检索结果-内蒙古大学图书馆

Can discovery of hidden charm strange pentaquark states help determine the spins of Pc(4440) and Pc(4457)?

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Liu, Ming-Zhu Pan, Ya-Wen Geng, Li-Sheng School of Physics Beihang University Beijing100191 China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China School of Physics and Microelectronics Zhengzhou University ZhengzhouHenan450001 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and Engineering Beihang University Beijing100191 China

The pentaquark states, Pc(4312), Pc(4440) and Pc(4457), could be nicely arranged into a multiplet of seven molecules of D¯(∗)Σc(∗) dictated by heavy quark spin symmetry. However, the spins of Pc(4440) and Pc(4457) are not yet fully determined. In this work we employ the contact-range effective field theory to investigate the SU(3)-flavor counterparts of D¯(∗)Σc(∗), and study the possibility whether their discovery can help determine the spins of Pc(4457) and Pc(4440). We find the existence of a complete hidden charm strange multiplet of D¯(∗)Ξc('∗) molecules irrespective of the spins of Pc(4440) and Pc(4457). On the other hand, we find that although molecules of D¯(∗)Ξc are also likely, depending on the realization of the underlying dynamics, their discovery can be more useful to determine the spins of Pc(4440) and Pc(4457) and to tell how the heavy quark and light quark interaction depends on the spin of the light quark pair. © 2020, CC BY.

关键词： Molecules

LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image

学校读者我要写书评

暂无评论

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ronghuan Zhang Jing Zhao Ming Li Qingzhi Zou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity.

关键词： Accuracy Computational modeling Memory management Neural networks Feature extraction Transformers Sensors data mining Computational complexity Remote sensing

Analysis of Epilepsy EEG and ECG Correlation Based on IRC Algorithm

学校读者我要写书评

暂无评论

Analysis of Epilepsy EEG and ECG Correlation Based on IRC Al...

International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Ting Sun Xin Zou Chuchu Ding Jiafei Dai Jin Li Jun Wang Fengzhen Hou Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Nanjing General Hospital of Nanjing Military Command Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi’an China Key Laboratory of Biomedical Functional Materials China Pharmaceutical University Nanjing China

ISBN: (纸本)9781538657393;9781538657386

In this paper, the correlation analysis of EEG signals and ECG signals between epileptic and normal population was carried out based on the improved synchronization algorithm IRC based on Kendall. In clinical animal experiments, it was found that when the epilepsy sample attacks, irregular heart rate changes often occur. In this experiment, an improved IRC algorithm was used to analyze the correlation between EEG signals and ECG signals to study whether the correlation between epilepsy patients was different from that of normal people. Experimental results show that using the IRC algorithm based on Kendall improvement can significantly distinguish the correlation between EEG signals and ECG signals in the left forehead and left anterior temporal regions of normal and epileptic populations, and further study on the correlation between brain and heart in epileptic patients.

关键词： Electroencephalography Electrocardiography Correlation Epilepsy Correlation coefficient Electrodes Forehead

Adversarial directed graph embedding

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Zhu, Shijie Li, Jianxin Peng, Hao Wang, Senzhang He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node's source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

Research on Freeway Travel Speed Prediction Based on Commercial Vehicle data

学校读者我要写书评

暂无评论

Research on Freeway Travel Speed Prediction Based on Commerc...

International Conference on Measuring Technology and Mechatronics Automation, ICMTMA

作者： Genyuan Cheng Dekun Xie Xiaochun Cheng Qin Chen Jiandong Zhao Tangshan Municipal Transportation Bureau Hebei Province Tangshan China Tandon School of Engineering New York University Brooklyn USA Beijing National Day School Beijing China Beijing Jiaotong University China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing Jiaotong University Beijing China

ISBN: (数字)9781728170817

ISBN: (纸本)9781728170824

With the rapid development of China's economy and the continuous improvement of people's living standards, the public's travel demand is increasing. However, rapid traffic demand growth is bound to bring problems such as traffic congestion and traffic accidents. Accurate travel speed prediction can provide a basis for traveler route selection and traffic guidance. This study uses the Beidou positioning navigation system data to take the Shanghai-Kunming freeway section in Jiangxi Province as a test section to study the travel speed prediction. The contents include:(1) Summarizing the main methods of short-term traffic flow prediction. (2) Design the preprocessing process for Beidou system data. (3) A travel speed prediction model based on support vector regression is established. (4) The SVR model is used to conduct empirical prediction research on different traffic flow states.

