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arXiv 2024年

作者： Lee, Dae Gwan Pfander, Götz E. Walnut, David Department of Mathematics and Big Data Science Kumoh National Institute of Technology Daehak-ro 61 Gumi Gyeongsangbuk-do 39177 Korea Republic of Katholische Universität Eichstätt–Ingolstadt Hohe-Schul-Str. 5 Ingolstadt85049 Germany Department of Mathematical Sciences George Mason University 4400 University Drive FairfaxVA22030 United States

The existence of a Fourier basis with frequencies in Rd for the space of square integrable functions supported on a given parallelepiped in Rd, has been well understood since the 1950s. In a companion paper, we derived necessary and sufficient conditions for a parallelepiped in Rd to permit an orthogonal basis of exponentials with frequencies constrained to be a subset of a prescribed lattice in Rd, a restriction relevant in many applications. In this paper, we investigate analogous conditions for parallelepipeds that permit a Riesz basis of exponentials with the same constraints on the frequencies. We provide a sufficient condition on the parallelepiped for the Riesz basis case which directly extends one of the necessary and sufficient conditions obtained in the orthogonal basis case. We also provide a sufficient condition which constrains the spectral norm of the matrix generating the parallelepiped, instead of constraining the structure of the *** Codes 42B05, 42C15 Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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Global cold-chain related SARS-CoV-2 transmission identified by pandemic-scale phylogenomics

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Zoological Research 2022年第5期43卷 871-874页

作者： Dalang Yu Junwei Zhu Jianing Yang Yi-Hsuan Pan Hailong Mu Ruifang Cao Bixia Tang Guangya Duan Zi-Qian Hao Long Dai Guo-Ping Zhao Ya-Ping Zhang Wenming Zhao Guoqing Zhang Haipeng Li National Genomics Data Center&Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghai 200031China National Genomics Data Center Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing 100101China Key Laboratory of Brain Functional Genomics of Ministry of Education School of Life ScienceEast China Normal UniversityShanghai 200062China State Key Laboratory of Genetic Resources and Evolution Yunnan Laboratory of Molecular Biology of Domestic AnimalsKunming Institute of ZoologyChinese Academy of SciencesKunmingYunnan 650223China Key Laboratory of Synthetic Biology CAS Center for Excellence in Molecular Plant SciencesInstitute of Plant Physiology and EcologyChinese Academy of SciencesShanghai 200032China School of Life and Health Sciences Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhouZhejiang 310024China Shanghai Southgene Technology Co.Ltd Shanghai 201203China University of Chinese Academy of Sciences Beijing 100049China

DEAR EDITOR,The COVID-19 pandemic caused by SARS-CoV-2 continues to pose a tremendous threat to human society. SARS-CoV-2is airborne and transmits primarily through social contact;however, whether cold chain-related transmission has occurred remains highly debated(Han & Liu, 2022;Lewis,2021;Ma et al., 2021;Mallapaty et al., 2021;Pang et al.,2020;Wu et al., 2021). Here, we present a novel method and identify two transmission routes based on lineage-specific reductions in the SARS-CoV-2 evolutionary rate.

关键词： primarily EDIT identif

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Neural Network-Based Wind Field Reconstruction Method with Limited Measurement Points for Urban Blocks

SSRN

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SSRN 2024年

作者： Wang, Ranpeng Liu, Yi Li, Congjian Gao, Hang Wang, Gangqiao Institute of Public Safety Research Department of Engineering Physics Tsinghua University Beijng100084 China School of Safety Science Tsinghua University Beijing100084 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China College of Artificial Intelligence Tianjin University of Science and Technology Tianjin300450 China Institute of Big Data intelligent Management and Decision College of Management Shenzhen University Shenzhen518055 China

The rapid reconstruction of wind fields in urban blocks plays a crucial role in retrofitting zero-energy buildings, guiding the selection and placement of wind turbines. Although numerical methods and the Proper Orthogonal Decomposition (POD) method can provide relatively accurate results, their high computational cost and associated discontinuities pose challenges for acquiring high-spatial-resolution data affected by buildings. In this study, we develop a deep learning approach that can reconstruct wind fields affected by complex building layouts using a limited number of measuring points. The model is meticulously crafted with considerations given to model structure, loss function, and point selection method. Extensive analysis and comparisons with conventional designs are conducted to validate the proposed model. The results demonstrate that our model outperforms the conventional methods, achieving a mean absolute error in velocity of only 0.12 m/s over the inlet velocity range of 1 to 20 m/s, with only ten measurement points. Furthermore, a comparison with the POD method reveals that our model excels in extracting and preserving the crucial features of wind fields affected by complex building layouts, enabling higher migration capability. Overall, the proposed method delivers the potential for accurately reconstructing wind fields in urban blocks. © 2024, The Authors. All rights reserved.

