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arXiv 2020年

作者： Hu, Hanjiang Wang, Hesheng Liu, Zhe Chen, Weidong Autonomous Robot Lab Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory of Robotics and System HIT The Department of Computer Science and Technology University of Cambridge United Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous driving. Image retrieval is an efficient and effective technique in image-based localization methods. Due to the drastic variability of environmental conditions, e.g. illumination, seasonal and weather changes, retrieval-based visual localization is severely affected and becomes a challenging problem. In this work, a general architecture is first formulated probabilistically to extract domain-invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. We also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised manner. The final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models without and with Grad-SAM loss. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons dataset. The strong generalization ability of our approach is verified on Robotcar dataset using models pre-trained on urban part of CMU-Seasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision, especially under the challenging environments with illumination variance, vegetation and night-time images. The code and pretrained models are available on https://***/HanjiangHu/DISAM. Copyright © 2020, The Authors. All rights reserved.

关键词： Image retrieval

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Learning to Detect Unacceptable Machine Translations for Downstream Tasks

arXiv

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arXiv 2020年

作者： Zhang, Meng Jiang, Xin Liu, Yang Liu, Qun Huawei Noah’s Ark Lab Institute for Artificial Intelligence State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology

The field of machine translation has progressed tremendously in recent years. Even though the translation quality has improved significantly, current systems are still unable to produce uniformly acceptable machine translations for the variety of possible use cases. In this work, we put machine translation in a cross-lingual pipeline and introduce downstream tasks to define task-specific acceptability of machine translations. This allows us to leverage parallel data to automatically generate acceptability annotations on a large scale, which in turn help to learn acceptability detectors for the downstream tasks. We conduct experiments to demonstrate the effectiveness of our framework for a range of downstream tasks and translation models. Copyright © 2020, The Authors. All rights reserved.

关键词： computer aided language translation

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Biochemical sensing in graphene-enhanced microfiber resonators with individual molecule sensitivity and selectivity

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Light(science & Applications) 2019年第1期8卷 173-182页

作者： Zhongxu Cao Baicheng Yao Chenye Qin Run Yang Yanhong Guo Yufeng Zhang Yu Wu Lei Bi Yuanfu Chen Zhenda Xie Gangding Peng Shu-Wei Huang Chee Wei Wong Yunjiang Rao Key Laboratory of Optical Fiber Sensing and Communications(Education Ministry of China) University of Electronic Science and Technology of ChinaChengdu 611731China State Key Lab of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of ChinaChengdu 611731China National Laboratory of Solid State Microstructures and School of Electronic Science and Engineering Nanjing UniversityNanjing 210093China School of Electrical Engineering and Telecommunications University of New South WalesSydneyNSW 2052Australia Department of Electrical Computerand Energy EngineeringUniversity of Colorado BoulderBoulderCO 80309USA Fang Lu Mesoscopic Optics and Quantum Electronics Laboratory University of CaliforniaLos AngelesCA 90095USA Ubiquitous Sensing Center Zhejiang LaboratoryHangzhou 310000China

Photonic sensors that are able to detect and track biochemical molecules offer powerful tools for information acquisition in applications ranging from environmental analysis to medical *** ultimate aim of biochemical sensing is to achieve both quantitative sensitivity and *** atomically thick films with remarkable optoelectronic tunability,graphene and its derived materials have shown unique potential as a chemically tunable platform for sensing,thus enabling significant performance enhancement,versatile functionalization and flexible device ***,we demonstrate a partially reduced graphene oxide(prGO)inner-coated and fiber-calibrated Fabry-Perot dye resonator for biochemical *** functionalization in the prGO film enables the intracavity fluorescent resonance energy transfer(FRET)to be chemically selective in the visible ***,by measuring the intermode interference via noise canceled beat notes and locked-in heterodyne detection with Hz-level precision,we achieved individual molecule sensitivity for dopamine,nicotine and single-strand DNA *** work combines atomic-layer nanoscience and high-resolution optoelectronics,providing a way toward high-performance biochemical sensors and systems.

