Bayesian Example Selection Using BaBiES

Active learning is widely used to select which examples from a pool should be labeled to give best results when learning predictive models. It is, however, sometimes desirable to choose examples before any labeling or machine learning has occurred. The optimal experimental design literature has many theoretically attractive optimality criteria for example selection, but most are intractable when working with large numbers of predictive features. We present the BaBiES criterion, an approximation of Bayesian A-optimal design for linear regression using binary predictors, which is both simple and extremely fast. Empirical evaluations demonstrate that, in spite of selecting all examples prior to learning, BaBiES is competitive with standard active learning methods for a variety of document classification tasks

Military spending and economic growth in China: a regime-switching analysis

This article has been made available through the Brunel Open Access Publishing Fund.This article investigates the impact of military spending changes on economic growth in China over the period 1953 to 2010. Using two-state Markov-switching specifications, the results suggest that the relationship between military spending changes and economic growth is state dependent. Specifically, the results show that military spending changes affect the economic growth negatively during a slower growth-higher variance state, while positively within a faster growth-lower variance one. It is also demonstrated that military spending changes contain information about the growth transition probabilities. As a policy tool, the results indicate that increases in military spending can be detrimental to growth during slower growth-higher growth volatility periods. © 2014 © 2014 The Author(s). Published by Taylor & Francis

Transport properties of single channel quantum wires with an impurity: Influence of finite length and temperature on average current and noise

The inhomogeneous Tomonaga Luttinger liquid model describing an interacting quantum wire adiabatically coupled to non-interacting leads is analyzed in the presence of a weak impurity within the wire. Due to strong electronic correlations in the wire, the effects of impurity backscattering, finite bias, finite temperature, and finite length lead to characteristic non-monotonic parameter dependencies of the average current. We discuss oscillations of the non-linear current voltage characteristics that arise due to reflections of plasmon modes at the impurity and quasi Andreev reflections at the contacts, and show how these oscillations are washed out by decoherence at finite temperature. Furthermore, the finite frequency current noise is investigated in detail. We find that the effective charge extracted in the shot noise regime in the weak backscattering limit decisively depends on the noise frequency $\omega$ relative to $v_F/gL$, where $v_F$ is the Fermi velocity, $g$ the Tomonaga Luttinger interaction parameter, and $L$ the length of the wire. The interplay of finite bias, finite temperature, and finite length yields rich structure in the noise spectrum which crucially depends on the electron-electron interaction. In particular, the excess noise, defined as the change of the noise due to the applied voltage, can become negative and is non-vanishing even for noise frequencies larger than the applied voltage, which are signatures of correlation effects.Comment: 28 pages, 19 figures, published version with minor change

The tunnel magnetoresistance in chains of quantum dots weakly coupled to external leads

We analyze numerically the spin-dependent transport through coherent chains of three coupled quantum dots weakly connected to external magnetic leads. In particular, using the diagrammatic technique on the Keldysh contour, we calculate the conductance, shot noise and tunnel magnetoresistance (TMR) in the sequential and cotunneling regimes. We show that transport characteristics greatly depend on the strength of the interdot Coulomb correlations, which determines the spacial distribution of electron wave function in the chain. When the correlations are relatively strong, depending on the transport regime, we find both negative TMR as well as TMR enhanced above the Julliere value, accompanied with negative differential conductance (NDC) and super-Poissonian shot noise. This nontrivial behavior of tunnel magnetoresistance is associated with selection rules that govern tunneling processes and various high-spin states of the chain that are relevant for transport. For weak interdot correlations, on the other hand, the TMR is always positive and not larger than the Julliere TMR, although super-Poissonian shot noise and NDC can still be observed

Effect of Palmitic Acid on the Electrical Conductivity of Carbon Nanotubes−Epoxy Resin Composites

We found that the palmitic acid allows an efficient dispersion of carbon nanotubes in the epoxy matrix. We have set up an experimental protocol in order to enhance the CNTs dispersion in epoxy resin. Electrical conductivity is optimal using a 1:1 CNTs to palmitic acid weight ratio. The associated percolation threshold is found between 0.05 and 0.1 wt % CNTs, i.e., between 0.03 and 0.06 vol %. The SEM image shows essentially individual CNTs which is inagreement with conductivity measurements. In comparison with composites without palmitic acid, the use of palmitic acid improves the electrical properties of CNTs-epoxy resin composites