Search

Occlusion handling is a key technical issue in augmented reality research. This paper proposes a new occlusion algorithm based on object contour detection to address issues such as poor real-time occlusion processing, high computational complexity in comparing the depth values of virtual and real objects, and the presence of jagged, blurry, and hollow edges in occluded areas. First, based on the depth and color information, we obtained aligned images of real scenes. Second, we extracted the maximum closed contour of the real object in the scene and overlaid it with the aligned image. Subsequently, we generated a virtual object and obtained a depth map of the virtual object. Finally, by comparing the depth values of the stacked images with the virtual objects, masks are generated in real time and optimized to present the occlusion processing results. Experimental comparisons demonstrated that the algorithm presented in this study not only improves real-time performance but also enhances accuracy at the intersection edges of virtual and real images. Simultaneously, it is no longer limited by the size of real scene images and can achieve real-time virtual and real occlusion effects. [ABSTRACT FROM AUTHOR]

To analyze the intensity of atmospheric turbulence in a satellite–ground laser communication link, it is important to consider the effect of increased atmospheric turbulence caused by wind speed. Atmospheric turbulence causes a change in the refractive index, which negatively impacts the quality and focusing ability of the laser beam by altering its phase front. To simulate the changes in amplitude and phase characteristics of laser beam propagation in atmospheric turbulence caused by wind speed, a transverse translation phase screen is used. To better understand and address the influence of atmospheric wind speed on the phase of optical signals in satellite–ground laser communication links, this paper proposes a Monte Carlo simulation method. This method utilizes the spatial and temporal variations in the refractive index in the atmosphere and integrates the principles of optical signal propagation in the atmosphere to simulate changes in the phase of optical signals under different wind speed conditions. By analyzing the variations in the received optical signal's power, the Monte Carlo method is employed to simulate phase screens and logarithmic amplitude screens. Additionally, it models the probability density of the statistical behavior of received optical signal's fluctuations, as well as the time autocorrelation coefficient of optical signals. This paper, under the coupling condition in satellite–ground laser communication links, conducted a Monte Carlo simulation experiment to analyze the characteristics of the optical signal's fluctuations in the link and discovered that atmospheric wind speed affects the shape of the power spectral density model of the received optical signal. Increasing wind speed leads to a decrease in the time autocorrelation coefficient of the received optical signal and affects the coupling efficiency. The paper then used a cubic spline interpolation fitting method to verify the models of the power spectral density and the autocorrelation time coefficient of the optical signal. This provides a theoretical foundation and practical guidance for the optimization of satellite–ground laser communication systems. [ABSTRACT FROM AUTHOR]

Recently, deep learning-based methods for quality assessment of omnidirectional images (OIs) have gained widespread attention. However, existing methods face challenges because most omnidirectional image quality assessment (OIQA) methods inadequately consider projection distortions and visual complexity. In response, a multiscale feature aggregation convolutional neural network is proposed for OIQA to explore the feasibility of using multiscale features to strengthen the perception of projection distortion information. Specifically, cubemap projection (CMP) is employed to generate viewport images from equirectangular projection (ERP) images to effectively preserve more omnidirectional information. Subsequently, a multiscale feature extraction (MFE) module is designed to extract features at different levels and enhance the representation of distortion information. Additionally, a feature aggregation (FA) module is introduced to fuse multiscale features and fully improve the interconnection capability of the network. Finally, a quality regression (QR) module is employed to map the features to a quality score. Extensive experiments demonstrate the effectiveness and superiority of the proposed network over other state-of-the-art methods for accurately assessing OI quality. [ABSTRACT FROM AUTHOR]

