PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows

Point cloud denoising aims to restore clean point clouds from raw observations corrupted by noise and outliers while preserving the fine-grained details. We present a novel deep learning-based denoising model, that incorporates normalizing flows and noise disentanglement techniques to achieve high denoising accuracy. Unlike existing works that extract features of point clouds for point-wise correction, we formulate the denoising process from the perspective of distribution learning and feature disentanglement. By considering noisy point clouds as a joint distribution of clean points and noise, the denoised results can be derived from disentangling the noise counterpart from latent point representation, and the mapping between Euclidean and latent spaces is modeled by normalizing flows. We evaluate our method on synthesized 3D models and real-world datasets with various noise settings. Qualitative and quantitative results show that our method outperforms previous state-of-the-art deep learning-based approaches

PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows

Point cloud upsampling aims to generate dense point clouds from given sparse ones, which is a challenging task due to the irregular and unordered nature of point sets. To address this issue, we present a novel deep learning-based model, called PU-Flow, which incorporates normalizing flows and weight prediction techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we exploit the invertible characteristics of normalizing flows to transform points between Euclidean and latent spaces and formulate the upsampling process as ensemble of neighbouring points in a latent space, where the ensemble weights are adaptively learned from local geometric context. Extensive experiments show that our method is competitive and, in most test cases, it outperforms state-of-the-art methods in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency. The source code will be publicly available at https://github.com/unknownue/pu-flow

A Review of the Effect of Reading Engagement on Reading Achievement

For decades, literacy research has placed a great deal of emphasis on reading engagement. It is widely acknowledged as a complex involving cognitive, behavioral, and emotional engagement. Learning engagement is an essential predictor of learning outcomes. It mediates educational intervention and learning outcomes. Although empirical studies proved the effectiveness of engagement on reading success, few studies have comprehensively reviewed the relationship between the subscales of engagement and how these subscales affect learning outcomes. To fill this gap, this review focused on exploring the interaction among subsets of engagement and how they affect reading achievement. The findings revealed that how engagement affects learning outcomes is determined by the intervention and how the outcomes are assessed and reported. The relationship between the engagement subscales is complicated: cognitive and behavioral engagement is a constant predictor of reading outcomes; emotion is the facilitator and affects behavioral or cognitive engagement. Behavioral engagement mediates cognitive engagement. Furthermore, learning outcomes could enhance emotional engagement, forming a natural learning cycle. This model is significant in understanding how learning engagement affects learning outcomes. It also demonstrated how the engagement subscales interacted and worked together to facilitate learning outcomes

Prevalence of porcine circovirus-like agent P1 in Jiangsu, China

Recently, we identified a novel porcine circovirus type 2-like agent P1 isolate from swine. The present study represents the first survey of P1 prevalence in swine herds from Jiangsu, China, by using PCR targeting the complete genome of P1. Prevalences of 50% and 19% were found among 6 herds and 248 animals, respectively. The results indicate a high prevalence of P1 in China pig populations

The Redox-Sensing Regulator Rex Contributes to the Virulence and Oxidative Stress Response of Streptococcus suis Serotype 2

Streptococcus suis serotype 2 (SS2) is an important zoonotic pathogen responsible for septicemia and meningitis. The redox-sensing regulator Rex has been reported to play critical roles in the metabolism regulation, oxidative stress response, and virulence of various pathogens. In this study, we identified and characterized a Rex ortholog in the SS2 virulent strain SS2-1 that is involved in bacterial pathogenicity and stress environment susceptibility. Our data show that the Rex-knockout mutant strain Δrex exhibited impaired growth in medium with hydrogen peroxide or a low pH compared with the wildtype strain SS2-1 and the complementary strain CΔrex. In addition, Δrex showed a decreased level of survival in whole blood and in RAW264.7 macrophages. Further analyses revealed that Rex deficiency significantly attenuated bacterial virulence in an animal model. A comparative proteome analysis found that the expression levels of several proteins involved in virulence and oxidative stress were significantly different in Δrex compared with SS2-1. Electrophoretic mobility shift assays revealed that recombinant Rex specifically bound to the promoters of target genes in a manner that was modulated by NADH and NAD+. Taken together, our data suggest that Rex plays critical roles in the virulence and oxidative stress response of SS2

Numerical Heat Transfer Coupled with Multidimensional Liquid Moisture Diffusion in Porous Textiles with a Measurable-Parameterized Model

This article reports on the numerical simulation of the transient heat transfer coupled with multidimensional liquid diffusion in porous textiles with a measurable-parameterized model. This model is developed with the incorporation of measured multidimensional liquid diffusion properties into the parameterization of the liquid transfer model by the investigation of physical mechanisms. An improved two-node model of human body is employed to simulate the thermoregulatory behaviors and the thermal responses between the textiles and body skin are considered through the boundary conditions for accuracy simulation of realistic wearing situations. The predicted results of this model are compared with the experimental data for validating the model accuracy. The influence of the multidimensional liquid diffusion property of porous textiles on the moisture performance of clothing during the wearing period is investigated through a series of computational experiments. This model offers the ability to predict the multidimensional liquid transfer capacity and is more effective in realistic application due to the measurable properties.Institute of Textiles and Clothin

Numerical simulation of thermal behaviors of a clothed human body with evaluation of indoor solar radiation

Solar radiation is a valuable green energy, which is important in achieving a successful building design for thermal comfort in indoor environment. This paper considers solar radiation indoors into the transient thermal transfer models of a clothed human body and offers a new numerical method to analyze the dynamic thermal status of a clothed human body under different solar radiation incidences. The evaluation model of solar radiation indoors and a group of coupled thermal models of the clothed human body are developed and integrated. The simulation capacities of these integrated models are validated through a comparison between the predicted results and the experimental data in reference. After that, simulation cases are also conducted to show the influence of solar radiation on the thermal status of individual clothed body segments when the human body is staying indoors in different seasons. This numerical simulation method provides a useful tool to analyze the thermal status of clothed human body under different solar radiation incidences indoors and thus enables the architect to efficiently utilize the green solar energy in building development.Institute of Textiles and Clothin