检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Liu, Jia Xuan, Wenjie Gan, Yuhang Zhan, Yibing Liu, Juhua Du, Bo Research Center for Graphic Communication Printing and Packaging Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Wuhan University Wuhan China Institute of Artificial Intelligence Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China Land Satellite Remote Sensing Application Center MNR Beijing China JD Explore Academy Beijing China

Change detection is a crucial but extremely challenging task in remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learning-based change detection methods try to elaborately design complicated neural networks with powerful feature representations. However, they ignore the universal domain shift induced by time-varying land cover changes, including luminance fluctuations and seasonal changes between pre-event and post-event images, thereby producing suboptimal results. In this paper, we propose an end-to-end supervised domain adaptation framework for cross-domain change detection named SDACD, to effectively alleviate the domain shift between bi-temporal images for better change predictions. Specifically, our SDACD presents collaborative adaptations from both image and feature perspectives with supervised learning. Image adaptation exploits generative adversarial learning with cycle-consistency constraints to perform cross-domain style transformation, which effectively narrows the domain gap in a two-side generation fashion. As for feature adaptation, we extract domain-invariant features to align different feature distributions in the feature space, which could further reduce the domain gap of cross-domain images. To further improve the performance, we combine three types of bi-temporal images for the final change prediction, including the initial input bi-temporal images and two generated bi-temporal images from the pre-event and post-event domains. Extensive experiments and analyses conducted on two benchmarks demonstrate the effectiveness and generalizability of our proposed framework. Notably, our framework pushes several representative baseline models up to new State-Of-The-Art records, achieving 97.34% and 92.36% on the CDD and WHU building datasets, respectively. The source code and models are publicly available at https://***/Perfect-You/SDACD. Copyright © 2022, The Aut

关键词： Change detection

来源：评论

学校读者我要写书评

暂无评论

SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention

arXiv

引用

arXiv 2023年

作者： Xu, Rui Liu, Zhi Luo, Yong Hu, Han Shen, Li Du, Bo Kuang, Kaiming Yang, Jiancheng The School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Hubei Luojia Laboratory Wuhan China The Department of Computer and Network Engineering The University of Electro-Communications 1-5-1 Chofugaoka Chofushi Tokyo182-8585 Japan The School of Information and Electronics Beijing Insititute of Technology Beijing100081 China The JD Explore Academy Beijing China The Dianei Technology Shanghai200120 China The Shanghai Jiao Tong University Shanghai China Dianei Technology Shanghai China EPFL Lausanne Switzerland

Lung cancer is the leading cause of cancer death worldwide. The best solution for lung cancer is to diagnose the pulmonary nodules in the early stage, which is usually accomplished with the aid of thoracic computed tomography (CT). As deep learning thrives, convolutional neural networks (CNNs) have been introduced into pulmonary nodule detection to help doctors in this labor-intensive task and demonstrated to be very effective. However, the current pulmonary nodule detection methods are usually domain-specific, and cannot satisfy the requirement of working in diverse real-world scenarios. To address this issue, we propose a slice grouped domain attention (SGDA) module to enhance the generalization capability of the pulmonary nodule detection networks. This attention module works in the axial, coronal, and sagittal directions. In each direction, we divide the input feature into groups, and for each group, we utilize a universal adapter bank to capture the feature subspaces of the domains spanned by all pulmonary nodule datasets. Then the bank outputs are combined from the perspective of domain to modulate the input group. Extensive experiments demonstrate that SGDA enables substantially better multi-domain pulmonary nodule detection performance compared with the state-of-the-art multi-domain learning methods. © 2023, CC BY.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing crop production in the Haihe Basin while addressing challenges related to water quantity and quality

引用

science of the Total Environment 2024年 955卷 176800页

作者： Wang, Gang Yang, Jing Liu, Xia Zhang, Haodi Xu, Xiangbo Luo, Jiafa Bai, Zhaohai Ma, Lin Key Laboratory of Agricultural Water Resources Hebei Key Laboratory of Soil Ecology Center for Agricultural Resources Research Institute of Genetic and Developmental Biology The Chinese Academy of Sciences 286 Huaizhong Road Hebei Shijiazhuang050021 China University of Chinese Academy of Science 19 A Yuquan Road Shijingshan District Beijing100049 China School of Mathematics and Science Hebei GEO University 136 Huai'an Road Hebei Shijiazhuang050031 China College of Computer Science and Software Engineering Shenzhen University China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China AgResearch Limited Ruakura Research Centre Private Bag 3123 Hamilton New Zealand State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University Jiangsu Nanjing210023 China

