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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yunrui Dong Guiduo Duan Tianxi Huang Yunhao Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China Chengdu Textile College College of Humanities and General Education Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of Multimodal Relation Extraction (MRE), we present the $\color{Red}{\text{W}}$atcher-$\color{Red}{\text{M}}$ediated $\color{Red}{\text{A}}$ttention $\color{Red}{\text{J}}$oint $\color{Red}{\text{L}}$earning Model ($\color{Red}{\text{WMAJL}}$), a novel approach addressing the challenges of modality alignment noise, cross-modal fusion disparity, preservation of textual relative position information, and the distinctiveness of classification labels. WMAJL employs an integrative framework leveraging contrastive learning and variational autoencoder constraints to mitigate modality alignment noise by prioritizing relevant semantic data and effectively reducing extraneous noise that does not contribute to the task. The model’s innovative architecture includes a mediator watcher, which facilitates enhanced cross-modal fusion by enabling nuanced information exchange between textual and visual modalities while preserving the unique characteristics of each modality. Additionally, the design of auxiliary tasks, such as Named Entity Recognition (NER), and output supervision constructs loss functions that preserve relative position information, ensuring a precise depiction of entity relationships throughout the multilayer encoding processes. A key differentiator of WMAJL is its label-centric self-information loss technique, inspired by InfoNCE, which trains the model to cluster similar relation labels in semantically coherent areas, thereby optimizing classification label uniqueness by discerning subtle differences among relation types. The synergistic application of these strategies has led to a significant enhancement of WMAJL’s performance, as evidenced by its state-of-the-art F1 score of $\color{Red}{84.93\%}$ on the MNRE dataset. This achievement surpasses existing benchmarks and sets a new standard for multimodal knowledge extraction, underscoring WMAJL’s potential to revolutionize the MRE landscape.

关键词： Resistance visualization Technological innovation Noise Autoencoders Semantics Contrastive learning Nonhomogeneous media Speech processing Standards

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Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation

arXiv

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arXiv 2025年

作者： Zheng, Hong Chen, Yucheng Mu, Nan Li, Xiaoning College of Computer Science Sichuan Normal University Chengdu610101 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Department of Cardiology West China Hospital Sichuan University Chengdu610041 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610066 China Sichuan 2011 Collaborative Innovation Center for Educational Big Data Chengdu610066 China

Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based segmentation methods have recently garnered significant attention due to their impressive performance. However, these segmentation methods are typically good at partitioning regularly shaped organs, such as the left ventricle (LV) and the myocardium (MYO), whereas they perform poorly on irregularly shaped organs, such as the right ventricle (RV). In this study, we argue that this limitation of segmentation models stems from their insufficient generalization ability to address the distribution shift of segmentation targets across slices, cardiac phases, and disease conditions. To overcome this issue, we present a Multi-Disease-Aware Training Strategy (MTS) and restructure the introduced CMRI datasets into multi-disease datasets. Additionally, we propose a specialized data processing technique for preprocessing input images to support the MTS. To validate the effectiveness of our method, we performed control group experiments and cross-validation tests. The experimental results show that (1) network models trained using our proposed strategy achieved superior segmentation performance, particularly in RV segmentation, and (2) these networks exhibited robust performance even when applied to data from unknown diseases. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Joint Optimization of UAV-Carried IRS for Urban Low Altitude mmWave Communications with Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Liu, Bei Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Kim, Dong In The College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication. Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Constructive Interference Precoding Empowered NOMA-ISAC Design

引用

IEEE Transactions on Wireless Communications 2025年

作者： Wang, Wei Dong, Chao Zhao, Nan Wu, Qihui Niyato, Dusit Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing210023 China Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Non-orthogonal multiple access (NOMA) can help integrated sensing and communication (ISAC) to accommodate more users and well manage interference. In this paper, we first propose a NOMA-ISAC scheme, in which a multiantenna base station (BS) transmits ISAC signal to detect a radar user (RU), and provide wireless service to the RU and the communication user (CU) simultaneously. The inter-user interference can be mitigated by the successive interference cancellation (SIC). We further investigate the trade-off between minimizing the beampattern matching error and maximizing the CU’s achievable signal-to-noise ratio (SNR), and propose a penalty-based semi-definite relaxation (SDR) method to solve this non-convex problem. Then, to mitigate the instantaneous NOMA-ISAC beampattern shaking and enhance its stability, we utilize constructive interference precoding (CIP) to assist the NOMA-ISAC beampattern design. Introducing CIP can convert the interference from RU into the beneficial signal to CU and the complex SIC can be avoided. Then, the corresponding trade-off can be transformed into a convex problem by the Taylor-series approximation, and an iterative algorithm is proposed to solve it. Moreover, the Manopt toolbox assisted initialization is utilized to accelerate its convergence speed. Simulation results verify that the proposed CIP-NOMA-ISAC scheme can effectively enhance the stability of instantaneous NOMA-ISAC beampattern over limited time slots, and provide higher SNR for CU. © 2002-2012 IEEE.

