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Using NSGA-Ⅲ for optimising biomedical ontology alignment

CAAI Transactions on Intelligence Technology 2019年第3期4卷 135-141页

作者： Xingsi Xue Jiawei Lu Junfeng Chen College of Information Science and Engineering Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Intelligent Information Processing Research Center Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Key Laboratory for Automotive Electronics and Electric Drive Fujian University of TechnologyFuzhouFujianPeople’s Republic of China College of IOT Engineering Hohai UniversityChangzhouJiangsuPeople’s Republic of China

To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on the similarity measures significantly reduces the alignment’s quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI’s participants show the effectiveness of the authors’ approach.

关键词： ontology ontologies transform together Ontology complex context effects

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Influence of dextran solution and corneal collagen crosslinking on Corneal Biomechanical Parameters Evaluated by Corvis ST

Research Square

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Research Square 2021年

作者： Qin, Xiao Tian, Lei Zhang, Hui Zhang, Di Guo, Li-Li Zhang, Hai-Xia Jie, Ying Li, Lin Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical University Beijing100069 China School of Biomedical Engineering Capital Medical University Beijing100069 China Beijing Institute of Ophthalmology Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology & Visual Sciences Key Laboratory Beijing100730 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Capital Medical University Beijing Tongren Hospital Beijing100730 China

Purpose: To analyze the influence of dextran solution and corneal collagen crosslinking (CXL) on corneal biomechanical parameters evaluated by Corneal Visualization Scheimpflug Technology (Corvis ST). Materials and Methods: Forty porcine eyes were included in this study. Twenty porcine eyes were soaked in dextran solution for 30 minutes (10 eyes in 2% dextran solution and 10 eyes in 20% dextran solution). CXL treatment was performed in 10 porcine eyes, the other 10 porcine eyes were regarded as a control group. Each eye was fixed on an experimental inflation platform to carry out Corvis measurements at different IOPs. Corneal biomechanical parameters were calculated based on Corvis measurement. Statistical analysis was used to analyze the influence of dextran solution and CXL on corneal biomechanical parameters based on Corvis parameters. Results: Corneal energy absorbed area (Aabsorbed) decreased after being soaked in dextran solution under IOP of 15 mmHg;Corneal elastic modulus (E) decreased after being soaked in 2% dextran solution and increased after being soaked in 20% dextran solution;SP-A1 increased after CXL. Conclusion: Both dextran solution and CXL can change corneal biomechanical properties;SP-A1 may be used as an effective parameter for the evaluation of CXL. © 2021, CC BY.

关键词： Visualization

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Kernelized multiview subspace analysis by self-weighted learning

arXiv

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arXiv 2019年

作者： Wang, Huibing Wang, Yang Zhang, Zhao Fu, Xianping Wang, Meng College of Information and Science Technology Dalian Maritime University Dalian Liaoning116021 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China

—With the popularity of multimedia technology, information is always represented or transmitted from multiple views. Most of the existing algorithms are graph-based ones to learn the complex structures within multiview data but overlooked the information within data representations. Furthermore, many existing works treat multiple views discriminatively by introducing some hyperparameters, which is undesirable in practice. To this end, abundant multiview based methods have been proposed for dimension reduction. However, there are still no research to leverage the existing work into a unified framework. To address this issue, in this paper, we propose a general framework for multiview data dimension reduction, named Kernelized Multiview Subspace Analysis (KMSA). It directly handles the multi-view feature representation in the kernel space, which provides a feasible channel for direct manipulations on multiview data with different dimensions. Meanwhile, compared with those graph-based methods, KMSA can fully exploit information from multiview data with nothing to lose. Furthermore, since different views have different influences on KMSA, we propose a self-weighted strategy to treat different views discriminatively according to their contributions. A co-regularized term is proposed to promote the mutual learning from multi-views. KMSA combines self-weighted learning with the co-regularized term to learn appropriate weights for all views. We also discuss the influence of the parameters in KMSA regarding the weights of multi-views. We evaluate our proposed framework on 6 multiview datasets for classification and image retrieval. The experimental results validate the advantages of our proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Multimedia systems

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Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based data Collection Method

Forecasting Chinese Stock Market Prices using Baidu Search I...

