Research on CPI Prediction Based on Natural Language Processing

In the past, the seed keywords for CPI prediction were often selected based on empirical summaries of research and literature studies, which were prone to select omitted and invalid variables. In this paper, we design a keyword expansion technique for CPI prediction based on the cutting-edge NLP model, PANGU. We improve the CPI prediction ability using the corresponding web search index. Compared with the unsupervised pre-training and supervised downstream fine-tuning natural language processing models such as BERT and NEZHA, the PANGU model can be expanded to obtain more reliable CPI-generated keywords by its excellent zero-sample learning capability without the limitation of the downstream fine-tuning data set. Finally, this paper empirically tests the keyword prediction ability obtained by this keyword expansion method with historical CPI data

Opportunities and Challenges Faced by the Trade Cooperation of China and Africa

This paper is based on the new normal background that the early days macroeconomic stimulation lead to the waste of manufacturability and hasted to find a new trade market, So we use the RCA Index proposed by Balassa in 1965 to compute the RCA Index and RMA Index of the mainly ten products of the export and import trade between China and Africa, Then specifies the competitive industries and the disadvantage industries in two countries, furthermore affirms the high growth potential trading products among the China and Africa. At the end of the paper, several opportunities and challenges are proposed according to the empirical results

Measurement of Investment activity in China based on Natural language processing technology

The purpose of this study is to propose a new index to measure and reflect China's investment activity in time, and to analyze the changes of China's investment activity in the past five years. This study first uses the NEZHA model for semantic representation, and expand the indicator system based on semantic similarity. Then we calculate China's investment activity index by using the network search data. This study shows that China's investment activity began to decline in 2019, rebounded for a period of time after the outbreak of COVID-19 in 2020, and then continued to maintain a downward trend. Private investment activity has declined significantly, while government investment activity has increased. Among the provinces in Chinese Mainland, the investment activity of economically developed provinces has decreased significantly, while the investment activity of some economically less developed provinces in the north and south is higher. After the outbreak of COVID-19, the investment period became shorter. Our research will provide timely investment information for the government, decision makers and managers, as well as provide other researchers who also pay attention to investment with a perspective other than investment in fixed asset

Joint-Individual Fusion Structure with Fusion Attention Module for Multi-Modal Skin Cancer Classification

Most convolutional neural network (CNN) based methods for skin cancer classification obtain their results using only dermatological images. Although good classification results have been shown, more accurate results can be achieved by considering the patient's metadata, which is valuable clinical information for dermatologists. Current methods only use the simple joint fusion structure (FS) and fusion modules (FMs) for the multi-modal classification methods, there still is room to increase the accuracy by exploring more advanced FS and FM. Therefore, in this paper, we design a new fusion method that combines dermatological images (dermoscopy images or clinical images) and patient metadata for skin cancer classification from the perspectives of FS and FM. First, we propose a joint-individual fusion (JIF) structure that learns the shared features of multi-modality data and preserves specific features simultaneously. Second, we introduce a fusion attention (FA) module that enhances the most relevant image and metadata features based on both the self and mutual attention mechanism to support the decision-making pipeline. We compare the proposed JIF-MMFA method with other state-of-the-art fusion methods on three different public datasets. The results show that our JIF-MMFA method improves the classification results for all tested CNN backbones and performs better than the other fusion methods on the three public datasets, demonstrating our method's effectiveness and robustnessComment: submitted to Pattern Recognition journal before 202

Metabolite profiles of ginsenosides Rk1 and Rg5 in zebrafish using ultraperformance liquid chromatography/quadrupole–time-of-flight MS

