Default Prediction of Internet Finance Users Based on Imbalance-XGBoost

Fast and accurate identification of financial fraud is a challenge in Internet finance. Based on the characteristics of imbalanced distribution of Internet financial data, this paper integrates machine learning methods and Internet financial data to propose a prediction model for loan defaults, and proves its effectiveness and generalizability through empirical research. In this paper, we introduce a processing method (link processing method) for imbalance data based on the traditional early warning model. In this paper, we conduct experiments using the financial dataset of Lending Club platform and prove that our model is superior to XGBoost, NGBoost, Ada Boost, and GBDT in the prediction of default risk

tSF: Transformer-based Semantic Filter for Few-Shot Learning

Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples. Most feature embedding modules in recent FSL methods are specially designed for corresponding learning tasks (e.g., classification, segmentation, and object detection), which limits the utility of embedding features. To this end, we propose a light and universal module named transformer-based Semantic Filter (tSF), which can be applied for different FSL tasks. The proposed tSF redesigns the inputs of a transformer-based structure by a semantic filter, which not only embeds the knowledge from whole base set to novel set but also filters semantic features for target category. Furthermore, the parameters of tSF is equal to half of a standard transformer block (less than 1M). In the experiments, our tSF is able to boost the performances in different classic few-shot learning tasks (about 2% improvement), especially outperforms the state-of-the-arts on multiple benchmark datasets in few-shot classification task

High MB solution degradation efficiency of FeSiBZr amorphous ribbon with surface tunnels

© 2020 by the authors. The as spun amorphous (Fe78Si9B13)99.5Zr0.5 (Zr0.5) and (Fe78Si9B13)99Zr1 (Zr1) ribbons having a Fenton-like reaction are proved to bear a good degradation performance in organic dye wastewater treatment for the first time by evaluating their degradation efficiency in methylene blue (MB) solution. Compared to the widely studied (Fe78Si9B13)100Zr0 (Zr0) amorphous ribbon for degradation, with increasing cZr (Zr atomic content), the as-spun Zr0, Zr0.5 and Zr1 amorphous ribbons have gradually increased degradation rate of MB solution. According to δc (characteristic distance) of as-spun Zr0, Zr0.5 and Zr1 ribbons, the free volume in Zr1 ribbon is higher Zr0 and Zr0.5 ribbons. In the reaction process, the Zr1 ribbon surface formed the 3D nano-porous structure with specific surface area higher than the cotton floc structure formed by Zr0 ribbon and coarse porous structure formed by Zr0.5 ribbon. The Zr1 ribbon\u27s high free volume and high specific surface area make its degradation rate of MB solution higher than that of Zr0 and Zr0.5 ribbons. This work not only provides a new method to remedying the organic dyes wastewater with high efficiency and low-cost, but also improves an application prospect of Fe-based glassy alloys

Macrocyclic colibactin induces DNA double-strand breaks via copper-mediated oxidative cleavage.

Colibactin is an assumed human gut bacterial genotoxin, whose biosynthesis is linked to the clb genomic island that has a widespread distribution in pathogenic and commensal human enterobacteria. Colibactin-producing gut microbes promote colon tumour formation and enhance the progression of colorectal cancer via cellular senescence and death induced by DNA double-strand breaks (DSBs); however, the chemical basis that contributes to the pathogenesis at the molecular level has not been fully characterized. Here, we report the discovery of colibactin-645, a macrocyclic colibactin metabolite that recapitulates the previously assumed genotoxicity and cytotoxicity. Colibactin-645 shows strong DNA DSB activity in vitro and in human cell cultures via a unique copper-mediated oxidative mechanism. We also delineate a complete biosynthetic model for colibactin-645, which highlights a unique fate of the aminomalonate-building monomer in forming the C-terminal 5-hydroxy-4-oxazolecarboxylic acid moiety through the activities of both the polyketide synthase ClbO and the amidase ClbL. This work thus provides a molecular basis for colibactin's DNA DSB activity and facilitates further mechanistic study of colibactin-related colorectal cancer incidence and prevention