关键词： Taylor series

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Interval quantile correlations with applications to testing high-dimensional quantile effects

引用

Journal of Econometrics 2025年 249卷

作者： Zhang, Yaowu Zhou, Yeqing Zhu, Liping Research Institute for Interdisciplinary Sciences School of Information Management and Engineering Shanghai University of Finance and Economics China School of Mathematical Sciences School of Economics and Management and Key Laboratory of Intelligent Computing and Applications Tongji University China Center for Applied Statistics and Institute of Statistics and Big Data Renmin University of China China

In this article, we propose interval quantile correlation and interval quantile partial correlation to measure the association between two random variables over an interval of quantile levels. We construct efficient estimators for the proposed correlations, and establish their asymptotic properties under the null and alternative hypotheses. We further use the interval quantile partial correlation to test for the significance of covariate effects in high-dimensional quantile regression when a subset of covariates are controlled. We calculate marginal interval quantile partial correlations for each covariate, then aggregate them to construct a sum-type test statistic. The null distribution of our proposed test statistic is asymptotically standard normal. We use extensive simulations and an application to illustrate that our proposed test, which pools information across an interval of quantile levels to enhance power performances, is very effective in detecting quantile effects. © 2024 Elsevier B.V.

关键词： Interval quantile Partial correlation Quantile regression

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Beyond Radio: Het-Medium Networks and Its Cross-Medium Communication Paradigms for 6G

引用

IEEE Network 2025年

作者： Wu, Mingming Han, Yufeng Xiao, Yue Gao, Yulan Li, Shiying Niyato, Dusit Karagiannidis, George K. University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China QianYuan National Laboratory Hangzhou310024 China KTH Royal Institute of Technology Division of Information Science and Engineering Stockholm100 44 Sweden Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Group Electrical and Computer Engineering Dept Thessaloniki54 124 Greece

In the evolving landscape of communication technologies, the integration of Heterogeneous Medium Networks (HetMNets) has emerged as a pivotal progression, signaling a departure from traditional, radio-centric paradigms. As we edge closer to the realization of 6G networks, understanding the intricate dynamics of cross-medium communication (CMC) becomes imperative. This article indicates the multifaceted nature of HetMNets and explores the transformative CMC paradigms shaping the roadmap toward 6G. First, we outline the key performance indicators (KPIs), potential scenarios, and the emerging technologies and applications of 6G wireless systems, which serve as the driving forces behind the development of HetMNets. Subsequently, various communication media are introduced, contrasting their propagation characteristics to underscore the indispensability of HetMNets for the 6G landscape. We then enumerate the general issues involving challenges/problems and potential technical solutions in HetMNets. Moreover, we present an air-sea coordinated CMC system model, serving as an example of HetMNets built upon CMC paradigms. Specifically, we tackle a power allocation challenge, optimizing the transmit power of both underwater acoustic and radio frequency (RF) communication media at the cross-medium bidirectional relay to curtail the outage probability. The corresponding solution highlights the efficacy of the proposed CMC paradigm in supporting the reliability and resource efficiency of HetMNets. © 1986-2012 IEEE.

关键词： Radio communication

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Utilizing Multipath Effects for Mobile Charging

引用

IEEE Transactions on Mobile computing 2025年

作者： Gao, Jing Wu, Die Zhang, Linglin Li, Jingwen Yang, Jin Peng, Jian Liu, Tang Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University College of Computer Science Chengdu610065 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Chengdu610068 China

Recently, Wireless Rechargeable Sensor Networks (WRSNs) have emerged as a promising solution to address the energy limitations of wireless sensor networks. In practical applications of WRSNs, environmental objects are ubiquitous, reflecting radio waves and causing them to reach sensors via multiple paths. These multipath effects significantly impact the power intensity received by sensors. In this paper, we study a fundamental issue of charGing schEduling with mulTipath effectS (GETS), that is, how to schedule a mobile charger by comprehensively considering the multipath effects to maximize the overall charging utility. To this end, we first establish a charging model with environmental objects to investigate the impact of multipath effects on power distribution. Then, we propose a charging scheduling scheme that not only selects a series of sojourn locations for the MC (Mobile Charger) to maximize the total power received by nearby sensors but also construct a charging path that avoids environmental objects. We conduct extensive simulations as well as indoor and outdoor field experiments to evaluate the performance of our scheme. The results demonstrate that, on average, our scheme outperforms baseline algorithms by 48.87%. © 2002-2012 IEEE.