To tackle the challenge of ensuring food security while managing water availability and quality constraints in the Haihe Basin, a critical grain-producing region in China with a dense population and severe water degradation issues, we have developed a comprehensive spatial optimization (CSO) strategy. This approach integrates various crop and soil management practices, land consolidation, and cutting-edge breeding technologies. It also uses a spatial planning model based on linear programming, considering water quantity and quality constraints. Our research shows that crop yields may drop by 14 % (3780 Kt) compared to levels in 2017 due to restrictions on groundwater extraction and nitrogen (N) concentrations in leaching or runoff. However, by strategically maximizing overall crop yields through the integrated CSO strategy without changing the crop production structure, the total crop yield could potentially increase by 81 % (21,817 Kt) while using 3.0 % (86 Kha) less cultivated land than in 2017. This would help to fulfill the projected food demand in 2050. If the integrated aims to minimize the sown area after fulfilling the demand for crops in 2050, around 28 % of the sown area, or 820 Kha cropland, could be reduced compared with 2017. This reduction may save more land for natural conversation. Furthermore, total N losses, including ammonia, nitrous oxide emissions, runoff, and leaching, could be reduced by 23 %–43 % (139–252 Kt), depending on the specific optimization strategy goals. The stepwise integrated optimization strategy provides a feasible framework for the Haihe Basin, enabling the maintenance or even enhancing current crop yields while safeguarding water quality and quantity. © 2024 Elsevier B.V.

关键词： Comprehensive spatial optimization Crop yield Haihe Basin Nitrogen losses Water security

来源：评论

学校读者我要写书评

暂无评论

Correction to: Fuzzy logic and CPSO-optimized key management for secure communication in decentralized IoT networks: A lightweight solution

引用

Peer-to-Peer Networking and Applications 2024年第6期17卷 4396-4396页

作者： Vellingiri, J. Vedhavathy, T.R. Senthil Pandi, S. Bala Subramanian, C. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India Department of Computer Science Engineering Kalasalingam Academy of Research and Education Krishnankoil India

来源：评论

学校读者我要写书评

暂无评论

Survey on the location privacy preservation in the internet of things

引用

Ruan Jian Xue Bao/Journal of software 2014年 25卷 1-10页

作者： Sun, Li-Min Li, Hong Wang, Xiao-Han He, Yun-Hua Beijing Key Laboratory of IOT Information Security Institute of Information Engineering The Chinese Academy of Sciences Beijing100093 China University of Chinese Academy of Sciences Beijing100049 China School of Software & Microelectronics Peking University Beijing102600 China School of Computer Science Xidian University Xi'an710071 China

In the Internet of Things, location information is considered as not only one of the basic elements of sensing information collected by sensors but also an essential prerequisite for providing location based services. On the one hand, location information can facilitate people's lives, but on the other hand, such information exposure may cause great harm to the users. The location privacy preservation for Internet of Things has become one of the hottest topics in the academic world. This paper first states the objectives and challenges of achieving location privacy preservation for the Internet of Things. Then, it provides an overview on the location privacy leakages and the corresponding privacy preserving techniques in localization, location based services and side information. Finally, it discusses the future directions on location privacy preservation for the Internet of Things. ©2014 ISCAS.

关键词： Location based services

来源：评论

学校读者我要写书评

暂无评论

Editorial

引用

International Journal of Internet Manufacturing and Services 2018年第2-3期5卷 101-102页

作者： He, Ting Li, Weiping Wang, Lei College of Computer Science and Technology Huaqiao University No. 668 Jimei Avenue Jimei District Xiamen Fujian Province361021 China School of Software and Microelectronics Peking University No. 5 Yiheyuan Road Haidian District Beijing100086 China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences No. 88 Keling Road Suzhou New District Jiangsu Province215163 China

