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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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Cloud-Based Framework for Scalable and Real-Time Multi-Robot SLAM

Cloud-Based Framework for Scalable and Real-Time Multi-Robot...

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IEEE International Conference on Web Services (ICWS)

作者： Pengfei Zhang Huaimin Wang Bo Ding Suning Shang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

In the past decade, multi-robot simultaneous localization and mapping (SLAM) has been widely studied. However, the problem of collaborative SLAM with a large number of robots, such as dozens of robots, is far from being well solved. The challenges stem from not only the computation complexity in large-scale map merging but also the inefficiency to enable the parallel computing in this process, which is indispensable for us to make avail of the frontier of computing technology such as powerful cloud infrastructure. To effectively address these challenges, especially the latter one, we propose a scalable and real-time multi-robot visual SLAM framework based on the cloud robotic paradigm. The prominent feature of our framework is that it can distribute the SLAM process to multiple computing hosts in a cluster, which enables map building in parallel. To eliminate the bottleneck from data sharing between different sub-tasks, we also introduce diversified messaging pattern for various messaging scenarios, as well as the consistency policies for map data. The evaluations on the prototype of our framework, have shown that our method can do support as many as 256 robot entities simultaneously, without any compromising on the precision of poses estimation and map building.

关键词： Simultaneous localization and mapping Cloud computing Robot kinematics Task analysis Instruction sets Feature extraction

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Software Ranker: A New Comprehensive Software Ranking Approach

Software Ranker: A New Comprehensive Software Ranking Approa...

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IEEE International Conference on Software Engineering and Service sciences (ICSESS)

作者： Yarong Zeng Xunhui Zhang Yaozong Li Tao Wang Gang Yin Huaimin Wang National Laboratory for Parallel and Distributed Processing National University of Defence Technology Changsha China

ISBN: (纸本)9781538665664;9781538665657

With the popularity of the open source, the open source community has accumulated a large number of open source project. While these massive projects provide developers with rich reusable resource, they also bring difficulties for users to choose the appropriate software. Therefore, it is very meaningful to rank the software and tell users which is better. In this paper, we propose a novel approach that ranks software based on a global perspective different from traditional software evaluation and ranking methods. We evaluate the software from four dimensions, namely community popularity, development activity, software health and team health. Each dimension contains some metrics. We demonstrate the effectiveness of our method through comparative experiments. This method has been integrated into to the OSSEAN platform to form a software leaderboard.

关键词： Mathematical model Open source software Tools Software quality Software measurement

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A Research on Area Coverage Algorithm for Robotics

A Research on Area Coverage Algorithm for Robotics

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IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Yaru Kang Dianxi Shi National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781538674178

Area coverage path planning is a special path planning method, which requires the robot to go through every point except obstacles in workspace. It has been used in many fields, such as lawn mowing, snow removal, search and rescue task, pesticide spraying, demining robots, cleaning robots and so on. This paper presents the achievements of the last two decades on area coverage. We propose a new Classification based on method of Choset. We analyze and summarize the recently research results by the new classification method and list the advantages and disadvantages. Finally, we give a summary of the classification methods and propose future research directions.

关键词： Path planning Task analysis Robot sensing systems Classification algorithms Approximation algorithms

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Simulation analysis of heavy-ion-induced single-event response for nanoscale bulk-Si FinFETs and conventional planar devices

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science China(Technological sciences) 2017年第3期60卷 459-466页

作者： YU JunTing CHEN ShuMing CHEN JianJun HUANG PengCheng College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

FinFET technologies are becoming the mainstream process as technology scales *** on 28-nm bulk-Si FinFETs and

关键词： charge collection bipolar amplification reversed bipolar effect single-event effect(SEE) single-event transient(SET) FinFET planar device

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Instance-Specific Algorithm Selection via Multi-Output Learning

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Tsinghua science and technology 2017年第2期22卷 210-217页

作者： Kai Chen Yong Dou Qi Lv Zhengfa Liang the National Laboratory for Parallel and Distributed Processing National University of Defense TechnologyChangsha 410037China the College of Computer National University of Defense TechnologyChangsha 410037China

Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more *** kinds of multi-output learning methods are used to predict the performances of the candidate algorithms：（1）multi-output regressor stacking;（2） multi-output extremely randomized trees; and（3） hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.

