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Classifying extended,localized and critical states in quasiperiodic lattices via unsupervised learning

Chinese Physics B 2025年第1期34卷 422-427页

作者： Bohan Zheng Siyu Zhu Xingping Zhou Tong Liu School of Computer Science and Technology School of SoftwareNanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Department of Applied Physics School of ScienceNanjing University of Posts and TelecommunicationsNanjing 210003China

Classification of quantum phases is one of the most important areas of research in condensed matter *** this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised ***,we choose two advanced unsupervised learning algorithms,namely,density-based spatial clustering of applications with noise(DBSCAN)and ordering points to identify the clustering structure(OPTICS),to explore the distinct phases of the Aubry–André–Harper model and the quasiperiodic p-wave *** unsupervised learning results match well with those obtained through traditional numerical ***,we assess similarity across different algorithms and find that the highest degree of similarity between the results of unsupervised learning algorithms and those of traditional algorithms exceeds 98%.Our work sheds light on applications of unsupervised learning for phase classification.

关键词： quantum phase quasiperiodic machine learning

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Preamble slice orderly queue access scheme in cell-free dense communication systems

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Digital Communications and Networks 2025年第1期11卷 126-135页

作者： Jun Sun Mengzhu Guo Jian Liu School of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Computer and Communication Engineering University of Science and TechnologyBeijing 100083China

High reliability applications in dense access scenarios have become one of the main goals of 6G *** solve the access collision of dense Machine Type Communication(MTC)devices in cell-free communication systems,an intelligent cooperative secure access scheme based on multi-agent reinforcement learning and federated learning is proposed,that is,the Preamble Slice Orderly Queue Access(PSOQA)*** this scheme,the preamble arrangement is combined with the access *** preamble arrangement is realized by preamble slices which is from the virtual preamble *** access devices learn to queue orderly by deep reinforcement *** orderly queue weakens the random and avoids collision.A preamble slice is assigned to an orderly access queue at each access *** orderly queue is determined by interaction information among multiple *** the federated reinforcement learning framework,the PSOQA scheme is implemented to guarantee the privacy and security of ***,the access performance of PSOQA is compared with other random contention schemes in different load *** results show that PSOQA can not only improve the access success rate but also guarantee low-latency tolerant performances.

关键词： Machine type communication Random access Reinforcement learning Access success probability Federated learning

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Exponential Stability of Fractional-Order Fuzzy Multilayer Networks With Short Memory and Non-Instantaneous Impulses Via Intermittent Control

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IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1639-1649页

作者： Xu, Yao Chen, Zui Li, Wenxue Wu, Yongbao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Department of Computer Science and Technology Weihai264209 China Department of Mathematics Weihai264209 China Southeast University School of Automation Nanjing210096 China

Many practical processes in the actual world may suffer from non-negligible instantaneous state resets and then persist for a set amount of time, which can be characterized by non-instantaneous impulses. In this paper, intermittently controlled fractional-order fuzzy multilayer complex networks with short memory and non-instantaneous impulses are considered, which give rise to a new, hybrid dynamical system that offers a wide range of applications. By employing a discontinuously intermittent control scheme, the exponential stability issue of the above-mentioned networks is studied and supported by the Lyapunov method and graph theory. In the analysis of exponential stability, stabilized and destabilized non-instantaneous impulsive effects are discussed respectively. Ultimately, main results are applied in the typical model of fractional-order competitive neural networks, and illustrative numerical simulations are conducted to show the effectiveness of theoretical analysis. © 1993-2012 IEEE.

关键词： Lyapunov methods

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FedADDP: Privacy-Preserving Personalized Federated Learning with Adaptive Dimensional Differential Privacy 24th

FedADDP: Privacy-Preserving Personalized Federated Learning ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Guo, Yangyang Zhang, Tao Mu, Xutong Li, Haoshuo Dong, Xuewen Li, Qi School of Computer Science and Technology Xidian University Xi’an China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9789819615476

Personalized Federated Learning (PFL) has gained significant attention for its superior capability in customizing model training. In response to the potential privacy risks associated with PFL, researchers apply differential privacy techniques to personalized federated learning, which protects privacy by clipping and adding noise to parameters. Existing methods utilize a uniform clipping threshold for all clients, which significantly reduces model accuracy. This reduction arises because these methods overlook the diversity in model parameters. To enhance the accuracy of personalized models under privacy constraints, we propose an Adaptive Dimensional Differential Privacy framework for Personalized Federated Learning (FedADDP). The framework utilizes Fisher information matrix to evaluate the sensitivity of parameters, effectively differentiating personalized parameters tailored to individual clients from global parameters consistent across all clients. Global parameters are handled through global consistency regularization and GlobalRobust Loss to ensure stability across different clients. Furthermore, we propose an adaptive dimensional differential privacy mechanism, which dynamically adjusts the clipping threshold for each dimension using historical gradient information, thus mitigating the accuracy loss of the personalized model. Experiments on the FEMNIST, SVHN, and CIFAR-10 datasets show that FedADDP improves accuracy by 1.67% to 23.12% across a variety of privacy levels and non-IID data distributions. The code is available at https://***/yyguo-xdu/FedADDP. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Impact of the chaotic semiconductor laser output power on time-delay-signature of chaos and random bit generation

