检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Wang, Chengzhi Li, Donglai Sun, Chen Li, Jingbo Wang, Zongguo Jin, Haibo Beijing Key Laboratory of Construction Tailorable Advanced Functional Materials and Green Applications School of Materials Science and Engineering Beijing Institute of Technology Beijing100081 China Computer Network Information Center Chinese Academy of Sciences Beijing100190 China

It is critical for solid-state alkaline metal batteries to solve the issues of large interfacial resistance and poor interfacial stability between the alkaline metal anode and the solid electrolyte. We tune the interface chemistry between Na metal and a NASICON-type solid electrolyte by a facile Cu2+-ion doping method. It is demonstrated that the Cu2+-ion incorporation into Na3Zr2Si2PO12 (NZSP) significantly reduces the interfacial resistance (Rinter ) and boostss the interfacial stability during charge/discharge cyles at room temperature. The optimal Na3.4Zr1.8Cu0.2Si2PO12 (NZSP-0.2Cu) realizes an increased critical current density of 0.5 mA cm-2 and an impressively low Rinter value of 19 Ω cm2 at 25 °C, which is only 1/35 of the undoped one (665 Ω cm2). Moreover, the symmetrical Na|NZSP-0.2Cu|Na cell maintains steady Na plating/stripping cycles for as long as 1450 h under 0.2 mA cm-2, clearly indicating the desired chemical compatibility at the Na metal anode interface. A self-formed dynamically stable Cu3PO4-dominant interphase layer at the Na metal/NZSP-0.2Cu interface is verified to explain for the outstanding interfacial performance. Furthermore, a room-temperature solid-state NaCrO2|NZSP-0.2Cu|Na metal battery is assembled, exhibiting excellent cycling performance at 5 C for 660 cycles with a capacity retention of 86.5% and an overall Coulombic efficiency of 99.5%. © 2022, The Authors. All rights reserved.

关键词： Solid electrolytes

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Towards practical and massively parallel quantum computing emulation for quantum chemistry

arXiv

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arXiv 2023年

作者： Shang, Honghui Fan, Yi Shen, Li Guo, Chu Liu, Jie Duan, Xiaohui Li, Fang Li, Zhenyu Institute of Computing Technology Chinese Academy of Sciences Beijing China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Software Shandong University Jinan China National Research Center of Parallel Computer Engineering and Technology Beijing China

Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. In the current noisy intermediate-scale quantum computing era, quantum resource is too scarce to support these explorations. Therefore, it is valuable to emulate quantum computing on classical computers for developing quantum algorithms and validating quantum hardware. However, existing simulators mostly suffer from the memory bottleneck so developing the approaches for large-scale quantum chemistry calculations remains challenging. Here we demonstrate a high-performance and massively parallel variational quantum eigensolver (VQE) simulator based on matrix product states, combined with embedding theory for solving large-scale quantum computing emulation for quantum chemistry on HPC platforms. We apply this method to study the torsional barrier of ethane and the quantification of the protein-ligand interactions. Our largest simulation reaches 1000 qubits, and a performance of 216.9 PFLOPS is achieved on a new Sunway supercomputer, which sets the state-of-the-art for quantum computing emulation for quantum chemistry. © 2023, CC BY.

关键词： Computational chemistry

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Synthetic frequency dimension state coupling in modulated LNOI ring cavity devices

Synthetic frequency dimension state coupling in modulated LN...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Armandas Balčytis Xuan Hiep Dinh Tomoki Ozawa Yasutomo Ota Toshihiko Baba Satoshi Iwamoto Arnan Mitchell Thach G. Nguyen Integrated Photonics and Applications Centre RMIT University Melbourne VIC Australia Advanced Institute for Materials Research Tohoku University Sendai Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama Japan Department of Electrical and Computer Engineering Yokohama National University Hodogaya-ku Yokohama Japan Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo Japan

We created a modulator-equipped thin-film lithium niobate integrated ring resonator simulating a synthetic frequency dimension tight-binding model. It produces frequency states spaced over a 600 GHz bandwidth. Reconfi... 详细信息

ISBN: (纸本)9781957171258

关键词： Electron beam lithography Frequency combs Frequency modulation Lithium niobate Ring resonators Waveguide modulators

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhan Tong Zhan Wu Yang Yang Weilong Mao Shijie Wang Yinsheng Li Yang Chen Laboratory of Image Science and Technology Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Laboratory of Image Science and Technology School of Computer Science and Engineering and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics.

