15,201 research outputs found
A Secure and Fair Resource Sharing Model for Community Clouds
Cloud computing has gained a lot of importance and has been one of the most discussed segment of today\u27s IT industry. As enterprises explore the idea of using clouds, concerns have emerged related to cloud security and standardization. This thesis explores whether the Community Cloud Deployment Model can provide solutions to some of the concerns associated with cloud computing. A secure framework based on trust negotiations for resource sharing within the community is developed as a means to provide standardization and security while building trust during resource sharing within the community. Additionally, a model for fair sharing of resources is developed which makes the resource availability and usage transparent to the community so that members can make informed decisions about their own resource requirements based on the resource usage and availability within the community. Furthermore, the fair-share model discusses methods that can be employed to address situations when the demand for a resource is higher than the resource availability in the resource pool. Various methods that include reduction in the requested amount of resource, early release of the resources and taxing members have been studied, Based on comparisons of these methods along with the advantages and disadvantages of each model outlined, a hybrid method that only taxes members for unused resources is developed. All these methods have been studied through simulations
Descartes' rule of signs and the identifiability of population demographic models from genomic variation data
The sample frequency spectrum (SFS) is a widely-used summary statistic of
genomic variation in a sample of homologous DNA sequences. It provides a highly
efficient dimensional reduction of large-scale population genomic data and its
mathematical dependence on the underlying population demography is well
understood, thus enabling the development of efficient inference algorithms.
However, it has been recently shown that very different population demographies
can actually generate the same SFS for arbitrarily large sample sizes. Although
in principle this nonidentifiability issue poses a thorny challenge to
statistical inference, the population size functions involved in the
counterexamples are arguably not so biologically realistic. Here, we revisit
this problem and examine the identifiability of demographic models under the
restriction that the population sizes are piecewise-defined where each piece
belongs to some family of biologically-motivated functions. Under this
assumption, we prove that the expected SFS of a sample uniquely determines the
underlying demographic model, provided that the sample is sufficiently large.
We obtain a general bound on the sample size sufficient for identifiability;
the bound depends on the number of pieces in the demographic model and also on
the type of population size function in each piece. In the cases of
piecewise-constant, piecewise-exponential and piecewise-generalized-exponential
models, which are often assumed in population genomic inferences, we provide
explicit formulas for the bounds as simple functions of the number of pieces.
Lastly, we obtain analogous results for the "folded" SFS, which is often used
when there is ambiguity as to which allelic type is ancestral. Our results are
proved using a generalization of Descartes' rule of signs for polynomials to
the Laplace transform of piecewise continuous functions.Comment: Published in at http://dx.doi.org/10.1214/14-AOS1264 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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Estimation of energy and material use of sintering-based construction for a lunar outpost - with the example of SinterHab module design
In this paper, we would revisit the usability of microwave for lunar regolith sintering through an in-depth experiment, and examine the minimum materials and energy required for sintering based on the SinterHab design. This will include the minimum layers to print, estimated printing time, minimum energy required for the sintering process and the potential energy sources
A novel spectral method for inferring general diploid selection from time series genetic data
The increased availability of time series genetic variation data from
experimental evolution studies and ancient DNA samples has created new
opportunities to identify genomic regions under selective pressure and to
estimate their associated fitness parameters. However, it is a challenging
problem to compute the likelihood of nonneutral models for the population
allele frequency dynamics, given the observed temporal DNA data. Here, we
develop a novel spectral algorithm to analytically and efficiently integrate
over all possible frequency trajectories between consecutive time points. This
advance circumvents the limitations of existing methods which require
fine-tuning the discretization of the population allele frequency space when
numerically approximating requisite integrals. Furthermore, our method is
flexible enough to handle general diploid models of selection where the
heterozygote and homozygote fitness parameters can take any values, while
previous methods focused on only a few restricted models of selection. We
demonstrate the utility of our method on simulated data and also apply it to
analyze ancient DNA data from genetic loci associated with coat coloration in
horses. In contrast to previous studies, our exploration of the full fitness
parameter space reveals that a heterozygote advantage form of balancing
selection may have been acting on these loci.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS764 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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