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Architectural distortion detection approach guided by mammary gland spatial pattern in digital breast tomosynthesis

Architectural distortion detection approach guided by mammar...

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conference on Medical Imaging - computer-Aided Diagnosis

作者： Li, Yue Xie, Zheng He, Zilong Ma, Xiangyuan Guo, Yanhui Chen, Weiguo Lu, Yao Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Southern Med Univ Nanfang Hosp Dept Radiol Guangzhou Peoples R China Univ Illinois Dept Comp Sci Springfield IL 62703 USA Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou Peoples R China

ISBN: (纸本)9781510633964

Architectural distortion (AD) is one of the most important potentially ominous signs of breast cancer. As a 3D imaging, digital breast tomosynthesis (DBT) is an accurate tool to detect AD. We developed a deep learning approach for AD detection guided by mammary gland spatial pattern (MGSP) in DBT. The approach consists of two stages: 2D detection and 3D aggregation. In 2D detection, prior MGSP information is obtained first. It includes 1) magnitude image and orientation field map produced from Gabor filters and 2) mammary gland convergence map. Second, Faster-RCNN detection network is employed. Region proposal network extracts features and determines locations of AD candidates and the soft classifier is used for reducing false positives. In 3D aggregation, a region fusion strategy is designed to fuse 2D candidates into 3D candidates. For evaluation, 265 DBT volumes (138 with ADs and 127 without any lesion) were collected from 68 patients. Free response receiver operating characteristic curve was obtained and the mean true positive fraction (MTPF) was used as the figure-of-merit of model performance. Compared with a baseline model based on convergence measure, the six-fold cross validation results showed that our proposed approach achieved MTPF of 0.50 +/- 0.04, while the baseline achieved 0.37 +/- 0.03. The improvement of our approach was statistically significant (p << 0.001).

关键词： Architectural distortion digital breast tomosynthesis computer aided detection mammary gland spatial pattern deep learning

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HpSpMV: A heterogeneous parallel computing scheme for SpMV on the sunway taihulight supercomputer 21

HpSpMV: A heterogeneous parallel computing scheme for SpMV o...

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21st IEEE International conference on high performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Chen, Yuedan Xiao, Guoqing Xiao, Zheng Yang, Wangdong College of Computer Science and Electronic Engineering Hunan University Changsha Hunan410082 China National Supercomputing Center in Changsha Changsha Hunan410082 China David R. Cheriton School of Computer Science University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9781728120584

Sparse matrix-vector multiplication (SpMV) is one of the most essential algorithms in various applications. This paper designs hpSpMV, a heterogeneous parallel computing scheme for SpMV, on the Sunway TaihuLight. There are three main contributions of the hpSpMV. (1) We propose a heterogeneous parallelization design for the SpMV based on the heterogeneous manycore architecture of the SW26010 of Sunway TaihuLight and the given sparse matrix formats. (2) We analyze the execution time of the proposed heterogeneous parallel SpMV on the Sunway. (3) We propose an auto-tuning framework to set the proper parameter of the heterogeneous parallel SpMV based on the execution time analysis on the Sunway. We test the hpSpMV's performance on the Sunway TaihuLight, the result analysis indicates that the hpSpMV has obvious performance improvement and good scalability on the Sunway TaihuLight. © 2019 IEEE.

关键词： Supercomputers

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Dynamically reconfigurable architecture for high-throughput hash function in key-value store 21

Dynamically reconfigurable architecture for high-throughput ...

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作者： Liu, Ke Zhao, Mengying Ju, Lei Jia, Zhiping School of Computer Science and Technology Shandong University China School of Software Shandong University China

ISBN: (纸本)9781728120584

Distributed in-memory key-value stores (KVSs), such as Redis and Memcached, are widely deployed in modern data centers. In Redis, the throughput of Murmurhash2 is important to the performance of the whole KVS, and further affects the whole server. In this paper, we propose a high-performance scalable architecture for Murmurhash2 to improve data locality and reduce the latency, and deploy partial reconfiguration scheme to maximize the ratio of performance/area. Experiment results on Xilinx SOC platform demonstrate that the proposed architecture can improve the throughput by approximate 1.4x and achieve up to 5.3x performance/watt gain compared with the typical Intel platform. © 2019 IEEE.

关键词： Hash functions

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TrackNetV2: Efficient Shuttlecock Tracking Network 1

TrackNetV2: Efficient Shuttlecock Tracking Network

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1st International conference on Pervasive Artificial Intelligence, ICPAI 2020

作者： Sun, Nien-En Lin, Yu-Ching Chuang, Shao-Ping Hsu, Tzu-Han Yu, Dung-Ru Chung, Ho-Yi Ik, Tsi-Ui National Chiao Tung University College of Computer Science Department of Computer Science 1001 University Road Hsinchu City Taiwan

