867 research outputs found
Mixing hetero- and homogeneous models in weighted ensembles
The effectiveness of ensembling for improving classification performance is well documented. Broadly speaking, ensemble design can be expressed as a spectrum where at one end a set of heterogeneous classifiers model the same data, and at the other homogeneous models derived from the same classification algorithm are diversified through data manipulation. The cross-validation accuracy weighted probabilistic ensemble is a heterogeneous weighted ensemble scheme that needs reliable estimates of error from its base classifiers. It estimates error through a cross-validation process, and raises the estimates to a power to accentuate differences. We study the effects of maintaining all models trained during cross-validation on the final ensemble's predictive performance, and the base model's and resulting ensembles' variance and robustness across datasets and resamples. We find that augmenting the ensemble through the retention of all models trained provides a consistent and significant improvement, despite reductions in the reliability of the base models' performance estimates
Network Synchronization, Diffusion, and the Paradox of Heterogeneity
Many complex networks display strong heterogeneity in the degree
(connectivity) distribution. Heterogeneity in the degree distribution often
reduces the average distance between nodes but, paradoxically, may suppress
synchronization in networks of oscillators coupled symmetrically with uniform
coupling strength. Here we offer a solution to this apparent paradox. Our
analysis is partially based on the identification of a diffusive process
underlying the communication between oscillators and reveals a striking
relation between this process and the condition for the linear stability of the
synchronized states. We show that, for a given degree distribution, the maximum
synchronizability is achieved when the network of couplings is weighted and
directed, and the overall cost involved in the couplings is minimum. This
enhanced synchronizability is solely determined by the mean degree and does not
depend on the degree distribution and system size. Numerical verification of
the main results is provided for representative classes of small-world and
scale-free networks.Comment: Synchronization in Weighted Network
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Radiative absorption enhancements by black carbon controlled by particle-to-particle heterogeneity in composition.
Black carbon (BC) absorbs solar radiation, leading to a strong but uncertain warming effect on climate. A key challenge in modeling and quantifying BC's radiative effect on climate is predicting enhancements in light absorption that result from internal mixing between BC and other aerosol components. Modeling and laboratory studies show that BC, when mixed with other aerosol components, absorbs more strongly than pure, uncoated BC; however, some ambient observations suggest more variable and weaker absorption enhancement. We show that the lower-than-expected enhancements in ambient measurements result from a combination of two factors. First, the often used spherical, concentric core-shell approximation generally overestimates the absorption by BC. Second, and more importantly, inadequate consideration of heterogeneity in particle-to-particle composition engenders substantial overestimation in absorption by the total particle population, with greater heterogeneity associated with larger model-measurement differences. We show that accounting for these two effects-variability in per-particle composition and deviations from the core-shell approximation-reconciles absorption enhancement predictions with laboratory and field observations and resolves the apparent discrepancy. Furthermore, our consistent model framework provides a path forward for improving predictions of BC's radiative effect on climate
Radiative absorption enhancements by black carbon controlled by particle-to-particle heterogeneity in composition
Black carbon (BC) absorbs solar radiation, leading to a strong but uncertain warming effect on climate. A key challenge in modeling and quantifying BC’s radiative effect on climate is predicting enhancements in light absorption that result from internal mixing between BC and other aerosol components. Modeling and laboratory studies show that BC, when mixed with other aerosol components, absorbs more strongly than pure, uncoated BC; however, some ambient observations suggest more variable and weaker absorption enhancement. We show that the lower-than-expected enhancements in ambient measurements result from a combination of two factors. First, the often used spherical, concentric core-shell approximation generally overestimates the absorption by BC. Second, and more importantly, inadequate consideration of heterogeneity in particle-to-particle composition engenders substantial overestimation in absorption by the total particle population, with greater heterogeneity associated with larger model–measurement differences. We show that accounting for these two effects—variability in per-particle composition and deviations from the core-shell approximation—reconciles absorption enhancement predictions with laboratory and field observations and resolves the apparent discrepancy. Furthermore, our consistent model framework provides a path forward for improving predictions of BC’s radiative effect on climate
Enhancing neural-network performance via assortativity
The performance of attractor neural networks has been shown to depend
crucially on the heterogeneity of the underlying topology. We take this
analysis a step further by examining the effect of degree-degree correlations
-- or assortativity -- on neural-network behavior. We make use of a method
recently put forward for studying correlated networks and dynamics thereon,
both analytically and computationally, which is independent of how the topology
may have evolved. We show how the robustness to noise is greatly enhanced in
assortative (positively correlated) neural networks, especially if it is the
hub neurons that store the information.Comment: 9 pages, 7 figure
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