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IEEE International Conference on Communications (ICC)

作者： Tzu–Hsuan Peng Cheng-Wei Huang Ai-Chun Pang Te-Chuan Chiu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan R.O.C Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan R.O.C High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan R.O.C Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan R.O.C Department of Computer Science National Tsing Hua University Hsinchu Taiwan R.O.C

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Federated Learning (FL) is a promising framework for enabling edge intelligence while preserving privacy and enhancing communication efficiency. In real-world scenarios, limited and heterogeneous hardware resources among users threaten the overall training efficiency in FL. To address this issue, several model heterogeneity frameworks have emerged in recent years. However, existing studies primarily focus on mitigating idle times on powerful devices, overlooking the fact that strong devices may not always have a positive impact and can potentially become a burden instead. Furthermore, as the resource gap among clients widens or the number of weak devices increases, the model may be hindered by the influence of strong devices. Directly applying existing model heterogeneity frameworks leads to model performance degradation due to the neglect of contributions from weak devices. To make a performance breakthrough, we proposed a novel FL framework called Progressive Width Mixing (PWM), which extracts diverse features and balances the contribution of strong and weak devices and mitigates straggler effects under scenarios with various device distributions. To our best knowledge, we are the first model heterogeneity work that considered straggler effects from the weak devices' point of view. The experiments show that PWM maintains stability over different datasets and outperforms all baselines across various device distributions and non-IID scenarios. PWM makes a leap forward and surpasses all baselines by 7%, and mitigates the negative impact from the strong devices.

关键词： Performance evaluation Training Privacy Philosophical considerations Federated learning Pulse width modulation System recovery

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Sensing With Communication Signals: From Information Theory to Signal Processing

arXiv

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arXiv 2025年

作者： Liu, Fan Liu, Ya-Feng Cui, Yuanhao Masouros, Christos Xu, Jie Han, Tony Xiao Buzzi, Stefano Eldar, Yonina C. Jin, Shi National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematicsand Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom Guangdong518172 China Huawei Technologies Co. Ltd China Department of Electrical and Information Engineering University of Cassino and Southern Lazio CassinoI-03043 Italy Department of Electronics Information and Bioengineering Politecnico di Milano MilanoI-20133 Italy ParmaI-43124 Italy Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel

The Integrated Sensing and Communications (ISAC) paradigm is anticipated to be a cornerstone of the upcoming 6G networks. In order to optimize the use of wireless resources, 6G ISAC systems need to harness the communication data payload signals, which are inherently random, for both sensing and communication (S&C) purposes. This tutorial paper provides a comprehensive technical overview of the fundamental theory and signal processing methodologies for ISAC transmission with random communication signals. We begin by introducing the deterministic-random tradeoff (DRT) between S&C from an information-theoretic perspective, emphasizing the need for specialized signal processing techniques tailored to random ISAC signals. Building on this foundation, we review the core signal models and processing pipelines for communication-centric ISAC systems, and analyze the average squared auto-correlation function (ACF) of random ISAC signals, which serves as a fundamental performance metric for multi-target ranging tasks. Drawing insights from these theoretical results, we outline the design principles for the three key components of communication-centric ISAC systems: modulation schemes, constellation design, and pulse shaping filters. The goal is to either enhance sensing performance without compromising communication efficiency or to establish a scalable tradeoff between the two. We then extend our analysis from a single-antenna ISAC system to its multi-antenna counterpart, discussing recent advancements in multiinput multi-output (MIMO) precoding techniques specifically designed for random ISAC signals. We conclude by highlighting several open challenges and future research directions in the field of sensing with communication signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal modulation

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Parallel algorithms for maximizing monotone one-sided σ-smooth functions

arXiv

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arXiv 2022年

作者： Zhang, Hongxiang Cheng, Yukun Wu, Chenchen Xu, Dachuan Du, Dingzhu Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing100124 China School of Business Suzhou University of Science and Technology Suzhou215009 China College of Science Tianjin University of Technology Tianjin 300384 China Department of Computer Science University of Texa Dallas Dallas75083 United States

