An interactive website to aid the academic and social transition of Chinese international students to Pepperdine University

As the number of Chinese international students studying in American universities has increased dramatically in recent years, more attention has been paid to the challenges these students face both academically and socially. To address problems Chinese international students face in their acculturation to U.S. culture generally, and Pepperdine University specifically, this strategic communication non-thesis project involved the development of an interactive website specifically designed for Chinese international students at Pepperdine University. Its aim is to help them better understand the American culture and find solutions when they encounter cultural difficulties and challenges in their academic and social life. The website not only elaborated on American culture such as slang and plagiarism rules, but also introduced campus resources and how to use them. In addition, the website included a Connect section facilitating opportunities for students to interact with other students. The development of the website involved both primary and secondary research to gain insights on Chinese international students\u27 college experience and how to help them make the transition to and their experience at Pepperdine University more smooth. Specifically, face-to-face interviews were conducted with different professors, an employee of Office of International Student Services, and the Chinese Student Association student chair. A focus group with Chinese international students was also conducted in order to explore their campus experience both academically and socially. A beta-test survey of Chinese international students revealed that the website was helpful in the transition process to American culture and adapting to campus. There were also indicators of attitude and behavior changes in terms of using campus resources, approaching professors, and views on plagiarism. Further development and improvements to the website, such as creating a Chinese counterpart website and adding more content on nearby restaurant guide and visa applications, will be completed by Pepperdine CSA members during summer 2017; the website could be relaunched as early as August 2017

Procrastination: Exploring the role of coping strategy

The current study examined the relationship between procrastination styles, coping styles, perceived stress, personality traits, and academic outcomes in a sample of undergraduate students (n = 42). Results suggest that active procrastination is associated with active coping and less perceived stress. In contrast, passive procrastination relates to greater perceived stress. In addition, the results indicate that active procrastination is positively associated with extroversion and conscientiousness. Passive procrastination is positively related to neuroticism. Moreover, procrastination styles are not associated with academic outcomes in the current study. Overall, the results suggest that procrastination style is associated with active coping, perceived stress, and different personality traits

Efficient View Synthesis with Neural Radiance Distribution Field

Recent work on Neural Radiance Fields (NeRF) has demonstrated significant advances in high-quality view synthesis. A major limitation of NeRF is its low rendering efficiency due to the need for multiple network forwardings to render a single pixel. Existing methods to improve NeRF either reduce the number of required samples or optimize the implementation to accelerate the network forwarding. Despite these efforts, the problem of multiple sampling persists due to the intrinsic representation of radiance fields. In contrast, Neural Light Fields (NeLF) reduce the computation cost of NeRF by querying only one single network forwarding per pixel. To achieve a close visual quality to NeRF, existing NeLF methods require significantly larger network capacities which limits their rendering efficiency in practice. In this work, we propose a new representation called Neural Radiance Distribution Field (NeRDF) that targets efficient view synthesis in real-time. Specifically, we use a small network similar to NeRF while preserving the rendering speed with a single network forwarding per pixel as in NeLF. The key is to model the radiance distribution along each ray with frequency basis and predict frequency weights using the network. Pixel values are then computed via volume rendering on radiance distributions. Experiments show that our proposed method offers a better trade-off among speed, quality, and network size than existing methods: we achieve a ~254x speed-up over NeRF with similar network size, with only a marginal performance decline. Our project page is at yushuang-wu.github.io/NeRDF.Comment: Accepted by ICCV202

Extended Ellipsoidal Outer-Bounding Set-Membership Estimation for Nonlinear Discrete-Time Systems with Unknown-but-Bounded Disturbances

This paper develops an extended ellipsoidal outer-bounding set-membership estimation (EEOB-SME) algorithm with high accuracy and efficiency for nonlinear discrete-time systems under unknown-but-bounded (UBB) disturbances. The EEOB-SME linearizes the first-order terms about the current state estimations and bounds the linearization errors by ellipsoids using interval analysis for nonlinear equations of process and measurement equations, respectively. It has been demonstrated that the EEOB-SME algorithm is stable and the estimation errors of the EEOB-SME are bounded when the nonlinear system is observable. The EEOB-SME decreases the computation load and the feasible sets of EEOB-SME contain more true states. The efficiency of the EEOB-SME algorithm has been shown by a numerical simulation under UBB disturbances

PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds

Semantic segmentation of point cloud usually relies on dense annotation that is exhausting and costly, so it attracts wide attention to investigate solutions for the weakly supervised scheme with only sparse points annotated. Existing works start from the given labels and propagate them to highly-related but unlabeled points, with the guidance of data, e.g. intra-point relation. However, it suffers from (i) the inefficient exploitation of data information, and (ii) the strong reliance on labels thus is easily suppressed when given much fewer annotations. Therefore, we propose a novel framework, PointMatch, that stands on both data and label, by applying consistency regularization to sufficiently probe information from data itself and leveraging weak labels as assistance at the same time. By doing so, meaningful information can be learned from both data and label for better representation learning, which also enables the model more robust to the extent of label sparsity. Simple yet effective, the proposed PointMatch achieves the state-of-the-art performance under various weakly-supervised schemes on both ScanNet-v2 and S3DIS datasets, especially on the settings with extremely sparse labels, e.g. surpassing SQN by 21.2% and 17.2% on the 0.01% and 0.1% setting of ScanNet-v2, respectively.Comment: 8 pages, 3 figure

Analysis of Gut Flora in a Mouse Model of Esophageal Squamous Cell Carcinoma in Situ

BackgroundWith the application and development of high-throughput sequencing-based approaches for gut flora analysis, increasing studies have confirmed that gut flora is closely related to the development of various cancers. The relationship of gut floras with esophageal squamous cell carcinoma (ESCC) , a common cancer threatening the health of Chinese people, has attracted extensive attention.ObjectiveTo analyze the diversity of gut floras between a rat model of ESCC in situ and normal mice, to identify the carcinoma-specific bacterial genus in ESCC.MethodsFrom August 2020 to May 2021, 20 female SPF C57BL/6 mice were randomly and equally divided into control group and model group. Rice in control group were routinely fed and given ordinary drinking water for 32 weeks, and those in model group were routinely fed and received water containing 0.1 mg/ml cancer inducer 4-nitroquinoline-1-oxide for 16 weeks, and then only fed with ordinary drinking water for another 16 weeks. Stool samples of both groups were collected, and DNA in faeces was extracted and amplified by PCR, followed by high-throughput sequencing. The obtained sequencing data were divided into operational taxonomic units (OTU) based on the similarity between sequences. The α-diversity, β-diversity and species abundance were further analyzed according to species annotation.ResultsNo death occurred in the experiment, and the modeling of ESCC was successfully established in model group. Compared with control group, the proportion of Bacteroidota and Firmicutes increased, while the proportion of Verrucomicrobiota and Proteobacteria decreased in model group. Analysis showed that the α-diversity measured by Shannon Diversity Index in model group was lower than that of control group (P<0.05) . As for β-diversity analysis, PCoA diagram showed that the gut floras of control and model groups clustered in different quadrants, suggesting a significant discrepancy between the groups (t=22.444, P=0.004) . At the phylum level, the abundances of unidentified bacteria, Cyanobacteria, Elusimicrobia and Campilobacterota were higher in model group than those in control group (P<0.05) . At the genus level, the abundances of Prevotellaceae_UCG-003, Bacteroides and Lachnospiraceae_NK4A136_group, Ruminococcus, Prevotellaceae_UCG-001, Prevotella, Colidextribacter, Lachnospiraceae_UCG-006 were higher while those of Romboutsia and Turicibacter were lower in model group than those in control group (P<0.05) . LEfSe analysis showed that, at the genus level, the abundances of Prevotellaceae_UCG-003, Escherichia-Shigella, Bacteroides, Lachnospiraceae_NK4A136_group were increased significantly in model group (P<0.05) , but the abundance of Romboutsia was increased significantly in control group DZ (P<0.05) .ConclusionBy comparing the composition of gut flora, we identified the rat model of ESCC may have less diversity of species and specially differentiated bacteria, and Prevotellaceae_UCG-003, Bacteroides, Lachnospiraceae_NK4A136_group, and Romboutsia could be used as biomarkers for ESCC

Pixel-level intra-domain adaptation for semantic segmentation

Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks. Despite such progress, existing works mainly focus on bridging the inter-domain gaps between the source and target domain, while only few of them noticed the intra-domain gaps within the target data. In this work, we propose a pixel-level intra-domain adaptation approach to reduce the intra-domain gaps within the target data. Compared with image-level methods, ours treats each pixel as an instance, which adapts the segmentation model at a more fine-grained level. Specifically, we first conduct the inter-domain adaptation between the source and target domain; Then, we separate the pixels in target images into the easy and hard subdomains; Finally, we propose a pixel-level adversarial training strategy to adapt a segmentation network from the easy to the hard subdomain. Moreover, we show that the segmentation accuracy can be further improved by incorporating a continuous indexing technique in the adversarial training. Experimental results show the effectiveness of our method against existing state-of-the-art approaches