The geometric distortion in panoramic images significantly mediates the performance of saliency detection method based on traditional CNN. The strategy of dynamically expanding convolution kernel can achieve good results, but it also produces a lot of computational overhead in the process of reading the adjacency list, which decreases the computational efficiency. The appearance of graph convolution provides a new way to solve such problems. Although using graph convolution can effectively extract the structural features of the graph, it reduces the accuracy of the model resulting from ignoring the spatial features of the image signal. To this end, this paper proposes a construction method of the multi-scale graph structure of the panoramic image and a panoramic image saliency detection model composed of an image saliency feature extraction network and multi-scale saliency feature fusion network combining the image structure information and spatial information in the panoramic image. First, we establish a graph structure consisting of root and leaf nodes obtained by super-pixel segmentation at different scales and spherical Fibonacci sampling, respectively. Then, a feature extraction network composed of two graph convolution layers and two one-dimensional auto-encoders with the same parameterization is used to extract the salient features of the multi-scale graph structure. Finally, the U-Net network fuses the multi-scale saliency features to get the final saliency map. The results show that the proposed model performs better than the state-of-the-art models in terms of calculation speed and accuracy. [ABSTRACT FROM AUTHOR]

When using Monte Carlo Path Tracing (MCPT) method to render 3D scenes, artifacts may occur due to insufficient sampling, and directly increasing the number of samples can increase the time cost of the rendering algorithm. An effective strategy is to adaptively allocate the number of samples per pixel in an iterative manner. However, iterative operations introduce additional computational overhead during the rendering process. To solve this problem, we proposed a rendering acceleration method that does not require iterative computation. This method combines the Just Noticeable Difference (JND) information and uses a neural network to predict the sampling matrix of the scene, which is adjusted based on the lighting information in the pre-rendered image. First, we extract the JND information of pre-rendered images during the preprocessing and estimate the fast convergence regions in the scene (such as environment map regions, light source regions, etc.). Then, we use Conv-LSTM to estimate the JND features of high-quality rendered images. We design a multi-feature fusion network to predict the number of samples required for each pixel during the rendering process. The network takes the preprocessed pre-rendered images as the input of the encoder, which are fused with the output of Conv-LSTM as the input of the decoder to output the corresponding sampling matrix. In addition, we noticed that areas with darker lighting are more difficult to converge during the rendering process. Therefore, we calculated the lighting clustering results of the pre-rendered image and adjusted the sampling matrix output by the sampling prediction model based on the lighting clustering results. The experimental results indicate that our method has better performance compared to the current methods. [ABSTRACT FROM AUTHOR]

In China, highly educated adults seek online cancer information more frequently than less educated adults. This health-related digital divide may impede the less-educated from effectively preventing cancer. To explicate the divide, we introduce informational subjective norms (ISN) and information sufficiency threshold (IST) as two socio-psychological mediators between education level and online cancer information seeking (OCIS) frequency. ISN represents one's perceived social pressure about seeking cancer information, while IST manifests individual evaluation of the amount of information needed to prevent cancer. An online survey supported a serial mediation effect of ISN and IST. ISN and IST also independently mediated the relationship between education level and OCIS frequency. Besides, the mediation effect of ISN was stronger than that of IST. The findings suggest that increasing ISN among less educated Chinese adults could facilitate their OCIS and to narrow the health-related digital divide. These implications may also inform other developing countries. [ABSTRACT FROM AUTHOR]

For orbital angular momentum (OAM) recognition in atmosphere turbulence, how to design a self-adapted model is a challenging problem. To address this issue, an efficient deep learning framework that uses a derived extreme learning machine (ELM) has been put forward. Different from typical neural network methods, the provided analytical machine learning model can match the different OAM modes automatically. In the model selection phase, a multilayer ELM is adopted to quantify the laser spot characteristics. In the parameter optimization phase, a fast iterative shrinkage-thresholding algorithm makes the model present the analytic expression. After the feature extraction of the received intensity distributions, the proposed method develops a relationship between laser spot and OAM mode, thus building the steady neural network architecture for the new received vortex beam. The whole recognition process avoids the trial and error caused by user intervention, which makes the model suitable for a time-varying atmospheric environment. Numerical simulations are conducted on different experimental datasets. The results demonstrate that the proposed method has a better capacity for OAM recognition. [ABSTRACT FROM AUTHOR]

