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Pseudocapacitive Heteroatom-Doped Carbon Cathode for Aluminum-Ion Batteries with Ultrahigh Reversible Stability

Energy & Environmental Materials 2024年第5期7卷 150-159页

作者： Jiahui Li Jehad KEl-Demellawi Guan Sheng Jonas Björk Fanshuai Zeng Jie Zhou Xiaxia Liao Junwei Wu Johanna Rosen Xingjun Liu Husam N.Alshareef Shaobo Tu School of Physics and Materials Science Nanchang University999 Xuefu RoadHonggutan DistrictNanchang 330031China School of Materials Science and Engineering and Institute of Materials Genome and Big DataHarbin Institute of TechnologyShenzhen 518055China KAUST Upstream Research Center(KURC) EXPEC-ARCSaudi AramcoThuwal 23955Saudi Arabia Materials Design Division Department of PhysicsChemistry and Biology(IFM)Linköping UniversitySE-58183LinköpingSweden Physical Science and Engineering(PSE)Division King Abdullah University of Science and TechnologyThuwal 23955Saudi Arabia

Aluminum(Al)-ion batteries have emerged as a potential alternative to conventional ion batteries that rely on less abundant and costly materials like ***,given the nascent stage of advancement in Al-ion batteries(AIBs),attaining electrode materials that can leverage both intercalation capacity and structural stability remains ***,we demonstrate a C3N4-derived layered N,S heteroatom-doped carbon,obtained at different pyrolysis temperatures,as a cathode material for AIBs,encompassing the diffusion-controlled intercalation and surface-induced capacity with ultrahigh *** developed layered N,S-doped corbon(N,S-C)cathode,synthesized at 900℃,delivers a specific capacity of 330 mAhg^(-1)with a relatively high coulombic efficiency of~85%after 500 cycles under a current density of 0.5 A g^(-1).Owing to its reinforced adsorption capability and enlarged interlayer spacing by doping N and S heteroatoms,the N,S-C900 cathode demonstrates outstanding energy storage capacity with excellent rate performance(61 mAhg^(-1)at 20 A g^(-1))and ultrahigh reversibility(90 mAhg^(-1)at 5Ag^(-1)after 10000cycles).

关键词： 2D carbon adsorption energy heteroatoms-doping high capacity long cycling life

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Discovering Cohesive Temporal Subgraphs with Temporal Density Aware Exploration

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Journal of computer science & Technology 2022年第5期37卷 1068-1085页

作者： Chun-Xue Zhu Long-Long Lin Ping-Peng Yuan Hai Jin National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Real-world networks,such as social networks,cryptocurrency networks,and e-commerce networks,always have occurrence time of interactions between *** networks are typically modeled as temporal *** cohesive subgraphs from temporal graphs is practical and essential in numerous data mining applications,since mining cohesive subgraphs gets insights into the time-varying nature of temporal ***,existing studies on mining cohesive subgraphs,such as Densest-Exact and k-truss,are mainly tailored for static graphs(whose edges have no temporal information).Therefore,those cohesive subgraph models cannot indicate both the temporal and the structural characteristics of *** this end,we explore the model of cohesive temporal subgraphs by incorporating both the evolving and the structural characteristics of temporal ***,the volume of time intervals in a temporal network is *** a result,the time complexity of mining temporal cohesive subgraphs is *** efficiently address the problem,we first mine the temporal density distribution of temporal *** by the distribution,we can safely prune many unqualified time intervals with the linear time ***,the remaining time intervals where cohesive temporal subgraphs fall in are examined using the greedy *** results of the experiments on nine real-world temporal graphs indicate that our model outperforms state-of-the-art solutions in efficiency and ***,our model only takes less than two minutes on a million-vertex DBLP and has the highest overall average ranking in EDB and TC metrics.

关键词： temporal network temporal feature distribution cohesive subgraph convex property

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PC-CSP: An Efficient Partial Convolution in Cross Stage Partial Network for Small Targets Detection 7

PC-CSP: An Efficient Partial Convolution in Cross Stage Part...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Xue, Junmin Rao, Yunbo Wu, Jiangang Huang, Mei School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Operation Data Center of Postal Savings Bank of China Beijing Beijing610000 China Chengdu Aerospace Science and Industry Big Data Research Institute Corporation Limited Chengdu610299 China

ISBN: (纸本)9798350350890

The common approach for small object detection does not work well in complex scenes with objects of multiple sizes. This is because the detection of small objects and objects of other sizes often difficult to take into account at the same time. To overcome these limitations, we introduce an efficient Partial Convolution in Cross Stage Partial (PC-CSP) network for detecting small targets. First, the introduction of the DCNv3 module improves the feature extraction for detection, slimneck is utilized to enhance the attention mechanism suitable for objects of varying sizes. Then, PC-CSP is employed to streamline the detection backbone network. Last, the loss function is refined by incorporating the concept of Inner. Through extensive experiments, our method demonstrates a significant increase in accuracy of 6.8%, and mAP@0.5 by 8.97% on the standard dataset compared to the original model. Overall, the proposed method exhibits excellent performance for small objects, such as: mobile phones, food, and faces. © 2024 IEEE.

