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A DRL-Framework for Full-Duplex WPCN Enabled Mobile Edge Computing

IEEE Transactions on Vehicular Technology 2025年

作者： Bao, Senlei Zhang, Shubin Chi, Kaikai Yu, Keping Mumtaz, Shahid Zhejiang University of Technology School of Computer Science and Technology China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Silesian University of Technology Department of Applied Informatics Poland Nottingham Trent University Department of Engineering United Kingdom

As the Internet of Things (IoT) continues to expand in both daily life and industrial production, the data generated by IoT devices is increasing rapidly. To address the challenges of limited battery life and computational capacity in these devices, two promising solutions have emerged: wireless power transfer (WPT) and mobile edge computing (MEC). By receiving radio frequency (RF) energy from a hybrid access point (HAP), wireless devices (WDs) can either offload data to the HAP or process it locally. In this paper, we explore the full-duplex WPT-MEC scenario and maximize the sum computation rate (SCR) by optimizing the offloading decision and resource allocation of WDs in different access modes and energy harvest models. This problem is formulated as a mixed-integer nonlinear programming (MINLP) problem. To address this, we decompose the problem into two sub-problems: one focuses on optimizing offloading decisions, and the other focuses on energy power and bandwidth allocation under given the offloading decisions. We propose a deep reinforcement learning approach based on policy gradients to make offloading decisions, and we design an optimization algorithm for efficient bandwidth and energy resource allocation. Simulation results show that our proposed algorithm achieves approximately 96% of the maximum possible computation rate while maintaining low computational complexity © 1967-2012 IEEE.

关键词： Reinforcement learning

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Preparation and physico-chemical characterization of AISI 304 stainless steel film deposited by DC magnetron sputtering

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Journal of Magnetism and Magnetic Materials 2025年 628卷

作者： Tomko, Samuel Pechoušek, Jiří Tuček, Jiří Kuzmann, Ernő Heger, Vítězslav Dubiel, Stanislaw M. Machala, Libor Department of Experimental Physics Faculty of Science Palacký University 17. Listopadu 1192/12 Olomouc779 00 Czech Republic Hella Autotechnik Nova s.r.o. Družstevní 16 Mohelnice789 85 Czech Republic Research Centre Faculty of Electrical Engineering and Informatics University of Pardubice Studentská 95 Pardubice530 02 Czech Republic Institute of Chemistry Eötvös Loránd University 1117 Budapest Pázmány Péter Sétány 1 /A Hungary Faculty of Physics and Applied Computer Science AGH University of Krakow Al. A. Mickiewicza 30 Kraków30-059 Poland

The present study reports on the preparation and physico-chemical investigation of a 2 µm thick film produced from AISI 304 stainless steel by DC magnetron sputtering. A phase transformation from the conventionally non-magnetic Face-Centered Cubic (FCC) phase of austenite (γ-Fe) nature to a predominantly Body-Centered Cubic (BCC) ferromagnetic single phase (α-Fe) during the deposition is observed upon the deposition process. The deposited films are characterized by various experimental techniques including Scanning Electron Microscopy (SEM) with Energy-Dispersive X-ray spectroscopy (EDX), X-Ray powder Diffraction (XRD), Grazing-Incidence X-Ray powder Diffraction (GIXRD), and Mössbauer Spectroscopy (MS) in both transmission and back-scattered geometry. For interpretation of the Mössbauer spectra of the film, a physico-chemical model based on distinct micromagnetic environments evolved due to alloying elements is suggested. The results, presented within this study, may stimulate further and detailed research in identifying the causes for the phase transition from both theoretical and experimental perspectives and understanding the underlying mechanisms. © 2025 Elsevier B.V.