The geometric distortion of the panoramic image makes the saliency detection method based on traditional 2D convolution invalid. "Mapped Convolution" can effectively solve this problem, which accepts a task- or domain-specific mapping function in the form of an adjacency list that dictates where the convolutional filters sample the input. However, when applied to panorama saliency detection, the method results in additional computational overhead due to repeatedly sampling overlapping regions of adjacent convolution positions along the longitude. In order to solve this problem, we improved the calculation process of "Mapped Convolution". Rather than accessing adjacency list during the convolution, we first sample the panorama based on the adjacency list for only once and obtain a sampled map. This sampling process is called the decoupled sampling of "Mapped Convolution". And then the map is convoluted in traditional 2D way, thus avoiding repeatedly sampling. In this paper, an interpolation method based on the Softmax function is also proposed and applied to the interpolation calculation of decoupled sampling. Compared with common interpolation methods such as linear interpolation, this interpolation method makes our network more efficient during training. We additionally introduce a new adaptive equator bias algorithm allowing for different attention distributions at different longitudes, which is more consistent with viewer's visual behavior. Combining the U-Autoencoder network containing the decoupled sampling with the adaptive equator bias algorithm, we construct a 360-degree visual saliency detection model. We map the original panorama into a cube, and then use the the cube isometric mapping method to remap it into a panorama and input it into the network for training. Then, the crude saliency map output by the decoder is combined with the equator bias map to obtain the final saliency map. The results show that the model proposed is superior to recent state-of-the-art models in terms of computational speed and saliency-map prediction. [ABSTRACT FROM AUTHOR]

The quality of images generated by computer graphics rendering algorithms is mainly affected by visible distortion at some pixel locations. Image quality assessment (IQA) metrics are commonly utilized to assess the quality of rendered images, but their results are a global difference value, which does not provide pixel‐wise differences to optimize the renderings. In contrast, visibility difference models including visual perception models and deep learning models can calculate pixel‐wise visibility difference between distorted images and reference images. However, they either are only applied to a single type of visible distortion or are seriously dependent on datasets. To this end, the authors propose a novel model, dubbed Human Visual Perception and Deep Learning Image Difference Metric (HPDL‐IDM), which combines the Human Visual System (HVS) model and deep learning. HPDL‐IDM primarily consists of two modules: (i) the visual perception feature calculation module, which calculates difference maps between various kinds of features extracted from the reference image and the distorted image according to the visual characteristics of human eyes and concatenates them, and (ii) the deep learning module, which utilizes a neural network of encoder–decoder structure to train on the LocvisVC and VisTexRes datasets whose input and output are these concatenated feature difference maps and the final image distortion visibility difference map respectively. Additionally, the authors pool the final difference map into a global difference value between 0 and 1 to apply their model to many image processing tasks related to Image quality metrics (IQMs). Experimental results show that HPDL‐IDM's generalization capacity and accuracy are improved by a large margin compared to other models. [ABSTRACT FROM AUTHOR]

Real-time 3D reconstruction of static scenes can be achieved based on the RGB-D image sequence fusion. It is a popular practice to divide the space into uniform voxels and use a truncated signed distance function to represent surface information. In order to represent a scene of large scale, the voxel hash algorithm which stores voxels compressively can be used, but most of the conventional methods do not consider the complexity and roughness of the object surface in the scene, so the scene is represented with a uniform resolution. It somewhat limits the range of scene representation and the speed of real-time reconstruction. In this paper, a large-scale scene reconstruction algorithm based on voxel hashing storage with LOD representation is proposed. The main contributions include the following two aspects: (1) By preprocessing the depth image with smooth filtering, which ensures the accuracy of the data, it can effectively reduce the distortion caused by the sensor itself and violent motion and provide better support for the stages of voxel hashing, model rendering, and frame-to-model camera position tracking. (2) The 3D reconstruction with LOD representation is realized. We take the view distance and the roughness of the model surface as criteria to control the adaptive division and representation of spatial voxel blocks. Finally, we carried out qualitative and quantitative evaluations of the algorithm, and confirmed that the algorithm can achieve real-time reconstruction with different levels of detail in the commercial graphics processing hardware environment, and achieve a good fusion effect in large-scale scenes. [ABSTRACT FROM AUTHOR]

