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Cryptanalysis of fair quantum blind signatures

Chinese Physics B 2012年第3期21卷 63-66页

作者：何立宝黄刘生杨威许瑞 National High Performance Computing Center Department of Computer Science and TechnologyUniversity of Science and Technology of China Suzhou Institute for Advanced Study University of Science and Technology of China

We investigate the fair quantum blind signature scheme proposed by Wang and Wen [Wang T Y and Wen Q Y 2010 Chin. Phys. B 19 060307], which uses the fundamental properties of quantum mechanics and the availability of a trusted arbitrator. However, in this paper, we find that the protocol cannot satisfy the property of non-forgeability even under the condition that the trusted arbitrator is totally credible. Moreover, a simple feasible suggestion for improving the protocol is proposed.

关键词： fair blind signature single photons hash function

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STUDIES ON STRUCTURAL AND MECHANICAL PROPERTIES UNDER ISOSTATIC COMPRESSION WITH LARGE-SCALE DISCRETE ELEMENT SIMULATIONS

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Acta Mechanica Solida Sinica 2014年第2期27卷 129-136页

作者： Jianguo Liu Qicheng Sun Feng Jin Qingkai Liu State Key Laboratory of Hydroscience and Engineering Tsinghua University High Performance Computing Center Institute of Applied Physics and Computational Mathematics

Granular systems undergo a jamming transition at point J simply by increasing the packing fraction. A large-scale parallel discrete element code （THDEM： TsingHua Discrete Element Method） was used to obtain a satisfying statistical description of the structural and me- chanical properties near point J. The isostatic compressions of 100,000 polydispersed frictionless particles were simulated on high performance computers to clearly observe the sophisticated con- figurations of force chains. The first peak of the pair correlation function, coordination number, spatial distribution of the packing fraction, and stress were calculated to analyze their variations with increasing packing fraction. The critical packing fraction at point J is determined to be 0.62. The incremental stress and coordination number from point J scale well with the power law, and coincide with previous theoretical predications. The distribution of the packing frac- tion is a normal distribution around the average value. The standard deviation decreases with increasing packing fraction, indicating the system is more uniform with a denser packing.

关键词： granular matter jamming transition force chains parallel computing

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Computation speedup in the dynamic simulation of MEMS by macromodels

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自然科学进展(英文版) 2003年第3期13卷 219-227页

作者： LIN Wuzhong LIANG Yanchun LEE Kwok Hong LIM Siak Piang LEE Heow Pueh Institute of High Performance Computing Singapore 117528 Institute of High Performance Computing Singapore 117528 College of Computer Science and Technology Jilin UniversityChangchun 130012 China Department of Mechanical Engineering National University of Singapore Singapore 119260

A doubly clamped microbeam actuated by electrostatic force with squeezed gas film damping is a well-known and standard micro-device in microelectromechanical system (MEMS) for many researchers to demonstrate how reduced-order dynamic macromodel is an effective way to faithfully capture the device behaviors. However it still takes time to directly recompute the time-dependant nonlinear terms in macromodels which are generated by a proper orthogonal decomposition (POD) method with Galerkin procedure at every time step during the macromodel simulation. This paper proposes two methods for speeding up the computation of macromodel simulations. In the first method, the computation speedup is achieved based on the concept of precomputation upon the basis functions are available. In the second method, cubic splines approximation is used to interpolate the basis functions and their first and second derivatives, and spatial integration is performed by application of the Gaussian quadrature. Numerical results show both methods could enhance the efficiency of the macromodel simulation compared with our previous computation results.

关键词： MEMS macromodels model order reduction proper orthogonal decomposition

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ESGSenticNet: A Neurosymbolic Knowledge Base for Corporate Sustainability Analysis

arXiv

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arXiv 2025年

作者： Ong, Keane Mao, Rui Xing, Frank Satapathy, Ranjan Sulaeman, Johan Cambria, Erik Mengaldo, Gianmarco College of Design and Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore School of Computing National University of Singapore 13 Computing Drive 117417 Singapore Asian Institute of Digital Finance National University of Singapore Innovation 4.0 3 Research Link #04-03 117602 Singapore College of Computing and Data Science Nanyang Technological University 50 Nanyang Ave 639798 Singapore Institute of High Performance Computing Agency for Science Technology and Research Fusionopolis Way #16-16 Connexis 138632 Singapore Sustainable Green Finance Institute National University of Singapore Innovation 4.0 3 Research Link #02-02 117602 Singapore NUS Business School National University of Singapore Mochtar Riady Building 15 Kent Ridge Dr Mocthar Riady Building 119245 Singapore Imperial College London United Kingdom

