Patients with damage to left anteromedial temporal cortex often show a striking deficit: they fail to recognize animals and other living things. This failure of recognition presents an important challenge to theories of the neural representation of conceptual knowledge. Here we propose that this lesion-behaviour association arises because polymodal neurons in anteromedial temporal cortex integr...

There are strong needs for both mobile and temporal properties in process algebras designed to specify and analyze distributed mobile real-time systems (DMRS). However there is no practical process algebra with both properties. For example, there is no mobile property in TCCS with the temporal, and, similarly, there is limited temporal property in d-Calculus with the mobile. In order to solve t...

It is known that the satisfiability problem for Metric Temporal Logic (MTL) is decidable over finite timed words. In this paper we study the satisfiability problem for extensions of this logic by various process-algebraic operators. On the negative side we show that satisfiability becomes undecidable when any of hiding, renaming, or asynchronous parallel composition are added to the logic. On t...

There are a number of aspects to be considered when modeling a business process, and research has mainly focused on the control flow perspective. The conceptual modeling of process-related information has been mainly left to traditional data models, like the ER model or UML class and object diagrams; these, however, are generic data models, and they do not offer means to express information fea...

Abstract We describe the re-annotation of selected types of named entities (persons, organizations, locations) from the MUC7 corpus. The focus of this annotation initiative is on recording the time needed for the linguistic process of named entity annotation. Annotation times are measured on two basic annotation units – sentences vs. complex noun phrases. We gathered evidence that decision time...

This paper presents an efficient Gaussian process inference scheme for modeling shortscale phenomena in spatio-temporal datasets. Our model uses a sum of separable, compactly supported covariance functions, which yields a full covariance matrix represented in terms of small sparse matrices operating either on the spatial or temporal domain. The proposed inference procedure is based on Gibbs sam...

Stochastic rainfall models are commonly used in practice for long-term flood risk management. One of the most widely model types is based on point processes. Despite widespread use such models, whether their known simplifications describing space–time structure will affect accuracy estimation has not been quantified. In this study, we quantify biases introduced by limitations to estimates two m...

We present a probabilistic factor analysis model which can be used for studying spatio-temporal datasets. The spatial and temporal structure is modeled by using Gaussian process priors both for the loading matrix and the factors. The posterior distributions are approximated using the variational Bayesian framework. High computational cost of Gaussian process modeling is reduced by using sparse ...