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A computational framework for quantifying blood flow dynamics across myogenically-active cerebral arterial networks

Biomechanics and modeling in mechanobiology 2025年 2025 May 9页

作者： Alberto Coccarelli Ioannis Polydoros Alex Drysdale Osama F Harraz Chennakesava Kadapa Zienkiewicz Institute for Modelling Data and AI Faculty of Science and Engineering Swansea University Swansea UK. alberto.coccarelli@swansea.ac.uk. Department of Mechanical Engineering Faculty of Science and Engineering Swansea University Swansea University Bay Campus Fabian Way Crymlyn Burrows Skewen Swansea SA1 8EN UK. alberto.coccarelli@swansea.ac.uk. Zienkiewicz Institute for Modelling Data and AI Faculty of Science and Engineering Swansea University Swansea UK. Department of Pharmacology Larner College of Medicine and Vermont Center for Cardiovascular and Brain Health University of Vermont Burlington USA. School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK.

Cerebral autoregulation plays a key physiological role by limiting blood flow changes in the face of pressure fluctuations. Although the underlying vascular cellular processes are chemo-mechanically driven, estimating the associated haemodynamic forces in vivo remains extremely difficult and uncertain. In this work, we propose a novel computational methodology for evaluating the blood flow dynamics across networks of myogenically-active cerebral arteries, which can modulate their muscular tone to stabilize flow (and perfusion pressure) as well as to limit vascular intramural stress. The introduced framework integrates a continuum mechanics-based, biologically-motivated model of the rat vascular wall with 1D blood flow dynamics. We investigate the time dependency of the vascular wall response to pressure changes at both single vessel and network levels. The dynamical performance of the vessel wall mechanics model was validated against different pressure protocols and conditions (control and absence of extracellular ). The robustness of the integrated fluid-structure interaction framework was assessed using different types of inlet signals and numerical settings in an idealized vascular network formed by a middle cerebral artery and its three generations. The proposed in-silico methodology aims to quantify how acute changes in upstream luminal pressure propagate and influence blood flow across a network of rat cerebral arteries. Weak coupling ensured accurate results with a lower computational cost for the vessel size and boundary conditions considered. To complete the analysis, we evaluated the effect of an upstream pressure surge on vascular network haemodynamics in the presence and absence of myogenic tone. This provided a clear quantitative picture of how pressure, flow and vascular constriction are re-distributed across each vessel generation upon inlet pressure changes. This work paves the way for future combined experimental-computational studies aiming to dec

关键词： 1D blood flow dynamics Autoregulation Biologically-motivated model Cerebral arterial networks Fluid-structure interaction Myogenic response

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Deep Reinforcement Learning for Energy Efficiency Maximization in RSMA-IRS-Assisted ISAC System

arXiv

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arXiv 2025年

作者： Ma, Zhangfeng Zhang, Ruichen Ai, Bo Lian, Zhuxian Zeng, Linzhou Niyato, Dusit School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Information and Communications Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China

This paper proposes a three-dimensional (3D) geometry-based channel model to accurately represent intelligent reflecting surfaces (IRS)-enhanced integrated sensing and communication (ISAC) networks using rate-splitting multiple access (RSMA) in practical urban environments. Based on this model, we formulate an energy efficiency (EE) maximization problem that incorporates transceiver beamforming constraints, IRS phase adjustments, and quality-of-service (QoS) requirements to optimize communication and sensing functions. To solve this problem, we use the proximal policy optimization (PPO) algorithm within a deep reinforcement learning (DRL) framework. Our numerical results confirm the effectiveness of the proposed method in improving EE and satisfying QoS requirements. Additionally, we observe that system EE drops at higher frequencies, especially under double-Rayleigh fading. © 2025, CC BY.

