Balanced Audiovisual Dataset for Imbalance Analysis

The imbalance problem is widespread in the field of machine learning, which also exists in multimodal learning areas caused by the intrinsic discrepancy between modalities of samples. Recent works have attempted to solve the modality imbalance problem from algorithm perspective, however, they do not fully analyze the influence of modality bias in datasets. Concretely, existing multimodal datasets are usually collected under specific tasks, where one modality tends to perform better than other ones in most conditions. In this work, to comprehensively explore the influence of modality bias, we first split existing datasets into different subsets by estimating sample-wise modality discrepancy. We surprisingly find that: the multimodal models with existing imbalance algorithms consistently perform worse than the unimodal one on specific subsets, in accordance with the modality bias. To further explore the influence of modality bias and analyze the effectiveness of existing imbalance algorithms, we build a balanced audiovisual dataset, with uniformly distributed modality discrepancy over the whole dataset. We then conduct extensive experiments to re-evaluate existing imbalance algorithms and draw some interesting findings: existing algorithms only provide a compromise between modalities and suffer from the large modality discrepancy of samples. We hope that these findings could facilitate future research on the modality imbalance problem.Comment: website:https://gewu-lab.github.io/Balanced-Audiovisual-Dataset

All-solid-state asymmetric supercapacitor based on porous cobalt selenide thin films

As a significant semiconductor material, cobalt selenide has enormous potential and extensive application prospects in the field of solar cells, photocatalysis and supercapacitor. In this paper, porous CoSe thin films were successfully fabricated on stainless-steel sheet using a facile, effective electrodeposition technique. Electrochemical tests reveal that the specific capacitance reaches as high as 510 F g−1 at the current density of 1 A g−1 with the capacitance retention of 91% over 5000 cycles. An asymmetric all-solid-state supercapacitor is fabricated using CoSe thin film as the positive electrode and activate carbon as the negative electrode. The combined solid device displays a high area specific capacitance of 18.1 mF cm−2 accompanied with good cycling stability, outstanding flexibility and satisfactory mechanical stability. Furthermore, the solid devices connected in series can power the red light-emitting diodes. The results show great potential for preparing large scale high energy density storage systems

DXVNet-ViT-Huge (JFT) Multimode Classification Network Based on Vision Transformer

Aiming at the problem that traditional CNN network is not good at extracting global features of images, Based on DXVNet network, Conditional Random Fields (CRF) component and pre-trained ViT-Huge (Vision Transformer) are adopted in this paper Transformer model expands and builds a brand new DXVNet-ViT-Huge (JFT) network. CRF component can help the network learn the constraint conditions of each word corresponding prediction label, improve the D-GRU method based word label prediction errors, and improve the accuracy of sequence annotation. The Transformer architecture of the ViT (Huge) model can extract the global feature information of the image, while CNN is better at extracting the local features of the image. Therefore, the ViT (Huge) Huge pre-training model and CNN pre-training model adopt the multi-modal feature fusion technology. Two complementary image feature information is fused by Bi-GRU to improve the performance of network classification. The experimental results show that the newly constructed Dxvnet-Vit-Huge (JFT) model achieves good performance, and the F1 values in the two real public data sets are 6.03% and 7.11% higher than the original DXVNet model, respectively

The Impact of Psychological Capital and Social Capital on Residents’ Mental Health and Happiness During COVID-19: Evidence From China

