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Corrigendum to “Szeged and Mostar root-indices of graphs”

applied mathematics and Computation 2025年 499卷

作者： Simon Brezovnik Matthias Dehmer Niko Tratnik Petra Žigert Pleteršek University of Ljubljana Faculty of Mechanical Engineering Slovenia Institute of Mathematics Physics and Mechanics Ljubljana Slovenia Swiss Distance University of Applied Sciences Brig Switzerland Department for Biomedical Computer Science and Mechatronics UMIT-Private University for Health Sciences Medical Informatics and Technology Eduard-Wallnfer-Zentrum 1 6060 Hall in Tyrol Tyrol Austria College of Artificial Intelligence Nankai University Tianjin China School of Science Xian Technological University Xian Shaanxi 710021 China University of Maribor Faculty of Natural Sciences and Mathematics Slovenia University of Maribor Faculty of Chemistry and Chemical Engineering Slovenia

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2024年 1057 LNNS卷 xiii-xiv页

作者： Bhateja, Vikrant Tang, Jinshan Sharma, Dilip Kumar Polkowski, Zdzislaw Ahmad, Afaq Department of Electronics Engineering Veer Bahadur Singh Purvanchal University Uttar Pradesh Jaunpur India Department of Health Administration and Policy College of Public Health George Mason University FairfaxVA United States GLA University Uttar Pradesh Mathura India Department of Humanities and Social Sciences The Karkonosze University of Applied Sciences Jelenia Góra Poland Department of Mathematics and Computer Science Modern College of Business and Science Muscat Oman

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Study on stability of roll over control of aircraft

Study on stability of roll over control of aircraft

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第二届电气、控制与自动化国际会议

作者： Jun Liu Chunyan Zhu Institute of Applied Mathematics Qujing Normal University College of Computer Science and Engineering Qujing Normal University

In this paper, studies the problem of rolling out of flight in air flight that may be out of control. First, the corresponding mathematical model is established through dynamic analysis. Secondly, the mathematical model is studied by using Lyapunov function method. Finally, the stability of the model is demonstrated in theory. The effectiveness and safety of the controller are designed.

关键词： Study on stability of roll over control of aircraft

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Load balancing for multi-physics simulations 21

Load balancing for multi-physics simulations

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Chen, Chunsen Li, Kenli Liu, Xu Li, Keqin College of Information Science and Engineering Hunan University Changsha China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing China Department of Computer Science State University of New York New York United States

ISBN: (纸本)9781728120584

Multi-physics simulations are usually essential to simplify researches on complex physical phenomena. In this paper, we extend the rectangular partitioning from single-physics simulations to multi-physics simulations. This work presents the Single-rectangle Tightly-coupled algorithm (ST) and Multirectangle Tightly-coupled algorithm (MT) based on rectangular partitioning. The MT algorithm is more flexible and achieves higher load balancing efficiency than the ST algorithm by greedily assigning the remaining unallocated most loaded rectangle to the least loaded processor and assigning k rectangles to each of the processors. To study the effect of machine characteristics on load balancing, the experiments use two types of machine parameters based on Tianhe-2 supercomputer. Inspired by the roofline model, this paper analyzes the relationship among the choice of the load balancing algorithms, the load characteristics of multi-physics application and the features of high performance parallel computer through the comparison experiment of looselycoupled algorithm and tightly-coupled algorithm. In the case of LARED-P, MT algorithm can further reduce the total simulation time compared with the ST algorithm. Through experiments, we show that whether the load balancing algorithm is effective depends on both the load characteristics of multi-physics simulation and the features of high performance parallel computer. © 2019 IEEE.

关键词： Supercomputers

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Corrections to “Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications”

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IEEE Access 2024年 12卷 135103-135103页

作者： Mohammed A. El-Shorbagy Anas Bouaouda Hossam A. Nabwey Laith Abualigah Fatma A. Hashim Department of Mathematics College of Science and Humanities in Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Faculty of Science and Technology Hassan II University of Casablanca Mohammedia Morocco Department of Basic Engineering Science Faculty of Engineering Menoufia University Shebin El-Kom Egypt Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Faculty of Information Technology Isra University Amman Jordan College of Engineering Yuan Ze University Taoyuan Taiwan Applied Science Research Center Applied Science Private University Amman Jordan Faculty of Engineering Helwan University Cairo Egypt

Presents corrections to the paper, Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications.

关键词： Metaheuristics

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Eighth power residue double circulant self-dual codes

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International Journal of Network Security 2020年第5期22卷 736-742页

作者： Jiang, Changsong Sun, Yuhua Liang, Xueting College of Science China University of Petroleum Qingdao Shandong266580 China Provincial Key Laboratory of Applied Mathematics Putian University Putian Fujian351100 China School of Computer Science and Engineering University of Electronic Science and Technology of China China

Self-dual codes are one of the most important classes of linear codes. Power residue classes are widely used in the constructions of linear codes and pseudo-random sequences. In this paper, we give new constructions of self-dual codes over GF(2) and GF(4) by eighth power residues. We get multiple pure double circulant codes and bordered double circulant codes. Some of these new self-dual codes have large minimum distances. © Femto Technique Co., Ltd.

