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arXiv 2019年

作者： Niu, Yulei Zhang, Hanwang Lu, Zhiwu Chang, Shih-Fu Beijing Key Laboratory of Big Data Management and Analysis Methods School of Information Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical Engineering Columbia University New YorkNY10027 United States

—We focus on grounding (i.e., localizing or linking) referring expressions in images, e.g., "largest elephant standing behind baby elephant". This is a general yet challenging vision-language task since it does not only require the localization of objects, but also the multimodal comprehension of context — visual attributes (e.g., "largest", "baby") and relationships (e.g., "behind") that help to distinguish the referent from other objects, especially those of the same category. Due to the exponential complexity involved in modeling the context associated with multiple image regions, existing work oversimplifies this task to pairwise region modeling by multiple instance learning. In this paper, we propose a variational Bayesian method, called Variational Context, to solve the problem of complex context modeling in referring expression grounding. Specifically, our framework exploits the reciprocal relation between the referent and context, i.e., either of them influences estimation of the posterior distribution of the other, and thereby the search space of context can be greatly reduced. In addition to reciprocity, our framework considers the semantic information of context, i.e., the referring expression can be reproduced based on the estimated context. We also extend the model to unsupervised setting where no annotation for the referent is available. Extensive experiments on various benchmarks show consistent improvement over state-of-the-art methods in both supervised and unsupervised settings. Copyright © 2019, The Authors. All rights reserved.

关键词： Bayesian networks

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An LDPC Approach for Chunked Network Codes

An LDPC Approach for Chunked Network Codes

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作者： Tang, Bin Yang, Shenghao National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China

Efficient communication through a multi-hop network with packet loss requires random linear network coding schemes with low computation cost and high throughput. In this paper, we propose a low-density parity-check (LDPC)-based framework for constructing chunked code, a variation of random linear network code with low encoding/decoding computational cost and small coefficient vector overhead. Two classes of chunked codes with LDPC structures, named uniform LDPC-chunked codes and overlapped LDPC-chunked (OLC) codes, are studied under a general chunk transfer matrix model. ULC codes achieve rates close to the optimum and perform better than existing chunked codes that employ parity-check constraints. OLC codes are overlapped chunked codes, where it is not necessary to generate new packets for encoding, and demonstrate much higher rates in certain scenarios than the state-of-the-art designs of overlapped chunked codes. These results justifies the feasibility of this LDPC approach for communication through multi-hop networks with packet loss. © 1993-2012 IEEE.

关键词： Belief propagation

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Attention-based Feature Enhanced Dehazing Network

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Journal of Physics: Conference Series 2021年第1期2035卷

作者： Yuanshun Cheng Anhui Tan Chuansheng Yang Yueting Yang Chao Wang Tianfu College of SWUFE Mianyang Sichuan 621000 China School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang 316022 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan Zhejiang 316022 China School of Mathematics and Statistics Beihua University Jilin Jilin 132013 P.R. China

Presence of haze in images obscures underlying information, which is undesirable in applications requiring accurate environment information. To recover such an image, a dehazing algorithm should enhance the feature information of the background while weakening the feature information of haze. In this paper, we propose an end-to-end attention-based feature enhanced dehazing network (AEDNet), which integrates enhancement strategy and attention mechanism, to achieve haze removal. The network is based on U-Net, which has the advantages of retaining information, obtaining multi-scale features and so on. In the training of the network, pixel loss and perceptual loss are used to preserve feature information and improve the overall quality of results. The extensive evaluation shows that the proposed model performs significantly better than previous dehazing methods on various benchmarks.

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Versatile hydrogel based on polyvinyl alcohol/chitosan/regenerated silk fibroin

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Journal of Physics: Conference Series 2021年第1期2011卷

作者： Qinyue Sun Jizhu Fu Heng Zhang Huihui Yang Yumin Wang Yi Xiong Wei Zeng Faniu Wang Chao Xu National Engineering Research Center for Agro-Ecological Big Data Analysis & Application School of Electronics and Information Engineering Anhui University Hefei 230601 Anhui People's Republic of China School of Physics and Technology MOE Key Laboratory of Artificial Micro- and Nano-Structures and Center for Electron Microscopy Wuhan University Wuhan 430072 Hubei People's Republic of China Science and Technology Institute Key Laboratory of Textile Fiber and Products Ministry of Education Laboratory for Electron Microscopy Wuhan Textile University Wuhan 430073 Hubei People's Republic of China

Versatile sensors have broad application prospects in human motion detection, health monitoring, wearable electronic devices and flexible electronic skin and other emerging fields. In this work, the enhanced hydrogel was prepared by freezing and thawing process with calcium ion crosslinking. This composite material shows excellent flexibility and elasticity, and after being cut in half, it can automatically heal well in a short time without external force. It shows great potential in flexible and wearable devices.

