Automated detection of galaxy-scale gravitational lenses in high resolution imaging data

Lens modeling is the key to successful and meaningful automated strong galaxy-scale gravitational lens detection. We have implemented a lens-modeling "robot" that treats every bright red galaxy (BRG) in a large imaging survey as a potential gravitational lens system. Using a simple model optimized for "typical" galaxy-scale lenses, we generate four assessments of model quality that are used in an automated classification. The robot infers the lens classification parameter H that a human would have assigned; the inference is performed using a probability distribution generated from a human-classified training set, including realistic simulated lenses and known false positives drawn from the HST/EGS survey. We compute the expected purity, completeness and rejection rate, and find that these can be optimized for a particular application by changing the prior probability distribution for H, equivalent to defining the robot's "character." Adopting a realistic prior based on the known abundance of lenses, we find that a lens sample may be generated that is ~100% pure, but only ~20% complete. This shortfall is due primarily to the over-simplicity of the lens model. With a more optimistic robot, ~90% completeness can be achieved while rejecting ~90% of the candidate objects. The remaining candidates must be classified by human inspectors. We are able to classify lens candidates by eye at a rate of a few seconds per system, suggesting that a future 1000 square degree imaging survey containing 10^7 BRGs, and some 10^4 lenses, could be successfully, and reproducibly, searched in a modest amount of time. [Abridged]Comment: 17 pages, 11 figures, submitted to Ap

Dynamical difference between the cD galaxy and the stellar diffuse component in simulated galaxy clusters

Member galaxies within galaxy clusters nowadays can be routinely identified in cosmological, hydrodynamical simulations using methods based on identifying self bound, locally over dense substructures. However, distinguishing the central galaxy from the stellar diffuse component within clusters is notoriously difficult, and in the center it is not even clear if two distinct stellar populations exist. Here, after subtracting all member galaxies, we use the velocity distribution of the remaining stars and detect two dynamically, well-distinct stellar components within simulated galaxy clusters. These differences in the dynamics can be used to apply an un-binding procedure which leads to a spatial separation of the two components into a cD and a diffuse stellar component (DSC). Applying our new algorithm to a cosmological, hydrodynamical simulation we find that -- in line with previous studies -- these two components have clearly distinguished spatial and velocity distributions as well as different star formation histories. We show that the DSC fraction -- which can broadly be associated with the observed intra cluster light -- does not depend on the virial mass of the galaxy cluster and is much more sensitive to the formation history of the cluster. We conclude that the separation of the cD and the DSC in simulations, based on our dynamical criteria, is more physically motivated than current methods which depend on implicit assumptions on a length scale associated with the cD galaxy and therefore represent a step forward in understanding the different stellar components within galaxy clusters. Our results also show the importance of analyzing the dynamics of the DSC to characterize its properties and understand its origin.Comment: 15 pages, 18 figures, MNRAS in pres

Thermal Comfort and its Relation to Ventilation Approaches in Non-Air-Conditioned Mosque Buildings

This paper reports the outcome of a thermal comfort study that assessed the satisfaction of occupants with their surrounding thermal conditions. The study was carried out in 10 mosque buildings around lowland Nibong Tebal, Penang and highland Cameron Highlands, Pahang. It involved determining the compliance level of thermal comfort parameters (i.e. air temperature, relative humidity and air speed) at lowland and highland and establishing relationships between ventilation systems with predicted mean vote and predicted percentage of dissatisfied at lowland and highland according to ASHRAE Standard-55. The study was conducted from 1200h to 1700h/1730h to assess the thermal conditions of the 10 mosques during Zohor/Friday and Asar prayer times. During prayer times, an active ventilation system was in operation, while before and after prayer times, only passive ventilation (windows and doors) was available. Overall, findings indicated that better thermal comfort conditions occurred during prayer time at highland compared with those at the lowland, with the thermal sensation conditions of mosques in the former ‘slightly warmer’ to ‘slightly cool’ and in the latter ‘slightly warm’ to ‘hot’. Moreover, most mosques at lowland did not provide good thermal comfort because the percentage of dissatisfied was high compared to that at highland

Evaluation of ocean color remote sensing algorithms for diffuse attenuation coefficients and optical depths with data collected on BGC-Argo floats

The vertical distribution of irradiance in the ocean is a key input to quantify processes spanning from radiative warming, photosynthesis to photo-oxidation. Here we use a novel dataset of thousands local-noon downwelling irradiance at 490 nm (Ed(490) and photosynthetically available radiation (PAR) profiles captured by 103 BGC-Argo floats spanning three years (from October 2012 to January 2016) in the world\u27s ocean, to evaluate several published algorithms and satellite products related to diffuse attenuation coefficient (Kd). Our results show: (1) MODIS-Aqua Kd(490) products derived from a blue-to-green algorithm and two semi-analytical algorithms show good consistency with the float-observed values, but the Chla-based one has overestimation in oligotrophic waters; (2) The Kd(PAR) model based on the Inherent Optical Properties (IOPs) performs well not only at sea-surface but also at depth, except for the oligotrophic waters where Kd(PAR) is underestimated below two penetration depth (2zpd), due to the model\u27s assumption of a homogeneous distribution of IOPs in the water column which is not true in most oligotrophic waters with deep chlorophyll-a maxima; (3) In addition, published algorithms for the 1% euphotic-layer depth and the depth of 0.415 mol photons m-2 d-1 isolume are evaluated. Algorithms based on Chla generally work well while IOPs-based ones exhibit an overestimation issue in stratified and oligotrophic waters, due to the underestimation of Kd(PAR) at depth

Gammatonegram Representation for End-to-End Dysarthric Speech Processing Tasks: Speech Recognition, Speaker Identification, and Intelligibility Assessment

Dysarthria is a disability that causes a disturbance in the human speech system and reduces the quality and intelligibility of a person's speech. Because of this effect, the normal speech processing systems can not work properly on impaired speech. This disability is usually associated with physical disabilities. Therefore, designing a system that can perform some tasks by receiving voice commands in the smart home can be a significant achievement. In this work, we introduce gammatonegram as an effective method to represent audio files with discriminative details, which is used as input for the convolutional neural network. On the other word, we convert each speech file into an image and propose image recognition system to classify speech in different scenarios. Proposed CNN is based on the transfer learning method on the pre-trained Alexnet. In this research, the efficiency of the proposed system for speech recognition, speaker identification, and intelligibility assessment is evaluated. According to the results on the UA dataset, the proposed speech recognition system achieved 91.29% accuracy in speaker-dependent mode, the speaker identification system acquired 87.74% accuracy in text-dependent mode, and the intelligibility assessment system achieved 96.47% accuracy in two-class mode. Finally, we propose a multi-network speech recognition system that works fully automatically. This system is located in a cascade arrangement with the two-class intelligibility assessment system, and the output of this system activates each one of the speech recognition networks. This architecture achieves an accuracy of 92.3% WRR. The source code of this paper is available.Comment: 12 pages, 8 figure