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An efficient quantum proactive incremental learning algorithm

Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 49-57页

作者： Lingxiao Li Jing Li Yanqi Song Sujuan Qin Qiaoyan Wen Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing University of Posts and Telecommunications

In scenarios where a large amount of data needs to be learned, incremental learning can make full use of old knowledge, significantly reduce the computational cost of the overall learning process, and maintain high performance. In this paper, taking the MaxCut problem as our example, we introduce the idea of incremental learning into quantum computing, and propose a Quantum Proactive Incremental Learning algorithm(QPIL). Instead of a one-off training of quantum circuit, QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices, proactively reducing large-scale problems to smaller ones to solve in steps, providing an efficient solution for MaxCut. Specifically, some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at first. Then, in each incremental phase, the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current phase. We perform experiments on 120 different small-scale graphs, and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR), time cost, anti-forgetting, and solving stability. In particular, QPIL's AR surpasses 20% of mainstream quantum baselines, and the time cost is less than 1/5 of them. The idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale Max Cut and other combinatorial optimization problems.

关键词： variational quantum algorithm incremental learning multi-phase training MaxCut quantum computing

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Actively tunable electromagnetically induced transparency in hybrid Dirac-VO2 metamaterials

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Optoelectronics Letters 2025年第1期21卷 13-20页

作者： DI Ke XIE Meng XIA Huarong CHENG Anyu LIU Yu DU Jiajia department of Optoelectronic Engineering Chongqing University of Post and Telecommunications Chongqing Key Laboratory of Autonomous Navigation and Microsystem Chongqing University of Posts and Telecommunications department of Automation Chongqing University of Post and Telecommunications

In this paper, we present a metamaterial structure of Dirac and vanadium dioxide（VO2） and investigate its optical properties using the finite-difference time-domain（FDTD） technique. Using the phase transition feature of VO2, the design can realize active tuning of the plasmon induced transparency（PIT） effect at terahertz frequency, thereby converting from a single PIT to a double-PIT. When VO2is in the insulating state, the structure is symmetric to obtain a single-band PIT effect. When VO2is in the metallic state, the structure turns asymmetric to realize a dual-band PIT effect. This design provides a reference direction for the design of actively tunable metamaterials. Additionally, it is discovered that the transparent window's resonant frequency and the Fermi level in this structure have a somewhat linear relationship. In addition, the structure achieves superior refractive index sensitivity in the terahertz band, surpassing 1 THz/RIU. Consequently, the design exhibits encouraging potential for application in refractive index sensors and optical switches.

关键词： Transparency

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The ground-state entanglement in the spin-XX chain with a magnetization current

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Chinese Physics B 2010年第11期19卷 90-95页

作者：张勇王川金光生 department of Physics Beijing University of Posts and Telecommunications

The ground-state entanglement in a transverse spin-1/2 XX chain with a magnetization current is studied. By introducing a magnetization current to the system, a quantum phase transition to current-carrying phase may be presented with the variation of the driving field λ for the magnetic field h 〉 1; and the ground-state entanglement arises simultaneously at the critical point of quantum phase transition. In our model, the introduction of magnetization current may result in more entanglement between any two nearest-neighbour spins.

关键词： entanglement current correlation concurrence

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Security analysis of a one-way hash function based on spatiotemporal chaos

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Chinese Physics B 2011年第9期20卷 79-85页

作者：王世红单鹏洋 School of Science Beijing University of Posts and Telecommunications International School Beijing University of Posts and Telecommunications

The collision and statistical properties of a one-way hash function based on spatiotemporal chaos are investigated. Analysis and simulation results indicate that collisions exist in the original algorithm and, therefo... 详细信息

关键词： collision spatiotemporal chaos hash function

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Local Gabor Dominant Direction Pattern for Face Recognition

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Chinese Journal of Electronics 2015年第2期24卷 245-250页

作者： LIU Jun JING Xiaojun SUN Songlin LIAN Zifeng beijing university of posts and telecommunications

We propose a novel face image representation – Local gabor dominant direction pattern(LGDDP)for face recognition. The face image is convolved with the Gabor filters, resulting in multiple response images of different orientations and scales. The response images' each pixel is encoded by the LGDDP descriptor from the pixel's dominant neighboring one or two pixels. The image formed by the LGDDP descriptor is partitioned into multiple regions and the histogram is extracted from each region. All the histograms are concatenated into the spatial *** nearest neighbor classifier and the weighted intersection histogram similarity measure are used for face image classification. The advantage of the proposed LGDDP method lies in the high recognition rate performance and the low computational complexity. Extensive experiments are performed on FERET face image database and the experimental results verify the LGDDP's effectiveness by comparing LGDDP with other well-known published face recognition methods.

关键词： Face recognition Spatial histogram Gabor filter

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Exploration of Multiple Access Interference Suppression Based on Multi-user Detection

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Chinese Journal of Electronics 2019年第4期28卷 835-840页

作者： WU Tao JING Xiaojun beijing university of posts and telecommunications

A multi-user detection algorithm for multiple access interference suppression combined with the minimum mean squared error and an artificial fish swarm algorithm is proposed. By taking advantage of the artificial fish swarm algorithm, the proposed algorithm can quickly converge to the optimal solution. The variation in the minimum mean squared error based test results is assigned to the artificial fish swarm algorithm, which is regarded as the initial state of artificial fish. The simulation results show that the proposed algorithm has a better multi-user capacity performance and lower bit-error rate than the minimum mean squared error detector.

