检索结果-内蒙古大学图书馆

Deep learning-based large-scale named entity recognition for anatomical region of mammalian brain

Quantitative Biology 2022年第3期10卷 253-263页

作者： Xiaokang Chai Yachao Di Zhao Feng Yue Guan Guoqing Zhang Anan Li Qingming Luo Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for OptoelectronicsMoE Key Laboratory for Biomedical PhotonicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Biomedical Engineering of Hainan Province School of Biomedical EngineeringHainan UniversityHaikou 570228China CAS Key Laboratory of Computational Biology Bio-Med Big Data CenterShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesShanghai 200031China Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging Chinese Academy of Medical SciencesHUST-Suzhou Institute for BrainsmaticsJITRISuzhou 215123China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Background:Images of anatomical regions and neuron type distribution,as well as their related literature are valuable assets for neuroscience *** are vital evidence and vehicles in discovering new phenomena and knowledge refinement through image and text big *** knowledge acquired from image data generally echoes with the literature accumulated over the *** knowledge within the literature can provide a comprehensive context for a deeper understanding of the image ***,it is quite a challenge to manually identify the related literature and summarize the neuroscience knowledge in the large-scale ***,neuroscientists are in dire need of an automated method to extract neuroscience knowledge from large-scale ***:A proposed deep learning model named BioBERT-CRF extracts brain region entities from the WhiteText *** model takes advantage of BioBERT and CRF to predict entity labels while ***:The proposed deep learning model demonstrated comparable performance against or even outperforms the previous models on the WhiteText *** BioBERT-CRF model has achieved the best average precision,recall,and F1 score of 81.3%,84.0%,and 82.6%,*** used the BioBERT-CRF model to predict brain region entities in a large-scale PubMed abstract dataset and used a rule-based method to normalize all brain region entities to three neuroscience ***:Our work shows that the BioBERT-CRF model can be well-suited for brain region entity *** rankings of different brain region entities by their appearance in the large-scale corpus indicate the anatomical regions that researchers are most concerned about.

关键词： brain region entity extraction literature mining WhiteText deep learning

来源：评论

学校读者我要写书评

暂无评论

Block-Structured Optimization for Subgraph Detection in Interdependent Networks

arXiv

引用

arXiv 2022年

作者： Jie, Fei Wang, Chunpai Chen, Feng Li, Lei Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University at Albany – SUNY AlbanyNY United States Erik Jonsson School of Engineering & Computer Science The University of Texas at Dallas DallasTX United States Mininglamp Academy of Sciences Mininglamp Technologies Beijing China Institute of Big Knowledge Science Hefei University of Technology Hefei China

We propose a generalized framework for block-structured nonconvex optimization, which can be applied to structured subgraph detection in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others. Specifically, we design an effective, efficient, and parallelizable projection algorithm, namely Graph Block-structured Gradient Projection (GBGP), to optimize a general non-linear function subject to graph-structured constraints. We prove that our algorithm: 1) runs in nearly-linear time on the network size;2) enjoys a theoretical approximation guarantee. Moreover, we demonstrate how our framework can be applied to two very practical applications and conduct comprehensive experiments to show the effectiveness and efficiency of our proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Functions

来源：评论

学校读者我要写书评

暂无评论

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

引用

Engineering 2021年第6期7卷 845-856页

作者： Yue Hou Qiuhan Li Chen Zhang Guoyang Lu Zhoujing Ye Yihan Chen Linbing Wang Dandan Cao Beijing Key Laboratory of Traffic Engineering Beijing University of TechnologyBeijing 100124China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong UniversityBeijing 100044China Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China National Center for Materials Service Safety University of Science and Technology BeijingBeijing 100083China School of Transportation Southeast UniversityNanjing 211189China Department of Civil and Environmental Engineering Virginia TechBlacksburgVA 24061USA

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and *** service quality and service life are of great importance for civil engineers as they directly affect the regular service for the ***,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation *** pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface *** technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning *** review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

关键词： Pavement monitoring and analysis The state-of-the-art review Intrusive sensing Image processing techniques Machine learning methods

来源：评论

学校读者我要写书评

暂无评论

Power Grid Critical State Search Based on Improved Particle Swarm Optimization 6th

Power Grid Critical State Search Based on Improved Particle ...

