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101P Circulating tumor DNA minimal residual disease predicts the risk of progression after long-term response to first-line immunotherapy in advanced NSCLC

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Annals of Oncology 2024年 35卷 S254-S254页

作者： F. Wu Y. Peng Y. Zeng Z. Qiu F. Ma L. Zhu C. Zhou X. Pu L. Zhang Y. Zhou J. Weng S. Zengmei J. Yu Z. Zhou R. Meng Q. Bu P. Liu J. Li S. Chen L. Cui Department of Oncology The Second Xiangya Hospital Central South University Changsha China Department of Radiation Oncology Stanford University Stanford CA USA Hunan Cancer Mega-Data Intelligent Application and Engineering Research Centre Changsha China Pneumology The Second Xiangya Hospital of Central South University Changsha China Respiratory Medicine State Key Laboratory of Respiratory Diseases - The First Affiliated Hospital of Guangzhou Medical University Guangzhou China Thoracic Medicine Department Hunan Cancer Hospital Changsha China Thoracic Department Hunan Cancer Hospital Changsha China Department of Respiratory and Critical Care Medicine The Third Xiangya Hospital Central South University Changsha China Department of Oncology Yueyang 1st People's Hospital Yueyang China Department of Hematology and Oncology The Third Hospital of Changsha Changsha China Department of Oncology Zhangjiajie People's Hospital Zhangjiajie China Department of Oncology The Second People's Hospital of Huaihua Huaihua China Department of Oncology Cancer Center Union Hospital Tongji Medical College Huazhong University of Science and Technology (HUST) Institute of Radiation Oncology Union Hospital Tongji Medical College HUST Hubei Key Laboratory of Precision Radiation Oncology Wuhan China Department of Oncology The First Affiliated Hospital of Guangxi Medical University Nanning China Respiratory Medicine The First Hospital of Changsha City Changsha China Department of Oncology Xiangya Changde Hospital Changde People’s Republic of China Changde China Department of Oncology Loudi Central Hospital Loudi China Medicine Geneplus-Beijing Institute Beijing China

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Non-autoregressive neural machine translation with enhanced decoder input

arXiv

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arXiv 2018年

作者： Guo, Junliang Tan, Xu He, Di Qin, Tao Xu, Linli Liu, Tie-Yan Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Microsoft Research School of EECS Peking University

Non-autoregressive translation (NAT) models, which remove the dependence on previous target tokens from the inputs of the decoder, achieve significantly inference speedup but at the cost of inferior accuracy compared to autoregressive translation (AT) models. Previous work shows that the quality of the inputs of the decoder is important and largely impacts the model accuracy. In this paper, we propose two methods to enhance the decoder inputs so as to improve NAT models. The first one directly leverages a phrase table generated by conventional SMT approaches to translate source tokens to target tokens, which are then fed into the decoder as inputs. The second one transforms source-side word embeddings to target-side word embeddings through sentence-level alignment and word-level adversary learning, and then feeds the transformed word embeddings into the decoder as inputs. Experimental results show our method largely outperforms the NAT baseline (Gu et al. 2017) by 5.11 BLEU scores on WMT14 English-German task and 4.72 BLEU scores on WMT16 English-Romanian task. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

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Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors

arXiv

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arXiv 2018年

作者： Li, Zhi Zhao, Hongke Liu, Qi Huang, Zhenya Mei, Tao Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China School of Software Engineering University of Science and Technology of China JD AI Research

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendations are becoming popular to explore the temporal characteristics of customers’ interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers’ long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users’ historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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A holistic approach to aligning geospatial data with multidimensional similarity measuring

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International Journal of Digital Earth 2018年第8期11卷 845-862页

作者： Li Yu Peiyuan Qiu Xiliang Liu Feng Lu Bo Wan State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China National Science Library Chinese Academy of SciencesBeijingPeople’s Republic of China Fujian Collaborative Innovation Center for Big Data Applications in Governments FuzhouPeople’s Republic of China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application NanjingPeople’s Republic of China Faculty of Information Engineering China University of GeosciencesWuhanPeople’s Republic of China

Semantically aligning the heterogeneous geospatial datasets(GDs)produced by different organizations demands efficient similarity matching ***,the strategies employed to align the schema(concept and property)and instances are usually not reusable,and the effects of unbalanced information tend to be neglected in GD *** solve this problem,a holistic approach is presented in this paper to integrally align the geospatial entities(concepts,properties and instances)***,lexical,structural and extensional similarity metrics are designed and automatically aggregated by means of approval *** presented approach is validated with real geographical semantic webs,Geonames and *** with the well-known extensional-based aligning system,the presented approach not only considers more information involved in GD alignment,but also avoids the artificial parameter setting in metric *** reduces the dependency on specific information,and makes the alignment more robust under the unbalanced distribution of various information.

