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Further Investigations on Nonlinear Complexity of Periodic Binary Sequences

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arXiv 2024年

作者： Yuan, Qin Li, Chunlei Zeng, Xiangyong Helleseth, Tor He, Debiao Faculty of Mathematics and Statistics Hubei Key Laboratory of Applied Mathematics Hubei University Hubei Wuhan430062 China Department of Informatics University of Bergen BergenN-5020 Norway Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Hubei Wuhan430072 China

Nonlinear complexity is an important measure for assessing the randomness of sequences. In this paper we investigate how circular shifts affect the nonlinear complexities of finite-length binary sequences and then reveal a more explicit relation between nonlinear complexities of finite-length binary sequences and their corresponding periodic sequences. Based on the relation, we propose two algorithms that can generate all periodic binary sequences with any prescribed nonlinear complexity. © 2024, CC BY.

关键词： Binary sequences

PROJECTION METHOD FOR QUASIPERIODIC ELLIPTIC EQUATIONS AND APPLICATION TO QUASIPERIODIC HOMOGENIZATION

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arXiv 2024年

作者： Jiang, Kai Li, Meng Zhang, Juan Zhang, Lei Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China School of Mathematical Sciences Institute of Natural Sciences MOE-LSC Shanghai Jiao Tong University Shanghai200240 China

In this study, we address the challenge of solving elliptic equations with quasiperiodic coefficients. To achieve accurate and efficient computation, we introduce the projection method, which enables the embedding of quasiperiodic systems into higher-dimensional periodic systems. To enhance the computational efficiency, we propose a compressed storage strategy for the stiffness matrix by its multi-level block circulant structure, significantly reducing memory requirements. Furthermore, we design a diagonal preconditioner to efficiently solve the resulting high-dimensional linear system by reducing the condition number of the stiffness matrix. These techniques collectively contribute to the computational effectiveness of our proposed approach. Convergence analysis shows the spectral accuracy of the proposed method. We demonstrate the effectiveness and accuracy of our approach through a series of numerical examples. Moreover, we apply our method to achieve a highly accurate computation of the homogenized coefficients for a quasiperiodic multiscale elliptic *** Codes 65D05, 65D15, 65F08, 35B15, 35B27 Copyright © 2024, The Authors. All rights reserved.

关键词： Stiffness matrix

Revisiting Spatio-Temporal Forecasting: Feature Propagation Carry More Weights Than How They Do

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Revisiting Spatio-Temporal Forecasting: Feature Propagation ...

International Conference on Intelligent Transportation

作者： Shen Fang Hongyan Liu Chengcheng Yu Tian Xie Wei Hua Zhejiang Lab Research Center for Intelligent Manufacturing Computing Hangzhou China Urban Mobility Institute Tongji University Shanghai China Key Laboratory of Road and Traffic Engineering Ministry of Education at Tongji University Tongji University Shanghai China Zhejiang Lab Research Center for Space Computing System Hangzhou China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Spatio-temporal forecasting, a prominent research focus for understanding the dynamics of data flow in various domains, has recently extended its significance to traffic prediction as a notable application. Considering topology of data flow, existing methods mainly utilize Graph Convolutional Networks (GCNs), where graph construction is the basic concern and generally determines how data features are propagated. However, through extensive investigations and theoretical evidence, it is revealed that graph construction strategy, which is usually regarded as the key step to success, has actually provided very little benefit, while the presence of feature propagation itself on spatiotemporal domain is more significant, i.e., feature propagation carry more weights than how they do. Thus, by making slight refinements of a feature normalization method, we propose a Spatio-Temporal Propagation (STP) module, which does not require intervention of a specific graph structure, yet simple and effective. Various experiments on public datasets verify that the proposed STP module is an on-the-shelf tool that can be accessed to the end of current models or even replace GCNs as an alternative for capturing spatiotemporal features, while achieving better predictions. All the source codes are open accessed on GitHub.

关键词： Network topology Graph convolutional networks Source coding Computational modeling Predictive models Spatiotemporal phenomena Topology Forecasting Intelligent transportation systems Software development management

Convergence analysis of PM-BDF2 method for quasiperiodic parabolic equations

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arXiv 2024年

作者： Jiang, Kai Li, Meng Zhang, Juan Zhang, Lei Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Department of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China School of Mathematical Sciences Institute of Natural Sciences MOE-LSC Shanghai Jiao Tong University Shanghai200240 China

Numerically solving parabolic equations with quasiperiodic coefficients is a significant challenge due to the potential formation of space-filling quasiperiodic structures that lack translational symmetry or decay. In this paper, we introduce a highly accurate numerical method for solving time-dependent quasiperiodic parabolic equations. We discretize the spatial variables using the projection method (PM) and the time variable with the second-order backward differentiation formula (BDF2). We provide a complexity analysis for the resulting PM-BDF2 method. Furthermore, we conduct a detailed convergence analysis, demonstrating that the proposed method exhibits spectral accuracy in space and second-order accuracy in time. Numerical results in both one and two dimensions validate these convergence results, highlighting the PMBDF2 method as a highly efficient algorithm for addressing quasiperiodic parabolic equations. Copyright © 2024, The Authors. All rights reserved.

