations. CoRR abs/2210.13488 (2022) - 2021
- [c33]Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh:
Nyströmformer: A Nyström-based Algorithm for Approximating Self-Attention. AAAI 2021: 14138-14148 - [c32]Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen:
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators. CVPR 2021: 3825-3834 - [c31]Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc V. Le, Norman P. Jouppi:
Searching for Fast Model Families on Datacenter Accelerators. CVPR 2021: 8085-8095 - [c30]Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong:
MoViNets: Mobile Video Networks for Efficient Video Recognition. CVPR 2021: 16020-16030 - [c29]Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong:
Robust and Accurate Object Detection via Adversarial Learning. CVPR 2021: 16622-16631 - [c28]Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan L. Yuille, Cihang Xie:
Shape-Texture Debiased Neural Network Training. ICLR 2021 - [c27]Mingxing Tan, Quoc V. Le:
EfficientNetV2: Smaller Models and Faster Training. ICML 2021: 10096-10106 - [c26]Arissa Wongpanich, Hieu Pham, James Demmel, Mingxing Tan, Quoc V. Le, Yang You, Sameer Kumar:
Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour. IPDPS Workshops 2021: 947-950 - [c25]Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan:
CoAtNet: Marrying Convolution and Attention for All Data Sizes. NeurIPS 2021: 3965-3977 - [i27]Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Hanxiao Liu, Gabriel Bender, Adam Kraft, Chen Liang, Quoc V. Le:
PyGlove: Symbolic Programming for Automated Machine Learning. CoRR abs/2101.08809 (2021) - [i26]Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh:
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention. CoRR abs/2102.03902 (2021) - [i25]Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc V. Le, Norman P. Jouppi:
Searching for Fast Model Families on Datacenter Accelerators. CoRR abs/2102.05610 (2021) - [i24]Yanqi Zhou, Xuanyi Dong, Berkin Akin, Mingxing Tan, Daiyi Peng, Tianjian Meng, Amir Yazdanbakhsh, Da Huang, Ravi Narayanaswami, James Laudon:
Rethinking Co-design of Neural Architectures and Hardware Accelerators. CoRR abs/2102.08619 (2021) - [i23]Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong:
MoViNets: Mobile Video Networks for Efficient Video Recognition. CoRR abs/2103.11511 (2021) - [i22]Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong:
Robust and Accurate Object Detection via Adversarial Learning. CoRR abs/2103.13886 (2021) - [i21]Mingxing Tan, Quoc V. Le:
EfficientNetV2: Smaller Models and Faster Training. CoRR abs/2104.00298 (2021) - [i20]Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan:
CoAtNet: Marrying Convolution and Attention for All Data Sizes. CoRR abs/2106.04803 (2021) - [i19]Hieu Pham, Zihang Dai, Golnaz Ghiasi, Hanxiao Liu, Adams Wei Yu, Minh-Thang Luong, Mingxing Tan, Quoc V. Le:
Combined Scaling for Zero-shot Transfer Learning. CoRR abs/2111.10050 (2021) - 2020
- [c24]Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, Quoc V. Le:
Adversarial Examples Improve Image Recognition. CVPR 2020: 816-825 - [c23]Yu Liu, Xuhui Jia, Mingxing Tan, Raviteja Vemulapalli, Yukun Zhu, Bradley Green, Xiaogang Wang:
Search to Distill: Pearls Are Everywhere but Not the Eyes. CVPR 2020: 7536-7545 - [c22]Mingxing Tan, Ruoming Pang, Quoc V. Le:
EfficientDet: Scalable and Efficient Object Detection. CVPR 2020: 10778-10787 - [c21]Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, Xiaodan Song:
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization. CVPR 2020: 11589-11598 - [c20]Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Yin Cui, Mingxing Tan, Quoc V. Le, Xiaodan Song:
Efficient Scale-Permuted Backbone with Learned Resource Distribution. ECCV (23) 2020: 572-586 - [c19]Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas S. Huang, Xiaodan Song, Ruoming Pang, Quoc Le:
BigNAS: Scaling up Neural Architecture Search with Big Single-Stage Models. ECCV (7) 2020: 702-717 - [c18]Michael S. Ryoo, A. J. Piergiovanni, Mingxing Tan, Anelia Angelova:
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures. ICLR 2020 - [c17]Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. ICML 2020: 11546-11555 - [c16]Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, Quoc Le:
PyGlove: Symbolic Programming for Automated Machine Learning. NeurIPS 2020 - [i18]Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas S. Huang, Xiaodan Song, Ruoming Pang, Quoc V. Le:
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models. CoRR abs/2003.11142 (2020) - [i17]Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen:
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators. CoRR abs/2004.14525 (2020) - [i16]Dan Kondratyuk, Mingxing Tan, Matthew Brown, Boqing Gong:
