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arXiv 2023年

作者： Zhang, Hailin Wang, Yujing Chen, Qi Chang, Ruiheng Zhang, Ting Miao, Ziming Hou, Yingyan Ding, Yang Miao, Xupeng Wang, Haonan Pang, Bochen Zhan, Yuefeng Sun, Hao Deng, Weiwei Zhang, Qi Yang, Fan Xie, Xing Yang, Mao Cui, Bin School of Computer Science Key Lab of High Confidence Software Technologies Peking University China Microsoft United States Aerospace Information Research Institute Key Laboratory of Target Cognition and Application Technology Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Carnegie Mellon University United States National University of Singapore Singapore National Engineering Laboratory for Big Data Analysis and Applications Peking University China

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with endto- end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Modelenhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embeddingbased models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Retinal vascular segmentation algorithm based on Atrous attention U-net

Retinal vascular segmentation algorithm based on Atrous atte...

引用

Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Yuchen Li Shenming Gu School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan China

ISBN: (纸本)9781665464697

The segmentation of retinal blood vessels is helpful in the diagnosis of diabetes, hypertension and other diseases. However, retinal blood vessel segmentation has become a big problem due to different vessel sizes, complex vessel structures, and limited annotated data sets. Deep learning is widely used in the field of medical image segmentation, among which U-net is widely used due to its good effect in small target segmentation and its scalable structure. In this paper, an improved model based on U-net, namely Atrous Attention U-net (AAU-net), is proposed, in which the Atrous convolution module and attention gate are added. Compared with the ordinary convolution module, the Atrous convolution module expands the receptive field and improves the correlation between objects. Before splicing the corresponding features in encoder and decoder, an integrated attention gate (AGs) is added, which can highlight the significant features transferred by skipping the connection. A series of experiments on the public DRIVE dataset show that the performance of this method is improved compared with the traditional U-shaped network, and the structure is relatively simple compared with other improved methods.

关键词： Training Image segmentation Correlation Convolution Splicing Logic gates Blood vessels

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Energy-Efficient Probabilistic Semantic Communication over Space-Air-Ground Integrated Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Zhao, Zhouxiang Yang, Zhaohui Chen, Mingzhe Zhu, Chen Xu, Wei Zhang, Zhaoyang Huang, Kaibin Zhejiang University College of Information Science and Electronic Engineering China Zhejiang Provincial Key Laboratory of Info. Proc. Commun Hangzhou310027 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Zhejiang University Polytechnic Institute Hangzhou310015 China Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

Space-air-ground integrated networks (SAGINs) are emerging as a pivotal element in the evolution of future wireless networks. Despite their potential, the joint design of communication and computation within SAGINs remains a formidable challenge. In this paper, the problem of energy efficiency in SAGIN-enabled probabilistic semantic communication (PSCom) system is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSCom technique to compress the transmitting data, while the GTs can automatically recover the missing information. The PSCom is underpinned by shared probabilistic graphs that serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Through analysis, the computation overhead function in PSCom is a piecewise function with respect to the semantic compression ratio. Therefore, it is important to make a balance between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, power, computation capacity, bandwidth, semantic compression ratio, and UAV location constraints. To solve this non-convex non-smooth problem, we propose an iterative algorithm where the closed-form solutions for computation capacity allocation and UAV altitude are obtained at each iteration. Numerical results show the effectiveness of the proposed algorithm. © 2002-2012 IEEE.

