North Carolina State University

About: North Carolina State University is a education organization based out in Raleigh, North Carolina, United States. It is known for research contribution in the topics: Population & Thin film. The organization has 44161 authors who have published 101744 publications receiving 3456774 citations. The organization is also known as: NCSU & North Carolina State University at Raleigh.

PRESS: PRedictive Elastic ReSource Scaling for cloud systems

Abstract: Cloud systems require elastic resource allocation to minimize resource provisioning costs while meeting service level objectives (SLOs). In this paper, we present a novel PRedictive Elastic reSource Scaling (PRESS) scheme for cloud systems. PRESS unobtrusively extracts fine-grained dynamic patterns in application resource demands and adjust their resource allocations automatically. Our approach leverages light-weight signal processing and statistical learning algorithms to achieve online predictions of dynamic application resource requirements. We have implemented the PRESS system on Xen and tested it using RUBiS and an application load trace from Google. Our experiments show that we can achieve good resource prediction accuracy with less than 5% over-estimation error and near zero under-estimation error, and elastic resource scaling can both significantly reduce resource waste and SLO violations.

Geminiviruses: masters at redirecting and reprogramming plant processes

Abstract: The family Geminiviridae is one of the largest and most important families of plant viruses. The small, single-stranded DNA genomes of geminiviruses encode 5-7 proteins that redirect host machineries and processes to establish a productive infection. These interactions reprogramme plant cell cycle and transcriptional controls, inhibit cell death pathways, interfere with cell signalling and protein turnover, and suppress defence pathways. This Review describes our current knowledge of how geminiviruses interact with their plant hosts and the functional consequences of these interactions.

Pollen nutritional content and digestibility for animals

Abstract: This paper reviews the literature concerning digestion and nutrient content of pollen. Four topics are addressed in detail: 1) The mechanism of pollen digestion by animals; 2) The efficiency of mechanical and digestive removal of pollen content by various animals; 3) Range and taxonomic distribution of pollen nutrients, and 4) Adaptive hypotheses proposed to associate pollen chemistry with pollinator reward. Studies on the mechanism(s) of pollen digestion remain inconclusive, but suggest that differences in digestibility among pollen types may reflect differences in pollen wall porosity, thickness, and composition. Although hummingbirds reportedly digest pollen very poorly, most animals studied, including those that do not regularly consume pollen, can digest 50–100% of ingested grains. Overlooked and recent research of pollen protein content shows that pollen grains may contain over 60% protein, double the amount cited in some studies of pollen-feeding animals. Adaptive hypotheses that associate pollen starch and pollen caloric content with pollinator reward remain unsubstantiated when critically viewed through the lens of phylogeny.

Best practices for justifying fossil calibrations

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Component selection and smoothing in multivariate nonparametric regression

Abstract: We propose a new method for model selection and model fitting in multivariate nonparametric regression models, in the framework of smoothing spline ANOVA. The "COSSO" is a method of regularization with the penalty functional being the sum of component norms, instead of the squared norm employed in the traditional smoothing spline method. The COSSO provides a unified framework for several recent proposals for model selection in linear models and smoothing spline ANOVA models. Theoretical properties, such as the existence and the rate of convergence of the COSSO estimator, are studied. In the special case of a tensor product design with periodic functions, a detailed analysis reveals that the COSSO does model selection by applying a novel soft thresholding type operation to the function components. We give an equivalent formulation of the COSSO estimator which leads naturally to an iterative algorithm. We compare the COSSO with MARS, a popular method that builds functional ANOVA models, in simulations and real examples. The COSSO method can be extended to classification problems and we compare its performance with those of a number of machine learning algorithms on real datasets. The COSSO gives very competitive performance in these studies.