检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Jin, Guangyin Liang, Yuxuan Fang, Yuchen Shao, Zezhi Huang, Jincai Zhang, Junbo Zheng, Yu National Innovative Institute of Defense Technology Beijing China National University of Defense Technology Changsha China Guangzhou China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Institute of Computing Technology University of Chinese Academy of Sciences Beijing China College of Systems Engineering National University of Defense Technology Changsha China JD Intelligent Cities Research and JD iCity JD Technology Beijing China

With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal data is an important yet demanding aspect of urban computing, which can enhance intelligent management decisions in various fields, including transportation, environment, climate, public safety, healthcare, and others. Traditional statistical and deep learning methods struggle to capture complex correlations in urban spatio-temporal data. To this end, Spatio-Temporal Graph Neural Networks (STGNN) have been proposed, achieving great promise in recent years. STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods. In this manuscript, we provide a comprehensive survey on recent progress on STGNN technologies for predictive learning in urban computing. Firstly, we provide a brief introduction to the construction methods of spatio-temporal graph data and the prevalent deep-learning architectures used in STGNNs. We then sort out the primary application domains and specific predictive learning tasks based on existing literature. Afterward, we scrutinize the design of STGNNs and their combination with some advanced technologies in recent years. Finally, we conclude the limitations of existing research and suggest potential directions for future work. © 2023, CC BY.

关键词： Complex networks

来源：评论

学校读者我要写书评

暂无评论

An Innovation Development Of Bandwidth Management In 4g Wireless Local Area Network (4g-Wlan) Using Smart Machine Learning Algorithm

An Innovation Development Of Bandwidth Management In 4g Wire...

引用

Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： B. Gopinathan Papiya Mukherjee Naveen Kumar N R. Nandhini S. Shanmugapriya S D Lalitha Department of Computer Science and Engineering Adhiyamaan College of Engineering Hosur Tamilnadu India Department of CSE Education Koneru Lakshmaiah Foundation Green Fields Guntur Andhra Pradesh India School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of ECE Veltech Multitech Dr Rangarajan Dr Shakuntala Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamilnadu India Department of CSE R.M.K. Engineering College Kavaraipettai Tamilnadu India

Currently, the vast use of existing fourth generation networks is further increasing its users. A major problem with this process is the allocation of equal bandwidth to everyone on the network. This varies depending on the secondary users who are aging. But the time difference between them allows both of them to be switched to get the required bandwidth utilization. An advanced machine learning algorithm is proposed in this paper. It is designed to reduce the hassle for the primary user by calculating the login times of the primary user and the secondary user. Also the switching schedule data for the secondary user is given to improve and fix them so that they do not have immediate connection problems. And the most important thing in this system is time management for primary and secondary users. Two are even better designed to manage this effectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Nonconvex optimization for optimum retrieval of the transmission matrix of a multimode fiber

引用

Advanced Photonics Nexus 2023年第6期2卷 40-50页

作者： Shengfu Cheng Xuyu Zhang Tianting Zhong Huanhao Li Haoran Li Lei Gong Honglin Liu Puxiang Lai The Hong Kong Polytechnic University Department of Biomedical EngineeringHong KongChina The Hong Kong Polytechnic University Shenzhen Research InstituteShenzhenChina Chinese Academy of Sciences Shanghai Institute of Optics and Fine MechanicsKey Laboratory for Quantum OpticsShanghaiChina University of Shanghai for Science and Technology School of Optical-Electrical and Computer EngineeringShanghaiChina University of Science and Technology of China Department of Optics and Optical EngineeringHefeiChina University of Chinese Academy of Sciences Center of Materials Science and Optoelectronics EngineeringBeijingChina The Hong Kong Polytechnic University Photonics Research InstituteHong KongChina

Transmission matrix(TM)allows light control through complex media,such as multimode fibers(MMFs),gaining great attention in areas,such as biophotonics,over the past *** have been taken to retrieve a complex-valued TM directly from intensity measurements with several representative phase-retrieval algorithms,which still see limitations of slow or suboptimum recovery,especially under noisy ***,we propose a modified nonconvex optimization *** numerical evaluations,it shows that the optimum focusing efficiency is approached with less running time or sampling *** comparative tests under different signal-to-noise levels further indicate its improved ***,the superior focusing performance of our algorithm is collectively validated by single-and multispot focusing;especially with a sampling ratio of 8,it achieves a 93.6%efficiency of the gold-standard holography *** on the recovered TM,image transmission through an MMF is realized with high *** to parallel operation and GPU acceleration,our nonconvex approach retrieves a 8685×1024 TM(sampling ratio is 8)with 42.3 s on average on a regular *** proposed method provides optimum efficiency and fast execution for TM retrieval that avoids the need for an external reference beam,which will facilitate applications of deep-tissue optical imaging,manipulation,and treatment.