When Ensembling Smaller Models is More Efficient than Single Large Models. CoRR abs/2005.00570 (2020) - [i15]Xuanyi Dong, Mingxing Tan, Adams Wei Yu, Daiyi Peng, Bogdan Gabrys, Quoc V. Le:
AutoHAS: Differentiable Hyper-parameter and Architecture Search. CoRR abs/2006.03656 (2020) - [i14]Cihang Xie, Mingxing Tan, Boqing Gong, Alan L. Yuille, Quoc V. Le:
Smooth Adversarial Training. CoRR abs/2006.14536 (2020) - [i13]Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. CoRR abs/2007.00811 (2020) - [i12]Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan L. Yuille, Cihang Xie:
Shape-Texture Debiased Neural Network Training. CoRR abs/2010.05981 (2020) - [i11]Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Yin Cui, Mingxing Tan, Quoc V. Le, Xiaodan Song:
Efficient Scale-Permuted Backbone with Learned Resource Distribution. CoRR abs/2010.11426 (2020) - [i10]Arissa Wongpanich, Hieu Pham, James Demmel, Mingxing Tan, Quoc V. Le, Yang You, Sameer Kumar:
Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour. CoRR abs/2011.00071 (2020)
2010 – 2019
- 2019
- [c15]Mingxing Tan, Quoc V. Le:
MixConv: Mixed Depthwise Convolutional Kernels. BMVC 2019: 74 - [c14]Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le:
MnasNet: Platform-Aware Neural Architecture Search for Mobile. CVPR 2019: 2820-2828 - [c13]Andrew Howard, Ruoming Pang, Hartwig Adam, Quoc V. Le, Mark Sandler, Bo Chen, Weijun Wang, Liang-Chieh Chen, Mingxing Tan, Grace Chu, Vijay Vasudevan, Yukun Zhu:
Searching for MobileNetV3. ICCV 2019: 1314-1324 - [c12]Mingxing Tan, Quoc V. Le:
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ICML 2019: 6105-6114 - [i9]Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam:
Searching for MobileNetV3. CoRR abs/1905.02244 (2019) - [i8]Mingxing Tan, Quoc V. Le:
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. CoRR abs/1905.11946 (2019) - [i7]Michael S. Ryoo, A. J. Piergiovanni, Mingxing Tan, Anelia Angelova:
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures. CoRR abs/1905.13209 (2019) - [i6]Mingxing Tan, Quoc V. Le:
MixConv: Mixed Depthwise Convolutional Kernels. CoRR abs/1907.09595 (2019) - [i5]Mingxing Tan, Ruoming Pang, Quoc V. Le:
EfficientDet: Scalable and Efficient Object Detection. CoRR abs/1911.09070 (2019) - [i4]Yu Liu, Xuhui Jia, Mingxing Tan, Raviteja Vemulapalli, Yukun Zhu, Bradley Green, Xiaogang Wang:
Search to Distill: Pearls are Everywhere but not the Eyes. CoRR abs/1911.09074 (2019) - [i3]Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, Quoc V. Le:
Adversarial Examples Improve Image Recognition. CoRR abs/1911.09665 (2019) - [i2]Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, Xiaodan Song:
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization. CoRR abs/1912.05027 (2019) - 2018
- [i1]Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Quoc V. Le:
MnasNet: Platform-Aware Neural Architecture Search for Mobile. CoRR abs/1807.11626 (2018) - 2017
- [j1]Gai Liu, Mingxing Tan, Steve Dai, Ritchie Zhao, Zhiru Zhang:
Architecture and Synthesis for Area-Efficient Pipelining of Irregular Loop Nests. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36(11): 1817-1830 (2017) - 2015
- [c11]Ritchie Zhao, Mingxing Tan, Steve Dai, Zhiru Zhang:
Area-efficient pipelining for FPGA-targeted high-level synthesis. DAC 2015: 157:1-157:6 - [c10]Mingxing Tan, Steve Dai, Udit Gupta, Zhiru Zhang:
Mapping-Aware Constrained Scheduling for LUT-Based FPGAs. FPGA 2015: 190-199 - [c9]Mingxing Tan, Gai Liu, Ritchie Zhao, Steve Dai, Zhiru Zhang:
ElasticFlow: A Complexity-Effective Approach for Pipelining Irregular Loop Nests. ICCAD 2015: 78-85 - [c8]Mingkai Huang, Dan He, Xianhua Liu, Mingxing Tan, Xu Cheng:
An Energy-Efficient Branch Prediction with Grouped Global History. ICPP 2015: 140-149 - 2014
- [c7]Steve Dai, Mingxing Tan, Kecheng Hao, Zhiru Zhang:
Flushing-Enabled Loop Pipelining for High-Level Synthesis. DAC 2014: 76:1-76:6 - [c6]Mingxing Tan, Bin Liu, Steve Dai, Zhiru Zhang:
Multithreaded pipeline synthesis for data-parallel kernels. ICCAD 2014: 718-725 - [c5]Gai Liu, Ye Tao, Mingxing Tan, Zhiru Zhang:
CASA: correlation-aware speculative adders. ISLPED 2014: 189-194 - [c4]Shreesha Srinath, Berkin Ilbeyi, Mingxing Tan, Gai Liu, Zhiru Zhang, Christopher Batten:
Architectural Specialization for Inter-Iteration Loop Dependence Patterns. MICRO 2014: 583-595 - 2012
- [c3]Mingxing Tan, Xianhua Liu, Zichao Xie, Dong Tong, Xu Cheng:
Energy-efficient branch prediction with Compiler-guided History Stack. DATE 2012: 449-454 - [c2]Mingxing Tan, Xianhua Liu, Tong Tong, Xu Cheng:
CVP: an energy-efficient indirect branch prediction with compiler-guided value pattern. ICS 2012: 111-120 - 2010
- [c1]Jiyu Zhang, Zhiru Zhang, Sheng Zhou, Mingxing Tan, Xianhua Liu, Xu Cheng, Jason Cong:
Bit-level optimization for high-level synthesis and FPGA-based acceleration. FPGA 2010: 59-68
Coauthor Index
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