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Security-Aware Task Scheduling for Improving the Cooperativity between the Private and Public Clouds

SSRN

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SSRN 2022年

作者： Wang, Bo Cao, Jie Wang, Changhai Huang, Wanwei Song, Ying Software Engineering College Zhengzhou University of Light Industry No.136 Kexue Road Zhengzhou450001 China Beijing Advanced Innovation Center for Materials Genome Engineering Computer School Beijing Information Science and Technology University China Beijing Key Laboratory of Internet Culture and Digital Dissemination Computer School Beijing Information Science and Technology University China

By combining the benefits of both the private and public clouds, hybrid cloud has become increasingly popular in both academia and industry. How to make the private and public clouds cooperate well is one of the key issues should be addressed for a high resource efficiency. In this paper, we focus on the task scheduling problem for improving the cooperativity between the private and public clouds. We first formulate the scheduling problem into a mixed binary non-linear programming for maximizing the user satisfaction with deadline and security constraints, and then propose a heuristic method to solve the problem in a polynomial time. Our proposed method has three procedures, where the first two procedures try to fully exploiting the resource abundance of the public cloud and the limited resources of the private cloud, respectively, by pre-scheduling as many tasks as possible to the public cloud and scheduling remaining unscheduled tasks to the private cloud, respectively. In the last procedure, the proposed method re-schedules tasks from the public cloud to the private cloud for saving the overall cost. Experiment results show that our proposed method has up to 64.1% better performance in user satisfaction, compared with six of classical methods and state-of-the-art methods. © 2022, The Authors. All rights reserved.

关键词： Heuristic methods

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Triangle Topology Enhancement for Multi-view Graph Clustering

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IEEE Transactions on Knowledge and Data engineering 2025年

作者： Wu, Danyang Wang, Penglei Lu, Jitao Hu, Zhanxuan Zhang, Hongming Nie, Feiping College of Information Engineering Northwest A & F University Shaanxi China Shaanxi Engineering Research Center for Intelligent Perception and Analysis of Agricultural Information Northwest A & F University Shaanxi China School of Software Engineering South China University of Technology Guangzhou510641 China School of Computer Science School of Artificial Intelligence OPtics and ElectroNics Northwestern Polytechnical University Xi’an710072 China Key Laboratory of Intelligent Interaction and Applications Northwestern Polytechnical University Ministry of Industry and Information Technology Xi’an710072 China School of Information Science and Technology Yunnan Normal University Kunming China

Most existing multi-view graph clustering models focus on integrating the topological structure of different views directly, which cannot efficiently stimulate the collaboration between multiple views. To alleviate this problem, this paper proposes a Triangle Topology Enhancement (T2E) module, which expands two topological structures based on the raw topology of each view, including the self-triangle enhanced topology that highlights the local view information and the cross-view triangle enhanced topology containing the global-local view information. Afterward, this paper designs a novel multi-view graph clustering model, named MGC-T2E, to integrate both the raw and derived topological structures and directly induce consistent clustering indicators based on a self-supervised clustering module. In the simulation, the experimental results demonstrate that MGC-T2E achieves state-of-the-art performances compared with a mass of current competitors. © 2025 IEEE. All rights reserved.

关键词： Topology

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Hyperbolic geometric latent diffusion model for graph generation 24

Hyperbolic geometric latent diffusion model for graph genera...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xingcheng Fu Yisen Gao Yuecen Wei Qingyun Sun Hao Peng Jianxin Li Xianxian Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China and Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies.