Evaluating corporate sustainability performance is essential to drive sustainable business practices, amid the need for a more sustainable economy. However, this is hindered by the complexity and volume of corporate sustainability data (i.e. sustainability disclosures), not least by the effectiveness of the NLP tools used to analyse them. To this end, we identify three primary challenges – immateriality, complexity, and subjectivity, that exacerbate the difficulty of extracting insights from sustainability disclosures. To address these issues, we introduce ESGSenticNet, a publicly available knowledge base for sustainability analysis. ESGSenticNet is constructed from a neurosymbolic framework that integrates specialised concept parsing, GPT-4o inference, and semi-supervised label propagation, together with a hierarchical taxonomy. This approach culminates in a structured knowledge base of 44k knowledge triplets – (‘halve carbon emission’, supports, ‘emissions control’), for effective sustainability analysis. Experiments indicate that ESGSenticNet, when deployed as a lexical method, more effectively captures relevant and actionable sustainability information from sustainability disclosures compared to state of the art baselines. Besides capturing a high number of unique ESG topic terms, ESGSenticNet outperforms baselines on the ESG relatedness and ESG action orientation of these terms by 26% and 31% respectively. These metrics describe the extent to which topic terms are related to ESG, and depict an action toward ESG. Moreover, when deployed as a lexical method, ESGSenticNet does not require any training, possessing a key advantage in its simplicity for non-technical stakeholders. © 2025, CC BY.

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Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Yan, Yunlu Feng, Chun-Mei Li, Yuexiang Li, Ping Goh, Rick Siow Mong Lei, Baiying Wang, Weiming Feng, David Dagan Zhu, Lei The Hong Kong University of Science and Technology (Guangzhou) ROAS Thrust Guangzhou China Technology and Research (A*STAR) Institute of High Performance Computing (IHPC) Agency for Science Singapore Guangxi Medical University Life Sciences Institute Nanning China The Hong Kong Polytechnic University Department of Computing the School of Design the Research Institute for Sports Science and Technology Hong Kong Shenzhen University School of Biomedical Engineering Shenzhen China Hong Kong Metropolitan University School of Science and Technology Hong Kong The University of Sydney Biomedical and Multimedia Information Technology Research Group School of Computer Science Sydney Australia The Hong Kong University of Science and Technology Department of ECE Hong Kong

While multi-modal learning has been widely used for MRI reconstruction, it relies on paired multi-modal data, which is difficult to acquire in real clinical scenarios. Especially in the federated setting, there is a common issue that several medical institutions suffer from missing modalities or even only have single-modal data. Therefore, it is infeasible to deploy a standard federated learning framework in such conditions. In this paper, we propose a novel communication-efficient federated learning framework (namely Fed-PMG) to address the missing modality challenge in federated multi-modal MRI reconstruction. Specifically, we utilize a pseudo modality generation mechanism to recover the missing modality for each single-modal client by sharing the distribution information of the amplitude spectrum in frequency space. However, the step of sharing the original amplitude spectrum leads to heavy communication costs. To reduce the communication cost, we introduce a clustering scheme to project the set of amplitude spectrum into a finite number of cluster centroids and share them among the clients. With such an elaborate design, our approach can effectively complete the missing modality within an acceptable communication cost. Extensive experimental results demonstrate that our proposed method can outperform state-of-the-art methods and reach a performance similar to the ideal scenario (i.e., all clients have the full set of modalities). © 2013 IEEE.

关键词： Federated learning modality missing MRI reconstruction multi-modal learning

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Numerical analysis of a semi-implicit Euler scheme for the Keller-Segel model

arXiv

引用

arXiv 2025年

作者： Huang, Xueling Goubet, Olivier Shen, Jie School of Mathematical Sciences Fujian Provincial Key Laboratory on Mathematical Modeling and High Performance Scientific Computing Xiamen University Fujian Xiamen361005 China Laboratoire Paul Painlevé CNRS UMR 8524 et équipe projet INRIA PARADYSE Université de Lille Lille59000 France School of Mathematical Science Eastern Institute of Technology Zhejiang Ningbo315200 China

We study the properties of a semi-implicit Euler scheme that is widely used in time discretization of Keller-Segel equations both in the parabolic-elliptic form and the parabolic-parabolic form. We prove that this linear, decoupled, first-order scheme preserves unconditionally the important properties of Keller-Segel equations at the semi-discrete level, including the mass conservation and positivity preserving of the cell density, and the energy dissipation. We also establish optimal error estimates in Lp-norm (1 Copyright © 2025, The Authors. All rights reserved.

关键词： Euler equations

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Measurement-Based Non-Stationary Markov Tapped Delay Line Channel Model for 5G-Railways

引用

IEEE Antennas and Wireless Propagation Letters 2025年

作者： Zhang, Xuejian He, Ruisi Yang, Mi Ding, Jianwen Chen, Ruifeng Gao, Shuaiqi Qi, Ziyi Zhang, Zhengyu Ai, Bo Zhong, Zhangdui Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Henan High Speed Railway Operation and Maintenance Engineering Research Center Zhengzhou451460 China China Academy of Railway Sciences Corporation Ltd. Institute of Computing Technology Beijing100081 China

5 G for Railways (5G-R) is globally recognized as a promising next-generation railway communication system designed to meet increasing demands. Channel modeling serves as foundation for communication system design, with tapped delay line (TDL) models widely utilized in system simulations due to their simplicity and practicality and serves as a crucial component of various standards like 3GPP. However, existing TDL models applicable to 5G-R systems are limited. Most fail to capture non-stationarity, a critical characteristic of railway communications, while others are unsuitable for the specific frequency bands and bandwidths of 5G-R. In this paper, a channel measurement campaign for 5G-R dedicated network is carried out, resulting in a measurement-based 5-tap TDL model utilizing a first-order two-state Markov chain to represent channel non-stationarity. Key model parameters, including number of taps, statistical distribution of amplitude, phase and Doppler shift, and state transition probability matrix, are extracted. The correlation between tap amplitudes is also obtained. Finally, the accuracy of model is validated through comparisons with measurement data and 3GPP model. These findings are expected to offer valuable insights for design and link-level simulation of 5G-R systems. © 2025 IEEE.

