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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenjun Tan Pan Liu Hui Guo Jinzhu Yang Xiaoxia Yin Yanchun Zhang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Centre for Applied Informatics College of Engineering and Science Victoria University Melbourne Australia

ISBN: (纸本)9781728118680

Due to the high degree of air pollution, high smoking rate and poor production protection conditions during the industrialization process, the incidence of pulmonary diseases such as pulmonary cancer and chronic obstructive pulmonary disease has been maintained at a high level in the world and has shown an increasing trend, which has become one of the diseases with the highest mortality rate. In recent years, with the rapid development of artificial intelligence technology, the intelligent assisted diagnosis and surgery technology of pulmonary CT images has made great progress, and played an important role in the early diagnosis and precise treatment of pulmonary diseases. This paper mainly discusses the development history of image-assisted diagnosis and surgery technology for pulmonary diseases, as well as the method of segmentation and evaluation of pulmonary tissue and lesion, and the clinical application of pulmonary intelligent assisted diagnosis and surgery systems.

关键词： biological tissues cancer computerised tomography image segmentation lung medical image processing surgery X-Ray Computed Tomography Medical image processing Lung TISSUES (BIOLOGY) Lung Diseases image segmentation Surgery Air contamination intelligent image processing Lung Cancer Chronic Obstructive Airway Disease Mortality

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Residual Networks for Light Field Image Super-Resolution

Residual Networks for Light Field Image Super-Resolution

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Shuo Zhang Youfang Lin Hao Sheng Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.

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Chinese Historical Term Translation Pairs Extraction Using Modern Chinese As a Pivot Language

Chinese Historical Term Translation Pairs Extraction Using M...

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第十八届中国计算语言学大会暨中国中文信息学会2019学术年会

作者： Xiaoting Wu Hanyu Zhao Lei Jing Chao Che State Grid Info & Telecom Group Beijing China-Power Information Technology Co. LTDBeijingChina Key Laboratory of Advanced Design and Intelligent Computing Ministry of EducationDalian University Chiping Vocational Education School LiaochengChina

ISBN: (纸本)9783030323806

Term translation of Chinese historical classics is very difficult and time-consuming work,and using term alignment methods to extract term translation pairs is of great help for historical term ***,the limited bilingual corpora resources of historical classics and special morphology of the ancient Chinese result in poor performance of term *** this end,this paper proposes a historical term alignment method using modern Chinese as a pivot *** method first identifies English terms by rules,then aligns them from English to modern Chinese and then from modern Chinese to ancient *** use of English-modern Chinese corpus and modern-ancient Chinese corpus instead of English-ancient Chinese corpus solves the shortage problem of the parallel ***,using modern Chinese as a pivot language effectively reduces the alignment errors caused by the abbreviations and the interchangeable characters of ancient *** the term alignment experiment on Shiji,our method outperformed the direct alignment method significantly,which proves the validity of our method.

关键词： Chinese Historical Classics Term Alignment Pivot Language

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Jacobi Spectral Collocation Method Based on Lagrange Interpolation Polynomials for Solving Nonlinear Fractional Integro-Differential Equations

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Advances in Applied Mathematics and Mechanics 2018年第6期10卷 1440-1458页

作者： Xingfa Yang Yin Yang Yanping Chen Jie Liu State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan UniversityChangsha 410082HunanChina Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationSchool of Mathematics and Computational ScienceXiangtan UniversityXiangtan 411105HunanChina School of Mathematical Sciences South China Normal UniversityGuangzhou 510631GuangdongChina Center for Research on Leading Technology of Special Equipment School of Mechanical and Electric EngineeringGuangzhou UniversityGuangzhou 510006GuangdongChina

In this paper,we study a class of nonlinear fractional integro-differential equations,the fractional derivative is described in the Caputo *** the properties of the Caputo derivative,we convert the fractional integro-differential equations into equivalent integral-differential equations of Volterra type with singular kernel,then we propose and analyze a spectral Jacobi-collocation approximation for nonlinear integro-differential equations of Volterra *** provide a rigorous error analysis for the spectral methods,which shows that both the errors of approximate solutions and the errors of approximate fractional derivatives of the solutions decay exponentially in L^(∞)-norm and weighted L^(2)-norm.

关键词： Spectral method nonlinear fractional derivative Volterra integro-differential equations Caputo derivative

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Constructing DNA Barcode Sets Based on Particle Swarm Optimization

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IEEE/ACM transactions on computational biology and bioinformatics 2018年第3期15卷 999-1002页

作者： Bin Wang Xuedong Zheng Shihua Zhou Changjun Zhou Xiaopeng Wei Qiang Zhang Ziqi Wei the Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China Department of Computing Science University of Alberta Edmonton AB Canada

Following the completion of the human genome project, a large amount of high-throughput bio-data was generated. To analyze these data, massively parallel sequencing, namely next-generation sequencing, was rapidly developed. DNA barcodes are used to identify the ownership between sequences and samples when they are attached at the beginning or end of sequencing reads. Constructing DNA barcode sets provides the candidate DNA barcodes for this application. To increase the accuracy of DNA barcode sets, a particle swarm optimization (PSO) algorithm has been modified and used to construct the DNA barcode sets in this paper. Compared with the extant results, some lower bounds of DNA barcode sets are improved. The results show that the proposed algorithm is effective in constructing DNA barcode sets.

