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TechRxiv 2023年

作者： Nguyen, Chi-Hieu Saputra, Yuris Mulya Hoang, Dinh Thai Nguyen, Diep N. Nguyen, Van-Dinh Xiao, Yong Dutkiewicz, Eryk The School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia Department of Electrical Engineering and Informatics Vocational College Universitas Gadjah Mada Yogyakarta55281 Indonesia The College of Engineering and Computer Science VinUniversity Vinhomes Ocean Park Hanoi100000 Viet Nam The School of Electronic Information and Communications The Huazhong University of Science and Technology Wuhan430074 China

Federated Learning (FL) plays a pivotal role in enabling artificial intelligence (AI)-based mobile applications in mobile edge computing (MEC). However, due to the resource heterogeneity among participating mobile users (MUs), delayed updates from slow MUs may deteriorate the learning speed of the MEC-based FL system, commonly referred to as the straggling problem. To tackle the problem, this work proposes a novel privacy-preserving FL framework that utilizes homomorphic encryption (HE) based solutions to enable MUs, particularly resource-constrained MUs, to securely offload part of their training tasks to the cloud server (CS) and mobile edge nodes (MENs). Our framework first develops an efficient method for packing batches of training data into HE ciphertexts to reduce the complexity of HE-encrypted training at the MENs/CS. On that basis, the mobile service provider (MSP) can incentivize straggling MUs to encrypt part of their local datasets that are uploaded to certain MENs or the CS for caching and remote training. However, caching a large amount of encrypted data at the MENs and CS for FL may not only overburden those nodes but also incur a prohibitive cost of remote training, which ultimately reduces the MSP’s overall profit. To optimize the portion of MUs’ data to be encrypted, cached, and trained at the MENs/CS, we formulate an MSP’s profit maximization problem, considering all MUs’ and MENs’ resource capabilities and data handling costs (including encryption, caching, and training) as well as the MSP’s incentive budget. We then show that the problem is convex and can be efficiently solved using an interior point method. Extensive simulations on a real-world human activity recognition dataset show that our proposed framework can achieve much higher model accuracy (improving up to 24.29%) and faster convergence rate (by 2.86 times) than those of the conventional FedAvg approach when the straggling probability varies between 20% and 80%. Moreover, the proposed

关键词： Mobile edge computing

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Electrochemical hydrogen charging treatment for hydrogenated 316 L stainless steel induced hydrogen therapy for orthopedic implantation

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Surface and Coatings Technology 2024年 479卷

作者： Lin, Yu-Chien Dhawan, Udesh Liu, Yen-Chen Lee, Yueh-Lien Liu, Xinke Yen, Hung-Wei Hu, Chih-Chien Chung, Ren-Jei Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan School of Materials Science and Engineering Nanyang Technological University Singapore Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow Glasgow United Kingdom Department of Engineering Science and Ocean Engineering National Taiwan University Taipei Taiwan College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhe China Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Materials Science and Engineering National Taiwan University Taipei Taiwan Bone and Joint Research Center Chang Gung Memorial Hospital Linko Taiwan Department of Orthopaedic Surgery Chang Gung Memorial Hospital Linko Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Taipei Taiwan

Post-implant inflammation is a leading factor in the failure of orthopedic implant surgery. Hydrogen therapy is viewed as a medium capable of reducing reactive oxygen species (ROS) to alleviate inflammatory responses. However, existing hydrogen therapy methods have limitations, often involving undesired inhalation, injection, or non-targeted exposure of hydrogen to the body. To address this, the concept of delivering hydrogen precisely using the implant itself has been conducted in this study. Traditional hydrogen treatments for metallic implant materials, such as 316 L stainless steel (316 L SS), can induce hydrogen embrittlement (HE), significantly restricting the potential applications of hydrogenated implants in the biomedical field. Therefore, in this study, the electrochemical cathodic hydrogen charging method was employed to diffuse a hydrogenated layer of approximately 216 ± 31 nm on the surface of 316 L SS. This renders the material minimized by HE and alters its surface properties. Through in vitro and in vivo experiments, an approximately 80 % free radical scavenging rate was achieved. Additionally, significant anti-inflammatory effects were observed in the histological analysis of Femur implantation. This demonstrates that the surface hydrogenation treatment can impart anti-inflammatory properties to metallic implant materials without compromising their inherent material properties, thereby enhancing the success rate of surgical procedures. © 2024

