Bayesian principal component analysis (BPCA), a probabilistic reformulation of PCA with Bayesian model selection, is a systematic approach to determining the number of essential p...
Networks are becoming a unifying framework for modeling complex systems and network inference problems are frequently encountered in many fields. Here, I develop and apply a gener...
Assuming that a set of source signals is sparsely representable in a given dictionary, we show how their sparse recovery fails whenever we can only measure a convolved observation...
Abstract: Model based development, like proposed by the OMG’s Model Driven Arre (MDA), aims to raise the level of abstraction from working on the code to working with models. For...
This study explores interruption patterns among software developers who program in pairs versus those who program solo. Ethnographic observations indicate that interruption length...