关键词： Numerical methods

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A highly efficient asymptotic preserving IMEX method for the quantum BGK equation

arXiv

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arXiv 2023年

作者： Li, Ruo Lu, Yixiao Wang, Yanli CAPT LMAM School of Mathematical Sciences Peking University Beijing100871 China Chongqing Research Institute of Big Data Peking University Chongqing401121 China HEDPS Center for Applied Physics and Technology School of Mathematical Sciences Peking University Beijing100871 China Beijing Computational Science Research Center China

This paper presents an asymptotic preserving (AP) implicit-explicit (IMEX) scheme for solving the quantum BGK equation using the Hermite spectral method. The distribution function is expanded in a series of Hermite polynomials, with the Gaussian function serving as the weight function. The main challenge in this numerical scheme lies in efficiently expanding the quantum Maxwellian with the Hermite basis functions. To overcome this, we simplify the problem to the calculation of polylogarithms and propose an efficient algorithm to handle it, utilizing the Gauss-Hermite quadrature. Several numerical simulations, including a 2D spatial lid-driven cavity flow, demonstrate the AP property and remarkable efficiency of this method. © 2023, CC BY.

关键词： Distribution functions

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Dual attention mechanism object tracking algorithm based on Fully-convolutional Siamese network

Dual attention mechanism object tracking algorithm based on ...

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2021 International Conference on Networking and Network Applications, NaNA 2021

作者： Ma, Sugang Zhang, Zixian Zhang, Lei Chen, Yanping Yang, Xiaobao Pu, Lei Hou, Zhiqiang Xi'an University of Posts and Telecommunications School of Computer Science and Technology Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Information and Navigation Air Force Engineering University Shaanxi Xi'an710077 China School of Information Engineering Chang'an University Shaanxi Xi'an710064 China

ISBN: (纸本)9781665441582

In an effort to the problem of insufficient tracking performance of the Fully-convolutional Siamese network (SiamFC) in complex scenarios, a dual attention mechanism object tracking algorithm based on the Fully-convolutional Siamese network is proposed to improve the generalization capability of the tracker by ameliorating the robustness of the template characteristics. Firstly, a global context attention module is appended after the backbone network of SiamFC to ameliorate the power of original feature extraction from two dimensions of spatial and channel. Then, a coordinate attention module is introduced to augment the capability of feature extraction in the channel dimension. Finally, the model of the proposed algorithm is trained on the Got-10k dataset. Five related algorithms are tested on the OTB2015 dataset, the results of experiments manifest that our algorithm outperforms the baseline trackers, the success and precision rate of the proposed algorithm are improved by 3.3% and 6.3%. The average tracking speed is 145FPS, which can demand the requirement of real-time tracking. © 2021 IEEE.

关键词： Feature extraction

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Multiagent Q-Learning for Multicrew Dynamic Scheduling and Routing in Road Network Restoration

Multiagent Q-Learning for Multicrew Dynamic Scheduling and R...

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第34届中国控制与决策会议

作者： Yufeng Shen Lianqi Duan Qianshun Zhu Zhaopin Su Guofu Zhang School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) Anhui Province Key Laboratory of Industry Safety and Emergency Technology (Hefei University of Technology)

Road network restoration is an important issue in the post-disaster disposal and rescue,especially when extraordinarily serious natural disasters(e.g.,floods and earthquakes) *** to this endeavour is the problem of determining how to reasonably schedule and route the repair crew to quickly restore the damaged road network and establish reliable supply lines from supply nodes to demand ***,most existing work focuses on the activities of the single repair crew in the static road network,which is unable to adapt to the dynamic changes of the road network caused by secondary disasters,such as rock slides and debris ***,this work is concentrated on multicrew dynamic scheduling and routing in road network ***,a model of multicrew dynamic scheduling and routing is first ***,the Markov decision process is adopted to construct a decision model for repair crews,based on which a multiagent Q-learning algorithm is developed to find the best scheduling and routing ***,experimental results demonstrate that the proposed method can make repair crews adjust their scheduling and routing strategies according to the dynamic changes of the damaged road network and provides a useful attempt to restore the damaged road network in complex,dynamic emergency scenarios of post-disaster.

关键词： Road Network Restoration Multiple Repair Crews Dynamic Scheduling and Routing Multiagent Q-Learning

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Penta-Encoder with Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

Penta-Encoder with Medical Transformer for Incomplete Multim...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Guohui Yu Lihui Wang Qijian Chen Li Wang Kun Tang Xinyu Cheng Yuemin Zhu State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China INSA Lyon CNRS Inserm CREATIS UMR 5220 U1206 Univ Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Accurate brain tumor segmentation of magnetic resonance imaging (MRI) images requires the integration of multimodal data in effective collaborative learning. However, clinical scenarios often face missing modalities, significantly impacting segmentation performance. In this paper, we propose the penta-encoder with medical transformer (Penta-MedTrans) for incomplete multimodal learning, which consists of four main components: a modality-specific encoder (MSE), a modality-dynamic encoder (MDE), a multimodal fusion module (MMFM) and a convolutional decoder. The MSE extracts unique features for each modality, while the MDE captures inter-modal semantics to address missing modalities. The MMFM establishes and aligns long-range correlations between modal-invariant features and global semantics of tumor regions. The convolutional decoder generates robust segmentation results by progressively upsampling and fusing modality-invariant features. We extensively validated our method on the BraTS 2020 and BraTS 2021 datasets for brain tumor segmentation. Results showed that our method outperforms SOTA for incomplete multimodal brain tumor segmentation across most subsets.