关键词： Bioinformatics DNA Particle Swarm Optimisation Particle Swarm Optimization Next Generation Sequencing DNA Barcode Sets DNA Sequential Analysis Algorithm Design And Analysis Next Generation Networking Particle Swarm Optimization Hamming Distance Filtering Algorithms DNA Barcodes Edit Distance Particle Swarm Optimization PSO Lower Bound

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Term Translation Extraction from Historical Classics Using Modern Chinese Explanation

Term Translation Extraction from Historical Classics Using M...

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第十七届全国计算语言学学术会议暨第六届基于自然标注大数据的自然语言处理国际学术研讨会(CCL 2018)

作者： Xiaoting Wu Hanyu Zhao Chao Che Dalian University Key Laboratory of Advanced Design and Intelligent ComputingMinistry of EducationDalian116622China

ISBN: (纸本)9783030017156

Extracting term translation pairs is of great help for Chinese histori-cal classics translation since term translation is the most time-consuming and challenging part in the translation of historical ***,it is tough to recognize the terms directly from ancient Chinese due to the flexible syntactic of ancient Chinese and the word segmentation errors of ancient Chinese will lead to more errors in term translation *** most of the terms in ancient Chinese are still reserved in modern Chinese and the terms in modern Chinese are more easily to be identified,we propose a term translation extract-ing method using multi-features based on character-based model to extract his-torical term translation pairs from modern Chinese-English corpora instead of ancient Chinese-English ***,we first employ character-based BiLSTM-CRF model to identify historical terms in modern Chinese without word segmentation,which avoids word segmentation error spreading to the term *** we extract English terms according to initial capitaliza-tion *** last,we align the English and Chinese terms based on co-occurrence frequency and transliteration *** experiment on Shiji demonstrates that the performance of the proposed method is far superior to the traditional method,which confirms the effectiveness of using modern Chinese as a substitute.

关键词： BiLSTM-CRF Co-occurrence frequency Transliteration features Term translation extraction

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Syntactically constrained bidirectional-asynchronous approach for emotional conversation generation

arXiv

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arXiv 2018年

作者： Li, Jingyuan Sun, Xiao Anhui Province Key Laboratory of Affective Computing Advanced Intelligent Machine Hefei University of Technology Hefei

Traditional neural language models tend to generate generic replies with poor logic and no emotion. In this paper, a syntactically constrained bidirectional-asynchronous approach for emotional conversation generation (ESCBA) is proposed to address this issue. In our model, pre-generated emotion keywords and topic keywords are asynchronously introduced into the process of decoding. It is much different from most existing methods which generate replies from the first word to the last. Through experiments, the results indicate that our approach not only improves the diversity of replies, but gains a boost on both logic and emotion compared with baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Computer circuits

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Controlled Cyclic Remote State Preparation of Arbitrary Qubit States

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Computers, Materials & Continua 2018年第5期55卷 321-329页

作者： Mingming Wang Chen Yang Reza Mousoli Shanxi Key Laboratory of Clothing Intelligence School of Computer ScienceXi’an Polytechnic UniversityXi’an 710048China State and Local Joint Engineering Research Center for Advanced Networking&Intelligent Information Services School of Computer ScienceXi’an Polytechnic UniversityXi’an 710048China School of Law Criminal Justice and ComputingCanterbury Christ Church UniversityCanterburyKentUK

Quantum secure communications could securely transmit quantum information by using quantum ***,novel applications such as bidirectional and asymmetric quantum protocols have been *** this paper,we propose a new method for generating entanglement which is highly useful for multiparty quantum communications such as teleportation and Remote State Preparation(RSP).As one of its applications,we propose a new type of quantum secure communications,*** RSP *** from a four-party controlled cyclic RSP protocol of one-qubit states,we show that this cyclic protocol can be generalized to a multiparty controlled cyclic RSP protocol for preparation of arbitrary qubit *** point out that previous bidirectional and asymmetric protocols can be regarded as a simpler form of our cyclic RSP protocols.

关键词： Quantum secure communications remote state preparation cyclic communications asymmetric communications bidirectional communications quantum control.

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Correction to: A method of VR‑EEG scene cognitive rehabilitation training

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Health information science and systems 2021年第1期9卷 7页

作者： Wenjun Tan Yang Xu Pan Liu Chunyan Liu Yujin Li Yanrui Du Chao Chen Yuping Wang Yanchun Zhang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang 110189 China. Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou 510006 China. Department of Neurology Xuanwu Hospital Capital Medical University Beijing 100053 China. Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin 300384 China. Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne VIC 8001 Australia.

[This corrects the article DOI: 10.1007/s13755-020-00132-6.].

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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