检索结果-内蒙古大学图书馆

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiangbo Shi Chang Jia Zeyu Gao Tieliang Gong Chunbao Wang Chen Li National Engineering Lab for Big Data Analytics Xi’an Jiao tong University Xi’an Shaanxi China School of Computer Science and Technology Xi’an Jiao tong University Xi’an Shaanxi China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi’an Jiao tong University Xi’an Shaanxi China the First Affiliated Hospital of Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (纸本)9781665429825

Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data. Then the corrected masks can be used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model in an end-to-end way. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. It even achieves comparable performance with the model training on supervised data in some noisy settings.

关键词： Training Learning systems Image segmentation Pathology Annotations Biological system modeling Optimization methods

来源：评论

学校读者我要写书评

暂无评论

Is heuristic sampling necessary in training deep object detectors?

arXiv

引用

arXiv 2019年

作者： Chen, Joya Liu, Dong Xu, Tong Wu, Shiwei Cheng, Yifei Chen, Enhong Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China School of Computer Science and Technology School of Data Science Department of Electronic Engineering and Information Science University of Science and Technology of China

To train accurate deep object detectors under the extreme foreground-background imbalance, heuristic sampling methods are always necessary, which either re-sample a subset of all training samples (hard sampling methods, e.g. biased sampling, OHEM), or use all training samples but re-weight them discriminatively (soft sampling methods, e.g. Focal Loss, GHM). In this paper, we challenge the necessity of such hard/soft sampling methods for training accurate deep object detectors. While previous studies have shown that training detectors without heuristic sampling methods would significantly degrade accuracy, we reveal that this degradation comes from an unreasonable classification gradient magnitude caused by the imbalance, rather than a lack of re-sampling/re-weighting. Motivated by our discovery, we propose a simple yet effective Sampling-Free mechanism to achieve a reasonable classification gradient magnitude by initialization and loss scaling. Unlike heuristic sampling methods with multiple hyperparameters, our Sampling-Free mechanism is fully data diagnostic, without laborious hyperparameters searching. We verify the effectiveness of our method in training anchor-based and anchor-free object detectors, where our method always achieves higher detection accuracy than heuristic sampling methods on COCO and PASCAL VOC datasets. Our Sampling-Free mechanism provides a new perspective to address the foreground-background imbalance. Our code is released at https://***/ChenJoya/sampling-free. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Learning pattern of hurricane damage levels using semantic web resources

Learning pattern of hurricane damage levels using semantic w...

引用

作者： Tran, Quang-Khai Song, Sa-Kwang Department of Big Data Science University of Science and Technology Korea Republic of Decision Support Technology Research Lab Disaster Management HPC Technology Research Center Convergence Technology Research Division Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of

This paper proposes an approach for hurricane damage level prediction using semantic web resources and matrix completion algorithms. Based on the statistical unit node set framework, streaming data from five hurricanes and damage levels from 48 counties in the USA were collected from the SRBench dataset and other web resources, and then trans-coded into matrices. At a time t, the pattern of possible highest damage levels at 6 hours into the future was estimated using a multivariate regression procedure based on singular value decomposition. We also applied soft-impute algorithm and k-nearest neighbours concept to improve the statistical unit node set framework in this research domain. Results showed that the model could deal with inaccurate, inconsistent and incomplete streaming data that were highly sparse, to learn future damage patterns and perform forecasting in near real-time. It was able to estimate the damage levels in several scenarios even if two-thirds of the relevant weather information was unavailable. The contributions of this work will be able to promote the applicability of the semantic web in the context of climate change. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Singular value decomposition

