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International Conference on computer Supported Cooperative Work in Design

作者： Jiawei Jiang Xiang Tian Yubing Han Chunyu Hu Bin Feng Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Information Science and Technology Taishan University Taian China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Owing to the easy deployment and mobile flexibility, Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (MEC) has been deemed as one potential technology for handling the computation-intensive tasks at terminal devices (TDs). In this work, a MEC architecture assisted by UAVs is designed which achieves efficient offloading, computing, and downloading for tasks from multiple TDs via aerial to aerial collaboration of two UAVs. In this architecture, the task offloading process contains two parts, i.e., the offloading from TDs to a mobile UAV which flies around TDs, and the offloading from the mobile UAV to a hovering UAV which hovers in the air. The computing tasks from TDs will be divided into three parts allocated to the TDs themselves, and both two UAVs. Upon completion of computation, the computation results are downloaded to the TDs. The optimization objective is to seek for an optimal task division strategy to attain the weighted total energy consumption minimization for all devices. Since the formulated optimization problem is not convex, we develop a two-step iteration algorithm which jointly optimizes computing frequency, task allocation volume, as well as UAV's trajectory based on the method of block coordinate descent. Simulation results confirm the effectiveness and performance advantages of the designed algorithm.

关键词： Energy consumption Simulation Collaboration computer architecture Autonomous aerial vehicles Minimization Resource management Jamming Trajectory optimization Radio spectrum management

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QoS-Aware and Cost-Efficient Dynamic Resource Allocation for Serverless ML Workflows

QoS-Aware and Cost-Efficient Dynamic Resource Allocation for...

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International Symposium on parallel and Distributed Processing (IPDPS)

作者： Hao Wu Junxiao Deng Hao Fan Shadi Ibrahim Song Wu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Inria Univ. Rennes CNRS IRISA France

Machine Learning (ML) workflows are increasingly deployed on serverless computing platforms to benefit from their elasticity and fine-grain pricing. Proper resource allocation is crucial to achieve fast and cost-efficient execution of serverless ML workflows (specially for hyperparameter tuning and model training). Unfortunately, existing resource allocation methods are static, treat functions equally, and rely on offline prediction, which limit their efficiency. In this paper, we introduce CE-scaling – a Cost-Efficient autoscaling framework for serverless ML work-flows. During the hyperparameter tuning, CE-scaling partitions resources across stages according to their exact usage to minimize resource waste. Moreover, it incorporates an online prediction method to dynamically adjust resources during model training. We implement and evaluate CE-scaling on AWS Lambda using various ML models. Evaluation results show that compared to state-of-the-art static resource allocation methods, CE-scaling can reduce the job completion time and the monetary cost by up to 63% and 41% for hyperparameter tuning, respectively; and by up to 58% and 38% for model training.

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2.5 Million-Atom Ab Initio Electronic-Structure Simulation of Complex Metallic Heterostructures with DGDFT

2.5 Million-Atom Ab Initio Electronic-Structure Simulation o...

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Supercomputing Conference

作者： Wei Hu Hong An Zhuoqiang Guo Qingcai Jiang Xinming Qin Junshi Chen Weile Jia Chao Yang Zhaolong Luo Jielan Li Wentiao Wu Guangming Tan Dongning Jia Qinglin Lu Fangfang Liu Min Tian Fang Li Yeqi Huang Liyi Wang Sha Liu Jinlong Yang University of Science and Technology of China Hefei Anhui China Pilot National Laboratory for Marine Science and Technology (Qingdao) China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Mathematical Sciences Peking University Beijing China Institute of Software Chinese Academy of Sciences Beijing China Qilu University of Technology Shandong Computer Science Center Jinan Shangdong China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665454452

Over the past three decades, ab initio electronic structure calculations of large, complex and metallic systems are limited to tens of thousands of atoms in computational accuracy and efficiency on leadership supercomputers. We present a massively parallel discontinuous Galerkin density functional theory (DGDFT) implementation, which adopts adaptive local basis functions to discretize the Kohn-Sham equation, resulting in a block-sparse Hamiltonian matrix. A highly efficient pole expansion and selected inversion (PEXSI) sparse direct solver is implemented in DGDFT to achieve O(N 1.5 ) scaling for quasi two-dimensional systems. DGDFT allows us to compute the electronic structures of complex metallic heterostructures with 2.5 million atoms (17.2 million electrons) using 35.9 million cores on the new Sunway supercomputer. The peak performance of PEXSI can achieve 64 PFLOPS (~5% of theoretical peak), which is un-precedented for sparse direct solvers. This accomplishment paves the way for quantum mechanical simulations into mesoscopic scale for designing next-generation electronic devices.