关键词： Feature extraction

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research on long and short-term social recommendation based on convolutional and gated recurrent units 29

Research on long and short-term social recommendation based ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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MetaCity: data-driven sustainable development of complex cities

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The Innovation 2025年第2期6卷 88-103,87页

作者： Yunke Zhang Yuming Lin Guanjie Zheng Yu Liu Nicholas Sukiennik Fengli Xu Yongjun Xu Feng Lu Qi Wang Yuan Lai Li Tian Nan Li Dongping Fang Fei Wang Tao Zhou Yong Li Yu Zheng Zhiqiang Wu Huadong Guo Department of Electronic Engineering Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China John Hopcroft Center for Computer Science Shanghai Jiao Tong UniversityShanghai 200240China University of Chinese Academy of Sciences Beijing 100049China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Civil and Environmental Engineering Northeastern UniversityBostonMA 02115USA School of Architecture Tsinghua UniversityBeijing 100084China School of Civil Engineering Tsinghua UniversityBeijing 100084China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China JD iCity JD Technology&JD Intelligent Cities ResearchBeijing 100176China College of Architecture and Urban Planning Tongji UniversityShanghai 200292China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China These authors contributed equally

Cities are complex systems that develop under complicated interactions among their human and environmental *** generates substantial outcomes and opportunities while raising challenges including congestion,air pollution,inequality,etc.,calling for efficient and reasonable solutions to sustainable ***,booming technologies generate large-scale data of complex cities,providing a chance to propose data-driven solutions for sustainable urban *** paper provides a comprehensive overview of data-driven urban sustainability *** this review article,we conceptualize MetaCity,a general framework for optimizing resource usage and allocation problems in complex cities with data-driven *** this framework,we decompose specific urban sustainable goals,e.g.,efficiency and resilience,review practical urban problems under these goals,and explore the probability of using data-driven technologies as potential solutions to the challenge of *** the basis of extensive urban data,we integrate urban problem discovery,operation of urban systems simulation,and complex decision-making problem solving into an entire cohesive framework to achieve sustainable development goals by optimizing resource allocation problems in complex cities.

关键词： resource usage sustainable development urbanization resilience data driven urban systems simulation complex cities complexity

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A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-Hoc Networks

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12621-12630页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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Unlearnable 3D point clouds: class-wise transformation is all you need 24

Unlearnable 3D point clouds: class-wise transformation is al...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xianlong Wang Minghui Li Wei Liu Hangtao Zhang Shengshan Hu Yechao Zhang Ziqi Zhou Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (纸本)9798331314385

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples; (ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC.

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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Modular Extremely Large-Scale Array Communication:Near-Field Modelling and Performance Analysis

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China Communications 2023年第4期20卷 132-152页

作者： Xinrui Li Haiquan Lu Yong Zeng Shi Jin Rui Zhang National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211111China The Chinese University of Hong Kong Shenzhen Research Institute of Big DataShenzhen 518172China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced *** module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical *** accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array *** on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)*** addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are ***,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of *** simulation results are presented to validate our findings.

关键词： modular extremely large-scale array practical deployment projected apertures non-uniform spherical wave near-field modelling

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Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on Retentive Network 7

Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on ...

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7th International Conference on computer Information science and Application Technology, CISAT 2024

作者： Su, Ruilin Zhang, Wanshan Yu, Dunhui Hubei University Wuhan China Wuhan430062 China Hubei University School of Computer Science and Information Engineering Wuhan430062 China Hubei University Hubei Key Laboratory of Big Data Intelligent Analysis and Application Wuhan430062 China Engineering and Technical Research Center of Hubei Province in Software Engineering Wuhan430062 China

ISBN: (纸本)9798350375107

Multi-hop Knowledge Reasoning is a task that involves generating an answer given a query and a knowledge graph. Existing sequence-to-sequence reasoning models use the Transformer to encode and decode sequences, but these models have some flaws, such as the inability to effectively handle long-sequence reasoning and susceptibility to exposure bias. To address these issues, we provides a sequence-to-sequence reasoning model named STSR, which is based on Retentive Network. Aiming at improving training efficiency through parallel training, this model leverages the advantages in Retentive Network, reduce time overhead, and enhance reasoning efficiency through iterative reasoning. It effectively mitigates the problems of high spatial overhead and low efficiency in long-sequence reasoning faced by the Transformer. Moreover, it retains and updates historical information during the encoding and decoding process, enhancing the model's memory and generalization capabilities. Additionally, the Scheduled Sampling method is adopted to alleviate the exposure bias introduced during reasoning, which involves using the model's own output as the input for the next moment with a certain probability during training, instead of using the true label. We conduct the experiment on six public datasets, with the results showing that the proposed model outperforms existing baseline models in terms of precision and generation quality. This paper provides a new solution for the task of sequence-to-sequence multihop knowledge reasoning and also demonstrates the potential application of the Retentive Network in natural language processing. © 2024 IEEE.

关键词： Iterative decoding

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