Thermodynamics of a Higher Order Phase Transition: Scaling Exponents and Scaling Laws

The well known scaling laws relating critical exponents in a second order phase transition have been generalized to the case of an arbitrarily higher order phase transition. In a higher order transition, such as one suggested for the superconducting transition in Ba$_{0.6}$K$_{0.4}$BiO$_3$ and in Bi$_2$Sr$_2$CaCu$_2$O$_8$, there are singularities in higher order derivatives of the free energy. A relation between exponents of different observables has been found, regardless of whether the exponents are classical (mean-field theory, no fluctuations, integer order of a transition) or not (fluctuation effects included). We also comment on the phase transition in a thin film.Comment: 10 pages, no figure

‘Retournement’ of the aedeagus in Curculionidae (Coleoptera, Curculionoidea)

Retournement or turning of the aedeagus about its longitudinal axis through about 180o during development is known in Chrysomeloidea (Coleoptera). This change in the orientation of the organ may be observed during the postembryonic development. This change produces certain morphological effects. By observing these morphological features in the imago the retournement may be inferred. Such morphological features in Curculionidae (Coleoptera) are here recorded. From this it has been inferred not only that retournement of the aedeagus is included in the ontogeny of curculionids, but also that the change of orientation of the organ occurs by the same mechanism as in Chrysomeloidea. These inferences attest the notion of a close phyletic relationship between the superfamilies Curculionoidea and Chrysomeloidea

Exploring Convolutional Networks for End-to-End Visual Servoing

Present image based visual servoing approaches rely on extracting hand crafted visual features from an image. Choosing the right set of features is important as it directly affects the performance of any approach. Motivated by recent breakthroughs in performance of data driven methods on recognition and localization tasks, we aim to learn visual feature representations suitable for servoing tasks in unstructured and unknown environments. In this paper, we present an end-to-end learning based approach for visual servoing in diverse scenes where the knowledge of camera parameters and scene geometry is not available a priori. This is achieved by training a convolutional neural network over color images with synchronised camera poses. Through experiments performed in simulation and on a quadrotor, we demonstrate the efficacy and robustness of our approach for a wide range of camera poses in both indoor as well as outdoor environments.Comment: IEEE ICRA 201