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We propose a novel end-to-end semi-supervised adversarial framework to generate photorealistic face images of new identities with wide ranges of expressions, poses, and illuminations conditioned by a 3D morphable model. Previous adversarial style-transfer methods either supervise their networks with large volume of paired data or use unpaired data with a highly under-constrained two-way generative framework in an unsupervised fashion. We introduce pairwise adversarial supervision to constrain two-way domain adaptation by a small number of paired real and synthetic images for training along with the large volume of unpaired data. Extensive qualitative and quantitative experiments are performed to validate our idea. Generated face images of new identities contain pose, lighting and expression diversity and qualitative results show that they are highly constraint by the synthetic input image while adding photorealism and retaining identity information. We combine face images generated by the proposed method with the real data set to train face recognition algorithms. We evaluated the model on two challenging data sets: LFW and IJB-A. We observe that the generated images from our framework consistently improves over the performance of deep face recognition network trained with Oxford VGG Face dataset and achieves comparable results to the state-of-the-art.

This paper presents a comparative study on organometal halide perovskite based solar cells using two different transparent conducting oxides such as TiO2 and ZnO as electron transporting materials. Simulation is an interesting tool for studying the behavior of every component of a solar cell device as well as for analyzing the performance of the full device. Solar cell capacitance simulator (SCAPS)‐1D has been the tool used for numerical simulation of such devices. Influences of thickness of absorber and ETMs, interface trap density of states and dopant concentration of ETMs on the performance of photovoltaic solar cells are studied. Comparable efficiencies of 22.35% and 22.49% for PV devices made of spiro‐MeOTAD/MAPbI3/TiO2 and spiro‐MeOTAD/MAPbI3/ZnO, respectively, with 400/400/90 nm thicknesses under 1 Sun illumination are observed. The performance of both devices indicates the replacement of the TiO2 by a ZnO layer. It could be a good option to reduce the cost and increase the mobility of ETM for this type of solar cells. Generally, increase in thickness of MAPbI3 perovskite up to around 700 nm results into increase in the power conversion efficiency (PCE) of the solar cells. On the other hand, TiO2 and ZnO layers thicker than 90 nm results into decrease in the efficiency of the devices. Furthermore, the increase in interface trap density is found to reduce the efficiency of the devices. (© 2015 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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, are observed in Biratnagar, Pokhara, Kathmandu, and Lukla respectively. Solar radiation available in any location is affected by topography and pollution. It is found that the more solar energy is available during spring than in summer in Lukla. The solar radiation is observed higher in Pokhara than in Kathmandu. It might be due to absorption of solar energy by air pollutants which are higher in Kathmandu as compared to Pokhara.In addition we also discussed the diurnal variation of measured and calculated data of solar radiation on clear sky day. The annual average solar energy measuring 4.95, 5.44, 5.19 and 4.61 kWh/m 2 /day is found in Biratnagar, Pokhara Kathmandu and Lukla respectively.

Rational and accurate solar energy databases, essential for designing, sizing and performing the solar energy systems in any part of the world, are not easily accessible in different localities of Nepal. In this study, daily global solar radiation, sunshine hours and meteorological data for Biratnagar, Kathmandu, Pokhara and Jumla have been used to derive the regression constants. The linear regression technique has been used to develop a model for Biratnagar, Kathmandu, Pokhara and Jumla. The model has calculated the global solar radiation for these locations. The values of global solar radiation estimated by the model are found to be in close agreement with measured values of respective sites. The estimated values were compared with Angstrom-Prescott model and examined using the root mean square error (RMSE), mean bias error (MBE), mean percentage error (MPE), coefficient of regression (R), coefficient of determinant (R 2) and correlation coefficient (CC) statistical techniques. Thus, the resultant correlations and linear regression relations may be then used for the locations of similar meteorological/geographical characteristics and also can be used to estimate the missing data of solar radiation for the respective site.

Abstract. This paper presents a novel method for hierarchically organizing large face databases, with application to efficient identity-based face retrieval. The method relies on metric learning with local binary pattern (LBP) features. On one hand, LBP features have proved to be highly resilient to various appearance changes due to illumination and contrast variations while being extremely efficient to calculate. On the other hand, metric learning (ML) approaches have been proved very successful for face verification 'in the wild', i.e. in uncontrolled face images with large amounts of variations in pose, expression, appearances, lighting, etc. While such ML based approaches compress high dimensional features into low dimensional spaces using discriminatively learned projections, the complexity of retrieval is still significant for large scale databases (with millions of faces). The present paper shows that learning such discriminative projections locally while organizing the database hierarchically leads to a more accurate and efficient system. The proposed method is validated on the standard Labeled Faces in the Wild (LFW) benchmark dataset with millions of additional distracting face images collected from photos on the internet.