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作者： Dong, Jichang Dai, Wei Liu, Ying Yu, Lean Wang, Jie School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management Beijing University of Chemical Technology Beijing100029 China Department of Civil and Environmental Engineering Stanford University StanfordCA94305 United States

In this study, to address search index selection and volatility problems, we propose a learning-based search index collection method that collects the search data fraction for modeling by learning the best criteria from robust statistics. Based on the fraction of collected search index from internet search engine (***) data sources, a novel model is formulated for Chinese stock market price forecasting. We empirically test our method on the two main Chinese stock market price indexes and discover that the prediction accuracy is equivalent or superior to the benchmarks from previous studies that used alternative search index collection methods or lagged data prediction models. All prediction results outstand the importance of an effective data collection method for the robustness of forecast models and demonstrate the utility of a learning-based collection method for addressing search index collection problem, leading to a significant improvement in Chinese stock market price prediction accuracy. © 2019 World Scientific Publishing Company.

关键词： Search engines

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Predicting response to patients with gastric cancer via a dynamic-aware model with longitudinal liquid biopsy data

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Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association 2025年 2025 Jun 17页

作者： Zifan Chen Jie Zhao Yanyan Li Xujiao Feng Yang Chen Yilin Li Xinyu Nan Huimin Liu Bin Dong Lin Shen Li Zhang Center for Data Science Peking University Haidian District Beijing 100080 China. National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China. Peking University Changsha Institute for Computing and Digital Economy Changsha China. Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education) Peking University Cancer Hospital and Institute Beijing China. Guangzhou Medical University Guangzhou China. Beijing International Center for Mathematical Research and the New Cornerstone Science Laboratory Peking University Beijing China. Center for Machine Learning Research Peking University Beijing China. Center for Data Science Peking University Haidian District Beijing 100080 China. zhangli_pku@***.

BACKGROUND:Gastric cancer (GC) presents challenges in predicting treatment responses due to its patient-specific heterogeneity. Recently, liquid biopsies have emerged as a valuable data modality, offering essential cellular and molecular insights while facilitating the capture of time-sensitive information. This study aimed to leverage artificial intelligence (AI) technology to analyze longitudinal liquid biopsy data. METHODS:We collected a dataset from longitudinal liquid biopsies of 91 patients at Peking Cancer Hospital, spanning from July 2019 to April 2022. This dataset included 1895 tumor-related cellular images and 1698 tumor marker indices. Subsequently, we introduced the Dynamic-Aware Model (DAM) to predict responses to GC treatment. DAM incorporates dynamic data through AI-engineered components, facilitating an in-depth longitudinal analysis. RESULTS:Utilizing threefold cross-validation, DAM exhibited superior performance compared to traditional cell-counting methods, achieving an AUC of 0.807 in predicting GC treatment responses. In the test set, DAM maintained stable efficacy with an AUC of 0.802. Besides, DAM showed the capability to accurately predict treatment responses based on early treatment data. Moreover, DAM's visual analysis of attention mechanisms identified six dynamic visual features related to focus areas, which were strongly associated with treatment-response. CONCLUSIONS:These findings represent a pioneering effort in applying AI technology to interpret longitudinal liquid biopsy data and employ visual analytics in GC. This approach provides a promising pathway toward precise response prediction and personalized treatment strategies for patients with GC.

关键词： Artificial intelligence Deep learning Gastric cancer Longitudinal liquid biopsy data Treatment response prediction

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LG-Umer: UNet-like Network Integrate Local-Global Feature with Novel Attention for Road Extraction from Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Niu, Penghui Cai, Taotao Zhang, Yajuan Zhang, Ping Xu, Wenjia Gu, Junhua Han, Jungong Hebei University of Technology School of Artificial Intelligence Tianjin300401 China University of Southern Queensland Toowoomba487- 535 Australia Hebei Prospecting Institute of Hydrogeology and Engineering Geological Shijiazhuang050021 China Hebei University of Technology Hebei Province Key Laboratory of Big Data Calculation Tianjin300401 China University of Sheffield Department of Computer Science S10 2TN United Kingdom