In this paper, we study the problem of maximizing a monotone normalized one-sided σ-smooth (OSS for short) function F (x), subject to a convex polytope (no need to downward-closed [1]). A function F (x) is one-sided σ-smooth if (Equation presented), for all x, u ≥ 0, x ≠ 0. This problem was first introduced by Mehrdad et al. [1] to characterize the multilinear extension of some set functions. Different with the serial algorithm with name Jump-Start Continuous Greedy Algorithm by Mehrdad et al. [1], we propose Jump-Start Parallel Greedy (JSPG for short) algorithm, the first parallel algorithm, for this problem. The approximation ratio of JSPG algorithm is proved to be (Equation presented) for any any number α ∈ (0, 1] and ǫ > 0, which improves the approximation ratio of JSCG algorithm in [1]. We also prove that our JSPG algorithm runs in (O(log n/ǫ2)) adaptive rounds and consumes O(n log n/ǫ2) queries, where the number of adaptive rounds and function evaluation queries approximately matches the known results for parallel submodular maximization. In addition, we study the stochastic version of maximizing monotone normalized OSS function, in which the objective function F (x) is defined as F (x) = Ey∼T f(x, y). Here f is a stochastic function with respect to the random variable Y, and y is the realization of Y drawn from a probability distribution T. For this stochastic version, we design Stochastic Parallel-Greedy (SPG) algorithm, which achieves a result of (Equation presented), with the same time complexity of JSPG algorithm. Here (Equation presented) is related to the preset parameters σ, L, D and time t. Copyright © 2022, The Authors. All rights reserved.

关键词： Parallel algorithms

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Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction

arXiv

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arXiv 2024年

作者： Papoutsellis, Evangelos da Costa-Luis, Casper Deidda, Daniel Delplancke, Claire Duff, Margaret Fardell, Gemma Gillman, Ashley Jorgensen, Jakob S. Kereta, Zeljko Ovtchinnikov, Evgueni Pasca, Edoardo Schramm, Georg Thielemans, Kris Finden Ltd Rutherford Appleton Laboratory Harwell Campus United Kingdom Scientific Computing Department Science & Technology Facilities Council Harwell Campus United Kingdom National Physical Laboratory Electricité de France Research and Development France Australian e-Health Res. Ctr. CSIRO BrisbaneQLD Australia Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Computer Science University College London United Kingdom Department of Imaging and Pathology Division of Nuclear Medicine KU Leuven Leuven Belgium Institute of Nuclear Medicine University College London United Kingdom

We introduce a stochastic framework into the open- source Core Imaging Library (CIL) which enables easy development of stochastic algorithms. Five such algorithms from the literature are developed, Stochastic Gradient Descent, Stochastic Average Gradient (-Amélioré), (Loopless) Stochastic Variance Reduced Gradient. We showcase the functionality of the framework with a comparative study against a deterministic algorithm on a simulated 2D PET dataset, with the use of the open-source Synergistic Image Reconstruction Framework. We observe that stochastic optimisation methods can converge in fewer passes of the data than a standard deterministic algorithm1. © 2024, CC BY.

关键词： Positron emission tomography

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SELF-DIFFUSIVE SPATIOTEMPORAL PATTERNS IN A FOOD CHAIN MODEL THROUGH WEAKLY NONLINEAR ANALYSIS

引用

Fractals 2023年第9期31卷 2350098-2350098页

作者： Shivam Singh, Teekam Kumar, Mukesh Hsieh, Sun-Yuan Singh, Kamred Udham Uttarakhand Dehradun248002 India 566/6 Bell Road Clement Town Uttarakhand Dehradun248002 India Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Institute of Medical Information Institute of Manufacturing Information and Systems National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan School of Computing Graphic Era Hill University Uttarakhand Dehradun248002 India

Density distributions of populations with self-diffusion and interaction in a spatial domain are dynamically visualized with coupled nonlinear reaction–diffusion equations. Incorporating self-diffusion terms creates a more pragmatic modeling paradigm and provides meaningful descriptions of influences on spatiotemporal pattern formation phenomena. This paper examines the effect of self-diffusion in a food chain system with a Holling type-IV functional response and the type of spatial structures forms on a geographical scale due to the random movement of species. We discussed the existence and uniqueness of a positive equilibrium solution and obtained the Turing instability conditions for the self-diffusive food chain model. Moreover, weakly nonlinear analysis close to the Turing bifurcation boundary is used to derive the amplitude equations. The stability of the amplitude equations and sufficient conditions for the emanation of spatiotemporal patterns (such as spots, stripes, and blended patterns) are investigated. The analytical results are verified with numerical simulations. The results are applicable to all environments and can be used to understand the effects of self-diffusion in other food chain models both qualitatively and quantitatively. © World scientific Publishing Company.

