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arXiv 2024年

作者： Pan, Gang Wang, Chen Sui, Zhijie Guo, Shuai Lv, Yaozhi Li, Honglie Sun, Di Xia, Zixia College of Intelligence and Computing Tianjin University Tianjin China Department of Municipal Engineering Hefei University of Technology Anhui China Key laboratory of Infrastructure Durability Tianjin Municipal Engineering Design and Research Institute Tianjin China West-East Gas Pipeline Company PipeChina Shanghai China College of Artificial Intelligence Tianjin University of Science and Technology Tianjin China Donald Bren School of Information and Computer Sciences University of California IrvineCA United States

The Quick-view (QV) technique serves as a primary method for detecting defects within sewerage systems. However, the effectiveness of QV is impeded by the limited visual range of its hardware, resulting in suboptimal image quality for distant portions of the sewer network. Image super-resolution is an effective way to improve image quality and has been applied in a variety of scenes. However, research on super-resolution for sewer images remains considerably unexplored. In response, this study leverages the inherent depth relationships present within QV images and introduces a novel Depth-guided, Reference-based Super-Resolution framework denoted as DSRNet. It comprises two core components: a depth extraction module and a depth information matching module (DMM). DSRNet utilizes the adjacent frames of the low-resolution image as reference images and helps them recover texture information based on the correlation. By combining these modules, the integration of depth priors significantly enhances both visual quality and performance benchmarks. Besides, in pursuit of computational efficiency and compactness, a super-resolution knowledge distillation model based on an attention mechanism is introduced. This mechanism facilitates the acquisition of feature similarity between a more complex teacher model and a streamlined student model, with the latter being a lightweight version of DSRNet. Experimental results demonstrate that DSRNet significantly improves PSNR and SSIM compared with other methods. This study also conducts experiments on sewer defect semantic segmentation, object detection, and classification on the Pipe dataset and Sewer-ML dataset. Experiments show that the method can improve the performance of low-resolution sewer images in these tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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MemX: An Atention-Aware Smart Eyewear System for Personalized Moment Auto-capture

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021年第2期5卷

作者： Chang, Yuhu Zhao, Yingying Dong, Mingzhi Wang, Yujiang Lu, Yutian Lv, Qin Dick, Robert P. Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai200438 China Shanghai Key Laboratory of Data Science Fudan University Shanghai200438 China Department of Computing Imperial College London London United Kingdom University of Colorado Boulder BoulderCO United States University of Michigan Ann ArborMI United States

This work presents MemX: a biologically-inspired attention-aware eyewear system developed with the goal of pursuing the long-awaited vision of a personalized visual Memex. MemX captures human visual attention on the fly, analyzes the salient visual content, and records moments of personal interest in the form of compact video snippets. Accurate attentive scene detection and analysis on resource-constrained platforms is challenging because these tasks are computation and energy intensive. We propose a new temporal visual attention network that unifies human visual attention tracking and salient visual content analysis. Attention tracking focuses computation-intensive video analysis on salient regions, while video analysis makes human attention detection and tracking more accurate. Using the YouTube-VIS dataset and 30 participants, we experimentally show that MemX significantly improves the attention tracking accuracy over the eye-tracking-alone method, while maintaining high system energy efficiency. We have also conducted 11 in-field pilot studies across a range of daily usage scenarios, which demonstrate the feasibility and potential benefits of MemX. © 2021 ACM.

关键词： Energy efficiency

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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LET: Linguistic knowledge enhanced graph transformer for Chinese short text matching

arXiv

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arXiv 2021年

作者： Lyu, Boer Chen, Lu Zhu, Su Yu, Kai State Key Laboratory of Media Convergence Production Technology and Systems MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Chinese short text matching is a fundamental task in natural language processing. Existing approaches usually take Chinese characters or words as input tokens. They have two limitations: 1) Some Chinese words are polysemous, and semantic information is not fully utilized. 2) Some models suffer potential issues caused by word segmentation. Here we introduce HowNet as an external knowledge base and propose a Linguistic knowledge Enhanced graph Transformer (LET) to deal with word ambiguity. Additionally, we adopt the word lattice graph as input to maintain multi-granularity information. Our model is also complementary to pre-trained language models. Experimental results on two Chinese datasets show that our models outperform various typical text matching approaches. Ablation study also indicates that both semantic information and multi-granularity information are important for text matching modeling. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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FAST discovery of an extremely radio-faint millisecond pulsar from the Fermi-LAT unassociated source 3FGL J0318.1+0252

