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arXiv 2019年

作者： Chen, Chen Qin, Chen Qiu, Huaqi Tarroni, Giacomo Duan, Jinming Bai, Wenjia Rueckert, Daniel Biomedical Image Analysis Group Department of Computing Imperial College London London United Kingdom Department of Computer Science City University of London London United Kingdom School of Computer Science University of Birmingham Birmingham United Kingdom Data Science Institute Imperial College London London United Kingdom Division of Brain Sciences Department of Medicine Imperial College London London United Kingdom

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Trajectory optimization for cooperative reentry of multiple hypersonic glide vehicle

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Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica 2020年 41卷 723776页

作者： Jiang, Peng Guo, Dong Han, Liang Li, Qingdong Ren, Zhang College of Systems Engineering National University of Defense Technology Changsha410073 China China Academy of Launch Vehicle Technology Beijing100076 China School of Automation Science and Electrical Engineering Beihang University Beijing100083 China School of Sino-French Engineer Beihang University Beijing100083 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China

This paper presents a trajectory optimization method for the cooperative reentry of multiple hypersonic glide vehicles. First, the nominal longitudinal trajectory is planned to satisfy the path and terminal constraints. Second, the trajectory tracking law is used to track the nominal longitudinal trajectory. Meanwhile, a multi-layer bounded corridor for heading error considering the initial lateral state is proposed to control the lateral maneuver, so as to meet the requirement of arrival time and the terminal constraints. Then the trajectory planning with arrival time constraints for a single vehicle is implemented. On this basis, the arrival time distribution and the flight capability are analyzed, and the analysis and the calculation methods for the minimum and the maximum arrival time are given. For the multiple hypersonic glide vehicle cooperative reentry scenario, the cooperative flight time decision-making is completed. Finally, numerical results show that the trajectory optimization method achieves a good performance on the arrival time and the terminal constraints, which indicates that the proposed method can realize the cooperative reentry of multiple hypersonic glide vehicle. The results in dispersed cases indicate that the trajectory optimization method has good calculation accuracy and robustness. © 2020, Beihang University Aerospace Knowledge Press. All right reserved.

关键词： Reentry

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Τ-FPL: Tolerance-constrained learning in linear time 32

Τ-FPL: Tolerance-constrained learning in linear time

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32nd AAAI Conference on Artificial Intelligence, AAAI 2018

作者： Zhang, Ao Li, Nan Pu, Jian Wang, Jun Yan, Junchi Zha, Hongyuan Shanghai Key Laboratory of Trustworthy Computing MOE International Joint Lab of Trustworthy Software School of Computer Science and Software Engineering East China Normal University Shanghai China Institute of Data Science and Technologies Alibaba Group Hangzhou China IBM Research China Georgia Institute of Technology Atlante United States

ISBN: (纸本)9781577358008

In many real-world applications, learning a classifier with false-positive rate under a specified tolerance is appealing. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which are of limitation in methodology since they do not directly incorporate the false-positive rate tolerance. In this paper, we propose a novel scoring-thresholding approach, τ-False Positive Learning (τ-FPL) to address this problem. We show that the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed τ-FPL over the existing approaches. Copyright © 2018, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mathematical transformations

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Modulus-based Matrix Multi-splitting Multi-Parameter Methods for a Class of Weakly Nonlinear Complementarity Problem

Modulus-based Matrix Multi-splitting Multi-Parameter Methods...

引用

2018 International Conference on Mathematics, Modelling, Simulation and Algorithms（MMSA2018）

作者： Chenliang Li Zhaohe Tian School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology

The modulus-based matrix multi-splitting multiparameter methods for solving a class of weakly nonlinear complementarity problem are presented. The weaker convergence conditions are analyzed. The numerical results show... 详细信息

ISBN: (纸本)9781510863064

关键词： weakly nonlinear complementarity problem modulus-based matrix multi-splitting multi-parameter

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Fully convolutional network ensembles for white matter hyperintensities segmentation in mr images

arXiv

引用

arXiv 2018年

作者： Li, Hongwei Jiang, Gongfa Zhang, Jianguo Wang, Ruixuan Wang, Zhaolei Zheng, Wei-Shi Menze, Bjoern School of Data and Computer Science Sun Yat-sen University China Computing School of Science and Engineering University of Dundee United Kingdom Department of Computer Science Technical University of Munich Germany

