检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Liu, Xianyuan Zhang, Jiayang Zhou, Shuo van der Plas, Thijs L. Vijayaraghavan, Avish Grishina, Anastasiia Zhuang, Mengdie Schofield, Daniel Tomlinson, Christopher Wang, Yuhan Li, Ruizhe van Zeeland, Louisa Tabakhi, Sina Demeocq, Cyndie Li, Xiang Das, Arunav Timmerman, Orlando Baldwin-McDonald, Thomas Wu, Jinge Bai, Peizhen Al Sahili, Zahraa Alwazzan, Omnia Do, Thao N. Suvon, Mohammod N.I. Wang, Angeline Cipolina-Kun, Lucia Moretti, Luigi A. Farndale, Lucas Jain, Nitisha Efremova, Natalia Ge, Yan Varela, Marta Lam, Hak-Keung Celiktutan, Oya Evans, Ben R. Coca-Castro, Alejandro Wu, Honghan Abdallah, Zahraa S. Chen, Chen Danchev, Valentin Tkachenko, Nataliya Lu, Lei Zhu, Tingting Slabaugh, Gregory G. Moore, Roger K. Cheung, William K. Charlton, Peter H. Lu, Haiping Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom The Alan Turing Institute London United Kingdom Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Applied AI Simula Research Laboratory Oslo Norway Information School University of Sheffield Sheffield United Kingdom NHS England Leeds United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Engineering King’s College London London United Kingdom Department of Computing Science University of Aberdeen Aberdeen United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Informatics King’s College London London United Kingdom Department of Earth Sciences University of Cambridge Cambridge United Kingdom Department of Computer Science University of Manchester Manchester United Kingdom Department of Computer Science Queen Mary University of London London United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science University of Bath Bath United Kingdom Department of Classics King’s College London London United Kingdom School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol United Kingdom School of Engineering University of the West of England Bristol United Kingdom Cancer Research UK Scotland Institute Glasgow United Kingdom School of Business and Management Queen Mary University of London London United Kingdom City St George’s University of London London United Kingdom British Antarctic Survey Cambridge United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom Chief Data & AI Office Lloyds Banking Group Lo

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact. © 2025, CC BY.

关键词： Decision making

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Modeling Soybean Cultivation Suitability in China and its Future Trends in Climate Change Scenarios

SSRN

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SSRN 2023年

作者： Zhu, Qinyan Wang, Fumin Yi, Qiuxiang Zhang, Xiaoyang Chen, Siting Zheng, Jueyi Li, Jiale Xu, Tianyue Peng, Dailiang Institute of Applied Remote Sensing & Information Technology Zhejiang University Hangzhou310058 China Key Laboratory of Agricultural Remote Sensing and Information System Zhejiang University Hangzhou310058 China School of Geomatics and Municipal Engineering Zhejiang University of Water Resources and Electric Power Hangzhou311228 China Geospatial Sciences Center of Excellence Department of Geography & Geospatial Sciences South Dakota State University BrookingsSD57007 United States Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health Zhejiang University Hangzhou310058 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Soybean is an important source of oil and vegetable protein and plays a key role in agricultural production and economy. A suitability evaluation of soybean cultivation is important for identifying potential soybean planting areas. Based on the raster data of soybean harvest ratio (FSHA) and climate-soil-topographic environmental factors, we used MaxEnt to simulate the soybean planting suitability and potential distribution in China and the future trends of soybean cultivation under climate change. The result shows that the suitability of soybean cultivation was primarily influenced by elevation, precipitation of warmest quarter, capacity of the clay fraction, slope, mean diurnal temperature range, accumulated temperature ≥10℃, solar radiation and topsoil gravel content. High-suitability and moderate-suitability area are respectively 44.61 Mha and 71.12 Mha in China. High-suitability areas for soybean are mainly concentrated in the Northeast Plain, the North China Plain and the northern parts of the middle and lower Yangtze River plain. There were many provinces with high soybean planting potential but low development degrees, including Hebei, Henan, Shandong, Tianjin, Jilin, Liaoning, Jiangsu, Hubei and Shaanxi. Under future climate scenarios (SSP126, SSP245 and SSP585) of multiple global circulation models in CMIP6, the area highly or moderately suitable for soybean cultivation is generally stable, while low suitability area shows growth evidently. The total increase of three suitability area is the most obvious under SSP245 during 2021-2040, accounting for more than 5% in the base of historical data. In the future, most parts of Northeast China (eastern Inner Mongolia, Heilongjiang and eastern and northern Jilin), parts of north-central China (northern Hebei, northern Shanxi and Shaanxi) and parts of southwest China (eastern Sichuan, Chongqing, northern Guizhou and Yunnan) may be more suitable for soybean cultivation. This study provides a practical reference for

关键词： Climate change

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An Improved Result on Stabilization of Switched Linear systems with Time-varying Delay ⁎

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IFAC-PapersOnLine 2021年第18期54卷 96-101页

作者： Tan Hou Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia P.O. Box 400743 Charlottesville VA 22904-4743 U.S.A.

