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2018 ISPRS TC III Mid-Term Symposium on Developments, Technologies and Applications in Remote Sensing

作者： Hong, S. Mao, B. College of Information Engineering Collaborative Innovation Centre for Modern Grain Circulation and Safety Jiangsu Key Laboratory of Grain Big-data Mining Nanjing University of Finance and Economics Nanjing210023 China

The logistics industry plays a very important role in the operation of modern cities. Meanwhile, the development of logistics industry has derived various problems that are urgent to be solved, such as the safety of logistics products. This paper combines the study of logistics industry traceability and logistics centre environment safety supervision with virtual reality technology, creates an interactive logistics centre information integration system. The proposed system utilizes the immerse characteristic of virtual reality, to simulate the real logistics centre scene distinctly, which can make operation staff conduct safety supervision training at any time without regional restrictions. On the one hand, a large number of sensor data can be used to simulate a variety of disaster emergency situations. On the other hand, collecting personnel operation data, to analyse the improper operation, which can improve the training efficiency greatly. © Authors 2018.

关键词： Virtual reality

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Adversarial multiarmed bandit problems in gradually evolving worlds 1st

Adversarial multiarmed bandit problems in gradually evolving...

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1st International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2017

作者： Lee, Chia-Jung Yang, Yalei Meng, Sheng-Hui Sung, Tien-Wen College of Information Science and Engineering Fujian University of Technology FuzhouFujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology FuzhouFujian China

ISBN: (纸本)9783319707297

We study the adversarial multi-armed bandit problem, in which a player must iteratively make online decisions with linear loss vectors and hopes to achieve a small total loss. We consider a natural measure on the loss vectors, called deviation, which is the sum of the distances between every two consecutive loss functions. We propose an online algorithm and the experimental results show that the proposed algorithm can achieve a small total loss when the loss functions have a small deviation. © Springer International Publishing AG 2018.

关键词： Iterative methods

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The characteristic ofcorvus pectoralis’s complete mitochondrial genome and phylogeny analysis

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Mitochondrial DNA Part B 2019年第2期4卷 3513-3514页

作者： Tian Huang Libo Zhou Zhenggang Xu College of Information and Electronic Engineering Hunan City University YiYang China Hunan Engineering Research Center for Internet of Animals Changsha China Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province Central South University of Forestry and Technology Changsha China

The Collared Crow (Corvus pectoralis), in the order Passeriformes, it widely distributed in large areas encompassing China and northern Vietnam. It is a vulnerable bird that is of international concern. In this study, we first sequenced and described the complete mitochondrial genome and phylogeny ofC. pectoralis. The results showed that the whole genome ofC. pectoraliswas 16,857 bp long and contains 13 PCGs, 2 ribosomal RNA genes, 23 transfer RNA genes, and 1 loop region. The overall base composition of the mitochondrial DNA was 31.13% for A, 29.52% for C, 24.46% for T, and 14.89% for G, with a GC content of 44.41%. The phylogenetic tree showed thatC. pectoraliswas clustered withC. brachyrhynchosand then together with other two crows in family Passeriformes. This information will be useful in the current understanding of the phylogeny and evolution of Passeriformes.

关键词： Corvus pectoralis Passeriformes mitochondrial genome phylogeny

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Publisher Correction: Small molecule agonist of mitochondrial fusion repairs mitochondrial dysfunction

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Nature chemical biology 2023年第4期19卷 530页

