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arXiv 2025年

作者： Luo, Cheng Xiang, Luping Hu, Jie Yang, Kun The School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China The State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China School of Intelligent Software and Engineering Nanjing University Suzhou Campus Suzhou China The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

Deep learning (DL) has emerged as a powerful tool for addressing the intricate challenges inherent in communication and sensing systems, significantly enhancing the intelligence of future sixth-generation (6G) networks. A substantial body of research has highlighted the promise of DL-based techniques in these domains. However, in addition to improving accuracy, new challenges must be addressed regarding the generalization and transferability of DL-based systems. To tackle these issues, this paper introduces a series of mathematically grounded and modularized models, referred to as bedrock models, specifically designed for integration into both communication and sensing systems. Due to their modular architecture, these models can be seamlessly incorporated into existing communication and sensing frameworks. For communication systems, the proposed models demonstrate substantial performance improvements while also exhibit strong transferability, enabling direct parameter sharing across different tasks, which greatly facilitates practical deployment. In sensing applications, the integration of the bedrock models into existing systems results in superior performance, reducing delay and Doppler estimation errors by an order of magnitude compared to traditional methods. Additionally, a pre-equalization strategy based on the bedrock models is proposed for the transmitter. By leveraging sensing information, the transmitted communication signal is dynamically adjusted without altering the communication model pre-trained in AWGN channels. This adaptation enables the system to effectively cope with doubly dispersive channels, restoring the received signal to an AWGN-like condition and achieving near-optimal performance. Simulation results substantiate the effectiveness and transferability of the proposed bedrock models, underscoring their potential to advance both communication and sensing systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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Unleashing the Power of Large Language Model for Denoising Recommendation

arXiv

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arXiv 2025年

作者： Wang, Shuyao Zheng, Zhi Sui, Yongduo Xiong, Hui School of Data Science University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Hefei China Tencent Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Recommender systems are crucial for personalizing user experiences but often depend on implicit feedback data, which can be noisy and misleading. Existing denoising studies involve incorporating auxiliary information or learning strategies from interaction data. However, they struggle with the inherent limitations of external knowledge and interaction data, as well as the non-universality of certain predefined assumptions, hindering accurate noise identification. Recently, large language models (LLMs) have gained attention for their extensive world knowledge and reasoning abilities, yet their potential in enhancing denoising in recommendations remains underexplored. In this paper, we introduce LLaRD, a framework leveraging LLMs to improve denoising in recommender systems, thereby boosting overall recommendation performance. Specifically, LLaRD generates denoising-related knowledge by first enriching semantic insights from observational data via LLMs and inferring user-item preference knowledge. It then employs a novel Chain-of-Thought (CoT) technique over user-item interaction graphs to reveal relation knowledge for denoising. Finally, it applies the Information Bottleneck (IB) principle to align LLM-generated denoising knowledge with recommendation targets, filtering out noise and irrelevant LLM knowledge. Empirical results demonstrate LLaRD’s effectiveness in enhancing denoising and recommendation accuracy. The code is available at https://***/shuyao-wang/LLaRD. Copyright © 2025, The Authors. All rights reserved.

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PVBF: A Framework for Mitigating Parameter Variation Imbalance in Online Continual Learning

arXiv

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arXiv 2025年

作者： Tao, Zelin Deng, Hao Liu, Mingqing Zhang, Lijun Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai201804 China College of Electronic and Information Engineering Tongji University Shanghai201804 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai201804 China

Online continual learning (OCL), which enables AI systems to adaptively learn from non-stationary data streams, is commonly achieved using experience replay (ER)-based methods that retain knowledge by replaying stored past during training. However, these methods face challenges of prediction bias, stemming from deviations in parameter update directions during task transitions. This paper identifies parameter variation imbalance as a critical factor contributing to prediction bias in ER-based OCL. Specifically, using the proposed parameter variation evaluation method, we highlight two types of imbalance: correlation-induced imbalance, where certain parameters are disproportionately updated across tasks, and layer-wise imbalance, where output layer parameters update faster than those in preceding layers. To mitigate the above imbalances, we propose the Parameter Variation Balancing Framework (PVBF), which incorporates: 1) a novel method to compute parameter correlations with previous tasks based on parameter variations, 2) an encourage-and-consolidate (E&C) method utilizing parameter correlations to perform gradient adjustments across all parameters during training, 3) a dual-layer copy weights with reinit (D-CWR) strategy to slowly update output layer parameters for frequently occuring sample categories. Experiments on short and long task sequences demonstrate that PVBF significantly reduces prediction bias and improves OCL performance, achieving up to 47% higher accuracy compared to existing ER-based methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Navy Droid: an efficient tool of energy inefficiency problem diagnosis for Android applications

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science China(Information sciences) 2018年第5期61卷 35-54页