关键词： Sputtering

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Programmable large-scale simulation of bosonic transport in optical synthetic frequency lattices

arXiv

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arXiv 2022年

作者： Senanian, Alen Wright, Logan G. Wade, Peter F. Doyle, Hannah K. McMahon, Peter L. Department of Physics Cornell University IthacaNY14853 United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States NTT Physics and Informatics Laboratories NTT Research Inc. SunnyvaleCA94085 United States School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States Kavli Institute Cornell for Nanoscale Science Cornell University IthacaNY14853 United States

Photonic simulators using synthetic frequency dimensions have enabled flexible experimental analogues of condensed-matter systems, realizing phenomena that are impractical to observe in real-space systems. However, to date such photonic simulators have been limited to small systems suffering from finite-size effects. Here, we present an analog simulator capable of simulating large 2D and 3D lattices, as well as lattices with non-planar connectivity, including a tree lattice that serves as a toy model in quantum gravity. Our demonstration is enabled by the broad bandwidth achievable in photonics, allowing our simulator to realize lattices with over 100,000 lattice sites. We explore these large lattices in a wide range of previously inaccessible regimes by using a novel method to excite arbitrary states. Our work establishes the scalability and flexibility of programmable simulators based on synthetic frequency dimensions in the optical domain. We anticipate that future extensions of this platform will leverage advances in high-bandwidth optoelectronics to support simulations of dynamic, non-equilibrium phases at the scale of millions of lattice sites, and Kerr-frequency-comb technology to simulate models with higher-order interactions, ultimately in regimes and at scales inaccessible to both digital computers and realizable materials. © 2022, CC BY.

关键词： Simulators

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MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing 24

MRP 2020: The Second Shared Task on Cross-Framework and Cros...

引用

Shared Task: Cross-Framework Meaning Representation Parsing at 24th Conference on Computational Natural Language Learning, CoNLL 2020

作者： Oepen, Stephan Abend, Omri Abzianidze, Lasha Bos, Johan Hajič, Jan Hershcovich, Daniel Li, Bin O’Gorman, Tim Xue, Nianwen Zeman, Daniel University of Oslo Department of Informatics Norway The Hebrew University of Jerusalem School of Computer Science and Engineering Israel Utrecht University UiL OTS Netherlands University of Groningen Center for Language and Cognition Netherlands Charles University Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Czech Republic University of Copenhagen Department of Computer Science Denmark Nanjing Normal University School of Chinese Language and Literature China University of Massachusetts at Amherst College of Information and Computer Sciences United States Brandeis University Department of Computer Science United States

ISBN: (纸本)9781952148644

The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization;for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://***. © 2020 Association for Computational Linguistics.

关键词： Directed graphs

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Critical success factors for client proposal selection: A vendor perspective in offshore software maintenance outsourcing

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AIP Conference Proceedings 2023年第1期2484卷

作者： Atif Ikram b) Masita Abdul Jalil Amir Bin Ngah Ahmad Salman Khan Tahir Iqbal 1School of Informatics and Applied Mathematics Universiti Malaysia Terengganu Malaysia 2Department of Computer Science & Information Technology The University of Lahore Pakistan 3Department of Software Engineering The University of Lahore Pakistan 4Department of Computer Science Bahria University Lahore Campus Pakistan

Offshore Software Maintenance Outsourcing (OSMO) is increasing by leaps and bounds. In offshore outsourcing, most of the highly-paid countries outsource software development and maintenance projects to such developing countries where skilled human resources are available at economical packages. The OSMO clients get quality-oriented services and OSMO vendors earn valuable foreign exchange if the OSMO process is executed successfully. But the existing literature advocates that all the OSMO business deals do not end successfully. The right combination of vendor and client plays a vital role. For a vendor, it is important to find a more suitable client among multiple options. The scope of the current paper is to find out such critical success factors which can help out OSMO vendor to select a more suitable OSMO client among many options. This practice will provide ease to OSMO vendors in the OSMO client selection and decision-making process to select a suitable client. The method used to conduct this study is the Literature Review (LR). The literature review found different critical success factors related to the research question. Some of these factors include the client's contract, the client's required team size, the domain of the required project, the size of the software maintenance project, etc. OSMO vendor organizations can use this information in the decision-making process to select more suitable OSMO clients for successful offshore business. This study also recommends the implementation of any suitable machine learning technique on the critical success factors (founded in the current study) in the decision-making process.

