Dickinsonneumann2706

Extensive experiments on moving and stationary target acquisition and recognition benchmark database demonstrate the effectiveness of our proposed framework. Compared with other state-of-the-art SAR ATR algorithms, RotANet will remarkably improve the recognition accuracy especially in the case of very limited training samples without performing any other data augmentation strategy.Hyperspectral imagery (HSI) contains rich spectral information, which is beneficial to many tasks. However, acquiring HSI is difficult because of the limitations of current imaging technology. As an alternative method, spectral super-resolution aims at reconstructing HSI from its corresponding RGB image. Recently, deep learning has shown its power to this task, but most of the used networks are transferred from other domains, such as spatial super-resolution. In this paper, we attempt to design a spectral super-resolution network by taking advantage of two intrinsic properties of HSI. The first one is the spectral correlation. Based on this property, a decomposition subnetwork is designed to reconstruct HSI. The other one is the projection property, i.e., RGB image can be regarded as a three-dimensional projection of HSI. Inspired from it, a self-supervised subnetwork is constructed as a constraint to the decomposition subnetwork. These two subnetworks constitute our end-to-end super-resolution network. In order to test the effectiveness of it, we conduct experiments on three widely used HSI datasets (i.e., CAVE, NUS, and NTIRE2018). Experimental results show that our proposed network can achieve competitive reconstruction performance in comparison with several state-of-the-art networks.A point cloud as an information-intensive 3D representation usually requires a large amount of transmission, storage and computing resources, which seriously hinder its usage in many emerging fields. In this paper, we propose a novel point cloud simplification method, Approximate Intrinsic Voxel Structure (AIVS), to meet the diverse demands in real-world application scenarios. The method includes point cloud pre-processing (denoising and down-sampling), AIVS-based realization for isotropic simplification and flexible simplification with intrinsic control of point distance. To demonstrate the effectiveness of the proposed AIVS-based method, we conducted extensive experiments by comparing it with several relevant point cloud simplification methods on three public datasets, including Stanford, SHREC, and RGB-D scene models. The experimental results indicate that AIVS has great advantages over peers in terms of moving least squares (MLS) surface approximation quality, curvature-sensitive sampling, sharp-feature keeping and processing speed. The source code of the proposed method is publicly available. (https//github.com/vvvwo/AIVS-project).Images captured in snowy days suffer from noticeable degradation of scene visibility, which degenerates the performance of current vision-based intelligent systems. Removing snow from images thus is an important topic in computer vision. In this paper, we propose a Deep Dense Multi-Scale Network (DDMSNet) for snow removal by exploiting semantic and depth priors. As images captured in outdoor often share similar scenes and their visibility varies with depth from camera, such semantic and depth information provides a strong prior for snowy image restoration. We incorporate the semantic and depth maps as input and learn the semantic-aware and geometry-aware representation to remove snow. In particular, we first create a coarse network to remove snow from the input images. Then, the coarsely desnowed images are fed into another network to obtain the semantic and depth labels. Finally, we design a DDMSNet to learn semantic-aware and geometry-aware representation via a self-attention mechanism to produce the final clean images. Experiments evaluated on public synthetic and real-world snowy images verify the superiority of the proposed method, offering better results both quantitatively and qualitatively. https//github.com/HDCVLab/Deep-Dense-Multi-scale-Network https//github.com/HDCVLab/Deep-Dense-Multi-scale-Network.The rotation, scale and translation invariance of extracted features have a high significance in image recognition. Local binary pattern (LBP) and LBP-based descriptors have been widely used in image recognition due to feature discrimination and computational efficiency. However, most of the existing LBP-based descriptors have been designed to achieve rotation invariance while fail to achieve scale invariance. Moreover, it is usually difficult to achieve a good trade-off between the feature discrimination and the feature dimension. In this work, a learning 2D co-occurrence LBP termed 2D-LCoLBP is proposed to address these issues. Firstly, a weighted joint histogram is constructed in different neighborhoods and scales of an image to represent the multi-neighborhood and multi-scale LBP (2D-MLBP) and achieve the rotation invariance. A feature learning strategy is then designed to learn the compact and robust descriptor (2D-LCoLBP) from LBP pattern pairs across different scales in the extracted 2D-MLBP to characterize the most stable local structures and achieve the scale invariance, as well as decrease the feature dimension and improve the noise robustness. Finally, a linear SVM classifier is employed for recognition. We applied the proposed 2D-LCoLBP on four image recognition tasks-texture, object, face and food recognition with ten image databases. Experimental results show that 2D-LCoLBP has obviously low feature dimension but outperforms the state-of-the-art LBP-based descriptors in terms of recognition accuracy under noise-free, Gaussian noise and JPEG compression conditions.Rainy weather is a challenge for many vision-oriented tasks (e.g., object detection and segmentation), which causes performance degradation. Image deraining is an effective solution to avoid performance drop of downstream vision tasks. However, most existing deraining methods either fail to produce satisfactory restoration