检索结果-内蒙古大学图书馆

Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke:development and validation of a hybrid machine learning model

引用

Stroke & Vascular Neurology 2024年第6期9卷 631-639页

作者： Ximing Nie Jinxu Yang Xinxin Li Tianming Zhan Dongdong Liu Hongyi Yan Yufei Wei Xiran Liu Jiaping Chen Guoyang Gong Zhenzhou Wu Zhonghua Yang Miao Wen Weibin Gu Yuesong Pan Yong Jiang Xia Meng Tao Liu Jian Cheng Zixiao Li Zhongrong Miao Liping Liu Department of Neurology Capital Medical UniversityBeijingChina China National Clinical Research Center for Neurological Diseases Beijing Tiantan HospitalBeijingChina School of Computer and Communication Engineering University of Science and TechnologyBeijingChina Department of Radiology Beijing Tiantan HospitalBeijingChina Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationSchool of Biological Science and Medical Engineering International Research Institute for Multidisciplinary ScienceBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Computer Science and EngineeringBeihang UniversityBeijingChina Department of Interventional Neuroradiology Beijing Tiantan HospitalCapital Medical UniversityBeijingChina

Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and ***,we present a novel risk stratification system based on hybrid machine learning method for predicting futile *** Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management *** models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following *** optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation *** Using a hybrid feature selection approach,we trained and tested multiple classifiers on two independent patient cohorts(n=1122)to develop a hybrid machine learning-based prediction *** model demonstrated superior discriminative ability compared with other models and scoring systems(area under the curve=0.80,95%CI 0.73 to 0.87)and was transformed into a web application(RESCUE-FR Index)that provides a risk stratification system for individual prediction(accessible online atfr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

关键词： database prediction optimal

来源：评论

学校读者我要写书评

暂无评论

Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

引用

Digital Communications and Networks 2024年第2期10卷 498-508页

作者： Zhenkun Jin Yixuan Geng Chenlu Zhu Yunzhi Xia Xianjun Deng Lingzhi Yi Xianlan Wang Wuhan Business University 430056China Wuhan Research Institute of Post and Telecommunication Wuhan430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecuritySchool of Cyber Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China Hubei Chutian Expressway Digital Technology Co.Ltd Wuhan430074China School of Information and Safety Engineering Zhongnan University of Economics and LawWuhan430073China

Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited *** energy harvesting WSN is a novel network architecture to address the limitation of traditional ***,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical *** considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual *** Confident Information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor *** the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation *** simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.

关键词： Energy harvesting WSN Deployment optimization Confident information coverage(CIC) Target perpetual coverage

来源：评论

学校读者我要写书评

暂无评论

Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

引用

big data Mining and Analytics 2024年第1期7卷 55-74页

作者： Guosheng Cui Ye Li Jianzhong Li Jianping Fan Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern *** has been widely used and studied in the multi-view clustering tasks because of its *** study proposes a general semi-supervised multi-view nonnegative matrix factorization *** algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different *** specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is *** on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

关键词： multi-view semi-supervised clustering discriminative information geometric information feature normalizing strategy

来源：评论

学校读者我要写书评

暂无评论

A Graph with Adaptive AdjacencyMatrix for Relation Extraction

引用

computers, Materials & Continua 2024年第9期80卷 4129-4147页

作者： Run Yang YanpingChen Jiaxin Yan Yongbin Qin Engineering Research Center of Text Computing&Cognitive Intelligence Ministry of EducationGuizhou UniversityGuiyang550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China College of Computer Science and Technology Guizhou UniversityGuiyang550025China

The relation is a semantic expression relevant to two named entities in a *** a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes dependency information specific to the two named *** related work,graph convolutional neural networks are widely adopted to learn semantic dependencies,where a dependency tree initializes the adjacency ***,this approach has two main ***,parsing a sentence heavily relies on external toolkits,which can be ***,the dependency tree only encodes the syntactical structure of a sentence,which may not align with the relational semantic *** this paper,we propose an automatic graph learningmethod to autonomously learn a sentence’s structural *** of using a fixed adjacency matrix initialized by a dependency tree,we introduce an Adaptive Adjacency Matrix to encode the semantic dependency between *** elements of thismatrix are dynamically learned during the training process and optimized by task-relevant learning objectives,enabling the construction of task-relevant semantic dependencies within a *** model demonstrates superior performance on the TACRED and SemEval 2010 datasets,surpassing previous works by 1.3%and 0.8%,*** experimental results show that our model excels in the relation extraction task,outperforming prior models.

