Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization

Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge is to precisely capture and sufficiently transfer characteristic cartoon styles (e.g., clear edges, smooth color shading, abstract fine structures, etc.). Existing advanced models try to enhance cartoonization effect by learning to promote edges adversarially, introducing style transfer loss, or learning to align style from multiple representation space. This paper demonstrates that more distinct and vivid cartoonization effect could be easily achieved with only basic adversarial loss. Observing that cartoon style is more evident in cartoon-texture-salient local image regions, we build a region-level adversarial learning branch in parallel with the normal image-level one, which constrains adversarial learning on cartoon-texture-salient local patches for better perceiving and transferring cartoon texture features. To this end, a novel cartoon-texture-saliency-sampler (CTSS) module is proposed to dynamically sample cartoon-texture-salient patches from training data. With extensive experiments, we demonstrate that texture saliency adaptive attention in adversarial learning, as a missing ingredient of related methods in image cartoonization, is of significant importance in facilitating and enhancing image cartoon stylization, especially for high-resolution input pictures.Comment: Proceedings of the 39th International Conference on Machine Learning, PMLR 162:7183-7207, 202

The Value of Alternative Data in Credit Risk Prediction: Evidence from a Large Field Experiment

Recently, the high penetration of mobile devices and internet access offers a new source of fine-grained user behavior data (aka “alternative data”) to improve the financial credit risk assessment. This paper conducts a comprehensive evaluation of the value of alternative data on microloan platforms with a large field experiment. Our machine-learning-based empirical analyses demonstrate that alternative data can significantly improve the prediction accuracy of borrowers’ default behavior and increase platform profits. Cellphone usage and mobility trace information perform the best among the multiple sources of alternative data. Moreover, we find that our proposed framework helps financial institutions extend their service to more lower-income and less-educated loan applicants from less-developed geographical areas – those historically disadvantaged population who have been largely neglected in the past. Our study demonstrates the tremendous potential of leveraging alternative data to alleviate such inequality in the financial service markets, while in the meantime achieving higher platform revenues

Modeling Mg II h, k and Triplet Lines at Solar Flare Ribbons

Observations from the \textit{Interface Region Imaging Spectrograph} (\textsl{IRIS}) often reveal significantly broadened and non-reversed profiles of the Mg II h, k and triplet lines at flare ribbons. To understand the formation of these optically thick Mg II lines, we perform plane parallel radiative hydrodynamics modeling with the RADYN code, and then recalculate the Mg II line profiles from RADYN atmosphere snapshots using the radiative transfer code RH. We find that the current RH code significantly underestimates the Mg II h \& k Stark widths. By implementing semi-classical perturbation approximation results of quadratic Stark broadening from the STARK-B database in the RH code, the Stark broadenings are found to be one order of magnitude larger than those calculated from the current RH code. However, the improved Stark widths are still too small, and another factor of 30 has to be multiplied to reproduce the significantly broadened lines and adjacent continuum seen in observations. Non-thermal electrons, magnetic fields, three-dimensional effects or electron density effect may account for this factor. Without modifying the RADYN atmosphere, we have also reproduced non-reversed Mg II h \& k profiles, which appear when the electron beam energy flux is decreasing. These profiles are formed at an electron density of $\sim 8\times10^{14}\ \mathrm{cm}^{-3}$ and a temperature of $\sim1.4\times10^4$ K, where the source function slightly deviates from the Planck function. Our investigation also demonstrates that at flare ribbons the triplet lines are formed in the upper chromosphere, close to the formation heights of the h \& k lines

Inferring Economic Condition Uncertainty from Electricity Big Data

Inferring the uncertainties in economic conditions are of significant importance for both decision makers as well as market players. In this paper, we propose a novel method based on Hidden Markov Model (HMM) to construct the Economic Condition Uncertainty (ECU) index that can be used to infer the economic condition uncertainties. The ECU index is a dimensionless index ranges between zero and one, this makes it to be comparable among sectors, regions and periods. We use the daily electricity consumption data of nearly 20 thousand firms in Shanghai from 2018 to 2020 to construct the ECU indexes. Results show that all ECU indexes, no matter at sectoral level or regional level, successfully captured the negative impacts of COVID-19 on Shanghai's economic conditions. Besides, the ECU indexes also presented the heterogeneities in different districts as well as in different sectors. This reflects the facts that changes in uncertainties of economic conditions are mainly related to regional economic structures and targeted regulation policies faced by sectors. The ECU index can also be easily extended to measure uncertainties of economic conditions in different fields which has great potentials in the future

Brake or Step On the Gas? Empirical Analyses of Credit Effects on Individual Consumption

Understanding the effects of credit on consumption is crucial for guiding users’ consumption behavior, designing financial marketing strategies, and identifying credit\u27s value in stimulating the economy. Whereas several studies have endeavored on this issue, most simply utilize observations of a single credit channel and/or focus on an overall effect without considering the potentially heterogeneous short-term and long-term consumption changes. This study, leveraging a quasi-experimental design with high-resolution transaction data, examines how people respond to credit in both short- and long-term periods. Results show that credit users’ consumption amount significantly expand by 51.74% after getting access to credit in the short term. However, they ultimately cut their consumption by 4.02% to cope with financial constraints in the long term. We also reveal and quantify the spillover effects of credit on consumption with savings channels. We draw on regulatory focus theory to rationalize the changes on consumers’ consumption behavior after credit activation

Insights from Niche Markets: Explainable and Predictive Values of Consumption Tendency on Credit Risks

The rapid development of FinTech drives the growing popularity of digital payment transactions. This phenomenon, especially given the increasing number of offline and online transactions being recorded in a real-time manner, offers great opportunities for financial service platforms to track consumers’ consumption tendencies and dynamically monitor and evaluate their creditworthiness. In our recent research, we first theorized the value of category-level consumption tendency based on the self-regulatory theory and employed econometric methods to empirically test the relationship between category-level consumption tendency and credit behavior. Then, we proposed a Deep Hierarchical Partial Attention-based Model (DHPAM) to predict credit default risk with full employment of product category features. We provided strong experimental evidence to show that the proposed DHPAM outperforms the state-of-the-art machine learning models. This paper, based on theories, empirical analyses, and a prediction model, offers comprehensive and practical guidance on the optimal utilization of consumption information in credit risk management