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Heterogeneous-length text topic modeling for reader-Aware multi-document summarization

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ACM Transactions on knowledge Discovery from data 2019年第4期13卷 42-42页

作者： Qiang, Jipeng Chen, Ping Ding, Wei Wang, Tong Xie, Fei Wu, Xindong Department of Computer Science Yangzhou University China Department of Computer Science University of Massachusetts Boston United States Department of Computer Science and Technology Hefei Normal University China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100084 China

More and more user comments like Tweets are available, which often contain user concerns. In order to meet the demands of users, a good summary generating from multiple documents should consider reader interests as reflected in reader comments. In this article, we focus on how to generate a summary from multi-document documents by considering reader comments, named as reader-Aware multi-document summarization (RA-MDS).We present an innovative topic-based method for RA-MDA, which exploits latent topics to obtain the most salient and lessen redundancy summary from multiple documents. Since finding latent topics for RA-MDS is a crucial step, we also present a Heterogeneous-length Text Topic Modeling (HTTM) to extract topics from the corpus that includes both news reports and user comments, denoted as heterogeneous-length texts. In this case, the latent topics extract by HTTM cover not only important aspects of the event, but also aspects that attract reader interests. Comparisons on summary benchmark datasets also confirm that the proposed RA-MDS method is effective in improving the quality of extracted summaries. In addition, experimental results demonstrate that the proposed topic modeling method outperforms existing topic modeling algorithms. © 2019 Association for Computing Machinery. All rights reserved.

关键词： heterogeneous-length text LDA multi-document summarization Topic modeling

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Quadruply charmed baryons as heavy quark symmetry partners of the Ds∗0(2317)

arXiv

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arXiv 2020年

作者： Wu, Tian-Wei Liu, Ming-Zhu Geng, Li-Sheng Hiyama, Emiko Valderrama, Manuel Pavon Wang, Wen-Ling School of Physics Beihang University Beijing100191 China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China School of Physics and Microelectronics Zhengzhou University Zhengzhou Henan450001 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and Engineering Beihang University Beijing100191 Department of Physics Kyushu University Fukuoka819-0395 Japan RIKEN Nishina Center RIKEN Wako351-0198 Japan

Both unitary chiral theories and lattice QCD simulations show that the DK interaction is attractive and can form a bound state, namely, Ds∗0(2317). Assuming the validity of the heavy antiquark-diquark symmetry (HADS), the ΞccK¯ interaction is the same as the DK interaction, which implies the existence of a ΞccK¯ bound state with a binding energy of 49 − 64 MeV. In this work, we study whether a ΞccΞccK¯ three-body system binds. The ΞccΞcc interaction is described by exchanging π, σ, ρ, and ω mesons, with the corresponding couplings related to those of the NN interaction via the quark model. We indeed find a ΞccΞccK¯ bound state, with quantum numbers JP = 0−, I = 21 , S = 1 and C = 4, and a binding energy of 80 − 118 MeV. It is interesting to note that this system is very similar to the well-known NNK¯ system, which has been studied extensively both theoretically and experimentally. Within the same framework, we show the existence of a NNK¯ state with a binding energy of 35 − 43 MeV, consistent with the results of other theoretical works and experimental data, which serves as a consistency check on the predicted ΞccΞccK¯ bound state. Copyright © 2020, The Authors. All rights reserved.

关键词： Binding energy

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Dynamic Harmonic Modeling and Characteristics Analysis of Photovoltaic Converter 3

Dynamic Harmonic Modeling and Characteristics Analysis of Ph...

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3rd IEEE Conference on Energy Internet and Energy System Integration, EI2 2019

作者： Wang, DIng Shen, Yangwu Mao, Xun Pi, Ao Zeng, Bing Zhang, Chengye State Grid Hunan Electric Power Company Limited Research Institute Power Grid Technology Center Changsha China State Grid Anhui Electric Power Co. Ltd. Research Institute Power Grid Technology Center Hefei China XEMC Wind Power Co. Ltd. Big Data Center State Key Laboratory of Offshore Wind Power Technology and Testing Changsha China Changsha University of Science and Technology School of Electrical and Information Engineering Changsha China

ISBN: (纸本)9781728131375

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. © 2019 IEEE.

