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The CCGG cooperative air sampling network effort began in 1967 at Niwot Ridge, Colorado. Today, the network is an international effort which includes regular discrete samples from the NOAA ESRL/GML baseline observatories, cooperative fixed sites, and commercial ships. Air samples are collected approximately weekly from a globally distributed network of sites. Samples are analyzed for Carbon Dioxide (CO2), Methane (CH4), Carbon Monoxide (CO), Hydrogen Gas (H2), Nitrous Oxide (N2O), and Sulfur Hexafluoride (SF6); and by INSTAAR for the stable isotopes of CO2 and CH4 and for many volatile organic compounds (VOC) such as ethane (C2H6), ethylene (C2H4) and propane (C3H8). Measurement data are used to identify long-term trends, seasonal variability, and spatial distribution of carbon cycle gases. 

Extensive literature has been proposed for the analysis of correlated survival data. Subjects within a cluster share some common characteristics, e.g., genetic and environmental factors, so their time-to-event outcomes are correlated. The frailty model under proportional hazards assumption has been widely applied for the analysis of clustered survival outcomes. However, the prediction performance of this method can be less satisfactory when the risk factors have complicated effects, e.g., nonlinear and interactive. To deal with these issues, we propose a neural network frailty Cox model that replaces the linear risk function with the output of a feed-forward neural network. The estimation is based on quasi-likelihood using Laplace approximation. A simulation study suggests that the proposed method has the best performance compared with existing methods. The method is applied to the clustered time-to-failure prediction within the kidney transplantation facility using the national kidney transplant registry data from the U.S. Organ Procurement and Transplantation Network. All computer programs are available at https://github.com/rivenzhou/deep_learning_clustered. 

Abstract East Asian inks are a major component of calligraphy, paintings, and prints in China, Japan, and Korea and are historically made from either pine soot or oil‐lamp soot mixed with a proteinaceous binder. Although the inks from the two different soot sources have different properties in East Asian works of art, no non‐destructive methods to differentiate them scientifically currently exist. Raman spectroscopy (RS) of carbonaceous materials is commonly used to extract information about their properties and has been applied here to East Asian inks. Soots used in making modern inks were collected from 10 sources in China and Japan and analyzed using RS. RS using 405‐, 633‐, and 785‐nm excitation has been able to differentiate pine soot from oil‐lamp soot, also called lampblack. In addition, principal component analysis (PCA) of only 785‐nm Raman spectra has been able to discriminate between two different soots used in a 19th‐century Japanese woodblock printing ofKaishien Gaden. In addition to allowing discrimination between inks on East Asian works of art, these results may be of use to other fields using carbonaceous materials. 

There has been growing research interest in developing methodology to evaluate healthcare centers' performance with respect to patient outcomes. Conventional assessments can be conducted using fixed or random effects models, as seen in provider profiling. We propose a new method, using fusion penalty to cluster healthcare centers with respect to a survival outcome. Without any prior knowledge of the grouping information, the new method provides a desirable data‐driven approach for automatically clustering healthcare centers into distinct groups based on their performance. An efficient alternating direction method of multipliers algorithm is developed to implement the proposed method. The validity of our approach is demonstrated through simulation studies, and its practical application is illustrated by analyzing data from the national kidney transplant registry. 

A beam splitter for phonons completes the toolbox required to develop a mechanically based quantum computing system.