Default Prediction of Internet Finance Users Based on Imbalance-XGBoost

Fast and accurate identification of financial fraud is a challenge in Internet finance. Based on the characteristics of imbalanced distribution of Internet financial data, this paper integrates machine learning methods and Internet financial data to propose a prediction model for loan defaults, and proves its effectiveness and generalizability through empirical research. In this paper, we introduce a processing method (link processing method) for imbalance data based on the traditional early warning model. In this paper, we conduct experiments using the financial dataset of Lending Club platform and prove that our model is superior to XGBoost, NGBoost, Ada Boost, and GBDT in the prediction of default risk

Wavelet theory and its applications in economics and finance

Wavelets orthogonally decompose data into different frequency components, and the temporal and frequency information of the data could be studied simultaneously. This analysis belongs within local nature analysis. Wavelets are therefore useful for managing time-varying characteristics found in most real-world time series and are an ideal tool for studying non-stationary or transient time series while avoiding the assumption of stationarity. Given the promising properties of wavelets, this thesis thoroughly discusses wavelet theory and adds three new applications of wavelets in economic and financial fields, providing new insights into three interesting phenomena. The second chapter introduces wavelet theory in detail and presents a thorough survey of the economic and financial applications of wavelets. In the third chapter, wavelets are applied in time series to extract business cycles or trend. They are useful for capturing the changing volatility of business cycles. The extracted business cycles and trend are linearly independent. We provide detailed comparisons with four alternative filters, including two of each detrending filters and bandpass filters. The result shows that wavelets are a good alternative filter for extracting business cycles or trend based on multiresolution wavelet analysis. The fourth chapter distinguishes contagion and interdependence. To achieve this purpose, we define contagion as a significant increase in short-run market commovement after a shock to one market. Following the application of wavelets to 27 global representative markets’ daily stock-return data series from 1996.1 to 1997.12, a multivariate GARCH model and a Granger-causality methodology are used on the results of wavelets to generate short-run pair-wise contemporaneous correlations and lead-lag relationships, respectively, both of which are involved in short-run relationships. The empirical evidence reveals no significant increase in interdependence during the financial crisis; contagion is just an illusion of interdependence. In addition, the evidence explains the phenomenon in which major negative events in global markets began to occur one month after the outbreak of the crisis. The view that contagion is regional is not supported. The fifth chapter studies how macroeconomic news announcements affect the U.S. stock market and how market participants’ responses to announcements vary over the business cycle. The arrival of scheduled macroeconomic announcements in the U.S. stock market leads to a two-stage adjustment process for prices and trading transactions. In a short first stage, the release of a news announcement induces a sharp and nearly instantaneous price change along with a rise in trading transactions. In a prolonged second stage, it causes significant and persistent increases in price volatility and trading transactions within about an hour. After allowing for different stages of the business cycle, we demonstrate that the release of a news announcement induces larger immediate price changes per interval in the expansion period, but more immediate price changes per interval in the contraction period, from the old equilibrium to the approximate new equilibrium. It costs smaller subsequent adjustments of stock prices along with a lower number of trading transactions across a shorter time in the contraction period, when the information contained in the news announcement is incorporated fully in stock prices. We use a static analysis to investigate the immediate effects of news announcements, as measured by the surprise in the news, on prices, and adopt a wavelet analysis to examine their eventual effects on prices. The evidence shows that only 6 out of 17 announcements have a significant immediate impact, but all announcements have an eventual impact over different time periods. The combination of the results of both analyses gives us the time-profile of each news announcement’s impact on stock prices, and shows that the impact is significant within about an hour, but is exhausted after a day

3D Volumetric Modeling with Introspective Neural Networks

In this paper, we study the 3D volumetric modeling problem by adopting the Wasserstein introspective neural networks method (WINN) that was previously applied to 2D static images. We name our algorithm 3DWINN which enjoys the same properties as WINN in the 2D case: being simultaneously generative and discriminative. Compared to the existing 3D volumetric modeling approaches, 3DWINN demonstrates competitive results on several benchmarks in both the generation and the classification tasks. In addition to the standard inception score, the Frechet Inception Distance (FID) metric is´ also adopted to measure the quality of 3D volumetric generations. In addition, we study adversarial attacks for volumetric data and demonstrate the robustness of 3DWINN against adversarial examples while achieving appealing results in both classification and generation within a single model. 3DWINN is a general framework and it can be applied to the emerging tasks for 3D object and scene modeling.