关键词： Drones

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A Profit-Oriented FDI Attack Strategy in Real-Time Peer-to-Peer Energy Trading Market

A Profit-Oriented FDI Attack Strategy in Real-Time Peer-to-P...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Aichao Zhang Shunbo Lei Chongyang Wan Haoran Zhang Yuan Luo School of Science and Engineering The Chinese University of Hong Kong Shenzhen P.R. China Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network School of Science and Engineering The Chinese University of Hong Kong Shenzhen P.R. China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen P.R. China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

The emergence of peer-to-peer (P2P) energy trading market brings both benefits and risks to modern power system. Relying on information and communication technology (ICT), transaction in P2P energy trading market is vulnerable to false data injection attacks (FDIAs). Specifically, the malicious redistribution of short-term forecasting load data will mislead the economic dispatch (ED) in the ex-ante real-time (RT) market. Hence, the actual generation outputs deviate from the schedule, and the corrupt result of ED can affect the trading volume in RT market through a P2P framework. Accordingly, targeting on a specific agent in P2P market, we propose a strategy to inject a malicious forecasting data in order to maximize the trading profits for this agent. Moreover, since the P2P model often solved in a distributed way, which brings heavy computational burden, we conduct a preferred locational marginal price (LMP) to give a one-time solution. Eventually, we demonstrate the effectiveness of our attacking strategy based on the IEEE 9-bus test system.

关键词： Economics Schedules Energy Internet System integration Electricity supply industry Real-time systems Peer-to-peer computing Information and communication technology Forecasting Optimization

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Multi-Temporal Relationship Inference in Urban Areas

arXiv

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arXiv 2023年

作者： Li, Shuangli Zhou, Jingbo Liu, Ji Xu, Tong Chen, Enhong Xiong, Hui School of Computer Science and Technology University of Science and Technology of China Baidu Research China Business Intelligence Lab Baidu Research China Big Data Lab Baidu Research China School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

Finding multiple temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. While some efforts have been made on finding static relationships among locations, little attention is focused on studying time-aware location relationships. Indeed, abundant location-based human activities are time-varying and the availability of these data enables a new paradigm for understanding the dynamic relationships in a period among connective locations. To this end, we propose to study a new problem, namely multi-Temporal relationship inference among locations (Trial for short), where the major challenge is how to integrate dynamic and geographical influence under the relationship sparsity constraint. Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL). SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. In addition, SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation © 2023, CC BY.

关键词： Location

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FEST: Feature Enhancement Swin Transformer for Remote Sensing Image Semantic Segmentation

FEST: Feature Enhancement Swin Transformer for Remote Sensin...

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International Conference on Computer Supported Cooperative Work in Design

作者： Ronghuan Zhang Jing Zhao Ming Li Qingzhi Zou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The global context is crucial for the precise segmentation of remote sensing images. However, the large volumes and high spatial resolutions of remote sensing images make efficient analysis of the entire scene challenging for most convolutional neural network (CNN)-based methods. To address this issue, we propose to design an innovative framework for semantic segmentation of remote sensing images called Feature Enhancement Swin Transformer (FEST). Firstly, we utilize the Swin Transformer as the encoder and incorporates a Global Information Enhancement Model (GIEM) within each Swin Transformer block to reduce information loss and enable encoding of more accurate spatial information. Secondly, we introduce an enhanced decoding structure called Enhanced Feature Fusion Module (EFFM) with added enhanced channel and spatial attention modules to retain localized information while obtaining extensive contextual information. Finally, for loss calculation, we utilize the dice and cross-entropy loss to jointly supervise the model, aiming to achieve a competitive performance. We comprehensively evaluated FEST on the ISPRS-Vaihingen and Potsdam datasets. The results indicate that our approach has achieved significant improvements in semantic segmentation tasks compared to existing methods.

关键词： Deep learning Federated learning Semantic segmentation Semantics Transformers Information retrieval Encoding

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耐瑞特:一种基于群体学习方法的新型硝酸盐制剂,可发挥更好的机体保护作用

