检索结果-内蒙古大学图书馆

Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

来源：评论

学校读者我要写书评

暂无评论

Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

Binary Bleed: Fast Distributed and Parallel Method for Autom...

引用

2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Boureima, Ismael Matuszek, Cynthia Alexandrov, Boian S. Theoretical Division Los Alamos National Laboratory Los Alamos United States University of Maryland Baltimore County Department of Computer Science and Electrical Engineering MD United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States

ISBN: (纸本)9798350387131

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as nonnegative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter k to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific k. The score is calculated for different trials over a range of k, and the ideal k is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good k value, visiting a range of k can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the k search space for these grid-search ML algorithms by truncating the target k values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal k and the accuracy of the Binary Bleed in identifying the correct k for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub. © 2024 IEEE.

关键词： Dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization 12

Cyber-Security Knowledge Graph Generation by Hierarchical No...

引用

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Wanna, Selma Solovyev, Nicholas Rasmussen, Kim Alexandrov, Boian S. Nicholas, Charles Matuszek, Cynthia Los Alamos National Laboratory Theoretical Division Los Alamos United States Los Alamos National Laboratory Advanced Research in Cyber Systems Los Alamos United States University of Maryland Department of Computer Science and Electrical Engineering Baltimore CountyMD United States

ISBN: (纸本)9798350330366

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Enhanced ferromagnetism in an artificially stretched lattice in quasi-two-dimensional Cr2Ge2Te6

引用

Physical Review B 2025年第2期111卷 L020402-L020402页

作者： Hiroshi Idzuchi Andres E. Llacsahuanga Allcca Anh Khoa Augustin Lu Mitsuhiro Saito Subhadip Das John F. Ribeiro Michel Houssa Ruishen Meng Kazutoshi Inoue WPI Advanced Institute for Materials Research (AIMR) Tohoku University Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (CSIS) Tohoku University Sendai 980-8577 Japan Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Mathematics for Advanced Materials Open Innovation Laboratory National Institute of Advanced Industrial Science and Technology Sendai 980-8577 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Institute of Engineering Innovation The University of Tokyo Tokyo 113-8656 Japan Institute of Physics and Astronomy and Villum Centers for Dirac Materials and for Hybrid Quantum Materials and Devices Aarhus University 8000 Aarhus-C Denmark School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Department of Physics and Astronomy Katholieke Universiteit Leuven 3001 Leuven Belgium

Crystal lattice is key in understanding magnetic interactions between atoms in solids. As the effective control of the lattice via tensile stress is limited, there are only a few demonstrations of controlling magnetic properties by expanding the lattice. This study reveals a clear correlation between enhanced magnetism, where Curie temperature (TC) is doubled from ∼60 to ∼120 K, and lattice expansion in prototypical van der Waals magnet Cr2Ge2Te6 with sputtered dielectric overlayers NiO and MgO. We ascribe the mechanism of this TC increase to a change in exchange interactions induced by strain, studied by several experimental probes and computational approaches.

关键词： Van der Waals forces

来源：评论

学校读者我要写书评

暂无评论

A conceptional structure for challenging the rebound effect of the product-service system 33

A conceptional structure for challenging the rebound effect ...

引用

33rd CIRP Design Conference

作者： Alfarisi, Salman Mitake, Yuya Tsutsui, Yusuke Wang, Hanfei Shimomura, Yoshiki Faculty of Systems Design Tokyo Metropolitan University Tokyo191-0065 Japan Human Augmentation Research Center National Institute of Advanced Industrial Science and Technology Chiba277-0882 Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University 719-1197 Japan

The product obsolescence rate is one of the product-service system rebound effect issues. Obsolescence can be generated by both planned obsolescence and post-production obsolescence. Planned obsolescence can increase sales and revenue artificially. However, the pattern of planned obsolescence has demonstrated that obsolescence is no longer limited to functional obsolescence but rather rapidly evolves to include quality and aesthetic aspects. Moreover, post-production consuming behaviours hasten the rate of product obsolescence, especially in the absence of ownership. At this point, rapid obsolescence contradicts the spirit of the Product-service System's (PSS) value proposition. During the stage of value co-creation, such topics must be considered. According to research on PSS and the co-creation process, the co-creation of value is inherent to PSS. Nevertheless, managing the process of value co-creation is another challenge. In addition, decision-making should also be prevented from being dominated by a high degree of centralisation. This paper presents a preliminary investigation into constructing a framework for managing the value co-creation process and decision-making to prevent the rebound effect based on conceptual review. This study identifies methods applicable to the co-creation procedure. A conceptional structure of co-creation of value and decision-making is presented. © 2023 The Authors. Published by Elsevier B.V.