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science China(Physics,Mechanics & Astronomy) 2021年第12期64卷 10-17页

作者： Pei Wang Di Li Colin J.Clark Pablo M.Saz Parkinson Xian Hou Weiwei Zhu Lei Qian Youling Yue Zhichen Pan Zhijie Liu Xuhong Yu Shanping You Xiaoyao Xie Qijun Zhi Hui Zhang Jumei Yao Jun Yan Chengmin Zhang Kwok Lung Fan Paul S.Ray Matthew Kerr David A.Smith Peter F.Michelson Elizabeth C.Ferrara David J.Thompson Zhiqiang Shen Na Wang FAST&Fermi-LAT Collaboration CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Jodrell Bank Centre for Astrophysics Department of Physics and AstronomyUniversity of ManchesterManchester M139PLUK Department of Physics and Laboratory for Space Research University of Hong KongHong Kong 999077China Santa Cruz Institute for Particle Physics University of CaliforniaSanta Cruz CA 95064USA Yunnan Observatories Chinese Academy of SciencesKunming 650216China Key Laboratory for the Structure and Evolution of Celestial Objects Chinese Academy of SciencesKunming 650216China School of Computer Science and Technology Key Laboratory of Information and Computing Guizhou ProvinceGuizhou Normal UniversityGuiyang 550001China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001China Physics Department Stanford UniversityStanford CA 94305USA UMCP/CRESST/GSFC NASAGreenbeltMaryland MD 20771USA GSFC NASAGreenbeltMaryland MD 20771USA Centre d’´Etudes Nucl´eaires de Bordeaux Gradignan IN2P3/CNRSUniversit´e BordeauxGradignan 33175France Space Science Division U.S.Naval Research LaboratoryWashington DC 20375-5352USA Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai 200030China Xinjiang Astronomical Observatory Urumqi 830011China Department of Physics University of MarylandCollege Park MD 20742USA NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-NatalDurban 4000South Africa 不详

High sensitivity radio searches of unassociated γ-ray sources have proven to be an effective way of finding new pulsars. Using the Five-hundred-meter Aperture Spherical radio Telescope(FAST) during its commissioning phase, we have carried out a number of targeted deep searches of Fermi Large Area Telescope(LAT) γ-ray sources. On February 27, 2018 we discovered an isolated millisecond pulsar(MSP), PSR J0318+0253, coincident with the unassociated γ-ray source 3 FGL J0318.1+0252. PSR J0318+0253 has a spin period of 5.19 ms, a dispersion measure(DM) of 26 pc cm-3 corresponding to a DM distance of about 1.3 kpc, and a period-averaged flux density of(~11±2) μJy at L-band(1.05-1.45 GHz). Among all high energy MSPs, PSR J0318+0253 is the faintest ever detected in radio bands, by a factor of at least ~4 in terms of L-band fluxes. With the aid of the radio ephemeris, an analysis of 9.6 years of Fermi-LAT data revealed that PSR J0318+0253 also displays strong γ-ray pulsations. Follow-up observations carried out by both Arecibo and FAST suggest a likely spectral turn-over around 350 MHz. This is the first result from the collaboration between FAST and the Fermi-LAT teams as well as the first confirmed new MSP discovery by FAST, raising hopes for the detection of many more MSPs. Such discoveries will make a significant contribution to our understanding of the neutron star zoo while potentially contributing to the future detection of gravitational waves, via pulsar timing array(PTA) experiments.