关键词： Geospatial data data alignment similarity matching semantic web

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Nonlinear Prediction with Deep Recurrent Neural Networks for Non-Blind Audio Bandwidth Extension

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China Communications 2018年第4期15卷 72-85页

作者： Lin Jiang Ruimin Hu Xiaochen Wang Weiping Tu Maosheng Zhang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan 430072 China Institute of Big Data and internet Innovation Hunan University of Commerce Changsha 410205 China Software College East China University of Technology Nanchang 330013 China Collaborative Innovation Center for Economics crime investigation and prevention technology Jiangxi Province Nanchang 330103China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan 430072 China Collaborative Innovation Center of Geospatial Technology Wuhan 430079 China

Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually generated by a duplication of the corresponding low frequencies and some parameters of high frequencies. However, the perception quality of coding will significantly degrade if the correlation between high frequencies and low frequencies becomes weak. In this paper, we quantitatively analyse the correlation via computing mutual information value. The analysis results show the correlation also exists in low frequency signal of the context dependent frames besides the current frame. In order to improve the perception quality of coding, we propose a novel method of high frequency coarse spectrum generation to improve the conventional replication method. In the proposed method, the coarse high frequency spectrums are generated by a nonlinear mapping model using deep recurrent neural network. The experiments confirm that the proposed method shows better performance than the reference methods.

关键词： audio coding non-blind audiobandwidth extension context correlation deeprecurrent neural network

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The complete mitochondrial genome ofPelecanus occidentalis(Pelecaniformes: Pelecanidae) and its phylogenetic analysis

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Mitochondrial DNA Part B 2018年第2期3卷 782-783页

作者： Tian Huang Jiao Peng Yunlin Zhao Zhenggang Xu College of Bioscience and Biotechnology Hunan Agricultural University Changsha ChinaCollege of Information and Electronic Engineering Hunan City University YiYang Hunan Province 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 College of Information and Electronic Engineering Hunan City University YiYang Hunan Province ChinaKey Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province Central South University of Forestry and Technology Changsha China

Pelecanus occidentalis, in the order Pelecaniformes, is one of the most abundant and widespread waterbird species in the coast of America. However, the phylogenetic relationships among Pelecaniformes, Suliformes, and Ciconiiformes remain unresolved, particularly in Pelecanidae and Ciconiidae. In this study, we first sequenced and described the complete mitochondrial genome and phylogeny ofP. occidentalis. The whole genome ofP. occidentaliswas 17,315 bp in length, and contained 13 protein-coding genes, 21 transfer RNA genes, two ribosome RNA genes, and one non-coding control region. The overall base composition of the mitochondrial DNA was 30.1% for A, 23.7% for T, 31.5% for C, and 14.6% for G, with a GC content of 46.1%. A phylogenetic tree confirmed thatP. occidentalis(Pelecaniformes) was sister toC. boyciana(Ciconiiformes), and Ardeidae and Threskiornithidae were both monophyletic group. This information will be useful in the current understanding of the phylogeny and evolution of Pelecaniformes.

关键词： Pelecanus occidentalis Pelecaniformes mitochondrial genome phylogeny

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Enhancing person-Job fit for talent recruitment: An ability-aware neural network approach

arXiv

引用

arXiv 2018年

作者： Qin, Chuan Zhu, Hengshu Xu, Tong Zhu, Chen Jiang, Liang Chen, Enhong Xiong, Hui Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology an Application China

The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person-Job Fit, which is the bridge for adapting the right job seekers to the right positions. Existing studies on Person-Job Fit have a focus on measuring the matching degree between the talent qualification and the job requirements mainly based on the manual inspection of human resource experts despite of the subjective, incomplete, and inefficient nature of the human judgement. To this end, in this paper, we propose a novel end-to-end Ability-aware Person-Job Fit Neural Network (APJFNN) model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results. The key idea is to exploit the rich information available at abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, four hierarchical ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measuring the different contribution of each job experience to a specific ability requirement. Finally, extensive experiments on a large-scale real-world data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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research on the Brain-inspired Cross-modal Neural Cognitive Computing Framework

arXiv

引用

arXiv 2018年

作者： Liu, Yang Key Laboratory of Big Data Analysis and Processing of Henan province Intelligent Technology and Application Engineering Research Centre of Henan province College of Computer Science and Information Engineering Henan University Kaifeng475004 China