关键词： Hydrogen

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Network Slicing with Flexible VNF Order: A Branch-and-Bound Approach

arXiv

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arXiv 2024年

作者： Luu, Quang-Trung Nguyen, Minh-Thanh Do, Tuan-Anh Kieffer, Michel Nguyen, Van-Dinh Nguyen, Tai-Hung Nguyen, Huu-Thanh School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi100000 Viet Nam Université Paris-Saclay CNRS CentraleSupélec – L2S Gif-sur-YvetteF-91192 France College of Engineering and Computer Science Center for Environmental Intelligence VinUniversity Vinhomes Ocean Park Hanoi100000 Viet Nam

Network slicing is a critical feature in 5G and beyond communication systems, enabling the creation of multiple virtual networks (i.e., slices) on a shared physical network infrastructure. This involves efficiently mapping each slice component, including virtual network functions (VNFs) and their interconnections (virtual links), onto the physical network. This paper considers slice embedding problem in which the order of VNFs can be adjusted, providing increased flexibility for service deployment on the infrastructure. This also complicates embedding, as the best order has to be selected. We propose an innovative optimization framework to tackle the challenges of jointly optimizing slice admission control and embedding with flexible VNF ordering. Additionally, we introduce a near-optimal branch-and-bound (BnB) algorithm, combined with the A* search algorithm, to generate embedding solutions efficiently. Extensive simulations on both small and large-scale scenarios demonstrate that flexible VNF ordering significantly increases the number of deployable slices within the network infrastructure, thereby improving resource utilization and meeting diverse demands across varied network topologies. © 2024, CC BY.

关键词： 5G mobile communication systems

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Experimental implementation of an efficient test of quantumness

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Physical Review A 2024年第1期109卷 012610-012610页

作者： Laura Lewis Daiwei Zhu Alexandru Gheorghiu Crystal Noel Or Katz Bahaa Harraz Qingfeng Wang Andrew Risinger Lei Feng Debopriyo Biswas Laird Egan Thomas Vidick Marko Cetina Christopher Monroe Institute for Quantum Information and Matter and Department of Computing and Mathematical Sciences California Institute of Technology Pasadena California 91125 USA Division of Physics Mathematics and Astronomy California Institute of Technology Pasadena California 91125 USA Joint Quantum Institute Departments of Physics and Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA IonQ Inc. College Park Maryland 20740 USA Departments of Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Institute for Theoretical Studies ETH Zürich Zürich CH 8001 Switzerland Duke Quantum Center and Department of Physics Duke University Durham North Carolina 27708 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Chemical Physics Program and Institute for Physical Science and Technology University of Maryland College Park Maryland 20742 USA

A test of quantumness is a protocol where a classical user issues challenges to a quantum device to determine if it exhibits nonclassical behavior, under certain cryptographic assumptions. Recent attempts to implement such tests on current quantum computers rely on either interactive challenges with efficient verification or noninteractive challenges with inefficient (exponential time) verification. In this paper, we execute an efficient noninteractive test of quantumness on an ion-trap quantum computer. Our results significantly exceed the bound for a classical device's success.

