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A Graph with Adaptive AdjacencyMatrix for Relation Extraction

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

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A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing

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Computer Modeling in engineering & sciences 2023年第10期137卷 363-383页

作者： Yanjun Zhang Yongqiang He Jingbo Zhang Yaru Zhao Zhihua Cui Wensheng Zhang School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China Department of Big Data and Intelligent Engineering Shanxi Institute of TechnologyYangquan045000China School of Computer Science Beijing University of TechnologyBeijing100124China The Institute of Automation Chinese Academy of Sciences(CAS)Beijing100049China

The video compression sensing method based onmulti hypothesis has attracted extensive attention in the research of video codec with limited ***,the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging *** resolve this problem,this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction *** includes the optimization of prediction blocks(OPBS),the selection of searchwindows and the use of neighborhood ***,the OPBS consists of two parts:the selection of blocks and the optimization of prediction *** combine the high-quality optimization reconstruction of foreground block with the residual reconstruction of the background block to improve the overall reconstruction effect of the video *** addition,most of the existing methods based on predictive residual reconstruction ignore the impact of search windows and reference frames on ***,Block-level search window(BSW)is constructed to cover the position of the optimal hypothesis block as much as *** maximize the availability of reference frames,Nearby reference frame information(NRFI)is designed to reconstruct the current *** proposed method effectively suppresses the influence of the fluctuation of the prediction block on reconstruction and improves the reconstruction *** results showthat the proposed compressed sensing-based high-quality adaptive video reconstruction optimization method significantly improves the reconstruction performance in both objective and supervisor quality.

关键词： Compressed sensing OPBS block-level search window nearby reference frame information evolutionary algorithm

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Medication Management Application for Directly Observed Treatment of TB Patients 6

Medication Management Application for Directly Observed Trea...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Tania, Marzia Hoque Rahman, Md. Asib Ahmed, Kadir Uddin Centre for Big Data Research in Health Unsw Sydney Sydney Australia Buet Dept. Computer Science and Engineering Dhaka Bangladesh Independent Researcher Sydney Australia

ISBN: (纸本)9798350385779

Tuberculosis (TB) poses global health challenges, requiring efficient management and monitoring of patients undergoing Directly Observed Therapy (DOT). This paper introduces TB-meds, a proposed software application to automate essential aspects of TB patient management. TB-meds aims to optimize resource utilization, improve patient care, and enhance TB control efforts. The application streamlines medication distribution, facilitates medication adherence monitoring, sends timely clinical test requirement's notifications, and enables convenient treatment status updates. By automating these processes, TB-meds has the potential to enhance efficiency, improve treatment outcomes, and reduce transmission rates. Its user-friendly interface promotes seamless communication among healthcare providers and TB patients. With tasks automation, the workload of DOT providers can be reduced, and more time can be dedicated to patient education, nutrition counseling, and lifestyle guidance. Additionally, TB-meds proposes to revolutionize TB surveillance by enhancing data collection and analysis capabilities, enabling informed decision-making and targeted interventions. The implementation of TB-meds holds promise for transforming TB patient management, contributing to better health outcomes, and advancing TB control efforts. © 2024 IEEE.

关键词： Telemedicine

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Service competition in two-tier healthcare markets with heterogeneous patients balking

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Journal of Management science and engineering 2025年第1期10卷 18-36页

作者： Yuanbing Miao Xiaoqing Cai Yanfei Lan Ruiqing Zhao Yinlian Zeng College of Management and Economics Tianjin UniversityTianjin300072China School of Data Science The Chinese University of Hong KongShenzhen518172China Shenzhen Research Institute of Big Data Shenzhen518172China College of Urban Transportation and Logistics Shenzhen Technology UniversityShenzhen518118China

