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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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EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in distributed Optimization 36

EF-BV: A Unified Theory of Error Feedback and Variance Reduc...

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Condat, Laurent Yi, Kai Richtarik, Peter King Abdullah Univ Sci & Technol KAUST Thuwal 239556900 Saudi Arabia

ISBN: (纸本)9781713871088

In distributed or federated optimization and learning, communication between the different computing units is often the bottleneck and gradient compression is widely used to reduce the number of bits sent within each communication round of iterative methods. There are two classes of compression operators and separate algorithms making use of them. In the case of unbiased random compressors with bounded variance (e.g., rand-k), the DIANA algorithm of Mishchenko et al. (2019), which implements a variance reduction technique for handling the variance introduced by compression, is the current state of the art. In the case of biased and contractive compressors (e.g., top-k), the EF21 algorithm of Richtarik et al. (2021), which instead implements an error-feedback mechanism, is the current state of the art. These two classes of compression schemes and algorithms are distinct, with different analyses and proof techniques. In this paper, we unify them into a single framework and propose a new algorithm, recovering DIANA and EF21 as particular cases. Our general approach works with a new, larger class of compressors, which has two parameters, the bias and the variance, and includes unbiased and biased compressors as particular cases. This allows us to inherit the best of the two worlds: like EF21 and unlike DIANA, biased compressors, like top-k, whose good performance in practice is recognized, can be used. And like DIANA and unlike EF21, independent randomness at the compressors allows to mitigate the effects of compression, with the convergence rate improving when the number of parallel workers is large. This is the first time that an algorithm with all these features is proposed. We prove its linear convergence under certain conditions. Our approach takes a step towards better understanding of two so-far distinct worlds of communication-efficient distributed learning.

关键词： Compressors

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Programming and Mapping for Mixed Heterogeneous Devices: The Case of Optical Flow

Programming and Mapping for Mixed Heterogeneous Devices: The...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Sergio Alonso Pascual Arturo Gonzalez-Escribano Departamento de Informática Universidad de Valladolid. ETSII Valladolid Spain

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Current parallel systems are increasingly heterogeneous, mixing devices of different types and computing capabilities. Exploiting multiple different devices for the same application continues to be a challenge that ranges from technical problems related to synchronizing and communicating diverse devices to problems of load distribution and flexibility to adjust the computation to the platform resources. In this work, we study the problem of using and extending a heterogeneous portability layer to program and adapt HSOpticalFlow to heterogeneous platforms. HSOpticalFlow is a streaming application to estimate the apparent movement of objects in a sequence of images. It is a simple but characteristic example of the structure of applications based on multilevel ILS (Iterative Loop Stencil), also known as multi-grid methods, applied to a sequence of inputs. Starting from the original CUDA reference code, we present a methodology and programming techniques based on the Controller programming model to implement it as a pipeline among multiple devices. We discuss a technique to determine a proper work partition and mapping for a set of devices. This allows for building very efficient parallel solutions, using similar devices or taking advantage of devices with lower computing power, to reduce the load and increase the productivity of more powerful ones. We present the results of an experimental study using several GPUs of different vendors, architectures, and generations, showing that this solution allows combinations of devices to be efficiently exploited to improve performance. Specifically, the results include speedups of 1.91x using two NVIDIA A100 GPUs and 1.21x using one NVIDIA V100 GPU and one AMD WX9100 GPU, which is about $3 x$ slower than the NVIDIA GPU for this application.

关键词： Productivity Performance evaluation Computational modeling Pipelines Graphics processing units Computer architecture Programming Streaming media Optical flow Tuning

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NTIRE 2022 Challenge on Perceptual image Quality Assessment

