Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data

Paucity of large curated hand-labeled training data for every domain-of-interest forms a major bottleneck in the deployment of machine learning models in computer vision and other fields. Recent work (Data Programming) has shown how distant supervision signals in the form of labeling functions can be used to obtain labels for given data in near-constant time. In this work, we present Adversarial Data Programming (ADP), which presents an adversarial methodology to generate data as well as a curated aggregated label has given a set of weak labeling functions. We validated our method on the MNIST, Fashion MNIST, CIFAR 10 and SVHN datasets, and it outperformed many state-of-the-art models. We conducted extensive experiments to study its usefulness, as well as showed how the proposed ADP framework can be used for transfer learning as well as multi-task learning, where data from two domains are generated simultaneously using the framework along with the label information. Our future work will involve understanding the theoretical implications of this new framework from a game-theoretic perspective, as well as explore the performance of the method on more complex datasets.Comment: CVPR 2018 main conference pape

Assessment of need of effective health education programme for improvement of personal hygiene among adolescent girl students in a slum area of Kolkata: a school based intervention study

Background: Personal hygiene aims at healthy living by maintaining cleanliness of the body. Adolescent girls being in the period of active growth and development are the ideal candidates to impart proper knowledge which in turn create a correct attitude followed by practice and it would be carried to next generation. The study aimed to assess the effectiveness of a health education programme in improving the knowledge, attitude and practice of personal hygiene among the adolescent girls in a slum area of Kolkata, West Bengal, India.Methods: A quasi-experimental study was conducted in two government Bengali medium secondary schools located in a slum area of Kolkata, West Bengal, India. First a baseline survey with the help of a predesigned pretested questionnaire and checklist was done to find out the socio-demographic information and existing K.A.P of personal hygiene of the students. This was followed by an intervention phase of 6 months during which weekly lecture and demonstration classes were taken in the study school. Impact of intervention was assessed by application of post-test questionnaire. Both the schools were followed for another 3 months to establish the sustainability of the programme.Results: There was statistically significant improvement in the mean scores of K.A.P of personal hygiene from the pre-test level to post-test level among the students of study school as compared to control school, though there was a significant decline in the mean scores at 9 months than 6 months revealing want of sustainability of the programme.Conclusions: Regular revision and reinforcement should be done to increase the effectiveness of a health education programme to improve personal hygiene and thereby resulting in a healthy living

C4Synth: Cross-Caption Cycle-Consistent Text-to-Image Synthesis

Generating an image from its description is a challenging task worth solving because of its numerous practical applications ranging from image editing to virtual reality. All existing methods use one single caption to generate a plausible image. A single caption by itself, can be limited, and may not be able to capture the variety of concepts and behavior that may be present in the image. We propose two deep generative models that generate an image by making use of multiple captions describing it. This is achieved by ensuring 'Cross-Caption Cycle Consistency' between the multiple captions and the generated image(s). We report quantitative and qualitative results on the standard Caltech-UCSD Birds (CUB) and Oxford-102 Flowers datasets to validate the efficacy of the proposed approach.Comment: To appear in the proceedings of IEEE Winter Conference on Applications of Computer Vision, WACV-201

Comparison of intrauterine insemination and timed intercourse following controlled ovarian hyperstimulation in unexplained infertility: a randomized controlled trial

Background: Being a diagnosis of exclusion the treatment options of unexplained infertility are often empiric. There is significant dilemma regarding the superiority of one over another. Despite increasing use of intrauterine insemination (IUI) in adjunct to controlled ovarian hyperstimulation (COH) there is scarcity of randomized controlled trials (RCT) from developing countries. Objective was to compare IUI and timed intercourse (TI) in super ovulated cycles among couples with unexplained infertility over one year.Methods: In this prospective randomized controlled trial total 85 patients were randomly assigned into group 1 (COH with IUI, N= 44) and group 2 (COH with TI, N=41). Patients underwent COH using sequential Clomiphene Citrate and injection human menopausal gonadotrophin followed by IUI in group 1 and timed intercourse in group 2. Either protocol was repeated for three consecutive cycles. Finally, both groups were compared for clinical pregnancy rate, adverse effects and acceptability of the treatment process and outcome. Comparison was done by Student’s unpaired t test for continuous and 2-tailed chi square test for categorical variables.Results: Clinical pregnancy rates following COH/IUI and COH/TI were 13.64% and 19.51% respectively. There was observable difference in the acceptability of the outcome (38.64% in IUI and 56.09% in TI group). All the results including complications and side effect rates were statistically insignificant.Conclusions: Present study failed to show any improvement of pregnancy rates following addition of IUI over TI and it raised the probability that the outcome of the procedure may not be well accepted