关键词： algorithm selection multi-output learning extremely randomized trees performance prediction constraint satisfaction

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Yu, Hao Zhang, Zhaoning Zhu, Zijie Wang, Xiong Zhang, Gen Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Meteorology and Oceanology National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781509060146

Similarity of nominal data plays fundamental roles in numerous fields of both machine learning and data mining. Unlike the similarity of numerical data, that of nominal data is much more difficult to describe, and few efforts have been done for it. Although existing nominal similarity measures can reveal a part of data properties, they suffer from low accuracy due to ignoring value relationships or integrating multi-view relationships inappropriately. In this paper, we propose a novel hierarchical measure for nominal data similarity (HNS). The HNS leverages the intrinsic data characteristics by considering low-level information both within and between attributes, and hierarchically seizes the value distributions, attribute interactions and attribute to object contributions. Meanwhile, it aggregates multi-view relationships trough a bottom to top framework, remaining consistency as well as complementary details. We theoretically analyzed this measure, and experiments on six UCI data sets demonstrate that the HNS outperforms the state-of-the-art nominal similarity measures in term of target alignment and clustering accuracy. © 2018 IEEE.

关键词： Data mining

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Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

Loss Rank Mining: A General Hard Example Mining Method for R...

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Yu, Hao Zhang, Zhaoning Qin, Zheng Wu, Hao Li, Dongsheng Zhao, Jun Lu, Xicheng Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Electronic and Engineering National University of Defense Technology Changsha China College of Meteorology and Oceanology National University of Defense Technology Changsha China

ISBN: (纸本)9781509060146

Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of back-grounds and become hard examples during training. Compared with those proposal-based ones, real-time detectors are in far more serious trouble since they renounce the use of region-proposing stage which is used to filter a majority of back-grounds for achieving real-time rates. Though foregrounds as hard examples are in urgent need of being mined from tons of backgrounds, a considerable number of state-of-the-art real-time detectors, like YOLO series, have yet to profit from existing hard example mining methods, as using these methods need detectors fit series of prerequisites. In this paper, we propose a general hard example mining method named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples. By using LRM, some elements representing easy examples in final feature map are filtered and detectors are forced to concentrate on hard examples during training. Extensive experiments validate the effectiveness of our method. With our method, the improvements of YOLOv2 detector on auto-driving related dataset KITTI and more general dataset PASCAL VOC are over 5% and 2% mAP, respectively. In addition, LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded. © 2018 IEEE.

关键词： Object detection

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Integrated high-speed optical SerDes over 100GBd based on optical time division multiplexing

Integrated high-speed optical SerDes over 100GBd based on op...

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作者： Xu, Shi Luo, Zhang Lai, Mingche Pang, Zhengbin Li, Renfa College of Computer Hunan University Changsha410082 China College of Computer National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha410073 China

An on-chip optical transceiver for transmission system over 100GBd is proposed based on optical time division multiplexing (OTDM) technology, and the performances, such as the insertion loss, the inter-symbol interference (ISI) crosstalk, and the potential symbol rate, are analyzed in detail. Co-designed with the double rail driver, on-chip Mach-Zehnder interferometer switch repeatedly generates extremely narrow sampling pulses of only 12ps full width at half maximum. Based on such narrow optical sampling pulse train, a four-stage cascaded optical switch divides the 25GHz clock cycle into four recurrent 9.5ps time slots and one blank time slot of 2ps. Thus, a 100GBd optical transmission channel is realized based on 4-bit 25Gbps bit-streams at the electrical interface. The ISI extinction ratio at the worst channel is 1.9dB with 10dB depth modulator, and the insertion loss caused by the OTDM mechanism is about 16dB. Further, taking advantages of dark modulation, an OTDM system with 5-bit 25Gbps bit-streams at the electrical interface is proposed to generate a 125GBd transmission utilizing the same optical sampling pulse. The ISI performance is much better and the extinction ratio at the worst channel is enhanced to 3.99dB. © 2018 ACM.

关键词： Timing circuits

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A GPU-based parallel WFST decoder on nnet3

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AIP Conference Proceedings 2019年第1期2073卷

作者： Yong Wang Jie Liu Chen Zhou Zhengbin Pang Shengguo Li Chunye Gong Xinbiao Gan Yurong Li 1Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China

One performance-intensive part of automatic speech recognition is the weighted finite-state transducer (WFST) decoding. To solve the problem, we expand parallel Graphics processing Units (GPU) computing to the decoding period. We describe extension work based on Kaldi toolkit for speech recognition research. Our work can support weighted finite-state transducer decoding on Kaldi neural nets with CUDA toolkit. Our paper also expands an efficient parallel Viterbi beam decoding algorithm to decrease the speech recognition Real Time Factor (RTF) value. Together with our optimization algorithm, we have reached 2.3x speed up on the AISHELL corpus decoding. We also implement nnet3 decoder that improves real-time speed up with no word error rate raise.

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