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Novel Hemorrhagic Risk Score in Elderly Patients with Coronary Artery Disease and Gastrointestinal Malignant Tumor Comorbidity:A 10-year Clinical Inpatient data analysis from 2 Medical Centers

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Cardiology Discovery 2021年第3期1卷 163-172页

作者： Nandi Bao Wanling Wang Huitao Wu Yabin Wang Hebin Che Wenwen Meng Jiaxin Miao Dong Han Fan Yin National Clinical Research Center for Geriatric Diseases The Second Medical Center of Chinese People's Liberation Army General HospitalBeijing 100853China Medical School of Chinese People's Liberation Army General Hospital Beijing 100853China National Engineering Laboratory for Medical Big Data Application Technology The First Medical Center of Chinese People's Liberation Army General HospitalBeijing 100853China Department of Oncology The Second Medical Center of Chinese People's Liberation Army General HospitalBeijing 100853China

Objective:Older patients with comorbidity,such as coronary heart disease(CHD)and malignant gastrointestinal tumors,are at a high risk of bleeding ***,risk prediction models based on risk factor assessment remain *** study aimed to establish an individualized bleeding risk assessment system based on the analysis of 10-year inpatient clinical big ***:Total clinical data of 56,819 patients with CHD and 25,988 patients with malignant digestive tract tumors(admitted from January 2008 to December 2017)were retrospectively collected at the First and Second Medical Centers of Chinese People's Liberation Army General *** them,1307 patients with CHD and malignant digestive tract tumors were screened as the derivation *** dependent variable was the occurrence of major clinical bleeding *** statistics and hypothesis tests of differences were performed for independent variables according to the occurrence of *** Tree,eXtreme Gradient Boosting(XGBoost),logistic regression,and random forest models were used for *** accuracy,sensitivity,specificity,and area under the receiver operating characteristic curve(AUC-ROC)were applied as the criteria for evaluating and verifying model *** evaluate this developed model,another cohort comprising 454 patients(admitted from January 2018 to December 2019)was prospectively enrolled as the validation cohort based on the same inclusion and exclusion ***:Among the 64 variables with<50%missing values,the recursive feature elimination method with a random forest model was used to screen the selected *** highest accuracy was obtained following the selection of 10 scalars,and the final model was constructed *** demonstrated the best performance *** AUC-ROC of this model was 0.981,with an accuracy,sensitivity,and specificity of 0.939,0.950,and 0.927,*** the validation cohort,the AUC-ROC,accurac

关键词： Coronary disease Gastrointestinal cancers Bleeding Multivariate analysis Risk factors Machine learning

Identification of winter wheat plantation based on sentinel-2 imagery

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Identification of winter wheat plantation based on sentinel-...

2019 ASABE Annual International Meeting

作者： Zhang, Dongyan Fang, Shengmei Duan, Hongrui Zhang, Huihui She, Bao Ding, Yang Yang, Yuying National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui University Hefei China Water Management and Systems Research Unit USDA Agricultural Research Service Fort CollinsCO United States School of Surveying and Mapping Anhui University of Science and Technology Huainan China

As an important food crop in the world, remote sensing identification of winter wheat (Triticum Aestivum L.) and extraction of planting areas are the basis of growth monitoring, disaster assessment and yield prediction. ESA Sentinel-2A/B satellite has significant advantages in crop identification with its high spatial resolution and short revisit period. In this study, northern and central Anhui, the main wheat production area in Anhui Province in China, were selected as the study area to explore the identification algorithm of winter wheat under different planting conditions. Based on the variation curve of the normalized difference vegetation index (NDVI) time series in the key phenological period of winter wheat, the specificity of its greenness was explored, and the screening rules were constructed accordingly. The spatial distribution of winter wheat in the study area was screened out by Decision Tree algorithm, and the extraction effect was improved by using a cultivated land distribution product. The extraction effect was evaluated by means of random samples, and the extraction area was tested with reference to the county-level wheat planting area data recorded in the statistical yearbook. The results showed that the estimation accuracy of winter wheat area in northern Anhui counties was 86.86%, and the Kappa coefficient was 0.80 (the Omission was 8.48%, the Commission was 9.58%). The estimation accuracy of winter wheat area in central Anhui counties was 80.00%, and the Kappa coefficient was 0.62 (the Omission was 17.30%, the Commission was 40.20%). The overall extraction effect is inferior to that of the main producing areas in northern Anhui. This study provides a valuable reference for mapping wheat production under fragmented and complicated land surface conditions, which will further contribute to decision-making by government and relevant departments. © 2019 ASABE Annual International Meeting. All rights reserved.