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Thermal analysis for convective transport of nanoparticles with effective phenomenon of damped shear-thermal flux: A fractional model

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Partial Differential Equations in applied Mathematics 2024年 12卷

作者： Ali, Qasim Khan, Sami Ullah Adnan Garalleh, Hakim AL Waqas, M. Zamri, Nurnadiah Abduvalieva, Dilsora Gupta, Manish Department of Mathematics University of Chakwal Chakwal 48800 Pakistan Department of Mathematics Namal University Mianwali 42250 Pakistan Department of Mathematics Mohi-ud-Din Islamic University Nerian Sharif AJ&K 12080 Pakistan Department of Mathematical Science College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia NUTECH School of Applied Sciences and Humanities National University of Technology Islamabad 44000 Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Besut Campus Terengganu Besut 22200 Malaysia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent 100070 Uzbekistan Division of Research and Development Lovely Professional University Phagwara India

The researchers are interested to enhance the temperature characteristics of convection heat transportation investigation. The studies based on nanofluids are accomplished because these phenomena exhibit an abnormal elevation in thermal transportation. This analysis captures historical impacts on heat and momentum transfer that traditional models ignore by using the Caputo fractional derivative in to model damped shear-thermal flux in nanofluid convection. In this manuscript, the natural convection flow, as well as heat transference of nanofluids, is studied. The classical expressions are fractionalized with the help of constitutive shearing stress expression along with Fourier relation. The Caputo kernel (power-law and non-local) engenders the damping on gradients of momentum and temperature;consequently, transportation approaches are affected by the histories entirely of the previous and current time. The fractional differential equations of velocity as well as temperature are solved analytically by invoking Laplace and the finite Sine-Fourier transform method. In a particular case, the solution to the classical model is also found. Moreover, these solutions are described in respect of the Lorenzo-Hartley G-function together with the Mittag-Leffler function. This investigation exposes that rising nanoparticle volume fraction improves the heat transfer rate, however the skin friction coefficient is affected by time and fractional parameters. The influences of diverse parameters are debated graphically. This study has numerous operations in thermal engineering. © 2024

关键词： Caputo time-fractional derivative Fourier law Free convection Heat transfer Laplace transform Nanofluid

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A Stability Analysis of Neural Networks and Its Application to Tsunami Early Warning

Journal of Geophysical Research: Machine Learning and Comput...

引用

Journal of Geophysical Research: Machine Learning and Computation 2024年第4期1卷

作者： Donsub Rim Sanah Suri Sanghyun Hong Kookjin Lee Randall J. LeVeque Department of Mathematics Washington University in St. Louis St. Louis MO USA School of Electrical Engineering and Computer Science Oregon State University Corvallis OR USA School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA Department of Applied Mathematics University of Washington Seattle WA USA

Neural networks (NNs) enable precise modeling of complicated geophysical phenomena but can be sensitive to small input changes. In this work, we present a new method for analyzing this instability in NNs. We focus our analysis on adversarial examples, test-time inputs with carefully crafted human-imperceptible perturbations that expose the worst-case instability in a model's predictions. Our stability analysis is based on a low-rank expansion of NNs on a fixed input, and we apply our analysis to a NN model for tsunami early warning which takes geodetic measurements as the input and forecasts tsunami waveforms. The result is an improved description of local stability that explains adversarial examples generated by a standard gradient-based algorithm, and allows the generation of other comparable examples. Our analysis can predict whether noise in the geodetic input will produce an unstable output, and identifies a potential approach to filtering the input that enable more robust forecasting. Neural networks (NNs) suffer from input instabilities, making them difficult to deploy in critical applications like natural hazard warning We derive a low-rank expansion for NNs, revealing an input-dependent low-rank matrix that describes local input stability We illustrate that the instabilities are closely related to the singular vectors derived from the low-rank matrix Deep learning models trained on empirical or simulated data can yield accurate predictions in real time. Consequently, their use has expanded to predicting complex physical phenomena. For example, a convolutional neural network can predict tsunami waveforms accurately using only geodetic measurements of the earthquake, thereby potentially informing early warning systems. However, indiscernible perturbations in the measurement data can incur instabilities in neural networks (NNs). Should the early warning systems rely on such models, the ramifications of unstable predictions are of great concern for safety and s

