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On the complete weight distributions of quantum error-correcting codes

Chinese Physics B 2023年第5期32卷 272-287页

作者：杜超马智熊茂胜 State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenghzhou 450001China Henan Key Laboratory of Network Cryptography Technology Zhenghzhou 450001China Department of Mathematics Hong Kong University of Science and TechnologyHong KongChina

In a recent paper, Hu et al. defined the complete weight distributions of quantum codes and proved the Mac Williams identities, and as applications they showed how such weight distributions may be used to obtain the singleton-type and hamming-type bounds for asymmetric quantum codes. In this paper we extend their study much further and obtain several new results concerning the complete weight distributions of quantum codes and applications. In particular, we provide a new proof of the Mac Williams identities of the complete weight distributions of quantum codes. We obtain new information about the weight distributions of quantum MDS codes and the double weight distribution of asymmetric quantum MDS codes. We get new identities involving the complete weight distributions of two different quantum codes. We estimate the complete weight distributions of quantum codes under special conditions and show that quantum BCH codes by the Hermitian construction from primitive, narrow-sense BCH codes satisfy these conditions and hence these estimate applies.

关键词： quantum codes complete weight distributions Mac Williams identities BCH codes

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Meta Transductive Linear Probing for Few-Shot Node Classification 5

Meta Transductive Linear Probing for Few-Shot Node Classific...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Lan, Mingjing Huang, Ningbo Zhou, Gang Xia, Yi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350355925

Graph few-shot learning aims to predict well by training with very few labeled data. Meta learning has been the most popular solution for few-shot learning problem. However, transductive linear probing shows that fine-tuning a simple linear classification head after a pretrained graph neural networks can outperforms most of the sophisticated-designed graph meta learning algorithms. Therefore, in the paper, we propose a meta transductive linear probing methods named Meta-TLP to incorporate the advantages of graph self-supervised and graph meta learning model. Specifically, the graph neural network is firstly pretrained with graph contrastive learning methods. Then we design an unsupervised meta training task construction methods to require meta tasks without relying on labeled data. Finally, we meta training the linear classification head on the meta training tasks to learn to fast adopt to novel classes. Experiment results show that our model can perform better than TLP on three real world datasets. © 2024 IEEE.

关键词： Self-supervised learning

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Optimal weight support vector regression ensemble with cluster-based subsampling for electricity price forecasting

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Journal of Intelligent and Fuzzy Systems 2024年第1期1卷 1-16页

作者： Li, Yuerong Zhang, Yuhua Che, Jinxing School of Science Nanchang Institute of Technology Jiangxi Nanchang China Key Laboratory of Engineering Mathematics and Advanced Computing Nanchang Institute of Technology Jiangxi Nanchang China

Accurate prediction of short-term electricity price is the key to obtain economic benefit and also an important index of power system planning and management. Support vector regression (SVR) based ensemble works have gained remarkable achievements in terms of high accuracy and steady performance, but they are highly dependent on data representativeness and have a high computational complexity O (k ∗ N 3) of data samples and parameter selection. To further improve the data representativeness and reduce its computational complexity, this paper develops a new approach to forecast electricity price via optimal weighted ensemble. In the model, the cluster-based subsampling algorithm is proposed to categorize the inputs being seasonally decomposed into several groups, and representative data are drawn from each group in a certain proportion to ensure that each subset trained with SVR has the same representativeness and features. Moreover, the optimal weighted combination method is presented to assign weights to the sub-SVRs to obtain the optimal support vector regression ensemble model (OWSSVRE). The experimental results show that the improved support vector regression ensemble model with the same features and representativeness of the subset has better performance in electricity price forecasting. As a result, it is suitable to support decision making in the energy and other sectors. © 2024 - IOS Press. All rights reserved.

关键词： K-means clustering

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RLGBG: A Reachable Landmark Grouping Based IP Geolocation Method for Unreachable Target 29th

RLGBG: A Reachable Landmark Grouping Based IP Geolocation Me...