关键词： Obsolescence

来源：评论

学校读者我要写书评

暂无评论

Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Scaling the "memory Wall" for Multi-Dimensional Seismic Processing with Algebraic Compression on Cerebras CS-2 systems 23

Scaling the "memory Wall" for Multi-Dimensional Seismic Proc...

引用

2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Ltaief, Hatem Hong, Yuxi Wilson, Leighton Jacquelin, Mathias Ravasi, Matteo Keyes, David Elliot Extreme Computing Research Center Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Cerebras Systems Inc. SunnyvaleCA United States

ISBN: (纸本)9798400701092

We exploit the high memory bandwidth of AI-customized Cerebras CS-2 systems for seismic processing. By leveraging low-rank matrix approximation, we fit memory-hungry seismic applications onto memory-austere SRAM wafer-scale hardware, thus addressing a challenge arising in many wave-equation-based algorithms that rely on Multi-Dimensional Convolution (MDC) operators. Exploiting sparsity inherent in seismic data in the frequency domain, we implement embarrassingly parallel tile low-rank matrix-vector multiplications (TLR-MVM), which account for most of the elapsed time in MDC operations, to successfully solve the Multi-Dimensional Deconvolution (MDD) inverse problem. By reducing memory footprint along with arithmetic complexity, we fit a standard seismic benchmark dataset into the small local memories of Cerebras processing elements. Deploying TLR-MVM execution onto 48 CS-2 systems in support of MDD gives a sustained memory bandwidth of 92.58PB/s on 35, 784, 000 processing elements, a significant milestone that highlights the capabilities of AI-customized architectures to enable a new generation of seismic algorithms that will empower multiple technologies of our low-carbon future. © 2023 Owner/Author(s).

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

引用

Neural Computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

来源：评论

学校读者我要写书评

暂无评论

A Data-Driven Rutting Depth Short-Time Prediction Model With Metaheuristic Optimization for Asphalt Pavements Based on RIOHTrack

引用

IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 1918-1932页

作者： Zhuoxuan Li Iakov Korovin Xinli Shi Sergey Gorbachev Nadezhda Gorbacheva Wei Huang Jinde Cao School of Mathematics Southeast UniversityNanjing 210096China Scientific Research Institute of Multiprocessor Computer Systems Southern Federal UniversityTaganrog 347928Russia School of Cyber Science and Engineering Southeast UniversityNanjing 210096China Russian Academy of Engineering Moscow 125009Russia Intelligent Transportation System Research Center Southeast UniversityNanjing 210096China Nanjing Modern Multimodal Transportation Laboratory Nanjing 211100China Yonsei Frontier Laboratory Yonsei UniversitySeoulKorea(South)

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements’ rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from19 asphalt pavements with different crude oil sources on a 2.038km long full-scale field accelerated pavement test track(Road Track Institute, RIOHTrack) in Tongzhou, Beijing. In addition,this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction(RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of average root mean squared error(MSE), average mean absolute error(MAE), and a verage mean absolute percentage error(MAPE) for 19 asphalt pavements reaching 1.742, 1.363, and 1.94% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters. It is a promising alternative method that facilitates the rapid assessment of pavement conditions and could be applied in the future to production processes in the oil and gas industry.

关键词： Extreme learning machine algorithm with residual correction(RELM) metaheuristic optimization oil-gas transportation RIOHTrack,rutting depth

来源：评论

学校读者我要写书评

暂无评论

SDN-Enabled Digital Twins: A Framework for Wireless SDN Simulation and Optimization

SDN-Enabled Digital Twins: A Framework for Wireless SDN Simu...

引用

Consumer Communications and Networking Conference, CCNC IEEE

作者： Angelo Trotta Mario Micciché Alexandre Heideker Marco Di Felice Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

The dynamic nature of modern wireless networks, combined with the need for efficient management, has heightened interest in Software-Defined Networking (SDN). This paper presents a simulation framework designed to effectively model SDN in wireless network environments. The framework addresses key challenges in managing heterogeneous networks, offering a versatile platform for the development and evaluation of SDN applications. A key feature of the framework is the integration of a Digital Twin (DT) module within the SDN controller, leveraging the controller's comprehensive view of the network. This integration allows for real-time construction and updating of a virtual representation of the network, enhancing the controller's decision-making capabilities. By predicting future network states through a neural network model, the DT facilitates proactive management strategies, such as routing adjustments and resource reallocation, which are essential for maintaining optimal network performance. The paper details the architectural design and implementation of the framework, including the integration of Mininet, OMNeT++, and the Ryu controller. Our results demonstrate the framework's effectiveness in simulating complex SDN scenarios and providing detailed analyses of network behavior.

关键词： Wireless networks Neural networks Decision making Routing Real-time systems Heterogeneous networks Digital twins Topology Software defined networking Thermal stability

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：