关键词： Performance evaluation Analytical models Leadership Quantum computing Computational modeling Supercomputers Mathematical models

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A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals

A Non-Intrusive Neural Quality Assessment Model for Surface ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Cho-Yuan Lee Kuan-Chen Wang Kai-Chun Liu Yu-Te Wang Xugang Lu Ping-Cheng Yeh Yu Tsao School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan College of Information and Computer Sciences University of Massachusetts Amherst MA USA National Institute of Information and Communications Technology Tokyo Japan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination is the presence of electrocardiogram (ECG) signals. To assess the quality of real-world sEMG data more effectively, this study proposes QASE-net, a new non-intrusive model that predicts the SNR of sEMG signals. QASE-net integrates a one-dimensional Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BLSTM) layer and attention mechanisms, following an end-to-end training strategy. Our experimental framework utilizes real-world sEMG and ECG data from two open-access databases, the Non-Invasive Adaptive Prosthetics Database and the MIT-BIH Normal Sinus Rhythm Database, respectively. The experimental results demonstrate the superiority of QASE-net over the baseline method, exhibiting significantly reduced prediction errors and notably higher linear correlations with the ground truth. These findings show the potential of QASE-net to substantially enhance the reliability and precision of sEMG quality assessment in practical applications.

关键词： Correlation Attention mechanisms Databases Surface contamination Electrocardiography Predictive models Electromyography Quality assessment Convolutional neural networks Signal to noise ratio

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Commercial Indian Bamboo Species Classification on matK DNA Barcode Sequences using Machine Learning Techniques with K-mer

Commercial Indian Bamboo Species Classification on matK DNA ...

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Ankush D. Sawarkar Deepti D. Shrimankar Lal Singh Anurag Agrahari Sagar Lachure Neeraj Dhanraj Bokde Department of Computer Science and Engineering Visvesvaraya National Institute of Technology (VNIT) Nagpur Maharashtra India Environmental Biotechnology And Genomics Division National Environmental and Engineering Research Institute (NEERI) Nagpur Maharashtra India Center for Quantitative Genetics and Genomics Aarhus University Aarhus Denmark

Bamboo, a grass, belongs to the Poaceae family, with 1642 species from 116 genera worldwide. It has exceptional physical, chemical, and mechanical qualities, which allow it to be employed in over a thousand different ways and contribute to a trade value of USD 2.76 billion. Bamboo is grown using rhizomes, tissue culture, or short branch cuttings without any other checks resulting in incorrect species identification and categorisation. Therefore, the classification or identification of these bamboo use its DNA barcode sequences with a K-mer based method, and machine learning (ML) is the most excellent strategy for resolving issues with the conventional or traditional categorisation of the species. A DNA barcode is a brief genetic signature that helps identify the species to which an organism belongs. It is possible to extract a useful feature from genome sequences using K-mer based approaches, which may then be used to increase comparison accuracy. In this research, we evaluate the classification performance of four supervised ML models on the DNA-barcode sequence of six Indian commercial bamboo species with a different K-mer combination. For this classification, we choose matK barcode region and supervised ML models such as Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) and Gradient Boosting Machine (GBM). The results analysis of these models on the matK DNA sequence with different K-mers demonstrates that the classification capabilities of the GBM approaches are quite promising, and it has an accuracy of 95.3% on average.