关键词： Equivalent circuits

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Structured Information Extraction of Pathology Reports with Attention-based Graph Convolutional Network

Structured Information Extraction of Pathology Reports with ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jialun Wu Kaiwen Tang Haichuan Zhang Chunbao Wang Chen Li School of Computer Science and Technology Xi’an Jiaotong University Xi’an Shaanxi China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an Shaanxi China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi’an Jiaotong University Xi’an Shaanxi China School of Computer Science and Technology Qian Xuesen College Xi’an Jiaotong University Xi’an Shaanxi China the First Affiliated Hospital of Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Electronic medical data contains biochemical, imaging, pathological information during diagnosis and treatment. The pathology report is a kind of highly liberalized unstructured textual data, which is the basis and gold standard of cancer diagnosis and is very important for the prognosis and treatment of patients. The application of information extraction technology to pathological reports can obtain structured data that can be understood and analyzed by computers, helping pathologists make appropriate decisions. In this work, we proposed an attention-based graph convolutional network (GCN) for converting unstructured pathological reports into a structured form suitable for computer analysis to improve the current pathologist's workflow, collected medical data from different platforms, and provided more accurate assistance for diagnosis and treatment. We used pathology reports data from TCGA (The Cancer Genome Atlas) database with fine-grained annotations on 3632 pathology reports including four types of cancers. Our method performs better in our pathology report dataset with higher F1 score than traditional methods and deep learning methods. The results indicate that our method is robust, thus may work with other types of cancer pathology report.

关键词： Pathology Cancer Task analysis Annotations Information retrieval Medical diagnostic imaging data mining

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Impact of temperature and relative humidity on the transmission of COVID-19: A modeling study in China and the United States

arXiv

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arXiv 2020年

作者： Wang, Jingyuan Tang, Ke Feng, Kai Lin, Xin Lv, Weifeng Chen, Kun Wang, Fei School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Social Sciences Tsinghua University China State Key Laboratory of Software Development Environment Beihang University China Department of Statistics University of Connecticut United States Center for Population Health University of Connecticut Health Center United States Department of Population Health Sciences Weill Cornell Medical College Cornell University United States

Objectives We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, and human mobility status. Design A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. Setting We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1,005 U.S. counties. Participants A total of 69,498 cases in China and 740,843 cases in the U.S. are used for calculating the effective reproductive numbers. Primary outcome measures Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). Results Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the U.S. Conclusions Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1 degree Celsius is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI [-0.0395,-0.0125]) in China and by 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.;an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI [-0.0108,-0.0045]) in China and by 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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Effects of annotation granularity in deep learning models for histopathological images

arXiv

引用

arXiv 2020年

作者： Shi, Jiangbo Gao, Zeyu Zhang, Haichuan Puttapirat, Pargorn Wang, Chunbao Zhang, Xiangrong Li, Chen National Engineering Lab for Big Data Analytics Xi'an Jiao tong University Xi'an Shaanxi710049 China School of Electronic and Information Engineering Xi'an Jiao tong University Xi'an Shaanxi710049 China Shaan xi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi'an Jiao tong University Xi'an Shaanxi710049 China Department of Pathology First Affiliated Hospital of Xi'an Jiaotong University Xi'an Shaanxi710061 China Institute of Intelligent Information Processing Xidian University Xi'an Shaanxi710071 China

Pathological examination is an important step in cancer diagnosis. Pathologists make diagnosis and pathology report based on observed cell and tissue structure on pathological slides. With the development of statistical machine learning, especially deep learning, automated classifiers are being used to analyze histopathological slides and assist pathologists in diagnosis. Currently, commonly used annotation methods in histopathological slides include image-wise, bounding box, ellipse-wise, pixel-wise. In order to verify the influence of annotation in pathological slide on deep learning model, we design corresponding experiments to test the performance based on annotations with different granularity annotation. In classification, all state-of-the-art deep learning based classifiers perform better when they are trained by the dataset with pixel-wise annotation. On average, precision, recall and F1-score improves by 7.87%, 8.83% and 7.85% respectively. Thus, it is suggested that finer annotations are better utilized by deep learning algorithms in classification tasks. Similarly, semantic segmentation algorithms can achieve 8.33% better segmentation accuracy when trained by pixel-wise annotations. Our study shows that finer-grained annotation can not only improve the performance of deep learning models, but help deep learning model extract more accurate phenotypic information from histopathological slides. An accurate acquisitions of phenotypic information can help pathologists to enquire the model based on which regions and features in the slide were mainly used to calculate the prediction, improve the reliability of the model prediction. The compartmentalized prediction approach similar to this work may contribute to phenotype and genotype association studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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