作者： Yi LIU Jue WANG Chang XU Xiaoxing MA Jian L State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing University

Energy inefficiency is an influential non-functional issue for smartphone applications, causing increased concerns from users. Locating these problems is labor-intensive, thus automated diagnosis tools are in demand. Some existing approaches detect energy inefficiency problems by exploring application states with the JPF framework, and get favorable results. However, the effects of these approaches are restricted because of their imprecise application execution models and incomplete energy inefficiency patterns. This paper introduces Navy Droid, an effective and efficient tool of energy inefficiency problem diagnosis for Android applications. We constructed a comprehensive application execution model in the form of a state machine,which accurately simulates the runtime behavior of Android applications. We designed a parallel algorithm to systematically explore an application’s state space. Our approach supports more energy inefficiency patterns, and is able to detect complicated wake lock misuses. We implemented our approach as a prototype tool and applied it to real-world applications. We evaluated Navy Droid with 19 real-world Android applications, and Navy Droid located more energy inefficiency bugs in these applications than the existing work E-Green Droid did. Also, Navy Droid reduced the analysis time with its parallel state exploration *** experimental results demonstrated the effectiveness and efficiency of our approach for detecting energy inefficiency bugs in Android applications.

关键词： energy inefficiency smartphone application wake lock

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Massive GaN micro-LED array based underwater wireless optical communication

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Optics Express 2025年第9期33卷 19527-19534页

作者： Lv, Zhijian Liu, Xinyi Wu, Shuang Zhang, Wenwei Yue, C. Patrick Liu, Zhaojun Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Integrated Circuit Design Center Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

High-speed underwater wireless optical communication technology is a much-needed communication technology in current underwater applications, and micro-LED is a promising light source option. Considering the limitation of transmission distance due to the low optical power of a single micro-LED pixel, an underwater wireless optical communication system based on a novel massive GaN micro-LED array includes more than 5000 pixels fabricated via flip-chip technology is proposed, which can achieve 165-Mbps and 90-Mbps at 37.7 m and 73.5 m, respectively. Therefore, the feasibility of realizing a high data rate and long transmission distance in an underwater wireless optical communication system based on a novel massive micro-LED array is confirmed. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Fiber to the x

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Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation

arXiv

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arXiv 2025年

作者： Wang, Yin Li, Mu Liu, Jiapeng Leng, Zhiying Li, Frederick W.B. Zhang, Ziyao Liang, Xiaohui B. State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Department of Computer Science University of Durham United Kingdom Zhongguancun Laboratory Beijing China

We address the challenging problem of fine-grained text-driven human motion generation. Existing works generate imprecise motions that fail to accurately capture relationships specified in text due to: (1) lack of effective text parsing for detailed semantic cues regarding body parts, (2) not fully modeling linguistic structures between words to comprehend text comprehensively. To tackle these limitations, we propose a novel fine-grained framework Fg-T2M++ that consists of: (1) an LLMs semantic parsing module to extract body part descriptions and semantics from text, (2) a hyperbolic text representation module to encode relational information between text units by embedding the syntactic dependency graph into hyperbolic space, and (3) a multi-modal fusion module to hierarchically fuse text and motion features. Extensive experiments on HumanML3D and KIT-ML datasets demonstrate that Fg-T2M++ outperforms SOTA methods, validating its ability to accurately generate motions adhering to comprehensive text semantics. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Low-Complexity Beamforming Design for Null Space-based Simultaneous Wireless Information and Power Transfer Systems

arXiv

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arXiv 2025年

作者： Luo, Cheng Hu, Jie Xiang, Luping Yang, Kun Sichuan Chengdu China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China School of Intelligent Software and Engineering Nanjing University Suzhou Campus Suzhou China School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

Simultaneous wireless information and power transfer (SWIPT) is a promising technology for the upcoming sixth-generation (6G) communication networks, enabling internet of things (IoT) devices and sensors to extend their operational lifetimes. In this paper, we propose a SWIPT scheme by projecting the interference signals from both intra-wireless information transfer (WIT) and inter-wireless energy transfer (WET) into the null space, simplifying the system into a point-to-point WIT and WET problem. Upon further analysis, we confirm that dedicated energy beamforming is unnecessary. In addition, we develop a low-complexity algorithm to solve the problem efficiently, further reducing computational overhead. Numerical results validate our analysis, showing that the computational complexity is reduced by 97.5% and 99.96% for the cases of KI = KE = 2, M = 4 and KI = KE = 16, M = 64, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Inductive power transmission

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Preventing the Popular Item Embedding Based Attack in Federated Recommendations

arXiv

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arXiv 2025年

作者： Zhang, Jun Li, Huan Rong, Dazhong Zhao, Yan Chen, Ke Shou, Lidan The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science Aalborg University Aalborg Denmark