关键词： Extraction

A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problem

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2020年第1期21卷 9253-9290页

作者： Peipei Tang Chengjing Wang Defeng Sun Kim-Chuan Toh School of Computer and Computing Science Zhejiang University City College Hangzhou China School of Mathematics and National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for solving these problems. Our key idea for making the proposed PMM to be efficient is to develop a sparse semismooth Newton method to solve the corresponding subproblems. By using the Kurdyka-Łojasiewicz property exhibited in the underlining problems, we prove that the PMM algorithm converges to a d-stationary point. We also analyze the oracle property of the initial subproblem used in our algorithm. Extensive numerical experiments are presented to demonstrate the high efficiency of the proposed PMM algorithm.

关键词： nonconvex square-root regression problems proximal majorization-minimization semismooth Newton method

Multispectral Pan-sharpening via Dual-Channel Convolutional Network with Convolutional LSTM Based Hierarchical Spatial-Spectral Feature Fusion

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arXiv 2020年

作者： Wang, Dong Bai, Yunpeng Li, Ying School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xian China School of Computing and Information Systems University of Melbourne VIC3010 Australia

Multispectral pan-sharpening aims at producing a high resolution (HR) multispectral (MS) image in both spatial and spectral domains by fusing a panchromatic (PAN) image and a corresponding MS image. In this paper, we propose a novel dual-channel network (DCNet) framework for MS pan-sharpening. In our DCNet, the dual-channel backbone involves a spatial channel to capture spatial information with a 2D CNN, and a spectral channel to extract spectral information with a 3D CNN. This heterogeneous 2D/3D CNN architecture can minimize causing spectral information distortion, which typically happens in conventional 2D CNN models. In order to fully integrate the spatial and spectral features captured from different levels, we introduce a multi-level fusion strategy. Specifically, a spatial-spectral CLSTM (S2-CLSTM) module is proposed for fusing the hierarchical spatial and spectral features, which can effectively capture correlations among multi-level features. The S2-CLSTM module attaches two fusion ways: the intra-level fusion via bi-directional lateral connections and inter-level fusion via the cell state in the S2-CLSTM. Finally, the ideal HR-MS image is recovered by a reconstruction module. Extensive experiments have been conducted at both simulated lower scale and the original scale of real-world datasets. Compared with the state-of-the-art methods, the proposed DCNet achieves superior or competitive performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

Magic Cube Bloom Filter: Answering Membership Queries for Multiple Sets

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Magic Cube Bloom Filter: Answering Membership Queries for Mu...

International Conference on big data and Smart Computing (bigCOMP)

作者： Zhouyi Sun Siang Gao Bingqing Liu Yufei Wang Tong Yang Bin Cui National Engineering Laboratory for Big Data Analysis Technology and Application (PKU) China Department of Computer Science Peking University China International School Beijing University of Posts and Telecommunications China

Given d sets without intersection, the multi-set membership query problem is to decide which set an incoming item e belongs to. Multi-set membership query is a fundamental problem in a variety of fields of computer science, especially computer networking. Although several approaches have been proposed in the literature to address this problem, they cannot achieve a high processing speed, a high accuracy, and a small memory usage at the same time. In this paper, we propose a novel data structure, namely Magic Cube Bloom Filter (MCBF), which outperforms the state-of-the-art in terms of accuracy and query speed with a limited memory usage. The key idea of the Magic Cube Bloom filter is that items from the same set are stored in different Bloom filters, improving the accuracy by the redistribution of items. The MC-BF also improves the query speed by utilizing spatial locality. Experimental results show that the MC-BF outperforms the state-of-the-art BUFFALO by up to 148.5 times in terms of accuracy, and up to 3.16 times in terms of query speed. We have made the source code of our MC-BF available on Github [1].

关键词： Random access memory Hash functions Servers Memory management Switches Arrays

ICSTrace: A Malicious IP Traceback Model for Attacking data of Industrial Control System

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arXiv 2019年

作者： Xiao, Feng Xu, Qiang Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Electronic Engineering Institute of Hefei China

Considering the attacks against industrial control system are mostly organized and premeditated actions, IP traceback is significant for the security of industrial control system. Based on the infrastructure of the Internet, we have developed a novel malicious IP traceback model-ICSTrace, without deploying any new services. The model extracts the function codes and their parameters from the attack data according to the format of industrial control protocol, and employs a short sequence probability method to transform the function codes and their parameter into a vector, which characterizes the attack pattern of malicious IP addresses. Furthermore, a Partial Seeded K-Means algorithm is proposed for the pattern’s clustering, which helps in tracing the attacks back to an organization. ICSTrace is evaluated basing on the attack data captured by the large-scale deployed honeypots for industrial control system, and the results demonstrate that ICSTrace is effective on malicious IP traceback in industrial control system. Copyright © 2019, The Authors. All rights reserved.

