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arXiv 2024年

作者： Cang, Yihan Chen, Ming Pan, Yijin Yang, Zhaohui Hu, Ye Sun, Haijian Chen, Mingzhe The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211100 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China Hangzhou310027 China The Department of Industrial and System Engineering University of Miami Coral GablesFL33146 United States The School of Electrical and Computer Engineering The University of Georgia AthensGA30602 United States The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy transfer

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CNN segmentation of skin melanoma in pre-processed dermoscopy images

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Procedia Computer science 2024年 235卷 2775-2782页

作者： Seifedine Kadry Elena Verdú Robertas Damasevicius Laith Abualigah Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu’s thresholding, (iii) Kapur’s thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database.

关键词： Watershed-algorithm skin melanoma segmentation Fuzzy-Tsallis entropy Moth-Flame algorithm evaluation

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Automatic liver-vessel examination from CT slice using Kapur’s thresholding and watershed algorithm

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Procedia Computer science 2024年 235卷 1824-1831页

作者： Seifedine Kadry Laith Abualigah Rubén González Crespo Elena Verdú Robertas Damasevicius Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

Computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur’s tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme.

关键词： liver CT liver-vessel Kapur’s thresholding hummingbird-optimizer watershed-algorithm

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions

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Procedia Computer science 2024年 235卷 3438-3446页

Computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays.

关键词： Covid19 X-ray Vision-transformer Patch-size Classification

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Junctional and somatic hypermutation-induced CX4C motif is critical for the recognition of a highly conserved epitope on HCV E2 by a human broadly neutralizing antibody

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Cellular & Molecular Immunology 2021年第3期18卷 675-685页

作者： Chunyan Yi Jing Xia Lan He Zhiyang Ling Xuesong Wang Yu Yan Jiangjun Wang Xinhao Zhao Weiguo Fan Xiaoyu Sun Ronghua Zhang Sheng Ye Rongguang Zhang Yongfen Xu Liyan Ma Yaguang Zhang Honglin Zhou Zhong Huang Junqi Niu Gang Long Junxia Lu Jin Zhong Bing Sun State Key Laboratory of Cell Biology CAS Center for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyChinese Academy of Sciences/University of Chinese Academy of SciencesShanghaiChina CAS Key Laboratory of Molecular Virology and Immunology Institut Pasteur of ShanghaiChinese Academy of Sciences/University of Chinese Academy of SciencesShanghaiChina School of Life Science and Technology ShanghaiTech UniversityShanghaiChina CAS Key Laboratory of Synthetic Chemistry of Natural Substances Shanghai Institute of Organic ChemistryChinese Academy of SciencesShanghaiChina College of Biology Hunan Provincial Key Laboratory of Medical VirologyHunan UniversityChangshaChina National Laboratory of Biophysics Institute of BiophysicsChinese Academy of Sciences/University of Chinese Academy of Sciences BdjingBeijingChina Interdisciplinary Innovation Institute of Medicine&Engineering Beijing Advanced Innovation Center for Big Data-Based Precision MedicineSchool of Biological Science and Medical EngineeringBeihang UniversityBeijingChina Nanjing Galaxy Biopharma Co. LtdNanjingChina Hepatology Section First HospitalUniversity of JilinChangchunChina

Induction of broadly neutralizing monoclonal antibodies(bNAbs)that bind to the viral envelope glycoproteins is a major goal of hepatitis C virus(HCV)vaccine *** study of bNAbs arising in natural infection is essential in this *** generated a human antibody,8D6,recognizing the E2 protein of HCV isolated from a chronic hepatitis C *** antibody shows broadly neutralizing activity,which covers a pan-genotypic panel of cell culture-derived HCV virions(HCVcc).Functional and epitope analyses demonstrated that 8D6 can block the interaction between E2 and CD81 by targeting a highly conserved epitope on *** describe how the 8D6 lineage evolved via somatic hypermutation to achieve broad *** found that the V(D)J recombination-generated junctional and somatic hypermutation-induced disulfide bridge(C-C)motif in the CDRH3 is critical for the broad neutralization and binding activity of *** motif is conserved among a series of broadly neutralizing HCV antibodies,indicating a common binding ***,the 8D6 inferred germline(iGL)was reconstructed and tested for its binding affinity and neutralization ***,8D6 iGL-mediated relatively strong inhibition of the 1b genotype PR79L9 strain,suggesting that PR79L9 may serve as a potential natural viral strain that provides E2 sequences that induce ***,our detailed epitope mapping and genetic studies of the HCV E2-specific mAb 8D6 have allowed for further refinement of antigenic sites on E2 and reveal a new mechanism to generate a functional CDRH3,while its iGL can serve as a probe to identify potential HCV vaccine strains.