关键词： Markov chains

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A Fast Algorithm for Mining Association Rules

引用

Journal of Computer Science & technology 2000年第6期15卷 619-624页

作者：黄刘生陈华平王洵陈国良 National High Performance Computing Center Department of Computer Science University of Science & Technology of China Hefei P.R. China

In this paper, the problem of discovering association rules between items in a large database of sales transactions is discussed, and a novel algorithm, BitMatrix, is proposed. The proposed algorithm is fundamentally different from the known algorithms Apriori and AprioriTid. Empirical evaluation shows that the algorithm outperforms the known ones for large databases. Scale-up experiments show that the algorithm scales linearly with the number of transactions.

关键词： database data mining large itemset association rule minimum support minimum confidence

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Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting

Quantum Kernel-Based Long Short-term Memory for Climate Time...

引用

Quantum Communications, Networking, and computing (QCNC), International Conference on

作者： Yu-Chao Hsu Nan-Yow Chen Tai-Yu Li Po-Heng Henry Lee Kuan-Cheng Chen NARlabs National Center for High-Performance Computing Hsinchu Taiwan Cross College Elite Program National Cheng Kung University Tainan Taiwan National Synchrotron Radiation Research Center Hsinchu Taiwan Department of Electrical and Electronic Engineering Imperial College London London UK Department of Civil Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

We present the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which integrates quantum kernel methods into classical LSTM architectures to enhance predictive accuracy and computational efficiency in climate time-series forecasting tasks, such as Air Quality Index (AQI) prediction. By embedding classical inputs into high-dimensional quantum feature spaces, QK-LSTM captures intricate nonlinear dependencies and temporal dynamics with fewer trainable parameters. Leveraging quantum kernel methods allows for efficient computation of inner products in quantum spaces, addressing the computational challenges faced by classical models and variational quantum circuit-based models. Designed for the Noisy Intermediate-Scale Quantum (NISQ) era, QK-LSTM supports scalable hybrid quantum-classical implementations. Experimental results demonstrate that QK-LSTM outperforms classical LSTM networks in AQI forecasting, showcasing its potential for environmental monitoring and resource-constrained scenarios, while highlighting the broader applicability of quantum-enhanced machine learning frameworks in tackling large-scale, high-dimensional climate datasets.

关键词： Quantum computing Accuracy Computational modeling Time series analysis Predictive models Computational efficiency Forecasting Kernel Long short term memory Meteorology

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Neuron Circuit Design and Signal Integrity Analysis of a Large-scale Memristor-based Crossbar Array

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IEEE Transactions on Components, Packaging and Manufacturing technology 2025年

作者： Li, Yan Fang, Lidan Li, Da Zhang, Ling Zhou, Haomiao Wang, Jiawei Liu, En-Xiao Li, Er-Ping China Jiliang University Key Laboratory of Electro-magnetic Wave Information Technology and Metrology of Zhejiang Province College of Information Engineering Hangzhou310018 China China Jiliang University Modern Science and Technology College Yiwu322000 China Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart System Hangzhou310027 China Urbana–Champaign Institute Zhejiang University Zhejiang University–the University of Illinois Haining314400 China A*STAR Institute of High Performance Computing 1 Fusionpolis Way Singapore Singapore

This paper addresses the limitations of standard chips in processing large-scale datasets by leveraging neuromorphic architectures, particularly spiking neural networks (SNNs), to simulate the pulsed signals of biological brains for enhanced power efficiency and performance. Initially, we developed a CMOS circuit based on the Integrate-and-Fire (IF) neuron model, which generates spike signals with the spatiotemporal dynamics typical of neurons. This approach provides a novel perspective for neuromorphic chip design. Next, we introduce the Partial Element Equivalent Circuit (PEEC) method to establish a unit circuit model for the memristor crossbar array, accounting for all resistance-inductance-capacitance (RLC) parasitic couplings. This model enables precise signal integrity (SI) analysis, offering deeper insights into the signal transmission mechanisms within memristor crossbar arrays and serving as a foundation for optimizing neuromorphic chip performance. Building upon this model, we designed a 24×24 crossbar array hardware prototype, experimentally validating the reliability and feasibility of the circuit. Finally, we perform SI analysis to demonstrate that the performance of the memristor crossbar array can be enhanced by optimizing the rise time, adjusting the memristor’s resistance state, and adding inductance at the output. These findings provide both a theoretical foundation and a technical pathway for improving the performance of neuromorphic chips in practical applications. © 2025 IEEE. All rights reserved.

关键词： Memristors

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