The largest virialized dark halo in the universe

Using semi-analytic approach, we present an estimate of the properties of the largest virialized dark halos in the present universe for three different scenarios of structure formation: SCDM, LCDM and OCDM models. The resulting virial mass and temperature increase from the lowest values of $1.6 \times 10^{15}h^{-1}M_{\odot}$ and 9.8 keV in OCDM, the mid-range values of $9.0 \times 10^{15}h^{-1}M_{\odot}$ and 31 keV in LCDM, to the highest values of $20.9 \times 10^{15}h^{-1}M_{\odot}$, 65 keV in SCDM. As compared with the largest virialized object seen in the universe, the richest clusters of galaxies, we can safely rule out the OCDM model. In addition, the SCDM model is very unlikely because of the unreasonably high virial mass and temperature. Our computation favors the prevailing LCDM model in which superclusters may be marginally regarded as dynamically-virialized systems.Comment: 5 pages, Accepted by Int. J. Mod. Phys.

Learning Garment DensePose for Robust Warping in Virtual Try-On

Virtual try-on, i.e making people virtually try new garments, is an active research area in computer vision with great commercial applications. Current virtual try-on methods usually work in a two-stage pipeline. First, the garment image is warped on the person's pose using a flow estimation network. Then in the second stage, the warped garment is fused with the person image to render a new try-on image. Unfortunately, such methods are heavily dependent on the quality of the garment warping which often fails when dealing with hard poses (e.g., a person lifting or crossing arms). In this work, we propose a robust warping method for virtual try-on based on a learned garment DensePose which has a direct correspondence with the person's DensePose. Due to the lack of annotated data, we show how to leverage an off-the-shelf person DensePose model and a pretrained flow model to learn the garment DensePose in a weakly supervised manner. The garment DensePose allows a robust warping to any person's pose without any additional computation. Our method achieves the state-of-the-art equivalent on virtual try-on benchmarks and shows warping robustness on in-the-wild person images with hard poses, making it more suited for real-world virtual try-on applications.Comment: 6 page

Shared memory parallel computing procedures for nonlinear dynamic analysis of super high rise buildings

The proposed parallel state transformation procedures (PSTP) of fiber beam-column elements and multi-layered shell elements, combined with the parallel factorization of Jacobian (PF), are incorporated into a finite element program. In PSTP, elements are classified into different levels of workload prior to state determination in order to balance workload among different threads. In PF, the multi-threaded version of OpenBLAS is adopted to compute super-nodes. A case study on four super high-rise buildings, i.e. S1~S4, has demonstrated that the combination of PSTP and PF does not have any observable influence on computational accuracy. As number of elements and DOFs increases, the ratio of time consumed in the formation of the Jacobian to that consumed in the solution of algebraic equations tends to decrease. The introduction of parallel solver can yield a substantial reduction in computational cost. Combination of PSTP and PF can give rise to a further significant reduction. The acceleration ratios associated with PSTP do not exhibit a significant decrease as PGA level increases. Even PGA level is equal to 2.0g, PSTP still can result in acceleration ratios of 2.56 and 1.92 for S1 and S4, respectively. It is verified that it is more effective to accelerate analysis by reducing the time spent in solving algebraic equations rather than reducing that spent in the formation of the Jacobian for super high-rise buildings

Application of RetCamⅡ in the screening of neonatal fundus disease

AIM: To investigate the safe and reliable examination method for neonatal fundus screening.<p>METHODS: Fundus information of 2 836 neonates performed by RetCamⅡ in our hospital from January 1, 2012 to December 31, 2012 were retrospectively analyzed, including 1 625 cases(57.30%)of premature infants which were first examined 1-4 weeks after birth and 1 211 cases(42.70%)of term infants which were first examined within 4 weeks after birth.<p>RESULTS: Totally 454 cases of abnormalfundus were found, including 207 cases(12.74%)of retinopathy of prematurity(ROP), ROPⅠ in 118 cases(57%), ROPⅡ in 58 cases(28.02%), ROPⅢ in 23 cases(11.11%), ROPⅣ in 8 cases(3.86%), no case of ROPV. A total of 247(20.40%)term infants had abnormal fundus, of which 68 cases(27.53%)were developmental or hereditary disease, retinoblastoma in 1 case(0.40%), retinal hemorrhage in 102 cases(41.30%), retinal exudative changes in 68 cases(27.53%), optic atrophy in 5 cases(2.02%)and optic disc edema in 3 cases(1.21%).<p>CONCLUSION: Neonatal fundus diseases were so various and harmful that early screening should be attended to. Premature infants and term infants with high risk are treated as focus group of fundus screening and RetCamII examination is safe and effective

