Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance

The prospect of detecting single-photon force effects in cavity optomechanics

Cavity optomechanical systems are approaching a strong-coupling regime where the coherent dynamics of nanomechanical resonators can be manipulated and controlled by optical fields at the single photon level. Here we propose an interferometric scheme able to detect optomechanical coherent interaction at the single-photon level which is experimentally feasible with state-of-the-art devices.Comment: 8 pages, 2 figure

Magnetic properties of a disordered Heisenberg binary spin system with long-range exchange

The influence of substitutional disorder on the magnetic properties of disordered Heisenberg binary spin systems with long-range exchange integrals is studied. The equation of motion for the magnon Green's function which is decoupled by the Tyablikov approximation is solved in the Blackman-Esterling-Berk(BEB) coherent potential approximation(CPA) framework, where the environmental disorder term is treated by virtual crystal approximation. The long-range exchange integrals include a power-law decaying and an oscillating Ruderman-Kittel-Kasuya-Yosida(RKKY) exchange interaction. The resulting spectral density, which is calculated by CPA self-consistent equation, is then used to estimate the magnetization and Curie temperature. The results show, in the case of the three-dimensional simple cubic systems, a strong influence of ferromagnetic long-range exchange integrals on the magnetization and Curie temperature of the systems, which is obviously different from the calculation of short-range interaction.Comment: 5 pages, 3 figures, to be accepted by PR

Magnetotransport and magnetocrystalline anisotropy in Ga1-xMnxAs epilayers

We present an analysis of the magnetic anisotropy in epitaxial Ga1-xMnxAs thin films through electrical transport measurements on multiterminal microdevices. The film magnetization is manipulated in 3D space by a three-axis vector magnet. Anomalous switching patterns are observed in both longitudinal and transverse resistance data. In transverse geometry in particular we observe strong interplay between the anomalous Hall effect and the giant planar Hall effect. This allows direct electrical characterization of magnetic transitions in the 3D space. These transitions reflect a competition between cubic magnetic anisotropy and an effective out-of-plane uniaxial anisotropy, with a reversal mechanism that is distinct from the in-plane magnetization. The uniaxial anisotropy field is directly calculated with high precision and compared with theoretical predictions

Data Unfolding with Wiener-SVD Method

Data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.Comment: 26 pages, 12 figures, match the accepted version by JINS