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arXiv 2019年

作者： Shi, Jia Li, Ruipeng Xi, Yuanzhe Saad, Yousef de Hoop, Maarten V. Department of Earth Environmental and Planetary Sciences Rice University United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Department of Mathematics Emory University AtlantaGA United States Department of Computer Science and Engineering University of Minnesota MN United States Simons Chair in Computational and Applied Mathematics and Earth Science Rice University HoustonTX United States

A new approach is presented to compute the seismic normal modes of a fully heterogeneous, rotating planet. Special care is taken to separate out the essential spectrum in the presence of a fluid outer core. The relevant elastic-gravitational system of equations, including the Coriolis force, is subjected to a mixed finite-element method, while self-gravitation is accounted for with the fast multipole method (FMM). To solve the resulting quadratic eigenvalue problem (QEP), the approach utilizes extended Lanczos vectors forming a subspace computed from a non-rotating planet – with the shape of boundaries of a rotating planet and accounting for the centrifugal potential – to reduce the dimension of the original problem significantly. The subspace is guaranteed to be contained in the space of functions to which the seismic normal modes belong. The reduced system can further be solved with a standard eigensolver. The computational accuracy is illustrated using all the modes with relative small meshes and also tested against standard perturbation calculations relative to a standard Earth model. The algorithm and code are used to compute the point spectra of eigenfrequencies in several Mars models studying the effects of heterogeneity on a large range of scales. Copyright © 2019, The Authors. All rights reserved.

关键词： Seismology

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Exploring Delay Tolerant Network: A Simulation Approach

Exploring Delay Tolerant Network: A Simulation Approach

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Mohammad Abdus Salam Arif Furkan Deniz Chinwe Aghadinuno Robert Kassouf-Short Alan G. Hylton Department of Computing Information and Mathematical Sciences and Technology Chicago State University Chicago IL USA Department of Computer Science Southern University and A&M College Baton Rouge LA USA Department of Electrical Engineering Southern University and A&M College Baton Rouge LA USA NASA Glenn Research Center Cleveland OH USA NASA Goddard Space Flight Center Greenbelt MD USA

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

This paper provides an in-depth exploration of Delay Tolerant Networks (DTNs), focusing particularly on the development and functionality of High Delay Tolerant Networks (HDTNs). The distinction between DTNs and HDTNs is illuminated, emphasizing the greater resilience and performance of HDTNs. The role and significance of HDTN simulators were thoroughly explained, explaining their essential role in modeling, and predicting the behavior of these advanced networks. A detailed walkthrough is offered on the installation, configuration, and simulation process of the HDTN Simulator, making this study a valuable resource for practitioners in the field. The paper also incorporates the use of DTNSim to further extend the simulation capabilities of HDTNs. The research also delves into the results and network topology obtained from the simulation, offering valuable insights into the practical applications and implications of HDTNs. The findings contribute to a growing body of knowledge surrounding the design, implementation, and management of high delay tolerant networks, thereby aiding future developments in this critical aspect of network engineering.

关键词： Knowledge engineering Network topology Instruments Focusing Predictive models Delays Optimization Resilience

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An integrated framework for parameter-based optimization of scientific workflows

An integrated framework for parameter-based optimization of ...

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18th ACM International Symposium on High Performance Distributed computing, HPDC 09, Co-located with the 2009 International Symposium on High Performance Distributed computing Conference, HPDC'09

作者： Kumar, Vijay S. Sadayappan, P. Mehta, Gaurang Vahi, Karan Deelman, Ewa Ratnakar, Varun Kim, Jihie Gil, Yolanda Hall, Mary Kurc, Tahsin Saltz, Joel Department of Computer Science and Engineering Ohio State University Columbus OH 43210 United States Advanced Systems Division Information Sciences Institute University of Southern California Marina del Rey CA 90292 United States Intelligent Systems Division Information Sciences Institute University of Southern California Marina del Rey CA 90292 United States School of Computing University of Utah Salt Lake City UT 84112 United States Center for Comprehensive Informatics Emory University Atlanta GA 30307 United States

ISBN: (纸本)9781605585871

Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not affect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework. Copyright 2009 ACM.