关键词： Convergence of numerical methods

Irrational-window-filter projection method and application to quasiperiodic Schrödinger eigenproblems

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arXiv 2024年

作者： Jiang, Kai Li, Xueyang Ma, Yao Zhang, Juan Zhang, Pingwen Zhou, Qi Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China School of Mathematics and Statistics Wuhan University Wuhan430072 China School of Mathematical Sciences Peking University Beijing100871 China

In this paper, we propose a new algorithm, the irrational-window-filter projection method (IWFPM), for quasiperiodic systems with concentrated spectral point distribution. Based on the projection method (PM), IWFPM filters out dominant spectral points by defining an irrational window and uses a corresponding index-shift transform to make the FFT available. The error analysis on the function approximation level is also given. We apply IWFPM to 1D, 2D, and 3D quasiperiodic Schrödinger eigenproblems (QSEs) to demonstrate its accuracy and efficiency. IWFPM exhibits a significant computational advantage over PM for both extended and localized quantum states. More importantly, by using IWFPM, the existence of Anderson localization in 2D and 3D QSEs is numerically *** Codes 35P05, 35J10, 65D15, 65T50, 81-08 © 2024, CC BY-NC-SA.

关键词： Wiener filtering

Fast and Continual Knowledge Graph Embedding via Incremental LoRA

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arXiv 2024年

作者： Liu, Jiajun Ke, Wenjun Wang, Peng Wang, Jiahao Gao, Jinhua Shang, Ziyu Li, Guozheng Xu, Zijie Ji, Ke Li, Yining School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Institute of Computing Technology Chinese Academy of Sciences China College of Software Engineering Southeast University China

Continual Knowledge Graph Embedding (CKGE) aims to efficiently learn new knowledge and simultaneously preserve old knowledge. Dominant approaches primarily focus on alleviating catastrophic forgetting of old knowledge but neglect efficient learning for the emergence of new knowledge. However, in real-world scenarios, knowledge graphs (KGs) are continuously growing, which brings a significant challenge to fine-tuning KGE models efficiently. To address this issue, we propose a fast CKGE framework (FastKGE), incorporating an incremental low-rank adapter (IncLoRA) mechanism to efficiently acquire new knowledge while preserving old knowledge. Specifically, to mitigate catastrophic forgetting, FastKGE isolates and allocates new knowledge to specific layers based on the fine-grained influence between old and new KGs. Subsequently, to accelerate fine-tuning, FastKGE devises an efficient IncLoRA mechanism, which embeds the specific layers into incremental low-rank adapters with fewer training parameters. Moreover, IncLoRA introduces adaptive rank allocation, which makes the LoRA aware of the importance of entities and adjusts its rank scale adaptively. We conduct experiments on four public datasets and two new datasets with a larger initial scale. Experimental results demonstrate that FastKGE can reduce training time by 34%-49% while still achieving competitive link prediction performance against state-of-the-art models on four public datasets (average MRR score of 21.0% vs. 21.1%). Meanwhile, on two newly constructed datasets, FastKGE saves 51%-68% training time and improves link prediction performance by 1.5%. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene Interactions

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CIMI4D: A Large Multimodal Climbing Motion Dataset under Hum...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ming Yan Xin Wang Yudi Dai Siqi Shen Chenglu Wen Lan Xu Yuexin Ma Cheng Wang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University

Motion capture is a long-standing research problem. Although it has been studied for decades, the majority of research focus on ground-based movements such as walking, sitting, dancing, etc. Off- grounded actions such as climbing are largely overlooked. As an important type of action in sports and firefighting field, the climbing movements is challenging to capture because of its complex back poses, intricate human-scene interactions, and difficult global localization. The research community does not have an indepth understanding of the climbing action due to the lack of specific datasets. To address this limitation, we collect CIMI4D, a large rock Climbing Motion dataset from 12 persons climbing 13 different climbing walls. The dataset consists of around 180,000 frames of pose inertial measurements, LiDAR point clouds, RGB videos, high-precision static point cloud scenes, and reconstructed scene meshes. Moreover, we frame-wise annotate touch rock holds to facilitate a detailed exploration of human-scene interaction. The core of this dataset is a blending optimization process, which corrects for the pose as it drifts and is affected by the magnetic conditions. To evaluate the merit of CIMI4D, we perform four tasks which include human pose estimations (with/without scene constraints), pose prediction, and pose generation. The experimental results demonstrate that CIMI4D presents great challenges to existing methods and enables extensive research opportunities. We share the dataset with the research community in http://***/cimi4d/.