Satellite remote sensing images contain adequate ground object information, making them distinguishable from natural images. Due to the constraint hardware capability of the satellite remote sensing imaging system, coupled with the surrounding complex electromagnetic noise, harsh natural environment, and other factors, the quality of the acquired image may not be ideal for follow-up research to make suitable judgment. In order to obtain clearer images, we propose a dual-path adversarial generation network model algorithm that particularly improves the accuracy of the satellite remote sensing image super-resolution. This network involves a dual-path convolution operation in a generator structure, a feature mapping attention mechanism that first extracts important feature information from a low-resolution image, and an enhanced deep convolutional network to extract the deep feature information of the image. The deep feature information and the important feature information are then fused in the reconstruction layer. Furthermore, we also improve the algorithm structure of the loss function and discriminator to achieve a relatively optimal balance between the output image and the discriminator, so as to restore the super-resolution image closer to human perception. Our algorithm was validated on the public UCAS-AOD datasets, and the obtained results showed significantly improved performance compared to other methods, thus exhibiting a real advantage in supporting various image-related field applications such as navigation monitoring. [ABSTRACT FROM AUTHOR]

Vector quantization is a viable solution to the problem as to full utilization of randomness provided by statistically dependent channel measurements in secret key extraction from randomly fluctuating channels. By aiming at atmospheric optical wireless channels, new vector quantization schemes based on sample grouping (SG) are proposed by using different ways, specifically, the $K\text {-dimensional}$ tree (KDT) partition and random placement, to separate samples of the dimension-reduced channel coefficient vector into a given number of groups, with each containing equal number of samples. Use of the KDT partition leads to both the basic KDT-partition-based quantization (B-KDTPQ) and improved KDT-partition-based quantization (I-KDTPQ) schemes. On the other hand, utilization of the random placement brings forth the random equisized grouping based quantization (REGQ) scheme. Performance evaluation shows that, due to use of quantization symbol modification, the I-KDTPQ scheme always has lower quantization symbol disagreement rate (QSDR) than the B-KDTPQ scheme; compared with the I-KDTPQ scheme, the REGQ scheme has more flexibility in accommodating itself to various conditions of channel coefficient fluctuations and noise levels. [ABSTRACT FROM AUTHOR]

In the process of film and television production, clear images can give the audience a real sensory experience, but high-resolution images require a massive amount of production time and highly specialized imaging equipment, which is not a cost-effective solution at the moment. To achieve a better cost efficiency during video production, we propose a multichannel featured superresolution network model that utilizes rendered low-resolution images according to their characteristics. This model includes a feature extraction layer, a series of subnetworks, and a reconstruction module. Inside the network model, a series of subnetworks are cascaded to improve the information flow from coarse to fine, which helps to fully extract the depth, normal vector, edge, and texture features from low-resolution rendered images to reconstruct the high-resolution image. Additionally, residual learning is introduced at each stage to further improve the reconstruction performance. We experiment with the model on the classic Disney Monte Carlo datasets and compare it with several related algorithms. The results show that our algorithm is able to reconstruct the image with clearer details and texture. Thus, our research not only helps to maintain the audience's sensory experience but also increases the efficiency of film and television production, which also brings considerable economic benefits. [ABSTRACT FROM AUTHOR]

Augmented reality is currently a research hotspot in the field of computer vision and computer graphics, and its applications are becoming more and more extensive. One of its key technologies is the three-dimensional registration of virtual objects. Three-dimensional registration requires accurate camera pose estimation and scene three-dimensional reconstruction. Therefore, this paper studies the 3D registration based on visual SLAM, and mainly contributes the following three aspects. (1) A dense matching method based on a depth camera is proposed, which can be well applied to scenes where the camera moves fast or rotates strongly, such as augmented reality. (2) A dense reconstruction method based on Voxel Hashing is designed, which alleviates the low computational efficiency and low precision of the existing RGB-D SLAM method. (3) A simple augmented reality system was designed to verify the effects of the registration and fusion of virtual objects. Experiments show that compared with the state-of-the-art methods, the algorithm proposed in this paper generates a more refined and smooth reconstructed model, and the virtual-real fusion effect based on this model is also more realistic. [ABSTRACT FROM AUTHOR]

