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The Volume-Audience-Match simulator, or VAM was applied to predict future activity on Twitter related to international economic affairs. VAM was applied to do timeseries forecasting to predict the: (1) number of total activities, (2) number of active old users, and (3) number of newly active users over the span of 24 hours from the start time of prediction. VAM then used these volume predictions to perform user link predictions. A user-user edge was assigned to each of the activities in the 24 future timesteps. VAM considerably outperformed a set of baseline models in both the time series and user-assignment tasks.

This paper summarizes the task of object state classification for cooking-related images, and introduces the reader to the architecture used in VGG19 convolutional neural network. Our work builds upon the VGG19 network by using its architecture as a starting point, and we use the ImageNet weights to initialize the neural network's weights for the applicable layers. And finally, we will discuss how the VGG19 was modified and trained to obtain our results.