The Dirichlet problem for quasilinear elliptic differential equations in unbounded domains

AbstractThis paper is devoted to the second order, quasilinear elliptic Dirichlet problem of nondivergence type. We mainly consider the existence and uniqueness of classical solutions which radially converge at infinity under certain hypotheses

Scene-Aware Feature Matching

Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene. This results in significant performance degradation when handling challenging scenes such as scenes with large viewpoint and illumination changes. To tackle this problem, we propose a novel model named SAM, which applies attentional grouping to guide Scene-Aware feature Matching. SAM handles multi-level features, i.e., image tokens and group tokens, with attention layers, and groups the image tokens with the proposed token grouping module. Our model can be trained by ground-truth matches only and produce reasonable grouping results. With the sense-aware grouping guidance, SAM is not only more accurate and robust but also more interpretable than conventional feature matching models. Sufficient experiments on various applications, including homography estimation, pose estimation, and image matching, demonstrate that our model achieves state-of-the-art performance.Comment: Accepted to ICCV 202

ParaFormer: Parallel Attention Transformer for Efficient Feature Matching

Heavy computation is a bottleneck limiting deep-learningbased feature matching algorithms to be applied in many realtime applications. However, existing lightweight networks optimized for Euclidean data cannot address classical feature matching tasks, since sparse keypoint based descriptors are expected to be matched. This paper tackles this problem and proposes two concepts: 1) a novel parallel attention model entitled ParaFormer and 2) a graph based U-Net architecture with attentional pooling. First, ParaFormer fuses features and keypoint positions through the concept of amplitude and phase, and integrates self- and cross-attention in a parallel manner which achieves a win-win performance in terms of accuracy and efficiency. Second, with U-Net architecture and proposed attentional pooling, the ParaFormer-U variant significantly reduces computational complexity, and minimize performance loss caused by downsampling. Sufficient experiments on various applications, including homography estimation, pose estimation, and image matching, demonstrate that ParaFormer achieves state-of-the-art performance while maintaining high efficiency. The efficient ParaFormer-U variant achieves comparable performance with less than 50% FLOPs of the existing attention-based models.Comment: Have been accepted by AAAI 202

Characterization of Laser-Resistant Port Wine Stain Blood Vessels Using In Vivo Reflectance Confocal Microscopy.

Background and objectivesPort wine stain (PWS) is a congenital vascular malformation of the human skin. Laser is the treatment of choice for PWS. Laser-resistant PWS is one crucial factor accounting for inadequate treatment outcome, which needs to be fully characterized. This study aims to quantitatively characterize the morphology of laser-resistant PWS blood vessels in the upper papillary dermis using in vivo reflectance confocal microscopy (RCM).Study design/materials and methodsA total of 42 PWS subjects receiving laser treatment from August 2016 through July 2018 were enrolled into this study. Thirty-three subjects had facial PWS; nine had extremity PWS. All subject's PWS received multiplex 585/1,064 nm laser treatment. RCM images were taken before and after treatment. The density, diameter, blood flow, and depth of PWS blood vessels were analyzed.ResultsWe found 44.4% PWS on the extremities (four out of nine subjects) were laser-resistant, which was significantly higher (P < 0.001) when compared with those PWS on the face (15.2%, 5 out of 33 subjects). The laser-resistant facial PWS blood vessels had significantly higher blood flow (1.35 ± 0.26 U vs. 0.89 ± 0.22 U, P < 0.001), larger blood vessel diameters (109.60 ± 18.24 µm vs. 84.36 ± 24.04 µm, P = 0.033) and were located deeper in the skin (106.01 ± 13.87 µm vs. 87.82 ± 12.57 µm, P < 0.001) in the skin when compared with laser-responsive PWS on the face. The average PWS blood vessel density (17.01 ± 4.63/mm2 vs. 16.61 ± 4.44/mm2 , P = 0.857) was not correlated to the laser resistance.ConclusionsLaser-resistant PWS blood vessels had significantly higher blood flow, larger diameters, and were located deeper in the skin. RCM can be a valuable tool for a prognostic evaluation on laser-resistant lesions before treatment, thereby providing guidance for tailored laser treatment protocols, which may improve the therapeutic outcome. The limitations for this study include relative small sample size and acquisitions of different blood vessels before and after 2 months of treatment. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc

Formulation, antileukemia mechanism, pharmacokinetics, and biodistribution of a novel liposomal emodin

Emodin is a multifunctional Chinese traditional medicine with poor water solubility. D-α-tocopheryl polyethylene glycol 1000 succinate (TPGS) is a pegylated vitamin E derivate. In this study, a novel liposomal-emodin-conjugating TPGS was formulated and compared with methoxypolyethyleneglycol 2000-derivatized distearoyl-phosphatidylethanolamine (mPEG2000–DSPE) liposomal emodin. TPGS improved the encapsulation efficiency and stability of emodin egg phosphatidylcholine/cholesterol liposomes. A high encapsulation efficiency of 95.2% ± 3.0%, particle size of 121.1 ± 44.9 nm, spherical ultrastructure, and sustained in vitro release of TPGS liposomal emodin were observed; these were similar to mPEG2000–DSPE liposomes. Only the zeta potential of −13.1 ± 2.7 mV was significantly different to that for mPEG2000–DSPE liposomes. Compared to mPEG2000–DSPE liposomes, TPGS liposomes improved the cytotoxicity of emodin on leukemia cells by regulating the protein levels of myeloid cell leukemia 1 (Mcl-1), B-cell lymphoma-2 (Bcl-2) and Bcl-2-associated X protein, which was further enhanced by transferrin. TPGS liposomes prolonged the circulation time of emodin in the blood, with the area under the concentration–time curve (AUC) 1.7 times larger than for free emodin and 0.91 times larger than for mPEG2000–DSPE liposomes. In addition, TPGS liposomes showed higher AUC for emodin in the lung and kidney than for mPEG2000–DSPE liposomes, and both liposomes elevated the amount of emodin in the heart. Overall, TPGS is a pegylated agent that could potentially be used to compose a stable liposomal emodin with enhanced therapeutics

Does Haze Removal Help CNN-based Image Classification?

Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images. When the ground-truth haze-free image is available for a hazy image, quantitative evaluation of image dehazing is usually based on objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks, such as automatic classification, recognition and categorization. One fundamental problem here is whether various dehazing methods can produce clearer images that can help improve the performance of the high-level tasks. In this paper, we empirically study this problem in the important task of image classification by using both synthetic and real hazy image datasets. From the experimental results, we find that the existing image-dehazing methods cannot improve much the image-classification performance and sometimes even reduce the image-classification performance