To estimate the prevalence of violent discipline at home against young children, and to explore the potential association between violent discipline at home and multifaceted health risks in children. A community-based cross-sectional survey was conducted in twenty rural counties of weight provinces in western China. The information about child neglect and socio-demographic factors, exposure to different forms of violent discipline at home and four health outcomes was collected by face-to-face interview. Before analysis, the included interviews were weighted by the double-weighted comprehensive weighting. The proportion of children reported by primary caregivers to have experienced different forms of violent discipline by gender were calculated. To adjust the clustering effect of the survey design, two-level univariate and multivariate logistic regression models were constructed to evaluate the potential association between a child's exposure to violent discipline at home and risk of suffering from diarrhea, fever, cough and injury. A total of 3,682 weighted interviews were finally included in the analysis. The prevalence of any violent discipline, psychological aggression, any physical punishment and severe physical punishment were 76.4%, 57.5%, 68.3% and 14.1%, respectively. In multivariate analysis, after adjusting for clustering, there was still a positive association between a child's exposure to psychological aggression and risk of diarrhea (adjusted OR: 1.47, 95%CI: 1.14-1.90) and injury (adjusted OR: 1.95, 95%CI: 1.36-2.80); a child's exposure to any physical punishment and risk of diarrhea (adjusted OR: 1.36, 95%CI: 1.04-1.77), cough (adjusted OR: 1.37, 95%CI: 1.14-1.66), and injury (adjusted OR: 2.05, 95%CI: 1.37-3.06); and a child's exposure to severe physical punishment and risk of injury (adjusted OR: 2.07, 95%CI: 1.41-3.05). Considering that using violent discipline at home is prevalent in rural western China, and it could threaten young children's health, effective measures to prevent young children from violent discipline are urgently needed. [ABSTRACT FROM AUTHOR]

The Conformer enhanced Transformer by using convolution serial connected to the multi-head self-attention (MHSA). The method strengthened the local attention calculation and obtained a better effect in auto speech recognition. This paper proposes a hybrid attention mechanism which combines the dynamic convolution CNNs and multi-head self-attention. This study focuses on generating local attention by embedding DY-CNNs in MHSA, followed by parallel computation of the globe and local attention inside the attention layer. Finally, concatenate the result of global and local attention to the output. In the experiments, we use the Aishell-1 (178 hours) Chinese database for training. In the testing folder dev/test, 4.5%/4.8% CER was obtained. The proposed method shows better performance in computation speed and the number of experimental parameters. The results are extremely close to the best result (4.4%/4.7%) of the Conformer. [ABSTRACT FROM AUTHOR]

Background: As reported, 27-93 % of pregnant women take at least one drug during pregnancy. However, drug exposure during pregnancy still lacks sufficient foetal safety evidence of human origin. It is urgent to fill the knowledge gap about medication safety during pregnancy for optimization of maternal disease treatment and pregnancy drug consultation.Methods and Analysis: The China Teratology Birth Cohort (CTBC) was established in 2019 and is a hospital-based open-ended prospective cohort study with the aim of assessing drug safety during pregnancy. Pregnant women who set up the pregnancy health records in the first trimester or who seek drug consultation regardless of gestational age in the member hospitals are recruited. Enrolled pregnant women need to be investigated four times, namely, 6-14 and 24-28 weeks of gestational age, before discharge after hospital delivery, and 28-42 days after birth. Maternal medication exposure during pregnancy is the focus of the CTBC. For drugs, information on the type, name, and route of medication; start and end time of medication; single dose; frequency of medication; dosage form; manufacturer; and reason for medication is collected. The adverse pregnancy outcomes collected in the study include birth defects, stillbirth, spontaneous abortion, preterm birth, post-term birth, low birth weight, macrosomia, small for gestational age, large for gestational age and low Apgar score. CTBC uses an electronic questionnaire for data collection and a cloud system for data management. Biological samples are collected if informed consents are obtained. Multi-level logistic regression, mixed-effect negative binomial distribution regression and spline function regression are used to explore the effect of drugs on the occurrence of birth defects.Discussion: The findings of the study will assist in further understanding the risk of birth defects and other adverse pregnancy outcomes associated with maternal drug exposure and developing the optimal treatment plans and drug counselling for pregnant women.Trial Registration: This study was approved by the Research Ethics Committee of the West China Second Hospital of Sichuan University and registered at the Chinese Clinical Trial Registry ( http://www.chictr.org.cn/index.aspx , registration number ChiCTR1900022569 ). [ABSTRACT FROM AUTHOR]

