SREFI: Synthesis of Realistic Example Face Images

In this paper, we propose a novel face synthesis approach that can generate an arbitrarily large number of synthetic images of both real and synthetic identities. Thus a face image dataset can be expanded in terms of the number of identities represented and the number of images per identity using this approach, without the identity-labeling and privacy complications that come from downloading images from the web. To measure the visual fidelity and uniqueness of the synthetic face images and identities, we conducted face matching experiments with both human participants and a CNN pre-trained on a dataset of 2.6M real face images. To evaluate the stability of these synthetic faces, we trained a CNN model with an augmented dataset containing close to 200,000 synthetic faces. We used a snapshot of this trained CNN to recognize extremely challenging frontal (real) face images. Experiments showed training with the augmented faces boosted the face recognition performance of the CNN

Nucleate boiling under different gravity values: numerical simulations & data-driven techniques.

Nucleate boiling is important in nuclear applications and cooling applications under earth gravity conditions. Under reduced gravity or microgravity environment, it is significant too, especially in space exploration applications. Although multiple studies have been performed on nucleate boiling, the effect of gravity on nucleate boiling is not well understood. This dissertation primarily deals with numerical simulations of nucleate boiling using an adaptive Moment-of-Fluid (MoF) method for a single vapor bubble (water vapor or Perfluoro-n-hexane) in saturated liquid for different gravity levels. Results concerning the growth rate of the bubble, specifically the departure diameter and departure time have been provided. The MoF method has been first validated by comparing results with a theoretical solution of vapor bubble growth in superheated liquid without any heat transfer from the wall. Next, bubble growth rate and heat transfer results under earth gravity, reduced gravity, and micro-gravity conditions are reported and they are in good agreement with experiments. A new method is proposed for estimating the bubble diameter at different gravity levels. This method is based on an analysis of empirical data at different gravity values and uses power-series curve fitting to obtain a generalized bubble growth curve irrespective of the gravity value. This method is shown to provide a good estimate of the bubble diameter for a specific gravity value and time. A new hybrid approach is proposed for calculating the contribution of the depletable liquid micro-layer trapped between the vapor bubble and the heater wall for numerical simulations in microgravity conditions is proposed in this work. This technique does not ``model\u27\u27 the micro-layer, but calculates the contribution of the vapor flux from the micro-layer into the bubble and distributes it over the cells where the micro-layer should be present. The micro-layer is depletable because an evaporation term is part of the equation which maintains the reduction in the thickness of the micro-layer consistent with the behavior reported in experiments. Results for nucleate boiling simulations under micro-gravity conditions are reported using the proposed micro-layer approach in comparison with experiments performed on the International Space Station. Results for bubble growth rate, bubble shape, and heat-flux are in good agreement with experiments and are verified with two different time instants in the bubble life cycle. Additionally, a data-driven model is proposed for the prediction of heat-flux from experimental parameters like wall super-heat, gravity, liquid sub-cooling, etc. Experimental data from multiple experiments under varying conditions for different liquids have been performed to date. Artificial Neural Networks (ANNs) have been used to predict nucleate boiling heat flux by learning from a dataset of twelve experimental parameters across 231 independent samples. An approach to reduce the number of parameters involved is proposed to increase model accuracy. The approach consists of two steps. In the first step, a feature importance study is performed to determine the most significant parameters. Only important features are used in the second step. In the second step, dimensional analysis is performed on these important parameters. Neural network analysis is then conducted based on the dimensionless parameters. The results indicate that the proposed feature importance study and dimensional analysis can significantly improve ANN performance. The results show that model errors based on the reduced dataset are considerably lower than those based on the initial dataset. The study based on other machine learning models also shows the reduced dataset generates better results. The results also show that ANN outperforms other machine learning algorithms and outperforms a well-known boiling correlation equation. The effect of parameters on heatflux has been quantified, and the effect of parameters on different physical sub-processes in nucleate boiling has been analyzed. The effect of parameters on the boiling regimes has also been investigated. Additionally, the feature importance study concludes that wall superheats, gravity and liquid subcooling are the three most significant parameters in the prediction of heat flux for nucleate boiling. The key contributions made in this work are listed below: MoF method simulations for nucleate boiling have been performed. Simulation results in earth gravity, and reduced gravity are in good agreement with experiments. \item A data-driven technique for the prediction of the effect of gravity on bubble growth rate has been proposed. A novel depletable microlayer approach for microgravity is proposed, results for bubble growth rate, bubble shape, and heat-flux are comparable to experiments performed on ISS. A novel data-driven technique has been used for heat flux prediction. ANN outperforms XGB (Extreme Gradient Boosting), RFR (Random Forest Regression), and Rohsenow correlation in heatflux prediction. Dimensional Analysis and Feature Importance techniques help in reducing ANN error from 25.7\% to 9.12\%. Gravity, Wall superheat, and Liquid subcooling are the three most significant parameters in heatflux prediction. Novel results of quantification of parameter contribution in each boiling regime have been reported

MATREX: the DCU MT system for WMT 2010

This paper describes the DCU machine translation system in the evaluation campaign of the Joint Fifth Workshop on Statistical Machine Translation and Metrics in ACL-2010. We describe the modular design of our multi-engine machine translation (MT) system with particular focus on the components used in this participation. We participated in the English–Spanish and English–Czech translation tasks, in which we employed our multiengine architecture to translate. We also participated in the system combination task which was carried out by the MBR decoder and confusion network decoder

Length-weight relationship of Mystus tengara (Ham.-Buch., 1822), a freshwater catfish of Indian subcontinent

Length-weight relationship is the most commonly used analysis which has been used for several purposes in fisheries field among which estimation of weight from length is the most popular one. The present study has been performed to analyze the length-weight relationship of Mystus tengara, a freshwater catfish of Indian subcontinent. Total Length and Body Weight of the studied specimens have been observed to vary from 7.2-11.3 cm (male), 7.3-11.7 cm (female) and 3.43-13.63 g (male), 2.83-14.88 g (female). The calculated regression coefficient (b) values are 2.941, 3.119 and 3.071 for male, female and combined sex, respectively; thus depicting negative allometric growth for male; while positive allometric growth for female and combined sex of this fish species. The correlation coefficient values (0.94, 0.95 and 0.95 for male, female and combined sex, respectively) are suggesting a significant relationship between length and weight of the studied fish. The present study provides the first baseline information on the length weight relationship of M. tengara which will be beneficial for future management of this fish species

Feeding and Breeding Biology of Amblypharyngodon mola – A Review

Amblypharyngodon mola is a popular food fish of Indian sub-continent due to its high nutritional value. Earlier many workers have carried out studies on feeding and breeding biology of this fish species but consolidated information on the same is not available. So, a survey of published literatures on the feeding and breeding biology of A. mola has been carried out to consolidate the available information.  Lacunae of information has been pointed out for further study mainly on age group wise variation in food preference and correlation of breeding periodicity with hydrological parameters and photoperiod

Eutropiichthys vacha (Hamilton, 1822), a threatened fish of Indian subcontinent

Eutropiichthys vacha (Batchwa vacha) is a freshwater catfish species having high economic value. It is a very popular table fish among the consumers due to high nutritional value and taste. Just recently small specimens of this species have also made their entry in ornamental fish markets. Recently due to number of reasons, populations of this fish species are facing the threat of extinction. It has already been documented as Endangered in India and Critically Endangered in Bangladesh. The present report has been prepared to summarize the information available on different aspects of this threatened fish species as well as to point out the possible measures that should be considered for its conservation