results or cost too much computation. In this work, considering both effectiveness and efficiency of image deraining, we propose a progressive coupled network (PCNet) to well separate rain streaks while preserving rain-free details. To this end, we investigate the blending correlations between them and particularly devise a novel coupled representation module (CRM) to learn the joint features and the blending correlations. By cascading multiple CRMs, PCNet extracts the hierarchical features of multi-scale rain streaks, and separates the rain-free content and rain streaks progressively. To promote computation efficiency, we employ depth-wise separable convolutions and a U-shaped structure, and construct CRM in an asymmetric architecture to reduce model parameters and memory footprint. Extensive experiments are conducted to evaluate the efficacy of the proposed PCNet in two aspects (1) image deraining on several synthetic and real-world rain datasets and (2) joint image deraining and downstream vision tasks (e.g., object detection and segmentation). Furthermore, we show that the proposed CRM can be easily adopted to similar image restoration tasks including image dehazing and low-light enhancement with competitive performance. The source code is available at https//github.com/kuijiang0802/PCNet.There are growing investigations on incorporating solid nanoparticles (NPs) into the shell of microbubbles (MBs), because NPs may endow the MBs with other bio-functions, such as multimodality imaging and drug delivery. These novel MBs have been developed as hybrid MBs contrast agents. Generally, the shell density of hybrid MBs was assumed to be the same as water in the studies of bubble dynamics. In fact, the NPs in the layer of MBs can change the density of the shell, which leads to the change of scattering characteristics of MBs under ultrasonic excitation. Thus, it is necessary to develop a new model to simulate dynamics of the hybrid MBs. Here, we have investigated scattering characteristics of the hybrid MB embedded with NPs based on a modified Rayleigh-Plesset model. The numerical and analytical solutions to this equation are obtained for oscillation response, harmonic-components and scattered cross section of hybrid MB at small amplitude oscillations. The results indicated that the shell density had a greater impact on the nonlinear harmonics than fundamental ones. Considering acoustic driving frequency and pulse lengths, the largest sub-harmonic amplitude is 14 times larger than the smallest value. Considering the effects of bubble equilibrium radius, the second scattering cross-section of hybrid MB increased first and then decreased with increasing bubble equilibrium radius. Therefore, the optimal values of shell density for hybrid MB can be predicted to obtain higher scattered signals. This also offers more accurate assessment of scattering characteristics for hybrid MB contrast agents.To investigate the role of the vasculature in pancreatic β-cell regeneration, we crossed a zebrafish β-cell ablation model into the avascular npas4l mutant (i.e. cloche). Surprisingly, β-cell regeneration increased markedly in npas4l mutants owing to the ectopic differentiation of β-cells in the mesenchyme, a phenotype not previously reported in any models. The ectopic β-cells expressed endocrine markers of pancreatic β-cells, and also responded to glucose with increased calcium influx. Through lineage tracing, we determined that the vast majority of these ectopic β-cells has a mesodermal origin. Notably, ectopic β-cells were found in npas4l mutants as well as following knockdown of the endothelial/myeloid determinant Etsrp. Together, these data indicate that under the perturbation of endothelial/myeloid specification, mesodermal cells possess a remarkable plasticity enabling them to form β-cells, which are normally endodermal in origin. Understanding the restriction of this differentiation plasticity will help exploit an alternative source for β-cell regeneration.Human embryogenesis entails complex signalling interactions between embryonic and extra-embryonic cells. However, how extra-embryonic cells direct morphogenesis within the human embryo remains largely unknown due to a lack of relevant stem cell models. Here, we have established conditions to differentiate human pluripotent stem cells (hPSCs) into yolk sac-like cells (YSLCs) that resemble the post-implantation human hypoblast molecularly and functionally. YSLCs induce the expression of pluripotency and anterior ectoderm markers in human embryonic stem cells (hESCs) at the expense of mesoderm and endoderm markers. This activity is mediated by the release of BMP and WNT signalling pathway inhibitors, and, therefore, resembles the functioning of the anterior visceral endoderm signalling centre of the mouse embryo, which establishes the anterior-posterior axis. Our results implicate the yolk sac in epiblast cell fate specification in the human embryo and propose YSLCs as a tool for studying post-implantation human embryo development in vitro.

To explore the effect of autonomy to choose exercise-therapy (ET) for nonspecific chronic low back pain (NSCLBP) on treatment adherence and clinical outcomes.

Forty-six students were recruited from Ariel University.

Every two gender-and-age-matched students were allocated to either self-selected exercise group (SSE) or pre-determined exercise group (PDE). selleck compound Subjects completed 4-weeks exercise and filled a training-log. Oswestry disability-index (ODI) and numerical pain-rating scores (NPRS) were measured, as well as exercise quality-performance.

ODI and NPRS improved in both groups, with no between-group differences. Exercise quality-performance was also similar between groups. A trend for better exercise-adherence was found in the SSE-group (75.3% vs 65.0% adherence,

 = 0.08, effect size

 = 0.59). Meaningful NPRS improvement was demonstrated in 54.5% of SSE-group participants compared with 33.3% in the PDE-group.

Autonomy may serve as a factor to enhance treatment adherence and clinical outcomes of ET for NSCLBP among students.