来源：评论

学校读者我要写书评

暂无评论

WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

arXiv

引用

arXiv 2022年

作者： Han, Chu Pan, Xipeng Yan, Lixu Lin, Huan Li, Bingbing Yao, Su Lv, Shanshan Shi, Zhenwei Mai, Jinhai Lin, Jiatai Zhao, Bingchao Xu, Zeyan Wang, Zhizhen Wang, Yumeng Zhang, Yuan Wang, Huihui Zhu, Chao Lin, Chunhui Mao, Lijian Wu, Min Duan, Luwen Zhu, Jingsong Hu, Dong Fang, Zijie Chen, Yang Zhang, Yongbing Li, Yi Zou, Yiwen Yu, Yiduo Li, Xiaomeng Li, Haiming Cui, Yanfen Han, Guoqiang Xu, Yan Xu, Jun Yang, Huihua Li, Chunming Liu, Zhenbing Lu, Cheng Chen, Xin Liang, Changhong Zhang, Qingling Liu, Zaiyi The Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The Department of Pathology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi541004 China The Department of Pathology Guangdong Provicial People's Hospital Ganzhou Hospital Ganzhou China The College of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The School of Computer Science and Engineering South China University of Technology Guangzhou510640 China The Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China The School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Research Institute of Beihang University in Shenzhen Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China The Department of Radiology Guangzhou First People's Hospital Hospital of South China University of Technology Guangzhou510180 China The Zhejiang Dahua Technology Co. Ltd Zhejiang310053 China The School of Biomedical Engineering University of Science and Technology of China Hefei230022 China The Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Electronic and Computer Engineering & Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong The School of Automation Science and Engineering Xi'An Jiaotong University Xian710049 Chi

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outstanding weakly-supervised semantic segmentation (WSSS) techniques for histopathology images of LUAD. Participants have to design the algorithm to segment tumor epithelial, tumor-associated stroma and normal tissue with only patch-level labels. This challenge includes 10,091 patch-level annotations (the training set) and over 130 million labeled pixels (the validation and test sets), from 87 WSIs (67 from GDPH, 20 from TCGA). All the labels were generated by a pathologist-in-the-loop pipeline with the help of AI models and checked by the label review board. Among 532 registrations, 28 teams submitted the results in the test phase with over 1,000 submissions. Finally, the first place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919). According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS. Cutmix data augmentation has been widely adopted to generate more reliable samples. With the success of this challenge, we believe that WSSS approaches with patch-level annotations can be a complement to the traditional pixel annotations while reducing the annotation efforts. The entire dataset has been released to encourage more researches on computational pathology in LUAD and more novel WSSS techniques. © 2022, CC BY.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework

arXiv

引用

arXiv 2024年

作者： Xu, Xusheng Cui, Jiangyu Cui, Zidong He, Runhong Li, Qingyu Li, Xiaowei Lin, Yanling Liu, Jiale Liu, Wuxin Lu, Jiale Luo, Maolin Lyu, Chufan Pan, Shijie Pavel, Mosharev Shu, Runqiu Tang, Jialiang Xu, Ruoqian Xu, Shu Yang, Kang Yu, Fan Zeng, Qingguo Zhao, Haiying Zheng, Qiang Zhou, Junyuan Zhou, Xu Zhu, Yikang Zou, Zuoheng Bayat, Abolfazl Cao, Xi Cui, Wei Li, Zhendong Long, Guilu Su, Zhaofeng Wang, Xiaoting Wang, Zizhu Wei, Shijie Wu, Re-Bing Zhang, Pan Yung, Man-Hong MindSpore Quantum Special Interest Group Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610051 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Department of Physical Chemistry University of the Basque Country UPV/EHU Apartado 644 Bilbao48080 Spain School of Physics and Astronomy Sun Yat-sen University Zhuhai519082 China Key Laboratory of Quantum Physics and Photonic Quantum Information Ministry of Education University of Electronic Science and Technology of China Chengdu611731 China Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Theoretical and Computational Photochemistry Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Beijing Academy of Quantum Information Sciences Beijing100193 China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China CAS Key Laboratory for Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China Shenzhen International Quantum Academy Shenzhen518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of MindSpore, an advanced open-source deep learning training/inference framework, MindSpore Quantum exhibits exceptional efficiency in the design and training of variational quantum algorithms on both CPU and GPU platforms, delivering remarkable performance. Furthermore, this framework places a strong emphasis on enhancing the operational efficiency of quantum algorithms when executed on real quantum hardware. This encompasses the development of algorithms for quantum circuit compilation and qubit mapping, crucial components for achieving optimal performance on quantum processors. In addition to the core framework, we introduce QuPack-a meticulously crafted quantum computing acceleration engine. QuPack significantly accelerates the simulation speed of MindSpore Quantum, particularly in variational quantum eigensolver (VQE), quantum approximate optimization algorithm (QAOA), and tensor network simulations, providing astonishing speed. This combination of cutting-edge technologies empowers researchers and practitioners to explore the frontiers of quantum computing with unprecedented efficiency and performance. © 2024, CC BY.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