关键词： Nonlinear analysis

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SCALSALE: Scalable SALE Benchmark Framework for Supercomputers

arXiv

引用

arXiv 2022年

作者： Harel, Re'em Rusanovsky, Matan Wagner, Ron Levin, Harel Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Israel Scientific Computing Center Nuclear Research Center Negev Israel Department of Physics Nuclear Research Center Negev Israel Israel Atomic Energy Commission Israel Department of Computer Science Technion Israel Institute of Technology Israel School of Physics and Astronomy Tel Aviv University Israel

Supercomputers worldwide provide the necessary infrastructure for groundbreaking research. As the demand for enhancing their performance constantly increases, new systems are built worldwide to supply this demand at an increased pace, volume, and power. However, most supercomputers are not designed equally due to different desired figure of merit, which is derived from the computational bounds of the targeted scientific applications’ portfolio. In turn, the design of such computers becomes a budget-constrained optimization process that strives to achieve the best performances possible in a multi-parameters search space, including heterogeneous hardware, middleware, and software. Therefore, verifying and evaluating whether a supercomputer can achieve its desired goal becomes a tedious and complex task. For this purpose, many full, mini, proxy, and benchmark applications have been introduced in the attempt to represent scientific applications partially. Nevertheless, as these benchmarks are hard to expand, update technologically, and most importantly, are over-simplified compared to scientific applications that tend to couple multiple scientific domains, they fail to represent the true scaling capabilities. We suggest a new physical scalable benchmark framework, namely SCALSALE, based on the well-known SALE scheme. SCALSALE’s main goal is to provide a simple, flexible, scalable infrastructure that can be easily expanded to include multi-physical schemes while maintaining scalable and efficient execution times. By expanding SCALSALE, the gap between the over-simplified benchmarks and scientific applications can be bridged. To achieve this goal, SCALSALE is implemented in Modern Fortran with simple object-oriented design patterns and supported by transparent MPI-3 blocking and non-blocking communication that allows such a scalable framework. In this manner, SCALSALE easily achieves almost identical performances in similar settings to common original benchmark suites of

关键词： Object oriented programming

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Robusta: Robust AutoML for Feature Selection via Reinforcement Learning

arXiv

引用

arXiv 2021年

作者： Wang, Xiaoyang Li, Bo Zhang, Yibo Kailkhura, Bhavya Nahrstedt, Klara Department of Computer Science University of Illinois at Urbana-Champaign United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

Several AutoML approaches have been proposed to automate the machine learning (ML) process, such as searching for the ML model architectures and hyper-parameters. However, these AutoML pipelines only focus on improving the learning accuracy of benign samples while ignoring the ML model robustness under adversarial attacks. As ML systems are increasingly being used in a variety of mission-critical applications, improving the robustness of ML systems has become of utmost importance. In this paper, we propose the first robust AutoML framework, Robusta–based on reinforcement learning (RL)–to perform feature selection, aiming to select features that lead to both accurate and robust ML systems. We show that a variation of the 0-1 robust loss can be directly optimized via an RL-based combinatorial search in the feature selection scenario. In addition, we employ heuristics to accelerate the search procedure based on feature scoring metrics, which are mutual information scores, tree-based classifiers feature importance scores, F scores, and Integrated Gradient (IG) scores, as well as their combinations. We conduct extensive experiments and show that the proposed framework is able to improve the model robustness by up to 22% while maintaining competitive accuracy on benign samples compared with other feature selection methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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X-ray simulations with gVXR as a useful tool for education, data analysis, set-up of CT scans, and scanner development 15

X-ray simulations with gVXR as a useful tool for education, ...

引用

15th SPIE Conference on Developments in X-Ray Tomography

作者： Vidal, Franck P. Afshari, Shaghayegh Ahmed, Sharif Atkins, Carolyn Béchet, Éric Bellot, Alberto Corbí Bosse, Stefan Chahid, Younes Chou, Cheng-Ying Culver, Robert Dixon, Lewis Friemann, Johan Garbout, Amin Hatton, Clémentine Henry, Audrey Leblanc, Christophe Leonardi, Alberto Létang, Jean Michel Lipscomb, Harry Manchester, Tristan Meere, Bas Middleburgh, Simon Mitchell, Iwan Perera, Liam Puig, Martí Tugwell-Allsup, Jenna Scientific Computing Ada Lovelace Centre Science Technology Facilities Council United Kingdom School of Computer Science & Engineering Bangor University United Kingdom Department of Biomechatronics Engineering National Taiwan University Taiwan DIAD Beamline Diamond Light Source United Kingdom UK Astronomy Technology Centre Royal Observatory Edinburgh United Kingdom Département d’Aérospatiale et Mécanique Université de Liège Belgium Escuela Superior de Ingeniería y Tecnología Universidad Internacional de La Rioja Spain Department of Computer Science University of Koblenz Koblenz Germany Department of Mechanical Engineering University of Siegen Siegen Germany The Manufacturing Technology Centre United Kingdom Department of Industrial and Materials Science Chalmers University of Technology Sweden Henry Royce Institute Henry Moseley X-Ray Imaging Facility Department of Materials The University of Manchester United Kingdom Scalian DS Rennes France INSA-Lyon Université Claude Bernard Lyon 1 CNRS Inserm CREATIS UMR 5220 U1294 Lyon France Department of Mechanical Engineering Eindhoven University of Technology Netherlands Department of Engineering Science University of Oxford United Kingdom Ysbyty Gwynedd United Kingdom

