检索结果-内蒙古大学图书馆

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Chunchen Wei Jingxin Wen Ran Bi Huabin Yang Yanqing Tao Yuhang Fan Yulong He Didi Cheng Yanru Zhang School of Computer Science and Engineering University of Electronic Science and Technology of China China Sichuan University Jinjiang College China School of Glasgow University of Electronic Science and Technology of China China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China China Chengdu Dishi Technology Co. Ltd. China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

Exercise at home has already been a common behavior in the current world, especially in the post-COVID-19 era, even some athletes need to do physical fitness at home to keep their state due to the quarantine. So, the importance of online physical training and evaluation is highly increasing. In this work, we build an online 8-form Tai Chi Chuan (TCC) training and evaluation system, which provides a platform for coaches and users to conduct TCC training and evaluation online. Coaches formulate an evaluation rule and upload coaching videos to the platform, then users watch videos online and submit their own recordings, finally, users will get a score of their recordings. To complete this task, we propose a video capture method to record users' sports exercise videos from different perspectives, construct a 3D pose estimation model to identify human pose from captured video, and propose an evaluation model which can judge users' performance and assign a score to each video. To test our proposed models, we make a dataset consisting of key pose frames of TCC, and the key pose frames are extracted from users' TCC exercise videos. We use the dataset to train our models and assign scores to key poses, then compare the results with scores given by professional TCC players. In addition, we add all key pose scores from every single user together and obtain the whole score of an exercise video. The experiment results show that the error between scores assigned by our models and scores given by professional players does not exceed 1.6 in most scoring of a whole exercise video, and Root Mean Square Error (RMSE) is about 0.75 in the scoring of each key pose.

关键词： Training Heating systems Solid modeling Three-dimensional displays Biological system modeling Pose estimation Recording

来源：评论

学校读者我要写书评

暂无评论

Fast Encoding of Extended Integrated Interleaved Codes Based on Two-Dimensional LCH-FFT

Fast Encoding of Extended Integrated Interleaved Codes Based...

引用

Topics in Coding (ISTC), International Symposium on

作者： Chao Chen Suihua Caiy Yunghsiang S. Han Nianqi Tang Baoming Bai State Key Laboratory of ISN Xidian University Xi'an China School of Computer Science and and Engineering Sun Yat-sen University Guangzhou China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Huawei Technologies Co. Ltd.

In this paper, we present fast encoding algorithms for extended integrated interleaved (EII) codes by re-defining them as a class of bivariate polynomial evaluation codes. Based on the recently proposed two-dimensional LCH-FFT algorithm, fast algorithms are derived for nonsystematic and systematic encoding. For an EII code of length N, both the encoding algorithms achieve the computational complexity O(N log (N)).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dear-DIA^(XMBD): Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

引用

Research 2024年第1期6卷 707-720页

作者： Qingzu He Chuan-Qi Zhong Xiang Li Huan Guo Yiming Li Mingxuan Gao Rongshan Yu Xianming Liu Fangfei Zhang Donghui Guo Fangfu Ye Tiannan Guo Jianwei Shuai Jiahuai Han Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)and Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang 325001China School of Life Sciences Xiamen UniversityXiamen 361102China State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkXiamen 361102China Department of Computer Science Xiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine School of MedicineXiamen UniversityXiamen 361102China Bruker(Beijing)Scientific Technology Co.Ltd. BeijingChina Westlake Laboratory of Life Sciences and Biomedicine Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University18 Shilongshan RoadHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024China Westlake Omics Ltd. Yunmeng Road 1HangzhouChina Department of Electronic Engineering Xiamen UniversityXiamen 361005China

Data-independent acquisition(DIA)technology for protein identification from mass spectrometry and related algorithms is developing *** spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising *** this paper,we proposed an untargeted analysis method,Dear-DIA^(XMBD),for direct analysis of DIA ***-DIA^(XMBD) first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms,then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes,and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and *** show that Dear-DIA^(XMBD) performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms.