ObjectiveThis paper studies the mediating and interactive effects of social capital on psychological capital and the feeling of happiness from the impact of COVID-19. Since its emergence, the COVID-19 pandemic has taken a toll on people’s mental health and affected their hopes for the future. Lifestyle and economic conditions have also been affected and have subsequently impacted people’s sense of confidence in life. This could increase the likelihood of many people developing mental health issues, such as anxiety or depression. Therefore, it is vital to study the influence of psychological capital and social capital on people’s subjective psychology and happiness experiences.Materials and MethodsUsing an ordered probit model, this paper studied the independent influence and interaction between psychological capital and social capital on people’s happiness. The ordered probit model was chosen because subjective well-being (SWB) is an ordered variable. We further used structural equation modeling (SEM) to study the mediating effects of social capital on psychological capital and happiness.ResultsThe regression results showed that both psychological capital and social capital were significantly positively correlated with happiness when controlling for other factors. In addition, psychological and social capital significantly interacted, in which the psychological capital promotes the effect of social capital on happiness. Moreover, the effect of psychological capital on happiness was greater than that of social capital, demonstrating that happiness is more greatly influenced by subjective psychological experience. The interaction coefficient of psychological and social capital was also significant, showing that the two have mutually reinforcing effects on happiness. Finally, health, income class, real estate, stranger trust, age, and urban household registration had significant positive effects on happiness, while the view of money, being female, education had a negative relationship with happiness. The SEM results showed that the mediating effect of psychological capital on happiness was partly transmitted through social capital: the total effect of psychological capital on happiness was highly significant (p < 0.0001), as was the total effect of social capital on happiness (p < 0.0001); however, the coefficient for psychological capital was greater than that for social capital. Through heterogeneity analysis, we found that the relationship between psychological capital, social capital, and happiness was significantly positive in each sub-sample group. There was also a significant interaction between psychological and social capital for men, women, urban and rural residents, and higher education background sample groups. However, the interaction was not significant in the sample group without higher education. In addition, the relationship between the happiness of rural residents and their educational background and gender was not significant.ConclusionWe found that psychological and social capital have significant positive relationships and effects on happiness. Psychological capital demonstrated both direct and indirect influences on happiness, and further strengthens the influence of social capital on happiness. These results support a scheme to emphasize psychological support during the COVID-19 pandemic period to enhance the mental health of citizens

Discovery of Shiga Toxin-Producing Escherichia coli (STEC)-Specific Bacteriophages From Non-fecal Composts Using Genomic Characterization

Composting is a complex biodegradable process that converts organic materials into nutrients to facilitate crop yields, and, if well managed, can render bactericidal effects. Majority of research focused on detection of enteric pathogens, such as Shiga toxin-producing Escherichia coli (STEC) in fecal composts. Recently, attention has been emphasized on bacteriophages, such as STEC-specific bacteriophages, associated with STEC from the fecal-contaminated environment because they are able to sustain adverse environmental condition during composting process. However, little is known regarding the isolation of STEC-specific bacteriophages in non-fecal composts. Thus, the objectives were to isolate and genomically characterize STEC-specific bacteriophages, and to evaluate its association with STEC in non-fecal composts. For bacteriophage isolation, the samples were enriched with non-pathogenic E. coli (3 strains) and STEC (14 strains), respectively. After purification, host range, plaque size, and phage morphology were examined. Furthermore, bacteriophage genomes were subjected to whole-genome sequencing using Illumina MiSeq and genomic analyses. Isolation of top six non-O157 and O157 STEC utilizing culture methods combined with PCR-based confirmation was also conducted. The results showed that various STEC-specific bacteriophages, including vB_EcoM-Ro111lw, vB_EcoM-Ro121lw, vB_EcoS-Ro145lw, and vB_EcoM-Ro157lw, with different but complementary host ranges were isolated. Genomic analysis showed the genome sizes varied from 42kb to 149kb, and most bacteriophages were unclassified at the genus level, except vB_EcoM-Ro111lw as FelixO1-like viruses. Prokka predicted less than 25% of the ORFs coded for known functions, including those essential for DNA replication, bacteriophage structure, and host cell lysis. Moreover, none of the bacteriophages harbored lysogenic genes or virulence genes, such as stx or eae. Additionally, the presence of these lytic bacteriophages was likely attributed to zero isolation of STEC and could also contribute to additional antimicrobial effects in composts, if the composting process was insufficient. Current findings indicate that various STEC-specific bacteriophages were found in the non-fecal composts. In addition, the genomic characterization provides in-depth information to complement the deficiency of biological features regarding lytic cycle of the new bacteriophages. Most importantly, these bacteriophages have great potential to control various serogroups of STEC