Road extraction from remote sensing images (RSIs) is a key research area in smart city development. While deep learning techniques have demonstrated remarkable effectiveness in this domain, existing approaches exhibit limitations: convolutional neural network (CNN)-based methods struggle to capture global contextual information for long-range road networks, vision transformer (ViT)-based methods fail to adequately extract multi-scale local features, and hybrid CNN-ViT architectures overlook the synergistic guidance between local and global features. To address these challenges, we propose LG-Umer, a UNet-like network that integrates Local-Global features with a novel attention mechanism, combining the complementary strengths of CNNs and ViTs within an encoder-decoder framework. Specifically, the encoder employs a Multi-scale Strip Deformational (MSD) module, which utilizes deformable convolutions to adaptively extract topological structures and variable-shaped local road features. In the decoder, a Multi-stage Gate Unit (MGU) module is introduced, incorporating a novel attention mechanism to model long-range dependencies by leveraging local features as attention operators for global feature refinement. Extensive experiments on three public benchmarks demonstrate the superiority of LG-Umer. It achieves IoU scores of 70.4%, 71.2% and 68.7% on the Massachusetts Road, DeepGlobe Road, and CHN6-CUG datasets, respectively, surpassing recent state-ofthe-art (SOTA) methods by 1.2%, 0.9%, and 1.1%. These results validate the effectiveness of our approach in balancing local detail preservation and global contextual modeling for road extraction tasks. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Research on KDT-DBSCAN-based Personal Semantic Location Acquisition

Research on KDT-DBSCAN-based Personal Semantic Location Acqu...

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第32届中国控制与决策会议

作者： Rong Nie Zixuan Yao Dong Wei Kaizhang Hou Jishuai Lin Beijing Engineering Research Center of Security Alarm and Security Check School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing Municipal Science and Technology Commission Haidian Branch of Beijing Public Security Bureau

With the development of science and technology in recent years,the planning and construction of smart cities have entered a new *** them,smart city safety management is the foundation that supports the stable development of the entire city,and location-based services are one of its important technical *** popularity of GPS-equipped devices provides a large amount of data for trajectory *** personal semantic locations by mining these data is an application based on location *** semantic locations are frequently visited by individual users and have significant semantic meaning to users（such as home,work place,etc.）.The discovery of the user’s personal semantic location involves obtaining the physical location and *** present,related research mostly uses clustering algorithms to obtain the physical location,of which the DBSCAN algorithm is the most commonly *** the traditional DBSCAN algorithm determines whether a sample belongs to a certain cluster,it will generate a large number of repeated calculations,which will reduce the program operation *** order to solve this problem,this paper proposes an improved DBSCAN algorithm *** algorithm uses a k-d tree to screen samples that need to be *** excluding samples that do not need to be calculated,the purpose of improving the calculation efficiency can be *** the problem of semantic location recognition,this paper pre-defines the daily behavior patterns of people,and identifies the location semantics based on the temporal characteristics of the samples in each *** calculating the distance between samples,the algorithm proposed here is 8.8 times more efficient than the traditional algorithm.

关键词： Personal Semantic Location DBSCAN K-D Tree

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ΛcN interaction in leading-order covariant chiral effective field theory

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Physical Review C 2020年第6期102卷 065208-065208页

作者： Jing Song Yang Xiao Zhi-Wei Liu Chun-Xuan Wang Kai-Wen Li Li-Sheng Geng School of Physics Beihang University Beijing 102206 China Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay 91405 France Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and Engineering Beihang University Key Laboratory of Big Data-Based Precision Medicine (Beihang University) Ministry of Industry and Information Technology Beijing 100191 China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 102206 China School of Physics and Microelectronics Zhengzhou University Zhengzhou Henan 450001 China

We study the ΛcN interaction in the covariant chiral effective field theory (ChEFT) at leading order. All the relevant low-energy constants are determined by fitting to the lattice QCD simulations from the HAL QCD Collaboration. Extrapolating the results to the physical point, we show that the ΛcN interaction is weakly attractive in the S01 channel, but in the S13 channel it is only attractive at extremely low energies and soon turns repulsive for larger laboratory energy. Furthermore, we show that the neglect of the S13−D13 coupling provided by the leading-order covariant ChEFT would result in an attractive interaction in the S13 channel at the physical point, which coincides with the previous nonrelativistic ChEFT study. As a byproduct, we predict the D13 phase shifts and the mixing angel ɛ1, which can be checked by future lattice QCD simulations. In addition, we compare the ΛcN interaction with the ΛN and NN interactions to study how the baryon-nucleon (BN) interactions evolve as a function of the baryon mass with the replacement of a light quark by a strange or charm quark in the baryon (B).