NTIRE 2022 Challenge on Perceptual Image Quality Assessment

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Gu, Jinjin Cai, Haoming Dong, Chao Ren, Jimmy S. Timofte, Radu Gong, Yuan Lao, Shanshan Shi, Shuwei Wang, Jiahao Yang, Sidi Wu, Tianhe Xia, Weihao Yang, Yujiu Cao, Mingdeng Heng, Cong Fu, Lingzhi Zhang, Rongyu Zhang, Yusheng Wang, Hao Song, Hongjian Wang, Jing Fan, Haotian Hou, Xiaoxia Sun, Ming Li, Mading Zhao, Kai Yuan, Kun Kong, Zishang Wu, Mingda Zheng, Chuanchuan Conde, Marcos, V Burchi, Maxime Feng, Longtao Zhang, Tao Li, Yang Xu, Jingwen Wang, Haiqiang Liao, Yiting Li, Junlin Xu, Kele Sun, Tao Xiong, Yunsheng Keshari, Abhisek Komal, Komal Thakur, Sadbhawana Jakhetiya, Vinit Subudhi, Badri N. Yang, Hao-Hsiang Chang, Hua-En Huang, Zhi-Kai Chen, Wei-Ting Kuo, Sy-Yen Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Tiwari, Anil Kumar Shanghai AI Lab Shanghai Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China SenseTime Res Beijing Peoples R China Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China UCL London England Netease Hangzhou Peoples R China Kuaishou Beijing Peoples R China Univ Wurzburg Comp Vis Lab Wurzburg Germany ByteDance Beijing Peoples R China Key Lab Parallel & Distributed Proc Beijing Peoples R China Indian Inst Technol Jammu Jagti Jammu & Kashmir India Indian Inst Technol Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata W Bengal India Indian Inst Technol Jodhpur Jodhpur Rajasthan India Natl Taiwan Univ New Taipei Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022. This challenge is held to address the emerging challenge of IQA by perceptual image processing algorithms. The output images of these algorithms have completely different characteristics from traditional distortions and are included in the PIPAL dataset used in this challenge. This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods. The challenge has 192 and 179 registered participants for two tracks. In the final testing stage, 7 and 8 participating teams submitted their models and fact sheets. Almost all of them have achieved better results than existing IQA methods, and the winning method can demonstrate state-of-the-art performance.

关键词： image quality Computer vision conferences Computational modeling Market research Distortion Pattern recognition

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Practical distributed Implementation of Very Large Scale Petri Net Simulations 41st

Practical Distributed Implementation of Very Large Scale Pe...

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41st International conference on Application and Theory of Petri Nets and Concurrency, Petri Nets 2020 and 20th International conference on Application of Concurrency to System Design, ACSD 2020

作者： Rafiev, Ashur Morris, Jordan Xia, Fei Yakovlev, Alex Naylor, Matthew Moore, Simon Thomas, David Bragg, Graeme Vousden, Mark Brown, Andrew School of Engineering Newcastle University Newcastle upon Tyne United Kingdom Computer Architecture Group Cambridge University Cambridge United Kingdom Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Electronics and Computer Science University of Southampton Southampton United Kingdom

ISBN: (纸本)9783662653029

With the continued increase of size and complexity of contemporary digital systems, there is a growing need for models of large size and high complexity, as well as methods of analyzing such models. This paper presents a method for simulating large-scale concurrent Petri net models using parallel distributed hardware platforms. By using POETS architecture, our method allows the mapping of concurrent Petri net executions onto 49,152 parallel processing hardware threads to achieve orders of magnitude (45 to 220 times) improvements of simulation speed, compared to conventional simulation methods using single processor systems. The presented method employs techniques including Petri net model partitioning, the use of max-step and locally-interleaving semantics, and the fair firing of transitions. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Petri nets

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An Analysis Model of English Text Coherence Based on RST Dependency Relationship

An Analysis Model of English Text Coherence Based on RST Dep...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Guimin Huang Wanying Shuai Qingkai Guo Nanxiao Deng Jianxing Lin Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

Text coherence analysis is an important and challenging task that is essential for subtasks such as automatic summarisation, viewpoint extraction and machine translation in natural language processing (NLP). A large body of previous work has used linguistic features such as lexical, syntactic and entity features to capture relatively shallow coherence features, while ignoring deeper logically relevant features hidden in the text. We propose an end-to-end English text coherence analysis model (RGCM for short) that incorporates RST and graph convolutional neural networks (GCN). The intra- and inter-sentence logical relations of the input text are first mapped onto a discourse relationship tree, and then transformed into an RST-dependent context graph by certain pruning strategies. Subsequently, we propose a GCN-based text coherence assessment framework to capture intra- and inter-sentence interactions to assess text coherence. We conducted experiments on two different coherence assessment datasets and achieved accuracy rates of 95.5% and 97.8% respectively.

