Long-term changes and variability of monthly extreme temperatures in India

Extreme temperatures are changing worldwide together with changes in the mean temperatures. This study investigates the long-term trends and variations of the monthly maximum and minimum temperatures and their effects on seasonal fluctuations in various climatological regions in India. The magnitude of the trends and their statistical significance were determined by parametric ordinary least square regression techniques and the variations were determined by the respective coefficient of variations. The results showed that the monthly maximum temperature increased, though unevenly, over the last century. Minimum temperature changes were more variable than maximum temperature changes, both temporally and spatially, with results of lesser significance. The results of this study are good indicators of Indian climate variability and its changes over the last century. © Springer-Verlag 2009.

Electronic and magnetic properties of Ti(2)O(3), Cr(2)O(3), and Fe(2)O(3) calculated by the screened exchange hybrid density functional.

The electronic and magnetic properties of the transition metal sesqui-oxides Cr(2)O(3), Ti(2)O(3), and Fe(2)O(3) have been calculated using the screened exchange (sX) hybrid density functional. This functional is found to give a band structure, bandgap, and magnetic moment in better agreement with experiment than the local density approximation (LDA) or the LDA+U methods. Ti(2)O(3) is found to be a spin-paired insulator with a bandgap of 0.22 eV in the Ti d orbitals. Cr(2)O(3) in its anti-ferromagnetic phase is an intermediate charge transfer Mott-Hubbard insulator with an indirect bandgap of 3.31 eV. Fe(2)O(3), with anti-ferromagnetic order, is found to be a wide bandgap charge transfer semiconductor with a 2.41 eV gap. Interestingly sX outperforms the HSE functional for the bandgaps of these oxides.

Dense active appearance models using a bounded diameter minimum spanning tree

We present a method for producing dense Active Appearance Models (AAMs), suitable for video-realistic synthesis. To this end we estimate a joint alignment of all training images using a set of pairwise registrations and ensure that these pairwise registrations are only calculated between similar images. This is achieved by defining a graph on the image set whose edge weights correspond to registration errors and computing a bounded diameter minimum spanning tree (BDMST). Dense optical flow is used to compute pairwise registration and we introduce a flow refinement method to align small scale texture. Once registration between training images has been established we propose a method to add vertices to the AAM in a way that minimises error between the observed flow fields and a flow field interpolated between the AAM mesh points. We demonstrate a significant improvement in model compactness using the proposed method and show it dealing with cases that are problematic for current state-of-the-art approaches.