关键词： Relation extraction graph convolutional neural network adaptive adjacency matrix

来源：评论

学校读者我要写书评

暂无评论

Improving Entity Linking in Chinese Domain by Sense Embedding Based on Graph Clustering

引用

Journal of computer science & Technology 2023年第1期38卷 196-210页

作者：张照博钟芷漫袁平鹏金海 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity *** is of great significance to some NLP(natural language processing)tasks,such as question *** English entity linking,Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words,which is more evident in certain *** Chinese domains,such as industry,the generated candidate entities are usually composed of long strings and are heavily *** addition,the meanings of the words that make up industrial entities are sometimes *** semantic space is a subspace of the general word embedding space,and thus each entity word needs to get its exact ***,we propose two schemes to achieve better Chinese entity ***,we implement an ngram based candidate entity generation method to increase the recall rate and reduce the nesting ***,we enhance the corresponding candidate entity ranking mechanism by introducing sense *** the contradiction between the ambiguity of word vectors and the single sense of the industrial domain,we design a sense embedding model based on graph clustering,which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with *** test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general *** confirm that our method can better learn candidate entities’fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.

关键词： natural language processing(NLP) domain entity linking computational linguistics word sense disambiguation knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Seer: Accelerating Block chain Transaction Execution by Fine-Grained Branch Prediction 51st

Seer: Accelerating Block chain Transaction Execution by Fine...

引用

51st International Conference on Very Large data Bases, VLDB 2025

作者： Zhang, Shijie Cheng, Ru Liu, Xinpeng Xiao, Jiang Jin, Hai Li, Bo National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Increasingly popular decentralized applications (dApps) with complex application logic incur significant overhead for executing smart contract transactions, which greatly limits public block chain performance. Pre-executing transactions off the critical path can mitigate substantial I/O and computation costs during execution. However, pre-execution does not yield any state transitions, rendering the system state inconsistent with actual execution. This inconsistency can lead to deviations in pre-execution paths when processing smart contracts with multiple state-related branches, thus diminishing pre-execution effectiveness. In this paper, we develop Seer, a novel public block chain execution engine that incorporates fine-grained branch prediction to fully exploit pre-execution effectiveness. Seerpredicts state-related branches using a two-level prediction approach, reducing inconsistent execution paths more efficiently than executing all possible branches. To enable effective reuse of pre execution results, Seer employs checkpoint-based fast-path execution, enhancing transaction execution for both successful and unsuccessful predictions. Evaluations with realistic block chain workloads demonstrate that Seer delivers an average of 27.7× transaction-level speedup and an overall 20.6× speedup in the execution phase over vanilla Ethereum, outperforming existing block chain execution acceleration solutions. © 2025, VLDB Endowment, All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Applying Hybrid Clustering in Pulsar Candidate Sifting with Multi-modality for FAST Survey

引用

research in Astronomy and Astrophysics 2024年第3期24卷 283-296页

作者： Zi-Yi You Yun-Rong Pan Zhi Ma Li Zhang Shuo Xiao Dan-Dan Zhang Shi-Jun Dang Ru-Shuang Zhao Pei Wang Ai-Jun Dong Jia-Tao Jiang Ji-Bing Leng Wei-An Li Si-Yao Li School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Key Laboratory of Information and Computing Guizhou Province Guizhou Normal UniversityGuiyang 550001China School of Big data and Computer Science Guizhou Normal UniversityGuiyang 550025China Guizhou Software Engineering Research Center Guiyang 550000China

Pulsar search is always the basis of pulsar navigation,gravitational wave detection and other research ***,the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope(FAST)shows an explosive growth rate that has brought challenges for its pulsar candidate filtering ***,the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification *** this study,a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented,which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for *** on both High Time Resolution Universe SurveyⅡ(HTRU2)and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars:On HTRU2,the precision and recall rates of its parallel mode reach0.981 and 0.988 *** FAST data,those of its parallel mode reach 0.891 and 0.961,meanwhile,the running time also significantly decreases with the increment of parallel nodes within ***,we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.