关键词： Charging stations

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ATHENA: An In-vehicle CAN Intrusion Detection Framework Based on Physical Characteristics of Vehicle Systems

arXiv

引用

arXiv 2025年

作者： Wang, Kai Sun, Zhen Wang, Bailing Fan, Qilin Li, Ming Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China Jinan Key Laboratory of Distributed Databases Shandong Inspur Database Technology Co. Ltd Jinan China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

With the growing interconnection between In-Vehicle Networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the resource-constrained vehicular environment. Specifically, in the cloud with sufficient resources, ATHENA uses the clustering method of multi-distribution mixture model combined with deep data mining technology to generate the raw Payload Rule Bank of IVN CAN messages, and then improves the rule quality with the help of exploitation on the first-principled physical knowledge of the vehicle system, after which the payload rules are periodically sent to the vehicle terminal. At the vehicle terminal, a simple LSTM component is used to generate the Time Rule Bank representing the long-term time series dependencies and the periodic characteristics of CAN messages, but not for any detection tasks as in traditional usage scenarios, where only the generated time rules are the candidates for further IVN intrusion detection tasks. Based on both the payload and time rules generated from cloud and vehicle terminal, ATHENA can achieve efficient intrusion detection capability by simple rule-base matching operations, rather than using complex black-box reasoning of resource-intensive neural network models, which is in fact only used for rule logic generation phase instead of the actual intrusion detection phase in our framework. Comparative experimental results on the ROAD dataset, which is current the most outstanding real-world in-vehicle CAN dataset covering new instances of sophisticated and stealthy masquerade attacks, demonstrate ATHENA significantly outperforms the state-of-the-art IVN intrusion detection methods in detecting complex attacks. We make the code available at https://***/wangkai-tech23/ATHENA. Copyright

关键词： Network intrusion

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Burst-Sensitive Traffic Forecast Via Multi-Property Personalized Fusion in Federated Learning

引用

IEEE Transactions on Mobile computing 2025年

作者： Xue, Jingjing Sun, Sheng Liu, Min Wang, Yuwei Meng, Xuying Wang, Jingyuan Zhang, JunBo Xu, Ke Chinese Academy of Sciences University of Chinese Academy of Sciences Institute of Computing Technology Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beihang University School of Computer Science and Engineering China Beihang University MIIT Key Laboratory of Data Intelligence and Management Beijing China JD Technology JD iCity China JD Intelligent Cities Research China Tsinghua University Department of Computer Science and Technology Beijing China Zhongguancun Laboratory Beijing China

For distributed network traffic prediction with data localization and privacy protection, Federated Learning (FL) enables collaborative training without raw data exchange across Base Stations (BSs). Nevertheless, traffic across BSs exhibit inherently heterogeneous trend burst and smooth fluctuation properties, but existing FL methods model single-scale series from only one view, which cannot simultaneously capture diverse trend and fluctuation properties, especially distinct burst distributions. In this paper, we propose Personalized Federated Forecasting with Multi-property Self-fusion (P2FMS), which can represent multi-scale traffic properties from different views. With precise multi-property representations, a fusion-level prediction decision is learned for each client in a personalized manner to promptly sense traffic bursts and improve forecasting performance in non-IID settings. Specifically, P2FMS decomposes the traffic series into distinct time scales, based on which, we effectively extract closeness, period, and trend properties from different views. The closeness and period are embedded through global-view representations with spatial correlations, while non-stationary trends are individually fitted from the client-side view. Furthermore, a personalized combiner is designed to accurately quantify the proportion of general fluctuation raws (i.e., closeness and period) and specific trend property in predictions, which enables multi-property self-fusion for each client to accommodate heterogeneous traffic patterns and enhance prediction accuracy. Besides, an alternant training mechanism is introduced to optimize property representation and fusion modules with the convergence guarantee. Extensive experiments on real-world datasets show that P2FMS outperforms status quo methods in both prediction performance and convergence time. © 2002-2012 IEEE.

关键词： Weather forecasting

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