Electronic, mechanical, and thermodynamic properties of americium dioxide

By performing density functional theory (DFT) +$U$ calculations, we systematically study the electronic, mechanical, tensile, and thermodynamic properties of AmO$_{2}$. The experimentally observed antiferromagnetic insulating feature [J. Chem. Phys. 63, 3174 (1975)] is successfully reproduced. It is found that the chemical bonding character in AmO$_{2}$ is similar to that in PuO$_{2}$, with smaller charge transfer and stronger covalent interactions between americium and oxygen atoms. The valence band maximum and conduction band minimum are contributed by 2$p-5f$ hybridized and 5$f$ electronic states respectively. The elastic constants and various moduli are calculated, which show that AmO$_{2}$ is less stable against shear forces than PuO$_{2}$. The stress-strain relationship of AmO$_{2}$ is examined along the three low-index directions by employing the first-principles computational tensile test method. It is found that similar to PuO$_{2}$, the [100] and [111] directions are the strongest and weakest tensile directions, respectively, but the theoretical tensile strengths of AmO$_{2}$ are smaller than those of PuO$_{2}$. The phonon dispersion curves of AmO$_{2}$ are calculated and the heat capacities as well as lattice expansion curve are subsequently determined. The lattice thermal conductance of AmO$_{2}$ is further evaluated and compared with attainable experiments. Our present work integrally reveals various physical properties of AmO$_{2}$ and can be referenced for technological applications of AmO$_{2}$ based materials.Comment: 23 pages, 8 figure

Exploring Unified Perspective For Fast Shapley Value Estimation

Shapley values have emerged as a widely accepted and trustworthy tool, grounded in theoretical axioms, for addressing challenges posed by black-box models like deep neural networks. However, computing Shapley values encounters exponential complexity in the number of features. Various approaches, including ApproSemivalue, KernelSHAP, and FastSHAP, have been explored to expedite the computation. We analyze the consistency of existing works and conclude that stochastic estimators can be unified as the linear transformation of importance sampling of feature subsets. Based on this, we investigate the possibility of designing simple amortized estimators and propose a straightforward and efficient one, SimSHAP, by eliminating redundant techniques. Extensive experiments conducted on tabular and image datasets validate the effectiveness of our SimSHAP, which significantly accelerates the computation of accurate Shapley values

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

Text Mining-Based Patent Analysis for Automated Rule Checking in AEC

Automated rule checking (ARC), which is expected to promote the efficiency of the compliance checking process in the architecture, engineering, and construction (AEC) industry, is gaining increasing attention. Throwing light on the ARC application hotspots and forecasting its trends are useful to the related research and drive innovations. Therefore, this study takes the patents from the database of the Derwent Innovations Index database (DII) and China national knowledge infrastructure (CNKI) as data sources and then carried out a three-step analysis including (1) quantitative characteristics (i.e., annual distribution analysis) of patents, (2) identification of ARC topics using a latent Dirichlet allocation (LDA) and, (3) SNA-based co-occurrence analysis of ARC topics. The results show that the research hotspots and trends of Chinese and English patents are different. The contributions of this study have three aspects: (1) an approach to a comprehensive analysis of patents by integrating multiple text mining methods (i.e., SNA and LDA) is introduced ; (2) the application hotspots and development trends of ARC are reviewed based on patent analysis; and (3) a signpost for technological development and innovation of ARC is provided