Generation of High-Energy Photons with Large Orbital Angular Momentum by Compton Backscattering

Usually, photons are described by plane waves with a definite 4-momentum. In addition to plane-wave photons, "twisted photons" have recently entered the field of modern laser optics; these are coherent superpositions of plane waves with a defined projection hbar*m of the orbital angular momentum onto the propagation axis, where m is integer. In this paper, we show that it is possible to produce high-energy twisted photons by Compton backscattering of twisted laser photons off ultra-relativistic electrons. Such photons may be of interest for experiments related to the excitation and disintegration of atoms and nuclei, and for studying the photo-effect and pair production off nuclei in previously unexplored experimental regimes.Comment: 4 pages; RevTe

Ca2+-induced fusion of Golgi-derived secretory vesicles isolated from rat liver

During the transport of plasma proteins from the cytoplasma of hepatocytes to the extracellular fluid srnall vesicles may act as shuttles between the Golgi complex and the plasma membrane. This type of intracellular transfer is weil established for various secretory cells and may be adopted also for the hepatocyte. Recent investigations have shown that secretory vesicles fuse with each other during secretion in mast cells [4] exocrine [5,6] and endocrine pancreatic tissue [7]. The intervesicular fusion provides a tool for studies on membrane fusion, since Golgi-derived vesicles can be isolated from the hepatocyte and their interaction with various agents, suggested to trigger membrane fusion, can be monitored by freeze-cleaving

Neural networks in geophysical applications

Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications. However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size and architecture

Learning Robot Control using a Hierarchical SOM-based Encoding

Hierarchical representations and modeling of sensorimotor observations is a fundamental approach for the development of scalable robot control strategies. Previously, we introduced the novel Hierarchical Self-Organizing Map-based Encoding algorithm (HSOME) that is based on a computational model of infant cognition. Each layer is a temporally augmented SOM and every node updates a decaying activation value. The bottom level encodes sensori-motor instances while their temporal associations are hierarchically built on the layers above. In the past, HSOME has shown to support hierarchical encoding of sequential sensor-actuator observations both in abstract domains and real humanoid robots. Two novel features are presented here starting with the novel skill acquisition in the complex domain of learning a double tap tactile gesture between two humanoid robots. During reproduction, the robot can either perform a double tap or prioritize to receive a higher reward by performing a single tap instead. Secondly, HSOME has been extended to recall past observations and reproduce rhythmic patterns in the absence of input relevant to the joints by priming initially the reproduction of specific skills with an input. We also demonstrate in simulation how a complex behavior emerges from the automatic reuse of distinct oscillatory swimming demonstrations of a robotic salamander