关键词： stability adversarial examples neural networks tsunami early warning low-rank expansions

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A new evolutionary algorithm: Learner performance based behavior algorithm

arXiv

引用

arXiv 2020年

作者： Rahman, Chnoor M. Rashid, Tarik A. Applied Computer Department College of Medicals and Applied Sciences Charmo University SulaimanyKRG Iraq Technical College of Informatics Sulaimany Polytechnic University SulaimanyKRG Iraq Computer Science and Engineering Department University of Kurdistan Hewler ErbilKRG Iraq

A novel evolutionary algorithm called learner performance based behavior algorithm (LPB) is proposed in this article. The basic inspiration of LPB originates from the process of accepting graduated learners from high school in different departments at university. In addition, the changes those learners should do in their studying behaviors to improve their study level at university. The most important stages of optimization;exploitation and exploration are outlined by designing the process of accepting graduated learners from high school to university and the procedure of improving the learner’s studying behavior at university to improve the level of their study. To show the accuracy of the proposed algorithm, it is evaluated against a number of test functions, such as traditional benchmark functions, CEC-C06 2019 test functions, and a real-world case study problem. The results of the proposed algorithm are then compared to the DA, GA, and PSO. The proposed algorithm produced superior results in most of the cases and comparative in some others. It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO. The overall results proved the ability of LPB in improving the initial population and converging towards the global optima. Moreover, the results of the proposed work are proved statistically. Copyright © 2020, The Authors. All rights reserved.

关键词： Genetic algorithms

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Pruning neural networks without any data by iteratively conserving synaptic flow

arXiv

引用

arXiv 2020年

作者： Tanaka, Hidenori Yamins, Daniel L.K. Kunin, Daniel Ganguli, Surya Physics & Informatics Laboratories NTT Reserach Inc Department of Applied Physics Stanford University Department of Psychology Department of Computer Science Stanford University Institute for Computational and Mathematical Engineering Stanford University Department of Applied Physics Stanford University

Pruning the parameters of deep neural networks has generated intense interest due to potential savings in time, memory and energy both during training and at test time. Recent works have identified, through an expensive sequence of training and pruning cycles, the existence of winning lottery tickets or sparse trainable subnetworks at initialization. This raises a foundational question: can we identify highly sparse trainable subnetworks at initialization, without ever training, or indeed without ever looking at the data? We provide an affirmative answer to this question through theory driven algorithm design. We first mathematically formulate and experimentally verify a conservation law that explains why existing gradient-based pruning algorithms at initialization suffer from layer-collapse, the premature pruning of an entire layer rendering a network untrainable. This theory also elucidates how layer-collapse can be entirely avoided, motivating a novel pruning algorithm Iterative Synaptic Flow Pruning (SynFlow). This algorithm can be interpreted as preserving the total flow of synaptic strengths through the network at initialization subject to a sparsity constraint. Notably, this algorithm makes no reference to the training data and consistently outperforms existing state-of-the-art pruning algorithms at initialization over a range of models (VGG and ResNet), datasets (CIFAR-10/100 and Tiny ImageNet), and sparsity constraints (up to 99.9 percent). Thus our data-agnostic pruning algorithm challenges the existing paradigm that data must be used to quantify which synapses are important. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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