Visual Confusion Label Tree For Image Classification

Convolution neural network models are widely used in image classification tasks. However, the running time of such models is so long that it is not the conforming to the strict real-time requirement of mobile devices. In order to optimize models and meet the requirement mentioned above, we propose a method that replaces the fully-connected layers of convolution neural network models with a tree classifier. Specifically, we construct a Visual Confusion Label Tree based on the output of the convolution neural network models, and use a multi-kernel SVM plus classifier with hierarchical constraints to train the tree classifier. Focusing on those confusion subsets instead of the entire set of categories makes the tree classifier more discriminative and the replacement of the fully-connected layers reduces the original running time. Experiments show that our tree classifier obtains a significant improvement over the state-of-the-art tree classifier by 4.3% and 2.4% in terms of top-1 accuracy on CIFAR-100 and ImageNet datasets respectively. Additionally, our method achieves 124x and 115x speedup ratio compared with fully-connected layers on AlexNet and VGG16 without accuracy decline.Comment: 9 pages, 5 figures, conferenc

A Study on the Impact of Urban Digitalization on the Urban-rural Income Gap

The empirical research topic for this paper is a panel dataset of 31 provinces and urban areas from my country from 2011 to 2020. On the one hand, it gauges the level of regional digital economic development. On the other side, we’ll talk about the structural impact of the level of digitalization on the urban-rural income difference and further debate whether the digital economy helps close or widen this gap. The findings show that the degree of digitization has a significant impact on reducing the income gap between urban and rural areas, while an increase in the Internet coverage index helps do so. However, the overall impact makes the digital economy unfavorable to reducing the income gap between urban and rural areas

Detection of Bacteroides forsythus and Porphyromonas gingivalis in infected root canals during periapical periodontitis by 16S rDNA

Periapical periodontitis is termed when inflammation of the periodontium is caused by irritants of endodontic origin. Bacterial strains in the root canals were not easy to be identified by the traditional agar culture. In this study a 16S rDNA-based polymerase chain reaction detection method was used to determine the occurrence of Bacteroides forsythus and Porphyromonas gingivalis in chronic periapical periodontitis among Chinese patients. 217 patients with chronic periapcial periodontitis were recruited and a total of 266 teeth were collected. The subjects had no systemic diseases, no antibiotics taken, no root canal treatment (RCT) performed on the infected teeth in the last 3 months. The DNA of bacteria in the root canal was extracted and amplified using universal 16S rDNA primers. The amplification was performed to detect B. forsythus and P. gingivalis using oligonucleotide primers designed from species-specific 16S rDNA signature sequences. B. forsythus and P. gingivalis were detected in 26 and 40% of the participants, respectively. 24 out of 217 infected root canals demonstrated the existence of both types of bacteria, the utility of a 16S rDNA-based PCR detection method showed high sensitivity and high specificity to directly detect B. forsythus, P. gingivalis or other pulpal microorganisms from samples of root canal infections. The results indicated that B. forsythus or P. gingivalis might be a member of the microbiota associated with chronic periapical periodontitis and there was a strong association between the studied species and periodontitis. © 2009 Academic Journals.published_or_final_versio