关键词： Effective field theory Hadron-hadron interactions Lattice QCD QCD in nuclear reactions

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Effect of Functional Groups of Hydration Heat Controlling Materials on Cement Hydration and its Mechanism

SSRN

引用

SSRN 2022年

作者： Liu, Xiao Xie, Hui Luo, Qifeng Xia, Chunlei Ding, Yungchin Chen, Yichen Gao, Ruijun Wei, Zhongyuan Bai, Xiabing He, Rui Zhou, Wu Zhou, Xiangzheng Wang, Ziming Cui, Suping Faculty of Materials and Manufacturing Key Laboratory of Advanced Functional Materials Ministry of Education Beijing University of Technology Beijing100024 China Beijing Municipal Engineering Research Institute Beijing100037 China Institute of Mineral Resources Engineering National Taipei University of Technology Taipei10608 Taiwan China Building Materials Academy Beijing100024 China Tangshan Longyi Tech. Co. Ltd. Tangshan063004 China Beijing NAURA Microelectronics Equipment Co. Ltd. Beijing100176 China Engineering Research Center of Transportation Materials of Ministry of Education Chang’ an University Xi'An710061 China Guangxi Keda Building Material Chemicals Co. Ltd. Nanning530221 China Faculty of Materials and Manufacturing National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China

Severe temperature rise of mass concrete caused by cement hydration can lead to thermal cracks which further reduce the durability of concrete. In this study, three types of hydration heat controlling materials (HHCMs) respectively containing pure hydroxyl (polyvinyl alcohol, PVA), carboxyl (polyacrylic acid, PAA) and ester groups (maleic acid-ethylene glycol ester, P(MA-EG)) were synthesized and mixed with cement-based materials, and the hydration heat and setting time of cement pastes and the adiabatic temperature rise and strength of mortars were tested. The results showed that PAA and P(MA-EG) reduced the peak value of hydration heat release rate of cement paste by 77.45% and 62.28% respectively, and prolonged 54.14h and 15.56h respectively. The cumulative heat release within 72h decreased by 42.42% and 64.50% respectively, and the central temperatures of mortars containing PAA and P(MA-EG) decreased by 11.9 °C and 18.5 °C respectively. The mechanism of HHCMs was further revealed in terms of adsorption capacity, hydrodynamic radius, and conductivity and hydration products of cement pastes. The HHCMs containing carboxyl or hydroxyl groups, adsorbed on cement particles by Ca 2+ complexation, while the HHCMs containing ester groups, i.e. P(MA-EG), continuously hydrolyzed to carboxyl groups in alkaline cement pastes, which hydrolysis rate changed with the hydration temperature of cement-based materials to further regulate the hydration process. This study aims to provide technical feasibility and theoretical basis for exploring new avenues of synthesizing HHCMs with different functional groups. These novel HHCMs with great potentials can be as new admixtures to reduce thermal cracks in concrete. © 2022, The Authors. All rights reserved.

关键词： Ethylene glycol

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Low-overhead pieceable fault-tolerant construction of logical controlled-phase circuit for degenerate quantum code*

Low-overhead pieceable fault-tolerant construction of logica...

引用

IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Chen Lin Guowu Yang Jinzhao Wu Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu P.R. China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

We propose a non-transversal but pieceable fault-tolerant conversion circuit that is used to convert encoded information between five-qubit code and seven-qubit CSS code. Since a syndrome extraction circuit requiring fewer ancillary qubit resources would facilitate the realization of large-scale quantum computations, we further adapt a flag-assisted fault-tolerant syndrome measurement scheme to reduce the cost of ancillary preparation. Numerical simulations are also performed to further analyze the performance of our conversion method.

关键词： Fault tolerance Fault tolerant systems Quantum computing Logic gates Circuit faults Error analysis Error correction codes

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