Discovering visual concept structure with sparse and incomplete tags

This work was partially supported by the China Scholarship Council, Vision Semantics Limited, and Royal Society Newton Advanced Fellowship Programme (NA150459)

Person re-identification by unsupervised video matching

This work was partially supported by National Basic Research Program of China (973 Project) 2012CB725405, the National Science and Technology Support Program (2014BAG03B01), National Natural Science Foundation China61273238, Beijing Municipal Science and Technology Project (D15110900280000), Tsinghua University Project (20131089307). Xiatian Zhu and Xiaolong Ma equally contributed to this work

KUNet: Imaging Knowledge-Inspired Single HDR Image Reconstruction

Recently, with the rise of high dynamic range (HDR) display devices, there is a great demand to transfer traditional low dynamic range (LDR) images into HDR versions. The key to success is how to solve the many-to-many mapping problem. However, the existing approaches either do not consider constraining solution space or just simply imitate the inverse camera imaging pipeline in stages, without directly formulating the HDR image generation process. In this work, we address this problem by integrating LDR-to-HDR imaging knowledge into an UNet architecture, dubbed as Knowledge-inspired UNet (KUNet). The conversion from LDR-to-HDR image is mathematically formulated, and can be conceptually divided into recovering missing details, adjusting imaging parameters and reducing imaging noise. Accordingly , we develop a basic knowledge-inspired block (KIB) including three subnetworks corresponding to the three procedures in this HDR imaging process. The KIB blocks are cascaded in the similar way to the UNet to construct HDR image with rich global information. In addition, we also propose a knowledge inspired jump-connect structure to fit a dynamic range gap between HDR and LDR images. Experimental results demonstrate that the proposed KUNet achieves superior performance compared with the state-of-the-art methods. The code, dataset and appendix materials are available at https://github.com/wanghu178/KUNet.git

PolarFormer: Multi-Camera 3D Object Detection with Polar Transformer

3D object detection in autonomous driving aims to reason “what” and “where” the objects of interest present in a 3D world. Following the conventional wisdom of previous 2D object detection, existing methods often adopt the canonical Cartesian coordinate system with perpendicular axis. However, we conjugate that this does not fit the nature of the ego car’s perspective, as each onboard camera perceives the world in shape of wedge intrinsic to the imaging geometry with radical (non perpendicular) axis. Hence, in this paper we advocate the exploitation of the Polar coordinate system and propose a new Polar Transformer (PolarFormer) for more accurate 3D object detection in the bird’s-eye-view (BEV) taking as input only multi-camera 2D images. Specifically, we design a cross-attention based Polar detection head without restriction to the shape of input structure to deal with irregular Polar grids. For tackling the unconstrained object scale variations along Polar’s distance dimension, we further introduce a multi-scale Polar representation learning strategy. As a result, our model can make best use of the Polar representation rasterized via attending to the corresponding image observation in a sequence-to-sequence fashion subject to the geometric constraints. Thorough experiments on the nuScenes dataset demonstrate that our PolarFormer outperforms significantly state-of-the-art 3D object detection alternatives

Application of machine learning risk prediction mathematical model in the diagnosis of Escherichia coli infection in patients with septic shock by cardiovascular color doppler ultrasound

this study was to explore the diagnosis of septic shock patients with Escherichia coli (E. coli) infection based on cardiovascular color Doppler ultrasound (CCDUS) images under the machine learning risk prediction mathematical model (risk prediction model). 120 septic shock patients with Escherichia coli (E. coli) infection, admitted to xxx hospital were selected as research subjects, and they were randomly divided into experimental group and control group, including 76 males and 44 females, with an average age of (45.47 ± 11.35) years old. The prediction model, random forest mathematical model (RF model), and feature combination were trained and applied in the CCDUS. The error rate, F1-score, and area under the curve (AUC) were compared. It was found that the prediction effect of the risk prediction model was better (P < 0.05). The receiver operating characteristic curve (ROC) was drawn based on the risk prediction model, and it was found that the AUC was 0.924, and the best cutoff value was 0.247. The consistency test between the predicted death result and the actual result showed that Kappa = 0.824, which was higher than 0.75. The pathogenic microorganisms of the patients were mainly Gram-positive bacteria (GPB) in 32 cases (53.33%). There were 19 cases whose pathogenic bacteria was E. coli, and 11 cases (57.9%) of which were acquired in the intensive care unit (ICU). The patient mortality rate was 41.67%. Finally, the acute physiology and chronic health II (APACH II) score and D-dimer of the patients were substituted into the Logistic regression model. The effect of the risk prediction model was better than the RF model and feature combination; the measurement results based on the risk prediction model had good consistency; the D-dimer and APACH II score were independent factors for death of the septic shock