Background: The influence of the safety and security of environments on early childhood development (ECD) has been under-explored. Although housing might be linked to ECD by affecting a child's health and a parent's ability to provide adequate care, only a few studies have examined this factor. We hypothesized that housing environment is associated with ECD in sub-Saharan Africa (SSA).Methods and Findings: From 92,433 children aged 36 to 59 months who participated in Multiple Indicator Cluster Survey (MICS) in 20 SSA countries, 88,271 were tested for cognitive and social-emotional development using the Early Childhood Development Index (ECDI) questionnaire and were thus included in this cross-sectional analysis. Children's mean age was 47.2 months, and 49.8% were girls. Children were considered developmentally on track in a certain domain if they failed no more than 1 ECDI item in that domain. In each country, we used conditional logistic regression models to estimate the association between improved housing (housing with finished building materials, improved drinking water, improved sanitation facilities, and sufficient living area) and children's cognitive and social-emotional development, accounting for contextual effects and socioeconomic factors. Estimates from each country were pooled using random-effects meta-analyses. Subgroup analyses were conducted by the child's gender, maternal education, and household wealth quintiles. On-track cognitive development was associated with improved housing (odds ratio [OR] = 1.15, 95% CI 1.06 to 1.24, p < 0.001), improved drinking water (OR = 1.07, 95% CI 1.00 to 1.14, p = 0.046), improved sanitation facilities (OR = 1.15, 95% CI 1.03 to 1.28, p = 0.014), and sufficient living area (OR = 1.06, 95% CI 1.01 to 1.10, p = 0.018). On-track social-emotional development was associated with improved housing only in girls (OR = 1.14, 95% CI 1.04 to 1.25, p = 0.006). The main limitations of this study included the cross-sectional nature of the datasets and the use of the ECDI, which lacks sensitivity to measure ECD outcomes.Conclusions: In this study, we observed that improved housing was associated with on-track cognitive development and with on-track social-emotional development in girls. These findings suggest that housing improvement in SSA may be associated not only with benefits for children's physical health but also with broader aspects of healthy child development. [ABSTRACT FROM AUTHOR]

Path tracing can generate realistic images based on virtual 3D scene models, but the images are prone to be noisy. To solve this problem, we developed a novel denoising algorithm framework. Firstly, according to the relative mean square error of the noisy pixels, we introduced a progressive adaptive sampling strategy to optimize the distribution of samples. Next, to enhance the quality of the final reconstructed images, we designed an improved bilateral filtering algorithm with use of the gradient feature to obtain the noise-free images. Experimental results demonstrate that our framework outperforms the state-of-the-art path tracing denoising methods in terms of the visual quality, numerical error , and time cost. [ABSTRACT FROM AUTHOR]

For multiclass imbalanced data online prediction, how to design a self-adapted model is a challenging problem. To address this issue, a novel dynamic multi-classification algorithm which uses two-stage game strategy has been put forward. Different from typical imbalanced classification methods, the proposed approach provided a self-updating model quantificationally, which can match the changes of arriving sample chunk automatically. In data generation phase, two dynamic ELMs with game theory are utilized for generating the lifelike minority class to equilibrate the distribution of different samples. In model update phase, both the current prediction performance and the cost sensitivity are taken into consideration simultaneously. According to the suffer loss and the shifty imbalance ratio, the proposed method develops the relationship between new weight and individual model, and an aggregate model of game theory is adopted to calculate the combination weight. These strategies help the algorithm reduce fitting error of sequence fragments. Also, alterative hidden-layer output matrix can be calculated according to the current fragment, thus building the steady network architecture in the next chunk. Numerical experiments are conducted on eight multiclass UCI datasets. The results demonstrate that the proposed algorithm not only has better generalization performance, but also improves the predictive ability of ELM method for minority samples. [ABSTRACT FROM AUTHOR]