关键词： Quantum cryptography Quantum information with trapped ions Quantum protocols Quantum verification

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Estimating uncertainty intervals from collaborating networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 11645-11691页

作者： Tianhui Zhou Yitong Li Yuan Wu David Carlson Department of Biostatistics and Bioinformatics Duke University Durham NC Department of Electrical and Computer Engineering Duke University Durham NC Departments of Civil and Environmental Engineering Biostatistics and Bioinformatics Electrical and Computer Engineering and Computer Science Duke University Durham NC

Effective decision making requires understanding the uncertainty inherent in a prediction. In regression, this uncertainty can be estimated by a variety of methods; however, many of these methods are laborious to tune, generate overconfident uncertainty intervals, or lack sharpness (give imprecise intervals). We address these challenges by proposing a novel method to capture predictive distributions in regression by defining two neural networks with two distinct loss functions. Specifically, one network approximates the cumulative distribution function, and the second network approximates its inverse. We refer to this method as Collaborating Networks (CN). Theoretical analysis demonstrates that a fixed point of the optimization is at the idealized solution, and that the method is asymptotically consistent to the ground truth distribution. Empirically, learning is straightforward and robust. We benchmark CN against several common approaches on two synthetic and six real-world datasets, including forecasting A1c values in diabetic patients from electronic health records, where uncertainty is critical. In the synthetic data, the proposed approach essentially matches ground truth. In the real-world datasets, CN improves results on many performance metrics, including log-likelihood estimates, mean absolute errors, coverage estimates, and prediction interval widths.

关键词： uncertainty estimation conditional distributions calibration consistency neural networks

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Adaptive pruning-based Newton’s method for distributed learning

arXiv

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arXiv 2023年

作者： Chen, Shuzhen Yuan, Yuan Tao, Youming Wang, Tianzhu Cai, Zhipeng Yu, Dongxiao College of Computer Science and Technology Ocean University of China Qingdao266100 China Shandong University Jinan250100 China School of Computer Science and Technology Shandong University Qingdao266237 China Department of Computer Science Georgia State University AtlantaGA30303 United States School of Electrical Engineering and Computer Science TU Berlin Berlin10587 Germany

Newton’s method leverages curvature information to boost performance, and thus outperforms first-order methods for distributed learning problems. However, Newton’s method is not practical in large-scale and heterogeneous learning environments, due to obstacles such as high computation and communication costs of the Hessian matrix, sub-model diversity, staleness of training, and data heterogeneity. To overcome these obstacles, this paper presents a novel and efficient algorithm named Distributed Adaptive Newton Learning (DANL), which solves the drawbacks of Newton’s method by using a simple Hessian initialization and adaptive allocation of training regions. The algorithm exhibits remarkable convergence properties, which are rigorously examined under standard assumptions in stochastic optimization. The theoretical analysis proves that DANL attains a linear convergence rate while efficiently adapting to available resources and keeping high efficiency. Furthermore, DANL shows notable independence from the condition number of the problem and removes the necessity for complex parameter tuning. Experiments demonstrate that DANL achieves linear convergence with efficient communication and strong performance across different datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Federated learning

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Improving conditional coverage via orthogonal quantile regression 21

Improving conditional coverage via orthogonal quantile regre...

引用

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Shai Feldman Stephen Bates Yaniv Romano Department of Computer Science Technion Israel Departments of Statistics and of EECS UC Berkeley Departments of Electrical and Computer Engineering and of Computer Science Technion Israel

ISBN: (纸本)9781713845393

We develop a method to generate prediction intervals that have a user-specified coverage level across all regions of feature-space, a property called conditional coverage. A typical approach to this task is to estimate the conditional quantiles with quantile regression—it is well-known that this leads to correct coverage in the large-sample limit, although it may not be accurate in finite samples. We find in experiments that traditional quantile regression can have poor conditional coverage. To remedy this, we modify the loss function to promote independence between the size of the intervals and the indicator of a miscoverage event. For the true conditional quantiles, these two quantities are independent (orthogonal), so the modified loss function continues to be valid. Moreover, we empirically show that the modified loss function leads to improved conditional coverage, as evaluated by several metrics. We also introduce two new metrics that check conditional coverage by looking at the strength of the dependence between the interval size and the indicator of miscoverage.