AbstractBackgroundIn the present study, metabolite profiles of ginsenosides Rk1 and Rg5 from red ginseng or red notoginseng in zebrafish were qualitatively analyzed with ultraperformance liquid chromatography/quadrupole–time-of-flight MS, and the possible metabolic were pathways proposed.MethodsAfter exposing to zebrafish for 24 h, we determined the metabolites of ginsenosides Rk1 and Rg5. The chromatography was accomplished on UPLC BEH C18 column using a binary gradient elution of 0.1% formic acetonitrile–0.1% formic acid water. The quasimolecular ions of compounds were analyzed in the negative mode. With reference to quasimolecular ions and MS2 spectra, by comparing with reference standards and matching the empirical molecular formula with that of known published compounds, and then the potential structures of metabolites of ginsenosides Rk1 and Rg5 were acquired.ResultsFour and seven metabolites of ginsenoside Rk1 and ginsenoside Rg5, respectively, were identified in zebrafish. The mechanisms involved were further deduced to be desugarization, glucuronidation, sulfation, and dehydroxymethylation pathways. Dehydroxylation and loss of C-17 residue were also metabolic pathways of ginsenoside Rg5 in zebrafish.ConclusionLoss of glucose at position C-3 and glucuronidation at position C-12 in zebrafish were regarded as the primary physiological processes of ginsenosides Rk1 and Rg5

Markov Switching Model With Bounce-Back Effect: An Application to Chinese Business Cycle

Based on Markov switching model with a bounce-back effect, this paper analyzes the data of the economic growth in China. The findings suggest that Markov switching model with a rebound effect fits the macroeconomic growth data in our country better. What’s more, we can also see that the economic fluctuation in our country not only is characterized by its obvious nonlinearity and asymmetry but has significant “bounce-back effect”

COAD: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking

Expert finding, a popular service provided by many online websites such as Expertise Finder, LinkedIn, and AMiner, benefits seeking consultants, collaborators, and candidate qualifications. However, its quality is suffered from a single source of support information for experts. This paper employs AMiner, a free online academic search and mining system, having collected more than over 100 million researcher profiles together with 200 million papers from multiple publication databases, as the basis for investigating the problem of expert linking, which aims at linking any external information of persons to experts in AMiner. A critical challenge is how to perform zero shot expert linking without any labeled linkages from the external information to AMiner experts, as it is infeasible to acquire sufficient labels for arbitrary external sources. Inspired by the success of self supervised learning in computer vision and natural language processing, we propose to train a self supervised expert linking model, which is first pretrained by contrastive learning on AMiner data to capture the common representation and matching patterns of experts across AMiner and external sources, and is then fine-tuned by adversarial learning on AMiner and the unlabeled external sources to improve the model transferability. Experimental results demonstrate that COAD significantly outperforms various baselines without contrastive learning of experts on two widely studied downstream tasks: author identification (improving up to 32.1% in HitRatio@1) and paper clustering (improving up to 14.8% in Pairwise-F1). Expert linking on two genres of external sources also indicates the superiority of the proposed adversarial fine-tuning method compared with other domain adaptation ways (improving up to 2.3% in HitRatio@1).Comment: TKDE under revie

Aphid Performance Changes with Plant Defense Mediated by \u3cem\u3eCucumber mosaic virus\u3c/em\u3e Titer

Background: Cucumber mosaic virus (CMV) causes appreciable losses in vegetables, ornamentals and agricultural crops. The green peach aphid, Myzus persicae Sulzer (Aphididae) is one of the most efficient vectors for CMV. The transmission ecology of aphid-vectored CMV has been well investigated. However, the detailed description of the dynamic change in the plant-CMV-aphid interaction associated with plant defense and virus epidemics is not well known. Results: In this report, we investigated the relationship of virus titer with plant defense of salicylic acid (SA) and jasmonic acid (JA) during the different infection time and their interaction with aphids in CMV-infected tobacco plants. Our results showed that aphid performance changed with virus titer and plant defense on CMV-inoculated plants. At first, plant defense was low and aphid number increased gradually. The plant defense of SA signaling pathway was induced when virus titer was at a high level, and aphid performance was correspondingly reduced. Additionally, the winged aphids were increased. Conclusion: Our results showed that aphid performance was reduced due to the induced plant defense mediated by Cucumber mosaic virus titer. Additionally, some wingless aphids became to winged aphids. In this way CMV could be transmitted with the migration of winged aphids. We should take measures to prevent aphids in the early stage of their occurrence in the field to prevent virus outbreak