关键词： Deep reinforcement learning

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Towards Depth-continuous Scene Representation with Displacement Field for Robust Light Field Depth Estimation

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IEEE Transactions on Multimedia 2025年

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences Macao999078 China

The light field (LF) captures both the spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods typically model surface depth only while ignoring the continuous nature of depth in 3D scenes. In this paper, we use the displacement field (DF) to describe this continuous property, and propose a novel depth-continuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speeds. Specifically, inspired by the signed distance field in 3D object descriptions, we aim to exploit the intrinsic depth-continuous property of 3D scenes via DF and define a novel depth-continuous scene representation. Then, we introduce a simple, yet general, learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-of-the-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D light field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multiview stereo depth inference, LF superresolution, and LF salient object detection. Our experiments demonstrate an improved performance when continuous scene representation is applied, suggesting that our framework can potentially provide insights for more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

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Privacy-Aware Edge Computation Offloading With Federated Learning in Healthcare Consumer Electronics System

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Yiming Fang, Hao Yan, Zheng Qi, Lianyong Bilal, Muhammad Li, Yang Xu, Xiaolong Zhou, Xiaokang Nanjing University of Information Science and Technology School of Software Nanjing210044 China NanJing University of Information Science and Technology Reading Academy Nanjing210044 China Xidian University School of Cyber Engineering Shaanxi Xi'an710026 China College of computer science and technology Qingdao China Lancaster University School of Computing and Communications United Kingdom Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China Kansai University Faculty of Business and Data Science Japan

The continuous iteration of consumer electronics has significantly promoted the development of medical devices, which has enabled the collection of large amounts of heterogeneous medical data. These data are offloaded from local devices to cloud servers for processing using traditional methods, which results in high transmission latency and the risk of privacy leakage. Additionally, researchers have employed federated learning to protect data privacy, but this approach can lead to a straggler effect due to the limited computational and communication resources of terminal devices. To address these issues, a computation offloading framework is designed to optimize task and resource allocation, addressing multi-optimization problems and mitigating the straggler effect in federated learning. Moreover, a novel computation offloading method within a federated learning framework assisted by edge computing, named DRWB, is proposed. Specifically, we develop a deep reinforcement learning-based approach to transfer lagging training tasks to idle edge servers, enhancing data processing speed, minimizing transmission delays, and protecting user privacy. Extensive experimental results demonstrate that the DRWB method outperforms baseline methods, showcasing superior performance in handling heterogeneous medical data tasks. © 1975-2011 IEEE.

关键词： Electronic health record

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PrivBox: Privacy-Preserving Deep Packet Inspection with Dual Double-masking Obfuscated Rule Generation

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Wu, Pengfei Ning, Jianting Huang, Xinyi Chen, Rongmao Zhang, Kai Liang, Kaitai Wuhan University School of Cyber Science and Engineering Wuhan China Singapore Management University School of Computing and Information Systems Singapore City University of Macau Faculty of Data Science Macau China Fujian Normal University College of Computer and Cyber Security Fuzhou China Jinan University College of Cyber Security Guangzhou China National University of Defense Technology College of Computer Changsha China Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

Many network middleboxes have been deployed to perform deep packet inspection (DPI) over packet payloads. However, such middleboxes cannot accomplish their tasks when the traffic is encrypted. BlindBox (SIGCOMM 2015) provided the first solution for performing DPI over encrypted traffic. To improve its efficiency, a later proposal PrivDPI (CCS 2019) introduced a practical technique to generate encrypted rules. However, a recent proposal P2DPI (ASIACCS 2021) showed that the rule generator in PrivDPI can comprise the user's privacy. In this paper, we present a new attack on P2DPI and show that the privacy of its endpoints can still be compromised by the rule generator. We comprehensively analyze the vulnerability of prior studies and present PrivBox, a new DPI system that achieves the same privacy guarantee as BlindBox while maintaining practical efficiency. This is based on a new technique called dual double-masking obfuscated rule generation. For a ruleset of 3,000, PrivBox achieves connection establishment time on the endpoint side comparable to PrivDPI and supports up to 4,672 token encryptions per second, which is sufficient for a number of real-world applications. Overall, our experiment demonstrates that PrivBox is practical and well-suited for short, frequently established sessions, especially when token repeating is common. ©2004-2012 IEEE.

