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Reconfigurable metasurface for code division multiplexing in high-capacity and low-crosstalk holographic encryption

Optics Express 2025年第4期33卷 7295-7306页

作者： Li, Rui Yuan, Huan Zhong, Zheqiang Zhang, Bin College of Electronics and Information Engineering Sichuan University Sichuan Chengdu610064 China Big Data Analysis and Fusion Application Technology Engineering Laboratory of Sichuan Province Chengdu610065 China

Metasurface multiplexing holography shows potential to enhance information capacity but faces challenges such as crosstalk and limited dynamic switching. Leveraging the orthogonality of code division multiplexing (CDM) technology, we propose a reconfigurable metasurface-based encryption scheme. Here, the CDM code acts as a decryption key, enabling dynamic holographic image switching and ensuring secure information transmission. The circular polarization information is encoded into the reconfigurable metasurface using simultaneous modulation of geometric and propagation phases. By controlling polarization states of incident light and crystallization levels of GSST, the high-capacity and low-crosstalk CDM holographic images can be generated. Furthermore, a detailed analysis is conducted on the capacity of CDM holography, the factors influencing the efficiency of metasurface diffraction, and the scalability and feasibility of practical manufacturing. This proposed scheme has the ability to significantly boost encryption security, paving the way for increased information storage capacity and optical encryption. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Crosstalk

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Adaptive Weighting Push-SUM for Decentralized Optimization with Statistical Diversity

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IEEE Transactions on Control of Network Systems 2025年

作者： Zhou, Yiming Cheng, Yifei Xu, Linli Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application Hefei China

Statistical diversity is a property of data distribution and can hinder the optimization of a decentralized network. However, the theoretical limitations of the Push-SUM protocol reduce the performance in handling the statistical diversity of optimization algorithms based on it. In this paper, we theoretically and empirically reduce the negative impact of statistical diversity on decentralized optimization using the Push-SUM protocol. Specifically, we propose the Adaptive Weighting Push-SUM protocol, a theoretical generalization of the original Push-SUM protocol where the latter is a special case of the former. Our theoretical analysis shows that, with sufficient communication, the upper bound on the consensus distance for the new protocol reduces to O(1N), whereas it remains at O(1) for the Push-SUM protocol. We adopt SGD and Momentum SGD on the new protocol and prove that the convergence rate of these two algorithms to statistical diversity is O(NT) on the new protocol, while it is O(NdT) on the Push-SUM protocol, where d is the parameter size of the training model. To address statistical diversity in practical applications of the new protocol, we develop the Moreau weighting method for its generalized weight matrix definition. This method, derived from the Moreau envelope, is an approximate optimization of the distance penalty of the Moreau envelope. We verify that the Adaptive Weighting Push-SUM protocol is practically more efficient than the Push-SUM protocol via deep learning experiments. © 2014 IEEE.

关键词： data communication systems

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Dual Test-Time Training for Out-of-Distribution Recommender System

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IEEE Transactions on Knowledge and data Engineering 2025年第6期37卷 3312-3326页

作者： Yang, Xihong Wang, Yiqi Chen, Jin Fan, Wenqi Zhao, Xiangyu Zhu, En Liu, Xinwang Lian, Defu National University of Defense Technology College of Computer Science and Technology Changsha410073 China The Hong Kong University of Science and Technology School of Business and Management Hong Kong Hong Kong City University of Hong Kong Department of Data Science Hong Kong University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology Hefei Anhui230000 China

Deep learning has been widely applied in recommender systems, which has recently achieved revolutionary progress. However, most existing learning-based methods assume that the user and item distributions remain unchanged between the training phase and the test phase. However, the distribution of user and item features can naturally shift in real-world scenarios, potentially resulting in a substantial decrease in recommendation performance. This phenomenon can be formulated as an Out-Of-Distribution (OOD) recommendation problem. To address this challenge, we propose a novel Dual Test-Time-Training framework for OOD Recommendation, termed DT3OR. In DT3OR, we incorporate a model adaptation mechanism during the test-time phase to carefully update the recommendation model, allowing the model to adapt specially to the shifting user and item features. To be specific, we propose a self-distillation task and a contrastive task to assist the model learning both the user's invariant interest preferences and the variant user/item characteristics during the test-time phase, thus facilitating a smooth adaptation to the shifting features. Furthermore, we provide theoretical analysis to support the rationale behind our dual test-time training framework. To the best of our knowledge, this paper is the first work to address OOD recommendation via a test-time-training strategy. We conduct experiments on five datasets with various backbones. Comprehensive experimental results have demonstrated the effectiveness of DT3OR compared to other state-of-the-art baselines. © 2025 IEEE.

