Fast Shape Recognition via the Restraint Reduction of Bone Point Segment

In computer science, and especially in computer vision, the contour of an object is used to describe its features; thus, the shape descriptor plays an indispensable role in target detection and recognition. Further, Fourier is an important mathematical description method, and the Fourier transform of a shape contour has symmetry. This paper will demonstrate the symmetry of shape contour in the frequency domain. In recent years, increasing numbers of shape descriptors have come to the fore, but many descriptors ignore the details of shape. It is found that the most fundamental reason affecting the performance of shape descriptors is structural restraints, especially feature structure restraint. Therefore, in this paper, the restraint of feature structure that intrinsically deteriorates recognition performance is shown, and a fast shape recognition method via the Bone Point Segment (BPS) restraint reduction is proposed. An approach using the inner distance to find bone shapes and segment the shape contour by these bones is proposed. Then, Fourier transform is performed on each segment to form the shape feature. Finally, the restraints of the shape feature are reduced in order to build a more effective shape feature. What is commendable is that its discriminability and robustness is strong, the process is simple, and matching speed is fast. More importantly, the experiment results show that the shape descriptor has higher recognition accuracy and the matching speed runs up to more than 1000 times faster than the existing description methods like CBW and TCD. More importantly, it is worth noting that the recognition accuracy approaches 100% in the self-build dataset

The crystal structure of [1-(4-(trifluoromethyl)phenyl)-3,4-dihydroquinolin-2(1H)-one], C16H12F3NO

C16H12F3NO, orthorhombic, P212121 (no. 19), a = 6.9928(6) Å, b = 8.9764(8) Å, c = 21.216(2) Å, V = 1331.7(2) Å3, Z = 2, Rgt(F) = 0.0583, wRref(F2) = 0.1552, T = 298 K

Pseudo-<inline-formula><math display="inline"><semantics><mrow><msub><mi mathvariant="script">L</mi><mn>0</mn></msub></mrow></semantics></math></inline-formula>-Norm Fast Iterative Shrinkage Algorithm Network: Agile Synthetic Aperture Radar Imaging via Deep Unfolding Network

A novel compressive sensing (CS) synthetic-aperture radar (SAR) called AgileSAR has been proposed to increase swath width for sparse scenes while preserving azimuthal resolution. AgileSAR overcomes the limitation of the Nyquist sampling theorem so that it has a small amount of data and low system complexity. However, traditional CS optimization-based algorithms suffer from manual tuning and pre-definition of optimization parameters, and they generally involve high time and computational complexity for AgileSAR imaging. To address these issues, a pseudo-L0-norm fast iterative shrinkage algorithm network (pseudo-L0-norm FISTA-net) is proposed for AgileSAR imaging via the deep unfolding network in this paper. Firstly, a pseudo-L0-norm regularization model is built by taking an approximately fair penalization rule based on Bayesian estimation. Then, we unfold the operation process of FISTA into a data-driven deep network to solve the pseudo-L0-norm regularization model. The network’s parameters are automatically learned, and the learned network significantly increases imaging speed, so that it can improve the accuracy and efficiency of AgileSAR imaging. In addition, the nonlinearly sparsifying transform can learn more target details than the traditional sparsifying transform. Finally, the simulated and data experiments demonstrate the superiority and efficiency of the pseudo-L0-norm FISTA-net for AgileSAR imaging

Rifampicin versus streptomycin for brucellosis treatment in humans: A meta-analysis of randomized controlled trials

<div><p>Brucellosis is a zoonotic disease with a high morbidity in developing countries, but there the optimal treatment is not yet determined. Therefore, the development of a simple and effective treatment is important. The aim of this study was to summarize the available evidences and compare rifampicin with streptomycin in human brucellosis with doxycycline as background regimen. We systematically searched PubMed, EmBase, and the Cochrane Library from their inception up through December 2016. We included studies with a randomized controlled design that evaluated the effect of streptomycin compared with rifampicin in human brucellosis patients who received doxycycline therapy as background regimen. The overall failure and relapse were summarized using random-effects model. Our meta-analysis included 1,383 patients with brucellosis from 14 trials. We found that patients who received rifampicin therapy had a higher risk of overall failure (RR: 2.36; 95% CI: 1.72–3.23; P<0.001) and relapse (RR: 2.74; 95% CI: 1.80–4.19; P<0.001) compared with streptomycin. Results of the sensitivity analysis were consistent with the overall analysis. Subgroup analysis indicated that mean age of the patients and percentage of male participants might influence the treatment effects. Furthermore, no publication bias was detected. The findings of this study indicated that rifampicin therapy significantly increased the risk of overall failure and relapse compared with streptomycin. Hence, it can be recommended to patients with human brucellosis receiving streptomycin therapy.</p></div

Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information

Deep learning is currently the mainstream method of object detection. Faster region-based convolutional neural network (Faster R-CNN) has a pivotal position in deep learning. It has impressive detection effects in ordinary scenes. However, under special conditions, there can still be unsatisfactory detection performance, such as the object having problems like occlusion, deformation, or small size. This paper proposes a novel and improved algorithm based on the Faster R-CNN framework combined with the Faster R-CNN algorithm with skip pooling and fusion of contextual information. This algorithm can improve the detection performance under special conditions on the basis of Faster R-CNN. The improvement mainly has three parts: The first part adds a context information feature extraction model after the conv5_3 of the convolutional layer; the second part adds skip pooling so that the former can fully obtain the contextual information of the object, especially for situations where the object is occluded and deformed; and the third part replaces the region proposal network (RPN) with a more efficient guided anchor RPN (GA-RPN), which can maintain the recall rate while improving the detection performance. The latter can obtain more detailed information from different feature layers of the deep neural network algorithm, and is especially aimed at scenes with small objects. Compared with Faster R-CNN, you only look once series (such as: YOLOv3), single shot detector (such as: SSD512), and other object detection algorithms, the algorithm proposed in this paper has an average improvement of 6.857% on the mean average precision (mAP) evaluation index while maintaining a certain recall rate. This strongly proves that the proposed method has higher detection rate and detection efficiency in this case