Protein subcellular locations, as an important property of proteins, are commonly learned using fluorescence microscopy. Previous work by our group has shown that automated analys...
Many applications refer to moving objects or phenomena and require spatio-temporal modelling and specific analysis. Unlike conventional data where attributes are simple values (nu...
Abstract. Dynamic Bayesian networks (DBNs) extend Bayesian networks from static domains to dynamic domains. The only known generic method for exact inference in DBNs is based on dy...
Mining massive temporal data streams for significant trends, emerging buzz, and unusually high or low activity is an important problem with several commercial applications. In th...
Learning temporal causal structures between time series is one of the key tools for analyzing time series data. In many real-world applications, we are confronted with Irregular T...