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Convolution and Attention Mixer for Synthetic Aperture Radar Image Change Detection

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IEEE Geoscience and Remote Sensing Letters 2023年 20卷 1页

作者： Zhang, Haopeng Lin, Zijing Gao, Feng Dong, Junyu Du, Qian Li, Heng-Chao Ocean University of China School of Computer Science and Technology Qingdao266100 China Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States Southwest Jiaotong University Sichuan Provincial Key Laboratory of Information Coding and Transmission Chengdu610031 China

Synthetic aperture radar (SAR) image change detection is a critical task and has received increasing attention in the remote sensing community. However, existing SAR change detection methods are mainly based on convolutional neural networks (CNNs), with limited consideration of global attention mechanism. In this letter, we explore Transformer-like architecture for SAR change detection to incorporate global attention. To this end, we propose a convolution and attention mixer (CAMixer). First, to compensate for the inductive bias for Transformer, we combine self-attention with shift convolution in a parallel way. The parallel design effectively captures the global semantic information via self-attention and performs local feature extraction through shift convolution simultaneously. Second, we adopt a gating mechanism in the feed-forward network to enhance the nonlinear feature transformation. The gating mechanism is formulated as the element-wise multiplication of two parallel linear layers. Important features can be highlighted, leading to high-quality representations against speckle noise. Extensive experiments conducted on three SAR datasets verify the superior performance of the proposed CAMixer. The source codes will be publicly available at https://***/summitgao/CAMixer. © 2004-2012 IEEE.

关键词： Feature extraction

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Improving Neural Radiance Fields Using Near-Surface Sampling With Point Cloud Generation

arXiv

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arXiv 2023年

作者： Yoo, Hye Bin Han, Hyun Min Hwang, Sung Soo Chun, Il Yong Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Information and Communication Engineering Handong Global University Pohang37554 Korea Republic of Departments of Artificial Intelligence Semiconductor Convergence Engineering and Display Convergence Engineering Center for Neuroscience Imaging Research Institute for Basic Science Sungkyunkwan University Suwon16419 Korea Republic of

Neural radiance field (NeRF) is an emerging view synthesis method that samples points in a three-dimensional (3D) space and estimates their existence and color probabilities. The disadvantage of NeRF is that it requires a long training time since it samples many 3D points. In addition, if one samples points from occluded regions or in the space where an object is unlikely to exist, the rendering quality of NeRF can be degraded. These issues can be solved by estimating the geometry of 3D scene. This paper proposes a near-surface sampling framework to improve the rendering quality of NeRF. To this end, the proposed method estimates the surface of a 3D object using depth images of the training set and performs sampling only near the estimated surface. To obtain depth information on a novel view, the paper proposes a 3D point cloud generation method and a simple refining method for projected depth from a point cloud. Experimental results show that the proposed near-surface sampling NeRF framework can significantly improve the rendering quality, compared to the original NeRF and three different state-of-the-art NeRF methods. In addition, one can significantly accelerate the training time of a NeRF model with the proposed near-surface sampling framework. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Deep learning-based multi-output quantile forecasting of PV generation

arXiv

引用

arXiv 2021年

作者： Dumas, Jonathan Cointe, Colin Fettweis, Xavier Cornélusse, Bertrand Departments of computer science electrical engineering geography University of Liège Belgium

—This paper develops probabilistic PV forecasters by taking advantage of recent breakthroughs in deep learning. A tailored forecasting tool, named encoder-decoder, is implemented to compute intraday multi-output PV quantiles forecasts to efficiently capture the time correlation. The models are trained using quantile regression, a non-parametric approach that assumes no prior knowledge of the probabilistic forecasting distribution. The case study is composed of PV production monitored on-site at the University of Liège (ULiège), Belgium. The weather forecasts from the regional climate model provided by the Laboratory of Climatology are used as inputs of the deep learning models. The forecast quality is quantitatively assessed by the continuous ranked probability and interval scores. The results indicate this architecture improves the forecast quality and is computationally efficient to be incorporated in an intraday decision-making tool for robust optimization. © 2021, CC BY-NC-SA.

关键词： Climate models

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