关键词：

Poisoning attack against estimating from pairwise comparisons

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

AutoSTL: Automated Spatio-Temporal Multi-Task Learning

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhang, Zijian Zhao, Xiangyu Miao, Hao Zhang, Chunxu Zhao, Hongwei Zhang, Junbo College of Computer Science and Technology Jilin University China School of Data Science City University of Hong Kong Hong Kong Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Department of Computer Science Aalborg University Denmark JD Intelligent Cities Research China JD iCity JD Technology China Hong Kong Institute for Data Science City University of Hong Kong Hong Kong

Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integrating their inseparable relationship. However, existing studies fail to address this joint learning problem well, which generally solve tasks individually or a fixed task combination. The challenges lie in the tangled relation between different properties, the demand for supporting flexible combinations of tasks and the complex spatio-temporal dependency. To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly. Firstly, we propose a scalable architecture consisting of advanced spatio-temporal operations to exploit the complicated dependency. Shared modules and feature fusion mechanism are incorporated to further capture the intrinsic relationship between tasks. Furthermore, our model automatically allocates the operations and fusion weight. Extensive experiments on benchmark datasets verified that our model achieves state-of-the-art performance. As we can know, AutoSTL is the first automated spatio-temporal multi-task learning method. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart city

FRACTIONAL PARTIAL DIFFERENTIAL VARIATIONAL INEQUALITY

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Cen, Jinxia Sousa, Vanterler Da C.J. Wu, Wei School of Mathematical Sciences Zhejiang Normal University Jinhua321004 China Aerospace Engineering PPGEA-UEMA Department of Mathematics DEMATIUEMA MA Sao Luís65054 Brazil Center for Applied Mathematics of Guangxi Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Guangxi Yulin537000 China

In this present paper, we introduce and study a dynamical systems involving fractional derivative operator and nonlocal condition, which is constituted of a fractional evolution equation and a time-dependent variational inequality, and is named as fractional partial differential variational inequality (FPDVI, for short). By employing the estimates involving the one-and two-parameter Mittag-Leffler functions, fixed-point theory for set-value mappings, and non-compactness measure theory, we develop a general framework to establish the existence of smooth solutions to (FPDVI).MSC Codes 47J20, 35R11, 49J40, 35J88, 26A33 Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

Time Delay Induces Pattern Dynamics on Directed Networks

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Song, Zhao Wang, Xinyu Shi, Juan Chang, Lili Wang, Zhen School of Mechanical Engineering Northwestern Polytechnical University Shaanxi Xi’an710072 China School of Automation Northwestern Polytechnical University Shaanxi Xi’an710072 China Complex Systems Research Center Shanxi University Shanxi Taiyuan030006 China Shanxi Key Laboratory of Mathematical Technique and Big Data Analysis on Disease Control and Prevention Shanxi Taiyuan030006 China

Pattern theory concerning networks has attracted substantial attention in recent years, proving its utility across diverse sub-disciplines, notably within the realm of reaction-diffusion (RD) systems. With real-world systems often characterized by directed connections, the traditional exploration of RD systems restricted to undirected networks has seamlessly extended to encompass directed networks. Prior studies have unveiled that in directed scenarios, the inherent asymmetry of the Laplacian matrix gives rise to intricate dispersion relations, thereby instigating the pursuit of further exploration and analysis. In this paper, we delve into time delay within RD systems operating on directed networks, which is not clearly revealed in previous work. Our investigations mainly focus on the emergence of spatially organized patterns resulting from the spatio-temporal dynamical processes. In detail, we linearize the RD system to derive the region of instability wherein patterns exhibit. This instability is time delay induced. Subsequent to this, we conduct extensive numerical simulations to illustrate wave patterns across different directed networks. Significantly, we find that network properties, encompassing average degree and degree distribution, wield minimal influence over pattern formation. In contrast, time delay significantly shapes pattern periodicity. Furthermore, we establish a compelling correlation between time delay and pattern periodicity, elegantly captured by an exponential-type function. © 2023, The Authors. All rights reserved.

关键词： Time delay