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White matter hyperintensities and relapse risk in late-life depression

Journal of Affective Disorders 2025年 383卷 298-305页

作者： Pearcy, Leigh B. Karim, Helmet T. Butters, Meryl A. Krafty, Robert Boyd, Brian D. Banihashemi, Layla Szymkowicz, Sarah M. Landman, Bennett A. Ajilore, Olusola Taylor, Warren D. Andreescu, Carmen Department of Psychiatry University of Pittsburgh Pittsburgh PA United States Department of Bioengineering University of Pittsburgh Pittsburgh PA United States Center for Cognitive Medicine Department of Psychiatry and Behavioral Science Vanderbilt University Medical Center Nashville TN United States Geriatric Research Education and Clinical Center Veterans Affairs Tennessee Valley Health System Nashville TN United States Department of Psychiatry University of Illinois-Chicago Chicago IL United States Department of Biostatistics and Bioinformatics Emory University Atlanta GA United States Departments of Computer Science Electrical Engineering and Biomedical Engineering Vanderbilt University United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center United States

Background: White matter hyperintensities (WMHs) are associated with late-life depression (LLD) and are considered a hallmark of MRI-defined vascular depression. However, their impact on depression recurrence in LLD is less well known. Methods: We investigated this relationship using data from a 2-year multi-site, longitudinal study, where baseline WMH volumes were obtained using 3 T FLAIR magnetic resonance imaging (MRI) from 145 participants, of which 102 had remitted LLD and 43 were control participants. We analyzed the effect of baseline WMH volume on LLD relapse over 2 years using regression and adjusting for total intracranial volume, age, sex, race, education, and study site. We performed survival analyses using a Cox proportional hazard model to determine whether baseline WMH volume was associated with time to relapse in LLD. Results: We found that participants with LLD had greater WMH volume at baseline than control participants, but not if accounting for peripheral cardiovascular disease. Participants with LLD who relapsed within 8 months of baseline had larger WMH volume than control participants but did not statistically differ from those that remained remitted;this effect was lost when expanding to participants that relapsed at any point in the 2-year study. WMH burden was not associated with time to relapse, suggesting greater WMH volumes are not indicative of faster relapse rates in LLD. Conclusion: Our results show that WMH burden may play a role in the early - but not the delayed - relapse of LLD, and they underscore the intricate dynamic of the biological markers underlying LLD treatment response and relapse. © 2025 The Authors

关键词： Depression Late-life LLD Recurrence Relapse Small vessel disease WMH

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DX2CT: Diffusion Model for 3D CT Reconstruction from Bi or Mono-planar 2D X-ray(s)

DX2CT: Diffusion Model for 3D CT Reconstruction from Bi or M...

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

作者： Yun Su Jeong Hye Bin Yoo Il Yong Chun Department of Electrical and Computer Engineering Sungkyunkwan University Republic of Korea Departments of Artificial Intelligence and Advanced Display Engineering Sungkyunkwan University Republic of Korea Center for Neuroscience Imaging Research Institute for Basic Science (IBS) Suwon Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Computational tomography (CT) provides high-resolution medical imaging, but it can expose patients to high radiation. X-ray scanners have low radiation exposure, but their resolutions are low. This paper proposes a new conditional diffusion model, DX2CT, that reconstructs three-dimensional (3D) CT volumes from bi or mono-planar X-ray image(s). Proposed DX2CT consists of two key components: 1) modulating feature maps extracted from two-dimensional (2D) X-ray(s) with 3D positions of CT volume using a new transformer and 2) effectively using the modulated 3D position-aware feature maps as conditions of DX2CT. In particular, the proposed transformer can provide conditions with rich information of a target CT slice to the conditional diffusion model, enabling high-quality CT reconstruction. Our experiments with the bi or mono-planar X-ray(s) benchmark datasets show that proposed DX2CT outperforms several state-of-the-art methods. Our codes and model will be available at: https://***/intyeger/DX2CT.

关键词： Three-dimensional displays Computed tomography Two-dimensional displays Diffusion models Transformers Feature extraction Speech processing X-ray imaging Image reconstruction Biomedical imaging

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IRS-Enhanced UAV Communication Networks: Securing Data with Hybrid Genetic and Gradient Descent Algorithms

IRS-Enhanced UAV Communication Networks: Securing Data with ...