来源：评论

学校读者我要写书评

暂无评论

Space technology:A powerful tool for safeguarding world heritage

引用

The Innovation 2023年第3期4卷 27-28页

作者： Lei Luo Jie Liu Francesca Cigna Damian Evans Mario Hernandez Deodato Tapete Peter Shadie Athos Agapiou Abdelaziz Elfadaly Min Chen Lanwei Zhu Bihong Fu Ruixia Yang Shahina Tariq Mohamed Ouessar Rosa Lasaponara Xinyuan Wang Huadong Guo International Research Center of Big Data for Sustainable Development Goals(CBAS) Beijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research Institute(AIR)Chinese Academy of Sciences(CAS)Beijing 100094China International Centre on Space Technologies for Natural and Cultural Heritage(HIST)Under the Auspices of UNESCO Beijing 100094China Institute of Atmospheric Sciences and Climate(ISAC) National Research Council(CNR)00133 RomeItaly École Française d’Extreme-Orient 22 Avenue du Président Wilson75116 ParisFrance International Society for Digital Earth(ISDE) Mexico CityMexico Italian Space Agency(ASI) 00133 RomeItaly Blue Mountains World Heritage Institute 16 Dunmore LaneKatoombaNSW 2780Australia Earth Observation Cultural Heritage Research Lab Department of Civil Engineering and GeomaticsCyprus University of TechnologyPO Box 503296 Limassol 3036Cyprus National Authority for Remote Sensing and Space Sciences Cairo 1564Egypt Key Laboratory of Virtual Geographic Environment Ministry of Education of PRCNanjing Normal UniversityNanjing 210023China COMSATS University Islamabad(CUI) Islamabad 45550Pakistan Institut des Regions Arides(IRA)-Medenine Medenine 4119Tunisia Institute of Methodologies for Environmental Analysis(IMAA) CNRC.da Santa LojaTito Scalo85050 PotenzaItaly

WORLD HERITAGE AND SPACE TECHNOLOGY The Convention Concerning the Protection of the World Cultural and Natural Heritage(WHC),adopted by United Nations Educational,Scientific and Cultural Organization(UNESCO)on November 16,1972,aims to ensure the identification,protection,conservation,presentation,and transmission to future generations of the world’s cultural and natural *** WHC works toward these goals by emphasizing the Outstanding Universal Value(OUV)of heritage sites and the unique contribution such places can make to conservation and human development agendas.1 As of the end of January 2023,theWHC has been signed by 194 state parties,covering 1,157 sites(including 900 cultural,218 natural,and 39 mixed properties),55 of which are considered to be in *** sites,totaling an area of more than 370 million hectares are designated as World Heritage(WH)sites(https://***/en/list/).WH sites have played a significant role in the sustainable development of society globally and helped effectively maintain and preserve the cultural diversity and global biodiversity of the Earth.

关键词： heritage maintain SPACE

来源：评论

学校读者我要写书评

暂无评论

Boundary stabilization and disturbance rejection for a time fractional order diffusion-wave equation ⁎

引用

IFAC-PapersOnLine 2020年第2期53卷 3695-3700页

作者： Hua-Cheng Zhou Ze-Hao Wu Bao-Zhu Guo Yangquan Chen School of Mathematics and Statistics Central South University Changsha 410075 PR China School of Mathematics and Big Data Foshan University Foshan 528000 PR China Academy of Mathematics and Systems Science Academia Sinica Beijing 100190 China Mechatronics Embedded Systems and Automation Lab University of California Merced 95343 CA USA

In this paper, we study the boundary stabilization and disturbance rejection for an unstable time fractional diffusion-wave equation involving Caputo time fractional derivative. When there is no boundary external disturbance, both state feedback control and output feedback control via boundary actuation are proposed by the classical backstepping method. It is proved that the state feedback makes the closed-loop system Mittag-Leffler stable while the output feedback makes the closed-loop system asymptotically stable. When there is boundary external disturbance, we propose a disturbance estimator which is constructed by two infinite dimensional auxiliary systems to recover the external disturbance. The resulting closed-loop system is Mittag-Leffler stable and the states of all subsystem involved are uniformly bounded. As a byproduct, we solve rigorously completely the two longtime unsolved problems raised in [Nonlinear Dynam., 38(2004), 339-354] where all the results are only verified by simulations.

