The operational efficiency of China's economic energy sector from the perspective of marginal analysis

The article presents the trends in the development of China's energy construction and analyzes the reasons for significant technological advances in this field of activity. Based on marginal analysis, the efficiency and sustainability of the functioning of China's energy field is shown

China’s financial innovation in response to evergrande’s bankruptcy

At certain specific times, financial institutions will adopt new processes and establish new channels to solve problems. Evergrande Group filed for bankruptcy protection in New York, USA. For the Chinese financial market, this is exactly the moment that requires financial innovation. In this situation, the relevant departments of the Chinese government adopted the method of issuing special bills by wholly-owned state-owned enterprises based on the principle of "not breaking down the trust of banks and not breaking down insurance licenses" to basically eliminate systemic financial risks

The operational efficiency of China's economic energy sector from the perspective of marginal analysis

The article presents the trends in the development of China's energy construction and analyzes the reasons for significant technological advances in this field of activity. Based on marginal analysis, the efficiency and sustainability of the functioning of China's energy field is shown

Higher-Order Orthogonal Causal Learning for Treatment Effect

Most existing studies on the double/debiased machine learning method concentrate on the causal parameter estimation recovering from the first-order orthogonal score function. In this paper, we will construct the $k^{\mathrm{th}}$-order orthogonal score function for estimating the average treatment effect (ATE) and present an algorithm that enables us to obtain the debiased estimator recovered from the score function. Such a higher-order orthogonal estimator is more robust to the misspecification of the propensity score than the first-order one does. Besides, it has the merit of being applicable with many machine learning methodologies such as Lasso, Random Forests, Neural Nets, etc. We also undergo comprehensive experiments to test the power of the estimator we construct from the score function using both the simulated datasets and the real datasets

The Causal Learning of Retail Delinquency

This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects and hence the estimation error can be magnificent. As such, we propose another approach to construct the estimators such that the error can be greatly reduced. The proposed estimators are shown to be unbiased, consistent, and robust through a combination of theoretical analysis and numerical testing. Moreover, we compare the power of estimating the causal quantities between the classical estimators and the proposed estimators. The comparison is tested across a wide range of models, including linear regression models, tree-based models, and neural network-based models, under different simulated datasets that exhibit different levels of causality, different degrees of nonlinearity, and different distributional properties. Most importantly, we apply our approaches to a large observational dataset provided by a global technology firm that operates in both the e-commerce and the lending business. We find that the relative reduction of estimation error is strikingly substantial if the causal effects are accounted for correctly.Comment: This paper was accepted and will be published in the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21

FDG PET-CT demonstration of metastatic neuroendocrine tumor of prostate

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: FDG PET-CT is generally not suitable for diagnosing prostate cancer because of low glycolysis of the tumor cells. Neuroendocrine differentiation of the prostate cancer is often associated with early visceral metastasis and dismal prognosis, which is resulted from changed metabolic and regulatory pathways. Case presentation: A case is reported in this paper that FDG PET-CT demonstrates intense uptake of neuroendocrine tumor of the prostate and multiple metastases. Conclusion: There is high glycolysis and strong FDG-avidity of neuroendocrine tumor of the prostate, which is similar to that of high grade of neuroendocrine tumor in other tissue and organs. In some selected cases of prostate neuroendocrine cancer, whole body FDG PET-CT may be helpful for detection of metastatic disease. Background Positron emission tomography (PET) is a new imaging modality which has been widely used for detection o

CE-BLAST makes it possible to compute antigenic similarity for newly emerging pathogens

Major challenges in vaccine development include rapidly selecting or designing immunogens for raising cross-protective immunity against different intra-or inter-subtypic pathogens, especially for the newly emerging varieties. Here we propose a computational method, Conformational Epitope (CE)-BLAST, for calculating the antigenic similarity among different pathogens with stable and high performance, which is independent of the prior binding-assay information, unlike the currently available models that heavily rely on the historical experimental data. Tool validation incorporates influenza-related experimental data sufficient for stability and reliability determination. Application to dengue-related data demonstrates high harmonization between the computed clusters and the experimental serological data, undetectable by classical grouping. CE-BLAST identifies the potential cross-reactive epitope between the recent zika pathogen and the dengue virus, precisely corroborated by experimental data. The high performance of the pathogens without the experimental binding data suggests the potential utility of CE-BLAST to rapidly design cross-protective vaccines or promptly determine the efficacy of the currently marketed vaccine against emerging pathogens, which are the critical factors for containing emerging disease outbreaks.Peer reviewe