关键词： resonator versatile enable

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Optimizing the size and electronic effects of core-shell heterostructures via well-constructed Ru clusters encapsulated in N-doped carbon layers

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Chinese Chemical Letters 2024年

作者： Min Jie Wang Jiao Yang Lishan Peng Yongjie Bai Zehui Liu Xiaoliang Yang Huijuan Lu Bingjie Zhou Ningtao Jiang Guoxu He Han-Ming Zhang Liwei Mi Yonghui Deng School of Chemistry and Environmental Engineering Pingdingshan University Pingdingshan 467000 China Key Laboratory of Rare Earths Chinese Academy of Sciences； Ganjiang Innovation Academy Chinese Academy of Sciences Ganzhou 341119 China China Automotive Technology and Research Center Co. Ltd. Tianjin 300300 China Department of Electrical and Computer Engineering North Dakota State University Fargo 58102 United States School of Materials Science and Engineering Hebei University of Science and Technology Shijiazhuang 050018 China Yaoshan laboratory Pingdingshan University Pingdingshan 467000 China Department of Chemistry State Key Laboratory of Molecular Engineering of Polymers Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials Collaborative Innovation Center of Chemistry for Energy Material (iChEM) Fudan University Shanghai 200433 China State Key Lab of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

The design and development of high-performance electrocatalysts for the hydrogen evolution reaction (HER) are essential for advancing the hydrogen economy. The electronic structure and core size of an electrocatalyst are pivotal for determining the intrinsic activity of the catalytic sites. Interfacial engineering, particularly the formation of well-controlled core-shell heterostructures, has emerged as a promising strategy, although significant challenges remain. Here, we present a series of Ru@NC heterostructures with size-controlled Ru cores encapsulated in N-doped graphene layers. Among these, Ru@NC-3h, with the best holistic effects, has superior durability and mass activity 7.03 times that of Pt/C. This high performance is attributed to the open porous structure, which enhances active site exposure and mass transfer, and the optimized adsorption and desorption of reaction intermediates by the strengthened hetero-interfacial interaction between the smaller Ru cores and thin N-doped shells. Attenuated total reflectance surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) reveals reinforced interfacial water interaction and reduced hydrogen adsorption. Density functional theory (DFT) calculations indicate that the size effect promotes interfacial H 2 O adsorption, whereas the electronic effect governs *H adsorption to collectively accelerate the HER kinetics. This novel strategy, introduced to regulate heterostructures through size and electronic effects, offers significant potential for various energy material applications.

关键词： Hydrogen evolution reaction Interfacial engineering Core-shell heterostructures Holistic effects Density functional theory

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A Blockchain-based Conditional Privacy-Preserving Traffic Data Sharing in Cloud

A Blockchain-based Conditional Privacy-Preserving Traffic Da...

引用

IEEE International Conference on Communications (ICC)

作者： Jingwei Liu Gaojian Zhang Rong Sun Xiaojiang Du Mohsen Guizani Shaanxi Key Laboratory of Blockchain and Secure Computing Xidian University Xi’an China State Key Lab of ISN Xidian University Xi’an China Department of Computer and Information Sciences Temple University Philadelphia PA USA Department of Computer Science and Engineering Qatar University Doha Qatar

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

As a typical application scenarios of Internet of Things (IoT), Internet of Vehicles (IoV) is playing an important role in the field of intelligent transportation. In such a system, vehicles are allowed to collect ambient sensing data and communicate with other vehicles, roadside infrastructure, cloud server, even personal mobile smart devices in order to exchange the relevant traffic information that usually includes the owners' sensitive information, such as geographical location or personally identifiable information. However, in open network environment, different wireless communication technologies make all these information vulnerable to various attacks and compromises. The users' private information is put at the risk of leaking at any time. To address this issue, a blockchain-based privacy-preserving traffic data sharing protocol with cloud storage is proposed based on a novel heterogeneous signcryption scheme between Public key Infrastructure (PKI) and Certificateless Cryptosystem (CLC). Through theoretical and simulation analysis in terms of security, complexity and time consumption, the proposed traffic data sharing protocol is proved to meet the security requirements of data tamper-proofing, anonymity and traceability with higher efficiency.

