Improved image inpainting exemplar-based algorithms by boundary priori-knowledge

Image inpainting plays an important role in restoration of cultural relics, pictures beautification. Criminisi algorithm creates good results in large-area inpainting. However, it does still have some deficiencies such as over-extending. In this paper, two improved algorithms based on prior knowledge of the boundary had been proposed by simulating the idea of manual repairing. An algorithm, by simulating the strategy that the next inpainted pixel will be near to the prior one, named nearer neighbor first algorithm, can void the random bounding of the to-be-inpainted pixle. Another algorithm, by simulating the strategy that the inpainting process, named no-inpainted first algorithm, will be in multiple directions, can void the inpainting process in a single direction. The results reveal that the neighborhood-first algorithm performs better than Criminsi algorithm in repairing the missing structure while the unrepaired-first algorithm performs better than Criminsi algorithm in repairing the missing texture.

A/B Contracts

This paper aims to improve the practical applicability of the classic theory of incentive contracts under moral hazard. We establish conditions under which the information provided by an A/B test of incentive contracts is sufficient for answering the question of how best to improve a status quo incentive contract, given a priori knowledge of the agent’s monetary preferences. We assess the empirical relevance of this result using data from DellaVigna and Pope’s (2018) study of a variety of incentive contracts. Finally, we discuss how our framework can be extended to incorporate additional considerations beyond those in the classic theory. (JEL D82, D86, D91)

Determination of biomarkers from microarray data using graph neural network and spectral clustering

In bioinformatics, the rapid development of gene sequencing technology has produced an increasing amount of microarray data. This type of data shares the typical characteristics of small sample size and high feature dimensions. Searching for biomarkers from microarray data, which expression features of various diseases, is essential for the disease classification. feature selection has therefore became fundemental for the analysis of microarray data, which designs to remove irrelevant and redundant features. There are a large number of redundant features and irrelevant features in microarray data, which severely degrade the classification effectiveness. We propose an innovative feature selection method with the goal of obtaining feature dependencies from a priori knowledge and removing redundant features using spectral clustering. In this paper, the graph structure is firstly constructed by using the gene interaction network as a priori knowledge, and then a link prediction method based on graph neural network is proposed to enhance the graph structure data. Finally, a feature selection method based on spectral clustering is proposed to determine biomarkers. The classification accuracy on DLBCL and Prostate can be improved by 10.90% and 16.22% compared to traditional methods. Link prediction provides an average classification accuracy improvement of 1.96% and 1.31%, and is up to 16.98% higher than the published method. The results show that the proposed method can have full use of a priori knowledge to effectively select disease prediction biomarkers with high classification accuracy.

How Does Social Media Influence College Students to Recognize Entrepreneurial Opportunities? - Evidence from China

The rapid development of information technology is having a profound impact on college students' entrepreneurial behavior,and accurately recognize entrepreneurial opportunities will affect the success or failure of individual entrepreneurship. The purpose of this study is to explore if and how social media influences college students to recognize entrepreneurial opportunities. A systematic review of relevant research results including social media in the field of entrepreneurship, entrepreneurship of college students, etc., this paper puts forward five dimensions and six hypotheses, including entrepreneurial alertness, priori knowledge, social capital, entrepreneurial opportunity recognition, and social media. A total of 508 valid questionnaires were obtained by designing questionnaires, organizing surveys, and screening data for college students. Through the reliability and validity test, correlation analysis, and hypothesis test analysis of the returned questionnaire data, all six hypotheses were verified. The empirical analysis shows that social media can significantly affect the recognition of entrepreneurial opportunities for college entrepreneurs. priori knowledge and entrepreneurial alertness play a mediating role and a moderating role respectively in this process. Meanwhile, priori knowledge plays a significant positive role in promoting entrepreneurial vigilance. Social capital has a direct and positive impact on college students' entrepreneurial opportunity recognition, and plays a moderating role in the impact of social media on college students' entrepreneurial opportunity recognition.

A novel weighted fuzzy c-means based on feature weight learning

In feature weighted fuzzy c-means algorithms, there exist two challenges when the feature weighting techniques are used to improve their performances. On one hand, if the values of feature weights are learnt in advance, and then fixed in the process of clustering, the learnt weights might be lack of flexibility and might not fully reflect their relevance. On the other hand, if the feature weights are adaptively adjusted during the clustering process, the algorithms maybe suffer from bad initialization and lead to incorrect feature weight assignment, thus the performance of the algorithms may degrade the in some conditions. In order to ease these problems, a novel weighted fuzzy c-means based on feature weight learning (FWL-FWCM) is proposed. It is a hybrid of fuzzy weighted c-means (FWCM) algorithm with Improved FWCM (IFWCM) algorithm. FWL-FWCM algorithm first learns feature weights as priori knowledge from the data in advance by minimizing the feature evaluation function using the gradient descent technique, then iteratively optimizes the clustering objective function which integrates the within weighted cluster dispersion with a term of the discrepancy between the weights and the priori knowledge. Experiments conducted on an artificial dataset and real datasets demonstrate the proposed approach outperforms the-state-of-the-art feature weight clustering methods. The convergence property of FWL-FWCM is also presented.

WTLS-Bayes Filter-based Relative Pose Estimation for Space Non-cooperative Unknown Target Without a Priori Knowledge

The relative pose estimation of a space noncooperative target is an attractive yet challenging task due to the complexity of the target background and illumination, and the lack of a priori knowledge. Unfortunately, these negative factors have a grave impact on the estimation accuracy and the robustness of filter algorithms. In response, this paper proposes a novel filter algorithm to estimate the relative pose to improve the robustness based on a stereovision system. First, to obtain a coarse relative pose, the weighted total least squares (WTLS) algorithm is adopted to estimate the relative pose based on several feature points. The resulting relative pose is fed into the subsequent filter scheme as observation quantities. Second, the classic Bayes filter is exploited to estimate the relative state except for moment-of-inertia ratios. Additionally, the one-step prediction results are used as feedback for WTLS initialization. The proposed algorithm successfully eliminates the dependency on continuous tracking of several fixed points. Finally, comparison experiments demonstrate that the proposed algorithm presents a better performance in terms of robustness and convergence time.