作者： Yingjie Guo Huan Zhang Chen Yan Birong Shen Yue Zhang Xiangyang Guo Sha Sun Fan Yu Jiayun Yan Ronghe Liu Qianping Zhang Di Zhang Haiyang Liu Yang Liu Yaoyao Zhang Wenlei Li Jiangyu Qin He Lv Zhaoxia Wang Yun Yuan Jie-Feng Yang Ya-Ting Zhong Song Gao Bing Zhou Lei Liu Deling Kong Xiaojiang Hao Junjie Hu Quan Chen State Key Laboratory of Membrane Biology Institute of Zoology Chinese Academy of Sciences Beijing China. State Key Laboratory of Medicinal Chemical Biology Haihe Laboratory of Cell Ecosystem College of Life Sciences Nankai University Tianjin China. University of Chinese Academy of Sciences Beijing China. National Laboratory of Biomacromolecules CAS Center for Excellence in Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing China. State Key Laboratory of Phytochemistry and Plant Resources Kunming Institute of Botany Chinese Academy of Sciences Kunming China. Department of Physiology and Pathophysiology Tianjin Medical University Tianjin China. Interdisciplinary Center of Cell Response College of Life Sciences Nankai University Tianjin China. Beijing Key Laboratory of Neurovascular Disease Discovery Department of Neurology Peking University First Hospital Beijing China. State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Sun Yat-sen University Cancer Center Guangzhou China. Beijing Advanced Innovation Center for Big Data-Based Interdisciplinary Innovation Institute of Medicine and Engineering Interdisciplinary Beihang University Beijing China. State Key Laboratory of Phytochemistry and Plant Resources Kunming Institute of Botany Chinese Academy of Sciences Kunming China. haoxj@***. University of Chinese Academy of Sciences Beijing China. huj@***. National Laboratory of Biomacromolecules CAS Center for Excellence in Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing China. huj@***. State Key Laboratory of Medicinal Chemical Biology Haihe Laboratory of Cell Ecosystem College of Life Sciences Nankai University Tianjin China. chenq@***. Interdisciplinary Center of Cell Response College of Life Sciences Nankai University Tianjin China. chenq@***.

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Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

arXiv

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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

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Bioinformatics Commons: The Cornerstone of Life and Health Sciences

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Genomics,Proteomics & Bioinformatics 2018年第4期16卷 223-225页

作者： Zhang Zhang Yu Xue Fangqing Zhao BIG Data Center and CAS Key Laboratory of Genome Sciences & Information Beijing Institute of Genomics Chinese Academy of Sciences Department of Bioinformatics & Systems Biology MOE Key Laboratory of Molecular Biophysics College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering Huazhong University of Science and Technology Beijing Institutes of Life Science Chinese Academy of Sciences

Bioinformatics, an interdisciplinary field that combines biology, mathematics, computer science, medicine, and health science, to integrate, analyze, and interpret biological data, is now becoming increasingly data-intensive. To dig out the treasure from big data powered by high-throughput sequencing technologies, it is highly dependent on Bioinformatics Commons that involves a variety of fundamental resources, includ-

关键词： Bioinformatics Commons The Cornerstone of Life and Health Sciences

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Correlation of Gut Microbiome Between ASD Children and Mothers and Potential Biomarkers for Risk Assessment

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Genomics, Proteomics & Bioinformatics 2019年第1期17卷 26-38页

作者： Ning Li Junjie Yang Jiaming Zhang Cheng Liang Ying Wang Bin Chen Changying Zhao Jingwen Wang Guangye Zhang Dongmei Zhao Yi Liu Lehai Zhang Jun Yang Guimei Li Zhongtao Gai Lei Zhang Guoping Zhao Shandong Children's Microbiome Center Qilu Children's Hospital of Shandong University Department of Pediatrics Shandong Provincial Hospital Affiliated to Shandong University Institute of Child Health Care Qilu Children's Hospital of Shandong University School of Information Science and Engineering Shandong Normal University Research Institute of Pediatrics Qilu Children's Hospital of Shandong University Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University College of Life Science Qilu Normal University School of Chemistry Beihang University CAS Key Laboratory of Synthetic Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences

Variation of maternal gut microbiota may increase the risk of autism spectrum disorders(ASDs) in offspring. Animal studies have indicated that maternal gut microbiota is related to neurodevelopmental abnormalities in mouse offspring, while it is unclear whether there is a correlation between gut microbiota of ASD children and their mothers. We examined the relationships between gut microbiome profiles of ASD children and those of their mothers, and evaluated the clinical discriminatory power of discovered bacterial biomarkers. Gut microbiome was profiled and evaluated by 16S ribosomal RNA gene sequencing in stool samples of 59 mother–child pairs of ASD children and 30 matched mother–child pairs of healthy children. Significant differences were observed in the gut microbiome composition between ASD and healthy children in our Chinese cohort. Several unique bacterial biomarkers, such as Alcaligenaceae and Acinetobacter, were identified. Mothers of ASD children had more Proteobacteria, Alphaproteobacteria, Moraxellaceae, and Acinetobacter than mothers of healthy children. There was a clear correlation between gut microbiome profiles of children and their mothers; however, children with ASD still had unique bacterial biomarkers, such as Alcaligenaceae, Enterobacteriaceae, and Clostridium. Candidate biomarkers discovered in this study had remarkable discriminatory power. The identified patterns of mother–child gut microbiome profiles may be important for assessing risks during the early stage and planning of personalized treatment and prevention of ASD via microbiota modulation.