关键词： FAST pulsar radio gamma rays

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Task Offloading with Multi-Tier computing Resources in Next Generation Wireless Networks

arXiv

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arXiv 2022年

作者： Wang, Kunlun Jin, Jiong Yang, Yang Zhang, Tao Nallanathan, Arumugam Tellambura, Chintha Jabbari, Bijan The Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China The School of Communication and Electronic Engineering East China Normal University Shanghai200241 China The School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia Terminus Group Beijing100027 China ShanghaiTech University Shanghai201210 China Peng Cheng Laboratory Shenzhen518055 China United States The School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom The Department of Electrical and Computer Engineering University of Alberta Canada The Department of Electrical and Computer Engineering George Mason University FairfaxVA United States

With the development of next-generation wireless networks, the Internet of Things (IoT) is evolving towards the intelligent IoT (iIoT), where intelligent applications usually have stringent delay and jitter requirements. In order to provide low-latency services to heterogeneous users in the emerging iIoT, multi-tier computing was proposed by effectively combining edge computing and fog computing. More specifically, multi-tier computing systems compensate for cloud computing through task offloading and dispersing computing tasks to multi-tier nodes along the continuum from the cloud to things. In this paper, we investigate key techniques and directions for wireless communications and resource allocation approaches to enable task offloading in multi-tier computing systems. A multi-tier computing model, with its main functionality and optimization methods, is presented in details. We hope that this paper will serve as a valuable reference and guide to the theoretical, algorithmic, and systematic opportunities of multi-tier computing towards next-generation wireless networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Resource allocation

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Reentrant quantum anomalous Hall effect in molecular beam epitaxy-grown MnBi2Te4 thin films

arXiv

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arXiv 2024年

作者： Li, Yuanzhao Bai, Yunhe Feng, Yang Luan, Jianli Gao, Zongwei Chen, Yang Tong, Yitian Liu, Ruixuan Chong, Su Kong Wang, Kang L. Zhou, Xiaodong Shen, Jian Zhang, Jinsong Wang, Yayu Chen, Chui-Zhen Xie, XinCheng Feng, Xiao He, Ke Xue, Qi-Kun Beijing Academy of Quantum Information Sciences Beijing100193 China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States Department of Physics and Astronomy University of California Los Angeles Los AngelesCA90095 United States State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200438 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200438 China Zhangjiang Fudan International Innovation Center Fudan University Shanghai201210 China Shanghai Research Center for Quantum Sciences Shanghai201315 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China School of Physical Science and Technology Soochow University Suzhou215006 China Institute for Advanced Study Soochow University Suzhou215006 China International Center for Quantum Materials School of Physics Peking University Beijing100871 China Southern University of Science and Technology Shenzhen518055 China Frontier Science Center for Quantum Information Beijing100084 China Hefei National Laboratory Hefei230088 China

In the realm of topological quantum physics, it is widely accepted that a global bandgap is necessary to achieve quantized Hall conductance, irrespective of its origin-magnetic field, exchange coupling or other mechanisms. A material exhibiting the quantum anomalous Hall (QAH) effect had been considered equivalent to a Chern insulator, a band insulator with non-zero Chern number. However, a Chern Anderson insulator (CAI), which arises in a conducting state with exchange-induced Berry curvature splitting and disorder-induced Anderson localization, can in principle exhibit the QAH effect. In this study, we investigate intrinsic magnetic topological insulator MnBi2Te4 thin films grown by molecular beam epitaxy (MBE). We observe a reentrant QAH effect when the Fermi energy enters the valance band and magnetic field equals zero, indicating the emergence of the CAI state. The discovery opens a new avenue for realizing the QAH effect and underscores the fundamental role of both Berry curvature and Anderson localization. © 2024, CC BY-NC-ND.