Statins for the Primary Prevention of Coronary Heart Disease

Object. The purpose of this study was to fully assess the role of statins in the primary prevention of coronary heart disease (CHD). Methods. We searched six databases (PubMed, the Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang Database, and Chinese Scientific Journal Database) to identify relevant randomized controlled trials (RCTs) from inception to 31 October 2017. Two review authors independently assessed the methodological quality and analysed the data using Rev Man 5.3 software. Risk ratios and 95% confidence intervals (95% CI) were pooled using fixed/random-effects models. Funnel plots and Begg’s test were conducted to assess publication bias. The quality of the evidence was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results. Sixteen RCTs with 69159 participants were included in this review. Statins can effectively decrease the occurrence of angina (RR=0.70, 95% CI: 0.58~0.85, I2 =0%), nonfatal myocardial infarction (MI) (RR=0.60, 95% CI: 0.51~0.69, I2 =14%), fatal MI (RR=0.49, 95% CI: 0.24~0.98, I2 =0%), any MI (RR=0.53, 95% CI: 0.42~0.67, I2 =0%), any coronary heart events (RR=0.73, 95% CI: 0.68~0.78, I2=0%), coronary revascularization (RR=0.66, 95% CI: 0.55~0.78, I2 = 0%), and any cardiovascular events (RR=0.77, 95% CI: 0.72~82, I2 = 0%). However, based on the current evidence, there were no significant differences in CHD deaths (RR=0.82, 95% CI: 0.66~1.02, I2=0%) and all-cause mortality (RR=0.88, 95% CI: 0.76 ~1.01, I2 =58%) between the two groups. Additionally, statins were more likely to result in diabetes (RR=1.21, 95% CI: 1.05~1.39, I2 =0%). There was no evidence of publication biases, and the quality of the evidence was considered moderate. Conclusion. Statins seemed to be beneficial for the primary prevention of CHDs but have no effect on CHD death and all-cause mortality

A Wheat WRKY Transcription Factor TaWRKY10 Confers Tolerance to Multiple Abiotic Stresses in Transgenic Tobacco

<div><p>WRKY transcription factors are reported to be involved in defense regulation, stress response and plant growth and development. However, the precise role of WRKY transcription factors in abiotic stress tolerance is not completely understood, especially in crops. In this study, we identified and cloned 10 WRKY genes from genome of wheat (<i>Triticum aestivum</i> L.). <i>TaWRKY10</i>, a gene induced by multiple stresses, was selected for further investigation. <i>TaWRKY10</i> was upregulated by treatment with polyethylene glycol, NaCl, cold and H₂O₂. Result of Southern blot indicates that the wheat genome contains three copies of <i>TaWRKY10</i>. The TaWRKY10 protein is localized in the nucleus and functions as a transcriptional activator. Overexpression of <i>TaWRKY10</i> in tobacco (<i>Nicotiana tabacum</i> L.) resulted in enhanced drought and salt stress tolerance, mainly demonstrated by the transgenic plants exhibiting of increased germination rate, root length, survival rate, and relative water content under these stress conditions. Further investigation showed that transgenic plants also retained higher proline and soluble sugar contents, and lower reactive oxygen species and malonaldehyde contents. Moreover, overexpression of the <i>TaWRKY10</i> regulated the expression of a series of stress related genes. Taken together, our results indicate that TaWRKY10 functions as a positive factor under drought and salt stresses by regulating the osmotic balance, ROS scavenging and transcription of stress related genes.</p></div