检索结果-内蒙古大学图书馆

A transition-based neural framework for Chinese nested entity and relation recognition

IEEE Access 2024年 1-1页

作者： Zhang, Junchi Liu, Mengchi School of Computer Science and Engineering Wuhan Institute of Technology Wuhan Hubei 430073 China Guangzhou Key Laboratory of Big Data and Intelligent School of Computer Science South China Normal University Guangzhou China

Entity and relation extraction for Chinese texts are typical performed in a pipelined fashion in the sense that by first segmenting sequence into words then recognizing entities and relations subsequently. However, this process often leads to the problem of error propagation and prevents cross-task information integration. To address this issue, we propose a novel transition-based model that performs nested entity recognition and relation extraction jointly without the need of word segmentation beforehand, which is achieved by leveraging the recent advance in using a lattice structure for Chinese sentence encodings. On standard ACE benchmarks, our model gives the best results in the literature. Further analyses show the effectiveness of the proposed architecture in capturing structural outputs. Author

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Energy Consumption Prediction of Public Buildings Based on MEA-BP Neural Network 33

Energy Consumption Prediction of Public Buildings Based on M...

引用

第33届中国控制与决策会议

作者： Shuai Wang Hongyan Ma Jiaming Dou Yongxue Ye Jingjian Yang Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education Institute of Distributed Energy Storage Safety Big Data

ISBN: (纸本)9781665431293

data mining technology is often adopted in construction engineering to provide relevant information and reference basis for public building energy consumption *** paper proposes an optimization algorithm applying back propagation(BP) neural network and mind evolutionary algorithm(MEA) to establish a model to predict building energy *** main weather data are used as inputs to the BP neural network and daily electricity consumption is used as an *** MEA is employed to optimise the structure of the network and to predict the energy consumption of *** results demonstrate that the optimized BP neural network has higher fitting accuracy,and the predicted load value is more consistent with the actual value.

关键词： Building energy consumption BP neural network Mind evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

Enhancing Drug-Drug Interaction Prediction by Knowledge Graph Embedding

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： CHAO LIU XIZHAO WANG QIANG LIU Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug).

关键词： Drugs Measurement Correlation Knowledge graphs Atoms Predictive models Graph neural networks Reliability Chemicals Cybernetics

来源：评论

学校读者我要写书评

暂无评论

A pruning method based on the dissimilarity of angle among channels and filters

arXiv

引用

arXiv 2022年

作者： Yao, Jiayi Li, Ping Kang, Xiatao Wang, Yuzhe Changsha University of Science and Technology China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transp China

Convolutional Neural Network (CNN) is more and more widely used in various fileds, and its computation and memory-demand are also increasing significantly. In order to make it applicable to limited conditions such as embedded application, network compression comes out. Among them, researchers pay more attention to network pruning. In this paper, we encode the convolution network to obtain the similarity of different encoding nodes, and evaluate the connectivity-power among convolutional kernels on the basis of the similarity. Then impose different level of penalty according to different connectivity-power. Meanwhile, we propose Channel Pruning base on the Dissimilarity of Angle (DACP). Firstly, we train a sparse model by GL penalty, and impose an angle dissimilarity (AD) constraint on the channels and filters of convolutional network to obtain a more sparse structure. Eventually, the effectiveness of our method is demonstrated in the section of experiment. On CIFAR-10, we reduce 66.86% FLOPs on VGG-16 with 93.31% accuracy after pruning, where FLOPs represents the number of floating-point operations per second of the model. Moreover, on ResNet-32, we reduce FLOPs by 58.46%, which makes the accuracy after pruning reach 91.76%. Our code is made public at: https://***/kangxiatao/prune_tf2_master. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

A Pruning Method Based on the Dissimilarity of Angle Among Channels and Filters

A Pruning Method Based on the Dissimilarity of Angle Among C...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jiayi Yao Ping Li Xiatao Kang Yuzhe Wang Changsha University of Science and Technology China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transp China

关键词： Convolution Encoding Convolutional neural networks Kernel Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion

arXiv

引用

arXiv 2024年

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024, CC BY.

关键词： Color image processing

来源：评论

学校读者我要写书评

暂无评论

Global, regional, and national burden of HIV-negative tuberculosis, 1990-2021: findings from the Global Burden of Disease Study 2021