ISBN: (纸本)9781665404839

TrackNet, a deep learning network, was proposed to track high-speed and tiny objects such as tennis balls and shuttlecocks from videos. To conquer low image quality issues such as blur, afterimage, and short-term occlusion, some number of consecutive images are input together to detect an flying object. In this work, TrackNetV2 is proposed to improve the performance of TrackNet from various aspects, especially processing speed, prediction accuracy, and GPU memory usage. First of all, the processing speed is improved from 2.6 FPS to 31.8 FPS. The performance boost is achieved by reducing the input image size and re-engineering the network from a Multiple-In Single-Out (MISO) design to a Multiple-In Multiple-Out (MIMO) design. Then, to improve the prediction accuracy, a comprehensive dataset from diverse badminton match videos is collected and labeled for training and testing. The dataset consists of 55563 frames from 18 badminton match videos. In addition, the network mechanisms are composed of not only VGG16 and upsampling layers but also U-net. Last, to reduce GPU memory usage, the data structure of the heatmap layer is remodeled from a pixel-wise one-hot encoding 3D array to a real-valued 2D array. To reflect the change of the heatmap representation, the loss function is redesigned from a RMSE-based function to a weighted cross-entropy based function. An overall validation shows that the accuracy, precision and recall of TrackNetV2 respectively reach 96.3%, 97.0% and 98.7% in the training phase and 85.2%, 97.2% and 85.4% in a test on a brand new match. The processing speed of the 3-in and 3-out version TrackNetV2 can reach 31.84 FPS. The dataset and source code of this work are available at https://***:234/open-source/TrackNetv2/. © 2020 IEEE.

关键词： Graphics processing unit

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Real-time vineyard trunk detection for a grapes harvesting robot via deep learning 13

Real-time vineyard trunk detection for a grapes harvesting r...

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13th International conference on Machine Vision, ICMV 2020

作者： Badeka, Eftichia Kalampokas, Theofanis Vrochidou, Eleni Tziridis, Konstantinos Papakostas, George Pachidis, Theodore Kaburlasos, Vassilis Department of Computer Science International Hellenic University Kavala Greece

ISBN: (纸本)9781510640405

Research and development in agricultural robots are continuously increasing. However, dynamically changing agricultural environments provide adverse conditions to robotics operability. In order to perform the agricultural tasks safely and accurately, reliable landmarks from the surrounding environment need to be identified. In this work, deep learning is employed for accurate and fast detection of high-level features of vineyards, the vine trunks. More specifically, Faster regions-convolutional neural network (Faster R-CNN), You Only Look Once version 3 (YOLOv3) and YOLOv5 are tested for real-time vine trunk detection. The models are trained with an in-house dataset designed for the needs of this study, containing 1927 annotated vine trunks in 899 different images. Comparative results indicate YOLOv5 as the configuration that allows the faster and most accurate vine trunk detection, achieving an overall Average Precision of 73.2% in 29.6 ms. The high precision combined with the fast runtime performance prove that the YOLOv5 detector is suitable for real-time vine trunk detection executed by an autonomous harvesting robot. © 2021 SPIE.

关键词： Object detection

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NUMA-aware FFT-based Convolution on ARMv8 Many-core CPUs

NUMA-aware FFT-based Convolution on ARMv8 Many-core CPUs

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

作者： Xiandong Huang Qinglin Wang Shuyu Lu Ruochen Hao Songzhu Mei Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China School of Computer Science National University of Defense Technology Changsha China University of Pittsburgh Pittsburgh USA

Convolutional Neural Networks (CNNs), one of the most representative algorithms of deep learning, are widely used in various artificial intelligence applications. Convolution operations often take most of the computational overhead of CNNs. The FFT-based algorithm can improve the efficiency of convolution by reducing its algorithm complexity, there are a lot of works about the high-performance implementation of FFT-based convolution on many-core CPUs. However, there is no optimization for the non-uniform memory access (NUMA) characteristics in many-core CPUs. In this paper, we present a NUMA-aware FFT-based convolution implementation on ARMv8 many-core CPUs with NUMA architectures. The implementation can reduce a number of remote memory access through the data reordering of FFT transformations and the three-level parallelization of the complex matrix multiplication. The experiment results on a ARMv8 many-core CPU with NUMA architectures demonstrate that our NUMA-aware implementation has much better performance than the state-of-the-art work in most cases.

关键词： Deep learning Recurrent neural networks Convolution computer architecture Complexity theory Convolutional neural networks Kernel

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Application of high-grade carbon produced from tyre waste using advanced thermo-chemical technology

Application of high-grade carbon produced from tyre waste us...