关键词： Image segmentation Correlation Convolution Magnetic resonance imaging Semantics Brain tumors Transformers Feature extraction Decoding Biomedical imaging

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Self-supervised fast reconstruction method for diffusion MRI based on multi-directional assistance

Self-supervised fast reconstruction method for diffusion MRI...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Yuxin Wu Jian Zhang Lihui Wang Ying Cao Linhai Wu Yunjia Wu Chen Ye Yuemin Zhu State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China INSA Lyon CNRS Inserm CREATIS UMR 5220 U1206 Univ Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Diffusion MRI (dMRI) is widely used for the detection and diagnosis of various pathologies due to its non-invasive nature. However, its signal acquisition process is relatively slow and susceptible to motions. In order to accelerate reconstruction process, many deep learning-based methods have been developed in recent years to achieve promising results, while most of them are supervised based methods and require fully sampled data which are difficult to obtain in practical applications to train the network. Self-supervised based methods were proposed to deal with this problem to a certain extent, however, they are devoted only to conventional MRI data, ignoring the correlation between the directions of dMRI data. To address this issue, we propose a self-supervised fast reconstruction method for dMRI based on multi-directional assistance. The method better utilizes the information between dMRI data directions by Partition-Replace operation, cleverly constructing data pairs and designing effective self-supervised strategy for bi-directional prediction, which can overcome the problem of data incompleteness and guide the model to reconstruct unsampled signals in spite of the difficulty of acquiring fully sampled data. Experimental results show that our method achieves better reconstruction in the absence of fully sampled data, even with an 8-fold acceleration factor.

关键词： Learning systems Pathology Correlation Magnetic resonance imaging Bidirectional control Reconstruction algorithms Signal processing Predictive models data models

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Hierarchical clustering by aggregating representatives in sub-minimum-spanning-trees

arXiv

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arXiv 2021年

作者： Xie, Wen-Bo Liu, Zhen Srivastava, Jaideep Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Science and Engineering University of Minnesota MinneapolisMN55455 United States Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China

One of the main challenges for hierarchical clustering is how to appropriately identify the representative points in the lower level of the cluster tree, which are going to be utilized as the roots in the higher level of the cluster tree for further aggregation. However, conventional hierarchical clustering approaches have adopted some simple tricks to select the "representative" points which might not be as representative as enough. Thus, the constructed cluster tree is less attractive in terms of its poor robustness and weak reliability. Aiming at this issue, we propose a novel hierarchical clustering algorithm, in which, while building the clustering dendrogram, we can effectively detect the representative point based on scoring the reciprocal nearest data points in each sub-minimum-spanning-tree. Extensive experiments on UCI datasets show that the proposed algorithm is more accurate than other benchmarks. Meanwhile, under our analysis, the proposed algorithm has O(n log n) time-complexity and O(log n) space-complexity, indicating that it has the scalability in handling massive data with less time and storage consumptions. Copyright © 2021, The Authors. All rights reserved.

关键词： Clustering algorithms

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Joint Base Station and IRS Deployment for Enhancing Network Coverage: A Graph-Based Modeling and Optimization Approach

arXiv

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arXiv 2022年

作者： Mei, Weidong Zhang, Rui The National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Intelligent reflecting surface (IRS) can be densely deployed in complex environment to create cascaded line-of-sight (LoS) paths between multiple base stations (BSs) and users via tunable IRS reflections, thereby significantly enhancing the coverage performance of wireless networks. To achieve this goal, it is vital to optimize the deployed locations of BSs and IRSs in the wireless network, which is investigated in this paper. Specifically, we divide the coverage area of the network into multiple non-overlapping cells and decide whether to deploy a BS/IRS in each cell given a total number of BSs/IRSs available. We show that to ensure the network coverage/communication performance, i.e., each cell has a direct/cascaded LoS path with at least one BS, as well as such LoS paths have the average number of IRS reflections less than a given threshold, there is a fundamental trade-off with the deployment cost or the number of BSs/IRSs needed. To optimally characterize this trade-off, we formulate a joint BS and IRS deployment problem based on graph theory, which, however, is difficult to be optimally solved due to the combinatorial optimization involved. To circumvent this difficulty, we first consider a simplified problem with given BS deployment and propose the optimal as well as an efficient suboptimal IRS deployment solution to it, by applying the branch-and-bound method and iteratively removing IRSs from the candidate locations, respectively. Next, an efficient sequential update algorithm is proposed for solving the joint BS and IRS deployment problem. Numerical results are provided to show the efficacy of the proposed design approach and optimization algorithms for the joint BS and IRS deployment. The trade-off between the network coverage performance and the number of deployed BSs/IRSs with different cost ratios is also unveiled. Copyright © 2022, The Authors. All rights reserved.

关键词： Base stations

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