Background: With great economic development and rapid urbanization in China, left-behind children whose parents migrate to big cities for job has become a large special population which requires more attention. The present study aims to explore the specific influence of migrant mothers on early child development, especially on social-emotional problems.Methods: The data of this study was obtained from a cross-sectional study in 8 counties of central and western rural China. Development status of 1880 children aged <60 months were assessed by Ages & Stages Questionnaire-Chinese Edition (ASQ) and the Ages and Stages Questionnaire: Social Emotional-Chinese Edition (ASQ: SE). Multivariate logistic regressions were used to analyze the association between being left behand by migrant mothers and developmental problems in various domains, while adjusting socio-demographic, socio-economic and perinatal co-variates, and effect modification analysis were conducted to explore the effect of age, gender and birth order.Results: Children left behind by migrant mothers were more likely to have overall suspected developmental delay (odds ratio (OR) = 1.24, 95% confidence interval (CI) = 1.13-1.35), developmental delay in personal social domain (OR = 1.55, 95% CI = 1.17-2.04) and socio-emotional delay compared with those living with their own mothers (OR = 1.49, 95% CI = 1.11-2.00) after adjusting for potential confounders. Additionally, girls increased the odds of social-emotional problems among children being left behind by migrating mother (P for interaction = 0.037).Conclusions: The study concluded that children left behind by migrant mothers were more likely to have suspected developmental delay compared with their peers living with mothers, especially on social emotional development. Future intervention is needed for this special population and should pay more attention to girls. [ABSTRACT FROM AUTHOR]

Up to now, a large amount of trajectory data have been collected by trusted servers because of the wide use of location-based services. One can extract useful information via an analysis of trajectory data. However, the privacy of trajectory bodies risks being inadvertently divulged to others. Therefore, the trajectory data should be properly processed for privacy protection before being released to unknown analysts. This paper proposes a privacy protection scheme for publishing the trajectories with personalized privacy requirements based on the translocation of trajectory points. The algorithm not only enables the published trajectory points to meet the personalized privacy requirements regarding desensitization and anonymity but also preserves the positions of all trajectory points. Our algorithm trades the loss in mobility patterns for the advantage in the similarity of trajectory distance. Related experiments on trajectory data sets with personalized privacy requirements have verified the effectiveness and the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

Water surface visualization is an important research topic in computer graphics. This paper presents a novel method of water surface simulation by Secondary Distorted Textures (SDT), which realistically simulates and visualizes the reflection and refraction of calm water in real-time using current consumer-level hardware. The proposed method renders water surface using two stages of texture maps. 1. The first texture map stores the 3D geometries’ perspective reflection with respect to 3D perspective view; 2. The second texture map stores the distortion results of the first reflection map with lighting effects. Perlin noise is used to generate random height map. Reflection and refraction are obtained and stored in the secondary distorted texture map with Fresnel effects for each frame. At rendering pass, the SDT is directly tiled on water surface. This paper also discussed the rendering of transparent geometry, which have view-dependent of lighting effects features. Experimental results demonstrated that our method can render realistic geometry nearby dynamic water surface at the frame rates of 70-100 FPS by NVIDIA Quadro k5000 graphic card. The existing texture mapping and bump mapping methods were compared for illustrating that our method produced high realistic water surface without aliasing reflection. [ABSTRACT FROM AUTHOR]

In this paper, we present a denoising network composed of a kernel prediction network and a deep generative adversarial network to construct an end-to-end overall network structure. The network structure consists of three parts: the Kernel Prediction Network (KPN), the Deep Generation Adversarial Network (DGAN), and the image reconstruction model. The kernel prediction network model takes the auxiliary feature information image as the input, passes through the source information encoder, the feature information encoder, and the kernel predictor, and finally generates a prediction kernel for each pixel. The generated adversarial network model is divided into two parts: the generator model and the multiscale discriminator model. The generator model takes the noisy Monte Carlo-rendered image as the input, passes through the symmetric encoder–decoder structure and the residual block structure, and finally outputs the rendered image with preliminary denoising. Then, the prediction kernel and the preliminarily denoised rendered image is sent to the image reconstruction model for reconstruction, and the prediction kernel is applied to the preliminarily denoised rendered image to obtain a preliminarily reconstructed result image. To further improve the quality of the result and to be more robust, the initially reconstructed rendered image undergoes four iterations of filtering for further denoising. Finally, after four iterations of the image reconstruction model, the final denoised image is presented as the output. This denoised image is applied to the loss function. We compared the results from our approach with state-of-the-art results by using the structural similarity index (SSIM) values and peak signal-to-noise ratio (PSNR) values, and we reported a better performance. [ABSTRACT FROM AUTHOR]