关键词： Cloud computing Public key Servers Transportation Protocols

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Preface

Synthesis Lectures on Computer Vision

引用

Synthesis Lectures on computer Vision 2023年 Part F825卷 v-vi页

作者： Wan, Jun Escalera, Sergio Li, Stan Z. Guo, Guodong Escalante, Hugo Jair State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Mathematics and Informatics University of Barcelona and Computer Vision Center Barcelona Spain AI Lab Westlake University Hangzhou China Department of Computer Science and Electrical Engineering West Virginia University Morgantown China Department of Computer Science Instituto Nacional de Astrofísica Optica y Electrónica Puebla Mexico

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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Open-Appositional-Synechial Anterior Chamber Angle Classification in AS-OCT Sequences 23rd

Open-Appositional-Synechial Anterior Chamber Angle Classific...

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Hao, Huaying Fu, Huazhu Xu, Yanwu Yang, Jianlong Li, Fei Zhang, Xiulan Liu, Jiang Zhao, Yitian Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangzhou China Glaucoma Artificial Intelligence Diagnosis and Imaging Analysis Joint Research Lab Guangzhou and Ningbo China

ISBN: (纸本)9783030597214

Anterior chamber angle (ACA) classification is a key step in the diagnosis of angle-closure glaucoma in Anterior Segment Optical Coherence Tomography (AS-OCT). Existing automated analysis methods focus on a binary classification system (i.e., open angle or angle-closure) in a 2D AS-OCT slice. However, clinical diagnosis requires a more discriminating ACA three-class system (i.e., open, appositional, or synechial angles) for the benefit of clinicians who seek better to understand the progression of the spectrum of angle-closure glaucoma types. To address this, we propose a novel sequence multi-scale aggregation deep network (SMA-Net) for open-appositional-synechial ACA classification based on an AS-OCT sequence. In our method, a Multi-Scale Discriminative Aggregation (MSDA) block is utilized to learn the multi-scale representations at slice level, while a ConvLSTM is introduced to study the temporal dynamics of these representations at sequence level. Finally, a multi-level loss function is used to combine the slice-based and sequence-based losses. The proposed method is evaluated across two AS-OCT datasets. The experimental results show that the proposed method outperforms existing state-of-the-art methods in applicability, effectiveness, and accuracy. We believe this work to be the first attempt to classify ACAs into open, appositional, or synechial types grading using AS-OCT sequences. © 2020, Springer Nature Switzerland AG.

关键词： Grading

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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On creating benchmark dataset for aerial image interpretation: Reviews, guidances and million-AID

arXiv

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arXiv 2020年

作者： Long, Yang Xia, Gui-Song Li, Shengyang Yang, Wen Yang, Michael Ying Zhu, Xiao Xiang Zhang, Liangpei Li, Deren State Key Lab. LIESMARS Wuhan University Wuhan China School of Computer Science State Key Lab. LIESMARS Wuhan University Wuhan China Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China School of Electronic Information State Key Lab LIESMARS Wuhan University Wuhan China Faculty of Geo-Information Science and Earth Observation University of Twente Hengelosestraat 99 Enschede Netherlands Technical University of Munich Germany

The past years have witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there is an increasing demand for the automatic interpretation of these images. In this context, the benchmark datasets serve as essential prerequisites for developing and testing intelligent interpretation algorithms. After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation. Specifically, we first analyze the current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations. We then present the general guidances on creating benchmark datasets in efficient manners. Following the presented guidances, we also provide an example on building RS image dataset, i.e., Million-AID 1, a new large-scale benchmark dataset containing a million instances for RS image scene classification. Several challenges and perspectives in RS image annotation are finally discussed to facilitate the research in benchmark dataset construction. We do hope this paper will provide the RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones. © 2020, CC BY.

关键词： Remote sensing

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