4,716 research outputs found
Efficiency Analysis of Simple Perturbed Pairwise Comparison Matrices
Efficiency, the basic concept of multi-objective optimization is investigated for the class of pairwise comparison matrices. A weight vector is called efficient if no alternative weight vector exists such that every pairwise ratio of the latterâs components is at least as close to the corresponding element of the pairwise comparison matrix as the one of the formerâs components is, and the latterâs approximation is strictly better in at least one position. A pairwise comparison matrix is called simple perturbed if it differs from a consistent pairwise comparison matrix in one element and its reciprocal. One of the classical weighting methods, the eigenvector method is analyzed. It is shown in the paper that the principal right eigenvector of a simple perturbed pairwise comparison matrix is efficient. An open problem is exposed: the search for a necessary and sufficient condition of that the principal right eigenvector is efficient
An M-estimator of spatial tail dependence
Tail dependence models for distributions attracted to a max-stable law are
fitted using observations above a high threshold. To cope with spatial,
high-dimensional data, a rank-based M-estimator is proposed relying on
bivariate margins only. A data-driven weight matrix is used to minimize the
asymptotic variance. Empirical process arguments show that the estimator is
consistent and asymptotically normal. Its finite-sample performance is assessed
in simulation experiments involving popular max-stable processes perturbed with
additive noise. An analysis of wind speed data from the Netherlands illustrates
the method.Comment: 25 pages; major revisio
Fitting stochastic predator-prey models using both population density and kill rate data
Most mechanistic predator-prey modelling has involved either parameterization
from process rate data or inverse modelling. Here, we take a median road: we
aim at identifying the potential benefits of combining datasets, when both
population growth and predation processes are viewed as stochastic. We fit a
discrete-time, stochastic predator-prey model of the Leslie type to simulated
time series of densities and kill rate data. Our model has both environmental
stochasticity in the growth rates and interaction stochasticity, i.e., a
stochastic functional response. We examine what the kill rate data brings to
the quality of the estimates, and whether estimation is possible (for various
time series lengths) solely with time series of population counts or biomass
data. Both Bayesian and frequentist estimation are performed, providing
multiple ways to check model identifiability. The Fisher Information Matrix
suggests that models with and without kill rate data are all identifiable,
although correlations remain between parameters that belong to the same
functional form. However, our results show that if the attractor is a fixed
point in the absence of stochasticity, identifying parameters in practice
requires kill rate data as a complement to the time series of population
densities, due to the relatively flat likelihood. Only noisy limit cycle
attractors can be identified directly from population count data (as in inverse
modelling), although even in this case, adding kill rate data - including in
small amounts - can make the estimates much more precise. Overall, we show that
under process stochasticity in interaction rates, interaction data might be
essential to obtain identifiable dynamical models for multiple species. These
results may extend to other biotic interactions than predation, for which
similar models combining interaction rates and population counts could be
developed
Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome
The biosynthetic capabilities of microbes underlie their growth and interactions, playing a prominent role in microbial community structure. For large, diverse microbial communities, prediction of these capabilities is limited by uncertainty about metabolic functions and environmental conditions. To address this challenge, we propose a probabilistic method, inspired by percolation theory, to computationally quantify how robustly a genome-derived metabolic network produces a given set of metabolites under an ensemble of variable environments. We used this method to compile an atlas of predicted biosynthetic capabilities for 97 metabolites across 456 human oral microbes. This atlas captures taxonomically-related trends in biomass composition, and makes it possible to estimate inter-microbial metabolic distances that correlate with microbial co-occurrences. We also found a distinct cluster of fastidious/uncultivated taxa, including several Saccharibacteria (TM7) species, characterized by their abundant metabolic deficiencies. By embracing uncertainty, our approach can be broadly applied to understanding metabolic interactions in complex microbial ecosystems.T32GM008764 - NIGMS NIH HHS; T32 GM008764 - NIGMS NIH HHS; R01 DE024468 - NIDCR NIH HHS; R01 GM121950 - NIGMS NIH HHS; DE-SC0012627 - Biological and Environmental Research; RGP0020/2016 - Human Frontier Science Program; NSFOCE-BSF 1635070 - National Science Foundation; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; R37DE016937 - NIDCR NIH HHS; R37 DE016937 - NIDCR NIH HHS; R01GM121950 - NIGMS NIH HHS; R01DE024468 - NIDCR NIH HHS; 1457695 - National Science FoundationPublished versio
Efficient Privacy Preserving Distributed Clustering Based on Secret Sharing
In this paper, we propose a privacy preserving distributed
clustering protocol for horizontally partitioned data based on a very efficient
homomorphic additive secret sharing scheme. The model we use
for the protocol is novel in the sense that it utilizes two non-colluding
third parties. We provide a brief security analysis of our protocol from
information theoretic point of view, which is a stronger security model.
We show communication and computation complexity analysis of our
protocol along with another protocol previously proposed for the same
problem. We also include experimental results for computation and communication
overhead of these two protocols. Our protocol not only outperforms
the others in execution time and communication overhead on
data holders, but also uses a more efficient model for many data mining
applications
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