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Rational evaluation of various epidemic models based on the COVID-19 data of China

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arXiv 2020年

作者： Yang, Wuyue Zhang, Dongyan Peng, Liangrong Zhuge, Changjing Hong, Liu School of Mathematics Sun Yat-Sen University Guangzhou510275 China Yau Mathematical Sciences Center Tsinghua University Beijing100084 China Beijing Institute for Scientific and Engineering Computing Faculty of Sciences Beijing University of Technology Beijing100124 China College of Mathematics and Data Science Minjiang University Fuzhou350108 China

In this paper, based on the Akaike information criterion, root mean square error and robustness coefficient, a rational evaluation of various epidemic models/methods, including seven empirical functions, four statistical inference methods and five dynamical models, on their forecasting abilities is carried out. With respect to the outbreak data of COVID-19 epidemics in China, we find that before the inflection point, all models fail to make a reliable prediction. The Logistic function consistently underestimates the final epidemic size, while the Gompertz’s function makes an overestimation in all cases. Towards statistical inference methods, the methods of sequential Bayesian and time-dependent reproduction number are more accurate at the late stage of an epidemic. And the transition-like behavior of exponential growth method from underestimation to overestimation with respect to the inflection point might be useful for constructing a more reliable forecast. Compared to ODE-based SIR, SEIR and SEIR-AHQ models, the SEIR-QD and SEIR-PO models generally show a better performance on studying the COVID-19 epidemics, whose success we believe could be attributed to a proper trade-off between model complexity and fitting accuracy. Our findings not only are crucial for the forecast of COVID-19 epidemics, but also may apply to other infectious diseases. Copyright © 2020, The Authors. All rights reserved.

关键词： Mean square error

Multiclass Single Label Model for Web Page Classification

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Multiclass Single Label Model for Web Page Classification

2019 International Conference on Recent Advances in Energy-efficient computing and Communication, ICRAECC 2019

作者： Kag, Aakash Jenila Livingston, L.M. Livingston Merlin, L.M. Agnel Livingston, L.G.X. Mirabel Technologies Big Data Analyst Hyderabad HyderabadTG500032 India VIT Chennai School of Computing Science and Engineering 600127 India Jeppiaar Institute of Technology ECE Dept SriperumbudurTamil Nadu631604 India St. Xaviers Catholic college of Engineering CSE Dept. Nagercoil629003 India

ISBN: (纸本)9781538694824

Web is a huge repository of information and there is a need of categorization of web pages to facilitate better search and retrieval of pages. Web page classification has become a challenging task due to the exponential growth of the World Wide Web and this study augments classification model with a general facility for automatically assigning class label (e.g., sport, news) to web pages based on the output of a Naive Bayes classifier. For the purpose of build classification model, yahoo Open Directory Project (ODP) data set has been used for create training and testing set. In this research work web page classification was done using Uniform Resource Locator (URL) features, Meta data, Meta keywords, Internal Links and text, which gives better result than URLs based method. © 2019 IEEE.

关键词： Websites

KPNet: Towards Minimal Face Detector

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arXiv 2020年

作者： Song, Guanglu Liu, Yu Zang, Yuhang Wang, Xiaogang Leng, Biao Yuan, Qingsheng SenseTime X-Lab Chinese University of Hong Kong Hong Kong School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 National Computer network Emergency Response technical Team/Coordination Center of China

The small receptive field and capacity of minimal neural networks limit their performance when using them to be the backbone of detectors. In this work, we find that the appearance feature of a generic face is discriminative enough for a tiny and shallow neural network to verify from the background. And the essential barriers behind us are 1) the vague definition of the face bounding box and 2) tricky design of anchor-boxes or receptive field. Unlike most top-down methods for joint face detection and alignment, the proposed KPNet detects small facial keypoints instead of the whole face by in the bottom-up manner. It first predicts the facial landmarks from a low-resolution image via the well-designed fine-grained scale approximation and scale adaptive soft-argmax operator. Finally, the precise face bounding boxes, no matter how we define it, can be inferred from the keypoints. Without any complex head architecture or meticulous network designing, the KPNet achieves state-of-the-art accuracy on generic face detection and alignment benchmarks with only ∼ 1M parameters, which runs at 1000fps on GPU and is easy to perform real-time on most modern front-end chips. Copyright © 2020, The Authors. All rights reserved.

关键词： Face recognition

Syntax-enhanced Pre-trained model

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arXiv 2020年

作者： Xu, Zenan Guo, Daya Tang, Duyu Su, Qinliang Shou, Linjun Gong, Ming Zhong, Wanjun Quan, Xiaojun Jiang, Daxin Duan, Nan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China Guangdong Key Laboratory Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens. Copyright © 2020, The Authors. All rights reserved.

关键词： Syntactics

Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19

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arXiv 2020年

作者： Shi, Feng Wang, Jun Shi, Jun Wu, Ziyan Wang, Qian Tang, Zhenyu He, Kelei Shi, Yinghuan Shen, Dinggang Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 United Imaging Intelligence CambridgeMA02140 United States Institute for Medical Imaging Technology School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200030 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing210093 China National Key Laboratory for Novel Software and Technology Nanjing University Nanjing China

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI ca n improve work efficiency by accurate delination of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

An efficient matching method for dispatching autonomous vehicles*

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An efficient matching method for dispatching autonomous vehi...

