Electro-Coalescence Fireworks

Electro-coalescence is the application of an electric field onto coalescing fluid bodies. The following fluid dynamics videos show a droplet coalescing into a fluid bath while embedded into a viscous medium and subject to a very high electric field. The concentration of electric stresses at the apex of the droplet cause it to break apart. The droplet is glycerol and the viscous medium is silicone oil

An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy

Etsy is a global marketplace where people across the world connect to make, buy and sell unique goods. Sellers at Etsy can promote their product listings via advertising campaigns similar to traditional sponsored search ads. Click-Through Rate (CTR) prediction is an integral part of online search advertising systems where it is utilized as an input to auctions which determine the final ranking of promoted listings to a particular user for each query. In this paper, we provide a holistic view of Etsy's promoted listings' CTR prediction system and propose an ensemble learning approach which is based on historical or behavioral signals for older listings as well as content-based features for new listings. We obtain representations from texts and images by utilizing state-of-the-art deep learning techniques and employ multimodal learning to combine these different signals. We compare the system to non-trivial baselines on a large-scale real world dataset from Etsy, demonstrating the effectiveness of the model and strong correlations between offline experiments and online performance. The paper is also the first technical overview to this kind of product in e-commerce context

Groundwater recovery simulation for determination of post-mining lake formation at the Sangan iron mine, Mashhad, Iran

A two-dimensional axisymetric finite element software SEEP/W was used to simulate the groundwater recovery process in wells entirely confined in an aquifer at Sangan iron mine in Iran. The simulation model predicted very well with the results obtained from both analytical method and field data for well recovery process. It was inferred from the results that the rate of groundwater recovery process is highest immediately after mine closure with no dewatering program. The paper presents a methodology for predicting how the natural groundwater regime can be established to its equilibrium conditions after mining operation has ceased

Style Conditioned Recommendations

We propose Style Conditioned Recommendations (SCR) and introduce style injection as a method to diversify recommendations. We use Conditional Variational Autoencoder (CVAE) architecture, where both the encoder and decoder are conditioned on a user profile learned from item content data. This allows us to apply style transfer methodologies to the task of recommendations, which we refer to as injection. To enable style injection, user profiles are learned to be interpretable such that they express users' propensities for specific predefined styles. These are learned via label-propagation from a dataset of item content, with limited labeled points. To perform injection, the condition on the encoder is learned while the condition on the decoder is selected per explicit feedback. Explicit feedback can be taken either from a user's response to a style or interest quiz, or from item ratings. In the absence of explicit feedback, the condition at the encoder is applied to the decoder. We show a 12% improvement on NDCG@20 over the traditional VAE based approach and an average 22% improvement on AUC across all classes for predicting user style profiles against our best performing baseline. After injecting styles we compare the user style profile to the style of the recommendations and show that injected styles have an average +133% increase in presence. Our results show that style injection is a powerful method to diversify recommendations while maintaining personal relevance. Our main contribution is an application of a semi-supervised approach that extends item labels to interpretable user profiles.Comment: 9 pages, 10 figures, Accepted to RecSys '1