关键词： Autism spectrum disorders Gut microbiome Biomarker Mother-child pair Microbiota-gut-immunebrain axis

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Adversarial Learning for Distant Supervised Relation Extraction

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Computers, Materials & Continua 2018年第4期55卷 121-136页

作者： Daojian Zeng Yuan Dai Feng Li R.Simon Sherratt Jin Wang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer&Communication EngineeringChangsha University of Science&TechnologyChangsha410114P.R.China Department of Biomedical Engineering University of ReadingUK. School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114P.R.China.

Recently,many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction(DSRE).These approaches generally use a softmax classifier with cross-entropy loss,which inevitably brings the noise of artificial class NA into classification *** address the shortcoming,the classifier with ranking loss is employed to *** randomly selecting a relation or heuristically selecting the highest score among all incorrect relations are two common methods for generating a negative class in the ranking loss ***,the majority of the generated negative class can be easily discriminated from positive class and will contribute little towards the *** by Generative Adversarial Networks(GANs),we use a neural network as the negative class generator to assist the training of our desired model,which acts as the discriminator in *** the alternating optimization of generator and discriminator,the generator is learning to produce more and more discriminable negative classes and the discriminator has to become better as *** framework is independent of the concrete form of generator and *** this paper,we use a two layers fully-connected neural network as the generator and the Piecewise Convolutional Neural Networks(PCNNs)as the *** results show that our proposed GAN-based method is effective and performs better than state-of-the-art methods.

关键词： Relation extraction generative adversarial networks distant supervision piecewise convolutional neural networks pair-wise ranking loss

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Correction to: Remote effect of model systematic bias in tropical SST on the cold bias over the Tibetan Plateau

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Climate Dynamics 2022年第11期60卷 4193-4193页

作者： Wu, Yuting Hu, Xiaoming Li, Zhenning Cai, Ming Lu, Mengmeng Yang, Song Chongqing Research Institute of Big Data Peking University Chongqing China School of Atmospheric Sciences Sun Yat-Sen University Zhuhai China Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Zhuhai China Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-Sen University Zhuhai China Division of Environment and Sustainability The Hong Kong University of Science and Technology Hong Kong China Department of Earth Ocean and Atmospheric Science Florida State University Tallahassee USA State Key Laboratory of Severe Weather Institute of Climate System Chinese Academy of Meteorological Sciences Beijing China

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Fully-convolutional intensive feature flow neural network for text recognition

arXiv

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arXiv 2019年

作者： Zhang, Zhao Tang, Zemin Zhang, Zheng Wang, Yang Qin, Jie Wang, Meng School of Computer Science and Technology Soochow University China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer and Information Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable;2) the traditional convolution operation optimizes slowly and the hierarchical features from different layers are not fully utilized. In this work, we address these problems by developing a novel deep network model called Fully-Convolutional Intensive Feature Flow Neural Network (IntensiveNet). Specifically, we design a further dense block called intensive block to extract the feature information, where the original inputs and two dense blocks are connected tightly. To encode data appropriately, we present the concepts of dense fusion block and further dense fusion operations for our new intensive block. By adding short connections to different layers, the feature flow and coupling between layers are enhanced. We also replace the traditional convolution by depthwise separable convolution to make the operation efficient. To prevent important feature information being lost to a certain extent, we use a convolution operation with stride 2 to replace the original pooling operation in the customary transition layers. The recognition results on large-scale Chinese string and MNIST datasets show that our IntensiveNet can deliver enhanced recognition results, compared with other related deep models. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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