Temperature dependence of optical spectral weights in quarter-filled ladder systems

The temperature dependence of the integrated optical conductivity I(T) reflects the changes of the kinetic energy as spin and charge correlations develop. It provides a unique way to explore experimentally the kinetic properties of strongly correlated systems. We calculated I(T) in the frame of a t-J-V model at quarter-filling for ladder systems, like NaV_2O_5, and show that the measured strong T dependence of I(T) for NaV_2O_5 can be explained by the destruction of short range antiferromagnetic correlations. Thus I(T) provides detailed information about super-exchange and magnetic energy scales.Comment: 4 pages, 5 figure

Theoretical study of the optical conductivity of α′−NaV2O5\alpha'-NaV_2O_5

Using finite temperature diagonalization techniques it is shown that the quarter-filled t-J-V model on a trellis lattice structure provides a quantitative explanation of the highly anisotropic optical conductivity of the $\alpha'-NaV_{2}O_{5}$ compound. The combined effects of the short-range Coulomb interaction and valence fluctuations of V-ions determine the main absorption and the fundamental gap. Inter-ladder hopping is necessary for the explanation of the anomalous in-gap absorption and generation of spectral weight at high energy. The role of valence fluctuations is explained in terms of the domain wall excitations of an anisotropic 2D Ising model in a transverse field close to criticality.Comment: 4 pages, 5 figure

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis

Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification.

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is neovascularisation, the growth of abnormal new vessels. This paper describes an automated method for the detection of new vessels in retinal images. Two vessel segmentation approaches are applied, using the standard line operator and a novel modified line operator. The latter is designed to reduce false responses to non-vessel edges. Both generated binary vessel maps hold vital information which must be processed separately. This is achieved with a dual classification system. Local morphology features are measured from each binary vessel map to produce two separate feature sets. Independent classification is performed for each feature set using a support vector machine (SVM) classifier. The system then combines these individual classification outcomes to produce a final decision. Sensitivity and specificity results using a dataset of 60 images are 0.862 and 0.944 respectively on a per patch basis and 1.00 and 0.90 respectively on a per image basis