Recently,public and private providers within two-tier healthcare markets have engaged in a fierce competition for patients,which is significantly influenced by patients'multifaceted sensitivities to delays and payments,particularly when patients'potential balking is ***,existing literature studying the two-tier healthcare market often presupposes patients as homogeneous or one-dimensionally heterogeneous,and neglects their balking *** stark contrast,this paper delves into the combined effects of patients’delay-and payment-sensitive heterogeneities,alongside their joining or balking decisions,on the service competition dynamics within the two-tier healthcare *** a game-theoretic framework,we initially derive subgame equilibria,both inclusive and exclusive of patient balking,uncovering that patient balking can lead to a subgame perfect Nash equilibrium in the presence of two-dimensional patient ***,our findings further suggest that the non-profit public provider may,counterintuitively,curtail its service capacity despite budgetary adequacy,contradicting the conventional assumption that such providers would fully allocate funds towards patient ***,from a patient welfare standpoint,we find heightened payment heterogeneity discrimination proves detrimental to patients,whereas increased differentiation in delay heterogeneity benefits *** revelation underscores a pressing social issue inherent in the two-tier healthcare market model:It has the potential to exacerbate healthcare disparities rooted in economic inequalities within the population.

关键词： OR in health services Queuing theory Game theory Joining-or-balking behavior

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The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

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China Communications 2024年第3期21卷 283-295页

作者： Zheng Quan Yan Wenliang Wu Rong Tan Xiaobin Yang Jian Yuan Liu Xu Zhenghuan Laboratory for Future Networks Department of AutomationUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230088China Suzhou Nuclear Power Research Institute Suzhou 215004China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data(NEL-PSRPC) China Academy of Electronics and Information Technology of CETCBeijing 100041China

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’***,the Information-Centric Networking(ICN)came into *** a technical point of view,ICN is a promising future network *** and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of *** current research on ICN pricing mechanism is focused on paid ***,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to *** this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN *** solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each *** extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

关键词： advertisers cache free content Information-Centric Networking pricing strategy

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SymILO: a symmetry-aware learning framework for integer linear optimization 24

SymILO: a symmetry-aware learning framework for integer line...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Qian Chen Tianjian Zhang Linxin Yang Qingyu Han Akang Wang Ruoyu Sun Xiaodong Luo Tsung-Hui Chang School of Science and Engineering The Chinese University of Hong Kong Shenzhen China and Shenzhen Research Institute of Big Data China Shenzhen Research Institute of Big Data China and School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China School of Data Science The Chinese University of Hong Kong Shenzhen China and Shenzhen International Center For Industrial And Applied Mathematics Shenzhen Research Institute of Big Data China

ISBN: (纸本)9798331314385

Integer linear programs (ILPs) are commonly employed to model diverse practical problems such as scheduling and planning. Recently, machine learning techniques have been utilized to solve ILPs. A straightforward idea is to train a model via supervised learning, with an ILP as the input and an optimal solution as the label. An ILP is symmetric if its variables can be permuted without changing the problem structure, resulting in numerous equivalent and optimal solutions. Randomly selecting an optimal solution as the label can introduce variability in the training data, which may hinder the model from learning stable patterns. In this work, we incorporate the intrinsic symmetry of ILPs and propose a novel training framework called SymILO. Specifically, we modify the learning task by introducing solution permutation along with neural network weights as learnable parameters and then design an alternating algorithm to jointly optimize the loss function. We conduct extensive experiments on ILPs involving different symmetries and the computational results demonstrate that our symmetry-aware approach significantly outperforms three existing methods—achieving 50.3%, 66.5%, and 45.4% average improvements, respectively.

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Remaining Useful Life Prediction of Degraded Systems Based on Multiple Performance Indicators Constructed by Multiple Features

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IEEE Sensors Journal 2025年第11期25卷 19613-19631页

作者： Wu, Bin Shi, Hui Zeng, Jianchao Zhang, Xiaohong Wang, Zhijian Wang, Zuolu Taiyuan University of Science and Technology School of Electronic Information Engineering Taiyuan030024 China North University of China institute for Big Data and Visual Computing Taiyuan030051 China University of Huddersfield Centre for Efficiency and Performance Engineering HuddersfieldHD1 3DH United Kingdom

The health status assessment of industrial equipment is critical to fault prediction and health management technologies. To fully reflect system degradation and accurately predict the remaining useful life (RUL) of a system, the construction of multiple performance indicators is important. Thus, a new state-space model and RUL prediction method is proposed herein based on multiple indicators, considering the three correlation relationships among features and degradation indicators. First, according to the extracted and selected feature signals, various relationships such as the correlations among multiple features, multiple features and indicators, and multiple indicators are analyzed. Then an intuitive multi-indicator state-space model is established considering the three correlations and the identified degradation patterns. On this basis, three failure modes of the system are defined according to the multi-indicator complex correlation conditions, and the corresponding probability density function of the system RUL is calculated. Finally, the PHM2012 Challenge and XJTU-SY datasets are used as examples for experimental verification. Compared with other prediction methods, the proposed method can intuitively reflect the correlation between features and indicators, to solve the problem caused by the hidden random correlation. © 2001-2012 IEEE.