关键词： K-means clustering

PSGR: Pixel-wise sparse graph reasoning for COVID-19 pneumonia segmentation in CT images

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arXiv 2021年

作者： Jia, Haozhe Tang, Haoteng Ma, Guixiang Cai, Weidong Huang, Heng Zhan, Liang Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Intel Labs 2111 NE 25th Ave HillsboroOR97124 United States School of Computer Science University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States JD Finance America Corporation Mountain View CaliforniaCA94043 United States

Automated and accurate segmentation of the infected regions in computed tomography (CT) images is critical for the prediction of the pathological stage and treatment response of COVID-19. Several deep convolutional neural networks (DCNNs) have been designed for this task, whose performance, however, tends to be suppressed by their limited local receptive fields and insufficient global reasoning ability. In this paper, we propose a pixel-wise sparse graph reasoning (PSGR) module and insert it into a segmentation network to enhance the modeling of long-range dependencies for COVID-19 infected region segmentation in CT images. In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the segmentation backbone, and then converted into a sparsely-connected graph by keeping only K strongest connections to each uncertain pixel. The long-range information reasoning is performed on the sparsely-connected graph to generate enhanced features. The advantages of this module are two-fold: (1) the pixel-wise mapping strategy not only avoids imprecise pixel-to-node projections but also preserves the inherent information of each pixel for global reasoning;and (2) the sparsely-connected graph construction results in effective information retrieval and reduction of the noise propagation. The proposed solution has been evaluated against four widely-used segmentation models on three public datasets. The results show that the segmentation model equipped with our PSGR module can effectively segment COVID-19 infected regions in CT images, outperforming all other competing models. Copyright © 2021, The Authors. All rights reserved.

关键词： Pixels

The Existence of a Class of Balanced Multi-output Rotation Symmetric Boolean Functions

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Chinese Journal of Electronics 2018年第5期27卷 995-1001页

作者： DU Jiao FU Shaojing QU Longjiang LI Chao PANG Shanqi College of Mathematics and Information Science Henan Normal University Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control Henan Normal University College of Computer Science National University of Defense Technology College of Liberal Arts and Science National University of Defense Technology

A new characterization of balanced rotation symmetric（n, m）-functions is presented. Based on the characterization, the nonexistence of balanced rotation symmetric（pr, m）-functions is determined, where p is an odd prime and m ≥ 2. And there exist balanced rotation symmetric（2r, m）-functions for 2 ≤ m ≤ 2r-r. With the help of these results, we also prove that there exist rotation symmetric resilient（2r, m）-functions for 2 ≤ m ≤ 2r-r-1.

关键词： Cryptography Rotation symmetric Orbit matrix Correlation immune Resilient functions

A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction

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arXiv 2020年

作者： Yang, Jun Li, Qing Sun, Yixuan Wang, Wei Li, Linchao Wang, Xuwei Jia, Shengyao Tong, Renyuan National and Local Joint Engineering research center for Disaster Monitoring Technologies and Instruments China Jiliang University Hangzhou310051 China Zhejiang Provincial Key Laboratory of Intelligent Manufacturing Quality Big Data Traceability Analysis and Application China Jiliang University Hangzhou310051 China Zhejiang PeckerAI Technology. Ltd Hangzhou310018 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Tailings ponds are places for storing industrial waste. The saturation line is the key factor of quantifying the safety of tailings pond. Existing saturation line time-series prediction methods are mainly based on statistical models or shallow machine learning models. Although these models aim to capture the time dependence of the sequence data, the channel and temporal are even unavailable in principle. In this paper, we presents a two-stage forecasting method, which embeds the channel and temporal attention into a hybrid CNN-LSTM model to predict the saturation line. In the first stage, the discrete wavelet transform (DWT) is applied to capture the refined sequence information. In the second stage, the CNN-LSTM model is utilized to learn the basic spatial and temporal features in the time series. Furthermore, the channel and temporal attention model are embedded into CNN-LSTM model to enhance the feature extracting ability in channel and temporal dimension. Consequently, our proposed model is shown to outperform classic models on five real-world datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting