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arXiv 2025年

作者： Gao, Chenyu Xu, Kaiwen Kim, Michael E. Zuo, Lianrui Li, Zhiyuan Archer, Derek B. Hohman, Timothy J. Moore, Ann Zenobia Ferrucci, Luigi Beason-Held, Lori L. Resnick, Susan M. Davatzikos, Christos Prince, Jerry L. Landman, Bennett A. Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Vanderbilt Memory and Alzheimer’s Center Vanderbilt University Medical Center NashvilleTN United States Department of Neurology Vanderbilt University Medical Center NashvilleTN United States Translational Gerontology Branch National Institute on Aging National Institutes of Health BaltimoreMD United States Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health BaltimoreMD United States AI2D Center for AI and Data Science University of Pennsylvania Philadelphia United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States

Defacing is often applied to head magnetic resonance image (MRI) datasets prior to public release to address privacy concerns. The alteration of facial and nearby voxels has provoked discussions about the true capability of these techniques to ensure privacy as well as their impact on downstream tasks. With advancements in deep generative models, the extent to which defacing can protect privacy is uncertain. Additionally, while the altered voxels are known to contain valuable anatomical information, their potential to support research beyond the anatomical regions directly affected by defacing remains uncertain. To evaluate these considerations, we develop a refacing pipeline that recovers faces in defaced head MRIs using cascaded diffusion probabilistic models (DPMs). The DPMs are trained on images from 180 subjects and tested on images from 484 unseen subjects, 469 of whom are from a different dataset. To assess whether the altered voxels in defacing contain universally useful information, we also predict computed tomography (CT)-derived skeletal muscle radiodensity from facial voxels in both defaced and original MRIs. The results show that DPMs can generate high-fidelity faces that resemble the original faces from defaced images, with surface distances to the original faces significantly smaller than those of a population average face (p -4). For shin muscle, the correlation is statistically significant (p 0.05) when any defacing method is applied, suggesting that defacing might not only fail to protect privacy but also eliminate valuable information. We advocate two solutions for data sharing that comply with privacy: 1) share skull-stripped images along with measurements of facial and cranial features extracted before skull-stripping for public access, while acknowledging that this approach inherently compromises many research potentials;or 2) share the unaltered images with privacy enforced through policy restrictions. © 2025, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Performance evaluation of file operations using Mutagen

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High-Confidence Computing 2025年

作者： Mahid Atif Hosain Sriram Chellappan Jannatun Noor Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Computer Science and Engineering University of South Florida USA Computing for Sustainability and Social Good (C2SG) Research Group Department of Computer Science and Engineering Brac University Dhaka 1212 Bangladesh

Docker is a vital tool in modern development, enabling the creation, deployment, and execution of applications using containers, thereby ensuring consistency across various environments. However, developers often face challenges, particularly with filesystem complexities and performance bottlenecks when working directly within Docker containers. This is where Mutagen comes into play, significantly enhancing the Docker experience by offering efficient network file synchronization, reducing latency in file operations, and improving overall data transfer rates in containerized environments. By exploring Docker’s architecture, examining Mutagen’s role, and evaluating their combined performance impacts, particularly in terms of file operation speeds and development workflow efficiencies, this research provides a deep understanding of these technologies and their potential to streamline development processes in networked and distributed environments.

关键词： Mutagen Docker OverlayFS Union mount filesystem Synchronization Fio Rsync

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The Age of Generative AI and AI-Generated Everything

arXiv

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arXiv 2023年

作者： Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Ping Cui, Shuguang Shen, Xuemin Mao, Shiwen Han, Zhu Jamalipour, Abbas Vincent Poor, H. Kim, Dong In School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative AI (GAI) has emerged as a significant advancement in artificial intelligence, renowned for its language and image generation capabilities. This paper presents "AIGenerated Everything" (AIGX), a concept that extends GAI beyond mere content creation to real-time adaptation and control across diverse technological domains. In networking, AIGX collaborates closely with physical, data link, network, and application layers to enhance real-time network management that responds to various system and service settings as well as application and user requirements. Networks, in return, serve as crucial components in further AIGX capability optimization through the AIGX lifecycle, i.e., data collection, distributed pre-training, and rapid decision-making, thereby establishing a mutually enhancing interplay. Moreover, we offer an in-depth case study focused on power allocation to illustrate the interdependence between AIGX and networking systems. Through this exploration, the article analyzes the significant role of GAI for networking, clarifies the ways networks augment AIGX functionalities, and underscores the virtuous interactive cycle they form. This article paves the way for subsequent future research aimed at fully unlocking the potential of GAI and networks. © 2023, CC BY.

关键词： Decision making

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Multiple Access Techniques for Intelligent and Multi-Functional 6G: Tutorial, Survey, and Outlook

arXiv

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arXiv 2024年

作者： Clerckx, Bruno Mao, Yijie Yang, Zhaohui Chen, Mingzhe Alkhateeb, Ahmed Liu, Liang Qiu, Min Yuan, Jinhong Wong, Vincent W.S. Montojo, Juan The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The School of Information Science and Technology ShanghaiTech University Shanghai201210 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Corporate R&D Qualcomm Inc. San DiegoCA United States

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, message, etc) to serve multiple users/devices/machines/services, ideally in the most efficient way. Given the increasing needs of multi-functional wireless networks for integrated communications, sensing, localization, computing, coupled with the surge of machine learning / artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this paper, we provide a tutorial, survey and outlook of past, emerging and future MA techniques and pay a particular attention to how wireless network intelligence and multi-functionality will lead to a re-thinking of those techniques. The paper starts with an overview of orthogonal, physical layer multicasting, space domain, power domain, rate-splitting, code domain MAs, and MAs in other domains, and highlight the importance of researching universal multiple access to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, receiver designs, user behavior predictions for different MA schemes, and MA for AI such as federated learning/edge intelligence and over the air computation. We then discuss MA for network multi-functionality and the interplay between MA and integrated sensing, localization, and communications, covering MA for joint sensing and communications, multimodal sensing-aided communications, multimodal sensing and digital twin-assisted communications, and communication-aided sensing/localization systems. We finish with studying MA for emerging intelligent applications such as semantic communications, metaverse, virtual realit

关键词： Wireless networks

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Bankrupting DoS Attackers

arXiv

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arXiv 2022年

作者： Chakraborty, Trisha Islam, Abir King, Valerie Rayborn, Daniel Saia, Jared Young, Maxwell Amazon Web Services MinneapolisMN55401 United States Department of Computer Science University of New Mexico AlbuquerqueNM87131 United States Department of Computer Science University of Victoria VictoriaBCV8P 5C2 Canada Department of Computer Science and Engineering Mississippi State University Mississippi StateMS39762 United States

Can we make a denial-of-service attacker pay more than the server and honest clients? Consider a model where a server sees a stream of jobs sent by either honest clients or an adversary. The server sets a price for servicing each job with the aid of an estimator, which provides approximate statistical information about the distribution of previously occurring good jobs. We describe and analyze pricing algorithms for the server under different models of synchrony, with total cost parameterized by the accuracy of the estimator. Given a reasonably accurate estimator, the algorithm’s cost provably grows more slowly than the attacker’s cost, as the attacker’s cost grows large. Additionally, we prove a lower bound, showing that our pricing algorithm yields asymptotically tight results when the estimator is accurate within constant factors. Copyright © 2022, The Authors. All rights reserved.

关键词： Computational cost

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Robustness of Dynamic Quantum Control: Differential Sensitivity Bounds

arXiv

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arXiv 2023年

作者： O'Neil, S.P. Weidner, C.A. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90007 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Faculty of Science and Engineering Physics Swansea University SwanseaSA2 8PP United Kingdom

Dynamic control via optimized, piecewise-constant pulses is a common paradigm for open-loop control to implement quantum gates. While numerous methods exist for the synthesis of such controls, there are many open questions regarding the robustness of the resulting control schemes in the presence of model uncertainty;unlike in classical control, there are generally no analytical guarantees on the control performance with respect to inexact modeling of the system. In this paper a new robustness measure based on the differential sensitivity of the gate fidelity error to parametric (structured) uncertainties is introduced, and bounds on the differential sensitivity to parametric uncertainties are used to establish performance guarantees for optimal controllers for a variety of quantum gate types, system sizes, and control implementations. Specifically, it is shown how a maximum allowable perturbation over a set of Hamiltonian uncertainties that guarantees a given fidelity error, can be reliably computed. This measure of robustness is inversely proportional to the upper bound on the differential sensitivity of the fidelity error evaluated under nominal operating conditions. Finally, the results show that the nominal fidelity error and differential sensitivity upper bound are positively correlated across a wide range of problems and control implementations, suggesting that in the high-fidelity control regime, rather than there being a trade-off between fidelity and robustness, higher nominal gate fidelities are positively correlated with increased robustness of the controls in the presence of parametric uncertainties. Copyright © 2023, The Authors. All rights reserved.

关键词： Robustness (control systems)

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A Master-Follower Teleoperation System for Robotic Catheterization: Design, Characterization, and Tracking Control

arXiv

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arXiv 2024年

作者： Nazari, Ali A. Catania, Jeremy Sadeghian, Soroush Jalali, Amir Masnavi, Houman Janabi-Sharifi, Farrokh Zareinia, Kourosh Department of Mechanical Industrial and Mechatronics Engineering Toronto Metropolitan University 350 Victoria St. TorontoONM5B 2K3 Canada Department of Computer Science Toronto Metropolitan University 350 Victoria St. TorontoONM5B 2K3 Canada

Background: Over the past two decades, telerobotic systems with robot-mediated, minimally invasive techniques, have mitigated radiation exposure for medical staff and extended medical services to remote areas. To enhance these services, master-follower telerobotic systems should offer transparency, enabling surgeons and clinicians to feel force interactions similar to those the follower device experiences with patients’ bodies. Methods: We present a three-degree-of-freedom master-follower teleoperated system for robotic catheterization. The follower device uses a grip-insert-release mechanism to prevent catheter buckling and torsion, mimicking real-world manual intervention. Performance is evaluated through open-loop path tracking on circular, infinity-like, and spiral paths. Results: Path tracking errors, mean Euclidean error (MEE) and mean absolute error (MAE), range from 0.64 to 1.53 cm (MEE) and 0.81 to 1.92 cm (MAE) for different paths. Conclusion: While the system meets precision and accuracy targets with an open-loop controller, closed-loop control is needed to address catheter hysteresis, dead zones, and nonlinearities. Copyright © 2024, The Authors. All rights reserved.

