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Deep hashing is being used to approximate nearest neighbor search for large-scale image recognition problems. However, CNN architectures have dominated similar applications. We present a Pyramid Transformer-based Triplet Hashing architecture to handle large-scale place recognition challenges in this study, leveraging the capabilities of Vision Transformer (ViT). For feature representation, we create a Siamese Pyramid Transformer backbone. We present a multi-scale feature aggregation technique to learn discriminative features for scale-invariant features. In addition, we observe that binary codes suitable for place recognition are sub-optimal. To overcome this issue, we use a self-restraint triplet loss deep learning network to create compact hash codes, further increasing recognition accuracy. To the best of our knowledge, this is the first study to use a triplet loss deep learning network to handle the deep hashing learning problem. We do extensive experiments on four difficult place datasets: KITTI, Nordland, VPRICE, and EuRoC. The experimental findings reveal that the suggested technique performs at the cutting edge of large-scale visual place recognition challenges. • A novel Pyramid Transformer Triplet Hashing framework with the Siamese architecture for the place recognition is presented. • A multi-scale feature aggregation method is proposed to learn discriminative features to improve the visual representation. • A self-restraint triplet loss deep learning network is used to learn compact hash codes to improve recognition accuracy. [ABSTRACT FROM AUTHOR]

• Executive ownership will limit corporate financial investment. • The proportion of executive ownership on the proportion of long-term financial asset holdings to reduce the role of the more obvious. • An increase in executive ownership has a more pronounced effect on the reduction in the proportion of financial assets held by private firms. The article selects A-share non-financial listed companies from 2012 to 2021 as a research sample to analyze the relationship between executive ownership and corporate financialization. It is found that executive ownership inhibits corporate financial investment; compared to the proportion of short-term financial asset holdings, a rise in executive ownership has a more pronounced effect on the reduction of the proportion of long-term financial asset holdings; the higher the proportion of executive ownership, the lower the reduction of the proportion of financial asset holdings in state-owned enterprises than in private enterprises. [ABSTRACT FROM AUTHOR]

This study aimed to explore the relationship between obesity-related glomerulopathy (ORG) and Helicobacter pylori (HP) infection in adults. Therefore, an artificial intelligence health data monitoring system was established based on the partial differential network mathematical model. The 13C breath test method was applied to detect HP infection in the research objects. A total of 130 patients were included in this study, and rolled into an experimental group (test results were negative) and a control group (test results were positive) according to the results of the 13C breath test. The results showed that there were 83 patients (63.85%) with HP-positive infection and 47 patients (36.15%) with HP-negative infection. The serum creatinine (SCr), 24-hour urine protein level, and the proportions of patients with damaged renal tubules, abnormal retinol-binding protein (RBP), and abnormal N-aceltyl-D-glucosaminidase (NAG) of the patients in the experimental group were markedly higher than those of the control group (P < 0.05); the proportions of patients with global sclerosis, segmental sclerosis, severe fibrosis, and moderate fibrosis in the experimental group were substantially higher than those of the control group (P < 0.05); and the proportions of patients with hypertriglyceridemia, hyperuricemia (HUA), and low high-density lipoprotein cholesterol (LDL-C) in the experimental group were greatly higher than the proportions of the control group (P < 0.05). In short, the partial differential network mathematical model-based artificial intelligence health data monitoring system under E-health can obtain the basic information of the patients in real time, and the HP-positive infection was highly expressed in the ORG patients. The renal function damage of patients with HP-positive infection in ORG was more serious than that of patients with HP-negative infection. Thus, HP infection might be related to the process of ORG. [ABSTRACT FROM AUTHOR]