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8th Latin American High Performance Computing conference, CARLA 2021

8th Latin American High Performance Computing Conference, CA...

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8th Latin American High Performance Computing conference, CARLA 2021

ISBN: (纸本)9783031042089

The proceedings contain 18 papers. The special focus in this conference is on Latin American High Performance Computing. The topics include: Accelerating Smart City Simulations;distributed Artificial Intelligent Model Training and Evaluation;large-Scale distributed Deep Learning: A Study of Mechanisms and Trade-Offs with PyTorch;wind Prediction Using Deep Learning and High Performance Computing;an Analysis of Neural Architecture Search and Hyper Parameter Optimization methods;Solving the Heat Transfer Equation by a Finite Difference Method Using Multi-dimensional Arrays in CUDA as in Standard C;High-Throughput of Measure-Preserving Integrators Derived from the Liouville Operator for Molecular Dynamics Simulations on GPUs;an Efficient parallel Model for Coupled Open-Porous Medium Problem Applied to Grain Drying processing;Energy Consumption Studies of WRF Executions with the LIMITLESS Monitor;Improving Performance of Long Short-Term Memory Networks for Sentiment Analysis Using Multicore and GPU Architectures;a Methodology for Evaluating the Energy Efficiency of Post-Moore Architectures;Understanding COVID-19 Epidemic in Costa Rica Through Network-Based Modeling;an Efficient Vectorized Auction Algorithm for Many-Core and Multicore Architectures;Green Energy HPC Data Centers to Improve processing Cost Efficiency;DICE: Generic Data Abstraction for Enhancing the Convergence of HPC and Big Data;a Comparative Study of Consensus Algorithms for distributed Systems.

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Algorithmic Implementation of Parking Spot Detector and Counter using MATLAB and OpenCV

Algorithmic Implementation of Parking Spot Detector and Coun...

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Innovation in Technology (ASIANCON), Asian conference on

作者： Neha Sable Chrisanne Rebello Kashyap Rathod Shawn Pereira Mrinmoyee Mukherjee Department of Information Technology St. Francis Institute of Technology Mumbai India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

In modern urban environments, efficient utilization of parking spaces has become a critical challenge. While recent technologies have made strides in parking management, the identification of problems in scenarios with sparse vehicle density remains a significant hurdle. This project addresses the issue of parking space occupancy detection within distributed parking ecosystems, with a particular focus on lined and commercial parking lots. It focuses on the exploration of computer vision methods to overcome the challenges posed by varying lighting conditions, environmental factors, and sparse vehicle distribution. The primary objective is to develop an advanced system using Computer Vision to identify vacant parking slots and a counter along with image processing to identify image properties using Matlab. The system's effectiveness is based on Computer Vision technologies like OpenCV as well as Matlab tools for image processing.

关键词： Computer vision Technological innovation image processing Urban areas Ecosystems Lighting Detectors Object recognition MATLAB Optimization

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Automated Identification of Traffic Accidents in images and Videos Employing Advanced Deep Learning methods 25

Automated Identification of Traffic Accidents in Images and ...

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26th International conference on distributed Computing and Networking, ICDCN 2025

作者： Kumari, Babli Dhir, Renu Chatterjee, Siddhartha Jain, Suchi Computer Science and Engineering Dr B R Ambedkar National Institute of Technology Punjab Jalandhar144011 India Computer Science and Engineering College of Engineering and Management Kolaghat West Bengal Purba Medinipur721171 India

