Impact of Metaverse Investment on the Market Performance of Public Enterprise in China

This paper provides evidence on the impact of Metaverse investment on the market performance of public enterprises in China. Our empirical analysis is based on event study and the data of 58 public firms which made Metaverse investment announcements between 2021 and 2022. The results show that Metaverse investment announcements have a significant positive impact on the market value of firms in the short term, but show a downward trend over time. Meanwhile, firms with larger size, stronger innovativeness and higher operation efficiency can obtain higher stoke price, resulting in more returns. Nevertheless, firms in different industries all get positive market feedback. Industry differences have no significant effect on the market performance of enterprise Metaverse investment

Research on Cross-Variety Arbitrage Strategy of Metal Futures in China

With the vigorous development of China’s futures market, the number of varieties is gradually increasing, the number of investors is increasing, and the liquidity of futures is getting better and better. The “T+0” trading mechanism of the futures market makes arbitrage traders prefer to carry out arbitrage in the futures market. Cross-variety arbitrage is one of the main ways of arbitrage in China’s futures market, and its key is to find the appropriate variety combination and arbitrage strategy. The purpose of this paper is to conduct scientific and detailed research and analysis on futures cross-variety arbitrage, so that investors can have a comprehensive and in-depth understanding of it to enhance investor education, and on this basis, find appropriate variety combinations and design cross-variety arbitrage strategies and empirically test the effect of the strategies, so as to provide reference for investors and enrich the existing research on futures cross-variety arbitrage strategies

Semantic-Enhanced Image Clustering

Image clustering is an important and open-challenging task in computer vision. Although many methods have been proposed to solve the image clustering task, they only explore images and uncover clusters according to the image features, thus being unable to distinguish visually similar but semantically different images. In this paper, we propose to investigate the task of image clustering with the help of a visual-language pre-training model. Different from the zero-shot setting, in which the class names are known, we only know the number of clusters in this setting. Therefore, how to map images to a proper semantic space and how to cluster images from both image and semantic spaces are two key problems. To solve the above problems, we propose a novel image clustering method guided by the visual-language pre-training model CLIP, named \textbf{Semantic-Enhanced Image Clustering (SIC)}. In this new method, we propose a method to map the given images to a proper semantic space first and efficient methods to generate pseudo-labels according to the relationships between images and semantics. Finally, we propose performing clustering with consistency learning in both image space and semantic space, in a self-supervised learning fashion. The theoretical result of convergence analysis shows that our proposed method can converge at a sublinear speed. Theoretical analysis of expectation risk also shows that we can reduce the expected risk by improving neighborhood consistency, increasing prediction confidence, or reducing neighborhood imbalance. Experimental results on five benchmark datasets clearly show the superiority of our new method

PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation

Today, scene graph generation(SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution. Thus, tackling the class imbalance trouble of SGG is critical and challenging. In this paper, we first discover that when predicate labels have strong correlation with each other, prevalent re-balancing strategies(e.g., re-sampling and re-weighting) will give rise to either over-fitting the tail data(e.g., bench sitting on sidewalk rather than on), or still suffering the adverse effect from the original uneven distribution(e.g., aggregating varied parked on/standing on/sitting on into on). We argue the principal reason is that re-balancing strategies are sensitive to the frequencies of predicates yet blind to their relatedness, which may play a more important role to promote the learning of predicate features. Therefore, we propose a novel Predicate-Correlation Perception Learning(PCPL for short) scheme to adaptively seek out appropriate loss weights by directly perceiving and utilizing the correlation among predicate classes. Moreover, our PCPL framework is further equipped with a graph encoder module to better extract context features. Extensive experiments on the benchmark VG150 dataset show that the proposed PCPL performs markedly better on tail classes while well-preserving the performance on head ones, which significantly outperforms previous state-of-the-art methods.Comment: To be appeared on ACMMM 202

Isolation and Characterization of Microsatellite Loci in Pistacia weinmannifolia (Anacardiaceae)

Fourteen polymorphic microsatellite loci were isolated from the genomic DNA of Pistacia weinmannifolia, using the Fast Isolation by AFLP of Sequences Containing repeats (FIASCO) method, and screened on 12 individuals from each of two wild populations. The 14 polymorphic loci had an average of 4.1 alleles per locus varying from 1 to 9. The observed (Ho) and expected (He) heterozygosities across the two populations ranged from 0.000 to 0.933 and from 0.000 to 0.906, respectively. Tests for departure from Hardy-Weinberg equilibrium (HWE) and genotypic linkage disequilibrium (LD) were conducted for each of the two populations separately. It was found that no locus significantly deviated from HWE proportions and no significant LD was detected between loci (p < 0.001). In the test of cross-species utility, we successfully amplified nine (64.2%) of 14 loci in P. chinensis and four (28.6%) in P. mexicana. The relatively high level of polymorphism for these markers will facilitate further studies of gene flow, population structure and evolutionary history of P. weinmannifolia and its congeners