This paper revisits the stabilization problem of switched linear systems with time-varying delay via state dependent switching strategies. In contrast to the existing works, the commonly adopted stable convex combination assumption is relaxed in this paper. For switched systems satisfying the relaxed assumption, we design a state dependent switching strategy, under which the considered system is asymptotically stable at the origin in the presence of time-varying delay. An optimization problem is formulated to estimate the upper bound of tolerable time delay. Numerical examples are presented to show that our method is applicable to a larger class of switched systems and leads to greater delay bound.

关键词： Asymptotic stability delay bound state dependent switching switched system time-varying delay

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Analyzing historical seismic data for region-specific earthquake prediction through deep neural networks

Research Square

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Research Square 2025年

作者： Nurtas, Marat Altaibek, Aizhan Nurakynov, Serik Nessipbay, Takhmina Laboratory of Satellite and Geodynamics Institute of Ionosphere Almaty050020 Kazakhstan Department of Mathematical and Computer Modeling International Information Technology University Manas Almaty050000 Kazakhstan School of Engineering and Digital Sciences Nazarbayev University Kabanbay Batyr Avenue Astana010000 Kazakhstan

This study addresses the problem of improving the accuracy of earthquake forecasting in Kazakhstan using deep learning methods. Special attention is paid to forecasting in zones with increased seismic activity, which is a unique feature of the regional plan. The main objective of the work is to develop a model based on the architecture of deep neural networks with direct propagation of signals to analyze historical data containing the time of occurrence, geographic coordinates and magnitude of earthquakes. This model is executed as a basis for classification and regression, permitting assessment of the level of agreement between predicted values and those that were encountered. Evidence of earthquake prediction potential is shown through utilizing this methodology utilizing computational capacity as well as advanced neural network techniques. The significance of this study resides in its contribution to the availability of processes and methods to facilitate the application of deep learning in the field of seismology to improve the accuracy and effectiveness of diagnosis and prevention of natural catastrophe. © 2025, CC BY.

关键词： Earthquake effects

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3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video

arXiv

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arXiv 2021年

作者： Wang, Guangming Zhong, Jiquan Zhao, Shijie Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. A novel unsupervised training framework is proposed with 3D hierarchical refinement and augmentation using explicit 3D geometry. In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer. The intermediate view image is proposed and synthesized by warping the pixels in an image with the estimated depth and coarse pose. Then, the residual pose transformation can be estimated from the new view image and the image of the adjacent frame to refine the coarse pose. The iterative refinement is implemented in a differentiable manner in this paper, making the whole framework optimized uniformly. Meanwhile, a new image augmentation method is proposed for the pose estimation by synthesizing a new view image, which creatively augments the pose in 3D space but gets a new augmented 2D image. The experiments on KITTI demonstrate that our depth estimation achieves state-of-the-art performance and even surpasses recent approaches that utilize other auxiliary tasks. Our visual odometry outperforms all recent unsupervised monocular learning-based methods and achieves competitive performance to the geometry-based method, ORB-SLAM2 with back-end optimization. Copyright © 2021, The Authors. All rights reserved.

关键词： Robots

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Loop Quantum Photonic Chip for Coherent Multi-Time-Step Evolution

arXiv

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arXiv 2024年

作者： Zhan, Yuancheng Zhang, Hui Erbanni, Rebecca Burger, Andreas Wan, Lingxiao Jiang, Xudong Chae, Sanghoon Liu, Aiqun Poletti, Dario Kwek, Leong Chuan Nanyang Technological University Singapore Nanyang Technological University Singapore Institute of Precision Optical Engineering School of Physics Science and Engineering Tongji University Shanghai200092 China MOE Key Laboratory of Advanced Micro-Structured Materials Shanghai Institute of Intelligent Science and Technology Shanghai Frontiers Science Center of Digital Optics Tongji University Shanghai200092 China Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Department of Computer Science University of Toronto Canada The Hong Kong Polytechnic University Hong Kong Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore National Institute of Education Nanyang Technological University Singapore

Quantum evolution is crucial for the understanding of complex quantum systems. However, current implementations of time evolution on quantum photonic platforms face challenges of limited light source efficiency due to propagation loss and merely single-layer complexity. In this work, we present a loop quantum photonic chip (Loop-QPC) designed to efficiently simulate quantum dynamics over multiple time steps in a single chip. Our approach employs a recirculating loop structure to reuse computational resources and eliminate the need for multiple quantum tomography steps or chip reconfigurations. We experimentally demonstrate the dynamics of the spin-boson model on a low-loss Silicon Nitride (SiN) integrated photonic chip. The Loop-QPC achieves a three-step unitary evolution closely matching the theoretical predictions. These results establish the Loop-QPC as a promising method for efficient and scalable quantum simulation, advancing the development of quantum simulation on programmable photonic circuits. Copyright © 2024, The Authors. All rights reserved.

