Modeling Trajectories Obtained from External Sensors for Location Prediction via NLP Approaches

Representation learning seeks to extract useful and low-dimensional attributes from complex and high-dimensional data. Natural language processing (NLP) was used to investigate the representation learning models to extract words' feature vectors using their sequential order in the text via word embeddings and language models that maintain their semantic meaning. Inspired by NLP, in this paper, we tackle the representation learning problem for trajectories, using NLP methods to encode external sensors positioned in the road network and generate the features' space to predict the next vehicle movement. We evaluate the vector representations of on-road sensors and trajectories using extrinsic and intrinsic strategies. Our results have shown the potential of natural language models to describe the space of features on trajectory applications as the next location prediction.

Are Noonan syndrome and Noonan-like/multiple giant cell lesion syndrome distinct entities?

We report on a family with typical clinical findings of Noonan syndrome associated with giant cell lesions in maxilla and mandible. We discuss the obvious clinical overlap between Noonan syndrome and Noonan-like/multiple giant cell lesion syndrome, and we give further clinical and molecular support that these two entities could be allelic conditions.