引用

Infectious Diseases of Poverty 2024年第4期13卷 101-102页

作者： Shun-Xian Zhang Feng-Yu Miao Jian Yang Wen-Ting Zhou Shan Lv Fan-Na Wei Yu Wang Xiao-Jie Hu Ping Yin Pei-Yong Zheng Ming Yang Mei-TiWang Xin-Yu Feng Lei Duan Guo-Bing Yang Ji-Chun Wang Zhen-Hui Lu Longhua Hospital Shanghai University of Traditional Chinese Medicine Shanghai 200032 China National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research) NHC Key Laboratory of Parasite and Vector Biology WHO Collaborating Centre for Tropical Diseases National Center for International Research On Tropical Diseases National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases Shanghai 200025 China School of Global Health Chinese Center for Tropical Diseases Research-Shanghai Jiao Tong University School of Medicine Shanghai 200025 China Beijing Municipal Health Big Data and Policy Research Center Beijing Institute of Hospital Management Beijing 101100 China Department of Science and Technology Chinese Center for Disease Control and Prevention National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases Beijing 102206 China National Health Commission (NHC) Key Laboratory of Biosafety National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases Beijing 102206 China Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Shanghai 200032 China Gansu Provincial Center for Disease Control and Prevention Lanzhou 730000 China

Background Tuberculosis(TB)is a major infectious disease with significant public health *** widespread transmission,prolonged treatment duration,notable side effects,and high mortality rate pose severe *** study examines the epidemiological characteristics of TB globally and across major regions,providing a scientific basis for enhancing TB prevention and control measures *** The ecological study used data from the Global Burden of Disease(GBD)Study *** assessed new incidence cases,deaths,disability-adjusted life years(DALYs),and trends in age-standardized incidence rates(ASIRs),mortality rates(ASMRs),and DALY rates for drug-susceptible tuberculosis(DS-TB),multidrug-resistant tuberculosis(MDR-TB),and extensively drug-resistant tuberculosis(XDR-TB)from 1990 to 2021.A Bayesian age-period-cohort model was applied to project ASIR and *** In 2021,the global ASIR for all HIV-negative TB was 103.00 per 100,000 population 95%uncertainty interval(UI):92.21,114.91 per 100,000 populationl,declining by 0.40%(95%UI:-0.43,-0.38%)compared to *** global ASMR was 13.96 per 100,000 population(95%UI:12.61,15.72 per 100,000 population),with a decline of 0.44%(95%UI:-0.61,-0.23%)since *** global age-standardized DALY rate for HIV-negative TB was 580.26 per 100,000 population(95%UI:522.37,649.82 per 100,000 population),showing a decrease of 0.65%(95%UI:-0.69,-0.57 per 100,000 population)from *** global ASIR of MDR-TB has not decreased since 2015,instead,it has shown a slow upward trend in recent *** ASIR of XDR-TB has exhibited significant increase in the past 30 *** projections indicate MDR-TB and XDR-TB are expected to see signifcant increases in both ASIR and ASMR from 2022 to 2035,highlighting the growing challenge of drug-resistant *** This study found that the ASIR of MDR-TB and XDR-TB has shown an upward trend in recent *** reduce the TB burden,it is essential to enhance health infrastru

关键词： Tuberculosis Epidemiology Global burden of disease Sociodemographic Index

来源：评论

学校读者我要写书评

暂无评论

Domain Adaptation Random Vector Functional Link for White Blood Cell Classification

Domain Adaptation Random Vector Functional Link for White Bl...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qin Wang Xi-Zhao Wang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells.

关键词： White blood cells Training Deep learning Adaptation models Neural networks Focusing Vectors data models Medical diagnosis Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

arXiv

引用

arXiv 2024年

作者： Deng, Kaijun Zheng, Dezhi Xie, Jindong Wang, Jinbao Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China Department of Computer Science University of Exeter United Kingdom

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code will be publicly available at https://***/CVI-SZU/DEGSTalk. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

来源：评论

学校读者我要写书评

暂无评论

A Multimodal Knowledge Representation Method for Fake News Detection

A Multimodal Knowledge Representation Method for Fake News D...

引用

Computer, Control and Robotics (ICCCR), International Conference on

作者： Fanhao Zeng Jiaxin Yao Yijie Xu Yanhua Liu Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Engineering Research Center of Big Data Intelligence Ministry of Education (Fuzhou University) Fuzhou China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

To address the key challenge encountered in fake news detection, i.e., multimodal data is difficult to be effectively semantically represented due to its intrinsic heterogeneity, this paper proposes a multimodal knowledge representation method for fake news detection. First, visual feature extraction is performed for fake news image data, the relevant images are sliced into multiple blocks, and then visual modal features are obtained by linear projection layer mapping. This simplifies the feature extraction process and reduces the computational cost, which helps to improve the fake news recognition performance. Second, to meet the actual fake news detection needs, a long text representation method based on topic words is investigated for the text data in fake news. Finally, the multimodal representation of the same fake news data is optimized by establishing a connection between two different modalities, visual and text, and inputting it into a BiLSTM-Attention based network to achieve the fusion of multimodal features. The experiment selects the same fake news data of EANN model and uses four classical classification methods to verify the effect of knowledge representation and compare it with the fusion model ViLT which is not optimized for long text. The experiment proves that the accuracy rate of fake news detection using the multimodal representation proposed in this paper is improved by 7.4% compared to the EANN model, and by 9.3% compared to the ViLT representation.

关键词： Digital control Visualization Accuracy Knowledge representation Machine learning Feature extraction data models

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：