关键词： Disturbance rejection fractional diffusion-wave equation Mittag-Leffler stability

来源：评论

学校读者我要写书评

暂无评论

StyleNAS: An empirical study of neural architecture search to uncover surprisingly fast end-to-end universal style transfer networks

arXiv

引用

arXiv 2019年

作者： An, Jie Xiong, Haoyi Ma, Jinwen Luo, Jiebo Huan, Jun School of Mathematical Sciences Peking University Big Data Lab Baidu Research Department of Computer Science University of Rochester

Neural Architecture Search (NAS) has been widely studied for designing discriminative deep learning models such as image classification, object detection, and semantic segmentation. As a large number of prior work have been obtained through the manual design of architectures in the fields, NAS is usually considered as a supplement approach. In this paper, we have significantly expanded the application areas of NAS by performing an empirical study of NAS to search generative models, or specifically, auto-encoder based universal style transfer, which lacks systematic exploration, if any, from the architecture design aspect. In our work, we first designed a search space where common operators for image style transfer such as VGG-based encoders, whitening and coloring transforms (WCT), convolution kernels, instance normalization operators, and skip connections were searched in a combinatorial approach. With a simple yet effective parallel evolutionary NAS algorithm with multiple objectives, we derived the first-of-its-kind end-to-end deep networks for universal photorealistic style transfer. Comparing to Random Search, a NAS method that is gaining popularity recently, we demonstrated that carefully designed search strategy leads to much better architecture design. Finally compared to existing universal style transfer networks for photorealistic rendering such as PhotoWCT that stacks multiple well-trained auto-encoders and WCT transforms in a non-end-to-end manner, the architectures designed by StyleNAS produce better style-transferred images with details preserving, using a tiny number of operators/parameters, and enjoying two orders of magnitude inference time speed-up. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019-nCoV

arXiv

引用

arXiv 2020年

作者： Zhou, Tao Liu, Quanhui Yang, Zimo Liao, Jingyi Yang, Kexin Bai, Wei Lu, Xin Zhang, Wei Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science Sichuan University Chengdu610065 China Beijing AiQiYi Science & Technology Co. Ltd. Beijing100080 China Shenzhen International Graduate School Tsinghua University Shenzhen518055 China CompleX Lab University of Electronic Science and Technology of China Chengdu611731 China College of Systems Engineering National University of Defense Technology Changsha410073 China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu610047 China

Objectives: To estimate the basic reproduction number of the Wuhan novel coronavirus (2019-nCoV). Methods: Based on the susceptible-exposed-infected-removed (SEIR) compartment model and the assumption that the infectious cases with symptoms occurred before 26 January, 2020 are resulted from free propagation without intervention, we estimate the basic reproduction number of 2019-nCoV according to the reported confirmed cases and suspected cases, as well as the theoretical estimated number of infected cases by other research teams, together with some epidemiological determinants learned from the severe acute respiratory syndrome. Results: The basic reproduction number fall between 2.8 to 3.3 by using the real-time reports on the number of 2019-nCoV infected cases from People's Daily in China and fall between 3.2 and 3.9 on the basis of the predicted number of infected cases from international colleagues. Using a newly reported epidemiological determinants for early 2019-nCoV, the estimated basic reproduction number is in the range [2.2,3.0]. Conclusions: The early transmission ability of 2019-nCoV is close to or slightly higher than SARS. It is a controllable disease with moderate-high transmissibility. Timely and effective control measures are needed to suppress the further transmissions. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Minimum Spanning Tree Clustering Based on Density Filtering 7th

Minimum Spanning Tree Clustering Based on Density Filtering

引用

7th CCF Academic Conference on bigdata, CCF bigdata 2019

作者： Wang, Ke Xie, Xia Sun, Jiayu Cao, Wenzhi National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hunan University of Technology and Business Changsha410205 China