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International conference on Cutting-Edge Research in Material Science and Chemistry (CRMSC)

作者： Sharma, Anita Sharma, Abhishek Joshi, Jyeshtharaj B. Jain, Rakesh Kumar Kasilingam, Rajkumar Veermata Jijabai Technol Inst Dept Tech & Appl Chem HR Mahajani Marg Matunga 19 India Manipal Univ Jaipur Dept Chem Engn Jaipur 303007 Rajasthan India Inst Chem Technol Dept Chem Engn Nathalal Parekh Marg Mumbai 19 Maharashtra India Int Ctr Inclus & Sustainable Ind Dev IC ISID UNIDO Joseph Stein Lane New Delhi 110003 India Indian Rubber Manufacturers Res Assoc Wagale Ind Estate Thana 400604 Maharashtra India

Advanced thermo-chemical treatment in inert environment (also known as pyrolysis) of end of life tyres results into generation of oil, non-condensable gases and carbonized product termed as char. Solid char accounts to 35-45 wt% of the total products depending upon the process conditions. The present study is directed towards evaluating the suitability of tyre char for certain commercial applications. Char contains additives (inorganics) which are used to enhance certain properties of commercial tyres. Characterization of char confirms the presence of 19% inorganics that constitutes of elements such as Zn, S, Mg, Si and Ca. The other characterization techniques such as Raman, SEM and TGA have assisted in examining the surface characteristics and stability of tyre char. Raman analysis has emphasized on the presence of graphitized carbon structure with lesser defects. TGA analysis has showed suitable thermal stability at higher temperatures. The direct application of tyre char without further treatment has been undertaken based on these analyses. Char obtained from tyre pyrolysis was evaluated in Styrene Butadiene Rubber based compounding formulation at a dose of 50 parts per hundred gram of Rubber in comparison with a standard carbon black grade of Semi Reinforcing type of carbon black with equivalent dose. It was found that the increase in tensile strength and associated properties of vulcanization of char sample and the char can be utilized in rubber products formulation without any negative impact. Significant improvement was observed on tear strength properties while adding the char or carbon black which proves that there exists the reinforcing characteristics of the char like standard carbon black. Based on these studies, it has been concluded that there is need for further upgradation of tyre char for application in dyes, coatings and activated carbon industries. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Pyrolysis Carbon black Rubber Char Thermo-chemical

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Local-Global Merge Tree Computation with Local Exchanges 19

Local-Global Merge Tree Computation with Local Exchanges

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International conference for high performance Computing, Networking, Storage and Analysis (SC)

作者： Nigmetov, Arnur Morozov, Dmitriy Graz Univ Technol Graz Austria Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (数字)9781450362290

ISBN: (纸本)9781450362290

A merge tree is a topological summary of a real-valued function on a graph. Merge trees can be used to find stable features in the data, report the number of connected components above any threshold, or compute other topological descriptors. A local-global merge tree provides a way of distributing a merge tree among multiple processors so that queries can be performed with minimal communication. While this makes them efficient in massively parallel setting, the only known algorithm for computing a local-global merge tree involves global reduction. Motivated by applications in cosmological simulations, we consider a restricted version of the problem: we compute a local-global tree down to a threshold fixed by the user. We describe two algorithms for computing such a tree via only local exchanges between processors. We present a number of experiments that show the advantage of our method on different simulations.

关键词： program processors Codes high performance computing Memory management Combustion

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Distributed multi-agent deep reinforcement learning for cooperative multi-robot pursuit

Distributed multi-agent deep reinforcement learning for coop...

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3rd Asian conference on Artificial Intelligence Technology, ACAIT 2019

作者： Yu, Chao Dong, Yinzhao Li, Yangning Chen, Yatong School of Data and Computer Science Sun Yat-Sen University Guangzhou510006 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China

As a popular research topic in the area of distributed artificial intelligence, the multi-robot pursuit problem is widely used as a testbed for evaluating coordinated and cooperative strategies in multi-robot systems. This study the problem of multi-robot pursuit game using reinforcement learning (RL) techniques is studied. Unlike most existing studies that apply fully centralised deep RL methods based on the centralised-learning and decentralised-execution scheme, the authors propose a fully decentralised multi-agent deep RL approach by modelling each agent as an individual deep RL agent that has its own individual learning system (i.e. individual action-value function, individual leaning update process, and individual action output). To realise coordination among agents, the limited information of other environmental agents is used as input of the learning process. Experimental results show that both distributed and centralised approaches can ultimately solve the pursuit-evasion problem in different dimensions, but the learning efficiency and coordination performance of the proposed distributed approach are much better than the traditional centralised approach. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Multipurpose robots

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Structures and Characteristics of high-performance Multi-bit Streaming Multiplayers

Structures and Characteristics of High-performance Multi-bit...

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International conference on Advanced computer Information Technologies (ACIT)

作者： Yaroslav Nykolaychuk Alina Davletova Petro Humennyi Natalia Vozna Igor Pitukh Oleg Zastavnyy Deparlament of Specialized Computer Systems Ternopil National Eonomic Univerity Ternopil Ukraine

ISBN: (数字)9781728167602

ISBN: (纸本)9781728167619

An application of multi-bit multipliers in universal computers and special processors for data encryption is analyzed. The use of single-bit half- and full adders with extremely minimal hardware and time complexity is substantiated. The structures of multi-bit streaming multipliers based on high-performance adder matrices with para-phase inputs and outputs are proposed. The characteristics of hardware and time complexity of this class of multipliers depending on the binary digits are investigated. The proposed structure and the system characteristics of multi-bit streaming multipliers with advanced functionality, which allow testing the correctness of the multiplication and cryptographic protection of the output information streaming of the multiplier by Galois codes are explored.

关键词： Adders Registers Periodic structures Delays Hardware Product codes program processors

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