With massive explosion of social media such as Twitter and Instagram, people daily share billions of multimedia posts, containing images and text. Typically, text in these posts is short, informal and noisy, leading to ambiguities which can be resolved using images. In this paper we explore text-centric Named Entity Recognition task on these multimedia posts. We propose an end to end model which learns a joint representation of a text and an image. Our model extends multi-dimensional self attention technique, where now image help to enhance relationship between words. Experiments show that our model is capable of capturing both textual and visual contexts with greater accuracy, achieving state-of-the-art results on Twitter multimodal Named Entity Recognition dataset.• Unrelated image : Text information do not match with an image, as we can see in Fig. 8(a), "Reddit" belongs to
Main objective of present study is to analyze the mixed convective peristaltic transport of water based nanofluids using five different nanoparticles i.e. (Al2O3, CuO, Cu, Ag and TiO2). Two thermal conductivity models namely the Maxwell's and Hamilton-Crosser's are used in this study. Hall and Joule heating effects are also given consideration. Convection boundary conditions are employed. Furthermore, viscous dissipation and heat generation/absorption are used to model the energy equation. Problem is simplified by employing lubrication approach. System of equations are solved numerically. Influence of pertinent parameters on the velocity and temperature are discussed. Also the heat transfer rate at the wall is observed for considered five nanofluids using the two phase models via graphs.
Serial crystallography experiments produce massive amounts of experimental data. Yet in spite of these large-scale data sets, only a small percentage of the data are useful for downstream analysis. Thus, it is essential to differentiate reliably between acceptable data (hits) and unacceptable data (misses). To this end, a novel pipeline is proposed to categorize the data, which extracts features from the images, summarizes these features with the `bag of visual words' method and then classifies the images using machine learning. In addition, a novel study of various feature extractors and machine learning classifiers is presented, with the aim of finding the best feature extractor and machine learning classifier for serial crystallography data. The study reveals that the oriented FAST and rotated BRIEF (ORB) feature extractor with a multilayer perceptron classifier gives the best results. Finally, the ORB feature extractor with multilayer perceptron is evaluated on various data sets including both synthetic and experimental data, demonstrating superior performance compared with other feature extractors and classifiers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.