关键词： Hepatitis C virus neutralizing antibody inferred germline CDRH3 disulfide bridge motif

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric sciences 2025年第6期42卷 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of St.Thomas School of EngineeringMinnesota 55105USA NSF National Center for Atmospheric Research BoulderColorado 80307USA University of Auckland Auckland 0630New Zealand National Oceanic and Atmospheric Administration National Centers for Environmental InformationSilver SpringMaryland 20910USA National Oceanic and Atmospheric Administration National Centers for Environmental InformationAshevilleNC 28801USA National Research Council(CNR)Institute of Marine Sciences(ISMAR) Rome 00133Italy Mercator Ocean International Toulouse 31400France Department of Earth and Environmental Science University of PennsylvaniaPhiladelphiaPennsylvania 19104USA Institute of Oceanology Chinese Academy of SciencesQingdao 266071China National Marine Environmental Forecasting Center Ministry of Natural Resources of ChinaBeijing 100081China North Carolina Institute for Climate Studies(NCICS) North Carolina State UniversityAshevilleNC 28804USA Oceanographic Data Center Chinese Academy of SciencesQingdao 266071China College of Oceanography Hohai UniversityNanjing 210098China National Marine Data and Information Service Tianjin 300171China Istituto Nazionale di Geofisica e Vulcanologia Sede di BolognaBologna 40128Italy South China Sea Institute of Oceanology Chinese Academy of SciencesGuangzhou 510301China ESSIC/CISESS-MD University of MarylandCollege ParkMD 20740USA Computer Network Information Center Chinese Academy of SciencesBeijing 100083China Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment AdministrationMinistry of Ecology and EnvironmentGuangzhou 510611China National Meteorological Information Center China Meteorological AdministrationBeijing 100081China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Xiamen Universit

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

关键词： ocean heat content sea surface temperature ocean temperature global warming climate

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Sending-or-not-sending twin-field quantum key distribution with redundant space

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Physical Review Research 2023年第2期5卷 023069-023069页

作者： Hai Xu Xiao-Long Hu Cong Jiang Zong-Wen Yu Xiang-Bin Wang State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing 100084 China Frontier Science Center for Quantum Information Beijing 100084 China School of Physics State Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Guangzhou 510275 China Jinan Institute of Quantum Technology SAICT Jinan 250101 China Data Communication Science and Technology Research Institute Beijing 100191 China Shanghai Branch CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 People's Republic of China Shenzhen Institute for Quantum Science and Engineering and Physics Department Southern University of Science and Technology Shenzhen 518055 China

We propose to adopt redundant space such as polarization mode in the sending-or-not-sending twin-field quantum key distribution (TF-QKD) in the Fock space. With the help of redundant space such as photon polarization, we can postselect events according to the outcome of the observation to the additional quantity. This compresses the bit-flip error rate in the postselected events of the SNS protocol. The calculation shows that the method using redundant space can greatly improve the performance in practical TF-QKD, especially when the total number of pulses is small.

关键词： Quantum communication Quantum cryptography

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Advances in magnetic particle imaging and perspectives on liver imaging

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iLIVER 2022年第4期1卷 237-244页

作者： Wei Li Xiaohua Jia Lin Yin Zhiyun Yang Hui Hui Jianlin Li Wenhui Huang Jie Tian Shuixing Zhang Department of Radiology The First Affiliated HospitalJinan UniversityNo.613Huangpu West RoadTianhe DistrictGuangzhou510627GuangdongChina CAS Key Laboratory of Molecular Imaging Institute of AutomationBeijing100190China Beijing Key Laboratory of Molecular Imaging Beijing100190China University of Chinese Academy of Sciences Beijing100080China School of Food Science and Pharmaceutical Engineering Nanjing Normal UniversityNanjing 210023China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing100083China Zhuhai Precision Medical Center Zhuhai People's HospitalAffiliated with Jinan UniversityZhuhai519000China

Magnetic particle imaging(MPI)is an emerging technique to visualize the spatial distribution of super-paramagnetic iron oxide with high temporal–spatial resolution,high sensitivity,unlimited image depth,and true quantitative *** is based on the nonlinear response of superparamagnetic iron oxide in an alter-nating magnetic field without tissue background *** is a promising imaging modality for various applica-tions,including vascular imaging,cell tracking,tumor imaging,and catheter *** applications of liver imaging could be improved or created with *** this review,we cover the principle and construction of MPI,we evaluate the features and advantages of MPI with relation to its own rationale and via comparison with other imaging modalities,and we review MPI liver imaging applications with a view toward assisting hepatic researchers in drawing inspiration.

关键词： Magnetic particle imaging Liver imaging SPIO

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Artificial intelligence in gastric cancer:applications and challenges

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Gastroenterology Report 2022年第1期10卷 227-242页

作者： Runnan Cao Lei Tang Mengjie Fang Lianzhen Zhong Siwen Wang Lixin Gong Jiazheng Li Di Dong Jie Tian School of Artificial Intelligence University of Chinese Academy of SciencesBeijingP.R.China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijingP.R.China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Radiology DepartmentPeking University Cancer Hospital&InstituteBeijingP.R.China College of Medicine and Biological Information Engineering School Northeastern UniversityShenyangLiaoningP.R.China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingP.R.China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxiP.R.China

Gastric cancer(GC)is one of the most common malignant tumors with high *** diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical ***,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer ***,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data *** are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including ***,we aim to provide a broad framework to summarize current research on AI in *** the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological *** the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype *** treatment decisions,AI can help with surgical margin determination and prognosis ***,current approaches are challenged by data scarcity and poor *** tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

关键词： gastric cancer artificial intelligence radiomics endoscopy computed tomography pathology

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data-Driven Control Based on Function Approximation with Binomial Coefficients

Data-Driven Control Based on Function Approximation with Bin...

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第33届中国控制与决策会议

作者： Guoliang Zhu Lei Chen Kexin Liu Jinhu Lü School of Automation Science and Electrical Engineering Beihang Uniersity Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates data-driven control methods based on function approximation with binomial coefficients and sampling *** propose two approximate models and corresponding controllers formed from sampled *** shows that the information truncated in the first approximate model is saved in the latter with parameter estimation *** results validate the effectiveness of proposed approaches.

关键词： Sampled-data driven systems Function approximation Approximate theory Adaptive control Binomial coefficients

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