关键词： Closed loop control systems

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An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering

arXiv

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arXiv 2023年

作者： Hu, Nan Wu, Yike Qi, Guilin Min, Dehai Chen, Jiaoyan Pan, Jeff Z. Ali, Zafar School of Computer Science and Engineering Southeast University 2 Dongda Rd Jiangsu Nanjing211189 China Department of Computer Science The University of Manchester Oxford Rd ManchesterM13 9PL United Kingdom School of Informatics The University of Edinburgh 10 Crichton St EdinburghEH8 9AB United Kingdom

Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP). It is now the consensus of the NLP community to adopt PLMs as the backbone for downstream tasks. In recent works on knowledge graph question answering (KGQA), BERT or its variants have become necessary in their KGQA models. However, there is still a lack of comprehensive research and comparison of the performance of different PLMs in KGQA. To this end, we summarize two basic KGQA frameworks based on PLMs without additional neural network modules to compare the performance of nine PLMs in terms of accuracy and efficiency. In addition, we present three benchmarks for larger-scale KGs based on the popular SimpleQuestions benchmark to investigate the scalability of PLMs. We carefully analyze the results of all PLMs-based KGQA basic frameworks on these benchmarks and two other popular datasets, WebQuestionSP and FreebaseQA, and find that knowledge distillation techniques and knowledge enhancement methods in PLMs are promising for KGQA. Furthermore, we test ChatGPT, which has drawn a great deal of attention in the NLP community, demonstrating its impressive capabilities and limitations in zero-shot KGQA. We have released the code and benchmarks to promote the use of PLMs on KGQA. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Performance Evaluation of FC-RDVM and LDIWM Router Placement Methods for WMNs by WMN-PSODGA Hybrid Simulation System Considering Load Balancing and Chi-Square Distribution of Mesh Clients 1

引用

37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Barolli, Admir Sakamoto, Shinji Kulla, Elis Barolli, Leonard Takizawa, Makoto Department of Information Technology Aleksander Moisiu University of Durres L.1 Rruga e Currilave Durres Albania Department of Information and Computer Science Kanazawa Institute of Technology 7-1 Ohgigaoka Ishikawa Nonoichi921-8501 Japan Department of System Management Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Fukuoka Higashi-Ku811-0295 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Fukuoka Higashi-Ku811-0295 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University 3-7-2 Kajino-machi Koganei-shi Tokyo184-8584 Japan

ISBN: (数字)9783031284519

ISBN: (纸本)9783031284502

In this paper, we present WMN-PSODGA hybrid simulation system for optimization of mesh routers in Wireless Mesh Networks (WMNs). We consider Chi-square distribution of mesh clients and compare the results of a Fast Convergence Rational Decrement of Vmax Method (FC-RDVM) with Linearly Decreacing Inertia Weight Method (LDIWM). The simulation results show that FC-RDVM has better load balancing than LDIWM. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： MESH networking

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Inversion-Free Sparse Bayesian Learning for Temporally Correlated Signal Recovery

TechRxiv

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TechRxiv 2023年

作者： Song, Yuhui Gong, Zijun Chen, Yuanzhu Li, Cheng Department of Computer Science Memorial University St. John’sNLA1B 3X5 Canada IOT Thrust HKUST Guangzhou China Department of ECE HKUST Hong Kong School of Computing Queen’s University KingstonONK7L 2N8 Canada Department of Electrical and Computer Engineering Department of Computer Science Memorial University St. John’sNLA1B 3X5 Canada

Compressed sensing recovers the sparse signal from far fewer samples than required by the well-known Nyquist–Shannon sampling theorem to speed up the measurement procedure. The sparse signal recovery performance can be significantly improved by exploiting the temporal correlation in the multiple measurement vector model within the framework of sparse Bayesian learning. However, it is inevitable to involve the matrix inversion for existing methods, making the application of these schemes to large datasets impractical. To overcome this bottleneck, in this letter, we propose an inversion-free sparse Bayesian learning algorithm for temporally correlated sparse signal recovery, which is free of any matrix inversion operation and only requires simple addition and multiplication operations. Specifically, utilizing a property for the convex quadratic function, we obtain a lower bound for the likelihood distribution that enables the computationally efficient model inference based on the expectation-maximization algorithm. Simulation results show its superior performance over other state-of-the-art methods in terms of the recovery performance and the running time. © 2023, CC BY.

关键词： Large dataset

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