关键词： Data handling

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Bayesian data assimilation for estimating epidemic evolution: A COVID-19 study

arXiv

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arXiv 2020年

作者： Yang, Xian Wang, Shuo Xing, Yuting Li, Ling Xu, Richard Yi Da Friston, Karl J. Guo, Yike Department of Computer Science Hong Kong Baptist University Hong Kong Special Administrative Region Hong Kong Data Science Institute Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China School of Computing University of Kent United Kingdom Faculty of Engineering and Information Technology University of Technology Sydney Australia Institute of Neurology University College London United Kingdom

The evolution of epidemiological parameters, such as instantaneous reproduction number Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to Rt estimation, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt;the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes;the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in revealing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for accurate and timely estimating transmission dynamics from reported data. Copyright © 2020, The Authors. All rights reserved.

关键词： Cell proliferation

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Merging Deep Learning, Chemistry, and Diffraction for High-Throughput Material Structure Prediction

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Microscopy and Microanalysis 2019年第S2期25卷 168-169页

作者： Matthew L. Gong Brandon D. Miller Ray R. Unocic Khallid Hattar Bryan Reed Dan Masiel Tolga Tasdizen Jeffery A. Aguiar Idaho National Laboratory Nuclear Materials Department Idaho Falls IdahoUSA. University of Utah Scientific Computing Imaging Institute Department of Electrical and Computer Engineering Salt Lake City UtahUSA. Oak Ridge National Laboratory Center for Nanophase Materials Science Oak Ridge TennesseeUSA. Sandia National Laboratories Center for Integrated Nanotechnologies Albuquerque New MexicoUSA. Integrated Dynamic Electron Solutions Pleasanton CaliforniaUSA.

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FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence

TechRxiv

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TechRxiv 2024年

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Xu, Ke Wang, Wen Jiang, Xuefeng Gao, Bo Lu, Jinda Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Computer and Information Technology The Engineering Research Center of Network Management Technology for High-Speed Railway Ministry of Education Beijing Jiaotong University Beijing China School of Information Science and Technology University of Science and Technology of China Hefei China

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos distributed among end devices in EI, Federated Learning (FL) is proposed for collaborative training of shared AI models across multiple devices without compromising data privacy. However, the prevailing FL approaches cannot guarantee model generalization and adaptation on heterogeneous clients. Recently, Personalized Federated Learning (PFL) has drawn growing awareness in EI, as it enables a productive balance between local-specific training requirements inherent in devices and global-generalized optimization objectives for satisfactory performance. However, most existing PFL methods are based on the Parameters Interaction-based Architecture (PIA) represented by FedAvg, which suffers from unaffordable communication burdens due to large-scale parameters transmission between devices and the edge server. In contrast, Logits Interaction-based Architecture (LIA) allows to update model parameters with logits transfer and gains the advantages of communication lightweight and heterogeneous on-device model allowance compared to PIA. Nevertheless, previous LIA methods attempt to achieve satisfactory performance either relying on unrealistic public datasets or increasing communication overhead for additional information transmission other than logits. To tackle this dilemma, we propose a knowledge cache-driven PFL architecture, named FedCache, which reserves a knowledge cache on the server for fetching personalized knowledge from the samples with similar hashes to each given on-device sample. During the training phase, ensemble distillation is applied to on-device models for constructive optimization with personalized knowledge transferred from the server-side knowledge cache. Empirical experiments on four datasets demonstrate that FedCache achiev

关键词： Federated learning

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Identification of Surface and Deep Layer Muscles Activity by Surface EMG

Identification of Surface and Deep Layer Muscles Activity by...

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SICE Annual Conference

作者： Toshiki Koshio Shigeru Sakurazawa Masashi Toda Junichi Akita Kazuaki Kondo Yuichi Nakamura Department of System Information Science Future University Hakodate Department of Electrical & Computer Engineering Kanazawa University Academic Center for Computing and Media Studies Kyoto University

ISBN: (纸本)9784907764401

In the surface EMG measurement, since signals generated from surface layer muscles are interfered with signals from deep layer muscles, it is difficult to identify each EMG. In this study, we aimed to identify surface layer and deep layer muscle activity by surface electrode. We focused on the intersection of the two muscles of surface layer and deep layer whose fibers run at right-angle each other. In such place, we placed electrodes along each muscle fiber. We observed the difference of each signals measured from each electrode sets for two kinds of body movements in which each muscle acts independently. At that time, it was confirmed that only the EMG signal along each muscle was detected when the muscle acted independently. Therefore, we thought that it can be identified surface or deep layer muscle activity by the difference of propagation directions of EMG signal along muscle fiber. Accordingly, we tried to identify the propagation direction of EMG using an electrode array. From this result, we found that the propagation direction can be visible by the electrode array. It is thought that we can identify muscle activity of deep layer muscle from the mixed signal from both of surface layer and deep layer by this method.