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Effects of Climate Variability on Habitat Range and Distribution of Chub Mackerel in the East China Sea

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Journal of Ocean University of China 2021年第6期20卷 1483-1494页

作者： YU Wei WEN Jian CHEN Xinjun LI Gang LI Yuesong ZHANG Zhong College of Marine Sciences Shanghai Ocean UniversityShanghai 201306China Laboratory for Marine Fisheries Science and Food Production Processes Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China National Engineering Research Center for Oceanic Fisheries Shanghai Ocean UniversityShanghai 201306China Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources Ministry of EducationShanghai Ocean UniversityShanghai 201306China Key Laboratory of Oceanic Fisheries Exploration Ministry of Agriculture and Rural AffairsShanghai 201306China Scientific Observing and Experimental Station of Oceanic Fishery Resources Ministry of Agriculture and Rural AffairsShanghai 201306China

Spatio-temporal distribution of chub mackerel Scomber japonicus is strongly susceptible to climate *** this study,a weighting-based habitat suitability index(HSI)model was established to assess the impacts of Pacific Decadal Oscillation(PDO)on habitat range and distribution of *** in the East China Sea(ECS)using vertical water temperature at depths of 2.5 m,25 m and 50 *** showed that the optimal HSI model selected from ten weighting scenarios can yield reliable *** analysis revealed a significant negative relationship between the PDO index and water temperature anomaly at different *** water temperatures at three different layers on the fishing ground of *** tended to be low in the warm PDO phase and high in the cold PDO phase,*** range of suitable habitats and its spatial distribution exhibited large differences between the warm and cold PDO *** a warm PDO phase,vertical water temperature became cool,and the monthly preferred water temperature at each depth for *** shifted ***,habitat quality dramatically decreased,and suitable habitat ranges also reduced and moved ***,in a cool PDO phase,with the warmer vertical water temperature and northwestward shift of the preferred water temperature,the suitable habitats enlarged and migrated in the same *** findings indicate that the PDO produced significant impacts on habitat range and distribution of *** in the East China Sea.

关键词： Scomber japonicus habitat pattern vertical water temperature Pacific Decadal Oscillation the East China Sea

Electrocatalytic ammonia synthesis catalyzed by mesoporous nickel oxide nanosheets loaded with Pt nanoparticles

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Chinese Journal of Catalysis 2022年第5期43卷 1371-1378页

作者： Wei Xiong Min Zhou Hao Li Zhao Ding Da Zhang Yaokang Lv Key Laboratory for Green Chemical Process(Ministry of Education) Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of EducationHubei Key Laboratory Of Novel Reactor&Green Chemical TechnologySchool of Chemistry and Environmental EngineeringWuhan Institute of TechnologyWuhan 430205HubeiChina Department of Physics Technical University of DenmarkLyngby 2800Denmark The State Key Laboratory of Refractories and Metallurgy Institute of Advanced Materials and NanotechnologyWuhan University of Science and TechnologyWuhan 430071HubeiChina Changjiang River Scientific Research Institute Wuhan 430071HubeiChina College of Chemical Engineering Zhejiang University of TechnologyHangzhou 310014ZhejiangChina

Owing to its cost‐effectiveness and adjustable eight‐electron distribution in the 3d orbital,nickel oxide(NiO)is considered an effective electrocatalyst for an ambient electrochemical nitrogen reduction reaction(NRR).However,because of the low conductivity of the transition metal oxide electrocatalyst,its application in this field is *** this study,we found that the doping of NiO nanosheets with a small amount(3–10 nm)of Pt nanoparticles(Pt/NiO‐NSs)leads to considerable improvements in the Faradaic efficiency(FE)and NH_(3) yield compared with those obtained using pure NiO,breaking the common perception that commercial Pt‐based electrocatalysts demonstrate little potential for NRR due to their high hydrogen evolution *** a 0.1 mol/L Na_(2)SO_(4) solution at−0.2 V ***,a typical Pt/NiO‐2 sample exhibits an optimum electrochemical NH_(3) yield of 20.59μg h^(–1)mg^(–1)*** an FE of 15.56%,which are approximately 5 and 3 times greater,respectively,than those of pure NiO nanosheets at the same applied potential.X‐ray photoelectron spectroscopy analysis revealed that Pt in Pt/NiO‐NSs exist as Pt0,Pt^(2+),and Pt^(4+)and that high‐valence Pt ions are more electropositive,thereby favoring chemisorption and the activation of N2 *** function theory calculations showed that the d‐band of Pt nanoparticles supported on NiO is significantly tuned compared to that of pure Pt,affording a more favorable electronic structure for *** results of this study show that Pt can be an effective NRR electrochemical catalyst when loaded on an appropriate *** importantly,it provides a new synthetic avenue for the fabrication of highly active Pt‐based NRR electrocatalysts.

关键词： Platinum nanoparticles NiO nanosheets Nitrogen reduction reaction Electrocatalysis