引用

6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Luo, Jie Deng, Hui-Qiong Li, Qin-Bin Zheng, Rong-Jin Li, Pei-Qiang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Fujian Provincial University Engineering Research Center of Smart Grid Simulation Analysis and Integrated Control Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

This article proposes a method to find the closest critical initial running state of power grid, aiming at the possible chain reaction failure caused by branch fault in power system. First of all, assuming the initial failure of power grid, considering the specific performance of relay protection, analyze the critical state of the branch in cascading trip in detail, the electrical distance between the current running state and the interlocking fault expressed in the form of nodal injection power is given. Secondly, on the basic of the relationship between the nodal injections power and the system safety, a safety level model that the system will not trigger cascading failures is established. Finally, the improved simplified mean particle swarm optimization algorithm is used to solve the model, and an example is analyzed on IEEE-14 node system. An example shows the effectiveness and feasibility of the proposed method, which provides a reference for further prevention of cascading trip accidents. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Outages

来源：评论

学校读者我要写书评

暂无评论

Does digital inclusive finance promote industrial global value chain upgrading? evidence from China’s advanced manufacturing industry

引用

Finance Research Letters 2025年 82卷

作者： Yuanyuan Cai Le Liang Yi Zhang Liangqun Qi Chengdong Wang School of Economics and Management Harbin University of Science and Technology Harbin 150080 PR China Heilongjiang Provincial Government Big Data Center Harbin 150080 PR China

This study examined digital inclusive finance (DIF), green technology innovation (GTI), digitalization, and the upgrading of industrial global value chain (GVC) in the context of a unified research framework to improve the existing research system by considering the theoretical and partial correlations among the constructs. To theoretically reveal the impact of DIF on the upgrading of the industrial GVC as well as the mediating role of GTI and the moderating role of digitalization, an empirical study was conducted with China’s advanced manufacturing industry as the object of study. The research revealed three main findings. First, DIF could effectively promote GTI in and the GVC upgrading of advanced manufacturing industries. Second, GTI plays a partial mediating role in the process of DIF’s promotion of the GVC upgrading of advanced manufacturing industries. Third, the level of digitalization positively moderates the relation between DIF and the GVC upgrading of advanced manufacturing industries. Based on the results, strategies for promoting the GVC upgrading of China’s advanced manufacturing industries are proposed.

关键词： Advanced manufacturing Digital inclusive finance Digitalization Green technology innovation Industrial GVC upgrading

来源：评论

学校读者我要写书评

暂无评论

TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

arXiv

引用

arXiv 2023年

作者： Yang, Xinquan Xie, Jinheng Li, Xuguang Li, Xuechen Li, Xin Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China National University of Singapore Singapore

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

Povm-Based Quantum Self-Attention Neural Network

Povm-Based Quantum Self-Attention Neural Network

引用

International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Jiachun Wei Zhimin He Chuangtao Chen Maijie Deng Haozhen Situ School of Mathematics and Big data Foshan University Foshan China School of Electronic and Information Engineering Foshan University Foshan China Faculty of Innovation Engineering Macau University of Science and Technology Macau China School of Mechatronic Engineering and Automation Foshan University Foshan China College of Mathematics and Informatics South China Agricultural University Guangzhou China

Quantum Self-Attention Neural Network (QSANN) has demonstrated remarkable potential. However, it is limited to measuring only partial information from qubits, thereby restricting its information extraction capability. Moreover, the measurement process requires multiple measurements for each qubit, leading to inefficiency in utilizing the feature space. To address these challenges, this paper introduces a novel approach called Positive Operator-Valued Measure based Quantum Self-Attention Neural Network (POVM-QSANN). It leverages POVM operators as observables, enabling the extraction of comprehensive QKV (Query-Key-Value) feature vectors from each qubit. This innovative mechanism significantly enhances information extraction capability and efficiently utilizes the feature space. The experimental results underscore the considerable advancements of POVM-QSANN on MC and RP datasets. Particularly noteworthy is its remarkable accuracy increase of 9.68% compared to QSANN on the RP dataset.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging

arXiv

引用

arXiv 2022年

作者： Chen, Yixiong Liu, Li Li, Jingxian Jiang, Hua Ding, Chris Zhou, Zongwei The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China The Hong Kong University of Science and Technology Guangzhou China Software School Fudan University Shanghai China Johns Hopkins University Baltimore United States

