Upper bound solution for pullout capacity of vertical anchors in sand using finite elements and limit analysis

The horizontal pullout capacity of vertical anchors embedded in sand has been determined by using an upper bound theorem of the limit analysis in combination with finite elements. The numerical results are presented in nondimensional form to determine the pullout resistance for various combinations of embedment ratio of the anchor (H/B), internal friction angle (ϕ) of sand, and the anchor-soil interface friction angle (δ). The pullout resistance increases with increases in the values of embedment ratio, friction angle of sand and anchor-soil interface friction angle. As compared to earlier reported solutions in literature, the present solution provides a better upper bound on the ultimate collapse load.

Generative Maximum Entropy Learning for Multiclass Classification

Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative in nature. In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature. To tackle the curse of dimensionality of large data sets, we employ conditional independence assumption (Naive Bayes) and we perform feature selection simultaneously, by enforcing a `maximum discrimination' between estimated class conditional densities. For two class problems, in the proposed method, we use Jeffreys (J) divergence to discriminate the class conditional densities. To extend our method to the multi-class case, we propose a completely new approach by considering a multi-distribution divergence: we replace Jeffreys divergence by Jensen-Shannon (JS) divergence to discriminate conditional densities of multiple classes. In order to reduce computational complexity, we employ a modified Jensen-Shannon divergence (JS(GM)), based on AM-GM inequality. We show that the resulting divergence is a natural generalization of Jeffreys divergence to a multiple distributions case. As far as the theoretical justifications are concerned we show that when one intends to select the best features in a generative maximum entropy approach, maximum discrimination using J-divergence emerges naturally in binary classification. Performance and comparative study of the proposed algorithms have been demonstrated on large dimensional text and gene expression datasets that show our methods scale up very well with large dimensional datasets.

Electronic band Structure and Photoemission Spectra of Graphene on Silicon Substrate

Synergizing graphene on silicon based nanostructures is pivotal in advancing nano-electronic device technology. A combination of molecular dynamics and density functional theory has been used to predict the electronic energy band structure and photo-emission spectrum for graphene-Si system with silicon as a substrate for graphene. The equilibrium geometry of the system after energy minimization is obtained from molecular dynamics simulations. For the stable geometry obtained, density functional theory calculations are employed to determine the energy band structure and dielectric constant of the system. Further the work function of the system which is a direct consequence of photoemission spectrum is calculated from the energy band structure using random phase approximations.