A universality theorem for ratios of random characteristic polynomials

We consider asymptotics of ratios of random characteristic polynomials associated with orthogonal polynomial ensembles. Under some natural conditions on the measure in the definition of the orthogonal polynomial ensemble we establish a universality limit for these ratios.     

Early Abortion and Personal Ontology

We are beings endowed with “personal capacities”—the capacity for reason, for a concept of self, perhaps more. Among ontologically salient views about what else we are, I focus on the “Big Three.” According to animalism, we are animals that have psychological properties only contingently. According to psychologistic materialism, we are material beings; according to substance dualism, we are either immaterial beings or composites of immaterial and material ones; but according to both psychologistic materialism and substance dualism, we essentially have some psychological properties. I argue that—contrary to what has been argued and is natural to think—none of the Big Three yields different assignments of moral status to early fetuses from any of the others, and consequently the moral status of early abortion doesn’t depend on which (if any) of these views of personal ontology is correct.     

Thermal Conductivity of Ionic Liquids: Recent Challenges Facing Theory and Experiment

Journal of Solution Chemistry - Recently ionic liquids have been considered as prospective substances for application as heat transfer fluids, which requires accurate knowledge of their thermal...     

Syntheses of chiral fused 4,5-diazafluorene–bis(nopinane) derivatives

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Hierarchical structure of SERS substrates possessing the silver ring morphology

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Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions

Distance weighted discrimination (DWD) is an appealing classification method that is capable of overcoming data piling problems in high-dimensional settings. Especially when various sparsity structures are assumed in these settings, variable selection in multicategory classification poses great challenges. In this paper, we propose a multicategory generalized DWD (MgDWD) method that maintains intrinsic variable group structures during selection using a sparse group lasso penalty. Theoretically, we derive minimizer uniqueness for the penalized MgDWD loss function and consistency properties for the proposed classifier. We further develop an efficient algorithm based on the proximal operator to solve the optimization problem. The performance of MgDWD is evaluated using finite sample simulations and miRNA data from an HIV study.