关键词： Multiple access interference suppression Minimum mean squared error Artificial fish swarm algorithm Multi-user detection algorithm Code division multiple access

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A Short Text Classification Algorithm Based on Semantic Extension

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Chinese Journal of Electronics 2021年第1期30卷 153-159页

作者： ZHOU Yajian DENG Dingpeng CHI Junhui beijing university of posts and telecommunications

A semantic-extension-based algorithm for short texts is proposed, by involving the Word2vec and the LDA model, to improve the performance of classification,which is frequently deteriorated by semantic dependencies and scarcity of features. For every keyword within a short text, weighted synonyms and related words can be generated by the Word2Vec and LDA model, respectively,and subsequently be inserted to extend the short text to a reasonable length. We not only have established a criterion by means of similarity estimation to determine whether a sentence should be extended, we designed a scheme to choose the number of extended words. The extended text will be classified. Experimental results show that, the classification performance of the proposed algorithm, in terms of the precision rate, is approximately 5% higher than that of the TF-IDF model and approximately 10% higher than that of the VSM method.

关键词： inference mechanisms extended words semantic dependencies extended text classification performance text analysis pattern classification natural language processing Word2Vec short text classification algorithm semantic-extension-based algorithm Word2vec related words LDA model semantic extension

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Multi-attribute-Based Access Control Policy for Supply Chain Data Service

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Chinese Journal of Electronics 2018年第6期27卷 1283-1290页

作者： ZOU Junwei LAN Jiewei WANG Xiaoke LUO Hong School of Electronic Engineering Beijing University of Posts and Telecommunications School of Computer Science Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing University of Posts and Telecommunications

The Electronic product code(EPC) network is established and maintained in worldwide-scale based on the EPC standard framework to guarantee the real-time information recognition, and provide efficient management for supply chain. In EPC network, a series of data services can be provided due to the requirements of users. Aiming to guarantee the security of data services, we propose a dynamic access control model for data services based on multiple attributes. We extract specific attribute sets from user, and calculate security level of user using certainty and uncertainty theories based on the attribute sets. The data can be provided to users according to the security level of user and data. The security of data in the supply chain can be guaranteed, and data acquisition can be dynamic and fine-grained. We deploy the proposed model to real supply chain management system we built to verify effectiveness and feasibility of the solution.

关键词： EPCglobal Supply chain Data service Access control

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K-Corruption Intermittent Attacks for Violating the Codiagnosability

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IEEE/CAA Journal of Automatica Sinica 2025年第1期 159-172页

作者： Ruotian Liu Yihui Hu Agostino Marcello Mangini Maria Pia Fanti IEEE the department of Electrical and Information Engineering Polytechnic University of Bari the School of Automation Xi'an University of Posts and Telecommunications

In this work, we address the codiagnosability analysis problem of a networked discrete event system under malicious attacks. The considered system is modeled by a labeled Petri net and is monitored by a series of sites, in which each site possesses its own set of sensors, without requiring communication among sites or to any coordinators. A net is said to be codiagnosable with respect to a fault if at least one site could deduce the occurrence of this fault within finite steps. In this context, we focus on a type of malicious attack that is called stealthy intermittent replacement attack. The stealthiness demands that the corrupted observations should be consistent with the system's normal behavior, while the intermittent replacement setting entails that the replaced transition labels must be recovered within a bounded of consecutive corrupted observations(called as K-corruption intermittent attack). Particularly, there exists a coordination between attackers that are separately effected on different sites, which holds the same corrupted observation for each common transition under attacks. From an attacker viewpoint, this work aims to design Kcorruption intermittent attacks for violating the codiagnosability of systems. For this purpose, we propose an attack automaton to analyze K-corruption intermittent attack for each site, and build a new structure called complete attack graph that is used to analyze all the potential attacked paths. Finally, an algorithm is inferred to obtain the K-corruption intermittent attacks, and examples are given to show the proposed attack strategy.

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FSCIL-EACA: Few-Shot Class-Incremental Learning Network Based on Embedding Augmentation and Classifier Adaptation for Image Classification

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Chinese Journal of Electronics 2024年第1期33卷 139-152页

作者： Ruru ZHANG Haihong E Meina SONG School of Computer Science Beijing University of Posts and Telecommunications Education department Information Network Engineering Research Center Beijing University of Posts and Telecommunications

The ability to learn incrementally is critical to the long-term operation of AI systems. Benefiting from the power of few-shot class-incremental learning(FSCIL), deep learning models can continuously recognize new classes with only a few samples. The difficulty is that limited instances of new classes will lead to overfitting and exacerbate the catastrophic forgetting of the old classes. Most previous works alleviate the above problems by imposing strong constraints on the model structure or parameters, but ignoring embedding network transferability and classifier adaptation(CA), failing to guarantee the efficient utilization of visual features and establishing relationships between old and new classes. In this paper, we propose a simple and novel approach from two perspectives: embedding bias and classifier bias. The method learns an embedding augmented(EA) network with cross-class transfer and class-specific discriminative abilities based on self-supervised learning and modulated attention to alleviate embedding bias. Based on the adaptive incremental classifier learning scheme to realize incremental learning capability,guiding the adaptive update of prototypes and feature embeddings to alleviate classifier bias. We conduct extensive experiments on two popular natural image datasets and two medical datasets. The experiments show that our method is significantly better than the baseline and achieves state-of-the-art results.

关键词： Training Adaptation models Visualization Image recognition Prototypes Self-supervised learning Power capacitors

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