引用

Science Bulletin 2023年第8期68卷 838-850,M0004页

作者：潘雯胡耿李韶容李国情冯晓宇吴志芳张栋秦力铮王雪胡亮徐骏疾胡磊贾翌江温欣王劲松张春梅周建李文斌王晓刚王玉记王松灵 Salivary Gland Disease Center and Beijing Key Laboratory of Tooth Regeneration and Function Reconstruction Beijing Laboratory of Oral Health and Beijing Stomatological HospitalCapital Medical University Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang University Immunology Research Center for Oral and Systemic Health Beijing Friendship HospitalCapital Medical University Department of Dental Implantology The Affiliated Stomatological Hospital of Nanjing Medical University Department of Pediatric Dentistry School of StomatologyCapital Medical University Beijing Key Laboratory of Tolerance Induction and Organ Protection in Transplantation Department of Oral Maxillofacial Surgery Capital Medical University School of Stomatology School of Pharmaceutical Sciences Capital Medical University Beijing Area Major Laboratory of Peptide and Small Molecular Drugs Engineering Research Center of Endogenous Prophylactic of Ministry of Education of China1 Department of Biochemistry and Molecular Biology School of Basic Medical SciencesCapital Medical University1 Laboratory for Oral and General Health Integration and Translation Beijing Tiantan HospitalCapital Medical University1 Research Units of Tooth Development and Regeneration Chinese Academy of Medical Sciences

硝酸盐是一种机体不可或缺的营养物质,已应用于多种疾病的预防和治疗研究.但由于半衰期短,硝酸盐的临床应用受限.为了提高硝酸盐可用性,突破传统的依赖大规模高通量生物实验的药物配伍研发瓶颈,本文报道了一种基于群体学习方法的药物配... 详细信息

硝酸盐是一种机体不可或缺的营养物质,已应用于多种疾病的预防和治疗研究.但由于半衰期短,硝酸盐的临床应用受限.为了提高硝酸盐可用性,突破传统的依赖大规模高通量生物实验的药物配伍研发瓶颈,本文报道了一种基于群体学习方法的药物配伍预测系统,该系统预测维生素C为与硝酸盐配伍的首选药物.通过采用微囊化技术,筛选并优化缓释制剂工艺,以维生素C、硝酸钠及壳聚糖3000为芯材,果胶与羧甲基纤维素钠为壁材制备硝酸盐纳米颗粒,命名为耐瑞特(Nanonitrator).耐瑞特的更长循环期显著提高了硝酸盐对放射性唾液腺损伤的疗效,同时维持了安全性.相同剂量的耐瑞特比硝酸盐(包含或不包含维生素C)可显著增强PI3K-Akt信号通路转导,更好地维持细胞内稳态,提示了其广泛的临床应用潜力.此外,本文还提供了一种将无机化合物加入缓释纳米颗粒的新方法.

关键词： Inorganic nitrate Artificial intelligence Nanomedicine Chitosan Irradiation-induced submandibular gland injury Homeostasis

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Attention-Based Fuzzy Neural Networks Designed for Early Warning of Financial Crises of Listed Companies

SSRN

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SSRN 2024年

作者： Zhao, Mengyang Song, Yan Huang, Hao Kim, Eun-Hu Shaanxi Key Laboratory of Green Preparation and Functionalization for Inorganic Materials China School of Economics and Management Harbin Engineering University Heilongjiang Harbin150001 China School of Electrical & Electronic Engineering The University of Suwon 17 Wauan-gil Bongdam-eup Gyeonggi-do Hwaseong-si18323 Korea Republic of Research Center for Big Data and Artificial Intelligence Linyi University Linyi276005 China

Developing an early warning model for company financial crises holds critical significance in robust risk management and ensuring the enduring stability of the capital market. Although the existing research has achieved rich results, the disadvantages of insufficient text information mining and poor model performance still exist. To alleviate the problem of insufficient text information mining, we collect related financial and annual report data from 820 listed companies in mainland China from 2018 to 2023 by using sophisticated web crawlers and advanced text sentiment analysis technologies and using missing value interpolation, standardization, and data balancing to build multi-source datasets of companies. Ranking the feature importance of multi-source data promotes understanding the formation of financial crises for companies. In the meantime, a novel Attention-based Fuzzy Neural Network (AFNN) was proposed to parse multi-source data to forecast financial crises among listed companies. Experimental results indicate that AFNN exhibits significantly improved performance compared to other advanced methods. © 2024, The Authors. All rights reserved.