An overview of domain-specific foundation model: key technologies, applications and challenges

arXiv

引用

arXiv 2024年

作者： Chen, Haolong Chen, Hanzhi Zhao, Zijian Han, Kaifeng Zhu, Guangxu Zhao, Yichen Du, Ying Xu, Wei Shi, Qingjiang Shenzhen Research Institute of Big Data Shenzhen518172 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China China Academy of Information and Communications Technology Beijing100191 China China Mobile Group Device Co. Ltd. Beijing100033 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China School of Software Engineering Tongji University Shanghai201804 China

The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application scenarios. This process, known as the customization of domain-specific foundation models, addresses the limitations of general-purpose models, which may not fully capture the unique patterns and requirements of domain-specific data. Despite its importance, there is a notable lack of comprehensive overview papers on building domain-specific foundation models, while numerous resources exist for general-purpose models. To bridge this gap, this article provides a timely and thorough overview of the methodology for customizing domain-specific foundation models. It introduces basic concepts, outlines the general architecture, and surveys key methods for constructing domain-specific models. Furthermore, the article discusses various domains that can benefit from these specialized models and highlights the challenges ahead. Through this overview, we aim to offer valuable guidance and reference for researchers and practitioners from diverse fields to develop their own customized foundation models. Copyright © 2024, The Authors. All rights reserved.

关键词： Building Information Model

来源：评论

学校读者我要写书评

暂无评论

Foreword

Proceedings of the 2016 International Conference on Image Pr...

引用

Proceedings of the 2016 International Conference on Image Processing, computer Vision, and Pattern Recognition, IPCV 2016 2016年

作者： Abd-Wahab, Mohd Helmy Al-Bakry, Abbas Al-Holou, Nizar Arabnia, Hamid R. Bhattacharya, Mahua Martinez Castillo, Juan Jose Daimi, Kevin Deligiannidis, Leonidas Djoudi, Lamia Atma Duong, Trung Eshaghian-Wilner, Mary Mehrnoosh Gravvanis, George A. Huang, Ruizhu Jandieri, George Kim, Byung-Gyu Kim, Tai-hoon Korovin, Iakov Lai, Guoming Lee, Hyo Jong Bin Mansor, Muhammad Naufal Marsh, Andrew Mostafaeipour, Ali Park, James J. Patil, Shashikant Ponalagusamy, R. Schaefer, Gerald Singh, Akash Solo, Ashu M.G. Swee, Sim Kok Thomas, Jaya Tinetti, Fernando G. Vladimir, Hahanov Wang, Shiuh-Jeng Yang, Mary Yoe, Hyun You, Jane Zhao, Wenbing Department of Computer Engineering University Tun Hussein Onn Malaysia Malaysia University of IT and Communications Baghdad Iraq Electrical and Computer Engineering Department IEEE/SEM-Computer Chapter University of Detroit Mercy DetroitMI United States University of Georgia United States ABV Indian Institute of Information Technology and Management MHRD Government of India India Acantelys Alan Turing Nikola Tesla Research Group and GIPEB Universidad Nacional Abierta Venezuela Computer Science and Software Engineering Programs Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States Department of Computer Information Systems Wentworth Institute of Technology BostonMA United States Synchrone Technologies France Rutgers University State University of New Jersey New Jersey United States University of Southern California California United States Electrical Engineering University of California Los Angeles Los Angeles [UCLA CA United States Advanced Scientific Computing Applied Math and Applications Research Group Applied Mathematics and Numerical Computing and Department of ECE School of Engineering Democritus University of Thrace Xanthi Greece Texas Advanced Computing Center University of Texas AustinTX United States Georgian Technical University Tbilisi Georgia Institute of Cybernetics Georgian Academy of Science Georgia Multimedia Processing CommunicationsLab.[MPCL Department of Computer Science and Engineering College of Engineering SunMoon University Korea Republic of School of Information and Computing Science University of Tasmania Australia Southern Federal University Russia Computer Science and Technology Sun Yat-Sen University Guangzhou China Center for Advanced Image and Information Technology Division of Computer Science and Engineering Chonbuk National University Korea Republic of Faculty of Engineering Technology Kampus Uniciti Alam Universiti Malaysia Perlis UniMAP Malaysi

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：