关键词： Molecular beam epitaxy

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Heterogeneous relational complement for vehicle re-identification

arXiv

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arXiv 2021年

作者： Zhao, Jiajian Zhao, Yifan Li, Jia Yan, Ke Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems Scse Beihang University Department of Computer Science and Technology Peking University Tencent Youtu Lab Shanghai China Peng Cheng Laboratory Shenzhen China

The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning viewpoint-invariant representations. In this paper, we propose to solve this problem from two aspects: constructing robust feature representations and proposing camerasensitive evaluations. We first propose a novel Heterogeneous Relational Complement Network (HRCN) by incorporating region-specific features and cross-level features as complements for the original high-level output. Considering the distributional differences and semantic misalignment, we propose graph-based relation modules to embed these heterogeneous features into one unified highdimensional space. On the other hand, considering the deficiencies of cross-camera evaluations in existing measures (i.e., CMC and AP), we then propose a Cross-camera Generalization Measure (CGM) to improve the evaluations by introducing position-sensitivity and cross-camera generalization penalties. We further construct a new benchmark of existing models with our proposed CGM and experimental results reveal that our proposed HRCN model achieves new state-of-the-art in VeRi-776, VehicleID, and VERI-Wild. Copyright © 2021, The Authors. All rights reserved.

关键词： Cameras

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Achievable Rate Maximization Pattern Design for Reconfigurable MIMO Antenna Array

arXiv

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arXiv 2022年

作者： Wang, Haonan Li, Ang Liu, Ya-Feng Qin, Qibo Song, Lingyang Li, Yonghui The School of Information and Communications Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China The State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Xi'An China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Wireless Network RAN Research Department Shanghai Huawei Technologies Co. Ltd. Shanghai201206 China The School of Electrical Engineering and Computer Science Peking University Beijing100871 China The School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia

Reconfigurable multiple-input multiple-output can provide performance gains over traditional MIMO by reshaping the channels, i.e., introducing more channel realizations. In this paper, we focus on the achievable rate maximization pattern design for reconfigurable MIMO systems. Firstly, we introduce the matrix representation of pattern reconfigurable MIMO (PR-MIMO), based on which a pattern design problem is formulated. To further reveal the effect of the radiation pattern on the wireless channel, we consider pattern design for both the single-pattern case where the optimized radiation pattern is the same for all the antenna elements, and the multi-pattern case where different antenna elements can adopt different radiation patterns. For the single-pattern case, we show that the pattern design is equivalent to a redistribution of gains among all scattering paths, and an eigenvalue optimization based solution is obtained. For the multi-pattern case, we propose a sequential optimization framework with manifold optimization and eigenvalue decomposition to obtain near-optimal solutions. Numerical results validate the superiority of PR-MIMO systems over traditional MIMO in terms of achievable rate, and also show the effectiveness of the proposed solutions. Copyright © 2022, The Authors. All rights reserved.

关键词： MIMO systems

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Hard instance learning for quantum adiabatic prime factorization

arXiv

引用

arXiv 2021年

作者： Lin, Jian Zhang, Zhengfeng Zhang, Junping Li, Xiaopeng State Key Laboratory of Surface Physics Institute of Nanoelectronics and Quantum Computing Department of Physics Fudan University Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Shanghai Qi Zhi Institute AI Tower Xuhui District Shanghai200232 China

Prime factorization is a difficult problem with classical computing, whose exponential hardness is the foundation of Rivest-Shamir-Adleman (RSA) cryptography. With programmable quantum devices, adiabatic quantum computing has been proposed as a plausible approach to solve prime factorization, having promising advantage over classical computing. Here, we find there are certain hard instances that are consistently intractable for both classical simulated annealing and un-configured adiabatic quantum computing (AQC). Aiming at an automated architecture for optimal configuration of quantum adiabatic factorization, we apply a deep reinforcement learning (RL) method to configure the AQC algorithm. By setting the success probability of the worst-case problem instances as the reward to RL, we show the AQC performance on the hard instances is dramatically improved by RL configuration. The success probability also becomes more evenly distributed over different problem instances, meaning the configured AQC is more stable as compared to the un-configured case. Through a technique of transfer learning, we find prominent evidence that the framework of AQC configuration is scalable—the configured AQC as trained on five qubits remains working efficiently on nine qubits with a minimal amount of additional training cost. Copyright © 2021, The Authors. All rights reserved.

关键词： Factorization

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