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Median Filtering Detection Based on Quaternion Convolutional Neural Network

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computers, Materials & Continua 2020年第10期65卷 929-943页

作者： Jinwei Wang Qiye Ni Yang Zhang Xiangyang Luo Yunqing Shi Jiangtao Zhai Sunil Kr Jha Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China Department of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijing100093China Department of Electrical Computer Engineering New Jersey Institute of TechnologyNew JerseyNJ07102USA

Median filtering is a nonlinear signal processing technique and has an advantage in the field of image ***,more attention has been paid to the forensics research of median *** this paper,a median filtering forensics method based on quaternion convolutional neural network(QCNN)is *** median filtering residuals(MFR)are used to preprocess the *** the output of MFR is expanded to four channels and used as the input of *** QCNN,quaternion convolution is designed that can better mix the information of different channels than traditional *** quaternion pooling layer is designed to evaluate the result of quaternion *** is proposed to features well combine the three-channel information of color image and fully extract forensics *** show that the proposed method has higher accuracy and shorter training time than the traditional convolutional neural network with the same convolution depth.

关键词： Median filtering forensics quaternion convolution layer quaternion pooling layer color image

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Design and Validation of a Hyper-converged Blockchain Hardware and software System Based on Domestic Chips 1st

Design and Validation of a Hyper-converged Blockchain Hardwa...

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1st Conference on Blockchain and Web3.0 technology Innovation and Application, BWTAC 2024

作者： Liu, Xiaolong Yuan, Bo Wu, Faguo Enterprise Technology Center Aerospace Legione Technology Co. Ltd Haidian Beijing100195 China School of Computer Science and Engineering Beihang University Haidian Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Haidian Beijing100191 China Institute of Artificial Intelligence Beihang University Haidian Beijing100191 China Beihang University Haidian Beijing100191 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Haidian Beijing100191 China

ISBN: (纸本)9789819794119

This paper proposes a hyper-converged blockchain hardware and software sys-tem based on domestic chips. By utilizing domestically firmware, the Linux operating system, X86 architecture Hygon processors, and ARM architecture Phytium processors, combined with servers, storage, network, and independently controllable blockchain technologies, the system achieves integrated hardware and software optimization. This significantly improves performance and scalability. Experimental results demonstrate that the system performs excellently in various high-security application scenarios, providing reliable solutions for government, finance, taxation, transportation, energy, and telecommunications sectors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Firmware

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DeepMA: End-to-end Deep Multiple Access for Wireless Image Transmission in Semantic Communication

arXiv

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arXiv 2023年

作者： Zhang, Wenyu Bai, Kaiyuan Zeadally, Sherali Zhang, Haijun Shao, Hua Ma, Hui Leung, Victor C.M. The School of Intelligence Science and Technology The Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China The China Telecom Research Institute Beijing100096 China The College of Communication and Information University of Kentucky LexingtonKY40506 United States Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Semantic communication is a new paradigm that exploits deep learning models to enable end-to-end communications processes, and recent studies have shown that it can achieve better noise resiliency compared with traditional communication schemes in a low signal-to-noise (SNR) regime. To achieve multiple access in semantic communication, we propose a deep learning-based multiple access (DeepMA) method by training semantic communication models with the abilities of joint source-channel coding (JSCC) and orthogonal signal modulation. DeepMA is achieved by a DeepMA network (DMANet), which is comprised of several independent encoder-decoder pairs (EDPs), and the DeepMA encoders can encode the input data as mutually orthogonal semantic symbol vectors (SSVs) such that the DeepMA decoders can recover their own target data from a received mixed SSV (MSSV) superposed by multiple SSV components transmitted from different encoders. We describe frameworks of DeepMA in wireless device-to-device (D2D), downlink, and uplink channel multiplexing scenarios, along with the training algorithm. We evaluate the performance of the proposed DeepMA in wireless image transmission tasks and compare its performance with the attention module-based deep JSCC (ADJSCC) method and conventional communication schemes using better portable graphics (BPG) and Low-density parity-check code (LDPC). The results obtained show that the proposed DeepMA can achieve effective, flexible, and privacy-preserving channel multiplexing process, and demonstrate that our proposed DeepMA approach can yield comparable bandwidth efficiency compared with conventional multiple access schemes. © 2023, CC BY.

关键词： Signal to noise ratio

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