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arXiv 2022年

作者： 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 Wu, 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 Ayazoglu, 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 Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

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 ×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. © 2022, CC BY.

关键词： Chemical activation

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P-99: Field Emission Properties of RNA-Carbon Nanotube Hybrid Film Using a Spray Method

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SID Symposium Digest of Technical Papers 2012年第1期40卷

作者： Woo-Sung Cho Yang Doo Lee Jung-Wan Yu Byeong- Kwon Ju Yong Churl Kim Jung-Ho Park Display and Nanosystem Lab. College of Engineering Korea University Anam-dong Seongbuk-gu Seoul 136-701 Republic of Korea Display Device and Material Lab. Samsung Advanced Institute of Technology Suwon 440-600 Republic of Korea Department of Electronics and Computer Engineering Korea University Anam-dong Seongbuk-gu Seoul 136-701 Republic of Korea

Singlewalled carbon nanotubes (SWNTs)-based field emitters are made by use of a spray method. They were dispersed in transfer-ribonucleic acid (t-RNA) aqueous solution and sprayed onto a photoresist-masked indium tin oxide glass substrate. After left-off process, well-defined t-RNA-SWNT hybrids pattern array is obtained and they were found to be tightly adhered to the ITO electrode. Good field emission properties were exhibited.

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Corrigendum to “Identification of autism spectrum disorder using multi-regional resting-state data through an attention learning approach” [Biomed. Signal Process. Control 69 (2021) 102833]

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Biomedical Signal Processing and Control 2022年 71卷

作者： Yaya Liu Lingyu Xu Jie Yu Jun Li Xuan Yu School of Computer Engineering and Science Shanghai University Shanghai 200072 China South China Academy of Advanced Optoelectronics South China Normal University Guangzhou 510006 China Key Lab for Behavioral Economic Science and Technology South China Normal University Guangzhou China

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Learnable low-rank latent dictionary for subspace clustering

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Pattern Recognition 2021年 120卷

作者： Yesong Xu Shuo Chen Jun Li Lei Luo Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu 210094 China RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan JD Finance America Corporation USA

Recently, Self-Expressive-based Subspace Clustering (SESC) has been widely applied in pattern clustering and machine learning as it aims to learn a representation that can faithfully reflect the correlation between data points. However, most existing SESC methods directly use the original data as the dictionary, which miss the intrinsic structure (e.g., low-rank and nonlinear) of the real-word data. To address this problem, we propose a novel Projection Low-Rank Subspace Clustering (PLRSC) method by integrating feature extraction and subspace clustering into a unified framework. In particular, PLRSC learns a projection transformation to extract the low-dimensional features and utilizes a low-rank regularizer to ensure the informative and important structures of the extracted features. The extracted low-rank features effectively enhance the self-expressive property of the dictionary. Furthermore, we extend PLRSC to a nonlinear version (i.e., NPLRSC) by integrating a nonlinear activator into the projection transformation. NPLRSC cannot only effectively extract features but also guarantee the data structure of the extracted features. The corresponding optimization problem is solved by the Alternating Direction Method (ADM), and we also prove that the algorithm converges to a stationary point. Experimental results on the real-world datasets validate the superior of our model over the existing subspace clustering methods.

关键词： Subspace clustering Low-rank Feature extraction Block diagonal representation

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

arXiv

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arXiv 2023年

作者： Teng, Siyu Hu, Xuemin Deng, Peng Li, Bai Li, Yuchen Yang, Dongsheng Ai, Yunfeng Li, Lingxi Xuanyuan, Zhe Zhu, Fenghua Chen, Long BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon 999077 Hong Kong The School of Computer Science and Information Engineering Hubei University Wuhan430062 China The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body China The College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China University of Chinese Academy of Sciences Beijing100049 China The School of Public Management/Emergency Management Jinan University Guangzhou510632 China Indianapolis United States Guangdong provincial key lab of IRADS BNU-HKBU United International College Zhuhai519087 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd. China

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This paper reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Electrocatalysis: Topotactic engineering of Ultrathin 2D Nonlayered Nickel Selenides for Full Water Electrolysis (Adv. Energy Mater. 14/2018)

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advanced Energy Materials 2018年第14期8卷

作者： Hao Wu Xin Lu Gengfeng Zheng Ghim Wei Ho Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Singapore Lab of Advanced Materials Department of Chemistry Fudan University Shanghai 200433 China Engineering Science Programme National University of Singapore 9 Engineering Drive 1 Singapore 117575 Singapore Institute of Materials Research and Engineering A*STAR (Agency for Science Technology and Research) 3 Research Link Singapore 117602 Singapore

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MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results 17th

MIPI 2022 Challenge on Under-Display Camera Image Restorati...