ISBN: (纸本)9798400710629

An accident detection is essential since it ensures emergency response and raises the bar for road safety in general. In this paper, A CNN (Convolutional Neural Network) based collision recognition technique using the Tensorflow framework has been proposed. Deep learning has been used to automatically pick up information and identify important characteristics from pictures or videos taken by in-car cameras or roadside cameras. By utilizing a CNN architecture, model can effectively identify various accident-related patterns and distinguish them from normal traffic scenes. To train the CNN model, a large-scale dataset consisting of both accident and non-accident samples. The dataset is carefully labelled to ensure accurate ground truth annotations. During the training phase, data augmentation techniques has been employed to enhance the generalization ability of the model and mitigate overfitting. This technique has been used such as random crop, platooning and rotation to artificially enhance training diversity samples. For cropping, flipping and rotation of the images we have used image augmentation which is a function of imageDataGenerator. After training, we evaluated the efficiency of our model on a separate test set, consisting of previously unseen accident and non-accident instances. Our model achieved 98% accuracy and robustness, demonstrating its effectiveness in accident detection. An OTP verification feature has been integrated to further enhance the system's functionality. This OTP verification confirms that emergency responders reach the accident location, adding a layer of security and ensuring follow-through in emergency responses. Overall, this study showcases the potential of deep learning techniques, specifically Convolutional Neural Networks, in addressing the crucial task of accident detection. This proposed method can integrate with the Computing Classification System (CCS), ensuring efficient categorization and real-time processing of data for acc

关键词： Convolutional neural networks

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A Power-Efficient, Analog Integrated, Sigmoid-Based Edge Detector

A Power-Efficient, Analog Integrated, Sigmoid-Based Edge Det...

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International conference on Microelectronics, ICM

作者： Vassilis Alimisis Christos Dimas Paul P. Sotiriadis Department of Electrical and Computer Engineering National Technical University of Athens Athens Greece and Archimedes/Athena RC Greece

ISBN: (数字)9798350379396

ISBN: (纸本)9798350379402

Edge detection is a powerful tool widely employed in numerous Computer Vision applications. One of its key functions is to identify and define the spatial boundaries of objects within an image, allowing for the segmentation and recognition of these objects. Like many Computer Vision techniques, edge detection is computationally intensive, but it lends itself well to parallel processing. As a result, hardware-specific implementations offer significant advantages over traditional CPU-based methods, providing faster and more efficient processing. In this paper, we introduce a hardware-optimized version of the Robert Cross operator based on sigmoid function circuit. This design, along with its building blocks, achieves notable efficiency in both power and area consumption. This circuit was tested on specific images, attaining a medium Structural Similarity Index Metric of 0.83, with each pixel requiring only $820\mu m^processing$ of chip area. The proposed edge detector was implemented and verified employing a TSMC 90nm CMOS process, with the tool Cadence IC Suite.

关键词： Semiconductor device modeling Computer vision image edge detection Detectors parallel processing CMOS process parallel architectures Microelectronics Object recognition Testing

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IMPROVING THE CLASSIFICATION OF PHONETIC SEGMENTS FROM RAW ULTRASOUND USING SELF-SUPERVISED LEARNING AND HARD EXAMPLE MINING 47

IMPROVING THE CLASSIFICATION OF PHONETIC SEGMENTS FROM RAW U...

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47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Xiong, Yunsheng Xu, Kele Jiang, Meng Cheng, Liang Dou, Yong Wang, Jinjia Natl Univ Def Technol Changsha Peoples R China Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China Changsha Med Univ Changsha Peoples R China Cent South Univ Xiangya Hosp Changsha Peoples R China Yanshan Univ Qinhuangdao Hebei Peoples R China

ISBN: (纸本)9781665405409

Ultrasound tongue imaging is an attractive way for speech production study as it provides an effective visualization for the vocal tract. Automatic classification of phonetic segments (tongue shapes) from raw ultrasound data is vital for further interpretation. Recently, deep learning-based approaches have been adopted in this task, which required a large-scale annotated dataset for the training, and it is not easy to be obtained in practical settings. Moreover, the data may contain many hard examples for the classification task, due to contamination of speckle noise. In this paper, we aim to address these issues: firstly, self-supervised learning is adopted to utilize the unlabeled datasets and extract the features without any human annotations;secondly, hard example mining is applied to imitate the learning path of the clinical linguists. To empirically demonstrate the proposed method's effectiveness, we evaluate the method on the Ultrax Typically Developing dataset (UXTD) under different scenarios. The results show that the proposed method outperforms the other methods and achieves superior performance. To better promote the study in this field, we release our code publicly at (1).

关键词： Ultrasound tongue imaging Automatic classification Self-supervised learning Hard example mining

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