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IEEE Conference on Wireless Communications and Networking

作者： Zina Chkirbene Ala Gouissem Ridha Hamila Unal Devrim Arafat Al-Dweik Kaya Kuru Electrical Engineering Qatar University Qatar College of Computing and Information Technology University of Doha for Science and Technology Qatar KINDI Center for Computing Research College of Engineering Qatar University Qatar Department of Computer and Information Engineering 4 KU 6G Research Center Khalifa University Abu Dhabi UAE School of Engineering University of Central Lancashire Preston UK

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In the rapidly advancing field of wireless communication, Unmanned Aerial Vehicles (UAVs) have become indispensable due to their extensive coverage capabilities and ability to access remote locations. Whether deployed as mobile base stations (BSs) or relays, UAVs significantly enhance network throughput and reliability. Alongside UAVs, Intelligent Reflecting Surfaces (IRS) have emerged as a cost-effective solution for improving communication quality through passive modulation arrays. Despite these advancements, the potential misuse of UAVs poses serious security risks, particularly in the form of communication eavesdropping. To address these challenges, this paper introduces a novel communication framework that integrates a UAV equipped with an adaptive IRS. The primary aim is to boost communication secrecy between BSs and multiple users, even in the presence of several UAV eavesdroppers. This objective is formulated as an optimization problem focused on maximizing the secrecy rate while considering UAV mobility constraints. To solve this non-convex problem, we propose a hybrid strategy that combines Genetic Algorithms and Gradient Descent techniques. This innovative approach efficiently determines suboptimal reflection angles and UAV trajectories for IRS-equipped UAVs, thereby enhancing the security of the communication network. This method not only addresses the complexity of the optimization but also provides a practical pathway to secure communications in environments with high eavesdropping risks.

关键词： Wireless networks Autonomous aerial vehicles Throughput Security Communication networks Reliability Relays Trajectory optimization Eavesdropping Testing

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Multiagent Deep Reinforcement Learning for Transactive Energy Management of MMGs Incorporating Battery Swapping Stations

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IEEE Transactions on Industrial Informatics 2025年

作者： Cai, Ting Zhang, You Wu, Yuxin Yan, Haoyuan Zhao, Tianyang Hubei University of Technology School of Computer Science Hubei Provincial Key Laboratory of Green Intelligent Computing Power Network Wuhan430068 China Hubei University of Arts and Science School of Computer Engineering Xiangyang441053 China Jinan University Energy and Electricity Research Center International Energy College Zhuhai519070 China Xi'an Jiaotong University School of Electrical Engineering Xi'an710049 China

Optimal energy management between microgrids (MGs) and battery swapping stations (BSSs) offers significant economic benefits. However, existing works face challenges in formulating optimal interaction strategies between MGs and BSSs, due to the temporal-spatial uncertainty of distributed renewables and emerging loads, as well as incomplete information. This article addresses the energy transaction problem between multi-MGs and multi-BSSs using a hybrid multiagent deep reinforcement learning approach to minimize operation costs. Specifically, a hierarchical transactive energy management community is introduced to facilitate energy exchange between MMGs and BSSs, considering different stakeholder interests. The problem is modeled as a partially observable Markov game. The proposed hybrid algorithm, combining multiagent proximal policy optimization (MAPPO) and double deep Q-network (DDQN), handles the continuous scheduling of MGs and the discrete operations of BSSs. Numerical results show that, averaged over the baselines, the proposed MAPPO-DDQN reduces 13.71% of MGs' operation costs and increases 14.62% of BSSs' profit. © 2005-2012 IEEE.

关键词： Deep reinforcement learning

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FIDSUS: Federated Intrusion Detection for Securing UAV Swarms in Smart Aerial computing

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IEEE Internet of Things Journal 2025年第9期12卷 11312-11328页

作者： Deng, Jiangtao Wang, Wei Wang, Lina Bashir, Ali Kashif Gadekallu, Thippa Reddy Feng, Hailin Lv, Meilei Fang, Kai Zhejiang A&F University College of Mathematics and Computer Science Hangzhou311300 China Quzhou University College of Electrical and Information Engineering Quzhou324000 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BX United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India Lovely Professional University Division of Research and Development Phagwara144411 India Chitkara University Center of Research Impact and Outcome Rajpura140401 India

The dynamic environment of UAV swarms in forest management is characterized by communication instability, heterogeneous nodes, and frequent topology changes due to challenging terrain. These systems are vulnerable to network attacks, requiring advanced intrusion detection technologies. Traditional methods struggle with rapid changes due to data privacy concerns and centralized computational limits, while existing federated learning (FL) algorithms lack robustness against client heterogeneity and dynamic data distribution, especially in complex forest environments. To address these challenges, we propose federated intrusion detection for securing UAV swarms (FIDSUS). FIDSUS improves intrusion detection systems by leveraging collaborative sensing among UAVs, enabling better monitoring and response to security threats in forestry. By quantifying the similarity between UAVs’ local feature extractors through an affinity matrix, FIDSUS guides the aggregation of feature extractors, improving detection capabilities. It also uses AI-driven aerial and distributed computing to enhance data processing efficiency and decision-making speed. The framework addresses data heterogeneity by cross-round feature fusion, improving detection in dynamic environments. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that FIDSUS outperforms existing FL methods with a 4%–34% accuracy improvement. FIDSUS shows robustness and accuracy in dynamic environments, providing an effective solution for securing UAV swarms in forestry. © 2014 IEEE.