关键词： Prediction models

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Accelerating Parallel Sampling of Diffusion Models 41

Accelerating Parallel Sampling of Diffusion Models

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41st International Conference on Machine Learning, ICML 2024

作者： Tang, Zhiwei Tang, Jiasheng Luo, Hao Wang, Fan Chang, Tsung-Hui School of Science and Engineering The Chinese University of Hong Kong Shenzhen China DAMO Academy Alibaba Group China Hupan Lab Zhejiang Province China Shenzhen Research Institute of Big Data Shenzhen China

Diffusion models have emerged as state-of-the-art generative models for image generation. However, sampling from diffusion models is usually time-consuming due to the inherent autoregressive nature of their sampling process. In this work, we propose a novel approach that accelerates the sampling of diffusion models by parallelizing the autoregressive process. Specifically, we reformulate the sampling process as solving a system of triangular nonlinear equations through fixed-point iteration. With this innovative formulation, we explore several systematic techniques to further reduce the iteration steps required by the solving process. Applying these techniques, we introduce ParaTAA, a universal and training-free parallel sampling algorithm that can leverage extra computational and memory resources to increase the sampling speed. Our experiments demonstrate that ParaTAA can decrease the inference steps required by common sequential sampling algorithms such as DDIM and DDPM by a factor of 4∼14 times. Notably, when applying ParaTAA with 100 steps DDIM for Stable Diffusion, a widely-used text-to-image diffusion model, it can produce the same images as the sequential sampling in only 7 inference steps. The code is available at https://***/TZW1998/ParaTAA-Diffusion. Copyright 2024 by the author(s)

关键词： Nonlinear equations

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Unleashing the Power of Federated Learning in Fragmented Digital Healthcare Systems: A Visionary Perspective 15

Unleashing the Power of Federated Learning in Fragmented Dig...

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15th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Tania, Marzia Hoque Clifton, David A. University of New South Wales Centre for Big Data Research in Health Sydney Australia Institute of Biomedical Engineering University of Oxford Department of Engineering Science Oxford United Kingdom Suzhou China

ISBN: (纸本)9798350316551

Digital healthcare landscape, including infrastructure, governance, interoperability, and user adoption, are continuously evolving, some taking more centralised approach, while others with higher degree of fragmentation. Attitude towards centralised healthcare systems in affluent countries are primarily influenced by historical development, infrastructure investments, and regulatory frameworks, which offers advantages with respect to standardised practises, centralised decision making, and economies of scale. In contrast, complexities due to diverse stakeholders, interoperability challenges, privacy and security concerns often pose challenges in achieving a completely centralised healthcare system even in high income countries such as the United Kingdom or in federal systems such as the United States. Moreover, decentralised healthcare systems are more prevalent in resource-poor countries. This paper presents our viewpoint and perspectives on the potential of federated learning in decentralised healthcare systems, especially in countries with infrastructure constraints and discusses its advantages, privacy and security concerns, and challenges. As data-hungry artificial intelligence-enabled systems are gradually changing the healthcare ecosystem, federated learning presents an opportunity for distributing the machine learning training process across multiple decentralised edge devices with reduced data transfer. Therefore, the decentralised digital healthcare system can leverage the collaborative model training while protecting highly sensitive and personal health information. However, challenges related to data heterogeneity, communication latency, and model aggregation need to be addressed for successful implementation of such systems. Adapting the federated learning framework to the specific needs and constraints of low and middle-income countries is crucial to unlock its potential in improving healthcare outcomes. © 2023 IEEE.

关键词： data transfer

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

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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

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