关键词： Fuzzy neural networks

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SIGNAL-COMPARISON-BASED DISTRIBUTED ESTIMATION UNDER DECAYING AVERAGE data RATE COMMUNICATIONS∗

arXiv

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arXiv 2024年

作者： Ke, Jieming Lu, Xiaodong Zhao, Yanlong Zhang, Ji-Feng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100149 China School of Automation and Electrical Engineering Zhongyuan University of Technology Henan Province Zhengzhou450007 China

The paper investigates the distributed estimation problem under low data rate communications. Based on the signal-comparison (SC) consensus protocol under binary-valued communications, a new consensus+innovations type distributed estimation algorithm is proposed. Firstly, the high-dimensional estimates are compressed into binary-valued messages by using a periodic compressive strategy, dithering noises and a sign function. Next, based on the dithering noises and expanding triggering thresholds, a new stochastic event-triggered mechanism is proposed to reduce the communication frequency. Then, a modified SC consensus protocol is applied to fuse the neighborhood information. Finally, a stochastic approximation estimation algorithm is used to process innovations. The proposed SC-based algorithm has the advantages of high effectiveness and low communication cost. For the effectiveness, the estimates of the SC-based algorithm converge to the true value in the almost sure and mean square sense, and a polynomial almost sure convergence rate is also obtained. For the communication cost, the local and global average data rates decay to zero at a polynomial rate. The trade-off between the convergence rate and the communication cost is established through event-triggered coefficients. A better convergence rate can be achieved by decreasing event-triggered coefficients, while lower communication cost can be achieved by increasing event-triggered coefficients. A simulation example is given to demonstrate the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost benefit analysis

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Microphysical Characteristics of the 2020 Record-Breaking Meiyu Rainfall in Anhui, China

SSRN

引用

SSRN 2024年

作者： Yang, Qiqi Zhang, Shuliang Chen, Yiheng Jin, Yuhan Fang, Hongyuan Nanjing Normal University China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou China Key Laboratory of VGE Ministry of Education Nanjing Normal University Nanjing China Suzhou Big Data Research Institute Suzhou China School of Water Conservancy and Transportation Zhengzhou University Zhengzhou China Department of Civil Engineering University of Bristol Bristol United Kingdom

The 2020 Meiyu season in Anhui, China, brought unprecedented rainfall, causing severe socio-economic and ecological disruptions. This study investigates the microphysical characteristics of raindrops during the 2020 record-breaking Meiyu event using minute-level data from six disdrometer stations. Light rain events lasted longer but contributed little to total rainfall, while brief, intense rain events delivered most of the precipitation. Throughout the entire event, the mean drop diameter (Dm) increased from 1.18 mm to 1.9 mm, and mean drop concentration (log10 Nw) rose from 2.92 to 4.08 with increasing rain rate. Novel μ–Λ and Z−R relationships tailored for this extreme event uncovered significant regional variability. The Z−R relationships for total, stratiform, and convective rainfall types revealed higher coefficients than those found in other Meiyu studies, indicating denser raindrop concentrations and larger drop sizes. Cutting-edge dual-polarization radar rainfall prediction models were developed, with relationships between Zdr, Zh, Kdp, and rain rate (R) showing exceptional accuracy, as evidenced by correlation coefficients nearing 1.0 and low RMSE and NMAE values. Furthermore, various KE–R relationships were established for estimating rainfall kinetic energy (KE), with Power Law relationships providing the most accurate representation for KEtime–R and Logarithmic relationships being most effective for KEmm–R, particularly for total and stratiform rainfall. These findings enhance understanding of rainfall microphysics in extreme Meiyu events, offering critical insights for improving flood management and soil erosion control in eastern China. © 2024, The Authors. All rights reserved.

关键词： Rain

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