ViP-CNN: Visual Phrase Guided Convolutional Neural Network

As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers' attention because of its descriptive power and clear structure. It detects the objects and captures their pair-wise interactions with a subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each visual relationship is considered as a phrase with three components. We formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase guided Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Phrase-guided Message Passing Structure (PMPS) to establish the connection among relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the state-of-art method both in speed and accuracy. We further pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task.Comment: 10 pages, 5 figures, accepted by CVPR 201

Adverse Selection in the Irish Tontines of 1773, 1775 and 1777

A tontine is a special kind of annuity in which all participants contribute equally to a subscription pool, and a fixed percent of the total capital raised is distributed equally among surviving nominees every year. In this paper, we examine the adverse selection in the Irish tontines of 1773, 1775 and 1777 because of the presence of a group of speculative investors, namely a group of Genevan bankers. These Genevan investors purportedly cherry picked nominees with greater expected longevity. Their existence allows us to study a rather unconventional aspect of adverse selection, which arises from the informational asymmetry among different types of buyers. Using a newly compiled data set on the nominees and their subsequent mortality, we estimate that these Genevan investors earned on average 8.5% more per share than the other subscribers of the Irish tontines. The result suggests that speculative investors with access to superior information may earn higher returns at the expense of average investors, a phenomenon implicit but difficult to quantify in other insurance markets