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Zu, Shuodi Liu, Chong Zhang, Fan Ding, Shichang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9783031429866

IP geolocation technology can estimate or determine the geographical location of terminal device through IP address, which is an important basis for location-based network services. The existing IP geolocation methods based on network measurement cannot accurately obtain the network environment when the target does not respond to the detection message, so it is difficult to achieve high-precision geolocation. To solve this problem, a reachable landmark grouping based IP geolocation method for unreachable target (RLGBG) is proposed in this manuscript. RLGBG first detects landmark data extensively, filters, evaluates and retains reliable and accessible landmarks. Secondly, RLGBG counts the topological relationship and geographical distribution among the reachable landmarks, groups the landmarks according to the common router, and divides the landmarks into different groups after checking by geographical location. Then, the continuous IP segments are divided into IP blocks according to landmark groups, and the location range of each IP block is calculated by the landmarks within each group. Finally, RLGBG counts the IP blocks containing the target, merges those IP blocks that are geographically coincident and topologically related, and estimates the target’s location by calculating the landmark center within the IP blocks. The geolocation experiments based on four cities in China show that RLGBG can effectively estimate the location of unreachable IP targets, and its accuracy and coverage are higher than the existing typical method and IP location databases. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Location

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Quantum computing control system configuration optimizer with adaptive genetic algorithm 9

Quantum computing control system configuration optimizer wit...

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9th International Symposium on Advances in Electrical, Electronics, and Computer engineering, ISAEECE 2024

作者： Huang, Shucheng Wang, Junchao Pang, Jianmin Shan, Zheng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China

ISBN: (纸本)9781510683303

As quantum computing continues to advance, the complexity of control systems for superconducting qubits also increases. This paper presents a genetic algorithm (GA) approach for optimizing the control systems in quantum computing. Focusing on minimizing costs, maximizing resource utilization, and reducing device count, the study demonstrates the algorithm's effectiveness in configuring control systems efficiently. Experimental results from five quantum chips, ranging from 64 to 1024 bits, demonstrate significant improvements in cost efficiency and device management after 500 generations of optimization. The findings highlight the algorithm's adaptability to different quantum system scales and its potential in enhancing the scalability and efficiency of quantum computing architectures. © 2024 SPIE.

关键词： Quantum computers

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A survey on federated learning:a perspective from multi-party computation

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Frontiers of Computer Science 2024年第1期18卷 93-103页

作者： Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG Institute of Artificial Intelligence and Key Laboratory of Mathematics Informatics Behavioral Semantics Beihang UniversityBeijing 100191China State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Pengcheng Laboratory Shenzhen 518055China Zhongguancun Laboratory Beijing 100190China Institute for Mathematical Sciences and Engineering Research Center of Financial Computing and Digital Engineering Renmin University of ChinaBeijing 100872China

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw *** enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model *** survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training *** also pinpoint future directions to deploy federated learning to a wider range of applications.

关键词： sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning

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Hydrodynamic Performance of An Integrated System of Breakwater and A Multi-Chamber OWC Wave Energy Converter

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China Ocean engineering 2024年第4期38卷 543-556页

作者： NING De-zhi ZHANG Xiang-yu WANG Rong-quan ZHAO Ming State Key Laboratory of Coastal and Offshore Engineering Dalian University of TechnologyDalian 116024China Dalian Key Laboratory of Offshore Renewable Energy Dalian University of TechnologyDalian 116024China School of Computing Engineering and MathematicsUniversity of Western SydneyPenrith NSW 2751Australia

A multi-chamber oscillating water column wave energy converter(OWC-WEC)integrated to a breakwater is *** hydrodynamic characteristics of the device are analyzed using an analytical model based on the linear potential flow theory.A pneumatic model is employed to investigate the relationship between the air mass flux in the chamber and the turbine *** effects of chamber width,wall draft and wall thickness on the hydrodynamic performance of a dual-chamber OWC-WEC are *** results demonstrate that the device,with a smaller front wall draft and a wider rear chamber exhibits a broader effective frequency *** device with a chamber-width-ratio of 1:3 performs better in terms of power ***,results from the analysis of a triplechamber OWC-WEC demonstrate that reducing the front chamber width and increasing the rearward chamber width can improve the total performance of the *** the number of chambers from 1 to 2 or 3 can widen the effective frequency bandwidth.