关键词： Contrastive Learning

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Salix-Leaf: Find Main Veins of Signal Clusters for Practical Parallel Decoding

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IEEE Transactions on Mobile Computing 2025年

作者： Li, Yajun Zhao, Jumin Li, Dengao Wu, Hejun Xu, Shuang Bai, Ruiqin Taiyuan University of Technology College of Electronic Information and Optical Engineering Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China Taiyuan University of Technology College of Computer Science and Technology College of Data Science Taiyuan China Sun Yat-sen University Department of Computer Science Guangzhou China

Parallel decoding of backscatter improves communication throughput by enabling concurrent transmission of backscatter tags. In practical applications of parallel decoding, it is extremely difficult to distinguish collided signals in superclusters where multiple signal clusters overlap. Existing methods are usually effective for superclusters with uniformly distributed signals. Nevertheless, there are many more scenarios in which signals in superclusters tend to gather unevenly, and existing methods cannot work. Such uneven clustering of signals occurs due to the following two possible causes: (1) signal-strengthdifferences (SSDs) among tags;or (2) cluster drifting (CD) driven by interferences from other objects within communication environments. This paper proposes a novel scheme called SalixLeaf, which aims to identify the main veins of signal clusters to address this problem of superclusters with unevenly distributed signals. Salix-Leaf identifies the main vein of each signal cluster for fine-grained clustering so that the direction of the main veins can be used to verify the accuracy of clustering. In addition, SalixLeaf employs a supercluster decomposer that divides signals into different segments for clustering analysis, enhancing robustness and practicability. Experimental results show that Salix-Leaf achieves a 1.2-fold increase in throughput and a 25% reduction in bit error rate (BER) compared to the state-of-the-art. © 2002-2012 IEEE.

关键词： Backscattering

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Understanding the mechanism of social tie in the propagation process of social network with communication channel

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Frontiers of Computer Science 2019年第6期13卷 1296-1308页

作者： Kai LI Guangyi LV Zhefeng WANG Qi LIU Enhong CHEN Lisheng QIAO Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China

The propagation of information in online social networks plays a critical role in modern life,and thus has been studied *** have proposed a series of propagation models,generally,which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his ***,the research on the mechanism how social ties between users play roles in propagation process is still ***,comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works,so that the existing models failed to clearly describe the process a user be activated by a *** this end,in this paper,we analyze the close correspondence between social tie in propagation process and communication channel,thus we propose to exploit the communication channel to describe the information propagation process between users,and design a social tie channel(STC)*** model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference,and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social *** experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.

关键词： information propagation social networks mechanism of social tie communication channel

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Exploiting Structural and Temporal Influence for Dynamic Social-Aware Recommendation

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Journal of Computer Science & Technology 2020年第2期35卷 281-294页

作者： Yang Liu Zhi Li Wei Huang Tong Xu En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230027China

Recent years have witnessed the rapid development of online social platforms,which effectively support the business intelligence and provide services for massive *** this line,large efforts have been made on the socialaware recommendation task,i.e.,leveraging social contextual information to improve recommendation *** existing methods have treated social relations in a static way,but the dynamic influence of social contextual information on users'consumption choices has been largely *** that end,in this paper,we conduct a comprehensive study to reveal the dynamic social influence on users'preferences,and then we propose a deep model called Dynamic Social-Aware Recommender System(DSRS)to integrate the users'structural and temporal social contexts to address the dynamic socialaware recommendation *** consists of two main components,i.e.,the social influence learning(SIL)and dynamic preference learning(DPL).Specifically,in the SIL module,we arrange social graphs in a sequential order and borrow the power of graph convolution networks(GCNs)to learn social ***,we design a structural-temporal attention mechanism to discriminatively model the structural social influence and the temporal social ***,in the DPL part,users'individual preferences are learned dynamically by recurrent neural networks(RNNs).Finally,with a prediction layer,we combine the users'social context and dynamic preferences to generate *** conduct extensive experiments on two real-world datasets,and the experimental results demonstrate the superiority and effectiveness of our proposed model compared with the state-of-the-art methods.

