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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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Performance, Energy and parallelism: Using Near Data processing in Utility and Cloud Computing

Performance, Energy and Parallelism: Using Near Data Process...

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IEEE International conference on Utility and Cloud Computing (UCC)

作者： Gul Agha Dipayan Mukherjee Atul Sandur Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL USA

Massive amounts of data are generated by sensor networks, edge computers, IoT devices, and enterprise networks. To process this volume of data requires (1) a scalable programming model that is not only concurrent and distributed, but supports the mobility of data and processes (actors), and (2) algorithms to distribute computations between nodes in a manner that improves overall performance while considering energy use in the system. With appropriate programming tools, we can distribute a given computation in a way that makes effective use of edge devices to improve performance while lowering energy consumption. The paper describes our work building on ideas based on the Actor model of computation. These include characterizing the relation of performance and energy consumption in parallel computation, and methods to support scalable placement mechanisms under dynamically changing network conditions and computational loads on edge devices. The paper will conclude with a presentation with a summary of open research problems.

关键词： Performance evaluation Energy consumption Cloud computing Solid modeling Computational modeling distributed databases Programming

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17th International conference on Intelligent Computing, ICIC 2021

17th International Conference on Intelligent Computing, ICIC...

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17th International conference on Intelligent Computing, ICIC 2021

ISBN: (纸本)9783030845315

The proceedings contain 130 papers. The special focus in this conference is on Intelligent Computing. The topics include: A Comparable Study on Dimensionality Reduction methods for Endmember Extraction;Hyperspectral image Classification with Locally Linear Embedding, 2D Spatial Filtering, and SVM;a Hierarchical Retrieval Method Based on Hash Table for Audio Fingerprinting;Automatic Extraction of Document Information Based on OCR and image Registration Technology;using Simplified Slime Mould Algorithm for Wireless Sensor Network Coverage Problem;super-Large Medical image Storage and Display Technology Based on Concentrated Points of Interest;person Re-identification Based on Hash;a Robust and Automatic Recognition Method of Pointer Instruments in Power System;partial Distillation of Deep Feature for Unsupervised image Anomaly Detection and Segmentation;an Evolutionary Neuron Model with Dendritic Computation for Classification and Prediction;Speech Recognition Method for Home Service Robots Based on CLSTM-HMM Hybrid Acoustic Model;serialized Local Feature Representation Learning for Infrared-Visible Person Re-identification;a Novel Decision Mechanism for image Edge Detection;Rapid Earthquake Assessment from Satellite imagery Using RPN and Yolo v3;attention-Based Deep Multi-scale Network for Plant Leaf Recognition;short Video Users’ Personal Privacy Leakage and Protection Measures;An Efficient Video Steganography Method Based on HEVC;analysis on the Application of Blockchain Technology in Ideological and Political Education in Universities;parallel Security Video Streaming in Cloud Server Environment;An Efficient Video Steganography Scheme for Data Protection in H.265/HEVC;an Improved Genetic Algorithm for distributed Job Shop Scheduling Problem;A Robust Lossless Steganography Method Based on H.264/AVC;detection of Pointing Position by Omnidirectional Camera.

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PIPArch: Programmable image processing Architecture Using Sliding Array

PIPArch: Programmable Image Processing Architecture Using Sl...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Feiyang Wu Zhuoran Song Jing Ke Li Jiang Naifeng Jing Xiaoyao Liang Shanghai Jiao Tong University Shanghai China

image processing arises as a promising domain for manifold applications requiring for heavy computing power and memory bandwidth with higher image resolution. Graphics processing unit (GPU) is widely used in image processing algorithms but suffers from its powerful programmability that costs high hardware overhead. Moreover, GPU consumes much energy to access data from high-capacity register files, making it hard to implement on wearable devices. Enabling low power and efficient architecture with low hardware overhead remains *** this paper, we propose a programmable image processing architecture (PIPArch) that explores the spatial locality in images to save energy while achieving high performance. We also design the instruction set architecture (ISA) to control the PIPArch. By supporting multiple parallel pipelines, we can keep the hardware utilization of PIPArch high. We evaluate the proposed PIPArch by developing the cycle-accurate simulator with some typical image processing algorithms. Compared to NVIDIA Tesla V100 GPU, PIPArch gains 23.63x speedup.

关键词： image processing Instruction sets Wearable computers Graphics processing units Computer architecture Programmable logic arrays Hardware

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17th International conference on Intelligent Computing, ICIC 2021

17th International Conference on Intelligent Computing, ICIC...

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17th International conference on Intelligent Computing, ICIC 2021

ISBN: (纸本)9783030845285

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Construction of Political Science Research Application Platform Based on Big Data Analysis Technology

Construction of Political Science Research Application Platf...

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Education, Knowledge and Information Management (ICEKIM), International conference on

作者： Qiao Li Dawei Zhang Hulunbuir University Hulunbuir Inner Mongolia China Hulunbuir Economic and Social Research Institute Hulunbuir Inner Mongolia China

ISBN: (纸本)9781665416672

Based on big data analysis technology, Hadoop and Spark big data parallel computing framework and HDFS distributed file storage database, we can complete the construction of political science research application platform. The political science research application platform pays attention to data capture, data cleaning, analysis and mining, visual display and other operations on the public comments and related responses of the masses under the government open platform and the opinions and contents published by netizens under the media social platform, which are involved in public opinion surveys under the current network environment, so as to complete the description and analysis of political phenomena with the help of the unique advantages of big data technology, and realize the innovation of political science research methods and the expansion of research fields. At the same time, it also makes an innovative attempt for the scientific and standardized research of political science.

