The incidence of human fatalities due to arrow injuries in the medical literature is rare. We report an incident involving a 46-year-old man who was found in his secured apartment with a fatal arrow wound of his chest and abdomen. The initial scene investigation suggested that the victim impaled himself with an arrow attached to a razor-sharp, 4-bladed broad-head hunting tip before collapsing on the floor. However, analysis of the bloodstain patterns suggested that the victim used the compound bow to propel the arrow. When investigating deaths due to bows and arrows, thorough scene investigation along with bloodstain pattern analysis is essential in determining the mechanism of injury and manner of death.

Traditional text classifiers are limited to predicting over a fixed set of labels. However, in many real-world applications the label set is frequently changing. For example, in intent classification, new intents may be added over time while others are removed.We propose to address the problem of dynamic text classification by replacing the traditional, fixed-size output layer with a learned, semantically meaningful metric space. Here the distances between textual inputs are optimized to perform nearest-neighbor classification across overlapping label sets. Changing the label set does not involve removing parameters, but rather simply adding or removing support points in the metric space. Then the learned metric can be fine-tuned with only a few additional training examples.We demonstrate that this simple strategy is robust to changes in the label space. Furthermore, our results show that learning a non-Euclidean metric can improve performance in the low data regime, suggesting that further work on metric spaces may benefit lowresource research. 1

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