Video Question Answering on Screencast Tutorials

This paper presents a new video question answering task on screencast tutorials. We introduce a dataset including question, answer and context triples from the tutorial videos for a software. Unlike other video question answering works, all the answers in our dataset are grounded to the domain knowledge base. An one-shot recognition algorithm is designed to extract the visual cues, which helps enhance the performance of video question answering. We also propose several baseline neural network architectures based on various aspects of video contexts from the dataset. The experimental results demonstrate that our proposed models significantly improve the question answering performances by incorporating multi-modal contexts and domain knowledge

Mission-Aware Spatio-Temporal Deep Learning Model for UAS Instantaneous Density Prediction

The number of daily sUAS operations in uncontrolled low altitude airspace is expected to reach into the millions in a few years. Therefore, UAS density prediction has become an emerging and challenging problem. In this paper, a deep learning-based UAS instantaneous density prediction model is presented. The model takes two types of data as input: 1) the historical density generated from the historical data, and 2) the future sUAS mission information. The architecture of our model contains four components: Historical Density Formulation module, UAS Mission Translation module, Mission Feature Extraction module, and Density Map Projection module. The training and testing data are generated by a python based simulator which is inspired by the multi-agent air traffic resource usage simulator (MATRUS) framework. The quality of prediction is measured by the correlation score and the Area Under the Receiver Operating Characteristics (AUROC) between the predicted value and simulated value. The experimental results demonstrate outstanding performance of the deep learning-based UAS density predictor. Compared to the baseline models, for simplified traffic scenario where no-fly zones and safe distance among sUASs are not considered, our model improves the prediction accuracy by more than 15.2% and its correlation score reaches 0.947. In a more realistic scenario, where the no-fly zone avoidance and the safe distance among sUASs are maintained using A* routing algorithm, our model can still achieve 0.823 correlation score. Meanwhile, the AUROC can reach 0.951 for the hot spot prediction

The Roles of Platelet GPIIb/IIIa and αvβ3 Integrins during HeLa Cells Adhesion, Migration, and Invasion to Monolayer Endothelium under Static and Dynamic Shear Flow

During their passage through the circulatory system, tumor cells undergo extensive interactions with various host cells including endothelial cells and platelets. Mechanisms mediating tumor cell adhesion, migration, and metastasis to vessel wall under flow condition are largely unknown. The aim of this study was to investigate the potential roles of GPIIb/IIIa and αvβ3 integrins underlying the HeLa-endothelium interaction in static and dynamic flow conditions. HeLa cell migration and invasion were studied by using Millicell cell culture insert system. The numbers of transmigrated or invaded HeLa cells significantly increased by thrombin-activated platelets and reduced by eptifibatide, a platelet inhibitor. Meanwhile, RGDWE peptides, a specific inhibitor of αvβ3 integrin, also inhibited HeLa cell transmigration. Interestingly, the presence of endothelial cells had significant effect on HeLa cell migration regardless of static or cocultured flow condition. The adhesion capability of HeLa cells to endothelial monolayer was also significantly affected by GPIIb/IIIa and αvβ3 integrins. The arrested HeLa cells increased nearly 5-fold in the presence of thrombin-activated platelets at shear stress condition (1.84 dyn/cm2 exposure for 1 hour) than the control (static). Our findings showed that GPIIb/IIIa and αvβ3 integrins are important mediators in the pathology of cervical cancer and provide a molecular basis for the future therapy, and the efficient antitumor benefit should target multiple receptors on tumor cells and platelets

Application of Volcano Plots in Analyses of mRNA Differential Expressions with Microarrays

Volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log10(p-value) from the t test). We review the basic and an interactive use of the volcano plot, and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide an unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility to apply volcano plots to other fields beyond microarray.Comment: 8 figure