关键词： recommender system social influence sequential recommendation

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HACAN:a hierarchical answer-aware and context-aware network for question generation

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Frontiers of Computer Science 2024年第5期18卷 45-55页

作者： Ruijun SUN Hanqin TAO Yanmin CHEN Qi LIU Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei230027China

Question Generation(QG)is the task of generating questions according to the given *** of the existing methods are based on Recurrent Neural Networks(RNNs)for generating questions with passage-level input for providing more details,which seriously suffer from such problems as gradient vanishing and ineffective information *** fact,reasonably extracting useful information from a given context is more in line with our actual needs during questioning especially in the education *** that end,in this paper,we propose a novel Hierarchical Answer-Aware and Context-Aware Network(HACAN)to construct a high-quality passage representation and judge the balance between the sentences and the whole ***,a Hierarchical Passage Encoder(HPE)is proposed to construct an answer-aware and context-aware passage representation,with a strategy of utilizing multi-hop ***,we draw inspiration from the actual human questioning process and design a Hierarchical Passage-aware Decoder(HPD)which determines when to utilize the passage *** conduct extensive experiments on the SQuAD dataset,where the results verify the effectivenesss of our model in comparison with several baselines.

关键词： question generation natural language generation natural language processing sequence to sequence

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Study on Information Diffusion analysis in Social Networks and Its applications

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International Journal of Automation and computing 2018年第4期15卷 377-401页

作者： Biao Chang Tong Xu Qi Liu En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer ScienceUniversity of Science and Technology of China

Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximiza- tion and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffu- sion analysis are listed for further study.

关键词： Information diffusion influence evaluation influence maximization information source detection social network.

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Towards Exploring Large Molecular Space:An Efficient Chemical Genetic Algorithm

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Journal of Computer Science & Technology 2022年第6期37卷 1464-1477页

作者： Jian-Fu Zhu Zhong-Kai Hao Qi Liu Yu Yin Cheng-Qiang Lu Zhen-Ya Huang En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China

Generating molecules with desired properties is an important task in chemistry and *** efficient method may have a positive impact on finding drugs to treat diseases like *** mining and artificial intelligence may be good ways to find an efficient ***,both the generative models based on deep learning and the work based on genetic algorithms have made some progress in generating molecules and optimizing the molecule’s ***,existing methods have defects in the experimental evaluation *** methods also need to be improved in efficiency and *** solve these problems,we propose a method named the Chemical Genetic Algorithm for Large Molecular Space(CALM).Specifically,CALM employs a scalable and efficient molecular representation called molecular *** we design corresponding crossover,mutation,and mask operators inspired by domain knowledge and previous *** apply our genetic algorithm to several tasks related to molecular property optimization and constraint molecular *** results of these tasks show that our approach outperforms the other state-of-the-art deep learning and genetic algorithm methods,where the z tests performed on the results of several experiments show that our method is more than 99%likely to be *** the same time,based on the experimental results,we point out the defects in the experimental evaluation standard which affects the fair evaluation of all previous *** these defects helps to objectively evaluate the performance of all work.

关键词： data mining molecular generation genetic algorithm drug discovery artificial intelligence

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Continuous and generalized visual odometry based on FLANN_PSC-RANSAC_LSTM

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 15-28页

作者： Li, Dengao Feng, Ran Wu, Fanming Zhao, Jinhua Zhao, Jumin College of Computer Science and Technology College of Data Science Taiyuan University of Technology Shanxi Taiyuan China College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan University of Technology Shanxi Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China

In the field of simultaneous localization and mapping (SLAM), visual odometry (VO) always has great application prospects. In recent years, with the progress in the field of machine learning, methods based on neural networks are constantly being updated and applied. In this paper, we propose a continuous and generalized monocular visual odometry method based on features and neural networks. First, the feature information of adjacent image sequences is extracted by matching and troubleshooting algorithm (FLANN_PSC-RANSAC), then it and the corresponding six-degree-of-freedom information are simultaneously input into the long short-term memory artificial neural network (LSTM) for model construction, which not only ensures the reliability of the mode but also eliminates the influence of illumination on the data. In the real environment test, it has been effectively proved in terms of trajectory recovery accuracy and generalization ability to different environments and different illuminations. © 2024 - IOS Press. All rights reserved.

关键词： Long short-term memory

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