Towards gravitationally assisted negative refraction of light by vacuum

Propagation of electromagnetic plane waves in some directions in gravitationally affected vacuum over limited ranges of spacetime can be such that the phase velocity vector casts a negative projection on the time-averaged Poynting vector. This conclusion suggests, inter alia, gravitationally assisted negative refraction by vacuum.Comment: 6 page

Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks

Exact calculation of electronic properties of molecules is a fundamental step for intelligent and rational compounds and materials design. The intrinsically graph-like and non-vectorial nature of molecular data generates a unique and challenging machine learning problem. In this paper we embrace a learning from scratch approach where the quantum mechanical electronic properties of molecules are predicted directly from the raw molecular geometry, similar to some recent works. But, unlike these previous endeavors, our study suggests a benefit from combining molecular geometry embedded in the Coulomb matrix with the atomic composition of molecules. Using the new combined features in a Bayesian regularized neural networks, our results improve well-known results from the literature on the QM7 dataset from a mean absolute error of 3.51 kcal/mol down to 3.0 kcal/mol.Comment: Under review ICANN 201

Developing Interventions for Children's Exercise (DICE): a pilot evaluation of school-based exercise interventions for primary school children aged 7 to 8 years.

BACKGROUND: Developing Interventions for Children's Exercise (DICE) is an initiative aimed at determining effective school-based exercise programs. To assess feasibility, we conducted a pilot study of exercise sessions which varied in duration and frequency. METHODS: Exercise interventions were delivered to Year 3 pupils (age 7-8 years; n = 73) in primary schools within Yorkshire, UK. Evaluations were conducted using focus group sessions, questionnaires and observations. RESULTS: The study revealed positive aspects of all interventions, including favorable effects on children's concentration during lessons and identified the value of incorporation of the DICE concept into curriculum lessons. Children appeared enthused and reported well-being and enjoyment. Areas requiring attention were the need for appropriate timetabling of sessions and ensuring the availability of space. CONCLUSION: The concept and sessions were well-accepted by teachers who confirmed their full support of any future implementation There appears to be potential for the encouragement and empowerment of teachers to support physical activity and healthy school environments, and to take an interest in the health of their pupils. Ultimately, these findings should assist in the design of successful exercise interventions in the school setting

Doubled CO2 Experiments With the Global Change Research Center Two-Dimensional Statistical Dynamical Climate Model

The zonally averaged response of the Global Change Research Center two-dimensional statistical dynamical climate model (GCRC 2-D SDCM) to a doubling of atmospheric carbon dioxide (350 parts per million by volume (ppmv) to 700 ppmv) is reported. The model solves the two-dimensional primitive equations in finite difference form (mass continuity, Newton\u27s second law, and the first law of thermodynamics) for the prognostic variables: zonal mean density, zonal mean zonal velocity, zonal mean meridional velocity, and zonal mean temperature on a grid that has 18 nodes in latitude and 9 vertical nodes (plus the surface). The equation of state, p=rhoRT, and an assumed hydrostatic atmosphere, Deltap=rhogDeltaz, are used to diagnostically calculate the zonal mean pressure and vertical velocity for each grid node, and the moisture balance equation is used to estimate the precipitation rate. The model includes seasonal variations in solar intensity, including the effects of eccentricity, and has observed land and ocean fractions set for each zone. Seasonally varying values of cloud amounts, relative humidity profiles, ozone, and sea ice are all prescribed in the model. Equator to pole ocean heat transport is simulated in the model by turbulent diffusion

RL-DOVS: Reinforcement Learning for Autonomous Robot Navigation in Dynamic Environments

Autonomous navigation in dynamic environments where people move unpredictably is an essential task for service robots in real-world populated scenarios. Recent works in reinforcement learning (RL) have been applied to autonomous vehicle driving and to navigation around pedestrians. In this paper, we present a novel planner (reinforcement learning dynamic object velocity space, RL-DOVS) based on an RL technique for dynamic environments. The method explicitly considers the robot kinodynamic constraints for selecting the actions in every control period. The main contribution of our work is to use an environment model where the dynamism is represented in the robocentric velocity space as input to the learning system. The use of this dynamic information speeds the training process with respect to other techniques that learn directly either from raw sensors (vision, lidar) or from basic information about obstacle location and kinematics. We propose two approaches using RL and dynamic obstacle velocity (DOVS), RL-DOVS-A, which automatically learns the actions having the maximum utility, and RL-DOVS-D, in which the actions are selected by a human driver. Simulation results and evaluation are presented using different numbers of active agents and static and moving passive agents with random motion directions and velocities in many different scenarios. The performance of the technique is compared with other state-of-the-art techniques for solving navigation problems in environments such as ours

Experimental Extraction of Secure Correlations from a Noisy Private State

We report experimental generation of a noisy entangled four-photon state that exhibits a separation between the secure key contents and distillable entanglement, a hallmark feature of the recently established quantum theory of private states. The privacy analysis, based on the full tomographic reconstruction of the prepared state, is utilized in a proof-of-principle key generation. The inferiority of distillation-based strategies to extract the key is exposed by an implementation of an entanglement distillation protocol for the produced state.Comment: 5 pages, 3 figures, final versio

Electronic structure of periodic curved surfaces -- topological band structure

Electronic band structure for electrons bound on periodic minimal surfaces is differential-geometrically formulated and numerically calculated. We focus on minimal surfaces because they are not only mathematically elegant (with the surface characterized completely in terms of "navels") but represent the topology of real systems such as zeolites and negative-curvature fullerene. The band structure turns out to be primarily determined by the topology of the surface, i.e., how the wavefunction interferes on a multiply-connected surface, so that the bands are little affected by the way in which we confine the electrons on the surface (thin-slab limit or zero thickness from the outset). Another curiosity is that different minimal surfaces connected by the Bonnet transformation (such as Schwarz's P- and D-surfaces) possess one-to-one correspondence in their band energies at Brillouin zone boundaries.Comment: 6 pages, 8 figures, eps files will be sent on request to [email protected]

Real-time information processing of environmental sensor network data using Bayesian Gaussian processes

In this article, we consider the problem faced by a sensor network operator who must infer, in real time, the value of some environmental parameter that is being monitored at discrete points in space and time by a sensor network. We describe a powerful and generic approach built upon an efficient multi-output Gaussian process that facilitates this information acquisition and processing. Our algorithm allows effective inference even with minimal domain knowledge, and we further introduce a formulation of Bayesian Monte Carlo to permit the principled management of the hyperparameters introduced by our flexible models. We demonstrate how our methods can be applied in cases where the data is delayed, intermittently missing, censored, and/or correlated. We validate our approach using data collected from three networks of weather sensors and show that it yields better inference performance than both conventional independent Gaussian processes and the Kalman filter. Finally, we show that our formalism efficiently reuses previous computations by following an online update procedure as new data sequentially arrives, and that this results in a four-fold increase in computational speed in the largest cases considered

Testing Error Correcting Codes by Multicanonical Sampling of Rare Events

The idea of rare event sampling is applied to the estimation of the performance of error-correcting codes. The essence of the idea is importance sampling of the pattern of noises in the channel by Multicanonical Monte Carlo, which enables efficient estimation of tails of the distribution of bit error rate. The idea is successfully tested with a convolutional code

A global information system for the conservation and sustainable use of Plant Genetic Resources for Food and Agriculture (PGRFA)

Poster presented at 2008 Annual Meeting of TD WG-Biodiversity Information Standards. Fremantle ( Australia), 19-24 Oct 200