关键词： methods data analysis-surveys-methods numerical

来源：评论

学校读者我要写书评

暂无评论

Hybrid models for accuracy and explainability in AI systems 3

Hybrid models for accuracy and explainability in AI systems

引用

3rd International Conference on Electronic Information engineering, big data, and computer Technology, EIBDCT 2024

作者： Yu, Yunxia School of Computer Engineering Jingchu University of Technology Hubei Jingmen448000 China Big Data Research Center Jingchu University of Technology Hubei Jingmen448000 China

ISBN: (纸本)9781510680449

Hybrid models for accuracy and explainability in AI systems are those that combine different types of AI techniques, such as machine learning, fuzzy logic, and knowledge representation, to achieve both high performance and transparency in decision making. In this paper, we propose or use a hybrid model that integrates these techniques and applies it to a specific domain and application. We evaluate our hybrid model using both empirical and theoretical methods, and compare our results with other approaches or baselines. We also conduct a user study to assess the usability and satisfaction of our hybrid model. We show that our hybrid model can achieve both high accuracy and explainability, and that it can provide different forms and levels of explanation, such as natural language, visualizations, or logic rules. We also discuss the implications and contributions of our paper, and outline the future directions and challenges for hybrid models. We hope that our paper can advance the state-of-the-art of hybrid models for accuracy and explainability in AI systems, and that it can inspire further research and development in this area. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

CDA-GNN: A Chain-driven Accelerator for Efficient Asynchronous Graph Neural Network 24

CDA-GNN: A Chain-driven Accelerator for Efficient Asynchrono...

引用

61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Yu, Hui Zhang, Yu He, Ligang He, Donghao Li, Qikun Zhao, Jin Liao, Xiaofei Jin, Hai Gu, Lin Liu, Haikun National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science University of Warwick United Kingdom

ISBN: (纸本)9798400706011

Asynchronous Graph Neural Network (AGNN) has attracted much research attention because it enables faster convergence speed than the synchronous GNN. However, existing software/hardware solutions suffer from redundant computation overhead and excessive off-chip communications for AGNN due to irregular state propagations along the dependency chains between vertices. This paper proposes a chain-driven asynchronous accelerator, CDA-GNN, for efficient AGNN inference. Specifically, CDA-GNN proposes a chain-driven asynchronous execution approach into novel accelerator design to regularize the vertex state propagations for fewer redundant computations and off-chip communications and also designs a chain-aware data caching method to improve data locality for AGNN. We have implemented and evaluated CDA-GNN on a Xilinx Alveo U280 FPGA card. Compared with the cutting-edge software solutions (i.e., Dorylus and AMP) and hardware solutions (i.e., BlockGNN and FlowGNN), CDA-GNN improves the performance of AGNN inference by an average of 1,173x, 182.4x, 10.2x, and 7.9x and saves energy by 2,241x, 242.2x, 12.4x, and 8.9x, respectively. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Atomic Spin Polarization Controllability Analysis:A Novel Controllability Determination Method for Spin-Exchange Relaxation-Free Co-Magnetometers

引用

IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 699-708页

作者： Zhuo Wang Sixun Liu Ruigang Wang Linlin Yuan Jiong Huang Yueyang Zhai Sheng Zou IEEE the Research Institute for Frontier Science the Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University the School of Instrumentation and Optoelectronic Engineering Beihang University the Research Institute of Frontier Science Beihang University

This paper investigates the atomic spin polarization controllability of spin-exchange relaxation-free co-magnetometers(SERFCMs).This is the first work in the field of controllability analysis for the atomic spin ensembles systems,whose dynamic behaviors of spin polarization are described by the Bloch *** on the Bloch equations,a state-space model of the atomic spin polarization for SERFCM is first established,which belongs to a particular class of nonlinear *** this class of nonlinear systems,a novel determination method for the global state controllability is proposed and ***,this method is implemented in the process of controllability analysis on the atomic spin polarization of an actual ***,a theoretically feasible and reasonable solution of the control input is proposed under some physical constraints,with whose limitation of realistic conditions,the controller design can be accomplished more practically and more ***,the simulation results demonstrate the feasibility and validation of the proposed controllability determination method.

关键词： Atomic spin polarization Bloch equations controllability determination nonlinear state-space model spin-exchange relaxation-free co-magnetometer(SERFCM)

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：