ISBN: (纸本)9781510679641

gVirtualXray (gVXR) is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gVXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and X-ray computed tomography (CT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and X-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gVXR has also been used to produce a high number of realistic simulated images in optimization problems and to train machine learning algorithms. This paper presents applications of gVXR related to XCT. © 2024 SPIE.

关键词： computerized tomography

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Using Deep Learning to Track Representational Flexibility Development of Children with Autism in a Virtual World

Using Deep Learning to Track Representational Flexibility De...

引用

Information and Education Technology (ICIET), International Conference on

作者： Zlatko Sokolikj Fengfeng Ke Shayok Chakraborty Jewoong Moon Department of Scientific Computing Florida State University Tallahassee FL U.S.A College of Education Florida State University Tallahassee FL U.S.A. Department of Computer Science Florida State University Tallahassee FL U.S.A. College of Education University of Alabama Tuscaloosa AL U.S.A.

Representational Flexibility (RF) is an individual cognitive ability to select, coordinate and create alternative representations during information processing. Students with autism spectrum disorder (ASD) typically lack representational flexibility and therefore are underrepresented in the field of stem education. Research has used virtual reality-based (VR) 3D simulations to promote RF development in ASD adolescent learners. However, to promote RF development through VR, the need to be able to track RF throughout the sessions is critical. In this paper we describe the development of neural network classifiers, designed to track RF subsets throughout the session. We trained these classifies using different modes of data collected from 178 session recordings with eight ASD participants. Our empirical results show the promise and potential of using deep learning to provide real-time tracking of the cognitive-affective states of students with ASD. To the best of our knowledge, this is the first research effort to combine VR-training, educational data mining and deep learning to provide a nurturing and adaptive learning environment for students with ASD.

关键词： Radio frequency Deep learning Training Autism Solid modeling Three-dimensional displays Neural networks

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Ad Hoc File Systems for High-Performance computing

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Journal of computer science & Technology 2020年第1期35卷 4-26页

作者： AndréBrinkmann Kathryn Mohror Weikuan Yu Philip Carns Toni Cortes Scott A.Klasky Alberto Miranda Franz-Josef Pfreundt Robert B.Ross Marc-AndréVef Zentrum für Datenverarbeitung Johannes Gutenberg University MainzMainz 55128Germany Center for Applied Scientific Computing Lawrence Livermore National LaboratoryLivermoreCA 94550U.S.A Department of Computer Science Florida State UniversityTallahasseeFL 32306U.S.A Mathematics and Computer Science Division Argonne National LaboratoryLemontIL 60439U.S.A Department of Computer Architecture Universitat Politecnica de CatalunyaBarcelona 08034Spain Computer Science and Mathematics Division Oak Ridge National LaboratoryOak RidgeTN 37831U.S.A Computer Science Department Barcelona Supercomputing CenterBarcelona 08034Spain Fraunhofer Institute for Industrial Mathematics ITWM Fraunhofer-Platz 1Kaiserslautern 67663Germany

Storage backends of parallel compute clusters are still based mostly on magnetic disks,while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory(NVRAM)are deployed within compute *** these new storage technologies into scientific workflows is unfortunately today a mostly manual task,and most scientists therefore do not take advantage of the faster storage *** approach to systematically include nodelocal SSDs or NVRAMs into scientific workflows is to deploy ad hoc file systems over a set of compute nodes,which serve as temporary storage systems for single applications or longer-running *** paper presents results from the Dagstuhl Seminar 17202"Challenges and Opportunities of User-Level File Systems for HPC"and discusses application scenarios as well as design strategies for ad hoc file systems using node-local storage *** discussion includes open research questions,such as how to couple ad hoc file systems with the batch scheduling environment and how to schedule stage-in and stage-out processes of data between the storage backend and the ad hoc file *** presented are strategies to build ad hoc file systems by using reusable components for networking and how to improve storage device *** interfaces and semantics are presented,for example those used by the three ad hoc file systems BeeOND,GekkoFS,and *** presentation covers a range from file systems running in production to cutting-edge research focusing on reaching the performance limits of the underlying devices.

关键词： parallel architectures distributed file system high-performance computing burst buffer POSIX(portable operating system interface)

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