Isolation and Characterization of Microsatellite Loci in Pistacia weinmannifolia (Anacardiaceae)

Fourteen polymorphic microsatellite loci were isolated from the genomic DNA of Pistacia weinmannifolia, using the Fast Isolation by AFLP of Sequences Containing repeats (FIASCO) method, and screened on 12 individuals from each of two wild populations. The 14 polymorphic loci had an average of 4.1 alleles per locus varying from 1 to 9. The observed (Ho) and expected (He) heterozygosities across the two populations ranged from 0.000 to 0.933 and from 0.000 to 0.906, respectively. Tests for departure from Hardy-Weinberg equilibrium (HWE) and genotypic linkage disequilibrium (LD) were conducted for each of the two populations separately. It was found that no locus significantly deviated from HWE proportions and no significant LD was detected between loci (p < 0.001). In the test of cross-species utility, we successfully amplified nine (64.2%) of 14 loci in P. chinensis and four (28.6%) in P. mexicana. The relatively high level of polymorphism for these markers will facilitate further studies of gene flow, population structure and evolutionary history of P. weinmannifolia and its congeners

Characterization of the chloroplast genome and its inference on the phylogenetic position of Incarvillea sinensis Lam. (Bignoniaceae)

Incarvillea sinensis Lam. is the type of the genus Incarvillea Juss., and it is widely distributed, relative to other members of the genus. In this paper, we sequenced, assembled and annotated the chloroplast genome of Incarvillea sinensis. The complete chloroplast genome is 162,088 bps in size, with overall GC content of 39.4%. We annotated 113 unique genes in the plastome sequence, including 79 protein coding genes, 30 tRNA genes, and four rRNA genes. The phylogenetic analysis based on chloroplast genome sequences resulted in a different resolution on the relationships among subgenera from the former

Semantic-Enhanced Image Clustering

Image clustering is an important and open challenging task in computer vision. Although many methods have been proposed to solve the image clustering task, they only explore images and uncover clusters according to the image features, thus being unable to distinguish visually similar but semantically different images. In this paper, we propose to investigate the task of image clustering with the help of visual-language pre-training model. Different from the zero-shot setting, in which the class names are known, we only know the number of clusters in this setting. Therefore, how to map images to a proper semantic space and how to cluster images from both image and semantic spaces are two key problems. To solve the above problems, we propose a novel image clustering method guided by the visual-language pre-training model CLIP, named Semantic-Enhanced Image Clustering (SIC). In this new method, we propose a method to map the given images to a proper semantic space first and efficient methods to generate pseudo-labels according to the relationships between images and semantics. Finally, we propose to perform clustering with consistency learning in both image space and semantic space, in a self-supervised learning fashion. The theoretical result of convergence analysis shows that our proposed method can converge at a sublinear speed. Theoretical analysis of expectation risk also shows that we can reduce the expectation risk by improving neighborhood consistency, increasing prediction confidence, or reducing neighborhood imbalance. Experimental results on five benchmark datasets clearly show the superiority of our new method

The complete plastome genome of Incarvillea compacta (Bignoniaceae), an alpine herb endemic to China

Incarvillea compacta is a threatened species endemic to the Hengduan Mountain and has been undergoing a successive reduction in the area of occupancy. In the present study, we assembled and characterised the complete chloroplast (cp) genomes of this species. The plastome genome was 150,154 bp in length, and overall GC content was about 40.5%. The circle molecular contained 110 genes including 77 protein-coding genes, 29 tRNA, and four rRNA. Phylogenetic analysis suggested that I. compacta is sister to Tecomaria capensis

Comparison on the complete plastome genome between the wild and cultivated Angelica sinensis (Apiaceae), a famous Chinese medicinal herb

Angelica sinensis is one of the most widely used Chinese medicinal herb. It has been cultivated for over 900 years, and the wild resource has been on the verge of extinction owing to over-exploitation. In the present study, we assembled and characterized the complete chloroplast (cp) genomes of two wild and one cultivated individuals of A. sinensis for future genetic studies. These plastome genomes varied from 142,484 to 142,529 bp in size, and overall GC contents were about 37.5%. Phylogenetic analysis and comparison on the cp genome structure reveal that the wild A. sinensis sampled should not be the wild ancestor, but the escape of the cultivated