ISBN: (纸本)9789811518980

Clustering analysis is an important method in data mining. In order to recognize clusters with arbitrary shapes as well as clusters with different density, we propose a new clustering approach: minimum spanning tree clustering based on density filtering. It masks the low-density points in the density filtering step, which reduces the interference of noise and makes the gap between clusters clearer. It uses relative values of adjacent distances to find mutations of density and changes between clusters to divide data sets. It is tested on multiple synthetic data sets and real-world data sets, the results of which show that the algorithm is able to detect clusters with arbitrary shape and it is insensitive to the imbalance of density between clusters. It has achieved great results on multiple data sets. © Springer Nature Singapore Pte Ltd 2019.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Early anomaly detection in wind turbine bolts breaking problem-Methodology and application 3

Early anomaly detection in wind turbine bolts breaking probl...

引用

3rd IEEE International Conference on big data Analysis, ICBDA 2018

作者： Wu, Chung-Wei Chen, Mei Big Data Science Lab Neucloud Technology Co. Beijing Ltd. Beijing China

ISBN: (纸本)9781538647936

Early anomaly detection plays an important role in many fields, such as fraud detection in financial data, a signal indicating machine unhealthy status, etc. It leads to fault diagnostics and even prognostics according to data analytics need. In this paper, we apply an early anomaly detection model in wind turbine bolts breaking problem with good detection result, which can be used to substitute prognostic model in a solid manner. Wind turbine bolts breaking is a common problem in wind turbine, which may cause serious impact but hard to detect, especially when no extra sensor on total 50 bolts for each blade. To solve this problem, a novel process is designed to answer following questions: (1) whether there are parameters whose behavior is influenced by the bolts breaking, (2) can we detect such parameters reflecting the breaking problem, and (3) find out how the impacts happen and how far the impacts grow by comparing different number of blots are broken. We develop a conditional detection model, which is able to detect only 2 bolts breaking by automatically extracting key features with 92% accuracy. © 2018 IEEE.

关键词： Wind turbines

来源：评论

学校读者我要写书评

暂无评论

Triplet Deep Subspace Clustering via Self-Supervised data Augmentation

Triplet Deep Subspace Clustering via Self-Supervised Data Au...

引用

IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Xianzhen Li Haijun Zhang Yi Yang Shuicheng Yan Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Computer Science and Technology Soochow University Suzhou China Harbin Institute of Technology (Shenzhen) Shenzhen China Centre for Artificial Intelligence University of Technology Sydney Sydney NSW Australia Sea AI Lab (SAIL) & National University of Singapore Singapore

ISBN: (纸本)9781665423991

Deep subspace clustering (DSC) with the auto-encoder and self-expression layer is of great concern due to encouraging performance. However, existing methods usually adopt a “single-task” strategy based on a single dataset, without considering other related tasks or data. As such, they cannot discover other useful information to improve the clustering task. Besides, the local structure preservation of the latent codes in mapping is usually ignored. In this paper, we therefore present an effective “multi-task” strategy via the self-supervised data augmentation, and propose a new end-to-end trainable Triplet Deep Subspace Clustering Network (TDSC-net). Specifically, TDSC-net firstly generates triplet data (i.e., anchor, positive and negative data) from input data by a spectral clustering module and a self-supervised data augmentation module. This can enable it to inherit the merits of self-supervised learning and multitask learning implicitly. After that, TDSC-net builds a triplet deep autoencoder network with a self-expression layer, which takes the triplet data as input, where they share the common network layers (i.e., autoencoder and self-expression layers) over the triple tasks for complementary learning and mutual supervision. A triplet loss is also included to retain the local information of deep latent codes, which will also benefit the self-expression. Furthermore, TDSC-net separates the self-expression layer from decoding process to improve the efficiency of reconstruction. Extensive results on several public datasets demonstrate the effectiveness of our triplet-task DSC strategy.

关键词： Codes Conferences Multitasking Decoding data mining Task analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：