关键词： Differential privacy

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A comparative study between android phone and TV apps

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Automated Software Engineering 2025年第2期32卷 1-30页

作者： Liu, Yonghui Chen, Xiao Liu, Yue Kong, Pingfan Bissyandé, Tegawendé F. Klein, Jacques Sun, Xiaoyu Li, Li Chen, Chunyang Grundy, John Department of Software Systems and Cybersecurity Monash University Melbourne Australia Faculty of Data Science City University of Macau Macau China School of Information and Physical Sciences University of Newcastle Newcastle Australia SnT University of Luxembourg Esch-sur-Alzette Luxembourg The School of Computing Australian National University Canberra Australia School of Software Beihang University Beijing China Department of Computer Science Technical University of Munich Munich Germany

Smart TVs have surged in popularity, leading developers to create TV versions of mobile apps. Understanding the relationship between TV and mobile apps is key to building consistent, secure, and optimized cross-platform experiences while addressing TV-specific SDK challenges. Despite extensive research on mobile apps, TV apps have been given little attention, leaving the relationship between phone and TV apps unexplored. Our study addresses this gap by compiling an extensive collection of 3445 Android phone/TV app pairs from the Google Play Store, launching the first comparative analysis of its kind. We examined these pairs across multiple dimensions, including non-code elements, code structure, security, and privacy aspects. Our findings reveal that while these app pairs could get identified with the same package names, they deploy different artifacts with varying functionality across platforms. TV apps generally exhibit less complexity in terms of hardware-dependent features and code volume but maintain significant shared resource files and components with their phone versions. Interestingly, some categories of TV apps show similar or even severe security and privacy concerns compared to their mobile counterparts. This research aims to assist developers and researchers in understanding phone-TV app relationships, highlight domain-specific concerns necessitating TV-specific tools, and provide insights for migrating apps from mobile to TV platforms.

关键词： Television applications

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Hyperbolic Variational Graph Auto-Encoder for Next POI Recommendation 25

Hyperbolic Variational Graph Auto-Encoder for Next POI Recom...

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Proceedings of the ACM on Web Conference 2025

作者： Yuwen Liu Lianyong Qi Xingyuan Mao Weiming Liu Fan Wang Xiaolong Xu Xuyun Zhang Wanchun Dou Xiaokang Zhou Amin Beheshti Shandong Key Laboratory of Intelligent Oil and Gas Industrial Software College of Computer Science and Technology China University of Petroleum(East China) Qingdao China College of Computer Science and Technology Zhejiang University Hangzhou China School of Software Nanjing University of Information Science and Technology Nanjing China School of Computing Macquarie University Sydney Australia Department of Computer Science and Technology Nanjing University Nanjing China Faculty of Business and Data Science Kansai University Osaka Japan

ISBN: (纸本)9798400712746

Next Point-of-Interest (POI) recommendation has become a crucial task in Location-Based Social Networks (LBSNs), which provide personalized recommendations by predicting the user's next check-in locations. Commonly used models including Recurrent Neural Networks (RNNs) and Graph Convolutional Networks (GCNs) have been widely explored. However, these models face significant challenges, including the difficulty of capturing the hierarchical and tree-like structure of POIs in Euclidean space and the sparsity problem inherent in POI recommendations. To address these challenges, we propose a Hyperbolic Variational Graph Auto-Encoder (HVGAE) for next POI recommendation. Specifically, we utilize a Hyperbolic Graph Convolutional Network (Hyperbolic GCN) to model hierarchical structures and tree-like relationships by converting node embeddings from euclidean space to hyperbolic space. Then we use Variational Graph Auto-Encoder (VGAE) to convert node embeddings to probabilistic distributions, enhancing the capture of deeper latent features and providing a more robust model structure. Furthermore, we combine the Mamba4Rec recommender and Rotary Position Embedding (RoPE) and propose Rotary Position Mamba (RPMamba) to effectively utilize POI embeddings rich in sequential information, which improves the accuracy of the next POI recommendation. Extensive experiments on three public datasets demonstrate the superior performance of the HVGAE model.

关键词： graph convolutional network

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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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...

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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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On the -differential spectra of two classes of power functions over finite fields

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Applicable Algebra in Engineering, Communication and computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

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