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Chinese Physics B 2025年第3期34卷 325-330页

作者： Chenpeng Xue Xu Wang Likai Zheng Haoyu Zhang Yanhua Hong Zuxing Zhang College of Electronic and Optical Engineering and College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science and Engineering Bangor UniversityBangor LL571UTUK

We experimentally analyze the effect of the optical power on the time delay signature identification and the random bit generation in chaotic semiconductor laser with optical *** to the inevitable noise during the photoelectric detection and analog-digital conversion,the varying of output optical power would change the signal to noise ratio,then impact time delay signature identification and the random bit *** results show that,when the optical power is less than-14 dBm,with the decreasing of the optical power,the actual identified time delay signature degrades and the entropy of the chaotic signal ***,the extracted random bit sequence with lower optical power is more easily pass through the randomness testing.

关键词： chaos time delay signature entropy estimation random bit generation

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Detector with Classifier2: An end-to-end Multi-Stream Feature Aggregation Network for Fine-Grained Object Detection in Remote Sensing Images

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IEEE Transactions on Image Processing 2025年 34卷 2707-2720页

作者： Zheng, Shangdong Wu, Zebin Xu, Yang He, Chengxun Wei, Zhihui Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense China

Fine-grained object detection (FGOD) fundamentally comprises two primary tasks: object detection and finegrained classification. In natural scenes, most FGOD methods benefit from higher instance resolution and fewer environmental variation, attributing more commonly associated with the latter task. In this paper, we propose an unified paradigm named Detector with Classifier2 (DC2), which provides a holistic paradigm by explicitly considering the end-to-end integration of object detection and fine-grained classification tasks, rather than prioritizing one aspect. Initially, our detection sub-network is restricted to only determining whether the proposal is a coarse-category and does not delve into the specific sub-categories. Moreover, in order to reduce redundant pixel-level calculation, we propose an instance-level feature enhancement (IFE) module to model the semantic similarities among proposals, which poses great potential for locating more instances in remote sensing images (RSIs). After obtaining the coarse detection predictions, we further construct a classification sub-network, which is built on top of the former branch to determine the specific sub-categories of the aforementioned predictions. Importantly, the detection network is performed on the complete image, while the classification network conducts secondary modeling for the detected regions. These operations can be denoted as the global contextual information and local intrinsic cues extractions for each instance. Therefore, we propose a multi-stream feature aggregation (MSFA) module to integrate global-stream semantic information and local-stream discriminative cues. Our whole DC2 network follows an end-to-end learning fashion, which effectively excavates the internal correlation between detection and finegrained classification networks. We evaluate the performance of our DC2 network on two benchmarks SAT-MTB and HRSC2016 datasets. Importantly, our method achieves the new state-of-theart results com

关键词： Object detection

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Optimized Cross-Chain Transactions With Aggregated Zero-Knowledge Proofs: Enhancing Efficiency and Security

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IEEE Internet of Things Journal 2025年第9期12卷 11495-11510页

作者： Hu, Xiaoxuan Chen, Xiangting Dong, Zhenjiang Sun, Yanfei Guo, Yufeng Fang, Bingyi Qi, Jin Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210049 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210049 China Jiangsu Engineering Research Center of HPC and Intelligent Processing Nanjing210023 China School of Nanjing University of Posts and Telecommunications Nanjing210049 China China Unicom Digital Technology Company Ltd. Nanjing210009 China

With the rapid development of the blockchain industry and the widespread adoption of IoT devices, which are often deployed on different blockchains, the need for cross-chain value and data exchange has become increasingly important. However, existing cross-chain transactions face challenges, such as low efficiency, high costs, and insufficient security. To address these issues, this article proposes a cross-chain transaction scheme based on aggregated zero-knowledge proofs (ZKPs). This scheme optimizes the allocation of computing resources in a distributed environment and employs a multibranch balanced Merkle tree to construct aggregated ZKPs, significantly reducing the verification costs for batch cross-chain transactions. To further enhance data privacy and integrity, this article introduces the secure aggregated block verification (SABV) algorithm and improves the system consistency and reliability through the local Merkle tree rebalance (LMTR) algorithm. In addition, this article analyses the basic security of the proposed scheme when implemented in adversarial environments and provides countermeasures for common threats in distributed systems. Finally, simulations and actual deployment on the Ethereum test network were conducted. The results indicate that our method reduces CPU usage, memory consumption, and time expenditure by 50.10%, 99.03%, and 99.47%, respectively, during the generation of ZKPs for batch cross-chain transactions. At the same time, building upon the performance improvements of the existing ZKPs, our approach also demonstrates significant enhancements in contract deployment and cross-chain transaction efficiency. © 2014 IEEE.