The spatiotemporal coherence properties of broadband Gaussian Schell-model (GSM) beams with different spectral bandwidths propagating through atmospheric turbulence are numerically calculated and analyzed. The results show that although the spatial coherence properties of an intermediate-broadband GSM beam almost do not depend on the spectral bandwidth, those of its ultra-broadband counterpart do. The temporal coherence of an ultra-broadband GSM beam not only has radial dependence in the observation plane, but also varies with the increasing propagation distance; however, the same behavior does not hold for an intermediate-broadband GSM beam of which the temporal coherence remains nearly invariable as the radial distance of the observation point or propagation distance changes. [ABSTRACT FROM AUTHOR]

With the development of handheld video capturing devices, video stabilization becomes increasingly important. The gyroscope-based video stabilization methods perform promising ability, since they can return more reliable three-dimensional (3D) camera rotation estimation, especially when there are many moving objects in scenes or there are serious motion blur or illumination changes. However, the gyroscope-based methods depend on the camera intrinsic parameters to execute video stabilization. Therefore, a self-calibrated spherical video stabilization method was proposed. It builds a virtual sphere, of which the spherical radius is calibrated automatically, and then projects each frame of the video to the sphere. Through the inverse rotation of the spherical image according to the rotation jitter component, the dependence on the camera intrinsic parameters is relaxed. The experimental results showed that the proposed method does not need to calibrate the camera and it can suppress the camera jitter by binding the gyroscope on the camera. Moreover, compared with other state-of-the-art methods, the proposed method can improve the peak signal-to-noise ratio, the structural similarity metric, the cropping ratio, the distortion score, and the stability score. [ABSTRACT FROM AUTHOR]

We propose a video stabilization algorithm based on the rotation of a virtual sphere. Unlike traditional video stabilization algorithms relying on two-dimensional motion models or reconstruction of (3D) camera motions, the proposed virtual sphere model stabilizes video by projecting each frame onto the sphere and performing corrective rotations. Specifically, matching feature points between two adjacent frames are first projected onto two virtual spheres to obtain pairs of spherical points. Then, the rotation matrix between the previous and current frame is calculated. The resulting 3D rotation matrix sequence is used to represent the camera motion, and it is smoothed using the geodesic distance on a Riemannian manifold. Finally, the difference between the smoothed and original path allows obtaining the rotation matrix that causes camera jitter, and the virtual spheres are rotated reversely to suppress jitter. Experimental results show that the proposed algorithm can effectively reduce random jitter, outperforming state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Early intervention of gestational diabetes mellitus (GDM) is effective in reducing pregnancy disorders. Fetal growth, measured by routine ultrasound scan a few weeks earlier before GDM diagnosis, might be useful to identify women at high risk of GDM. In the study, generalized estimating equations were applied to examine the associations between ultrasonic indicators of abnormal fetal growth at 22–24 weeks and the risk of subsequent GDM diagnosis. Of 44,179 deliveries, 8324 (18.8%) were diagnosed with GDM between 24 and 28 weeks. At 22–24 weeks, fetal head circumference (HC) < 10th, fetal femur length (FL) < 10th, and estimated fetal weight (EFW) < 10th percentile were associated with 13% to 17% increased risks of maternal GDM diagnosis. Small fetal size appeared to be especially predictive of GDM among women who were parous. Fetal growth in the highest decile of abdominal circumference (AC), HC, FL and EFW was not associated with risk of subsequent GDM. The observed mean difference in fetal size across gestation by GDM was small; there was less than 1 mm difference for AC, HC, and FL, and less than 5 g for EFW before 24 weeks. Despite similar mean fetal growth among women who were and were not later diagnosed with GDM, mothers with fetuses in the lowest decile of HC, FL and EFW at 22–24 weeks tended to have higher risk of GDM. [ABSTRACT FROM AUTHOR]