To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for multimedia and multimodal information. The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-modal Neural Cognitive Computing (CNCC) framework was proposed based on MNCC model, and formal description and analysis for CNCC framework was given. It would effectively improve the performance of semantic processing for multimedia and cross-modal information, and has far-reaching significance for exploration and realization brain-inspired computing. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Leaf area index estimation of winter pepper based on canopy spectral data and simulated bands of satellite 4th

Leaf area index estimation of winter pepper based on canopy ...

引用

4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2016

作者： Li, Dan Jiang, Hao Chen, Shuisen Wang, Chongyang Huang, Siyu Liu, Wei Guangdong Open Laboratory of Geospatial Information Technology and Application Guangdong Key Laboratory of Remote Sensing & GIS Application & Engineer Technology Research Center for Remote Sensing Big Data Application of Guangdong Province Guangzhou Institute of Geography Guangzhou510070 China

ISBN: (纸本)9789811039652

Leaf area index (LAI) is an important indicator of crop growth status. In this paper, the relationships between canopy reflectance at 400–2500 nm and leaf area index (LAI) in pepper crop were studied. 102 pair of canopy reflectance and LAI of pepper were collected in 2014–2015. Reflectance of canopy were measured in the field over a spectral range of 400–2500 nm. Simultaneously, the LAI were collected by the LAI-2000. Estimation models of LAI were developed based on the whole spectrum range by partial least squares regression (PLSR) and support vector regression (SVR), respectively. Then the field canopy spectra were resampled according to the band response functions of seven satellite sensors. They were the Vegetation and environment monitoring on a new micro-satellite (VENlS), Worldview-2 (WV-2), RapidEye-1 (RE-1), HJ1/CCD1, Sentinel-2, Landsat 8/OLI and GaoFen (GF) 1/WFV1. The values of common used spectral indices were calculated based on the simulated sensor bands, respectively. Prediction models were also developed based on the spectral indices and simulated bands. The results showed that the PLSR model by whole spectrum had the good accuracy of LAI estimation with the R2c = 0.726, RMSEc = 0.462, R2cv = 0.635, RMSEcv = 0.538. For the simulated satellite datasets, the better LAI estimation were obtained by Sentinel-2 and Venls bands with the R2cv greater than 0.600 and RMSEcv less than 0.557. The Estimation model by simulated WV-2 bands, and RE-1 bands had the lowest performance with the R2cv between 0.50 and 0.55, and RMSEcv between 0.600 and 0.623. The inversion results demonstrated the potential of the multispectral remote sensing data to calibrate the LAI estimation model of winter pepper for the precision agriculture application. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Remote sensing

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Dynamic Harmonic Modeling and Characteristics Analysis of Photovoltaic Converter

Dynamic Harmonic Modeling and Characteristics Analysis of Ph...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Ding Wang Yangwu Shen Xun Mao Ao Pi Bing Zeng Chengye Zhang Power grid technology center State Grid Hunan Electric Power Company Limited Research Institute Changsha China Power grid technology center State Grid Anhui Electric Power Co. Ltd. Research Institute Hefei China Big data center state key laboratory of offshore wind power technology and testing XEMC Wind power Co. Ltd. Changsha China School of Electrical & Information Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9781728131375

ISBN: (纸本)9781728131382

Photovoltaic converters whose dynamic characteristics determine the behavior of grid-connected device are important equipment for new energy generation. Therefore, to analyze the harmonic characteristics generated by grid-connected photovoltaic devices, it is necessary to firstly study the mechanism of harmonic generation, then build the dynamic harmonic model of photovoltaic power generation unit, and discuss the influencing factors. In this paper, based on the deep comprehension of harmonic characteristics, a Norton equivalent circuit that described the harmonic characteristics of grid-connected converter of photovoltaic power generation unit is established. Besides, the effects of the power output of photovoltaic power generation unit and the filter structure on converter's harmonic output are investigated. The accuracy of the proposed model is verified by comparing the results between the theoretical calculation and simulation.

关键词： Power harmonic filters Harmonic analysis Modulation Photovoltaic systems

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