In medical image analysis, transfer learning is a powerful method for deep neural networks (DNNs) to generalize well on limited medical data. Prior efforts have focused on developing pre-training algorithms on domains such as lung ultrasound, chest X-ray, and liver CT to bridge domain gaps. However, we find that model fine-tuning also plays a crucial role in adapting medical knowledge to target tasks. The common fine-tuning method is manually picking transferable layers (e.g., the last few layers) to update, which is labor-expensive. In this work, we propose a meta-learning-based LR tuner, named MetaLR, to make different layers automatically co-adapt to downstream tasks based on their transferabilities across domains. MetaLR learns appropriate LRs for different layers in an online manner, preventing highly transferable layers from forgetting their medical representation abilities and driving less transferable layers to adapt actively to new domains. Extensive experiments on various medical applications show that MetaLR outperforms previous state-of-the-art (SOTA) fine-tuning strategies. Codes are released. © 2022, CC BY.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

MedLSAM: Localize and Segment Anything Model for 3D CT Images

arXiv

引用

arXiv 2023年

作者： Lei, Wenhui Xu, Wei Li, Kang Zhang, Xiaofan Zhang, Shaoting School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Shanghai AI Lab Shanghai China School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China

Recent advancements in foundation models have shown significant potential in medical image analysis. However, there is still a gap in models specifically designed for medical image localization. To address this, we introduce MedLAM, a 3D medical foundation localization model that accurately identifies any anatomical part within the body using only a few template scans. MedLAM employs two self-supervision tasks: unified anatomical mapping (UAM) and multi-scale similarity (MSS) across a comprehensive dataset of 14,012 CT scans. Furthermore, we developed MedLSAM by integrating MedLAM with the Segment Anything Model (SAM). This innovative framework requires extreme point annotations across three directions on several templates to enable MedLAM to locate the target anatomical structure in the image, with SAM performing the segmentation. It significantly reduces the amount of manual annotation required by SAM in 3D medical imaging scenarios. We conducted extensive experiments on two 3D datasets covering 38 distinct organs. Our findings are twofold: 1) MedLAM can directly localize anatomical structures using just a few template scans, achieving performance comparable to fully supervised models;2) MedLSAM closely matches the performance of SAM and its specialized medical adaptations with manual prompts, while minimizing the need for extensive point annotations across the entire dataset. Moreover, MedLAM has the potential to be seamlessly integrated with future 3D SAM models, paving the way for enhanced segmentation performance. Our code is public at https://***/openmedlab/MedLSAM. © 2023, CC BY.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Deep Q-Learning Strategy for Routing Schemes in SDN-Based data Centre Networks

An Adaptive Deep Q-Learning Strategy for Routing Schemes in ...

引用

Advances in Informatics, Electronics and Education (ISAIEE), International Symposium on

作者： Jian Li Shuoyu Wang Yubo Huang Kai Liao Feili Bi Xia Lou College of Mathematics and Information Science Guiyang University Guiyang China College of Information Science and Technology Tibet University Lasa China College of Civil Engineering Southwest Jiaotong University Chengdu China College of Big Data and Information Engineering Guizhou University Guiyang China Guizhou Vocational College of Applied Technology Fuquan China

The enhancing size of uses on the cloud has improvised the requirement for dependable and elite execution network engineering in datacentres. Programming Defined Networking has worked on the adaptability, postponement, and throughput of networks in contrast with static arrangements. To adjust to the quick advancement of distributed computing, enormous information, and different innovations, the mix of server farm rout and SDN is anticipated to type network the executives more advantageous and adaptable. With this benefit, routing methodologies have been widely concentrated by specialists. In any case, the systems in the regulator chiefly depend on manual plan, the ideal arrangements are hard to be acquired in the powerful network climate. A few routing calculations, for example, network geographies with repetitive connections, for example, Fat-tree to give proficient burden adjusting, be that as it may, the failure of these plans to adjust to quickly changing traffic conduct restricts their exhibition. This research suggests DL-based networking plan for SDN. We utilize an adaptive deep Q-network (ADQN) to fabricate the deep reinforcement learning routing plan. We exhibit the adequacy of the proposed framework through broad reproductions. Profiting from nonstop learning with a worldwide view, the proposed framework has lower stream fruition time, throughput and better burden stability as well as better vigor, contrasted with OSPF.

关键词： Technological innovation Regulators Q-learning Organizations Programming Throughput Routing

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：