Quantum optical effective-medium theory for loss-compensated metamaterials

A central aim in metamaterial research is to engineer sub-wavelength unit cells that give rise to desired effective-medium properties and parameters, such as a negative refractive index. Ideally one can disregard the details of the unit cell and employ the effective description instead. A popular strategy to compensate for the inevitable losses in metallic components of metamaterials is to add optical gain material. Here we study the quantum optics of such loss-compensated metamaterials at frequencies for which effective parameters can be unambiguously determined. We demonstrate that the usual effective parameters are insufficient to describe the propagation of quantum states of light. Furthermore, we propose a quantum-optical effective-medium theory instead and show that it correctly predicts the properties of the light emerging from loss-compensated metamaterials.Comment: 6 pages, 3 figures. Accepted for Physical Review Letter

FIRST RECORD OF DISEPYRIS KIEFFER (HYMENOPTERA: BETHYLIDAE) FROM IRAN

Disepyris Kieffer, a small genus of Bethylidae (Hymenoptera), is recorded for the first time from Iran, represented by D. niveus Lim & Azevedo. Members of this genus can be easily recognized by the protarsi of females with delicate long spines and forewing with short 2r-rs&Rs vein. The diagnostic characters of species are briefly presented according to the newly collected specimens with reference to the respective illustrations

Comparisons of atmospheric data and reduction methods for the analysis of satellite gravimetry observations

[1] The Gravity Recovery and Climate Experiment (GRACE) derived gravity solutions contain errors mostly due to instrument noise, anisotropic spatial sampling, and temporal aliasing. Improving the quality of satellite gravimetry observations, in terms of using more sensitive sensors and/or increasing the spatial isotropy, has been discussed in the context of the designed scenarios of future satellite gravimetry missions. Temporal aliasing caused by incomplete reducing of background models, however, is still a factor that affects the quality of the gravity field solutions. This paper specifically explores the possible physical, geometrical, and numerical modifications of the three‒dimensional (3‒D) integration approach to eliminate the high‒frequency atmospheric effects from satellite gravimetry observations. The new modified 3‒D approach is then applied to compute new sets of atmospheric dealiasing products, using atmospheric fields from the European Centre for Medium‒Range Weather Forecasts (ECMWF) operational analysis model and ERA‒Interim reanalysis. Impacts of modifications are compared to the prelaunch baseline and the current error‒curve of GRACE as well as an error‒curve of a Bender‒type multiorbit satellite configuration. Specifically, we found that using latitude‒dependent radius, latitude‒ and altitude‒dependent gravity accelerations along with numerical modifications have a considerable impact on the 3‒D integral. Comparing the new products to those of GRACE Atmosphere and Ocean Dealiasing level‒1B shows a nonnegligible difference with respect to the prelaunch baseline of GRACE and a possible Bender‒type mission up to harmonic degrees 13 and 50, respectively. A big difference is also found between the derived dealiasing products from ECMWF operational analysis and ERA‒Interim indicating the importance of input parameters on the final atmospheric dealiasing products

Wavelet Filter Banks for Super-Resolution SAR Imaging

This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem

Comparisons of atmospheric mass variations derived from ECMWF reanalysis and operational fields, over 2003 to 2011

There are two spurious jumps in the atmospheric part of the Gravity Recovery and Climate Experiment-Atmosphere and Ocean De-aliasing level 1B (GRACE-AOD1B) products, which occurred in January-February of the years 2006 and 2010, as a result of the vertical level and horizontal resolution changes in the ECMWFop (European Centre for Medium-Range Weather Forecasts operational analysis). These jumps cause a systematic error in the estimation of mass changes from GRACE time-variable level 2 products, since GRACE-AOD1B mass variations are removed during the computation of GRACE level 2. In this short note, the potential impact of using an improved set of 6-hourly atmospheric de-aliasing products on the computations of linear trends as well as the amplitude of annual and semi-annual mass changes from GRACE is assessed. These improvements result from 1) employing a modified 3D integration approach (ITG3D), and 2) using long-term consistent atmospheric fields from the ECMWF reanalysis (ERA-Interim). The monthly averages of the new ITG3D-ERA-Interim de-aliasing products are then compared to the atmospheric part of GRACE-AOD1B, covering January 2003 to December 2010. These comparisons include the 33 world largest river basins along with Greenland and Antarctica ice sheets. The results indicate a considerable difference in total atmospheric mass derived from the two products over some of the mentioned regions. We suggest that future GRACE studies consider these through updating uncertainty budgets or by applying corrections to estimated trends and amplitudes/phases

Optimal partial-arcs in VMAT treatment planning

Purpose: To improve the delivery efficiency of VMAT by extending the recently published VMAT treatment planning algorithm vmerge to automatically generate optimal partial-arc plans. Methods and materials: A high-quality initial plan is created by solving a convex multicriteria optimization problem using 180 equi-spaced beams. This initial plan is used to form a set of dose constraints, and a set of partial-arc plans is created by searching the space of all possible partial-arc plans that satisfy these constraints. For each partial-arc, an iterative fluence map merging and sequencing algorithm (vmerge) is used to improve the delivery efficiency. Merging continues as long as the dose quality is maintained above a user-defined threshold. The final plan is selected as the partial arc with the lowest treatment time. The complete algorithm is called pmerge. Results: Partial-arc plans are created using pmerge for a lung, liver and prostate case, with final treatment times of 127, 245 and 147 seconds. Treatment times using full arcs with vmerge are 211, 357 and 178 seconds. Dose quality is maintained across the initial, vmerge, and pmerge plans to within 5% of the mean doses to the critical organs-at-risk and with target coverage above 98%. Additionally, we find that the angular distribution of fluence in the initial plans is predictive of the start and end angles of the optimal partial-arc. Conclusions: The pmerge algorithm is an extension to vmerge that automatically finds the partial-arc plan that minimizes the treatment time. VMAT delivery efficiency can be improved by employing partial-arcs without compromising dose quality. Partial arcs are most applicable to cases with non-centralized targets, where the time savings is greatest

Learning to Generate Natural Language Rationales for Game Playing Agents

Many computer games feature non-player charactert (NPC) teammates and companions; however, playing with or against NPCs can be frustrating when they perform unexpectedly. These frustrations can be avoided if the NPC has the ability to explain its actions and motivations. When NPC behavior is controlled by a black box AI system it can be hard to generate the necessary explanations. In this paper, we present a system that generates human-like, natural language explanations—called rationales—of an agent\u27s actions in a game environment regardless of how the decisions are made by a black box AI. We outline a robust data collection and neural network training pipeline that can be used to gather think-aloud data and train a rationale generation model for any similar sequential turn based decision making task. A human-subject study shows that our technique produces believable rationales for an agent playing the game, Frogger. We conclude with insights about how people perceive automatically generated rationales