[Transitional strength under plasma] Precise estimations of astrophysically relevant electromagnetic transitions of Ar7+^{7+}, Kr7+^{7+}, Xe7+^{7+}, and Rn7+^{7+} under plasma atmosphere

The growing interest in atomic structures of moderately-stripped alkali-like ions in diagnostic study and modeling of astrophysical and laboratory plasma makes an accurate many-body study of atomic properties inevitable. This work presents transition line parameters in the absence or presence of plasma atmosphere for astrophysically important candidates, Ar$^{7+}$, Kr$^{7+}$, Xe$^{7+}$, and Rn$^{7+}$. We employ relativistic coupled-cluster (RCC) theory, a well-known correlation exhaustive method. In the case of a plasma environment, we use Debye Model. Our calculations agree with experiments available in the literature for ionization potentials, transition strengths of allowed and forbidden selections, and lifetimes of several low-lying states. The unit ratios of length and velocity forms of transition matrix elements are the critical estimation of the accuracy of the transition data presented here, especially for a few presented first time in the literature. We do compare our findings with the available recent theoretical results. Our reported data can be helpful to the astronomer in estimating the density of the plasma environment around the astronomical objects or in the discovery of observational spectra corrected by that environment. The present results should be advantageous in the modeling and diagnostics laboratory plasma, whereas the calculated ionisation potential depression parameters reveal important characteristics of atomic structure

A Boosted Machine Learning Framework for the Improvement of Phase and Crystal Structure Prediction of High Entropy Alloys Using Thermodynamic and Configurational Parameters

The reason behind the remarkable properties of High-Entropy Alloys (HEAs) is rooted in the diverse phases and the crystal structures they contain. In the realm of material informatics, employing machine learning (ML) techniques to classify phases and crystal structures of HEAs has gained considerable significance. In this study, we assembled a new collection of 1345 HEAs with varying compositions to predict phases. Within this collection, there were 705 sets of data that were utilized to predict the crystal structures with the help of thermodynamics and electronic configuration. Our study introduces a methodical framework i.e., the Pearson correlation coefficient that helps in selecting the strongly co-related features to increase the prediction accuracy. This study employed five distinct boosting algorithms to predict phases and crystal structures, offering an enhanced guideline for improving the accuracy of these predictions. Among all these algorithms, XGBoost gives the highest accuracy of prediction (94.05%) for phases and LightGBM gives the highest accuracy of prediction of crystal structure of the phases (90.07%). The quantification of the influence exerted by parameters on the model's accuracy was conducted and a new approach was made to elucidate the contribution of individual parameters in the process of phase prediction and crystal structure prediction

RetroKD : Leveraging Past States for Regularizing Targets in Teacher-Student Learning

Several recent works show that higher accuracy models may not be better teachers for every student, and hence, refer this problem as student-teacher "knowledge gap". Further, they propose techniques, which, in this paper, we discuss are constrained to certain pre-conditions: 1). Access to Teacher Model/Architecture 2). Retraining Teacher Model 3). Models in Addition to Teacher Model. Being well known that for a lot of settings, these conditions may not hold true challenges the applicability of such approaches. In this work, we propose RetroKD, which smoothes out the logits of a student network by leveraging students' past state logits with the ones from the teacher. By doing so, we hypothesize that the present target will no longer be as hard as the teacher target and not as more uncomplicated as the past student target. Such regularization on learning the parameters alleviates the needs as required by other methods. Our extensive set of experiments comparing against the baselines for CIFAR 10, CIFAR 100, and TinyImageNet datasets and a theoretical study further help in supporting our claim. We performed crucial ablation studies such as hyperparameter sensitivity, the generalization study by showing the flatness on loss landscape and feature similarly with teacher network