Self-Supervised Visual Representation Learning with Semantic Grouping

In this paper, we tackle the problem of learning visual representations from unlabeled scene-centric data. Existing works have demonstrated the potential of utilizing the underlying complex structure within scene-centric data; still, they commonly rely on hand-crafted objectness priors or specialized pretext tasks to build a learning framework, which may harm generalizability. Instead, we propose contrastive learning from data-driven semantic slots, namely SlotCon, for joint semantic grouping and representation learning. The semantic grouping is performed by assigning pixels to a set of learnable prototypes, which can adapt to each sample by attentive pooling over the feature and form new slots. Based on the learned data-dependent slots, a contrastive objective is employed for representation learning, which enhances the discriminability of features, and conversely facilitates grouping semantically coherent pixels together. Compared with previous efforts, by simultaneously optimizing the two coupled objectives of semantic grouping and contrastive learning, our approach bypasses the disadvantages of hand-crafted priors and is able to learn object/group-level representations from scene-centric images. Experiments show our approach effectively decomposes complex scenes into semantic groups for feature learning and significantly benefits downstream tasks, including object detection, instance segmentation, and semantic segmentation. Code is available at: https://github.com/CVMI-Lab/SlotCon.Comment: Accepted at NeurIPS 202

Optimization of Small Crispy Meat Paste Formulation by Response Surface-Principal Component Analysis

In order to reduce the fat content of small crispy chicken, this experiment was conducted by adding Kudzuvine root powder to the basic formulation of small crispy chicken batter, and the quality of small crispy pork after deep-frying was tested by single factor experiments with sensory score, water content, oil content and color difference as indicators. The batter formulation was optimized by combining response surface-principal component analysis. The results showed that, based on the total weight of the paste, when the amount of wheat flour added was 20%, the amount of kudzu flour added was 4%, the amount of baking powder added was 0.6%, and the amount of water added was 59%, the sensory score was 88, the moisture content of meat core was 62.24%, the oil content was 4.38%, the overall moisture content was 50.16%, the oil content was 14.22%, and the moisture content increased by 80.62% compared with that without adding Kudzuvine root powder, and the oil content decreased by 61.46%, with a normalized composite score of 1.60, which was basically consistent with the predicted values. This batter formulation was a guideline for the development of highly nutritious, low-fat fried batter products

Loss of endothelial hypoxia inducible factor-prolyl hydroxylase 2 induces cardiac hypertrophy and fibrosis

BACKGROUND: Cardiac hypertrophy and fibrosis are common adaptive responses to injury and stress, eventually leading to heart failure. Hypoxia signaling is important to the (patho)physiological process of cardiac remodeling. However, the role of endothelial PHD2 (prolyl-4 hydroxylase 2)/hypoxia inducible factor (HIF) signaling in the pathogenesis of cardiac hypertrophy and heart failure remains elusive. METHODS AND RESULTS: Mice with Egln1Tie2Cre (Tie2-Cre-mediated deletion of Egln1 [encoding PHD2]) exhibited left ventricular hypertrophy evident by increased thickness of anterior and posterior wall and left ventricular mass, as well as cardiac fibrosis. Tamoxifen-induced endothelial Egln1 deletion in adult mice also induced left ventricular hypertrophy and fibrosis. Additionally, we observed a marked decrease of PHD2 expression in heart tissues and cardiovascular endothelial cells from patients with cardiomyopathy. Moreover, genetic ablation of Hif2a but not Hif1a in Egln1Tie2Cre mice normalized cardiac size and function. RNA sequencing analysis also demonstrated HIF-2α as a critical mediator of signaling related to cardiac hypertrophy and fibrosis. Pharmacological inhibition of HIF-2α attenuated cardiac hypertrophy and fibrosis in Egln1Tie2Cre mice. CONCLUSIONS: The present study defines for the first time an unexpected role of endothelial PHD2 deficiency in inducing cardiac hypertrophy and fibrosis in an HIF-2α– dependent manner. PHD2 was markedly decreased in cardiovascular endothelial cells in patients with cardiomyopathy. Thus, targeting PHD2/HIF-2α signaling may represent a novel therapeutic approach for the treatment of pathological cardiac hypertrophy and failure

Integrin β3 Mediates the Endothelial-to-Mesenchymal Transition via the Notch Pathway