关键词： Blockchain

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Exposing Audio-Visual Forgeries in Frequency Domain 18th

Exposing Audio-Visual Forgeries in Frequency Domain

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Wan, Yuanfei Wang, Jian Cui, Jinrong Sun, Yunlian School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China South China Agricultural University Guangzhou China

ISBN: (纸本)9789819610679

Recently, the rapid development of deepfake technology attracted strong attention from the community. Some previous work on deepfake detection achieved good results in the frequency domain, which inspires us to combine frequency-domain information with temporal and spatial domains of visual to detect deepfakes. In addition, the audio signal can be represented in the frequency domain, so we can explore multimodal frequency-domain cues by combining audio and visual modalities. In this paper, we propose a Frequency-aware Audio-Visual Deepfake Detection(FAVDD) method. Specifically, we design a Frequency-Temporal-Spatial(FTS) visual encoder that extracts spatial, frequency, and temporal forgery cues and embeds them into visual features to form a unified representation. In addition, we project the audio signal into the frequency domain by Fourier transform and capture the forgery traces, which are later combined with visual features for deepfake detection. The results show that our proposed framework effectively combines multiple cues and achieves good results on three multimodal deepfake datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Audio acoustics

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Universal Face Manipulation Proactive Defense by Frequency-Driven Imperceptible Adversarial Attack 18th

Universal Face Manipulation Proactive Defense by Frequency-...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Wen, Jinchang Wang, Jian Sun, Yunlian Tistarelli, Massimo School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Department of Biomedical Sciences and Information Technology University of Sassari Sassari Italy

ISBN: (纸本)9789819610709

Currently, highly realistic face-swapped videos/images pose significant potential security risks to society, which forces researchers to develop an effective defense mechanism against deepfakes, especially in a proactive manner. This involves adding imperceptible adversarial perturbations to facial images, which render deepfake models ineffective. Although existing proactive defense methods based on adversarial perturbations show promising performance in white-box scenarios, most of them suffer from significant performance degradation in black-box scenarios. Moreover, we observe that adversarial perturbations appearing in low-frequency areas (e.g., cheeks) are more easily noticed by the human eye. To this end, we propose a novel Frequency-Aware Multi-Teacher Distillation (FMTD) framework, which aims to generate a universal and imperceptible perturbation with remarkable generalization property against various facial manipulation methods. To extract universal property, we propose a multi-teacher distillation block for learning valuable shared knowledge from different perturbation generators. Moreover, we design a Frequency-aware Mean Squared Error (FMSE) loss based on wavelet transform to prefer to generate imperceptible perturbations in high-frequency regions. Experimental results demonstrate the superior performance of our proposed proactive defense method to state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mean square error

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Mining Topics towards ChatGPT Using a Disentangled Contextualized-neural Topic Model 25

Mining Topics towards ChatGPT Using a Disentangled Contextua...

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18th ACM International Conference on Web Search and Data Mining, WSDM 2025

作者： Wang, Rui Liu, Xing Wang, Yanan Chang, Shuyu Yao, Yuanzhi Huang, Haiping School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China

ISBN: (纸本)9798400713293

Mining topics relevant to the advanced AI dialogue system, such as ChatGPT, from short-length posts on social media poses several challenges for existing topic-mining approaches. Firstly, Bag-Of-Words approaches, including probabilistic topic models and their embedding-based variants, may struggle to extract interpretable topics due to insufficient word co-occurrence. Secondly, contextualized based approaches, built on the autoencoding framework, often yield entangled topic spaces, resulting in the mixing of irrelevant words into topics. To address these limitations, we propose a novel Disentangled Contextualized-neural Topic Model (DisCTM) based on textual representation learning. DisCTM leverages a pre-trained transformer language model to incorporate word sequence information and deal with the sparsity in short text. Additionally, it employs a topic disentangling mechanism to decorrelate dimensions of the latent topic space, effectively separating semantically irrelevant words into different topics. Extensive experiments have been conducted on three publicly available text corpora, and the results demonstrate the effectiveness of DisCTM in extracting high-quality topics, as measured by topic coherence and diversity metrics. © 2025 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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