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Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

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Computational and Mathematical Methods in Medicine 2021年第1期2021卷 9025470页

作者： Tufail, Ahsan Bin Ma, Yong-Kui Kaabar, Mohammed K. A. Martínez, Francisco Junejo, A.R. Ullah, Inam Khan, Rahim School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China Department of Electrical and Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Gofa Camp Near Gofa Industrial College and German Adebabay Nifas Silk-Lafto Addis Ababa26649 Ethiopia Institute of Mathematical Sciences Faculty of Science University of Malaya Kuala Lumpur50603 Malaysia Department of Applied Mathematics and Statistics Technological University of Cartagena Cartagena30203 Spain School of Control Science and Control Engineering Harbin Institute of Technology Harbin150001 China Changzhou Campus 213022 China

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods. © 2021 Ahsan Bin Tufail et al.

关键词： Forecasting

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Global guidance network for breast lesion segmentation in ultrasound images

arXiv

引用

arXiv 2021年

作者： Xue, Cheng Zhu, Lei Fu, Huazhu Hu, Xiaowei Li, Xiaomeng Zhang, Hai Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Applied Mathematics and Theoretical Physics University of Cambridge Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Shenzhen People's Hospital Second Clinical College of Jinan University First Affiliated Hospital of Southern University of Science and Technology Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accurately from ultrasound image is a challenging task due to the inherent speckle artifacts, blurry breast lesion boundaries, and inhomogeneous intensity distributions inside the breast lesion regions. Recently, convolutional neural networks (CNNs) have demonstrated remarkable results in medical image segmentation tasks. However, the convolutional operations in a CNN often focus on local regions, which suffer from limited capabilities in capturing long-range dependencies of the input ultrasound image, resulting in degraded breast lesion segmentation accuracy. In this paper, we develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection (BD) modules for boosting the breast ultrasound lesion segmentation. The GGB utilizes the multi-layer integrated feature map as a guidance information to learn the long-range non-local dependencies from both spatial and channel domains. The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement. Experimental results on a public dataset and a collected dataset show that our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation. Moreover, we also show the application of our network on the ultrasound prostate segmentation, in which our method better identifies prostate regions than state-of-the-art networks. © 2021, CC BY.

关键词： Deep neural networks

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CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

arXiv

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arXiv 2023年

作者： Graham, Simon Vu, Quoc Dang Jahanifar, Mostafa Weigert, Martin Schmidt, Uwe Zhang, Wenhua Zhang, Jun Yang, Sen Xiang, Jinxi Wang, Xiyue Rumberger, Josef Lorenz Baumann, Elias Hirsch, Peter Liu, Lihao Hong, Chenyang Aviles-Rivero, Angelica I. Jain, Ayushi Ahn, Heeyoung Hong, Yiyu Azzuni, Hussam Xu, Min Yaqub, Mohammad Blache, Marie-Claire Piégu, Benoît Vernay, Bertrand Scherr, Tim Böhland, Moritz Löffler, Katharina Li, Jiachen Ying, Weiqin Wang, Chixin Snead, David Raza, Shan E. Ahmed Minhas, Fayyaz Rajpoot, Nasir M. Tissue Image Analytics Centre University of Warwick Coventry United Kingdom Histofy Ltd Birmingham United Kingdom Institute of Bioengineering School of Life Sciences Epfl Lausanne Switzerland Dresden Germany The Department of Computer Science The University of Hong Kong Hong Kong Tencent Ai Lab Shenzhen China College of Biomedical Engineering Sichuan University Chengdu China Department of Precision Instruments Tsinghua University Beijing China College of Computer Science Sichuan University Chengdu China Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association Berlin Germany Humboldt University of Berlin Faculty of Mathematics and Natural Sciences Berlin Germany Charité University Medicine Berlin Germany University of Bern Bern Switzerland Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Softsensor.ai BridgewaterNJ United States PRR.ai TX United States Department of R&d Center Arontier Co. Ltd Seoul Korea Republic of Computer Vision Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Cnrs Ifce Inrae Université de Tours Prc Nouzilly3780 France Institut de Génétique et de Biologie Moléculaire et Cellulaire Illkirch France Centre National de la Recherche Scientifique UMR7104 Illkirch France Institut National de la Santé et de la Recherche Médicale Inserm U1258 Illkirch France Université de Strasbourg Strasbourg France Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany School of Software Engineering South China University of Technology Guangzhou China Department of Pathology University Hospitals Coventry Warwickshire Nhs Trust Coventry United Kingdom Division of Biomedical Sciences Warwick Medical School University of Warwick Coventry United Kingdom

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery. © 2023, CC BY-NC-SA.

关键词： Grading

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

引用

arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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