关键词： transmission matrix phase retrieval multimode fiber imaging wavefront shaping

来源：评论

学校读者我要写书评

暂无评论

Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

Hybrid genetic-discretized algorithm to handle data uncertai...

引用

作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Abdar, Moloud Beykikhoshk, Adham Khosravi, Abbas Nahavandi, Saeid Plawiak, Pawel Tan, Ru San Acharya, U Rajendra Deakin University GeelongVIC Australia Department of Engineering Fasa Branch Islamic Azad University Fars Fasa Iran Centre for Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 st. F-3 31-155 Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland National Heart Centre Singapore Singapore Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z-Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper-parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries. © 2020 John Wiley & Sons, Ltd.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Retraction Note: Unlocking the power of recalling enhanced recurrent neural network: channel estimation and data analysis for 5G MIMO communication system

引用

Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Alqahtani, Abdullah Saleh Pathak, Pankaj Pandiaraj, Saravanan Indrapriyadarsini, H. Arun Kumar, U. Nataraju, Susheela Department of Self-Development Skills-IT CFY Deanship King Saud University Riyadh Saudi Arabia Symbiosis Institute of Digital and Telecom Management (SIDTM) Symbiosis International (Deemed University) Lavale Pune India Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India Department of Electrical and Electronics Engineering Faculty of Engineering Karpagam Academy of Higher Education (Deemed to Be University) Coimbatore India Department of Computer Science Engineering G Madegowda Institute of Technology Bharathinagara (K M Doddi) India

来源：评论

学校读者我要写书评

暂无评论

TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems

arXiv

引用

arXiv 2024年

作者： Yang, Jing Zhao, Yu Yang, Linyao Wang, Xiao Chen, Long Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha410000 China Zhejiang Lab Hangzhou311121 China School of Artificial Intelligence Anhui University Hefei266114 China Department of Computer and Information Science University of Macau 999078 China Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

Temporal relation extraction (TRE) aims to grasp the evolution of events or actions, and thus shape the workflow of associated tasks, so it holds promise in helping understand task requests initiated by requesters in crowdsourcing systems. However, existing methods still struggle with limited and unevenly distributed annotated data. Therefore, inspired by the abundant global knowledge stored within pre-trained language models (PLMs), we propose a multi-task prompt learning framework for TRE (TemPrompt), incorporating prompt tuning and contrastive learning to tackle these issues. To elicit more effective prompts for PLMs, we introduce a task-oriented prompt construction approach that thoroughly takes the myriad factors of TRE into consideration for automatic prompt generation. In addition, we design temporal event reasoning in the form of masked language modeling as auxiliary tasks to bolster the model’s focus on events and temporal cues. The experimental results demonstrate that TemPrompt outperforms all compared baselines across the majority of metrics under both standard and few-shot settings. A case study on designing and manufacturing printed circuit boards is provided to validate its effectiveness in crowdsourcing scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Event Stream-Based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline

