Discovery of Water Maser Emission in Five AGN and a Possible Correlation Between Water Maser and Nuclear 2-10 keV Luminosities

We report the discovery of water maser emission in five active galactic nuclei (AGN) with the 100-m Green Bank Telescope (GBT). The positions of the newly discovered masers, measured with the VLA, are consistent with the optical positions of the host nuclei to within 1 sigma (0.3 arcsec radio and 1.3 arcsec optical) and most likely mark the locations of the embedded central engines. The spectra of three sources, 2MASX J08362280+3327383, NGC 6264, and UGC 09618 NED02, display the characteristic spectral signature of emission from an edge-on accretion disk with maximum orbital velocity of ~700, ~800, and ~1300 km s^-1, respectively. We also present a GBT spectrum of a previously known source MRK 0034 and interpret the narrow Doppler components reported here as indirect evidence that the emission originates in an edge-on accretion disk with orbital velocity of ~500 km s^-1. We obtained a detection rate of 12 percent (5 out of 41) among Seyfert 2 and LINER systems with 10000 km s^-1 < v_sys < 15000 km s^-1. For the 30 nuclear water masers with available hard X-ray data, we report a possible relationship between unabsorbed X-ray luminosity (2-10 keV) and total isotropic water maser luminosity, L_{2-10} proportional to L_{H2O}^{0.5+-0.1}, consistent with the model proposed by Neufeld and Maloney in which X-ray irradiation and heating of molecular accretion disk gas by the central engine excites the maser emission.Comment: 16 pages, 5 tables, 3 figures, to appear in the November 10, 2006, v651n2 issue of the Astrophysical Journa

Change in hippocampal theta oscillation associated with multiple lever presses in a bimanual two-lever choice task for robot control in rats.

Hippocampal theta oscillations have been implicated in working memory and attentional process, which might be useful for the brain-machine interface (BMI). To further elucidate the properties of the hippocampal theta oscillations that can be used in BMI, we investigated hippocampal theta oscillations during a two-lever choice task. During the task body-restrained rats were trained with a food reward to move an e-puck robot towards them by pressing the correct lever, ipsilateral to the robot several times, using the ipsilateral forelimb. The robot carried food and moved along a semicircle track set in front of the rat. We demonstrated that the power of hippocampal theta oscillations gradually increased during a 6-s preparatory period before the start of multiple lever pressing, irrespective of whether the correct lever choice or forelimb side were used. In addition, there was a significant difference in the theta power after the first choice, between correct and incorrect trials. During the correct trials the theta power was highest during the first lever-releasing period, whereas in the incorrect trials it occurred during the second correct lever-pressing period. We also analyzed the hippocampal theta oscillations at the termination of multiple lever pressing during the correct trials. Irrespective of whether the correct forelimb side was used, the power of hippocampal theta oscillations gradually decreased with the termination of multiple lever pressing. The frequency of theta oscillation also demonstrated an increase and decrease, before and after multiple lever pressing, respectively. There was a transient increase in frequency after the first lever press during the incorrect trials, while no such increase was observed during the correct trials. These results suggested that hippocampal theta oscillations reflect some aspects of preparatory and cognitive neural activities during the robot controlling task, which could be used for BMI

Aggregate Selection in Evolutionary Robotics

Can the processes of natural evolution be mimicked to create robots or autonomous agents? This question embodies the most fundamental goals of evolutionary robotics (ER). ER is a field of research that explores the use of artificial evolution and evolutionary computing for learning of control in autonomous robots, and in autonomous agents in general. In a typical ER experiment, robots, or more precisely their control systems, are evolved to perform a given task in which they must interact dynamically with their environment. Controllers compete in the environment and are selected and propagated based on their ability (or fitness) to perform the desired task. A key component of this process is the manner in which the fitness of the evolving controllers is measured. In ER, fitness is measured by a fitness function or objective function. This function applies some given criteria to determine which robots or agents are better at performing the task for which they are being evolved. Fitness functions can introduce varying levels of a priori knowledge into evolving populations. Som

Post-Privatisation Corporate Performance in Poland. Evidence from Companies Privatized in 2008-2011

The study concerns the effects of Polish privatisation program conducted in the years 2008-2011. After drawing a broad picture of this process we investigate the performance of 59 privatised companies, and finally focus on a deeper analysis of three companies, which is the core part of our study. We test the hypotheses that privatisation increases a company's profitability, labour productivity, capital investment spending, plow-back ratio and leverage. In case studies, we additionally explore the effect of privatization on each company's value. The outcomes concerning the larger group of companies are partly ambiguous (with four hypotheses confirmed and four rejected). Profitability has been not visibly improved, although a number of positive initiatives and improvements in performance occurred. By contrast, the three case studies showed a significant improvement of profitability and all other performance indicators observed, as well as a considerable increase of company value. Our results show that privatisation works, though its full effects need time to occur

Neural Network based Guide Robot Navigation: An Evolutionary Approach

AbstractAbstract— Intelligent robot navigation in urban environments is still a challenge. In this paper we test if it is possible to train neural networks to control the robot to reach the target location in urban dynamic environments. The robot has to rely on GPS and compass sensor to navigate from the starting point to the goal location in an environment with moving obstacles. We compare the performance of three neural architectures in different environments settings. The results show that neural controller with separated hidden neurons has a fast response to sensory input. The performance of evolved neural controllers is also tested in real robot navigation. In addition to the neural network based navigation, the robot has also to switch between other navigation algorithms to reach the target location in the university campus