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LiV2O4 is a mixed-valent spinel oxide and one of a few transition-metal compounds to host a heavy fermion phase at low temperatures. Although numerous experimental studies have attempted to elucidate how its 3d electrons undergo giant mass renormalization, spectroscopic probes that may provide crucial hints, such as scanning tunneling microscopy (STM), remain to be applied. A prerequisite is atomically flat and pristine surfaces, which, in the case of LiV2O4, are difficult to obtain by the cleavage of small, three-dimensional crystals. We report the epitaxial growth of LiV2O4 thin films with bulklike properties on SrTiO3(111) via pulsed laser deposition and stable STM imaging of the LiV2O4(111) surface. The as-grown films were transferred ex situ to a room-temperature STM, where subsequent annealing with optional sputtering in ultrahigh vacuum enabled compact islands with smooth surfaces and a hexagonal 1 × 1 atomic lattice to be resolved. Our STM measurements provide insights into the growth mechanisms of LiV2O4 on SrTiO3(111) as well as demonstrate the feasibility of performing surface-sensitive measurements of this heavy fermion compound. [ABSTRACT FROM AUTHOR]

Attrition or dropout is the most severe missingness problem in longitudinal cohort study data where some participants do not show up for follow‐up examinations. Dropouts result in biased data and cause the reduction of 1ata set size. Moreover, they limit the power of statistical analysis and the validity of study findings. Visualization can play a strong role in analysing and displaying the missingness patterns. In this work, we present VIVID, a framework for the visual analysis of missing values in cohort study data. VIVID is inspired by discussions with epidemiologists and adds visual components to their current statistics‐based approaches. VIVID provides functions for exploration, imputation and validity check of imputations. The main focus of this paper is multiple imputation to fix the missing data. [ABSTRACT FROM AUTHOR]

A prerequisite of personalized medicine is the identification of groups of people who share specific risk factors towards an outcome. We investigate the potential of subspace clustering for finding such groups in epidemiological data. We propose a workflow that encompasses clusterability assessment before cluster discovery and quality assessment after learning the clusters. Epidemiological usually do not have a ground truth for the verification of clusters found in subspaces. Hence, we introduce quality assessment through juxtaposition of the learned models to “models-of-randomness”, i.e. models that do not reflect a true cluster structure. On the basis of this workflow, we select subspace clustering methods, compare and discuss their performance. We use a dataset with hepatic steatosis as outcome, but our findings apply on arbitrary epidemiological cohort data that have tenths of variables and exhibit class skew.