关键词： oscillating water column power extraction efficiency potential flow theory wave energy converter multi-chamber

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Numerical Simulation Study of Oil-Water Emulsion Separation in an Ultrasonic Field:Effect of Coupling between Acoustic and Flow Field Parameters

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China Petroleum Processing & Petrochemical Technology 2024年第1期26卷 116-125页

作者： Wang Heping Lin Yinchao Li Yanggui Zhang Xiaohang Wu Yi Key Laboratory of Engineering Mathematics and Advanced Computing of Nanchang Institute of Technology School of SciencesNanchang Institute of TechnologyNanchang 330099People’s Republic of China Dongguan Key Laboratory of Kunpeng Computing School of Computer ScienceGuangdong University of Science&TechnologyDongguan 523083China State Key Laboratory of Plateau Ecology and Agriculture Qinghai UniversityXining 810016China

In this study,the separation and coalescence of oil-in-water emulsions are explored in an ultrasonic field using the lattice Boltzmann *** simulating the propagation of ultrasonic waves,this study focuses on examining the effects of acoustic wave frequency,the ratio of oil to water components,and the aspect ratio of the boundary on the emulsification and separation processes of oil-water *** following conclusions are drawn.①Frequency affects the speed of oil droplet separation,leading to an increase in droplet size over *** droplets are found near the source,while smaller droplets are distributed throughout the wave web.②As the boundary aspect ratio increases,the emulsification efficiency of the droplets weakens,and the system takes longer to stabilize.③Emulsions with a higher component of oil can better resist acoustic waves.④At the same acoustic frequency,longer wavelength ultrasonic fields promote the formation of uniformly distributed,smaller oil droplets,which is beneficial to the storage of *** numerical simulation results offer insights for optimizing conditions for oil-in-water separation and serve as a numerical reference for the study of oil-in-water emulsion separation in ultrasonic environments.

关键词： lattice Boltzmann method phase separation ultrasound field

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Multiscale method for identifying and marking the multiform fractures from visible-light rock-mass images

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Underground Space 2024年第3期16卷 279-300页

作者： Yongbo Pan Junzhi Cui Zhenhao Xu School of Mathematics and Statistics Zhengzhou UniversityZhengzhou 450001China The State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Geotechnical and Structural Engineering Research Center Shandong UniversityJinan 250100China

Multiform fractures have a direct impact on the mechanical performance of rock *** accurately identify multiform fractures,the distribution patterns of grayscale and the differential features of fractures in their neighborhoods are *** on this,a multiscale processing algorithm is *** multiscale process is as *** the neighborhood of pixels,a grayscale continuous function is constructed using bilinear interpolation,the smoothing of the grayscale function is realized by Gaussian local filtering,and the grayscale gradient and Hessian matrix are calculated with high *** small-scale blocks,the pixels are classified by adaptively setting the grayscale threshold to identify potential line segments and *** the global image,potential line segments and mini-fillings are spliced together by progressing the block frontier layer-by-layer to identify and mark multiform *** accuracy of identifying multiform fractures is improved by constructing a grayscale continuous function and adaptively setting the grayscale thresholds on small-scale *** the layer-by-layer splicing algorithm is performed only on the domain of the 2-layer small-scale blocks,reducing the *** using rock mass images with different fracture types as examples,the identification results show that the proposed algorithm can accurately identify the multiform fractures,which lays the foundation for calculating the mechanical parameters of rock masses.

关键词： Visible light rock-mass images Continuous grayscale function Small-scale blocks Multiform fractures

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TS-GGNN: Combining Graph and Sequence Features for Vulnerability Detection in Source Code 5

TS-GGNN: Combining Graph and Sequence Features for Vulnerabi...

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5th International Conference on Communications, Information System and Computer engineering, CISCE 2023

作者： Zhou, Xin Pang, Jianmin Zhang, Chunyan Yue, Feng Wang, Junchao Liu, Guangming State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350326796

Software vulnerability detection is crucial for maintaining the security and stability of software systems. In this paper, we propose a novel neural network model called TS-GGNN to address the problem of vulnerability detection in source code slices. The TS-GGNN model effectively captures both local and global features of vulnerable code by fusing sequence features with graph features. To achieve this, we utilize graph structure and sequence structure learning approaches to comprehensively extract valuable information from the source code slices. Our experiments are conducted on the SARD dataset, which consists of 61,638 code samples annotated for the presence or absence of vulnerabilities. The results demonstrate that TS-GGNN has the best vulnerability detection performance, with an accuracy of 99.4%, a precision of 98.81%, and an F1 score as high as 99.4% thereby validating the effectiveness of the TS-GGNN model in capturing features relevant to software vulnerabilities. © 2023 IEEE.

关键词： Network coding

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