Background/Aims: Neointimal hyperplasia is responsible for stenosis, which requires corrective vascular surgery, and is also a major morphological feature of many cardiovascular diseases. This hyperplasia involves the endothelial-to-mesenchymal transition (EndMT). We investigated whether integrin β3 can modulate the EndMT, as well as its underlying mechanism. Methods: Integrin β3 was overexpressed or knocked down in human umbilical vein endothelial cells (HUVECs). The expression of endothelial markers and mesenchymal markers was determined by real-time reverse transcription PCR (RT-PCR), immunofluorescence staining, and western blot analysis. Notch signaling pathway components were detected by real-time RT-PCR and western blot analysis. Cell mobility was evaluated by wound-healing, Transwell, and spreading assays. Fibroblast-specific protein 1 (FSP-1) promoter activity was determined by luciferase assay. Results: Transforming growth factor (TGF)-β1 treatment or integrin β3 overexpression significantly promoted the EndMT by downregulating VE-cadherin and CD31 and upregulating smooth muscle actin α and FSP-1 in HUVECs, and by enhancing cell migration. Knockdown of integrin β3 reversed these effects. Notch signaling was activated after TGF-β1 treatment of HUVECs. Knockdown of integrin β3 suppressed TGF-β1-induced Notch activation and expression of the Notch downstream target FSP-1. Conclusion: Integrin β3 may promote the EndMT in HUVECs through activation of the Notch signaling pathway

How Influential is Elon Musk? An Event Study Analysis of Tweets on Auto Stock Returns

In the past few years, the social media platform Twitter has become a popular space for speculation on stock prices. Elon Musk, the CEO of Tesla, has become notable for his opinions on a variety of stocks and cryptocurrency. With his large following of 80 million users on Twitter, many of his tweets about Tesla have gone viral. Some of these viral tweets turn out to be misleading "troll" messages, but they still draw investor attention, and Tesla's stock price often moves substantially as a result. In this paper, we apply the event study methodology to analyze the effects of Elon Musk's tweets on abnormal returns of Tesla and other auto companies. We perform sentiment analysis using NLP techniques and present three models: the Abnormal Returns Model, Multivariate Regression Model, and Panel Event Study Model, which are based on OLS linear regression. The first two models reveal that Elon Musk's tweets have a statistically significant effect on Tesla and electric vehicle rival companies. There are event-induced abnormal returns on the days of events, but there is limited to no evidence that returns on the days before and after are affected. We find that some abnormal returns are confounded by other firm-specific events. Our findings have implications on the reach of Musk's influence: while his tweets affect abnormal returns of firms in the electric vehicle sector, his Twitter activity does not impact the abnormal returns for non-EV auto firms

Housing Sources of Second-Tier Urban Residents Based on Multivariate Correspondence

To explore the relationship between different housing sources and individual attributes in second-tier cities, finding the key point of contradiction between supply and demand in the real estate market is important. Multivariate correspondence analysis (MCA) is a powerful method to solve the complex problem of the relationship between classified variables. Also known as corresponding analysis, it is a multivariate statistical analysis method that aims to describe the relationship between two classification variables in a corresponding table in a low-dimensional space. MCA has unique advantages when dealing with categorical variable data. It combines the advantages of factor analysis and multidimensional scaling. On the basis of combining and analysing the existing literature, this paper makes an analysis of the data obtained from a housing questionnaire survey collected in Jinan, Shandong Province, in 2017. The research results show that the housing sources of second-tier urban residents are closely related to age, annual family income, marriage, education level, and individual characteristics of the permanent family population but not to gender

Modelling epidemics on d-cliqued graphs

Since social interactions have been shown to lead to symmetric clusters, we propose here that symmetries play a key role in epidemic modelling. Mathematical models on d-ary tree graphs were recently shown to be particularly effective for modelling epidemics in simple networks. To account for symmetric relations, we generalize this to a new type of networks modelled on d-cliqued tree graphs, which are obtained by adding edges to regular d-trees to form d-cliques. This setting gives a more realistic model for epidemic outbreaks originating within a family or classroom and which could reach a population by transmission via children in schools. Specifically, we quantify how an infection starting in a clique (e.g. family) can reach other cliques through the body of the graph (e.g. public places). Moreover, we propose and study the notion of a safe zone, a subset that has a negligible probability of infection

Optimal Design of Multi-section Proportional Directional Valve Throttle Grooves with Artificial Neural Networks

This paper presents a method for design multi-section proportional directional valve Throttle grooves with ANN method, which aims at getting a better flow stability. There exists a coupling matter during the opening and closing process between the throttling notches, so that it’s difficult to parameterize the complex flow field characteristics Cd and the structure boundary of the spool grooves. However, in this paper, an ANN was built with data from CFD results, while the typical structural parameters (U type, the O-type and C-type), operating parameters was input vectors, the discharge coefficient as output vectors. Meanwhile, all of the needed data is taken from the three-dimensional CFD analysis, which are organized properly and verified by a bench scale test on a rig. Then, with throttling stiffness as optimization objective to evaluate flow stability, an optimal design process is carried out to optimize to optimize the structure of coupling grooves with ANN models and genetic algorithm. Ultimately, the optimized structure is verified better by the physical test on test rig, therefore, the significance of design method is proved