Preparation and characterization of a sulfonated carbon-based solid acid microspheric material (SCSAM) and its use for the esterification of oleic acid with methanol

In this study, a sulfonated group (-SO3H) rich carbon-based solid acid microspheric material was prepared by hydrothermal method followed by sulfonation using glucose as the raw material. Such a green, non-corrosive, and renewable carbon material was used as a heterogeneous catalyst for the esterification of oleic acid with methanol for the production of biodiesel. The carbon microspheres were characterized systematically. It was found that the carbon microspheres prepared under the optimal reaction conditions exhibited smooth surfaces, uniform particle sizes and good dispersion. The sulfonated carbon-based solid acid microspheric materials showed high acidity and good catalytic activities for the esterification of oleic acid with methanol. The influence of reaction operating conditions on the performance of esterification was studied. The optimal esterification reaction conditions were found to be: methanol/oleic acid molar ratio 12:1, catalyst loading 0.25 g (0.05 mmol H+), reaction temperature 65 °C, reaction time 8 h and mechanical stirring rate 360 rpm. It was found that the catalyst demonstrated very good reusability although there was noticeable loss in acidity due to the leaching of active sites

Generative Model Based Noise Robust Training for Unsupervised Domain Adaptation

Target domain pseudo-labelling has shown effectiveness in unsupervised domain adaptation (UDA). However, pseudo-labels of unlabeled target domain data are inevitably noisy due to the distribution shift between source and target domains. This paper proposes a Generative model-based Noise-Robust Training method (GeNRT), which eliminates domain shift while mitigating label noise. GeNRT incorporates a Distribution-based Class-wise Feature Augmentation (D-CFA) and a Generative-Discriminative classifier Consistency (GDC), both based on the class-wise target distributions modelled by generative models. D-CFA minimizes the domain gap by augmenting the source data with distribution-sampled target features, and trains a noise-robust discriminative classifier by using target domain knowledge from the generative models. GDC regards all the class-wise generative models as generative classifiers and enforces a consistency regularization between the generative and discriminative classifiers. It exploits an ensemble of target knowledge from all the generative models to train a noise-robust discriminative classifier and eventually gets theoretically linked to the Ben-David domain adaptation theorem for reducing the domain gap. Extensive experiments on Office-Home, PACS, and Digit-Five show that our GeNRT achieves comparable performance to state-of-the-art methods under single-source and multi-source UDA settings

Prediction Calibration for Generalized Few-shot Semantic Segmentation

Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of (e.g., 1-5) training images per class. Compared to the widely studied Few-shot Semantic Segmentation FSS, which is limited to segmenting novel classes only, GFSS is much under-studied despite being more practical. Existing approach to GFSS is based on classifier parameter fusion whereby a newly trained novel class classifier and a pre-trained base class classifier are combined to form a new classifier. As the training data is dominated by base classes, this approach is inevitably biased towards the base classes. In this work, we propose a novel Prediction Calibration Network PCN to address this problem. Instead of fusing the classifier parameters, we fuse the scores produced separately by the base and novel classifiers. To ensure that the fused scores are not biased to either the base or novel classes, a new Transformer-based calibration module is introduced. It is known that the lower-level features are useful of detecting edge information in an input image than higher-level features. Thus, we build a cross-attention module that guides the classifier's final prediction using the fused multi-level features. However, transformers are computationally demanding. Crucially, to make the proposed cross-attention module training tractable at the pixel level, this module is designed based on feature-score cross-covariance and episodically trained to be generalizable at inference time. Extensive experiments on PASCAL-$5^{i}$ and COCO-$20^{i}$ show that our PCN outperforms the state-the-the-art alternatives by large margins.Comment: Technical Repor