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An accurate weight binarization scheme for CNN object detectors with two scaling factors

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IEIE Transactions on Smart Processing and Computing 2020年第6期9卷 497-503页

作者： Nguyen, Xuan Truong Nguyen, Tuan Nghia Lee, Hyuk-Jae Kim, Hyun Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Electrical and Information Engineering Research Center for Electrical and Information Technology Seoul National University of Science and Technology Seoul01811 Korea Republic of

Recently, convolutional neural network (CNN)-based object detectors such as You Only Look Once (YOLO) have been intensively studied for applications in robotics, drones, and autonomous driving. Although YOLO can run in real time by using a graphics processing unit, the YOLO hardware implementation has received a great deal of interest due to its power efficiency and the potential for massive chip production. However, extensive memory access and high computation complexity are widely known as bottlenecks in YOLO hardware implementation. A common and intuitive approach is to apply quantization, especially binarization, to object detectors. However, the existing binarization methods suffer from substantial degradation in detection performance. To address the problem, this study proposes an accurate weight binarization scheme using two scaling factors. Specifically, a new binary weight optimization problem is formulated, and an analytical solution is derived. Experimental results with well-known PASCAL Visual Object Classes show that the proposed method reduces the detection accuracy degradation by up to 32.18% while meeting the memory and computation requirements of state-of-the-art methods. Copyrights © 2020 The Institute of Electronics and Information Engineers

关键词： Convolutional neural networks

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Microscopic-based analysis of nuclei in spheroids via SUNSHINE: An on-chip workflow integrating optical clearing, fluorescence calibration and supervoxel segmentation

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computers in Biology and Medicine 2025年 187卷 109761-109761页

作者： Lin, Chia-Hsiang Leng, Zi-Chao Yu, Chien-Hsin Deori Bharali, Lui Kirtan Lin, Cheng-Li Mao, Bin-Hsu Tu, Ting-Yuan Department of Electrical Engineering National Cheng Kung University Tainan City70101 Taiwan Institute of Computer and Communication Engineering Department of Electrical Engineering National Cheng Kung University Tainan City70101 Taiwan Department of Biomedical Engineering College of Engineering National Cheng Kung University Tainan City70101 Taiwan Department of Biotechnology and Medical Engineering National Institute of Technology Odisha Rourkela769008 India Department of Orthopedic Surgery National Cheng Kung University Hospital Tainan City704 Taiwan Skeleton Materials and Biocompatibility Core Lab Research Center of Clinical Medicine National Cheng Kung University Hospital Tainan City704 Taiwan National Cheng Kung University Hospital Tainan City704 Taiwan Musculoskeletal Research Center Innovative Headquarter National Cheng Kung University Tainan City70101 Taiwan Miin Wu School of Computing National Cheng Kung University Tainan City70101 Taiwan Meta-nanoPhotonics Center National Cheng Kung University Tainan City70101 Taiwan Medical Device Innovation Center National Cheng Kung University Tainan City70101 Taiwan

Multicellular spheroids (MCSs) are increasingly employed as 3D cell culture models in biomedical research due to their ability to effectively replicate in vivo cell interactions, making them suitable for high-throughput drug screening. Accurate cell counting is critical for data normalization, therapeutic evaluation, and exploration of culture conditions;however, affordable software solutions for 3D cell counting using microscopic images are limited. To fill this gap, we created SUNSHINE, an innovative on-chip analytical workflow that uniquely merges optical clearing, histogram matching (HM)-assisted fluorescence calibration, and simple linear iterative clustering (SLIC) supervoxel segmentation. This tool offers an efficient method for analyzing the characteristics and counts of fluorescence-labeled nuclei within MCSs. While optical clearing improves the penetration depth of microscopic imaging, deeper regions of thicker samples often yield faint fluorescence signals. SUNSHINE resolves this issue through the HM image post-processing algorithm. Moreover, SLIC is an effective alternative to traditional contour-wise segmentation, enabling the identification of irregularly shaped fluorescent nuclei. We found that SUNSHINE generated results comparable to commercial software like Imaris and machine learning (ML)-based tools, such as StarDist and Cellpose, in our analysis of the effects of seeding density and cell type on spheroid growth. We also used it to measure the volume and spatial distribution of nuclei, focusing on the hypoxic and peripheral regions of spheroids. Overall, this study finds that SUNSHINE serves as a valuable and economical approach for characterizing cellular activity and interactions in 3D, diminishing the reliance on costly proprietary software. © 2025

