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  1. A small area typically refers to a subpopulation or domain of interest for which a reliable direct estimate, based only on the domain-specific sample, cannot be produced due to small sample size in the domain. While traditional small area methods and models are widely used nowadays, there have also been much work and interest in robust statistical inference for small area estimation (SAE). We survey this work and provide a comprehensive review here.We begin with a brief review of the traditional SAE methods. We then discuss SAEmethods that are developed under weaker assumptions and SAE methods that are robust inmore »certain ways, such as in terms of outliers or model failure. Our discussion also includes topics such as nonparametric SAE methods, Bayesian approaches, model selection and diagnostics, and missing data. A brief review of software packages available for implementing robust SAE methods is also given.« less
  2. Abstract Amongst the rare-earth perovskite nickelates, LaNiO 3 (LNO) is an exception. While the former have insulating and antiferromagnetic ground states, LNO remains metallic and non-magnetic down to the lowest temperatures. It is believed that LNO is a strange metal, on the verge of an antiferromagnetic instability. Our work suggests that LNO is a quantum critical metal, close to an antiferromagnetic quantum critical point (QCP). The QCP behavior in LNO is manifested in epitaxial thin films with unprecedented high purities. We find that the temperature and magnetic field dependences of the resistivity of LNO at low temperatures are consistent withmore »scatterings of charge carriers from weak disorder and quantum fluctuations of an antiferromagnetic nature. Furthermore, we find that the introduction of a small concentration of magnetic impurities qualitatively changes the magnetotransport properties of LNO, resembling that found in some heavy-fermion Kondo lattice systems in the vicinity of an antiferromagnetic QCP.« less
  3. In this paper, we present an approach that detects the level of food in store-bought containers using deep convolutional neural networks (CNNs) trained on RGB images captured using an off-the-shelf camera. Our approach addresses three challenges—the diversity in container geometry, the large variations in shapes and appearances of labels on store-bought containers, and the variability in color of container contents—by augmenting the data used to train the CNNs using printed labels with synthetic textures attached to the training bottles, interchanging the contents of the bottles of the training containers, and randomly altering the intensities of blocks of pixels in themore »labels and at the bottle borders. Our approach provides an average level detection accuracy of 92.4% using leave-one-out cross-validation on 10 store-bought bottles of varying geometries, label appearances, label shapes, and content colors.« less