关键词： Surface EMG Surface layer Deep layer Electrode array Propagation direction of EMG

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NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI

TechRxiv

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TechRxiv 2022年

作者： Xu, Junshen Moyer, Daniel Gagoski, Borjan Iglesias, Juan Eugenio Grant, P. Ellen Golland, Polina Adalsteinsson, Elfar Department of Electrical Engineering and Computer Science MIT CambridgeMA United States MIT CambridgeMA United States Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital BostonMA United States Harvard Medical School BostonMA United States The Center for Medical Image Computing UCL London United Kingdom The Martinos Center for Biomedical Imaging Harvard Medical School BostonMA United States

Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects, e.g., fetal MRI. However, existing slice-to-volume reconstruction methods are time-consuming, especially when a high-resolution volume is desired. Moreover, they are still vulnerable to severe subject motion and when image artifacts are present in acquired slices. In this work, we present NeSVoR, a resolution-agnostic slice-to-volume reconstruction method, which models the underlying volume as a continuous function of spatial coordinates with implicit neural representation. To improve robustness to subject motion and other image artifacts, we adopt a continuous and comprehensive slice acquisition model that takes into account rigid inter-slice motion, point spread function, and bias fields. NeSVoR also estimates pixel-wise and slice-wise variances of image noise and enables removal of outliers during reconstruction and visualization of uncertainty. Extensive experiments are performed on both simulated and in vivo data to evaluate the proposed method. Results show that NeSVoR achieves state-of-the-art reconstruction quality while providing two to ten-fold acceleration in reconstruction times over the state-of-the-art algorithms. © 2022, CC BY.

关键词： Magnetic resonance imaging

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FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence

arXiv

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arXiv 2023年

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Xu, Ke Wang, Wen Jiang, Xuefeng Gao, Bo Lu, Jinda The Institute of Computing Technology Chinese Academy of Sciences Beijing China The University of Chinese Academy of Sciences Beijing China The Zhongguancun Laboratory Beijing China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Computer and Information Technology The Engineering Research Center of Network Management Technology for High-Speed Railway Ministry of Education Beijing Jiaotong University Beijing China The School of Information Science and Technology University of Science and Technology of China Hefei China

关键词： Distillation

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An interpretable decision-support model for breast cancer diagnosis using histopathology images

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Journal of Pathology Informatics 2023年 14卷 100319-100319页

作者： Krishna, Sruthi Suganthi, S.S. Bhavsar, Arnav Yesodharan, Jyotsna Krishnamoorthy, Shivsubramani Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India Valeo Navalur Chennai India School of Computing and Electrical Engineering IIT Mandi Himachal Pradesh India Department of Pathology Amrita Institute of Medical Science Kochi India Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

Microscopic examination of biopsy tissue slides is perceived as the gold-standard methodology for the confirmation of presence of cancer cells. Manual analysis of an overwhelming inflow of tissue slides is highly susceptible to misreading of tissue slides by pathologists. A computerized framework for histopathology image analysis is conceived as a diagnostic tool that greatly benefits pathologists, augmenting definitive diagnosis of cancer. Convolutional Neural Network (CNN) turned out to be the most adaptable and effective technique in the detection of abnormal pathologic histology. Despite their high sensitivity and predictive power, clinical translation is constrained by a lack of intelligible insights into the prediction. A computer-aided system that can offer a definitive diagnosis and interpretability is therefore highly desirable. Conventional visual explanatory techniques, Class Activation Mapping (CAM), combined with CNN models offers interpretable decision making. The major challenge in CAM is, it cannot be optimized to create the best visualization map. CAM also decreases the performance of the CNN models. To address this challenge, we introduce a novel interpretable decision-support model using CNN with a trainable attention mechanism using response-based feed-forward visual explanation. We introduce a variant of DarkNet19 CNN model for the classification of histopathology images. In order to achieve visual interpretation as well as boost the performance of the DarkNet19 model, an attention branch is integrated with DarkNet19 network forming Attention Branch Network (ABN). The attention branch uses a convolution layer of DarkNet19 and Global Average Pooling (GAP) to model the context of the visual features and generate a heatmap to identify the region of interest. Finally, the perception branch is constituted using a fully connected layer to classify images. We trained and validated our model using more than 7000 breast cancer biopsy slide images from an

关键词： Attention branch network Breast cancer computer-aided diagnosis Convolutional neural networks Histopathology images

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