关键词： Visualization Technological innovation Statistical analysis Memory Big Data parallel processing Media

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17th International conference on Intelligent Computing, ICIC 2021

17th International Conference on Intelligent Computing, ICIC...

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17th International conference on Intelligent Computing, ICIC 2021

ISBN: (纸本)9783030845216

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image Distillation Based Screening for X-ray Crystallography Diffraction images

Image Distillation Based Screening for X-ray Crystallography...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Lingyao Chen Kang Xu Xiaoying Zheng Yongxin Zhu Yi Jing Shanghai Information Technology Research Center Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China

The modern synchrotron radiation facilities are producing massive diffraction images, which present a severe problem for data processing due to the high dimensionality of imaging data. Feature recognition and selection based deep learning methods have been developed to analyze data automatically. One crucial step is to use AI to screen out the diffraction images without Bragg spots. This paper proposes a feature distillation based approach for screening. It helps to reduce over 40% raw data volume and greatly alleviates the post processing workload faced by scientists.

关键词： Heating systems Deep learning Diffraction Imaging X-ray diffraction Data processing Crystallography

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parallel Shift-Invert Spectrum Slicing on distributed Architectures with GPU Accelerators 20

Parallel Shift-Invert Spectrum Slicing on Distributed Archit...

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49th International conference on parallel processing (ICPP)

作者： Williams-Young, David B. Yang, Chao Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA

ISBN: (纸本)9781450388160

The solution of large scale eigenvalue problems (EVP) is often the computational bottleneck for many scientific and engineering applications. Traditional eigensolvers, such as direct (e.g. ScaLAPACK) and Krylov subspace (e.g. Lanczos) methods, have struggled in achieving high scalability on large computing resources due to communication and synchronization bottlenecks which are inherent in their implementation. This includes a difficulty in developing well-performing ports of these algorithms to architectures which rely on the use of accelerators, such as graphics processing units (GPU), for the majority of their floating point operations. Recently, there has been significant research into the development of eigensolvers based on spectrum slicing, in particular shift-invert spectrum slicing, to alleviate the communication and synchronization bottlenecks of traditional eigensolvers. In general, spectrum slicing trades the global EVP for many smaller, independent EVPs which may be combined to assemble some desired subset of the entire eigenspectrum. The result is a method which utilizes more floating point operations than traditional eigensolvers, but in a way which allows for the expression of massive concurrency leading to an overall improvement in time-to-solution on large computing resources. In this work, we will examine the performance of parallel shift-invert spectrum slicing on modern GPU clusters using state-of-the-art linear algebra software.

关键词： Graphics processing unit

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Sparse parallel Training of Hierarchical Dirichlet Process Topic Models

Sparse Parallel Training of Hierarchical Dirichlet Process T...

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conference on Empirical methods in Natural Language processing (EMNLP)

作者： Terenin, Alexander Magnusson, Mans Jonsson, Leif Imperial Coll London London England Uppsala Univ Uppsala Sweden Aalto Univ Espoo Finland Ericsson AB Stockholm Sweden Linkoping Univ Linkoping Sweden

ISBN: (纸本)9781952148606

To scale non-parametric extensions of probabilistic topic models such as Latent Dirichlet allocation to larger data sets, practitioners rely increasingly on parallel and distributed systems. In this work, we study data-parallel training for the hierarchical Dirichlet process (HDP) topic model. Based upon a representation of certain conditional distributions within an HDP, we propose a doubly sparse data-parallel sampler for the HDP topic model. This sampler utilizes all available sources of sparsity found in natural language-an important way to make computation efficient. We benchmark our method on a well-known corpus (PubMed) with 8m documents and 768m tokens, using a single multi-core machine in under four days.

关键词： Statistics

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Mutual Contrastive Low-rank Learning to Disentangle Whole Slide image Representations for Glioma Grading 33

Mutual Contrastive Low-rank Learning to Disentangle Whole Sl...

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33rd British Machine Vision conference Proceedings, BMVC 2022

作者： Zhang, Lipei Wei, Yiran Fu, Ying Price, Stephen Schönlieb, Carola-Bibiane Li, Chao Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Clinical Neuroscience University of Cambridge United Kingdom School of Computer Science and Technology Beijing Institute of Technology China

Whole slide images (WSI) provide valuable phenotypic information for histological assessment and malignancy grading of tumors. The WSI-based grading promises to provide rapid diagnostic support and facilitate digital health. Currently, the most commonly used WSIs are derived from formalin-fixed paraffin-embedded (FFPE) and Frozen section. The majority of automatic tumor grading models are developed based on FFPE sections, which could be affected by the artifacts introduced from tissue processing. The frozen section exists problems such as low quality that might influence training within single modality as well. To overcome these problems in the single modal training and achieve better multi-modal and discriminative representation disentanglement in brain tumor, we propose a mutual contrastive low-rank learning (MCL) scheme to integrate FFPE and frozen sections for glioma grading. We first design a mutual learning scheme to jointly optimize the model training based on FFPE and frozen sections. In this proposed scheme, we design a normalized modality contrastive loss (NMC-loss), which could promote to disentangle multi-modality complementary representation of FFPE and frozen sections from the same patient. To reduce intra-class variance, and increase inter-class margin at intra- and inter-patient levels, we conduct a low-rank (LR) loss. Our experiments show that the proposed scheme achieves better performance than the model trained based on each single modality or mixed modalities without reducing the efficiency of inference, and even improves the feature extraction in classical attention-based multiple instances learning methods (MIL). The combination of NMC-loss and low-rank loss outperforms other typical contrastive loss functions. The source code is in https://***/uceclz0/MCL_glioma_grading. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Grading

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