Event Stream-Based Visual Object Tracking: A High-Resolution...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiao Wang Shiao Wang Chuanming Tang Lin Zhu Bo Jiang Yonghong Tian Jin Tang School of Computer Science and Technology Anhui University Hefei China University of Chinese Academy of Sciences Beijing China Institute of Optics and Electronics CAS Chengdu China Beijing Institute of Technoloay Beijing China Peng Cheng Laboratory Shenzhen China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Tracking with bio-inspired event cameras has garnered increasing interest in recent years. Existing works either utilize aligned RGB and event data for accurate tracking or directly learn an event-based tracker. The former incurs higher inference costs while the latter may be susceptible to the impact of noisy events or sparse spatial resolution. In this paper, we propose a novel hierarchical knowledge distillation framework that can fully utilize multimodal / multi-view information during training to facilitate knowledge transfer, enabling us to achieve high-speed and low-latency visual tracking during testing by using only event signals. Specifically, a teacher Transformer-based multimodal tracking framework is first trained by feeding the RGB frame and event stream simultaneously. Then, we design a new hierarchical knowledge distillation strategy which includes pairwise similarity, feature representation, and response maps-based knowledge distillation to guide the learning of the student Transformer network. In particular, since existing event-based tracking datasets are all low-resolution (346 × 260), we propose the first large-scale high-resolution (1280 × 720) dataset named EventVOT. It contains 1141 videos and covers a wide range of categories such as pedestrians, vehicles, UAVs, ping pong, etc. Ex-tensive experiments on both low-resolution (FE240hz, Vi-sEvent, COESOT), and our newly proposed high-resolution EventVOT dataset fully validated the effectiveness of our proposed method.

关键词： Knowledge engineering Training Visualization Pedestrians Self-supervised learning Transformers Pattern recognition

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

Adaptive Traffic Forecasting on Daily Basis: A Spatio-Temporal Context Learning Approach

引用

IEEE Transactions on Knowledge and Data engineering 2025年

作者： Li, Xiaoyu Zhang, Yitian Long, Guodong Hu, Yupeng Lu, Wenpeng Chen, Meng Zhang, Chengqi Gong, Yongshun Shandong University School of Software Jinan250100 China McGill University Electrical and Computer Engineering MontrealQCH3A 0G4 Canada University of Technology Sydney School of Computer Science SydneyNSW2007 Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250316 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250100 China

Traffic forecasting plays a crucial role in establishing an Intelligent Transportation System (ITS) by providing essential insights. Existing traffic forecasting relies on the assumption that there is a hidden invariant spatial-temporal pattern in the large-scale dataset. However, the traffic patterns are easily influenced by many unpredictable external factors, such as policy interventions and climate changes. Due to the dynamic nature of these exogenous factors, the traffic network's spatial-temporal patterns are also changed, thus impacting the performance of traffic forecasting models. Thus, there is an urgent need to rethink the traffic forecasting model in a fast-adaptive manner. To solve this challenge, this paper proposes an Adaptive Spatio Temporal Context Learning framework named ASTCL, which achieves desired forecasting accuracy using daily basis traffic data collected from dozens of sensors. ASTCL constructs adaptive spatio-temporal contexts for target locations in the traffic network and generates dynamic sequence graphs based on semantic similarities. The adaptive contexts aggregate valuable information from available data, while the graphs reveal dynamic trends in traffic properties. Further, ASTCL introduces a joint convolution and attention mechanism to model intricate spatio-temporal relationships from multiple perspectives. Extensive experiments conducted on four real-world datasets demonstrate that ASTCL achieves remarkable fast adaptability and outperforms other state-of-the-art methods by a significant margin. © 1989-2012 IEEE.

关键词： Global warming

来源：评论

学校读者我要写书评

暂无评论

Big Data in Agricultural and Environmental Assessment: Opportunities and Difficulties

Big Data in Agricultural and Environmental Assessment: Oppor...

引用

Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Mrinmoy Kumar Chini Sipra Sagar D. Bhanu Vishal Sharma M. Vigenesh Santosh Kumar Department of Chemistry TMU Department of Applied Sciences Invertis University Bareilly Uttar Pradesh Department of Information Technology Karpagam Institute of Technology Coimbatore School of Media Film & Television Studies IIMT University Meerut Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore AI & Ds Vishwakarma Institute of Information Technology Pune

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Big Data in Agriculture and Ecological Analysis: Possibilities and Challenges The same capabilities that gave rise to the Internet and are revamping medicines are now being used as agriculture stands on the verge of a digitalization. In general, the cheap cost of gathering growth, together with data from temperature readings and data gathered by aircraft and satellites, is what is driving this digital agrarian economy. These technologies promise to generate more food with less resources, less land, less labor, and a lower environmental impact. The inability to gather and analyses data in a manner that produces usable farmers, as well as the need to teach farmers how to utilize such tools, are now obstacles to fulfilling this promise. The promise and obstacles to fulfilling to change agriculture are reviewed in this essay.

关键词： Natural resources Temperature distribution Costs Satellites Production Pricing Big Data Agriculture Manufacturing Monitoring

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：