关键词： Cell culture

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Pioneering Deterministic Scheduling and Network Structure Optimization for Time-Critical Computing Tasks in Industrial IoT

arXiv

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arXiv 2024年

作者： Hu, Yujiao Zhu, Yining Zhang, Huayu Pan, Yan Jia, Qingmin Xie, Renchao Yang, Gang Yu, F. Richard Future Network Research Center Purple Mountain Laboratories Nanjing211111 China The School of Computer Science Northwestern Polytechnical University Xi’an710029 China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology China The State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Systems and Computer Engineering Carleton University Ottawa Canada

The Industrial Internet of Things (IIoT) has become a critical technology to accelerate the process of digital and intelligent transformation of industries. As the cooperative relationship between smart devices in IIoT becomes more complex, getting deterministic responses of IIoT periodic time-critical computing tasks becomes a crucial and nontrivial problem. However, few current works in cloud/edge/fog computing focus on this problem. This paper is a pioneer to explore the deterministic scheduling and network structural optimization problems for IIoT periodic time-critical computing tasks. We first formulate the two problems and derive theorems to help quickly identify computation and network resource sharing conflicts. Based on this, we propose a deterministic scheduling algorithm, IIoTBroker, which realizes deterministic response for each IIoT task by optimizing the fine-grained computation and network resources allocations, and a network optimization algorithm, IIoTDeployer, providing a cost-effective structural upgrade solution for existing IIoT networks. Our methods are illustrated to be cost-friendly, scalable, and deterministic response guaranteed with low computation cost from our simulation results. © 2024, CC BY-NC-ND.

关键词： Internet of things

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Triangular Formation Cluster Space Control for Multi-Agent Entrapment/Escorting Tasks

Triangular Formation Cluster Space Control for Multi-Agent E...

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Control technology and Applications (CCTA),

作者： Ernesto Monroy Cruz Luis Rodolfo Garcia Carrillo Eduardo Steed Espinoza Quesada National Technological of Mexico (TecNM) Hidalgo Mexico Klipsch School of Electrical and Computer Engineering New Mexico State University New Mexico USA Center of Research and Advanced Studies of the National Polytechnic Institute Mexico National Council of Humanities Science and Technology Mexico

The task of enabling a multi-agent system for entrapping and escorting static/dynamic targets in the context of cluster space control is addressed. The proposed methodology consists of two main strategies. On one hand, the entrapping and escorting problems are specified in terms of cluster parameter trajectories, resulting in individual robot motions capable of satisfying the required multi-agent formation behavior. On the other hand, a fuzzy logic controller is designed in order to deal with the uncertainty in the model of the platform used in laboratory tests. Experimental results showing a team of three ground mobile robots entrapping a static target, as well as escorting a dynamic target demonstrate the effectiveness of the proposed control scheme.

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Fast and Scalable Gate-Level Simulation in Massively parallel Systems

Fast and Scalable Gate-Level Simulation in Massively Paralle...

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IEEE International Conference on computer-Aided Design

作者： Haichuan Hu Zichen Xu Yuhao Wang Fangming Liu Services Computing Technology and System Lab Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Mathematics and Computer Science Nanchang University Nanchang China Pengcheng Laboratory Shenzhen China

The natural bijection between a proposed circuit design and its graph representation shall allow any graph optimization algorithm deploying into many-core systems efficiently. However, this process suffers from the exponentially growing overhead and heavy memory footprint with the signal propagation. To conquer the unique challenge, we systematically study the simulation with millions of gates, and identify that the processing complexity could grow exponentially from the signal inputs, the skewness of the computational graph stays. Thus, we present ZhouBi, a fast and scalable gate-level simulation framework to fully exploit the parallelism from many-core systems. ZhouBi contributes in threefolds, (I) a graph representation that colors gate-level netlists and identifies skew partitions based on the graph skewness; (II) A set of heuristic algorithms that picks opportunistic and conservative algorithms to accelerate the simulation; (III) A system facility that supports selective mapping between simulation and many-core, providing a tradeoff between the risk of concurrent simulation fail and performance gain. We have prototyped ZhouBi and evaluated it with practical baselines. ZhouBi can achieve a 27.6× performance gain, as compared to the state-of-the-practice Veriwell without compromising any correctness. Our framework supports large graphs enabling scale-out gate-level simulations for chip design.

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