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arXiv 2021年

作者： Xu, Kele Csapó, Tamás Gábor Feng, Ming National Key Lab of Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Budapest Hungary MTA-ELTE Lendület Lingual Articulation Research Group Budapest Hungary Tongji University Shanghai China

Ultrasound tongue imaging is widely used for speech production research, and it has attracted increasing attention as its potential applications seem to be evident in many different fields, such as the visual biofeedback tool for second language acquisition and silent speech interface. Unlike previous studies, here we explore the feasibility of age estimation using the ultrasound tongue image of the speakers. Motivated by the success of deep learning, this paper leverages deep learning on this task. We train a deep convolutional neural network model on the UltraSuite dataset. The deep model achieves mean absolute error (MAE) of 2.03 for the data from typically developing children, while MAE is 4.87 for the data from the children with speech sound disorders, which suggest that age estimation using ultrasound is more challenging for the children with speech sound disorder. The developed method can be used a tool to evaluate the performance of speech therapy sessions. It is also worthwhile to notice that, although we leverage the ultrasound tongue imaging for our study, the proposed methods may also be extended to other imaging modalities (e.g. MRI) to assist the studies on speech production. © 2021, CC BY-NC-SA.

关键词： Ultrasonics

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Feature and Performance Comparison of FaaS Platforms

Feature and Performance Comparison of FaaS Platforms

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

作者： Penghui Ma Peichang Shi Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.

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Deep learning-based age estimation using B-mode ultrasound tongue imaging

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The Journal of the Acoustical Society of America 2021年第4_SUPPLEMENT期150卷 A190-A190页

作者： Kele Xu Tamás G. Csapó Ming Feng National Key Lab. of Parallel and Distributed Processing (PDL) 107 Yanwachi Changsha 410073 China kelele.xu@*** Budapest Univ. of Technol. and Economics Budapest Hungary Tongji Univ. Shanghai China

The feasibility of age estimation is explored using the ultrasound tongue image of the speakers. Motivated by the success of deep learning, a deep convolutional neural network model is trained on the UltraSuite dataset. The deep model achieves mean absolute error (MAE) of 2.03 years for the data from typically developing children, while MAE is 4.87 for the data from the children with speech sound disorders, which suggest that age estimation using ultrasound is more challenging for the children with speech sound disorder. Also, we explore to visualize what does the deep model learn for the age estimation task. We firstly visualize the convolutional layers in the learned convolutional neural networks. We observe that the deep model not only focuses on the contour in the ultrasound tongue image, but also pays more attention to the regions corresponding to the tendon and tongue root regions, which may provide guidance for future ultrasound tongue imaging interpretation tasks. The developed method can be used a tool to evaluate the performance of speech therapy sessions.

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Strategy of transcription analysis for alternative splicing research

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Guofang Keji Daxue Xuebao/Journal of national University of Defense Technology 2006年第4期28卷 37-42页

作者： Wang, Zheng-Zhi Li, Zhi-Feng Hang, Xing-Yi Mao, Yi-Qing Luo, Zhi-Gang Zhao, Dong-Sheng Zhang, Cheng-Gang College of Mechatronics Engineering and Automation National Univ. of Defense Technology Changsha 410073 China Institute of Radiation Medicine Academy of Military Medical Sciences Beijing 100850 China Institute of Health Administration and Medical Information Academy of Military Medical Sciences Beijing 100850 China National Lab of Parallel and Distributed Processing National Univ. of Defense Technology Changsha 410073 China

Experiments on transcription analysis have resulted huge genes expression data related with specific temporal and spatial information. These data can be used in new genes identification, analysis of genes express patterns and the annotation of genomes, which may provide the reference standard for experiment design and result analysis of transcription experiments. Since the alternative splicing of eukaryotic genes have found to be universal and play an important role in physiology and pathology, systematic analysis of alternative splicing is becoming a new hotspot of functional genome research. Facing the immense and exponential increase of experimental express data and more new genomes getting sequenced, there is exigent of the strategy which can handle transcription sequences in large scale and automatic way. We elucidate the algorithms, the computing requirements and programs in different transcription sequences analysis systems and propose a strategy more suitable for large scale analysis of alternative splicing.

关键词： Alternative splicing EST assembly EST clustering High performance computing Transcripton

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Journal of Information Science and Engineering: Editorial notice

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Journal of Information Science and Engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

No abstract availab.e

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Correction to: A compressed matrix sequence method for solving normal equations of bundle adjustment

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Machine Vision and Applications 2021年第4期32卷 1-1页

作者： Peng, Jiaxin Liu, Jie Wei, Hua Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Changsha China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Changsha China Algorithm Lab Changsha Morale Network Technology Co. Ltd. Changsha China Water and Land Resources Research Center of the Middle Reaches of Yangtze River School of Resources Environment Science and Engineering Hubei University of Science and Technology Xianning China

A correction to this paper has been published: https://***/10.1007/s00138-021-01222-5

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QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

arXiv

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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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