Privacy concerns have led to the rise of federated recommender systems (FRS), which can create personalized models across distributed clients. However, FRS is vulnerable to poisoning attacks, where malicious users manipulate gradients to promote their target items intentionally. Existing attacks against FRS have limitations, as they depend on specific models and prior knowledge, restricting their real-world applicability. In our exploration of practical FRS vulnerabilities, we devise a model-agnostic and prior-knowledge-free attack, named PIECK (Popular Item Embedding based Attack). The core module of PIECK is popular item mining, which leverages embedding changes during FRS training to effectively identify the popular items. Built upon the core module, PIECK branches into two diverse solutions: The PIECKIPE solution employs an item popularity enhancement module, which aligns the embeddings of targeted items with the mined popular items to increase item exposure. The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items. Upon identifying PIECK, we evaluate existing federated defense methods and find them ineffective against PIECK, as poisonous gradients inevitably overwhelm the cold target items. We then propose a novel defense method by introducing two regularization terms during user training, which constrain item popularity enhancement and user embedding approximation while preserving FRS performance. We evaluate PIECK and its defense across two base models, three real datasets, four top-tier attacks, and six general defense methods, affirming the efficacy of both PIECK and its defense. © 2025, CC BY.

关键词： Federated learning

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Discriminative Word Alignment over Multiple Word Segmentations

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Chinese Journal of Electronics 2014年第2期23卷 263-270页

作者： XI Ning DAI Xinyu HUANG Shujian CHEN Jiajun State Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing University

Conventional bilingual word alignment is conducted on sentence pairs with single word segmentation for languages such as Chinese, viz. Single-segmentationbased word alignment(SSWA). However, SSWA may run the risk of losing optimal word segmentation granularities or causing data sparseness in word alignment. This paper proposes Multiple-segmentation-based word alignment(MSWA). In MSWA, diverse and complementary knowledge in multiple word segmentations can be employed to lower the above risks in word alignment. Given k word segmentations of a Chinese sentence, a skeleton segmentation is firstly constructed. The alignment between the skeleton segmentation and the parallel English sentence is loglinearly modeled, where various features defined over multiple word segmentations are incorporated. The Viterbi alignment, the alignment with the highest score, is mapped back to k word alignments based on k segmentations respectively. Experimentally, MSWA outperformed SSWA on all k segmentations in both alignment quality and translation performance.

关键词： Natural language processing Word align-ment Multiple word segmentation Machine translation

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PolarBEVU: Multi-Camera 3D Object Detection in Polar Bird's-Eye View via Unprojection

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Hou, Minghui Lyu, Chuanhao Wang, Gang Ma, Baorui Xu, Rongtao Hu, Jue Fan, Xiaopeng Jilin University College of Computer Science and Technology Changchun China Tsinghua University School of Software Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China Harbin Institute of Technology National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin150080 China Harbin Institute of Technology Centre for Composite Materials and Structures Harbin150001 China Harbin Institute of Technology Faculty of Computing Harbin150001 China

3D object detection from a Bird's Eye View (BEV) has emerged as a novel perception paradigm for autonomous driving scenarios. While most current 3D object detection methods still rely on the conventional Cartesian coordinates, they fail to align with the non-aligned coordinate system inherent in image geometry. The Polar coordinates, on the other hand, better fit with the geometric shape corresponding to the perception of cameras. However, transforming between coordinate systems introduces distortions in the perception information, resulting in issues such as "Weak Adaptability to Heatmap Distribution"and "Offset in the Center Point of the Bounding Box."To address these challenges, this paper proposes a cutting-edge 3D object detection model named PolarBEVU, which leverages the bird's-eye view under the Polar coordinates along with multi-camera unprojection. The model introduces an innovative "Deformable Uniform Heatmap Distribution"method that adjusts heatmap computations based on box shapes, generating high-quality heatmaps and effectively resolving the issue of "Weak Adaptability to Heatmap Distribution."Moreover, the model incorporates the concept of "Dynamic High-risk Regression Region"to enhance the accuracy and robustness of the center point regression at the bounding box, thus mitigating the issue of "Offset in the Center Point of the Bounding Box."In extensive experiments on the nuScenes dataset, PolarBEVU achieves impressive results with 49.9% mAP and 57.4% NDS on the test set, surpassing other comparative approaches and reaching the state-of-the-art (SOTA) performance among methods utilizing Polar coordinates. This clearly demonstrates the efficacy and superiority of PolarBEVU. In addition, the model is successfully deployed on Nvidia Jetson AGX Orin, showcasing real-time inference speeds of 31.42ms. These findings affirm PolarBEVU's potential for practical applications. Code is available at https: //***/JLUrob/PolarBEVU. © 2025 IEEE.

关键词： Object tracking

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