关键词： Deep Auto Independent

来源：评论

学校读者我要写书评

暂无评论

An Early-Termination Method for the Welch-Berlekamp Algorithm

An Early-Termination Method for the Welch-Berlekamp Algorith...

引用

IEEE International Symposium on Information Theory

作者： Chao Chen Yunghsiang S. Han Nianqi Tang Sian-Jheng Lin Baoming Bai Xiao Ma State Key Laboratory of ISN Xidian University Xi’an China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Huawei Technologies Co. Ltd. School of Computer Science and and Engineering Sun Yat-sen University Guangzhou China

This paper presents an early-termination method for the Welch-Berlekamp algorithm. Specifically, if e ≤ t (where e is the number of errors and t is the error correction capability), the Welch–Berlekamp algorithm can be terminated at latest at the completion of the (t + e)-th iteration. Based on the early-termination mechanism, a new variant of the Welch–Berlekamp algorithm called eFDMA is presented, and a systolic architecture is designed for the eFDMA algorithm. This provides an efficient implementation for the key equation solver for a new class of Reed–Solomon codes recently proposed by Lin et al. [9].

关键词：

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Clustered Cell-Free Networking with Access Point Selection

arXiv

引用

arXiv 2024年

作者： Zhou, Ouyang Wang, Junyuan Liu, Fuqiang Wang, Jiangzhou College of Electronic and Information Engineering Tongji University Shanghai201804 China Institute of Advanced Study Tongji University Shanghai200092 China College of Computer Science and Technology Huaibei Normal University Huaibei235000 China School of Engineering University of Kent CanterburyCT2 7NZ United Kingdom

Ultra-densely deploying access points (APs) to support the increasing data traffic would significantly escalate the cell-edge problem resulting from traditional cellular networks. By removing the cell boundaries and coordinating all APs for joint transmission, the cell-edge problem can be alleviated, which in turn leads to unaffordable system complexity and channel measurement overhead. A new scalable clustered cell-free network architecture has been proposed recently, under which the large-scale network is flexibly partitioned into a set of independent subnetworks operating parallelly. In this paper, we study the energy-efficient clustered cell-free networking problem with AP selection. Specifically, we propose a user-centric ratio-fixed AP-selection based clustering (UCR-ApSel) algorithm to form subnetworks dynamically. Following this, we analyze the average energy efficiency achieved with the proposed UCRApSel scheme theoretically and derive an effective closed-form upper-bound. Based on the analytical upper-bound expression, the optimal AP-selection ratio that maximizes the average energy efficiency is further derived as a simple explicit function of the total number of APs and the number of subnetworks. Simulation results demonstrate the effectiveness of the derived optimal AP-selection ratio and show that the proposed UCR-ApSel algorithm with the optimal AP-selection ratio achieves around 40% higher energy efficiency than the baselines. The analysis provides important insights to the design and optimization of future ultra-dense wireless communication systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection

arXiv

引用

arXiv 2025年

作者： Xu, Meiyan Chen, Qingqing Chen, Duo Ding, Yi Wang, Jingyuan Gu, Peipei Pan, Yijie Huang, Deshuang Guo, Jiayang Zhang, Xun Department of Computer Science The Institute of Human Factors Engineering Minnan Normal University Zhangzhou363000 China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing210023 China College of Computing and Data Science Nanyang Technological University Singapore School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing100088 China College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450000 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo315200 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China

EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data’s non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. Thus, we propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel’s features. With exact fit attention optimization, the network captures inter-channel correlations through a trainable adjacency matrix. The interpretability is enhanced by revealing critical areas of brain activity and their interrelations in various mental states. In validations on two public datasets, NHGNet outperforms the SOTAs. Specifically, in the intra-subject, NHGNet improved detection accuracy by at least 2.34% and 3.42%, and in the inter-subjects, it improved by at least 2.09% and 15.06%. Visualization research on the model revealed that the central parietal area plays an important role in detecting fatigue levels, whereas the frontal and temporal lobes are essential for maintaining vigilance. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

DPA-2:a large atomic model as a multitask learner

引用

npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

来源：评论

学校读者我要写书评

暂无评论

Can thermal nonreciprocity improve the radiative cooling efficiency?