White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans. The algorithm was evaluated and ranked 1st in the WMH Segmentation Challenge at MICCAI 2017. In the evaluation stage, the implementation of the algorithm was submitted to the challenge organizers, who then independently tested it on a hidden set of 110 cases from 5 scanners. Averaged dice score, precision and robust Hausdorff distance obtained on held-out test datasets were 80%, 84% and 6.30mm respectively. These were the highest achieved in the challenge, suggesting the proposed method is the state-of-the-art. Detailed descriptions and quantitative analysis on key components of the system were provided. Furthermore, a study of cross-scanner evaluation is presented to discuss how the combination of modalities affect the generalization capability of the system. The adaptability of the system to different scanners and protocols is also investigated. A quantitative study is further presented to show the effect of ensemble size and the effectiveness of the ensemble model. Additionally, software and models of our method are made publicly available. The effectiveness and generalization capability of the proposed system show its potential for real-world clinical practice. Copyright © 2018, The Authors. All rights reserved.

关键词： Scanning

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Efficient Time-Evolving Stream Processing at Scale

arXiv

引用

arXiv 2018年

作者： Huang, Yu Services Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

—Time-evolving stream datasets exist ubiquitously in many real-world applications where their inherent hot keys often evolve over times. Nevertheless, few existing solutions can provide efficient load balance on these time-evolving datasets while preserving low memory overhead. In this paper, we present a novel grouping approach (named FISH), which can provide the efficient time-evolving stream processing at scale. The key insight of this work is that the keys of time-evolving stream data can have a skewed distribution within any bounded distance of time interval. This enables to accurately identify the recent hot keys for the real-time load balance within a bounded scope. We therefore propose an epoch-based recent hot key identification with specialized intra-epoch frequency counting (for maintaining low memory overhead) and inter-epoch hotness decaying (for suppressing superfluous computation). We also propose to heuristically infer the accurate information of remote workers through computation rather than communication for cost-efficient worker assignment. We have integrated our approach into Apache Storm. Our results on a cluster of 128 nodes for both synthetic and real-world stream datasets show that FISH significantly outperforms state-of-the-art with the average and the 99th percentile latency reduction by 87.12% and 76.34% (vs. W-Choices), and memory overhead reduction by 99.96% (vs. Shuffle Grouping). Copyright © 2018, The Authors. All rights reserved.

关键词： Fish

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A review on image processing for fish disease detection

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Journal of Physics: Conference Series 2021年第1期1997卷

作者： S N Pauzi M G Hassan N Yusoff N H Harun A H Abu Bakar B C Kua School of Technology Management and Logistics Universiti Utara Malaysia 06010 Sintok Kedah Malaysia Department of Data Science Universiti Malaysia Kelantan 16100 Kota Bharu Kelantan Malaysia School of Computing Universiti Utara Malaysia 06010 Sintok Kedah Malaysia Faculty of Electronic Engineering Technology Universiti Malaysia Perlis 01000 Kangar Perlis Malaysia National Fish Health Research Centre Fisheries Research Institute Department of Fisheries Malaysia 11960 Batu Maung Penang Malaysia

Fish disease is considered the main cause for production and economic losses by fish farmers. Fish disease detection and health monitoring is a demanding task by manual method of human visualization. Therefore, any potential approach that is fast, reliable and possesses high automation supports an interest in this issue. Nowadays, with the current emergence in the technology revolution, image processing has been extensively used in disease detection field, especially in human and plant, aiding the human experts in providing the right treatment. Image processing technique offers opportunities to improve the traditional approach in achieving accurate results. Besides, several steps in image processing are adopted including image acquisition, image pre-processing, image segmentation, object detection, feature extraction and classification. The objective of this paper is to briefly review the work established in the fish disease detection field with the use of numerous classification techniques of image processing, including rule-based expert system, machine learning, deep learning, statistical method and hybrid method. The present review recognizes the need for improvement in these image processing approaches that would be valuable for further advancement in terms of performance.

关键词：

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A Reliable Road Segmentation and Edge Extraction for Sparse 3D Lidar data

A Reliable Road Segmentation and Edge Extraction for Sparse ...

引用

IEEE Intelligent Vehicles Symposium

作者： Jianfeng Gu Yuehui Wang Long Chen Zhihao Zhao Zhe Xuanyuan Kai Huang Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University

ISBN: (纸本)9781538644539

Precise segmentation of road areas using cheap Lidar is a tough and critical task due to data sparsity problem. With sparse point clouds, reliable perception of environment is difficult due to the lack of available information and loss of object features. This paper presents a new approach to use sparse 3D Lidar data for road segmentation by fusing multiple frames of point cloud. With registration of multiple frames into a same coordinate system, reliable data can be provided for later ground segmentation and edge extraction. The accuracies of extensive experiments on three kinds of roads demonstrate that the proposed approach obtains high precision and reliability.

