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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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Nonuniformly Spaced Control Points Based on Variational Cardiac Image Registration 26th

Nonuniformly Spaced Control Points Based on Variational Card...

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26th International conference on Medical Image Computing and computer-Assisted Intervention (MICCAI)

作者： Su, Haosheng Yang, Xuan Shenzhen Univ Shenzhen 518060 Guangdong Peoples R China Guangdong Prov Key Lab Popular High Performance C Shenzhen Guangdong Peoples R China Shenzhen Key Lab Serv Comp & Applicat Shenzhen Guangdong Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Non-uniformly spaced control points located on the interface of different objects are beneficial for constructing an accurate displacement field for image registration. However, extracting features of non-uniformly spaced control points in images is challenging for convolutional neural networks (CNNs). We extend a probabilistic image registration model using uniformed-spaced control points by employing non-uniformly-spaced control points. We construct a network to extract the image and spatial features of non-uniformly-spaced control points. Moreover, a variational Bayesian (VB) model using a factorized prior is employed to estimate the distribution of latent variables. In theory, we analyze the KL divergence between the posterior and the two separated priors. We found that the factorized prior has the advantage of decreasing the KL divergence, but too more factorized priors, such as the standard normal, might deteriorate registration accuracy. Moreover, we analyze the relationship between the uncertainty of the displacement field and the spatial distribution of control points. Experimental results on four public datasets show that our network outperforms the state-of-arts registration networks and can provide registration uncertainty.

关键词： Cardiac image registration Variational Bayesian Non-uniformly-spaced control points

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high-Rate Structural Health Monitoring: Part-III Algorithms 1

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41st IMAC, A conference and Exposition on Structural Dynamics, 2023

作者： Laflamme, Simon Hu, Chao Department of Civil Construction and Environmental Engineering Department of Electrical and Computer Engineering Iowa State University AmesIA United States Department of Mechanical Engineering University of Connecticut StorrsCT United States

ISBN: (数字)9783031349461

ISBN: (纸本)9783031349454

The real-time estimation of the structural health of high-rate systems, termed "high-rate structural health monitoring" (HRSHM), is critical in designing decision mechanisms that can ensure structural integrity and performance. However, this is a difficult task, because three aspects uniquely characterize these systems: (1) large uncertainties in the external loads, (2) high levels of nonstationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations. In addition, because these systems are experiencing events of high amplitudes (often beyond 100 gn) over short durations (under 100 ms), a successful feedback mechanism is one that can operate under 1 ms. A solution to the unique system characteristics and temporal constraint is the design and application of real-time learning algorithms. Part III of this III part tutorial will provide attendees with an organized discussion of challenges and opportunities in designing real-time learning algorithms for HRSHM applications. In particular, we will explore fast real-time learning for time-series prediction using conventional machine learning and deep learning algorithms, with an emphasis on crafting the input space to improve algorithmic efficiency, and discuss a path to rapid real-time state estimation. © 2023, The Society for Experimental Mechanics, Inc.

关键词： Time series

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Effects of EEG Electrode Numbers on Deep Learning-Based Source Imaging 16th

Effects of EEG Electrode Numbers on Deep Learning-Based Sour...

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16th International conference on Brain Informatics, BI 2023

作者： Rong, Jesse Sun, Rui Guo, Yuxin He, Bin Department of Biomedical Engineering Carnegie Mellon University PittsburghPA15213 United States Neural Computation Program Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9783031430749

Electrophysiological source imaging (ESI) allows spatio-temporal imaging of brain activity and functional connectivity with high density EEG. The relationship between the performance of ESI methods and the number of scalp EEG electrodes has been a topic of ongoing investigation. Research has shown that high-density EEG is necessary for obtaining accurate and reliable ESI results using conventional ESI solutions, limiting their applications when only low-density EEG is available. In recent years, deep learning-based ESI methods have demonstrated strong performance by learning spatiotemporal patterns of brain activities directly from data. In this work, we examined the effect of the EEG electrode numbers on a recently proposed Deep Learning-based Source Imaging Framework (DeepSIF). We evaluated the ESI performance with 16, 21, 32, 64, and 75 channel EEG configurations using DeepSIF and conventional ESI methods in computer simulations. Our findings suggest that DeepSIF can provide accurate estimations of the source location and extent across various numbers of channels and noise levels, outperforming conventional methods, which indicates its merits for wide applications of ESI, especially for centers/labs without high density EEG devices. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Zephyr: A high-performance Framework for Graph Attention Networks on Heterogeneous Data 23

Zephyr: A High-Performance Framework for Graph Attention Net...