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17th European Conference on computer Vision, ECCV 2022

作者： Feng, Ruicheng Li, Chongyi Zhou, Shangchen Sun, Wenxiu Zhu, Qingpeng Jiang, Jun Yang, Qingyu Loy, Chen Change Gu, Jinwei Zhu, Yurui Wang, Xi Fu, Xueyang Hu, Xiaowei Hu, Jinfan Liu, Xina Chen, Xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, Xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, Xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie Xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Jiang, Biao Ding, Yuhan Li, WanZhang Feng, Xiaoyue Chen, Sijing Zhong, Tianheng Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, Xiang Nanyang Technological University Singapore Singapore SenseBrain San Jose United States Shanghai AI Laboratory Shanghai China SenseTime Research and Tetras.AI Beijing China University of Science and Technology of China Hefei China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology Hefei China School of Artificial Intelligence Xidian University Xi’an China School of Remote Sensing and Information Engineering Wuhan University Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Harbin Institute of Technology Harbin China University of Macau China Changsha Research Institute of Mining and Metallurgy Changsha China Tianjin University Tianjin China Computer Vision Lab University of Wurzburg Würzburg Germany Couger Inc. Tokyo Japan School of Statistics and Data Science Nankai University Tianjin China Founding Minds Software Thiruvananthapuram India Department of Electronics and Communication SRM University AP Amaravati India Fudan University Shanghai China Shenyang Aerospace University Shenyang China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783031250712

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image reconstruction

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AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results 16th

AIM 2020 Challenge on Efficient Super-Resolution: Methods an...

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Workshops held at the 16th European Conference on computer Vision, ECCV 2020

作者： Zhang, Kai Danelljan, Martin Li, Yawei Timofte, Radu Liu, Jie Tang, Jie Wu, Gangshan Zhu, Yu He, Xiangyu Xu, Wenjie Li, Chenghua Leng, Cong Cheng, Jian Wu, Guangyang Wang, Wenyi Liu, Xiaohong Zhao, Hengyuan Kong, Xiangtao He, Jingwen Qiao, Yu Dong, Chao Luo, Xiaotong Chen, Liang Zhang, Jiangtao Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Li, Xiaochuan Lang, Zhiqiang Nie, Jiangtao Wei, Wei Zhang, Lei Muqeet, Abdul Hwang, Jiwon Yang, Subin Kang, JungHeum Bae, Sung-Ho Kim, Yongwoo Qu, Yanyun Jeon, Geun-Woo Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Marty, Steven Marty, Eric Xiong, Dongliang Chen, Siang Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Wang, Haicheng Bhaskara, Vineeth Levinshtein, Alex Tsogkas, Stavros Jepson, Allan Kong, Xiangzhen Zhao, Tongtong Zhao, Shanshan Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Nan, Nan Liu, Shuai Cai, Jie Meng, Zibo Ding, Jiaming Ho, Chiu Man Wang, Xuehui Yan, Qiong Zhao, Yuzhi Chen, Long Sun, Long Wang, Wenhao Liu, Zhenbing Lan, Rushi Umer, Rao Muhammad Micheloni, Christian Computer Vision Lab ETH Zurich Zürich Switzerland State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China MAICRO Nanjing China University of Electronic Science and Technology of China Nanjing China McMaster University Hamilton Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China Xiamen University Xiamen China Indian Institute of Technology Madras Chennai India Nanjing University of Aeronautics and Astronautics Nanjing China School of Computer Science Northwestern Polytechnical University Xi’an China Kyung Hee University Seoul Korea Republic of Sang Myung University Seoul Korea Republic of Yonsei University Seoul Korea Republic of ETH Zurich Zürich Switzerland Zhejiang University Hangzhou China Shandong China Xidian University Xi’an China Samsung AI Centre Toronto Canada Wuhan China Dalian Maritime University Dalian China China Everbright Bank Co. Ltd. Beijing China College of Engineering Trivandrum India Jinan China InnoPeak Technology Inc. Palo Alto United States SenseTime Research Hong Kong China Sun Yat-sen University Guangzhou China City University of Hong Kong Kowloon Hong Kong Guilin University of Electronic Technology Guilin541004 China University of Udine Udine Italy

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor × 4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution. © 2020, Springer Nature Switzerland AG.

关键词： Optical resolving power

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Dipole-matter interactions governed by the asymmetry of Maxwell equations

arXiv

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arXiv 2023年

作者： Zhong, Yuhan Wang, Chan Bian, Chenxu Chen, Xuhuinan Chen, Jialin Zhu, Xingjian Hu, Hao Low, Tony Chen, Hongsheng Zhang, Baile Lin, Xiao Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy at Zhejiang University Zhejiang University Haining314400 China Jinhua Institute of Zhejiang University Zhejiang University Jinhua321099 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa32000 Israel School of Physics Zhejiang University Hangzhou310027 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Department of Physics University of Minnesota MinneapolisMN55455 United States Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing312000 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore637371 Singapore

Directionally molding the near-field and far-field radiation lies at the heart of nanophotonics and is crucial for applications such as on-chip information processing and chiral quantum networks. The most fundamental model for radiating structures is a dipolar source located inside a homogeneous matter. However, the influence of matter on the directionality of dipolar radiation is oftentimes overlooked, especially for the near-field radiation. We show that the dipole-matter interaction is intrinsically asymmetric and does not fulfill the duality principle, originating from the inherent asymmetry of Maxwell’s equations, i.e., electric charge and current are ubiquitous but their magnetic counterparts are non-existent to elusive. Moreover, we find that the asymmetric dipole-matter interaction could offer an enticing route to reshape the directionality of not only the near-field radiation but also the far-field radiation. As an example, both the near-field and far-field radiation directionality of Huygens dipole (located close to a dielectric-metal interface) would be reversed, if the dipolar position is changed from the dielectric region to the metal region. © 2023, CC BY.

关键词： Maxwell equations

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