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Abstract To ensure privacy protection and alleviate computational burden, we propose a fast subsmaling procedure for the Cox model with massive survival datasets from multi-centered, decentralized sources. The proposed estimator is computed based on optimal subsampling probabilities that we derived and enables transmission of subsample-based summary level statistics between different storage sites with only one round of communication. For inference, the asymptotic properties of the proposed estimator were rigorously established. An extensive simulation study demonstrated that the proposed approach is effective. The methodology was applied to analyze a large dataset from the U.S. airlines. 

The evolving focus in statistics and data science education highlights the growing importance of computing. This paper presents the Data Jamboree, a live event that combines computational methods with traditional statistical techniques to address real-world data science problems. Participants, ranging from novices to experienced users, followed workshop leaders in using open-source tools like Julia, Python, and R to perform tasks such as data cleaning, manipulation, and predictive modeling. The Jamboree showcased the educational benefits of working with open data, providing participants with practical, hands-on experience. We compared the tools in terms of efficiency, flexibility, and statistical power, with Julia excelling in performance, Python in versatility, and R in statistical analysis and visualization. The paper concludes with recommendations for designing similar events to encourage collaborative learning and critical thinking in data science. 

Abstract The coloration mechanism of Oregon sunstone is a classic and controversial topic in mineralogy because of the unique coexistence of anisotropic (green-red) and isotropic (red) color zones within single feldspar crystals. After nearly 50 years of research, no models proposed to date have satisfactorily accounted for all observed optical phenomena. Here, we present high-resolution transmission electron microscopy analyses of samples prepared by focused ion beam extraction along specific crystal directions. In both the anisotropic and the isotropic color zones, we observed Cu nanoparticles (NPs) included within plagioclase but with different geometries. In the isotropic (red) zone, NPs were randomly distributed nano-spheres or nano-ellipsoids (8.7–12 nm in diameter) with an aspect ratio of 1–1.3. In contrast, in dichroic (green/red) zones, NPs were directionally aligned nano-rods (8.5–21 nm along the long axis) with an aspect ratio of ∼2.5. We applied localized surface plasmon resonance (LSPR) theory to simulate absorption spectra and developed a model to explain the observed optical properties. LA-ICP-MS and polarized UV-Vis spectroscopy were also performed to confirm our conclusions. This study systematically reveals the existence and optical influence of variably shaped metal-NP inclusions in feldspar crystals. Furthermore, it demonstrates the necessity of including LSPR in the canon of mineral coloration mechanisms. Cu-NP-bearing labradorite has been shown to exhibit third-order nonlinear optical properties, and approaches that incorporate NP shapes and sizes will assist in designing NP-embedded optical materials with tailored optical properties. 

Subsampling is a practical strategy for analyzing vast survival data, which are progressively encountered across diverse research domains. While the optimal subsampling method has been applied to inferences for Cox models and parametric accelerated failure time (AFT) models, its application to semi‐parametric AFT models with rank‐based estimation have received limited attention. The challenges arise from the non‐smooth estimating function for regression coefficients and the seemingly zero contribution from censored observations in estimating functions in the commonly seen form. To address these challenges, we develop optimal subsampling probabilities for both event and censored observations by expressing the estimating functions through a well‐defined stochastic process. Meanwhile, we apply an induced smoothing procedure to the non‐smooth estimating functions. As the optimal subsampling probabilities depend on the unknown regression coefficients, we employ a two‐step procedure to obtain a feasible estimation method. An additional benefit of the method is its ability to resolve the issue of underestimation of the variance when the subsample size approaches the full sample size. We validate the performance of our estimators through a simulation study and apply the methods to analyze the survival time of lymphoma patients in the surveillance, epidemiology, and end results program.