关键词： Network intrusion

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

Lightweight Authenticated Task Offloading in 6G-Cloud Vehicu...

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IEEE Conference on Wireless Communications and Networking

作者： Sarah Al–Shareeda Fusun Ozguner Keith Redmill Trung Q. Duong Berk Canberk Electrical and Computer Engineering Department Center for Automotive Research (CAR) The Ohio State University USA BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen's University Belfast UK Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University UK

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50 % due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].

关键词： 6G mobile communication Performance evaluation Identity-based encryption Authentication Deep reinforcement learning Reproducibility of results Resource management Cryptography Optimization Software development management

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Multifidelity Simulation-based Inference for Computationally Expensive Simulators

arXiv

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arXiv 2025年

作者： Krouglova, Anastasia N. Johnson, Hayden R. Confavreux, Basile Deistler, Michael Gonçalves, Pedro J. Departments of Computer Science and Electrical Engineering KU Leuven Belgium Belgium Gatsby Computational Neuroscience Unit UCL London United Kingdom University of Tübingen Germany Tübingen AI Center Tübingen Germany Leuven Belgium

Across many domains of science, stochastic models are an essential tool to understand the mechanisms underlying empirically observed data. Models can be of different levels of detail and accuracy, with models of high-fidelity (i.e., high accuracy) to the phenomena under study being often preferable. However, inferring parameters of high-fidelity models via simulation-based inference is challenging, especially when the simulator is computationally expensive. We introduce MF-NPE, a multifidelity approach to neural posterior estimation that leverages inexpensive low-fidelity simulations to infer parameters of high-fidelity simulators within a limited simulation budget. MF-NPE performs neural posterior estimation with limited high-fidelity resources by virtue of transfer learning, with the ability to prioritize individual observations using active learning. On one statistical task with analytical ground-truth and two real-world tasks, MF-NPE shows comparable performance to current approaches while requiring up to two orders of magnitude fewer high-fidelity simulations. Overall, MF-NPE opens new opportunities to perform efficient Bayesian inference on computationally expensive simulators. © 2025, CC BY.

关键词： Stochastic models

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Flexible Intelligent Metasurfaces for Enhancing MIMO Communications

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IEEE Transactions on Communications 2025年

作者： An, Jiancheng Han, Zhu Niyato, Dusit Debbah, Merouane Yuen, Chau Hanzo, Lajos Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of Nanyang Technological University College of Computing and Data Science Singapore Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Flexible intelligent metasurfaces (FIMs) show great potential for improving the wireless network capacity in an energy-efficient manner. An FIM is a soft array consisting of several low-cost radiating elements. Each element can independently emit electromagnetic signals, while flexibly adjusting its position even perpendicularly to the overall surface to 'morph' its 3D shape. More explicitly, compared to a conventional rigid antenna array, an FIM is capable of finding an optimal 3D surface shape that provides improved signal quality. In this paper, we study point-to-point multiple-input multiple-output (MIMO) communications between a pair of FIMs. In order to characterize the capacity limits of FIM-aided MIMO transmissions over frequency-flat fading channels, we formulate a transmit optimization problem for maximizing the MIMO channel capacity by jointly optimizing the 3D surface shapes of the transmitting and receiving FIMs as well as the MIMO transmit covariance matrix, subject to the total transmit power constraint and to the maximum perpendicular morphing range of the FIM. To solve this problem, we develop an efficient block coordinate descent (BCD) algorithm. The BCD algorithm iteratively updates the 3D surface shapes of the FIMs and the transmit covariance matrix, while keeping the other fixed, to find a locally optimal solution. Numerical results verify that FIMs can achieve higher MIMO capacity than that of the conventional rigid arrays. In particular, the MIMO channel capacity can be doubled by the proposed BCD algorithm under some setups. © 1972-2012 IEEE.

关键词： Covariance matrix

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The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

arXiv

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arXiv 2025年

作者： Luo, Ziyuan Rocha, Anderson Shi, Boxin Guo, Qing Li, Haoliang Wan, Renjie Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Computing University of Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China A*STAR Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware Key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness. The source code is available at: https://***/luo-ziyuan/NeRF_Signature. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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