Accurate prognosis and prediction of a patient's current disease state is critical in an ICU. The use of vast amounts of digital medical information can help in predicting the best course of action for the diagnosis and treatment of patients. The proposed technique investigates the strength of using a combination of latent variable models (latent dirichlet allocation) and structured data to transform the information streams into potentially actionable knowledge. In this project, I use Apache Spark to predict mortality among ICU patients so that it can be used as an acuity surrogate to help physicians identify the patients in need of immediate care.
The goal of the Angry Birds AI competition is to build an intelligent agent which can complete the levels of the game better than human players. This task is very challenging, because humans have a good prediction about the physic world, while for computers it is hard to reason about an unknown environment. In this paper we describe our DualHEX AI agent, which is based on the AngryHEX agent of participants of the Angry Birds competition 2013. Our agent models the knowledge of the game by means of Answer Set Programming. In this project we improved the AngryHEX approach by extending the knowledge base of the domain. Our DualHEX agent plans a shot taking into consideration the current and the next bird. It compares the damage probability of both birds to discard targets that suits more the next bird features.
Este artigo trata de um resumo das características principais da linguagem Ruby,
desde a sua arquitetura até a sua sintaxe. Abordando principalmente temas relativos
ao contexto de Linguagens de Programação é possível avaliar uma linguagem de
forma correta. Este artigo tenta fazer isto de uma forma neutra, avaliando cada ponto
da linguagem Ruby.
Keywords: Linguagem Ruby, Ruby, Linguagens de Scripts