关键词： Edge detection Road Lidar segmentation Segmentation Coordinate system

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Time-aware conversion prediction

引用

Frontiers of Computer science 2017年第4期11卷 702-716页

作者： Wendi JI Xiaoling WANG Feida ZHU Shanghai Key Laboratory of Trustworthy Computing Institute for Data Science and Engineering East China Normal University Shanghai 200062 China School of Information Systems Singapore Management University Singapore 188065 Singapore

The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The ＂conversion＂ here refers to actu- ally a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product＇s ac- tual conversion period and the application＇s target conversion period has been the subtle culprit compromising many exist- ing recommendation algorithms. The challenging question： what products should be recom- mended for a given time period to maximize conversion--is what has motivated us in this paper to propose a rank-based time-aware conversion prediction model （rTCP）, which con- siders both recommendation relevance and conversion time. We adopt lifetime models in survival analysis to model the conversion time and personalize the temporal prediction by incorporating context information such as user preference. A novel mixture lifetime model is proposed to further accom- modate the complexity of conversion intervals. Experimental results on two real-world data sets illustrate the high good- ness of fit of our proposed model rTCP and demonstrate its effectiveness in time-aware conversion rate prediction for ad- vertising and product recommendation.

关键词： conversion time survival analysis product rec-ommendation advertising

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data-Driven Modeling of Telluric Features and Stellar Variability with ***

arXiv

引用

arXiv 2024年

作者： Gilbertson, Christian Ford, Eric B. Halverson, Samuel Fitzmaurice, Evan Blake, Cullen H. Stefánsson, Guðmundur Mahadevan, Suvrath Wright, Jason T. Luhn, Jacob K. Ninan, Joe P. Robertson, Paul Roy, Arpita Schwab, Christian Terrien, Ryan C. Center for Computing Research Sandia National Laboratories 1450 Innovation Pkwy SE AlbuquerqueNM87123 United States Department of Astronomy & Astrophysics Penn State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Center for Exoplanets and Habitable Worlds Penn State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Penn State Astrobiology Research Center University ParkPA16802 United States Center for Astrostatistics Penn State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Jet Propulsion Laboratory 4800 Oak Grove Drive PasadenaCA91109 United States Institute for Computational and Data Sciences Penn State University 224B Computer Building University ParkPA16802 United States Department of Physics & Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA19104 United States NASA Sagan United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08540 United States ETH Zurich Institute for Particle Physics & Astrophysics Zurich Switzerland Penn State Extraterrestrial Intelligence Center Penn State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai400005 India Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Department of Physics and Astronomy Johns Hopkins University 3400 N Charles St BaltimoreMD21218 United States School of Mathematical and Physical Sciences Macquarie University Balaclava Road North RydeNSW2109 Australia Carleton College One North College St. Nor

A significant barrier to achieving the radial velocity (RV) measurement accuracy and precision required to characterize terrestrial mass exoplanets is the existence of time-variable features in the measured spectra, from both telluric absorption and stellar variability, which affect measured line shapes and can cause apparent RV shifts. Reaching the desired accuracy using traditional techniques often requires avoiding lines contaminated by stellar variability and/or changing tellurics, and thus discarding a large fraction of the spectrum, lowering precision. New data-driven methods can help achieve extremely precise and accurate RVs by enabling the use of a larger fraction of the available data. While there exist methods for modeling telluric features or the stellar variability individually, there is a need for additional tools that are capable of modeling them simultaneously at the spectral level. Here we present *** (SSOF), a Julia package for measuring Doppler shifts and creating data-driven models (with fast, physically-motivated Gaussian Process regularization) for the time-variable spectral features for both the telluric transmission and stellar spectrum, while accounting for the wavelength-dependent instrumental line-spread function. We demonstrate SSOF's state-of-the-art performance on data from the NEID RV spectrograph on the WIYN 3.5m Telescope for multiple stars. We show SSOF's,ability to accurately identify and characterize spectral variability and provide ∼2-6x smaller photon-limited errors over the NEID CCF-based pipeline and match the performance of SERVAL, a leading template-based pipeline, using only observed EPRV spectra. © 2024, CC BY.

关键词： Stars

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