International Conference on Intelligent Transportation

作者： Ming Li Nan Zheng Xinkai Wu Xiang Huo School of Transportation Science and Engineering Beihang University China Department of Civil Engineering Monash University Australia Beijing Advanced Innovation Center for Big Data and Brain Computing School of Transportation Science and Engineering Beihang University China

ISBN: (纸本)9781538670255

Autonomous vehicles (AVs), as an emerging transportation tool, are widely discussed for improving traffic mobility. Particularly, when combining with prevalent shared transportation modes, i.e., car-sharing and ride-sharing, AVs show promising future. However, the efficiency of searching for an optimal match between shared autonomous vehicles and passengers has long become a critical problem. This research tackles this issue by developing a model which has better efficiency. The essential task is to dispatch vehicles to serve passengers, which is similar to vehicle routing problem. But unlike existing vehicle routing models, our method makes use of the similarity of passengers' demand instead of directly matching vehicles and passengers. In this way, a cluster-based algorithm is proposed to find near-optimal solutions. Numerical experiments on large-size are performed to present the validity of the optimization model and efficiency of the proposed algorithm. Results indicate that the cluster-based algorithm could bring benefit to minimizing the number of vehicles and total travel distance.

关键词： Intelligent transportation systems Conferences

The importance of environmental factors in forecasting Australian power demand

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arXiv 2019年

作者： Eshragh, Ali Ganim, Benjamin Perkins, Terry Bandara, Kasun School of Information and Physical Sciences University of Newcastle NSW Australia International Computer Science Institute BerkeleyCA United States School of Computing and Information Systems Melbourne Centre for Data Science University of Melbourne VIC Australia

We develop a time series model to forecast weekly peak power demand for three main states of Australia for a yearly time-scale, and show the crucial role of environmental factors in improving the forecasts. More precisely, we construct a seasonal autoregressive integrated moving average (SARIMA) model and reinforce it by employing the exogenous environmental variables including, maximum temperature, minimum temperature, and solar exposure. The estimated hybrid SARIMA-regression model exhibits an excellent mean absolute percentage error (MAPE) of 3.41%. Moreover, our analysis demonstrates the importance of the environmental factors by showing a remarkable improvement of 46.3% in MAPE for the hybrid model over the crude SARIMA model which merely includes the power demand variables. In order to illustrate the efficacy of our model, we compare our outcome with the state-of-the-art machine learning methods in forecasting. The results reveal that our model outperforms the latter *** Codes 62M10, 97K80 Copyright © 2019, The Authors. All rights reserved.

关键词： Electric power utilization

Improving neural relation extraction with implicit mutual relations

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arXiv 2019年

作者： Kuang, Jun Cao, Yixin Zheng, Jianbing He, Xiangnan Gao, Ming Zhou, Aoying School of Data Science and Engineering East China Normal University Shanghai China School of Computing National University of Singapore Singapore School of Information Science and Technology University of Science and Technology of China Hefei China KLATASDS-MOE School of Statistics East China Normal University Shanghai China

Relation extraction (RE) aims at extracting the relation between two entities from the text corpora. It is a crucial task for Knowledge Graph (KG) construction. Most existing methods predict the relation between an entity pair by learning the relation from the training sentences, which contain the targeted entity pair. In contrast to existing distant supervision approaches that suffer from insufficient training corpora to extract relations, our proposal of mining implicit mutual relation from the massive unlabeled corpora transfers the semantic information of entity pairs into the RE model, which is more expressive and semantically plausible. After constructing an entity proximity graph based on the implicit mutual relations, we preserve the semantic relations of entity pairs via embedding each vertex of the graph into a low-dimensional space. As a result, we can easily and flexibly integrate the implicit mutual relations and other entity information, such as entity types, into the existing RE methods. Our experimental results on a New York Times and another Google Distant Supervision datasets suggest that our proposed neural RE framework provides a promising improvement for the RE task, and significantly outperforms the state-of-the-art methods. Moreover, the component for mining implicit mutual relations is so flexible that can help to improve the performance of both CNN-based and RNN-based RE models significant. Copyright © 2019, The Authors. All rights reserved.

关键词： Knowledge graph

Innovative informatics methods for process mining in health care

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Journal of Biomedical Informatics 2020年 109卷 103551-103551页

作者： Munoz-Gama, Jorge Martin, Niels Fernandez-Llatas, Carlos Johnson, Owen Sepúlveda, Marcos Department of Computer Science School of Engineering Pontificia Universidad Católica de Chile Santiago Chile Research Group Business Informatics Hasselt University Hasselt Belgium Data Analytics Laboratory Vrije Universiteit Brussel Brussels Belgium ITACA Institute – Process Mining 4 Health Lab Universitat Politècnica de Valencia Valencia Spain Karolinska Institutet Stockholm Sweden School of Computing Leeds University Leeds United Kingdom