arXiv

引用

arXiv 2024年

作者： Liu, Mengqi Jin, Shenghao Zhou, Chenglong Wang, Boxiang Zhao, Changying Qiu, Cheng-Wei Institute of Engineering Thermophysics MOE Key Laboratory for Power Machinery and Engineering School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore School of Energy Science and Engineering Harbin Institute of Technology Harbin150001 China 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China School of Graduate Study University of Chinese Academy of Sciences Beijing100049 China

关键词： Radiative Cooling

来源：评论

学校读者我要写书评

暂无评论

Improved Multisignature Scheme for Authenticity of Digital Document in Digital Forensics Using Edward-Curve Digital Signature Algorithm

引用

Security and Communication Networks 2023年第1期2023卷

作者： Shankar, Gauri Ai-Farhani, Liwa H. Anitha Christy Angelin, P. Singh, Parvinder Alqahtani, Abdullah Singh, Abha Kaur, Gaganpreet Samori, Issah Abubakari Chandigarh University Mohali Punjab Mohali140413 India System Analysis Control and Information Processing Academy of Engineering RUDN University Moscow Russia PSNA College of Engineering and Technology Tamilnadu Dindigul624622 India Central University of Punjab Bathinda Punjab 151001 India Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Basic Science College of Science and Theoretical Study Dammam-Female Branch Saudi Electronic University Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Engineering Sciences University of Ghana Accra Ghana

At the moment, digital documents are just as important as paper documents. As a result, authenticity is essential, especially in legal situations and digital forensics. As technology advances, these digital signature algorithms become weaker, necessitating the development of digital authentication schemes capable of withstanding current security threats. This study proposed a scheme based on an asymmetric key cryptosystem and the user's biometric credentials to generate keys for digital signatures. A single document can be signed by multiple signatories at the same time under this scheme. The primary goal of this article is to create a safe and cost-effective multiignature scheme. To create keys for document signing and verification, the Edwards-curve Digital Signature Algorithm (EdDSA), especially Ed25519, is employed. The Edwards-curve Digital Signature Algorithm is used with blockchain technology to sign crypto wallets. The Python implementation of a scheme that enables platform independence. We performed performance, security, and comparative analysis to ensure maximum usability. The article's main findings are that the Ed25519 algorithm can be used in blockchain. © 2023 Gauri Shankar et al.

关键词： Digital forensics

来源：评论

学校读者我要写书评

暂无评论

Balanced Self-Paced Learning for AUC Maximization

arXiv

引用

arXiv 2022年

作者： Gu, Bin Zhang, Chenkang Xiong, Huan Huang, Heng MBZUAI United Arab Emirates School of Computer & Software Nanjing University of Information Science & Technology China Institute for Advanced Study in Mathematics Harbin Institute of Technology China Department of Electrical & Computer Engineering University of Pittsburgh PA United States

Learning to improve AUC performance is an important topic in machine learning. However, AUC maximization algorithms may decrease generalization performance due to the noisy data. Self-paced learning is an effective method for handling noisy data. However, existing self-paced learning methods are limited to pointwise learning, while AUC maximization is a pairwise learning problem. To solve this challenging problem, we innovatively propose a balanced self-paced AUC maximization algorithm (BSPAUC). Specifically, we first provide a statistical objective for self-paced AUC. Based on this, we propose our self-paced AUC maximization formulation, where a novel balanced self-paced regularization term is embedded to ensure that the selected positive and negative samples have proper proportions. Specially, the sub-problem with respect to all weight variables may be non-convex in our formulation, while the one is normally convex in existing self-paced problems. To address this, we propose a doubly cyclic block coordinate descent method. More importantly, we prove that the sub-problem with respect to all weight variables converges to a stationary point on the basis of closed-form solutions, and our BSPAUC converges to a stationary point of our fixed optimization objective under a mild assumption. Considering both the deep learning and kernel-based implementations, experimental results on several large-scale datasets demonstrate that our BSPAUC has a better generalization performance than existing state-of-the-art AUC maximization methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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：