关键词： Silicon nitride

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Sub-milliwatt, widely-tunable coherent microcomb generation with feedback-free operation

arXiv

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arXiv 2021年

作者： Shu, Haowen Chang, Lin Lao, Chenghao Shen, Bitao Xie, Weiqiang Zhang, Xuguang Jin, Ming Tao, Yuansheng Chen, Ruixuan Tao, Zihan Yu, Shaohua Yang, Qi-Fan Wang, Xingjun Bowers, John E. State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States State Key Laboratory for Artificial Microstructure and Mesoscopic Physics School of Physics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China

Microcombs are revolutionizing optoelectronics by providing parallelized, mutually coherent wavelength channels for time-frequency metrol-ogy and information processing. To implement this essential function in integrated photonic sys-tems, it is desirable to drive microcombs directly with an on-chip laser in a simple and flexible way. However, two major difficulties are prevent-ing this goal: 1) generating mode-locked comb states usually requires a significant amount of pump power and 2) the requirement to align laser and resonator frequency significantly complicates operation and limits the tunability of the comb lines. Here, we address these problems by using microresonators on an AlGaAs on-insulator plat-form to generate dark-pulse microcombs. This highly nonlinear platform dramatically relaxes fabrication requirements and leads to a record-low pump power of less than 1 mW for coher-ent comb generation. Dark-pulse microcombs fa-cilitated by thermally-controlled avoided mode-crossings are accessed by direct DFB laser pump-ing. Without any feedback or control circuitries, the comb shows good coherence and stability. This approach also leads to an unprecedented wide chirping range of all the comb lines. Our work provides a route to realize power-efficient, simple and reconfigurable microcombs that can be seamlessly integrated with a wide range of pho-tonic systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Aluminum gallium arsenide

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

引用

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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Research of Insect Recognition Based on Improved YOLOv5

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Journal on Artificial Intelligence 2021年第4期3卷 145-152页

作者： Zhong Yuan Wei Fang Yongming Zhao Victor S.Sheng School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing100081China China Meteorological Administration Training Center Beijing100081China Department of Computer Texas Tech UniversityLubbockTX 79409USA

Insects play an important role in the natural ecology,it is of great significance for ecology to research on ***,the invasion of alien species has brought serious troubles and a lot of losses to local ***,there is still much room for improvement in the accuracy of insect recognition to effectively prevent the invasion of alien *** the latest target detection algorithm,YOLOv5 has been used in various scene detection tasks,because of its powerful recognition capabilities and extremely high *** the problem of imbalance of feature maps at different scales will affect the accuracy of recognition,we propose that adding an attention mechanism based on *** channel attention module and the spatial attention module are added to highlight the important information in the feature map and weaken the secondary information,enhancing the recognition ability of the *** training on self-made insect data sets,experimental results show that the mAP@0.5 value reaches 92.5%and the F1 score reaches *** with YOLOv5,the map has increased by 1.7%,and the F1 score has increased by 0.02,proving the effectiveness of insect recognition based on improved *** conclusion,we provide effective technical support for insect identification,especially for pest identification.

关键词： YOLOv5 attention mechanism insect identification

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MetAug: Contrastive Learning via Meta Feature Augmentation

arXiv

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arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Xiong, Hui Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Guangdong China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Guangzhou China Department of Computer Science Engineering The Hong Kong University of Science and Technology Hong Kong

What matters for contrastive learning? We argue that contrastive learning heavily relies on informative features, or "hard" (positive or negative) features. Early works include more informative features by applying complex data augmentations and large batch size or memory bank, and recent works design elaborate sampling approaches to explore informative features. The key challenge toward exploring such features is that the source multi-view data is generated by applying random data augmentations, making it infeasible to always add useful information in the augmented data. Consequently, the informativeness of features learned from such augmented data is limited. In response, we propose to directly augment the features in latent space, thereby learning discriminative representations without a large amount of input data. We perform a meta learning technique to build the augmentation generator that updates its network parameters by considering the performance of the encoder. However, insufficient input data may lead the encoder to learn collapsed features and therefore malfunction the augmentation generator. A new margin-injected regularization is further added in the objective function to avoid the encoder learning a degenerate mapping. To contrast all features in one gradient back-propagation step, we adopt the proposed optimization-driven unified contrastive loss instead of the conventional contrastive loss. Empirically, our method achieves state-of-the-art results on several benchmark datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Backpropagation

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