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23rd IEEE International conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Ding, Wenxiu Liu, Muzhi Cai, Yuxuan Chen, Mingxin Yan, Zheng Wang, Mingjun School of Cyber Engineering Xidian University Xi'an China Xidian University School of Computer Science and Technology Xi'an China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China

The Graph Attention Network (GAT), based on the foundational principles of Graph Neural Network (GNN), represents a significant advancement in the accurate representation and in-depth understanding of complex data structures. However, GAT can't proficiently handle heterogeneous data, which negatively affects model performance. Existing solutions incur substantial computational overhead when addressing these challenges, compromising efficiency and impeding practical deployment. In this paper, we propose Zephyr to enable GAT to efficiently process heterogeneous data with high performance. In Zephyr, we introduce Dynamic Multi-matrix Design and Spatial Multi-activation Design to eliminate the effects of finiteness of matrix representation capability and difference in heterogeneous spaces. Remarkably, Zephyr shows superior capability in processing heterogeneous data while requiring minimal storage space. Through comprehensive simulations and comparisons with existing schemes, Zephyr demonstrates a substantial performance improvement, increasing accuracy by over 8% on heterogeneous datasets and enhancing fitting speed by approximately 20%. Moreover, with heterogeneous data, it maintains memory overhead below 1.05x and curtails time overhead to 70%. © 2024 IEEE.

关键词： Fitting Enhancement Mechanism Graph Attention Networks Heterogeneous Data Processing

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libcrpm: Improving the Checkpoint performance of NVM 22

libcrpm: Improving the Checkpoint Performance of NVM

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59th ACM/IEEE Design Automation conference (DAC) - From Chips to Systems - Learn Today, Create Tomorrow

作者： Ren, Feng Chen, Kang Wu, Yongwei Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781450391429

libcrpm is a new programming library to improve the checkpoint performance for applications running in NVM. It proposes the failure-atomic differential checkpointing protocol, which addresses two problems simtdtaneously that exist in the current NVM-based checkpoint-recovery libraries: (1) high write amplification when page-granularity incremental checkpointing is used, and (2) high persistence costs from excessive memory fence instructions when line-grained undo-log or copy-on-write is used. Evaluation results show that libcrpm reduces the checkpoint overhead in realistic workloads. For MPI-based parallel applications such as LULESH, the checkpoint overhead of libcrpm is only 44.78% of FTI, an application-level checkpoint-recovery library.

关键词： computer programming

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E-CVP-MVSNet: Effective Hierarchical Multi-View 3D Reconstruction Based on Light-Weight Network 5

E-CVP-MVSNet: Effective Hierarchical Multi-View 3D Reconstru...

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5th International conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Zhang, Xin Huang, Hongbo Lu, Lu Yan, Xiaoxu Bao, Zhongman Zhao, Qisheng Gong, Meihui Mechanical Electrical Engineering School Beijing Information Science and Technology University Beijing China Computer School Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350357950

Multi-view stereo based on cost volume pyramid has achieved good results in depth inference using high-resolution images. However, most approaches require a lot of computing resources, in part because convolutional layers extract redundant features and pay too much attention to some unwanted feature information. In this work, we propose a pyramid network for feature extraction that is both effective and efficient. There are two main mechanisms in the proposed approach. An effective convolution module called SCConv (Spatial and Channel reconstruction Convolution) to reduce duplicate features and encourage representative feature learning. To encode spatial data and processes high-resolution photos more efficiently, Conv2Former is also introduced into the model to enhance feature extraction capabilities. Experiments show that our method can obviously decrease the parameters, complexity and computational costs, thus improve the performance and generative ability remarkably. © 2023 IEEE.

关键词： Attention mechanism Convolution Depth estimation Feature extraction Multi-view stereo

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C3LRSO: A Chinese Corpus for Complex Logical Reasoning in Sentence Ordering 31

C3LRSO: A Chinese Corpus for Complex Logical Reasoning in Se...

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31st International conference on Computational Linguistics, COLING 2025

作者： Guo, Xiaotao Li, Jiang Su, Xiangdong Zhang, Fujun College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Sentence ordering is the task of rearranging a set of unordered sentences into a coherent and logically consistent sequence. Recent work has primarily used pre-trained language models, achieving significant success in the task. However, existing sentence ordering corpora are predominantly in English, and comprehensive benchmark datasets for non-English languages are unavailable. Meanwhile, current datasets often insert specific markers into paragraphs, inadvertently making the logical sequence between sentences more apparent and reducing the models' ability to handle genuinely unordered sentences in real applications. To address these limitations, we develop C3LRSO, a high-quality Chinese sentence ordering dataset that overcomes the aforementioned shortcomings by providing genuinely unordered sentences without artificial segmentation cues. Furthermore, given the outstanding performance of large language models on NLP tasks, we evaluate these models on our dataset for this task. Additionally, we propose a simple yet effective parameter-free approach that outperforms existing methods on this task. Experiments demonstrate the challenging nature of the dataset and the strong performance of our proposed method. These findings highlight the potential for further research in sentence ordering and the development of more robust language models. Our dataset is freely available at https://***/JasonGuo1/C3LRSO. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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BEAM: A Multi-Channel Optical Interconnect for Multi-GPU Systems

BEAM: A Multi-Channel Optical Interconnect for Multi-GPU Sys...

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2025 Design, Automation and Test in Europe conference, DATE 2025

作者： Yang, Chongyi Hu, Bohan Chen, Peiyu Liu, Yinyi Zhang, Wei Xu, Jiang Microelectronics Thrust The Hong Kong University of Science and Technology Guangzhou China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9783982674100

high-performance computing and AI applications necessitate high-bandwidth communication between GPUs. Traditional electrical interconnects for GPU-to-GPU communication face challenges over longer distances, including high power consumption, crosstalk noise, and signal loss. In contrast, optical interconnects excel in this domain, offering high bandwidth and consistent power dissipation over long distance. This paper proposes BEAM, a Bandwidth-Enhanced optical interconnect Architecture for Multi-GPU systems. BEAM extends electrical-optical interfaces into the GPU package, positioning them close to GPU compute logic and memory. Unlike existing single-channel approaches, each BEAM optical interface incorporates multiple parallel optical channels, further enhancing bandwidth. An arbitration scheme manages channel usage among data transfers. Evaluation on Rodinia benchmarks and LLM training kernels demonstrates that BEAM achieves a speedup of 1.14 - 1.9× and reduces energy consumption by 29 - 44% compared to the electrical-interconnected system and state-of-the-art schemes, while maintaining comparable chip area consumption. © 2025 EDAA.

关键词： Bandwidth

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Importance Weighted Variational Cardiac MRI Registration Using Transformer and Implicit Prior 26th

Importance Weighted Variational Cardiac MRI Registration Usi...

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26th International conference on Medical Image Computing and computer-Assisted Intervention (MICCAI)

作者： Xu, Kangrong Huang, Qirui Yang, Xuan Shenzhen Univ Shenzhen 518060 Guangdong Peoples R China Guangdong Prov Key Lab Popular High Performance C Shenzhen Guangdong Peoples R China Shenzhen Key Lab Serv Comp & Applicat Shenzhen Guangdong Peoples R China

ISBN: (纸本)9783031439988;9783031439995

The variational registration model takes advantage of explaining uncertainties of registration results. However, most existing variational registration models are based on convolutional neural networks (CNNs), which cannot capture distant information in images. Besides, the evidence lower bound (ELBO) and the commonly used standard prior cannot close the gap between the real posterior and the variational posterior in the vanilla variational registration model. This paper proposes a network in a variational image registration model for cardiac motion estimation to effectively capture the spatial correspondence of long-distance images and solve the shortcomings of CNNs. Our proposed network comprises a Transformer with a T2T module and the cross attention between the moving and the fixed images. To close the gap between the real posterior and the variational posterior, the importance-weighted evidence lower bound (iwELBO) is introduced into the variational registration model with an implicit prior. The coefficients of a parametric transformation using multi-supports CSRBFs are latent variables in our variational registration model, which improve registration accuracy significantly. Experimental results show that the proposed method outperforms state-of-arts research on public cardiac datasets.

关键词： Variational inference Transformer Cross attention Compact support radial basis function (CSRBF)

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PDC Course Development and Assessment Process for the betterment of Teaching-Learning Process 29

PDC Course Development and Assessment Process for the better...

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29th IEEE International conference on high performance Computing, Data and Analytics (HiPC)

作者： Bayyapu, Neelima Manipal Acad Higher Educ MAHE Manipal Inst Technol MIT Dept Comp Sci & Engn Manipal 576104 Karnataka India

ISBN: (纸本)9798350333886

Outcome-based education is being adapted in most of the engineering colleges across India. This process is being accredited by a national body called National Board of Accreditation (NBA) in India. This accreditation process involves a continuous process of defining program Outcomes (POs) and Course Outcomes (COs) and these are evaluated to check the OBE outcome level. This course evaluation includes course content, evaluation methods used for students' evaluation, pedagogy of teaching, Bloom's taxonomy mapping of learning levels, feedback from stakeholders and results and outcome analysis. If the required level is not attained, the COs and POs are reviewed and accordingly curriculum is revised. There are a lot of challenges being faced by faculty in this process of creating and assessing the curriculum as per NBA standards. These challenges are unique and non-trivial for PDC/HPC (Parallel and Distributed Computing/high-performance Computing) courses. The preparation of OBE-based PDC/HPC course involves a lot of stakeholders and brainstorming over multiple sessions. Many universities are adopting PDC/HPC courses more recently across the world. With an intention to create a pointer for developing, delivering and reviewing a PDC/HPC course, this paper presents the course development process for the benefit of various stakeholders and specially PDC/HPC educators and institutions. This research paper presents the undergraduate teaching experience of parallel computing (PC) course with a critical evaluation based on course Outcomes (COs) and Bloom's taxonomy mapping of learning levels. This paper compiles a list of challenges faced by PDC/HPC educators and stakeholders focusing on the Indian education scenario. It lists the activities and the suggestions that can be applied to address these challenges suitably. The research and teaching experience-based discussions and strategies proposed in this paper help other PDC/HPC educators and stakeholders in the CS (computer Sci

关键词： Undergraduate Education Parallel and Distributed Computing (PDC) Education high-performance Computing (HPC) Education computer Science Community Indian Education Bloom's Taxonomy Learning Levels Course Outcomes Parallel Computing Course

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